diff --git a/.gitignore b/.gitignore index 80c3560..a10d445 100644 --- a/.gitignore +++ b/.gitignore @@ -75,6 +75,9 @@ TCGA_BRCA_DATA/ !bioneuralnet/datasets/lgg/ !bioneuralnet/datasets/lgg/**/*.csv +!bioneuralnet/datasets/paad/ +!bioneuralnet/datasets/paad/**/*.csv + feature_testing @@ -89,6 +92,7 @@ env.bak/ venv.bak/ .venv/ bioneuralnet-env/ +.localenv/ # IDE and editor directories .vscode/ diff --git a/CHANGELOG.md b/CHANGELOG.md index ce315a2..89d4202 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -197,6 +197,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/). ### Left to Do - **(DONE)Test Suite Completion**: Refactor remaining tests (`gnn_embedding`, `subject_representation`, `hybrid_louvain`) to align with new `utils` imports and other major changes. -- **Documentation**: Update ReadTheDocs API reference and `README.md` to reflect the split `utils` submodules and new `graph_tools`. All jupyter notebook examples will need to be updates. +- **(DONE)Documentation**: Update ReadTheDocs API reference and `README.md` to reflect the split `utils` submodules and new `graph_tools`. All jupyter notebook examples will need to be updates. - **(DONE)Release Prep**: Bump version to `1.2.0` in `setup.py`. - **(DONE)Errors**: Errors with tests and doc-build are expected. They will be addresed in following smaller versions `1.2.1` and so on. + diff --git a/TCGA-Notebooks/TCGA-BRCA.ipynb b/TCGA-Notebooks/TCGA-BRCA.ipynb new file mode 100644 index 0000000..819feaa --- /dev/null +++ b/TCGA-Notebooks/TCGA-BRCA.ipynb @@ -0,0 +1,1101 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "d182cc95", + "metadata": {}, + "source": [ + "# TCGA-BRCA Analysis: *Cancer Subtype* classification\n", + "\n", + "### Dataset Source\n", + "\n", + "- **Omics Data**: [FireHose BRCA](http://firebrowse.org/?cohort=BRCA)\n", + "- **Clinical and PAM50 Data**: [TCGAbiolinks](http://bioconductor.org/packages/release/bioc/html/TCGAbiolinks.html)\n", + "\n", + "##### Dataset Overview\n", + "\n", + "- **Original Data**:\n", + "\n", + " - **Methylation**: 20,107 × 885\n", + " - **mRNA**: 18,321 × 1,212\n", + " - **miRNA**: 503 × 1,189\n", + " - **PAM50**: 1,087 × 1\n", + " - **Clinical**: 1,098 × 101\n", + "\n", + "- **PAM50 Subtype Counts**:\n", + "\n", + " - **LumA**: 419\n", + " - **LumB**: 140\n", + " - **Basal**: 130\n", + " - **Her2**: 46\n", + " - **Normal**: 34\n", + "\n", + "### Patients in Every Dataset\n", + "\n", + "- Total patients present in methylation, mRNA, miRNA, PAM50, and clinical: **769**\n", + "\n", + "### Final Shapes (Per-Patient)\n", + "\n", + "After aggregating multiple aliquots by mean, all modalities align on 769 patients:\n", + "\n", + "- **Methylation**: 769 × 20,106\n", + "- **mRNA**: 769 × 20,531\n", + "- **miRNA**: 769 × 503\n", + "- **PAM50**: 769 × 1\n", + "- **Clinical**: 769 × 119\n", + "\n", + "### Data Summary Table\n", + "\n", + "| Stage | Clinical | Methylation | miRNA | mRNA | PAM50 (Subtype Counts) | Notes |\n", + "| ------------------------------ | ----------- | ------------ | ----------- | -------------- | -------------------------------------------------------------- | --------------------------------------- |\n", + "| **Original Raw Data** | 1,098 × 101 | 20,107 × 885 | 503 × 1,189 | 18,321 × 1,212 | LumA: 509
LumB: 209
Basal: 192
Her2: 82
Normal: 40 | Raw FireHose & TCGAbiolinks files |\n", + "| **Patient-Level Intersection** | 769 × 101 | 769 × 20,107 | 769 × 1,046 | 769 × 20,531 | LumA: 419
LumB: 140
Basal: 130
Her2: 46
Normal: 34 | Patients with complete data in all sets |\n", + "\n", + "### Reference Links\n", + "\n", + "- [FireHose BRCA](http://firebrowse.org/?cohort=BRCA)\n", + "- [TCGAbiolinks](http://bioconductor.org/packages/release/bioc/html/TCGAbiolinks.html)\n", + "- [Direct Download BRCA](http://firebrowse.org/?cohort=BRCA&download_dialog=true)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9c0bda23", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "from pathlib import Path\n", + "root = Path(\"/home/vicente/Github/BioNeuralNet/TCGA_BRCA_DATA\")\n", + "\n", + "mirna_raw = pd.read_csv(root/\"BRCA.miRseq_RPKM_log2.txt\", sep=\"\\t\",index_col=0,low_memory=False) \n", + "rna_raw = pd.read_csv(root / \"BRCA.uncv2.mRNAseq_RSEM_normalized_log2.txt\", sep=\"\\t\",index_col=0,low_memory=False)\n", + "meth_raw = pd.read_csv(root/\"BRCA.meth.by_mean.data.txt\", sep='\\t',index_col=0,low_memory=False)\n", + "clinical_raw = pd.read_csv(root / \"BRCA.clin.merged.picked.txt\",sep=\"\\t\", index_col=0, low_memory=False)\n", + "\n", + "# display all shapes and first few rows of each dataset\n", + "display(mirna_raw.iloc[:3,:5])\n", + "display(mirna_raw.shape)\n", + "\n", + "display(rna_raw.iloc[:3,:5])\n", + "display(rna_raw.shape)\n", + "\n", + "display(meth_raw.iloc[:3,:5])\n", + "display(meth_raw.shape)\n", + "\n", + "display(clinical_raw.iloc[:3,:5])\n", + "display(clinical_raw.shape)" + ] + }, + { + "cell_type": "markdown", + "id": "aacae339", + "metadata": {}, + "source": [ + "## TCGA-BioLink: Pam50\n", + "\n", + "This section demonstrates how to use the `TCGAbiolinks` R package to access and download clinical and molecular subtype data. It begins by ensuring `TCGAbiolinks` is installed, then loads the package. It retrieves PAM50 molecular subtype labels using `TCGAquery_subtype()` and writes them to a CSV file. Additionally, it downloads clinical data using `GDCquery_clinic()` and formats it with `GDCprepare_clinic()`, saving the result as another CSV file." + ] + }, + { + "cell_type": "markdown", + "id": "a445601f", + "metadata": {}, + "source": [ + "```R\n", + " # Install TCGAbiolinks\n", + " if (!requireNamespace(\"TCGAbiolinks\", quietly = TRUE)) {\n", + " if (!requireNamespace(\"BiocManager\", quietly = TRUE))\n", + " install.packages(\"BiocManager\")\n", + " BiocManager::install(\"TCGAbiolinks\")\n", + " }\n", + "\n", + " # Load the library\n", + " library(TCGAbiolinks)\n", + "\n", + " # Download PAM50 subtype labels\n", + " pam50_df <- TCGAquery_subtype(tumor = \"BRCA\")[ , c(\"patient\", \"BRCA_Subtype_PAM50\")]\n", + " write.csv(pam50_df, file = \"BRCA_PAM50_labels.csv\", row.names = FALSE, quote = FALSE)\n", + "\n", + " # Download clinical data\n", + " clin_raw <- GDCquery_clinic(project = \"TCGA-BRCA\", type = \"clinical\")\n", + " clin_df <- GDCprepare_clinic(clin_raw, clinical.info = \"patient\")\n", + " write.csv(clin_df, file = \"BRCA_clinical_data.csv\", row.names = FALSE, quote = FALSE)\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5a6eb71e", + "metadata": {}, + "outputs": [], + "source": [ + "pam50 = pd.read_csv(root /\"BRCA_PAM50_labels.csv\",index_col=0)\n", + "clinical_biolinks = pd.read_csv(root /\"BRCA_clinical_data.csv\",index_col=1)\n", + "\n", + "display(pam50.iloc[:5,:5])\n", + "display(pam50.shape)\n", + "display(clinical_biolinks.iloc[:5,:5])\n", + "display(clinical_biolinks.shape)" + ] + }, + { + "cell_type": "markdown", + "id": "fd066d50", + "metadata": {}, + "source": [ + "## Data Processing Summary\n", + "\n", + "- **Transpose Data**: All raw data (miRNA, RNA, etc.) is flipped so rows represent patients and columns represent features.\n", + "- **Standardize Patient IDs**: Patient IDs in all tables are cleaned to the 12-character TCGA format (e.g., `TCGA-AB-1234`) for matching.\n", + "- **Handle Duplicates**: Duplicate patient rows are averaged in the omics data. The first entry is kept for duplicate patients in the clinical data.\n", + "- **Impute Missing Values (KNN)**: Missing data (NaNs) in the omics datasets are estimated and filled using **K-Nearest Neighbors (KNN)** imputation.\n", + "- **Find Common Patients**: The script identifies the largest common cohort of patients that exist in all datasets.\n", + "- **Subset Data**: All data tables are filtered down to only this common list of patients, ensuring perfect alignment.\n", + "- **Extract Target**: The **PAM50 subtype** column is pulled from the corresponding data table to be used as the final prediction target (y-variable)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "128f63dd", + "metadata": {}, + "outputs": [], + "source": [ + "import bioneuralnet as bnn\n", + "\n", + "meth = meth_raw.T\n", + "rna = rna_raw.T\n", + "mirna = mirna_raw.T\n", + "clinical_firehose = clinical_raw.T\n", + "\n", + "print(f\"miRNA (samples, features): {mirna.shape}\")\n", + "print(f\"RNA (samples, features): {rna.shape}\")\n", + "print(f\"Methylation (samples, features): {meth.shape}\")\n", + "print(f\"Clinical (samples, features): {clinical_firehose.shape}\")\n", + "\n", + "def trim_barcode(idx):\n", + " return idx.to_series().str.slice(0, 12)\n", + "\n", + "# standardize patient IDs across all files\n", + "meth.index = trim_barcode(meth.index)\n", + "rna.index = trim_barcode(rna.index)\n", + "mirna.index = trim_barcode(mirna.index)\n", + "clinical_firehose.index = clinical_firehose.index.str.upper()\n", + "clinical_firehose.index.name = \"Patient_ID\"\n", + "\n", + "meth = meth.apply(pd.to_numeric, errors='coerce')\n", + "meth = meth.drop(columns=[\"Composite Element REF\"], errors=\"ignore\")\n", + "\n", + "rna = rna.apply(pd.to_numeric, errors='coerce')\n", + "mirna = mirna.apply(pd.to_numeric, errors='coerce')\n", + "\n", + "meth = meth.groupby(meth.index).mean()\n", + "rna = rna.groupby(rna.index).mean()\n", + "mirna = mirna.groupby(mirna.index).mean()\n", + "\n", + "clinical = clinical_firehose[~clinical_firehose.index.duplicated(keep='first')]\n", + "\n", + "for df in [meth, rna, mirna]:\n", + " df.columns = df.columns.str.replace(r\"\\?\", \"unknown_\", regex=True)\n", + " df.columns = df.columns.str.replace(r\"\\|\", \"_\", regex=True)\n", + " df.columns = df.columns.str.replace(\"-\", \"_\", regex=False)\n", + " df.columns = df.columns.str.replace(r\"_+\", \"_\", regex=True)\n", + " df.columns = df.columns.str.strip(\"_\")\n", + "\n", + "print(f\"\\nMethylation shape: {meth.shape}\")\n", + "print(f\"RNA shape: {rna.shape}\")\n", + "print(f\"miRNA shape: {mirna.shape}\")\n", + "print(f\"Clinical shape: {clinical.shape}\")\n", + "\n", + "meth.columns = pd.Index(meth.columns.tolist())\n", + "rna.columns = pd.Index(rna.columns.tolist())\n", + "mirna.columns = pd.Index(mirna.columns.tolist())\n", + "\n", + "# impute missing values using KNN on the finalized omics data\n", + "meth = bnn.utils.data.impute_omics_knn(meth, n_neighbors=5)\n", + "rna = bnn.utils.data.impute_omics_knn(rna, n_neighbors=5)\n", + "mirna = bnn.utils.data.impute_omics_knn(mirna, n_neighbors=5)\n", + "\n", + "# handle duplicate patient IDs in clinical data (keeping first occurrence)\n", + "clinical_biolinks = clinical_biolinks[~clinical_biolinks.index.duplicated(keep='first')]\n", + "clinical_firehose = clinical_firehose[~clinical_firehose.index.duplicated(keep='first')]\n", + "\n", + "# patients common to both clinical sources and merge\n", + "common_clinical_patients = clinical_biolinks.index.intersection(clinical_firehose.index)\n", + "clinical_biolinks = clinical_biolinks.loc[common_clinical_patients]\n", + "clinical_firehose = clinical_firehose.loc[common_clinical_patients]\n", + "clinical = pd.concat([clinical_biolinks, clinical_firehose], axis=1)\n", + "clinical.index.name = \"Patient_ID\"\n", + "\n", + "\n", + "# final list of patients present in ALL datasets\n", + "common_patients = sorted(\n", + " set(meth.index) & \n", + " set(rna.index) & \n", + " set(mirna.index) & \n", + " set(pam50.index) & \n", + " set(clinical.index)\n", + ")\n", + "\n", + "print(f\"\\nFound: {len(common_patients)} patients across all data types.\")\n", + "\n", + "meth = meth.loc[common_patients]\n", + "rna = rna.loc[common_patients]\n", + "mirna = mirna.loc[common_patients]\n", + "pam50 = pam50.loc[common_patients]\n", + "clinical = clinical.loc[common_patients]\n", + "\n", + "\n", + "targets = pam50['BRCA_Subtype_PAM50'] \n", + "\n", + "print(\"\\nFinal shapes:\")\n", + "print(f\"meth: {meth.shape}\")\n", + "print(f\"rna: {rna.shape}\")\n", + "print(f\"mirna: {mirna.shape}\")\n", + "print(f\"pam50: {pam50.shape}\")\n", + "print(f\"clinical: {clinical.shape}\")\n", + "print(f\"targets: {targets.shape}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ee114802", + "metadata": {}, + "outputs": [], + "source": [ + "# drop unwanted columns from clinical data\n", + "clinical.drop(columns=[\"Composite Element REF\"], errors=\"ignore\", inplace=True)\n", + "\n", + "# we transform the methylation beta values to M-values and drop unwanted columns\n", + "meth_m = meth.drop(columns=[\"Composite Element REF\"], errors=\"ignore\")\n", + "\n", + "# convert beta values to M-values using bioneuralnet utility with small epsilon to avoid log(0)\n", + "meth_m = bnn.utils.beta_to_m(meth_m, eps=1e-6) \n", + "\n", + "# lastly we turn the target labels into numerical classes\n", + "mapping_brca = {\n", + " 'LumA': 0, \n", + " 'Her2': 1, \n", + " 'LumB': 2, \n", + " 'Basal': 3, \n", + " 'Normal': 4\n", + "}\n", + "target_labels = targets.map(mapping_brca).to_frame(name=\"target\")\n", + "\n", + "X_meth = meth_m.loc[targets.index]\n", + "X_rna = rna.loc[targets.index]\n", + "X_mirna = mirna.loc[targets.index]\n", + "Y_labels = target_labels.loc[targets.index]\n", + "clinical_processed = clinical.loc[targets.index]\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4f35bd67", + "metadata": {}, + "outputs": [], + "source": [ + "display(X_meth.iloc[:3,:5])\n", + "display(X_meth.shape)\n", + "\n", + "display(X_rna.iloc[:3,:5])\n", + "display(X_rna.shape)\n", + "\n", + "display(X_mirna.iloc[:3,:5])\n", + "display(X_mirna.shape)\n", + "\n", + "display(clinical_processed.iloc[:3,:5])\n", + "display(clinical_processed.shape)\n", + "\n", + "display(Y_labels.value_counts())\n" + ] + }, + { + "cell_type": "markdown", + "id": "09265512", + "metadata": {}, + "source": [ + "## Feature Selection Methodology for BRCA\n", + "\n", + "### Supported Methods and Interpretation\n", + "\n", + "BioNeuralNet provides three techniques for feature selection, allowing for different views of the data's statistical profile:\n", + "\n", + "* Variance Thresholding: Identifies features with the highest overall variance across all samples.\n", + "\n", + "* ANOVA F-test: Pinpoints features that best distinguish between the target classes (LumA, LumB, Her2, Basal, and Normal).\n", + "\n", + " Random Forest Importance: Assesses feature utility based on its contribution to a predictive non-linear model.\n", + "\n", + "### BRCA Cohort Selection Strategy\n", + "\n", + "A dimensionality reduction step was essential for managing the high-feature-count omics data, given the complexity of the BRCA network:\n", + "\n", + "* High-Feature Datasets: Both DNA Methylation (20,106 features) and RNA (18,321 features) required significant feature reduction.\n", + "\n", + "* Filtering Process: The top 6,000 features were initially extracted from the Methylation and RNA datasets using all three methods.\n", + "\n", + "* Final Set: A consensus set was built by finding the intersection of features selected by the ANOVA F-test and Random Forest Importance, ensuring both statistical relevance and model-based utility.\n", + "\n", + "* Low-Feature Datasets: The miRNA data (503 features) was passed through without selection, as its feature count was already manageable." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fa70dcca", + "metadata": {}, + "outputs": [], + "source": [ + "import bioneuralnet as bnn\n", + "\n", + "# feature selection\n", + "meth_highvar = bnn.utils.select_top_k_variance(X_meth, k=6000)\n", + "rna_highvar = bnn.utils.select_top_k_variance(X_rna, k=6000)\n", + "\n", + "meth_af = bnn.utils.top_anova_f_features(X_meth, Y_labels, max_features=6000)\n", + "rna_af = bnn.utils.top_anova_f_features(X_rna, Y_labels, max_features=6000)\n", + "\n", + "meth_rf = bnn.utils.select_top_randomforest(X_meth, Y_labels, top_k=6000)\n", + "rna_rf = bnn.utils.select_top_randomforest(X_rna, Y_labels, top_k=6000)\n", + "\n", + "meth_var_set = set(meth_highvar.columns)\n", + "meth_anova_set = set(meth_af.columns)\n", + "meth_rf_set = set(meth_rf.columns)\n", + "\n", + "rna_var_set = set(rna_highvar.columns)\n", + "rna_anova_set = set(rna_af.columns)\n", + "rna_rf_set = set(rna_rf.columns)\n", + "\n", + "meth_inter1 = list(meth_anova_set & meth_var_set)\n", + "meth_inter2 = list(meth_rf_set & meth_var_set)\n", + "meth_inter3 = list(meth_anova_set & meth_rf_set)\n", + "meth_all_three = list(meth_anova_set & meth_var_set & meth_rf_set)\n", + "\n", + "rna_inter4 = list(rna_anova_set & rna_var_set)\n", + "rna_inter5 = list(rna_rf_set & rna_var_set)\n", + "rna_inter6 = list(rna_anova_set & rna_rf_set)\n", + "rna_all_three = list(rna_anova_set & rna_var_set & rna_rf_set)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cc981cdb", + "metadata": {}, + "outputs": [], + "source": [ + "print(\"FROM THE 6000 Methylation feature selection:\\n\")\n", + "print(f\"Anova-F & variance selection share: {len(meth_inter1)} features\")\n", + "print(f\"Random Forest & variance selection share: {len(meth_inter2)} features\")\n", + "print(f\"Anova-F & Random Forest share: {len(meth_inter3)} features\")\n", + "print(f\"All three methods agree on: {len(meth_all_three)} features\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "da639dd6", + "metadata": {}, + "outputs": [], + "source": [ + "print(\"\\nFROM THE 6000 RNA feature selection:\\n\")\n", + "print(f\"Anova-F & variance selection share: {len(rna_inter4)} features\")\n", + "print(f\"Random Forest & variance selection share: {len(rna_inter5)} features\")\n", + "print(f\"Anova-F & Random Forest share: {len(rna_inter6)} features\")\n", + "print(f\"All three methods agree on: {len(rna_all_three)} features\")" + ] + }, + { + "cell_type": "markdown", + "id": "88c6d4a4", + "metadata": {}, + "source": [ + "## Feature Selection Summary: ANOVA-RF Intersection\n", + "\n", + "The final set of features was determined by the **intersection** of those highlighted by the **ANOVA F-test** and **Random Forest Importance**. This methodology provides a balanced filter, capturing features with both high class-separability (ANOVA) and significant predictive value in a non-linear model (Random Forest). The resulting feature pool is considered highly relevant for the subsequent modeling tasks.\n", + "\n", + "### Feature Overlap Results\n", + "\n", + "The table below quantifies the shared features identified by the different selection techniques for each omics type.\n", + "\n", + "| Omics Data Type | ANOVA-F & Variance | RF & Variance | ANOVA-F & Random Forest (Selected) | All Three Agree |\n", + "| :--- | :--- | :--- | :--- | :--- |\n", + "| **Methylation** | 2,092 features | 1,870 features | **2,203 features** | 814 features |\n", + "| **RNA** | 2,359 features | 2,191 features | **2,500 features** | 1,124 features |" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3bc89f38", + "metadata": {}, + "outputs": [], + "source": [ + "X_meth_selected = X_meth[meth_inter3]\n", + "X_rna_selected = X_rna[rna_inter6]\n", + "\n", + "print(\"\\nFinal Shapes for Modeling\")\n", + "print(f\"Methylation (X1): {X_meth_selected.shape}\")\n", + "print(f\"RNA-Seq (X2): {X_rna_selected.shape}\")\n", + "print(f\"miRNA-Seq (X3): {X_mirna.shape}\")\n", + "print(f\"Labels (Y): {Y_labels.shape}\")" + ] + }, + { + "cell_type": "markdown", + "id": "c2168980", + "metadata": {}, + "source": [ + "## Data Availability\n", + "\n", + "To facilitate rapid experimentation and reproduction of our results, the fully processed and feature-selected dataset used in this analysis has been made available directly within the package.\n", + "\n", + "Users can load this dataset, bypassing all preceding data acquisition, preprocessing, and feature selection steps. This allows users to proceed immediately from this step." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2d0340f6", + "metadata": {}, + "outputs": [], + "source": [ + "from bioneuralnet.datasets import DatasetLoader\n", + "\n", + "tgca_brca = DatasetLoader(\"brca\")\n", + "display(tgca_brca.shape)\n", + "\n", + "dna_meth = tgca_brca[\"meth\"]\n", + "rna = tgca_brca[\"rna\"]\n", + "mirna = tgca_brca[\"mirna\"]\n", + "clinical = tgca_brca[\"clinical\"]\n", + "target = tgca_brca[\"target\"]\n", + "\n", + "display(dna_meth.iloc[:3,:5])\n", + "display(rna.iloc[:3,:5])\n", + "display(mirna.iloc[:3,:5])\n", + "display(clinical.iloc[:3,:5])\n", + "display(target.iloc[:3,:5])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f8f9346e", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import bioneuralnet as bnn\n", + "\n", + "# subset from dna_methylation and rna to reduce dimensionality from 5000+ to 2525\n", + "dna_meth = bnn.utils.select_top_k_variance(dna_meth, k=1011)\n", + "rna = bnn.utils.select_top_k_variance(rna, k=1011)" + ] + }, + { + "cell_type": "markdown", + "id": "1d8d776c", + "metadata": {}, + "source": [ + "## Reproducibility and Seeding\n", + "\n", + "To ensure our experimental results are fully reproducible, a single global seed is set at the beginning of the analysis.\n", + "\n", + "This utility function propagates the seed to all sources of randomness, including `random`, `numpy`, and `torch` (for both CPU and GPU). Critically, it also configures the PyTorch cuDNN backend to use deterministic algorithms." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c052f712", + "metadata": {}, + "outputs": [], + "source": [ + "import bioneuralnet as bnn\n", + "\n", + "SEED = 8183\n", + "bnn.utils.set_seed(SEED)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b9b161bc", + "metadata": {}, + "outputs": [], + "source": [ + "# Identified several columns in the clinical data that could lead to data leakage\n", + "dataleak_columns = [\n", + " 'treatments_pharmaceutical_days_to_treatment_end',\n", + " 'treatments_pharmaceutical_days_to_treatment_start',\n", + " 'treatments_pharmaceutical_treatment_id',\n", + " 'treatments_pharmaceutical_treatment_type',\n", + " 'treatments_pharmaceutical_regimen_or_line_of_therapy',\n", + " 'treatments_pharmaceutical_treatment_effect',\n", + " 'treatments_pharmaceutical_therapeutic_agents',\n", + " 'treatments_pharmaceutical_treatment_or_therapy',\n", + " 'treatments_pharmaceutical_initial_disease_status',\n", + " 'treatments_pharmaceutical_treatment_intent_type',\n", + " 'treatments_pharmaceutical_treatment_anatomic_site',\n", + " 'treatments_pharmaceutical_treatment_outcome',\n", + " 'treatments_pharmaceutical_prescribed_dose_units',\n", + " 'treatments_pharmaceutical_treatment_dose',\n", + " 'treatments_pharmaceutical_route_of_administration',\n", + " 'treatments_pharmaceutical_number_of_cycles',\n", + " 'treatments_pharmaceutical_prescribed_dose',\n", + " 'treatments_pharmaceutical_treatment_dose_units',\n", + " 'treatments_pharmaceutical_clinical_trial_indicator',\n", + " 'treatments_pharmaceutical_treatment_anatomic_sites',\n", + " 'treatments_pharmaceutical_margin_status',\n", + " 'treatments_pharmaceutical_course_number',\n", + " 'treatments_pharmaceutical_number_of_fractions',\n", + " 'treatments_radiation_days_to_treatment_end',\n", + " 'treatments_radiation_days_to_treatment_start',\n", + " 'treatments_radiation_treatment_id',\n", + " 'treatments_radiation_treatment_type',\n", + " 'treatments_radiation_regimen_or_line_of_therapy',\n", + " 'treatments_radiation_treatment_effect',\n", + " 'treatments_radiation_therapeutic_agents',\n", + " 'treatments_radiation_treatment_or_therapy',\n", + " 'treatments_radiation_initial_disease_status',\n", + " 'treatments_radiation_treatment_intent_type',\n", + " 'treatments_radiation_treatment_anatomic_site',\n", + " 'treatments_radiation_treatment_outcome',\n", + " 'treatments_radiation_prescribed_dose_units',\n", + " 'treatments_radiation_treatment_dose',\n", + " 'treatments_radiation_route_of_administration',\n", + " 'treatments_radiation_number_of_cycles',\n", + " 'treatments_radiation_prescribed_dose',\n", + " 'treatments_radiation_treatment_dose_units',\n", + " 'treatments_radiation_clinical_trial_indicator',\n", + " 'treatments_radiation_treatment_anatomic_sites',\n", + " 'treatments_radiation_margin_status',\n", + " 'treatments_radiation_course_number',\n", + " 'treatments_radiation_number_of_fractions',\n", + " 'radiation_therapy',\n", + " 'patient',\n", + " 'ajcc_pathologic_stage',\n", + " 'ajcc_pathologic_t',\n", + " 'ajcc_pathologic_n',\n", + " 'ajcc_pathologic_m',\n", + " 'age_at_diagnosis',\n", + " 'primary_diagnosis',\n", + " 'tissue_or_organ_of_origin',\n", + " 'days_to_last_follow_up',\n", + " 'year_of_diagnosis',\n", + " \n", + " 'vital_status',\n", + " 'days_to_death',\n", + " 'year_of_death',\n", + " 'days_to_last_follow_up',\n", + " 'days_to_last_known_alive',\n", + " 'last_known_disease_status',\n", + " 'days_to_last_known_disease_status',\n", + " 'days_to_recurrence',\n", + " 'progression_or_recurrence',\n", + " 'age_at_diagnosis',\n", + " 'days_to_birth', \n", + "]\n", + "\n", + "clinical_clean = clinical.drop(columns=dataleak_columns, errors='ignore')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a013bcb2", + "metadata": {}, + "outputs": [], + "source": [ + "from bioneuralnet.utils import preprocess_clinical\n", + "\n", + "clinical_for_model = preprocess_clinical(\n", + " clinical_clean, \n", + " top_k=7, \n", + " scale=False,\n", + ")\n", + "\n", + "print(clinical_for_model.columns)\n", + "display(clinical_for_model.iloc[:5,:5])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8b099471", + "metadata": {}, + "outputs": [], + "source": [ + "from bioneuralnet.utils import gen_threshold_graph, gen_correlation_graph, gen_similarity_graph, gen_gaussian_knn_graph\n", + "\n", + "omics_brca = pd.concat([dna_meth, rna, mirna], axis=1)\n", + "\n", + "threshold_10 = gen_threshold_graph(omics_brca, b=6.2, k=10)\n", + "correlation_10 = gen_correlation_graph(omics_brca, k=10, method='pearson', signed=False)\n", + "similarity_10 = gen_similarity_graph(omics_brca, k=10, metric='cosine')\n", + "gaussian_15 = gen_gaussian_knn_graph(omics_brca, k=15, sigma=None)\n", + "\n", + "comparison_runs = [\n", + " {\"name\":\"threshold_10\", \"graph\": threshold_10},\n", + " {\"name\":\"correlation_10\", \"graph\": correlation_10},\n", + " {\"name\":\"similarity_10\", \"graph\": similarity_10},\n", + " {\"name\":\"gaussian_15\", \"graph\": gaussian_15},\n", + "]" + ] + }, + { + "cell_type": "markdown", + "id": "27f4e2c7", + "metadata": {}, + "source": [ + "## Classification using DPMON: Training and Evaluation" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1a2f5323", + "metadata": {}, + "outputs": [], + "source": [ + "from bioneuralnet.downstream_task import DPMON\n", + "\n", + "\n", + "output_dir_base = Path(\"/home/vicente/Github/BioNeuralNet/dpmon_results_GAT_FINAL/brca\")\n", + "all_results = []\n", + "\n", + "for run_config in comparison_runs:\n", + " graph_name = run_config[\"name\"]\n", + " A_full = run_config[\"graph\"]\n", + " \n", + " current_output_dir = output_dir_base / graph_name\n", + " current_output_dir.mkdir(parents=True, exist_ok=True)\n", + "\n", + " dpmon_params_base = {\n", + " \"adjacency_matrix\": A_full,\n", + " \"omics_list\": omics_brca,\n", + " \"phenotype_data\": target,\n", + " \"phenotype_col\": \"target\",\n", + " \"clinical_data\": clinical_for_model,\n", + " \"model\": 'GAT',\n", + " \"tune\": True, \n", + " \"cv\": True, \n", + " \"n_folds\": 5,\n", + " \"repeat_num\":5,\n", + " \"gpu\": True,\n", + " \"cuda\": 0,\n", + " \"seed\": SEED,\n", + " \"output_dir\": current_output_dir\n", + " }\n", + " \n", + " dpmon_tunned = DPMON(**dpmon_params_base)\n", + " predictions_df, metrics, embeddings = dpmon_tunned.run()\n", + "\n", + " all_results.append({\n", + " \"graph\": graph_name,\n", + " \"predictions\": predictions_df,\n", + " \"metrics\": metrics,\n", + " \"embeddings\": embeddings\n", + " })\n", + "\n", + "for res in all_results:\n", + " graph_name = res[\"graph\"]\n", + " graph_metrics = res[\"metrics\"]\n", + " \n", + " acc_row = graph_metrics.loc[graph_metrics['Metric'] == 'Accuracy'].iloc[0]\n", + " f1_macro_row = graph_metrics.loc[graph_metrics['Metric'] == 'F1 Macro'].iloc[0]\n", + " f1_weighted_row = graph_metrics.loc[graph_metrics['Metric'] == 'F1 Weighted'].iloc[0]\n", + " recall_row = graph_metrics.loc[graph_metrics['Metric'] == 'Recall'].iloc[0]\n", + " auc_row = graph_metrics.loc[graph_metrics['Metric'] == 'AUC'].iloc[0]\n", + " \n", + " acc_avg, acc_std = acc_row['Average'], acc_row['StdDev']\n", + " f1_macro_avg, f1_macro_std = f1_macro_row['Average'], f1_macro_row['StdDev']\n", + " f1_weighted_avg, f1_weighted_std = f1_weighted_row['Average'], f1_weighted_row['StdDev']\n", + " recall_avg, recall_std = recall_row['Average'], recall_row['StdDev']\n", + " auc_avg, auc_std = auc_row['Average'], auc_row['StdDev']\n", + "\n", + " print(f\"\\nResults for: {graph_name}\")\n", + " print(f\"Accuracy (Avg +/- Std): {acc_avg:.4f} +/- {acc_std:.4f}\")\n", + " print(f\"F1 Macro (Avg +/- Std): {f1_macro_avg:.4f} +/- {f1_macro_std:.4f}\")\n", + " print(f\"F1 Weighted (Avg +/- Std): {f1_weighted_avg:.4f} +/- {f1_weighted_std:.4f}\")\n", + " print(f\"Recall: {recall_avg:.4f} +/- {recall_std:.4f}\")\n", + " print(f\"AUC: {auc_avg:.4f} +/- {auc_std:.4f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "43396d92", + "metadata": {}, + "outputs": [], + "source": [ + "output_dir_base = Path(\"/home/vicente/Github/BioNeuralNet/dpmon_results_SAGE_FINAL/brca\")\n", + "all_results = []\n", + "\n", + "for run_config in comparison_runs:\n", + " graph_name = run_config[\"name\"]\n", + " A_full = run_config[\"graph\"]\n", + " \n", + " current_output_dir = output_dir_base / graph_name\n", + " current_output_dir.mkdir(parents=True, exist_ok=True)\n", + "\n", + " dpmon_params_base = {\n", + " \"adjacency_matrix\": A_full,\n", + " \"omics_list\": omics_brca,\n", + " \"phenotype_data\": target,\n", + " \"phenotype_col\": \"target\",\n", + " \"clinical_data\": clinical_for_model,\n", + " \"model\": 'SAGE',\n", + " \"tune\": True, \n", + " \"cv\": True, \n", + " \"n_folds\": 5,\n", + " \"repeat_num\":5,\n", + " \"gpu\": True,\n", + " \"cuda\": 0,\n", + " \"seed\": SEED,\n", + " \"output_dir\": current_output_dir\n", + " }\n", + " \n", + " dpmon_tunned = DPMON(**dpmon_params_base)\n", + " predictions_df, metrics, embeddings = dpmon_tunned.run()\n", + "\n", + " all_results.append({\n", + " \"graph\": graph_name,\n", + " \"predictions\": predictions_df,\n", + " \"metrics\": metrics,\n", + " \"embeddings\": embeddings\n", + " })\n", + "\n", + "for res in all_results:\n", + " graph_name = res[\"graph\"]\n", + " graph_metrics = res[\"metrics\"]\n", + " \n", + " acc_row = graph_metrics.loc[graph_metrics['Metric'] == 'Accuracy'].iloc[0]\n", + " f1_macro_row = graph_metrics.loc[graph_metrics['Metric'] == 'F1 Macro'].iloc[0]\n", + " f1_weighted_row = graph_metrics.loc[graph_metrics['Metric'] == 'F1 Weighted'].iloc[0]\n", + " recall_row = graph_metrics.loc[graph_metrics['Metric'] == 'Recall'].iloc[0]\n", + " auc_row = graph_metrics.loc[graph_metrics['Metric'] == 'AUC'].iloc[0]\n", + " \n", + " acc_avg, acc_std = acc_row['Average'], acc_row['StdDev']\n", + " f1_macro_avg, f1_macro_std = f1_macro_row['Average'], f1_macro_row['StdDev']\n", + " f1_weighted_avg, f1_weighted_std = f1_weighted_row['Average'], f1_weighted_row['StdDev']\n", + " recall_avg, recall_std = recall_row['Average'], recall_row['StdDev']\n", + " auc_avg, auc_std = auc_row['Average'], auc_row['StdDev']\n", + "\n", + " print(f\"\\nResults for: {graph_name}\")\n", + " print(f\"Accuracy (Avg +/- Std): {acc_avg:.4f} +/- {acc_std:.4f}\")\n", + " print(f\"F1 Macro (Avg +/- Std): {f1_macro_avg:.4f} +/- {f1_macro_std:.4f}\")\n", + " print(f\"F1 Weighted (Avg +/- Std): {f1_weighted_avg:.4f} +/- {f1_weighted_std:.4f}\")\n", + " print(f\"Recall: {recall_avg:.4f} +/- {recall_std:.4f}\")\n", + " print(f\"AUC: {auc_avg:.4f} +/- {auc_std:.4f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cb69e019", + "metadata": {}, + "outputs": [], + "source": [ + "output_dir_base = Path(\"/home/vicente/Github/BioNeuralNet/dpmon_results_GCN_FINAL/brca\")\n", + "\n", + "all_results = []\n", + "\n", + "for run_config in comparison_runs:\n", + " graph_name = run_config[\"name\"]\n", + " A_full = run_config[\"graph\"]\n", + " \n", + " current_output_dir = output_dir_base / graph_name\n", + " current_output_dir.mkdir(parents=True, exist_ok=True)\n", + "\n", + " dpmon_params_base = {\n", + " \"adjacency_matrix\": A_full,\n", + " \"omics_list\": omics_brca,\n", + " \"phenotype_data\": target,\n", + " \"phenotype_col\": \"target\",\n", + " \"clinical_data\": clinical_for_model,\n", + " \"model\": 'GCN',\n", + " \"tune\": True, \n", + " \"cv\": True, \n", + " \"n_folds\": 5,\n", + " \"repeat_num\":5,\n", + " \"gpu\": True,\n", + " \"cuda\": 0,\n", + " \"seed\": SEED,\n", + " \"output_dir\": current_output_dir\n", + " }\n", + " \n", + " dpmon_tunned = DPMON(**dpmon_params_base)\n", + " predictions_df, metrics, embeddings = dpmon_tunned.run()\n", + "\n", + " all_results.append({\n", + " \"graph\": graph_name,\n", + " \"predictions\": predictions_df,\n", + " \"metrics\": metrics,\n", + " \"embeddings\": embeddings\n", + " })\n", + "\n", + "for res in all_results:\n", + " graph_name = res[\"graph\"]\n", + " graph_metrics = res[\"metrics\"]\n", + " \n", + " acc_row = graph_metrics.loc[graph_metrics['Metric'] == 'Accuracy'].iloc[0]\n", + " f1_macro_row = graph_metrics.loc[graph_metrics['Metric'] == 'F1 Macro'].iloc[0]\n", + " f1_weighted_row = graph_metrics.loc[graph_metrics['Metric'] == 'F1 Weighted'].iloc[0]\n", + " recall_row = graph_metrics.loc[graph_metrics['Metric'] == 'Recall'].iloc[0]\n", + " auc_row = graph_metrics.loc[graph_metrics['Metric'] == 'AUC'].iloc[0]\n", + " \n", + " acc_avg, acc_std = acc_row['Average'], acc_row['StdDev']\n", + " f1_macro_avg, f1_macro_std = f1_macro_row['Average'], f1_macro_row['StdDev']\n", + " f1_weighted_avg, f1_weighted_std = f1_weighted_row['Average'], f1_weighted_row['StdDev']\n", + " recall_avg, recall_std = recall_row['Average'], recall_row['StdDev']\n", + " auc_avg, auc_std = auc_row['Average'], auc_row['StdDev']\n", + "\n", + " print(f\"\\nResults for: {graph_name}\")\n", + " print(f\"Accuracy (Avg +/- Std): {acc_avg:.4f} +/- {acc_std:.4f}\")\n", + " print(f\"F1 Macro (Avg +/- Std): {f1_macro_avg:.4f} +/- {f1_macro_std:.4f}\")\n", + " print(f\"F1 Weighted (Avg +/- Std): {f1_weighted_avg:.4f} +/- {f1_weighted_std:.4f}\")\n", + " print(f\"Recall: {recall_avg:.4f} +/- {recall_std:.4f}\")\n", + " print(f\"AUC: {auc_avg:.4f} +/- {auc_std:.4f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7178390e", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.model_selection import StratifiedKFold, ParameterSampler, RepeatedStratifiedKFold\n", + "from sklearn.metrics import accuracy_score, f1_score, recall_score, roc_auc_score, average_precision_score\n", + "from sklearn.base import clone\n", + "from sklearn.preprocessing import StandardScaler, label_binarize\n", + "from sklearn.pipeline import Pipeline\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.neural_network import MLPClassifier\n", + "from scipy.stats import loguniform, randint\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "from sklearn.tree import DecisionTreeClassifier\n", + "from xgboost import XGBClassifier\n", + "from sklearn.svm import SVC\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "X = pd.concat([dna_meth, rna, mirna, clinical_for_model], axis=1)\n", + "y = target['vital_status']\n", + "print(f\"Successfully created X matrix with shape: {X.shape}\")\n", + "print(f\"Successfully created y vector with shape: {y.shape}\")\n", + "\n", + "\n", + "pipe_lr = Pipeline([\n", + " ('scaler', StandardScaler()),\n", + " ('model', LogisticRegression(solver='lbfgs', max_iter=1000, penalty=None, random_state=SEED))\n", + "])\n", + "\n", + "pipe_mlp = Pipeline([\n", + " ('scaler', StandardScaler()),\n", + " ('model', MLPClassifier(max_iter=500, early_stopping=True, n_iter_no_change=10, random_state=SEED))\n", + "])\n", + "\n", + "pipe_xgb = Pipeline([\n", + " ('scaler', StandardScaler()),\n", + " ('model', XGBClassifier(eval_metric='logloss', tree_method='hist', max_bin=128, random_state=SEED))\n", + "])\n", + "pipe_rf = Pipeline([\n", + " ('scaler', StandardScaler()),\n", + " ('model', RandomForestClassifier(random_state=SEED))\n", + "])\n", + "\n", + "pipe_svm = Pipeline([\n", + " ('scaler', StandardScaler()),\n", + " ('model', SVC(probability=True, random_state=SEED))\n", + "])\n", + "\n", + "pipe_dt = Pipeline([\n", + " ('scaler', StandardScaler()),\n", + " ('model', DecisionTreeClassifier(random_state=SEED))\n", + "])\n", + "\n", + "params_lr = {'model__penalty': ['l2'], 'model__C': loguniform(1e-4, 1e2)}\n", + "\n", + "params_mlp = {\n", + " 'model__hidden_layer_sizes': [(100,), (100, 50), (50, 50)],\n", + " 'model__activation': ['relu', 'tanh'],\n", + " 'model__alpha': loguniform(1e-5, 1e-1),\n", + " 'model__learning_rate_init': loguniform(1e-4, 1e-2)\n", + "}\n", + "params_xgb = {\n", + " 'model__n_estimators': randint(50, 200),\n", + " 'model__learning_rate': loguniform(0.01, 0.3),\n", + " 'model__max_depth': randint(3, 7),\n", + " 'model__subsample': [0.8, 1.0], \n", + " 'model__colsample_bytree': [0.8, 1.0]\n", + "}\n", + "params_rf = {\n", + " 'model__n_estimators': randint(100, 300),\n", + " 'model__max_depth': [10, 20, 30, None],\n", + " 'model__min_samples_split': randint(2, 10),\n", + " 'model__min_samples_leaf': randint(1, 5),\n", + " 'model__max_features': ['sqrt', 'log2']\n", + "}\n", + "params_svm = {\n", + " 'model__C': loguniform(1e-2, 1e3),\n", + " 'model__kernel': ['rbf', 'linear'],\n", + " 'model__gamma': ['scale', 'auto']\n", + "}\n", + "\n", + "params_dt = {\n", + " 'model__max_depth': randint(3, 15),\n", + " 'model__min_samples_split': randint(2, 20),\n", + " 'model__criterion': ['gini', 'entropy']\n", + "}\n", + "\n", + "models_to_tune = {\n", + " \"LogisticRegression\": (pipe_lr, params_lr),\n", + " \"SVM\": (pipe_svm, params_svm),\n", + " \"MLP\": (pipe_mlp, params_mlp),\n", + " \"XGBoost\": (pipe_xgb, params_xgb),\n", + " \"RandomForest\": (pipe_rf, params_rf),\n", + " \"DecisionTree\": (pipe_dt, params_dt),\n", + "}\n", + "\n", + "all_results = {\n", + " \"LogisticRegression\": {\"acc\": [], \"f1_w\": [], \"f1_m\": [], \"recall\": [], \"auc\": []},\n", + " \"MLP\": {\"acc\": [], \"f1_w\": [], \"f1_m\": [], \"recall\": [], \"auc\": []},\n", + " \"XGBoost\": {\"acc\": [], \"f1_w\": [], \"f1_m\": [], \"recall\": [], \"auc\": []},\n", + " \"RandomForest\": {\"acc\": [], \"f1_w\": [], \"f1_m\": [], \"recall\": [], \"auc\": []},\n", + " \"SVM\": {\"acc\": [], \"f1_w\": [], \"f1_m\": [], \"recall\": [], \"auc\": []},\n", + " \"DecisionTree\": {\"acc\": [], \"f1_w\": [], \"f1_m\": [], \"recall\": [], \"auc\": []},\n", + "}\n", + "\n", + "\n", + "for model_name, (pipeline, param_dist) in models_to_tune.items():\n", + " print(f\"Evaluating model with nested CV: {model_name}\")\n", + " \n", + " outer_cv = RepeatedStratifiedKFold(n_splits=5, n_repeats=5, random_state=SEED)\n", + " \n", + " # inner folds are for finding the best hyperparameters\n", + " for fold_idx, (train_idx, test_idx) in enumerate(outer_cv.split(X, y), start=1):\n", + " X_train_outer, X_test_outer = X.iloc[train_idx], X.iloc[test_idx]\n", + " y_train_outer, y_test_outer = y.iloc[train_idx], y.iloc[test_idx]\n", + " inner_cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=SEED)\n", + " \n", + " best_score_fold = -np.inf\n", + " best_params_fold = None\n", + " # May or may not want to set a seed here. A fix seed = same hyperparamters each fold.\n", + " # No seed = different hyperparameters each fold. This adds more randomness, and may yield better generalization.\n", + " param_sampler = list(ParameterSampler(param_dist, n_iter=20))\n", + " \n", + " for params in param_sampler:\n", + " inner_scores = []\n", + " \n", + " for inner_train_idx, inner_val_idx in inner_cv.split(X_train_outer, y_train_outer):\n", + " X_train_inner = X_train_outer.iloc[inner_train_idx]\n", + " X_val_inner = X_train_outer.iloc[inner_val_idx]\n", + " y_train_inner = y_train_outer.iloc[inner_train_idx]\n", + " y_val_inner = y_train_outer.iloc[inner_val_idx]\n", + " \n", + " inner_pipeline = clone(pipeline)\n", + " inner_pipeline.set_params(**params)\n", + " inner_pipeline.fit(X_train_inner, y_train_inner)\n", + " \n", + " y_val_pred = inner_pipeline.predict(X_val_inner)\n", + " score = f1_score(y_val_inner, y_val_pred, average='weighted', zero_division=0)\n", + " inner_scores.append(score)\n", + " \n", + " mean_score = np.mean(inner_scores)\n", + " if mean_score > best_score_fold:\n", + " best_score_fold = mean_score\n", + " best_params_fold = params\n", + " \n", + " print(f\"Outer fold {fold_idx}: best params (inner CV F1-W={best_score_fold:.4f})\")\n", + " print(f\"{best_params_fold}\")\n", + " \n", + " final_pipeline = clone(pipeline)\n", + " final_pipeline.set_params(**best_params_fold)\n", + " final_pipeline.fit(X_train_outer, y_train_outer)\n", + " \n", + " preds = final_pipeline.predict(X_test_outer)\n", + " \n", + " if hasattr(final_pipeline, \"predict_proba\"):\n", + " proba = final_pipeline.predict_proba(X_test_outer)\n", + " else:\n", + " proba = None\n", + " \n", + " acc = accuracy_score(y_test_outer, preds)\n", + " f1_w = f1_score(y_test_outer, preds, average='weighted', zero_division=0)\n", + " f1_m = f1_score(y_test_outer, preds, average='macro', zero_division=0)\n", + " recall = recall_score(y_test_outer, preds, average='macro', zero_division=0)\n", + " \n", + " auc = np.nan\n", + " \n", + " if proba is not None:\n", + " try:\n", + " if len(np.unique(y)) == 2:\n", + " auc = roc_auc_score(y_test_outer, proba[:, 1])\n", + " else:\n", + " auc = roc_auc_score(y_test_outer, proba, multi_class='ovr', average='macro')\n", + " y_test_bin = label_binarize(y_test_outer, classes=np.unique(y))\n", + " except Exception:\n", + " auc = np.nan\n", + "\n", + " print(f\"Fold {fold_idx} results: Acc={acc:.4f}, F1-W={f1_w:.4f}, \"\n", + " f\"F1-M={f1_m:.4f}, Recall={recall:.4f}, AUC={auc:.4f}\")\n", + " \n", + " all_results[model_name][\"acc\"].append(acc)\n", + " all_results[model_name][\"f1_w\"].append(f1_w)\n", + " all_results[model_name][\"f1_m\"].append(f1_m)\n", + " all_results[model_name][\"recall\"].append(recall)\n", + " all_results[model_name][\"auc\"].append(auc)\n", + "\n", + "print(\"\\nFINAL BASELINE RESULTS\\n\")\n", + "for model_name, metrics in all_results.items():\n", + " avg_acc = np.mean(metrics[\"acc\"])\n", + " std_acc = np.std(metrics[\"acc\"])\n", + " avg_f1_w = np.mean(metrics[\"f1_w\"])\n", + " std_f1_w = np.std(metrics[\"f1_w\"])\n", + " avg_f1_m = np.mean(metrics[\"f1_m\"])\n", + " std_f1_m = np.std(metrics[\"f1_m\"])\n", + " avg_recall = np.mean(metrics[\"recall\"])\n", + " std_recall = np.std(metrics[\"recall\"])\n", + " avg_auc = np.nanmean(metrics[\"auc\"])\n", + " std_auc = np.nanstd(metrics[\"auc\"])\n", + " \n", + " print(f\"\\n{model_name}:\")\n", + " print(f\"Accuracy: {avg_acc:.4f} +/- {std_acc:.4f}\")\n", + " print(f\"F1 Weighted: {avg_f1_w:.4f} +/- {std_f1_w:.4f}\")\n", + " print(f\"F1 Macro: {avg_f1_m:.4f} +/- {std_f1_m:.4f}\")\n", + " print(f\"Recall: {avg_recall:.4f} +/- {std_recall:.4f}\")\n", + " print(f\"AUC: {avg_auc:.4f} +/- {std_auc:.4f}\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".enviroment", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/TCGA-Notebooks/TCGA-Biomarkers.ipynb b/TCGA-Notebooks/TCGA-Biomarkers.ipynb new file mode 100644 index 0000000..8365508 --- /dev/null +++ b/TCGA-Notebooks/TCGA-Biomarkers.ipynb @@ -0,0 +1,3443 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "d408841e", + "metadata": {}, + "source": [ + "# TCGA-KIPAN Analysis: Using *BioNeuralNet* for Interpretability and Biomarker Discovery\n", + "\n", + "- **Cohort**: Focuses on the TCGA-KIPAN dataset, a vital resource merging three major kidney cancer subtypes:\n", + "\n", + " - `Kidney Renal Clear Cell Carcinoma` (KIRC)\n", + " - `Kidney Renal Papillary Cell Carcinoma` (KIRP)\n", + " - `Kidney Chromophobe `(KICH)\n", + "\n", + "- **Goal**: Leverage Subgraph Detection, one of the modules included in *BioNeuralNet*, to identify densely connected omics from a larger Multi-Omics Network.\n", + "\n", + "- These densely connected omics represent biologically meaningful **communities** and are highly correlated to their respective phenotype.\n", + "\n", + "- We will also leverage BioNeuralNet visualization tools to explore the discovered communities further.\n", + "\n", + "**Data Source:** [Broad Institute FireHose](`http://firebrowse.org/?cohort=KIPAN`)\n", + "\n", + "*For the end-to-end analysis see TCGA-KIPAN*" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "40c36cd7", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-11-26 00:57:19,265 - bioneuralnet.utils.preprocess - INFO - [Inf]: Replaced 0 infinite values\n", + "2025-11-26 00:57:19,266 - bioneuralnet.utils.preprocess - INFO - [NaN]: Replaced 0 NaNs after median imputation\n", + "2025-11-26 00:57:19,266 - bioneuralnet.utils.preprocess - INFO - [Zero-Var]: 0 columns dropped due to zero variance\n", + "2025-11-26 00:57:19,274 - bioneuralnet.utils.preprocess - INFO - Selected top 300 features by variance\n", + "2025-11-26 00:57:19,604 - bioneuralnet.utils.preprocess - INFO - [Inf]: Replaced 0 infinite values\n", + "2025-11-26 00:57:19,604 - bioneuralnet.utils.preprocess - INFO - [NaN]: Replaced 0 NaNs after median imputation\n", + "2025-11-26 00:57:19,604 - bioneuralnet.utils.preprocess - INFO - [Zero-Var]: 0 columns dropped due to zero variance\n", + "2025-11-26 00:57:19,613 - bioneuralnet.utils.preprocess - INFO - Selected top 300 features by variance\n", + "2025-11-26 00:57:19,685 - bioneuralnet.utils.preprocess - INFO - [Inf]: Replaced 0 infinite values\n", + "2025-11-26 00:57:19,685 - bioneuralnet.utils.preprocess - INFO - [NaN]: Replaced 0 NaNs after median imputation\n", + "2025-11-26 00:57:19,685 - bioneuralnet.utils.preprocess - INFO - [Zero-Var]: 0 columns dropped due to zero variance\n", + "2025-11-26 00:57:19,687 - bioneuralnet.utils.preprocess - INFO - Selected top 300 features by variance\n", + "2025-11-26 00:57:20,259 - bioneuralnet.utils.graph_tools - INFO - GRAPH ANALYSIS: threshold_10\n", + "\n", + "2025-11-26 00:57:20,259 - bioneuralnet.utils.graph_tools - INFO - Nodes: 900 | Edges: 8,628\n", + "2025-11-26 00:57:20,260 - bioneuralnet.utils.graph_tools - INFO - Avg degree: 19.17\n", + "2025-11-26 00:57:20,260 - bioneuralnet.utils.graph_tools - INFO - Connected components: 1\n", + "2025-11-26 00:57:20,261 - bioneuralnet.utils.graph_tools - INFO - Largest component: 900 nodes (100.0%)\n", + "2025-11-26 00:57:20,345 - bioneuralnet.utils.graph_tools - INFO - Avg clustering coefficient: 0.016\n", + "2025-11-26 00:57:20,357 - bioneuralnet.utils.graph_tools - INFO - GRAPH ANALYSIS: correlation_10\n", + "\n", + "2025-11-26 00:57:20,357 - bioneuralnet.utils.graph_tools - INFO - Nodes: 900 | Edges: 8,628\n", + "2025-11-26 00:57:20,357 - bioneuralnet.utils.graph_tools - INFO - Avg degree: 19.17\n", + "2025-11-26 00:57:20,358 - bioneuralnet.utils.graph_tools - INFO - Connected components: 1\n", + "2025-11-26 00:57:20,358 - bioneuralnet.utils.graph_tools - INFO - Largest component: 900 nodes (100.0%)\n", + "2025-11-26 00:57:20,443 - bioneuralnet.utils.graph_tools - INFO - Avg clustering coefficient: 0.108\n", + "2025-11-26 00:57:20,455 - bioneuralnet.utils.graph_tools - INFO - GRAPH ANALYSIS: similarity_10\n", + "\n", + "2025-11-26 00:57:20,455 - bioneuralnet.utils.graph_tools - INFO - Nodes: 900 | Edges: 8,930\n", + "2025-11-26 00:57:20,456 - bioneuralnet.utils.graph_tools - INFO - Avg degree: 19.84\n", + "2025-11-26 00:57:20,456 - bioneuralnet.utils.graph_tools - INFO - Connected components: 1\n", + "2025-11-26 00:57:20,456 - bioneuralnet.utils.graph_tools - INFO - Largest component: 900 nodes (100.0%)\n", + "2025-11-26 00:57:20,552 - bioneuralnet.utils.graph_tools - INFO - Avg clustering coefficient: 0.127\n", + "2025-11-26 00:57:20,567 - bioneuralnet.utils.graph_tools - INFO - GRAPH ANALYSIS: gaussian_15\n", + "\n", + "2025-11-26 00:57:20,567 - bioneuralnet.utils.graph_tools - INFO - Nodes: 900 | Edges: 11,543\n", + "2025-11-26 00:57:20,567 - bioneuralnet.utils.graph_tools - INFO - Avg degree: 25.65\n", + "2025-11-26 00:57:20,568 - bioneuralnet.utils.graph_tools - INFO - Connected components: 1\n", + "2025-11-26 00:57:20,568 - bioneuralnet.utils.graph_tools - INFO - Largest component: 900 nodes (100.0%)\n", + "2025-11-26 00:57:20,711 - bioneuralnet.utils.graph_tools - INFO - Avg clustering coefficient: 0.298\n" + ] + } + ], + "source": [ + "from bioneuralnet.utils import gen_threshold_graph, gen_correlation_graph, gen_similarity_graph, gen_gaussian_knn_graph\n", + "from bioneuralnet.utils import graph_analysis\n", + "from bioneuralnet.utils import select_top_k_variance\n", + "\n", + "top_meth = select_top_k_variance(dna_meth, k=300)\n", + "top_rna = select_top_k_variance(rna, k=300)\n", + "top_mirna = select_top_k_variance(mirna, k=300)\n", + "\n", + "omics_kipan = pd.concat([top_meth, top_rna, top_mirna], axis=1)\n", + "\n", + "threshold_10 = gen_threshold_graph(omics_kipan, b=6.5, k=10, self_loops=True)\n", + "correlation_10 = gen_correlation_graph(omics_kipan, k=10, method='pearson', signed=False, self_loops=True)\n", + "similarity_10 = gen_similarity_graph(omics_kipan, k=10, metric='cosine', self_loops=True)\n", + "gaussian_15 = gen_gaussian_knn_graph(omics_kipan, k=15, sigma=None, self_loops=True)\n", + "\n", + "comparison_runs = [\n", + " {\"name\":\"threshold_10\", \"graph\": threshold_10},\n", + " {\"name\":\"correlation_10\", \"graph\": correlation_10},\n", + " {\"name\":\"similarity_10\", \"graph\": similarity_10},\n", + " {\"name\":\"gaussian_15\", \"graph\": gaussian_15},\n", + "]\n", + "\n", + "for run in comparison_runs:\n", + " graph_analysis(run[\"graph\"], run[\"name\"])\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "4906eda5", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-11-26 00:57:20,716 - bioneuralnet.downstream_task.dpmon - INFO - Output directory set to: /home/vicente/Github/BioNeuralNet/dpmon_results_GAT_FINAL/kipan/threshold_10\n", + "2025-11-26 00:57:20,717 - bioneuralnet.downstream_task.dpmon - INFO - Initialized DPMON with model: GAT\n", + "2025-11-26 00:57:20,717 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 00:57:20,718 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 00:57:20,718 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 00:57:20,719 - bioneuralnet.downstream_task.dpmon - INFO - Random seed set to: 8183\n", + "2025-11-26 00:57:20,726 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 00:57:20,727 - bioneuralnet.downstream_task.dpmon - INFO - Running in Cross-Validation mode (cv=True) with 5 folds.\n", + "2025-11-26 00:57:20,728 - bioneuralnet.downstream_task.dpmon - INFO - CV Setup: Standard 5-fold split.\n", + "2025-11-26 00:57:20,729 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 1/5\n", + "2025-11-26 00:57:20,731 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 00:57:20,739 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 00:57:24,910 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 00:57:24,922 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (526, 901)\n", + "2025-11-26 00:57:24,926\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 00:57:36,598\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0077s.\n", + "2025-11-26 00:57:36,759 - bioneuralnet.downstream_task.dpmon - INFO - Fold 1 best config: {'gnn_layer_num': 3, 'gnn_hidden_dim': 128, 'lr': 0.002995945702914729, 'weight_decay': 7.542726756559785e-05, 'nn_hidden_dim1': 256, 'nn_hidden_dim2': 32, 'ae_encoding_dim': 4, 'num_epochs': 2048, 'gnn_dropout': 0.0, 'gnn_activation': 'elu', 'dim_reduction': 'mlp'}\n", + "2025-11-26 00:57:36,760 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 1 using train split only\n", + "2025-11-26 00:57:36,765 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 00:57:40,906 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 00:57:45,459 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 1 on test set\n", + "2025-11-26 00:57:45,470 - bioneuralnet.downstream_task.dpmon - INFO - Fold 1 results:\n", + "2025-11-26 00:57:45,470 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9394\n", + "2025-11-26 00:57:45,470 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9290\n", + "2025-11-26 00:57:45,470 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9398\n", + "2025-11-26 00:57:45,471 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9566\n", + "2025-11-26 00:57:45,471 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9918\n", + "2025-11-26 00:57:45,471 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9803\n", + "\n", + "2025-11-26 00:57:45,473 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 2/5\n", + "2025-11-26 00:57:45,474 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 00:57:45,482 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 00:57:49,590 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 00:57:49,600 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (526, 901)\n", + "2025-11-26 00:57:49,603\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 00:58:00,206\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0192s.\n", + "2025-11-26 00:58:00,330 - bioneuralnet.downstream_task.dpmon - INFO - Fold 2 best config: {'gnn_layer_num': 2, 'gnn_hidden_dim': 64, 'lr': 0.0007034204356990857, 'weight_decay': 5.358735876893429e-05, 'nn_hidden_dim1': 512, 'nn_hidden_dim2': 64, 'ae_encoding_dim': 8, 'num_epochs': 1024, 'gnn_dropout': 0.2, 'gnn_activation': 'relu', 'dim_reduction': 'ae'}\n", + "2025-11-26 00:58:00,331 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 2 using train split only\n", + "2025-11-26 00:58:00,335 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 00:58:04,454 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 00:58:06,182 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 2 on test set\n", + "2025-11-26 00:58:06,187 - bioneuralnet.downstream_task.dpmon - INFO - Fold 2 results:\n", + "2025-11-26 00:58:06,187 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9697\n", + "2025-11-26 00:58:06,188 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9487\n", + "2025-11-26 00:58:06,188 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9701\n", + "2025-11-26 00:58:06,188 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9579\n", + "2025-11-26 00:58:06,188 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9940\n", + "2025-11-26 00:58:06,188 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9493\n", + "\n", + "2025-11-26 00:58:06,189 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 3/5\n", + "2025-11-26 00:58:06,191 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 00:58:06,203 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 00:58:10,296 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 00:58:10,306 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (526, 901)\n", + "2025-11-26 00:58:10,310\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 00:58:21,272\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0142s.\n", + "2025-11-26 00:58:21,422 - bioneuralnet.downstream_task.dpmon - INFO - Fold 3 best config: {'gnn_layer_num': 3, 'gnn_hidden_dim': 128, 'lr': 0.0006980653261883734, 'weight_decay': 6.059927981082565e-05, 'nn_hidden_dim1': 256, 'nn_hidden_dim2': 128, 'ae_encoding_dim': 8, 'num_epochs': 2048, 'gnn_dropout': 0.0, 'gnn_activation': 'elu', 'dim_reduction': 'ae'}\n", + "2025-11-26 00:58:21,423 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 3 using train split only\n", + "2025-11-26 00:58:21,427 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 00:58:25,530 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 00:58:29,913 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 3 on test set\n", + "2025-11-26 00:58:29,918 - bioneuralnet.downstream_task.dpmon - INFO - Fold 3 results:\n", + "2025-11-26 00:58:29,919 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9545\n", + "2025-11-26 00:58:29,919 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9459\n", + "2025-11-26 00:58:29,919 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9544\n", + "2025-11-26 00:58:29,919 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9297\n", + "2025-11-26 00:58:29,919 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9963\n", + "2025-11-26 00:58:29,919 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9895\n", + "\n", + "2025-11-26 00:58:29,920 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 4/5\n", + "2025-11-26 00:58:29,921 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 00:58:29,926 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 00:58:34,048 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 00:58:34,057 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (527, 901)\n", + "2025-11-26 00:58:34,061\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 00:58:45,497\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0174s.\n", + "2025-11-26 00:58:45,665 - bioneuralnet.downstream_task.dpmon - INFO - Fold 4 best config: {'gnn_layer_num': 1, 'gnn_hidden_dim': 128, 'lr': 0.0019123736530165902, 'weight_decay': 0.0006168499043610002, 'nn_hidden_dim1': 256, 'nn_hidden_dim2': 128, 'ae_encoding_dim': 8, 'num_epochs': 1024, 'gnn_dropout': 0.4, 'gnn_activation': 'relu', 'dim_reduction': 'mlp'}\n", + "2025-11-26 00:58:45,666 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 4 using train split only\n", + "2025-11-26 00:58:45,671 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 00:58:49,803 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 00:58:51,152 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 4 on test set\n", + "2025-11-26 00:58:51,157 - bioneuralnet.downstream_task.dpmon - INFO - Fold 4 results:\n", + "2025-11-26 00:58:51,158 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9466\n", + "2025-11-26 00:58:51,158 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9505\n", + "2025-11-26 00:58:51,158 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9466\n", + "2025-11-26 00:58:51,158 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9607\n", + "2025-11-26 00:58:51,158 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9945\n", + "2025-11-26 00:58:51,159 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9885\n", + "\n", + "2025-11-26 00:58:51,159 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 5/5\n", + "2025-11-26 00:58:51,160 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 00:58:51,165 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 00:58:55,257 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 00:58:55,268 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (527, 901)\n", + "2025-11-26 00:58:55,271\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 00:59:08,036\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0139s.\n", + "2025-11-26 00:59:08,254 - bioneuralnet.downstream_task.dpmon - INFO - Fold 5 best config: {'gnn_layer_num': 2, 'gnn_hidden_dim': 64, 'lr': 0.001685812648195555, 'weight_decay': 0.0023773937538678585, 'nn_hidden_dim1': 256, 'nn_hidden_dim2': 128, 'ae_encoding_dim': 8, 'num_epochs': 512, 'gnn_dropout': 0.2, 'gnn_activation': 'relu', 'dim_reduction': 'linear'}\n", + "2025-11-26 00:59:08,254 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 5 using train split only\n", + "2025-11-26 00:59:08,260 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 00:59:12,418 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 00:59:13,329 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 5 on test set\n", + "2025-11-26 00:59:13,334 - bioneuralnet.downstream_task.dpmon - INFO - Fold 5 results:\n", + "2025-11-26 00:59:13,335 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9618\n", + "2025-11-26 00:59:13,335 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9515\n", + "2025-11-26 00:59:13,335 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9618\n", + "2025-11-26 00:59:13,335 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9506\n", + "2025-11-26 00:59:13,335 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9988\n", + "2025-11-26 00:59:13,335 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9970\n", + "\n", + "2025-11-26 00:59:13,348 - bioneuralnet.downstream_task.dpmon - INFO - Successfully saved global best CV model state to: /home/vicente/Github/BioNeuralNet/dpmon_results_GAT_FINAL/kipan/threshold_10/best_cv_model.pt\n", + "2025-11-26 00:59:13,349 - bioneuralnet.downstream_task.dpmon - INFO - Successfully saved global best CV model state to: /home/vicente/Github/BioNeuralNet/dpmon_results_GAT_FINAL/kipan/threshold_10/best_cv_model_embds.pt\n", + "2025-11-26 00:59:13,349 - bioneuralnet.downstream_task.dpmon - INFO - Cross-Validation Results:\n", + "\n", + "2025-11-26 00:59:13,349 - bioneuralnet.downstream_task.dpmon - INFO - Avg Accuracy: 0.9544 +/- 0.0120\n", + "2025-11-26 00:59:13,349 - bioneuralnet.downstream_task.dpmon - INFO - Avg F1 Macro: 0.9451 +/- 0.0093\n", + "2025-11-26 00:59:13,349 - bioneuralnet.downstream_task.dpmon - INFO - Avg F1 Weighted: 0.9545 +/- 0.0120\n", + "2025-11-26 00:59:13,349 - bioneuralnet.downstream_task.dpmon - INFO - Avg Recall: 0.9511 +/- 0.0125\n", + "2025-11-26 00:59:13,349 - bioneuralnet.downstream_task.dpmon - INFO - Avg AUC: 0.9951 +/- 0.0026\n", + "2025-11-26 00:59:13,350 - bioneuralnet.downstream_task.dpmon - INFO - Avg AUPR: 0.9809 +/- 0.0187\n", + "2025-11-26 00:59:13,350 - bioneuralnet.downstream_task.dpmon - INFO - DPMON run completed.\n", + "2025-11-26 00:59:13,351 - bioneuralnet.downstream_task.dpmon - INFO - Output directory set to: /home/vicente/Github/BioNeuralNet/dpmon_results_GAT_FINAL/kipan/correlation_10\n", + "2025-11-26 00:59:13,351 - bioneuralnet.downstream_task.dpmon - INFO - Initialized DPMON with model: GAT\n", + "2025-11-26 00:59:13,351 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 00:59:13,351 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 00:59:13,351 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 00:59:13,351 - bioneuralnet.downstream_task.dpmon - INFO - Random seed set to: 8183\n", + "2025-11-26 00:59:13,355 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 00:59:13,357 - bioneuralnet.downstream_task.dpmon - INFO - Running in Cross-Validation mode (cv=True) with 5 folds.\n", + "2025-11-26 00:59:13,358 - bioneuralnet.downstream_task.dpmon - INFO - CV Setup: Standard 5-fold split.\n", + "2025-11-26 00:59:13,358 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 1/5\n", + "2025-11-26 00:59:13,359 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 00:59:13,364 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 00:59:17,533 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 00:59:17,543 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (526, 901)\n", + "2025-11-26 00:59:17,546\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 00:59:28,510\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0097s.\n", + "2025-11-26 00:59:28,689 - bioneuralnet.downstream_task.dpmon - INFO - Fold 1 best config: {'gnn_layer_num': 3, 'gnn_hidden_dim': 128, 'lr': 0.002995945702914729, 'weight_decay': 7.542726756559785e-05, 'nn_hidden_dim1': 256, 'nn_hidden_dim2': 32, 'ae_encoding_dim': 4, 'num_epochs': 2048, 'gnn_dropout': 0.0, 'gnn_activation': 'elu', 'dim_reduction': 'mlp'}\n", + "2025-11-26 00:59:28,690 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 1 using train split only\n", + "2025-11-26 00:59:28,694 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 00:59:32,822 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 00:59:37,372 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 1 on test set\n", + "2025-11-26 00:59:37,377 - bioneuralnet.downstream_task.dpmon - INFO - Fold 1 results:\n", + "2025-11-26 00:59:37,378 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9394\n", + "2025-11-26 00:59:37,378 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9287\n", + "2025-11-26 00:59:37,378 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9399\n", + "2025-11-26 00:59:37,378 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9558\n", + "2025-11-26 00:59:37,378 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9930\n", + "2025-11-26 00:59:37,378 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9919\n", + "\n", + "2025-11-26 00:59:37,380 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 2/5\n", + "2025-11-26 00:59:37,381 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 00:59:37,387 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 00:59:41,482 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 00:59:41,492 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (526, 901)\n", + "2025-11-26 00:59:41,496\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 00:59:52,119\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0186s.\n", + "2025-11-26 00:59:52,225 - bioneuralnet.downstream_task.dpmon - INFO - Fold 2 best config: {'gnn_layer_num': 4, 'gnn_hidden_dim': 64, 'lr': 0.00024165542558361394, 'weight_decay': 0.0002652189086272745, 'nn_hidden_dim1': 256, 'nn_hidden_dim2': 128, 'ae_encoding_dim': 8, 'num_epochs': 512, 'gnn_dropout': 0.1, 'gnn_activation': 'relu', 'dim_reduction': 'mlp'}\n", + "2025-11-26 00:59:52,226 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 2 using train split only\n", + "2025-11-26 00:59:52,231 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 00:59:56,361 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 00:59:57,746 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 2 on test set\n", + "2025-11-26 00:59:57,751 - bioneuralnet.downstream_task.dpmon - INFO - Fold 2 results:\n", + "2025-11-26 00:59:57,751 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9697\n", + "2025-11-26 00:59:57,752 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9487\n", + "2025-11-26 00:59:57,752 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9701\n", + "2025-11-26 00:59:57,752 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9579\n", + "2025-11-26 00:59:57,752 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9905\n", + "2025-11-26 00:59:57,752 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9331\n", + "\n", + "2025-11-26 00:59:57,753 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 3/5\n", + "2025-11-26 00:59:57,754 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 00:59:57,759 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:00:01,882 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:00:01,892 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (526, 901)\n", + "2025-11-26 01:00:01,896\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:00:12,425\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0233s.\n", + "2025-11-26 01:00:12,573 - bioneuralnet.downstream_task.dpmon - INFO - Fold 3 best config: {'gnn_layer_num': 3, 'gnn_hidden_dim': 128, 'lr': 0.0006980653261883734, 'weight_decay': 6.059927981082565e-05, 'nn_hidden_dim1': 256, 'nn_hidden_dim2': 128, 'ae_encoding_dim': 8, 'num_epochs': 2048, 'gnn_dropout': 0.0, 'gnn_activation': 'elu', 'dim_reduction': 'ae'}\n", + "2025-11-26 01:00:12,574 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 3 using train split only\n", + "2025-11-26 01:00:12,578 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:00:16,743 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:00:21,070 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 3 on test set\n", + "2025-11-26 01:00:21,076 - bioneuralnet.downstream_task.dpmon - INFO - Fold 3 results:\n", + "2025-11-26 01:00:21,077 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9545\n", + "2025-11-26 01:00:21,077 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9459\n", + "2025-11-26 01:00:21,077 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9544\n", + "2025-11-26 01:00:21,077 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9297\n", + "2025-11-26 01:00:21,077 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9968\n", + "2025-11-26 01:00:21,078 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9934\n", + "\n", + "2025-11-26 01:00:21,079 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 4/5\n", + "2025-11-26 01:00:21,080 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:00:21,086 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:00:25,234 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:00:25,245 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (527, 901)\n", + "2025-11-26 01:00:25,249\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:00:36,494\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0065s.\n", + "2025-11-26 01:00:36,639 - bioneuralnet.downstream_task.dpmon - INFO - Fold 4 best config: {'gnn_layer_num': 1, 'gnn_hidden_dim': 128, 'lr': 0.002226976717919125, 'weight_decay': 0.00016678702979522166, 'nn_hidden_dim1': 128, 'nn_hidden_dim2': 64, 'ae_encoding_dim': 16, 'num_epochs': 512, 'gnn_dropout': 0.3, 'gnn_activation': 'relu', 'dim_reduction': 'ae'}\n", + "2025-11-26 01:00:36,639 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 4 using train split only\n", + "2025-11-26 01:00:36,644 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:00:40,787 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:00:41,407 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 4 on test set\n", + "2025-11-26 01:00:41,411 - bioneuralnet.downstream_task.dpmon - INFO - Fold 4 results:\n", + "2025-11-26 01:00:41,412 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9466\n", + "2025-11-26 01:00:41,412 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9505\n", + "2025-11-26 01:00:41,412 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9466\n", + "2025-11-26 01:00:41,412 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9607\n", + "2025-11-26 01:00:41,412 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9934\n", + "2025-11-26 01:00:41,412 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9805\n", + "\n", + "2025-11-26 01:00:41,413 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 5/5\n", + "2025-11-26 01:00:41,414 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:00:41,419 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:00:45,531 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:00:45,541 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (527, 901)\n", + "2025-11-26 01:00:45,544\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:00:58,694\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0108s.\n", + "2025-11-26 01:00:58,924 - bioneuralnet.downstream_task.dpmon - INFO - Fold 5 best config: {'gnn_layer_num': 2, 'gnn_hidden_dim': 64, 'lr': 0.001685812648195555, 'weight_decay': 0.0023773937538678585, 'nn_hidden_dim1': 256, 'nn_hidden_dim2': 128, 'ae_encoding_dim': 8, 'num_epochs': 512, 'gnn_dropout': 0.2, 'gnn_activation': 'relu', 'dim_reduction': 'linear'}\n", + "2025-11-26 01:00:58,925 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 5 using train split only\n", + "2025-11-26 01:00:58,929 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:01:03,090 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:01:03,987 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 5 on test set\n", + "2025-11-26 01:01:03,991 - bioneuralnet.downstream_task.dpmon - INFO - Fold 5 results:\n", + "2025-11-26 01:01:03,991 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9618\n", + "2025-11-26 01:01:03,992 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9514\n", + "2025-11-26 01:01:03,992 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9617\n", + "2025-11-26 01:01:03,992 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9497\n", + "2025-11-26 01:01:03,992 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9977\n", + "2025-11-26 01:01:03,992 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9940\n", + "\n", + "2025-11-26 01:01:03,996 - bioneuralnet.downstream_task.dpmon - INFO - Successfully saved global best CV model state to: /home/vicente/Github/BioNeuralNet/dpmon_results_GAT_FINAL/kipan/correlation_10/best_cv_model.pt\n", + "2025-11-26 01:01:03,997 - bioneuralnet.downstream_task.dpmon - INFO - Successfully saved global best CV model state to: /home/vicente/Github/BioNeuralNet/dpmon_results_GAT_FINAL/kipan/correlation_10/best_cv_model_embds.pt\n", + "2025-11-26 01:01:03,997 - bioneuralnet.downstream_task.dpmon - INFO - Cross-Validation Results:\n", + "\n", + "2025-11-26 01:01:03,997 - bioneuralnet.downstream_task.dpmon - INFO - Avg Accuracy: 0.9544 +/- 0.0120\n", + "2025-11-26 01:01:03,997 - bioneuralnet.downstream_task.dpmon - INFO - Avg F1 Macro: 0.9450 +/- 0.0094\n", + "2025-11-26 01:01:03,997 - bioneuralnet.downstream_task.dpmon - INFO - Avg F1 Weighted: 0.9545 +/- 0.0119\n", + "2025-11-26 01:01:03,997 - bioneuralnet.downstream_task.dpmon - INFO - Avg Recall: 0.9507 +/- 0.0124\n", + "2025-11-26 01:01:03,998 - bioneuralnet.downstream_task.dpmon - INFO - Avg AUC: 0.9943 +/- 0.0029\n", + "2025-11-26 01:01:03,998 - bioneuralnet.downstream_task.dpmon - INFO - Avg AUPR: 0.9786 +/- 0.0260\n", + "2025-11-26 01:01:03,998 - bioneuralnet.downstream_task.dpmon - INFO - DPMON run completed.\n", + "2025-11-26 01:01:03,999 - bioneuralnet.downstream_task.dpmon - INFO - Output directory set to: /home/vicente/Github/BioNeuralNet/dpmon_results_GAT_FINAL/kipan/similarity_10\n", + "2025-11-26 01:01:03,999 - bioneuralnet.downstream_task.dpmon - INFO - Initialized DPMON with model: GAT\n", + "2025-11-26 01:01:03,999 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 01:01:04,000 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 01:01:04,000 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 01:01:04,000 - bioneuralnet.downstream_task.dpmon - INFO - Random seed set to: 8183\n", + "2025-11-26 01:01:04,005 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:01:04,006 - bioneuralnet.downstream_task.dpmon - INFO - Running in Cross-Validation mode (cv=True) with 5 folds.\n", + "2025-11-26 01:01:04,008 - bioneuralnet.downstream_task.dpmon - INFO - CV Setup: Standard 5-fold split.\n", + "2025-11-26 01:01:04,009 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 1/5\n", + "2025-11-26 01:01:04,010 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:01:04,015 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:01:08,133 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:01:08,144 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (526, 901)\n", + "2025-11-26 01:01:08,147\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:01:20,813\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0121s.\n", + "2025-11-26 01:01:21,000 - bioneuralnet.downstream_task.dpmon - INFO - Fold 1 best config: {'gnn_layer_num': 3, 'gnn_hidden_dim': 128, 'lr': 0.002995945702914729, 'weight_decay': 7.542726756559785e-05, 'nn_hidden_dim1': 256, 'nn_hidden_dim2': 32, 'ae_encoding_dim': 4, 'num_epochs': 2048, 'gnn_dropout': 0.0, 'gnn_activation': 'elu', 'dim_reduction': 'mlp'}\n", + "2025-11-26 01:01:21,001 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 1 using train split only\n", + "2025-11-26 01:01:21,005 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:01:25,276 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:01:29,966 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 1 on test set\n", + "2025-11-26 01:01:29,971 - bioneuralnet.downstream_task.dpmon - INFO - Fold 1 results:\n", + "2025-11-26 01:01:29,972 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9394\n", + "2025-11-26 01:01:29,972 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9290\n", + "2025-11-26 01:01:29,972 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9398\n", + "2025-11-26 01:01:29,972 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9566\n", + "2025-11-26 01:01:29,972 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9957\n", + "2025-11-26 01:01:29,972 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9935\n", + "\n", + "2025-11-26 01:01:29,974 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 2/5\n", + "2025-11-26 01:01:29,975 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:01:29,981 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:01:34,133 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:01:34,146 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (526, 901)\n", + "2025-11-26 01:01:34,149\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:01:46,408\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0098s.\n", + "2025-11-26 01:01:46,566 - bioneuralnet.downstream_task.dpmon - INFO - Fold 2 best config: {'gnn_layer_num': 2, 'gnn_hidden_dim': 64, 'lr': 0.0007034204356990857, 'weight_decay': 5.358735876893429e-05, 'nn_hidden_dim1': 512, 'nn_hidden_dim2': 64, 'ae_encoding_dim': 8, 'num_epochs': 1024, 'gnn_dropout': 0.2, 'gnn_activation': 'relu', 'dim_reduction': 'ae'}\n", + "2025-11-26 01:01:46,567 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 2 using train split only\n", + "2025-11-26 01:01:46,571 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:01:50,727 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:01:52,514 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 2 on test set\n", + "2025-11-26 01:01:52,519 - bioneuralnet.downstream_task.dpmon - INFO - Fold 2 results:\n", + "2025-11-26 01:01:52,520 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9621\n", + "2025-11-26 01:01:52,520 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9315\n", + "2025-11-26 01:01:52,520 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9623\n", + "2025-11-26 01:01:52,520 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9322\n", + "2025-11-26 01:01:52,520 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9934\n", + "2025-11-26 01:01:52,520 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9490\n", + "\n", + "2025-11-26 01:01:52,521 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 3/5\n", + "2025-11-26 01:01:52,522 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:01:52,527 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:01:56,688 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:01:56,700 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (526, 901)\n", + "2025-11-26 01:01:56,703\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:02:07,390\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0206s.\n", + "2025-11-26 01:02:07,536 - bioneuralnet.downstream_task.dpmon - INFO - Fold 3 best config: {'gnn_layer_num': 3, 'gnn_hidden_dim': 128, 'lr': 0.0006980653261883734, 'weight_decay': 6.059927981082565e-05, 'nn_hidden_dim1': 256, 'nn_hidden_dim2': 128, 'ae_encoding_dim': 8, 'num_epochs': 2048, 'gnn_dropout': 0.0, 'gnn_activation': 'elu', 'dim_reduction': 'ae'}\n", + "2025-11-26 01:02:07,536 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 3 using train split only\n", + "2025-11-26 01:02:07,540 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:02:11,675 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:02:16,004 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 3 on test set\n", + "2025-11-26 01:02:16,009 - bioneuralnet.downstream_task.dpmon - INFO - Fold 3 results:\n", + "2025-11-26 01:02:16,009 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9545\n", + "2025-11-26 01:02:16,010 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9459\n", + "2025-11-26 01:02:16,010 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9544\n", + "2025-11-26 01:02:16,010 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9297\n", + "2025-11-26 01:02:16,010 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9937\n", + "2025-11-26 01:02:16,011 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9911\n", + "\n", + "2025-11-26 01:02:16,012 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 4/5\n", + "2025-11-26 01:02:16,014 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:02:16,020 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:02:20,128 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:02:20,139 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (527, 901)\n", + "2025-11-26 01:02:20,142\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:02:31,003\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0166s.\n", + "2025-11-26 01:02:31,137 - bioneuralnet.downstream_task.dpmon - INFO - Fold 4 best config: {'gnn_layer_num': 1, 'gnn_hidden_dim': 128, 'lr': 0.002226976717919125, 'weight_decay': 0.00016678702979522166, 'nn_hidden_dim1': 128, 'nn_hidden_dim2': 64, 'ae_encoding_dim': 16, 'num_epochs': 512, 'gnn_dropout': 0.3, 'gnn_activation': 'relu', 'dim_reduction': 'ae'}\n", + "2025-11-26 01:02:31,137 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 4 using train split only\n", + "2025-11-26 01:02:31,143 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:02:35,446 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:02:36,071 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 4 on test set\n", + "2025-11-26 01:02:36,076 - bioneuralnet.downstream_task.dpmon - INFO - Fold 4 results:\n", + "2025-11-26 01:02:36,076 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9389\n", + "2025-11-26 01:02:36,076 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9449\n", + "2025-11-26 01:02:36,076 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9390\n", + "2025-11-26 01:02:36,076 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9554\n", + "2025-11-26 01:02:36,076 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9943\n", + "2025-11-26 01:02:36,077 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9862\n", + "\n", + "2025-11-26 01:02:36,077 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 5/5\n", + "2025-11-26 01:02:36,079 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:02:36,084 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:02:40,268 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:02:40,280 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (527, 901)\n", + "2025-11-26 01:02:40,283\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:02:52,747\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0184s.\n", + "2025-11-26 01:02:52,935 - bioneuralnet.downstream_task.dpmon - INFO - Fold 5 best config: {'gnn_layer_num': 4, 'gnn_hidden_dim': 128, 'lr': 0.0013804780705269182, 'weight_decay': 0.0037674454696866636, 'nn_hidden_dim1': 128, 'nn_hidden_dim2': 64, 'ae_encoding_dim': 4, 'num_epochs': 1024, 'gnn_dropout': 0.0, 'gnn_activation': 'elu', 'dim_reduction': 'ae'}\n", + "2025-11-26 01:02:52,936 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 5 using train split only\n", + "2025-11-26 01:02:52,941 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:02:57,095 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:02:59,798 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 5 on test set\n", + "2025-11-26 01:02:59,803 - bioneuralnet.downstream_task.dpmon - INFO - Fold 5 results:\n", + "2025-11-26 01:02:59,803 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9466\n", + "2025-11-26 01:02:59,804 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9398\n", + "2025-11-26 01:02:59,804 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9462\n", + "2025-11-26 01:02:59,804 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9373\n", + "2025-11-26 01:02:59,804 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9952\n", + "2025-11-26 01:02:59,804 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9880\n", + "\n", + "2025-11-26 01:02:59,818 - bioneuralnet.downstream_task.dpmon - INFO - Successfully saved global best CV model state to: /home/vicente/Github/BioNeuralNet/dpmon_results_GAT_FINAL/kipan/similarity_10/best_cv_model.pt\n", + "2025-11-26 01:02:59,819 - bioneuralnet.downstream_task.dpmon - INFO - Successfully saved global best CV model state to: /home/vicente/Github/BioNeuralNet/dpmon_results_GAT_FINAL/kipan/similarity_10/best_cv_model_embds.pt\n", + "2025-11-26 01:02:59,819 - bioneuralnet.downstream_task.dpmon - INFO - Cross-Validation Results:\n", + "\n", + "2025-11-26 01:02:59,819 - bioneuralnet.downstream_task.dpmon - INFO - Avg Accuracy: 0.9483 +/- 0.0100\n", + "2025-11-26 01:02:59,819 - bioneuralnet.downstream_task.dpmon - INFO - Avg F1 Macro: 0.9382 +/- 0.0077\n", + "2025-11-26 01:02:59,819 - bioneuralnet.downstream_task.dpmon - INFO - Avg F1 Weighted: 0.9483 +/- 0.0100\n", + "2025-11-26 01:02:59,819 - bioneuralnet.downstream_task.dpmon - INFO - Avg Recall: 0.9422 +/- 0.0129\n", + "2025-11-26 01:02:59,819 - bioneuralnet.downstream_task.dpmon - INFO - Avg AUC: 0.9945 +/- 0.0010\n", + "2025-11-26 01:02:59,819 - bioneuralnet.downstream_task.dpmon - INFO - Avg AUPR: 0.9815 +/- 0.0184\n", + "2025-11-26 01:02:59,820 - bioneuralnet.downstream_task.dpmon - INFO - DPMON run completed.\n", + "2025-11-26 01:02:59,821 - bioneuralnet.downstream_task.dpmon - INFO - Output directory set to: /home/vicente/Github/BioNeuralNet/dpmon_results_GAT_FINAL/kipan/gaussian_15\n", + "2025-11-26 01:02:59,821 - bioneuralnet.downstream_task.dpmon - INFO - Initialized DPMON with model: GAT\n", + "2025-11-26 01:02:59,821 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 01:02:59,821 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 01:02:59,821 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 01:02:59,821 - bioneuralnet.downstream_task.dpmon - INFO - Random seed set to: 8183\n", + "2025-11-26 01:02:59,826 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:02:59,827 - bioneuralnet.downstream_task.dpmon - INFO - Running in Cross-Validation mode (cv=True) with 5 folds.\n", + "2025-11-26 01:02:59,828 - bioneuralnet.downstream_task.dpmon - INFO - CV Setup: Standard 5-fold split.\n", + "2025-11-26 01:02:59,829 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 1/5\n", + "2025-11-26 01:02:59,830 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:02:59,838 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:03:04,025 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:03:04,039 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (526, 901)\n", + "2025-11-26 01:03:04,042\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:03:17,429\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0133s.\n", + "2025-11-26 01:03:17,636 - bioneuralnet.downstream_task.dpmon - INFO - Fold 1 best config: {'gnn_layer_num': 3, 'gnn_hidden_dim': 128, 'lr': 0.002995945702914729, 'weight_decay': 7.542726756559785e-05, 'nn_hidden_dim1': 256, 'nn_hidden_dim2': 32, 'ae_encoding_dim': 4, 'num_epochs': 2048, 'gnn_dropout': 0.0, 'gnn_activation': 'elu', 'dim_reduction': 'mlp'}\n", + "2025-11-26 01:03:17,637 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 1 using train split only\n", + "2025-11-26 01:03:17,641 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:03:21,783 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:03:26,807 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 1 on test set\n", + "2025-11-26 01:03:26,812 - bioneuralnet.downstream_task.dpmon - INFO - Fold 1 results:\n", + "2025-11-26 01:03:26,812 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9394\n", + "2025-11-26 01:03:26,812 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9216\n", + "2025-11-26 01:03:26,813 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9405\n", + "2025-11-26 01:03:26,813 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9566\n", + "2025-11-26 01:03:26,813 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9950\n", + "2025-11-26 01:03:26,813 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9943\n", + "\n", + "2025-11-26 01:03:26,815 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 2/5\n", + "2025-11-26 01:03:26,816 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:03:26,821 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:03:30,970 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:03:30,983 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (526, 901)\n", + "2025-11-26 01:03:30,986\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:03:41,107\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0289s.\n", + "2025-11-26 01:03:41,224 - bioneuralnet.downstream_task.dpmon - INFO - Fold 2 best config: {'gnn_layer_num': 2, 'gnn_hidden_dim': 64, 'lr': 0.0007034204356990857, 'weight_decay': 5.358735876893429e-05, 'nn_hidden_dim1': 512, 'nn_hidden_dim2': 64, 'ae_encoding_dim': 8, 'num_epochs': 1024, 'gnn_dropout': 0.2, 'gnn_activation': 'relu', 'dim_reduction': 'ae'}\n", + "2025-11-26 01:03:41,225 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 2 using train split only\n", + "2025-11-26 01:03:41,229 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:03:45,495 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:03:47,199 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 2 on test set\n", + "2025-11-26 01:03:47,204 - bioneuralnet.downstream_task.dpmon - INFO - Fold 2 results:\n", + "2025-11-26 01:03:47,204 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9621\n", + "2025-11-26 01:03:47,204 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9315\n", + "2025-11-26 01:03:47,204 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9623\n", + "2025-11-26 01:03:47,204 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9322\n", + "2025-11-26 01:03:47,205 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9929\n", + "2025-11-26 01:03:47,205 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9469\n", + "\n", + "2025-11-26 01:03:47,206 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 3/5\n", + "2025-11-26 01:03:47,207 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:03:47,212 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:03:51,382 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:03:51,395 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (526, 901)\n", + "2025-11-26 01:03:51,399\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:04:02,747\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0162s.\n", + "2025-11-26 01:04:02,915 - bioneuralnet.downstream_task.dpmon - INFO - Fold 3 best config: {'gnn_layer_num': 1, 'gnn_hidden_dim': 256, 'lr': 0.0016239332393902155, 'weight_decay': 1.2898372523486065e-05, 'nn_hidden_dim1': 512, 'nn_hidden_dim2': 128, 'ae_encoding_dim': 4, 'num_epochs': 512, 'gnn_dropout': 0.2, 'gnn_activation': 'elu', 'dim_reduction': 'ae'}\n", + "2025-11-26 01:04:02,916 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 3 using train split only\n", + "2025-11-26 01:04:02,921 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:04:07,093 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:04:07,908 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 3 on test set\n", + "2025-11-26 01:04:07,912 - bioneuralnet.downstream_task.dpmon - INFO - Fold 3 results:\n", + "2025-11-26 01:04:07,913 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9545\n", + "2025-11-26 01:04:07,913 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9459\n", + "2025-11-26 01:04:07,913 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9544\n", + "2025-11-26 01:04:07,913 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9297\n", + "2025-11-26 01:04:07,913 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9967\n", + "2025-11-26 01:04:07,913 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9949\n", + "\n", + "2025-11-26 01:04:07,915 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 4/5\n", + "2025-11-26 01:04:07,916 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:04:07,922 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:04:12,095 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:04:12,109 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (527, 901)\n", + "2025-11-26 01:04:12,112\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:04:23,475\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0193s.\n", + "2025-11-26 01:04:23,614 - bioneuralnet.downstream_task.dpmon - INFO - Fold 4 best config: {'gnn_layer_num': 1, 'gnn_hidden_dim': 128, 'lr': 0.002226976717919125, 'weight_decay': 0.00016678702979522166, 'nn_hidden_dim1': 128, 'nn_hidden_dim2': 64, 'ae_encoding_dim': 16, 'num_epochs': 512, 'gnn_dropout': 0.3, 'gnn_activation': 'relu', 'dim_reduction': 'ae'}\n", + "2025-11-26 01:04:23,615 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 4 using train split only\n", + "2025-11-26 01:04:23,620 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:04:27,796 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:04:28,417 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 4 on test set\n", + "2025-11-26 01:04:28,421 - bioneuralnet.downstream_task.dpmon - INFO - Fold 4 results:\n", + "2025-11-26 01:04:28,422 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9389\n", + "2025-11-26 01:04:28,422 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9449\n", + "2025-11-26 01:04:28,422 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9390\n", + "2025-11-26 01:04:28,422 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9554\n", + "2025-11-26 01:04:28,422 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9919\n", + "2025-11-26 01:04:28,423 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9839\n", + "\n", + "2025-11-26 01:04:28,424 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 5/5\n", + "2025-11-26 01:04:28,425 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:04:28,431 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:04:32,603 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:04:32,616 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (527, 901)\n", + "2025-11-26 01:04:32,619\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:04:45,848\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0215s.\n", + "2025-11-26 01:04:46,065 - bioneuralnet.downstream_task.dpmon - INFO - Fold 5 best config: {'gnn_layer_num': 2, 'gnn_hidden_dim': 64, 'lr': 0.001685812648195555, 'weight_decay': 0.0023773937538678585, 'nn_hidden_dim1': 256, 'nn_hidden_dim2': 128, 'ae_encoding_dim': 8, 'num_epochs': 512, 'gnn_dropout': 0.2, 'gnn_activation': 'relu', 'dim_reduction': 'linear'}\n", + "2025-11-26 01:04:46,066 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 5 using train split only\n", + "2025-11-26 01:04:46,071 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:04:50,196 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:04:51,249 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 5 on test set\n", + "2025-11-26 01:04:51,254 - bioneuralnet.downstream_task.dpmon - INFO - Fold 5 results:\n", + "2025-11-26 01:04:51,254 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9542\n", + "2025-11-26 01:04:51,254 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9456\n", + "2025-11-26 01:04:51,255 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9540\n", + "2025-11-26 01:04:51,255 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9435\n", + "2025-11-26 01:04:51,255 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9975\n", + "2025-11-26 01:04:51,255 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9938\n", + "\n", + "2025-11-26 01:04:51,259 - bioneuralnet.downstream_task.dpmon - INFO - Successfully saved global best CV model state to: /home/vicente/Github/BioNeuralNet/dpmon_results_GAT_FINAL/kipan/gaussian_15/best_cv_model.pt\n", + "2025-11-26 01:04:51,259 - bioneuralnet.downstream_task.dpmon - INFO - Successfully saved global best CV model state to: /home/vicente/Github/BioNeuralNet/dpmon_results_GAT_FINAL/kipan/gaussian_15/best_cv_model_embds.pt\n", + "2025-11-26 01:04:51,260 - bioneuralnet.downstream_task.dpmon - INFO - Cross-Validation Results:\n", + "\n", + "2025-11-26 01:04:51,260 - bioneuralnet.downstream_task.dpmon - INFO - Avg Accuracy: 0.9498 +/- 0.0102\n", + "2025-11-26 01:04:51,260 - bioneuralnet.downstream_task.dpmon - INFO - Avg F1 Macro: 0.9379 +/- 0.0110\n", + "2025-11-26 01:04:51,260 - bioneuralnet.downstream_task.dpmon - INFO - Avg F1 Weighted: 0.9500 +/- 0.0100\n", + "2025-11-26 01:04:51,260 - bioneuralnet.downstream_task.dpmon - INFO - Avg Recall: 0.9435 +/- 0.0126\n", + "2025-11-26 01:04:51,260 - bioneuralnet.downstream_task.dpmon - INFO - Avg AUC: 0.9948 +/- 0.0024\n", + "2025-11-26 01:04:51,260 - bioneuralnet.downstream_task.dpmon - INFO - Avg AUPR: 0.9827 +/- 0.0206\n", + "2025-11-26 01:04:51,261 - bioneuralnet.downstream_task.dpmon - INFO - DPMON run completed.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Results for: threshold_10\n", + "Accuracy (Avg +/- Std): 0.9544 +/- 0.0120\n", + "F1 Macro (Avg +/- Std): 0.9451 +/- 0.0093\n", + "F1 Weighted (Avg +/- Std): 0.9545 +/- 0.0120\n", + "Recall: 0.9511 +/- 0.0125\n", + "AUC: 0.9951 +/- 0.0026\n", + "\n", + "Results for: correlation_10\n", + "Accuracy (Avg +/- Std): 0.9544 +/- 0.0120\n", + "F1 Macro (Avg +/- Std): 0.9450 +/- 0.0094\n", + "F1 Weighted (Avg +/- Std): 0.9545 +/- 0.0119\n", + "Recall: 0.9507 +/- 0.0124\n", + "AUC: 0.9943 +/- 0.0029\n", + "\n", + "Results for: similarity_10\n", + "Accuracy (Avg +/- Std): 0.9483 +/- 0.0100\n", + "F1 Macro (Avg +/- Std): 0.9382 +/- 0.0077\n", + "F1 Weighted (Avg +/- Std): 0.9483 +/- 0.0100\n", + "Recall: 0.9422 +/- 0.0129\n", + "AUC: 0.9945 +/- 0.0010\n", + "\n", + "Results for: gaussian_15\n", + "Accuracy (Avg +/- Std): 0.9498 +/- 0.0102\n", + "F1 Macro (Avg +/- Std): 0.9379 +/- 0.0110\n", + "F1 Weighted (Avg +/- Std): 0.9500 +/- 0.0100\n", + "Recall: 0.9435 +/- 0.0126\n", + "AUC: 0.9948 +/- 0.0024\n" + ] + } + ], + "source": [ + "from bioneuralnet.downstream_task import DPMON\n", + "\n", + "output_dir_base = Path(\"/home/vicente/Github/BioNeuralNet/dpmon_results_GAT_FINAL/kipan\")\n", + "\n", + "all_results_GAT = []\n", + "\n", + "for run_config in comparison_runs:\n", + " graph_name = run_config[\"name\"]\n", + " A_full = run_config[\"graph\"]\n", + "\n", + " current_output_dir = output_dir_base / graph_name\n", + " current_output_dir.mkdir(parents=True, exist_ok=True)\n", + "\n", + " dpmon_params_base = {\n", + " \"adjacency_matrix\": A_full,\n", + " \"omics_list\": omics_kipan,\n", + " \"phenotype_data\": target,\n", + " \"phenotype_col\": \"target\",\n", + " \"clinical_data\": clinical_for_model,\n", + " \"model\": 'GAT',\n", + " \"tune\": True, \n", + " \"cv\": True, \n", + " \"n_folds\": 5,\n", + " \"repeat_num\": 1,\n", + " \"gpu\": True,\n", + " \"cuda\": 0,\n", + " \"seed\": SEED,\n", + " \"output_dir\": current_output_dir\n", + " }\n", + " \n", + " dpmon_tunned = DPMON(**dpmon_params_base)\n", + " predictions_df, metrics, embeddings = dpmon_tunned.run()\n", + "\n", + " all_results_GAT.append({\n", + " \"graph\": graph_name,\n", + " \"predictions\": predictions_df,\n", + " \"metrics\": metrics,\n", + " \"embeddings\": embeddings\n", + " })\n", + "\n", + "for res in all_results_GAT:\n", + " graph_name = res[\"graph\"]\n", + " graph_metrics = res[\"metrics\"]\n", + " \n", + " acc_row = graph_metrics.loc[graph_metrics['Metric'] == 'Accuracy'].iloc[0]\n", + " f1_macro_row = graph_metrics.loc[graph_metrics['Metric'] == 'F1 Macro'].iloc[0]\n", + " f1_weighted_row = graph_metrics.loc[graph_metrics['Metric'] == 'F1 Weighted'].iloc[0]\n", + " recall_row = graph_metrics.loc[graph_metrics['Metric'] == 'Recall'].iloc[0]\n", + " auc_row = graph_metrics.loc[graph_metrics['Metric'] == 'AUC'].iloc[0]\n", + " \n", + " acc_avg, acc_std = acc_row['Average'], acc_row['StdDev']\n", + " f1_macro_avg, f1_macro_std = f1_macro_row['Average'], f1_macro_row['StdDev']\n", + " f1_weighted_avg, f1_weighted_std = f1_weighted_row['Average'], f1_weighted_row['StdDev']\n", + " recall_avg, recall_std = recall_row['Average'], recall_row['StdDev']\n", + " auc_avg, auc_std = auc_row['Average'], auc_row['StdDev']\n", + "\n", + " print(f\"\\nResults for: {graph_name}\")\n", + " print(f\"Accuracy (Avg +/- Std): {acc_avg:.4f} +/- {acc_std:.4f}\")\n", + " print(f\"F1 Macro (Avg +/- Std): {f1_macro_avg:.4f} +/- {f1_macro_std:.4f}\")\n", + " print(f\"F1 Weighted (Avg +/- Std): {f1_weighted_avg:.4f} +/- {f1_weighted_std:.4f}\")\n", + " print(f\"Recall: {recall_avg:.4f} +/- {recall_std:.4f}\")\n", + " print(f\"AUC: {auc_avg:.4f} +/- {auc_std:.4f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fac7426c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " 0 1 2 3 4\n", + "MGP 0.019121 0.024070 0.035583 0.007485 0.009774\n", + "ADH1C 0.000000 0.049013 0.000000 0.000000 0.097422\n", + "LOC440354 0.018648 0.010866 0.014588 0.011661 0.019968\n", + "C1orf110 0.000000 0.050925 0.000000 0.002459 0.074045\n", + "CLDN8 0.001006 0.015970 0.009054 0.009751 0.065529" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# we have emebeddings for each of the 4 graphs\n", + "# we will select the last one from the list (threshold_10) for visualization\n", + "res = all_results_GAT[0] \n", + "tensor_data = res[\"embeddings\"]\n", + "\n", + "# .detach().cpu().numpy() to get the raw numbers out of pytorch\n", + "GAT_embeddings_df = pd.DataFrame(\n", + " tensor_data.detach().cpu().numpy(),\n", + " index=omics_kipan.columns\n", + ")\n", + "\n", + "display(GAT_embeddings_df.iloc[:5,:5])" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "b1200e4e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Plotting Feature Embeddings for graph: threshold_10\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from bioneuralnet.metrics import plot_embeddings\n", + "\n", + "# for a more interpretable embedding plot we will map the node labels to omics types\n", + "node_labels = []\n", + "GAT_embeddings_array = GAT_embeddings_df.values\n", + "feature_names = GAT_embeddings_df.index\n", + "\n", + "# using the sets from the orginal to serve as a loopup table\n", + "dna_feats = set(dna_meth.columns)\n", + "rna_feats = set(rna.columns)\n", + "mirna_feats = set(mirna.columns)\n", + "\n", + "for feat in feature_names:\n", + " if feat in dna_feats:\n", + " # 1 for DNA methylation\n", + " node_labels.append(1)\n", + " elif feat in rna_feats:\n", + " # 2 for RNA \n", + " node_labels.append(2)\n", + " elif feat in mirna_feats:\n", + " # and 3 for miRNA \n", + " node_labels.append(3)\n", + " else:\n", + " node_labels.append(0)\n", + "\n", + "# our plotting function needs an array to squeeze the embedings into 2Dspace\n", + "node_labels = np.array(node_labels)\n", + "\n", + "# feature(omics) embeddings colored by omics type.\n", + "print(f\"Plotting Feature Embeddings for graph: {res['graph']}\")\n", + "plot_embeddings(GAT_embeddings_array, node_labels, legend_labels=[\"DNA_Methylation\", \"RNA\", \"miRNA\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "98442c32", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-11-26 09:50:43,480 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 09:50:43,481 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 09:50:43,481 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 09:50:43,482 - bioneuralnet.clustering.hybrid_louvain - INFO - Initializing HybridLouvain...\n", + "2025-11-26 09:50:43,482 - bioneuralnet.clustering.hybrid_louvain - INFO - Input G is a DataFrame; converting adjacency matrix to NetworkX graph.\n", + "2025-11-26 09:50:43,494 - bioneuralnet.clustering.hybrid_louvain - INFO - Initialized HybridLouvain with 900 graph nodes, 900 omics columns, 658 phenotype rows; max_iter=3, k3=0.2, k4=0.8, tune=False\n", + "2025-11-26 09:50:43,495 - bioneuralnet.clustering.hybrid_louvain - INFO - \n", + "Iteration 1/3: Running Correlated Louvain...\n", + "2025-11-26 09:50:43,504 - bioneuralnet.clustering.correlated_louvain - INFO - CorrelatedLouvain(k3=0.2, k4=0.8, nodes=900, edges=8930, features=900)\n", + "2025-11-26 09:50:43,504 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 09:50:43,505 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 09:50:43,505 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 09:50:43,505 - bioneuralnet.clustering.correlated_louvain - INFO - Running standard community detection...\n", + "2025-11-26 09:50:43,615 - bioneuralnet.clustering.correlated_louvain - INFO - Computed quality (supervised): Q = 0.7372, avg_corr = 0.5264, combined = 0.5686\n", + "2025-11-26 09:50:43,615 - bioneuralnet.clustering.correlated_louvain - INFO - Final quality: 0.5686\n", + "2025-11-26 09:50:43,616 - bioneuralnet.clustering.correlated_louvain - INFO - CorrelatedLouvain found 9 communities (nodes=900)\n", + "2025-11-26 09:50:43,669 - bioneuralnet.clustering.correlated_louvain - INFO - Computed quality (supervised): Q = 0.7372, avg_corr = 0.5264, combined = 0.5686\n", + "2025-11-26 09:50:43,669 - bioneuralnet.clustering.hybrid_louvain - INFO - Iteration 1: Louvain Quality = 0.5686\n", + "2025-11-26 09:50:43,722 - bioneuralnet.clustering.hybrid_louvain - INFO - Selected seed community of size 159 with correlation -0.8356\n", + "2025-11-26 09:50:43,722 - bioneuralnet.clustering.correlated_pagerank - INFO - Initialized PageRank with the following parameters:\n", + "2025-11-26 09:50:43,723 - bioneuralnet.clustering.correlated_pagerank - INFO - Graph: NetworkX Graph with 900 nodes and 8930 edges.\n", + "2025-11-26 09:50:43,723 - bioneuralnet.clustering.correlated_pagerank - INFO - Omics Data: DataFrame with shape (658, 900).\n", + "2025-11-26 09:50:43,724 - bioneuralnet.clustering.correlated_pagerank - INFO - Phenotype Data: Series with 658 samples.\n", + "2025-11-26 09:50:43,724 - bioneuralnet.clustering.correlated_pagerank - INFO - Alpha: 0.9\n", + "2025-11-26 09:50:43,724 - bioneuralnet.clustering.correlated_pagerank - INFO - Max Iterations: 100\n", + "2025-11-26 09:50:43,724 - bioneuralnet.clustering.correlated_pagerank - INFO - Tolerance: 1e-06\n", + "2025-11-26 09:50:43,725 - bioneuralnet.clustering.correlated_pagerank - INFO - K (Composite Score Weight): 0.5\n", + "2025-11-26 09:50:43,725 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 09:50:43,726 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 09:50:43,726 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 09:50:43,726 - bioneuralnet.clustering.correlated_pagerank - INFO - Initialized Correlated PageRank. device=cpu\n", + "2025-11-26 09:50:45,149 - bioneuralnet.clustering.correlated_pagerank - INFO - Generated personalization vector for seed nodes: ['LOC645638', 'MIR190', 'MIR657', 'CAPG', 'GGTLC2', 'FABP7_2173', 'SLC6A3_6531', 'PTGER3_5733', 'CP_1356', 'CA9_768', 'HMGCS2_3158', 'C6orf223_221416', 'CYP2J2_1573', 'CREB3L3_84699', 'ANGPTL4_51129', 'MYEOV_26579', 'PCSK6_5046', 'C1QL1_10882', 'LGI4_163175', 'UBE2QL1_134111', 'MCHR1_2847', 'ACTG2_72', 'SORCS2_57537', 'APOB_338', 'FOLH1_2346', 'MAP7D2_256714', 'ACAN_176', 'TFAP2A_7020', 'CA4_762', 'VAT1L_57687', 'AVPR1A_552', 'CCL20_6364', 'PRDM16_63976', 'FHL5_9457', 'LOX_4015', 'PCDH17_27253', 'APLNR_187', 'COL21A1_81578', 'LOC100131551_100131551', 'ACTC1_70', 'ANGPT2_285', 'ADAMTS4_9507', 'ESM1_11082', 'CASQ2_845', 'ADAMTSL1_92949', 'PCOLCE2_26577', 'CDCA2_157313', 'COL5A3_50509', 'PDE1C_5137', 'CPXM2_119587', 'RGS5_8490', 'CREB3L1_90993', 'PRRX1_5396', 'KCNN3_3782', 'CALCR_799', 'ADCY1_107', 'COX4I2_84701', 'CD36_948', 'STC1_6781', 'C2orf72_257407', 'ALDOC_230', 'CDH13_1012', 'CDH11_1009', 'PPFIA4_8497', 'PDE3A_5139', 'ANGPT1_284', 'CXCL9_4283', 'CXCL11_6373', 'APLN_8862', 'HOXD10_3236', 'CA8_767', 'TRPC6_7225', 'AGTR1_185', 'DDR2_4921', 'B4GALNT1_2583', 'OR51E1_143503', 'BDKRB2_624', 'DGKI_9162', 'MYH11_4629', 'SEMA5B_54437', 'BARX2_8538', 'TMEM45A_55076', 'MPPED2_744', 'COL25A1_84570', 'PLAC9_219348', 'RASGRF2_5924', 'FLT1_2321', 'C6orf105_84830', 'RASD2_23551', 'C3orf70_285382', 'EGLN3_112399', 'CNN1_1264', 'CSPG4_1464', 'CXCL10_3627', 'COL6A3_1293', 'APOLD1_81575', 'HMCN1_83872', 'ZNF521_25925', 'SLC35F1_222553', 'C4orf6_10141', 'DACH1_1602', 'SPARCL1_8404', 'C17orf55_284185', 'BDKRB1_623', 'COL14A1_7373', 'KLHDC8A_55220', 'PDZD2_23037', 'GABRE_2564', 'FATE1_89885', 'BTNL9_153579', 'ADAMTS5_11096', 'GPR4_2828', 'GPR116_221395', 'C7orf68_29923', 'ANO3_63982', 'COL8A1_1295', 'B3GNT4_79369', 'NGF_4803', 'ADAMTS12_81792', 'hsa_mir_217', 'hsa_mir_122', 'hsa_mir_885', 'hsa_mir_675', 'hsa_mir_577', 'hsa_mir_653', 'hsa_mir_503', 'hsa_mir_1247', 'hsa_mir_126', 'hsa_mir_489', 'hsa_mir_224', 'hsa_mir_424', 'hsa_mir_134', 'hsa_mir_1_2', 'hsa_mir_199b', 'hsa_mir_145', 'hsa_mir_199a_2', 'hsa_mir_199a_1', 'hsa_mir_153_2', 'hsa_mir_3676', 'hsa_mir_139', 'hsa_mir_379', 'hsa_mir_127', 'hsa_mir_452', 'hsa_mir_450a_1', 'hsa_mir_450a_2', 'hsa_mir_374c', 'hsa_mir_1271', 'hsa_mir_450b', 'hsa_mir_1274b', 'hsa_mir_326', 'hsa_mir_195', 'hsa_mir_33b', 'hsa_mir_26a_1', 'hsa_mir_497', 'hsa_mir_3605', 'hsa_mir_320b_2', 'hsa_mir_504', 'hsa_mir_1296', 'hsa_mir_212']\n", + "2025-11-26 09:50:45,159 - bioneuralnet.clustering.correlated_pagerank - INFO - PageRank computation completed.\n", + "2025-11-26 09:51:02,712 - bioneuralnet.clustering.correlated_pagerank - INFO - Sweep cut resulted in cluster of size 293 with conductance 0.098 and correlation -0.84.\n", + "2025-11-26 09:51:02,712 - bioneuralnet.clustering.correlated_pagerank - INFO - PageRank clustering completed successfully.\n", + "2025-11-26 09:51:02,713 - bioneuralnet.clustering.hybrid_louvain - INFO - Iteration 1: cluster size=293, Conductance=0.098 Correlation=-0.840 score=-0.371\n", + "2025-11-26 09:51:02,722 - bioneuralnet.clustering.hybrid_louvain - INFO - \n", + "Iteration 2/3: Running Correlated Louvain...\n", + "2025-11-26 09:51:02,728 - bioneuralnet.clustering.correlated_louvain - INFO - CorrelatedLouvain(k3=0.2, k4=0.8, nodes=293, edges=2701, features=900)\n", + "2025-11-26 09:51:02,729 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 09:51:02,730 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 09:51:02,730 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 09:51:02,730 - bioneuralnet.clustering.correlated_louvain - INFO - Running standard community detection...\n", + "2025-11-26 09:51:02,816 - bioneuralnet.clustering.correlated_louvain - INFO - Computed quality (supervised): Q = 0.6016, avg_corr = 0.6149, combined = 0.6123\n", + "2025-11-26 09:51:02,817 - bioneuralnet.clustering.correlated_louvain - INFO - Final quality: 0.6123\n", + "2025-11-26 09:51:02,817 - bioneuralnet.clustering.correlated_louvain - INFO - CorrelatedLouvain found 8 communities (nodes=293)\n", + "2025-11-26 09:51:02,850 - bioneuralnet.clustering.correlated_louvain - INFO - Computed quality (supervised): Q = 0.6016, avg_corr = 0.6149, combined = 0.6123\n", + "2025-11-26 09:51:02,851 - bioneuralnet.clustering.hybrid_louvain - INFO - Iteration 2: Louvain Quality = 0.6123\n", + "2025-11-26 09:51:02,881 - bioneuralnet.clustering.hybrid_louvain - INFO - Selected seed community of size 66 with correlation -0.8259\n", + "2025-11-26 09:51:02,881 - bioneuralnet.clustering.correlated_pagerank - INFO - Initialized PageRank with the following parameters:\n", + "2025-11-26 09:51:02,882 - bioneuralnet.clustering.correlated_pagerank - INFO - Graph: NetworkX Graph with 293 nodes and 2701 edges.\n", + "2025-11-26 09:51:02,882 - bioneuralnet.clustering.correlated_pagerank - INFO - Omics Data: DataFrame with shape (658, 900).\n", + "2025-11-26 09:51:02,883 - bioneuralnet.clustering.correlated_pagerank - INFO - Phenotype Data: Series with 658 samples.\n", + "2025-11-26 09:51:02,883 - bioneuralnet.clustering.correlated_pagerank - INFO - Alpha: 0.9\n", + "2025-11-26 09:51:02,883 - bioneuralnet.clustering.correlated_pagerank - INFO - Max Iterations: 100\n", + "2025-11-26 09:51:02,883 - bioneuralnet.clustering.correlated_pagerank - INFO - Tolerance: 1e-06\n", + "2025-11-26 09:51:02,884 - bioneuralnet.clustering.correlated_pagerank - INFO - K (Composite Score Weight): 0.5\n", + "2025-11-26 09:51:02,884 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 09:51:02,885 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 09:51:02,885 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 09:51:02,885 - bioneuralnet.clustering.correlated_pagerank - INFO - Initialized Correlated PageRank. device=cpu\n", + "2025-11-26 09:51:03,374 - bioneuralnet.clustering.correlated_pagerank - INFO - Generated personalization vector for seed nodes: ['FATE1_89885', 'CASQ2_845', 'FOLH1_2346', 'hsa_mir_1247', 'BARX2_8538', 'NGF_4803', 'CA4_762', 'C2orf72_257407', 'hsa_mir_1_2', 'RASD2_23551', 'OR51E1_143503', 'ANGPT2_285', 'ADAMTS4_9507', 'ZNF521_25925', 'CXCL10_3627', 'GABRE_2564', 'C6orf223_221416', 'hsa_mir_153_2', 'PDE3A_5139', 'MCHR1_2847', 'AGTR1_185', 'ESM1_11082', 'CXCL9_4283', 'hsa_mir_489', 'hsa_mir_885', 'B4GALNT1_2583', 'ACAN_176', 'CSPG4_1464', 'hsa_mir_224', 'COL5A3_50509', 'FLT1_2321', 'GPR4_2828', 'hsa_mir_126', 'DACH1_1602', 'BTNL9_153579', 'PCDH17_27253', 'APLNR_187', 'COL21A1_81578', 'SLC35F1_222553', 'STC1_6781', 'PLAC9_219348', 'ADAMTS12_81792', 'hsa_mir_139', 'ANO3_63982', 'COL8A1_1295', 'APLN_8862', 'LGI4_163175', 'ACTG2_72', 'hsa_mir_653', 'CDH13_1012', 'CALCR_799', 'APOLD1_81575', 'KLHDC8A_55220', 'RGS5_8490', 'DDR2_4921', 'COL25A1_84570', 'FHL5_9457', 'CD36_948', 'HMCN1_83872', 'C3orf70_285382', 'KCNN3_3782', 'ADAMTS5_11096', 'COX4I2_84701', 'CXCL11_6373', 'PRRX1_5396', 'TRPC6_7225']\n", + "2025-11-26 09:51:03,379 - bioneuralnet.clustering.correlated_pagerank - INFO - PageRank computation completed.\n", + "2025-11-26 09:51:05,727 - bioneuralnet.clustering.correlated_pagerank - INFO - Sweep cut resulted in cluster of size 227 with conductance 0.085 and correlation -0.83.\n", + "2025-11-26 09:51:05,728 - bioneuralnet.clustering.correlated_pagerank - INFO - PageRank clustering completed successfully.\n", + "2025-11-26 09:51:05,728 - bioneuralnet.clustering.hybrid_louvain - INFO - Iteration 2: cluster size=227, Conductance=0.085 Correlation=-0.830 score=-0.372\n", + "2025-11-26 09:51:05,735 - bioneuralnet.clustering.hybrid_louvain - INFO - \n", + "Iteration 3/3: Running Correlated Louvain...\n", + "2025-11-26 09:51:05,740 - bioneuralnet.clustering.correlated_louvain - INFO - CorrelatedLouvain(k3=0.2, k4=0.8, nodes=227, edges=2085, features=900)\n", + "2025-11-26 09:51:05,741 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 09:51:05,741 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 09:51:05,742 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 09:51:05,742 - bioneuralnet.clustering.correlated_louvain - INFO - Running standard community detection...\n", + "2025-11-26 09:51:05,781 - bioneuralnet.clustering.correlated_louvain - INFO - Computed quality (supervised): Q = 0.5419, avg_corr = 0.6823, combined = 0.6542\n", + "2025-11-26 09:51:05,782 - bioneuralnet.clustering.correlated_louvain - INFO - Final quality: 0.6542\n", + "2025-11-26 09:51:05,782 - bioneuralnet.clustering.correlated_louvain - INFO - CorrelatedLouvain found 7 communities (nodes=227)\n", + "2025-11-26 09:51:05,808 - bioneuralnet.clustering.correlated_louvain - INFO - Computed quality (supervised): Q = 0.5419, avg_corr = 0.6823, combined = 0.6542\n", + "2025-11-26 09:51:05,808 - bioneuralnet.clustering.hybrid_louvain - INFO - Iteration 3: Louvain Quality = 0.6542\n", + "2025-11-26 09:51:05,832 - bioneuralnet.clustering.hybrid_louvain - INFO - Selected seed community of size 58 with correlation -0.8271\n", + "2025-11-26 09:51:05,833 - bioneuralnet.clustering.correlated_pagerank - INFO - Initialized PageRank with the following parameters:\n", + "2025-11-26 09:51:05,834 - bioneuralnet.clustering.correlated_pagerank - INFO - Graph: NetworkX Graph with 227 nodes and 2085 edges.\n", + "2025-11-26 09:51:05,834 - bioneuralnet.clustering.correlated_pagerank - INFO - Omics Data: DataFrame with shape (658, 900).\n", + "2025-11-26 09:51:05,834 - bioneuralnet.clustering.correlated_pagerank - INFO - Phenotype Data: Series with 658 samples.\n", + "2025-11-26 09:51:05,835 - bioneuralnet.clustering.correlated_pagerank - INFO - Alpha: 0.9\n", + "2025-11-26 09:51:05,835 - bioneuralnet.clustering.correlated_pagerank - INFO - Max Iterations: 100\n", + "2025-11-26 09:51:05,835 - bioneuralnet.clustering.correlated_pagerank - INFO - Tolerance: 1e-06\n", + "2025-11-26 09:51:05,836 - bioneuralnet.clustering.correlated_pagerank - INFO - K (Composite Score Weight): 0.5\n", + "2025-11-26 09:51:05,836 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 09:51:05,837 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 09:51:05,838 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 09:51:05,838 - bioneuralnet.clustering.correlated_pagerank - INFO - Initialized Correlated PageRank. device=cpu\n", + "2025-11-26 09:51:06,070 - bioneuralnet.clustering.correlated_pagerank - INFO - Generated personalization vector for seed nodes: ['FATE1_89885', 'CASQ2_845', 'FOLH1_2346', 'hsa_mir_1247', 'BARX2_8538', 'HOXD10_3236', 'NGF_4803', 'CA4_762', 'C2orf72_257407', 'RASD2_23551', 'OR51E1_143503', 'ANGPT2_285', 'ADAMTS4_9507', 'ZNF521_25925', 'GABRE_2564', 'C6orf223_221416', 'hsa_mir_153_2', 'PDE3A_5139', 'MCHR1_2847', 'AGTR1_185', 'ESM1_11082', 'hsa_mir_489', 'hsa_mir_885', 'B4GALNT1_2583', 'ACAN_176', 'CSPG4_1464', 'hsa_mir_224', 'COL5A3_50509', 'FLT1_2321', 'GPR4_2828', 'DACH1_1602', 'BTNL9_153579', 'PCDH17_27253', 'APLNR_187', 'COL21A1_81578', 'SLC35F1_222553', 'STC1_6781', 'PLAC9_219348', 'ANO3_63982', 'COL8A1_1295', 'APLN_8862', 'LGI4_163175', 'hsa_mir_653', 'CDH13_1012', 'CALCR_799', 'APOLD1_81575', 'KLHDC8A_55220', 'RGS5_8490', 'DDR2_4921', 'COL25A1_84570', 'FHL5_9457', 'CD36_948', 'HMCN1_83872', 'C3orf70_285382', 'KCNN3_3782', 'ADAMTS5_11096', 'COX4I2_84701', 'TRPC6_7225']\n", + "2025-11-26 09:51:06,074 - bioneuralnet.clustering.correlated_pagerank - INFO - PageRank computation completed.\n", + "2025-11-26 09:51:08,534 - bioneuralnet.clustering.correlated_pagerank - INFO - Sweep cut resulted in cluster of size 94 with conductance 0.177 and correlation -0.83.\n", + "2025-11-26 09:51:08,535 - bioneuralnet.clustering.correlated_pagerank - INFO - PageRank clustering completed successfully.\n", + "2025-11-26 09:51:08,535 - bioneuralnet.clustering.hybrid_louvain - INFO - Iteration 3: cluster size=94, Conductance=0.177 Correlation=-0.830 score=-0.327\n", + "2025-11-26 09:51:08,538 - bioneuralnet.clustering.hybrid_louvain - INFO - Hybrid Louvain completed after 4 iterations.\n", + "2025-11-26 09:51:08,542 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 09:51:08,543 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 09:51:08,543 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 09:51:08,544 - bioneuralnet.clustering.hybrid_louvain - INFO - Initializing HybridLouvain...\n", + "2025-11-26 09:51:08,544 - bioneuralnet.clustering.hybrid_louvain - INFO - Input G is a DataFrame; converting adjacency matrix to NetworkX graph.\n", + "2025-11-26 09:51:08,564 - bioneuralnet.clustering.hybrid_louvain - INFO - Initialized HybridLouvain with 900 graph nodes, 900 omics columns, 658 phenotype rows; max_iter=3, k3=0.2, k4=0.8, tune=False\n", + "2025-11-26 09:51:08,565 - bioneuralnet.clustering.hybrid_louvain - INFO - \n", + "Iteration 1/3: Running Correlated Louvain...\n", + "2025-11-26 09:51:08,606 - bioneuralnet.clustering.correlated_louvain - INFO - CorrelatedLouvain(k3=0.2, k4=0.8, nodes=900, edges=8930, features=900)\n", + "2025-11-26 09:51:08,607 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 09:51:08,608 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 09:51:08,609 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 09:51:08,609 - bioneuralnet.clustering.correlated_louvain - INFO - Running standard community detection...\n", + "2025-11-26 09:51:08,749 - bioneuralnet.clustering.correlated_louvain - INFO - Computed quality (supervised): Q = 0.7372, avg_corr = 0.4985, combined = 0.5462\n", + "2025-11-26 09:51:08,750 - bioneuralnet.clustering.correlated_louvain - INFO - Final quality: 0.5462\n", + "2025-11-26 09:51:08,750 - bioneuralnet.clustering.correlated_louvain - INFO - CorrelatedLouvain found 9 communities (nodes=900)\n", + "2025-11-26 09:51:08,850 - bioneuralnet.clustering.correlated_louvain - INFO - Computed quality (supervised): Q = 0.7372, avg_corr = 0.4985, combined = 0.5462\n", + "2025-11-26 09:51:08,850 - bioneuralnet.clustering.hybrid_louvain - INFO - Iteration 1: Louvain Quality = 0.5462\n", + "2025-11-26 09:51:08,932 - bioneuralnet.clustering.hybrid_louvain - INFO - Selected seed community of size 48 with correlation -0.8391\n", + "2025-11-26 09:51:08,932 - bioneuralnet.clustering.correlated_pagerank - INFO - Initialized PageRank with the following parameters:\n", + "2025-11-26 09:51:08,933 - bioneuralnet.clustering.correlated_pagerank - INFO - Graph: NetworkX Graph with 900 nodes and 8930 edges.\n", + "2025-11-26 09:51:08,933 - bioneuralnet.clustering.correlated_pagerank - INFO - Omics Data: DataFrame with shape (658, 900).\n", + "2025-11-26 09:51:08,934 - bioneuralnet.clustering.correlated_pagerank - INFO - Phenotype Data: Series with 658 samples.\n", + "2025-11-26 09:51:08,934 - bioneuralnet.clustering.correlated_pagerank - INFO - Alpha: 0.9\n", + "2025-11-26 09:51:08,934 - bioneuralnet.clustering.correlated_pagerank - INFO - Max Iterations: 100\n", + "2025-11-26 09:51:08,934 - bioneuralnet.clustering.correlated_pagerank - INFO - Tolerance: 1e-06\n", + "2025-11-26 09:51:08,935 - bioneuralnet.clustering.correlated_pagerank - INFO - K (Composite Score Weight): 0.5\n", + "2025-11-26 09:51:08,935 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 09:51:08,936 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 09:51:08,936 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 09:51:08,937 - bioneuralnet.clustering.correlated_pagerank - INFO - Initialized Correlated PageRank. device=cpu\n", + "2025-11-26 09:51:09,133 - bioneuralnet.clustering.correlated_pagerank - INFO - Generated personalization vector for seed nodes: ['BAAT', 'SCARNA4', 'RPL23AP32', 'LOH3CR2A', 'C4orf6', 'SNORA1', 'CP', 'C10orf62', 'C3orf24', 'GBP5', 'LCN2_3934', 'SLPI_6590', 'CKMT1B_1159', 'TJP3_27134', 'CHL1_10752', 'C19orf46_163183', 'CKMT1A_548596', 'C6orf168_84553', 'MAL_4118', 'BNC1_646', 'CAMK2B_816', 'CNTN3_5067', 'LOC554202_554202', 'C1orf116_79098', 'CST6_1474', 'WFDC2_10406', 'DEFB1_1672', 'CCNO_10309', 'ANO5_203859', 'CCDC78_124093', 'STK33_65975', 'IGF2BP2_10644', 'EPS8L1_54869', 'CBLC_23624', 'C4orf48_401115', 'CNTD2_79935', 'OGDHL_55753', 'FAM155B_27112', 'ANXA3_306', 'hsa_mir_375', 'hsa_mir_183', 'hsa_mir_96', 'hsa_mir_182', 'hsa_mir_34c', 'hsa_mir_200b', 'hsa_mir_429', 'hsa_mir_651', 'hsa_mir_200a']\n", + "2025-11-26 09:51:09,144 - bioneuralnet.clustering.correlated_pagerank - INFO - PageRank computation completed.\n", + "2025-11-26 09:51:26,800 - bioneuralnet.clustering.correlated_pagerank - INFO - Sweep cut resulted in cluster of size 159 with conductance 0.217 and correlation -0.83.\n", + "2025-11-26 09:51:26,801 - bioneuralnet.clustering.correlated_pagerank - INFO - PageRank clustering completed successfully.\n", + "2025-11-26 09:51:26,801 - bioneuralnet.clustering.hybrid_louvain - INFO - Iteration 1: cluster size=159, Conductance=0.217 Correlation=-0.830 score=-0.307\n", + "2025-11-26 09:51:26,809 - bioneuralnet.clustering.hybrid_louvain - INFO - \n", + "Iteration 2/3: Running Correlated Louvain...\n", + "2025-11-26 09:51:26,816 - bioneuralnet.clustering.correlated_louvain - INFO - CorrelatedLouvain(k3=0.2, k4=0.8, nodes=159, edges=1292, features=900)\n", + "2025-11-26 09:51:26,819 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 09:51:26,819 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 09:51:26,820 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 09:51:26,821 - bioneuralnet.clustering.correlated_louvain - INFO - Running standard community detection...\n", + "2025-11-26 09:51:26,870 - bioneuralnet.clustering.correlated_louvain - INFO - Computed quality (supervised): Q = 0.5196, avg_corr = 0.5933, combined = 0.5785\n", + "2025-11-26 09:51:26,871 - bioneuralnet.clustering.correlated_louvain - INFO - Final quality: 0.5785\n", + "2025-11-26 09:51:26,871 - bioneuralnet.clustering.correlated_louvain - INFO - CorrelatedLouvain found 6 communities (nodes=159)\n", + "2025-11-26 09:51:26,895 - bioneuralnet.clustering.correlated_louvain - INFO - Computed quality (supervised): Q = 0.5196, avg_corr = 0.5933, combined = 0.5785\n", + "2025-11-26 09:51:26,896 - bioneuralnet.clustering.hybrid_louvain - INFO - Iteration 2: Louvain Quality = 0.5785\n", + "2025-11-26 09:51:26,918 - bioneuralnet.clustering.hybrid_louvain - INFO - Selected seed community of size 45 with correlation -0.8283\n", + "2025-11-26 09:51:26,919 - bioneuralnet.clustering.correlated_pagerank - INFO - Initialized PageRank with the following parameters:\n", + "2025-11-26 09:51:26,920 - bioneuralnet.clustering.correlated_pagerank - INFO - Graph: NetworkX Graph with 159 nodes and 1292 edges.\n", + "2025-11-26 09:51:26,920 - bioneuralnet.clustering.correlated_pagerank - INFO - Omics Data: DataFrame with shape (658, 900).\n", + "2025-11-26 09:51:26,920 - bioneuralnet.clustering.correlated_pagerank - INFO - Phenotype Data: Series with 658 samples.\n", + "2025-11-26 09:51:26,920 - bioneuralnet.clustering.correlated_pagerank - INFO - Alpha: 0.9\n", + "2025-11-26 09:51:26,921 - bioneuralnet.clustering.correlated_pagerank - INFO - Max Iterations: 100\n", + "2025-11-26 09:51:26,921 - bioneuralnet.clustering.correlated_pagerank - INFO - Tolerance: 1e-06\n", + "2025-11-26 09:51:26,921 - bioneuralnet.clustering.correlated_pagerank - INFO - K (Composite Score Weight): 0.5\n", + "2025-11-26 09:51:26,922 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 09:51:26,922 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 09:51:26,923 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 09:51:26,923 - bioneuralnet.clustering.correlated_pagerank - INFO - Initialized Correlated PageRank. device=cpu\n", + "2025-11-26 09:51:27,114 - bioneuralnet.clustering.correlated_pagerank - INFO - Generated personalization vector for seed nodes: ['C4orf48_401115', 'CKMT1B_1159', 'OGDHL_55753', 'hsa_mir_21', 'hsa_mir_183', 'CST6_1474', 'EPS8L1_54869', 'hsa_mir_429', 'C1orf116_79098', 'hsa_mir_200b', 'C19orf46_163183', 'GBP5', 'hsa_mir_31', 'CNTD2_79935', 'C6orf168_84553', 'CHL1_10752', 'CBLC_23624', 'CNTN3_5067', 'CKMT1A_548596', 'hsa_mir_200a', 'AKR1C2_1646', 'SLPI_6590', 'WFDC2_10406', 'hsa_mir_375', 'ANXA3_306', 'hsa_mir_149', 'BNC1_646', 'CCDC78_124093', 'MAL_4118', 'CAMK2B_816', 'FAM155B_27112', 'CCNO_10309', 'STK33_65975', 'LOC554202_554202', 'TJP3_27134', 'hsa_mir_34c', 'C3orf24', 'IGF2BP2_10644', 'hsa_mir_182', 'CLSTN2_64084', 'ANO5_203859', 'hsa_mir_96', 'hsa_mir_205', 'LCN2_3934', 'LOH3CR2A']\n", + "2025-11-26 09:51:27,117 - bioneuralnet.clustering.correlated_pagerank - INFO - PageRank computation completed.\n", + "2025-11-26 09:51:28,052 - bioneuralnet.clustering.correlated_pagerank - INFO - Sweep cut resulted in cluster of size 63 with conductance 0.229 and correlation -0.85.\n", + "2025-11-26 09:51:28,052 - bioneuralnet.clustering.correlated_pagerank - INFO - PageRank clustering completed successfully.\n", + "2025-11-26 09:51:28,053 - bioneuralnet.clustering.hybrid_louvain - INFO - Iteration 2: cluster size=63, Conductance=0.229 Correlation=-0.850 score=-0.311\n", + "2025-11-26 09:51:28,055 - bioneuralnet.clustering.hybrid_louvain - INFO - \n", + "Iteration 3/3: Running Correlated Louvain...\n", + "2025-11-26 09:51:28,058 - bioneuralnet.clustering.correlated_louvain - INFO - CorrelatedLouvain(k3=0.2, k4=0.8, nodes=63, edges=490, features=900)\n", + "2025-11-26 09:51:28,059 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 09:51:28,060 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 09:51:28,060 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 09:51:28,061 - bioneuralnet.clustering.correlated_louvain - INFO - Running standard community detection...\n", + "2025-11-26 09:51:28,085 - bioneuralnet.clustering.correlated_louvain - INFO - Computed quality (supervised): Q = 0.3730, avg_corr = 0.6817, combined = 0.6200\n", + "2025-11-26 09:51:28,085 - bioneuralnet.clustering.correlated_louvain - INFO - Final quality: 0.6200\n", + "2025-11-26 09:51:28,086 - bioneuralnet.clustering.correlated_louvain - INFO - CorrelatedLouvain found 5 communities (nodes=63)\n", + "2025-11-26 09:51:28,104 - bioneuralnet.clustering.correlated_louvain - INFO - Computed quality (supervised): Q = 0.3730, avg_corr = 0.6817, combined = 0.6200\n", + "2025-11-26 09:51:28,105 - bioneuralnet.clustering.hybrid_louvain - INFO - Iteration 3: Louvain Quality = 0.6200\n", + "2025-11-26 09:51:28,135 - bioneuralnet.clustering.hybrid_louvain - INFO - Selected seed community of size 17 with correlation -0.8340\n", + "2025-11-26 09:51:28,136 - bioneuralnet.clustering.correlated_pagerank - INFO - Initialized PageRank with the following parameters:\n", + "2025-11-26 09:51:28,136 - bioneuralnet.clustering.correlated_pagerank - INFO - Graph: NetworkX Graph with 63 nodes and 490 edges.\n", + "2025-11-26 09:51:28,136 - bioneuralnet.clustering.correlated_pagerank - INFO - Omics Data: DataFrame with shape (658, 900).\n", + "2025-11-26 09:51:28,137 - bioneuralnet.clustering.correlated_pagerank - INFO - Phenotype Data: Series with 658 samples.\n", + "2025-11-26 09:51:28,137 - bioneuralnet.clustering.correlated_pagerank - INFO - Alpha: 0.9\n", + "2025-11-26 09:51:28,138 - bioneuralnet.clustering.correlated_pagerank - INFO - Max Iterations: 100\n", + "2025-11-26 09:51:28,138 - bioneuralnet.clustering.correlated_pagerank - INFO - Tolerance: 1e-06\n", + "2025-11-26 09:51:28,138 - bioneuralnet.clustering.correlated_pagerank - INFO - K (Composite Score Weight): 0.5\n", + "2025-11-26 09:51:28,139 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 09:51:28,140 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 09:51:28,141 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 09:51:28,141 - bioneuralnet.clustering.correlated_pagerank - INFO - Initialized Correlated PageRank. device=cpu\n", + "2025-11-26 09:51:28,205 - bioneuralnet.clustering.correlated_pagerank - INFO - Generated personalization vector for seed nodes: ['C4orf48_401115', 'CAMK2B_816', 'TMEM204', 'CCNO_10309', 'CORO6_84940', 'LOC554202_554202', 'TJP3_27134', 'IGF2BP2_10644', 'C1orf162', 'CNTD2_79935', 'C6orf168_84553', 'CHL1_10752', 'hsa_mir_96', 'BNC1_646', 'CCDC78_124093', 'LCN2_3934', 'LOH3CR2A']\n", + "2025-11-26 09:51:28,209 - bioneuralnet.clustering.correlated_pagerank - INFO - PageRank computation completed.\n", + "2025-11-26 09:51:28,455 - bioneuralnet.clustering.correlated_pagerank - INFO - Sweep cut resulted in cluster of size 32 with conductance 0.246 and correlation -0.86.\n", + "2025-11-26 09:51:28,456 - bioneuralnet.clustering.correlated_pagerank - INFO - PageRank clustering completed successfully.\n", + "2025-11-26 09:51:28,457 - bioneuralnet.clustering.hybrid_louvain - INFO - Iteration 3: cluster size=32, Conductance=0.246 Correlation=-0.860 score=-0.307\n", + "2025-11-26 09:51:28,458 - bioneuralnet.clustering.hybrid_louvain - INFO - Hybrid Louvain completed after 4 iterations.\n", + "2025-11-26 09:51:28,460 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 09:51:28,461 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 09:51:28,462 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 09:51:28,462 - bioneuralnet.clustering.hybrid_louvain - INFO - Initializing HybridLouvain...\n", + "2025-11-26 09:51:28,462 - bioneuralnet.clustering.hybrid_louvain - INFO - Input G is a DataFrame; converting adjacency matrix to NetworkX graph.\n", + "2025-11-26 09:51:28,482 - bioneuralnet.clustering.hybrid_louvain - INFO - Initialized HybridLouvain with 900 graph nodes, 900 omics columns, 658 phenotype rows; max_iter=3, k3=0.2, k4=0.8, tune=False\n", + "2025-11-26 09:51:28,483 - bioneuralnet.clustering.hybrid_louvain - INFO - \n", + "Iteration 1/3: Running Correlated Louvain...\n", + "2025-11-26 09:51:28,498 - bioneuralnet.clustering.correlated_louvain - INFO - CorrelatedLouvain(k3=0.2, k4=0.8, nodes=900, edges=8930, features=900)\n", + "2025-11-26 09:51:28,500 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 09:51:28,501 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 09:51:28,502 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 09:51:28,503 - bioneuralnet.clustering.correlated_louvain - INFO - Running standard community detection...\n", + "2025-11-26 09:51:28,640 - bioneuralnet.clustering.correlated_louvain - INFO - Computed quality (supervised): Q = 0.7372, avg_corr = 0.4314, combined = 0.4926\n", + "2025-11-26 09:51:28,640 - bioneuralnet.clustering.correlated_louvain - INFO - Final quality: 0.4926\n", + "2025-11-26 09:51:28,641 - bioneuralnet.clustering.correlated_louvain - INFO - CorrelatedLouvain found 9 communities (nodes=900)\n", + "2025-11-26 09:51:28,715 - bioneuralnet.clustering.correlated_louvain - INFO - Computed quality (supervised): Q = 0.7372, avg_corr = 0.4314, combined = 0.4926\n", + "2025-11-26 09:51:28,716 - bioneuralnet.clustering.hybrid_louvain - INFO - Iteration 1: Louvain Quality = 0.4926\n", + "2025-11-26 09:51:28,776 - bioneuralnet.clustering.hybrid_louvain - INFO - Selected seed community of size 76 with correlation 0.8164\n", + "2025-11-26 09:51:28,777 - bioneuralnet.clustering.correlated_pagerank - INFO - Initialized PageRank with the following parameters:\n", + "2025-11-26 09:51:28,777 - bioneuralnet.clustering.correlated_pagerank - INFO - Graph: NetworkX Graph with 900 nodes and 8930 edges.\n", + "2025-11-26 09:51:28,778 - bioneuralnet.clustering.correlated_pagerank - INFO - Omics Data: DataFrame with shape (658, 900).\n", + "2025-11-26 09:51:28,778 - bioneuralnet.clustering.correlated_pagerank - INFO - Phenotype Data: Series with 658 samples.\n", + "2025-11-26 09:51:28,778 - bioneuralnet.clustering.correlated_pagerank - INFO - Alpha: 0.9\n", + "2025-11-26 09:51:28,779 - bioneuralnet.clustering.correlated_pagerank - INFO - Max Iterations: 100\n", + "2025-11-26 09:51:28,779 - bioneuralnet.clustering.correlated_pagerank - INFO - Tolerance: 1e-06\n", + "2025-11-26 09:51:28,779 - bioneuralnet.clustering.correlated_pagerank - INFO - K (Composite Score Weight): 0.5\n", + "2025-11-26 09:51:28,780 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 09:51:28,780 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 09:51:28,781 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 09:51:28,781 - bioneuralnet.clustering.correlated_pagerank - INFO - Initialized Correlated PageRank. device=cpu\n", + "2025-11-26 09:51:29,691 - bioneuralnet.clustering.correlated_pagerank - INFO - Generated personalization vector for seed nodes: ['ALDH1A1', 'C21orf131', 'ADAM28', 'C21orf129', 'TMEM71', 'C4orf26', 'SLC22A2', 'TINAG', 'ACSM5', 'CIDEC', 'C20orf118', 'RHCG_51458', 'ATP6V0A4_50617', 'SLC4A1_6521', 'KLK1_3816', 'ATP6V0D2_245972', 'CLDN8_9073', 'FOXI1_2299', 'DMRT2_10655', 'HEPACAM2_253012', 'CLCNKB_1188', 'ATP6V1B1_525', 'CFTR_1080', 'CKMT2_1160', 'GCGR_2642', 'SLC26A7_115111', 'CLCNKA_1187', 'FGF9_2254', 'BMPR1B_658', 'TMPRSS2_7113', 'DHRS2_10202', 'BSND_7809', 'AQP6_363', 'CWH43_80157', 'TRIM50_135892', 'C16orf89_146556', 'SLC9A2_6549', 'CALCA_796', 'OLIG1_116448', 'CLNK_116449', 'CYP11A1_1583', 'RANBP3L_202151', 'DIRAS1_148252', 'C1orf168_199920', 'INPP5J_27124', 'C15orf59_388135', 'CCBE1_147372', 'EPB41L4B_54566', 'GCOM1_145781', 'C9orf84_158401', 'ATP6V1G3_127124', 'VWA5B1_127731', 'LRRTM1_347730', 'KRTAP5_8_57830', 'ST8SIA5_29906', 'B4GALNT3_283358', 'CNTNAP5_129684', 'CEND1_51286', 'LOC643008_643008', 'CEL_1056', 'CR1L_1379', 'ASB5_140458', 'CTNNA2_1496', 'MARCH10_162333', 'SEZ6L_23544', 'hsa_mir_891a', 'hsa_mir_141', 'hsa_mir_187', 'hsa_mir_200c', 'hsa_mir_129_1', 'hsa_mir_129_2', 'hsa_mir_874', 'hsa_mir_206', 'hsa_mir_218_1', 'hsa_mir_618', 'hsa_mir_30c_1']\n", + "2025-11-26 09:51:29,704 - bioneuralnet.clustering.correlated_pagerank - INFO - PageRank computation completed.\n", + "2025-11-26 09:51:46,043 - bioneuralnet.clustering.correlated_pagerank - INFO - Sweep cut resulted in cluster of size 69 with conductance 0.109 and correlation 0.81.\n", + "2025-11-26 09:51:46,044 - bioneuralnet.clustering.correlated_pagerank - INFO - PageRank clustering completed successfully.\n", + "2025-11-26 09:51:46,044 - bioneuralnet.clustering.hybrid_louvain - INFO - Iteration 1: cluster size=69, Conductance=0.109 Correlation=0.810 score=-0.350\n", + "2025-11-26 09:51:46,046 - bioneuralnet.clustering.hybrid_louvain - INFO - \n", + "Iteration 2/3: Running Correlated Louvain...\n", + "2025-11-26 09:51:46,049 - bioneuralnet.clustering.correlated_louvain - INFO - CorrelatedLouvain(k3=0.2, k4=0.8, nodes=69, edges=585, features=900)\n", + "2025-11-26 09:51:46,050 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 09:51:46,051 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 09:51:46,052 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 09:51:46,052 - bioneuralnet.clustering.correlated_louvain - INFO - Running standard community detection...\n", + "2025-11-26 09:51:46,074 - bioneuralnet.clustering.correlated_louvain - INFO - Computed quality (supervised): Q = 0.3798, avg_corr = 0.7822, combined = 0.7017\n", + "2025-11-26 09:51:46,075 - bioneuralnet.clustering.correlated_louvain - INFO - Final quality: 0.7017\n", + "2025-11-26 09:51:46,075 - bioneuralnet.clustering.correlated_louvain - INFO - CorrelatedLouvain found 4 communities (nodes=69)\n", + "2025-11-26 09:51:46,093 - bioneuralnet.clustering.correlated_louvain - INFO - Computed quality (supervised): Q = 0.3798, avg_corr = 0.7822, combined = 0.7017\n", + "2025-11-26 09:51:46,094 - bioneuralnet.clustering.hybrid_louvain - INFO - Iteration 2: Louvain Quality = 0.7017\n", + "2025-11-26 09:51:46,114 - bioneuralnet.clustering.hybrid_louvain - INFO - Selected seed community of size 9 with correlation 0.8318\n", + "2025-11-26 09:51:46,116 - bioneuralnet.clustering.correlated_pagerank - INFO - Initialized PageRank with the following parameters:\n", + "2025-11-26 09:51:46,117 - bioneuralnet.clustering.correlated_pagerank - INFO - Graph: NetworkX Graph with 69 nodes and 585 edges.\n", + "2025-11-26 09:51:46,118 - bioneuralnet.clustering.correlated_pagerank - INFO - Omics Data: DataFrame with shape (658, 900).\n", + "2025-11-26 09:51:46,118 - bioneuralnet.clustering.correlated_pagerank - INFO - Phenotype Data: Series with 658 samples.\n", + "2025-11-26 09:51:46,119 - bioneuralnet.clustering.correlated_pagerank - INFO - Alpha: 0.9\n", + "2025-11-26 09:51:46,120 - bioneuralnet.clustering.correlated_pagerank - INFO - Max Iterations: 100\n", + "2025-11-26 09:51:46,121 - bioneuralnet.clustering.correlated_pagerank - INFO - Tolerance: 1e-06\n", + "2025-11-26 09:51:46,121 - bioneuralnet.clustering.correlated_pagerank - INFO - K (Composite Score Weight): 0.5\n", + "2025-11-26 09:51:46,122 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 09:51:46,124 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 09:51:46,125 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 09:51:46,126 - bioneuralnet.clustering.correlated_pagerank - INFO - Initialized Correlated PageRank. device=cpu\n", + "2025-11-26 09:51:46,159 - bioneuralnet.clustering.correlated_pagerank - INFO - Generated personalization vector for seed nodes: ['SEZ6L_23544', 'LRRTM1_347730', 'hsa_mir_187', 'KLK1_3816', 'CTNNA2_1496', 'CASS4', 'hsa_mir_618', 'CFTR_1080', 'ATP6V0D2_245972']\n", + "2025-11-26 09:51:46,162 - bioneuralnet.clustering.correlated_pagerank - INFO - PageRank computation completed.\n", + "2025-11-26 09:51:46,457 - bioneuralnet.clustering.correlated_pagerank - INFO - Sweep cut resulted in cluster of size 34 with conductance 0.211 and correlation 0.81.\n", + "2025-11-26 09:51:46,457 - bioneuralnet.clustering.correlated_pagerank - INFO - PageRank clustering completed successfully.\n", + "2025-11-26 09:51:46,457 - bioneuralnet.clustering.hybrid_louvain - INFO - Iteration 2: cluster size=34, Conductance=0.211 Correlation=0.810 score=-0.300\n", + "2025-11-26 09:51:46,459 - bioneuralnet.clustering.hybrid_louvain - INFO - \n", + "Iteration 3/3: Running Correlated Louvain...\n", + "2025-11-26 09:51:46,461 - bioneuralnet.clustering.correlated_louvain - INFO - CorrelatedLouvain(k3=0.2, k4=0.8, nodes=34, edges=235, features=900)\n", + "2025-11-26 09:51:46,462 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 09:51:46,462 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 09:51:46,463 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 09:51:46,463 - bioneuralnet.clustering.correlated_louvain - INFO - Running standard community detection...\n", + "2025-11-26 09:51:46,479 - bioneuralnet.clustering.correlated_louvain - INFO - Computed quality (supervised): Q = 0.3141, avg_corr = 0.7568, combined = 0.6683\n", + "2025-11-26 09:51:46,480 - bioneuralnet.clustering.correlated_louvain - INFO - Final quality: 0.6683\n", + "2025-11-26 09:51:46,480 - bioneuralnet.clustering.correlated_louvain - INFO - CorrelatedLouvain found 6 communities (nodes=34)\n", + "2025-11-26 09:51:46,493 - bioneuralnet.clustering.correlated_louvain - INFO - Computed quality (supervised): Q = 0.3141, avg_corr = 0.7568, combined = 0.6683\n", + "2025-11-26 09:51:46,493 - bioneuralnet.clustering.hybrid_louvain - INFO - Iteration 3: Louvain Quality = 0.6683\n", + "2025-11-26 09:51:46,505 - bioneuralnet.clustering.hybrid_louvain - INFO - Selected seed community of size 7 with correlation 0.8084\n", + "2025-11-26 09:51:46,506 - bioneuralnet.clustering.correlated_pagerank - INFO - Initialized PageRank with the following parameters:\n", + "2025-11-26 09:51:46,507 - bioneuralnet.clustering.correlated_pagerank - INFO - Graph: NetworkX Graph with 34 nodes and 235 edges.\n", + "2025-11-26 09:51:46,507 - bioneuralnet.clustering.correlated_pagerank - INFO - Omics Data: DataFrame with shape (658, 900).\n", + "2025-11-26 09:51:46,507 - bioneuralnet.clustering.correlated_pagerank - INFO - Phenotype Data: Series with 658 samples.\n", + "2025-11-26 09:51:46,508 - bioneuralnet.clustering.correlated_pagerank - INFO - Alpha: 0.9\n", + "2025-11-26 09:51:46,508 - bioneuralnet.clustering.correlated_pagerank - INFO - Max Iterations: 100\n", + "2025-11-26 09:51:46,508 - bioneuralnet.clustering.correlated_pagerank - INFO - Tolerance: 1e-06\n", + "2025-11-26 09:51:46,508 - bioneuralnet.clustering.correlated_pagerank - INFO - K (Composite Score Weight): 0.5\n", + "2025-11-26 09:51:46,509 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 09:51:46,510 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 09:51:46,510 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 09:51:46,510 - bioneuralnet.clustering.correlated_pagerank - INFO - Initialized Correlated PageRank. device=cpu\n", + "2025-11-26 09:51:46,532 - bioneuralnet.clustering.correlated_pagerank - INFO - Generated personalization vector for seed nodes: ['SEZ6L_23544', 'ST8SIA5_29906', 'LRRTM1_347730', 'CTNNA2_1496', 'hsa_mir_618', 'CFTR_1080', 'CR1L_1379']\n", + "2025-11-26 09:51:46,534 - bioneuralnet.clustering.correlated_pagerank - INFO - PageRank computation completed.\n", + "2025-11-26 09:51:46,625 - bioneuralnet.clustering.correlated_pagerank - INFO - Sweep cut resulted in cluster of size 16 with conductance 0.345 and correlation 0.83.\n", + "2025-11-26 09:51:46,625 - bioneuralnet.clustering.correlated_pagerank - INFO - PageRank clustering completed successfully.\n", + "2025-11-26 09:51:46,626 - bioneuralnet.clustering.hybrid_louvain - INFO - Iteration 3: cluster size=16, Conductance=0.345 Correlation=0.830 score=-0.242\n", + "2025-11-26 09:51:46,626 - bioneuralnet.clustering.hybrid_louvain - INFO - Hybrid Louvain completed after 4 iterations.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Driver modules (one-vs-rest):\n", + " KIRP: 293 features\n", + " KIRC: 159 features\n", + " KICH: 69 features\n" + ] + } + ], + "source": [ + "from bioneuralnet.clustering import HybridLouvain\n", + "\n", + "labels = target[\"target\"] \n", + "unique_phenotypes = labels.unique()\n", + "multiclass_results = {}\n", + "summary = []\n", + "label_map = {0: \"KIRC\", 1: \"KIRP\", 2: \"KICH\"}\n", + "\n", + "for phenotype in unique_phenotypes:\n", + " y_binary = (labels == phenotype).astype(int)\n", + "\n", + " hl = HybridLouvain(\n", + " G=similarity_10,\n", + " B=omics_kipan,\n", + " Y=y_binary,\n", + " k3=0.2,\n", + " k4=0.8,\n", + " max_iter=3,\n", + " seed=SEED,\n", + " )\n", + "\n", + " subnetworks = hl.run(as_dfs=True)\n", + "\n", + " name = label_map.get(phenotype, str(phenotype))\n", + "\n", + " if subnetworks:\n", + " df = subnetworks[0]\n", + " multiclass_results[name] = df\n", + " summary.append((name, df.shape[1]))\n", + " else:\n", + " summary.append((name, 0))\n", + "\n", + "print(\"\\nDriver modules (one-vs-rest):\")\n", + "for name, n_feats in summary:\n", + " print(f\" {name}: {n_feats} features\")\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "id": "04f74f33", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Omic Degree\n", + "Index \n", + "5 INPP5J_27124 12\n", + "26 SLC26A7_115111 9\n", + "12 HEPACAM2_253012 7\n", + "16 CLNK_116449 7\n", + "27 RHCG_51458 6\n", + "3 CLCNKB_1188 6\n", + "13 DMRT2_10655 6\n", + "18 LOC643008_643008 5\n", + "10 SLC4A1_6521 5\n", + "9 CLCNKA_1187 5\n", + "4 FGF9_2254 5\n", + "11 C9orf84_158401 4\n", + "8 ATP6V1B1_525 4\n", + "22 KRTAP5_8_57830 4\n", + "15 FOXI1_2299 3\n", + "23 ATP6V0A4_50617 3\n", + "21 ATP6V0D2_245972 1\n", + "24 TMPRSS2_7113 1\n", + "25 GCOM1_145781 1\n", + "1 hsa_mir_129_1 1\n", + "20 hsa_mir_200c 1\n", + "19 KLK1_3816 1\n", + "17 hsa_mir_891a 1\n", + "2 hsa_mir_129_2 1\n", + "7 EPB41L4B_54566 1\n", + "6 TRIM50_135892 1\n", + "14 hsa_mir_141 1\n" + ] + } + ], + "source": [ + "from bioneuralnet.metrics import louvain_to_adjacency, plot_network\n", + "\n", + "kirp_df = multiclass_results[\"KICH\"]\n", + "kirp_adj = louvain_to_adjacency(kirp_df)\n", + "kirp_mapping = plot_network(kirp_adj, weight_threshold=0.76, show_labels=True, show_edge_weights=True)\n", + "print(kirp_mapping)" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "id": "66890c59", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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OmicDegreeSource
Index
5INPP5J_2712412RNA
26SLC26A7_1151119RNA
12HEPACAM2_2530127RNA
16CLNK_1164497RNA
27RHCG_514586RNA
3CLCNKB_11886RNA
13DMRT2_106556RNA
18LOC643008_6430085RNA
10SLC4A1_65215RNA
9CLCNKA_11875RNA
4FGF9_22545RNA
11C9orf84_1584014RNA
8ATP6V1B1_5254RNA
22KRTAP5_8_578304RNA
15FOXI1_22993RNA
23ATP6V0A4_506173RNA
21ATP6V0D2_2459721RNA
24TMPRSS2_71131RNA
25GCOM1_1457811RNA
1hsa_mir_129_11miRNA
20hsa_mir_200c1miRNA
19KLK1_38161RNA
17hsa_mir_891a1miRNA
2hsa_mir_129_21miRNA
7EPB41L4B_545661RNA
6TRIM50_1358921RNA
14hsa_mir_1411miRNA
\n", + "
" + ], + "text/plain": [ + " Omic Degree Source\n", + "Index \n", + "5 INPP5J_27124 12 RNA\n", + "26 SLC26A7_115111 9 RNA\n", + "12 HEPACAM2_253012 7 RNA\n", + "16 CLNK_116449 7 RNA\n", + "27 RHCG_51458 6 RNA\n", + "3 CLCNKB_1188 6 RNA\n", + "13 DMRT2_10655 6 RNA\n", + "18 LOC643008_643008 5 RNA\n", + "10 SLC4A1_6521 5 RNA\n", + "9 CLCNKA_1187 5 RNA\n", + "4 FGF9_2254 5 RNA\n", + "11 C9orf84_158401 4 RNA\n", + "8 ATP6V1B1_525 4 RNA\n", + "22 KRTAP5_8_57830 4 RNA\n", + "15 FOXI1_2299 3 RNA\n", + "23 ATP6V0A4_50617 3 RNA\n", + "21 ATP6V0D2_245972 1 RNA\n", + "24 TMPRSS2_7113 1 RNA\n", + "25 GCOM1_145781 1 RNA\n", + "1 hsa_mir_129_1 1 miRNA\n", + "20 hsa_mir_200c 1 miRNA\n", + "19 KLK1_3816 1 RNA\n", + "17 hsa_mir_891a 1 miRNA\n", + "2 hsa_mir_129_2 1 miRNA\n", + "7 EPB41L4B_54566 1 RNA\n", + "6 TRIM50_135892 1 RNA\n", + "14 hsa_mir_141 1 miRNA" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from bioneuralnet.metrics import find_omics_modality\n", + "\n", + "# for those features in the KIRP module, we will identify their omics source\n", + "omics_df_list = [top_meth, top_rna, top_mirna]\n", + "source_names = [\"DNA_Methylation\", \"RNA\", \"miRNA\"]\n", + "\n", + "kirp_mapping_with_source = find_omics_modality(kirp_mapping, omics_df_list, source_names)\n", + "display(kirp_mapping_with_source)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "074f3225", + "metadata": {}, + "outputs": [], + "source": [ + "dna_feats = set(dna_meth.columns)\n", + "rna_feats = set(rna.columns)\n", + "mirna_feats = set(mirna.columns)\n", + "\n", + "def get_omics_type(feat):\n", + " if feat in dna_feats:\n", + " return \"DNA_Methylation\"\n", + " elif feat in rna_feats:\n", + " return \"RNA\"\n", + " elif feat in mirna_feats:\n", + " return \"miRNA\"\n", + " else:\n", + " return \"Unknown\"\n", + "\n", + "# kirp_mapping is the DF returned by plot_network\n", + "kirp_mapping[\"OmicsType\"] = kirp_mapping[\"Omic\"].map(get_omics_type)\n", + "\n", + "print(kirp_mapping)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "81315c47", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-11-26 01:05:57,049 - bioneuralnet.downstream_task.dpmon - INFO - Output directory set to: /home/vicente/Github/BioNeuralNet/dpmon_results_SAGE_FINAL/kipan/threshold_10\n", + "2025-11-26 01:05:57,050 - bioneuralnet.downstream_task.dpmon - INFO - Initialized DPMON with model: SAGE\n", + "2025-11-26 01:05:57,051 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 01:05:57,052 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 01:05:57,052 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 01:05:57,052 - bioneuralnet.downstream_task.dpmon - INFO - Random seed set to: 8183\n", + "2025-11-26 01:05:57,063 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:05:57,068 - bioneuralnet.downstream_task.dpmon - INFO - Running in Cross-Validation mode (cv=True) with 5 folds.\n", + "2025-11-26 01:05:57,071 - bioneuralnet.downstream_task.dpmon - INFO - CV Setup: Standard 5-fold split.\n", + "2025-11-26 01:05:57,073 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 1/5\n", + "2025-11-26 01:05:57,075 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:05:57,085 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:06:01,170 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:06:01,192 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (526, 901)\n", + "2025-11-26 01:06:01,195\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:06:11,311\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0313s.\n", + "2025-11-26 01:06:11,512 - bioneuralnet.downstream_task.dpmon - INFO - Fold 1 best config: {'gnn_layer_num': 2, 'gnn_hidden_dim': 512, 'lr': 0.0017134878267294578, 'weight_decay': 0.00037228516397726333, 'nn_hidden_dim1': 512, 'nn_hidden_dim2': 128, 'ae_encoding_dim': 4, 'num_epochs': 2048, 'gnn_dropout': 0.2, 'gnn_activation': 'elu', 'dim_reduction': 'mlp'}\n", + "2025-11-26 01:06:11,512 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 1 using train split only\n", + "2025-11-26 01:06:11,518 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:06:15,594 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:06:18,738 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 1 on test set\n", + "2025-11-26 01:06:18,755 - bioneuralnet.downstream_task.dpmon - INFO - Fold 1 results:\n", + "2025-11-26 01:06:18,755 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9470\n", + "2025-11-26 01:06:18,755 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9324\n", + "2025-11-26 01:06:18,756 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9470\n", + "2025-11-26 01:06:18,756 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9423\n", + "2025-11-26 01:06:18,756 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9960\n", + "2025-11-26 01:06:18,756 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9907\n", + "\n", + "2025-11-26 01:06:18,757 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 2/5\n", + "2025-11-26 01:06:18,758 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:06:18,763 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:06:22,834 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:06:22,844 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (526, 901)\n", + "2025-11-26 01:06:22,848\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:06:32,649\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0301s.\n", + "2025-11-26 01:06:32,793 - bioneuralnet.downstream_task.dpmon - INFO - Fold 2 best config: {'gnn_layer_num': 2, 'gnn_hidden_dim': 64, 'lr': 0.0007034204356990857, 'weight_decay': 5.358735876893429e-05, 'nn_hidden_dim1': 512, 'nn_hidden_dim2': 64, 'ae_encoding_dim': 8, 'num_epochs': 1024, 'gnn_dropout': 0.2, 'gnn_activation': 'relu', 'dim_reduction': 'ae'}\n", + "2025-11-26 01:06:32,794 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 2 using train split only\n", + "2025-11-26 01:06:32,799 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:06:36,856 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:06:37,956 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 2 on test set\n", + "2025-11-26 01:06:37,961 - bioneuralnet.downstream_task.dpmon - INFO - Fold 2 results:\n", + "2025-11-26 01:06:37,961 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9697\n", + "2025-11-26 01:06:37,961 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9487\n", + "2025-11-26 01:06:37,962 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9701\n", + "2025-11-26 01:06:37,962 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9579\n", + "2025-11-26 01:06:37,962 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9915\n", + "2025-11-26 01:06:37,962 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9353\n", + "\n", + "2025-11-26 01:06:37,963 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 3/5\n", + "2025-11-26 01:06:37,964 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:06:37,969 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:06:42,023 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:06:42,033 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (526, 901)\n", + "2025-11-26 01:06:42,036\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:06:51,939\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0300s.\n", + "2025-11-26 01:06:52,121 - bioneuralnet.downstream_task.dpmon - INFO - Fold 3 best config: {'gnn_layer_num': 3, 'gnn_hidden_dim': 128, 'lr': 0.0006980653261883734, 'weight_decay': 6.059927981082565e-05, 'nn_hidden_dim1': 256, 'nn_hidden_dim2': 128, 'ae_encoding_dim': 8, 'num_epochs': 2048, 'gnn_dropout': 0.0, 'gnn_activation': 'elu', 'dim_reduction': 'ae'}\n", + "2025-11-26 01:06:52,122 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 3 using train split only\n", + "2025-11-26 01:06:52,127 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:06:56,224 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:06:58,746 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 3 on test set\n", + "2025-11-26 01:06:58,751 - bioneuralnet.downstream_task.dpmon - INFO - Fold 3 results:\n", + "2025-11-26 01:06:58,751 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9545\n", + "2025-11-26 01:06:58,751 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9459\n", + "2025-11-26 01:06:58,751 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9544\n", + "2025-11-26 01:06:58,751 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9297\n", + "2025-11-26 01:06:58,751 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9966\n", + "2025-11-26 01:06:58,752 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9935\n", + "\n", + "2025-11-26 01:06:58,753 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 4/5\n", + "2025-11-26 01:06:58,754 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:06:58,761 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:07:02,888 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:07:02,900 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (527, 901)\n", + "2025-11-26 01:07:02,903\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:07:13,708\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0323s.\n", + "2025-11-26 01:07:13,923 - bioneuralnet.downstream_task.dpmon - INFO - Fold 4 best config: {'gnn_layer_num': 4, 'gnn_hidden_dim': 128, 'lr': 0.0008628455289617886, 'weight_decay': 0.005542594076906398, 'nn_hidden_dim1': 512, 'nn_hidden_dim2': 128, 'ae_encoding_dim': 16, 'num_epochs': 2048, 'gnn_dropout': 0.2, 'gnn_activation': 'elu', 'dim_reduction': 'linear'}\n", + "2025-11-26 01:07:13,924 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 4 using train split only\n", + "2025-11-26 01:07:13,928 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:07:18,066 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:07:21,166 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 4 on test set\n", + "2025-11-26 01:07:21,170 - bioneuralnet.downstream_task.dpmon - INFO - Fold 4 results:\n", + "2025-11-26 01:07:21,171 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.8931\n", + "2025-11-26 01:07:21,171 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.8940\n", + "2025-11-26 01:07:21,171 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.8929\n", + "2025-11-26 01:07:21,171 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9236\n", + "2025-11-26 01:07:21,171 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9934\n", + "2025-11-26 01:07:21,171 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9925\n", + "\n", + "2025-11-26 01:07:21,172 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 5/5\n", + "2025-11-26 01:07:21,174 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:07:21,183 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:07:25,311 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:07:25,322 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (527, 901)\n", + "2025-11-26 01:07:25,325\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:07:35,948\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0072s.\n", + "2025-11-26 01:07:36,170 - bioneuralnet.downstream_task.dpmon - INFO - Fold 5 best config: {'gnn_layer_num': 2, 'gnn_hidden_dim': 64, 'lr': 0.001685812648195555, 'weight_decay': 0.0023773937538678585, 'nn_hidden_dim1': 256, 'nn_hidden_dim2': 128, 'ae_encoding_dim': 8, 'num_epochs': 512, 'gnn_dropout': 0.2, 'gnn_activation': 'relu', 'dim_reduction': 'linear'}\n", + "2025-11-26 01:07:36,170 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 5 using train split only\n", + "2025-11-26 01:07:36,176 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:07:40,293 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:07:40,839 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 5 on test set\n", + "2025-11-26 01:07:40,844 - bioneuralnet.downstream_task.dpmon - INFO - Fold 5 results:\n", + "2025-11-26 01:07:40,844 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9618\n", + "2025-11-26 01:07:40,844 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9514\n", + "2025-11-26 01:07:40,844 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9617\n", + "2025-11-26 01:07:40,845 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9497\n", + "2025-11-26 01:07:40,845 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9969\n", + "2025-11-26 01:07:40,845 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9881\n", + "\n", + "2025-11-26 01:07:40,847 - bioneuralnet.downstream_task.dpmon - INFO - Successfully saved global best CV model state to: /home/vicente/Github/BioNeuralNet/dpmon_results_SAGE_FINAL/kipan/threshold_10/best_cv_model.pt\n", + "2025-11-26 01:07:40,848 - bioneuralnet.downstream_task.dpmon - INFO - Successfully saved global best CV model state to: /home/vicente/Github/BioNeuralNet/dpmon_results_SAGE_FINAL/kipan/threshold_10/best_cv_model_embds.pt\n", + "2025-11-26 01:07:40,848 - bioneuralnet.downstream_task.dpmon - INFO - Cross-Validation Results:\n", + "\n", + "2025-11-26 01:07:40,848 - bioneuralnet.downstream_task.dpmon - INFO - Avg Accuracy: 0.9452 +/- 0.0303\n", + "2025-11-26 01:07:40,848 - bioneuralnet.downstream_task.dpmon - INFO - Avg F1 Macro: 0.9345 +/- 0.0238\n", + "2025-11-26 01:07:40,848 - bioneuralnet.downstream_task.dpmon - INFO - Avg F1 Weighted: 0.9452 +/- 0.0305\n", + "2025-11-26 01:07:40,848 - bioneuralnet.downstream_task.dpmon - INFO - Avg Recall: 0.9406 +/- 0.0141\n", + "2025-11-26 01:07:40,848 - bioneuralnet.downstream_task.dpmon - INFO - Avg AUC: 0.9949 +/- 0.0024\n", + "2025-11-26 01:07:40,849 - bioneuralnet.downstream_task.dpmon - INFO - Avg AUPR: 0.9800 +/- 0.0251\n", + "2025-11-26 01:07:40,849 - bioneuralnet.downstream_task.dpmon - INFO - DPMON run completed.\n", + "2025-11-26 01:07:40,850 - bioneuralnet.downstream_task.dpmon - INFO - Output directory set to: /home/vicente/Github/BioNeuralNet/dpmon_results_SAGE_FINAL/kipan/correlation_10\n", + "2025-11-26 01:07:40,850 - bioneuralnet.downstream_task.dpmon - INFO - Initialized DPMON with model: SAGE\n", + "2025-11-26 01:07:40,850 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 01:07:40,850 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 01:07:40,850 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 01:07:40,850 - bioneuralnet.downstream_task.dpmon - INFO - Random seed set to: 8183\n", + "2025-11-26 01:07:40,855 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:07:40,856 - bioneuralnet.downstream_task.dpmon - INFO - Running in Cross-Validation mode (cv=True) with 5 folds.\n", + "2025-11-26 01:07:40,857 - bioneuralnet.downstream_task.dpmon - INFO - CV Setup: Standard 5-fold split.\n", + "2025-11-26 01:07:40,857 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 1/5\n", + "2025-11-26 01:07:40,858 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:07:40,865 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:07:44,953 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:07:44,964 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (526, 901)\n", + "2025-11-26 01:07:44,967\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:07:54,922\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0290s.\n", + "2025-11-26 01:07:55,109 - bioneuralnet.downstream_task.dpmon - INFO - Fold 1 best config: {'gnn_layer_num': 4, 'gnn_hidden_dim': 256, 'lr': 0.001257123304842143, 'weight_decay': 0.0003745702077936848, 'nn_hidden_dim1': 128, 'nn_hidden_dim2': 128, 'ae_encoding_dim': 8, 'num_epochs': 1024, 'gnn_dropout': 0.4, 'gnn_activation': 'relu', 'dim_reduction': 'ae'}\n", + "2025-11-26 01:07:55,109 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 1 using train split only\n", + "2025-11-26 01:07:55,114 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:07:59,376 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:08:00,931 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 1 on test set\n", + "2025-11-26 01:08:00,937 - bioneuralnet.downstream_task.dpmon - INFO - Fold 1 results:\n", + "2025-11-26 01:08:00,937 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9545\n", + "2025-11-26 01:08:00,937 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9482\n", + "2025-11-26 01:08:00,937 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9546\n", + "2025-11-26 01:08:00,938 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9679\n", + "2025-11-26 01:08:00,938 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9956\n", + "2025-11-26 01:08:00,938 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9900\n", + "\n", + "2025-11-26 01:08:00,939 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 2/5\n", + "2025-11-26 01:08:00,940 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:08:00,946 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:08:05,060 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:08:05,072 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (526, 901)\n", + "2025-11-26 01:08:05,075\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:08:15,288\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0300s.\n", + "2025-11-26 01:08:15,451 - bioneuralnet.downstream_task.dpmon - INFO - Fold 2 best config: {'gnn_layer_num': 2, 'gnn_hidden_dim': 64, 'lr': 0.0007034204356990857, 'weight_decay': 5.358735876893429e-05, 'nn_hidden_dim1': 512, 'nn_hidden_dim2': 64, 'ae_encoding_dim': 8, 'num_epochs': 1024, 'gnn_dropout': 0.2, 'gnn_activation': 'relu', 'dim_reduction': 'ae'}\n", + "2025-11-26 01:08:15,452 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 2 using train split only\n", + "2025-11-26 01:08:15,456 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:08:19,574 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:08:20,611 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 2 on test set\n", + "2025-11-26 01:08:20,616 - bioneuralnet.downstream_task.dpmon - INFO - Fold 2 results:\n", + "2025-11-26 01:08:20,616 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9697\n", + "2025-11-26 01:08:20,616 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9487\n", + "2025-11-26 01:08:20,616 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9701\n", + "2025-11-26 01:08:20,616 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9579\n", + "2025-11-26 01:08:20,617 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9927\n", + "2025-11-26 01:08:20,617 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9484\n", + "\n", + "2025-11-26 01:08:20,617 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 3/5\n", + "2025-11-26 01:08:20,619 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:08:20,624 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:08:24,748 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:08:24,759 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (526, 901)\n", + "2025-11-26 01:08:24,762\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:08:35,204\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0243s.\n", + "2025-11-26 01:08:35,374 - bioneuralnet.downstream_task.dpmon - INFO - Fold 3 best config: {'gnn_layer_num': 1, 'gnn_hidden_dim': 128, 'lr': 0.002302454339377372, 'weight_decay': 1.3521341174248578e-05, 'nn_hidden_dim1': 512, 'nn_hidden_dim2': 128, 'ae_encoding_dim': 4, 'num_epochs': 2048, 'gnn_dropout': 0.3, 'gnn_activation': 'relu', 'dim_reduction': 'ae'}\n", + "2025-11-26 01:08:35,375 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 3 using train split only\n", + "2025-11-26 01:08:35,380 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:08:39,498 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:08:41,284 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 3 on test set\n", + "2025-11-26 01:08:41,288 - bioneuralnet.downstream_task.dpmon - INFO - Fold 3 results:\n", + "2025-11-26 01:08:41,288 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9545\n", + "2025-11-26 01:08:41,288 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9478\n", + "2025-11-26 01:08:41,288 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9546\n", + "2025-11-26 01:08:41,289 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9482\n", + "2025-11-26 01:08:41,289 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9956\n", + "2025-11-26 01:08:41,289 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9939\n", + "\n", + "2025-11-26 01:08:41,289 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 4/5\n", + "2025-11-26 01:08:41,291 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:08:41,299 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:08:45,432 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:08:45,442 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (527, 901)\n", + "2025-11-26 01:08:45,445\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:08:55,587\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0224s.\n", + "2025-11-26 01:08:55,774 - bioneuralnet.downstream_task.dpmon - INFO - Fold 4 best config: {'gnn_layer_num': 4, 'gnn_hidden_dim': 128, 'lr': 0.0008628455289617886, 'weight_decay': 0.005542594076906398, 'nn_hidden_dim1': 512, 'nn_hidden_dim2': 128, 'ae_encoding_dim': 16, 'num_epochs': 2048, 'gnn_dropout': 0.2, 'gnn_activation': 'elu', 'dim_reduction': 'linear'}\n", + "2025-11-26 01:08:55,775 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 4 using train split only\n", + "2025-11-26 01:08:55,779 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:09:00,043 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:09:03,096 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 4 on test set\n", + "2025-11-26 01:09:03,101 - bioneuralnet.downstream_task.dpmon - INFO - Fold 4 results:\n", + "2025-11-26 01:09:03,101 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.8855\n", + "2025-11-26 01:09:03,101 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.8402\n", + "2025-11-26 01:09:03,101 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.8960\n", + "2025-11-26 01:09:03,102 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9137\n", + "2025-11-26 01:09:03,102 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9857\n", + "2025-11-26 01:09:03,102 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9669\n", + "\n", + "2025-11-26 01:09:03,103 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 5/5\n", + "2025-11-26 01:09:03,104 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:09:03,110 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:09:07,251 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:09:07,262 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (527, 901)\n", + "2025-11-26 01:09:07,265\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:09:17,331\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0317s.\n", + "2025-11-26 01:09:17,571 - bioneuralnet.downstream_task.dpmon - INFO - Fold 5 best config: {'gnn_layer_num': 1, 'gnn_hidden_dim': 64, 'lr': 0.001483781276337717, 'weight_decay': 2.5485016069955917e-05, 'nn_hidden_dim1': 128, 'nn_hidden_dim2': 128, 'ae_encoding_dim': 4, 'num_epochs': 1024, 'gnn_dropout': 0.2, 'gnn_activation': 'elu', 'dim_reduction': 'mlp'}\n", + "2025-11-26 01:09:17,571 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 5 using train split only\n", + "2025-11-26 01:09:17,577 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:09:21,713 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:09:22,694 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 5 on test set\n", + "2025-11-26 01:09:22,699 - bioneuralnet.downstream_task.dpmon - INFO - Fold 5 results:\n", + "2025-11-26 01:09:22,699 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9695\n", + "2025-11-26 01:09:22,699 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9572\n", + "2025-11-26 01:09:22,699 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9694\n", + "2025-11-26 01:09:22,699 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9558\n", + "2025-11-26 01:09:22,700 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9990\n", + "2025-11-26 01:09:22,700 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9971\n", + "\n", + "2025-11-26 01:09:22,702 - bioneuralnet.downstream_task.dpmon - INFO - Successfully saved global best CV model state to: /home/vicente/Github/BioNeuralNet/dpmon_results_SAGE_FINAL/kipan/correlation_10/best_cv_model.pt\n", + "2025-11-26 01:09:22,703 - bioneuralnet.downstream_task.dpmon - INFO - Successfully saved global best CV model state to: /home/vicente/Github/BioNeuralNet/dpmon_results_SAGE_FINAL/kipan/correlation_10/best_cv_model_embds.pt\n", + "2025-11-26 01:09:22,703 - bioneuralnet.downstream_task.dpmon - INFO - Cross-Validation Results:\n", + "\n", + "2025-11-26 01:09:22,703 - bioneuralnet.downstream_task.dpmon - INFO - Avg Accuracy: 0.9467 +/- 0.0351\n", + "2025-11-26 01:09:22,703 - bioneuralnet.downstream_task.dpmon - INFO - Avg F1 Macro: 0.9284 +/- 0.0495\n", + "2025-11-26 01:09:22,703 - bioneuralnet.downstream_task.dpmon - INFO - Avg F1 Weighted: 0.9489 +/- 0.0306\n", + "2025-11-26 01:09:22,704 - bioneuralnet.downstream_task.dpmon - INFO - Avg Recall: 0.9487 +/- 0.0208\n", + "2025-11-26 01:09:22,704 - bioneuralnet.downstream_task.dpmon - INFO - Avg AUC: 0.9937 +/- 0.0050\n", + "2025-11-26 01:09:22,704 - bioneuralnet.downstream_task.dpmon - INFO - Avg AUPR: 0.9793 +/- 0.0209\n", + "2025-11-26 01:09:22,705 - bioneuralnet.downstream_task.dpmon - INFO - DPMON run completed.\n", + "2025-11-26 01:09:22,705 - bioneuralnet.downstream_task.dpmon - INFO - Output directory set to: /home/vicente/Github/BioNeuralNet/dpmon_results_SAGE_FINAL/kipan/similarity_10\n", + "2025-11-26 01:09:22,705 - bioneuralnet.downstream_task.dpmon - INFO - Initialized DPMON with model: SAGE\n", + "2025-11-26 01:09:22,705 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 01:09:22,706 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 01:09:22,706 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 01:09:22,706 - bioneuralnet.downstream_task.dpmon - INFO - Random seed set to: 8183\n", + "2025-11-26 01:09:22,711 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:09:22,712 - bioneuralnet.downstream_task.dpmon - INFO - Running in Cross-Validation mode (cv=True) with 5 folds.\n", + "2025-11-26 01:09:22,713 - bioneuralnet.downstream_task.dpmon - INFO - CV Setup: Standard 5-fold split.\n", + "2025-11-26 01:09:22,714 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 1/5\n", + "2025-11-26 01:09:22,715 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:09:22,722 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:09:26,854 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:09:26,865 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (526, 901)\n", + "2025-11-26 01:09:26,868\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:09:37,243\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0253s.\n", + "2025-11-26 01:09:37,453 - bioneuralnet.downstream_task.dpmon - INFO - Fold 1 best config: {'gnn_layer_num': 2, 'gnn_hidden_dim': 512, 'lr': 0.0017134878267294578, 'weight_decay': 0.00037228516397726333, 'nn_hidden_dim1': 512, 'nn_hidden_dim2': 128, 'ae_encoding_dim': 4, 'num_epochs': 2048, 'gnn_dropout': 0.2, 'gnn_activation': 'elu', 'dim_reduction': 'mlp'}\n", + "2025-11-26 01:09:37,454 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 1 using train split only\n", + "2025-11-26 01:09:37,459 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:09:41,579 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:09:44,803 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 1 on test set\n", + "2025-11-26 01:09:44,820 - bioneuralnet.downstream_task.dpmon - INFO - Fold 1 results:\n", + "2025-11-26 01:09:44,821 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9470\n", + "2025-11-26 01:09:44,821 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9344\n", + "2025-11-26 01:09:44,821 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9476\n", + "2025-11-26 01:09:44,821 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9618\n", + "2025-11-26 01:09:44,821 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9899\n", + "2025-11-26 01:09:44,822 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9574\n", + "\n", + "2025-11-26 01:09:44,823 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 2/5\n", + "2025-11-26 01:09:44,824 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:09:44,830 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:09:48,915 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:09:48,926 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (526, 901)\n", + "2025-11-26 01:09:48,929\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:09:58,642\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0321s.\n", + "2025-11-26 01:09:58,800 - bioneuralnet.downstream_task.dpmon - INFO - Fold 2 best config: {'gnn_layer_num': 2, 'gnn_hidden_dim': 64, 'lr': 0.0007034204356990857, 'weight_decay': 5.358735876893429e-05, 'nn_hidden_dim1': 512, 'nn_hidden_dim2': 64, 'ae_encoding_dim': 8, 'num_epochs': 1024, 'gnn_dropout': 0.2, 'gnn_activation': 'relu', 'dim_reduction': 'ae'}\n", + "2025-11-26 01:09:58,801 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 2 using train split only\n", + "2025-11-26 01:09:58,805 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:10:03,027 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:10:04,075 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 2 on test set\n", + "2025-11-26 01:10:04,080 - bioneuralnet.downstream_task.dpmon - INFO - Fold 2 results:\n", + "2025-11-26 01:10:04,080 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9773\n", + "2025-11-26 01:10:04,080 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9648\n", + "2025-11-26 01:10:04,080 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9777\n", + "2025-11-26 01:10:04,080 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9835\n", + "2025-11-26 01:10:04,080 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9921\n", + "2025-11-26 01:10:04,080 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9552\n", + "\n", + "2025-11-26 01:10:04,081 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 3/5\n", + "2025-11-26 01:10:04,082 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:10:04,088 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:10:08,219 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:10:08,231 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (526, 901)\n", + "2025-11-26 01:10:08,234\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:10:18,090\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0282s.\n", + "2025-11-26 01:10:18,287 - bioneuralnet.downstream_task.dpmon - INFO - Fold 3 best config: {'gnn_layer_num': 3, 'gnn_hidden_dim': 128, 'lr': 0.0006980653261883734, 'weight_decay': 6.059927981082565e-05, 'nn_hidden_dim1': 256, 'nn_hidden_dim2': 128, 'ae_encoding_dim': 8, 'num_epochs': 2048, 'gnn_dropout': 0.0, 'gnn_activation': 'elu', 'dim_reduction': 'ae'}\n", + "2025-11-26 01:10:18,288 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 3 using train split only\n", + "2025-11-26 01:10:18,292 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:10:22,422 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:10:24,946 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 3 on test set\n", + "2025-11-26 01:10:24,951 - bioneuralnet.downstream_task.dpmon - INFO - Fold 3 results:\n", + "2025-11-26 01:10:24,951 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9621\n", + "2025-11-26 01:10:24,952 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9621\n", + "2025-11-26 01:10:24,952 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9622\n", + "2025-11-26 01:10:24,952 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9535\n", + "2025-11-26 01:10:24,953 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9952\n", + "2025-11-26 01:10:24,953 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9900\n", + "\n", + "2025-11-26 01:10:24,953 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 4/5\n", + "2025-11-26 01:10:24,956 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:10:24,963 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:10:29,116 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:10:29,127 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (527, 901)\n", + "2025-11-26 01:10:29,130\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:10:38,870\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0212s.\n", + "2025-11-26 01:10:39,028 - bioneuralnet.downstream_task.dpmon - INFO - Fold 4 best config: {'gnn_layer_num': 1, 'gnn_hidden_dim': 128, 'lr': 0.0019123736530165902, 'weight_decay': 0.0006168499043610002, 'nn_hidden_dim1': 256, 'nn_hidden_dim2': 128, 'ae_encoding_dim': 8, 'num_epochs': 1024, 'gnn_dropout': 0.4, 'gnn_activation': 'relu', 'dim_reduction': 'mlp'}\n", + "2025-11-26 01:10:39,029 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 4 using train split only\n", + "2025-11-26 01:10:39,033 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:10:43,160 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:10:44,156 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 4 on test set\n", + "2025-11-26 01:10:44,160 - bioneuralnet.downstream_task.dpmon - INFO - Fold 4 results:\n", + "2025-11-26 01:10:44,160 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9389\n", + "2025-11-26 01:10:44,160 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9449\n", + "2025-11-26 01:10:44,160 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9390\n", + "2025-11-26 01:10:44,161 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9554\n", + "2025-11-26 01:10:44,161 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9970\n", + "2025-11-26 01:10:44,161 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9949\n", + "\n", + "2025-11-26 01:10:44,161 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 5/5\n", + "2025-11-26 01:10:44,162 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:10:44,168 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:10:48,309 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:10:48,320 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (527, 901)\n", + "2025-11-26 01:10:48,324\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:10:58,591\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0247s.\n", + "2025-11-26 01:10:58,842 - bioneuralnet.downstream_task.dpmon - INFO - Fold 5 best config: {'gnn_layer_num': 4, 'gnn_hidden_dim': 64, 'lr': 0.0007025528058103543, 'weight_decay': 0.0018355753249756474, 'nn_hidden_dim1': 512, 'nn_hidden_dim2': 128, 'ae_encoding_dim': 4, 'num_epochs': 1024, 'gnn_dropout': 0.0, 'gnn_activation': 'relu', 'dim_reduction': 'mlp'}\n", + "2025-11-26 01:10:58,842 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 5 using train split only\n", + "2025-11-26 01:10:58,848 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:11:02,948 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:11:04,466 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 5 on test set\n", + "2025-11-26 01:11:04,471 - bioneuralnet.downstream_task.dpmon - INFO - Fold 5 results:\n", + "2025-11-26 01:11:04,472 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9618\n", + "2025-11-26 01:11:04,472 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9514\n", + "2025-11-26 01:11:04,472 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9617\n", + "2025-11-26 01:11:04,473 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9497\n", + "2025-11-26 01:11:04,473 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9979\n", + "2025-11-26 01:11:04,473 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9942\n", + "\n", + "2025-11-26 01:11:04,476 - bioneuralnet.downstream_task.dpmon - INFO - Successfully saved global best CV model state to: /home/vicente/Github/BioNeuralNet/dpmon_results_SAGE_FINAL/kipan/similarity_10/best_cv_model.pt\n", + "2025-11-26 01:11:04,476 - bioneuralnet.downstream_task.dpmon - INFO - Successfully saved global best CV model state to: /home/vicente/Github/BioNeuralNet/dpmon_results_SAGE_FINAL/kipan/similarity_10/best_cv_model_embds.pt\n", + "2025-11-26 01:11:04,476 - bioneuralnet.downstream_task.dpmon - INFO - Cross-Validation Results:\n", + "\n", + "2025-11-26 01:11:04,477 - bioneuralnet.downstream_task.dpmon - INFO - Avg Accuracy: 0.9574 +/- 0.0149\n", + "2025-11-26 01:11:04,477 - bioneuralnet.downstream_task.dpmon - INFO - Avg F1 Macro: 0.9515 +/- 0.0125\n", + "2025-11-26 01:11:04,477 - bioneuralnet.downstream_task.dpmon - INFO - Avg F1 Weighted: 0.9576 +/- 0.0149\n", + "2025-11-26 01:11:04,477 - bioneuralnet.downstream_task.dpmon - INFO - Avg Recall: 0.9608 +/- 0.0135\n", + "2025-11-26 01:11:04,478 - bioneuralnet.downstream_task.dpmon - INFO - Avg AUC: 0.9944 +/- 0.0034\n", + "2025-11-26 01:11:04,478 - bioneuralnet.downstream_task.dpmon - INFO - Avg AUPR: 0.9783 +/- 0.0202\n", + "2025-11-26 01:11:04,479 - bioneuralnet.downstream_task.dpmon - INFO - DPMON run completed.\n", + "2025-11-26 01:11:04,479 - bioneuralnet.downstream_task.dpmon - INFO - Output directory set to: /home/vicente/Github/BioNeuralNet/dpmon_results_SAGE_FINAL/kipan/gaussian_15\n", + "2025-11-26 01:11:04,479 - bioneuralnet.downstream_task.dpmon - INFO - Initialized DPMON with model: SAGE\n", + "2025-11-26 01:11:04,480 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 01:11:04,480 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 01:11:04,480 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 01:11:04,481 - bioneuralnet.downstream_task.dpmon - INFO - Random seed set to: 8183\n", + "2025-11-26 01:11:04,486 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:11:04,487 - bioneuralnet.downstream_task.dpmon - INFO - Running in Cross-Validation mode (cv=True) with 5 folds.\n", + "2025-11-26 01:11:04,488 - bioneuralnet.downstream_task.dpmon - INFO - CV Setup: Standard 5-fold split.\n", + "2025-11-26 01:11:04,488 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 1/5\n", + "2025-11-26 01:11:04,490 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:11:04,497 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:11:08,732 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:11:08,747 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (526, 901)\n", + "2025-11-26 01:11:08,750\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:11:19,358\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0249s.\n", + "2025-11-26 01:11:19,570 - bioneuralnet.downstream_task.dpmon - INFO - Fold 1 best config: {'gnn_layer_num': 4, 'gnn_hidden_dim': 256, 'lr': 0.001257123304842143, 'weight_decay': 0.0003745702077936848, 'nn_hidden_dim1': 128, 'nn_hidden_dim2': 128, 'ae_encoding_dim': 8, 'num_epochs': 1024, 'gnn_dropout': 0.4, 'gnn_activation': 'relu', 'dim_reduction': 'ae'}\n", + "2025-11-26 01:11:19,571 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 1 using train split only\n", + "2025-11-26 01:11:19,576 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:11:23,732 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:11:25,523 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 1 on test set\n", + "2025-11-26 01:11:25,533 - bioneuralnet.downstream_task.dpmon - INFO - Fold 1 results:\n", + "2025-11-26 01:11:25,534 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9545\n", + "2025-11-26 01:11:25,534 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9482\n", + "2025-11-26 01:11:25,534 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9546\n", + "2025-11-26 01:11:25,535 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9679\n", + "2025-11-26 01:11:25,535 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9943\n", + "2025-11-26 01:11:25,535 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9887\n", + "\n", + "2025-11-26 01:11:25,536 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 2/5\n", + "2025-11-26 01:11:25,538 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:11:25,543 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:11:29,653 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:11:29,667 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (526, 901)\n", + "2025-11-26 01:11:29,670\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:11:39,430\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0402s.\n", + "2025-11-26 01:11:39,573 - bioneuralnet.downstream_task.dpmon - INFO - Fold 2 best config: {'gnn_layer_num': 2, 'gnn_hidden_dim': 64, 'lr': 0.0007034204356990857, 'weight_decay': 5.358735876893429e-05, 'nn_hidden_dim1': 512, 'nn_hidden_dim2': 64, 'ae_encoding_dim': 8, 'num_epochs': 1024, 'gnn_dropout': 0.2, 'gnn_activation': 'relu', 'dim_reduction': 'ae'}\n", + "2025-11-26 01:11:39,574 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 2 using train split only\n", + "2025-11-26 01:11:39,579 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:11:43,701 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:11:44,838 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 2 on test set\n", + "2025-11-26 01:11:44,843 - bioneuralnet.downstream_task.dpmon - INFO - Fold 2 results:\n", + "2025-11-26 01:11:44,843 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9773\n", + "2025-11-26 01:11:44,843 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9648\n", + "2025-11-26 01:11:44,844 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9777\n", + "2025-11-26 01:11:44,844 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9835\n", + "2025-11-26 01:11:44,844 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9923\n", + "2025-11-26 01:11:44,844 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9447\n", + "\n", + "2025-11-26 01:11:44,845 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 3/5\n", + "2025-11-26 01:11:44,846 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:11:44,851 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:11:48,972 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:11:48,986 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (526, 901)\n", + "2025-11-26 01:11:48,989\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:11:59,032\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0306s.\n", + "2025-11-26 01:11:59,220 - bioneuralnet.downstream_task.dpmon - INFO - Fold 3 best config: {'gnn_layer_num': 3, 'gnn_hidden_dim': 128, 'lr': 0.0006980653261883734, 'weight_decay': 6.059927981082565e-05, 'nn_hidden_dim1': 256, 'nn_hidden_dim2': 128, 'ae_encoding_dim': 8, 'num_epochs': 2048, 'gnn_dropout': 0.0, 'gnn_activation': 'elu', 'dim_reduction': 'ae'}\n", + "2025-11-26 01:11:59,221 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 3 using train split only\n", + "2025-11-26 01:11:59,226 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:12:03,330 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:12:05,781 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 3 on test set\n", + "2025-11-26 01:12:05,785 - bioneuralnet.downstream_task.dpmon - INFO - Fold 3 results:\n", + "2025-11-26 01:12:05,786 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9697\n", + "2025-11-26 01:12:05,786 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9773\n", + "2025-11-26 01:12:05,786 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9697\n", + "2025-11-26 01:12:05,786 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9773\n", + "2025-11-26 01:12:05,786 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9936\n", + "2025-11-26 01:12:05,786 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9928\n", + "\n", + "2025-11-26 01:12:05,786 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 4/5\n", + "2025-11-26 01:12:05,788 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:12:05,795 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:12:09,897 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:12:09,910 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (527, 901)\n", + "2025-11-26 01:12:09,913\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:12:20,492\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0219s.\n", + "2025-11-26 01:12:20,669 - bioneuralnet.downstream_task.dpmon - INFO - Fold 4 best config: {'gnn_layer_num': 1, 'gnn_hidden_dim': 128, 'lr': 0.002226976717919125, 'weight_decay': 0.00016678702979522166, 'nn_hidden_dim1': 128, 'nn_hidden_dim2': 64, 'ae_encoding_dim': 16, 'num_epochs': 512, 'gnn_dropout': 0.3, 'gnn_activation': 'relu', 'dim_reduction': 'ae'}\n", + "2025-11-26 01:12:20,670 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 4 using train split only\n", + "2025-11-26 01:12:20,675 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:12:24,817 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:12:25,254 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 4 on test set\n", + "2025-11-26 01:12:25,258 - bioneuralnet.downstream_task.dpmon - INFO - Fold 4 results:\n", + "2025-11-26 01:12:25,258 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9389\n", + "2025-11-26 01:12:25,258 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9449\n", + "2025-11-26 01:12:25,259 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9390\n", + "2025-11-26 01:12:25,259 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9554\n", + "2025-11-26 01:12:25,259 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9934\n", + "2025-11-26 01:12:25,259 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9742\n", + "\n", + "2025-11-26 01:12:25,259 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 5/5\n", + "2025-11-26 01:12:25,261 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:12:25,266 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:12:29,434 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:12:29,448 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (527, 901)\n", + "2025-11-26 01:12:29,451\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:12:39,858\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0232s.\n", + "2025-11-26 01:12:40,073 - bioneuralnet.downstream_task.dpmon - INFO - Fold 5 best config: {'gnn_layer_num': 4, 'gnn_hidden_dim': 128, 'lr': 0.0013804780705269182, 'weight_decay': 0.0037674454696866636, 'nn_hidden_dim1': 128, 'nn_hidden_dim2': 64, 'ae_encoding_dim': 4, 'num_epochs': 1024, 'gnn_dropout': 0.0, 'gnn_activation': 'elu', 'dim_reduction': 'ae'}\n", + "2025-11-26 01:12:40,073 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 5 using train split only\n", + "2025-11-26 01:12:40,079 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:12:44,207 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:12:45,599 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 5 on test set\n", + "2025-11-26 01:12:45,604 - bioneuralnet.downstream_task.dpmon - INFO - Fold 5 results:\n", + "2025-11-26 01:12:45,604 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9695\n", + "2025-11-26 01:12:45,604 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9572\n", + "2025-11-26 01:12:45,604 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9694\n", + "2025-11-26 01:12:45,604 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9558\n", + "2025-11-26 01:12:45,605 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9978\n", + "2025-11-26 01:12:45,605 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9941\n", + "\n", + "2025-11-26 01:12:45,607 - bioneuralnet.downstream_task.dpmon - INFO - Successfully saved global best CV model state to: /home/vicente/Github/BioNeuralNet/dpmon_results_SAGE_FINAL/kipan/gaussian_15/best_cv_model.pt\n", + "2025-11-26 01:12:45,608 - bioneuralnet.downstream_task.dpmon - INFO - Successfully saved global best CV model state to: /home/vicente/Github/BioNeuralNet/dpmon_results_SAGE_FINAL/kipan/gaussian_15/best_cv_model_embds.pt\n", + "2025-11-26 01:12:45,608 - bioneuralnet.downstream_task.dpmon - INFO - Cross-Validation Results:\n", + "\n", + "2025-11-26 01:12:45,608 - bioneuralnet.downstream_task.dpmon - INFO - Avg Accuracy: 0.9620 +/- 0.0153\n", + "2025-11-26 01:12:45,608 - bioneuralnet.downstream_task.dpmon - INFO - Avg F1 Macro: 0.9585 +/- 0.0131\n", + "2025-11-26 01:12:45,608 - bioneuralnet.downstream_task.dpmon - INFO - Avg F1 Weighted: 0.9621 +/- 0.0154\n", + "2025-11-26 01:12:45,608 - bioneuralnet.downstream_task.dpmon - INFO - Avg Recall: 0.9680 +/- 0.0126\n", + "2025-11-26 01:12:45,608 - bioneuralnet.downstream_task.dpmon - INFO - Avg AUC: 0.9943 +/- 0.0021\n", + "2025-11-26 01:12:45,608 - bioneuralnet.downstream_task.dpmon - INFO - Avg AUPR: 0.9789 +/- 0.0207\n", + "2025-11-26 01:12:45,609 - bioneuralnet.downstream_task.dpmon - INFO - DPMON run completed.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Results for: threshold_10\n", + "Accuracy (Avg +/- Std): 0.9452 +/- 0.0303\n", + "F1 Macro (Avg +/- Std): 0.9345 +/- 0.0238\n", + "F1 Weighted (Avg +/- Std): 0.9452 +/- 0.0305\n", + "Recall: 0.9406 +/- 0.0141\n", + "AUC: 0.9949 +/- 0.0024\n", + "\n", + "Results for: correlation_10\n", + "Accuracy (Avg +/- Std): 0.9467 +/- 0.0351\n", + "F1 Macro (Avg +/- Std): 0.9284 +/- 0.0495\n", + "F1 Weighted (Avg +/- Std): 0.9489 +/- 0.0306\n", + "Recall: 0.9487 +/- 0.0208\n", + "AUC: 0.9937 +/- 0.0050\n", + "\n", + "Results for: similarity_10\n", + "Accuracy (Avg +/- Std): 0.9574 +/- 0.0149\n", + "F1 Macro (Avg +/- Std): 0.9515 +/- 0.0125\n", + "F1 Weighted (Avg +/- Std): 0.9576 +/- 0.0149\n", + "Recall: 0.9608 +/- 0.0135\n", + "AUC: 0.9944 +/- 0.0034\n", + "\n", + "Results for: gaussian_15\n", + "Accuracy (Avg +/- Std): 0.9620 +/- 0.0153\n", + "F1 Macro (Avg +/- Std): 0.9585 +/- 0.0131\n", + "F1 Weighted (Avg +/- Std): 0.9621 +/- 0.0154\n", + "Recall: 0.9680 +/- 0.0126\n", + "AUC: 0.9943 +/- 0.0021\n" + ] + } + ], + "source": [ + "output_dir_base = Path(\"/home/vicente/Github/BioNeuralNet/dpmon_results_SAGE_FINAL/kipan\")\n", + "all_results_SAGE = []\n", + "\n", + "for run_config in comparison_runs:\n", + " graph_name = run_config[\"name\"]\n", + " A_full = run_config[\"graph\"]\n", + " \n", + " current_output_dir = output_dir_base / graph_name\n", + " current_output_dir.mkdir(parents=True, exist_ok=True)\n", + "\n", + " dpmon_params_base = {\n", + " \"adjacency_matrix\": A_full,\n", + " \"omics_list\": omics_kipan,\n", + " \"phenotype_data\": target,\n", + " \"phenotype_col\": \"target\",\n", + " \"clinical_data\": clinical_for_model,\n", + " \"model\": 'SAGE',\n", + " \"tune\": True, \n", + " \"cv\": True, \n", + " \"n_folds\": 5,\n", + " \"repeat_num\": 1,\n", + " \"gpu\": True,\n", + " \"cuda\": 0,\n", + " \"seed\": SEED,\n", + " \"output_dir\": current_output_dir\n", + " }\n", + " \n", + " dpmon_tunned = DPMON(**dpmon_params_base)\n", + " predictions_df, metrics, embeddings = dpmon_tunned.run()\n", + "\n", + " all_results_SAGE.append({\n", + " \"graph\": graph_name,\n", + " \"predictions\": predictions_df,\n", + " \"metrics\": metrics,\n", + " \"embeddings\": embeddings\n", + " })\n", + "\n", + "for res in all_results_SAGE:\n", + " graph_name = res[\"graph\"]\n", + " graph_metrics = res[\"metrics\"]\n", + " \n", + " acc_row = graph_metrics.loc[graph_metrics['Metric'] == 'Accuracy'].iloc[0]\n", + " f1_macro_row = graph_metrics.loc[graph_metrics['Metric'] == 'F1 Macro'].iloc[0]\n", + " f1_weighted_row = graph_metrics.loc[graph_metrics['Metric'] == 'F1 Weighted'].iloc[0]\n", + " recall_row = graph_metrics.loc[graph_metrics['Metric'] == 'Recall'].iloc[0]\n", + " auc_row = graph_metrics.loc[graph_metrics['Metric'] == 'AUC'].iloc[0]\n", + " aupr_row = graph_metrics.loc[graph_metrics['Metric'] == 'AUPR'].iloc[0]\n", + " \n", + " acc_avg, acc_std = acc_row['Average'], acc_row['StdDev']\n", + " f1_macro_avg, f1_macro_std = f1_macro_row['Average'], f1_macro_row['StdDev']\n", + " f1_weighted_avg, f1_weighted_std = f1_weighted_row['Average'], f1_weighted_row['StdDev']\n", + " recall_avg, recall_std = recall_row['Average'], recall_row['StdDev']\n", + " auc_avg, auc_std = auc_row['Average'], auc_row['StdDev']\n", + " aupr_avg, aupr_std = aupr_row['Average'], aupr_row['StdDev']\n", + "\n", + " print(f\"\\nResults for: {graph_name}\")\n", + " print(f\"Accuracy (Avg +/- Std): {acc_avg:.4f} +/- {acc_std:.4f}\")\n", + " print(f\"F1 Macro (Avg +/- Std): {f1_macro_avg:.4f} +/- {f1_macro_std:.4f}\")\n", + " print(f\"F1 Weighted (Avg +/- Std): {f1_weighted_avg:.4f} +/- {f1_weighted_std:.4f}\")\n", + " print(f\"Recall: {recall_avg:.4f} +/- {recall_std:.4f}\")\n", + " print(f\"AUC: {auc_avg:.4f} +/- {auc_std:.4f}\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "80c83c6e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " 0 1 2 3 4\n", + "MGP 0.0 0.0 0.923418 0.000000 0.295637\n", + "ADH1C 0.0 0.0 0.521837 0.005931 0.421344\n", + "LOC440354 0.0 0.0 0.851917 0.000000 0.216582\n", + "C1orf110 0.0 0.0 0.625036 0.009498 0.256967\n", + "CLDN8 0.0 0.0 0.638092 0.000000 0.230229" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#This time we will select the gaussian_15 graph embeddings\n", + "res = all_results_SAGE[3] \n", + "tensor_data = res[\"embeddings\"]\n", + "\n", + "# .detach().cpu().numpy() to get the raw numbers out of pytorch\n", + "SAGE_embeddings_df = pd.DataFrame(\n", + " tensor_data.detach().cpu().numpy(),\n", + " index=omics_kipan.columns\n", + ")\n", + "\n", + "display(SAGE_embeddings_df.iloc[:5,:5])" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "1e1ed982", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Plotting Feature Embeddings for graph: correlation_10\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from bioneuralnet.metrics import plot_embeddings\n", + "\n", + "# for a more interpretable embedding plot we will map the node labels to omics types\n", + "SAGE_node_labels = []\n", + "SAGE_embeddings_array = SAGE_embeddings_df.values\n", + "feature_names = SAGE_embeddings_df.index\n", + "\n", + "# using the sets from the orginal to serve as a loopup table\n", + "dna_feats = set(dna_meth.columns)\n", + "rna_feats = set(rna.columns)\n", + "mirna_feats = set(mirna.columns)\n", + "\n", + "for feat in feature_names:\n", + " if feat in dna_feats:\n", + " # 1 for DNA methylation\n", + " SAGE_node_labels.append(1)\n", + " elif feat in rna_feats:\n", + " # 2 for RNA \n", + " SAGE_node_labels.append(2)\n", + " elif feat in mirna_feats:\n", + " # and 3 for miRNA \n", + " SAGE_node_labels.append(3)\n", + " else:\n", + " SAGE_node_labels.append(0)\n", + "\n", + "# our plotting function needs an array to squeeze the embedings into 2Dspace\n", + "SAGE_node_labels = np.array(SAGE_node_labels)\n", + "\n", + "# feature(omics) embeddings colored by omics type.\n", + "print(f\"Plotting Feature Embeddings for graph: {res['graph']}\")\n", + "plot_embeddings(SAGE_embeddings_array, SAGE_node_labels, legend_labels=[\"DNA_Methylation\", \"RNA\", \"miRNA\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "9a15b24b", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-11-26 01:12:46,329 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 01:12:46,330 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 01:12:46,330 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 01:12:46,330 - bioneuralnet.clustering.hybrid_louvain - INFO - Initializing HybridLouvain...\n", + "2025-11-26 01:12:46,331 - bioneuralnet.clustering.hybrid_louvain - INFO - Input G is a DataFrame; converting adjacency matrix to NetworkX graph.\n", + "2025-11-26 01:12:46,343 - bioneuralnet.clustering.hybrid_louvain - INFO - Initialized HybridLouvain with 900 graph nodes, 900 omics columns, 658 phenotype rows; max_iter=3, k3=0.2, k4=0.8, tune=False\n", + "2025-11-26 01:12:46,343 - bioneuralnet.clustering.hybrid_louvain - INFO - \n", + "Iteration 1/3: Running Correlated Louvain...\n", + "2025-11-26 01:12:46,352 - bioneuralnet.clustering.correlated_louvain - INFO - CorrelatedLouvain(k3=0.2, k4=0.8, nodes=900, edges=8628, features=900)\n", + "2025-11-26 01:12:46,352 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 01:12:46,353 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 01:12:46,353 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 01:12:46,353 - bioneuralnet.clustering.correlated_louvain - INFO - Running standard community detection...\n", + "2025-11-26 01:12:46,464 - bioneuralnet.clustering.correlated_louvain - INFO - Computed quality (supervised): Q = 0.6936, avg_corr = 0.4879, combined = 0.5290\n", + "2025-11-26 01:12:46,464 - bioneuralnet.clustering.correlated_louvain - INFO - Final quality: 0.5290\n", + "2025-11-26 01:12:46,465 - bioneuralnet.clustering.correlated_louvain - INFO - CorrelatedLouvain found 11 communities (nodes=900)\n", + "2025-11-26 01:12:46,521 - bioneuralnet.clustering.correlated_louvain - INFO - Computed quality (supervised): Q = 0.6936, avg_corr = 0.4879, combined = 0.5290\n", + "2025-11-26 01:12:46,522 - bioneuralnet.clustering.hybrid_louvain - INFO - Iteration 1: Louvain Quality = 0.5290\n", + "2025-11-26 01:12:46,574 - bioneuralnet.clustering.hybrid_louvain - INFO - Selected seed community of size 108 with correlation -0.8495\n", + "2025-11-26 01:12:46,575 - bioneuralnet.clustering.correlated_pagerank - INFO - Initialized PageRank with the following parameters:\n", + "2025-11-26 01:12:46,575 - bioneuralnet.clustering.correlated_pagerank - INFO - Graph: NetworkX Graph with 900 nodes and 8628 edges.\n", + "2025-11-26 01:12:46,576 - bioneuralnet.clustering.correlated_pagerank - INFO - Omics Data: DataFrame with shape (658, 900).\n", + "2025-11-26 01:12:46,576 - bioneuralnet.clustering.correlated_pagerank - INFO - Phenotype Data: Series with 658 samples.\n", + "2025-11-26 01:12:46,576 - bioneuralnet.clustering.correlated_pagerank - INFO - Alpha: 0.9\n", + "2025-11-26 01:12:46,576 - bioneuralnet.clustering.correlated_pagerank - INFO - Max Iterations: 100\n", + "2025-11-26 01:12:46,577 - bioneuralnet.clustering.correlated_pagerank - INFO - Tolerance: 1e-06\n", + "2025-11-26 01:12:46,577 - bioneuralnet.clustering.correlated_pagerank - INFO - K (Composite Score Weight): 0.5\n", + "2025-11-26 01:12:46,577 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 01:12:46,578 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 01:12:46,578 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 01:12:46,579 - bioneuralnet.clustering.correlated_pagerank - INFO - Initialized Correlated PageRank. device=cpu\n", + "2025-11-26 01:12:47,133 - bioneuralnet.clustering.correlated_pagerank - INFO - Generated personalization vector for seed nodes: ['ALDH1A1', 'KRTAP3_3', 'TAS2R42', 'ADAM28', 'C21orf71', 'LOC645638', 'MIR196A1', 'ALDOB', 'TMEM71', 'HSD17B14', 'C4orf26', 'DGCR10', 'MIR942', 'BCAN', 'LOC100128288', 'ADM2', 'IFITM1', 'PSORS1C3', 'TMEM88', 'MIR26A1', 'ALDOC', 'C8orf80', 'MIR130A', 'MIR429', 'CCDC17', 'NDUFA4L2', 'ALOX12P2', 'ATP8B1', 'C14orf129', 'FOXS1', 'SLC10A6', 'C3orf24', 'GGTLC2', 'C10orf10', 'ART4', 'IL18BP', 'MIR554', 'DDR2', 'C15orf28', 'C7orf29', 'USHBP1', 'TMEM204', 'NME3', 'PTGER3_5733', 'HMGCS2_3158', 'SLPI_6590', 'CXCL6_6372', 'FOXJ1_2302', 'HGFAC_3083', 'UBE2QL1_134111', 'SORCS2_57537', 'BCHE_590', 'DACT2_168002', 'ELFN2_114794', 'C19orf21_126353', 'CCDC135_84229', 'CPNE7_27132', 'VAT1L_57687', 'AVPR1A_552', 'PRDM16_63976', 'ACTC1_70', 'ADAMTSL1_92949', 'PDE1C_5137', 'CPXM2_119587', 'SULT4A1_25830', 'ADCY1_107', 'CORO6_84940', 'ANGPT1_284', 'CLDN3_1365', 'CA8_767', 'RASGEF1C_255426', 'CCDC108_255101', 'BDKRB2_624', 'ACSM1_116285', 'DGKI_9162', 'C19orf51_352909', 'B3GALT5_10317', 'CLSTN2_64084', 'C1orf114_57821', 'MPPED2_744', 'RASGRF2_5924', 'SPARCL1_8404', 'C2orf62_375307', 'GPR116_221395', 'C6orf174_387104', 'AKR1C2_1646', 'ADORA1_134', 'C2orf89_129293', 'hsa_mir_31', 'hsa_mir_885', 'hsa_mir_577', 'hsa_mir_203', 'hsa_mir_135b', 'hsa_mir_582', 'hsa_mir_139', 'hsa_mir_335', 'hsa_mir_21', 'hsa_mir_100', 'hsa_mir_181d', 'hsa_mir_219_1', 'hsa_mir_3934', 'hsa_mir_181c', 'hsa_mir_625', 'hsa_mir_676', 'hsa_mir_34a', 'hsa_mir_1254', 'hsa_mir_3615', 'hsa_mir_3127']\n", + "2025-11-26 01:12:47,144 - bioneuralnet.clustering.correlated_pagerank - INFO - PageRank computation completed.\n", + "2025-11-26 01:13:01,127 - bioneuralnet.clustering.correlated_pagerank - INFO - Sweep cut resulted in cluster of size 857 with conductance 0.079 and correlation 0.83.\n", + "2025-11-26 01:13:01,127 - bioneuralnet.clustering.correlated_pagerank - INFO - PageRank clustering completed successfully.\n", + "2025-11-26 01:13:01,128 - bioneuralnet.clustering.hybrid_louvain - INFO - Iteration 1: cluster size=857, Conductance=0.079 Correlation=0.830 score=-0.375\n", + "2025-11-26 01:13:01,167 - bioneuralnet.clustering.hybrid_louvain - INFO - \n", + "Iteration 2/3: Running Correlated Louvain...\n", + "2025-11-26 01:13:01,196 - bioneuralnet.clustering.correlated_louvain - INFO - CorrelatedLouvain(k3=0.2, k4=0.8, nodes=857, edges=8206, features=900)\n", + "2025-11-26 01:13:01,198 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 01:13:01,199 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 01:13:01,199 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 01:13:01,200 - bioneuralnet.clustering.correlated_louvain - INFO - Running standard community detection...\n", + "2025-11-26 01:13:01,310 - bioneuralnet.clustering.correlated_louvain - INFO - Computed quality (supervised): Q = 0.6811, avg_corr = 0.5019, combined = 0.5378\n", + "2025-11-26 01:13:01,310 - bioneuralnet.clustering.correlated_louvain - INFO - Final quality: 0.5378\n", + "2025-11-26 01:13:01,311 - bioneuralnet.clustering.correlated_louvain - INFO - CorrelatedLouvain found 11 communities (nodes=857)\n", + "2025-11-26 01:13:01,369 - bioneuralnet.clustering.correlated_louvain - INFO - Computed quality (supervised): Q = 0.6811, avg_corr = 0.5019, combined = 0.5378\n", + "2025-11-26 01:13:01,369 - bioneuralnet.clustering.hybrid_louvain - INFO - Iteration 2: Louvain Quality = 0.5378\n", + "2025-11-26 01:13:01,423 - bioneuralnet.clustering.hybrid_louvain - INFO - Selected seed community of size 73 with correlation -0.8235\n", + "2025-11-26 01:13:01,423 - bioneuralnet.clustering.correlated_pagerank - INFO - Initialized PageRank with the following parameters:\n", + "2025-11-26 01:13:01,424 - bioneuralnet.clustering.correlated_pagerank - INFO - Graph: NetworkX Graph with 857 nodes and 8206 edges.\n", + "2025-11-26 01:13:01,425 - bioneuralnet.clustering.correlated_pagerank - INFO - Omics Data: DataFrame with shape (658, 900).\n", + "2025-11-26 01:13:01,425 - bioneuralnet.clustering.correlated_pagerank - INFO - Phenotype Data: Series with 658 samples.\n", + "2025-11-26 01:13:01,425 - bioneuralnet.clustering.correlated_pagerank - INFO - Alpha: 0.9\n", + "2025-11-26 01:13:01,426 - bioneuralnet.clustering.correlated_pagerank - INFO - Max Iterations: 100\n", + "2025-11-26 01:13:01,426 - bioneuralnet.clustering.correlated_pagerank - INFO - Tolerance: 1e-06\n", + "2025-11-26 01:13:01,426 - bioneuralnet.clustering.correlated_pagerank - INFO - K (Composite Score Weight): 0.5\n", + "2025-11-26 01:13:01,427 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 01:13:01,427 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 01:13:01,428 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 01:13:01,428 - bioneuralnet.clustering.correlated_pagerank - INFO - Initialized Correlated PageRank. device=cpu\n", + "2025-11-26 01:13:01,771 - bioneuralnet.clustering.correlated_pagerank - INFO - Generated personalization vector for seed nodes: ['ALDH1A1', 'ADAM28', 'LOC645638', 'TMEM71', 'C4orf26', 'C14orf129', 'GGTLC2', 'PTGER3_5733', 'HMGCS2_3158', 'SLPI_6590', 'CXCL6_6372', 'HGFAC_3083', 'UBE2QL1_134111', 'SORCS2_57537', 'BCHE_590', 'ELFN2_114794', 'C19orf21_126353', 'TFAP2A_7020', 'CCDC135_84229', 'CPNE7_27132', 'VAT1L_57687', 'AVPR1A_552', 'PRDM16_63976', 'ACTC1_70', 'ADAMTSL1_92949', 'PDE1C_5137', 'CPXM2_119587', 'KCNN3_3782', 'SULT4A1_25830', 'ADCY1_107', 'CORO6_84940', 'ANGPT1_284', 'CLDN3_1365', 'HOXD10_3236', 'CA8_767', 'RASGEF1C_255426', 'CCDC108_255101', 'BDKRB2_624', 'ACSM1_116285', 'DGKI_9162', 'C19orf51_352909', 'B3GALT5_10317', 'CLSTN2_64084', 'MPPED2_744', 'RASGRF2_5924', 'SPARCL1_8404', 'C2orf62_375307', 'GPR116_221395', 'C6orf174_387104', 'AKR1C2_1646', 'ADORA1_134', 'C2orf89_129293', 'hsa_mir_31', 'hsa_mir_577', 'hsa_mir_203', 'hsa_mir_135b', 'hsa_mir_582', 'hsa_mir_139', 'hsa_mir_211', 'hsa_mir_335', 'hsa_mir_21', 'hsa_mir_100', 'hsa_mir_10b', 'hsa_mir_181d', 'hsa_mir_219_1', 'hsa_mir_3934', 'hsa_mir_181c', 'hsa_mir_625', 'hsa_mir_676', 'hsa_mir_34a', 'hsa_mir_1254', 'hsa_mir_3615', 'hsa_mir_3127']\n", + "2025-11-26 01:13:01,782 - bioneuralnet.clustering.correlated_pagerank - INFO - PageRank computation completed.\n", + "2025-11-26 01:13:14,769 - bioneuralnet.clustering.correlated_pagerank - INFO - Sweep cut resulted in cluster of size 420 with conductance 0.112 and correlation 0.62.\n", + "2025-11-26 01:13:14,770 - bioneuralnet.clustering.correlated_pagerank - INFO - PageRank clustering completed successfully.\n", + "2025-11-26 01:13:14,770 - bioneuralnet.clustering.hybrid_louvain - INFO - Iteration 2: cluster size=420, Conductance=0.112 Correlation=0.620 score=-0.254\n", + "2025-11-26 01:13:14,782 - bioneuralnet.clustering.hybrid_louvain - INFO - \n", + "Iteration 3/3: Running Correlated Louvain...\n", + "2025-11-26 01:13:14,790 - bioneuralnet.clustering.correlated_louvain - INFO - CorrelatedLouvain(k3=0.2, k4=0.8, nodes=420, edges=3651, features=900)\n", + "2025-11-26 01:13:14,790 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 01:13:14,791 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 01:13:14,791 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 01:13:14,792 - bioneuralnet.clustering.correlated_louvain - INFO - Running standard community detection...\n", + "2025-11-26 01:13:14,891 - bioneuralnet.clustering.correlated_louvain - INFO - Computed quality (supervised): Q = 0.6275, avg_corr = 0.4688, combined = 0.5005\n", + "2025-11-26 01:13:14,892 - bioneuralnet.clustering.correlated_louvain - INFO - Final quality: 0.5005\n", + "2025-11-26 01:13:14,892 - bioneuralnet.clustering.correlated_louvain - INFO - CorrelatedLouvain found 8 communities (nodes=420)\n", + "2025-11-26 01:13:14,926 - bioneuralnet.clustering.correlated_louvain - INFO - Computed quality (supervised): Q = 0.6275, avg_corr = 0.4688, combined = 0.5005\n", + "2025-11-26 01:13:14,926 - bioneuralnet.clustering.hybrid_louvain - INFO - Iteration 3: Louvain Quality = 0.5005\n", + "2025-11-26 01:13:14,957 - bioneuralnet.clustering.hybrid_louvain - INFO - Selected seed community of size 88 with correlation -0.8538\n", + "2025-11-26 01:13:14,957 - bioneuralnet.clustering.correlated_pagerank - INFO - Initialized PageRank with the following parameters:\n", + "2025-11-26 01:13:14,958 - bioneuralnet.clustering.correlated_pagerank - INFO - Graph: NetworkX Graph with 420 nodes and 3651 edges.\n", + "2025-11-26 01:13:14,958 - bioneuralnet.clustering.correlated_pagerank - INFO - Omics Data: DataFrame with shape (658, 900).\n", + "2025-11-26 01:13:14,958 - bioneuralnet.clustering.correlated_pagerank - INFO - Phenotype Data: Series with 658 samples.\n", + "2025-11-26 01:13:14,958 - bioneuralnet.clustering.correlated_pagerank - INFO - Alpha: 0.9\n", + "2025-11-26 01:13:14,959 - bioneuralnet.clustering.correlated_pagerank - INFO - Max Iterations: 100\n", + "2025-11-26 01:13:14,959 - bioneuralnet.clustering.correlated_pagerank - INFO - Tolerance: 1e-06\n", + "2025-11-26 01:13:14,959 - bioneuralnet.clustering.correlated_pagerank - INFO - K (Composite Score Weight): 0.5\n", + "2025-11-26 01:13:14,960 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 01:13:14,960 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 01:13:14,961 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 01:13:14,961 - bioneuralnet.clustering.correlated_pagerank - INFO - Initialized Correlated PageRank. device=cpu\n", + "2025-11-26 01:13:15,438 - bioneuralnet.clustering.correlated_pagerank - INFO - Generated personalization vector for seed nodes: ['BCHE_590', 'ACSM1_116285', 'hsa_mir_181c', 'ALDH1A1', 'RASD2_23551', 'HSD17B14', 'ADAMTSL1_92949', 'hsa_mir_1180', 'VAT1L_57687', 'RASGRF2_5924', 'C19orf21_126353', 'CPXM2_119587', 'UBE2QL1_134111', 'PDE1C_5137', 'ELFN2_114794', 'hsa_mir_335', 'CXCL6_6372', 'PTGER3_5733', 'ADCY1_107', 'hsa_mir_219_1', 'hsa_mir_203', 'CCDC135_84229', 'hsa_mir_181d', 'hsa_mir_3615', 'ADAMTS5_11096', 'B3GALT5_10317', 'CLDN3_1365', 'HGFAC_3083', 'C2orf62_375307', 'DACT2_168002', 'SULT4A1_25830', 'hsa_mir_10b', 'hsa_mir_1254', 'FOXJ1_2302', 'hsa_mir_3934', 'DACH1_1602', 'TMEM71', 'AKR1C2_1646', 'hsa_mir_676', 'BDKRB2_624', 'hsa_mir_577', 'hsa_mir_139', 'AVPR1A_552', 'C19orf51_352909', 'C3orf24', 'hsa_mir_100', 'HOXD10_3236', 'RASGEF1C_255426', 'CA4_762', 'C14orf129', 'hsa_mir_625', 'ADORA1_134', 'SLPI_6590', 'BTNL9_153579', 'C6orf174_387104', 'C1orf114_57821', 'C2orf89_129293', 'SPARCL1_8404', 'CLSTN2_64084', 'hsa_mir_135b', 'C4orf26', 'DGKI_9162', 'SORCS2_57537', 'CDH17_1015', 'C2orf72_257407', 'ANGPT1_284', 'CORO6_84940', 'CCDC108_255101', 'BCAN', 'hsa_mir_21', 'GPR116_221395', 'CA8_767', 'hsa_mir_551b', 'CPNE7_27132', 'hsa_mir_31', 'PRDM16_63976', 'hsa_mir_211', 'HMGCS2_3158', 'PCDH17_27253', 'ACTC1_70', 'hsa_mir_582', 'GGTLC2', 'MPPED2_744', 'DDR2_4921', 'PDZD2_23037', 'KCNN3_3782', 'CNTN6_27255', 'LOC645638']\n", + "2025-11-26 01:13:15,444 - bioneuralnet.clustering.correlated_pagerank - INFO - PageRank computation completed.\n", + "2025-11-26 01:13:19,148 - bioneuralnet.clustering.correlated_pagerank - INFO - Sweep cut resulted in cluster of size 176 with conductance 0.225 and correlation -0.76.\n", + "2025-11-26 01:13:19,149 - bioneuralnet.clustering.correlated_pagerank - INFO - PageRank clustering completed successfully.\n", + "2025-11-26 01:13:19,149 - bioneuralnet.clustering.hybrid_louvain - INFO - Iteration 3: cluster size=176, Conductance=0.225 Correlation=-0.760 score=-0.267\n", + "2025-11-26 01:13:19,154 - bioneuralnet.clustering.hybrid_louvain - INFO - Hybrid Louvain completed after 4 iterations.\n", + "2025-11-26 01:13:19,158 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 01:13:19,159 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 01:13:19,160 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 01:13:19,160 - bioneuralnet.clustering.hybrid_louvain - INFO - Initializing HybridLouvain...\n", + "2025-11-26 01:13:19,160 - bioneuralnet.clustering.hybrid_louvain - INFO - Input G is a DataFrame; converting adjacency matrix to NetworkX graph.\n", + "2025-11-26 01:13:19,180 - bioneuralnet.clustering.hybrid_louvain - INFO - Initialized HybridLouvain with 900 graph nodes, 900 omics columns, 658 phenotype rows; max_iter=3, k3=0.2, k4=0.8, tune=False\n", + "2025-11-26 01:13:19,181 - bioneuralnet.clustering.hybrid_louvain - INFO - \n", + "Iteration 1/3: Running Correlated Louvain...\n", + "2025-11-26 01:13:19,196 - bioneuralnet.clustering.correlated_louvain - INFO - CorrelatedLouvain(k3=0.2, k4=0.8, nodes=900, edges=8628, features=900)\n", + "2025-11-26 01:13:19,197 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 01:13:19,198 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 01:13:19,198 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 01:13:19,198 - bioneuralnet.clustering.correlated_louvain - INFO - Running standard community detection...\n", + "2025-11-26 01:13:19,325 - bioneuralnet.clustering.correlated_louvain - INFO - Computed quality (supervised): Q = 0.6936, avg_corr = 0.4574, combined = 0.5046\n", + "2025-11-26 01:13:19,325 - bioneuralnet.clustering.correlated_louvain - INFO - Final quality: 0.5046\n", + "2025-11-26 01:13:19,326 - bioneuralnet.clustering.correlated_louvain - INFO - CorrelatedLouvain found 11 communities (nodes=900)\n", + "2025-11-26 01:13:19,384 - bioneuralnet.clustering.correlated_louvain - INFO - Computed quality (supervised): Q = 0.6936, avg_corr = 0.4574, combined = 0.5046\n", + "2025-11-26 01:13:19,384 - bioneuralnet.clustering.hybrid_louvain - INFO - Iteration 1: Louvain Quality = 0.5046\n", + "2025-11-26 01:13:19,438 - bioneuralnet.clustering.hybrid_louvain - INFO - Selected seed community of size 59 with correlation 0.8725\n", + "2025-11-26 01:13:19,438 - bioneuralnet.clustering.correlated_pagerank - INFO - Initialized PageRank with the following parameters:\n", + "2025-11-26 01:13:19,439 - bioneuralnet.clustering.correlated_pagerank - INFO - Graph: NetworkX Graph with 900 nodes and 8628 edges.\n", + "2025-11-26 01:13:19,439 - bioneuralnet.clustering.correlated_pagerank - INFO - Omics Data: DataFrame with shape (658, 900).\n", + "2025-11-26 01:13:19,439 - bioneuralnet.clustering.correlated_pagerank - INFO - Phenotype Data: Series with 658 samples.\n", + "2025-11-26 01:13:19,440 - bioneuralnet.clustering.correlated_pagerank - INFO - Alpha: 0.9\n", + "2025-11-26 01:13:19,440 - bioneuralnet.clustering.correlated_pagerank - INFO - Max Iterations: 100\n", + "2025-11-26 01:13:19,440 - bioneuralnet.clustering.correlated_pagerank - INFO - Tolerance: 1e-06\n", + "2025-11-26 01:13:19,440 - bioneuralnet.clustering.correlated_pagerank - INFO - K (Composite Score Weight): 0.5\n", + "2025-11-26 01:13:19,441 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 01:13:19,442 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 01:13:19,442 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 01:13:19,442 - bioneuralnet.clustering.correlated_pagerank - INFO - Initialized Correlated PageRank. device=cpu\n", + "2025-11-26 01:13:19,655 - bioneuralnet.clustering.correlated_pagerank - INFO - Generated personalization vector for seed nodes: ['BAAT', 'CAPG', 'CP', 'FABP7_2173', 'SLC6A3_6531', 'CP_1356', 'CA9_768', 'CKMT1B_1159', 'CYP2J2_1573', 'CREB3L3_84699', 'ANGPTL4_51129', 'MYEOV_26579', 'TJP3_27134', 'PCSK6_5046', 'C1QL1_10882', 'MCHR1_2847', 'C19orf46_163183', 'APOB_338', 'CKMT1A_548596', 'MAP7D2_256714', 'CCL20_6364', 'LOX_4015', 'C6orf168_84553', 'MAL_4118', 'LOC100131551_100131551', 'CNTN3_5067', 'PCOLCE2_26577', 'CDCA2_157313', 'C1orf116_79098', 'WFDC2_10406', 'ALDOC_230', 'ANO5_203859', 'PPFIA4_8497', 'STK33_65975', 'EPS8L1_54869', 'CBLC_23624', 'SEMA5B_54437', 'TMEM45A_55076', 'OGDHL_55753', 'EGLN3_112399', 'C4orf6_10141', 'FAM155B_27112', 'C7orf68_29923', 'B3GNT4_79369', 'hsa_mir_122', 'hsa_mir_599', 'hsa_mir_183', 'hsa_mir_96', 'hsa_mir_210', 'hsa_mir_182', 'hsa_mir_374c', 'hsa_mir_1271', 'hsa_mir_200b', 'hsa_mir_429', 'hsa_mir_875', 'hsa_mir_3941', 'hsa_mir_200a', 'hsa_mir_3678', 'hsa_mir_3912']\n", + "2025-11-26 01:13:19,666 - bioneuralnet.clustering.correlated_pagerank - INFO - PageRank computation completed.\n", + "2025-11-26 01:13:34,489 - bioneuralnet.clustering.correlated_pagerank - INFO - Sweep cut resulted in cluster of size 451 with conductance 0.087 and correlation 0.89.\n", + "2025-11-26 01:13:34,489 - bioneuralnet.clustering.correlated_pagerank - INFO - PageRank clustering completed successfully.\n", + "2025-11-26 01:13:34,490 - bioneuralnet.clustering.hybrid_louvain - INFO - Iteration 1: cluster size=451, Conductance=0.087 Correlation=0.890 score=-0.401\n", + "2025-11-26 01:13:34,504 - bioneuralnet.clustering.hybrid_louvain - INFO - \n", + "Iteration 2/3: Running Correlated Louvain...\n", + "2025-11-26 01:13:34,512 - bioneuralnet.clustering.correlated_louvain - INFO - CorrelatedLouvain(k3=0.2, k4=0.8, nodes=451, edges=3950, features=900)\n", + "2025-11-26 01:13:34,513 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 01:13:34,514 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 01:13:34,514 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 01:13:34,514 - bioneuralnet.clustering.correlated_louvain - INFO - Running standard community detection...\n", + "2025-11-26 01:13:34,616 - bioneuralnet.clustering.correlated_louvain - INFO - Computed quality (supervised): Q = 0.6249, avg_corr = 0.6253, combined = 0.6252\n", + "2025-11-26 01:13:34,616 - bioneuralnet.clustering.correlated_louvain - INFO - Final quality: 0.6252\n", + "2025-11-26 01:13:34,617 - bioneuralnet.clustering.correlated_louvain - INFO - CorrelatedLouvain found 10 communities (nodes=451)\n", + "2025-11-26 01:13:34,661 - bioneuralnet.clustering.correlated_louvain - INFO - Computed quality (supervised): Q = 0.6249, avg_corr = 0.6253, combined = 0.6252\n", + "2025-11-26 01:13:34,662 - bioneuralnet.clustering.hybrid_louvain - INFO - Iteration 2: Louvain Quality = 0.6252\n", + "2025-11-26 01:13:34,701 - bioneuralnet.clustering.hybrid_louvain - INFO - Selected seed community of size 57 with correlation 0.8714\n", + "2025-11-26 01:13:34,702 - bioneuralnet.clustering.correlated_pagerank - INFO - Initialized PageRank with the following parameters:\n", + "2025-11-26 01:13:34,702 - bioneuralnet.clustering.correlated_pagerank - INFO - Graph: NetworkX Graph with 451 nodes and 3950 edges.\n", + "2025-11-26 01:13:34,703 - bioneuralnet.clustering.correlated_pagerank - INFO - Omics Data: DataFrame with shape (658, 900).\n", + "2025-11-26 01:13:34,703 - bioneuralnet.clustering.correlated_pagerank - INFO - Phenotype Data: Series with 658 samples.\n", + "2025-11-26 01:13:34,703 - bioneuralnet.clustering.correlated_pagerank - INFO - Alpha: 0.9\n", + "2025-11-26 01:13:34,703 - bioneuralnet.clustering.correlated_pagerank - INFO - Max Iterations: 100\n", + "2025-11-26 01:13:34,704 - bioneuralnet.clustering.correlated_pagerank - INFO - Tolerance: 1e-06\n", + "2025-11-26 01:13:34,704 - bioneuralnet.clustering.correlated_pagerank - INFO - K (Composite Score Weight): 0.5\n", + "2025-11-26 01:13:34,704 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 01:13:34,705 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 01:13:34,706 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 01:13:34,706 - bioneuralnet.clustering.correlated_pagerank - INFO - Initialized Correlated PageRank. device=cpu\n", + "2025-11-26 01:13:34,936 - bioneuralnet.clustering.correlated_pagerank - INFO - Generated personalization vector for seed nodes: ['CAPG', 'CP', 'FABP7_2173', 'SLC6A3_6531', 'CP_1356', 'CA9_768', 'CKMT1B_1159', 'CYP2J2_1573', 'CREB3L3_84699', 'ANGPTL4_51129', 'MYEOV_26579', 'TJP3_27134', 'PCSK6_5046', 'C1QL1_10882', 'MCHR1_2847', 'C19orf46_163183', 'APOB_338', 'CKMT1A_548596', 'MAP7D2_256714', 'CCL20_6364', 'LOX_4015', 'C6orf168_84553', 'MAL_4118', 'LOC100131551_100131551', 'CNTN3_5067', 'PCOLCE2_26577', 'CDCA2_157313', 'C1orf116_79098', 'CST6_1474', 'WFDC2_10406', 'ALDOC_230', 'ANO5_203859', 'PPFIA4_8497', 'STK33_65975', 'IGF2BP2_10644', 'EPS8L1_54869', 'CBLC_23624', 'SEMA5B_54437', 'TMEM45A_55076', 'OGDHL_55753', 'EGLN3_112399', 'C4orf6_10141', 'FAM155B_27112', 'C7orf68_29923', 'B3GNT4_79369', 'hsa_mir_122', 'hsa_mir_183', 'hsa_mir_96', 'hsa_mir_210', 'hsa_mir_182', 'hsa_mir_374c', 'hsa_mir_1271', 'hsa_mir_200b', 'hsa_mir_429', 'hsa_mir_3941', 'hsa_mir_200a', 'hsa_mir_3678']\n", + "2025-11-26 01:13:34,942 - bioneuralnet.clustering.correlated_pagerank - INFO - PageRank computation completed.\n", + "2025-11-26 01:13:39,443 - bioneuralnet.clustering.correlated_pagerank - INFO - Sweep cut resulted in cluster of size 193 with conductance 0.221 and correlation 0.9.\n", + "2025-11-26 01:13:39,444 - bioneuralnet.clustering.correlated_pagerank - INFO - PageRank clustering completed successfully.\n", + "2025-11-26 01:13:39,445 - bioneuralnet.clustering.hybrid_louvain - INFO - Iteration 2: cluster size=193, Conductance=0.221 Correlation=0.900 score=-0.340\n", + "2025-11-26 01:13:39,451 - bioneuralnet.clustering.hybrid_louvain - INFO - \n", + "Iteration 3/3: Running Correlated Louvain...\n", + "2025-11-26 01:13:39,456 - bioneuralnet.clustering.correlated_louvain - INFO - CorrelatedLouvain(k3=0.2, k4=0.8, nodes=193, edges=1530, features=900)\n", + "2025-11-26 01:13:39,457 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 01:13:39,458 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 01:13:39,459 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 01:13:39,460 - bioneuralnet.clustering.correlated_louvain - INFO - Running standard community detection...\n", + "2025-11-26 01:13:39,506 - bioneuralnet.clustering.correlated_louvain - INFO - Computed quality (supervised): Q = 0.5349, avg_corr = 0.7495, combined = 0.7066\n", + "2025-11-26 01:13:39,507 - bioneuralnet.clustering.correlated_louvain - INFO - Final quality: 0.7066\n", + "2025-11-26 01:13:39,507 - bioneuralnet.clustering.correlated_louvain - INFO - CorrelatedLouvain found 7 communities (nodes=193)\n", + "2025-11-26 01:13:39,533 - bioneuralnet.clustering.correlated_louvain - INFO - Computed quality (supervised): Q = 0.5349, avg_corr = 0.7495, combined = 0.7066\n", + "2025-11-26 01:13:39,533 - bioneuralnet.clustering.hybrid_louvain - INFO - Iteration 3: Louvain Quality = 0.7066\n", + "2025-11-26 01:13:39,558 - bioneuralnet.clustering.hybrid_louvain - INFO - Selected seed community of size 58 with correlation 0.8576\n", + "2025-11-26 01:13:39,559 - bioneuralnet.clustering.correlated_pagerank - INFO - Initialized PageRank with the following parameters:\n", + "2025-11-26 01:13:39,559 - bioneuralnet.clustering.correlated_pagerank - INFO - Graph: NetworkX Graph with 193 nodes and 1530 edges.\n", + "2025-11-26 01:13:39,560 - bioneuralnet.clustering.correlated_pagerank - INFO - Omics Data: DataFrame with shape (658, 900).\n", + "2025-11-26 01:13:39,560 - bioneuralnet.clustering.correlated_pagerank - INFO - Phenotype Data: Series with 658 samples.\n", + "2025-11-26 01:13:39,560 - bioneuralnet.clustering.correlated_pagerank - INFO - Alpha: 0.9\n", + "2025-11-26 01:13:39,560 - bioneuralnet.clustering.correlated_pagerank - INFO - Max Iterations: 100\n", + "2025-11-26 01:13:39,561 - bioneuralnet.clustering.correlated_pagerank - INFO - Tolerance: 1e-06\n", + "2025-11-26 01:13:39,561 - bioneuralnet.clustering.correlated_pagerank - INFO - K (Composite Score Weight): 0.5\n", + "2025-11-26 01:13:39,561 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 01:13:39,562 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 01:13:39,563 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 01:13:39,563 - bioneuralnet.clustering.correlated_pagerank - INFO - Initialized Correlated PageRank. device=cpu\n", + "2025-11-26 01:13:39,832 - bioneuralnet.clustering.correlated_pagerank - INFO - Generated personalization vector for seed nodes: ['C4orf48_401115', 'FATE1_89885', 'BARX2_8538', 'FOLH1_2346', 'NGF_4803', 'hsa_mir_3678', 'C2orf72_257407', 'hsa_mir_503', 'OR51E1_143503', 'ANGPT2_285', 'CXCL10_3627', 'GABRE_2564', 'C6orf223_221416', 'ESM1_11082', 'CXCL9_4283', 'KLF14', 'hsa_mir_489', 'CNTD2_79935', 'CSPG4_1464', 'hsa_mir_224', 'CHL1_10752', 'CREB3L3_84699', 'FLT1_2321', 'COL5A3_50509', 'GPR4_2828', 'hsa_mir_126', 'hsa_let_7b', 'hsa_mir_561', 'BTNL9_153579', 'PCDH17_27253', 'COL21A1_81578', 'SLC35F1_222553', 'STC1_6781', 'ANXA3_306', 'APLN_8862', 'BNC1_646', 'LGI4_163175', 'hsa_mir_653', 'TRIM58', 'CAMK2B_816', 'CDH13_1012', 'CALCR_799', 'hsa_mir_3912', 'APOLD1_81575', 'CCNO_10309', 'KLHDC8A_55220', 'STK33_65975', 'RGS5_8490', 'LOC554202_554202', 'HMCN1_83872', 'C3orf70_285382', 'CD36_948', 'ADAMTS5_11096', 'COX4I2_84701', 'CXCL11_6373', 'TRPC6_7225', 'LCN2_3934', 'LOH3CR2A']\n", + "2025-11-26 01:13:39,836 - bioneuralnet.clustering.correlated_pagerank - INFO - PageRank computation completed.\n", + "2025-11-26 01:13:41,069 - bioneuralnet.clustering.correlated_pagerank - INFO - Sweep cut resulted in cluster of size 82 with conductance 0.135 and correlation 0.87.\n", + "2025-11-26 01:13:41,069 - bioneuralnet.clustering.correlated_pagerank - INFO - PageRank clustering completed successfully.\n", + "2025-11-26 01:13:41,070 - bioneuralnet.clustering.hybrid_louvain - INFO - Iteration 3: cluster size=82, Conductance=0.135 Correlation=0.870 score=-0.367\n", + "2025-11-26 01:13:41,072 - bioneuralnet.clustering.hybrid_louvain - INFO - Hybrid Louvain completed after 4 iterations.\n", + "2025-11-26 01:13:41,076 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 01:13:41,077 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 01:13:41,077 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 01:13:41,078 - bioneuralnet.clustering.hybrid_louvain - INFO - Initializing HybridLouvain...\n", + "2025-11-26 01:13:41,078 - bioneuralnet.clustering.hybrid_louvain - INFO - Input G is a DataFrame; converting adjacency matrix to NetworkX graph.\n", + "2025-11-26 01:13:41,100 - bioneuralnet.clustering.hybrid_louvain - INFO - Initialized HybridLouvain with 900 graph nodes, 900 omics columns, 658 phenotype rows; max_iter=3, k3=0.2, k4=0.8, tune=False\n", + "2025-11-26 01:13:41,100 - bioneuralnet.clustering.hybrid_louvain - INFO - \n", + "Iteration 1/3: Running Correlated Louvain...\n", + "2025-11-26 01:13:41,124 - bioneuralnet.clustering.correlated_louvain - INFO - CorrelatedLouvain(k3=0.2, k4=0.8, nodes=900, edges=8628, features=900)\n", + "2025-11-26 01:13:41,125 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 01:13:41,126 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 01:13:41,126 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 01:13:41,127 - bioneuralnet.clustering.correlated_louvain - INFO - Running standard community detection...\n", + "2025-11-26 01:13:41,261 - bioneuralnet.clustering.correlated_louvain - INFO - Computed quality (supervised): Q = 0.6936, avg_corr = 0.3912, combined = 0.4517\n", + "2025-11-26 01:13:41,262 - bioneuralnet.clustering.correlated_louvain - INFO - Final quality: 0.4517\n", + "2025-11-26 01:13:41,262 - bioneuralnet.clustering.correlated_louvain - INFO - CorrelatedLouvain found 11 communities (nodes=900)\n", + "2025-11-26 01:13:41,327 - bioneuralnet.clustering.correlated_louvain - INFO - Computed quality (supervised): Q = 0.6936, avg_corr = 0.3912, combined = 0.4517\n", + "2025-11-26 01:13:41,328 - bioneuralnet.clustering.hybrid_louvain - INFO - Iteration 1: Louvain Quality = 0.4517\n", + "2025-11-26 01:13:41,385 - bioneuralnet.clustering.hybrid_louvain - INFO - Selected seed community of size 81 with correlation 0.7998\n", + "2025-11-26 01:13:41,386 - bioneuralnet.clustering.correlated_pagerank - INFO - Initialized PageRank with the following parameters:\n", + "2025-11-26 01:13:41,387 - bioneuralnet.clustering.correlated_pagerank - INFO - Graph: NetworkX Graph with 900 nodes and 8628 edges.\n", + "2025-11-26 01:13:41,387 - bioneuralnet.clustering.correlated_pagerank - INFO - Omics Data: DataFrame with shape (658, 900).\n", + "2025-11-26 01:13:41,387 - bioneuralnet.clustering.correlated_pagerank - INFO - Phenotype Data: Series with 658 samples.\n", + "2025-11-26 01:13:41,388 - bioneuralnet.clustering.correlated_pagerank - INFO - Alpha: 0.9\n", + "2025-11-26 01:13:41,388 - bioneuralnet.clustering.correlated_pagerank - INFO - Max Iterations: 100\n", + "2025-11-26 01:13:41,388 - bioneuralnet.clustering.correlated_pagerank - INFO - Tolerance: 1e-06\n", + "2025-11-26 01:13:41,389 - bioneuralnet.clustering.correlated_pagerank - INFO - K (Composite Score Weight): 0.5\n", + "2025-11-26 01:13:41,389 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 01:13:41,390 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 01:13:41,391 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 01:13:41,391 - bioneuralnet.clustering.correlated_pagerank - INFO - Initialized Correlated PageRank. device=cpu\n", + "2025-11-26 01:13:41,824 - bioneuralnet.clustering.correlated_pagerank - INFO - Generated personalization vector for seed nodes: ['IL18', 'RHCG_51458', 'ATP6V0A4_50617', 'SLC4A1_6521', 'KLK1_3816', 'ATP6V0D2_245972', 'CLDN8_9073', 'FOXI1_2299', 'DMRT2_10655', 'HEPACAM2_253012', 'CLCNKB_1188', 'ATP6V1B1_525', 'CFTR_1080', 'GCGR_2642', 'SLC26A7_115111', 'CLCNKA_1187', 'FGF9_2254', 'BMPR1B_658', 'TMPRSS2_7113', 'BSND_7809', 'CD70_970', 'AQP6_363', 'CWH43_80157', 'TRIM50_135892', 'COL23A1_91522', 'C16orf89_146556', 'SLC9A2_6549', 'CALCA_796', 'OLIG1_116448', 'CLNK_116449', 'CYP11A1_1583', 'RANBP3L_202151', 'DIRAS1_148252', 'C1orf168_199920', 'INPP5J_27124', 'C15orf59_388135', 'CCBE1_147372', 'EPB41L4B_54566', 'GCOM1_145781', 'C9orf84_158401', 'ATP6V1G3_127124', 'DGCR5_26220', 'VWA5B1_127731', 'CHST9_83539', 'KRTAP5_8_57830', 'DEFB1_1672', 'ST8SIA5_29906', 'LOC643008_643008', 'CEL_1056', 'C6orf105_84830', 'CR1L_1379', 'ASB5_140458', 'MARCH10_162333', 'ANGPTL1_9068', 'hsa_mir_891a', 'hsa_mir_141', 'hsa_mir_187', 'hsa_mir_200c', 'hsa_mir_375', 'hsa_mir_592', 'hsa_mir_129_1', 'hsa_mir_129_2', 'hsa_mir_184', 'hsa_mir_218_2', 'hsa_mir_206', 'hsa_mir_33a', 'hsa_mir_362', 'hsa_mir_218_1', 'hsa_mir_188', 'hsa_mir_363', 'hsa_mir_20b', 'hsa_mir_651', 'hsa_mir_3200', 'hsa_mir_660', 'hsa_mir_1468', 'hsa_mir_501', 'hsa_let_7f_2', 'hsa_mir_500b', 'hsa_mir_30c_1', 'hsa_mir_532', 'hsa_mir_23c']\n", + "2025-11-26 01:13:41,836 - bioneuralnet.clustering.correlated_pagerank - INFO - PageRank computation completed.\n", + "2025-11-26 01:13:57,321 - bioneuralnet.clustering.correlated_pagerank - INFO - Sweep cut resulted in cluster of size 448 with conductance 0.116 and correlation -0.85.\n", + "2025-11-26 01:13:57,322 - bioneuralnet.clustering.correlated_pagerank - INFO - PageRank clustering completed successfully.\n", + "2025-11-26 01:13:57,323 - bioneuralnet.clustering.hybrid_louvain - INFO - Iteration 1: cluster size=448, Conductance=0.116 Correlation=-0.850 score=-0.367\n", + "2025-11-26 01:13:57,336 - bioneuralnet.clustering.hybrid_louvain - INFO - \n", + "Iteration 2/3: Running Correlated Louvain...\n", + "2025-11-26 01:13:57,345 - bioneuralnet.clustering.correlated_louvain - INFO - CorrelatedLouvain(k3=0.2, k4=0.8, nodes=448, edges=3766, features=900)\n", + "2025-11-26 01:13:57,345 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 01:13:57,346 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 01:13:57,347 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 01:13:57,347 - bioneuralnet.clustering.correlated_louvain - INFO - Running standard community detection...\n", + "2025-11-26 01:13:57,433 - bioneuralnet.clustering.correlated_louvain - INFO - Computed quality (supervised): Q = 0.6664, avg_corr = 0.5211, combined = 0.5502\n", + "2025-11-26 01:13:57,433 - bioneuralnet.clustering.correlated_louvain - INFO - Final quality: 0.5502\n", + "2025-11-26 01:13:57,433 - bioneuralnet.clustering.correlated_louvain - INFO - CorrelatedLouvain found 9 communities (nodes=448)\n", + "2025-11-26 01:13:57,476 - bioneuralnet.clustering.correlated_louvain - INFO - Computed quality (supervised): Q = 0.6664, avg_corr = 0.5211, combined = 0.5502\n", + "2025-11-26 01:13:57,476 - bioneuralnet.clustering.hybrid_louvain - INFO - Iteration 2: Louvain Quality = 0.5502\n", + "2025-11-26 01:13:57,514 - bioneuralnet.clustering.hybrid_louvain - INFO - Selected seed community of size 84 with correlation 0.8241\n", + "2025-11-26 01:13:57,514 - bioneuralnet.clustering.correlated_pagerank - INFO - Initialized PageRank with the following parameters:\n", + "2025-11-26 01:13:57,515 - bioneuralnet.clustering.correlated_pagerank - INFO - Graph: NetworkX Graph with 448 nodes and 3766 edges.\n", + "2025-11-26 01:13:57,515 - bioneuralnet.clustering.correlated_pagerank - INFO - Omics Data: DataFrame with shape (658, 900).\n", + "2025-11-26 01:13:57,516 - bioneuralnet.clustering.correlated_pagerank - INFO - Phenotype Data: Series with 658 samples.\n", + "2025-11-26 01:13:57,516 - bioneuralnet.clustering.correlated_pagerank - INFO - Alpha: 0.9\n", + "2025-11-26 01:13:57,516 - bioneuralnet.clustering.correlated_pagerank - INFO - Max Iterations: 100\n", + "2025-11-26 01:13:57,517 - bioneuralnet.clustering.correlated_pagerank - INFO - Tolerance: 1e-06\n", + "2025-11-26 01:13:57,517 - bioneuralnet.clustering.correlated_pagerank - INFO - K (Composite Score Weight): 0.5\n", + "2025-11-26 01:13:57,518 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 01:13:57,519 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 01:13:57,519 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 01:13:57,519 - bioneuralnet.clustering.correlated_pagerank - INFO - Initialized Correlated PageRank. device=cpu\n", + "2025-11-26 01:13:57,967 - bioneuralnet.clustering.correlated_pagerank - INFO - Generated personalization vector for seed nodes: ['ASB5_140458', 'C1orf168_199920', 'OLIG1_116448', 'FOXI1_2299', 'LOC643008_643008', 'hsa_mir_592', 'BMPR1B_658', 'CD70_970', 'hsa_mir_1258', 'FGF9_2254', 'C15orf59_388135', 'ATP6V1B1_525', 'C9orf84_158401', 'hsa_mir_218_2', 'GCGR_2642', 'GALNT14_79623', 'DHRS2_10202', 'CNTNAP5_129684', 'CFTR_1080', 'ATP6V0A4_50617', 'hsa_mir_1468', 'hsa_mir_129_1', 'LRRTM1_347730', 'TRIM50_135892', 'DGCR5_26220', 'DMRT2_10655', 'hsa_mir_651', 'ALPK2_115701', 'hsa_mir_874', 'ANGPTL1_9068', 'hsa_mir_891a', 'C3_718', 'VWA5B1_127731', 'CLDN8_9073', 'RANBP3L_202151', 'hsa_mir_129_2', 'CYP11A1_1583', 'hsa_mir_141', 'CHST9_83539', 'CR1L_1379', 'CEND1_51286', 'hsa_mir_206', 'hsa_mir_23c', 'C17orf55_284185', 'SLC9A2_6549', 'hsa_mir_218_1', 'IL18', 'hsa_mir_200c', 'CWH43_80157', 'CKMT2_1160', 'hsa_mir_618', 'DEFB1_1672', 'BSND_7809', 'SLC26A7_115111', 'ABCC3_8714', 'CCDC78_124093', 'hsa_mir_30c_1', 'CLNK_116449', 'CALCA_796', 'TMPRSS2_7113', 'RHCG_51458', 'CLCNKB_1188', 'DIRAS1_148252', 'ST8SIA5_29906', 'C6orf105_84830', 'hsa_mir_187', 'SLC4A1_6521', 'CLCNKA_1187', 'CEL_1056', 'AQP6_363', 'MARCH10_162333', 'ATP6V0D2_245972', 'C16orf89_146556', 'GCOM1_145781', 'SEZ6L_23544', 'INPP5J_27124', 'EPB41L4B_54566', 'HEPACAM2_253012', 'COL23A1_91522', 'CCBE1_147372', 'ATP6V1G3_127124', 'KLK1_3816', 'CTNNA2_1496', 'KRTAP5_8_57830']\n", + "2025-11-26 01:13:57,974 - bioneuralnet.clustering.correlated_pagerank - INFO - PageRank computation completed.\n", + "2025-11-26 01:14:02,424 - bioneuralnet.clustering.correlated_pagerank - INFO - Sweep cut resulted in cluster of size 211 with conductance 0.185 and correlation -0.85.\n", + "2025-11-26 01:14:02,425 - bioneuralnet.clustering.correlated_pagerank - INFO - PageRank clustering completed successfully.\n", + "2025-11-26 01:14:02,425 - bioneuralnet.clustering.hybrid_louvain - INFO - Iteration 2: cluster size=211, Conductance=0.185 Correlation=-0.850 score=-0.333\n", + "2025-11-26 01:14:02,431 - bioneuralnet.clustering.hybrid_louvain - INFO - \n", + "Iteration 3/3: Running Correlated Louvain...\n", + "2025-11-26 01:14:02,435 - bioneuralnet.clustering.correlated_louvain - INFO - CorrelatedLouvain(k3=0.2, k4=0.8, nodes=211, edges=1490, features=900)\n", + "2025-11-26 01:14:02,436 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 01:14:02,437 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 01:14:02,437 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 01:14:02,438 - bioneuralnet.clustering.correlated_louvain - INFO - Running standard community detection...\n", + "2025-11-26 01:14:02,491 - bioneuralnet.clustering.correlated_louvain - INFO - Computed quality (supervised): Q = 0.5780, avg_corr = 0.5743, combined = 0.5750\n", + "2025-11-26 01:14:02,491 - bioneuralnet.clustering.correlated_louvain - INFO - Final quality: 0.5750\n", + "2025-11-26 01:14:02,492 - bioneuralnet.clustering.correlated_louvain - INFO - CorrelatedLouvain found 10 communities (nodes=211)\n", + "2025-11-26 01:14:02,527 - bioneuralnet.clustering.correlated_louvain - INFO - Computed quality (supervised): Q = 0.5780, avg_corr = 0.5743, combined = 0.5750\n", + "2025-11-26 01:14:02,527 - bioneuralnet.clustering.hybrid_louvain - INFO - Iteration 3: Louvain Quality = 0.5750\n", + "2025-11-26 01:14:02,561 - bioneuralnet.clustering.hybrid_louvain - INFO - Selected seed community of size 34 with correlation -0.8628\n", + "2025-11-26 01:14:02,561 - bioneuralnet.clustering.correlated_pagerank - INFO - Initialized PageRank with the following parameters:\n", + "2025-11-26 01:14:02,562 - bioneuralnet.clustering.correlated_pagerank - INFO - Graph: NetworkX Graph with 211 nodes and 1490 edges.\n", + "2025-11-26 01:14:02,562 - bioneuralnet.clustering.correlated_pagerank - INFO - Omics Data: DataFrame with shape (658, 900).\n", + "2025-11-26 01:14:02,563 - bioneuralnet.clustering.correlated_pagerank - INFO - Phenotype Data: Series with 658 samples.\n", + "2025-11-26 01:14:02,563 - bioneuralnet.clustering.correlated_pagerank - INFO - Alpha: 0.9\n", + "2025-11-26 01:14:02,563 - bioneuralnet.clustering.correlated_pagerank - INFO - Max Iterations: 100\n", + "2025-11-26 01:14:02,563 - bioneuralnet.clustering.correlated_pagerank - INFO - Tolerance: 1e-06\n", + "2025-11-26 01:14:02,564 - bioneuralnet.clustering.correlated_pagerank - INFO - K (Composite Score Weight): 0.5\n", + "2025-11-26 01:14:02,564 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 01:14:02,565 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 01:14:02,566 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 01:14:02,566 - bioneuralnet.clustering.correlated_pagerank - INFO - Initialized Correlated PageRank. device=cpu\n", + "2025-11-26 01:14:02,682 - bioneuralnet.clustering.correlated_pagerank - INFO - Generated personalization vector for seed nodes: ['CEND1_51286', 'ST8SIA5_29906', 'LRRTM1_347730', 'BDKRB1_623', 'LOC440354', 'EPGN', 'hsa_mir_222', 'C17orf55_284185', 'hsa_mir_874', 'ANGPTL1', 'IL18', 'CKMT2_1160', 'hsa_mir_618', 'C1orf168', 'HOXA7', 'KCNRG', 'hsa_mir_1258', 'SEZ6L_23544', 'hsa_mir_130a', 'hsa_mir_221', 'HULC', 'GALNT14_79623', 'CYS1_192668', 'MIR744', 'CNTNAP5_129684', 'MIR618', 'CHST9_83539', 'CTNNA2_1496', 'C14orf53', 'hsa_mir_33a', 'BICC1_80114', 'ABP1_26', 'hsa_mir_766', 'CR1L_1379']\n", + "2025-11-26 01:14:02,685 - bioneuralnet.clustering.correlated_pagerank - INFO - PageRank computation completed.\n", + "2025-11-26 01:14:04,038 - bioneuralnet.clustering.correlated_pagerank - INFO - Sweep cut resulted in cluster of size 202 with conductance 0.058 and correlation -0.85.\n", + "2025-11-26 01:14:04,038 - bioneuralnet.clustering.correlated_pagerank - INFO - PageRank clustering completed successfully.\n", + "2025-11-26 01:14:04,039 - bioneuralnet.clustering.hybrid_louvain - INFO - Iteration 3: cluster size=202, Conductance=0.058 Correlation=-0.850 score=-0.396\n", + "2025-11-26 01:14:04,044 - bioneuralnet.clustering.hybrid_louvain - INFO - Hybrid Louvain completed after 4 iterations.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Driver modules (one-vs-rest):\n", + " 1: 857 features\n", + " 0: 451 features\n", + " 2: 448 features\n" + ] + } + ], + "source": [ + "from bioneuralnet.clustering import HybridLouvain\n", + "\n", + "labels = target[\"target\"]\n", + "unique_phenotypes = labels.unique()\n", + "\n", + "multiclass_results = {}\n", + "summary = []\n", + "\n", + "for phenotype in unique_phenotypes:\n", + " # one-vs-rest encoding\n", + " y_binary = (labels == phenotype).astype(int)\n", + "\n", + " hl = HybridLouvain(\n", + " G=correlation_10,\n", + " B=omics_kipan,\n", + " Y=y_binary,\n", + " k3=0.2,\n", + " k4=0.8,\n", + " max_iter=3,\n", + " seed=SEED,\n", + " )\n", + "\n", + " subnetworks = hl.run(as_dfs=True)\n", + "\n", + " if subnetworks:\n", + " df = subnetworks[0]\n", + " multiclass_results[phenotype] = df\n", + " summary.append((phenotype, df.shape[1]))\n", + " else:\n", + " summary.append((phenotype, 0))\n", + "\n", + "print(\"\\nDriver modules (one-vs-rest):\")\n", + "for pheno, n_feats in summary:\n", + " print(f\" {pheno}: {n_feats} features\")" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "efbff52f", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-11-26 01:14:04,056 - bioneuralnet.downstream_task.dpmon - INFO - Output directory set to: /home/vicente/Github/BioNeuralNet/dpmon_results_GCN_FINAL/kipan/threshold_10\n", + "2025-11-26 01:14:04,057 - bioneuralnet.downstream_task.dpmon - INFO - Initialized DPMON with model: GCN\n", + "2025-11-26 01:14:04,057 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 01:14:04,058 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 01:14:04,058 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 01:14:04,059 - bioneuralnet.downstream_task.dpmon - INFO - Random seed set to: 8183\n", + "2025-11-26 01:14:04,064 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:14:04,067 - bioneuralnet.downstream_task.dpmon - INFO - Running in Cross-Validation mode (cv=True) with 5 folds.\n", + "2025-11-26 01:14:04,068 - bioneuralnet.downstream_task.dpmon - INFO - CV Setup: Standard 5-fold split.\n", + "2025-11-26 01:14:04,069 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 1/5\n", + "2025-11-26 01:14:04,071 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:14:04,081 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:14:08,191 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:14:08,215 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (526, 901)\n", + "2025-11-26 01:14:08,218\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:14:20,851\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0286s.\n", + "2025-11-26 01:14:21,044 - bioneuralnet.downstream_task.dpmon - INFO - Fold 1 best config: {'gnn_layer_num': 2, 'gnn_hidden_dim': 64, 'lr': 0.0006037895979188083, 'weight_decay': 0.004195106610298857, 'nn_hidden_dim1': 512, 'nn_hidden_dim2': 128, 'ae_encoding_dim': 16, 'num_epochs': 1024, 'gnn_dropout': 0.1, 'gnn_activation': 'elu', 'dim_reduction': 'ae'}\n", + "2025-11-26 01:14:21,045 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 1 using train split only\n", + "2025-11-26 01:14:21,050 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:14:25,269 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:14:26,773 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 1 on test set\n", + "2025-11-26 01:14:26,778 - bioneuralnet.downstream_task.dpmon - INFO - Fold 1 results:\n", + "2025-11-26 01:14:26,778 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9394\n", + "2025-11-26 01:14:26,779 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9287\n", + "2025-11-26 01:14:26,779 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9399\n", + "2025-11-26 01:14:26,779 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9566\n", + "2025-11-26 01:14:26,779 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9908\n", + "2025-11-26 01:14:26,780 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9839\n", + "\n", + "2025-11-26 01:14:26,780 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 2/5\n", + "2025-11-26 01:14:26,782 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:14:26,788 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:14:30,936 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:14:30,947 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (526, 901)\n", + "2025-11-26 01:14:30,951\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:14:41,518\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0283s.\n", + "2025-11-26 01:14:41,641 - bioneuralnet.downstream_task.dpmon - INFO - Fold 2 best config: {'gnn_layer_num': 2, 'gnn_hidden_dim': 64, 'lr': 0.0007034204356990857, 'weight_decay': 5.358735876893429e-05, 'nn_hidden_dim1': 512, 'nn_hidden_dim2': 64, 'ae_encoding_dim': 8, 'num_epochs': 1024, 'gnn_dropout': 0.2, 'gnn_activation': 'relu', 'dim_reduction': 'ae'}\n", + "2025-11-26 01:14:41,642 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 2 using train split only\n", + "2025-11-26 01:14:41,647 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:14:45,770 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:14:47,251 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 2 on test set\n", + "2025-11-26 01:14:47,256 - bioneuralnet.downstream_task.dpmon - INFO - Fold 2 results:\n", + "2025-11-26 01:14:47,257 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9773\n", + "2025-11-26 01:14:47,257 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9648\n", + "2025-11-26 01:14:47,257 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9777\n", + "2025-11-26 01:14:47,257 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9835\n", + "2025-11-26 01:14:47,257 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9928\n", + "2025-11-26 01:14:47,258 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9488\n", + "\n", + "2025-11-26 01:14:47,258 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 3/5\n", + "2025-11-26 01:14:47,260 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:14:47,265 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:14:51,373 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:14:51,385 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (526, 901)\n", + "2025-11-26 01:14:51,388\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:15:03,121\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0079s.\n", + "2025-11-26 01:15:03,293 - bioneuralnet.downstream_task.dpmon - INFO - Fold 3 best config: {'gnn_layer_num': 1, 'gnn_hidden_dim': 256, 'lr': 0.0016239332393902155, 'weight_decay': 1.2898372523486065e-05, 'nn_hidden_dim1': 512, 'nn_hidden_dim2': 128, 'ae_encoding_dim': 4, 'num_epochs': 512, 'gnn_dropout': 0.2, 'gnn_activation': 'elu', 'dim_reduction': 'ae'}\n", + "2025-11-26 01:15:03,293 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 3 using train split only\n", + "2025-11-26 01:15:03,299 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:15:07,440 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:15:08,042 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 3 on test set\n", + "2025-11-26 01:15:08,046 - bioneuralnet.downstream_task.dpmon - INFO - Fold 3 results:\n", + "2025-11-26 01:15:08,047 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9621\n", + "2025-11-26 01:15:08,047 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9621\n", + "2025-11-26 01:15:08,048 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9622\n", + "2025-11-26 01:15:08,048 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9535\n", + "2025-11-26 01:15:08,048 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9955\n", + "2025-11-26 01:15:08,048 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9937\n", + "\n", + "2025-11-26 01:15:08,049 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 4/5\n", + "2025-11-26 01:15:08,051 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:15:08,058 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:15:12,200 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:15:12,211 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (527, 901)\n", + "2025-11-26 01:15:12,214\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:15:23,504\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0155s.\n", + "2025-11-26 01:15:23,665 - bioneuralnet.downstream_task.dpmon - INFO - Fold 4 best config: {'gnn_layer_num': 4, 'gnn_hidden_dim': 128, 'lr': 0.0008628455289617886, 'weight_decay': 0.005542594076906398, 'nn_hidden_dim1': 512, 'nn_hidden_dim2': 128, 'ae_encoding_dim': 16, 'num_epochs': 2048, 'gnn_dropout': 0.2, 'gnn_activation': 'elu', 'dim_reduction': 'linear'}\n", + "2025-11-26 01:15:23,665 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 4 using train split only\n", + "2025-11-26 01:15:23,670 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:15:27,775 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:15:32,476 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 4 on test set\n", + "2025-11-26 01:15:32,482 - bioneuralnet.downstream_task.dpmon - INFO - Fold 4 results:\n", + "2025-11-26 01:15:32,482 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9389\n", + "2025-11-26 01:15:32,482 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9449\n", + "2025-11-26 01:15:32,482 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9390\n", + "2025-11-26 01:15:32,482 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9554\n", + "2025-11-26 01:15:32,482 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9940\n", + "2025-11-26 01:15:32,482 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9919\n", + "\n", + "2025-11-26 01:15:32,483 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 5/5\n", + "2025-11-26 01:15:32,485 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:15:32,490 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:15:36,751 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:15:36,762 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (527, 901)\n", + "2025-11-26 01:15:36,765\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:15:49,425\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0172s.\n", + "2025-11-26 01:15:49,636 - bioneuralnet.downstream_task.dpmon - INFO - Fold 5 best config: {'gnn_layer_num': 2, 'gnn_hidden_dim': 64, 'lr': 0.001685812648195555, 'weight_decay': 0.0023773937538678585, 'nn_hidden_dim1': 256, 'nn_hidden_dim2': 128, 'ae_encoding_dim': 8, 'num_epochs': 512, 'gnn_dropout': 0.2, 'gnn_activation': 'relu', 'dim_reduction': 'linear'}\n", + "2025-11-26 01:15:49,637 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 5 using train split only\n", + "2025-11-26 01:15:49,643 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:15:53,783 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:15:54,563 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 5 on test set\n", + "2025-11-26 01:15:54,568 - bioneuralnet.downstream_task.dpmon - INFO - Fold 5 results:\n", + "2025-11-26 01:15:54,568 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9695\n", + "2025-11-26 01:15:54,569 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9673\n", + "2025-11-26 01:15:54,569 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9694\n", + "2025-11-26 01:15:54,569 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9763\n", + "2025-11-26 01:15:54,569 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9986\n", + "2025-11-26 01:15:54,569 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9967\n", + "\n", + "2025-11-26 01:15:54,571 - bioneuralnet.downstream_task.dpmon - INFO - Successfully saved global best CV model state to: /home/vicente/Github/BioNeuralNet/dpmon_results_GCN_FINAL/kipan/threshold_10/best_cv_model.pt\n", + "2025-11-26 01:15:54,572 - bioneuralnet.downstream_task.dpmon - INFO - Successfully saved global best CV model state to: /home/vicente/Github/BioNeuralNet/dpmon_results_GCN_FINAL/kipan/threshold_10/best_cv_model_embds.pt\n", + "2025-11-26 01:15:54,572 - bioneuralnet.downstream_task.dpmon - INFO - Cross-Validation Results:\n", + "\n", + "2025-11-26 01:15:54,572 - bioneuralnet.downstream_task.dpmon - INFO - Avg Accuracy: 0.9574 +/- 0.0175\n", + "2025-11-26 01:15:54,572 - bioneuralnet.downstream_task.dpmon - INFO - Avg F1 Macro: 0.9536 +/- 0.0165\n", + "2025-11-26 01:15:54,572 - bioneuralnet.downstream_task.dpmon - INFO - Avg F1 Weighted: 0.9576 +/- 0.0175\n", + "2025-11-26 01:15:54,572 - bioneuralnet.downstream_task.dpmon - INFO - Avg Recall: 0.9651 +/- 0.0138\n", + "2025-11-26 01:15:54,572 - bioneuralnet.downstream_task.dpmon - INFO - Avg AUC: 0.9943 +/- 0.0029\n", + "2025-11-26 01:15:54,573 - bioneuralnet.downstream_task.dpmon - INFO - Avg AUPR: 0.9830 +/- 0.0197\n", + "2025-11-26 01:15:54,573 - bioneuralnet.downstream_task.dpmon - INFO - DPMON run completed.\n", + "2025-11-26 01:15:54,573 - bioneuralnet.downstream_task.dpmon - INFO - Output directory set to: /home/vicente/Github/BioNeuralNet/dpmon_results_GCN_FINAL/kipan/correlation_10\n", + "2025-11-26 01:15:54,574 - bioneuralnet.downstream_task.dpmon - INFO - Initialized DPMON with model: GCN\n", + "2025-11-26 01:15:54,574 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 01:15:54,574 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 01:15:54,574 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 01:15:54,575 - bioneuralnet.downstream_task.dpmon - INFO - Random seed set to: 8183\n", + "2025-11-26 01:15:54,579 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:15:54,581 - bioneuralnet.downstream_task.dpmon - INFO - Running in Cross-Validation mode (cv=True) with 5 folds.\n", + "2025-11-26 01:15:54,581 - bioneuralnet.downstream_task.dpmon - INFO - CV Setup: Standard 5-fold split.\n", + "2025-11-26 01:15:54,582 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 1/5\n", + "2025-11-26 01:15:54,583 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:15:54,591 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:15:58,722 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:15:58,734 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (526, 901)\n", + "2025-11-26 01:15:58,737\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:16:10,921\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0197s.\n", + "2025-11-26 01:16:11,103 - bioneuralnet.downstream_task.dpmon - INFO - Fold 1 best config: {'gnn_layer_num': 4, 'gnn_hidden_dim': 256, 'lr': 0.001257123304842143, 'weight_decay': 0.0003745702077936848, 'nn_hidden_dim1': 128, 'nn_hidden_dim2': 128, 'ae_encoding_dim': 8, 'num_epochs': 1024, 'gnn_dropout': 0.4, 'gnn_activation': 'relu', 'dim_reduction': 'ae'}\n", + "2025-11-26 01:16:11,104 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 1 using train split only\n", + "2025-11-26 01:16:11,109 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:16:15,261 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:16:18,073 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 1 on test set\n", + "2025-11-26 01:16:18,078 - bioneuralnet.downstream_task.dpmon - INFO - Fold 1 results:\n", + "2025-11-26 01:16:18,079 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9470\n", + "2025-11-26 01:16:18,079 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9273\n", + "2025-11-26 01:16:18,079 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9481\n", + "2025-11-26 01:16:18,079 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9627\n", + "2025-11-26 01:16:18,079 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9984\n", + "2025-11-26 01:16:18,080 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9982\n", + "\n", + "2025-11-26 01:16:18,081 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 2/5\n", + "2025-11-26 01:16:18,082 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:16:18,088 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:16:22,220 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:16:22,231 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (526, 901)\n", + "2025-11-26 01:16:22,234\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:16:34,076\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0191s.\n", + "2025-11-26 01:16:34,229 - bioneuralnet.downstream_task.dpmon - INFO - Fold 2 best config: {'gnn_layer_num': 4, 'gnn_hidden_dim': 64, 'lr': 0.00024165542558361394, 'weight_decay': 0.0002652189086272745, 'nn_hidden_dim1': 256, 'nn_hidden_dim2': 128, 'ae_encoding_dim': 8, 'num_epochs': 512, 'gnn_dropout': 0.1, 'gnn_activation': 'relu', 'dim_reduction': 'mlp'}\n", + "2025-11-26 01:16:34,229 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 2 using train split only\n", + "2025-11-26 01:16:34,235 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:16:38,349 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:16:39,516 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 2 on test set\n", + "2025-11-26 01:16:39,521 - bioneuralnet.downstream_task.dpmon - INFO - Fold 2 results:\n", + "2025-11-26 01:16:39,521 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9697\n", + "2025-11-26 01:16:39,521 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9487\n", + "2025-11-26 01:16:39,521 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9701\n", + "2025-11-26 01:16:39,522 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9579\n", + "2025-11-26 01:16:39,522 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9917\n", + "2025-11-26 01:16:39,522 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9461\n", + "\n", + "2025-11-26 01:16:39,522 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 3/5\n", + "2025-11-26 01:16:39,523 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:16:39,529 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:16:43,626 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:16:43,637 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (526, 901)\n", + "2025-11-26 01:16:43,639\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:16:54,182\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0281s.\n", + "2025-11-26 01:16:54,337 - bioneuralnet.downstream_task.dpmon - INFO - Fold 3 best config: {'gnn_layer_num': 1, 'gnn_hidden_dim': 128, 'lr': 0.002302454339377372, 'weight_decay': 1.3521341174248578e-05, 'nn_hidden_dim1': 512, 'nn_hidden_dim2': 128, 'ae_encoding_dim': 4, 'num_epochs': 2048, 'gnn_dropout': 0.3, 'gnn_activation': 'relu', 'dim_reduction': 'ae'}\n", + "2025-11-26 01:16:54,338 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 3 using train split only\n", + "2025-11-26 01:16:54,343 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:16:58,476 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:17:00,722 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 3 on test set\n", + "2025-11-26 01:17:00,727 - bioneuralnet.downstream_task.dpmon - INFO - Fold 3 results:\n", + "2025-11-26 01:17:00,727 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9621\n", + "2025-11-26 01:17:00,727 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9621\n", + "2025-11-26 01:17:00,727 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9622\n", + "2025-11-26 01:17:00,727 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9535\n", + "2025-11-26 01:17:00,728 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9975\n", + "2025-11-26 01:17:00,728 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9970\n", + "\n", + "2025-11-26 01:17:00,728 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 4/5\n", + "2025-11-26 01:17:00,730 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:17:00,737 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:17:04,871 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:17:04,883 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (527, 901)\n", + "2025-11-26 01:17:04,886\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:17:15,818\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0171s.\n", + "2025-11-26 01:17:15,993 - bioneuralnet.downstream_task.dpmon - INFO - Fold 4 best config: {'gnn_layer_num': 1, 'gnn_hidden_dim': 128, 'lr': 0.0019123736530165902, 'weight_decay': 0.0006168499043610002, 'nn_hidden_dim1': 256, 'nn_hidden_dim2': 128, 'ae_encoding_dim': 8, 'num_epochs': 1024, 'gnn_dropout': 0.4, 'gnn_activation': 'relu', 'dim_reduction': 'mlp'}\n", + "2025-11-26 01:17:15,994 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 4 using train split only\n", + "2025-11-26 01:17:15,999 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:17:20,122 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:17:21,350 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 4 on test set\n", + "2025-11-26 01:17:21,355 - bioneuralnet.downstream_task.dpmon - INFO - Fold 4 results:\n", + "2025-11-26 01:17:21,355 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9389\n", + "2025-11-26 01:17:21,355 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9449\n", + "2025-11-26 01:17:21,355 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9390\n", + "2025-11-26 01:17:21,355 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9554\n", + "2025-11-26 01:17:21,355 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9962\n", + "2025-11-26 01:17:21,356 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9940\n", + "\n", + "2025-11-26 01:17:21,356 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 5/5\n", + "2025-11-26 01:17:21,357 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:17:21,363 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:17:25,520 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:17:25,531 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (527, 901)\n", + "2025-11-26 01:17:25,534\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:17:38,250\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0174s.\n", + "2025-11-26 01:17:38,447 - bioneuralnet.downstream_task.dpmon - INFO - Fold 5 best config: {'gnn_layer_num': 2, 'gnn_hidden_dim': 64, 'lr': 0.001685812648195555, 'weight_decay': 0.0023773937538678585, 'nn_hidden_dim1': 256, 'nn_hidden_dim2': 128, 'ae_encoding_dim': 8, 'num_epochs': 512, 'gnn_dropout': 0.2, 'gnn_activation': 'relu', 'dim_reduction': 'linear'}\n", + "2025-11-26 01:17:38,447 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 5 using train split only\n", + "2025-11-26 01:17:38,453 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:17:42,581 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:17:43,369 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 5 on test set\n", + "2025-11-26 01:17:43,374 - bioneuralnet.downstream_task.dpmon - INFO - Fold 5 results:\n", + "2025-11-26 01:17:43,374 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9695\n", + "2025-11-26 01:17:43,374 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9572\n", + "2025-11-26 01:17:43,374 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9694\n", + "2025-11-26 01:17:43,375 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9558\n", + "2025-11-26 01:17:43,375 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9986\n", + "2025-11-26 01:17:43,375 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9967\n", + "\n", + "2025-11-26 01:17:43,378 - bioneuralnet.downstream_task.dpmon - INFO - Successfully saved global best CV model state to: /home/vicente/Github/BioNeuralNet/dpmon_results_GCN_FINAL/kipan/correlation_10/best_cv_model.pt\n", + "2025-11-26 01:17:43,378 - bioneuralnet.downstream_task.dpmon - INFO - Successfully saved global best CV model state to: /home/vicente/Github/BioNeuralNet/dpmon_results_GCN_FINAL/kipan/correlation_10/best_cv_model_embds.pt\n", + "2025-11-26 01:17:43,378 - bioneuralnet.downstream_task.dpmon - INFO - Cross-Validation Results:\n", + "\n", + "2025-11-26 01:17:43,378 - bioneuralnet.downstream_task.dpmon - INFO - Avg Accuracy: 0.9574 +/- 0.0139\n", + "2025-11-26 01:17:43,378 - bioneuralnet.downstream_task.dpmon - INFO - Avg F1 Macro: 0.9480 +/- 0.0134\n", + "2025-11-26 01:17:43,378 - bioneuralnet.downstream_task.dpmon - INFO - Avg F1 Weighted: 0.9578 +/- 0.0137\n", + "2025-11-26 01:17:43,379 - bioneuralnet.downstream_task.dpmon - INFO - Avg Recall: 0.9571 +/- 0.0035\n", + "2025-11-26 01:17:43,379 - bioneuralnet.downstream_task.dpmon - INFO - Avg AUC: 0.9965 +/- 0.0028\n", + "2025-11-26 01:17:43,379 - bioneuralnet.downstream_task.dpmon - INFO - Avg AUPR: 0.9864 +/- 0.0226\n", + "2025-11-26 01:17:43,379 - bioneuralnet.downstream_task.dpmon - INFO - DPMON run completed.\n", + "2025-11-26 01:17:43,380 - bioneuralnet.downstream_task.dpmon - INFO - Output directory set to: /home/vicente/Github/BioNeuralNet/dpmon_results_GCN_FINAL/kipan/similarity_10\n", + "2025-11-26 01:17:43,380 - bioneuralnet.downstream_task.dpmon - INFO - Initialized DPMON with model: GCN\n", + "2025-11-26 01:17:43,380 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 01:17:43,380 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 01:17:43,381 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 01:17:43,381 - bioneuralnet.downstream_task.dpmon - INFO - Random seed set to: 8183\n", + "2025-11-26 01:17:43,386 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:17:43,387 - bioneuralnet.downstream_task.dpmon - INFO - Running in Cross-Validation mode (cv=True) with 5 folds.\n", + "2025-11-26 01:17:43,388 - bioneuralnet.downstream_task.dpmon - INFO - CV Setup: Standard 5-fold split.\n", + "2025-11-26 01:17:43,388 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 1/5\n", + "2025-11-26 01:17:43,390 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:17:43,397 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:17:47,498 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:17:47,510 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (526, 901)\n", + "2025-11-26 01:17:47,513\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:18:00,506\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0235s.\n", + "2025-11-26 01:18:00,689 - bioneuralnet.downstream_task.dpmon - INFO - Fold 1 best config: {'gnn_layer_num': 2, 'gnn_hidden_dim': 64, 'lr': 0.0006037895979188083, 'weight_decay': 0.004195106610298857, 'nn_hidden_dim1': 512, 'nn_hidden_dim2': 128, 'ae_encoding_dim': 16, 'num_epochs': 1024, 'gnn_dropout': 0.1, 'gnn_activation': 'elu', 'dim_reduction': 'ae'}\n", + "2025-11-26 01:18:00,690 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 1 using train split only\n", + "2025-11-26 01:18:00,695 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:18:04,943 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:18:06,393 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 1 on test set\n", + "2025-11-26 01:18:06,398 - bioneuralnet.downstream_task.dpmon - INFO - Fold 1 results:\n", + "2025-11-26 01:18:06,398 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9621\n", + "2025-11-26 01:18:06,399 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9458\n", + "2025-11-26 01:18:06,399 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9629\n", + "2025-11-26 01:18:06,399 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9731\n", + "2025-11-26 01:18:06,399 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9831\n", + "2025-11-26 01:18:06,399 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9689\n", + "\n", + "2025-11-26 01:18:06,399 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 2/5\n", + "2025-11-26 01:18:06,401 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:18:06,406 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:18:10,545 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:18:10,557 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (526, 901)\n", + "2025-11-26 01:18:10,560\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:18:21,270\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0070s.\n", + "2025-11-26 01:18:21,414 - bioneuralnet.downstream_task.dpmon - INFO - Fold 2 best config: {'gnn_layer_num': 2, 'gnn_hidden_dim': 64, 'lr': 0.0007034204356990857, 'weight_decay': 5.358735876893429e-05, 'nn_hidden_dim1': 512, 'nn_hidden_dim2': 64, 'ae_encoding_dim': 8, 'num_epochs': 1024, 'gnn_dropout': 0.2, 'gnn_activation': 'relu', 'dim_reduction': 'ae'}\n", + "2025-11-26 01:18:21,415 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 2 using train split only\n", + "2025-11-26 01:18:21,420 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:18:25,548 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:18:27,026 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 2 on test set\n", + "2025-11-26 01:18:27,031 - bioneuralnet.downstream_task.dpmon - INFO - Fold 2 results:\n", + "2025-11-26 01:18:27,032 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9773\n", + "2025-11-26 01:18:27,032 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9648\n", + "2025-11-26 01:18:27,032 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9777\n", + "2025-11-26 01:18:27,032 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9835\n", + "2025-11-26 01:18:27,032 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9926\n", + "2025-11-26 01:18:27,032 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9360\n", + "\n", + "2025-11-26 01:18:27,033 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 3/5\n", + "2025-11-26 01:18:27,034 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:18:27,039 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:18:31,171 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:18:31,183 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (526, 901)\n", + "2025-11-26 01:18:31,186\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:18:41,965\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0160s.\n", + "2025-11-26 01:18:42,128 - bioneuralnet.downstream_task.dpmon - INFO - Fold 3 best config: {'gnn_layer_num': 3, 'gnn_hidden_dim': 128, 'lr': 0.0006980653261883734, 'weight_decay': 6.059927981082565e-05, 'nn_hidden_dim1': 256, 'nn_hidden_dim2': 128, 'ae_encoding_dim': 8, 'num_epochs': 2048, 'gnn_dropout': 0.0, 'gnn_activation': 'elu', 'dim_reduction': 'ae'}\n", + "2025-11-26 01:18:42,129 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 3 using train split only\n", + "2025-11-26 01:18:42,134 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:18:46,248 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:18:49,771 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 3 on test set\n", + "2025-11-26 01:18:49,776 - bioneuralnet.downstream_task.dpmon - INFO - Fold 3 results:\n", + "2025-11-26 01:18:49,777 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9470\n", + "2025-11-26 01:18:49,777 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9318\n", + "2025-11-26 01:18:49,777 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9468\n", + "2025-11-26 01:18:49,778 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9244\n", + "2025-11-26 01:18:49,778 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9966\n", + "2025-11-26 01:18:49,778 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9917\n", + "\n", + "2025-11-26 01:18:49,779 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 4/5\n", + "2025-11-26 01:18:49,781 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:18:49,788 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:18:53,915 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:18:53,926 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (527, 901)\n", + "2025-11-26 01:18:53,929\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:19:05,812\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0167s.\n", + "2025-11-26 01:19:05,972 - bioneuralnet.downstream_task.dpmon - INFO - Fold 4 best config: {'gnn_layer_num': 3, 'gnn_hidden_dim': 64, 'lr': 0.0013470053780947797, 'weight_decay': 0.00030758808386390933, 'nn_hidden_dim1': 256, 'nn_hidden_dim2': 128, 'ae_encoding_dim': 16, 'num_epochs': 1024, 'gnn_dropout': 0.4, 'gnn_activation': 'relu', 'dim_reduction': 'ae'}\n", + "2025-11-26 01:19:05,973 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 4 using train split only\n", + "2025-11-26 01:19:05,978 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:19:10,184 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:19:12,014 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 4 on test set\n", + "2025-11-26 01:19:12,019 - bioneuralnet.downstream_task.dpmon - INFO - Fold 4 results:\n", + "2025-11-26 01:19:12,019 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9389\n", + "2025-11-26 01:19:12,019 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9449\n", + "2025-11-26 01:19:12,019 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9390\n", + "2025-11-26 01:19:12,019 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9554\n", + "2025-11-26 01:19:12,020 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9963\n", + "2025-11-26 01:19:12,020 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9941\n", + "\n", + "2025-11-26 01:19:12,020 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 5/5\n", + "2025-11-26 01:19:12,021 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:19:12,027 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:19:16,138 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:19:16,150 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (527, 901)\n", + "2025-11-26 01:19:16,153\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:19:28,943\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0206s.\n", + "2025-11-26 01:19:29,171 - bioneuralnet.downstream_task.dpmon - INFO - Fold 5 best config: {'gnn_layer_num': 4, 'gnn_hidden_dim': 64, 'lr': 0.0007025528058103543, 'weight_decay': 0.0018355753249756474, 'nn_hidden_dim1': 512, 'nn_hidden_dim2': 128, 'ae_encoding_dim': 4, 'num_epochs': 1024, 'gnn_dropout': 0.0, 'gnn_activation': 'relu', 'dim_reduction': 'mlp'}\n", + "2025-11-26 01:19:29,172 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 5 using train split only\n", + "2025-11-26 01:19:29,177 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:19:33,346 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:19:35,544 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 5 on test set\n", + "2025-11-26 01:19:35,549 - bioneuralnet.downstream_task.dpmon - INFO - Fold 5 results:\n", + "2025-11-26 01:19:35,549 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9313\n", + "2025-11-26 01:19:35,550 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9080\n", + "2025-11-26 01:19:35,550 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9331\n", + "2025-11-26 01:19:35,550 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9473\n", + "2025-11-26 01:19:35,550 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9938\n", + "2025-11-26 01:19:35,550 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9915\n", + "\n", + "2025-11-26 01:19:35,553 - bioneuralnet.downstream_task.dpmon - INFO - Successfully saved global best CV model state to: /home/vicente/Github/BioNeuralNet/dpmon_results_GCN_FINAL/kipan/similarity_10/best_cv_model.pt\n", + "2025-11-26 01:19:35,553 - bioneuralnet.downstream_task.dpmon - INFO - Successfully saved global best CV model state to: /home/vicente/Github/BioNeuralNet/dpmon_results_GCN_FINAL/kipan/similarity_10/best_cv_model_embds.pt\n", + "2025-11-26 01:19:35,553 - bioneuralnet.downstream_task.dpmon - INFO - Cross-Validation Results:\n", + "\n", + "2025-11-26 01:19:35,553 - bioneuralnet.downstream_task.dpmon - INFO - Avg Accuracy: 0.9513 +/- 0.0185\n", + "2025-11-26 01:19:35,553 - bioneuralnet.downstream_task.dpmon - INFO - Avg F1 Macro: 0.9390 +/- 0.0210\n", + "2025-11-26 01:19:35,554 - bioneuralnet.downstream_task.dpmon - INFO - Avg F1 Weighted: 0.9519 +/- 0.0183\n", + "2025-11-26 01:19:35,554 - bioneuralnet.downstream_task.dpmon - INFO - Avg Recall: 0.9567 +/- 0.0230\n", + "2025-11-26 01:19:35,554 - bioneuralnet.downstream_task.dpmon - INFO - Avg AUC: 0.9925 +/- 0.0055\n", + "2025-11-26 01:19:35,554 - bioneuralnet.downstream_task.dpmon - INFO - Avg AUPR: 0.9764 +/- 0.0248\n", + "2025-11-26 01:19:35,555 - bioneuralnet.downstream_task.dpmon - INFO - DPMON run completed.\n", + "2025-11-26 01:19:35,555 - bioneuralnet.downstream_task.dpmon - INFO - Output directory set to: /home/vicente/Github/BioNeuralNet/dpmon_results_GCN_FINAL/kipan/gaussian_15\n", + "2025-11-26 01:19:35,555 - bioneuralnet.downstream_task.dpmon - INFO - Initialized DPMON with model: GCN\n", + "2025-11-26 01:19:35,555 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 01:19:35,556 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 01:19:35,556 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 01:19:35,556 - bioneuralnet.downstream_task.dpmon - INFO - Random seed set to: 8183\n", + "2025-11-26 01:19:35,561 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:19:35,562 - bioneuralnet.downstream_task.dpmon - INFO - Running in Cross-Validation mode (cv=True) with 5 folds.\n", + "2025-11-26 01:19:35,563 - bioneuralnet.downstream_task.dpmon - INFO - CV Setup: Standard 5-fold split.\n", + "2025-11-26 01:19:35,564 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 1/5\n", + "2025-11-26 01:19:35,566 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:19:35,573 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:19:39,710 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:19:39,725 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (526, 901)\n", + "2025-11-26 01:19:39,728\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:19:52,665\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0205s.\n", + "2025-11-26 01:19:52,846 - bioneuralnet.downstream_task.dpmon - INFO - Fold 1 best config: {'gnn_layer_num': 3, 'gnn_hidden_dim': 128, 'lr': 0.002995945702914729, 'weight_decay': 7.542726756559785e-05, 'nn_hidden_dim1': 256, 'nn_hidden_dim2': 32, 'ae_encoding_dim': 4, 'num_epochs': 2048, 'gnn_dropout': 0.0, 'gnn_activation': 'elu', 'dim_reduction': 'mlp'}\n", + "2025-11-26 01:19:52,847 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 1 using train split only\n", + "2025-11-26 01:19:52,851 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:19:56,959 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:20:00,808 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 1 on test set\n", + "2025-11-26 01:20:00,813 - bioneuralnet.downstream_task.dpmon - INFO - Fold 1 results:\n", + "2025-11-26 01:20:00,813 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9621\n", + "2025-11-26 01:20:00,814 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9538\n", + "2025-11-26 01:20:00,814 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9622\n", + "2025-11-26 01:20:00,814 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9740\n", + "2025-11-26 01:20:00,814 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9945\n", + "2025-11-26 01:20:00,814 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9854\n", + "\n", + "2025-11-26 01:20:00,815 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 2/5\n", + "2025-11-26 01:20:00,817 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:20:00,822 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:20:04,918 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:20:04,932 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (526, 901)\n", + "2025-11-26 01:20:04,935\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:20:15,891\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0178s.\n", + "2025-11-26 01:20:16,037 - bioneuralnet.downstream_task.dpmon - INFO - Fold 2 best config: {'gnn_layer_num': 4, 'gnn_hidden_dim': 64, 'lr': 0.00024165542558361394, 'weight_decay': 0.0002652189086272745, 'nn_hidden_dim1': 256, 'nn_hidden_dim2': 128, 'ae_encoding_dim': 8, 'num_epochs': 512, 'gnn_dropout': 0.1, 'gnn_activation': 'relu', 'dim_reduction': 'mlp'}\n", + "2025-11-26 01:20:16,037 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 2 using train split only\n", + "2025-11-26 01:20:16,042 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:20:20,158 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:20:21,370 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 2 on test set\n", + "2025-11-26 01:20:21,375 - bioneuralnet.downstream_task.dpmon - INFO - Fold 2 results:\n", + "2025-11-26 01:20:21,376 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9697\n", + "2025-11-26 01:20:21,376 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9487\n", + "2025-11-26 01:20:21,376 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9701\n", + "2025-11-26 01:20:21,376 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9579\n", + "2025-11-26 01:20:21,376 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9898\n", + "2025-11-26 01:20:21,377 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9414\n", + "\n", + "2025-11-26 01:20:21,377 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 3/5\n", + "2025-11-26 01:20:21,379 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:20:21,384 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:20:25,595 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:20:25,609 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (526, 901)\n", + "2025-11-26 01:20:25,612\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:20:36,485\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0081s.\n", + "2025-11-26 01:20:36,637 - bioneuralnet.downstream_task.dpmon - INFO - Fold 3 best config: {'gnn_layer_num': 3, 'gnn_hidden_dim': 128, 'lr': 0.0006980653261883734, 'weight_decay': 6.059927981082565e-05, 'nn_hidden_dim1': 256, 'nn_hidden_dim2': 128, 'ae_encoding_dim': 8, 'num_epochs': 2048, 'gnn_dropout': 0.0, 'gnn_activation': 'elu', 'dim_reduction': 'ae'}\n", + "2025-11-26 01:20:36,637 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 3 using train split only\n", + "2025-11-26 01:20:36,642 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:20:40,760 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:20:44,494 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 3 on test set\n", + "2025-11-26 01:20:44,499 - bioneuralnet.downstream_task.dpmon - INFO - Fold 3 results:\n", + "2025-11-26 01:20:44,499 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9470\n", + "2025-11-26 01:20:44,499 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9318\n", + "2025-11-26 01:20:44,499 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9468\n", + "2025-11-26 01:20:44,500 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9244\n", + "2025-11-26 01:20:44,500 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9970\n", + "2025-11-26 01:20:44,500 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9936\n", + "\n", + "2025-11-26 01:20:44,500 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 4/5\n", + "2025-11-26 01:20:44,502 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:20:44,509 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:20:48,666 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:20:48,680 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (527, 901)\n", + "2025-11-26 01:20:48,683\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:20:59,905\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0143s.\n", + "2025-11-26 01:21:00,060 - bioneuralnet.downstream_task.dpmon - INFO - Fold 4 best config: {'gnn_layer_num': 4, 'gnn_hidden_dim': 128, 'lr': 0.0008628455289617886, 'weight_decay': 0.005542594076906398, 'nn_hidden_dim1': 512, 'nn_hidden_dim2': 128, 'ae_encoding_dim': 16, 'num_epochs': 2048, 'gnn_dropout': 0.2, 'gnn_activation': 'elu', 'dim_reduction': 'linear'}\n", + "2025-11-26 01:21:00,061 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 4 using train split only\n", + "2025-11-26 01:21:00,066 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:21:04,216 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:21:09,152 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 4 on test set\n", + "2025-11-26 01:21:09,157 - bioneuralnet.downstream_task.dpmon - INFO - Fold 4 results:\n", + "2025-11-26 01:21:09,157 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9542\n", + "2025-11-26 01:21:09,157 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9398\n", + "2025-11-26 01:21:09,157 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9549\n", + "2025-11-26 01:21:09,157 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9659\n", + "2025-11-26 01:21:09,158 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9964\n", + "2025-11-26 01:21:09,158 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9957\n", + "\n", + "2025-11-26 01:21:09,158 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 5/5\n", + "2025-11-26 01:21:09,160 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:21:09,166 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:21:13,268 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:21:13,282 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (527, 901)\n", + "2025-11-26 01:21:13,285\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:21:25,473\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0135s.\n", + "2025-11-26 01:21:25,665 - bioneuralnet.downstream_task.dpmon - INFO - Fold 5 best config: {'gnn_layer_num': 4, 'gnn_hidden_dim': 64, 'lr': 0.0007025528058103543, 'weight_decay': 0.0018355753249756474, 'nn_hidden_dim1': 512, 'nn_hidden_dim2': 128, 'ae_encoding_dim': 4, 'num_epochs': 1024, 'gnn_dropout': 0.0, 'gnn_activation': 'relu', 'dim_reduction': 'mlp'}\n", + "2025-11-26 01:21:25,665 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 5 using train split only\n", + "2025-11-26 01:21:25,671 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:21:29,789 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:21:32,020 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 5 on test set\n", + "2025-11-26 01:21:32,026 - bioneuralnet.downstream_task.dpmon - INFO - Fold 5 results:\n", + "2025-11-26 01:21:32,026 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9618\n", + "2025-11-26 01:21:32,026 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9432\n", + "2025-11-26 01:21:32,027 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9622\n", + "2025-11-26 01:21:32,027 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9506\n", + "2025-11-26 01:21:32,027 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9970\n", + "2025-11-26 01:21:32,027 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9899\n", + "\n", + "2025-11-26 01:21:32,029 - bioneuralnet.downstream_task.dpmon - INFO - Successfully saved global best CV model state to: /home/vicente/Github/BioNeuralNet/dpmon_results_GCN_FINAL/kipan/gaussian_15/best_cv_model.pt\n", + "2025-11-26 01:21:32,030 - bioneuralnet.downstream_task.dpmon - INFO - Successfully saved global best CV model state to: /home/vicente/Github/BioNeuralNet/dpmon_results_GCN_FINAL/kipan/gaussian_15/best_cv_model_embds.pt\n", + "2025-11-26 01:21:32,030 - bioneuralnet.downstream_task.dpmon - INFO - Cross-Validation Results:\n", + "\n", + "2025-11-26 01:21:32,030 - bioneuralnet.downstream_task.dpmon - INFO - Avg Accuracy: 0.9590 +/- 0.0087\n", + "2025-11-26 01:21:32,030 - bioneuralnet.downstream_task.dpmon - INFO - Avg F1 Macro: 0.9434 +/- 0.0084\n", + "2025-11-26 01:21:32,030 - bioneuralnet.downstream_task.dpmon - INFO - Avg F1 Weighted: 0.9592 +/- 0.0088\n", + "2025-11-26 01:21:32,030 - bioneuralnet.downstream_task.dpmon - INFO - Avg Recall: 0.9546 +/- 0.0190\n", + "2025-11-26 01:21:32,031 - bioneuralnet.downstream_task.dpmon - INFO - Avg AUC: 0.9949 +/- 0.0030\n", + "2025-11-26 01:21:32,031 - bioneuralnet.downstream_task.dpmon - INFO - Avg AUPR: 0.9812 +/- 0.0226\n", + "2025-11-26 01:21:32,031 - bioneuralnet.downstream_task.dpmon - INFO - DPMON run completed.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Results for: threshold_10\n", + "Accuracy (Avg +/- Std): 0.9574 +/- 0.0175\n", + "F1 Macro (Avg +/- Std): 0.9536 +/- 0.0165\n", + "F1 Weighted (Avg +/- Std): 0.9576 +/- 0.0175\n", + "Recall: 0.9651 +/- 0.0138\n", + "AUC: 0.9943 +/- 0.0029\n", + "\n", + "Results for: correlation_10\n", + "Accuracy (Avg +/- Std): 0.9574 +/- 0.0139\n", + "F1 Macro (Avg +/- Std): 0.9480 +/- 0.0134\n", + "F1 Weighted (Avg +/- Std): 0.9578 +/- 0.0137\n", + "Recall: 0.9571 +/- 0.0035\n", + "AUC: 0.9965 +/- 0.0028\n", + "\n", + "Results for: similarity_10\n", + "Accuracy (Avg +/- Std): 0.9513 +/- 0.0185\n", + "F1 Macro (Avg +/- Std): 0.9390 +/- 0.0210\n", + "F1 Weighted (Avg +/- Std): 0.9519 +/- 0.0183\n", + "Recall: 0.9567 +/- 0.0230\n", + "AUC: 0.9925 +/- 0.0055\n", + "\n", + "Results for: gaussian_15\n", + "Accuracy (Avg +/- Std): 0.9590 +/- 0.0087\n", + "F1 Macro (Avg +/- Std): 0.9434 +/- 0.0084\n", + "F1 Weighted (Avg +/- Std): 0.9592 +/- 0.0088\n", + "Recall: 0.9546 +/- 0.0190\n", + "AUC: 0.9949 +/- 0.0030\n" + ] + } + ], + "source": [ + "output_dir_base = Path(\"/home/vicente/Github/BioNeuralNet/dpmon_results_GCN_FINAL/kipan\")\n", + "\n", + "all_results_GCN = []\n", + "\n", + "for run_config in comparison_runs:\n", + " graph_name = run_config[\"name\"]\n", + " A_full = run_config[\"graph\"]\n", + "\n", + " current_output_dir = output_dir_base / graph_name\n", + " current_output_dir.mkdir(parents=True, exist_ok=True)\n", + "\n", + " dpmon_params_base = {\n", + " \"adjacency_matrix\": A_full,\n", + " \"omics_list\": omics_kipan,\n", + " \"phenotype_data\": target,\n", + " \"phenotype_col\": \"target\",\n", + " \"clinical_data\": clinical_for_model,\n", + " \"model\": 'GCN',\n", + " \"tune\": True, \n", + " \"cv\": True, \n", + " \"n_folds\": 5,\n", + " \"repeat_num\": 1,\n", + " \"gpu\": True,\n", + " \"cuda\": 0,\n", + " \"seed\": SEED,\n", + " \"output_dir\": current_output_dir\n", + " }\n", + " \n", + " dpmon_tunned = DPMON(**dpmon_params_base)\n", + " predictions_df, metrics, embeddings = dpmon_tunned.run()\n", + "\n", + " all_results_GCN.append({\n", + " \"graph\": graph_name,\n", + " \"predictions\": predictions_df,\n", + " \"metrics\": metrics,\n", + " \"embeddings\": embeddings\n", + " })\n", + "\n", + "for res in all_results_GCN:\n", + " graph_name = res[\"graph\"]\n", + " graph_metrics = res[\"metrics\"]\n", + " \n", + " acc_row = graph_metrics.loc[graph_metrics['Metric'] == 'Accuracy'].iloc[0]\n", + " f1_macro_row = graph_metrics.loc[graph_metrics['Metric'] == 'F1 Macro'].iloc[0]\n", + " f1_weighted_row = graph_metrics.loc[graph_metrics['Metric'] == 'F1 Weighted'].iloc[0]\n", + " recall_row = graph_metrics.loc[graph_metrics['Metric'] == 'Recall'].iloc[0]\n", + " auc_row = graph_metrics.loc[graph_metrics['Metric'] == 'AUC'].iloc[0]\n", + " aupr_row = graph_metrics.loc[graph_metrics['Metric'] == 'AUPR'].iloc[0]\n", + " \n", + " acc_avg, acc_std = acc_row['Average'], acc_row['StdDev']\n", + " f1_macro_avg, f1_macro_std = f1_macro_row['Average'], f1_macro_row['StdDev']\n", + " f1_weighted_avg, f1_weighted_std = f1_weighted_row['Average'], f1_weighted_row['StdDev']\n", + " recall_avg, recall_std = recall_row['Average'], recall_row['StdDev']\n", + " auc_avg, auc_std = auc_row['Average'], auc_row['StdDev']\n", + " aupr_avg, aupr_std = aupr_row['Average'], aupr_row['StdDev']\n", + "\n", + " print(f\"\\nResults for: {graph_name}\")\n", + " print(f\"Accuracy (Avg +/- Std): {acc_avg:.4f} +/- {acc_std:.4f}\")\n", + " print(f\"F1 Macro (Avg +/- Std): {f1_macro_avg:.4f} +/- {f1_macro_std:.4f}\")\n", + " print(f\"F1 Weighted (Avg +/- Std): {f1_weighted_avg:.4f} +/- {f1_weighted_std:.4f}\")\n", + " print(f\"Recall: {recall_avg:.4f} +/- {recall_std:.4f}\")\n", + " print(f\"AUC: {auc_avg:.4f} +/- {auc_std:.4f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "d36f4ced", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " 0 1 2 3 4\n", + "MGP 0.035549 0.001088 0.0 0.0 0.003548\n", + "ADH1C 0.000000 0.000253 0.0 0.0 0.026202\n", + "LOC440354 0.000000 0.002382 0.0 0.0 0.000000\n", + "C1orf110 0.000000 0.000261 0.0 0.0 0.060484\n", + "CLDN8 0.000000 0.001527 0.0 0.0 0.004444" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#This time we will select the gaussian_15 graph embeddings\n", + "res = all_results_GCN[3] \n", + "tensor_data = res[\"embeddings\"]\n", + "\n", + "# .detach().cpu().numpy() to get the raw numbers out of pytorch\n", + "GCN_embeddings_df = pd.DataFrame(\n", + " tensor_data.detach().cpu().numpy(),\n", + " index=omics_kipan.columns\n", + ")\n", + "\n", + "display(GCN_embeddings_df.iloc[:5,:5])" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "db08276e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Plotting Feature Embeddings for graph: similarity_10\n" + ] + }, + { + "data": { + "image/png": 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nz2dYPiQkhAEDBjBgwADCw8MpV64cn3zyyWMl3U/q66+/ZtSoUQwePJj33nvvseqYN28eAI0aNXpgWT8/P3r37k3v3r1JSEigVq1ajBo1yiXpdjqdXLx40eVLlr+/V5YuXYrJZGLjxo0ue5fPmjXL5X758+fH6XRy8uRJypQpk2FM+fPnB1KTzge99zLD3ffLuXPnKFq0aNrx27dvExMTk25V+cxWvnx5fvjhh3RTQO5Oebg70iQsLIzo6GiKFy+ero6xY8cyduxY/vjjj3s+179asmQJycnJD/UeEUIIIauXCyGEeAw3btxw2aorLi6OuXPnUqZMmbTexQ4dOrB37142btyY7vqYmBjsdjsATZs2xW63880336Sddzgc6eYih4SEUKZMGebMmUNsbGza8c2bN3Py5MlMa9vdRGLatGkux/8ej8PhcIkDUhO97Nmzp9sq6mlYtGgRr7/+Ol27dmXSpEmPVcfWrVv56KOPCA0NpWvXrvct+/dV8T09PSlQoECGbf/r1lKqqjJ16lT0ej0vvPACkNrLqyiKy1ZXly9fZsWKFS71tGrVCo1Gw5gxY9Kt6n23R7hRo0Z4e3szduxYbDZbulju7sOdWZo2bQqQbpXzu7+DjFa9z0wtW7bEaDQya9Ysl2cyffp0IHXNA4DXX3+d5cuXu7y+++47IHW1/uXLl6cbOp+RI0eOMHjwYHx9fXnttdeyoEVCCPHvIz3dQgjxL7J+/XpOnz6d7ni1atUee55xRgoVKkTfvn05cOAA2bJlY+bMmdy+fdulZ/Kdd95h1apVNG/enF69elG+fHkSExM5duwYS5Ys4fLlywQEBPDiiy9SvXp1hg4dyuXLl9P2/P57Qgswbtw4mjVrRo0aNejTpw9RUVFMmTKF4sWLpy0o9aTKly9P27Zt+eKLL4iMjKRKlSrs2LEjrXf2bu9+fHw8OXPmpF27dpQuXRpPT09++eUXDhw4wMSJEzMlloe1f/9+evTogb+/Py+88AILFixwOZ/R7//ue8Vut3P79m22bt3K5s2byZMnD6tWrcJkMt33nsWKFaNOnTqUL18ePz8/Dh48yJIlS1wWTQMwmUxs2LCBnj17UrlyZdavX8/atWt5//3303phmzVrxqRJk2jcuDFdunQhPDycr7/+mgIFCnD06NG0ugoUKMDw4cP56KOPqFmzJm3atMFoNHLgwAGyZ8/OuHHj8Pb25ptvvqF79+6UK1eOTp06ERgYSFhYGGvXrqV69eqZur906dKl6dmzJ99//z0xMTHUrl07bVuuVq1aUbdu3Uy7V0aCg4MZPnw4I0aMoHHjxrRq1YojR47www8/0LlzZypWrAhAuXLlKFeunMu1d4eZFy9enFatWqWre+fOnaSkpOBwOIiMjGT37t2sWrUKs9nM8uXLMxy+L4QQIj1JuoUQ4l9kxIgRGR6fNWtWpibdBQsWZMqUKbzzzjucOXOG0NBQFi1a5DLc1N3dnR07djB27FgWL17M3Llz8fb2plChQowePTptoSiNRsOqVasYPHgw8+fPR1EUWrRowcSJEylbtqzLfRs3bszixYv54IMPGDZsGPnz52fWrFmsXLmS7du3Z1r75s6dS3BwMD/99BPLly+nfv36LFq0iMKFC6clo+7u7gwYMIBNmzaxbNkynE4nBQoUYNq0aS4rsT8NJ0+exGq1EhERQZ8+fdKdz+j3f/e9YjAY8PPzo2TJknzxxRf07t37oRbIev3111m1ahWbNm3CYrGQJ08ePv74Y9555x2Xclqtlg0bNvDqq6/yzjvv4OXlxciRI13eq/Xq1WPGjBl8+umnDB48mNDQUMaPH8/ly5ddkm5IXcAtNDSUKVOmMHz4cNzd3SlVqpTL/PUuXbqQPXt2Pv30Uz777DMsFgs5cuSgZs2a9O7d+8EP9BFNnz6dfPnyMXv27LRkdNiwYen2Is8qH3zwAb6+vkyZMoXBgwe7JOJP4quvvgJAr9fj4+ND0aJFGT16NP3797/nAolCCCHSU9THWTlGCCGE+I85fPgwZcuWZf78+Q8cei1S9erViyVLlmTaKAQhhBDieSRzuoUQQoi/SU5OTnfsiy++QKPRUKtWrWcQkRBCCCGeVzK8XAghhPibCRMm8Pvvv1O3bl10Oh3r169n/fr1vPTSS5myx7EQQggh/jsk6RZCCCH+plq1amzevJmPPvqIhIQEcufOzahRoxg+fPizDk0IIYQQzxmZ0y2EEEIIIYQQQmQRmdMthBBCCCGEEEJkEUm6hRBCCCGEEEKILPLcz+l2Op3cuHEDLy8vFEV51uEIIYQQQgghhPgPUFWV+Ph4smfPjkZz7/7s5z7pvnHjhqwkK4QQQgghhBDimbh69So5c+a85/nnPun28vICUhvq7e39jKMRQgghhBBCCPFfEBcXR65cudJy0nt57pPuu0PKvb29JekWQgghhBBCCPFUPWiasyykJoQQQgghhBBCZBFJuoUQQgghhBBCiCwiSbcQQgghhBBCCJFFnvs53UIIIYQQQojnm8PhwGazPeswhHCh1+vRarVPXI8k3UIIIYQQQohnQlVVbt26RUxMzLMORYgM+fj4EBwc/MDF0u5Hkm4hhBBCCCHEM3E34Q4KCsLd3f2JEhshMpOqqiQlJREeHg5ASEjIY9clSbcQQgghhBDiqXM4HGkJt7+//7MOR4h03NzcAAgPDycoKOixh5rLQmpCCCGEEEKIp+7uHG53d/dnHIkQ93b3/fkkaw5I0i2EEEIIIYR4ZmRIufgny4z3pyTdQgghhBBCCCFEFpGkWwghhBBCCCH+QWbPno2Pj8+zDkNkEkm6hRBCCCGEEOIRXb16lT59+pA9e3YMBgN58uThjTfeIDIy8onr7tixI2fPns2EKF316tULRVHu+cqbN2+m31NI0i2EEEIIIYQQj+TixYtUqFCBc+fO8dNPP3H+/Hm+/fZbtmzZQtWqVYmKinqi+t3c3AgKCsqkaP/05ZdfcvPmzbQXwKxZs9J+PnDgQKbfU0jSLYQQQgghhBCP5LXXXsNgMLBp0yZq165N7ty5adKkCb/88gvXr19n+PDhaWXz5s3Lxx9/TI8ePfD09CRPnjysWrWKiIgIWrZsiaenJ6VKleLgwYNp12Q0vHz16tVUrFgRk8lEQEAArVu3Tjs3bdo0ChYsiMlkIlu2bLRr1y7DuM1mM8HBwWkvAB8fH4KDg3n//ffp3bu3S3mbzUZQUBAzZswAoE6dOgwcOJCBAwdiNpsJCAjgww8/RFXVtGssFgtDhgwhR44ceHh4ULlyZbZv3/5Yz/nfQpJuIYQQQgghhHhIUVFRbNy4kQEDBqTt43xXcHAwXbt2ZdGiRS6J6OTJk6levTp//PEHzZo1o3v37vTo0YNu3bpx6NAh8ufPT48ePVyu+au1a9fSunVrmjZtyh9//MGWLVuoVKkSAAcPHuT1119nzJgxnDlzhg0bNlCrVq1Hble/fv3YsGFDWg84wJo1a0hKSqJjx45px+bMmYNOp2P//v18+eWXTJo0ienTp6edHzhwIHv37mXhwoUcPXqU9u3b07hxY86dO/fIMf1b6J51AEIIIYQQQgjxvDh37hyqqlK0aNEMzxctWpTo6GgiIiLShog3bdqUl19+GYARI0bwzTffULFiRdq3bw/Ae++9R9WqVbl9+3ZaD/RfffLJJ3Tq1InRo0enHStdujQAYWFheHh40Lx5c7y8vMiTJw9ly5Z95HZVq1aNwoULM2/ePN59910gdeh5+/bt8fT0TCuXK1cuJk+ejKIoFC5cmGPHjjF58mT69+9PWFgYs2bNIiwsjOzZswMwZMgQNmzYwKxZsxg7duwjx/VvID3dQgghhBBCCPGI7tUrnZFSpUql/f9s2bIBULJkyXTHwsPDM7z+8OHDvPDCCxmea9CgAXny5CFfvnx0796dBQsWkJSU9NCx/VW/fv2YNWsWALdv32b9+vX06dPHpUyVKlVc9q6uWrUq586dw+FwcOzYMRwOB4UKFcLT0zPttWPHDi5cuPBYMf0bSNIthBBCCCGEEA+pQIECKIrCqVOnMjx/6tQpfH19CQwMTDum1+vT/v/dhDWjY06nM8M6/z6M/a+8vLw4dOgQP/30EyEhIYwYMYLSpUsTExPz0G26q0ePHly8eJG9e/cyf/58QkNDqVmz5kNfn5CQgFar5ffff+fw4cNpr1OnTvHll18+cjz/FpJ0CyGEEEIIIcRD8vf3p0GDBkybNo3k5GSXc7du3WLBggV07NjRpTf4SZUqVYotW7bc87xOp6N+/fpMmDCBo0ePcvnyZbZu3frI9/H396dVq1bMmjWL2bNnp1tYDWDfvn0uP//2228ULFgQrVZL2bJlcTgchIeHU6BAAZdXRsPm/ytkTrcQQgghhBBCPIKpU6dSrVo1GjVqxMcff0xoaCgnTpzgnXfeIUeOHHzyySeZer+RI0fywgsvkD9/fjp16oTdbmfdunW89957rFmzhosXL1KrVi18fX1Zt24dTqeTwoULP9a9+vXrR/PmzXE4HPTs2TPd+bCwMN566y1efvllDh06xJQpU5g4cSIAhQoVomvXrvTo0YOJEydStmxZIiIi2LJlC6VKlaJZs2ZP9ByeV5J0CyGEEEIIIcQjKFiwIAcPHmTkyJF06NCBqKgogoODadWqFSNHjsTPzy9T71enTh0WL17MRx99xKeffoq3t3faCuU+Pj4sW7aMUaNGkZKSQsGCBfnpp58oXrz4Y92rfv36hISEULx48bTF0P6qR48eJCcnU6lSJbRaLW+88QYvvfRS2vlZs2bx8ccf8/bbb3P9+nUCAgKoUqUKzZs3f7zG/wso6qOsAPAPFBcXh9lsJjY2Fm9v72cdjhBCCCGEEOIhpKSkcOnSJUJDQzGZTM86HPF/CQkJ5MiRg1mzZtGmTRuXc3Xq1KFMmTJ88cUXzya4Z+B+79OHzUWlp1sIIYQQQggh/uOcTid37txh4sSJ+Pj40KJFi2cd0r+GJN1CCCGEEEII8R8XFhZGaGgoOXPmZPbs2eh0kipmFnmSQgghhMgyly9fZu7cuezcvZsUi4W8uXPTuVMnGjZsKB/ohBDiHyRv3rwP3Ht8+/btTyeYfxn5104IIYQQmc7pdPLxxx/z5ddTcRiMuBUpiMbDjdPHjrJq/ToK5c/PwgU/kj9//mcdqhBCCJGlJOkWQgghRKb76KOPmDR1Kn6N6hNYoxoavZ7kW7eJ3HcAh+rk6OkzVK9Zk182baJEiRLPOlwhhBAiy2iedQBCCCGE+He5ePEiX339NX6N6pOtbm1Uh4NLs+dwcdIE3E5vp37+azStpaDaI6hdqwrjxo3D6XQ+67CFEEKILCE93UIIIYTIVHPnzsVhMhJYoxpOm41L02fgEXOJsR8H0ai+Gb1eASDmThI/LYpkylfjSEhI4JNPPnnGkQshhBCZT3q6hRBCCJGpduzahVuRwmj0eu7s3Qc3LzB9Sg6aN/FJS7gBfPxM9O/uw6gh/sya+Q1Hjhx5hlELIYQQWUOSbiGEEEJkKovVisZoQFVVYn7bTdN67pQq4X7P8l3a+pMjGGbNmvUUoxRCCCGeDkm6hRBCCJGpcufIgfXGLaxR0djvhPNiU58My6lOJwpgMOho1cSLHds3PdU4hRBCPJrLly+jKAqHDx9+pnVkZj1PgyTdQgghhMhUXTp3xnIljKRr11EUMJu1GZZzWq3o9Xp0Oh0+PjqSkpKecqRCiH8Lh8PBiT1n2L1iPyf2nMHhcGTp/Xr16oWiKCiKgl6vJ1u2bDRo0ICZM2e6LAyZN29eFEXht99+c7l+8ODB1KlTJ129165dw2AwPNauDnfj+fu9LBYL/v7+KIrySPts9+rVi1atWj1yHFkho1hy5crFzZs3n4sdMCTpFkIIIUSmatKkCQVDQwnfuBmHEy5dsaQr40hOQbXZMRgMqKqTi5dTCAgIfAbRCiGed/vWHeK1CkN5v8knjOv2Je83+YTXKgxl37pDWXrfxo0bc/PmTS5fvsz69eupW7cub7zxBs2bN8dut6eVM5lMvPfeew9V5+zZs+nQoQNxcXHs27fvkWPKlStXuqk6y5cvx9PT85Hr+qfTarUEBwej0/3z1waXpFsIIYQQmUqv1/PTggUE6w3Y7bDw5yhwOsHpxJGUjC02DqfVAgokJCZy4dJtFq+KpknTls86dCHEc2bfukOM7/4V187dRGfU4e7ljs6o49q5m4zv/lWWJt5Go5Hg4GBy5MhBuXLleP/991m5ciXr169n9uzZaeVeeuklfvvtN9atW3ff+lRVZdasWXTv3p0uXbowY8aMR46pZ8+eLFy4kOTk5LRjM2fOpGfPnunKXr16lQ4dOuDj44Ofnx8tW7bk8uXLAIwaNYo5c+awcuXKtB70v/aSX7x4kbp16+Lu7k7p0qXZu3cvAImJiXh7e7NkyRKXe61YsQIPDw/i4+PTxeFwOOjbty+hoaG4ublRuHBhvvzyy7Tz94olo+HlO3bsoFKlShiNRkJCQhg6dKjLFyB16tTh9ddf591338XPz4/g4GBGjRr1KI/4sUjSLYQQQohMcenSJSZOnMi7777LggULGD1iBM0aNObgoSRmzbyBLT4ep82GxqBH5+GJzssL1ejOmMnRRERZWb5yBREREc+6GUKI54TD4WDW8J+wWuy4e7uh0+tQNAo6vQ53bzesFjuzPvgpy4ea/1W9evUoXbo0y5YtSzsWGhrKK6+8wrBhw1yGnv/dtm3bSEpKon79+nTr1o2FCxeSmJj4SPcvX748efPmZenSpQCEhYXx66+/0r17d5dyNpuNRo0a4eXlxc6dO9m9ezeenp40btwYq9XKkCFD6NChQ1pv/s2bN6lWrVra9cOHD2fIkCEcPnyYQoUK0blzZ+x2Ox4eHnTq1Cldb/usWbNo164dXl5e6WJ2Op3kzJmTxYsXc/LkSUaMGMH777/Pzz//DPDAWO66fv06TZs2pWLFihw5coRvvvmGGTNm8PHHH7uUmzNnDh4eHuzbt48JEyYwZswYNm/e/EjP+VH98/vihRBCCPGPFhkZyRuDB7Nu40acBgN6P1+cKSnYo2MoEBpK23bdmPD1Txw/baVb10BKlzZhd8D2bXHMnh/F/mNOQjp24eK2Xxnw2mss/v8HLSGEuJ/T+85z8+JtjO4GFEVxOacoCkZ3Azcv3Ob0vvMUr1b4qcVVpEgRjh496nLsgw8+YNasWSxYsCBdAnzXjBkz6NSpE1qtlhIlSpAvXz4WL15Mr169Hun+ffr0YebMmXTr1o3Zs2fTtGlTAgNdp+8sWrQIp9PJ9OnT057drFmz8PHxYfv27TRs2BA3NzcsFgvBwcHp7jFkyBCaNWsGwOjRoylevDjnz5+nSJEi9OvXj2rVqnHz5k1CQkIIDw9n3bp1/PLLLxnGq9frGT16dNrPoaGh7N27l59//pkOHTrg6el531jumjZtGrly5WLq1KkoikKRIkW4ceMG7733HiNGjECjSe1vLlWqFCNHjgSgYMGCTJ06lS1bttCgQYNHeMqPRpJuIYQQQjy2mJgYmrdswamr1whs0xLfMqXQ6PWoqkrSlTDCNm4mbOMGHLizYies3n4DvVZFdYLNqcGQO5Rc/ZrgVSA/ek8PtixayunTpylSpMizbpoQ4h8uJjwWh8OBUWvM8LxWq8XisBITHvtU41JVNd2XAIGBgQwZMoQRI0bQsWPHdNfExMSwbNkydu3alXasW7duzJgx45GT7m7dujF06FAuXrzI7Nmz+eqrr9KVOXLkCOfPn0/X85ySksKFCxceeI9SpUql/f+QkBAAwsPDKVKkCJUqVaJ48eLMmTOHoUOHMn/+fPLkyUOtWrXuWd/XX3/NzJkzCQsLIzk5GavVSpkyZR6yxalOnTpF1apVXZ599erVSUhI4Nq1a+TOnTtd7HfjDw8Pf6R7PSpJuoUQQgjx2MaNG8epK2Hkfe1lTIEBaccVRcEjbx7y9evN+RmzSTh8lBLvDsceG0fKnUgUjQaPXDlxyx6Sdo25RAnCV69nyZIlfPDBB8+iOUKI54hPkBmtVovD4UCnSZ/WOBwOtFoNPkHmpxrXqVOnCA0NTXf8rbfeYtq0aUybNi3duR9//JGUlBQqV66cdkxVVZxOJ2fPnqVQoUIPfX9/f3+aN29O3759SUlJoUmTJunmUickJFC+fHkWLFiQ7vq/94pnRK/Xp/3/u0nuX4fO9+vXj6+//pqhQ4cya9Ysevfune6LiLsWLlzIkCFDmDhxIlWrVsXLy4vPPvvssRaSexh/jf1u/Pcb9p8ZZE63EEIIIR5LXFwcCxYtxFytskvC/VeKVktAowag1ZJ89RpeBQsQWLUyAZUruiTcABqdFn2gPzdu3Hga4QshnnNFKhcgJF82LElWVFV1OaeqKpYkKyH5s1GkcoGnFtPWrVs5duwYbdu2TXfO09OTDz/8kE8++SRdEjxjxgzefvttDh8+nPY6cuQINWvWZObMmY8cR58+fdi+fTs9evRAq02/bWO5cuU4d+4cQUFBFChQwOVlNqd+SWEwGB57Pny3bt24cuUKX331FSdPnsxwIbe7du/eTbVq1RgwYABly5alQIEC6XrbHyaWokWLsnfvXpf3wu7du/Hy8iJnzpyP1Y7MIkm3EEIIIR7Lzp07iUtIxL9ShfuWMwYGYMydi+ijxx9YpzPFgslkcjmWkJDA0qVLmTZtGpMnT2bevHls27aNK1euPFH8Qojnm1arpfcnnTEYdSTFJWO32VGdKnabnaS4ZAxGHb0/7pxh0pkZLBYLt27d4vr16xw6dIixY8fSsmVLmjdvTo8ePTK85qWXXsJsNvPjjz+mHTt8+DCHDh2iX79+lChRwuXVuXNn5syZ47IC98No3LgxERERjBkzJsPzXbt2JSAggJYtW7Jz504uXbrE9u3bef3117l27RqQusf40aNHOXPmDHfu3MFmsz30/X19fWnTpg3vvPMODRs2vG/SW7BgQQ4ePMjGjRs5e/YsH374IQcOHHAp8zCxDBgwgKtXrzJo0CBOnz7NypUrGTlyJG+99VbafO5nRZJuIYQQQjyW+Ph4nKqK3tv7vuUMegNaby9Sbt9/zlzK7XCsN29Ro0YNAKxWK2PGjKFc2eK88koPhr//Jh98MISBr/WibetGVKpYmk6dOrBnz55Ma5MQ4vlSuWk53pv3OjkLhmC32EmKT8ZusZOzUAjvzXudyk3LZdm9N2zYQEhICHnz5qVx48Zs27aNr776ipUrV94z0dfr9Xz00UekpKSkHZsxYwbFihXLcC2L1q1bpy1E9igURSEgIACDwZDheXd3d3799Vdy585NmzZtKFq0aNpwdO///ze9f//+FC5cmAoVKhAYGMju3bsfKYa+fftitVrp06fPfcu9/PLLtGnTho4dO1K5cmUiIyMZMGCAS5mHiSVHjhysW7eO/fv3U7p0aV555RX69u37j5iupKh/H4vxnImLi8NsNhMbG5v2BhFCCCFE1tuwYQMdunUj/7tvYvT3v2/Z459Nxh4eTqlPRqPJ4EOgqqpcmf8T5vA7HDt8GEVR6N69C7/t2UTDOm5s3BZN7uxaenUyU6OSCYtVZf9hC4tXWzhxRsPESV9nuDiREOKfKyUlhUuXLhEaGppuhMujcjgcnN53npjwWHyCzBSpXCDLerjFw5k3bx5vvvkmN27cuGfy/zy43/v0YXPRLO3pbtGiBblz58ZkMhESEkL37t3TzdM6evQoNWvWxGQykStXLiZMmJCVIQkhhBAik9SqVQs/Hx/u7Dtw33LJN25CeARuGi1nv/mBm6fPEh4ezp2IOyQkJJASHUPYwsVYT57ms08/xWAwMGXKFPbu3sSXn2Rn++4Yqlc0snFRTnp3NlMwv5HiRYy0auLBrC/86djSyJC3B3Hs2LGn1HIhxD+NVquleLXCVG9VieLVCkvC/QwlJSVx4cIFPv30U15++eXnOuHOLFmadNetW5eff/6ZM2fOsHTpUi5cuEC7du3SzsfFxdGwYUPy5MnD77//zmeffcaoUaP4/vvvszIsIYQQQmQCd3d3enbtSvze/SRdz3jxM6fNxo2Vq/Hx9ASbk4TTZ7k+9Vtuz/2JiNXruDxrHsdHfYT16HF++OYbWrRogc1mY+6c6XRu7cX5Syk4HE6mjc+GyfTnxxZFUfD21ILiZOSQbOQIdvDDDz88raYLIcRTNXbsWDw9PTN8NWnS5FmH52LChAkUKVKE4OBghg0b9qzD+Ud4qsPLV61aRatWrbBYLOj1er755huGDx/OrVu30r4BGTp0KCtWrOD06dMPVacMLxdCCCGencTERFq1acOB48fxfaEO/hUroHN3Q3U6iTt1hogt21Bv3sKeZCGHkp/CHiUJt97guuUKFixoVS0Opw2LKZHZc2fTokULdu3aRYf2zdiwMB8vvXWeGpX1TBgRlOH9o2LsaDRGflyWwuffJnPq1AXc3NwyLGuz2YiMjEyb6yg9YUI8W5k5vPzfLioqiqioqAzPubm5kSNHjqcc0X9HZgwvf2r7dEdFRbFgwQKqVauWtjfa3r17qVWrlsuQg0aNGjF+/Hiio6Px9fV9WuEJIYQQ4jF4eHiwbMkS3nnnHRYtXcrttetTF1az2tDYrJQrXYZLcQlokgIo4VUeRVHI6RZKTrc/97BVVZWDsb8y8sORNGvWjMjISMBJnlxGrt20UKKI5z3vr9WAw+mkRFF3rJZoIiMj062Se+XKFWbMmMHPi+YTFxcDQEhIDrr36EvPnj3l84YQ4h/Pz88PPz+/Zx2GeExZvnr5e++9h4eHB/7+/oSFhbFy5cq0c7du3SJbtmwu5e/+fOvWrQzrs1gsxMXFubyEEEII8WzExcXx6aefsn7tBhxJVtT4ZCzXb6NJTKJVs+b079uXqDvRFHQvjqIoGdahKAoFPEpw+dIVtm7dioeHB6AhMsqO0aAhMdl5z/s71dTrExJT92/9+9zBXbt2Uf+FmixbPIXubR3MmZKdGV+EULdqFFO+HEnDhnW5ePFipj0PIYQQ4u8eOekeOnQoiqLc9/XXoeHvvPMOf/zxB5s2bUKr1dKjR490m9c/inHjxmE2m9NeuXLleuy6hBBCCPH4YmNjafFiS7776gd84oKp7dWMFwO60dCvDXnUwqxevJb33n0PN9zx1t+/N9lX748RE7///jtVqlTBw8PMktV3qFzeizWbEjO8xuFQsVpVjEYTazZFExpagMDAwLTzly5donevLpQvZWHvuiIMG5yLF2r50KiuLxNGhrJjZSHcdFfp0rk9SUlJmfpsHtf169c5ceIEYWFhT/R5SQghxD/HIyfdb7/9NqdOnbrvK1++fGnlAwICKFSoEA0aNGDhwoWsW7eO3377DYDg4GBu377tUv/dn4ODgzO8/7Bhw4iNjU17Xb169VGbIIQQQohM8O6773L89xNUdK9DEc9SuGs9URQFN60HhT1LUcmjLhG37mC12B6qPo2ixW634+npSfsO3Zi9MJbG9Xw5csLClp2uibcKqb3bioZLV52s35JEr979XXrTf/jhB9xNCfwwMR8eHunnb2cPNjLrq1CuXTvH8uXLn+hZPAmn08nixYtp1KARpUqUpma1WpQtXY66deqyYMEC7HZ7htc5HA5u3brF9evXsVqtTzlqIYQQD+uR53QHBga6fIv8KJzO1OFhFosFgKpVqzJ8+HBsNlvaPO/NmzdTuHDhe86vMhqNGI3Gx7q/EEIIITLHzZs3WbF0Bfn0RfHW+WRYxktnJliXi+vJl0m2JeGmd79nfSmOJJIdieTJkwdIHSn3644tTPr2DIULuPPqu+FMGBHAiw09UVVISHKQnKJy4qyWd0ZdoVDhcnTp0uXP+lJSWPzzfPp39c4w4b4rNI+JejXcmTd3Jl27dn28h/EEHA4HgwYO4qf5C/EhgGKG8rjpPbE4krj6+yUGvjKIDRs2MHPmzLTPSlFRUcyePZuZc+Zw4+ZNALy9venWqRP9+vUjb968T70dQggh7i3L5nTv27ePqVOncvjwYa5cSZ2j1blzZ/Lnz0/VqlUB6NKlCwaDgb59+3LixAkWLVrEl19+yVtvvZVVYQkhhBAiE6xcuRJbip2cptD7livuVR6H4uBC/P13JbmcfB5PsyctW7YEwNfXl2XLV1OoyAucPq8hJg76DL5FufqXeXPEbUZ/HkWnlyLpNeg2ufNW4aeFi/8/FzzVrVu3SExMoEqFB+9sUq2iJ+fPn32IVme+iRMn8uO8nyhuqEgl7zpkN+XBV+9PsCkXFcy1KG2syprlaxk5ciQAFy9epG79+oz+7DOiQrKRrVtnsvXsirN0Sb6eN4+adevy66+/PpO2CCGEyFiWrV7u7u7OsmXLGDlyJImJiYSEhNC4cWM++OCDtJ5qs9nMpk2beO211yhfvjwBAQGMGDGCl156KavCEkIIIUQmCA8Px6R1R68x3Lecu84DN707lx2nCbIEE2TMnq7MrZRrhDnO8ubLg122XAkKCuLnxUs5deoUS5Ys4dixY1y4cJ69f1gx+5gpVro44z7vQfXq1dMt0nb354eZF+1UVTSaLF9bNp3ExES++fpbcmkKkMOUJ+14siORa8mXSHYmo1U0+KvBzJw+iwEDBtC+Y0euJSeRf8hgDD7mtGvMRQrjeKEuV+b9SNeePdn+yy/kz5//qbdJCCFEelmWdJcsWZKtW7c+sFypUqXYuXNnVoUhhBBCiCzg7u6O3WlDVdV7rkoOqUmvm96N7PmDOXxxD/6WbOQ05sOkcU9NLi0XiVLCadW+JcOGDcuwjqJFi/Lhhx8+UnzZs2fH19efrTtjqVnFfN+yW3cmULx4pQzPHT58mD179mCxWMiePTtNmzbFy8vrkWKB1Kl1q1atYuHCRVwLu4bJzURgUACREXco4Z06AtDmtHI0bj/Xk6+gQYMJDxzYSSEJR7Kdvn37cu7SJfL9LeG+S2s0kLdHF85NmMwPP/zAp59++shxCvG8cjidHL59k8ikJPzd3SmTLQRtFn6Z1qtXL+bMmQOATqcjZ86ctG/fnjFjxqTt5awoCkajkTNnzqRNnQFo1aoVPj4+zJ4926XOvXv3UqNGDRo3bszatWuzLHbx9D21fbqFEEII8e9Rp04dxn40jgjrLYKMIfcsF2kLx6618uVXX3Lp0iVm/DCDYyf2oTqdKBoNJcoVZ/RLH9C5c2e02nvPvX5Uer2ezl16Mn/u5wzsa8PfT59huSMnEtlzIJlp3/R1OX7w4EGGDR/OwT8O4dTp0RoN2BMSMXt60KdHT4YPH55ue7J7OX78OJ07deHq5av4EICnxpsYZxKHbUdIsiZzWXeGgh4l2Ru9lXhrDPkpTpCSE52S+jEtXo3hkvMUW7Zuw1yuDKbAgHveS2Mw4F2xPD/+vIiRI0fi5ub2kE9MiOfXtssX+XzPLsJiY3GqTjSKhtxmM0Oq1aBu3nwPruAxNW7cmFmzZmGz2fj999/p2bMniqIwfvz4tDKKojBixIi0BP1+ZsyYwaBBg5gxYwY3btwge/b0I4PE80mSbiGEEEI8svLly1OqTEnO/34cf0MQWiV9wuxUnZxLPkaREkWoVasWtWvXpmfPnly8eJHY2Fh8fX3JmzfvfXvKn0T//v35edF8ur56gdlT8hEc5JokHz+VSJ83LlOiRAWaNWuWdnzXrl2079wZq78fwd264F20MIpGgzU2lsjf9jP52285efoUP85fkLa4GcCJEyfYsGEDsbGxmM1mmjVrhtFopFWLVqSE26nu0QhP3Z/D5+Pii3LedopzCSeJsNwi3hZLKaUqXoqPS5xeig+FlbJEKptRQrKhOlUUzb2fmWf+UG5s/5Xbt2/LomriX2/b5Yu8tXEdVocDk06HTqPD7nRyOSaatzauY1KjplmWeBuNxrQdl3LlykX9+vXZvHmzS9I9cOBAJk2axDvvvEOJEiXuWVdCQgKLFi3i4MGD3Lp1i9mzZ/P+++9nSdzi6ZOkWwghhBCPTFEUJk6aSIvmLTkYt4Oi7uXw1vuknY+3x3Ay8Q9sHilMnDwxLbFWFOWpzTXOli0bCxcto0vndlRtcoZmDTyoXN4Tu13ll1/j2LEnmeLFyzN/wcK05Dk5OZne/frhyBFC/t490Oj+/KhkMJsJadQAz3z52DRrLtOmTeONN97gwoULvD54MHv37UM1GdF5emGLj+OT8eMxe3kRfyuemuamGP42/91kMJFdyYviVLloPUUupWC6hBtARQUF9IoB1eEkOSUZd/d7rwSvOp0okGVfZgjxT+FwOvl8zy6sDgeeBkPae16v1aLTaEiwWpm4dze1cufN0qHmkDqiZc+ePS7DyAGqV6/O2bNnGTp0KGvWrLnn9T///DNFihShcOHCdOvWjcGDBzNs2DD5e/wvIUm3EEIIIR5L+fLlWbp8Cf369mdv2Ga8kswYVDesSgrxSgzZc2Vn/g+z0nYteRaKFSvGjl/3snDhQubPm8WqTRfRKBpKlSrLF1/2oWXLli5bka5cuZLbkXfI3+fPhFtVVZwWC6pTRWsy4lUwPx5lS/HDzJk0btyY5i1bEolKUOcOmIsXRdFqcdodRB89xtWVq1EcydiclnRJt8FoRKvV4u70AhQCyXgoqV21odFo8NX5EXP+Iim1qt836Y47dQZ/Pz8Zmir+9Q7fvklYbCwmnS7DxRRNOh1XYmI4fPsm5UNyZPr916xZg6enJ3a7HYvFgkajYerUqenKjRs3Lm0dq5o1a2ZY14wZM+jWrRuQOmw9NjaWHTt2UKdOnUyPWzx9knQLIYQQ4rFVqVKFPw4fYsOGDaxdu5bIyEj8/Pxo2rQpTZs2dRl+/ayYzWZefvllXn75ZZxOJ4qi3LP3aNmKFRjzhWL098NhsRJ18Hci9uwl+dZtAHTu7vhXqoBXkcJc+/0wvXr3JkqBfANeQef+5/xpjU6LZ/GiBHp7EzlvIYej91Pd54V09/P28iIi+iaKqkGvpn9WdtWGEydmL29ClUL8dnknyVevg59fhvHbEhJIOHSYV1555R/x7IXISpFJSThVJzpNximNVqPBabcTmZSUJfevW7cu33zzDYmJiUyePBmdTkfbtm3TlStWrBg9evRg6NCh7N69O935M2fOsH//fpYvXw6kLszWsWNHZsyYIUn3v4Qk3UIIIYR4Inq9nhdffJEXX3zxWYfyQA/aGizizh30vr7Y4uI49/1Mkm/dxq1IIfyrV0PR6bBcvUbEvgNE7N6LJsXCsRMnyNmnp0vCfZeqqmg93PGpXZuIJcuJt8fipXNdddxoMuHj6QPxKskkYVTdUFBQUXHiAMDb2ws3N3dC1Fzo47XcWbwUHz9fPHLldKnLGhNL2Ox5hPj40K9fvyd7UEI8B/zd3dEoGuxOJ/oMFmJ0OFMXVfO/z8iQJ+Hh4UGBAgUAmDlzJqVLl2bGjBn07ds3XdnRo0dTqFAhVqxYke7cjBkzsNvtLqNTVFXFaDQydepUzOb778Ag/vkk6RZCCCGE+D8fb29st29yfsYcrAkJhLz2EoZs2dLOe5Qohs8LdYhYuJjEI8dw8/LCXLSwSx1Ou4PY4ycI37OXxGs3QFVxKHZOxR+mvE+NdIvOZfPIgZKgEKncwlfrj1N1oCga3E0euLu7odWmflyzOFPQ6zXkCgjkytRvMeXLi2exoigaDYmXLpN88jQ5s2Vj8aKFaYs7CfFvViZbCLnNZi7HRKPTaFxGsKiqSordTqivH2Wy3XuHhcyi0Wh4//33eeutt+jSpUu6nQNy5crFwIEDef/9913WtbDb7cydO5eJEyfSsGFDl2tatWrFTz/9xCuvvJLl8YuslbUrCgghhBBCPEeaNG5M4olTJF29RmCn9i4J910agwG/1i3QB/jjREX5Sw+bLS6OM199zcV5C7DZ7XhVr4JXjaoY8+fjqnqNHdEbSHYkutQXa49Ca9ASYwxHMasEBWUjMDAQLy+vtITbqTo5kXCQgEB/du7YwazvvqNCUDDJv2wjceMv5Lc7+fzjj9mzcyfFihXL2ockxD+EVqNhSLUaGLRaEqxWbA4HTlXF5nCQYLVi0Gp5u2r1LF9E7a727duj1Wr5+uuvMzw/bNgwbty4wS+//JJ2bM2aNURHR9O3b19KlCjh8mrbti0zZsx4KrGLrCVJtxBCCCHE/3Xs2BHsdvQh2TDmzpVhGdXhQFHBXLki1sQkbAkJADhtNs7/MBNLXBzBL/UluF9vfOrUwrtWDQI7dyCkXx8SfRX2xG7D5rQCkOxI5FjyfipUKE/dBnU4lLyLc4knsDhTUu+lqkRYbrE/dhuJ7jF898N3+Pn50bZtW9atWcONq1e5ee0au3fupH///jIMVfzn1M2bj0mNmpLXxxerw0mi1YrV4STU1y9LtwvLiE6nY+DAgUyYMIHExMR05/38/HjvvfdISUlJOzZjxgzq16+f4d/dtm3bcvDgQY4ePZqlcYusp6iqqj7rIJ5EXFwcZrOZ2NhYvL29H3yBEEIIIf4RTp8+zdq1a9P+DW/SpAnFixd/1mGRO18+4gvlx1y3Nlqj8c+ebFXFabPhtFjQaXXoE+I5+9kX5GrXmuC6tbmzbz9Xfl5GyICXMIT8ObzbYbXiSEpGq2hwRsdz87sfKKQUQKNoualeIWf+HKxctYKgoCBGjBjB/LkLSE5Ixk3rjs1pw6GxU7RYET6d8Ok9Vz4W4nmUkpLCpUuXCA0NxWQyPVFdDqeTw7dvEpmUhL+7O2WyhTy1Hm7x73a/9+nD5qIyp1sIIYQQT9WVK1d4+6032LPnV8xeKtkC9URE2pg0cSwVK1Zl0uSvntpe3hkxe3tjN5nQOFXsiYkoGg0oCjidoKoYjSbMZjMpKSlotVqid+0loHJFInb/hqlwQZeEW3U6Ua1WDAY9iqJg8/XEWLQgp48cI1dIDl7p8RKvv/46gYGBAEyYMIFhw4axcuVKbty4gdFopFq1alSpUkX26xXiPrQaTZZsCyZEZpCkWwghhBBPTVhYGC1bNMHdeJtp47PR5AVfANZvieaL726we9cmypcvTqNGLRg06HWqV6/+1JPNksWLs+n4MXI1b4bFkoLVakVVQavV4GZyQ6tL7fmOO3MWX7MZT4OBC1O/JfH6DfzbtEytRFVx2u04LRY0KPj6+qLVarHb7WjKlSHy4iX27tuT4YJnvr6+9OrV6ym2WAghRFaSMRdCCCGEeGQ2m42wsDAuX77sMj/xQYYMeRM3wy1WzClAi8b+RETaaNrpBAPePY+3l5M3X/bhlR4eXDm/lg7tm9O5cwfi4+OzsCXp9e7VC9uNmyReuoTJZMLb2xuz2RtPT8+0hNthsRK37yCdO3Rg84YNVCtUGNVqxWmzYU9IwJ6QgDM5GYNOj7+/H9r/D1HX6XQYTCZ0Wi1Go/GptksIIcSzIT3dQgghhHhot2/fZvr06cyZPZ2bt26hqnY0ioKfXwDtO3TljTfeIGfOnBlee+7cOXbt3MaUcdkI8NcTF2+nU/8zWK021i7IQeniqXPlUixOBvWzc/SUgTc/3EyfPj356aef0emezseWevXqUbViRfYtWEiu3j1wz+k6ZNWRksLluQvwVJ289tpr5MuXj7WrV1OoaFGibofj4eaGRtFgNBkzjDnh8mV8/fxkLRohhPiPkJ5uIYQQQjyUU6dO0bBBHaZ89Ql37lwlJMjJq728+OBNM81fSGLhj19SoUIp5s+fn+H169evx8tTpVn91CHlcxeFc/1mCgu/z56WcAMYDRq0GoXqFU18PzE3u3dtYfPmzU+ljQBarZYF8+ZRvlBhLk/9hoszZnNn336ifj/E1WUrOfPxp9jOXyR/3lA++PBDPvnkE65cucIr/ftjO3UGN40GD0+PDBNuR0oKCYcO06tbt7TebyGEEP9uknQLIYQQ4oHi4uLo0rkdTvs1FGy83s+HP37Jw/gPghgywJ8vPsnGnjU56dAchrw9kFWrVmVYh7+vDoNBg8OhMu/ncFo29iRvLr1LOUUBrUbB6XRSo7I35UoZmD376e5V6+/vz9rVq/l+ylRKeXoRs3ItkUtWkPjbfuzxCaju7px02Nh2/SqffTON8pUqcf78efw9PbkyZwH25OR0dTosVq7M+xGzwUjPnj2fanuEEEI8OzK8XAghhBAPtHDhQiLvXMXLw0GbZp58+Ja/ywJnGkUhJFjPsDd8iI6N4aMxH9KsWTOX3lyz2UxktB2r1UlktJ3rt6xpC6n9laqCw6li/P92P01e8Gbyd/uyvpF/YzQa6dSpE506dSIxMZGOnTqx89Dv5OzUHp9SJdH8f36302rlzr4DLFi6lPo1anDo6FEufP4FXhXK4124EGgU4s+eI+7A73gBC+bOI1eujPcAF0II8e8jSbcQQgghHmjB/NkUL6zjyIlkBvYJznBFcY2i4O6mpVcnT7oPvMzWrVtp0KABAE6nEx8fH67fSKJApYMAxMXbWbMpgTLFjQQG/PmRxGJ14lRJW2jMZNRgs9mfQivvbfny5ezc9xu5XuqLZ2hel3Mag4GgmtXReXqyZdESvv/6a44cOcL8hQu5/utOADw9POjfvgMvv/zyM90OTQghxNMnSbcQQggh7ktVVS5evEC5kgqhufUULmC4Z1mDQaFoIT1BARoOHTpEgwYNSElJ4eWX+7N502rKldLTuJ6JQH8d+/9IZsX6BDbtSOSHicHUqOyO06mSkOjEoDeg16cOOz98LPGZ9gyrqsoPM2dgKlQwXcL9V75lShG1/VfWrV/P7FmzGD58ONeuXUNVVXLmzImHh8fTC1oIIcQ/hszpFkIIIcQDaTQa7A4Vo+EBe2arqX8YjRrs9tTe6UGDBrLr19XM/DIHvywtQd8ufrRs5Mm49wPYsjQHZUsY6f3GLQ6fSCEqxoHTqeDtbQbgTqSN1ZsS6dK1Vxa27v4iIyM5fvwEPmVL37ecoih4li7Jlu3bAHB3d6dQoUIULlxYEm4h/uO2b9+OoijExMQ861DEMyBJtxBCCCHuS1EUSpUqQ/gdOxcu24iMctyzrMXqJDJK5cZNG3nz5uXIkSOsXbOU8SNCaFjHF51Oh5+fPxqdAasNPNw1TBwdQFCAlsnfRaHR6PHz80en0xEXb6f/W5fw8AqiU6dOT7HFrpKTk1EBrZvbA8tq3dxISU5BVdWsD0wIkUZVHajWQ6gpm1P/VO/936lnoVq1aty8eROzOfULxbtJ+N1XYGAgTZs25dixYy7X9erVC0VR+PTTT12Or1ixIsNpPgBFihTBaDRy69atrGmMeGSSdAshhBDigXr07MPV6yoOJ/y0PC7DMg6HSnKKyrJ1KRhNZlq0aMGcOXPIEazQsrFfWjmtVoufrx8BAYG4ubmjKBo6tPBk49YkTp3Xcea8lS+/u0G91mc4c9GTuXMX4ufnl+E9nwY/Pz/0ej0pt8MfWDbl1i2CgoLu+WFYCJH5VMt21Mg2qNF9UWOGpP4Z2QbVsv1Zh5bGYDAQHJx+PYwzZ85w8+ZNNm7ciMVioVmzZlitVpcyJpOJ8ePHEx0d/cD77Nq1i+TkZNq1a8ecOXMytQ3i8UnSLYQQQogHatmyJaVKVcTu0PDZ11Fs253kct7uUImOdbB9j4XpCxLo1ftlvLy8OH7sEHVruKPVpk9CdTodPj4++PsHUK+mLxarSquel2jU8SJTZlqo26AP6zdsoXz58k+rmRny8PCgZfPmxO47cN8ebKfVSuLhY3Tp2PEpRifEf5tq2Y4a8w7YLwEGUDxT/7RfRo15J8sS7zp16jBo0CAGDx6Mr68v2bJl44cffiAxMZHevXvj5eVFgQIFWL9+PXDv4eVBQUEEBwdTrlw5Bg8ezNWrVzl9+rRLmfr16xMcHMy4ceMeGNeMGTPo0qUL3bt3Z+bMmZnWXvFkJOkWQgghxAMZDAbmL1hIyZJViY6Fjv1v0P21G6zeFM/u/YksWRXHa+9F8NbIOBo36cDQoUMBcDgcaDX37/XV6/X4+5txd/dg7NiJrF23jcNHTjFx4kRCQ0OfRvMeqH+/fqhR0dxYuyHDxFt1OAj7eSluQI8ePZ5+gEL8B6mqAzV+MqgWULxA0YOi/P9PT1CtqPGTs2yo+Zw5cwgICGD//v0MGjSIV199lfbt21OtWjUOHTpEw4YN6d69O0lJSQ+sKzY2loULFwKp/739K61Wy9ixY5kyZQrXrl27Zx3x8fEsXryYbt260aBBA2JjY9m5c+eTNVJkCkm6hRBCCPFQgoKCWLd+E3PmLiRP3pKs2Wyl66u3ebH7bV7/IJ6opDJMmvwd3333PTpd6gYp+QsUYf8fyQ+c47z3YDyKoqdx48aULVsWb2/vp9Gkh1axYkXGffQRCbv3cmn6bGJPncZps+GwWIk+fIQL077Hefos33/zjezBLcTTYjsCjjBQ3FKT7b9SFFBMqedtR7Lk9qVLl+aDDz6gYMGCDBs2DJPJREBAAP3796dgwYKMGDGCyMhIjh49es86cubMiaenJz4+Pvz444+0aNGCIkWKpCvXunVrypQpw8iRI+9Z18KFCylYsCDFixdHq9XSqVMnZsyYkSltFU9GtgwTQgghxEPT6/W0bduWtm3boqoqV65cITY2Fh8fH3Lnzp1uvmK3bt3p1HExu/fHU6Nyxom006ky66dIataqS548eZ5GMx7Lyy+/TM6cOfls0iSOzF3ANacTAL1GS41q1Rg67RuqVav2jKMU4j/EGQmqE5R7pTTa1PPOyCy5falSpf68k1aLv78/JUuWTDuWLVs2AMLDw+/5ReLOnTtxd3fnt99+Y+zYsXz77bf3vN/48eOpV68eQ4YMyfD8zJkz6datW9rP3bp1o3bt2kyZMgUvL69HapvIXJJ0CyGEEOKxKIpC3rx571umRo0alCtXhTeG7+fHb/NSuIC7y3mHQ+WDsVc4csLJop8HZ12wmaRZs2Y0bdqUI0eOcP78eTQaDcWLF6dw4cLPOjQh/ns0/qBoADugz6CAI/W8xj9Lbq/Xu95TURSXY3e/hHT+/wu6jISGhuLj40PhwoUJDw+nY8eO/PrrrxmWrVWrFo0aNWLYsGH06tXL5dzJkyf57bff2L9/P++9917acYfDwcKFC+nfv/+jNk9kIkm6hRBCCJFlNBoNs2bPo1OndjTueJjmDT14sZEv7m5ajp9OZP7iGMKua/h84lRq1KjxyPXHxMSwaNEifpz/I1evXsVkMlGzdk169+5N5cqVs2QVcUVRKFOmDGXKlMn0uoUQj0BfGrS5wX4Z0LkOMVdVUFNAlze13HPgtddeY9y4cSxfvpzWrVtnWObTTz+lTJky6b7omzFjBrVq1eLrr792OT5r1ixmzJghSfczJkm3EEIIIbJUYGAgK1euZf78+cydM4Pl6y4AoNcbaP5iJ6Z93/+xEth9+/bRtXM3IsMjCSAYX1127E4bqxas5eefFlOpakUKFixIcnIygYGBtGnThooVK8p2XkL8SyiKFrzeTF29XE0ATIAWcKQm3IoBxevN1HLPAXd3d/r378/IkSNp1apVhv+tKlmyJF27duWrr75KO2az2Zg3bx5jxoyhRIkSLuX79evHpEmTOHHiBMWLF8/yNoiMyUJqQgghhMhynp6evPLKK+zavY/fD51gz95DnDx1ga+//vqxEu4zZ87QoV1HbOEqtTybUc5cgwIexSjiVZqCxpJYLA5++fVX5vyymVUnjvHDsqU0bNaMeg0acPny5UxvnxDi2VCMdVB8Pkvt0cYKamLqn7q8KD6foRjrPNsAH9HAgQM5deoUixcvvmeZMWPGuAxZX7VqFZGRkRn2jhctWpSiRYvKgmrPmKI+aDnRf7i4uDjMZjOxsbH/uJVOhRBCiOdVYmIiy5cvZ+mSpYTfjsDL25NGjRvRpUuXtMWBMtuZM2dYvHgxN2/exGg0Ur16dZo3b47RaExX9uWXXmblj2uo7tUQnebPOZS3Uq6yL2EXxmKFMNephcbfTEBgAFqNhvhz57m1cg3BBiMb162TVcaFeMZSUlK4dOkSoaGhmEymJ6pLVR2pq5Q7I1PncOtLPzc93OKf7X7v04fNRSXpFkIIIYSLXbt20btnHyJuR+BLIG6KJ1ZnClFKOEYPPWM/HUvv3r0z7X6RkZEMeHUAv2zegsauxV31xKE4SFBjyRYSxKcTPnXpwQkPD6dUiVLkshYin8efW+s4VAebolagFAslW+u2KBoFi2rB3cMdb+/UlXtt8QlcnPINberWZcb06ZnWBiHEo8vMpFuIrJIZSbfM6RZCCCH+wxITE1m7di1Xr15Fr9djNpt5f+hw9PFu1PRsgrvWM62szWnldMIRhrw5BIPBQNeuXZ/4/nFxcbRs0ZIzR85R1FCOEK/caJTU2W8J9jjO3jxKvz79sdlsdOjQAYATJ06Qkmwh2CMnqurE6VTRKAo3LWGkaKzkqFMPRZNahwYNNps17X56L0/8alVn9dq13Lp1i+Dg4CdugxBCCHE/knQLIYQQ/0E2m40JEybww3fTiY2KxaAx4lAdxKXE4uX0obxvXdy0bi7X6DUGSnhVwBZr5c3Bb/HHH3+g1WrJnz8/7dq1w8/P75HjmDhxIqePnqWyRz08da69BJ46b8p6V+do/D7efvNtGjZsiI+PD3a7HYfdQXxcPPGWBFRAAc5zEsXfncQzp7B4e+NRqEjqLkJ/G9PnV6Ec4avXsmPHDjp27PjIMQshhBCPQhZSE0IIIf5j7HY7/fv35/NxE/GOCaSmZxPqeL1IBVMtFIdCDmcocdGxWCwpLtepqsrFpNPccFwnMjGe6atXMXPdWoZ8+AHFS5dixIgR2Gy2h44jKSmJeXPmE6LkSZdw36UoCkU8ypAQm8iiRYtQVZV169aRnJLMnZRwtOhIUhM4qhzgji4Sh9VCzKGDhK9bw5WvvyJ6+zY0f1sBWGsyoWi1JCYmPvrDE0IIIR6R9HQLIYQQ/zFz585l5dJVlDZVJZsxR9rxKHsEGrQEaXLiwE5sTCyBgYa0odpnEo5y2nYSzyoV8atYFs+QIMxmM/aERO78to8vv/2Wm7du8d2336LRPPh7/QMHDhAdGU0R9wr3LWfUmvBVA9m4YSNGo5HZM+bgqTMT4biOSXXjuPYgutCcBFZtiKFgXnSeXthjYkk4+Dtxu/eixMfj07cXijZ1USVrdAw4nAQEBDz+QxRCCCEekvR0CyGEEP8hqqry/bffE6AGuyTckLoQmQYtGkWDXtHjVFWSU5IBiLFFctpyAnP9egQ0aITObE4btq3z9CC4fj1Cunbk5+XLWLFixUPFkpCQgKo6MWoevICSQTERGxPL5xM+J9CZgzLmysSokRzV7MdQtADBXbrhUaAQigqqw4HOx4y5fl0Cu3Yi/tx5wn/dlVbXnd/24edj5oUXXni4h/aci4iI4OTJk1y6dMllmyEh/inkfSn+yTLj/Sk93UIIIcR/yKlTpzh79hxlTNXSnXPXeuDATrKaiJvigQYNKckpuLt7cCnpLFo/H3wqVQFUVFQ0Wtfv7n1KFCcqNJTpM2fSpk2bB8bi7++PotGQ6IjHoEm/LdhfJZOIU/Xj+tXrVHZ/AbPejxxuubnCVbwbvYCqTZ3XDQpOSwoaNzcURcGjUAGSS5UkYvdegmrXJOnqNWJ3/8bgl17Cw8Pj4R/cc2jr1q388MO37Ni+BXACCrlzh9KjZ1969eqFu7v7sw5R/McZDAY0Gg03btwgMDAQg8GA8rfpIEI8K6qqYrVaiYiIQKPRYDAYHrsuSbqFEEKILKSqKseOHSMyMhIPDw/KlCnzRP9wP6moqChUh4q70TPduWzGHBg0Bm44L5NfKY6CkvYN/03HDTxKVULRaHBgB1Tc3NzS1eFToRz7li4nMjISf3//+8ZSoUIFcuXORdiV8/jq7z3UO9EeTyyR5Mtfm2O/n8CsT12wzarY8ChQCL3ZjF39cy656lBxJiWjMRpQDQY8K5Tj9h+HubJoMcknTlOjQgWGDh36MI/ruTVx4kQmfv4xZUromDg6gIL53IiLd7Bi3U0mfDqMVSuXsXDREnx8fJ51qOI/TKPREBoays2bN7lx48azDkeIDLm7u5M7d+6HmjZ1L5J0CyGEEFnA4XAwd+5cvv3he86eP4/j/9taZQsMpGe3bgwaNAgvL6+nHpeXlxeKRiHFmYwHrvfXKjpCPYpwNv4YZtUfM35pHzLsTitaD09UnNhVO0ajEZ0u/ccIvbcXThXi4+MfmHTrdDr6v9yPke+PIsJyk0BjSLoyDtXO8cQDBGfPRunSpVmxaBWqqqIoChas6H39MChGVCW19/1uH5nFacGZYsFpsaDo9TitNpwnTvF6v/68//77GX5h8G+xdOlSJn7+EUMH+fFa3xAURUFFxWKxUKaEHx1autFr0G6aNm3Eli3b/9XPQvzzGQwGcufOnborgcPxrMMRwoVWq0Wn0z3xCAxJuoUQQohM5nA4eHXAABYtX45b8aJk79MTY2AA9oREog4eYsLXU9mweRMrly3H19f3qcZWvHhxcuXOydXLF/E3BJHsSCTCeguHaseoMZHfvQjxthhOphzEVw3EXwniVnwYTtWJJS4Kq2pBp9dj9jFnWL81OgaNojx0D+orr7zC3j17Wb96A7msBcjjXhB3rQdO1cktyzUuWk6heDuZNWceDocDVeMgyhaBvyEIvaojKTEBAOX//4PUwe+KAp5enmg1WpITkjCZjCxc8CP16tXLlOf4T6WqKlO+mkTDOiYG9ssOpG4PFxMTg8NpR69TKFZI4aOhPvR/6yBFixZk2rTvadq06TOOXPyXKYqCXq9Hr9c/61CEyBKSdAshhBCZ7Msvv2TR8uWEdO6AT6kSaccNPj6458xBcpWKHP1uJq8OGMDCn37KtPtaLBZWr17N8hUrCI+IwNvbi0YNGtKxY0fM5tQkWafT0bd/Xz4cNoKdkbFEW+/8v4c4tTdUrzGQxy0/7loPbnODcPU2Gp0bTp1KwuEjeNeqhtHogUbJeJhdzIGD1KlV86GTbr1ez5y5cxg/fjwzfpjJzshz6BR96qJuOoWqtavw8ScfU6ZMGZxOJ4WKFOLCiVP46QMJNuTgxOkTOJKT0Lr9OT/ZodpRFAV3dw80GoXI4yfIGZKdGjVqPPEz/qc7dOgQZ8+eYMyQ3ADY7Taio6PQalV8vHXo9alfTLRr7s3n02Kw22Po368HM2ctoFGjRs8ydCGE+NdSVFVVn3UQTyIuLg6z2UxsbCze3hnv8SmEEEI8LRaLheKlS5NSMD85W714z3JRfxzmzs/L2LtzJ4ULF37i++7fv5+effpw/dYtjKF50Pn54UhIIOX8RbxNJiZ99hkdOnQA4Pjx41StXA2SNORSChCs5Ean6EhWE7mhXiFMPYtDD+ZSJchWrw6mPLmxRkRybvJXeFauiHet6ri5ueNjNsNfRtzd2XeAO8tXsXDePBo3bvzIbUhMTGTDhg3cvHkTo9FI9erVKVasmEuZNWvW0LNbL7I5clPAoxi/RK/CVKkMAY2aoiipK7DbseHp5YWnpwfJN25y5ZvveXfgIN5///0nesbPg8WLF/PG6324eKAkBoOGqKgonE4rfr7adPuVv/ruLaJjdbi56Th62p/9Bw5LT6MQQjyCh81FpadbCCGEyESbN28m/M4d8vXqet9yPiVLErFmA4sWLWLEiBFPdM8jR47QpkMHLAH+5BvyBqbAwLRztvh4bq7dwCsDB6LX62nZsiWvvPQKXoqZou4VcKQ4cGBHVR1o0RFEDq7qLuJZvhTZ27fBy5z6IcKUKwe5WrXg6opV2OPj8SxXFn3hgnh4eJAScYeIXbuJ/+0AA/r1e+weUw8PD9q2bXvfMs2bN2fyV5MY8tY7RCRex18TwK39B3FYUvCsXgW9vx8eHh64Gw1EHvidiLXrKVu0GK+//vpjxfS8uTvP3mJ1otE4sdos+HilT7gBrDYVnU7h3YHZadD+Ahs2bODFF+/9RZEQQojHI0m3EEIIkYmuX7+OxmBwSXwzotFp0QUFcv369Se+5wcjRpDs5UH+fr3Q/K2nUu/lRa6O7bhis/PusGF4eXlx4vhJyrvVwt8QiNPhIDklGYfDiaLAHft1FL0J/xdeIDklBU9vLxRFISkpCaVoIXysjYjbuZvwI8e44+aG3mBAY7EQHBTEsFGjeO2117J8y58ePXpQtWpV5syZw+JFS3C/pSf52DFSTp3CI1dOEk0m7BF3UCwWWjRpwpSvvsLTM/1q7f9GpUuXBvRs2h5Dk3puKIDRmH4qQEKik937U+jXzZeihdwpWtDAr7/+Kkm3EEJkAUm6hRBCiExkMBhwOuw47Q40Ou39C9tsTzyc99SpU+zeu5fAju3SJdx3KYpCcOP6XPz8S7744gvcVA/89KlfCmi0Wjw8/kxIb0ffxL1EMfRunlidFiwWK1aLhaSkJDRo8SlRFt9iZYjcuYOYrVtRFBPuPm7Mnz2bKlWqPFFbHkXBggX5+OOP+fjjj1FVFZvNxpo1a9i7dy9Wq5WAgADKlClDUFAQCQkJaXPa/+3y5ctHzVr1+G7OdupVz4GiQEbfgcz9OZakZJUubVPfB95eGpKTk7MsLqfTyZEjR7hz5w4eHh6UL18eo/H+e7MLIcS/xeNvNiaEEEKIdKpUqYJOhdiTJ+9bzhIVheXqNapWrfpE99uzZw92RcFcovh9y5kCAzFkD+H8+fMYHe737I22qlZ03ua0lcAtKSkkJSWhU/ToFQMaNCgaLV5FS6DTm6hkroMpxZM+vfuSkpLyRG15XIqiYDAYaNOmDW+//TZeXl7MnD6Lnl170rhBE0qXLE2H9h3YsWPHM4nvaXv33WGcv6xn0PvXiYl14HD8uXyPw6Eyb3Esn06Jpm/XYLIHG7HbVS5ftREUFJTpsTgcDn744QdqVK9Ms6Z16dmjLe3aNqVc2eJ8/PHHxMXFZfo9hRDin0Z6uoUQQohMVLRoUapWrsxv237FXLTIPXufb/2yDT8fH1q3bv1E97NYLGj0+gf3qgOK0YjG7sCh+TM5tlqtWCwp/9/7WoMWLY7ERO6maRarBQ0atH/7yOBIiAdVxU3rTin3yuy6uoFp06ZRpEgRdDodJUuWJCQk/b7bWencuXO0btma22ERhGjyUMhUDq2iJcYWyd71B9jyS1s+nTCO/v37P9W4nrby5cszc9YC+vfrQe1W12nTzIMSRYzExDpZtTGBqzccdGsXyAdv5QJg47ZobkfwwPn0j8put/Pyyy+xccNSWjb24PMP8xCa20hktJ0lq+8wd/bnbNmykaVLV+Ln55ep9xZCiH8SSbqFEEKITDZm1ChebN2ay7PnkbNdGwy+Pmnn7MnJ3Nr0Cyl/HGbCxEm4u7vfu6KHEBwcjDMlBUtUNEa/jPf8tsXHE3P8JIkXL5G/YEGuqdeJs0Rji3dgs9vTdrhWAW/Vh5vHjmOuWxsMCk6HE71iSFdn/LGjeCpeeGq9uWW5hjXZxvvvDcdkNAFgMBlo2rwJ7777broVyLOC1Wqlc8fORIXFUc2zISatW9o5L52ZnKZQTiX8wdB3h1GoUCFq166d5TE9S/Xq1WPf/sN06dKFBcu2EuCbjNlbR80qvkz7LIiyJVOnFFy7YWHUhFtUrVqT4sXvP1riUX3xxRds2riU6ZNz0rDOn+/NbEEGRhTOTec2ybTve5yBA1/lxx8XZeq9hRDin0S2DBNCCCGywK5du+jZpw8R0dGYChbAEOCHPSGR5FNnMGk0jB0zhr59+z7xfRITEyleujSO0iXI0dR1my5bQgLXV64m6fhh9M4U3PROnBiJjU1BSXGjpFoND42Xy57bic54Dmh34F6rCoENX8Bms2FQjCh/mZGWfDWM2/PmU1JfGhUnJ+IO4a36kl2Xl7wB+VFVJ7cs1wiznUdn1rBo8UKqVav2xG29n2XLltGnR1+qeTTAS+eTYRlVVdkb9wtVGldg8ZLFWRrPP0VycjKdO7Xn6JFd9OzoTZe2geTJaeJOlI3FK+8w48do3D3zsWLlWoKDgzPtvikpKZQrW5z2zW2MfCf3Pcut2hDJgPdus3XbHooUKZJp9xdCiKfhYXNRmdMthBBCZIEaNWpw5NAhvp40iSpB2cgRGU1Jg5GR77zD0UOHMiXhhtRttvr16kXsrr3EnT2XdtwWH8/FqVPxuvY7I97wYveqHBzeXpATO4sz5j1/suW2cEy/g3g1Ju0aVVWxKhZ0dg1xu3YTsXottujotKHmjpQUYg/u5/ZPPxFEAJ5ab07EHSIXBSimVCBQG4xRY8KkdSeveyGqeTdEG2uge9fuREdHZ0p772XB/AX4KP73TLghde53LkN+tm3dxrVr17I0nn8KNzc3Fi5aQp9+Q1i0Sk/tlufJW/4oFRqc5osfUnihYS9Wr9mQqQk3pG6dFxMTQc+O958n3uQFXwL9YdEi6ekWQvx7SU+3EEII8Zyz2Wx079GD9Vu34lG2FP5VKnNrwwZ8o0+zYFoI2YNU9Fodfn5+2O12oqKiuHnTSZcB17l02YGPmg0depJJIJkkzHpfPLRehDkugVaLITAIrd6ILSICUmzk1uellHdFDsbsJCElnrJKTaxY8PT0TLc1l8WRwo6ENYz97BNeffXVx27j5cuX2bhxY9q/+40bNyZ37j97UMuXKY/1ooZiXmXvW0+8PZa9yZtZv2ndU11t/Z8gJSWFnTt3EhkZiaenJzVq1MDHxydL7vXtt98y6fOhnN5TIsPzTqeT5ORkkpOT6DP4Ott2O6hVswY9e/WlVatWmEymLIlLCCEy08PmojKnWwghhHjO6fV65s2dyzfffMP3M2Zw+atpOFJiGT0ygJzZwM3NHU8PTzQaDRaLBVTIk92ddwcEMfjD27ipbiiKBi9NdnK6heKvD0JFJTbuDrkK5OTMybME6nLjoy9CTt98uGndsThTuJ1ynQKUxIEdBXB3c0sXm1FrIoAQflrw02Ml3deuXeOdd99l0y+/YNdq0Lm7Y09M5P0PP6RJo0Z8NmECwcHB6PR6klXrA+tzqo60Z/ZfYzKZaNCgwVO5l9FoxGJxYrM50etdB1babDZiYqJxOh2YjAo2m0q1ikb8vY7x9lsvMX36tyxYsIhs2bI9lViFECKryfByIYQQIgPnzp1j69at7Nq1i9jY2GcdzgPp9Xpef/11Dv/+O/179SJboBud2uQkKDAQby9vNJrUf/JVVU1bOK1JHS/8fbT4GQKp7FuHMuYqBBiyoSgKGkWDUWuifv365MmbmyjnbRQ0GJXUvZUtjmRUwIQ7Dhx4enmi0Wa8grqXzuexhnNfvXqVRk2bsmH/Pvxat6DwyOEUfP9dCo0Yjk/LZqzZtZOGTZpw8+ZNqlavQqR6C6fqTFdPiiOZeHssKY5kbqZcxexjlvnDWaxKlSrY7Do2botxOe5wOIiOjkajcRLgryM2TuX4aSs9O2Zj7tcF2LioAFHhR+jatSNW64O/RBFCiOeB9HQLIYQQf7F27Vq+mTaFQ4f2A6m9oiaTJy1bdeCtt94iV65cj123qqocP36ca9euYTAYKFOmDP7+/pkUeSqdTofZbCZndjd8zOl7njUaDSoqKmA0aMgWpCP+Qvr9ta0OK3HJscyYPhOn3UmKPYWjln0cjz+At94PvWLArtqwaWx4eXnh4eFxz5jsTttjDRce9MYb3ExJJt9rL6P/y7A9rdFAQOVKeBcpzKWvv+PNt95i2NChzJs9j2spl8jtlh9VVbmecpmLKWeJckSSujy7CqqTFpWbP/Gq8eL+ihYtSqVK1Zg64zca1PbBaEz90icpKQlw4GvWoSjwxffReHnqaN00dcuwYoXdmfllXpp2OcTatWufeEs9IYT4J5CebiGEEOL/JkyYQO9endA49jJxlDsbFwawel42XuutY/uW2TRtUp9Tp049Vt3Lli2j7gsvUOuFenTs3p3WHTtSvHQpXnvtNS5fvpyp7fDw8CA6xo7DkX7ZFpPJCErqMGunUyU61oFOcR1q7XQ4OHvnOCm2FEIceajp0YSmgZ2o4dOYEF0eEpyxuOfUky04G8lusfdNuFVVJUK9Tu26j7ZF1+nTp/l11y4CG9V3Sbj/ymA2E9DgBTZv2YLZbKZzt86ctv7BlaRzHIzdxYHk30jM70NA+zZk69UDc5vm6AuGsnHbVga/+SZOZ/pe8X8yh8PBvn37WL16NVu2bCEuLu5Zh3RfI0d9xLlLBnoOusDV6xZUVSU5OQk3k4b4BCcffnqHn1cl8MFbuXFz+3OURKniHlSv5Mac2TOeYfRCCJF5pKdbCCGEAObPn88nH3/A269682pPMwZD6vfSFquTzq11tGyUjZffuUOPHp3ZvXs/BkP6vavvZdy4cYyfNAlDoQIE9+yGe84cOK02Yo4d56eNG9j4yy+sWLqUEiUyXnTqUdWvX59JEz9h665YGtT2cTmn0+kxGAxYLVb2H7ATHuGkolsOlzI3o29w2XGWEFMuSnpXTDse5B5CkHsIN1PCOHZ7P/Ua1mXbph3E22Px0pkzjOVayiUs2hT69OnzSG1Yu3YtqtGI+QHPxLdMKSJWr2P16tVMnjwZRVGY/sN0bFoNAe3b4F6o0P979p145Q0hd52axB09zuwfF5A7Vy7efvvtR4rrWXA4HEyfPp2ZM77l6tVLgBNQcHPzom27LgwZMoSgoPuvEv4slC1blgU/LqF//55Ua3aGWlVMBAXYSExysnVnMg6Hwsfv56FL28B019ap5smX0489g6iFECLzSU+3EEKI/7zY2FjefHMQ1SsZePc1P/z99Hh5avHy1OLvp8PHW4unh41xw324fu0ia9aseei6V69ezfhJk/Bt3IB8fXpiLlIYvacnRj9fstWuSb43BhJjMtC5a9fURc4yQZkyZShdugKff32LpCRHuvNmsxmLDSZND0frcMNL6wOAzWnlfPwJ/rDuQqfVUs5cPcP6Q0y5yUYuTp04RbFSRTiYtINwy03+uiGKQ3VwKeksp21/0LV7FypUqPBIbYiKikJn9kaj0+K0O7DGxGCNiUV1uLZHo9ej8/IkOjoaq9VKvnz5cCjgVas6xkL5cGAHDXh6eRIQEIBOp8OvXBm8q1dlyjff/H+4c8ZiYmL4/fff+f3337N8y7N7cTgcvPrqK3w05l0ql77Nyrl5ObmrGLtWF2RALz3r1/xA82YN/7FboFWrVo0DB47w2effEmcpx8yfEjh9TuX1/jnZv6kMvTplvFiaRqvwnG+wI4QQaaSnWwghxH/exIkTSU6Ko1+34LQe7rsUwGTUoCiQU3VQpbyBhQsX0KZNm4eq++tvvsEQmpdsdWpleF7n7kbOzh25+PkXrFq1ivbt2z9pcwD47PPJtG7djA79zzNySHYqlPFEUVITmcPHkxk1IYbjZxRUA2xLWIlBY8TqtGBzWFF1KnX8mmHSpp8Tfldet0Lsu76Fj8d+zPQfpvPbnj2YLO54qT44cRLDHVS9gz4v92bcuHEoivJI8Xt5eWGNieHa6rVE7j+I/f/Jsd7Tk4DKlQisXhW92Run3YE9IRFFUWj24ovsO3gQp16Pd9UqaD08wOHAabORkJCIVqPFzT21TQHVqnJh115Wr15Nx44dXe59+vRppk6dytLFy0hJTp3vbnIz0bptKwYNGkTRokUfqS2P6tq1a8ybN4/du3Zw8eJ5wq6G8dYrwQx/M2faSuDeXjrefCUHHVsF0K7PRfr17cX6DZsf+Tk/De7u7nTp0oU2bdpQskQhWjTW8PpL2e97zZ79CRQsWOopRSiEEFlLkm4hhBD/WtHR0SxatIhdO3eRlJRE9hzZad++PTVr1kxbzdvhcPDzogUYjQplit97sS+jQYNe56Rwfg2/HrjyUPe/cOEC+w8eJKhLh/uWMwUGYMoXyk8LF2Za0l2iRAmWLl3Nq6/0o3Wvs+TPoyMkWMut2w7OX7aTN29Btmz9noCAADZt2kRsbCy+vr6sXLGSo7+cxqS7/0Jj3jofHA4HycnJrF23lt9++4358+dz4dwFdDod5Su2o0ePHuTPn/+x4s+VKxeJEXdI3vMbnhXKYcoXCkDyuQvc2rWbO/v2U+ClPljuRKFYLGzesoVjly/hXbY08Tdvoff1Sa1Ip0NjMOBIsRAbF4dWq8VgNGD088Xg68OFCxdc7vvrr7/StXM3bLEOcujyEWQKASDCcotlc1eyasVq5i2YS926dR+rXfejqiqTJk1i8qRP8XC30aCWG/mzWzh7Uc/0+TfZsCWKWVMKUbTQn7+b7MFGPv0wO11e+Z39+/dTuXLlDOvdvXs3Cxcu5NrVyxiMJipVqkK3bt0IDg7O9Hbci8lkon2Hbvy47Fte7eXAwyPj1e4vXE5my85Exk/onrra/j/wiwQhhHgUknQLIYT4V5o1axYfDv+QxLgkfNQAtOjYpznIgrk/UqJkcebOn0toaCg3btzgTmQEOq1CUtL9F9YyGRUSEmwYjQ+3EvfNmzdxqE7csoc8sKwhezBhmTxEuFSpUuzc9Rs7d+5kzZo1xMbGkqegmTHjmlGrVq20Lx769u2bds2vv/6aOiT7AeyqDUVRMBqNKIpC1apVqVq1aqbEffv2bUaOGYMpfyh+7dug9/dPS7zcCxfCp24twucs4Nx3M9AZDRiB3w4cIFvPbtguXUb9+wJpioLWZMTudJCQmIifMXU+vupwoP3LNmfXr1+ne9ce6GJNVPaugVb582OSt96XvGohDsXtome3nuzau4vcuXNnSnvvmjRpEhM/H8Nbr/jxSs9gtFo70TFR+PvquHDJyusfhNOx32lWzS9G3tx/vgdrVPYmNJeGn3/+OV3SfePGDfr07sHRowcpGKqjVDEDSckOvvvmF778YgIDB73NO++8k/ZeyGovvfQSSxYvoO+bF5k+OR+ef0u812yK4uW3L5KQpPLeu4MZNfJ9mjZrRe/evSlXrtxTiVEIITKbJN1CCCH+dWbOnMmQN98h2JmbCp4lMWpTExRVVYmyRXDiyEFebNaCTb9sxG63o9NqMBo0rNmcQLHCxnvWa7OprN+aRMcuD7cSt9FoBBQcKQ+eq+1MseDmdu/h3I9Lo9FQu3Ztatd+uJirV6/OisUrSXEk33d4+Y2UKxhNBqpUqZJZoaaZPXs2EQnxFHh7MHEpKTgSk9CYjKn7gCsKWnd3Ajq159rnX2BJSkZV3NFm90cbkp2U6GhsERFYI+5gCAxAdTpxJiahqk4UgwGr1YLD7sBy+xb2uDhKlSrlct/E6CRqedV1Sbjv0ipaynpVZ0fsGmbPns2IESMyrc23bt3ii8njGfySL2+9mrqwXVKyBQXQ6RSKFDSy6LvsNO50jYnTrjPl0z9HEGg0CoUL6Lh584ZLnVFRUbRr2xK75QILv89N9UpeaV9exCfYmT7/NhO/GIvdbmf48OGZ1pb7yZs3L7Nm/0jvXl2p0vg0nVp7U7m8F1arg7GTr3HsVBIF8xno0TGY7MEmbty0smjljyxdsoC3hwx/Lha+E0KIv5OF1IQQQvyrREVF8cH7HxLszENJ74ppCTeAoij4G4Ko5FmX8LAIPv30U4KCgjCZ3ChS0J35S+K5HXHvXt4fl8URfsdJjx49HiqW4sWL42s2E33k6H3LOe12kk+dpnaNGg/XyCzUoUMHPM2enE08fs8yNqeVy7azNG7WmJw5c2bq/Z1OJ7PnzcOzTGlMZm/8/PzQazQ4k5KwJyRiT0zElpCAqtHgUaI4nnozHgYzbnlCMWpMmIuURGMyEbNtBzG/7uL65ClcHT+RaxMmc+2zyURv3kLCrZvc3vorubLnoEGDBkDqFzLz5swjm5ITvUZ/z/h0Gh3BmtzMnT03U7ccW7BgAUaDhZd7/jncW0FBBVRn6oJivj5aenc2s2ZTFHcibS7XxyeqmEyuX5JMnTqVqMhz/Dw9PzUqe7sM0/byTJ0TPuz1AL6eOolLly5lWlsepHr16mzZupNOXd/kpxU6er9+k24Dwjh1LoWxw7Ozb2MZBvXLTqsmZgb0CeTX1YV49zUfJn7+EbNnz35qcQohRGaRpFsIIcS/ysKFC0lOSKGQ5723mjJp3cipy8+Sn5disVh4sUU77kSBXq+lQ78bHDvl2jNttapMXxDDiAlRVKv+AoULF36oWNzd3eneuTMJB37HGhN7z3IRu/eiSU6hV69eD1VvVvL29mbMx6O5rQnjWNxBrE7XZxFni2Z//HY8Ak18+OGHmX7/uLg4wsPD8SyQDwCdXod/gD/+fv54uLvhZjCiqCpaRYtnnnxYVQv/z0wB0OoMmMtVJGHfAWI2bcGYJzcBndoT2K0TXpUrkXz2POc+/4LEw0cY+cEH6HSpPdrJycncuROJj97/gTH66P2JiYklISEh09q9d88u6lZ3w8vzzx52g8GAgkKK5c9VvFs08sRmd3Lo2J/3vh1uZd8hCzX+8qVNSkoKP/04hy5tzOTOee/RG/26ZcPX7GTOnDmZ1paHkStXLkaMGMGx42fY+9shsgUFMLBvMC/3zE5CQjwREeFERt1JfUXeoVcnTzq0cGPypPHYbLYH30AIIf5BZHi5EEKIf5Vt27bhowZg1Nx/3nVOU14uJpzkwIEDvPzyy6xc8TP1qus5dzGFJp2vUa6UkdLFjCQlq2zekcTV6zYMJjPz589/pHgGDRrE6nXruPzddHJ0bId7ntxpPY5Oq5WI3XuJ2vgLgwcMoECBAo/d7szUu3dvVFXlg/c/YEf8FXzUAPSKkSQSiFeiyZs/L/MXzKNgwYKZfm+NRpO6ZPzfepH1Bj16g56U5BSSk5LQK3pS/p+L+mh9CTt3HrVh6nZiiWdOoc8WhH+Hduj9fVF0etAoGPPkxrN8WaJWrkZ39brLvuh6vR5FAacz/RZrf+dUHWnXZBaLJRmPANe+EK1Wi9FoJCkpBZNRQaNR8PRIfe+kpPz5fL747gZ6vTft2rVLO3bixAliYyNp2Tj0vvc1GjU0ecGdnb9uAUZlWnselk6nY+/evSQmRtOjQ16ioiLRasHLU0Gv14IKFquT5JRE2r9oYOGKq6xfv54WLVo89ViFEOJxSU+3EEKIf5WkhNSE7H7sTjs2pxWH00FSUhIlSpTgy6++Zcuu1OSuU+sgdDojW3Za2bQ9hcho8A/IzoYNmwkICHikeIKCgli1fDnFQrIT9u10Lnz1NWGLl3J5wULOfjKB+F+28vagQYwcOfJJmp3p+vTpw7ETxxjz6WhKNyhGnqoh1G1Tg3k/zuXAwf0uCWtm8vLyIk/uPMSdPpvheavVioKCgobk8+fw0poJdS+EMzaehJMnSDxzGuudOwR27YwxRwiKTodqt6FaLDitVvQmI4X69kLj68O3336bVq9er6dU6dLctl9/YIy3rdcoXqJYps7BD8mem+NnrOn2pvb09MKpaoiOdWCzqxw/bQUgR4iR6Bg7H467wrwliYwY+THe3t5p1yUnJwMq3l4ZrxD+V2YvHcnJ996vPKudOHGC0Nx6vDySMBnB31eHu5sWvU5Br1fw9NDi76ujUH49vmY7hw4demaxCiHE45CebiGEEP8YKSkpXLp0CafTSc6cOTGbzY9cR/ac2TnCiQzPRdsiuZh4mhspV3CoDhyqnQ/e/4CIiAi6du3KipXrmTp1Cj+vWvv/Hk8tJpM3Xbp1YtCgQeTNm/ex2pU7d252bN3K9u3b+fGnn7gcFobR3ZNqr7xCjx49Mn0V7McVFhbG+fPn0Wg0FClShODgYAYOHMjAgQOfWgyKotCvd2+GjR6NpWF9jH6+LudTk1IFa2QkyafPUMhYDrPej5y63Fxbtw6Nnw/GfKEYgrOhoqI1pY54cCQlo3E68PXxRafX4V2pAouWLqVRo0aoqoq/vz+9+/Ri4P5BxNjuPcw81hZNJLcZ1T9zFx7r2LEj3bv9zKGjiZQv7Zl2XKfT4evrR0xMDJHRdr6fF4NOp2HazJts252CqrjzydhJ9OzZ06W+1K3AtJw+n0yeXPcf9XHqnIWQkMydm/8oVFXFbreh0aTuP57RDmEajYKPtxYFlaNH779GghBC/NMo6t+/Un3OxMXFYTabiY2NdfmGVwghxPPjxo0bfPvtt8z76SdiYmNQVTAZDbRp0ZIBAwY8Uq/qpk2b6Ni2E5Xc6rokTmFJ5zkcuw8TbgSTBxPuODUOkkyxRCq3KF2+NEuWLsbf35/bt28TFhaGRqOhYMGC//p/X/bu3ctXX01mx/Yt8P/twjQaAw0aNuOtt96mZMmSTzWe2NhY6r7wApcT4snTuwfGgD9/j/HxCcRcCSN60XLco+3U9mmMTqPHodr5PXY3YfbLeNevi7l2rf8PU1fBbkdRFHx9fDAYDTidKpFnznLl629xV/RoNBoUjYZcuXNis9uIuBpJabcq+BuypU0FSF35PpyjyfsoUbE4a9etydSebofDwQv1amFPOc3iGfnJFmRwOa+qKnMW3mDIqBvkzl2IgoUKUqfOC3Tq1Al//4y/IGjapCFm9yMs+Obe0wCuXE2hxotnmTjpezp16pQpbVFVlZSUFFRVxc3N7YH7bE+fPp333nmFfRtykS/PveefXwqzUqlxGCHZC3L8+KlMiVUIIZ7Ew+aiknQLIYR4pk6ePEnrdu24nZCAV/mymIsXRdFqSbh4idh9BzEkJTLz+x9o2rTpQ9XncDioWrkq10/fopJXXQwaIxGWm+yN2kIwuSmglETFiQ0b3t5euLt7EGeL5veknVSqXYHVa1Y/MEn4N1m6dCmD33iFYoVU+nQJoHolL5xO2Lorllk/RhJ2w8jMWQuoW7fuU43r0qVLtOvQgXNXLuNWuDCeBfODqhJ3+ixRR47i4XSjtk8j3LV/9gqrqsq66CUY6lTCq1oVVFKHjbu7ueHm7oZGo8HpdBIVGUXilTAiZs2nmkdt/A1BJDriCUs+zy31Gh7ebiTGJ+GpeuOrCQIgWo0gUYmjSrXKzJs/75GnGTyMy5cv07bNi9gs1+jRwYeWTfzw8tBy6lwycxfdYeP2ZHr1HsAnn3zyUO/RFStWMODVXnw0NJDenbOlOx+fYKfbgIuE3fDnt32HcHNz48iRI8yaNYs9u7eRkpJCcHB22nfoQocOHR448iQmJoYff/yReXNncuXKZUAlV67cdO/Rl65du+Lr65vhdceOHaNKlTK8O8CXD9++93P9YFwEcxfHozcEcunyg6cBCCFEVpOkWwghxD9efHw8VapX57bqJG+/3ug8PVzOO+0Own5aBOcvsm3zZooWLfpQ9Z45c4YWzVsQczOeXLoC3Ei5gs1qo7RSFQdOnDhwczNhNvukXRNhuclh2x5Wr1tF9erVM7OZ/1gnT56kcaO6tGmq5bNRoWi1romcxeLk5SEX2XPQwM5d+wgJCXmq8SUkJLB06VJmzp7NmbNnURSFYkWLEh8by6UTV6jsWQ8PnZfLNb/FbCMyu0JAzy54eHrg5eV6PjoqGovFSuKu30javpfGvq3R/WWLsHDLTQ6n7KZNx9bY7XaOHTkGQPGSxenZsyf/Y++uo6M62geOf+963AUL7u4uxSnu7k5xipXitAWKe7HgENzd2kJbihZ3CBLitpvNZu3+/kgJzS8QoC3Wdz7nvOe87M69d+Zm0+xzZ+Z5PvvsM5TK1++T/rtCQ0OZPXs227ZuwGiMJzk1u5J8+QvRq9cXtG7d+o0fCsmyzIQJE1i2dC6f13CgS1sfihZwItFkZ8/haFasjyI6zo2Nm7ZTpEgRRo8ezbq1y8nkDw3ruODilLw8/cBxI05O3qwMXEf58uVfeq27d+/SpnUzIsIf0qiOM1UrJgfop36LY+eBBDy9srBh41by5cuX5tjHjx9TsGBeHHUWZk/2oXXj1N/nZFlm9eZ4vv4ukkpl3bh804Wbt95fiTNBEIRXEUG3IAiC8NFbuXIlQ0aNIufIoWjc3V/axm61cff7WXRq0JA5c+a88bmDg4P59ttv2b51OzHRMeSlON6KDCiVSpycHHF0TB3gy7LMKf1BGrWvnyrB1uuEhoaybt06du3cQnR0FC4uLtSq3YDOnTt/NNnIX2XIkCGcOrma0/vyo1K9PJAzJNgoVesGPXp9xYgRI95zD194/nVFkiQiIiJo1LARd67dI6OUjQCHnGgUWow2A9f1F3kqPyVTj65kKFY01f5gq8VKZGQkiiQbz5YsI5spA0VcS6e51nX9BRI8Y7h6/QqOjo7va4ipxMfH88cff/w52+xPoUKF/tYKDFmW2bRpE4sXzePu3RuADZBQKnXUrtOAkSNHkSdPHsaMGcOa1QuYMtqPds18Uj2ACQs3M3BMMBevati95xAFChRIdY24uDhqVK+Mi0MIaxdlJ6N/6iXiYeFmOn5xn6g4P44d/xlPT89U71utVkqXKopaesSTZyaKFtTSqrELGXxVPA21snFHPNdvmene3p+zlxLx8q/J+vWb3vpeCIIg/NtE0C0IgiB89KpUq8Zt2Ua2Tu3TbRd67ATmU79y58YNnJyc0m37/23dupWuHbtSQVcbR5ULGo2a5M2+aV2O/x2/Eu6c/PFkqtfNZjNxcXE4OTmlCsJ27NjB4MH90KgSaVTHiayZtYRHWth10EBUjIKRo8YxcODAj3K5utlsJl/eHAzopmFQ74zpth3zTTCHfnLjwsWr76l3rxcTE8OMGTNYv3YDsTGxyHYZhVJBxkwZkBUQYtCTpXMHnLK+SFKn1+uJD48kdvtueBhGVbc6aWbKARKsBk4lHGDVukCaNGnyHkf17siyzIULF3j8+DEajYYSJUr8mWwt+QFVhfIl+XqoO707+b/0eKPRxudtb5MhoDq9e/cFIG/evGTKlInFixcz9dtRnN6XJ03A/VxYuJkK9W8xZNhkBg4cmOb9WbNmsWDeBCaN9Gf/0RhOnk6uay9JUKOyO13a+qFQQLs+j1i1egu1a9f+F+6KIAjCP/OmsajIXi4IgiB8MA+CH+JQ+fVLuZ2yZSXu6AlCQ0PJmTPnW13Dy8sLpUqFRqtFo9Sk29Yu21EqX/xpPHv2LMuXL+fA/l1YrRZAokKFynTu0g2tVkv/L3rSooGWSaPy4eL84rivh9pZsOIZ06aOx8nJiR49erxVn9+H6OhoTCYjhfK7v7ZtofyOrN4Sis1me6dLq9+Gh4cH33zzDaNHj+bMmTMYDAa8vLwoW7YsBoOBtu3b88uipehyZMe5YH4USiXRt++gv3gZnUVFOZeqLw24AZxUzqgkNSEhIe95VO+OJEnkzZuX6Oho9Ho9N2/exNPTE41Gw9q1a3FxttKppc8rj1epbLRpomP4xO2cPHEApVKJJKmpWasef1w6T8PaTq8MuAH8fDU0ruvE2jUrGDBgQJoHUd26dWPL5g0sCnzADzOysmxWLvQGG64uKrRaiWM/xTFwzBMqVa5JjRo1/rX7IgiC8D6IoFsQBEH4YFRKFbLN9tp2stWKRHL5pNex2+08efIEo9GIt7c3BQoUQKvVEJYUQnbHPK8+TrYTK0XQoHgtAObOncu0qRPJmVVi1EB3sgfoiIq2snXPL/TqeQylyoFKZRTMnJQNhSJ1AKHRKBjaNxNR0Va+nz6Ftm3bvvUM/bumVifvY04wvv7+GxJsqFXqj3LG3tnZOU0Q5uHhwZ5du9i7dy/LV67k/OFj2Gw21AoFzmY1VT3ro1W8uoyWXbZjs1vR6dIvtfWpiI2N5fvvv2fL5nUYDLGAHVDg5eVPh47dOHf2DFXL63BwePkDFZPJRFxcLJ9V1ODkKDFjQkY+q+TGydPxrFi/n9t3YvmyT7bX9qNqBTc2736CwWBIs9fe3d2doM3b6dC+NXVaX6dcSR3lSjpgscgc/SmBW/esVKlam2XLVnw0D34EQRDelOJDd0AQBEH431W6VCkSrt98bbu4a9fx8/UlY8ZXL4NOSkpi2bJlVKlcnnJli1H9s/IUKZyXIUMGUrxUcR5b7mKTra88PsQUjFVppkuXLgQFBTFt6gSG9XXj5K589OmcgTqfedCuuQ/bV+Vm+BdexMdF0rWtS5qA+6/6dvUnISGaHTt2vHaM74Msy5w8eZKOnTpRsEgRYuMMbN4ZQnx8PFZr2nsjyzK/no3nu7lPidfHkzmzL4UL5WXcuHHcu3fvA4zgzanVapo2bcq+PXt49uQJYSEhzJ8zB1ltx2q3pHvss6THKNQKKlSo8J56++5ERUXRuNHnbNuykO5tJU7tyc3tXwtxdGtOmtQxsmTRd5w7/ztazcu/ElqtVuLiYtFpwd9XhUol4eKsJKO/lnbNfdi1JjdqFRiNCdhsr/79Al5af/uvAgICOHb8J5b8sBaVQyWC9jiz64g7OfM1ZvOWvWzcGJQmWBcEQfgUiJluQRAE4YPp1qULhzp2QH/nHi65X75s3Bwbh+HiZQYPGZIyO/v/6fV62rdrzaVLp/m8hiNjB2fEw03J3Qcm1m09zqXLZhIVcD7+FMVcKqBRvFhmLssyYUlPuWG+SPO2zShYsCA9e3SmYW0dQ/pkeun1tBoF3p5KiuQHm92GUvHymbfMGbUUzKvj4sWLdOjQ4S3vzr/LarUyZMgQ1m7ahNLPF9ca1ZAfP+HQT79x4XIs+XIbcXVxTdmzbrfLfDXlIauCwsmcQUn/7l74+uh4+MjE5i3zCVy5hOnfz6Vt27YfdFxvQpIkJEmiUaNGjPEbw+2wyxRzrfDSmXubbOWB6QaValR8aabtT82gQf2Jjb7B3nW5yJHtxcx9vtyOTByZlYZ19NRqcYPfzmuRZTnNPTEajSgUMq4uak7+YgQgW5YX53F2VpE/twNnzifSukliukHxj7/EkylTZpydnV/ZRq1W06hRIxo1avR3hywIgvDREUG3IAiC8MHUrFmTzypV5uS6DWRs3waX3LlSfek3hYXzaM16svv7061bt1eeZ8CAfty+9Qs7VmWneOHnX+hlihTU0rC2I0vXRvDtnCgMihh+1O/Fh4y4qT2xyhbCrU9IUOhp0LQ+8+fP5+eff+bRo/vMn5LtldezWmUcdAokCRITE3F2enUQodGAxWLhzJkzXL9+HVmWyZMnDxUqVECheH8LziZOnMjqoCD8WzbDo0QxJEnCbjZzLyqcniMeM22sJ+WLxaFQKtBpdXw75wlrNoczfpgHrZt44eHpifRnAroR/e2Mn/aIYUP74+npSZ06dd7bOP4JnU7H1OlT6dWjN3/oz5DfqRha5YsA0mCN52rCORTuMGnypA/X0X/J3bt3OX7sEPO+9U8VcP9VqWIudGntTeCmcH78JZZqFV/U0paRMZkScXRQADKBG+MokNeJwgVSZ3Tv2NqPKTMfMaBHPEUKOfOyRIVh4WZ2HjAwZNjIj3KbgiAIwrskspcLgiAIH1R8fDydu3Th+M8/o/b3wyl/PiSlAuODYEx375E3Z042b9pE9uzZX3r8zZs3qf5ZBeZ960ez+l4AmM1JxMfrk5dM//lXbvikcHYdMlOtWk2ePQ0lJCQEjUZD5aqV6Nq1K9WqVePSpUt89dVX/PrLcZrU86BsSRdaN/HG0yP1DPvug1H0G3GX3WszkiObC+5/qff9V4YEG8U+u4pC5Y0xIQa1yg6Axaoge/Y8DB02kubNm/87NzIdYWFhFClRHIdqVfCvXi3Ve9aEBIJXr8EcfJecmWUqlXbAZlezJiiCoX3dGdTTHzc39zSBkt0u06HvHSLi83H06I+fVCAVFBTEsCHDSIg34iH7oJF0JJJAHFFkyJyB1WtWUaZMmQ/dzX9s2rRprAmcyvmj+dG8Yvk4QFh4ErnLXSJ7gI5tgXnIld0BSM6PEB4RjpuLxA+r45ixOIZF03PRqK5XquPj4q2UqX0JT3eJbasKvbxkWP/7RMW+vGSYIAjCp0pkLxcEQRA+Ca6urmzbupUTJ06wavVqzl64gM1mo1TOnHQZOoxGjRqlm9Bqw4YN+HhBw9rJM3RJSSZiYmKRZAmVpEahUCIBnZp5s/fQY3489hOt27fi93NnUhIyhYeH07xZY37//TT+PjYa1nbAajXx/YJYvl/whKF9M/FF9wwpgWWdzzzwcFOzYVs8Y4e9WE4ryzIXLidw624iANduxhMalkCd6lqG9AmgYpnktucuGVi65hED+vfg2bNn9O/f/13c2hQbN27EJMtkKV82zXsqJydy9O2D4f4DHh48xK0NN/D19cfVRU2/rtlwd3d46TkVCok+Xfxo1+cK586do3TptPWuP1atW7emTp06BAUFcejQYeLj4vH186FZs2Y0bNgQrfbVWbg/JaGhoWTPqk434Abw89WSNYszSRZ36rW5R7P6zjSu54mLk4JfzxnYud/A5etmvuyXOU3ADeDmqqJ7B38mz3hKpQa3aVTHiaoV3QD4+dc4dh1MwN0jMxs2bhUBtyAI/5NE0C0IgiB8cAqFgho1avytUkAPHtyneGE1arUCu91ObGwckqxArdCkWuRavKAOlQrsSRrWrVmHRqNh5syZmM1mWjRvjD7uJqvmZaJSWQdi46LxdFNiSJBZvCqWqfMeY7bIDO2bvMdbq1XQoZUv0+c9pkQRd9q1cOfAsRhmLXrCjTvJ+17tdpkEox0HnYJenfyoVPbFE/DSxV0oVcyZWYtD+PabcZQtW/adBq3Xr19HkzkzKoeXB9CSJOGSMwfOfXtzfcwEsmXLiZeLCR/vl7d/rlJZV9QqG1evXv2kgm5Izpbdu3dvevfu/aG78s7odDoMhtcvaLRaZcxmmZ69+6JQKFi/LpD12x4DMgaDEW9PBasX5KFGFfdXnuPcpUSqVatO/QaNWbN6OVv3BgOQOXMWhg3vQbt27UTALQjC/ywRdAuCIAifNKVShSUxObBITExEttvRKHQpAbcMWO1mTFYrduxkym7ExUXBpo3LOH7sIAULFScs9Cb71uf8c9+rjMqgIiHRhoeHijFDvHBwkJi95CnNG3iR9c8kUlq1hE3WMXxSFPOWR/HgkZHqlRxZ1d+P4kW0JCbKnLmQyNY9CXQfdIep47LRoYVvSr8lSWJIn4zsOhDPihUr3mnQKsvy61NHJ3cKSUpun15W9r80R6GQsNvt/0IvhTdhs9k4fvw4u3fvJjo6CmdnF2rVqvXSGfrKlSuzetUirt8yUiCv40vP9yzMzNjvgnkWamDmjO/QaDSULFWGXr37U7p0ac6dO8fkSSNwd3v1V8Zfz8bzy1kTixb3pUmTJvTt2zf5d1GWcXR0/KS2HgiCILwLomSYIAiC8EkrVqwYv51PIl5vxWQyIaFMSfglAxZ7ErJk5eylRLQaiWXfZ2JXYFYObsxI7apGDh7cRbPPtX9JNCXh7OxCklnGYLAhy9C3szsuThLrtkQgyzJbdkcy64dohg0bwYiRE7l5N4lOLV2YNcmbQgW0yLIKlUpB7WrObFiSgS6tXfhqykNu3Dam6rtCIdG2uTv79+3EbDa/dqw2mw2LJf1yVy+TN29eLE+fYktKSred4f4DFHaZggUL8se1JIyvqeF94XICSWaJXLlyvXWfhLd39epVKlcqR+dOLbj+x0a08jFCgrczcEA3SpYszJEjR1K1r127Nn5+mZm+IAS7Pe2M90+/xlG10WWO/BhNjw7uzJniy4ThriisZ5g8aRRTp35Dy5YtKVmyEp2+eMjhkzGpzmOzyew7Ek23wY+oWKkG9evXB5IfKDk6OuLk5CQCbkEQBETQLQiCIHzi2rZti8WqI3BDOHa7PSXghuTyT7Jkw8lRwaogPSUKOZInhxYJBZkzqGjTxAsHLdSsosb6lxrDOp0OVxc3jIkykdEWbHaZWtUc2bgjgtotbzFkbBgtWnbl66+/Jjg4mHy5nPlmTF68vXzw9vbBy8sbJFCpkktVTRjuja+XklWbwtP0P2c2HVarmdjY2JeOLzExkfXr11O71mdkyeJH1oAMlCldjDlz5hAREfFG96hNmzaorTaifj/3yjayLBP582ny5s7FyJEjSTCqCdoVme55l68LIyAgJ5UrV36jfgh/361bt2jRvBEuDg/Ysy47R7bmY/mc3Oxak5efduWhZKF4unVtz7Fjx1KOUalUTJ02i2M/W/li5AOePnvx0OX6LSOdvrhNsUJqjm/LwrRxOWndxIeubf3YujIPW5Zn5erlEwwY0I+16zZSpHhNug0KoUqjm4ya/JCRkx5SueFNen/5jDLl6hMYuOaVJf0EQRD+14mgWxAEQfik+fn50av3AGYsjiJoZzw2W/JSZ5nkoNtsgWETIrh8LYmB3bz/PCp5uXWiSUajkXB1kUg0JqY6r6OjI15eXmi1ThgTQaeVeBJiJSBnAzYF7WL27NnIssyunVto18wNjUaFWq1GpVQhSclFk57PCqpUEm2bubBjXyT/v2hIdIwVUODk5JRmbGFhYTRoUJcRw/vi73mD6WO9mDXJm0qlIpg3ZzzVqlXgwoULr71HmTNnpkO7dkQdPEzc9Rtp3pftdp4dOoL51m1GfjmcgIAAWrRsx5RZ4Zw8HZe2vSyzYHkIew4nMnjI8Pda+ux/1dixX+HvHcfm5bn+UhYvWY5sOpbPzknV8hLDvxyUnLX/T3Xq1OGHpas4+auW8p/fom2v2wwec5+G7W/g6S4xc6IfWTJ7pwmYy5d2Zc6UTJw8cZibN2+yadMWdu0+TMmy7bh0MzeXb+emXKWO7N13jDVr1qVbe1sQBOF/ndjTLQiCIHzyvvrqK4xGIxNnzuGHNTE0q+eGq7OCe4+NHDxuRJYlpo7OQGy8jSVro7DJVvLm1pA/nwsg8TTERtbMJlxcXFKdV6VS4+qqxtXVhdCIeKrXqENg4KqU9w0GA0lJJnJmd/t/PZLQarWYTEk4OiYH4DmzqTEm2jEa7Tg5KVNabt0TQ7lyFdIE3RaLhfbtWxMTcZnDm3ORP8+LPbmtGnszepCFboPv06F9Kw4fOUnmzJnTvUfTpk4lOjqanWvWE5UtK24liqF0ciIpPIK4s+chJpYp4yfQrFmz5PbTphMVFUmHfnupUl5Hi4aeeHmouB9sYsO2GG7csTPsy7G0adPmjX9OQjKLxcKBAwdYv34NDx/cRalUUqx4Wbp06ULp0qXTLMm+c+cOp34+wfzv/HBxfvlXN6VSYuSATNRpfY8jR45Qr169lPdq1qzJ4SMnOXbsGMePH+X2o3Bi408zor8fmTP5vXIJeM0q7uQICGXVqlUpyf4+tYR5giAIHwMRdAuCIAifPIVCwbfffku1atVo2rgp85bGo0BBkmyiayt3MmXQMG1RBBHRFrw9ldhliI6xk9EvFk8PFUG79JQrnXam+bknIWZOnE5k2vTUAaaDgwNIEjFx1jTHODg4EhNrwmRKzmAeE2dHkpIznz935MdYfjufxJIfeqQ5ft++fVy/doEDG3OmCrif8/JUs3ZhTio1uMWyZcuYOHFiuvdIo9GwcsUKmu3dy4rAQH7ZuRe7LKPVaGjdsCHdu3enVKlSKe21Wi2BgavZsWMHq1etYOBXZwEZhVJFrVoNmPhtDypVqpTuNYW0goOD6dC+Nffu3aBsCR2Nazlgttg5fDKIJjs2UqduYxYtWpz82frTjz/+iEZtpX5Nj3TPXTCfI3lzajhx4gR169bl2LFjrFq1khMnjiDb7SiUSmrVqkejxs24fPkstar5pLvnWqGQqFbRkVPnL/1bwxcEQfifJIJuQRAE4T+jdu3azF84n+FDh+Nm9iZK8ZCYOBvbD0bSqpEz3dv7kT+3GkmSuHrLwtI1sWzZredBsInPKhno0TE5u7jJZCcqxoJWo8BBJzFkbDDuHv40bdo01fW0Wi3ly1Vkx77fUmUmT35Pg4POkXi9EbtdZvs+PZXKuqJSSSQk2Ni0M5Ips8KpU7dJSgKqv1qzeiUVSjtQuMCrHwa4uqho08SVdZvWMHr06HTrmQMolUoaN25M48aNSUhIwGg04ubmhkajeWl7lUpFy5YtadmyJTExMSQkJODh4fHSpfDC60VFRdGyRRPU0iMOb86ZKqP410Nl9h+NYei4nfTpY2PVqjUpAbHRaMTZSfXaetsA7m4KEhISGD58OBvWL6dIATWTR7qTwU/DkxAzm3bsY8/u7YAVheL15cQUCinNlghBEATh7YigWxAEQfhP6datGzqdjnFfj8P02Ma+43qmjPKiT+fkJeCSJCEpFBTJr2XBd35kz6Ji/IwoRk6K4sRpCyDz61k9SWY7FqsMSCgULmzdthxHx7Qzzp27dKdP75Mc/TGWmlXd//KOhKubKwqFxJ5DkZz6LZEihVS07X2Hi1eSMCaqad2mO999NxWlUpnmvNevX2FAt9fvk61WyY1Fqx4TEhJCjhw53vg+OTk5vVXw7OHhgYdH+jOtQvqWLVtGXOwDjm3LTUb/1OW9FAqJBrU9UakkegzZw+nTp1NWEvj6+hIbbyUi0oKP96uTlVmtMg8fW0iw3OHa1d+YOTEDrZv4pGrTrZ0vc34IYdzURxz7OZLu7bO88nyyLHP6dyO58xT8B6MWBEEQROYTQRAE4T+nXbt2XL1+lTz58pE7h5qubVyRJAUKRfL//lrDu2tbV3IEaPDPmIO9h2O5cl1Pr04uzP/Oh1kTffi8hgtqtYXvvp1MTExMmmvVr1+f2nUa02vYY1YHhZOY+KLMVqLRzpY9iYz6xkDBwqXJX7gVbj6N6dXna878fomZM2e9cpb5TWcXU8YiZiM/ahaLhQ3rV9GigUuagPuv6nzmTt6cKgIDV6a8Vq9ePTQaFzZuTz9b/dGfYgkNl7lx4zJ9O3ukCbjheX34TOTMpmPl+gjM5leXhTv9u54bd6x06tT5DUYoCIIgvIqY6RYEQRD+kxQKBbExEQzt7Yk+QcZmt+PgoECl/LN+t9lOgtGOxSrRroUnU2bdpl83fyZ8mQFJSg5gNRo1HVqpuHojgXZ9ztGjexe2btuZah+sUqlk6dLljBjhwdffrWfa/HDKFNciyzJnL5nRG1S0aduDqVOnvVVJpTx58nPq9wv07Zp+u9O/x+Pk5ELGjBn/zm0S3pOHDx8SGRlGvZqvnlmG5KD485ourN/xS8prbm5utGjZnoWBS6lSwZVihdKugAgJTWLC9GdkzpyN0NB7dGsfkO51Jo4MoEPf2wwYfY/53+VKs3T9+i0jA0Y/oWTJ8mLvviAIwj8kgm5BEAThP0mv12OzWcmdywsnJw1GYwIJiVYUf8bLdhlUSjXu7i5k8DWhVMD4YVlwdEz7p7FQfidmT85E5wE/8fvvv1O2bNlU72s0GubMmcuQIUNZv349t27dREKiS7f8tG/fnixZ0g+0XqZjp64MHnSaW3eN5M2Vdlk7QEKCjQ3b42nZqk+qxFvCx+d5GS+d9vWLDHVaBWazJdVr48eP5+qVP2jc8Rca1namU2tfShd3JjrGyubdkSxbG4NGl5UKFapw5cJT/H1fvoLiucb1vMjo78TOAybO/XGTds3cKFLQCaPRxt7DsRz+0UiBAqVYtXqdKAknCILwD4mgWxAEQfhPcnJyAkkiItKKs7MnTk5OJCWZsFptSJKEWq1Go1Fjtdl4FmbCyVGBg0PavdXPfVbJjRwBz1i7dm2aoPu5rFmz8tVXX/0r/W/UqBHz5xWg26DbrF+cg2wBqZOk6Q1Wegy5j9HkQq9evf6VawrvToYMGVCpNFy6mkCJIunv1b94xUhAQIGUf0dGRrJ48WIePLiDMdHK+m0RrA4KB0lCpZCQFEr8/LPQvlM7wsLCeNOdBq6uapo075a8WmPdBhISQgCJPHnyMWlyD9q0aSMe5giCIPwLRNAtCIIg/CfpdDqqVavBlt3H6dzaF0mS0OnSBhBWi5Ud+wzUqOKOQpF++aTypR24cuf6u+x2Cp1Ox4aNW2jTujmfNb1F/VpO1K7mhkol8fsFA5t3xyPjweo1G8iWLdt76ZPw97m7u1OnbkPWbt5J59a+KJUv/6w9fZbE4R8T+ObbTgA8evSIli2aEBd7nzZNXGlYJy9ajZ0z5yNZtzWWc5eSKJzbgZw54lkwbwpGkwKllMTTZ0lkyvDqveO37hp5FmalSpUqNGzYkMmTk3MWaDQa3Nzc0i0lJgiCILwdsV5IEARB+M/q0qU7l65a2L4v6pVtNu2I5uZdCy0aer/2fDabjFL5/p5XZ8mShQMHj/LV19O5dCMT/UaG02tYGNv2a+nYeTjHT5yifPny760/r6PX6wkLCyMpKelDd+Wj1KdPX+4FS3z1TTA2W9rp6Ng4Kz2HPsDbOzPNmzfHZrPRuVM7lARzZEtuxn0ZQOECOrw9TDSq7ciBDZkZN8yLh09MtG3mzdnD+ejYwgF9gplFK5+k25cfVofi45ORunXrAqBWq/H19cXd3V0E3IIgCP8yMdMtCIIg/GfVqlWLlq06M/jrVTwINtG5tS/eXsnJzMLCzazcEMbCwBiQtMQbrOmey2qV+fGXRKrXLvo+up7C1dWV3r1706tXrz/3qdtwdXV9aZmxD8FqtbJz505Wr1rB+fO/AzJqtZb6DZrSvXt3SpYs+aG7+NEoWbIkM2ctYNjQ/ly8covOrT0pXdwFs9nO4ZOxrNsah9XuzcZNQTg7O3PkyBFu3brCnnXZU2atDXoDkmTDw12FQiExtI8Hv180sTjwGbvWFuD7Cdm5ct3IosAIcmRzols7v1RBtN0us3DFMzbvNvLd1O/eKrmfIAiC8PdI8ideYyQ+Ph43Nzfi4uJwdXX90N0RBEEQPjI2m43vvvuOFcsXYbclkD+PFlmGG3eSUKtd6PfFEC5fvkTw3f0c2JQX7SsSXW3bG8mgMeEcOfozBQv+O3WL7927x8aNG7l//z4qlYqiRYvSpk0bvLy8/pXzv0txcXFs3LiROXNm8/jxQ/Lm1NCrsx/ZA3TcvZ/I+m2xPHwMEydNpWfPnh+6ux+VM2fOsGjRAo4eOYAsJz/scXR0oUXL9vTt25eoqCjWr1/Ptq2byZ4llp2rc+Do6ACSRGREBC7OEo5/yT+w/5iBXsPCObGzMLlzOBAWnkS+CpdRq1Xkz62jdRN3/HzVPA0xE7QrjuAnMsO+HMOwYcM+1C0QBEH4T3jTWFQE3YIgCML/hNjYWLZu3crNmzeRJIkCBQrQvHlzXF1duXz5Mo0a1qFKOZn532XDxfnFQrDoGAu7D0YzcUYoteq0YvnyFf+4LwaDgcGDB7F/3w483GSKFdJgtsicvZiEjANf9B/Kl19++VFmjZZlmVmzZrFwwSwSEqIokEeBo4OCm3fMyeXXmvkwcWQASqXEd3OesHi1nqXL1tCgQYMP3fWPTlhYGI8fP0apVJIrVy6sViu9e3Xn1KkTZMkoERaRQLP6joz4whNZllBrNJiTkvD1VqXKPxAabqVU7WBWL8hLjSruALTrfZv4pJJkyZKFgwf3YDFb0Gq1NGjYjK5du1KiRIkPNGpBEIT/jjeNRd/Z8vKHDx8yefJkjh8/TmhoKBkzZqRDhw6MGTMGjeZFGYvLly/zxRdfcPbsWXx8fBgwYAAjRox4V90SBEEQ/sNu377N7t27iY6OxsnJidq1a1OqVCkkScLd3Z0ePXq89LgiRYqwMnA9vXp2oVStmzSv74wMnDgVy43biQAolWpu3bxGYGAg7du3T/W37G2YzWY6dmjLtas/M3OiH03qeaXMrsfEWlmxPpQ5s6dgMBiYNGnS37rGuzR+/HiWL5tL386uNK7rT46sGhwdFMTrbazfHs/0+eGER1r4YWYuvhqSmVv37jJ79nTq168v9gr/P35+fvj5+QFgNBpp0bwJTx9fYMWcjNSs4k6D9teQ7RI+XiqMiXb0hiRkWU5zH01Jz+vKv3jdzVWBPdGJH35YiizLJCUlodVqxc9AEAThA3hnQffNmzex2+388MMP5MqVi6tXr9KzZ08SEhKYMWMGkPxkoHbt2tSsWZMlS5Zw5coVunXrhru7uyh/IgiCILyxkJAQBg8awKlTJ3B3lcnoryYqxsrCBTMoULAIs2bNp0iRIumeo3r16pw6/Ttr1qxh7txZxMdFULKojpkTM5AvjwsJRonte4MZN3Ywe/fsYs3a9cllyd7SunXrOH/+J7auyEapYi6p3vNwV/HlF5nxcFczfvoCmjdvTtGi73cPeXrOnj3L8mULmDzKlxYNHElMNOCgS35g4OqipG9nD3Jl09B1UCi7DkbTrL4XPTr40q7PFS5cuCD2d6dj9erV3Lxxjn0bclIgb3Jd9pJFXNh7OAKrFZwclUgSxMXbMCXZU+47wIFjBjRqBYXyJX8eZVnm1l0rRUpmBPgzc78u7UUFQRCE9+KdrVurW7cugYGB1K5dmxw5ctCoUSO+/PJLtm/fntJm/fr1mM1mVq5cScGCBWnTpg0DBw5k1qxZ76pbgiAIwn9MaGgojRrW5eG9Eyyc6sfF4/k5vCUPZw/nZ/3izCjt12jWtAF//PHHa8/l7+9P5syZke0mpo3PxvEdxenRMRuVynpR5zNPfpiZk60rsnHl8kkGDx741n2VZZlVgcuo+5ljmoD7r7q08SWTP6xateqtr/EuBQYGki2LROfWvlitVtRqif8/cVqrqhMVyziwelMYABVKuwJWbt++/f47/Imw2+2sWb2CRnWcUgJugE6tfYmMtrN+ezwADjolKqWEIcGW0iYu3kbgxnga1fXCwz15LuXMeQO371tp2bIlkPy5O3/+PCtWrGDp0qUcPXoUqzX9xIGCIAjCv+e9bhaLi4vD09Mz5d+//vorVapUSbVEr06dOty6dYuYmJj32TVBEAThE/XVV6OxWx6zc00uGtfzQq1O/tOmUEhUreDGtpW5yZszkf5f9OZ1aUzsdjvz582iUR0H+nTO8NI2pYu7MHGEH/v27uDevXsvbfP48WOWLVvGjBkzWLp0KcHBwQA8efKEu3dv0bxh+onSlEqJJvVcOHH84OuG/14dPbKPFg1ck/cTp7NKuWUjF87/YSA2zppyz8Wy5ld7/PgxwcH3aVzPM9XreXI60KGFDxOmR7Fuaxw2m4xOJ2G1ypiS7Dx6YqFd32cYE2Fw7+RZ7fAIMyMnPSFfvsJUrFiR48ePU6vWZzRsUJPJE4cw7bvhdOrYnLJlihMYGPja3wlBEAThn3tvJcPu3r3L/PnzU5aWQ/LsRPbs2VO1e763KTQ0FA8PjzTnSUpKSlX/Mz4+/h31WBAEQfjYhYSEcPjQXqaM9sHf9+V7rB0dlXw9JCPNu93k1KlTVK5c+ZXn++WXXwgOvsu8ydnSvW6Tel58MzucDRs2MHbsWIKDg1m7di1Hjxzg1q3bGI16XF2UeHvpiIu3MnHCV9SoWZfu3XsCMh5ury/35eGhIiEh4bXt3hdZljEkJODtnRwYqlVqjEYTdllG8f8Cah/P5PHpDTbOXjIAqn8t4/t/kclkAmRcXdJ+LqZ8lQ2bDUZNiWDu0lhqVnVArZK4ftvMz78l4u2lZtX8POh0ChYHPmP5+mhQZGTHprXs3LmTgQN6Ub6UgnWLMlOlfPIDkyvXE1i5IYwxXw3m8ePHjBs37v0PWhAE4X/IWwfdo0aNYtq0aem2uXHjBvny5Uv599OnT6lbty4tW7b8x2VDvvvuOyZOnPiPziEIgiD8Nxw+fBilwkyz+unPHJcp4Uy2LEoOHDiQbtB99+5dlAobJYqkv1dbq1VQrJCGe/fuMX36dObOmY6DzkxioglvD4n+X7pTs4ojzk5qtFo39h+LZ84P+xkw4Dw2G9x7aEp3eTnAg+AkfHz8efToERs3buTu3bsoFAoKFSpEmzZt8PHxSff4f5skSXh6eBL8KPnBt4ODAwkJBhIT7Tg5pg4Wg59YkKTkfd5L14RTrFhpChcu/F77+ynx8fFBkpTcvJuY5nOhUkl8PzE7nVr7snpTGBt3RJJgVJMlIDvOLnHExsfTtMt9QEKjcaJhow6MHj0au93OkMFf0Ly+lpmTsqXKdl64gBOzp+SgcIEwxk2bTfny5alVq9Z7HrUgCML/jrcOuocNG0aXLl3SbZMjR46U/x8SEsJnn31GhQoVWLp0aap2/v7+hIWFpXrt+b/9/f1feu7Ro0czdOjQlH/Hx8eTJUuWtxmCIAiC8B8RHx+Pi7MSZ6f0Z44lScLfV0VcXFy67ZRKJXYZbDZQveYvpMUic+fGDQ4f2sGXfT05c0HPvQcWdq3JRAY/FVarTJzehskUS4uGXnxW0Y0mne/i4ODE+m3RtG7y6qBZb7CyfZ+eTFmyUb5cCVycbRQtoMYuw+xDm5jx/Tf07NWf0aNHo1S+ftb839K4SUu27lrMiAF21GolDg6OGBISUCoktDoFEskz4uu3xVOtohtTZj3mt/M21q4b9d76+Cny9PSkRs26rN1yiPbNfV66FL9wASe+HhbAjgMJjJ8wmYEDB2K1Wvntt98IDw/HwcGBsmXLpmzj+/bbb3HQJfLNV/lTBdx/1a2dHzsPxLJ8+Q8i6BYEQXiH3jro9vHxeeOn60+fPuWzzz6jZMmSBAYGpqk3Wr58ecaMGYPFYkGtVgNw5MgR8ubN+9Kl5QBarRatVvu23RYEQRD+g9zd3Yk3WInXW3F1efWfNFmWCQm1kr+oe7rnK1asGLKs4vipWGpXe/nfIYDYOCtnLpiI019n9EAvGtfzYsaip8ye7EMGv+R+qFQSHm5KomNsGAwG/HzdGTnAjz7Dn/L7BVi2NpSeHdM+YLZaZUZMDCYi0ozNdpnJo/xo1cgbxz9nk+PirazaGM7Mxd8TGxvL999//972S3fp0oVVgT8wZdZjJowIwMXFBbtsJ1afSPB1E0+fWTlxysjZSyY83CVsdgWz5yymevXq76V/n7KePXvTutV+Zi0OYWjfjGl+pmazncFfP0Slcadt27YAqFQqKlWq9NLz7dyxmab1XFI+N6/Svrknw8afICoqCi+v9FeMCIIgCH/PO0uk9vTpU6pVq0ZAQAAzZswgIiKC0NBQQkNDU9q0a9cOjUZD9+7duXbtGkFBQcydOzfVTLYgCIIgvEqdOnUAB7buiUq33enf9Tx6aqNBgwbptitcuDDFi5dhcWA4VuurE0yt3BBGghHcXKBXJ3/2HYnG2UmiUe3Uy9IVCglHR4mkJBM2u436tTzw8lBSslRFJs6IpP+o+1y4bECWZaxWmcMnY2jZ/TZb9ySg0ykInJeVLm38UgVObq4qBvXOyLSx/mxYv4Lffvvt9TfqX5I7d26++XYGKzYk0G3QXc5eNHD+skzPoVE06RxC7+FhrNumx2IBF7dszF+wlFatWr23/n3KKleuzFdjJjH7h1g69rvL8Z9jSUiwERtnJWhnBPXb3eKn3ySWLl31RpMfUVGRZM3y+kmK7AE6wE5UVPq/Q4IgCMLf984SqR05coS7d+9y9+5dMmfOnOq955ky3dzcOHz4MF988QUlS5bE29ubcePGiRrdgiAIwhvx8/Oj3ueNmb98CzWruBOQOW2QoTdYmTQzhHz5ClOuXLnXnnP0V2Np26YZg79+wNSxWVMtXbfZZNZsDmf2D9H4ZwigdOEIXF1UxMRZ8fVSotWmfZat1SqIN1ixmC3odDoy+KkpUaIE7dq1Y8H8Wezs+AC1yo7NLmO3qyhRoix580aTNWMwVSu4vbKfbZp6s2RVJKtWBVK+fPk3vGP/XJcuXfD09GTqd5Op2+YaVksSlcromDHRhzLFHNFoHfntvJnl60IZNLAPTk5O1KxZ873171PWv39/AgICmD9vFp36/wFYSU4Tr+az6rWYMXckxYsXf6NzOTk5ER2T9Np2UTHJ13B2dv4nXRcEQRDSIcmfeK2I+Ph43NzciIuLw9XV9UN3RxAEQXjPIiIiaNzocxIT7jK0jw9NP/fC0VGJ1Spz6EQMsxaH8SzCjR0795E/f/43OueePXsYMKA3GpWRJvWcyZFVR2yclR379TwOkenarS9Xr1wiwPcic7/NwewlT1m+NoRLx7OhVqdeFmyXZcIjrbi7uaNW6yhZ8wYdOo9ixIgR2Gw2Tp8+zf3791GpVBQtWpTMmTNTsEAu5n3r+9oEcfOXhTB/pZW79x797fv3d507d476n9egc2sHvh7ij1qtQqPRpiyLNpvt9B3+gJ/OqDh1+ncyZHh5CTYhLVmWuXz5Mg8ePECpVFKkSBGyZs36VucYPHgwp39cwy/786NUvnr7QfdB93gUnpujR38UZd0EQRDe0pvGou+1TrcgCIIg/Nt8fHzYtXs/RUs0ZNSUaIpVv0GlBrcoUu06vb8MxcWjLDt37X/jgBugYcOG/PLLOXr2HsPPZ/2YsTiJDTu1lKvUgX37j/HNN9+QOUs2rtwwI8sytat5EKeXOfJT2hJfVkvys22FUsnxU3FERsvUrl0bSE7cVqVKFbp06UKHDh0oXLjwn2XCZLw8Xr8YzcNDhTHR+EFqLa9YsYJc2VR8OyYnzs7OaLW6VEGbRqNgzjdZUUh61q1b99779ymTJImiRYvSpEkTGjZs+NYBN0DXrl15Gpq8FeJVfj0bz5GfjHTu3EME3IIgCO+QmOkWBEEQ/jMeP37Mnj17iI6OxtnZmdq1a1OgQIF3cq1Tp07RqmUDNi8PoEJpV5p0uk58vImdqzOlqrccG2/FalWi1XnQrMtdNI7FOXDwyCvPazAYyJcvBxOHu9G1rV+6fZg88xHb9jty+crNf21cb8JoNJI/Xw5GDnCiT+f0Z7BHT37I8V+9OHvuj/fUu/+miIgI4uLicHFxwc8v/c/Fc5MmTWLJ4pn07+ZBjw5+eHslJ61NTLSxdW8Uk2aEUbJ0Tdav35SS0FYQBEF4c28ai4qgWxAEQRD+BlmWqVO7OvExf7BlRU6MiXaadLpOBl8FXw32pFoFR0xJMrHxVq7eVDJjcQyPQ1zeaNa9a9cuPLq3l8Nb8r5yBjIpyU6pWjdo0XogEydOfBdDfKXHjx9TtkxRNv6QicrlXr3vHGB1UDhjp8Xy+HGYmE19S7Iss2fPHlauWMrvv/8K2AEFJUuWoUvXHjRt2jSlMkxCQgI7duxg29YgwsJCcHR0omq1WiQlJbF+3UrstgSKF9aiVsOVG2b0BhVNmrZixoyZODg4fNBxCoIgfKreNBZ9Z4nUBEEQBOG/TJIkVgaupUXzxtRpdZf2zV2ZMSE70+Y/oV2fUHy8FPh4KYmKVRAVoyZ/gcLs3PXDGy1z79GjJy1b7GbB8mcM6Jkxzft2u8y4aY+IN6jp3LnzuxheupJLd0rE623ptpNlmZjYRJJMJvr370+WLFlo3bo12bNnfz8d/YTZ7XaGDh3C5qDVVCitZd63vmTy1xAabiZo1wUG9O/GoUMHWbRoMefPn6dbtw7ExYbxWUVHSlTVEhMXyvo1l9EnqOjbbzDe3t5cvnwZu91O1wrZadu2LQEBAR96mIIgCP8TxEy3IAiCIPwDkZGRzJ07ly2b1xEfH4Ms27FaZVxdvciXvyClS5emfv36lCpV6q1memfOnMnMGZNpWNuBHh38KFHECVmGn3+LZ8mqME79bmPW7EW0bt36HY7u5WRZplrViuTJ+oAfZuZ8aRtjohF9fDzNuz3l0VOZMiXcuHnHTGy8gnqfN2b27Dni73Y6Zs6cyexZk5j7TUaafp42od6BYzH0HfGERo07cGD/booVTGL25Kxkzvgig39ioo3Fq0KZtSSGUaMnMXDgwPc5BEEQhP88sbxcEARBEN4jo9HI5cuXMZlM+Pn5kS9fvn+8nDooKIg5s78nOPguapUdWQarTUn+AoUZNWostWrV+pd6//YCAwMZN3Ywe9bmoEjB1PXJE4wJ6PXxnD5jZMBXkaxbnJfqld0xmezsOhjFxBlh5MxVli1bd+Do6PiBRvDxMhgMFCqYh5qV7QzsmYH8eRxfmoF81uKnfDM7jEL5tBwMyoeDg/IlZ4NvZj1m+QYzFy9dw9PT8113XxAE4X+GCLoFQRAE4T/Abrdz6tQp7t27hyRJFCpUiJIlS37w/dEmk4nmzRrz4N4ZZk7KRK2q7igUEjabjWfPwjnyYwJTZkdTvbIHP8zMhULxor9XrifQpPMDBg2ZwODBgz/cID4yFouFwMBAZs2axtMnD3F2UiBJEJBJR8dWvvTo4Ida/aLwzLPwRIpW/YPmDbxZ/H2uV543OsZCqVo3GTHqO/r16/c+hiIIgvA/QQTdgiAIgiC8U3FxcfTs0Y1Tp46TNbOCsiW0JCaaOfZzPAlGmeYNvPl+Qna02rQVSkdMfMCJXz058/tFVCqRYsZsNtO1ayd+/ukgtauqaFRHS6F8OkJCrWzeFc+ugwmUKeHK6gV50OmS72diYiKfNblOlQqeTB+f/j75Dn1vo3aux+rVq9/HcIT/ASaTiQMHDvDgwQPUajXFixenYsWKH/yBoCC8TyKRmiAIgiAI75SbmxtBm7dy4cIF1q5dy727Nzl79iwBmbTsWZ+bvLlevXS8ZSNvNmwP5vr16xQpUuQ99vrjNHHiRE7/fIA1CwIoUVhJYqIBHy8V2bKoqVDagdZNEun4xTPGTQ1m+oQXAbbFKqN7yUON/89BJ5FoSXqXQxD+R9jtdubOncvSHxYQFxeBj5cas9lOnF4mR468jB03iTp16nzobgrCR+X1/5UWBEEQBEF4BUmSKFmyJHPmzGHP3oNkyxZA47qe6QbcAN6eakBGr9e/n45+xGJjY9mwPpBBPb2oUt4NlVqFzS5jsb5YjFi+lAPD+nmwZXckkVEWAB49sfAg2JKqLvzL2GwyV29ayZxZZCsX/hlZlhkyZAgzvh9PywYWTu3Jw8Xj+bn6cwG2rshK1gwP6da1HVu2bPnQXRWEj4oIugVBEARBeGMWi4WdO3fSqmVzypQuSoXyJenXrx9nz55FlmW8vHwJfmJ+7XkePjYBCry80mbm/l+zc+dO7LYE2rfwAZJLsikVShKMNv66B7BtE1cUCplt+6IAWLouEhk1wY/Tn8E+fiqOxyF22rRp81b9SkpKYtu2bbRs0ZRyZYtTuVIZhg4dypUrV97qPMJ/x/bt29myeTXzvs3EhBEBZAvQAckP38qVcmH1gly0bqzjy2EDCQkJ+cC9FYSPhwi6BUEQBEF4Iw8fPqRa1Yr069sJm+kUTWrHUadKBJfOBdG4UW26detK3XoNOHTCmDIb+yobtkWRN29B8ubN+556//F68uQJmTJo8PZSAyAh4ezsgilJxmCw8Tz7jrubkmxZ1AQ/NjFr8VOCdhpp3qIdOw4YCdoZ8dJz339oYtTkp5QpU5HixYu/cZ9u3bpF5UrlGNC/K1h+oWGNaKqUfsZPx1dRp3ZVBg8ejMWS/s9Y+O8JXLmMKuW1Ly1jB6BQSEwYEYBWk8i6devec+8E4eMl9nQLgiAIgvBaERERtGzZBJ3yMUe25CJ/nhfLx8cMkdl3JIah43ZgTkpCrXVj5KRHLJmRPVW27ef2HYnm4AkjU6f1FkmXALVaTaLJhizLKffDwcEBWbaj1+tJNFlw0ClQKCBOb2PVpigUCkdGjZ7EgAEDGDnSm2Hjl7L/aBwdWnqTO7uOOL2N7XsjCdqlxz9DfpYuW/nG9zokJIRWLZvg7R7ByZ25yZXdIeW98cNltuyO5KtvAgGYM2fOv34/hI/T06dPuXDhdxZP90+3nbOTkkZ1nNi1cwsjRox4T70ThI+bmOkWBEEQBOG1li5dij4umE3LcqYKuCF5dqthHU/mfpOJEycO0L//UI6flmnZ4w5HfozFZkueqn0QbGLC9Ef0HfGUho3a0L59+w8xlI9OmTJlCIuwc+FyQqrXHR2d8PLyxsHBCVOSxNmLJu4HW6larRHHT5xm4MCBSJLEtGnTmDN3KaHR+ek6MIRKDe9Qv90Ddhx0oHPX4ezZexBfX9837s+8efPAHsbGH3KmCrgBVCqJts18+OYrfzYHreHatWv/yj34r5FlmaSkJD7xIkGpREdHA3YCMmtf2zZrZh0xMdHvvlOC8IkQJcMEQRAE4X+U0Whkz5493LhxA4D8+fNTtmxZDh06RGhoKDqdjooVK1K6dGlKlihMs3omJox4dTIuWZap3vQWWXPVpWzZ8qwKXEFw8F2cnSQ0GgXxehvu7r5079GXwYMHo1SmnwDsf4XdbqdihTLkCnhM4LzUNc2fk2WZXkPvcemGzyvLrMmyzJ07dwgPD8fBwYHChQuj0Wjeqi8Gg4FiRfPTp5OKoX0zvbKd1SpTtu51atbpwffff/9W1/gvu3z5MitXrmTvnu0YjUbUGjW1a9enS5euVKhQ4ZNe2fHw4UMqlC9B4LyM1Krqnm7bcVODOXLKizO/X3ovfROED0WUDBMEQRAE4aVkWWbhwoUsmD8TvT6KHFm1yLLMgvl6TCYbOq2afHmcSDDamTvHTsaM2YiIeEa9GjnSPa/NZqNaRSWzlwRx9MgeIHm5tLtHVipWqkqtWrWoXbs2Op3u/Qz0E6FQKJgw8Ru6dW3P0LEPmDAiAHe3F1/R4vVWpsx6zIHjZpb88M0r65pLkkSePHnIkyfP3+7L9evXMRr1r/1Zq1QSdT9z5rezp//2tf5rFi1axJTJY8nkL9OnkxtZMnkTGWVh866dtGyxjS5d+zJlyhQUik9zoWnWrFnJkyc/W3bdTzfoNpvt7DxgoFnLru+vc4LwkRNBtyAIgiD8D5FlmfHjx7N82Vy6t3OlR4e8ZM6oITo6muAnTmzepWftFj3VKzkyfngWfjunZ/z0R9wyGlEorK88r9liJjYmBrXShrubgp9250GjkTh70UDgxlC2bV1P0aJFRcD9CnXq1GHBwmUMGfIFew7foF4NRzL6aQgNt7D/WAI22ZHZcxbTqFGjd9oPs9kMyDjo3qD2t4Piz/ZCUFAQUyaPYVBPN4b2zYRS+WJGu08Xf9ZtjeCrbxbi5eXFsGHDPmBP/z5JkujcpQfjxg7h1Jl4KpV9+azevGUhRMcq6Ny583vuoSB8vD7NR22CIAiCIPwtp0+fZvmy+Uwe5cvEkVnJkkmLwWDAajVTOJ+OqV/7MmW0N8vXhfLLWT3lS7uydmFOlEqJX3+PfOkeVbtsJzYmBpVK5tY9C4XyORKQWYu/r4aGdTzZFpibPp1dGDd2BL/88ssHGPWnoWnTppw7d5lhw7/h/tMC7D3uye1HeRk0ZBJnz/5B69at33kfAgICACWXria8tu3FK4lkCcj+zvv0sbPZbHw//Rsa1dHx5RepA25IDlY7tvSlXxd3Fi2cTXx8/Afq6T/XoUMHKlWuTZcBwSxbG0q8/sWDuODHJkZNfsicpbGMGj2enDlzfsCeCsLHRezpFgRBEIT/IIPBQEREBCqViowZM6bsn+7WrSsP7+zhyNa8SJKELMtERITjoAMX5+Q2sixTo/kTcuVwZums3AC07XWT+w8NHNteAGfn1InUjMYE9Pp4jIkyVZs84duvs9GhRerEXbIsU6fVLTJn/5xVq9a8hzsg/F0tmjclKeE0O1fnTrUH2W6X+eWsntv3EgkNMzNnaSQrVm6gcePGH7C3H96RI0fo3KkF+zfkoEhBp1e2Cws3U7beLSZOmkPXrp/u0uvg4GA6duzIhfO/o1Ta8XBX4e2lISTUhqurDyNHjf2kxycIb+NNY1Ex0y0IgiAI/yGXL1+mf//+FCyQi4oVSlC2TFHKlC7GnDlziIyM5Mjh/bRq7J4STJnNZuyyPdVyYkmSaNXYhcMnY7Fak5/ND+2biTv3LYyd+gi7PfXz+sTERBJNdvqNDMfHS03Temlr+EqSRKfWnhw9coCoqKh3eAeEf6pvv/6c/8PKtHlPU1Y2bN0TSZWGl2nT8waTZgQzf/lTLBYzs2dNZ//+/a89p81mIz4+/j9Z2/v69et4uivTDbgB/Hw1FMij/WQzvtvtdqZNm0aliqV59vQyLRu70rqJG77eSh4EJ5EhQw4OHDwmAm5BeAmxp1sQBEEQ/iO2bdvGkMH9yORvZcQX7hQr5Ispyc7+o9HMmzOejRvWYrGayeD3IqP186BK8f8SiWf0V2G1yhgTbbi6qChd3IXypV1ZtUnPtdu36NzKk1LFnDGbZbbvjWTLbj0KhZJNy/Lh5KTAYrUg2+1IkgKVSoUkSRTK54jdHk5YWBheXmkDc+HjUKNGDcaO+4bJk8bwy7kEfL0V7D8Szec1HPnmK3/y5VIjSUpu3NWwfN1denTvwNRpc+jUqVOac128eJGVK1eyZ882zElJSAoFVatWp0uX7tSqVeuTzub9XHJ99TdrK0nJ7S0WCydOnODhw4eoVCpKlChBsWLF3mk//6lvv/2WRQu/Z1hfT3p0yIGLc3IYIcsyp87EM2zcYzq0b83efYfw8PD4wL0VhI+LCLoFQRAE4T/g3LlzDB7Ul+b1VUwfnwuV6kUU8Fkld77oZqJNr3sYE4yEhCWlvPc86LHbQPGXbwVPQy2oVBKODi+icRdnNeUrVMXPz49RUw4gy+EAGAxGShZxYNWC3Hh52ImMjMJqezGjqVQocXBwRG+wAtJbl7ES3r++ffuSN29evv12MkE7fmJYX3d6d3ZDqVCic3DAycmJqr5KqpR3Z9zUR3w1ehhlypQhX758Ked4ns07IBMM7eVG1izuREZb2Lb3OF06H6JJ03bMmzfvldnY/w5Zljl//jz3799HpVJRtGjRd763OG/evETF2Lhx25imhv1fRUVbuHbThKdfKGVKFyMs7AmODhIWix2LVUnRoqUY8/V4KlWq9E77+3fcvXuXRQtnM2awN327Zkj1niRJVC7nxublWuq1vc3ChQv5+uuvP1BPBeHjJJaXC4IgCMJ/wIIF88iTw8708dlTBdzPZQvQ8cPMbNjtMuu3RqXMcGs0GhSSgkSTPaWtLMsE7dBTt7pHyrkeP03i9FkTnTp1YfXqtVy4eI1du4+wb/8JWrXuRJJFhZNjInp9HCqlFQ83Jd6eKjzdVWg1dhIS9Gzd/QxvH3+yZs36fm6K8I9Ur16dnDlzU6ywG6MH58DbyxsfHx9cXVxR/rk0QpIkxn2ZBW9PG6tXr045dtu2bUyZPIaBPVw4tTcf/XtkpGEdT7q29WPv+rwsmpaBvXvWM3HixL/dP7PZzIMHD7h//z6JiYls27aNz6pVolHDmgwe1J3+X3SmcqXStGrVnPPnz//j+/EqNWvWxM8vM8vWhqbbbnVQGHF6O8eP7aFGxViObMnJ7d8Kcff3wqyalwmt4hJt2zR7o+X679uaNWvwdLfTvb3fK9tkC9DRurErGzesJikp6ZXtBOF/kQi6BUEQBOETFxYWxtEjB+jcxvOlAfdzRQs6UaSQI1dvJLJyQ/IstSRJODg4Yky0Y/lz//by9XHcfWilc+vkZGhWq8y4aY9xcfGmSZMmAPj5+VG6dGmKFy9Or169uH0vkV37o3BzVeLupkKrUaBSSmjUEq4uKuINMtv2xqPROKJWq9/tDRHemM1mIyoqiri4uDSZ6ZOSkti7dwctGjii1+uJjY0lOjqGhIQE7PYXD2nUagWtG7uyY3sQkLz3d+aMqXxeQ8vw/plQKNJ+JhvV9WJ4P2/WrF5GeHj4W/U5JCSEiRMnUrRIfipWKEGliiXJni0jnTu3x8v1NpuWBvDgXGFu/VKI+d/5ERvxI82a1ufo0aN/4w69nlqtZuCgL9m828iS1c9emuF/14Eops4LR6W0MWNCBqaPz54yK65UStSs6s7m5Xn4vIaK/l/0IiIi4p309XWuXLnCl19+Sb26NalT+zO++OILfvvtN3768Sif13RCo0k/dGjyuScxMRGf7L51QXhXRNAtCIIgCJ+44OBg7HYLZUu4vLbt59XdcHH1Yvz0cL7+NpiHj0w4OzuhUmn442oiIyaFM+H7KPp2yUC5Ui78dk5P+753OHFaZsHCpTg6pl0+W6RIEWRZzddTI9m+z4DF8iLokGWZMxcSad/nGe5uap48vs/du3f/1fELb+/JkydMnjyZIoXzUbhQHvLny0HFCmVYsmQJ8fHxyLLM7NmziY2JJqOfDY3aik4no1RaMBjiiYyMSDWbmT2rLiVR2qlTp3j48A69O/unu2e7YysflIpEgoKC3rjff/zxB7VrVSNowxzaNbEStCwLGxb706uDgqyZZC5f0yNJyQ8CnJyUNP3ciz3r8vBZRejdu+tbB/hvqkuXLgwYOJIps2Ko2fwWy9aGcvhkDGu3hNOo4y2+GPUMNzcfalR2pW0zn5eeQ6WSmDo2AGQ9GzZseCf9fJXExER69uxBndpVOXl0JQVy3KBInjv8cW4TzZrW5fr166jTeaD3nJuLCpAxmUzvvtOC8AkRe7oFQRAE4ROnUCQ/Q3+eaTw9VptMlswZ6NipB/PnzWBV0G2yZtYgy3Dnvp6kJBsOOjVHfjSy48BNQsNtZM+eh/UbZlK5cuWXnvPo0aNoNHbqfubFiEmRzFgUTfVKjmjUEuf+MHH9tplC+Z3YOjMXDdrfIygoiDFjxvyr90B4c6dPn6Zrl/YopVhaN3GldHF/LBY7R358ynffjGTtmpU0adqSObO/Q6uRsNnA1eXFV0abXUavtxEbG4OHhycajYaoGAtqjRqVSsXNmzdx0MmUKJJ+Nm9XFxXFCum4efPmG/U7Ojqajh1akzVTDGsX5sXdLblPMTExFMzrwZdfeNFzaCjdB93m8JbCBGTWAskB+OzJ2ShZ8wYbN25k0KBBf/POvZokSYwePZqqVauyYsVypszeh80WA5JElSqfMW16I0aOGET7Fv7pnsfNVUXD2k7s3LH5nfTzZWw2G7179+T0z3uY921GGtV5sWJGlmVOnIqjz5f3WLM5ka8GZ8bBQfnKc928mwgo8fdPf5yC8L9GBN2CIAiC8InLkycPOp0TR3+MTTeRk90uc+RkAkVKfE7fvn3p2rUr+/btSwl68uXLR8mSJdm/fz+hoaHodDoqV65MpUqV0p2xfPz4Ma4uKlbMzc3NO0bWbo7g4hUDVquNbAHOfDXElyrlXVEoJPLn0fD48eN//R4Ib+b+/ft06dyW4oWSWDYrX0oGakhe8j2sr4m2ve8yedJYenfy5s4DJdv2JdCmqVtKO6VCws1VRXSsFb1Bj6eHJ9v3xlG9ev23zkb+kpXYr7Rhwwb08c9YGZQvJeC22+0kmZNwdVbg6KBg2Ux/ytV7xOqgMMYOC0g51s1VRaM6TmzdsuGdBrMVKlSgQoUKJCUlER8fj7OzMw4ODly6dAmwkz1A+9pzZA/QcfyX97e8/NixYxw9spfV87NQo4p7qvckSaJ6ZXc2/JCNeq3vsCgwlGH9Mr3yXGs2R1KiRBly5MiR6vWHDx+ybt06rl+/hizbyZevAB06dHjnSe4E4WMhgm5BEARB+MS5urrSuEkr1mxeTec2vqlmJf/q0IlYHjy2M2t+ch1dnU5H8+bN07Tr16/fW11fo9GQlGTDapXJl9uRb8a8OlGaMVEmo/b1gYfwbixduhRnRwMrZufFySntjGW2AB0Lp/lTs/ktcuXQUaGMC1+MvMvRnxKoWeXFzLUkgZOjgth4M5t3hnPjjo1xk7sBkD9/fhJNEuf/MFCq2Ku3PMTFW/njmoka9fK/Ud83rF9FozpO+Hi/yAlgt9sAOWVm1tlJQZsmzmzcEcHoQVlS5TjIncOBAyfC3uha/5RWq8XH58UycmdnZ0AiKsb62mMjoy04O7u/u879P6tWraBYIXWagPuvShb1pG4NJ2YsekrvTn44O6f9b0zgxjB+/i2JxUv6prxmNpsZMWI4m4PW4e5qp2wJDZIEW4MO88OSuTRu0opZs2bj4ODwLoYmCB8NsadbEARBEP4DBg4cSILJnU797xMeYU71nizLHP85lsFfP6VGzc8pU6bMv3rtsmXLYrYoOfZzbLrtHj9N4tJV079+feHNGI1Gtm5ZT/vmbi8NuJ/LmVVJ1fIO7DsSTYPantSo7E6PoWEsXReLPsGW0i4pSSZwYxxDxz2lZatOVKlSBYCKFSuSLVtuflgd9tKkYs+t2xKBze5A69atX9t3q9XKw+CHlPn/eQv+nFmX7S+uU6aEA3HxNmJiUwe48XrbBwvucuTIQbZsudi6Jyrddmaznd2HDNSo+fl76hn8+sspGtR2TbeNJEk0b+BHQoKdWi1usWZzOFHRFhISbPxyNp7ew+4xdmoEvfsMoVGjRkDyKoS+ffuwc/sqvvnKi3NH8rNibm6Wz8nN2cP5mTHBh8MHN9KjRzesVismk4kTJ06wa9cuTp48+doM6BaLhdDQUEJDQ7FaX/8wQxA+JDHTLQiCIAj/AdmzZ2f9hi107tyWsnVv8XlNR4oVciIpyc6+o3qu3LBQ7bO6LFmy9K2XAL9OoUKFKFmyHAtWnKN6JTfU6pc/05+/PARnZy+aNm36r15fSC0xMZGdO3dy+vRpkpKSyJAhA61atUKr1WI0GqhYJiDd42VkKpR2YNk6PROmP+LMBT0xsVaGjQtnysxIShd3QKmAi1eTCIuwUe2zusycOTPlc6VQKPhy+Gj6f9GdafOeMrx/JpTKF585WZbZdSCa6Qsj6dptUKoZ4VdRKBRIkpSSYf85lVKJSqnClGRDq03+3D1vo/zLcwW7XWbXQT2VqzR8o3v4b1MoFHTq3J3vvhlFlzYGShZ1fmm7JatCiYxW0Llz5zc+t9Vq5bfffiM8PBwHBwfKlSuHh4fHGx0ryzJmixlnp/T33wO4uWpwdnYia66afP3dSb76JhSQASW5cuVn5qyBtGnTJuVzcPToUfbv28b44T5kyajh/GUDRQo44uKsQqNR0KapDxn9NbTrvY+OHTty6eLvxMVFAnZAgYeHD23bdWHo0KGpEjg+e/aMFStWsHHDamJikh9ieHn50K59F7p164af36vLmgnChyKCbkEQBEH4jyhZsiSnTv1OUFAQGzes4fCPwajVakqXqc2ar7pSvXr1lKRr/7ax4ybSskUjeg69z7SxAfj5alLeMyTYmL3kKRu2G/lu6rcvzYAu/DuCgoIYP240en0kxQtrcXaUOH/GyvJlCyhStCQ2m/21+6iVCiVWm0x4hIXdByPo2saFzyo5EhllY9naOE6dSSTJLNOoridHfjTRr98XqFSpv1I2a9aM8PBwJk0cw66DN2nb1I1sAVoio61s2R3LlRsWmjXvyLhx495oXAqFgiKFi3H0xyt0auX7l3eSS94ZDPE4WmTUaokjJxPIklGTsu8bYMP2CB49tbOwS5c3vJPpS0xM5Pjx4ymBbpUqVciYMWO6x3Tt2pUD+/fQod9pxn/pR5N6Xuh0yb+PYeFmlqwOZdm6eIYO+5rcuXO/tg82m40ffviBFcsX8+zZY8AGSGg0TjRu0opRo0aRIUOGlx5rMBg4fvw4UVFRODg4cPVGwmuvd+N2IjoHJ+bMmcuhQ4e4du0aDg4O1KlTh4oVK6Z6mCfLMpMnT0S2JzLp+0ckB+fg5KikeQNvBvXKiJ+vhkrlXChZBPbv28qwfhlp3SQXGfw0hISa2bQjglUrZ/DrLz8TtHkbzs7OXLx4kQ7tW2GzRtCqkSuVy2VEluGnX+NYtXIaG9avYsPGrRQuXPi14xGE90mS01v38wmIj4/Hzc2NuLg4XF3TXxojCIIgCMK7c/LkSXr36kpiYjQ1KjmQI6uWiCgLB44bMSVpGDtuCr169frQ3fzP2rBhA8OGfkHlskpKFHHGw11FnpwOlC/lwrGf4xjzbQj3HyYysIcPIwd6k5iYiN1uQ5Ik1BoNjg6OaDRqLBYLjTpc4+qNJK79nB1Xl9RL0U0mO72Hh3HohBFPrwAuX7mBRqN5aZ8uXbpEYGAgu3dtJSnJhKRQUL16bTp16kLNmjXfatXFpk2bGDa0Fwc35aJgvhcPbmRZJjo6GpvNTFy8nTqtnzK8fxb6ds2AIcHGmqBwps6PpG27Xnz//fd/7+b+yWw28/3337N82ULi46JQKGxYrWC1SeTLV4T5CxZSrly5Vx5vMBgYMmQQ+/buxN3VTsF8aszm5FUDarULw778ir59+772viRnHO/FoYNbadnQmY6tfMmdXUec3saOfVGs2BCDpMzIjp37yJr1RY6FxMREvvvuOzZtXI3BEINKCbFxSbg42/llXy4yZnBHqUy79cBmk6nU4AZJtizExoRjNifwfEba1dWLdu27MmLECHQ6HbIsM3HCBGbNmsLnNZ3o1taNgnm1GBLs7DlsYM3meFQqJVtX5sfdzcTmnRGMnx7NvbOlcHRMfe3L1xJo3esBtet24Ouvx1K9eiWyZ45lzYKcqR6qAETHWOj4xX2ehnlx/MQpvL29X/8DFYR/6E1jURF0C4IgCILwr4mPj2fbtm3s2L6FyMgwnJ1dqVX7czp06PDKWTfhn4uOjiZfvlw4ahMwW+xoNRKSBKYkmayZdQzqnYkyxZ2p0fwasXFmzhwIwMdLiUolIctgMslY7TJ6g4bFq6NYvDKCyuUc6NDSjeb1XXByTL1CIirGSpk6j8hboDoHDx5M9d7FixdZvXo1Fy/8htVqJVv2XLRu3Y6KFSvi6uqKWq3m7zCZTDRoUJeo8D9YOTcbRQu+WBJtt9u58Ec4PYc+IfiJjSb1PECS+OVsEsZENT16fsHYsWNfGlC+KbPZTPv2bTj10346tHSkTRMX8uTQYEiQ2XVAz6JVsdwPtjNv/g9069Yt3XM9ePCATZs28fDhQ1QqFSVLlqRFixZv/F123rx5fD99LMtmZaZ2teSl5AkJNg4cjyHkmRlTkp2N22Pw9i/K0aM/IkkSRqOR1q2ac+P6r/Ro70675j5kzqjl1l0j1ZtepVIZLTMn+ZHB3yfVfbLbZUZOesjSNRFk8NcxqKcPLRp64+Wp4tGTJDZsiyBwUxyFCldkU9BW9u7dy6CBPRneT0Ovju446FJ/dsIirLTqEQKSio1LfPj9YiL9R0fyx8nieHmm/WwsXxfKlNl6unTtzbo1s/lxV3Y83dWo1eo0KywiIi2U//wmg4ZMem8l14T/bSLoFgRBEARB+B/RsWNHdmzfQNPPnenT2Z1yJXUAXLicxLJ1sew9ksCX/TISGW1g7tIYKpd3YO38jHh5JgdXSWaZ0VPCCdqlByBvLg1qlcT120k4OigY2MOdXh3dsdvBaLKTZJZZHGhgyz5Hrly9jVqtJikpiYEDB7Bn9xYyZ5CoVdUJrVbB2UuJnP8jidy5C7J23SYCAtLfU56e8PBwOrRvw9Wr5yhfyoHqlZxBgl9+T+DkL0b8/LJSrnxloqIiUKlUFCtWgvbt27926febmDp1KnNnT+CHGV7UrJI8tr/OR0fFWGnc6SlXbtg4fuI0pUuX/lvXSUxM5Pz58xgMBry8vChRokSqINhisVC6VFHqVInnu7HZsFjsfL/gKWu3hGNIsOLpriTRJKM32EkwwsxZC+jduzdjxowhaONCgpZlo3jh1HvKj/8cS/dBd/D3VdCptQeN62VCqZA4fVZP4MZITp+Jx8tDzcld+cmUIW31gQuXDbTp9ZCWrfty5swpAvzuMHWMCw46cHFO+6Dj4hUTDTo8ZfZkb56F2Zi3LI6bv5Z8aT6IeL2V3GX/ICnJTufWjowe5JnynkatxdnZOdVKi2HjHnDqnDdnz/3xt+6/ILwNEXQLgiAIgiD8D7hx4wblyhajd0cnvvva56VLk2ctiWbm4hi+/cqLUVOicNApUChkmtRzpkQRLUtWx3LvoYXBvdxpXNeZTBnUODoqeRJiZsnqWFZtiqdnRzf6dHZHrVLj6OjEqd9NdB34jIuXbuDr60vPnj04dmQr30/ISOO6nqmSp125nkDfEY+wkZX9B47g5eX1t8drsVjYv38/q1et4Pr1K8iyTO7c+ejYqSuNGzdGp9P97XO/islkonChvDSqFc/EET44Orw8N8KZC4nUa/uEUqWrcvTo8be6RlxcHHPmzGHjhtXEx0cBMlarHUlS4+zsSkBAFipUrErevHkZNXIAR7bkJHcOB3oOucPJ07H06uhGx5auZM6oxmaTOX7ayIyF0Zy/AmvWBDFoYB/6dlYztO/L62xfvpbAyMkPOPWbHhcXhz/zP6goXKQk58/9xpqFAdSt/uoEbXOWPGXOciPmpCQ2LM5E8cISSUmJ+Hipedlq+c/bPcbDXcm9BxYqlPFg1uQcaRsB0+c/4ds5j5AkiRWz/Wj6efL3/aQkOwmJdqxWcHf3QPtnKcJteyMZNCaS+w+evJPPgiD81ZvGoiKRmiB8hCwWCwcPHuT8+fNYLBayZctGs2bN/tGXFEEQhE/JrVu3uH//PkqlkkKFCv0rM5X/VatWrSKDn4IR/T1SBdx2WcZkkrFY7HRt68LmXfHsOGBAqYRvxmQjItLChm3hBG6MR0Zm9Xw/alR2Ii4+uSyYBGTJqGHKaF98vFTMWBRD84aZKZw/uea02ZwISKhUKn777Tf279vOomkZaVQ37d+qwgWcCFqWg5ot7rBs2TJGjRr1t8erVqtp3LgxjRs3/tvneFs//vgjMTFhtGzkm2a59F+VKa6jUD4tv/76Cw8fPiRbtmxvdP6oqCiaN2tE6LPrtG/mSpPPs6FSJhD8xMTO/QZ2HnhKZFgEe3Ze44cwKyZTEjmz6Vi5IYzjp2JZOSf5Z/ecUilRq4oTxQpoGDAmir59u2O1GGnf/NU10YsUdGLv+oKUqnWdwsXq06VLF3LlysWqVasIf3aemunU8QZo28ybqfOvYjRa8PM1kpioQJZl4vVWXFyUKP5f5F0wr4ZdBxNINMFnldyZveQpMbFWXJyV1KrmTrFCzhz9MZZ5y57yZT9PFq+KQ6GQUgJ4nU6BVqsgLt5KbGws3j7eKBVKbH9WtXtXSSMF4e8QQbcgfGS2bNnC+EmTePLsGRpvLySVCnNkJGMnTqBLh45MmjTplQlrBEEQPnVHjhxh3txZnD9/BkiuvatQaKlZqx7Dhg0XWYn/H1mW2bljM22auPLX6URjoh1Dgg1ZllGpkgOVFg2dmfB9NJIk4+2pom0zHwb0zEDtllfJ5A8N/6zVrDfYSEqy4/RnUisJ+KKrB2u36NmwNYrvxibXyj58IpaAgKx4eHiwalUgObMqaFjHM00fn8uUQUuLBi6sXxfIsGHD/vbe7g8hIiICsJMnx8tnbZ+TJIk8OdVcvGLk+vXrbxx09+/fl+jI6+xem4PsARqioqJQKiBnVidqV3WmRcMEug8Oo0dHH7w94KtvnzDmm4f8clZP47pOqQLuv1KqJCYM96V68ycAqaoKvLS9Mrn/zs7O1KxZE0he0p89QI1KlV5yNxmdzoSD1orBYEelIHmZe5Ido9GO1Sbj6KBAp1WQ/MDGTkysncdPLeTO4UDf4XdxdZbw91URFWNj7tKnFC3oDMgUKaCle3tX9h8zcfyUkSb1XtRqlyRwdVESEWUl0ZiIWq1m/5EwEowmihTOS86ceWjXvhNNmzYVVROED0oE3YLwEVm1ahVDRoxAWzA/2Vs3xyGDPwDWhAQiz5xlcWAgwY8esWb16k/qy4ogCILJZGLXrl1sWL+GBw/uolQqKVa8NJ07d6VKlSooFApWrlzJ12O+pFxJFUtn+lO2hAtWq8yRn2JZuX4fjRsdY/WaTVSuXPmtrm0wGEhISMDNze0/t9zUYrGg1+vJmc0Vs0XGapUxW+zE6204OEg4OypRKpOTpRUpoMXRMfm1afOfUK6UC3qDjRu3ExnS+0UZLpVKwmyRsfxZggtArZZo9rkz2/fH8N3YbAQ/NrHncAIjRvVAoVDw+5lTtGrg8tqs2/VreRK46RH3798nb9687/Te/JscHR2R7RAXb8fdLf22sXF2ZCTsdvtL35dlmfPnz3Pw4EH0ej1ms5njxw+xZHpGcmV3IC4uDkmy4eGuQqFIvp/VKznRo70b67eG8/vhIiQkJPLt3HBUKpgx4eXLxW225J9hQGZnqldyYM+heEwme0qZsleJiZPJnMMh1djj4l8+lucMCQnExydgs4GDg4KjPxnp28UDjUaBTpv8eYyNsyFJNpAkEhPtHDqZiFKlQqGwseBbHz6v6YxGIyUvjT9lZOy0SC5eMRGQSU2jjs+w2RRs2qGnfzd38uR8sa9coZDQaSUMBj2PQywcPK6nXnUPKpVV8PvFPxgxvC9z58xg/YbNb1SKTRDeBbHuQhA+Ek+ePGHEV1/hWLYUWdu3SQm4AVROTvhXr0amLh3Yf/Qo69at+4A9FQRBeDv37t2jWtWKDBncE0f1Obq2stC2sZFH9/bRrm0T2rZtxYkTJ/h6zHB6d3Jmy4rcfF7TEy9PNX6+Gjq08GX/xryUK2Gje7eOf846ps9ut7N7926aN2tMntzZKF4sP7lzZaV792788ssvadqeOHGC7j16ULV6dWrWrs2YMWO4devWu7ol/xq1Wo1ao0ZvkFAqlMTpbegNNhwdFLg6K1Ptq46KtqFSSqxf5M/jpyYWrghFb7ABMr5eyfMwMiDbQZKUxMZZMVteBFveXkoMCTZu3DbSrs99MmbKQ4cOHYDk4D+9ZdfP6XQSIGOxWP7N2/DOVaxYEVlSs2N/Qrp1zh8/tfDL2US0Gg25cuVK8/7169epU7s6jRrWYtvmWVz8PZAN65fg4phIuRLJ98VkSvxzz33qBxgdWrgSr7dy7Kd42rf0xt1NgTHRTrYsaR/Cy0CC0QYo0DnoyB7gABLsOhiV7jhv3DZy/ZaZzz77LOW1qlWrcuOOmRu3jS89RpZljAkJ/PxbInZZ4vOaHgRujCcmNnmdt1ajwMdL/WcGfAlXFzd2HbQTF68gTw4H1i7MQNWKDij+km/tp1+NPH5qIUeAmoa1HWle35lC+VTYZZlynz9i7xF9qrEqlfDgkZmBX0WQO4cDP8zMSb9uGVg1Pxc/786Dk/YxrVs1JTw8PN3xC8K7IoJuQfhIrF27FrNCQcZ6dV85U+CaJzcO+fOybOUKPvEciB8No9HIo0ePCAsLe+WshCAIf19ERAStWjZBpwrmx125Wb84N4N6Z+TLLzJzeEte1izIzB8Xj9KzRzdyZ4cxQ7K89L+BOp2CBVOzYTHHsHHjxnSvabFY6N27F316d8Se9BszJnixal5Gxg514d7N3bRo3oAZM2YAEBoaSq06dWjSqhW7fvuVO1o117CzaP06yleuzOAhQz7qAFGSJKpXr8P2fXG4urlhscjIMjg5vriHspwcGG3ZY6BkES3lSjnRurELG7eH/7mEXCL4SfIYzWY7Vju4u7uhUGqIjrURHWPFkGDj/kMz8Xo7tVreRa3LS9Dm7SmJg7Jkycof1xJf298/rhlRKNSfXPk4Pz8/qlevy7L1cTx5Zn5pG1mWmTo/GrMZypevQL58+VK9f/36dZo2qY/dfIV1izJz9nB+DgbloVEdN4oU0KJUWImOjkaW5T+XYaeWNYsaV2cFIaFmvLxcqVnFEbsdQkKtqdrZ7DJ6vRWjScbF1QWFpCAqxoaHhzcLlkcQF29Nc25InhmfNj8EX99M1KtXL+X1unXr4ueXmWnzQ7DZ0n73MJlMGBKsLF0bR8UyrkwelY0ks0TrXiHcuvviXjk7KTEl2VmyKpzpC6PRapV8+UUG8uTywWKRiIq2Eh1r5eupEawKimPicC/OHMzCiP6eDO3rwZoFGfj9UFZKFtHStvczWvZ4yqqgOFZtjKXXsDAadgxBrVazfkk+XJxV3LhtZPTkh/QaeockcxL3799m5MiRWK0vH78gvEsi6BaEj8SuvXtxLFQApTb9/VYepUty49Zt7t27h9ls5sSJE2zbto3Dhw+j1+vTPVZ44eeff6Zq1ap4enmTK09eAgKykjt3HubPn4/BYPjQ3ROE/4xly5Zh0D9m4w85yJnNIdV7kiRRvbI7C6dmIjz8EaWK69LM7v2Vu5uKxnWd2ByU/mqfcePGcfjgFpbPzsy2wNy0aepDzaru9Ojgz7HteRk90INZM6ewbNkymjRvxoX798jSqxs5B/cnoEVTsrZuQe7RI/BsVJ/ADRsYMmTIR/2gs2vXbty4Y2P3QT0qlRqdNvke2u0yNpuM3S5z+KSRMxeSaNnIBYPRRvOGzkTFWAgJNVOmhDMbd8RjtcrE621o1Bq0Wi2enp64u3kgKTRExcgE7Tbg5Z2D+QsCOXL0JFmyZEnpQ5u2HTnyUwJPnyW9sp92u8y6LdHUql3/k0wMOnv2HKJidDTrEsLJ08ZUn4ngxxa+GBVG0E49SDqGDhuR6lhZlhk4sC9ZMujZvio31Sq6pXzWHR0UJJpkPD2USJIdkF+6b9xikUk0yWi1ClRKFR4ejkiSxNqtcUTFWImLtxITZyUyykqiCVxd3HB0cCQu3sqhE0a6dOlJjN6Tlt3v8vsFfar+33uYSI8h9zh+ysb3M+am2sKmVquZ/v0cjp+y0XvYfR4+MqXq16WrBnoMCSMsQmbiyAACMmvZtCwf8XqJGi0e07zbU8ZOjeDLCRFUb/aUb+dGU75CTVyclTSp54lWq8XHxxdXVzeehEis3aJn1ABPurd3x8lRiUKRXG8eIEeAhi0rMpI/j5YzF8xM+D6aCTOiOHTCSJniLhwMKkgGPzVDx96nVourHD4ZRYkiKmpU0lK6mJod29ZSo3oVgoOD/+nHQRDeitjTLQgfidi4ONSZX//kX+Pmht1uZ+7cuRw+dozQiHDssoyEhLurC+1atWbkyJF4eLy6rMf/ugEDBrBo6VIkjQbHggVQOjpijYom+P59hgz7ksCVgRw6fAgfH58P3VVB+KRZLBY2blhNq0Yu6SZwqlTWmcL5Ndy4lbx89d7DRG7fMyFJkC+XA9kCXuzDzp/Hkd2Hn77yXGFhYaxft5KRA7xfWt5IkiS+6J6BG3eMfPPNBKKTbOQcPACtd+ogUKFS4l2+LJJazfrNQXTp0oVSpUq97S14LypXrkyr1p35ckIgvTtq6djKGTdXBTKgN9gJ2hnPd3Nj+LymJ00+9yfBqEelAKtV5sJlA6WKuTBr8VNWboyleQMP3N09gOTkazqdDq1Wy/QFwch4cODgIbJmzZqmD82bN2fWzKn0/vIh6xfnxM019VdMu11m/LRH3LwL337f9/3cmH9ZlixZOHb8FLVqVaNx56fkyakmXy4NcfF2fr9oIskMSpUjs2fPT0lC9ty5c+e4fu0y6xdnxtkpdd3qcqVc2LI7gkdPrXh7KtAbbFiscqqtAQCHTiZgsUL5UsmJxK7dspAvfxF2H7pDm6b8meRNwtlZi4NDcskvWZaZNu8JdhwZMGAA7dq1o1fPrjTreoM8OVQEZFYQFSNz8UoSXl4ZCFy1gFq1aqUZe61atVixch1DBn9BpYa3KVNci7enkodPrFy6koDVauWn3YXIlzs5WVn+PI78vLcIB47FsHlXJL+cNaPRSkgKNe3adyV79uw8vPcTjs+T9UkSDg6O7DkchZuLgm5t3ZEkCUmRvGfbZn3xgMDdVUWvjm5MmR3DmYNF2LQ9mClzovn262w4OCgY8vUDdh2IZNpYb1o1cknJS5BktvPzGSPjpt2gVcum7Nt/GG9v73/4qRCENyOCbkH4SHi4uxMXE/vadqaISBITEli9OQjXMqXJ1q4VWi9PLHoD0ecusGT9ek789BN7d+0SQeNLTJo0iUVLl+JStgzeVaqjdHiRzdQSHU3Egb1cvnaNJo2b8POpn0XJEeE/ITIykidPnqBSqciZMycODg6vP+hf8OjRI6Kiwqn9WZZ02ykUCmpWdWTlBj0tu93g13N6kndqAkhUKe/G4N4ZKVPChYQEW7rJ0IKCglCrTLRvnj3da/bo4Mf6rddQ5yuaJuD+K88SxYg6dpKVK1d+tEG3JEnMnDkTDw8PZsz6jkWBMZQv5YBCAZeuJmFKkmnb1IfJo7OiVivQ6dRcuhpJoklm/PQIVColWp0nY6fFE/xUTfd2zuTIpkOWZX6/YGDxqlCO/mRh5qyFLw24AZydnVm7Log2rZtRvdktOrX0oE51d7QaBef+MBC4MYorN+x8N3U2ZcuWfc936O+7e/cujx49Qq1WU6hQIQoXLszDh0+ZMmUKa9as4saBCCTAwdGZ1m1a0adPH0qUKJHmPAcPHsTfV6JyubR1fBvX9WLSjEfMWhLNnMm+6A02jIm2VEvMTSY7C1bEUqaEM/nzOHLlegIXLptZuGgcSxYvoPvg84z70o/Gdb1SEqXdf2hizg8hbN9vYvr38/Dx8cHHx4eTP57m1KlT7Nixg6ioSAJyOtO9Ty0aNGiQbnWUOnXqcOHiVXbt2sXRo0cxGOLJW9CbGnUyM2/ud/z/HVpqtYJGdb1SSsg9fGSicqPbVKlSBb1eT0yslYQEG05/eQhx7g89n1VyxOHPOugSEnY7WK12TCY7Wq0CSYL6NZ0ZPz2Kid8/IWhXNGVKuFCskDMXrxjYuieSmRO9ad049b2WJCiUT8umpb7UbfOAJUuW8PXXX6f/ARCEf4kIugXhI9GscWOmzJ6NrbEJZTpfKJ/s3A1aDQE9u+GcPVvK61ovTzLUqYlnyeLcXryMfl98wZbNm9Mcb7FYePToERaLhYwZM6bsx/tfYDQamT5rFs4lS+Bb+3MkKXVArfb0xL9VW56uXsnZc+dTlqALwsfgecbjkydPkpiYiK+vL02aNMHPz++Vx5w/f56FCxdw5PA+bLbkvZUuLu60at2Rfv36vfN9tc/3Tmo16We0VioV6DQKomOsJJmTWDjVh0plHJFlmRO/JLJifRytetxkwdQc7D6kp0LFV9dnvn37NoXz63B1Sf8rTqF8OpydwP6aVUGSQoFTofz8eOrUK9vIsozJZMJut+Po6PjaDN7vglKpZPz48Xh5efH111/i6KjD0UFBny5etGrshYebHZMpgaQkCY1Gw+5DJgoWKsaKFWtwdHQkc+bM/PDDDyxZPI/VQbfx9lRhscjE6WWyZ8/DsuXjqV+/frp9KFKkCAcOHmPevHnMX7mJ7xfdJ/nhiYpqn9UkaOIAKlWq9F7uxz916NAhFi6Yy7lzv5Fcuk5Co3GkUeOWDBkyhClTpjB58mSio6NJTEzEy8sr3YdZ8fHx+HqrXrp9QqdTMGFEVoZ8fR9Hh0i6tnHBTVKSYLTh6Kgk+LGFkZMjuPvAytbAXISFmxkw+hE5cuSjYcOGlC5dmnbtWtNv5EW+/u4JeXNpMZuVXL+dhKenP3PnzaVly5Yp11MoFFSpUoUqVaq89X3R6XS0bt2a1q1bp7xmtVrZtnUji1c9Y9H0nC/9/MuyzIIVT0hMtDFi+GCsNhvxcYkE7XxGl3YZUfz599hikVO2RwCYLXbsdlBrtMTqzSgTbKhVEqYkG4kmO9v2JeLtHUAG3+RVMmuCwsmSUUmLBi5p+mCxJK8KzJxJl5zTYMNqhg8fjlarTdNWEP5tIugWhI9Ehw4dmDlnDk/27CNjk0YoJAVKpTK5QOqfoi9cwhQWjt/ndVMF3H+l9fbCp0E9jm3exq1bt1JKskRGRrJ8+XI2rF9FZGQYAGq1lnqfN6FPnz4UK1bsHY/ww1uxYgWJZjNZKlZKE3A/p1Cr8ahQkdCtm5k/f74IuoWPwrlz5xg1ahjXr13G0x1cXVQ8CzMzZfJYGjZqztSp09I8QNu6dStDBvcjZ1Y7E4Z7ULqYM2aLzOETsWzYuoDdu7ayKWg7+fPnf2f9zpAhA2q1lnN/JC9hfu75XtLnX86tVisXrpjw8VKyeVnGVDN8LRu60KSuMwPHhNF72F1sdgemTOv6ymtKkoT9DfZf2+12ZBkUqtd/FVJqtSSZ0ybP0uv1BAUFsXzlSu7cuweAv58f3Tp3pmPHjvj6+qY55l3r0qULCxfMxmozMGdKdkwmAyZTLHq9jFIhIcsyew4Z2bwrkt59ulCwYMGUY/v370/Pnj05dOgQ9+/fR6VSUbRoUSpWrPjGq36yZs3KzJkzGT9+PDdv3sRqtZI1a1YyZXp5WauP0cKFC/lmyteUL6Xmhxn+lCzqjMlk58CxGAI3ruPI4X0Ebd5BkSJF3nhvuru7O6HhVmy2tMvGAVo28sZikRnzzUNWbYqjdjUX3N1knoTY+O18Im6uaiaPzsrhE7Gs2xqHRpeJzVvWM3XqVFauWIzVaiB/bgdCw0z89IsBrdaRlq3aMWfOnNeWyZNlmQsXLrBv3z5iYmJwdXWlbt26lCtX7o0eIKlUKkaNHsfAAT3J6P+YkQMyo9G8+LwkJVmYuegBy9dFkSenEz06OKJUSsxYqGfGomeUKa4gXx5fVCoVWTJp+eNa8n5zWQaDwY5arcbL0xOr1YoxMRGb1cblGwmoVFrmzV/MxYsXWbtmIQ+CjZw5H8/nNZ3S3GNZBmOijE7ngEJS0KC2J8vWPeTOnTsUKlTojX6GgvBPSPLHnBnkDcTHx+Pm5kZcXNz/1Iyd8N9z8eJFvvzyS06eOoVDwfy4liuLLnMmHJ2cUFutRP12hohDR7EDhSZ8jdrt1Z93u9XGnW+n8WWv3owZM4aHDx/SsmUT4mMf0qKBC3VreKDTKjj/h4G1m2N4FKJkztzFNG/e/P0N+ANo1qwZB65eJkvn7kjp5JGUrVbuT/+O7Bkycufu7ffYQ0FI69dff6Vt2+YUymthaB9/qpR3RaGQiNdb2bonihkLIwjIVoLtO3bj7OwMwIULF2jcqC4tGqiZPj5bmi+gUdEW2va5R0x8Rn76+VecnJzeWf/79evHpXNBHNueG6s1iUSjEastuZSQSqnEwcGRh4+NVGt8j/Ff+tCqsSNurko0GkXKM0dZhpg4KyVrBePjV5SzZ8++MhhYsmQJ330zgvNH8+PpkbaU0nO/noujbuvruFarS5YmDdMdw8O1G8inUHHy+PGU1x4/fkzzVi25ee8+jgXy4ZI/H5JCgeH+AxL+uIyfmzubN278IA80T506RYcOLcmeSU+P9i58XtMJF2clwU8srN4cz/J1ceh0KixWF1at3kiNGjXeex8/VsePH6dD+xYM7uXKsH6Z0nzO4vVW2vW5R0i4L7/8ehZHR8dXnCm1P/74g3p1qxE4LxO1qrq/st3SNaGMmhxKufLliQgPJTw8EoNBj06nQJIUODu70bJVB/r378+UKVPYvWsdA7p70KmVL74+yUvDHz4ysXRNKGu2GBgydAzDhw9/5fXu3LnDgP59uXz5PP6+CjL6qwgLt/I01Ea+fIWZv2BJqgcz6Vm+fDnjx43C091Gi4YuZMqgJTTczLot4TwJMdOrkz/fT8iWck9Dw83Ua3MNq8XKkD6etG2Wld8vJtC+z002LPanQF4tdruEp6cXqv/3cKxj31uc/FXCbrciyxb0+kQK51ejN8h0aePCsL4vHobIMsTprSQlgZeXFyqVmhu3jdRqeZ+9+46/dDuAILypN41FRdAtCB+BoKAgBn4xEIVJjZPsRrgtlETJhMLZCVRK7Ho9Pp4eFCtUmF+vXSPvuNGvPee9RT/QtmJlZsyYQbWqFVHK99m0LCcZ/FLv17JaZUZMfMC2fVZ27jpAyZIl39UwP7iGDRty/OEDMrVpn27QDXB/2rdk8/Xlzt0776l3gpCWxWKhdKmi5M4WxdqFOVPNHj1347aRZl3v06rNACZPngxA7969uHF5O0e35UWlenlwGvzYRKWGt5n+/WLat2//zsZw5coV6tWrTp0qVsZ/6YGzswLtn+Mwm+08DbXSc1gYT5/ZuPJTcSzmeMwWMyqlhFoNyJBklrHLMH+5gd1HXLl2/dW/l9HR0ZQoXpAvuuoY1u/ls6uyLNNt0F2O/GgnycuXXIP7vzKIt8TFc2fqDGZ/9x3du3cHIDExkc9q1OB2ZARZu3dJsyfcakjg4aq1eCSa+PH4cTJmzPg37tw/M2jQIAJXLsJBBzqthFYrYUiw4+6qolt7f/p19afviAecuejI+QtX3umDl09J69YtMMT8yO61eV75mXj0JImKDW7x/YwltGvX7o3P3bBBPeKifmfH6tx4uKddYRESmkSjDncpXLwBq1evTXn9/v37hISEoNFoKFiwIE5OTuzZs4fevTqyaFqGlD3T/9+C5SFMnR/LwUMnKVKkSJr37927R6OGdfH1jGHMEP+UjOqyLHP6dz1TZoXw8IkzO3bue+PA++7du6xevZq9e7YRHRODUqEgPCKS1fNz0LR+2n6GhpvpP/Ieh0/G4uOtI1MGB65ejyNzRgXrF2chf17vNAH33KWPGDf1KblyOPLV4ExUKuPKnfuJdBlwm8goC5/XdCJwXgYkKXlJuTFRxm4HN3d3dNrkWf+gnREMGx/JhYvX8Pf3f6OxCcLLvGksKjIECcIHdubMGQb0G4BHkj+VXOtS0r0idTybUtGpGvmSspNN74ujVUXWjJlp0KABdrMZu9X22vPKpiR0Oh179+7l4cNbLJ2VNU3ADaBSSUwfn52cWe0sXrzoXQzxo5EnTx4sYeFYrenX3LVER2G3mMmXP1+67QThXZBlmbNnzzJw4EDKlyvJvXt3cHaUuXHn5TWQ8+dxpEtrdzYHrSUhIYHY2FgO7N9Fx1Yerwy4AbJm0VGjsiMb1q95V0NJvk7WrDg6urJlTzyte4WyZbeB2/fMXLtpZsGKOFr2eMalq0kM6plcHsjT0xNPDy/UagesNjU2uxqdzglvLx+KFfYgJiaGpKRXl6Xy9PSkV+8BzFkaxYZtEWlKfVksdqbMesyRHy0MHDQMOTyCkH0HX1oSzGYy8Wj9Jvw8PVPtid2xYwfX79wmoEvHlyZhUzk7ka1bJyISDKxcufIf3L2/x2w2c+L4Ifp39+fY9sKMG56VwX0ys/j73Jw7WpyhfTOh0ymZMjoLBkMk27dvf+99/BiFhITw808n6NTKK91l1QGZtVSv5MimjemXrvv/5s5bSLTei0YdbrNzfxRmc3LmscREG5t2RNC44z1U2mxMnz4j1XE5cuSgUqVKlClTJuXhyKrA5ZQrqX1lwA3Qt2sGMvlDYGDgS98fMXwoXm7RbF+Vi+qV3VP2m0uSRKWyrmxbmZusmQwMHvTFG5fMy5UrF5MnT+bipesEBz+jUKGifF7T/aUBN4C/r4atgfnp0MIHs9WF6rX7MXDwaFS6XLTuHcW3c0K4dNXA3QeJ7D4YRfOutxg39SlFCzrx+6EiNG/gjZ+vhkrl3Di9ryg1q3lw6ISRK9dNRMfYMCTIaDQOeHp5pQTcsiyzZnM01T6rKQJu4b0RQbcgfGDz589HneRAYZfSKYlEFJICP20m8rkUpZhbWcq51uDmtZsolUqUNhtx16+ne87EkGdYwsKpVKkSG9avoWIZB/LmevUSOJVKomMrDw4d3ENkZOS/Or6PSdeuXVEkGkm4dwuZV3+BiL14DsluZ8yYMe+xd4KQ/MS8TZuWNG5Um3O/rad8sRA6tnTi6g099dteo+uA2yQkpH3o1qapN3p9ND///DMhISFYrWZKFHn9zGXJIo4EB99/F0NJsXnzZkyJkWxalo+iBV35dk4M9ds/pUmXp6zfbqBxXR+cHRW4OEsYE40kJ6zS4Obmhpen1/+xd9bhUVzfH35nXbIbFywJ7l5ocXd3J1gpUENKlVL7FmhpKdCixd3dihZ3dw+SELfdbNbn90cgNI1AW6y/zvs8ffqwc+fOubPZmXvuPedz8PHxxWg0olAoMKe6kMllmWoIZ8fHH39Mj56D+PDrGOq3u8rUOQ9Yvi6W73++zxvNrjBzoYX/ffsDH3/8MeP+9z9SDx7m1vRZJJ49jyM5BVt8AjH7DnBz0hT0ySksXbQo0w7GnHlz0RYrhiYw55xthU6HR6UKzF24EIcj94W+Z82xY8eIi4uiW3t/ShXX0bdbIIPC8tCqiU+GsjVAvjxqar2uZfPmjS/UvleVBw8eAC7KlnxyyHjZkhoiI+/9pf4LFSrEpk3byRdSj3c+iaZc3UtUa3aFcnUvMfKreMpUaMHGTdtyFUcESEpK4vDhA3Ruk7sIoFwu0Km1kW1b12c5duXKFQ4f3s/wwQFZyrs9Qq+X8/F7QVy8eJZTp049/UD/wPnzZ2hQK6uo2Z/p0MoP0W1n0KBBfP311+zevZ+evUewYqOKlj3Cqdv2JkM+iubSDSMB/lp2rSmTJW0mMEDFgl+KUSCfmpFfx+FwqvEPCMDT0xOlIv2ZIYoi4ybd5+xFF4MGvf23xiQh8XeQhNQkJF4iUVFR/LZ1O4WVZTIc7uzwUvpisHix9/e9VK1cmSPbd6EKCUGhUaNSqTKtyItuNw9+20H+vHlp1qwZX37xCd3aPHkC8XolAy5XLPfu3ft/W7eyTJky1KxWnf2/7UDpH4DOJxCBzC9t842rpBw9SvkyZf5VZW0k/v3YbDZ6dO/CjWuHmTMpHw1re5GUnIhMsGPQy9m8M5WRX8UyYNh1Fk7NHDaeP68aEElKSqJgwfRSWXb7k3em7A4xS+jms2bB/Nk0q6+jaX1vmtb3JinZyf1IG3K5QMFgDRqNjNMXzOzca6FNUwseej2Q/S7j+m3JVKtW84miXjKZjO+++442bdowb95cfpi+BbvdjtFgpG27gYSFhWUIyA0cOJCCBQsycfJkDi9byQO3O70ElFpN51atGD58OMWKFcvU/6XLV9A3qPvEsRuLFSPq8DFiYmJeqJBYcnIy4CZfnpzLPz0iXx4FF278/11s/Ss8KpeVanE/oSWYU925ltfKidDQUFasXM3169f57bffSElJwdvbm+bNm+dYju3PpKSkACJ5AnNffIJ0R9RkTkIUxUxzhe3bt2PwcNOsQe6Oe53qngT6R7Jt27a/lX7mdovZRtzExjnYujuRhAQHer0coyG9bJjrod6Dj48Po0aNYuTIkVy6dAmbzYavry/NmjZgcB9/lMrsnwE6nZyFU4vTtMsFmna9xeA+eWnW0Bu1Ssapc+ml646fcTL6izF/S71dQuLvIjndEhIvkfDwcJwOJ77aJyvcGgUfft+zFxERU0IcN2fOxrtJI9SBAWh1Wjw8PHAkJRO5aSvu6zf5Yd48lEolCoUCm+3JEwi7I73N856Av2yWLl6cnos5dx76ypXwKFMWhVaPIzGBlDOnST17juCgPOzZs+ellP2R+O+yatUqTp06yPoFhahULl0QTSbIcLvSd6xaN/HA21NGt0EP2LorkVZNfDLOfRBtBwSMRiMFCxbE29uPbbsTeb1yzjtMoiiydZeJSpXrPrcxORwObty8zuBej0NLvTwVeHlmfs6EdQlk2Oc3OXXeQqO6YrZllfYeSubEGRszf+33VNcWBIEaNWpQo0YNRFHE6XTmuEPeqFEjGjVqxPXr1wkPD0epVFK6dGn8/f3/wmhfHTw9PQGBB9H2LPf6z0TFODF6+uTa5r9CsWLF8Pb2Z9P2BCqX98ixncslsmWnmVr1/r74aNGiRSlatOjfOtfLywuQcT8yq6L+n4mItONpNGZ5n5lMJrw9FTk6r4+QyQR8fRSYTKa/ZWuRIkU5cuI6fbul794nJTv5fOwdNm1PQBRFvL1kpJjcJJvcuN1qLBZLpvPVajUVK1YEID4+HosllVLFcw8JL1tKz6fDgvnkm0gmzExj3M/xD4/IqVatFgsWvkvDhg3/1ngkJP4uUni5hMRL5JGD6xZzz9EW3W4sFgtxsXEEWApQzaMeyvB4YmfOI3reYu4tX8Wln6dy/bsf0N6PYO6sWTRr1gyA8hWqsGNv6hPzsbbvScLDw5PChQs/m8G9ovj7+3Ng3z7e7dsP2bkLRM2Yyd2JE4iaNxfFjZu8PWAAVy5fkoQZJV448+b+SoNaugyHG0CtUWN3ijid6b/fWm/oqFpRw4Ll0ZnOXb4uDr3ei1q1aqFSqejWPYzl61OIjsl5Ur5zXzJXbjjp2/fpnNi/zVOkgnZo6UuhEC0DR0SzZWciLtfjk5xOkTWb43lz+D3q1W+e8Wz7KwiC8MSQdEh3hBo1akTdunVzdbhLlSxB6vWbT+wv5fp1vL29X3jpsCpVquDjE8jydbnvYD+ItrP3sJXmzVu+IMtebdRq9cPfjomIBznrBixfF0dkNPTr95x/OzlgNBqpVbsuS9cm5NrO4XCzcmMKrVp3zHLM29ubuAQnaWm5zz8cDjdR0Q68n1DPPid69urLb79buB9pIynZSfuwy/x+MJFP3vfm7J4QTu8K5fzeEMZ95kuRggIdOrTi5s3sf1uP6mmbs0mx+TM6rQwvbwOnTl1k5aotLF22gYOHTrJ6zXrJ4ZZ4KUhOt4TES6RYsWLoPfRE2SJybZeUnEys+wF5VMGUMJQnnzaExj5tqaqrQUAEqM/cx37pOtUqVuLC2bO0avW4/E2fPn25ftvJjr1JOfafkOhgyZpkOnXu+dTlT/7NeHl58eOPP3Lv9m12bdvGptVrOLxvP7FRUUyePDnjxS4h8aIwmUxcvHieVk28EEWRtLQ0EhISMJtMiCKkmJ0ZtadbNfHg6CkTbnf6v6/fSmP2kkQ6de6JwZC+sz1w4ED0HgXoOvAm129lFmATRZHf9iTyzsf3adCwBTVq1Hhu41IoFAQGBrJ7fzxJyUkkpyRjtVqzaCoolTKqVjKQZtUxaGQUNVpcYfjntxj8wQ2KVj1D3/fC8fYtQvfuPZ+brX+Ffn36knbtGtbomBzbOC1pmE+epm+vXk/l8D9L1Go13Xv0YcmaFM5fSs22jcsl8uX399Bqvf/fl4v8KwwaNAhPr4J0efMWp8+bMx1zONzMXx7Dp2Oi6NylN2XLln1JVkK/fm9y6pyDZWtjsz0uiiITpkUSHSsQFhaW5XiLFi1IsypYvy13x/23PUkkJAm0bt36b9nZsWNH/P1D6PvebT788jax8TbWzsvLwF5eeBrluEURm02kdVMjm5eWwM8zgSGDB2a7UeDh4UHZshXY+FvSE6+78bcU3ni9Bj4+PtSoUYM6depkpN5ISLwMJKdbQuIlYjQa6dSlI5Gu2zjc2e9IuVwuIq13sAtWinmUyfhcJsjIpwmhqlcd6vu0oIz+Nc6fvUBaWuYJdvXq1WnQsAXvfRrBzr1JWV5kEQ9s9Bh8E1EIYPDgwc9+kAkwdegAAQAASURBVK8wOp2O2rVr06xZM6pUqYJcLn/ZJkn8R3mkxq3VQFxcHMkpSQjY0WpEtGoBm00kMclJmtWFSglWq5sN2xKYvyyaDn1vkSdfWT7++OOM/gICAlixch12d0HqtbtOt7euM2FaBOMm3ad+u6v0HxrBG9VbMH36zCfmR/9dwsPDadG8Cbdu32b91iQiH5hx2tNISk4kLjY2kwJ5XLyDzTssfPLpaLZs3cPrNbqzaZeaZeuTsDvSha3cjtu8OaAHNapX4bfffnsuNj8t7dq1o2TRYtydtxBbXHyW487UVMLnzMdf7/HSdkOHDh1KseJV6TLwNnOXRmMyO4F0Z+zkWTNh79xg624nkyZPy1iskQA/Pz9WrlqHWleSVj1v06L7VUaNucMHX9ymSuPLfDYmjs5d3mT8+PEv1c7GjRvTs9dARn4VxRff3eHOPSuQroT+255Ewt65xuRZSYz6/JsM/YI/EhISQsNGzfhhSkyOu/qxcQ7GTY7mjTdqZtvH02AwGFiydCUxCb4sWxfHgJ5GCuRTYLO7Mae6iIt34nAKeHl54e+r4fMRQZw/fypH4bY+fQew+4CFU+fM2R4H2H8kmWOnrYT1eTm/PQmJ7JDqdEtIvGTCw8NpUK8B9liRSh7V0cgf7zSLosid5BuctRwhUJOP173r5ZhnbHfb+N20kW+//x96vZ7Dhw9jt9vJmzcvrVq1YvLkiezZvZVSxRQ0rW9Ao5Zx6pyFHfss+PnlZ9HiFU9dh1NCQuLZ4nA4KFY0hLBODob09cTLKM8kPmS1uUlKdiKK8OP0RKbPS0alFHC6BIqXKMemTZvJkydPln5tNhsbN25k8aL5hIffRKFQUKFiVfr06Uv16tWfm27B/fv3adWyCTpVFB++E8DXP97D0yCyaGoefH3kmM0u7A7w8vLGZJbR+52bPIj1Z+eufXh5edGjR1dOHNvJ8EF+dG3nh4+3ElEUOX0+lUkzH7D7gIspU2fRtm3b52L/03Dv3j3ad+rI1Vu30ZUqgaFkCQS5DPPN26SePUeA0ZMVS5dm5KM+D1wuF/v27ePKlSsAlCxZktq1a2cspJhMJj766EM2bliNWmUnOJ8Kc6qL+w9cBAcX4tsx42nQoMFzs+/fjMvlYvfu3SxZsoi7d26hVCqp/Fo1wsLCsgjr/R3cbjf79+/nzJkzOJ1OChUqRLNmzdBoNLmeJ4oip06d4urVq7jdbs6cOcPmTWtISIhBLnNjTrXjdqfrInh6+tC33yAGDhxI/vz5s/QVHR1N61bNsFtvM/Qtf9o390Wvl2O1ulm/LZ6JM2Kxu/KwfsNWgoOD/9F4p02bxldfDGff+rx4PMygkQkyNBotOr0Ohfxhup1bpFqzKzRtOYRvvvkmSz92u51OHdtx7cpBJnyTj0Z1Hpc6c7lENu9IYORXD6hcpTGLFi35f69TI/HyeVpfVHK6JSReAU6fPk23Lt2IjozBjyCMCh9cootYdySJ9jgEt0BTv84oZLm/PH5LWIXgIcOFgDq4AKiUOKNjwJxKnZo16dO7N+vXr+XE8cM4HA5CQwvRrXtvOnTogIdHzqIxEhISz59q1aqRGHeKUztD0Kgf7z673CKJiY/Dy2u3uU/7Fl50befLms1mFq0yUa58TZYuW4lWq31Z5meif/9+nDu1jk2Li+Lvp+T6rTS6v3WVhEQ7bZp6ULe6jtQ0F4eO29i2x4WHIS+Ll6ykTJkyTJ06le/GfsqS6cFUq5L1ve52iwwbFc6mnXDy1Hl8fF6eCJjJZGL58uXMmjOH6w/zUIMCA+kXFkavXr2eay738uXLmfDjOO7du43Hw+pw5lQICSnCiA8+pmPHx3m8Dx48YM2aNURGRqLRaKhevTr16tV7blEO/yVEUeTQoUNs27YNk8mEl5cXrVq1olKlSjkuam3evJlv//cl4eHX8TIKqFQyYuIceHr689agd3nvvfey/W62bdvGDz+M5dLFc8CjMnRKgvIEE3H/Dj5eTrq3N1LjdSMKhZL9h1NYujYFN74sWryCSpUqZekzOjqajz/+iB3bN6OQ2/HxVpCU7MRmV1CvfhPGjfs+W4f9rzJ58mRmTP2Sc3tL4nanC7fKZLJs71HHftfJE9KRKVOmZNtXSkoKb701gL2/7yC0gIxqr2kQRdh/JI2IKJEmTVvzyy9TM2qaQ7qzfuvWLZxOJ3nz5n2pzw2J/19ITreExL+MlJQUVqxYwcL5C7lz5y5KpYJqNaohk8nYuno7dQwtMl5OZmcKEdZw7G4bckFBkDo/ifY4TluP41OzGgWaN0Hl5QWA6HKRfOky0es3UyJfXrZu2vxQ+VRCQuJVwWKxUKpUUcwp0fTsaGT8F/4ZNWgTkpy4XCIeeoH3P4tlx14LmxYXonTJdKGvk2fNdB14m85dhzB27NiXOQwg3cGrWqU8X3/kRViXx05nYpKTxatiWLgihogoOy6XSKpFpEvXMMaNG4e/vz8ul4tqb1SmeqUYJnxTKMdrJCQ6eK3RFT78eCxDhgx5EcPKFVEUsVqtuFwu9Hr9c6988PPPPzN2zGhaNdYwsHcgFcqkOxenzqXy68JoNu2wMurzb1+Je/P/hejoaBYvXszGDatJTIzHw8NIhYpVOXH8MHfv3qRAXjkB/goiHzh5EOOmbNlK/DJlehaF8qVLlzJi+Ns0rK3knf5BVC7vgSAIhN+1Mm9ZDLOXJNOpc19++umnTH9H8+fP55OPh1H7DQUDewdS8/X0Oe/aLfEMGXmDN17TMHtiYQL8M6cKJKc46f3OTW7d9eL3vYdyFAiMiIhg69atJCUlYTQaady4MaGhoc/s/s2dO5cvR7/PpQOl0WpzTuUSRZG6ba5So+4Axo0bl2u7U6dOMX/+fK5fu4QgCJQoWZawsDDKly+f0S42NpYZM2awdMl8EhPjABG5XEXjJi0ZPHgIr7322jMbo8R/E8nplpD4f8Lu3bvp0LYjr6nroJPrOW06QrQzCkGnQeHpictiwZViwuWyo6tQlmL9+qBUZRXtscbGEf7LdIa9+SZffPHFSxiJhIRETpw9e5ZmTeswfJA3k2ZGUKqYin7dPan2mob4BAdnLthYtMrEjdsOfvrGn5qvG/Dzezx5njQjkp/n2Dhz9vJLfxcuXbqUD0YM5PLBMnjos06uRVHEZE5XH+7Y/waly/dg0qRJAJw5c4bmzeqxek5wruXOAAaPvMn92LJs3rL92Q/iFebkyZO0atmI4YM8GT44a+1vURQZ/0sEk2eZ2LJ1FxUqVHjxRv4/Y926dbz//mAUMgutGuvJn1dNdIyNFevjSEx2MbB3EN+NDkUQBNxukX2HU/jmxwdEx3uxcdNvGVVB7t69S80aVejaVsnYUSHZLs6s3hTH+59FMW36fNq0aQPAxYsXadyoDv26afnyw+BM54356R6LVkaxfWV+lAoBH19flIrMc4CERAevN73Cu+9/xdChQ5/fjcqFu3fvUu2NSnw/2pdu7XOuDHDyrJk2vcNZtHgN9evX/0fXvH37Np06tsFsukuXNkaa1vdGo0mv171wRSI3wgV++PFnunbt+o+uI/Hf5ml9USmuSELiFadu3boULlqYy6mn2Ze8g1hDGr4d2hI8dBj53hxIgXffw69HF1QhBbDevI09KirbfjT+fhiqVGL+4sVZxNYkJCReLk5nushV84beLJtZAi9PDcM+j6VSwzvUax/BqLHx5A1SsGp2PhrW1mcRROzW3g+73cTmzZtfhvmZMJvNaNSybB1uSC/fZTQoMBoUBPjKSE19rK6dkpICuMkbpHridfIGKUlKyl15+f8js2fPJrSAwNC38mZ7XBAERgzJR4G86buLEv+MHTt28M7bA2jdSODkzpJM+KYQwwfn5cN3jBzYlJ8PhnizZHUMi1alq4jLZAJ1a3iyZl4RfD0T+ejDDzL6WrhwITqtjdEjCuQYDdGhpR81qqqZM3tmxmezZs0iT4Cbz0dkdrjdbpFla2Pp1NpAviAlMpmYpc41gI+3krbNPFi0cM6zui1/meDgYBo0bMrkX2OJi3dk28ZudzNmYiQhIUWoW7fuP7qe3W6nR/fO6FQR7F5TjC9Gpi/klS+tp2+3QHasKk739mo+GPEuR48e/UfXkpB4GiSnW0LiFUcmk/HjhB+IF2OwGETyhPXCULoswiNxEBkoQwvgF9YDVd4gbi9aivgwX+rP+FSqSHxiYo6qoBISEi+H/PnzIwgKTp9PpVoVI0tnluDApnLUrOpJqWIqjmwNYf7PealcXoPDISKXZ9Z3CPBX4e2pIDo6OocrvDh8fX1Js4q51giH9B3Z23edeHh4kJSUhMvlerhLIPAgOvdzASKjHHh5/bfyMp1OJ5s3r6V7+8fiUdkhlwt0a+fJhvWrMvJnJf46oijyzdejqVlVxk//K4jRkP67s9sdOJwO/HwUjBrmR9e2Br6bdA+r9fG99jQqGD44gEOH9nLt2jUANm5YTbtmBnS63CtldG/vw/HjR4iKisLlcrF+3Uq6tffMJK4IkJziIiHJSdVKGgQBtBoZ1jQrkDWItWolA5GRkdjtT/5tPQucTidbt25l5MiRDBkyhC+//JLu3XtidQTSvs8NduxNwuVKt1MURU6cMdF14A1OX1QwafLUf6w3sHnzZsLDrzLjxxCCArIu4snlAt9+GkLxwiLTpmWfOy4h8SyRnG4JiX8BJUqUQOOhx1izOm6DBrtowyHasYs27KINBJCr1fg0a4ItPh7TtRvZ9qPQp++QZbcSLiEh8fIIDAykfoMmzFsen1F/OzRYQ7EiWtQqGfnypIeLOp0iNoeYRTDN4XBjSXO9EkJqDRs2RKs1smRN+s5fWpqL6Bg7Fosro01qqotRY+9w/lIKSxbPpVTJwpQtU5x169YREJA3U+1ht+jGYkklISGeuLhY4uPjuBeRxLbdqTRv0eaFj+9lkpqaisNuJzi/+oltQwqosdms0vP+H3Do0CFu3LjMuwOCMi1y2Gw25DJQqdKn0e/09yYpxcn6bZnLxzVv6I2H3s327ekpEImJCeTP++QojgL51ICbxMREUlNTsVrTKByaVdX8UZVLpzP9maGQC4iIGaKLf8TpFEHguesNAOzZs4eqVSrSv19XTh6ZR9Td1WxYM5l+fbvh7x+I2qMMfd+L4PUml2kXdo3ara/SNuwOMYkhLF++lqpVq/5jG5YsXkCNqlq0GjljfrpH/bbnqNroDE27XGDa3AckJDqQywXCuvqwc8fWV2LBUuL/N5KOvoTEv4CdO3fiBArXq41DELBZbbhFNzKZDI1Gg8WSht3lQlUgPwo/P5LOn8dYImtJE2tcHDJBwM/P78UPQkJCIlcGDRpC507b+PL7u3z5YTAymUCtN4wsWhnNxatWShRRk5TiQi5TZCkr9NueJNKscmrXrv2SrH+M0WikU+ee/DTjF34/mMypc2Ye+QC1q3nSppkPU+dEcuV6Gm2bedCuZRAqZXqe5fKlk4iNh1UbnXRuY6JiWRXJyUmIohu1SkCpEnA63Xzz4wNi4y3/uV1cnU6HIJMRm0N47h+JS3Aik8tfiYWYfysnT57E0yDwxmuZ9QVEMb0k1yP3NbSAklLFVJw+n0qXto/zlZVKGd6eCkwmEwAeHh7Exqc88boxcQ5AwGAwpH9/gkBSiitLO4OHnNACanbstdCmqYGH63U8tuwx239PplSpMiiVWTVfniW7d+8mrHdXar0uMH9SYUqXSC+D6nKJ7NqfxJffn8Phzs/8BSs4dOgQ8fHxlDEY+LZRo0zl7v4p4eE3yevvpEaLsxj0Ai0b6/H3lRN+z8n4X+4xYVoEv3xXmMrlPHC7Y7h//z6BgYHP5NoSEtkh7XRLSPwLSE5ORq5Wo9Dp0Gq1eHl74ePjg5eXFxqNBq1Wg+hygigiNxhwpVmz7SfhyDGKFiqUSdlTQkLi1aBGjRqMGTuBucssNOt2lSWrY/DzduGhF/j2p3iiY+04HG7kchl2x+MQUYvFxeRfY6hSpRolS5Z8iSNIRxRFRLcbk8lOQmIao4Z5M3tiAGM/88VkSmPQiBtcumZhzdy8zJlcgnbN/WjRyIfPRwRzdFsJGtZRYjK76DrwBpNmhJOW5sTPR4GXp4Kb4Q6GjY5jy840WjT0YuyY0SxduvRlD/mFoVQqqVOnPqs3JeXaThRFVmxIokGDJsjluYcyS+SM0+lErc5a1komE3C5RP64oaxSCTgcmXeYLRYXcQlOvL29AWjarA1rt5hxOHJfLFq+Lp5SpcuSL18+lEolNWrUZu3mpCztBEGgV5dANu9IJfyeA6vVjUqlymLvlesWdu230KfPgL8w+r+O3W5n6PtDqFtdxryfi2Q43JAezt24rjdr5xVBdN5j1aqV1KhRg9KlS1OwYEEMBsMz3YVPSEzmwNFkhvTx5OSOEL4fHcDIt32ZMi6QE9tDqFdDw1sjbnD0VPqCiFTPW+J5IzndEhL/Ary8vHDbbDgt2QugaTQa5DIZTosFV0oycp0uS5uk8xewXLjIoIEDpdqsEhKvKGFhYaxYuQEvvzoMHnmbJl1uEBXtYtOOVD75Np4UkxtRdJCYmEBqaiqXr1noPvgm4fd1fDvmu5dtPgBTp05l4cIZ/PS/YDYtLkzbZgYqlFbTpJ6OXycE8v1oP5QKgf3Hsk509Xo5syYUpkhBNckmGWMnJ1CvfQQNO97n9aZ3aNkzgivXXfz6U1GW/Vqczq21fP3VKKzW7Bca/z/St+8AzlxwsH5rfI5t1myO5+IVB71793lxhv0/JCQkhNh4B3fv2zJ9rtZocItgs6c7zyazm+u3HIT8Kex/7ZZ4rDYFLVu2BKB3797Exgv8POtBjtfcdziZXfvT6NPnzQwnNCysH8fP2NixNylL+27t/MifV03nNyO5dN2GTpv5/X/hciq9hoRTrHh5OnTo8JfvwV9hy5YtxMVFMmp4viz5548IDFDSt5uBpUvm0qN7O8Z/9xH/+3o4rVo2pGHDOuzatesf25GamkpKciLd2xn4+H1ftNrMcx4fbzlTvwukUlkV3/98H73eSJEiRf7xdSUkckOaeUtIPAeSkpK4ceMG9+/ffybhj40aNUKnUpFw4mS2xwVBwNvLG8fde9gfRCFXq3E/VEO2Rsdwb816Ihcvp0u79vTp0+cf2yMhIfH8qFatGinJSRQO1bNwajH2bSzHginFOH7GQcNOEQz5KIbR4+Jo3fMq9dtfJSI6iOUr1lKmTJmXbTpWq5Upv0ygTxcjfboG4enphb9/AEajF3q9EblCQYeWBt7t782cxTGYzM4sfWi1cnp18sTpsDB5TCE+GRpMnRq+tG4awNyfi3NwS3maNfBGEATeezMvycmxrF+//iWM9uXQsGFD2nfoyXufRfLzr5EkJT++h4lJTibNiGT46Ad06hz2j0su/ddp3rw5BoMvC1bEZPpcqVCiUqowm9243CIrN5qw2kQ6tXmcunUvwsaE6bE0btKC/PnzA1C8eHE++ng0E6Yn8sk34Zmc+RSTk+nzH9Dn3bvUq98yUxmr5s2b06RpO94acY+5S6NJS3P9wRaBti38uHrDQbs+MfQfeo9pcx8wbe4Durx5naZdb+LlV5alS1c991SDHTt2UKGMmqKFcr6OyWSmUR05KpXIyLf9uHakDDeOlWXxtPz4elyhd68urF69+h/ZsW7dOpQKN2Fdjdhs2c/BFAqBwWGeREbZqV2nEXq9/h9dU0LiSUixFBISz5BDhw4xffp0tm35DafTiSAIFC5ciP5v9qd3795P/cK7c+cOCxcuZOP6jSQnJ+Pj40PBkBAu79mHZ+mSqH19s5wjE91YDxzCS++B+eBhLh84hCCXI7hcBAUEMPKzz3j33XelUEMJiVecffv2cf78SVbNDsnIJS1XWk/jut6s25rAjt8TSUx2EX7fTkhISQ4eOvLc8zSfls2bN5OUFMuAno81JWQyWcazz2JJRaUW6NvNk+kLklm7JYHenQOy9FO1kga5HMqW0lOhjEeO1wsN1lCmhIZjx47RpUuXZz+gVxBBEJg4cSK+vr5MmDGDiTMvU66UGhE4d9GGiJYBbw5n1KhRL0Q06/8zWq2WAW++zcSfvqZ8aT2tmjxWy/f09CIhIZ4dv5v55sd4WjfxJU+gCpPZyepN8fw8Kw6NrjDffTc+U5/vvfceHh4ejP/+WxatvkrJomqUSoFrN+04nGq6dH2Tb78dkykKRCaTMWPGTD780JPR3y1m/JRYqlZMF2Q7dtqOyaygT99BVKtWnaVLFjJ59mUEQaBEiSpMmdqfFi1aoFI9WcDtn2I2m/HzyflvzmazkWoxkz+PAr1WhreX4uH4BOpU96TWG0Y++CKc4cPeoWrVqhQoUOBv2XHixAkqlNVRKMSD5BQrGElPE/hDG6dLpHxpNXqdjOLFi/+t60hI/BUkp1tC4hkxdepUPv9sNBqnjoKKkhiUXjjcdh5cvcMnH3zK6lWrWbFyBV5eXrn2M2vWLD79+DNcVjcBQj60Mm/iI83EiPexuNK48fM0Aps2xrtiBeRqFaLbTfKlK8Ru34nRamPj7t1otVrWr19PZGQkfn5+dOvWjdDQ0BdyHyQkJP4ZS5YspmRRBa9Xzuxs6nRyunfwp3uHdKGmrbsSeXP4bW7duvVcJo3h4eEsWrSIixfPI4oixYqVoEePHrle69q1a+TPoyI0OKvS8h8JClBQtKCS6zezT5l5VIdcIX+y06jTCi+sDNKrgkKh4KuvvuLdd99l+fLlXL16FYCmrUrQpUsXfLNZmJX4ewwbNoxbt24y5KOlrN6UQLf2vgTnUxMd62DZuhTWbUnEkiawaUcqB49fJjHJgVvU0KRpe8aMGUtAQOZFJUEQ6N+/P927d2f9+vWcOXMGp9NJy3YF6dSpU5b2j1CpVEycOInhw0ewePFirly5DEBY35L06NGD4OBgALp37/58b0gu+Pj4cPqYG1EUs13wsVhSUSoEkpJFHE4RX+/MbohMJvC/T4LZuvsyixYt4pNPPvlbdtjtdjRqAU9PL5KTk0hKsaKQu9Go021yOEXsdhGZTI7BoMPDI+eFPQmJZ4XkdEtIPAM2b97M559+Tj53YUoYymd62QRp8pPsSODE4X3079efVatX5bj7sHTpUj784CPyugtS3FgOhfD4J+pw2zlnOsb92FvErF5H7OatKI1GXKkWsNmoWrkyP/34I1FRUfw04ScOHTiMy5kegvbTDz/RtEVTPvjgA8qVK/d8b4aEhMQ/4k74DSqX1zxxl7JyOT3g5M6dO8/U6XY4HHz00YcsW7oAT4OLaq+pkckF1q/Zyaxff6F5i3ZMnvwzumy0IwRByCh5lh0KhRKb3YaHHtxuyGmIB49ZcLlyPv4Iq9XN1Rt2qtf9ezti/3b8/Px4++23X7YZ/6+Ry+X88ssUateuw+xZM+g/9CzgBgQKFizKt2O+oW7duuzdu5fk5GS8vb1p1qwZefLkybVfrVZL165dM4WRPw3BwcF/2xl93rRt25bly+Zy4oyZKhUzK7673W5sdjueBhlzlyah18mpX8srSx86nZz2zQ2sW7vib48zODiY3Tvs2GwiXl5e2O0OLBYLFqsd0e3OWNS7fddGXHwaUVFROJ1OSUxN4rki/XVJvPJEREQQGxuLXq+nUKFCr1x4tCiK/DD+RwxOH0oYy2c7UfZU+lBKXZk9u3/nzJkzVKxYMdNxm83GmjVreHvw2xhS/SigKYzoFOEPEaNKmYpKxhq4UlwY82sY+NZAEhIS0Ov1NGzYkHLlyjF//nyGDx2B3mGklLoyvppARNxE2yLYuWYPu3bsZvHSRdStW/c53xUJCYm/i0KhxG5/shaE3SECwjOdKLrdbt5+ewi/bV3BNx8H0K2dPxpNuvyLw+Fm3dYERo1dTZ8+SSxevCxLWHvp0qWZHO3k6g0LxYv80SlPrx2s1WpJSrZyI9zG9dt2BoZlddxTU10sXWtGrfFg7ZYESpfIOddy/bZ4kk0yOnfu/EzGLyGRHTKZjK5du9KlSxfCw8NJSEjAYDBQpEiRDGHSokWLvmQrXz61atWicOESfDb2BqtmF8FoePxsSte3Ebl83cavi5Lp3CYAD33287nQYDVxG2P/th1dunRh0sTvWbc1nq7t/FGpVIiiiM1mQxBEtBoZSqXAmpmJCLj5deZErl27wuzZc6XcbonnhuR0S7xU0tLSWL9+PfMWLODSlfQcpPJly9Gnd28A5sybx6EjR3A/DFUqGBLCgL596du373MRBImMjGT16tVERESgUqmoXr06DRs2zHVSe/78ec6eOUs5zRu57kwFqvNxzaRm0aJFmZzuTZs2MXzocCLuR2C3OSlGZdIsViyWNFRKJZ5eXhkLDYIgUERbihPheyldunQm5/nkyZN8MOwDgpzBlDZWzmRLiK4o+cVCnEo5QJ/efTh6/KhUj1JC4hWlUuXXWb/mOHa7G5UqZ73TrbsSUSq1lC1b9plde8+ePWzauJLp4/PRsrFPpmNKpYxOrf3In0dFpwE7WLNmTZY86qZNm+Lvn5cZ86OY8E0hXC4XljQLaWlp6ZPuh5vgU+ckoVbJaNM08zVSTE7eHH6L1DQjAwZ0YdbiGVSt5EHjut5ZbL1wOZWvxkfTtFk7KX1G4oUgCAIFCxakYMGCL9uUVxKZTMbMX+fSvl1LWvW4zrtv+tOqsQ9qtYy4BCezFiUzf3kKxYvo+fi9/Dn2k5Do/Ech36GhobRo2ZavflhNiaJaShdXk5SUiEoFnkYFMkFgw29mlqwxM2p4MJXK6xkwbDtvvz2YefMW/O3rSkjkhuR0S7w07ty5Q6euXbhy4wbaokXQ1agGosjhq9fZ3qcPLrsdY4ni+HfpgNrfD2eqhQcnT/PJV1+xfuNGVi5fjtFozPUaOeUV/Rmz2cwHI0eyeu1aHHIZSl9fRJuNSdOmEpwvP9+PHUuzZs2yPffWrVu4HC58tNnnYT1CJsgwuL25cf1GxmcbN26kX1g/PO1+5JGHkCDEYZSlTy7dohuHw0FCQgK+vj7IZOmOt7fSD6VVxcmTJzM53TNnzkRhV2dxuB8hF+RUMFTj94RNLFmyhGHDhj3xvkhISLx4evfuzexZU1iyJpY+XR8vjrndImlpbrRaGZY0N/OWJdCseWf8/f2f2bXnz59LmRJKWjTK6uQ+oloVI3Wqa5k/b3YWp1upVDJ8xMd88vH75A26S4/2KuRyEY1Ghkohw+UWmbkwmTlLU3C6YOCIWzRr4IVKJXD6XCqrNpmQK3yYv2AxVapUISEhngHDVtG8QQI9OvoRkl9DbLyDlRviWLXRTPESVZg4cdIzG7+EhMQ/o2TJkmzYuI3PP/+UoaN2M/LLSPQ6OSlmFyazneC8Kpb/WgJ9DrvcLpfI6k0p1G/Q7h/ZMWHCRLp3i6R9n0M0ra+gVWMtRUJVnDhjY8maFHbus9CuuR/vvpkHmUxg/BdOhny0kTNnzlChQoV/dG0JieyQnG6Jl0JycjIdOnXiVlIihYa9hybwscNqeL0KymvXiV+zHqfdhmfpUsgehjAaixXFUvM+R2bNZcjbb7No4cIsfZ86dYq5c+eycf0mUlNTMRqNdOjUnr59+1KyZMks7S0WCx07d+bI2bP4Nm+CT+WKyDXpIkCW+xFEbd9Fzz59mDV9Ou3aZX0JyGQyEEDkyeGgbtzIH+6ap6WlMfS9oXjZ/alorMF503Fkf6jiJxNkqFBhd9kwmcx4enoC6SvtMkGG0/m4TIzZbGb92g3kVxTJdZFBKVPhT14WL1wsOd0SEq8oRYsWpXuPfnw1/lc0aoF8QWoWroxh++9JOJ0ichkolXJcojcLRozIOE8URW7cuEF8fDwGg4ESJUr8pXQcURTZu3cXH7/r+cTFyvYtvHn/s5OkpKRkWfwMCwvj+vXrfP3DOBatTC//lT+vgphYF8vXmwi/56R35wA27Uhh5z4Hew6m15sODAxk4KD36NWrF0FBQQDMmDGTBQtqMmf2DLq9dZVHubSBgfl4+91hDB48ONvccgkJiZdH0aJFWbZsJbdu3eL3338nNTUVf39/kpOT+ebrDzl7KZXqVbLfNJm9OJrIaP5xeVODwcCKlWuYMGECY8d8yeqNySiVAiBQqpiO778oRJe2fshk6c+6Fo18yD8xhnnz5jFx4sR/dG0JieyQnG6Jl8LixYu5fucOhT54H7XPH8ILRUhNTUVdID+BfXrxYMoMEk+fwbdqlYwmugL5CWzdki2r1nLt2jWKFUsvTSOKImPGjGHC+J9QOtUEyQsQJNNhiTczb+oC5syay//GfMOgQYMy2TJlyhQOnz5FyFsD0BXIHO6ky5+Pgn16cnfZKt4dOpR69eplUR8vV64cSqWCaFsEBbSFchyzS3SSLMRTqVJ6aPm6deuIj02gpr4ZgiCgk3tgwYxDdKAUHuVJCshRYLVaMRgMyGQyLK5UrO60TOGUcXFxOOx2jIqcd6ce4an05n7k9Se2k5CQeHmMHTsWh8PBWyNmIAgOypRQMXSgJ/6+cu5GOFi5IZUHMamsXr2asmXLsnbtWg4e3EdcbHTGxDJfvmB69e7PwIED0WhyVxMHcLlcOBwOvIxPdtTTczVF0tLSso04stvt5MurpdYbHsxcmEiqxY1GLaNZA28mfhvAaxUMDOiZSvPut/hlyiyaN2+erY1yuZy+ffvSp08fLl26RHx8PB4eHpQtW/YflUmzWq0kJSWh0+meGDElISHx9yhUqBCFCj2eFzkcDnbv3kHYOzsYNTyQTq180enSnzdx8Q5mLYrmlzmJDBo8gvLly//j62u1WqpXr47BoGXhlGD0Ojm+PgoKh2YVqpTLBepU13L24tl/fF0JieyQnG6JF44oisyaOxdd2dKZHW7AZrfjcrmQazTIgwLRFC1C7KEjmZxuAK/y5Yjd8htLly7liy++ANJLdv0w7kcKy0tTyFgi0wO1uFiOq+ZzfPrxZ3h7e2eERDocDubMn49HpYpZHO5HCDIZeVs15/rY8Sxfvpy33nor0/HQ0FAaNGrA/i2HyasJQS5kP2G9k3YTQSXSs2dPAHbt2oVR9EavSM9byq8pyGXTGaLEuxQQCmecJxfkON3O9BIYGg3hlmt4+XjRsmXLjDYajQYEAZfoyPnGP8TpdqBWP3kCLiEh8fJQKpUULFgQvV7B58P96NBCC4KAXCZDq9Ux8h01P/96j6/HjMLlAoOHkJ5Og0CJIjq6t/fn4tU4Jvwwmh3bt7Jk6QoMBkOu11QoFPh4+3AthzJef+TGrTSUKnW2JRBTU1NZvWoJQ/r4MWxQPiBdhE2hEDI9l8uV1vN6JQ1Llyyiffv2uV5PEARKly79RLuy45FSsSAIHDt2jNmzZ7F1ywacTjsgULlyVfr0HUCbNm0k9WIJieeIUqlk3ryFjBz5AaPGLue7n2MpW1KFwyFy+rwNmVzPByO/eKaReI/SDEsU1ZI3SJ1rW7kMRPHJUYsSEn+HnBVaJCSeE4mJidy5cwdjqayh3i6XExEQHoZE6koUw3I/AtGd+SEoU8hR5gni9u3bQPokb/x3P5CXUArrS2ZZwZQJMkp4lMfHGciY/43JCM0+efIkD6Kj8a1SOVeblQYPtMWKsGnLlmyPf/TxR+Dh4lTKARzuzPViRVHkXtotbjjO03dA34wdarPJjAJVRjuNXEuwthB3uEqiGPeHHgRARBTdPLDe4777Jm8NHphJYTMwMJAiRYsQabub6zhEUSTaHUHturVybSchIfFyMZvNTJ3yE4PCfBjcN5SAgEAC/APw9fVDp9OSmmqme3s1PTt6kDdIxpWDodw9VZh5k4PQalx88+NdWjTyZs28UK5dPcyIEU83ie3YqTsr1puw2XKeeLrdIotXJ9GyZXvU6qyT2Hv37pGWlkqtNx7vICuVsmxD1utU03PlyoWnsu2vkJKSwuzZs6lfrxYhwXkIDc1LmdKlaNG8ARfPruaT9z2YNzkvP33jj1Zxinff6UffvmHYbLZnbouExIvg0eLSq45Wq+WXX6Zw5Ohp+g34BN887chXsBOffT6e02cuMXz48KfS4nlaihQpAig4eMyUaztRFDl4LI2ixf7e4p6ExJOQnG6JF47L5Up3rBVZd4QFHj5oH7085PIsLxKXy4XNasNps5Gamgqkh2onJSRRSJ/Vkc/oWxAorCvF3fB77N69GwCTyYRbFFF6Pjm8UGE0kpScnO2xSpUqsWjJQlzeVn43beJMyhFupl7hqvkc+01bueI6Tc++Pfj2228zzvEP8Mcms2QaXxljFfzUQVwQj3LJfYJ4MRqTmEwCsZxNO8J5+1E6de/IyJEjs4xtwJv9iReiSHEk5jiGB7Z7pMlM9OvX74njlZCQeHmsW7cOiyWRAT2zVhlIS7OSZrVgNMgZ0tcTk9nNnoNpKBQCTevrWTMnH3VraBjy4Q0KBmsYPSKQzZvWER4e/sTrhoWFYbao+fibO7hcWSfxoijy7U/3uHNfoH///v94nM/DT7h27Rr169Xkqy+HUzT4Jl+ONNCiAdy/f5VBvVWsmePHgJ7+NKzjRafWfiybWZRFU4M5uH8TI0d+8OwNknhluXTpEh999BEVKlemWKlS1K5Xj2nTppGYmPN79FXi5MmTvPvuu5QoXpD8+QMoW6Y4n332Gdevv/opZAUKFODDDz9k+vTpTJkyhYEDB2YbOfNPCQ4Opk7dBsxZEpftM+0Rew4kc/OOi94Pq+dISDxrJKdb4oXj7e2Nt5cX5lu3sxyTA+5UCy6rFQBb+B3Uvr4IMhkOh4PExCRiY2OJi4gg5dp1tmzaSvNmzdm4cSMeMiM6ee71Fb2UPqgENRcupO+seHl5IRME7IlJT7TbkZSEj7c3KSkpzJ07l4EDBxIWFsYnn3zC6dOnqV+/PsdPHufzbz7DWExNtD6cVL8EWndvweZtm5g8eXKmHMR27dphEcwkOh7taos4bA6KCxUoJC9JqmDioniMk+7fuSKcIrRKPqbOnML06dOzDYHs0aMHFatU5IRlH9G2iEzOvFt0czftJhdtx2nboS21a9d+4nglJCReHpcvX6ZYIU024ZAiFksqapWARi1QrJCKPIEKrt54vEOrVAp8/7k/NpubFevjaNvMB6OHixUrVjzxugULFmTipGms3mynfd9rbN6RgNXqxm53s2tfEt3fus6MBWa+/uY7KlWqlG0fBQoUQKfzYN/hlCdeb+9hMyVLPruSZ3FxcXTt0h5PfSQHNhZj2vhChHUJ4PylVNo19+CzYb6IopOkxMRMz8i6NTwZ/UEgq1ct5c6dO8/MHolXE7fbzZdffknNunWZuWI5cfnyYCtXmmu4+ejLL6j42mvs3bv3ZZuZI6Io8vXXX9OqZSNOHFnCgO7w7SfedG5pZeO6qdSrW525c+e+bDNfGd57bxiXrgmM/PI2DkfWKJ7zl1IZOiqCatXq8Prrr78ECyX+C0jJSxL/mJiYGOLj49HpdAQHBz8xLEihUBDWsycTfv0VV6MGyNRqUi5dIfbgYVKuXsMtpqvTaosUIu36TfK3ao7NZicpMRFEUKDEfPwsSoeMcrqqXNx/jTjnA9Q8XZ6yW3STnJyM1WqlYsWKFMiXj/ijx9GHBOd4jj05Gev1G/gWL0mZUmUxJZkwit7IRDkWuZkZ02ZSo2Z1Zs2exbBhw54qH6lu3boUL1GMS5dOUkmsSWqKBZfLjYBAAPnxd+cjhkhucI6WrVuwZs2aXO+tTqdj5aoV9O3Tl/17D6C2aTGKPoiIJAmxuBROuvXuyoSfJjzT0C0JCYkXh9PpwuF04G2UPyp5jUzIumPs76egcV0d67bE82avIIoXUXH3bu7pJ49o164dfn5+/DB+HG99cBi49/CIgjJlKjJ7zsgcSygC6PV6OnTszuLVvzKgZyAnz5rZtjuJFJMTb08FLRr7UL2KgbMXUzl22sb0GWF/+T7kxLx58zCl3GfzwmIEBqSn7xw5YeJmeBrffZ4XlVKGl6dAfKIDq9WKVqvNOLdTK1/GTY5hyZIlfPLJJ8/MJolXj++//56fpkzBt3kT/KpXQ/aHyDuH2cy95avo1qsXm9evp2LFii/R0uyZNGkS06f9yJcj/enXPTBDgRvgw3fdfPvTfT77dAS+vr60bt36JVr6alCtWjUmTprOsKFD2H/kCj06eFK6pB6LxcWGbUns2JdKuXLVmDV7njQ/knhuCOK/JQkkB1JSUvD09CQ5OVlSIH2BiKLIb7/9xsxff2Xv/v24RBGZIFCsSBEG9OtHr169ss31e8S9e/eoVbcuZh9v5AYPEk6dRpUvL/qKFZAbDDgSEjAfO4H9QRRB9euiqvEGMlGGwi0n+cRxEnfsopSqDMU9yiKKImcTj3HTepEaPo0J0mQVRHOLbh5Y73I97RJxrhh0ej06rZaWzZtjNBiYs2QJ+fuHYSicVX1cdLkIX7gU17XruNIcBIkhFNOXRiPXZfQdY4vksu0UBUuFsO23bXh7P1lFHODKlSs0adSExIgU8lOIICEEhaDAKlp4IN4hgtvIZTLUBhWLli6iadOmT/XdHD9+nIULF3L18lXkCjmVKleiV69elChR4qnskpCQeLksWLCAzz59l6PbihMU8Fj7wW63k5AYj5+3Apkcrt+00qBjBL+MDaRts8xCaeMmx7NuaxpHfqtAi+7XKFG2Bz/99NNfsuPy5ctcvHgRURQpVqwY5cqVe6pJ6e3bt6lbpzpWaxwyAYqEKgkKVBDxwEn4PQcF8qpJMcvIH1yZLVu3/y0l8ujoaFasWMGNGzeQyWSUKVOGyZN+oFldE2NGhWa0m788htHjbhN+olCG7YlJTtwo8fXxzdRnryHXURtbMGfOnL9sj0TuxMXFsXTpUnbs2oXJbCZPYCBt27ShWLFiuN1u8ubNm1Eq7nkSHR1NuUoV0dSsTp7GDbNt43Y4uPnLdOqVKsWqFSufu01/hZSUFCpWKEWfznI+G14g2zaiKDJg6A2uhgdz4ODR9NKmEly+fJlZs2axds1yrFYLIFCiZCnCwgbQpUuXp6ryICHxZ57WF5V2uiX+Mo/Cmn765ReUwfnx6dAWjb8fztRUwk+eYcRnn7J+40aWLl6Mh4dHtn0UKFCAxQsW0KJ1axKtafi2b4tHpQrp/TudqPLnQ1emFKknTxO9Yxeae/fQ+AdhuXAJd7KJ4uqSFNOXAdLzmct5vkaU7R4XTCeyON1u0c2J5P1EuCNRFSyAb7nq+OTNgz0+nnWHDuKOjSMkX17uzF2Ad93a+L5eFaXBA1EUMd+4SfSuPQj3I3DZHOQVC1LGmFl0TSbICNLkx6Dw5MilXfz00098/fXXT3UvS5QoQeEihTny4Ci33Ze5JV5GLspx4UIpU1JIV4Li+nKcNh3kg+Ef0KBBgydOTgVBoGrVqlStWvWpbJCQkHj1aNeuHd98PYo5i6P5dNjjifUjp9EtisgRmL0kBR8vOc0bZH3Wxie68NDLuXvfxrlLVnr3/+vPhJIlS1KyZM5aGTmRlJSEy+2ibAkVI9/2pmpFDSqVLF2s6GgaX0+I50GMi1/nfINcLufgwYPcvHkTmUxG+fLlKVs255Bzh8PBZ599yrKlC1AqrJQqrsbthhXL00hOTuOND0IgXTkEAOFhJIDbDY/KlqvVAiazI1M7SBeJk3a6nj1z587l088/x+J0oClWFJlOx6kL51ixdi24XGh1OlRKJQ3q1WPI4MHUrVv3udmyZMkSrG6RArVq5NhGplTiW7sGu1et49atW5nKXr1sVq9ejcNhYkDP4jm2EQSBwX2DaBt2nf3791OnTp0XaOGrS8mSJfnxxx8ZN24cCQkJqNVqPD09pd+8xAtBcrol/jLz589nwi+/4NeyGf41q2c65lmqJKnhd9g3Zz7vDxvG7F9/zbGfsmXLotFoUFV7HX3JErgtFgBkMhkeeh1ajRZb/bo44hMwHz2O6kYcoYq8hHrWwqj0ytSXTC4nRFWES7bT3E27SbD2ccmtc6bjRMqi8evUEVWREAxGQ4byt3+tGkTv2sOdHbupW6MGxw8e4fqu31F5eeKy2RAtaZQuWYKKHTqyZN4yihtyngjqFQbyyEJYtGAxH3/8MTqd7on38tKlS5w9fY7XPGthVPgQZ4/CJTpRy7UEqvKhkKX/RIvrynH43k62bdtGq1atntivhITEvxuDwcCgwe8z4cevKRiioWs7PwRBQKFUIJfJSUtzMW99KotXmxj5ts/D2tyPsVjcbN6RSs9OQfwyOxKDwY82bdq8ENtFUWT4sHcpX0pk2cwyiG4baWkW0h4qolcq78GqOb50e+s+w4a+h1IpJzz8OjKZC1EEUVRQoUIVPvr4syzOgsvlYuDAAezeuY6P3/Oje/uCD2uGw9kLZhp0uIDdnobJZM4okVamhA63G/YfTaNu9UfP5fSqEH/EZHZy/IydIe9I6sXPCrvdzvTp0xn99dfoX69C0aaNENRqEhISUbtdGJJTSNy0FXt0ND71arP76hV2du7M/778kiFDhjwXm06ePImqYCiKP6QWZIdnmTI8WLGaU6dOvVJO9/nz5yldXJ2RPpETlct7YDQIXLhwQXK6/4RSqSQwMKtIpYTE80RyuiX+Ei6XiwmTJqErXzaLw/0IfWgIAS2bs3btej775JMcX1arV6/G4nBQtEFdFAYDblf6hEwml/No0VGhVGB8rTJpJ85QWleBfJqQHG3zNwShEpVctJ8gwh5OPlUoAgK3bdcxNm+IqkgIer0+U6ktQRAIalgf64No7kVGcv70aTZv3kxkZCQqlYpq1arxxhtv0KJZC7xFf5Sy3F9y+bUFOZJwk1OnTlGzZs1c2wLs378f0QmBuvzIBBl6RZFs2xmV3uitRg4cOCA53RIS/xGGDRtGTEwMI7+aydxlCXRt60Wgv5Jb4WksWhnL3UgXg8MC6dpWQ3KKE6NBnpHb+fPsRFLMIonJTpautTBm7LdPtRD4tLhcLnbt2sXhw4ex2WzkzZuXDh06kCdPHo4cOcLVqxdYNjMYvU4JKPHw0D8ULntcq7tQiIKVG87Sua0fP30ZwmsVPHC5YM/BZKbPO02P7h35ZcqvtG3bNuO6a9as4bdt65j/cwEa1PbKZFOp4joC/JRcuGyj5utmNBoNSqWSimX1lCquZ/r8JGq/oUUmE7A73MjlCv64y71oZSw2u4oePXpkO2abzYbT6USn00k7Y0/g+vXrzJo1i8ULlxAVH4O+6mtoalXHJoqkJSbicruR6/UoDAYC+/biwfRZpIbfpfA7g3iwbQefffklhQsXpkmTJs/cNofTmW31lD8jyOWIYvrf+quEKIoZERu5IQgCcpnwytkvIfFfRXK6Jf4S+/bt4879+wS3aZFrO++K5Ynd+htLlixh1KhR2ba5evUq6gB/lA/zH+Q5vARV/n7IDR6YbNmX63qE0+1Eq9Px408/sH7devbvO4AlzYKoVeJdpRIe3t455pn7167BzWm/cv78+WzLRZjNZtSyJ+f6qAQ1olskLS3tiW0BrFYrCpkCmfDkfCuFqHjqfiUkJP79yGQyxo0bR9OmTZk3bw5f/vAbbpfr4cKkD1qNidIlDej1OixpJmLjnSQmu5izJIU5S1NQKpUs32Bn9Bdj6dOnzzOza9u2bXw+6iMiIsIJLaBEp5URfs/Od+O+plXrDvj5+ZMnUE6Nqn/MMRcyOar7jySzbXcCH73rzfsD86DXp4fHKxTQqI4X9Wt6MmL0bd55+012797NhfOnsFrTuHPnHiEFoNprBv6MUimjcxt/Vm+MoU9XTywWS0bo6Mh38tPvvWt8OiaOL0b4YrOJGAw6XG4X1jQr67cl8s2EGNq07ZlpB8xqtbJ69Wrmzf2VixcvACK+vn506x5GWFgY+fLle2b39f8L27dvp29YPxxmJ2qnHplChXeN2ohOSEkxASJyvR7hYZ6xTK3Gs1Z14tduwB6fQJ6mjUi7c4dJP//8XJzugqGh7Dh+DNHtzrAhOyx376GQyQgJyXmx/2VQuHBh1q+1kWJyZkR5ZMeV6xYSk10P61RLSEi8bCRlBYm/xO3btxFlAvoCWcXK/ohMqUSZN0+udWHdbjdPo+KnUqvSFc2foPkXabtD4SKF6d27N2vXreVW+E06dGyPV7Ei+AYF5SrspgsuAApFRm1Lt9tNdHQ0ERER2Gw2gvIEkSqacr2+w+1IL9WFmGk3PTeCgoKwu21YXZZc27lFNxbML0Rk5lUmLS2N06dPc/ToUe7fv/+yzZGQeO4IgkC9evWYP38ht29HcPHSjfT/X7xCnXpteP+zGGq2us37nyXTb1gCddpEMnNhKgEBoQwdNprDh08xaNCgZ2bP+vXr6d+vJyULR7FlSSEObCrB9pXFOLWrJF996MmuHUtZvHghvt7yXHeDZy+KplRxFQN6eOLO5tkul8Mn7/sikMTyZb/yWplwmtaO5fWKadwKT6VGi3OcOmfOcl6/7oE4XQLDR8eSkPj4udqojhfjvyzI4tUmKjQIZ9KvySxbG8sPP9+gUccrvP3hPex2FxvWr6Bli6acP3+euLg42rRuwUcfDiGP7xV+/MqXX8YG0KaxhQXzvqde3RocOHDg2dzY/ydcvHiRPr37ojEZqG1ogUqmQh0SgtrghVJQIUMGMjn86W9DVyZdo8V86xaCIOBbvRpHjh17LvWmu3fvjmgyk3zxUq7t4g4eoljhwq+cPkrnzp1xubUsXhWba7vZi6MJCMhHo0aNXpBlEhISuSHtdEv8JeRyOaJbfOIKMQAud5Z60larlTVr1jB71myOHTuGRXQReeMWxqAAtFodcvnjPkVRxGq1YY+OxWUykSxLQBSzF7lJdiQSL0TxwYAxGSqdRqMxXcjtKfX5RdGN1Wpl6tSpzJ41h/Db4SCCTq+jfMVyJLhjSHWa0Csy77CYnMncTL3E/bRw7G47ghy6delG957deeuttwgNDc3xms2aNcPgaeCO+QbFPcrl2O6B9S5uhYuOHTs+3WD+nxEdHc3UqVNZvmwBKSmJDz+VU7NWXQYNepv69eu/VPskJF4ESqUyozKCUqlk/vxFXL9+neXLl3Pv3j0ClEq69HyNjh075ihi+U9ITk5m+LB3aNtMxaRvC2YqU+Shl9OnayCVynpQv/0VbiPH4XCjVGZ9T6SYnOzan8Q3H/sgimSrrGwym0G00LWtgd37bYz7PBSny0lcnBKz2c2IL+PoMegq6xeWoljhx7m5wfnVzJlUlM4DrtCg4z36d3dSvaoBpwvOXUxFoVSTbFYzfX4qDkcier2MxnW9GDc6iGqvGdh9IJkJ007Qtk1z8ubLjynpKluXFqZ0iceh+W2b+zLyHSeDRt6mT1g3tmzdRbFixZ7x3f53MmXKFNxmqGisjlyQ4xJdyNSPvx8REUGuQIRM73NBqQCZDLfDCYA+JBin282dO3coWrToM7WxXLlyNKhTh51rN6D29UWbN0+WNjH7DmC9eJlhkye/csrfAQEB9OjZj+9/mUKRQloa1fHK0mbu0miWrjXz9Tdf/a3KABISEs8eyemWyBaz2cz69eu5fv06MpmM0qVL07JlSypWrIhCEEi5fBXP0jkr2jrMZmx371K+X/+Mz+Li4ujSuSsnj53EhwBKKipyLu0UpmMnkdWrhSXVgpe3FyqlCnOqGUuqBbfbTcL+g+ASuW+7Tbw9hnLG18mvDQXSX9rRtggu2U5S6fWK9OrVK5MdZcqUYcnatTgtFhS55DOab95CcLqYOWMmEeGR+At5Ka2qglyQk2iO59jvJ7HZbRxN3kNt7+YZAmextgccS9yLXFSQj0J4CJ54aPUkpyQw+5e5rFi2ghWrVlC2bFm2bNnCsqXLuBN+F41WQ526tenduze9+/Ri2qTp+Nvz4KPyz2JbqtPENfs5GjSr/5+c2N2+fZuOHdqQlnqPnu2NtGhcEJ1WxvnLFuYt20fPHnsY9fn/npvojoTEq0zRokVzTOF5Em63m7Nnz5KQkICHhwcVK1ZEpcpZt2L58uU4HSmMHlEik8P9R8qV1tOptSdLVsexZWcCLRr5olBkbpuQ6EQUoUBeJSCgUWdO3XG5XFhSU9HrZJQoqmbF+lSAh2k4AnmDlCyakodm3e7zw5T7zJyQ2SmrVsVI57Z+LFljZ/FaOdPmRwIQGJiH994fxr1799i6eR4rfi1BmZKZ87Ob1POm9htGGnS4zPlzJ9i6vGQmh/sRBg8FsyYUom7bq0ydOpWJEyfmeN/+KyQlJbF29TryKwojF9LTxbRyHTFRMX9aqE9fCf+j0+2IiQW3G6WXJwBupwMBnpvD+OvMmbTv2JFTU2agK1cGn0oVkOu0WKNjSTx6HMedu3zw/vt07dqVxMREli1bxuatW0lOScHf15e2bdrQvn3757K49TR89dVXREdH0fe91dSoGkvnNt4E+CkJv2djyepEzl928tag4fTv3//JnUlISLwQpDrdEplwu91MmDCByVOnkGQyo/LzBVHEHhdPgJ8fn374IUuXL+d0XAyF3xqA8Gc1DzH9dfpg6284jp3g0rlzeHt743Q6adG8BacPnaWSriaeSh8ArpkvcNF+Hu9mTdBWLIsgB5VKjc1qReYWMB05RsruvVTQvIbcruRm2hXiicJPHYhR4Y1JSMAms1K7bi3mzJ2Dj49PJnPi4uIoU6E86hrVydO4QbZjFkWR23PmY7t8FbVdTRWPungoMv8tuUU3J5MOcM92Cy+VN0W1ZfBU+LI/fhsG0ZtilAcBtFoNnp5eADjcdk6Y9iF4u/D09ORu+D088cFD8MQpOoknClRuBg0ZxPlz59m/5wBBhBCiLYJO4YHNZeWe9RYR7lsULVWYDZs24O+f1Sn//4zD4aBe3ZoIrhssn1U4U71iSP/uxv8SweRZKcxfsFwKo5OQeAqcTiezZ89m3txfuXPnBuAGZPj5BdG9Rx/eeeedbJ2Jdm1b4aU9zuxJOeeIiqLIwhUxDBxxE5VSwEMvw9tLQdtmfvTu4k/xIjqiY+xUrH+a70f70rKxH15eXpn6MJvNWCwm/HwVTJqRyK+LTVw6mF6qMTExAbfbjq+3grnLkvlyfDxHf6vwp1rmbl5rdJn2nd7l888/Jy4uDplMhq+vL2lpaVSsUIpBvRUMH5xzPnbrnpcwma1sW14KtTpnPY9fZkXy00wrZ89d+c/PQc6dO0fdWvWooq6LlzK9/nmiPY69pu34d+uKvkhRHKIdl+BGrk9fyJA9nEPEb9qC7dIVyoz6BJlCTvSevaT9vo+LZ8/h6+ub4zX/CampqcyaNYvZ8+ZxL+I+opguqxecLx8OhwOL1Yrd4SA5MRGUCvSlSqH0NOJITMR6/SZ+Xl7MnjmTevXqPRf7noTL5WL9+vXMnfMrJ08eA0QEQUb9Bk3o27e/FAEmIfGCkOp0S/xlHjx4QO/evdl/7BieNasTUqsGen8/AKyxccT8vo/hH31Ej86dOXvuHHeWriB/h7YoNFqsNhsWiwVbWhrmk6dJ3rGLBrVqkZycjLe3Nzt37uTY4eO8pq2T4XADFNWXxu62cmPLNswnTqApXxqZXodosmA5fQ5XfCKlNGUJ1RcDPeTxyM/p5CM8EO9Q8rWilCtfn169elG5cuVsw879/Px4Z9Bgxk+ehMrLiE+V1zK1cztdRGzaTNrlq4hWBxUM9bI43JC+w1LZqyapSSY8gjRcSzyLKdGMzC2nsFAauVyG7k/K6EqZipK6iuyO2IAl2sbrxvp4qXwyctlcoovblqtMmTSFd4e9S/Ua1Zk7ey5HHuzE7XYjyAQ8vTx5s2d/Pvzww4yw0v8S27dv59atK2xbViiLww08FEjKx5GTFqZN/VlyuiUknoDD4eDNN/uza+d62jbV8+PoEEKD1cTGOVi5IY5ZM8exe9d2Vq5ai6enZ6ZzU1KSKBma886jyyXy0Ve3Wbo2lvKl1bRv7oGPl4wHMU7WbI5l4cpoRg0vgCiC1QabdqTRvWPW563dYUelFECENVvM1Kvx2A6dTk9ikg1LmouWDfV8Pi6OU+fMNG+Y/l4RRZFvfrxHkklBWFgYSqWSPHkehw/v2bMHiyWZru1yrnEMEBVtp3kjLVarLVenu051T8b9nEB4eDjlyuWcIvRf4FEYtviHnC4vpS++Mj/it25F1csfhZcRl9uK2+FEpkyfglouXcF87CR5mzREppDjTEsj+cgxurdt99wcbgC9Xs/777/P22+/zc2bNzly5Aiff/kltx9EoitdCkWAP2nJybguX8WVmISHQk6+1i2RKeTYk5KIWLOBbr16snblKqpVq/bc7MwJuVxO+/btad++PYmJiZhMJry8vP7ziz8SEq8qktMtQXJyMiM//JAVK1disljwadcabdnSmFxOrPHxGI1GNP5+BHdqT5S3F0tXruTLUaMYN34817/9HnmxogieBlxWK2kXLuFKMaELLsCRixepVqsWs6ZPZ9HCRRhEzyzh04IgUNpQmSBHAcLjrxG+bQcgoJZpyKcsQEHPKngr/TLaK5RKKvq8QbIpjiZNm/Dxxx8/cXyffPIJSUlJzF4wn4T9hzBULI/SwwNrXBzmU2dQWK2UKlaMiAsxKG0a4lPi00tyyGRotBo0Gi2CICATZASri3DPfJXN2zbTplVbvEwB+On9UatV/LH0DIDFlcqJxAOoRA1FHOWxJjqIEWLQarXo9DrkcgVF9KUgFab9Mo3jp44zbNgwDh48SEJCAgaDgWrVqmXUmn1ZPAqGeRklcpYuXUTl8mrKlMxZmE4QBPp09WXIRwcIDw/PNYdeQuK/zvfff8+eXeuZO6kA9Wt5ZXweFKCibCk93dpb6NT/FEOHvsfcufMznevt7cf9BzkLW42ddJ8VG+IYP9qferU0eHv7YE2zYrNZ6dfNkwnTE/ngi3B0OiOvVanDwePHOHYqlTf+rEQuiggyWLrWxO27Tn746rGauFqtRq/zIMVsxukUcbvB4RARRZFjp8xMmxfFzn0Ovvt+EoULF85iY1JSEjKZSJ7A3Ms/iojIhPToL4mnIzQ0FA+DBzGmyIz3tiAIvGasycHknUTOmYXhtdfQlC6BW5OGmJqK5cw5Us+dx7tcWYIa1MOWkMj9JcvxEmQMGzbshditUChwu92M+vJLbP6+FO3xNgq9B7GxsShkAj7NmmA5f4G41esRFApCu3VG5eVFSO8e3Jr+K5+MGsWenTtfahk5b2/v/+TCvITEv4lXSx1C4oWTkpJCq7ZtWLFlM05PI5oihfCs/gYKDw9kWi0Ol5uEhAQcdgcAgfXrgtHArVu3OHX8OCVCQkg5dhzTwSPYr1zHp2wZSn04jFIjh1F81EeIRQrRb+CbHD1yFG95YLY2CIKAnyqQUoaKyJ0yijvL0dSzHZU8q2dyuB+hkCnxF/KyZtWapxqjXC5n/PjxbFyzlrbVa+DYf4jEdRtRnb/E4G7d2b/ndyLvP0CVpsFsNuNyuBCdIg67k+TkFGJjY3HY7QD4KQOxWe2YzWacDgcBurwPVdEzv2yTHPH8HruJZGc8eSmIVtCjRIUgyrFYLMTHxWN/2GdBXTFEu8DChQtRqVTUq1ePDh060Lhx45fmcKekpDB79mxq1ahFnsA85A3KS5NGTVi+fDlWq/WF2XHv7m0qlnlyqbYKZTwAF/fu3Xv+RklI/EtJTU1lwfxfGdjLK5PD/UdKFtMx+oMgftu2iVu3bmU61qJla/YctBAVY89yXmycg9mLohgxyIuWjXUo5ArUajVeXl74+fnj4+vF1x+H0KKRF8WKFWPLli3UqNmIsHfvsGxtLDZbunN7K9zK/BXJ9HkviqGfx9CptS9VK2UOdTcYPDAaPDlzwUGqxc3w0fcpVfMiHfrd4ea9EGb+uiCLvscjjEYjbrdAXLwz13tVoqiO/UesCDnkrj9i/5EUVCrtK1dW6mXg4eFBtx5diXTfweF+/Deileuo5dWYQvb8WPYeIWraTKJ++pnoGbMxnziFR0gwmoAAwucv4ub3E/BzOFmzcmW2iybPix9+/JFUlZKQPr1Renhgs1lxuV3I1WoEQUBfriw+LZsRf+IkaVFRAMgUcvwb1OXs+XOcOnXqhdn6Z6xWK8uXL6dzpw7UrvU6jRvV4+uvv861eoyEhMSLR3K6/+OMGTOGc9dvkL9vb2yJiXhUrpR+QBCQKZUo9DpEmYyk5CQQQZDJMFSqyLpNm0hMTOT6rVsU7NOLSt9/S7kvPiWkcwd0D+uWypRKgrt2xunjQ3xiAk9aA7a77QiCgFbQPXHFWCf3ICEh4anHKQgCNWvWZPasWdy/c4eoiAhuXrvGmDFj2L9/P9FRUYCAWtCgFFQoBCVKQYVKUIMbEhITcTodiLgfi7sIAm6y7oLY3FaOJOxB7lYgR0kA+ZAhQybIUAgKVIIGRIGkxERcLhdyQYEPARzY/+xLz4iiSHx8PPfv38diyb0s2SOuXbtGrZq1+eD9kUSdiyfEUYIC9mJcPxzOW/0H0bhRY6IeTjqeNwqFEqvtyTtNNnt6G0mlVUIiZ7Zt24bZnECvzgG5tmvT1Acvo5sVK1Zk+rxjx45otT6MGnMPpzOzHMyK9XHI5SLdOxhIs4podTqEh099uVyOTqvDw8ODt/vn49atq5w+fZp58xZSv2FnPvgyjrK1L1C4ygkqNzzN2EkxHD+Tvri3dVcS/5twD7v9j88BAZ1Ox/ptDgoVKs7wD8bw/rAxLF+xkf0HjtCyZcscx1anTh3Uag9WrI/L9R7UreHJiTNWLl5x5dgmLc3FghWJtGnbOUso/n+VwYMHY/DTccK0D5vr8QKtWqahrLEKZTQVkDmcKAUBrUqFWhCQP4hGfuYsFY1eTJkwgRNHj1KhQoUXZnN0dDQbN2/Gu/obyNXpERB2uwNBJsukW+NRqQIyvZ64Q0czPjMWL4aoUnP48OEXZu8fOXv2LNXeqMywoW8iOA9Qv9oDShe+xtJFE6herTLjxo3jXy7dJCHx/wbJ6f4Pk5KSwqJlS/GsUQ2VZ3oOkOLPuUCCgFyjweVyYbPZAFB6GTGbTMyfPx9Rr8e7QvkcryFTyPGpWQ2r00GMMyJXe5SCElF04xDsyP8s0PYnrC4LRuPfm+QIgpBRyiwlJYWvv/wGvcxAMvFZXk4CAkpBBSKYTCaibZHoPPSUL1+eQoUKEmPPWiv6btpNHG47wUK60rgceZYFB6WgQhRF0h46wnJBgc1q+1vjyQ673c6CBQuoW6cuxYsWp3yZChQKLcxbb73FiRMncjwvLi6O9u06EHcrgZoezahkrEmorhgFdcWp4lmHarqGXD19nc6dOr+QHe8qVWuwY68FhyN3x3vz9gS0WgOlSpV67jZJSPxbiYiIwNtTQf686lzbqdUyihZSEhkZmelzg8HAz7/MYPteF50HXOPA0eSMZ+bJs2bKllThdoNSpUavyz4lpEZVAyqlm4sXL6LT6ZgxYyazZi/EbFEQ6K9g0rf5uHygFGd2F2Hnynz07uzB3CVR9H77WibHe86SaLbutvLpZ1/w7rvvMmTIEGrVqvXE8k7e3t60aduZmQsTuHMv+2eYw+Fmx94UZHIt730axeVrWRcszakuBo64RXySTqqc8AdCQ0NZuXolmiAFe82bOJ18mHDLNW6lXmFn/DpOWA4i8/fGv2lj8nTrTEDbVoi+PiQlJVOqRAm6d++eSRvlRXDhwgWsDgeeZUohut2kXLtO3I5dJO3YTfK+AziTkgEQ5HK0xYqSeu/xe1+QyZCplBnzoxfJjRs36NK5HXkDYti/oRhLZxRl9AfB/Ph1QU7uLMVH73gyedJYfvjhhxdu26uI2+3mwoULHDx4kAsXLkipIxIvHCmn+z/Mnj17SDGnUvT1Ksgeru46k5OztBPkcpDJsdqsqDVqHEnJeBgMnLtwAXXhgsgUuTvIxhLFkauUJFsTSbTH4a3KGjIOoJJpEGQCifJoBKFsjv25RBcxYgTvtP3nE52VK1diTjZT2vAaJ5IOkEgsPmTdBZKjIM1q4b5wk15de+Dp6Un/N/vz8YhPMDtTMomv3bHcwI88eAieIIKZZDSCNkufMuRY0tLwMHhgFpOpEvpsRHhMJhPdu3XnwO8H8SGAEurKKBUqTPYkNizexOqVq/lu/HfZlhKZN28eD+5GUdOjCRp51jI5RqU3lXQ1OXx6J+vXr6dLly7PxOacCAsLY8H8mSxfH0fPjtnvziUkOli4Kom27fpJAjISErmgUqmw2ty4XCJyee7RRKlpYqbyYSkpKSxZsoQF82djt9vYtS+NHXsTMXqoyBuk5dpNE7WrqdBq9RgMhlyjlQRByHDWHQ4HX3zxKZXKyVk2sywe+vT3icvlAkQG9FDweiU1/YfG8OX4u9R6w5OFK+PYd9jGoMHD6dy581+6B1FRUbRr1469v++iXZ8bfDYsiFaNfVCpZIiiyIkzZr7/5QEnzspYsHApkyb9SJMup2hYW0fT+l5o1AKnz6eyfH0KLreRufMWUbx47qJs/zUqVarEkWNHWLp0KfPmzif8zmUcTgepcht+9eoS3KYNcuXjeUNA3dokHD/J7IUL8fb25vPPP3+h9qb/rUFq+B0itm7HFheHzGBAptGQev4CiTt2oytdEr82rRDkskzOmiM5BafJTL58OSvhPy+++24cPp7JLJ5WFKMh83Reo5Hx7pt5cThFJk0aT+/evQkMzD7F7/87DoeD2bNn8+ucOdwKv41bFJEJAoVCC/Jmv370799fipKTeCFITvd/mKSkJERBQPlwl9tYrCjmU2cwVKmctbFMwO0WEd1uTCfP0LtVK65cv87T6IYICCjkCgoWDeHMrUNUltXGqPDK1MbpdnDadBC9UU+KPYFY2wP81Xmy7e+a+TyCGnr37v1Xh5yFo0ePYhS9yasJwV99gyu2U5TiNbyEzAsDLpxcFk8h1wm88847AHTv3p15c+dx4sI+Kuqq46n0wS26sThN5KUgOlGPHg8ecBc/IetYZMhwuh0k2OIxC8l07979H48HYMjgIRz+/SivaetkEq4LUOehkFiCS+bTfDjiI/Lly0fTpk0fj9HlYt6ceQSQL1uH+xFGpTfe+DNn1pzn7nSXLFmSrt368PnY2cgEgc5t/DLV/L19x8qgkeE43QEMHTo0y/l/rAMrIfFf54033sCSJrD7QDKN6njl2O5WuJVLV20MeucNAMLDw+napQMPIm/QsrGeEW/lRSbA/iNJrNqYTFSsjBo163Pt+mHkcn2Ov7l0pzYFqy29vjjAjh07iLh/mzk/Fs5wuCE9JN3Hx5fU1FTKlpTRroWen2dFMX95KpUrv870GUNo1arVU/++d+zYwcyZ0zh4YC/gQhTBLQoMGHqPPIFRhBRQkZzi5m6Ek9DQoixZOpEaNWrQsGFDVq1axbx5vzJ89CVAxNvbh569P6BPnz7kz5//qa7/X8PHx4e3336bt99+G7fbTY1atbgpugnt0C7LdyYIAr5VX8OenMyUGdMZPHgwfn7ZL84/DwoWLIjodHJr/mLUhQsR2LYVqgL5EUUR0W4n9dwFknfu5sGc+eBwYCzw+DuPO3IMTw89zZs3f2H2QnpI/LatG/hypE8Wh/uPDOgZyPT5V1i0aBEjRox4gRa+GthsNnr27sX2339HW7o0fr26o/T0xJViIuL0GT76YjS79uxm0YKFD/V5JCSeH5LT/R/GaDSCKOIwmVAaDPjXrM7NOfMxnzqNR6WKmRu7RWRKgahde8Bkol+/fsyfP59j69Yhut0IuYT0mW7cQCGTMWPmDD768COOnNqJrxBIoDI/MkFGoiOOKPc99N46Vs1fyaxfZ7F1428UcpYkWFsElSx9t8XsTOFG6iVi5RH875tvKFSo0D++BzabDZkoRxAEqnjV5ljiXs7Zj2AUvfEjDzJkmEgilkjsgo1PB3+ScV2DwcDqNavp0qkLR87uwkvwxUvwx4ULJzYcgoMC8iJcdZ3lvvsm+WVZRWHsoo2r1lOUKF2chg0b/uPxXLhwgS2btlBS/VoWpXhIn9yU8qiIOSWJH3+YkMnpjouL48GDKMqoqj7xOv7KPJw/f/6FOLXfffcdbrebD79ewE/TY2nWQI9eJ+fspTT2HU4jT55QVqxcToECBQA4ceIEc+fOZcf2TZhTU/Hy9KRN286EhYVRokSJ52qrhMSrTIUKFShf/jV+nnWGutWNKJVZn9uiKDJxRiTe3oG0atUKs9lMt64dURDO3vXFCM7/eGLappkvn7zvIOzdW9y6fZ3UNAVL18YyoGdQpj7tdnt6SUmblV9mxWAypTFu7P+IihrA9u3bqVBGRekSWRf6ZDIZBoMBDw8P+vfQsGzdbcb/8DM9e/b8S+P+7rvvmDRxHK9VUDLpW3+KFdKSbHKxbks8qze7EGW+FC/TAl9fX+rWrZspTF2r1dKrVy969epFWloaDocDDw+PJ4axSzzm6NGjXL52jXwD+uT6vvCvUY3rv+9n2bJlGYvbL4L8+fPjFkXURQoR2LtHRh632+0GlQpDlcqogwsQPXMObrMZv07tATBdv0HS3v28/9ZbLzzK6uTJk7hcVlo0DM21ndGgoE41NUePHgKe7HTfv3+fHTt2kJKSgpeXF02bNv1X75CPHj2a337fi0+XjghBgTjcIg4AL0/0jeqjL1uG7ctW8MUXXzBu3LiXba7E/3Mkp/s/TN26dTHotMQfP0lQ/bp4liqJ3xtViVu7EXt0LMY3qqLw9kJ0uXDExmI5ex77uQuM/uQTypUrR69evZizcAFJ5y/iXT77cHDR7Sb+wGFq16jBG2+8wdZtW1mxYgWzf53N5ctnQASfAG+G9nmPsLAwChQoQM2aNfnss89YtGARt02X0cuMiLgxu1PwD/Rj4hc/PZNdbkh/2VrkJtyiG6VMRTWfBkTZ7hNuucYd+1VE3GjlHoSqixIh3qZ27dqZzs+bNy+7f9/N5s2bmTd3HpcvXkblVJJkT6C4V1mC1IG4TU6umy+S6I4lD6HoMeLGSZR4j2jZPQqGhLBk2ZKMPPM/Exsby9KlS9m6ZQPJyYl4efnQomVbunXrho+PT6a2ixcvRuFSkUdXIMcxC4JAiLoYp04c5fz585Qtm/7dPQqZexonWkB4YflQSqWSiRMn0q9fP+bNm8fvR/fjcNjJl68cEyf1pHXr1mg0GkRR5KuvvmLmjMmEFhAY1MuIv58Pd+/bWLlxBgvm/8rX32QfVi8h8V/hy6/+R6eOrWna5TI1X/fA06igWGEtTep5kWpx893k+6zZYmXipImo1WqWLFnC/fvX2Lsus8P9CF8fJQunFKZ6iysUKVKWMRPPUTBYQ4PaXkC6YrrJnIJcBvNXpPDb72m82cufBzFnGfr+myhVBlrUz30qIggCxQobkMuFv7wbtWLFCiZNHMtnQ30Z3DdzxFHN143072Gh68DbRD24z+TJk3PtS6vVotVmTRWSyJ2LFy/ilsvwKJz7QrlCp0OVPx8XL158QZals379epDL8KpfF7fDkaEpIxNkuHEjiiJKfz/0FcphOnQEURS5s2wllnMXaFKvHqNGjXqh9gIPq5+I6HS5p/cB6HVy4mNyzzmPjIzk008/ZueOrchlNowGBckmJ5+P0tKseRu+/XbMC40+eBbEx8czd8ECVK9XgYBAZG45ckEOCIi4cTlduP180VR7g/mLFvHhhx9mmVM9a0RR5MSJEyxZsoR7d8NRqlS89lpVevToQVBQ0JM7kPhXIznd/2G8vb3p3L4D89aswbtcWdR+vgR3aIfK05PovfsxHT6CwssLt8OBKzGRQsEhfDp+PH369AHSd02aNWrMljXrUXoa8QjNXDJFdLm4t2Y9PIhixOSfAdDpdPTp04ewsDAsFgtOpxODwZBp10ClUjF+/Hg+/PBDVq5cSXh4OEqlkgoVKtC6detnGgLUtWtXpk+ZTrQtgjyaAsgEGXk1weTVBKeP4eFO7tmUoxQIzk+dOnWy9KFUKmnbti1t27YFYPny5bzVfxBWmQU1GkoZKqJx67hlucpF8Rjp5cVEnDgJCPBnyrQpFCxYMFv7Vq5cyciR7yOIqTStryNvBRX3H0Qy/rtj/DB+DBMnTaVNmzYZ7a9evYbB7Y1MyH0Xxk8VhDPFxc2bNzOcbj8/P7y8PImPjyZQnXt+WoIzhiIli7zQ0O1y5coxYcKEHI9PmjSJmTN+4qsPA+jbLQDZH0r9fPC2m3GT7/P5qJH4+PjQrl27F2GyhMQrhSiKXL58GY1Gy+nz8dyPTEUuF4iLd6FQCDhdCnR6T8b/8HNGrvSihXNpUldPaHDOpfu8vRR0bGlg3W/R1K7bmrB3N1C9SiytmuhQq6zExLhYtSmVG7cdfPxufoYOSn++7D+STKseV3kQk3M6yyPiEhyAgIeHxxPb/nG8v/z8E03rabI43I8oWUzH91/kpf/QPZw5c+aFqmb/VxBFMUPJ/kkIMuGFq21v3rIFXZHC+IQEk5KSgtPpQqZSZkTwiXYHosOBrmxpUg4c4upPPyNXKPDx9ibQ35/Jkyez+/ffuXrtGoJMRsXy5egb1ofGjRvnuJj+T0lPa5Bz/lIq1arkvMsuiiLnLtkpVT44xzb379+ndatmyIlg7Gd+tGvui04nJ8XkZNXGeH6etZLWrU6zYePWf5XjvXbtWpIsFgLLlIYEE/akZJDJUAcGItfpkQlyXLhQlSxB3N79rFu3jn79+j03e2JiYnhzQF+OHz9EaAEZ5UursFrdTJvyGxN/+o4hbw/jo48+emIUjSiKnDp1ikOHDmG1WsmTJw+tWrWSKij8C3huTve3337L5s2bOXPmDCqViqSkpCxt7t69y+DBg9mzZw8eHh6EhYUxduzY5/aQksjK6NGjOXzsKNem/Ypf00Z4VyhHnsYN8a9di6gdO0k8dhJ5aipfjvqcESNGZBLWAZgxfTrde/Zk7/RZaIoWxrN8OeQaDWmRDzCdOIkiLY0pkyZRq1atTOcJgvBEhVJ/f//nrgpbtmxZ6tSrw/4dhzAoPDMJokG6nRFp4URxlzHvfPtUYhtt27Zl+rQZnDpxgIq6GsjSFOjSPCknvIFNSMMkJnFbvIJSpsKdItCrR2/WrFtN5cqZc+k3btzI++8NoksbDZ+PKImX5+PfRWKSky++u8uQwf3RarU0btwYAJkgIJL7hMXhthNuuY7VbuG7cd+yadMm2rdvT+PGjekV1ospP06lqLsMSpkq2/MtLjNxYhQf93918sNSUlL45ecfGRTmRf8eWUPhlEoZo4YX4O79m3w37hvatGkjhYdK/Ccwm82sWbOGY8eOceLECS5ePEP7FkZGvlOW0PwCVquVqzfSWLQqmZUbrYSFvUmPHj2A9Mnd1WtX6NXe64nXqV3dk7nLIvnuu+852r4Dc+fO4p2PfkMud2PwkNO0vg9jRwVkchBqveFJ13Y+rNoYx71ICwXy5ux8r9oQj0ZjoHr16k899hMnTnDjxmW+/ShnhwOgYW0v8ueJYsmSJZLT/RwoVqwYuFxY7t5DH5Lzd+GyWrHdj6Rol6Iv0Lr0kqByoxGdTodCocBisWC12uDhu1Qul6PUaBA9jellU0uWwKdsGdJiopk2Zw6i6MZYsgSeVSqB283vV66xPaw31apUZfHChfj6+j5zmytXrkzhwiWYv/xOrk734RMmrt1y8r/vctaMGfr+O6jkEaydV4TAgMfvfaNBQb/ugTSo5Um7Pjf48MORzJkz95mO43ly9OhRUCpJWLcR2/0/KM7L5eiLl8C7ei1Uvr4odR6g1z3XuuYpKSl06tiWlKTLLPglH3VreGZsDJjMTmYviubHn8fhcDgYPXp0jv0cO3aMUZ99zIULpzEaQK+TERvvZPTnH9Gla2+++OILNJqcF0glXi7Pzbu12+106tSJatWqMXv27CzHXS4XLVq0ICgoiEOHDvHgwQN69+6NUqlkzJgxz8ssiT/h4+PDpvUbGDpsGNvWrCd201aU3l64LWm4UlIoW7Qok376iddffz3b8w0GA2tWrWL16tXMmjOH02vWI4oiOq2WPh06MGDAgFe+jNPMX2fStk1bjpzbRRDB5NcWRCVTYXaauGe7QRxR9O7bi0GDBj1Vf2q1muUrltG1SzcOHt6OxuaBrxCIDDnJxJFALB5KA29410ct03A8cR89e/Ti9JlTGQ9Lp9PJl198StN6Sn74KjTLjrK3l4KJ3xYkKeUGX4z+lIYNGyKTyShfoTy//7YPl+hELmT9ed9Nu8kV61EUGjstGqnJk+cet++EM6D/SvLnL8S3Y75nwbyFnIw7QGVDzSyOt9Vl4ZT5AMGFgunUqdPfvOPPnjVr1mC3p/Bmz5xVhAVBYFCfINr0vsXevXupV6/eC7QwZ+Li4ti0aRMxMTFotVrq1KlDuXLPRsle4r/N3LlzGTvmSyyWRMqXUuJntJEvUGTH70mIbpg0phA+PjqqVYVqVfNRpuQDvp04hZYtW+b4zH8Scrmctm3bEhgYyNEj+/n1x3zUr+WJVpt9GOz/PglhyZpY/vfjXab/UDzb6JmIBzZmL0mkbbu+f2k3J30S7aJKhdx3x+VygYpllYSH33rqviWenpo1a1I4NJTIfQfQ98rZ+Ys/dgK1KD4zUdGnxcfbG1dUenk8lUqFSqXC7XbjdrsREHA4nSQlJeG2pCFTKsnToC76kGCu/PQzygB/fDt3QGE0YDAY0jcTGtbHfDucowuX0r1nTzZt2PDM1bEFQWDwkPf4YMQQFqyIoXfnrNU97kfaGDH6PmXKVKRGjRrZ9nP58mUOHdrH9PFBmRzuPxJSQMOwQf58NmYTERERL0Wp/e+wZ88enCYTyrxB+HXumC6O53KSduUq5qPHiVg4jzydu6LKmweX3U5ERO5lbf8J06dPJzLiEluWFqZwaOYUFYOHgqGD8qHRyvjfhEl06dIl24oI+/fvp2ePTpQt6crkuMfGOVi6JpbJs6Zy/doVFi9ZJonCvaI8t62er776imHDhmWErv6Z7du3c+nSJRYtWpQeptysGd988w1Tpkx5mKsi8aLw9/dn8aJFnDh6lNHDh/Nmq9YM69uXTWvXcvjgwSdOvpRKJV27dmXn9u3cvn6dqxcvcvvGDSZMmPDKO9yQHla9ZesWhn88FGeQmWPW3exP3cpZ5yHylPPjl2k/M3HixEw7o3a7nX379rFhwwb27NlDWlpapj4DAgLY9ttWggsWwCpPJVIWzn3ZDUSVSEWvatT1a4FeYUAhU1JOX5UH9x+k55U9ZOfOnTx4cJf3B+bJMYRbJhN4f2Ae7ty5wd69ewHSBYZUbu6mZZ083ku7xSXbQbp0ULNrVT5mTijAz2OLsGlxcX5bXhhfz/u8+85bfDd+HDJfN/vNW7hkOkW0LYIo633OpRzjgHkbPqFerFy14pUqz3XhwgVKF1fnOGkAEEU3Fcpo8TQILzxnMDvMZjNDhw6lcqUyfPH5+6xY8j1Tfh5N0yZ1aNG8CWfPnn3ZJkr8i5k2bRqffTqc9s1cHNlanIVT8zBnYhCnd4cy+Vt/TpxJocubV0hNdWWcM6hPEIVDZMyZMwdIn9gXL16C/YdNT7ze3oPJ+PkFZOREXr9+HYXCTbOG3jk63AB+vkoqlzOwcoOZIR/e4mb442epyyXy255E2ve5id5QiE8++eQv3YNHUXM2+5P1J2x2UYqye07I5XI++uADrBcv82D7rmzDx5MvXiZu23Z69+xJnjzZpwI8L1q2aIHt9h1scfEZn8lkMhQKBXKFHLPZBHI5louXkOu0GIoUJu7QEewpKQT27YUmfz4ElQqz2YzoTh+bR8FQ8vfpyeHjx9m0adNzsbtbt2706/8On34bQ++3b7BjbxL3ImxcumphzE/3aNL5OoKiIHPnLcpxHrFhwwZ8vESa1PPK9Vrtm/uiUtqf21ieNQcOHOBuZCTGWjXwD+uJrkwpFJ5GlD4+GKtXI2jwQJRB/kStXoH1zh1cySnPLXTe4XCweNFcOrUyZHG4/0j/7oH4+4osWLAgy7G0tDQGDepH9Soiq+cUpX4tr4ydcn8/Je8NzMuS6aGcOL6bX3755bmMQ+Kf89LeMIcPH6Zs2bKZVBGbNGnC4MGDuXjxIhUrVsz2PJvNhs32WBAiJSXludv6X6FgwYIMHz78H/XxKjlifwWj0chnn33GyJEjOXv2LBaLBX9/f0qWLJnpZWW1Wpk4cSJzZ88lJioWt9uNTCbg4+dLWN/eDBs2DIPBAKRPOqMeRFPFqw4BOZQ/s7osOEUHercny5ctzyjBdezYMYLzKSlbKvcQ/Erl9AT6yzl27Bj16tWjYMGC9Ozdk/mzFqC2qsmjDkYQBJyik6vWY3Ruq+eLET6IMjFTbmTpEjqWzihMy+7XWL9+LfsO7GXOnDksmLeAyKRwAPIUCOLT/p8QFhb2t15OJpOJDRs2cOvWLQRBoHz58jRt2vSZ7AC4XK5MOdyPEEWRtLQ00tIsOJxOAGw2C5s3b6Zly5aEhob+42v/HVJTU+nUsR23bh7no3d96do2FC9PBS6XyK79SUyYdpL27VqwYuX6LGkHEhJP4v79+3z77WgGhxn5bHgBRERiohPx0AuoVQLtWxgoUURF694RzFgQxfDB6TtXgiDQtZ0n30/dgNM5BYVCQa9e/fh81PuE37XmmNedkOhg1SYT/d98J8NxfbRI+TTpuXmDVKj0VTh48g4b21ynXCk1RoOMm7cdPIgRqVq1BtOm/4q/f9aKDLmRPo9QsW13Ep1a5/zMMpmdHDxm463Br/2l/iWeni5duhAZGck3Y8diPncez6qvofbzxWlOJenUGezh4bRv2YqxLyHSsU2bNoz64gsi1m0ktE8vZIrHi0R2ux2n04kzOQXz8ZMEVq+GIJcTe/gourJlUHh7AyBTqXDa7aRZ09Dp0tMk9MEF0BQqyKw5c56LjoggCHzzzTdUrFiRmTOm0ve904ALEDAavenc9T3ee++9XN/XCQkJ5A1SZlvJ4I/o9XL8fZXEx8fn2u5VYeq0aajy58NYuyY4HCDP7O7INBp8O3Yg8qfJxP22DcEtUqlSpediy5UrV4iNfUDb5iG5tlMqZbRoqGff3h3At5mOrV+/nqTEaMZ8WizH76pqJQPd2hlZuGA27733nlR7/BXkpTndUVFRWcoQPPp3VFRUjueNHTuWr7766rnaJvHfRaVSUaVKlWyPWSwWOnfqzKF9R8hDMG9oy6GV67G607gXf5PJ439mz+7fWb9hHUajkejoaNwuNwZN1oWIaFsEt1KvEGt7gAi4RRc7tu/gl19+oW/fvtjtdnTaJweiCIKAViPD4XBkfDZ+/HjSLGmsXL6KW/bLBMoLkOxIRFSmMTDMB+QiXp5eWfLzjQYFQ/r5M/Kr7bjdY/niiy/49NNPiY+PRxAE/Pz8MlRd/woul4vx48czbcp0UhJT0Mr0iIikianky5+XUaNH0a1bt7/c7x8pWrQo69ZYSUp2ZuS+u91uEhMTcDodqFUCOoOMqzftpJhcXDy/n/r1avLrrPk0aNDgH1377/Ddd99x4/pxVs8pSJmSjxdW5HKBxnW9qfW6kR6DbzLwzb4cOXpSenlK/CUWLlyIXutg2KC8QPrikwjI/7AwVaq4mo6tDCxeFcO7A/JkTOTy51XjsCdhsVgwGo106tSJmTOm0GvILRZNK0hIgcyOd2ycgz7v3UKjDcoQ2QQoXbo0breCvYeSqV/LK0dbTWYnx8/YGfJOM95++202btzI/v37sVqtNC8bQKdOnf52ukVoaCh16jZg5oJdtG7ig1qd/TN13tIYrDZVRi67xPNh2LBhVKtWjVmzZ7Nx82aSnE4EQaD6G28wYNTntGzZ8m+9Y/4pGo2GWTNm0KVHd27NnE1go/p4FCmMIAhYTSZMJ05jPnQEXWAAQY0b4DSZsScl4VnycQiwIJMhyOU4HM5MfXuUKsGpnXueW3lNQRDo0KED7du3T1/oj4pCo9FQtmzZp1La9/DwID7R9UT7HA43SSnOjE2FV5mYmBi279qJT5NGCDIZosOJW7AiU6nhD2NUGA1oihbGcuY8Bo32L+lF/BUeRUJ6Gp/schkN8iyRkwAbN66nRlVtlufvn+nW3o/5K25z9OhRatas+fcMlnhu/KXw8o8//hhBEHL978qVK8/LVgA++eQTkpOTM/67d+/ec72ehMQjPv/8cw7tPcJrmtqUMb6GUemNUqbCoPCklKESVbR1OX/ifEa0gE6nQxAEHO7M6RJXzOc4krCHNFsaRSlPRaEmpYUqeNuCGP3xF7Rq2RpfX19u37WRnOLMzpQM4uIdRDywZ8qxUiqVTJ8xnTXrV9OgXV1i9feIdN2mcgU1xYp64ufnn6PQRpumPoiijUOHDmX0FRQURGBg4N+aDImiyPvvv893//sez+QAank0p5ahGbUNzamm/T/2zjpAirKP45+Z7b3Y6yLu4I7uBmnpVGnpRkBERQxQwcAAFcWkWxqkS7obpOsODrjO3dvenXn/ODy99w4wAGs/99/O7DPPzM7NPL/6/lrguCUw8rnn+fbbbwt899y5c/zwww+sW7eO69ev3/c43bp1Q5J1LFqRkntcZDKzMpEkJwH+SvwMSnRakcUrjRSN0PDT7go0quti8KA+fyjV/OLFi4wbN44O7VvTvl0rRo8ezfHjx3+T6q7ZbGbZ0gUMfNYvn8H9a3Q6Be+/UZTExJts3779d8/Pw3+bH7dvpm0zr7x2QoKQqx3tlvLfn107+JCc6uDClV8WecmpDkSFIm/B7uXlxfdLViArStLk6asMH3udlevTWL0xnVcmxFGv7SUSUoL4fsnKfI70qlWrUrFiNaYvSEGS7v1/sWhFKnZHrsGr0Wjo0qULX3zxBdOnT+e999770/oGr702jth4DYNejCU9w5lvm8slM3dJMpO/TmfYcy889rTm/yJ169Zl1syZ3IyN5eLZs9y4do31a9fy1FNP/SUG9880btyYNStWUsbLizuz53N10sdcm/I5NyZ/RtbmrfhVKEep54ag0GiQ5bvtNRWFGVH573VBoXgs7TUFQaB06dI0atSI2rVr/+bWdi1atCAx2c2Bo/cvIdm8IxNTjkiLFi0exnQfKUlJSbjcEoaoKERBQBREZIcLt9mMZLMhOexINhtusxlVUDCiKNLx6Y4UK3bvVqt/htznosilqwWN6f/nynU7YWEFa+YzM9IoGv5go71YEQ0gkZmZ+Qdm6uFR87si3WPGjMnnyS6MkiXv34fxZ8LCwjh69Gi+z5KTk/O23QuNRuMRCPDw2ElPT+f7RUsooSyLv7rwVC1flT8xqoqsXbOOCRNuUaVKFQICA7idHkd5VW4K2m1rHJdNPxFFWYoJ0XmeZTVaIryKI2mcHD+2F51eiyTrWPpDGsP63vv/YcnqVESFV167sp8RBIEmTZrQpEkTAAYMGIDLvBEf7/t7qXU6BWqVUKin9Y8wb9485s2ZT5SyDJH6GHSKX9SJfVV+VDc8wUXTad4a/zbNmjWjdOnSbN26lU+mfMrJ4ydwOXPrTZVqJU2ebMKrr44tVGMgKCiI3n0G8cm3XxFTUkfT+nqcTgcBBgUqZW4LmrlLjSxZY2Liq5H4+qr4dnJJmnW+zFdffVWo0V8YNpuNl156kbU/LCc0GBrV1SKKAkcOHWfF8gU0aPAkM2bOxs/P755j7N27F5Mpg2c73Vv0DaB8GT2Vy6tYt24dbdu2/U3z8/Dfxel0smXLFk6ePMnly1fw1TrJyHQS4K9CILe/tc1mR68nr3lTYECukZNzt65blmWW/ZBF8+Zt82VXREVFsWXrDpYvX878ebNYv+0aABEREYx+6VV69epVIIVVEARefW0c/fp25/X3bvD+G5Go1b/4+WVZZtWGdD76Mo3+A14okPn2sKhatSrzFyxlyOB+1GxxibbN9ZQuqcOY42LdlhwSU6D/gJGMGzfukRz/v4zZbGbVqlXMXTCfK1evIYoCVSpVZmD//rRv3/6R/eZ/lHr16rF/z14OHTrE/v37sdlspKamMm/xYsKaNUFx11mt0HshqtVYb9xEE5O75hUA3BLK/zPELTduEhkZ+Vjba/4e6tSpQ9mylfjwi0usnO1VqP6C0eRi6ncp1K3bgLJly/4Fs/x9qNVqBAEkpwOfoECM2dkoBAUCAm7XLxoWChTgdKJQKnjrrbce2XwiIyOpWbMui1aeoEOre/cBT0pxsG1PDn36VmPhwoUolUqqV69OmTJl8PYxkJp+/yAMQHJqbmvFf2qp57+d32V0BwcH/+6aqntRr149Jk2aREpKCiEhuaqL27dvx9fX9x8hvuXhv8W6deuwmW0U94m+735FtCW4lnOe1atXM3r0aPr278OXn3xNpKsUeoU3V3LOEUgoxcWYvO+4ZCcCoNfpEBXelNdU5/CBw7Tt0IZPvl5P1Qpe1KlR0Fjef8TI5zPSebbXSPzv1pXdi7CwMHZucyNJcqG1zz8Td9OKw3l/x9dv4dSpU3z++Wf8sGY5CoWbBM5yy3gef6EIMfqKBKp/UVot412ZJFM88+fPJzo6mrEvv4q3y4+KmjoE6kIBmRR7Ake2HuepfU8xZ96cQo3QiRMnkpKSzOCXVlC9EjzdWkeJKDW3bjv5frWJc5fsDOsXntdSTKMR6dvNnw+nrSEjY1KeANS9kCSJ4cOHsWfXD3z6Thid2gXmpeRKksyOfdm8/NZOej7bndVr1t4zmyC3faJM0Yh7i779TPEiSjIz/xk1dB7+OpYvX84HkyaSknKHqGIqioTaOHTcRs3mp+nZOZgJY4uj0+vJzLRhtUp5pSuxNx2AQFBA7lJg6Zo0Ll5189a7BXvV+vr6MnjwYAYPHozdbkeSJLRa7X2NiebNmzP18295Zcwotuy8SLenfCkZqSUr28WqDdlcvu6ma7f+TJgw4ZFcl59p1KgRh4+cZOnSpaxcsYT9x5LQ6Xxo0rwr/fv3v6fgq4c/zvXr1+navTvXbt5EV7Y0Xk0aIksSRy9fZe/QodSsWpWl33+ft/77uyAIAk888UReqrHT6eTHXbtI3XeQ4l07YbPayDYa0ZYrS86JU/g2rI+gViE5nciylM9Z5TQaMZ87z4CJD7ckUpZlXC7XQyk7EgSBqZ9/Radn2tNtyDXefCmC2tW9EQQBSZLZf8TIe58mkpLhx6z5nz6E2T96oqOjCQsJJevMWYpFlwRkTEYTkiwjIgK5rVXdkgvrpUs806FDblu7R8jQYSMYOqQ3sxcnF9rW1Gx28VSfS5hMTubN/QaFQiI3QUJF3boNqFipKvPmbic5xXFfwdgVa9MwGIKoXbv2ozsZD3+YR1bTHR8fT0ZGBvHx8bjdbk6fPg1ATEwM3t7etGzZkvLly9OnTx8mT55MUlISb775JiNHjvREsj387UhKSkKn0KMW739vKkUlOkFPYmIiAC+88AKbNm7i6IXdFFNGY3JlU0kod3dvGZfswo0bHx8fxLupdSGaIqhMGoKCgqlesxk9hu7k6TZ6uj8dTHioijtJDpauTmPdNjP1G7Ri4sSJD5x/ly5dmD/vO3YfKFhfKcsyNpsNi9XCN3OSyc62Mv7tt7l+/To9e/b83T1Gt23bxpDBfSlZ3MmkN/xoWNMHhajg8EkLC1Ykc/x8AhXdDSiiiwJAFERChKIsWbwUk9FIuBRFed9q+RbzRXRRhGuLc9p0kKGDh3L46GGKFi2a77gqlYrp02ewfn0HBg/qy8GjaWi1IoIATzbw47UXomjaIP+5N6rny7ufphEXF/dAo3vnzp1s3bKWOV8UpWWT/E4OURRo0diPxd+q6NDnMEuWLGHAgAEFrvOVK1dISEjA5ZJISnEQEXb/+ykp1U1oUb/77uPh38nFixc5cOAANpuN0NBQWrduXWg95dy5cxk/7mWebqPl+UHRlC2lx2Ixc/NWJtt2W/h8egq3ExzM+jwGvc4LU44Zt1tGrxNZvMpIuVI6DD4KPvz8Ft/My6J3n2E0btw4b/yrV6+ydOlS4uPjUSqV1KxZk65du/7mSEq3bt2oWbMm8+fPZ8WqpaRnpKPVaGn6ZEfe/XAADRo0eCxRQH9/f4YPH87w4cMf+bH+66Snp/NMly7csVmJfuVFNEG/eoc0aYT51m1OzV9Ej5492bJpUwF9kb8TKpWKF0eN4tU3x5MYGoJQKgYRBQE163L73AXSV6wmoPPT4HKBLJCVlUVAYCA47Nycv4iIoGB69OjxUOZy4sQJ5syZw5bN67BacwXb2rZ7mgEDBtxTfPi3UKVKFVauWscLo4bTeeAlooopCAlSkpDk4naiRLnylfhh7UxKlXq8/dP/KCqVin69ezNp6lSyatdAHRCAn78fLpcLu92OLMuIogL7xUvo7A7Gjh37yOfUvn17ho8Yw4TJn3LkRA79nw2meiVvjCYXm3dkMnHKbbKynfTsHMigXuFUreiFyyWzZWcm380/yLy5R3BLWt799DZfflii0ODJpasWFq7Mpnffl39zeYGHx4sg/5YixD9A//79mT9/foHPd+3alZfyevPmTYYPH87u3bvx8vKiX79+fPTRR7+rbYfRaMRgMJCdne1Jp/DwyPjss8/4cMLHNPXpeN8FoizL7MvZzHNjhuRFb5KTkxnQbwB79+7DbnXwBC0RBQWSICEIAj7e3ui98tf2Hs/aR9VW5VmydAkzZ85k/ryZ3L59E5AAkcjIaPr1H8ygQYN+k7dblmU6tG9DatJRVs2NzjP2csXGMnE4nRw+aee5V1ORoiqiNhiwnD1PiJ8fSxcv/s0K2vHx8TRuVI9mDSS+mFSM7OwMVKgRhdzImsslM35yEivX2njCpx0+Sj8AYs2XuOg4hY9ooKFvm3teY5fkYk/OBsaMe+m+7YPq1a3Ok/VSGdYvDIOvAh/vwp8pF69YaNE1lvUbdjzwHPv06UXKnW1sWlL6vvfA0Jevc+1WSXbt3p+rHO9ysXDhQubOmcG1a5eQZYmcHDOhwUrGPl+U/j1CClUjvX7DSuOnrvLlV3Pp3Lnzfefm4d/D2bNneevNNzh69CAatRu9TkGW0YVe78ezPfszfvz4PMd0fHw8TzxRg4E9dEwYWyzvvpRkibTUVFQqmVNnHfR/IYn3x0XRt1swOTlmzBYzW3eaePntNCLC9WSbQKMxMOy5FxgzZgyiKGI0GnnhhZFs27qRQH+ZCmVU2B1w4ic7KpUPY14Zx/Dhw3+3wZzb8eGRdSv18Dfg008/5d1PPiH6lRdR+xXeV91y+w43vvyWOd99R5cuXR7zDH8fsizz2muv8dmXX6IpXhxDtRoo/QOwXL9G5v69KHx88KlZHa/IaJxuG874W7jOXyTEy4uVy5b9aV0CWZb56KOP+HLaFKKKCXTtYCAsVEVCkoPla43cSpAZ88p4Xn755T/lwJIkiQMHDrBlyxays7Px8/OjQ4cO1K5d+2+bHl8Y33//PR9OnszFS5cQ/f0IeLoDmiIRqFRqfHy8USmUZBw/Qeq6jfTu0oWvvvrqsZyfLMssW7aMaV98yvkL53C7nEhSrsilgEyjer7Ur2MAGcJC1TzVOoCgQBVOp8SQl2PZuV/C7XbRsomaV0ZEUKFsbrme1epm7ZYMJk1NIbxoFdasWf+PELz7N/FbbdFHZnQ/LjxGt4fHwbFjx2jVvDVVVE/cs/0XQKYzjWO23axYvZzmzZvnfS7LMp999hlvvv4WTyhbohTVqNUqdFodQiEL0KOZe6jToRpLly0FchXAz58/n/cirFChwu9euN65c4dnnm6PzXKDAT386dwxAGQT1+OsrN5iY+naHISSFYnq1w9RqcCZk0P8/MX4mHLY9eOPREbev90FwPvvv8/iBZ9wbFs5tFpISUlFiQqF8EudmNMp06RLHEJqaSr75qZAnTee5Ir1LJW8axHjde/ykixnOsez9uPS2mjRogVVq1Wld+/eBVp/DRjQn1txG9m67P4G8rfzEpn8lZnTZy4+MEW/ZIkijHlOy/AB9xdb2rAtg+fGJvPT2csYDAYGDx7Izh3rafOkjh7PBFEkXM212EyWr01nxz4rDeoYmDutVD5lZZdLZtCL1zl9wcDxEz95sn/+I5w4cYJuXZ+mRFELo4aE0vpJP1QqkcRkB4tXpvDtvExq1WnOzJlzOH/+PDNnzuTA3lWc2lkenS6/Y8lut5OVlYlSCWMmpHIrAX5cVZFrcTbmLU1m7vcZFC1ejubNW1C2bFk6deqU9w61WCx06fwMcdeP8O7rYXRoGZBXj52c4uC7+UnMXGRkzCtvMWbMmMd+nTz8fXG73VSuVo3MiDCKd+10331jZ8yhRlAwmzdufEyz++MsXbqUQf0H4ysEkiGl5xZxy+AleKFBQzbZuHEjyRIybp4fPpyxY8fmEzn9o0yfPp13Jr7G+BcDGdYvLF+U0+2W+Xp2IpO/zmDSB1MLZFj9GkmSOHz4MLGxsSgUCqpWrUq5cuXuuf8/kcmTJ/PBlCloK1XAr2plErdsw5KQiCo0BFVEBLLTiXwzHpXdQZ+ePflkypTH2h3k8uXLPNujM2mpN2jfwosnantjsVjZfySHTTvMOJ0QHaUiPUMCBDq1D+S91yOx2SVqtrhE+459OHhgN0lJt4iOUuHtJRIX78SUI9KyVQc+//wLDIbCHV0eHh2/1Rb9y1qGefDwT6JmzZpUqlKRayfOEaQOzYvc/hpJlrhiPkuJUiVo2rRpvm0/t/X48P0PsQo5FNPdW3DQKTkxihlUr/FLz0iFQvGnveVFihRhw8atuR7zOcv46MuL2O02ZIUK0TcA3ybtCG3aJK9Hqcrbm8iBfbn+yedMnz6dD35D/9SlSxbQ7SnfPNVklVqFy+7KZ3SrVAI9nvZl2nfXkeTcvrjJ8i0USgW+ysINX6fk4ETWfpLtCahlNT5Of05tOc/urXv5/LPPGTBoAB988EHey7NXr970enYlO/am8kQtb1RqFSpl/her1epm4fIsOnR89oEGtyzL2B0OvL3v3zcdwNtLAeSm7E+bNo3dO9cx/8viNKn/y4swOkpDraoqDp/IYeTrKbz14U0mTyyBLMuc/MnMlK8TOXwC5s3/1mNw/we4du0aq1ev5rNPp+BvsPD2KyWpX8c3z2EUHqrmlZFFqVzeix5DN1C2bEkE3JhMOfTr7o3ZnIkse6HX6/hZJk2j0eDvH0BOjom2zfUMfimFkjXP4nQJBAaG8cb4sYwaNapQ593s2bO5cP4waxeUpFL5/Pd8aIiaCWOL4+tzh08/+YBnnnnmNwuoevj3k5aWRmJSEqHNmz5wX+9yZTj9CNtpPUz27NlDgDKEJwzNsbttOGQbSkGNVtTd7VLixCaZcUsSh8zbqVOnzkMxuK1WK59PnUy/bj6FOnwVCoEXhkYQf8fO1M8+plevXoWm6/8cYY2Lu4IguMkNt6moWbMur70+nvr16//puf7V7Nu3jw8/+QT/Vs0JbZpbImMoVxbj5SukHT6KPTEJR0YmosXCmh9+yBcYeRxkZGTwbI/O+PsksWFhecJC1FitVrKyZDq11fHWywEMGJ2MywV71hZj049mpk5P5/oNG0tnlKVtcy8uXviJo8dOs23bNg4cOIDdbqd5m3A6d+5cIPjg4e+HJ8fLg4ffgCAIfDz5Y1zedo4b92J25W+vYXWbOWU8gEVnZMqnkwttfRIVFUWzFs244biCW3YX2P4zN6xXUGhFevfu/dDPIyQkhM8++4xTpy9Qtnwt3P7FKDJkJKXeGE94i2Z5BvfPKHU6fGrWYPGypVgslvuObbfbycjIyEt5AvDS65GRCpxvuRgNbpy4ZCeXc35CUrnQajQ4Zcf/D4tbdnE4cyfp9lTKCTWoKTSllKIKNf0a0sS3PSXkCsz+bg4vvvgikiQxY8YMXnjpJYxmB8PG3ODg0VTS09JJz0jH4cgdP8fsZuiYWFIzdDz//PMPvG6CIBAeFs7Fy/e/BpCbsq5SaVAqlSxaOJsXhgTkM7gBRFEkICCA+rX9GN7fwHfzk2je5RINO1zmqb43iE8syqLFK3jyyScfeDwPf5y/OtErMTGRHj260qhhbWZOf4/iRXJQq9z0GHqJVl3Pc+a8OW/f5BQH730aT4CfTN+uAtuWl6BohIqoYiqUSjcmUzbZ2UZ+3bJIrVYTEBBI6ZggdDotffq9wKzZSzhx8iyjR48u1OB2u90smD+Lp9t4FTC4f82IAeH4GyQWLFjwUK+Jh382ee2xFA9eXgqi+FjaaT0MrFYrSjk3TqVRaPFR+qFT6POcBSpRhY/SDz91AKJQeK/lP8L69esxGlMZ0uf+Su/D+oWRlpbA5s2bC2ybMmUKL704jPLRt1g1J5KbJysTd7wSMz4NQ3Ic49kendj4D8g2eBAzZs5EERpCSJNGeZ8JooihXFmiB/Sl/NiXqPzuW6gD/Nm/f/9jn9/ChQvJyrzFom9KEhaiRpZljEYjGo2An5+C6Cg1i74OIyHJxbbdZp7r58eS78I5dzGHL2clEBOlIS0tGaVSSdu2bZk0aRKffPIJY8aM8Rjc/xA8RrcHD7+RunXrsmzFUlRhAvvNWzicvZNT2Yc4krWLvTmbkILsLFg0n2bNmt1zjNffeB3BR+KEcT92yZZvmyRL3LBcIc59kZGjRhAREfHIzsVgMJCclkZA7Zr4li5VwNjOt2+5MhiNJm7evHnfMZVKJYIoYLZIuNwuTCYTFosVQRRxyg6ckjPPyLFYJSQZzhqPc1u4zjvvvUPlKpVJtBc8xk3rdTId6VQS6hAshCMJEhpNridfISgpqS9DOVV1vl+4hN69ezN23Dgyw0Mp+eKLGP1L0X1EGsPHpbN8XSYLl8czblIstVpe5OgZLXPnff+bW6B0696b1ZtyMOUUbNshSTJ7DmYzYNQV3vzgJhaLmebNGpOVlUH7FoVH0UVRgb+/P4N6ReHjrSIlI4ywoo34ePKXHDx0jEaNGhX6PQ9/jrNnz/LSSy9RrmxJihYNoXy5aF599VUuXLjwWOeRnJxMx45tuHZpB199GMq+tcXYtLgIJ7ZHsvibcJRKF10HXuT0uRxkWea5sdewWl1sXlqU0UMMRJdQEuivIjnVjZ+vEoOPApvNgtlsAWQcDjtZWVmkpqZw5VoGDoeLiIgImjVrdl/hqitXrnDnTjxdOhbeGvFnNBqRjq28+XH7pod8ZTz8kwkKCsJgMJATG/fAfc2xcUSXLPm3j3JDbq9lq2h+oKPO7DKBID80Vfbz589TMlJDVPHCu2H8TKmSOopFqDl//ny+z3fv3s3Uzz5g3OhApn8aTZ0aPoiigEol0rZ5AKvnlqbNkwqeHzmUxMREZFlm//79vPDCC3Tr1pk+fXrz3Xff3e268ffFZDKxeds2DLVq3Pd+ElUqvKtVYdnKlY9xdrkO3oULZvN0G+889XGrzYosS3h7iXltHIsXVdGmmZ5FK4wAVK+spcfTPixemUJahhO9/sHZdh7+vniMbg8e7sOVK1dYs2YNq1ev5vz58zRq1IgzZ08zffZ31O9Ym5L1i1C3Qw2mffMFZ8+fpXXr1vcdr1q1any/dDEEOthj2sip7INczTnHBdNJ9ho3cU0+x7CRQ3nzzTcf+blJkgS/ZbEjisjAwYMHee211xg5ciRvvfUWx48fz7cAUSgU1KxZh1UbUkhLTcOcY8Zpd4GUewyX7MQu2bBLdjbszMIlu1EVlZj29Rc8//zzDB4ymAxSyHCk5o0pyzI3zFcIJBQfwQ9JdiMjo9fr802xiDYKwSmyav06Qjo/RWT3LvhElyR6+HMEde7FgVvhvPZRDqPfzuCr2Wl06zGK3bsP/i7Dtk+fPrglH14cfxOH45fojNXqZuALV+n13CWu38jhxWF+THglkNpV0lEqnHQecIkTZ3IKGVHGbDaDbKRIGMTfvMLhQ9t57dXR9OjRlTNnzvzmuXl4MLIs8+mnn9KqZWP27ZrPgO4SH4zzp09nN9u3zKR5s4Z89dVXj20+48ePw2W/ydqFMTzdNhClUkahyFXCb/yEnlWzIyhbSskLb8Ry4kwOx06Z+PDNIKKjcsskJEmibfMA1m01Y7ZIaLUiOq2A2WImMzOTjMwMXC4bWq3M2s0mvL3gvXdfo2mTBsTF3dsgMpvNgEyA34OrzwL8lZjNhd3bHv6rqFQqevfoQc7xk7jt9nvu58jKxnrxMgP69XuMs/vjdO7cGYdoI9WReN/9blivEhgceF/n++8hKysLt8uJxWLBZrPd1+gXRaHA9lmzZlC5vJLhAwpvA6pUCkyeUBylwsxXX31FixZN6Na1PWeOf4+/7gCSdRsfTnqNalUrMHv27IdyTo+CzMxMJElCE5zrLHRZraTsO0Dcwu+JnbeQ2+s2YE1MAkATFERaWtpjzXQyGo0kJNyh8RO/ZL3ZbTY0agGFIv/v1qS+nms3HDiduZ/17ORLWoaTJWsykFEzYsQI3n77bU6dOvWXZ2t5+H14aro9eCiEAwcO8PGUKew/eBCnlJsarRQV1KpenbFjxtC9e3e6d+/+h8Zu3LgxJ06dYOnSpSz5fimJCQnodHqebdGN/v37U7Vq1Yd4JvemXJkyHIq78cD9si5dwWI2M+bFV9DjjQoNNix8Pe0bqtWoysxZM/Hz82Px4sXs2b0fSTJx9KSe2tX0iIKIQsh9zEiyG6fs4NwVO3uPOBkx8gU++uijvDrs7t27s3zZcg7t3k9ZqTrhmmLYJRsmVzbFhBjcsgsXLnQ6LWp1/jpnQRBwSRKqqCgCa9fK+1xUqQisXZPA2jWRXG5cRhPXpnxGeHj47663Cw8PZ/qMuQwe1Id2Pa8wsGcgTev7MnbiDfYfzebz94JpUEeHr8GAWqUmK1vDS0O9GP9ROr2eu8gP80tTprQvwl0FnuxsIzabBa0296X78vAwXhtVlE07Mvl2zm6eefowixavyOsX6+HPMWfOHD795D1eez6AEQPDUSh+cTi9PDyCL2Yk8MGkt/D396dXr16PdC5JSUls3bKeiWODCA/NjXoIgsivM211OpE3Xwqk04AEPv8ugWIRSp5soM/bRxREencN5tt5CXzweTrvvxGETqvAbHEiS278fRWoNSLbdpnZe9jKx2+XpGZVb4a8FEu3rk+zecsOgoIKRrNzPxOJi7dTtpS+wPZfc/2GjaCgBwssevhvMWjQIOYvXszNBYuJ7NsbhSZ/ZoXTlMPNeQspGhr6h9+jj5tatWpRo1YNzh4+ha/SD62i4P9Gmj2JO1Isrwwe86dbNl27do133pnAhvXrELBwLTadkGAloiCi0+vx9vLOF9G9ectG/B17vn7TGRkZ7Nq5jclvB943+uvjraRjaz3ffD2VcqV1rJhVnLo1ffK+k5buZNrMBN568xUkSWLIkCF/6tweBV5eXgjk3lvJu/aQsPVHJLcLTfFiCEol2cfjSN6zD0O5sujCwvDy9v5LMix+fUhJklEqc9cvsiQjy4XHQSLCFDidMukZNsxpN0k8l4pNsPDVtK+pXac2M2ZOp3jx4o/vJDz8YTzq5R7+NUiSxL59+5g7bx4HDh3C4XQQWaw4fXr1olu3br9Z0XHt2rUMee45pNAQAhs+gW/ZMiCI5Fy7Ttr+A7hvxPPZ5Mn079//0Z7QI2bNmjX0GzKEqNEj0UcUrshtM1u4+OFkNGlW6vo3w0+Z+/KWZZlURyKX7WdQByhQKlSkJqbibfPHosjGO9jMpHGBNHlCi1IpohY0yDLsPmzilfeSCAgqw+EjxwuIhBmNRkYMH8HmjZtRutRo3V4k2OKpKNTGBz8EnYzeS49W4YVazF3Iud0ucpwmduRsJKD7M5Ro3PC+531j8VLKyAJ7du36Q9ft1KlTTJ36KTt+3ILDYcVms/HlB8G0bxmAl5cXdrsNi8V6t7JWQqUUaPPsHapV0vLBuDD8/f1xOBxkG7Mw+CiIvemkVffbzPisFG2b5/YKt9kk+r9wnXNXfDh69DTe3t5/aK4ecrHZbFSvVoH2ze189FbUPfd7cXwse4/6cez4mUeqaLtgwQLeHP88Z/eUz2tnZ7FYMJmyCQpQ5jkEZFmm0VO3yDJCraoq5nweTo7ZjcUKQcHBiILIopUpvP7uDdo00zO0j4Gi4Qr8/RRkG2UWrjDy1ewsWjb159spMSgUAonJDp7sdIWBg9/gtddeK3R+bdu0xE//E4u+jbnnOaRnOKnZ4iKvj/vY0/vaQwH2799Pz759MUlufGrWwCemJLIkkX3xEuZTPxHu58fqFSv+UerZt27dol3b9iTFJVNMGUNRbQnUogaz28gNyzUS5Ru0aNOcxd8v/lPPjwsXLtDpmfYE+WUzsGcAH3x+i/7dfXh1VABWq4TFKqFWa/Dz888zHCd8fJOVG1WcPHU+z+C/cuUKTRrXYfXc4tSufv8WUl/MiGPCx0lcPlQjL/35/5k4OZ75y52cOHm2UIfdX03L1q05eOki9qwsfOrVwbdhfZR3W2fJbjeW8xfJ2LAJKSeHft17MGvWrMc2N0mSqFG9Ei0aZue9gzIzMwAHfgYlsiTd7R8uMOqNFM5csLNvXSRut8yps1aad72Nr7M49QNb5o4nS6Q6ErlkO01Q8QC2bt/yUIT7PPwxfqst6kkv91AomZmZXL9+neTk5H9E+orVaqX/gAE81aULG44dxVGlImK9OlxBYsz48dSqW5cTJ048cJwbN24wbORIFOXLEj1iKP5Vq6DQalFo1BgqlKPk0EHo69Tilddf4/Tp04/+xB4h7dq1o2K5ctxesBh7WnqB7ZLdwfV5C3CnZ1HXvzn+qqC8F7wgCIRoIqikq0PCrQSyb+VQXWhEKbEy1eQmSKlhjHotnfa9knhvahoTP0+gda+bDBmTQlaaFpVSl1dXajKZmDt3Lk8/9TRtWrclMzOT50Y+R/eBXan0ZFkUKgVJinjOqA9z2L2HncbNbMlYxeH0XcSlXCM1NY2UzGRkZBR+flgt1l9rSRVAExREcmrqvXd4ANWqVWPBgkUcOXqaBg1bUCbGhx6dIgkICMBms2G2WBG0GpQ+PgiiAoVCoF93A9t2W0jPcpCRkYHZnINWLaDVCHw5K5PQYDUtm/xS+63Vinz2bnGM2SmsXr36D8/VQy7r168nKyuV5/oVnmL5M8P6hZGScoetW7c+0vkYjUa89fn7x2t1WgRBJMfszrt9BUEgPFSJ2w1mi4zLLWOxSmi1urwOCr27hPD1x9H8dN5Fmx63adb5Nk92uk3t1vHMWGhkcJ9wvv44Os+QDw9V06W9D4sXzcXpdBY6v0GDh7H7oI1lPxT+f+J0Srz2bjwqjR89evR4eBfGw7+GBg0asGfHDkb06o1w6gyJcxaQNG8RXtfieHXECHbv2PGPMrgBihUrxrbtW+k+oCsJmlj25KxnW9ZKDlm2I4VbGDfhjT9tcLvdbgYN7EvxCCMbvi9F/2dDGdYvnG/nZ7FqgwkfbwV+BgUOhx2zObfGfNHKFGZ/b2TEyJfyRdi9vLwAgSxjQR2SXyNJEmnpNvx8Ffc0uAFeHBaBKFhYsmTJHz6/R0mHdu2wJCbi07ABAW1b5xncAIJCgVfligT36YksCHevzeNDFEV69xnI6o05pKXnPnc1Gi0Oh4zklhFEEUEQuJ3gZMN2M8+08yYzy0VqupOlP5jApaGO/y/CqqIgEqopQh3vJ0mNT2PihImP9Xw8/DE86eUe8pBlme3btzNj1kx27dmLJMuIQLWqVRk8cCBdunRBqfz73TKyLDPy+edZu20b4b17YKhQPl/akDPbSPyiJXTt0YMft269b3ub+fPnYxMESnfthFCIArkgCBTp2I6rl68we/Zsvvzyy0dyTo8DtVrN8iVL6NS1K5enTkNfoQKGiuURFApybt4k+8hxLCmpVNXVwl8VWOgYd2w3UMkayrirIssCIgqUgpLycm1M9iwSr9xgZVwGOXIW/ooIanrXwO62ceH6cZKTkzl37hxDBw8lMy0Lf4LRCnqS5HQO7j2M3kfL+LfGc/j4MdJs2fhUrUZo6XIIKiXWhDukHj9BYsoRysgV0eGNhIRksZKdnY3dYcdg8Cs0VctlNuP9EF64RYsWJTMjmVZNfVCrVTidTqxWK6JWi6jOXXSJGi02q4W6NTQ4nDI3khT4ersRkAGB8R+ksWG7ha8+ikapzD/ZiDANTzbQs3rVcvr27fun5/tf5ty5c0RHPViMqFxpPRGhSs6dO0f79u0f6hycTic//vgjV69e5cSJE6Rm2MnKduJnuHuvCCK+vgaysrOQs114eylQKCAhyUVkMQ0Hj+Vw5bqdkCBNgcyHp9oE0r5lAEUrH6VoERUN6wZQIlJHx1YB+PoUfGa3bubP3KW3iI+PJzo6usD2Tp06cejQIcZMmMnx0zkM7BlKudJ6XC6Z7Xuy+HZeCmcvKpg1e/YD2+15+O9SokQJJk2axFtvvUVycjKiKBIWFvZY+yI/bMLCwvjyyy+ZOHEiBw4cwGKxEBQURKNGje4rUPhb+fHHH7l58yqbvi+Z97/70nMRJKc4GDMhhYUrjHR/ygdfH5FbCWY2/ujm7EUX/QeMLNCJIyIiglKlyrJ6Q1w+p+7/Y7c7WLclh2aN7v+/7GdQ0rS+lr17dzFq1Kg/fa4Pm4yMDNS+vvjUqIbkcCCoVHlrQVmSkRwOFAZfDDVr8OOuXTidzsd6L/bt25cF82fRe/g1Zn5elEB/BSBgynFj8FWSkibRf3QKgQFKOrcPRJIFLl3NYdkaK5GqiijFgs9yrUJHpKo069au47333yMs7P6OZQ9/LX8/C8rDY8XtdmMymVAqlXz00Ud8NX06qqJFCHi6A+rAAFwmE2dPnGLYqFFs3LSJ2bNmPZQXy8Pk5MmTrFm/jtBuXfCrWKHAdpXBN7ff9NQvmTZtGp9//vk9x/p+2TK8q1dBvM+DWBBFfGvVYOWaNXzyySf/6D7KRYsW5cetW1m0aBGz5s7l5pLlyIBWo8FbkrAJCm44r5OWlUKkNppQTZG8CJtbdhNvuUa4EIlCUOX2Wr07riAI+OKPr+APbvhJOoQgKghUh5BmTwJXrkLq8yNGobf40tC7Lbpf1cjZJRsXTacY/dLLaMJDCe3bE40hEKWgRJZlVOHh+FarQfr2rVw+ehKD0wfBLWO/eAXvEjHYrDYUChM+PvlT6iSXC/PZ87R5SOI9LpcLlSr3rC0WC4hCnsENIKiUKNAhSU6sVolvZqVSpZyK5GQHO/ZZcbkFPn47iqfbFu7UiCmhYf2OW0yfPp3ExEQ0Gg1PPPEEDRs2LLTdk4fCkSQJpeK31e8plQ+3jZEsy0yfPp1pX3xCRkYygf5K3G4Zu81OnVZn+OjtEjxz9/fXarX44YfJZCQt08Wpn+xcuGLH36AgK1ti3tIcPno7vNDf3m6XcDhkWjXxZczIyLxjHz9tYtGKVK5ctyIIUL60nupVcp1O94p0C4LAlClTiImJYfp3X7JkzXU0anC7ZVxuJTVq1GHZ8gnUrVv3oV0nD/9etFotkZH/rtr/wMBAOnbs+NDHXb16NRXLqqhc4RfHsCgKfDwhihZN/Zm3JJk3JqXjlmRMJon6DZqwcNE4nnzyyQI1yoIg0K//YCa8/RInzuRQo0rhZUpL1qQTF+/iy48erLhu8FWScsv8wP3+Crbt2IF/jWrofX1zW7bZ7XD3WSm7JQQBfLy88alXl9szZnPp0iUqVar02OYXGxtLQHAE+w9do07LCzzTzpva1TQgyxw5aWfzDgtBASqWzChPYICaRctv8eG0FIScEEr73nueRbUluGw8w+7duz2ZR39zPEb3f5QbN24wZ84cFi1ZQnZ2NjabDYfbTdgzTxHRpGG+h7d/1SoYL11h7YLFhIwbx6effPIXzrwg8+fPRzAY8K9y74eSUqfDUKcWy1ev4p133im0vtvlcpGeno5/wwcLV+lCQzE6HGRlZREaev/+mX93fH19GTFiBM899xxJSUm8MW4c6zZtxK5Wo69bExRqMm7fJvHWfgxWX+r6Nkav8MbmtuCQHPgLIbnGtlBQOfVn/AnhlvMaAEZ3Ngqlks8/+xy1WUcNQ8M8Q/5nNKKWYHUE193X8XuqA/7FimIympBkCVEWERFBFPBv0RzbnXgybyZQQlOKWz+dw1C9JorQICwWS67YjPjLvZy8aw8Ku41+D8noLhldhqOnbgDgcDgQCskEEVQqzlwVcKFk/zV/9pzMxpllYli/UF4dVYywe6Tzmc1uftiUzvUbSXz0wasUi1CTY5H4cpqbqKhSTPpgMk2bNn0o5/FvJyYmhvnz7CSnOO6bPhl/286tBHuh0d8/QnJyMj179uT4sT30eMabnp38KF1Sg1qj4fRZC7MXpzPy1WtkZLoY1Cv3OaLVatFoNKRnWJjyzXV8DSEMHDICWZb57tvPMPgm8MLQ8Hyp6Tfibbz8djySrOL85VyHQbbRxbAx19h/JJuoYiqeqKVFkmDPwQzmL0vG4VTh5+d3z7kLgsBzzz3HoEGD2L17N/Hx8SiVSmrWrEmFCgWdmx48ePjzpKel5HUo+DWCINCisR8tGvthsbjJyHJSp/UlBg8eel+l9F69erFu7Rp6j9jHe6+H8VTrAFSq3Pet0eRi7vcpTPk6FQQVet2DHblX4xwE30MD5q8mJycHVXAgBoMv3t5eWK1W3K67Qrh6JTqdDlEUsVlzNVdyOzU8HrZs2UK/QQNxBwUR3rMXjswsluw6ysI1mUhOJ7JbQqFQEhSoYeRrt4i/4yA13Y5s0dEssDlK8d6BIJWoRiEoyckpvJNEUlISixYtYs3qZaSnp+Hl5UXzFu3o37//P67E45+Ox+j+D7J9+3b6DRqEBfCpUY3AIuHcXLYSfflyiOVKk5GRgb+/f75oim/Z0gS1as7C7xfz6tixfytD8+jJk+jKlkZ4QOTPUKEcN7bv5PLly9SuXbvAdoVCgUqlwmW1PvCYbpsVQRD+tELp3wlBEJgwcSI/bNlMSOenUZWKwWKxoRE0gIAtMYHUNWs4kLGTRn4tke9WnuZqcYNWrcZmt6OkkAUDADKyLHPHFUvdhnXYv+cAVXRPFDC4fybefh1t8UiUwUEoFAr8/P0xGrNxupx5EXVE8K5Vi+yETZTxroTZlEPS4kX4t2qFukxJbDYbOr0Op9FI8p59mA4c5q3XXiMm5t4CUb+HXr36MKD/ek7+lEPR8MKlR91umcUrs/GqUJmSgwdhvnWH65M/RK8T72lw22wS3YdcIjnVzruvhdKvR1F8fZR3I5c5TP3uJn37dGfmrAUPbFPnAZ555hnefWc885el8Oqoovfcb97SZHx8Hk4E6/Lly7Rr25Lk5NtMeiOQoX39AAGnU8JqtVO+tIJP3wlh6vQM3ng/juqVvahWyTsvhfuzb5NJTA1ky5aNlC9fPjfDQ6Vi8scfMHV6MjElVMREaXFKAgeO2AgKiuDFl4YzZ/bnXL9h5aU347geZ2bO52E0b6RHvOt8cjpllv5g5K2Ps3j55dEsWLD4vmVDKpWKFi1a/Onr4cGDhwfj5e1DRmauoSjJd8W1BDFfIESvV2DKcSMI4gNrkzUaDQsXfc8LL4zixTfX8f5nyVSrpMblkjl8wo7TpWX4yNdYu3Ylc75PpEQxO2537vFVKjV6vR61Ro2AwPlLFk7+5GDW6G6P7gL8CUJCgklOSwNy13P3EiC1p6ahEASCg4Mfy7wSExMZNHQoQqkYSj7bHVGZW7oot26Z21ZPlknavZfsHbupVqsToaGhPBMezvnz5/lh0XqUwv1T4G1uC27ZRUBAQIFt69at44UXnkOlsNCxlRclI7WkZ5pYs2kmCxfMYtQLr/D666//JUru/0U8+Yn/Mc6ePUvfgQNwRxan9LixFGnfBlGhRHI6MTSsj9LLC6fLRVZmVgExqoDaNbEDS5cu/Sumfk9cLmeh9df/j6BQAjIuV+GiIoIg0KRRI0xnzj5wrKxTP1GtSpV/lWL+kSNHWPnDGkK7dCKwZg10ej0gI5EbOdOGRxDWqzcWjZNY8yV0Cj0qQUWGnIIgCHj75LbgcMqOAmNnkY6P0sAF0ymsQg4+Pj7ITpkg1b2dNzlyDtpixRAQcDqcaLUadHcFpFSiGpWoRiNq8S4ahSCK2CUb9QxNCbMHkrF6LYlffcvNeQu4/u1Mrn74CfKpn/hg4kTGjBnz0K5Zs2bNKFe+CiNejedOkhv57mLlZ9xumQ+mJHL6gpvgJk0AsCUng6hlxfocrsUV7uD5anYCZ87nMOfzMEYMLJZX2ycIArWq+bDwm1K0aqLghVHPYTKZHtr5/Fvx9fVl8JCRfDUnkx82FRQNBFj2QyozF2Uz7LkX/rQzzWw206tnVyzmZOpU1zJyoD9qlYhaJeClVxAYoER7tyrlpWGh+BtEmne+RIP2l6nS5AJDXk5E71uLH9Zuonz58pjNZkaNep5vvp6Kv59IudJarDY3m3ZksfeggwEDR3H02GnGjh1LUFBROvW/wqmzJhZ9E07LJl55Brcsg8XqpmUTL+Z8EcXuXVseuWicBw8efjuNGzdh32EzFy4lk5KcQmpKKskpyWRlZeFw/PJuXbk+HbXai/r16z9wTB8fH+bOncfuPYfp1HU0srolKu+2jBr9DseO/0SnTp1ISU5l6Zp0tu/OxksPeh1Ikp3MrAyyMjPJyHLw8oR4ihePoWXLlvnGl2WZo0ePMnLkSBo2aULDJk0YPnw4hw8ffqxCvN06d8F66QpOo/G++6UfPkq1KlXuq+/zMFm4cCFmt5tiXTvlGdyQW6qo1OlQ6vUUadMKTdEiuNxuJk2axPPPP0+vXr2wCxbSnSn3Hf+m9Rq+fr40b9483+e7d+9mxPBBtGsGx7eXZcrEEgwfEM6bLxfj8OayjH/Rjy+nffSP1ib6p+Exuv9jfDFtGnYvLyJ798irW7alpSHqdKhDQxAUCkSdDofTgd2R33hS6nSow8KIjY39K6Z+T8rElMJ+I/6B+5njbqASFURFRd1zn4EDBuBKSCTr7Ll77pMTG4ft2nUGDxz4R6b7t2XevHkI/v55afoqlQqVWoVLduZFtVUGA15Vq3DDGYuASKi2CInyTZQaJUplbrqqLMg4JDtuOdcAtcg5pMlJmCUTVxzncIgSqzdswOi2sCNrA9fMF/L2/TUCArIkAb9KWxdyo+YKlChQIiAiu6W8/VWimjp+jWnm1w61yU4pUUnnmrX47IMPuHj2LCNHjnyoHl2lUsnChUtR60rRqX8y705O5uAhIydPm1m0JJ0O3eOYv9JCaOdu+MREI8syWYeP0qJFc4oWr0zXQbFs3pGJy/XLwiQh0c4XMxLo3N6bJ2oHoijEoaRQCLz7ejGs1gxWrlz50M7n38yrr77KM5368vwbiXTqf5VlP6Sy91A2S1an0rHPZcZMSKZnr6GMHj36Tx9rzZo1JCTEIctuenXxLaTWEnx9lChE0GoV9OsRhpeXD206vMDQ4W+zbfte1m/YTLly5bBarfTo3oVtWxYz4RUDp3aWY9/6ipzZXZ2dayrQsomWeXOns337dry9vVm0eDm3EySeqKklpqQKh1PG6ZKxWN2kZ7qw2cHHx5emDQOoWVXD/Plz/vT5evDg4c/jdDrZuWsXmdl2ps5IQ9BoEPU6BI0GmyO380VOTg5JKQ5mf5/JU093+11ChqVLl2bixIksWLCQefPm8eKLL+JyuejRvRNlY+w81TqQse+mMWFyOtdvOPHxzn33LFyeRvNO57mT5MfceYvyZcYYjUa69ehOy3btWPLjdmK9tMR6aVm2cwetO3SgS9euZGdnP/RrVRjdunUjwMeHWyvWILkKrikA0o+fxH71GkMHD35s0d3vly1DX7kiCu29hTwFQcBQuyYbt2zBeNdpUL9+fSpUqsAl6ykcUsFgBkC2M4Pb0nV69+2VLwgkyzKT3n+H2tUEPn+/RL5yJACVSmT4gHCe6+fHF59Pzjumh0eLJ738P0RqairrNm7Av1ULxF89NAVByOsRKAgColKJJCqwWi1oNP+X/ipJfzsBpz69e7OhT2/M8bfwKl6s0H1kSSLz4GFaNGtGRETEPcdq1qwZnTp2ZNXSFThNOQTWqpHnnJBcbrLO/ETy2vU0a9iQzp07P5Lz+avYd/Ag3hUr5EvT9zP4kZ6RjsNtR4kSUVDgVbYcyYePcsF0knQ5GVnt5iqn8XbWx+l0oVIpcTlduCQHOZKVCxzHJTixqhX4VqhEcP26yF5eZCenYDt3iXNnz5KYdZt6hqb56pb8hQASLl/Bq2E9JLMbhyyhVKmQ70bfxbs+Q8u1KyhkER/lL3X6LtmFSqPigw8+eOSpsREREWzctI3vvvuOSZMmsmhVPKJahSwq8apQhagRDfGKLI4syyRu2Y77TgKjP/2MKlWqMPy5IQx5eTfhIYlUKKvEaoN9h8xYrBI9O4XeN3UwLERN0/p6Nm1az4ABAx7pOf4bUCgUTJs2jVatWjFv7mzGTNhLbjqPSMNGTZgzdzCtWrV6KAux7xfP54maWg4es1O8SOGpgYIAOp1AjtlGZDENkmxh9OjRbN68ma1bt7Jz505q1arFvn37OH/uECvnRFG1Yv50yaoVvZk7LZpRb8Tx0ksjady4McWKFUOjVdOxjS9WK5gtrryspVzNfDCasjGbc2jVRMMHX+xF+hs+1z14+K8xbvx41m3bSkDzVizfvAdJSGHksBCKFVWDWo3LamPLzlQmf2VCqY5i3Lhxf/qY3377LUgpLJleBoOvgukLkpizOJmlP9zJ20eSIMcsMHvOpHw1wE6nk959+7L3+HHC+jyLoVzZvPWDLMsYL15ix/LV9OjZk7Vr1jxyEV6DwcDcWbPo0bs317+ZTlDjhhgqlEdUKrAmJJJ64BA5x08yqE8funV7fCnyycnJ6Co/WAdDGxJChttNeno6vr65ztpZc2bRvm17Dif/SAl1GSK0kSgEJXa3jXjrNW66r1KtTlXGjx+fb6xTp05x/vxpFnxVJK9dZGEM7RPKrMWXWb58OYMHD/7T5+rh/niM7v8QsbGx2J0uIkrlr2fVFy2CbLdjvxmPNipXYVRQKnA686dhO7ON2BMSqFy58mOb82+hefPmVKlYkXOLlxE5dACawPxK0LIkcWv1WkhNY9T/tdT4f0RR5Ltvv8V7rDeLliwhffsONCWiQBBw3LyFYDbzdNu2fPP11//otieF4XA48ilvAyiUCgIDAzEaTdjtNpCduJQSLlwkq2/Rp1cvWrduTa9ne7M1dRWhQlEMciASEhmkkEoCCo2ATudLkZ7dCaheFcht3+HQ6fCKLImrWg2Sv/+e06bD1DQ0zDt2EW0kccnbSfz6G7hbEqDQ6tCVL4t39ero/UNw2+yYjp+guDISlZj7QpdlmeuW80RFR/Lkk0/yODAYDLz22mu0bNmSpzt3JtPlxL9ubQKqV0NUKcn86SwZB4/guhnPpIkT8+a1fMVqzp49y9KlS7l9+zYGHxXdn/Vi+bI5xJQ0APc3AMNDlJy8WHi6tIeCCIJA+/btad++PdnZ2WRnZ+Pn5/fQy0Ti4q4zsIc3B49lk5p+7x65KpWIjIuERAd2u4uaNSphsWQSHKjCZpP40ChhtzsZ3NtQwOD+GVEUmPBKUTb+eJkVK1bQsWNHREEgOMib4GA/LFYLOSYTgiCj14m5avsy2B0SCtFBdnYWsbGxD03nwIMHD7+fxMRE5i9cSFCrFoQ0bkhGsaKsXruatVviqFZJg5+vwNU4F3FxNlQqHUuXfMLu3buRZZnSpUtTrVq13+0wtFgsrFi+iAHdDfj75ZoDw/uHM6R3GIeOG0lIcqDViNSu7k3fkXFs3bqF7t27531/7dq17N6/n+LPDca7RFS+sQVBwFC+HMoBfTjw7UzWrFmT77v34ubNmyxcuJCNW7aQk5NDcHAQXTt1pkePHr8pqt+kSRPWr1nDuDff5OD8RcRKUq6H0+0m0M+P8WPHMnbs2Mdaw6zV6XBbLPk+kyUJ09VrZJ7+CZfZjEKjQentBbKcr7ypTJkybNm2hfHjxvPj9h+5aDyFUlDhlB14+XjRv2df3nnnnQL166dPn0atctOkfkHR4F8TEqymWkUNp0+ffmjn6+HeeIxuD3hHl0QbFET2vgNoIovf82GUvHcfBi+vv12EV6lUsmTRYp7p0pnLU7/Cq0ol/KpUQlSpsNy6TfaRY4hZ2Xw5dSpPPPFgZXK1Ws20L77gxdGjWbhwIWfPnUNGpmz9hvTu3ftfq/YYWbw4Z28nFPhcoVBgMPhitapwOJzY09PRa7UcPHSAUqVKMXLkSGSXTIAmmHRXMknSLQD0Ci8ilMW57b6JoXpV/KtVzRtTEAX0ej1msxl1kXD8W7TgzroNlHPl4KX0xuTK5qT5MIKXDn2FcujLlkVUq7HduEHO8ZPk/HSOgNatMB89gTLbRilDrhfZKTm5mHOKLFUaU9+bU2hq9qOkWrVq7Nm5k6lTp7J89Wridu1BlkEpitSrU4fRkz4oUA9XqVKlfG1LDh48yIrl87mT6CDA//6OncQUF37+hbcb83B/DAZDoV0M/gwZGRls2rSJnJwcXC4Vtap5s2JdDt063sOol2WcTonPvk1GqRDo382Pvt3LEBGmQZZlZixIZtykOFo1UWKz2dDeIz0xJFjNkw10bNywlr59+6LT6Tl/yULTBt7kmExo1GDwVeXT+dNoRG4nuFAqZfr368WOnXv/0e0PPXj4J7NkyRIcgkBgnVoABNSohl+lCmSePsO5K9eQUp2oInzwiYCUnbvp3KMH8t1/aJVCpFKFCrz2yljatWtX6Pgmk4mVK1eyYeNGMrKyCPD3o0a16hiNWTRvHJVvX6VSoGHd/M/GFo29WbnpeL7PZs+diza6ZAGD+9d4RRZHGxPNzNmz72t0y7LM119/zcT338elUqKvUB5lWAipaem8NnECH06ezOwZM35T5lpcXBznz5xF6RTwcRtQiipkwJpl5IupXxAZGflYW2u1fPJJVu7dg9yiGYIgYE1IJHbhYmwpqSiDglAG+CNlZmG7GY9WoeDixYv5+m1HR0ezdNlS4uLi2LVrF2azmaCgINq0aXPPDhROpxOlUszT87gfajX31Dry8HDxGN3/IWJiYtCqVRgvX0Eb+ks/RkEUKdKuDdfnLyRj/Sb827REdrlQ3U0FkiWJlL37MR04xPtvT/hbiocVKVKEbZu3MGfOHGbPm0fCydOAjFqlpnP79gwbOpQaNWr8rjFLlizJhAkTHsl8/4707tmTF14diyMzC7W/HwCSJGMymbDZrEiyDLJM5qEj4HDw4Ucf0a5tW5YuXkZFTS2K6KKQZAmn5EAQBFSCmkR7PHcsCWirVMJms+bz4Hr7+OByu7Db7GjKl0b+UUOc5RJhmqIcNO5CjgigWJ+eqA2+2O12EBWoixfDu25t0patJHX5CgQZiqmiuG27gdVtJo1EVHolX34+7ZH0UP0tREVF8cUXXzBx4kTOnz+Pw+GgePHivzmSWKtWLYKDw1n2QxqVyt87vTwpxcGuAxbefa/Dw5q6hz9ITk4Ob7/9NqtXLUGSLFgsNjZsExnUy5fxHxjZvDOHNk8WjFTbHTKvv5eOw+Fi0XcxtGn2i5quIAiUiNSg04oUK6IkOzsLtTrknmng4SEqbp3NRKVS8fQz3fl+1Vz6dPFCFHN76/6/L9VikVi5Pod+3UNYsf4iGzZs+Ns5VD14+K9w5coV1EUi8tX9imo1gbVrEVg71xBPP3qcG8tWoo4uSVCTRgRXqYwggOnadS7u3U+fAQOY8uGHDBo0KN/YmzZtYsSoUWSYjGhjYlAafHHdvsWWH3egcOUgy877zk2SJSTJyc2bN2lYvyEGPwOt27Tm6LFj+D/V/r7fdbvcqEtHs2fZKsJDw/Hx8aVt+zYMGDCAKlWq5O03e/Zs3nznHXwbNyCsWVPEX6WiO3NyuL3qB3r378eaFSvzBU9+FnG7evUqoiiSkZHBxLcmEuSKoK6hel4GHIBTcnAx5xQjhz+PXq9/bGuE/v37s3zNajJOnMSrWDEufz0d0c+X0MED0BQvllvi6XZju3ET64FD9Ojdi+XfL6Fx48b5xilRogQlSpT4TccsWbIkFqvMhcsWypfR33M/q9XN2YsOBjzx28b18OcQ5McpLfgIMBqNGAwGsrOz/5bG4N+NYcOGsWLXTmJefiGvVvln0o4cJX7lGlCp0Jcvh6FIEWSrlZwzP4HRxJjRoxk3btzfvrWAy+Xi9u3bOBwOwsLCPPfFb8RkMlG7Xj1SlQqiBvdH1GjJyMjA5XYhqNUICiVZ27ZjOnSEkAb1sVy8BNlGNFYNjQxtC70vrpkvcF68TPjLo1Cqc1PVf40sg8VixmK2kDh/Ee7rN1AolTgVAiVfeRG/okUQEHA4HFisFhwOB7IMostF0nczqRFTCrvVTnZ2Nv7+/jzT+Rl69+5937r9fwJTpkzh268nsWJ2FNUqFTTW3G6ZEa/GsuewhhMnz+Lj4/MXzNID5BrcXbs8Q+z1Y4waFEiPZ4I4ddZMv+cvM/uzEJb8kMPuAxZeGeFP7y4G/Ay52RcJSU6mzcrgqzlGuj/lz+wvyhYY+8gJE50HXGDdggiKhCvx9va9Z51//1FXsbieYOWqNVy8eJGWLRrTuqmDKW8H5anf/4zTKTPy9WR27LOy64fKvPpuPA65Fj+s3fDwL5AHDx4eyPDhw1l57CjRw4cUut2WnMKFT6air1YV3yaN8PMzoNf/YkzJskzChs1YDx3hx61bqVq1KgA7duygR+9eKEqXIuKp9qh/ld1jSUjg+uSPefcVAyMGRaJRF8x0sdvtZGVlMfjlRPbvgyKqUtglK+kkY3JZKN63N6F1axU6Z6vVSna2Eculy2QsW00VfQ1ckosk6RYulYOXxrzI+PHjMZvNlK9cGXe5MhTr9FShY0kuN7HfzaRGWDhbN28GcgUrp3w8hUsXL+N2upFkCbvDRhDh1PZvjEZTMDNIlmVOGPfjFaXm5OkT922X+LCQZZkXX3yReUuX4NLpkBQKwocNRtRpQZaRXC4kmw21UoWfwZe4OQuIcLo4eezYH87Wczqd1K5Vlab1svjknXsb1ItXpvD6+xkcPHSCyMjIP3qK/3l+qy3qiXT/xxg9ejQbtmzhxoLviezdA8Wv0gmD6tRGodVyc/5ipDNncVyLxcfHm84dOjJgwIC/XS33vVAqlfdVKPdQOD4+PixdvJhOXbty/bNpqKtUQlkiEoW3N47rcZiOHMVx+w7FOz1NcP16OJ9swrlPP0c22e/piBHI9eCKsoDT6USS5HzpToIAXl5eeOm9yFSpaNqxPbE3bpDorce/2C/9lNUaNer/E/Vz1K3NrbMXuHz+/CMXaHncjBo1in17d9Fj6AFeGRFM96eD/q9PdyIHjsHMWd95DO6/mA8//JDr146xcnYJKpbLNYibNjBQv7aBl95O44tJwQT6i0z5JpOp07OoUFaN0ylz8ic7JrOESinSpb0vTpcTlTK/I7R6ZS9Cg9UsX5fD6y/4Y7NZCzW6c7MerLz7Xm7kply5crw85g3eHP8K8bedDO7lR4M6OmQZdu63MHdJNtfiXHz3aQzFi2qoV8OLOcuuPPqL5cHDfxRZljl16hQLFizg6pULAJQtV4l+/fpRuXJlKlasyPdr1uCyWFDqC0YmUw8cQtDpcjMR7fYCxqIgCES0a83VCxeZPXs2X375JZIk8eobb0BkcSJ7P5tPJBVAHxGBd7WaLF59iu5PG4kID+LXOiJ2h53MzExib7g4dMRJRX0jiupyDTib28rmjFVkxsViqFoZrTa/wW6z2cnOykZEgZycgVbUEa0vjyAIlJYrEWe5zKcff4qvry8Gg4GsHBMxTza55/UTlQqCmzbm6KIlnDlzhn379vH2+An4uYOppmtAoFcISfbbHLHtIoISZGVmYfAzoNXmb/8oCAIx+gocvbmTHTt20KpVqwf+dr+FuLg4FixYwIo1a8jIyMDX15cObdrQv39/KlSowKefforD4WDGnDkEdH46N3vAYgG3G2QZjUaLn8GAIAqEt2nJja+/Y8eOHQXK0X4rKpWKESNfZMLbr1C1Ugq9u4QU2OfYKRPvfppCh47dPQb3Y8IjV/ofo3z58iyePx9NYhJXP5jC7R/Wk3HiJGmHjxI7cw6J3y+naYMGxMfGknjrFlcuXGTq1Kn/GIPbw5+jSpUq7Ni2je4tW5G+eSvJM+aSOO0b0letQavTUWrYYILr1wNA5euLX/OmmLGQ7cwodDx/VRDYHdhu3ASZe/bstGdm4kpK5plnniEpORltVCRWqxWrxYrD7ijQMx7AJzqa7OxsUlNTH9r5/13QarUsWbqCdh36M+lzE9WbXaLJU5ep1fISz/S/SXxSJPMXLKV169Z/9VT/05hMJpYvW8iQ3v55BjfkipvN+jyGSuV9GDA6mTPnHXR7yptqlby4fM3JnoMWLFaZJvUNqFUQHOgiPT2NrKysfP8jKpVIn64hLF9r4sw5O5JU8B/B5ZJ568Nb6HQBdOnSJe/z1q1bo9PpkWQVo99MpUaLm9RseZPX3kujSLgXq+aVo1XTXGEitySjUHiWAx48PAqMRiO9ez9L+3bNOLx/IWUiz1O6+Hn27JhL61aN6d+/L+3bt0enVJB64FChY6SfOIV39WrIbjdKpbJQR7MgivjWqsGylSvZvn07n332GVeuXSOsRbMCBvfPhDRtwpVbAm99mERycgYZGelkZKRjMpkwZhtJTnbz4vg01G4D4dried/TKnSU0pTFcvInstPTC7yjc0wmBEQUboGcU6cprorKc86Lgki0VzmKCNF8MvlTDh06hKZIEdR+99fY8C1XBrcgsHz5cia8NZFilKKmb0OC1KEIgkCWMx2toCdAEYKASHZ2Nm53wdZhfqoAtIKOY8eO3fd4v5WFCxdS+4knmDp7FlnFi6Bt2ghz6WhmrVpJw6ZNmTJlCgqFgpo1a6I3GAiuXg2NQoFGVKDX6QkKDMLf3w/hbkBCX6woyqAgduzY8afmNXjwYPoPGMnr76XQecBVVm9M59TZHH7ck8Vzr1yn6+CbVK7ShM8+m/owLoOH34An0v0fpEmTJhzat48FCxYwf9Ei0o4eRwBq1qjBoG/H8tRTT/3rIocefjtRUVHUrVuXxatWUaRPTxRqFeqAADSBAfl3FEBfKoYsX2/u2G5gUAUUGMtfFYRB9CP70EECI7vcU9QjedceAgwGoqOjycrOQp9thKxfensqFAq8vL3yp9NJuS/Tf2urIy8vLz7//AvGjRt/t+9zAhqNhieeeIJGjRr9a8/7n0SuqE0Wz3YqU2Cbj7eSJTPKsOdgNvOXpjBvSQZOl4hSAWNGFOWN0UW4fsNOmx7ncLoEDD4KjCYrWdky/n5+/BxxGj4gnP1HjfR5PolRgwMY3Nsff7/crIfDx018PiOJwydg9pwZ+bIeoqKiCAwM5MkGMP2TGK7G2RCAsqV0FI3IH5X6cW8OlSo/WGTSgwcPvw+n00m/vr24eGEfMz6NoFVT/7wWTm63zMbtGbz67jpefdXCc4OHMPXrr1EbDATUqpFnpEouF26bFYWfL7hceN9DPMty+w7ZP/2EPTuFvn0652aX2Z0kbdlCSLNm+JQqqCuiNhgQff1ZtjaBk2dj6dvVl8oVNVgsMjv2mlm1wYLT5Ett72YohPypziX0ZbiedZW0tRsw9OuN1iv3/exwOHC5XCglBakb14PRQgm/gs/IaH1Z9mbFcvnyZQTVg80RQRQRlUp2796NxqWjjE+lfFl2ua1Ec+eoEtQ4ZBtWiwXvQrLBREGBw1F47+vfw7p16xg9Zgy6WjUo0aFtvrJNuXVLknfv5f3Jk/Hx8cHhcKDUafG9q5lzP0S9juPHj9OyRUtux99Go9XSuGkj+vfvn1c68CAEQWDSpEnUr1+fObNn8MK4/YAECMTElGHiO0Po3bu3Z73/GPEY3f9RihYtyrhx43jjjTew2Wwolcp/XQssD3+c9PR01D7e+FW4v1K73tsb0c8XW6Kt0O2CIFBBV5W917Zj3LyV4J7d86XOSU4nST/uxHLsBH0HDaJLp67IdhnH9RtoauUaARIybrcLY7YRl8uVVy9jvHSZ0JAQgoODCz32v4WQkBCGDRv2V0/DQyFkZGSgVEBEWOGq36Io0LSBH00b+NGg/U9ci7OzY1UFKpTN/R8oX0ZBkTANK9aZqFdThyhAptGG3e7IUxLXaES+mRxNuSfO8Mk3Vr6Ze5Ei4WrMFjep6RLR0WVZtPjjAqI7Op2Ort168/3qrxnWL4wWjf0KneOBo0Z+uuBg/uv9H9p18eDBQy6rV6/m6NG9rJkXRc2q+Y0/hUKgY+tAAvxV9Bi6nWef7c2gPn2Yu2ghGfsO4Fu9KkovL2xpaUg2G26TGV9fX7RaLfLdrJefo6PGS1e4vWAOZYq66D0+iGfaFcVqs7DvcDaLf4jjxKzphHbuTmDtmnnHd1ksxE3/jqJe2Qx7M4Tzl51Mm2XE6ZJyu24oICtbpogiDC9lQcPVS+lDHe8GHDi7k+vTviakSSN8S5fCYrWSc+481uNnkBJTqeX9BN7KgnWuWoUeH9EPh8OBMykZyeksoDX0a2zJKUh2OxfOXSBGUalAWZte4YUNCw7ZjlrQIKLAarUWMLrtkg2r20zx4sX5M0iSxDvvv4+ydAxFn+lYYD6CQkFYs6Y4s4189MknTBg/HrfVijMnB5V34S0gARxWG8bbdzh49hIR6ih8FAacsoulsctZOG8R/Qb2ZfLkyQVKDLKysrh27RqyLFOiRAmCgoIQBIF27drRrl07kpOTSU9Px8vLi+LFC3YqcjqdJCcnc+rUKTZt2sSlC5cAqFy1Mn379qV69ep/e02nvzseo/s/jiAI+RSlPXiA3Ciry2pDcrkQ7yM0otPqkCwWMhxGJFlCFPJHX2VZJs2ZjJdag+pGPFcnTUZXoRyawECcOTlYzp5H6XQy/tVX+e6b6ahMOqp6lebk9WPYk1PQhIYiIiAKatyyC4vZgkqlRul0kHPqJ1586aXHIoTiwUNh+Pj44HLLZGa58vrcFoYsy1yLs9G2mXeewQ25i+6+3UP55Otb9OrsS/UqWlRKCYvFkmd0y7LMlzMT8fHxY/OWnRw5ciQv66FevXrUr1//nguh5557jjWrl9F7xHVmTy1BaEj+iMbRkyaee+UWtWs3emw97T14+C8xf95smjyhK2Bw/5oGdXypW0PD/HmzWb1mHZ06deK7GTPYtHkzDqcTvV5HdFQUadeuIzVtTGpKKu67mV4KhQK1y82dhfNo+YSCKW8H461TEhigw2yWaNPMSdv2wUyaksSC1cvRRYSjL1oEgJQ9+9BZk5m/OJIIg4NBvXxwT9SQmOzE7rDg5+1g/WYbb354jWxn2UKz2cK0xQi0BRKi9yJpw2bS3OtxOJw4rDaKqSIp5dOMAPW9HeOCLFCqVCkuXr1K5qkz+ZwC/0/qgUMEBwaQmZReqBFfRBvFeeNJkuR4igulEBDyrtOvibdeR+utpVOnTvc81m9h7969XIuLo/hzg+9rjIY0acT1o8dxOp14abSkHz1O2P/Vr7ucLiwWC3aHHdPFSzizsimrKU85n2oohdx3iyRX5ZY1lrkz56FSqfj444+BXOX7L7/8khVr1mC12wHQqlR0bN+eUc8/n1ceGhoaSmhoaIH5JScnM2fOHObMn0/8rVu4XC4EWcAPAwZlABdPXGHhvEW0adea6TOmF+gJ7uG341mtevDgoQDNmzdH8fbbZP10joDqVe+5n+32bZQWC24tHDHuJEpThhBNBAIC6Y5k4myXMSrTmTx5Ml26dGHJkiWsWLWK1IuX8fXxod2QIfTt25ddu3aRkZpBQ++2qEUNsfYrJC9bSmj3HmjuviQUghK37MaYmIjxh/UUDQqif//+j+eCePjPcu7cOebOncuP2zdiMpkICAjgqae70adPHxo3boxK5cXydWkM6xt2zzGOnjRhNrvp2KpgT/XBvUP5cW8mPYcn8sboANo8qQdyF07X4qxMm5HI6k02PvxoKhUqVKBChQq/ee5FixZlydJV9OndnbptLtO2uRc1q3phd0hs25XD0VM2atVqwLz5Cx97T3sPHv7tWCwWTp8+yafvFPy//386tPJj/IeHMBqNbNq0ib07d+OyOFChxJ5tJcloxuxyoDx+Au8y5VAKuRFhySWRtP8gPiobH74dhVrhyCvD0uq0mHJMCJKL8a+Gs+9wLKn79hP5bHcklxvjsUP07+BD0RAB2QFarQ5RFPHxVmKxyBizHXRpa+CbeVlcT7hEKWVFJElCEAQ0Gg0ajQan5MIlOmnYoAFOp5Pbt29jMpk4deI05byq4qvyu+c5OyUHOWRTrVo1EAR+2LgZXXgY+l+JqP5MxqnTmI4e54VXXmHKx5/glAqmhqtFDZH6GG6Yr+Ij++GLfwFjONWexA3XJYb2H0JAQH4ngsVi4YcffmDp8uXcSUhAr9fTrEkT+vbtS8mSJQsc78yZM4g6HfrI+0fMNQH+aMJCuX79Os927cqc5cvwLVMafZEIkHNr/i1WCwgibquV7J17UOj0XHNc407Gber5NsagCkAURCL1MUgWN7NnzGbo0KEkJSXRo3dvzAoFhiYNCS1TGkQB07VYVu3dw/pNG5k3a/Y99V/Onz9P527dSMzKQlm2NH5P1EEpqLDfjCfn1Gls5iTq6hvjkG1sWbeNvrZ+LFu21JMZ+wfxtAzz4MFDoXTu0oVd585S8vnnUBaSDSG53MTNnE2UQsWUjz9m8seTOXLoCC6nG2RQqBRUqlKRV8a+QocO9+8l3aZ1Wy7vj6WWoREANreFQ8bdZJGNLqYkuuhSIApY429ivnCe6MhI1q1ZQ5kyBevEPHh4GMiyzMcff8y0LyYTHgKd2vkQHKQi/radVRtM5Fg0fDx5KgcOHODA3iVsWBxdaJq5yyXTY8gVtu/JZO3CsjSp71dgH4vFzevv3eCHzekolVAsQolS6cXl63YCA8N56+336Nat2x8+l+zsbJYtW8biRfO4cTMOpUJB9eq16dtvAK1bt/YsoDx4eARkZmZSoXwMMz4NpW3zglHiX7N6YzqjxqVQoXw1Th09QzExmuK6GHQKPQ6XnYupP3FdvoCsVxHYqhW+larm9bJOnP45fVo5eWOUAa1Gg7+/P8JdTYjs7GwsNisKvZ65izKY9JWJYq+8hjsri+QZX7NsVjiVYkCv1WH4VTsxSZJITU1BlBRM+SaDefNdVHQ2yu1IcvdPoRDJUCZzwXESnUaHt2hAJalxCDZSrcmEE0nNwAb3fL5cN1/kluoqP507k1sO0707h0+dRF+pIgG1aqDy9saelk764aPYr16l37M9+fzzz2nZohXXj9yktqFJgTHdsptjWXtJsSXgLwdTVFOCIEMINreFW7ZY0oUkWrRpzsJFC/PVMp84cYJeffuSkJKMNiYGTVgIbqsVy4VLKOx2xox+kTfeeCOfEf/ZZ5/x/pfTKP3m6w+8F2K/+o4+TzbjnXfe4elOnTh+/jyG+vXQVCiHQ5nr8DSfu4Bx3wGQoUivfiC5SflhDcLtNBobWuWl+LtlN3uMG+g7rDer1q7FHOhPVL8+KP6vw4vkchP//VKE2Bvs372b6OjofNszMzOp36gRyciE9e6B2elCJWgQ72psu202kletQI69w5N+bTG7czhh28v8RfN46qnCW7v9FoxGI8uXL2ftD6vIzEzDx8dAy1bt6NmzZ6GR+H8CnpZhHjx4uCeyLHPp0iUyMzPx8fGhfPnyBSJdH3/0Ea3atiXu25mEdWiHd0zJvBeO+dZtkjZuRpGUwlerVlGnTh2aNGnC+fPnOX/+PLIsExMT85trgJISE/EWf3lQaRV6Gvm1IsF2g7hr18i6vA2Q0YleqGX45ssvPQb3XWRZRpZlj7DaQ+bbb79l2hcfMm50EEP7hqFU/nIfj3tR4t1P4nllzPNM+eRLjh09SOtuV2jXwodK5fWUidFTvbIXZ86b+fjLRI6eEfD2MXDxirVQo1uvVzDtw2heH12Ufs9f5fwVNQMHDmbUyzVp3779nxa6MRgMDB06lKFDh/6pcTx48PDb8fHxwcvbmwuXLQ80ui9ctiC5JU4ePkUtr6b4/SqV22lzEioUI4QiHLXsJG3DBrJ37UETVRwZAUdKCqWjAhFkGY1ak2dwA/j45gp4Oc1mYiJFcDrITkjAmZyM5HKgE+2oVd4FDAVRFNFqdVjMFry8BGQk1MIvTkUZmSxnKpecp1GKSup6NcNPlRvRl2WZc8Jxruac52KaN+WDq6L8v3aIKfYErrsuMHBQ/zxD64fVq5k5cyaz580jftZcJBkUgkDVypUZ9tXXdOvWDUEQGDxkEMOPjiDNnkSQ5pcMoyxnBvHWa7hxo1AoSXbfwiRmoLQqEUSBmLLRvDrsI/r375/PEXD58mU6d+uG2eBL9NiX84nGSk4nKfsO8PHUz1CpVIwdOzZvW3R0NC5TDrbUVLT30ZZxWazYk5KIjo7G29ubH1av5oMPPmDuwoXc3rARUa0BZARRRBMTTfCTLVD55DpAwnr05M6smVzJOUc1Q27nGIWgwJ9gNm7YSKbFQqkXRxYwuCG3zVrxZ7tx9aNPmDNnDpMmTcq3fcmSJdxOSabUa69gdDoR7/79jEKrJbRzV25/9SVxliuU96mGwRbIrJmz/rDRvX37dkaMGIzdlkXzhjpqVlCTknabr6Yd4/Opk3l/0mT69Onzh8b+J+CJdHvw8B/C7XazYMECZnw3g8uXryC7ZQRRIDKqOIOHDmbw4MF5taSQ+zIaPHQoZy9cQOHvj+hnQMrJwZWaSonikXz71VfUq1fvT8+rwRMNSPvJRBVDnUK3//yYynSmccKxl937dv2n29i53W62b9/OnNlzOLD/AA6Hk4iIcHr37U2fPn0IC7t3qrOHB2MymaherQLPPg0TXy08dVCWZQa8cI0zFwMJDgnh+LFDCIILAZBkUCoVSJKSmJjSfPLpNJYvX86JI0vYu65snnrx/2M0uajZ4hLDR77FmDFjHt0JevDg4ZFz48YN+vXrR0riMXaviUKnU6PT6Qo4uG02iZotL3D7jkRJsQJlvCvl256amgJuAaWgIkNO4ax0mGL6aFyyAxlIl+OZMCaAPl19EFUiQUFBed+1WCxkG40gCuzYncNzr6bgUOpBBrXbwmfvBFGujBcatTcRYZp8nQ0yMzOxWW28PCGFTZskKkhPoEGHAxtJ8i2S5HhkJDQKHS1DOuXTdJFlmZ+Mx7huvoCfIpAY3/LoFd7YJRt37HFkCWm0e6otc+bMKeBUdLvdXLp0iZycHPzuKrU7HA5CQ0MJCQnB6XTSq2cvfty8kzKqyoRoinDKdIhkdxKiny+qYkWQ3C7c8bfQCQLPdOzISy+9RPny5Qt1TvcfMIB1B/YTPXokCq220N8yceuPWPcf4Oyp03lOArvdTqWqVTGXjKJYp1+MUGe2kcwzP+E0mhA1alwmE+4zZzl76nQ+4dexY8fyzbx5hLRpiVKnQ1MiihyXC7Wgzec4yTx8kJztu2nl/zRqMff3OZN9hDvKm/jUq0PxLs8UOuefSdi8FeHUT1y7dCmfs6FmnTrc8fai+LNdSU5ORokKRSGx2LQd23EcPEnrgE7EW68RK14gKSXxd5ckHThwgGd7dKJZA5EP3yxGSPAvv7vR5OLjaXeYv9zMF9Om07Vr19819l+NJ9LtAchVM1y6dClrf1hHelo6fv4G2ndoT8+ePfM9mD0UxOFwcPbsWaxWK2FhYcTEFGy38U/C6XQyZMgQ1q5aRxBhVNU8gU7jhU2ycvt6LG+9/jZbt2xl6bKleTVhZcqUYe/u3Rw6dIh169aRmZmJt7c3rVu35sknn3xodaAtWrXgy5++xi27UAgFH0s/R8tv224QXjSMcuXur6r+b8ZisTBo4CC2bNqKj2QgXFkSpaAi+0YGH73zMV9/+TXzFsyjSZMmf/VU/7GsXr0amy2bYf3unU0hCAIDe/rTsfdlfPUJLJkRQ4M6euw2O4dP5DB/aSZ7DrsYNHg4jRo1wmAw8MOaZbz14U3eHxdZoH2e3S7xwrgbIPrSu3fvR3yGHjx4+COkpaWxZMkSfli3jqzsbPwMBp7q0IFnn302z6Cy2+288soYVq9agkbtICPTwdsfJ/D2mADM5hx0Oj0+vj4ICDidEi+9FUdGpgBuiPTKv86QJQnJLaEk11jyJxid4IVaVFPDtz4AR7J3sHlnKn27GXC5XMjIuSJibjdGkxFRrULUatl+MBNVcCjFnnoGQRCIX7GSNyalotWkAwKCIFCvpg9D+4XzZAMf7HY76ekCO/bacUtqTssH8ualEbWES1EEy2GckQ6RZL9FhDYyb7sgCFT2rYVaVHPZeoZL7lOIUu4xqtapxpCh79G5c+c8IdSLFy9y7do1RFGkQoUKFCtWjJkzZzJ39lwSEhJBBlEh0rxFM4aPGM78BfN58cUXWblsJScyDoK3F/7tnkIbE42gVOCl16NXqUg9dJiV69cRGBjI5MmTgV9qt7dt28bthAT27NmDT93aGM1mlE4nOp2ugEBrcKP6XN1/gEWLFuU5RDUaDS+MHMm4iRNJLxqBoWJFbq1eQ+aZsyCKKHy8cZstuI1GihcpSmJiYj6j+8Tp0/hWq0Jo44ZArphaTloaEhIKfllbeZcpS9aPO8l2ZhCsCUeWZYxk4nK58Cpe7IH3rL54MVL2HiArKyvv+C6Xixs3buD3VHt+ib0W7gzWFY8k58BhnJIDpaBClmWcTufvWv/JssyECeOpXkniu0+iUanyOz98fZS8P644ppw43pk4no4dO+YLAP1b8Bjd/2I2bNjAiOdGYMrKIYBQdKIXGdJtJhx6h48//JipX0z9U3WC/1ays7P5+uuvmT93PqnJqch3H/bValRj+Ijn6NSp0z+ybcLHH3/M2pXrqaKtR6imSN7n3vgSpA4lw5HKgV37ePXVV/nqq68wGo0kJCSgVCqpVasWTzzx6Pr49uvXjy+/+Ipr5osFvPw/Y3JlkcItXhvw6n+6BnXEiBFs27CdqtonCNFE5NvmlKpyOuMQvZ7tzdbtW6hYseJfNMt/NmfOnKFCGQ3hofdO63a5XZQu4STQX0HXjgG0auoPgJdeQ9sWvrRpHs4HU2/zzsRx1KxZkxo1ajB5yheMfWUU5y5fYeCzQdSr6YNbktmxL5u536dz846GOXPnF6hrW7lyJR98MInY6xdxu92o1RrqPdGEDz744D+d8eHBw+NkzZo1jHzhBcxOJ7pyZVAVCeNOtpHjH0zioylTmDZ1Kk8//TSDBw/kwL4NfDA+lK4dgti8M5MXx1/n5m0XvTr7ULWCC7XGycmzErMXp3M1Tkmnzs+yfOEKtIr7d5MRBAGtrMcu/dKmM1JThlNnb7PviJkn6vxiqFisFmRAodFw+aqNLTstBLZug1dkMeK++45AZTZdevjydGtvggIVXL7mZN4yIwNGXWbUkFC6tFPx2vsZYNfTMrgjZrcJu9tGjttIijWRTCENI5mILpFb1rh8RvfPcy3jXZlE+SYvvPI8vXr1wtvbm5CQkLx9tm/fzmeffsbRw0dxOd0IQm5atEIpYrc4iVBEUk1TH6WowujK4sCmw2zf+iPvvD+R6dOnU6xYMSZ9+ilhA/qgiQhDqVTdzSbINerCnmyCQqdj1rx59OjRg6SkJJ4fPZq0rEzEIkVwa1QoixXFeOoM5tg4Ap9qjzk8DK1Gi8FgyGvJptTp0ESX5PCRI/nOceTIkdy6dYvvZs3i5tKVoFFjaNkcr8oVQVQgOx1It+5gPHSEth07snbVKmrUqAHkBkJEr19+b6VKiUqtwuVwoRBEfjaChbsOAAkJgAxnChbBhFajRXI6H3jfyk4ngkC+jIK8NayU23VGEARkSQKhoCEtS1Led7JdGQSGBvxug/jEiRNcOH+GRd8ULWBw/3pOo4eFs3rTVTZs2EDnzp1/1zH+CXiM7n8pO3bsYGD/gRjswVT3boJG8UvKjEOyczHnFMOHjUCr1dKxY8e/cKaPj8TERJYvX058fDwqlYqaNWvSoUOHfA+PlJQUnn7qaS6fu0oYxampq4haUGNyZRN75DqDjw3hxIkTTJo06R9leOfk5DBz+iyKidH5DO5fE6AOJtpVge8XfY8x28j2bT9it9kRBAgOCabfgH4MGTLkkWRIREVF8cb413n/nUm4TU6ivcqjEXPvWUmWSLbf4aL9JBWrV2D48OEP/fj/FE6fPs36H9ZTTlOzgMENoBLVVPdtwAHjVj777DPmzJnzF8zyn48kSTyoE53VYkUUQa8XKaxISxAE3nixKJt3XmL27NnUqFGDnj17UrRoUb6c9jnPv7EbuAMIiKKaFi3bM+3bMfmMaJfLRbNmzThxfD+RxZS8MNgbfz8FsTcdrN64lXp1tzDmlXG8++67D/P0PXjw8H9s2bKFwc89h7pSBUo91R6l/pfWfy6LhYR1Gxk2YgSnTp1ix48bWPRNcZrUz63LfaZtIGHBKr6YkcBr76XjckvY7TIajRctWrbjw09e5sKFC3y/YCku2ZXXIgpAEEVEhYjklhDvRj+dOPNlhIVoIvCzFWPU+Hg+GB9Mr66gVILNZgeFkv2Hc3jt7SSk4OIE1KxB3HffUlSXwbx5JYkIkpHsNjRqgZLF1XRo6cWXs7L44IskZswXyE7XUsOrORqFFjduzpiPkS1noywairpoWVwuN67Ll7htikebfYRKvrXypZmLgohKyDX2/l8BfM6cOYwd8yreTj8qamoTqAtFQiI2/TLxzus4BSeh/kXy6rb9VIEU05bksvksE96cQGRkJD+sX4+hWhVCy5e9528XVKcWmXv28c6773Lg8CGUZcsQ1rMbDrUKFEpklwt3Tg4ZGzaRtnwVIf37YDMISJmZ+Af8ooAuqJTYHflV0wVB4KOPPiI2Npb1u3YS2LM7quAgkGVUooje3x9deARStSrEzZzLoKFDOX7kCEqlkhKRkVw8fSrfeF5e3mQ5Mn91HwjYExMRZBm9whuzy8RZ61Fq1K6OxkvH8XMXCK5//xK/7LPnKR1TKl/as0KhoGLFCly8dJmgenXQ6fRYzBaU5B7zF2TM164gynDWeIxEZzzDOgy95/r3Z3X7/99+9OhRvL1kGtW7fxlwdJSOcqU0HDlyxGN0e/hnIEkS414fh95qoKqhXoHeyWpRQ2WfOpw07mfc6+No06bNvzpyaLFYeP2111m2ZBkuu4SX4IOEm++k6YSEBTPx3Yn07NkTWZYZOGAg187GUterWb4+kF5KH8K0Rblpuca3X35H6dKl/1HtqjZu3Eh2ZjZVvBvcdz8BEWO2iU3LtxKlK42/OggJicTEW3z6wWcsW7qcNT+spkSJEg99ji+//DIqlYqPPviY26ZYfOUARFnEosjBpbDTpGUTZsycgY/Pvfud/tuZP38+SpeGcP29U8oUgoJiqhg2rttIcnLyP1YN9HFz+fJlzp07hyRJqNVqLl6xYzS58PUp/DVptVpJSHKRmeWmRGThdYCiKNCrsx+Tv16D7bPP0Gq1NGrUiEaNGhEbG8v169cRRZFy5coREVHQidKxY0fOnNrPx28FMaS3Xz4xt3dfdfPy2yl8+skkwsLCGDFixMO5EB48eMiH2+3mjTffRBFTkuLduyD8X12wUq+nWLfOxFmtfPvtVzRvqM0zuH+mXi1f6tXy5Ua8jSuxVt547xbRZVqwePESILeHskqtIMF6g+L6/Cnmer0ekykHJUpyZBNmjJTXVM3bLgoiZb2qsSvrFmPfzebruRdpUl+H3Wbl4Ek7126BOjKaEn37YbkZjzvpFpNnFaVokZ8jnwJ2mxWHww0CdH/Gmz2HLGzZaaG+bxMC1MHYJRsHsndg81cR9nR/1OHhed91tGiK5ex54rbtQDJKeYJfAC7JiU2yFnDWHz9+nNdeeY1wdyTlfX8RXHU4HPi7QwkSinCZU5zI2s+TwR3RKXKdHIIgUMarEtnGdKZ8PIXYuDhC+/a87+8niCJelSuxdet2fKtWpki3LqRnZiBoNIgKBS6XC1VIMCH9epM8cw5Z234ktH8fHGYLVosVvZceWZZxJSQRVbFKgfFNJhOHjh0ltFlTQiuUR7obPRYVv3I+qNWEd2xH3NffsXPnTlq2bEnvXr1Yu2kj5vhbeWniWq0GH18fTEYTkiwhIpJ9/Bg++HDDcoVkblOqfDSLFi/i0KFD7B86lJy4G3iXiCr03K1JSVgvXGLIRx8VMIQHDxjIiJdewpqUhD4oCKvFglN2ohJUgIAbF7bsDExnf0LprSEh0IkzXcWcBQtIS0/ny2nT8uqYlyxZwpz584mLi8stLahUkUEDBvLMM8+g1Wqx2+1oNSIyEjk5Vmw2G5IsIQgiWq0GvU6fl66u0wk4f0ME/5+Ix+j+F3LgwAGuXr5KdX2jAgb3zwiCQIy+Aodv7WD79u20bdv2Mc/y8eBwOOj5bE/27dhPtKoCRb1LohJzHQw5LiPXks7z/IhRWCwWqlWrxsH9h6isrZvP4P41kfoYMrKT+erLr+nbt+8/RjE6Pj4ejajLe3EVRqYjjbPGo4RRnAqqGvh5+eVtC1KHYnWX59j1PXTv2p19B/Y99HobQRB44YUX6NOnD0uWLOH48eM4HA6KFy9Ojx49PGm0wOmTp/EXgu/5f/0zoZoIrpjPcOnSJY/R/QAOHjzIlMkfcuTIASD3Re92i9hsNhatSGTEwIIODhkZSZZYvMpEUICKlk387jl+TEkdTmcm2dnZaH8l0lOyZMlCe7/+zOXLl9mzZxuvj/JneH//AtsNvgq+mxJGQvJt3n9vgsfo9uDhEbFr1y5ib96k+IihBQzunxFEkaCG9Yk7cYSOre6dDRZVXEtUcS3Xb9iY/PVBZFlGEASKFy9OyzYt2bF2N2FSsTzBLCAvCml324jjIlqFjlB1EdLsScRZr5LsTsQuWVFrNXTt1gtZljh//RrHT5zAovUnamhPvEuWQBAE0o8epXxpNdWq/LIWEBQigiDi5+8HMggCDOuvYce+qyTY4gnWhHPdfBGL1kVErwGo/PwAGUmSclOelSJ+NWqiVGq4uW49JZyl85TMb9tuoNSKBTIqZ86YidKhyWdwQ64zU0BAKSopI1fliPwjN63XKOv9y/tfEAQiNaU4c+YgklpRaEvT/0dWKnA6nYS3aYnVbgNBRPFzurUoIjudiDodvo0akr5iNc70DERvbywWS67T4+o13OkZPPvsswXG3r17N8YcMzF1aiGK4j3XhfpiRVGFhbF+/XpatmxJs2bNqFiuHBeXLCdq2CDUd0XjvLy8UCpVmHNySNu1G8u1K6BQIxex89rAsQwdOhQ/Pz86dOhAo3r12D9/MRE9uuJTplS+a5kTe4M73y+laoUKdO/ePd9cYmNjuXjxIpLDwaWvviPk2W7oIyKw2204ZAkBEXtGGumr1qD08iJ0UH8Ueh2+Xl5YLl5i3eatxD/zDJ9Mnky/gQO5lZyEvkJ5DB3aIrvd/HTpCsNeGMU3079jxdJlFC1alJRUGz+dSyQsVImgVCIolEiSTI7ZjNlsxtfHF0HQcC3OSZMWhWdk/tPxGN3/Qo4ePYpSVhOgyhVMMLmySLLdxik7UYsawjXF8FL6YFAFoLd5c+TIkX+t0T1nzhz27txHdV1DAtUh+bZ5K32p4luXCzknGf/6eJ7u/DRqt5YQr4IRp18TqSvN8at7OHToEPXr13+U039oqFQqJNmV94IvjGvmC2hlL6KF8igKeWnoFHqqedXj4KXtj7Text/f32NA3ANJyn0ZPoiflU+lu7VYHgpnw4YNDH9uIJXLy3w7OYxmDQ0oFAKHT5gY+Vos735yh5KRKlo3y68GLwBrNppYsNzIxNciUavv/ZtkG12AgO43LAx/zVtvvYW3Hp7r63fPfVQqgRED/Og1PInVq1fTqVOn33UMDx48PJijR4+i8PVBX6zofffTBQcjigLeXg9uChTgp8TpdOZGWe9mGk6aNInjR1txJHEXZbVVCVKHIggCoiii8BW5kHmCNDmJMEURDmRuJ400lCFB6CrWxUuvQ2G2sGT9OvSCyMzvvuPChQu8P3Uq+iIRee99KSudqnXUCOTW6sqAZHcAMm63G51OhyiI1KqqQKtVcsd8g+KOaG46Y/GqUfWuwQ0ggABu2Ylwt9WUT6XKZO/bR5zpCtUM9chxGYl1XqBjpw4UKfKLEWU2m1n7wzqKKksVWI+4Xa6895dSUBEiF+GW5Xo+oxtyy+EkmwyyhC0lBa+o/PXk/481KRlRrUYXFkZOaiqCSpnrXQAUGjVuqw1J4UBfoRwZaxTYrsfiXbMGLqsFS0oqiSvXULtGDerWrVtg7MzMTGRBQG0wFNj2/wi+vmRkZuYeV6Hg+4WLeKpTJ2I//wqfGtXxq1IJhUaD5U4CmYeOIMXfYszo0QwYMICYmJh8WakqlYrFixbRf8AAds1biDIkGF3pGEDAGhuHMyGR2tWrs3jhQry8vPK+N336dMZPmIBLrcK7dg2yz10gccYcNCVLoI0ugSCK2GLjsF69jtLfj6Be3dH5++Ht7YVKpUJXuyb6YkU5+dW3tO3QAXdQIDGvjcl3/sH162FNSOTc3AV0e7YHo58fhcnsYOVGMy++UCTv2gMga5DsdoxGI1t3uzHlKP61elMeo/tfwOXLl1mwYAEHDxzC6XRiNufgcrvIcRn5yXyMVHcKgk6LQqvDbTFzPus0ocoIqvrURoHiX5vGIUkSs2bOJkgOL2Bw/0xuqlJlEk3xHDx4EC/Z94G12n6qQCSLxO3btx/FtB8JtWrVwi26SXemEKQuGPm0SzaS7LeIksuSLiST6nRhz8r1OPso/fBV+hGiicBH6YcfQcyft+BfWW/zd6dc+XJcPb3xvs4TgDRHMkql4r6R1P86t27d4vnnh9KuuYJpH5TMl7rd+AkDJ36sTP12Z+n5XBxtm2fT45lgggNV3Lxt5/vVGew7lI2fQc2wvvfPJFizKZNq1Wr+7paWP/30E3VraAkMKPw1LQPIMq2aeqFR5woSeYxuDx4ePk6nE1GtfuDaQKHXISMQF++4734A1+Ns+BkM+YyoqKgoNmzawOBBgzl95gAauw6NpMMlOskhm7CYULq2eo7ly1eQnmPCv2M7fCtXxsvLC51OhyAKuDu05dby1QwcMoRZ06ejE0XurN9EsS65iuWyIGK3SUhSrmNAdruQXS5EBEzZRsw5Zvz9/bHZJdQqNZHlYzhyaRdW2Y7f3a4huZk+bly4kAUZAXDJLhSiEn35cqQcOsNV83niXVcpU6kUU6ZMyXfuaWlpOB1OfJV+BS+MIOQ+3O5eai98SZYKrrUk2Y0oiFStUpVzh48RUKvmPX8fl8WK+ew5NP65x5NlOZ/RJ6rVuSrxNjuyUgEKBa6sLBxJSeScOEnGxSuUKV6M+XPnFnoMX19fkGWcJhOqB5S/yTk5GH71LoiMjGT7li18++23zF+0iFsHDuWK3wkCTRo2ZPjHk2nRosU9xzMYDKxauZIDBw4wb/58Tv/0EwDlKleh3yef/o+9uw6zomwfOP6dOX22znawdHd3IyiohLQ0AiIGBi8GBr6vidgFKt0lDQZI59KdC0tsd5w+M/P7Y2FxfxugIkvM57q81DNzZp6Z3T1n7ifuu0CVmQULFvD622/h3bI54Y90QtTpkN1ukvcfJHH7TjJ+34jsciMAlkc64dOwPl5+fvj4+uaLk03hYQj+FjISk6j11FD0vgWv2xQRTunhQzj81Xe8PuENCAxn2sJUGjTMoU2rP+0vCIhGI8dO25n4SRxdHh1EmTKFl+q816lB9z3M4/EwYcIEpv80A41Hi78SgkbQkO5OIcuZye/ulejDwghq2wuvKlURNBpktxvr6ZMkbdnK1ozfQePJ1wN5Pzlz5gwXoi9Qz1j8aLRW1BFEOInxCZiVm/dUSooH4J5aB9+iRQuq1ajK+aMnCNAVnJ5s9eTglt1c1J7DJbjQizJaUcbPR8ApwMUsAb3Dhwr6Olg0gUSfO19CV/JgGzxkMEsWLSHZlUCI4dqaOkW5tjZKQBBEZEXmsus8Dz36EGXLFt/7/yCbO3cuRr2NT9+tni/gvs5g0LD7lzrUa3+Mnft0bNmVCMiASKvW7Xj1tUZ8/dXHrP09nW6dAws9x86oLLbusvPFl0/95fZJkoROV7BdiqLk/QMgCgqCCL/8sp5NmzbRoUOHv3wulUpVtNKlS+NKS8OTY0Xr7VXkfrLLhYSORSuzeWGUUqAs4HVut8yS1Vn07PVcgW2VK1dmy9bcMp2rVq0iJSUFb29vHnroIbp06UJCQgILly6hdK8ehLRpVSAI1BgMlHmyL9FTfmD6jBl89dlnjHnhBS5mZxPaoR1KcBgbd8TwlkPCIEooLhcaNOhEfW7gKLlIT09n3QYPOr2ZZT8vY+LEicxfuhRJq+BU7LknEsBgMOLt7YXNZsNhdyApHmS9QI6URaLpEoP6DeDdd9/Nq7V9ndFoRBDAoxTsnNDrdLj/lKzMgztf+azr4p1XMRj1vPD884wcM4bETVsIe6h9wZ+JR+LK4qUYBREEAUWSEEUR6f/NAtMYjQgaDe6UNOQcK5lbtpO1fRe4Xbwy9kVef/11AgICCv15tmvXDh+zidR9Bwjr0K7QfQDscfG4Y+Po0qVLvteDg4N55513ePXVVzl9+jQul4vw8HBKl755OTAAURRp3bo1rVu3LnY/l8vFf99/H2Pd2kQ81iXvd8ejKFC+HKHlyiGiJWnlcuxnzhHYtCWyoGCz2ZBkCYvFPy/wlj0SzvQMzHVrg7HopYbmiHB05csSc/wklcc+S8qOnTzzylEe72SmZ3cLpcJ1pKZ5WLM+k2VrcshIh/btH7ql674XqUH3Pez1119n+g8zqKytQ1mfSnmBlKLI/Ja4EmeYicDB/TF5+edN1xF1Onxq18VUtjyxs2YgZOXQu3fvkryMYkmSxB9//MGMmTPZu28fkixRvmw5hg4eTO/evYsdPcrJyUGRFYyawpMc5WbFvsolezSpzkTcLidW7NhcVsz6or9Y451X0Btys5/fKwRB4P0P3qd/3/4czt5NTe+GednBAaKtJ5EMItrIEMypsXTv7MPQJwOpWU0PHg8nT1pZ+HMOy9fuxugOIURXcI3pX+VyuVi/fj1z58wl+nw0Wq2WJs2aMHz4cBo1KrrX+kHWokULWrZuyZ4tUdSVm6N1GXA4HLkj34BGqyFGPIPTbOPFl14s6ebe1ZYtXUCvx3wwm4uuNarTibz8TCjvfZ7Fjp37kCSJgIAAAgMDURSFmJiLvPjmYpJTPQzoGYTJlHssl0tmxfpU3v44kdZtHvlbI9BlypTh4NFL17Ic5/4tKEruOkqE3IEaQRDYd9iJy6XgbUpl0MDe/O+9Txg5cuTfuykqlaqAHj168ObEd0iJ2ldsUJUatR9vLy8ux3r49LtYxj9fqsD3mCwrvP3xZdIzdUUmYxUEgRYtWhRapnPevHl4tDqCmjUt8jtS1GoIat2SbYt/5pNJk5g7cyZvv/suZ7+dilMQsDscLF2cyMBevmjQohV1uU+IgoBO0JOaaefHOUk0avwwv/76K1WrVsWk16HJysSnUgUEQcRg0OeNoPr5+eHjk1vXOystnVo1a7D5jz/w9y/8OSEkJISq1aoSd+wS4cb8I5omswmr1YqkeBDRkEwcQYb8s4k8socr7vM89sRj9OrVi5iYGN77+GPsl68S1KoFXuXLoUgeMk+cInX7TnRpaXw6aRKvTphA5olTmCqUJzsnB8WYf8aYqNNhP3UKrclE+SEDSPx1A62rVM2r710Uf39/+vbsxazly/GvWxtDYMFOWNkjEb9mPZEREXTu3LnQ4xiNRurVq1fsuf6J9evXE5+URIVB/fOuW1EUMtIzQBHQC3pAwKdZE2ynTmOPvoBX5SqIiDgdTmw2a940dU92NpLdgaFCeRS5+OUUxooVyTh2Aq+KFXD5+5O+J4TlOw+w/Jc4REFBUUAx+6Cv1xpT9AVOnjz5r92DkqYG3feo48ePM3PaLKpo61LOXDnftgxPOnatnaAOXRFNJjyKO69kw3Wirxc+bVqQ8+vvuP5fCQRZltm7dy+xsbHo9XqaNGlCWFj+NY13QlZWFsOGD+ePbdvQlYrAu0lDNFotp2Mu89Lrr/HZl1+yZOFCatasWej7/f39EUUBm2TF5/9NY7J6stmbvplsTxY+WAhSIpAUN/Fc5njqQap51bkW0Of/UvPIHmJcZ3m428P33PSXDh06MGPWDMaMHsPWzHUEEYpR9CLDnUqcHItPi+Z4DkcxdqSF50eH3Pgy0mipUVPgf5X1VCpr5YOv42hUrmAGz7/i4sWL9OvTj7Onz+FHIH5iAA5klp9dxaL5i+nesxtTpkzJl3hKlfswNnvObFq2aMW2M78QSBghQgQadOSQSbzrEnZyaFy38b/65X0/SEpOonJFy033q1LRhCSlodPp8k3XFwSBb7/9jgkT/Hh38kw+m5JM0wYGNCLsO+wiNV2ga7e+fPnlV39rVsyECRPo1vVhlq7JYlBvv9y1l4qSF2xf//OcNi8TjUZk3++1+fS7ON55+1WqVatGq1bFVypQqVS3JiAggKEDBzFl1iy8SpfGp3LFAvtkR18g7Y/NPD10GOXLl+f9997k1DkHIweF0LyRD4oCW3dl8sOcJHbtk/js8++oVKlSIWcr3pGjR9GXK4PGoC92P9/q1YmTJY4fP07Pnj3p0qULjzzyCFFb92MQQpj0dSomvZ6+j1n488S3+AQPL/43iVPn7Jy7uoWNu/cgajW4JJm4pSsQnC7COrQrkFBOFEVEuwPPhRjGff55kQE35H5+jRg1gnFj/0OGOzUv6RqARqPFbDZjtdnIUBKwkk0dc5O87Q7JzuGc3egsGsaPHw/AuHHjKFu2LJ9/9SUnp8/KHcUWQCdqaN+mDW+8/jqNGjVizbp1bFuzjjJPj0QQQLI70JqMeVPNHZevkLV9J8HNm+DOyERJSWXM58/c0s/l7bffZtfePZz7/ieCunTCv24dRJ0ORVHIuXCRpN82oklM4odFi0psluShQ4fQBwViDL2x3NLhcORW7BAMXH/eNYSFo/H2wpkQfy3o1iCiwWa14WX2AuH6jCs5L+dAsa59V2VmZOLxSAQ0bobQsDmupCQkuw1Rr0cfFoZH9JB45hypqan/0h0oeWrQfRdyOp3Ex8cDEBERka+g/XWzZ89G49FRxrfgh3+84xIaP1/MFSshISEpHjSCBgEBBQVJkZCR8KtXG9f2nfzyyy88/fTTKIrCrFmz+HbKFKIvXkRSZAQEjDodjz/6KBPeeONvfUn8HbIsM/ypp9i0dy+RI4biU/lP520LrowMLs+ZT88+fdi0YUOhU+QrVqxIrTq1uHIwOl9taodkZ1faRhRJob7QGh/BD6fiwMfHhwA5mNPZhxGtGirK1fC33JhOZJOsHM3Zi84i8uZbb/6r1/9vefzxx2l+tDmLFy9m+c/LSU1JxZ1ow8uvLBrJQ6kIkWdHBRfoQRf1eiSPm6H9vVm6PqPIaXO3Ijk5mR7dnyDpQirNzZ3w1VnytimKQpzzMquWrkGWZWbNmqWOeP8/u3fvJjE+kRBjOE7JyRnPESC3VFgpczn8dUGcOX6Ml156iR9//LGEW3v3MhmNZGV5brpfRmbRidB0Oh2TJ0/mhRdeYP78+Zw4cRyPItO7XzUGDhz4jz4vH3roIcqUrciEDy9SqbyeJg2MoCgI4o2A+7sZGSxZnU3/niEYDBomvBzJtr1n+fHHqWrQrVLdRv/973+JvniBDTPnYKpVk4AmDdFbLLgyM0mL2o/t2Ak6tmnN+++/j8FgICIigi8+/4R+o04iilLuaJ6ipWbNesyaPaHYdbrFkWUZNEXPzrlOuFauSpIkIDcojrsaRzlTFap41eZ49j7e+ugMU2Zl8OhDZrzMGk6dd/LHditWu4RQqiwRT3TDUr0qgiCQdTWO2M2biV27HmdyCmX798n33eyx27myYDERISE3ndmTnZ1NmzZtaNSsIQf2bKeGoSGhhlJ5szXNPmauui9yxnkEjVZDsjOeDHcqOVIGKSQQGBLAvAWLqX5tjTlA79696dWrF/v37+fixYtotVrq1q1LxYo3npF/nDqVx7p25fyUH/Bu3AgqVUDxeCNbrVgPHSVn336MwUFIOTZS9v7Cy889V+So9P8XGBjIutVrePGll/j151Ukr/kFnb8FyW5HzsyiauVKfLVsWaGJ2O4Uj8eDoM0f9jkcDgTEfAlaNWhAEJGs1huvCVrckhOX241er0Pn64MAuK5cRXeTa3LEXALAlpKCyS83s78syujCgjEgcj0qFx1uXMkpXLp06TZc7d1JDbrvIpcvX+bHH39k/twFZGVmAgKWAAtDhg5m5MiR+QLLbVu2EySEFVo6yCW70Pr65k79UQRkRcKluPLGbDUaDT5mH8xeZtK9vXIzLyoK48eP56fZszDVrkXkMyMxl4pAdrpIP3KUldu3sWXbNlb+/PMdKd20bds2/ti6lfBhg/IH3NfoLRbKPTWM6M+/YurUqbz33nsF9hEEgVFPj+KFMWOJd1wh3Ji7PibadgqX5KSh0BaDYMKtuBCE3IfqqkJtEBROZR8hznaR0kp5DFojVjmbNJIICQth3oIF1KhR41+/B/+WwMBAnn32WZ599lkcDgdlypcnsHlTUn5dS5+nfBE1AvkymVwjaHXIspOBvQL4aloULper0A6hm/nhhx+IvRBHC++HC5QwEwSBUsayCAisWbmW3bt3FzrF7kGlKAqfTPoEX08ATfzbAeBSnMiKjF40oBFyH8ZEu8jPS5czfvx4KleuXPQBH2Ct2zzEyl/W8NyI8GI7dlasT6d6jZoEFjJl8LoyZcrwxhtv3PY2bt68jUYN69F1cCyPdTTTp6s3gQEazl90M2dJFnsPOmjSwJfvJ+WOwAuCwNC+Abz23q8kJSURElJ4AkmVSvXXGAwGFsybz7Rp0/hpxgwuTJuFgoKAQIVy5Rg5cSKjRo3KG8Xs0aMH3bt3JyoqirNnzyIIAjVq1KB+/fr/qCO5cqVKbIiKQvZIiNqig++c6AtoBTFf0ClLudUvREGkjm9TyrorE5NwjvkL4pEVN1rFTLbTgal+PSxdO+MTcmPGm29kBJqePUiICCd55Vo0BgNhD7VH9rjJOHaCzN1RWASBhUuW5MuUfZ2iKGzcuJHp06bzx8Y/kCUFBDD5GjmYuQMvpze+BKIoMpliKrJJotcTPfEP8Gfblu1YHVmUiizFq4Nepm/fvvgVkilcEAQaN25M48aNC70noaGh/LJuHR999BGLf15GxqbNuFxuZEkCWUar00JyKmFmb16aPJmhQ4f+pZ9VcHAwC+bP58KFC6xYsYKUlBTMZjPt27enZcuWJT6AUL58eZzJybhzctB5ewPXfyf+3xKIHCtSVhb2S5dQJAlBoylQESU7+gK4PdgOHSGjXVsMPt4Y9LlruwVRzOsYdqak4jofjVYQsB48gqZd/nXnAuQucRC05Bw7huB0c/L4SRwOx30501FQrmdjuUdlZWXlFWf/q9lh7yZRUVH07/sk2Sk5hItlCdKHAQrJrnji5csEhFlYsmxJ3pTR+nXrI13UUd2nXoFjncg+yAVTHJHPv4AiKrgUF95eXuj0OjQaDTqtDgSQ3W7O/O9DPn77Hfz8/Bjz4lhCevYgsEnBtcoeu52Yn2YSjsCBffv+VrD1VwwdNoy1+/dR6aXni/2gil3/K5ojxzl17FihI1GyLDN69GiWLfyZSLECpU0V2ZH6G8FyJOWFakiKB0WQsVgsGP70B57tzmRb5nosgRbCwkIJCw+jX/9+9OzZs9AvlHtVUlIS1WrVIrh/H2Lnz+bL/wXzWBffa6mR81M8bhSHk8Mn9Iwal8TRY2cICiq6HmlhnE4ntWvURp/sQ02fhkXupygKO7J/5fH+Xfjxp+JHa2VZZtu2bcyePZtjR44BUKtOLYYNG0abNm3umVrqt+LgwYN0bN+JuroWNxKpFUJSJLZmreW5/4zh3XffvXMNvIds376dfn278c1HYTzxaOEB9f7D2fQcHsNHH3/L4MGD73ALc2VlZTF27FgWL5qLKCqIIsgyBAZoGdrPwoujAtFoNJhMJvR6PcdO2ujy5EV+/W2bWttepfoXyLLMiRMnyMzMxM/Pjxo1auTLEP1vOnXqFC3btiWob08C6tcrdB9FUbg4YzbVDEa2btqc9wzVvVt3jmw6SVO/ggnHAM7kHOUUZ4l44Vkw6QgOCSkwq83pcBL903TsZ85i8vJGAExGA7269+CVV17JF+T/uT1vvvkmU7/7AS/Zl1La8pg0XjhkG3HuGDLkFMpXLk+piFK5I9T16jJkyJB/dWZlZmYmO3bsICcnh8zMTLRaLYIgUL58eVq3bn3Hfp53UlpaGjXr1sHQqgVhHTtcey0dj9ONTriRDC1t62ZytubWkNeWj8TSoiXGCuXxCG68NVqSd+8mdfM2BIcrt1RapfIE9uyOYDBcm24uYDSa0LvdXJ01l1IGIydPnEDR6wnq3wdzzRq5mfQVBcXtRnG7cV2JJW3hMkLc/mRqUvnve+8SERFBnTp18s1ouFvdaiyqjnTfBeLi4niy35NIKdDap0tuFslrgg3hVJJrsT9hK/379mfHrh0EBQVRpmwZjkYXnmwgzBDJ2cxTZMecQ18ud3TXarWCTcBoNOLt5YVWpyX9yFE0HolHHnmEwcOGYqxerdCAG0BrMhHZrzcXPv+atWvXFpg+dOTIEebMmcOJU6cAqFm9OkOHDv3bD337DhzAq1rVm5fvqlGdK9t2EhMTU+gfpiiKTJkyhUqVKvHDlB/ZnvQLDsmBRQjAjQutTouvjx96Q/7siz46P8oZqmAIFdh/cP/fuoZ7gbe3N6Ig4LZaEUQNqekeRFHMzYysKHmxd95PQRRJSfeAIPytzocLFy6QkpJKA0PxMwUEQSBICGf3rt3F7peens7gQYPZuX0XZsUbfyF3ZG/D2U2sWr6alq1bMGfunCKzjt5rzp07h+SRCPIqvkyVRtDgo1g4c+bMHWrZvadVq1Y80XMAr7w9j9Q0NwN7BeclQnO7ZdZuSGfCB/E0btyWfv36lVg7fX19mTVrFkeP7KNRrRQeamsiPFimSkUdep2AILhxu93YHXZ0Wj05Vg0g3FPVFVSqe4koitSuXbtEzl29enUe69yZNSvXYvD3L1CfWlEU4n/dgPt8NP+ZNj3fM9SQoUMYsWkkWe6MfMu6rrvqvoy5fg0w6jAajYUuIzMYDYS3bU3i1Ti+++orSpUqRe3atYudCfTdd9/x/TdTqKatR1nv/DOvypgqEuu4xInz++jVqxdvv/32X7wjf4+fnx+PPfbYHTnX3SIgIIARQ4fx9U8/YYoIx69GdYwGA1lOZ96sjZzTp8jauYuqhmqEGCI4duUAyQsXoRh1CDoNOF14HE6ChWDqW1qwL3sbaeejSZj6E14N62MoWxYkD6lnzmE7epzI4CAiK1TkdGws+vBwUpetwHb8BN4N6qOxWJBycrAeOIj1yDEMLpFsJRObw8Y7EyYiCiJavZZmLZoy4c0JtGxZfCWie4EadN8FZs2aRWZyNm18Hs0XcF+nF/U09GnNtrh1zJ8/nxdffJEBAwewddMzWD3ZeGnz18dzSDYkj5uMTZsJHTgQrcGMKAhIiozD7sDpcOCj05H6xxY+wb/mAAEAAElEQVQ6duiA1Wrl5OnThA8fUtis4jzG0BCM5cuxaMmSvKA7JyeHZ8aMYd1vvyH4+GCoUA6AqJUrmTF3Lo898ghTp0zB+9pUllslyTLCLa1byt3H4yl6baZWq+W1117jhRde4JtvvuF/E9/DR+dLgDEAXTEj9hpBi8tl+0vtvteYzWbat2vLloOHMVetwcp1Jxk6MDD3i1rIP+lIdnswGg0sXZ1I69btCp1ZcDPXf07Xp0EXRyNosLuKriHvdDrp368/B3cdpr6xFUH60HwZOVNciezdtpf+/fqzZu0aDIaiy1rcK/Kur7CpCP+PolDo8hNVLkEQ+PLLL/H29ua/n07n86kpNG9oQKsV2H/ERWIyPNK5O998892/PrPnVjRv0Y69O+cw8T9e+HjrMJtFxLzfh9yM6VnZLpatzsA/IOiO5d9QqVR31vfffUf6gAFs/2E6xqpVsDSoi9ZkwpGYRMbefSgpqXzw7n/p1q1bvvc9/vjjVKtRlUMnd9LYux1mTf6Oc6fiRG/xAYFiO9UNgYEgCNSsWfOmCTsdDgdffv4VEZSjrLnwpU6ljGXJ9mTyw5QfefHFF+/pWat3u4kTJ3Ll6lVWzllAWpXK+DVsgEv24MjMwXbkOI5z0ZTWlKaqdx1EQaSdvgux9hgO23ZRuXw5zp46R0PvNkQay7E7czM5ZonwroNwxl4l+9ARcnZHAaC3+OFVKoL0pGS279xJUI+u6CtUxHnsNJkH95M8f1Fem3T+/hjLlcN25iy+koXyYg0qWaqgCJDojOXotpP02teLaTOn8fjjj5fUrbst1KC7hMmyzJxZcwgVIgsNuGVFJtmVm0RC49bzxedf8vTTT9O9e3fef+8DDsfsorFPO/RibkCR7cngYOYuguUwMq+mkjR/IQHtO2AuXwGtqEUjQea5U8Rv3kwZH18++uAD5s+fj9VmJ1sUsSUmIooiZrMJk8mMRpP/oV0XEkx8QgIAbrebIUOH8see3YT164WlTu28jJaKLJNx9Bhrl69i6PBhLFqw8C+NvFSpVIm9F2Nuul/OxRiMBv0t1TM0m80MGTKEyZMm48JebMANkCVlUKPs/f/gOmrESDYMHIhPy+ac3OFh5ZoMnuiWP/Oo7HKBLLNpu5ODR93MmPn3ShJFRESg1WkLZCwtTKaURpUK5Yvcvnz5cvbuiqKpuUOBYwmCQLAhjAZiK/bu3sTy5ct58skn/1ab7ya1atVCq9OS5IzLy1FQGI/sIVtMp1btWnewdbfHyZMnWbx4MVeuXMmrnnCz8oB/l06nY9KkSTz33HPMmzeP48ePYnNJPNq1CoMGDbqrprU99NBDzJk9lc077QzomX89owAY9CI5Ng9L12RRo1ZDdaRbpbpP+fj48POyZSxcuJBpM2dycuFSFECn1dK9SxdGjRxJ8+bNC7zPYDCwaMkiej7Ri13nfyNYKUWYMRIRDenuFJyyDY3Vip/Fkru+uQju7GwEQcDHx6fIfa5bv349KUkptPIqvsRqOVNlLmed5eeff2b48OE3Pa7q79HpdMyYPp1HFi/mx2nTOLpoCU6XC4fdjlExUMtUm0peNREEAUmRiHdc5qzrKPUb1cdkMpGuzaa0qTxprmSSpASCu/bFq0pVvKtUJaBNOyS7FRduvAMD8PXz5fSX3+K8dJny9euSlZ2DT726+NSthzstDdlhR9Qb0ARacGSn4bwQQ6AcSpg5AlHMHZiJMJYhzBDJ4azdjB41mn0H9hEREVHCd/HvU4PuEhATE8PcuXM5eOAgToeTS5cvUc1QP7fW7p+mAsXaYzhhP4ING6K3F4oB0pITqV67NmOffZaFixbQ64le7Iz/jUhNBcKNZTibcwKNrKWqUA+rlE1M3BmSFyxE9PVB4+WFlJ2NnGNFkSW69uvPf8aP5/c//sgtsaDIiEYDiiSTk2PFarXhb7Gg/1NpCsnhwGTKXfu8evVqft+0Cf/2bZDdHmyXr2AuWya3nI0o4l+vLlpvbzb8NJO1a9fyxBNP3PI9Gjp4MFufeQZ7QiKmsMKn0soeiYw9UfR9vCsWi+WWjhseHs5DHR9i1/ooIo0Vipy+bvVkk04Sgwa/e8ttvld16tSJYQMHMn3ePITACCa8d5XYOBdP9g0k0CIiu1zEx9r49Pts1m6wUrlyLWJjY/PWs/0VgYGBPPp4Fzb8vIkySm5teUmRyPFkIisyRo0Zk8aM1ZNDGkkMHvJOkceaPm0GAYQUG7xbdIEEEMJPP/50XwTdNWrUoGnzJhzbdjpfttf/77L9PIJeYdCgQXe4hX9fWloazz33DFu3bCQ0GGpU0ZLhgHfXLeSD99/h9TcmMnLkyH8lGU2ZMmWYMGHCbT/u7XTw4EGMBi1vfpiKIAj0ftwHne7GvTh41MHzbySi1YpcijmH0+m8L2Z3qFSqggwGA8OGDWPo0KEkJSVht9sJDAy8aSBcrlw5/ti0kZkzZzJj2gyOxe0FFIxmEw2q1+XU+QvoNcXPkErff5AqFStSvnzRneLXnTt3DqNoLjAj8/8zakx4ib6cP3/+pscsKR6Ph40bN3L48GEkSaJixYp07dr1nsvzo9FoGDBgAE8++SRXr14lKyuLPXv2MHnSZC7EnSIpOxYNWuzkIGklHu7aiffee49mTZpTVV8PgEv282gC/DFXujF7QdBo0Hr7guLG6XIhiCI+TRuRc+EigtWGVqvF7XGhE/To/7QcwaU40fr4oTGacDmdmM1eKIpCujsFh2xDI2ip5lWPXdm/M2/ePF599dU7fctuGzWR2h3kdruZMGECM6fPBJeInxKIgECiLRaX4CTEEEEjS2v0ooEY21kO2/djrFUdS7MWGMMjkBQPjswUOHOOrN17eGrgIF566SW+/PJLli5ehjXbSo4thzJUoaKhGl5e3uj1etLdKSQ4r+bV644wliXGdo547SUEby/C+/fm4twFeDVrgv+15AqKoiDZ7QiSRGBAIFqdFsnp5NwHk3j9hbG0bduWHj17kpKRgfinoNwUHk54x/b417tRx/nCD9NpFBLK+rVrb/leOZ1OWrVpQ3RmBuVGDUf//4Jq2SNxedESOHee39f/8pfWjm/dupUnuj1BmLsclU210Om0aDQ3+p9cspN92VuxlPVh3/6ovzWN+l4jSRKTJk3i26lTSU1LQ4sLk1GhZhUdickSV2I96HQC1SubMZmNRF90otF6M2z4aCZMmIBWe+v9d/v27eOxzo9hcYZgEE1ccl3AiTO3VqasEKQJxqnYCK8Yyt6oPZjN5gLHcLlclAovRSWlDmVMBRO3/NllezTnhaNcjbt6XwQh27Zto/cTvfF3hVLbp0m+qfrXS66dcO5j5JgRTJ48uQRbemuOHTvGwoULmT9/NkZdOu+/EUHXzsHodbnXlZjk4tvp8cxclM2EN9/n+eefL+EWl4z27VrSsEYMbo/C0tUphAZr6NjGhF4ncOCok6MnnVSrZObVsZE89WIsa9dtokGDBiXdbJVKdZfyeDwkJCTg8XgICQkhPj6eJi1a4NWmJRGdHy70PTkXYrgybQafvv8Bo0aNuuk5Jk+ezKfvfUE7n5tPC96Z/RvDxg7m/fff/8vX8m9btmwZ7773HlfiYtH4+iJqtbjS0vH39eW50aMZN27cPZ98zeVysXbtWnbu3InT6SQ8PJy+fftSuXJlYmJiaFivIfW0LQkyhLEt4zcc9UoT8mjXAseRFA+S4CE0LJS0uHiiP55M5adH4FOtGmnpaUgeKbf2t6ABFNyyC1ESiP3sS2rI1dDp9EQ7z2IlB0UQQAGtImKUDZSqHMqxE8fu/M25CTWR2l1GURRefvllFsxeSCVtLcr4VkQj5N7+ZHcSKZ5ELjpPsjttE/X8mnHEdgDv5k0I7Phw3siOjIQxKIigalVJi4xg5vz5dOjQgS+//JJ3332X7du3M2jAYEppIwkw3+hFCtAHE6APztceraglx2Gn0rDB+FWvRmCjhqRE7cenUUO0Fj8EQUBrMuGxWsmxWrFY/EjctBWdrBAeHk73Xr3I9vEmqEsnvGrWRNCIOGIukbVrDxfmLqBUWjphHdoB4Fu3NlGr1+F2u295yqPBYGDp4sX06NWLC599jbleHbyqVsEtS9gvXyF7/0F0Njs/fv/9Xwq4jx07xowZM3B73Jy1HyPRHku4UBZ/fRBGs4EUKZFY6QKWMF8WL1n0QATckNvzOWHCBJ5//nl+/vlnjh49ypkzZ7hw4QJJKTGMHhLEsyNKUaFsbgCcnOJm7tIkvvrxM+LiYvn++ym3nCW8cePGvP3u27zyn/FgMuLTvD5+1Wsg6vQ4EuJIO3AA99VMnukxqtCAG3K/HBTlWj3Jm12boEVRct9zPwTdbdq0YepPU3luzPNsyVpDmFgGH40vLsVFgnQFu5hD3wF9+PDDD0u6qcWKi4tj9NOj2bVjF067Cx8/B/O+jqBiOYX4hGR27FH4eV06sXEu9HqBCmUE3vvf23Tv3v2WlpPcbxwOOxY/LRNeLs2owWHMWZLEwaM5SB6FMpFmZn5Tjg6t/Lh81QkoOByOkm6ySqW6i2m1WiIjI/P+v2LFirwzYQIT338fyWYjpF1bDAG5S80kp5O0/QdJ+fV3OrRsxdChQ2/pHLVq1cKl2Ml0p+On8y9yP6snhxw5i1q17r4lUdOnT+c/b7yBvkY1yvbqhvla+V5XegbJO3fz/uTJXL58mW+++abEy4L9E3q9np49exZaZ93Pzw9BFLHJN2p3F5VaRkFGuJaET6fVgqzgyshEo9UQGBiI3WbHZrPhkVx573FEXwCXmzRNCgmuRLzq1SasXgN0QcHIdjs5p06QuXcvZ6LPc/nyZcqUKXNbr/1OUUe675Bdu3bxeJeuVNc0INKUf0qOzWYlKysbFw6OKDux6INI87JT+oWxiNeCVAUZl+LE188Pszk3ELwwdRpNwiNYu3o1kFsCoWL5SlSjPqVM5Yptz+bUtWQFi9R9ewKCRsSdnc2Zr75DQiGgRzeM5cshCAKy04UnIwP5+Emyd+7m5eee48fp05EqlMf4UDs03l55bYTczoWMTVvI2rKNKs+OxqdiBdIOHSF16XJizp+/pTVAf5aamsrkyZP5+rvvcLpcgICoCJgUA+gU/AMtTJo8if79+9/0WNu2bWNA/4HIWVBaVwkRkRjbOdLcSchKbu1BXz8fhj41lBdffJGyZcve5Ij3t+PHj/Nwp7aMf9aPF0cXvoZm3YY0Rv8nlu+nzKZHjx63dFxJkmjf8SGOXL5M4JN9kU3GvA9vQRQxGo1k79yFdfsuFs2dyyOPPFLgGIqiULVyVfRJxZceAziRfQBncBZnzp25r8qHXbx4kdmzZ7NowSJSU1PR6XQ81Okhhg8fTvv27e/qL//ExEQeebgzCdGJVNLX5rRzL0MG6HhzbCinztt5dkIsKeke2rUwU6eGAZtdYd1GK2ejXdSt14zNm7c8cGuWn+jRFW/9PmZ/W3yeifUb03h6XAI7dx24pemfKpVKdZ2iKMyYMYP3PvqItMxMDBHhoNHgTkpC55Ho27s3kz/5pMgO8f/P7XZTv259nJcVGvgVnX36WNY+HIFZnDh1/K4a7Lhw4QJNWrTA2LQRpbo+Vuj3atqhIyQuXsbsn3665eege1GvXr3Z9/tBmvt25GhWFDHmREo/98L/S3qs4FSceHmZ8fH1ISVqPzGz5+Ffrw4VRg6/cf+U3JxWkiyREp9IyrxFaK+k49C6Cer1BN7VCla4cWSlkTh/Hs2qVmPzxo131TOOOtJ9l5k1cxZG2Uwpr3IFtplMJmw2G4LHRABhJMpx+NZpnhfMysi4ldzyVtfXUwNYmjRi99LlJCYmEhoaiq+vL9WqVSXh6JWbBt1ZShZeVRsjXFu7o/PxofKzo7kwaw5JM+egCw5CFxaG7HBiP30GP7OJ9995h5ycHGyKTOW+vUjJzESRJPjTw68gCFg6tMN+8jRJ23bgU7EC9vh4fH18cLvdTJs2jVPXyopVq1aNPn36FLse22q1smrFKvxkCxW9auCrtWDUmNGJOuySjTPpR3jumdxa3sWV9UlKSmLo4KHoskw09G2dNyW3jLkiVk82Vk82MfZzZAkpDBw48IEPuAFmzpxJeIjCcyOKrgf9WKcAWjROZdbMabf8ZbN582aOHD9BmWdG4lWuLLIkI0kSCOTVyvTt8ggXLl/lq2++KTToFgSBwUMH89Wkb6gq10ErFh6AeWQPCfIVXhz2wn0VcAOUL1+ed999l3fffRePx4NGo7mrvoQKEx8fz+LFi5kxYwbnT0VTxVwHvahHEh083jGQK3EuRo6PpUwpDT/PDKdcaR2iRkQA3no5kDlLMpjw4T5effVVvvjii5K+nDuqT98nGf+frVyJdVK6VNEzNuYsSaVhw6ZqwK1SPeAkSWLTpk3MmTOHUydyn7vqN6jP0GFDadmyZaHfF4IgMGLECPr378+KFSs4ePAgHo+HsmXL0q9fv3wj47dCp9Px1jtv8ezo5ziVfTgvK/Z1siJzwXaaeOESH73+4V0VcAPMnj0bj15PRJdHivx+Dahfl4yo/fw0ffp9HXSPHDmCzRs3cdkeTVlTJS5knifn1El8al0vn6fgUdwgKJjMJmSPRPrO3bRs1pRjJ09xeeFSIh7rjM7PFwQQNSKu1FRSf16O8/JVRI0ZrwZ1Cw24QUH0MRPaoxuHFy9j165d92QJsfvrKfQutumPTYSIpYr4kBPx9w9Ao9XgTzCS4kH09cGjuHEpTtyKE61Oi79/QL736wP8kVHIyMi4dhyBEaNGkEoime70ItuS4kpEwlMge7chwJ9qL4+lyjNP41umDBqbHa3bjVaRmTNjJi+88AKLli7Fq25tNEYDZpMJxe3OrU2U73oEvBs3JPPEKdzZ2WTvO0i50qWpVa8er7z5JnP+2MicPzYy7q03qVGnNh9++GFu0FWI9997n7TYTJr6tqeUqSw+Oj901wIsk8ZMXd9mBHnCeW38a2RnZxd5zQsWLCAzNYt6Ps0LlKvy0voQYoygsaUNereJH3/4scjjPEh++3U1vbv6otUWH8j16+FPVNRuUlNTb+m48xcsQBceirls7vQgUSOi0+vQ6XR5v9+CIBDYohl7oqKKTKwydOhQzH5GDmXvQlIK/v5IisSh7J2Y/Iy3PBXuXnW9s+Ju5XQ6eeWVl2nSuC7ffj2REEs0zZoIxGkOsT/nDxQkLH4afpifitEI878Po3xpHaDkfb5otQJ9uvny7vhAFi+andd596Do0aMHgYERvDDhEjnWwj8vp81LYMdeJ6OeHnOHW6dSqe4mSUlJPNr5Ufr26sfmldtxngfHeVi3+Fe6PdaN3r37kJWVVeT7vby8GDRoEJ9//jlff/0148aN+8sB93X9+/fn/Y/eI057kW3Z6ziVfZhLtvOcyTnK9uz1xAineW3Cq4wePfrvXu6/5udVq/CuVzvfjM7CWJo0ZM/evSQmJt6hlt15nTt3ZsTTIzjtPsQV+wVCxTBS16/HFn0+b3BQQsLXxxdBkrm8YBG6jAw+/+xzpk2dijb6Auc+/pQL02dzafEyoqf8xIXPvsI3Mxu9KOIUXPjWL3zmoqRIKCgE1qiOJjCAJUuW3OGrvz3UoPsOcTic6IRiakJrNAQFBmLxtgACHms2siih02vxs1gIDAwoUL7LnZmFgJBvKkP//v1p0KQBB2zbSHYm8OfVA4qikOC4yhH7bkqFh+O+dIX/v7pAEAR8Klek3IB+VHn2aQIaNcDH24eGDXP/EJKSkjCGhABgujbNXXI4CgTeuuAgFEXm0uJluNPT2X/sGNqmjaj8xn+o+MIYKr4whspvvIquWRMmffEF48aNK9CWxMREVq9cTRldZQyikcIIgkBV77pkpmWxbNmyIu/v/LnzCSYir7RaUccqpa3AqpWriw3gHxRZWdmEhtx8Cm9YiB6Qb/meRV+8iKF05E2DRHPZ0nhkmUuXLhW6vUyZMsyaOwunj5UdWb8SbT1FlieDbE8G0dbT7Mj6FaePldlzZ92z63/uBx6PhxEjhrPi5xm8M86PnWvLM/ubMJZMLcvW5eXo19OIR5Y4dsbKL5uzGdrXF39L/uRw10mSwhOP+hEanDsC8SAxm83MnDWfM9FedO53hpkLE0lKdpGd42HH3ixGvhTNu5NTGPPsuAL1eVUq1YPDarXSp3dfDu0+QkNDG1r6PkwNnwbU9GlAK5/O1NE1Z+tv2xg0cBBut/uOtOnZZ59l+85tDB0zGGtgGtHicbL8k+n3VB82btrA66+/fld2HGdmZhRI5FsYvcWSbxDsfiQIApMmTeK/H7yLIziLTCEF0e4kbsE8rs6eTvbBgyjRF0jb8AfnPvwE8eIlZk+fQcOGDenZsycnjh7ly48/pl3pMtTR6OhcoyYzf/iB0ydOUL1mdSRBRhMcwJ8XiyvXRs89uDGbzegNerRhocTGxZXcjfgH1Onld0h4eBhZZzKK30kQcGrsGHU6lPPRBPfphagtOlFUxv4DNKhXj7CwsLzXTCYTS5YuZvCgwezeuQOz0xsLwSgopCtJODV2Oj6au+7zySFDyDkfjU/lwtcIyh6JjN17ebxz57xzGE1GPHY7kDu6ZvHzIyMjE49kQ9DrEDQaBASk7BxkpxP36bMIWi1hvXoQ2CR/nUadjzfhj3RCHxjArPnz6dq1Kw899FDe9l27dmG3OYj0LVfsbTNpzPgq/mzbtq3I+o5xcXFE6G5ec9tP589Fl4uUlJS/vP78fmOx+BEbd/NkTFfjnIB4y+XDdDodsttz0/0UtwdBoNi1ux06dGDDH7/z9ddfs2rFKqLtJwAwmgz06N+dF154gZo1a95Su1T/jqVLl7J50zrmTylDm+Z+2Gw2MjNBQCAkUMtHr4ezbY+V2UvTcbpkOrfPXSt4+ryLeT9ncfyUC1mB8qW1dHnIi7YtQ+jU1sz+fbtK+MruvIYNG7Jm7W988skk3p28hrc/TiT3AUVLlSo1+OLLsfTt27ekm6lSqUrQokWLOHrwKM28OuKrteTbJggCoYZSaAUd27ZsZf369XTv3v2OtKt69ep88sknfPLJJwVK5N6tfH19ySxmRsB17sxMBIS/XEb1XiOKImPHjuXpp59m3bp1nDt3jujoaE6fPcu5vfuwejwEBwczZswYhgwZki/pqa+vL0899RRPPfVUgeOOf3U8Q0aNwuW0IpkUBHJ/NxRkEAS8vLzw8c59JpcdDkzGwgfi7nZq0H2HPDnwSd5/50PcsgudWPiIt6IoxHou8sgjD7Nt104S/9hM+CMdC9038+QpHOeiGfXttwU+uAIDA1m7bi07duxg9uzZnDx+CkEQaFu/K8OGDaNJkyYoikLzJk3Ys2gppUcMwxyRf92u7PFwZfEytBkZjH3hhbzXO7bvwPLt21A6dsitx42AKIpIkgfZ5uHa3wk5hw5j0hto3qwp+69eJaBx0cmuAho2IH3nbmbOmpUv6Lbb7SiKgla4+WirBh02q63I7QaDEY/t5j26HtkNgnBfZLn+px7v2otlq6fy6gsyOl3Rk2IWrkinVet2+PsXnZn0z5o1bszBefOQPVLxnUrHjmMyGG6azbRGjRpMnTqVDz74gAsXLgBQoUIFAgOLrt+tujMURWHmjJ/o0MpEm+a5DyPX19Zff+gSBIGXRgYz/v04TGYRUQMvvZ3E8vU5BAdqaNfCjKiBqIMOlq/PoUl9O5UqGHG7XcWd+r5VpUoVpk2bTkJCAocOHcLtdhMZGUn9+vXviYdYlUr171EUhWk/TiOIsAIB958F6kPwcwQy7adpdyzo/rO7/bNKkiS2bNmCRoHkXXsxNG6E3mTEbDKj0xd8Jk3fd5DGDRvmGwS7nxmNRnr16pXvNUVRkCTpL5WQva5du3ZYvL3RXI3Fq35dPB4JAdDqdJhNJsRrM33d2Tk4L16i5bCCgfu9QA2675CBAwfy5edfcjhjNw18WxVYV6woCqdzjuDQ2nj11VdpFxXFxPffx52VRWj7thiCcgMIj9VKyp4o0v7YQs/HH6d3796Fnk8URdq0aUObNm0K3S4IAvPmzKFPv37s/+Z7zNWr4Vu7Fhq9HtvVq2TtO4jB7Wba1B/yppYDDBs6lGUrV5B24CDmmjXIzMwCjQatlxcIIoos4bxyFce5aLSKwrYdOwnr2b3YD1hBEPBt2IB1a9Yzfvx4ftnwO9YcK3q9HrfkItWVRLCh6A8yRVFwCFZCw0KL3KdNu9Zs+HkzlZWaxbYlznmJitUqPDAfnMUZOnQos2f9yORvY3njpcKngy9ZlcL+wy6mTR/xl4475aefSN0bRXDL5oXu47HbydgdxcAeTxAQEJBvm8PhQKvVFvhgDwwMVAPtu0xiYiLHjx/h+ck3/jb1Bn1uR53iQUvuw0ufx/34YV4KZy86efa1JKJj3HzydhC9u3qj0wkoCsiywrY9TsZNTGbvwRzaP9S6pC7rrhAWFkaXLl1KuhkqleoukpGRwblz56hmKL6qB0CothT7ovbdM6POd0piYiKDBg5mf9R+9B4jkieL9K078WvTErvNjsFoxHKttC5AxvGTOKOjGfHduBJueckSBOFvBdyQ+33W9bHHWLFtK0GNGqItYqZpwh+b8DYYbqli0d1IXdN9h4SGhjJz9kwc3jnsytrAZXs0LtmJW3aT6IwlKmsLsWI0H3z0Ps2bN+fFF1/ki08+wXTxEtGTvyD686+J/uo7zn34CY5tO3h+xAh++vFHNJqb1ykuSmBgIOvWrOGrSZ9QCYHUpT+TOG8hyr6DjO7Xj22bNtG1a/7C9y1atGDIkwNIWLaSqxs2oSgyWrMJQasFFGwnT5O8YDE+lSpQqk9PHE4nHq0Wl8uFLBdenU5RFLLPnCUrO5ufli0lo1wZaNaYrNKlkEwGtmdtINZR+LpegDR3MnbRWmQHBMDw4cOxiznEO68UuU+WO50U4hkxasR9l+n676hatSrvTPyA72dl8fzrFzhx+sZMgktXHPx38mXGTYxnwMCRf+nhv0qVKjw1ZAgp634lZe8+FFnOt92Zls6l6bPxF0VeeeUVIDfr9aRJk6hRuzaRZcsSVqoUnR55hMWLF+N0Om/PBT9gFEXhwIEDvP766wwdOpTRo0cze/ZscnJybts5co8lExx4Y2RAFERMZhOSIuWV6hMEgaF9AnC74cARJ1+8F0y/Hj5oNAK5vx4CGo2GDq28mPNNGB5JJigo5La1U6VSqe4HLpcLRQENN3821KBBkqQiE9k+iLKzs+ndqzdH9hyloaENbS2PUcfcEOu2XWT8sgElLQunw0FGRgburGzif99I/PxF9OnxRLHPoKqbe3PCBAIEkZgfp2OPi8+3zWOzcXX1Wqx79vG/d94pturR3Uyt032HHTx4kE8mfcLGDRtxuzyggFanoV6D+oz7zys8+uij+fZ3OBysXr2aI0eOIEkS5cuXp3fv3rd9RE9RFDIzM3G5XPj7+xe7jtbj8dCpUye27dqFxuKHsXw5EEVcV64iZWdjqVGD8N49sLvdRE98H0vH9vg2b4ogCBiNJsxmEy6XC6fDiawoZO3eQ+rGzVjat6XiY48i6m58WWSmpBK7fBWOwydo6dOeEEP+etFOycHenM1UqluOrdu3FhksK4rCyJEjWb54JdV0dYk0VcgrW6EoCsmueI479lGrUU3WrluDl5fXP7+p94nFixfzyaT3iY+/TFiIFq1GIDbBhY9PEM+MGcvYsWP/cieF2+1m/PjxzF6wAMHXB3PN6mgMRuyxsTjOnqdUaCgL582jXr167Nixg0FDh5LhsONVry5eZUsjezxkHTuJ4/x5mjVsxKIFCwqMiKuKFh8fz+inR7B//25KR4hUraQhO0dh32EnZnMAE9/9gIEDB/7j86SlpVGrZmU++28Q/XoE572uKDJpaem4XW40ghYNGj76NonvZmZQp5aG3xaXwttLRBBEBOHGVERJUkjPlHjtvRROx1Ri584otYNMpVKprnG73VSqUBlLVijVvOsWu+/RrCh0ZWWOHj96h1p39/v+++9589W3aO7VCR/tjfXZMbZznLIfxSG60Ab4I4ugsVnxM3vx9FNP8eabbxb73Ky6NSdPnmTQkCFEx8SgKx2JLjgIye7Aee4cJq2O/73zDk8//XRJN7OAW41F1aC7hMTExHD8+HEkSaJixYrUrFn8tOe7TaOmTbliNmIIDMB2NRZFljGGBBPUtAmKnx9Z2bnTzpNmz8eTlUWpV8aCoiC7XCDLuWvBEZEdTmK/n4JXg3pY2rUhICAAveHGmndFgfS0NK7OnodwLpbWfp3w0Vlwyy6uOi5yxXOeoNIBrFm3hgoVKhTbZpfLxSsvv8LCBYvQeLRYlGBERDKFVJyinZatWzBr9ix1inIhPB4PGzdu5NixY3m/s48//vg/rql5+PBhZs2axbZdO3E4nJQtXZoB/fvTs2dPvLy8OHv2LA898giOkCDKDh6A9v+dz3rlKldmzqF5rdqsXb36H838eFCkpKTQ9fHOeJwX+PDNCNq38kMUcz974hKcfDE1joUrbHz08Ze3pdRa3769sGVuY/XcqvleV5TcjPd2ux2rTaJDz1iSUyVef8GfkYN80etFzCYRnTZ3ernDKWN3yAiChgPHNIx8OZF9+49RqlSpf9xGlUqlul+88cYbTP9mJm18HyuwlPE6t+xiS/Za3vzvG4wb92BPi75OlmUaN2pC1lk7pY0VsUnZCIKIRReIr9aCpEjEOy6T4U7jivM81etV4/cNv9/3ydPuNLfbze+//86SpUuJT0jAy2ymfbt2DBgwgKCgoJJuXqHUoFv1rypfuRJKk0aEdWiX73WXy0VaWhqCXo/GaCR7/0GSFy3Bv8sjWDq0Q5YkZLsDZBmDYCRr/35S/vidsDFPo/PxAQECAgLyJapQFEg6fIQr02dhEnSIiCCC2ctE9ye689Zbb/2l+pGnTp1i3rx5HDxwELfbQ9VqVRg8eDBNmza9pzo+HgQvvvgic9etpeIrL6IxFJ6AMPt8NFenzWL5okX5EvGpCvfGG2+wesVU1i+sROlSBRMGKorCWx9eZuEqiQMHjv3jTqjffvuN4cP688GEYIb2Kzgl3OOReO1/F5m71Iogirz/eiBD+gZis1lxulxcLx8iCiImkxmzl5moA1b6jLzCjp0HbtrZplKpVA+Sc+fO0bZ1O7xy/Kjn2yJvVt91HtnDgeztCIESe6J2ExpadD6cB0l8fDyVK1XBoIjIGidarYKigMct4CuEUsVUn0B97nfYeetJkr2vcPlq0UsfVQ+OW41F1URqqr/Fx8eHtELqMlutVhA1aK6l89d4eSGIIlmbt+JOScW3RTP04WFIVhv2hDjSd+9AFxaKwceCVtDiUpxkZWcTGHhjqrAgQEjd2qT6+fL0oME0btwYk8lE8+bNCQ4OLtCGm6levToffPDB37941R2RmZnJkuXL8W3TssiAG8C7YgV04aHMmj1bDbpvIjs7m2VL5/P0IEuhATfkTuUe92wEC1ecZvHixTz77LP/6JwPP/wwT414jjc//Ibjp6yMGBhKtcpmFEVh9/5spsxMZPNON99+9xPffP0Zp8/FYTAYMBgMuesNZQmB3AQt1zvFTpyxodXq79peb5VKpSoplStXZvrMaTw1bAQ7s38jUluBIH0YiqKQ5Ioj1nMRvUXLwgUL1ID7TyZPnowo2Oj6mC+De5WiTjUjHklh004r0xems//wBuop7Qg1lEIjaPB47kyNc9X9Qw26VX9Lt0cf47u5c5Ef64J4LVuhLMk4nS5E442H+ZxDh/EqWxZL08YkbtxEwtHjiGYziseDnGNF6wFNQGBeWTCNoMPtcuF2u/OtjxFEEUHUULlyZfr161dku6Kjo5kzZw5Re6PweCQqVqrAoEGDaNmypTqKfY+5cOECdoeD4KqVi91PEARMlStx9PjxO9Sye9f+/fuxWjPp+Vjx9zTAX0eHViY2b974j4NuQRB47733KFu2LN99+wULV0Tj5yPikRSsNqhUqTozZk6kc+fOJCQk8PWXE3l7nAeLnxaNRlNgyYAsK8xbmk7nLj3V2U13OafTycWLF/F4PERERKh5F1SqO6RLly788tt6vv3mW9asXss5+zEAjCYjfQb34vnnn6dq1ao3OcqD45dffmHRwhlM/E8gg3oGoBVyn2v1okDndj481NKbl96N47eN22irfYJMdxqlKqhLm1R/jRp0q/6W3LJPP5L4x5a8WuKSLAEKwrWHZHv0Beynz1Dmie4YatVErFABT0wcruQkbNHRKFlXqWCuwrn4i8huN6JOhwYRD+B25Q+67XHx4HZRvnz5Qtvjdrt58803mfHTDAS3BgtBiGg4secUSxYsoXGzJsyZO1vt1b0n3byzREDgHl8pc0dYrVZAIcD/5h/9/hYNCRcLzmb5OwRBYNSoUQwbNoyNGzdy4cIFRFGkbt26NG/ePK9DbMCAAXz/3Ze88MZFpn1REYMh/7RIRVF477MrRF+Cz76++5KpqHIlJiby448/snDBbDIyUgEFjUbPI527MmbMs/nKUKpUqn9HvXr1mDZ9GklJScTExCAIApUqVcLf37+km3bXmfbTDzSpr+epgaHYbXY0gjbfk4dOJ/Dha2Fs3X2Ri7YzJBHH88PeLanmqu5RatCt+lsqV67MOxPezK0lnp1NaPu2aK6NOsl2O9Yjx8jYuAnfKpUJbNqEbGsOgkaLV6XKeFWqjOxwIF1KoaypEucyTpFz4ji+9eqTG2AJKOQPoJJ27CIyPIL27dsXaIuiKLmZsKfPobK2NmV8K+UlD1EUhRRXIod2RdGzR09++e2XB2507PLly8yZM4ffN27EarMRGhJC39696d27N97e3iXdvCKVL18eo0FP9rnzmEtFFLuvPfoCLWvUvEMtu3flLscQOX/RQcO6xf/so2PcBIfc3nr1Op2u2PJyISEhTJs+h2FDn6RzvzMMHxDIw+0saDUCew5kM2NBClGHPHzw4Wc0btz4trZNdXtER0fTp3d3bNar9O/hS+cOZTAYRA4ezWHO4lX06L6GTz/7ptgZSyqV6vYJCQkhJEQtsViU+Ph4du/ezjcfhWA2m7Hb7XhkF1pRny/w9vPV8HgnLxYtP0pIeARPPvlkibVZdW9Sg27V3zZ27Fi8vb15/+OPid5/AH1YGDanE3dKCgICgY0aULpnd0StBo2ouRZIKyiygu3kKUqJQXhpfSilK0Pshg3ogoIwRpYCFDTijSmlKbv3Yjt4iP9+8CE6nY7Lly+TmJiIwWCgWrVqHDt2jLmz5lJNV58ypor52igIAsGGMBpr2rH72EamTZuWV/v5fqcoCl9//TXvffQRHq0WU41qaP3DuJqcwo7x43nvww+ZO2sWLVq0KOmmFspisdCrew8Wbvid4JbNEYsox5ETcwlXbCzDP5l8h1t472nUqBGRkeWYvyyp2KD7bLSdqEMOpky983VH27Rpw6rVv/L555/y1kfrmfBBwrUtWpo0acHceS+ra/fvUk6nk4ED+uJrjmfd3CqEhtzIxVC3phdD+obw+nsxjHvleSpUqKB2nKhUqhKXnJwMSFQsa0Sn0+UlxHLLTjSCBuFazXMZiTKlBdBKzFswT80povrL1Ozlqn/MZrOxatUqDh06xM6dOzl44gSVXnwOrzKl8/bxeCRSkpPRCjpsx0+RumIlbXw6EaAPxiN72Ju5hWQhBUP1Kpjr1CQoshSu1DTS9+7Dfekyz44aRevWrZny/RR27diFJMkIgkBIaDABgQFEH4uhre9jxa7bPpK5F20ZiUNHDj0Q9RS///57JkyciG+71oR1aIeov/EA7MrI5OriZegSk1i/ejV16xZfz7OknDp1io6dO+OOjKDswCcLJFSzxydwefosGlWpyq/r16slw27BlClT+PCDN5j2RSSd2loKbM+xSgx45jxxSWHsjTpYon8rcXFxnDhxAlmWqVChApUrF78WXVWyli5dyotjR7BlZWUqlS+8nKAkKTzc5wzlq3RlxoyZd7iFKpVKld/58+dp07oxC6ZG0qZ5bvkvl8uF1WrF6XTmltABEAS+npbFL1sCOHL0dAm2WHW3UUuGqUpEamoqnR55hJisTEoN6IdX6UgUSSLz5CkS9u7HEReHOy6BEE0YLf075Y1oS4rE8az9RLtOozMa0BsMaASBli1a8PTIkRw/fpxPPpqMj2ShtKESvlo/3LKbWMclzluPU1qsTIPgZohi0UFXijOBQ56d7InaTZUqVe7ULSkRGRkZ1KhTB7F+HSK7PV7oPrLbTfQ3U2hXvQY/L1t2h1t46zZv3szg4cPJkSV8GtTHXLY0ittD5vET2E+fpUGtWixZtEidPneLJEni6adH8ftvPzO4ty9D+4dQqbwRh0Nm9W9pTJmZQmKqL4uXrKBevXol3VzVPeSJHl3RC1Es/KH4zpHZi5N4++N0Dh85pY4WqVSqEiVJEi1bNKZJnQS+/KBCgW2S5AEEFEWk+aNnqFrjYfr164efnx8tW7bEeK1aj+rBpZYMU5WIwMBAVi5fTp9+/Tj59XeI/v7Y09ORXC70oaFogwIR9HqSr8bxW/oK6pobYdJ4cdlxnmQhjn59+vDuf98lMTGRpKQkfH19OXPmDJ98NJnyQnUq+dXId74AfTBXbNEIskBGegYBxdQU1oo6UHKnQN7vFi9eTI7TSeX27YrcR9TpCGzbms3LVnD+/HkqVap0x9r3V7Rv356dW7cya9Ys5syfT8quPSAIVK9WlZGffELfvn0xm80l3cx7hkaj4YcffuTrr2sxc8YPzFp8Hq1GwSMpCIKBdu0f5seZE6lWrVpJN1V1j4mJiWZAj5v/LTaq640sJ3HlyhU16FapVCVKo9EwZOhIJn30BiMGWqldwyvfNo1Gg6zIfDftMucvZHP+ym+s/2MjoiAQ5B/A8CFDeOWVV9TnENVNqUG36raRZZm1a9fy0/TpnI+ORpZkrFeuIppMBHTqSFDrFphNZpxOJxmXLpO+8Q92n9mCl85IpcqVeWn0+7Rr145PP/2UVStWYbc5ALDb7WjR4WPxK/S8Zq03DrcNl9uNy+VCry+8pnOWOx2NVvNAZDCPiopCX6Y0Op/ik2VZ6tQibvEy9u/ff9cG3QBly5Zl4sSJvPXWW2RnZ6PT6TCbzWoZuL9Jq9Xyyiuv8Pzzz7Nlyxbi4uIwGo00a9aMcuXKlXTzVPcorVaL0ynfdD+nK3efB2GZj0qluvsNHz6ctWtW0v/pKD58M5xHO/qj0+VWz0jLcPHNjzF8+WMamjKVKD90IHo/P1zpGaRG7efT779j244dLF+27K5OTqsqeWrQrbotPB4PL4wdy4KlS9GXK0tAr+7YETCnZ2A7fpyMzVvQ2e349uqBTu+NV80aBFcsz6UZswn3yOzdt4cjR47waOfHsKc6idRWJsxcCtkjc9l6kUTlKnvSNlPbrzEVzPlrS5Y2VeSE+wBllCrY7fZCg25FUbjivkCnrh0fiGnILrcb4U8PtIqi4HK5URQZQRDR63UIgoCg1SKIAi6XqwRbe+s0Gg0Wi6Wkm3Hf0Ov1PPzwwyXdDNV9on6Dpvy+ZTlvvKQU2yH226Z0fH39qVixYpH7qFQq1Z1iMplYtHgZzz33DM+9/ishQYnUqaHF7Yatu7LIyPLg17o9ZXo/gSDmBuPG0BBKdX0UW/26RP04gwkTJvD111+X8JWo7mbizXdRqW7uo48+Yv6yZYT170PFZ0biW7cO2sgIfJo0JHz0SAJ6dCN5TxSJm7YAIAhgMJmI6NKZ2Ph4NmzYwJP9nkROFWjh8zAVvarhpfVBj4FAwqgntqQUFTiWuY8UV2K+c5c2VUAn6jmnHMXlcRRom6IonMk5il2bwzNjnrkTt6PElSldGk9CIrLbQ3Z2NslJyaSnpZGRnkF6WhrJSclkZ+dguxqLiEBkZGRJN1mlUt3jhg0bzvkYiV83ZRS5T0qqm4UrsujbbzAmU+HJ1lQqlepO8/X1Ze7cBfyxaSfdnngeDI+g6DuQZdMT0rMPZfv2ygu4/8wcWYqAju1Z8vPPJCUllUDLVfcKNehW/WOZmZlM+ekn/Nu1wb9eHQBcThcIAqI2dzKFT8P6+DRvQsKWbch/GlX1Kl8W0c+PH374gZTEVOp5N0cn/mnKoSCAkPuvCkJ1vPDhgvVUvvPrRT1N/NuSKaRxyLOTi7Yz2CUbTtlBguMqUVmbuSqe570P/0ebNm3+/RtyF+jXrx9KdjZXo/ZhzbEiyAJ6wYBeMKITDAiygDUnh6ubthAZHv7A3BeVSvXvad68OZ0e7sqLb8by66Z0/n+e1pjLDgY8E41GF8YzzzwYHaAqlereUr16df73v/8xZ85cWrVqhdbHm+AWzYp9T0Cjhtg9HlatWnWHWqm6F6nTy1X/2M8//4zV6aRSi6Z5rymKci1gvjHF0KdpE7J3R5F+9DiBjRoAuXW0NSYjhw4eIogwjJr8iSh0Oi0CudnNNYKWcKUs0Y4TOGUHBvFGxkg/bQBeRjOVapTn0qUznM05CoBGK9K4ZWNefOlFunTp8i/ehbtLnTp18Pf1JXbNOsIHDUUbdGNKvYCAKOixHT9C1r4D1G/WHK1W/ShQqVT/jCAITJnyA2PGwMiX11ClQiKdO3hj0IscPGZj0w4b4eHlWLxkCRERESXdXJVKpSpWfHw8On9/NAZDsftpzSZ0Fj8SExOL3U/1YFOftFX/WHR0NPrgIHQ+PnmviRoRZAVFubG2TxcYgMbPF2dSct5+stuNOz0D2WYnSFu6wLFFUYPBaMTpcKJBi7fgh6IoOCV7vqA7xn4WrUnL8hXL0ev1HD16FI/HQ7ly5ahevfq/ePV3F0VRiIqKYsqUKcReuooMxM2Yjk/9+vjUrI3GZMKdlkbWoYPYT58lSAnkxPGTJCUlPRBr3VWq+4EkSRw4cIDU1FS8vb1p1KjRXTNV22w2M2vWHHbu3MmsWTNZsnYPHo+HsmXrM/nTwfTo0UPN8qtSqe4JJpMJ2eHI9yxbGEWWkRwOtXyYqlhq0K36xwRBADn/NEKjwUgW2SguN4LhT4nNZBnEGx9c6UeOIbpc+Fv8kVI8hR7f29sbl9OJW3EikbuPKOTW45YVmRj7OS5IJ3np5RcpXTo3cO/UqdPtvMR7QkZGBk8Nf4otm7YiOrWESqURgKSsODJ27iRrzx5EQYOggJ9ooYapCWH6SLbmrGXlypU8/fTTJX0JKpWqELGxsSxZsoQLFy5w8uRJLl86j9WaASgoCnh7W+jXfwjjx4/Hz6/wKg93kiAItGrVilatWpV0U1Qqlepva926NZ9+9RXWmEt4ly9X5H7Z56NRbHb1M09VLDXoVv1jtWvXxvnDVJypqRiu1ckWNSImkxG7w4Gg1SBoNLjiE5CyszGXyp1W6ExJJeWX3+nSqRM+3t6snr+OqkqdAr2JWq0W/4AA0tPTSfBcAUHhsi0aCQ8pJCBp3Tw7dgxvvfXWHb/2u4XT6eTJ/k+yb8cB6hibYZJ9sLvt6AUDVZX6JEqxnPMcxlfvR32/5vhq/fPus1FjJiEhoYSvQKVS/X92u51XXx3PiuWLMRldOJxOHHYPTzzqTa/HfSkdqSMl1cPaDTnMnvkJc+fOYOHCZbRo0aKkm65SqVT3vNatW1O5QgVift+I18jhCBpNgX1kj0TShk3UrlGDpk2bFnIUlSqXmkhN9Y9169aNIP8AkrZuz/e6r48vOq0WyWbDY3eQuX0HOl9fzJGlSNi4iYvf/UDl8DC++vJLhg8fjlNjJ85xqdBz6HQ6jBY9qbp4AsMDyA5IxlBR4KnnhrJj13Y++OADNIV8GD4olixZwp4de2lobk2ooRTin2YTCIJAmBhJLbEpme50rFJOXsCtKAoe2a1OiVKp7jJut5uhQwexfu1c/vuqH/26eyMis/incL75MJgm9fVYfKBWNT3/HR/EmrkR6MUUHnmkAytXrizp5qtU97XU1FT27NnDrl271HW89zFRFPls8mTE2Dhi5szHmZqWb7sjOYWYWXPQJiUzedKkYqegq1TqSLfqHzOZTLwxfjzjJ0wgwc+P0PZtEUQRQRQI8A8gKzOThA0bydl3AJ1Oz/mPP8PbbGZ4r168/fbbBAQEEBQURN/+fVg8fwmS4iHSVAFRyO0TkmWJY9n7iXadAa1IpsOBKDhxeNxkZGTg7e19S+3MzMxk8eLF7N+/H7fbTZkyZejfv/89v+ZbURSm/TSNAEKw6HJnGuj1enKw5tXlBvAXgvFV/ImxnSXCWAaAVFciHtGtTolSqe4ys2fPZs+ujcz6JpQKZRQ6fZXMiIE+tG6a20Hm4y2i0ShYbTLeZg11ahj55J1gnnopgTHPjCAyMpJGjRqV8FWoVPeX06dP8+WXX7JyxVIcThsooNMbeOyxbrzwwos0bNiwpJuous3atGnDgjlzeXrMGKInf46hXDk0fr5IGZk4Yy4RFhzMtAULaNas+AznKpWg/P+aHveYrKws/Pz8yMzMxNfXt6Sb88BSFIXPPvuMDz/5BHy88a5fD52fL670dHIOHUWwWunXqxfNmzfHz8+Ptm3bYrFY8h3D5XLxn//8h/lzFqCRtPgrIQiIXHFF49BK+DZqQKl2bTGXCkey20k/eJj0nbsJNZpYu2oVlSpVKrJt33//PR9MmkSO04mhTCSCVoc7IQHBauORjh2Z8v33Bdpzr8jKyqJCuYpUpR6RpvJ5r6ekJCO7FXTijTX1sfJFLgoneTx0AAoKezM3U7pOGDt27VB7aFWqu4SiKLRq2ZRKpc/z8Vv+bNhm5fX3Uti1tjSlS2lRFLj+zZ2aLmEyavDx1uB2KzR65BJWm0jrtr2YPXtuyV6ISnUf2bFjB31698DtygRBwajP/c50uhQUBATBixkz59K1a9cSbqnq32C321m1ahVr160jPSODAH9/uj7+ON26dVNnCz7gbjUWVYNu1W117NgxZsyYwer168jOysbPYqF3jx4MHTqUatWqYbfbWbt2LTExMWg0GurXr0/btm0RxRsrHU6dOsXs2bPZF7Wfy5cvcTkpkcjBAwiuXw/+X1zozskh5ofpVLb4s3P79kKnmH/22Wf87+OP8WnZjNB2bfKyrMseiYxjx0lavZYGVaqyZtUqvLy8/s3b869ISkqiRtUa1BSbEGaMzHvd5XKSnpaOgIhW0CEgkCBf4RxHaB/UldO2w9i9slm2fCktW7YswStQqVR/Fh0dTeNGtfjxU38e6+TNjAWZfDMtnRPbywHXyjEqCrIMOTYZtwuCAnUA9BkZR0amyJkLOvYfOEZYWFiJXotKdT9ITEykbp2a2O3pPPqQF6OHWGjV1IQgwJ4DDn6am8Gq33IQRC92795H1apVS7rJKpXqDrnVWFSdXq66rWrXrs0XX3zBF198ke91SZL47LPP+O6b70lLScMgmlAUGbfgomLlirz734k8/vjjAFSvXp2PP/4Yj8dD/UaNCKzQlOAG9Qo9n87bm4jeT3Bqyk9s2bKFhx56KN/2ixcv8uEnn2Dp0Jbwhzvm2yZqNQTUr4spNISD3//IDz/8wCuvvHL7bsYd4u/vj8lsIjMnjTBuBN16vQGLvz+ZmRm4ZCeiIpKlpCELMrtsvxMUGsSCafMfyIBbURT279/PihUrSE5OxsvLiw4dOtClSxd0Ol1JN0/1gLt69Soej4vSEVoMehG9XsDhVHC7FXS6a32PgoAgKIiCgPynvvMcq0yZSBOnzju4cOGCGnSrVLfBZ599RnZOOmNHWHj/jaB8M8OaNzLRrKGRyt+k8dHXaXzwwQfMmTOnBFurUqnuRmoiNdW/SpIk3G43L730Eu9P/ABzmj+tvLrQzudx2vl0pZGhLWmnsxg6eBgLFy7M9949e/ZwOTaWwBbFZ4M0lymNLjSEJUuWFNg2Z84cJIOe0PZti3y/KSIcc93azJg9G4+n8LJldzOdTkff/n2Jly4hK3K+bQaDgeDgEPz8/BANAsmaOKrVrsrUaVM4dvwo7du3L6FWl5yYmBg6PtSRRzp2ZsY3s9mwYAvLpq9kyMCh1KtTjw0bNpR0E1UPuD179iDLkJQiAdCikQmXW+H3rbZ8+wkCSH8q13jhkotjp5zUq+UFCA90ckmV6naaM2cWZSO1vDs+qNClWIIg8NrzAVSrpGPVquW4XK4SaKVKpbqbqUG36razWq3MmjWLtq3bEhYSRmBAID98/yOVlNrU8K6HlzY38ZkgCATog2nk14YQTyQvj32ZK1eu5B0nISEBWZExhYUWez5BENCEBBFfSAbRXzZswFyzBuJNRi8DGtYnNi6OM2fO/I0rLnkjR45EMMPR7L0FAm9BENAb9cRoThEQamHdunX0798fk8lUQq0tOVevXuWxLo9xMuoMdXUtaOPzKE0t7Wnp+zDNzZ2wX/Ew8MlBauCtKlFWqxWzScvCFdkAVK9ioFE9I99Oz8DpVK6t6c79t9OhoBFBVmQ+n5qOxVeDzW4lKyuHQQP70Ktnd1avXo3b7S7hq1Kp7k0ej4fs7DT69/BBpys694koCgzq7YvbbScpKekOtlClUt0L1KBbdVtdvXqVjh068vLzrxB7OImKcm00Dj0WORizw4+UlJQCo8mCIFDDuwEeu8y8efPyXjeZTAgISDb7Tc+r2J2YCwkirTk5aG9hnbbWywuZ3Ifde1H16tX5fup3ZBiS2J21gSv2C9glG3bJyiXbOXZl/Y7dO5sZs2YQGRl58wPepyZOnEjKlTSaencgxBCeb8TCV2uhoW9rvB0Wxj4/Vh2pUJUYvV6Pn5+eLbvsrFifG3i/My6I8xddDB2bSMyV3N/N7BwZWYGsbIlX/5vMivXZPPeUL4tXZtKwrhcvjdKhuHbzzOjBPP5YZ5KTk0vyslSqe5LH40FRFMqXufnSo/JldAjkJt1SqVSqP1ODbtVtY7PZ6NOrDxdPXKKF18M09G1NiCEcu2SjlFgOvWBAlhTS09OQ5fyjsVpRS7AQwfJly/Nea9q0KWajgbRDh4s9rzs7B8eFi7RrW3AKeWhICK6UlJu23ZGcgkYQCA4OvrWLvQv17NmTlWtW0uLRppxVDrPNuo5t1vVcEE/QsWd71v+6jk6dOpV0M0tMQkICa1etpayuCgZN4ZlGRUGkmlc94mMTWL9+/R1uoUqVq2HDhuRYBTq0sjD2zSQmfpKCn4/A7G9COXXWRYvHrtJzeDxvfZTKuInJtO1xlZ/X5TBujIVN2+1YbTD104qMGR7OshlVWDu/PEkJhxg0sD9Op7OkL0+luqfo9Xp0Wh3JqdJN901K8SCIIhEREXegZSqV6l6iJlJT3TY///wzp06cpoXXw3hrc7P3ueTcBzwDuaPWekGPU3Jit9nw+n/1tc0ab1JT4/L+PygoiF7de7Dgl/UENGyAzqfwetwJG/7A22Cgf//+Bbb17d2bqLffxp2dnZe1vDBpe/bSqEEDypcvX+Q+94LmzZvTvHlzLl26xPnz5xEEgWrVqqkPAMCWLVtw2J1E+hb/M/bR+uGNHxs3bqRHjx53pnEq1Z906tSJ8PAyGAzJjHs2kp/mJjBtfgYB/hpcThmnS2b3/tyRNIdTodsjXnh7i3w2JYMcq8KX71egSsUbM3/q1fJm1tdl6fLkPlavXk2fPn1K6tJUqnuOKIo0bNSMn9fuZcxQPwyGwnMluD0Ky9bkUK1qzXuyEopKpfp3qSPdqttmxvQZBBKKj9Yv7zWtkFsj2sX10RUBDRpsNluB9zskG76+fvlee+ONNwgzexHzwzRyYi7x5wp37uxsrixfiW3ffj74738LrbPdr18/Ary9ubp0BbKn8F7q1Kj9OM9fYPSoUX/xiu9eZcuW5aGHHqJDhw5qwH2N1WpFRMxXt7woOkVPTk7OHWiVSlWQVqtl4rsf8MsmNwlJbtbMK89HbwYxcqCFl0YHsOSnUvR/wgenC4x6gU077Ow/5KRtcxOyIvPGBxfpNugkV+NujGrXruFFm+Ym5syeUYJXplLdm954401OnpVZvCobh1Pmz7V2FcDlkln9axZRh1y8+tqEkmqmSqW6i6kj3arbQlEUTp44STld9XyvmzVe+OosJLqvECTklq4REfHIbmRZzqvPLSkSSUosY3qMzvf+yMhI1q1ezaChQzgxdRrakGC0IcHIDgfOCzF4Gwx88clkhg0bVmi7/Pz8mDV9Ov0HDSL6u6kEtW2NX62aCBoRe1wcyTt2Yz14mNHDh9OzZ8/bf2NUd42goCAUQcEuWTFpih6FUBQFh2gjKCjoDrZOpcqve/fuWK3f8vprr7Dg5wt0fdhIudI6Yi5LfD41HbcHJr0dTN9u3piMoNeLZGRKbNtjZ8wwC+s2WOk59BRr5tcgNCS3o6lTW1/enXwQRVEKzcCsUqkK17FjR/r0HcKr780hPkmiX3cfAvw1CEBGpsTy9TlM+iaDRzr3pFevXiXdXJVKdRcSlD8PHd6DbrUguerfJcsy4aHhlPPUoJy5cr5tMbZzHM3cS02hCQFCCLIi4cFNcEgwopg7TetU9mHitBfZE7WbSpUqFXr8bdu2sWTJEuITEjCZTLRt04b+/fvj5+dXYP//b//+/fz3vffYuXs3bllG0IgIskzpiFKMfe45Ro0apT6E3udycnKoWb0WlqxQqnnXLXK/FFcih1zbWbN+zQNZw1x1d0lISKDjQx3IyYomMECLxVdLl44BPNIWSkVo8PPRXOvAFMixylRvFcP3k8JoUt/IYwNjadXUwlcfVgRg3rIk3vggnStXEvM6PFUq1a1xu91MnPgOM2dMRad10KC2DkGAg8fcOJx6BgwYxqRPJqPX33w2lUqlun/caiyqjnSrbgtRFKlSpQrxRxMpR/6gu4ypIonOq5x07KesUoVgItBotIiiiNWTzTnrCZI1sbz33v8KDbivH79du3a0a9fub7WvUaNGrFm1ilOnTnHw4EFcLhdlypShbdu2aLXqn8GDwNvbmyHDBvP9l1MIdUfirwsssI9LdnLacYhadWvRokWLEmilSpVfWFgYDz/SmV3bZrNjbTVEUcBut5GVlYmPlya3WDe5MzT2HnQAuRmUw0K0jBzoyyffpjFxfBkC/HVEHcihfLnyasCtUv0NOp2ODz/8iLFjX2T+/PmcOnUKRVF4tm0VBgwYQOnSpUu6iSqV6i6mjnSrbpvp06fznxfH08q7M2ZN/qRnkiJxPGs/l+3nkWQJiz4AnUFHjpxFUGgQb739ZpFTxFWq28Vut9OrZy/2bo+itKYSZUyVMGnMeGQPsY4YYtxn8Av3Zu36tVSuXPnmB1Sp7oADBw7Q9fGOzPgqgofb+ZORkY4sOwmw5HYYyrKMLMsMeT6R9EyZ9QsiEQSB5BQP9Tte4vtPKtG0gQ/Nupzh9QkfM2bMmBK+IpVKpVKp7g+3GouqQbfqtsnOzqZN67Yknk+mkXdbTBpzvu0u2cnerC14zHb69O2DxWKhbt26dOvWDaOx8BJOKtXtZrPZ+OCDD5g7ex6ZGZloBR2S7EFn1NL50c689957lCtXrqSbqVLlURSFfn17cfL4Zhb+UJ6IUAcCLix+N4LuSd+k8e2MDH78LJTHOuZWapAkhbINLzDxP2VY8Usm8cnBbN22u9CkkyrV/SQuLo558+axdctG7HYbIaHh9O7dj65du2IwGEq6eSqV6j6iBt2qEnH+/Hl69ezNlQtXCCaCEEMEAiIprgQSlasEhvmzaMkiGjRoUNJN/cusVitbtmwhLS0NHx8f2rRpQ0BAQEk3S/U3ZWdns2HDBpKTk/Hy8qJt27bq9EDVXSs9PZ0n+/fh1Ml9dGqrpXN7A5EROk6dczFvaRanzrkYO8qf4f19MBlF9DqB6Esu2na/itlsxM9SioWLfqZWrVolfSkq1b9GURS++eYbPvnkPcxGNw+3NePnq+HsBRc7o+yEhpZm5qz51KtXr6SbWqLUZIoq1e2jBt2qEpOens68efOYPm0GV69cASA4OJhhTw1j6NChhIWFlXAL/xqbzcakSZOYM2su6anpKLICAnj5mOnVpxdvv/02ISEhJd1MlUp1n7PZbMyZM4dvv/mCmJjzeJlFdDqBjm0sjBwcRpP6Zmw2G3aHHUWR+XxKOj/MyeGVcW8wevRotXyg6r73/fff8/57Exg70sKzT4Xj7XWjpvaFGAcvvX2J8zE+rF7zK1WqVCnBlt55sbGxzJ07l/mLFpGYmIjBaKB9m7Y8NXw4bdu2VYNwlepvUoNuVYlTFIWsrCwkScJisfwryXvsdjsrV65k1cpVpKSkYrH40eXRLvTp0yfv9+Hy5ctcuXIFvV5P9erV8fb2vslRb7BarfTu1ZuonfsoJVSgrLkSJtELl+zkiuMCVzzniaxcivW/rLvnOhNUKtW9ye1206xpI4y6yyyYWpHSpfIvz1EUhd37Mhn47GVGPf0qb7311m07d1ZWFkePHsXpdBIZGUnVqlVv27FVqn8iNTWVBvVrMWKAnrdeKXzWUnaOh0f7n6VKzW7MnDnrzjawBP3666889fTT2GQJr7p1MIaHIdntZB8+ipSUTJ8nnuD7775Dp9OVdFNVqnuOGnSr7nvbt2/nqWFPkZyYgkUJwiiYcSp2MoQUfP19GDV6FAf2H2D71u1IHhkE8PXzZcCgJ3nhhRduadRn/PjxzJgyk0bmtlgKyXZtk6xE5WyiRcdmrFi54t+4zPvOsWPH2L17N06nk4iICDp37oyXV9F1s2+XhIQEVq9enTedvEOHDtSpU+dfP69K9W84deoUT/R4jCBLBs+NCKZ750CMRpGrcU7mL0vmx7npNGr8EPMXLLotJYxiY2P5/PPPWbliMXZ79rVXNdSt25Axz75At27d/vE5VKriJCYmcuTIETweD2XLlqVGjRr5Rme/++47Jk+awIGN1fG3FF2VZM6SJN76KJ29UYcpVarUnWh6iYqKiqLbE09A5YqU7tsLzZ/WtCuKQsaRY8Qv+ZnhTz7JV19+WXINVanuUWrQrbqv7d27lx7dnsBo9aamVyO8tDdGrx2SjSMZUVx1XcRfH0gVc238dUFIioc45xXi5IuERAaxfOXyYkdp0tPTqVWjNqG2MlT2LnodZJzjMiel/WzftY0aNWrc1uu8nxw4cIB33p7AoUNRmIwyRoOGjCwP3t4BDB4yktdee+1f6WXPyMjg1VdfZdXyVbgdHowaEy7JhaKVaNy0MZ9+9qm6zlV1Tzp37hzvvvsOmzf9jii6MOhF7A4FH58ABgwczuuvv35bkkadO3eO3r26IZLIkD4WHn/EHy+zhmOnbMxelMKWXQ7Gvvg6r7/++m24KpUqv9OnTzP5009ZvWYNOVYrkiQhAKUjI3nt1VcZNWoUbrebhx/uRIjfEb6bFI4oihgNRgwGQ4Fp0xmZHmq1OcGUqfPo3r17yVzUHdSrT2+2nD5NxefHIGo1he6TvGsP6WvWs3/PHipUqHCHW6hS3dvUOt2q+5aiKLz+2htocww08muDKOSfti5KGsq4quJUHNglKxHGsnn7+Or8KS9VYd+VLfTv9yR7o/YUOQr0yy+/YMu2Uca78Nrh14UZIjmTfYTly5ffNOhWFIWDBw8yd+5cTp05gyAK1KtdhyFDhtzXAfuOHTsYNLAPNap4mP5lBB3bWNBoBGLjncxdkswPP0zmzJlTzJgx67YG3pmZmXTr2o2Th05TQVedSJ/y6EQ9siKT5Izj2M7jPNblcVatWfnAJ9ZR3XsqV67M/PkLiYmJYfv27djtdkJCQujUqdNtmz3idrsZNKgfvl7xzPgijAB/EVF0YjSa6NjWj05tLUydHc/7n0+iRo0a6oi36rbat28fvfr1I9VmRfBYMZsl6tUyYNDDiZOXefHFZ/j6qy9xu2ViYqLp282E2+EBFOw2OxqNBovFku973suc+zzgdDrzXrPb7WRmZmI2m2/rAE5GRgZ2ux1/f/8SqdJy8eJFNm/dRlDvHkUG3ACBjRuSuuEP5s6dy8SJE+9gC1WqB4cadKvuOQcPHuTIoSPUNTUvEHBD7jpsURApL1TnoLSdROdVwo1l8rYbNEbqejVn1/nfWbt2LT179iz0PElJSehEPQZN8V+UoiBiwkxSUlKx+2VlZTFy1Cg2bN6E4OeHoVxZQGHv4kX8OGMGvZ94gq+/+uq+K59ms9l4+ulhNG8kM/Oryuj1N35mpcINvP5iJM0b+zD0hbX89NNPPPvss7ft3P/73/84eeg0Tbza46P1y3tdFETCjJEE6cPYl76ZkSNGsTdqDxpN0Q8lKtXdqly5cv9ambv33nuPM6eOsnpuOP4WDwLgcUO6w45Wo8XPYuGZoeFs35PD1CnfqkG36rbJyspiwODBpHncaN3ZDO/vy/AhQYSF5nbMOp0y69en8v7ks6SnaAjRlOHk6QR0gg5BEJAVGY/kJi0tjYDAAPS63MD72CkboCEiIoI9e/Ywffo0fv1lDZLkBgQaNWrKsOEj6d69+9/6TnC73SxbtoxZM6dx7NhhQEGnM/DY408wcuTIO1q95dixY7hlGd/q1YvdT9TpMFQoz5GjR+9Qy1SqB8/tz2z1N3z33XeUK1cOo9FI06ZNiYqKKukmqe5iO3fuRJQ1BOvDC2yTJAmX04UGLd6CHya8SHElFtjPR+uHRQhk3tx5RZ7HbDbjkd3IilzwPIqHy/ZotqX/xtrUpcS54vjlt99YuXIlbre7wP5Op5P+Awbw+66dhA7sT+VXX6Fs/z6U7d+Xyq+PJ7BXdxavXs3IUaOQJOkv3pG724oVK8jMSGLS22XyBdx/1raFHz0f9WLWzB9v2/VnZmayaMFiSmsq5Qu4/0wraqlmrk/0uWg2b958W86rUt0vfvnlFz7/bBLNGhlo1shMgEWLxU9LYICWQIsWQZBIT0vD4/EwtF8Qhw/v58yZMyXdbNV9YtmyZSQkJ0NOOi+MsvDG+PC8gBvAYBDp3j2Qmd+EY/aRMGpMRF/0sOegDcjtXNWJBgRFICszC8hdTTlncRKlS5dn37599HziUc6eWMlbL/sw+5sIPv9fMAbxAM8/N5ynnhqGy+X6S222Wq08+WRf/jPuGUIsJ/jmoxBmfxPBq8+ZObx/MV0ff5iZM2fetnt0M9dXkArizTOTC6KIJBd83lGpVLdHiQfdixcv5pVXXmHixIkcPHiQunXr8sgjj9x01FD14HI6nWgFbaHlLWRJQoG8EXAtWiSl8CDOR7BwKeZSkedp27Ytgk4g3nkl3+tWTzab09dzwBGFtXIAXg+3wffhDqR6mRkyciQPd+5MYmL+QH/p0qXs3LuX0k8NxVKrJsKfMrmLWg2BjRoSMbAfa379lY0bN97qrbgnrFq1nDbNTERGFL+2dGDvEK5ejeHQoUP/6HzZ2dkcPHiQH374geysbMqYKha7v0UbiFnxYe3atf/ovCrV/cTtdvPaqy/jb4GGdU1oNfk/b3U6AX8/DaKokJ2dRe3qXoBEXFxcibRXdf+Zv3Ahio83QRYYNTy44A6KAgrUrOVD9y5m0uVYzEogEz5KIi4ht/NbADSCFo/Hg8vtZtmaFJattVK/QVMmf/I/xo3xY9OKqowaHMZDbSz07R7E4p8qM+fb0mzdvJrXXnv1L7X5+eef4+jhzSybXo7Z31biiUcDeaiNhTHDw9m2uhqjBpl5c8I4fvvtt39+g25BpUqV0IoiOdEXi91PkSRcly5TtXLlO9IulepBVOJB9+eff86oUaMYPnw4NWrUYOrUqZjNZmbMmFHSTVPdpcLDw3HKDhySveDGvEBcQVIk7FgxacyFHkdSJHS6orP6Vq1aldZtWnHBeRK3nPsF7pJd7MrajD1QT6nRzxDetx9eTRpiadGMKs+Optzzz3D4QjT9nnwSh8OR2xJF4acZ0zFWrYxXmcLLmAD4Va+GrlQE02ZMv7UbcY9IS02mXOn89zk7x8OK9an8NDeBBT8nExvvpEwpAyCTmpr6t84TExPD+PHjqVGtJg+168i7E/+L0+bEke3A7S56tMIu27A5rCxZPI/q1SrQpHFd3nzzTc6ePfu32qFS3Q/Wr19PSkoc5UobsNkKH/0SRQEvs4jT5SIjK/dv7HZkSlepAGLj4xGdOTz+sBdGQ8HH1etZgAVRpGsnb1yilSqm+iRdNdP9qct8OS2ZcxedJKfK7D1gZ/QrF3jprXj69R/O0SP7eayjgZefKVVoB36H1hbeHhfC0iXzuXr16i2199SpU/z262o+fDOcpg19CmwXRYG3x5WmVVMdX37x6V+6F39XzZo1adygASk7dlJc3uSM4ydRsnMYPHjwHWmXSvUgKtGg2+VyceDAATp27Jj3miiKdOzYkd27d5dgy1R3s65du+Ll68Vle3SBbVqtFlEQkBSJZCUWCQ+RxvIF9pMVmVQSaNm6RbHn+ujjjzAE6ojK3ky6O5VL9nNYtQ7CnhyILigAt+JCERR8/XxBAHPpSEoPH8LBo0dZtWoVkDvN+fiJE/jVu3l5Kt+6tdmxc1exX473Gm9vX5JTczstbDaJtz+6RKOOh3nh9fN88s1lXvvfBZp3OcIz/zmPR+Iv1VG/7vDhw3Ts0JHZU+YSmFOKpsaHqKrPvd859hzSUtNwOAp20lyxX2B71kq8grLo/bjAc8MEOrdNZ83K72nXtjlffPHFffWzUKlu1ZYtW6hVzUiH1hZ+32LD6Sw88DYaRAQUVv+SgsnkQ+3ate9wS1X3K6PBgOJx428pYl21cuM/AvxFRCF3VLuZTxeMGdX4fpqdLgMv07p7DEOeT2Tnfi8mf/o9TzzRk5iY84waHFrs+ft2C8LL7GbhwoW31N558+YRGgxdH/Yvch9BEBgxMJgjR/Zz7NixWzruP/X0yJHYTp3h1KdfErv+V7JOn0X50zRy66XLJC5fSeeOHdW/X5XqX1SiidRSUlKQJInQ0PwffKGhoZw+fbrQ9zidznwZJ7Oysv7VNqruPn5+fgweOogfvvmRQFcIgfqQvG2CIGAym0jOSeQCpwgzlsZLW7DH+ar9Ah6Nm2HDhhV7rurVq7N67SqGD3uKqLObsDkdmBrWRfEz4VKciKKIv8WCVqslOzsHu82GrBGRI8J59vnnkWWZli1boijkq41ZFI3RgNvtRpIktNr7I8/hw488yicfb+dqnINnX73A6XNWRg/xY0BPX8JDtVhtMsvXZfPlj+k47Ar+/kU/sBQmKyuLJ/s9iTNJopVPZ3Ri7kibUWPiTM4x0pQkwihNZkYm2kAt2mvZ0RMcVznh2MmTvcz857kQIiIC85LYvfmyzDfT4pn8yX/x8fFh5MiRt/emqFR3Obvdjp8vDO4Twk9zE5i5KItnhloK7CcIkJouMXdpBr16P6uW7lTdNu1at+bsjFNcuVowTwqQO3dcAcXt4UqshCwLGEQjBo2R2r6NqS7XJ8OdglN2csyzl5dfeZWBAwcyY8YMdFqZhnWL7+D18tJQr5aBc+fO3VJ7z507Q5P6enS64sezWjXxATycO3fuXw1yHQ4HEydOZM6C+Sg6Hc7sbBJ37CL+t43oLX4ENGqIOzUV++mztGralB+mTv3X2qJSqe6C6eV/1UcffYSfn1/eP6VLFz1dV3X/mjhxIm07tuGgYztHs6LIcKfhkp1keTK4pJzluBCFR3BR0Zy/DJesyFyyneeM5wiDhg68pS+82rVrszdqD9NnTUNj0OBdoyoGkwGLxUJwSDAgkJKSgjUnB2QBLTq8qlQlMyOLZ0aOYcRTIzDo9djjE256LltcPCEhIfdNwA3Qv39/EL3oO+IMp89ZWfJTOOPGBBAemnuNXmaRJ5/wZcHUMGpWNTB27Ji/NLq8ZMkSEmITaeDdMi/gBjCIRkoZy3KVaCQ8gIDVlptgR1EUzjoO0LGdgbdf8cfbR4fBeKNTRKcTefmZCDq21jP+P6/QsWNbunR+iHHjxnFUze6qusfEx8fz9ddfM27cOF577TWWLFmSt/ylKCEhIVyI8VC2tIFRg8P48Ks0ps7OKDDifeyUg6deTMTp9uell176F69C9aAZPnw4KBrWb8gmPcNTYLsgCKDIyB43K9ZZ8RYC8dLc6GTXilqCDGG4FSdGsyGvUsn16eS38jUjy0qh088LIwgCt5KH7Po+t3rcv8PlcjFw0CCmzpmNoU0rakx8k1pvvUGF8a8QPGQAQoA/8b/8RlBGFt98+ikrfv5Z7TBTqf5lJRp0BwUFodFoCiSdSkxMJCwsrND3vPHGG2RmZub9c+XKlUL3U93fjEYji5cs5s13JyBGuolybGJz9ir22DbiDMlm9AujqFG7OvudW9iT+Qcnsg9yLGsfW7PWcU45ypARg/n001tfU6XRaGjTpg0mkxkfPz8sFj+MJiOSRyIjPR1kAb1gRCfo0aBFFHVoBR1NzR04vOcoRp2erKj9KMVk5pacLqyHjjLoySdvxy26awQEBDBx4gccO21ncB9valW7EdwqCtgdMmkZHoICjHz0ThlOnjjC3r17b/n4c2fPJYgwjIWs3a/p0xC91sBhZSepSgI2uxVFkUlxJWAX0hnazxtBAxY/CwI3HoDSMzz0GXGa37ekU660i1qVTlOjwmm2/jGTzo+0ZcSIp7BdC+BVqruV3W7nxRfH0qRxXb7+8h1OHpnL/t0zeOnFkTSoX5O5c+cW+d6ePXsSn6SwaUcmb48rzYiBYbz/RRqNHr7MK+8k8fbHyXQfGstDva9y+rzM8hVriYiIuINXp7rf1a1bl+FDhpCd5eG/71/F7cofJSseD7Ldzi9/5LBxq50y+moFAtlsTwbR7pN079md8PDciie1atXC7dGwM6r4mZKZWR4OHHFSs2bNW2pv7dp12RnlxOEoPvL+Y3sGoKNGjRrF7vdP/Pjjj2zcvp3I4UMI69AOrbcXOr0Oi7+FMg3qU/3Z0QQ1b0pWdjbdu3fHcAsz8VQq1T9TosNper2ehg0b8scff9CjRw8AZFnmjz/+4Pnnny/0PQaDQf1wUAG5vz/jxo1j7NixREVFkZ6ejq+vL02aNMFoNOJ2u1m/fj3z5s3nYvRFDAYzj7UaxLBhw275S/TP/P398bdYsF6IwVIz98vSarWiKGAQ9PCnoM1x9TJmjTf++iBqK03Zn7QVjUnHlRWrKd2ze77s5QCyR+LyoqWYRfG+TGRisVjwNuvp0cWPlHQPOo2AIILHoyArYDAY8fP1o3VTgXKl41m+fDnNmjW7pWNfunSJQG1kodsMGiOtAh7mcOZuzjqPIsoiAVmBZLsz8QtUaNrICz8/v3zJnxwOmUFjznAl1s7c78KoW1OPXm/G19cPj0dh7e9pvPq/5Ywe7WTWrDlqbW/VXcntdjN06CAO7NvIxP+E0KdbID7euV/5MZcdfDMtntdeHYvNZmP06NEF3l+vXj0aNmzGOx/vpVY1MxPHl2FI3xDmLk1iz/5s3G7p/9i778Aoir6B49+9fsml90IavYTepPcqHeld0EdRXxXEhqhgQyxYaNJ7RzpI7733EkoIpPfk+t3u+0c0EpMAKk3dz/PP4+3c7MzmuNvfzsxv0GpVqJQaXnj1dapUqfK4uyj7D/juu+9IS0tjzdrlpKRcZegAb+rXcUWJyLUbFlasMzJ/WTZWmxqTkIvZaUSncMEsmrhlvsYd53UqVi/PhAkTkCSJI0eOcPnyZbx9Apg8K4EGddwQhKLHn+YtTcYp6vNmaz2Avn37MnnStyxZncKgXkWvF3c4JGYsTKVu3QaULVv2L1+Xe3E6nfw0cyYuVaJxKxlV6LggCKjUakI7d+Tq5xNYunRpkd8BMpns4Xric1jffPNNBg4cSM2aNalduzYTJ07EaDTmTSuSyR6AWq2mfv36Rb7eqVMnOnXq9FDOo1KpGNC3L9/OmIGzZTMEjQaLxYISJXcH3A5jLqaz56mozQvsfTUBuFk9KVUxgnMnTnEtMQmfBs9gKFUSRJHsS1dI338QdWYms2fO+lcumUhNTcXVVU2lCkFYrBbsNhuSBGqNAr1OX2A6fUQJJampqQ9ct0qlQixmWzjIW9td17sZt0wxnLYcomP/9sTGxnLz2n78/Hy5+28HsPjnFM5dMrJ+QQjR5bWkZTjypyGqVAKd2/ng7qZkwCvr2bZtG61bt/5T10ImexzmzJnDoYPbWDItgro1C+a1iAjT8fXYSLw94xg3djRt2rQhPDy8UB1Tp82gc+f2tO8Tw7B+XnR71ofRb5bg5i0rC1cmM3dpNvXqt2DMmDGPq1uy/xiVSsWSJUtYufI53ntvFC++GYdeC1qNEotNib9/CO+89zZpaWksXriY2MwriKKIQqHAw8uDF/oP5Z133mHHjh188/V4YmIuolA4MZsd3Lhh480PLjPunQgMBn3+OSVJYsnPqYz7Jomw8NIMHNAHrVZHvfqN6NevX7EzOiIjI+nZqz8fT5iNn4+adi28Coy8Wywib318k9PnBZYsffuRXbPTp09z6/ZtQtvf+7dJ7WZAX7Y0q9eulYNumewxeOJBd8+ePUlJSWHMmDEkJiZStWpVNm/eXCi5mkz2NBg8eDCz5s3j5ryFhPTuiSRJKITfRzqdZjPJK5ajtSvxUwSTnZ0FgJvdk6yMLH5evpyvv/2WvUtXkvDrwi61Ukmr5s0ZOWIENWrUeCL9etRcXV2x2kRMJhFXVz16nb7YsumZEqWDXfP/Oysri+TkZLRaLaGhoSj+MEvgmfrPsGP1HkpLle65Ri7DkUZUZBQ//PADGzdu5H8v7uP6TStREbr8MpIksWBZMq2auBBdXosoSjidElpdwdHsZg09qRadxOzZM+SgW/bUEUWRObOn0665S6GA+25vvhTMwlUXmT9/PqNHjy50PCQkhPXrf+HTTz9lwuTlfDoxBRABJZ6evgwZOpKRI0fmJyCUyR6Vbt260a1bN06dOsXx48ex2WyEh4fTokWL/JlK77//Prt37yY7OxsPDw8aN26Mm5sbkydP5pNx79OysZbP3gmjbg03JAnGfBHL5NkJbNh6lr7dfSlbyo30DAcLlqdz9lIuOq2SQJ87VCiZQq5RZMZP+/j+uwm88eY7vPnmm0X+3nz55QRyc3N5ceQKKpZLoXNbdzzdVVy5ZmbZmmxMFj2TJs8ocqDgYcnKykKUJDRenvctq/b0ID0t45G1RSaT/U6Q/uH74fz25ZqVlSUngZA9Fvv376d3//5kOewoKpTDNbwUCglM16+Re/IUqlw75RxVcMUdxa+jqDekyyQob/LW2yP58MMPiY2NJSYmBkEQKF++PGFhYU+4V49WfHw8tWtV4ZN3vRjQw7/YcleumWnW9SpTps4nMDCQqVOnsnH9Jux2OwICEVERPD90CAMHDsTVNS8w37FjB906d6eK5hkCtCFF1mt05HLQtIX3PnqXESNGYLVaqV6tEp1bWxj37u8jfOkZdio3Psnk8f50bG3AaHKSa5Tw9fNDqSgYeP80L5HPfzBy82b8I02II5P9WVevXqVxo9osnBJK43oe9yz77ribHDgZwt59986jkJ6ezr59+8jNzcXX15dGjRrJwbbsqZCdnc2WLVtIS0vD1dWVpk2bEhKS91tw9OhROnVszf8Nc2fk8MJ7cl+OMTH4taucu2jGYHBDpdZgt9koEwU/fB5O5Yq/ZzjPNTr5aV4i30xN580RHzBy5Mgi2yOKIjt37mTu3Nns2bMDm82Gr48vz/XoS//+/YmIiPhb/XU6nezYsYMNGzaQmZmJh4cHbdu2pUWLFqhUKo4fP07zNm0o8b+huEYUnsFytxvzFlHd4MamDRse+PySJLF//34WLVrE9dhYdBoNdevUoX///vnXXSb7L3nQWPSJj3TLZP809evXZ/f27Xz//ff8MHkKuexHIShQo6GEFIyPPQgXwYBKUAECkiSRQwYGwZ1JEyeTkpzClKlTKFWq1JPuymMTHBxMixZt+WHGGurXFHAzSEiASqXERe+CTqdDFOHz7+7g5xfCtWvX6Nu7Lw6rE0FSoBJUuKs9Sb2UwfujRrNs6XJWrFyOr68vTZo0oU271mxZvw1RqkGgtkSBG6tMezqnTQeJKhvJkCFDgLzcEC/+71XGf/EBVSql0r2DLwDWXxP1uLoI2GwiuUYRnc6lUMANYHBVYrfbkaQHz24rkz0OeVtpSvj7qu9b1t9PTU5O1n3LeXt707Fjx4fQOpns4cjNzWXcuHEsXLqErFwjSo0Gp92GTqmiXZs2fDJuHDNmTKdkuFBkwA1QtpQLu1ZHU6vVRTp2eRmTycSu7XNZMas0fn/492NwVfLmS3lB5cRvx9OnT58ip5orFAqaN29O8+bNkSQJURQfWu6PEydO8PJLw7h1K4bypdUEByo4f1Ni+bI5hIREMGnydKpXr06JkBDSjh6/Z9DtyDVivnSJDh9+9MDnj4+Pp//AgRw/dQqlny+akGBEYw67fvyBrydO5P9eeYX33nuv0Iw0mUwmB90y2V8SGRnJt99+S05ODqsXraOWoTEaUUtmRhYqQYVS+P2fVhZpGMnhGfdmOCQHy5Ysp0PHDjz77LNPsAePV3Z2NvGJSVy4YqTX/+L44C1/atfQYxdFMrOyiD2XzpQ5RnYekHi2Q2PeHvk2SkmFvxCKK244JAeptnjMmAjUhnD+2AUGDxrM2nVrUSgUzJw1kxdffJENazdy1XoOH0UgAgJZUhrZZFCpckUWL11cYA/wV155hRs3bvD66NksX5tB327eBPrn3WQdP22hUjkNGo222KeWFy6bCAwIlG8uZE8dHx8fQEFcvJXyZQpn9b9b3B0rXl5y1nHZP0tubi5dunXj6LmzeDSoT5k6tVB7uOO0Wkk/cYq1u/ZyqHVrcrNSGP1G3tpqp9OJ2WzGbDYjik4EQUCj1eKid6FHRzcWrliE1Wrl9WGehQLuu70wIJCf5ucty3j77XuvzRYE4aEF3GfOnOG57p0oX9rC5M8jqVrp91H4sxeMfDThDj17dGb5irUMHTyYMZ9/Rm6tGhiKCLwlUeTOug24aXX0fsAdU9LT0+nUpQvX0lIJGToIQ8mo/AcZTquVlH0HmPDddzgcDj766KOH0meZ7N9Enl4uk/0NFy5coGXzVmhzXCkpVsRudaIRfs+unytlcVY6jJvGnQberREEgYPZ26jZshqrVq18gi1/fBwOB926d2f38WP4tW9D+t49OJLuEFlCSVSYitR0J6fPGJEkNR9//Bnvv/s+BqsXFRQ1C1xLSZJIkm5zlTP4aYPI1WSw8ZcN+VnOf8tMO2fOHI4dOYbT6aRUmVIMGDCA1q1bo1YXvomSJIk1a9Ywa+ZPHDt2GHCSnW0hMkxg77pyuLm5FDk6kmt0UrPlRZ4f9u59b7pkssdNkiRatWxKkM8l5vxQ/Iya7BwHNVpc5JXXPuKNN954jC2Uyf6eUaNG8dPCBYS9+DwuRUxptufmcu2HKYhJcayYVZoGdfVkZWYiiaBA8WvGcgmn5AQBNu6w8vbYbLRa2LeuNBFh9146Mfzta9xJrcbadRsfUQ8LkiSJtm1aItlPsWp2afT6woG81Sry3NCrGK1l2bR5Gz169WL3kcP4tGiGd+2aKLU6EARMcXEkbduBM+Y6M6ZOpUuXLg/Uhk8++YSvpk4l8rWX0fp4F9FIiN++g/RNWxj34Ye0a9fukWVol8meJg8ai8pBt0z2N+3YsYOB/QeRlpBGIOG4C96IkpNUEkgnGU+1N3W9m6FV5P2I3zLFEKM4y81bN/PXJf+bbdq0iV4D+hP6/GDcSpdEkiRyr98g4+RpHLm5KDQaXMJKkLp5KyW8vbl9IYHqQiPUCk2R9d0Rb3CN87jp3ekxqBuTJk16KO1MSEggLS2N+Ph4nh/Sjx4d1XzxQQRKZcGg22YTeXHkdfYdUbN332F5b2LZU2nJkiWMePN/TPkymGdbFb5BFkWJEWNusvoXkSNHTsnJS2VPNVEUuXLlCrm5uahUKtp36ojqmToEtWxe7HsyTp8lbvpUpn0VRvOGShSSApVCjXDXjhUSIEoOZixJ5YsfjBgMak7vLI+P972XZrw99iZnrpTmly07H1YX7+nkyZO0b9eMOd+H0KKxZ7Hl9h3OptcLt1j182aqVKnCy8OHs3zVKqySiNLDA8luR8rJISQwiB8mTnzgGXdWq5UKlStjLVea0I6F32M2mck1GnFYrcT/MAXBaMTg5kaDevX44P33qVWr1l/tukz21JPXdMtkj0mzZs3YtWcnlSpW4rbtOpIYg4CAp9qbqq51CdVHFJhurlHoEEWJ3Nzc/0TQPWvObDShobiVLgnkTbdzKxlVaP9Qc2Iil7fvpiQVUCuKv+EJFMKIla4g2SVirsQ8tHYGBQURFBSEr68vw195k2+/+YLzly8ztJ8f9Wu54RQlduzLYvaidG7EqZk5a44ccMueWj169GD37l0Mf2cxp84ZGdDDn7BQLZIkcehYDj/OTGLPIScTvvqenTt3Ehsbi0qlonr16jRu3FheNiF7KthsNmbOnMnMGbO4fu06kijhcNqxCBJlqkTnRc3FpNTwjK7ILbWOZWtSadEgEJVCU6ioAChQsm6zGZtNRJIELlwx0bDuvRMQXrxqJSj48W3vuWPHDrw8oGmDe7erfm03AvwUbN++nYyMDH7ZuhWFqwsGP18UanXeGvOcXFJT05g+Y0Z+hve75ebmcvLkSSwWCwEBAURHR3P+/HnS0tMpUa1qoXPm5ORgNBpBpUbl7oahWhUct27h16oFB/bsp0OXziyYM5cWLVo8zEsik/3jyEG3TPY3Xbt2jTFjxuCwO/OepAsaRETMThM5jiz+eEdgcuaiVqvw8Lj3j+e/xbHjJ3CtU/O+5Qxly5K8YzeuFL/FEYBSUOIl+ZEppqB4SGvlAHbt2sW0aZPZvWs74AScHDvlZP+RWFxdlCgUAgqFlpat2jNx0ptUqVLloZ1bJnvYFAoFP/44iaioksycMZlp8y7j56PGahXJypEoWaocrdtU4KMP38VkyiDQT4PNLvL1VyIREaX5YMxY2rZt+6S7IfsPM5lM9O7Vm7079+FLEFW19dEqdMQYzxOrisfoFBGzsvJ+S4sIvAWFApVfALsPxnLkhI0GNbWFCwEbtudw8YoDtaDF4ObFvKWp9wy6z100cuKMjZmvP9ha6IfBZDLh4a4qNPPqjwRBwNtTxbVr1/hhymQoVYpyvbqj+MPyqtzrN9k9dwHDXniBxYsWIQgCycnJfPvtt3mJ6XJykCRQKgQqlCtHy2bNESUR5R92LLBarBiNRhQ6HYpft25TaLVIdgdeVavgUakSsfMXMmTYME4eO/ZrvgmZ7L9JDrplsr/h0qVLdGjfgZwkExXVNXGzeaFXuGKRzCRIsdwwXibLnk5d72YoBSWSJHHHeZO2Xdr+Z7bbsTscaB4kOFbmjawpEHBKzgKzA/5IgQIbVqIrV+Lo0aNYrVZCQ0P/8lYsefu4jqZqJSXfjPUjurwLJrPIul/SWfxzFhptAB+MGUujRo3k0W3ZP4ZSqeStt95i+PDhbNy4kdjYWNRqNVWrVmXBgvls2riMlwZ6MaBnWYID80bBT5wx8sOMWzw/pB8Tv5tCjx49nnQ3ZP9RI0eOZN+O/dTQN8Jb45f/urfGj1v22yjsEmbMKFUqDIaiZ40JSiVmM7z0dhIfvO6kQ0t39Lq835oco5Pl67KYMCUdf6EUVsFGQEAAm3acY+GKZPp2L7y9ZVq6nddHxxERUYaWLVs+mo4XwdfXl6QUO0ajE1fX4n9PLRaRO4l2skxnsHt4ULJ3DxSqInbfiIogsHtnflm0lBMnTuDv70+HTp24mZqKR91aRFatglKvw5yYRMzBI5z54QfsZjOm+Hh0fr759RhNRlAq8wNuAFtSMupfBxUUKiUlenTj6mcTWLJkCcOHD3+IV0Um+2eR13TLZH+RKIrUq1uPuAvx1HZrhkJUkJaaigIlKiHvqXKWlM5Z6RAlDeWo4FadGOMFbnKJNetX06BBgyfcg8ejUZMmXJGcRAzoe89y8b9s487qdVQWauHh8CmQRO1ukiRxSNyGWZFDWFgAFouRvDmGSurUqc9LL79Kq1atHrh9W7ZsYdDAXrw21J23Xim8rczteCs9h11D71aRrVt3PbRMtDLZk7J48WJGjvgfP30dStvmXoWOi6LE22NvsmK9yKHDJwgKCnoCrZT9l92+fZvqVaoTKVYgwqVMgWMmZy5bM9bi1akDLpUrICkk/Pz9Cn13m+ITiPn6OySTjRLaCJKcN/D2hppVtYgiHDlhJSdbIFhZlopuNTietY/az1YlqmQUc2ZPpm0zPQN7+VGhjJ5cY95D2NmL03ESyIqVaylduvRjux537tyhTu2qfPqeF/2fK/ww4DfL1qTy+gdJWJ1KfLp0xLdObew5OeReu45os6N2d8dQqiQKlRJJFLk64Vv6tm7DmfPnOHv7NhEvDkXjWXiUP+3ocWIXLELn50eF0e8A4HSKpKQko9Dr80fS7ekZxH/7A+E9uuJbp3b++2MXLaOUw8ne3bsf8pWRyZ68B41F5UVbMtlftGvXLi5fukIFfQ00Cg0qlQo3dzecOLGLNiRJxEPwJpgIbhivcDxzH9fFC4x6963/TMANMGjAAMwXL2PLyCy2jOhwkHP8JCGBgaQpE5CQsEu2IsvGizewq7OIDFfzYn81W5dHsW9daSaPDwD7YQYN7Ml33333wO2b9ON31K2hKjLgBggN1vLD52FcuniGHTt2PHC9MtnTSJIkZs2cRvOG+iIDbgCFQmDMyBJo1GYWLlz4mFsok8GyZctw2iRCdZGFjrkoDQSogsjavx8sdkRRxGqxFigjOZ0kbtxMkL8/Op2OEtpSNHTrjCG7Esd2+HNiVwA+5io0cu9KtHtekq9cIZNSpUvx6aef8vU3U4iJi6TXC3FUbnKReu2v8M00M42aDWTDxq2PNeAGCAkJoU3bjnw1KYVrN81FlomNszD+h2QqV66BUwKdvz835i/i7LjPuT5/ETeXLufq9Jmc+/QLErftAElCVzKKvQcOcPrsOYK6dyky4AbwqVUDjxrVMSUkkrR7LwCi6ATypvEDiBYLqStWoTa44lW1aoH3a/19SUhKekhXQyb7Z5Knl8v+Mex2OwcPHiQ1NRVXV1eeeeaZJzq7YfXq1bhIBrzUv0+1cnFxRaFQkpuTg81pQ5DAl0BixSs4fc18N24i/fv3f2JtfhK6d+/O1xMnEjt3AZFDB6P6wzRA0eHk1uJlaK1W3hs9mvffeZ80IR5fazBWyYLi1/9JQLaYwTX1KaIralk7vxIB/r+PhkeE6ejQ2puJ0+IZ/8VHlCtXjtatW9+zbTExMRw9epCpEwKLDLh/U72ygcoVNCxatOCxTimUyR622NhYzp8/w5sT7z167WZQ0aGVK+vWrmTkyJGPqXUyWZ64uDhccUNVTFLNaENN9qRtIXH+fNxaNsVx157V5vgEEjZuhtg4ZixcyOj3RnPj7GVqeTSmglu1IutLsNzCqXLQt29fBEGgd+/e9OrVi1OnTpGQkIBWq6V69ep4eRX9oOpxmDDhKzp3ukin/hd5cYAXvbr44eerJj3DztLVqUydm46bRyleemk4B4YM4dqsuUgqFZ6tWuBauRIKvR57cgo5R45xZ/NWjHG3Ubm6kpKUhNLPF0NU4Qccdwto1IDck6dJWbsB0/UbeNatjajXIzmdmC9dIefQESSTmdIvDEGpLbj7iMNkwsPV5VFeHpnsqScH3bKnnt1u54cffmDG7NncSUxElEQEBNwNrvR+rgdvv/02vr6+96/oIUtJTkErFt7HWafTodPpsNmsOOwODJIBrVXLR2M/YsCAAY+9nU+au7s7yxYvpluPHsR8PRG3GtXwqFABQakg9/oNso4cQ2MyM3v6dNq2bYvT6WTM+x+SIaTgL4agsmtxiDZSpUSSFXfQ6wVmfVe2QMD9G0EQeP3FYA4cNTJ50vf3Dbpv3boFOKlRxXDPcgDVo3UcPnP9r14GmeypkJGRAYiUCC56+cbdQoM0bNuXft9ykiRx5MgR5s+fz4XLlxEEgWqVKzNw4ECio6MfQqtl/zUqlQpREIs97qpyo6FHS44k7yVlwSJMQYFo/f2RTCbsCYmEBgczadEimjRpwoi3RjB08DAu556ljGulQr/ZGbZULtlO0q5zW8qXL5//uiAIVKtWjWrVig7UHzcvLy/WrN3Ixx9/zMTpSxn/4wU0agGbXUKjMdCxU38+/PBDjEYjVosZlZcnQS8MQXnXLimaoEB8Oj2LvmxpUhYtQ61WEejmjjMw4J4PngFcgoNQadS8OGgwh48e5ezcheSaTEgCKDVaPCtVILhNK3QBBae/iw4nxjPn6Svnh5D9x8lBt+ypZrfb6T9gAJt27EBfrQp+7Vqj9vZCtFgwnb/AjGVL2bl7N+vXriUwMPCxts3gZsBB0VOgATQaLRqNFqtoQelQ/qdzDlSsWJEdW7cybdo05i1axJ39B5Ek0Gm19OncmZdeeomKFSsCMHz4cCpXrsy0adP4ZdMWnGonggBh4WEEajwJ8YujYrnig2RBEBjY05f/vXWQ69evExUVVWxZ9a/r0KzW4m/ufmO1iahU8lem7J8t73tIQWKyjQpl7z3ylJRix93d755lMjMzGTRkCLv27kXh7YUuMgJJkjj58ypmzptH144d+fGHH9Dr9Q+xF7J/uxo1ajBdnIHRkYOrqugdLQwqdyq5VOe4ZQ+dGzZCo9FgMBho0aIFrVu3zv9+79q1K7du3WLcR5+QkhNPqCoSg8oDu2gj3nqTNCGJ+o3rMXnK5MfZxb/E09OTb7/9lg8//JAdO3aQmZmJu7s7zZo1w9vbG4AzZ86gUKrwbNsapUvR/8ZdypbBpUo0uQcOofL0wpKSgiSK+VPFi+K02hAQaNKkCRMmTOD06dNMnTqV+UuXEPb8QDzKlS3yfan7DyAYjQwcOPDvXwCZ7B9MTqQme6p9+umnfD5xIp7dO6MKDQFBAEEBkgiShMJkIX3xUhpUimb92rWPtW1Llizhf0NfooFrm2JvCgCuGS9yR3uNs+fPPJER+aeNxWIhNjYWURQJCQm557/btLQ0kpOT0el0hIWF0bRJPZrUSeCjUWH3PEfcHSvPtLvCkqXraNSoUbHl0tPTqV6tIm+97MJLg4ufbmu3i9RufYlnO73Ep59+WuCYKIrynsayfwxJkmjcqB7lIm8y9auSxZYzm53UaHmRfgNGMnr06CLLWCwWOnbuzJELFwh6rgvu5crm37RLTicZp8+S9PMa2jdtxvx58+QkhLIHZjabqVQxGlWyC1U96hZZRpREjmTtJKxqMHv27bnvSO2+fft+f5jrcIAgUK5cWZ4f9jx9+/ZFq73/7I9/guHDh7Nox3bc+/ZCUKtR6XX8dqMvSRKSzYZos2FNTCJlxhzUej12swl9UBChHdvjGV2pyHqT9x3A+Ms2zpw8SUBAAJD3d+rUpQtHzp3Dv3MHPKMr5WdLd5hMJO/ZR+bOPYx49VU+/PDDx9F9meyxe9BYVB62kT21zGYzk3/6CXXVyqjCwlDotHn7MgsCSBKiw4EoKNA1bsjW5Stp3rw5kZGRdOjQgXbt2uU/5X5UOnXqxOj3RnMp5TTV3eoX+YNvcZq45bhKt95d5ID7VzqdjrJli34i/kc+Pj4F9vVUqzWYLfcflf6tzP0+A97e3nTo2J05SxbSt7sf7m5FfyUu/jmVlDQYMGAAkiSxf/9+Zs+exa6dWzFbzPh4+9Cla08GDRp0z5F1mexJEwSBwUNeYMwHb7DvcDYN6hS+QZAkia8m3yHXpL5nDorFixdz6MQJIoa/iEtoSMHzKJV4V6+KUq9jw9yFbNu27b7LPWSy3+j1ej78aAyvv/oGF3JOUMZQGdVd20jaRBvnc49i1ucw9pOx9w24ARo0aECDBg3IysoiNTUVvV5PUFDQA733nyQhMRFNgD9eXp5kZmZhz8lFUKtAoUCy25GcThRqDbrwMBQaDSHdu2BSKsg5coxrc+YT0fM5fGrXLFCnw2QmY98Buj/7bH7ADXl/p+VLl/LSyy+zedlKUjZsRhUcCE4ntpu30KuUfPD224wYMeJxXwaZ7KkjD8/InlpbtmwhOTUV1+pVUbq6oFCp8gJuAEFAUKkQ9Do0URFIrq4cirnK6uPH6P/881StUYPDhw8/0vbp9Xomfj+RLHUKJ7L3kevIzj8mSRLJ1ngO5+4kIMKfMWPGPNK2/FfUqduQrbtN2O33Drw3bE1Hr3fLn7J+L2+++Sa5Zi/6vnSN2/EFM+A6nRJLfk7hw/FJ9Oo9iJIlS/LGG2/Q47kOXL+8lteHaRg/2pvnnrWwavkPNGn8DEuXLv1bfZTJHrV+/frRsFFrBr4Sy9S5CWRlO/KPxdww88boG0ybl8NHH31OeHh4kXVIksTMObNxKV+2UMB9N4/y5dCEBDNz9uyH3g/Zv9uAAQP4bPynJGni2J29jtPZh7mce4aTWQfYnbMOk1sWM2bNoGnTpn+qXg8PD0qWLElwcPC/KuC22WxcvHgRm82G02xGrVKjEAQQJSSrHclsyQu4dToUWg2ixYKEhFKjwbd8OXw6d8SlahVil6/Emp6RX685MYmbM2bhrVDy7jvvFDqvh4cHixYuZP/u3bzSrx9tSpbm2fIV+eT99zl36jRvvfWWPBtMJkOeXi57io0aNYpvpk0j7KP38wLuu0iShCiKIAhIdjspcxeid3Wh1LAhmBMTiV+9HnVSMut+/pkaNWo80nZu2LCBN19/k6SEZNwED5SiGovChE1hoWbtmsyYOZ2wsHtPh5Y9mEuXLtGsaT0+edeHQb0CiiyTlm6n5XNXadV2KF9++eUD1Xv69GkGDuhNauptmjVwoXIFPblGJxu3GbmdINKj50AmTJjAuHHjmD3rOyZ8GMRzHX3yb9gkJMwmBx+Oj2PJGitz5y2lRYsWD63fMtnDZrVaGT36fZYtXYBSYSYqQovdJnH1hhUfnyDee/8jevfuXez709PTKV2uHL49u+FVtco9z5W8ey/mHbuJj4v7VwU5ssfj1q1bzJs3jw3rNpCdnY2vry/dnutG79698fO7d86B/4LU1FSmTZvG3AULSElLxWq2YJNEgl99GYWrKxpBhwBYJSuSUoHy1/wK2QcOkbl1GxGvv4pvaAjGXCNZ6ekkfD8JrbcXHuXKYktKxnrzFhElSrBo/nwqVSp66rlM9l/2oLGoHHTLnlodO3Zk444dhI8dkzc16i6iU0QCBEXeVPOEH6eiUasoP+L1vON2O9emTqeqXwDbtmx55Dd6NpuN9evXs3PnTsxmM/7+/nTr1o3q1avLN5kP2ahRo1i8aBofj/Knbzc/1Orfn6BfuWZm+NuxpGb6sWHjVkJDQx+4XqPRyM8//8zSJQu5c+cWWq2WOnUbMWjQIKpWrUpSUhI1a0Qzargbw5/PW//tFJ2YTWZMJhOiKCKKEv8blcyVm35s376bEiVKFDiHyWTi7NmzWCwWAgMDKVOmjPz5kD1RKSkprFixgtjYWFQqFTVq1KB9+/ZoNJp7vi8hIYGKVaoQMKAPHuXLFVvOmp7BreUrsZw/Q3hYCdzc3GjTthMDBw6Ul2LIZH/TzZs36dy1KzeTkzFUr4JndDSSKHJl8lT0VaLxbNEcjUKLQlBiFc0o9HoElQqn0Uji1Blog4Px7tgOfz8/FEoFTqfIzWUrsJ88TeXKlQkJCqJXz56PZcmeTPZPJQfdsn+8/v37s2jFCvz69MRQtXL+67+NcguCAIKAPS2d+Anf4F2tKiWH/J4dM+vCRRLnL2L75l+oXr36k+iC7BFwOBy89957LJg/gwA/aN3EBRcXJWcuWDhw1EyJEqWYN3/xA68bf1Dffvstk374mGNby+HupsLusJORnoHoFFGgRCHkBf+7D+Uy+I14PD2CWL12NTVr1iQ1NZXvv/+eZUvnk52dAUiAkujoarzw4st07dpVDr5l/yhWq5XI0qXRNHiGwOZFT+9NO3KUxFUr8NBa6dzahbKlvEhMtrF6Uy6Z2UreHz2Wl19++TG3XCb7d7Db7TRo3JiraalEvPA8Gk+P/GNxO3aRtG4jrtWr4la3FjqDN3ZsCHo99jsJpK9Zj2gyEdxvIJKHDncPd1x+zXSetHM3HDrKjZiYJ9U1mewfRU6kJvvHCwsLQ6VWkbVnHy7ly6L4NbNo/nMiQUCSJLK270RyihhKFszE616uLAlaLXv37pWD7qfcb3/TBwk8VSoVX375JYMHD2bevHkcOrgHm81KSGg4kyb3e6BROoDbt2+zZMkSYmJiUCgUREdH07Nnz/xtV/7o/Pnz1Kqqwd1NhSiKZKRnIDklNIK2QLsb13ZHr0nGkm6j53M9Wbp8KcNffoGcrBv07+ZO53ZRuBmUXLxqZv6y87z6yvOcOnWKsWMfLBmQTPY00Gq19OjalXnr1xHQpBGCUok1LZ2Mk6ewZ+dgTc/Acuk0A3u48/oQHwL83DEY8rb6G/2myLdT4/lk3PsYDAYGDBjwhHsjk/3zbNq0iYtXrhDxf8MLBNwA7jWqY7Paydyxk9xjJ9BHRSLotDjSM7Anp6Dx8SW4dz80nl5YJQui+Pv4mz07G2/34ndkkclkf40cdMueWu3bt+f7yZOxpWeQNGcBPp2eRRP4+zpeZ3YOmdt2YDx1GoVahfddo+EAgkKBUqfDbDY/7qbLHkBubi7Lly9n9ty5XLl6FUEQqBwdzZBBg+jUqRM6ne6e7y9fvjyff/75nz6v1Wpl1FujWLxwMaJNwlXyACQWs5RxH3/C/73xWpGJX/K2Bsv7/2azGdHpzFsr94dAWaHIy/cXrivLjeRL9OnTA2/3TH5ZVoqQoN+3pAkN1tKysSdzliQx+vMfqFChwj3X0MpkT5uhQ4eyaNlSYpesQLKZMV08h4dBIjRIxcULRto01vPuy64oFIoC+3RrtQre+b9QUtPtfPH5WHr06HHff+8ymayg+QsWoA0PwyW48HaXCkHAULUyHuWjSd28iewTJ1ALaiQvA0HP9UIfEfnr9n4SIOUlXANEhwPjmXMM6lf8rgUymeyvkYNu2VPrmWeeoWrlypy9cwdbdjYJk6aiCQ1F5e2N05iL5foNBIUChVaLoUxpjJKIkJWFVqtFq9XhNJuwZ2cX2N5C9nS4du0a3Xr04HrcLVzKl8O1VXMk4OSlKwx7ZTg/TJ7M8iVLCAoqfu/sv8LhcDB40GA2r/uFUupKlDCURKXI+xq0ihZumC7zxSfjycrK4rPPPivw3lKlSjF/jg2LRcRkMqFAWeTI9PEzZux2AQ+dN242LxITrjN7YoUCAffdBvUK4ODRXKZM/p5evXrJo92yf4xKlSox/tPPGD78RQL9BT4a7U/b1l6cOGVi+BtG3njRE61GwO6QcDodhfbpHj4kiCWrr7Bu3Tqee+65J9QLmeyf6dqNG+jCShR5TKvVYjQaEXQavBo2wnzuAqVUpYkxxoBC8WvADU6c+eUBkrbvQmG20K9fP/bs2cPcefM4d/ECgqCgaqVKDBw4kLp168q/UzLZXyDn8Jc9tQRB4KepU/EWBHQGAwGNG+Li4Y6QlYVks6EvVxZBq0Xh4oJbk8ZYzTYsJguZGZmkpCSTdOAQLmo1HTp0eNJdkd0lPT2dLt27E2c2UXLkG0QM6Itfg3r4N6hH1NBBRL42nPO343iuV6+HPkthxYoVbFq/maq6ekS6lM0PuAG0Ch3lDFUoo6rMtMnTOHbsWIH39unTh6wcJcvXpeB0OvPXcN9NkiTmrchAJ3rgo/bHJlooV1pN7Wqu92xX/x6+XLp0ljFjxrBo0SJiY2MfTodlsj/BZDIRGxtLfHw8Tqfzgd5z6dIlQoN1LJ1egk7NtagdJk6fyMTHS0ntajq8PFVo1QJZWVn8MYVMZLiOsiW1nDp16hH0Rib7d1OqlEhOR5HHNBoNKpUKh+TILxOoLUGAEEDy0qVkHjmMw2LGITnQ6rQ4srK4teJnsnbuZuTrrzNy1Cg6duvGmkMHuePtRZyXO8t37aRtx44816MH2dnZRZ5XJpMVTx7plj3Vypcvz4a1a3lz5EgOHTwCWi0qVxfMySnYjUZ0JaMI6NQFjcEd+G37JhHTndskbd5Ch6bN8PX1fbKdkBUwb948YhMTKDnyjULr0AD0wUGEDR7Ame9+5Oeff6ZPnz4P5bySJDH9pxl4SX74aYsfQQ/XlyY2+yqzZ8+mZs2a+a9HRETQtVsvPp6wAHdXA/WqF1zzJkkSk+elsWGbmYramgiCgENhpnJ5bbGjApIkkWvMJcjPhM1q5tsJE1EqlWi0alq1acVHH39E6dKlH0r/ZbLiXLx4kWnTprFm9TKsVgsAISEl6D/geQYOHIiHR+F/p5CXPGbF8oW8PMiHGlUCcTgdOB1OtForGrUxf2TbzaAkNcOB1WotNI1crRaw2+2PtoMy2b/QM7Vqs2DTRqQO7fNHrvMJeftnp6enk33lAgpRwFXpTrCmBNm5GaRt2kjazm1oAvzRazTYk5Px8fTiw08+YfnKlRy7fIngIQNwK13q960xJYnsi5fYtnQF/fr3Z+WKFXJGc5nsT5BHumVPvfLly7Npwwb2bN/OB2+8wWt9+lIxIgKlCGJiKrmnTmPPzEJyOrGnp5O+cyep85egNjo5f/a8fEP3FBFFkZlz5uBaObrIgPs3+qBAdKVLM2vO7Id27tTUVE6fOkWwJvye5QRBIFBZgk0bNhc69tVXX1O3XluGvJ7MC2/dYd3WbPYeMTJ/ZQbPDrjF11MyiVRWJ1QfCYDNacNilVAoCgfdkiSRmZmJMScXiwkUKKnu2oAWHl0pRWW2r91Fm1ZtOH/+/MO5ADJZEdauXUub1k3Zv3serw/TseSnUOZ8H0yDmql8+/UHtG3Tgr1793Lo0CHOnTuHw/H7yNru3bsxm7Pp1SVvr2SVUoVWq6VkpCtJqQ7i7uR996pUAmqVgMViKXDurGwHMTesREREPLb+ymT/FgMHDkTKyiLj9Jkij6s1atx1OkwnT+EULWxOWcaJzP0oHAo87B6ocu04Y2/jabEyfuw4Lpw5g16v58jJk4Q9PxD3MqULPDAWBAGPCuUJHdifXfv3s379+sfVVZnsX0Ee6Zb9Y0RHRxMdHc2dO3f48bsfqexaG5M9h1s795O1e09eIQk0kopSmihCPMM5nLiTjRs30qlTpyfbeBmQN7X8Tnw8/k0a3LesW7kynNm05dcEZn//+aDRaEQSQaO6f8ImrUKHyWQs9LpOp2PevAW8/rovM6dPY+feZAQBkBT4KsKo5VIWH01eDgGz04gNO/uOCOTkOnEzFPy6NRqNWC1WVIKWHXuzEB0qvFx8UAkqwvQlCdKW4EjKLgb2H8ihI4dQqeSva9nDdfLkSV4ZPowOrZR8M7ZcgT3vmzX0IDw0gXFfn6J9++YYXPWAguDgvBHwF1544dcppiKB/gVHu9q38OLD8SrmLs1i9Jt5M42UCpAksUC5RatScIp6eT23TPYXVK9enW6dOrN85WoEQcCzSuUCQbI1LZ07CxbjrdaQq9HhafcnUl0WV6U7Op0OpUpJsjWeS0knmf7TDLp27cqMWbPQlyuDS0hIsec1REWgi4pkxqxZdOnS5XF0VSb7V5Dv4mT/OLt378ZqsRHhXgqVQk15sSqptiTskg2NoMVXG4hKyPtoG8webN++XQ66nxKimHfTLSiU9ykJglKJJEmF1oH+Vd7e3iiUCozOHPwIvGfZXEcOPoFFL0v4bcuykydOcuX0NSrpa+Gt9iuwPtzoyOGEcR+RJaPIykpi+vwk3nzp95sYSZLyk7GZjBJzlmbhqwhHp3TJL6NWaKjoUpMjV3ewfft2Wrdu/TevgExW0KRJPxJZwsk3Y0sWCLidTonX37/O6k2ptG/pQruWLlQq50tOroIV61KZ+M2HbN2yiQEDhwAK4hNtBRIFurgoGdIngO+nx1O5oo6OrQ04RVCqfj/HnoNZTPgxlZ69XsDPz+9xdlsm+1cQBIFJP/6IJEmsXLKClC3bca1YHoVag/n2bSxXYggNCCDLIRIolKCqb71CS50CdaG4q704FLONsWPHcv7iRXy6db7vud0qVeDIxl9wOp2FEiRC3h7iGzZsYMH8OVy5chFBgIqVqjJgwGBatGghP0SW/SfJn3rZP47RaEQpqFAp8kZX1AoNQbqiM3iqRDW5ubmPs3kPLCEhgYULF3L02DEcTgeR4RH06dOHatWq/Wszg3p7e+Pl5UXOtWt4VCx/z7K5164TFRVV5A/6X+Hu7k6rNi3ZvWYf4VKpYq+xU3KQJMXxaq/hxdbl4uLC8hXL6fFcD06e3oe3xR9fdSAgkO5IIk1KIrJ0JCtWLmfFihV88+2nqFUCw/oHotMpsNlsiE4n8XcE3v40gVs3FdR1q1LoPJ5qb1xN7qxatUoOumUPVWpqKr9sXsdHb3kVCLgBfpyRwJrNaUz6IoCOrQ2kZzgQFE4qlPWgTg03+j9npNeLh1i/3g9XV0+W/JzKiJcLjoy98b8Qbt228vLbSSxZnU27Fq6UKakhKyeTpT+nsWW3iabNnuWTTz59nN2Wyf5VtFotM6ZPZ9jQocyePZsDhw9js9spHx5Ov2+/xWQy8fab71DD0LTY3zwXpSuhipKsWvkzkiii0Nx/nbZCq0GSpCKD7jt37tC3Tw+uXDlLvVp6Bj3ngijBzv07GDJ4M9WrP8PceQvx8fF5KNdAJvunkINu2T+Or68vThyYnSb0d40M/pEkSVgV5qduFEUURT777DO+nzQJqyCgiYxAoVax89QpZs6bS+MGDZk5ffq/8gdJpVIxqF8/vv5pGo6WzVHdtXfv3WyZWZjOX2DI2HEP9fwvvPACmzds5prpIqVcKxQ6LkkS53OOo9IrGDhw4D3rCg4OZtv2baxevZpZM2dz7uxZAEpXKs3o59+me/fuGAwG3nrrLZxOJ+O//4qpc9Np08wVvU7k9PkcDh6xo7S7UtPQFIPKvcjz6CRXkhKT/n7nZbK7xMbG4nTaqFujYEJAq1VkxoJEBvV0p2NrAwBqjYDF8vta7soVXflwZAAjPtxI5y49mbloMe1belGu9O/fx0qlwMRPo6hXy51RY2+yYasZNzczoKB8hUp89vlQ+vTpI494yWR/kyAI1K1bl7p16xY61r9ff9wl73veKwGU0EdxPecCOg8XTLfv4Bld6Z7lTbfv4Ofnh0ajKfB6dnY2PXt0xW65yi9LS1Gx3O/nffMlOHIihxdGHKZ/v96sXrO+0Ptlsn8z+ddO9kTZ7Xbi4uKw2+0EBQXh7l448EhPTyc5ORmdTkeJEiVo0aIFnl6exGbHUM5Qudi6U21JWBXmp27N0ZgxY/h+2jS8WzQlvEE9lL9m85VEkayLl9i9cg3dnnuO9WvXYjAYnnBrH74hQ4Ywe948YucsIGJw//z+/8aek0Ps7HmUCAikV69eD/XcjRs3ZuTbI/ny8wlkZ2UQ6VIOL7UPkiSRakvkuvkSudpMJv344wMld9LpdPTq1YtevXrlT4P/42iCIAi8++679O7dm3nz5rFv704SkxK4ejmNivo6RHiWQSkUP5rvwIabu1uxx2Wyv+K3PAlOZ8HlG1t2ZZKRZWdQr7u+iyXgD5/rzm19GPd1Mn5+foSUqEz3IWcY8bIv3Tv44GZQIYoSuw9ksXBVOmqND4sWTyMiIgI3NzfCw8P/tbN5ZLKnSU5ODmq09y2nUeSVqV+3LjuPHUds2RxFMQ/EnBYLxpNneGV44dlgCxcuJO7WJXb+XJqIsML5U2pXd2POD+E82/cQa9eupXv37n+yRzLZP5ecvVz2RKSlpTF+/HgqV6tGjbp1qduwAWUqlOell17i9OnTABw4cIABAwdStmIFnmnYkGq1a1O9Vi3mzJlDj17PcdsZQ4Y9tcj6raKFi5aTVK4aTZ06dR5n1+7p3LlzTP7pJ3yfbUtgi2YFAk5BocCzYgXCXhjMyYsXmDlz5hNs6aMTEhLCkoULMWRlc3X819xZv5GsS5fJunCRuFVruDZhIgGCghVLlxa7VdHf8c477/DD5O8xlNRw1LKTrVmr2Jq1klP2A4TXCGbx0kX07NnzT9crCMI9A4mIiAjGjBnDlq072b37IN7efoiI9wy4zU4TWUIazZs3/9PtkcnupVSpUuj1Brbtzizw+u14K+4GBVHheSNQEmC1SahVBaecarUKypfRkJKSwqpVa2nUtAcfTciiWrOLNHj2MtWaXaT/8DtYxYqsWLmODh06EB0dTUREhBxwy2SPiZ+/HxahcFLQPzI6c1AoFHTp0gWdU+TW4mWIDmehcqLdTuyipbir1QwYMKDAMUmSmD9vJh1auRYZcP+maiUDDevqmTvn33mPI5MVR5AeVpaiJyQ7OxsPDw+ysrKKHCWVPX1u3rxJ527duJmYiGvVynhGV0RQqzHG3iLr0BHURiNdOnRk2apVCH6+eNatjUtwEE6LlYxTpzGdPU90mTJo1WpOHTlNqKIk4frS6JUu2EU7dyw3uGm/gneIBxs2bSAqKuqx9k+SJE6ePMncuXPZv3c/NpuN8MgI+vXry8GDB5m7dg1l3hmJcI+1yreWrsQ3JZUTR4/+a6dfxsXFMWvWLOYuWEBmVhYAAf7+DB4wgEGDBuHv7/9Iz+90OtmzZw8xMTEolUoqVapErVq1HltA8Prrr7NoxhLqGloUOfVPkiRO5xzC6pXD2fNncXOTR7tlD9eIESPYuXU2O34ug7tb3vfM9PmJfPHdLa4cjESpFLBaRTKynXh7eaPRaHE4HJjNZhwOBz2GxWFxRrNkyRJKlChBYmIia9asITk5GRcXF5o0aUL16tXlIFsme0I2btxI3559qaNvjofau9hyp7IOkeWaTO26dYi9dYsrMTFoggLxbVAP97JlkCSR7EtXyDx4GJ3ZwsK5c2natGmBOtLT06lUsTTTvgqgfcvizwUwZ0kSH36Zza24RPn7QfaP96CxqBx0yx4rm81Gg8aNiElPJ+KFIWg8PQscl5xOrk6dTtaFiwS2bkVoh3aFvpDNiUncmj6b2uXLU7N6dRbMW0hWRiYKlDglJ1q9hnbPtmPs2LGEhYU9xt7lTZd//fXXWbJwCWqnFl8hCCUqssUMMkjBJjjwbNKIsM4d7llPztUY4mfN5fC+/ZQpU+Yxtf7JsNvtpKSkIAgCfn5+/9qHDH+UlJRE61ZtSLyWRDldNfw1wfmfdaMjh8vGM6Srk5g2fao8Be8vys3NZcOGDdy+fRuNRkOdOnWKfbCSmprK0qVLOXv2LJIkUbp0aXr37k3IPbbO+ae7efMmrVo2pVRELrMmRuLvp+Hk2Vw69D3PnO8DaVhXT1a2E7Vai6enF1nZWVgsFhSCREqak2Zdb6NSa1AoXOjTdzCfffYZavX9kzDJZLLHw+FwUKtmbVJjMqjl1hi1ovAa6nPZx7hkPYfGYMCtYnlUri6Yb90h98YNQEKrd0GpVKBTa+jUoQP/99prVKxYsVA9ycnJVK1SjtnfB9Oysec927VoZQqjxqURF5f00JKlymRPyoPGov+Nu1vZY5ednU1cXBwA4eHh+WuTN2zYwKWrMUS+/kqhgBvytokSHQ50pUphaFh4ewsAfWAAQT26cnj2fD4aM4Z3332XrVu3kpqaiqurK02aNCE4OPiR9q84b775JovmLqa8phohLpEohN9XcOQ6stmSuQarJBa7zcZvlC56JAnMZvPjaPYTpVarn9jf60kKCAhg/YZ1PD9kKEcPH0Jj1aETXXAKDnLIxMfPm+lf/0TXrl2fdFP/UWw2G2vWrOHjsWOJuX4dp8OJBhUalRZBBdFVKvHpZ5/SoEHeXvFOp5PPP/+c6T/9iEIwU6OKFkGA6TtsTPx2PN2f68P48V+i1d5/XeQ/hdlsZsWKFcyZM52srAx2H8ilVO0Mosu70LWDN96ear6ekk7ZUgG4uurw8PAkMzMTm82Cu0GJXqfg22mZeHup2bO2Emt/yeCTb34iOzuLqVOnySNXMtlTQqVSMXfeHDp37MzB1G1EasoRrAtHKSgxOXM5nXWYO+JtPOo/Q2TnjqgNrvnvtSSnELd4KarUdL6ZMIFWrVrdMzGtl5cX7u4eHD+Ve9+g+9ipHMJKhMkBt+w/RR7p/pdISUlh8+bNpKWlYTAYaN68OZGRkY+9HVeuXGHy5MksW7UK068Bo8HVhV7dn+Pll1/mzZEjORB3i6j/DS3y/eaERC589S0+z3VFFxGBv79ffsKfu0mSRMzX39GrWXMmTZr0SPv0oM6ePUuThk0po6hCmL5kkWW2Z6zHWimYEr173PPzmn78BGkrVnPu9GmCgoIeVZNlTwFJkjhx4gQrVqwgMTERvV5Po0aN6NSpE/pisrvLinbp0iV69e3DxStXUJYIRRsYDHYHlouXEXOMBCmDcWLHbjAzb8E8WrZsyahRo1i86CfefNGbQb0D8PTIexZtNDpZtjaVT75JpmHjDsyaNedfMQsjKSmJPn16cPnSSVo0cqF1Uw80aoFDxzJZuiaD9Awnehd37DYzndoY+PKjSAwuTrKyM/HyUOJ0wMSfMpg8J5Nx70YwuHcAAOt+SeelUfHMX7BSzkEgkz1lLl26xJgPxrBj2w4cNidKhQqHaMfktOJepyYl+/VBoSj8sMxptXHtxyk0q1SJFcuW3/c8H3zwAatX/sihTeXQ64sOqNMz7NRqdYk3Rozjtdde+9t9k8meNHl6+X9EZmYmI0eOZM3qNdjMdnRKPTbRhlIt0Lxlc8Z/Of6BsjA/DHv37qV3//6YlErc69TErXQpALIvXSH76DHcBQUKQUCsXoWg1i2L7s/Zc1ybM5+Qt94EJHx8fIqdrhi7dAXRChVbNm9+VF36U0aMGMGCnxbRyK19gRHuu53PPs5F+wU8WzcnoGxZ3EqXRCjiocK1SdNoGBnFqpUrH3WzZbJ/hbi4OFq0bk2i04GhbWtc/IJQkHfTJzkcZJ08TubWbZRUlMKGGYeXmekzpzNwQA++HONPn25Fj+Ds2p9Fv5dv8cOPs+jWrdsjabvdbic2NjZ/FwfPImYBPazzPNu+DcmJJ1gwOZLyZQrmEsjOcTDszeucvqDn/dFjGTfuA+zWLJrUUxAeqsBoktiw1UiuSeLd10vw0qDfHwhKkkTbXlcILNGaefMWPJL2y2Syv+f69evs3bsXi8XCpUuXmDF/PqXeHoHW26vY96QfP0HqitUcPXiQkiWLHlD4zc2bN2nerAH1azmYOiEKna7g/U12joOBr1zn6k139uw9hK+v70Ppl0z2JMnTy//lJElixYoVvPS/l8hMz0KBAoWgxFXjTjl93v7Duzfuo/XJ1mzcvPG+X5R/V1xcHH0HDsQWFECpAX1R3LX3omtYCfwb1efmnAVkX7iIT27xbREUv94ki85fg9HipylKTicqTfEZMh+3QwcO40NgkQF3hi2VC8ZTpDiTQSmQuW0n2bv2ovXxJqBJI3zr1UUSJRxOJ6l79mKPu83/vhj/BHohk/0zff311ySbTXj174NCo88PuAEElQrPWnVAkri2eSuN3FtwLH0Pn3wyjlIRSnp3Lf7Gr0l9DxrW1TFn9oyHHnSnp6czY8YMZs+bR2JyEpIEGrWaDu3a8fJLL1GjRo2Her5ffvmFs2ePsW5B4YAbwN1NxayJJWnc6TLnzp3j6NHTzJ07l9GjRxEcoCA0SEv/HoH0e86fEiEFp9sLgkCX9h588cN2JEmSp5jLZE+hqKio/OSyL7/8MtoSofcMuAE8K0eTsHwVBw4cuO+9ZEREBDNmzuf5If1o3OkS/Z/zpH4dd0QRdu7LZOHKLKx2LxYuWioH3LL/HHnLsH8gURQZNWoUA/sOREpXUV6oQVVFfcoJ1RBtcDLrIMm2eOoYmpGTYGLY0GE86gkNc+bMIdtuI7x/nwIB92+UOh3hA/si6HRkHj9ZbHv0IcEIggLT+YsIggKVqujpSaLDgTXmGjWrV3+o/fg77HYbiiK2f0q2JrA3exuZQUp8unch/K1RBI8agW//3iiCAold+TNXFy8l9tgxrs6YxZ2f14LNzrZt27h169YT6IlM9s+SlZXFslWrMNSuCRpNsduwuVergWBwIcEahzf+nD51jO4d3O8bIHbv4MXx40fIzMws8HpaWhqTJk2iYePGlCpXjio1qjNq1CguXLhw3zbHxcXRqk0bPps4kezwEgQPGUiJ/w3FrXULVh/YT+v27Vm8eHGh9zkcDkRRvG/9RZk/fw61qmqpFm0otoyrq5J+3T1YtXIxarWa3r174+5m4IcvSrFtVTTvvl6iUMD9Gy8PFXabHYfD8ZfaJ5PJHh+r1YrwALkqFGo1glKJ1Wp9oHqbNm3Kps07qN94AN/+ZKFDv1g6DYhl+kKR9h1fYvMvOx76A0WZ7J9AHul+yl2/fp25c+eyedMacnJy8PT0wsc3iN3b9xDhqECQIgyl8Puf0V8IIVVK5KL5ODqFnrK6Kpw6cZDjx49Ts2bNR9JGp9PJ3AULMFSvivIeX+AqvR7PmtVJ3b2X7AuX8KhYvlAZjacH7mVLk3PgEL7VqhV7M5x2+CiC2VJon8gnKbJkJAcvHSvwmk20cjR3H+pyJQns2h1BpcKJA6VkRx8ehiYwEHVgABkbNiHsOYi7ypNoTVUA5kyZx+pVq1m1ehWVKlV6Aj2SQd5Drv3797NhwwYyMzPx8PCgbdu2NGrUqMh8A7LH78yZMxjNJkIrVsQICMXMkFGo1ejLliH19C18FL7YHXa8PO//M+jjpQYkjEZj/tTv7du3M3joUDJNJlwqVUBXrTJpJhPTly9jxpw5/N/w4XzwwQdFfkbsdjs9+/TmemYGkW+8WmCkyTUiHN96dYlbtYZX33iD8PBwSpUqxcKFC1m0cA534u+gUCioVrUGAwc9T8eOHdEU8aCzKJcunmNAd9f7lmtUz4OvpsQSFxdHVFQUao2WmOvm+yZHunrNjKenh5zBXCb7BwgKCsKxcyeSKBa5zO03lpQUcDj/VH6ZsmXLMnHiRMaOHcvt27cRBIHw8HBcXArPsJHJ/ivkoPspNnnyZD79ZAxeHk46tTEQ6K/h1p0Elqw6g1OUkJAKBNy/8RUCCZNKc9N0ldKuldBYtaxevfqRBd05OTlkZGbgGx5+37JuUZFk7jtA/JLl0KcH7uXKFgisbRmZODIycKakkrJyFS59e6E2/D4qI4kiGSdPkbphM8/360epUqX+VFsvXrzIjRs3UKvVREdHExgY+KfeXxxJkqhXrx7r127gmvEiIboIdEo9cebr2NUSoe2fRVCpAAmn5ESj1eJmMGCzpuFerTq2SzHorqfT1Kt9/vWIFMtyNHE3vXr05tCRg/kZ4GWPz7lz53hh2AtcOn8JreSCRtJhEyz8NGU6pcuW5qfp06hateqTbuZ/ntVqRZJAqdWA3Y6EVOzCFIVGg4iIVbKg1Wi5eev+ozfXYy0olKr8gPvYsWP0HTgAKSKcMj26oXL9PZAV27Uldf8BvvnxR3Q6HW+//Xah+rZs2cL5S5cIf+UltF5e2Gw2RKeIIAioNRoUCgUlunbi2u07fPzxx8TevIrdlkaXdgaqRftgtYn8svMEr726n9mznmHBwiV4ed17imiePz/jSavV0r59FxauXMqLAwOLTLYEYLOJLFubTbfur/zpc8hkssevR48eTPrpJ7IuXMKzUoViy6UeOIS/ry8tWrT40+dwd3enQoXi65bJ/kvkoPspNXfuXD4Z9z6vPu/B6y8Go9XmPYU0W8y80E/Dd1Ozmbf0HDqbC35C4aePQUI4t6Sr3LHcRIsL6enpj6yteVs+CEi/TSmU8m6CzWYzDqcTQRDQaNS46F2QnA5cXV1p1rAhW+cuJCXAH9eKFVCoVZhuxWG+dJkSgUG889NPfDh2LDGfT8ClYgV0QYE4rVZyz5xDysigXYuWlCpVim+//RZ/f3/at29/z+RDmzZt4ruJ33H08FGcjrypmVqdhnYd2jFq1CjKly886v6gVq5cyffffc/Z0+dw2BycthzmfPYJgvVhZImZ6KPLonI1ABIOyY6EiKurK0aTEQEBjaDBvVoNUmNWYnIacVXlBdcahZZqhnrsvbWJVatWPVWj+v8FFy5coEP7DtjTJKrrG+Gt9kMQBByinSvGc5w7fYG69etTpXJlOnfsSP/+/QkNDX3Szf5PCg0NRalQYEtKRunni9PpLHKpB4AtPh5PSUe6kETNWvVYtvYob70iotEUPdIjSRKLVmbQqlUHXH8Nrsd9+il2b29K9u+L4g9LYBQqJf6NG+K0WPn2h+8ZMmRIoW125i1YgCY0FMnLk5SUFJxOZ/4xQRDQ6/UYDAYMlSqwd80qWjf1YubE8vmZ1QEG9Qrg5NlcBr16hEED+7Hq57X33X6nbLlK7D20jzf+d89i7D2YjU7nkv95Hjp0KM/+vJTPJ97mvTdCC81AEkWJ9z+LJStHw6BBg+5duUwmeypUrlyZhvXqsXfNOvSBAWh9fQqVybpwkZzDR3n9rVH/qm0TZbInQZ4b+RSyWCyM/2IsvTq78PZrofkBN4DT4cRVr+TDNwJo01zPbdX5ItdHawQtrriR68jGjhU3N7dH1l6DwUDZMqXJvnARp8NJaloqGZkZWBx2nIKAAwmjyURKagppJ09TpXJlli5ezM/LltG2eg3Up8/CoaNUUGv5/ssJHNq/n/79+3Ps8GE+H/MhUXYn0qEjuFy8TIuq1ahUphy/bPiF0aPG8MWHX/LKi69SsXwlRo4cidFoLNS+yZMn0693Py7uj6GSsg5N3DrQ2NCeCGd5Ni/fSuuWbTh48OBf6vvnn3/OsCEvEHcikSrqerTx6U4dVXPCKE2KKYkMZzoqH2+cOLBJVpw4cXd3R61WYbFYUaICBNQ+vqAQsIoF9+V2URrwIYD58+b/pfbJ/ro3Xn8Ta5qT2m5N8dH4IwgCGbZUtmWs47LjEqqyUehq1+KyQmD8jz9SrVZNJk6c+MjzJ8gKK1euHDWqViX9wCH0Li6IOBEpvO7ZmpiANe4OSCI6g5Zx48aRma3moy9vFft3mzQzgUsxIs8/n7fN4ZUrV9i7fz++jRsUCrjv5tewPmans8h12VdjYpAC/MnJzkFyglrQohF0aAQtCkmJyWQmLS0N4+3bhAYpmf5NGJ4eKkRJxOl0Ikp5fasWbWDKhBIcPbqfnTt33vc69e8/iCMnLZw6l1tsGZPJyYIVmXTp2iv/d6N69ep8PPYLpszNYcDwGHYfyMLhkLDbRbbsyqDH0KssWW3hq69/eORJO2Uy2cMzfdo0SgcEcuPHKcRv/AVLcgoOoxHjzVhilywnfv5iurRrz+uvv/6kmyqT/ePJI91PoXXr1pGZmcIrz5cptowgKHi+txdbdsWT4UjFm8Lb3YhIWEUzZpWRVq1aPbL2CoLA0MFDeP3tt0m6chWFrzdKV1eEu0ddJAnzrTiyz1/gRkAg77zzDm3btmXunDnFrov19vbm5Zdf5uWXXwbg1KlTdO7YGVu6SEVtTQJdS6AQFFidFm6ZYpg9dQ4Xzl9gxcoV+euG9u3bxwfvjyFELEk59yoFRmgiXMoQKkZxPHMP/fr04/jJ439qq54NGzYw4YuviBTKU8rj9+lTQb5a3I0e+BmDOSztxG7KxS7aUKpUuBsM6F30OOwOkCSEXzOdixYzSBKqIpYLuCu9ib0R+8Dtkv19p0+f5tiRo1TS1kGtyFufmm3P5EDODoSIYEKf7YDayxsRJ3bJhqfBQPr+A3z46aeo1WqGDx/+hHvw3/PaK68w4PnnyT1yFHWVythtVlSoUQhKBAQcuTkkr1mN0ilh1Gfxw9ffU6dOHcZ/OZGRI14h9nYM/xvkT/3a7ggCnDhjZMaCJNZtMTNi5AfUr18fgJMnT2J3OvGoeO8pkyoXPZrwME6cOFHoWGpaGg6DC2pBUyDLOgioBAVKVFhM2ZgvnmX4YDc0Gifp6enYbLb8klqtFhcXF56paSC6vJp58+bcd/pnmzZtqFixOkNfP8WCKZGUK11wfWVOroMXRtwg22jgpZdeKnBs2LBhBAUF8e23X9L3pbPAb8nSVFSrVpsFC9+hadOm9zy/TCZ7PERRfKCcIwEBAWzesIFvvvmGBUsWc2PPXiQJFIJARFgYL3z0ES+88AIqlRwuyGR/l/yv6Cl08uRJypXSEhFWeDsstTpvXbCESLWKOry9FOSY0vEWCgbdZsmIiRwUIkSViqJJkyaPtM09e/bkw48/JnHhYvz790EVdtc6bEnCcvMWKUuXo3Rz49bteGZPms/0qTMoXaYUU3+aSvX7ZCF3OBwMHjQEMV3gGbfmqBS/J+rRKnWUNlTCzx7E4X27+fLLL/noo48AmDplKnq7a6GA+zcqhYqqhnrsTlnP0qVLefHFFx+4z5MnTcZd9KKUe8Gbb9EpYjFbUEoq/B2BJJ+/iGfTpoiCSFZWFg6nA71e/9vVASD3/Dn0ggsGlUeh84iSE7VGTkz0OG3btg2FU4W/S3D+axeNpxD9PAnp2Ts/Q7+CvG3tHEBwm1ZIDgfjPv+c3r174+3t/WQa/x/VsWNH3h05ks+/+gr11WvoqkTj8PRAstkxX75C7rETiNk5hIeG8uWEL/O3/+rTpw/e3t6M/+ITer94HrXKiUIhYLUJhIWV5OtvRtK7d+/88zgcDiQo+FCxOEoVNru9wEtWq5Xs9AwsFitCszZFzjcTECAzF8FupX4tD3JychGUShQ6HSgEJFHEardjzcjAxdWFZg1cWbb+1H2bo1arWbBwCX16P0eL7idoWk9Nk/o6tBqB0+dtrN5sQan2Zc7chUXmy3j22Wdp3749J06c4OrVqwiCQIUKFYiOjr7/tZDJZI/U2bNnmT17NqtX5SXedXNzo2PnDgwaNOieuUe8vb355JNPePfddzl69ChGoxE/Pz9q1Khx3yUrMpnswclB91PI4XCgVhedrEaj1aJUKfPKKDRoVAJ2HIhSwTWMt8VriIITtYeSyVMmPfIvTkmSEJFQAEnTZ6ENK4GuZBSSJGG+eg1b3G20QUEEdOpKwqxZ+FkDCdFGcOnSKTp16Mza9WuoVq1asfVv27aNm9duUtulWYGA+26eah+CLZHMmzOfUaNGYTKZ+GXzFkqqK91zSyCtUocvQSxasOiBg+6bN29y6OBhKmgKbnvhdDpIz0gHMW9NdoRUjsTsHeQeP45v3YY4JCfG3Lwp8AqlAqfTiT0lDeOZs1TQlCu0x7ckSaRI8bSv3+aB2iV7OIxGIxqFJv/vYXLmkuiIx+uZDn/YEi8vT/ZvU5MDmjbmyoHDLFmyJH+Ghuzxefvtt6lQoQKTp07l8Jr12J1OHA4HGqWSmuXKMWzYMPr161cou3abNm1o3bo1R48e5fz584iiSKlSpWjYsGGh0aLw8HBUCgXGW3EYIopPHik6nDjiE4hsUnD0d+fOnThtDkRLFsbLlzCUL3rEXLLakZxOUIBSr0O4q80CgEaDaLNhNJpwOOwPvKzB1dUVH18/TGaRLbuMbNqeA0LeyJbNrqBMGU9CQkKKfb8gCNSoUUPe8kcme4p89913jPt4HEqbmkBlGP6KcCwZJpbMXMb8uQt49/13GDFixD3vhVxdXR/5AI1M9l8mB91PoYiICFYss5KV7cDDveCfSEDAYHAjKyuLm/EmUtKdhCgN2EU7gpR343WHm9zmGqElQlmybAl16tR55G0+d+4cuSYzZV75H9a0dFIPHMJ07AROUUTt40NAl264liyNoFSiL1+O1OPXiNbUoo66KYezdvD6/73Ort27iv1BWLNmDa6SG57qe48ehutLsS91M7t37yY8PBynw4m71vO+7XdTeXL79p0H7m9CQgKiU8RdUzBjcG5uLpKYt6YewFVww9PuSebWHShsIh616qDUqTDmGtFq1GRcOE/2pm14OlyJ9ChX+DzWOCwKE4MGD3rgtsn+Ph8fHyyiBYfkQCWoSLelICoFXMv+8W+Ut4vAb9utqFxd0UZFcuDAATnofkI6dOhAhw4diImJIT4+Ho1GQ6VKle6b/V8QBGrXrk3t2rXvWa5evXqUiozk9t4D9wy6s86dQzAa6du3b4HXExMTUaAkRBlA/Lp1CEolLqXLFPjuc+Rkk759G6JD4thZO5VrFv2gUaHRgCiyc18iJUve/3teFEWGDRvCqRPbWTo9ihaNPLBYRBxOCYOrkktXzbzwZiw9e3Rl0+Zt8mwNmewfYN68eXw8ZixhlKa0e6UCD+9LS5W4ZrzAp2M/w8vLi+eff/4JtlQm+2+Tg+6nUPfu3Rn/xVgWrUrhpUGFM5Pr9XokSeTbqbFYbE4cOju5Qjq51hwSpTgcCivPD3qeSZMe/Qj3b/LWGkooXfR4hUbjVSUah91BamoqKkGD8q51i4JanZ/kSCmoKK2L5tTpAxw5cqTYBwRpaWloJH2Rx+7mojQgiiLp6emUKVMGQQCHaL/v+xyiHZ2u8HT+4uh0OgTAIf1etyiKWCyWX5Oj/c4NLyx2M7m79pN14CDayHAklQLbnTs4MjJRiUoquDfLXzsMeSOn8dZbXLAep0O3Z6lXr94Dt0329z377LN89MFHxFtiCdOXzPu8Cnn7PN/NSV7W6bs/OwqNGutda29lT0apUqX+9JaCD0KhUPDG//0fL7/+Osl79uHfqEGhMqY7d0havY42zZsX2i5Hr9eDAFXc6iDlHCZh6TJUAf64VCiPoFJhS0jAfPESCgdYnQoW/Wykbx+xQELNu1285uTIKTOvvnr/HRh27NjB7l1bmD8plKYNPH9tz+/fzeXLuLBkehTNul5l1qxZjBw5ssD7RVHk9OnTpKenYzAYqFat2gPvES6TyR4+u93O559+jq8zmLIelQsdVwgKShsqYco28sVnX9C3b98/da8jk8keHjl7+VPI39+fHj378dWkVPYczCqyzLY9Fhb+bOeZ+k0gxEqCy3WkEhaGvTaEw8cOM3Xq1Me6Fic4OBiloMB8Jz7/Nas1b/9b5R8+ZvakJPTC7wG0ryYQtahh69atxdbv4eGBnfsHMmanEafoYO/evWzevBkvby/uWG7e8z15U7jv0Lhpo/vW/5sKFSrg6+dboG673f5rApLfr7soiaQJiYRqI6juqEe4MQzD2UykUzGoU01EqKJAEtmfuZX9GVu4kHOSc9nH2ZuzkQuOY3Tu0ZFp06bdc0qY7OELDw+n7bNtuWY7j9GRi17hiiCCNSkxv4yEiENyoNVpUf265CM7M4vs6zfYumUL9erW45tvviEpKekJ9kT2KPTp04eRr71GxsZfuDb5J9KPn8R0+w7ZV64Su3gZsZN/olaFikybOrXQe5955hk0WjVJ1tvU9mhEfUMz/FM1WHcdxrR1L9oLCVTRVCNIGYQqwJ+biUpGvncbi7VwNvabsVZefTsBh6R5oB0q5s2bQ6VyaprUL5w74jchQVq6tTewYP4s7L+uR3c4HPz000/Ur1eb9u2a0r9fF7p0bk2N6tF8/vnn5OYWnw1dJpM9Ops2bSIhPpFSrvd+6FbSpTwpSamsX7/+MbVMJpP9kTzS/ZT69NPPSEiIp9/LG2jZyIVeXX3w91UTd8fK4p/T2H3AQtduA/nuu++eikQXpUqVonbNmpw4cBjP6EoAiJL064rX3wNGW2oqlhs3idbVzX9NEATUgqbI7b5+07JlS5YvXoHRkYOrqvDNpSRJXDdd5rz5FDaFyJL161i8dg12owm7zYq70Zso17JF1n3bch2r0sKQIUMeuL9arZb+g/rz3Zc/EOEo++ve2nlrKoW7+hsv3cSODU+rHxp0RCrypidfFk9hU1uo5dWIys467M/cQpaQhlcJd1z1eprW7cLgwYOpXr26HHA/AEmSuHr1av4IXLly5f52ttWvv/6aSxfac+TSDkooS6NHT/bJ42iDg3FKTpw4UKoUeLh7kJtrJDc3B8v1WOzJ6YSpS5F2LodPzn3G1xO+Zsq0KXTs2PEh9Vb2pAmCwAcffECtWrWY9tNP7FnxM05JQvg14+/QD8YwePDg/F0U7hYWFkbrdq3ZvnoXQbow/LSB+GkDC5SRJIlrxoso1XpCeg5i88J5nOhwnZ6d3ahcSY/NJrF9Vw4btxtxeASh8bIVWqdelOPHDjGsj9t9v1PatvBi3vLb3Llzh5CQEF54YSjbtq6hU2sXvh4TTkSYlpRUO8vXpjJz+nh2bN/C8hU//6ndH2Qy2d937lxeElY3lec9yxlU7rgoDJw/f57u3bs/nsbJZLIC5KD7KaXRaJg9ey6LFi1i7pwZDH7tPCACCqpVq8X3PwyjW7duT1VA9srLL9Nv8GCSdu4moGljFArFryte84Jvp8lEypqfcZVcCNKF5b/PKTmxShZ8fHyKrbtTp06MDvyAC4knqOHesMCaJUmSOJdznBjHFfS1KhNU/xn8y5RGkiRyrsQQ98tWTlw8hNmZSwW334NYp+TkljmGGMc5+g3u+6cTAw0fPpx1a9Zx9NIuKulr4aHIW98tSiISIvHSTW5yiVB1FHqHK2pBk9/eXLLwVOatl9QqtTTwas2enPUMGjKQESNG/Kl2/JeJosjixYv5adpPnDtzDlHMC3zCwkswZOgQhg0bdlem+D/Hz8+PjZs38sEHH/DzytXYLLlYT55AVSIY18qV0Ol0uLu5YbZYyM3JQczMJWvDFvyUflRxr4MgCNhFG+dyjzJ0yFCWrVgmJ6n5l2nTpg1t2rQhMTGRlJQUXFxciIiIuO+D0A8//JCD+w9yJGUXlVxq4nFXrgqL08xl42msKjNClh19UCBRb4wgZf8BflxwBNGaDYDK2xevlq3Qlwjl9k8zqVix4n3b63A40Gjv/5BWq8n7frXb7Xz11Vds37aamd+WoEVjz/wygf4aoiu40ruriR5DT/F///cKc+cuuG/dMpns4RFFMX/r0fsRBAFRLDxjRiaTPR6C9KApT59S2dnZeHh4kJWVhbu7+5NuziMhSRKxsbHk5OTg5eVFaGjok25SscaPH8/nX32FpmQUnnVrYdbrUNgkzJevknP0GOosK/Xdm+Gu/j0B2R3zTS5KJzh05CClS5cutu6tW7fSr08/XMwelHGpnJ9ULd5yi0PGvXi0b4VHrRr4+HgXyDjsdDiIWbiE7ENH8FH54KXwRUQkk1QktZPBQwfz2WefPdBI0R/Fx8fTr28/Th4/hYtkQGnW4HA6yRbScOKgpKECvqZgFJISlZBXf5aUxmnpIM94N8df+/ua/dPZh1GHiZw9f/ZPt+O/yOl08uorr7J4wRK8JX9K6EriqnTDKpqJs9wghTvUbVCHpcuWPtDU23tJTk7ml19+Ye7cuRw4dgxDxQr41quL1s+P5Pg7mM5dxHz8NK4WFfU9WqBX/j7CKUoiR7J2ElUrnB07tz9VD8pkT8758+fp17c/N6/dwCB5oscVu2QjU0jF4OHK2E/G8vGnn+IoX5bQzh2AvIzoTpMRQalE+esoeuyipfimZXDq+PH7zu5o0bwxkcFXmPZ1yXuWmzIngS9/NHLk6GkaN6pL3y4i779Zotjyy9em8sYHyezdd5SSJe9dt0wme3iWLl3Ki8//j4aGtrgoi08WaXaa2Ju7ke+nfEf//v0fYwtlsn+/B41F5aBb9tCtXbuW73/8keOnTmEym3HYHWgkFSXUEZRxjf51KnYes9PE4dwdNGxTnxUrlt+37h07dvDG/73BrZtx6HFFhYZkawKKyFBChw7G08MDhbLwU1/RZuPi2M+pEBaGp7snarWamrVrMmDAAKKiooC8UZ1ffvmFPXv2YDabCQgIoHv37pQrVzireIG6RZH9+/ezcOFCjhw6wvnz5/FVBlLdsz56wYXk5GRUqFEISiySiTPSQbRqHY192hUIwOLM17gqnOFOwh05OdED+Oabbxg35hMqaWsRrCucRTrDnsZx0x669OzEjJkz/tI5EhIS2LZtG9nZ2Xh5edGyZUu2bdvGlGnTOHvhPFabDavFig4dEeqSlDZURKsonKQm2RrPaftBtu7YIm+1JMtnt9vZunUrq1atIikhCYObgWbNm9GjRw88PDyYPHky7344Bu82rfBv1CA/Sz6A5HSSsGU7OXv2MuX7H+jVq9d9zzdr1iw++vANDmwoQ3Cgtpg2iTTtcplqtXrRokULhr88kAMbyhIWWnR5AKtVpGbLi/QbOIp33333z18ImUz2l5hMJipViMYl3ZOKbsX/tlzIOUm2RwrnLpx94IfQd+7cYevWreTk5ODp6UmbNm3w8/N7WE2Xyf415KBb9sRdvHiRCxcuMPr90STHpRKpLkcJfUk0Cg120cZtyw1u2i8TXDKITZs3EhRUOFN7URwOB9u3b2fXrl2kp6ezYPFiAnp2x7/uvbf6iVu1Bt+EJE6fOFHo2C+//MKIN0ZwO+4OroI7KkmFGSNOpYOmzZswecpk/P39H6h9n3/+ORO++Ap3pxehmijsmXnTudJIJJFbaFU66nm3KPRUOib3PJcdp+nUqRN2h53IyEh69+5N1apVH+i8/yUWi4VKFaPRJrtTyb34G41bphhihLMcPXGUiIiIB64/ISGB90ePZv3GjVicThQaDaLViotWQ/fOXRg7diyJiYmMHj2a/ZsO0sCrLSqh+FFGSZLYlr2KLyeOZ+jQoX+mq7L/MEmS+PTTT/nm++/B0wO3alVQe3hgS88g58RJFLlGxn74IcOHD3+g+rKzs2nUsC5BfiksnFIST4+Cn1mHQ2LkhzdYvdnJho3b2L17N5N/+IBze+8/db3b4KuERD7Hjz/++Jf6KpPJ/ppvv/2WsWPGUk5dnTB94Zkmt803uGA/zrsfvMOoUaPuW198fDzvvfceG9dvxG5xoFKosYs29K46unTrwrhx4+65HFAm+6+Rg27ZUyMtLe3XdbE/YzZaUAlqHKIdrYuWjp068MmnnxAQEPCX6r569Sq169cjZNjzGKIi7lk2ec8+rLv2cic2tsDrGzZsYNCAQbhZvSnrUjl/6rsoiSRY47hiPU1khXA2bd70wPvWrl+/nimTp3DowGFyc3KQRNAp9YS5lKKUa4UCo6GSJHEp9zQXc06hVqoJ0ISgEJTkClnYVVaaNG3M9BnT5R+5u6xdu5YBfQZS37U1BlXx/+6dkoNdOet5a/QI3n777Qeq+86dO7Rt35647Cy8mzTCu0Y1lDodDpOJtKPHydi1l/JhJdiwdh2jR49mw/wt1HVvVmx9vz1gOpN7mKo1qtK8eXN69OhBlSpV/nS/Zf9Nx48fZ86cOazZsAGT0YibuzvdOnVi8ODBD7SW+25nz56lV8+uqBRp9O3mTovGnmjUCg6fyGHe0gxuxCn5/ItvMBqNfPfdV9yKvU6pSC31a7szoKc/ZovI0ZO52GwSIcEaOrb2xsNdRZueV6hUrR/ffPPNI7oKMpmsKKIo8s477zBj2kxcne6EqCPRK12wOE3ccdwkV8hkwJABfPPNNwWW3hXl9u3btG3TjuSbKUSoyxGqi0ClUGMTbcSZrxPruExwVCD9+vdDFEW8vb1p3749wcHBj6m3MtnTRw66ZU+dlJQUtm7dSkZGBh4eHrRo0YLAwMD7v/EeEhISqFilCn69n8OrcvQ9y97ZuBnXy1e5dO58/mtGo5HKlSqjSNVSza1+kettjY4cDhm38/zwwYwfP/5Pte/GjRtMmzaNSd9Npp5bq/x16Hc7n3OcmNwLhBBFWY9KuLnkbecjSiLJ1jtcsJ6gQvXyrN+w7m+vTf63mDRpEh++8zHN3Tvft+yh7O10GNj2gUfgOnXpwt6zZ4l8+QXUHoW/UywpqcROmU731q0pXaoUX479isbuzxY50n3DdJnz5tPYlSKq8BD07m5IKalIObk0ql+f6dOmPfAMCpkM8m6w73fjfD9xcXFMmTKF5csWYDTmJWZTKjW0btOBBg0aMv6LcZiMqbRuqiMsxInTCeu3GrkcY0MUJSJKqHF1UZCQ5ESlUtCuhRcrN+Ty/Q+zeO655x5GN2Uy2Z8gSRJbt25lxowZ7Ny+E9EpISgEmjZrwpDnh9CmTZsHyifSuVNnDm0/Qh1DM3TKgrsv5DqyOZ59gBQxCbXBgKuXF47cXDRAx2efZfwXX+Dr6/uIeiiTPb3koFv2nyBJEk2aN+OSzUrk4AHFl3M6ufLFVwzp0pWvv/46//UFCxbwyv9evW8Sksu5Z0h3S+D8xXN/OvDNzc2lTes2XDl9jWou9fBU/z5inW3PZEfqOsIpTbimdN5I+h9+GLMdmRw27WD0x+/x5ptv/qlz/1tNmzaNd0e8Twv3Lve9kTiQvZXuQzvz7bff3rfeixcvUr9xY/x6PYdXleIf4qTsP0jOpi1sWLuWdq3bUVKqRLhLwSSA14wXOWs5iaFOLQzP1EbpYchbDyeJZF24RNLqtZQNDmbzho3yVkuyJ8JoNHL9+nUcDgclSpQgJSWFDs+2ololGz9+HoGfr5q09DREpw2HQ2TTdhMfTkijT1d3xn/gT3KqgwUrsvl6SjoWq5642wkYDMV/j8pkskcvJyeH7Oxs3Nzc/tR98cWLF2nwTEMqKGsUypOS48hkb9Y2nH7ueDRqgKZsFP6BgUhWC3c2bCZt/0FUgkBoaCiVK1Zk0MCBtG7d+i8lqJXJ/mkeNBb9e4/LZbInTBAEXnh+KJYrV8m5GlNsueQ9+xByjQwaNKjA61t+2YKH5H3PgBughD6K7Mxs9u/f/6fbaDAYWLFyBRVqlOOweQcHs7dxOfcMl3LPsD9zCypJRQlNSby8vAoF3ADuKk/8CWH2zNk4HI4/ff4nRZIkjEYjubm5POxnezVr1gSlRLIt/p7lTM5ccqRMatWq9UD1rlmzBkmvx6NihXuW865RDaskcfr0abr16MZV+1nSbSn5xy1OE+fNp3Cr/wxerVogGPS4uroiCCAoFHhWqkD4i0O5FHuL77777oHaJpM9bK6urkRHR1OtWjV8fX2ZMOFLAn1NzP4uCj/fvJtlg6sBh0NCp1MwuLcHY9/2YeHKbK7H2vD1VjK0rweTx/uj14ksXLgQSZK4ePEie/bs4fjx49hstifcS5nsv8XNzY2QkJA/PRC1Zs0alE4lgdqCOxVIksTxnAOIgV4EDxqMW4VKCAoFpvR0rs2cQ+qhw6hCg9HWq0NmyUh2X4+h76BBNG/VkoSEhIfZNZnsH00OumX/eD169KBN8xbcnruA5H0HcFos+cfsWdncWbeR9F+2MuL//o/o6IKjl1lZWWgonG36j7QKfd6+3zk5f6mNgYGBbN22hQWL51OrZTXsQTk4gnPQuKoI1Ufi6+NbIDPxH4XoIrhzO56rV6/+pfM/TpmZmUyaNImaNWoRXiKCiLBIqlWtznfffUdaWtpDOUf16tWpWr0K180XEaXi9x29mnseb19vOnfu/ED1pqWlofb0QKG6917GSp0OlcFAamoq33zzDQ2a1eeYZQ8nsvaTZL3DpdwzOHUqXJ+phUOy4+Lqgqtrwal6Oj9f3GpWZ97ChVju+szKZE9CYmIiW7dsYEhfb1xcfv/8OxwOFAoBg6sCUZTo1ckNTw8l0xdkkpLmwGyRaNk0kC7t3Jkw4QuaNW1I82b16NWzAx2ebU6N6tF89tlnZGdnFzhfTk4Os2fPpnHDxgQHBhMSFEr7du1ZsWKFHKjLZE9AWloaOsEFxR/2/c6wp5IpZuLdogVKnQ4BARxObs1biCk5hYDBAwh++UW8WjbHpW5tIl8cRtgr/+PcrTi69ejxl++bZLJ/Gznolv3jqdVq5s2Zw6CevcjZvIWrn03g+pTpXPtxKjFffIV06gzjPhjDe++9V+i9vn6+WATTfc9hcuagUAgPnEituHY+++yzrFy1kvOXznP+4nkCAgLQq12Ae0+R1ggaJEnCbDb/5fM/DjExMTRu1ITRb48h96qVslSlHNUwX3fy8ftjadywMRcvXvzb5xEEgbHjxmJzNXEyZz9WsWDQ6hAdXMg5QbLyNh9+PAYXF5diairIYDDgMBrvOzIvOpw4zWYMBgMuLi4sX7GcL7/5Au/yBs46DnHDfgVtuVJo3Q14enkWO+LgXaMaqRkZnCgio75M9jidOXMGm83EnXgrPYdeolP/C7z0Vgxbd6WhUoFKpURQKNBqFTStr+fMBTsGgxt+fn7otHqSU+2kJN8iPPAKCyaHcnBjGTYsiqRzaxOzZ35Jp07t8x+6XblyhQb1G/LmqyO4cyqFUGtpgs1RnN97haGDhtGmVRuSk5Of8BWRyf5b3NzcsGEt9PsXb4lF6emBPjzi11ckjJcuY74Tj3+/3uiiIgEQ1GokwGK14loilLChgzh/5QqLFy9+rP2QyZ5WctAt+1fQarV8N3Eip4+fYMyIEfSo+wx9Gzdh4vjxXDhzhtdee63Itb+dOnUiR8gk2555z/pjTTH4B/rTsGHDv9S+rKwsVq1axcyZM1m+fDmpqakABAYFYnRm3+fdkOPIQqlUPNVJt7KysujWtTsp19NoaGhDVfdnCNVHEqKPoKp7XRoa2pFxK4fuXbvn9//vaNCgAfMWzMPuaWJ3znqOZ+3jYs4pTmUdYnfOepI0cXw2/lMGDhz4wHU2b94cMSub3Os37lku69x5FHY7LVq0AECj0TBs2DAOHj7I0RNHqRhdCTcfH7x9vNHpip9JoXJ1RZIkTKb7P/iRyR4VSZJYvXo1ublmZi1KxM3gICoMrt3MYfg78XQZGM+NWBsKQUChUKBRKxAEFa6uBhQKJdPmJrL/SBbfjvNjylfhNKnvQYkQLVUquvLx2+FsWFSStORzvPS/YaSkpNC1czdSr6fTwNCGGu4NiHQpS5RrOeq4N6WOS3POHbtAzx695BFvmewxatGiBTbBSpotqcDrNsmK0tMjfzaeEyfGU6fRl4xCWyI0v5wgCAgCSGLe7DOdvx/6CuWYMWvWQ19iJpP9ExW/saxM9g8UHBz8p5KNtW3blhLhJTh/8xi13JsUmYE63ZZCArG8NWQkGo3mT7UnOzubsWPHsnj5MrKNJhQqJaLDgatOR7dOnWnbvi2fHv0Mi9OMTqkvtp44+3XqNalHaGhosWWetMWLF3Pr+i0aGNqiV7oWOq5T6qlpaMTeuI0sWLCA119//W+fs1WrVpw6c4olS5awcvlKUlNT8XfzYNCzvenXrx8lSpS4fyV3qV+/PhXKleXKpi24vvg8iiKSwDjMZlK27aBBvXqUK1euwDGFQkFUVBRRERHcuX3rvuezJKegFIS8BGsy2RMyadIkVq1cwPDnPXjjBS8C/PI+95Ikcey0mZEfpfDc0HjWzQ8lKEDJ8dMWKpXP22XBbheZsSCRXp3daNPMtdDUVIBSkXo+fS+IF0fu4osvviAhLpEGhtaFsiMDeKq9qe7SgEPHtrNu3Tq6dev2aDsvk8kAeOaZZ6hQsTxXzpzFS+OL8tf7IbWgwZmTlhc4C+Bw2rAlJuL9bLsC75ckCUnKy5r+G49KFbm2dAWZmZl5eWtksv8wOXu57D9v/fr1DBn8PGKuRCldRUroS6IQFFicJmLNMdxyXqVhswYsW74MrVb7wPVmZWXRoXMnzlyNwbNhPXxq10Tt7p6/33Pmnn2Ee/uQkZqGIl1DDfdGKIXCa4mvGy9xTTrP/EXzaN++/cPs+kNVq2ZtEs4n4a7yxiE50Cq0hOgi8PjDNmmnsw+hCRc4ffbUA21h8qjFx8ezZs0aUlNTcXFxISgoiLfefRebrzeB7drgEh6G02gi9chRss5dwHzrFnqFkiWLFtG6desi61yyZAkvvPoKJd96A+099le/OX8xETY7B/fvfyquhey/JykpiVo1K/Nifx0v9HdBEOx4earyF7yIkkRSso1n+ybQuJ6eLu3c6P2/BJbNLE+9Wu5s3Z3J4Fcvs2p2EJFhOnz9/PLWfP6BwyFRt81F4uKV+FpDqex+7+SGh7N2UqFhaTZu2vgIei2TyYpy8uRJOrTviJCtppy+Cl5qX1Jtiew37sR/YH9UoYEgidz+eiLenTviVqNa/ntFmw3RYsXPzxelMu9eJvP8BZIXLOHSuXNP9Uw9mezveNBYVB7plv1n7du3j2+/m8jO3XuwOR3YlFaOmg5wMvcgrhoDouDEzcONlwb9jw8++OBPBdwAb7/zDmdiYgh/aRj6wID811UuLgQ0bohndCVuTvmJqqVKcuXiFQ5kbSFCW4ZAbRhKQUmGPZVY8xVSFYm8+dYbT3XAvXv3bk6dOwMoyfAAhYsLjsxUrmRfwlfpR3W3erj8OvrtpwniYvwJcnJynuiDsoyMDEaOHMna1WuxmmyoBDUiTgS1QJmypckxm4idMh2HUgGmTFy1EvWqadGHKbkU42DwoN60bvMs3333Y6F+dOrUiQ/HjuX2slVEPj8QRREzJDJOncF8/gIvfvmlHHDLnpiFCxeiVlkY/nwkGrWDzKwMjEYnrq5KBPI2VPD3VTGgpxvfTM1k+z4z1aINPFMzb+vE2/FWlAoIC1Hh4uJSZMANoFIJVCir5PylHCq6B+e/bhUt3DbfIMeRBYCH2otQXSS+qiBOnzrzyPsvk8l+V61aNdasW81LL77EsSu70VtcUElaJKeT1B1bCRrQD29/P5I9PbHG3c4PuiVRRLTa0Gq1+QE3gDH2Fu7u7vIot0yGHHTL/qMWLVrEq2+8gRAUgG/3LhiiIpGcTjLOnidt/0HIzWXEa2/ywgsvsGvXLt59911sNhuhoaH07NmTqKioe9aflJTEz2vW4N2qeYGA+25aby/82rXm9LJVzJ4+nUULF7F963bOZx8HQKlUUKp8aT5+YzS9e/d+6NfgYdm/fz89+/RBUzIK7yZN0QeVQBAEJKcT07UY0rduZW/6Fhp5tkavdOFpmFqTmZlJuzbtOHvqHCIKHAonIIIEKovAxVOXcfMx0LR+ffbt/YXhL3oxuJcPQYEGNBo1drvE2l/S+eCLtfTtk8LyFT8XWLut1+tZMHcuXXv0IOaHKfg0bohXlWgElQpLYhIpBw5hPHacPs8996fWnMtkD9v+fbtp1kCPu5sKUOHmdCcnNxurTUKvU6BWCYiiQPOGLnw0IR1XFxUzJ5ZGEASycxwcPJqD0SwCGlxcCi8ruZvJnJcyUkBAlEQu5p7kjuMSWhcnFcprEUWJC1evEJN9HIMYgKh1Po5LIJPJ7qLVamnQqAEJaclkZGSgUVupUKost+LjyV6xCn2bVvjWqUXCjl3YWzRDUKuQrDYQRUTRicViQafVITrs5B4/ySv9B8j7dctkyNPLZf9BJ06coHW7dqirV6VEl46FtupyWm3cnDMP4dZtvNx15OamE11eh4te4HKMjYwsxa8jnD8U+5mbNWsWb7z3LqVHv4NKX/xabdHh4Oqn43n3lVcZNWoUN2/e5MSJE9jtdsLDw6lduzaKe2wl9qQ5HA6q1axJokaFe6cOSJICtVBwVNeRm0P8rFkE53pQ07Mhp7IPoYsQOHXmyU0vf+2115g2dTqSRoOhahXcqlZD7eGJ02gk59wZco+fQMw1otU6+GJMGC8NCiqyntPnjXQecJ133vuCl156qdDxc+fO8fG4sWzfuQub04mgVKKQRIIDAnn5xRd5+eWXC4wKyGSPW/t2rSgXcZ4JH0Xmv2a1WTEZTdhsVqRfH5Pl5krUbnMLQdBQoawLer2Cm7ds5JoUWC1mZk4Mp3M732LPk5Rso06bS5jMWoJsEVgkE5nqa/zfME96dfTEyyNvDCA5zcGClRlMmpOGu2cQ5y9cQKfTyTftMtkjJkkSX3/9NZ99+SWSwRVDtSpo3N2xZmSQe/I0gtGEj7c3qRkZOLQaTGnp6CLD8enaGaVOjxIlIiISImqVkqx1G1Hfjmffrl1ERkbevwEy2T+UPL1cJivGTz/9hNPDnag/BNySJGG6FUfKwcPk3IpDmZNCl1aevPlyBKWj3AABq1Vk7S/pfDxhDc91v82qn9fi6lp4dCc5ORmVwe2eATeAQqVC5eVFSkoKABEREURERDzM7j5SW7Zs4dad24S/+jK4uZGdnY2EiHDXxggqgxse9esRv3ELmbY0UqQ7fDh0zBMLuDMzM5kzew6iVk1Qrz7o77reShcXfJo2xy26MnFTJxPoL9C/u2exdVWp6EqHVq7MmzuTF198sdADkkqVKrF86TKuX7/O4cOHsVqthISE0KRJEzmIkD0V/AOCuRRzssBrWo0WrUaLKDpxOkUEQeBGnAkXFwNvv/Mx6enp2Gw2ujwXSrdu3Xj1lZeZPGs3bZp5o9MV/ZDwx5kJqDXuDO7dm59+mI5CbeaHjwNo27TgDYq/j4r/G+aNn7/I6C/iiYwIQaPV0KBBYwYOHEKbNm3kB1Uy2UNks9mIj49n4cKFfDnxW7xaNCOgaWOEu/6diW1bE7/pF9IOHKZ7p04sXrgYN7sWS0wcqT/Nxa1mDVyiSoKgwBR7jaxjR1EZTaxdtUoOuGWyX8lBt+w/JScnh9Xr1+HZrEmBgNtptXFz4WIyz19A4eaG0pjJsAHejHzJEwETael2vDy90GoVPNfRl4plXeg04DiTJ0/mrbfeKnQeV1dXnBYz0q+jm8WRJAnRZCoycP8n2LJlC+rAQFxCgpFECaPJiN1hQy1oCgTehorRpP/yC0dydhMSFUy/fv2eWJvXr1+P0WrFq1mzAgH33TS+fujddXRsLeFwWIDiH550fdaHVRuvcePGDUqWLFlkmaioqPsuSZDJ/i6Hw8H27dtZsGAeMVcvolAoqBRdnYEDB/HMM88U+aCrR4+ePD/kZ85eMBJdoeD3kEKhRKHI+/6atzSFqKiyvPrqq4UeLr0/egxdu7Rn0GvX+PrjMEKCfs9/YTQ6+X56PLOX5PDx2Am0bNmSqVMmU7OKijZN3Qr3QXLgkGz07Gxg8c+5eHoq6NXFm5837mXY0G20at2JadOm/+kcGzKZrKDExESmT5/OvDnzSEtNI9tixPBMbVxq10SU4O47F4VKRciz7biZkcWylSvxVvjzjH8LjM4crhn/n737Do+iahs4/JvZvtlN7wGS0EKXjlQpUqUqUhRQBHuvqK/ltetnwY4KSK8qIAgISpHeS+gQQggJpJftdeb7IxDNm0Iv6txeXpfunpk5M0l255lzzvMcIn31eopXry1pK4uECEG4tMJFVxBRKP7Jbtx5qwrFVZCbm4vb48VQLa70NVmSSJ02g+JjKYQPuQNTi2YEB6t45KFoVAEBqIwGvF4fBYWFpbUmGyQZGdLfzKyZU/B6veWO07VrV0SPl+IDh6rsj/1EGv6iYrp163ZlT/QasVqtCAElZX8EUSAkJBRRLeKR3XhlD358+PEh6UQktYAhRMcPP80nrIqM3lfbhg0bQK0iqGnzKtsJokBQoAqv11dlu9BgNSBjt9uvYC8ViouTlZVF717dGX3vEAqyf6NP53x6dMzhUPICBt/Rh7vuGorVai23Xffu3UlIqMszr52ioLD8ZxnAj0vyWLLSwdj7H65wuUvTpk2ZOesH9h8107bPEe55LIU3P0rn6VdSadH9EBOmO/jPK+8wduxYIiIiMBjU3N7XjEdy45W8+GU/ftmPR3Ljkz0YDQKR4VpGDA7kyDEno4ZEsGhaXaZ+Xp0/1vzMc889e8Wvn0Lxb3LgwAG63NKFzz74Al1uEDFSIoJKQ1DLNjjsTvLy8/C4PWW2EQSBsI7tsDudBKvCEQQBkzqQm4La0CtkEJ0De9E5sCe9Qm+nY2hPtLKeGTNmXKczVChuPErQrfhX0Wq1CIDkdpe+VnzwEJYjR4kYdicBjRvhSzlCr65GzGYVCCCo1aiMRrxeL06ns3S7wf3DyMk5TXJy+Qy7DRs2pE2rVuT+vhr/X471V5LPT/aK30iqXZv27dtf8XO9FkJDQ5GKiksfRqjVKsLCwjEHBiKoBXx48eFFctnRIPDue+9Qv37969pnt9uNaDCgMpcfZfsrIcBMeub5EzkdP+kCVEqtbcV1Y7VaGTrkdgrz9vLz9ESWzKzLf56pzqvP1mDNoiSmfF6N3TtXMmbMvfh8ZR8iqdVqpkydSU5BGL2GHuObaWfIyvFgs/vZtsvKYy+m8tQrZxg6bHSVSf/atWvHtm17eO/9r7B5WvH7xigOp9VjzP0vsXXrHh599FEEQcBms6FWq0hMCMFoMiKL0p+fE4KETisQaFYjCAIR4So8Xhmfr+Tz5dZbgnljXBQ//TiH1NTUq3pNFYp/quLiYoYMHoI100kHcy8ampsjIaGLiEQfEoFW0IEkUFhUiN9X9jtQGxONaDKV26dG1BKsCSVYE4ZW1CIKImYphEMHyw88WCwWDhw4wMGDB7HZbFftPBWKG40SdCv+VWJiYoivUYOi5P2lr+Vu3Iy2WjUMdWqXvOB2ERZasvKidDKmSkRQq3E4HHA2sVBEmAaQK/3S+PjDDzG53Jz4bjL2tJP8NWehI/M0aZOnoMrK4bPx42/oZGlV6d+/P/78Auwn0kpfE0WBgAAjERHhREVFERUVBcdTCQ0OYtCgQdevs2c1bNgQfD58voofhpyjTWrEkpU27M4qmzHrxwJuvrk9MTEVJ1tTKK62GTNmcDLtAHO/q0mLm8reEAuCQPdbgvnu4xpsWP87v/32W7ntk5KS+GXpStq0H84HX9hp2f0w9drt5/bRJ9lzKI533v2UTz4Zf948DCaTiZEjR7Jw0RI2bNzOipWrGTduHHFxf84sCgoKQlSpyTzjI9AcSGRkJBEREYSEhCAIYDSqSo9zKtNHgFFEo/nz8/HOfuGEBEnMnDnzci6ZQvGvNWfOHE6fOkMLUwd04rmqGzKcvQ8RENAIWmSJs/c8ZQmicElVSI4ePcpTTz1F/aQGdGzXiQ5tO9KgXgPGjRtHWlraJZ+PQvF38fe801coLpFKpWLs6NE49u3HlZ0DgC31BMZGDf5sZDRx8pQXZMrcZIoaNT6fD0kq+bpJO+UGxEqnSjdo0IAlCxeSqDOQ/s0kUj75nOOTp5Iy/gtOfvE1UR4fP82bx80333zVzvdqa9++PQ3qJZH18y/4nOWjU0EQcGVlU7R+E8PvHEJQUNB16GVZPXr0QCVJ2E+kICNV0kpGHRdDscXPmx8V4PVW3G7q3Gy27HQzZuyDV6/DCkUVJEli+rRJ9OsRQM0EfaXtOrQJpHkTLVOmTKrw/fj4eL766it27trP91PmM+GbmSz6eSUbNm5l9OjRVyzxoV6vp3v3PsxdWLJcRxAEVCoVklTyN6bTltyW+Hwy8xZZ6dcjtMz2Op3IzS20HD588Ir0R6H4t5k6ZRrhxKBXGUtfC1CZ8ebl4T8bZAsIqFDhcDr5a40jf1ExfqsNv1z1siu/7MciFtCgYcm91bp16+jW5VbmTJpPlKMGLXW30FJ7C6HWWKZ8PY0ut3Rl27ZtV/5kFYobiBJ0K/51Ro0aReOkepyc+D2WYynIPj+i9myZK1lGW68hq9Y5yMv3wl9vNM/+97kR61k/5lKrVlLJyGklmjRpwtbNm1k4bx6jevbitrr1uKtrN+ZMm87uHTv+ttPKzxFFkcnfTSTQ7SH1q28p2L0H6ez0VZ/DQfYf6zn57SSa1q3La6+9dp17W6JJkya0a9MG2+atuL0O/Pig9Lm9jIQft+TGtnsXQSFRrN4ocsd9x1j2ewE+n4wsy+zZb+PJl1N55b0cxt7/BH369Lmep6T4F8vNzSU9PY0+3UPO27bPrYHs2L6lyjbh4eH06tWLAQMGXLWShWPGjOXQMT/fzciu8H1Zlvl4QgGns/3cOzyq3PvXq/KBQvF3J8syJ1JTCdWUXQ5VzZCI6JWw7NlV+pooiMiSVPpADKBgy1b0Wi02qZCqKg5nutKQ1H5GjhxJeno6I+4eibpYT4fAXtQxNSJEE06INpwkUxM6mHoh5QsMHzqc7OyKPxMUin8CJehW/OuYzWYW/PADrZLqcXryNCSfD1f6KfxOJz6bHUPd2rhFA2++n43X++eXiuz3gyAgiiIr1xay9HcH94158Lw3gKIo0rVrV8aPH8/UqVP5/PPP6dOnD2r1P6N4QP369fl16VI6N2hI3vwFHHn9bY6+/QFH33of5+q1jBp0O4sXLaqyduG19uEHH2Cy2SlctBhXYS5u2YVHduGW3bhshRStWIGYmsZ333zDvPmLELWteODZLBJbJpPYYh99705j695I3n3vM9544w0lCFBcNx5PSbIjQyWluv5KrxPxen1V3ixfCx06dOCxx5/nrY/zeObVVA4ddZR+Hm7f7eSRcdl8MbmI/zxdnUb1y2ZU93gktux0k5R0fXNDKBR/V6IoIv/PBHGdqCdRU5viP9ZhP3oE+PNR9Llvt/xtO7Bu3saYe+7BpXeQbN2KJJefBZbjPs0Rzx7uHDqYOnXqMGXKFByFTpqb26MWyt/3aEQtzc0dKMotVpaNKP7RBPl6f/tepgstSK5Q/C9Jkli/fj3Pv/ACe44epdpTj6E3mzEYDTiOp5I57XtaNxK4/94wOrY3g9NObq7IwuVOJs8upmevwXz77XdKzdi/OHbsGKtWrcJutxMeHs5tt91GeHj49e5WhTZu3Mi9Y8ZwJjcXdY3qqENC8NtteI+fIDgggPEffcSdd95Z2v7gwYPs3r0br9dLYmIiHTp0UH72iuvO5XLRoH4tnhij44kHYqts+8yrqWxLjmXT5h3XqHeVk2W55CHkZx+RnZ1BWIgKu91BYbGX2okGnn2kGkMGlP/smLMgl+ffyGX9hu2VluhTKBSV69OrDwc3HKNNUJcyr0uyxE7LRjL8GehrJWJo0hB1oAmjLFO0fSfeUxncf++9fPDBB/z444888diTSE6ZGCEeszoIr+why3cKC4X06d+byZMno9FoSKqThD4vmIbmqiuG7LVsRVNDYt+BfVfz9BWKK+5CY1El6Fb862VnZ9O6bVucUZEk3HM3okYDlKz1zvrlFzwZaRg1PjQqGa9PS0hIFKPve5BnnnnmHzNa/W/ldDpZvHgxPy1cSG5eHsGBgfTu1YshQ4YQHBx8vbunUFyQp59+mvVrprFxab0yScf+qqDQS6seh3nmubd5/PHHr3EPK+f1evn99985evQoR48eZd7cGYy5K5C3XopHrS47g2TtxmLGPp1Orz538fXXE65TjxWKv7cffviB+0c/QBtjV4I1ZXPSyLJMpiuNFOch8vw56AwGDHo9nTt2ZOyYMfTq1at0ZtfRo0eZOnUqc2bNwWq1IQgC7Tu0474x93HbbbehUqkoKCigbu26NBBaEqOvUWW/TjmPc0xIJvNMJtpzS/4Uir8BJehWKC7CH3/8wV2jRuHU6whpdzOB9ZMQRBXWYynkrl6LPyeXoXfeSb9+/ejZsyd6feUJixSKfyqXy8XPP//MsmVLsRQXEhQcym239aV///7odLrr3b1/rUOHDtGrZxf6dhf55M2EcoG3ze5n9BPHOXjMzPoNW2/Y2ScAs2bNYtwLTxEV7ueuO4KonWjAYvWxcFkRW3a66XZrXyZOnKx8BisUl8jj8dC7Vx/2bdtPU2M7QjRlPw9sPgu77BuISAxj+oxpxMfHExJSec4ISZJwOBzodDo0ZwctzrFYLNRMqEU9mhFnSCjznixLyLKM1+PF4XRwypXKMZJp2LAho+4dxYgRI0qqnygUNzgl6Fb8q/j9fmRZvqyR5wMHDjD+009Z/MsvuH0l2cvVokj3bt148oknaNu27RXssULx97JmzRoee+wBCguyadtST0ykmswsH1t3uQgLi+HrCZPo2LHj9e7mv9bixYt57NH7qVnDzz3DQmnXyozPJ7N6fTHT5xdisQcyY+Z8Wrdufb27el4HDx5k8uTJLFo4H6fTAYLAzW3acc+9Y+jbt6+yrEOhuEy5ubncNfwutm/dQRBhhKuiASj05VJADnWSajP/x/kkJiZe1nFkWabdze3IO1BM86AOyLKE0+nE4XDg8/nP5peQERA5Iu/GpXYQratGLpkEhpmZMWsGHTp0uAJnrFBcPUrQrfjHc7lcLFiwgO+nTmFv8j5kWSY+Pp7Ro0Zx1113ERoaev6dVCA7O5ujR48iSRI1a9akevXqV7jnCsXfy4YNG7hr+B3c0lbgjReqkVDjz1HG1DQXr35wik3bBebNX/S3LoH3d7dr1y6+/PILVq74BUnyAqDTGRkwcAiPPfYYtWvXvs49vDh+vx+bzYZer1dmUigUV5jX62X58uV8P/l7kvcmI0kSdZOSGH3fvQwcOBCDwXBFjjNp0iSef+oFbjZ0w2Px4/P5EBER5JIZOTISVrmQvWymrqkxjQJb4JU87LZuxBfkZuXvK6hfX0mcqLhxKUG34h8tOzubIcOGsXv/Pgx162Kqn4SoErGlpuHYd4CYsDDmz5lDkyZNrndXFYq/NUmS6NSxLdFhJ5g1oXaFa4a9XokhY49hcSaxes16JZv7dZaTk8PJkycRRZHatWsTFBR0vbukUCj+pWw2Gz2792TftgPUlZsSIkYgIJSWIrPKhRxkJ148qAQVt0T0wawOwif72GhZwaCR/ZkwQcnhoLhxKUG34h/L4/HQo1cv9qadoMboURhiY8q877VaOTl1BqFuL2tXrSI2tuqMvhdLlmV27drF7t278fl8JCQk0K1bt3Jrma4nWZbZsGEDy5cvp7i4mODgYPr06UO7du3+VgGRx+Nh6dKlTJ82nWNHj6FWq2nRqgWjR4+mffv2f6tz+btav349Q4f046fv42nTwlx5uy3FDH/wFAsW/qqMdisUCsUNwOPxsGzZMv744w+cTidRUVEMHjyYxo0bV7mdz+dj5cqVrF69GrvdTkREBLfffjtNmza9pH5MnTqV+8c8gCirCCaMIMKQZZki8rBQiJlg6gstSGYT0cY4mgaVLOc7bj9Epi6lNKP5iRMnAKhVq9Ylz2ZUKK40JehW/GMtWLCAex94gIQnHsYYF1dhG5/dTsqHn/LsAw/w6quvXrFjr1u3jv++/l/27NqL7JMRRRV+fMRVi+XxJx/nwQfPX7f7aktOTuahBx7i8MHD6GQjWvR4cOEWnTRs1IBvJ35LgwYNrmsfL0RaWhrDhgzj8MEjJV/SqnAk2U++nIVLtNOjVw8mTZ6EyWS63l39R3vnnXdY8MMnbFtRr8rfbVmWadb1ECPvfYnnn3/+GvZQoVAoFP9r2bJlPPv0s5zJzMIkBKGW1TgFOz6Vh3Yd2/Pdd98SExNTbrs1a9bwxONPknHyFEYCUaPBhR2vysPNN7fh24nfXvSyu0EDB7Hz92TidbVJcxyjyJUPQABBxIoJhBKJKIikS8c4JaTQM/IONKIWm8/CeutyunbvwpbNW3C7PADoDXoG3TGQxx9//IaZem61WsnIyEAURWrUqHHFpucrbnwXGosq9Y4U192+ffuYMmUKv69chcNhJyYmhqHDhzJ8+HDCwsLKtf9+6lT0NRMrDbgB1AEBmFo0ZerMmYwbN+6KlJ9YsmQJY0ePRecM4CZDWyICYhAEAYu3iBOnjvDi8y9x4sQJ3n///esWeO/bt4/+ffvjK4Dmhk6EaiIQBAFZlsn35nB47x769unL0uVLb5gvqooUFBRw+6A7OHMsi7bGWwnU/Jk5VZZlst2Z/LZ0FWPuG8OcuXMQxYrLJCkun9PpJMisOu/vtCAImE0qnE7nNeqZQqFQKCqyZMkS7rvnPgI9YbQL6IFZXbLERJIlst2ZbFuzndv69OXXFcuJjIws3W716tUMH3oXRmcgbYzdCNKElm6X4z7Nrg176dP7Nlas/PWiZhEe3H+QMFUksfp4YvXx5OXlIftk1ELZGYLBQjhp8hGcfjsaUUueJxuHy8GmlVuJ19UlwhCNDOS6z7Bg+s8sXrSEWXNmcsstt1z+RbtEhw8fZsKECcxfsACXywUCBAUGMXL4cB588EGqVat23fqmuLEod6qK60aSJF555RU6d+zCrO/mIKdrCMyPJHtvIa+9+DotmrVg9erV5bZL3r+PgLrnTwgUVC+JgsJCTp8+fdl9zc7O5uEHHybQFU6boK5E6mJLg5BATTA3BbWhvqY5302YyJIlSy77eJdClmWeePxJvAUybcxdCdNGlvZREATCtVG0MXfBnefj2WeevS59vFCTJ0/mZEo6LU2dywTcUHIu0fpqNNa3ZuWvv7FmzZrr1Mt/h+joaNIzPdjt/irbWaw+zmR7lRIvCoVCcR3Z7XaeeuIpgj0RtAjsWBpwA4iCSIy+Oq1NXTmVksG7775b+p7X6+WxRx7D6AykZWCn0oD73HbR+mq0MXUhOy2X/77+34vqkyAI/HVarVqtRkKq+jx8VvYWbyWcGDoG9aRmQD3M6mAC1cHUCqhPh8CeaCwGRo24h8zMzIvqz5Xy+++/07VHD2Ys/QVdp/ZUe2gscfePwX9TI76cPo2u3buTnJx8XfqmuPEoQbfiunnvvff46rOvqCk0pFPgbTQwN6eOqRHNgtpxi7kvYoGOu4ePYOfOnWW2k2UZLmhk88qNNs+aNQuHxUVjcytEoeJj1zDUIkgO5btvv7tix70Yu3btYu/uvdTVN0YtVjyJRSNqqaVryNbNW9m/f/817uGF8Xq9TP1+KpFUw6gKqLRdpDaWAMnM1KlTr13n/oUGDRqEy63hp6X5Vbb7YXE+Pr+OgQMHXpuOKRQKhaKchQsXkp9bQD1T00pnKBlVAVQXa/HDvB8pKioCSqajn844Q/2AppXe5+hVRuLVdVj882Kys7MvuE+NmzahQPqzvdFoQEZGkssG3gVyDmpBjVFlJtVxBFEWaaRviVpVfraiSlDTzNweW6Gd6dOnX3BfrpTjx49zz5gx+GtUo/ZzTxHdtTMBCfGYaiYQ16cXtZ57igKdhiHDh5deY8W/mxJ0K66L7OxsPv/0C+LFetQ0JpX7gNeKOloEdkS0a3j33ffKvFc/qR6O46nnPYY1JYVAs5no6OjL7u+P838knBg0YtXT1Ktpa7Fl05br8tR15cqVaCQtEdrya7T+KkoXh+hXs3LlymvUs4uTmZlJ1pksYnRVT8kSBIEIVSybN26+Rj37d4qLi6PPbQP5vy9yOXzMUWGbA4cdfDwhl37971BGuhUKheI6+u233wiSwzCqqs53Us1QE7vVzoYNG0q3MxGEWR1c5XbVDTVx2l2sXbv2gvs0evS9WCmkwJMLgFarQ6vV4MODfHYM3Cd7ySKd6saaCMBx+0GihGoEmiqvvqARNUQJ1Zg+dTrXOkXVxIkTcYgi8SOGI1aQSFdtNBI/agSn83KZO3fuNe2b4sakBN2K62L27Nn4nD5qGpMqbSMKIgnauqxdvZaUlJTS10ffcw/Oo8dwZedUuq3f5cK6Yzcjhg1Dr9dX2u5C5ebmEqCqPHPzOQEqM5Ikk59f9ajg1WCz2dCIuvOuvRUFsSRBic12jXp2cbxeLzIgCios3kL2WbazIX8lG/JXkmzZRrG3oLStKKjw+aqe9qy4fB9++BGx1W9i0L2pvPfpKU6cdOF2S6SmuXjnk1PcPjqVhMSWvP/+B9e7qwqFQvGvZrPa0HD+PDY6UY8sydjt9pLtbDbU0vmrsGhELSLiRd1D9OjRg9ZtW7PXuZmis9/hwcEhqDVqPLIbh2Rjn7QVn+BFQGCtZSl+/EQFxJ63MkyIJpzc3DzcbvcF9+dyud1uZs2bR2Cr5hUG3OdoggIxNmzA1OswEq+48SiJ1BTXxa5duwiUQs87chytr87+4u3s2bOH2rVL1nEPHDiQ8Z9/xrEp0wm+uTWyJCFqtQTWqY0+KhK/y0XatJkEqlTcf//9V6S/JpMZT875P9A9kgtB4Lpk1A4NDcXld+CX/agEVaXtfLIPt+SqMEndjSA6OhqtVsueoi3YfBa06AgmDBA47UnnhP0oMfpqNA9qT7Evn8TEhOvd5X+8oKAgFi5cwocffsiMeTP4asoxQAJEAgPDGDHqaZ577jkCAipfDqBQKBSKqy8iMgKXsPO87ex+K4JKKC29FRYWhkflQpblKh/eO/0OZEG6qHsItVrNrNmzGDZ0GFu3riKECCI1cYgGgRyyOO09iU/wYDQGYDHncdfgocyYOoNCKZf0wlTssh1REAkTw0k01CVEE17aR7/sQxBLjnGt5ObmYrfbiY6PP2/bgIR4Tvz623mvq+KfTwm6FdeF1+NFqGTN0F+JiCCX1IwsfU0Uadu6DXsnTcLy0yJEowF8fmS/H02gGbXfT5jJzJyZMwkODiYjI4OgoCDM5vOPVFemT9/eTPx8MpLcrNK1TgAZrhPUblCHhISESz7Wperfvz/vvv0eZ1zpVDMkVtrutOskogb69u17DXt34QwGA+YgM6dyT5EkNiOC2NJrLskSeZzhmGsfm6VVOFRW/nPvC9e5x/8OZrOZN998kxdffJGNGzdSXFxMUFAQHTp0UEqjKBQKxQ1iwIABzJs1j2JvQZlkaP/rpCOFyKgIOnXqVLrd9xOnUODNJUwbWfl2zhSCQoLp1q3bRfUrPDycpcuWsmjRIr6f9D27du0GZGLrxPLO6Lfo3bs3wcHBhIaGcuLECb6fNo3jUirGOnXRxTZE9no5ffgwp/J+I05dneaB7VAJKrK8GbRo3+KaBt3njiX7zz/TTvb5UKnVSsCtUIJuxfVRs1ZN1oh/IMlSlUFsoTcXlUYsDWLdbjfDhg9n9eZNRPbuScBNjfFq1PjcHpxHj2HdtAW11cbI4cN5+6232bplG7Iko1KJdO/VnbFjx9KlS5eL7u+oUaP4dsK3pNgPUtfUqMI2BZ5c8oQzvHD/U9elhFWdOnXoemtX1i1fT5g2CoPKWK6Nw2/juOcAvQb2Iv4CntBWJSMjgyNHjgBQt27di67bWZlly5aRfTqbemIzQs7W7jxHFEQiiUOLjt3uDUTHRnHHHXdckeNeDr/fz9q1a5k7Zy5paSfR63W0bdeWkSNHXrHrcqMwGo107979endDoVAoFBXo3r07ibVrcuDYTlqZO6MRy09/zvfkcIY0nh/zHDqdDoAOHTrQoGF9Du3bRRt11wpnIhZ5C8jwp/DQqAcvaSBDp9MxdOhQhg4dit/vR5KkctPHT58+zaDBg1FFRxHVtzfG0OiSARgg9JYu2A8fInPxEoTiTdQ01qOIPMaMfbeiw101ERERxMXGkn/gIEENqy6/aj10hJubNbtGPVPcyAT5WmceuMIutCC54sayb98+OnfsTANVS2L1lQd/O4vXE94giE1bNiGKIh988AHvjv+E6mNGY6qZUNpOkkoyYEoeD0e++gZnygnitNWI0yaiEw04/FYyvSdwqG089cyTvPrqqxf91PGjjz7inTffJVZOpHZAffRng1qf7CPTmcYxbzIdurbnhx9/uCJ1wS/FmTNn6N2rD6ePnyFRU484fSIaUYNX8pDhOkGa9wjxSdVZunxpmdqcF2PHjh18/Mkn/LZqFZ6zT3k1okj3bt149plnaNWq1WWdw6CBg9j5ezLNdO0pLioCBFSoEAUVIOOX/Uj4OSjvJKldTdZvWH9Zx7tc6enpjLh7BPv27scomTERhF/2USTkIejgyaef4OWXX1ZqiSsUCoXimkhOTmZAv4G48r3U1NYjRl8DlaDC6Xdw0nmMU/4Ubrm1E3PnzS0NuqGk5nTfPn2x57hI1CYRq49HJahx+Z2kO4+T7j9Gy3bNWbBwwVVbTvTyyy8zYdYsEp9+DKvHg9fjQyNozgbeJfdt1gP7yFuwEKOgpV3ntiz6edEl3XfJssyuXbvYtm0bXq+X6tWr06tXrwuavfXpp5/y+gfvU/OZJ9GFhlTYxp52kpMTJjJj8mQGDBhw0f1T/D1caCyqBN3/ctdzjcnQIUNZtWwNLY2dKpwClWI/yAnpIF9M+IJWrVrhcDi4Y8gQ3El1qDawX4X7tFisFJ1II3fiVG42diRWX6P0PVmWOeE4Qoq0n4/Gf8iYMWMuqr+yLDNhwgTef/cDrEVWzEIwgixiFywIGplBgwcxfvz4676uNTs7m5deeolfFi/F6/KiEbV4JA96g5YBgwbw9jtvExERcUn7Xrp0KWMeeABvSDCh7W/GXLcuANajxyjYtAVVfgGTvvmG/v37X9L+fT4fMVGx1JQakGCsi9frxWG343K5Smt8ioKAwWggX8wmVbWfU5mnrkiyvEuRk5NDj+49yTqeQ2ND6zLrzHySjxOOw5yQDvPMC0/z7LPPkpWVhUqlIjb2/MlhFAqFQqG4VIcOHeKVV17lj9Vr8XslVIIar+whOCSYe++7h5dffrlMwH3O8ePH+c/L/+H3lb+XbueTvJiCTNw1Yjivv/76VbvPsdls1G/cGLl5U6J7dEMQBIqLivF4PAgIpSPefsnHmW++I1ZnYPfuXQQHB1/0sTZu3Mirr7zKnl17wS+gFtV4JDeh4aE89MiDPPPMM1VOWS8qKuLWHj1ILSqk+j13Y/ifSjnW46lkzpxL28aNWbxokfKd/w+mBN2KSh06dIipU6fy46JFFBcVYTKb6de7N6NHj6Z58+bXrB9FRUXcOfhOdm7dRQSxxOoSSrJq+4rJ8KZiFYpo0641J0+cJDs7pyQAk7zEP/EIIQkJaLRlP8Akv0Rubi6irCJn+kwiMgVaBXcsd9w9xZvR1JDZvXf3JX0IWiwWfvrpJ3bt2oXX6yUhIYFhw4Zdl3XcVTl9+jQrV64s/Rvp1avXZZVzSktLo23Hjsi1EqkxfCiiumyyNtnvJ33uD8hHU9iwdm1p4ruL4XA4qFEtniSallmXLksSPr8fQQCVqmRt1GlXOofkHaSkplzSF+6V8Oqrr/LNp9/SNqBHhdP5AfZbdpDqPURwUDAetwcQiIqO5N777uW+++4jPDz82nZaoVAoFP8aKSkprF+/HqfTSVRUFD179rygZK8nTpxg7dq12O12IiIi6NWrF0FBlZfvulyZmZm88cYbfD9jBpFjR6ONikQQBQwGAxq1BrfbjdfnBQQ0GjXFq9YSl1/Ijq1bL/pYv/32GyPvHoXGbqCWoT6R2lgEQcDus5HmPEqmlModw27n22+/RaWqPDHtyZMnGTJ8OIeOHkVfqybGmgnIkoT9yDG8mZm0b3Mzs2bMICSk4pFwxT+DEnQrKvTtt9/y8muvIRmNmJs3RRsSjNdixbp7DxRbeOaJJ/jPf/5zzUa/HQ4HU6ZMYfLE7zmZloYsg6gSaN2mNSnHUsjPLiSKasToa5DnyeKg9wBxLzwNGhVBgUEYjH9OAbLb7VgtVrSCnpwlPxOQnEWHkPJrTy3eQjY7f2fO/Nn07t37mpznP8Hrr7/OZ1OmUPfl5ystkSH5fBx790MeGTGCd95556KPIcsytWvWxpgfSgNz1WugDtuSKQw8w4m01Cq/FK8Wh8NBw/qNMBeGU9/ctMI2Z1yn2FG0HlESqaGrTYy5OpIske3OIJsMqiXGsXDRAmrUqIEsy8qTcIVCoVD86+zdu5c777iT7Mxc7LKbuIcfQhsejiRL+PEjiBASElJmCnnm0uWEncxgz87zZ2r/q+LiYm5qfBOqAj3NAztUmFcoy3WKZM9Wxn/xCffee2+V+0tJSeHTTz9l7bp15OXlYQoMpGWzZoy+9166det2TRO8Ka6PC41FlUWG/yLz58/nxVdfwdjuZuq8+ByxfXoS3rYNMT1vpc4LzxDU81Y+/OxTJkyYcM36ZDQaefTRR9m+cxvbdm5j3cY/2JO8h+KiYixZdtoFdKdxYCvCtVEEqUNLphc5fYiyimJLyZSjc3w+PwIiAgKS3Y6GigOYQE0IGrQcP378ip3H3/zZ1XnJssyM2bMxt2haZU1KELj8OgABAABJREFUUa3G3LI5s+bNK11nfzEEQWD43cPJktLxy5VnBZVkiTP+NIbfNey6BNxQkpegqLCIuEpyEhR7C9lZtJ5QOZIWQhdiSSBcG0WkLobGga1ob+zB6aPZNL2pGdGR0cRGx9KiWUu++OKL61LnXaFQKBSKa62goIChdw7FmeWlnflWVLKI90wWIirUggatoANJoLCwCP9fsoW7T2VSK7HySi2VmT9/PsUFFhqZWlWayDdaX50wovnum+8qvb9LT09n5KhR3Ny+PdPnzyejoAC7x4PdbicmOpq2bdsqAbeiDCXo/pfw+Xy8/d57aBvUJ/a2XuWmBguiSFTnTphvbsP/ffIJdrv9mvZPpVJRq1YtGjVqxN69ezmw/yA3GdtiVP05BSpcG4VaErHt349G0CDIQpl+nhub91qKcaWmEaWLq/BYsiwjI192YqsdO3bw6KOPUrdOPHFxETSoX5uXXnqJw4cPX9Z+b0QOh4NiiwVDXOx52xpiY7BaLdhstks61ujRo1EHqEi2bkWSywfukiyxz7oNwcB5n0BfTW63G1mWUVeQGRbguP0gGllHPaE5KkHkr9/bkuTHUeQk0dMAl81FiDuK2nITbMfcvP7Sf+nQrgPJycnX6EwUCoVCobhwhw4d4qWXXuLm1m1p2qQZfW/ry6xZs3A4HBe9r9mzZ5NzOpcWpo4EakKIUsdg2bED+eyDewEBjaBFlqTS/TuzsnCdSGPkiBEXfJwDBw7w/PPP8/yLL+LwethmWUea4xg+yVth+xq6Whw+fIRDhw6Ve+/EiRP07NOHpZs3ETzgNpL++wr13niFuq++iK5Te6b88AP9Bw3CYrFc9PVQ/HMpQfe/xJo1aziZcYqoLrdUOXU8olMHCoqLWbRo0bXr3P+YMWMGgXIIwf+TXE0jaqmujse6dRveomJUghq3243fX/LBrNaokSQ/BWtWofWrqKZPKLdvl9/BXssWLK5iFi5cyGuvvXbRQbIsy7zxxhv079eD3dvn8tBIkfdfCeXuQV6W//It3bp24D//+Q8fffQRb7/9NpMnTyYvL++Sr8eNQKvVIgCSy33etpLHjYBQYYKWC1GrVi0mfPs1xbpctlh+J9OZhkdy45E8ZLpOssWyinxtFl989Tn161ddquNqiomJQaVSYfEWlnvPK3k47TpJDPGIgohMSdk6AGSZwsJCfD4fQWIo4UIMdr+VGoZaNA1qS0dTH2yZLu68407OnDlzjc9KoVAoFIqK+f1+Xn75ZTq068jkL6ZgOeRCStNw4I9jPPrgY7Rs0Yrdu3df1D6nfj+VCGLRqUoSotY21MeXcZr81b+XCbxVqHA6nHiLLWTMnEtSrVr06dPngvr84osv0rFLFyb99CPCTY0xdW6HIymM3Z6d/Fa4mEJP+Xs0o8qE7JcoKCgo87osy9z/4INk+7wkPvYQ4W1ao9KVTHvXmExEd+1M/ENj2XPkCK+//vpFXQvFP5sy7+FfYt++fYgBAcjBweTl5eH3SwiCgE6nxWg0lq4l1YWGoIuK5MCBA9etrylHUwgSy2czB6hvakpe8UrOzJhOaI/uqGvVwO/3oVJpodhCweIluJIP0cbcqcwIpCzLHLLt4bj9EJIkESpGkrLpJHu37OOrL77mtr59+PKrLy8oL8Bnn33Gt998whsvRDJ6eCSi+OdDjCfud/L2xyf57NP3UUlmArRmXH4Hr7z8KsPuGso777yD0Vhxwq0bmUajoXWrVuxI3kd42zZVti3eu58WzZtfctAN0K9fP0IXhPLp+E/5Y+069lt9yJQ8WOnQtT3PPf8cHTuWT5J3LdWpU4eWrVpwdPNxovVla3E7fDb8skQQociyhIRUWoLE5Xbh9frQCjoEBAIJ5bQvtXRbvcpAS3Mn1p9ZzuTJk3nllVeu6XkpFAqFQlGRN954gwlffENtdWMSAuuUmZ5t99nYe2ozg28fzIrfVlxQMlWfz8fJkyeprW1S+lqELpqb/C3Zu3kr7pMnMbdshS4mBr/PjfXIIfIPH6VGeDjz5sy5oDJhr732GhO+/57wvr0Jv7k1BYVF+L0SGkGLt7iY3J8XsunkGjoF9cCs/jNRnFtyIYhiuaRzu3btYuee3UTfMwJNJQnpDDHRBN/Sgbk//shrr72mJFJTAErQ/a9x5MgR7A4HVpsNQaMGrQZZlnG63DidToxGI4HmwJI52qJYZt3MtaZWa3DIFY+o6lR6OgTdyg7rRnLn/QBBRmzVqyF4vHjOZGHUapFUKtySq0w5tH2W7ZxwHKU6tYgUqhEeHIFer0eSJU67TrJs4XKG5Q1jwcIFVZafslgsfPnFxzx8TzBj7i6bCdztdmO1FvPk2GAyMn2sWqmhQ0AvvLKHdOdxpk+cwdEjR/lpwU/XrcTV5Rh7331seOABrMdTMdeqWWEb24k0XCnHGfP1swB4PB6WLl3KunXrcDqdREdHc+edd9KwYcPy29pszJ8/n++nTeXw4SMAJCXV5bkXnqV27doYDAaSkpKoU6fOBfdZkiQKCwvx+/2EhoZe8fVVjzz2CKO33Ueq/TA1A+rh9/ux22wUOAuRZQkffjyyBwS5dF2Yw+FAPJt7AEBGKv3vc7SijmixOlOnTGPcuHFKgjWFQqFQXFdpaWlM+HICNVUNqGlMKvd+gNpEK/MtbMpdyYcffsi3335b4X4kqWTQRxAERFFEEAQkqew9Z4KxLiZ1ECnZB8n+eQmyKCDJErLs47mnn+bpp5++oGosaWlpfDNxImG9uhPRvi0AOr0Om9eGjIwmKIjoocPJnDyRI8X7aBnUoXTbU85U4uJjadSoUZl9Llq0CCEwkMC6Vd+LhLVuybGVq/j1118ZPnz4efuq+Oe7qtPL161bR79+/YiNLUnF/79TlmVZ5rXXXiMmJgaDwcCtt97KsWPHrmaX/pV27tzJvB9/xG93ILldqAwGVDodKr0etSkAUa/H4XBgtVnxWm24s7MvqdzTxcrIyODDDz/koYce4uGHH+brr7+moKCANm1bU0B2het5AfQqIx2Cu1NTUxu908PAps0Z1b0H0ydN4lRaGs+98Cyp8gHWW5dxxJbMQcsujtsPkUBd4oREwoLCSoNeURCpZkikubEjWzZsZcaMGVX2+aeffsLjsTB2RNkPe1mWKSoqQpBFtCo99w0Lwy1ayfWcQSvqqB3QgBbGTmxev5XPPvvsylzAa6xfv35069SJzGmzKEzeVzrtC0pKehXt20/G1Jl0bt+eQYMGsWTJEho3bco999/PjN9/Y8GuHXw2bRodunRhwKBBZGdnl26flpZG565defrFF0kRILj/bQT3v43josh748fz3v/9Hw0bNrzggLu4uJgvv/ySFs1bklQniQZJDahXtx5vvvkmp06dumLXZMCAATzz/NMcl/aztXANx3OPYHEUo5bUCAjkcQYEGQGR4mILVqsFr9dXWmsUoIBsgjVh5fYdqY2lML9QmWKuUCgUiutu5syZyB6BBGPdSttoRC3V1LVZvHAxubm5pa8XFRXxzTff0Kb1zURHRhMTFUOP7j354YcfaNykCbne8t9z4doobg7uQs+Qgdxi6k6sGEO92nXp3LkzO3bs4MCBA+dNYjt9+nT8Oh3h7W4ufc1gMIIg4JO9gIyo02Fu1ZrTvgxcfmdJf70F5AgZ3Df2vnIP6/Py8lCHhCBUkhfI6/VSXGyh2OXGLUuMHz+e33777boOZiluDFe1ZNjy5cvZuHEjLVq04Pbbb2fhwoUMHDiw9P0PPviA9957j2nTppGYmMirr77Kvn37OHjw4AWPBColw87vjsGDWXPoIM7CIvSNGhDWr/waGL/bjez24N+TjG/LNg4mJ1+R6TBer5elS5cyfdp0jh45ikqlolmLZtjsdtauW4dPo0YTGwOSjPtUBgFaDXcOup0Z02bSUNWSOENChfv1SB42Wlcw8sG7+Pjjj8u9v2PHDqZMmcKyX5aTnZ2F2qOlfUAPjEZTpaOdu4o3ElrfxOatmytNsvbss89yYM8Mls0p+6XjcDooLipGJ+gRBAFZlmnVO5UQe3PqBPw5qrvPsh051sXefXsvaFrUjcZqtfLAgw/y6++/IQQFoa+VCAi4Uk8gFxXRo1s3Jn77HWvXrmX02LGo69UlplcP9FGRQEkt7+IDh8hespTaEZEsX7oUrVZL527dSLMUEz/mHnRhZQNQd34B6d9Po4bJzNpVq85bJzQtLY3Btw8m9egJIoglUheHiEi+N4csKR1zmIlZc2bSrl27K3JNZFlm3rx5PDD2QZx2JypBBQj48aGRtbQSu6IT9PhlHz58CIAKNSpBTZGcR7K8hTYhnYnWVyuz31x3Fnt9m9i1dyfx8RVnSFcoFAqF4lroe1tfDq89QYvg9lW2c/rtrLMt44eF8+nevTsHDhxgyOAhnD51hghiCNVEIiGT6z1NIbnE1Yjl1MkM2gV0J0hT8dLCLFcGWyyr0Zr0CKqSeziVKNL8ppt46okn6NevX4Xb9enbl11OOwl3DS3zusPhwFJsOZshXY3PYiXjiy+42dAer+zlqCeZVu1bsGDhgnJLAl944QW+X/oLtZ95oszrsixTXFyMy+UCQUSWZTL/72P0gYGoXC6aNW7C7JkziY09f0Jaxd/LhcaiV3V6ee/evSutgyzLMp9++imvvPIKAwYMAEqeSEVFRbFo0SKGDRt2Nbv2r3H8+HHWrFtH+J234y0uJuOXZWijIjG3blmmnajVYk3ej/X3VYx78qkrEnCfPHmSYUOHcWj/YYIII1gVjl/yMe/Ij3j1AuG9epLQqSNqQ8naX5/NTu7GTUydM4fExBocTNmJIAjE6GqUSf7m9NvZbdtEUJSJJ554osJjt2zZkpYtW8JX0KBeA9SnTQSag6vsb5w+gX3HtpKRkUGNGjUqbCNJEipV+UR0bpe7ZMrw2X4KgoBKJZR7ClvDUJutZ1axbds2OnToUG4/Nzqz2czsWbPYvXs306dPJ/nAAUCmce8+jBo1iubNm2O323ni6afRNKxP/F1DyzwNFlQqgps0wlAtlmNffMMHH3xA7dq1SUlPp9azT6ILK/+FqwsLpcaYezn+0Xhmz57Nww8/XGn/nE4nQwYPIePoGdqbepbJfh+tr0ZdqTG78tdz17C7WbtuDQkJCZd9TQRBwGQyISDQJuQWREGFgIhBDGBL0Sr2+7fRkJboBSPS2fXdEhJWOZ+D8k7CtZFE6sp/Ced6zhAUGkR0dHSFx7VYLPz0008kJycjSRI1a9Zk6NChlbZXKBQKheJSeTzesw+VqyaebeP1esnOzmbw7YOxZDroZOqDXvVnAJtI3ZLSmunrUWlU7LSvp1lAB0L+Z+ZXuvM4220b0cREENGtKyGNGyJo1NhPnGTfps2MGnMfb7zyarn7QavVSmZmJi6vm/xtOwhMqosmqCQgMhqNCIKA1WLFI7mRVH4k2c8exxZ0Bg0Dh/Zn/PjxFebgufXWW/l2yvfYT2UQUP3Ph+XFRcW43G5EgwFRrca6aw+CIFLn8YfxWW0kz5nPoMGDWbl8+XkHDxT/TNdtTfeJEyfIysri1ltvLX0tKCiINm3asHnzZiXovkL27t2LV/IT1LA+olaLp7CInCVLse3cjalFM9QhIfitFmy79uJMSaFmbNwVSdxUUFDA7YPuIPPoGdoabyVQUxLEpzuP4/cLRA8djjo+FqvTQYheBwKoTQHE9OyO2mQifckyOnZtz4Y/NnLcc5AIIRZRUGHxF1JANjHVYpg7f84FjQA6HA7CKknM9lcaQYssy1WWvKhTpw6LFrgotvgICvzzz0eSyq7LPXbCTUGRn1hN2SdeRlVA6TrjvytBEGjevDnNmzev8P2ffvqJAquF2n37VDr9ShcaSlDbNsz5YT5R4REENKxfYcD9Z/sQAho1ZPLUqTz00EOVZuBftGgRRw8fo11AjzIB9zkaUUMLc0fWFy5n4sSJvPPOOxdwxuc3b948ggglzlC2ZmjbkG5sKVzDdv8awuQozATjk73kk41DsBKmjaJVSOdytUK9kocsOZ2HRz1YLiGdLMt8+eWX/N8nH2OxO9DGxoAo4vlhPu+8/z4j776b99599285k0KhUCgUN6bEmgkc2HKoTL6cihR58xHVIjVq1GDixInkZubTwdwLnVh+BmuQJoTmxg5ssf9OdM0otqWtJsgRSpg6umSJlvcMGd5TGJLqUPfBsWjPJiQFCGpYn8AG9chauYr/vv02TZo0oXPnztjtdt59912mz55FTl4+flHAPu8HBFFFcKMGVBvQF21wMAaDAb1ej8vlovBUBmq1igceHMtjjz1W5QP5bt26kVi9BpnLfiVxzGhEtQqP24PL7SoJuDUa/A4Hlj/WE1Q/CV1YGLqwMOLvv4/Dn37J999/z9NPP31ZPwvF39N1KxmWlZUFUC4RQlRUVOl7FXG73VgsljL/Kirn8/kAEFRqBEGg2qD+1B47GoPZTMGSpeRMn0n+wsVo1SpMiQk0bdbsiiSbmjJlCmlH02hluqU04JZlmVTXUQx1ahOQUAuNoMXtduP2lE2aFt62DUJICNWrV+eX5Uu4bWgvfHE2bOG5JLaJY/yXn7B1+xaaNGlS0aHLiYyMxOY7/++JzW9BVImEh4dX2ubOO+9Ekg3M/CG3zOuCKPDXMe0ZPxWi8gcQpSt5Cmr3WUm1H+aQdQ8+n/e865D+zn77/Xd0iQlog6t+khvWqgUWm52jKSmYkipfI3aOOakuJ9LSqnwoMnXKVEKJLJOB9H+pRQ3RQg1mzZhdMg3sCjh18hQBQvkpRYGaELqE96VxUEu8GjfpwjFOCIfxmVwYtUZuCrwZrVg2OPZKHnZaN2AOMzFmzJhy+3z33Xd55c03kG9qTO0Xn6PW4w9T69EHqfPyC5h6dGPijBncN2YMPp8PWZbZs2cPK1as4I8//lA+LxUKhUJxSYYPH45DsFHgza2y3UlXCk2b3UTdunWZPnU6UUL1CgPuc4I1oYSI4VSvVo3ps6bRrFtjioLPUBh8GnOCEb3ZTN3R95QJuM8RBIHoHt0QY6KZ8O232O12bh88mC+nTEFs1YI6L4+j2gvPUG3cswT37oEl7SSHP/sKd35B6fZ6vR7X3n10aNuWDz/88Lwz4FQqFV9+/jmq02c4MXkKRaknsFgsyAjIkoTzWArZk6aAx0v1gf1Lt9OFhxFwU2MmT51aem+u+Hf522Uvf++993jjjTeudzf+NmrWrIlKELGfSMNcpxaCIBBUvx5B9evhd7nwOZyIOh3OjAyOfzOJPwoKqdeoEYnx8YwaMYKBAweWljq6UD6fjymTpxApVCsz2uiWXBT5Cwlv3AUAERUCAg6Ho8xoniCKmJs2YdnKlXz11VeXvfZ26PChvPv6e3glDxqx8tG/DG8q3Xp3rTLojoiIYMTIMXz09ZfUStTTq2vJAwW9To/bVYwkScxeVMycBVZqadrg9NtJtu0g23cGtGrQqfEjcP/DD7NixQreeecdgoODL+v8bjQWqxVVQMB526lNAUiyjHCep+alzo6aV/XA4tDBw0SqK14a8FcR2mgyLcfJysq6IlPMDUYjPqniGxGNqCXRmESiMQmbz8JGxwq+mTiBjz/8mM1HfiNCiCFSe3bduSeHbPkUQRFmZs+dXW4mR3JyMh9/9hmhvXoQ1blTmffUBgORnTqgj4xkydQZPPXUU+zauYvDB48g+SUQwBxoZvjdw3jmmWeUaegKhUKhuGCdOnWiafOm7N+xjVaqzhXOJjtmP0CxKo8nnvyArKws8vMKuElXPtP5/wpXxXBg/wH69+9P//5/Bqqdu3YlLyIEbVDZh9qSJOFyukqSkwlgatGM339Zzrhx49iavJf4B8dgrBYHMrgL8vH6/Jhbt8TYoD7Zk6dyYuYckp54BIDsVWvwpqbyxKTJF3YvAnTo0IGH73+A9z/6kIJ9+1FHRqIymfAVFOArLEIXFkrdRx9CF152qnxwk0Zk7tpDRkbGFbn3UPy9XLeR7nM3fH/NYHzu/6u6GXzppZcoLi4u/fdKZiL+J2rRogUN6iWRt2FTufdUej0aUwDpc3/g6ISJCMFBaFq3wtmwHjsL8nnwicdp064dhw8fvqhjZmZmcuZ0FtG6srWL/bIPBFAZ/gzIRFR4Pd5y+1CbAnDY7Rd13MqMGDECY5CBZOvWSjOip9gPYldZeODBB867v//+97/06HUnY5/O4M4xx5j/cx5bd7n56Rcbg+4/yesf5hMrNCZCG8O64pXkhbgJGzSAas8+Q8zTj1H7P+MIuLULs5Yspu+AARQVFV2R87xRRIaH4ys4//R5d34+KlEomYmQknre9raU48TGxBBQRUAvCAIy559FcKXnGXS6pSMFQg4+qeqn15muk5gDzfTp04ffV//OO//3FuY6eg7Luzgob8cXbePpl55k/cb1tGlTvh76999/D2YzkZ06IMsyfr+ELJU9m8B6dRHiq/Ptd5PI2VdIU017Opv70d7YkzBbHFMnTKf7rT1IS0u7kpdAoVAoFP9goigyc9YMqifFsdn+O/stOyn05mH1FZPpOsmW4lWkyYd58T/jGDhwIFJphZMLCWQFJKn8N3PmmTPoY/6MCWRZxmKxkJObi8Vqxe5yYnc68QQYKbJamDl3DsGdOpQE3GcPHRwcgkoU8dvtCFoNwT1vxZ52kjMrV5E64TuKV63hlXEvlgn2z+err77i0y+/JLB5c2oMvh19jeqoAs3oayZibNgAr81Gwc5d5a+hVgvIeDyeCz6W4p/juo10JyYmEh0dzapVq2jatClQkhho69atVSZK0ul05dY4KionCALPPPkUYx56iONTpiN5vfhsdkSdlqD69bAeO471eCpht/cnqFFDQkP/XFfrzssnfcZsBg0ezKqVKy8442LJtBkZ1f+sU9WKOgQZvIUFGP7yhK+iIMmdl0fY2RHn7Oxsfv75Z7KystDr9XTo0IG2bdte8BPJqKgoJk6eyL2jRrO5+HcS9UlE6aohIlLgzSXNeZQCMYtx/xlHt27dzrs/jUbDt99+x5Il/Zjy/USeeW0zIOPz+bHZJALlMKJN1dhp24Q/0kzMyFEIBh0+vGi0GoJCQxEiIwhMqsv+bybyyiuv8OWXX17QufwdDBw4kB8WLcJ5+gyG2JhK2+Vt3kpMZBT333cfb370Id7e3dFUkvXRa7Vh3bOXIcOGc/DgQerVq4dKVT6hS5OmTdi39iC1aVBlH3M8pwkJD71iWURHjhzJJx+N57jjEEmmxhW2cfmdnJZOMGr4iNLslo888ggPP/wwFosFv99PUFBQhed1ztIVv2Jo0pBiiwWXyw2yDAJoNVqMRiN6vR6H04k2KQn7vsM0NLUoTVyjE/UkmRoT76/F1rQ1jLx7JH+s/6PSTP0KhUKhUPxVXFwcK35bwddff830KdPZkfcHsiwjqkTad2nHw488XJpAOTo6GnOgmfyibCJ0ZQfTZFmmyJtPljsDn+wl25NB3XrlS9XqdbrSZWCyXFJ6zO32IOi0iFpt6X2gVxCR/RJWh5O4JmXraqtUImFhoTjsDhzOkjwogtFA7tLl9OrZk4ff+4AePXpc8DVITk7mtTfewNy5I7G9SrZT5eXjA9TGkpmhxRs2kbXiNwLr1MFcp1bpto6MTLQa7QXVGFf881zVuy2bzcaePXvYs2cPUJI8bc+ePaSnpyMIAk899RRvv/02ixcvZt++fYwaNYrY2NgyZcUUl69GjRrotVoKdu3GZXcgREbg12rJWPorhXv2oK9Xl8CGDQgOLpuxXBceRsLY0ZyxWJgwYcIFHy8qKgqdXk+hN7/M6xpRS5Q6BuvuXaVThGUkVKqyz34krxfb7mQG9evHY48/TpNmzXj+tdf4fPYs3p/wNX0GDKB9p45s3LjxgvvUq1cvFi1eSLMujTno38lvRT+xovgHdrnXEVrPxNfffc24ceMueH+iKDJgwAAW/fwLhw6nsm17MsdTM1jyyy/ENYhhq3M1ef5czLd2xm8Q8Qk+9AYDoSGhpV8S+qhIQjp34sdFC8nLy7vgY9/oevbsSXz16mQu+Bm/211hG+vxVGw7dnHfPfdw7733EhceQdr30/FabWUbymDNy+Pw19/iLCxiyqSpdGzXiRbNW/LFF1+UW5N9332jKZLzKfqf372/8khusqR07hk96oolG6tWrRovvPg8J6UjHLEl45XKzt4o8uazzbaWyOoRPPvss2XeEwSBoKAgQkNDqwy4AXKyc3Aj4Ha6UclqNIIWNRq8Hh9FRUUUFRVht9tQB5gQBRGfXH7kXa8y0tjQmv37DrBu3brLP3mFQqFQ/GuEhobyyiuvsO/gPlb/sYpff1vOrj07WbxkcZmKRXq9nrtH3sUZ+SS+v3wnFnsL+aPoV/6wruSYLp2ToUVYjD72HT7EHYMHk5mZWdq26y23YN9/sGSttMOB2+1GNBpQ6XRlBl7sBw6iDghANBpwyHLJkqq/EEURk9lEREQE4RERmOPiGHz77SxcsOCiAm4omXEmB5qJ6fFnImijwYDs8yGfHd0PbN8WTWQEuRv/nGUqSxLFW7fT/7bblOzl/1JXtU732rVr6dKlS7nX77nnHqZOnYosy7z++ut89913FBUV0aFDB77++mvq1j1/UqVzlDrdVUtOTqZP//64w8OI6N8Xv1GP2+0BZHx2B9YNm7Dv3UfCsMGEt25V4T4yl/2Kau9+DiYnV1g+oSKPP/44P0xZQMfA3mUyM+e4z7DJtobgHt0JatMGj+wiMCiwdL+yJHHqp0V49+6jXt267EtJIfTWLoS2aoHaYECWZWwpqWT/tgp1djZzZ86ic+fOF3VNDh06RHJyMj6fj8TERG6++eYrOtonSRL33HMPC9euocZTj6PSqNHrDahU5Y/hczg4+tZ7fPXxJ4wcOfKK9eFqS01NZeHCheTn52M0GunatWuZ2Qe7d+9mwB13YNPrCO96C8GNGyGq1XiKisjbvI3iDZu4tUMH5syejVar5cCBAwweOpTTBQUENG1CYN3agEDevv0U7diF6PRyk7ElEboY3JKLDGcqucJpbu7Qhnnz52E2mwHweDz07NGLAzsO0sLYsTSJ3zkuv5Ndtg0YY7Ss+WPNFa2XKcsyH330ER9+8BF+l0SwHI5a0OAQLFjlYqLjorhzyJ1ERkbSqFEjbrnllov6vZs7dy4j7rkXc7u2RN7ak/+dsifhxyt7kGUZ94EjFC78hd6ht1eYwEaWZTZaVtBvRB+++eabyz11hUKhUCjKSUtLo3OnLlCgorm5Iw6/lQ2WVcixYYTc0hlDzUR8gg9BkFGfySLv19+JCwhgxfLlxMbGsnv3brr27EnYgL7ItWriF4TS0eRzvLl5nJnwHeaEBCzHjxP7xKMEhoQQYKp8KVrK+C+4t1dvPvroo4s6H1mWia9ZE6F1izJBtyTJ5OXlIgkCqrPlyCybtlC04jeavvsmgkrFqYWL8ezaw4qlS2nRosXFXUjFDe1CY9GrGnRfC0rQXbWBgwax/ugRaj320Nm1JCVBoeSXKCgsRFKJFC1bgevwUZq8/nJpm7+yp5/i1NffsX71aho3rnjq7P/av38/3bt1x2wP46bAm8sE3oesezjsPYi+SQPMrVtQrUkTQMaacpzcteuR0k5ya+fOrFi/jhoPjv1zbc5fSD4/adNmEJhfyL49ey462dvV9thjjzF/yyZqPnz+NeJH33qP159+hieffPIa9Ozy5OTk8MSTT7Jy1SokrRZNcBB+hwPJaqNRg/p89sn40i+TgwcP8p9XXuGPDRvwCaDSaPG7XIQEBTJ65CheeumlMktFcnJymD59Ot9Pm0Z2djYutxu300V1dTxNAluXqe8JUOjNZ6djHbcPG8jESRNLX8/OzmbInUPZu2svIUQSqYkpWUrgyyVHPk10XCTzf5x/wb/LF+v06dPMnDmT9evW43A4cbtdnDpzmkKLBbUpANkvIblc1EpI4NWXX2bQoEHn3aff76d1qzac2H8Sh1mg+uNPIGo05dr5ZC9e2UvBjHmEZHppF1z5condxZuo0zGepcuXXtb5KhQKhUIBJUHptm3bWLZsWen9efXq1Xnzjbew5dtxez3I1SKIGnE3aNVI+FGpVYSGhKJSq/AWW0j96lv6tm/PjOnTkWWZZ599lokzpqPt0A5z2zaoz97vybKM63gq+Qt+Rms0En/XUA6P/5yQ23oT2KQx4f+TxOwcR+ZpTn7xNXOmTadPnz4XdX5ut5u4GjUIub0/YS3LBs5ej5eCwkJkQNBqcKWkkjdnHtUH9seyZy9yVg5fjB/PXXfddUnXVnHjutBY9G+XvfzvTpIkCgoKkCSJ0NDQK1KeqzJHjx5l3caNhA+5o0wwLYoioigiOZ248/LRJcZj27WHgt17CW9TfrRbPNtHr7d8wrPKNGrUiG+++4YH73+QDZYVVNPUJEwTgYQMMuhlFZ79B7AcS+HwDwuRZQlRkmnSsCHjvv+eJ555BlPrlhUG3CV9UhE3oB/HPxrP4sWLGTp06EVenavLbDbjt9nPW89S8njwOZ2lI7U3sry8PG7r14+UnGwiBg8kuEljRI3m7OyD4xxe8Tv9Bg1i0Y8/0rp1axo0aMDCBQs4duwYGzZswOl0EhUVRc+ePTGZymc9jYyM5LnnnuPpp58mKyuLDu07YvAH0Tio4hkYIZow6moas2jBIl559ZXSTN9RUVGsWPkrixYtYtLESSTvTUaWZRLqJvDkmLcYNmxYmdwFV1psbCwvvPACL7zwAm+88QaffPkFAS2akdixPYbo6JJa8OmnyFyzjvsefJCCgoIKS4P91YYNG0hNSaWRqRU7bJvIX/M74d17lfvdUgkqinbvwHXyJNGGlth8FowqU7k64FASoBuMN9bDKoVCoVD8PR08eJCHH3qYfXv3o5V0aNHjxoVXdJNUry5xreNYtHQp4d06IWkFVCowG80YjIbSWV+aoEDCut3C8sVLOXXqFNWrV+f//u//yM3NZda8eVg3bUFfpzaCWo3nVAa+vHwCEmpQ696RaAIDCUyqi2XDJgLq1ALKB92y30/W8hVUj4276GnlUDI4IEkSzoJCvF4vmr88/NZoNYSFhWG323C5XHjz8vA7XRQtW0H3W2/lia8fo3379pd8fRV/f0rQfY3k5+czbdo0pkyewpnTZwAICw9j1L2jGD169BWd5nrOpk2b8AHBjcsmlXDl5pH1+2ryd+1G9vsBAcnrIePnJWgCzQTVr1emve1kOhqVimrVql3U8QcMGECNGjWY8PUEFv+8mOOufSAImIPNPDbyUR544AFSU1M5fvw4KpWKRo0a0apVK9auXUteQT4JrVpWuX9deBi6xASW/PLLDRd0d+/ena8nTcJxMp2AhPhK2xXu2YsG4YISuF1v//3vf0nJyiLh0QfRhf0ZtAqCgLlObQIS4jkxaQoPPPQQO7dvL12fXKdOHerUqXPBx1GpVOzcuZPCvEIaBLSusm2sIYFj1v3MmzePF154ofR1vV7PsGHDGDZsWMnMDkm6qg+4KrJmzRo+/epLwvr2IbLDn2XvBEEgIL4GCffcTeYvyxj3n//QoUMHkpIqL6ty7NgxBEkgRl+Dm2Qvu7dsw19sIejmtuirVUcQBDy5ORTt3IZl+3Zkj4dkzw4EYSdGVQAJxjokGJPQiCU3CF7JQ5GQR4eOHa76dVAoFArFP9uhQ4fo26cv7jw/TfXtCNdGl1QTkWVyPVkcPbCXw0cOY4qJpnrLFgiiUPIwuIIxiZCmTcn5eSkrVqxg7NixZGVlcfhQSRUdXbVq+AuKkCU/mohwgrt1ITipLmpzyehi9YH92f/u/5E9fTbGYXdiqplY+nDamZXFmaUr4GQ6X8yafVH3BAcOHOD999/n16W/YrNZcGzeiqpRQzRaLSZTAHp9yTIutVpFUFAQZrOZ4qMpdO7YkenTpl30/bPin0kJuq+Bo0ePcsftg8lMO00kcTTQtUJAIC8rm/HvfcqUyVOY98O8K77Gw+12I6rViOo/kzPZT2Vw7NtJoNEQ1K0L+qS6iBo1zuOp2LfvImXSFKoP6Edkp5KbcVmSKNq8ldt69iQyMrLSY8myTE5ODk6nk9DQ0NLpFc2aNeO7id/x3vvvcerUKVQqFTVr1iwt+xQfH0/Lli3xer0EBQUhCALFxcVIsow2JPi856gOCiS/oOAyrtLV0blzZ+okJnJy2QoSH7ivdLbAX3ltNvLXrKPXrbeWq8d8o8nLy+PHRQsJ7nJLmYD7r0SNhpi+fTjx1Tf8/vvv9OzZ85KPd+rUKdSCBpO66iUjakFNAGbS09MrbXNuZse1NnHSJFTR0US0b1vh+4IgENu7F8f27mPatGm8++67le5LFMXSHP/xxtpoRS0Hj+wl+8g0BL0eQRDwOR1IPi86n44a1CdYDMePjxx/JoetyWS40mgXcis6lZ4U+0G0Rm3pNDdJkvD7/WWe2isUCoVCcSGefPxJXHk+bjZ3K324CyXfc5G6GEI0YazI+wkhRodKXXXCUJVOiybASHFxMQUFBQwaMIj0w5mYxUAEtZbYu88Nssj4ZT9OpxMEgaCgQNQmE3q9jghRRebEKajCQhFDQ5DtdjyZZ4iLjuarmbMqzDdVmY0bNzJsyHD8xTK1NI3RmLVsy9mAa+9BVE0bU1RYhMlsxvSXNeRFe/chncli3IcfKQG3opQSdF9lFouFOwcPIT+tiI6mXmXWpUbrq1FXaszOnHUMvXMo6zasu6Ij3rGxsUgeT8kU8vAw/C4XxydPRRUWStSoEYgGPZJfQkbGWC+JgEYNsa7bwKmfl2CIicZUqyanFvyMkF/Ao488UuExPB4Pc+fOZdL337P/4EFkQK1ScVuvXjxw//20a1cywhcWFkZY2J9TfZxOJ/Pnz2fS999z8PBhkGXMgYHcPXQo9evXRxQEvEXFqKIqD/QBfEUWQpOu/CyByyWKIl9/+SUD77iDE999T3Sfnhjja5Q8+ZUkLEeOkv3LciI1Wt6rIti6UaxevRq7y0W1VlU/GDJWr4YmKorly5dfVtCtVquRZOm80/MBJKQbLli0Wq2sXLWKoNvKTwH/K1GtwtTsJuYvWFBl0N2wYUMEFeR7sgnXRROjr0G0rjp5nmyKfQUUefJJd6VSk3rEEI8oCoCARtASIkRQTa7FPu8WthauJUIbzSkhhddeepUNGzYwecoUtu/YgSRJxMTEcM+IEYwcOVIpaaJQKBSK89qzZw87d+ykka5NmYD7rzSilnB1FGcKC/G6PWh0lVcO8bvdeO0OgoOD+eabbzhxNI225u6ccWWwb/8eHI1vwphYExBQCWqQwelwYNDryFr8C2adni0bN3Ls2LHShK8mk4kePXrQu3fvi7pfKC4uZtSIUaiKdbQJ7IhKUCPLEvHORNKW/oq/qJiAls2xARqNGtHjJW/TForWrmPUsGGXNIVd8c+lBN1X2bx580hPTaeDqXe5RFAAWlFLS3Mn1uUsY8qUKfznP/+5Ysfu3r07EaGh5G7aTLX+fSnYuRuvzUbcA2MQDXqQZQRRQPKUlDkQZAjq2hnnseOk/7iwJMlVfgFfjB9PmzZtyu3fZrNx94gRrN24EX29JCKG34naYMCVk8sv27axeOlS3nj1VR5//PEy2+Xl5TFk2DB2JCdjqFeXsMGDELUaHOkZfD1zJnq/H6NOT/72HcT1LZvkwu/z4/a4kWUZX1ExrhNp9H2mbAmmG0Xr1q1Z9NNPPPr44xz9ZhLq0BBEUwD+omJkq5VWzVvw3Tff3PCj3FDy8EhUa1AHVJ4N9BzBbKK4uPiyjteyZUtQyeR4ThOlq3hdP4DDb8MqF9GqVcXrvq+X4uJiJElCdwFrx3WhoRQVFlb5gKF169Y0aFif1OTDhGmjEAQBQRCI0EUTro1iXf5ywoUoqlELWZQIDQmhuLgYj9+NiIAOPTXlBuz3bMNtsPPCuOf5ffVq1m/ejL5mIubePRA1GvLTT/HWJ5/w1TffMHPaNGX9mUKhUCiqtHLlSlR+DZHGqgdAagc0JKNwBQWHDhPVtEml7Qp27UEnCHTt2pVePXoRRXWMKhOJxrrkFp8ha948gjp1JPCmZqgCAhARcZ8+Q+rPS1ClZ/DtV18RFRVFVFQUHTpc3hKqefPmUZBbSCfTbagENQ6HA7vNRrQ/AT8SmX9sxrJ5C6rIMHLVKlQWCyadjucef5yXX375vIMGin8XJei+yqZ8P5VwYjCqKg9WNKKWKKE606ZMY9y4cRe0zuTw4cNMnz6dVX+sxeV0ERsTw13DhnH77beXTt3W6/U8MGYM737yMcV1apO/fSf6unUQA834/f6SHUkystuNWlSB5Edyegm4qTEFP//CbbffzrPPPFNpQPPY44/zx47tVH9oLKa/rFs216lNeLubyVq5itfefJO4uDhuv/32ksNJEiPvuYfdKcdIePxhjLExpdsFN2qI1L0r6XPmY9u9F2HzNkKaN8MYG4Pf58diteJ2u5GRQZLIX/AzLpuNTZs2cdttt92Q2etbt27N1s2b+eOPP/jtt9+wWq2EhoYyYMAAmjVr9rf5QA4KCkLyefHabGgqSIJ2jizLSMUWgoODL+t4zZs3p2nzm0jZdogIbUyFicAAjtkOEBoeysCBAy/pODt37mTKlCn8umwFDoediIgIhgwbwsiRI6lRo8Yl999sNiMIAl6b9bxtvVYrASZTlb8LgiDw8isvM/LuUey37qChuUXpNbH4CinyFlCf5kiCH1NAABqtlvCIcNwuN06XE79fIpwoguQQBt7Rj63btrFxzx5qPHx/mb/dsNYt8d3Wi/QZcxg2YgSrVqy4qBKOCoVCofh3sVgsaEVdpd/T54RpI1EjkrdiJWFJdUqzkP+Vp6iYgtVrGdC7Nw6Hg9ycPFroGwAgCiKtgjpx0LqLtFXrKP5jHeqgICSfD29xETqNmjnz55WpFX65Zs+aQzjR6FUGrFYrdrsdFSp0op5aNCReqku28xTZaRkUkMOQoUOYMGECISEh59+54l/n2i90/BeRJImUY8cI01Q9RRogXBtFQX4BBedZnyxJEq+99hrtOnViwpzZZAQHUZgYz86iAh595hmat2rFzp07S9s/88wzDOjVm9PTZ5ckRIuOQpakkmDb40FyOOBsoqkAYwDhYeFEJNXFZDbz0osvVhpwHzlyhMVLlxLR77YyN+0AyCXTznWtmuOtFsewu+6iUYNGvP/++/z4449s2raN2GFDygTc54gaDTXuGoY2OgqzRsPJbyeRtW4juWdO4/Z6EHRa/PkF5C9YjOdkOmHtb2bGwoX0HzgQi8Vy3ut8PYiiSJcuXXj33Xf54osveOONN2jevPnfJuAGuPXWWzEZDORv24HX48Xj8eD3+cu1c6SfwpebR9++fS/reIIg8OZbb+IJcLDbuhG35Crzvk/ycdC6ixxVBq/999ULrh9/jiRJvPzyy/To1pOfpi0iID+MOHcdvGki49/7jDatbmbBggWX3P+goCDatmlD8Y7dZd+QS8p/+Xw+JElCliRsu5PpdwE3CX369OHTz8eTpzvNH9ZfOGjdTbojhWP2A/hlH0bMGI1GTKZzmfAFdHo9wcEhZ5d3hBOmieLA/oOs3bCB2OF3lv/bBdQGA/H3jMCuUvHll19e8jVQKBQKxT9faGgoLsmJJEtVtpOQMKh16IqtpH79HUXJ+88m8wXJ6yV/2w5OfPUN8SGhfPD++yWDLLKMWvhzIEolqGgc2IqeIQO5SdOMRGskdZzViFfFEx9X7YoG3ABZp89gUgXjcbux2+2oUaMWNAhnM8CpBQ1xYk3qijehRsPKX1eSn59/Rfug+OdQRrqvMkEQSkZmz0subV+Vd955h8++mUBY756Et2tbJkmau6CQjLnzuX3IEFYuW0ZSUhIajYbvJ0+mZ8+erFm/HsnuQLI7SrdRoUItavDLPqxWKyqVClGSEUUBbQU1u8+ZPXs2stFISNObyp6FLFNUVIzb5UJAIKhlK3IOHcWe5uXDtz/GJTvR1KiGqXbNSvctqlWEtG9L8fIVDOjZi7k//ICk1aCNiEByu/FbLOjCw6n9wH0E1q2D8/QZ9nw7mf/+97988sknVV4/xaXxer3UiI1j968r8UZFojk7bVqr1WIMCECv1+F3e8hasoy6NWvSuXPnyz5mhw4dmDZjGvePuZ8/in4hnGgMogm35CKfLES9wDtvvc2999570fv+4IMP+PqLCdRV30SCuU6Zvzuf1Iz9tu08dP9DhISEXFTClb8ae999rL9/LEX7DxDUsAFOhxOHw4HP5ytpIIBr7z68eXmMHj36gvY5cuRI2rRpw7Rp05g/dz5ZRWnIOhm1R01QYBBmfdWzPSTZz5ms06ijIjHXrTyjvEqnJejmVvywcAFvvvnmZc9cUCgUCsU/U79+/XjnrXc540onzpBQabvTrpOotSrmzZ7Nx+PHs2XOfLJ0WtQBAfisNlReL727d+eTjz8mKioKWZZRqUWKfQUEasqOHGtFHYnGP2dhbS/+gxrxld9XXiq9QY9X9uBwOBDOrSGvgA8voqACj8C0adN46623rnhfFH9/ykj3VSSKIg0bNSTXe+a8bbPdp4mOia6yfvCpU6f47KuvCOnejchOHcoE3AC60BAS7rsHq0rk/z788M99Z2ezb88+woVwPIeOoZbVaAUtOkGPRtAiIKAW1IiI2Gw2CvfuJSoiktq1a1fal6PHjqGpFluuD8XFFtwuF2pBi1bQE1A9EUGlIlpfjc7mvvh9AkJsDD6vr8rrYa5TG69fokvnzuj1esJbtSSkXl0iWjSnzoNjaTjuWQLPBg2G2BiCb+nA3B9/oKioqMr9Ki7eyZMn6dG9JykHUtFY/eRPn4trzwFEj4zX46MwP5+sHTtJnfAdhqJiJn77bWm5MICCggKmTJnCO++8w8cff8zGjRuR5Qt5EAU9e/ZkT/Ie3vm/t4lvHQs1XEQ0CeKZl59i5+4dPPzwwxd9Pnl5eXz+6RckiEkkGuuWe9ClFtU0MbfB4DLz9ltvX3Bf/1f//v25c+AgTs+aR+qSpRRl5yD5ZNSCFsHmxrJ6A7lLluF1uNiyZcsF77du3bq88847HDt+jKycLPbu24sp0ESBlFPldn7ZR5GQh1eWMNSpdd4HfIFJdXG63Bw/fvyC+6ZQKBSKf5c6derQuWtnjnn24/I7Kmzj9Ds47jlA957d6datG8t++YX1a9bw2tPP8MRdd/PWiy+yc9s2Zs+aRXR0NADR0dF07daVU57UKr+HHX4bBeQwbPhQfv31V+4eMYLW7drRrlMnnnrqKfbu3XvJ59alWxdypExcLheqKsYpc+RMDCojMaoEfpz/4yUfT/HPpox0X2X3jbmPx7Y/jtVXjFkdVGEbl99BDpm8MvblMsHK/5o8eTJOWSakQX3y8/MRRRG9Xo/+bMkgAJVeT0j7tixZupSsrCyio6OZM2cOkkemibkFG/PX4Dx0GHPDxv+z95IneM78XCw7dvP4U09VmeFRrVYjS2U/BH1eHy6nE7WgQUXJeciyDHLJOR6y7cWPD1mWyS8owGQyYTQYEFXln/2c+4Bdt24d6rBQ4ofcgVBF2aew1i05tnIVK1asuOFqdv+d+f1+7r7rbrKO59De1BNREEm2buf0kl8pXPk7apMZn9OOz2GjSaOGTF88h8aNS363nE4nr732GjPnzsHu8aINDsLvciO//z4N6iXxf++9f0FJTkJDQ3nkkUd4pJIM+hdr7ty5uO1uEs2V18UWBZFEQz1279rC3r17adq06UUfRxRFJnz9NRs3bODo8t9Qrd6ALjoaWZLwZmWh9os00TXFL/t59T+vUb16dfr3739Rx1CpVMTFxdGrT09WLVxLNTmh0ifxJ53HQSsREhKCpaLiqOVOQECGS37ooFAoFIp/h88//4zevfqw5cQqEjX1idMnoBbV+CQvma40Ur2Hia0VzfhPx5du07BhQxo2bFjlfh959BFW/TaYI7ZkkkxNyj0s9koe9to2ExEVwcTJk9l/+BCauDj08dWRfX4OL1nCtNmzuXPQIL74/POSBMEXYfTo0UydPI1sMqhGrQrb2GUrOWRS19gInagnrSDjoo6h+PdQgu6r7Pbbb+frr75m1771NA/oWC7wdvjt7LKtJy4hllGjRlW6n8WLF/Phhx8hNqyHTxYQPBI+/LhdbqyileDgYLRnSzAEN2lE7pJl7Nmzh169enH48GECpCDCtdFUV8dzaskvyD4/5kaNEf4S5HtOZ5G78EdiAgJ44IEHqjyvJo0bs2TV7/hdLlR6fcm5OB2AUBpwA9hTjiF5PBzwJ6Mym1CZQ3GlnURSiVhtNux2G0FBQejP7uMcy5GjGHTakvU8IcFVBtwAGpMJUacjLy+vynaKi7N69WoO7DtIK0NnDGez77cK6ojTbyfTdRKPzYVKUJMrniE0KJRGjRoBJTXih991F2u2bCGk6y3EtW6F2hSALMvYjqdy7PfV3DF0CDOnTqN79+7X9JySk5MxyyFoxaq/fCO0MUhO6ZKDboADBw6QcyaH1gEd8MhubGeKAYEgbXOq6RPQiCW/41ZLEeM/+ZR+/frh9XrJzMwsLeF1IevVx40bx9rVf7CjeD1NzW3RiX/+PcmyzClXKim+fTzwyP1kZGayYl/yefdpPXYcvVZDQkLCJZ27QqFQKP4d4uLi+HXFcsaNG8evy37lqHUvGlGLR/Kg0anofUdvPvjgg9JR7AvVuXNn3nr3TV59+VWKLPnE62oTrAkvWSrlPkWGLxVDqBaNUceh7CxqPPIgATWql24vSxKFu/cyb+HP+Hw+Jk+adFH5dJo0acKo0SOZ8Pk3yMhUo2bpg21Zlikkl6PyXkyaQGoa65HqOExAQOXJZhX/bkrQfZUZjUZ++PEH7hx8J5v2rSSUSCI0MQiI5HuzyeMM1ROr88OP8wkPD69wH8uXL2fMvWPAq0Kt1qEV/gwWZCS8kpfCwkJCQ0PRaDUIGg0yJcnM4M914oIg0CywLYJlC+mLl1D0x1oMSUkIKhXujAw8GaeR/R4ef+/dKqe5A9x999383ycfk7d5K1FdbgHA6/UhIsLZUTTZ7yd/5QpktUhQl86E3NwOV0YGp+fNwnMqg4C6dZDcboqKigkJEUqfQEpeL8VbtjGs/wCMRiN+m/2819nv9iB5PJiqyKytuHAWi4WioiKmTJlKgBRIiLbs76ZBFUDtgAal/x/kCmXPrq0cOnSIBg0a8NVXX7Fm8yaqjx2NKTGhtJ0gCJhr18KUmEDa9Fk88PDD7Nuz56r/3CRJwuVyYTAYkCSpNAlKVYSz/0hS1clhqjJr1iw0fh3VzImVZnYVBIEEfV1279zIo48+yopVqygoLARkTAEBDL9zCA888ECVyz0aNWrEnHmzuWfkPfyR+wsRxGBSB+OTvOTImXhVLu4ZM4p33nmHNWvW8Mtdd2E7kVbmZ/NXks9P0eat3Nm3X6WfSwqFQqFQnBMbG8uMGTNIT09nxYoVWCwWgoKC6NWrF9WqVbvk/T7yyCPUqlWLzz/7nC2btuJ3+0EAg9HAnSNux2QyMWH6NOIffxRdaEhJiqSzX/GCKBLaohmCSmTB3B8Zu3kzbdu2paCgAJ/PV3Lf/D+zOo8ePcq0adP4beXvOB0OomOiCYkIJjX3IJmkEixHICJipRAnDsK0EbQM7oRKUJMlpTOk/+DLuIqKfzIl6L4G4uLi+H3V7yxYsIDvJ3/Pgf0HkGWZWkm1eWHMUwwdOrTSclder5fnn30BsyeUII2aMxmny9TzFRDRClo8sgeLxUJAQAD5Bw7hcjh44/U32b59O5GRkdjEIrySB42opXlQO2p565PmOErBjhQkZMKFAII1DTmpOUrHjh3Pe06xsbE8OGYsX3z3HeqAAEJbtSjzvuTxkLN0Cd7CfILbtSO0QycA9PHx6GvEU/DTItQjhqOvUR2f5MBqtaHT6fC7XJycORejx8Pjjz9Oeno6E6dNxXEqA2P1yj+0C/fsQSsKdOvW7UJ/LIoKrF27lkmTJrFy+Ur8fgmb3UqMnIBL50SvL1/e45xgTRh+t0RGRgZ16tRh8tSpBDRrWmlQJ6hUxA3qT8r7H/PTTz9xzz33XPFzkWWZ9evXM3nyZFYsX4HP50ev11E9vjoWuRC/7Kt0KjZAoTcPVFCrVsVTyi7EsaPHMEnB5y2lokaDze1g+sIFhLRpTWy9JBAFbMdTmfjDfGbPn8+sadO45ZZbKt1Hhw4d2LFrB3PnzmX2rDlkZmRiMOi5o+tARo8eTYsWLRDO1j5t1awZO2fNpcbYezH8z8iD5PWSPmc+WrudRx999JLPXaFQKBT/PjVq1OD++++/ovvs2bMnPXv25NixY5w8eRKNRkOjRo1IT0+nW48eCA3qYfH7IDcXlUqFwWjEaDQgnp0lGXxTE3J/W824ceNwO92kHk9FBkwmE3eNGM6YMWOoVasW7777LuM/Ho/gVRFBLBrByImTGdh8JYM/YdooJMGPX/YSoY4h3lCbEE04giBw3H4Yr8pzScldFf8Ogvw3X7B37klacXHxDVmnGUpu/vPy8nC5XISGhmIwGEqyMlaxfvucZcuWcffQu7lJ25ZTzhOkSWlEjByOMT4BUVCjOjuy7JN9+GQPCAL5Py5CPnSCaup4CoRs/CovXq+XutqbyoxO/m8ftxWvpUazGP5Y/8cFTb/x+Xw899xzTJs9CzEsDHXdOvhVKqTcAqy7diM5XaAWiR46nICkeqX79LhsZP/4A97TZ9DVTMRQPwkkCVWRBee+fQTp9MycOpVOnTrh8/lo0bo1p9UiiWNGl0vcBuC12Tjx5Tfc1rYdM6ZPP2+/FeXJssy7777LJx9+gsFnIk6TSIDKzJ7iLRh9ZmqJjdDrdWezWJf/3bD7bGxwLGfh4gUYDAZ69etH9UcfJKCKByUAqZOn0jG2Gj/9eGUTj0iSxLhx45j83fcYJBMxqhroRD0Ov40M33EKnYU01reibkijSvexq3gDgXWNbN+xrfSL+2INvmMwu389QMvgyh9keSQPqwqX4I0LotaDYzCFhZU9F4+HtBmz0Z3JYt3qNSQmJl5SX/4qJyeHO4bcya69yagiwjHExqANCwVZxrZrDwZJZsrEifTs2fOyj6VQKBQKxZX2888/M/a+sRTYrESPGokxoeS70S/7kfCjUqsJDQ1BpVIh+SWOL/qZ4lV/UFNbiyhtNVSCmkJvHmf8J1GbVNzWrw8/zP2RWqqG1DTWK/Ow3OmzsTr/F7ySh/oBzUg0JaEVS5Z0uvwOjjsOkykf5+nnn+a11167LtdDcf1caCyqjHRfRW63mzlz5jB54mQOHTyELINWp6X/wH488MADtGjR4rz72LBhA36PxBbPOmSDDlE0ULB0Oaq7h6EOCsKHgEbQ4JO9CAg4tu3GeziFFgE3U91QE7/sJ81xlP3e7Rzy7UaLjurGmmWCakmWOGDdhVVbyEv/+fyC17uo1WrGjx/P0KFDmTJ1Kr8sW0bu6dMIKjWCWo06NBhJ8pH98wK04RGEdemGsWZN0GmIu+duhPQMcjdtpmjZr/idTqIiInn5iScZOXIksbGxpcf46vPPGTxsKCcmTyGmT6/SEW9ZkrAcPUbOkmVEanW88/bbl/BTUgBMnz6djz74mFpiQ2oG/vmAJNYQT5rtGKIs4nK5sVisFX6gnHGnYzAauOmmm9i2bRuSLKMNCT7vcdXBweSdpzb9pfi///s/Jk6YRH1tc6oHlP19ry03ZI13CYecuzGpA4k11yizrSzLHHccIk/M4o3nvrzkgBugWfNmrF3xBz7ZV6bW6F+dch7HqfYSfeftGIKCy70varXEj7iLlA8+ZvLkybz9dklGda/XW2VZv3OOHDlCeno6arWaxo0bEx4ezs6dO/G4PSDLuHJycebkIrndqASBHl278umnn1Y5nV2hUCgUiutl9+7dPHj/gxicgaiwIxVZ8eUXogkNQyOokJHw+DwUFhQSFh5GYWEhsqhCJ+hpFti+dD9RujjqyI3YbdnI1MlTSTTWr3BwyqA20SPidlbmLeCoN5l061FMQhAIMlapGHOwmf8+/1+eeOKJa3kZFH8zStB9lVgsFoYNHcbmDVsII4r62hZoBC0WdxGLZy1lwQ8L+L+P/++89XnXrVuHQ/QS2rUbQS1bI7ndnJ4zk9xpMzHe1ARD3dp4NBq8ufk4dyXjST1JPV0DqhtK6hX6vX7CvNFU99XhOAfYXryOI9Zk4rSJmPRm7H4rWfIp0EmM/+QTevfufVHnKQgCbdu2pXXr1kgPPcT0efMwtm5JcLNWiEEmfKKELysHy7oNZP00n7B+fdHXq405MBRtiwhCWzRDliSOvPkuL78wjscff7zcMTp06MCPc+fx6BNPkPbVN6gjIlAFBOArLES2WGjRtCkTv/2OGjVqVNBDxfn4fD4+/vBjIqRYapnrl3kvwVCH47aDZHGKWOJxOhyYTAGI4l8S8EluMnzHGTJyMKGhoQQGBiIIAt5iC5rzrNX2FRcTGhVzRc+nqKiILz77knhVXWoYyk8NFwWRW8L6sDL3J7bZ1xInxROnS0Ar6rH7LGT4TuBUWRn38gsMHz78svoyYsQIPv34U046UqgVUK/CNifcKRgaJ2EMC0NVwUwOKKmbbW7ZnElTpnDs6DHW/bEOn89HYGAgw+4axr333kudOmXrbi9fvpxPP/+c7Tt34ju7Lt2o09Gwfn127N6Nrm4dEh6+H1OtmsgyODIzyduwifVbtzJ37lxeeeWVyzp3hUKhUCiuhi8+/wKXxYNXAB9+cpb/giCKaMPDCWzWgsCbmqFRafH63NhsNrxeL/6sHAIEE263G5VKRK0uWcutElQkqW4iV87GK7lLj+GTfZx2naTQk4eERIDKRL2AJqRykKeeeZLCwkJEUaRRo0YMHjwYs9l8vS6H4m9CCbqvkgceeIBt63fQytiFEM2f00UjdDHUlOtx0LaL5555nmrVqlWavTktLY09+/YR2K0zIW3bAwKiVkvcyHso2roFa/JebFu3I3k9CH6ZYH8oDY2tSTCXjFA57HYsVisCAnFCTU7LaZi1wTj9Ng65d6HyqYiOjuG+u+/h3nvvpV69ioOCCzF79mx+WLSIhHtHIVeLxevxIMl+BAn0NRPQJcST/9NC8pYuJanRs6WZ1gHceflIThfx8fGV7r9Dhw7s2r6dVatW8dtvv2Gz2QgLC2PQoEE0b978orJRKspas2YNp05m0NrYtdx7AWozNQPqk2o/hCALhBGN0+kszc5p81nYa99CULSZZ599FoCWLVsSExlJ/vYdGOMqL4HlLbbgOnacvveNvaLn88MPP+CwOkg0VV4STC1qaBPSjc22lZgT9RzK2InslxFVKnr06879D9xP586dL7sv8fHxjH1wLN98+S0ap4bq+rKj7h6/m2J/ISE1bq4ymZwsA5ER5OblsW7pRuI0ddCKWmz5FiZ+PplJ303m8y8/Ky2X98UXX/DaW2+hSYgn8u5hBNSohuTzUbh3HxtWr0XyeqnevSum+JIHVYIApurVMA0fQk5cLB9//hnt27enS5cul30NFAqFQqG4UrKyspgzdy4+USSgQW0iG/dCDDIje73Yd+0hb9VvOFJTiR5UUmrWYXfgs9hwHjxCtLsWhZ5CoGQmZUBAAAaDAb9PIoYETrmP4ZZcnHGlc8C5F4/oQxsdhaBW4805Ck43kuyjTp06l/1QXvHvowTdV8GePXv47dffaKBrVSbgPkcQBBqYmmO1FPPJx59UGnTPnDkT0WTC3LI5fvyozv64VAYjYZ27EtqhE668bAq3b8Gz4wD1aU6suWTU0O12YbFaUaFCLWhAgDApGq/golfUnRR7C9lhX0fjJo147733zhu0ut1uVq9eTVZWFgaDgXbt2pWOLMuyzLeTJqGvn0ToTY2QZRmn04XDbsfj9SJ5vQgaDSF9e+M6fBR78n7MXTuX7jt3wyaiIyLo1atXlX1QqVT06NGDHj16VNlOcXFSUlJQoSJYU3HG+gbmZshIpNoPclI+QqQjjgDJhE0qpog8qidUZ+68OaWlpbRaLaNHjeK9zz7F0aJ5hQnwZEkic8lSggMCuPPOO6/o+Rw8eBCzEIxOpa+yXZg2AoM6gLEPjGXIkCHY7XZCQ0PPrlu/fHl5eWzfvp2WLVvSo88Jfvv1N9IsR4hUVUMjaLD6isiRT4MgY9Dr0Wg1le7LarXgdjpRoaK1uTN61Z9lxJLkJuy3buexhx8jPLwkocvrb71FYOdORPfoVuZvO6Blc6JqJpC/4GdSv59Gw5eeLy35d05Ex/ZYdu9h4qRJStCtUCgUihvKa6+9hlclEjVkGKZadZDw45HcCFothjq1cR5LIW/2PPJW/07IrV1xO2wULFyMxqsmVkhAI2iQZBm/z0dxcTFerxdZhiBCOSlLHLEmk+pLIaBlcyJvbocmJAQAye3Guj+ZvFUr+eTT8dxxxx0XtMRLoThHCbqvglmzZqH26Yg2Vp5EShAEEnR12LZlGwcPHqRBg/JrSNauW0dAg3oYTCZcLheCIJ4tyXV2H2o12uhozDe3JnfPAXxiSRsAu82OiFgScJ+lQo1bLplmGqQJoZGhJZs2bGb79u20bt26wn56vV7Gjx/PpG8nkZOTC5KALEto9Rp69O7Bf//7X2RZ5sChQ0SPuqv03IxGA0ajgaKiIpwuN6JajcZkwtCgHoW79xJ9NujO37EL27YdvPjaa8qH13UiiiKyLJfJil/mfUGkcWArEox12FC4Ek+4jbBoE3XjGzP8ruH079+/tNzbOY8//jir165l68QphPW8ldAWzUqDO3v6KbJ/W42UeoKJEydenQSIFzjx4VzpsKioqCt26PT0dD744AMW/LgQp91Zcl1FgejYaKKioziTcZpCt5vo6CgeHvEqvyxbSvLRFOhScWZyv9+Pw+HAnXICo2hCK5YNkkVBpLG5NVuLV/PBex9gDglEFRdLdI9ugIDL5cLpdOL3+fH6fIgmE+FDBnN6/OcU7NpDRLuby14TQSC4dStW/rIci8VywyaoVCgUCsW/S3FxMYuWLCGwU3uMZyuLiKjQCFq8Xg9+vx99YjyBt3TAsnY9slaNfe8eKLDRXGqPRii5zxQFAREtfrnk+1Wr1SALErIskeo5hvmWjoR1LvvQWdTpMLdojhAZwuGfFjJlyhQefPDBa30JFH9jStB9FRw5fIRAOeS8ZYLCtFH4LH5SUlIqDLqdbheiOYDAoCB8fj9erxsVqrNljgRkZCTZh3A2WNUbSgIfn8+Hx+tFQ9mRMxvFBKj+nMIaqY1F5zIwc+bMCoNur9fL6HtHs2zxcmJIoL2xOSZ1ID7Jx2n3SVb9vJYtm3rx9rtvI8kyugpqewcFBSFJRXgcDiS1BlVQEPYjRznyxQRsJ9KQ3W6qx8URGBiI3W4nICDgvNdXcWU1atQIWZTI9+YQrq08+NQIOlRakf++8fp5y4EYjUZ+nD+f5194gZ8WLSTv15VoQ0ORPB58hUUk1qjB/02fflVmLSQlJWGVinFLLnRi5aPdRd4CfHhJSqp8GvrFOnr0KAP6DaAgs4hq6lpUMyWgFrRYfIWcPH2M5NPJjLx3BJ9+9mlpgrbq1asz+sEHsZ/KqDDbu9PhxFdsxZl8gAbaehV+rpTU+k5i+7bNyBqRiCF34Pf7KSwswu/zISCWPGCQZRAExEAz+rp1yN++s1zQDaALD0OSJAoLC5WgW6FQKBQ3hIULF+Ly+wludhOS7C/9PlQJagQEfJIPyenCUL8+Rb+vpnjVakRJxEQgZjG43P5UggpJ9pd8X5JbkvXcZCakfYcKj++XfQQkxCM3bsSk77/n/vvvv6xkq4p/F+U35SoQRRGZ81dik5FBoNI/2OqxcXiyshFFgbDQUAJMAciijEd245FdeGU3qEDKy0eW/RhUJQGr3+8r6YfwZ1Imq1yElSLi/5JYShAEzISQciylwuN/9dVXLF28jJt0bWkU2AKTuuTmWy2qqWGoRTtzd9y5ft588y0EwGuxltuHIAiEhIQQGBiIyu/Dtns33uJi7FnZBLZuSWTvntijI3n8uedo2aYNe/fuPe91U1xZ7dq1I6l+EqmOQ1RVQfCE4whGs/GCp4ObzWa+mTCBvTt38c7L/+H+/gN44u4R/Dh7Nju3bbtqywSGDBmCwaQnzXGsynapjkNUj69O167l17JfCr/fz8i7R1KcaaetuTu1AxqgVxlRi2pCtRE0C2xHQ01LZkydydSpU0u369evH62bNydj6gzsaSfL7deelU3enPkYvVoSjHUrPX6kLhavx4fX70cXHU1BQSGSz49G0KEVdCVP+AWBko8dAU10FK5KMsd7bTYEqHKduUKhUCgU11J6ejra0BBM4eH48Ze51xZRoRV06EQ9BlMw2uBQkCQaB7bAJTgoIq/CfapR4/F5yBEykNQCxkYNEDXll3v5ZV9JQrWAAEJbtSDlxAlSUiq+f1YoKqKMdF8FNzW9iY2rNuOX/aiEymtxZ7sz0WjUNGzYsML3hw8bxrIxY3BmZWGIjsZsNmMymfB6vEiyhCiKaDRaUhYtQQCy3acJ1ITw59xaGRBwyy4Oy7sx/z979xkYRbU2cPw/s303vRNKgNAJvTeliHSQ3rsNu9fert4rei1XvSh2ehOR3nvvvUNIh5Bed7N9d+b9EI3EJIC+NHV+fnLn7JkzQ7LZZ845z6P2I0xXudQ5JNlbksHxWm63mxnfzSCcqoTpIssdn0bUEmNsycErWwmODCPv2HF8a5fNFi0IxTOfWavX4i00EzF0MJEd26O+5kPNmZdP6qIfGThkCFs3baJmzZoV3rc7TZIkrly5gsPhICwsjMCf9/f8VQiCwFtvv8XY0eM4azlKbVMMaY4ULruScMgOREFEJ2mxioW8/cI/f/ee58jISJ566qnbM/hyBAUF8diUR/nfx9PwsftS2VC91HFZlomzniNXlc7nr36OWn1rPga3bdvGpYtxtDZ2qXCGvbKhOlmuq3z91TdMmDABURTRarUsXrSIUWPGcOjr79FGVcNUrw6CIGBNTKLgzDlEp0T7wH5oRV25/f6WtbAAOSgYraArWUIPoEKF1+0GrRbZ6UIWRXJzc1GpVOj1enQ6PYIABUdP0LJFC4KDy+akUCgUCoXiblCpVMgeDyaTCYfdgVtyoRG0pf7OCYjIePF63GgELbWMDcl2ZnDBdZyGtMJfKL0q04OHi/IJ9EFaPE4vmHS4ZdfP398FZCS8shf554DbYDSAjw+yLGOxlJ1sUigqosx03wbjxo1D1ni5Yk+ssI0kS6Q4L9H1ga7UqFGj3Da9e/cmOiqKqz/8hMdqBYoDJK1Oi16vR6vVkrNvP97EZIYPHUIyFzlUuJ0cbzpO2YZZKiBJushxeTeoZNoEdim1NNUreygQcmjRsnmZcx88eJCrqWlUN9Quc+xa/pogfAggPCQE2+mz5c7UAdjSM8jef5DAHt2pfF/HUgE3gC4okKjJ4ymQJT799NPrnvNOsVqtfPXVV7Rv15J2bZvRpXNbYhrWZvz4sezdu/duD++W6t27N9O++B/pYgrr8pZw0n0CR8MI1F1aIbdtQJ6/C4+6ODmY1+u928O9oTfffJMxE0dz3nOU/YVbSLZdIt1xhXjrefaYN3BZvMQb/3yDMWPG3LJzLlmyBJPsR0A5yROvFWWoQ0JcPEePHi15LSQkhHVr1rBg9mw6Va+BePgYHDxCM/9AHurVC5PGiOGa5GnlyXFlotFqCA4MJP/kKVSoSn0RgeKldEgSXqcT27nz6KpUwe1047S7KMgvIDsri7Qjx8g/c5Zz58/To1cvZs+erXyxUCgUCsVd17JlSzyFhTjTMwgMCkRQUbLy0yO78chuXLIDa0YqUmEh/pogvF4PDbTNMYo+nJL2cUraz1UpiXQphUvSKQ7JWykQcnj19Vdp0qQJos0OKnDLrpJ+NVo1AYEB+PoVlwVz5ucjCoLyYFrxuyhB920QHR3N6HGjueQ+RZojpcySXY/k5oRlH5KPm5dfebnCfjQaDYsWLCBYkkn433Qyd+zCmZuHp8iKOfYSSbPnkbt2A889+STz589n3sK5NOhUhwvycU6q9nFM3kW6kEJVUw3uC+6JSV26hmCKPQG0EuPGjStz7uzsbCSvhEl9g/2csowBEwH+AdzXti2XZ84le98BvM5fax06snNInDkHldFIpU4dUKnKn/1XGwz4t23N0pUryM3Nvf55b7O8vDweGtCXjz54jdaNM5g3PZKVc6vx/htBXE3ZyLCh/fnqq6/u6hhvtWbNmqH3M+HToC6Rz04h8KG++HVsS6W+PYl56zUihwzk61kzmTp16t0e6g2pVCo+//xzFi9dTIc+bUgUz3FePkKaLoH+o/qwfuO6khJnt8rV1KsYuXGdTl+1P16PREZGRqnXNRoN/fr1Y/myZSTGxZEUH8+mDRv48MMPEbQyqfbkCvuUZZlE2wWialTD38eHouMncRXk4pHdpZbfCYiIqLAePYYnNw9jvXqAgFbQgtNNweEjXF24GHVgAELLZpwssvDsKy/TvFUrjhw58kdvjUKhUCgU/2/dunUjqkpVMrftQK1WExoSip+/H6JGhSR6kUUJjU6D68QpAv39MbsLyMzJxGV1U09qTi2hEV7ZS4J8jjjOYFblEaILJzAwgAYNGhAeEkLR8ZNI584TYDAQHBxMaGgoQcFB6K+p9JF38AjNmjS5bqlbheK3BPl6mzj/BMxmM/7+/hQWFt5TCX/cbjdPPvEkS5csxSD5EK6qikbQYPYUkiWnYvDTMXP2THr06HHDvlJSUvjkk0/4acVyrHYHACpRoFGDhjz1xBMMHTq0VNbphIQE9u7dy0svvITe7ktzvw6llrvKskyqI5EL7hM8MuVhPvroozLn3LRpE8MHj+A+n94le8V/4fF4sNlsOOzFmZkvyMcxVFPz5ZdfsnbdOpYuX45bpUIbForsduPMyMTjcmFq2Zzo0SOue63O3FwSP/qM1cuWcf/95Wdzvt1kWWbI4IeIi93ND9/WoH4dY5nj//3yKtO+L+T7GQvo06fPXRnnrTZ23DjWHTlMrWeeQKwgk3zmjl1Ytm7nxJGjVK1a9Q6P8I9zOp1YrVZ8fX3RlLNX61YYPHgIJzaeoVXA9X5uZQpsBewqWkutOtGEhobRvkM7xo8fT506Fe/XfuKJJ1g870ea6tsTqqtUukdZ5pT5EEnuS2iNBsTAAGy5uYg+PgT164M2MhKNqEEtqPG6XFhOnyR382ZwuECvQ1s5AkFU40xPQ/Z68WnciNC+vQgJDwPAVVDIlR+WYMjLZ9O6ddSvX/9W3C6FQqFQKH631atXM+HhhzG0bE5kn56lyl56nU7S1m2k6OBhNAgU5pmJEupQU/w1WbEke3HLbkRRwC/Ijz2WDeiD9dicTmSdDofHg9dmQ9RoCG7ZnKoP9Sv1nSj/5GkyFi/hu+lfMnz48Dt67Yp7083GokrQfRvJssyuXbuYNWsWO7fvwul0EB4ezuixoxk7diyRkeXvlYbioD0vLw+NRkNgYCCCIJCXl8fJkydxuVxERkbSqFGj69bX3rt3L+PGjCM/p5BQKuGrDsAtuciSruJSOxgzbjSffPJJuUFIYWEhDevHEGqtSh2fmJLX7TYbZrMZABE1btnJEXk7Bq0RQS8zcsxIXn75ZZYvX16c8EKrpWXLlkybPp0kHwNVBz103XvmLjQT9/5HLF+8mAceeOAGd/j2OHr0KP37dWf255Xpfn9AuW1kWWb0lHgKrI3YuGnrTfUrSdI9m+UyNTWVpi1bENCvDyFtyy8fB+B1uoh7/0NeefIpXnvttTs4wtvDarVy7NgxbDYboaGhNGvW7A//G33++ee8/fq/6OzbD61Y9qFFcTbxfC6740iWY6lpqgsI5AtZeNUeJj8yiffff7/cPeYOh4OJEyayYd1GAgimkiYKraCl0FPAZXccZo8FfWQEUSOGYqhenYzERPKWr8aVlYUmLBRNpQgEr4wjNg7ZYiPSU5Uo6nBC3oNX5cUh2zHWq0dYv/4IviY8sovgkBA0muKxeJ1OEqZ9yYAOHZk9a9Yfuj8KhUKhUNwKCxcu5B8vv4wTMDSsjzYgAFdhIfaz59DKEOznjznVRogYQWzRGapSi6pCLdTCL39fZQqkPGKFE9g0Nvzq1yP8wW741orG6XKRm5aO7ew5zHv341OtGrUfm4y70Ez2/gOY9x9izLBhTP/ii3v2O53izrrZWFRJpHYbCYJA586d6dy5M0CFdZCvFR8fz6xZs1i0YBEWSxGCADWjo3n4kcmMGjWKrl27IssyBw4cYMrjUzh75hwI0KhxDOPHj6dly5Z4vV60Wi0dO3bk6PGjLF68mIULFpGeloZer2dgl/5MnDiRVq1aVTgef39/hg4fwoLvF1HVWxODyojT6aDQbEaFCrWgQZZlEohHLWroHNiXbFc6i+b9QEBAAO+9916p/tatX8/FY0fLPde1rCmXUYviXV2ys2DBAqpXFenWyb/CNoIgMGlkKBOeOca5c+cqTIZ39uxZZs2axYo1a7CYzRhNJvr37s3EiRNp0aLF7bqE3+3kyZO4PF78Y8qWrruWSqdFF12TIzfxb3kvy83N5dNPP2XB4sUUmAuR5eK6nbVr1uTxRx9l4sSJv+uPaXx8PMnJydgcRZy0H6Smqj56gx6DwYhKpUKSJPLy8nB47GTIV6hurENT/3bAz/kd7HF8//UMvF4vn3zySZn+9Xo98xfMZ/Xq1cyaOYv9+w7gcNrxuD3FnysBvgQMH4I3JARJljCGhKAeNwZvylUKT53EfvES3oICgjwh1BE64SP6USSbceEkQluZDF0+EUOG/fw0X8YDuFzOkqBbpdMR2KEdazesJz09nUqVKpUZo0KhUCgUd8Lo0aPp0qULCxYsYMXq1eSnXSQyIICHnnmWRo0aMWbkWJro2xOqjUAURGKLTpMmJxEkh6NGgw0L+XI2HlEisHEToidPRFQXb33U6/WEVI7E4ueHtnIk2fN/4Oxr/0StUhESFMTbr77Kc889pwTcit9Nmem+h2zevJmJ4yfhLvJQSYgiQBOMhJcMZyo5pBPTtCGzZs/i5ZdeZteO3ei8RgKFEGRZJsNzBatsRVSrissZBAUxZuRIxo8fT/Xq1cs9n81mY+XKlSxcsJDkpGR0Oh0dOnVgwoQJNG/enKysLHo82JOMhCzq65shmjVIXhmNoMUh20iWY8niKs0C2lHt51Jk8dbzXNXEc+rsKcLDf635vGXLFoaMGkW1xx/GVL38gFqWZRK/nUnLsHA2rFt3y+/vzRrQvw9R4Sf433s1kKSfy7oJAvwmKZXZ4qFBx/N88+0C+vfvX6af6dOn8/a//43s64NP86ZoAwNxF5qxnDgJBYW88OyzvP766zd8EHMnrFq1ijGTJ1HvX2+VWqpVnuRFP9LS5Mva1avv0OhurYyMDPoNGEBcWhp+bVoS1LwZKqMRZ3Y2OQcOYT9zjhGDB/PVl19WmH/gWtOnT+edf/4LwSWi9mjJdWZRWahJZaqjFjX4+/njcrvJsqaRyAVklUSn4J4Yf7NtI8UWR5x8hl17dxITE1PB2Yq3m/TvN4DUxFTCqEyyOwlT144EdOiAFw+yIGM0GLHabMUFDJCRHE7Sv/gKLHYM+FBJqEaOnI6gAb3ahDUmnPABA0vO4ZQd+PiaSpUMc1ssxE39kB/nz6dXr16/+74rFAqFQnG7vfvuu0z/6Cvu9+1b8v3K7rWRYosj25WOV/aiVxnRiXoSpARqvfwPAqtWKbcvl8tF6orVCOfOM/1/0+jXr1+pvd0KBSgz3X86Z86cYfzYCZis/rTza1+q1FikPgqLp5AjJ3bRrk07cIk01rclzBSJV/ZyxLwbq+BCW70G+gZ10BhN2C0WPpsxg29nzOD7b74ps+/4/PnzDB82givJVwgiDF9VIC7Zw0/xy1k4fxEjRg7nf9P+x7r1a5k0cTIH9+9FcoMP/ngFD2by0Ygamvu1o+o1tb+jDLVILrrIokWLeP7550te79q1K00aNuTs4p+IenQSuqDSJRtkWSZ90xY8KZd59t27m6jL6/VitbnIyspGkiSgODGX0WjEYDCUPN10u4ufV5W3HHjRokW8+a9/4de5E5W6d0O4JniLeKALWbv38tH//kdQUBBTpky5A1d1fdWqVUMtilgvX8GvTsUZ62VJwpV6leief86gS5ZlJk6eTHxONjWenoIu+NefQ42vDz41a5B/+gyLf1hCo5iYG5Y6mzNnDv98422qyrWo7ReDiEiC7QIXLCfJlK/g7w1GzFdhE8xYZQv+mkBaB3YrE3ADVDVEk2i+wJw5c/jvf/9b7vncbjcjR4wkMzGb9j49sHrMJHgT8a0fg0pQo0KFW3YXB9zICIioUCPq9Zjq1Uc8loSv5E+SfAGVoKJLQD9OFR1GUP/681mceE1GFEs/cBDUauSfx6BQKBQKxb3IbDajE/SlJjQMKiP1fJtQjyYlrx3I346+Zg001ymDqtVqiejYnpQTp6hUqZIScCv+X5S1EfeI6dOng1WgmV+Hcmt7+6r9iVBVpTDfTIy6NeG6ygiCwHHzfrLUeYSPHkXlsRMIbNEGXb06BHfvRp3XX0KKrsnkRx8tlXn48uXLDBwwkPxkMx1NPWnlfz/1fBpTz6cJdbSNCHKHM3f2PB599FEqV67Mxk0beO6FZ3GrHegMOnwNfjT1b8uDoYNLBdxQXLvbVw7gwoULpV5XqVQsWrCAmoFBJE37ktSVa7Amp2BPSyf36DESpn9D0a69TH37bXr27Hl7bvJNOH78OAcOHWXrbjN2r4Bo0CMa9HhFAbPFQm5uLh6vB4DNOwsQBC2NGjUq1Yfb7ea9Dz5A3ziGyJ4Plgq4AQRRJLzzffi0aclHn36KzWa7Y9dXkaZNm9KoQUNy9u6/bjvLpXikvHxGjx59h0Z2ax0/fpyDRw4TMbB/qYD7WoGNG2Fq0Zyvv/vuugGm3W7n3X9PJcxbhXq+TVAJKgRBoJapAQ+GDqKeXxPUehV5YgZmOZ/G/q3pHNIXnwoqAoiCSIhQiT27Ki5Ht2nTJs6fvUBTYzuMKhNe2QMC16xOEIr3rMkyoqxCK+oQBRUyMqJOjywIVBfqUVtoDECR14xJNOFKvVpSZcErexAEAb2+dE1w25VU1KLwp0qgp1AoFIq/l4CAAByyHUmWrtvOiQt1gP8Nl4lrgwKRZJn8/PxbOUzF35ASdN8D8vLyWLViNVXU0aXqaP9WtjOdYMLRuQwAFLjzSPOmEtS3D8YaNQEQUSEgYrPZELVaqo0chjsokE+uqX09bdo08jMKaeV7Pya1L5IscdFyis1ZyzlcsJur9hQ8Tg8L5i2kf7/+JCcnU716dfRaI60D7qdlQCeijLWweS2cMR9hf95WDuRt47zlOFaPBWShTJk0gCpVqrBpwwZefuJJjPGJXPlmBimff0XeslV0qVuXZT/+yJNPPnmL7+7NS0tLY9jIkUhhYeRZNazcZEfQaBA0GlQGA2ofE15ZJj8vH7vDw8yFuXR7oGeZIGTLli2kpqUR3vm+654v7P5O5Bbks2rVqtt5WTdFEASee+YZXJfiydi+s9x/P0dmFulLl9O+TRtatWp1F0b5//fjjz8i+PvjV7fiTOEAoe3bkpqexq5duypss3r1anKzcqllKrsPXqfSU9vUkPZBD9DItxUiIoFiyHV/vwHUggbnNeX2fmvhgoX4EYi/Jujn8xgQJBlXTnZJG69cXEddhRqB4mBeJYp4cnIwoEdAoLJYA3+CSbLGUs1QC09WDvaUZCQkvHhKrej4Re6+AzRq0JCmTZte9xoUCoVCobhb+vXrh1flJsuZdt12Hq8Tr6UI3W8eMP+Wu7AQUSjOdaRQ/H8oy8vvAUlJSTgdTkL04RW28cgeCt15RAsxuD3FM60ptjjEAD986pYu4aNChdvtKk4OpVYR1L4tW1asJiUlhcDAQJYs/olIsQZaUYckSxwu2EW2I41KVKeSEIUBE17BS7qczN5NB3jwgQf517v/QlRBnjsbf3Ugxwv3k+64ghYdfgQhI5PkjCOu6DyoZGrVqlVqTF6vl507d7J9+3ZsNhujR4ygWbNmVKlShUqVKt0TiZlmzZpFjrWIWq+8SNr6Dbz/6V5MRpF+vQMQRQFEEZXRQE6amWfeiCPpio7Ppr9Upp9z586h8vPDEFl8TbIsI0kSgiCUCmR0QUHowsI4d+7cHbvG6xk0aBCJiYm899GHWC/EEti2FfpKlfDa7eSfOIX15Gka1anD3Nmz74l96H9EWloaqrAwhBs82dZXikD6uX1FTp48iY/oh0l9/drcYbpIZCDfmUOQIfS6bS1SATFR9So8HncpHj/h1xn6AHUwvoIv5pPHMURVB4qDbpHi1RUyxZkIXDk5OFMu09i3LWqbGrfHSRiViXOdpqnQDj/Bn6zVKwkZMxxTeDi+fqWvKXPHLpyxcTz7zTd/2n97hUKhUPz1NW7cmDZt23Bi72mCtKFoxbJBtd1rxY0Dd1IyXksRqoCKA+rcI8cICQqmdeuKK7soFDdDCbrvAb8EYsV7Kcv3yzKZX75MAxTKBeija5ZZvvxrvq/ir9x+9eqQKUnExsYSEBBAkaWIGH3x7Gyc9SzZjjQaCq0JFH4NCNSoqUQUYVQmPvMUn336P2rXrU3C+Qu4vS7yXbnUFZoSSmTJ7J1XLg7UEzznOHXqVEmJrH379vHMc88Rn5yMOigQldGIOz8f4Us7nTp04Osvv/z/3cBbwO12M2fBAnyaN0NtNFDloQFccbt58e3DfD0rj349fDGZROITHKxaV4jTqWLNmhU0adKkTF+SJCGIAm63u7ieucNRMnOsVqtL9oYLggBC+asC7pYXX3yRJk2a8PU337Br6Uq8sozX60WjUqH2ysRfiKV7t+6MGDWCcePGXbfs3b1Ir9cju1w3bCe53CDL6HQVPwH3eDwIN7FYyKT2Ra1Sc9WTTE25XoVBq9VjIZ9sxox5t8K+NBoNNtle8v+CIBCtr8uJM8ew1K6Db4OY4qXk14zL63CQvXYNRklPpCEKQQ9FRUUU2fR4JA87LWsQRRGhyEP+vIXIrVuibd4MlV6HPT2T/IOH8Vy+zGsvvsjgwYNveL0KhUKhUNxNX379Jb169OJA2laitQ2opK+GSlDhkT2k2ZNJcJ+nep3q5FsKSVuznqjRw8t9GG9PS6foyDEee3yKsp9b8f+mBN33gOjoaIwmI1n29JJlo7+lFtSoRQ0WqYBKmuKAuaJYTZaLg92SL/c/B8WyLBfvUZVBJaiQZIlk6yUiqFYq4P7teRsaW3Hk0g6eef5pPjn3CS6nh2ZiB/yF4FJtBSBUqIxBZ2Lj2o3s2LEDnU7HkBHD8VaqRLXHH8YYVQ1BEJA8XgrPn2fPuo307tuXTRs2lMp2fqfl5uaSl5dHeJ3iGXpBgIiePbA1aEDaqVN8sSgeye1A7eePULMmuuQUOnXqVG5ftWvXxp6TS2Z8AurgIAStFlGlAlnG4/ZgNpux2+34qNQ4s7LLrAq427p370737t25fPkyr732GmtXr0PrNBChropW1mFJMvPx1E/4YtoXfDfjuzJJ+u5l7dq1Y8nq1bgLzWj8K84wWXDqNFqVmrZt21bYpkaNGlglMy7JVW5d7l9YPRbUGhVWsZBY6xnqmhqVCbxdkpOT1gNUrV6VAQMGVNhXm3atWXx+CZIslTzsijLUJt+dS/KKldiTEjE0jUEMi0ByuSiKvYj50CHEHAtt/LoVv0cAXz8/rNpCTE4j//noP9SuXZvGjRszZ84cZs+bR+rhY8iAShDo2L49T0x9767mWlAoFAqF4mbVqFGDjZs38sILL7Jz+04uWk6iFbU4vU5UWoGeA3vy3//+l6NHjzJh8mSSZs8n4sFuGH/OYu51usg/cZKcDZtpXr9BqcTACsUfpZQMu0c899xz/DBjCR19e6AWNeW2OZS/kyx7Gg8EP4RBZ+RE4QFS/QqoMuWJkid0MjIu2YHJx4Svb/ES0bzjJ8n5aTnHDh1CpVLRvElz6grN0Io6DuRtp7lwHz5C2Xvnlp1odVoCAgPZW7iJfmN6sXPHLvIvWagvtkCFCgEBGZDwICGj1+vx9/djv3kL9/XvwKmzZ8ky6KgxaUJJDcRruQoKSZr+NcN79OSbb775f9/Hy5cv8+OPP5KYmIharaZp06YMHTr0hj8bGRkZNGjcmOAhD+HMziHn4GHcFgtQnKQquFULwjp2QBcSTPbe/di2bCftypVyZy23bt1Kz7598WnflpCH+hVH8NeQvV68NhvWfQfRnj3Prm3bCAwMJCgo6J5auvvWW28x/bMvqadtRlV9zVJj80huTlsOYzHksnzVctq3b38XR3rzzGYzDRo3Rm5Yj6qDHiq3jdfhIGH6N/Rs3oJFCxdW2FdWVhZNYpoQ6Youd1/3L85ZjmEPLuTJp5/gvX+/h48nkGq6WgRqgotXhzguk+pNJCDCjxWrVlRY8x3gxIkTPNDlAeqJzaliqFHyuizLJNpiSXDGYpEtyAKIiKgkgUrqSOr7NMVXXXr53DHzHiIaBbNn355S/7Zut5vY2FgcDgfh4eFK4jSFQqFQ/GnFxcWxefNmzGYzgYGB9O7dm2rVqpUc37JlCy++8grJV66gDgpE1Olw5+Wh9ngY0Lcfn3366Z86vlDcfjcbiypB9z0iPj6eB7p2R85T0dy3Q5k9KHmubI5ad+OU7ERqqtPMtz2Fnjx2mTcTMmwoPnXrATJu2YUsyISEhKBSq5AliYQvv+H+WnVYtnQpAIMHD+HIpmNU1tTgROFBOgq9yyR4kpFwyS4CAwPQ6fQcK9hLvc41OXjgELW8jQjwhuJwOEqm27VaLUajEd3Py2/iredJFi7gVgnUePbJkv3N5cnavRfb1h2cPnGCsLCwP3T/7HY7L7z4IkuWLsWj0aCtFIHs9eJMvYpRo6Zpo8bkm83YbDYiIsIZPmQow4YNK/mZ8Xg81KlXj9TcHNBoMDVtgqFOLQRRxJGUQtGx4wheL9ETx5OzZx8NjSa2b9lS7lgGDR7M5kMHcRYVEdSvDz6tWpQJpi3HT5Lz41KMggqtVocgQM2aNZn08CRGjx5d8sDkbklLS6Np42ZU9dSuMKCUZImD5m007RzDmrVryhw3m80sX76cpKQk1Go1TZo0oVevXmg05T9UulNmz57NP155GVPb1kR0fwC10VByzJGVzdUlyzBZiti4di316lW8vxrgpZdeYsbXs2imb0+orvTPeKE7nyRbLKmeRB6d8giffvopmzdv5ovPv+DAvoN4vV4EBIw+RoYOH8Lzzz9PVFT5Nex/Icsyjz/2OD8tWkoDXUsiddVK/WxZPWb2FWzF7C4gSh9NjF8rDCojbslFqiOJK85knDiRZQmHZOHd997lxRdf/AN3UaFQKBSKvwav18u2bdvYu3cvTqeTSpUqMXjwYOWhs+KmKEH3n9DBgwcZPXI0+dmFhFGZAE0wEl6y3FcpEHLp0Kk9EydN5Oknn0awaqiirkmqK5k8XREhI4ahqRwOAgQEBqDT6ZA8Xq4sW4H79FlWLVtGhw4dANi5cyeDBw7BxxlApiONtsIDaIVf96rIPwfvKrWKkJBgQOBgwXZa9WrKjm07aaRqQ7iucnFLSS7Zm3yty7Z4TtoPYKodTa3nrl/r2GOzE/uvqcz48iuGDx/+u++b2+1mxMiRbN2/j5DePQlq3gyVTossy6SuWE3Gzl2IBgMBzZpi8PfHlZ2N7UIsIf7+zJs9m44dO2KxWKhTvz75GjXhk8ej+U2WSsnpJPuHJbhSrqCRvHz7xfRyy2YlJSXRok0bgocOxJZ8max9+9FWqoRPy+aoAvzxWiwUHTuBM+UKyDJ+TgON/Vrhlb1kulLJltNo0Lg+y5YvIyIi4nffi1vl448/5oN/fcT9vn3RVLDyAiDNkcJ57zH2HthD/frFCf08Hg/vv/8+386cSZHdjjY4qLi+d24elSMiePvNNxkxYsSdupRyzZgxgzffeQe714O+Th1URgPunBycSSlUjYxkwdy5NGvW7Ib9uFwuJk6YyPo1GwiSw6iiq4HNayXBcRGzbEZWiajVKgw6PY0bNuSlF16gX79+xMfHc+XKFTQaDTExMQRcp07obzmdTp588kmW/7QCnddAqBiJSlBR6M0jjywqV42ke4/uzJ01j8rUIEAdzEnbEVxqL4Y6dVAHBeK2F+GKjcNHpeLtN9/kqaeeuqdWWSgUCoVCoVD8GdxsLKrs6b6HtG3blgOHDrBw4ULmzp5LwtUzqFQqWnVoxeSHJ9OnTx80Gg01a9Zk2rRpbFi3AZfHhddqJ33uHHzq1SW4RTPsOTnkXU3HfPQYWqeTb6ZPLwm4ATp37syHH3/ASy+8jNfuIZ0rVBNqgSzjxYuEF1GlIjAwEBCwe22YhTwefPBB9u3Zj8VZ+HPQLSCI5X9RN3sK0el0CD4+N7xutdGAoFZTVFT0h+7bggUL2Lp7N1UenoBvdM2S1zO2bCNr336CevXA1LI5eL2EBIeg1qhxF5q5smQZw0aNYv3q1Rw/fpwip5OwMSMQVGpkSSqVVEPU6QgZNpTU/36Kv8nEwIEDyx1LfHw8HknCLzqaoGZN8atfl+x9B8hfs74kUZ62ahVCHuqPM+kymuPJP99LiNRXo8hj5ujpXYwYPpItWzfftVnhU6dO4ScFXjfgBgjXVeF04SHOnDlD/fr1kSSJKU88wZKVKwno3Ila7dqg+fkDyJ6RQeb2XUx55mkKCwt57LHH7sSllOvhhx+mf//+/PDDD2zZto0iq5WIWnUY9tob9O3bF6224j3a19JqtcydN5cffviBGd/P4Pixvdg8LnQ1qxPevg/BjRqiNxgoSkzk4p59jJs8mQ/efZfHHnvsD+/l1+l0fP/99zzyyCPMnj2bfXv2YXe5qVU9irHj3mTQoEEYjUaio6OZ+u/3uFB4BkO9ukT07oXoYwQBDHoDJr2ezO07ef3tt0lNTeXpp5+mSpUqf2hMCoVCoVAoFIqKKTPd9zCv11s6IdpvpKenk5CQgNvt5tKlS/y0bBknTp0CWcZoNDJy2DAmTpxYMgP5W3v37mXSxElcjU+nsdAeraBDFEWMRgNGowlRFJFlmVOWgzgDLZw5d4ZXXnmF5fNWcZ9f7wrH5ZU97DSvpU7jWsTm51HrhWevO4vmNpu59N5HzP3uOwYNGvS77pEsy3S8/z4SBKg+7teZZ7eliDPvvo9vh3YEdu9WnMSsyIrRoC/5OZHcbhK++JouDRuSmZlFnCBTZcQw8vLzizOQa9QIKjUgI3k8yB4PRXv2YbwYR8KlS+UGZtu2bWPg8OHUeu1FtNfMXnodDrwOB0UOFy5ZRifoyN6wDu2JFO4PKJ2gKt+dyxH7dhb+uJDevXv/rvtxq4waOYqDq4/RKuD+67aTZInNBUv5dtY3jBgxgiVLlvDIk09QafQIAhrFlGkvyzJp6zZgP3CYQ/v2ER0dfbsu4Y5LSkqidfv2iA3qUXXoYNSa0s80ZVkmbf1GrHsPsHn9elq2bHnbx9S7b1/2JcRTaeJYBJUalVpVnDkfAavVis1uJ3/LViz7D+Hv60uXzp156skn6dKly20fm0KhUCgUv5ckSezZs4fZs2dz5OAR3B430bVqMW78WB566CEMBsONO1EobqGbjUVvXO9GcdeoVKrrBquVKlWiY8eOdOnShccee4ytmzeTkpDApQsXSIyL46OPPqow4Abo2LEju/fsplrdqiToT4Ofh9CQEHx8fBFFEZu3iJOWA+So0/novx/h6+vLww8/jFfr4nzRiXJLXUmyxGnLYTQmNc888wzenFysKZeve505B4/g72PigQceuPmb87MrV65w4WIsgS2al3o998hREAT8OrQrfkEQEDRqHA5nSRtRoyH4/o5s37mLi5cuYapRHbVGTUhIML6+PoiShORwIDmcqAF/Pz/CYmKw2mxkZWWVO54GDRqgU6spPH+x1OsqvR61nx9uZFSokL0S9rgEAoTAMn0EaoLxJZA5c+b87vtxq0TXiqZILCwpVVeRXFcWKo2qJHieMWsWulrR5QbcUFziqlKP7ni0WubNm3fLx303zZs3D7dGTdTQQWUCbii+9sjePRGCApkxY8ZtH09sbCwHDx8m4oEuBAQH4x/gj4+PD7Isk5Obi9VuA40a/66dEY0GNI0asDshnsHDh98wqaHT6SQrKwur1Xrbr0OhUCgUCigudzlq5CgG9hvIlmU7UKf7YMoOIXZvAlMeeYJ2bdsTGxt7t4epUJRLCbr/Ynx8fAgODr7pZckRERFs3LSBmDb1OSMdYrdlA4fzd3KgYCt7ijbgCbbxzXdfM2zYMACaN2/Oh//9kHQxmcPmHWQ6r+KVPXgkN2mOFA6at1Goy+bb779h2LBh1K1dm4yVa/A6HOWe356eQeG+A4wePuIPrVSwWq3IsozaZCz9elIKuupRqIzXvF5OTeyARjG4JS9ejwdZ+rkWuihiMpkIDQ0lPDyM8PBwQkJCMBqN8HMQqvptbfSfVapUid49e5K//yCS213qmNfrRZZlREFF0cXzSIVmqhtrl9tPoBDKxd8E7nfS6NGj8ao8pDlSKmwjyzLJjlgaNKxPy5YtSU9P5+jx4wS2bFFue8krYbPasDocaOvXZdGPP95TNcpvxOPxsG3bNmbOnMmcOXM4depUqeM/LFmCT7MmiNf53RMEAb/WLVmxZg12u73CdrfC4cOHcUsS/jG/PgCRZcjPL0BCRmUyodLr0QQEoIuqBjLUfPIxTB3b8fo//8n27dvL9HngwAEmT55MVNUoGtRtQPVq1Rk0cBBr165Fkq7/gEahUCgUij9KkiQeefgRtqzbRmNtOzr4Pkg9nybU8Ymhlf/9dDT1JDshl8EDB5OZmXm3h6tQlKEE3QqioqLYtn0b6zetY8zjI+k4uA29xnRn+jdfcPb8WYYOHVqq/aRJk1i4eAHRratzxnOQLYXL2WpewXnvMZp1acTyVcvp378/oigy6/vv8XM4SfjyW/JPnkLyeAHw2Gxk7txN8jff07ROHd54440/NPagoCBEQcCZm1vqddnrRfht8CMV1y+/lqBWI8tQrUoVimIvlelfEIRSOeLMF2OJCA8nNLT8uuYAL/zjH+itNpLnLcLzm8BKlmVs8XHkrltPZXXVCuuyy8hlxnon1atXj34D+nLRdZI8V3aZ47Isc8l6hgJVDv948R8kJiby4YcfYrPbcKlV2Gx2ZKk4oJYlmcLCQrKyszGbzdiKbHh1OhKTkuh8f2cOHTp0py/vd5FlmdmzZ9O2TXPGjhnE1H//g3+++Qy9enamV8/u7N27F6/XS05ODvrwG2ff14eF4na7KSgouK3jdrvdCCqxVKk+p9OBx+tBZTCUylkgaDTIHk/JbLyqciRffvVVyXFZlvn444/p26sfG5ZspoqrDg3F1kRLjTi69SRjR41jyuNTcP/mQZNCoVAoFLfCnj172LR+EzG6VoTrKpdZCWpS+9LS534yrmTx7bff3qVRKhQVUxKpKYDi4LJt27a0bdv2ptr37NmTHj16cPbsWS5duoQoijRo0IC6deuWatewYUPWr1nDK6+9xp4fl5GxbCUqvR6P1YZRo2b8oMFMnTr1D+/HDw8Pp1OHDuw7fIyga5aYa4ODKDp7riQhmizLyG4PBh9TqffbLl9BJYoMHjSIj6dNw3b1KsbKlcs9l9tspujEaZ57/nnU6op/dRo3bsyiefMYN3Eice9/hKlxIwyVI/E6HGQdPIznagaVVVVo7t+u3PfLskyunEm3ptffT327Tf9yOrm5uezdtYsgeziVdVFoRB1FnkJSPYk4VDZeee1lVq5axcOPP45Xo8Ht8WLNzUMKCcZiseDjY8LhcOB2uVGhQSUU13bHYscomEg8fpmH+g/kp2VL6Nix41293vLIssw///lPZs74nGH9TUwcWZNGDUx4vTLb9xby9eyTjBwxiK++nolWp8Nru/HstddmRxC47fvOqlatiiBJ2NPSS0r22W12BJUK4ZqVGrLXizsjE/9mTYDiz4LAdm3YsXQFly9fplq1aixcuJD/vPsfqov1qeXboNSXnShqke64zJJFPxEaFsrUqVNv63UpFAqF4u9nzpw5GGTfkuSz5dGrDEQI1Zg7ex6vvPIKOp2uwrYKxZ2mJFJT3DGxsbFs374dm81GSEgIffr0ISQk5P/d77p16xg9YQLBA/oS2q4NANYrqVz83xeEjByGqX694uXtHg8hIaGoVL/O8CUvXEykxcreXbvo3bcvp5ISqTpxbJnA25mXz+U584lQqdm5bdt1Z7p/kZmZyYIFC5i/aBFpaWlodTp8DAYykzPp7N8frar8DNnZznROuPaxfNWyu57QyuVysWjRImbNmMX5c+eRZRmVWk2vPj0ZO3YsH/33vxw+d5bQvr0JbNqYS19+g1erJWzsKCSnC8nlAllGK+oRf15YI3k8XPl8GtGuqjTwbcYR8y70ldWcOn3ynvsDuX79eh6ePIr3Xg9j/PCys9gej8wzryeycadAq9Yd2X7uLLWef/q6uRgSZ8yhiZ8/WzZtuqkxXL58mfnz57N1xw5sdhuVIyoxfNgwBgwYgF6vr/B9brebpi1akB8ZQbWhxQkKs7NzkFQiqmveZz1zlpwly2jwwnMlwbkjO5ukT6axYdVqWrduTYtmLbAleWju36HccwHEWc+Rrkvk9NnTN/X7oVAoFH82brcblUp1V1ei/V01rN8QdZoP9XyaXLddniubY67d7D+0j3r16t2h0Sn+zpREaop7Tt26dZkyZQovvPAC48ePvyUBN0Dv3r154pFHyFm1hpQfl2JPS8dUtQq+taLJW7kGe8plZLcHfz//UgF39oFD2M+c5Zknn8RoNPLjDz/QtGY0yZ9/TeJ3s8javZfsvftJnreQhI8/pbJWx/KffrrpgCI8PJwXXniBk8eOkZmWRmpyMuvXrSMgLIDjRXtwSc4y78lzZXPacYj7Onfi/vvv7kw3FJfEmjBhArv27OLcxXMcPXGU+MQ45s6dy9mzZzl08iTVHplIcMvmiGo1oR3a4YhPwHbhIiqDHkFbvMT/lxBUlmXyd++EIhvVjbURBZGGphZkXM1g7dq1f3icp06d4p133uHJJ5/k5ZdfZsOGDXg8nv/39c+c8R1tmuvKDbgB1GqBD96KQiVYCQ0JwZuVTf7J0xX2Z4lPwBmfwMOTJt3w3LIs8+mnn9K8dWs+/vprLgoyV4OD2Hv1Co889STNWrbk+PHjFb5fo9Hw5OOPYz12grxjJwCKt0pc85jVlZlJ3toN+NWtUxJwA0huDwICWq2WHTt2cDn5CjWNFSdlBIgy1MZpdbFkyZIbXptCoVD8WaSlpfHhhx/SrGlDoqpVomrVcAb078OKFSuULTV3kMfjKXl4fz2CIJa0VyjuJcrycsWfniAIvPfee9SoUYP/ffEFyZ9/iaDV4XW7kMwWsmfOJbB1C3xatcJuNODIyiL/4BFciUk89ehjjB8/HigOkjeuX8+aNWuYOXs2p7bvQpIkateqxaQPP2LIkCH4+vr+4TEC1KxZkyVLf2Tk8JHsylpLqFCZQHUIEl6yPFcpIJcO97Vn7ry599STdEEQCA8PL/l/j8fDzDlzMDVpVGpVQFDzZpgvxJLz4zJcndIxtmyBKIp4ZS/e7GwKDu7HfuosjQ3N8VEXPw30UfvhRyBr165l8ODBv2tcqampPDHlUY4ePUBEqED1ahryCyQWzP+WSpWq8cmnX9C5c+fr9pGcnMy8efNYt3EjRUVFhIaEMGzIELp27cqBA3v4/P3r79P281XTv4cPR04dZdTQoSz8aRkei4XgNq1R6bTIsozTbqfg5Cly126kR+fON1Ua7/PPP+df//kPAV3uJ6zzfah0v66McObkkvrjUgYNHcrGdesqfJr/+OOPE3vpEnMWLqDw9Bn0MTF4fU14gKKTp7GePIUhNIQao0eUel/B6TP4+fpQr1495s6diwo1ARXkH/iFVtTiIwRw6VLZ3AgKhULxZ7R7924mTx6LSCGD+/jSuGEINruXDduO8uQTe5k3937mzlugrLS8A6Kjo7mYnnDDdvmubLQ6LZUr2CqoUNwtStCt+EsQBIFHHnmECRMmsGPHDhITE1Gr1dSuXZsjR44we948Ur+biQyoBIE2rVrx+Otv0L9//1JLgXU6HUOGDGHIkCFA8Wzj9ZYK/xGtWrXiwKEDLFy4kHlz5pFw5SxqtYqWHVoy+eHJ9OnT56azz98KdrudlStXsviHxVxNvYrRaKRz186MHz++3DrabrebxYsXk5SSQuUHu5a6R4IoUn3UcLSBgWTu3Ufh7n2ofH3A7UEutGJAT3NjG6oZSverkw3k5eb9rnGnp6czoH9vtKqrzJoWSbdOAahUxeM4e8HKf6alMXbMMObOW0zXrl3LvF+WZb744gv+/f77eDQajDEN0FSpRG5OLq/+618Yp05F8rioUe3GS96jq+vZsCObadOm4efnx4w5c8jduh2xciQer4QjJQXJXIRaEEmIS2DWrFlMnjy5wtwAOTk5/Ofjj/Hr1J5KPcqW0tOFBFN98ngSPv+K/3zwAXMrKC8niiL/++wz2rVty9fffcvJn5ZRZLMhqFVoAwKI6Hwf4Z3vK7Xc3G0pwnLkOFNGjcLHx+fnf1v5Jn8Xbv3vi0KhUNwNFy5cYML4kbRr4eGrj+rh6/Pr5/WEEeEcPm5hwjO7eeSRSSxe/JPy2XebjZswjsf3TqHIYy55aP9bkiyR6klkwPD+BAaWLcmqUNxNyp5uxd+C2+0mNjYWu91OaGgo1atXv9tDKuH1ehFF8a78wT5x4gSjR40h7UoagYTiI/rhltzkkoGs9fLcP57j9ddfRxRFnE4n06dPZ+acOaRcuYLD6SBiyqPoQkMwGoyYfEylrsFWUEjagUPYTp5BSkmnlV9HInRVEIWyM/gHC7fReXBH5s6de9Njf+yxxzh8YAnrF9UmPKzs/niPR2bSswmciQ3k6LHTZR5kfP/997z0+uv4de5ERLcupUp9eYqspCxajP3scX6aWZseXa+/FWLqp1dYtdmXY8fPAnDmzBn69+tP2tV0DIKJEE04UYbaCILAFUcCWVxlwOD+zJgxo9zA+4svvuDN96ZS+41XUBuNZY7/IvvAIcxrN3Dy2LEbPtWXZZm4uDjeeOMNNmzbRsTgAQS3alkqqZojM4srCxcTLqrYumkTkZGR7Nq1i4f6DaSl7n6CtBVvrXB6HewqWsuHn37Ao48+et2xKBQKxb3uscce48yJpWxdWhe9vvyVZ1t3FTDhmVSWLltP+/bt7/AI/17sdjvt23UgMz6HVj73o1eVTkYqyRKnzYcoNGSzYfMGmjVrdpdGqvi7udlYVJnpVvwtaDQaYq6pV3wvqajm9+0WFxfH4IFDcOdIdDT1xKT+dem8V/aSZIvlkw8/RaVS8dxzzzF85Eh2HTyIT4umVOlyH4lzFyDZ7chqNUVWK06Xk6DAIASxOPA2+Pvh27wprpQrGNOcROqrlTsOq8dCIXn07t37pseemZnJhvUrefP54HIDbijeb/3G85F0GxzPhg0b6N+/f8kxi8XCu/95H1PrlkT2fBCP24OtqAhJkhAEEb1OR/WJ4zn36nkWr8i8btDtdkusWG/mwV6/LtF+b+p7FOXY6BY0AF+1f6n2wdowMp1XWbV0NQ0afMrLL79cps8DBw+irVnzugE3QGCTxmStXMORI0duGHQLgkCdOnVYtGgRL774IvN/+IG87bswNKiHqNbgvHoVR2Iy0VFRLF60iMjISAA6depEdO1oEi9eIFATUuHDoSR7LEZfI8OGDbvuOBQKheJel5OT8/PfmMAKA26Abvf5U6t6OnPnzlGC7tvMYDCw5KcfGThgEPsubyJCrEqEriqiIJLvyiHVk4hs8PD1d18rAbfinnTvbBpVKBR31IcffIgtx0Er3/tLBdwAKkFFLVMDosS6fPbJ//jHCy+w69Ahqj4ykaqDHiKgcQzGypWwHj+JSq9HZTLi9ngoNJtL+hAEAa3Xi+3CBULU5e+LlmSJ89bjhIaHMmDAgJse+86dO/F4bAwbcP0Z6Lq1jDRuoGPz5s2lXl++fDkFRUWEdO5EXl4eOTk5FFms2K12rEVWcnNzKSgsxKdFa9ZutnDoWEGF55ixMJPMbKEkN8DFixfZsnkrdbVNygTcvwjXVaayWJNvv/4Ou71smTGny4movfEWA1GrBWRcLhcpKSnMnTuXr776iiVLlmC+5t/iWhqNhmnTprFz61YmDHiIyrkFhFy5SoeqUcz57jsOHThQqvSfKIq88ebrFGiyuVB0AkmWSvUnyzIptnhSvJd4+tmnCAgIuOG4FQqF4l4WFxeHx+Ogc4fyP8N/IQgCnTuYuHD+1B0a2d9b7dq12bZjK8++8jSOUDPHXXs46tzFFW0c/Uf3YeOWjQwcOPBuD1OhKJcy061Q/A1lZmayZvUaojT10IjlzxQD1DTWI8Ucy8IffiCodw98qkcBxV80wjp1IPnHpVhPn8HUuBGiTofD4cDr9UGlUiF5vORv3IIGSPMm42PzpbK+OmpRgyzL5LmzibOfxWm08sOMRdctf/VbVqsVrUbAz/fGH2FhIQIWi6XUa0ePHkUbGUmRx4vXK6EWNKhQUZxnXUZCwuN2Y+jUnowD+xn1eBLvvV6Fgb2D0emKn1Xm5Lr5bl4GX80p4IknX6JBgwYA/PTTT6g8air5lj+z/4vqhtrszd7Ili1bSs3CA1SrUhX36dM33EdtT01FkOG7779nytNP45ZlRK0GyeHEz8fE6OEj+Oc//4nJZCrz3saNG/Ppp5/e8P4BDBw4kJycHF57+TWu5qUQIVTFqPJBUnnIkK7gUNl49IlHyp21VygUij+bX3Zeijex7UsUf22vuP3Cw8N56623ePnll0lOTsbj8VC5cmXlga/inqcE3QrF39CxY8dw2l1U8rl+YKgRNWg8OmxqGyGtW5U6FtSqJZaERHJ+WoHj8hV8W7VENBiwW614LqeSs2MX6pwcfly0iFWrVrF65Rriis5iEIx4cOPEQe36tfnk09l06tTpd40/ODgYlxsyslxEVLC8HH6ehb0i0aJtcKnX3W43LknC65XQCjoErv1iJSCiQiOIuNUg6wxUqdaQF9+5yHufZdGovgaXG46dciKqTLz08js899xzJe++evUqBtmn3L3r1zKpfVELaq5evVrm2IgRI5i9YAGWuHj86tSusI+sPftxOZ0cS04i5KG+BDZtgqjV4jabyTl0hG8XLODk6dMsX7q03MD7ZmVnZ3Py5EkQweo1c8l7BlmWEUWBuvXq8vF/v6Nnz55KIiGFQvGXEB0djShq2XPITM3qFT8QlmWZPQdt1K7T8A6OTgHFiW+vXZWlUNzrlKBbofgbcrvdyLKMSrjxfnJJllD5+qD2KR20CYJA1PCh6MPCyNq9l6JDRwABlSBg0Gho07Il73zzLW3btqV///5ceecKK1asIDMzE4PBQKdOnejUqdMfKo3WtWtXjEZ/Fi7N5oUnKt7LfOREEXFJHt794KFSr/v5+WG/ehWVFwR1+YGigICUW4jX4eShhwYxaNAgFi1aRHJyMiqVitf7NWP48OFlnq7rdDokwXvDa5BkCa/kRacrmx29devWtGnZksNLV6B77GF0wWXLdeUePkLekaPoAvyJfuaJUvu/NX5+VOreDf96dTn43Szef/993nvvvRuOqTxZWVn07d2X5NjLVFfVp2pITbSiDrvXSqo9mcspcbz773dp06YNQUHXLyumUCgUfwbh4eF0f7APcxevYdSgEDSa8v9O7T9i4UKch7f+Pf4Oj1ChUPzZKEG3QvE3VKVKFVRqFQXuPEJ1ERW2k2UZl+AEd/nl0wRRJKJbF8Lu74T5/EWuLF7CA23bMXXqVBo1alSqbdWqVXnmmWfKnCM1NZUdO3ZgtVoJDg6mR48eN6xE4Ovry/AR4/hu/pc82DmARg3KzuKaLR7e/OAqtWrVp2PHjqWOBQYGIlms2C7E4teoSYXnKTp+AtErk5eXR40aNXjjjTeuOy6Adu3aMXfmPKweS5m98tfKcKYiakTatm1b5pggCMydPZt+AwYQ9/lX+LZqTmDzZqj0ehyZmeQdPIz1/EVEWaba2FEVJlwzVq1CQMd2zFu0kFdfffUP1Zl/9tlnSb54mTY+XTGqfEpeN6hM1PZpSCVPVQ6f2cFLL73EzJkzf3f/CoVCcS965plneWjARp55PYlp79VAqy0deJ+PtfHUq1do0aId9913310apUKh+LNQEqkpFH9DzZs3p36DeqQ4Ll23Xa47C9QSKreboviECtuJajWaAH/UksyTTz5ZJuAuT0pKCmPHjqVZk+Y8/fgzvPHSWzw84REa1o/h5Zdfpqio6Lrvf/3116ldpzVDH07ii+/TyMl1A+B0Sixdk0O/0XGkpvvx7Xezysym63Q6tIKa/E2bcaSnldu/5dwZLEeO4iP4YLVab3g9vxgwYADBYcFcsp4tZ5+fjCRJeCQ3iY4LtOvQtmQv+G9FRESwacMGnnv4YdRnL5Dy+VckfPhfLn8/G++lePSiiCokGE3lylxvO2Fw29YUWorYunXrTV/DLxITE9mycQu1tDGlAu5r+aj9qKmpz5pVa8pdKq9QKBR/Rs2aNePrb2axaSe07XWRj6ensnF7PivX5/LI8wn0GplARGRz5sxd8IdWbCkUir8X5VNCofgbEgSBZ557hjwxi0RbbLltrJ4iztqP0LJ1S5rExJC1dQeSp/xl07IkkbllOzWqVaNbt243PH9iYiI9H+zJlhXbiJZi6OY3kAf8BtLZtx8hRZWZ+dVsBj006LqBt8lkYslPyxkw8BH+972D5g9coNF956nf4RzPvZlNZFQX1q7bTP369cu8NzAwEJ1aS4DDSMbcuWStW409JRlXdjbWuEukL1lM7opVRInV0WjUvytBi8Fg4P3/vEeuOp2zliO4JCderweLxUxWZhZXMlPYnbmJXHcWrVq3Kjd7+S+CgoJ45513OH/6NE8/9hgaCdRuAd+iQNyFEmJgIIWFheRkZ+N2u8vtQ+vvj6BWk5ube9PX8IuVK1eCRyRSH3XddlX0NfA4JFavXv27z6FQKBT3qt69e7N12x769J/CzB/g4eczeOq1LBKvRvPu1GmsXLWW4ODgG3ekUCj+9pTl5QrF39SwYcOIjY3lf59MI7swjWq6WvipA3DLbq46ksmQLlOzXnUWLFxAUlISg4cPI3neAqoMHIA2MKCkH3ehmaur1yElJvHx/Pk3rDsuyzKTJz1MYVoRbX0fQCf+mqRGrzJQx6cR4e7KHDm4i6lTp/LBBx9U2JePjw8ff/wxb7zxBhs3biQrKwuTyUSXLl2oWbNmhe/r0aMHWoOWqp4oIpFIOhFP5vFTIAggywSIgcQYWqMV9OR60ujXr9/N31hg+PDhuFwuXnnpFbYVrMLH6Y9G1uLAQQE5qEQVwaowPvv4f+zds5cfl/x43f3QS5cu5evpXxPurUZd/8ZoRR3HC/eRbnWgFXS4vW7y8vIJDgpCrSn9se51OJA8Hnx8yp+pvp7c3Fz0gvGGe//Voga9yvCHAnuFQqG4l9WqVYv33nuPd955h8LCQrRaLb6+vkriSIVC8bsoQbdC8TclCAJvvfUWTZo04euvvubIoSNILgkECAoJ4pkJT/HUU08RFBREZGQki+bN5+HHHiP+w0/Q1aqJxt8fj8WCIy6eQB9fZsyYQffu3W943sOHD3PqxCma6tqXCriv5a8JoqqqFgvnL+L111+/4R7vgIAARowYcdPXHhkZSd/+fVi7ZD1tfLoSbaxPkacQj+xBK+rwUfvhklwcsmynaasmNG/e/Kb7/sXYsWOJjIxk4EMDMbvzMYo+6FR6munb/Vw6TU2hO49jB/cwbuw4Vq9ZXe4SRYvFwpuvv0Wopwoxfi1LvuiF66qQkrYfd3YumtAQ3JITS5GFwMDAUu/PO3ocg0ZD586df/c1GI1G3LLzhqXLJFnCLTkxVrC3XKFQKP7sNBoNISEhd3sYCoXiT0oJuhWKvzFBEBgwYAADBgwgISGB9PR0DAYDDRs2LFM3u2vXrpw5eZIVK1awavVqcvLyCIqsTJ/JjzBkyJAbBsa/WLlyJVpJT4i24gRuAFGGaJIKLrBp0yaGDh36h6+xIh988AGnT57m0KXtRGsaEGmojlpQI8kSVx0pJDjP4RNu4Jtvv/nDMxrz5s3DR/anQ1iPckuI+WuCaKRvzb49+9i3bx+dOnVCkiT27t3L0qVLSc/IID0tjeysbDr7tyk1jkq6qhhsOvJ2biN80FBUKjVOpxOvx4tKXTwz7bZYyNu9j4G9exMRcf37XZ5u3brxyUefkuPKvG7CvSxnGpLKe1NbCxQKhUKhUCj+bpQ93QrF31BeXh4XLlwgISEBj8cDFNcl7dixIy1atCgTcP/CZDIxZswYflqyhAVz59KqRUumfTaNurXrUiOqJo88/Aj79u0rJ4HYr3Jzc9FLxhsGsnqVEbWoIScn549f6HWEhYWxbsM6eg56kHjxDDvMq9ltWc8OyyouSMdo3bUFGzZtoE6dOn+o/4yMDDas20BVTa3r1uwO0UZglH2YO3cuiYmJ3NelC/0HD2bh1i3szkzndJEZlxp2mTdx2f5rMjtREGlmaoPzQjwZy37Ck5ULMrjcLmRJovBiLElff08lo5F//+tff+ga2rRpQ0yjhsQ5zuCVy9/P75E8xDvP0bJVS5o0qTgTvEKhUCgUCsXflTLTrbhn2O128vLy0Ol0BAcHK/ulboMDBw7w9TffsGHzJtweL4IgULlSJSaNH8/DDz9807PV69at49GHH8VhcREmVKGqui5uh4t1P25k2U/LGTN+NJ9++ilqddmPGJPJhEd03fAcHsmNV/pje5FvVnh4OPPmzePy5cusW7eOvLw8fH19efDBB6lXr97/q+/4+HjcTg+hxuvPMAuCQAAhHD92gr4DBpDpcVPl0UmYalRHEAQK8guwZudStPcAx48dAqCaIbp4/LrKtOU+Tlw6TNql7xHDgygMDESwWJDNZlo0bcaM776jSpUqf+gaBEHgs2mfMaDfAA6bd1Lf2JQATXHSIFmWyXfncNF+EnWgwCefffKHzqFQKBT3GrPZzPr168nIyECv13PfffeVqTQhyzJHjhxh9uzZHDywC4fDQWRkFYYNH83w4cN/VwJOhULx1yfI15uS+hMwm834+/tTWFh40wGD4t5y8uRJvv/+e5avWoXT5UIA6tevz8MTJzJixIgKZ10Vv8/MmTN56bXXEEJDCGjbGmPlSCSXi/yTp7GeOkOD6JqsWLrshsuQDxw4wMABA/G1BdHYtw1qUVNyTJZlUh1JXHAfZ8rTj/P++++Xef/atWsZM3Is7QwP4KcJLHP8Fym2OBLFcxw/dfwPB4130759++jbsx/tjQ/io77+Z9MZ81HyTGk4jQZqPvMkGt9fHzSYzWZsVjtatORsXI/z6Bl6BD6ERtSWtJFkiUTbRc7ajvJA9240bdqUQYMG0bx581vy8OrIkSM8+shjJCcmY5R90UhaXKIDu2Cldp1afD/ze2WWW6FQ/Ok5HA7+/e9/M3/uAiyFFrSiDq/kATW0adea/3zwH5o0aYLb7eb5559n+bKF1Kgq0vdBX3x9VJy9aGPDNhtGUwiz5yykbdu2d/uSFArFbXazsagSdCvuqvnz5/OPl17C6+eHf+sWGCpVwutwUHDqNI6Ll2jfqhWLFy3C39//bg/1T23btm0MHTkSU/u2RPbtVSYQc2TnkPL9LFpE12Lzxo3XrTk6oP8Ajm8/TVu/bhUum06wXuCyKrbcgNntdtOsSXOsl5209Luv3D6ckoODlq10H9iN+fPn/4Ervvuys7Np1LARVd11qWmsW2E7WZbZVbiWIpWV8IH9CbuvY6njbpeb3NxcNIIWyWIj9YvPaaJtRo3f9HnGfAQ50sHps6fRaDTcal6vl+3bt7N+/XoKCwsJCAigb9++dO7cWalRq1Ao/vScTifDhw1n97Y9VFPVJspQC73KiCRLZDpTSXCcRwyA5SuX8cMPP7Dsp5l8/E4lBvUJRhR//ZuanePmqdeSOXlOx+o1m8otW6lQKP46lKBbcc/bsWMHQ0aMQN+yOVUe6ofwmy/u1iupXJk5l+7t2rHkxx+V5eb/D/0GDODAlctEP/FohffRkpDI1e9ns2rpUu6///5Sx/Lz89m6dSuxsbF89J+PaKRtSxVj9QrP55E87LSs4aU3X+DVV18tc3znzp2MGDYCg82PusYm+P884y3LMtmuDGKdJ/GP9GHj5o1ERV2/RvS97NFHHmXND+vp4NejwrJbGY5Ujtn3IOvV1Hv7DTTlLKfPy83D5XKjFbRkLF5MUIKdNoGdr+njCqddh3j73X/y3HPP3Z6LUSgUir+wjz76iA/e/ZDm+k4Ea8PKHPfKHo6Yd6GNUGG15vD+GyGMG1a2HYDN5qXHsEs0aDKQ77+fcbuHrlAo7qKbjUWV6QnFXfPJZ58hVqlcbsANYKpahYghA9m6cwcnTpy4CyP8a0hISGDfgQMEd2h73QcXPjVroAoLZf6CBSWvFRQU8Nxzz9GwfgwPT3iED6Z+iN3mQDRrKSwsRJKkcvtSCSq0bgOrV69m2bJlJCUllToeEhLC/V3uJ0vMYGvhGlZnL2J7zhp2mddxyr2fui1qs27Duj91wA3w7HPPovITOGbei1tylzme68rinPMIMY0bIggiaoOh3H4CAgJQq1U4PTbcDhvZrnQO5G3nYN529udv4ZTzIIOHD+Lpp5++3ZekUCgUfzkul4tZM2YRQVS5ATeASlDT0NSCK5cvE+DnYcRDFZcPMxpVTBodxMYNq8nMzLxdw1YoFH8iStCtuCsuXbrE/oMHCerQrtyA+xf+Deoh+Pszb968Ozi6v5akpCQ8koSpRvXrthMEAV1UNeISijNk5+fn06dXHxbMWESEozpd/PrT2NgalaBCLatx2O3k5eWWCrxlWSbZFse2/DVkC9kcPXOaCY89Sos2bRg2fDhHjx7l+eef576uXdl85DD+nToQ+mA3tPVrYtYWIfjBtzO+Ye26NQiCwOXLl3G7ywarfxYNGzZkwaL5eAMd7LKs5Yz5CFfsCSTZLnHQvJ2jjl106NqeN954A5Uo4MjMKrcfUSWizsoi49uZuK5eRaoeRl5dExkhDjLIJCQylBdeeAGVqvzZdIVCoVBUbP/+/WSmZxFlqHXddr7qADSiyH1tNWi11/8K3aNLAF6vg7Nnz97KoSoUij8pJXu54q6Ij4/HI0n41qp53XaCKKKrHsXFuLg7NLK/HrVajQDInvJLPl1L9nhKMo6/9NJLXDoTTxtT15JEYCaVHyBQJBQSIITg9jgxFxYSEBiILMucsxwn3nMJQ6OGhLZoTGB0TUxGAwVnzrFt9142PPAAkiBQachAglu1QLgmSHRkZpEyfxGPP/kkWkGNy+kEBCIqhTNh0gQmTZpEcHDwbbhDt1fnzp05cHA/8+fPZ/7c+cRlnUYQBdp2bMukyZPo06cPAJHhEeQcPETVQQ+V6aPwwkUS58xHF12TkOGDCapeHZVajSgIOK+kkrF6Lf0HDmTT+vXUrHn93ymFQqFQlJaXl4ckSRhVN66WoRJEVDfx7VmjLg7KfynLqVAo/t6UmW7FXfFL4iVZunFKAVmSUCmJmv6wmJgYtBoNuadO43Q6K/wCIHm8OOLiad2iBWlpaaxeuYaamvqlMm8HakLw0wRwVU4CGVRocDqdeL0e0p2XiXfHEti7JyED+qGrHInRxweVTkdwy+ZEPf4IUtXKuIGAJo1KBdwAQoA/voMGYJFlpAKBpuoONFa3RUrV8sG/P6Jrl24kJibezlt121SuXJlXX32VcxfOcTX9KhlZGaxes5qHHnoIjUaDRqPh0cmTKTp6nMILF0u9V/J4SVmyDF10TYIG9MUQEY7JZEKv16HVafGtVZMajz9MruTljTffvEtXqFAoFPc+WZY5cOAAjz36GK1atKZl85aMGT2G2NhYBFHAKdlv2IfkUXH8lJMbpUQ6eqoIUFO9evVbM3iFQvGnpkQyiruiQYMGaEQR828CjN+SPF6ciUk0bdz4Do3sr+XMmTO88MILFBUUkLljN7kZmeRk55CXl4fTWbpWdt6x4whWG+PGjWP16tV4HB4q62uUaiMIAnV8GpFHFilyLCIisiyTY83iovUM2uga+DZvhkd2o9frUWt+nQ5weNwE9euDLEDekWOl+vW4PRTkF6A2mPBv2ZoiwYq/JogwXSSN/VrRwdSD7MRchg0djt1+4y9F9zKtVltutu+nn36a/j16kjp3AYlLlpKXmITdZiPv2HFc+fn4dWiPSqUurv36m635aqOR4C73s3nbNpKTk+/IdSgUCsWfSVFREaNGjqJPz76s+WE99jgvznjYtWofH773ES63k8v2hOv2Yfda8aq8xCZ42LY7C5vNVu6DbFmWmbUomxYt2lC3bsXVKxQKxd+HEnQr7opq1arRvVs38vbuR7rOsuf84ycQrDbGjh17B0f317B792569ejNlmXbqa1pjCrPSv7S1WB14nZ6yM/Pw2azI0sSecdPkr16LWNGjKB+/frk5OSgVxnRiGVLT1XWR9HQrzkpchz7pI0cFney17mVXCkHfdOGuCQnKrWqTJk3l8uNOiAAQ/165B0vnRjParMiy6ARtJjq1MMjeDF7CkqOG1RGmpk6EB8bz6pVq27L/brbTp06RVZ2Nl67g9xde0n49HPOvvImibPmog4OxrdqZYKDg0qW//9WYNPGuGWZnTt33tmBKxQKxT3O4/EwYfwENq/dSiN1Gzr69qShbwsa+DanvX93Whk6gySSYL1Akbuw3D4kycv+/G248GBzSjz16mVOncslJ6f4QfYv+UdkWeaDaakcOOrh6Weev4NXqVAo7mXKnm7FXfPSiy+yc0B/Uhb8QLURQ1Dp9SXHZFmm8NwFslatZdSQIbjdbl588UXOnDqDJMs0aFifsWPH0qJFC6WUWDmysrIYN2YcGrOBFn6dUAkqQp0RHEjYQeoXX2CoVw9NRBiFLheeuARURUWMGDyY//73vwCYTCbckhNJlsqtox1lqEWKI4ECdRGGRg0whIdRuG0nuiqVETRqvF4vFosFP/9fl6bLsgyigCY0BEfq1V9fl2QcdgcqVICAqFEjA5JcOjO6j9qPIMKYM3suI0aMuC337W7Zt28fQ0eOxBngT7XJE/CtV4eihETsaelkbN2BymQqXs4vVpwoTdRqEbVabDbbHRy5QqFQ3Pu2bNnCts3baabvSKguoszxIG0oHf0fZEfeWvYXbqWhqQWV9NVK/v4VuvLYX7gVm8ZN8H33EdKsGekrljHk0TQG9DTS434DJn0RVzPULFxu4Xysl7ff+Q8PPvjgnb5UhUJxj1KCbsVd06JFC+bPnsPEhx/m0vsf4dO0MfpKlZAcDiynz+BJz6Rvjx7YrFY6d+qM2qslQA4BBM4eOsf8OQvo2r0rs2bNVGq0/8bChQsx5xVxv29fVIIKt+TikvUMXpcbg0uL92QcLuECEhKy5KFGjeo8/9xzaLVaALp27crUf00ly5lGhL5Kmf5PWA5SZHATOXo8YkQIersdy87d4PGgNpmQXC6sdhsqtRqTyQjwc8krD5LdjqjR4PF48HolJMmLLMslX26cGekIkoxRZSpz3gB1CHGX/lpJ9Ww2GxMmT8ZTKYLoiWMRNcWrC/zr18O/fj3chWZyT5/Bbrej0WpL7udvuQvNSA4HISEVl7FRKBSKv6NZM2fhK/uXG3D/IlAbQjVDTfI12VyQjxNrOYUBE1485LtzcGsEosaPIbRFMwAMTz5J9t79/Lh9P4tW5iG7XUhemYceGso7U5+kY8eOd+ryFArFn4ASdCvuqu7du3P4wAHmz5/Pgh9+IOPYSTRaLQ926sT4j8cxe9ZsNq7cSD1tcyKNUSWBmSzLZDpT2blxFyOGj2DFyhXodLq7fDX3joXzFxIqRKIVtUiyxOGCXRQ4c2kstiNQ+Dkok8Ere7Bi5nJGLEMGDWHbjm2Eh4fTpEkTWrZuydn95wjRRqAWf/2oKPKYSXOnEty3P6qIMFQaEb/Qamj8/LCeOoO+ehSiVovs9WK1WjEajQgCGAwG7Lm52E6fw1S/LjnZOSV9SrKMBw9qwHz0KCHqUExq3zLXJcsSKtVfa1fMypUryczNIXryuJKA+1pBzZuStW8/zitXsGk0mIzGMnu6AXIOHcHPx0TPnj3vwKgVCoXiz+PA/oOEq6Nu2K6KrgYFUg5r1q1m586dZGZmotPpWLl6NeZK4SUBN4DaYKBS925EdO2MMzcPZ24uqbPn0bt335sOuCVJYu/evZw7dw5Zlqlbty6dO3dWyj8qFH9Bf61vr4o/pcjISF555RXOnDxJVno6aZcvs2jhQjweD5vWb6axvh1VDDVKLXMWBIEIfVWaGztyYM9Bli5dehev4N6TlpaGvzoQgCxnGjnOTOoLLX4NuH8mIKLDSAtjJzKuZPH999+XHPvk009QB8IRyw7yXTklmVov2xMQfAxo69UCQcbfzx9RrSK0bRusp07j+rnWtKjVIkleXC4nADqdHuvR47jz8tDVqoVa0KIVdGgFHWrUeCUvObt34ExKobahQbnXlSNl0LxF81t+v+6mZcuXo4+ORhcUVO5xY1Q1TFWrUrB5G67CwnLrlluvpFKwZx9jRoxUVn0oFArFb3g8blTCjeeZREENMlStWpXXX3+dadOmMXr0aLJycwlu27rc9wgqFfqwUPzr18NQpw5Lly27qTGtWLGC1u3a0X/wYF59911ef+89Bo0YQYvWrfnhhx9+1/UpFIp7nxJ0K+4p1+7PnjVrNr4EEKarVGH7QE0IgYTy/Xczbli+4+9Ep9PjkYqDs2R7HL74EyiEltOy+J7pVQYihKrMnT0Xl6s4q3lMTAyr1qwivG4IR5w72WvZxKGCHSTaL6CqFIZKpyEoKAiNtnh2Nuz+juhDQsicPQ/b+YsgCICA1+PFY7OTum4DBVu2o/JAwfI1FO7agyszC3dePo4LseQu+BHzzt2oPQIh2rJLAHNcmRRRyMRJE2/LPbtbsnJy0ASXH3BD8e9EjbEjETweMmfPI3v/ATw/Z3B3FRSSvmkLV76bRbumTXnrrbfu1LAVCoXi/y09PZ2PP/6Y1u3aUateXZq3asXbb79NQsL1s4j/XlWrVaPQnXvDdgXuXPQGfaltOjk5OUiyjO4mtu5oQoLIyM6+Ybvvv/+eSY89xmWtmiqPP0y9f79FvX+/RbUnHiXD35cpzzzDp59+esN+FArFn4eyvFxxT5IkiX179lJVVeeGbStpq3H2zMnixF3KLB8Ane7vyLYVO6klN6TAlUME5S+r8+JFrVYhiirCdVU4kbuXy5cvU6tWLQCaNm3KwcMH2blzJ2vXrqWgoIBjx46RKkBoaGipZc4qvZ7ajz9M0vxFZP/wIyp/f9ShIRQKIlJqKk5LEX6SkY7BPUiwX+Dy7oOY9+xFBgRJxt8bQFVvA5KFi6Q7LlPF8Gu5slxXFqfsB+jYuQMPPPDA7bx1d5yfry8ec8F12+iCg6kxchjx07/BsnEL+Rs2IajV4PES4OfLkxMn8sYbb2A0lr/fW6FQKO41y5Yt46lnn8UmSRgbNURXvSqZFgufz57N9G++4V9vvcVTTz11S841bvxY/vnaO1g8hWQ4U8l0pOKW3egEPZGGKKroqyMKKtK8yYwaMRyDwVDyXpPJhCAIeKxFaAP8r3MW8BZZ8fctuzXqWufOnePVN9/Ap0NbIvv0KjXZYKpWFdPoEWRs3c7UDz6gQ4cOtGnT5v938QqF4p6gBN2Ke5LX68Xr9aIup2TVb6kFDUgyDodDCbp/NmnSJNauWkuG8woyMkI5i1pkZCQkfIzFXxBERJApU3NUpVLRrVs3unXrBsCsWbN47tVXcZvNaPxL32+Njw91pjyK9fIV0tZvxHLmHJ0feIDgBg1Z+tMywjVVkASJRn6tqC81w+zJR0LCIJrQoSc/P49cdwanzYdwSk5kJLK9aVjIp8P9HZi/YP5fbq9brx492Dt1Kh6rFbWpbPK4X5gvxVEtKortmzdz4MABrFYrQUFBdOvWDd9rvuTFx8ezevVq8vLy8PHxoXv37jRv3lzJ8q9QKO4Zmzdv5tEnnkDXOIY6D/UrVb1E6tubjK07ePNf/8JkMjFx4v9/ddPIkSOZ+u+pbMlagVrQEEw4PhhxYOe06xDnLcfx1wQh+sGjjz5a6r3NmjUjNDiYvKMnMFauXOE5PHY7tguxVOrVi88//xy1Wk3r1q3LVFmZOXMmso8Pkb16VPi5HN6tC5aTp5kxc6YSdCsUfxGC/Cdfk2s2m/H396ewsFAJuP5i6tetj5hmIMav5XXbxRadIdcnlcTkRDTlJKL6O5JlmcmTJ7NyySpkp4DB60OM+Ot+NEmW8OBCrSleIi4IAvHW86TpEomNu3jd3yWz2UyDxo2hcQxVBvQtt43k8ZLwzfdEeLyIskBKUgoOm6M4sBcEQnURNPBtToCm9LJqWZY4nX+EdFUyfv7+qNUqmrdsweTJk3jwwQcrrFH9Z5abm0tMkyaIjWKoMnhAuV/C7BmZpHz1HS9OmcKbb75Zbj/p6ek888yzbN+6HcEjYhCNOCUHHtFN0+ZN+PyLz4mJibndl6NQKBTXJcsy7Tp0IEH2UnPSeASx/J2OKT8tRzx/kS8//xwfHx+aNm36h6szLFu2jMkTJuNnDaEqtdALRkSh+AGuXSoiTj5DnpDJ+x++z0svvVTm/VOnTuW/X39N9ScfQx8eVua45PEQO+1LPCnx+PnqCQnS4HBKWG0C9Rs04p133qNTp07IskxUdDRCy+ZU6nH9VVuZu/Zg37aTy0lJJZVFFArFvedmY9G/3jdYxV/G6LGj+eyDadSTmpbKnn0tSZZI9yYzZsSoWxpwy3LxzLnH42H16tUsXrKEtPR0jEYj3Tp3Zvz48dSoUePGHd0lgiDw9ddfo9fpmTd3PlaPBbNUgEEwISMhI6PVagkMCEAQBCRZ4qo3iSFDB93w4ZWfnx9vvPIKr/7zn6Qb9IR3ub9U1m2P1UrqspU4EhNJcctUFqvTWtcZl11CLWjIJYNUZwL7XJtpG9SFYG34NeMWEdQCjRo3Zu++Pbft/txLgoOD+fiDD3jmH//gsttFRPdu6EKCgeKHFwVnzpC1Zj2NatfimWeeKbePjIwMevXsTXpCBvW0zajkWw2VoEKWZbJcacQeOUvf3n1Zs24NjRo1upOXp1AoFKXs27ePi3FxVH5kYoUBd96JkxQlJmHLymTUhAmo1WqMOh0D+/fnxRdfJDo6ulR7WZaxWCyIoliyHPwXLpeL1199nVC5Mo1DW2Oz2bDbHXjk4lVdakFDjK41J717mTVzFn5+fsTExNC+ffuSfp555hk2bdnC2W9nEta/N/4xMYjq4qDdejmVuC++wCgV8djEAEYPDqZ2LT80Gi17DliYPvMio0YO5rvv59K1a9fiVUrXyePxC11wMIVuN2azWSkFqVD8BSgz3Yp71pUrV2jXph16sy/N/DqUyl4OxQH3acthCvRZ7Ny9g/r16wPFf3wPHjzInDlz2LFtBw6Hg4iICEaNGcXo0aOL9yJXID4+ntmzZ7N40WJyc/MoctoQtVp869fDGFkJr8OO7dxFVC4nLz3/D1555ZV7ftnu8ePH6d2rD65cDw1VrTFpfDAaDWg0xU/OJVnipHk/RaYCtm7fQsOGDW/YpyzLfPrpp7z/0Ud4dTpMjRqiNplw5uRgO3cBjSzjtNhpYmhLFUMNZFkmOysLQRZRCxq8spdz8mFsYhHdQx8q2UYgyRK7LGuZ/NRE/vOf/9zW+3KvWbJkCa+99RbZubnoKkeCVosnOxvBZqfHAw/w5fTpBFWQ4XzixIms+2kDbX0ewKAqu6/bI3k4ZNlOVKPK7N2/957/mVUoFH9dX3zxBW99+CF133mj3M+itA2bSd+6DX3tWhgbNyKgRnVMej0Fp8+Qv/8QgYLAiqVLadKkCVlZWcyfP5/ZM2eTkZEJQI2aNZg0eSKjRo3C39+f5cuXM2ncZDqYeuCjLv6eKMsSHo8XWZZIdSWR4jmHS7Rg8pHR6fQ4nCqio+vxyqtv0rdv8YquvLw8HpsyhW07diCbjKjDQnGkZ+DMuIpRL/DNf8Po0dmExwtuj4xapSYgIBBQ8dSriWzbp+bw4ZPENGmCvnMnwrvcf937lHPwMAWr13ElObnUHnOFQnFvudlYVAm6Ffe0TZs2MWHcRASrmihNLcJ0kYBAjiuDy654nDorX37zJUOHDgXA7Xbz/PPPs2j+D+i8BsJVVVALWiyefLJJwzfQhznz5tClS5cy51q+fDlPPP4kXrtEgBTKFU8y6hpVCezXG5W/Lzq9noAAf2SPh6xde8jfup1/vvoaL7zwwh2+K79fXFwcgwYOJj05g3ChChG6KoiCijx3Nle9SQhGmRmzvqdPnz6/q9+EhATmzZvH+k2bsBQVERYSwrAhQ1i0cBGZZ/Jo6X9fSVuLxYzNakMr6AABh2zjiLydJv5tiDLWBiDJFksC59h3YC/16tW7lbfgT8HhcLB69WoOHDiA0+kkMjKSYcOGUadOxQkF09LSaNq4GTW89alurLhdjiuTE649rF63+qZryCoUCsWt9tlnn/HutP9R563XyhzLO3WGpLkL8H+wG/6dOuC12TBodfj/nD/EY7eTPGMOYV6JWd9/z/ix48lJzyVMqEKwNhyX10GSPZZCuQCDyUDPHj2QvF72bzhER/+eZc533nKCVPkMQ/ubGDs4kOho8PH14exFma/nZLBtj5sPPvwf48aNK3nPhQsXWLJkCdu2bePM6UME+sP97fXM/OzXSitut4zZ4sUriQQHB1FQKNOy+wVeee0DDh85wtojh4l+7qnrPgBN+PJb7qsZzTKlJKpCcU9Tgm7FX8axY8f47NPP2LRhE27Xz8vBNCru79qZf/zjeTp06FDS9qWXXmLmN7Oor2lOZX310kvMJBenLAdw+lpZt2EtTZs2LTm2d+9eBg0YRKArnEa+rTlauIesIBeVJ00urjeNF7fswmAw4P9z9tK0jZtx7j/EmRMnCAsru8frXpOVlcWMGTOYO3suOdk5yDLoDXqGDBvMY489dlMz3DfjwoULdGzXkcbqdj8/JCkmSV5yc/OQvBIaQYOAyFnpMKIW2gZ1I8UeT7znDI8+8QgffvjhLRnL38GcOXN4/ql/0MV3AJrrJB6UZZk9lg1MemY877333h0coUKhUPxqxYoVjH/kEaJfev7XrTReCUuRhZQZsxE0GsImjgNZRrLa0Go1BAUGIYjFf8+dubnEf/QpPmot6iI9LXw7oRV0xFnPcdFxBkmvQV87GnRq3JlZeDOzwCnxQGC/kplugCxnOiccW3jrH0FMGFq8isgpOfDx88HH5IMsy/zzg8vMXeJg564DJVU9oHjWu3nzGHp3lVi5Po9508Pp2rF0IkxJksnN96DRGAgICGDKSwmkZDTgjTffof+gQYQMGkBw6/Jz1uSfPkPGoiUsmjPndz8MVygUd5ayp1vxl9GiRQsWLFxAamoqFy5cAKBWrVpl9lQnJSUxe+ZsaqljSpWb+oVW1NLcryP7zVv4+KOPWbhoYcmxjz78CJ3TRGO/NjgkOxmedALb9kH8OXmJiAq1oMFut2PyMaFWqwm7vxNxe/ezaNEinnvuudt3A26RsLAwXn/9dV588UVSU1PxeDxUqlSpVObrWyE7OxuvV8JHX/qDRxRVBAUFkp9fgMvjQkRAh4FM9xV2mdcia7w8/tRjvPvuu7d0PH91hYWFaETtdQNuKN7nr0VHYWHhHRpZ8R+iZcuWcfbsWSRJonbt2gwfPpzg4OA7NgaFQnFv6dWrF6FBQWTv20+VAf2QvBK5ebk4s3NxpaYRPGwwAgKSxwOyjNvlJi8vrzjppyigCw5GXT2K3NMX6BM8DK2o41LRWc67zuB3fwf827RDpdcjI+GSncg5uWQuX8nerK3cF9ADo6o4OE52XKRRAy3jhwQCxQ8mZWRUYvFebUEQeOuFqqzedIG5c+eW+tu0ePFikKz071GFletziapS9vNXFAWMBpEiqwOv5KV6VR0nzufQsWNHJo0bx4z583DlFxDaoR1qn+IxeWx2cg4eIn/LdoYPGkSvXr1u9z+HQqG4Q5SgW/GnUaVKFapUqVLh8fnz54NLpJpfrQrbqAQVUdrabN64mcuXL1OtWjViY2PZt2c/DXQtEAWRfHcOkgpMdUsvb1ahwoMHu82Or58vaoMBbc0aHDx06JZd452g1WqpWbPmbevfaDQiCAIuyYlR5VPqmEqlJiQkGKfTid1ux+two/fV8fjTjzJ+/PhSMwmKmxMQEIBbcuGWXGjEijPcyrKMEwf+/tevM/t7OZ1O1q5dy44dO7BarYSGhjJgwAAOHDjAtM8+p6iwCB/BH0EQKJIKefdfU3n40cm8/fbbf8ls9AqF4vr0ej1PTZnCO/95n7xqVRGiovB6JASrEwBtRDiy14vsdKIS1KgENW63E7OleDYJGYTgIFSCBq2ow+61cdFxBr/7OxJ0f+eS8wiIxf9FhBM6egTZM+cTW3SaZv7tcEtu8rypvNg/uGRFnEf2IIoier2upA+tVmRYfz+WrFhSKujet28PndrqqFypuG1Wjpfo6uVdq4jF6sHtcpOV48bX1w9BEPjoo48ICgpi+jdfE7d7D9rISBDAlZaOQVTx1COP8M477yBWkGhOoVD8+SjfeBR/GcePHSdADkElXL+Oc7iuMhcsxzhz5gzVqlXj4sWLeNweQg3F+7Ek2QuAUCYbuoCIgPuaOtaiRoPL5bql1/Fn16hRI8LCQ0nNSCZAU96MpoBOp0elUWEVCnnplRd57bWye/sUN6dHjx5o9RpSHUnUMNatsF2OKxOX6ChJCnQrbNiwgeeeeY7M9Cx8CUAlq3EIVr6Y9gWSR6a2sSHNfe5D/3NyN5fkJNkWx/TPviQrM4uvv/la+VKpUPwNPfvssyQnJzNrwQKkyEr4NW2G4JaQJQmvxYKo1yMiohE0gIAKNQ67o3hllgxehxPtzw8ZL9vjkfUaAtq2K3MeERUerwdTcDDW1s24vGUvDaXmeGQ3gihTrXLx33mv7EXCg8nog/CbpK1R1XTk5echy3JJgO6w2wgPUFGvtoHKETp+Wm2hXcuyyc5EAZDBUuRi3RYrkx4pXiquUql44403mDJlCj/++CMXLlxAlmXq1KnD8OHD/xRb1hQKxe+jfNtR/GV4vd4yfyzLIyIiy8Xty2NUmRBkGWdGegU9FKdBkCUJd3oGUdWq/dEh/yXpdDrGTRhHBpcp8pgrbBdnPYdKLzJmzJg7OLq/noiICPo91I9kdyw2b1G5bdySm1jHKRrGNKBt27a35LwbNmxg3JjxeNIFOph60N6/O20CutBQ3wrJLVNDrk+Yoyoarpk1EnXU8YkhRteaJYt/Yt26dbdkLAqF4s9FFEX+97//MaBXL9yxCeQtWUHOipXILhe2s+fRoEEraIHiIFclqEtKeUpeL/bzF/GneNVOtjsTfd06iDpdhefz8/fHr1EMbtHD8YL92DxFSDJk57txSy48shudXo9Go8FqtWK1WnG5nIBMTo4b088ruH4RHlGZ2Hg3oghjhoaxckMRJ844So7LgCTLuFwSkiTx8fRUrqYXkZaWRnJyckm7oKAgpkyZwueff84XX3zB008/rQTcCsVflBJ0K/4yatWuhUXI50a5AXNdWag1qpIl1nXq1EGtUZPjKi43EqQJwwcfzMeO/OadxRWuNeriJ+OWuHikvHxGjBhxy68FIDc3l+nTp9OmfXuqVI8ium4dJk2ezJ49e254jXfb008/Tf1GdTli3UmG4wqSLJUcc3jtnDUfI5V43vznG1StWvUujvSv4f3336danSocKtrOZXtCSf1ZSZZId1zhkGUbuhAV337/7S0pF+Z0OnnumefwcwbTwq9TqeREyfZLGPGlqhCNJBXXzv2tSH01/ORAZs2c9f8ei0Kh+HMSBIHw8HDCtGG0U3elmdyOKlIU9uNn8JqL+CXgBhAQAAFJksg7eBjJYinZTuMVvCX5V35LwotGrUYUBYIjItBoNXgCbRx37cXtlVi2oQBBLaDT63C73RTk52MxW7CYLeTl5pGZmc3ilXk80L10MrNhw4ZxIc7D0ZNFPDI2gkYNfBj5eDoLlhZitXqRpOJZ+9gEJ//+NJ9vf7ChrRfDvFUr6di5Mzt37rxNd1WhUNyrlKBb8ZcxZswYXKKDTOfVCtvIskyKM47mLVsQExMDQP369WnTrjXJztiS5WO19PWxnzmP+cSxkvd68QIyBoMBZ04u6UtX0LZVK1q3bn3Lr2Xfvn20atuWN6ZO5bLJgPb+TniaNmHVgf30GzSIRx599J5e1u7n58eKVSvo8EA7znoPs9uynsP5uzhYsJ3dReso8MnkvQ/f4+mnn77bQ/1LCAsLY92GdfR4qDvxnGanZQ17ijaw07Kac97DNOkUw7oN62jQoMEtOd+aNWvITM+inqlJqSBelmUyHKmEUwVBEFGhwulwIEllV5VEaqqzZ/fecoNyhULx1+dwONixYweF1kJku4zR60uUVAetRSZj/nxsV1KQZQkoTnAmuV3k7N5HzvqNdGrfngIhB6fkwIABV2Zmmf5lJCQkDMbi7S2OjAz0Wh3Lli9jw+b1vPjyKxw7BYeOu3E6HOAFjaBDJ+rRino0go4lK82cjy0iNDS0VN/33XcfdevG8OLbqZgtHhZ+U5cuHQN57b1cWvZIYeD4qzw47CqdH7rKonVewgePpO6Tj1Pn1RdxRVZizITxXLp06U7cZoVCcY9QSoYp/jJkWWbwoMHs2bKPFsb78NcEljl+segUV8UE5i+aT+/evUuO7dixg6GDhxHqrkyMb0sEBM5ajpLgiUdXpxa+zZoiBPujRsYTl0DR8ZPUqVqVNStXEh4efkuv4/z58zzYuzfO8FCiRo0oyWr6yzUUnDpDxk/LGDNkKF9On35Lz307nDp1isWLF5OSkkJmZiZGo5H69evToEEDBg0apPze3mLJycmsW7eO3NxcfHx86N69O40aNbql53jiiSdYM28D7f27l3pdkiXWZiyiNo2JEIu3XTglBwEB/ugNpfc7ZjqvcsZ7iHMXzhIREXFLx6dQKO5tkiQxceJEli9ZgcvhopnYCT+h+G+2Q7ZxRjhCkboIbaUItJGRSB431ksXkaw2fHQGRo8Zxbat27GlO6mqieaY4zCRjzyMLrz4s6Q4c7kLtVpFcEhxsrTk+T9Q3eXmwL59CIKA1+ulb5/eHD2yjVeeDGFIr0B8fYpzwuTkeVi4Ip8vZxcg2AMR/WT2HdhL7dq1S64hOTmZgQ/1ASmNSSODGNAriMwcK9/NSWP/MScJKR70NWtR+4nHUV2z9F1yu4n/+DPG9x/AZ599dgfvukKhuB2UOt2Kv6X8/HyGDhnKsUMnCCWCSH111IIGizufZEccFrmAmCYxdOjQgUGDBtGhQ4eSRE6LFy/m2aefBbtIJVUUvip/slxpXHYl4RLcqDQatBoNIiBKYNDriapenXHjxzJy5EgCAwOvP7ibNPnhh1mxdw+1nn2ywiVzuYePkLtiDft27aJ+/fq35Ly30+zZs/nvx++Tn59JzSgdahUkXnah0fgycdLjvPrqq0om6z+R8ePHs/unA7QO7Fzm2PrMH6kkRVFNqIMbFy7JSYh/MEZT6Uz2SbZYUtQXSUpJwvjzTJRCofh72LFjB4MGDCZG3ZrzRSfQeLTECG0Qf87LIssyuVIm6aTgUDmwymZ0go42gZ3JdWVyRUqgSs1I7DY7WWnZONwuhMhQwkePQjDokJBQq9UEBgaiUqvI2LqdrLUbGDV8OEOGDKFr165otVpGjRrF2qVrEdUefHxlGtXX4vHKnD7vwmVXUU0TQy1jQ3YXreOxZx9h6tSpeL1eRFFEEATS09P58MMPWbVyCU5nEQ6HA7dXRhNWiYAO9xHasT1COckiM7Zux7XvILHnzt3ysp0KheLOUoJuxd+WzWZj3rx5zJwxi8T4RNxuN3aHHVEQ8NcEYxJ8cKrsOAQrDRs1ZPbc2SWlqs6fP8/MmTP56cefsNlsCIJAq9at6PZAN9auWcvRI8fwFwIJU1dBJagpdOeSTRoh4cH88OMPtGzZ8v819szMTBo1bYpvz+6EdmxfYTvJ4yXug4+ZMnIU77///v/rnLfbtGnT+PCDtxnxkIkpEyOIrl4845mZ5WL+T1l8MTOfvv1G8uWXXymZrP8kXnjhBRZ99yOdfHuV2SN+LH8vqfYkVGodbsENgF7QU1Nfm+qGOuhUemRZZp9lEw8M6sLcuXPvxiUoFIq7aOzYsWxfuZsOvg+S68rkQP52/OUgagoNMAl+yLKMJEkUkEsS53GJTjoGP1iygq3IY+awbQdde3WmQ8cOfP3V18QlJSIG+OPftg0hLZtj8vcn/8xZMtasQTbn4WNUYzTqcLkFgoMjGDV6Ap/+91NqSg0J11Xhij2BIq8ZAQE/dSBVDTXRisUz1GcLj5Glu0JISDA5ObnodFq6de/GpEmTuO+++ygoKODIkSOMnzQJ6tel2rDB5Qbbv7Amp5D67Qz279r9p3hwrlAoKqYE3Yq/PUmSmDdvHv949h/4e0Jo6NMCk7r4ibIsy+S5szlvP45fpJGNmzdSvXr1kvd6PB4sFgt6vR69Xs/YMWPZsGoTjfVtCNNFljqP0+vgRNE+xGCZrdu3EB0d/YfHvG3bNgYOH06tV19EGxhw3bYpPy6lkahm88aNf/h8t9uFCxfo1rUj/3jcj39MqVxumzWb8pjy8lW+/mYeAwYMuMMj/Htzu91kZ2cjiiIhISE3vdpg79699O/Tn2baToRof91ekefKZr95B04D+DZrhrFajeKMw8mJ2E6dQedS0d6vC9nOdJI4z8o1K+nUqdPtujyFQnGPqhFVk8CCCGr7FOdWyXamc7xwPw6vHR/80chanNgpwowXD52CehCuL/035LI9gXhOc/TEUaKiooiLi+Ozzz5jxZrVWO0OPB4PuIpo0kDHU5PDGdQ3HJ1ORXySnTk/ZDFvSSG5eS7amh4gTB9Z3jCB4r3nl/LPEiefpq5PY3zVAbhlFxney9hFKyNGD2fatGloNBrqNGiAs1FDKj3Y7brXX5SUzNXvZnJwz17q1q241KNCobj33WwsqkwrKf6y3G437099nyBvOK387y8JuKE4a2qwNow2vl0oSDPz9ttvl3rvL8vSDAYDhw8fZv3aDTTUtSwTcBe688l0XSVMXZnCrEI+//zz/9eYJak4y7eguvGvpqBSlaoZfi+aM2cO4aESTz9c6f/YO+/4KMrtDz8z22sqaUAKoXcIvTepIkoH6WDvXa8Vr/dn71cslNAEpCpFQar03mtogZDes9m+M/P7IxCNSSiKXsV9Pv4hO++8ZTY7M+e853xPpW369wqmXUs9iTOm/okz+2dz8eJFXn31VerUqkujBo1pUK8hTRo14d133yU7O/ua57dv355GTRpxwrkft1xSJscp2dlp+wmxRlWqP/ww1m6d0NaMwVi7JlV69aHqgw/ii7CwMf97zkhHefKZJ/0Gtx8/N5nc3FzWrl3LqlWrOHjw4F+u0oWiKDgcDlwuJ2pRU/p5FV0kt1W5i5ZBHTFrLMjImIUA4oS6qNGUqZBwhSh9DF6XjwkTJjBs+HAmT55MrVq12Lh2HfNnzSIy2EKPToGsXdyIEYOi0OlK8rVrxhl4418xfPV+NfQ6Hymuc5XO1+NxU1hQgBcPepWJhtYWxBhrUtNUn/aWXtRXJbBgzje8/PLLADRt3AhH0ulrXoeik6ewWqxE+0uO+vHzj8GfROnnlmX58uVkZWTT3tSr0jJJWlFHjKYOP6z6gdTUVKpWLb8bO3PmTHSSgQhTtdLPst3pnHAcJk/ORVGV9K0oEl98NZWWLVv+5trTNWrUQC2KFJ87T1DTJpW2UxQF94WL1OnUucLjBQUFfPPNN+zcuROXy0W1atUYPnw4zZs3vyklo66XH77/lmH9rWg0V3ciDLsziMde3EFubi4hISF/0uz+mezcuZMRw0ZSnGcnQqhOjLYeCgqZl9J4a/LbzEqczZJli6+6+yIIAjMSZ9CvTz92pK0jVlObYl8RXoNI1cGDQadBxoeCgg8vPsULJg1BQ+4i8/OvuHPQAP71r3/9iav24+fW5sKFC7zzzjusXLEEr9dx+VMVtWvX54EHH2Xo0KF/6r3/12RlZTFnzhy+nptIRkY6tuIiTklFKEB1fQ00ogZREInSx2D1BWP3OdAKOi7JZ1GJKnSivkx/HtnN3qKt2CQnO5NOYfK6UXwSK9av56333qVHl644nYV8+O/aGI0Vv+r27h7MwNstLFxyHkVpX+H1KS4uBgVyySBCV63MMUEQqGqIxSW7mDF1Bg8//DDjx45jzZjRFJ9PxhwXW+G4PqcT2579PDhqFIZfCUzm5eVx5MgRvF4v0dHR1K5d+/ovsh8/fv7S+I1uP7csGzZswEIgZvXVRUqq6WM5WXSALVu2VFhze9+e/QQL4aUP5FRnMnsdO9DEVqdK6+4Y42uCIOC4dIGC3Tt58LFHycvL49FHH73hOcfHx9O+bVt2bNtJYJPGlb4k2U6fQcrOYfTo0WU+VxSFr776in9P/jf2IgdWJRgVajaKm5n+1QzatGtN4szEm664XhmFhUVEhJuv2S4yXAvIFBUV+Y3uP5CUlBRGDBuJnCvS0dKntM4tQLiuKi6pIXsv/sSQwUPZum3LVcOk4uPjWf3jal599VW+X/kDee5CLC3bIukEBFHGbDRhNpvxeLz4fCW53eqgQHwtmnPq9On/qQHgx8+txIkTJxgyZAAGTQ7PPhzE7bdVxWgQOHbKxexvzvHE4/dx7NgxJk+e/D/53R04cIBRdw/F7cpmYD8zrRPCKLZb+HFDHms37iSl6CQtLd0xqkqeFSVzVFAUhXQuEKWPLrMr7pO9bC/YQKHJQ2jfwYQ0bYw1oORe5XM6yd6yjSXLltC7s4G4GH1FUyplzLBwFn53mjP249QyNyhzzOfz4vF4ySYVJw7ijBUbwDGGmpy3neDrr7/mqaeeok2LluyeO59qY0dhiq5epq3XVsyFWXMJ1um47777Sj8/f/4877//PksWLcXpcIICKrWK1m1b8cijj9CnT5/rvt5+/Pj5a+I3uv3ccqSkpDBr1izmfT0Pvd1KpjMTjVaD0WhEr9cDZV861KIGERGHw1Fhf7IsIV5+UXFIxeyz78TQvBFV+vYvI5RijI5BVS0c38HDvPbGG7Ro0YJ27SoXQ6uMJx57jG3Dh5O6fBVV+/ctJ8bizMgkfeFS2rZqVa7/zz//nJdeeJlIOZYW5gboVCUvHIqikOVJY++W/fTv15/VP64mODj4hud2owQGBpCW7rxmu0tpHkAkICDgD5/TP5np06djyy2mk6VvGYP7CnqVgebmjmw9/wOLFi1i4sSJV+0vNjaWWbNmcfjwYdp37kRQ3ToEBAWi0+lKX+51Oi063c9jmWvGc2bF90iShEpVEu5pt9v59ttvOXToEJIkER8fz5AhQ8rVxvXjx09ZvF4vY8eMJDI0j5mfVEerceP1FeL1KtSpIfDea8G0beHg1Xc/oXHjxgwePPg3jeNyuVi+fDmLly4lIzMTi9lMr9tuY8SIEVf9naampnL3yCHExxSS+HEdgoNKjGdJlujZScOFCT7ufzqLvRfX0z6gHypBjVarQ1ZsnFGO4BKc1DDWLdPnOccpCtTFRNw9FqFKIHrDz4a12mAgsmcPCrZvoXqUB5/Pi1qtoTLq1rKi1apJchxGEARiDDVL7412dzEX5dOkCxeoYapLsLbidapFNVY5iKNHj6JWq5k3dy7DR45k12dfoo+vgaV+XQS1GnvyBRxHjhEeFMSCBQtKQ8sPHz7MwDsHYsuyU00dT6SxOipBRb43l+Nbkhi1YxSvvv7qb3LkV8S5c+fYs2cPbreb6tWr07FjR3/1ED9+/gT8Od1+binWr19P+zbt+eitj9G49LhwICoqfB6JgoJC8vLyURS5zDkOqRhFUAgNDa2wzzp161Cg5AKQ7DiNYtQS2qu8MSwrMoIoENWzB2KVUKZOm1bmuKIoHDhwgBdeeIGxY8dy3333MWvWrJLwtV/QrVs33n/7bZy79nDmg0/I2rqd4nPnKTxxkgvzF5L86RQaxcYwe+bMMrsW6enpTH71daoqNWhoTSg1uKFk5yBcV5VWpi6cO5nMxx9/fOMX9zfQ7/aBLFllw+uVr9puwbI82rbt+Kc4Av6peL1e5syaS6QYXaHBfQWjykQoESROT7zuvqOiotBoSoxrvV5/1d00RVEQBAFBEFAUhS+//JIGTZrwwOOPk/jD98xZv44X/v06jZo25fnnn8fr9d7QOv34+Sfx/fffcynlLG88H4AoFCOKPgItIkEBKixmAcnnpt9tIp3bqvjyi//+phzvPXv20DQhgUkPPsimc2c5Y9Cxt6iQl9/8Pxo1a0ZiYuX3iunTp4Ocy+z/xpca3AAqUUVQUCAx0Wo+eSsEtzqP845TuCQHWb5UTgh7ucQ5GllbEKT9+dksKzLnPWcwNWmIWCUYtVqNtoLSmprQKuQXyTgcJU5fn8+H3W6nuLgYu8OOJEkA5BdI6HR67hjYn1TtGTbZVrKzaD07itaxzbWGFM5Qx9yIhpaEa1wlAVkqec6FhISwasUKEr/8koTQKhSvWUfB8lVEFdr4z0svsWPrVpo3bw6UVFsZMXwk7iyJ9pZe1DTVx6S2oFcZidRXp5W1K9WVWkx+ZTLr16+/ru+rMg4fPsyQwUNoldCKeyfcxyMPPMrAAYNo3jSBr7766i+X/+/Hz63GH+raevPNN1m6dCknT57EYDDQrl073n777TK5gi6Xi6eeeooFCxbgdrvp1asXU6ZM+dPCX/3cOhw5coTRd49BX2yms6U7Bb48tuWuxSYUEiSEIisyHo+HgoLCMjW1LzjOEBQSRI8ePSrsd8zYMXy/4gfyPTmkeJMxNW+MqPm151xBwodep0elVhHQqgWrVq/GZrNhsVjIzMxkwqRJbN+1E8FqRR0WhuJ2M3/ZUl5+7TUG3H47BQUF5GTnEhgYQO8+vVm6cCFz5s5l+apV5Pu8CAjExsQw6ZVXGTNmDGZz2bDtuXPn4nP6qGVpWOk1MqktRIrRzJk1l+eff75cPtnNQpIkCgsLueuuu5iZ+CXvfZbK849Vq9AYW7oql90HPHw1ddIfMhc/JeTk5FBQUEB17bWVcoM1YZw5c7LUQL5m++BgoiIjyTtxisCGDa7atvjkKerXr4coirz77ru88c47mFu1IL5LJ3TBJb9Ln8NJ7q7dTEmcQWpaGokzZvh3Yvz4qYCFCxfQrJFAdFUIClCh1ZZ1BhsMYLdL3NlHx8Mv7OXkyZM3VKLq6NGjDBw6FFdIEPHPPIEu9Of0H5/DSfqPa3nyuWdRq9Xl0p08Hg/z581i5F1WAqzlf79arY7gkGAa6ux0apfPmo27Oes7jkqjokmnxhw5fJRM9yVCvGFYr5QKk4pwKHZC6tUBQSkNK/815rr1WbvhNFk5dnySD4/bgyKUOKAVWcFGSXWSRd8VYbUGM23aNGw2GwsWLODs2bOIoojZbOazTz4jRBt21fugpEgUqwqoVbvWL9amZdCgQQwaNKi0/NmVyJ5fsmzZMlIvptLB1KdCZ6ggCNQ2NSLPlsWUKZ/TvfvVVdErY8eOHQwZNBS5EOromhEZUB0RFYW+fJKTT/HsU89x8uRJ3n//fX/qjx8/fxB/6FvMTz/9xEMPPUTLli3x+Xz861//omfPnhw/fhyTyQTAE088wapVq1i0aBEBAQE8/PDDDBw4kG3btv2RU/NzC/LxRx+jFAs0t3ZAJagI0YQRoAnijPcITWiHVtChUTS43W68Xg8ajZZcTxaX5LM8PvExjEZjhf12796dxs0ac2j/DlySk8CQX++IK3gVL4pA6d+1PjyMfJ+PvLw8vF4vt99xB2eys4gYNRJrvTqlu+T27GxSvl/DlK++IpAAQjTheJTT/LByNcGhQfx3yn/54P33ycrKQqfTUbVq1UprWa9ft55gJRyNWHkoHUA1fRw7885x4MCB3xT+fjVSU1NJTExkzqw55ObmAgIms5kPv8whNcPDQxMiqVe75DpfSnMzc0EWX87OZ+iwCfTr1++mzsVPWa783VzPboaCgiiKeL1e7HY7JpOpwt2kX/Y9YexYJr/7Lt6ePdBU8iLsyszCeeo0kz78kOPHj/Pmu+8S1KMrET26lWmnNhoI79oZfUQ4y2d9zZIlSxg2bNgNrNaPn38GyedP06KhiMVc3uCGkmQqs0lF43pafD43ly5duqrRrSgKxcXFaLVadDodr05+DYfRQPzEcYi/ugeojQaqDehPisfLS6+9xsCBA0ufgQAZGRkUFubTsW3F5SIBNGoNgYGB9O7uYO8RGzNnzic+Pp64uDh2797NxAmT2HFhHWZHADrFQLFciISEymQgKDio0vtSSOuWnFy1nM+m5/HcY6GIBj3CZWe5IsvIXi9JZ218NSedtu3vQq/XYzQaeeyxx8pci80/beb8gVOEaiMqNUZTXcnIaqlSAVVBECo0uAHmfT2PYMIwqSvXPhEEgeqaeH7asKlSwderUVxczJhRY1AX6WgR0AmV8POrf6AmmKYBbUlxniNx6kxatWpVobaNHz9+fj9/aHj56tWrGTduHA0aNKBJkybMnDmTixcvsm/fPgAKCwuZPn06H3zwAd26dSMhIYHExES2b9/Ozp07/8ip+bnFyMzMZMXylVTXxKMSSh5ugiDQMrATikrmgLKFVPk8MnKJEmlxNsdt+9nv3EKn7h157rnnKu1brVaz4Jv51GgYiw8vXqcNGQkZCZ/ixa24kQWZwIAANNqSh7rP7kAAjEYj77//PqfT0oi9/x4CGtQrNbi9Hi/FkkxQz54EtG2HAxf1zc1oHdiVTua+iLl6xo0Zx65du6hZsybVq1ev1OAGsNmKrxo2fAWNqEORFZzOa+da3wg7d+6kY/tOfPjWx6gzzNQXWlJXaI4u34LTLjBvSSE9Bp+mVc8TtO93inb9TvH1UoEnnnyFDz744B/lXfd6vaxdu5bp06cza9Ysjh079oePGRoaSmRkBFmetGu2zXBdQlSJRFeLplZ8LapXrc7o0aPZtGlTpUb72LFjiQ4LI3n6TNx5+eWOOzMyuThjNg3q1GbgwIHMnDkTxWwivGvFCvwAAfXqoqsZz7QZM65/oX78/IPIyyvA4ZQx6K/+OifJIEkyp09XXM4qOTmZyZMnU7NOHWJr1qRqdDTtO3VkzZofCe7QrpzBfQVBEIi4rTv5RUUsXbr0N69DEER0Wh09evQgLi4OgFatWrH/wD7mzp/D7aN6k9CvEbcN6obRYMDo9V7VEag2m3HLaqbPt/HGJ4Vk5pUY0bIk45MUftrhYuwTOWTlCyxbvpzut91GampqubU98+wzFKlzOVFccfm1HE8mpzyHGDj4LmrWrHnD6065mIJZvLaWiVUThCTJpKen3/AYS5cuJTszh8bm1mUM7is4pGK0og69z1SyeeEPM/fj5w/hT43XKywsBCjN29y3bx9er7dMWG/dunWJjo5mx44dtGnTplwfbrcbt9td+u+ioqI/eNZ+/g6cPn0aj8tDFWPZetAmtYWOIb04bjvAedcJzirHSh4oboWqYVV5ctITPPPMM+h0uqv2HxkZyZof19C7Tx8OHDuGuW2r0rxUg0GP0WhCo/n555S//wCNGjXCYDAwd8ECrK1boAspm69cVFQEioBW0BLcoRMp+w9y0XWWWqYGGFQmmlnasbdoC08/+TT7Duy7ZnhtVFQkqYcPXrWNrMgkO5Jwep28OnkyX3z5Jd27dWPYsGFlQu5vlOTk5FJV7M6WfmWM/6r6GGr7GrOneBNWq4UBg0YhiiLx8fH079+/zM7IrY4sy3z55Zd89ulnpKakgiKiKDIqTYlK7WuTX6NVq1Z/yNgqlYrxE8fzf6++iUtqiF5VcWRHRsEl0l0pWHKt1NDXx6gy4vQ62PDtZlYt/577H7qPN954o5wDKCQkhCWLFjF42DDOvvshxvp1MdeMR5Flik+dxpmURMO6dVm04BuMRiPLv/8ec5PGCJXsAF0hqEUz9n2zhIyMDCIiIm7a9fDj51bAYLSy7qeLuFwyBkPlhvf36x3IMhU6bteuXcu4SZOwyzKWhGaEVq+K7PFwYv8hnD4f2dt3EtCwAWpjxelI2qBAtNWrsX379jIh5hEREQQEBLFlRxGd2l7dsNyys5g6dRqX+1yj0XD77bdz++23l352x513smPXHoKbNy3ZtfZ4EDWaMveSopNJKD4fhg6d+HrlIb759iwJTXRYTCLHkrykZsqI0XFY+tak8McNHDh7ljsHDmTNDz+U0Rbp378//3nrP7z0wkvk2NKpqorDog7AI7tJ81ygQMihe69uv1knRavV4VR812wnyT4EQbiqo6EyFi1cRDBhGFRln7V5nmySHEfJ9KUjiwIKMln7Mxlw5528+sorJCRcK4/djx8/N8KfZnTLsszjjz9O+/btadiwJOc0IyMDrVZLYGBgmbbh4eFkZGRU2M+bb77J5MmT/+jp+vmbIlB+t9SoMtMisCMuyUG2J4Nkx2kCYozsP7j/hnKarVYr7737Lv0GDEB9MYXgFgmIKrHcDm3xufO4kk4z6cOP2LdvHwVFhcQlNC/Txuv14vV60QhaQEBlMKKvXYvM42nUMpXkxIqCSG1jI3Ynb2DdunX07t37qvMbPGQwa1evo9CbT4E3hwx3KhISWkFLNX0setHI7uKt2LChi4vhrE5D0qUU1rz6Cq//3//x9n/+Uy4n73qZOnUqxXl2Olr6VhjeblKbSTB3ZHvaj9SuXfsfGSqsKArPPPMM07+aQYQSTRtjD6yaIGRFJtOdyuHNxxnQ/07mzpvzm/P2rsXYsWOZmTiLPcmbSTB3KC3Rc4VMWxr7HFsxqEx0DepfpkxPnFKHi84zTPn0c8LCwnj88cfL9V+7dm22/vQTixYtYsbMmSSt/AFBEGjQoAGTPvqYu+66qzSNw2azobNevZwfgMZqRUHBZrP5jW4/fn5FrVq1WH3mENO+LuSRSRU7TjOzfcxZWIQgqsppgRw+fJgxE8Yjx8ZSe+TQMjvahoYN0J1MIm/pt5ybOYdaD9xT5nknSRKSTyqJYddqymyIQEle8/ARY1gw/yMeuceH1VLxK+fpc042bnPw/gcTrmvND9x3HxtHjuTE+x/jys5G9noRBBFrnVpUad8Wc62aXPxmEeoAK7FDBuJz9SNly1Z2pqaBTUFV3UrgHU3RVY1Cdrsp3LgZa8vmnN6xmylTpvDSSy+VGe/++++nRYsWTJ06le+WLcfr9SIIAs3aNGXSPf9m4MCBaMrpvFwfnbp05OvT85EVGVGo3GmS5r5ASJXgMppI10t6Wjomsey9Ns11gb32HYhRYQS3ugNDTBwKMrbzSWw+fJR+A+4gceo0f6kyP35uIn+a0f3QQw9x9OhRtm7d+rv6eeGFF3jyySdL/11UVET16tWvcoaffwI1a9ZEo1OT7cnAVEldbr3KSDV9HMneU3Ts1PE3iYi1bduWsXffTeK8echOJ6FtW6PSl6iEyz4f+QcOkbV8FT06dWbYsGGsXbsWRQH1r/LFPR4PAOIvMjxUBgM+ynq8AzXBGFwmtm7dek2je8CAATz91DOsz1qJoNGgqxGDymjCl5tLStoWJJ8XbUQYYXeNp0qtmhgu71p4i4tJX7OOR596ClEUufvuu2/omrjdbubNnU+kEHPVfHKLOoAgqjBj+ox/pNG9ePFiZkxNpL6mOdUN8UBJ5IHNV4hO1NPY1JqT9oNMHD+Rg4cPlnNG3gxCQ0NZsnQxQ4cMY+vZ1YQQTrAmHAWFHG86qY4LaEUdXUL7lTG4oSTUMsZYi2JbER9/+An33HNPhVEKVquViRMnMnHiRGS5RM23ot21oKAgCioIQ/817rw8REH4XZEYfvzcqrRt25Z1P37HW5/mIUkK94wOxGT8Wb/hwBE3j7+UhccrYDQYSjc9rvDRxx/jMZmIHzUc8VfRVCpRhTYqgpCBA8j+egHFZ85hqRWPx+PBbrfjdnsABUWSKUq+wEGPjx07drBv3z6WLl5Kbm4uOp2OvEKR0Q+eYeanNQkKLDvG+Qsuxj1ynho16jNgwIDrWnNKSgqSJOHOz8fcuhXasCrITif2g4c5/dV01FodotuNqUYcKrUau+zE3Lwp6k4d4FdOclGnQ2U0IggC5uZNmTl3Ls888wxqtZq8vDygJDqzRYsWtGjRgo8++oiCggIMBsNNuUePHz+eWTNmk+I8S4yxRIjNJTmwS8UICJjVAbgkO8nu01QVI2nTqg0mk4kePXswduzY0lD8q2Eym7HJuaX/tvkK2Vu8A13TBoT1618aISAjY2rciOBOHUlbvJSJ993H9s2biY2N/d3r9OPHz59kdD/88MOsXLmSzZs3U61atdLPIyIiLqtJF5S5eWVmZla6o6HT6a4ZCuznn0dERAR9+vVh7ZINRCvxlXqMsz0ZuAQ7Y8aO+U3jCILAe++9h9ls5qvp08nb8BPa2GgEQcRz6RKi08XgAQP45KOP0Gg0hIWFIQoCrpwczOafDZSSnCmBX9YM9+bmYhH05cZUo8Hlcl1zbuvWrcPmcmBo2oCAzp3QBYQgCioEoDj9Armrf8RXUIjBZCjjcNCYzVQfOIAUSeKFl19mwIAB5XZDfonP52P9+vXMmj2bw0eP4vZ4SMtIo66uyjW99SHqcE6eOHXNtdxqKIrCF59/SaAcSnVDPD7Zy1nHSZI9Z3AqzpIXQUUhUAikOKuABQsWcP/99/8hc6lduzZbtm5m8eLFJE5P5PTpUyUl5WLCyTyppqO1N4ZKQs8B4ox12ZL7Pd999x0jR4686lhX0yAYctddfDhtGnK/3hVUA/iZgt376NS+Q6Ul/fz4+SczYsQI3n/v/2jawMcHXxbwxexCurY3YDaJHDnh4ehJNzXjDMTFaFBUTWnSpEnpuVlZWaz8/nsC+/QsZ3ADaHU6VKKIEB2Npkoo2Tt2oq5WtSRVUBQR9ToEtRrH8ZPIxXaSsrPo2LUrRkFLuKo6BtFEoezCIcms31JMQo8jDL8ziNYJFnySwrqfCvlhg4Pq1eswb/6i63KEf/vttzz30kuEdO9CUNfO2B1OJMmHAhga1sd5/CSFK7+nbu3aJBcVoigKHo8bQa0pZ3ADyF4vstOJqNdjrVeXC9t28Oyzz7Jp449kZ2cBClWqhDPy7nGMGzeO8PDwm1r1o1GjRoybOJYZXyVSVFSIXS4m05eOIgIoICt4PS7UghpvGkgqDTmKjf8ensJ/P/mMZ59/hmeeeeaqmig9e93Gh/s/wif7UItqkh1JYDUS1vf2MiH5kuJDVIlo9Hqihw8h6f/eYdasWbz66qs3bb1+/PyT+UOF1BRF4eGHH2bZsmVs2LChnEcuISEBjUZTpvbgqVOnuHjxIm3btv0jp+bnFuSxxx5DMfk4WLQdSZHKHS/w5nLUtZsOnTvQvn373zyOWq3mjTfe4ND+/bz85JP0qVWHnnE1eGLCRHZt28b0qVNLdwCbNWtGjdhYcrfvKtOHKKoo0YguESzx5ObgTr5ANV1smXaS4sOhFBMZWTZX/dd4PB6eevZZ9A3qUWvcGIzhVfAJXjyKC5fiQhUeRti40agDrRSs/4lfR+ELgkBEzx4UFNtYvHhxpePk5OTQr39/ho66mx+PHaWoVjyu+nURY6pz3HuMzQVrcEmOSs9XUCp677klKSoqYtasWbz88ss89thj7N61i2raODyym60F6zihnERsUZ/wcWOJuu8+QocOwVkzBDcSb7751h8qZmOxWBg/fjybt24mLSON1PRUhgwdQoA2CKsm8KrnGlUmTCoLJ0+e/F1zGDt2LBqfj9QV31e61tzde/BeTGHSxIm/ayw/fm5VwsPDuefehzlwVOK5R6sxfkQEmTkiJ04rRFcz8dUHtWjWyMzRkwLPPf9iGePszJkzuH1eLL8odfVLBAFMRhP4vOjiYnGkZ1BYWIigUaM2GRG1Wnz5BeR/vxpzzXiCJ4xFX7MmkiJS39ycepamNA1oQ4/AO6ku1CMrW2HhSnjo+SweezGH42djeOXV91m9Zv11RSzKssz/vfUW2tq1iOrXB6PJRJXQUIKDgwkMCCAwIJDoTh2JvL0vFy5dwpdfQPGZcyX3l0qeO44jx1B8PgIb1MdTUIjPWcjK5Yn061bM9I8imP5RZMn/T32THt07ceTIkd/0PV2Nt99+m663deGU5xiZoW4s/XsSdt8EQu8Zh7FHJzRhVdBoDMQb61PP0pQm1tZ0sfanulSLN99465r55KNHj0bQCZxxHENWZC56kzE3a4bwC0eLgoyMhNFgRBBA1GiwNG/GnHnz/MJqfvzcJP7Qne6HHnqIefPm8d1332GxWErztAMCAjAYDAQEBDBx4kSefPJJgoODsVqtPPLII7Rt27ZCETU/fq5G8+bNmZ44nUkT7uEn20oihRgCNcH4FB8ZnhTyyKJN+9bMmj3rpihlR0ZG8vTTT1+1jUql4v577uGZl16k4GhDAhvWB0Cv12ErEko8y16FnFWrMMh6IvXRZc5PdV0AjcygQYNKP5NlGbvdjk6nKxVVWblyJelZWdQYPQKNTkuQTovkk/B4vXg8bhwOJ2qrhYCOHchd9h2u7Bz0VcruHGoDA9DGRLN161bGjRtXbi0ul4thI0aw/3QS1e+diLlGiRNNUUCT0AxPSjq5S79lR+FGOgb2Qi2Wv73k+jKIjonm3XffxWazERQUxO23306tWhW/9P0dkSSJt956i8+nfoXN4UQbGorP58Uh+9jv2IXOrsNukokcPQFNYCDFx45QeGQ/ckEuCAK68FAuZaQzdepU7r333j98vr/1t/B7f0OxsbF88M47PPbUU5wvKCCsWxdMMdEIgoArK5vsrdux7drD/RMm0Ldv3981lh8/two2m42UlBQEQSA6OhqTycSLL75IUVERb348nfgYFQP7BRIYqOZcspNnXkvD7tTx0cdf0K1bt2sP8CtMJhNen5cCrw93Tg7uS6noq1fFk5WN/eBhivfuQ2uxENi/D4JGR8Sdg0j55BMuOs9S21wSyq4WNTQJaI1SpKAILg4fOYjJZEKv19/QfWT79u0knT1LtXsn/nyeQDlxsdA2rclfv4lQi4XMH9YQOHQgSgXjSHY7hZs2Y61TG1GvI+2bebRoouWbabUIr/LzbnavrkE8+YCX0Q+d4+6RQ9iwcetvjrwpKipi8eLFzJw9m+SLF1CpVNStVZude/YQ2r4dIX174ZUlFKXkWaIJq0JoQhsyFi1k5/mf6B50OzpRj0pQUcvUgMKCPP71rxf58JNP8Pp8REVGcvfw4dx9992EhYUBEB0dzYsv/4vJr7yOx+bGq3gICA+/PCMFCRmf4kWtUZdJGTJUjSJ3xy6cTmelJVX9+PFz/fyhRvfnn38OQJcuXcp8npiYWPpS/+GHHyKKIoMGDcLtdtOrVy+mTJnyR07Lzy1M37592bJtM4mJiXw9Zx5ptnOAQKMWjfj3Pa8waNAg9PryIdx/JBMnTmT3nj0s+noBtpYJhLZrgz48DJ1aTe7+g9h37kHILKStpWtpuTMAm6+AM56j9L2rL7GxsZw/f57ExEQWfjOXoqJCQKB58xaMHTeRn376CW1UJPqwKqXnq9QqDGoVKAoOwYkgCJgaNST32xXYkk6XM7oBBK2u0lJiy5YtY8/BA8Q+8gDGX9QJFQQwGA3IVcMJHzGC9KnTSHGdI85Yu8z5ac4LpHpSyDmay5GLyajNZryFRfz7zTfp1rkzH3/00Q3XH/2rIcsyjz76KHMXLSKgUwdqtmuDJsCKz+sj/fRp7Lv2U7B3D5a4RgiiQGbi56id+fTqZCShqQnJB+s229hbIPPc888waNAgQkJC/pS516tXD6dsp9hXhFldcZ1tALuvGLtko379+r97zNGjRxMYGMjkN97gzBfTEPR6BLUKn62YiCpVeOG113jooYf+UeXk/PipiFOnTvH555+zcOlSHJfv0VaLmbuHDee+++7j3XffZfDgwSQmJvLFnDXYHQVUCQ1l5Oh7GT16dIV5uTVr1kSn1lT6PABAgABrAGkpl5BtxWTPmYegLnlOqfQGqrRqQXDnjhQ5S0KgRYMOY6MGXNh/htr8nD8uCAK1TA3YlrmGTZs2MXjw4Bu+BidPnkRRqTDFxZSUvXQ5cTqdSJKEQImyt9FoRGM0oK1ejZY14tmxezc58xaia9cGU4N6iCoViiThTDpN/pp1CD4f0YPuJHfnboxKMV++H1vG4L5CcJCGWZ/WoG3fk8yZM4cnnnjihud/9OhRho0cyaWMDAz16mDo0A7F52PT2vVIGg2hPbtjCQhAEAUkn0R2djZqQYOoUxN+1yBSPv2EFOc5aprqIykSBwq3c0m+hBgeiqdpIwxWK2lpGbz2zjt89OmnzJoxo/T9+7HHHkOr1fLG6//B5/bi9TjwKO7L0XYKWp2OwMCSsa8gu90I8JtF4vz48VOWP9Tovp6QFL1ez2effcZnn332R07Fzz+I+Ph43njjDV577TVsNhsajeaqOcp/NCqVii+/+IIG9evzxdSpnNuxC68i4/N4kL0+8Eno0ZPpTsWkMuNRPKQ4z5GmJFO/WV0+/uRjVq1axUMPTsJicjFqoJWG9cKwO2RW/HiAxx6diN4QhBJcsXEmqkQEQJEkBI0aQaNG9nrLtVNkGSk7h8i27SrsZ3riDPS1apUxuK9gNJpwOpwooYHo69bh/InTxBpqlRpLac6L7Cj+CW21KKoN6E9AvToIKhWyz0fBkaNsWL2O3n37suaHH4iKivrtF/t/zMqVK5m7cCERwwcT1PTn3Em1Wo0+NBRNr+4IVhNFm7cipZ6nUayXT96Jo3rVKzs1CuPHhLBvfzEPP5nCgDv6sX7DT3+KjkXfvn0JjwzjbMYJmlhbV9runOMkwaHB3HHHHTdl3P79+9OvXz+2bNnCoUOHkCSJGjVq0Lt3b79+h59bGrvdTmZmJiqViqioqEqNm02bNnH32LE41WqsHdoSUjMeFIWiU0l8sWA+3yxezKIFC2jTps0NRQmGhYVxe9++fLdtKyGtW1aY1w1QlJQEBQUYTSZCB9yOLjgYlU6LMToalU6L3W4Hp6tUGFQXEUmhfKhcP2a1FbNoZe/evb/J6L7yTun1eikoKECSZQS1GkGjQVEUnB43TpcTvd4AKISEhLDi22954KGH2D13PvmBgWiCApGKipCKizHFRBM3ciLa4GByN6xlZC8jMdUqdziGhmi4q6+ZuXNm8Pjjj9+QMzA1NZWBQ4aQp1UT/9yTaC/rGEluD+k/rsPapjVeWaKgoICgoCA8l5/RIpcdHCYTxob1uXDoLDVN9TlctJtLYjpV7hqEpk4N9AYdgUElffr69+Xi/G8YOWYMq1eupHHjxgiCwIMPPsjIkSNp2749aadPE9SyOSqVCoNBX+5vT3K5KNh3gITmzf1Gtx8/N4k/NKfbj5//JWq1mqCgoP+pwX0FlUrFE088wSsvvohKUtA6FKopVWlpbEtTfTusBHPcdoCV2QvYal9NcXAuDz5xPytXreTs2bM8cP8EeneF3Wvq8a8nqnNH7xBGDKzCvC9qsWxmHEjZOE4nIUvlc9m1Wh2iqEL2ePEVFKJ4PGis5V8sbKfPIOfnM3To0HLHvF4vBw8dxtqw4t1NlUokKDgIQQXa2nHk+3JJdp7mguMMuws2sbN4I9pqkdR77GECG9YvFW8R1WqCmzUl7sF7uVRczDPPPvs7r/T/lqnTp6ONjSljcAMggNFoREHB0r4NgkpFkNrG1E+q/cLghituyhYtLHz6ZhUOH95DWGgY9erU4/XXXyc5OfkPm7tOp+OZ554hU0jhjP14Oaepoiicc5winfM89cyTN1VMSBRFOnfuzKOPPsoTTzzBgAEDsNvtbNmyhY0bN3L+/PmbNpYfP/9rTpw4wRNPPEGtevVIaNOGZi1b0rhZM9555x1ycnLKtE1OTmb0+PF4q0ZR85nHiejeFVNMNKbYGCJ73Ub8U49RZDEzbORIsrOzb3gujz/2GFq7nQtzFyBfrqrxS+wXU0j/ZjHtWrVGrdGgCw4isGF9LLVqotKV3LtKbhU/C4Mql0t4VYSIqrR6x41St25dFK+XzCPHkAUBtdmM2mhEpdOh0utRm0yIBgOOggJsZ89Tp04dmjRpwrYtW5j+xRcY3W6k9AwMVUKpOXE8NSeNx5WTw7lpifiKiujYNvCakXDtW1lJT08rcTTcAJ9//jlZDjuxE8eVGtwAvmIbss+HLroaot6A2+O+fH1K9qB/adbrIqOwS3ZsvgIueM8T3Ls35nr1EQWhVB8GQG00EDvmbtxmE+++916ZeQQGBvLaK6+gpFxCW1iI1WopNaoVSSJv337OfPpfjr/4LxynjnBo/04efPBB9u3bd0Pr9ePHT3n8RrcfP38SW7Zs4fFHnyBCjqZXyBBaB3Ul1lSHWkH16BzZmw6BPTFpzDRv2Zyjx4/w+uuvY7Va+fjjD6kVJ/Hxf+LQ6cr/ZFs2s/Dxf6qjkpxk/1S+JF+JGI4RxeuhaOduVDodAQ3qlWnjKSgkY9lyWjZvTsuWLcv14fOVqMOK6l8onfokHHYHxcV2HA4HKlFFaGgoRosFBIUT3n0cce/CE+hA1KiJvrM/GnP5ElMAmgAroT26sGbtWi5cuHCDV/avQV5eHtt37iSwRbMKjxtNxhI1b0VBq5IY1M9EaMiVHYSfRfUQSqIOmjXS06KxAa1Xh5hm5JN3/kvb1u1Yvnz5H7aGiRMn8uwLz5DMSbbYfuC0/RiXnOc5Yz/OxoIVHHXsRq3V8J9//x8N6jXk5Zdf5uzZszd1DmfPnuXhhx+mQb2G3NH3Du66YyAtmrdkwB0DWLdu3U0dy4+fP5vVq1fTrWdPZi1fjrZ9G6ImjSNi3CiKYqrzfx9/TLfbbuPcuXOl7WfMmIFNkogZNaJClX+1wUDs2FFkFxUyb968G55P48aNmT0jEdWFiyS9+S6pK74n/+Ahcnfv4dy0mVyY8iVtGzVm3ty5xNeoQcGh8kJiKtUVYdCSEoH2EycIVVUp184re3EoNqKjo8sdux7at2+PQaejaPceRIMB4dfVEQQBUaPBcew4Xrudpk2bXv5YYOzYsezdvZsxQ4eizcomddZckib/Hxmz5lJHq8dkMqHVXjv1TJKvDHX9u9wOh4O5C+ZjbZGA+ldlFq84oBWvF1GjBlGFw+lApSqpPCL/wphWfD5EQSTZcRrRasbSqDElEmjK5e/gZ0SNhqB2bfjhxzWkpqaWOXbXXXfRqU0bUmbOoeDYcRRZRvZ6OZ84k6yFc2kTeYn3X7Iy+9OqvPREEAf3fkP/23syderU616zHz9+yuM3uv34+ZN4/7330btNNLa0qqCslkCYMYrGplYcOnCIU6dKymqlpqayYf0aJt4dgkZT+c+1T49QasbqSF/1A56CwnLHTSYTSmYWRZu3ojabcWVmIblcuPPySF+7nvOfTCHaYiVx+vQKXyb0ej3hYWHYL6Qg+STy8/PJzs6mqKiIYlsxRYVFZGVnU1hYROGuPYgqFYJehy4ynEIBfIrChYVLyN66vdK0k6CmTfCKAmvWrLn+i/oXoqioCFlR0AYEVHhcEARMZjNSfgGi5KVTW8NlU/vnPQpBABRQfBICcFtHM7LKRUNrAl2st2OxB3PPhHvYurW8c+VmIAgCgwcP5pXJL9O8UxNSNWc5qRzgtHQYu1SESWUhwhtDVXdNhFQdX3z4FW1btyMxMfGmjH/gwAF69ujJoplLiXLXoJ2xFx1NfaknJnBgw1GGDR5e+uJXWFjItGnTeOKJJ3j88ceZMmVKuV1CP37+Shw/fpzx90xCqRFLzWefIKJHNyzxNbDWqU31gQOo8eSjpDodDB0+HJfLhc/nY878+VgSmpXuKleE2mzC1KgRM+fMueYcLly4wBtvvEHbDh1o0Lgxnbt14+TJk6z89luemDgJ7fGT5CxcSv63K2lssTL1v5/x3bJlBAcHM2n8eJzHTuDMyCzTp16vQxDFkmob587ivZRGnL52ubEvuc4j6sQKo6muh8LCQjxuN86TSRSu34Qiy+Xa2I8ep3DTZjQGPatWrSpzrGbNmnz15ZccOXCQJfPnM/3zz5k9fQazZ86kTu26bNxadM05bNxaSGxsjRsSFjt//jwFhUUE1K9b7pjGakUbFITjeEk1CEGtxuPxotVoUalVSIrv57WdOEGIKpR8OR99zXgElQoJGVAqjDwKqF8Pj0/i2LFjZT7XarXM+/prerRpS+ac+Zx+90NOvPUOYvJhEt8P5qu3QxnSP4Q7b6/GgxOi2Ly8Lg+MNfPqK8+xcuXK6163Hz9+yvKn1On24+efztmzZ9myeSv1dAlX9ZCH66qRZDvM7NmzadasGUlJSSiKl05tK88zAxBFgR6dA0lZUMz5T6cQ0L4tIQnNUVvMuLNzyNm5G9vuvTSsVQufJJEy5SskRUZAwGIyMX7QIJ577jnCSxVNyyIIAmNHjeI/H31EdotmoNGhFjSoUAEloW2SIpG5ajVFp5IIateG6j26oQ8Po6iwiMLUVOwHDnFx2Xd4i4uJ6t2z/Bq0WtRGY0kN2L8hAQEBiIKAJ78A2ePBmZ6B7POhDQxAd1kMzWg0UIACioJGI1wunyZc/k9AvvwSqXi9iIiXHS0lJrlKUNPE2oadRet55+136NChw02d/6ZNm3j/vffZvm0HkrckTUGtURFXI5bTJ09TR9+Umqb6ZRxGdZWmnLAd5OknniEwMJC77rrrN49vs9kYOXwknmyZ9paeaMSfjQyjykSULpoTxQd47pnn2b9/P8u/X4XN5UYXVVJOz7NgAa//33+4Z/wEXnnlFX8eop+/HJ9//jluvZ6aI4dVXBc7MIDqY0eR9MHHfPfdd3Tr1o3CwkLC4mKu2bcpLpqUQ4fx+XyoK8nN/uKLL3h58mS8KhXGhvVRR4aRm5fPS//5Dxadji+nTOHll1/Gbrej1WrLaSqMGjWKed8s4Mi0RKJGDMVcIw5BEBAEAaNeT/b+AxSuWE24GE6Yrqw2R54nm7PeowwbMfQ363YcO3YMWRSI6N6VzJ824zh6DHNCM9TBwUh2O/aDh/FcukRQk8ao9Hp27tlTYT/p6eksW7aMZUu+xX1ZLExn0HH+XAHPPOQkNrri1JmUVDcrf7TzwosTr/ocT0lJ4cyZMwiCQN26dZEup30JFXwvgihSpW1r0n5ch7dzB8Qr6XBCibO8qLAISfHhTr5Q4swwdiTJdQxEEeWy6rhWp63wfndlF12qIO3MarWyaOFC9u7dyyeffMJ3337Dq6+E0qVdEEaj4XJ/JWsURYF/PUxhmMgAAHudSURBVFGNk2fO8OGH79CvXz+/uKUfP78Bv9Htx8+fQFJSEpJXItRSsVF7BVEQsSohnDxx4zWQRVGkcaNGNG7SgoVLl3B6zTpkRUElCFQJDeXhxx/nySefRBRFdu/eTXZ2NkajkdatW2OtIMf714wZM4bJb7xB9pLviBw0DJX259uHgID7/AVsO3cR2LMHoZ3aow8KKjkmCmhCQgi9sz+akGDS167HWrcO5tiSF0nZ68WefAFvkQ1XQcFfIgf/txAUFETrFi3YtHwlKd+tRHL9rAJvrhFHeJdOBDaoj7VKGLk+mf1H3LRqJ5Ym7SmKAoqM7PaAJKEWdOw95MQg/PzdiIJIrK4227Zs5+TJk9StW37n5Lcwd+5cHn/0CYweC/V1CYRYwlEUhXR3CgcP7CCCGGKCa5WL0FAJKhpYmuMqcvD6a69zxx13lAtzvF4WL15MRmomHc19yhjcVxAEgbqmpiTnnmbG3LmE9+pBrQ7t0FgsAPgcDnJ27OKTL78kMyuLLz7/vCSc34+fvwBFRUUs/vZbAjp3qFSwDEAfVgV9zZrMnD2bnj17lohgen3l2vmcTvL3HcB+MQVFkvAWFyPIcqV/87NmzeKFl1/G3KEtcT17IP6izJbPbufS0uWMnzSJRQsW0Llz5wr7MJvNLP5mIaPHjmXX1ERU4WEY4mJRZBln0mkc6RkoHh8qjYpsTzpGlRm35OSS+zzZpNGha3veu5xjfOTIEebPn8/5c+dRa9QkJCQwYsSISh2/cNl4VKBKh3YENWlE1tbt5G/YhHLZqLTWrkX18WMIqF+PS8tX4nO6y/WxZMkSHrr/IXCqqKaOJ1ATgqxIXLIlc8mXzeAJJ/hmWh3iY8uGgSdfdDH24fNERMYzcuTICue3c+dOPvnkQzZtXAeUfGeiqKVzl9tQCVB87jzGauWFSEPbtSF3zz4yE+cQPOB2zNWrAWDQG3A6nBQeP0H+tysxuXW4fV4EQcCZfB635EKj0xD4ixzxKyiSRMaGjbjtdt597z3mzJ3LbT16MHjwYCyX75mCINCyZUtq165N9aomxg2PrTCF7Urbe0aHMfL+I+zfv5+EhISKvyQ/fvxUit/o9nNLckXdVKvVYrVab6pXtqCggFWrVpGZmYnBYKBz587XLJ9UOv61Bf2vnAFA7dq1EQQNm3cUMfyu8jlyV5BlhS07nbRsm8DHH3/Ma6+9xtatW7Hb7YSEhNCpU6cyuxbt2lWsUH41MjMzUSsCntMXSf3ySywtEjDGxoEg4kw+T+76dairhBLQshVutwdZkhFVInqdHrvdjixJWDu0o3jfAbK3bscQGUHG2g3k7NqNz+FAkWQUn5f3P/qI4uJiHnnkkT+9vNvv4cKFC5xMSsLr9mBp3RJTk0aIWi2ejAxsu/ZydsYsonr1RJF8+CSRBUuKGD3EhsGkAUFAliQUr6+kRIugJTXdx/rNDmpompYZJ0xblcNFuzh+/PhNMboPHjzIk489SZi3Gg2tLcr8VlSIiKioShyFBQWEVglFFMsa1YIgEG+sz57zG9m4cSM9evT4TfOY9/U8ggnHoKo47x8gz5uFS/AS0Ls3ET17lDEw1EYjEd27oqsSyjfzFjLgjjvo16/fb5qLHz83m9TUVFwuF6Fxsddsa4yL4fT+gwQGBhIXF8elY8cJatoYKHHOZf20hfQ1a5G8PnTVqoJajTslBcHhZNI99/DxRx+VGlZQklP82htvYEhoStXby9e8V5tMxNw9jLNfTefl117lpw0bK31mhoWF8cOqVWzevJnZc+ZwIikJtUpFsz59GT16NCdOnGDql1M5cmwnskdBEATiasXx2KR/M3HiRJxOJ2PHjGXDug2oJS0m2YqMzKplP/DW/73N408+xrPPPluh8yAmJgaVKGK/cKFEgDMmmtjhQ5DcbkSttowzw33xEjWbltXX2LdvHw/e9yCBrjAaW1uXcSJW0UVS3RPHzqQ1tO1zjAG9Q+jUzoIgwObtNtZsslO1ai0WfLO4Qif10qVLeezR+6lXS+aD16vQvpUFWYaN2wpJnLcGxesie8MmQtu1LaONAiV5+bXuv4ekKV+SOTURZ8147DXicNrt2E8l4cvKQeUDh6Kw37cZCQklRwVp6YQkNCtT5gvAkXKJs7Pm4srKwhBTneOKxJGzp1m59kde+/e/+eDddxk0aFBp+6SkU7Roqq3U4L5Cu5ZWwEdSUpLf6Pbj5zfgN7r93FKcOXOG6dOn8/U331BcXIwgQN06dZg0fgLDhw//XYrLDoeD1157ja/nzMNhc6AVdfhkL6ihbfs2vPnWmzRq1KjCc+vVq4daqyLLk0p1Q3ylY8iKTAHZNGlaYixUrVqVbt17Mf3rNQy6vfK87h83FXDhksx/x44FSnZd+/fv/5vXWhHLly/HIJhoH9CRs8UnuLTuJwrZAIAgCwj4CGiWgEbU4lFcuNyukpqpWg0ajQavy4XKZMKc0Iz89ZtwZWfjzMzG3KI5pqaNEdRqRKcL54mTvPH++/y0ZQsLFyy4ody5/xWSJDFy9GgKBIh99EE8GnXJS6BWi6ZKKMaGDSjcuJlLK1dhUGt44bnnSZzxBa+8lc//vRiCVgOCXBLurxV15ORKPPSvdASvhWrW2DJjXXkZlivIZ/wtTJs2DZVHQwNL+dSHAl8eJixYxADciguHw1lhJEKQJgSNS8vhw4d/s9GdciEFqyqowmOyIpPlTuOgbSeq8FCMjRridLowGY1l5H0VBUx16iBGRvDmW2/RsWPH64ri8OPnj0YUxVKRxGuhSBKiqEIQBCaNH8+zr76KKzsHfZVQMtZuIG3Nj1jatMbasT1qq6Vkp7uwECH5IktXryZ9+HCWLVlS6rT87rvvyC3IJ77b+ErHFESRsK6dOZI4h71791YoqHn69Glmz57Nnv37kXw+asXH895bb9GuXbvSe0fLli0ZPXo0p0+fJj8/H4vFQp06dVCpVNjtdgYNHMzh3Ueor2tJhLFaqeHrlT2cc5zk7f+8g8vl4rXXXis3fmxsLJ07dGDrtp0ENW1SEtquUqH+1TPCnnwBb1o6o997v8znn376KSqXrpzBfYVQbQRtLX3YbvuR/cdCWPNTFoqiUKd2XV7/94Qyu8S/5MSJEzz+2AMM6qfm3dfiUKl+vimNGRrG8DtDuefJMyxZmcH52XOJGzOqnOENChqViqiYaBrWqcuPa9ficXmJUEVR29qLYE0YdsmGS3bilTzsLt5MxpJlWKMiMFUtCddXZAVnRiZJX0xFDAwgfOI4wuvWKXW4ewoLSV+1mkn334/L5WLkyJGl6QHXU+L3Sht/aLkfP78Nf+ydn1uGVatW0aFLF75YMB8SmhI2ajghQwZyVoDHnnuOfv37/2ahJafTyZDBQ5g2ZQZhjmg6mfvRxdKfbtY7qa9qwYGfDtOvz+2VltWIiYmhx209uOA5g6xU/tKV5rqApPYxZsyY0s8ef/xJziSrefRf53G7y5+754CNJ19JpUvXnjRv3vw3re96yMvLQ4cBqyaQZgFt6R10F12sveli7UWPoP6IghpNQBDC5dIxsvzzQzwgIABRAcluRzSb8Tns2NPSCb17GNZOJXlsGqORKrVrUX3gAKLvGc+WvXt4+eWX/7D13EzWr1/P0RPHqTpiKCHVqmExW8Drw1dcjK+4GKnYjimhKYYaNYivUYPXX3+dGYlfs22PjoHjcliwzMexU7D7gJM3/5tF77svkHRSQwtzN9Ri2Vy9HE8GKrWKmjVr/u55O51OlixaSpQqrsKXUAUF4fJjQoUKp9NZrs0VRMTf5QjQ6rT4lPJhtHmebNZnL2dn3kaKFXtJBIEgYisqIjs7G6/Hi6KAvdhOdnY2efl5qOrWZvuuXdRt2IBnnnnmN5VS8uPnZhIbG0tQYBCFx09cs6395CkSLitv33333dSvGc/FaTPJP3KUtDU/EtC1M8H9epcY3D4fksOBzmAgon1bqk8ax/a9e5kxY0Zpf3v37kUXGYkuJPiq41pq1UTWaNi7d2+Zz71eL088+SSt27fn09mzOOh2chSZbzasp9+dd9L39tvL/MYEQaB27dq0bt2a+vXrl6aczJgxgwO7D5Bg7ESUPrrMPUcjaqljbky8qiGffvxfjh8/XuEcH33kEaTUNFJXfl+hoejOzSN1/iKaNGxAt27dSj/Pysri+5U/UF1To8J73RVCtGGEqiOJr1mPixczSEnJZMPGLYwfP75CgxtKHJdVgr28/UpsGYP7ClqtyGdvxxMZbsB19Bhn3vuQjA2bsJ09R1HSaS4u+ZZz739CtNnCow8/guT1ojglOul70SawKyHacARBwKy2EqoNJ9JQna6BfZELbZz58FNOz5zDhe07uLB3L6e+moai0xEyZDAh8fGlBrfP58MlCOi6dcEXF8PYiRNo074dM2bMoFat2uza78HhKJ/7/Us2bS8C1DRo0OCq7fz48VMxfqPbzy3BgQMHmHjvvVArnlrPP01U754ENqhPcLOmxI65m7hHHmDf6SR69OzJjBkzWL16NW53+Xyvynj//ffZuWUXLYydqG1uiF5VsmMuCiJR+mjaWHtAgYrx4ybg9Xor7OPpZ55GNnk4aNteoXGR5U7jhOcAdw2+s0y4ekJCAlM+n87qjdCq1wn+78MUlq/OZf7SbEbcd5q7xp2nXv1OfPHF1D/UA202m/HiKX3R0YhaAjXBBGpCMKiMCAhIxTZKYugVxF/MRa1WExwSjAoBb24uiiRj7dYFTUQ4iiKDIpeEyF3u2xQbQ3C3Lnz9zQJyc3P/sDXdLObNn48mKgpTdHUQwGw2ERZWhcCAAMwGI2ajkaCgIKp270pKaiqnTp3itttuY/WajXTqNo5PprsZ/VA2ox7KYPocB1ZbI9pZ+mFRB5YZR1EUkl1JNG3ehCZNmlQ8mRsgLy8Pj9uD9VfjXMGssuLAhlfxlAi9VSDIA2D3lezA1KhR4zfPpWPnjuQoaWVepPO9uezIW49a0tBUaI8KNRqjBQ1aNIIOWVLIy8sjLy8PW3ExikpEZTKhCQ1FZdAjNmvK1IXf0KtvX9LT03/z3Pz4+b3odDrGjByJbe8BvMXFlbaznT6DNy2DCeNLdqUtFgtLFi6iftWqJH81AzQazK1bIrnd+Ox2JIcDjVpDUFBQSYWE6OoYGzZgWmJiqRPM5/PBVfLIryCIIqJaXeYZpigKjz/+ODO+/pqg/n2p/a9niR0xlJihg6j59ONETRjDzuPHGDhkMDabrdK+JUli+rQZVCGKAE3FES0AccbaqHxqZs2aVeHxrl278u6bb+LYvoszH/2XnB27cKRcwnb2HClLv+P8x58RHxLCvDlzy+hLXLx4Ea/HS7Am7JrXIVAM4UzSaURRvKYuhNfrZdnSbxg5KOCqFUbMJhXDBgRSPSqM0X374d68jbRpiWQkziEwJZXubdtSmJvPS8+/zOqVPxLgDcFbLJGdlYXdXv7vxaoJoqq6Oj6Hg8I9+8iav4jsufPxZmZhbdcGUa/F7nDg8/pwudzk5ubicDoRtFoCb+sOGi2nCgp44oXn+f6HH7AVi8xbWrlzUpYVvpyVRdOmLSuN6PPjx8/V8Rvdfm4JPv7kE7wBVqKHDy0nUuPz+XAZDBh69uDI8eM8+uBjDB8ygsYNG/Pee+9VaiRfweVykTh9JpFCLEGa0ArbqAU19Y0JXEy+yI8//lhhmxYtWjBj5gwc5iJ+KlrJ0aJ9XHSe5ZzjFDuK1nHQvZ3e/Xvy6aeflju3X79+bNy0jbsGP8LcpSoefC6LZ17Pxe5pxiefzmDhoiV/eBhtr169cAsO8rzlH8yiIBKhjqT44MFSh8KvlW89Hg8+yYfj0BEEtQprqxaoDUbUZjMqkwmfLJOXl4d8uRBqSKsWOLw+vvvuuz90XTeDc8nJ6KqVVeQVBAGDwYDZYsZsMaPX6zFHV8cny6SkpABQp04dPvjgA44cTWLb9v306TsQUTAQposqdexcQVEUjhcfwKbO5+lnnr4pDhadTocggE+p+DdQzRAHAqQrF64sqsJ25xynqBIeSt++5fNFr5fx48fjVrm45DoPlKz3cOEu9IqJRkIbAsRg1KjxFRYiCAIiIlpBiyIreDweRKMBlcFQUkYnPx9BpSKqT0/iHn6A5Lw8Jt1772+emx8/N4P77ruPCKuV5Gkz8eQXlDtuO3OW1K+/oUv79mV2aatWrcrGdeuIjo7G0rgRoqIgeH3o1GqCAoMICQ4uYxwGt2zO+QvJnD59GoDo6Gi8WdlIbs9V5+fOyUVyOMrU0d69ezfzFi0kfNAAqrRrU+b5KggC1tq1iLlnAodPnmLmzJmV9n3u3DlSLlykqj7uqnMQBZEqVOXH1WsrbTNx4kRWfvstfZsnULDyBy5+9iVp0xKxXkjhxccf58fVq6lWrVrZfkWxRJSO6wjvR75uQci8vDxcLgcN61WuRXGFhvWMFBQW8MEHH3D6xAn27tjJ/t27GT5kCBvWbsSYF0g7Y09ERSBEiEQr6BAUEZutGJutbDkzr+whR8pBFRFOg8mv0OT1V6jarw8qoxFLywTUZjMykJuXR0FBAYpKXfKs1evQRYSji6mOPjKC2Ece5GR6GjpDAP/5MJvlq3PLRRB4PDLPvZ7Mrv0STz/z/HVdFz9+/JTHn9Pt529PZmYmq374gcB+vcvkSXkKC8nevpPcw0eRPR5UBiNqs5UQRwANjQkkZyTxn9f+j0OHDpGYmFhpmZUtW7aQm5NLe2OLq84jQBOExRHAd999V6mAU58+fdi+cxuzZ8/m6zlfcyb3IqJKpGO3jkyYOIFevXpV+rCvcTksefLkyaUlXbTaymu33mzat29PnXp1OHXsEK01XVEJZa9XnKE2aWmbKDp8kOBWCah+8V3IsozNZsNx5BjenFw0Vaqg/kV+vaBWIxiNSHY7tuJiAgKsqE0mtIEBpKWl/Wlr/K3otFpkz9WdN1Ci1C4IlPveTCYT8fHxzJw5k1GjRrNx7UaCnGFEaaPRiFpsvkLSfMl4tS7efuet32Xc/pKQkBDq1qtL+uGLROnLlyXSiXpijbU4b09Cq+iJ1FdDURRsvgI8ige1oKHQm0eacp7XH5v8u4TvEhISGHH3cObNmo+syJjVVgq8eTQQWqISVCiKTIhUheyDBwlu3wFUJUaGAghaTWl5HEVRKN63n8AG9RE1GnTBQYQPuJ3tc+dx8OBBml4O2/Xj588mMjKSZYsXM3jYMM688z6GunUxxUYjSxL24yfxXLpE53btmT1rVrnngEajQaVWYwkJISzs6ru1apMJRYHiyzvqQ4cO5c133yFv/wGqtG1d6XnZ23YQGhSEx+Ph888/R6/Xs2bNGoSgIIKaNa30PH14GMZGJbvrDz74YIXPMIfDgaIoaCuoTPBrtKIOpyPvqm3atWtHu3btyMnJISMjA41GQ40aNSotFVirVi3MVjMZtksEaCoPs1cUhRw5gztaXp8IY4lzWbhmaDaA3SGjVqkRRRGz2YzZbGb37t18/MEnxIsNiDfVK00/K0nSElALGgRFwG53oNPp0GpLnNkXnWfxaCTChg9GFxyIKIqo9XoEjQbx8jVQGQ34Lv8NaAz6Mk5TUadD9ngwREYQNWIoKV/NoEu7Tjz43BY+S8xhYL8AAq0qzpxzsXB5EQVFGj74cEoZZ5AfP35uDL/R7edvz9mzZ/FIPiy1fhYoy9qyjUvLV6GIIoZ6dUp2UvPykC5dItVbSLxUl4bWFlRxR7Lq2++Z0nIKjz76aIX95+XlocgKRtW1S1npFCM5OVcPh46JieHll1/m5Zdfxuv1olKpbqi0kSAI/5OyWqIo8tnnn3HH7QPYVbSROobGBGvCEAQBWZGRFRmtLJC/6nu0bjemzh3QBgQAUJSRSf6mzdj37sdULQqPy42iKGV2awVRRNBqcbmcWCwlqrGSy/23UDBv16YNexITkX2+q5YDyj98BKNeX2l4nslkYuHCb1i8eDHTpk7j0MGDKJKCVqflzhEDmDRp0k1VjRUEgYn3TOTJR56iwJtLoCakXJsGlgTyPTkc9+zlkucMUpYPl+wCRUFGBhE6denIpEmTfvdcPvroI9RqNXNnfY27yINKUWElCA9uFBSqamLJKthJ3uafCO7SDVmQSmqcqzUlqQmCQNHW7Xizsqky8Oea4dZ6dci0Wlm0aJHf6PbzP6VevXrs3LaNxYsXM3P2bM5t2YZapaZLQnMmvvkWPXr0qNQBHBYaStJ1pNu4c3IRBYHQ0JLIrOrVqzP4roEsWP4dxsgITLHlHWx5Bw+Tu3kLRlHNvRPuQy2okWQJm+QgqHtXfJKM5irPqaBmTbg4cy4pKSnExsaWn3tYGKIoYvMVXtXoBSiWComKuHp5zSuEhoaWrvNqWCwWRtw9gsQps4mT61Zq/Ge4L+FWORk3ftx1jR8QEEDduvVZseYMA/qUv3/+khU/FtKmbccyz70ZM2aglfTUsJRUohAFEZPaSr4vhwhKIg5UghpJkXA4HKVG93n3GfSN6qINDCrNUdcEWFG8Hrx5+WiCS9INBLUaxVc2nU2RZbw5uZjrhuLKykYTEIAmIpzAoGDmfr2UWbMSeevTdXg9XgIDAxg0+GHGjh17U3RE/Pj5J+M3uv387Sl5gAml5biyd+wi5dvlmNu2xtq5Iyr9zzuqnuwcClasYsepn+ggdCdcV5UwVzWmfjmVBx98sMKXHbPZjCAIuGUXBtXVlbQ9uLBaKxZbqYjKvPJ/VZo3b853K77loQceYv/JLehcRjRocePEq/LQtlNbWrVuyZz58zizYye6yzsyhRcvIssyVW/viyEqkrMzZuG5lIqu+q9CADUafG43Xq8Hz8UUFIfjN5U3+7MZM2YM//38c3K27ySsU4cK2/gcTgp37mbMwEEEBVWe06jRaBgxYgTDhw+nsLAQp9NJYGDg71LevxojRoxg4TcL2bN1Cw10LQjXVf1ZIV2RueQ6j1NVTGh4CAU5hQRJYcQK9dALRhSNRK6Yyf5dBxk2dBgLvlmAyXTtMMvK0Gg0fPzxx9x///3ce++9HNlxHJVKRKVWYzAY0Oki8NndHNm6HW9BAZaWLRCjqoAo4E7PoHjnbuwHDxHZo1sZJ5wgiqhCQ8nIyPjd18uPn9+LxWJh/PjxjB9fuZp4RQwdPJgX33gDn92O+iq/s7xde2iZkEBMzM/G9fvvvcel1FS2fDUDY8MGBLdsjtpiwZObR+6u3RQePgpuL5G6WsSZa2NUmZEVmZV5C5FFkby8XIKDgtFoK35mqXQ6QKlUbDEyMpIOnTqwf/2RkrSVSnDLLnJI54kRD13fRbkBHnroIZYuXsrezJ9oZm5f5nmuKApZnjSOefbSp39v2rRpc119CoLA2HGTeOnFRzl6wl5pmPmWnYXsPehm6rQJZcZc8d0KIlWxZQzxWGMtjhXtx6U40Qsl930VKtwu92Vnp0KxVIQ1Lhqj0VBawSGgfj1UegPFe/YS1Ou2klcilQq8vpKIoMv9O06ewpudQ05BATm7dpesQ1Sx0baJ/376Kd26dUNRFHw+39/uHcWPn78yfqPbz9+eWrVqYdBqKTp5Ck1AAKkrv8eU0Iyg3j3L5SapTAaqDLyL7K+/4VjqATpqexJjrMXulA3s3LmTDh3KG0wdO3bEEmAhpfgstc2VC4jYfTaKhHz69Olz09f4VyIhIYHtO7ezbds21qxZg81mIzg4mDvuuIOmTZsiCALPP/88y5Yt49SpUwCsXLWKjCArkbd1R5FldEFBFGz8ibBRIxB+uXtyxdjz+chct4EG9erStm3b/8Uyb4j4+HjumzSJz6ZNQxBFQtq0LpPq4M7JJWXeAkI1Wp544onr6lMQBAIDAwkMDPyDZl2CXq9nwTcLmDB+AhvXb+KUR49VCQYUCoQcJJWP2g1rcfr4GVoHdCVMG1UifgdkeFMRXKD3Glm7Zh3Dhg3j22+/rXSn7nqpV68eI0aM4NiBVwiwBJRRcI831UMn6jlx7DCZR4+h6DWIOi2yy402IIDoQXcRWkEIreL+e0RN+PFTGcOHD+ft997j0pJvibl7eGlKxS/J3bsP99lz3PvFs2U+N5lMLFm0iOnTpzMtMZHzM2YhK6ASBEKCgnB4FVpaehCm/1mbQhREzKIFb3YeyAL5BQVUqVKlQmkHR1o6apWKiIiISud/3/33cfemUZyzn6SGqW6545Iicdi2i4BgKyNGjLiBK3N9REdHs3jpYoYPHcGW1O8JESIIUAUjKxLZUhp20Uavvj35aupXN6SZMWzYMBbMn8vI+/fy0RtV6dohoPR8SVL4fl0+z0xOo1PnXvTq1av0PLfbjdvtwSCWdeZXN8Rz1n6CY9JuGtIanaC/XBUEZEXBrbjwKT4EQcBo+PlcUaulSttWZPy0FX3NePQ1yjs3PNnZ5C5bAaJIYJ9e6KKikL0e7IeOkHnoCD379GbZ4iVERESwf/9+Fi9eTGZmBgaDkY4dOzJw4MC/RRlPP37+igjK9RTn+wtTVFREQEAAhYWF/nqs/2Duv/9+Fm5YT1CHtlxcupyqTz6KGGC9rIZd8rBSJB+y04lW0OE4dZqchYvoau2NSWVlXdFSZs5N5K677qqw/6eeeopZX86hjak7JnX5nWxZkdlftA0hzMuRY0f+sF3JvyuTJk3i2z27qfnogwAUHj/B2RmzMdSvS1DfXqgv/3YVScKdlo5ryzZUF1NY8s3CCh0hf0V8Ph8vvfQSX82YgWIyYaxfF1GnxZ2WgevMWWKqVmX+11/TsGHD//VUK0RRFPbv38/s2bNJOpWEIIjExsUQEhLCZ//9DKMrgISADuhVBnLcGewr3IZLcmIhAC16nIodGwXUqV+H2XNm/e7ydZcuXaJ5k+bEyQ2INdaqcL4pzrPstm3F0KQBUR3aE9SgXoWGiDsnl7PvfchXn/6X4cOH/655+fHzv2T16tWMnTABKSKcKl06Ya1XB0EUcaZnkL1tB8V79nHP2LG89957lRqOkiSRlJREcXExVquVQXcNwndRRZOA8s6qM/bjHJWOUu3RR5AMKgIDA9EbyjqvFEXhzIef0q9FS2ZXojp+hTfeeIP33/mAUDmCGENtgjShyMhkuFJIdp9CsUjMW/A1Xbp0+c3X6FoUFRWxePFiZiXO4sKFC2jUGtq0b8P48ePp0qXLDaV7XSE/P5+JE8ayc+dmasaqad/KgCQr/LTdSUqazG09b2fKlC/KRAIpikK1qGqEO2KoZS77XLD5CtiRtwG35KIKUQQSgoyMz+gii1ScuDA3b0qNsaPKnCf7JM5On0nRmbOYWzTH2Kg+Kqu1JLR//0GKtmxH8fmo+uSjaIICS8/zFRejKraTt2AxtcPCCLQaOXJ4P7HVRWrVUFNYJLPnoBuLJZR/v/E2Q4YMueFr5MfPrcr12qJ+o9vPLcGJEyfo0bs3+R43QnAwkfdPQpblkrxhBBRJQnY5EZXLiseSzIV336GxugkRumpsKf6eb5YsKOOF/iV5eXn06dWH8ycuUFvbmMhf1Bgt9OZxynEYu76QOV/PrrSPfzKrV69m2OhRxDz8AMZqVQEoOHyU5AULkTxe9DXjUVkteHNz8Zw5R/XISKZ+8QXdu3f/H8/8xjlx4gSzZs1i09ateNxuqlerxsjhwxkwYMDfZqc1OTmZF55/gbU/rsXt8CK5JRShJAc/RBtGjjuTAEKoIdTHLPx8382Vs0jTnEFTRcXK71fQuHHj3zWPiRMnsvyblbQydcWsLn9/t/tsrMv/DlW1cOo/9RiqCq6voihcnL8QY0oqRw8d8jvE/Pzt2bp1K5P//W/2HtiPhICoVqN4PERFRPDIgw9y//33X7fhuH37dvr1vp1W+q4Vajq4ZRfr8lcg1qxO0KAB6C0mgn5hrCmKQtqq1di37WDFsmW0b9/+quMpisL8+fP56IOPOJN0BskngwBqrZou3brwr3+9cFN1K34riqKwc+dOfvjhB4qKiggMDKRfv360aNGiUmeGoijs2LGDOXPmcDrpGIIg0KBhM8aOHVsaBfZrHnzwQZbO/o5O1r7ljrtlFxccZ0h2JGHzFaLRaoiPr8H4ieMRBIHX3nqL+KcfL1d/XfZJZK7fSNbW7Xjy80AQETUaZJ8XUaen6pOPog74+X4qe33ITgfBwcE4kk5z4csvaFTXyDuvRtOhtRVRLJnXpTQ3732WyuKVLj786HOGDRtW6fXzer0UFRVhMBj8O+N+bnn8RreffxybN2+m7+23o1SvRtjYUSCKKIqM4pNAkhAR0Qja0jCtC++9SwOlLrIik21K4diJowRcFv6qiKysLB566GE2rNuAyqdCjwlJ8OFQiqkeW533P3iP22677c9a7t8Kn89Hm3btSHY6iL13ImpjieEjuVzk7d1PwZFjuHJy8WRmcPew4Xz22We/KzfYz2/n3Llz9O3dl4I0GzW0dQmQQnEWOxEQyVQukaycRMJHG6EnOrGsketV3GgMGo6xh4Zt67Jm7ZrfNZe8vDz6396fU4dPE62qSbShJjqVHrfsIsV5jou+00TGhVNQbMMRYKXqoDsxREX+PJ/CItJW/4j74GG+mjKFwYMH/675+PHzV+LgwYPs378fr9dLTEwM3bt3v+Ec3BUrVjBqxGi6W+5EU4m4WI4nkx22TchVrAS0bUXVNq0QVCrs55PJ3bYD6UIKb/7739x3333XPa4sy+zcuZMLFy6gVqtp1qzZX0ao69ixY9z/4IMcOX4cMSAAldWCVFiEUlREk8aN+XLKFOrUqXNTxtq7dy+9evQmlrrUNNWvsM15+ylOy4eZM28Offv2Ldm1ttno0q0b521FRI0egU9vwOv1gAJqtRq1z0f6nHkYiotx2B34AgNw5uQQctcALAnNSvtWfD4kpxOtRktwcBAXFizEfG47y+fEUKdmlXJzURSFp145z8r1avbvP1ruvfvQoUNMnz6dxcuW4Xa7QRBo3bIlkyZM4I477vjdqUd+/PwV8Rvdfv6RPPfcc/x39mzCHroPRQBZLtnp1ogaVKi4Emruycsl9fPPaapL4LzvBKMmjeTDDz+8rjFOnTrFkiVLyMrKwmAw0KlTJ2677Tb/w+QanDhxgv533kmeIhPUoR1BzZqi0utwZmSSs30n9n0HGDJgAF9+8cV110j1c3NRFIUe3XtwYk8Src3d0Io6bEVF2O12NOiQkbEpBRxnL8GEUU9VNoTco7gxmgzYNUUclXax4acNNGnS5HfNqaCggMmTJ7NwwSKKi4oREZGRMVlMDBk2mFdffZWUlBRGjxvHhZQUNNWroQ4JRrY7cJ09R4DRyHtvv33VXRk/fv6pbNiwgYEDBtHe2KvCaJIrFHrz2Za/Fq/Oh/ZyRIlaFGndsiVPPPYYPXv2/LOm/Idy/Phx+vbvT7HRQHi/3phrxiMIAoosYzt9hsxVqwnweFm9ahW1a9e+KWO++eabvPPmu4Qr1Yk31iv9Huw+G+cdp0jlPA8/9hD//ve/y+yGHzt2jI5dulDkdGKsVxdDjXgQBJwXknEcOYpOFFm1fDlqtZrBw4aRnZdH5GMPoa1SovYue70okoROqyUoMAjJ4SDpP//m+fv0TBwRQJXQ8kY3QGaWh1a9T/L6vz8uIwaYmJjIMy88j2yxYG2ZgD48DMnhpPDAIdznztO3Z08SZ8z420R8+fFzvfiNbj//SJKSkmjToQOhg+8isFkTiopsOB0OBAREQYVKUCMikrP+R5zb9mJRm4isGc6aH1cTHn59JUr8/HbOnz/Pv994g5Xff4/T60FRQCUKVIuM4v577qm0xqufP4e9e/fSs3svmmjaEqaLwuFwUFRUhCIraAQNMiWl4TK5xAVO0UroXrrbraDgUdwEBgai1WnZaFvO868+S2xsLHPnzePCxYtodVo6tGnL+PHjbzi3vaCggPXr15fe67t3715GBd7r9bJmzRqWLl1KRlYWFrOZ23r0YOjQof5ngx8/lWC322lQryHWgirUszSttJ1LcrK5eBX/evUFmjZtiiRJxMXF3bQd378CiqLQo2dPDqanUeOBeypMV/E5nZz77Eva1qzFqhUrbtq406dP55233iErIxujygQIOKRigkODefLpJ3jooYfKGNySJDFyxEjWrlpHKFHkyjnYZRsARtFEuDqKXDmd2HrR/LjuR7Zt28bgkSMJnTQOVXBJOLpWq8FoNKLT6RGEkpKWWV/PYNOyakSEqKhylVJsd9+fhNrUkzlz5gIlKWQjx4zB2KYlVfv3KyuQChQlnSZ19jxG3HknX3z++U25bn78/FXwG91+/rHcPWoU361fT8CwwairhCCIqpLSGbKEIiu4ks6Qt/RbtJJCh44dSZw5g6pVq/6vp/2PIj09nZ07d+JyuYiIiKBDhw7+0iR/AV555RW+/HAanS398Ljd5BcUoEKFrEiAgKAIyMgICOxkLfE0JEqIRgF8ihdZkFGrVRgNRnb61qEJ0mJ3udDXiENXtUQl13H8FEJxMWPvvpt33nnH/7378fM/5sUXX+TLj6dWKhSqKAqHbbuwW/M5evzqaVh/Z/bv30/33r2IGHM3AfXKq6tfIf/wEbLnL2Lrpk3Ur19xSPhvwe12s2rVKo4fPw5A7dq16d+/f4U6FGvXrmXooGE01bYjTFeiOC8pEgAqocRxbffZ2G5fyytvvMS4ceOo16gh+s4dCevSuSTm71cp5rl795G7eC77f6xKgNlw1e/54efPklnQiiVLv0NRFLr26MGxYhs17hlfac57zq495H27gj07dhAfH19hGz9+/o5cry3qj4f1c8vRrGlTFi5eTNasuZhbNsfcrCkqkwlvbi5Fu/bgOHwEg1rDipXf0aVLlxsqDeLn5hAZGVmpUryf/x15eXnoMSAIArbiklButaBBRoVXcaMgAApqtKjR4MODrCjISMiCXPKyJwlkFKWTpy7AZIwh/qH70If9HKao3CGRu2cfM+Z9jSAIfPDBB/+7Bf8JuN1ucnNz0Wq1hISE+O83fv5yPP3002xYt4HdxzZSR9eECF31UqHQYl8RSfYj5GkymfLBZ7eswQ3w448/gtGEtc7Vw8YD6tcnU6tlzZo1N9Xo1ul0DBw4kIEDB16zbeKMREyytdTghp+N7SuY1BbChChmTJvBww8/zJC7BjJn1SpC27aucBdfY7YgSQrJyR7atgoud/yXnE32EVurpDzcwYMHOXTkMBFjR131/hbcvCm5q9fy9ddf88orr1xzjX783GrceF0EP37+wly6dIm333+fsNu6E9m9C+7jJ8n4chqpH3xM1qy5kJtHVK+eqExG9u/f738B9uPnF5jNZry48Xjc+Hw+VJf9sqJQIkKoCAoKCl48+PAiIODFgyRIaAQNGkGLWtCQzAlUgVaChgxEHVz25U1QqQht04rQ/v2YOXcuJ06c+F8s9Q/n2LFjPP7449SIjadRg8bUrVWX9m3bM336dBwOxx86ttPpZMGCBYwbN4677hrIPffcw4oVK/B6vX/ouH7+ngQFBfHdiu/o2LM9x6V9bLatYmfhBrYX/cg2+xoI9/DVtC9veV0Em82G2mwqFxr9a0S1CrXZRHFxMXC5QsLFi5w4cYKsrKw/Y6rs3LGLMFXUNdtF6qJJvZRGeno6jz32GAGCQPKM2XhttjLtFEVB9njwuiW+Xe24agTSoWN2jpzwlApTJiUl4ZNkLNcQwhM1GjTR1Thx8uR1rNCPn1sP/063n1uKOXPm4FWJxPbphUqnJeK2HjguXUJ2uVFbTBiiohAEAcnlYsasWTz66KP+8FY/fi5z22238eWUr8h1ZSEomjJlh0RBhV7Q45bdZCmXUJAJIKRELwERlVDyOCmU88lX5xHYohuiwYjDbscaUD7cKqRlC/LWb2LWrFm89dZbf9oa/wwWL17MQw88jOASiRRjCdQE41O8ZBxL4enHnuHrufNYtHghISHlSzT9XtasWcNDDzxEdmYOViUILXo8ootFCxZTPaY6iTNn0KJFi5s+rp+/N2FhYSxZuoTjx4+zaNEi0tPT0el0tG/fnjvuuOMfIX4VHByMt7AQ2SchqivXFpG9XnxFNiwWCzNmzGBa4gxOnkpCQUEUBLp07MS999xTrnxocnJyaVpVVFQUXbt2/c3vH16vF1G49iu8KKgABa/XS3x8PIsXLGDEqFGcfvNdjA3qY6xWFdnrpfjocXwZmdSr25hl359hQJ8iOrcrH9VQWOTj2ckpBAaGcfHiRRYuXEhBQcHlo9eRraqA6N/s8PMPxZ/T7eeWolPXrpxWi8QMH3LVdvbkC6R8MY1Na9fStGnTP2dyfvz8xZEkidat2pB2LIt4dyMMqvL1VT2Ki33yZlw4iKcBIYSjFfTIgkyGcpGLymk8Opnq9z+AKjQYWZAICwtDEMu/aF1cvIx4p5stmzb9Ieux2+2sXLmSc+fOoVaradq0Kd26dftDxfp27NjBgNsHEOQOp7G1dWmY7hWKvPnsdWymdeeWrFi54qZG26xYsYK7R9yN16MgI4MgYBANxGrjCdZU4YzrGAT4WPnDyt9dQ92Pn1uNU6dO0bZjR6oMG0RQ08qrLuTu3kvu0u9o0bw5uw4cwFC/LkHNm6E2GXHl5FCwaw++lFQef/hhXnnlFU6cOMHk1yazfu16vG5fyW9ehMioCO574D4eeeSRG74ndWzfkYxDeTQPuHpd9DP246TpznH6bBJmsxmA/Px8vvnmG+Z8/TUXUlLQajW0b9OW8ePG0b59eyZMGMdPG1cwaoiFUYNDqB1vxu0WWPZ9Lp9MTefkGTeK2ohKp0WRJDSiCrvNRvVRIwht06rSucgeD0n/9w7P3P8AL7744g2t14+fvzL+nG4//0jsdjvqqhHXbKc2m1FQSsPD/PjxAyqViimff0a/Prdz3L2XeLkBAUJwSckcRSGPLM4px5GRCSWSCyRxlmOolJJyfGpBTRV9JBlKBqJaiyiokBQfPklCI5Z/3IhaDe5CW/mJ/E4kSeL9999nyn8/pyCvAIPKhKxIeBQ3MTVieG3yq9x55503fVyAjz/6GI3bUKHBDWDVBNFI34rtW7axbds2OnTocFPGTUlJYfjIu3ErMuZG9TDWqgWiCtelFE4eOoymOImWlg6cKjzE8889z/c/fH9TxvXj51ahTp06dO3UiY0/rMUcF4emgggdT0EBOWvXExIYyK4jR6h+7wTMcbGlx02xMYS0SCBr63Y++O9/URSFxOkz8eRL1NY2ISowBhEVNqmQ5LTTTH7pdY4dO8YXN1gqc9SYUTz/1Au4JAf6CpyjALIikyqdZ8jQQaUGN5SkE9x///3cf//9ZdvLMlOnTuXIoWPk5sl88lU2n8/IQqMRABGPV8AtiwR36ExEj67ogoPxOZ3k7zuA88d1JM9fiLlGXBkNj1+Su3svKo+Xu++++7rX6cfPrYQ/p9vPLUVYlSp4cvOu2c6VnY0oiIRepSSGHz//RFq1asWKVcvxmhwcULawV9nIIXk7u5X1HFP2oNfoaaxuQz2xOdHUQhQE6lmb0iKwAz3DBtLY0hJBBndG+i96rTigyn0pjbiY6Js6f1mWefTRR3nz9bewFITS0dyXTua+dLH0p5WhK7azLiaOm8Ts2bOvu09FUZBl+ZrtUlJSWLd2HTHamhUa3FcI1UagV0zMmT3nuudwNdxuN31uvx2vyUjVe+8jfOBgLI2aYGnQkCq9+lDtkUcgNoI9tq1UU8exa8dujh07dlPG9uPnVuLjjz6iqsnEuc++JGfXbiS3BwDJ5SJ7+07Of/YlEUYTuQUFhPXvU8bg/iVhHdphaNyQt997DzlPoJ2lB9UN8agENYIgYFUH0tjakka61ixesITExMQbmufw4cMJjwpjf/E2vLKn3HFZkTls2wUGiXvvvfea/cmyzOOPP87zT7+AO1mhk7UPvQNH01DTjQBXTYqLRByKhrhHHyZm6CB0l7U61AYDVTq0o+ZjD6EOCuTEBx8jeX3l+i84eozs71czeuRIYmMrvmZ+/Nzq+I1uP7cUgwcOxHkyCW9h0VXb5e3aQ6P69W+pGqN+/Nws2rRpw5yv52AwGVBrNFgMAVQzxdEppDcdQ3pj0QSQo6RzUThNDWM9apsbUdUQi0bUYlJbCFVVoWjfXiS5JJRSpSq/y+1MS8d94SKjRt7cXY9ly5Yxb858GuhaUt/S7HLN2xICNSEkWDsQLlXnmSefITk5udJ+nE4n8+bNo0f3HkSGRxIRFkHbNu346quvKCqq+P5y9uxZfF6JEG34VecoCAKBhHLi+M0RkVuxYgVnzp+nytDB6EPDyh1XGYyEDxmGz6zBJhWBT+Cnn366KWP78XMrERUVxSsvvUSIABnz5nD8mac4/MSTHP3XqxSu/IE7u3Tlzv79UfR6gpo1LXOuIit4PB7cbjc+rw9ryxZ4fD6qamNRixXnbkfqq1NFieTLL766LsfeFQICApj/zXy0VUS22lZzqvgIhd48inwFJDuS2G77kUJ9Np9/+TmNGjW6Zn9z5sxhTuJcGmha0DSgLYGaEDSihmqGOJoGtEWl1mNp1xav1UpFSammkBCqDboLn62Yk5PfIPX71eTu3U/Wlm2c+WQKGXMXMKT/Hbz7zjvXvUY/fm41/Ea3n1uKIUOGEGK1krJ4KbKvvLcVIP/gIVwnk7jvnnv86uV+/FTCgAED+OiTD8HswybkoxG1OGQ7yc4kjiq7OKHsI0QbRkNreVGumoZ6eM5fIH/rZnR6HeKv8rm9hUVcmvcNdeLj6d27902d97Sp0wiQQ6iqj6nwuCAI1LM0Q3IpzJ07t8I26enp9LytJw/e+xDndqUQ7a1LnNSAnKOFPPfk87Rv14GTFSjwXhGeux6lFAWljFDd7yFx1ix08XFow8ob3FdQ6fWYmjXlovc8KkGF0+m8KWP78XOrYLPZGD58CI89eg+x1Yp4+ckoXn06nL6d9Vi0HmKrhfPySy+RnZ2NJioS8bIImizJFBXZyMrOIi83j/y8fHJycvBYzAgaTWn97MqINtTi7OmzHDhw4Ibm26xZM9ZvXM/YB0aTa05ll2sDO53rOK86Qc/B3Vm1etV1leZUFIXPP/ucUCWCqobYcscz3al4RYmgZglIkoTb5aqwn5AG9bBWr0bD2Dg0h4+Rv/Q7iteso3v9Biz8+mumfvWVX7jWzz8af063n1sKq9VK4rRpDB81irNTviK0S0cC6tdHVKtwZmSSs30Htt17GT1sOCNGjPhfT9ePn780EyZMoGXLlkyfPp0li5ZwyelCVAm07dmWfftkstPTWZ/9HQaViUh9NNUNNdCKWkK1EViKTeT/tAUxMxO5XVuM1ashezzkHz6Cbc9+qgUF8c38+Tf1JSw9PZ09u/ZQV5dw1XZqQU2YUJWli5fy0ksvlTnmcrkYOmQoSQfP0tZ0G1Z1YOmxWGrhlOzsS97C4IFD2PjTBqpU+Tl/sU6dOuj0OrI8qcSpK4+ikRWZfLLo1bzLb1rnrzl6/Bim5k1RUCgJ5a/YmWiMq4FtyzZckkxISAjnzp3D6/USGRnpFyL1849GkiQmThzH4YPrmTulOp3bWcs45c+cdzLp8WSGDrmTBg2bweVdacknkZeXhyTJqFBdrpUtoCDj9blRZJk07wXijHUqdPK7JReSLOH1eklLSyMh4er3rl8TGxvLu+++y8svv8yZM2eQZZno6GjCruKA+zWHDh0i6dRpmukrFmVzyy4EtRqNNQiP4sbpcqI3lFezF0QRdWgI9erVY/v06Xg8HjQazU1zLvrx83fHb3T7ueXo0qULK5Yt47XXX2fH/EWkAaJKheL1EhUeznMvv8LDDz/sfxD48XMdNGrUiI8++ogPPvgAm83Gp59+ygfvf4DL4cKqBGHwmfH6PBx17+VY0T7C9VEUi4UYQ/W88OR/2L5jBxsXL0NSFAQgMCCAR8aO5eGHHyY8/Oph2DdKQUEBsqxgVJuu2dagMpGXn1bu82+//ZYjB4/S1tijjMH9y/NamDuz9eJqZs+ezVNPPVV6LDw8nP533M6qb1YTrcSXllH7NenuFDwqN2PHjr3+xV2DEueFgoRUWl+9IiRZQlYJvPfuf/jXC09cPldHn753cv/99/urOfj5R7Jhwwa2blnP/C+r07FN+VJZNeMMLJgaT9e7kigsrIH74kV8xXYKXE5kSUYr6BB+4ewSUOE9n4Lglcn2ZpLiOke0Ib70eK4nizOOE2T40pAFBUnx8sDDD3Po0CHuvffeGzKaoWTDoXnz5r9p7VlZWciSjNlQft1Q4qRUJAnZ40HQCMhS5WHwitOF0WhEEAR0Ot1vmo8fP7cqfqPbzy1Jy5YtWbViBcePH2f37t14vV6qVatGjx49/OFNfvz8BlauXMmzL7zA6bNnEdUaBK2GIsGGpMhUk2sQJ9UlWTlJqvsCI0eN4PXXXycmJoanKBEYS01NRaPRUK9ePYzGitV2fy8WiwVBFHDJjqu2c0p2Up0XkNQunnvuORISEkprEc+YnkgwYVg1QZWer1cZCBOqkjg9kccff7yM6vATTz7B6h/WsK9oK80s7dCI2jLnZrnTOOHez+0D+900A7d+3XrsO5dMULOmuN0eREFEqCB7rOjEMVSCm5BguOM2F316VEevE9l3qJg5CxdxR/9lfPjRFAYNGnRT5uXHz9+FWbMSadJAU6HBfYWIMC2Db7ewbM1RjBotaRs3oW2RgEbQljG4ARRZpmjXLqySFYNg5GzxCarrayAIAhccpzno2IMqKpyghD6oI8KQZC9cSuW9L75g4ZIlfLd0KXFxcX/0sgEwGAwIgoBX8aDHUO54qC4C0aFQfPwYhib1K03Lc2Vm4bmUSrdu3f7oKfvx87fEX6fbjx8/fvxcla+++ornXnoRX7VqWFq0wBxdAxBwpVykcPdOXCeSaKRvRpy+Frttm4lsEMqOXTv+dM0ERVHo3q07yXtSaRnQudxxn+LjsG03KZ5kJL0KY7WqaAQBT0YmVUJCeO2ll3j26Wep7qlNnPHqIosZrksclXdz/OSxcjv2mzdvZsyoMdjyiqkiVCVQHYxP8ZHpu0SxWEivPj2ZkTjjqs6HzMxMFi1aRHJyMmq1moSEBPr3749eXz6sc+HChUx68EFiHn0Qp1aLz+dDhRqVoEJAQEHBYy8i/cP3aFBLzXdzGlI1suwulM+n8Ozk8yxZ5ePb73644TBXP37+ztSvF8+9dys8em/UVdtt3VXE8HtTGDf+Ad7/9FMs3boQnNAG4RdikbLbTfaPP+A6cJS6viaIisBx9tE5tA8+xce2ovWY2rYipPttIIJHcWMymbBYLXgKC7nw1QxqBASybcsWtFrtVWZzcyguLqZh/UYEFIRR29wISfGhEtRlKjDsKthEVqCTkHEjCQgLw2wuG02kyDLJs+cRkJPL0UOH/pR5+/HzV+F6bVG/0e3Hjx8/firl0KFDdOt5G9pWLdG2aoFW1CP+YhdVURTyNm3AtmU7XQJ6ISk+9ru3sPz75TetBvWNMH/+fB689yGa6NoSrqta+rmkSOwo3ECuuhBr9y7oG9YlrGoUarUad04uGes34jp4CMnppp66ObHG2lcdJ9OdyhFpF8dOHCUiIqL88cxM5s6dy5xZc0hLS0Oj0dC+Q3smTJzAbbfdVmlNXpfLxfMvvMC8bxbgVkAXVgVFknD/wjEwevToMue43W669ujOyYwMqk8Yg1evx+lwcuXxLrvcZM6cg74onS0rG9KwrqXCsX0+hZ5DThFf9w6mT59x1fX78XMrUad2LI9OVPHA+Mirttu938bA8RfZuGknffv05UJqKpqgEEyNGqIyGPHm5+E4chTR6aW5uS3BchVyCnPYq2ykWWA7MjyXyI2AqHHjkUXwKV7UahXBIcGlKW/OtHTOf/wZs6dN48477/wTVg9jxoxh4YLFIIgogIhAlLoascbahGrDsftsrMtfAZHB1BgxDEvNGqVOVVd2Dunfr0ZKOs2cxJn06dPnT5mzHz9/Fa7XFvWHl/vx48ePn0qZMWMGitVKUNdOOB2uMgY3lKiBB3fuiuPYcc7bkmhqbYPGrWPHjh3/E6N76NChrF69muVLVhDrrUuMsSY6Uc95xymyhTyqjBiOplokFqsFtbrkEagLDSFm2GBSjUYyf1xHhpx6TaM7x5NJYGggISEhFR4PDw/nqaee4qmnnkKW5TIaErIss23btlIF9Dp16tCuXTskSWLUmNGs3bKFkJ49iG6ZgNpQEu55xTHw6FNPYrfbuf/++0v70+l0LJy/gEFDh3Liw/9irF8XS726+GQFR3Iy9kOH0Tnt9OgUXKnBDaBWC4wZFsSr76wkJyeH0NDQ67vofvz8zYmJiWPfofIVCX7NvkPFaDQ6oqKiqF+/Pp50BWOxmYyt+3EqPnSijjqaWsQE1cSgKolkMSlGKFI44tyNR5YISuiPR/CCoqDVagkMDCxzfzBERaKLi2XO3Ll/uNGtKAqTJ09m+Q/fI0RVwdi4ETprEFKRjYyDB7mUtZ4a6poIiOh0akJ1etKmz0QVGoIYFITidOJJuUR4aCif+A1uP36uit/o9uPHjx8/FSJJEouXLcPavg2CULnwoCCKmJs15dL6LTSlDSpBjdvt/hNn+jMqlYpp06YxufpkZkxL5ILtFAbBTLYnG32zhuiiq2KxWDAYy+cuRvbqQc627aTlJbO/cDsG0Ui4ripBmtAyofIeycVF7xnizDG0btkaQRBo0qwJ48aNo0OHDuVEGn/572+//ZY3/+8tTp9MQpFL+hREhVp1a9OyVQvWbvqJahPHYqkZX6aPUseAycRLr71G7969iY2NBcDr9bJ//37iY2MpLigk49gJ0g+UhHiGhYXx5BNP8vXcGbROuPZ30qqZBUnKJiUlxW90+/nHMPLusbz80mNcSnNTLapiATCvV2bu4gLuGDASi8VC+w7t2b5pJ60snWlaSR1ugDyyCA4NYfTYUXzyxRdY4qLRm4wYDPpKNWb00dU4c+7cTVnb1ZgyZQoffvZfQm7vS3Cb1hQWFuD1eBEQMLZshm3vPpLW/IhFZ+Dtt9/i3nvvZfv27SxdupScnByMRiPd/9WdO+644//bu+/4Ksv7/+Ov++xzsncCJOwpIEORIFgRRS0igiCOOqgLHHWVorZqrfUr1f60ah1QFVur4Aa3CAoOQAHZyt4rBDJOcpKzr98fqdFIQFRiGO+njzwe5r6v3PfnPtfjkLzPdd/XpYnTRH6Apm8WEZF6BQIBgqEg7swMbHYb5n//1ceZnkGMGIFYBdWxAE2bNq233S/B6XTy17/+leVfLeP+f/yNsy48A7vbQXZhb7Kys+sN3BG/n03P/Rdb0E92NqS0XkcoYznzq9/l0/K3KI3sAaAyUs77xa8SDFWxZ10ZkfV2wuss3n95JkPOOodhQ4dRXl5eb10TJ07k8ssup3hFKT3cJ3Fa8jBOSx5GD/dJ7FpRyoR//QtH2zZ7Be7vyjv9VKJOB8899xwAy5Yt4/jjenHJRZfy6RvziG62kRxIwxG147Y5+MMttzBu3Di8Xi+RSN2+q7n9vO62cKRmZuJv7gIQORoMHz6czMx8Lr9pA3tKInvtj0Ti3HTHRrYXObn66qsB+M1vfoPdY2N91ap9HjccD7E1uo7h553LBRdcgNfjITHBR3Jy0n4ndTXRaIO/B6uqqvj7P/5BYu9eZPftg8NhJyMjg/SMdDw+Ly6Pi8x+fUjvdyLpOZmMGjUKm81G3759efDBB/nPf/7Dk08+yYgRIxS4RQ6AfquKiEi9vF4vFhbRQBUpHi8VFZXETAxHPUthxaqqsIDt1RvxJnp/sWcR9yc9PZ0rrriCbt26MeW110jITKe+ud0iFZWsf/xxchx7+Ms92Qzs58HrsRENx/jiyxCPPl3O54vfIZ0CtoU24jJuTkzpT7bv20mXjDEUh3fy6cw5XHjBhUydNrXOH9VLlizh9ltvp0m8FR1TutUZOc9wZeOy3Gwp34yjfVsikcg+/yC3OZ34unTmjbff5vzzz+ecs88hVByrWeLsOzOuh+JBVlcuY+zNY3E6nRzb7Ximz3qdsddFqa6uprq6mng8Dha4nC58Ph9uj5sPZpWRmJhCq1atDkIPiBweEhMTeWHyy4w8bygnn7OaC4Ymc9rJqTidFl98WclzL5WydaeTfz72Lzp37gxAs2bNuPn3N/G3e+/HqoSWCR3q/NtYES1jSeBzUvOSGTt2LBkZGSQnJVK2bAW+/XwoaeJxqr5eReHA0xv0mt988032lJXSul/dx4BcLledidA8A/qz/u//4K233tLKBiI/g0a6RUSkXk6nk5N/dRL+RUuw2W14vR5iRIiz9zqtlcuWkmKlsim+hosv/Q1paftecuuXlpaWht2yCO8prXf/tjfeJNPazfNPFTD49EQSvA4yMzJJTkmizwk+nn44i+OPs7GLDTjsDvqmD6wTuKHm2fZsdx7dvSfy2cdzeOutt+rsf+aZZ3BEXHRIPLbeWd2jRLBbduwJiVRV7X/JM2dyEuV+P3/5y1+oKg7RK+nkvZY4c9s8dE46jqxYM/50+58YPnwEX68J8uobm6msCmAcdmweD5bbTTgWo7SsjA0bd/PfV8oZcd5vSEj44bXORY4kHTt25P3pHzF85O/472s2hl62mbMu2sR9j1TSted5vPnWdAYPHlznZ/7whz8w9rbfs8Wxltn+t1hUPpdl/gXMK5/JnMAHZLVOZ+obU2nevDmJiYn85vwLqJi/kGhlYJ91lC5ZhikrY9SoUQ16vV9//TWuzEzcGen7befJysSVkV47B4WI/DQK3SIisk9X/PZywlu3Ubp0GcnJybhcLiImRMSEiRPHYKhYuYLgli1UWX76n96fu+++u7HLrqNVq1Z0PqYTJV/M32tfpKKSquWLueqyNJo1cWKiUbxeDzabjcTERLKysyjIz+H/7miN02lIs2eS4t73H6nprixSrQyefurp2m2xWIxXXnqFbNOMSDhCNLL37asuq+b2TOOvJFgd/P5d33WES0pJSkzgvbffo7mz3V5rgX/DsizaJXTBX1rBZ599RqA6zu/v2cOcxQbL7cZyObG5XNgTfOzyOxh1wxZ27oozZswY1q5dy6effsrChQsJBoP7LkbkCJKXl8fdd9/N4iVf8+FHc/lgxqcsWbqSJ554gq5du+7V3mazcfvttzN/4RfcMO568nvlkNbZR+Gg4/n3888yf8EXdOzYsbb9NddcQ7YvgQ1PTSK0Z0+dYxljKF28lKJXX2fY2UPo0aNHg1/vATus1zkSOTTo9nIREdmngQMHMnLYMF6c8grhklIyTjieoNtNdVUVoSo/gSXLKJ/5ESkJPu688w7GjBlzyK3RalkWo6+8ijE33EDp0mWkde1Su8//9UocJsyQs1KJBYNYWHi93z7zbWFh2ex075xA21YOtn1ds/2b28n3hIuImRheu4+mnhZ47F6yHc2Y/8V84vE4xcXFjB8/nl1Fu0ijKaXVNaPtDoeDhISE2nP57Imk2tIILFmCu11LjDH1jojHgkGqlq+g04BTWbtiDU2Tm+/32r12H8kmjRcmT8adX0A4OZErbvqKY9rt5vRTEnG7bSxZVs0Hs6sIWR5i4SgjzjuPVWvXEjNxLCzS09K49KKLuP7660lP3/+omMiRwOPx0KFDhwNu36JFC+644w7uuOOO/bZr1qwZU199lfPOP591DzyEt107PM2aEI9GCSz/inhJKeeefTaP/fOf9b7/D6aOHTsS3r2bUEkJ7v28r0O79xAuKaF9+/YNWo/IkU6hW0RE9slms/HYP/9Jeloaz/z735TMnIWrWVPAENy0BXssxsXnnccTTzyBx+Np7HL36fzzz2f2xx8zZfLLVG3aQmaf3rgz0olWV+NxQ6I9DJHY/5bvqbuGdtzECQaDNMmx8/WyUpaWf06J2UbYVklutp0En41tOyOs9S8k296KVEcm8XicDRs2MGzouWxbvx2Lmono3JabuDHEolHKy8uJRCIkJydjWRatPO1YsGoeVStXQc7ea3+beJxtU9/EZ7PTq1cv3njtTez1PF//ffG4Ycv27TS74Dwyeh1Hxeq1rJ0zh6//s4F4LIYzPZOUQSfgbNua9Q8/zpIN6ym4aCTe3FxiwSCli5fw0L/+xZtvv80bU6fSpEmTHzyniNSvY8eOfD53LlOnTuW5559nw4qVuJxOfn1yf0aNGsUJJ5zQ4IEbYPDgwWT86Y8Uf/wZzc4ZvM92uz7+lKyMDM4666wGr0nkSGaZmulLD1sHuiC5iIj8PDt37uSFF15g1aqa2Xrbt2/PhRdeSG7u3gHxUBSLxXjwwQd5fOJE9pSV4khMJFjuxxXz88V7Lclvlvq9WXgNlZUBAoEA8ViMIZfuYMnSOE5PlAH9vFx7WQY9OydisywqKmO8/l45D04spaLESdPWBWRmZrDqy7X0SuzP4vJ5VIeq6G77dtKimIkRJUJKcjJenw9jDO/sfpGwx5BzSv/aDwaMMVSu38Cuj2YTX7+RiY8/TnJyMucNO4/evtNIdqTu85qNMcwofZ0qZ4hOd/0JV2pKve2qq6spLy+n7IMPMbv30PGm6+vsD5WUsGnC03Rv2YoZ06f/IqFARBrWo48+yp/uvpvMsweR2ad3nfe1MYbiTz5jzzvvMf4v9zBmzJhGrFTk0HWgWVQj3SIickByc3O5+eabG7uMn8xutzN27Fiuu+463nnnHTZt2kQkEuHhf9zPB5/EGXNZ3cDt9/sJBKqw42Dlyhhr18VxOSzOOT2Zv92RiWWPEzNRLMtJUqKdS4anc+wxHs6/ZjMpqcksXbyMnt5+eO0+Wvra8XloFsVmO1lWzUix3bITNzECVQG8Ph87QptxuO1cMHI4M2bNYt1nc3AkJWEiUUwwSPu2bbjnuecYOHAgkUiE3KZ5bNq2hi7Jx+/zmovDO4jYwjgcTthPTg4EAuBwYDkc1PdZvDs9nbwRw1j41LN89tln9O3bt56jiMjh5LrrrqOoqIjHJk6g7PP5pPTsjjMlhXB5Of4FXxLfvYdbrv8do0ePbuxSRQ57Ct0iInJU8Xq9dZa+2bZtG09Meo4zT0mjRUHNLfKhUJiqqmqclpNwyMZ9/9yBFXPh8QW57bos3HYHMWLETBSbsWG37BigY3s7FwxNZvK0r/DGk0h3ZgOQ425KM28LVlYvImiqyLOa47CcOHBQFQmwyr+UTWY15180ksefeJxgMFj7wYDD4aBnz56ceOKJtSNRTqeTq0dfxd13/IWsYB65nmZ7XWd1LMDXwS/p3r0by1evpGLNOjKO23typkg4QjQaxfJ4CK7fQErLFvW+bomtW2HPyuSFF15Q6BY5AliWxT333MMZZ5zB0888wzvvvUc0FsXpcHLOmWfy21GjOPHEExu7TJEjgkK3iIgc1f785z/z5cLPGXrZasZem805Z6YTClURj8InC4M88vQeli4FD0n07WcnO9OBAWzYif8veBtMzai3zeKSkU34z8urcUXTakOyZVl0T+mDy+ZmY9VqNps1JJlUAErNbnw2L2OuHc3dd9+NzWbD5/MxfPjw/dZ93XXXsWzZMl576XV2hppS4G1DkiOFcDzE1uAGtsU20KxNUya/OJkbb76JDz+bS3qPbli2uguXxOIxDBBat4FoSQmZF46s93yWZeFsksemLVt+7ksuIocIy7Lo27cvffv2JRKJUFVVhc/nw+l0NnZpIkcUhW4RETmqpaenM3Xa24wd+3vG3fMWf36giARvkAo/lJRCosmgV2JvVlTPpWmuE5vNwhiDMWDDRtREMZbB6/OSkJCAw2mwWRCh7tJgNstGl+TjaZtwDJur1+GPlhEzMWwxePSxR/jNb37zo+p2OBxMnDiR448/nglPTGDBhtnEq+JYNovklCRGXXgpv//978nOzuaWm25m1tChbHnpVZoNH4bN8e1kccZAePtO9rw2lYSWLfEW5O/znPFIBHfKoTU7vYgcHE6nk5SU+ud9EJGfR6FbRESOepmZmUya9CxbtmzhxRdf5M477iLHakavxGNIdWRgWRb2Kjfbd1ZjWRaWZWEAY2zYsMjKysJurwmyO4oCWDYH1ZafcDyM63vraHvsPtol1ixbtj6wkqDLz2mnnfaT6rbb7YwePZorr7yS+fPnU1xcjM/n4/jjj68zoUvv3r2Z8NhjjLn+etb87e8kH9cTT5M8AmVllC1eSvXq1dhTU0kedAa7i3fj8XpISkrC9p1R8VgwSGjNOgrPOvsn1SoiInK0UugWERH5n/z8fG688Ub+3wP/j/RQFmnOzNp9Oc4CPvpsO0W7I+RkOr8zL5mF3f5tOJ3y+m5yc5uyu7iStYEVdErqXuccxhhKI7upjPpZG1rBkCGDycnJ+Vl12+12evfuvd82w4YNo2PHjkyaNInnp0xhw+7dxKJRvFYCqSaZKsuBy+bFGDvVVUEi4Qjp6enY/ndtuz75DLcxP3pEXkRE5Ghn++EmIiIiRw+Hw8HgswezI7apzkzezTwtiYXc3PfPYmIxgwFiJorX6+GbqcEXLatkytQKLr3sKm7/021sZR1fVywmEo9gjGFD1Spmlr7J7IoPmB+cS4VVzbsfTOfGG29kx44dDX5tHTt25P7772fwr39NouXjlORBDMocwQlpJ2PfU0nRlCmYskpclotoNEa5v5x4JELRR7MpmzmLm373O/Ly8hq8ThERkSOJ1ukWERH5nnnz5jHojLNoQQfaJHSq3b4juIWl1bPpf5KLqy9JocsxDjIzM6gO2nhx6m7+/ngxHTudyEsvv4bb7eaRRx7h//56H5GqCLEwBBzV+Lp2JqFbVzy5OSS5PfiXL6d87hc0SUri7TfeoEWLFg16bUVFRRzb5Vjyw+1oldChdntpZA+f+2dTbQvhadMaZ3Ym0VA1bNyEIxTmpt/9jttvv73OLeciIiJHswPNogrdIiIi9Rg/fjx/+7/7yY43o5WvA8nOVAA2BFaxMjgf4wjRPN9HeqqHLdvDxOIefv3rIbRt156PPvyAiooyUlMz6HdSfz799FPemTGDjKFDSD62M16vF7fbw/8mNyfi97NhwtN0zM7mk1mzGzTYPv300/z+hj/QP2kwzu89bx41UbYHN7E5tJ4qU00gVs6vTurLk08+SevWrRusJhERkcPRgWZRPdMtIiJSj3HjxpGTk8MDf3uAuds+wB2sWcM7GK8mMy+P/qecTEFBAfF4nCZNmmCz2bj33rt4/70XGXiyl+wOTrZu38gj//iE0vIwiW060PzkfvWey5mcTJMRw1j+5FPMnj2b/v37N9h17dq1C4/ds1fgBnBYDgq8rSnw1gTsORUf0KNHDwVuERGRn0H3iImIiNTDsixGjRrFkmVL+O/k5xhxybk0aZ0LlqFkdwmvvzKVf0/6D5Zl4fV6uevOcQw702LBBx154oE23D2uOU8/3IZP3mzNoAEuIlvW4l+9Zp/nS2hegCMnmxdffLFBr8vn8xGJR4ib+H7bGWOImDA+n69B6xERETnSKXSLiIjsh9PppLi4mCnPT6F4TSmdnMdR6D2V49y/IqEkjUf//hjXX3c1vx7g5IE/tyAtte5NZFkZNv52ZxYnHe+k6M032NdTXZZl4czJZuv27Q16PaeccgrGHmNXaNt+25VEiglZ1Q066i4iInI0UOgWERHZj08++YSxN48lJ9qcPsmnUeBtTbIzjTRnJp2SetDGeQyYCJeN3NeIsIXDYXH1bzOI7tqOf+06QqEQoVCIWCxWp2U8GMLr8TTo9XTp0oVevXuxNvQV0Xi03jZxE2dN9TLatm9L3759G7QeERGRI51Ct4iIyH48+sijeCNJHJPUA8uy9tpfES2nbQsPLQtshEKhvfa7XE4sC47r6iI1GYqWLae0pJTSklKKi4spLS0lEo4QraoitG49J/Wr/7nvg+n+B+7HngZfVHxEWWRPnX3+SCnz/bMJJ1Tz8CP/0GzlIiIiP5MmUhMREdmHbdu28eHMD2nn6lZv4IaaUeEEnw0bNqqqq/B8b6TaZrPjdDoJR8J43BaVMXBZNW3ixAgHw5SESgjO/Ryvw8EFF1zQ4NfVpUsXpr0xlct/ewVfrP0IX1UirriXiC1EpVVOs4Jm/HfiJAoLCxu8FhERkSOdQreIiMg+bNmyhVg0Tpo3c59tvHYfGzZHqK4GmyO21/5oNEokHKF4d4TinUHsBRYWNQHejgNCEfbM/QT/p59x1+23k5m573P9WPF4nI8//pgZM2YQCATIzMxk6NChdOrUie7duzN/wRd8+OGHvPnmm5SUlJCcnMyZZ57JGWecgdPpPGh1iIiIHM0UukVERPbhm+AZM3uH6W8087ZkQ/li3ppRycihe6/RWVVVBQamvR0kGITI7E8IbtiAMzuLWDBEcPUabKEoXpxEo/U/Y/1TzJs3j99d9zvWrF6LBx9OXARNFQ8+8BAn9juRJ558nKZNm3Laaadx2mmnHbTzioiISF2W2dc0qoeJA12QXERE5MeqrKykU4djSK/Io31i1322W1j+McHk9Ux+soATe+d+Z4+hqGgXny8IMvr3xaSHO5LpymVLcD3VBLFjI8uRS3NvG9ZXfU1VRilr16/d563sB2rOnDmcO3Q4roCXdt6upDkzsSyLuIlTFNrK6tBSsltm8f7098jLy/tZ5xIRETlaHWgW1Ui3iIjIPiQmJnLhby7gmceepUW8HW7bt89rB6IVVETLMECCLZVtJXDxdbsYOSTIeedkkpPlZPO2IM/8t5ip71SRFMnnmOSe2CwbeZ78vc6V4khnR/kmAoEAiYmJP7nmaDTKmNHX4Ar46JV8Mjbr24nQas5dQJozk7kbZnDnHXfyr6f+9ZPPJSIiIj9MI90iIiL7sW3bNk495VT82wJ0S+hDOB5iVeUydoVq1tM2Jk6MGM0KmnHW4EF89ulHFBVtAwxgUV4eINtqxXEp/eoE4O/bVLWWdbZlbN+5/Wc9T/3+++9z/vAL6OU9hVRn+j7bra9axWbHKpYuX0JOTs5PPp+IiMjRSiPdIiIiB0HTpk15beprnD/yAmatfotIKEIiKbSxOpNMOliGalclu/ds56XJL/HwPx+mbdu2+P1+0tLSuHXcrayes2G/gRtgZ2Qzx/c7/icF7ng8zvz589m5cyfPPvssbuPbb+AGyPe0ZLV/CTNnzuTCCy/80ecUERGRA6PQLSIi8gM6duzISy+/SOEJfUiLZtPG6ozdsuN0ufD5fDidTtqYjiyt+IKbbriJj2Z/xHHHHQfA5VdczlVzrqYkXEy6K6ve4+8J76LM2sMVV17xo+oyxvDss8/yr4mPsXHjWiBGKBQmGIvzZfmntE/oRoKj/lvVnTYXDpuTioqKH3VOERER+XH2/7G7iIiIAPDiiy9ii9rpnX0yOdm5ZGZlkZKSUjsybbNsdE3qham2eOaZZ2p/bujQoZzUvx+Lqj9lR3AL332qK27ibA9uZnH1HPqf2p/BgwcfcD3GGG6++Sb+ePuNdO+0ndefbc6qOZ2Z83Yr7vx9Ku4mm5hX+Q7+aFm9Px+KBYnGI6Slpf20F0REREQOiJ7pFhEROQDt27bHWZTIMUk999tuVeVSSpK2s27DutpA7vf7ufLKK/ngvRm44h5STc1a3GXWbsK2IGf8+nQmTJxAUlISUHO7+LJlyygtLSUpKYmuXbvuddv5s88+y+233cA//prH8MHfru0dDoco2VNCpd/BqBu3s26ll34pZ+91e/uayuUU+Taz4uvlpKam/tyXR0RE5KijZ7pFREQOknA4TElJCW0de886/n3JjjS2BtZSXl5OZmZNGE5OTmbKlCl8+eWX/Oc//2H5shUAnNG1P5dccgndu3fHsiyi0SiTJk1iwlNPsXb9eowxWJZFk9xcfnvppVxzzTX4fD7i8Tj/mvg4Z5/uY/jgTIwxBENBYtGa9cRtdjuJyXHuuz2bc0ZtpSi4FYfNVbNUmakiHo/hj5dw8YW/UeAWERFpYArdIiIi9TDGMG/ePF588UU2bdxERWUlW+0byXLl4bF79/lzURMBy8LtdtfZblkWPXv2pGfP+kfKI5EIl19xBdPefRdv5040veIy3BkZRCoqKFmwkHv+39+ZPmMGr7z0EmvWrGHDhtU88KcCKgOVBAJVxONxLJsFpmZGdYA2bSy6dXEy74tPiDksHFlZ2HOziYeDhNeX8vobb3Dsscdy9dVX/+y1wUVERKR+Ct0iInLUWrp0Kc8//zxfrVyJzWajW9euXHzxxSQkJHDpJZex4IsFuGNeEkwy6bEcdoW38UHoddoldqFdQud6g+rO8BaO7XVs7a3iB+r+++9n2nvv0eSSC0np2KF2uystlYSCfKpOOJ7PJz7DTbfcwrBzzgHiNM2LUlERxuZy4nB5wVZzC7mJxYiHwsQjEdq2sTNnaYi0Cy7Cnd8Mm82Gx+vBa7Oz66NZ3HrHHcTjca655pqf81KKiIjIPih0i4jIUaeiooLRY8bwzvTpWEmJuJoXYIxh9rOTeOTxx3HZ7JhK6ObtQ2ZCLpZlEQoFKS4ppshsZWXFEgyGDold6xy3JFxMCbv4v6vu/lH1BAIBJj7zDMl9C+sE7u/yNW1K1q/PYOq0NxjQvz+xaIyiXQEKWiZhueo+723Z7dh9XkxljMqAwVPQnMxjOmGzWXg8Hmz/C+fNzj6LbTYbd997L+eee67W6xYREWkAmr1cRESOKuFwmAsvuoi3Z80ie+Rw2o67hRYXnU/Liy6gxS034hpwMnsCldgjLpJJrx3Ndrs9JHgSaWq1JJ82rK5YSiBaCdTMQr4juJlF1Z/S7+S+DBs27EfV9M4771DqLyersPd+26V3P5aY08m6desIReJMn129V+CuZQwV/hgzPwvhadOOxMQEfD5fbeD+Rs6A/gTjcV544YUfVbOIiIgcGIVuERE5qrz00kt8PG8u+aMuJq1bVyy7nUgkwu7duynz+/Ec04mMoUPZbe1hfclqdu/eTTQaASA1NRWvx0MO+WAs5pXOZFH5XD6peIflsfmcetYAnn/heVwu14+qadu2bTgSEnClpe63nc3lwpmdxbZt2wiGLf7zSiXFuyP1tjWxGM9MLqMi5MB9TKd9HtPh9eJp15YPZ836UTWLiIjIgVHoFhGRo4Yxhn898zSedm1JaNEcgGgkSklJCbFoDDs1o8aJbTvgaprHLrYTi8Yo2VNCNBrBsixSUlPJycom19kMEmO0OrEpF1wxkpkfzWDy5Mk/aflKl8tFPBLFxOM/fA3hMPF4HLfXy87KBC4bvZkly6rqrP/t98d46LFdPDGpHHefk7Al+vZ7TLvXTaCq6kfXLSIiIj9Mz3SLiMhRo6ysjOUrVpAx4tzabeX+coiD03JjiIOpmWk84ZhjKNvyIS7jJmxC+Mv9pGdkAOBwOPG5E8lpk85777/3s+s64YQTsCJh/KvXkNKh/T7bBYt2Ed5ZRO/evXl16lSSTz2V9V98znmjtnBMexed2jmpDMSZNSdIRbVFdcxFVvOCH5yZPLJrN007d/nZ1yEiIiJ700i3iIgcNYLBIMaA3VOznFckEiESjuCwnFhYQE04NRhsHg9x4hhjcOAgHIkQjXx7K3fA+GnSNO+g1NWjRw+6delK8cxZxP+31vb3hf0VbJz8Ej6Xi+zsbHr26EFg1Vra3nILeZddxVpnF6YuyWXGhua4+w2i3R/vwNskj8CChbjdbqqrq6kKVFFdXU38OyPq1Tt2Etq8hfNGjDgo1yIiIiJ1aaRbRESOGmlpabhdLoI7i0jp2IFQKARY2P73GbQNCwsbcaKEi4tx4cayLCzsWCZCKBTC4XQSiFZQRjEjzx95UOqyLIt777mHYeeNYNNzz9Ns2Dk4U2puUw+XlbNt6htULlmI04pi3E6uuvIiHE4v/j1+SpeuIL1bl3pnPXdlZFC+dDl72rTB26kDFhYGg2VZeD0evHYH2156lVYFBZxxxhkH5VpERESkLoVuERE5ang8HoYNGcLkDz4g+1f9MHHzv7Htb26/tnDYHISCASoXL6Yg1vQ794RZxI0hEo+wNPA5TfKbMmTIkINSVzwep0+fPvz32X9zxdVXs3b833EW5BMsLSW6fTP5eTZuviGF4YMzKMhPY/PWCM+/uosnn42z+emnCA4aRFbfPjgTEwkW76b40zmUf/01VZu3YlmwZ9pbJG7bTnKv43Dm5hCtrKT4s7kEPl9ArsfD89Om4HTuYxZ0ERER+Vks892ZVw5Dfr+flJQUysvLf9LkNSIicnRZtGgRp515Bu7je5I24BQqKytxWx5qby2PRdn55lQqFy8mJ9qUAlsbkkihygSo8pRTZNuCJ8PFq6+/Qo8ePQ7onNFolBkzZvD+++9TWVlJWloaXbt2ZcmSJbw6bRr+8nJ8CQmcdcYZjBgxgokTJ/LqtGlYROnQ2sF/nmhKVrodE41is1mkpqTidrv5fKGf4b9dg7/KhXE5icejWMEA6SnQJNdBqT9OUamNuCeJiLETqzNZmoUtFmPQwIG89uqrB/+FFhEROcIdaBZV6BYRkaPOv//9b24aOxYrvxmOzp1IKGiJDQdV69bi/+JzYlt2UOBswe7wLgLRCoyJEydOSmoyZw05i9tvv5127dod0LnmzJnD6GuvZdPWLThzcrASEqjauJFgWRnOtDQy+/TGk5lBxF9BxZdLCO3YQdyySOl1HFWLPmfSP/PoW5hUczBjiFUHIRYlPT0dl9PF3x7ZylMvRLEsOy7HbsZem8HggWmEwwEst5uFyyM88kQxc5fGyTx9EI6EBGxOB4ktW1K+ciVl095m0YIF5OfnN+ArLiIicuRR6BYREdmPGTNm8P8eeogPZ88mGotjt2xYMch25NDe14UMVzbGGLYHN7Os6nO69TqWyVMm06RJkwM+x5w5cxh23nnEmuSSN+hMfM2asmfBQjZOfomkEwtJKuyN3W4jPT0dh8NBNBBgyZ/uJqFnd5wOB7lF85k+tRU2W93Zx2OBAE67g4z0dLbtCNG9/1ISEpzMmtqB5vkeKgOVVFRW4khMAgvC4Tijb9jCZyu9tLv1dmwOe81xQmHW3Duev952O9ddd91BfX1FRESOdAeaRTV7uYiIHJVOPfVU3n37bWa8+y4tmjTBG3fS3t2J45P7keHKJhIPs6F6NavDS2jTqQ0vvfzSjwrc8Xic3914I7G8HFpePgpfs6aYeJzt707H1/kY0k4/DUdyEnHA768AoOTLxeCwk9zvRIKlpbRu4dgrcAPYXC4i4TDRaIS8HCcuZ5wTe3lpnu+pPbdl2WofVXe5bPzhpmyoKMX/1de1x7G7XTiSk9m9e/dPfh1FRERk/xS6RUTkqNavXz/mzp3D+Zecxy73Zj70T6v92uJYxdALh/Due++Qk5Pzo447a9Ys1mzYQO4ZA2tHlv1fryJcVkZy3z41s6JbFja3m3A4RDQaJbBxE+6CfJwZ6eB0UOaP13tsy+nEAOFIhDJ/NaFwnL4nfPsJu82y4Hs3snVo56VLRxdly1fUbjPxOLGqahISEn7UtYmIiMiB0+zlIiJy1MvOzmbCxAn85Z6/MH36dMrLy0lJSWHgwIE/Omx/Y+bMmTjS0/A1L6jdVrV9OzZfAu6m346Y2xwO4liEQ2FMLIblcGDZbHhatmLx9GVs2hyieYG7/pMYeOv9MoIh+PWp6bWbXW43VAYw0SiW49tf9XnZNlbu+HYyNf+q1VBdxYABA37SNYqIiMgPU+gWERH5n5ycHC6++OKDcqxAIIDN58OyvnN7uDFY379d3LLAqlmOzJWeRvmCDZhYDF/H9pTMTuThx3fx9/9rVvc281jNCHigCp54tgQsGx73t/tdTicOp4NoKITd7qi9zXzrjhh2X82odjwcZtf0mfQ4thvdu3c/KNcsIiIie9Pt5SIiIg0gMzOTaFkZJhar3ebJziZWWUl4V3HtNmMMxsSx2WxkHH8c8UCAwLIVYLPIOGsIb8wIMu5PW9myNVz7M9FQiOUrw1w0ZjNlFemkp6Uz5fXvPpdtkZKcghU3xKqqMLEYy7+q4qvVEVK7diawaTPrJz6Dz1/Bg3//e90PBkREROSgUugWERFpAEOGDIFAFeVfr6zdltK5E87ERCrmzqvdFg+HsSwLj8eNNy+XlI4dKHnjbaLFxWQf14MmF1zCtNkWA4du4LKrN3LzHzYy7OJN/Oaa3QSjrZn2xruMPP9iJvynhDXrq2uP63Q6SU9Pww5U7qnk3vHbCMds7Hp3Opuf+BctnC6mvfYaxx577C/5soiIiBx1FLpFREQaQJcuXTixd292vfUukXI/UPP8ds4pJ1O54Ev8874gHo1iwmG8Hg82W82v5NSuXYhXVlDy3GS2vjoVZ3Iyza8ajafXScxa7OSVN6sIxzswecobfPzJXDp06MCdd95JXrMuDLtsPZMmF1FRGa05n83Joq8cjB5bytIVNk47aQCXnflrXpsyhc/nzqVHjx6N9vqIiIgcLbROt4iISAPZunUrZw4axNYKP+m/6kf6cT2wud1sfuV1ij/9DEd2NsnH9SAjP59oZSVlC74kvHkLw4cMoUP79jz73HNs27EDA9gti25duzL6qqsYMWLEXreEl5WVMW7cH3jn7ak4HSFys12UlUcp80PXrj25/4EH6dq1a+O8ECIiIkegA82iCt0iIiINaMeOHfzpjjt48+23qY5GsTudxMIhXDY72ZmZFO/ZQ9wYLMvipBNP5LejRjFo0CBsNhuRSISVK1cSCATIysqiVatWP/j89c6dO3njjTcoLi7G5/PRv39/unXr9stcrIiIyFFEoVtEROQQsnPnTj788EP8fj/p6ekMHDiQ1NRUAoEA5eXlJCYm/uDvscrKSl577TXmzp1LKBQiLy+PkSNHagRbRESkESh0i4iIHEH++9//8se77qLU78eV3wzL7SJaVAyVlZx04on8a8IEsrOzG7tMERGRo8aBZlGt0y0iInKImzRpEjeP+wPe7t1oc9oAXGmpAJhYjPKvV/Lx1DcZfM45vPf226SlpTVusSIiIlKHZi8XERE5hBUXF3P7HXfgO/448kcMqw3cAJbdTmrnY2gx+kpWbd7MQw891HiFioiISL0UukVERA5hkydPpioWJe+MgfucRM2dmUFSr548N/kFqqqqfuEKRUREZH8UukVERA5h02fMwNOuLQ6fd7/t0nv2oKSsjIULF/5ClYmIiMiBUOgWERE5hFVUVmL3+X6wnSMhAWMgEAj8AlWJiIjIgVLoFhEROYTlZGcT2bPnB9uFindjsyyysrJ+gapERETkQDVo6H7iiSfo2rUrycnJJCcnU1hYyLvvvlu7PxgMcu2115KRkUFiYiLnnnsuRUVFDVmSiIjIYWX4sGGE1m8ktHv/wXvPvC9o3bIl3bt3/4UqExERkQPRoKG7WbNmjB8/noULF7JgwQJOOeUUhgwZwooVKwC46aabePPNN3n55ZeZPXs227dvZ9iwYQ1ZkoiIyGFlyJAhNM3NZevLrxILhettU7b8K6qWLePqK67AZtNNbCIiIocSyxhjfskTpqen88ADDzB8+HCysrJ44YUXGD58OAArV66kY8eOzJ07l969ex/Q8Q50QXIREZHD1YIFCxg6YgQBr4fMk/uR2rULNqeT4K5iij+bQ+UXCxl+9tlMnDABu93e2OWKiIgcFQ40izp+qYJisRgvv/wygUCAwsJCFi5cSCQS4dRTT61t06FDBwoKCvYbukOhEKFQqPZ7v9/f4LWLiIg0puOOO4733nqLu+7+Mx++MpUdL72GZbdDLEZedja33HorN9xwgwK3iIjIIajBQ/eyZcsoLCwkGAySmJjI66+/TqdOnVi8eDEul4vU1NQ67XNycti5c+c+j3ffffdx9913N3DVIiIih5ZjjjmGV156mXXr1jFv3jxCoRBNmjThlFNOweVyNXZ5IiIisg8NHrrbt2/P4sWLKS8v55VXXuHSSy9l9uzZP/l4t912GzfffHPt936/n/z8/INRqoiIyCGvdevWtG7durHLEBERkQPU4KHb5XLRpk0bAHr27Mn8+fN5+OGHGTlyJOFwmLKysjqj3UVFReTm5u7zeG63G7fb3dBli4iIiIiIiPxsv/gUp/F4nFAoRM+ePXE6ncycObN236pVq9i8eTOFhYW/dFkiIiIiIiIiB12DjnTfdtttnHnmmRQUFFBRUcELL7zArFmzeP/990lJSeHyyy/n5ptvJj09neTkZK6//noKCwsPeOZyERERERERkUNZg4buXbt2cckll7Bjxw5SUlLo2rUr77//PqeddhoADz30EDabjXPPPZdQKMTpp5/O448/3pAliYiIiIiIiPxifvF1ug82rdMtIiIiIiIiv7QDzaK/+DPdIiIiIiIiIkcLhW4RERERERGRBqLQLSIiIiIiItJAFLpFREREREREGohCt4iIiIiIiEgDUegWERERERERaSAK3SIiIiIiIiINRKFbREREREREpIEodIuIiIiIiIg0EIVuERERERERkQai0C0iIiIiIiLSQBS6RURERERERBqIQreIiIiIiIhIA1HoFhEREREREWkgCt0iIiIiIiIiDUShW0RERERERKSBKHSLiIiIiIiINBCFbhEREREREZEGotAtIiIiIiIi0kAcjV3Az2WMAcDv9zdyJSIiIiIiInK0+CaDfpNJ9+WwD90VFRUA5OfnN3IlIiIiIiIicrSpqKggJSVln/st80Ox/BAXj8fZvn07SUlJWJbV2OUINZ/45Ofns2XLFpKTkxu7HPkJ1IeHP/Xh4U39d/hTHx7+1IeHP/Xh4e1w6D9jDBUVFTRp0gSbbd9Pbh/2I902m41mzZo1dhlSj+Tk5EP2DSIHRn14+FMfHt7Uf4c/9eHhT314+FMfHt4O9f7b3wj3NzSRmoiIiIiIiEgDUegWERERERERaSAK3XLQud1u7rrrLtxud2OXIj+R+vDwpz48vKn/Dn/qw8Of+vDwpz48vB1J/XfYT6QmIiIiIiIicqjSSLeIiIiIiIhIA1HoFhEREREREWkgCt0iIiIiIiIiDUShW0RERERERKSBKHTLQXX22WdTUFCAx+MhLy+Piy++mO3bt9dps3TpUvr164fH4yE/P5/777+/kaqV79u4cSOXX345LVu2xOv10rp1a+666y7C4XCddurDQ9u9995Lnz598Pl8pKam1ttm8+bNDBo0CJ/PR3Z2NmPHjiUajf6yhco+PfbYY7Ro0QKPx8MJJ5zAF1980dglyT58/PHHDB48mCZNmmBZFlOnTq2z3xjDnXfeSV5eHl6vl1NPPZU1a9Y0TrGyl/vuu4/jjz+epKQksrOzOeecc1i1alWdNsFgkGuvvZaMjAwSExM599xzKSoqaqSK5fueeOIJunbtSnJyMsnJyRQWFvLuu+/W7lf/HV7Gjx+PZVnceOONtduOhD5U6JaDqn///rz00kusWrWKV199lXXr1jF8+PDa/X6/n4EDB9K8eXMWLlzIAw88wJ///GcmTpzYiFXLN1auXEk8HmfChAmsWLGChx56iCeffJLbb7+9to368NAXDocZMWIEY8aMqXd/LBZj0KBBhMNh5syZw7///W+effZZ7rzzzl+4UqnPiy++yM0338xdd93Fl19+ybHHHsvpp5/Orl27Grs0qUcgEODYY4/lscceq3f//fffzyOPPMKTTz7J559/TkJCAqeffjrBYPAXrlTqM3v2bK699lrmzZvHBx98QCQSYeDAgQQCgdo2N910E2+++SYvv/wys2fPZvv27QwbNqwRq5bvatasGePHj2fhwoUsWLCAU045hSFDhrBixQpA/Xc4mT9/PhMmTKBr1651th8RfWhEGtC0adOMZVkmHA4bY4x5/PHHTVpamgmFQrVtxo0bZ9q3b99YJcoPuP/++03Lli1rv1cfHj4mTZpkUlJS9tr+zjvvGJvNZnbu3Fm77YknnjDJycl1+lUaR69evcy1115b+30sFjNNmjQx9913XyNWJQcCMK+//nrt9/F43OTm5poHHnigdltZWZlxu91m8uTJjVCh/JBdu3YZwMyePdsYU9NfTqfTvPzyy7Vtvv76awOYuXPnNlaZ8gPS0tLMU089pf47jFRUVJi2bduaDz74wPzqV78yN9xwgzHmyHkPaqRbGkxJSQnPP/88ffr0wel0AjB37lxOOukkXC5XbbvTTz+dVatWUVpa2lilyn6Ul5eTnp5e+7368PA3d+5cunTpQk5OTu22008/Hb/fXzsyII0jHA6zcOFCTj311NptNpuNU089lblz5zZiZfJTbNiwgZ07d9bpz5SUFE444QT15yGqvLwcoPb33sKFC4lEInX6sEOHDhQUFKgPD0GxWIwpU6YQCAQoLCxU/x1Grr32WgYNGlSnr+DIeQ8qdMtBN27cOBISEsjIyGDz5s1Mmzatdt/OnTvr/KEP1H6/c+fOX7RO+WFr167l0Ucf5eqrr67dpj48/KkPD127d+8mFovV2z/qm8PPN32m/jw8xONxbrzxRk488UQ6d+4M1PShy+Xaa34M9eGhZdmyZSQmJuJ2uxk9ejSvv/46nTp1Uv8dJqZMmcKXX37Jfffdt9e+I6UPFbrlB916661YlrXfr5UrV9a2Hzt2LIsWLWL69OnY7XYuueQSjDGNeAXyY/sQYNu2bZxxxhmMGDGCK6+8spEql2/8lD4UEZEDd+2117J8+XKmTJnS2KXIj9S+fXsWL17M559/zpgxY7j00kv56quvGrssOQBbtmzhhhtu4Pnnn8fj8TR2OQ3G0dgFyKHvlltu4bLLLttvm1atWtX+f2ZmJpmZmbRr146OHTuSn5/PvHnzKCwsJDc3d6/ZBr/5Pjc396DXLjV+bB9u376d/v3706dPn70mSFMfNo4f24f7k5ubu9ds2OrDQ0NmZiZ2u73e95j65vDzTZ8VFRWRl5dXu72oqIhu3bo1UlVSn+uuu4633nqLjz/+mGbNmtVuz83NJRwOU1ZWVmekTe/JQ4vL5aJNmzYA9OzZk/nz5/Pwww8zcuRI9d8hbuHChezatYsePXrUbovFYnz88cf885//5P333z8i+lChW35QVlYWWVlZP+ln4/E4AKFQCIDCwkL++Mc/EolEap/z/uCDD2jfvj1paWkHp2DZy4/pw23bttG/f3969uzJpEmTsNnq3hCjPmwcP+d9+H2FhYXce++97Nq1i+zsbKCmD5OTk+nUqdNBOYf8NC6Xi549ezJz5kzOOeccoObf0ZkzZ3Ldddc1bnHyo7Vs2ZLc3FxmzpxZG7L9fn/taJw0PmMM119/Pa+//jqzZs2iZcuWdfb37NkTp9PJzJkzOffccwFYtWoVmzdvprCwsDFKlgMQj8cJhULqv8PAgAEDWLZsWZ1to0aNokOHDowbN478/Pwjow8beyY3OXLMmzfPPProo2bRokVm48aNZubMmaZPnz6mdevWJhgMGmNqZiDMyckxF198sVm+fLmZMmWK8fl8ZsKECY1cvRhjzNatW02bNm3MgAEDzNatW82OHTtqv76hPjz0bdq0ySxatMjcfffdJjEx0SxatMgsWrTIVFRUGGOMiUajpnPnzmbgwIFm8eLF5r333jNZWVnmtttua+TKxRhjpkyZYtxut3n22WfNV199Za666iqTmppaZ7Z5OXRUVFTUvscA8+CDD5pFixaZTZs2GWOMGT9+vElNTTXTpk0zS5cuNUOGDDEtW7Y01dXVjVy5GGPMmDFjTEpKipk1a1ad33lVVVW1bUaPHm0KCgrMhx9+aBYsWGAKCwtNYWFhI1Yt33Xrrbea2bNnmw0bNpilS5eaW2+91ViWZaZPn26MUf8djr47e7kxR0YfKnTLQbN06VLTv39/k56ebtxut2nRooUZPXq02bp1a512S5YsMX379jVut9s0bdrUjB8/vpEqlu+bNGmSAer9+i714aHt0ksvrbcPP/roo9o2GzduNGeeeabxer0mMzPT3HLLLSYSiTRe0VLHo48+agoKCozL5TK9evUy8+bNa+ySZB8++uijet9vl156qTGmZtmwO+64w+Tk5Bi3220GDBhgVq1a1bhFS619/c6bNGlSbZvq6mpzzTXXmLS0NOPz+czQoUPrfBgtjeu3v/2tad68uXG5XCYrK8sMGDCgNnAbo/47HH0/dB8JfWgZoxmuRERERERERBqCZi8XERERERERaSAK3SIiIiIiIiINRKFbREREREREpIEodIuIiIiIiIg0EIVuERERERERkQai0C0iIiIiIiLSQBS6RURERERERBqIQreIiIiIiIhIA1HoFhEREREREWkgCt0iIiIiIiIiDUShW0RERERERKSBKHSLiIiIiIiINJD/D07ciuj/QKI8AAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from bioneuralnet.metrics import plot_embeddings\n", + "\n", + "# for a more interpretable embedding plot we will map the node labels to omics types\n", + "GCN_node_labels = []\n", + "GCN_embeddings_array = GCN_embeddings_df.values\n", + "feature_names = GCN_embeddings_df.index\n", + "\n", + "# using the sets from the orginal to serve as a loopup table\n", + "dna_feats = set(dna_meth.columns)\n", + "rna_feats = set(rna.columns)\n", + "mirna_feats = set(mirna.columns)\n", + "\n", + "for feat in feature_names:\n", + " if feat in dna_feats:\n", + " # 1 for DNA methylation\n", + " GCN_node_labels.append(1)\n", + " elif feat in rna_feats:\n", + " # 2 for RNA \n", + " GCN_node_labels.append(2)\n", + " elif feat in mirna_feats:\n", + " # and 3 for miRNA \n", + " GCN_node_labels.append(3)\n", + " else:\n", + " GCN_node_labels.append(0)\n", + "\n", + "# our plotting function needs an array to squeeze the embedings into 2Dspace\n", + "GCN_node_labels = np.array(GCN_node_labels)\n", + "\n", + "# feature(omics) embeddings colored by omics type.\n", + "print(f\"Plotting Feature Embeddings for graph: {res['graph']}\")\n", + "plot_embeddings(GCN_embeddings_array, GCN_node_labels, legend_labels=[\"DNA_Methylation\", \"RNA\", \"miRNA\"])" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".enviroment", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/TCGA-Notebooks/TCGA-KIPAN.ipynb b/TCGA-Notebooks/TCGA-KIPAN.ipynb new file mode 100644 index 0000000..ba7fa9d --- /dev/null +++ b/TCGA-Notebooks/TCGA-KIPAN.ipynb @@ -0,0 +1,4348 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "d408841e", + "metadata": {}, + "source": [ + "# TCGA-KIPAN Analysis: Using *BioNeuralNet* for Interpretability and Biomarker Discovery\n", + "\n", + "- **Cohort**: Focuses on the TCGA-KIPAN dataset, a vital resource merging three major kidney cancer subtypes:\n", + " - `Kidney Renal Clear Cell Carcinoma` (KIRC)\n", + " - `Kidney Renal Papillary Cell Carcinoma` (KIRP)\n", + " - `Kidney Chromophobe `(KICH)\n", + "\n", + "- **Goal**: Perform histological subtype classification.\n", + "- **Prediction Target**: Predict the specific kidney cancer subtype (`KIRC`, `KIRP`, or `KICH`) from its multi-omics profile.\n", + "\n", + "**Data Source:** Broad Institute FireHose (`http://firebrowse.org/?cohort=KIPAN`)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1c56af27", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "from pathlib import Path\n", + "root = Path(\"/home/vicente/Github/BioNeuralNet/KIPAN\")\n", + "\n", + "mirna_raw = pd.read_csv(root/\"KIPAN.miRseq_RPKM_log2.txt\", sep=\"\\t\",index_col=0,low_memory=False) \n", + "rna_raw = pd.read_csv(root / \"KIPAN.uncv2.mRNAseq_RSEM_normalized_log2.txt\", sep=\"\\t\",index_col=0,low_memory=False)\n", + "meth_raw = pd.read_csv(root/\"KIPAN.meth.by_mean.data.txt\", sep='\\t',index_col=0,low_memory=False)\n", + "clinical_raw = pd.read_csv(root / \"KIPAN.clin.merged.picked.txt\",sep=\"\\t\", index_col=0, low_memory=False)\n", + "\n", + "# display all shapes and first few rows of each dataset\n", + "display(mirna_raw.iloc[:3,:5])\n", + "display(mirna_raw.shape)\n", + "\n", + "display(rna_raw.iloc[:3,:5])\n", + "display(meth_raw.shape)\n", + "\n", + "display(meth_raw.iloc[:3,:5])\n", + "display(meth_raw.shape)\n", + "\n", + "display(clinical_raw.iloc[:3,:5])\n", + "display(clinical_raw.shape)" + ] + }, + { + "cell_type": "markdown", + "id": "e13c6054", + "metadata": {}, + "source": [ + "## Data Processing Summary\n", + "\n", + "1. **Transpose Data:** All raw data (miRNA, RNA, etc.) is flipped so rows represent patients and columns represent features.\n", + "2. **Standardize Patient IDs:** Patient IDs in all tables are cleaned to the 12-character TCGA format (e.g., `TCGA-AB-1234`) for matching.\n", + "3. **Handle Duplicates:** Duplicate patient rows are averaged in the omics data. The first entry is kept for duplicate patients in the clinical data.\n", + "4. **Find Common Patients:** The script identifies the list of patients that exist in *all* datasets.\n", + "5. **Subset Data:** All data tables are filtered down to *only* this common list of patients, ensuring alignment.\n", + "6. **Extract Target:** The `histological_type` column is pulled from the processed clinical data to be used as the prediction target (y-variable)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "33acffac", + "metadata": {}, + "outputs": [], + "source": [ + "mirna = mirna_raw.T\n", + "rna = rna_raw.T\n", + "meth = meth_raw.T\n", + "clinical = clinical_raw.T\n", + "\n", + "print(f\"miRNA (samples, features): {mirna.shape}\")\n", + "print(f\"RNA (samples, features): {rna.shape}\")\n", + "print(f\"Methylation (samples, features): {meth.shape}\")\n", + "print(f\"Clinical (samples, features): {clinical.shape}\")\n", + "\n", + "def trim_barcode(idx):\n", + " return idx.to_series().str.slice(0, 12)\n", + "\n", + "# standarized patient IDs across all files\n", + "meth.index = trim_barcode(meth.index)\n", + "rna.index = trim_barcode(rna.index)\n", + "mirna.index = trim_barcode(mirna.index)\n", + "clinical.index = clinical.index.str.upper()\n", + "clinical.index.name = \"Patient_ID\"\n", + "\n", + "# convert all data to numeric, coercing errors to NaN\n", + "meth = meth.apply(pd.to_numeric, errors='coerce')\n", + "rna = rna.apply(pd.to_numeric, errors='coerce')\n", + "mirna = mirna.apply(pd.to_numeric, errors='coerce')\n", + "\n", + "# for any duplicate columns in the omics data, we average their values\n", + "meth = meth.groupby(meth.index).mean()\n", + "rna = rna.groupby(rna.index).mean()\n", + "mirna = mirna.groupby(mirna.index).mean()\n", + "\n", + "# for any duplicate rows in the clinical data, we keep the first occurrence\n", + "clinical = clinical[~clinical.index.duplicated(keep='first')]\n", + "\n", + "print(f\"\\nMethylation shape: {meth.shape}\")\n", + "print(f\"RNA shape: {rna.shape}\")\n", + "print(f\"miRNA shape: {mirna.shape}\")\n", + "print(f\"Clinical shape: {clinical.shape}\")\n", + "\n", + "for df in [meth, rna, mirna]:\n", + " df.columns = df.columns.str.replace(r\"\\?\", \"unknown_\", regex=True)\n", + " df.columns = df.columns.str.replace(r\"\\|\", \"_\", regex=True)\n", + " df.columns = df.columns.str.replace(\"-\", \"_\", regex=False)\n", + " df.columns = df.columns.str.replace(r\"_+\", \"_\", regex=True)\n", + " df.columns = df.columns.str.strip(\"_\")\n", + " \n", + " df.fillna(df.mean(), inplace=True)\n", + "\n", + "# to see which pateints are common across all data files\n", + "common_patients = sorted(list(set(meth.index)&set(rna.index)&set(mirna.index)&set(clinical.index)))\n", + "\n", + "print(f\"\\nFound: {len(common_patients)} patients across all data types.\")\n", + "\n", + "# subset to only common patients\n", + "meth_processed = meth.loc[common_patients]\n", + "rna_processed= rna.loc[common_patients]\n", + "mirna_processed = mirna.loc[common_patients]\n", + "clinical_processed = clinical.loc[common_patients]\n", + "\n", + "# extract target labels from clinical data\n", + "targets = clinical_processed['histological_type']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4ca526c4", + "metadata": {}, + "outputs": [], + "source": [ + "display(mirna_processed.iloc[:3,:5])\n", + "display(mirna_processed.shape)\n", + "\n", + "display(rna_processed.iloc[:3,:5])\n", + "display(rna_processed.shape)\n", + "\n", + "display(meth_processed.iloc[:3,:5])\n", + "display(meth_processed.shape)\n", + "\n", + "display(clinical_processed.iloc[:3,:5])\n", + "display(clinical_processed.shape)\n", + "\n", + "display(targets.value_counts())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e924114f", + "metadata": {}, + "outputs": [], + "source": [ + "import bioneuralnet as bnn\n", + "\n", + "# drop unwanted columns from clinical data\n", + "clinical_processed.drop(columns=[\"Composite Element REF\"], errors=\"ignore\", inplace=True)\n", + "\n", + "# we transform the methylation beta values to M-values and drop unwanted columns\n", + "meth_m = meth_processed.drop(columns=[\"Composite Element REF\"], errors=\"ignore\")\n", + "\n", + "# convert beta values to M-values using bioneuralnet utility with small epsilon to avoid log(0)\n", + "meth_m = bnn.utils.beta_to_m(meth_m, eps=1e-6) \n", + "\n", + "# lastly we turn the target labels into numerical classes\n", + "mapping = {\"kidney clear cell renal carcinoma\": 0, \"kidney papillary renal cell carcinoma\": 1, \"kidney chromophobe\": 2}\n", + "target_labels = targets.map(mapping).to_frame(name=\"target\")\n", + "\n", + "# as a safety check we align the indices once more\n", + "X_meth = meth_m.loc[common_patients]\n", + "X_rna = rna_processed.loc[common_patients]\n", + "X_mirna = mirna_processed.loc[common_patients]\n", + "Y_labels = target_labels.loc[common_patients]\n", + "clinical_final = clinical_processed.loc[common_patients]\n", + "\n", + "print(f\"\\nDNA_Methylation shape: {X_meth.shape}\")\n", + "print(f\"RNA shape: {X_rna.shape}\")\n", + "print(f\"miRNA shape: {X_mirna.shape}\")\n", + "print(f\"Clinical shape: {clinical_final.shape}\")\n", + "print(Y_labels.value_counts())" + ] + }, + { + "cell_type": "markdown", + "id": "173f6145", + "metadata": {}, + "source": [ + "## Feature Selection Methodology\n", + "\n", + "### Supported Methods and Interpretation\n", + "\n", + "**BioNeuralNet** provides three techniques for feature selection, allowing for different views of the data's statistical profile:\n", + "\n", + "- **Variance Thresholding:** Identifies features with the **highest overall variance** across all samples.\n", + "\n", + "- **ANOVA F-test:** Pinpoints features that best **distinguish between the target classes** (KIRC, KIRP, and KICH).\n", + "\n", + "- **Random Forest Importance:** Assesses **feature utility** based on its contribution to a predictive non-linear model.\n", + "\n", + "### KIPAN Cohort Selection Strategy\n", + "\n", + "A dimensionality reduction step was essential for managing the high-feature-count omics data:\n", + "\n", + "- **High-Feature Datasets:** Both DNA Methylation (20,116) and RNA (18,272) required significant feature reduction.\n", + "\n", + "- **Filtering Process:** The **top 6,000 features** were initially extracted from the Methylation and RNA datasets using all three methods.\n", + "\n", + "- **Final Set:** A consensus set was built by finding the intersection of features selected by the ANOVA F-test and Random Forest Importance, ensuring both statistical relevance and model-based utility.\n", + "\n", + "- **Low-Feature Datasets:** The miRNA data (472 features) was passed through **without selection**, as its feature count was already manageable." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "241a4bb0", + "metadata": {}, + "outputs": [], + "source": [ + "import bioneuralnet as bnn\n", + "\n", + "# feature selection\n", + "meth_highvar = bnn.utils.select_top_k_variance(X_meth, k=6000)\n", + "rna_highvar = bnn.utils.select_top_k_variance(X_rna, k=6000)\n", + "\n", + "meth_af = bnn.utils.top_anova_f_features(X_meth, Y_labels, max_features=6000)\n", + "rna_af = bnn.utils.top_anova_f_features(X_rna, Y_labels, max_features=6000)\n", + "\n", + "meth_rf = bnn.utils.select_top_randomforest(X_meth, Y_labels, top_k=6000)\n", + "rna_rf = bnn.utils.select_top_randomforest(X_rna, Y_labels, top_k=6000)\n", + "\n", + "meth_var_set = set(meth_highvar.columns)\n", + "meth_anova_set = set(meth_af.columns)\n", + "meth_rf_set = set(meth_rf.columns)\n", + "\n", + "rna_var_set = set(rna_highvar.columns)\n", + "rna_anova_set = set(rna_af.columns)\n", + "rna_rf_set = set(rna_rf.columns)\n", + "\n", + "meth_inter1 = list(meth_anova_set & meth_var_set)\n", + "meth_inter2 = list(meth_rf_set & meth_var_set)\n", + "meth_inter3 = list(meth_anova_set & meth_rf_set)\n", + "meth_all_three = list(meth_anova_set & meth_var_set & meth_rf_set)\n", + "\n", + "rna_inter4 = list(rna_anova_set & rna_var_set)\n", + "rna_inter5 = list(rna_rf_set & rna_var_set)\n", + "rna_inter6 = list(rna_anova_set & rna_rf_set)\n", + "rna_all_three = list(rna_anova_set & rna_var_set & rna_rf_set)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b02d2358", + "metadata": {}, + "outputs": [], + "source": [ + "print(\"FROM THE 6000 Methylation feature selection:\\n\")\n", + "print(f\"Anova-F & variance selection share: {len(meth_inter1)} features\")\n", + "print(f\"Random Forest & variance selection share: {len(meth_inter2)} features\")\n", + "print(f\"Anova-F & Random Forest share: {len(meth_inter3)} features\")\n", + "print(f\"All three methods agree on: {len(meth_all_three)} features\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3abea9c8", + "metadata": {}, + "outputs": [], + "source": [ + "print(\"\\nFROM THE 6000 RNA feature selection:\\n\")\n", + "print(f\"Anova-F & variance selection share: {len(rna_inter4)} features\")\n", + "print(f\"Random Forest & variance selection share: {len(rna_inter5)} features\")\n", + "print(f\"Anova-F & Random Forest share: {len(rna_inter6)} features\")\n", + "print(f\"All three methods agree on: {len(rna_all_three)} features\")" + ] + }, + { + "cell_type": "markdown", + "id": "ee3a3e98", + "metadata": {}, + "source": [ + "## Feature Selection Summary: ANOVA-RF Intersection\n", + "\n", + "The final set of features was determined by the **intersection** of those highlighted by the **ANOVA F-test** and **Random Forest Importance**. This methodology provides a balanced filter, capturing features with both high class-separability (ANOVA) and significant predictive value in a non-linear model (Random Forest). The resulting feature pool is considered highly relevant for the subsequent modeling tasks.\n", + "\n", + "### Feature Overlap Results\n", + "\n", + "The table below quantifies the shared features identified by the different selection techniques for each omics type.\n", + "\n", + "| Omics Data Type | ANOVA-F & Variance | RF & Variance | ANOVA-F & Random Forest (Selected) | All Three Agree |\n", + "| :--- | :--- | :--- | :--- | :--- |\n", + "| **Methylation** | 1,875 features | 1,656 features | **2,102 features** | 666 features |\n", + "| **RNA** | 2,271 features | 2,141 features | **2,284 features** | 943 features |" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cba88e86", + "metadata": {}, + "outputs": [], + "source": [ + "X_meth_selected = X_meth[meth_inter3]\n", + "X_rna_selected = X_rna[rna_inter6]\n", + "\n", + "print(\"\\nFinal Shapes for Modeling\")\n", + "print(f\"Methylation (X1): {X_meth_selected.shape}\")\n", + "print(f\"RNA-Seq (X2): {X_rna_selected.shape}\")\n", + "print(f\"miRNA-Seq (X3): {X_mirna.shape}\")\n", + "print(f\"Labels (Y): {Y_labels.shape}\")" + ] + }, + { + "cell_type": "markdown", + "id": "f0d52489", + "metadata": {}, + "source": [ + "## Data Availability\n", + "\n", + "To facilitate rapid experimentation and reproduction of our results, the fully processed and feature-selected dataset used in this analysis has been made available directly within the package.\n", + "\n", + "Users can load this dataset, bypassing all preceding data acquisition, preprocessing, and feature selection steps. This allows users to proceed immediately from this step." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "78e99139", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'mirna': (658, 472),\n", + " 'target': (658, 1),\n", + " 'clinical': (658, 19),\n", + " 'rna': (658, 2284),\n", + " 'meth': (658, 2102)}" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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C19orf22C15orf41APOL5ADCY10MIR762
patient
TCGA-2K-A9WE0.362406-0.2286361.8236981.240816-2.290156
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ARAP3_64411GPRC5C_55890RASGEF1C_255426PLAC9_219348C9orf169_375791
patient
TCGA-2K-A9WE7.04455711.8462875.4312493.8462834.402456
TCGA-2Z-A9J15.89414413.0868473.6311043.4466805.747207
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hsa_let_7a_1hsa_let_7a_2hsa_let_7a_3hsa_let_7bhsa_let_7c
patient
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TCGA-2Z-A9J211.83227812.83838811.84072511.0387188.360210
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" + ], + "text/plain": [ + " hsa_let_7a_1 hsa_let_7a_2 hsa_let_7a_3 hsa_let_7b hsa_let_7c\n", + "patient \n", + "TCGA-2K-A9WE 12.933499 13.933025 12.938528 12.861969 11.474055\n", + "TCGA-2Z-A9J1 12.535658 13.536437 12.531655 12.710724 10.355773\n", + "TCGA-2Z-A9J2 11.832278 12.838388 11.840725 11.038718 8.360210" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "(658, 472)" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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years_to_birthvital_statusdays_to_deathdays_to_last_followuptumor_tissue_site
Patient_ID
TCGA-2K-A9WE53.00NaN214.0kidney
TCGA-2Z-A9J171.00NaN2298.0kidney
TCGA-2Z-A9J271.00NaN1795.0kidney
\n", + "
" + ], + "text/plain": [ + " years_to_birth vital_status days_to_death \\\n", + "Patient_ID \n", + "TCGA-2K-A9WE 53.0 0 NaN \n", + "TCGA-2Z-A9J1 71.0 0 NaN \n", + "TCGA-2Z-A9J2 71.0 0 NaN \n", + "\n", + " days_to_last_followup tumor_tissue_site \n", + "Patient_ID \n", + "TCGA-2K-A9WE 214.0 kidney \n", + "TCGA-2Z-A9J1 2298.0 kidney \n", + "TCGA-2Z-A9J2 1795.0 kidney " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "(658, 19)" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "target\n", + "0 318\n", + "1 274\n", + "2 66\n", + "Name: count, dtype: int64" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import pandas as pd\n", + "from pathlib import Path\n", + "from bioneuralnet.datasets import DatasetLoader\n", + "\n", + "tgca_kipan = DatasetLoader(\"kipan\")\n", + "display(tgca_kipan.shape)\n", + "\n", + "# the dataset is returned as a dictionary. \n", + "# We extract each file independetly based on the name (Key).\n", + "dna_meth = tgca_kipan[\"meth\"]\n", + "rna = tgca_kipan[\"rna\"]\n", + "mirna = tgca_kipan[\"mirna\"]\n", + "clinical = tgca_kipan[\"clinical\"]\n", + "target = tgca_kipan[\"target\"]\n", + "\n", + "display(dna_meth.iloc[:3,:5])\n", + "display(dna_meth.shape)\n", + "display(rna.iloc[:3,:5])\n", + "display(rna.shape)\n", + "display(mirna.iloc[:3,:5])\n", + "display(mirna.shape)\n", + "display(clinical.iloc[:3,:5])\n", + "display(clinical.shape)\n", + "display(target[\"target\"].value_counts())" + ] + }, + { + "cell_type": "markdown", + "id": "4c68ddb7", + "metadata": {}, + "source": [ + "## Reproducibility and Seeding\n", + "\n", + "To ensure our experimental results are fully reproducible, a single global seed is set at the beginning of the analysis.\n", + "\n", + "This utility function propagates the seed to all sources of randomness, including `random`, `numpy`, and `torch` (for both CPU and GPU). Critically, it also configures the PyTorch cuDNN backend to use deterministic algorithms.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "339c3c5d", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-11-26 00:57:18,920 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 00:57:18,921 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 00:57:18,922 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n" + ] + } + ], + "source": [ + "import bioneuralnet as bnn\n", + "import numpy as np\n", + "\n", + "SEED = 8183\n", + "bnn.utils.set_seed(SEED)" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "6c20cfda", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "histological_type\n", + "kidney clear cell renal carcinoma 318\n", + "kidney papillary renal cell carcinoma 274\n", + "kidney chromophobe 66\n", + "Name: count, dtype: int64" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "clinical[\"histological_type\"].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "e22f2aea", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-11-26 00:57:18,937 - bioneuralnet.utils.preprocess - INFO - Ignoring 8 columns.\n", + "2025-11-26 00:57:18,939 - bioneuralnet.utils.preprocess - INFO - Dropping 5 numeric columns due to >50.0% missing values.\n", + "2025-11-26 00:57:18,945 - bioneuralnet.utils.preprocess - INFO - Selecting top 7 features by variance.\n", + "2025-11-26 00:57:18,946 - bioneuralnet.utils.preprocess - INFO - Clinical data cleaning complete. Final shape: (658, 7)\n" + ] + }, + { + "data": { + "text/html": [ + "
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years_to_birthradiation_therapy_nogender_maleethnicity_not hispanic or latinorace_white
Patient_ID
TCGA-2K-A9WE53.01111
TCGA-2Z-A9J171.01111
TCGA-2Z-A9J271.01011
\n", + "
" + ], + "text/plain": [ + " years_to_birth radiation_therapy_no gender_male \\\n", + "Patient_ID \n", + "TCGA-2K-A9WE 53.0 1 1 \n", + "TCGA-2Z-A9J1 71.0 1 1 \n", + "TCGA-2Z-A9J2 71.0 1 0 \n", + "\n", + " ethnicity_not hispanic or latino race_white \n", + "Patient_ID \n", + "TCGA-2K-A9WE 1 1 \n", + "TCGA-2Z-A9J1 1 1 \n", + "TCGA-2Z-A9J2 1 1 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from bioneuralnet.utils import preprocess_clinical\n", + "\n", + "clinical_for_model = preprocess_clinical(\n", + " clinical, \n", + " top_k=7, \n", + " scale=False,\n", + " ignore_columns=[\n", + " # target-related or outcome-related or irrelevant columns\n", + " \"histological_type\",\n", + " \"pathologic_stage\",\n", + " \"pathology_M_stage\",\n", + " \"pathology_N_stage\",\n", + " \"pathology_T_stage\",\n", + " \"date_of_initial_pathologic_diagnosis\",\n", + " \"days_to_last_followup\",\n", + " \"vital_status\"\n", + " ]\n", + ")\n", + "display(clinical_for_model.iloc[:3,:5])" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "40c36cd7", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-11-26 00:57:19,265 - bioneuralnet.utils.preprocess - INFO - [Inf]: Replaced 0 infinite values\n", + "2025-11-26 00:57:19,266 - bioneuralnet.utils.preprocess - INFO - [NaN]: Replaced 0 NaNs after median imputation\n", + "2025-11-26 00:57:19,266 - bioneuralnet.utils.preprocess - INFO - [Zero-Var]: 0 columns dropped due to zero variance\n", + "2025-11-26 00:57:19,274 - bioneuralnet.utils.preprocess - INFO - Selected top 300 features by variance\n", + "2025-11-26 00:57:19,604 - bioneuralnet.utils.preprocess - INFO - [Inf]: Replaced 0 infinite values\n", + "2025-11-26 00:57:19,604 - bioneuralnet.utils.preprocess - INFO - [NaN]: Replaced 0 NaNs after median imputation\n", + "2025-11-26 00:57:19,604 - bioneuralnet.utils.preprocess - INFO - [Zero-Var]: 0 columns dropped due to zero variance\n", + "2025-11-26 00:57:19,613 - bioneuralnet.utils.preprocess - INFO - Selected top 300 features by variance\n", + "2025-11-26 00:57:19,685 - bioneuralnet.utils.preprocess - INFO - [Inf]: Replaced 0 infinite values\n", + "2025-11-26 00:57:19,685 - bioneuralnet.utils.preprocess - INFO - [NaN]: Replaced 0 NaNs after median imputation\n", + "2025-11-26 00:57:19,685 - bioneuralnet.utils.preprocess - INFO - [Zero-Var]: 0 columns dropped due to zero variance\n", + "2025-11-26 00:57:19,687 - bioneuralnet.utils.preprocess - INFO - Selected top 300 features by variance\n", + "2025-11-26 00:57:20,259 - bioneuralnet.utils.graph_tools - INFO - GRAPH ANALYSIS: threshold_10\n", + "\n", + "2025-11-26 00:57:20,259 - bioneuralnet.utils.graph_tools - INFO - Nodes: 900 | Edges: 8,628\n", + "2025-11-26 00:57:20,260 - bioneuralnet.utils.graph_tools - INFO - Avg degree: 19.17\n", + "2025-11-26 00:57:20,260 - bioneuralnet.utils.graph_tools - INFO - Connected components: 1\n", + "2025-11-26 00:57:20,261 - bioneuralnet.utils.graph_tools - INFO - Largest component: 900 nodes (100.0%)\n", + "2025-11-26 00:57:20,345 - bioneuralnet.utils.graph_tools - INFO - Avg clustering coefficient: 0.016\n", + "2025-11-26 00:57:20,357 - bioneuralnet.utils.graph_tools - INFO - GRAPH ANALYSIS: correlation_10\n", + "\n", + "2025-11-26 00:57:20,357 - bioneuralnet.utils.graph_tools - INFO - Nodes: 900 | Edges: 8,628\n", + "2025-11-26 00:57:20,357 - bioneuralnet.utils.graph_tools - INFO - Avg degree: 19.17\n", + "2025-11-26 00:57:20,358 - bioneuralnet.utils.graph_tools - INFO - Connected components: 1\n", + "2025-11-26 00:57:20,358 - bioneuralnet.utils.graph_tools - INFO - Largest component: 900 nodes (100.0%)\n", + "2025-11-26 00:57:20,443 - bioneuralnet.utils.graph_tools - INFO - Avg clustering coefficient: 0.108\n", + "2025-11-26 00:57:20,455 - bioneuralnet.utils.graph_tools - INFO - GRAPH ANALYSIS: similarity_10\n", + "\n", + "2025-11-26 00:57:20,455 - bioneuralnet.utils.graph_tools - INFO - Nodes: 900 | Edges: 8,930\n", + "2025-11-26 00:57:20,456 - bioneuralnet.utils.graph_tools - INFO - Avg degree: 19.84\n", + "2025-11-26 00:57:20,456 - bioneuralnet.utils.graph_tools - INFO - Connected components: 1\n", + "2025-11-26 00:57:20,456 - bioneuralnet.utils.graph_tools - INFO - Largest component: 900 nodes (100.0%)\n", + "2025-11-26 00:57:20,552 - bioneuralnet.utils.graph_tools - INFO - Avg clustering coefficient: 0.127\n", + "2025-11-26 00:57:20,567 - bioneuralnet.utils.graph_tools - INFO - GRAPH ANALYSIS: gaussian_15\n", + "\n", + "2025-11-26 00:57:20,567 - bioneuralnet.utils.graph_tools - INFO - Nodes: 900 | Edges: 11,543\n", + "2025-11-26 00:57:20,567 - bioneuralnet.utils.graph_tools - INFO - Avg degree: 25.65\n", + "2025-11-26 00:57:20,568 - bioneuralnet.utils.graph_tools - INFO - Connected components: 1\n", + "2025-11-26 00:57:20,568 - bioneuralnet.utils.graph_tools - INFO - Largest component: 900 nodes (100.0%)\n", + "2025-11-26 00:57:20,711 - bioneuralnet.utils.graph_tools - INFO - Avg clustering coefficient: 0.298\n" + ] + } + ], + "source": [ + "from bioneuralnet.utils import gen_threshold_graph, gen_correlation_graph, gen_similarity_graph, gen_gaussian_knn_graph\n", + "from bioneuralnet.utils import graph_analysis\n", + "from bioneuralnet.utils import select_top_k_variance\n", + "\n", + "top_meth = select_top_k_variance(dna_meth, k=300)\n", + "top_rna = select_top_k_variance(rna, k=300)\n", + "top_mirna = select_top_k_variance(mirna, k=300)\n", + "\n", + "omics_kipan = pd.concat([top_meth, top_rna, top_mirna], axis=1)\n", + "\n", + "threshold_10 = gen_threshold_graph(omics_kipan, b=6.5, k=10, self_loops=True)\n", + "correlation_10 = gen_correlation_graph(omics_kipan, k=10, method='pearson', signed=False, self_loops=True)\n", + "similarity_10 = gen_similarity_graph(omics_kipan, k=10, metric='cosine', self_loops=True)\n", + "gaussian_15 = gen_gaussian_knn_graph(omics_kipan, k=15, sigma=None, self_loops=True)\n", + "\n", + "comparison_runs = [\n", + " {\"name\":\"threshold_10\", \"graph\": threshold_10},\n", + " {\"name\":\"correlation_10\", \"graph\": correlation_10},\n", + " {\"name\":\"similarity_10\", \"graph\": similarity_10},\n", + " {\"name\":\"gaussian_15\", \"graph\": gaussian_15},\n", + "]\n", + "\n", + "for run in comparison_runs:\n", + " graph_analysis(run[\"graph\"], run[\"name\"])\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "4906eda5", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-11-26 00:57:20,716 - bioneuralnet.downstream_task.dpmon - INFO - Output directory set to: /home/vicente/Github/BioNeuralNet/dpmon_results_GAT_FINAL/kipan/threshold_10\n", + "2025-11-26 00:57:20,717 - bioneuralnet.downstream_task.dpmon - INFO - Initialized DPMON with model: GAT\n", + "2025-11-26 00:57:20,717 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 00:57:20,718 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 00:57:20,718 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 00:57:20,719 - bioneuralnet.downstream_task.dpmon - INFO - Random seed set to: 8183\n", + "2025-11-26 00:57:20,726 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 00:57:20,727 - bioneuralnet.downstream_task.dpmon - INFO - Running in Cross-Validation mode (cv=True) with 5 folds.\n", + "2025-11-26 00:57:20,728 - bioneuralnet.downstream_task.dpmon - INFO - CV Setup: Standard 5-fold split.\n", + "2025-11-26 00:57:20,729 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 1/5\n", + "2025-11-26 00:57:20,731 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 00:57:20,739 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 00:57:24,910 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 00:57:24,922 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (526, 901)\n", + "2025-11-26 00:57:24,926\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 00:57:36,598\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0077s.\n", + "2025-11-26 00:57:36,759 - bioneuralnet.downstream_task.dpmon - INFO - Fold 1 best config: {'gnn_layer_num': 3, 'gnn_hidden_dim': 128, 'lr': 0.002995945702914729, 'weight_decay': 7.542726756559785e-05, 'nn_hidden_dim1': 256, 'nn_hidden_dim2': 32, 'ae_encoding_dim': 4, 'num_epochs': 2048, 'gnn_dropout': 0.0, 'gnn_activation': 'elu', 'dim_reduction': 'mlp'}\n", + "2025-11-26 00:57:36,760 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 1 using train split only\n", + "2025-11-26 00:57:36,765 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 00:57:40,906 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 00:57:45,459 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 1 on test set\n", + "2025-11-26 00:57:45,470 - bioneuralnet.downstream_task.dpmon - INFO - Fold 1 results:\n", + "2025-11-26 00:57:45,470 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9394\n", + "2025-11-26 00:57:45,470 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9290\n", + "2025-11-26 00:57:45,470 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9398\n", + "2025-11-26 00:57:45,471 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9566\n", + "2025-11-26 00:57:45,471 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9918\n", + "2025-11-26 00:57:45,471 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9803\n", + "\n", + "2025-11-26 00:57:45,473 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 2/5\n", + "2025-11-26 00:57:45,474 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 00:57:45,482 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 00:57:49,590 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 00:57:49,600 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (526, 901)\n", + "2025-11-26 00:57:49,603\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 00:58:00,206\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0192s.\n", + "2025-11-26 00:58:00,330 - bioneuralnet.downstream_task.dpmon - INFO - Fold 2 best config: {'gnn_layer_num': 2, 'gnn_hidden_dim': 64, 'lr': 0.0007034204356990857, 'weight_decay': 5.358735876893429e-05, 'nn_hidden_dim1': 512, 'nn_hidden_dim2': 64, 'ae_encoding_dim': 8, 'num_epochs': 1024, 'gnn_dropout': 0.2, 'gnn_activation': 'relu', 'dim_reduction': 'ae'}\n", + "2025-11-26 00:58:00,331 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 2 using train split only\n", + "2025-11-26 00:58:00,335 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 00:58:04,454 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 00:58:06,182 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 2 on test set\n", + "2025-11-26 00:58:06,187 - bioneuralnet.downstream_task.dpmon - INFO - Fold 2 results:\n", + "2025-11-26 00:58:06,187 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9697\n", + "2025-11-26 00:58:06,188 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9487\n", + "2025-11-26 00:58:06,188 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9701\n", + "2025-11-26 00:58:06,188 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9579\n", + "2025-11-26 00:58:06,188 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9940\n", + "2025-11-26 00:58:06,188 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9493\n", + "\n", + "2025-11-26 00:58:06,189 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 3/5\n", + "2025-11-26 00:58:06,191 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 00:58:06,203 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 00:58:10,296 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 00:58:10,306 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (526, 901)\n", + "2025-11-26 00:58:10,310\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 00:58:21,272\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0142s.\n", + "2025-11-26 00:58:21,422 - bioneuralnet.downstream_task.dpmon - INFO - Fold 3 best config: {'gnn_layer_num': 3, 'gnn_hidden_dim': 128, 'lr': 0.0006980653261883734, 'weight_decay': 6.059927981082565e-05, 'nn_hidden_dim1': 256, 'nn_hidden_dim2': 128, 'ae_encoding_dim': 8, 'num_epochs': 2048, 'gnn_dropout': 0.0, 'gnn_activation': 'elu', 'dim_reduction': 'ae'}\n", + "2025-11-26 00:58:21,423 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 3 using train split only\n", + "2025-11-26 00:58:21,427 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 00:58:25,530 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 00:58:29,913 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 3 on test set\n", + "2025-11-26 00:58:29,918 - bioneuralnet.downstream_task.dpmon - INFO - Fold 3 results:\n", + "2025-11-26 00:58:29,919 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9545\n", + "2025-11-26 00:58:29,919 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9459\n", + "2025-11-26 00:58:29,919 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9544\n", + "2025-11-26 00:58:29,919 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9297\n", + "2025-11-26 00:58:29,919 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9963\n", + "2025-11-26 00:58:29,919 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9895\n", + "\n", + "2025-11-26 00:58:29,920 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 4/5\n", + "2025-11-26 00:58:29,921 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 00:58:29,926 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 00:58:34,048 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 00:58:34,057 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (527, 901)\n", + "2025-11-26 00:58:34,061\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 00:58:45,497\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0174s.\n", + "2025-11-26 00:58:45,665 - bioneuralnet.downstream_task.dpmon - INFO - Fold 4 best config: {'gnn_layer_num': 1, 'gnn_hidden_dim': 128, 'lr': 0.0019123736530165902, 'weight_decay': 0.0006168499043610002, 'nn_hidden_dim1': 256, 'nn_hidden_dim2': 128, 'ae_encoding_dim': 8, 'num_epochs': 1024, 'gnn_dropout': 0.4, 'gnn_activation': 'relu', 'dim_reduction': 'mlp'}\n", + "2025-11-26 00:58:45,666 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 4 using train split only\n", + "2025-11-26 00:58:45,671 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 00:58:49,803 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 00:58:51,152 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 4 on test set\n", + "2025-11-26 00:58:51,157 - bioneuralnet.downstream_task.dpmon - INFO - Fold 4 results:\n", + "2025-11-26 00:58:51,158 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9466\n", + "2025-11-26 00:58:51,158 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9505\n", + "2025-11-26 00:58:51,158 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9466\n", + "2025-11-26 00:58:51,158 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9607\n", + "2025-11-26 00:58:51,158 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9945\n", + "2025-11-26 00:58:51,159 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9885\n", + "\n", + "2025-11-26 00:58:51,159 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 5/5\n", + "2025-11-26 00:58:51,160 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 00:58:51,165 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 00:58:55,257 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 00:58:55,268 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (527, 901)\n", + "2025-11-26 00:58:55,271\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 00:59:08,036\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0139s.\n", + "2025-11-26 00:59:08,254 - bioneuralnet.downstream_task.dpmon - INFO - Fold 5 best config: {'gnn_layer_num': 2, 'gnn_hidden_dim': 64, 'lr': 0.001685812648195555, 'weight_decay': 0.0023773937538678585, 'nn_hidden_dim1': 256, 'nn_hidden_dim2': 128, 'ae_encoding_dim': 8, 'num_epochs': 512, 'gnn_dropout': 0.2, 'gnn_activation': 'relu', 'dim_reduction': 'linear'}\n", + "2025-11-26 00:59:08,254 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 5 using train split only\n", + "2025-11-26 00:59:08,260 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 00:59:12,418 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 00:59:13,329 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 5 on test set\n", + "2025-11-26 00:59:13,334 - bioneuralnet.downstream_task.dpmon - INFO - Fold 5 results:\n", + "2025-11-26 00:59:13,335 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9618\n", + "2025-11-26 00:59:13,335 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9515\n", + "2025-11-26 00:59:13,335 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9618\n", + "2025-11-26 00:59:13,335 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9506\n", + "2025-11-26 00:59:13,335 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9988\n", + "2025-11-26 00:59:13,335 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9970\n", + "\n", + "2025-11-26 00:59:13,348 - bioneuralnet.downstream_task.dpmon - INFO - Successfully saved global best CV model state to: /home/vicente/Github/BioNeuralNet/dpmon_results_GAT_FINAL/kipan/threshold_10/best_cv_model.pt\n", + "2025-11-26 00:59:13,349 - bioneuralnet.downstream_task.dpmon - INFO - Successfully saved global best CV model state to: /home/vicente/Github/BioNeuralNet/dpmon_results_GAT_FINAL/kipan/threshold_10/best_cv_model_embds.pt\n", + "2025-11-26 00:59:13,349 - bioneuralnet.downstream_task.dpmon - INFO - Cross-Validation Results:\n", + "\n", + "2025-11-26 00:59:13,349 - bioneuralnet.downstream_task.dpmon - INFO - Avg Accuracy: 0.9544 +/- 0.0120\n", + "2025-11-26 00:59:13,349 - bioneuralnet.downstream_task.dpmon - INFO - Avg F1 Macro: 0.9451 +/- 0.0093\n", + "2025-11-26 00:59:13,349 - bioneuralnet.downstream_task.dpmon - INFO - Avg F1 Weighted: 0.9545 +/- 0.0120\n", + "2025-11-26 00:59:13,349 - bioneuralnet.downstream_task.dpmon - INFO - Avg Recall: 0.9511 +/- 0.0125\n", + "2025-11-26 00:59:13,349 - bioneuralnet.downstream_task.dpmon - INFO - Avg AUC: 0.9951 +/- 0.0026\n", + "2025-11-26 00:59:13,350 - bioneuralnet.downstream_task.dpmon - INFO - Avg AUPR: 0.9809 +/- 0.0187\n", + "2025-11-26 00:59:13,350 - bioneuralnet.downstream_task.dpmon - INFO - DPMON run completed.\n", + "2025-11-26 00:59:13,351 - bioneuralnet.downstream_task.dpmon - INFO - Output directory set to: /home/vicente/Github/BioNeuralNet/dpmon_results_GAT_FINAL/kipan/correlation_10\n", + "2025-11-26 00:59:13,351 - bioneuralnet.downstream_task.dpmon - INFO - Initialized DPMON with model: GAT\n", + "2025-11-26 00:59:13,351 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 00:59:13,351 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 00:59:13,351 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 00:59:13,351 - bioneuralnet.downstream_task.dpmon - INFO - Random seed set to: 8183\n", + "2025-11-26 00:59:13,355 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 00:59:13,357 - bioneuralnet.downstream_task.dpmon - INFO - Running in Cross-Validation mode (cv=True) with 5 folds.\n", + "2025-11-26 00:59:13,358 - bioneuralnet.downstream_task.dpmon - INFO - CV Setup: Standard 5-fold split.\n", + "2025-11-26 00:59:13,358 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 1/5\n", + "2025-11-26 00:59:13,359 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 00:59:13,364 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 00:59:17,533 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 00:59:17,543 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (526, 901)\n", + "2025-11-26 00:59:17,546\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 00:59:28,510\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0097s.\n", + "2025-11-26 00:59:28,689 - bioneuralnet.downstream_task.dpmon - INFO - Fold 1 best config: {'gnn_layer_num': 3, 'gnn_hidden_dim': 128, 'lr': 0.002995945702914729, 'weight_decay': 7.542726756559785e-05, 'nn_hidden_dim1': 256, 'nn_hidden_dim2': 32, 'ae_encoding_dim': 4, 'num_epochs': 2048, 'gnn_dropout': 0.0, 'gnn_activation': 'elu', 'dim_reduction': 'mlp'}\n", + "2025-11-26 00:59:28,690 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 1 using train split only\n", + "2025-11-26 00:59:28,694 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 00:59:32,822 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 00:59:37,372 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 1 on test set\n", + "2025-11-26 00:59:37,377 - bioneuralnet.downstream_task.dpmon - INFO - Fold 1 results:\n", + "2025-11-26 00:59:37,378 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9394\n", + "2025-11-26 00:59:37,378 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9287\n", + "2025-11-26 00:59:37,378 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9399\n", + "2025-11-26 00:59:37,378 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9558\n", + "2025-11-26 00:59:37,378 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9930\n", + "2025-11-26 00:59:37,378 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9919\n", + "\n", + "2025-11-26 00:59:37,380 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 2/5\n", + "2025-11-26 00:59:37,381 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 00:59:37,387 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 00:59:41,482 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 00:59:41,492 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (526, 901)\n", + "2025-11-26 00:59:41,496\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 00:59:52,119\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0186s.\n", + "2025-11-26 00:59:52,225 - bioneuralnet.downstream_task.dpmon - INFO - Fold 2 best config: {'gnn_layer_num': 4, 'gnn_hidden_dim': 64, 'lr': 0.00024165542558361394, 'weight_decay': 0.0002652189086272745, 'nn_hidden_dim1': 256, 'nn_hidden_dim2': 128, 'ae_encoding_dim': 8, 'num_epochs': 512, 'gnn_dropout': 0.1, 'gnn_activation': 'relu', 'dim_reduction': 'mlp'}\n", + "2025-11-26 00:59:52,226 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 2 using train split only\n", + "2025-11-26 00:59:52,231 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 00:59:56,361 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 00:59:57,746 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 2 on test set\n", + "2025-11-26 00:59:57,751 - bioneuralnet.downstream_task.dpmon - INFO - Fold 2 results:\n", + "2025-11-26 00:59:57,751 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9697\n", + "2025-11-26 00:59:57,752 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9487\n", + "2025-11-26 00:59:57,752 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9701\n", + "2025-11-26 00:59:57,752 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9579\n", + "2025-11-26 00:59:57,752 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9905\n", + "2025-11-26 00:59:57,752 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9331\n", + "\n", + "2025-11-26 00:59:57,753 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 3/5\n", + "2025-11-26 00:59:57,754 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 00:59:57,759 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:00:01,882 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:00:01,892 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (526, 901)\n", + "2025-11-26 01:00:01,896\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:00:12,425\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0233s.\n", + "2025-11-26 01:00:12,573 - bioneuralnet.downstream_task.dpmon - INFO - Fold 3 best config: {'gnn_layer_num': 3, 'gnn_hidden_dim': 128, 'lr': 0.0006980653261883734, 'weight_decay': 6.059927981082565e-05, 'nn_hidden_dim1': 256, 'nn_hidden_dim2': 128, 'ae_encoding_dim': 8, 'num_epochs': 2048, 'gnn_dropout': 0.0, 'gnn_activation': 'elu', 'dim_reduction': 'ae'}\n", + "2025-11-26 01:00:12,574 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 3 using train split only\n", + "2025-11-26 01:00:12,578 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:00:16,743 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:00:21,070 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 3 on test set\n", + "2025-11-26 01:00:21,076 - bioneuralnet.downstream_task.dpmon - INFO - Fold 3 results:\n", + "2025-11-26 01:00:21,077 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9545\n", + "2025-11-26 01:00:21,077 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9459\n", + "2025-11-26 01:00:21,077 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9544\n", + "2025-11-26 01:00:21,077 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9297\n", + "2025-11-26 01:00:21,077 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9968\n", + "2025-11-26 01:00:21,078 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9934\n", + "\n", + "2025-11-26 01:00:21,079 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 4/5\n", + "2025-11-26 01:00:21,080 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:00:21,086 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:00:25,234 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:00:25,245 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (527, 901)\n", + "2025-11-26 01:00:25,249\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:00:36,494\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0065s.\n", + "2025-11-26 01:00:36,639 - bioneuralnet.downstream_task.dpmon - INFO - Fold 4 best config: {'gnn_layer_num': 1, 'gnn_hidden_dim': 128, 'lr': 0.002226976717919125, 'weight_decay': 0.00016678702979522166, 'nn_hidden_dim1': 128, 'nn_hidden_dim2': 64, 'ae_encoding_dim': 16, 'num_epochs': 512, 'gnn_dropout': 0.3, 'gnn_activation': 'relu', 'dim_reduction': 'ae'}\n", + "2025-11-26 01:00:36,639 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 4 using train split only\n", + "2025-11-26 01:00:36,644 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:00:40,787 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:00:41,407 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 4 on test set\n", + "2025-11-26 01:00:41,411 - bioneuralnet.downstream_task.dpmon - INFO - Fold 4 results:\n", + "2025-11-26 01:00:41,412 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9466\n", + "2025-11-26 01:00:41,412 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9505\n", + "2025-11-26 01:00:41,412 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9466\n", + "2025-11-26 01:00:41,412 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9607\n", + "2025-11-26 01:00:41,412 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9934\n", + "2025-11-26 01:00:41,412 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9805\n", + "\n", + "2025-11-26 01:00:41,413 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 5/5\n", + "2025-11-26 01:00:41,414 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:00:41,419 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:00:45,531 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:00:45,541 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (527, 901)\n", + "2025-11-26 01:00:45,544\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:00:58,694\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0108s.\n", + "2025-11-26 01:00:58,924 - bioneuralnet.downstream_task.dpmon - INFO - Fold 5 best config: {'gnn_layer_num': 2, 'gnn_hidden_dim': 64, 'lr': 0.001685812648195555, 'weight_decay': 0.0023773937538678585, 'nn_hidden_dim1': 256, 'nn_hidden_dim2': 128, 'ae_encoding_dim': 8, 'num_epochs': 512, 'gnn_dropout': 0.2, 'gnn_activation': 'relu', 'dim_reduction': 'linear'}\n", + "2025-11-26 01:00:58,925 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 5 using train split only\n", + "2025-11-26 01:00:58,929 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:01:03,090 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:01:03,987 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 5 on test set\n", + "2025-11-26 01:01:03,991 - bioneuralnet.downstream_task.dpmon - INFO - Fold 5 results:\n", + "2025-11-26 01:01:03,991 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9618\n", + "2025-11-26 01:01:03,992 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9514\n", + "2025-11-26 01:01:03,992 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9617\n", + "2025-11-26 01:01:03,992 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9497\n", + "2025-11-26 01:01:03,992 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9977\n", + "2025-11-26 01:01:03,992 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9940\n", + "\n", + "2025-11-26 01:01:03,996 - bioneuralnet.downstream_task.dpmon - INFO - Successfully saved global best CV model state to: /home/vicente/Github/BioNeuralNet/dpmon_results_GAT_FINAL/kipan/correlation_10/best_cv_model.pt\n", + "2025-11-26 01:01:03,997 - bioneuralnet.downstream_task.dpmon - INFO - Successfully saved global best CV model state to: /home/vicente/Github/BioNeuralNet/dpmon_results_GAT_FINAL/kipan/correlation_10/best_cv_model_embds.pt\n", + "2025-11-26 01:01:03,997 - bioneuralnet.downstream_task.dpmon - INFO - Cross-Validation Results:\n", + "\n", + "2025-11-26 01:01:03,997 - bioneuralnet.downstream_task.dpmon - INFO - Avg Accuracy: 0.9544 +/- 0.0120\n", + "2025-11-26 01:01:03,997 - bioneuralnet.downstream_task.dpmon - INFO - Avg F1 Macro: 0.9450 +/- 0.0094\n", + "2025-11-26 01:01:03,997 - bioneuralnet.downstream_task.dpmon - INFO - Avg F1 Weighted: 0.9545 +/- 0.0119\n", + "2025-11-26 01:01:03,997 - bioneuralnet.downstream_task.dpmon - INFO - Avg Recall: 0.9507 +/- 0.0124\n", + "2025-11-26 01:01:03,998 - bioneuralnet.downstream_task.dpmon - INFO - Avg AUC: 0.9943 +/- 0.0029\n", + "2025-11-26 01:01:03,998 - bioneuralnet.downstream_task.dpmon - INFO - Avg AUPR: 0.9786 +/- 0.0260\n", + "2025-11-26 01:01:03,998 - bioneuralnet.downstream_task.dpmon - INFO - DPMON run completed.\n", + "2025-11-26 01:01:03,999 - bioneuralnet.downstream_task.dpmon - INFO - Output directory set to: /home/vicente/Github/BioNeuralNet/dpmon_results_GAT_FINAL/kipan/similarity_10\n", + "2025-11-26 01:01:03,999 - bioneuralnet.downstream_task.dpmon - INFO - Initialized DPMON with model: GAT\n", + "2025-11-26 01:01:03,999 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 01:01:04,000 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 01:01:04,000 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 01:01:04,000 - bioneuralnet.downstream_task.dpmon - INFO - Random seed set to: 8183\n", + "2025-11-26 01:01:04,005 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:01:04,006 - bioneuralnet.downstream_task.dpmon - INFO - Running in Cross-Validation mode (cv=True) with 5 folds.\n", + "2025-11-26 01:01:04,008 - bioneuralnet.downstream_task.dpmon - INFO - CV Setup: Standard 5-fold split.\n", + "2025-11-26 01:01:04,009 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 1/5\n", + "2025-11-26 01:01:04,010 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:01:04,015 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:01:08,133 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:01:08,144 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (526, 901)\n", + "2025-11-26 01:01:08,147\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:01:20,813\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0121s.\n", + "2025-11-26 01:01:21,000 - bioneuralnet.downstream_task.dpmon - INFO - Fold 1 best config: {'gnn_layer_num': 3, 'gnn_hidden_dim': 128, 'lr': 0.002995945702914729, 'weight_decay': 7.542726756559785e-05, 'nn_hidden_dim1': 256, 'nn_hidden_dim2': 32, 'ae_encoding_dim': 4, 'num_epochs': 2048, 'gnn_dropout': 0.0, 'gnn_activation': 'elu', 'dim_reduction': 'mlp'}\n", + "2025-11-26 01:01:21,001 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 1 using train split only\n", + "2025-11-26 01:01:21,005 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:01:25,276 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:01:29,966 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 1 on test set\n", + "2025-11-26 01:01:29,971 - bioneuralnet.downstream_task.dpmon - INFO - Fold 1 results:\n", + "2025-11-26 01:01:29,972 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9394\n", + "2025-11-26 01:01:29,972 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9290\n", + "2025-11-26 01:01:29,972 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9398\n", + "2025-11-26 01:01:29,972 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9566\n", + "2025-11-26 01:01:29,972 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9957\n", + "2025-11-26 01:01:29,972 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9935\n", + "\n", + "2025-11-26 01:01:29,974 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 2/5\n", + "2025-11-26 01:01:29,975 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:01:29,981 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:01:34,133 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:01:34,146 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (526, 901)\n", + "2025-11-26 01:01:34,149\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:01:46,408\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0098s.\n", + "2025-11-26 01:01:46,566 - bioneuralnet.downstream_task.dpmon - INFO - Fold 2 best config: {'gnn_layer_num': 2, 'gnn_hidden_dim': 64, 'lr': 0.0007034204356990857, 'weight_decay': 5.358735876893429e-05, 'nn_hidden_dim1': 512, 'nn_hidden_dim2': 64, 'ae_encoding_dim': 8, 'num_epochs': 1024, 'gnn_dropout': 0.2, 'gnn_activation': 'relu', 'dim_reduction': 'ae'}\n", + "2025-11-26 01:01:46,567 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 2 using train split only\n", + "2025-11-26 01:01:46,571 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:01:50,727 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:01:52,514 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 2 on test set\n", + "2025-11-26 01:01:52,519 - bioneuralnet.downstream_task.dpmon - INFO - Fold 2 results:\n", + "2025-11-26 01:01:52,520 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9621\n", + "2025-11-26 01:01:52,520 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9315\n", + "2025-11-26 01:01:52,520 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9623\n", + "2025-11-26 01:01:52,520 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9322\n", + "2025-11-26 01:01:52,520 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9934\n", + "2025-11-26 01:01:52,520 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9490\n", + "\n", + "2025-11-26 01:01:52,521 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 3/5\n", + "2025-11-26 01:01:52,522 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:01:52,527 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:01:56,688 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:01:56,700 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (526, 901)\n", + "2025-11-26 01:01:56,703\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:02:07,390\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0206s.\n", + "2025-11-26 01:02:07,536 - bioneuralnet.downstream_task.dpmon - INFO - Fold 3 best config: {'gnn_layer_num': 3, 'gnn_hidden_dim': 128, 'lr': 0.0006980653261883734, 'weight_decay': 6.059927981082565e-05, 'nn_hidden_dim1': 256, 'nn_hidden_dim2': 128, 'ae_encoding_dim': 8, 'num_epochs': 2048, 'gnn_dropout': 0.0, 'gnn_activation': 'elu', 'dim_reduction': 'ae'}\n", + "2025-11-26 01:02:07,536 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 3 using train split only\n", + "2025-11-26 01:02:07,540 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:02:11,675 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:02:16,004 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 3 on test set\n", + "2025-11-26 01:02:16,009 - bioneuralnet.downstream_task.dpmon - INFO - Fold 3 results:\n", + "2025-11-26 01:02:16,009 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9545\n", + "2025-11-26 01:02:16,010 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9459\n", + "2025-11-26 01:02:16,010 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9544\n", + "2025-11-26 01:02:16,010 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9297\n", + "2025-11-26 01:02:16,010 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9937\n", + "2025-11-26 01:02:16,011 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9911\n", + "\n", + "2025-11-26 01:02:16,012 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 4/5\n", + "2025-11-26 01:02:16,014 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:02:16,020 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:02:20,128 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:02:20,139 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (527, 901)\n", + "2025-11-26 01:02:20,142\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:02:31,003\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0166s.\n", + "2025-11-26 01:02:31,137 - bioneuralnet.downstream_task.dpmon - INFO - Fold 4 best config: {'gnn_layer_num': 1, 'gnn_hidden_dim': 128, 'lr': 0.002226976717919125, 'weight_decay': 0.00016678702979522166, 'nn_hidden_dim1': 128, 'nn_hidden_dim2': 64, 'ae_encoding_dim': 16, 'num_epochs': 512, 'gnn_dropout': 0.3, 'gnn_activation': 'relu', 'dim_reduction': 'ae'}\n", + "2025-11-26 01:02:31,137 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 4 using train split only\n", + "2025-11-26 01:02:31,143 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:02:35,446 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:02:36,071 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 4 on test set\n", + "2025-11-26 01:02:36,076 - bioneuralnet.downstream_task.dpmon - INFO - Fold 4 results:\n", + "2025-11-26 01:02:36,076 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9389\n", + "2025-11-26 01:02:36,076 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9449\n", + "2025-11-26 01:02:36,076 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9390\n", + "2025-11-26 01:02:36,076 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9554\n", + "2025-11-26 01:02:36,076 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9943\n", + "2025-11-26 01:02:36,077 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9862\n", + "\n", + "2025-11-26 01:02:36,077 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 5/5\n", + "2025-11-26 01:02:36,079 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:02:36,084 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:02:40,268 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:02:40,280 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (527, 901)\n", + "2025-11-26 01:02:40,283\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:02:52,747\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0184s.\n", + "2025-11-26 01:02:52,935 - bioneuralnet.downstream_task.dpmon - INFO - Fold 5 best config: {'gnn_layer_num': 4, 'gnn_hidden_dim': 128, 'lr': 0.0013804780705269182, 'weight_decay': 0.0037674454696866636, 'nn_hidden_dim1': 128, 'nn_hidden_dim2': 64, 'ae_encoding_dim': 4, 'num_epochs': 1024, 'gnn_dropout': 0.0, 'gnn_activation': 'elu', 'dim_reduction': 'ae'}\n", + "2025-11-26 01:02:52,936 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 5 using train split only\n", + "2025-11-26 01:02:52,941 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:02:57,095 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:02:59,798 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 5 on test set\n", + "2025-11-26 01:02:59,803 - bioneuralnet.downstream_task.dpmon - INFO - Fold 5 results:\n", + "2025-11-26 01:02:59,803 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9466\n", + "2025-11-26 01:02:59,804 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9398\n", + "2025-11-26 01:02:59,804 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9462\n", + "2025-11-26 01:02:59,804 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9373\n", + "2025-11-26 01:02:59,804 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9952\n", + "2025-11-26 01:02:59,804 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9880\n", + "\n", + "2025-11-26 01:02:59,818 - bioneuralnet.downstream_task.dpmon - INFO - Successfully saved global best CV model state to: /home/vicente/Github/BioNeuralNet/dpmon_results_GAT_FINAL/kipan/similarity_10/best_cv_model.pt\n", + "2025-11-26 01:02:59,819 - bioneuralnet.downstream_task.dpmon - INFO - Successfully saved global best CV model state to: /home/vicente/Github/BioNeuralNet/dpmon_results_GAT_FINAL/kipan/similarity_10/best_cv_model_embds.pt\n", + "2025-11-26 01:02:59,819 - bioneuralnet.downstream_task.dpmon - INFO - Cross-Validation Results:\n", + "\n", + "2025-11-26 01:02:59,819 - bioneuralnet.downstream_task.dpmon - INFO - Avg Accuracy: 0.9483 +/- 0.0100\n", + "2025-11-26 01:02:59,819 - bioneuralnet.downstream_task.dpmon - INFO - Avg F1 Macro: 0.9382 +/- 0.0077\n", + "2025-11-26 01:02:59,819 - bioneuralnet.downstream_task.dpmon - INFO - Avg F1 Weighted: 0.9483 +/- 0.0100\n", + "2025-11-26 01:02:59,819 - bioneuralnet.downstream_task.dpmon - INFO - Avg Recall: 0.9422 +/- 0.0129\n", + "2025-11-26 01:02:59,819 - bioneuralnet.downstream_task.dpmon - INFO - Avg AUC: 0.9945 +/- 0.0010\n", + "2025-11-26 01:02:59,819 - bioneuralnet.downstream_task.dpmon - INFO - Avg AUPR: 0.9815 +/- 0.0184\n", + "2025-11-26 01:02:59,820 - bioneuralnet.downstream_task.dpmon - INFO - DPMON run completed.\n", + "2025-11-26 01:02:59,821 - bioneuralnet.downstream_task.dpmon - INFO - Output directory set to: /home/vicente/Github/BioNeuralNet/dpmon_results_GAT_FINAL/kipan/gaussian_15\n", + "2025-11-26 01:02:59,821 - bioneuralnet.downstream_task.dpmon - INFO - Initialized DPMON with model: GAT\n", + "2025-11-26 01:02:59,821 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 01:02:59,821 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 01:02:59,821 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 01:02:59,821 - bioneuralnet.downstream_task.dpmon - INFO - Random seed set to: 8183\n", + "2025-11-26 01:02:59,826 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:02:59,827 - bioneuralnet.downstream_task.dpmon - INFO - Running in Cross-Validation mode (cv=True) with 5 folds.\n", + "2025-11-26 01:02:59,828 - bioneuralnet.downstream_task.dpmon - INFO - CV Setup: Standard 5-fold split.\n", + "2025-11-26 01:02:59,829 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 1/5\n", + "2025-11-26 01:02:59,830 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:02:59,838 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:03:04,025 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:03:04,039 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (526, 901)\n", + "2025-11-26 01:03:04,042\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:03:17,429\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0133s.\n", + "2025-11-26 01:03:17,636 - bioneuralnet.downstream_task.dpmon - INFO - Fold 1 best config: {'gnn_layer_num': 3, 'gnn_hidden_dim': 128, 'lr': 0.002995945702914729, 'weight_decay': 7.542726756559785e-05, 'nn_hidden_dim1': 256, 'nn_hidden_dim2': 32, 'ae_encoding_dim': 4, 'num_epochs': 2048, 'gnn_dropout': 0.0, 'gnn_activation': 'elu', 'dim_reduction': 'mlp'}\n", + "2025-11-26 01:03:17,637 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 1 using train split only\n", + "2025-11-26 01:03:17,641 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:03:21,783 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:03:26,807 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 1 on test set\n", + "2025-11-26 01:03:26,812 - bioneuralnet.downstream_task.dpmon - INFO - Fold 1 results:\n", + "2025-11-26 01:03:26,812 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9394\n", + "2025-11-26 01:03:26,812 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9216\n", + "2025-11-26 01:03:26,813 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9405\n", + "2025-11-26 01:03:26,813 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9566\n", + "2025-11-26 01:03:26,813 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9950\n", + "2025-11-26 01:03:26,813 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9943\n", + "\n", + "2025-11-26 01:03:26,815 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 2/5\n", + "2025-11-26 01:03:26,816 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:03:26,821 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:03:30,970 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:03:30,983 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (526, 901)\n", + "2025-11-26 01:03:30,986\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:03:41,107\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0289s.\n", + "2025-11-26 01:03:41,224 - bioneuralnet.downstream_task.dpmon - INFO - Fold 2 best config: {'gnn_layer_num': 2, 'gnn_hidden_dim': 64, 'lr': 0.0007034204356990857, 'weight_decay': 5.358735876893429e-05, 'nn_hidden_dim1': 512, 'nn_hidden_dim2': 64, 'ae_encoding_dim': 8, 'num_epochs': 1024, 'gnn_dropout': 0.2, 'gnn_activation': 'relu', 'dim_reduction': 'ae'}\n", + "2025-11-26 01:03:41,225 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 2 using train split only\n", + "2025-11-26 01:03:41,229 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:03:45,495 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:03:47,199 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 2 on test set\n", + "2025-11-26 01:03:47,204 - bioneuralnet.downstream_task.dpmon - INFO - Fold 2 results:\n", + "2025-11-26 01:03:47,204 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9621\n", + "2025-11-26 01:03:47,204 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9315\n", + "2025-11-26 01:03:47,204 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9623\n", + "2025-11-26 01:03:47,204 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9322\n", + "2025-11-26 01:03:47,205 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9929\n", + "2025-11-26 01:03:47,205 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9469\n", + "\n", + "2025-11-26 01:03:47,206 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 3/5\n", + "2025-11-26 01:03:47,207 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:03:47,212 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:03:51,382 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:03:51,395 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (526, 901)\n", + "2025-11-26 01:03:51,399\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:04:02,747\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0162s.\n", + "2025-11-26 01:04:02,915 - bioneuralnet.downstream_task.dpmon - INFO - Fold 3 best config: {'gnn_layer_num': 1, 'gnn_hidden_dim': 256, 'lr': 0.0016239332393902155, 'weight_decay': 1.2898372523486065e-05, 'nn_hidden_dim1': 512, 'nn_hidden_dim2': 128, 'ae_encoding_dim': 4, 'num_epochs': 512, 'gnn_dropout': 0.2, 'gnn_activation': 'elu', 'dim_reduction': 'ae'}\n", + "2025-11-26 01:04:02,916 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 3 using train split only\n", + "2025-11-26 01:04:02,921 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:04:07,093 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:04:07,908 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 3 on test set\n", + "2025-11-26 01:04:07,912 - bioneuralnet.downstream_task.dpmon - INFO - Fold 3 results:\n", + "2025-11-26 01:04:07,913 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9545\n", + "2025-11-26 01:04:07,913 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9459\n", + "2025-11-26 01:04:07,913 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9544\n", + "2025-11-26 01:04:07,913 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9297\n", + "2025-11-26 01:04:07,913 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9967\n", + "2025-11-26 01:04:07,913 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9949\n", + "\n", + "2025-11-26 01:04:07,915 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 4/5\n", + "2025-11-26 01:04:07,916 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:04:07,922 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:04:12,095 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:04:12,109 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (527, 901)\n", + "2025-11-26 01:04:12,112\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:04:23,475\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0193s.\n", + "2025-11-26 01:04:23,614 - bioneuralnet.downstream_task.dpmon - INFO - Fold 4 best config: {'gnn_layer_num': 1, 'gnn_hidden_dim': 128, 'lr': 0.002226976717919125, 'weight_decay': 0.00016678702979522166, 'nn_hidden_dim1': 128, 'nn_hidden_dim2': 64, 'ae_encoding_dim': 16, 'num_epochs': 512, 'gnn_dropout': 0.3, 'gnn_activation': 'relu', 'dim_reduction': 'ae'}\n", + "2025-11-26 01:04:23,615 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 4 using train split only\n", + "2025-11-26 01:04:23,620 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:04:27,796 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:04:28,417 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 4 on test set\n", + "2025-11-26 01:04:28,421 - bioneuralnet.downstream_task.dpmon - INFO - Fold 4 results:\n", + "2025-11-26 01:04:28,422 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9389\n", + "2025-11-26 01:04:28,422 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9449\n", + "2025-11-26 01:04:28,422 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9390\n", + "2025-11-26 01:04:28,422 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9554\n", + "2025-11-26 01:04:28,422 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9919\n", + "2025-11-26 01:04:28,423 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9839\n", + "\n", + "2025-11-26 01:04:28,424 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 5/5\n", + "2025-11-26 01:04:28,425 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:04:28,431 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:04:32,603 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:04:32,616 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (527, 901)\n", + "2025-11-26 01:04:32,619\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:04:45,848\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0215s.\n", + "2025-11-26 01:04:46,065 - bioneuralnet.downstream_task.dpmon - INFO - Fold 5 best config: {'gnn_layer_num': 2, 'gnn_hidden_dim': 64, 'lr': 0.001685812648195555, 'weight_decay': 0.0023773937538678585, 'nn_hidden_dim1': 256, 'nn_hidden_dim2': 128, 'ae_encoding_dim': 8, 'num_epochs': 512, 'gnn_dropout': 0.2, 'gnn_activation': 'relu', 'dim_reduction': 'linear'}\n", + "2025-11-26 01:04:46,066 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 5 using train split only\n", + "2025-11-26 01:04:46,071 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:04:50,196 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:04:51,249 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 5 on test set\n", + "2025-11-26 01:04:51,254 - bioneuralnet.downstream_task.dpmon - INFO - Fold 5 results:\n", + "2025-11-26 01:04:51,254 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9542\n", + "2025-11-26 01:04:51,254 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9456\n", + "2025-11-26 01:04:51,255 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9540\n", + "2025-11-26 01:04:51,255 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9435\n", + "2025-11-26 01:04:51,255 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9975\n", + "2025-11-26 01:04:51,255 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9938\n", + "\n", + "2025-11-26 01:04:51,259 - bioneuralnet.downstream_task.dpmon - INFO - Successfully saved global best CV model state to: /home/vicente/Github/BioNeuralNet/dpmon_results_GAT_FINAL/kipan/gaussian_15/best_cv_model.pt\n", + "2025-11-26 01:04:51,259 - bioneuralnet.downstream_task.dpmon - INFO - Successfully saved global best CV model state to: /home/vicente/Github/BioNeuralNet/dpmon_results_GAT_FINAL/kipan/gaussian_15/best_cv_model_embds.pt\n", + "2025-11-26 01:04:51,260 - bioneuralnet.downstream_task.dpmon - INFO - Cross-Validation Results:\n", + "\n", + "2025-11-26 01:04:51,260 - bioneuralnet.downstream_task.dpmon - INFO - Avg Accuracy: 0.9498 +/- 0.0102\n", + "2025-11-26 01:04:51,260 - bioneuralnet.downstream_task.dpmon - INFO - Avg F1 Macro: 0.9379 +/- 0.0110\n", + "2025-11-26 01:04:51,260 - bioneuralnet.downstream_task.dpmon - INFO - Avg F1 Weighted: 0.9500 +/- 0.0100\n", + "2025-11-26 01:04:51,260 - bioneuralnet.downstream_task.dpmon - INFO - Avg Recall: 0.9435 +/- 0.0126\n", + "2025-11-26 01:04:51,260 - bioneuralnet.downstream_task.dpmon - INFO - Avg AUC: 0.9948 +/- 0.0024\n", + "2025-11-26 01:04:51,260 - bioneuralnet.downstream_task.dpmon - INFO - Avg AUPR: 0.9827 +/- 0.0206\n", + "2025-11-26 01:04:51,261 - bioneuralnet.downstream_task.dpmon - INFO - DPMON run completed.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Results for: threshold_10\n", + "Accuracy (Avg +/- Std): 0.9544 +/- 0.0120\n", + "F1 Macro (Avg +/- Std): 0.9451 +/- 0.0093\n", + "F1 Weighted (Avg +/- Std): 0.9545 +/- 0.0120\n", + "Recall: 0.9511 +/- 0.0125\n", + "AUC: 0.9951 +/- 0.0026\n", + "\n", + "Results for: correlation_10\n", + "Accuracy (Avg +/- Std): 0.9544 +/- 0.0120\n", + "F1 Macro (Avg +/- Std): 0.9450 +/- 0.0094\n", + "F1 Weighted (Avg +/- Std): 0.9545 +/- 0.0119\n", + "Recall: 0.9507 +/- 0.0124\n", + "AUC: 0.9943 +/- 0.0029\n", + "\n", + "Results for: similarity_10\n", + "Accuracy (Avg +/- Std): 0.9483 +/- 0.0100\n", + "F1 Macro (Avg +/- Std): 0.9382 +/- 0.0077\n", + "F1 Weighted (Avg +/- Std): 0.9483 +/- 0.0100\n", + "Recall: 0.9422 +/- 0.0129\n", + "AUC: 0.9945 +/- 0.0010\n", + "\n", + "Results for: gaussian_15\n", + "Accuracy (Avg +/- Std): 0.9498 +/- 0.0102\n", + "F1 Macro (Avg +/- Std): 0.9379 +/- 0.0110\n", + "F1 Weighted (Avg +/- Std): 0.9500 +/- 0.0100\n", + "Recall: 0.9435 +/- 0.0126\n", + "AUC: 0.9948 +/- 0.0024\n" + ] + } + ], + "source": [ + "from bioneuralnet.downstream_task import DPMON\n", + "\n", + "output_dir_base = Path(\"/home/vicente/Github/BioNeuralNet/dpmon_results_GAT_FINAL/kipan\")\n", + "\n", + "all_results_GAT = []\n", + "\n", + "for run_config in comparison_runs:\n", + " graph_name = run_config[\"name\"]\n", + " A_full = run_config[\"graph\"]\n", + "\n", + " current_output_dir = output_dir_base / graph_name\n", + " current_output_dir.mkdir(parents=True, exist_ok=True)\n", + "\n", + " dpmon_params_base = {\n", + " \"adjacency_matrix\": A_full,\n", + " \"omics_list\": omics_kipan,\n", + " \"phenotype_data\": target,\n", + " \"phenotype_col\": \"target\",\n", + " \"clinical_data\": clinical_for_model,\n", + " \"model\": 'GAT',\n", + " \"tune\": True, \n", + " \"cv\": True, \n", + " \"n_folds\": 5,\n", + " \"repeat_num\": 1,\n", + " \"gpu\": True,\n", + " \"cuda\": 0,\n", + " \"seed\": SEED,\n", + " \"output_dir\": current_output_dir\n", + " }\n", + " \n", + " dpmon_tunned = DPMON(**dpmon_params_base)\n", + " predictions_df, metrics, embeddings = dpmon_tunned.run()\n", + "\n", + " all_results_GAT.append({\n", + " \"graph\": graph_name,\n", + " \"predictions\": predictions_df,\n", + " \"metrics\": metrics,\n", + " \"embeddings\": embeddings\n", + " })\n", + "\n", + "for res in all_results_GAT:\n", + " graph_name = res[\"graph\"]\n", + " graph_metrics = res[\"metrics\"]\n", + " \n", + " acc_row = graph_metrics.loc[graph_metrics['Metric'] == 'Accuracy'].iloc[0]\n", + " f1_macro_row = graph_metrics.loc[graph_metrics['Metric'] == 'F1 Macro'].iloc[0]\n", + " f1_weighted_row = graph_metrics.loc[graph_metrics['Metric'] == 'F1 Weighted'].iloc[0]\n", + " recall_row = graph_metrics.loc[graph_metrics['Metric'] == 'Recall'].iloc[0]\n", + " auc_row = graph_metrics.loc[graph_metrics['Metric'] == 'AUC'].iloc[0]\n", + " \n", + " acc_avg, acc_std = acc_row['Average'], acc_row['StdDev']\n", + " f1_macro_avg, f1_macro_std = f1_macro_row['Average'], f1_macro_row['StdDev']\n", + " f1_weighted_avg, f1_weighted_std = f1_weighted_row['Average'], f1_weighted_row['StdDev']\n", + " recall_avg, recall_std = recall_row['Average'], recall_row['StdDev']\n", + " auc_avg, auc_std = auc_row['Average'], auc_row['StdDev']\n", + "\n", + " print(f\"\\nResults for: {graph_name}\")\n", + " print(f\"Accuracy (Avg +/- Std): {acc_avg:.4f} +/- {acc_std:.4f}\")\n", + " print(f\"F1 Macro (Avg +/- Std): {f1_macro_avg:.4f} +/- {f1_macro_std:.4f}\")\n", + " print(f\"F1 Weighted (Avg +/- Std): {f1_weighted_avg:.4f} +/- {f1_weighted_std:.4f}\")\n", + " print(f\"Recall: {recall_avg:.4f} +/- {recall_std:.4f}\")\n", + " print(f\"AUC: {auc_avg:.4f} +/- {auc_std:.4f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "fac7426c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " 0 1 2 3 4\n", + "MGP 0.019121 0.024070 0.035583 0.007485 0.009774\n", + "ADH1C 0.000000 0.049013 0.000000 0.000000 0.097422\n", + "LOC440354 0.018648 0.010866 0.014588 0.011661 0.019968\n", + "C1orf110 0.000000 0.050925 0.000000 0.002459 0.074045\n", + "CLDN8 0.001006 0.015970 0.009054 0.009751 0.065529" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "# we have emebeddings for each of the 4 graphs\n", + "# we will select the last one from the list (threshold_10) for visualization\n", + "res = all_results_GAT[0] \n", + "tensor_data = res[\"embeddings\"]\n", + "\n", + "# .detach().cpu().numpy() to get the raw numbers out of pytorch\n", + "GAT_embeddings_df = pd.DataFrame(\n", + " tensor_data.detach().cpu().numpy(),\n", + " index=omics_kipan.columns\n", + ")\n", + "\n", + "display(GAT_embeddings_df.iloc[:5,:5])" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "b1200e4e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Plotting Feature Embeddings for graph: threshold_10\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from bioneuralnet.metrics import plot_embeddings\n", + "\n", + "# for a more interpretable embedding plot we will map the node labels to omics types\n", + "node_labels = []\n", + "GAT_embeddings_array = GAT_embeddings_df.values\n", + "feature_names = GAT_embeddings_df.index\n", + "\n", + "# using the sets from the orginal to serve as a loopup table\n", + "dna_feats = set(dna_meth.columns)\n", + "rna_feats = set(rna.columns)\n", + "mirna_feats = set(mirna.columns)\n", + "\n", + "for feat in feature_names:\n", + " if feat in dna_feats:\n", + " # 1 for DNA methylation\n", + " node_labels.append(1)\n", + " elif feat in rna_feats:\n", + " # 2 for RNA \n", + " node_labels.append(2)\n", + " elif feat in mirna_feats:\n", + " # and 3 for miRNA \n", + " node_labels.append(3)\n", + " else:\n", + " node_labels.append(0)\n", + "\n", + "# our plotting function needs an array to squeeze the embedings into 2Dspace\n", + "node_labels = np.array(node_labels)\n", + "\n", + "# feature(omics) embeddings colored by omics type.\n", + "print(f\"Plotting Feature Embeddings for graph: {res['graph']}\")\n", + "plot_embeddings(GAT_embeddings_array, node_labels, legend_labels=[\"DNA_Methylation\", \"RNA\", \"miRNA\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "98442c32", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-11-26 09:50:43,480 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 09:50:43,481 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 09:50:43,481 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 09:50:43,482 - bioneuralnet.clustering.hybrid_louvain - INFO - Initializing HybridLouvain...\n", + "2025-11-26 09:50:43,482 - bioneuralnet.clustering.hybrid_louvain - INFO - Input G is a DataFrame; converting adjacency matrix to NetworkX graph.\n", + "2025-11-26 09:50:43,494 - bioneuralnet.clustering.hybrid_louvain - INFO - Initialized HybridLouvain with 900 graph nodes, 900 omics columns, 658 phenotype rows; max_iter=3, k3=0.2, k4=0.8, tune=False\n", + "2025-11-26 09:50:43,495 - bioneuralnet.clustering.hybrid_louvain - INFO - \n", + "Iteration 1/3: Running Correlated Louvain...\n", + "2025-11-26 09:50:43,504 - bioneuralnet.clustering.correlated_louvain - INFO - CorrelatedLouvain(k3=0.2, k4=0.8, nodes=900, edges=8930, features=900)\n", + "2025-11-26 09:50:43,504 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 09:50:43,505 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 09:50:43,505 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 09:50:43,505 - bioneuralnet.clustering.correlated_louvain - INFO - Running standard community detection...\n", + "2025-11-26 09:50:43,615 - bioneuralnet.clustering.correlated_louvain - INFO - Computed quality (supervised): Q = 0.7372, avg_corr = 0.5264, combined = 0.5686\n", + "2025-11-26 09:50:43,615 - bioneuralnet.clustering.correlated_louvain - INFO - Final quality: 0.5686\n", + "2025-11-26 09:50:43,616 - bioneuralnet.clustering.correlated_louvain - INFO - CorrelatedLouvain found 9 communities (nodes=900)\n", + "2025-11-26 09:50:43,669 - bioneuralnet.clustering.correlated_louvain - INFO - Computed quality (supervised): Q = 0.7372, avg_corr = 0.5264, combined = 0.5686\n", + "2025-11-26 09:50:43,669 - bioneuralnet.clustering.hybrid_louvain - INFO - Iteration 1: Louvain Quality = 0.5686\n", + "2025-11-26 09:50:43,722 - bioneuralnet.clustering.hybrid_louvain - INFO - Selected seed community of size 159 with correlation -0.8356\n", + "2025-11-26 09:50:43,722 - bioneuralnet.clustering.correlated_pagerank - INFO - Initialized PageRank with the following parameters:\n", + "2025-11-26 09:50:43,723 - bioneuralnet.clustering.correlated_pagerank - INFO - Graph: NetworkX Graph with 900 nodes and 8930 edges.\n", + "2025-11-26 09:50:43,723 - bioneuralnet.clustering.correlated_pagerank - INFO - Omics Data: DataFrame with shape (658, 900).\n", + "2025-11-26 09:50:43,724 - bioneuralnet.clustering.correlated_pagerank - INFO - Phenotype Data: Series with 658 samples.\n", + "2025-11-26 09:50:43,724 - bioneuralnet.clustering.correlated_pagerank - INFO - Alpha: 0.9\n", + "2025-11-26 09:50:43,724 - bioneuralnet.clustering.correlated_pagerank - INFO - Max Iterations: 100\n", + "2025-11-26 09:50:43,724 - bioneuralnet.clustering.correlated_pagerank - INFO - Tolerance: 1e-06\n", + "2025-11-26 09:50:43,725 - bioneuralnet.clustering.correlated_pagerank - INFO - K (Composite Score Weight): 0.5\n", + "2025-11-26 09:50:43,725 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 09:50:43,726 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 09:50:43,726 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 09:50:43,726 - bioneuralnet.clustering.correlated_pagerank - INFO - Initialized Correlated PageRank. device=cpu\n", + "2025-11-26 09:50:45,149 - bioneuralnet.clustering.correlated_pagerank - INFO - Generated personalization vector for seed nodes: ['LOC645638', 'MIR190', 'MIR657', 'CAPG', 'GGTLC2', 'FABP7_2173', 'SLC6A3_6531', 'PTGER3_5733', 'CP_1356', 'CA9_768', 'HMGCS2_3158', 'C6orf223_221416', 'CYP2J2_1573', 'CREB3L3_84699', 'ANGPTL4_51129', 'MYEOV_26579', 'PCSK6_5046', 'C1QL1_10882', 'LGI4_163175', 'UBE2QL1_134111', 'MCHR1_2847', 'ACTG2_72', 'SORCS2_57537', 'APOB_338', 'FOLH1_2346', 'MAP7D2_256714', 'ACAN_176', 'TFAP2A_7020', 'CA4_762', 'VAT1L_57687', 'AVPR1A_552', 'CCL20_6364', 'PRDM16_63976', 'FHL5_9457', 'LOX_4015', 'PCDH17_27253', 'APLNR_187', 'COL21A1_81578', 'LOC100131551_100131551', 'ACTC1_70', 'ANGPT2_285', 'ADAMTS4_9507', 'ESM1_11082', 'CASQ2_845', 'ADAMTSL1_92949', 'PCOLCE2_26577', 'CDCA2_157313', 'COL5A3_50509', 'PDE1C_5137', 'CPXM2_119587', 'RGS5_8490', 'CREB3L1_90993', 'PRRX1_5396', 'KCNN3_3782', 'CALCR_799', 'ADCY1_107', 'COX4I2_84701', 'CD36_948', 'STC1_6781', 'C2orf72_257407', 'ALDOC_230', 'CDH13_1012', 'CDH11_1009', 'PPFIA4_8497', 'PDE3A_5139', 'ANGPT1_284', 'CXCL9_4283', 'CXCL11_6373', 'APLN_8862', 'HOXD10_3236', 'CA8_767', 'TRPC6_7225', 'AGTR1_185', 'DDR2_4921', 'B4GALNT1_2583', 'OR51E1_143503', 'BDKRB2_624', 'DGKI_9162', 'MYH11_4629', 'SEMA5B_54437', 'BARX2_8538', 'TMEM45A_55076', 'MPPED2_744', 'COL25A1_84570', 'PLAC9_219348', 'RASGRF2_5924', 'FLT1_2321', 'C6orf105_84830', 'RASD2_23551', 'C3orf70_285382', 'EGLN3_112399', 'CNN1_1264', 'CSPG4_1464', 'CXCL10_3627', 'COL6A3_1293', 'APOLD1_81575', 'HMCN1_83872', 'ZNF521_25925', 'SLC35F1_222553', 'C4orf6_10141', 'DACH1_1602', 'SPARCL1_8404', 'C17orf55_284185', 'BDKRB1_623', 'COL14A1_7373', 'KLHDC8A_55220', 'PDZD2_23037', 'GABRE_2564', 'FATE1_89885', 'BTNL9_153579', 'ADAMTS5_11096', 'GPR4_2828', 'GPR116_221395', 'C7orf68_29923', 'ANO3_63982', 'COL8A1_1295', 'B3GNT4_79369', 'NGF_4803', 'ADAMTS12_81792', 'hsa_mir_217', 'hsa_mir_122', 'hsa_mir_885', 'hsa_mir_675', 'hsa_mir_577', 'hsa_mir_653', 'hsa_mir_503', 'hsa_mir_1247', 'hsa_mir_126', 'hsa_mir_489', 'hsa_mir_224', 'hsa_mir_424', 'hsa_mir_134', 'hsa_mir_1_2', 'hsa_mir_199b', 'hsa_mir_145', 'hsa_mir_199a_2', 'hsa_mir_199a_1', 'hsa_mir_153_2', 'hsa_mir_3676', 'hsa_mir_139', 'hsa_mir_379', 'hsa_mir_127', 'hsa_mir_452', 'hsa_mir_450a_1', 'hsa_mir_450a_2', 'hsa_mir_374c', 'hsa_mir_1271', 'hsa_mir_450b', 'hsa_mir_1274b', 'hsa_mir_326', 'hsa_mir_195', 'hsa_mir_33b', 'hsa_mir_26a_1', 'hsa_mir_497', 'hsa_mir_3605', 'hsa_mir_320b_2', 'hsa_mir_504', 'hsa_mir_1296', 'hsa_mir_212']\n", + "2025-11-26 09:50:45,159 - bioneuralnet.clustering.correlated_pagerank - INFO - PageRank computation completed.\n", + "2025-11-26 09:51:02,712 - bioneuralnet.clustering.correlated_pagerank - INFO - Sweep cut resulted in cluster of size 293 with conductance 0.098 and correlation -0.84.\n", + "2025-11-26 09:51:02,712 - bioneuralnet.clustering.correlated_pagerank - INFO - PageRank clustering completed successfully.\n", + "2025-11-26 09:51:02,713 - bioneuralnet.clustering.hybrid_louvain - INFO - Iteration 1: cluster size=293, Conductance=0.098 Correlation=-0.840 score=-0.371\n", + "2025-11-26 09:51:02,722 - bioneuralnet.clustering.hybrid_louvain - INFO - \n", + "Iteration 2/3: Running Correlated Louvain...\n", + "2025-11-26 09:51:02,728 - bioneuralnet.clustering.correlated_louvain - INFO - CorrelatedLouvain(k3=0.2, k4=0.8, nodes=293, edges=2701, features=900)\n", + "2025-11-26 09:51:02,729 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 09:51:02,730 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 09:51:02,730 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 09:51:02,730 - bioneuralnet.clustering.correlated_louvain - INFO - Running standard community detection...\n", + "2025-11-26 09:51:02,816 - bioneuralnet.clustering.correlated_louvain - INFO - Computed quality (supervised): Q = 0.6016, avg_corr = 0.6149, combined = 0.6123\n", + "2025-11-26 09:51:02,817 - bioneuralnet.clustering.correlated_louvain - INFO - Final quality: 0.6123\n", + "2025-11-26 09:51:02,817 - bioneuralnet.clustering.correlated_louvain - INFO - CorrelatedLouvain found 8 communities (nodes=293)\n", + "2025-11-26 09:51:02,850 - bioneuralnet.clustering.correlated_louvain - INFO - Computed quality (supervised): Q = 0.6016, avg_corr = 0.6149, combined = 0.6123\n", + "2025-11-26 09:51:02,851 - bioneuralnet.clustering.hybrid_louvain - INFO - Iteration 2: Louvain Quality = 0.6123\n", + "2025-11-26 09:51:02,881 - bioneuralnet.clustering.hybrid_louvain - INFO - Selected seed community of size 66 with correlation -0.8259\n", + "2025-11-26 09:51:02,881 - bioneuralnet.clustering.correlated_pagerank - INFO - Initialized PageRank with the following parameters:\n", + "2025-11-26 09:51:02,882 - bioneuralnet.clustering.correlated_pagerank - INFO - Graph: NetworkX Graph with 293 nodes and 2701 edges.\n", + "2025-11-26 09:51:02,882 - bioneuralnet.clustering.correlated_pagerank - INFO - Omics Data: DataFrame with shape (658, 900).\n", + "2025-11-26 09:51:02,883 - bioneuralnet.clustering.correlated_pagerank - INFO - Phenotype Data: Series with 658 samples.\n", + "2025-11-26 09:51:02,883 - bioneuralnet.clustering.correlated_pagerank - INFO - Alpha: 0.9\n", + "2025-11-26 09:51:02,883 - bioneuralnet.clustering.correlated_pagerank - INFO - Max Iterations: 100\n", + "2025-11-26 09:51:02,883 - bioneuralnet.clustering.correlated_pagerank - INFO - Tolerance: 1e-06\n", + "2025-11-26 09:51:02,884 - bioneuralnet.clustering.correlated_pagerank - INFO - K (Composite Score Weight): 0.5\n", + "2025-11-26 09:51:02,884 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 09:51:02,885 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 09:51:02,885 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 09:51:02,885 - bioneuralnet.clustering.correlated_pagerank - INFO - Initialized Correlated PageRank. device=cpu\n", + "2025-11-26 09:51:03,374 - bioneuralnet.clustering.correlated_pagerank - INFO - Generated personalization vector for seed nodes: ['FATE1_89885', 'CASQ2_845', 'FOLH1_2346', 'hsa_mir_1247', 'BARX2_8538', 'NGF_4803', 'CA4_762', 'C2orf72_257407', 'hsa_mir_1_2', 'RASD2_23551', 'OR51E1_143503', 'ANGPT2_285', 'ADAMTS4_9507', 'ZNF521_25925', 'CXCL10_3627', 'GABRE_2564', 'C6orf223_221416', 'hsa_mir_153_2', 'PDE3A_5139', 'MCHR1_2847', 'AGTR1_185', 'ESM1_11082', 'CXCL9_4283', 'hsa_mir_489', 'hsa_mir_885', 'B4GALNT1_2583', 'ACAN_176', 'CSPG4_1464', 'hsa_mir_224', 'COL5A3_50509', 'FLT1_2321', 'GPR4_2828', 'hsa_mir_126', 'DACH1_1602', 'BTNL9_153579', 'PCDH17_27253', 'APLNR_187', 'COL21A1_81578', 'SLC35F1_222553', 'STC1_6781', 'PLAC9_219348', 'ADAMTS12_81792', 'hsa_mir_139', 'ANO3_63982', 'COL8A1_1295', 'APLN_8862', 'LGI4_163175', 'ACTG2_72', 'hsa_mir_653', 'CDH13_1012', 'CALCR_799', 'APOLD1_81575', 'KLHDC8A_55220', 'RGS5_8490', 'DDR2_4921', 'COL25A1_84570', 'FHL5_9457', 'CD36_948', 'HMCN1_83872', 'C3orf70_285382', 'KCNN3_3782', 'ADAMTS5_11096', 'COX4I2_84701', 'CXCL11_6373', 'PRRX1_5396', 'TRPC6_7225']\n", + "2025-11-26 09:51:03,379 - bioneuralnet.clustering.correlated_pagerank - INFO - PageRank computation completed.\n", + "2025-11-26 09:51:05,727 - bioneuralnet.clustering.correlated_pagerank - INFO - Sweep cut resulted in cluster of size 227 with conductance 0.085 and correlation -0.83.\n", + "2025-11-26 09:51:05,728 - bioneuralnet.clustering.correlated_pagerank - INFO - PageRank clustering completed successfully.\n", + "2025-11-26 09:51:05,728 - bioneuralnet.clustering.hybrid_louvain - INFO - Iteration 2: cluster size=227, Conductance=0.085 Correlation=-0.830 score=-0.372\n", + "2025-11-26 09:51:05,735 - bioneuralnet.clustering.hybrid_louvain - INFO - \n", + "Iteration 3/3: Running Correlated Louvain...\n", + "2025-11-26 09:51:05,740 - bioneuralnet.clustering.correlated_louvain - INFO - CorrelatedLouvain(k3=0.2, k4=0.8, nodes=227, edges=2085, features=900)\n", + "2025-11-26 09:51:05,741 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 09:51:05,741 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 09:51:05,742 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 09:51:05,742 - bioneuralnet.clustering.correlated_louvain - INFO - Running standard community detection...\n", + "2025-11-26 09:51:05,781 - bioneuralnet.clustering.correlated_louvain - INFO - Computed quality (supervised): Q = 0.5419, avg_corr = 0.6823, combined = 0.6542\n", + "2025-11-26 09:51:05,782 - bioneuralnet.clustering.correlated_louvain - INFO - Final quality: 0.6542\n", + "2025-11-26 09:51:05,782 - bioneuralnet.clustering.correlated_louvain - INFO - CorrelatedLouvain found 7 communities (nodes=227)\n", + "2025-11-26 09:51:05,808 - bioneuralnet.clustering.correlated_louvain - INFO - Computed quality (supervised): Q = 0.5419, avg_corr = 0.6823, combined = 0.6542\n", + "2025-11-26 09:51:05,808 - bioneuralnet.clustering.hybrid_louvain - INFO - Iteration 3: Louvain Quality = 0.6542\n", + "2025-11-26 09:51:05,832 - bioneuralnet.clustering.hybrid_louvain - INFO - Selected seed community of size 58 with correlation -0.8271\n", + "2025-11-26 09:51:05,833 - bioneuralnet.clustering.correlated_pagerank - INFO - Initialized PageRank with the following parameters:\n", + "2025-11-26 09:51:05,834 - bioneuralnet.clustering.correlated_pagerank - INFO - Graph: NetworkX Graph with 227 nodes and 2085 edges.\n", + "2025-11-26 09:51:05,834 - bioneuralnet.clustering.correlated_pagerank - INFO - Omics Data: DataFrame with shape (658, 900).\n", + "2025-11-26 09:51:05,834 - bioneuralnet.clustering.correlated_pagerank - INFO - Phenotype Data: Series with 658 samples.\n", + "2025-11-26 09:51:05,835 - bioneuralnet.clustering.correlated_pagerank - INFO - Alpha: 0.9\n", + "2025-11-26 09:51:05,835 - bioneuralnet.clustering.correlated_pagerank - INFO - Max Iterations: 100\n", + "2025-11-26 09:51:05,835 - bioneuralnet.clustering.correlated_pagerank - INFO - Tolerance: 1e-06\n", + "2025-11-26 09:51:05,836 - bioneuralnet.clustering.correlated_pagerank - INFO - K (Composite Score Weight): 0.5\n", + "2025-11-26 09:51:05,836 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 09:51:05,837 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 09:51:05,838 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 09:51:05,838 - bioneuralnet.clustering.correlated_pagerank - INFO - Initialized Correlated PageRank. device=cpu\n", + "2025-11-26 09:51:06,070 - bioneuralnet.clustering.correlated_pagerank - INFO - Generated personalization vector for seed nodes: ['FATE1_89885', 'CASQ2_845', 'FOLH1_2346', 'hsa_mir_1247', 'BARX2_8538', 'HOXD10_3236', 'NGF_4803', 'CA4_762', 'C2orf72_257407', 'RASD2_23551', 'OR51E1_143503', 'ANGPT2_285', 'ADAMTS4_9507', 'ZNF521_25925', 'GABRE_2564', 'C6orf223_221416', 'hsa_mir_153_2', 'PDE3A_5139', 'MCHR1_2847', 'AGTR1_185', 'ESM1_11082', 'hsa_mir_489', 'hsa_mir_885', 'B4GALNT1_2583', 'ACAN_176', 'CSPG4_1464', 'hsa_mir_224', 'COL5A3_50509', 'FLT1_2321', 'GPR4_2828', 'DACH1_1602', 'BTNL9_153579', 'PCDH17_27253', 'APLNR_187', 'COL21A1_81578', 'SLC35F1_222553', 'STC1_6781', 'PLAC9_219348', 'ANO3_63982', 'COL8A1_1295', 'APLN_8862', 'LGI4_163175', 'hsa_mir_653', 'CDH13_1012', 'CALCR_799', 'APOLD1_81575', 'KLHDC8A_55220', 'RGS5_8490', 'DDR2_4921', 'COL25A1_84570', 'FHL5_9457', 'CD36_948', 'HMCN1_83872', 'C3orf70_285382', 'KCNN3_3782', 'ADAMTS5_11096', 'COX4I2_84701', 'TRPC6_7225']\n", + "2025-11-26 09:51:06,074 - bioneuralnet.clustering.correlated_pagerank - INFO - PageRank computation completed.\n", + "2025-11-26 09:51:08,534 - bioneuralnet.clustering.correlated_pagerank - INFO - Sweep cut resulted in cluster of size 94 with conductance 0.177 and correlation -0.83.\n", + "2025-11-26 09:51:08,535 - bioneuralnet.clustering.correlated_pagerank - INFO - PageRank clustering completed successfully.\n", + "2025-11-26 09:51:08,535 - bioneuralnet.clustering.hybrid_louvain - INFO - Iteration 3: cluster size=94, Conductance=0.177 Correlation=-0.830 score=-0.327\n", + "2025-11-26 09:51:08,538 - bioneuralnet.clustering.hybrid_louvain - INFO - Hybrid Louvain completed after 4 iterations.\n", + "2025-11-26 09:51:08,542 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 09:51:08,543 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 09:51:08,543 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 09:51:08,544 - bioneuralnet.clustering.hybrid_louvain - INFO - Initializing HybridLouvain...\n", + "2025-11-26 09:51:08,544 - bioneuralnet.clustering.hybrid_louvain - INFO - Input G is a DataFrame; converting adjacency matrix to NetworkX graph.\n", + "2025-11-26 09:51:08,564 - bioneuralnet.clustering.hybrid_louvain - INFO - Initialized HybridLouvain with 900 graph nodes, 900 omics columns, 658 phenotype rows; max_iter=3, k3=0.2, k4=0.8, tune=False\n", + "2025-11-26 09:51:08,565 - bioneuralnet.clustering.hybrid_louvain - INFO - \n", + "Iteration 1/3: Running Correlated Louvain...\n", + "2025-11-26 09:51:08,606 - bioneuralnet.clustering.correlated_louvain - INFO - CorrelatedLouvain(k3=0.2, k4=0.8, nodes=900, edges=8930, features=900)\n", + "2025-11-26 09:51:08,607 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 09:51:08,608 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 09:51:08,609 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 09:51:08,609 - bioneuralnet.clustering.correlated_louvain - INFO - Running standard community detection...\n", + "2025-11-26 09:51:08,749 - bioneuralnet.clustering.correlated_louvain - INFO - Computed quality (supervised): Q = 0.7372, avg_corr = 0.4985, combined = 0.5462\n", + "2025-11-26 09:51:08,750 - bioneuralnet.clustering.correlated_louvain - INFO - Final quality: 0.5462\n", + "2025-11-26 09:51:08,750 - bioneuralnet.clustering.correlated_louvain - INFO - CorrelatedLouvain found 9 communities (nodes=900)\n", + "2025-11-26 09:51:08,850 - bioneuralnet.clustering.correlated_louvain - INFO - Computed quality (supervised): Q = 0.7372, avg_corr = 0.4985, combined = 0.5462\n", + "2025-11-26 09:51:08,850 - bioneuralnet.clustering.hybrid_louvain - INFO - Iteration 1: Louvain Quality = 0.5462\n", + "2025-11-26 09:51:08,932 - bioneuralnet.clustering.hybrid_louvain - INFO - Selected seed community of size 48 with correlation -0.8391\n", + "2025-11-26 09:51:08,932 - bioneuralnet.clustering.correlated_pagerank - INFO - Initialized PageRank with the following parameters:\n", + "2025-11-26 09:51:08,933 - bioneuralnet.clustering.correlated_pagerank - INFO - Graph: NetworkX Graph with 900 nodes and 8930 edges.\n", + "2025-11-26 09:51:08,933 - bioneuralnet.clustering.correlated_pagerank - INFO - Omics Data: DataFrame with shape (658, 900).\n", + "2025-11-26 09:51:08,934 - bioneuralnet.clustering.correlated_pagerank - INFO - Phenotype Data: Series with 658 samples.\n", + "2025-11-26 09:51:08,934 - bioneuralnet.clustering.correlated_pagerank - INFO - Alpha: 0.9\n", + "2025-11-26 09:51:08,934 - bioneuralnet.clustering.correlated_pagerank - INFO - Max Iterations: 100\n", + "2025-11-26 09:51:08,934 - bioneuralnet.clustering.correlated_pagerank - INFO - Tolerance: 1e-06\n", + "2025-11-26 09:51:08,935 - bioneuralnet.clustering.correlated_pagerank - INFO - K (Composite Score Weight): 0.5\n", + "2025-11-26 09:51:08,935 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 09:51:08,936 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 09:51:08,936 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 09:51:08,937 - bioneuralnet.clustering.correlated_pagerank - INFO - Initialized Correlated PageRank. device=cpu\n", + "2025-11-26 09:51:09,133 - bioneuralnet.clustering.correlated_pagerank - INFO - Generated personalization vector for seed nodes: ['BAAT', 'SCARNA4', 'RPL23AP32', 'LOH3CR2A', 'C4orf6', 'SNORA1', 'CP', 'C10orf62', 'C3orf24', 'GBP5', 'LCN2_3934', 'SLPI_6590', 'CKMT1B_1159', 'TJP3_27134', 'CHL1_10752', 'C19orf46_163183', 'CKMT1A_548596', 'C6orf168_84553', 'MAL_4118', 'BNC1_646', 'CAMK2B_816', 'CNTN3_5067', 'LOC554202_554202', 'C1orf116_79098', 'CST6_1474', 'WFDC2_10406', 'DEFB1_1672', 'CCNO_10309', 'ANO5_203859', 'CCDC78_124093', 'STK33_65975', 'IGF2BP2_10644', 'EPS8L1_54869', 'CBLC_23624', 'C4orf48_401115', 'CNTD2_79935', 'OGDHL_55753', 'FAM155B_27112', 'ANXA3_306', 'hsa_mir_375', 'hsa_mir_183', 'hsa_mir_96', 'hsa_mir_182', 'hsa_mir_34c', 'hsa_mir_200b', 'hsa_mir_429', 'hsa_mir_651', 'hsa_mir_200a']\n", + "2025-11-26 09:51:09,144 - bioneuralnet.clustering.correlated_pagerank - INFO - PageRank computation completed.\n", + "2025-11-26 09:51:26,800 - bioneuralnet.clustering.correlated_pagerank - INFO - Sweep cut resulted in cluster of size 159 with conductance 0.217 and correlation -0.83.\n", + "2025-11-26 09:51:26,801 - bioneuralnet.clustering.correlated_pagerank - INFO - PageRank clustering completed successfully.\n", + "2025-11-26 09:51:26,801 - bioneuralnet.clustering.hybrid_louvain - INFO - Iteration 1: cluster size=159, Conductance=0.217 Correlation=-0.830 score=-0.307\n", + "2025-11-26 09:51:26,809 - bioneuralnet.clustering.hybrid_louvain - INFO - \n", + "Iteration 2/3: Running Correlated Louvain...\n", + "2025-11-26 09:51:26,816 - bioneuralnet.clustering.correlated_louvain - INFO - CorrelatedLouvain(k3=0.2, k4=0.8, nodes=159, edges=1292, features=900)\n", + "2025-11-26 09:51:26,819 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 09:51:26,819 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 09:51:26,820 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 09:51:26,821 - bioneuralnet.clustering.correlated_louvain - INFO - Running standard community detection...\n", + "2025-11-26 09:51:26,870 - bioneuralnet.clustering.correlated_louvain - INFO - Computed quality (supervised): Q = 0.5196, avg_corr = 0.5933, combined = 0.5785\n", + "2025-11-26 09:51:26,871 - bioneuralnet.clustering.correlated_louvain - INFO - Final quality: 0.5785\n", + "2025-11-26 09:51:26,871 - bioneuralnet.clustering.correlated_louvain - INFO - CorrelatedLouvain found 6 communities (nodes=159)\n", + "2025-11-26 09:51:26,895 - bioneuralnet.clustering.correlated_louvain - INFO - Computed quality (supervised): Q = 0.5196, avg_corr = 0.5933, combined = 0.5785\n", + "2025-11-26 09:51:26,896 - bioneuralnet.clustering.hybrid_louvain - INFO - Iteration 2: Louvain Quality = 0.5785\n", + "2025-11-26 09:51:26,918 - bioneuralnet.clustering.hybrid_louvain - INFO - Selected seed community of size 45 with correlation -0.8283\n", + "2025-11-26 09:51:26,919 - bioneuralnet.clustering.correlated_pagerank - INFO - Initialized PageRank with the following parameters:\n", + "2025-11-26 09:51:26,920 - bioneuralnet.clustering.correlated_pagerank - INFO - Graph: NetworkX Graph with 159 nodes and 1292 edges.\n", + "2025-11-26 09:51:26,920 - bioneuralnet.clustering.correlated_pagerank - INFO - Omics Data: DataFrame with shape (658, 900).\n", + "2025-11-26 09:51:26,920 - bioneuralnet.clustering.correlated_pagerank - INFO - Phenotype Data: Series with 658 samples.\n", + "2025-11-26 09:51:26,920 - bioneuralnet.clustering.correlated_pagerank - INFO - Alpha: 0.9\n", + "2025-11-26 09:51:26,921 - bioneuralnet.clustering.correlated_pagerank - INFO - Max Iterations: 100\n", + "2025-11-26 09:51:26,921 - bioneuralnet.clustering.correlated_pagerank - INFO - Tolerance: 1e-06\n", + "2025-11-26 09:51:26,921 - bioneuralnet.clustering.correlated_pagerank - INFO - K (Composite Score Weight): 0.5\n", + "2025-11-26 09:51:26,922 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 09:51:26,922 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 09:51:26,923 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 09:51:26,923 - bioneuralnet.clustering.correlated_pagerank - INFO - Initialized Correlated PageRank. device=cpu\n", + "2025-11-26 09:51:27,114 - bioneuralnet.clustering.correlated_pagerank - INFO - Generated personalization vector for seed nodes: ['C4orf48_401115', 'CKMT1B_1159', 'OGDHL_55753', 'hsa_mir_21', 'hsa_mir_183', 'CST6_1474', 'EPS8L1_54869', 'hsa_mir_429', 'C1orf116_79098', 'hsa_mir_200b', 'C19orf46_163183', 'GBP5', 'hsa_mir_31', 'CNTD2_79935', 'C6orf168_84553', 'CHL1_10752', 'CBLC_23624', 'CNTN3_5067', 'CKMT1A_548596', 'hsa_mir_200a', 'AKR1C2_1646', 'SLPI_6590', 'WFDC2_10406', 'hsa_mir_375', 'ANXA3_306', 'hsa_mir_149', 'BNC1_646', 'CCDC78_124093', 'MAL_4118', 'CAMK2B_816', 'FAM155B_27112', 'CCNO_10309', 'STK33_65975', 'LOC554202_554202', 'TJP3_27134', 'hsa_mir_34c', 'C3orf24', 'IGF2BP2_10644', 'hsa_mir_182', 'CLSTN2_64084', 'ANO5_203859', 'hsa_mir_96', 'hsa_mir_205', 'LCN2_3934', 'LOH3CR2A']\n", + "2025-11-26 09:51:27,117 - bioneuralnet.clustering.correlated_pagerank - INFO - PageRank computation completed.\n", + "2025-11-26 09:51:28,052 - bioneuralnet.clustering.correlated_pagerank - INFO - Sweep cut resulted in cluster of size 63 with conductance 0.229 and correlation -0.85.\n", + "2025-11-26 09:51:28,052 - bioneuralnet.clustering.correlated_pagerank - INFO - PageRank clustering completed successfully.\n", + "2025-11-26 09:51:28,053 - bioneuralnet.clustering.hybrid_louvain - INFO - Iteration 2: cluster size=63, Conductance=0.229 Correlation=-0.850 score=-0.311\n", + "2025-11-26 09:51:28,055 - bioneuralnet.clustering.hybrid_louvain - INFO - \n", + "Iteration 3/3: Running Correlated Louvain...\n", + "2025-11-26 09:51:28,058 - bioneuralnet.clustering.correlated_louvain - INFO - CorrelatedLouvain(k3=0.2, k4=0.8, nodes=63, edges=490, features=900)\n", + "2025-11-26 09:51:28,059 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 09:51:28,060 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 09:51:28,060 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 09:51:28,061 - bioneuralnet.clustering.correlated_louvain - INFO - Running standard community detection...\n", + "2025-11-26 09:51:28,085 - bioneuralnet.clustering.correlated_louvain - INFO - Computed quality (supervised): Q = 0.3730, avg_corr = 0.6817, combined = 0.6200\n", + "2025-11-26 09:51:28,085 - bioneuralnet.clustering.correlated_louvain - INFO - Final quality: 0.6200\n", + "2025-11-26 09:51:28,086 - bioneuralnet.clustering.correlated_louvain - INFO - CorrelatedLouvain found 5 communities (nodes=63)\n", + "2025-11-26 09:51:28,104 - bioneuralnet.clustering.correlated_louvain - INFO - Computed quality (supervised): Q = 0.3730, avg_corr = 0.6817, combined = 0.6200\n", + "2025-11-26 09:51:28,105 - bioneuralnet.clustering.hybrid_louvain - INFO - Iteration 3: Louvain Quality = 0.6200\n", + "2025-11-26 09:51:28,135 - bioneuralnet.clustering.hybrid_louvain - INFO - Selected seed community of size 17 with correlation -0.8340\n", + "2025-11-26 09:51:28,136 - bioneuralnet.clustering.correlated_pagerank - INFO - Initialized PageRank with the following parameters:\n", + "2025-11-26 09:51:28,136 - bioneuralnet.clustering.correlated_pagerank - INFO - Graph: NetworkX Graph with 63 nodes and 490 edges.\n", + "2025-11-26 09:51:28,136 - bioneuralnet.clustering.correlated_pagerank - INFO - Omics Data: DataFrame with shape (658, 900).\n", + "2025-11-26 09:51:28,137 - bioneuralnet.clustering.correlated_pagerank - INFO - Phenotype Data: Series with 658 samples.\n", + "2025-11-26 09:51:28,137 - bioneuralnet.clustering.correlated_pagerank - INFO - Alpha: 0.9\n", + "2025-11-26 09:51:28,138 - bioneuralnet.clustering.correlated_pagerank - INFO - Max Iterations: 100\n", + "2025-11-26 09:51:28,138 - bioneuralnet.clustering.correlated_pagerank - INFO - Tolerance: 1e-06\n", + "2025-11-26 09:51:28,138 - bioneuralnet.clustering.correlated_pagerank - INFO - K (Composite Score Weight): 0.5\n", + "2025-11-26 09:51:28,139 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 09:51:28,140 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 09:51:28,141 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 09:51:28,141 - bioneuralnet.clustering.correlated_pagerank - INFO - Initialized Correlated PageRank. device=cpu\n", + "2025-11-26 09:51:28,205 - bioneuralnet.clustering.correlated_pagerank - INFO - Generated personalization vector for seed nodes: ['C4orf48_401115', 'CAMK2B_816', 'TMEM204', 'CCNO_10309', 'CORO6_84940', 'LOC554202_554202', 'TJP3_27134', 'IGF2BP2_10644', 'C1orf162', 'CNTD2_79935', 'C6orf168_84553', 'CHL1_10752', 'hsa_mir_96', 'BNC1_646', 'CCDC78_124093', 'LCN2_3934', 'LOH3CR2A']\n", + "2025-11-26 09:51:28,209 - bioneuralnet.clustering.correlated_pagerank - INFO - PageRank computation completed.\n", + "2025-11-26 09:51:28,455 - bioneuralnet.clustering.correlated_pagerank - INFO - Sweep cut resulted in cluster of size 32 with conductance 0.246 and correlation -0.86.\n", + "2025-11-26 09:51:28,456 - bioneuralnet.clustering.correlated_pagerank - INFO - PageRank clustering completed successfully.\n", + "2025-11-26 09:51:28,457 - bioneuralnet.clustering.hybrid_louvain - INFO - Iteration 3: cluster size=32, Conductance=0.246 Correlation=-0.860 score=-0.307\n", + "2025-11-26 09:51:28,458 - bioneuralnet.clustering.hybrid_louvain - INFO - Hybrid Louvain completed after 4 iterations.\n", + "2025-11-26 09:51:28,460 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 09:51:28,461 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 09:51:28,462 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 09:51:28,462 - bioneuralnet.clustering.hybrid_louvain - INFO - Initializing HybridLouvain...\n", + "2025-11-26 09:51:28,462 - bioneuralnet.clustering.hybrid_louvain - INFO - Input G is a DataFrame; converting adjacency matrix to NetworkX graph.\n", + "2025-11-26 09:51:28,482 - bioneuralnet.clustering.hybrid_louvain - INFO - Initialized HybridLouvain with 900 graph nodes, 900 omics columns, 658 phenotype rows; max_iter=3, k3=0.2, k4=0.8, tune=False\n", + "2025-11-26 09:51:28,483 - bioneuralnet.clustering.hybrid_louvain - INFO - \n", + "Iteration 1/3: Running Correlated Louvain...\n", + "2025-11-26 09:51:28,498 - bioneuralnet.clustering.correlated_louvain - INFO - CorrelatedLouvain(k3=0.2, k4=0.8, nodes=900, edges=8930, features=900)\n", + "2025-11-26 09:51:28,500 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 09:51:28,501 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 09:51:28,502 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 09:51:28,503 - bioneuralnet.clustering.correlated_louvain - INFO - Running standard community detection...\n", + "2025-11-26 09:51:28,640 - bioneuralnet.clustering.correlated_louvain - INFO - Computed quality (supervised): Q = 0.7372, avg_corr = 0.4314, combined = 0.4926\n", + "2025-11-26 09:51:28,640 - bioneuralnet.clustering.correlated_louvain - INFO - Final quality: 0.4926\n", + "2025-11-26 09:51:28,641 - bioneuralnet.clustering.correlated_louvain - INFO - CorrelatedLouvain found 9 communities (nodes=900)\n", + "2025-11-26 09:51:28,715 - bioneuralnet.clustering.correlated_louvain - INFO - Computed quality (supervised): Q = 0.7372, avg_corr = 0.4314, combined = 0.4926\n", + "2025-11-26 09:51:28,716 - bioneuralnet.clustering.hybrid_louvain - INFO - Iteration 1: Louvain Quality = 0.4926\n", + "2025-11-26 09:51:28,776 - bioneuralnet.clustering.hybrid_louvain - INFO - Selected seed community of size 76 with correlation 0.8164\n", + "2025-11-26 09:51:28,777 - bioneuralnet.clustering.correlated_pagerank - INFO - Initialized PageRank with the following parameters:\n", + "2025-11-26 09:51:28,777 - bioneuralnet.clustering.correlated_pagerank - INFO - Graph: NetworkX Graph with 900 nodes and 8930 edges.\n", + "2025-11-26 09:51:28,778 - bioneuralnet.clustering.correlated_pagerank - INFO - Omics Data: DataFrame with shape (658, 900).\n", + "2025-11-26 09:51:28,778 - bioneuralnet.clustering.correlated_pagerank - INFO - Phenotype Data: Series with 658 samples.\n", + "2025-11-26 09:51:28,778 - bioneuralnet.clustering.correlated_pagerank - INFO - Alpha: 0.9\n", + "2025-11-26 09:51:28,779 - bioneuralnet.clustering.correlated_pagerank - INFO - Max Iterations: 100\n", + "2025-11-26 09:51:28,779 - bioneuralnet.clustering.correlated_pagerank - INFO - Tolerance: 1e-06\n", + "2025-11-26 09:51:28,779 - bioneuralnet.clustering.correlated_pagerank - INFO - K (Composite Score Weight): 0.5\n", + "2025-11-26 09:51:28,780 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 09:51:28,780 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 09:51:28,781 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 09:51:28,781 - bioneuralnet.clustering.correlated_pagerank - INFO - Initialized Correlated PageRank. device=cpu\n", + "2025-11-26 09:51:29,691 - bioneuralnet.clustering.correlated_pagerank - INFO - Generated personalization vector for seed nodes: ['ALDH1A1', 'C21orf131', 'ADAM28', 'C21orf129', 'TMEM71', 'C4orf26', 'SLC22A2', 'TINAG', 'ACSM5', 'CIDEC', 'C20orf118', 'RHCG_51458', 'ATP6V0A4_50617', 'SLC4A1_6521', 'KLK1_3816', 'ATP6V0D2_245972', 'CLDN8_9073', 'FOXI1_2299', 'DMRT2_10655', 'HEPACAM2_253012', 'CLCNKB_1188', 'ATP6V1B1_525', 'CFTR_1080', 'CKMT2_1160', 'GCGR_2642', 'SLC26A7_115111', 'CLCNKA_1187', 'FGF9_2254', 'BMPR1B_658', 'TMPRSS2_7113', 'DHRS2_10202', 'BSND_7809', 'AQP6_363', 'CWH43_80157', 'TRIM50_135892', 'C16orf89_146556', 'SLC9A2_6549', 'CALCA_796', 'OLIG1_116448', 'CLNK_116449', 'CYP11A1_1583', 'RANBP3L_202151', 'DIRAS1_148252', 'C1orf168_199920', 'INPP5J_27124', 'C15orf59_388135', 'CCBE1_147372', 'EPB41L4B_54566', 'GCOM1_145781', 'C9orf84_158401', 'ATP6V1G3_127124', 'VWA5B1_127731', 'LRRTM1_347730', 'KRTAP5_8_57830', 'ST8SIA5_29906', 'B4GALNT3_283358', 'CNTNAP5_129684', 'CEND1_51286', 'LOC643008_643008', 'CEL_1056', 'CR1L_1379', 'ASB5_140458', 'CTNNA2_1496', 'MARCH10_162333', 'SEZ6L_23544', 'hsa_mir_891a', 'hsa_mir_141', 'hsa_mir_187', 'hsa_mir_200c', 'hsa_mir_129_1', 'hsa_mir_129_2', 'hsa_mir_874', 'hsa_mir_206', 'hsa_mir_218_1', 'hsa_mir_618', 'hsa_mir_30c_1']\n", + "2025-11-26 09:51:29,704 - bioneuralnet.clustering.correlated_pagerank - INFO - PageRank computation completed.\n", + "2025-11-26 09:51:46,043 - bioneuralnet.clustering.correlated_pagerank - INFO - Sweep cut resulted in cluster of size 69 with conductance 0.109 and correlation 0.81.\n", + "2025-11-26 09:51:46,044 - bioneuralnet.clustering.correlated_pagerank - INFO - PageRank clustering completed successfully.\n", + "2025-11-26 09:51:46,044 - bioneuralnet.clustering.hybrid_louvain - INFO - Iteration 1: cluster size=69, Conductance=0.109 Correlation=0.810 score=-0.350\n", + "2025-11-26 09:51:46,046 - bioneuralnet.clustering.hybrid_louvain - INFO - \n", + "Iteration 2/3: Running Correlated Louvain...\n", + "2025-11-26 09:51:46,049 - bioneuralnet.clustering.correlated_louvain - INFO - CorrelatedLouvain(k3=0.2, k4=0.8, nodes=69, edges=585, features=900)\n", + "2025-11-26 09:51:46,050 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 09:51:46,051 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 09:51:46,052 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 09:51:46,052 - bioneuralnet.clustering.correlated_louvain - INFO - Running standard community detection...\n", + "2025-11-26 09:51:46,074 - bioneuralnet.clustering.correlated_louvain - INFO - Computed quality (supervised): Q = 0.3798, avg_corr = 0.7822, combined = 0.7017\n", + "2025-11-26 09:51:46,075 - bioneuralnet.clustering.correlated_louvain - INFO - Final quality: 0.7017\n", + "2025-11-26 09:51:46,075 - bioneuralnet.clustering.correlated_louvain - INFO - CorrelatedLouvain found 4 communities (nodes=69)\n", + "2025-11-26 09:51:46,093 - bioneuralnet.clustering.correlated_louvain - INFO - Computed quality (supervised): Q = 0.3798, avg_corr = 0.7822, combined = 0.7017\n", + "2025-11-26 09:51:46,094 - bioneuralnet.clustering.hybrid_louvain - INFO - Iteration 2: Louvain Quality = 0.7017\n", + "2025-11-26 09:51:46,114 - bioneuralnet.clustering.hybrid_louvain - INFO - Selected seed community of size 9 with correlation 0.8318\n", + "2025-11-26 09:51:46,116 - bioneuralnet.clustering.correlated_pagerank - INFO - Initialized PageRank with the following parameters:\n", + "2025-11-26 09:51:46,117 - bioneuralnet.clustering.correlated_pagerank - INFO - Graph: NetworkX Graph with 69 nodes and 585 edges.\n", + "2025-11-26 09:51:46,118 - bioneuralnet.clustering.correlated_pagerank - INFO - Omics Data: DataFrame with shape (658, 900).\n", + "2025-11-26 09:51:46,118 - bioneuralnet.clustering.correlated_pagerank - INFO - Phenotype Data: Series with 658 samples.\n", + "2025-11-26 09:51:46,119 - bioneuralnet.clustering.correlated_pagerank - INFO - Alpha: 0.9\n", + "2025-11-26 09:51:46,120 - bioneuralnet.clustering.correlated_pagerank - INFO - Max Iterations: 100\n", + "2025-11-26 09:51:46,121 - bioneuralnet.clustering.correlated_pagerank - INFO - Tolerance: 1e-06\n", + "2025-11-26 09:51:46,121 - bioneuralnet.clustering.correlated_pagerank - INFO - K (Composite Score Weight): 0.5\n", + "2025-11-26 09:51:46,122 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 09:51:46,124 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 09:51:46,125 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 09:51:46,126 - bioneuralnet.clustering.correlated_pagerank - INFO - Initialized Correlated PageRank. device=cpu\n", + "2025-11-26 09:51:46,159 - bioneuralnet.clustering.correlated_pagerank - INFO - Generated personalization vector for seed nodes: ['SEZ6L_23544', 'LRRTM1_347730', 'hsa_mir_187', 'KLK1_3816', 'CTNNA2_1496', 'CASS4', 'hsa_mir_618', 'CFTR_1080', 'ATP6V0D2_245972']\n", + "2025-11-26 09:51:46,162 - bioneuralnet.clustering.correlated_pagerank - INFO - PageRank computation completed.\n", + "2025-11-26 09:51:46,457 - bioneuralnet.clustering.correlated_pagerank - INFO - Sweep cut resulted in cluster of size 34 with conductance 0.211 and correlation 0.81.\n", + "2025-11-26 09:51:46,457 - bioneuralnet.clustering.correlated_pagerank - INFO - PageRank clustering completed successfully.\n", + "2025-11-26 09:51:46,457 - bioneuralnet.clustering.hybrid_louvain - INFO - Iteration 2: cluster size=34, Conductance=0.211 Correlation=0.810 score=-0.300\n", + "2025-11-26 09:51:46,459 - bioneuralnet.clustering.hybrid_louvain - INFO - \n", + "Iteration 3/3: Running Correlated Louvain...\n", + "2025-11-26 09:51:46,461 - bioneuralnet.clustering.correlated_louvain - INFO - CorrelatedLouvain(k3=0.2, k4=0.8, nodes=34, edges=235, features=900)\n", + "2025-11-26 09:51:46,462 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 09:51:46,462 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 09:51:46,463 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 09:51:46,463 - bioneuralnet.clustering.correlated_louvain - INFO - Running standard community detection...\n", + "2025-11-26 09:51:46,479 - bioneuralnet.clustering.correlated_louvain - INFO - Computed quality (supervised): Q = 0.3141, avg_corr = 0.7568, combined = 0.6683\n", + "2025-11-26 09:51:46,480 - bioneuralnet.clustering.correlated_louvain - INFO - Final quality: 0.6683\n", + "2025-11-26 09:51:46,480 - bioneuralnet.clustering.correlated_louvain - INFO - CorrelatedLouvain found 6 communities (nodes=34)\n", + "2025-11-26 09:51:46,493 - bioneuralnet.clustering.correlated_louvain - INFO - Computed quality (supervised): Q = 0.3141, avg_corr = 0.7568, combined = 0.6683\n", + "2025-11-26 09:51:46,493 - bioneuralnet.clustering.hybrid_louvain - INFO - Iteration 3: Louvain Quality = 0.6683\n", + "2025-11-26 09:51:46,505 - bioneuralnet.clustering.hybrid_louvain - INFO - Selected seed community of size 7 with correlation 0.8084\n", + "2025-11-26 09:51:46,506 - bioneuralnet.clustering.correlated_pagerank - INFO - Initialized PageRank with the following parameters:\n", + "2025-11-26 09:51:46,507 - bioneuralnet.clustering.correlated_pagerank - INFO - Graph: NetworkX Graph with 34 nodes and 235 edges.\n", + "2025-11-26 09:51:46,507 - bioneuralnet.clustering.correlated_pagerank - INFO - Omics Data: DataFrame with shape (658, 900).\n", + "2025-11-26 09:51:46,507 - bioneuralnet.clustering.correlated_pagerank - INFO - Phenotype Data: Series with 658 samples.\n", + "2025-11-26 09:51:46,508 - bioneuralnet.clustering.correlated_pagerank - INFO - Alpha: 0.9\n", + "2025-11-26 09:51:46,508 - bioneuralnet.clustering.correlated_pagerank - INFO - Max Iterations: 100\n", + "2025-11-26 09:51:46,508 - bioneuralnet.clustering.correlated_pagerank - INFO - Tolerance: 1e-06\n", + "2025-11-26 09:51:46,508 - bioneuralnet.clustering.correlated_pagerank - INFO - K (Composite Score Weight): 0.5\n", + "2025-11-26 09:51:46,509 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 09:51:46,510 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 09:51:46,510 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 09:51:46,510 - bioneuralnet.clustering.correlated_pagerank - INFO - Initialized Correlated PageRank. device=cpu\n", + "2025-11-26 09:51:46,532 - bioneuralnet.clustering.correlated_pagerank - INFO - Generated personalization vector for seed nodes: ['SEZ6L_23544', 'ST8SIA5_29906', 'LRRTM1_347730', 'CTNNA2_1496', 'hsa_mir_618', 'CFTR_1080', 'CR1L_1379']\n", + "2025-11-26 09:51:46,534 - bioneuralnet.clustering.correlated_pagerank - INFO - PageRank computation completed.\n", + "2025-11-26 09:51:46,625 - bioneuralnet.clustering.correlated_pagerank - INFO - Sweep cut resulted in cluster of size 16 with conductance 0.345 and correlation 0.83.\n", + "2025-11-26 09:51:46,625 - bioneuralnet.clustering.correlated_pagerank - INFO - PageRank clustering completed successfully.\n", + "2025-11-26 09:51:46,626 - bioneuralnet.clustering.hybrid_louvain - INFO - Iteration 3: cluster size=16, Conductance=0.345 Correlation=0.830 score=-0.242\n", + "2025-11-26 09:51:46,626 - bioneuralnet.clustering.hybrid_louvain - INFO - Hybrid Louvain completed after 4 iterations.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Driver modules (one-vs-rest):\n", + " KIRP: 293 features\n", + " KIRC: 159 features\n", + " KICH: 69 features\n" + ] + } + ], + "source": [ + "from bioneuralnet.clustering import HybridLouvain\n", + "\n", + "labels = target[\"target\"] \n", + "unique_phenotypes = labels.unique()\n", + "multiclass_results = {}\n", + "summary = []\n", + "label_map = {0: \"KIRC\", 1: \"KIRP\", 2: \"KICH\"}\n", + "\n", + "for phenotype in unique_phenotypes:\n", + " y_binary = (labels == phenotype).astype(int)\n", + "\n", + " hl = HybridLouvain(\n", + " G=similarity_10,\n", + " B=omics_kipan,\n", + " Y=y_binary,\n", + " k3=0.2,\n", + " k4=0.8,\n", + " max_iter=3,\n", + " seed=SEED,\n", + " )\n", + "\n", + " subnetworks = hl.run(as_dfs=True)\n", + "\n", + " name = label_map.get(phenotype, str(phenotype))\n", + "\n", + " if subnetworks:\n", + " df = subnetworks[0]\n", + " multiclass_results[name] = df\n", + " summary.append((name, df.shape[1]))\n", + " else:\n", + " summary.append((name, 0))\n", + "\n", + "print(\"\\nDriver modules (one-vs-rest):\")\n", + "for name, n_feats in summary:\n", + " print(f\" {name}: {n_feats} features\")\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "id": "04f74f33", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Omic Degree\n", + "Index \n", + "5 INPP5J_27124 12\n", + "26 SLC26A7_115111 9\n", + "12 HEPACAM2_253012 7\n", + "16 CLNK_116449 7\n", + "27 RHCG_51458 6\n", + "3 CLCNKB_1188 6\n", + "13 DMRT2_10655 6\n", + "18 LOC643008_643008 5\n", + "10 SLC4A1_6521 5\n", + "9 CLCNKA_1187 5\n", + "4 FGF9_2254 5\n", + "11 C9orf84_158401 4\n", + "8 ATP6V1B1_525 4\n", + "22 KRTAP5_8_57830 4\n", + "15 FOXI1_2299 3\n", + "23 ATP6V0A4_50617 3\n", + "21 ATP6V0D2_245972 1\n", + "24 TMPRSS2_7113 1\n", + "25 GCOM1_145781 1\n", + "1 hsa_mir_129_1 1\n", + "20 hsa_mir_200c 1\n", + "19 KLK1_3816 1\n", + "17 hsa_mir_891a 1\n", + "2 hsa_mir_129_2 1\n", + "7 EPB41L4B_54566 1\n", + "6 TRIM50_135892 1\n", + "14 hsa_mir_141 1\n" + ] + } + ], + "source": [ + "from bioneuralnet.metrics import louvain_to_adjacency, plot_network\n", + "\n", + "kirp_df = multiclass_results[\"KICH\"]\n", + "kirp_adj = louvain_to_adjacency(kirp_df)\n", + "kirp_mapping = plot_network(kirp_adj, weight_threshold=0.76, show_labels=True, show_edge_weights=True)\n", + "print(kirp_mapping)" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "id": "66890c59", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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OmicDegreeSource
Index
5INPP5J_2712412RNA
26SLC26A7_1151119RNA
12HEPACAM2_2530127RNA
16CLNK_1164497RNA
27RHCG_514586RNA
3CLCNKB_11886RNA
13DMRT2_106556RNA
18LOC643008_6430085RNA
10SLC4A1_65215RNA
9CLCNKA_11875RNA
4FGF9_22545RNA
11C9orf84_1584014RNA
8ATP6V1B1_5254RNA
22KRTAP5_8_578304RNA
15FOXI1_22993RNA
23ATP6V0A4_506173RNA
21ATP6V0D2_2459721RNA
24TMPRSS2_71131RNA
25GCOM1_1457811RNA
1hsa_mir_129_11miRNA
20hsa_mir_200c1miRNA
19KLK1_38161RNA
17hsa_mir_891a1miRNA
2hsa_mir_129_21miRNA
7EPB41L4B_545661RNA
6TRIM50_1358921RNA
14hsa_mir_1411miRNA
\n", + "
" + ], + "text/plain": [ + " Omic Degree Source\n", + "Index \n", + "5 INPP5J_27124 12 RNA\n", + "26 SLC26A7_115111 9 RNA\n", + "12 HEPACAM2_253012 7 RNA\n", + "16 CLNK_116449 7 RNA\n", + "27 RHCG_51458 6 RNA\n", + "3 CLCNKB_1188 6 RNA\n", + "13 DMRT2_10655 6 RNA\n", + "18 LOC643008_643008 5 RNA\n", + "10 SLC4A1_6521 5 RNA\n", + "9 CLCNKA_1187 5 RNA\n", + "4 FGF9_2254 5 RNA\n", + "11 C9orf84_158401 4 RNA\n", + "8 ATP6V1B1_525 4 RNA\n", + "22 KRTAP5_8_57830 4 RNA\n", + "15 FOXI1_2299 3 RNA\n", + "23 ATP6V0A4_50617 3 RNA\n", + "21 ATP6V0D2_245972 1 RNA\n", + "24 TMPRSS2_7113 1 RNA\n", + "25 GCOM1_145781 1 RNA\n", + "1 hsa_mir_129_1 1 miRNA\n", + "20 hsa_mir_200c 1 miRNA\n", + "19 KLK1_3816 1 RNA\n", + "17 hsa_mir_891a 1 miRNA\n", + "2 hsa_mir_129_2 1 miRNA\n", + "7 EPB41L4B_54566 1 RNA\n", + "6 TRIM50_135892 1 RNA\n", + "14 hsa_mir_141 1 miRNA" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from bioneuralnet.metrics import find_omics_modality\n", + "\n", + "# for those features in the KIRP module, we will identify their omics source\n", + "omics_df_list = [top_meth, top_rna, top_mirna]\n", + "source_names = [\"DNA_Methylation\", \"RNA\", \"miRNA\"]\n", + "\n", + "kirp_mapping_with_source = find_omics_modality(kirp_mapping, omics_df_list, source_names)\n", + "display(kirp_mapping_with_source)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "074f3225", + "metadata": {}, + "outputs": [], + "source": [ + "dna_feats = set(dna_meth.columns)\n", + "rna_feats = set(rna.columns)\n", + "mirna_feats = set(mirna.columns)\n", + "\n", + "def get_omics_type(feat):\n", + " if feat in dna_feats:\n", + " return \"DNA_Methylation\"\n", + " elif feat in rna_feats:\n", + " return \"RNA\"\n", + " elif feat in mirna_feats:\n", + " return \"miRNA\"\n", + " else:\n", + " return \"Unknown\"\n", + "\n", + "# kirp_mapping is the DF returned by plot_network\n", + "kirp_mapping[\"OmicsType\"] = kirp_mapping[\"Omic\"].map(get_omics_type)\n", + "\n", + "print(kirp_mapping)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "81315c47", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-11-26 01:05:57,049 - bioneuralnet.downstream_task.dpmon - INFO - Output directory set to: /home/vicente/Github/BioNeuralNet/dpmon_results_SAGE_FINAL/kipan/threshold_10\n", + "2025-11-26 01:05:57,050 - bioneuralnet.downstream_task.dpmon - INFO - Initialized DPMON with model: SAGE\n", + "2025-11-26 01:05:57,051 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 01:05:57,052 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 01:05:57,052 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 01:05:57,052 - bioneuralnet.downstream_task.dpmon - INFO - Random seed set to: 8183\n", + "2025-11-26 01:05:57,063 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:05:57,068 - bioneuralnet.downstream_task.dpmon - INFO - Running in Cross-Validation mode (cv=True) with 5 folds.\n", + "2025-11-26 01:05:57,071 - bioneuralnet.downstream_task.dpmon - INFO - CV Setup: Standard 5-fold split.\n", + "2025-11-26 01:05:57,073 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 1/5\n", + "2025-11-26 01:05:57,075 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:05:57,085 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:06:01,170 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:06:01,192 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (526, 901)\n", + "2025-11-26 01:06:01,195\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:06:11,311\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0313s.\n", + "2025-11-26 01:06:11,512 - bioneuralnet.downstream_task.dpmon - INFO - Fold 1 best config: {'gnn_layer_num': 2, 'gnn_hidden_dim': 512, 'lr': 0.0017134878267294578, 'weight_decay': 0.00037228516397726333, 'nn_hidden_dim1': 512, 'nn_hidden_dim2': 128, 'ae_encoding_dim': 4, 'num_epochs': 2048, 'gnn_dropout': 0.2, 'gnn_activation': 'elu', 'dim_reduction': 'mlp'}\n", + "2025-11-26 01:06:11,512 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 1 using train split only\n", + "2025-11-26 01:06:11,518 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:06:15,594 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:06:18,738 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 1 on test set\n", + "2025-11-26 01:06:18,755 - bioneuralnet.downstream_task.dpmon - INFO - Fold 1 results:\n", + "2025-11-26 01:06:18,755 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9470\n", + "2025-11-26 01:06:18,755 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9324\n", + "2025-11-26 01:06:18,756 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9470\n", + "2025-11-26 01:06:18,756 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9423\n", + "2025-11-26 01:06:18,756 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9960\n", + "2025-11-26 01:06:18,756 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9907\n", + "\n", + "2025-11-26 01:06:18,757 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 2/5\n", + "2025-11-26 01:06:18,758 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:06:18,763 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:06:22,834 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:06:22,844 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (526, 901)\n", + "2025-11-26 01:06:22,848\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:06:32,649\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0301s.\n", + "2025-11-26 01:06:32,793 - bioneuralnet.downstream_task.dpmon - INFO - Fold 2 best config: {'gnn_layer_num': 2, 'gnn_hidden_dim': 64, 'lr': 0.0007034204356990857, 'weight_decay': 5.358735876893429e-05, 'nn_hidden_dim1': 512, 'nn_hidden_dim2': 64, 'ae_encoding_dim': 8, 'num_epochs': 1024, 'gnn_dropout': 0.2, 'gnn_activation': 'relu', 'dim_reduction': 'ae'}\n", + "2025-11-26 01:06:32,794 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 2 using train split only\n", + "2025-11-26 01:06:32,799 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:06:36,856 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:06:37,956 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 2 on test set\n", + "2025-11-26 01:06:37,961 - bioneuralnet.downstream_task.dpmon - INFO - Fold 2 results:\n", + "2025-11-26 01:06:37,961 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9697\n", + "2025-11-26 01:06:37,961 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9487\n", + "2025-11-26 01:06:37,962 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9701\n", + "2025-11-26 01:06:37,962 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9579\n", + "2025-11-26 01:06:37,962 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9915\n", + "2025-11-26 01:06:37,962 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9353\n", + "\n", + "2025-11-26 01:06:37,963 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 3/5\n", + "2025-11-26 01:06:37,964 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:06:37,969 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:06:42,023 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:06:42,033 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (526, 901)\n", + "2025-11-26 01:06:42,036\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:06:51,939\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0300s.\n", + "2025-11-26 01:06:52,121 - bioneuralnet.downstream_task.dpmon - INFO - Fold 3 best config: {'gnn_layer_num': 3, 'gnn_hidden_dim': 128, 'lr': 0.0006980653261883734, 'weight_decay': 6.059927981082565e-05, 'nn_hidden_dim1': 256, 'nn_hidden_dim2': 128, 'ae_encoding_dim': 8, 'num_epochs': 2048, 'gnn_dropout': 0.0, 'gnn_activation': 'elu', 'dim_reduction': 'ae'}\n", + "2025-11-26 01:06:52,122 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 3 using train split only\n", + "2025-11-26 01:06:52,127 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:06:56,224 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:06:58,746 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 3 on test set\n", + "2025-11-26 01:06:58,751 - bioneuralnet.downstream_task.dpmon - INFO - Fold 3 results:\n", + "2025-11-26 01:06:58,751 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9545\n", + "2025-11-26 01:06:58,751 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9459\n", + "2025-11-26 01:06:58,751 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9544\n", + "2025-11-26 01:06:58,751 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9297\n", + "2025-11-26 01:06:58,751 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9966\n", + "2025-11-26 01:06:58,752 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9935\n", + "\n", + "2025-11-26 01:06:58,753 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 4/5\n", + "2025-11-26 01:06:58,754 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:06:58,761 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:07:02,888 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:07:02,900 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (527, 901)\n", + "2025-11-26 01:07:02,903\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:07:13,708\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0323s.\n", + "2025-11-26 01:07:13,923 - bioneuralnet.downstream_task.dpmon - INFO - Fold 4 best config: {'gnn_layer_num': 4, 'gnn_hidden_dim': 128, 'lr': 0.0008628455289617886, 'weight_decay': 0.005542594076906398, 'nn_hidden_dim1': 512, 'nn_hidden_dim2': 128, 'ae_encoding_dim': 16, 'num_epochs': 2048, 'gnn_dropout': 0.2, 'gnn_activation': 'elu', 'dim_reduction': 'linear'}\n", + "2025-11-26 01:07:13,924 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 4 using train split only\n", + "2025-11-26 01:07:13,928 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:07:18,066 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:07:21,166 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 4 on test set\n", + "2025-11-26 01:07:21,170 - bioneuralnet.downstream_task.dpmon - INFO - Fold 4 results:\n", + "2025-11-26 01:07:21,171 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.8931\n", + "2025-11-26 01:07:21,171 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.8940\n", + "2025-11-26 01:07:21,171 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.8929\n", + "2025-11-26 01:07:21,171 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9236\n", + "2025-11-26 01:07:21,171 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9934\n", + "2025-11-26 01:07:21,171 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9925\n", + "\n", + "2025-11-26 01:07:21,172 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 5/5\n", + "2025-11-26 01:07:21,174 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:07:21,183 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:07:25,311 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:07:25,322 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (527, 901)\n", + "2025-11-26 01:07:25,325\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:07:35,948\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0072s.\n", + "2025-11-26 01:07:36,170 - bioneuralnet.downstream_task.dpmon - INFO - Fold 5 best config: {'gnn_layer_num': 2, 'gnn_hidden_dim': 64, 'lr': 0.001685812648195555, 'weight_decay': 0.0023773937538678585, 'nn_hidden_dim1': 256, 'nn_hidden_dim2': 128, 'ae_encoding_dim': 8, 'num_epochs': 512, 'gnn_dropout': 0.2, 'gnn_activation': 'relu', 'dim_reduction': 'linear'}\n", + "2025-11-26 01:07:36,170 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 5 using train split only\n", + "2025-11-26 01:07:36,176 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:07:40,293 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:07:40,839 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 5 on test set\n", + "2025-11-26 01:07:40,844 - bioneuralnet.downstream_task.dpmon - INFO - Fold 5 results:\n", + "2025-11-26 01:07:40,844 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9618\n", + "2025-11-26 01:07:40,844 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9514\n", + "2025-11-26 01:07:40,844 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9617\n", + "2025-11-26 01:07:40,845 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9497\n", + "2025-11-26 01:07:40,845 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9969\n", + "2025-11-26 01:07:40,845 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9881\n", + "\n", + "2025-11-26 01:07:40,847 - bioneuralnet.downstream_task.dpmon - INFO - Successfully saved global best CV model state to: /home/vicente/Github/BioNeuralNet/dpmon_results_SAGE_FINAL/kipan/threshold_10/best_cv_model.pt\n", + "2025-11-26 01:07:40,848 - bioneuralnet.downstream_task.dpmon - INFO - Successfully saved global best CV model state to: /home/vicente/Github/BioNeuralNet/dpmon_results_SAGE_FINAL/kipan/threshold_10/best_cv_model_embds.pt\n", + "2025-11-26 01:07:40,848 - bioneuralnet.downstream_task.dpmon - INFO - Cross-Validation Results:\n", + "\n", + "2025-11-26 01:07:40,848 - bioneuralnet.downstream_task.dpmon - INFO - Avg Accuracy: 0.9452 +/- 0.0303\n", + "2025-11-26 01:07:40,848 - bioneuralnet.downstream_task.dpmon - INFO - Avg F1 Macro: 0.9345 +/- 0.0238\n", + "2025-11-26 01:07:40,848 - bioneuralnet.downstream_task.dpmon - INFO - Avg F1 Weighted: 0.9452 +/- 0.0305\n", + "2025-11-26 01:07:40,848 - bioneuralnet.downstream_task.dpmon - INFO - Avg Recall: 0.9406 +/- 0.0141\n", + "2025-11-26 01:07:40,848 - bioneuralnet.downstream_task.dpmon - INFO - Avg AUC: 0.9949 +/- 0.0024\n", + "2025-11-26 01:07:40,849 - bioneuralnet.downstream_task.dpmon - INFO - Avg AUPR: 0.9800 +/- 0.0251\n", + "2025-11-26 01:07:40,849 - bioneuralnet.downstream_task.dpmon - INFO - DPMON run completed.\n", + "2025-11-26 01:07:40,850 - bioneuralnet.downstream_task.dpmon - INFO - Output directory set to: /home/vicente/Github/BioNeuralNet/dpmon_results_SAGE_FINAL/kipan/correlation_10\n", + "2025-11-26 01:07:40,850 - bioneuralnet.downstream_task.dpmon - INFO - Initialized DPMON with model: SAGE\n", + "2025-11-26 01:07:40,850 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 01:07:40,850 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 01:07:40,850 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 01:07:40,850 - bioneuralnet.downstream_task.dpmon - INFO - Random seed set to: 8183\n", + "2025-11-26 01:07:40,855 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:07:40,856 - bioneuralnet.downstream_task.dpmon - INFO - Running in Cross-Validation mode (cv=True) with 5 folds.\n", + "2025-11-26 01:07:40,857 - bioneuralnet.downstream_task.dpmon - INFO - CV Setup: Standard 5-fold split.\n", + "2025-11-26 01:07:40,857 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 1/5\n", + "2025-11-26 01:07:40,858 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:07:40,865 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:07:44,953 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:07:44,964 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (526, 901)\n", + "2025-11-26 01:07:44,967\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:07:54,922\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0290s.\n", + "2025-11-26 01:07:55,109 - bioneuralnet.downstream_task.dpmon - INFO - Fold 1 best config: {'gnn_layer_num': 4, 'gnn_hidden_dim': 256, 'lr': 0.001257123304842143, 'weight_decay': 0.0003745702077936848, 'nn_hidden_dim1': 128, 'nn_hidden_dim2': 128, 'ae_encoding_dim': 8, 'num_epochs': 1024, 'gnn_dropout': 0.4, 'gnn_activation': 'relu', 'dim_reduction': 'ae'}\n", + "2025-11-26 01:07:55,109 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 1 using train split only\n", + "2025-11-26 01:07:55,114 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:07:59,376 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:08:00,931 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 1 on test set\n", + "2025-11-26 01:08:00,937 - bioneuralnet.downstream_task.dpmon - INFO - Fold 1 results:\n", + "2025-11-26 01:08:00,937 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9545\n", + "2025-11-26 01:08:00,937 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9482\n", + "2025-11-26 01:08:00,937 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9546\n", + "2025-11-26 01:08:00,938 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9679\n", + "2025-11-26 01:08:00,938 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9956\n", + "2025-11-26 01:08:00,938 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9900\n", + "\n", + "2025-11-26 01:08:00,939 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 2/5\n", + "2025-11-26 01:08:00,940 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:08:00,946 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:08:05,060 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:08:05,072 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (526, 901)\n", + "2025-11-26 01:08:05,075\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:08:15,288\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0300s.\n", + "2025-11-26 01:08:15,451 - bioneuralnet.downstream_task.dpmon - INFO - Fold 2 best config: {'gnn_layer_num': 2, 'gnn_hidden_dim': 64, 'lr': 0.0007034204356990857, 'weight_decay': 5.358735876893429e-05, 'nn_hidden_dim1': 512, 'nn_hidden_dim2': 64, 'ae_encoding_dim': 8, 'num_epochs': 1024, 'gnn_dropout': 0.2, 'gnn_activation': 'relu', 'dim_reduction': 'ae'}\n", + "2025-11-26 01:08:15,452 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 2 using train split only\n", + "2025-11-26 01:08:15,456 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:08:19,574 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:08:20,611 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 2 on test set\n", + "2025-11-26 01:08:20,616 - bioneuralnet.downstream_task.dpmon - INFO - Fold 2 results:\n", + "2025-11-26 01:08:20,616 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9697\n", + "2025-11-26 01:08:20,616 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9487\n", + "2025-11-26 01:08:20,616 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9701\n", + "2025-11-26 01:08:20,616 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9579\n", + "2025-11-26 01:08:20,617 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9927\n", + "2025-11-26 01:08:20,617 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9484\n", + "\n", + "2025-11-26 01:08:20,617 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 3/5\n", + "2025-11-26 01:08:20,619 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:08:20,624 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:08:24,748 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:08:24,759 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (526, 901)\n", + "2025-11-26 01:08:24,762\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:08:35,204\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0243s.\n", + "2025-11-26 01:08:35,374 - bioneuralnet.downstream_task.dpmon - INFO - Fold 3 best config: {'gnn_layer_num': 1, 'gnn_hidden_dim': 128, 'lr': 0.002302454339377372, 'weight_decay': 1.3521341174248578e-05, 'nn_hidden_dim1': 512, 'nn_hidden_dim2': 128, 'ae_encoding_dim': 4, 'num_epochs': 2048, 'gnn_dropout': 0.3, 'gnn_activation': 'relu', 'dim_reduction': 'ae'}\n", + "2025-11-26 01:08:35,375 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 3 using train split only\n", + "2025-11-26 01:08:35,380 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:08:39,498 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:08:41,284 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 3 on test set\n", + "2025-11-26 01:08:41,288 - bioneuralnet.downstream_task.dpmon - INFO - Fold 3 results:\n", + "2025-11-26 01:08:41,288 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9545\n", + "2025-11-26 01:08:41,288 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9478\n", + "2025-11-26 01:08:41,288 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9546\n", + "2025-11-26 01:08:41,289 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9482\n", + "2025-11-26 01:08:41,289 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9956\n", + "2025-11-26 01:08:41,289 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9939\n", + "\n", + "2025-11-26 01:08:41,289 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 4/5\n", + "2025-11-26 01:08:41,291 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:08:41,299 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:08:45,432 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:08:45,442 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (527, 901)\n", + "2025-11-26 01:08:45,445\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:08:55,587\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0224s.\n", + "2025-11-26 01:08:55,774 - bioneuralnet.downstream_task.dpmon - INFO - Fold 4 best config: {'gnn_layer_num': 4, 'gnn_hidden_dim': 128, 'lr': 0.0008628455289617886, 'weight_decay': 0.005542594076906398, 'nn_hidden_dim1': 512, 'nn_hidden_dim2': 128, 'ae_encoding_dim': 16, 'num_epochs': 2048, 'gnn_dropout': 0.2, 'gnn_activation': 'elu', 'dim_reduction': 'linear'}\n", + "2025-11-26 01:08:55,775 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 4 using train split only\n", + "2025-11-26 01:08:55,779 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:09:00,043 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:09:03,096 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 4 on test set\n", + "2025-11-26 01:09:03,101 - bioneuralnet.downstream_task.dpmon - INFO - Fold 4 results:\n", + "2025-11-26 01:09:03,101 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.8855\n", + "2025-11-26 01:09:03,101 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.8402\n", + "2025-11-26 01:09:03,101 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.8960\n", + "2025-11-26 01:09:03,102 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9137\n", + "2025-11-26 01:09:03,102 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9857\n", + "2025-11-26 01:09:03,102 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9669\n", + "\n", + "2025-11-26 01:09:03,103 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 5/5\n", + "2025-11-26 01:09:03,104 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:09:03,110 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:09:07,251 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:09:07,262 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (527, 901)\n", + "2025-11-26 01:09:07,265\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:09:17,331\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0317s.\n", + "2025-11-26 01:09:17,571 - bioneuralnet.downstream_task.dpmon - INFO - Fold 5 best config: {'gnn_layer_num': 1, 'gnn_hidden_dim': 64, 'lr': 0.001483781276337717, 'weight_decay': 2.5485016069955917e-05, 'nn_hidden_dim1': 128, 'nn_hidden_dim2': 128, 'ae_encoding_dim': 4, 'num_epochs': 1024, 'gnn_dropout': 0.2, 'gnn_activation': 'elu', 'dim_reduction': 'mlp'}\n", + "2025-11-26 01:09:17,571 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 5 using train split only\n", + "2025-11-26 01:09:17,577 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:09:21,713 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:09:22,694 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 5 on test set\n", + "2025-11-26 01:09:22,699 - bioneuralnet.downstream_task.dpmon - INFO - Fold 5 results:\n", + "2025-11-26 01:09:22,699 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9695\n", + "2025-11-26 01:09:22,699 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9572\n", + "2025-11-26 01:09:22,699 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9694\n", + "2025-11-26 01:09:22,699 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9558\n", + "2025-11-26 01:09:22,700 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9990\n", + "2025-11-26 01:09:22,700 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9971\n", + "\n", + "2025-11-26 01:09:22,702 - bioneuralnet.downstream_task.dpmon - INFO - Successfully saved global best CV model state to: /home/vicente/Github/BioNeuralNet/dpmon_results_SAGE_FINAL/kipan/correlation_10/best_cv_model.pt\n", + "2025-11-26 01:09:22,703 - bioneuralnet.downstream_task.dpmon - INFO - Successfully saved global best CV model state to: /home/vicente/Github/BioNeuralNet/dpmon_results_SAGE_FINAL/kipan/correlation_10/best_cv_model_embds.pt\n", + "2025-11-26 01:09:22,703 - bioneuralnet.downstream_task.dpmon - INFO - Cross-Validation Results:\n", + "\n", + "2025-11-26 01:09:22,703 - bioneuralnet.downstream_task.dpmon - INFO - Avg Accuracy: 0.9467 +/- 0.0351\n", + "2025-11-26 01:09:22,703 - bioneuralnet.downstream_task.dpmon - INFO - Avg F1 Macro: 0.9284 +/- 0.0495\n", + "2025-11-26 01:09:22,703 - bioneuralnet.downstream_task.dpmon - INFO - Avg F1 Weighted: 0.9489 +/- 0.0306\n", + "2025-11-26 01:09:22,704 - bioneuralnet.downstream_task.dpmon - INFO - Avg Recall: 0.9487 +/- 0.0208\n", + "2025-11-26 01:09:22,704 - bioneuralnet.downstream_task.dpmon - INFO - Avg AUC: 0.9937 +/- 0.0050\n", + "2025-11-26 01:09:22,704 - bioneuralnet.downstream_task.dpmon - INFO - Avg AUPR: 0.9793 +/- 0.0209\n", + "2025-11-26 01:09:22,705 - bioneuralnet.downstream_task.dpmon - INFO - DPMON run completed.\n", + "2025-11-26 01:09:22,705 - bioneuralnet.downstream_task.dpmon - INFO - Output directory set to: /home/vicente/Github/BioNeuralNet/dpmon_results_SAGE_FINAL/kipan/similarity_10\n", + "2025-11-26 01:09:22,705 - bioneuralnet.downstream_task.dpmon - INFO - Initialized DPMON with model: SAGE\n", + "2025-11-26 01:09:22,705 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 01:09:22,706 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 01:09:22,706 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 01:09:22,706 - bioneuralnet.downstream_task.dpmon - INFO - Random seed set to: 8183\n", + "2025-11-26 01:09:22,711 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:09:22,712 - bioneuralnet.downstream_task.dpmon - INFO - Running in Cross-Validation mode (cv=True) with 5 folds.\n", + "2025-11-26 01:09:22,713 - bioneuralnet.downstream_task.dpmon - INFO - CV Setup: Standard 5-fold split.\n", + "2025-11-26 01:09:22,714 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 1/5\n", + "2025-11-26 01:09:22,715 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:09:22,722 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:09:26,854 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:09:26,865 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (526, 901)\n", + "2025-11-26 01:09:26,868\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:09:37,243\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0253s.\n", + "2025-11-26 01:09:37,453 - bioneuralnet.downstream_task.dpmon - INFO - Fold 1 best config: {'gnn_layer_num': 2, 'gnn_hidden_dim': 512, 'lr': 0.0017134878267294578, 'weight_decay': 0.00037228516397726333, 'nn_hidden_dim1': 512, 'nn_hidden_dim2': 128, 'ae_encoding_dim': 4, 'num_epochs': 2048, 'gnn_dropout': 0.2, 'gnn_activation': 'elu', 'dim_reduction': 'mlp'}\n", + "2025-11-26 01:09:37,454 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 1 using train split only\n", + "2025-11-26 01:09:37,459 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:09:41,579 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:09:44,803 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 1 on test set\n", + "2025-11-26 01:09:44,820 - bioneuralnet.downstream_task.dpmon - INFO - Fold 1 results:\n", + "2025-11-26 01:09:44,821 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9470\n", + "2025-11-26 01:09:44,821 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9344\n", + "2025-11-26 01:09:44,821 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9476\n", + "2025-11-26 01:09:44,821 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9618\n", + "2025-11-26 01:09:44,821 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9899\n", + "2025-11-26 01:09:44,822 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9574\n", + "\n", + "2025-11-26 01:09:44,823 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 2/5\n", + "2025-11-26 01:09:44,824 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:09:44,830 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:09:48,915 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:09:48,926 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (526, 901)\n", + "2025-11-26 01:09:48,929\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:09:58,642\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0321s.\n", + "2025-11-26 01:09:58,800 - bioneuralnet.downstream_task.dpmon - INFO - Fold 2 best config: {'gnn_layer_num': 2, 'gnn_hidden_dim': 64, 'lr': 0.0007034204356990857, 'weight_decay': 5.358735876893429e-05, 'nn_hidden_dim1': 512, 'nn_hidden_dim2': 64, 'ae_encoding_dim': 8, 'num_epochs': 1024, 'gnn_dropout': 0.2, 'gnn_activation': 'relu', 'dim_reduction': 'ae'}\n", + "2025-11-26 01:09:58,801 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 2 using train split only\n", + "2025-11-26 01:09:58,805 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:10:03,027 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:10:04,075 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 2 on test set\n", + "2025-11-26 01:10:04,080 - bioneuralnet.downstream_task.dpmon - INFO - Fold 2 results:\n", + "2025-11-26 01:10:04,080 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9773\n", + "2025-11-26 01:10:04,080 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9648\n", + "2025-11-26 01:10:04,080 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9777\n", + "2025-11-26 01:10:04,080 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9835\n", + "2025-11-26 01:10:04,080 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9921\n", + "2025-11-26 01:10:04,080 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9552\n", + "\n", + "2025-11-26 01:10:04,081 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 3/5\n", + "2025-11-26 01:10:04,082 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:10:04,088 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:10:08,219 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:10:08,231 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (526, 901)\n", + "2025-11-26 01:10:08,234\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:10:18,090\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0282s.\n", + "2025-11-26 01:10:18,287 - bioneuralnet.downstream_task.dpmon - INFO - Fold 3 best config: {'gnn_layer_num': 3, 'gnn_hidden_dim': 128, 'lr': 0.0006980653261883734, 'weight_decay': 6.059927981082565e-05, 'nn_hidden_dim1': 256, 'nn_hidden_dim2': 128, 'ae_encoding_dim': 8, 'num_epochs': 2048, 'gnn_dropout': 0.0, 'gnn_activation': 'elu', 'dim_reduction': 'ae'}\n", + "2025-11-26 01:10:18,288 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 3 using train split only\n", + "2025-11-26 01:10:18,292 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:10:22,422 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:10:24,946 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 3 on test set\n", + "2025-11-26 01:10:24,951 - bioneuralnet.downstream_task.dpmon - INFO - Fold 3 results:\n", + "2025-11-26 01:10:24,951 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9621\n", + "2025-11-26 01:10:24,952 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9621\n", + "2025-11-26 01:10:24,952 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9622\n", + "2025-11-26 01:10:24,952 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9535\n", + "2025-11-26 01:10:24,953 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9952\n", + "2025-11-26 01:10:24,953 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9900\n", + "\n", + "2025-11-26 01:10:24,953 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 4/5\n", + "2025-11-26 01:10:24,956 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:10:24,963 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:10:29,116 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:10:29,127 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (527, 901)\n", + "2025-11-26 01:10:29,130\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:10:38,870\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0212s.\n", + "2025-11-26 01:10:39,028 - bioneuralnet.downstream_task.dpmon - INFO - Fold 4 best config: {'gnn_layer_num': 1, 'gnn_hidden_dim': 128, 'lr': 0.0019123736530165902, 'weight_decay': 0.0006168499043610002, 'nn_hidden_dim1': 256, 'nn_hidden_dim2': 128, 'ae_encoding_dim': 8, 'num_epochs': 1024, 'gnn_dropout': 0.4, 'gnn_activation': 'relu', 'dim_reduction': 'mlp'}\n", + "2025-11-26 01:10:39,029 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 4 using train split only\n", + "2025-11-26 01:10:39,033 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:10:43,160 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:10:44,156 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 4 on test set\n", + "2025-11-26 01:10:44,160 - bioneuralnet.downstream_task.dpmon - INFO - Fold 4 results:\n", + "2025-11-26 01:10:44,160 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9389\n", + "2025-11-26 01:10:44,160 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9449\n", + "2025-11-26 01:10:44,160 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9390\n", + "2025-11-26 01:10:44,161 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9554\n", + "2025-11-26 01:10:44,161 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9970\n", + "2025-11-26 01:10:44,161 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9949\n", + "\n", + "2025-11-26 01:10:44,161 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 5/5\n", + "2025-11-26 01:10:44,162 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:10:44,168 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:10:48,309 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:10:48,320 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (527, 901)\n", + "2025-11-26 01:10:48,324\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:10:58,591\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0247s.\n", + "2025-11-26 01:10:58,842 - bioneuralnet.downstream_task.dpmon - INFO - Fold 5 best config: {'gnn_layer_num': 4, 'gnn_hidden_dim': 64, 'lr': 0.0007025528058103543, 'weight_decay': 0.0018355753249756474, 'nn_hidden_dim1': 512, 'nn_hidden_dim2': 128, 'ae_encoding_dim': 4, 'num_epochs': 1024, 'gnn_dropout': 0.0, 'gnn_activation': 'relu', 'dim_reduction': 'mlp'}\n", + "2025-11-26 01:10:58,842 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 5 using train split only\n", + "2025-11-26 01:10:58,848 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:11:02,948 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:11:04,466 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 5 on test set\n", + "2025-11-26 01:11:04,471 - bioneuralnet.downstream_task.dpmon - INFO - Fold 5 results:\n", + "2025-11-26 01:11:04,472 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9618\n", + "2025-11-26 01:11:04,472 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9514\n", + "2025-11-26 01:11:04,472 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9617\n", + "2025-11-26 01:11:04,473 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9497\n", + "2025-11-26 01:11:04,473 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9979\n", + "2025-11-26 01:11:04,473 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9942\n", + "\n", + "2025-11-26 01:11:04,476 - bioneuralnet.downstream_task.dpmon - INFO - Successfully saved global best CV model state to: /home/vicente/Github/BioNeuralNet/dpmon_results_SAGE_FINAL/kipan/similarity_10/best_cv_model.pt\n", + "2025-11-26 01:11:04,476 - bioneuralnet.downstream_task.dpmon - INFO - Successfully saved global best CV model state to: /home/vicente/Github/BioNeuralNet/dpmon_results_SAGE_FINAL/kipan/similarity_10/best_cv_model_embds.pt\n", + "2025-11-26 01:11:04,476 - bioneuralnet.downstream_task.dpmon - INFO - Cross-Validation Results:\n", + "\n", + "2025-11-26 01:11:04,477 - bioneuralnet.downstream_task.dpmon - INFO - Avg Accuracy: 0.9574 +/- 0.0149\n", + "2025-11-26 01:11:04,477 - bioneuralnet.downstream_task.dpmon - INFO - Avg F1 Macro: 0.9515 +/- 0.0125\n", + "2025-11-26 01:11:04,477 - bioneuralnet.downstream_task.dpmon - INFO - Avg F1 Weighted: 0.9576 +/- 0.0149\n", + "2025-11-26 01:11:04,477 - bioneuralnet.downstream_task.dpmon - INFO - Avg Recall: 0.9608 +/- 0.0135\n", + "2025-11-26 01:11:04,478 - bioneuralnet.downstream_task.dpmon - INFO - Avg AUC: 0.9944 +/- 0.0034\n", + "2025-11-26 01:11:04,478 - bioneuralnet.downstream_task.dpmon - INFO - Avg AUPR: 0.9783 +/- 0.0202\n", + "2025-11-26 01:11:04,479 - bioneuralnet.downstream_task.dpmon - INFO - DPMON run completed.\n", + "2025-11-26 01:11:04,479 - bioneuralnet.downstream_task.dpmon - INFO - Output directory set to: /home/vicente/Github/BioNeuralNet/dpmon_results_SAGE_FINAL/kipan/gaussian_15\n", + "2025-11-26 01:11:04,479 - bioneuralnet.downstream_task.dpmon - INFO - Initialized DPMON with model: SAGE\n", + "2025-11-26 01:11:04,480 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 01:11:04,480 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 01:11:04,480 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 01:11:04,481 - bioneuralnet.downstream_task.dpmon - INFO - Random seed set to: 8183\n", + "2025-11-26 01:11:04,486 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:11:04,487 - bioneuralnet.downstream_task.dpmon - INFO - Running in Cross-Validation mode (cv=True) with 5 folds.\n", + "2025-11-26 01:11:04,488 - bioneuralnet.downstream_task.dpmon - INFO - CV Setup: Standard 5-fold split.\n", + "2025-11-26 01:11:04,488 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 1/5\n", + "2025-11-26 01:11:04,490 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:11:04,497 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:11:08,732 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:11:08,747 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (526, 901)\n", + "2025-11-26 01:11:08,750\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:11:19,358\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0249s.\n", + "2025-11-26 01:11:19,570 - bioneuralnet.downstream_task.dpmon - INFO - Fold 1 best config: {'gnn_layer_num': 4, 'gnn_hidden_dim': 256, 'lr': 0.001257123304842143, 'weight_decay': 0.0003745702077936848, 'nn_hidden_dim1': 128, 'nn_hidden_dim2': 128, 'ae_encoding_dim': 8, 'num_epochs': 1024, 'gnn_dropout': 0.4, 'gnn_activation': 'relu', 'dim_reduction': 'ae'}\n", + "2025-11-26 01:11:19,571 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 1 using train split only\n", + "2025-11-26 01:11:19,576 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:11:23,732 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:11:25,523 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 1 on test set\n", + "2025-11-26 01:11:25,533 - bioneuralnet.downstream_task.dpmon - INFO - Fold 1 results:\n", + "2025-11-26 01:11:25,534 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9545\n", + "2025-11-26 01:11:25,534 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9482\n", + "2025-11-26 01:11:25,534 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9546\n", + "2025-11-26 01:11:25,535 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9679\n", + "2025-11-26 01:11:25,535 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9943\n", + "2025-11-26 01:11:25,535 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9887\n", + "\n", + "2025-11-26 01:11:25,536 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 2/5\n", + "2025-11-26 01:11:25,538 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:11:25,543 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:11:29,653 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:11:29,667 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (526, 901)\n", + "2025-11-26 01:11:29,670\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:11:39,430\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0402s.\n", + "2025-11-26 01:11:39,573 - bioneuralnet.downstream_task.dpmon - INFO - Fold 2 best config: {'gnn_layer_num': 2, 'gnn_hidden_dim': 64, 'lr': 0.0007034204356990857, 'weight_decay': 5.358735876893429e-05, 'nn_hidden_dim1': 512, 'nn_hidden_dim2': 64, 'ae_encoding_dim': 8, 'num_epochs': 1024, 'gnn_dropout': 0.2, 'gnn_activation': 'relu', 'dim_reduction': 'ae'}\n", + "2025-11-26 01:11:39,574 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 2 using train split only\n", + "2025-11-26 01:11:39,579 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:11:43,701 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:11:44,838 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 2 on test set\n", + "2025-11-26 01:11:44,843 - bioneuralnet.downstream_task.dpmon - INFO - Fold 2 results:\n", + "2025-11-26 01:11:44,843 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9773\n", + "2025-11-26 01:11:44,843 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9648\n", + "2025-11-26 01:11:44,844 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9777\n", + "2025-11-26 01:11:44,844 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9835\n", + "2025-11-26 01:11:44,844 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9923\n", + "2025-11-26 01:11:44,844 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9447\n", + "\n", + "2025-11-26 01:11:44,845 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 3/5\n", + "2025-11-26 01:11:44,846 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:11:44,851 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:11:48,972 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:11:48,986 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (526, 901)\n", + "2025-11-26 01:11:48,989\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:11:59,032\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0306s.\n", + "2025-11-26 01:11:59,220 - bioneuralnet.downstream_task.dpmon - INFO - Fold 3 best config: {'gnn_layer_num': 3, 'gnn_hidden_dim': 128, 'lr': 0.0006980653261883734, 'weight_decay': 6.059927981082565e-05, 'nn_hidden_dim1': 256, 'nn_hidden_dim2': 128, 'ae_encoding_dim': 8, 'num_epochs': 2048, 'gnn_dropout': 0.0, 'gnn_activation': 'elu', 'dim_reduction': 'ae'}\n", + "2025-11-26 01:11:59,221 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 3 using train split only\n", + "2025-11-26 01:11:59,226 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:12:03,330 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:12:05,781 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 3 on test set\n", + "2025-11-26 01:12:05,785 - bioneuralnet.downstream_task.dpmon - INFO - Fold 3 results:\n", + "2025-11-26 01:12:05,786 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9697\n", + "2025-11-26 01:12:05,786 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9773\n", + "2025-11-26 01:12:05,786 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9697\n", + "2025-11-26 01:12:05,786 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9773\n", + "2025-11-26 01:12:05,786 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9936\n", + "2025-11-26 01:12:05,786 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9928\n", + "\n", + "2025-11-26 01:12:05,786 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 4/5\n", + "2025-11-26 01:12:05,788 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:12:05,795 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:12:09,897 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:12:09,910 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (527, 901)\n", + "2025-11-26 01:12:09,913\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:12:20,492\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0219s.\n", + "2025-11-26 01:12:20,669 - bioneuralnet.downstream_task.dpmon - INFO - Fold 4 best config: {'gnn_layer_num': 1, 'gnn_hidden_dim': 128, 'lr': 0.002226976717919125, 'weight_decay': 0.00016678702979522166, 'nn_hidden_dim1': 128, 'nn_hidden_dim2': 64, 'ae_encoding_dim': 16, 'num_epochs': 512, 'gnn_dropout': 0.3, 'gnn_activation': 'relu', 'dim_reduction': 'ae'}\n", + "2025-11-26 01:12:20,670 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 4 using train split only\n", + "2025-11-26 01:12:20,675 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:12:24,817 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:12:25,254 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 4 on test set\n", + "2025-11-26 01:12:25,258 - bioneuralnet.downstream_task.dpmon - INFO - Fold 4 results:\n", + "2025-11-26 01:12:25,258 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9389\n", + "2025-11-26 01:12:25,258 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9449\n", + "2025-11-26 01:12:25,259 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9390\n", + "2025-11-26 01:12:25,259 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9554\n", + "2025-11-26 01:12:25,259 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9934\n", + "2025-11-26 01:12:25,259 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9742\n", + "\n", + "2025-11-26 01:12:25,259 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 5/5\n", + "2025-11-26 01:12:25,261 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:12:25,266 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:12:29,434 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:12:29,448 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (527, 901)\n", + "2025-11-26 01:12:29,451\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:12:39,858\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0232s.\n", + "2025-11-26 01:12:40,073 - bioneuralnet.downstream_task.dpmon - INFO - Fold 5 best config: {'gnn_layer_num': 4, 'gnn_hidden_dim': 128, 'lr': 0.0013804780705269182, 'weight_decay': 0.0037674454696866636, 'nn_hidden_dim1': 128, 'nn_hidden_dim2': 64, 'ae_encoding_dim': 4, 'num_epochs': 1024, 'gnn_dropout': 0.0, 'gnn_activation': 'elu', 'dim_reduction': 'ae'}\n", + "2025-11-26 01:12:40,073 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 5 using train split only\n", + "2025-11-26 01:12:40,079 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:12:44,207 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:12:45,599 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 5 on test set\n", + "2025-11-26 01:12:45,604 - bioneuralnet.downstream_task.dpmon - INFO - Fold 5 results:\n", + "2025-11-26 01:12:45,604 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9695\n", + "2025-11-26 01:12:45,604 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9572\n", + "2025-11-26 01:12:45,604 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9694\n", + "2025-11-26 01:12:45,604 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9558\n", + "2025-11-26 01:12:45,605 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9978\n", + "2025-11-26 01:12:45,605 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9941\n", + "\n", + "2025-11-26 01:12:45,607 - bioneuralnet.downstream_task.dpmon - INFO - Successfully saved global best CV model state to: /home/vicente/Github/BioNeuralNet/dpmon_results_SAGE_FINAL/kipan/gaussian_15/best_cv_model.pt\n", + "2025-11-26 01:12:45,608 - bioneuralnet.downstream_task.dpmon - INFO - Successfully saved global best CV model state to: /home/vicente/Github/BioNeuralNet/dpmon_results_SAGE_FINAL/kipan/gaussian_15/best_cv_model_embds.pt\n", + "2025-11-26 01:12:45,608 - bioneuralnet.downstream_task.dpmon - INFO - Cross-Validation Results:\n", + "\n", + "2025-11-26 01:12:45,608 - bioneuralnet.downstream_task.dpmon - INFO - Avg Accuracy: 0.9620 +/- 0.0153\n", + "2025-11-26 01:12:45,608 - bioneuralnet.downstream_task.dpmon - INFO - Avg F1 Macro: 0.9585 +/- 0.0131\n", + "2025-11-26 01:12:45,608 - bioneuralnet.downstream_task.dpmon - INFO - Avg F1 Weighted: 0.9621 +/- 0.0154\n", + "2025-11-26 01:12:45,608 - bioneuralnet.downstream_task.dpmon - INFO - Avg Recall: 0.9680 +/- 0.0126\n", + "2025-11-26 01:12:45,608 - bioneuralnet.downstream_task.dpmon - INFO - Avg AUC: 0.9943 +/- 0.0021\n", + "2025-11-26 01:12:45,608 - bioneuralnet.downstream_task.dpmon - INFO - Avg AUPR: 0.9789 +/- 0.0207\n", + "2025-11-26 01:12:45,609 - bioneuralnet.downstream_task.dpmon - INFO - DPMON run completed.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Results for: threshold_10\n", + "Accuracy (Avg +/- Std): 0.9452 +/- 0.0303\n", + "F1 Macro (Avg +/- Std): 0.9345 +/- 0.0238\n", + "F1 Weighted (Avg +/- Std): 0.9452 +/- 0.0305\n", + "Recall: 0.9406 +/- 0.0141\n", + "AUC: 0.9949 +/- 0.0024\n", + "\n", + "Results for: correlation_10\n", + "Accuracy (Avg +/- Std): 0.9467 +/- 0.0351\n", + "F1 Macro (Avg +/- Std): 0.9284 +/- 0.0495\n", + "F1 Weighted (Avg +/- Std): 0.9489 +/- 0.0306\n", + "Recall: 0.9487 +/- 0.0208\n", + "AUC: 0.9937 +/- 0.0050\n", + "\n", + "Results for: similarity_10\n", + "Accuracy (Avg +/- Std): 0.9574 +/- 0.0149\n", + "F1 Macro (Avg +/- Std): 0.9515 +/- 0.0125\n", + "F1 Weighted (Avg +/- Std): 0.9576 +/- 0.0149\n", + "Recall: 0.9608 +/- 0.0135\n", + "AUC: 0.9944 +/- 0.0034\n", + "\n", + "Results for: gaussian_15\n", + "Accuracy (Avg +/- Std): 0.9620 +/- 0.0153\n", + "F1 Macro (Avg +/- Std): 0.9585 +/- 0.0131\n", + "F1 Weighted (Avg +/- Std): 0.9621 +/- 0.0154\n", + "Recall: 0.9680 +/- 0.0126\n", + "AUC: 0.9943 +/- 0.0021\n" + ] + } + ], + "source": [ + "output_dir_base = Path(\"/home/vicente/Github/BioNeuralNet/dpmon_results_SAGE_FINAL/kipan\")\n", + "all_results_SAGE = []\n", + "\n", + "for run_config in comparison_runs:\n", + " graph_name = run_config[\"name\"]\n", + " A_full = run_config[\"graph\"]\n", + " \n", + " current_output_dir = output_dir_base / graph_name\n", + " current_output_dir.mkdir(parents=True, exist_ok=True)\n", + "\n", + " dpmon_params_base = {\n", + " \"adjacency_matrix\": A_full,\n", + " \"omics_list\": omics_kipan,\n", + " \"phenotype_data\": target,\n", + " \"phenotype_col\": \"target\",\n", + " \"clinical_data\": clinical_for_model,\n", + " \"model\": 'SAGE',\n", + " \"tune\": True, \n", + " \"cv\": True, \n", + " \"n_folds\": 5,\n", + " \"repeat_num\": 1,\n", + " \"gpu\": True,\n", + " \"cuda\": 0,\n", + " \"seed\": SEED,\n", + " \"output_dir\": current_output_dir\n", + " }\n", + " \n", + " dpmon_tunned = DPMON(**dpmon_params_base)\n", + " predictions_df, metrics, embeddings = dpmon_tunned.run()\n", + "\n", + " all_results_SAGE.append({\n", + " \"graph\": graph_name,\n", + " \"predictions\": predictions_df,\n", + " \"metrics\": metrics,\n", + " \"embeddings\": embeddings\n", + " })\n", + "\n", + "for res in all_results_SAGE:\n", + " graph_name = res[\"graph\"]\n", + " graph_metrics = res[\"metrics\"]\n", + " \n", + " acc_row = graph_metrics.loc[graph_metrics['Metric'] == 'Accuracy'].iloc[0]\n", + " f1_macro_row = graph_metrics.loc[graph_metrics['Metric'] == 'F1 Macro'].iloc[0]\n", + " f1_weighted_row = graph_metrics.loc[graph_metrics['Metric'] == 'F1 Weighted'].iloc[0]\n", + " recall_row = graph_metrics.loc[graph_metrics['Metric'] == 'Recall'].iloc[0]\n", + " auc_row = graph_metrics.loc[graph_metrics['Metric'] == 'AUC'].iloc[0]\n", + " aupr_row = graph_metrics.loc[graph_metrics['Metric'] == 'AUPR'].iloc[0]\n", + " \n", + " acc_avg, acc_std = acc_row['Average'], acc_row['StdDev']\n", + " f1_macro_avg, f1_macro_std = f1_macro_row['Average'], f1_macro_row['StdDev']\n", + " f1_weighted_avg, f1_weighted_std = f1_weighted_row['Average'], f1_weighted_row['StdDev']\n", + " recall_avg, recall_std = recall_row['Average'], recall_row['StdDev']\n", + " auc_avg, auc_std = auc_row['Average'], auc_row['StdDev']\n", + " aupr_avg, aupr_std = aupr_row['Average'], aupr_row['StdDev']\n", + "\n", + " print(f\"\\nResults for: {graph_name}\")\n", + " print(f\"Accuracy (Avg +/- Std): {acc_avg:.4f} +/- {acc_std:.4f}\")\n", + " print(f\"F1 Macro (Avg +/- Std): {f1_macro_avg:.4f} +/- {f1_macro_std:.4f}\")\n", + " print(f\"F1 Weighted (Avg +/- Std): {f1_weighted_avg:.4f} +/- {f1_weighted_std:.4f}\")\n", + " print(f\"Recall: {recall_avg:.4f} +/- {recall_std:.4f}\")\n", + " print(f\"AUC: {auc_avg:.4f} +/- {auc_std:.4f}\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "80c83c6e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " 0 1 2 3 4\n", + "MGP 0.0 0.0 0.923418 0.000000 0.295637\n", + "ADH1C 0.0 0.0 0.521837 0.005931 0.421344\n", + "LOC440354 0.0 0.0 0.851917 0.000000 0.216582\n", + "C1orf110 0.0 0.0 0.625036 0.009498 0.256967\n", + "CLDN8 0.0 0.0 0.638092 0.000000 0.230229" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#This time we will select the gaussian_15 graph embeddings\n", + "res = all_results_SAGE[3] \n", + "tensor_data = res[\"embeddings\"]\n", + "\n", + "# .detach().cpu().numpy() to get the raw numbers out of pytorch\n", + "SAGE_embeddings_df = pd.DataFrame(\n", + " tensor_data.detach().cpu().numpy(),\n", + " index=omics_kipan.columns\n", + ")\n", + "\n", + "display(SAGE_embeddings_df.iloc[:5,:5])" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "1e1ed982", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Plotting Feature Embeddings for graph: correlation_10\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from bioneuralnet.metrics import plot_embeddings\n", + "\n", + "# for a more interpretable embedding plot we will map the node labels to omics types\n", + "SAGE_node_labels = []\n", + "SAGE_embeddings_array = SAGE_embeddings_df.values\n", + "feature_names = SAGE_embeddings_df.index\n", + "\n", + "# using the sets from the orginal to serve as a loopup table\n", + "dna_feats = set(dna_meth.columns)\n", + "rna_feats = set(rna.columns)\n", + "mirna_feats = set(mirna.columns)\n", + "\n", + "for feat in feature_names:\n", + " if feat in dna_feats:\n", + " # 1 for DNA methylation\n", + " SAGE_node_labels.append(1)\n", + " elif feat in rna_feats:\n", + " # 2 for RNA \n", + " SAGE_node_labels.append(2)\n", + " elif feat in mirna_feats:\n", + " # and 3 for miRNA \n", + " SAGE_node_labels.append(3)\n", + " else:\n", + " SAGE_node_labels.append(0)\n", + "\n", + "# our plotting function needs an array to squeeze the embedings into 2Dspace\n", + "SAGE_node_labels = np.array(SAGE_node_labels)\n", + "\n", + "# feature(omics) embeddings colored by omics type.\n", + "print(f\"Plotting Feature Embeddings for graph: {res['graph']}\")\n", + "plot_embeddings(SAGE_embeddings_array, SAGE_node_labels, legend_labels=[\"DNA_Methylation\", \"RNA\", \"miRNA\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "9a15b24b", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-11-26 01:12:46,329 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 01:12:46,330 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 01:12:46,330 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 01:12:46,330 - bioneuralnet.clustering.hybrid_louvain - INFO - Initializing HybridLouvain...\n", + "2025-11-26 01:12:46,331 - bioneuralnet.clustering.hybrid_louvain - INFO - Input G is a DataFrame; converting adjacency matrix to NetworkX graph.\n", + "2025-11-26 01:12:46,343 - bioneuralnet.clustering.hybrid_louvain - INFO - Initialized HybridLouvain with 900 graph nodes, 900 omics columns, 658 phenotype rows; max_iter=3, k3=0.2, k4=0.8, tune=False\n", + "2025-11-26 01:12:46,343 - bioneuralnet.clustering.hybrid_louvain - INFO - \n", + "Iteration 1/3: Running Correlated Louvain...\n", + "2025-11-26 01:12:46,352 - bioneuralnet.clustering.correlated_louvain - INFO - CorrelatedLouvain(k3=0.2, k4=0.8, nodes=900, edges=8628, features=900)\n", + "2025-11-26 01:12:46,352 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 01:12:46,353 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 01:12:46,353 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 01:12:46,353 - bioneuralnet.clustering.correlated_louvain - INFO - Running standard community detection...\n", + "2025-11-26 01:12:46,464 - bioneuralnet.clustering.correlated_louvain - INFO - Computed quality (supervised): Q = 0.6936, avg_corr = 0.4879, combined = 0.5290\n", + "2025-11-26 01:12:46,464 - bioneuralnet.clustering.correlated_louvain - INFO - Final quality: 0.5290\n", + "2025-11-26 01:12:46,465 - bioneuralnet.clustering.correlated_louvain - INFO - CorrelatedLouvain found 11 communities (nodes=900)\n", + "2025-11-26 01:12:46,521 - bioneuralnet.clustering.correlated_louvain - INFO - Computed quality (supervised): Q = 0.6936, avg_corr = 0.4879, combined = 0.5290\n", + "2025-11-26 01:12:46,522 - bioneuralnet.clustering.hybrid_louvain - INFO - Iteration 1: Louvain Quality = 0.5290\n", + "2025-11-26 01:12:46,574 - bioneuralnet.clustering.hybrid_louvain - INFO - Selected seed community of size 108 with correlation -0.8495\n", + "2025-11-26 01:12:46,575 - bioneuralnet.clustering.correlated_pagerank - INFO - Initialized PageRank with the following parameters:\n", + "2025-11-26 01:12:46,575 - bioneuralnet.clustering.correlated_pagerank - INFO - Graph: NetworkX Graph with 900 nodes and 8628 edges.\n", + "2025-11-26 01:12:46,576 - bioneuralnet.clustering.correlated_pagerank - INFO - Omics Data: DataFrame with shape (658, 900).\n", + "2025-11-26 01:12:46,576 - bioneuralnet.clustering.correlated_pagerank - INFO - Phenotype Data: Series with 658 samples.\n", + "2025-11-26 01:12:46,576 - bioneuralnet.clustering.correlated_pagerank - INFO - Alpha: 0.9\n", + "2025-11-26 01:12:46,576 - bioneuralnet.clustering.correlated_pagerank - INFO - Max Iterations: 100\n", + "2025-11-26 01:12:46,577 - bioneuralnet.clustering.correlated_pagerank - INFO - Tolerance: 1e-06\n", + "2025-11-26 01:12:46,577 - bioneuralnet.clustering.correlated_pagerank - INFO - K (Composite Score Weight): 0.5\n", + "2025-11-26 01:12:46,577 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 01:12:46,578 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 01:12:46,578 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 01:12:46,579 - bioneuralnet.clustering.correlated_pagerank - INFO - Initialized Correlated PageRank. device=cpu\n", + "2025-11-26 01:12:47,133 - bioneuralnet.clustering.correlated_pagerank - INFO - Generated personalization vector for seed nodes: ['ALDH1A1', 'KRTAP3_3', 'TAS2R42', 'ADAM28', 'C21orf71', 'LOC645638', 'MIR196A1', 'ALDOB', 'TMEM71', 'HSD17B14', 'C4orf26', 'DGCR10', 'MIR942', 'BCAN', 'LOC100128288', 'ADM2', 'IFITM1', 'PSORS1C3', 'TMEM88', 'MIR26A1', 'ALDOC', 'C8orf80', 'MIR130A', 'MIR429', 'CCDC17', 'NDUFA4L2', 'ALOX12P2', 'ATP8B1', 'C14orf129', 'FOXS1', 'SLC10A6', 'C3orf24', 'GGTLC2', 'C10orf10', 'ART4', 'IL18BP', 'MIR554', 'DDR2', 'C15orf28', 'C7orf29', 'USHBP1', 'TMEM204', 'NME3', 'PTGER3_5733', 'HMGCS2_3158', 'SLPI_6590', 'CXCL6_6372', 'FOXJ1_2302', 'HGFAC_3083', 'UBE2QL1_134111', 'SORCS2_57537', 'BCHE_590', 'DACT2_168002', 'ELFN2_114794', 'C19orf21_126353', 'CCDC135_84229', 'CPNE7_27132', 'VAT1L_57687', 'AVPR1A_552', 'PRDM16_63976', 'ACTC1_70', 'ADAMTSL1_92949', 'PDE1C_5137', 'CPXM2_119587', 'SULT4A1_25830', 'ADCY1_107', 'CORO6_84940', 'ANGPT1_284', 'CLDN3_1365', 'CA8_767', 'RASGEF1C_255426', 'CCDC108_255101', 'BDKRB2_624', 'ACSM1_116285', 'DGKI_9162', 'C19orf51_352909', 'B3GALT5_10317', 'CLSTN2_64084', 'C1orf114_57821', 'MPPED2_744', 'RASGRF2_5924', 'SPARCL1_8404', 'C2orf62_375307', 'GPR116_221395', 'C6orf174_387104', 'AKR1C2_1646', 'ADORA1_134', 'C2orf89_129293', 'hsa_mir_31', 'hsa_mir_885', 'hsa_mir_577', 'hsa_mir_203', 'hsa_mir_135b', 'hsa_mir_582', 'hsa_mir_139', 'hsa_mir_335', 'hsa_mir_21', 'hsa_mir_100', 'hsa_mir_181d', 'hsa_mir_219_1', 'hsa_mir_3934', 'hsa_mir_181c', 'hsa_mir_625', 'hsa_mir_676', 'hsa_mir_34a', 'hsa_mir_1254', 'hsa_mir_3615', 'hsa_mir_3127']\n", + "2025-11-26 01:12:47,144 - bioneuralnet.clustering.correlated_pagerank - INFO - PageRank computation completed.\n", + "2025-11-26 01:13:01,127 - bioneuralnet.clustering.correlated_pagerank - INFO - Sweep cut resulted in cluster of size 857 with conductance 0.079 and correlation 0.83.\n", + "2025-11-26 01:13:01,127 - bioneuralnet.clustering.correlated_pagerank - INFO - PageRank clustering completed successfully.\n", + "2025-11-26 01:13:01,128 - bioneuralnet.clustering.hybrid_louvain - INFO - Iteration 1: cluster size=857, Conductance=0.079 Correlation=0.830 score=-0.375\n", + "2025-11-26 01:13:01,167 - bioneuralnet.clustering.hybrid_louvain - INFO - \n", + "Iteration 2/3: Running Correlated Louvain...\n", + "2025-11-26 01:13:01,196 - bioneuralnet.clustering.correlated_louvain - INFO - CorrelatedLouvain(k3=0.2, k4=0.8, nodes=857, edges=8206, features=900)\n", + "2025-11-26 01:13:01,198 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 01:13:01,199 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 01:13:01,199 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 01:13:01,200 - bioneuralnet.clustering.correlated_louvain - INFO - Running standard community detection...\n", + "2025-11-26 01:13:01,310 - bioneuralnet.clustering.correlated_louvain - INFO - Computed quality (supervised): Q = 0.6811, avg_corr = 0.5019, combined = 0.5378\n", + "2025-11-26 01:13:01,310 - bioneuralnet.clustering.correlated_louvain - INFO - Final quality: 0.5378\n", + "2025-11-26 01:13:01,311 - bioneuralnet.clustering.correlated_louvain - INFO - CorrelatedLouvain found 11 communities (nodes=857)\n", + "2025-11-26 01:13:01,369 - bioneuralnet.clustering.correlated_louvain - INFO - Computed quality (supervised): Q = 0.6811, avg_corr = 0.5019, combined = 0.5378\n", + "2025-11-26 01:13:01,369 - bioneuralnet.clustering.hybrid_louvain - INFO - Iteration 2: Louvain Quality = 0.5378\n", + "2025-11-26 01:13:01,423 - bioneuralnet.clustering.hybrid_louvain - INFO - Selected seed community of size 73 with correlation -0.8235\n", + "2025-11-26 01:13:01,423 - bioneuralnet.clustering.correlated_pagerank - INFO - Initialized PageRank with the following parameters:\n", + "2025-11-26 01:13:01,424 - bioneuralnet.clustering.correlated_pagerank - INFO - Graph: NetworkX Graph with 857 nodes and 8206 edges.\n", + "2025-11-26 01:13:01,425 - bioneuralnet.clustering.correlated_pagerank - INFO - Omics Data: DataFrame with shape (658, 900).\n", + "2025-11-26 01:13:01,425 - bioneuralnet.clustering.correlated_pagerank - INFO - Phenotype Data: Series with 658 samples.\n", + "2025-11-26 01:13:01,425 - bioneuralnet.clustering.correlated_pagerank - INFO - Alpha: 0.9\n", + "2025-11-26 01:13:01,426 - bioneuralnet.clustering.correlated_pagerank - INFO - Max Iterations: 100\n", + "2025-11-26 01:13:01,426 - bioneuralnet.clustering.correlated_pagerank - INFO - Tolerance: 1e-06\n", + "2025-11-26 01:13:01,426 - bioneuralnet.clustering.correlated_pagerank - INFO - K (Composite Score Weight): 0.5\n", + "2025-11-26 01:13:01,427 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 01:13:01,427 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 01:13:01,428 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 01:13:01,428 - bioneuralnet.clustering.correlated_pagerank - INFO - Initialized Correlated PageRank. device=cpu\n", + "2025-11-26 01:13:01,771 - bioneuralnet.clustering.correlated_pagerank - INFO - Generated personalization vector for seed nodes: ['ALDH1A1', 'ADAM28', 'LOC645638', 'TMEM71', 'C4orf26', 'C14orf129', 'GGTLC2', 'PTGER3_5733', 'HMGCS2_3158', 'SLPI_6590', 'CXCL6_6372', 'HGFAC_3083', 'UBE2QL1_134111', 'SORCS2_57537', 'BCHE_590', 'ELFN2_114794', 'C19orf21_126353', 'TFAP2A_7020', 'CCDC135_84229', 'CPNE7_27132', 'VAT1L_57687', 'AVPR1A_552', 'PRDM16_63976', 'ACTC1_70', 'ADAMTSL1_92949', 'PDE1C_5137', 'CPXM2_119587', 'KCNN3_3782', 'SULT4A1_25830', 'ADCY1_107', 'CORO6_84940', 'ANGPT1_284', 'CLDN3_1365', 'HOXD10_3236', 'CA8_767', 'RASGEF1C_255426', 'CCDC108_255101', 'BDKRB2_624', 'ACSM1_116285', 'DGKI_9162', 'C19orf51_352909', 'B3GALT5_10317', 'CLSTN2_64084', 'MPPED2_744', 'RASGRF2_5924', 'SPARCL1_8404', 'C2orf62_375307', 'GPR116_221395', 'C6orf174_387104', 'AKR1C2_1646', 'ADORA1_134', 'C2orf89_129293', 'hsa_mir_31', 'hsa_mir_577', 'hsa_mir_203', 'hsa_mir_135b', 'hsa_mir_582', 'hsa_mir_139', 'hsa_mir_211', 'hsa_mir_335', 'hsa_mir_21', 'hsa_mir_100', 'hsa_mir_10b', 'hsa_mir_181d', 'hsa_mir_219_1', 'hsa_mir_3934', 'hsa_mir_181c', 'hsa_mir_625', 'hsa_mir_676', 'hsa_mir_34a', 'hsa_mir_1254', 'hsa_mir_3615', 'hsa_mir_3127']\n", + "2025-11-26 01:13:01,782 - bioneuralnet.clustering.correlated_pagerank - INFO - PageRank computation completed.\n", + "2025-11-26 01:13:14,769 - bioneuralnet.clustering.correlated_pagerank - INFO - Sweep cut resulted in cluster of size 420 with conductance 0.112 and correlation 0.62.\n", + "2025-11-26 01:13:14,770 - bioneuralnet.clustering.correlated_pagerank - INFO - PageRank clustering completed successfully.\n", + "2025-11-26 01:13:14,770 - bioneuralnet.clustering.hybrid_louvain - INFO - Iteration 2: cluster size=420, Conductance=0.112 Correlation=0.620 score=-0.254\n", + "2025-11-26 01:13:14,782 - bioneuralnet.clustering.hybrid_louvain - INFO - \n", + "Iteration 3/3: Running Correlated Louvain...\n", + "2025-11-26 01:13:14,790 - bioneuralnet.clustering.correlated_louvain - INFO - CorrelatedLouvain(k3=0.2, k4=0.8, nodes=420, edges=3651, features=900)\n", + "2025-11-26 01:13:14,790 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 01:13:14,791 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 01:13:14,791 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 01:13:14,792 - bioneuralnet.clustering.correlated_louvain - INFO - Running standard community detection...\n", + "2025-11-26 01:13:14,891 - bioneuralnet.clustering.correlated_louvain - INFO - Computed quality (supervised): Q = 0.6275, avg_corr = 0.4688, combined = 0.5005\n", + "2025-11-26 01:13:14,892 - bioneuralnet.clustering.correlated_louvain - INFO - Final quality: 0.5005\n", + "2025-11-26 01:13:14,892 - bioneuralnet.clustering.correlated_louvain - INFO - CorrelatedLouvain found 8 communities (nodes=420)\n", + "2025-11-26 01:13:14,926 - bioneuralnet.clustering.correlated_louvain - INFO - Computed quality (supervised): Q = 0.6275, avg_corr = 0.4688, combined = 0.5005\n", + "2025-11-26 01:13:14,926 - bioneuralnet.clustering.hybrid_louvain - INFO - Iteration 3: Louvain Quality = 0.5005\n", + "2025-11-26 01:13:14,957 - bioneuralnet.clustering.hybrid_louvain - INFO - Selected seed community of size 88 with correlation -0.8538\n", + "2025-11-26 01:13:14,957 - bioneuralnet.clustering.correlated_pagerank - INFO - Initialized PageRank with the following parameters:\n", + "2025-11-26 01:13:14,958 - bioneuralnet.clustering.correlated_pagerank - INFO - Graph: NetworkX Graph with 420 nodes and 3651 edges.\n", + "2025-11-26 01:13:14,958 - bioneuralnet.clustering.correlated_pagerank - INFO - Omics Data: DataFrame with shape (658, 900).\n", + "2025-11-26 01:13:14,958 - bioneuralnet.clustering.correlated_pagerank - INFO - Phenotype Data: Series with 658 samples.\n", + "2025-11-26 01:13:14,958 - bioneuralnet.clustering.correlated_pagerank - INFO - Alpha: 0.9\n", + "2025-11-26 01:13:14,959 - bioneuralnet.clustering.correlated_pagerank - INFO - Max Iterations: 100\n", + "2025-11-26 01:13:14,959 - bioneuralnet.clustering.correlated_pagerank - INFO - Tolerance: 1e-06\n", + "2025-11-26 01:13:14,959 - bioneuralnet.clustering.correlated_pagerank - INFO - K (Composite Score Weight): 0.5\n", + "2025-11-26 01:13:14,960 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 01:13:14,960 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 01:13:14,961 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 01:13:14,961 - bioneuralnet.clustering.correlated_pagerank - INFO - Initialized Correlated PageRank. device=cpu\n", + "2025-11-26 01:13:15,438 - bioneuralnet.clustering.correlated_pagerank - INFO - Generated personalization vector for seed nodes: ['BCHE_590', 'ACSM1_116285', 'hsa_mir_181c', 'ALDH1A1', 'RASD2_23551', 'HSD17B14', 'ADAMTSL1_92949', 'hsa_mir_1180', 'VAT1L_57687', 'RASGRF2_5924', 'C19orf21_126353', 'CPXM2_119587', 'UBE2QL1_134111', 'PDE1C_5137', 'ELFN2_114794', 'hsa_mir_335', 'CXCL6_6372', 'PTGER3_5733', 'ADCY1_107', 'hsa_mir_219_1', 'hsa_mir_203', 'CCDC135_84229', 'hsa_mir_181d', 'hsa_mir_3615', 'ADAMTS5_11096', 'B3GALT5_10317', 'CLDN3_1365', 'HGFAC_3083', 'C2orf62_375307', 'DACT2_168002', 'SULT4A1_25830', 'hsa_mir_10b', 'hsa_mir_1254', 'FOXJ1_2302', 'hsa_mir_3934', 'DACH1_1602', 'TMEM71', 'AKR1C2_1646', 'hsa_mir_676', 'BDKRB2_624', 'hsa_mir_577', 'hsa_mir_139', 'AVPR1A_552', 'C19orf51_352909', 'C3orf24', 'hsa_mir_100', 'HOXD10_3236', 'RASGEF1C_255426', 'CA4_762', 'C14orf129', 'hsa_mir_625', 'ADORA1_134', 'SLPI_6590', 'BTNL9_153579', 'C6orf174_387104', 'C1orf114_57821', 'C2orf89_129293', 'SPARCL1_8404', 'CLSTN2_64084', 'hsa_mir_135b', 'C4orf26', 'DGKI_9162', 'SORCS2_57537', 'CDH17_1015', 'C2orf72_257407', 'ANGPT1_284', 'CORO6_84940', 'CCDC108_255101', 'BCAN', 'hsa_mir_21', 'GPR116_221395', 'CA8_767', 'hsa_mir_551b', 'CPNE7_27132', 'hsa_mir_31', 'PRDM16_63976', 'hsa_mir_211', 'HMGCS2_3158', 'PCDH17_27253', 'ACTC1_70', 'hsa_mir_582', 'GGTLC2', 'MPPED2_744', 'DDR2_4921', 'PDZD2_23037', 'KCNN3_3782', 'CNTN6_27255', 'LOC645638']\n", + "2025-11-26 01:13:15,444 - bioneuralnet.clustering.correlated_pagerank - INFO - PageRank computation completed.\n", + "2025-11-26 01:13:19,148 - bioneuralnet.clustering.correlated_pagerank - INFO - Sweep cut resulted in cluster of size 176 with conductance 0.225 and correlation -0.76.\n", + "2025-11-26 01:13:19,149 - bioneuralnet.clustering.correlated_pagerank - INFO - PageRank clustering completed successfully.\n", + "2025-11-26 01:13:19,149 - bioneuralnet.clustering.hybrid_louvain - INFO - Iteration 3: cluster size=176, Conductance=0.225 Correlation=-0.760 score=-0.267\n", + "2025-11-26 01:13:19,154 - bioneuralnet.clustering.hybrid_louvain - INFO - Hybrid Louvain completed after 4 iterations.\n", + "2025-11-26 01:13:19,158 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 01:13:19,159 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 01:13:19,160 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 01:13:19,160 - bioneuralnet.clustering.hybrid_louvain - INFO - Initializing HybridLouvain...\n", + "2025-11-26 01:13:19,160 - bioneuralnet.clustering.hybrid_louvain - INFO - Input G is a DataFrame; converting adjacency matrix to NetworkX graph.\n", + "2025-11-26 01:13:19,180 - bioneuralnet.clustering.hybrid_louvain - INFO - Initialized HybridLouvain with 900 graph nodes, 900 omics columns, 658 phenotype rows; max_iter=3, k3=0.2, k4=0.8, tune=False\n", + "2025-11-26 01:13:19,181 - bioneuralnet.clustering.hybrid_louvain - INFO - \n", + "Iteration 1/3: Running Correlated Louvain...\n", + "2025-11-26 01:13:19,196 - bioneuralnet.clustering.correlated_louvain - INFO - CorrelatedLouvain(k3=0.2, k4=0.8, nodes=900, edges=8628, features=900)\n", + "2025-11-26 01:13:19,197 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 01:13:19,198 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 01:13:19,198 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 01:13:19,198 - bioneuralnet.clustering.correlated_louvain - INFO - Running standard community detection...\n", + "2025-11-26 01:13:19,325 - bioneuralnet.clustering.correlated_louvain - INFO - Computed quality (supervised): Q = 0.6936, avg_corr = 0.4574, combined = 0.5046\n", + "2025-11-26 01:13:19,325 - bioneuralnet.clustering.correlated_louvain - INFO - Final quality: 0.5046\n", + "2025-11-26 01:13:19,326 - bioneuralnet.clustering.correlated_louvain - INFO - CorrelatedLouvain found 11 communities (nodes=900)\n", + "2025-11-26 01:13:19,384 - bioneuralnet.clustering.correlated_louvain - INFO - Computed quality (supervised): Q = 0.6936, avg_corr = 0.4574, combined = 0.5046\n", + "2025-11-26 01:13:19,384 - bioneuralnet.clustering.hybrid_louvain - INFO - Iteration 1: Louvain Quality = 0.5046\n", + "2025-11-26 01:13:19,438 - bioneuralnet.clustering.hybrid_louvain - INFO - Selected seed community of size 59 with correlation 0.8725\n", + "2025-11-26 01:13:19,438 - bioneuralnet.clustering.correlated_pagerank - INFO - Initialized PageRank with the following parameters:\n", + "2025-11-26 01:13:19,439 - bioneuralnet.clustering.correlated_pagerank - INFO - Graph: NetworkX Graph with 900 nodes and 8628 edges.\n", + "2025-11-26 01:13:19,439 - bioneuralnet.clustering.correlated_pagerank - INFO - Omics Data: DataFrame with shape (658, 900).\n", + "2025-11-26 01:13:19,439 - bioneuralnet.clustering.correlated_pagerank - INFO - Phenotype Data: Series with 658 samples.\n", + "2025-11-26 01:13:19,440 - bioneuralnet.clustering.correlated_pagerank - INFO - Alpha: 0.9\n", + "2025-11-26 01:13:19,440 - bioneuralnet.clustering.correlated_pagerank - INFO - Max Iterations: 100\n", + "2025-11-26 01:13:19,440 - bioneuralnet.clustering.correlated_pagerank - INFO - Tolerance: 1e-06\n", + "2025-11-26 01:13:19,440 - bioneuralnet.clustering.correlated_pagerank - INFO - K (Composite Score Weight): 0.5\n", + "2025-11-26 01:13:19,441 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 01:13:19,442 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 01:13:19,442 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 01:13:19,442 - bioneuralnet.clustering.correlated_pagerank - INFO - Initialized Correlated PageRank. device=cpu\n", + "2025-11-26 01:13:19,655 - bioneuralnet.clustering.correlated_pagerank - INFO - Generated personalization vector for seed nodes: ['BAAT', 'CAPG', 'CP', 'FABP7_2173', 'SLC6A3_6531', 'CP_1356', 'CA9_768', 'CKMT1B_1159', 'CYP2J2_1573', 'CREB3L3_84699', 'ANGPTL4_51129', 'MYEOV_26579', 'TJP3_27134', 'PCSK6_5046', 'C1QL1_10882', 'MCHR1_2847', 'C19orf46_163183', 'APOB_338', 'CKMT1A_548596', 'MAP7D2_256714', 'CCL20_6364', 'LOX_4015', 'C6orf168_84553', 'MAL_4118', 'LOC100131551_100131551', 'CNTN3_5067', 'PCOLCE2_26577', 'CDCA2_157313', 'C1orf116_79098', 'WFDC2_10406', 'ALDOC_230', 'ANO5_203859', 'PPFIA4_8497', 'STK33_65975', 'EPS8L1_54869', 'CBLC_23624', 'SEMA5B_54437', 'TMEM45A_55076', 'OGDHL_55753', 'EGLN3_112399', 'C4orf6_10141', 'FAM155B_27112', 'C7orf68_29923', 'B3GNT4_79369', 'hsa_mir_122', 'hsa_mir_599', 'hsa_mir_183', 'hsa_mir_96', 'hsa_mir_210', 'hsa_mir_182', 'hsa_mir_374c', 'hsa_mir_1271', 'hsa_mir_200b', 'hsa_mir_429', 'hsa_mir_875', 'hsa_mir_3941', 'hsa_mir_200a', 'hsa_mir_3678', 'hsa_mir_3912']\n", + "2025-11-26 01:13:19,666 - bioneuralnet.clustering.correlated_pagerank - INFO - PageRank computation completed.\n", + "2025-11-26 01:13:34,489 - bioneuralnet.clustering.correlated_pagerank - INFO - Sweep cut resulted in cluster of size 451 with conductance 0.087 and correlation 0.89.\n", + "2025-11-26 01:13:34,489 - bioneuralnet.clustering.correlated_pagerank - INFO - PageRank clustering completed successfully.\n", + "2025-11-26 01:13:34,490 - bioneuralnet.clustering.hybrid_louvain - INFO - Iteration 1: cluster size=451, Conductance=0.087 Correlation=0.890 score=-0.401\n", + "2025-11-26 01:13:34,504 - bioneuralnet.clustering.hybrid_louvain - INFO - \n", + "Iteration 2/3: Running Correlated Louvain...\n", + "2025-11-26 01:13:34,512 - bioneuralnet.clustering.correlated_louvain - INFO - CorrelatedLouvain(k3=0.2, k4=0.8, nodes=451, edges=3950, features=900)\n", + "2025-11-26 01:13:34,513 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 01:13:34,514 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 01:13:34,514 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 01:13:34,514 - bioneuralnet.clustering.correlated_louvain - INFO - Running standard community detection...\n", + "2025-11-26 01:13:34,616 - bioneuralnet.clustering.correlated_louvain - INFO - Computed quality (supervised): Q = 0.6249, avg_corr = 0.6253, combined = 0.6252\n", + "2025-11-26 01:13:34,616 - bioneuralnet.clustering.correlated_louvain - INFO - Final quality: 0.6252\n", + "2025-11-26 01:13:34,617 - bioneuralnet.clustering.correlated_louvain - INFO - CorrelatedLouvain found 10 communities (nodes=451)\n", + "2025-11-26 01:13:34,661 - bioneuralnet.clustering.correlated_louvain - INFO - Computed quality (supervised): Q = 0.6249, avg_corr = 0.6253, combined = 0.6252\n", + "2025-11-26 01:13:34,662 - bioneuralnet.clustering.hybrid_louvain - INFO - Iteration 2: Louvain Quality = 0.6252\n", + "2025-11-26 01:13:34,701 - bioneuralnet.clustering.hybrid_louvain - INFO - Selected seed community of size 57 with correlation 0.8714\n", + "2025-11-26 01:13:34,702 - bioneuralnet.clustering.correlated_pagerank - INFO - Initialized PageRank with the following parameters:\n", + "2025-11-26 01:13:34,702 - bioneuralnet.clustering.correlated_pagerank - INFO - Graph: NetworkX Graph with 451 nodes and 3950 edges.\n", + "2025-11-26 01:13:34,703 - bioneuralnet.clustering.correlated_pagerank - INFO - Omics Data: DataFrame with shape (658, 900).\n", + "2025-11-26 01:13:34,703 - bioneuralnet.clustering.correlated_pagerank - INFO - Phenotype Data: Series with 658 samples.\n", + "2025-11-26 01:13:34,703 - bioneuralnet.clustering.correlated_pagerank - INFO - Alpha: 0.9\n", + "2025-11-26 01:13:34,703 - bioneuralnet.clustering.correlated_pagerank - INFO - Max Iterations: 100\n", + "2025-11-26 01:13:34,704 - bioneuralnet.clustering.correlated_pagerank - INFO - Tolerance: 1e-06\n", + "2025-11-26 01:13:34,704 - bioneuralnet.clustering.correlated_pagerank - INFO - K (Composite Score Weight): 0.5\n", + "2025-11-26 01:13:34,704 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 01:13:34,705 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 01:13:34,706 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 01:13:34,706 - bioneuralnet.clustering.correlated_pagerank - INFO - Initialized Correlated PageRank. device=cpu\n", + "2025-11-26 01:13:34,936 - bioneuralnet.clustering.correlated_pagerank - INFO - Generated personalization vector for seed nodes: ['CAPG', 'CP', 'FABP7_2173', 'SLC6A3_6531', 'CP_1356', 'CA9_768', 'CKMT1B_1159', 'CYP2J2_1573', 'CREB3L3_84699', 'ANGPTL4_51129', 'MYEOV_26579', 'TJP3_27134', 'PCSK6_5046', 'C1QL1_10882', 'MCHR1_2847', 'C19orf46_163183', 'APOB_338', 'CKMT1A_548596', 'MAP7D2_256714', 'CCL20_6364', 'LOX_4015', 'C6orf168_84553', 'MAL_4118', 'LOC100131551_100131551', 'CNTN3_5067', 'PCOLCE2_26577', 'CDCA2_157313', 'C1orf116_79098', 'CST6_1474', 'WFDC2_10406', 'ALDOC_230', 'ANO5_203859', 'PPFIA4_8497', 'STK33_65975', 'IGF2BP2_10644', 'EPS8L1_54869', 'CBLC_23624', 'SEMA5B_54437', 'TMEM45A_55076', 'OGDHL_55753', 'EGLN3_112399', 'C4orf6_10141', 'FAM155B_27112', 'C7orf68_29923', 'B3GNT4_79369', 'hsa_mir_122', 'hsa_mir_183', 'hsa_mir_96', 'hsa_mir_210', 'hsa_mir_182', 'hsa_mir_374c', 'hsa_mir_1271', 'hsa_mir_200b', 'hsa_mir_429', 'hsa_mir_3941', 'hsa_mir_200a', 'hsa_mir_3678']\n", + "2025-11-26 01:13:34,942 - bioneuralnet.clustering.correlated_pagerank - INFO - PageRank computation completed.\n", + "2025-11-26 01:13:39,443 - bioneuralnet.clustering.correlated_pagerank - INFO - Sweep cut resulted in cluster of size 193 with conductance 0.221 and correlation 0.9.\n", + "2025-11-26 01:13:39,444 - bioneuralnet.clustering.correlated_pagerank - INFO - PageRank clustering completed successfully.\n", + "2025-11-26 01:13:39,445 - bioneuralnet.clustering.hybrid_louvain - INFO - Iteration 2: cluster size=193, Conductance=0.221 Correlation=0.900 score=-0.340\n", + "2025-11-26 01:13:39,451 - bioneuralnet.clustering.hybrid_louvain - INFO - \n", + "Iteration 3/3: Running Correlated Louvain...\n", + "2025-11-26 01:13:39,456 - bioneuralnet.clustering.correlated_louvain - INFO - CorrelatedLouvain(k3=0.2, k4=0.8, nodes=193, edges=1530, features=900)\n", + "2025-11-26 01:13:39,457 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 01:13:39,458 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 01:13:39,459 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 01:13:39,460 - bioneuralnet.clustering.correlated_louvain - INFO - Running standard community detection...\n", + "2025-11-26 01:13:39,506 - bioneuralnet.clustering.correlated_louvain - INFO - Computed quality (supervised): Q = 0.5349, avg_corr = 0.7495, combined = 0.7066\n", + "2025-11-26 01:13:39,507 - bioneuralnet.clustering.correlated_louvain - INFO - Final quality: 0.7066\n", + "2025-11-26 01:13:39,507 - bioneuralnet.clustering.correlated_louvain - INFO - CorrelatedLouvain found 7 communities (nodes=193)\n", + "2025-11-26 01:13:39,533 - bioneuralnet.clustering.correlated_louvain - INFO - Computed quality (supervised): Q = 0.5349, avg_corr = 0.7495, combined = 0.7066\n", + "2025-11-26 01:13:39,533 - bioneuralnet.clustering.hybrid_louvain - INFO - Iteration 3: Louvain Quality = 0.7066\n", + "2025-11-26 01:13:39,558 - bioneuralnet.clustering.hybrid_louvain - INFO - Selected seed community of size 58 with correlation 0.8576\n", + "2025-11-26 01:13:39,559 - bioneuralnet.clustering.correlated_pagerank - INFO - Initialized PageRank with the following parameters:\n", + "2025-11-26 01:13:39,559 - bioneuralnet.clustering.correlated_pagerank - INFO - Graph: NetworkX Graph with 193 nodes and 1530 edges.\n", + "2025-11-26 01:13:39,560 - bioneuralnet.clustering.correlated_pagerank - INFO - Omics Data: DataFrame with shape (658, 900).\n", + "2025-11-26 01:13:39,560 - bioneuralnet.clustering.correlated_pagerank - INFO - Phenotype Data: Series with 658 samples.\n", + "2025-11-26 01:13:39,560 - bioneuralnet.clustering.correlated_pagerank - INFO - Alpha: 0.9\n", + "2025-11-26 01:13:39,560 - bioneuralnet.clustering.correlated_pagerank - INFO - Max Iterations: 100\n", + "2025-11-26 01:13:39,561 - bioneuralnet.clustering.correlated_pagerank - INFO - Tolerance: 1e-06\n", + "2025-11-26 01:13:39,561 - bioneuralnet.clustering.correlated_pagerank - INFO - K (Composite Score Weight): 0.5\n", + "2025-11-26 01:13:39,561 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 01:13:39,562 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 01:13:39,563 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 01:13:39,563 - bioneuralnet.clustering.correlated_pagerank - INFO - Initialized Correlated PageRank. device=cpu\n", + "2025-11-26 01:13:39,832 - bioneuralnet.clustering.correlated_pagerank - INFO - Generated personalization vector for seed nodes: ['C4orf48_401115', 'FATE1_89885', 'BARX2_8538', 'FOLH1_2346', 'NGF_4803', 'hsa_mir_3678', 'C2orf72_257407', 'hsa_mir_503', 'OR51E1_143503', 'ANGPT2_285', 'CXCL10_3627', 'GABRE_2564', 'C6orf223_221416', 'ESM1_11082', 'CXCL9_4283', 'KLF14', 'hsa_mir_489', 'CNTD2_79935', 'CSPG4_1464', 'hsa_mir_224', 'CHL1_10752', 'CREB3L3_84699', 'FLT1_2321', 'COL5A3_50509', 'GPR4_2828', 'hsa_mir_126', 'hsa_let_7b', 'hsa_mir_561', 'BTNL9_153579', 'PCDH17_27253', 'COL21A1_81578', 'SLC35F1_222553', 'STC1_6781', 'ANXA3_306', 'APLN_8862', 'BNC1_646', 'LGI4_163175', 'hsa_mir_653', 'TRIM58', 'CAMK2B_816', 'CDH13_1012', 'CALCR_799', 'hsa_mir_3912', 'APOLD1_81575', 'CCNO_10309', 'KLHDC8A_55220', 'STK33_65975', 'RGS5_8490', 'LOC554202_554202', 'HMCN1_83872', 'C3orf70_285382', 'CD36_948', 'ADAMTS5_11096', 'COX4I2_84701', 'CXCL11_6373', 'TRPC6_7225', 'LCN2_3934', 'LOH3CR2A']\n", + "2025-11-26 01:13:39,836 - bioneuralnet.clustering.correlated_pagerank - INFO - PageRank computation completed.\n", + "2025-11-26 01:13:41,069 - bioneuralnet.clustering.correlated_pagerank - INFO - Sweep cut resulted in cluster of size 82 with conductance 0.135 and correlation 0.87.\n", + "2025-11-26 01:13:41,069 - bioneuralnet.clustering.correlated_pagerank - INFO - PageRank clustering completed successfully.\n", + "2025-11-26 01:13:41,070 - bioneuralnet.clustering.hybrid_louvain - INFO - Iteration 3: cluster size=82, Conductance=0.135 Correlation=0.870 score=-0.367\n", + "2025-11-26 01:13:41,072 - bioneuralnet.clustering.hybrid_louvain - INFO - Hybrid Louvain completed after 4 iterations.\n", + "2025-11-26 01:13:41,076 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 01:13:41,077 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 01:13:41,077 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 01:13:41,078 - bioneuralnet.clustering.hybrid_louvain - INFO - Initializing HybridLouvain...\n", + "2025-11-26 01:13:41,078 - bioneuralnet.clustering.hybrid_louvain - INFO - Input G is a DataFrame; converting adjacency matrix to NetworkX graph.\n", + "2025-11-26 01:13:41,100 - bioneuralnet.clustering.hybrid_louvain - INFO - Initialized HybridLouvain with 900 graph nodes, 900 omics columns, 658 phenotype rows; max_iter=3, k3=0.2, k4=0.8, tune=False\n", + "2025-11-26 01:13:41,100 - bioneuralnet.clustering.hybrid_louvain - INFO - \n", + "Iteration 1/3: Running Correlated Louvain...\n", + "2025-11-26 01:13:41,124 - bioneuralnet.clustering.correlated_louvain - INFO - CorrelatedLouvain(k3=0.2, k4=0.8, nodes=900, edges=8628, features=900)\n", + "2025-11-26 01:13:41,125 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 01:13:41,126 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 01:13:41,126 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 01:13:41,127 - bioneuralnet.clustering.correlated_louvain - INFO - Running standard community detection...\n", + "2025-11-26 01:13:41,261 - bioneuralnet.clustering.correlated_louvain - INFO - Computed quality (supervised): Q = 0.6936, avg_corr = 0.3912, combined = 0.4517\n", + "2025-11-26 01:13:41,262 - bioneuralnet.clustering.correlated_louvain - INFO - Final quality: 0.4517\n", + "2025-11-26 01:13:41,262 - bioneuralnet.clustering.correlated_louvain - INFO - CorrelatedLouvain found 11 communities (nodes=900)\n", + "2025-11-26 01:13:41,327 - bioneuralnet.clustering.correlated_louvain - INFO - Computed quality (supervised): Q = 0.6936, avg_corr = 0.3912, combined = 0.4517\n", + "2025-11-26 01:13:41,328 - bioneuralnet.clustering.hybrid_louvain - INFO - Iteration 1: Louvain Quality = 0.4517\n", + "2025-11-26 01:13:41,385 - bioneuralnet.clustering.hybrid_louvain - INFO - Selected seed community of size 81 with correlation 0.7998\n", + "2025-11-26 01:13:41,386 - bioneuralnet.clustering.correlated_pagerank - INFO - Initialized PageRank with the following parameters:\n", + "2025-11-26 01:13:41,387 - bioneuralnet.clustering.correlated_pagerank - INFO - Graph: NetworkX Graph with 900 nodes and 8628 edges.\n", + "2025-11-26 01:13:41,387 - bioneuralnet.clustering.correlated_pagerank - INFO - Omics Data: DataFrame with shape (658, 900).\n", + "2025-11-26 01:13:41,387 - bioneuralnet.clustering.correlated_pagerank - INFO - Phenotype Data: Series with 658 samples.\n", + "2025-11-26 01:13:41,388 - bioneuralnet.clustering.correlated_pagerank - INFO - Alpha: 0.9\n", + "2025-11-26 01:13:41,388 - bioneuralnet.clustering.correlated_pagerank - INFO - Max Iterations: 100\n", + "2025-11-26 01:13:41,388 - bioneuralnet.clustering.correlated_pagerank - INFO - Tolerance: 1e-06\n", + "2025-11-26 01:13:41,389 - bioneuralnet.clustering.correlated_pagerank - INFO - K (Composite Score Weight): 0.5\n", + "2025-11-26 01:13:41,389 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 01:13:41,390 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 01:13:41,391 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 01:13:41,391 - bioneuralnet.clustering.correlated_pagerank - INFO - Initialized Correlated PageRank. device=cpu\n", + "2025-11-26 01:13:41,824 - bioneuralnet.clustering.correlated_pagerank - INFO - Generated personalization vector for seed nodes: ['IL18', 'RHCG_51458', 'ATP6V0A4_50617', 'SLC4A1_6521', 'KLK1_3816', 'ATP6V0D2_245972', 'CLDN8_9073', 'FOXI1_2299', 'DMRT2_10655', 'HEPACAM2_253012', 'CLCNKB_1188', 'ATP6V1B1_525', 'CFTR_1080', 'GCGR_2642', 'SLC26A7_115111', 'CLCNKA_1187', 'FGF9_2254', 'BMPR1B_658', 'TMPRSS2_7113', 'BSND_7809', 'CD70_970', 'AQP6_363', 'CWH43_80157', 'TRIM50_135892', 'COL23A1_91522', 'C16orf89_146556', 'SLC9A2_6549', 'CALCA_796', 'OLIG1_116448', 'CLNK_116449', 'CYP11A1_1583', 'RANBP3L_202151', 'DIRAS1_148252', 'C1orf168_199920', 'INPP5J_27124', 'C15orf59_388135', 'CCBE1_147372', 'EPB41L4B_54566', 'GCOM1_145781', 'C9orf84_158401', 'ATP6V1G3_127124', 'DGCR5_26220', 'VWA5B1_127731', 'CHST9_83539', 'KRTAP5_8_57830', 'DEFB1_1672', 'ST8SIA5_29906', 'LOC643008_643008', 'CEL_1056', 'C6orf105_84830', 'CR1L_1379', 'ASB5_140458', 'MARCH10_162333', 'ANGPTL1_9068', 'hsa_mir_891a', 'hsa_mir_141', 'hsa_mir_187', 'hsa_mir_200c', 'hsa_mir_375', 'hsa_mir_592', 'hsa_mir_129_1', 'hsa_mir_129_2', 'hsa_mir_184', 'hsa_mir_218_2', 'hsa_mir_206', 'hsa_mir_33a', 'hsa_mir_362', 'hsa_mir_218_1', 'hsa_mir_188', 'hsa_mir_363', 'hsa_mir_20b', 'hsa_mir_651', 'hsa_mir_3200', 'hsa_mir_660', 'hsa_mir_1468', 'hsa_mir_501', 'hsa_let_7f_2', 'hsa_mir_500b', 'hsa_mir_30c_1', 'hsa_mir_532', 'hsa_mir_23c']\n", + "2025-11-26 01:13:41,836 - bioneuralnet.clustering.correlated_pagerank - INFO - PageRank computation completed.\n", + "2025-11-26 01:13:57,321 - bioneuralnet.clustering.correlated_pagerank - INFO - Sweep cut resulted in cluster of size 448 with conductance 0.116 and correlation -0.85.\n", + "2025-11-26 01:13:57,322 - bioneuralnet.clustering.correlated_pagerank - INFO - PageRank clustering completed successfully.\n", + "2025-11-26 01:13:57,323 - bioneuralnet.clustering.hybrid_louvain - INFO - Iteration 1: cluster size=448, Conductance=0.116 Correlation=-0.850 score=-0.367\n", + "2025-11-26 01:13:57,336 - bioneuralnet.clustering.hybrid_louvain - INFO - \n", + "Iteration 2/3: Running Correlated Louvain...\n", + "2025-11-26 01:13:57,345 - bioneuralnet.clustering.correlated_louvain - INFO - CorrelatedLouvain(k3=0.2, k4=0.8, nodes=448, edges=3766, features=900)\n", + "2025-11-26 01:13:57,345 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 01:13:57,346 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 01:13:57,347 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 01:13:57,347 - bioneuralnet.clustering.correlated_louvain - INFO - Running standard community detection...\n", + "2025-11-26 01:13:57,433 - bioneuralnet.clustering.correlated_louvain - INFO - Computed quality (supervised): Q = 0.6664, avg_corr = 0.5211, combined = 0.5502\n", + "2025-11-26 01:13:57,433 - bioneuralnet.clustering.correlated_louvain - INFO - Final quality: 0.5502\n", + "2025-11-26 01:13:57,433 - bioneuralnet.clustering.correlated_louvain - INFO - CorrelatedLouvain found 9 communities (nodes=448)\n", + "2025-11-26 01:13:57,476 - bioneuralnet.clustering.correlated_louvain - INFO - Computed quality (supervised): Q = 0.6664, avg_corr = 0.5211, combined = 0.5502\n", + "2025-11-26 01:13:57,476 - bioneuralnet.clustering.hybrid_louvain - INFO - Iteration 2: Louvain Quality = 0.5502\n", + "2025-11-26 01:13:57,514 - bioneuralnet.clustering.hybrid_louvain - INFO - Selected seed community of size 84 with correlation 0.8241\n", + "2025-11-26 01:13:57,514 - bioneuralnet.clustering.correlated_pagerank - INFO - Initialized PageRank with the following parameters:\n", + "2025-11-26 01:13:57,515 - bioneuralnet.clustering.correlated_pagerank - INFO - Graph: NetworkX Graph with 448 nodes and 3766 edges.\n", + "2025-11-26 01:13:57,515 - bioneuralnet.clustering.correlated_pagerank - INFO - Omics Data: DataFrame with shape (658, 900).\n", + "2025-11-26 01:13:57,516 - bioneuralnet.clustering.correlated_pagerank - INFO - Phenotype Data: Series with 658 samples.\n", + "2025-11-26 01:13:57,516 - bioneuralnet.clustering.correlated_pagerank - INFO - Alpha: 0.9\n", + "2025-11-26 01:13:57,516 - bioneuralnet.clustering.correlated_pagerank - INFO - Max Iterations: 100\n", + "2025-11-26 01:13:57,517 - bioneuralnet.clustering.correlated_pagerank - INFO - Tolerance: 1e-06\n", + "2025-11-26 01:13:57,517 - bioneuralnet.clustering.correlated_pagerank - INFO - K (Composite Score Weight): 0.5\n", + "2025-11-26 01:13:57,518 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 01:13:57,519 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 01:13:57,519 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 01:13:57,519 - bioneuralnet.clustering.correlated_pagerank - INFO - Initialized Correlated PageRank. device=cpu\n", + "2025-11-26 01:13:57,967 - bioneuralnet.clustering.correlated_pagerank - INFO - Generated personalization vector for seed nodes: ['ASB5_140458', 'C1orf168_199920', 'OLIG1_116448', 'FOXI1_2299', 'LOC643008_643008', 'hsa_mir_592', 'BMPR1B_658', 'CD70_970', 'hsa_mir_1258', 'FGF9_2254', 'C15orf59_388135', 'ATP6V1B1_525', 'C9orf84_158401', 'hsa_mir_218_2', 'GCGR_2642', 'GALNT14_79623', 'DHRS2_10202', 'CNTNAP5_129684', 'CFTR_1080', 'ATP6V0A4_50617', 'hsa_mir_1468', 'hsa_mir_129_1', 'LRRTM1_347730', 'TRIM50_135892', 'DGCR5_26220', 'DMRT2_10655', 'hsa_mir_651', 'ALPK2_115701', 'hsa_mir_874', 'ANGPTL1_9068', 'hsa_mir_891a', 'C3_718', 'VWA5B1_127731', 'CLDN8_9073', 'RANBP3L_202151', 'hsa_mir_129_2', 'CYP11A1_1583', 'hsa_mir_141', 'CHST9_83539', 'CR1L_1379', 'CEND1_51286', 'hsa_mir_206', 'hsa_mir_23c', 'C17orf55_284185', 'SLC9A2_6549', 'hsa_mir_218_1', 'IL18', 'hsa_mir_200c', 'CWH43_80157', 'CKMT2_1160', 'hsa_mir_618', 'DEFB1_1672', 'BSND_7809', 'SLC26A7_115111', 'ABCC3_8714', 'CCDC78_124093', 'hsa_mir_30c_1', 'CLNK_116449', 'CALCA_796', 'TMPRSS2_7113', 'RHCG_51458', 'CLCNKB_1188', 'DIRAS1_148252', 'ST8SIA5_29906', 'C6orf105_84830', 'hsa_mir_187', 'SLC4A1_6521', 'CLCNKA_1187', 'CEL_1056', 'AQP6_363', 'MARCH10_162333', 'ATP6V0D2_245972', 'C16orf89_146556', 'GCOM1_145781', 'SEZ6L_23544', 'INPP5J_27124', 'EPB41L4B_54566', 'HEPACAM2_253012', 'COL23A1_91522', 'CCBE1_147372', 'ATP6V1G3_127124', 'KLK1_3816', 'CTNNA2_1496', 'KRTAP5_8_57830']\n", + "2025-11-26 01:13:57,974 - bioneuralnet.clustering.correlated_pagerank - INFO - PageRank computation completed.\n", + "2025-11-26 01:14:02,424 - bioneuralnet.clustering.correlated_pagerank - INFO - Sweep cut resulted in cluster of size 211 with conductance 0.185 and correlation -0.85.\n", + "2025-11-26 01:14:02,425 - bioneuralnet.clustering.correlated_pagerank - INFO - PageRank clustering completed successfully.\n", + "2025-11-26 01:14:02,425 - bioneuralnet.clustering.hybrid_louvain - INFO - Iteration 2: cluster size=211, Conductance=0.185 Correlation=-0.850 score=-0.333\n", + "2025-11-26 01:14:02,431 - bioneuralnet.clustering.hybrid_louvain - INFO - \n", + "Iteration 3/3: Running Correlated Louvain...\n", + "2025-11-26 01:14:02,435 - bioneuralnet.clustering.correlated_louvain - INFO - CorrelatedLouvain(k3=0.2, k4=0.8, nodes=211, edges=1490, features=900)\n", + "2025-11-26 01:14:02,436 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 01:14:02,437 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 01:14:02,437 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 01:14:02,438 - bioneuralnet.clustering.correlated_louvain - INFO - Running standard community detection...\n", + "2025-11-26 01:14:02,491 - bioneuralnet.clustering.correlated_louvain - INFO - Computed quality (supervised): Q = 0.5780, avg_corr = 0.5743, combined = 0.5750\n", + "2025-11-26 01:14:02,491 - bioneuralnet.clustering.correlated_louvain - INFO - Final quality: 0.5750\n", + "2025-11-26 01:14:02,492 - bioneuralnet.clustering.correlated_louvain - INFO - CorrelatedLouvain found 10 communities (nodes=211)\n", + "2025-11-26 01:14:02,527 - bioneuralnet.clustering.correlated_louvain - INFO - Computed quality (supervised): Q = 0.5780, avg_corr = 0.5743, combined = 0.5750\n", + "2025-11-26 01:14:02,527 - bioneuralnet.clustering.hybrid_louvain - INFO - Iteration 3: Louvain Quality = 0.5750\n", + "2025-11-26 01:14:02,561 - bioneuralnet.clustering.hybrid_louvain - INFO - Selected seed community of size 34 with correlation -0.8628\n", + "2025-11-26 01:14:02,561 - bioneuralnet.clustering.correlated_pagerank - INFO - Initialized PageRank with the following parameters:\n", + "2025-11-26 01:14:02,562 - bioneuralnet.clustering.correlated_pagerank - INFO - Graph: NetworkX Graph with 211 nodes and 1490 edges.\n", + "2025-11-26 01:14:02,562 - bioneuralnet.clustering.correlated_pagerank - INFO - Omics Data: DataFrame with shape (658, 900).\n", + "2025-11-26 01:14:02,563 - bioneuralnet.clustering.correlated_pagerank - INFO - Phenotype Data: Series with 658 samples.\n", + "2025-11-26 01:14:02,563 - bioneuralnet.clustering.correlated_pagerank - INFO - Alpha: 0.9\n", + "2025-11-26 01:14:02,563 - bioneuralnet.clustering.correlated_pagerank - INFO - Max Iterations: 100\n", + "2025-11-26 01:14:02,563 - bioneuralnet.clustering.correlated_pagerank - INFO - Tolerance: 1e-06\n", + "2025-11-26 01:14:02,564 - bioneuralnet.clustering.correlated_pagerank - INFO - K (Composite Score Weight): 0.5\n", + "2025-11-26 01:14:02,564 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 01:14:02,565 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 01:14:02,566 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 01:14:02,566 - bioneuralnet.clustering.correlated_pagerank - INFO - Initialized Correlated PageRank. device=cpu\n", + "2025-11-26 01:14:02,682 - bioneuralnet.clustering.correlated_pagerank - INFO - Generated personalization vector for seed nodes: ['CEND1_51286', 'ST8SIA5_29906', 'LRRTM1_347730', 'BDKRB1_623', 'LOC440354', 'EPGN', 'hsa_mir_222', 'C17orf55_284185', 'hsa_mir_874', 'ANGPTL1', 'IL18', 'CKMT2_1160', 'hsa_mir_618', 'C1orf168', 'HOXA7', 'KCNRG', 'hsa_mir_1258', 'SEZ6L_23544', 'hsa_mir_130a', 'hsa_mir_221', 'HULC', 'GALNT14_79623', 'CYS1_192668', 'MIR744', 'CNTNAP5_129684', 'MIR618', 'CHST9_83539', 'CTNNA2_1496', 'C14orf53', 'hsa_mir_33a', 'BICC1_80114', 'ABP1_26', 'hsa_mir_766', 'CR1L_1379']\n", + "2025-11-26 01:14:02,685 - bioneuralnet.clustering.correlated_pagerank - INFO - PageRank computation completed.\n", + "2025-11-26 01:14:04,038 - bioneuralnet.clustering.correlated_pagerank - INFO - Sweep cut resulted in cluster of size 202 with conductance 0.058 and correlation -0.85.\n", + "2025-11-26 01:14:04,038 - bioneuralnet.clustering.correlated_pagerank - INFO - PageRank clustering completed successfully.\n", + "2025-11-26 01:14:04,039 - bioneuralnet.clustering.hybrid_louvain - INFO - Iteration 3: cluster size=202, Conductance=0.058 Correlation=-0.850 score=-0.396\n", + "2025-11-26 01:14:04,044 - bioneuralnet.clustering.hybrid_louvain - INFO - Hybrid Louvain completed after 4 iterations.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Driver modules (one-vs-rest):\n", + " 1: 857 features\n", + " 0: 451 features\n", + " 2: 448 features\n" + ] + } + ], + "source": [ + "from bioneuralnet.clustering import HybridLouvain\n", + "\n", + "labels = target[\"target\"]\n", + "unique_phenotypes = labels.unique()\n", + "\n", + "multiclass_results = {}\n", + "summary = []\n", + "\n", + "for phenotype in unique_phenotypes:\n", + " # one-vs-rest encoding\n", + " y_binary = (labels == phenotype).astype(int)\n", + "\n", + " hl = HybridLouvain(\n", + " G=correlation_10,\n", + " B=omics_kipan,\n", + " Y=y_binary,\n", + " k3=0.2,\n", + " k4=0.8,\n", + " max_iter=3,\n", + " seed=SEED,\n", + " )\n", + "\n", + " subnetworks = hl.run(as_dfs=True)\n", + "\n", + " if subnetworks:\n", + " df = subnetworks[0]\n", + " multiclass_results[phenotype] = df\n", + " summary.append((phenotype, df.shape[1]))\n", + " else:\n", + " summary.append((phenotype, 0))\n", + "\n", + "print(\"\\nDriver modules (one-vs-rest):\")\n", + "for pheno, n_feats in summary:\n", + " print(f\" {pheno}: {n_feats} features\")" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "efbff52f", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-11-26 01:14:04,056 - bioneuralnet.downstream_task.dpmon - INFO - Output directory set to: /home/vicente/Github/BioNeuralNet/dpmon_results_GCN_FINAL/kipan/threshold_10\n", + "2025-11-26 01:14:04,057 - bioneuralnet.downstream_task.dpmon - INFO - Initialized DPMON with model: GCN\n", + "2025-11-26 01:14:04,057 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 01:14:04,058 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 01:14:04,058 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 01:14:04,059 - bioneuralnet.downstream_task.dpmon - INFO - Random seed set to: 8183\n", + "2025-11-26 01:14:04,064 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:14:04,067 - bioneuralnet.downstream_task.dpmon - INFO - Running in Cross-Validation mode (cv=True) with 5 folds.\n", + "2025-11-26 01:14:04,068 - bioneuralnet.downstream_task.dpmon - INFO - CV Setup: Standard 5-fold split.\n", + "2025-11-26 01:14:04,069 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 1/5\n", + "2025-11-26 01:14:04,071 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:14:04,081 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:14:08,191 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:14:08,215 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (526, 901)\n", + "2025-11-26 01:14:08,218\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:14:20,851\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0286s.\n", + "2025-11-26 01:14:21,044 - bioneuralnet.downstream_task.dpmon - INFO - Fold 1 best config: {'gnn_layer_num': 2, 'gnn_hidden_dim': 64, 'lr': 0.0006037895979188083, 'weight_decay': 0.004195106610298857, 'nn_hidden_dim1': 512, 'nn_hidden_dim2': 128, 'ae_encoding_dim': 16, 'num_epochs': 1024, 'gnn_dropout': 0.1, 'gnn_activation': 'elu', 'dim_reduction': 'ae'}\n", + "2025-11-26 01:14:21,045 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 1 using train split only\n", + "2025-11-26 01:14:21,050 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:14:25,269 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:14:26,773 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 1 on test set\n", + "2025-11-26 01:14:26,778 - bioneuralnet.downstream_task.dpmon - INFO - Fold 1 results:\n", + "2025-11-26 01:14:26,778 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9394\n", + "2025-11-26 01:14:26,779 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9287\n", + "2025-11-26 01:14:26,779 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9399\n", + "2025-11-26 01:14:26,779 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9566\n", + "2025-11-26 01:14:26,779 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9908\n", + "2025-11-26 01:14:26,780 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9839\n", + "\n", + "2025-11-26 01:14:26,780 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 2/5\n", + "2025-11-26 01:14:26,782 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:14:26,788 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:14:30,936 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:14:30,947 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (526, 901)\n", + "2025-11-26 01:14:30,951\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:14:41,518\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0283s.\n", + "2025-11-26 01:14:41,641 - bioneuralnet.downstream_task.dpmon - INFO - Fold 2 best config: {'gnn_layer_num': 2, 'gnn_hidden_dim': 64, 'lr': 0.0007034204356990857, 'weight_decay': 5.358735876893429e-05, 'nn_hidden_dim1': 512, 'nn_hidden_dim2': 64, 'ae_encoding_dim': 8, 'num_epochs': 1024, 'gnn_dropout': 0.2, 'gnn_activation': 'relu', 'dim_reduction': 'ae'}\n", + "2025-11-26 01:14:41,642 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 2 using train split only\n", + "2025-11-26 01:14:41,647 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:14:45,770 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:14:47,251 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 2 on test set\n", + "2025-11-26 01:14:47,256 - bioneuralnet.downstream_task.dpmon - INFO - Fold 2 results:\n", + "2025-11-26 01:14:47,257 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9773\n", + "2025-11-26 01:14:47,257 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9648\n", + "2025-11-26 01:14:47,257 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9777\n", + "2025-11-26 01:14:47,257 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9835\n", + "2025-11-26 01:14:47,257 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9928\n", + "2025-11-26 01:14:47,258 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9488\n", + "\n", + "2025-11-26 01:14:47,258 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 3/5\n", + "2025-11-26 01:14:47,260 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:14:47,265 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:14:51,373 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:14:51,385 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (526, 901)\n", + "2025-11-26 01:14:51,388\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:15:03,121\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0079s.\n", + "2025-11-26 01:15:03,293 - bioneuralnet.downstream_task.dpmon - INFO - Fold 3 best config: {'gnn_layer_num': 1, 'gnn_hidden_dim': 256, 'lr': 0.0016239332393902155, 'weight_decay': 1.2898372523486065e-05, 'nn_hidden_dim1': 512, 'nn_hidden_dim2': 128, 'ae_encoding_dim': 4, 'num_epochs': 512, 'gnn_dropout': 0.2, 'gnn_activation': 'elu', 'dim_reduction': 'ae'}\n", + "2025-11-26 01:15:03,293 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 3 using train split only\n", + "2025-11-26 01:15:03,299 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:15:07,440 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:15:08,042 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 3 on test set\n", + "2025-11-26 01:15:08,046 - bioneuralnet.downstream_task.dpmon - INFO - Fold 3 results:\n", + "2025-11-26 01:15:08,047 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9621\n", + "2025-11-26 01:15:08,047 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9621\n", + "2025-11-26 01:15:08,048 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9622\n", + "2025-11-26 01:15:08,048 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9535\n", + "2025-11-26 01:15:08,048 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9955\n", + "2025-11-26 01:15:08,048 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9937\n", + "\n", + "2025-11-26 01:15:08,049 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 4/5\n", + "2025-11-26 01:15:08,051 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:15:08,058 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:15:12,200 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:15:12,211 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (527, 901)\n", + "2025-11-26 01:15:12,214\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:15:23,504\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0155s.\n", + "2025-11-26 01:15:23,665 - bioneuralnet.downstream_task.dpmon - INFO - Fold 4 best config: {'gnn_layer_num': 4, 'gnn_hidden_dim': 128, 'lr': 0.0008628455289617886, 'weight_decay': 0.005542594076906398, 'nn_hidden_dim1': 512, 'nn_hidden_dim2': 128, 'ae_encoding_dim': 16, 'num_epochs': 2048, 'gnn_dropout': 0.2, 'gnn_activation': 'elu', 'dim_reduction': 'linear'}\n", + "2025-11-26 01:15:23,665 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 4 using train split only\n", + "2025-11-26 01:15:23,670 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:15:27,775 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:15:32,476 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 4 on test set\n", + "2025-11-26 01:15:32,482 - bioneuralnet.downstream_task.dpmon - INFO - Fold 4 results:\n", + "2025-11-26 01:15:32,482 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9389\n", + "2025-11-26 01:15:32,482 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9449\n", + "2025-11-26 01:15:32,482 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9390\n", + "2025-11-26 01:15:32,482 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9554\n", + "2025-11-26 01:15:32,482 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9940\n", + "2025-11-26 01:15:32,482 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9919\n", + "\n", + "2025-11-26 01:15:32,483 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 5/5\n", + "2025-11-26 01:15:32,485 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:15:32,490 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:15:36,751 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:15:36,762 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (527, 901)\n", + "2025-11-26 01:15:36,765\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:15:49,425\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0172s.\n", + "2025-11-26 01:15:49,636 - bioneuralnet.downstream_task.dpmon - INFO - Fold 5 best config: {'gnn_layer_num': 2, 'gnn_hidden_dim': 64, 'lr': 0.001685812648195555, 'weight_decay': 0.0023773937538678585, 'nn_hidden_dim1': 256, 'nn_hidden_dim2': 128, 'ae_encoding_dim': 8, 'num_epochs': 512, 'gnn_dropout': 0.2, 'gnn_activation': 'relu', 'dim_reduction': 'linear'}\n", + "2025-11-26 01:15:49,637 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 5 using train split only\n", + "2025-11-26 01:15:49,643 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:15:53,783 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:15:54,563 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 5 on test set\n", + "2025-11-26 01:15:54,568 - bioneuralnet.downstream_task.dpmon - INFO - Fold 5 results:\n", + "2025-11-26 01:15:54,568 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9695\n", + "2025-11-26 01:15:54,569 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9673\n", + "2025-11-26 01:15:54,569 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9694\n", + "2025-11-26 01:15:54,569 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9763\n", + "2025-11-26 01:15:54,569 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9986\n", + "2025-11-26 01:15:54,569 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9967\n", + "\n", + "2025-11-26 01:15:54,571 - bioneuralnet.downstream_task.dpmon - INFO - Successfully saved global best CV model state to: /home/vicente/Github/BioNeuralNet/dpmon_results_GCN_FINAL/kipan/threshold_10/best_cv_model.pt\n", + "2025-11-26 01:15:54,572 - bioneuralnet.downstream_task.dpmon - INFO - Successfully saved global best CV model state to: /home/vicente/Github/BioNeuralNet/dpmon_results_GCN_FINAL/kipan/threshold_10/best_cv_model_embds.pt\n", + "2025-11-26 01:15:54,572 - bioneuralnet.downstream_task.dpmon - INFO - Cross-Validation Results:\n", + "\n", + "2025-11-26 01:15:54,572 - bioneuralnet.downstream_task.dpmon - INFO - Avg Accuracy: 0.9574 +/- 0.0175\n", + "2025-11-26 01:15:54,572 - bioneuralnet.downstream_task.dpmon - INFO - Avg F1 Macro: 0.9536 +/- 0.0165\n", + "2025-11-26 01:15:54,572 - bioneuralnet.downstream_task.dpmon - INFO - Avg F1 Weighted: 0.9576 +/- 0.0175\n", + "2025-11-26 01:15:54,572 - bioneuralnet.downstream_task.dpmon - INFO - Avg Recall: 0.9651 +/- 0.0138\n", + "2025-11-26 01:15:54,572 - bioneuralnet.downstream_task.dpmon - INFO - Avg AUC: 0.9943 +/- 0.0029\n", + "2025-11-26 01:15:54,573 - bioneuralnet.downstream_task.dpmon - INFO - Avg AUPR: 0.9830 +/- 0.0197\n", + "2025-11-26 01:15:54,573 - bioneuralnet.downstream_task.dpmon - INFO - DPMON run completed.\n", + "2025-11-26 01:15:54,573 - bioneuralnet.downstream_task.dpmon - INFO - Output directory set to: /home/vicente/Github/BioNeuralNet/dpmon_results_GCN_FINAL/kipan/correlation_10\n", + "2025-11-26 01:15:54,574 - bioneuralnet.downstream_task.dpmon - INFO - Initialized DPMON with model: GCN\n", + "2025-11-26 01:15:54,574 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 01:15:54,574 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 01:15:54,574 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 01:15:54,575 - bioneuralnet.downstream_task.dpmon - INFO - Random seed set to: 8183\n", + "2025-11-26 01:15:54,579 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:15:54,581 - bioneuralnet.downstream_task.dpmon - INFO - Running in Cross-Validation mode (cv=True) with 5 folds.\n", + "2025-11-26 01:15:54,581 - bioneuralnet.downstream_task.dpmon - INFO - CV Setup: Standard 5-fold split.\n", + "2025-11-26 01:15:54,582 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 1/5\n", + "2025-11-26 01:15:54,583 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:15:54,591 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:15:58,722 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:15:58,734 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (526, 901)\n", + "2025-11-26 01:15:58,737\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:16:10,921\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0197s.\n", + "2025-11-26 01:16:11,103 - bioneuralnet.downstream_task.dpmon - INFO - Fold 1 best config: {'gnn_layer_num': 4, 'gnn_hidden_dim': 256, 'lr': 0.001257123304842143, 'weight_decay': 0.0003745702077936848, 'nn_hidden_dim1': 128, 'nn_hidden_dim2': 128, 'ae_encoding_dim': 8, 'num_epochs': 1024, 'gnn_dropout': 0.4, 'gnn_activation': 'relu', 'dim_reduction': 'ae'}\n", + "2025-11-26 01:16:11,104 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 1 using train split only\n", + "2025-11-26 01:16:11,109 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:16:15,261 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:16:18,073 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 1 on test set\n", + "2025-11-26 01:16:18,078 - bioneuralnet.downstream_task.dpmon - INFO - Fold 1 results:\n", + "2025-11-26 01:16:18,079 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9470\n", + "2025-11-26 01:16:18,079 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9273\n", + "2025-11-26 01:16:18,079 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9481\n", + "2025-11-26 01:16:18,079 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9627\n", + "2025-11-26 01:16:18,079 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9984\n", + "2025-11-26 01:16:18,080 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9982\n", + "\n", + "2025-11-26 01:16:18,081 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 2/5\n", + "2025-11-26 01:16:18,082 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:16:18,088 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:16:22,220 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:16:22,231 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (526, 901)\n", + "2025-11-26 01:16:22,234\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:16:34,076\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0191s.\n", + "2025-11-26 01:16:34,229 - bioneuralnet.downstream_task.dpmon - INFO - Fold 2 best config: {'gnn_layer_num': 4, 'gnn_hidden_dim': 64, 'lr': 0.00024165542558361394, 'weight_decay': 0.0002652189086272745, 'nn_hidden_dim1': 256, 'nn_hidden_dim2': 128, 'ae_encoding_dim': 8, 'num_epochs': 512, 'gnn_dropout': 0.1, 'gnn_activation': 'relu', 'dim_reduction': 'mlp'}\n", + "2025-11-26 01:16:34,229 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 2 using train split only\n", + "2025-11-26 01:16:34,235 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:16:38,349 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:16:39,516 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 2 on test set\n", + "2025-11-26 01:16:39,521 - bioneuralnet.downstream_task.dpmon - INFO - Fold 2 results:\n", + "2025-11-26 01:16:39,521 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9697\n", + "2025-11-26 01:16:39,521 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9487\n", + "2025-11-26 01:16:39,521 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9701\n", + "2025-11-26 01:16:39,522 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9579\n", + "2025-11-26 01:16:39,522 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9917\n", + "2025-11-26 01:16:39,522 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9461\n", + "\n", + "2025-11-26 01:16:39,522 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 3/5\n", + "2025-11-26 01:16:39,523 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:16:39,529 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:16:43,626 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:16:43,637 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (526, 901)\n", + "2025-11-26 01:16:43,639\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:16:54,182\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0281s.\n", + "2025-11-26 01:16:54,337 - bioneuralnet.downstream_task.dpmon - INFO - Fold 3 best config: {'gnn_layer_num': 1, 'gnn_hidden_dim': 128, 'lr': 0.002302454339377372, 'weight_decay': 1.3521341174248578e-05, 'nn_hidden_dim1': 512, 'nn_hidden_dim2': 128, 'ae_encoding_dim': 4, 'num_epochs': 2048, 'gnn_dropout': 0.3, 'gnn_activation': 'relu', 'dim_reduction': 'ae'}\n", + "2025-11-26 01:16:54,338 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 3 using train split only\n", + "2025-11-26 01:16:54,343 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:16:58,476 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:17:00,722 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 3 on test set\n", + "2025-11-26 01:17:00,727 - bioneuralnet.downstream_task.dpmon - INFO - Fold 3 results:\n", + "2025-11-26 01:17:00,727 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9621\n", + "2025-11-26 01:17:00,727 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9621\n", + "2025-11-26 01:17:00,727 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9622\n", + "2025-11-26 01:17:00,727 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9535\n", + "2025-11-26 01:17:00,728 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9975\n", + "2025-11-26 01:17:00,728 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9970\n", + "\n", + "2025-11-26 01:17:00,728 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 4/5\n", + "2025-11-26 01:17:00,730 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:17:00,737 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:17:04,871 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:17:04,883 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (527, 901)\n", + "2025-11-26 01:17:04,886\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:17:15,818\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0171s.\n", + "2025-11-26 01:17:15,993 - bioneuralnet.downstream_task.dpmon - INFO - Fold 4 best config: {'gnn_layer_num': 1, 'gnn_hidden_dim': 128, 'lr': 0.0019123736530165902, 'weight_decay': 0.0006168499043610002, 'nn_hidden_dim1': 256, 'nn_hidden_dim2': 128, 'ae_encoding_dim': 8, 'num_epochs': 1024, 'gnn_dropout': 0.4, 'gnn_activation': 'relu', 'dim_reduction': 'mlp'}\n", + "2025-11-26 01:17:15,994 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 4 using train split only\n", + "2025-11-26 01:17:15,999 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:17:20,122 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:17:21,350 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 4 on test set\n", + "2025-11-26 01:17:21,355 - bioneuralnet.downstream_task.dpmon - INFO - Fold 4 results:\n", + "2025-11-26 01:17:21,355 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9389\n", + "2025-11-26 01:17:21,355 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9449\n", + "2025-11-26 01:17:21,355 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9390\n", + "2025-11-26 01:17:21,355 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9554\n", + "2025-11-26 01:17:21,355 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9962\n", + "2025-11-26 01:17:21,356 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9940\n", + "\n", + "2025-11-26 01:17:21,356 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 5/5\n", + "2025-11-26 01:17:21,357 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:17:21,363 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:17:25,520 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:17:25,531 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (527, 901)\n", + "2025-11-26 01:17:25,534\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:17:38,250\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0174s.\n", + "2025-11-26 01:17:38,447 - bioneuralnet.downstream_task.dpmon - INFO - Fold 5 best config: {'gnn_layer_num': 2, 'gnn_hidden_dim': 64, 'lr': 0.001685812648195555, 'weight_decay': 0.0023773937538678585, 'nn_hidden_dim1': 256, 'nn_hidden_dim2': 128, 'ae_encoding_dim': 8, 'num_epochs': 512, 'gnn_dropout': 0.2, 'gnn_activation': 'relu', 'dim_reduction': 'linear'}\n", + "2025-11-26 01:17:38,447 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 5 using train split only\n", + "2025-11-26 01:17:38,453 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:17:42,581 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:17:43,369 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 5 on test set\n", + "2025-11-26 01:17:43,374 - bioneuralnet.downstream_task.dpmon - INFO - Fold 5 results:\n", + "2025-11-26 01:17:43,374 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9695\n", + "2025-11-26 01:17:43,374 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9572\n", + "2025-11-26 01:17:43,374 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9694\n", + "2025-11-26 01:17:43,375 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9558\n", + "2025-11-26 01:17:43,375 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9986\n", + "2025-11-26 01:17:43,375 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9967\n", + "\n", + "2025-11-26 01:17:43,378 - bioneuralnet.downstream_task.dpmon - INFO - Successfully saved global best CV model state to: /home/vicente/Github/BioNeuralNet/dpmon_results_GCN_FINAL/kipan/correlation_10/best_cv_model.pt\n", + "2025-11-26 01:17:43,378 - bioneuralnet.downstream_task.dpmon - INFO - Successfully saved global best CV model state to: /home/vicente/Github/BioNeuralNet/dpmon_results_GCN_FINAL/kipan/correlation_10/best_cv_model_embds.pt\n", + "2025-11-26 01:17:43,378 - bioneuralnet.downstream_task.dpmon - INFO - Cross-Validation Results:\n", + "\n", + "2025-11-26 01:17:43,378 - bioneuralnet.downstream_task.dpmon - INFO - Avg Accuracy: 0.9574 +/- 0.0139\n", + "2025-11-26 01:17:43,378 - bioneuralnet.downstream_task.dpmon - INFO - Avg F1 Macro: 0.9480 +/- 0.0134\n", + "2025-11-26 01:17:43,378 - bioneuralnet.downstream_task.dpmon - INFO - Avg F1 Weighted: 0.9578 +/- 0.0137\n", + "2025-11-26 01:17:43,379 - bioneuralnet.downstream_task.dpmon - INFO - Avg Recall: 0.9571 +/- 0.0035\n", + "2025-11-26 01:17:43,379 - bioneuralnet.downstream_task.dpmon - INFO - Avg AUC: 0.9965 +/- 0.0028\n", + "2025-11-26 01:17:43,379 - bioneuralnet.downstream_task.dpmon - INFO - Avg AUPR: 0.9864 +/- 0.0226\n", + "2025-11-26 01:17:43,379 - bioneuralnet.downstream_task.dpmon - INFO - DPMON run completed.\n", + "2025-11-26 01:17:43,380 - bioneuralnet.downstream_task.dpmon - INFO - Output directory set to: /home/vicente/Github/BioNeuralNet/dpmon_results_GCN_FINAL/kipan/similarity_10\n", + "2025-11-26 01:17:43,380 - bioneuralnet.downstream_task.dpmon - INFO - Initialized DPMON with model: GCN\n", + "2025-11-26 01:17:43,380 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 01:17:43,380 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 01:17:43,381 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 01:17:43,381 - bioneuralnet.downstream_task.dpmon - INFO - Random seed set to: 8183\n", + "2025-11-26 01:17:43,386 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:17:43,387 - bioneuralnet.downstream_task.dpmon - INFO - Running in Cross-Validation mode (cv=True) with 5 folds.\n", + "2025-11-26 01:17:43,388 - bioneuralnet.downstream_task.dpmon - INFO - CV Setup: Standard 5-fold split.\n", + "2025-11-26 01:17:43,388 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 1/5\n", + "2025-11-26 01:17:43,390 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:17:43,397 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:17:47,498 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:17:47,510 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (526, 901)\n", + "2025-11-26 01:17:47,513\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:18:00,506\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0235s.\n", + "2025-11-26 01:18:00,689 - bioneuralnet.downstream_task.dpmon - INFO - Fold 1 best config: {'gnn_layer_num': 2, 'gnn_hidden_dim': 64, 'lr': 0.0006037895979188083, 'weight_decay': 0.004195106610298857, 'nn_hidden_dim1': 512, 'nn_hidden_dim2': 128, 'ae_encoding_dim': 16, 'num_epochs': 1024, 'gnn_dropout': 0.1, 'gnn_activation': 'elu', 'dim_reduction': 'ae'}\n", + "2025-11-26 01:18:00,690 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 1 using train split only\n", + "2025-11-26 01:18:00,695 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:18:04,943 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:18:06,393 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 1 on test set\n", + "2025-11-26 01:18:06,398 - bioneuralnet.downstream_task.dpmon - INFO - Fold 1 results:\n", + "2025-11-26 01:18:06,398 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9621\n", + "2025-11-26 01:18:06,399 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9458\n", + "2025-11-26 01:18:06,399 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9629\n", + "2025-11-26 01:18:06,399 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9731\n", + "2025-11-26 01:18:06,399 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9831\n", + "2025-11-26 01:18:06,399 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9689\n", + "\n", + "2025-11-26 01:18:06,399 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 2/5\n", + "2025-11-26 01:18:06,401 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:18:06,406 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:18:10,545 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:18:10,557 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (526, 901)\n", + "2025-11-26 01:18:10,560\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:18:21,270\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0070s.\n", + "2025-11-26 01:18:21,414 - bioneuralnet.downstream_task.dpmon - INFO - Fold 2 best config: {'gnn_layer_num': 2, 'gnn_hidden_dim': 64, 'lr': 0.0007034204356990857, 'weight_decay': 5.358735876893429e-05, 'nn_hidden_dim1': 512, 'nn_hidden_dim2': 64, 'ae_encoding_dim': 8, 'num_epochs': 1024, 'gnn_dropout': 0.2, 'gnn_activation': 'relu', 'dim_reduction': 'ae'}\n", + "2025-11-26 01:18:21,415 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 2 using train split only\n", + "2025-11-26 01:18:21,420 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:18:25,548 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:18:27,026 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 2 on test set\n", + "2025-11-26 01:18:27,031 - bioneuralnet.downstream_task.dpmon - INFO - Fold 2 results:\n", + "2025-11-26 01:18:27,032 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9773\n", + "2025-11-26 01:18:27,032 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9648\n", + "2025-11-26 01:18:27,032 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9777\n", + "2025-11-26 01:18:27,032 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9835\n", + "2025-11-26 01:18:27,032 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9926\n", + "2025-11-26 01:18:27,032 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9360\n", + "\n", + "2025-11-26 01:18:27,033 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 3/5\n", + "2025-11-26 01:18:27,034 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:18:27,039 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:18:31,171 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:18:31,183 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (526, 901)\n", + "2025-11-26 01:18:31,186\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:18:41,965\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0160s.\n", + "2025-11-26 01:18:42,128 - bioneuralnet.downstream_task.dpmon - INFO - Fold 3 best config: {'gnn_layer_num': 3, 'gnn_hidden_dim': 128, 'lr': 0.0006980653261883734, 'weight_decay': 6.059927981082565e-05, 'nn_hidden_dim1': 256, 'nn_hidden_dim2': 128, 'ae_encoding_dim': 8, 'num_epochs': 2048, 'gnn_dropout': 0.0, 'gnn_activation': 'elu', 'dim_reduction': 'ae'}\n", + "2025-11-26 01:18:42,129 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 3 using train split only\n", + "2025-11-26 01:18:42,134 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:18:46,248 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:18:49,771 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 3 on test set\n", + "2025-11-26 01:18:49,776 - bioneuralnet.downstream_task.dpmon - INFO - Fold 3 results:\n", + "2025-11-26 01:18:49,777 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9470\n", + "2025-11-26 01:18:49,777 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9318\n", + "2025-11-26 01:18:49,777 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9468\n", + "2025-11-26 01:18:49,778 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9244\n", + "2025-11-26 01:18:49,778 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9966\n", + "2025-11-26 01:18:49,778 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9917\n", + "\n", + "2025-11-26 01:18:49,779 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 4/5\n", + "2025-11-26 01:18:49,781 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:18:49,788 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:18:53,915 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:18:53,926 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (527, 901)\n", + "2025-11-26 01:18:53,929\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:19:05,812\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0167s.\n", + "2025-11-26 01:19:05,972 - bioneuralnet.downstream_task.dpmon - INFO - Fold 4 best config: {'gnn_layer_num': 3, 'gnn_hidden_dim': 64, 'lr': 0.0013470053780947797, 'weight_decay': 0.00030758808386390933, 'nn_hidden_dim1': 256, 'nn_hidden_dim2': 128, 'ae_encoding_dim': 16, 'num_epochs': 1024, 'gnn_dropout': 0.4, 'gnn_activation': 'relu', 'dim_reduction': 'ae'}\n", + "2025-11-26 01:19:05,973 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 4 using train split only\n", + "2025-11-26 01:19:05,978 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:19:10,184 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:19:12,014 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 4 on test set\n", + "2025-11-26 01:19:12,019 - bioneuralnet.downstream_task.dpmon - INFO - Fold 4 results:\n", + "2025-11-26 01:19:12,019 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9389\n", + "2025-11-26 01:19:12,019 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9449\n", + "2025-11-26 01:19:12,019 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9390\n", + "2025-11-26 01:19:12,019 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9554\n", + "2025-11-26 01:19:12,020 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9963\n", + "2025-11-26 01:19:12,020 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9941\n", + "\n", + "2025-11-26 01:19:12,020 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 5/5\n", + "2025-11-26 01:19:12,021 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:19:12,027 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:19:16,138 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:19:16,150 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (527, 901)\n", + "2025-11-26 01:19:16,153\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:19:28,943\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0206s.\n", + "2025-11-26 01:19:29,171 - bioneuralnet.downstream_task.dpmon - INFO - Fold 5 best config: {'gnn_layer_num': 4, 'gnn_hidden_dim': 64, 'lr': 0.0007025528058103543, 'weight_decay': 0.0018355753249756474, 'nn_hidden_dim1': 512, 'nn_hidden_dim2': 128, 'ae_encoding_dim': 4, 'num_epochs': 1024, 'gnn_dropout': 0.0, 'gnn_activation': 'relu', 'dim_reduction': 'mlp'}\n", + "2025-11-26 01:19:29,172 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 5 using train split only\n", + "2025-11-26 01:19:29,177 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:19:33,346 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:19:35,544 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 5 on test set\n", + "2025-11-26 01:19:35,549 - bioneuralnet.downstream_task.dpmon - INFO - Fold 5 results:\n", + "2025-11-26 01:19:35,549 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9313\n", + "2025-11-26 01:19:35,550 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9080\n", + "2025-11-26 01:19:35,550 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9331\n", + "2025-11-26 01:19:35,550 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9473\n", + "2025-11-26 01:19:35,550 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9938\n", + "2025-11-26 01:19:35,550 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9915\n", + "\n", + "2025-11-26 01:19:35,553 - bioneuralnet.downstream_task.dpmon - INFO - Successfully saved global best CV model state to: /home/vicente/Github/BioNeuralNet/dpmon_results_GCN_FINAL/kipan/similarity_10/best_cv_model.pt\n", + "2025-11-26 01:19:35,553 - bioneuralnet.downstream_task.dpmon - INFO - Successfully saved global best CV model state to: /home/vicente/Github/BioNeuralNet/dpmon_results_GCN_FINAL/kipan/similarity_10/best_cv_model_embds.pt\n", + "2025-11-26 01:19:35,553 - bioneuralnet.downstream_task.dpmon - INFO - Cross-Validation Results:\n", + "\n", + "2025-11-26 01:19:35,553 - bioneuralnet.downstream_task.dpmon - INFO - Avg Accuracy: 0.9513 +/- 0.0185\n", + "2025-11-26 01:19:35,553 - bioneuralnet.downstream_task.dpmon - INFO - Avg F1 Macro: 0.9390 +/- 0.0210\n", + "2025-11-26 01:19:35,554 - bioneuralnet.downstream_task.dpmon - INFO - Avg F1 Weighted: 0.9519 +/- 0.0183\n", + "2025-11-26 01:19:35,554 - bioneuralnet.downstream_task.dpmon - INFO - Avg Recall: 0.9567 +/- 0.0230\n", + "2025-11-26 01:19:35,554 - bioneuralnet.downstream_task.dpmon - INFO - Avg AUC: 0.9925 +/- 0.0055\n", + "2025-11-26 01:19:35,554 - bioneuralnet.downstream_task.dpmon - INFO - Avg AUPR: 0.9764 +/- 0.0248\n", + "2025-11-26 01:19:35,555 - bioneuralnet.downstream_task.dpmon - INFO - DPMON run completed.\n", + "2025-11-26 01:19:35,555 - bioneuralnet.downstream_task.dpmon - INFO - Output directory set to: /home/vicente/Github/BioNeuralNet/dpmon_results_GCN_FINAL/kipan/gaussian_15\n", + "2025-11-26 01:19:35,555 - bioneuralnet.downstream_task.dpmon - INFO - Initialized DPMON with model: GCN\n", + "2025-11-26 01:19:35,555 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 01:19:35,556 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 01:19:35,556 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 01:19:35,556 - bioneuralnet.downstream_task.dpmon - INFO - Random seed set to: 8183\n", + "2025-11-26 01:19:35,561 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:19:35,562 - bioneuralnet.downstream_task.dpmon - INFO - Running in Cross-Validation mode (cv=True) with 5 folds.\n", + "2025-11-26 01:19:35,563 - bioneuralnet.downstream_task.dpmon - INFO - CV Setup: Standard 5-fold split.\n", + "2025-11-26 01:19:35,564 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 1/5\n", + "2025-11-26 01:19:35,566 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:19:35,573 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:19:39,710 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:19:39,725 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (526, 901)\n", + "2025-11-26 01:19:39,728\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:19:52,665\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0205s.\n", + "2025-11-26 01:19:52,846 - bioneuralnet.downstream_task.dpmon - INFO - Fold 1 best config: {'gnn_layer_num': 3, 'gnn_hidden_dim': 128, 'lr': 0.002995945702914729, 'weight_decay': 7.542726756559785e-05, 'nn_hidden_dim1': 256, 'nn_hidden_dim2': 32, 'ae_encoding_dim': 4, 'num_epochs': 2048, 'gnn_dropout': 0.0, 'gnn_activation': 'elu', 'dim_reduction': 'mlp'}\n", + "2025-11-26 01:19:52,847 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 1 using train split only\n", + "2025-11-26 01:19:52,851 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:19:56,959 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:20:00,808 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 1 on test set\n", + "2025-11-26 01:20:00,813 - bioneuralnet.downstream_task.dpmon - INFO - Fold 1 results:\n", + "2025-11-26 01:20:00,813 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9621\n", + "2025-11-26 01:20:00,814 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9538\n", + "2025-11-26 01:20:00,814 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9622\n", + "2025-11-26 01:20:00,814 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9740\n", + "2025-11-26 01:20:00,814 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9945\n", + "2025-11-26 01:20:00,814 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9854\n", + "\n", + "2025-11-26 01:20:00,815 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 2/5\n", + "2025-11-26 01:20:00,817 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:20:00,822 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:20:04,918 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:20:04,932 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (526, 901)\n", + "2025-11-26 01:20:04,935\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:20:15,891\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0178s.\n", + "2025-11-26 01:20:16,037 - bioneuralnet.downstream_task.dpmon - INFO - Fold 2 best config: {'gnn_layer_num': 4, 'gnn_hidden_dim': 64, 'lr': 0.00024165542558361394, 'weight_decay': 0.0002652189086272745, 'nn_hidden_dim1': 256, 'nn_hidden_dim2': 128, 'ae_encoding_dim': 8, 'num_epochs': 512, 'gnn_dropout': 0.1, 'gnn_activation': 'relu', 'dim_reduction': 'mlp'}\n", + "2025-11-26 01:20:16,037 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 2 using train split only\n", + "2025-11-26 01:20:16,042 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:20:20,158 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:20:21,370 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 2 on test set\n", + "2025-11-26 01:20:21,375 - bioneuralnet.downstream_task.dpmon - INFO - Fold 2 results:\n", + "2025-11-26 01:20:21,376 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9697\n", + "2025-11-26 01:20:21,376 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9487\n", + "2025-11-26 01:20:21,376 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9701\n", + "2025-11-26 01:20:21,376 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9579\n", + "2025-11-26 01:20:21,376 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9898\n", + "2025-11-26 01:20:21,377 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9414\n", + "\n", + "2025-11-26 01:20:21,377 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 3/5\n", + "2025-11-26 01:20:21,379 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:20:21,384 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:20:25,595 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:20:25,609 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (526, 901)\n", + "2025-11-26 01:20:25,612\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:20:36,485\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0081s.\n", + "2025-11-26 01:20:36,637 - bioneuralnet.downstream_task.dpmon - INFO - Fold 3 best config: {'gnn_layer_num': 3, 'gnn_hidden_dim': 128, 'lr': 0.0006980653261883734, 'weight_decay': 6.059927981082565e-05, 'nn_hidden_dim1': 256, 'nn_hidden_dim2': 128, 'ae_encoding_dim': 8, 'num_epochs': 2048, 'gnn_dropout': 0.0, 'gnn_activation': 'elu', 'dim_reduction': 'ae'}\n", + "2025-11-26 01:20:36,637 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 3 using train split only\n", + "2025-11-26 01:20:36,642 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:20:40,760 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:20:44,494 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 3 on test set\n", + "2025-11-26 01:20:44,499 - bioneuralnet.downstream_task.dpmon - INFO - Fold 3 results:\n", + "2025-11-26 01:20:44,499 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9470\n", + "2025-11-26 01:20:44,499 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9318\n", + "2025-11-26 01:20:44,499 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9468\n", + "2025-11-26 01:20:44,500 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9244\n", + "2025-11-26 01:20:44,500 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9970\n", + "2025-11-26 01:20:44,500 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9936\n", + "\n", + "2025-11-26 01:20:44,500 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 4/5\n", + "2025-11-26 01:20:44,502 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:20:44,509 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:20:48,666 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:20:48,680 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (527, 901)\n", + "2025-11-26 01:20:48,683\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:20:59,905\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0143s.\n", + "2025-11-26 01:21:00,060 - bioneuralnet.downstream_task.dpmon - INFO - Fold 4 best config: {'gnn_layer_num': 4, 'gnn_hidden_dim': 128, 'lr': 0.0008628455289617886, 'weight_decay': 0.005542594076906398, 'nn_hidden_dim1': 512, 'nn_hidden_dim2': 128, 'ae_encoding_dim': 16, 'num_epochs': 2048, 'gnn_dropout': 0.2, 'gnn_activation': 'elu', 'dim_reduction': 'linear'}\n", + "2025-11-26 01:21:00,061 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 4 using train split only\n", + "2025-11-26 01:21:00,066 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:21:04,216 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:21:09,152 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 4 on test set\n", + "2025-11-26 01:21:09,157 - bioneuralnet.downstream_task.dpmon - INFO - Fold 4 results:\n", + "2025-11-26 01:21:09,157 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9542\n", + "2025-11-26 01:21:09,157 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9398\n", + "2025-11-26 01:21:09,157 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9549\n", + "2025-11-26 01:21:09,157 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9659\n", + "2025-11-26 01:21:09,158 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9964\n", + "2025-11-26 01:21:09,158 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9957\n", + "\n", + "2025-11-26 01:21:09,158 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 5/5\n", + "2025-11-26 01:21:09,160 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:21:09,166 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:21:13,268 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:21:13,282 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (527, 901)\n", + "2025-11-26 01:21:13,285\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:21:25,473\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0135s.\n", + "2025-11-26 01:21:25,665 - bioneuralnet.downstream_task.dpmon - INFO - Fold 5 best config: {'gnn_layer_num': 4, 'gnn_hidden_dim': 64, 'lr': 0.0007025528058103543, 'weight_decay': 0.0018355753249756474, 'nn_hidden_dim1': 512, 'nn_hidden_dim2': 128, 'ae_encoding_dim': 4, 'num_epochs': 1024, 'gnn_dropout': 0.0, 'gnn_activation': 'relu', 'dim_reduction': 'mlp'}\n", + "2025-11-26 01:21:25,665 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 5 using train split only\n", + "2025-11-26 01:21:25,671 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:21:29,789 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:21:32,020 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 5 on test set\n", + "2025-11-26 01:21:32,026 - bioneuralnet.downstream_task.dpmon - INFO - Fold 5 results:\n", + "2025-11-26 01:21:32,026 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9618\n", + "2025-11-26 01:21:32,026 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9432\n", + "2025-11-26 01:21:32,027 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9622\n", + "2025-11-26 01:21:32,027 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9506\n", + "2025-11-26 01:21:32,027 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9970\n", + "2025-11-26 01:21:32,027 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9899\n", + "\n", + "2025-11-26 01:21:32,029 - bioneuralnet.downstream_task.dpmon - INFO - Successfully saved global best CV model state to: /home/vicente/Github/BioNeuralNet/dpmon_results_GCN_FINAL/kipan/gaussian_15/best_cv_model.pt\n", + "2025-11-26 01:21:32,030 - bioneuralnet.downstream_task.dpmon - INFO - Successfully saved global best CV model state to: /home/vicente/Github/BioNeuralNet/dpmon_results_GCN_FINAL/kipan/gaussian_15/best_cv_model_embds.pt\n", + "2025-11-26 01:21:32,030 - bioneuralnet.downstream_task.dpmon - INFO - Cross-Validation Results:\n", + "\n", + "2025-11-26 01:21:32,030 - bioneuralnet.downstream_task.dpmon - INFO - Avg Accuracy: 0.9590 +/- 0.0087\n", + "2025-11-26 01:21:32,030 - bioneuralnet.downstream_task.dpmon - INFO - Avg F1 Macro: 0.9434 +/- 0.0084\n", + "2025-11-26 01:21:32,030 - bioneuralnet.downstream_task.dpmon - INFO - Avg F1 Weighted: 0.9592 +/- 0.0088\n", + "2025-11-26 01:21:32,030 - bioneuralnet.downstream_task.dpmon - INFO - Avg Recall: 0.9546 +/- 0.0190\n", + "2025-11-26 01:21:32,031 - bioneuralnet.downstream_task.dpmon - INFO - Avg AUC: 0.9949 +/- 0.0030\n", + "2025-11-26 01:21:32,031 - bioneuralnet.downstream_task.dpmon - INFO - Avg AUPR: 0.9812 +/- 0.0226\n", + "2025-11-26 01:21:32,031 - bioneuralnet.downstream_task.dpmon - INFO - DPMON run completed.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Results for: threshold_10\n", + "Accuracy (Avg +/- Std): 0.9574 +/- 0.0175\n", + "F1 Macro (Avg +/- Std): 0.9536 +/- 0.0165\n", + "F1 Weighted (Avg +/- Std): 0.9576 +/- 0.0175\n", + "Recall: 0.9651 +/- 0.0138\n", + "AUC: 0.9943 +/- 0.0029\n", + "\n", + "Results for: correlation_10\n", + "Accuracy (Avg +/- Std): 0.9574 +/- 0.0139\n", + "F1 Macro (Avg +/- Std): 0.9480 +/- 0.0134\n", + "F1 Weighted (Avg +/- Std): 0.9578 +/- 0.0137\n", + "Recall: 0.9571 +/- 0.0035\n", + "AUC: 0.9965 +/- 0.0028\n", + "\n", + "Results for: similarity_10\n", + "Accuracy (Avg +/- Std): 0.9513 +/- 0.0185\n", + "F1 Macro (Avg +/- Std): 0.9390 +/- 0.0210\n", + "F1 Weighted (Avg +/- Std): 0.9519 +/- 0.0183\n", + "Recall: 0.9567 +/- 0.0230\n", + "AUC: 0.9925 +/- 0.0055\n", + "\n", + "Results for: gaussian_15\n", + "Accuracy (Avg +/- Std): 0.9590 +/- 0.0087\n", + "F1 Macro (Avg +/- Std): 0.9434 +/- 0.0084\n", + "F1 Weighted (Avg +/- Std): 0.9592 +/- 0.0088\n", + "Recall: 0.9546 +/- 0.0190\n", + "AUC: 0.9949 +/- 0.0030\n" + ] + } + ], + "source": [ + "output_dir_base = Path(\"/home/vicente/Github/BioNeuralNet/dpmon_results_GCN_FINAL/kipan\")\n", + "\n", + "all_results_GCN = []\n", + "\n", + "for run_config in comparison_runs:\n", + " graph_name = run_config[\"name\"]\n", + " A_full = run_config[\"graph\"]\n", + "\n", + " current_output_dir = output_dir_base / graph_name\n", + " current_output_dir.mkdir(parents=True, exist_ok=True)\n", + "\n", + " dpmon_params_base = {\n", + " \"adjacency_matrix\": A_full,\n", + " \"omics_list\": omics_kipan,\n", + " \"phenotype_data\": target,\n", + " \"phenotype_col\": \"target\",\n", + " \"clinical_data\": clinical_for_model,\n", + " \"model\": 'GCN',\n", + " \"tune\": True, \n", + " \"cv\": True, \n", + " \"n_folds\": 5,\n", + " \"repeat_num\": 1,\n", + " \"gpu\": True,\n", + " \"cuda\": 0,\n", + " \"seed\": SEED,\n", + " \"output_dir\": current_output_dir\n", + " }\n", + " \n", + " dpmon_tunned = DPMON(**dpmon_params_base)\n", + " predictions_df, metrics, embeddings = dpmon_tunned.run()\n", + "\n", + " all_results_GCN.append({\n", + " \"graph\": graph_name,\n", + " \"predictions\": predictions_df,\n", + " \"metrics\": metrics,\n", + " \"embeddings\": embeddings\n", + " })\n", + "\n", + "for res in all_results_GCN:\n", + " graph_name = res[\"graph\"]\n", + " graph_metrics = res[\"metrics\"]\n", + " \n", + " acc_row = graph_metrics.loc[graph_metrics['Metric'] == 'Accuracy'].iloc[0]\n", + " f1_macro_row = graph_metrics.loc[graph_metrics['Metric'] == 'F1 Macro'].iloc[0]\n", + " f1_weighted_row = graph_metrics.loc[graph_metrics['Metric'] == 'F1 Weighted'].iloc[0]\n", + " recall_row = graph_metrics.loc[graph_metrics['Metric'] == 'Recall'].iloc[0]\n", + " auc_row = graph_metrics.loc[graph_metrics['Metric'] == 'AUC'].iloc[0]\n", + " aupr_row = graph_metrics.loc[graph_metrics['Metric'] == 'AUPR'].iloc[0]\n", + " \n", + " acc_avg, acc_std = acc_row['Average'], acc_row['StdDev']\n", + " f1_macro_avg, f1_macro_std = f1_macro_row['Average'], f1_macro_row['StdDev']\n", + " f1_weighted_avg, f1_weighted_std = f1_weighted_row['Average'], f1_weighted_row['StdDev']\n", + " recall_avg, recall_std = recall_row['Average'], recall_row['StdDev']\n", + " auc_avg, auc_std = auc_row['Average'], auc_row['StdDev']\n", + " aupr_avg, aupr_std = aupr_row['Average'], aupr_row['StdDev']\n", + "\n", + " print(f\"\\nResults for: {graph_name}\")\n", + " print(f\"Accuracy (Avg +/- Std): {acc_avg:.4f} +/- {acc_std:.4f}\")\n", + " print(f\"F1 Macro (Avg +/- Std): {f1_macro_avg:.4f} +/- {f1_macro_std:.4f}\")\n", + " print(f\"F1 Weighted (Avg +/- Std): {f1_weighted_avg:.4f} +/- {f1_weighted_std:.4f}\")\n", + " print(f\"Recall: {recall_avg:.4f} +/- {recall_std:.4f}\")\n", + " print(f\"AUC: {auc_avg:.4f} +/- {auc_std:.4f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "d36f4ced", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " 0 1 2 3 4\n", + "MGP 0.035549 0.001088 0.0 0.0 0.003548\n", + "ADH1C 0.000000 0.000253 0.0 0.0 0.026202\n", + "LOC440354 0.000000 0.002382 0.0 0.0 0.000000\n", + "C1orf110 0.000000 0.000261 0.0 0.0 0.060484\n", + "CLDN8 0.000000 0.001527 0.0 0.0 0.004444" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#This time we will select the gaussian_15 graph embeddings\n", + "res = all_results_GCN[3] \n", + "tensor_data = res[\"embeddings\"]\n", + "\n", + "# .detach().cpu().numpy() to get the raw numbers out of pytorch\n", + "GCN_embeddings_df = pd.DataFrame(\n", + " tensor_data.detach().cpu().numpy(),\n", + " index=omics_kipan.columns\n", + ")\n", + "\n", + "display(GCN_embeddings_df.iloc[:5,:5])" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "db08276e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Plotting Feature Embeddings for graph: similarity_10\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from bioneuralnet.metrics import plot_embeddings\n", + "\n", + "# for a more interpretable embedding plot we will map the node labels to omics types\n", + "GCN_node_labels = []\n", + "GCN_embeddings_array = GCN_embeddings_df.values\n", + "feature_names = GCN_embeddings_df.index\n", + "\n", + "# using the sets from the orginal to serve as a loopup table\n", + "dna_feats = set(dna_meth.columns)\n", + "rna_feats = set(rna.columns)\n", + "mirna_feats = set(mirna.columns)\n", + "\n", + "for feat in feature_names:\n", + " if feat in dna_feats:\n", + " # 1 for DNA methylation\n", + " GCN_node_labels.append(1)\n", + " elif feat in rna_feats:\n", + " # 2 for RNA \n", + " GCN_node_labels.append(2)\n", + " elif feat in mirna_feats:\n", + " # and 3 for miRNA \n", + " GCN_node_labels.append(3)\n", + " else:\n", + " GCN_node_labels.append(0)\n", + "\n", + "# our plotting function needs an array to squeeze the embedings into 2Dspace\n", + "GCN_node_labels = np.array(GCN_node_labels)\n", + "\n", + "# feature(omics) embeddings colored by omics type.\n", + "print(f\"Plotting Feature Embeddings for graph: {res['graph']}\")\n", + "plot_embeddings(GCN_embeddings_array, GCN_node_labels, legend_labels=[\"DNA_Methylation\", \"RNA\", \"miRNA\"])" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".enviroment", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/TCGA-Notebooks/TCGA-LGG.ipynb b/TCGA-Notebooks/TCGA-LGG.ipynb new file mode 100644 index 0000000..7a37802 --- /dev/null +++ b/TCGA-Notebooks/TCGA-LGG.ipynb @@ -0,0 +1,940 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "b42de981", + "metadata": {}, + "source": [ + "# TCGA-LGG Analysis: *Vital Status* classification\n", + "\n", + "- **Cohort**: Focuses on the TCGA-LGG dataset for Lower-Grade Glioma (LGG).\n", + "- **Goal**: Classification\n", + "- **Prediction Target**: Predict `Vital Status` based on their omics profiles.\n", + "\n", + "**Data Source:** Broad Institute FireHose (http://firebrowse.org/?cohort=LGG)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "027dc20c", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "from pathlib import Path\n", + "root = Path(\"/home/vicente/Github/BioNeuralNet/LGG\")\n", + "\n", + "mirna_raw = pd.read_csv(root/\"LGG.miRseq_RPKM_log2.txt\", sep=\"\\t\",index_col=0,low_memory=False) \n", + "rna_raw = pd.read_csv(root / \"LGG.uncv2.mRNAseq_RSEM_normalized_log2.txt\", sep=\"\\t\",index_col=0,low_memory=False)\n", + "meth_raw = pd.read_csv(root/\"LGG.meth.by_mean.data.txt\", sep='\\t',index_col=0,low_memory=False)\n", + "clinical_raw = pd.read_csv(root / \"LGG.clin.merged.picked.txt\",sep=\"\\t\", index_col=0, low_memory=False)\n", + "\n", + "# display shapes and first few rows-columns of each file\n", + "display(mirna_raw.iloc[:3,:5])\n", + "display(mirna_raw.shape)\n", + "\n", + "display(rna_raw.iloc[:3,:5])\n", + "display(meth_raw.shape)\n", + "\n", + "display(meth_raw.iloc[:3,:5])\n", + "display(meth_raw.shape)\n", + "\n", + "display(clinical_raw.iloc[:3,:5])\n", + "display(clinical_raw.shape)" + ] + }, + { + "cell_type": "markdown", + "id": "cbacccaf", + "metadata": {}, + "source": [ + "## Data Processing Summary\n", + "\n", + "1. **Transpose Data:** All raw data (miRNA, RNA, etc.) is flipped so rows represent patients and columns represent features.\n", + "2. **Standardize Patient IDs:** Patient IDs in all tables are cleaned to the 12-character TCGA format (e.g., `TCGA-AB-1234`) for matching.\n", + "3. **Handle Duplicates:** Duplicate patient rows are averaged in the omics data. The first entry is kept for duplicate patients in the clinical data.\n", + "4. **Find Common Patients:** The script identifies the list of patients that exist in *all* datasets.\n", + "5. **Subset Data:** All data tables are filtered down to *only* this common list of patients, ensuring alignment.\n", + "6. **Extract Target:** The `vital_status` column is pulled from the processed clinical data to be used as the prediction target (y-variable)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "820583f3", + "metadata": {}, + "outputs": [], + "source": [ + "mirna = mirna_raw.T\n", + "rna = rna_raw.T\n", + "meth = meth_raw.T\n", + "clinical = clinical_raw.T\n", + "\n", + "print(f\"miRNA (samples, features): {mirna.shape}\")\n", + "print(f\"RNA (samples, features): {rna.shape}\")\n", + "print(f\"Methylation (samples, features): {meth.shape}\")\n", + "print(f\"Clinical (samples, features): {clinical.shape}\")\n", + "\n", + "def trim_barcode(idx):\n", + " return idx.to_series().str.slice(0, 12)\n", + "\n", + "# standarized patient IDs across all files\n", + "meth.index = trim_barcode(meth.index)\n", + "rna.index = trim_barcode(rna.index)\n", + "mirna.index = trim_barcode(mirna.index)\n", + "clinical.index = clinical.index.str.upper()\n", + "clinical.index.name = \"Patient_ID\"\n", + "\n", + "# convert all data to numeric, coercing errors to NaN\n", + "meth = meth.apply(pd.to_numeric, errors='coerce')\n", + "rna = rna.apply(pd.to_numeric, errors='coerce')\n", + "mirna = mirna.apply(pd.to_numeric, errors='coerce')\n", + "\n", + "# for any duplicate columns in the omics data, we average their values\n", + "meth = meth.groupby(meth.index).mean()\n", + "rna = rna.groupby(rna.index).mean()\n", + "mirna = mirna.groupby(mirna.index).mean()\n", + "\n", + "# for any duplicate rows in the clinical data, we keep the first occurrence\n", + "clinical = clinical[~clinical.index.duplicated(keep='first')]\n", + "\n", + "print(f\"\\nMethylation shape: {meth.shape}\")\n", + "print(f\"RNA shape: {rna.shape}\")\n", + "print(f\"miRNA shape: {mirna.shape}\")\n", + "print(f\"Clinical shape: {clinical.shape}\")\n", + "\n", + "for df in [meth, rna, mirna]:\n", + " df.columns = df.columns.str.replace(r\"\\?\", \"unknown_\", regex=True)\n", + " df.columns = df.columns.str.replace(r\"\\|\", \"_\", regex=True)\n", + " df.columns = df.columns.str.replace(\"-\", \"_\", regex=False)\n", + " df.columns = df.columns.str.replace(r\"_+\", \"_\", regex=True)\n", + " df.columns = df.columns.str.strip(\"_\")\n", + " \n", + " df.fillna(df.mean(), inplace=True)\n", + "\n", + "# to see which pateints are common across all data files\n", + "common_patients = sorted(list(set(meth.index)&set(rna.index)&set(mirna.index)&set(clinical.index)))\n", + "\n", + "print(f\"\\nFound: {len(common_patients)} patients across all data types.\")\n", + "\n", + "# subset to only common patients\n", + "meth_processed = meth.loc[common_patients]\n", + "rna_processed= rna.loc[common_patients]\n", + "mirna_processed = mirna.loc[common_patients]\n", + "clinical_processed = clinical.loc[common_patients]\n", + "\n", + "# extract target labels from clinical data\n", + "targets = clinical_processed['vital_status']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d9c562ba", + "metadata": {}, + "outputs": [], + "source": [ + "display(mirna_processed.iloc[:3,:5])\n", + "display(mirna_processed.shape)\n", + "\n", + "display(rna_processed.iloc[:3,:5])\n", + "display(rna_processed.shape)\n", + "\n", + "display(meth_processed.iloc[:3,:5])\n", + "display(meth_processed.shape)\n", + "\n", + "display(clinical_processed.iloc[:3,:5])\n", + "display(clinical_processed.shape)\n", + "\n", + "display(targets.value_counts())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "edc9e7f8", + "metadata": {}, + "outputs": [], + "source": [ + "import bioneuralnet as bnn\n", + "\n", + "# drop unwanted columns from clinical data\n", + "clinical_processed.drop(columns=[\"Composite Element REF\"], errors=\"ignore\", inplace=True)\n", + "\n", + "# we transform the methylation beta values to M-values and drop unwanted columns\n", + "meth_m = meth_processed.drop(columns=[\"Composite Element REF\"], errors=\"ignore\")\n", + "\n", + "# convert beta values to M-values using bioneuralnet utility with small epsilon to avoid log(0)\n", + "meth_m = bnn.utils.beta_to_m(meth_m, eps=1e-6) \n", + "\n", + "# lastly we turn the target labels into numerical classes\n", + "mapping = {\"astrocytoma\": 0, \"oligodendroglioma\": 1, \"oligoastrocytoma\": 2}\n", + "target_labels = targets.map(mapping).to_frame(name=\"target\")\n", + "\n", + "# as a safety check we align the indices once more\n", + "X_meth = meth_m.loc[common_patients]\n", + "X_rna = rna_processed.loc[common_patients]\n", + "X_mirna = mirna_processed.loc[common_patients]\n", + "Y_labels = target_labels.loc[common_patients]\n", + "clinical_final = clinical_processed.loc[common_patients]\n", + "\n", + "print(f\"\\nDNA_Methylation shape: {X_meth.shape}\")\n", + "print(f\"RNA shape: {X_rna.shape}\")\n", + "print(f\"miRNA shape: {X_mirna.shape}\")\n", + "print(f\"Clinical shape: {clinical_final.shape}\")\n", + "print(Y_labels.value_counts())" + ] + }, + { + "cell_type": "markdown", + "id": "0643ec13", + "metadata": {}, + "source": [ + "## Feature Selection Methodology\n", + "\n", + "### Supported Methods and Interpretation\n", + "\n", + "**BioNeuralNet** provides three techniques for feature selection, allowing for different views of the data's statistical profile:\n", + "\n", + "- **Variance Thresholding:** Identifies features with the **highest overall variance** across all samples.\n", + "\n", + "- **ANOVA F-test:** Pinpoints features that best **distinguish between the target classes** (KIRC, KIRP, and KICH).\n", + "\n", + "- **Random Forest Importance:** Assesses **feature utility** based on its contribution to a predictive non-linear model.\n", + "\n", + "### LGG Cohort Selection Strategy\n", + "\n", + "A dimensionality reduction step was essential for managing the high-feature-count omics data:\n", + "\n", + "- **High-Feature Datasets:** Both DNA Methylation (20,114) and RNA (18,328) required significant feature reduction.\n", + "\n", + "- **Filtering Process:** The **top 6,000 features** were initially extracted from the Methylation and RNA datasets using all three methods.\n", + "\n", + "- **Final Set:** A consensus set was built by finding the intersection of features selected by the ANOVA F-test and Random Forest Importance, ensuring both statistical relevance and model-based utility.\n", + "\n", + "- **Low-Feature Datasets:** The miRNA data (548 features) was passed through **without selection**, as its feature count was already manageable." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "978e9281", + "metadata": {}, + "outputs": [], + "source": [ + "import bioneuralnet as bnn\n", + "\n", + "# feature selection\n", + "meth_highvar = bnn.utils.select_top_k_variance(X_meth, k=6000)\n", + "rna_highvar = bnn.utils.select_top_k_variance(X_rna, k=6000)\n", + "\n", + "meth_af = bnn.utils.top_anova_f_features(X_meth, Y_labels, max_features=6000)\n", + "rna_af = bnn.utils.top_anova_f_features(X_rna, Y_labels, max_features=6000)\n", + "\n", + "meth_rf = bnn.utils.select_top_randomforest(X_meth, Y_labels, top_k=6000)\n", + "rna_rf = bnn.utils.select_top_randomforest(X_rna, Y_labels, top_k=6000)\n", + "\n", + "meth_var_set = set(meth_highvar.columns)\n", + "meth_anova_set = set(meth_af.columns)\n", + "meth_rf_set = set(meth_rf.columns)\n", + "\n", + "rna_var_set = set(rna_highvar.columns)\n", + "rna_anova_set = set(rna_af.columns)\n", + "rna_rf_set = set(rna_rf.columns)\n", + "\n", + "meth_inter1 = list(meth_anova_set & meth_var_set)\n", + "meth_inter2 = list(meth_rf_set & meth_var_set)\n", + "meth_inter3 = list(meth_anova_set & meth_rf_set)\n", + "meth_all_three = list(meth_anova_set & meth_var_set & meth_rf_set)\n", + "\n", + "rna_inter4 = list(rna_anova_set & rna_var_set)\n", + "rna_inter5 = list(rna_rf_set & rna_var_set)\n", + "rna_inter6 = list(rna_anova_set & rna_rf_set)\n", + "rna_all_three = list(rna_anova_set & rna_var_set & rna_rf_set)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "467d9ff1", + "metadata": {}, + "outputs": [], + "source": [ + "print(\"FROM THE 6000 Methylation feature selection:\\n\")\n", + "print(f\"Anova-F & variance selection share: {len(meth_inter1)} features\")\n", + "print(f\"Random Forest & variance selection share: {len(meth_inter2)} features\")\n", + "print(f\"Anova-F & Random Forest share: {len(meth_inter3)} features\")\n", + "print(f\"All three methods agree on: {len(meth_all_three)} features\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6fb1e10b", + "metadata": {}, + "outputs": [], + "source": [ + "print(\"\\nFROM THE 6000 RNA feature selection:\\n\")\n", + "print(f\"Anova-F & variance selection share: {len(rna_inter4)} features\")\n", + "print(f\"Random Forest & variance selection share: {len(rna_inter5)} features\")\n", + "print(f\"Anova-F & Random Forest share: {len(rna_inter6)} features\")\n", + "print(f\"All three methods agree on: {len(rna_all_three)} features\")" + ] + }, + { + "cell_type": "markdown", + "id": "be2ce6f8", + "metadata": {}, + "source": [ + "## Feature Selection Summary: ANOVA-RF Intersection\n", + "\n", + "The chosen strategy for feature selection is based on the **overlap** between features identified by the **ANOVA F-test** and **Random Forest Importance**. This approach offers comprehensive filtering by balancing class-based relevance (ANOVA) with non-linear model importance (Random Forest). The resulting feature sets are considered the most robust for downstream analysis.\n", + "\n", + "### Feature Overlap Results\n", + "\n", + "The following table details the number of features resulting from the intersection of different selection methods for each omics data type.\n", + "\n", + "| Omics Data Type | ANOVA-F & Variance | RF & Variance | ANOVA-F & Random Forest (Selected) | All Three Agree |\n", + "| :--- | :--- | :--- | :--- | :--- |\n", + "| **Methylation** | 2,704 features | 1,768 features | **1,823 features** | 809 features |\n", + "| **RNA** | 2,183 features | 1,977 features | **2,127 features** | 763 features |" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a1934672", + "metadata": {}, + "outputs": [], + "source": [ + "X_meth_selected = X_meth[meth_inter3]\n", + "X_rna_selected = X_rna[rna_inter6]\n", + "\n", + "print(\"\\nFinal Shapes for Modeling\")\n", + "print(f\"Methylation (X1): {X_meth_selected.shape}\")\n", + "print(f\"RNA-Seq (X2): {X_rna_selected.shape}\")\n", + "print(f\"miRNA-Seq (X3): {X_mirna.shape}\")\n", + "print(f\"Labels (Y): {Y_labels.shape}\")" + ] + }, + { + "cell_type": "markdown", + "id": "9348ca2a", + "metadata": {}, + "source": [ + "## Data Availability\n", + "\n", + "To facilitate rapid experimentation and reproduction of our results, the fully processed and feature-selected dataset used in this analysis has been made available directly within the package.\n", + "\n", + "Users can load this dataset, bypassing all preceding data acquisition, preprocessing, and feature selection steps. This allows users to proceed immediately from this step." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "08ec1120", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "from pathlib import Path\n", + "import bioneuralnet as bnn\n", + "\n", + "tgca_lgg = bnn.datasets.DatasetLoader(\"lgg\")\n", + "display(tgca_lgg.shape)\n", + "\n", + "# The dataset is returned as a dictionary. We extract each file independetly based on the name( Key).\n", + "dna_meth = tgca_lgg[\"meth\"]\n", + "rna = tgca_lgg[\"rna\"]\n", + "mirna = tgca_lgg[\"mirna\"]\n", + "clinical = tgca_lgg[\"clinical\"]\n", + "target = tgca_lgg[\"target\"]\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "95296b47", + "metadata": {}, + "outputs": [], + "source": [ + "dna_meth = bnn.utils.select_top_k_variance(dna_meth, k=500)\n", + "rna = bnn.utils.select_top_k_variance(rna, k=500)\n", + "mirna = bnn.utils.select_top_k_variance(mirna, k=500)\n", + "\n", + "print(dna_meth.shape)\n", + "print(rna.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "09c76e4f", + "metadata": {}, + "outputs": [], + "source": [ + "from bioneuralnet.utils import preprocess_clinical\n", + "clinical_for_model = preprocess_clinical(\n", + " X=clinical, \n", + " top_k=10,\n", + " scale=False,\n", + " ignore_columns=[\n", + " # target-related or outcome-related or irrelevant columns\n", + " \"days_to_last_followup\", \n", + " \"days_to_death\",\n", + " \"date_of_initial_pathologic_diagnosis\",\n", + " \"histological_type\"\n", + " ]\n", + ")\n", + "display(clinical_for_model.iloc[:5,:5])" + ] + }, + { + "cell_type": "markdown", + "id": "61a2b14e", + "metadata": {}, + "source": [ + "## Reproducibility and Seeding\n", + "\n", + "To ensure our experimental results are fully reproducible, a single global seed is set at the beginning of the analysis.\n", + "\n", + "This utility function propagates the seed to all sources of randomness, including `random`, `numpy`, and `torch` (for both CPU and GPU). Critically, it also configures the PyTorch cuDNN backend to use deterministic algorithms." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3f28d80e", + "metadata": {}, + "outputs": [], + "source": [ + "import bioneuralnet as bnn\n", + "\n", + "SEED = 1883\n", + "bnn.utils.set_seed(SEED)" + ] + }, + { + "cell_type": "markdown", + "id": "88084af0", + "metadata": {}, + "source": [ + "## Classification using DPMON: Training and Evaluation" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "54f23044", + "metadata": {}, + "outputs": [], + "source": [ + "from bioneuralnet.utils import find_optimal_graph\n", + "\n", + "omics_lgg = pd.concat([mirna, dna_meth, rna], axis=1)\n", + "\n", + "optimal_graph, best_params, results_df = find_optimal_graph(\n", + " omics_data=omics_lgg,\n", + " y_labels=target,\n", + " methods=['correlation','threshold'],\n", + " seed=SEED,\n", + " verbose=False,\n", + " trials=10,\n", + " omics_list=[mirna, dna_meth, rna],\n", + " centrality_mode=\"eigenvector\",\n", + ")\n", + "display(optimal_graph.iloc[:5,:5])\n", + "display(best_params)\n", + "\n", + "results_df.sort_values(\"score\", ascending=False, inplace=True)\n", + "display(results_df)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7adc097f", + "metadata": {}, + "outputs": [], + "source": [ + "# Graph analysis: The optimal gaph uses a proxy to evaluate its properties, but for the final output\n", + "# the graph is built without the target variable. The search only helps to find the best graph parameters..\n", + "from bioneuralnet.utils import graph_analysis\n", + "\n", + "graph_analysis(optimal_graph, graph_name=\"Optimal\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8bbf7958", + "metadata": {}, + "outputs": [], + "source": [ + "from pathlib import Path\n", + "from bioneuralnet.downstream_task import DPMON\n", + "\n", + "output_dir_base = Path(\"/home/vicente/Github/BioNeuralNet/dpmon_results_GAT_FINAL/lgg\")\n", + "\n", + "current_output_dir = output_dir_base / \"Gat_best_graph\"\n", + "current_output_dir.mkdir(parents=True, exist_ok=True)\n", + "\n", + "dpmon_params_base = {\n", + " \"adjacency_matrix\": optimal_graph,\n", + " \"omics_list\": omics_lgg,\n", + " \"phenotype_data\": target,\n", + " \"phenotype_col\": \"target\",\n", + " \"clinical_data\": clinical_for_model,\n", + " \"model\": 'GAT',\n", + " \"tune\": True, \n", + " \"cv\": True, \n", + " \"n_folds\": 5,\n", + " 'repeat_num': 1,\n", + " \"gpu\": True,\n", + " \"cuda\": 0,\n", + " \"seed\": SEED,\n", + " \"output_dir\": current_output_dir\n", + "}\n", + "\n", + "dpmon_tunned = DPMON(**dpmon_params_base)\n", + "predictions_df, metrics, embeddings = dpmon_tunned.run()\n", + "\n", + "graph_metrics = metrics\n", + "\n", + "acc_row = graph_metrics.loc[graph_metrics['Metric'] == 'Accuracy'].iloc[0]\n", + "f1_macro_row = graph_metrics.loc[graph_metrics['Metric'] == 'F1 Macro'].iloc[0]\n", + "f1_weighted_row = graph_metrics.loc[graph_metrics['Metric'] == 'F1 Weighted'].iloc[0]\n", + "recall_row = graph_metrics.loc[graph_metrics['Metric'] == 'Recall'].iloc[0]\n", + "auc_row = graph_metrics.loc[graph_metrics['Metric'] == 'AUC'].iloc[0]\n", + "aupr_row = graph_metrics.loc[graph_metrics['Metric'] == 'AUPR'].iloc[0]\n", + "\n", + "acc_avg, acc_std = acc_row['Average'], acc_row['StdDev']\n", + "f1_macro_avg, f1_macro_std = f1_macro_row['Average'], f1_macro_row['StdDev']\n", + "f1_weighted_avg, f1_weighted_std = f1_weighted_row['Average'], f1_weighted_row['StdDev']\n", + "recall_avg, recall_std = recall_row['Average'], recall_row['StdDev']\n", + "auc_avg, auc_std = auc_row['Average'], auc_row['StdDev']\n", + "aupr_avg, aupr_std = aupr_row['Average'], aupr_row['StdDev']\n", + "\n", + "print(f\"Accuracy (Avg +/- Std): {acc_avg:.4f} +/- {acc_std:.4f}\")\n", + "print(f\"F1 Macro (Avg +/- Std): {f1_macro_avg:.4f} +/- {f1_macro_std:.4f}\")\n", + "print(f\"F1 Weighted (Avg +/- Std): {f1_weighted_avg:.4f} +/- {f1_weighted_std:.4f}\")\n", + "print(f\"Recall: {recall_avg:.4f} +/- {recall_std:.4f}\")\n", + "print(f\"AUC: {auc_avg:.4f} +/- {auc_std:.4f}\")\n", + "print(f\"AUPR: {aupr_avg:.4f} +/- {aupr_std:.4f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "313822d2", + "metadata": {}, + "outputs": [], + "source": [ + "from bioneuralnet.downstream_task import DPMON\n", + "\n", + "\n", + "output_dir_base = Path(\"/home/vicente/Github/BioNeuralNet/dpmon_results_GCN_FINAL/lgg\")\n", + "\n", + "current_output_dir = output_dir_base / \"Gcn_best_graph\"\n", + "current_output_dir.mkdir(parents=True, exist_ok=True)\n", + "\n", + "dpmon_params_base = {\n", + " \"adjacency_matrix\": optimal_graph,\n", + " \"omics_list\": omics_lgg,\n", + " \"phenotype_data\": target,\n", + " \"phenotype_col\": \"target\",\n", + " \"clinical_data\": clinical_for_model,\n", + " \"model\": 'GCN',\n", + " \"tune\": True, \n", + " \"cv\": True, \n", + " \"n_folds\": 5,\n", + " 'repeat_num': 1,\n", + " \"gpu\": True,\n", + " \"cuda\": 0,\n", + " \"seed\": SEED,\n", + " \"output_dir\": current_output_dir\n", + "}\n", + "\n", + "dpmon_tunned = DPMON(**dpmon_params_base)\n", + "predictions_df, metrics, embeddings = dpmon_tunned.run()\n", + "graph_metrics = metrics\n", + "\n", + "acc_row = graph_metrics.loc[graph_metrics['Metric'] == 'Accuracy'].iloc[0]\n", + "f1_macro_row = graph_metrics.loc[graph_metrics['Metric'] == 'F1 Macro'].iloc[0]\n", + "f1_weighted_row = graph_metrics.loc[graph_metrics['Metric'] == 'F1 Weighted'].iloc[0]\n", + "recall_row = graph_metrics.loc[graph_metrics['Metric'] == 'Recall'].iloc[0]\n", + "auc_row = graph_metrics.loc[graph_metrics['Metric'] == 'AUC'].iloc[0]\n", + "aupr_row = graph_metrics.loc[graph_metrics['Metric'] == 'AUPR'].iloc[0]\n", + "\n", + "acc_avg, acc_std = acc_row['Average'], acc_row['StdDev']\n", + "f1_macro_avg, f1_macro_std = f1_macro_row['Average'], f1_macro_row['StdDev']\n", + "f1_weighted_avg, f1_weighted_std = f1_weighted_row['Average'], f1_weighted_row['StdDev']\n", + "recall_avg, recall_std = recall_row['Average'], recall_row['StdDev']\n", + "auc_avg, auc_std = auc_row['Average'], auc_row['StdDev']\n", + "aupr_avg, aupr_std = aupr_row['Average'], aupr_row['StdDev']\n", + "\n", + "print(f\"Accuracy (Avg +/- Std): {acc_avg:.4f} +/- {acc_std:.4f}\")\n", + "print(f\"F1 Macro (Avg +/- Std): {f1_macro_avg:.4f} +/- {f1_macro_std:.4f}\")\n", + "print(f\"F1 Weighted (Avg +/- Std): {f1_weighted_avg:.4f} +/- {f1_weighted_std:.4f}\")\n", + "print(f\"Recall: {recall_avg:.4f} +/- {recall_std:.4f}\")\n", + "print(f\"AUC: {auc_avg:.4f} +/- {auc_std:.4f}\")\n", + "print(f\"AUPR: {aupr_avg:.4f} +/- {aupr_std:.4f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9a77d244", + "metadata": {}, + "outputs": [], + "source": [ + "from bioneuralnet.downstream_task import DPMON\n", + "output_dir_base = Path(\"/home/vicente/Github/BioNeuralNet/dpmon_results_GIN_FINAL/lgg\")\n", + "\n", + "\n", + "current_output_dir = output_dir_base / \"gin_best_graph\"\n", + "current_output_dir.mkdir(parents=True, exist_ok=True)\n", + "\n", + "dpmon_params_base = {\n", + " \"adjacency_matrix\": optimal_graph,\n", + " \"omics_list\": omics_lgg,\n", + " \"phenotype_data\": target,\n", + " \"phenotype_col\": \"target\",\n", + " \"clinical_data\": clinical_for_model,\n", + " \"model\": 'GIN',\n", + " \"tune\": True, \n", + " \"cv\": True, \n", + " \"n_folds\": 5,\n", + " 'repeat_num': 1,\n", + " \"gpu\": True,\n", + " \"cuda\": 0,\n", + " \"seed\": SEED,\n", + " \"output_dir\": current_output_dir\n", + "}\n", + "\n", + "dpmon_tunned = DPMON(**dpmon_params_base)\n", + "predictions_df, metrics, embeddings = dpmon_tunned.run()\n", + "graph_metrics = metrics\n", + "\n", + "acc_row = graph_metrics.loc[graph_metrics['Metric'] == 'Accuracy'].iloc[0]\n", + "f1_macro_row = graph_metrics.loc[graph_metrics['Metric'] == 'F1 Macro'].iloc[0]\n", + "f1_weighted_row = graph_metrics.loc[graph_metrics['Metric'] == 'F1 Weighted'].iloc[0]\n", + "recall_row = graph_metrics.loc[graph_metrics['Metric'] == 'Recall'].iloc[0]\n", + "auc_row = graph_metrics.loc[graph_metrics['Metric'] == 'AUC'].iloc[0]\n", + "aupr_row = graph_metrics.loc[graph_metrics['Metric'] == 'AUPR'].iloc[0]\n", + "\n", + "acc_avg, acc_std = acc_row['Average'], acc_row['StdDev']\n", + "f1_macro_avg, f1_macro_std = f1_macro_row['Average'], f1_macro_row['StdDev']\n", + "f1_weighted_avg, f1_weighted_std = f1_weighted_row['Average'], f1_weighted_row['StdDev']\n", + "recall_avg, recall_std = recall_row['Average'], recall_row['StdDev']\n", + "auc_avg, auc_std = auc_row['Average'], auc_row['StdDev']\n", + "aupr_avg, aupr_std = aupr_row['Average'], aupr_row['StdDev']\n", + "\n", + "print(f\"Accuracy (Avg +/- Std): {acc_avg:.4f} +/- {acc_std:.4f}\")\n", + "print(f\"F1 Macro (Avg +/- Std): {f1_macro_avg:.4f} +/- {f1_macro_std:.4f}\")\n", + "print(f\"F1 Weighted (Avg +/- Std): {f1_weighted_avg:.4f} +/- {f1_weighted_std:.4f}\")\n", + "print(f\"Recall: {recall_avg:.4f} +/- {recall_std:.4f}\")\n", + "print(f\"AUC: {auc_avg:.4f} +/- {auc_std:.4f}\")\n", + "print(f\"AUPR: {aupr_avg:.4f} +/- {aupr_std:.4f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c8b344bd", + "metadata": {}, + "outputs": [], + "source": [ + "from bioneuralnet.downstream_task import DPMON\n", + "output_dir_base = Path(\"/home/vicente/Github/BioNeuralNet/dpmon_results_SAGE_FINAL/lgg\")\n", + "\n", + "current_output_dir = output_dir_base / \"sage_best_graph\"\n", + "current_output_dir.mkdir(parents=True, exist_ok=True)\n", + "\n", + "dpmon_params_base = {\n", + " \"adjacency_matrix\": optimal_graph,\n", + " \"omics_list\": omics_lgg,\n", + " \"phenotype_data\": target,\n", + " \"phenotype_col\": \"target\",\n", + " \"clinical_data\": clinical_for_model,\n", + " \"model\": 'SAGE',\n", + " \"tune\": True, \n", + " \"cv\": True, \n", + " \"n_folds\": 5,\n", + " 'repeat_num': 1,\n", + " \"gpu\": True,\n", + " \"cuda\": 0,\n", + " \"seed\": SEED,\n", + " \"output_dir\": current_output_dir\n", + "}\n", + "\n", + "dpmon_tunned = DPMON(**dpmon_params_base)\n", + "predictions_df, metrics, embeddings = dpmon_tunned.run()\n", + "graph_metrics = metrics\n", + "\n", + "acc_row = graph_metrics.loc[graph_metrics['Metric'] == 'Accuracy'].iloc[0]\n", + "f1_macro_row = graph_metrics.loc[graph_metrics['Metric'] == 'F1 Macro'].iloc[0]\n", + "f1_weighted_row = graph_metrics.loc[graph_metrics['Metric'] == 'F1 Weighted'].iloc[0]\n", + "recall_row = graph_metrics.loc[graph_metrics['Metric'] == 'Recall'].iloc[0]\n", + "auc_row = graph_metrics.loc[graph_metrics['Metric'] == 'AUC'].iloc[0]\n", + "aupr_row = graph_metrics.loc[graph_metrics['Metric'] == 'AUPR'].iloc[0]\n", + "\n", + "acc_avg, acc_std = acc_row['Average'], acc_row['StdDev']\n", + "f1_macro_avg, f1_macro_std = f1_macro_row['Average'], f1_macro_row['StdDev']\n", + "f1_weighted_avg, f1_weighted_std = f1_weighted_row['Average'], f1_weighted_row['StdDev']\n", + "recall_avg, recall_std = recall_row['Average'], recall_row['StdDev']\n", + "auc_avg, auc_std = auc_row['Average'], auc_row['StdDev']\n", + "aupr_avg, aupr_std = aupr_row['Average'], aupr_row['StdDev']\n", + "\n", + "print(f\"Accuracy (Avg +/- Std): {acc_avg:.4f} +/- {acc_std:.4f}\")\n", + "print(f\"F1 Macro (Avg +/- Std): {f1_macro_avg:.4f} +/- {f1_macro_std:.4f}\")\n", + "print(f\"F1 Weighted (Avg +/- Std): {f1_weighted_avg:.4f} +/- {f1_weighted_std:.4f}\")\n", + "print(f\"Recall: {recall_avg:.4f} +/- {recall_std:.4f}\")\n", + "print(f\"AUC: {auc_avg:.4f} +/- {auc_std:.4f}\")\n", + "print(f\"AUPR: {aupr_avg:.4f} +/- {aupr_std:.4f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "00140d16", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.model_selection import StratifiedKFold, ParameterSampler, RepeatedStratifiedKFold\n", + "from sklearn.metrics import accuracy_score, f1_score, recall_score, roc_auc_score, average_precision_score\n", + "from sklearn.base import clone\n", + "from sklearn.preprocessing import StandardScaler, label_binarize\n", + "from sklearn.pipeline import Pipeline\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.neural_network import MLPClassifier\n", + "from scipy.stats import loguniform, randint\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "from sklearn.tree import DecisionTreeClassifier\n", + "from xgboost import XGBClassifier\n", + "from sklearn.svm import SVC\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "X = pd.concat([dna_meth, rna, mirna, clinical_for_model], axis=1)\n", + "y = target['target']\n", + "print(f\"Successfully created X matrix with shape: {X.shape}\")\n", + "print(f\"Successfully created y vector with shape: {y.shape}\")\n", + "\n", + "\n", + "pipe_lr = Pipeline([\n", + " ('scaler', StandardScaler()),\n", + " ('model', LogisticRegression(solver='lbfgs', max_iter=1000, penalty=None, random_state=SEED))\n", + "])\n", + "\n", + "pipe_mlp = Pipeline([\n", + " ('scaler', StandardScaler()),\n", + " ('model', MLPClassifier(max_iter=500, early_stopping=True, n_iter_no_change=10, random_state=SEED))\n", + "])\n", + "\n", + "pipe_xgb = Pipeline([\n", + " ('scaler', StandardScaler()),\n", + " ('model', XGBClassifier(eval_metric='logloss', tree_method='hist', max_bin=128, random_state=SEED))\n", + "])\n", + "pipe_rf = Pipeline([\n", + " ('scaler', StandardScaler()),\n", + " ('model', RandomForestClassifier(random_state=SEED))\n", + "])\n", + "\n", + "pipe_svm = Pipeline([\n", + " ('scaler', StandardScaler()),\n", + " ('model', SVC(probability=True, random_state=SEED))\n", + "])\n", + "\n", + "pipe_dt = Pipeline([\n", + " ('scaler', StandardScaler()),\n", + " ('model', DecisionTreeClassifier(random_state=SEED))\n", + "])\n", + "\n", + "params_lr = {'model__penalty': ['l2'], 'model__C': loguniform(1e-4, 1e2)}\n", + "\n", + "params_mlp = {\n", + " 'model__hidden_layer_sizes': [(100,), (100, 50), (50, 50)],\n", + " 'model__activation': ['relu', 'tanh'],\n", + " 'model__alpha': loguniform(1e-5, 1e-1),\n", + " 'model__learning_rate_init': loguniform(1e-4, 1e-2)\n", + "}\n", + "params_xgb = {\n", + " 'model__n_estimators': randint(50, 200),\n", + " 'model__learning_rate': loguniform(0.01, 0.3),\n", + " 'model__max_depth': randint(3, 7),\n", + " 'model__subsample': [0.8, 1.0], \n", + " 'model__colsample_bytree': [0.8, 1.0]\n", + "}\n", + "params_rf = {\n", + " 'model__n_estimators': randint(100, 300),\n", + " 'model__max_depth': [10, 20, 30, None],\n", + " 'model__min_samples_split': randint(2, 10),\n", + " 'model__min_samples_leaf': randint(1, 5),\n", + " 'model__max_features': ['sqrt', 'log2']\n", + "}\n", + "params_svm = {\n", + " 'model__C': loguniform(1e-2, 1e3),\n", + " 'model__kernel': ['rbf', 'linear'],\n", + " 'model__gamma': ['scale', 'auto']\n", + "}\n", + "\n", + "params_dt = {\n", + " 'model__max_depth': randint(3, 15),\n", + " 'model__min_samples_split': randint(2, 20),\n", + " 'model__criterion': ['gini', 'entropy']\n", + "}\n", + "\n", + "\n", + "models_to_tune = {\n", + " \"LogisticRegression\": (pipe_lr, params_lr),\n", + " \"SVM\": (pipe_svm, params_svm),\n", + " \"MLP\": (pipe_mlp, params_mlp),\n", + " \"XGBoost\": (pipe_xgb, params_xgb),\n", + " \"RandomForest\": (pipe_rf, params_rf),\n", + " \"DecisionTree\": (pipe_dt, params_dt),\n", + "}\n", + "\n", + "all_results = {\n", + " \"LogisticRegression\": {\"acc\": [], \"f1_w\": [], \"f1_m\": [], \"recall\": [], \"auc\": [], \"aupr\": []},\n", + " \"MLP\": {\"acc\": [], \"f1_w\": [], \"f1_m\": [], \"recall\": [], \"auc\": [], \"aupr\": []},\n", + " \"XGBoost\": {\"acc\": [], \"f1_w\": [], \"f1_m\": [], \"recall\": [], \"auc\": [], \"aupr\": []},\n", + " \"RandomForest\": {\"acc\": [], \"f1_w\": [], \"f1_m\": [], \"recall\": [], \"auc\": [], \"aupr\": []},\n", + " \"SVM\": {\"acc\": [], \"f1_w\": [], \"f1_m\": [], \"recall\": [], \"auc\": [], \"aupr\": []},\n", + " \"DecisionTree\": {\"acc\": [], \"f1_w\": [], \"f1_m\": [], \"recall\": [], \"auc\": [], \"aupr\": []},\n", + "}\n", + "\n", + "\n", + "for model_name, (pipeline, param_dist) in models_to_tune.items():\n", + " print(f\"Evaluating model with nested CV: {model_name}\")\n", + " \n", + " outer_cv = RepeatedStratifiedKFold(n_splits=5, n_repeats=1, random_state=SEED)\n", + " \n", + " # inner folds are for finding the best hyperparameters\n", + " for fold_idx, (train_idx, test_idx) in enumerate(outer_cv.split(X, y), start=1):\n", + " X_train_outer, X_test_outer = X.iloc[train_idx], X.iloc[test_idx]\n", + " y_train_outer, y_test_outer = y.iloc[train_idx], y.iloc[test_idx]\n", + " inner_cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=SEED)\n", + " \n", + " best_score_fold = -np.inf\n", + " best_params_fold = None\n", + " # May or may not want to set a seed here. A fix seed = same hyperparamters each fold.\n", + " # No seed = different hyperparameters each fold. This adds more randomness, and may yield better generalization.\n", + " param_sampler = list(ParameterSampler(param_dist, n_iter=20))\n", + " \n", + " for params in param_sampler:\n", + " inner_scores = []\n", + " \n", + " for inner_train_idx, inner_val_idx in inner_cv.split(X_train_outer, y_train_outer):\n", + " X_train_inner = X_train_outer.iloc[inner_train_idx]\n", + " X_val_inner = X_train_outer.iloc[inner_val_idx]\n", + " y_train_inner = y_train_outer.iloc[inner_train_idx]\n", + " y_val_inner = y_train_outer.iloc[inner_val_idx]\n", + " \n", + " inner_pipeline = clone(pipeline)\n", + " inner_pipeline.set_params(**params)\n", + " inner_pipeline.fit(X_train_inner, y_train_inner)\n", + " \n", + " y_val_pred = inner_pipeline.predict(X_val_inner)\n", + " score = f1_score(y_val_inner, y_val_pred, average='weighted', zero_division=0)\n", + " inner_scores.append(score)\n", + " \n", + " mean_score = np.mean(inner_scores)\n", + " if mean_score > best_score_fold:\n", + " best_score_fold = mean_score\n", + " best_params_fold = params\n", + " \n", + " print(f\"Outer fold {fold_idx}: best params (inner CV F1-W={best_score_fold:.4f})\")\n", + " print(f\"{best_params_fold}\")\n", + " \n", + " final_pipeline = clone(pipeline)\n", + " final_pipeline.set_params(**best_params_fold)\n", + " final_pipeline.fit(X_train_outer, y_train_outer)\n", + " \n", + " preds = final_pipeline.predict(X_test_outer)\n", + " \n", + " if hasattr(final_pipeline, \"predict_proba\"):\n", + " proba = final_pipeline.predict_proba(X_test_outer)\n", + " else:\n", + " proba = None\n", + " \n", + " acc = accuracy_score(y_test_outer, preds)\n", + " f1_w = f1_score(y_test_outer, preds, average='weighted', zero_division=0)\n", + " f1_m = f1_score(y_test_outer, preds, average='macro', zero_division=0)\n", + " recall = recall_score(y_test_outer, preds, average='macro', zero_division=0)\n", + " \n", + " auc = np.nan\n", + " aupr = np.nan\n", + " \n", + " if proba is not None:\n", + " try:\n", + " if len(np.unique(y)) == 2:\n", + " auc = roc_auc_score(y_test_outer, proba[:, 1])\n", + " aupr = average_precision_score(y_test_outer, proba[:, 1])\n", + " else:\n", + " auc = roc_auc_score(y_test_outer, proba, multi_class='ovr', average='macro')\n", + " y_test_bin = label_binarize(y_test_outer, classes=np.unique(y))\n", + " aupr = average_precision_score(y_test_bin, proba, average='weighted')\n", + " except Exception:\n", + " auc = np.nan\n", + " aupr = np.nan\n", + "\n", + " print(f\"Fold {fold_idx} results: Acc={acc:.4f}, F1-W={f1_w:.4f}, \"\n", + " f\"F1-M={f1_m:.4f}, Recall={recall:.4f}, AUC={auc:.4f}, AUPR={aupr:.4f}\")\n", + " \n", + " all_results[model_name][\"acc\"].append(acc)\n", + " all_results[model_name][\"f1_w\"].append(f1_w)\n", + " all_results[model_name][\"f1_m\"].append(f1_m)\n", + " all_results[model_name][\"recall\"].append(recall)\n", + " all_results[model_name][\"auc\"].append(auc)\n", + " all_results[model_name][\"aupr\"].append(aupr)\n", + "\n", + "print(\"\\nFINAL BASELINE RESULTS\\n\")\n", + "for model_name, metrics in all_results.items():\n", + " avg_acc = np.mean(metrics[\"acc\"])\n", + " std_acc = np.std(metrics[\"acc\"])\n", + " avg_f1_w = np.mean(metrics[\"f1_w\"])\n", + " std_f1_w = np.std(metrics[\"f1_w\"])\n", + " avg_f1_m = np.mean(metrics[\"f1_m\"])\n", + " std_f1_m = np.std(metrics[\"f1_m\"])\n", + " avg_recall = np.mean(metrics[\"recall\"])\n", + " std_recall = np.std(metrics[\"recall\"])\n", + " avg_auc = np.nanmean(metrics[\"auc\"])\n", + " std_auc = np.nanstd(metrics[\"auc\"])\n", + " avg_aupr = np.nanmean(metrics[\"aupr\"])\n", + " std_aupr = np.nanstd(metrics[\"aupr\"])\n", + " \n", + " print(f\"\\n{model_name}:\")\n", + " print(f\"Accuracy: {avg_acc:.4f} +/- {std_acc:.4f}\")\n", + " print(f\"F1 Weighted: {avg_f1_w:.4f} +/- {std_f1_w:.4f}\")\n", + " print(f\"F1 Macro: {avg_f1_m:.4f} +/- {std_f1_m:.4f}\")\n", + " print(f\"Recall: {avg_recall:.4f} +/- {std_recall:.4f}\")\n", + " print(f\"AUC: {avg_auc:.4f} +/- {std_auc:.4f}\")\n", + " print(f\"AUPR: {avg_aupr:.4f} +/- {std_aupr:.4f}\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".enviroment", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/TCGA-Notebooks/TCGA-PAAD.ipynb b/TCGA-Notebooks/TCGA-PAAD.ipynb new file mode 100644 index 0000000..1b1b002 --- /dev/null +++ b/TCGA-Notebooks/TCGA-PAAD.ipynb @@ -0,0 +1,1058 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "dc820e6e", + "metadata": {}, + "source": [ + "# TCGA-PAAD Analysis: *Overall Survival* classification\n", + "\n", + "- **Cohort**: Focuses on the **TCGA-PAAD** (Pancreatic Adenocarcinoma) dataset, a vital cohort for studying pancreatic cancer.\n", + "\n", + "- **Goal**: Perform survival prediction using a multi-omics profile.\n", + "- **Prediction Target**: Predict **Overall Survival (OS)** based on the patient's omics data (RNA, Methylation, CNV, and clinical features).\n", + "\n", + "**Data Sources:**\n", + "- Omics Data and Target: [https://xenabrowser.net/datapages/](https://xenabrowser.net/datapages/)\n", + "- Clinical Data: [Broad Institute FireHose](`http://firebrowse.org/?cohort=PAAD`)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ec5a7a8a", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "from pathlib import Path\n", + "root = Path(\"/home/vicente/Github/BioNeuralNet/PAAN\")\n", + "\n", + "cnv_gistic_raw = pd.read_csv(root/\"Gistic2_CopyNumber_Gistic2_all_thresholded_by_genes.txt\", sep=\"\\t\",index_col=0,low_memory=False) \n", + "rna_raw = pd.read_csv(root / \"HiSeqV2.txt\", sep=\"\\t\",index_col=0,low_memory=False)\n", + "meth_raw = pd.read_csv(root/\"HumanMethylation450.txt\", sep='\\t',index_col=0,low_memory=False)\n", + "clinical_raw = pd.read_csv(root / \"PAAD.clin.merged.picked.txt\",sep=\"\\t\", index_col=0, low_memory=False)\n", + "target = pd.read_csv(root / \"survival_PAAD_survival.txt\", sep=\"\\t\", index_col=0, low_memory=False)\n", + "probe_map_meth = pd.read_csv( root / \"probeMap_illuminaMethyl450_hg19_GPL16304_TCGAlegacy.txt\", sep=\"\\t\", index_col=0, low_memory=False)\n", + "\n", + "# display all shapes and first few rows of each dataset\n", + "display(cnv_gistic_raw.iloc[:5,:5])\n", + "display(cnv_gistic_raw.shape)\n", + "\n", + "display(rna_raw.iloc[:5,:5])\n", + "display(rna_raw.shape)\n", + "\n", + "display(meth_raw.iloc[:5,:5])\n", + "display(meth_raw.shape)\n", + "display(probe_map_meth.iloc[:5,:5])\n", + "display(probe_map_meth.shape)\n", + "\n", + "display(clinical_raw.iloc[:5,:5])\n", + "display(clinical_raw.shape)\n", + "\n", + "display(target.iloc[:5,:5])\n", + "display(target.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "85ddcf20", + "metadata": {}, + "outputs": [], + "source": [ + "import bioneuralnet as bnn\n", + "cnv_gistic_processed = cnv_gistic_raw.apply(pd.to_numeric, errors=\"coerce\")\n", + "\n", + "# collapse GISTIC to -1 / 0 / +1 per gene\n", + "cnv_signed = cnv_gistic_processed.copy()\n", + "cnv_signed[cnv_signed > 0] = 1\n", + "cnv_signed[cnv_signed < 0] = -1\n", + "\n", + "cnv_signed_T = cnv_signed.T \n", + "rna_numeric = rna_raw.apply(pd.to_numeric, errors=\"coerce\")\n", + "rna_T = rna_numeric.T\n", + "\n", + "# make numeric (beta values)\n", + "meth_numeric = meth_raw.apply(pd.to_numeric, errors=\"coerce\")\n", + "\n", + "# build gene map from probe_map_meth\n", + "gene_map = probe_map_meth[['gene']].copy()\n", + "gene_map = gene_map.dropna(subset=['gene'])\n", + "gene_map = gene_map[gene_map['gene'] != '.']\n", + "\n", + "gene_map['gene'] = gene_map['gene'].str.split(',')\n", + "gene_map_exploded = gene_map.explode('gene')\n", + "\n", + "meth_with_gene = meth_numeric.join(gene_map_exploded['gene'], how='inner')\n", + "\n", + "data_cols = meth_with_gene.columns.drop('gene')\n", + "meth_gene_beta = (\n", + " meth_with_gene\n", + " .groupby('gene')[data_cols]\n", + " .mean()\n", + " .T\n", + ")\n", + "\n", + "meth_gene_m = bnn.utils.beta_to_m(meth_gene_beta, eps=1e-8)\n", + "\n", + "clinical = clinical_raw.copy()\n", + "clinical = clinical.T\n", + "clinical.index = clinical.index.str.upper().str.slice(0, 12)\n", + "clinical.index.name = \"Patient_ID\"\n", + "clinical = clinical[~clinical.index.duplicated(keep='first')]\n", + "clinical = clinical.drop(columns=[\"Hybridization REF\", \"Composite Element REF\"], errors=\"ignore\")\n", + "\n", + "outcomes = target.copy()\n", + "outcomes = outcomes.set_index('_PATIENT')\n", + "outcomes.index = outcomes.index.str.upper().str.slice(0, 12)\n", + "outcomes.index.name = \"Patient_ID\"\n", + "outcomes = outcomes[~outcomes.index.duplicated(keep='first')]\n", + "\n", + "cnv_signed_T.index = cnv_signed_T.index.str.upper().str.slice(0, 12)\n", + "rna_T.index = rna_T.index.str.upper().str.slice(0, 12)\n", + "meth_gene_m.index = meth_gene_m.index.str.upper().str.slice(0, 12)\n", + "\n", + "cnv_signed_T = cnv_signed_T.groupby(cnv_signed_T.index).mean()\n", + "rna_T = rna_T.groupby(rna_T.index).mean()\n", + "meth_gene_m = meth_gene_m.groupby(meth_gene_m.index).mean()\n", + "\n", + "common_patients = sorted(\n", + " set(cnv_signed_T.index)\n", + " & set(rna_T.index)\n", + " & set(meth_gene_m.index)\n", + " & set(outcomes.index)\n", + ")\n", + "X_cnv = cnv_signed_T.loc[common_patients]\n", + "X_rna = rna_T.loc[common_patients]\n", + "X_meth = meth_gene_m.loc[common_patients]\n", + "Y_labels = outcomes.loc[common_patients, \"OS\"]\n", + "clinical = clinical.loc[common_patients]\n", + "\n", + "def clean_cols(df, prefix):\n", + " cols = df.columns\n", + " cols = cols.str.replace(r\"\\?\", \"unknown_\", regex=True)\n", + " cols = cols.str.replace(r\"\\|\", \"_\", regex=True)\n", + " cols = cols.str.replace(\"-\", \"_\", regex=False)\n", + " cols = cols.str.replace(r\"_+\", \"_\", regex=True)\n", + " cols = cols.str.strip(\"_\")\n", + " df.columns = cols\n", + " return df.add_prefix(prefix)\n", + "\n", + "X_cnv = clean_cols(X_cnv, \"cnv_\")\n", + "X_rna = clean_cols(X_rna, \"rna_\")\n", + "X_meth = clean_cols(X_meth, \"meth_\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "81713f21", + "metadata": {}, + "outputs": [], + "source": [ + "display(X_cnv.iloc[:5,:5])\n", + "display(X_cnv.shape)\n", + "\n", + "display(X_rna.iloc[:5,:5])\n", + "display(X_rna.shape)\n", + "\n", + "display(X_meth.iloc[:5,:5])\n", + "display(X_meth.shape)\n", + "\n", + "display(clinical.iloc[:5,:5])\n", + "display(clinical.shape)" + ] + }, + { + "cell_type": "markdown", + "id": "7767b027", + "metadata": {}, + "source": [ + "## Feature Selection Methodology\n", + "\n", + "### Supported Methods and Interpretation\n", + "\n", + "**BioNeuralNet** provides three techniques for feature selection, allowing for different views of the data's statistical profile:\n", + "\n", + "- **Variance Thresholding:** Identifies features with the **highest overall variance** across all samples.\n", + "\n", + "- **ANOVA F-test:** Pinpoints features that best **distinguish between the target classes** (e.g., Alive vs. Deceased).\n", + "\n", + "- **Random Forest Importance:** Assesses **feature utility** based on its contribution to a predictive non-linear model.\n", + "\n", + "### PAAD Cohort Selection Strategy\n", + "\n", + "A dimensionality reduction step was essential for managing the high-feature-count omics data:\n", + "\n", + "- **High-Feature Datasets:** DNA Methylation (34,013), RNA (20,530), CNV Amplification (24,776), and CNV Deletion (24,776) all required significant feature reduction.\n", + "\n", + "- **Filtering Process:** As an example strategy, the **top 6,000 features** could be extracted from each high-feature omics dataset using all three methods.\n", + "\n", + "- **Final Set:** A consensus set could be built for each omics type by finding the intersection of features selected by the ANOVA F-test and Random Forest Importance, ensuring both statistical relevance and model-based utility.\n", + "\n", + "- **Low-Feature Datasets:** The **Clinical** data (19 features) was passed through **without selection**, as its feature count was already manageable." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "be3941c5", + "metadata": {}, + "outputs": [], + "source": [ + "import bioneuralnet as bnn\n", + "\n", + "print(f\"METH before: {X_meth.shape}\")\n", + "X_meth = bnn.impute_omics_knn(X_meth, n_neighbors=5)\n", + "print(f\"METH after: {X_meth.shape}\\n\")\n", + "\n", + "print(f\"RNA before: {X_rna.shape}\")\n", + "X_rna = bnn.impute_omics_knn(X_rna, n_neighbors=5)\n", + "print(f\"RNA after: {X_rna.shape}\\n\")\n", + "\n", + "print(f\"CNV before: {X_cnv.shape}\")\n", + "X_cnv = bnn.impute_omics_knn(X_cnv, n_neighbors=5)\n", + "print(f\"CNV after: {X_cnv.shape}\\n\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8a96f822", + "metadata": {}, + "outputs": [], + "source": [ + "import bioneuralnet as bnn\n", + "\n", + "meth_highvar = bnn.utils.select_top_k_variance(X_meth, k=6000)\n", + "meth_af = bnn.utils.top_anova_f_features(X_meth, Y_labels, max_features=6000)\n", + "meth_rf = bnn.utils.select_top_randomforest(X_meth, Y_labels, top_k=6000)\n", + "\n", + "rna_highvar = bnn.utils.select_top_k_variance(X_rna, k=6000)\n", + "rna_af = bnn.utils.top_anova_f_features(X_rna, Y_labels, max_features=6000)\n", + "rna_rf = bnn.utils.select_top_randomforest(X_rna, Y_labels, top_k=6000)\n", + "\n", + "cnv_highvar = bnn.utils.select_top_k_variance(X_cnv, k=6000)\n", + "cnv_af = bnn.utils.top_anova_f_features(X_cnv, Y_labels, max_features=6000)\n", + "cnv_rf = bnn.utils.select_top_randomforest(X_cnv, Y_labels, top_k=6000)\n", + "\n", + "meth_var_set = set(meth_highvar.columns)\n", + "meth_anova_set = set(meth_af.columns)\n", + "meth_rf_set = set(meth_rf.columns)\n", + "\n", + "rna_var_set = set(rna_highvar.columns)\n", + "rna_anova_set = set(rna_af.columns)\n", + "rna_rf_set = set(rna_rf.columns)\n", + "\n", + "cnv_var_set = set(cnv_highvar.columns)\n", + "cnv_anova_set = set(cnv_af.columns)\n", + "cnv_rf_set = set(cnv_rf.columns)\n", + "\n", + "meth_inter1 = list(meth_anova_set & meth_var_set)\n", + "meth_inter2 = list(meth_rf_set & meth_var_set)\n", + "meth_inter3 = list(meth_anova_set & meth_rf_set)\n", + "meth_all_three = list(meth_anova_set & meth_var_set & meth_rf_set)\n", + "\n", + "rna_inter1 = list(rna_anova_set & rna_var_set)\n", + "rna_inter2 = list(rna_rf_set & rna_var_set)\n", + "rna_inter3 = list(rna_anova_set & rna_rf_set)\n", + "rna_all_three = list(rna_anova_set & rna_var_set & rna_rf_set)\n", + "\n", + "cnv_inter1 = list(cnv_anova_set & cnv_var_set)\n", + "cnv_inter2 = list(cnv_rf_set & cnv_var_set)\n", + "cnv_inter3 = list(cnv_anova_set & cnv_rf_set)\n", + "cnv_all_three = list(cnv_anova_set & cnv_var_set & cnv_rf_set)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f6d3d1c8", + "metadata": {}, + "outputs": [], + "source": [ + "print(\"FROM THE 6000 Methylation feature selection:\\n\")\n", + "print(f\"Anova-F & variance selection share: {len(meth_inter1)} features\")\n", + "print(f\"Random Forest & variance selection share: {len(meth_inter2)} features\")\n", + "print(f\"Anova-F & Random Forest share: {len(meth_inter3)} features\")\n", + "print(f\"All three methods agree on: {len(meth_all_three)} features\")\n", + "\n", + "print(\"\\nFROM THE 6000 RNA feature selection:\\n\")\n", + "print(f\"Anova-F & variance selection share: {len(rna_inter1)} features\")\n", + "print(f\"Random Forest & variance selection share: {len(rna_inter2)} features\")\n", + "print(f\"Anova-F & Random Forest share: {len(rna_inter3)} features\")\n", + "print(f\"All three methods agree on: {len(rna_all_three)} features\")\n", + "\n", + "print(\"\\nFROM THE 6000 CNV feature selection:\\n\")\n", + "print(f\"Anova-F & variance selection share: {len(cnv_inter1)} features\")\n", + "print(f\"Random Forest & variance selection share: {len(cnv_inter2)} features\")\n", + "print(f\"Anova-F & Random Forest share: {len(cnv_inter3)} features\")\n", + "print(f\"All three methods agree on: {len(cnv_all_three)} features\")" + ] + }, + { + "cell_type": "markdown", + "id": "1d61f83a", + "metadata": {}, + "source": [ + "## Feature Selection Summary: ANOVA-RF Intersection\n", + "\n", + "The final set of features was determined by the **intersection** of those highlighted by the **ANOVA F-test** and **Random Forest Importance**. This methodology provides a balanced filter, capturing features with both high class-separability (ANOVA) and significant predictive value in a non-linear model (Random Forest). The resulting feature pool is considered highly relevant for the subsequent modeling tasks.\n", + "\n", + "### Feature Overlap Results\n", + "\n", + "The table below quantifies the shared features identified by the different selection techniques for each omics type.\n", + "\n", + "| Omics Data Type | ANOVA-F & Variance | RF & Variance | ANOVA-F & Random Forest (Selected) | All Three Agree |\n", + "| :--- | :--- | :--- | :--- | :--- |\n", + "| **DNA Methylation** | 1,410 features | 1,076 features | **1,152 features** | 280 features |\n", + "| **RNA** | 1,910 features | 1,815 features | **1,910 features** | 589 features |\n", + "| **CNV** | 1,884 features | 1,516 features | **1,035 features** | 477 features |" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "11a83aea", + "metadata": {}, + "outputs": [], + "source": [ + "# Subset each omics dataframe using the selected feature lists\n", + "X_meth_selected = X_meth[meth_inter3]\n", + "X_rna_selected = X_rna[rna_inter3]\n", + "X_cnv_selected = X_cnv[cnv_inter3]\n", + "\n", + "print(\"\\nFinal Shapes for Modeling\")\n", + "print(f\"Methylation (X_meth_selected): {X_meth_selected.shape}\")\n", + "print(f\"RNA-Seq (X_rna_selected): {X_rna_selected.shape}\")\n", + "print(f\"CNV (X_cnv_selected): {X_cnv_selected.shape}\")\n", + "print(f\"Clinical (clinical_selected): {clinical.shape}\")\n", + "print(f\"Labels (Y_labels): {Y_labels.shape}\")" + ] + }, + { + "cell_type": "markdown", + "id": "9706f2e3", + "metadata": {}, + "source": [ + "## Data Availability\n", + "\n", + "To facilitate rapid experimentation and reproduction of our results, the fully processed and feature-selected dataset used in this analysis has been made available directly within the package.\n", + "\n", + "Users can load this dataset, bypassing all preceding data acquisition, preprocessing, and feature selection steps. This allows users to proceed immediately from this step." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b20ce715", + "metadata": {}, + "outputs": [], + "source": [ + "# for training puroses we will do variance selection to reduce the number of features\n", + "import bioneuralnet as bnn\n", + "\n", + "tgca_paad = bnn.datasets.DatasetLoader(\"paad\")\n", + "display(tgca_paad.shape)\n", + "\n", + "# The dataset is returned as a dictionary. We extract each file independetly based on the name (Key).\n", + "cnv = tgca_paad[\"cnv\"]\n", + "clinical = tgca_paad[\"clinical\"]\n", + "target = tgca_paad[\"target\"]\n", + "dna_meth = tgca_paad[\"meth\"]\n", + "rna = tgca_paad[\"rna\"]\n", + "\n", + "display(cnv.iloc[:3,:5])\n", + "display(dna_meth.iloc[:3,:5])\n", + "display(rna.iloc[:3,:5])\n", + "display(clinical.iloc[:3,:5])\n", + "display(target.iloc[:2,:5])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0bca75bf", + "metadata": {}, + "outputs": [], + "source": [ + "# Variance selection\n", + "cnv = bnn.utils.select_top_k_variance(cnv, k=500)\n", + "dna_meth = bnn.utils.select_top_k_variance(dna_meth, k=500)\n", + "rna = bnn.utils.select_top_k_variance(rna, k=500)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4cbd23b0", + "metadata": {}, + "outputs": [], + "source": [ + "from bioneuralnet.utils import preprocess_clinical\n", + "\n", + "dataleak_columns = [\n", + " \"vital_status\",\n", + " \"days_to_death\",\n", + " \"days_to_last_followup\",\n", + " \"date_of_initial_pathologic_diagnosis\",\n", + " ]\n", + "\n", + "clinical_for_model = preprocess_clinical(\n", + " clinical, \n", + " top_k=10, \n", + " scale=False, \n", + " ignore_columns=dataleak_columns,\n", + " nan_threshold=0.6)\n", + "\n", + "display(clinical_for_model.iloc[:5,:5])" + ] + }, + { + "cell_type": "markdown", + "id": "03ecd4a7", + "metadata": {}, + "source": [ + "## Reproducibility and Seeding\n", + "\n", + "To ensure our experimental results are fully reproducible, a single global seed is set at the beginning of the analysis.\n", + "\n", + "This utility function propagates the seed to all sources of randomness, including `random`, `numpy`, and `torch` (for both CPU and GPU). Critically, it also configures the PyTorch cuDNN backend to use deterministic algorithms.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2b8428d6", + "metadata": {}, + "outputs": [], + "source": [ + "import bioneuralnet as bnn\n", + "import pandas as pd\n", + "\n", + "SEED = 1883\n", + "bnn.utils.set_seed(SEED)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c25ce1a8", + "metadata": {}, + "outputs": [], + "source": [ + "from bioneuralnet.utils import find_optimal_graph\n", + "\n", + "omics_paad = pd.concat([cnv, dna_meth, rna], axis=1)\n", + "\n", + "optimal_graph, best_params, results_df = find_optimal_graph(\n", + " omics_data=omics_paad,\n", + " y_labels=target,\n", + " methods=['threshold', 'correlation','similarity','gaussian'],\n", + " seed=SEED,\n", + " verbose=False,\n", + " trials=30,\n", + " omics_list=[cnv, dna_meth, rna],\n", + " centrality_mode=\"eigenvector\",\n", + ")\n", + "display(optimal_graph.iloc[:5,:5])\n", + "display(best_params)\n", + "\n", + "results_df.sort_values(\"score\", ascending=False, inplace=True)\n", + "display(results_df)\n", + "## {'k': 20,'metric': 'cosine','mutual': False, 'linkage_mode': 'eigen_corr', 'epsilon': 0.0001}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ccaf3cd7", + "metadata": {}, + "outputs": [], + "source": [ + "# Graph analysis: The optimal gaph uses a proxy to evaluate its properties, but for the final output\n", + "# the graph is built without the target variable. The search only helps to find the best graph parameters..\n", + "from bioneuralnet.utils import graph_analysis\n", + "\n", + "graph_analysis(optimal_graph, graph_name=\"Optimal\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5c9f3a3a", + "metadata": {}, + "outputs": [], + "source": [ + "from pathlib import Path\n", + "from bioneuralnet.downstream_task import DPMON\n", + "\n", + "output_dir_base = Path(\"/home/vicente/Github/BioNeuralNet/dpmon_results_GAT_FINAL/paad\")\n", + "\n", + "current_output_dir = output_dir_base / \"Gat_best_graph\"\n", + "current_output_dir.mkdir(parents=True, exist_ok=True)\n", + "\n", + "dpmon_params_base = {\n", + " \"adjacency_matrix\": optimal_graph,\n", + " \"omics_list\": omics_paad,\n", + " \"phenotype_data\": target,\n", + " \"phenotype_col\": \"target\",\n", + " \"clinical_data\": clinical_for_model,\n", + " \"model\": 'GAT',\n", + " \"tune\": True, \n", + " \"cv\": True, \n", + " \"n_folds\": 5,\n", + " 'repeat_num': 1,\n", + " \"gpu\": True,\n", + " \"cuda\": 0,\n", + " \"seed\": SEED,\n", + " \"output_dir\": current_output_dir\n", + "}\n", + "\n", + "dpmon_tunned = DPMON(**dpmon_params_base)\n", + "predictions_df, metrics, embeddings = dpmon_tunned.run()\n", + "\n", + "graph_metrics = metrics\n", + "\n", + "acc_row = graph_metrics.loc[graph_metrics['Metric'] == 'Accuracy'].iloc[0]\n", + "f1_macro_row = graph_metrics.loc[graph_metrics['Metric'] == 'F1 Macro'].iloc[0]\n", + "f1_weighted_row = graph_metrics.loc[graph_metrics['Metric'] == 'F1 Weighted'].iloc[0]\n", + "recall_row = graph_metrics.loc[graph_metrics['Metric'] == 'Recall'].iloc[0]\n", + "auc_row = graph_metrics.loc[graph_metrics['Metric'] == 'AUC'].iloc[0]\n", + "aupr_row = graph_metrics.loc[graph_metrics['Metric'] == 'AUPR'].iloc[0]\n", + "\n", + "acc_avg, acc_std = acc_row['Average'], acc_row['StdDev']\n", + "f1_macro_avg, f1_macro_std = f1_macro_row['Average'], f1_macro_row['StdDev']\n", + "f1_weighted_avg, f1_weighted_std = f1_weighted_row['Average'], f1_weighted_row['StdDev']\n", + "recall_avg, recall_std = recall_row['Average'], recall_row['StdDev']\n", + "auc_avg, auc_std = auc_row['Average'], auc_row['StdDev']\n", + "aupr_avg, aupr_std = aupr_row['Average'], aupr_row['StdDev']\n", + "\n", + "print(f\"Accuracy (Avg +/- Std): {acc_avg:.4f} +/- {acc_std:.4f}\")\n", + "print(f\"F1 Macro (Avg +/- Std): {f1_macro_avg:.4f} +/- {f1_macro_std:.4f}\")\n", + "print(f\"F1 Weighted (Avg +/- Std): {f1_weighted_avg:.4f} +/- {f1_weighted_std:.4f}\")\n", + "print(f\"Recall: {recall_avg:.4f} +/- {recall_std:.4f}\")\n", + "print(f\"AUC: {auc_avg:.4f} +/- {auc_std:.4f}\")\n", + "print(f\"AUPR: {aupr_avg:.4f} +/- {aupr_std:.4f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5a9e86d6", + "metadata": {}, + "outputs": [], + "source": [ + "from bioneuralnet.downstream_task import DPMON\n", + "\n", + "\n", + "output_dir_base = Path(\"/home/vicente/Github/BioNeuralNet/dpmon_results_GCN_FINAL/paad\")\n", + "\n", + "current_output_dir = output_dir_base / \"Gcn_best_graph\"\n", + "current_output_dir.mkdir(parents=True, exist_ok=True)\n", + "\n", + "dpmon_params_base = {\n", + " \"adjacency_matrix\": optimal_graph,\n", + " \"omics_list\": omics_paad,\n", + " \"phenotype_data\": target,\n", + " \"phenotype_col\": \"target\",\n", + " \"clinical_data\": clinical_for_model,\n", + " \"model\": 'GCN',\n", + " \"tune\": True, \n", + " \"cv\": True, \n", + " \"n_folds\": 5,\n", + " 'repeat_num': 1,\n", + " \"gpu\": True,\n", + " \"cuda\": 0,\n", + " \"seed\": SEED,\n", + " \"output_dir\": current_output_dir\n", + "}\n", + "\n", + "dpmon_tunned = DPMON(**dpmon_params_base)\n", + "predictions_df, metrics, embeddings = dpmon_tunned.run()\n", + "graph_metrics = metrics\n", + "\n", + "acc_row = graph_metrics.loc[graph_metrics['Metric'] == 'Accuracy'].iloc[0]\n", + "f1_macro_row = graph_metrics.loc[graph_metrics['Metric'] == 'F1 Macro'].iloc[0]\n", + "f1_weighted_row = graph_metrics.loc[graph_metrics['Metric'] == 'F1 Weighted'].iloc[0]\n", + "recall_row = graph_metrics.loc[graph_metrics['Metric'] == 'Recall'].iloc[0]\n", + "auc_row = graph_metrics.loc[graph_metrics['Metric'] == 'AUC'].iloc[0]\n", + "aupr_row = graph_metrics.loc[graph_metrics['Metric'] == 'AUPR'].iloc[0]\n", + "\n", + "acc_avg, acc_std = acc_row['Average'], acc_row['StdDev']\n", + "f1_macro_avg, f1_macro_std = f1_macro_row['Average'], f1_macro_row['StdDev']\n", + "f1_weighted_avg, f1_weighted_std = f1_weighted_row['Average'], f1_weighted_row['StdDev']\n", + "recall_avg, recall_std = recall_row['Average'], recall_row['StdDev']\n", + "auc_avg, auc_std = auc_row['Average'], auc_row['StdDev']\n", + "aupr_avg, aupr_std = aupr_row['Average'], aupr_row['StdDev']\n", + "\n", + "print(f\"Accuracy (Avg +/- Std): {acc_avg:.4f} +/- {acc_std:.4f}\")\n", + "print(f\"F1 Macro (Avg +/- Std): {f1_macro_avg:.4f} +/- {f1_macro_std:.4f}\")\n", + "print(f\"F1 Weighted (Avg +/- Std): {f1_weighted_avg:.4f} +/- {f1_weighted_std:.4f}\")\n", + "print(f\"Recall: {recall_avg:.4f} +/- {recall_std:.4f}\")\n", + "print(f\"AUC: {auc_avg:.4f} +/- {auc_std:.4f}\")\n", + "print(f\"AUPR: {aupr_avg:.4f} +/- {aupr_std:.4f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6c2c8381", + "metadata": {}, + "outputs": [], + "source": [ + "from bioneuralnet.downstream_task import DPMON\n", + "output_dir_base = Path(\"/home/vicente/Github/BioNeuralNet/dpmon_results_GIN_FINAL/paad\")\n", + "\n", + "\n", + "current_output_dir = output_dir_base / \"gin_best_graph\"\n", + "current_output_dir.mkdir(parents=True, exist_ok=True)\n", + "\n", + "dpmon_params_base = {\n", + " \"adjacency_matrix\": optimal_graph,\n", + " \"omics_list\": omics_paad,\n", + " \"phenotype_data\": target,\n", + " \"phenotype_col\": \"target\",\n", + " \"clinical_data\": clinical_for_model,\n", + " \"model\": 'GIN',\n", + " \"tune\": True, \n", + " \"cv\": True, \n", + " \"n_folds\": 5,\n", + " 'repeat_num': 1,\n", + " \"gpu\": True,\n", + " \"cuda\": 0,\n", + " \"seed\": SEED,\n", + " \"output_dir\": current_output_dir\n", + "}\n", + "\n", + "dpmon_tunned = DPMON(**dpmon_params_base)\n", + "predictions_df, metrics, embeddings = dpmon_tunned.run()\n", + "graph_metrics = metrics\n", + "\n", + "acc_row = graph_metrics.loc[graph_metrics['Metric'] == 'Accuracy'].iloc[0]\n", + "f1_macro_row = graph_metrics.loc[graph_metrics['Metric'] == 'F1 Macro'].iloc[0]\n", + "f1_weighted_row = graph_metrics.loc[graph_metrics['Metric'] == 'F1 Weighted'].iloc[0]\n", + "recall_row = graph_metrics.loc[graph_metrics['Metric'] == 'Recall'].iloc[0]\n", + "auc_row = graph_metrics.loc[graph_metrics['Metric'] == 'AUC'].iloc[0]\n", + "aupr_row = graph_metrics.loc[graph_metrics['Metric'] == 'AUPR'].iloc[0]\n", + "\n", + "acc_avg, acc_std = acc_row['Average'], acc_row['StdDev']\n", + "f1_macro_avg, f1_macro_std = f1_macro_row['Average'], f1_macro_row['StdDev']\n", + "f1_weighted_avg, f1_weighted_std = f1_weighted_row['Average'], f1_weighted_row['StdDev']\n", + "recall_avg, recall_std = recall_row['Average'], recall_row['StdDev']\n", + "auc_avg, auc_std = auc_row['Average'], auc_row['StdDev']\n", + "aupr_avg, aupr_std = aupr_row['Average'], aupr_row['StdDev']\n", + "\n", + "print(f\"Accuracy (Avg +/- Std): {acc_avg:.4f} +/- {acc_std:.4f}\")\n", + "print(f\"F1 Macro (Avg +/- Std): {f1_macro_avg:.4f} +/- {f1_macro_std:.4f}\")\n", + "print(f\"F1 Weighted (Avg +/- Std): {f1_weighted_avg:.4f} +/- {f1_weighted_std:.4f}\")\n", + "print(f\"Recall: {recall_avg:.4f} +/- {recall_std:.4f}\")\n", + "print(f\"AUC: {auc_avg:.4f} +/- {auc_std:.4f}\")\n", + "print(f\"AUPR: {aupr_avg:.4f} +/- {aupr_std:.4f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f96dcd75", + "metadata": {}, + "outputs": [], + "source": [ + "from bioneuralnet.downstream_task import DPMON\n", + "\n", + "output_dir_base = Path(\"/home/vicente/Github/BioNeuralNet/dpmon_results_SAGE_FINAL/paad\")\n", + "\n", + "current_output_dir = output_dir_base / \"sage_best_graph\"\n", + "current_output_dir.mkdir(parents=True, exist_ok=True)\n", + "\n", + "dpmon_params_base = {\n", + " \"adjacency_matrix\": optimal_graph,\n", + " \"omics_list\": omics_paad,\n", + " \"phenotype_data\": target,\n", + " \"phenotype_col\": \"target\",\n", + " \"clinical_data\": clinical_for_model,\n", + " \"model\": 'SAGE',\n", + " \"tune\": True, \n", + " \"cv\": True, \n", + " \"n_folds\": 5,\n", + " 'repeat_num': 1,\n", + " \"gpu\": True,\n", + " \"cuda\": 0,\n", + " \"seed\": SEED,\n", + " \"output_dir\": current_output_dir\n", + "}\n", + "\n", + "dpmon_tunned = DPMON(**dpmon_params_base)\n", + "predictions_df, metrics, embeddings = dpmon_tunned.run()\n", + "graph_metrics = metrics\n", + "\n", + "acc_row = graph_metrics.loc[graph_metrics['Metric'] == 'Accuracy'].iloc[0]\n", + "f1_macro_row = graph_metrics.loc[graph_metrics['Metric'] == 'F1 Macro'].iloc[0]\n", + "f1_weighted_row = graph_metrics.loc[graph_metrics['Metric'] == 'F1 Weighted'].iloc[0]\n", + "recall_row = graph_metrics.loc[graph_metrics['Metric'] == 'Recall'].iloc[0]\n", + "auc_row = graph_metrics.loc[graph_metrics['Metric'] == 'AUC'].iloc[0]\n", + "aupr_row = graph_metrics.loc[graph_metrics['Metric'] == 'AUPR'].iloc[0]\n", + "\n", + "acc_avg, acc_std = acc_row['Average'], acc_row['StdDev']\n", + "f1_macro_avg, f1_macro_std = f1_macro_row['Average'], f1_macro_row['StdDev']\n", + "f1_weighted_avg, f1_weighted_std = f1_weighted_row['Average'], f1_weighted_row['StdDev']\n", + "recall_avg, recall_std = recall_row['Average'], recall_row['StdDev']\n", + "auc_avg, auc_std = auc_row['Average'], auc_row['StdDev']\n", + "aupr_avg, aupr_std = aupr_row['Average'], aupr_row['StdDev']\n", + "\n", + "print(f\"Accuracy (Avg +/- Std): {acc_avg:.4f} +/- {acc_std:.4f}\")\n", + "print(f\"F1 Macro (Avg +/- Std): {f1_macro_avg:.4f} +/- {f1_macro_std:.4f}\")\n", + "print(f\"F1 Weighted (Avg +/- Std): {f1_weighted_avg:.4f} +/- {f1_weighted_std:.4f}\")\n", + "print(f\"Recall: {recall_avg:.4f} +/- {recall_std:.4f}\")\n", + "print(f\"AUC: {auc_avg:.4f} +/- {auc_std:.4f}\")\n", + "print(f\"AUPR: {aupr_avg:.4f} +/- {aupr_std:.4f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6d8f2d74", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.model_selection import StratifiedKFold, ParameterSampler, RepeatedStratifiedKFold\n", + "from sklearn.metrics import accuracy_score, f1_score, recall_score, roc_auc_score, average_precision_score\n", + "from sklearn.base import clone\n", + "from sklearn.preprocessing import StandardScaler, label_binarize\n", + "from sklearn.pipeline import Pipeline\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.neural_network import MLPClassifier\n", + "from scipy.stats import loguniform, randint\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "from sklearn.tree import DecisionTreeClassifier\n", + "from xgboost import XGBClassifier\n", + "from sklearn.svm import SVC\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "X = pd.concat([dna_meth, rna, cnv, clinical_for_model], axis=1)\n", + "y = target['target']\n", + "print(f\"Successfully created X matrix with shape: {X.shape}\")\n", + "print(f\"Successfully created y vector with shape: {y.shape}\")\n", + "\n", + "pipe_lr = Pipeline([\n", + " ('scaler', StandardScaler()),\n", + " ('model', LogisticRegression(solver='lbfgs', max_iter=1000, penalty=None, random_state=SEED))\n", + "])\n", + "\n", + "pipe_mlp = Pipeline([\n", + " ('scaler', StandardScaler()),\n", + " ('model', MLPClassifier(max_iter=500, early_stopping=True, n_iter_no_change=10, random_state=SEED))\n", + "])\n", + "\n", + "pipe_xgb = Pipeline([\n", + " ('scaler', StandardScaler()),\n", + " ('model', XGBClassifier(eval_metric='logloss', tree_method='hist', max_bin=128, random_state=SEED))\n", + "])\n", + "pipe_rf = Pipeline([\n", + " ('scaler', StandardScaler()),\n", + " ('model', RandomForestClassifier(random_state=SEED))\n", + "])\n", + "\n", + "pipe_svm = Pipeline([\n", + " ('scaler', StandardScaler()),\n", + " ('model', SVC(probability=True, random_state=SEED))\n", + "])\n", + "\n", + "pipe_dt = Pipeline([\n", + " ('scaler', StandardScaler()),\n", + " ('model', DecisionTreeClassifier(random_state=SEED))\n", + "])\n", + "\n", + "params_lr = {'model__penalty': ['l2'], 'model__C': loguniform(1e-4, 1e2)}\n", + "\n", + "params_mlp = {\n", + " 'model__hidden_layer_sizes': [(100,), (100, 50), (50, 50)],\n", + " 'model__activation': ['relu', 'tanh'],\n", + " 'model__alpha': loguniform(1e-5, 1e-1),\n", + " 'model__learning_rate_init': loguniform(1e-4, 1e-2)\n", + "}\n", + "params_xgb = {\n", + " 'model__n_estimators': randint(50, 200),\n", + " 'model__learning_rate': loguniform(0.01, 0.3),\n", + " 'model__max_depth': randint(3, 7),\n", + " 'model__subsample': [0.8, 1.0], \n", + " 'model__colsample_bytree': [0.8, 1.0]\n", + "}\n", + "params_rf = {\n", + " 'model__n_estimators': randint(100, 300),\n", + " 'model__max_depth': [10, 20, 30, None],\n", + " 'model__min_samples_split': randint(2, 10),\n", + " 'model__min_samples_leaf': randint(1, 5),\n", + " 'model__max_features': ['sqrt', 'log2']\n", + "}\n", + "params_svm = {\n", + " 'model__C': loguniform(1e-2, 1e3),\n", + " 'model__kernel': ['rbf', 'linear'],\n", + " 'model__gamma': ['scale', 'auto']\n", + "}\n", + "\n", + "params_dt = {\n", + " 'model__max_depth': randint(3, 15),\n", + " 'model__min_samples_split': randint(2, 20),\n", + " 'model__criterion': ['gini', 'entropy']\n", + "}\n", + "\n", + "models_to_tune = {\n", + " \"LogisticRegression\": (pipe_lr, params_lr),\n", + " \"SVM\": (pipe_svm, params_svm),\n", + " \"MLP\": (pipe_mlp, params_mlp),\n", + " \"XGBoost\": (pipe_xgb, params_xgb),\n", + " \"RandomForest\": (pipe_rf, params_rf),\n", + " \"DecisionTree\": (pipe_dt, params_dt),\n", + "}\n", + "\n", + "all_results = {\n", + " \"LogisticRegression\": {\"acc\": [], \"f1_w\": [], \"f1_m\": [], \"recall\": [], \"auc\": [], \"aupr\": []},\n", + " \"MLP\": {\"acc\": [], \"f1_w\": [], \"f1_m\": [], \"recall\": [], \"auc\": [], \"aupr\": []},\n", + " \"XGBoost\": {\"acc\": [], \"f1_w\": [], \"f1_m\": [], \"recall\": [], \"auc\": [], \"aupr\": []},\n", + " \"RandomForest\": {\"acc\": [], \"f1_w\": [], \"f1_m\": [], \"recall\": [], \"auc\": [], \"aupr\": []},\n", + " \"SVM\": {\"acc\": [], \"f1_w\": [], \"f1_m\": [], \"recall\": [], \"auc\": [], \"aupr\": []},\n", + " \"DecisionTree\": {\"acc\": [], \"f1_w\": [], \"f1_m\": [], \"recall\": [], \"auc\": [], \"aupr\": []},\n", + "}\n", + "\n", + "\n", + "for model_name, (pipeline, param_dist) in models_to_tune.items():\n", + " print(f\"Evaluating model with nested CV: {model_name}\")\n", + " \n", + " outer_cv = RepeatedStratifiedKFold(n_splits=5, n_repeats=1, random_state=SEED)\n", + " \n", + " # inner folds are for finding the best hyperparameters\n", + " for fold_idx, (train_idx, test_idx) in enumerate(outer_cv.split(X, y), start=1):\n", + " X_train_outer, X_test_outer = X.iloc[train_idx], X.iloc[test_idx]\n", + " y_train_outer, y_test_outer = y.iloc[train_idx], y.iloc[test_idx]\n", + " inner_cv = StratifiedKFold(n_splits=5, shuffle=True)\n", + " \n", + " best_score_fold = -np.inf\n", + " best_params_fold = None\n", + " # May or may not want to set a seed here. A fix seed = same hyperparamters each fold.\n", + " # No seed = different hyperparameters each fold. This adds more randomness, and may yield better generalization.\n", + " param_sampler = list(ParameterSampler(param_dist, n_iter=20))\n", + " \n", + " for params in param_sampler:\n", + " inner_scores = []\n", + " \n", + " for inner_train_idx, inner_val_idx in inner_cv.split(X_train_outer, y_train_outer):\n", + " X_train_inner = X_train_outer.iloc[inner_train_idx]\n", + " X_val_inner = X_train_outer.iloc[inner_val_idx]\n", + " y_train_inner = y_train_outer.iloc[inner_train_idx]\n", + " y_val_inner = y_train_outer.iloc[inner_val_idx]\n", + " \n", + " inner_pipeline = clone(pipeline)\n", + " inner_pipeline.set_params(**params)\n", + " inner_pipeline.fit(X_train_inner, y_train_inner)\n", + " \n", + " y_val_pred = inner_pipeline.predict(X_val_inner)\n", + " score = f1_score(y_val_inner, y_val_pred, average='weighted', zero_division=0)\n", + " inner_scores.append(score)\n", + " \n", + " mean_score = np.mean(inner_scores)\n", + " if mean_score > best_score_fold:\n", + " best_score_fold = mean_score\n", + " best_params_fold = params\n", + " \n", + " print(f\"Outer fold {fold_idx}: best params (inner CV F1-W={best_score_fold:.4f})\")\n", + " print(f\"{best_params_fold}\")\n", + " \n", + " final_pipeline = clone(pipeline)\n", + " final_pipeline.set_params(**best_params_fold)\n", + " final_pipeline.fit(X_train_outer, y_train_outer)\n", + " \n", + " preds = final_pipeline.predict(X_test_outer)\n", + " \n", + " if hasattr(final_pipeline, \"predict_proba\"):\n", + " proba = final_pipeline.predict_proba(X_test_outer)\n", + " else:\n", + " proba = None\n", + " \n", + " acc = accuracy_score(y_test_outer, preds)\n", + " f1_w = f1_score(y_test_outer, preds, average='weighted', zero_division=0)\n", + " f1_m = f1_score(y_test_outer, preds, average='macro', zero_division=0)\n", + " recall = recall_score(y_test_outer, preds, average='macro', zero_division=0)\n", + " \n", + " auc = np.nan\n", + " aupr = np.nan\n", + " \n", + " if proba is not None:\n", + " try:\n", + " if len(np.unique(y)) == 2:\n", + " auc = roc_auc_score(y_test_outer, proba[:, 1])\n", + " aupr = average_precision_score(y_test_outer, proba[:, 1])\n", + " else:\n", + " auc = roc_auc_score(y_test_outer, proba, multi_class='ovr', average='macro')\n", + " y_test_bin = label_binarize(y_test_outer, classes=np.unique(y))\n", + " aupr = average_precision_score(y_test_bin, proba, average='weighted')\n", + " except Exception:\n", + " auc = np.nan\n", + " aupr = np.nan\n", + "\n", + " print(f\"Fold {fold_idx} results: Acc={acc:.4f}, F1-W={f1_w:.4f}, \"\n", + " f\"F1-M={f1_m:.4f}, Recall={recall:.4f}, AUC={auc:.4f}, AUPR={aupr:.4f}\")\n", + " \n", + " all_results[model_name][\"acc\"].append(acc)\n", + " all_results[model_name][\"f1_w\"].append(f1_w)\n", + " all_results[model_name][\"f1_m\"].append(f1_m)\n", + " all_results[model_name][\"recall\"].append(recall)\n", + " all_results[model_name][\"auc\"].append(auc)\n", + " all_results[model_name][\"aupr\"].append(aupr)\n", + "\n", + "print(\"\\nFINAL BASELINE RESULTS\\n\")\n", + "for model_name, metrics in all_results.items():\n", + " avg_acc = np.mean(metrics[\"acc\"])\n", + " std_acc = np.std(metrics[\"acc\"])\n", + " avg_f1_w = np.mean(metrics[\"f1_w\"])\n", + " std_f1_w = np.std(metrics[\"f1_w\"])\n", + " avg_f1_m = np.mean(metrics[\"f1_m\"])\n", + " std_f1_m = np.std(metrics[\"f1_m\"])\n", + " avg_recall = np.mean(metrics[\"recall\"])\n", + " std_recall = np.std(metrics[\"recall\"])\n", + " avg_auc = np.nanmean(metrics[\"auc\"])\n", + " std_auc = np.nanstd(metrics[\"auc\"])\n", + " avg_aupr = np.nanmean(metrics[\"aupr\"])\n", + " std_aupr = np.nanstd(metrics[\"aupr\"])\n", + " \n", + " print(f\"\\n{model_name}:\")\n", + " print(f\"Accuracy: {avg_acc:.4f} +/- {std_acc:.4f}\")\n", + " print(f\"F1 Weighted: {avg_f1_w:.4f} +/- {std_f1_w:.4f}\")\n", + " print(f\"F1 Macro: {avg_f1_m:.4f} +/- {std_f1_m:.4f}\")\n", + " print(f\"Recall: {avg_recall:.4f} +/- {std_recall:.4f}\")\n", + " print(f\"AUC: {avg_auc:.4f} +/- {std_auc:.4f}\")\n", + " print(f\"AUPR: {avg_aupr:.4f} +/- {std_aupr:.4f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c265a190", + "metadata": {}, + "outputs": [], + "source": [ + "import bioneuralnet as bnn\n", + "# plotting function, the values below were enter manually\n", + "\n", + "baseline_data = {\n", + " \"Accuracy\": {\n", + " \"GIN\": (0.7005, 0.0583),\n", + " \"GraphSAGE\": (0.7005, 0.0934),\n", + " \"GAT\": (0.6894, 0.0646),\n", + " \"GCN\": (0.6608, 0.0469),\n", + " \"LogReg\": (0.7062, 0.0560),\n", + " \"SVM\": (0.6778, 0.0620),\n", + " \"XGBoost\": (0.6779, 0.0721),\n", + " \"MLP\": (0.6710, 0.0667),\n", + " \"Random Forest\": (0.6665, 0.0830),\n", + " \"Decision Tree\": (0.5715, 0.0654),\n", + " },\n", + " \"F1 Weighted\": {\n", + " \"GIN\": (0.6973, 0.0601),\n", + " \"GraphSAGE\": (0.6925, 0.0984),\n", + " \"GAT\": (0.6774, 0.0701),\n", + " \"GCN\": (0.6515, 0.0487),\n", + " \"LogReg\": (0.7025, 0.0586),\n", + " \"SVM\": (0.6740, 0.0630),\n", + " \"XGBoost\": (0.6715, 0.0740),\n", + " \"MLP\": (0.6678, 0.0690),\n", + " \"Random Forest\": (0.6592, 0.0870),\n", + " \"Decision Tree\": (0.5663, 0.0659),\n", + " },\n", + " \"F1 Macro\": {\n", + " \"GIN\": (0.6966, 0.0602),\n", + " \"GraphSAGE\": (0.6924, 0.0911),\n", + " \"GAT\": (0.6762, 0.0629),\n", + " \"GCN\": (0.6505, 0.0435),\n", + " \"LogReg\": (0.7008, 0.0597),\n", + " \"SVM\": (0.6725, 0.0637),\n", + " \"XGBoost\": (0.6693, 0.0751),\n", + " \"MLP\": (0.6662, 0.0697),\n", + " \"Random Forest\": (0.6568, 0.0883),\n", + " \"Decision Tree\": (0.5648, 0.0668),\n", + " },\n", + "}\n", + "\n", + "baseline_data_cont = {\n", + " \"Recall\": {\n", + " \"GIN\": (0.7002, 0.0589),\n", + " \"GraphSAGE\": (0.7032, 0.0734),\n", + " \"GAT\": (0.6883, 0.0553),\n", + " \"GCN\": (0.6607, 0.0427),\n", + " \"LogReg\": (0.7032, 0.0585),\n", + " \"SVM\": (0.6755, 0.0631),\n", + " \"XGBoost\": (0.6737, 0.0736),\n", + " \"MLP\": (0.6686, 0.0682),\n", + " \"Random Forest\": (0.6620, 0.0846),\n", + " \"Decision Tree\": (0.5700, 0.0671),\n", + " },\n", + " \"AUC\": {\n", + " \"GIN\": (0.7471, 0.0573),\n", + " \"GraphSAGE\": (0.7482, 0.0772),\n", + " \"GAT\": (0.7418, 0.0784),\n", + " \"GCN\": (0.7390, 0.0779),\n", + " \"LogReg\": (0.7905, 0.0590),\n", + " \"SVM\": (0.7602, 0.0604),\n", + " \"XGBoost\": (0.7454, 0.0852),\n", + " \"MLP\": (0.7363, 0.0720),\n", + " \"Random Forest\": (0.7509, 0.0813),\n", + " \"Decision Tree\": (0.5802, 0.0747),\n", + " },\n", + " \"AUPR\": {\n", + " \"GIN\": (0.7634, 0.0743),\n", + " \"GraphSAGE\": (0.7812, 0.0722),\n", + " \"GAT\": (0.7570, 0.0872),\n", + " \"GCN\": (0.7661, 0.0739),\n", + " \"LogReg\": (0.8042, 0.0689),\n", + " \"SVM\": (0.7824, 0.0574),\n", + " \"XGBoost\": (0.7624, 0.0836),\n", + " \"MLP\": (0.7533, 0.0824),\n", + " \"Random Forest\": (0.7770, 0.0734),\n", + " \"Decision Tree\": (0.5869, 0.0559),\n", + " },\n", + "}\n", + "\n", + "bnn.metrics.plot_multiple_metrics(\n", + " baseline_data,\n", + " title_map={\n", + " \"Accuracy\": \"GNNs vs Baselines: Accuracy\",\n", + " \"F1 Weighted\": \"GNNs vs Baselines: F1 Weighted\",\n", + " \"F1 Macro\": \"GNNs vs Baselines: F1 Macro\",\n", + " }\n", + ")\n", + "\n", + "bnn.metrics.plot_multiple_metrics(\n", + " baseline_data_cont,\n", + " title_map={\n", + " \"Recall\": \"GNNs vs Baselines: Recall\",\n", + " \"AUC\": \"GNNs vs Baselines: AUC\",\n", + " \"AUPR\": \"GNNs vs Baselines: AUPR\",\n", + " }\n", + ")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".enviroment", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/bioneuralnet/datasets/paad/clinical.csv b/bioneuralnet/datasets/paad/clinical.csv new file mode 100644 index 0000000..2172775 --- /dev/null +++ b/bioneuralnet/datasets/paad/clinical.csv @@ -0,0 +1,178 @@ +patient,years_to_birth,vital_status,days_to_death,days_to_last_followup,tumor_tissue_site,pathologic_stage,pathology_T_stage,pathology_N_stage,pathology_M_stage,gender,date_of_initial_pathologic_diagnosis,radiation_therapy,histological_type,number_pack_years_smoked,year_of_tobacco_smoking_onset,residual_tumor,number_of_lymph_nodes,race,ethnicity +TCGA-2J-AAB1,65,1,66,,pancreas,stage iib,t3,n1,m0,male,2012,no,pancreas-adenocarcinoma-other subtype,25,1962,r0,7,white, +TCGA-2J-AAB4,48,0,,729,pancreas,stage iib,t2,n1,m0,male,2012,no,pancreas-adenocarcinoma-other subtype,,,r0,0,white, +TCGA-2J-AAB6,75,1,293,,pancreas,stage iia,t3,n0,m0,male,2012,no,pancreas-adenocarcinoma ductal type,,,r0,0,white, +TCGA-2J-AAB8,71,0,,80,pancreas,stage iib,t3,n1,m0,male,2012,,pancreas-adenocarcinoma ductal type,,,r1,3,white, +TCGA-2J-AAB9,70,1,627,,pancreas,stage iib,t3,n1,m0,female,2012,yes,pancreas-adenocarcinoma ductal type,,,r0,6,white, +TCGA-2J-AABA,55,1,607,,pancreas,stage iib,t3,n1,m0,male,2012,no,pancreas-adenocarcinoma ductal type,51,,r1,2,white, +TCGA-2J-AABE,73,0,,676,pancreas,stage iia,t3,n0,m0,male,2012,yes,pancreas-adenocarcinoma ductal type,2.2,1960,r0,0,white, +TCGA-2J-AABF,73,1,691,,pancreas,stage iib,t3,n1,m0,male,2012,yes,pancreas-adenocarcinoma ductal type,7.5,1960,r1,4,white, +TCGA-2J-AABH,61,0,,1287,pancreas,stage iia,t3,n0,m0,male,2011,no,pancreas-adenocarcinoma-other subtype,,,r0,0,white, +TCGA-2J-AABI,55,0,,969,pancreas,stage iia,t3,n0,m0,female,2011,yes,pancreas-adenocarcinoma-other subtype,42,1973,r0,0,white, +TCGA-2J-AABK,71,0,,484,pancreas,stage iib,t3,n1,m0,male,2012,yes,pancreas-adenocarcinoma ductal type,,,r1,2,white, +TCGA-2J-AABO,43,0,,440,pancreas,stage iib,t3,n1,m0,male,2013,yes,pancreas-adenocarcinoma ductal type,7.5,1993,r0,8,white, +TCGA-2J-AABP,58,0,,463,pancreas,stage iib,t3,n1,m0,female,2013,no,pancreas-undifferentiated carcinoma,,,r0,1,white, +TCGA-2J-AABR,60,0,,438,pancreas,stage iia,t3,n0,m0,female,2013,no,pancreas-adenocarcinoma ductal type,,,r0,0,white, +TCGA-2J-AABT,72,0,,319,pancreas,stage iib,t3,n1,m0,female,2012,,pancreas-adenocarcinoma ductal type,,,r0,1,white, +TCGA-2J-AABU,56,1,277,,pancreas,stage iib,t3,n1,m0,male,2012,no,pancreas-adenocarcinoma ductal type,,,r0,2,white, +TCGA-2J-AABV,74,1,652,,pancreas,stage iib,t3,n1,m0,male,2012,no,pancreas-adenocarcinoma ductal type,30,,r1,13,white, +TCGA-2L-AAQA,76,1,143,,pancreas,stage iib,t3,n1,mx,male,2010,no,pancreas-adenocarcinoma ductal type,30,,r1,6,white, +TCGA-2L-AAQE,56,1,684,,pancreas,stage iib,t3,n1,m0,male,2011,no,pancreas-adenocarcinoma ductal type,30,,r0,1,white, +TCGA-2L-AAQI,66,1,103,,pancreas,stage iib,t3,n1,mx,male,2011,no,pancreas-adenocarcinoma ductal type,,,r1,6,white, +TCGA-2L-AAQJ,49,1,394,,pancreas,stage iii,t4,n0,mx,female,2010,no,pancreas-adenocarcinoma ductal type,,,r1,0,white, +TCGA-2L-AAQL,82,1,292,,pancreas,stage iib,t3,n1,mx,male,2011,no,pancreas-adenocarcinoma ductal type,,,rx,3,white, +TCGA-2L-AAQM,52,0,,1383,pancreas,stage iib,t3,n1,mx,male,2010,no,pancreas-adenocarcinoma-other subtype,,,r1,16,white, +TCGA-3A-A9I5,57,0,,1794,pancreas,,,n0,mx,male,2009,yes,pancreas-adenocarcinoma ductal type,,,r0,0,white,not hispanic or latino +TCGA-3A-A9I7,45,0,,1323,pancreas,stage iib,t3,n1,mx,male,2010,yes,pancreas-adenocarcinoma ductal type,,,r0,2,black or african american,not hispanic or latino +TCGA-3A-A9I9,67,1,634,,pancreas,stage iia,t3,n0,mx,male,2011,yes,pancreas-adenocarcinoma ductal type,75,,r0,0,white,not hispanic or latino +TCGA-3A-A9IB,69,1,224,,pancreas,stage iib,t3,n1,mx,female,2011,no,pancreas-adenocarcinoma ductal type,5,1962,r0,5,white,not hispanic or latino +TCGA-3A-A9IC,61,1,738,,pancreas,stage iib,t3,n1,mx,female,2011,yes,pancreas-adenocarcinoma ductal type,,,r0,4,white,not hispanic or latino 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iib,t3,n1,m0,male,2009,no,pancreas-adenocarcinoma-other subtype,,,r0,2,white, +TCGA-F2-6880,70,0,,295,pancreas,stage iib,t3,n1,m0,male,2009,no,pancreas-adenocarcinoma ductal type,15,,r0,1,white, +TCGA-F2-7273,54,1,592,,pancreas,stage iib,t3,n1,m0,male,2011,yes,pancreas-adenocarcinoma ductal type,,,r1,3,black or african american,not hispanic or latino +TCGA-F2-7276,64,1,216,,pancreas,stage iib,t3,n1,m0,male,2011,no,pancreas-adenocarcinoma ductal type,,,r0,9,white,not hispanic or latino +TCGA-F2-A44G,79,1,233,,pancreas,stage iib,t3,n1,m0,female,2012,yes,pancreas-adenocarcinoma ductal type,,,r1,3,white,not hispanic or latino +TCGA-F2-A44H,65,0,,586,pancreas,stage iia,t3,n0,m0,male,2012,yes,pancreas-adenocarcinoma ductal type,,,r0,0,white,not hispanic or latino +TCGA-F2-A7TX,77,1,95,,pancreas,stage iib,t3,n1,m0,male,2013,no,pancreas-adenocarcinoma ductal type,,,r1,7,white, +TCGA-F2-A8YN,76,0,,517,pancreas,stage iia,t3,n0,m0,male,2013,no,pancreas-adenocarcinoma ductal type,60,,r1,0,white,not hispanic or latino +TCGA-FB-A4P5,69,1,179,,pancreas,stage iib,t3,n1,mx,female,2012,,pancreas-adenocarcinoma ductal type,40,1968,r0,4,white,not hispanic or latino +TCGA-FB-A545,72,1,732,,pancreas,stage iib,t3,n1,m0,female,2012,yes,pancreas-adenocarcinoma ductal type,,,,4,white,not hispanic or latino +TCGA-FB-A5VM,74,1,498,,pancreas,stage ib,t2,n0,m0,male,2012,no,pancreas-adenocarcinoma ductal type,24,1953,r0,0,white,not hispanic or latino +TCGA-FB-A78T,71,1,375,,pancreas,stage iib,t3,n1,m0,female,2013,no,pancreas-adenocarcinoma ductal type,2,1980,r0,4,white,not hispanic or latino +TCGA-FB-A7DR,48,1,353,,pancreas,stage iia,t3,nx,m0,male,2012,no,pancreas-colloid (mucinous non-cystic) carcinoma,20,1993,r0,,asian,not hispanic or latino +TCGA-FB-AAPP,71,1,485,,pancreas,stage iib,t3,n1,mx,male,2009,yes,pancreas-adenocarcinoma ductal type,,,r0,3,white,not hispanic or latino +TCGA-FB-AAPQ,65,1,1130,,pancreas,stage iib,t3,n1,mx,male,2008,no,pancreas-adenocarcinoma ductal type,25,1983,r0,4,white,not hispanic or latino +TCGA-FB-AAPS,62,0,,228,pancreas,stage iib,t2,n1,mx,female,2008,yes,pancreas-adenocarcinoma ductal type,10,1960,r0,1,white,not hispanic or latino +TCGA-FB-AAPU,41,1,381,,pancreas,stage iia,t3,n0,mx,female,2008,no,pancreas-adenocarcinoma ductal type,,,r1,0,asian,not hispanic or latino +TCGA-FB-AAPY,71,1,1059,,pancreas,stage iib,t2,n1,mx,male,2007,yes,pancreas-adenocarcinoma ductal type,25,1960,r0,2,white,not hispanic or latino +TCGA-FB-AAPZ,54,0,,716,pancreas,stage iib,t2,n1,mx,male,2008,no,pancreas-adenocarcinoma ductal type,,,r0,1,white,not hispanic or latino +TCGA-FB-AAQ0,68,1,473,,pancreas,stage iia,t3,n0,mx,male,2011,no,pancreas-adenocarcinoma ductal type,,,r0,0,white,not hispanic or latino +TCGA-FB-AAQ1,49,1,123,,pancreas,stage iib,t3,n1,mx,male,2011,no,pancreas-adenocarcinoma ductal type,,,r1,3,white,not hispanic or latino +TCGA-FB-AAQ2,81,1,153,,pancreas,stage iib,t3,n1,mx,female,2011,no,pancreas-adenocarcinoma ductal type,,,r1,2,white,not hispanic or latino +TCGA-FB-AAQ3,65,1,31,,pancreas,stage iib,t3,n1,mx,female,2010,no,pancreas-adenocarcinoma ductal type,,,r0,1,white,not hispanic or latino +TCGA-FB-AAQ6,85,1,244,,pancreas,stage ia,t1,n0,mx,male,2011,no,pancreas-adenocarcinoma ductal type,,,r0,0,white,not hispanic or latino +TCGA-H6-8124,56,0,,392,pancreas,stage iib,t3,n1,m0,female,2011,yes,pancreas-adenocarcinoma ductal type,3.5,1969,r0,2,white,not hispanic or latino +TCGA-H6-A45N,88,1,421,,pancreas,stage iib,t3,n1,mx,female,2012,no,pancreas-adenocarcinoma-other subtype,1,,,1,white,not hispanic or latino +TCGA-H8-A6C1,53,0,,671,pancreas,stage iia,t3,n0,mx,male,2012,no,pancreas-adenocarcinoma-other subtype,30,1982,r0,0,white,not hispanic or latino +TCGA-HV-A5A3,50,1,128,,pancreas,stage iia,t3,nx,mx,male,2012,no,pancreas-adenocarcinoma ductal type,,,,5,asian, +TCGA-HV-A5A4,72,0,,232,pancreas,stage iia,t3,n0,mx,female,2012,no,pancreas-adenocarcinoma ductal type,,,,1,asian, +TCGA-HV-A5A5,61,0,,289,pancreas,stage iib,t3,n1,m0,female,2012,no,pancreas-adenocarcinoma ductal type,,,,2,asian, +TCGA-HV-A5A6,65,1,2036,,pancreas,stage iib,t3,n1b,m0,female,2001,no,pancreas-adenocarcinoma ductal type,,,r0,2,asian, +TCGA-HV-A7OL,70,0,,252,pancreas,stage iia,t3,n0,mx,male,2012,no,pancreas-adenocarcinoma ductal type,,,r2,,asian, +TCGA-HV-A7OP,72,0,,978,pancreas,stage iib,t3,n1,m0,male,2012,yes,pancreas-adenocarcinoma-other subtype,,,r0,7,asian, +TCGA-HV-AA8V,50,0,,920,pancreas,stage iib,t3,n1,mx,male,2012,no,pancreas-adenocarcinoma ductal type,60,,r2,3,asian, +TCGA-HV-AA8X,75,1,532,,pancreas,stage iib,t2,n1,mx,female,2013,no,pancreas-adenocarcinoma ductal type,,,r2,3,asian, +TCGA-HZ-7289,77,1,661,,pancreas,stage iib,t3,n1,mx,male,2010,no,pancreas-adenocarcinoma-other subtype,,,rx,1,white,not hispanic or latino +TCGA-HZ-7918,72,0,,969,pancreas,stage iib,t3,n1,m0,male,2011,yes,pancreas-adenocarcinoma ductal type,,,r0,11,white,not hispanic or latino +TCGA-HZ-7919,52,1,593,,pancreas,stage iib,t3,n1,m0,female,2011,,pancreas-adenocarcinoma ductal type,,,r1,2,white,not hispanic or latino +TCGA-HZ-7920,71,1,236,,pancreas,stage ib,t2,n0,mx,male,2011,yes,pancreas-adenocarcinoma ductal type,,,r0,0,white,not hispanic or latino +TCGA-HZ-7922,61,0,,4,pancreas,stage iib,t3,n1,mx,female,2011,,pancreas-adenocarcinoma ductal type,,,r0,3,white,not hispanic or latino +TCGA-HZ-7923,65,0,,314,pancreas,stage iia,t3,n0,mx,male,2011,no,pancreas-adenocarcinoma ductal type,,,r0,0,white,not hispanic or latino +TCGA-HZ-7924,60,0,,840,pancreas,stage iia,t3,n0,mx,female,2011,no,pancreas-adenocarcinoma ductal type,,,r0,0,white,not hispanic or latino +TCGA-HZ-7925,66,1,614,,pancreas,stage iib,t3,n1,mx,male,2011,no,pancreas-adenocarcinoma ductal type,,,r0,2,white,not hispanic or latino +TCGA-HZ-7926,57,1,518,,pancreas,stage iib,t3,n1,mx,male,2011,,pancreas-adenocarcinoma ductal type,,,rx,2,black or african american,not hispanic or latino +TCGA-HZ-8001,69,0,,706,pancreas,stage iii,t4,n0,mx,male,2011,no,pancreas-adenocarcinoma ductal type,,,r0,0,white,not hispanic or latino +TCGA-HZ-8002,61,1,366,,pancreas,stage iib,t3,n1,mx,male,2011,no,pancreas-adenocarcinoma ductal type,,,r0,4,white,not hispanic or latino +TCGA-HZ-8003,65,1,596,,pancreas,stage iib,t3,n1,mx,female,2011,no,pancreas-adenocarcinoma ductal type,,,r0,1,white,not hispanic or latino +TCGA-HZ-8005,81,1,120,,pancreas,stage iib,t3,n1,mx,male,2012,no,pancreas-adenocarcinoma ductal type,,,r1,8,white,not hispanic or latino +TCGA-HZ-8315,54,1,299,,pancreas,stage iia,t3,n0,mx,female,2012,,pancreas-adenocarcinoma ductal type,,,r1,0,white,not hispanic or latino +TCGA-HZ-8317,69,1,378,,pancreas,stage iib,t3,n1,mx,female,2012,yes,pancreas-adenocarcinoma ductal type,,,r1,4,white,not hispanic or latino +TCGA-HZ-8519,73,0,,454,pancreas,stage ia,t1,n0,mx,male,2012,yes,pancreas-adenocarcinoma-other subtype,,,r0,0,white,not hispanic or latino +TCGA-HZ-8636,58,1,545,,pancreas,stage iv,t3,n0,m1,female,2012,,pancreas-adenocarcinoma ductal type,,,r1,0,white,not hispanic or latino +TCGA-HZ-8637,76,1,517,,pancreas,stage iib,t3,n1,mx,female,2012,yes,pancreas-adenocarcinoma ductal type,,,r0,1,white,hispanic or latino +TCGA-HZ-8638,84,1,151,,pancreas,stage ia,t1,n0,mx,female,2012,no,pancreas-colloid (mucinous non-cystic) carcinoma,,,r0,0,white,not hispanic or latino +TCGA-HZ-A49G,58,0,,660,pancreas,stage iib,t2,n1,mx,female,2012,yes,pancreas-adenocarcinoma ductal type,,,r1,3,white,not hispanic or latino +TCGA-HZ-A49H,68,0,,491,pancreas,stage iib,t3,n1,mx,female,2012,no,pancreas-adenocarcinoma ductal type,,,r0,1,white,not hispanic or latino +TCGA-HZ-A49I,77,1,308,,pancreas,stage iib,t3,n1,mx,male,2012,yes,pancreas-adenocarcinoma ductal type,,,r1,3,white,not hispanic or latino +TCGA-HZ-A4BH,75,0,,194,pancreas,stage iib,t3,n1b,mx,male,2011,,pancreas-adenocarcinoma ductal type,,,r1,4,white, +TCGA-HZ-A4BK,72,0,,657,pancreas,stage iib,t3,n1,mx,male,2012,yes,pancreas-adenocarcinoma-other subtype,,,r1,2,white,not hispanic or latino +TCGA-HZ-A77O,77,1,160,,pancreas,stage iib,t2,n1,mx,female,2013,no,pancreas-adenocarcinoma ductal type,,,r0,4,white,not hispanic or latino +TCGA-HZ-A77P,77,0,,330,pancreas,stage iib,t3,n1,mx,male,2013,no,pancreas-adenocarcinoma-other subtype,,,r0,1,white,not hispanic or latino +TCGA-HZ-A77Q,55,0,,33,pancreas,stage iib,t3,n1,mx,female,2013,,pancreas-adenocarcinoma-other subtype,15,1983,r0,1,white,not hispanic or latino +TCGA-HZ-A8P0,76,0,,0,pancreas,stage iib,t3,n1,mx,male,2013,,pancreas-adenocarcinoma ductal type,20,1968,r0,15,white,not hispanic or latino +TCGA-HZ-A8P1,81,0,,7,pancreas,stage ib,t2,n0,mx,male,2013,,pancreas-adenocarcinoma ductal type,15,1952,r0,0,white,not hispanic or latino +TCGA-HZ-A9TJ,70,0,,603,pancreas,stage iv,t3,n0,m1,male,2013,no,pancreas-adenocarcinoma-other subtype,,,r0,0,white,not hispanic or latino +TCGA-IB-7644,65,1,394,,pancreas,stage iv,t3,n1,m1,female,2011,no,pancreas-adenocarcinoma ductal type,30,1981,r1,3,white,not hispanic or latino +TCGA-IB-7645,44,1,1502,,pancreas,stage iib,t3,n1,m0,female,2007,no,pancreas-adenocarcinoma ductal type,,,r0,5,, +TCGA-IB-7646,60,1,145,,pancreas,stage iib,t3,n1,m0,male,2007,no,pancreas-adenocarcinoma ductal type,,,r1,6,white,not hispanic or latino +TCGA-IB-7647,41,1,666,,pancreas,stage iib,t3,n1,m0,male,2008,no,pancreas-adenocarcinoma ductal type,,,r1,14,white,not hispanic or latino +TCGA-IB-7649,73,1,467,,pancreas,stage iib,t3,n1,m0,female,2007,no,pancreas-adenocarcinoma ductal type,57,1951,r1,5,, +TCGA-IB-7651,64,1,603,,pancreas,stage iib,t3,n1,m0,female,2009,no,pancreas-adenocarcinoma ductal type,20,1989,r0,3,white,not hispanic or latino +TCGA-IB-7652,49,0,,1116,pancreas,stage iib,t3,n1,m0,female,2011,no,pancreas-adenocarcinoma ductal type,,,r1,1,asian,not hispanic or latino +TCGA-IB-7654,80,1,476,,pancreas,stage iib,t2,n1,m0,male,2008,no,pancreas-adenocarcinoma ductal type,,,r0,2,white,not hispanic or latino +TCGA-IB-7885,78,0,,1257,pancreas,stage iib,t3,n1,m0,female,2010,no,pancreas-adenocarcinoma ductal type,20,1960,r0,3,white,not hispanic or latino +TCGA-IB-7886,80,1,123,,pancreas,stage iib,t3,n1,m0,male,2010,no,pancreas-adenocarcinoma ductal type,35,1975,r1,4,white,not hispanic or latino +TCGA-IB-7887,62,1,110,,pancreas,stage iib,t3,n1,m0,female,2009,no,pancreas-adenocarcinoma ductal type,20,,r1,3,white,not hispanic or latino +TCGA-IB-7888,66,1,1332,,pancreas,stage iia,t3,n0,m0,female,2008,no,pancreas-adenocarcinoma ductal type,,,r0,0,black or african american,not hispanic or latino +TCGA-IB-7889,85,1,481,,pancreas,stage iib,t3,n1,m0,female,2011,no,pancreas-adenocarcinoma ductal type,,,r0,3,white,not hispanic or latino +TCGA-IB-7890,73,1,598,,pancreas,stage ib,t2,n0,m0,male,2008,no,pancreas-adenocarcinoma ductal type,,,r0,0,, +TCGA-IB-7891,49,1,913,,pancreas,stage iib,t3,n1,m0,female,2011,no,pancreas-adenocarcinoma ductal type,29,1980,r0,7,white,not hispanic or latino +TCGA-IB-7893,64,1,117,,pancreas,stage iia,t3,n0,m0,male,2009,no,pancreas-adenocarcinoma ductal type,,,r0,0,white,not hispanic or latino +TCGA-IB-7897,53,1,486,,pancreas,stage iib,t3,n1,m0,female,2008,no,pancreas-adenocarcinoma ductal type,,,r0,2,white,not hispanic or latino +TCGA-IB-8126,79,0,,462,pancreas,stage iii,t4,n1,m0,female,2011,no,pancreas-adenocarcinoma ductal type,4,1957,r1,5,white,not hispanic or latino +TCGA-IB-8127,59,0,,522,pancreas,stage iib,t3,n1,m0,male,2011,no,pancreas-adenocarcinoma ductal type,40,1971,r1,3,white,not hispanic or latino +TCGA-IB-A5SO,71,1,365,,pancreas,stage iib,t3,n1,m0,male,2012,no,pancreas-adenocarcinoma ductal type,,,r0,3,white, +TCGA-IB-A5SP,77,0,,482,pancreas,stage iia,t3,n0,m0,male,2011,no,pancreas-adenocarcinoma ductal type,,,r1,0,white, +TCGA-IB-A5SQ,56,1,219,,pancreas,stage ib,t2,n0,m0,female,2012,no,pancreas-adenocarcinoma ductal type,,,r0,0,white,not hispanic or latino +TCGA-IB-A5SS,64,1,460,,pancreas,stage iib,t3,n1,m0,female,2011,no,pancreas-adenocarcinoma ductal type,20,1991,r0,5,white,not hispanic or latino +TCGA-IB-A5ST,64,0,,635,pancreas,stage iib,t3,n1,m0,female,2012,no,pancreas-adenocarcinoma ductal type,,,r0,2,white,not hispanic or latino +TCGA-IB-A6UF,63,0,,666,pancreas,stage iib,t3,n1,m0,male,2012,no,pancreas-adenocarcinoma ductal type,40,1973,r1,7,white,not hispanic or latino +TCGA-IB-A6UG,65,1,41,,pancreas,stage iib,t3,n1,m0,male,2012,no,pancreas-adenocarcinoma ductal type,33.75,1968,r1,16,white,not hispanic or latino +TCGA-IB-A7LX,57,1,250,,pancreas,stage iib,t3,n1,mx,male,2012,no,pancreas-adenocarcinoma ductal type,,,r0,4,white,not hispanic or latino +TCGA-IB-A7M4,81,0,,483,pancreas,stage iib,t3,n1,mx,male,2013,no,pancreas-adenocarcinoma ductal type,52,1956,r0,1,white,not hispanic or latino +TCGA-IB-AAUM,76,0,,8,pancreas,stage iib,t2,n1,m0,male,2013,no,pancreas-adenocarcinoma ductal type,,,r0,1,white,not hispanic or latino +TCGA-IB-AAUN,74,1,144,,pancreas,stage ib,t2,n0,m0,female,2013,no,pancreas-adenocarcinoma ductal type,,,r1,0,white,not hispanic or latino +TCGA-IB-AAUO,64,1,239,,pancreas,stage iib,t3,n1,m0,female,2013,no,pancreas-adenocarcinoma ductal type,,,r1,5,white,not hispanic or latino +TCGA-IB-AAUP,68,0,,431,pancreas,stage iib,t3,n1,m0,male,2013,no,pancreas-adenocarcinoma ductal type,,,r1,7,white,not hispanic or latino +TCGA-IB-AAUQ,50,1,183,,pancreas,stage iib,t3,n1,m0,male,2013,no,pancreas-adenocarcinoma ductal type,69,1978,r1,13,white,not hispanic or latino +TCGA-IB-AAUR,67,0,,338,pancreas,stage iib,t3,n1,m0,male,2013,no,pancreas-adenocarcinoma ductal type,,,r1,9,white,not hispanic or latino +TCGA-IB-AAUS,84,0,,225,pancreas,stage iib,t3,n1,m0,female,2013,no,pancreas-adenocarcinoma ductal type,40,1948,r1,2,white,not hispanic or latino +TCGA-IB-AAUT,65,0,,287,pancreas,stage iib,t2,n1,m0,male,2013,no,pancreas-colloid (mucinous non-cystic) carcinoma,15,1988,r1,7,white,not hispanic or latino +TCGA-IB-AAUU,35,0,,245,pancreas,stage iib,t3,n1,m0,male,2013,no,pancreas-adenocarcinoma ductal type,15,,r0,2,white,not hispanic or latino +TCGA-IB-AAUV,49,0,,404,pancreas,stage iib,t3,n1,m0,male,2013,no,pancreas-adenocarcinoma ductal type,50,1980,r0,3,white,not hispanic or latino +TCGA-IB-AAUW,63,1,230,,pancreas,stage iib,t3,n1,m0,female,2013,no,pancreas-adenocarcinoma ductal type,,,r0,1,white,not hispanic or latino +TCGA-L1-A7W4,48,1,278,,pancreas,stage iib,t3,n1,m0,female,2013,no,pancreas-adenocarcinoma ductal type,,,r2,2,white,not hispanic or latino +TCGA-LB-A7SX,74,1,393,,pancreas,stage iib,t3,n1,mx,female,2013,no,pancreas-adenocarcinoma ductal type,40,1952,r1,7,black or african american,not hispanic or latino +TCGA-LB-A8F3,64,0,,379,pancreas,stage iia,t3,n0,mx,female,2013,yes,pancreas-adenocarcinoma ductal type,,,r0,0,black or african american,not hispanic or latino +TCGA-LB-A9Q5,63,1,313,,pancreas,stage iib,t3,n1,mx,female,2013,yes,pancreas-adenocarcinoma ductal type,0.3,1971,r0,3,white,not hispanic or latino +TCGA-M8-A5N4,48,0,,584,pancreas,stage iia,t3,n0,m0,female,,yes,pancreas-adenocarcinoma ductal type,,,r1,0,, +TCGA-OE-A75W,75,1,267,,pancreas,stage iia,t3,n0,m0,male,2013,yes,pancreas-adenocarcinoma ductal type,30,1956,r0,0,white,not hispanic or latino +TCGA-PZ-A5RE,44,1,470,,pancreas,stage iib,t3,n1,mx,female,2012,yes,pancreas-adenocarcinoma ductal type,,,,3,white,not hispanic or latino +TCGA-Q3-A5QY,58,0,,416,pancreas,stage iib,t3,n1,mx,male,2012,no,pancreas-adenocarcinoma ductal type,,,r0,4,white,not hispanic or latino +TCGA-Q3-AA2A,64,0,,95,pancreas,stage ib,t3,n1,mx,female,2013,no,pancreas-adenocarcinoma ductal type,,,rx,9,white,not hispanic or latino +TCGA-RB-A7B8,81,1,466,,pancreas,stage iib,t3,n1,mx,female,2013,no,pancreas-adenocarcinoma-other subtype,,,r0,8,white,not hispanic or latino +TCGA-RB-AA9M,43,0,,286,pancreas,stage iib,t1,n1,mx,male,2013,yes,pancreas-adenocarcinoma ductal type,,,r0,1,white,not hispanic or latino +TCGA-RL-AAAS,60,0,,9,pancreas,stage ib,t2,n0,m0,female,2013,no,pancreas-adenocarcinoma ductal type,,,r0,0,white,not hispanic or latino +TCGA-S4-A8RM,67,0,,737,pancreas,stage iib,t3,n1,mx,male,2013,no,pancreas-adenocarcinoma ductal type,25,1966,r0,1,white,not hispanic or latino +TCGA-S4-A8RO,75,0,,525,pancreas,stage iib,t3,n1,mx,female,2013,yes,pancreas-adenocarcinoma ductal type,,,r1,1,white,not hispanic or latino +TCGA-S4-A8RP,77,1,702,,pancreas,stage iib,t3,n1,mx,female,2010,yes,pancreas-adenocarcinoma ductal type,26,,r0,2,white, +TCGA-US-A774,76,1,695,,pancreas,stage iib,t3,n1,mx,female,2011,no,pancreas-adenocarcinoma ductal type,4.65,1955,r0,2,white,not hispanic or latino +TCGA-US-A776,61,0,,1216,pancreas,stage iia,t3,n0,mx,male,2011,no,pancreas-colloid (mucinous non-cystic) carcinoma,,,r0,0,white,not hispanic or latino +TCGA-US-A779,54,1,511,,pancreas,stage iib,t3,n1b,mx,female,2011,,pancreas-adenocarcinoma ductal type,,,r0,2,white,not hispanic or latino +TCGA-US-A77E,73,1,430,,pancreas,stage iib,t3,n1,mx,male,2012,no,pancreas-adenocarcinoma-other subtype,,,r1,2,white,not hispanic or latino +TCGA-US-A77G,64,1,12,,pancreas,stage iib,t3,n1,mx,male,2010,no,pancreas-adenocarcinoma ductal type,,,r0,3,white,not hispanic or latino +TCGA-US-A77J,81,1,568,,pancreas,stage iib,t3,n1b,mx,female,2010,,pancreas-adenocarcinoma ductal type,,,r0,4,white,not hispanic or latino +TCGA-XD-AAUG,66,0,,420,pancreas,stage iv,t3,n1,m1,female,2013,no,pancreas-adenocarcinoma ductal type,,,r0,1,white,not hispanic or latino +TCGA-XD-AAUH,57,0,,395,pancreas,stage iib,t3,n1,m0,female,2013,yes,pancreas-adenocarcinoma ductal type,,,r2,7,white,not hispanic or latino +TCGA-XD-AAUI,50,1,366,,pancreas,stage iib,t3,n1,mx,female,2013,no,pancreas-adenocarcinoma ductal type,,1979,r1,10,white,not hispanic or latino +TCGA-XD-AAUL,56,0,,498,pancreas,stage iia,t3,n0,mx,male,2013,no,pancreas-adenocarcinoma ductal type,30,1984,r0,0,white,not hispanic or latino +TCGA-XN-A8T3,67,0,,951,pancreas,stage ib,t2,n0,m0,male,2012,yes,pancreas-adenocarcinoma ductal type,,,r1,0,white,hispanic or latino +TCGA-XN-A8T5,53,0,,720,pancreas,stage ib,t2,n0,m0,female,2012,yes,pancreas-adenocarcinoma-other subtype,15.2,1974,r1,0,white,hispanic or latino +TCGA-YB-A89D,59,0,,350,pancreas,stage iib,t3,n1,mx,male,2013,yes,pancreas-adenocarcinoma ductal type,,,r0,4,white,not hispanic or latino +TCGA-YH-A8SY,73,0,,388,pancreas,stage iii,t3,n1,mx,female,2011,yes,,,,,16,white,not hispanic or latino +TCGA-YY-A8LH,61,0,,2016,pancreas,stage iib,t3,n1,mx,female,2009,yes,pancreas-adenocarcinoma ductal type,46,1963,r0,2,white,not hispanic or latino +TCGA-Z5-AAPL,74,0,,467,pancreas,stage iia,t3,n0,m0,female,2013,no,pancreas-adenocarcinoma ductal type,,,r0,0,white,not hispanic or latino diff --git a/bioneuralnet/datasets/paad/cnv.csv b/bioneuralnet/datasets/paad/cnv.csv new file mode 100644 index 0000000..8a3d440 --- /dev/null +++ b/bioneuralnet/datasets/paad/cnv.csv @@ -0,0 +1,178 @@ 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.2,cnv_CTTN,cnv_MAP2K3,cnv_LRRCC1,cnv_TBX4,cnv_MITD1,cnv_MIR497HG,cnv_SLC52A2,cnv_RN7SL616P,cnv_RN7SL786P,cnv_C6orf164,cnv_ULBP3,cnv_PTPRU,cnv_CKB,cnv_LINC00545,cnv_RPL39L,cnv_KLHL35,cnv_MYH10,cnv_REXO1L1,cnv_OR6F1,cnv_RAD21,cnv_STK24,cnv_SNORA14B,cnv_IL22RA2,cnv_MIR4644,cnv_TRAPPC6A,cnv_FAM111A,cnv_ATP5I,cnv_RN7SL437P,cnv_LIPT1,cnv_SNORD5_ENSG00000238854.1,cnv_MIR4420,cnv_CDRT7,cnv_WDR64,cnv_RARRES3,cnv_OR2AK2,cnv_ADGB,cnv_OPRD1,cnv_C1orf74,cnv_RN7SL643P,cnv_OR5B17,cnv_SLC33A1,cnv_SGCE,cnv_NACAP1,cnv_CRIP2,cnv_C17orf64,cnv_GPR126,cnv_LYRM2,cnv_PON2,cnv_OR2T8,cnv_CASC8,cnv_KATNAL2,cnv_IFT88,cnv_MFI2,cnv_SCRIB,cnv_GH2,cnv_UCHL5,cnv_C8orf87,cnv_MS4A6E,cnv_MIR4677,cnv_WDR49,cnv_RNA5SP188,cnv_CA13,cnv_RN7SKP215,cnv_C1orf115,cnv_MIR181B1,cnv_ZNF876P,cnv_KLHL5,cnv_ACTN2,cnv_IRG1,cnv_TCF4,cnv_ERCC8,cnv_ZNF695,cnv_TAB2,cnv_C1orf53,cnv_CAMK1G,cnv_SDCCAG8,cnv_EPB41L2,cnv_YTHDF2,cnv_U8_ENSG00000251740.1,cnv_DCAF13,cnv_DNAJA3,cnv_IARS2,cnv_PRDM8,cnv_RN7SL685P,cnv_SNORA81_ENSG00000253092.1,cnv_HBS1L,cnv_KMO,cnv_CFHR5,cnv_SQLE,cnv_TPCN2,cnv_CYB5D2,cnv_TMEM150C,cnv_OR2G6,cnv_RYR2,cnv_ARHGAP24,cnv_MAPK4,cnv_C4orf6,cnv_CLCF1,cnv_BCLAF1,cnv_RCE1,cnv_KCNIP3,cnv_LPP,cnv_SNORA40_ENSG00000212273.1,cnv_OR14A16,cnv_SNORD39_ENSG00000264997.1,cnv_CDC73,cnv_ABCA5,cnv_MIR3664,cnv_SIVA1,cnv_RN7SKP132,cnv_OR2M2,cnv_FAHD2A,cnv_CD55,cnv_MTR,cnv_RN7SL193P,cnv_SEC62,cnv_MIR548W,cnv_RAP2B,cnv_SNORA70_ENSG00000206637.1,cnv_MIR4729,cnv_RN7SKP245,cnv_PFKFB3,cnv_SULF1,cnv_RNF165,cnv_GLB1L2,cnv_SHANK2,cnv_GPR37L1,cnv_SMAD4,cnv_SRSF1,cnv_TFRC,cnv_CTNND1,cnv_MIR3676,cnv_TCP10,cnv_CANT1,cnv_PLCB3,cnv_HEATR1,cnv_CCDC170,cnv_FOS,cnv_NUDT16P,cnv_DESI2,cnv_UBE2T,cnv_FAM43A,cnv_LYG1,cnv_MARK1,cnv_OR2W5,cnv_NR5A2,cnv_QKI,cnv_C3orf36,cnv_SHARPIN,cnv_LYPLAL1,cnv_OR5AK2,cnv_DDO,cnv_ST3GAL4,cnv_ASTL,cnv_MEIOB,cnv_SMAD2,cnv_DHX36,cnv_MIR4446,cnv_C8orf59,cnv_RN7SL399P,cnv_CORIN,cnv_REPS1,cnv_HPS3,cnv_PBX2P1,cnv_ASPG,cnv_SCAF8,cnv_EEF1D,cnv_SFTA1P,cnv_PTPRVP,cnv_RN7SL464P,cnv_PSKH2,cnv_SNAP91,cnv_RN7SL173P,cnv_TCTE3,cnv_OR2M4,cnv_SLC30A10,cnv_CFHR4,cnv_CACNG1,cnv_LINC00501,cnv_USH2A,cnv_CIAO1,cnv_NEURL3,cnv_RN7SKP55,cnv_DCTN5,cnv_CRB1,cnv_C6orf211,cnv_SLC35G6,cnv_CPT1A,cnv_MAGEF1,cnv_ZNF385C,cnv_SNORA25_ENSG00000252290.1,cnv_SLITRK1,cnv_CDX2,cnv_LINC00638,cnv_DHRS7C,cnv_NEK7,cnv_TGFB2,cnv_MIR603,cnv_OR14I1,cnv_OR2T10,cnv_EMC6,cnv_SNORA47,cnv_MIR548F3,cnv_GPR149,cnv_OR2M7,cnv_CD300C,cnv_GSC,cnv_RNA5SP81,cnv_FAM203B,cnv_GLRX5,cnv_LINC00911,cnv_QSER1,cnv_METTL12,cnv_LPXN,cnv_IRF2BP2,cnv_PHOX2B,cnv_PAG1,cnv_SNORA7B,cnv_STARD7,cnv_MRPL13,cnv_ELAC2,cnv_PIAS2,cnv_CACNG3,cnv_RN7SL738P,cnv_PROM2,cnv_ARRDC3,cnv_RNU4ATAC18P,cnv_KLF3,cnv_TGFB3,cnv_RPL38,cnv_HNRNPA1P30,cnv_GDI2,cnv_USP35,cnv_TBCE,cnv_C8orf76,cnv_MIR433,cnv_C1orf101,cnv_FOXN3,cnv_PSMA8,cnv_NHSL1,cnv_SETBP1,cnv_ARHGEF18,cnv_MYCT1,cnv_VASP,cnv_QRSL1,cnv_OR2T33,cnv_MIR4529,cnv_TPTE2P6,cnv_FH,cnv_RN7SKP29,cnv_COL14A1,cnv_KANSL3,cnv_QRICH2,cnv_E2F5,cnv_N4BP2L2,cnv_ISY1,cnv_AZIN1,cnv_IRF6,cnv_KCTD12,cnv_RN7SKP231,cnv_EDARADD,cnv_PACS2,cnv_CFH,cnv_SLIRP,cnv_CASP1,cnv_CD200,cnv_FAM229B,cnv_TRAF3IP3,cnv_COIL,cnv_ATAD2,cnv_YIPF7,cnv_MIR181A1HG,cnv_MIR5681A,cnv_DHX33,cnv_MIR3124,cnv_ETHE1,cnv_YBX2,cnv_MYH13,cnv_snoU13_ENSG00000238576.1,cnv_EIF5B,cnv_TMEM100,cnv_POU3F2,cnv_SNORD112_ENSG00000251918.1,cnv_MRPL47,cnv_MT1HL1,cnv_ARID4B,cnv_MIR181A1,cnv_SNRNP200,cnv_TROVE2,cnv_POP1,cnv_AADAC,cnv_ZACN,cnv_RNU4ATAC6P,cnv_BST1,cnv_KIF1C,cnv_OR2T2,cnv_GPR63,cnv_DLST,cnv_LRRC37A16P,cnv_MIR301A,cnv_THUMPD1,cnv_EXO1,cnv_U8_ENSG00000212144.1,cnv_RN7SL47P,cnv_U2SURP,cnv_TSR1,cnv_BCL6,cnv_IL2RA,cnv_RN7SL805P,cnv_TTC39B,cnv_snoU13_ENSG00000238522.1,cnv_PSPN,cnv_RPS6KA2,cnv_MARK4,cnv_CDKN2B,cnv_snoU13_ENSG00000239134.1,cnv_GAS7,cnv_snoU13_ENSG00000238628.1,cnv_TCEB3CL,cnv_SPATA17,cnv_TCEB3C,cnv_CHRNA9,cnv_TNFAIP3,cnv_C4BPA,cnv_PEX2,cnv_NPIPB4,cnv_PNMA1,cnv_MIR3916,cnv_PGBD2,cnv_HEBP2,cnv_MIR5704,cnv_RN7SKP180,cnv_U6_ENSG00000266839.2,cnv_ZNF692,cnv_TRPV1,cnv_TRAPPC9,cnv_U8_ENSG00000239142.1,cnv_TTC19,cnv_OR2T27,cnv_DSCAML1,cnv_LINC00381,cnv_MSLNL,cnv_ZBTB18,cnv_RN7SL724P,cnv_TRAPPC4,cnv_RN7SL448P,cnv_MIR4428,cnv_IFNGR1,cnv_PPP1R14B,cnv_OLIG3,cnv_ZNF287,cnv_MARC1,cnv_MRPL30,cnv_USP17L22,cnv_C3orf56,cnv_ZNF532,cnv_MIR4706,cnv_PRM1,cnv_HSD11B1,cnv_CCDC7,cnv_ZNF124,cnv_DVL3,cnv_MSC,cnv_PPWD1,cnv_MDN1,cnv_KCTD3,cnv_OR2W3,cnv_ARG1,cnv_FOLR1,cnv_ZNF296,cnv_RN7SKP104,cnv_C16orf89,cnv_ADAMTS6,cnv_FAM172A,cnv_GABARAP,cnv_OR2L3,cnv_LYPD8,cnv_PTGER2,cnv_RNA5SP155,cnv_LRRC34,cnv_KALRN,cnv_SLC22A9,cnv_SERTM1,cnv_NBEA,cnv_STK32B,cnv_MIR22HG,cnv_OR10Q1,cnv_MIR3610,cnv_RN7SL668P,cnv_snoU13_ENSG00000238383.1,cnv_C1orf143,cnv_WDR7,cnv_DEFB131,cnv_TARBP1,cnv_PBOV1,cnv_MIR3668,cnv_RMND1,cnv_MATN1,cnv_SNORD60_ENSG00000206630.1,cnv_FGF9,cnv_MIR4527,cnv_snoU13_ENSG00000238938.1,cnv_B3GALNT2,cnv_C18orf25,cnv_AGO2,cnv_SLC14A2,cnv_SNORA40_ENSG00000212587.1,cnv_OR14K1,cnv_MTMR4,cnv_MIR1253,cnv_SDK2,cnv_ZNF496,cnv_PLD5,cnv_MYO3A,cnv_RNY4P4,cnv_RN7SKP115,cnv_PRKCQ,cnv_snoU13_ENSG00000238799.1,cnv_LAPTM5,cnv_MIR4671,cnv_PMAIP1,cnv_RNF167,cnv_ZNF557,cnv_SAMD7,cnv_MIR44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diff --git a/bioneuralnet/datasets/paad/meth.csv b/bioneuralnet/datasets/paad/meth.csv new file mode 100644 index 0000000..ee95ad1 --- /dev/null +++ b/bioneuralnet/datasets/paad/meth.csv @@ -0,0 +1,178 @@ +patient,meth_CACYBPP2,meth_CCDC172,meth_BAALC_AS2,meth_CAMK2D,meth_AC104655.2,meth_TTC6,meth_BHLHE41,meth_CA6,meth_AC008592.3,meth_BPIFA3,meth_ATP6V1G1,meth_AC102953.4,meth_MIR3146,meth_SDS,meth_CCDC110,meth_TAF5L,meth_AC006994.2,meth_OR52N1,meth_AC022173.2,meth_ABCC3,meth_AC009487.4,meth_ZNF710,meth_ARMCX1,meth_AFG3L2,meth_B3GAT3,meth_BRI3P1,meth_CHGA,meth_AC079586.1,meth_SOX3,meth_BEST3,meth_AQP1,meth_CYP4F11,meth_ARHGEF19,meth_AC126755.2,meth_BFSP1,meth_ACAA1,meth_RP1_159A19.4,meth_ART3,meth_AMN,meth_AC226119.5,meth_AC092839.3,meth_AF121898.3,meth_LTN1,meth_EIF2S2P4,meth_AC002978.1,meth_BCKDHB,meth_BPGM,meth_AL590723.1,meth_ATP1B2,meth_CACTIN_AS1,meth_FTH1P4,meth_BIRC6,meth_AC091153.4,meth_ATG4B,meth_ATF4P3,meth_AD000091.2,meth_ZPBP2,meth_C10orf55,meth_AC007557.5,meth_AC096558.1,meth_AC005522.7,meth_AC004471.9,meth_ARHGAP19,meth_AC083949.1,meth_CCDC174,meth_C1S,meth_AL078471.5,meth_PTBP3,meth_OR9Q2,meth_C9orf89,meth_APOBR,meth_RP11_392O1.4,meth_RP11_519G16.5,meth_AES,meth_RP11_1000B6.3,meth_FBXL19,meth_HOTAIRM1,meth_POU4F1,meth_AC079767.3,meth_B3GNT5,meth_ANGPT1,meth_AC011551.3,meth_C11orf86,meth_AL158147.2,meth_AP000251.3,meth_ACTL6B,meth_AC021037.1,meth_ANKDD1A,meth_AC096649.3,meth_AC017104.3,meth_LINC01333,meth_NMRAL1,meth_ALDH7A1,meth_AC144652.1,meth_BCAT1,meth_RAD51D,meth_BHLHE40,meth_ANKRD30B,meth_ZAN,meth_AC005772.2,meth_RP11_642P15.1,meth_TAS2R42,meth_AC138649.1,meth_BSN_AS2,meth_KCNMA1_AS3,meth_C1RL,meth_BCAM,meth_AC068946.1,meth_CALM2P4,meth_SCGB3A1,meth_AQP5,meth_AC003986.7,meth_RP11_420C9.1,meth_ABTB2,meth_ARPC1A,meth_AC008041.1,meth_FGF12_AS2,meth_AC079154.1,meth_ANKRD10,meth_ACADM,meth_CTD_2526A2.2,meth_SNRPGP5,meth_ACSF3,meth_ATOX1,meth_AC009487.5,meth_AP000640.2,meth_AP003419.11,meth_CHMP2A,meth_ATP5F1,meth_BMF,meth_AC108004.1,meth_ACSF2,meth_A2ML1_AS1,meth_AC097713.4,meth_AC090673.1,meth_CCDC148,meth_AC096574.5,meth_AC007952.5,meth_RGS1,meth_AC073465.3,meth_AC009133.15,meth_ITGB6,meth_NHP2,meth_NPC1,meth_TMEM105,meth_AC010226.4,meth_AC112198.1,meth_KL,meth_AC113607.3,meth_BCL2,meth_AK4P3,meth_ARMCX2,meth_AC003005.4,meth_ATP6V1H,meth_BVES_AS1,meth_C9orf142,meth_RP11_9G1.3,meth_ALYREF,meth_RP11_977G19.5,meth_BPIFA4P,meth_GMCL1P1,meth_AHSG,meth_ARHGAP5_AS1,meth_C20orf24,meth_CAPN8,meth_AC109925.1,meth_AC009948.7,meth_ANO9,meth_C15orf38_AP3S2,meth_ZSCAN25,meth_AC006026.10,meth_AC007318.5,meth_AC005740.3,meth_CTA_392C11.2,meth_AF235103.1,meth_DDX24,meth_AC140481.8,meth_CACNA2D3_AS1,meth_C3orf67_AS1,meth_ABCC13,meth_AC015688.3,meth_RP5_867C24.1,meth_EMP3,meth_OR8G5,meth_MXD4,meth_C4orf26,meth_ATF3,meth_BPIFB3,meth_AC022201.5,meth_ALPK1,meth_CTC_338M12.1,meth_ACOXL,meth_ASH1L_AS1,meth_CTD_2330K9.2,meth_ACADL,meth_BPIFB6,meth_AF001548.5,meth_LEPROT,meth_AC010091.1,meth_RP11_379L18.2,meth_AREG,meth_AC114730.8,meth_AC005037.3,meth_AC105247.1,meth_AC244205.1,meth_CAD,meth_AC011286.1,meth_RP11_392P7.6,meth_OSGEP,meth_AC004775.5,meth_ABCD1P5,meth_RP11_796G6.1,meth_CADM2_AS1,meth_AC004980.8,meth_RP11_626H12.1,meth_AMOTL1,meth_TSPO,meth_RP11_632K20.7,meth_APPL1,meth_AL450992.2,meth_BX571818.1,meth_MTHFD2L,meth_AC027601.1,meth_AKAP4,meth_RP1_101K10.4,meth_AP000253.1,meth_CAB39,meth_RP11_2E11.5,meth_AC007880.1,meth_AC005740.6,meth_BRD1,meth_BCAS2,meth_C12orf60,meth_C2orf48,meth_BTBD7,meth_RP11_416N13.1,meth_ZFP64P1,meth_AC008063.3,meth_RP11_930O11.2,meth_ACSM2A,meth_C4orf3,meth_AC141586.5,meth_PLEKHH2,meth_RP11_672L10.2,meth_AADACP1,meth_BUD31,meth_RP1_78O14.1,meth_C8A,meth_BHLHE23,meth_C21orf91,meth_AC003682.17,meth_LINC00899,meth_C12orf4,meth_AIFM1P1,meth_ABHD15,meth_CCDC130,meth_AC011242.6,meth_EDN1,meth_RP11_142A5.1,meth_AL031777.2,meth_ANO1_AS2,meth_AC004754.3,meth_AK4P2,meth_ANAPC4,meth_AFG3L2P1,meth_CTD_3074O7.2,meth_RP11_74E22.8,meth_C15orf56,meth_RP6_109B7.4,meth_CALHM2,meth_BRIX1,meth_AC002395.1,meth_APOL6,meth_ACP6,meth_TMEM53,meth_AC093702.1,meth_AC114812.5,meth_RP11_310N16.1,meth_C5orf38,meth_B3GNTL1,meth_AC137932.4,meth_CALR4P,meth_RP11_68I18.10,meth_CACNG8,meth_AC074212.5,meth_ART4,meth_ANP32B,meth_AC093620.5,meth_ARL8B,meth_RRS1_AS1,meth_BMP2,meth_AC022182.1,meth_SWAP70,meth_BHLHB9,meth_AP000697.6,meth_AC092610.12,meth_AL591893.1,meth_ARHGDIB,meth_CASKIN2,meth_MCF2L_AS1,meth_AC018730.4,meth_FAM120A,meth_ADRA1D,meth_CES1P1,meth_AC007395.3,meth_AC099344.3,meth_C5orf46,meth_MCFD2,meth_AC091180.1,meth_CCDC138,meth_AC090286.2,meth_ATP13A5,meth_GAB3,meth_BOLL,meth_AC110781.3,meth_AC005730.2,meth_ACCS,meth_AC024028.1,meth_AK4P1,meth_ACTA2,meth_RP11_14K3.3,meth_AC123886.2,meth_C5orf28,meth_AC061992.2,meth_CBX6,meth_AP4B1_AS1,meth_C14orf1,meth_AC007391.2,meth_ACTL9,meth_AL589743.1,meth_TEX12,meth_AC099754.1,meth_RP11_137N23.1,meth_AC004112.4,meth_CTD_2517M22.14,meth_ARMC2,meth_AL022328.1,meth_CARTPT,meth_AC072061.2,meth_ATP13A1,meth_REG1A,meth_CAPN9,meth_ISL2,meth_AC016768.1,meth_CACNA1C_AS1,meth_RP11_569G13.2,meth_VTN,meth_C1orf127,meth_AC128709.2,meth_OR6M1,meth_TXN,meth_NTN3,meth_ACA64,meth_APOPT1,meth_AL590410.1,meth_BCL11B,meth_CBR4,meth_AZU1,meth_AC003001.1,meth_AC092570.2,meth_PHYHIPL,meth_BTBD11,meth_SUCLA2P1,meth_HIST1H3F,meth_PRKRIRP2,meth_ANKRD12,meth_AC011309.1,meth_AC012640.1,meth_AP002884.2,meth_AC007680.2,meth_PICALM,meth_EIF4EBP2,meth_ACOT7,meth_AC006160.5,meth_ARL6,meth_ANO1,meth_CHST4,meth_ATP13A4_AS1,meth_BRD7P2,meth_POLR1E,meth_AC007557.2,meth_AC007292.3,meth_TBC1D9B,meth_AC073283.7,meth_AL355388.1,meth_EMC3,meth_AC005104.3,meth_U91328.1,meth_LRRTM1,meth_MIA,meth_AC142528.1,meth_ANKRD13B,meth_ATP6V0A1,meth_AC009784.3,meth_HERC2P10,meth_HYKK,meth_RP11_964E11.2,meth_AC009227.2,meth_RP11_81A1.6,meth_MIR4448,meth_ASIP,meth_TUG1_4,meth_CCDC108,meth_ACSL3,meth_APC,meth_TTC32,meth_AC142119.1,meth_SYT15,meth_BMS1P3,meth_C1orf110,meth_AL359737.1,meth_A2MP1,meth_ATAT1,meth_AVL9,meth_C11orf40,meth_AC092168.2,meth_AFAP1L2,meth_AC013472.3,meth_B4GALT6,meth_MPG,meth_ATP13A4,meth_AL354933.1,meth_RP11_941H19.2,meth_AP000439.5,meth_AC004945.1,meth_ACTA2_AS1,meth_SPAG16,meth_CXCL11,meth_ANKRD13D,meth_MTUS2_AS1,meth_LOXL1,meth_AC017028.6,meth_ADCY8,meth_AC093110.3,meth_ZCCHC9,meth_RP11_536C12.1,meth_MIR4767,meth_AC010168.1,meth_ECHS1,meth_ANAPC7,meth_C7orf49,meth_ATP10D,meth_PTGES3P2,meth_ATXN8OS,meth_AP001888.1,meth_AC002480.3,meth_CCDC106,meth_AC064834.3,meth_AOC2,meth_AC138517.4,meth_CMTR1,meth_BNIP3P1,meth_AC008753.6,meth_ATL3,meth_RP11_486O13.2,meth_ADIRF_AS1,meth_RP11_447B18.1,meth_AC005229.7,meth_C9orf41,meth_ATAD3B,meth_GS1_421I3.2,meth_AMPD2,meth_AFF3,meth_NOXO1,meth_EPO,meth_CAPG,meth_ARX,meth_AB019441.29,meth_LINC00276,meth_HTR3D,meth_AC011718.4,meth_GRHL1,meth_AC008074.3,meth_CALHM3,meth_RP11_474O21.5,meth_AC006019.3,meth_AC058791.1,meth_B3GALT1,meth_CAPS,meth_AC093887.1,meth_ASCC2,meth_B4GALT1_AS1,meth_FAM230A,meth_AHSA1,meth_AGPAT3,meth_BARX2,meth_HOXD3,meth_ABCB10P4,meth_MIR7_3,meth_DHRS2,meth_ASB17,meth_RP11_630D6.5,meth_AC003986.6,meth_AC104134.2,meth_RP11_1166P10.7,meth_BAZ2B,meth_AF001550.7,meth_PCBP2,meth_ATP6AP2,meth_AP000908.1,meth_BPIFB1,meth_AC093609.1,meth_KIAA2026,meth_AC002116.7,meth_ARHGAP8,meth_MIR4326,meth_RP11_849I19.1,meth_ATP5L2,meth_C22orf29,meth_AC007255.8,meth_DCLK2,meth_AC006947.1,meth_PDZD8,meth_ARPC5L,meth_BAZ2A,meth_CCAT1,meth_BMP7,meth_RP3_428L16.2,meth_BOK,meth_RP11_709P2.1,meth_AC007557.3,meth_AHR,meth_RPS19,meth_CTD_2062F14.3,meth_AC005082.12,meth_AC005775.2,meth_AC116366.5,meth_AC008937.3,meth_SALL3,meth_BRWD1_AS1,meth_AC079584.2,meth_RP11_1299A16.3,meth_REPIN1,meth_SNORD20,meth_ASPA,meth_PMCHL2,meth_CLSTN1,meth_CACNB1,meth_AC003006.7,meth_NIFKP2,meth_PIGHP1,meth_AL139139.1,meth_CCDC180,meth_CPA1,meth_ANXA2,meth_CA8,meth_CALY,meth_IL7,meth_AC007326.9,meth_BECN1,meth_AC009108.2,meth_C2orf80,meth_ARRDC1,meth_AC011239.1,meth_AC009336.19,meth_RP11_67L3.4,meth_ANKRD20A8P,meth_ABHD14B,meth_AC005329.7,meth_SMIM24,meth_AC007405.6,meth_AC078814.1,meth_TSPAN4,meth_AL589822.1,meth_C1orf43,meth_AL031777.1,meth_BCKDHA,meth_KIAA1522,meth_C17orf104,meth_RP11_829H16.3,meth_C6orf203,meth_C1orf195,meth_C19orf70,meth_ARHGAP21,meth_AL021918.1,meth_AC005702.2,meth_ALX4,meth_KIAA0930,meth_RP11_239E10.2,meth_AP000525.10,meth_AP1S3,meth_ANKRD36BP2,meth_AC068282.3,meth_CTD_2006M22.2,meth_AVPI1,meth_AC011742.3,meth_AC011899.10,meth_AC132217.4,meth_AC024592.9,meth_TMEM68,meth_AC140542.2,meth_FAM221B,meth_CSPG4,meth_BMS1P17,meth_BICD1,meth_AP1S1,meth_AC138647.1,meth_AXL,meth_MIR129_1,meth_AC016739.2,meth_ANO6,meth_SOX14,meth_AC142472.6,meth_ALDH3A1,meth_AC002368.4,meth_C1QTNF9,meth_ACTBP2,meth_BIRC7,meth_AC005796.2,meth_ARF4,meth_ADPRM,meth_AC096772.6,meth_AC074011.2,meth_BAALC,meth_C5orf66_AS1,meth_AHNAK,meth_ANKRD61,meth_AC023137.2,meth_AC137932.5,meth_AC000111.6,meth_ATIC,meth_AADACL2_AS1,meth_BACH1,meth_CASC4P1,meth_C11orf57,meth_ASS1P1,meth_AC135048.13,meth_AC025335.1,meth_ARL17B,meth_AC003090.1,meth_AGAP1_IT1,meth_RNA5SP492,meth_SH3RF1,meth_CLN3,meth_BTN2A3P,meth_MIR4727,meth_CASP10,meth_RCAN1,meth_AC005176.2,meth_HMGN1P32,meth_AP4B1,meth_BAG3,meth_ALDH3B1,meth_CAPS2,meth_ALPP,meth_BTBD6,meth_C17orf59,meth_AP003064.2,meth_AP001626.1,meth_BIK,meth_RP11_219B4.5,meth_ACBD5,meth_AC007566.10,meth_FAM210B,meth_AC016723.4,meth_B4GALT4_AS1,meth_AC009014.3,meth_ARL2BP,meth_APOA1,meth_AC093673.5,meth_AC114814.4,meth_SETD1B,meth_C8orf86,meth_ZACN,meth_AC019011.1,meth_IL12RB1,meth_BNIP1,meth_CCNA2,meth_ADCYAP1,meth_RP11_876N24.3,meth_BBS12,meth_MROH1,meth_AL136419.6,meth_AAED1,meth_HOXA11_AS1_5,meth_PSEN2,meth_AC025280.1,meth_C7,meth_C8B,meth_BTNL10,meth_ASRGL1,meth_AL021807.1,meth_AL450226.2,meth_AL138999.1,meth_AC005702.3,meth_BPIFA1,meth_RP11_896J10.3,meth_ANXA9,meth_AC073343.13,meth_ATL2,meth_DENND5A,meth_AOX3P,meth_AC009506.1,meth_AC018816.3,meth_CPNE8,meth_C11orf94,meth_CCDC105,meth_C12orf75,meth_AC097713.3,meth_ALDH9A1,meth_AK1,meth_AC007381.2,meth_ARMC10P1,meth_CIRBP_AS1,meth_ADPRHL2,meth_AC005387.2,meth_CREBL2,meth_AC011899.9,meth_AUTS2,meth_ITGB3,meth_C3orf67,meth_AC129492.1,meth_ACTB,meth_AC007682.4,meth_RP13_266I11.1,meth_LINC01347,meth_FBXO30,meth_AC005355.1,meth_AMER2,meth_AC007405.8,meth_C9orf24,meth_C1RL_AS1,meth_AGXT2,meth_TVP23A,meth_AL008723.1,meth_ANKRD29,meth_ABHD11_AS1,meth_RFKP1,meth_AC005523.3,meth_ADTRP,meth_CAMK2N2,meth_AC012506.4,meth_AC084357.1,meth_ADAM5,meth_ALDH1A3,meth_DUSP22,meth_HS6ST1P1,meth_RP11_168O16.2,meth_B3GAT3P1,meth_UTS2R,meth_AMIGO2,meth_AURKC,meth_BCL2L15,meth_ASXL2,meth_CASP6,meth_ZNF461,meth_BX842568.2,meth_SLC17A7,meth_AL672043.1,meth_AC079250.1,meth_AC004231.2,meth_GPX1P1,meth_C9orf152,meth_PRR23D2,meth_ATP5L,meth_ABLIM1,meth_ACTR3,meth_AC109642.1,meth_ADAM9,meth_AC004510.3,meth_RGL3,meth_AC009133.24,meth_JAG1,meth_AMACR,meth_AP1G1,meth_MT1G,meth_AC231657.1,meth_AC011516.1,meth_AC009487.6,meth_AC093323.1,meth_AJ003147.8,meth_B4GALT4,meth_ADAM10,meth_WISP2,meth_C1orf143,meth_KCNH6,meth_HIST1H1B,meth_RP11_3D4.4,meth_BARHL2,meth_BCAT2,meth_RP11_424I19.2,meth_AC004448.2,meth_ARHGAP5,meth_AF111168.2,meth_AEN,meth_BBC3,meth_NUDT4,meth_C1QTNF7,meth_ATP6V1E2,meth_AC003075.4,meth_AP000647.3,meth_BRWD1_AS2,meth_ASS1P9,meth_ANKRD26P3,meth_BEX4,meth_RPS17P1,meth_B4GALT5,meth_AC007365.1,meth_CCDC178,meth_RP11_434C1.4,meth_AC005786.5,meth_AC024592.12,meth_C1orf216,meth_BRF1,meth_AFMID,meth_AC023669.1,meth_AC073046.25,meth_AP3S2,meth_ABCD1P4,meth_MYH16,meth_AC068134.8,meth_AQP11,meth_AC022007.5,meth_TCAF1,meth_RNASE6,meth_AC006547.13,meth_ADAMTS19_AS1,meth_ERC2_IT1,meth_AC012454.4,meth_AC078883.4,meth_RPUSD2,meth_RANGRF,meth_ARHGAP31_AS1,meth_AL928654.1,meth_SLC25A45,meth_AC015849.15,meth_AC004870.5,meth_AGRN,meth_C6orf136,meth_B4GALT1,meth_ACTBP13,meth_AC124944.5,meth_KRT18P1,meth_AC092338.5,meth_C19orf80,meth_BARX1,meth_AC004854.4,meth_ARHGAP29,meth_AC008982.2,meth_AC000095.9,meth_CTA_243E7.3,meth_CENPJ,meth_ACKR3,meth_RALGPS1,meth_BX842568.4,meth_ATG2A,meth_CARKD,meth_RP11_317L10.1,meth_C19orf83,meth_APOBEC3B,meth_AC069285.1,meth_AC005702.1,meth_ARL14,meth_AC009404.2,meth_ATP5SL,meth_ALMS1,meth_CCDC184,meth_AC017028.7,meth_C12orf5,meth_AL023806.1,meth_AP001046.6,meth_BX470102.3,meth_AC090587.2,meth_C1orf106,meth_CCKAR,meth_RP11_97N19.2,meth_BTF3P12,meth_LINC00243,meth_PRDM1,meth_BCAN,meth_AC013470.6,meth_C9orf173,meth_ANKRD22,meth_BCL2L12,meth_ALKBH3,meth_CTD_2291D10.3,meth_MCM7,meth_BCS1L,meth_AC023590.1,meth_C15orf57,meth_RP11_381N20.1,meth_PPP3CC,meth_SARNP,meth_AC105402.3,meth_ADIRF,meth_AC005387.3,meth_REST,meth_ACOT13,meth_BRDT,meth_AC104777.4,meth_AHCY,meth_RP11_328K15.1,meth_AC024560.2,meth_AC007040.11,meth_ASB14,meth_BEND3,meth_AC097468.7,meth_AC005944.2,meth_AC011995.3,meth_PPARG,meth_OPRD1,meth_AC021087.1,meth_ALG1L,meth_C2orf76,meth_FTH1P12,meth_ADCY3,meth_C10orf91,meth_TRDC,meth_AC005537.2,meth_CCDC144CP,meth_CACNA2D3,meth_UCA1,meth_AC140725.4,meth_AC008060.8,meth_AC005702.4,meth_BNIP3,meth_CTB_158D10.3,meth_AC092573.2,meth_ARF6,meth_AC007387.2,meth_NT5C3AP1,meth_ACTG1,meth_ALDH3B2,meth_CTD_2537I9.18,meth_AC005593.2,meth_ATG16L1,meth_RP11_473M14.3,meth_AC090952.1,meth_RP11_958F21.1,meth_AC009237.8,meth_RP13_766D20.2,meth_AC007563.5,meth_AC004009.3,meth_ANKRD50,meth_AC055876.1,meth_HAGLR,meth_AC079355.1,meth_AC011431.1,meth_AC008427.2,meth_AC017104.2,meth_AC127383.1,meth_CBFB,meth_BST1,meth_CCDC144A,meth_AJUBA,meth_RP11_425D10.10,meth_ANXA13,meth_AC132008.1,meth_CACNB3,meth_SNRPD3,meth_AC010886.2,meth_AC114812.9,meth_ARHGEF37,meth_ARSK,meth_APRT,meth_ZC2HC1A,meth_ATL1,meth_MIR4267,meth_BHLHE40_AS1,meth_AC004051.2,meth_RNPEPL1,meth_METTL14,meth_BARX1_AS1,meth_RP11_958J22.1,meth_OR2M4,meth_AC097381.1,meth_AP3M1,meth_AC037459.4,meth_APAF1,meth_ZNF75D,meth_AC114498.1,meth_ATP6V1G2_DDX39B,meth_TMEM249,meth_PHOX2A,meth_RP11_661A12.9,meth_ARL15,meth_HLF,meth_NOVA1,meth_RP11_311F12.2,meth_CTB_113P19.3,meth_C1QTNF9B_AS1,meth_AC005498.3,meth_AC131951.1,meth_AC018647.3,meth_APOBEC3A,meth_APTR,meth_AC018804.7,meth_ANKRD60,meth_RP11_336K24.12,meth_CAPN6,meth_AC007563.3,meth_AC137723.5,meth_ASAP2,meth_CABP7,meth_BATF,meth_MAFG_AS1,meth_C6orf99,meth_BCL2L14,meth_LDHAL6B,meth_CAPRIN2,meth_CTD_2561B21.7,meth_ARHGAP42P1,meth_RP11_446J8.1,meth_RP11_1029J19.5,meth_CARS,meth_CABLES1,meth_ALX3,meth_CADM2,meth_GK5,meth_RP11_498B4.5,meth_CA12,meth_CCDC166,meth_E2F4P1,meth_RP11_365O16.1,meth_CASP1,meth_AC074183.4,meth_ACTR5,meth_SFTA1P,meth_C11orf80,meth_CCDC175,meth_AC004987.9,meth_BAMBI,meth_AQPEP,meth_ACTL10,meth_AP002490.1,meth_ADAMTS19,meth_C8orf58,meth_TXNRD3,meth_CTC_484M2.1,meth_AC087651.2,meth_ANAPC1P1,meth_AC097499.1,meth_AC010504.2,meth_CALML4,meth_PIGG,meth_AC068533.7,meth_DPPA2,meth_RP11_93H12.4,meth_LIFR,meth_BLOC1S4,meth_AC019131.1,meth_BHLHA15,meth_ABCD1P3,meth_H1F0,meth_AC097721.2,meth_KIAA1551,meth_BANF1P2,meth_AC005391.2,meth_RP13_766D20.4,meth_RP11_6O2.4,meth_BX322557.10,meth_C1orf111,meth_AC000041.10,meth_AC061992.1,meth_AC000089.3,meth_AC093230.2,meth_ARPIN,meth_AZIN2,meth_ARPC3,meth_RPL35P4,meth_AC069155.1,meth_C14orf39,meth_AC005088.1,meth_AC104532.4,meth_AC074183.3,meth_ARSD_AS1,meth_ACTG1P9,meth_RP11_786A20.1,meth_RPL29P30,meth_AC093628.1,meth_NAV2_AS3,meth_C1QL2,meth_AC002454.1,meth_AC079779.6,meth_CAMLG,meth_CACNA1C,meth_ATP6V1B1,meth_AC012360.6,meth_C2CD3,meth_C11orf53,meth_BFSP2,meth_AC011893.3,meth_MIR139,meth_ANKRD20A19P,meth_BTC,meth_RP11_528A10.1,meth_AC011530.4,meth_INTS6P1,meth_AC004870.3,meth_RP11_215E13.2,meth_C16orf70,meth_FSHB,meth_C9orf173_AS1,meth_AC087793.1,meth_AP001350.4,meth_CA13,meth_AJ003147.9,meth_SNX18,meth_BOK_AS1,meth_AC012531.25,meth_LINC01033,meth_BTLA,meth_BCCIP,meth_AC004819.1,meth_ARNTL2,meth_AL031601.5,meth_USP34,meth_C15orf39,meth_AC027307.1,meth_CARS2,meth_AMOTL2,meth_AC007228.5,meth_GPR22,meth_MKRN9P,meth_CBLN4,meth_AP001615.9,meth_LINC00964,meth_AC008155.1,meth_ASB7,meth_ADAM2,meth_BOD1L1,meth_TRPA1,meth_C11orf91,meth_GALR1,meth_ARHGDIG,meth_AC007381.3,meth_AC093901.1,meth_ALDOB,meth_AKR1C3,meth_AC091633.3,meth_CASC21,meth_AC123788.1,meth_AC144831.3,meth_C16orf90,meth_AC078842.4,meth_ARHGEF2,meth_AC009970.1,meth_AC092614.2,meth_AC006014.8,meth_BVES,meth_SURF4,meth_ANP32BP1,meth_AOX1,meth_NEFL,meth_AC008060.5,meth_AC006116.21,meth_E2F3P1,meth_ACPP,meth_ASH1L,meth_BZW2,meth_RP11_443G13.2,meth_C1orf141 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diff --git a/bioneuralnet/datasets/paad/rna.csv b/bioneuralnet/datasets/paad/rna.csv new file mode 100644 index 0000000..2df94d0 --- /dev/null +++ b/bioneuralnet/datasets/paad/rna.csv @@ -0,0 +1,178 @@ +patient,rna_IRF9,rna_DRG2,rna_AGXT2L2,rna_XRCC2,rna_FTSJD2,rna_GABRG1,rna_KCNC4,rna_C17orf95,rna_KRTAP2_2,rna_KGFLP1,rna_KIAA1529,rna_UBXN7,rna_GALR2,rna_SNORA74A,rna_CDK12,rna_C20orf12,rna_BRIP1,rna_CLPP,rna_TMEM97,rna_LAGE3,rna_CBX3,rna_DIAPH3,rna_SEMA6C,rna_ZNF441,rna_SPPL2A,rna_CCL20,rna_HAUS7,rna_NDRG4,rna_OR3A2,rna_ZNF470,rna_ZNF345,rna_ROD1,rna_FAM128B,rna_IL32,rna_SNHG10,rna_FAM183B,rna_FAM72A,rna_KPNA4,rna_PTTG3P,rna_LEPREL2,rna_GALNTL1,rna_SNRPN,rna_DOT1L,rna_DPF1,rna_NEK7,rna_SPTBN1,rna_ZNF414,rna_KDM2B,rna_EDA,rna_HFE,rna_PRCD,rna_SLC22A12,rna_SLC39A10,rna_CMPK1,rna_LOC284578,rna_FAM66C,rna_ASTN1,rna_LOC389634,rna_NKX2_5,rna_MPHOSPH10,rna_GPR162,rna_PCYT1A,rna_GPSM2,rna_NUP98,rna_FAM188A,rna_ZBTB38,rna_SIN3B,rna_ZNF121,rna_PPAPDC1A,rna_TLR3,rna_MUC20,rna_ACTR2,rna_LRRFIP2,rna_HS3ST4,rna_SAFB,rna_LOC284440,rna_E2F8,rna_RASD1,rna_TUSC5,rna_OTUD3,rna_ATP6V1G2,rna_ZSCAN1,rna_ALMS1P,rna_PTPRT,rna_PIK3CB,rna_C9orf163,rna_CCDC38,rna_PMEPA1,rna_SLC23A2,rna_TBKBP1,rna_ACTL6B,rna_C21orf2,rna_CORO7,rna_C9orf167,rna_FBXL16,rna_FIG4,rna_L3MBTL,rna_TSPYL1,rna_FBXL8,rna_SERINC2,rna_DPPA4,rna_C5orf27,rna_LAPTM4A,rna_ADAP1,rna_KLHL32,rna_ELP2P,rna_TMCO5A,rna_ROGDI,rna_FAM72D,rna_FSIP1,rna_PPIG,rna_ZNF439,rna_PARN,rna_PRPF39,rna_CAPG,rna_USP27X,rna_NRXN2,rna_C6orf150,rna_SERPINB2,rna_SFRS18,rna_POLRMT,rna_WASH2P,rna_MAP1S,rna_PPP1R9B,rna_PDE11A,rna_VMAC,rna_NIPA1,rna_WNT1,rna_HIST1H2BN,rna_PDXDC1,rna_MRPL49,rna_LOC100128788,rna_GPR142,rna_KDM3A,rna_PLVAP,rna_PDE4DIP,rna_SP100,rna_LIMA1,rna_MX2,rna_CSPG5,rna_DHH,rna_RACGAP1P,rna_ITGA7,rna_CEBPD,rna_NOLC1,rna_C17orf100,rna_RAD18,rna_MBNL3,rna_NCL,rna_ACOT4,rna_TTC21A,rna_CCDC15,rna_CCDC6,rna_C11orf57,rna_TERF2IP,rna_RNF175,rna_ARHGAP4,rna_TRPC7,rna_TFG,rna_GNRH2,rna_RAC1,rna_CHST2,rna_PHYHIPL,rna_TMEM176B,rna_P2RY8,rna_POU4F3,rna_UNC13D,rna_C7orf55,rna_PTPN14,rna_RFX2,rna_REXO1,rna_PHKG1,rna_NARF,rna_DEFB124,rna_C11orf82,rna_MGC4473,rna_C16orf73,rna_ANLN,rna_STRN,rna_TMEM31,rna_RPSAP52,rna_KRT25,rna_TCEA1,rna_NPY5R,rna_CEACAM19,rna_SGOL1,rna_ATG16L2,rna_ABHD8,rna_RHOF,rna_BZW2,rna_MDP1,rna_MYCNOS,rna_FLJ26850,rna_WDR19,rna_MECP2,rna_CLCN1,rna_SCN3A,rna_C2orf81,rna_RBM10,rna_ZNF582,rna_MRPL3,rna_PTPRZ1,rna_TAS2R20,rna_ACRC,rna_KCNA6,rna_TMCC1,rna_HDAC10,rna_VEPH1,rna_PASK,rna_HDHD2,rna_CCDC58,rna_DNAI2,rna_C3orf17,rna_LIPE,rna_GJA4,rna_MMEL1,rna_C9orf142,rna_S100P,rna_EIF2AK2,rna_PFDN4,rna_GPR144,rna_EVI5L,rna_ATG4D,rna_C19orf23,rna_HHIP,rna_DLG4,rna_YAP1,rna_RRAGA,rna_LOC25845,rna_LAMB4,rna_SOX4,rna_ARHGAP26,rna_RMRP,rna_WDR5B,rna_TBC1D10C,rna_CPNE3,rna_CCDC57,rna_TUFT1,rna_PHLDA1,rna_TMEM129,rna_WASL,rna_PCDHA1,rna_C20orf186,rna_DNAJC25_GNG10,rna_HAR1A,rna_SEPT10,rna_C6orf226,rna_ZNF8,rna_LRRC8A,rna_ZNF571,rna_SLC46A1,rna_PIPOX,rna_GIGYF2,rna_FAM78A,rna_NRXN1,rna_WDR12,rna_IL10RA,rna_MTHFD1,rna_YIPF6,rna_E2F3,rna_SFTA2,rna_VN1R1,rna_RSPH9,rna_ADCY1,rna_BRCA2,rna_ANXA11,rna_VPS35,rna_SPRYD3,rna_LASS3,rna_KIAA0913,rna_TUBA1C,rna_ARNTL2,rna_APBA3,rna_SPRR1A,rna_RASA4,rna_LOC100216001,rna_LOC100128842,rna_ZNF324,rna_SPINK2,rna_NCRNA00115,rna_SGMS2,rna_DSCR3,rna_SRRM4,rna_CNPY3,rna_GSPT1,rna_LOC127841,rna_LOC728743,rna_KLHDC3,rna_MAP3K2,rna_H1FNT,rna_PCDHA8,rna_GOLGA8G,rna_B3GNT8,rna_AAAS,rna_PCSK6,rna_LOC100129387,rna_CDCP1,rna_HOXA13,rna_C14orf132,rna_CITED2,rna_DHRS12,rna_C7orf50,rna_ZNF419,rna_BCL6,rna_C1orf112,rna_RNF7,rna_FAM71F2,rna_EID2B,rna_LOC401588,rna_SRCIN1,rna_C17orf101,rna_PTGS2,rna_ROM1,rna_HIST2H2BF,rna_MMP14,rna_TIPARP,rna_RNF103,rna_DMPK,rna_FAM111B,rna_C10orf99,rna_MLLT6,rna_PKD1,rna_RNASEH1,rna_SLC7A8,rna_ZNF860,rna_TUBE1,rna_TCTA,rna_CDCA5,rna_MYO5C,rna_F3,rna_MUM1,rna_PPP1R1C,rna_PLAC9,rna_unknown_8225,rna_AURKB,rna_TSPAN14,rna_F2RL2,rna_MKLN1,rna_RAI2,rna_FAM72B,rna_MAP1LC3A,rna_CCDC116,rna_C1orf93,rna_GFOD2,rna_AXL,rna_NDFIP2,rna_CC2D1A,rna_MIER1,rna_DLL1,rna_RND2,rna_MLL4,rna_CCNE2,rna_TFAP2A,rna_TUBA1A,rna_E2F6,rna_CN5H6.4,rna_ANKK1,rna_PTDSS2,rna_PMS2L4,rna_B3GNT3,rna_TCEAL3,rna_C19orf21,rna_C19orf20,rna_ELAVL3,rna_MSI1,rna_PI4KAP1,rna_DDOST,rna_CAPSL,rna_LMNB1,rna_CLP1,rna_KLHL22,rna_LYSMD2,rna_LSM12,rna_ASGR2,rna_NAP1L5,rna_THSD4,rna_KCNJ15,rna_CLCN3,rna_PSMB6,rna_KPNA7,rna_SEMA6D,rna_ANKRA2,rna_ALPL,rna_ASMTL,rna_DHCR24,rna_HRH1,rna_TMEM105,rna_IGSF3,rna_EIF4H,rna_MICAL1,rna_FOXD1,rna_MBD3,rna_ARIH1,rna_REM1,rna_ARHGAP29,rna_FAM169A,rna_TUBB2B,rna_ADAM10,rna_LPPR3,rna_KCMF1,rna_C2orf67,rna_BTBD2,rna_PSMA4,rna_CNRIP1,rna_C9orf139,rna_FRS3,rna_BRD8,rna_PSMA2,rna_CLDN1,rna_C1orf35,rna_NMU,rna_RASAL3,rna_KCTD2,rna_OGFR,rna_C2orf85,rna_SMAD3,rna_NPPC,rna_CKS1B,rna_CHST7,rna_SERPINB7,rna_OR7E91P,rna_DCX,rna_PRKAR1B,rna_ERGIC1,rna_GRIN2C,rna_CRCP,rna_FGFBP1,rna_TMCC2,rna_C19orf43,rna_THSD1,rna_CYB5B,rna_RPN1,rna_TGM2,rna_XAF1,rna_FNDC5,rna_FAM125B,rna_CRABP2,rna_C7orf43,rna_CHST8,rna_SNORD15A,rna_TNNT1,rna_FAM172A,rna_FAM174A,rna_GGA3,rna_PTPN12,rna_KCNE3,rna_CAP1,rna_RAB6B,rna_ITGB6,rna_LONRF3,rna_CDC16,rna_RSPH3,rna_VPS37C,rna_KCNE1L,rna_ASF1B,rna_STX8,rna_CCDC159,rna_SAMD12,rna_CDC73,rna_ALPK1,rna_CT45A1,rna_RNF216,rna_CNPY2,rna_unknown_653553,rna_HBP1,rna_CYB561D2,rna_DCTD,rna_SC4MOL,rna_LOC100240726,rna_LPCAT2,rna_GABBR1,rna_AURKA,rna_LOC440957,rna_EIF2A,rna_CAV2,rna_NES,rna_LOC389333,rna_KCNAB2,rna_RSPH4A,rna_ADCY8,rna_KDM4DL,rna_TMEM149,rna_WSCD1,rna_HYMAI,rna_JMJD5,rna_E2F7,rna_RHPN1,rna_ZFP57,rna_DNAJA4,rna_PLD3,rna_LOC283856,rna_MET,rna_ADRBK2,rna_ERLIN1,rna_SCAF1,rna_OR2A1,rna_CPLX3,rna_C3orf71,rna_HCST,rna_HELLS,rna_KIF25,rna_ACTR3B,rna_FIGF,rna_CLDN4,rna_LOC84740,rna_FLJ43860,rna_NCRNA00204B,rna_TSTA3,rna_UBE2QL1,rna_LRRC29,rna_MAGED4,rna_STS,rna_C16orf7,rna_KCNAB3,rna_MXD1,rna_UBE2G2,rna_SDC1,rna_DENND4B,rna_RHO,rna_LRFN1,rna_LASS1,rna_TAOK3,rna_KCTD19,rna_LRWD1,rna_C3orf35,rna_PTER,rna_ZDHHC3,rna_RAB42,rna_TMEM115,rna_ILKAP,rna_DDX60L,rna_PABPC1L2A,rna_ACD,rna_TMEM92,rna_FAM13A,rna_RAB40A,rna_NUDT14,rna_TRIM44,rna_SPTA1,rna_WDR17,rna_ZNF80,rna_FUS,rna_CCDC154,rna_NFKBID,rna_TSPAN19,rna_LGALS9B,rna_C1orf58,rna_C1orf141,rna_SPTBN5,rna_F7,rna_RFX1,rna_PELP1,rna_FAM66E,rna_KIAA1199,rna_MARK4,rna_HRH3,rna_GOLGA8A,rna_ATOH8,rna_HOXA1,rna_C4orf38,rna_VANGL1,rna_FAM69B,rna_OLIG1,rna_LOC100130093,rna_LPCAT1,rna_CTNS,rna_VEGFB,rna_PPP3CA,rna_GPRIN2,rna_ZNF146,rna_HDDC2,rna_GRPEL2,rna_NPEPPS,rna_C6orf48,rna_C2orf29,rna_HERC2P4,rna_CCDC92,rna_GNA12,rna_SMURF2,rna_ETV6,rna_C10orf119,rna_C9orf140,rna_CHKB_CPT1B,rna_TMC2,rna_CCND1,rna_KIAA1609,rna_ZC3H10,rna_WNT2,rna_LOC284233,rna_CDKL4,rna_FBRS,rna_NKAIN2,rna_LOC100133331,rna_TRIM46,rna_C1orf170,rna_POC1A,rna_DAZL,rna_TMEM170B,rna_EPB41L1,rna_ANO1,rna_SLC25A35,rna_KNDC1,rna_C20orf111,rna_IFT27,rna_SLC22A16,rna_DPF3,rna_SUN1,rna_SAT1,rna_CALM2,rna_KRTCAP3,rna_ASGR1,rna_FAM196B,rna_DPCR1,rna_DDX60,rna_MCF2L,rna_MAGEH1,rna_HRASLS2,rna_GRASP,rna_TCL1B,rna_NUDCD1,rna_CAMKK1,rna_GOLGA1,rna_UPK2,rna_ZMYND17,rna_TM9SF3,rna_PAX9,rna_C2CD4D,rna_HSD17B3,rna_VPS16,rna_TELO2,rna_CAPN10,rna_LAMA3,rna_MRPS22,rna_LGR4,rna_PLA2G6,rna_SRY,rna_SLAIN2,rna_HEATR1,rna_ITGA6,rna_MPZ,rna_TRIM41,rna_CCL28,rna_NIPSNAP3B,rna_ARHGAP9,rna_CHODL,rna_SAT2,rna_KHDRBS1,rna_KIAA0391,rna_GINS1,rna_RIT1,rna_DGKZ,rna_FAM66A,rna_OPA1,rna_HSD17B11,rna_SLC25A11,rna_SKIV2L,rna_FAM160B2,rna_RABEP1,rna_OSTF1,rna_ITGA2,rna_ZNF833,rna_FLJ39653,rna_ACCN3,rna_ASAP2,rna_GSDMC,rna_MEG3,rna_BCR,rna_LRRC24,rna_CACNB2,rna_RAB2A,rna_SSFA2,rna_LRRN1,rna_LY6G6C,rna_TBPL2,rna_HN1,rna_TAOK2,rna_C16orf90,rna_ANKLE1,rna_PTCD3,rna_ACAT1,rna_DHX8,rna_MAGED1,rna_ALPP,rna_MGAT3,rna_N6AMT1,rna_TREM1,rna_TBC1D17,rna_C1orf213,rna_C1orf70,rna_TMEM121,rna_PRH1,rna_PALB2,rna_DSCR9,rna_CECR1,rna_PPP1R14D,rna_RNFT2,rna_ODZ2,rna_GNA15,rna_C4orf35,rna_PTPRVP,rna_KCNA5,rna_NDC80,rna_C3orf54,rna_LRRFIP1,rna_BIVM,rna_WNT10B,rna_C11orf2,rna_ANAPC2,rna_CYP2S1,rna_TRIM21,rna_LRRTM2,rna_SLC2A11,rna_TMSL3,rna_TUB,rna_RAB4B,rna_NGFRAP1,rna_SBF2,rna_GDF1,rna_SLC39A11,rna_ABCB8,rna_FAM118B,rna_MBOAT1,rna_NKAPL,rna_CLCN7,rna_ZDHHC14,rna_MDM1,rna_LTBP3,rna_FAM104B,rna_SGTA,rna_C10orf114,rna_KLHDC4,rna_POLR2J3,rna_EXOC3L,rna_DACT2,rna_FAM48A,rna_GRIK1,rna_FAM91A1,rna_LRRC50,rna_CABP1,rna_LMBR1L,rna_ZNF692,rna_KIAA0895L,rna_CPNE9,rna_MAGED4B,rna_NRN1,rna_FBXO34,rna_UBL7,rna_TMEM179,rna_LSG1,rna_AURKC,rna_ZBTB17,rna_ADAM17,rna_OSGEP,rna_YES1,rna_THSD1P1,rna_CFB,rna_LRSAM1,rna_NDUFA13,rna_RAPH1,rna_PPPDE1,rna_CHD5,rna_GTPBP5,rna_C3orf75,rna_BRF1,rna_PKP3,rna_FOXM1,rna_PRKACA,rna_VPS45,rna_ADAM11,rna_COL7A1,rna_SHC2,rna_B4GALT5,rna_UNC80,rna_GABRP,rna_ACOT9,rna_TMEM91,rna_TTC18,rna_GGCT,rna_PAQR5,rna_COL10A1,rna_LRPAP1,rna_BCL9L,rna_MGC42105,rna_PAPD7,rna_C9orf169,rna_RARRES3,rna_TTLL3,rna_PLOD2,rna_IPO13,rna_PSG10,rna_C16orf75,rna_DNAI1,rna_GALNT12,rna_ZNF578,rna_FGFR1,rna_GALR1,rna_NGF,rna_MYO1E,rna_ZBED4,rna_WBSCR26,rna_LRRC4B,rna_NRP2,rna_ATP1A3,rna_NUFIP2,rna_ABAT,rna_EPB41L3,rna_MYO16,rna_ROPN1L,rna_CPO,rna_C14orf119,rna_GPR126,rna_GIN1,rna_NRL,rna_TAB2,rna_LOC100130015,rna_COL19A1,rna_KIAA1429,rna_EIF1AX,rna_DULLARD,rna_FAM104A,rna_CLK3,rna_TAPT1,rna_C2orf64,rna_RTN3,rna_CLIC3,rna_CDK2,rna_C21orf56,rna_MMAB,rna_CFP,rna_FLRT3,rna_CTCFL,rna_NICN1,rna_GOLGB1,rna_SRPX2,rna_CCNA1,rna_MAGT1,rna_LOC121952,rna_LOC100128977,rna_C4orf39,rna_NAGLU,rna_LRPPRC,rna_PCDHA2,rna_KLHL30,rna_NSUN6,rna_CRYGC,rna_ZNF559,rna_IFT88,rna_C4orf31,rna_MTDH,rna_C1RL,rna_PBX2,rna_FAM25A,rna_SYTL4,rna_WASH3P,rna_DLGAP3,rna_VPS37B,rna_ADAMTS19,rna_IKZF4,rna_SGSM2,rna_TSSK6,rna_FBLL1,rna_ATP8B3,rna_GTPBP1,rna_DNAH10,rna_SLC16A3,rna_RAB10,rna_TMEM80,rna_RNF13,rna_C1orf135,rna_LQK1,rna_LASS6,rna_PTPRR,rna_DNAJB11,rna_SF1,rna_SIDT2,rna_FAM157B,rna_LRRC67,rna_TMPRSS2,rna_SLC26A10,rna_CLEC3B,rna_SMPD1,rna_FAM70B,rna_MBOAT2,rna_FBF1,rna_VKORC1L1,rna_RAD54B,rna_S100A3,rna_CLK1,rna_PCDHA3,rna_MFNG,rna_CHD3,rna_FIGNL2,rna_SDR16C5,rna_LOC100133985,rna_GPAT2,rna_DEPDC5,rna_KIAA1683,rna_TMEM176A,rna_PTPRK,rna_RMND5A,rna_DNAJC28,rna_SMARCD3,rna_GMNN,rna_ACBD4,rna_FBXL15,rna_SBK2,rna_LPPR2,rna_SRPK3,rna_TRIM66,rna_B4GALNT3,rna_CROCCL2,rna_LOC349114,rna_GEFT,rna_C2orf84,rna_TMEM87A,rna_SS18L1,rna_MAPKBP1,rna_ATP5D,rna_LRRC45,rna_REEP3,rna_NT5M,rna_NCRNA00116,rna_ANKFN1,rna_SNRPD3,rna_GGA1,rna_ZNF671,rna_CEPT1,rna_ZNF653,rna_C16orf3,rna_HPCA,rna_DDX47,rna_PARP4,rna_SERGEF,rna_MMAA,rna_RPS15,rna_LPCAT4,rna_TMEM14B,rna_CKLF,rna_ZNF302,rna_MYCBP,rna_FOSL2,rna_KRT16,rna_KIF24,rna_SHBG,rna_RALBP1,rna_LOC92973,rna_ZNF335,rna_MARCH11,rna_GRIA2,rna_TMEM8B,rna_ZSCAN2,rna_ZCCHC3,rna_ALKBH5,rna_FAM118A,rna_C15orf59,rna_FLJ45445,rna_ATG16L1,rna_B4GALT1,rna_ZC3H15,rna_SGK494,rna_LIG4,rna_EFHC1,rna_MS4A6E,rna_MRPS26,rna_LNPEP,rna_SPRR1B,rna_SLC10A7,rna_WNT7A,rna_SERPINB1,rna_IFFO1,rna_CNTD1,rna_ABI1,rna_IL13,rna_MTFR1,rna_DCAF13,rna_AHNAK,rna_ARHGAP42,rna_ISCU,rna_CMTM7,rna_TK1,rna_NT5E,rna_MYO5B,rna_NCBP2,rna_RBM14,rna_LCN10,rna_KLK11,rna_CCDC108,rna_CSNK2A1P,rna_KIF20B,rna_ITCH,rna_ATP6AP1,rna_GCNT3,rna_ATP13A3,rna_FLJ45340,rna_SHANK1,rna_MCOLN1,rna_ZNF193,rna_NOG,rna_GGT6,rna_GTSE1,rna_ZNF789,rna_ZDHHC9,rna_C14orf34,rna_IGFBP2,rna_DNAJA3,rna_WRB,rna_BEX1,rna_MBNL2,rna_DLGAP2,rna_LIN9,rna_LY75,rna_LOX,rna_LOC100133161,rna_SLC39A13,rna_CAPN5,rna_TSPAN15,rna_KRTAP10_6,rna_SSB,rna_LIPJ,rna_DGKH,rna_FAM169B,rna_PEX2,rna_RND3,rna_TBC1D13,rna_IL15RA,rna_LOC100128811,rna_GGA2,rna_SP1,rna_PTOV1,rna_SSTR2,rna_PRKAA1,rna_CGB5,rna_SIK3,rna_ZFR2,rna_C4orf34,rna_COX6B2,rna_TSSC4,rna_C2orf39,rna_MPZL2,rna_NOTCH2,rna_LUZP1,rna_C7orf30,rna_C3orf18,rna_SLC35F1,rna_COLQ,rna_HCN3,rna_PWRN1,rna_EDNRA,rna_TMEM63B,rna_WNT4,rna_SPTBN4,rna_SEL1L3,rna_OBSL1,rna_LOC440944,rna_CPLX1,rna_MTHFD2,rna_SLC6A20,rna_DYNC1I1,rna_ACOX2,rna_CES8,rna_PIM2,rna_PTPRCAP,rna_RASA2,rna_CDK5RAP3,rna_MARK2,rna_PDCD6IP,rna_METTL7B,rna_ST3GAL2,rna_LOC282997,rna_LOC100134713,rna_RSAD1,rna_TMEM88,rna_SETDB2,rna_PLCD4,rna_PLA2G2F,rna_ABLIM3,rna_ST6GALNAC6,rna_FABP12,rna_ATP1A4,rna_NDUFS7,rna_SEC61A2,rna_SEP15,rna_SLFN14,rna_SDK2,rna_CNTNAP4,rna_BIN1,rna_LDLR,rna_MTHFR,rna_PEBP1,rna_GOLPH3L,rna_CCDC56,rna_PDE3B,rna_FAM73B,rna_ITGA3,rna_BEX4,rna_SLMAP,rna_LOC100132287,rna_GNAL,rna_CABP4,rna_KIAA0892,rna_NUDT15,rna_CCNF,rna_MRPL42P5,rna_DCLK2,rna_TOMM20,rna_INPPL1,rna_SRI,rna_FAM43A,rna_KISS1R,rna_PSCA,rna_RSBN1L,rna_MLLT1,rna_SLC44A1,rna_OXT,rna_MBD1,rna_CCM2,rna_LPAR5,rna_SFRS14,rna_SPCS3,rna_RNF139,rna_PCSK4,rna_PSPC1,rna_WASH5P,rna_BMP4,rna_KPNA1,rna_POU3F1,rna_FAM49B,rna_ZNF784,rna_ADSS,rna_PRAF2,rna_FAM155B,rna_KCNA4,rna_ARHGAP27,rna_UCA1,rna_ARFRP1,rna_HILS1,rna_ARHGAP21,rna_NCRNA00152,rna_XPA,rna_DKFZP686I15217,rna_FBXL18,rna_UBE3C,rna_CELF4,rna_TREML3,rna_SLC26A11,rna_PARD6A,rna_GALR3,rna_ZNF222,rna_LOC162632,rna_USP20,rna_GUSBP1,rna_STAT4,rna_RTKN2,rna_GNAS,rna_AP2A2,rna_GNG7,rna_ZNF286B,rna_CUX2,rna_PBX3,rna_unknown_26823,rna_AGFG1,rna_CHFR,rna_PPIL6,rna_APC2,rna_C15orf23,rna_LDHA,rna_CNTROB,rna_NOVA2,rna_DLGAP5,rna_HAVCR1,rna_MYH7B,rna_FBXO32,rna_LAMB3,rna_PATL1,rna_ZNF786,rna_SYT3,rna_TAX1BP1,rna_C20orf54,rna_MRFAP1,rna_A1BG,rna_KCNA3,rna_DPY19L2P4,rna_RAD51L3,rna_GPR123,rna_DCAF12L2,rna_UFSP1,rna_TCTEX1D4,rna_MAST1,rna_ATP6V0E2,rna_MED21,rna_NSMCE2,rna_NBEAL1,rna_RC3H2,rna_PIGX,rna_LOC285456,rna_LY6G5C,rna_TMOD2,rna_ANPEP,rna_MAMLD1,rna_CLNS1A,rna_ANKH,rna_TKT,rna_SMS,rna_KLHDC1,rna_ZFP28,rna_DDX21,rna_SMNDC1,rna_ENO3,rna_C6orf62,rna_AJAP1,rna_TMEM145,rna_DTNA,rna_CRY2,rna_COX4I2,rna_ODZ1,rna_CCDC71,rna_CTNNB1,rna_ATG4B,rna_ATP2B2,rna_OLFM1,rna_LEMD2,rna_PDLIM5,rna_NMD3,rna_ERBB2,rna_C19orf26,rna_NCRNA00219,rna_MSH2,rna_PLCB3,rna_PHPT1,rna_C14orf139,rna_TMEM159,rna_CYFIP1,rna_STK11IP,rna_GPS1,rna_B2M,rna_SPATA24,rna_TSKU,rna_OAS2,rna_SYT10,rna_LTBP4,rna_NOTCH4,rna_INSIG2,rna_TDRD9,rna_REV3L,rna_EZR,rna_SLC7A7,rna_CTSL2,rna_OAS3,rna_SLC25A14,rna_C1QTNF8,rna_MAGED2,rna_C15orf41,rna_CLDN7,rna_GLYAT,rna_BCL2L13,rna_RRAS,rna_ASPH,rna_RAB6A,rna_HNF4G,rna_PTPRE,rna_SPINK13,rna_NCRNA00158,rna_MAD2L1,rna_COL28A1,rna_EFHA2,rna_NDUFS1,rna_GNG3,rna_IGFBP3,rna_EPM2A,rna_CCDC151,rna_TMEM107,rna_KRT13,rna_ZNF319,rna_NKX6_3,rna_CLIC1,rna_PGBD5,rna_TCN1,rna_TMX2,rna_NISCH,rna_PMPCA,rna_KIAA0922,rna_LOC729082,rna_GNAI3,rna_YY1,rna_TNFRSF11B,rna_LOC728989,rna_LONRF2,rna_C17orf107,rna_C15orf27,rna_ATP1A2,rna_CLCN5,rna_TPH1,rna_DMTF1,rna_ZNF549,rna_PGM2,rna_ZNF367,rna_C15orf48,rna_ZNF761,rna_TCEAL6,rna_SLC35F2,rna_GNG2,rna_ZNF777,rna_CDK5RAP1,rna_CDK10,rna_ESCO2,rna_SGSM1,rna_ENOSF1,rna_KLKB1,rna_LOC285359,rna_ENGASE,rna_SRR,rna_FIZ1,rna_ASPRV1,rna_PPP3CB,rna_SMAP2,rna_STIL,rna_RHPN2,rna_TRA2A,rna_LOC283267,rna_RAB43,rna_SLC22A18AS,rna_LCMT2,rna_C7orf51,rna_POU4F2,rna_DDX25,rna_ZNF772,rna_RUNDC3A,rna_PCGF2,rna_ITGAV,rna_LOC100289341,rna_S100A6,rna_TPPP,rna_IFIT3,rna_PPWD1,rna_PPIA,rna_CELSR2,rna_REXO2,rna_MAP3K10,rna_ARIH2,rna_CLPX,rna_NEK9,rna_C17orf85,rna_ZNF879,rna_LOC90784,rna_C17orf108,rna_SPATS2L,rna_REV1,rna_OSTM1,rna_FUK,rna_GNG12,rna_C12orf35,rna_PPCS,rna_ANKS1B,rna_FAM154B,rna_C3orf52,rna_MAP6,rna_CCDC157,rna_KIAA1009,rna_TMEM187,rna_CCDC3,rna_GUSBP3,rna_C20orf72,rna_SEC31B,rna_FAM151A,rna_GDI1,rna_CLMN,rna_HNRNPM,rna_IFIT2,rna_TMEM84,rna_EFCAB10,rna_SGOL2,rna_C10orf12,rna_DDX24,rna_NKX2_2,rna_KIAA1524,rna_FER1L6,rna_E2F2,rna_BCAT2,rna_NUAK2,rna_COQ6,rna_NOVA1,rna_FBXO15,rna_WNT6,rna_CCDC37,rna_LBXCOR1,rna_OMP,rna_ZNF853,rna_GADL1,rna_C2orf66,rna_ZNF814,rna_ACCN4,rna_OR2L13,rna_OAS1,rna_ZNF416,rna_GIGYF1,rna_MT1G,rna_MGC16025,rna_RNF123,rna_ZAP70,rna_RAB39B,rna_GPSM1,rna_XRN2,rna_PRSS22,rna_C16orf5,rna_MCM10,rna_CXorf42,rna_RLF,rna_C8orf42,rna_C19orf29,rna_FRMD6,rna_SPIN2B,rna_OLA1,rna_PTPRH,rna_TMEM123,rna_UBE2NL,rna_RUNX2,rna_PHF10,rna_ZNF346,rna_ACTL6A,rna_BFAR,rna_PHC1,rna_USP6,rna_CLDN5,rna_CHMP4C,rna_MAP3K4,rna_CLINT1,rna_LOC339047,rna_RNF141,rna_SRFBP1,rna_GOLGA8B,rna_ADRA2C,rna_CCDC13,rna_EFNB2,rna_NANOS1,rna_IL1RAPL2,rna_OASL,rna_ZNF331,rna_N4BP2L2,rna_ZNF71,rna_TUBB4Q,rna_ACBD3,rna_ZNF695,rna_KRT7,rna_VAMP1,rna_CCDC51,rna_CDC6,rna_ZCCHC18,rna_UBE2L6,rna_R3HCC1,rna_KIAA1409,rna_LRRC49,rna_ZNF418,rna_SPRN,rna_DUSP28,rna_C3orf38,rna_LRFN3,rna_LOC653501,rna_SPATA16,rna_C6orf64,rna_AIRE,rna_TMC3,rna_VPS24,rna_RHOXF1,rna_ZNF708,rna_SYT5,rna_LGI4,rna_LOC284749,rna_PNRC1,rna_DYNC1I2,rna_SMURF1,rna_UNC13A,rna_PCNA,rna_ENO1,rna_TMEM175,rna_TRAF6,rna_EPM2AIP1,rna_MDM4,rna_ACADSB,rna_SIVA1,rna_CPE,rna_ZNF780B,rna_SCAND2,rna_SSTR5,rna_RTN1,rna_PECR,rna_IRF6,rna_MED22,rna_ZSWIM3,rna_DPEP3,rna_PPPDE2,rna_TENC1,rna_ORC1L,rna_RFESD,rna_HSBP1L1,rna_FOSL1,rna_PSAP,rna_ITGAE,rna_LOC646498,rna_MEMO1,rna_NXPH4,rna_DHPS,rna_EIF3E,rna_GMEB2,rna_RNF157,rna_NHEDC2,rna_C16orf72,rna_C1orf56,rna_SLC25A24,rna_KLRA1,rna_CLCN6,rna_LOC284009,rna_POT1,rna_CLEC14A,rna_ZNF554,rna_UBTF,rna_TTLL6,rna_TRIM67,rna_PPT2,rna_LOC100129637,rna_NUDT18,rna_TTC7B,rna_INO80B,rna_GTPBP10,rna_DPP7,rna_NCAPG2,rna_NSUN3,rna_NCRNA00235,rna_SFTPA2,rna_CFLP1,rna_EXT2,rna_ACACB,rna_PPIAL4C,rna_OSGIN2,rna_OVGP1,rna_SERPINA10,rna_CHRNA2,rna_ABCE1,rna_RUFY3,rna_GCH1,rna_C12orf70,rna_TMEM178,rna_KRR1,rna_TUBG2,rna_LOC646471,rna_MARK1,rna_FRMD5,rna_TTLL9,rna_B3GAT1,rna_KRT19,rna_ANKRD28,rna_C2orf62,rna_PTPRF,rna_PLAUR,rna_LOC285419,rna_N6AMT2,rna_ATP6V0A1,rna_ITGAD,rna_CAMK1,rna_MRPL34,rna_PDCD10,rna_KCTD17,rna_SNRNP70,rna_LMX1B,rna_JUB,rna_HMGN2,rna_APOBEC3B,rna_ZNF709,rna_ZBTB49,rna_NCRNA00086,rna_ZSCAN21,rna_AZI1,rna_CD9,rna_ORAI2,rna_TRPA1,rna_GABRA1,rna_TOP2A,rna_SNHG11,rna_CARS,rna_PTPRN,rna_AGPAT1,rna_RELA,rna_TNFSF10,rna_BCAT1,rna_RTN4RL2,rna_CENPF,rna_NBPF3,rna_KCNC3,rna_DNAJB14,rna_CCDC17,rna_TOB2,rna_NENF,rna_MAL,rna_KCNK5,rna_NUP160,rna_TRIM29,rna_DEK,rna_PLEKHM2,rna_KRT72,rna_E2F1,rna_VPS4B,rna_SHANK3,rna_OTUD7A,rna_GPER,rna_B3GNT5,rna_TYR,rna_SYPL1,rna_ADCY4,rna_ATR,rna_CCDC97,rna_ASPSCR1,rna_LAD1,rna_STXBP1,rna_FAM100A,rna_C6orf132,rna_NCKAP1,rna_UNG,rna_SLC22A10,rna_MTCP1NB,rna_TARS,rna_PCNT,rna_HMGCLL1,rna_KIF23,rna_PTPN6,rna_DUSP26,rna_PIGA,rna_PTCH2,rna_FGFRL1,rna_PFDN5,rna_IZUMO1,rna_ZNF831,rna_PSD,rna_GPATCH3,rna_EREG,rna_B4GALT7,rna_PARK2,rna_B3GNT1,rna_LYRM7,rna_TMEM132E,rna_OR2C1,rna_ERF,rna_ACTR3,rna_SMPD2,rna_CELF5,rna_PRRT1,rna_ZNF114,rna_SRRT,rna_OLFM2,rna_GDE1,rna_HMGN3,rna_MECOM,rna_ST3GAL3,rna_FOXL1,rna_KIAA1522,rna_MAP2K2,rna_NUP37,rna_NUDT17,rna_U2AF1L4,rna_CLK4,rna_BMP2,rna_LOC100128573,rna_SLC44A2,rna_PLCXD2,rna_NAMPT,rna_PRR3,rna_FAM21C,rna_CSNK1G2,rna_EHMT2,rna_XPC,rna_BRD9,rna_NBN,rna_HMGN5,rna_CCT5,rna_C7orf59,rna_PITPNA,rna_NUDT9P1,rna_HS3ST1,rna_C13orf34,rna_TRIM34,rna_PMM1,rna_ERGIC2,rna_STAB1,rna_ADAT3,rna_PTK6,rna_SCN3B,rna_TTC7A,rna_C20orf203,rna_ACADVL,rna_SGK196,rna_ZNF17,rna_PVRIG,rna_LY6D,rna_B3GAT3,rna_KLF11,rna_HOXB9,rna_WDR52,rna_CASP8,rna_LOC100270804,rna_C19orf28,rna_C4orf43,rna_MCM2,rna_NDUFB11,rna_ATP2A3,rna_ADAD2,rna_DEFB132,rna_RRAGB,rna_BCL2,rna_SH2B1,rna_INPP4B,rna_PIGH,rna_GSDMB,rna_HSF2,rna_SERPINB5,rna_BIRC2,rna_FAM5B,rna_CYP46A1,rna_C16orf93,rna_ZNF165,rna_FAM78B,rna_MRPL41,rna_WNK4,rna_LOC729799,rna_C1QL2,rna_ZNF157,rna_LGALS9,rna_AHR,rna_KIAA0114,rna_PPFIA3,rna_C17orf53,rna_C9orf123,rna_DCLRE1B,rna_CFD,rna_ERCC6L,rna_ZNF597,rna_B3GNT7,rna_TMEM108,rna_MINK1,rna_PLSCR1,rna_LOC649330,rna_RPGRIP1,rna_LOXL2,rna_FAM3C,rna_LOC168474,rna_PI4KA,rna_C15orf61,rna_ERBB2IP,rna_ZNF589,rna_DLGAP4,rna_MAP1B,rna_MAP3K12,rna_BICC1,rna_C21orf7,rna_SYT2,rna_CH25H,rna_PLAC8L1,rna_KYNU,rna_ELK4,rna_S100A1,rna_TRIM59,rna_OIP5,rna_RPGR,rna_FAIM3,rna_WHAMML1,rna_NCRNA00085,rna_C16orf79,rna_CDC45,rna_TFPT,rna_DFFA,rna_ZNF837,rna_CCDC52,rna_ZNF488,rna_RRAGC,rna_ANKRD13D,rna_PRSS50,rna_PMM2,rna_JMJD6,rna_NPEPL1,rna_PPP4R1L,rna_KLHDC2,rna_AZIN1,rna_SQLE,rna_MIER2,rna_DIAPH2,rna_GFRA2,rna_CRYGS,rna_IP6K1,rna_FAM64A,rna_ZNF821,rna_ARL5B,rna_DBF4,rna_HAP1,rna_CRIP3,rna_C2orf63,rna_GAB3,rna_HABP4,rna_LRRC27,rna_LGALS3,rna_ITM2A,rna_MUC21,rna_PNP,rna_KCNA1,rna_HERC2P2,rna_LOC729176,rna_PDXP,rna_ZMAT1,rna_KRT17,rna_LRRC48,rna_DDX28,rna_HTATIP2,rna_SEMA7A,rna_TTC8,rna_TRIM5,rna_MGC23284,rna_HOXA10,rna_WDR83,rna_C8orf44,rna_CHRAC1,rna_FAM123C,rna_LOC401463,rna_LGALS9C,rna_CMTM6,rna_SNAPC4,rna_RNASEH2C,rna_CRAT,rna_DNAJC4,rna_PTPRS,rna_NRXN3,rna_FBXO31,rna_C4orf36,rna_CYFIP2,rna_CAV1,rna_CHMP2B,rna_STAG3L4,rna_DENND2A,rna_TIMM22,rna_ZNF575,rna_CRIP1,rna_SNORD115_26,rna_CNGB1,rna_PLA2G4F,rna_VPS37D,rna_IKZF2,rna_DNAJC6,rna_PSME4,rna_MRO,rna_LTBP1,rna_KLK6,rna_TCEA2,rna_PCDHA9,rna_C15orf43,rna_BLMH,rna_GPR151,rna_TUBA3E,rna_FBXL13,rna_MOCOS,rna_CWF19L1,rna_TPRKB,rna_DDX19B,rna_DCUN1D1,rna_LOC401387,rna_ZBTB7C,rna_ATP8B2,rna_ZNF767,rna_ZNF397OS,rna_HIST1H2BB,rna_LIFR,rna_C19orf25,rna_AGPS,rna_LOC286094,rna_BRCC3,rna_HLX,rna_ATP8B1,rna_AKIRIN2,rna_ZFP64,rna_ABI3,rna_CMTM1,rna_KIAA1161,rna_POMT2,rna_ATXN7L2,rna_ZFAND1,rna_CALM1,rna_TPX2,rna_FAM71F1,rna_BEGAIN,rna_ZNF276,rna_USP26,rna_SUDS3,rna_CCNE1,rna_ZNF207,rna_CHMP6,rna_FLJ35390,rna_CSNK1D,rna_AKR1B10,rna_FBXL17,rna_B3GNT2,rna_PCDHB17,rna_CXorf1,rna_ACTN4,rna_SCARF1,rna_NUDT16L1,rna_SELO,rna_PRHOXNB,rna_C17orf81,rna_ELMOD3,rna_SKA3,rna_GGT7,rna_GTPBP8,rna_EPS8,rna_KCNC2,rna_SFI1,rna_FBXO18 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+TCGA-XN-A8T5,0 +TCGA-YB-A89D,0 +TCGA-YH-A8SY,0 +TCGA-YY-A8LH,0 +TCGA-Z5-AAPL,0 diff --git a/bioneuralnet/downstream_task/dpmon.py b/bioneuralnet/downstream_task/dpmon.py index 32777c8..27904b2 100644 --- a/bioneuralnet/downstream_task/dpmon.py +++ b/bioneuralnet/downstream_task/dpmon.py @@ -1,3 +1,5 @@ +from __future__ import annotations + import os import re import statistics @@ -24,6 +26,7 @@ from ray import train from ray import tune from ray.tune import Checkpoint +#from bioneuralnet.utils.ray_compatability import train, tune, Checkpoint from ray.tune import CLIReporter from ray.tune.error import TuneError from ray.tune.stopper import TrialPlateauStopper @@ -35,8 +38,8 @@ from sklearn.metrics import f1_score, roc_auc_score, recall_score,average_precision_score, matthews_corrcoef from bioneuralnet.utils import get_logger -from bioneuralnet.utils.reproducibility import set_seed -from bioneuralnet.network_embedding.gnn_models import GCN, GAT, SAGE, GIN +from bioneuralnet.utils import set_seed +from bioneuralnet.network_embedding import GCN, GAT, SAGE, GIN logger= get_logger(__name__) diff --git a/bioneuralnet/utils/preprocess.py b/bioneuralnet/utils/preprocess.py index edeb051..d2cfd54 100644 --- a/bioneuralnet/utils/preprocess.py +++ b/bioneuralnet/utils/preprocess.py @@ -1,3 +1,5 @@ +from __future__ import annotations + import pandas as pd import numpy as np import networkx as nx @@ -415,7 +417,7 @@ def key_fn(k: str) -> float: def select_top_randomforest(X: pd.DataFrame, y: pd.Series, top_k: int = 1000, seed: int = 119) -> pd.DataFrame: """Select top-k features using RandomForest feature importances. - Non-numeric columns are rejected, the remaining data are cleaned and zero-variance features removed, a RandomForest classifier or regressor is fitted depending on y, and the top_k most important features are selected based on feature_importances_. + Non-numeric columns are rejected, the remaining data are cleaned and zero-variance features removed, a RandomForest classifier or regressor is fitted depending on y, and the top_k most important features are selected based on ``feature_importances_``. Args: diff --git a/docs/source/Quick_Start.ipynb b/docs/source/Quick_Start.ipynb index 3fe4bf3..77dc3b0 100644 --- a/docs/source/Quick_Start.ipynb +++ b/docs/source/Quick_Start.ipynb @@ -19,6 +19,21 @@ "- This setup is useful for quickly testing the full BioNeuralNet pipeline, from preprocessing and graph construction to model training and evaluation.\n" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "vscode": { + "languageId": "shellscript" + } + }, + "outputs": [], + "source": [ + "#!pip install bioneuralnet\n", + "#!pip install torch\n", + "#!pip install torch_geometric" + ] + }, { "cell_type": "code", "execution_count": 3, @@ -202,7 +217,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Example 2: Similarity Graph\n", + "## Example 2: Similarity Network\n", "\n", "Build a normalized k-nearest neighbors (kNN) similarity graph from feature vectors. \n", "\n", @@ -684,7 +699,7 @@ "metadata": {}, "outputs": [], "source": [ - "from bioneuralnet.clustering import HybridLouvain\n", + "from bioneuralnet.clustering import HybridLouvain\n", "import networkx as nx\n", "\n", "merged_omics = pd.concat([omics1, omics2], axis=1)\n", diff --git a/docs/source/_autosummary/bioneuralnet.clustering.correlated_louvain.rst b/docs/source/_autosummary/bioneuralnet.clustering.correlated_louvain.rst index 8ba2fde..254c773 100644 --- a/docs/source/_autosummary/bioneuralnet.clustering.correlated_louvain.rst +++ b/docs/source/_autosummary/bioneuralnet.clustering.correlated_louvain.rst @@ -12,6 +12,7 @@ bioneuralnet.clustering.correlated\_louvain get_logger original_modularity pearsonr + set_seed .. rubric:: Classes diff --git a/docs/source/_autosummary/bioneuralnet.clustering.correlated_pagerank.rst b/docs/source/_autosummary/bioneuralnet.clustering.correlated_pagerank.rst index b0a1d84..e0a6834 100644 --- a/docs/source/_autosummary/bioneuralnet.clustering.correlated_pagerank.rst +++ b/docs/source/_autosummary/bioneuralnet.clustering.correlated_pagerank.rst @@ -10,6 +10,7 @@ bioneuralnet.clustering.correlated\_pagerank get_logger pearsonr + set_seed .. rubric:: Classes diff --git a/docs/source/_autosummary/bioneuralnet.clustering.hybrid_louvain.rst b/docs/source/_autosummary/bioneuralnet.clustering.hybrid_louvain.rst index d23b655..5428af5 100644 --- a/docs/source/_autosummary/bioneuralnet.clustering.hybrid_louvain.rst +++ b/docs/source/_autosummary/bioneuralnet.clustering.hybrid_louvain.rst @@ -9,6 +9,7 @@ bioneuralnet.clustering.hybrid\_louvain .. autosummary:: get_logger + set_seed .. rubric:: Classes diff --git a/docs/source/_autosummary/bioneuralnet.clustering.rst b/docs/source/_autosummary/bioneuralnet.clustering.rst index e142848..2eb3174 100644 --- a/docs/source/_autosummary/bioneuralnet.clustering.rst +++ b/docs/source/_autosummary/bioneuralnet.clustering.rst @@ -1,4 +1,4 @@ -bioneuralnet.clustering +bioneuralnet.clustering ======================= .. automodule:: bioneuralnet.clustering diff --git a/docs/source/_autosummary/bioneuralnet.datasets.rst b/docs/source/_autosummary/bioneuralnet.datasets.rst index 4d546e3..6a47c09 100644 --- a/docs/source/_autosummary/bioneuralnet.datasets.rst +++ b/docs/source/_autosummary/bioneuralnet.datasets.rst @@ -1,9 +1,20 @@ -bioneuralnet.datasets +bioneuralnet.datasets ===================== .. automodule:: bioneuralnet.datasets + .. rubric:: Functions + + .. autosummary:: + + load_brca + load_example + load_kipan + load_lgg + load_monet + load_paad + .. rubric:: Classes .. autosummary:: diff --git a/docs/source/_autosummary/bioneuralnet.downstream_task.dpmon.rst b/docs/source/_autosummary/bioneuralnet.downstream_task.dpmon.rst index 5e78517..740cc4d 100644 --- a/docs/source/_autosummary/bioneuralnet.downstream_task.dpmon.rst +++ b/docs/source/_autosummary/bioneuralnet.downstream_task.dpmon.rst @@ -8,29 +8,49 @@ bioneuralnet.downstream\_task.dpmon .. autosummary:: - build_omics_networks_tg + average_precision_score + f1_score get_logger + label_binarize + matthews_corrcoef + pointbiserialr + prepare_node_features + recall_score + roc_auc_score run_hyperparameter_tuning run_standard_training + set_seed setup_device slice_omics_datasets train_model + train_test_split .. rubric:: Classes .. autosummary:: ASHAScheduler - Autoencoder + AutoEncoder + BasicVariantGenerator CLIReporter Checkpoint DPMON - DimensionAveraging DownstreamTaskNN GAT GCN GIN + MLPProjection NeuralNetwork Path + RepeatedStratifiedKFold SAGE + ScalarProjection + StratifiedKFold + TrialPlateauStopper + + .. rubric:: Exceptions + + .. autosummary:: + + TuneError \ No newline at end of file diff --git a/docs/source/_autosummary/bioneuralnet.downstream_task.rst b/docs/source/_autosummary/bioneuralnet.downstream_task.rst index ab5776f..b91213a 100644 --- a/docs/source/_autosummary/bioneuralnet.downstream_task.rst +++ b/docs/source/_autosummary/bioneuralnet.downstream_task.rst @@ -1,4 +1,4 @@ -bioneuralnet.downstream\_task +bioneuralnet.downstream\_task ============================= .. automodule:: bioneuralnet.downstream_task diff --git a/docs/source/_autosummary/bioneuralnet.external_tools.rst b/docs/source/_autosummary/bioneuralnet.external_tools.rst index 761a07b..511ac7d 100644 --- a/docs/source/_autosummary/bioneuralnet.external_tools.rst +++ b/docs/source/_autosummary/bioneuralnet.external_tools.rst @@ -1,4 +1,4 @@ -bioneuralnet.external\_tools +bioneuralnet.external\_tools ============================ .. automodule:: bioneuralnet.external_tools diff --git a/docs/source/_autosummary/bioneuralnet.metrics.evaluation.rst b/docs/source/_autosummary/bioneuralnet.metrics.evaluation.rst deleted file mode 100644 index c7ca687..0000000 --- a/docs/source/_autosummary/bioneuralnet.metrics.evaluation.rst +++ /dev/null @@ -1,33 +0,0 @@ -bioneuralnet.metrics.evaluation -=============================== - -.. automodule:: bioneuralnet.metrics.evaluation - - - .. rubric:: Functions - - .. autosummary:: - - accuracy_score - evaluate_f1m - evaluate_f1w - evaluate_model - evaluate_rf - f1_score - get_logger - r2_score - train_test_split - - .. rubric:: Classes - - .. autosummary:: - - RandomForestClassifier - RandomForestRegressor - - .. rubric:: Exceptions - - .. autosummary:: - - DataConversionWarning - \ No newline at end of file diff --git a/docs/source/_autosummary/bioneuralnet.metrics.plot.rst b/docs/source/_autosummary/bioneuralnet.metrics.plot.rst index 9c674c4..1b62312 100644 --- a/docs/source/_autosummary/bioneuralnet.metrics.plot.rst +++ b/docs/source/_autosummary/bioneuralnet.metrics.plot.rst @@ -11,6 +11,7 @@ bioneuralnet.metrics.plot cast cluster_correlation compare_clusters + find_omics_modality get_logger plot_embeddings plot_multiple_metrics diff --git a/docs/source/_autosummary/bioneuralnet.metrics.rst b/docs/source/_autosummary/bioneuralnet.metrics.rst index 5a680d8..9db6f24 100644 --- a/docs/source/_autosummary/bioneuralnet.metrics.rst +++ b/docs/source/_autosummary/bioneuralnet.metrics.rst @@ -1,4 +1,4 @@ -bioneuralnet.metrics +bioneuralnet.metrics ==================== .. automodule:: bioneuralnet.metrics @@ -10,10 +10,6 @@ cluster_correlation compare_clusters - evaluate_f1m - evaluate_f1w - evaluate_model - evaluate_rf louvain_to_adjacency omics_correlation plot_embeddings @@ -31,5 +27,4 @@ :recursive: correlation - evaluation plot diff --git a/docs/source/_autosummary/bioneuralnet.network_embedding.gnn_models.rst b/docs/source/_autosummary/bioneuralnet.network_embedding.gnn_models.rst index 0fdb7ec..960d542 100644 --- a/docs/source/_autosummary/bioneuralnet.network_embedding.gnn_models.rst +++ b/docs/source/_autosummary/bioneuralnet.network_embedding.gnn_models.rst @@ -10,6 +10,7 @@ bioneuralnet.network\_embedding.gnn\_models get_activation process_dropout + set_seed .. rubric:: Classes diff --git a/docs/source/_autosummary/bioneuralnet.network_embedding.rst b/docs/source/_autosummary/bioneuralnet.network_embedding.rst index 0f0a1ca..d7680f6 100644 --- a/docs/source/_autosummary/bioneuralnet.network_embedding.rst +++ b/docs/source/_autosummary/bioneuralnet.network_embedding.rst @@ -1,4 +1,4 @@ -bioneuralnet.network\_embedding +bioneuralnet.network\_embedding =============================== .. automodule:: bioneuralnet.network_embedding diff --git a/docs/source/_autosummary/bioneuralnet.rst b/docs/source/_autosummary/bioneuralnet.rst new file mode 100644 index 0000000..b817ef4 --- /dev/null +++ b/docs/source/_autosummary/bioneuralnet.rst @@ -0,0 +1,45 @@ +bioneuralnet +============ + +.. automodule:: bioneuralnet + + + .. rubric:: Functions + + .. autosummary:: + + get_logger + load_brca + load_example + load_kipan + load_lgg + load_monet + load_paad + set_seed + + .. rubric:: Classes + + .. autosummary:: + + CorrelatedLouvain + CorrelatedPageRank + DPMON + DatasetLoader + GNNEmbedding + HybridLouvain + SmCCNet + SubjectRepresentation + +.. rubric:: Modules + +.. autosummary:: + :toctree: + :recursive: + + clustering + datasets + downstream_task + external_tools + metrics + network_embedding + utils diff --git a/docs/source/_autosummary/bioneuralnet.utils.data.rst b/docs/source/_autosummary/bioneuralnet.utils.data.rst index ff679f4..0eb167c 100644 --- a/docs/source/_autosummary/bioneuralnet.utils.data.rst +++ b/docs/source/_autosummary/bioneuralnet.utils.data.rst @@ -8,23 +8,10 @@ bioneuralnet.utils.data .. autosummary:: - beta_to_m correlation_summary explore_data_stats expression_summary get_logger - impute_omics - impute_omics_knn - normalize_omics - set_seed variance_summary zero_fraction_summary - - .. rubric:: Classes - - .. autosummary:: - - KNNImputer - MinMaxScaler - StandardScaler \ No newline at end of file diff --git a/docs/source/_autosummary/bioneuralnet.utils.graph.rst b/docs/source/_autosummary/bioneuralnet.utils.graph.rst index 4b470a7..bc34ddb 100644 --- a/docs/source/_autosummary/bioneuralnet.utils.graph.rst +++ b/docs/source/_autosummary/bioneuralnet.utils.graph.rst @@ -22,4 +22,5 @@ bioneuralnet.utils.graph .. autosummary:: GraphicalLasso + StandardScaler \ No newline at end of file diff --git a/docs/source/_autosummary/bioneuralnet.utils.graph_tools.rst b/docs/source/_autosummary/bioneuralnet.utils.graph_tools.rst new file mode 100644 index 0000000..7f0b1dd --- /dev/null +++ b/docs/source/_autosummary/bioneuralnet.utils.graph_tools.rst @@ -0,0 +1,29 @@ +bioneuralnet.utils.graph\_tools +=============================== + +.. automodule:: bioneuralnet.utils.graph_tools + + + .. rubric:: Functions + + .. autosummary:: + + cross_val_score + find_optimal_graph + gen_correlation_graph + gen_gaussian_knn_graph + gen_similarity_graph + gen_threshold_graph + get_logger + graph_analysis + repair_graph_connectivity + + .. rubric:: Classes + + .. autosummary:: + + ParameterGrid + RidgeClassifier + StandardScaler + StratifiedKFold + \ No newline at end of file diff --git a/docs/source/_autosummary/bioneuralnet.utils.preprocess.rst b/docs/source/_autosummary/bioneuralnet.utils.preprocess.rst index 1ea9552..fc8e387 100644 --- a/docs/source/_autosummary/bioneuralnet.utils.preprocess.rst +++ b/docs/source/_autosummary/bioneuralnet.utils.preprocess.rst @@ -8,13 +8,18 @@ bioneuralnet.utils.preprocess .. autosummary:: + beta_to_m clean_inf_nan + clean_internal f_classif f_regression get_logger + impute_omics + impute_omics_knn multipletests network_remove_high_zero_fraction network_remove_low_variance + normalize_omics preprocess_clinical prune_network prune_network_by_quantile @@ -27,7 +32,11 @@ bioneuralnet.utils.preprocess .. autosummary:: + KNNImputer + MinMaxScaler RandomForestClassifier RandomForestRegressor RobustScaler + SimpleImputer + StandardScaler \ No newline at end of file diff --git a/docs/source/_autosummary/bioneuralnet.utils.reproducibility.rst b/docs/source/_autosummary/bioneuralnet.utils.reproducibility.rst new file mode 100644 index 0000000..15075f6 --- /dev/null +++ b/docs/source/_autosummary/bioneuralnet.utils.reproducibility.rst @@ -0,0 +1,13 @@ +bioneuralnet.utils.reproducibility +================================== + +.. automodule:: bioneuralnet.utils.reproducibility + + + .. rubric:: Functions + + .. autosummary:: + + get_logger + set_seed + \ No newline at end of file diff --git a/docs/source/_autosummary/bioneuralnet.utils.rst b/docs/source/_autosummary/bioneuralnet.utils.rst index db9d9c3..e4493e0 100644 --- a/docs/source/_autosummary/bioneuralnet.utils.rst +++ b/docs/source/_autosummary/bioneuralnet.utils.rst @@ -1,4 +1,4 @@ -bioneuralnet.utils +bioneuralnet.utils ================== .. automodule:: bioneuralnet.utils @@ -13,6 +13,7 @@ correlation_summary explore_data_stats expression_summary + find_optimal_graph gen_correlation_graph gen_gaussian_knn_graph gen_lasso_graph @@ -21,6 +22,7 @@ gen_snn_graph gen_threshold_graph get_logger + graph_analysis impute_omics impute_omics_knn network_remove_high_zero_fraction @@ -30,6 +32,7 @@ prune_network prune_network_by_quantile rdata_to_df + repair_graph_connectivity select_top_k_correlation select_top_k_variance select_top_randomforest @@ -46,6 +49,8 @@ data graph + graph_tools logger preprocess rdata_convert + reproducibility diff --git a/docs/source/_static/BioNeuralNet.png b/docs/source/_static/BioNeuralNet.png index 342e99a..2e6f133 100644 Binary files a/docs/source/_static/BioNeuralNet.png and b/docs/source/_static/BioNeuralNet.png differ diff --git a/docs/source/_static/BioNeuralNet1-0.png b/docs/source/_static/BioNeuralNet1-0.png new file mode 100644 index 0000000..342e99a Binary files /dev/null and b/docs/source/_static/BioNeuralNet1-0.png differ diff --git a/docs/source/_static/bioneuralnet_api.png b/docs/source/_static/bioneuralnet_api.png new file mode 100644 index 0000000..5266a9e Binary files /dev/null and b/docs/source/_static/bioneuralnet_api.png differ diff --git a/docs/source/_static/emb_kipan.png 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a/docs/source/_static/rosmap_net2.png b/docs/source/_static/rosmap_net2.png new file mode 100644 index 0000000..324b627 Binary files /dev/null and b/docs/source/_static/rosmap_net2.png differ diff --git a/docs/source/clustering.rst b/docs/source/clustering.rst deleted file mode 100644 index ecd8fb6..0000000 --- a/docs/source/clustering.rst +++ /dev/null @@ -1,95 +0,0 @@ -Correlated Clustering -===================== - -BioNeuralNet provides **correlated clustering methods** designed specifically to identify biologically relevant communities within multi-omics networks. By integrating **phenotype correlations**, these approaches enhance traditional community detection methods, capturing biologically meaningful network modules strongly associated with clinical or phenotypic outcomes. - -Key Features ------------- -- **Phenotype-Aware Clustering**: Incorporates external phenotype information directly into clustering algorithms, resulting in communities that are both structurally cohesive and biologically meaningful. -- **Flexible Application**: Methods are applicable to any network data represented as adjacency matrices, facilitating diverse research scenarios including biomarker discovery and functional module identification. -- **Integration with Downstream Analysis**: Clusters obtained can directly feed into downstream tasks such as disease prediction, feature selection, and biomarker identification. - -Supported Clustering Methods ----------------------------- - -Correlated PageRank -------------------- -A variant of PageRank that biases node rankings toward phenotype-relevant nodes, prioritizing features with strong phenotype associations: - -.. math:: - - \mathbf{r} = \alpha \cdot \mathbf{M} \mathbf{r} + (1 - \alpha) \mathbf{p} - -- :math:`\mathbf{M}`: Normalized adjacency (transition probability matrix). -- :math:`\mathbf{p}`: Phenotype-informed personalization vector (based on correlation). -- Ideal for ranking biologically impactful nodes. - -Correlated Louvain ------------------- -Modifies Louvain community detection to balance structural modularity and phenotype correlation, optimizing: - -.. math:: - - Q^* = k_L \cdot Q + (1 - k_L) \cdot \overline{\lvert \rho \rvert} - -- :math:`Q`: Newman-Girvan modularity, measuring network structural cohesiveness. -- :math:`\overline{\lvert \rho \rvert}`: Mean absolute Pearson correlation between cluster features and phenotype. -- :math:`k_L`: User-defined parameter balancing structure and phenotype relevance. -- Efficient for identifying phenotype-enriched communities. - -Hybrid Louvain (Iterative Refinement) -------------------------------------- -Combines Correlated Louvain with Correlated PageRank iteratively to refine community assignments: - -1. Initial clustering using Correlated Louvain identifies phenotype-associated modules. -2. Clusters iteratively refined by expanding highly correlated modules using Correlated PageRank. -3. Repeated until convergence, producing optimized phenotype-associated communities. - -.. figure:: _static/hybrid_clustering.png - :align: center - :alt: Hybrid Clustering Workflow - - Workflow: Hybrid Louvain iteratively integrates Correlated PageRank and Correlated Louvain to produce refined phenotype-associated clusters. - -Comparison of Methods ---------------------- -The figure below illustrates the difference between standard and correlated clustering methods, highlighting BioNeuralNet's ability to extract biologically meaningful modules. - -.. figure:: _static/clustercorrelation.png - :align: center - :alt: Clustering Method Comparison - - Comparison: Standard (SmCCNet) versus Correlated Louvain clusters. - -Applications and Use Cases --------------------------- -BioNeuralNet correlated clustering is versatile and suitable for diverse network analyses: - -- **Multi-Omics Networks**: Extract biologically relevant gene/protein modules associated with clinical phenotypes. -- **Neuroimaging Networks**: Identify functional brain modules linked to neurological diseases. -- **Disease Networks**: Reveal patient or epidemiological network communities strongly linked to clinical outcomes. - -Integration into BioNeuralNet Workflow --------------------------------------- -Clustering outputs seamlessly feed into downstream BioNeuralNet modules: - -- **GNN Embedding Generation**: Train Graph Neural Networks on phenotype-enriched clusters. -- **Disease Prediction (DPMON)**: Utilize phenotype-associated modules for improved predictive accuracy. -- **Biomarker Discovery**: Extract features or modules strongly predictive of disease status. - -User Recommendations --------------------- -- **Correlated PageRank**: Best for prioritizing individual high-impact features or nodes. -- **Correlated Louvain**: Ideal for extracting phenotype-associated functional communities efficiently. -- **Hybrid Louvain**: Recommended for maximal biological interpretability, particularly in complex multi-omics scenarios. - -Reference and Further Reading ------------------------------ -For detailed methodology and benchmarking, refer to our publication: - -- Abdel-Hafiz et al., Frontiers in Big Data, 2022. [1]_ - -Return to :doc:`../index` - -.. [1] Abdel-Hafiz, M., Najafi, M., et al. "Significant Subgraph Detection in Multi-omics Networks for Disease Pathway Identification." *Frontiers in Big Data*, 5 (2022). DOI: `10.3389/fdata.2022.894632 `_. - diff --git a/docs/source/conf.py b/docs/source/conf.py index 81b4d90..66cfbf7 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -2,8 +2,13 @@ import sys from importlib import metadata +#sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '../..'))) sys.path.insert(0, os.path.abspath("../../")) +# CONF_DIR = os.path.dirname(__file__) +# PROJECT_ROOT = os.path.abspath(os.path.join(CONF_DIR, "..", "..")) +# sys.path.insert(0, PROJECT_ROOT) + try: release = metadata.version("bioneuralnet") except metadata.PackageNotFoundError: diff --git a/docs/source/datasets.rst b/docs/source/datasets.rst new file mode 100644 index 0000000..c2c715f --- /dev/null +++ b/docs/source/datasets.rst @@ -0,0 +1,331 @@ +Datasets Guide +============== + +BioNeuralNet ships with several built-in multi-omics datasets that can be loaded via convenience functions or through the :class:`bioneuralnet.datasets.DatasetLoader` class. + +Each dataset is loaded as a collection of :class:`pandas.DataFrame` objects: + +- Keys are table names (e.g., ``"rna"``, ``"mirna"``, ``"clinical"``, ``"target"``). +- Values are the corresponding data tables. + +In BioNeuralNet, **rows** represent subjects/patients and **columns** represent omics features or related variables. + +Feature Selection Summary +------------------------- + +Because omics data are typically **high-dimensional**, most built-in datasets use a filtering step based on both: + +- **ANOVA F-test**: To capture features with strong class separability under a linear model. +- **Random Forest importance**: To capture non-linear dependencies and interaction effects. + +We retain features in the **intersection** of these criteria, providing a compact set that is both discriminative and predictive. For a detailed mathematical description of this procedure, see :ref:`feature-selection-details` below. + +Example overlap for selected omics types: + +.. list-table:: Feature overlap across selection methods + :header-rows: 1 + + * - Omics Data Type + - ANOVA-F & Variance + - RF & Variance + - ANOVA-F & Random Forest (Selected) + - All Three Agree + * - Methylation + - 2,092 features + - 1,870 features + - **2,203 features** + - 814 features + * - RNA + - 2,359 features + - 2,191 features + - **2,500 features** + - 1,124 features + +Quick Usage +----------- + +You can use either the convenience loader functions or the lower-level +:class:`DatasetLoader`: + +.. code-block:: python + + from bioneuralnet.datasets import ( + load_example, + load_monet, + load_brca, + load_lgg, + load_kipan, + load_paad, + ) + + brca = load_brca() + print(brca.keys()) + # dict_keys(['mirna', 'target', 'clinical', 'rna', 'meth']) + + from bioneuralnet.datasets import DatasetLoader + + loader = DatasetLoader("kipan") + print(loader.shape) + # {'mirna': (658, 472), 'target': (658, 1), 'clinical': (658, 19), + # 'rna': (658, 2284), 'meth': (658, 2102)} + +API Summary +----------- + +Convenience loaders +^^^^^^^^^^^^^^^^^^^ + +The following functions are provided in `bioneuralnet.datasets`: + +.. code-block:: python + + from bioneuralnet.datasets import ( + load_example, + load_monet, + load_brca, + load_lgg, + load_kipan, + load_paad, + ) + +Each function returns a ``dict[str, pandas.DataFrame]`` mapping table names to loaded DataFrames: + +- :func:`load_example` keys: ``"X1"``, ``"X2"``, ``"Y"``, ``"clinical"`` +- :func:`load_monet` keys: ``"gene"``, ``"mirna"``, ``"phenotype"``, ``"rppa"``, ``"clinical"`` +- :func:`load_brca` keys: ``"mirna"``, ``"target"``, ``"clinical"``, ``"rna"``, ``"meth"`` +- :func:`load_lgg` keys: ``"mirna"``, ``"target"``, ``"clinical"``, ``"rna"``, ``"meth"`` +- :func:`load_kipan` keys: ``"mirna"``, ``"target"``, ``"clinical"``, ``"rna"``, ``"meth"`` +- :func:`load_paad` keys: ``"cnv"``, ``"target"``, ``"clinical"``, ``"rna"``, ``"meth"`` + +DatasetLoader +^^^^^^^^^^^^^ + +The :class:`DatasetLoader` class provides a unified interface to any of the built-in datasets: + +.. code-block:: python + + from bioneuralnet.datasets import DatasetLoader + + loader = DatasetLoader("paad") + data = loader.data + print(loader.shape) + # {'cnv': (177, 1035), 'target': (177, 1), 'clinical': (177, 19), + # 'rna': (177, 1910), 'meth': (177, 1152)} + +Valid ``dataset_name`` values (case-insensitive): + +- ``"example"`` +- ``"monet"`` +- ``"brca"`` +- ``"lgg"`` +- ``"kipan"`` +- ``"paad"`` + +The :pyattr:`DatasetLoader.shape ` property returns a mapping from table name to ``(n_rows, n_cols)`` for each loaded table. + +Built-in Datasets +----------------- + +Below are the built-in datasets exactly as defined in the :class:`bioneuralnet.datasets.DatasetLoader` implementation. + +example +^^^^^^^ + +Synthetic example dataset. + +Tables and shapes: + +- ``X1``: ``(358, 500)`` +- ``X2``: ``(358, 100)`` +- ``Y``: ``(358, 1)`` +- ``clinical``: ``(358, 6)`` + +Loaded via: + +.. code-block:: python + + from bioneuralnet.datasets import load_example + example = load_example() + +monet +^^^^^ + +MONET multi-omics dataset included with the package. + +Tables and shapes: + +- ``gene``: ``(107, 5039)`` +- ``mirna``: ``(107, 789)`` +- ``phenotype``: ``(106, 1)`` +- ``rppa``: ``(107, 175)`` +- ``clinical``: ``(107, 5)`` + +Loaded via: + +.. code-block:: python + + from bioneuralnet.datasets import load_monet + monet = load_monet() + +brca +^^^^ + +Breast Invasive Carcinoma (BRCA) dataset. + +Tables and shapes: + +- ``mirna``: ``(769, 503)`` +- ``target``: ``(769, 1)`` +- ``clinical``: ``(769, 103)`` +- ``rna``: ``(769, 2500)`` +- ``meth``: ``(769, 2203)`` + +Loaded via: + +.. code-block:: python + + from bioneuralnet.datasets import load_brca + brca = load_brca() + +lgg +^^^ + +Brain Lower Grade Glioma (LGG) dataset. + +Tables and shapes: + +- ``mirna``: ``(511, 548)`` +- ``target``: ``(511, 1)`` +- ``clinical``: ``(511, 13)`` +- ``rna``: ``(511, 2127)`` +- ``meth``: ``(511, 1823)`` + +Loaded via: + +.. code-block:: python + + from bioneuralnet.datasets import load_lgg + lgg = load_lgg() + +paad +^^^^ + +Pancreatic Adenocarcinoma (PAAD) dataset. + +Tables and shapes: + +- ``cnv``: ``(177, 1035)`` +- ``target``: ``(177, 1)`` +- ``clinical``: ``(177, 19)`` +- ``rna``: ``(177, 1910)`` +- ``meth``: ``(177, 1152)`` + +Loaded via: + +.. code-block:: python + + from bioneuralnet.datasets import load_paad + paad = load_paad() + +kipan +^^^^^ + +Pan-kidney cohort (KIPAN: KICH + KIRC + KIRP). + +Tables and shapes: + +- ``mirna``: ``(658, 472)`` +- ``target``: ``(658, 1)`` +- ``clinical``: ``(658, 19)`` +- ``rna``: ``(658, 2284)`` +- ``meth``: ``(658, 2102)`` + +Loaded via: + +.. code-block:: python + + from bioneuralnet.datasets import load_kipan + kipan = load_kipan() + + +.. _feature-selection-details: + +Feature Selection +----------------- + +To reduce the dimensionality of the high-feature omics datasets, we employed two complementary criteria: statistical class separation via ANOVA and model-based predictive utility via Random Forests. The final feature set was obtained from the intersection of these two methods, ensuring that retained variables exhibit both strong discriminative structure and demonstrable predictive value, a more conservative and robust strategy than relying on either criterion alone. + +**ANOVA F-test.** +For each feature :math:`x_j`, class separability was quantified using a one-way ANOVA. + +Let :math:`\bar{x}_{k,j}` denote the class means, :math:`\bar{x}_j` the global mean, and :math:`n_k` the class sizes. The between-class and within-class mean squares are + +.. math:: + + MS_{\text{between},j} + \;=\; + \frac{1}{K-1} + \sum_{k=1}^{K} n_k\bigl(\bar{x}_{k,j}-\bar{x}_j\bigr)^2, + +.. math:: + + MS_{\text{within},j} + \;=\; + \frac{1}{n-K} + \sum_{k=1}^{K} + \sum_{x_{ij}\in X_{k,j}} + \bigl(x_{ij}-\bar{x}_{k,j}\bigr)^2. + +The ANOVA statistic is then + +.. math:: + + F_j \;=\; \frac{MS_{\text{between},j}}{MS_{\text{within},j}}. + +Features were ranked by :math:`F_j` after Benjamini-Hochberg FDR correction. + +**Random Forest Importance.** +Predictive relevance was quantified using the mean decrease in Gini impurity. +A node :math:`t` with class proportions :math:`p_k(t)` has impurity + +.. math:: + + G(t) \;=\; 1 - \sum_{k=1}^K p_k(t)^2. + +A split on feature :math:`j` produces an impurity reduction + +.. math:: + + \Delta G(j,t) + \;=\; + G(t) + - + \Bigl( + \frac{N_{t_L}}{N_t} G(t_L) + + + \frac{N_{t_R}}{N_t} G(t_R) + \Bigr), + +and the forest-level importance is the average of :math:`\Delta G` over all trees: + +.. math:: + + I(j) + \;=\; + \frac{1}{B} + \sum_{b=1}^{B} + \sum_{t:\,\mathrm{feat}(t)=j} + \Delta G(j,t). + +**Consensus Selection.** +For methylation and RNA, the top :math:`6{,}000` ANOVA and Random Forest features were extracted, and their intersection was taken: + +.. math:: + + \mathcal{S}_{\text{final}} + \;=\; + \mathcal{S}_{\mathrm{ANOVA}} + \,\cap\, + \mathcal{S}_{\mathrm{RF}}. + +This approach retains features that are simultaneously statistically discriminative and useful to a non-linear classifier, yielding a stable and biologically meaningful subset. The lower-dimensional miRNA panel (472 features) was included in full. diff --git a/docs/source/index.rst b/docs/source/index.rst index 7495dc4..a1f19ec 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -50,128 +50,89 @@ Get started quickly with these end-to-end examples demonstrating the BioNeuralNe `View BioNeuralNet Workflow. `_ -Citation --------- +What is BioNeuralNet? +--------------------- -If you use BioNeuralNet in your research, we kindly ask that you cite our paper: +BioNeuralNet is a flexible and modular Python framework tailored for **end-to-end network-based multi-omics data analysis**. It leverages Graph Neural Networks (GNNs) to learn biologically meaningful low-dimensional representations from multi-omics networks, converting complex molecular interactions into versatile embeddings. - Ramos, V., Hussein, S., et al. (2025). - `BioNeuralNet: A Graph Neural Network based Multi-Omics Network Data Analysis Tool `_. - *arXiv preprint arXiv:2507.20440* | `DOI: 10.48550/arXiv.2507.20440 `_. +**Core Analytical Modules:** -For your convenience, you can use the following BibTeX entry: +- **Network Construction**: Build informative networks from raw tabular data using strategies like **Similarity**, **Correlation**, **Neighborhood-based**, or **Phenotype-driven** (e.g., SmCCNet) approaches. +- **Biomarker Discovery**: Identify biological modules and key molecular interactions that drive disease phenotypes. +- **Disease Prediction**: Implement end-to-end supervised disease classification using the **DPMON** (Disease Prediction using Multi-Omics Networks) module. +- **Subject Representation**: Generate enhanced subject-level embeddings for stratification and clustering. -.. code-block:: bibtex +**Visualizing Multi-Omics Networks** - @misc{ramos2025bioneuralnetgraphneuralnetwork, - title={BioNeuralNet: A Graph Neural Network based Multi-Omics Network Data Analysis Tool}, - author={Vicente Ramos and Sundous Hussein and Mohamed Abdel-Hafiz and Arunangshu Sarkar and Weixuan Liu and Katerina J. Kechris and Russell P. Bowler and Leslie Lange and Farnoush Banaei-Kashani}, - year={2025}, - eprint={2507.20440}, - archivePrefix={arXiv}, - primaryClass={cs.LG}, - url={https://arxiv.org/abs/2507.20440}, - doi={10.48550/arXiv.2507.20440} - } +BioNeuralNet allows you to inspect the topology of your constructed networks. The visualization below, from our **TCGA Kidney Cancer (KIPAN)** analysis, highlights a module of highly interacting genes and proteins. -What is BioNeuralNet? ---------------------- +.. figure:: _static/kipan_net.png + :align: center + :alt: Multi-Omics Network Visualization + :width: 90% + + *Network visualization of a highly connected gene module identified in the KIPAN dataset.* + +**Top Identified Biomarkers (Hub Omics)** -BioNeuralNet is a flexible, modular Python framework developed to facilitate end-to-end network-based multi-omics analysis using **Graph Neural Networks (GNNs)**. It addresses the complexities associated with multi-omics data, such as high dimensionality, sparsity, and intricate molecular interactions, by converting biological networks into meaningful, low-dimensional embeddings suitable for downstream tasks. +The table below lists the top hub features identified in the network above, ranked by their degree centrality. -BioNeuralNet provides: +.. list-table:: Omics with high degree + :widths: 50 25 + :header-rows: 1 + :align: center -- **Network Construction**: Easily build informative networks from multi-omics datasets to capture biologically relevant molecular interactions. -- **GNN Embeddings**: Transform complex biological networks into versatile embeddings, capturing both structural relationships and molecular interactions. -- **Phenotype-Aware Analysis**: Integrate phenotype or clinical variables to enhance the biological relevance of the embeddings. -- **Disease Prediction**: Utilize network-derived embeddings for accurate and scalable predictive modeling of diseases and phenotypes. -- **Interoperability**: Outputs structured as **Pandas DataFrames**, ensuring compatibility with common Python tools and seamless integration into existing bioinformatics pipelines. + * - Feature Name (Omic) + - Degree + * - INPP5J_27124 + - 12 + * - SLC26A7_115111 + - 9 + * - HEPACAM2_253012 + - 7 + * - CLNK_116449 + - 7 + * - RHCG_51458 + - 6 + * - CLCNKB_1188 + - 6 -BioNeuralNet emphasizes usability, reproducibility, and adaptability, making advanced network-based multi-omics analyses accessible to researchers working in precision medicine and systems biology. Why Graph Neural Networks for Multi-Omics? ------------------------------------------ -Traditional machine learning methods often struggle with the complexity and high dimensionality of multi-omics data, particularly their inability to effectively capture intricate molecular interactions and dependencies. BioNeuralNet overcomes these limitations by using **graph neural networks (GNNs)**, which naturally encode biological structures and relationships. - -BioNeuralNet supports several state-of-the-art GNN architectures optimized for biological applications: +Traditional statistical methods typically represent multi-omics data as high-dimensional tabular matrices, often overlooking the intricate relationships and interactions between biomolecular entities. BioNeuralNet overcomes these limitations by using **Graph Neural Networks (GNNs)** to explicitly model multi-omics data as biological networks. -- **Graph Convolutional Networks (GCN)**: Aggregate biological signals from neighboring molecules, effectively modeling local interactions such as gene co-expression or regulatory relationships. -- **Graph Attention Networks (GAT)**: Use attention mechanisms to dynamically prioritize important molecular interactions, highlighting the most biologically relevant connections. -- **GraphSAGE**: Facilitate inductive learning, enabling the model to generalize embeddings to previously unseen molecular data, thereby enhancing predictive power and scalability. -- **Graph Isomorphism Networks (GIN)**: Provide powerful and expressive graph embeddings, accurately distinguishing subtle differences in molecular interaction patterns. +BioNeuralNet supports several GNN architectures suited to different biological contexts: -For detailed explanations of BioNeuralNet's supported GNN architectures and their biological relevance, see :doc:`gnns`. +* **GCN**: Effective for uniformly connected graphs. +* **GAT**: Uses attention mechanisms to highlight key biological relationships. +* **GraphSAGE**: Designed for large or dynamic datasets. +* **GIN**: Sensitive to subtle feature variations in molecular structures. -Example: Network-Based Multi-Omics Analysis for Disease Prediction ------------------------------------------------------------------- +**Network Embeddings** -`View full-size image: Network-Based Multi-Omics Analysis for Disease Prediction `_ +By projecting high-dimensional omics networks into latent spaces, BioNeuralNet distills complex, nonlinear molecular relationships into compact vectorized representations. The t-SNE projection below reveals distinct clusters corresponding to different omics modalities (e.g., DNA Methylation, RNA, miRNA). -.. figure:: _static/Overview.png +.. figure:: _static/emb_kipan.png :align: center - :alt: BioNeuralNet's workflow for network-based multi-omics analysis + :alt: t-SNE visualization of Network Embeddings + :width: 90% - **BioNeuralNet Workflow**: Network-Based Multi-Omics Analysis for Disease Prediction + *2D projection of Network Embeddings showing distinct separation between omics modalities.* -Below is a concise example demonstrating the following key steps: +For detailed explanations of BioNeuralNet's supported GNN architectures, see :doc:`gnns`. -1. **Data Preparation**: - - - Load your multi-omics data (e.g., transcriptomics, proteomics) along with phenotype and clinical covariates. +Key Considerations for Robust Analysis +-------------------------------------- -2. **Network Construction**: - - - Here, we construct the multi-omics network using an external R package, **SmCCNet** [1]_. - - BioNeuralNet provides convenient wrappers for external tools (like SmCCNet) through `bioneuralnet.external_tools`. Note: R must be installed for these wrappers. +To ensure scientific rigor and optimal performance when using BioNeuralNet, users should consider the following best practices: -3. **Disease Prediction with DPMON**: - - - **DPMON** [2]_ integrates omics data and network structures to predict disease phenotypes. - - It provides an end-to-end pipeline, complete with built-in hyperparameter tuning, and outputs predictions directly as pandas DataFrames for easy interoperability. - -**Example Usage**: - -.. code-block:: python - - import pandas as pd - from bioneuralnet.external_tools import SmCCNet - from bioneuralnet.downstream_task import DPMON - from bioneuralnet.datasets import DatasetLoader - - # Step 1: Load the dataset and access individual omics modalities - example = DatasetLoader("example") - omics_genes = example.data["X1"] - omics_proteins = example.data["X2"] - phenotype = example.data["Y"] - clinical = example.data["clinical"] - - # Step 2: Network Construction with SmCCNet - smccnet = SmCCNet( - phenotype_df=phenotype, - omics_dfs=[omics_genes, omics_proteins], - data_types=["Genes", "Proteins"], - kfold=5, - summarization="PCA", - ) - global_network, clusters = smccnet.run() - print("Adjacency matrix generated.") - - # Step 3: Disease Prediction using DPMON - dpmon = DPMON( - adjacency_matrix=global_network, - omics_list=[omics_genes, omics_proteins], - phenotype_data=phenotype, - clinical_data=clinical, - model="GCN", - repeat_num=5, - tune=True, - gpu=True, - cuda=0, - output_dir="./output" - ) - predictions, avg_accuracy = dpmon.run() - print("Disease phenotype predictions:\n", predictions) +* **Network Topology Sensitivity**: Performance is inherently tied to the quality of the constructed network. We recommend comparing multiple network construction strategies (e.g., Correlation vs Similarity) within BioNeuralNet to benchmark different topologies. +* **Feature Selection Impact**: Results depend heavily on input features. BioNeuralNet supports various preselection strategies (Top-k Variance, ANOVA-F, Random Forest), which often yield complementary biological insights. +* **Handling Missing Data**: Incomplete multi-omics data is common. BioNeuralNet provides utilities like ``impute_omics`` and ``impute_omics_knn`` to handle missing values within modalities. +* **Computational Scalability**: While optimized for standard omics datasets, extremely large networks may require aggressive feature reduction or subgraph detection strategies to maintain computational efficiency. +* **Interpretability Scope**: BioNeuralNet provides network-level interpretability (identifying key modules and hub features). However, full node-level explainability remains an active area of research. Explore BioNeuralNet's Documentation ------------------------------------ @@ -184,7 +145,7 @@ For detailed examples and tutorials, visit: **Documentation Sections:** - :doc:`gnns`: Overview of supported GNN architectures (GCN, GAT, GraphSAGE, GIN) and embedding generation. -- :doc:`clustering`: How to identify biologically relevant functional modules using correlated clustering methods. +- :doc:`subgraph`: How to identify biologically relevant functional modules using correlated clustering methods. - :doc:`downstream_tasks`: Performing downstream analyses such as subject representation and phenotype prediction (DPMON). - :doc:`metrics`: Methods for visualization, quality evaluation, and performance benchmarking. - :doc:`utils`: Tools for preprocessing, feature selection, network construction, and data summarization. @@ -217,6 +178,14 @@ We welcome contributions to BioNeuralNet! If you have ideas for new features, im pre-commit install pytest --cov=bioneuralnet +Citation +-------- + +If you use BioNeuralNet in your research, we kindly ask that you cite our paper: + + Ramos, V., Hussein, S., et al. (2025). + `BioNeuralNet: A Graph Neural Network based Multi-Omics Network Data Analysis Tool `_. + *arXiv preprint arXiv:2507.20440* | `DOI: 10.48550/arXiv.2507.20440 `_. .. toctree:: :maxdepth: 2 @@ -225,11 +194,13 @@ We welcome contributions to BioNeuralNet! If you have ideas for new features, im installation gnns - clustering + subgraph metrics utils downstream_tasks + datasets Quick_Start.ipynb + quick_start_bio notebooks/index examples/index external_tools/index @@ -244,4 +215,4 @@ Indices and References * :ref:`search` .. [1] Liu, W., Vu, T., Konigsberg, I. R., Pratte, K. A., Zhuang, Y., & Kechris, K. J. (2023). "Network-Based Integration of Multi-Omics Data for Biomarker Discovery and Phenotype Prediction." *Bioinformatics*, 39(5), btat204. DOI: `10.1093/bioinformatics/btat204 `_. -.. [2] Hussein, S., Ramos, V., et al. "Learning from Multi-Omics Networks to Enhance Disease Prediction: An Optimized Network Embedding and Fusion Approach." In *2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)*, Lisbon, Portugal, 2024, pp. 4371-4378. DOI: `10.1109/BIBM62325.2024.10822233 `_. +.. [2] Hussein, S., Ramos, V., et al. "Learning from Multi-Omics Networks to Enhance Disease Prediction: An Optimized Network Embedding and Fusion Approach." In *2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)*, Lisbon, Portugal, 2024, pp. 4371-4378. DOI: `10.1109/BIBM62325.2024.10822233 `_. \ No newline at end of file diff --git a/docs/source/installation.rst b/docs/source/installation.rst index d22b588..5679c19 100644 --- a/docs/source/installation.rst +++ b/docs/source/installation.rst @@ -3,6 +3,29 @@ Installation BioNeuralNet is fully compatible with Python 3.10, 3.11 and 3.12 and supports Windows, macOS, and Linux platforms. Follow these steps to install BioNeuralNet along with necessary dependencies. +Recommended System Requirements +------------------------------- + +.. list-table:: Recommended system requirements for running BioNeuralNet. + :header-rows: 1 + + * - Component + - Recommendation + * - Operating System + - Linux, macOS, or Windows + * - Python Version + - 3.10 - 3.12 + * - Processor (CPU) + - Modern multi-core CPU (Intel i5/i7, AMD Ryzen 5/7); for large datasets/models: high-core CPUs like Intel Xeon or AMD EPYC + * - Memory (RAM) + - Minimum 8GB; 16GB recommended for standard workflows; 32GB+ for large-scale models + * - Storage + - Minimum 64GB for system and software; SSD recommended; 500GB+ for large datasets + * - GPU (Optional but recommended) + - NVIDIA GPU with `>=` 6GB VRAM or Intel Arc GPU for faster training; ensure drivers and CUDA/cuDNN versions match PyTorch + * - Notes + - GPU acceleration significantly improves training speed; hardware requirements increase with model and dataset size + 1. **Install BioNeuralNet via pip** The core modules, including graph embeddings, disease prediction (DPMON), and clustering, can be installed directly: @@ -11,22 +34,31 @@ BioNeuralNet is fully compatible with Python 3.10, 3.11 and 3.12 and supports Wi pip install bioneuralnet -2. **Install PyTorch and PyTorch Geometric (Required)** + This will also install the required Ray components for hyperparameter tuning (Ray Tune and Ray Train) if your environment honors package extras. + +2. **Install PyTorch and PyTorch Geometric (Required for GNNs and DPMON)** + + BioNeuralNet utilizes PyTorch and PyTorch Geometric for graph neural network computations (e.g., GNN embeddings and the DPMON model). These are **not** pinned automatically because GPU/CPU builds and CUDA versions vary by system, so you should install them separately. - BioNeuralNet utilizes PyTorch and PyTorch Geometric for graph neural network computations. Install these separately: + Basic installation (CPU-only or default build): .. code-block:: bash pip install torch pip install torch_geometric - For GPU-enabled installations or advanced configurations, please refer to the official documentation: + We recommend the following tested versions for DPMON and GNN-based workflows: + + .. code-block:: bash + + pip install "torch==2.7.0+cu118" + pip install "torch_geometric==2.6.1" + + For GPU-enabled installations or advanced configurations, please refer to the official documentation and choose the appropriate build based on your system and GPU availability: - `PyTorch Installation Guide `_ - `PyTorch Geometric Installation Guide `_ - Choose the appropriate build based on your system and GPU availability. - .. figure:: _static/pytorch.png :align: center :alt: PyTorch Installation Guide Example @@ -35,7 +67,38 @@ BioNeuralNet is fully compatible with Python 3.10, 3.11 and 3.12 and supports Wi :align: center :alt: PyTorch Geometric Installation Guide Example -3. **Optional: Install R and External Tools (e.g., SmCCNet)** +3. **Ray for Hyperparameter Tuning (DPMON and Other HPO Workflows)** + + BioNeuralNet uses `Ray Tune` and `Ray Train` for hyperparameter optimization and checkpointing in components such as DPMON. + + If you installed BioNeuralNet via pip in a standard environment, Ray with the necessary extras should be installed automatically. For reproducibility or if your environment strips extras, you can explicitly install the tested Ray version: + + .. code-block:: bash + + pip install "ray[tune,train]==2.46.0" + + This ensures compatibility with the hyperparameter tuning utilities used throughout the library. + +4. **Core Python Dependencies** + + BioNeuralNet is built on a standard scientific Python stack. While the installer handles these automatically, we are transparent about the specific versions used in our testing environment: + + .. code-block:: text + + pandas = 2.2.3 + numpy = 1.26.4 + scipy = 1.13.1 + matplotlib = 3.10.3 + scikit-learn = 1.6.1 + statsmodels = 0.14.4 + networkx = 3.4.2 + python-louvain = 0.16 + pydantic >= 2.5 + ray[tune,train] = 2.46.0 + + *Note: The installer is flexible. It will attempt to install these tested versions (or compatible newer releases) to ensure stability, but will not strictly force them if your environment requires slight deviations.* + +5. **Optional: Install R and External Tools (e.g., SmCCNet)** For advanced network construction using external R tools like **SmCCNet**, follow these additional steps: @@ -54,6 +117,7 @@ BioNeuralNet is fully compatible with Python 3.10, 3.11 and 3.12 and supports Wi See :doc:`external_tools/index` for further details on external tools. + Next Steps ---------- @@ -61,4 +125,3 @@ After installation, explore our step-by-step tutorials: - :doc:`notebooks/index` - :doc:`Quick_Start` - diff --git a/docs/source/metrics.rst b/docs/source/metrics.rst index 0162a8a..8edcb07 100644 --- a/docs/source/metrics.rst +++ b/docs/source/metrics.rst @@ -1,153 +1,148 @@ -Metrics -======= +Metrics and Visualization +========================= -The metrics module includes functions that perform statistical analyses and generate visualizations. Functions from the metrics module allow users to assess and explore network-based embeddings, evaluate clustering performance, and explore the tabular data and network structure. -The following examples and plots are from our :doc:`Quick_Start`. +The ``metrics`` module provides a suite of tools for statistical analysis, quality control, and visualization. These functions allow users to assess data variance, explore network structures, evaluate clustering concordance, and visualize downstream performance metrics. -Visualization -------------- +To see the full code used to generate the plots below (specifically for the Kidney Cancer analysis), refer to our **TCGA KIPAN Biomarker Discovery** tutorial in :doc:`notebooks/index`. -The module also contains several plotting functions: +.. contents:: Table of Contents + :local: + :depth: 2 -- :func:`bioneuralnet.metrics.plot.plot_network` displays the network graph from an adjacency matrix. -- :func:`bioneuralnet.metrics.plot.plot_embeddings` visualizes high-dimensional embeddings by projecting them into a 2 dimensional space. +Performance Evaluation +---------------------- -Example Usage -------------- +BioNeuralNet provides specialized plotting functions to compare model performance (e.g., Accuracy, F1-Score) across different experiments. -**Network Visualization** -Visualization of network showing labels but no edge weights. +* :func:`bioneuralnet.metrics.plot_multiple_metrics`: Visualizes multiple metrics on the same figure for comprehensive benchmarking. +* :func:`bioneuralnet.metrics.plot_performance`: Plots a standard performance comparison. +* :func:`bioneuralnet.metrics.plot_performance_three`: Compares three distinct metric sets side-by-side. -.. image:: _static/plot_network.png - :align: center - :alt: Network Visualization of Louvain Clusters exampleOne - -**Table Output:** - -+-------+-----------+--------+ -| Index | Omic | Degree | -+=======+===========+========+ -| 11 | Gene_411 | 6 | -+-------+-----------+--------+ -| 16 | Gene_7 | 5 | -+-------+-----------+--------+ -| 24 | Gene_174 | 5 | -+-------+-----------+--------+ -| 3 | Gene_1 | 4 | -+-------+-----------+--------+ -| 28 | Gene_114 | 4 | -+-------+-----------+--------+ - -Visualization of network showing labels and edge weights. - -.. image:: _static/plot_network2.png - :align: center - :alt: Network Visualization of Louvain Clusters exampleTwo - -**Table Output:** - -+--------+-----------+--------+ -| Index | Omic | Degree | -+========+===========+========+ -| 1 | Gene_7 | 4 | -+--------+-----------+--------+ -| 2 | Gene_6 | 4 | -+--------+-----------+--------+ -| 3 | Gene_1 | 4 | -+--------+-----------+--------+ -| 4 | Gene_446 | 4 | -+--------+-----------+--------+ -| 5 | Gene_53 | 4 | -+--------+-----------+--------+ - -.. code-block:: python +**Example: Benchmarking GNNs vs Baselines** - from bioneuralnet.metrics import plot_network - from bioneuralnet.metrics import louvain_to_adjacency +The following plots demonstrate a comprehensive benchmarking analysis from our **TCGA KIPAN** case study, comparing BioNeuralNet GNN architectures (GIN, GraphSAGE, GAT, GCN) against standard baseline models across six different performance metrics. - cluster1 = hybrid_result[0] - cluster2 = hybrid_result[1] +.. image:: _static/metrics_plot_1.png + :align: center + :alt: GNNs vs Baselines: Accuracy and F1 Scores - # Convert Louvain clusters into adjacency matrices - louvain_adj1 = louvain_to_adjacency(cluster1) - louvain_adj2 = louvain_to_adjacency(cluster2) +.. image:: _static/metrics_plot_2.png + :align: center + :alt: GNNs vs Baselines: Recall, AUC, and AUPR - # Plot using the converted adjacency matrices - cluster1_mapping = plot_network(louvain_adj1, weight_threshold=0.1, show_labels=True, show_edge_weights=False) - print(cluster1_mapping.head()) - cluster2_mapping = plot_network(louvain_adj2, weight_threshold=0.01, show_labels=True, show_edge_weights=True) - print(cluster2_mapping.head()) +For implementation details and parameters, refer to the API documentation: :func:`bioneuralnet.metrics.plot_multiple_metrics`. +Embedding Visualization +----------------------- -**Embeddings Visualization** +:func:`bioneuralnet.metrics.plot_embeddings` visualizes high-dimensional embeddings (e.g., from GCN or GAT models) by projecting them into a 2-dimensional space using t-SNE or UMAP. -Visualizing a 16 dimension embedding space projected into a 2 dimensional space. +The example below shows embeddings from the **TCGA KIPAN** dataset, revealing distinct clusters corresponding to different omics modalities (DNA Methylation, RNA, miRNA). -.. image:: _static/embeddings.png +.. image:: _static/emb_kipan.png :align: center - :alt: 2D t-SNE Projection of Embeddings + :alt: 2D Projection of Embeddings from KIPAN Analysis .. code-block:: python from bioneuralnet.metrics import plot_embeddings - # Using our embeddings instance, we get the necessary labels for the graph. - node_labels = gnn._prepare_node_labels() + # Retrieve embeddings from the trained GNN model embeddings_array = embeddings.values - embeddings_plot = plot_embeddings(embeddings_array, node_labels) + + # Prepare labels (e.g., Omics Type or Phenotype) for coloring + node_labels = gnn._prepare_node_labels() + + # Generate t-SNE projection + plot_embeddings(embeddings_array, node_labels, title="Embeddings in 2D space") + +Network Visualization & Clustering +---------------------------------- -Correlation Metrics -------------------- +These functions allow you to inspect the topology of your constructed networks and the modules identified by clustering algorithms. -- :func:`bioneuralnet.metrics.correlation.cluster_correlation` computes the Pearson correlation for a cluster of features with a phenotype. Clusters with fewer than two features or with zero variance are handled properly. +Network Graph +~~~~~~~~~~~~~ -**Cluster Comparison** -Hybrid louvain with the SmCCNet clusters. +:func:`bioneuralnet.metrics.plot_network` displays the network graph from an adjacency matrix. It allows for filtering edges by weight to focus on strong connections (hubs). -.. image:: _static/clusters.png +.. image:: _static/kipan_net.png :align: center - :alt: clusters Graph Comparison + :alt: Network Visualization of Hub Genes in KIPAN + :width: 80% .. code-block:: python - # Lets compare hytbrid louvain with the SmCCNet clusters - print("Number of clusters:", len(hybrid_result)) + from bioneuralnet.metrics import plot_network, louvain_to_adjacency - compare_clusters(hybrid_result, clusters, phenotype, merged_omics) + # Convert identified clusters (e.g., from Hybrid Louvain) to adjacency + louvain_adj = louvain_to_adjacency(cluster_result) -Evaluation ----------- + # Visualize the top connected module, filtering weak edges + plot_network( + louvain_adj, + weight_threshold=0.7, + show_labels=True, + show_edge_weights=True + ) -- :func:`bioneuralnet.metrics.evaluation.evaluate_rf` trains and evaluates a Random Forest model over multiple runs. -- :func:`bioneuralnet.metrics.plot.plot_multiple_metrics` plots multiple metrics on the same figure. +Cluster Comparison +~~~~~~~~~~~~~~~~~~ -**Plotting Multiple Performance Metrics** -Visualizing performance metrics comparison. +:func:`bioneuralnet.metrics.compare_clusters` allows for the visual and statistical comparison of different clustering results (e.g., comparing Hybrid Louvain results against SmCCNet results). -.. image:: _static/performance.png - :align: center - :alt: plot of multiple performance metrics +.. code-block:: python + + from bioneuralnet.metrics import compare_clusters + + # Compare Hybrid Louvain results with SmCCNet clusters + compare_clusters(hybrid_result, smccnet_clusters, phenotype, merged_omics) + + +Correlation & Data Quality +-------------------------- + +These utilities help validate input data quality and quantify relationships between features and phenotypes. + +Feature & Cluster Correlation +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +* :func:`bioneuralnet.metrics.omics_correlation`: Computes correlation between raw omics features. +* :func:`bioneuralnet.metrics.cluster_correlation`: Computes the Pearson correlation for a specific cluster of features against a phenotype. .. code-block:: python - from bioneuralnet.metrics import evaluate_rf, plot_multiple_metrics + from bioneuralnet.metrics import cluster_correlation + + # Calculate correlation of a specific cluster with the phenotype + score = cluster_correlation(cluster_features, phenotype_data) + +Data Variance +~~~~~~~~~~~~~ + +BioNeuralNet includes tools to inspect the variance within your omics datasets, which is crucial for feature selection. + +* :func:`bioneuralnet.metrics.plot_variance_distribution`: Plots the distribution of variances across all features. +* :func:`bioneuralnet.metrics.plot_variance_by_feature`: Visualizes variance for individual features, helping to identify low-information features. + +.. code-block:: python + + from bioneuralnet.metrics import plot_variance_distribution, plot_variance_by_feature + + # Visualize the distribution of variance in the dataset + plot_variance_distribution(omics_df) - # raw omics evaluation - X_raw = merged_omics.values - y_global = phenotype.values - rf_acc, rd_f1w, rf_f1m = evaluate_rf(X_raw, y_global, n_estimators=100, runs=5, mode="classification") + # Inspect specific feature variances to guide filtering + plot_variance_by_feature(omics_df, top_n=20) - # metrics dictionary - metrics = { - "Accuracy": {"Raw": rf_acc,"DPMON": dpmon_acc_tuple}, - "F1-Weighted": {"Raw": rd_f1w,"DPMON": dpmon_f1w_tuple}, - "F1-Macro": {"Raw": rf_f1m,"DPMON": dpmon_f1m_tuple} - } - plot_multiple_metrics(metrics) +Utilities +--------- +* :func:`bioneuralnet.metrics.louvain_to_adjacency`: A helper function that converts the output of Louvain clustering algorithms into an adjacency matrix format suitable for plotting with ``plot_network``. -Further Information -------------------- +Reference +--------- -For more details on each function and its parameters, see the metrics reference: https://bioneuralnet.readthedocs.io/en/latest/_autosummary/bioneuralnet.metrics.html +For more details on each function and its parameters, see the full API reference: +.. `bioneuralnet.metrics API `_ \ No newline at end of file diff --git a/docs/source/notebooks/TCGA-BRCA.ipynb b/docs/source/notebooks/TCGA-BRCA.ipynb index a421a92..819feaa 100644 --- a/docs/source/notebooks/TCGA-BRCA.ipynb +++ b/docs/source/notebooks/TCGA-BRCA.ipynb @@ -5,138 +5,65 @@ "id": "d182cc95", "metadata": {}, "source": [ - "# TCGA-BRCA Demo\n", + "# TCGA-BRCA Analysis: *Cancer Subtype* classification\n", "\n", - "## Dataset Source\n", + "### Dataset Source\n", "\n", "- **Omics Data**: [FireHose BRCA](http://firebrowse.org/?cohort=BRCA)\n", "- **Clinical and PAM50 Data**: [TCGAbiolinks](http://bioconductor.org/packages/release/bioc/html/TCGAbiolinks.html)\n", "\n", - "## Dataset Overview\n", + "##### Dataset Overview\n", "\n", - "**Original Data**:\n", + "- **Original Data**:\n", "\n", - "- **Methylation**: 20,107 × 885\n", - "- **mRNA**: 18,321 × 1,212\n", - "- **miRNA**: 503 × 1,189\n", - "- **PAM50**: 1,087 × 1\n", - "- **Clinical**: 1,098 × 101\n", + " - **Methylation**: 20,107 × 885\n", + " - **mRNA**: 18,321 × 1,212\n", + " - **miRNA**: 503 × 1,189\n", + " - **PAM50**: 1,087 × 1\n", + " - **Clinical**: 1,098 × 101\n", "\n", - "- **Note: Omics matrices are features × samples; clinical matrices are samples × fields.**\n", + "- **PAM50 Subtype Counts**:\n", "\n", - "**PAM50 Subtype Counts**:\n", + " - **LumA**: 419\n", + " - **LumB**: 140\n", + " - **Basal**: 130\n", + " - **Her2**: 46\n", + " - **Normal**: 34\n", "\n", - "- **LumA**: 419\n", - "- **LumB**: 140\n", - "- **Basal**: 130\n", - "- **Her2**: 46\n", - "- **Normal**: 34\n", - "\n", - "## Patients in Every Dataset\n", + "### Patients in Every Dataset\n", "\n", "- Total patients present in methylation, mRNA, miRNA, PAM50, and clinical: **769**\n", "\n", - "## Final Shapes (Per-Patient)\n", + "### Final Shapes (Per-Patient)\n", "\n", "After aggregating multiple aliquots by mean, all modalities align on 769 patients:\n", "\n", - "- **Methylation**: 769 × 20,107\n", + "- **Methylation**: 769 × 20,106\n", "- **mRNA**: 769 × 20,531\n", "- **miRNA**: 769 × 503\n", "- **PAM50**: 769 × 1\n", "- **Clinical**: 769 × 119\n", "\n", - "## Data Summary Table\n", + "### Data Summary Table\n", "\n", "| Stage | Clinical | Methylation | miRNA | mRNA | PAM50 (Subtype Counts) | Notes |\n", "| ------------------------------ | ----------- | ------------ | ----------- | -------------- | -------------------------------------------------------------- | --------------------------------------- |\n", "| **Original Raw Data** | 1,098 × 101 | 20,107 × 885 | 503 × 1,189 | 18,321 × 1,212 | LumA: 509
LumB: 209
Basal: 192
Her2: 82
Normal: 40 | Raw FireHose & TCGAbiolinks files |\n", "| **Patient-Level Intersection** | 769 × 101 | 769 × 20,107 | 769 × 1,046 | 769 × 20,531 | LumA: 419
LumB: 140
Basal: 130
Her2: 46
Normal: 34 | Patients with complete data in all sets |\n", "\n", - "## Reference Links\n", + "### Reference Links\n", "\n", "- [FireHose BRCA](http://firebrowse.org/?cohort=BRCA)\n", "- [TCGAbiolinks](http://bioconductor.org/packages/release/bioc/html/TCGAbiolinks.html)\n", "- [Direct Download BRCA](http://firebrowse.org/?cohort=BRCA&download_dialog=true)\n" ] }, - { - "cell_type": "markdown", - "id": "c9698b74", - "metadata": {}, - "source": [ - "## Raw Data Overview\n", - "\n", - "Let's take a look at the data from FireHose directly after download:\n", - "\n", - "Some of the first things we noticed were:\n", - "\n", - "- **Different sample sizes** for each data type\n", - "- **Multi-index structure** in some datasets\n", - "- **Presence of NaN values**, especially in clinical data\n", - "\n", - "**Dataset Shapes:**\n", - "- `mirna` shape: **(503, 1189)**\n", - "- `rna` shape: **(18321, 1212)**\n", - "- `meth` shape: **(20107, 885)**\n", - "- `clinical` shape: **(18, 1097)**\n", - "\n", - "**Additional Notes:**\n", - "- `mirna`, `rna`, and `meth` use gene names as index and patient/sample IDs as columns.\n", - "- `meth` and `clinical` datasets include metadata rows (e.g., \"Beta_Value\", \"value\") as part of a multi-index.\n", - "- `clinical` data contains missing values (e.g., in \"days_to_death\"), which will require preprocessing." - ] - }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "9c0bda23", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "mirna shape: (503, 1189), rna shape: (18321, 1212), meth shape: (20107, 885), clinical shape: (18, 1097)\n", - " TCGA-3C-AAAU-01 TCGA-3C-AALI-01 ... TCGA-E2-A10E-01 TCGA-E2-A10F-01\n", - "gene ... \n", - "hsa-let-7a-1 13.129765 12.918069 ... 14.060268 12.990403\n", - "hsa-let-7a-2 14.117933 13.922300 ... 15.047592 14.006035\n", - "hsa-let-7a-3 13.147714 12.913194 ... 14.074978 13.018659\n", - "hsa-let-7b 14.595135 14.512657 ... 16.370741 15.439239\n", - "hsa-let-7c 8.414890 9.646536 ... 10.885520 11.385638\n", - "\n", - "[5 rows x 1189 columns]\n", - " TCGA-3C-AAAU-01 TCGA-3C-AALI-01 ... TCGA-Z7-A8R5-01 TCGA-Z7-A8R6-01\n", - "gene ... \n", - "?|100133144 4.032489 3.211931 ... 1.178747 2.783771\n", - "?|100134869 3.692829 4.119273 ... 2.866572 4.631075\n", - "?|10357 5.704604 6.124231 ... 6.410173 7.388457\n", - "?|10431 8.672694 9.139279 ... 10.155173 9.970921\n", - "?|155060 10.213110 9.011343 ... 7.977670 7.894918\n", - "\n", - "[5 rows x 1212 columns]\n", - " TCGA-3C-AAAU-01 TCGA-3C-AALI-01 ... TCGA-Z7-A8R5-01 TCGA-Z7-A8R6-01\n", - "Hybridization REF ... \n", - "Composite Element REF Beta_Value Beta_Value ... Beta_Value Beta_Value\n", - "A1BG 0.483716119676 0.637191226131 ... 0.617859586161 0.568150149265\n", - "A1CF 0.295827203492 0.458972998571 ... 0.691835387189 0.224696596211\n", - "A2BP1 0.187699869591 0.240515847704 ... 0.522169978143 0.33955834608\n", - "A2LD1 0.62958551322 0.666272288675 ... 0.791229999577 0.637764188841\n", - "\n", - "[5 rows x 885 columns]\n", - " tcga-5l-aat0 tcga-5l-aat1 ... tcga-xx-a89a tcga-z7-a8r6\n", - "Hybridization REF ... \n", - "Composite Element REF value value ... value value\n", - "years_to_birth 42 63 ... 68 46\n", - "vital_status 0 0 ... 0 0\n", - "days_to_death NaN NaN ... NaN NaN\n", - "days_to_last_followup 1477 1471 ... 488 3256\n", - "\n", - "[5 rows x 1097 columns]\n" - ] - } - ], + "outputs": [], "source": [ "import pandas as pd\n", "from pathlib import Path\n", @@ -148,11 +75,17 @@ "clinical_raw = pd.read_csv(root / \"BRCA.clin.merged.picked.txt\",sep=\"\\t\", index_col=0, low_memory=False)\n", "\n", "# display all shapes and first few rows of each dataset\n", - "print(f\"mirna shape: {mirna_raw.shape}, rna shape: {rna_raw.shape}, meth shape: {meth_raw.shape}, clinical shape: {clinical_raw.shape}\")\n", - "print(mirna_raw.head())\n", - "print(rna_raw.head())\n", - "print(meth_raw.head())\n", - "print(clinical_raw.head())" + "display(mirna_raw.iloc[:3,:5])\n", + "display(mirna_raw.shape)\n", + "\n", + "display(rna_raw.iloc[:3,:5])\n", + "display(rna_raw.shape)\n", + "\n", + "display(meth_raw.iloc[:3,:5])\n", + "display(meth_raw.shape)\n", + "\n", + "display(clinical_raw.iloc[:3,:5])\n", + "display(clinical_raw.shape)" ] }, { @@ -193,1140 +126,954 @@ ] }, { - "cell_type": "markdown", - "id": "3505b6b8", + "cell_type": "code", + "execution_count": null, + "id": "5a6eb71e", "metadata": {}, + "outputs": [], "source": [ - "## Preprocessing: Phase 1\n", + "pam50 = pd.read_csv(root /\"BRCA_PAM50_labels.csv\",index_col=0)\n", + "clinical_biolinks = pd.read_csv(root /\"BRCA_clinical_data.csv\",index_col=1)\n", "\n", - "- Loaded raw data from FireHose and TCGABiolinks\n", - "- Transposed `mirna`, `meth`, and `rna` to have samples as rows\n", - "- Standardized sample IDs (e.g., trimmed barcodes, uppercased indices)\n", - "- Aligned clinical data from both sources and merged them\n", - "- Filtered to patients present in all datasets" + "display(pam50.iloc[:5,:5])\n", + "display(pam50.shape)\n", + "display(clinical_biolinks.iloc[:5,:5])\n", + "display(clinical_biolinks.shape)" + ] + }, + { + "cell_type": "markdown", + "id": "fd066d50", + "metadata": {}, + "source": [ + "## Data Processing Summary\n", + "\n", + "- **Transpose Data**: All raw data (miRNA, RNA, etc.) is flipped so rows represent patients and columns represent features.\n", + "- **Standardize Patient IDs**: Patient IDs in all tables are cleaned to the 12-character TCGA format (e.g., `TCGA-AB-1234`) for matching.\n", + "- **Handle Duplicates**: Duplicate patient rows are averaged in the omics data. The first entry is kept for duplicate patients in the clinical data.\n", + "- **Impute Missing Values (KNN)**: Missing data (NaNs) in the omics datasets are estimated and filled using **K-Nearest Neighbors (KNN)** imputation.\n", + "- **Find Common Patients**: The script identifies the largest common cohort of patients that exist in all datasets.\n", + "- **Subset Data**: All data tables are filtered down to only this common list of patients, ensuring perfect alignment.\n", + "- **Extract Target**: The **PAM50 subtype** column is pulled from the corresponding data table to be used as the final prediction target (y-variable)." ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "128f63dd", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Initial shapes\n", - "meth: (20107, 885)\n", - "rna: (18321, 1212)\n", - "mirna: (503, 1189)\n", - "pam50: (1087, 1)\n", - "clinical TCGABioLinks: (1098, 101)\n", - "clinical FireHose: (1097, 18)\n", - "\n", - "After tranpose\n", - "meth: (885, 20107)\n", - "rna: (1212, 18321)\n", - "mirna: (1189, 503)\n", - "Patients in both clinical datasets: 1097\n", - "Combined Clinical shape (1097, 119)\n", - "Patients in every dataset: 769\n", - "\n", - "Final shapes:\n", - "meth: (863, 20107)\n", - "rna: (865, 18321)\n", - "mirna: (855, 503)\n", - "pam50: (769, 1)\n", - "clinical: (769, 119)\n", - "\n" - ] - } - ], + "outputs": [], "source": [ - "import pandas as pd\n", + "import bioneuralnet as bnn\n", "\n", - "# from Firehose\n", - "mirna = pd.read_csv(root/\"BRCA.miRseq_RPKM_log2.txt\", sep=\"\\t\",index_col=0,low_memory=False)\n", - "meth = pd.read_csv(root/\"BRCA.meth.by_mean.data.txt\", sep='\\t',index_col=0,low_memory=False) \n", - "rna = pd.read_csv(root / \"BRCA.uncv2.mRNAseq_RSEM_normalized_log2.txt\", sep=\"\\t\",index_col=0,low_memory=False)\n", - "clinical_firehose = pd.read_csv(root / \"BRCA.clin.merged.picked.txt\",sep=\"\\t\", index_col=0, low_memory=False).T\n", + "meth = meth_raw.T\n", + "rna = rna_raw.T\n", + "mirna = mirna_raw.T\n", + "clinical_firehose = clinical_raw.T\n", "\n", - "# from TCGABiolinks\n", - "pam50 = pd.read_csv(root /\"BRCA_PAM50_labels.csv\",index_col=0)\n", - "clinical_biolinks = pd.read_csv(root /\"BRCA_clinical_data.csv\",index_col=1)\n", + "print(f\"miRNA (samples, features): {mirna.shape}\")\n", + "print(f\"RNA (samples, features): {rna.shape}\")\n", + "print(f\"Methylation (samples, features): {meth.shape}\")\n", + "print(f\"Clinical (samples, features): {clinical_firehose.shape}\")\n", "\n", - "print(\"Initial shapes\")\n", - "print(f\"meth: {meth.shape}\")\n", - "print(f\"rna: {rna.shape}\")\n", - "print(f\"mirna: {mirna.shape}\")\n", - "print(f\"pam50: {pam50.shape}\")\n", - "print(f\"clinical TCGABioLinks: {clinical_biolinks.shape}\")\n", - "print(f\"clinical FireHose: {clinical_firehose.shape}\")\n", + "def trim_barcode(idx):\n", + " return idx.to_series().str.slice(0, 12)\n", "\n", - "meth = meth.T\n", - "rna = rna.T\n", - "mirna = mirna.T\n", + "# standardize patient IDs across all files\n", + "meth.index = trim_barcode(meth.index)\n", + "rna.index = trim_barcode(rna.index)\n", + "mirna.index = trim_barcode(mirna.index)\n", + "clinical_firehose.index = clinical_firehose.index.str.upper()\n", + "clinical_firehose.index.name = \"Patient_ID\"\n", "\n", - "print(\"\\nAfter tranpose\")\n", - "print(f\"meth: {meth.shape}\")\n", - "print(f\"rna: {rna.shape}\")\n", - "print(f\"mirna: {mirna.shape}\")\n", + "meth = meth.apply(pd.to_numeric, errors='coerce')\n", + "meth = meth.drop(columns=[\"Composite Element REF\"], errors=\"ignore\")\n", "\n", - "def trim(idx):\n", - " return idx.to_series().str.extract(r'(^TCGA-\\w\\w-\\w\\w\\w\\w)')[0]\n", + "rna = rna.apply(pd.to_numeric, errors='coerce')\n", + "mirna = mirna.apply(pd.to_numeric, errors='coerce')\n", "\n", - "meth.index = trim(meth.index)\n", - "rna.index = trim(rna.index)\n", - "mirna.index = trim(mirna.index)\n", - "pam50.index = pam50.index.str.upper()\n", - "clinical_biolinks.index = clinical_biolinks.index.str.upper()\n", - "clinical_firehose.index = clinical_firehose.index.str.upper()\n", + "meth = meth.groupby(meth.index).mean()\n", + "rna = rna.groupby(rna.index).mean()\n", + "mirna = mirna.groupby(mirna.index).mean()\n", "\n", - "idx1 = clinical_biolinks.index\n", - "idx2 = clinical_firehose.index\n", + "clinical = clinical_firehose[~clinical_firehose.index.duplicated(keep='first')]\n", "\n", - "# intersection and unique counts\n", - "common = idx1.intersection(idx2)\n", - "only_in_1 = idx1.difference(idx2)\n", - "only_in_2 = idx2.difference(idx1)\n", + "for df in [meth, rna, mirna]:\n", + " df.columns = df.columns.str.replace(r\"\\?\", \"unknown_\", regex=True)\n", + " df.columns = df.columns.str.replace(r\"\\|\", \"_\", regex=True)\n", + " df.columns = df.columns.str.replace(\"-\", \"_\", regex=False)\n", + " df.columns = df.columns.str.replace(r\"_+\", \"_\", regex=True)\n", + " df.columns = df.columns.str.strip(\"_\")\n", + "\n", + "print(f\"\\nMethylation shape: {meth.shape}\")\n", + "print(f\"RNA shape: {rna.shape}\")\n", + "print(f\"miRNA shape: {mirna.shape}\")\n", + "print(f\"Clinical shape: {clinical.shape}\")\n", + "\n", + "meth.columns = pd.Index(meth.columns.tolist())\n", + "rna.columns = pd.Index(rna.columns.tolist())\n", + "mirna.columns = pd.Index(mirna.columns.tolist())\n", "\n", - "print(f\"Patients in both clinical datasets: {len(common)}\")\n", - "common = clinical_biolinks.index.intersection(clinical_firehose.index)\n", - "clinical_biolinks = clinical_biolinks.loc[common]\n", - "clinical_firehose = clinical_firehose.loc[common]\n", + "# impute missing values using KNN on the finalized omics data\n", + "meth = bnn.utils.data.impute_omics_knn(meth, n_neighbors=5)\n", + "rna = bnn.utils.data.impute_omics_knn(rna, n_neighbors=5)\n", + "mirna = bnn.utils.data.impute_omics_knn(mirna, n_neighbors=5)\n", "\n", + "# handle duplicate patient IDs in clinical data (keeping first occurrence)\n", + "clinical_biolinks = clinical_biolinks[~clinical_biolinks.index.duplicated(keep='first')]\n", + "clinical_firehose = clinical_firehose[~clinical_firehose.index.duplicated(keep='first')]\n", + "\n", + "# patients common to both clinical sources and merge\n", + "common_clinical_patients = clinical_biolinks.index.intersection(clinical_firehose.index)\n", + "clinical_biolinks = clinical_biolinks.loc[common_clinical_patients]\n", + "clinical_firehose = clinical_firehose.loc[common_clinical_patients]\n", "clinical = pd.concat([clinical_biolinks, clinical_firehose], axis=1)\n", + "clinical.index.name = \"Patient_ID\"\n", + "\n", "\n", - "print(f\"Combined Clinical shape {clinical.shape}\")\n", + "# final list of patients present in ALL datasets\n", + "common_patients = sorted(\n", + " set(meth.index) & \n", + " set(rna.index) & \n", + " set(mirna.index) & \n", + " set(pam50.index) & \n", + " set(clinical.index)\n", + ")\n", "\n", - "common = sorted(set(meth.index) & set(rna.index) & set(mirna.index) & set(pam50.index) & set(clinical.index))\n", - "print(f\"Patients in every dataset: {len(common)}\")\n", + "print(f\"\\nFound: {len(common_patients)} patients across all data types.\")\n", "\n", - "meth = meth.loc[common]\n", - "rna = rna.loc[common]\n", - "mirna = mirna.loc[common]\n", - "pam50 = pam50.loc[common]\n", - "clinical = clinical.loc[common]\n", + "meth = meth.loc[common_patients]\n", + "rna = rna.loc[common_patients]\n", + "mirna = mirna.loc[common_patients]\n", + "pam50 = pam50.loc[common_patients]\n", + "clinical = clinical.loc[common_patients]\n", + "\n", + "\n", + "targets = pam50['BRCA_Subtype_PAM50'] \n", "\n", "print(\"\\nFinal shapes:\")\n", "print(f\"meth: {meth.shape}\")\n", "print(f\"rna: {rna.shape}\")\n", "print(f\"mirna: {mirna.shape}\")\n", "print(f\"pam50: {pam50.shape}\")\n", - "print(f\"clinical: {clinical.shape}\\n\")" + "print(f\"clinical: {clinical.shape}\")\n", + "print(f\"targets: {targets.shape}\")" ] }, { - "cell_type": "markdown", - "id": "32ba4b2c", + "cell_type": "code", + "execution_count": null, + "id": "ee114802", "metadata": {}, + "outputs": [], "source": [ - "## Handling Multiple Aliquots per Sample\n", - "\n", - "To ensure each patient appears only once across datasets:\n", - "\n", - "- Identified and counted patients with multiple aliquots in `meth`, `rna`, and `mirna`\n", - "- Converted all data to numeric (with coercion for errors)\n", - "- Aggregated duplicate rows by computing the mean per patient\n", - "- Aligned all datasets to retain only shared patients across `meth`, `rna`, `mirna`, `pam50`, and `clinical`\n", - "\n", - "**Duplicate summary:**\n", - "- meth: 91 patients with multiple aliquots (94 extra rows)\n", - "- rna: 93 patients (96 extra rows)\n", - "- mirna: 84 patients (86 extra rows)\n", - "\n", - "**Final shapes after aggregation and filtering:**\n", - "- meth: (769, 20107)\n", - "- rna: (769, 18321)\n", - "- mirna: (769, 503)\n", - "- pam50: (769, 1)\n", - "- clinical: (769, 119)" + "# drop unwanted columns from clinical data\n", + "clinical.drop(columns=[\"Composite Element REF\"], errors=\"ignore\", inplace=True)\n", + "\n", + "# we transform the methylation beta values to M-values and drop unwanted columns\n", + "meth_m = meth.drop(columns=[\"Composite Element REF\"], errors=\"ignore\")\n", + "\n", + "# convert beta values to M-values using bioneuralnet utility with small epsilon to avoid log(0)\n", + "meth_m = bnn.utils.beta_to_m(meth_m, eps=1e-6) \n", + "\n", + "# lastly we turn the target labels into numerical classes\n", + "mapping_brca = {\n", + " 'LumA': 0, \n", + " 'Her2': 1, \n", + " 'LumB': 2, \n", + " 'Basal': 3, \n", + " 'Normal': 4\n", + "}\n", + "target_labels = targets.map(mapping_brca).to_frame(name=\"target\")\n", + "\n", + "X_meth = meth_m.loc[targets.index]\n", + "X_rna = rna.loc[targets.index]\n", + "X_mirna = mirna.loc[targets.index]\n", + "Y_labels = target_labels.loc[targets.index]\n", + "clinical_processed = clinical.loc[targets.index]\n" ] }, { "cell_type": "code", - "execution_count": 4, - "id": "b841497a", + "execution_count": null, + "id": "4f35bd67", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "meth:\n", - "patients with >1 aliquot: 91\n", - "total duplicate rows: 94\n", - "\n", - "rna:\n", - "patients with >1 aliquot: 93\n", - "total duplicate rows: 96\n", - "\n", - "mirna:\n", - "patients with >1 aliquot: 84\n", - "total duplicate rows: 86\n", - "\n", - "Post-aggregation shapes:\n", - "meth: (769, 20107)\n", - "rna: (769, 18321)\n", - "mirna: (769, 503)\n", - "Patients in every dataset: 769\n", - "\n", - "Final shapes\n", - "meth: (769, 20107)\n", - "rna: (769, 18321)\n", - "mirna: (769, 503)\n", - "pam50: (769, 1)\n", - "clinical:(769, 119)\n" - ] - } - ], + "outputs": [], "source": [ - "for name, df in [(\"meth\", meth), (\"rna\", rna), (\"mirna\", mirna)]:\n", - " counts = df.index.value_counts()\n", - " n_multiple = (counts > 1).sum()\n", - " total_duplicates = counts[counts > 1].sum() - n_multiple\n", - " \n", - " print(f\"{name}:\")\n", - " print(f\"patients with >1 aliquot: {n_multiple}\")\n", - " print(f\"total duplicate rows: {total_duplicates}\\n\")\n", + "display(X_meth.iloc[:3,:5])\n", + "display(X_meth.shape)\n", "\n", - "meth = meth.apply(pd.to_numeric, errors=\"coerce\")\n", - "rna = rna .apply(pd.to_numeric, errors=\"coerce\")\n", - "mirna = mirna.apply(pd.to_numeric, errors=\"coerce\")\n", - "\n", - "meth = meth.groupby(level=0).mean()\n", - "rna = rna.groupby(level=0).mean()\n", - "mirna = mirna.groupby(level=0).mean()\n", - "\n", - "# Now each has one row per patient\n", - "print(\"Post-aggregation shapes:\")\n", - "print(f\"meth: {meth.shape}\")\n", - "print(f\"rna: {rna.shape}\")\n", - "print(f\"mirna: {mirna.shape}\")\n", + "display(X_rna.iloc[:3,:5])\n", + "display(X_rna.shape)\n", "\n", - "common = sorted( set(meth.index) & set(rna.index) & set(mirna.index)& set(pam50.index) & set(clinical.index) )\n", - "print(f\"Patients in every dataset: {len(common)}\")\n", + "display(X_mirna.iloc[:3,:5])\n", + "display(X_mirna.shape)\n", "\n", - "meth = meth.loc[common]\n", - "rna = rna.loc[common]\n", - "mirna = mirna.loc[common]\n", - "pam50 = pam50.loc[common]\n", - "clinical = clinical.loc[common]\n", + "display(clinical_processed.iloc[:3,:5])\n", + "display(clinical_processed.shape)\n", "\n", - "print(\"\\nFinal shapes\")\n", - "print(f\"meth: {meth.shape}\")\n", - "print(f\"rna: {rna.shape}\")\n", - "print(f\"mirna: {mirna.shape}\")\n", - "print(f\"pam50: {pam50.shape}\")\n", - "print(f\"clinical:{clinical.shape}\")" + "display(Y_labels.value_counts())\n" ] }, { "cell_type": "markdown", - "id": "9d8dac23", + "id": "09265512", "metadata": {}, "source": [ - "## Review Data:\n", - "\n", - "After preprocessing (phase 1), the datasets are aligned and filtered to include only patients present in all sources.\n", - "\n", - "**Sample views:**\n", - "- `meth`: Methylation data with 20,107 features\n", - "- `rna`: Gene expression data with 18,321 features\n", - "- `mirna`: miRNA expression with 503 features\n", - "- `clinical`: Demographic and clinical information with 119 columns\n", - "- `pam50`: Subtype distribution \n", - " - LumA: 419 \n", - " - LumB: 140 \n", - " - Basal: 130 \n", - " - Her2: 46 \n", - " - Normal: 34\n", - "\n", - "All datasets now share a consistent set of sample ids." + "## Feature Selection Methodology for BRCA\n", + "\n", + "### Supported Methods and Interpretation\n", + "\n", + "BioNeuralNet provides three techniques for feature selection, allowing for different views of the data's statistical profile:\n", + "\n", + "* Variance Thresholding: Identifies features with the highest overall variance across all samples.\n", + "\n", + "* ANOVA F-test: Pinpoints features that best distinguish between the target classes (LumA, LumB, Her2, Basal, and Normal).\n", + "\n", + " Random Forest Importance: Assesses feature utility based on its contribution to a predictive non-linear model.\n", + "\n", + "### BRCA Cohort Selection Strategy\n", + "\n", + "A dimensionality reduction step was essential for managing the high-feature-count omics data, given the complexity of the BRCA network:\n", + "\n", + "* High-Feature Datasets: Both DNA Methylation (20,106 features) and RNA (18,321 features) required significant feature reduction.\n", + "\n", + "* Filtering Process: The top 6,000 features were initially extracted from the Methylation and RNA datasets using all three methods.\n", + "\n", + "* Final Set: A consensus set was built by finding the intersection of features selected by the ANOVA F-test and Random Forest Importance, ensuring both statistical relevance and model-based utility.\n", + "\n", + "* Low-Feature Datasets: The miRNA data (503 features) was passed through without selection, as its feature count was already manageable." ] }, { "cell_type": "code", - "execution_count": 5, - "id": "4f35bd67", + "execution_count": null, + "id": "fa70dcca", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Hybridization REF Composite Element REF A1BG ... psiTPTE22 tAKR\n", - "0 ... \n", - "TCGA-3C-AAAU NaN 0.483716 ... 0.247304 0.506404\n", - "TCGA-3C-AALI NaN 0.637191 ... 0.163022 0.623865\n", - "TCGA-3C-AALJ NaN 0.656092 ... 0.252328 0.504451\n", - "TCGA-3C-AALK NaN 0.615194 ... 0.471956 0.682468\n", - "TCGA-4H-AAAK NaN 0.612080 ... 0.314877 0.744877\n", - "\n", - "[5 rows x 20107 columns]\n", - "gene ?|100133144 ?|100134869 ... ZZZ3|26009 psiTPTE22|387590\n", - "0 ... \n", - "TCGA-3C-AAAU 4.032489 3.692829 ... 10.205129 0.785174\n", - "TCGA-3C-AALI 3.211931 4.119273 ... 8.667973 9.855788\n", - "TCGA-3C-AALJ 3.538886 3.206237 ... 8.992994 5.143969\n", - "TCGA-3C-AALK 3.595671 3.469873 ... 9.453001 6.057699\n", - "TCGA-4H-AAAK 2.775430 3.850979 ... 9.784147 7.548699\n", - "\n", - "[5 rows x 18321 columns]\n", - "gene hsa-let-7a-1 hsa-let-7a-2 ... hsa-mir-99a hsa-mir-99b\n", - "0 ... \n", - "TCGA-3C-AAAU 13.129765 14.117933 ... 7.024602 15.506461\n", - "TCGA-3C-AALI 12.918069 13.922300 ... 7.885299 13.626182\n", - "TCGA-3C-AALJ 13.012033 14.010002 ... 7.580704 15.013822\n", - "TCGA-3C-AALK 13.144697 14.141721 ... 10.031619 14.554783\n", - "TCGA-4H-AAAK 13.411684 14.413518 ... 10.078201 14.650338\n", - "\n", - "[5 rows x 503 columns]\n", - " project synchronous_malignancy ... race ethnicity\n", - "TCGA-3C-AAAU TCGA-BRCA No ... white not hispanic or latino\n", - "TCGA-3C-AALI TCGA-BRCA No ... black or african american not hispanic or latino\n", - "TCGA-3C-AALJ TCGA-BRCA No ... black or african american not hispanic or latino\n", - "TCGA-3C-AALK TCGA-BRCA No ... black or african american not hispanic or latino\n", - "TCGA-4H-AAAK TCGA-BRCA No ... white not hispanic or latino\n", - "\n", - "[5 rows x 119 columns]\n", - "BRCA_Subtype_PAM50\n", - "LumA 419\n", - "LumB 140\n", - "Basal 130\n", - "Her2 46\n", - "Normal 34\n", - "Name: count, dtype: int64\n" - ] - } - ], + "outputs": [], "source": [ - "print(meth.head())\n", - "print(rna.head())\n", - "print(mirna.head())\n", - "print(clinical.head())\n", - "print(pam50.value_counts())" + "import bioneuralnet as bnn\n", + "\n", + "# feature selection\n", + "meth_highvar = bnn.utils.select_top_k_variance(X_meth, k=6000)\n", + "rna_highvar = bnn.utils.select_top_k_variance(X_rna, k=6000)\n", + "\n", + "meth_af = bnn.utils.top_anova_f_features(X_meth, Y_labels, max_features=6000)\n", + "rna_af = bnn.utils.top_anova_f_features(X_rna, Y_labels, max_features=6000)\n", + "\n", + "meth_rf = bnn.utils.select_top_randomforest(X_meth, Y_labels, top_k=6000)\n", + "rna_rf = bnn.utils.select_top_randomforest(X_rna, Y_labels, top_k=6000)\n", + "\n", + "meth_var_set = set(meth_highvar.columns)\n", + "meth_anova_set = set(meth_af.columns)\n", + "meth_rf_set = set(meth_rf.columns)\n", + "\n", + "rna_var_set = set(rna_highvar.columns)\n", + "rna_anova_set = set(rna_af.columns)\n", + "rna_rf_set = set(rna_rf.columns)\n", + "\n", + "meth_inter1 = list(meth_anova_set & meth_var_set)\n", + "meth_inter2 = list(meth_rf_set & meth_var_set)\n", + "meth_inter3 = list(meth_anova_set & meth_rf_set)\n", + "meth_all_three = list(meth_anova_set & meth_var_set & meth_rf_set)\n", + "\n", + "rna_inter4 = list(rna_anova_set & rna_var_set)\n", + "rna_inter5 = list(rna_rf_set & rna_var_set)\n", + "rna_inter6 = list(rna_anova_set & rna_rf_set)\n", + "rna_all_three = list(rna_anova_set & rna_var_set & rna_rf_set)" ] }, { - "cell_type": "markdown", - "id": "17f7d599", + "cell_type": "code", + "execution_count": null, + "id": "cc981cdb", "metadata": {}, + "outputs": [], "source": [ - "## Preprocessing: Phase 2\n", - "\n", - "After reviewing the data, we applied the following steps to prepare it for downstream analysis.\n", - "\n", - "1. **Methylation (B -> M-value)**\n", - " - Clip B-values to \\[E, 1-E] and apply logit transform: M = log_2(B / (1-B)).\n", - " - Drop the original `Composite Element REF` column.\n", - "\n", - "2. **mRNA & miRNA:**\n", - " - Already in log_2 scale (RSEM normalized and RPKM).\n", - "\n", - "3. **Quality Control:**\n", - " - Count samples with all-zero rows in each modality.\n", - " - Compute NaN counts post-transformation, then replace all NaNs with 0.\n", - "\n", - "4. **Column Name Cleaning:**\n", - " - Replace all `-` and `|` characters with `_`.\n", - " - Replace `?` with `unknown`.\n", - "\n", - "5. **Label Encoding:**\n", - " - Map `PAM50` subtypes to integers: \n", - " - Normal = 0\n", - " - Basal = 1 \n", - " - Her2 = 2\n", - " - LumA = 3\n", - " - LumB = 4\n", - "\n", - "6. **Alignment & Aggregation:**\n", - " - Trim barcodes to patient level.\n", - " - Aggregate duplicate aliquots by mean per patient.\n", - " - Drop the `project` column from clinical.\n", - " - Subset all tables to the common patient set (no missing or all-zero samples).\n", - " - Set up a commong index across all files.\n", - "\n", - "7. **Final Output Shapes:**\n", - " - Methylation M-value: 769 × 20,107\n", - " - mRNA (log_2): 769 × 20,531\n", - " - miRNA (log_2): 769 × 503\n", - " - PAM50 labels: 769 × 1\n", - " - Clinical covariates: 769 × 101" + "print(\"FROM THE 6000 Methylation feature selection:\\n\")\n", + "print(f\"Anova-F & variance selection share: {len(meth_inter1)} features\")\n", + "print(f\"Random Forest & variance selection share: {len(meth_inter2)} features\")\n", + "print(f\"Anova-F & Random Forest share: {len(meth_inter3)} features\")\n", + "print(f\"All three methods agree on: {len(meth_all_three)} features\")" ] }, { "cell_type": "code", - "execution_count": 6, - "id": "5bb6450e", + "execution_count": null, + "id": "da639dd6", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "All zeros: meth: 0, rna: 0, mirna: 0\n", - "nan_meth: 0, nan_rna: 0, nan_mirna: 0, nan_clinical: 0, nan_pam50: 0\n", - "NaN counts after filling:\n", - "0 0 0 46476 0\n", - "new shapes: meth: (769, 20106), rna: (769, 18321), mirna: (769, 503), pam50: (769, 1), clinical: (769, 118)\n", - "Hybridization REF A1BG A1CF ... psiTPTE22 tAKR\n", - "patient ... \n", - "TCGA-3C-AAAU -0.094004 -1.251175 ... -1.605783 0.036955\n", - "TCGA-3C-AALI 0.812517 -0.237291 ... -2.360128 0.729981\n", - "TCGA-3C-AALJ 0.931878 -0.059301 ... -1.567104 0.025686\n", - "TCGA-3C-AALK 0.676913 0.741678 ... -0.162004 1.103860\n", - "TCGA-4H-AAAK 0.657963 0.044649 ... -1.121575 1.545812\n", - "\n", - "[5 rows x 20106 columns]\n", - "gene unknown_100133144 unknown_100134869 ... ZZZ3_26009 psiTPTE22_387590\n", - "patient ... \n", - "TCGA-3C-AAAU 4.032489 3.692829 ... 10.205129 0.785174\n", - "TCGA-3C-AALI 3.211931 4.119273 ... 8.667973 9.855788\n", - "TCGA-3C-AALJ 3.538886 3.206237 ... 8.992994 5.143969\n", - "TCGA-3C-AALK 3.595671 3.469873 ... 9.453001 6.057699\n", - "TCGA-4H-AAAK 2.775430 3.850979 ... 9.784147 7.548699\n", - "\n", - "[5 rows x 18321 columns]\n", - "gene hsa_let_7a_1 hsa_let_7a_2 ... hsa_mir_99a hsa_mir_99b\n", - "patient ... \n", - "TCGA-3C-AAAU 13.129765 14.117933 ... 7.024602 15.506461\n", - "TCGA-3C-AALI 12.918069 13.922300 ... 7.885299 13.626182\n", - "TCGA-3C-AALJ 13.012033 14.010002 ... 7.580704 15.013822\n", - "TCGA-3C-AALK 13.144697 14.141721 ... 10.031619 14.554783\n", - "TCGA-4H-AAAK 13.411684 14.413518 ... 10.078201 14.650338\n", - "\n", - "[5 rows x 503 columns]\n", - " synchronous_malignancy ajcc_pathologic_stage ... race ethnicity\n", - "patient ... \n", - "TCGA-3C-AAAU No Stage X ... white not hispanic or latino\n", - "TCGA-3C-AALI No Stage IIB ... black or african american not hispanic or latino\n", - "TCGA-3C-AALJ No Stage IIB ... black or african american not hispanic or latino\n", - "TCGA-3C-AALK No Stage IA ... black or african american not hispanic or latino\n", - "TCGA-4H-AAAK No Stage IIIA ... white not hispanic or latino\n", - "\n", - "[5 rows x 118 columns]\n", - "pam50\n", - "3 419\n", - "4 140\n", - "1 130\n", - "2 46\n", - "0 34\n", - "Name: count, dtype: int64\n" - ] - } - ], + "outputs": [], "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "\n", - "def beta_to_m(df, eps=1e-6):\n", - " B = np.clip(df.values, eps, 1.0 - eps)\n", - " M = np.log2(B / (1 - B))\n", - " return pd.DataFrame(M, index=df.index, columns=df.columns)\n", - "\n", - "# find rows that are all 0s\n", - "zeros_meth = (meth == 0).all(axis=1).sum()\n", - "zeros_rna = (rna == 0).all(axis=1).sum()\n", - "zeros_mirna = (mirna == 0).all(axis=1).sum()\n", - "print(f\"All zeros: meth: {zeros_meth}, rna: {zeros_rna}, mirna: {zeros_mirna}\")\n", - "\n", - "# find rows with all nans\n", - "nan_meth = meth.isna().all(axis=1).sum()\n", - "nan_rna = rna.isna().all(axis=1).sum()\n", - "nan_mirna = mirna.isna().all(axis=1).sum()\n", - "nan_clinical = clinical.isna().all(axis=1).sum()\n", - "nan_pam50 = pam50.isna().all(axis=1).sum()\n", - "print(f\"nan_meth: {nan_meth}, nan_rna: {nan_rna}, nan_mirna: {nan_mirna}, nan_clinical: {nan_clinical}, nan_pam50: {nan_pam50}\")\n", - "\n", - "# map PAM50 subtypes to integers\n", - "mapping = {\"Normal\":0, \"Basal\":1, \"Her2\":2, \"LumA\":3, \"LumB\":4}\n", - "pam50 = pam50[\"BRCA_Subtype_PAM50\"].map(mapping).to_frame(name=\"pam50\")\n", - "\n", - "# drop and transform methylation\n", - "meth_clean = meth.drop(columns=[\"Composite Element REF\"], errors=\"ignore\")\n", - "meth_m = beta_to_m(meth_clean)\n", - "clinical = clinical.drop(columns=[\"project\"], errors=\"ignore\")\n", - "\n", - "# clean column names and fill nans\n", - "for df in [meth_m, rna, mirna]:\n", - " df.columns = df.columns.str.replace(r\"\\?\", \"unknown_\", regex=True)\n", - " df.columns = df.columns.str.replace(r\"\\|\", \"_\", regex=True)\n", - " df.columns = df.columns.str.replace(\"-\", \"_\", regex=False)\n", - " df.columns = df.columns.str.replace(r\"_+\", \"_\", regex=True)\n", - " df.columns = df.columns.str.strip(\"_\")\n", - " df.fillna(0, inplace=True)\n", - "\n", - "# check for nans after filling\n", - "print(\"NaN counts after filling:\")\n", - "print(meth_m.isna().sum().sum(),rna.isna().sum().sum(),mirna.isna().sum().sum(),clinical.isna().sum().sum(),pam50.isna().sum().sum())\n", - "\n", - "# align index to PAM50\n", - "X_meth = meth_m.loc[pam50.index]\n", - "X_rna = rna.loc[pam50.index]\n", - "X_mirna = mirna.loc[pam50.index]\n", - "clinical= clinical.loc[pam50.index]\n", - "\n", - "print(f\"new shapes: meth: {X_meth.shape}, rna: {X_rna.shape}, mirna: {X_mirna.shape}, pam50: {pam50.shape}, clinical: {clinical.shape}\")\n", - "print(X_meth.head())\n", - "print(X_rna.head())\n", - "print(X_mirna.head())\n", - "print(clinical.head())\n", - "print(pam50.value_counts())" + "print(\"\\nFROM THE 6000 RNA feature selection:\\n\")\n", + "print(f\"Anova-F & variance selection share: {len(rna_inter4)} features\")\n", + "print(f\"Random Forest & variance selection share: {len(rna_inter5)} features\")\n", + "print(f\"Anova-F & Random Forest share: {len(rna_inter6)} features\")\n", + "print(f\"All three methods agree on: {len(rna_all_three)} features\")" ] }, { "cell_type": "markdown", - "id": "54fae854", + "id": "88c6d4a4", "metadata": {}, "source": [ - "## Save & Load\n", - "\n", - "Our data is clean and consistently structured across all modalities.\n", + "## Feature Selection Summary: ANOVA-RF Intersection\n", "\n", - "- All-zero rows: meth: 0, rna: 0, mirna: 0 \n", - "- All-NaN rows: meth: 0, rna: 0, mirna: 0, clinical: 0, pam50: 0 \n", - "- NaN values exist in clinical data:\n", - " - A total of 46,476 NaN entries\n", - " - Will be addressed in the next step\n", + "The final set of features was determined by the **intersection** of those highlighted by the **ANOVA F-test** and **Random Forest Importance**. This methodology provides a balanced filter, capturing features with both high class-separability (ANOVA) and significant predictive value in a non-linear model (Random Forest). The resulting feature pool is considered highly relevant for the subsequent modeling tasks.\n", "\n", - "**Final dataset shapes:**\n", - "- meth: 769 × 20,106 \n", - "- rna: 769 × 18,321 \n", - "- mirna: 769 × 503 \n", - "- clinical: 769 × 118 \n", - "- pam50: 769 × 1\n", + "### Feature Overlap Results\n", "\n", - "**Saving files:** \n", - "Set a common patient index across all datasets and saved each one as a `.csv` file.\n", + "The table below quantifies the shared features identified by the different selection techniques for each omics type.\n", "\n", - "**Verifying saved files:** \n", - "Loaded each `.csv` and printed the head to confirm successful read/write with preserved structure and content.\n" + "| Omics Data Type | ANOVA-F & Variance | RF & Variance | ANOVA-F & Random Forest (Selected) | All Three Agree |\n", + "| :--- | :--- | :--- | :--- | :--- |\n", + "| **Methylation** | 2,092 features | 1,870 features | **2,203 features** | 814 features |\n", + "| **RNA** | 2,359 features | 2,191 features | **2,500 features** | 1,124 features |" ] }, { "cell_type": "code", - "execution_count": 7, - "id": "2f0714e8", + "execution_count": null, + "id": "3bc89f38", "metadata": {}, "outputs": [], "source": [ - "# Setting up a commong index and saving to csv\n", - "X_meth.index.name = \"patient\"\n", - "X_rna.index.name = \"patient\"\n", - "X_mirna.index.name = \"patient\"\n", - "pam50.index.name = \"patient\"\n", - "clinical.index.name = \"patient\"\n", - "\n", - "X_meth.to_csv(root / \"meth.csv\", index=True)\n", - "X_rna.to_csv(root / \"rna.csv\", index=True)\n", - "X_mirna.to_csv(root / \"mirna.csv\", index=True)\n", - "pam50.to_csv(root / \"pam50.csv\", index=True)\n", - "clinical.to_csv(root / \"clinical.csv\", index=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "ef2982ef", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " A1BG A1CF ... psiTPTE22 tAKR\n", - "patient ... \n", - "TCGA-3C-AAAU -0.094004 -1.251175 ... -1.605783 0.036955\n", - "TCGA-3C-AALI 0.812517 -0.237291 ... -2.360128 0.729981\n", - "TCGA-3C-AALJ 0.931878 -0.059301 ... -1.567104 0.025686\n", - "TCGA-3C-AALK 0.676913 0.741678 ... -0.162004 1.103860\n", - "TCGA-4H-AAAK 0.657963 0.044649 ... -1.121575 1.545812\n", - "\n", - "[5 rows x 20106 columns]\n", - " unknown_100133144 unknown_100134869 ... ZZZ3_26009 psiTPTE22_387590\n", - "patient ... \n", - "TCGA-3C-AAAU 4.032489 3.692829 ... 10.205129 0.785174\n", - "TCGA-3C-AALI 3.211931 4.119273 ... 8.667973 9.855788\n", - "TCGA-3C-AALJ 3.538886 3.206237 ... 8.992994 5.143969\n", - "TCGA-3C-AALK 3.595671 3.469873 ... 9.453001 6.057699\n", - "TCGA-4H-AAAK 2.775430 3.850979 ... 9.784147 7.548699\n", - "\n", - "[5 rows x 18321 columns]\n", - " hsa_let_7a_1 hsa_let_7a_2 ... hsa_mir_99a hsa_mir_99b\n", - "patient ... \n", - "TCGA-3C-AAAU 13.129765 14.117933 ... 7.024602 15.506461\n", - "TCGA-3C-AALI 12.918069 13.922300 ... 7.885299 13.626182\n", - "TCGA-3C-AALJ 13.012033 14.010002 ... 7.580704 15.013822\n", - "TCGA-3C-AALK 13.144697 14.141721 ... 10.031619 14.554783\n", - "TCGA-4H-AAAK 13.411684 14.413518 ... 10.078201 14.650338\n", - "\n", - "[5 rows x 503 columns]\n", - " synchronous_malignancy ajcc_pathologic_stage ... race.1 ethnicity.1\n", - "patient ... \n", - "TCGA-3C-AAAU No Stage X ... white not hispanic or latino\n", - "TCGA-3C-AALI No Stage IIB ... black or african american not hispanic or latino\n", - "TCGA-3C-AALJ No Stage IIB ... black or african american not hispanic or latino\n", - "TCGA-3C-AALK No Stage IA ... black or african american not hispanic or latino\n", - "TCGA-4H-AAAK No Stage IIIA ... white not hispanic or latino\n", - "\n", - "[5 rows x 118 columns]\n", - " pam50\n", - "patient \n", - "TCGA-3C-AAAU 3\n", - "TCGA-3C-AALI 2\n", - "TCGA-3C-AALJ 4\n", - "TCGA-3C-AALK 3\n", - "TCGA-4H-AAAK 3\n" - ] - } - ], - "source": [ - "# To confirm our data saved and loads properly:\n", - "meth = pd.read_csv(root / \"meth.csv\", index_col=0)\n", - "rna = pd.read_csv(root / \"rna.csv\", index_col=0)\n", - "mirna = pd.read_csv(root / \"mirna.csv\", index_col=0)\n", - "pam50 = pd.read_csv(root / \"pam50.csv\", index_col=0)\n", - "clinical = pd.read_csv(root / \"clinical.csv\", index_col=0)\n", - " \n", - "print(meth.head())\n", - "print(rna.head())\n", - "print(mirna.head())\n", - "print(clinical.head())\n", - "print(pam50.head())" + "X_meth_selected = X_meth[meth_inter3]\n", + "X_rna_selected = X_rna[rna_inter6]\n", + "\n", + "print(\"\\nFinal Shapes for Modeling\")\n", + "print(f\"Methylation (X1): {X_meth_selected.shape}\")\n", + "print(f\"RNA-Seq (X2): {X_rna_selected.shape}\")\n", + "print(f\"miRNA-Seq (X3): {X_mirna.shape}\")\n", + "print(f\"Labels (Y): {Y_labels.shape}\")" ] }, { "cell_type": "markdown", - "id": "09265512", + "id": "c2168980", "metadata": {}, "source": [ - "## Feature Selection: Phase 1\n", - "\n", - "To explore different ways of selecting informative features, we evaluated three built-in methods:\n", - "- variance thresholding \n", - "- ANOVA F-test \n", - "- random forest importance \n", - "\n", - "Each method highlights different statistical properties: overall variability, class-based separability, and model-derived relevance. Here, we applied all three and compared the overlap between selected features to assess their agreement.\n", - "\n", - "**Methods applied:**\n", - "- Selected the top 6000 features for both methylation and RNA datasets using each method \n", - "- Compared feature overlap across methods \n", - "- miRNA was excluded due to its limited feature count (503 total) \n", - "- Selection was necessary for methylation and RNA, which originally had over 20,000 and 18,000 features, respectively\n", - "\n", - "**Methylation feature selection:**\n", - "- ANOVA F-test & variance share: 2,091 features \n", - "- Random forest & variance share: 1,871 features \n", - "- ANOVA F-test & random forest share: 2,201 features \n", - "- All three methods agree on: 815 features\n", - "\n", - "**RNA feature selection:**\n", - "- ANOVA F-test & variance share: 2,152 features \n", - "- Random forest & variance share: 1,829 features \n", - "- ANOVA F-test & random forest share: 2,216 features \n", - "- All three methods agree on: 805 features\n", - "\n", - "These overlaps suggest that while each method captures unique aspects of the data, there is meaningful agreement, particularly between ANOVA and random forest." + "## Data Availability\n", + "\n", + "To facilitate rapid experimentation and reproduction of our results, the fully processed and feature-selected dataset used in this analysis has been made available directly within the package.\n", + "\n", + "Users can load this dataset, bypassing all preceding data acquisition, preprocessing, and feature selection steps. This allows users to proceed immediately from this step." ] }, { "cell_type": "code", - "execution_count": 9, - "id": "fa70dcca", + "execution_count": null, + "id": "2d0340f6", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2025-05-28 11:30:33,011 - bioneuralnet.utils.preprocess - INFO - [Inf]: Replaced 0 infinite values\n", - "2025-05-28 11:30:33,011 - bioneuralnet.utils.preprocess - INFO - [NaN]: Replaced 0 NaNs after median imputation\n", - "2025-05-28 11:30:33,011 - bioneuralnet.utils.preprocess - INFO - [Zero-Var]: 0 columns dropped due to zero variance\n", - "2025-05-28 11:30:33,115 - bioneuralnet.utils.preprocess - INFO - Selected top 6000 features by variance\n", - "2025-05-28 11:30:35,816 - bioneuralnet.utils.preprocess - INFO - [Inf]: Replaced 0 infinite values\n", - "2025-05-28 11:30:35,817 - bioneuralnet.utils.preprocess - INFO - [NaN]: Replaced 0 NaNs after median imputation\n", - "2025-05-28 11:30:35,817 - bioneuralnet.utils.preprocess - INFO - [Zero-Var]: 0 columns dropped due to zero variance\n", - "2025-05-28 11:30:35,911 - bioneuralnet.utils.preprocess - INFO - Selected top 6000 features by variance\n", - "2025-05-28 11:30:38,886 - bioneuralnet.utils.preprocess - INFO - [Inf]: Replaced 0 infinite values\n", - "2025-05-28 11:30:38,886 - bioneuralnet.utils.preprocess - INFO - [NaN]: Replaced 0 NaNs after median imputation\n", - "2025-05-28 11:30:38,886 - bioneuralnet.utils.preprocess - INFO - [Zero-Var]: 0 columns dropped due to zero variance\n", - "2025-05-28 11:30:39,052 - bioneuralnet.utils.preprocess - INFO - Selected 6000 features by ANOVA (task=classification), 17514 significant, 0 padded\n", - "2025-05-28 11:30:41,772 - bioneuralnet.utils.preprocess - INFO - [Inf]: Replaced 0 infinite values\n", - "2025-05-28 11:30:41,772 - bioneuralnet.utils.preprocess - INFO - [NaN]: Replaced 0 NaNs after median imputation\n", - "2025-05-28 11:30:41,773 - bioneuralnet.utils.preprocess - INFO - [Zero-Var]: 0 columns dropped due to zero variance\n", - "2025-05-28 11:30:41,923 - bioneuralnet.utils.preprocess - INFO - Selected 6000 features by ANOVA (task=classification), 16864 significant, 0 padded\n", - "2025-05-28 11:30:44,909 - bioneuralnet.utils.preprocess - INFO - [Inf]: Replaced 0 infinite values\n", - "2025-05-28 11:30:44,910 - bioneuralnet.utils.preprocess - INFO - [NaN]: Replaced 0 NaNs after median imputation\n", - "2025-05-28 11:30:44,910 - bioneuralnet.utils.preprocess - INFO - [Zero-Var]: 0 columns dropped due to zero variance\n", - "2025-05-28 11:30:53,200 - bioneuralnet.utils.preprocess - INFO - [Inf]: Replaced 0 infinite values\n", - "2025-05-28 11:30:53,201 - bioneuralnet.utils.preprocess - INFO - [NaN]: Replaced 0 NaNs after median imputation\n", - "2025-05-28 11:30:53,201 - bioneuralnet.utils.preprocess - INFO - [Zero-Var]: 0 columns dropped due to zero variance\n" - ] - } - ], + "outputs": [], "source": [ - "from bioneuralnet.utils.preprocess import select_top_k_variance\n", - "from bioneuralnet.utils.preprocess import top_anova_f_features\n", - "from bioneuralnet.utils.preprocess import select_top_randomforest\n", + "from bioneuralnet.datasets import DatasetLoader\n", "\n", - "# feature selection\n", - "meth_highvar = select_top_k_variance(meth, k=6000)\n", - "rna_highvar = select_top_k_variance(rna, k=6000)\n", - "\n", - "meth_af = top_anova_f_features(meth, pam50, max_features=6000)\n", - "rna_af = top_anova_f_features(rna, pam50, max_features=6000)\n", - "\n", - "meth_rf = select_top_randomforest(meth, pam50, top_k=6000)\n", - "rna_rf = select_top_randomforest(rna, pam50, top_k=6000)\n", - "\n", - "meth_var = list(meth_highvar.columns)\n", - "meth_anova = list(meth_af.columns)\n", - "meth_rf = list(meth_rf.columns)\n", - "\n", - "rna_var = list(rna_highvar.columns)\n", - "rna_anova = list(rna_af.columns)\n", - "rna_rf = list(rna_rf.columns)\n", - "\n", - "inter1 = []\n", - "for x in meth_anova:\n", - " if x in meth_var:\n", - " inter1.append(x)\n", - "\n", - "inter2 = []\n", - "for x in meth_rf:\n", - " if x in meth_var:\n", - " inter2.append(x)\n", - "\n", - "inter3 = []\n", - "for x in meth_anova:\n", - " if x in meth_rf:\n", - " inter3.append(x)\n", - "\n", - "meth_all_three = []\n", - "for x in meth_anova:\n", - " if x in meth_rf and x in meth_var:\n", - " meth_all_three.append(x)\n", - "\n", - "inter4 = []\n", - "for x in rna_anova:\n", - " if x in rna_var:\n", - " inter4.append(x)\n", - "\n", - "inter5 = []\n", - "for x in rna_rf:\n", - " if x in rna_var:\n", - " inter5.append(x)\n", - "\n", - "inter6 = []\n", - "for x in rna_anova:\n", - " if x in rna_rf:\n", - " inter6.append(x)\n", - "\n", - "rna_all_three = []\n", - "for x in rna_anova:\n", - " if x in rna_rf and x in rna_var:\n", - " rna_all_three.append(x)\n" + "tgca_brca = DatasetLoader(\"brca\")\n", + "display(tgca_brca.shape)\n", + "\n", + "dna_meth = tgca_brca[\"meth\"]\n", + "rna = tgca_brca[\"rna\"]\n", + "mirna = tgca_brca[\"mirna\"]\n", + "clinical = tgca_brca[\"clinical\"]\n", + "target = tgca_brca[\"target\"]\n", + "\n", + "display(dna_meth.iloc[:3,:5])\n", + "display(rna.iloc[:3,:5])\n", + "display(mirna.iloc[:3,:5])\n", + "display(clinical.iloc[:3,:5])\n", + "display(target.iloc[:3,:5])" ] }, { "cell_type": "code", - "execution_count": 10, - "id": "cc981cdb", + "execution_count": null, + "id": "f8f9346e", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Methylation feature selection:\n", - "\n", - "Anova-F & variance selection share: 2091 features\n", - "Random Forest & variance selection share: 1871 features\n", - "Anova-F & Random Forest share: 2201 features\n", - "All three methods agree on: 815 features\n" - ] - } - ], + "outputs": [], "source": [ - "print(\"Methylation feature selection:\\n\")\n", - "print(f\"Anova-F & variance selection share: {len(inter1)} features\")\n", - "print(f\"Random Forest & variance selection share: {len(inter2)} features\")\n", - "print(f\"Anova-F & Random Forest share: {len(inter3)} features\")\n", - "print(f\"All three methods agree on: {len(meth_all_three)} features\")" + "import pandas as pd\n", + "import bioneuralnet as bnn\n", + "\n", + "# subset from dna_methylation and rna to reduce dimensionality from 5000+ to 2525\n", + "dna_meth = bnn.utils.select_top_k_variance(dna_meth, k=1011)\n", + "rna = bnn.utils.select_top_k_variance(rna, k=1011)" ] }, { - "cell_type": "code", - "execution_count": 11, - "id": "da639dd6", + "cell_type": "markdown", + "id": "1d8d776c", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "RNA feature selection:\n", - "\n", - "Anova-F & variance selection share: 2340 features\n", - "Random Forest & variance selection share: 2218 features\n", - "Anova-F & Random Forest share: 2546 features\n", - "All three methods agree on: 1134 features\n" - ] - } - ], "source": [ - "print(\"\\nRNA feature selection:\\n\")\n", - "print(f\"Anova-F & variance selection share: {len(inter4)} features\")\n", - "print(f\"Random Forest & variance selection share: {len(inter5)} features\")\n", - "print(f\"Anova-F & Random Forest share: {len(inter6)} features\")\n", - "print(f\"All three methods agree on: {len(rna_all_three)} features\")" + "## Reproducibility and Seeding\n", + "\n", + "To ensure our experimental results are fully reproducible, a single global seed is set at the beginning of the analysis.\n", + "\n", + "This utility function propagates the seed to all sources of randomness, including `random`, `numpy`, and `torch` (for both CPU and GPU). Critically, it also configures the PyTorch cuDNN backend to use deterministic algorithms." ] }, { "cell_type": "code", - "execution_count": 12, - "id": "1c371389", + "execution_count": null, + "id": "c052f712", "metadata": {}, "outputs": [], "source": [ - "out_dir = Path(\"/home/vicente/Github/BioNeuralNet/TCGA_BRCA_DATA/ANOVA\")\n", + "import bioneuralnet as bnn\n", "\n", - "rna_af.to_csv(out_dir / \"rna_anova.csv\")\n", - "meth_af.to_csv(out_dir / \"meth_anova.csv\")" + "SEED = 8183\n", + "bnn.utils.set_seed(SEED)" ] }, { - "cell_type": "markdown", - "id": "c2168980", + "cell_type": "code", + "execution_count": null, + "id": "b9b161bc", "metadata": {}, + "outputs": [], "source": [ - "## Data Accessibility: Using `DatasetLoader`\n", - "\n", - "To make this dataset easy to use, we've packaged it into the `DatasetLoader` component. Due to GitHub and PyPI file size limits, we included only the top 6,000 features from Methylation, RNA. Selected using the ANOVA F-test from the previous step.\n", - "\n", - "If you have additional preprocessed or raw datasets you would like to contribute, feel free to reach out and we are happy to help expand the platform." + "# Identified several columns in the clinical data that could lead to data leakage\n", + "dataleak_columns = [\n", + " 'treatments_pharmaceutical_days_to_treatment_end',\n", + " 'treatments_pharmaceutical_days_to_treatment_start',\n", + " 'treatments_pharmaceutical_treatment_id',\n", + " 'treatments_pharmaceutical_treatment_type',\n", + " 'treatments_pharmaceutical_regimen_or_line_of_therapy',\n", + " 'treatments_pharmaceutical_treatment_effect',\n", + " 'treatments_pharmaceutical_therapeutic_agents',\n", + " 'treatments_pharmaceutical_treatment_or_therapy',\n", + " 'treatments_pharmaceutical_initial_disease_status',\n", + " 'treatments_pharmaceutical_treatment_intent_type',\n", + " 'treatments_pharmaceutical_treatment_anatomic_site',\n", + " 'treatments_pharmaceutical_treatment_outcome',\n", + " 'treatments_pharmaceutical_prescribed_dose_units',\n", + " 'treatments_pharmaceutical_treatment_dose',\n", + " 'treatments_pharmaceutical_route_of_administration',\n", + " 'treatments_pharmaceutical_number_of_cycles',\n", + " 'treatments_pharmaceutical_prescribed_dose',\n", + " 'treatments_pharmaceutical_treatment_dose_units',\n", + " 'treatments_pharmaceutical_clinical_trial_indicator',\n", + " 'treatments_pharmaceutical_treatment_anatomic_sites',\n", + " 'treatments_pharmaceutical_margin_status',\n", + " 'treatments_pharmaceutical_course_number',\n", + " 'treatments_pharmaceutical_number_of_fractions',\n", + " 'treatments_radiation_days_to_treatment_end',\n", + " 'treatments_radiation_days_to_treatment_start',\n", + " 'treatments_radiation_treatment_id',\n", + " 'treatments_radiation_treatment_type',\n", + " 'treatments_radiation_regimen_or_line_of_therapy',\n", + " 'treatments_radiation_treatment_effect',\n", + " 'treatments_radiation_therapeutic_agents',\n", + " 'treatments_radiation_treatment_or_therapy',\n", + " 'treatments_radiation_initial_disease_status',\n", + " 'treatments_radiation_treatment_intent_type',\n", + " 'treatments_radiation_treatment_anatomic_site',\n", + " 'treatments_radiation_treatment_outcome',\n", + " 'treatments_radiation_prescribed_dose_units',\n", + " 'treatments_radiation_treatment_dose',\n", + " 'treatments_radiation_route_of_administration',\n", + " 'treatments_radiation_number_of_cycles',\n", + " 'treatments_radiation_prescribed_dose',\n", + " 'treatments_radiation_treatment_dose_units',\n", + " 'treatments_radiation_clinical_trial_indicator',\n", + " 'treatments_radiation_treatment_anatomic_sites',\n", + " 'treatments_radiation_margin_status',\n", + " 'treatments_radiation_course_number',\n", + " 'treatments_radiation_number_of_fractions',\n", + " 'radiation_therapy',\n", + " 'patient',\n", + " 'ajcc_pathologic_stage',\n", + " 'ajcc_pathologic_t',\n", + " 'ajcc_pathologic_n',\n", + " 'ajcc_pathologic_m',\n", + " 'age_at_diagnosis',\n", + " 'primary_diagnosis',\n", + " 'tissue_or_organ_of_origin',\n", + " 'days_to_last_follow_up',\n", + " 'year_of_diagnosis',\n", + " \n", + " 'vital_status',\n", + " 'days_to_death',\n", + " 'year_of_death',\n", + " 'days_to_last_follow_up',\n", + " 'days_to_last_known_alive',\n", + " 'last_known_disease_status',\n", + " 'days_to_last_known_disease_status',\n", + " 'days_to_recurrence',\n", + " 'progression_or_recurrence',\n", + " 'age_at_diagnosis',\n", + " 'days_to_birth', \n", + "]\n", + "\n", + "clinical_clean = clinical.drop(columns=dataleak_columns, errors='ignore')" ] }, { "cell_type": "code", - "execution_count": 13, - "id": "2d0340f6", + "execution_count": null, + "id": "a013bcb2", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "TGCA BRCA dataset shape: {'mirna': (769, 503), 'pam50': (769, 1), 'clinical': (769, 118), 'rna': (769, 6000), 'meth': (769, 6000)}\n" - ] - } - ], + "outputs": [], "source": [ - "from bioneuralnet.datasets import DatasetLoader\n", + "from bioneuralnet.utils import preprocess_clinical\n", "\n", - "tgca_brca = DatasetLoader(\"brca\")\n", + "clinical_for_model = preprocess_clinical(\n", + " clinical_clean, \n", + " top_k=7, \n", + " scale=False,\n", + ")\n", "\n", - "print(f\"TGCA BRCA dataset shape: {tgca_brca.shape}\")\n", - "brca_meth = tgca_brca.data[\"meth\"]\n", - "brca_rna = tgca_brca.data[\"rna\"]\n", - "brca_mirna = tgca_brca.data[\"mirna\"]\n", - "brca_clinical = tgca_brca.data[\"clinical\"]\n", - "brca_pam50 = tgca_brca.data[\"pam50\"]\n" + "print(clinical_for_model.columns)\n", + "display(clinical_for_model.iloc[:5,:5])" ] }, { - "cell_type": "markdown", - "id": "0ddf042e", + "cell_type": "code", + "execution_count": null, + "id": "8b099471", "metadata": {}, + "outputs": [], "source": [ - "## Feature Selection: Phase 2\n", + "from bioneuralnet.utils import gen_threshold_graph, gen_correlation_graph, gen_similarity_graph, gen_gaussian_knn_graph\n", "\n", - "We used `preprocess_clinical` to reduce the clinical dataset to the top 10 most informative features based on random forest importance.\n", + "omics_brca = pd.concat([dna_meth, rna, mirna], axis=1)\n", "\n", - "- Dropped samples with missing PAM50 labels \n", - "- Subset all datasets to matched patients \n", - "- Ignored non-informative age-related columns \n", - "- No scaling applied\n", + "threshold_10 = gen_threshold_graph(omics_brca, b=6.2, k=10)\n", + "correlation_10 = gen_correlation_graph(omics_brca, k=10, method='pearson', signed=False)\n", + "similarity_10 = gen_similarity_graph(omics_brca, k=10, metric='cosine')\n", + "gaussian_15 = gen_gaussian_knn_graph(omics_brca, k=15, sigma=None)\n", "\n", - "**Result:**\n", - "- Clinical data reduced to 10 features across the 769 patients " + "comparison_runs = [\n", + " {\"name\":\"threshold_10\", \"graph\": threshold_10},\n", + " {\"name\":\"correlation_10\", \"graph\": correlation_10},\n", + " {\"name\":\"similarity_10\", \"graph\": similarity_10},\n", + " {\"name\":\"gaussian_15\", \"graph\": gaussian_15},\n", + "]" ] }, { - "cell_type": "code", - "execution_count": 14, - "id": "338dc995", + "cell_type": "markdown", + "id": "27f4e2c7", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2025-05-28 11:31:03,616 - bioneuralnet.utils.preprocess - INFO - [Inf]: Replaced 0 infinite values\n", - "2025-05-28 11:31:03,616 - bioneuralnet.utils.preprocess - INFO - [NaN]: Replaced 31384 NaNs after median imputation\n", - "2025-05-28 11:31:03,617 - bioneuralnet.utils.preprocess - INFO - [Zero-Var]: 39 columns dropped due to zero variance\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "RNA shape: (769, 6000)\n", - "METH shape: (769, 6000)\n", - "miRNA shape: (769, 503)\n", - "Clinical shape: (769, 118)\n", - "Phenotype shape: (769, 1)\n", - "Phenotype counts:\n", - "pam50\n", - "3 419\n", - "4 140\n", - "1 130\n", - "2 46\n", - "0 34\n", - "Name: count, dtype: int64\n", - "Nan values in pam50 0\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2025-05-28 11:31:03,978 - bioneuralnet.utils.preprocess - INFO - Selected top 10 features by RandomForest importance\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " year_of_diagnosis number_of_lymph_nodes ... laterality_Right primary_diagnosis_Infiltrating duct carcinoma, NOS\n", - "patient ... \n", - "TCGA-3C-AAAU 2004.0 4.0 ... False False \n", - "TCGA-3C-AALI 2003.0 1.0 ... True True \n", - "TCGA-3C-AALJ 2011.0 1.0 ... True True \n", - "TCGA-3C-AALK 2011.0 0.0 ... True True \n", - "TCGA-4H-AAAK 2013.0 4.0 ... False False \n", - "\n", - "[5 rows x 10 columns]\n" - ] - } - ], "source": [ - "from bioneuralnet.utils.preprocess import preprocess_clinical\n", - "\n", - "#shapes\n", - "print(f\"RNA shape: {brca_rna.shape}\")\n", - "print(f\"METH shape: {brca_meth.shape}\")\n", - "print(f\"miRNA shape: {brca_mirna.shape}\")\n", - "print(f\"Clinical shape: {brca_clinical.shape}\")\n", - "print(f\"Phenotype shape: {brca_pam50.shape}\")\n", - "print(f\"Phenotype counts:\\n{brca_pam50.value_counts()}\")\n", - "\n", - "#check nans in pam50\n", - "print(f\"Nan values in pam50 {brca_pam50.isna().sum().sum()}\")\n", - "brca_pam50 = brca_pam50.dropna()\n", - "\n", - "X_rna = brca_rna.loc[brca_pam50.index]\n", - "X_meth = brca_meth.loc[brca_pam50.index]\n", - "X_mirna = brca_mirna.loc[brca_pam50.index]\n", - "clinical = brca_clinical.loc[brca_pam50.index]\n", - "\n", - "# for more details on the preprocessing function, see bioneuralnet.utils.preprocess\n", - "clinical = preprocess_clinical(clinical, brca_pam50, top_k=10, scale=False, ignore_columns=[\"days_to_birth\", \"age_at_diagnosis\", \"days_to_last_followup\", \"age_at_index\", \"years_to_birth\"])\n", - "print(clinical.head())" + "## Classification using DPMON: Training and Evaluation" ] }, { - "cell_type": "markdown", - "id": "89cb8500", + "cell_type": "code", + "execution_count": null, + "id": "1a2f5323", "metadata": {}, + "outputs": [], "source": [ - "## Graph Construction\n", + "from bioneuralnet.downstream_task import DPMON\n", "\n", - "We built a k-NN cosine similarity graph to capture relationships across omics\n", "\n", - "- Selected 1,000 features each from methylation and RNA, and all 503 from miRNA \n", - "- Combined into `X_train_full` (769 × 2,503), no NaNs found \n", - "- Transposed the matrix to treat features as nodes \n", - "- Constructed a cosine similarity graph with `k=15`\n", - "- Graph shape: 2,503 × 2,503 (features × features)" + "output_dir_base = Path(\"/home/vicente/Github/BioNeuralNet/dpmon_results_GAT_FINAL/brca\")\n", + "all_results = []\n", + "\n", + "for run_config in comparison_runs:\n", + " graph_name = run_config[\"name\"]\n", + " A_full = run_config[\"graph\"]\n", + " \n", + " current_output_dir = output_dir_base / graph_name\n", + " current_output_dir.mkdir(parents=True, exist_ok=True)\n", + "\n", + " dpmon_params_base = {\n", + " \"adjacency_matrix\": A_full,\n", + " \"omics_list\": omics_brca,\n", + " \"phenotype_data\": target,\n", + " \"phenotype_col\": \"target\",\n", + " \"clinical_data\": clinical_for_model,\n", + " \"model\": 'GAT',\n", + " \"tune\": True, \n", + " \"cv\": True, \n", + " \"n_folds\": 5,\n", + " \"repeat_num\":5,\n", + " \"gpu\": True,\n", + " \"cuda\": 0,\n", + " \"seed\": SEED,\n", + " \"output_dir\": current_output_dir\n", + " }\n", + " \n", + " dpmon_tunned = DPMON(**dpmon_params_base)\n", + " predictions_df, metrics, embeddings = dpmon_tunned.run()\n", + "\n", + " all_results.append({\n", + " \"graph\": graph_name,\n", + " \"predictions\": predictions_df,\n", + " \"metrics\": metrics,\n", + " \"embeddings\": embeddings\n", + " })\n", + "\n", + "for res in all_results:\n", + " graph_name = res[\"graph\"]\n", + " graph_metrics = res[\"metrics\"]\n", + " \n", + " acc_row = graph_metrics.loc[graph_metrics['Metric'] == 'Accuracy'].iloc[0]\n", + " f1_macro_row = graph_metrics.loc[graph_metrics['Metric'] == 'F1 Macro'].iloc[0]\n", + " f1_weighted_row = graph_metrics.loc[graph_metrics['Metric'] == 'F1 Weighted'].iloc[0]\n", + " recall_row = graph_metrics.loc[graph_metrics['Metric'] == 'Recall'].iloc[0]\n", + " auc_row = graph_metrics.loc[graph_metrics['Metric'] == 'AUC'].iloc[0]\n", + " \n", + " acc_avg, acc_std = acc_row['Average'], acc_row['StdDev']\n", + " f1_macro_avg, f1_macro_std = f1_macro_row['Average'], f1_macro_row['StdDev']\n", + " f1_weighted_avg, f1_weighted_std = f1_weighted_row['Average'], f1_weighted_row['StdDev']\n", + " recall_avg, recall_std = recall_row['Average'], recall_row['StdDev']\n", + " auc_avg, auc_std = auc_row['Average'], auc_row['StdDev']\n", + "\n", + " print(f\"\\nResults for: {graph_name}\")\n", + " print(f\"Accuracy (Avg +/- Std): {acc_avg:.4f} +/- {acc_std:.4f}\")\n", + " print(f\"F1 Macro (Avg +/- Std): {f1_macro_avg:.4f} +/- {f1_macro_std:.4f}\")\n", + " print(f\"F1 Weighted (Avg +/- Std): {f1_weighted_avg:.4f} +/- {f1_weighted_std:.4f}\")\n", + " print(f\"Recall: {recall_avg:.4f} +/- {recall_std:.4f}\")\n", + " print(f\"AUC: {auc_avg:.4f} +/- {auc_std:.4f}\")" ] }, { "cell_type": "code", - "execution_count": 15, - "id": "b4646135", + "execution_count": null, + "id": "43396d92", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2025-05-28 11:31:04,871 - bioneuralnet.utils.preprocess - INFO - [Inf]: Replaced 0 infinite values\n", - "2025-05-28 11:31:04,871 - bioneuralnet.utils.preprocess - INFO - [NaN]: Replaced 0 NaNs after median imputation\n", - "2025-05-28 11:31:04,871 - bioneuralnet.utils.preprocess - INFO - [Zero-Var]: 0 columns dropped due to zero variance\n", - "2025-05-28 11:31:04,914 - bioneuralnet.utils.preprocess - INFO - Selected 1000 features by ANOVA (task=classification), 6000 significant, 0 padded\n", - "2025-05-28 11:31:05,822 - bioneuralnet.utils.preprocess - INFO - [Inf]: Replaced 0 infinite values\n", - "2025-05-28 11:31:05,822 - bioneuralnet.utils.preprocess - INFO - [NaN]: Replaced 0 NaNs after median imputation\n", - "2025-05-28 11:31:05,823 - bioneuralnet.utils.preprocess - INFO - [Zero-Var]: 0 columns dropped due to zero variance\n", - "2025-05-28 11:31:05,866 - bioneuralnet.utils.preprocess - INFO - Selected 1000 features by ANOVA (task=classification), 6000 significant, 0 padded\n", - "2025-05-28 11:31:05,945 - bioneuralnet.utils.preprocess - INFO - [Inf]: Replaced 0 infinite values\n", - "2025-05-28 11:31:05,945 - bioneuralnet.utils.preprocess - INFO - [NaN]: Replaced 0 NaNs after median imputation\n", - "2025-05-28 11:31:05,945 - bioneuralnet.utils.preprocess - INFO - [Zero-Var]: 0 columns dropped due to zero variance\n", - "2025-05-28 11:31:05,948 - bioneuralnet.utils.preprocess - INFO - Selected 503 features by ANOVA (task=classification), 465 significant, 38 padded\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Nan values in X_train_full: 0\n", - "Nan value in X_train_full after dropping: 0\n", - "X_train_full shape: (769, 2503)\n", - "\n", - "Network shape: (2503, 2503)\n" - ] - } - ], + "outputs": [], "source": [ - "from bioneuralnet.utils.preprocess import top_anova_f_features\n", - "from bioneuralnet.utils.graph import gen_similarity_graph\n", - "\n", - "meth_sel = top_anova_f_features(X_meth, brca_pam50, max_features=1000)\n", - "rna_sel = top_anova_f_features(X_rna, brca_pam50 ,max_features=1000)\n", - "mirna_sel = top_anova_f_features(X_mirna, brca_pam50,max_features=503)\n", - "X_train_full = pd.concat([meth_sel, rna_sel, mirna_sel], axis=1)\n", - "\n", - "# we check again for nan values then drop if any\n", - "print(f\"Nan values in X_train_full: {X_train_full.isna().sum().sum()}\")\n", - "X_train_full = X_train_full.dropna()\n", - "print(f\"Nan value in X_train_full after dropping: {X_train_full.isna().sum().sum()}\")\n", - "\n", - "print(f\"X_train_full shape: {X_train_full.shape}\")\n", - "# building the graph using the similarity graph function with k=15\n", - "A_train = gen_similarity_graph(X_train_full.T, k=15)\n", + "output_dir_base = Path(\"/home/vicente/Github/BioNeuralNet/dpmon_results_SAGE_FINAL/brca\")\n", + "all_results = []\n", "\n", - "print(f\"\\nNetwork shape: {A_train.shape}\")" + "for run_config in comparison_runs:\n", + " graph_name = run_config[\"name\"]\n", + " A_full = run_config[\"graph\"]\n", + " \n", + " current_output_dir = output_dir_base / graph_name\n", + " current_output_dir.mkdir(parents=True, exist_ok=True)\n", + "\n", + " dpmon_params_base = {\n", + " \"adjacency_matrix\": A_full,\n", + " \"omics_list\": omics_brca,\n", + " \"phenotype_data\": target,\n", + " \"phenotype_col\": \"target\",\n", + " \"clinical_data\": clinical_for_model,\n", + " \"model\": 'SAGE',\n", + " \"tune\": True, \n", + " \"cv\": True, \n", + " \"n_folds\": 5,\n", + " \"repeat_num\":5,\n", + " \"gpu\": True,\n", + " \"cuda\": 0,\n", + " \"seed\": SEED,\n", + " \"output_dir\": current_output_dir\n", + " }\n", + " \n", + " dpmon_tunned = DPMON(**dpmon_params_base)\n", + " predictions_df, metrics, embeddings = dpmon_tunned.run()\n", + "\n", + " all_results.append({\n", + " \"graph\": graph_name,\n", + " \"predictions\": predictions_df,\n", + " \"metrics\": metrics,\n", + " \"embeddings\": embeddings\n", + " })\n", + "\n", + "for res in all_results:\n", + " graph_name = res[\"graph\"]\n", + " graph_metrics = res[\"metrics\"]\n", + " \n", + " acc_row = graph_metrics.loc[graph_metrics['Metric'] == 'Accuracy'].iloc[0]\n", + " f1_macro_row = graph_metrics.loc[graph_metrics['Metric'] == 'F1 Macro'].iloc[0]\n", + " f1_weighted_row = graph_metrics.loc[graph_metrics['Metric'] == 'F1 Weighted'].iloc[0]\n", + " recall_row = graph_metrics.loc[graph_metrics['Metric'] == 'Recall'].iloc[0]\n", + " auc_row = graph_metrics.loc[graph_metrics['Metric'] == 'AUC'].iloc[0]\n", + " \n", + " acc_avg, acc_std = acc_row['Average'], acc_row['StdDev']\n", + " f1_macro_avg, f1_macro_std = f1_macro_row['Average'], f1_macro_row['StdDev']\n", + " f1_weighted_avg, f1_weighted_std = f1_weighted_row['Average'], f1_weighted_row['StdDev']\n", + " recall_avg, recall_std = recall_row['Average'], recall_row['StdDev']\n", + " auc_avg, auc_std = auc_row['Average'], auc_row['StdDev']\n", + "\n", + " print(f\"\\nResults for: {graph_name}\")\n", + " print(f\"Accuracy (Avg +/- Std): {acc_avg:.4f} +/- {acc_std:.4f}\")\n", + " print(f\"F1 Macro (Avg +/- Std): {f1_macro_avg:.4f} +/- {f1_macro_std:.4f}\")\n", + " print(f\"F1 Weighted (Avg +/- Std): {f1_weighted_avg:.4f} +/- {f1_weighted_std:.4f}\")\n", + " print(f\"Recall: {recall_avg:.4f} +/- {recall_std:.4f}\")\n", + " print(f\"AUC: {auc_avg:.4f} +/- {auc_std:.4f}\")" ] }, { - "cell_type": "markdown", - "id": "376f8a5b", + "cell_type": "code", + "execution_count": null, + "id": "cb69e019", "metadata": {}, + "outputs": [], "source": [ - "## DPMON Run Summary\n", - "\n", - "We evaluated **DPMON** for PAM50 subtype classification using multi-omics data (RNA, methylation, miRNA), a feature graph, and clinical covariates.\n", - "\n", - "**Performance Metrics:**\n", - "- Accuracy: 0.9870\n", - "- F1-Weighted: 0.9875 \n", - "- F1-Macro: 0.9651\n", + "output_dir_base = Path(\"/home/vicente/Github/BioNeuralNet/dpmon_results_GCN_FINAL/brca\")\n", "\n", - "DPMON is an end-to-end optimized pipeline that fuses multi-omics data and network structure for disease prediction using GNNs. \n", + "all_results = []\n", "\n", - "For implementation details, see the [documentation](https://bioneuralnet.readthedocs.io/en/latest/gnns.html#how-dpmon-uses-gnns-differently). \n", - "\n", - "For the full paper, see:\n", - "[2] Hussein, S., Ramos, V., et al. *Learning from Multi-Omics Networks to Enhance Disease Prediction: An Optimized Network Embedding and Fusion Approach.* \n", - "**IEEE BIBM 2024**, Lisbon, Portugal, pp. 4371-4378. DOI: [10.1109/BIBM62325.2024.10822233](https://doi.org/10.1109/BIBM62325.2024.10822233)" + "for run_config in comparison_runs:\n", + " graph_name = run_config[\"name\"]\n", + " A_full = run_config[\"graph\"]\n", + " \n", + " current_output_dir = output_dir_base / graph_name\n", + " current_output_dir.mkdir(parents=True, exist_ok=True)\n", + "\n", + " dpmon_params_base = {\n", + " \"adjacency_matrix\": A_full,\n", + " \"omics_list\": omics_brca,\n", + " \"phenotype_data\": target,\n", + " \"phenotype_col\": \"target\",\n", + " \"clinical_data\": clinical_for_model,\n", + " \"model\": 'GCN',\n", + " \"tune\": True, \n", + " \"cv\": True, \n", + " \"n_folds\": 5,\n", + " \"repeat_num\":5,\n", + " \"gpu\": True,\n", + " \"cuda\": 0,\n", + " \"seed\": SEED,\n", + " \"output_dir\": current_output_dir\n", + " }\n", + " \n", + " dpmon_tunned = DPMON(**dpmon_params_base)\n", + " predictions_df, metrics, embeddings = dpmon_tunned.run()\n", + "\n", + " all_results.append({\n", + " \"graph\": graph_name,\n", + " \"predictions\": predictions_df,\n", + " \"metrics\": metrics,\n", + " \"embeddings\": embeddings\n", + " })\n", + "\n", + "for res in all_results:\n", + " graph_name = res[\"graph\"]\n", + " graph_metrics = res[\"metrics\"]\n", + " \n", + " acc_row = graph_metrics.loc[graph_metrics['Metric'] == 'Accuracy'].iloc[0]\n", + " f1_macro_row = graph_metrics.loc[graph_metrics['Metric'] == 'F1 Macro'].iloc[0]\n", + " f1_weighted_row = graph_metrics.loc[graph_metrics['Metric'] == 'F1 Weighted'].iloc[0]\n", + " recall_row = graph_metrics.loc[graph_metrics['Metric'] == 'Recall'].iloc[0]\n", + " auc_row = graph_metrics.loc[graph_metrics['Metric'] == 'AUC'].iloc[0]\n", + " \n", + " acc_avg, acc_std = acc_row['Average'], acc_row['StdDev']\n", + " f1_macro_avg, f1_macro_std = f1_macro_row['Average'], f1_macro_row['StdDev']\n", + " f1_weighted_avg, f1_weighted_std = f1_weighted_row['Average'], f1_weighted_row['StdDev']\n", + " recall_avg, recall_std = recall_row['Average'], recall_row['StdDev']\n", + " auc_avg, auc_std = auc_row['Average'], auc_row['StdDev']\n", + "\n", + " print(f\"\\nResults for: {graph_name}\")\n", + " print(f\"Accuracy (Avg +/- Std): {acc_avg:.4f} +/- {acc_std:.4f}\")\n", + " print(f\"F1 Macro (Avg +/- Std): {f1_macro_avg:.4f} +/- {f1_macro_std:.4f}\")\n", + " print(f\"F1 Weighted (Avg +/- Std): {f1_weighted_avg:.4f} +/- {f1_weighted_std:.4f}\")\n", + " print(f\"Recall: {recall_avg:.4f} +/- {recall_std:.4f}\")\n", + " print(f\"AUC: {auc_avg:.4f} +/- {auc_std:.4f}\")" ] }, { "cell_type": "code", "execution_count": null, - "id": "43396d92", + "id": "7178390e", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2025-05-29 13:09:32,081 - bioneuralnet.downstream_task.dpmon - INFO - Output directory set to: /home/vicente/Github/BioNeuralNet/dpmon_output\n", - "2025-05-29 13:09:32,081 - bioneuralnet.downstream_task.dpmon - INFO - Initialized DPMON with the provided parameters.\n", - "2025-05-29 13:09:32,082 - bioneuralnet.downstream_task.dpmon - INFO - Starting DPMON run.\n", - "2025-05-29 13:09:32,096 - bioneuralnet.downstream_task.dpmon - INFO - Running hyperparameter tuning for DPMON.\n", - "2025-05-29 13:09:32,096 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", - "2025-05-29 13:09:32,177 - bioneuralnet.downstream_task.dpmon - INFO - Number of nodes in network: 2503\n", - "2025-05-29 13:09:34,491 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (769, 2504)\n", - "2025-05-29 13:10:23,658 - bioneuralnet.downstream_task.dpmon - INFO - Best trial config: {'gnn_layer_num': 4, 'gnn_hidden_dim': 64, 'lr': 0.02435222881645533, 'weight_decay': 0.0005853927207500042, 'nn_hidden_dim1': 64, 'nn_hidden_dim2': 128, 'num_epochs': 2048}\n", - "2025-05-29 13:10:23,659 - bioneuralnet.downstream_task.dpmon - INFO - Best trial final loss: 0.9653064608573914\n", - "2025-05-29 13:10:23,659 - bioneuralnet.downstream_task.dpmon - INFO - Best trial final accuracy: 0.9466840052015605\n", - "2025-05-29 13:10:23,661 - bioneuralnet.downstream_task.dpmon - INFO - gnn_layer_num gnn_hidden_dim ... nn_hidden_dim2 num_epochs\n", - "0 4 64 ... 128 2048\n", - "\n", - "[1 rows x 7 columns]\n", - "2025-05-29 13:10:23,663 - bioneuralnet.downstream_task.dpmon - INFO - Best tuned parameters: {'gnn_layer_num': 4, 'gnn_hidden_dim': 64, 'lr': 0.02435222881645533, 'weight_decay': 0.0005853927207500042, 'nn_hidden_dim1': 64, 'nn_hidden_dim2': 128, 'num_epochs': 2048}\n", - "2025-05-29 13:10:23,663 - bioneuralnet.downstream_task.dpmon - INFO - Best tuned parameters: {'gnn_layer_num': 4, 'gnn_hidden_dim': 64, 'lr': 0.02435222881645533, 'weight_decay': 0.0005853927207500042, 'nn_hidden_dim1': 64, 'nn_hidden_dim2': 128, 'num_epochs': 2048}\n", - "2025-05-29 13:10:23,663 - bioneuralnet.downstream_task.dpmon - INFO - Running standard training with tuned parameters.\n", - "2025-05-29 13:10:23,663 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", - "2025-05-29 13:10:23,746 - bioneuralnet.downstream_task.dpmon - INFO - Number of nodes in network: 2503\n", - "2025-05-29 13:10:26,097 - bioneuralnet.downstream_task.dpmon - INFO - Training iteration 1/5\n", - "2025-05-29 13:10:36,562 - bioneuralnet.downstream_task.dpmon - INFO - Training Accuracy: 0.8362\n", - "2025-05-29 13:10:36,565 - bioneuralnet.downstream_task.dpmon - INFO - Model saved to /home/vicente/Github/BioNeuralNet/dpmon_output/dpm_model_iter_1.pth\n", - "2025-05-29 13:10:36,567 - bioneuralnet.downstream_task.dpmon - INFO - Training iteration 2/5\n", - "2025-05-29 13:10:47,086 - bioneuralnet.downstream_task.dpmon - INFO - Training Accuracy: 0.5878\n", - "2025-05-29 13:10:47,090 - bioneuralnet.downstream_task.dpmon - INFO - Model saved to /home/vicente/Github/BioNeuralNet/dpmon_output/dpm_model_iter_2.pth\n", - "2025-05-29 13:10:47,093 - bioneuralnet.downstream_task.dpmon - INFO - Training iteration 3/5\n", - "2025-05-29 13:10:57,548 - bioneuralnet.downstream_task.dpmon - INFO - Training Accuracy: 0.9870\n", - "2025-05-29 13:10:57,551 - bioneuralnet.downstream_task.dpmon - INFO - Model saved to /home/vicente/Github/BioNeuralNet/dpmon_output/dpm_model_iter_3.pth\n", - "2025-05-29 13:10:57,553 - bioneuralnet.downstream_task.dpmon - INFO - Training iteration 4/5\n", - "2025-05-29 13:11:08,005 - bioneuralnet.downstream_task.dpmon - INFO - Training Accuracy: 0.7802\n", - "2025-05-29 13:11:08,007 - bioneuralnet.downstream_task.dpmon - INFO - Model saved to /home/vicente/Github/BioNeuralNet/dpmon_output/dpm_model_iter_4.pth\n", - "2025-05-29 13:11:08,010 - bioneuralnet.downstream_task.dpmon - INFO - Training iteration 5/5\n", - "2025-05-29 13:11:18,421 - bioneuralnet.downstream_task.dpmon - INFO - Training Accuracy: 0.8700\n", - "2025-05-29 13:11:18,423 - bioneuralnet.downstream_task.dpmon - INFO - Model saved to /home/vicente/Github/BioNeuralNet/dpmon_output/dpm_model_iter_5.pth\n", - "2025-05-29 13:11:18,426 - bioneuralnet.downstream_task.dpmon - INFO - Best Accuracy: 0.9870\n", - "2025-05-29 13:11:18,426 - bioneuralnet.downstream_task.dpmon - INFO - Average Accuracy across 5 models: 0.8122\n", - "2025-05-29 13:11:18,426 - bioneuralnet.downstream_task.dpmon - INFO - Standard Deviation across all models: 0.1465\n", - "2025-05-29 13:11:18,427 - bioneuralnet.downstream_task.dpmon - INFO - Returning best model predictions and average accuracy (predictions, avg_accuracy).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "DPMON results:\n", - "Accuracy: 0.9869960988296489\n", - "F1 weighted: 0.9874857727588546\n", - "F1 macro: 0.9695114345114344\n" - ] - } - ], + "outputs": [], "source": [ - "from bioneuralnet.downstream_task import DPMON\n", - "from sklearn.metrics import accuracy_score, f1_score\n", - "\n", - "save = Path(\"/home/vicente/Github/BioNeuralNet/dpmon_output\")\n", - "brca_pam50 = brca_pam50.rename(columns={\"pam50\": \"phenotype\"})\n", - "\n", - "dpmon = DPMON(\n", - " adjacency_matrix=A_train,\n", - " omics_list=[meth_sel, rna_sel, mirna_sel],\n", - " phenotype_data=brca_pam50,\n", - " clinical_data=clinical,\n", - " repeat_num=5,\n", - " tune=True,\n", - " gpu=True, \n", - " cuda=0,\n", - " output_dir=Path(save),\n", - ")\n", - "\n", - "predictions_df, avg_accuracy = dpmon.run()\n", - "actual = predictions_df[\"Actual\"]\n", - "pred = predictions_df[\"Predicted\"]\n", - "\n", - "dpmon_acc = accuracy_score(actual, pred)\n", - "dpmon_f1w = f1_score(actual, pred, average='weighted')\n", - "dpmon_f1m = f1_score(actual, pred, average='macro')\n", + "from sklearn.model_selection import StratifiedKFold, ParameterSampler, RepeatedStratifiedKFold\n", + "from sklearn.metrics import accuracy_score, f1_score, recall_score, roc_auc_score, average_precision_score\n", + "from sklearn.base import clone\n", + "from sklearn.preprocessing import StandardScaler, label_binarize\n", + "from sklearn.pipeline import Pipeline\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.neural_network import MLPClassifier\n", + "from scipy.stats import loguniform, randint\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "from sklearn.tree import DecisionTreeClassifier\n", + "from xgboost import XGBClassifier\n", + "from sklearn.svm import SVC\n", + "import numpy as np\n", + "import pandas as pd\n", "\n", - "print(f\"\\nDPMON results:\")\n", - "print(f\"Accuracy: {dpmon_acc}\")\n", - "print(f\"F1 weighted: {dpmon_f1w}\")\n", - "print(f\"F1 macro: {dpmon_f1m}\")" + "X = pd.concat([dna_meth, rna, mirna, clinical_for_model], axis=1)\n", + "y = target['vital_status']\n", + "print(f\"Successfully created X matrix with shape: {X.shape}\")\n", + "print(f\"Successfully created y vector with shape: {y.shape}\")\n", + "\n", + "\n", + "pipe_lr = Pipeline([\n", + " ('scaler', StandardScaler()),\n", + " ('model', LogisticRegression(solver='lbfgs', max_iter=1000, penalty=None, random_state=SEED))\n", + "])\n", + "\n", + "pipe_mlp = Pipeline([\n", + " ('scaler', StandardScaler()),\n", + " ('model', MLPClassifier(max_iter=500, early_stopping=True, n_iter_no_change=10, random_state=SEED))\n", + "])\n", + "\n", + "pipe_xgb = Pipeline([\n", + " ('scaler', StandardScaler()),\n", + " ('model', XGBClassifier(eval_metric='logloss', tree_method='hist', max_bin=128, random_state=SEED))\n", + "])\n", + "pipe_rf = Pipeline([\n", + " ('scaler', StandardScaler()),\n", + " ('model', RandomForestClassifier(random_state=SEED))\n", + "])\n", + "\n", + "pipe_svm = Pipeline([\n", + " ('scaler', StandardScaler()),\n", + " ('model', SVC(probability=True, random_state=SEED))\n", + "])\n", + "\n", + "pipe_dt = Pipeline([\n", + " ('scaler', StandardScaler()),\n", + " ('model', DecisionTreeClassifier(random_state=SEED))\n", + "])\n", + "\n", + "params_lr = {'model__penalty': ['l2'], 'model__C': loguniform(1e-4, 1e2)}\n", + "\n", + "params_mlp = {\n", + " 'model__hidden_layer_sizes': [(100,), (100, 50), (50, 50)],\n", + " 'model__activation': ['relu', 'tanh'],\n", + " 'model__alpha': loguniform(1e-5, 1e-1),\n", + " 'model__learning_rate_init': loguniform(1e-4, 1e-2)\n", + "}\n", + "params_xgb = {\n", + " 'model__n_estimators': randint(50, 200),\n", + " 'model__learning_rate': loguniform(0.01, 0.3),\n", + " 'model__max_depth': randint(3, 7),\n", + " 'model__subsample': [0.8, 1.0], \n", + " 'model__colsample_bytree': [0.8, 1.0]\n", + "}\n", + "params_rf = {\n", + " 'model__n_estimators': randint(100, 300),\n", + " 'model__max_depth': [10, 20, 30, None],\n", + " 'model__min_samples_split': randint(2, 10),\n", + " 'model__min_samples_leaf': randint(1, 5),\n", + " 'model__max_features': ['sqrt', 'log2']\n", + "}\n", + "params_svm = {\n", + " 'model__C': loguniform(1e-2, 1e3),\n", + " 'model__kernel': ['rbf', 'linear'],\n", + " 'model__gamma': ['scale', 'auto']\n", + "}\n", + "\n", + "params_dt = {\n", + " 'model__max_depth': randint(3, 15),\n", + " 'model__min_samples_split': randint(2, 20),\n", + " 'model__criterion': ['gini', 'entropy']\n", + "}\n", + "\n", + "models_to_tune = {\n", + " \"LogisticRegression\": (pipe_lr, params_lr),\n", + " \"SVM\": (pipe_svm, params_svm),\n", + " \"MLP\": (pipe_mlp, params_mlp),\n", + " \"XGBoost\": (pipe_xgb, params_xgb),\n", + " \"RandomForest\": (pipe_rf, params_rf),\n", + " \"DecisionTree\": (pipe_dt, params_dt),\n", + "}\n", + "\n", + "all_results = {\n", + " \"LogisticRegression\": {\"acc\": [], \"f1_w\": [], \"f1_m\": [], \"recall\": [], \"auc\": []},\n", + " \"MLP\": {\"acc\": [], \"f1_w\": [], \"f1_m\": [], \"recall\": [], \"auc\": []},\n", + " \"XGBoost\": {\"acc\": [], \"f1_w\": [], \"f1_m\": [], \"recall\": [], \"auc\": []},\n", + " \"RandomForest\": {\"acc\": [], \"f1_w\": [], \"f1_m\": [], \"recall\": [], \"auc\": []},\n", + " \"SVM\": {\"acc\": [], \"f1_w\": [], \"f1_m\": [], \"recall\": [], \"auc\": []},\n", + " \"DecisionTree\": {\"acc\": [], \"f1_w\": [], \"f1_m\": [], \"recall\": [], \"auc\": []},\n", + "}\n", + "\n", + "\n", + "for model_name, (pipeline, param_dist) in models_to_tune.items():\n", + " print(f\"Evaluating model with nested CV: {model_name}\")\n", + " \n", + " outer_cv = RepeatedStratifiedKFold(n_splits=5, n_repeats=5, random_state=SEED)\n", + " \n", + " # inner folds are for finding the best hyperparameters\n", + " for fold_idx, (train_idx, test_idx) in enumerate(outer_cv.split(X, y), start=1):\n", + " X_train_outer, X_test_outer = X.iloc[train_idx], X.iloc[test_idx]\n", + " y_train_outer, y_test_outer = y.iloc[train_idx], y.iloc[test_idx]\n", + " inner_cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=SEED)\n", + " \n", + " best_score_fold = -np.inf\n", + " best_params_fold = None\n", + " # May or may not want to set a seed here. A fix seed = same hyperparamters each fold.\n", + " # No seed = different hyperparameters each fold. This adds more randomness, and may yield better generalization.\n", + " param_sampler = list(ParameterSampler(param_dist, n_iter=20))\n", + " \n", + " for params in param_sampler:\n", + " inner_scores = []\n", + " \n", + " for inner_train_idx, inner_val_idx in inner_cv.split(X_train_outer, y_train_outer):\n", + " X_train_inner = X_train_outer.iloc[inner_train_idx]\n", + " X_val_inner = X_train_outer.iloc[inner_val_idx]\n", + " y_train_inner = y_train_outer.iloc[inner_train_idx]\n", + " y_val_inner = y_train_outer.iloc[inner_val_idx]\n", + " \n", + " inner_pipeline = clone(pipeline)\n", + " inner_pipeline.set_params(**params)\n", + " inner_pipeline.fit(X_train_inner, y_train_inner)\n", + " \n", + " y_val_pred = inner_pipeline.predict(X_val_inner)\n", + " score = f1_score(y_val_inner, y_val_pred, average='weighted', zero_division=0)\n", + " inner_scores.append(score)\n", + " \n", + " mean_score = np.mean(inner_scores)\n", + " if mean_score > best_score_fold:\n", + " best_score_fold = mean_score\n", + " best_params_fold = params\n", + " \n", + " print(f\"Outer fold {fold_idx}: best params (inner CV F1-W={best_score_fold:.4f})\")\n", + " print(f\"{best_params_fold}\")\n", + " \n", + " final_pipeline = clone(pipeline)\n", + " final_pipeline.set_params(**best_params_fold)\n", + " final_pipeline.fit(X_train_outer, y_train_outer)\n", + " \n", + " preds = final_pipeline.predict(X_test_outer)\n", + " \n", + " if hasattr(final_pipeline, \"predict_proba\"):\n", + " proba = final_pipeline.predict_proba(X_test_outer)\n", + " else:\n", + " proba = None\n", + " \n", + " acc = accuracy_score(y_test_outer, preds)\n", + " f1_w = f1_score(y_test_outer, preds, average='weighted', zero_division=0)\n", + " f1_m = f1_score(y_test_outer, preds, average='macro', zero_division=0)\n", + " recall = recall_score(y_test_outer, preds, average='macro', zero_division=0)\n", + " \n", + " auc = np.nan\n", + " \n", + " if proba is not None:\n", + " try:\n", + " if len(np.unique(y)) == 2:\n", + " auc = roc_auc_score(y_test_outer, proba[:, 1])\n", + " else:\n", + " auc = roc_auc_score(y_test_outer, proba, multi_class='ovr', average='macro')\n", + " y_test_bin = label_binarize(y_test_outer, classes=np.unique(y))\n", + " except Exception:\n", + " auc = np.nan\n", + "\n", + " print(f\"Fold {fold_idx} results: Acc={acc:.4f}, F1-W={f1_w:.4f}, \"\n", + " f\"F1-M={f1_m:.4f}, Recall={recall:.4f}, AUC={auc:.4f}\")\n", + " \n", + " all_results[model_name][\"acc\"].append(acc)\n", + " all_results[model_name][\"f1_w\"].append(f1_w)\n", + " all_results[model_name][\"f1_m\"].append(f1_m)\n", + " all_results[model_name][\"recall\"].append(recall)\n", + " all_results[model_name][\"auc\"].append(auc)\n", + "\n", + "print(\"\\nFINAL BASELINE RESULTS\\n\")\n", + "for model_name, metrics in all_results.items():\n", + " avg_acc = np.mean(metrics[\"acc\"])\n", + " std_acc = np.std(metrics[\"acc\"])\n", + " avg_f1_w = np.mean(metrics[\"f1_w\"])\n", + " std_f1_w = np.std(metrics[\"f1_w\"])\n", + " avg_f1_m = np.mean(metrics[\"f1_m\"])\n", + " std_f1_m = np.std(metrics[\"f1_m\"])\n", + " avg_recall = np.mean(metrics[\"recall\"])\n", + " std_recall = np.std(metrics[\"recall\"])\n", + " avg_auc = np.nanmean(metrics[\"auc\"])\n", + " std_auc = np.nanstd(metrics[\"auc\"])\n", + " \n", + " print(f\"\\n{model_name}:\")\n", + " print(f\"Accuracy: {avg_acc:.4f} +/- {std_acc:.4f}\")\n", + " print(f\"F1 Weighted: {avg_f1_w:.4f} +/- {std_f1_w:.4f}\")\n", + " print(f\"F1 Macro: {avg_f1_m:.4f} +/- {std_f1_m:.4f}\")\n", + " print(f\"Recall: {avg_recall:.4f} +/- {std_recall:.4f}\")\n", + " print(f\"AUC: {avg_auc:.4f} +/- {std_auc:.4f}\")" ] } ], diff --git a/docs/source/notebooks/TCGA-Biomarkers.ipynb b/docs/source/notebooks/TCGA-Biomarkers.ipynb new file mode 100644 index 0000000..8ff2add --- /dev/null +++ b/docs/source/notebooks/TCGA-Biomarkers.ipynb @@ -0,0 +1,1167 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "d408841e", + "metadata": {}, + "source": [ + "# TCGA-KIPAN Analysis: Using *BioNeuralNet* for Interpretability and Biomarker Discovery\n", + "\n", + "- **Cohort**: Focuses on the TCGA-KIPAN dataset, a vital resource merging three major kidney cancer subtypes:\n", + "\n", + " - `Kidney Renal Clear Cell Carcinoma` (KIRC)\n", + " - `Kidney Renal Papillary Cell Carcinoma` (KIRP)\n", + " - `Kidney Chromophobe `(KICH)\n", + "\n", + "- **Goal**: Leverage Subgraph Detection, one of the modules included in *BioNeuralNet*, to identify densely connected omics from a larger Multi-Omics Network.\n", + "\n", + "- These densely connected omics represent biologically meaningful **communities** and are highly correlated to their respective phenotype.\n", + "\n", + "- We will also leverage BioNeuralNet visualization tools to explore the discovered communities further.\n", + "\n", + "**Data Source:** [Broad Institute FireHose](`http://firebrowse.org/?cohort=KIPAN`)\n", + "\n", + "*For the end-to-end analysis see TCGA-KIPAN Analysis*" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fac7426c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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MGP0.0191210.0240700.0355830.0074850.009774
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" + ], + "text/plain": [ + " 0 1 2 3 4\n", + "MGP 0.019121 0.024070 0.035583 0.007485 0.009774\n", + "ADH1C 0.000000 0.049013 0.000000 0.000000 0.097422\n", + "LOC440354 0.018648 0.010866 0.014588 0.011661 0.019968\n", + "C1orf110 0.000000 0.050925 0.000000 0.002459 0.074045\n", + "CLDN8 0.001006 0.015970 0.009054 0.009751 0.065529" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# we have emebeddings for each of the 4 graphs\n", + "# we will select the last one from the list (threshold_10) for visualization\n", + "res = all_results_GAT[0] \n", + "tensor_data = res[\"embeddings\"]\n", + "\n", + "# .detach().cpu().numpy() to get the raw numbers out of pytorch\n", + "GAT_embeddings_df = pd.DataFrame(\n", + " tensor_data.detach().cpu().numpy(),\n", + " index=omics_kipan.columns\n", + ")\n", + "\n", + "display(GAT_embeddings_df.iloc[:5,:5])" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "b1200e4e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Plotting Feature Embeddings for graph: threshold_10\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from bioneuralnet.metrics import plot_embeddings\n", + "\n", + "# for a more interpretable embedding plot we will map the node labels to omics types\n", + "node_labels = []\n", + "GAT_embeddings_array = GAT_embeddings_df.values\n", + "feature_names = GAT_embeddings_df.index\n", + "\n", + "# using the sets from the orginal to serve as a loopup table\n", + "dna_feats = set(dna_meth.columns)\n", + "rna_feats = set(rna.columns)\n", + "mirna_feats = set(mirna.columns)\n", + "\n", + "for feat in feature_names:\n", + " if feat in dna_feats:\n", + " # 1 for DNA methylation\n", + " node_labels.append(1)\n", + " elif feat in rna_feats:\n", + " # 2 for RNA \n", + " node_labels.append(2)\n", + " elif feat in mirna_feats:\n", + " # and 3 for miRNA \n", + " node_labels.append(3)\n", + " else:\n", + " node_labels.append(0)\n", + "\n", + "# our plotting function needs an array to squeeze the embedings into 2Dspace\n", + "node_labels = np.array(node_labels)\n", + "\n", + "# feature(omics) embeddings colored by omics type.\n", + "print(f\"Plotting Feature Embeddings for graph: {res['graph']}\")\n", + "plot_embeddings(GAT_embeddings_array, node_labels, legend_labels=[\"DNA_Methylation\", \"RNA\", \"miRNA\"])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "98442c32", + "metadata": {}, + "outputs": [], + "source": [ + "from bioneuralnet.clustering import HybridLouvain\n", + "\n", + "labels = target[\"target\"] \n", + "unique_phenotypes = labels.unique()\n", + "multiclass_results = {}\n", + "summary = []\n", + "label_map = {0: \"KIRC\", 1: \"KIRP\", 2: \"KICH\"}\n", + "\n", + "for phenotype in unique_phenotypes:\n", + " y_binary = (labels == phenotype).astype(int)\n", + "\n", + " hl = HybridLouvain(\n", + " G=similarity_10,\n", + " B=omics_kipan,\n", + " Y=y_binary,\n", + " k3=0.2,\n", + " k4=0.8,\n", + " max_iter=3,\n", + " seed=SEED,\n", + " )\n", + "\n", + " subnetworks = hl.run(as_dfs=True)\n", + "\n", + " name = label_map.get(phenotype, str(phenotype))\n", + "\n", + " if subnetworks:\n", + " df = subnetworks[0]\n", + " multiclass_results[name] = df\n", + " summary.append((name, df.shape[1]))\n", + " else:\n", + " summary.append((name, 0))\n", + "\n", + "print(\"\\nDriver modules (one-vs-rest):\")\n", + "for name, n_feats in summary:\n", + " print(f\" {name}: {n_feats} features\")\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "d44680c5", + "metadata": {}, + "source": [ + "Driver modules (one-vs-rest):\n", + " KIRP: 293 features\n", + " KIRC: 159 features\n", + " KICH: 69 features" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "id": "04f74f33", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Omic Degree\n", + "Index \n", + "5 INPP5J_27124 12\n", + "26 SLC26A7_115111 9\n", + "12 HEPACAM2_253012 7\n", + "16 CLNK_116449 7\n", + "27 RHCG_51458 6\n", + "3 CLCNKB_1188 6\n", + "13 DMRT2_10655 6\n", + "18 LOC643008_643008 5\n", + "10 SLC4A1_6521 5\n", + "9 CLCNKA_1187 5\n", + "4 FGF9_2254 5\n", + "11 C9orf84_158401 4\n", + "8 ATP6V1B1_525 4\n", + "22 KRTAP5_8_57830 4\n", + "15 FOXI1_2299 3\n", + "23 ATP6V0A4_50617 3\n", + "21 ATP6V0D2_245972 1\n", + "24 TMPRSS2_7113 1\n", + "25 GCOM1_145781 1\n", + "1 hsa_mir_129_1 1\n", + "20 hsa_mir_200c 1\n", + "19 KLK1_3816 1\n", + "17 hsa_mir_891a 1\n", + "2 hsa_mir_129_2 1\n", + "7 EPB41L4B_54566 1\n", + "6 TRIM50_135892 1\n", + "14 hsa_mir_141 1\n" + ] + } + ], + "source": [ + "from bioneuralnet.metrics import louvain_to_adjacency, plot_network\n", + "\n", + "kirp_df = multiclass_results[\"KICH\"]\n", + "kirp_adj = louvain_to_adjacency(kirp_df)\n", + "kirp_mapping = plot_network(kirp_adj, weight_threshold=0.76, show_labels=True, show_edge_weights=True)\n", + "print(kirp_mapping)" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "id": "66890c59", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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OmicDegreeSource
Index
5INPP5J_2712412RNA
26SLC26A7_1151119RNA
12HEPACAM2_2530127RNA
16CLNK_1164497RNA
27RHCG_514586RNA
3CLCNKB_11886RNA
13DMRT2_106556RNA
18LOC643008_6430085RNA
10SLC4A1_65215RNA
9CLCNKA_11875RNA
4FGF9_22545RNA
11C9orf84_1584014RNA
8ATP6V1B1_5254RNA
22KRTAP5_8_578304RNA
15FOXI1_22993RNA
23ATP6V0A4_506173RNA
21ATP6V0D2_2459721RNA
24TMPRSS2_71131RNA
25GCOM1_1457811RNA
1hsa_mir_129_11miRNA
20hsa_mir_200c1miRNA
19KLK1_38161RNA
17hsa_mir_891a1miRNA
2hsa_mir_129_21miRNA
7EPB41L4B_545661RNA
6TRIM50_1358921RNA
14hsa_mir_1411miRNA
\n", + "
" + ], + "text/plain": [ + " Omic Degree Source\n", + "Index \n", + "5 INPP5J_27124 12 RNA\n", + "26 SLC26A7_115111 9 RNA\n", + "12 HEPACAM2_253012 7 RNA\n", + "16 CLNK_116449 7 RNA\n", + "27 RHCG_51458 6 RNA\n", + "3 CLCNKB_1188 6 RNA\n", + "13 DMRT2_10655 6 RNA\n", + "18 LOC643008_643008 5 RNA\n", + "10 SLC4A1_6521 5 RNA\n", + "9 CLCNKA_1187 5 RNA\n", + "4 FGF9_2254 5 RNA\n", + "11 C9orf84_158401 4 RNA\n", + "8 ATP6V1B1_525 4 RNA\n", + "22 KRTAP5_8_57830 4 RNA\n", + "15 FOXI1_2299 3 RNA\n", + "23 ATP6V0A4_50617 3 RNA\n", + "21 ATP6V0D2_245972 1 RNA\n", + "24 TMPRSS2_7113 1 RNA\n", + "25 GCOM1_145781 1 RNA\n", + "1 hsa_mir_129_1 1 miRNA\n", + "20 hsa_mir_200c 1 miRNA\n", + "19 KLK1_3816 1 RNA\n", + "17 hsa_mir_891a 1 miRNA\n", + "2 hsa_mir_129_2 1 miRNA\n", + "7 EPB41L4B_54566 1 RNA\n", + "6 TRIM50_135892 1 RNA\n", + "14 hsa_mir_141 1 miRNA" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from bioneuralnet.metrics import find_omics_modality\n", + "\n", + "# for those features in the KIRP module, we will identify their omics source\n", + "omics_df_list = [top_meth, top_rna, top_mirna]\n", + "source_names = [\"DNA_Methylation\", \"RNA\", \"miRNA\"]\n", + "\n", + "kirp_mapping_with_source = find_omics_modality(kirp_mapping, omics_df_list, source_names)\n", + "display(kirp_mapping_with_source)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "074f3225", + "metadata": {}, + "outputs": [], + "source": [ + "dna_feats = set(dna_meth.columns)\n", + "rna_feats = set(rna.columns)\n", + "mirna_feats = set(mirna.columns)\n", + "\n", + "def get_omics_type(feat):\n", + " if feat in dna_feats:\n", + " return \"DNA_Methylation\"\n", + " elif feat in rna_feats:\n", + " return \"RNA\"\n", + " elif feat in mirna_feats:\n", + " return \"miRNA\"\n", + " else:\n", + " return \"Unknown\"\n", + "\n", + "# kirp_mapping is the DF returned by plot_network\n", + "kirp_mapping[\"OmicsType\"] = kirp_mapping[\"Omic\"].map(get_omics_type)\n", + "\n", + "print(kirp_mapping)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "81315c47", + "metadata": {}, + "outputs": [], + "source": [ + "output_dir_base = Path(\"/home/vicente/Github/BioNeuralNet/dpmon_results_SAGE_FINAL/kipan\")\n", + "all_results_SAGE = []\n", + "\n", + "for run_config in comparison_runs:\n", + " graph_name = run_config[\"name\"]\n", + " A_full = run_config[\"graph\"]\n", + " \n", + " current_output_dir = output_dir_base / graph_name\n", + " current_output_dir.mkdir(parents=True, exist_ok=True)\n", + "\n", + " dpmon_params_base = {\n", + " \"adjacency_matrix\": A_full,\n", + " \"omics_list\": omics_kipan,\n", + " \"phenotype_data\": target,\n", + " \"phenotype_col\": \"target\",\n", + " \"clinical_data\": clinical_for_model,\n", + " \"model\": 'SAGE',\n", + " \"tune\": True, \n", + " \"cv\": True, \n", + " \"n_folds\": 5,\n", + " \"repeat_num\": 1,\n", + " \"gpu\": True,\n", + " \"cuda\": 0,\n", + " \"seed\": SEED,\n", + " \"output_dir\": current_output_dir\n", + " }\n", + " \n", + " dpmon_tunned = DPMON(**dpmon_params_base)\n", + " predictions_df, metrics, embeddings = dpmon_tunned.run()\n", + "\n", + " all_results_SAGE.append({\n", + " \"graph\": graph_name,\n", + " \"predictions\": predictions_df,\n", + " \"metrics\": metrics,\n", + " \"embeddings\": embeddings\n", + " })\n", + "\n", + "for res in all_results_SAGE:\n", + " graph_name = res[\"graph\"]\n", + " graph_metrics = res[\"metrics\"]\n", + " \n", + " acc_row = graph_metrics.loc[graph_metrics['Metric'] == 'Accuracy'].iloc[0]\n", + " f1_macro_row = graph_metrics.loc[graph_metrics['Metric'] == 'F1 Macro'].iloc[0]\n", + " f1_weighted_row = graph_metrics.loc[graph_metrics['Metric'] == 'F1 Weighted'].iloc[0]\n", + " recall_row = graph_metrics.loc[graph_metrics['Metric'] == 'Recall'].iloc[0]\n", + " auc_row = graph_metrics.loc[graph_metrics['Metric'] == 'AUC'].iloc[0]\n", + " aupr_row = graph_metrics.loc[graph_metrics['Metric'] == 'AUPR'].iloc[0]\n", + " \n", + " acc_avg, acc_std = acc_row['Average'], acc_row['StdDev']\n", + " f1_macro_avg, f1_macro_std = f1_macro_row['Average'], f1_macro_row['StdDev']\n", + " f1_weighted_avg, f1_weighted_std = f1_weighted_row['Average'], f1_weighted_row['StdDev']\n", + " recall_avg, recall_std = recall_row['Average'], recall_row['StdDev']\n", + " auc_avg, auc_std = auc_row['Average'], auc_row['StdDev']\n", + " aupr_avg, aupr_std = aupr_row['Average'], aupr_row['StdDev']\n", + "\n", + " print(f\"\\nResults for: {graph_name}\")\n", + " print(f\"Accuracy (Avg +/- Std): {acc_avg:.4f} +/- {acc_std:.4f}\")\n", + " print(f\"F1 Macro (Avg +/- Std): {f1_macro_avg:.4f} +/- {f1_macro_std:.4f}\")\n", + " print(f\"F1 Weighted (Avg +/- Std): {f1_weighted_avg:.4f} +/- {f1_weighted_std:.4f}\")\n", + " print(f\"Recall: {recall_avg:.4f} +/- {recall_std:.4f}\")\n", + " print(f\"AUC: {auc_avg:.4f} +/- {auc_std:.4f}\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "20a324ce", + "metadata": {}, + "source": [ + "Results for: threshold_10\n", + "Accuracy (Avg +/- Std): 0.9452 +/- 0.0303\n", + "F1 Macro (Avg +/- Std): 0.9345 +/- 0.0238\n", + "F1 Weighted (Avg +/- Std): 0.9452 +/- 0.0305\n", + "Recall: 0.9406 +/- 0.0141\n", + "AUC: 0.9949 +/- 0.0024\n", + "\n", + "Results for: correlation_10\n", + "Accuracy (Avg +/- Std): 0.9467 +/- 0.0351\n", + "F1 Macro (Avg +/- Std): 0.9284 +/- 0.0495\n", + "F1 Weighted (Avg +/- Std): 0.9489 +/- 0.0306\n", + "Recall: 0.9487 +/- 0.0208\n", + "AUC: 0.9937 +/- 0.0050\n", + "\n", + "Results for: similarity_10\n", + "Accuracy (Avg +/- Std): 0.9574 +/- 0.0149\n", + "F1 Macro (Avg +/- Std): 0.9515 +/- 0.0125\n", + "F1 Weighted (Avg +/- Std): 0.9576 +/- 0.0149\n", + "Recall: 0.9608 +/- 0.0135\n", + "AUC: 0.9944 +/- 0.0034\n", + "\n", + "Results for: gaussian_15\n", + "Accuracy (Avg +/- Std): 0.9620 +/- 0.0153\n", + "F1 Macro (Avg +/- Std): 0.9585 +/- 0.0131\n", + "F1 Weighted (Avg +/- Std): 0.9621 +/- 0.0154\n", + "Recall: 0.9680 +/- 0.0126\n", + "AUC: 0.9943 +/- 0.0021" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "80c83c6e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " 0 1 2 3 4\n", + "MGP 0.0 0.0 0.923418 0.000000 0.295637\n", + "ADH1C 0.0 0.0 0.521837 0.005931 0.421344\n", + "LOC440354 0.0 0.0 0.851917 0.000000 0.216582\n", + "C1orf110 0.0 0.0 0.625036 0.009498 0.256967\n", + "CLDN8 0.0 0.0 0.638092 0.000000 0.230229" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#This time we will select the gaussian_15 graph embeddings\n", + "res = all_results_SAGE[3] \n", + "tensor_data = res[\"embeddings\"]\n", + "\n", + "# .detach().cpu().numpy() to get the raw numbers out of pytorch\n", + "SAGE_embeddings_df = pd.DataFrame(\n", + " tensor_data.detach().cpu().numpy(),\n", + " index=omics_kipan.columns\n", + ")\n", + "\n", + "display(SAGE_embeddings_df.iloc[:5,:5])" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "1e1ed982", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Plotting Feature Embeddings for graph: correlation_10\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from bioneuralnet.metrics import plot_embeddings\n", + "\n", + "# for a more interpretable embedding plot we will map the node labels to omics types\n", + "SAGE_node_labels = []\n", + "SAGE_embeddings_array = SAGE_embeddings_df.values\n", + "feature_names = SAGE_embeddings_df.index\n", + "\n", + "# using the sets from the orginal to serve as a loopup table\n", + "dna_feats = set(dna_meth.columns)\n", + "rna_feats = set(rna.columns)\n", + "mirna_feats = set(mirna.columns)\n", + "\n", + "for feat in feature_names:\n", + " if feat in dna_feats:\n", + " # 1 for DNA methylation\n", + " SAGE_node_labels.append(1)\n", + " elif feat in rna_feats:\n", + " # 2 for RNA \n", + " SAGE_node_labels.append(2)\n", + " elif feat in mirna_feats:\n", + " # and 3 for miRNA \n", + " SAGE_node_labels.append(3)\n", + " else:\n", + " SAGE_node_labels.append(0)\n", + "\n", + "# our plotting function needs an array to squeeze the embedings into 2Dspace\n", + "SAGE_node_labels = np.array(SAGE_node_labels)\n", + "\n", + "# feature(omics) embeddings colored by omics type.\n", + "print(f\"Plotting Feature Embeddings for graph: {res['graph']}\")\n", + "plot_embeddings(SAGE_embeddings_array, SAGE_node_labels, legend_labels=[\"DNA_Methylation\", \"RNA\", \"miRNA\"])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9a15b24b", + "metadata": {}, + "outputs": [], + "source": [ + "from bioneuralnet.clustering import HybridLouvain\n", + "\n", + "labels = target[\"target\"]\n", + "unique_phenotypes = labels.unique()\n", + "\n", + "multiclass_results = {}\n", + "summary = []\n", + "\n", + "for phenotype in unique_phenotypes:\n", + " # one-vs-rest encoding\n", + " y_binary = (labels == phenotype).astype(int)\n", + "\n", + " hl = HybridLouvain(\n", + " G=correlation_10,\n", + " B=omics_kipan,\n", + " Y=y_binary,\n", + " k3=0.2,\n", + " k4=0.8,\n", + " max_iter=3,\n", + " seed=SEED,\n", + " )\n", + "\n", + " subnetworks = hl.run(as_dfs=True)\n", + "\n", + " if subnetworks:\n", + " df = subnetworks[0]\n", + " multiclass_results[phenotype] = df\n", + " summary.append((phenotype, df.shape[1]))\n", + " else:\n", + " summary.append((phenotype, 0))\n", + "\n", + "print(\"\\nDriver modules (one-vs-rest):\")\n", + "for pheno, n_feats in summary:\n", + " print(f\" {pheno}: {n_feats} features\")" + ] + }, + { + "cell_type": "markdown", + "id": "308099e1", + "metadata": {}, + "source": [ + "Driver modules (one-vs-rest):\n", + " 1: 857 features\n", + " 0: 451 features\n", + " 2: 448 features" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "efbff52f", + "metadata": {}, + "outputs": [], + "source": [ + "output_dir_base = Path(\"/home/vicente/Github/BioNeuralNet/dpmon_results_GCN_FINAL/kipan\")\n", + "\n", + "all_results_GCN = []\n", + "\n", + "for run_config in comparison_runs:\n", + " graph_name = run_config[\"name\"]\n", + " A_full = run_config[\"graph\"]\n", + "\n", + " current_output_dir = output_dir_base / graph_name\n", + " current_output_dir.mkdir(parents=True, exist_ok=True)\n", + "\n", + " dpmon_params_base = {\n", + " \"adjacency_matrix\": A_full,\n", + " \"omics_list\": omics_kipan,\n", + " \"phenotype_data\": target,\n", + " \"phenotype_col\": \"target\",\n", + " \"clinical_data\": clinical_for_model,\n", + " \"model\": 'GCN',\n", + " \"tune\": True, \n", + " \"cv\": True, \n", + " \"n_folds\": 5,\n", + " \"repeat_num\": 1,\n", + " \"gpu\": True,\n", + " \"cuda\": 0,\n", + " \"seed\": SEED,\n", + " \"output_dir\": current_output_dir\n", + " }\n", + " \n", + " dpmon_tunned = DPMON(**dpmon_params_base)\n", + " predictions_df, metrics, embeddings = dpmon_tunned.run()\n", + "\n", + " all_results_GCN.append({\n", + " \"graph\": graph_name,\n", + " \"predictions\": predictions_df,\n", + " \"metrics\": metrics,\n", + " \"embeddings\": embeddings\n", + " })\n", + "\n", + "for res in all_results_GCN:\n", + " graph_name = res[\"graph\"]\n", + " graph_metrics = res[\"metrics\"]\n", + " \n", + " acc_row = graph_metrics.loc[graph_metrics['Metric'] == 'Accuracy'].iloc[0]\n", + " f1_macro_row = graph_metrics.loc[graph_metrics['Metric'] == 'F1 Macro'].iloc[0]\n", + " f1_weighted_row = graph_metrics.loc[graph_metrics['Metric'] == 'F1 Weighted'].iloc[0]\n", + " recall_row = graph_metrics.loc[graph_metrics['Metric'] == 'Recall'].iloc[0]\n", + " auc_row = graph_metrics.loc[graph_metrics['Metric'] == 'AUC'].iloc[0]\n", + " aupr_row = graph_metrics.loc[graph_metrics['Metric'] == 'AUPR'].iloc[0]\n", + " \n", + " acc_avg, acc_std = acc_row['Average'], acc_row['StdDev']\n", + " f1_macro_avg, f1_macro_std = f1_macro_row['Average'], f1_macro_row['StdDev']\n", + " f1_weighted_avg, f1_weighted_std = f1_weighted_row['Average'], f1_weighted_row['StdDev']\n", + " recall_avg, recall_std = recall_row['Average'], recall_row['StdDev']\n", + " auc_avg, auc_std = auc_row['Average'], auc_row['StdDev']\n", + " aupr_avg, aupr_std = aupr_row['Average'], aupr_row['StdDev']\n", + "\n", + " print(f\"\\nResults for: {graph_name}\")\n", + " print(f\"Accuracy (Avg +/- Std): {acc_avg:.4f} +/- {acc_std:.4f}\")\n", + " print(f\"F1 Macro (Avg +/- Std): {f1_macro_avg:.4f} +/- {f1_macro_std:.4f}\")\n", + " print(f\"F1 Weighted (Avg +/- Std): {f1_weighted_avg:.4f} +/- {f1_weighted_std:.4f}\")\n", + " print(f\"Recall: {recall_avg:.4f} +/- {recall_std:.4f}\")\n", + " print(f\"AUC: {auc_avg:.4f} +/- {auc_std:.4f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "d36f4ced", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " 0 1 2 3 4\n", + "MGP 0.035549 0.001088 0.0 0.0 0.003548\n", + "ADH1C 0.000000 0.000253 0.0 0.0 0.026202\n", + "LOC440354 0.000000 0.002382 0.0 0.0 0.000000\n", + "C1orf110 0.000000 0.000261 0.0 0.0 0.060484\n", + "CLDN8 0.000000 0.001527 0.0 0.0 0.004444" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#This time we will select the gaussian_15 graph embeddings\n", + "res = all_results_GCN[3] \n", + "tensor_data = res[\"embeddings\"]\n", + "\n", + "# .detach().cpu().numpy() to get the raw numbers out of pytorch\n", + "GCN_embeddings_df = pd.DataFrame(\n", + " tensor_data.detach().cpu().numpy(),\n", + " index=omics_kipan.columns\n", + ")\n", + "\n", + "display(GCN_embeddings_df.iloc[:5,:5])" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "db08276e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Plotting Feature Embeddings for graph: similarity_10\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from bioneuralnet.metrics import plot_embeddings\n", + "\n", + "# for a more interpretable embedding plot we will map the node labels to omics types\n", + "GCN_node_labels = []\n", + "GCN_embeddings_array = GCN_embeddings_df.values\n", + "feature_names = GCN_embeddings_df.index\n", + "\n", + "# using the sets from the orginal to serve as a loopup table\n", + "dna_feats = set(dna_meth.columns)\n", + "rna_feats = set(rna.columns)\n", + "mirna_feats = set(mirna.columns)\n", + "\n", + "for feat in feature_names:\n", + " if feat in dna_feats:\n", + " # 1 for DNA methylation\n", + " GCN_node_labels.append(1)\n", + " elif feat in rna_feats:\n", + " # 2 for RNA \n", + " GCN_node_labels.append(2)\n", + " elif feat in mirna_feats:\n", + " # and 3 for miRNA \n", + " GCN_node_labels.append(3)\n", + " else:\n", + " GCN_node_labels.append(0)\n", + "\n", + "# our plotting function needs an array to squeeze the embedings into 2Dspace\n", + "GCN_node_labels = np.array(GCN_node_labels)\n", + "\n", + "# feature(omics) embeddings colored by omics type.\n", + "print(f\"Plotting Feature Embeddings for graph: {res['graph']}\")\n", + "plot_embeddings(GCN_embeddings_array, GCN_node_labels, legend_labels=[\"DNA_Methylation\", \"RNA\", \"miRNA\"])" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".enviroment", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/source/notebooks/TCGA-GBMLGG.ipynb b/docs/source/notebooks/TCGA-GBMLGG.ipynb deleted file mode 100644 index a95ed01..0000000 --- a/docs/source/notebooks/TCGA-GBMLGG.ipynb +++ /dev/null @@ -1,1791 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "b42de981", - "metadata": {}, - "source": [ - "# TCGA-GBMLGG Analysis Demo\n", - "\n", - "- **Cohort**: Focuses on the TCGA-GBMLGG dataset, a vital resource merging Glioblastoma Multiforme (GBM) and Lower-Grade Glioma (LGG).\n", - "- **Goal**: Perform histological subtype classification.\n", - "- **Prediction Target**: Predict whether a tumor is an `astrocytoma`, `oligodendroglioma`, or `oligoastrocytoma` based on its multi-omics profile.\n", - "\n", - "**Data Source:** Broad Institute FireHose (http://firebrowse.org/?cohort=GBMLGG)" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "027dc20c", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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TCGA-06-0125-01TCGA-06-0125-02TCGA-06-0152-01TCGA-06-0152-02TCGA-06-0171-01
Hybridization REF
Composite Element REFBeta_ValueBeta_ValueBeta_ValueBeta_ValueBeta_Value
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The first entry is kept for duplicate patients in the clinical data.\n", - "4. **Find Common Patients:** The script identifies the list of patients that exist in *all* datasets.\n", - "5. **Subset Data:** All data tables are filtered down to *only* this common list of patients, ensuring alignment.\n", - "6. **Extract Target:** The `histological_type` column is pulled from the processed clinical data to be used as the prediction target (y-variable)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "820583f3", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "miRNA (samples, features): (531, 548)\n", - "RNA (samples, features): (701, 18328)\n", - "Methylation (samples, features): (685, 20115)\n", - "Clinical (samples, features): (1110, 14)\n", - "\n", - "Methylation shape: (658, 20115)\n", - "RNA shape: (681, 18328)\n", - "miRNA shape: (517, 548)\n", - "Clinical shape: (1110, 14)\n", - "\n", - "Found: 511 patients across all data types.\n" - ] - } - ], - "source": [ - "mirna = mirna_raw.T\n", - "rna = rna_raw.T\n", - "meth = meth_raw.T\n", - "clinical = clinical_raw.T\n", - "\n", - "print(f\"miRNA (samples, features): {mirna.shape}\")\n", - "print(f\"RNA (samples, features): {rna.shape}\")\n", - "print(f\"Methylation (samples, features): {meth.shape}\")\n", - "print(f\"Clinical (samples, features): {clinical.shape}\")\n", - "\n", - "def trim_barcode(idx):\n", - " return idx.to_series().str.slice(0, 12)\n", - "\n", - "# standarized patient IDs across all files\n", - "meth.index = trim_barcode(meth.index)\n", - "rna.index = trim_barcode(rna.index)\n", - "mirna.index = trim_barcode(mirna.index)\n", - "clinical.index = clinical.index.str.upper()\n", - "clinical.index.name = \"Patient_ID\"\n", - "\n", - "# convert all data to numeric, coercing errors to NaN\n", - "meth = meth.apply(pd.to_numeric, errors='coerce')\n", - "rna = rna.apply(pd.to_numeric, errors='coerce')\n", - "mirna = mirna.apply(pd.to_numeric, errors='coerce')\n", - "\n", - "# for any duplicate columns in the omics data, we average their values\n", - "meth = meth.groupby(meth.index).mean()\n", - "rna = rna.groupby(rna.index).mean()\n", - "mirna = mirna.groupby(mirna.index).mean()\n", - "\n", - "# for any duplicate rows in the clinical data, we keep the first occurrence\n", - "clinical = clinical[~clinical.index.duplicated(keep='first')]\n", - "\n", - "print(f\"\\nMethylation shape: {meth.shape}\")\n", - "print(f\"RNA shape: {rna.shape}\")\n", - "print(f\"miRNA shape: {mirna.shape}\")\n", - "print(f\"Clinical shape: {clinical.shape}\")\n", - "\n", - "for df in [meth, rna, mirna]:\n", - " df.columns = df.columns.str.replace(r\"\\?\", \"unknown_\", regex=True)\n", - " df.columns = df.columns.str.replace(r\"\\|\", \"_\", regex=True)\n", - " df.columns = df.columns.str.replace(\"-\", \"_\", regex=False)\n", - " df.columns = df.columns.str.replace(r\"_+\", \"_\", regex=True)\n", - " df.columns = df.columns.str.strip(\"_\")\n", - " \n", - " df.fillna(df.mean(), inplace=True)\n", - "\n", - "# to see which pateints are common across all data files\n", - "common_patients = sorted(list(set(meth.index)&set(rna.index)&set(mirna.index)&set(clinical.index)))\n", - "\n", - "print(f\"\\nFound: {len(common_patients)} patients across all data types.\")\n", - "\n", - "# subset to only common patients\n", - "meth_processed = meth.loc[common_patients]\n", - "rna_processed= rna.loc[common_patients]\n", - "mirna_processed = mirna.loc[common_patients]\n", - "clinical_processed = clinical.loc[common_patients]\n", - "\n", - "# extract target labels from clinical data\n", - "targets = clinical_processed['histological_type']" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "d9c562ba", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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Epsilon: 1e-06\n", - "2025-11-08 13:06:13,301 - bioneuralnet.utils.data - INFO - Beta-to-M conversion complete.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Methylation shape: (511, 20114)\n", - "RNA shape: (511, 18328)\n", - "miRNA shape: (511, 548)\n", - "Clinical shape: (511, 13)\n", - "target\n", - "0 193\n", - "1 191\n", - "2 127\n", - "Name: count, dtype: int64\n" - ] - } - ], - "source": [ - "import bioneuralnet as bnn\n", - "\n", - "# drop unwanted columns from clinical data\n", - "clinical_processed.drop(columns=[\"Composite Element REF\"], errors=\"ignore\", inplace=True)\n", - "\n", - "# we transform the methylation beta values to M-values and drop unwanted columns\n", - "meth_m = meth_processed.drop(columns=[\"Composite Element REF\"], errors=\"ignore\")\n", - "\n", - "# convert beta values to M-values using bioneuralnet utility with small epsilon to avoid log(0)\n", - "meth_m = bnn.utils.beta_to_m(meth_m, eps=1e-6) \n", - "\n", - "# lastly we turn the target labels into numerical classes\n", - "mapping = {\"astrocytoma\": 0, \"oligodendroglioma\": 1, \"oligoastrocytoma\": 2}\n", - "target_labels = targets.map(mapping).to_frame(name=\"target\")\n", - "\n", - "# as a safety check we align the indices once more\n", - "X_meth = meth_m.loc[common_patients]\n", - "X_rna = rna_processed.loc[common_patients]\n", - "X_mirna = mirna_processed.loc[common_patients]\n", - "Y_labels = target_labels.loc[common_patients]\n", - "clinical_final = clinical_processed.loc[common_patients]\n", - "\n", - "print(f\"\\nDNA_Methylation shape: {X_meth.shape}\")\n", - "print(f\"RNA shape: {X_rna.shape}\")\n", - "print(f\"miRNA shape: {X_mirna.shape}\")\n", - "print(f\"Clinical shape: {clinical_final.shape}\")\n", - "print(Y_labels.value_counts())" - ] - }, - { - "cell_type": "markdown", - "id": "0643ec13", - "metadata": {}, - "source": [ - "## Feature Selection Methodology\n", - "\n", - "### Supported Methods and Interpretation\n", - "\n", - "**BioNeuralNet** provides three techniques for feature selection, allowing for different views of the data's statistical profile:\n", - "\n", - "- **Variance Thresholding:** Identifies features with the **highest overall variance** across all samples.\n", - "\n", - "- **ANOVA F-test:** Pinpoints features that best **distinguish between the target classes** (KIRC, KIRP, and KICH).\n", - "\n", - "- **Random Forest Importance:** Assesses **feature utility** based on its contribution to a predictive non-linear model.\n", - "\n", - "### GBMLGG Cohort Selection Strategy\n", - "\n", - "A dimensionality reduction step was essential for managing the high-feature-count omics data:\n", - "\n", - "- **High-Feature Datasets:** Both DNA Methylation (20,114) and RNA (18,328) required significant feature reduction.\n", - "\n", - "- **Filtering Process:** The **top 6,000 features** were initially extracted from the Methylation and RNA datasets using all three methods.\n", - "\n", - "- **Final Set:** A consensus set was built by finding the intersection of features selected by the ANOVA F-test and Random Forest Importance, ensuring both statistical relevance and model-based utility.\n", - "\n", - "- **Low-Feature Datasets:** The miRNA data (548 features) was passed through **without selection**, as its feature count was already manageable." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "978e9281", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2025-11-08 13:15:33,012 - bioneuralnet.utils.preprocess - INFO - [Inf]: Replaced 0 infinite values\n", - "2025-11-08 13:15:33,012 - bioneuralnet.utils.preprocess - INFO - [NaN]: Replaced 0 NaNs after median imputation\n", - "2025-11-08 13:15:33,012 - bioneuralnet.utils.preprocess - INFO - [Zero-Var]: 0 columns dropped due to zero variance\n", - "2025-11-08 13:15:33,085 - bioneuralnet.utils.preprocess - INFO - Selected top 6000 features by variance\n", - "2025-11-08 13:15:35,778 - bioneuralnet.utils.preprocess - INFO - [Inf]: Replaced 0 infinite values\n", - "2025-11-08 13:15:35,779 - bioneuralnet.utils.preprocess - INFO - [NaN]: Replaced 0 NaNs after median imputation\n", - "2025-11-08 13:15:35,779 - bioneuralnet.utils.preprocess - INFO - [Zero-Var]: 0 columns dropped due to zero variance\n", - "2025-11-08 13:15:35,843 - bioneuralnet.utils.preprocess - INFO - Selected top 6000 features by variance\n", - "2025-11-08 13:15:38,824 - bioneuralnet.utils.preprocess - INFO - [Inf]: Replaced 0 infinite values\n", - "2025-11-08 13:15:38,824 - bioneuralnet.utils.preprocess - INFO - [NaN]: Replaced 0 NaNs after median imputation\n", - "2025-11-08 13:15:38,824 - bioneuralnet.utils.preprocess - INFO - [Zero-Var]: 0 columns dropped due to zero variance\n", - "2025-11-08 13:15:38,962 - bioneuralnet.utils.preprocess - INFO - Selected 6000 features by ANOVA (task=classification), 12015 significant, 0 padded\n", - "2025-11-08 13:15:41,666 - bioneuralnet.utils.preprocess - INFO - [Inf]: Replaced 0 infinite values\n", - "2025-11-08 13:15:41,666 - bioneuralnet.utils.preprocess - INFO - [NaN]: Replaced 0 NaNs after median imputation\n", - "2025-11-08 13:15:41,666 - bioneuralnet.utils.preprocess - INFO - [Zero-Var]: 0 columns dropped due to zero variance\n", - "2025-11-08 13:15:41,773 - bioneuralnet.utils.preprocess - INFO - Selected 6000 features by ANOVA (task=classification), 10820 significant, 0 padded\n", - "2025-11-08 13:15:44,759 - bioneuralnet.utils.preprocess - INFO - [Inf]: Replaced 0 infinite values\n", - "2025-11-08 13:15:44,760 - bioneuralnet.utils.preprocess - INFO - [NaN]: Replaced 0 NaNs after median imputation\n", - "2025-11-08 13:15:44,760 - bioneuralnet.utils.preprocess - INFO - [Zero-Var]: 0 columns dropped due to zero variance\n", - "2025-11-08 13:15:49,982 - bioneuralnet.utils.preprocess - INFO - [Inf]: Replaced 0 infinite values\n", - "2025-11-08 13:15:49,982 - bioneuralnet.utils.preprocess - INFO - [NaN]: Replaced 0 NaNs after median imputation\n", - "2025-11-08 13:15:49,983 - bioneuralnet.utils.preprocess - INFO - [Zero-Var]: 0 columns dropped due to zero variance\n" - ] - } - ], - "source": [ - "import bioneuralnet as bnn\n", - "\n", - "# feature selection\n", - "meth_highvar = bnn.utils.select_top_k_variance(X_meth, k=6000)\n", - "rna_highvar = bnn.utils.select_top_k_variance(X_rna, k=6000)\n", - "\n", - "meth_af = bnn.utils.top_anova_f_features(X_meth, Y_labels, max_features=6000)\n", - "rna_af = bnn.utils.top_anova_f_features(X_rna, Y_labels, max_features=6000)\n", - "\n", - "meth_rf = bnn.utils.select_top_randomforest(X_meth, Y_labels, top_k=6000)\n", - "rna_rf = bnn.utils.select_top_randomforest(X_rna, Y_labels, top_k=6000)\n", - "\n", - "meth_var_set = set(meth_highvar.columns)\n", - "meth_anova_set = set(meth_af.columns)\n", - "meth_rf_set = set(meth_rf.columns)\n", - "\n", - "rna_var_set = set(rna_highvar.columns)\n", - "rna_anova_set = set(rna_af.columns)\n", - "rna_rf_set = set(rna_rf.columns)\n", - "\n", - "meth_inter1 = list(meth_anova_set & meth_var_set)\n", - "meth_inter2 = list(meth_rf_set & meth_var_set)\n", - "meth_inter3 = list(meth_anova_set & meth_rf_set)\n", - "meth_all_three = list(meth_anova_set & meth_var_set & meth_rf_set)\n", - "\n", - "rna_inter4 = list(rna_anova_set & rna_var_set)\n", - "rna_inter5 = list(rna_rf_set & rna_var_set)\n", - "rna_inter6 = list(rna_anova_set & rna_rf_set)\n", - "rna_all_three = list(rna_anova_set & rna_var_set & rna_rf_set)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "467d9ff1", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "FROM THE 6000 Methylation feature selection:\n", - "\n", - "Anova-F & variance selection share: 2704 features\n", - "Random Forest & variance selection share: 1768 features\n", - "Anova-F & Random Forest share: 1823 features\n", - "All three methods agree on: 809 features\n" - ] - } - ], - "source": [ - "print(\"FROM THE 6000 Methylation feature selection:\\n\")\n", - "print(f\"Anova-F & variance selection share: {len(meth_inter1)} features\")\n", - "print(f\"Random Forest & variance selection share: {len(meth_inter2)} features\")\n", - "print(f\"Anova-F & Random Forest share: {len(meth_inter3)} features\")\n", - "print(f\"All three methods agree on: {len(meth_all_three)} features\")" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "6fb1e10b", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "FROM THE 6000 RNA feature selection:\n", - "\n", - "Anova-F & variance selection share: 2183 features\n", - "Random Forest & variance selection share: 1977 features\n", - "Anova-F & Random Forest share: 2127 features\n", - "All three methods agree on: 763 features\n" - ] - } - ], - "source": [ - "print(\"\\nFROM THE 6000 RNA feature selection:\\n\")\n", - "print(f\"Anova-F & variance selection share: {len(rna_inter4)} features\")\n", - "print(f\"Random Forest & variance selection share: {len(rna_inter5)} features\")\n", - "print(f\"Anova-F & Random Forest share: {len(rna_inter6)} features\")\n", - "print(f\"All three methods agree on: {len(rna_all_three)} features\")" - ] - }, - { - "cell_type": "markdown", - "id": "be2ce6f8", - "metadata": {}, - "source": [ - "## Feature Selection Summary: ANOVA-RF Intersection\n", - "\n", - "The chosen strategy for feature selection is based on the **overlap** between features identified by the **ANOVA F-test** and **Random Forest Importance**. This approach offers comprehensive filtering by balancing class-based relevance (ANOVA) with non-linear model importance (Random Forest). The resulting feature sets are considered the most robust for downstream analysis.\n", - "\n", - "### Feature Overlap Results\n", - "\n", - "The following table details the number of features resulting from the intersection of different selection methods for each omics data type.\n", - "\n", - "| Omics Data Type | ANOVA-F & Variance | RF & Variance | ANOVA-F & Random Forest (Selected) | All Three Agree |\n", - "| :--- | :--- | :--- | :--- | :--- |\n", - "| **Methylation** | 2,704 features | 1,768 features | **1,823 features** | 809 features |\n", - "| **RNA** | 2,183 features | 1,977 features | **2,127 features** | 763 features |" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "a1934672", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Final Shapes for Modeling\n", - "Methylation (X1): (511, 1823)\n", - "RNA-Seq (X2): (511, 2127)\n", - "miRNA-Seq (X3): (511, 548)\n", - "Labels (Y): (511, 1)\n" - ] - } - ], - "source": [ - "X_meth_selected = X_meth[meth_inter3]\n", - "X_rna_selected = X_rna[rna_inter6]\n", - "\n", - "print(\"\\nFinal Shapes for Modeling\")\n", - "print(f\"Methylation (X1): {X_meth_selected.shape}\")\n", - "print(f\"RNA-Seq (X2): {X_rna_selected.shape}\")\n", - "print(f\"miRNA-Seq (X3): {X_mirna.shape}\")\n", - "print(f\"Labels (Y): {Y_labels.shape}\")" - ] - }, - { - "cell_type": "markdown", - "id": "9348ca2a", - "metadata": {}, - "source": [ - "## Data Availability\n", - "\n", - "To facilitate rapid experimentation and reproduction of our results, the fully processed and feature-selected dataset used in this analysis has been made available directly within the package.\n", - "\n", - "Users can load this dataset, bypassing all preceding data acquisition, preprocessing, and feature selection steps. This allows users to proceed immediately from this step." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "08ec1120", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'mirna': (511, 548),\n", - " 'target': (511, 1),\n", - " 'clinical': (511, 13),\n", - " 'rna': (511, 2127),\n", - " 'meth': (511, 1823)}" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import bioneuralnet as bnn\n", - "\n", - "tgca_lgg = bnn.datasets.DatasetLoader(\"lgg\")\n", - "display(tgca_lgg.shape)\n", - "\n", - "# The dataset is returned as a dictionary. We extract each file independetly based on the name( Key).\n", - "dna_meth = tgca_lgg.data[\"meth\"]\n", - "rna = tgca_lgg.data[\"rna\"]\n", - "mirna = tgca_lgg.data[\"mirna\"]\n", - "clinical = tgca_lgg.data[\"clinical\"]\n", - "target = tgca_lgg.data[\"target\"]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "09c76e4f", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2025-11-08 13:33:26,861 - bioneuralnet.utils.preprocess - INFO - [Inf]: Replaced 0 infinite values\n", - "2025-11-08 13:33:26,862 - bioneuralnet.utils.preprocess - INFO - [NaN]: Replaced 717 NaNs after median imputation\n", - "2025-11-08 13:33:26,862 - bioneuralnet.utils.preprocess - INFO - [Zero-Var]: 1 columns dropped due to zero variance\n", - "2025-11-08 13:33:26,944 - bioneuralnet.utils.preprocess - INFO - Selected top 7 features by RandomForest importance\n" - ] - }, - { - "data": { - "text/html": [ - "
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histological_type_oligodendrogliomahistological_type_oligoastrocytomakarnofsky_performance_scoreradiation_therapy_noradiation_therapy_yes
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" - ], - "text/plain": [ - " histological_type_oligodendroglioma \\\n", - "Patient_ID \n", - "TCGA-CS-4938 False \n", - "TCGA-CS-4941 False \n", - "TCGA-CS-4942 False \n", - "\n", - " histological_type_oligoastrocytoma karnofsky_performance_score \\\n", - "Patient_ID \n", - "TCGA-CS-4938 False 90.0 \n", - "TCGA-CS-4941 False 90.0 \n", - "TCGA-CS-4942 False 70.0 \n", - "\n", - " radiation_therapy_no radiation_therapy_yes \n", - "Patient_ID \n", - "TCGA-CS-4938 True False \n", - "TCGA-CS-4941 False True \n", - "TCGA-CS-4942 False True " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# BioNeuralNet provides a preprocessing function to handle clinical data\n", - "clinical = tgca_lgg.data[\"clinical\"]\n", - "\n", - "# For more details on the preprocessing functions, see `bioneuralnet.utils.preprocess``\n", - "clinical_preprocessed = bnn.utils.preprocess_clinical(\n", - " clinical, \n", - " target, \n", - " top_k=7, \n", - " scale=False, \n", - " ignore_columns=[ \"days_to_last_followup\", \"years_to_birth\", \"days_to_death\", \"date_of_initial_pathologic_diagnosis\"])\n", - "\n", - "display(clinical_preprocessed.iloc[:3,:5])" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "fb76a376", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Nan values in X_train_full: 0\n", - "Nan value in X_train_full after dropping: 0\n", - "X_train_full shape: (511, 4498)\n", - "\n", - "Network shape: (4498, 4498)\n" - ] - } - ], - "source": [ - "import pandas as pd\n", - "\n", - "X_train_full = pd.concat([dna_meth, rna, mirna], axis=1)\n", - "\n", - "print(f\"Nan values in X_train_full: {X_train_full.isna().sum().sum()}\")\n", - "X_train_full = X_train_full.dropna()\n", - "print(f\"Nan value in X_train_full after dropping: {X_train_full.isna().sum().sum()}\")\n", - "\n", - "print(f\"X_train_full shape: {X_train_full.shape}\")\n", - "# building the graph using the similarity graph function with k=15\n", - "A_train = bnn.utils.gen_similarity_graph(X_train_full, k=15)\n", - "\n", - "print(f\"\\nNetwork shape: {A_train.shape}\")" - ] - }, - { - "cell_type": "markdown", - "id": "61a2b14e", - "metadata": {}, - "source": [ - "## Reproducibility and Seeding\n", - "\n", - "To ensure our experimental results are fully reproducible, a single global seed is set at the beginning of the analysis.\n", - "\n", - "This utility function propagates the seed to all sources of randomness, including `random`, `numpy`, and `torch` (for both CPU and GPU).\n", - "\n", - "Critically, it also configures the PyTorch cuDNN backend to use deterministic algorithms." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "3f28d80e", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2025-11-08 16:33:32,623 - bioneuralnet.utils.data - INFO - Setting global seed for reproducibility to: 118\n", - "2025-11-08 16:33:32,633 - bioneuralnet.utils.data - INFO - CUDA available. Applying seed to all GPU operations\n", - "2025-11-08 16:33:32,634 - bioneuralnet.utils.data - INFO - Seed setting complete\n" - ] - } - ], - "source": [ - "import bioneuralnet as bnn\n", - "\n", - "SEED = 118\n", - "bnn.utils.set_seed(SEED)" - ] - }, - { - "cell_type": "markdown", - "id": "eb5e0121", - "metadata": {}, - "source": [ - "## Hyperparameter Tuning\n", - "\n", - "- First step on our benchmark analysis is dedicated to **hyperparameter tuning** for:\n", - " - **SAGE:** GraphSAGE (Graph Sample and Aggregate)\n", - " - **GCN:** Graph Convolutional Network\n", - " - **GAT:** Graph Attention Network\n", - " \n", - "- We use `tune=True` and `repeat_num=5` within **DPMON** (our classification framework) to let the framework automatically search for the best architectural and training parameters.\n", - "\n", - "- After the tuning phase, we will manually set the best-performing parameters for each model.\n", - "- We then use these optimal configurations in the final cross-validation to rigorously test each model performance and stability." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1a6d4721", - "metadata": {}, - "outputs": [], - "source": [ - "from pathlib import Path\n", - "\n", - "# We rename the target to phenotype so we keep it consistent with DPMON expectations.\n", - "target = target.rename(columns={\"target\": \"phenotype\"})\n", - "\n", - "dpmon_sage_tune = bnn.downstream_task.DPMON(\n", - " adjacency_matrix=A_train,\n", - " omics_list=[dna_meth, rna, mirna],\n", - " phenotype_data=target,\n", - " clinical_data=clinical_preprocessed,\n", - " model=\"SAGE\",\n", - " tune=True,\n", - " repeat_num=5,\n", - " gpu=True, cuda=0,\n", - " output_dir=Path(\"/home/vicente/Github/BioNeuralNet/dpmon_tuning/GBM_SAGE\"),\n", - ")\n", - "\n", - "dpmon_sage_tune.run()\n", - "\n", - "dpmon_gcn_tune = bnn.downstream_task.DPMON(\n", - " adjacency_matrix=A_train,\n", - " omics_list=[dna_meth, rna, mirna],\n", - " phenotype_data=target,\n", - " clinical_data=clinical_preprocessed,\n", - " model=\"GCN\",\n", - " tune=True,\n", - " repeat_num=5,\n", - " gpu=True, cuda=0,\n", - " output_dir=Path(\"/home/vicente/Github/BioNeuralNet/dpmon_tuning/GBM_GCN\"),\n", - ")\n", - "\n", - "dpmon_gcn_tune.run()\n", - "\n", - "\n", - "dpmon_gat_tune = bnn.downstream_task.DPMON(\n", - " adjacency_matrix=A_train,\n", - " omics_list=[dna_meth, rna, mirna],\n", - " phenotype_data=target,\n", - " clinical_data=clinical_preprocessed,\n", - " model=\"GAT\",\n", - " tune=True,\n", - " repeat_num=5,\n", - " gpu=True, cuda=0,\n", - " output_dir=Path(\"/home/vicente/Github/BioNeuralNet/dpmon_tuning/GBM_GAT\"),\n", - ")\n", - "\n", - "dpmon_gcn_tune.run()\n", - "\n", - "# Due to the extensive length, the terminal output has been omitted. To see the full terminal output for the results below please visit: https://github.com/UCD-BDLab/BioNeuralNet/TCGA-Notebooks/TCGA-GBMLGG.ipynb" - ] - }, - { - "cell_type": "markdown", - "id": "383740ac", - "metadata": {}, - "source": [ - "## Analysis of Hyperparameter Optimization\n", - "\n", - "**Hyperparameter tuning using `SEED = 118`**\n", - "\n", - "### Tuning Search Space\n", - "\n", - "**DPMON** parameter search space is extensive, offering over 12,000 discrete combinations plus a continuous space for learning rates. By sampling **50 trials** from this space, we are effectively probing it to find the distinct, optimal architecture best suited for each GNN (SAGE, GCN, and GAT).\n", - "\n", - "\n", - "| Parameter | SAGE (GraphSAGE) | GCN (Graph Convolutional) | GAT (Graph Attention) |\n", - "| :--- | :--- | :--- | :--- |\n", - "| **gnn\\_layer\\_num** | 2 | 64 | 16 |\n", - "| **gnn\\_hidden\\_dim** | 128 | 16 | 4 |\n", - "| **lr (Learning Rate)** | 0.005197 | 0.004397 | 0.003226 |\n", - "| **weight\\_decay** | 0.046079 | 0.000405 | 0.046863 |\n", - "| **nn\\_hidden\\_dim1** | 1 | 64 | 16 |\n", - "| **nn\\_hidden\\_dim2** | 32 | 8 | 128 |\n", - "| **num\\_epochs** | 4096 | 1024 | 512 |\n", - "\n", - "### SAGE Model Configuration\n", - "The optimal SAGE configuration was **shallow but wide**, requiring an unusually long training time. This completely reverses the previous \"deep and narrow\" result, indicating that a high-dimensional feature representation was prioritized over recursive aggregation in this specific run.\n", - "\n", - "### GCN Model Configuration\n", - "The GCN model favored a highly atypical architecture: **deep yet narrow**. The success of this extremely deep GCN suggests the model learned highly specialized, low-dimensional features over many layers, despite the typical risk of over-smoothing.\n", - "\n", - "### GAT Model Configuration\n", - "The GAT model found a balanced, efficient architecture: **moderate depth and narrow**. It was the fastest to converge, suggesting GAT's attention mechanism effectively selected features, allowing it to perform well with a minimal, low-dimensional representation." - ] - }, - { - "cell_type": "markdown", - "id": "b44593d0", - "metadata": {}, - "source": [ - "---" - ] - }, - { - "cell_type": "markdown", - "id": "88084af0", - "metadata": {}, - "source": [ - "## Cross-Validation Results with 5 folds\n", - "\n", - "| Model | Avg. Accuracy | Avg. F1 Weighted | Avg. F1 Macro |\n", - "| :--- | :--- | :--- | :--- |\n", - "| **GAT** | **0.9213** +/- 0.1321 | **0.9045** +/- 0.1656 | **0.8906** +/- 0.1910 |\n", - "| **GCN** | 0.8821 +/- 0.1353 | 0.8639 +/- 0.1666 | 0.8644 +/- 0.1620 |\n", - "| **SAGE** | 0.7567 +/- 0.1846 | 0.7311 +/- 0.2160 | 0.7135 +/- 0.2361 |\n", - "\n", - "The cross-validation results demonstrate that the optimal architectures found with `SEED = 118` performed best with GAT, followed by GCN, and then SAGE.\n", - "\n", - "### 1. GAT Performance\n", - "\n", - "The GAT model achieved the highest average accuracy and F1 scores, suggesting its attention mechanism effectively utilized the narrow-but-moderately-deep architecture (16 layers, 4 hidden dim) for classification.\n", - "\n", - "- Avg. Accuracy: 0.9213 (+/- 0.1321)\n", - "- Avg. F1 Weighted: 0.9045 (+/- 0.1656)\n", - "\n", - "### 2. GCN Performance\n", - "\n", - "GCN performed very strongly, showing that its highly unusual deep-and-narrow architecture (64 layers, 16 hidden dim) was successful. However, like the other models, it exhibited high standard deviations, indicating its performance was also unstable across the folds.\n", - "\n", - "- Avg. Accuracy: 0.8821 (+/- 0.1353)\n", - "- Avg. F1 Weighted: 0.8639 (+/- 0.1666)\n", - "\n", - "### 3. SAGE Performance\n", - "\n", - "SAGE yielded the lowest average performance and the highest variability (largest +/- values). This suggests that the shallow-but-wide architecture (2 layers, 128 hidden dim) was less stable and less effective for this specific classification task compared to the other two models.\n", - "\n", - "- Avg. Accuracy: 0.7567 (+/- 0.1846)\n", - "- Avg. F1 Weighted: 0.7311 (+/- 0.2160)\n", - "\n", - "\n", - "### Summary\n", - "\n", - "While the GAT and GCN models achieved high average performance, all three models suffered from high standard deviations. This indicates that while the architectures are capable of high accuracy, their performance is highly sensitive.\n", - "\n", - "**These results are shown below**" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9a77d244", - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import numpy as np\n", - "from sklearn.model_selection import StratifiedKFold\n", - "from sklearn.metrics import f1_score, accuracy_score\n", - "from bioneuralnet.downstream_task import DPMON\n", - "\n", - "X_meth_full = dna_meth\n", - "X_rna_full = rna\n", - "X_mirna_full = mirna\n", - "C_full = clinical_preprocessed\n", - "Y_full = target\n", - "A_full = A_train\n", - "\n", - "patient_indices = np.arange(len(Y_full))\n", - "y_target_classes = Y_full.squeeze()\n", - "\n", - "sage_params = {\n", - " 'layer_num': 2,\n", - " 'gnn_hidden_dim': 128,\n", - " 'lr': 0.005197038211089667,\n", - " 'weight_decay': 0.04607928341516355,\n", - " 'nn_hidden_dim1': 16,\n", - " 'nn_hidden_dim2': 32,\n", - " 'num_epochs': 4096\n", - "}\n", - "\n", - "N_FOLDS = 5\n", - "skf = StratifiedKFold(n_splits=N_FOLDS, shuffle=True, random_state=SEED) \n", - "output_dir_base_sage = Path(\"/home/vicente/Github/BioNeuralNet/dpmon_cv_results_SAGE_FINAL\")\n", - "\n", - "accuracy_scores_sage = []\n", - "f1_macro_scores_sage = []\n", - "f1_weighted_scores_sage = []\n", - "\n", - "for fold_num, (train_index, test_index) in enumerate(skf.split(patient_indices, y_target_classes)):\n", - " print(f\"\\nRunning DPMON SAGE Fold {fold_num + 1}/{N_FOLDS}\")\n", - "\n", - " X_train_omics = [X_meth_full.iloc[train_index], X_rna_full.iloc[train_index], X_mirna_full.iloc[train_index]]\n", - " Y_train = Y_full.iloc[train_index]\n", - " C_train = C_full.iloc[train_index]\n", - " A_train_fold = A_full.iloc[train_index, train_index] \n", - " \n", - " dpmon_fold = DPMON(\n", - " adjacency_matrix=A_train_fold, omics_list=X_train_omics, phenotype_data=Y_train,\n", - " clinical_data=C_train, repeat_num=1, tune=False, gpu=True, cuda=0,\n", - " output_dir=output_dir_base_sage / f\"fold_{fold_num + 1}\",\n", - " **sage_params\n", - " )\n", - "\n", - " predictions_df, _ = dpmon_fold.run() \n", - " actual = predictions_df[\"Actual\"]\n", - " pred = predictions_df[\"Predicted\"]\n", - "\n", - " test_f1_macro = f1_score(actual, pred, average='macro')\n", - " test_f1_weighted = f1_score(actual, pred, average='weighted')\n", - " test_acc = accuracy_score(actual, pred)\n", - "\n", - " accuracy_scores_sage.append(test_acc)\n", - " f1_macro_scores_sage.append(test_f1_macro)\n", - " f1_weighted_scores_sage.append(test_f1_weighted)\n", - " \n", - " print(f\"SAGE Fold {fold_num + 1} | Accuracy: {test_acc:.4f} | F1-Macro: {test_f1_macro:.4f}\")\n", - "\n", - "mean_acc_sage = np.mean(accuracy_scores_sage)\n", - "std_acc_sage = np.std(accuracy_scores_sage)\n", - "\n", - "mean_f1_macro_sage = np.mean(f1_macro_scores_sage)\n", - "std_f1_macro_sage = np.std(f1_macro_scores_sage)\n", - "\n", - "mean_f1_weighted_sage = np.mean(f1_weighted_scores_sage)\n", - "std_f1_weighted_sage = np.std(f1_weighted_scores_sage)\n", - "\n", - "print(\"\\nClassification with SAGE and 5-FOLD Cross-Validation\")\n", - "print(f\"Avg. Accuracy: {mean_acc_sage:.4f} +/- {std_acc_sage:.4f}\")\n", - "print(f\"Avg. F1 Weighted: {mean_f1_weighted_sage:.4f} +/- {std_f1_weighted_sage:.4f}\")\n", - "print(f\"Avg. F1 Macro: {mean_f1_macro_sage:.4f} +/- {std_f1_macro_sage:.4f}\")\n", - "\n", - "# Due to the extensive length, the terminal output has been omitted. To see the full terminal output for the results below please visit: https://github.com/UCD-BDLab/BioNeuralNet/TCGA-Notebooks/TCGA-GBMLGG.ipynb" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c8b344bd", - "metadata": {}, - "outputs": [], - "source": [ - "output_dir_base_gcn = Path(\"/home/vicente/Github/BioNeuralNet/dpmon_cv_results_GCN_FINAL\")\n", - "\n", - "gcn_params = {\n", - " 'layer_num': 64,\n", - " 'gnn_hidden_dim': 16,\n", - " 'lr': 0.004397636847528492,\n", - " 'weight_decay': 0.00040569459559797747,\n", - " 'nn_hidden_dim1': 4,\n", - " 'nn_hidden_dim2': 8,\n", - " 'num_epochs': 1024\n", - "}\n", - "\n", - "skf_gcn = StratifiedKFold(n_splits=N_FOLDS, shuffle=True, random_state=SEED) \n", - "\n", - "accuracy_scores_gcn = []\n", - "f1_macro_scores_gcn = []\n", - "f1_weighted_scores_gcn = []\n", - "\n", - "for fold_num, (train_index, test_index) in enumerate(skf_gcn.split(patient_indices, y_target_classes)):\n", - " print(f\"\\nDPMON GCN Fold {fold_num + 1}/{N_FOLDS}\")\n", - "\n", - " X_train_omics = [X_meth_full.iloc[train_index], X_rna_full.iloc[train_index], X_mirna_full.iloc[train_index]]\n", - " Y_train = Y_full.iloc[train_index]\n", - " C_train = C_full.iloc[train_index]\n", - " A_train_fold = A_full.iloc[train_index, train_index] \n", - " \n", - " dpmon_fold = DPMON(\n", - " adjacency_matrix=A_train_fold, omics_list=X_train_omics, phenotype_data=Y_train,\n", - " clinical_data=C_train, repeat_num=1, tune=False, gpu=True, cuda=0,\n", - " output_dir=output_dir_base_gcn / f\"fold_{fold_num + 1}\",\n", - " **gcn_params\n", - " )\n", - "\n", - " predictions_df, _ = dpmon_fold.run() \n", - " actual = predictions_df[\"Actual\"]\n", - " pred = predictions_df[\"Predicted\"]\n", - "\n", - " test_acc = accuracy_score(actual, pred)\n", - " test_f1 = f1_score(actual, pred, average='macro')\n", - " test_f1w = f1_score(actual, pred, average='weighted')\n", - "\n", - " accuracy_scores_gcn.append(test_acc)\n", - " f1_macro_scores_gcn.append(test_f1)\n", - " f1_weighted_scores_gcn.append(test_f1w)\n", - " \n", - " print(f\"GCN Fold {fold_num + 1} | Accuracy: {test_acc:.4f} | F1-Macro: {test_f1:.4f}\")\n", - "\n", - "mean_acc_gcn = np.mean(accuracy_scores_gcn)\n", - "std_acc_gcn = np.std(accuracy_scores_gcn)\n", - "\n", - "mean_f1_macro_gcn = np.mean(f1_macro_scores_gcn)\n", - "std_f1_macro_gcn = np.std(f1_macro_scores_gcn)\n", - "\n", - "mean_f1_weighted_gcn = np.mean(f1_weighted_scores_gcn)\n", - "std_f1_weighted_gcn = np.std(f1_weighted_scores_gcn)\n", - "\n", - "print(\"Classification with GCN and 5-FOLD Cross-Validation\")\n", - "print(f\"Avg. Accuracy: {mean_acc_gcn:.4f} +/- {std_acc_gcn:.4f}\")\n", - "print(f\"Avg. F1 Weighted: {mean_f1_weighted_gcn:.4f} +/- {std_f1_weighted_gcn:.4f}\")\n", - "print(f\"Avg. F1 Macro: {mean_f1_macro_gcn:.4f} +/- {std_f1_macro_gcn:.4f}\")\n", - "\n", - "# Due to the extensive length, the terminal output has been omitted. To see the full terminal output for the results below please visit: https://github.com/UCD-BDLab/BioNeuralNet/TCGA-Notebooks/TCGA-GBMLGG.ipynb" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b6a40cf6", - "metadata": {}, - "outputs": [], - "source": [ - "output_dir_base_gat = Path(\"/home/vicente/Github/BioNeuralNet/dpmon_cv_results_GAT_FINAL\")\n", - "\n", - "gat_params = {\n", - " 'layer_num': 16,\n", - " 'gnn_hidden_dim': 4,\n", - " 'lr': 0.003226646486974539,\n", - " 'weight_decay': 0.04686394640851084,\n", - " 'nn_hidden_dim1': 16,\n", - " 'nn_hidden_dim2': 128,\n", - " 'num_epochs': 512\n", - "}\n", - "\n", - "skf_gat = StratifiedKFold(n_splits=N_FOLDS, shuffle=True, random_state=SEED) \n", - "\n", - "accuracy_scores_gat = []\n", - "f1_macro_scores_gat = []\n", - "f1_weighted_scores_gat = []\n", - "\n", - "for fold_num, (train_index, test_index) in enumerate(skf_gat.split(patient_indices, y_target_classes)):\n", - " print(f\"\\nDPMON GAT Fold {fold_num + 1}/{N_FOLDS}\")\n", - "\n", - " X_train_omics = [X_meth_full.iloc[train_index], X_rna_full.iloc[train_index], X_mirna_full.iloc[train_index]]\n", - " Y_train = Y_full.iloc[train_index]\n", - " C_train = C_full.iloc[train_index]\n", - " A_train_fold = A_full.iloc[train_index, train_index] \n", - " \n", - " dpmon_fold = DPMON(\n", - " adjacency_matrix=A_train_fold, omics_list=X_train_omics, phenotype_data=Y_train,\n", - " clinical_data=C_train, repeat_num=1, tune=False, gpu=True, cuda=0,\n", - " output_dir=output_dir_base_gat / f\"fold_{fold_num + 1}\",\n", - " **gat_params\n", - " )\n", - "\n", - " predictions_df, _ = dpmon_fold.run() \n", - " actual = predictions_df[\"Actual\"]\n", - " pred = predictions_df[\"Predicted\"]\n", - "\n", - " test_acc = accuracy_score(actual, pred)\n", - " test_f1 = f1_score(actual, pred, average='macro')\n", - " test_f1w = f1_score(actual, pred, average='weighted')\n", - "\n", - " accuracy_scores_gat.append(test_acc)\n", - " f1_macro_scores_gat.append(test_f1)\n", - " f1_weighted_scores_gat.append(test_f1w)\n", - " \n", - " print(f\"GAT Fold {fold_num + 1} | Accuracy: {test_acc:.4f} | F1-Macro: {test_f1:.4f}\")\n", - "\n", - "mean_acc_gat = np.mean(accuracy_scores_gat)\n", - "std_acc_gat = np.std(accuracy_scores_gat)\n", - "\n", - "mean_f1_macro_gat = np.mean(f1_macro_scores_gat)\n", - "std_f1_macro_gat = np.std(f1_macro_scores_gat)\n", - "\n", - "mean_f1_weighted_gat = np.mean(f1_weighted_scores_gat)\n", - "std_f1_weighted_gat = np.std(f1_weighted_scores_gat)\n", - "\n", - "print(\"Classification with GAT and 5-FOLD Cross-Validation\")\n", - "print(f\"Avg. Accuracy: {mean_acc_gat:.4f} +/- {std_acc_gat:.4f}\")\n", - "print(f\"Avg. F1 Weighted: {mean_f1_weighted_gat:.4f} +/- {std_f1_weighted_gat:.4f}\")\n", - "print(f\"Avg. F1 Macro: {mean_f1_macro_gat:.4f} +/- {std_f1_macro_gat:.4f}\")\n", - "\n", - "# Due to the extensive length, the terminal output has been omitted. To see the full terminal output for the results below please visit: https://github.com/UCD-BDLab/BioNeuralNet/TCGA-Notebooks/TCGA-GBMLGG.ipynb" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": ".enviroment", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.3" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/source/notebooks/TCGA-KIPAN.ipynb b/docs/source/notebooks/TCGA-KIPAN.ipynb index 3c3c48c..68b9ef2 100644 --- a/docs/source/notebooks/TCGA-KIPAN.ipynb +++ b/docs/source/notebooks/TCGA-KIPAN.ipynb @@ -5,7 +5,7 @@ "id": "d408841e", "metadata": {}, "source": [ - "# TCGA-KIPAN Analysis Demo\n", + "# TCGA-KIPAN Analysis\n", "\n", "- **Cohort**: Focuses on the TCGA-KIPAN dataset, a vital resource merging three major kidney cancer subtypes:\n", " - `Kidney Renal Clear Cell Carcinoma` (KIRC)\n", @@ -23,372 +23,7 @@ "execution_count": null, "id": "1c56af27", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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Hybridization REF
Composite Element REFBeta_ValueBeta_ValueBeta_ValueBeta_ValueBeta_Value
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tcga-kl-8328tcga-kl-8339tcga-km-8439tcga-km-8441tcga-km-8442
Hybridization REF
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years_to_birth6067306138
vital_status01000
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years_to_birthradiation_therapy_nogender_maleethnicity_not hispanic or latinorace_white
Patient_ID
TCGA-2K-A9WE53.01111
TCGA-2Z-A9J171.01111
TCGA-2Z-A9J271.01011
\n", + "
" + ], + "text/plain": [ + " years_to_birth radiation_therapy_no gender_male \\\n", + "Patient_ID \n", + "TCGA-2K-A9WE 53.0 1 1 \n", + "TCGA-2Z-A9J1 71.0 1 1 \n", + "TCGA-2Z-A9J2 71.0 1 0 \n", + "\n", + " ethnicity_not hispanic or latino race_white \n", + "Patient_ID \n", + "TCGA-2K-A9WE 1 1 \n", + "TCGA-2Z-A9J1 1 1 \n", + "TCGA-2Z-A9J2 1 1 " + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "import bioneuralnet as bnn\n", - "\n", - "# feature selection\n", - "meth_highvar = bnn.utils.select_top_k_variance(X_meth, k=6000)\n", - "rna_highvar = bnn.utils.select_top_k_variance(X_rna, k=6000)\n", - "\n", - "meth_af = bnn.utils.top_anova_f_features(X_meth, Y_labels, max_features=6000)\n", - "rna_af = bnn.utils.top_anova_f_features(X_rna, Y_labels, max_features=6000)\n", - "\n", - "meth_rf = bnn.utils.select_top_randomforest(X_meth, Y_labels, top_k=6000)\n", - "rna_rf = bnn.utils.select_top_randomforest(X_rna, Y_labels, top_k=6000)\n", - "\n", - "meth_var_set = set(meth_highvar.columns)\n", - "meth_anova_set = set(meth_af.columns)\n", - "meth_rf_set = set(meth_rf.columns)\n", - "\n", - "rna_var_set = set(rna_highvar.columns)\n", - "rna_anova_set = set(rna_af.columns)\n", - "rna_rf_set = set(rna_rf.columns)\n", - "\n", - "meth_inter1 = list(meth_anova_set & meth_var_set)\n", - "meth_inter2 = list(meth_rf_set & meth_var_set)\n", - "meth_inter3 = list(meth_anova_set & meth_rf_set)\n", - "meth_all_three = list(meth_anova_set & meth_var_set & meth_rf_set)\n", + "from bioneuralnet.utils import preprocess_clinical\n", "\n", - "rna_inter4 = list(rna_anova_set & rna_var_set)\n", - "rna_inter5 = list(rna_rf_set & rna_var_set)\n", - "rna_inter6 = list(rna_anova_set & rna_rf_set)\n", - "rna_all_three = list(rna_anova_set & rna_var_set & rna_rf_set)" + "clinical_for_model = preprocess_clinical(\n", + " clinical, \n", + " top_k=7, \n", + " scale=False,\n", + " ignore_columns=[\n", + " # target-related or outcome-related or irrelevant columns\n", + " \"histological_type\",\n", + " \"pathologic_stage\",\n", + " \"pathology_M_stage\",\n", + " \"pathology_N_stage\",\n", + " \"pathology_T_stage\",\n", + " \"date_of_initial_pathologic_diagnosis\",\n", + " \"days_to_last_followup\",\n", + " \"vital_status\"\n", + " ]\n", + ")\n", + "display(clinical_for_model.iloc[:3,:5])" ] }, { "cell_type": "code", - "execution_count": 6, - "id": "b02d2358", + "execution_count": 5, + "id": "40c36cd7", "metadata": {}, "outputs": [ { - "name": "stdout", + "name": "stderr", "output_type": "stream", "text": [ - "FROM THE 6000 Methylation feature selection:\n", + "2025-11-26 00:57:19,265 - bioneuralnet.utils.preprocess - INFO - [Inf]: Replaced 0 infinite values\n", + "2025-11-26 00:57:19,266 - bioneuralnet.utils.preprocess - INFO - [NaN]: Replaced 0 NaNs after median imputation\n", + "2025-11-26 00:57:19,266 - bioneuralnet.utils.preprocess - INFO - [Zero-Var]: 0 columns dropped due to zero variance\n", + "2025-11-26 00:57:19,274 - bioneuralnet.utils.preprocess - INFO - Selected top 300 features by variance\n", + "2025-11-26 00:57:19,604 - bioneuralnet.utils.preprocess - INFO - [Inf]: Replaced 0 infinite values\n", + "2025-11-26 00:57:19,604 - bioneuralnet.utils.preprocess - INFO - [NaN]: Replaced 0 NaNs after median imputation\n", + "2025-11-26 00:57:19,604 - bioneuralnet.utils.preprocess - INFO - [Zero-Var]: 0 columns dropped due to zero variance\n", + "2025-11-26 00:57:19,613 - bioneuralnet.utils.preprocess - INFO - Selected top 300 features by variance\n", + "2025-11-26 00:57:19,685 - bioneuralnet.utils.preprocess - INFO - [Inf]: Replaced 0 infinite values\n", + "2025-11-26 00:57:19,685 - bioneuralnet.utils.preprocess - INFO - [NaN]: Replaced 0 NaNs after median imputation\n", + "2025-11-26 00:57:19,685 - bioneuralnet.utils.preprocess - INFO - [Zero-Var]: 0 columns dropped due to zero variance\n", + "2025-11-26 00:57:19,687 - bioneuralnet.utils.preprocess - INFO - Selected top 300 features by variance\n", + "2025-11-26 00:57:20,259 - bioneuralnet.utils.graph_tools - INFO - GRAPH ANALYSIS: threshold_10\n", + "\n", + "2025-11-26 00:57:20,259 - bioneuralnet.utils.graph_tools - INFO - Nodes: 900 | Edges: 8,628\n", + "2025-11-26 00:57:20,260 - bioneuralnet.utils.graph_tools - INFO - Avg degree: 19.17\n", + "2025-11-26 00:57:20,260 - bioneuralnet.utils.graph_tools - INFO - Connected components: 1\n", + "2025-11-26 00:57:20,261 - bioneuralnet.utils.graph_tools - INFO - Largest component: 900 nodes (100.0%)\n", + "2025-11-26 00:57:20,345 - bioneuralnet.utils.graph_tools - INFO - Avg clustering coefficient: 0.016\n", + "2025-11-26 00:57:20,357 - bioneuralnet.utils.graph_tools - INFO - GRAPH ANALYSIS: correlation_10\n", + "\n", + "2025-11-26 00:57:20,357 - bioneuralnet.utils.graph_tools - INFO - Nodes: 900 | Edges: 8,628\n", + "2025-11-26 00:57:20,357 - bioneuralnet.utils.graph_tools - INFO - Avg degree: 19.17\n", + "2025-11-26 00:57:20,358 - bioneuralnet.utils.graph_tools - INFO - Connected components: 1\n", + "2025-11-26 00:57:20,358 - bioneuralnet.utils.graph_tools - INFO - Largest component: 900 nodes (100.0%)\n", + "2025-11-26 00:57:20,443 - bioneuralnet.utils.graph_tools - INFO - Avg clustering coefficient: 0.108\n", + "2025-11-26 00:57:20,455 - bioneuralnet.utils.graph_tools - INFO - GRAPH ANALYSIS: similarity_10\n", + "\n", + "2025-11-26 00:57:20,455 - bioneuralnet.utils.graph_tools - INFO - Nodes: 900 | Edges: 8,930\n", + "2025-11-26 00:57:20,456 - bioneuralnet.utils.graph_tools - INFO - Avg degree: 19.84\n", + "2025-11-26 00:57:20,456 - bioneuralnet.utils.graph_tools - INFO - Connected components: 1\n", + "2025-11-26 00:57:20,456 - bioneuralnet.utils.graph_tools - INFO - Largest component: 900 nodes (100.0%)\n", + "2025-11-26 00:57:20,552 - bioneuralnet.utils.graph_tools - INFO - Avg clustering coefficient: 0.127\n", + "2025-11-26 00:57:20,567 - bioneuralnet.utils.graph_tools - INFO - GRAPH ANALYSIS: gaussian_15\n", "\n", - "Anova-F & variance selection share: 1875 features\n", - "Random Forest & variance selection share: 1656 features\n", - "Anova-F & Random Forest share: 2102 features\n", - "All three methods agree on: 666 features\n" + "2025-11-26 00:57:20,567 - bioneuralnet.utils.graph_tools - INFO - Nodes: 900 | Edges: 11,543\n", + "2025-11-26 00:57:20,567 - bioneuralnet.utils.graph_tools - INFO - Avg degree: 25.65\n", + "2025-11-26 00:57:20,568 - bioneuralnet.utils.graph_tools - INFO - Connected components: 1\n", + "2025-11-26 00:57:20,568 - bioneuralnet.utils.graph_tools - INFO - Largest component: 900 nodes (100.0%)\n", + "2025-11-26 00:57:20,711 - bioneuralnet.utils.graph_tools - INFO - Avg clustering coefficient: 0.298\n" ] } ], "source": [ - "print(\"FROM THE 6000 Methylation feature selection:\\n\")\n", - "print(f\"Anova-F & variance selection share: {len(meth_inter1)} features\")\n", - "print(f\"Random Forest & variance selection share: {len(meth_inter2)} features\")\n", - "print(f\"Anova-F & Random Forest share: {len(meth_inter3)} features\")\n", - "print(f\"All three methods agree on: {len(meth_all_three)} features\")" + "from bioneuralnet.utils import gen_threshold_graph, gen_correlation_graph, gen_similarity_graph, gen_gaussian_knn_graph\n", + "from bioneuralnet.utils import graph_analysis\n", + "from bioneuralnet.utils import select_top_k_variance\n", + "\n", + "top_meth = select_top_k_variance(dna_meth, k=300)\n", + "top_rna = select_top_k_variance(rna, k=300)\n", + "top_mirna = select_top_k_variance(mirna, k=300)\n", + "\n", + "omics_kipan = pd.concat([top_meth, top_rna, top_mirna], axis=1)\n", + "\n", + "threshold_10 = gen_threshold_graph(omics_kipan, b=6.5, k=10, self_loops=True)\n", + "correlation_10 = gen_correlation_graph(omics_kipan, k=10, method='pearson', signed=False, self_loops=True)\n", + "similarity_10 = gen_similarity_graph(omics_kipan, k=10, metric='cosine', self_loops=True)\n", + "gaussian_15 = gen_gaussian_knn_graph(omics_kipan, k=15, sigma=None, self_loops=True)\n", + "\n", + "comparison_runs = [\n", + " {\"name\":\"threshold_10\", \"graph\": threshold_10},\n", + " {\"name\":\"correlation_10\", \"graph\": correlation_10},\n", + " {\"name\":\"similarity_10\", \"graph\": similarity_10},\n", + " {\"name\":\"gaussian_15\", \"graph\": gaussian_15},\n", + "]\n", + "\n", + "for run in comparison_runs:\n", + " graph_analysis(run[\"graph\"], run[\"name\"])\n" ] }, { "cell_type": "code", - "execution_count": 7, - "id": "3abea9c8", + "execution_count": 6, + "id": "4906eda5", "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-11-26 00:57:20,716 - bioneuralnet.downstream_task.dpmon - INFO - Output directory set to: /home/vicente/Github/BioNeuralNet/dpmon_results_GAT_FINAL/kipan/threshold_10\n", + "2025-11-26 00:57:20,717 - bioneuralnet.downstream_task.dpmon - INFO - Initialized DPMON with model: GAT\n", + "2025-11-26 00:57:20,717 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 00:57:20,718 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 00:57:20,718 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 00:57:20,719 - bioneuralnet.downstream_task.dpmon - INFO - Random seed set to: 8183\n", + "2025-11-26 00:57:20,726 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 00:57:20,727 - bioneuralnet.downstream_task.dpmon - INFO - Running in Cross-Validation mode (cv=True) with 5 folds.\n", + "2025-11-26 00:57:20,728 - bioneuralnet.downstream_task.dpmon - INFO - CV Setup: Standard 5-fold split.\n", + "2025-11-26 00:57:20,729 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 1/5\n", + "2025-11-26 00:57:20,731 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 00:57:20,739 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 00:57:24,910 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 00:57:24,922 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (526, 901)\n", + "2025-11-26 00:57:24,926\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 00:57:36,598\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0077s.\n", + "2025-11-26 00:57:36,759 - bioneuralnet.downstream_task.dpmon - INFO - Fold 1 best config: {'gnn_layer_num': 3, 'gnn_hidden_dim': 128, 'lr': 0.002995945702914729, 'weight_decay': 7.542726756559785e-05, 'nn_hidden_dim1': 256, 'nn_hidden_dim2': 32, 'ae_encoding_dim': 4, 'num_epochs': 2048, 'gnn_dropout': 0.0, 'gnn_activation': 'elu', 'dim_reduction': 'mlp'}\n", + "2025-11-26 00:57:36,760 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 1 using train split only\n", + "2025-11-26 00:57:36,765 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 00:57:40,906 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 00:57:45,459 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 1 on test set\n", + "2025-11-26 00:57:45,470 - bioneuralnet.downstream_task.dpmon - INFO - Fold 1 results:\n", + "2025-11-26 00:57:45,470 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9394\n", + "2025-11-26 00:57:45,470 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9290\n", + "2025-11-26 00:57:45,470 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9398\n", + "2025-11-26 00:57:45,471 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9566\n", + "2025-11-26 00:57:45,471 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9918\n", + "2025-11-26 00:57:45,471 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9803\n", + "\n", + "2025-11-26 00:57:45,473 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 2/5\n", + "2025-11-26 00:57:45,474 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 00:57:45,482 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 00:57:49,590 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 00:57:49,600 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (526, 901)\n", + "2025-11-26 00:57:49,603\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 00:58:00,206\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0192s.\n", + "2025-11-26 00:58:00,330 - bioneuralnet.downstream_task.dpmon - INFO - Fold 2 best config: {'gnn_layer_num': 2, 'gnn_hidden_dim': 64, 'lr': 0.0007034204356990857, 'weight_decay': 5.358735876893429e-05, 'nn_hidden_dim1': 512, 'nn_hidden_dim2': 64, 'ae_encoding_dim': 8, 'num_epochs': 1024, 'gnn_dropout': 0.2, 'gnn_activation': 'relu', 'dim_reduction': 'ae'}\n", + "2025-11-26 00:58:00,331 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 2 using train split only\n", + "2025-11-26 00:58:00,335 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 00:58:04,454 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 00:58:06,182 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 2 on test set\n", + "2025-11-26 00:58:06,187 - bioneuralnet.downstream_task.dpmon - INFO - Fold 2 results:\n", + "2025-11-26 00:58:06,187 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9697\n", + "2025-11-26 00:58:06,188 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9487\n", + "2025-11-26 00:58:06,188 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9701\n", + "2025-11-26 00:58:06,188 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9579\n", + "2025-11-26 00:58:06,188 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9940\n", + "2025-11-26 00:58:06,188 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9493\n", + "\n", + "2025-11-26 00:58:06,189 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 3/5\n", + "2025-11-26 00:58:06,191 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 00:58:06,203 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 00:58:10,296 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 00:58:10,306 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (526, 901)\n", + "2025-11-26 00:58:10,310\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 00:58:21,272\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0142s.\n", + "2025-11-26 00:58:21,422 - bioneuralnet.downstream_task.dpmon - INFO - Fold 3 best config: {'gnn_layer_num': 3, 'gnn_hidden_dim': 128, 'lr': 0.0006980653261883734, 'weight_decay': 6.059927981082565e-05, 'nn_hidden_dim1': 256, 'nn_hidden_dim2': 128, 'ae_encoding_dim': 8, 'num_epochs': 2048, 'gnn_dropout': 0.0, 'gnn_activation': 'elu', 'dim_reduction': 'ae'}\n", + "2025-11-26 00:58:21,423 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 3 using train split only\n", + "2025-11-26 00:58:21,427 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 00:58:25,530 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 00:58:29,913 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 3 on test set\n", + "2025-11-26 00:58:29,918 - bioneuralnet.downstream_task.dpmon - INFO - Fold 3 results:\n", + "2025-11-26 00:58:29,919 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9545\n", + "2025-11-26 00:58:29,919 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9459\n", + "2025-11-26 00:58:29,919 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9544\n", + "2025-11-26 00:58:29,919 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9297\n", + "2025-11-26 00:58:29,919 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9963\n", + "2025-11-26 00:58:29,919 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9895\n", + "\n", + "2025-11-26 00:58:29,920 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 4/5\n", + "2025-11-26 00:58:29,921 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 00:58:29,926 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 00:58:34,048 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 00:58:34,057 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (527, 901)\n", + "2025-11-26 00:58:34,061\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 00:58:45,497\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0174s.\n", + "2025-11-26 00:58:45,665 - bioneuralnet.downstream_task.dpmon - INFO - Fold 4 best config: {'gnn_layer_num': 1, 'gnn_hidden_dim': 128, 'lr': 0.0019123736530165902, 'weight_decay': 0.0006168499043610002, 'nn_hidden_dim1': 256, 'nn_hidden_dim2': 128, 'ae_encoding_dim': 8, 'num_epochs': 1024, 'gnn_dropout': 0.4, 'gnn_activation': 'relu', 'dim_reduction': 'mlp'}\n", + "2025-11-26 00:58:45,666 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 4 using train split only\n", + "2025-11-26 00:58:45,671 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 00:58:49,803 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 00:58:51,152 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 4 on test set\n", + "2025-11-26 00:58:51,157 - bioneuralnet.downstream_task.dpmon - INFO - Fold 4 results:\n", + "2025-11-26 00:58:51,158 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9466\n", + "2025-11-26 00:58:51,158 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9505\n", + "2025-11-26 00:58:51,158 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9466\n", + "2025-11-26 00:58:51,158 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9607\n", + "2025-11-26 00:58:51,158 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9945\n", + "2025-11-26 00:58:51,159 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9885\n", + "\n", + "2025-11-26 00:58:51,159 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 5/5\n", + "2025-11-26 00:58:51,160 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 00:58:51,165 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 00:58:55,257 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 00:58:55,268 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (527, 901)\n", + "2025-11-26 00:58:55,271\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 00:59:08,036\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0139s.\n", + "2025-11-26 00:59:08,254 - bioneuralnet.downstream_task.dpmon - INFO - Fold 5 best config: {'gnn_layer_num': 2, 'gnn_hidden_dim': 64, 'lr': 0.001685812648195555, 'weight_decay': 0.0023773937538678585, 'nn_hidden_dim1': 256, 'nn_hidden_dim2': 128, 'ae_encoding_dim': 8, 'num_epochs': 512, 'gnn_dropout': 0.2, 'gnn_activation': 'relu', 'dim_reduction': 'linear'}\n", + "2025-11-26 00:59:08,254 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 5 using train split only\n", + "2025-11-26 00:59:08,260 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 00:59:12,418 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 00:59:13,329 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 5 on test set\n", + "2025-11-26 00:59:13,334 - bioneuralnet.downstream_task.dpmon - INFO - Fold 5 results:\n", + "2025-11-26 00:59:13,335 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9618\n", + "2025-11-26 00:59:13,335 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9515\n", + "2025-11-26 00:59:13,335 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9618\n", + "2025-11-26 00:59:13,335 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9506\n", + "2025-11-26 00:59:13,335 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9988\n", + "2025-11-26 00:59:13,335 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9970\n", + "\n", + "2025-11-26 00:59:13,348 - bioneuralnet.downstream_task.dpmon - INFO - Successfully saved global best CV model state to: /home/vicente/Github/BioNeuralNet/dpmon_results_GAT_FINAL/kipan/threshold_10/best_cv_model.pt\n", + "2025-11-26 00:59:13,349 - bioneuralnet.downstream_task.dpmon - INFO - Successfully saved global best CV model state to: /home/vicente/Github/BioNeuralNet/dpmon_results_GAT_FINAL/kipan/threshold_10/best_cv_model_embds.pt\n", + "2025-11-26 00:59:13,349 - bioneuralnet.downstream_task.dpmon - INFO - Cross-Validation Results:\n", + "\n", + "2025-11-26 00:59:13,349 - bioneuralnet.downstream_task.dpmon - INFO - Avg Accuracy: 0.9544 +/- 0.0120\n", + "2025-11-26 00:59:13,349 - bioneuralnet.downstream_task.dpmon - INFO - Avg F1 Macro: 0.9451 +/- 0.0093\n", + "2025-11-26 00:59:13,349 - bioneuralnet.downstream_task.dpmon - INFO - Avg F1 Weighted: 0.9545 +/- 0.0120\n", + "2025-11-26 00:59:13,349 - bioneuralnet.downstream_task.dpmon - INFO - Avg Recall: 0.9511 +/- 0.0125\n", + "2025-11-26 00:59:13,349 - bioneuralnet.downstream_task.dpmon - INFO - Avg AUC: 0.9951 +/- 0.0026\n", + "2025-11-26 00:59:13,350 - bioneuralnet.downstream_task.dpmon - INFO - Avg AUPR: 0.9809 +/- 0.0187\n", + "2025-11-26 00:59:13,350 - bioneuralnet.downstream_task.dpmon - INFO - DPMON run completed.\n", + "2025-11-26 00:59:13,351 - bioneuralnet.downstream_task.dpmon - INFO - Output directory set to: /home/vicente/Github/BioNeuralNet/dpmon_results_GAT_FINAL/kipan/correlation_10\n", + "2025-11-26 00:59:13,351 - bioneuralnet.downstream_task.dpmon - INFO - Initialized DPMON with model: GAT\n", + "2025-11-26 00:59:13,351 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 00:59:13,351 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 00:59:13,351 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 00:59:13,351 - bioneuralnet.downstream_task.dpmon - INFO - Random seed set to: 8183\n", + "2025-11-26 00:59:13,355 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 00:59:13,357 - bioneuralnet.downstream_task.dpmon - INFO - Running in Cross-Validation mode (cv=True) with 5 folds.\n", + "2025-11-26 00:59:13,358 - bioneuralnet.downstream_task.dpmon - INFO - CV Setup: Standard 5-fold split.\n", + "2025-11-26 00:59:13,358 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 1/5\n", + "2025-11-26 00:59:13,359 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 00:59:13,364 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 00:59:17,533 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 00:59:17,543 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (526, 901)\n", + "2025-11-26 00:59:17,546\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 00:59:28,510\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0097s.\n", + "2025-11-26 00:59:28,689 - bioneuralnet.downstream_task.dpmon - INFO - Fold 1 best config: {'gnn_layer_num': 3, 'gnn_hidden_dim': 128, 'lr': 0.002995945702914729, 'weight_decay': 7.542726756559785e-05, 'nn_hidden_dim1': 256, 'nn_hidden_dim2': 32, 'ae_encoding_dim': 4, 'num_epochs': 2048, 'gnn_dropout': 0.0, 'gnn_activation': 'elu', 'dim_reduction': 'mlp'}\n", + "2025-11-26 00:59:28,690 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 1 using train split only\n", + "2025-11-26 00:59:28,694 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 00:59:32,822 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 00:59:37,372 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 1 on test set\n", + "2025-11-26 00:59:37,377 - bioneuralnet.downstream_task.dpmon - INFO - Fold 1 results:\n", + "2025-11-26 00:59:37,378 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9394\n", + "2025-11-26 00:59:37,378 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9287\n", + "2025-11-26 00:59:37,378 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9399\n", + "2025-11-26 00:59:37,378 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9558\n", + "2025-11-26 00:59:37,378 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9930\n", + "2025-11-26 00:59:37,378 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9919\n", + "\n", + "2025-11-26 00:59:37,380 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 2/5\n", + "2025-11-26 00:59:37,381 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 00:59:37,387 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 00:59:41,482 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 00:59:41,492 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (526, 901)\n", + "2025-11-26 00:59:41,496\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 00:59:52,119\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0186s.\n", + "2025-11-26 00:59:52,225 - bioneuralnet.downstream_task.dpmon - INFO - Fold 2 best config: {'gnn_layer_num': 4, 'gnn_hidden_dim': 64, 'lr': 0.00024165542558361394, 'weight_decay': 0.0002652189086272745, 'nn_hidden_dim1': 256, 'nn_hidden_dim2': 128, 'ae_encoding_dim': 8, 'num_epochs': 512, 'gnn_dropout': 0.1, 'gnn_activation': 'relu', 'dim_reduction': 'mlp'}\n", + "2025-11-26 00:59:52,226 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 2 using train split only\n", + "2025-11-26 00:59:52,231 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 00:59:56,361 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 00:59:57,746 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 2 on test set\n", + "2025-11-26 00:59:57,751 - bioneuralnet.downstream_task.dpmon - INFO - Fold 2 results:\n", + "2025-11-26 00:59:57,751 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9697\n", + "2025-11-26 00:59:57,752 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9487\n", + "2025-11-26 00:59:57,752 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9701\n", + "2025-11-26 00:59:57,752 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9579\n", + "2025-11-26 00:59:57,752 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9905\n", + "2025-11-26 00:59:57,752 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9331\n", + "\n", + "2025-11-26 00:59:57,753 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 3/5\n", + "2025-11-26 00:59:57,754 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 00:59:57,759 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:00:01,882 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:00:01,892 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (526, 901)\n", + "2025-11-26 01:00:01,896\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:00:12,425\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0233s.\n", + "2025-11-26 01:00:12,573 - bioneuralnet.downstream_task.dpmon - INFO - Fold 3 best config: {'gnn_layer_num': 3, 'gnn_hidden_dim': 128, 'lr': 0.0006980653261883734, 'weight_decay': 6.059927981082565e-05, 'nn_hidden_dim1': 256, 'nn_hidden_dim2': 128, 'ae_encoding_dim': 8, 'num_epochs': 2048, 'gnn_dropout': 0.0, 'gnn_activation': 'elu', 'dim_reduction': 'ae'}\n", + "2025-11-26 01:00:12,574 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 3 using train split only\n", + "2025-11-26 01:00:12,578 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:00:16,743 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:00:21,070 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 3 on test set\n", + "2025-11-26 01:00:21,076 - bioneuralnet.downstream_task.dpmon - INFO - Fold 3 results:\n", + "2025-11-26 01:00:21,077 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9545\n", + "2025-11-26 01:00:21,077 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9459\n", + "2025-11-26 01:00:21,077 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9544\n", + "2025-11-26 01:00:21,077 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9297\n", + "2025-11-26 01:00:21,077 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9968\n", + "2025-11-26 01:00:21,078 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9934\n", + "\n", + "2025-11-26 01:00:21,079 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 4/5\n", + "2025-11-26 01:00:21,080 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:00:21,086 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:00:25,234 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:00:25,245 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (527, 901)\n", + "2025-11-26 01:00:25,249\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:00:36,494\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0065s.\n", + "2025-11-26 01:00:36,639 - bioneuralnet.downstream_task.dpmon - INFO - Fold 4 best config: {'gnn_layer_num': 1, 'gnn_hidden_dim': 128, 'lr': 0.002226976717919125, 'weight_decay': 0.00016678702979522166, 'nn_hidden_dim1': 128, 'nn_hidden_dim2': 64, 'ae_encoding_dim': 16, 'num_epochs': 512, 'gnn_dropout': 0.3, 'gnn_activation': 'relu', 'dim_reduction': 'ae'}\n", + "2025-11-26 01:00:36,639 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 4 using train split only\n", + "2025-11-26 01:00:36,644 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:00:40,787 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:00:41,407 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 4 on test set\n", + "2025-11-26 01:00:41,411 - bioneuralnet.downstream_task.dpmon - INFO - Fold 4 results:\n", + "2025-11-26 01:00:41,412 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9466\n", + "2025-11-26 01:00:41,412 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9505\n", + "2025-11-26 01:00:41,412 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9466\n", + "2025-11-26 01:00:41,412 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9607\n", + "2025-11-26 01:00:41,412 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9934\n", + "2025-11-26 01:00:41,412 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9805\n", + "\n", + "2025-11-26 01:00:41,413 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 5/5\n", + "2025-11-26 01:00:41,414 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:00:41,419 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:00:45,531 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:00:45,541 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (527, 901)\n", + "2025-11-26 01:00:45,544\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:00:58,694\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0108s.\n", + "2025-11-26 01:00:58,924 - bioneuralnet.downstream_task.dpmon - INFO - Fold 5 best config: {'gnn_layer_num': 2, 'gnn_hidden_dim': 64, 'lr': 0.001685812648195555, 'weight_decay': 0.0023773937538678585, 'nn_hidden_dim1': 256, 'nn_hidden_dim2': 128, 'ae_encoding_dim': 8, 'num_epochs': 512, 'gnn_dropout': 0.2, 'gnn_activation': 'relu', 'dim_reduction': 'linear'}\n", + "2025-11-26 01:00:58,925 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 5 using train split only\n", + "2025-11-26 01:00:58,929 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:01:03,090 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:01:03,987 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 5 on test set\n", + "2025-11-26 01:01:03,991 - bioneuralnet.downstream_task.dpmon - INFO - Fold 5 results:\n", + "2025-11-26 01:01:03,991 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9618\n", + "2025-11-26 01:01:03,992 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9514\n", + "2025-11-26 01:01:03,992 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9617\n", + "2025-11-26 01:01:03,992 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9497\n", + "2025-11-26 01:01:03,992 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9977\n", + "2025-11-26 01:01:03,992 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9940\n", + "\n", + "2025-11-26 01:01:03,996 - bioneuralnet.downstream_task.dpmon - INFO - Successfully saved global best CV model state to: /home/vicente/Github/BioNeuralNet/dpmon_results_GAT_FINAL/kipan/correlation_10/best_cv_model.pt\n", + "2025-11-26 01:01:03,997 - bioneuralnet.downstream_task.dpmon - INFO - Successfully saved global best CV model state to: /home/vicente/Github/BioNeuralNet/dpmon_results_GAT_FINAL/kipan/correlation_10/best_cv_model_embds.pt\n", + "2025-11-26 01:01:03,997 - bioneuralnet.downstream_task.dpmon - INFO - Cross-Validation Results:\n", + "\n", + "2025-11-26 01:01:03,997 - bioneuralnet.downstream_task.dpmon - INFO - Avg Accuracy: 0.9544 +/- 0.0120\n", + "2025-11-26 01:01:03,997 - bioneuralnet.downstream_task.dpmon - INFO - Avg F1 Macro: 0.9450 +/- 0.0094\n", + "2025-11-26 01:01:03,997 - bioneuralnet.downstream_task.dpmon - INFO - Avg F1 Weighted: 0.9545 +/- 0.0119\n", + "2025-11-26 01:01:03,997 - bioneuralnet.downstream_task.dpmon - INFO - Avg Recall: 0.9507 +/- 0.0124\n", + "2025-11-26 01:01:03,998 - bioneuralnet.downstream_task.dpmon - INFO - Avg AUC: 0.9943 +/- 0.0029\n", + "2025-11-26 01:01:03,998 - bioneuralnet.downstream_task.dpmon - INFO - Avg AUPR: 0.9786 +/- 0.0260\n", + "2025-11-26 01:01:03,998 - bioneuralnet.downstream_task.dpmon - INFO - DPMON run completed.\n", + "2025-11-26 01:01:03,999 - bioneuralnet.downstream_task.dpmon - INFO - Output directory set to: /home/vicente/Github/BioNeuralNet/dpmon_results_GAT_FINAL/kipan/similarity_10\n", + "2025-11-26 01:01:03,999 - bioneuralnet.downstream_task.dpmon - INFO - Initialized DPMON with model: GAT\n", + "2025-11-26 01:01:03,999 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 01:01:04,000 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 01:01:04,000 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 01:01:04,000 - bioneuralnet.downstream_task.dpmon - INFO - Random seed set to: 8183\n", + "2025-11-26 01:01:04,005 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:01:04,006 - bioneuralnet.downstream_task.dpmon - INFO - Running in Cross-Validation mode (cv=True) with 5 folds.\n", + "2025-11-26 01:01:04,008 - bioneuralnet.downstream_task.dpmon - INFO - CV Setup: Standard 5-fold split.\n", + "2025-11-26 01:01:04,009 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 1/5\n", + "2025-11-26 01:01:04,010 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:01:04,015 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:01:08,133 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:01:08,144 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (526, 901)\n", + "2025-11-26 01:01:08,147\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:01:20,813\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0121s.\n", + "2025-11-26 01:01:21,000 - bioneuralnet.downstream_task.dpmon - INFO - Fold 1 best config: {'gnn_layer_num': 3, 'gnn_hidden_dim': 128, 'lr': 0.002995945702914729, 'weight_decay': 7.542726756559785e-05, 'nn_hidden_dim1': 256, 'nn_hidden_dim2': 32, 'ae_encoding_dim': 4, 'num_epochs': 2048, 'gnn_dropout': 0.0, 'gnn_activation': 'elu', 'dim_reduction': 'mlp'}\n", + "2025-11-26 01:01:21,001 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 1 using train split only\n", + "2025-11-26 01:01:21,005 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:01:25,276 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:01:29,966 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 1 on test set\n", + "2025-11-26 01:01:29,971 - bioneuralnet.downstream_task.dpmon - INFO - Fold 1 results:\n", + "2025-11-26 01:01:29,972 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9394\n", + "2025-11-26 01:01:29,972 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9290\n", + "2025-11-26 01:01:29,972 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9398\n", + "2025-11-26 01:01:29,972 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9566\n", + "2025-11-26 01:01:29,972 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9957\n", + "2025-11-26 01:01:29,972 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9935\n", + "\n", + "2025-11-26 01:01:29,974 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 2/5\n", + "2025-11-26 01:01:29,975 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:01:29,981 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:01:34,133 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:01:34,146 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (526, 901)\n", + "2025-11-26 01:01:34,149\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:01:46,408\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0098s.\n", + "2025-11-26 01:01:46,566 - bioneuralnet.downstream_task.dpmon - INFO - Fold 2 best config: {'gnn_layer_num': 2, 'gnn_hidden_dim': 64, 'lr': 0.0007034204356990857, 'weight_decay': 5.358735876893429e-05, 'nn_hidden_dim1': 512, 'nn_hidden_dim2': 64, 'ae_encoding_dim': 8, 'num_epochs': 1024, 'gnn_dropout': 0.2, 'gnn_activation': 'relu', 'dim_reduction': 'ae'}\n", + "2025-11-26 01:01:46,567 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 2 using train split only\n", + "2025-11-26 01:01:46,571 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:01:50,727 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:01:52,514 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 2 on test set\n", + "2025-11-26 01:01:52,519 - bioneuralnet.downstream_task.dpmon - INFO - Fold 2 results:\n", + "2025-11-26 01:01:52,520 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9621\n", + "2025-11-26 01:01:52,520 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9315\n", + "2025-11-26 01:01:52,520 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9623\n", + "2025-11-26 01:01:52,520 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9322\n", + "2025-11-26 01:01:52,520 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9934\n", + "2025-11-26 01:01:52,520 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9490\n", + "\n", + "2025-11-26 01:01:52,521 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 3/5\n", + "2025-11-26 01:01:52,522 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:01:52,527 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:01:56,688 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:01:56,700 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (526, 901)\n", + "2025-11-26 01:01:56,703\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:02:07,390\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0206s.\n", + "2025-11-26 01:02:07,536 - bioneuralnet.downstream_task.dpmon - INFO - Fold 3 best config: {'gnn_layer_num': 3, 'gnn_hidden_dim': 128, 'lr': 0.0006980653261883734, 'weight_decay': 6.059927981082565e-05, 'nn_hidden_dim1': 256, 'nn_hidden_dim2': 128, 'ae_encoding_dim': 8, 'num_epochs': 2048, 'gnn_dropout': 0.0, 'gnn_activation': 'elu', 'dim_reduction': 'ae'}\n", + "2025-11-26 01:02:07,536 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 3 using train split only\n", + "2025-11-26 01:02:07,540 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:02:11,675 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:02:16,004 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 3 on test set\n", + "2025-11-26 01:02:16,009 - bioneuralnet.downstream_task.dpmon - INFO - Fold 3 results:\n", + "2025-11-26 01:02:16,009 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9545\n", + "2025-11-26 01:02:16,010 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9459\n", + "2025-11-26 01:02:16,010 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9544\n", + "2025-11-26 01:02:16,010 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9297\n", + "2025-11-26 01:02:16,010 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9937\n", + "2025-11-26 01:02:16,011 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9911\n", + "\n", + "2025-11-26 01:02:16,012 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 4/5\n", + "2025-11-26 01:02:16,014 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:02:16,020 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:02:20,128 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:02:20,139 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (527, 901)\n", + "2025-11-26 01:02:20,142\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:02:31,003\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0166s.\n", + "2025-11-26 01:02:31,137 - bioneuralnet.downstream_task.dpmon - INFO - Fold 4 best config: {'gnn_layer_num': 1, 'gnn_hidden_dim': 128, 'lr': 0.002226976717919125, 'weight_decay': 0.00016678702979522166, 'nn_hidden_dim1': 128, 'nn_hidden_dim2': 64, 'ae_encoding_dim': 16, 'num_epochs': 512, 'gnn_dropout': 0.3, 'gnn_activation': 'relu', 'dim_reduction': 'ae'}\n", + "2025-11-26 01:02:31,137 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 4 using train split only\n", + "2025-11-26 01:02:31,143 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:02:35,446 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:02:36,071 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 4 on test set\n", + "2025-11-26 01:02:36,076 - bioneuralnet.downstream_task.dpmon - INFO - Fold 4 results:\n", + "2025-11-26 01:02:36,076 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9389\n", + "2025-11-26 01:02:36,076 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9449\n", + "2025-11-26 01:02:36,076 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9390\n", + "2025-11-26 01:02:36,076 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9554\n", + "2025-11-26 01:02:36,076 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9943\n", + "2025-11-26 01:02:36,077 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9862\n", + "\n", + "2025-11-26 01:02:36,077 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 5/5\n", + "2025-11-26 01:02:36,079 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:02:36,084 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:02:40,268 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:02:40,280 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (527, 901)\n", + "2025-11-26 01:02:40,283\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:02:52,747\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0184s.\n", + "2025-11-26 01:02:52,935 - bioneuralnet.downstream_task.dpmon - INFO - Fold 5 best config: {'gnn_layer_num': 4, 'gnn_hidden_dim': 128, 'lr': 0.0013804780705269182, 'weight_decay': 0.0037674454696866636, 'nn_hidden_dim1': 128, 'nn_hidden_dim2': 64, 'ae_encoding_dim': 4, 'num_epochs': 1024, 'gnn_dropout': 0.0, 'gnn_activation': 'elu', 'dim_reduction': 'ae'}\n", + "2025-11-26 01:02:52,936 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 5 using train split only\n", + "2025-11-26 01:02:52,941 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:02:57,095 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:02:59,798 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 5 on test set\n", + "2025-11-26 01:02:59,803 - bioneuralnet.downstream_task.dpmon - INFO - Fold 5 results:\n", + "2025-11-26 01:02:59,803 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9466\n", + "2025-11-26 01:02:59,804 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9398\n", + "2025-11-26 01:02:59,804 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9462\n", + "2025-11-26 01:02:59,804 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9373\n", + "2025-11-26 01:02:59,804 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9952\n", + "2025-11-26 01:02:59,804 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9880\n", + "\n", + "2025-11-26 01:02:59,818 - bioneuralnet.downstream_task.dpmon - INFO - Successfully saved global best CV model state to: /home/vicente/Github/BioNeuralNet/dpmon_results_GAT_FINAL/kipan/similarity_10/best_cv_model.pt\n", + "2025-11-26 01:02:59,819 - bioneuralnet.downstream_task.dpmon - INFO - Successfully saved global best CV model state to: /home/vicente/Github/BioNeuralNet/dpmon_results_GAT_FINAL/kipan/similarity_10/best_cv_model_embds.pt\n", + "2025-11-26 01:02:59,819 - bioneuralnet.downstream_task.dpmon - INFO - Cross-Validation Results:\n", + "\n", + "2025-11-26 01:02:59,819 - bioneuralnet.downstream_task.dpmon - INFO - Avg Accuracy: 0.9483 +/- 0.0100\n", + "2025-11-26 01:02:59,819 - bioneuralnet.downstream_task.dpmon - INFO - Avg F1 Macro: 0.9382 +/- 0.0077\n", + "2025-11-26 01:02:59,819 - bioneuralnet.downstream_task.dpmon - INFO - Avg F1 Weighted: 0.9483 +/- 0.0100\n", + "2025-11-26 01:02:59,819 - bioneuralnet.downstream_task.dpmon - INFO - Avg Recall: 0.9422 +/- 0.0129\n", + "2025-11-26 01:02:59,819 - bioneuralnet.downstream_task.dpmon - INFO - Avg AUC: 0.9945 +/- 0.0010\n", + "2025-11-26 01:02:59,819 - bioneuralnet.downstream_task.dpmon - INFO - Avg AUPR: 0.9815 +/- 0.0184\n", + "2025-11-26 01:02:59,820 - bioneuralnet.downstream_task.dpmon - INFO - DPMON run completed.\n", + "2025-11-26 01:02:59,821 - bioneuralnet.downstream_task.dpmon - INFO - Output directory set to: /home/vicente/Github/BioNeuralNet/dpmon_results_GAT_FINAL/kipan/gaussian_15\n", + "2025-11-26 01:02:59,821 - bioneuralnet.downstream_task.dpmon - INFO - Initialized DPMON with model: GAT\n", + "2025-11-26 01:02:59,821 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 01:02:59,821 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 01:02:59,821 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 01:02:59,821 - bioneuralnet.downstream_task.dpmon - INFO - Random seed set to: 8183\n", + "2025-11-26 01:02:59,826 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:02:59,827 - bioneuralnet.downstream_task.dpmon - INFO - Running in Cross-Validation mode (cv=True) with 5 folds.\n", + "2025-11-26 01:02:59,828 - bioneuralnet.downstream_task.dpmon - INFO - CV Setup: Standard 5-fold split.\n", + "2025-11-26 01:02:59,829 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 1/5\n", + "2025-11-26 01:02:59,830 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:02:59,838 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:03:04,025 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:03:04,039 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (526, 901)\n", + "2025-11-26 01:03:04,042\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:03:17,429\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0133s.\n", + "2025-11-26 01:03:17,636 - bioneuralnet.downstream_task.dpmon - INFO - Fold 1 best config: {'gnn_layer_num': 3, 'gnn_hidden_dim': 128, 'lr': 0.002995945702914729, 'weight_decay': 7.542726756559785e-05, 'nn_hidden_dim1': 256, 'nn_hidden_dim2': 32, 'ae_encoding_dim': 4, 'num_epochs': 2048, 'gnn_dropout': 0.0, 'gnn_activation': 'elu', 'dim_reduction': 'mlp'}\n", + "2025-11-26 01:03:17,637 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 1 using train split only\n", + "2025-11-26 01:03:17,641 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:03:21,783 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:03:26,807 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 1 on test set\n", + "2025-11-26 01:03:26,812 - bioneuralnet.downstream_task.dpmon - INFO - Fold 1 results:\n", + "2025-11-26 01:03:26,812 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9394\n", + "2025-11-26 01:03:26,812 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9216\n", + "2025-11-26 01:03:26,813 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9405\n", + "2025-11-26 01:03:26,813 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9566\n", + "2025-11-26 01:03:26,813 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9950\n", + "2025-11-26 01:03:26,813 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9943\n", + "\n", + "2025-11-26 01:03:26,815 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 2/5\n", + "2025-11-26 01:03:26,816 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:03:26,821 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:03:30,970 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:03:30,983 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (526, 901)\n", + "2025-11-26 01:03:30,986\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:03:41,107\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0289s.\n", + "2025-11-26 01:03:41,224 - bioneuralnet.downstream_task.dpmon - INFO - Fold 2 best config: {'gnn_layer_num': 2, 'gnn_hidden_dim': 64, 'lr': 0.0007034204356990857, 'weight_decay': 5.358735876893429e-05, 'nn_hidden_dim1': 512, 'nn_hidden_dim2': 64, 'ae_encoding_dim': 8, 'num_epochs': 1024, 'gnn_dropout': 0.2, 'gnn_activation': 'relu', 'dim_reduction': 'ae'}\n", + "2025-11-26 01:03:41,225 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 2 using train split only\n", + "2025-11-26 01:03:41,229 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:03:45,495 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:03:47,199 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 2 on test set\n", + "2025-11-26 01:03:47,204 - bioneuralnet.downstream_task.dpmon - INFO - Fold 2 results:\n", + "2025-11-26 01:03:47,204 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9621\n", + "2025-11-26 01:03:47,204 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9315\n", + "2025-11-26 01:03:47,204 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9623\n", + "2025-11-26 01:03:47,204 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9322\n", + "2025-11-26 01:03:47,205 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9929\n", + "2025-11-26 01:03:47,205 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9469\n", + "\n", + "2025-11-26 01:03:47,206 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 3/5\n", + "2025-11-26 01:03:47,207 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:03:47,212 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:03:51,382 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:03:51,395 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (526, 901)\n", + "2025-11-26 01:03:51,399\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:04:02,747\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0162s.\n", + "2025-11-26 01:04:02,915 - bioneuralnet.downstream_task.dpmon - INFO - Fold 3 best config: {'gnn_layer_num': 1, 'gnn_hidden_dim': 256, 'lr': 0.0016239332393902155, 'weight_decay': 1.2898372523486065e-05, 'nn_hidden_dim1': 512, 'nn_hidden_dim2': 128, 'ae_encoding_dim': 4, 'num_epochs': 512, 'gnn_dropout': 0.2, 'gnn_activation': 'elu', 'dim_reduction': 'ae'}\n", + "2025-11-26 01:04:02,916 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 3 using train split only\n", + "2025-11-26 01:04:02,921 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:04:07,093 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:04:07,908 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 3 on test set\n", + "2025-11-26 01:04:07,912 - bioneuralnet.downstream_task.dpmon - INFO - Fold 3 results:\n", + "2025-11-26 01:04:07,913 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9545\n", + "2025-11-26 01:04:07,913 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9459\n", + "2025-11-26 01:04:07,913 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9544\n", + "2025-11-26 01:04:07,913 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9297\n", + "2025-11-26 01:04:07,913 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9967\n", + "2025-11-26 01:04:07,913 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9949\n", + "\n", + "2025-11-26 01:04:07,915 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 4/5\n", + "2025-11-26 01:04:07,916 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:04:07,922 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:04:12,095 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:04:12,109 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (527, 901)\n", + "2025-11-26 01:04:12,112\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:04:23,475\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0193s.\n", + "2025-11-26 01:04:23,614 - bioneuralnet.downstream_task.dpmon - INFO - Fold 4 best config: {'gnn_layer_num': 1, 'gnn_hidden_dim': 128, 'lr': 0.002226976717919125, 'weight_decay': 0.00016678702979522166, 'nn_hidden_dim1': 128, 'nn_hidden_dim2': 64, 'ae_encoding_dim': 16, 'num_epochs': 512, 'gnn_dropout': 0.3, 'gnn_activation': 'relu', 'dim_reduction': 'ae'}\n", + "2025-11-26 01:04:23,615 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 4 using train split only\n", + "2025-11-26 01:04:23,620 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:04:27,796 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:04:28,417 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 4 on test set\n", + "2025-11-26 01:04:28,421 - bioneuralnet.downstream_task.dpmon - INFO - Fold 4 results:\n", + "2025-11-26 01:04:28,422 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9389\n", + "2025-11-26 01:04:28,422 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9449\n", + "2025-11-26 01:04:28,422 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9390\n", + "2025-11-26 01:04:28,422 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9554\n", + "2025-11-26 01:04:28,422 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9919\n", + "2025-11-26 01:04:28,423 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9839\n", + "\n", + "2025-11-26 01:04:28,424 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 5/5\n", + "2025-11-26 01:04:28,425 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:04:28,431 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:04:32,603 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:04:32,616 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (527, 901)\n", + "2025-11-26 01:04:32,619\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:04:45,848\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0215s.\n", + "2025-11-26 01:04:46,065 - bioneuralnet.downstream_task.dpmon - INFO - Fold 5 best config: {'gnn_layer_num': 2, 'gnn_hidden_dim': 64, 'lr': 0.001685812648195555, 'weight_decay': 0.0023773937538678585, 'nn_hidden_dim1': 256, 'nn_hidden_dim2': 128, 'ae_encoding_dim': 8, 'num_epochs': 512, 'gnn_dropout': 0.2, 'gnn_activation': 'relu', 'dim_reduction': 'linear'}\n", + "2025-11-26 01:04:46,066 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 5 using train split only\n", + "2025-11-26 01:04:46,071 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:04:50,196 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:04:51,249 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 5 on test set\n", + "2025-11-26 01:04:51,254 - bioneuralnet.downstream_task.dpmon - INFO - Fold 5 results:\n", + "2025-11-26 01:04:51,254 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9542\n", + "2025-11-26 01:04:51,254 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9456\n", + "2025-11-26 01:04:51,255 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9540\n", + "2025-11-26 01:04:51,255 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9435\n", + "2025-11-26 01:04:51,255 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9975\n", + "2025-11-26 01:04:51,255 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9938\n", + "\n", + "2025-11-26 01:04:51,259 - bioneuralnet.downstream_task.dpmon - INFO - Successfully saved global best CV model state to: /home/vicente/Github/BioNeuralNet/dpmon_results_GAT_FINAL/kipan/gaussian_15/best_cv_model.pt\n", + "2025-11-26 01:04:51,259 - bioneuralnet.downstream_task.dpmon - INFO - Successfully saved global best CV model state to: /home/vicente/Github/BioNeuralNet/dpmon_results_GAT_FINAL/kipan/gaussian_15/best_cv_model_embds.pt\n", + "2025-11-26 01:04:51,260 - bioneuralnet.downstream_task.dpmon - INFO - Cross-Validation Results:\n", + "\n", + "2025-11-26 01:04:51,260 - bioneuralnet.downstream_task.dpmon - INFO - Avg Accuracy: 0.9498 +/- 0.0102\n", + "2025-11-26 01:04:51,260 - bioneuralnet.downstream_task.dpmon - INFO - Avg F1 Macro: 0.9379 +/- 0.0110\n", + "2025-11-26 01:04:51,260 - bioneuralnet.downstream_task.dpmon - INFO - Avg F1 Weighted: 0.9500 +/- 0.0100\n", + "2025-11-26 01:04:51,260 - bioneuralnet.downstream_task.dpmon - INFO - Avg Recall: 0.9435 +/- 0.0126\n", + "2025-11-26 01:04:51,260 - bioneuralnet.downstream_task.dpmon - INFO - Avg AUC: 0.9948 +/- 0.0024\n", + "2025-11-26 01:04:51,260 - bioneuralnet.downstream_task.dpmon - INFO - Avg AUPR: 0.9827 +/- 0.0206\n", + "2025-11-26 01:04:51,261 - bioneuralnet.downstream_task.dpmon - INFO - DPMON run completed.\n" + ] + }, { "name": "stdout", "output_type": "stream", "text": [ "\n", - "FROM THE 6000 RNA feature selection:\n", + "Results for: threshold_10\n", + "Accuracy (Avg +/- Std): 0.9544 +/- 0.0120\n", + "F1 Macro (Avg +/- Std): 0.9451 +/- 0.0093\n", + "F1 Weighted (Avg +/- Std): 0.9545 +/- 0.0120\n", + "Recall: 0.9511 +/- 0.0125\n", + "AUC: 0.9951 +/- 0.0026\n", + "\n", + "Results for: correlation_10\n", + "Accuracy (Avg +/- Std): 0.9544 +/- 0.0120\n", + "F1 Macro (Avg +/- Std): 0.9450 +/- 0.0094\n", + "F1 Weighted (Avg +/- Std): 0.9545 +/- 0.0119\n", + "Recall: 0.9507 +/- 0.0124\n", + "AUC: 0.9943 +/- 0.0029\n", + "\n", + "Results for: similarity_10\n", + "Accuracy (Avg +/- Std): 0.9483 +/- 0.0100\n", + "F1 Macro (Avg +/- Std): 0.9382 +/- 0.0077\n", + "F1 Weighted (Avg +/- Std): 0.9483 +/- 0.0100\n", + "Recall: 0.9422 +/- 0.0129\n", + "AUC: 0.9945 +/- 0.0010\n", "\n", - "Anova-F & variance selection share: 2271 features\n", - "Random Forest & variance selection share: 2141 features\n", - "Anova-F & Random Forest share: 2284 features\n", - "All three methods agree on: 943 features\n" + "Results for: gaussian_15\n", + "Accuracy (Avg +/- Std): 0.9498 +/- 0.0102\n", + "F1 Macro (Avg +/- Std): 0.9379 +/- 0.0110\n", + "F1 Weighted (Avg +/- Std): 0.9500 +/- 0.0100\n", + "Recall: 0.9435 +/- 0.0126\n", + "AUC: 0.9948 +/- 0.0024\n" ] } ], "source": [ - "print(\"\\nFROM THE 6000 RNA feature selection:\\n\")\n", - "print(f\"Anova-F & variance selection share: {len(rna_inter4)} features\")\n", - "print(f\"Random Forest & variance selection share: {len(rna_inter5)} features\")\n", - "print(f\"Anova-F & Random Forest share: {len(rna_inter6)} features\")\n", - "print(f\"All three methods agree on: {len(rna_all_three)} features\")" + "from bioneuralnet.downstream_task import DPMON\n", + "\n", + "output_dir_base = Path(\"/home/vicente/Github/BioNeuralNet/dpmon_results_GAT_FINAL/kipan\")\n", + "\n", + "all_results_GAT = []\n", + "\n", + "for run_config in comparison_runs:\n", + " graph_name = run_config[\"name\"]\n", + " A_full = run_config[\"graph\"]\n", + "\n", + " current_output_dir = output_dir_base / graph_name\n", + " current_output_dir.mkdir(parents=True, exist_ok=True)\n", + "\n", + " dpmon_params_base = {\n", + " \"adjacency_matrix\": A_full,\n", + " \"omics_list\": omics_kipan,\n", + " \"phenotype_data\": target,\n", + " \"phenotype_col\": \"target\",\n", + " \"clinical_data\": clinical_for_model,\n", + " \"model\": 'GAT',\n", + " \"tune\": True, \n", + " \"cv\": True, \n", + " \"n_folds\": 5,\n", + " \"repeat_num\": 1,\n", + " \"gpu\": True,\n", + " \"cuda\": 0,\n", + " \"seed\": SEED,\n", + " \"output_dir\": current_output_dir\n", + " }\n", + " \n", + " dpmon_tunned = DPMON(**dpmon_params_base)\n", + " predictions_df, metrics, embeddings = dpmon_tunned.run()\n", + "\n", + " all_results_GAT.append({\n", + " \"graph\": graph_name,\n", + " \"predictions\": predictions_df,\n", + " \"metrics\": metrics,\n", + " \"embeddings\": embeddings\n", + " })\n", + "\n", + "for res in all_results_GAT:\n", + " graph_name = res[\"graph\"]\n", + " graph_metrics = res[\"metrics\"]\n", + " \n", + " acc_row = graph_metrics.loc[graph_metrics['Metric'] == 'Accuracy'].iloc[0]\n", + " f1_macro_row = graph_metrics.loc[graph_metrics['Metric'] == 'F1 Macro'].iloc[0]\n", + " f1_weighted_row = graph_metrics.loc[graph_metrics['Metric'] == 'F1 Weighted'].iloc[0]\n", + " recall_row = graph_metrics.loc[graph_metrics['Metric'] == 'Recall'].iloc[0]\n", + " auc_row = graph_metrics.loc[graph_metrics['Metric'] == 'AUC'].iloc[0]\n", + " \n", + " acc_avg, acc_std = acc_row['Average'], acc_row['StdDev']\n", + " f1_macro_avg, f1_macro_std = f1_macro_row['Average'], f1_macro_row['StdDev']\n", + " f1_weighted_avg, f1_weighted_std = f1_weighted_row['Average'], f1_weighted_row['StdDev']\n", + " recall_avg, recall_std = recall_row['Average'], recall_row['StdDev']\n", + " auc_avg, auc_std = auc_row['Average'], auc_row['StdDev']\n", + "\n", + " print(f\"\\nResults for: {graph_name}\")\n", + " print(f\"Accuracy (Avg +/- Std): {acc_avg:.4f} +/- {acc_std:.4f}\")\n", + " print(f\"F1 Macro (Avg +/- Std): {f1_macro_avg:.4f} +/- {f1_macro_std:.4f}\")\n", + " print(f\"F1 Weighted (Avg +/- Std): {f1_weighted_avg:.4f} +/- {f1_weighted_std:.4f}\")\n", + " print(f\"Recall: {recall_avg:.4f} +/- {recall_std:.4f}\")\n", + " print(f\"AUC: {auc_avg:.4f} +/- {auc_std:.4f}\")" ] }, { - 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The resulting feature pool is considered highly relevant for the subsequent modeling tasks.\n", - "\n", - "### Feature Overlap Results\n", + "# we have emebeddings for each of the 4 graphs\n", + "# we will select the last one from the list (threshold_10) for visualization\n", + "res = all_results_GAT[0] \n", + "tensor_data = res[\"embeddings\"]\n", "\n", - "The table below quantifies the shared features identified by the different selection techniques for each omics type.\n", + "# .detach().cpu().numpy() to get the raw numbers out of pytorch\n", + "GAT_embeddings_df = pd.DataFrame(\n", + " tensor_data.detach().cpu().numpy(),\n", + " index=omics_kipan.columns\n", + ")\n", "\n", - "| Omics Data Type | ANOVA-F & Variance | RF & Variance | ANOVA-F & Random Forest (Selected) | All Three Agree |\n", - "| :--- | :--- | :--- | :--- | :--- |\n", - "| **Methylation** | 1,875 features | 1,656 features | **2,102 features** | 666 features |\n", - "| **RNA** | 2,271 features | 2,141 features | **2,284 features** | 943 features |" + "display(GAT_embeddings_df.iloc[:5,:5])" ] }, { "cell_type": "code", - "execution_count": 8, - "id": "cba88e86", + "execution_count": 21, + "id": "b1200e4e", "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Plotting Feature Embeddings for graph: threshold_10\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from bioneuralnet.metrics import plot_embeddings\n", + "\n", + "# for a more interpretable embedding plot we will map the node labels to omics types\n", + "node_labels = []\n", + "GAT_embeddings_array = GAT_embeddings_df.values\n", + "feature_names = GAT_embeddings_df.index\n", + "\n", + "# using the sets from the orginal to serve as a loopup table\n", + "dna_feats = set(dna_meth.columns)\n", + "rna_feats = set(rna.columns)\n", + "mirna_feats = set(mirna.columns)\n", + "\n", + "for feat in feature_names:\n", + " if feat in dna_feats:\n", + " # 1 for DNA methylation\n", + " node_labels.append(1)\n", + " elif feat in rna_feats:\n", + " # 2 for RNA \n", + " node_labels.append(2)\n", + " elif feat in mirna_feats:\n", + " # and 3 for miRNA \n", + " node_labels.append(3)\n", + " else:\n", + " node_labels.append(0)\n", + "\n", + "# our plotting function needs an array to squeeze the embedings into 2Dspace\n", + "node_labels = np.array(node_labels)\n", + "\n", + "# feature(omics) embeddings colored by omics type.\n", + "print(f\"Plotting Feature Embeddings for graph: {res['graph']}\")\n", + "plot_embeddings(GAT_embeddings_array, node_labels, legend_labels=[\"DNA_Methylation\", \"RNA\", \"miRNA\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "98442c32", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-11-26 09:50:43,480 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 09:50:43,481 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 09:50:43,481 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 09:50:43,482 - bioneuralnet.clustering.hybrid_louvain - INFO - Initializing HybridLouvain...\n", + "2025-11-26 09:50:43,482 - bioneuralnet.clustering.hybrid_louvain - INFO - Input G is a DataFrame; converting adjacency matrix to NetworkX graph.\n", + "2025-11-26 09:50:43,494 - bioneuralnet.clustering.hybrid_louvain - INFO - Initialized HybridLouvain with 900 graph nodes, 900 omics columns, 658 phenotype rows; max_iter=3, k3=0.2, k4=0.8, tune=False\n", + "2025-11-26 09:50:43,495 - bioneuralnet.clustering.hybrid_louvain - INFO - \n", + "Iteration 1/3: Running Correlated Louvain...\n", + "2025-11-26 09:50:43,504 - bioneuralnet.clustering.correlated_louvain - INFO - CorrelatedLouvain(k3=0.2, k4=0.8, nodes=900, edges=8930, features=900)\n", + "2025-11-26 09:50:43,504 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 09:50:43,505 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 09:50:43,505 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 09:50:43,505 - bioneuralnet.clustering.correlated_louvain - INFO - Running standard community detection...\n", + "2025-11-26 09:50:43,615 - bioneuralnet.clustering.correlated_louvain - INFO - Computed quality (supervised): Q = 0.7372, avg_corr = 0.5264, combined = 0.5686\n", + "2025-11-26 09:50:43,615 - bioneuralnet.clustering.correlated_louvain - INFO - Final quality: 0.5686\n", + "2025-11-26 09:50:43,616 - bioneuralnet.clustering.correlated_louvain - INFO - CorrelatedLouvain found 9 communities (nodes=900)\n", + "2025-11-26 09:50:43,669 - bioneuralnet.clustering.correlated_louvain - INFO - Computed quality (supervised): Q = 0.7372, avg_corr = 0.5264, combined = 0.5686\n", + "2025-11-26 09:50:43,669 - bioneuralnet.clustering.hybrid_louvain - INFO - Iteration 1: Louvain Quality = 0.5686\n", + "2025-11-26 09:50:43,722 - bioneuralnet.clustering.hybrid_louvain - INFO - Selected seed community of size 159 with correlation -0.8356\n", + "2025-11-26 09:50:43,722 - bioneuralnet.clustering.correlated_pagerank - INFO - Initialized PageRank with the following parameters:\n", + "2025-11-26 09:50:43,723 - bioneuralnet.clustering.correlated_pagerank - INFO - Graph: NetworkX Graph with 900 nodes and 8930 edges.\n", + "2025-11-26 09:50:43,723 - bioneuralnet.clustering.correlated_pagerank - INFO - Omics Data: DataFrame with shape (658, 900).\n", + "2025-11-26 09:50:43,724 - bioneuralnet.clustering.correlated_pagerank - INFO - Phenotype Data: Series with 658 samples.\n", + "2025-11-26 09:50:43,724 - bioneuralnet.clustering.correlated_pagerank - INFO - Alpha: 0.9\n", + "2025-11-26 09:50:43,724 - bioneuralnet.clustering.correlated_pagerank - INFO - Max Iterations: 100\n", + "2025-11-26 09:50:43,724 - bioneuralnet.clustering.correlated_pagerank - INFO - Tolerance: 1e-06\n", + "2025-11-26 09:50:43,725 - bioneuralnet.clustering.correlated_pagerank - INFO - K (Composite Score Weight): 0.5\n", + "2025-11-26 09:50:43,725 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 09:50:43,726 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 09:50:43,726 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 09:50:43,726 - bioneuralnet.clustering.correlated_pagerank - INFO - Initialized Correlated PageRank. device=cpu\n", + "2025-11-26 09:50:45,149 - bioneuralnet.clustering.correlated_pagerank - INFO - Generated personalization vector for seed nodes: ['LOC645638', 'MIR190', 'MIR657', 'CAPG', 'GGTLC2', 'FABP7_2173', 'SLC6A3_6531', 'PTGER3_5733', 'CP_1356', 'CA9_768', 'HMGCS2_3158', 'C6orf223_221416', 'CYP2J2_1573', 'CREB3L3_84699', 'ANGPTL4_51129', 'MYEOV_26579', 'PCSK6_5046', 'C1QL1_10882', 'LGI4_163175', 'UBE2QL1_134111', 'MCHR1_2847', 'ACTG2_72', 'SORCS2_57537', 'APOB_338', 'FOLH1_2346', 'MAP7D2_256714', 'ACAN_176', 'TFAP2A_7020', 'CA4_762', 'VAT1L_57687', 'AVPR1A_552', 'CCL20_6364', 'PRDM16_63976', 'FHL5_9457', 'LOX_4015', 'PCDH17_27253', 'APLNR_187', 'COL21A1_81578', 'LOC100131551_100131551', 'ACTC1_70', 'ANGPT2_285', 'ADAMTS4_9507', 'ESM1_11082', 'CASQ2_845', 'ADAMTSL1_92949', 'PCOLCE2_26577', 'CDCA2_157313', 'COL5A3_50509', 'PDE1C_5137', 'CPXM2_119587', 'RGS5_8490', 'CREB3L1_90993', 'PRRX1_5396', 'KCNN3_3782', 'CALCR_799', 'ADCY1_107', 'COX4I2_84701', 'CD36_948', 'STC1_6781', 'C2orf72_257407', 'ALDOC_230', 'CDH13_1012', 'CDH11_1009', 'PPFIA4_8497', 'PDE3A_5139', 'ANGPT1_284', 'CXCL9_4283', 'CXCL11_6373', 'APLN_8862', 'HOXD10_3236', 'CA8_767', 'TRPC6_7225', 'AGTR1_185', 'DDR2_4921', 'B4GALNT1_2583', 'OR51E1_143503', 'BDKRB2_624', 'DGKI_9162', 'MYH11_4629', 'SEMA5B_54437', 'BARX2_8538', 'TMEM45A_55076', 'MPPED2_744', 'COL25A1_84570', 'PLAC9_219348', 'RASGRF2_5924', 'FLT1_2321', 'C6orf105_84830', 'RASD2_23551', 'C3orf70_285382', 'EGLN3_112399', 'CNN1_1264', 'CSPG4_1464', 'CXCL10_3627', 'COL6A3_1293', 'APOLD1_81575', 'HMCN1_83872', 'ZNF521_25925', 'SLC35F1_222553', 'C4orf6_10141', 'DACH1_1602', 'SPARCL1_8404', 'C17orf55_284185', 'BDKRB1_623', 'COL14A1_7373', 'KLHDC8A_55220', 'PDZD2_23037', 'GABRE_2564', 'FATE1_89885', 'BTNL9_153579', 'ADAMTS5_11096', 'GPR4_2828', 'GPR116_221395', 'C7orf68_29923', 'ANO3_63982', 'COL8A1_1295', 'B3GNT4_79369', 'NGF_4803', 'ADAMTS12_81792', 'hsa_mir_217', 'hsa_mir_122', 'hsa_mir_885', 'hsa_mir_675', 'hsa_mir_577', 'hsa_mir_653', 'hsa_mir_503', 'hsa_mir_1247', 'hsa_mir_126', 'hsa_mir_489', 'hsa_mir_224', 'hsa_mir_424', 'hsa_mir_134', 'hsa_mir_1_2', 'hsa_mir_199b', 'hsa_mir_145', 'hsa_mir_199a_2', 'hsa_mir_199a_1', 'hsa_mir_153_2', 'hsa_mir_3676', 'hsa_mir_139', 'hsa_mir_379', 'hsa_mir_127', 'hsa_mir_452', 'hsa_mir_450a_1', 'hsa_mir_450a_2', 'hsa_mir_374c', 'hsa_mir_1271', 'hsa_mir_450b', 'hsa_mir_1274b', 'hsa_mir_326', 'hsa_mir_195', 'hsa_mir_33b', 'hsa_mir_26a_1', 'hsa_mir_497', 'hsa_mir_3605', 'hsa_mir_320b_2', 'hsa_mir_504', 'hsa_mir_1296', 'hsa_mir_212']\n", + "2025-11-26 09:50:45,159 - bioneuralnet.clustering.correlated_pagerank - INFO - PageRank computation completed.\n", + "2025-11-26 09:51:02,712 - bioneuralnet.clustering.correlated_pagerank - INFO - Sweep cut resulted in cluster of size 293 with conductance 0.098 and correlation -0.84.\n", + "2025-11-26 09:51:02,712 - bioneuralnet.clustering.correlated_pagerank - INFO - PageRank clustering completed successfully.\n", + "2025-11-26 09:51:02,713 - bioneuralnet.clustering.hybrid_louvain - INFO - Iteration 1: cluster size=293, Conductance=0.098 Correlation=-0.840 score=-0.371\n", + "2025-11-26 09:51:02,722 - bioneuralnet.clustering.hybrid_louvain - INFO - \n", + "Iteration 2/3: Running Correlated Louvain...\n", + "2025-11-26 09:51:02,728 - bioneuralnet.clustering.correlated_louvain - INFO - CorrelatedLouvain(k3=0.2, k4=0.8, nodes=293, edges=2701, features=900)\n", + "2025-11-26 09:51:02,729 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 09:51:02,730 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 09:51:02,730 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 09:51:02,730 - bioneuralnet.clustering.correlated_louvain - INFO - Running standard community detection...\n", + "2025-11-26 09:51:02,816 - bioneuralnet.clustering.correlated_louvain - INFO - Computed quality (supervised): Q = 0.6016, avg_corr = 0.6149, combined = 0.6123\n", + "2025-11-26 09:51:02,817 - bioneuralnet.clustering.correlated_louvain - INFO - Final quality: 0.6123\n", + "2025-11-26 09:51:02,817 - bioneuralnet.clustering.correlated_louvain - INFO - CorrelatedLouvain found 8 communities (nodes=293)\n", + "2025-11-26 09:51:02,850 - bioneuralnet.clustering.correlated_louvain - INFO - Computed quality (supervised): Q = 0.6016, avg_corr = 0.6149, combined = 0.6123\n", + "2025-11-26 09:51:02,851 - bioneuralnet.clustering.hybrid_louvain - INFO - Iteration 2: Louvain Quality = 0.6123\n", + "2025-11-26 09:51:02,881 - bioneuralnet.clustering.hybrid_louvain - INFO - Selected seed community of size 66 with correlation -0.8259\n", + "2025-11-26 09:51:02,881 - bioneuralnet.clustering.correlated_pagerank - INFO - Initialized PageRank with the following parameters:\n", + "2025-11-26 09:51:02,882 - bioneuralnet.clustering.correlated_pagerank - INFO - Graph: NetworkX Graph with 293 nodes and 2701 edges.\n", + "2025-11-26 09:51:02,882 - bioneuralnet.clustering.correlated_pagerank - INFO - Omics Data: DataFrame with shape (658, 900).\n", + "2025-11-26 09:51:02,883 - bioneuralnet.clustering.correlated_pagerank - INFO - Phenotype Data: Series with 658 samples.\n", + "2025-11-26 09:51:02,883 - bioneuralnet.clustering.correlated_pagerank - INFO - Alpha: 0.9\n", + "2025-11-26 09:51:02,883 - bioneuralnet.clustering.correlated_pagerank - INFO - Max Iterations: 100\n", + "2025-11-26 09:51:02,883 - bioneuralnet.clustering.correlated_pagerank - INFO - Tolerance: 1e-06\n", + "2025-11-26 09:51:02,884 - bioneuralnet.clustering.correlated_pagerank - INFO - K (Composite Score Weight): 0.5\n", + "2025-11-26 09:51:02,884 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 09:51:02,885 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 09:51:02,885 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 09:51:02,885 - bioneuralnet.clustering.correlated_pagerank - INFO - Initialized Correlated PageRank. device=cpu\n", + "2025-11-26 09:51:03,374 - bioneuralnet.clustering.correlated_pagerank - INFO - Generated personalization vector for seed nodes: ['FATE1_89885', 'CASQ2_845', 'FOLH1_2346', 'hsa_mir_1247', 'BARX2_8538', 'NGF_4803', 'CA4_762', 'C2orf72_257407', 'hsa_mir_1_2', 'RASD2_23551', 'OR51E1_143503', 'ANGPT2_285', 'ADAMTS4_9507', 'ZNF521_25925', 'CXCL10_3627', 'GABRE_2564', 'C6orf223_221416', 'hsa_mir_153_2', 'PDE3A_5139', 'MCHR1_2847', 'AGTR1_185', 'ESM1_11082', 'CXCL9_4283', 'hsa_mir_489', 'hsa_mir_885', 'B4GALNT1_2583', 'ACAN_176', 'CSPG4_1464', 'hsa_mir_224', 'COL5A3_50509', 'FLT1_2321', 'GPR4_2828', 'hsa_mir_126', 'DACH1_1602', 'BTNL9_153579', 'PCDH17_27253', 'APLNR_187', 'COL21A1_81578', 'SLC35F1_222553', 'STC1_6781', 'PLAC9_219348', 'ADAMTS12_81792', 'hsa_mir_139', 'ANO3_63982', 'COL8A1_1295', 'APLN_8862', 'LGI4_163175', 'ACTG2_72', 'hsa_mir_653', 'CDH13_1012', 'CALCR_799', 'APOLD1_81575', 'KLHDC8A_55220', 'RGS5_8490', 'DDR2_4921', 'COL25A1_84570', 'FHL5_9457', 'CD36_948', 'HMCN1_83872', 'C3orf70_285382', 'KCNN3_3782', 'ADAMTS5_11096', 'COX4I2_84701', 'CXCL11_6373', 'PRRX1_5396', 'TRPC6_7225']\n", + "2025-11-26 09:51:03,379 - bioneuralnet.clustering.correlated_pagerank - INFO - PageRank computation completed.\n", + "2025-11-26 09:51:05,727 - bioneuralnet.clustering.correlated_pagerank - INFO - Sweep cut resulted in cluster of size 227 with conductance 0.085 and correlation -0.83.\n", + "2025-11-26 09:51:05,728 - bioneuralnet.clustering.correlated_pagerank - INFO - PageRank clustering completed successfully.\n", + "2025-11-26 09:51:05,728 - bioneuralnet.clustering.hybrid_louvain - INFO - Iteration 2: cluster size=227, Conductance=0.085 Correlation=-0.830 score=-0.372\n", + "2025-11-26 09:51:05,735 - bioneuralnet.clustering.hybrid_louvain - INFO - \n", + "Iteration 3/3: Running Correlated Louvain...\n", + "2025-11-26 09:51:05,740 - bioneuralnet.clustering.correlated_louvain - INFO - CorrelatedLouvain(k3=0.2, k4=0.8, nodes=227, edges=2085, features=900)\n", + "2025-11-26 09:51:05,741 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 09:51:05,741 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 09:51:05,742 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 09:51:05,742 - bioneuralnet.clustering.correlated_louvain - INFO - Running standard community detection...\n", + "2025-11-26 09:51:05,781 - bioneuralnet.clustering.correlated_louvain - INFO - Computed quality (supervised): Q = 0.5419, avg_corr = 0.6823, combined = 0.6542\n", + "2025-11-26 09:51:05,782 - bioneuralnet.clustering.correlated_louvain - INFO - Final quality: 0.6542\n", + "2025-11-26 09:51:05,782 - bioneuralnet.clustering.correlated_louvain - INFO - CorrelatedLouvain found 7 communities (nodes=227)\n", + "2025-11-26 09:51:05,808 - bioneuralnet.clustering.correlated_louvain - INFO - Computed quality (supervised): Q = 0.5419, avg_corr = 0.6823, combined = 0.6542\n", + "2025-11-26 09:51:05,808 - bioneuralnet.clustering.hybrid_louvain - INFO - Iteration 3: Louvain Quality = 0.6542\n", + "2025-11-26 09:51:05,832 - bioneuralnet.clustering.hybrid_louvain - INFO - Selected seed community of size 58 with correlation -0.8271\n", + "2025-11-26 09:51:05,833 - bioneuralnet.clustering.correlated_pagerank - INFO - Initialized PageRank with the following parameters:\n", + "2025-11-26 09:51:05,834 - bioneuralnet.clustering.correlated_pagerank - INFO - Graph: NetworkX Graph with 227 nodes and 2085 edges.\n", + "2025-11-26 09:51:05,834 - bioneuralnet.clustering.correlated_pagerank - INFO - Omics Data: DataFrame with shape (658, 900).\n", + "2025-11-26 09:51:05,834 - bioneuralnet.clustering.correlated_pagerank - INFO - Phenotype Data: Series with 658 samples.\n", + "2025-11-26 09:51:05,835 - bioneuralnet.clustering.correlated_pagerank - INFO - Alpha: 0.9\n", + "2025-11-26 09:51:05,835 - bioneuralnet.clustering.correlated_pagerank - INFO - Max Iterations: 100\n", + "2025-11-26 09:51:05,835 - bioneuralnet.clustering.correlated_pagerank - INFO - Tolerance: 1e-06\n", + "2025-11-26 09:51:05,836 - bioneuralnet.clustering.correlated_pagerank - INFO - K (Composite Score Weight): 0.5\n", + "2025-11-26 09:51:05,836 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 09:51:05,837 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 09:51:05,838 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 09:51:05,838 - bioneuralnet.clustering.correlated_pagerank - INFO - Initialized Correlated PageRank. device=cpu\n", + "2025-11-26 09:51:06,070 - bioneuralnet.clustering.correlated_pagerank - INFO - Generated personalization vector for seed nodes: ['FATE1_89885', 'CASQ2_845', 'FOLH1_2346', 'hsa_mir_1247', 'BARX2_8538', 'HOXD10_3236', 'NGF_4803', 'CA4_762', 'C2orf72_257407', 'RASD2_23551', 'OR51E1_143503', 'ANGPT2_285', 'ADAMTS4_9507', 'ZNF521_25925', 'GABRE_2564', 'C6orf223_221416', 'hsa_mir_153_2', 'PDE3A_5139', 'MCHR1_2847', 'AGTR1_185', 'ESM1_11082', 'hsa_mir_489', 'hsa_mir_885', 'B4GALNT1_2583', 'ACAN_176', 'CSPG4_1464', 'hsa_mir_224', 'COL5A3_50509', 'FLT1_2321', 'GPR4_2828', 'DACH1_1602', 'BTNL9_153579', 'PCDH17_27253', 'APLNR_187', 'COL21A1_81578', 'SLC35F1_222553', 'STC1_6781', 'PLAC9_219348', 'ANO3_63982', 'COL8A1_1295', 'APLN_8862', 'LGI4_163175', 'hsa_mir_653', 'CDH13_1012', 'CALCR_799', 'APOLD1_81575', 'KLHDC8A_55220', 'RGS5_8490', 'DDR2_4921', 'COL25A1_84570', 'FHL5_9457', 'CD36_948', 'HMCN1_83872', 'C3orf70_285382', 'KCNN3_3782', 'ADAMTS5_11096', 'COX4I2_84701', 'TRPC6_7225']\n", + "2025-11-26 09:51:06,074 - bioneuralnet.clustering.correlated_pagerank - INFO - PageRank computation completed.\n", + "2025-11-26 09:51:08,534 - bioneuralnet.clustering.correlated_pagerank - INFO - Sweep cut resulted in cluster of size 94 with conductance 0.177 and correlation -0.83.\n", + "2025-11-26 09:51:08,535 - bioneuralnet.clustering.correlated_pagerank - INFO - PageRank clustering completed successfully.\n", + "2025-11-26 09:51:08,535 - bioneuralnet.clustering.hybrid_louvain - INFO - Iteration 3: cluster size=94, Conductance=0.177 Correlation=-0.830 score=-0.327\n", + "2025-11-26 09:51:08,538 - bioneuralnet.clustering.hybrid_louvain - INFO - Hybrid Louvain completed after 4 iterations.\n", + "2025-11-26 09:51:08,542 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 09:51:08,543 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 09:51:08,543 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 09:51:08,544 - bioneuralnet.clustering.hybrid_louvain - INFO - Initializing HybridLouvain...\n", + "2025-11-26 09:51:08,544 - bioneuralnet.clustering.hybrid_louvain - INFO - Input G is a DataFrame; converting adjacency matrix to NetworkX graph.\n", + "2025-11-26 09:51:08,564 - bioneuralnet.clustering.hybrid_louvain - INFO - Initialized HybridLouvain with 900 graph nodes, 900 omics columns, 658 phenotype rows; max_iter=3, k3=0.2, k4=0.8, tune=False\n", + "2025-11-26 09:51:08,565 - bioneuralnet.clustering.hybrid_louvain - INFO - \n", + "Iteration 1/3: Running Correlated Louvain...\n", + "2025-11-26 09:51:08,606 - bioneuralnet.clustering.correlated_louvain - INFO - CorrelatedLouvain(k3=0.2, k4=0.8, nodes=900, edges=8930, features=900)\n", + "2025-11-26 09:51:08,607 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 09:51:08,608 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 09:51:08,609 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 09:51:08,609 - bioneuralnet.clustering.correlated_louvain - INFO - Running standard community detection...\n", + "2025-11-26 09:51:08,749 - bioneuralnet.clustering.correlated_louvain - INFO - Computed quality (supervised): Q = 0.7372, avg_corr = 0.4985, combined = 0.5462\n", + "2025-11-26 09:51:08,750 - bioneuralnet.clustering.correlated_louvain - INFO - Final quality: 0.5462\n", + "2025-11-26 09:51:08,750 - bioneuralnet.clustering.correlated_louvain - INFO - CorrelatedLouvain found 9 communities (nodes=900)\n", + "2025-11-26 09:51:08,850 - bioneuralnet.clustering.correlated_louvain - INFO - Computed quality (supervised): Q = 0.7372, avg_corr = 0.4985, combined = 0.5462\n", + "2025-11-26 09:51:08,850 - bioneuralnet.clustering.hybrid_louvain - INFO - Iteration 1: Louvain Quality = 0.5462\n", + "2025-11-26 09:51:08,932 - bioneuralnet.clustering.hybrid_louvain - INFO - Selected seed community of size 48 with correlation -0.8391\n", + "2025-11-26 09:51:08,932 - bioneuralnet.clustering.correlated_pagerank - INFO - Initialized PageRank with the following parameters:\n", + "2025-11-26 09:51:08,933 - bioneuralnet.clustering.correlated_pagerank - INFO - Graph: NetworkX Graph with 900 nodes and 8930 edges.\n", + "2025-11-26 09:51:08,933 - bioneuralnet.clustering.correlated_pagerank - INFO - Omics Data: DataFrame with shape (658, 900).\n", + "2025-11-26 09:51:08,934 - bioneuralnet.clustering.correlated_pagerank - INFO - Phenotype Data: Series with 658 samples.\n", + "2025-11-26 09:51:08,934 - bioneuralnet.clustering.correlated_pagerank - INFO - Alpha: 0.9\n", + "2025-11-26 09:51:08,934 - bioneuralnet.clustering.correlated_pagerank - INFO - Max Iterations: 100\n", + "2025-11-26 09:51:08,934 - bioneuralnet.clustering.correlated_pagerank - INFO - Tolerance: 1e-06\n", + "2025-11-26 09:51:08,935 - bioneuralnet.clustering.correlated_pagerank - INFO - K (Composite Score Weight): 0.5\n", + "2025-11-26 09:51:08,935 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 09:51:08,936 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 09:51:08,936 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 09:51:08,937 - bioneuralnet.clustering.correlated_pagerank - INFO - Initialized Correlated PageRank. device=cpu\n", + "2025-11-26 09:51:09,133 - bioneuralnet.clustering.correlated_pagerank - INFO - Generated personalization vector for seed nodes: ['BAAT', 'SCARNA4', 'RPL23AP32', 'LOH3CR2A', 'C4orf6', 'SNORA1', 'CP', 'C10orf62', 'C3orf24', 'GBP5', 'LCN2_3934', 'SLPI_6590', 'CKMT1B_1159', 'TJP3_27134', 'CHL1_10752', 'C19orf46_163183', 'CKMT1A_548596', 'C6orf168_84553', 'MAL_4118', 'BNC1_646', 'CAMK2B_816', 'CNTN3_5067', 'LOC554202_554202', 'C1orf116_79098', 'CST6_1474', 'WFDC2_10406', 'DEFB1_1672', 'CCNO_10309', 'ANO5_203859', 'CCDC78_124093', 'STK33_65975', 'IGF2BP2_10644', 'EPS8L1_54869', 'CBLC_23624', 'C4orf48_401115', 'CNTD2_79935', 'OGDHL_55753', 'FAM155B_27112', 'ANXA3_306', 'hsa_mir_375', 'hsa_mir_183', 'hsa_mir_96', 'hsa_mir_182', 'hsa_mir_34c', 'hsa_mir_200b', 'hsa_mir_429', 'hsa_mir_651', 'hsa_mir_200a']\n", + "2025-11-26 09:51:09,144 - bioneuralnet.clustering.correlated_pagerank - INFO - PageRank computation completed.\n", + "2025-11-26 09:51:26,800 - bioneuralnet.clustering.correlated_pagerank - INFO - Sweep cut resulted in cluster of size 159 with conductance 0.217 and correlation -0.83.\n", + "2025-11-26 09:51:26,801 - bioneuralnet.clustering.correlated_pagerank - INFO - PageRank clustering completed successfully.\n", + "2025-11-26 09:51:26,801 - bioneuralnet.clustering.hybrid_louvain - INFO - Iteration 1: cluster size=159, Conductance=0.217 Correlation=-0.830 score=-0.307\n", + "2025-11-26 09:51:26,809 - bioneuralnet.clustering.hybrid_louvain - INFO - \n", + "Iteration 2/3: Running Correlated Louvain...\n", + "2025-11-26 09:51:26,816 - bioneuralnet.clustering.correlated_louvain - INFO - CorrelatedLouvain(k3=0.2, k4=0.8, nodes=159, edges=1292, features=900)\n", + "2025-11-26 09:51:26,819 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 09:51:26,819 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 09:51:26,820 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 09:51:26,821 - bioneuralnet.clustering.correlated_louvain - INFO - Running standard community detection...\n", + "2025-11-26 09:51:26,870 - bioneuralnet.clustering.correlated_louvain - INFO - Computed quality (supervised): Q = 0.5196, avg_corr = 0.5933, combined = 0.5785\n", + "2025-11-26 09:51:26,871 - bioneuralnet.clustering.correlated_louvain - INFO - Final quality: 0.5785\n", + "2025-11-26 09:51:26,871 - bioneuralnet.clustering.correlated_louvain - INFO - CorrelatedLouvain found 6 communities (nodes=159)\n", + "2025-11-26 09:51:26,895 - bioneuralnet.clustering.correlated_louvain - INFO - Computed quality (supervised): Q = 0.5196, avg_corr = 0.5933, combined = 0.5785\n", + "2025-11-26 09:51:26,896 - bioneuralnet.clustering.hybrid_louvain - INFO - Iteration 2: Louvain Quality = 0.5785\n", + "2025-11-26 09:51:26,918 - bioneuralnet.clustering.hybrid_louvain - INFO - Selected seed community of size 45 with correlation -0.8283\n", + "2025-11-26 09:51:26,919 - bioneuralnet.clustering.correlated_pagerank - INFO - Initialized PageRank with the following parameters:\n", + "2025-11-26 09:51:26,920 - bioneuralnet.clustering.correlated_pagerank - INFO - Graph: NetworkX Graph with 159 nodes and 1292 edges.\n", + "2025-11-26 09:51:26,920 - bioneuralnet.clustering.correlated_pagerank - INFO - Omics Data: DataFrame with shape (658, 900).\n", + "2025-11-26 09:51:26,920 - bioneuralnet.clustering.correlated_pagerank - INFO - Phenotype Data: Series with 658 samples.\n", + "2025-11-26 09:51:26,920 - bioneuralnet.clustering.correlated_pagerank - INFO - Alpha: 0.9\n", + "2025-11-26 09:51:26,921 - bioneuralnet.clustering.correlated_pagerank - INFO - Max Iterations: 100\n", + "2025-11-26 09:51:26,921 - bioneuralnet.clustering.correlated_pagerank - INFO - Tolerance: 1e-06\n", + "2025-11-26 09:51:26,921 - bioneuralnet.clustering.correlated_pagerank - INFO - K (Composite Score Weight): 0.5\n", + "2025-11-26 09:51:26,922 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 09:51:26,922 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 09:51:26,923 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 09:51:26,923 - bioneuralnet.clustering.correlated_pagerank - INFO - Initialized Correlated PageRank. device=cpu\n", + "2025-11-26 09:51:27,114 - bioneuralnet.clustering.correlated_pagerank - INFO - Generated personalization vector for seed nodes: ['C4orf48_401115', 'CKMT1B_1159', 'OGDHL_55753', 'hsa_mir_21', 'hsa_mir_183', 'CST6_1474', 'EPS8L1_54869', 'hsa_mir_429', 'C1orf116_79098', 'hsa_mir_200b', 'C19orf46_163183', 'GBP5', 'hsa_mir_31', 'CNTD2_79935', 'C6orf168_84553', 'CHL1_10752', 'CBLC_23624', 'CNTN3_5067', 'CKMT1A_548596', 'hsa_mir_200a', 'AKR1C2_1646', 'SLPI_6590', 'WFDC2_10406', 'hsa_mir_375', 'ANXA3_306', 'hsa_mir_149', 'BNC1_646', 'CCDC78_124093', 'MAL_4118', 'CAMK2B_816', 'FAM155B_27112', 'CCNO_10309', 'STK33_65975', 'LOC554202_554202', 'TJP3_27134', 'hsa_mir_34c', 'C3orf24', 'IGF2BP2_10644', 'hsa_mir_182', 'CLSTN2_64084', 'ANO5_203859', 'hsa_mir_96', 'hsa_mir_205', 'LCN2_3934', 'LOH3CR2A']\n", + "2025-11-26 09:51:27,117 - bioneuralnet.clustering.correlated_pagerank - INFO - PageRank computation completed.\n", + "2025-11-26 09:51:28,052 - bioneuralnet.clustering.correlated_pagerank - INFO - Sweep cut resulted in cluster of size 63 with conductance 0.229 and correlation -0.85.\n", + "2025-11-26 09:51:28,052 - bioneuralnet.clustering.correlated_pagerank - INFO - PageRank clustering completed successfully.\n", + "2025-11-26 09:51:28,053 - bioneuralnet.clustering.hybrid_louvain - INFO - Iteration 2: cluster size=63, Conductance=0.229 Correlation=-0.850 score=-0.311\n", + "2025-11-26 09:51:28,055 - bioneuralnet.clustering.hybrid_louvain - INFO - \n", + "Iteration 3/3: Running Correlated Louvain...\n", + "2025-11-26 09:51:28,058 - bioneuralnet.clustering.correlated_louvain - INFO - CorrelatedLouvain(k3=0.2, k4=0.8, nodes=63, edges=490, features=900)\n", + "2025-11-26 09:51:28,059 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 09:51:28,060 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 09:51:28,060 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 09:51:28,061 - bioneuralnet.clustering.correlated_louvain - INFO - Running standard community detection...\n", + "2025-11-26 09:51:28,085 - bioneuralnet.clustering.correlated_louvain - INFO - Computed quality (supervised): Q = 0.3730, avg_corr = 0.6817, combined = 0.6200\n", + "2025-11-26 09:51:28,085 - bioneuralnet.clustering.correlated_louvain - INFO - Final quality: 0.6200\n", + "2025-11-26 09:51:28,086 - bioneuralnet.clustering.correlated_louvain - INFO - CorrelatedLouvain found 5 communities (nodes=63)\n", + "2025-11-26 09:51:28,104 - bioneuralnet.clustering.correlated_louvain - INFO - Computed quality (supervised): Q = 0.3730, avg_corr = 0.6817, combined = 0.6200\n", + "2025-11-26 09:51:28,105 - bioneuralnet.clustering.hybrid_louvain - INFO - Iteration 3: Louvain Quality = 0.6200\n", + "2025-11-26 09:51:28,135 - bioneuralnet.clustering.hybrid_louvain - INFO - Selected seed community of size 17 with correlation -0.8340\n", + "2025-11-26 09:51:28,136 - bioneuralnet.clustering.correlated_pagerank - INFO - Initialized PageRank with the following parameters:\n", + "2025-11-26 09:51:28,136 - bioneuralnet.clustering.correlated_pagerank - INFO - Graph: NetworkX Graph with 63 nodes and 490 edges.\n", + "2025-11-26 09:51:28,136 - bioneuralnet.clustering.correlated_pagerank - INFO - Omics Data: DataFrame with shape (658, 900).\n", + "2025-11-26 09:51:28,137 - bioneuralnet.clustering.correlated_pagerank - INFO - Phenotype Data: Series with 658 samples.\n", + "2025-11-26 09:51:28,137 - bioneuralnet.clustering.correlated_pagerank - INFO - Alpha: 0.9\n", + "2025-11-26 09:51:28,138 - bioneuralnet.clustering.correlated_pagerank - INFO - Max Iterations: 100\n", + "2025-11-26 09:51:28,138 - bioneuralnet.clustering.correlated_pagerank - INFO - Tolerance: 1e-06\n", + "2025-11-26 09:51:28,138 - bioneuralnet.clustering.correlated_pagerank - INFO - K (Composite Score Weight): 0.5\n", + "2025-11-26 09:51:28,139 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 09:51:28,140 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 09:51:28,141 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 09:51:28,141 - bioneuralnet.clustering.correlated_pagerank - INFO - Initialized Correlated PageRank. device=cpu\n", + "2025-11-26 09:51:28,205 - bioneuralnet.clustering.correlated_pagerank - INFO - Generated personalization vector for seed nodes: ['C4orf48_401115', 'CAMK2B_816', 'TMEM204', 'CCNO_10309', 'CORO6_84940', 'LOC554202_554202', 'TJP3_27134', 'IGF2BP2_10644', 'C1orf162', 'CNTD2_79935', 'C6orf168_84553', 'CHL1_10752', 'hsa_mir_96', 'BNC1_646', 'CCDC78_124093', 'LCN2_3934', 'LOH3CR2A']\n", + "2025-11-26 09:51:28,209 - bioneuralnet.clustering.correlated_pagerank - INFO - PageRank computation completed.\n", + "2025-11-26 09:51:28,455 - bioneuralnet.clustering.correlated_pagerank - INFO - Sweep cut resulted in cluster of size 32 with conductance 0.246 and correlation -0.86.\n", + "2025-11-26 09:51:28,456 - bioneuralnet.clustering.correlated_pagerank - INFO - PageRank clustering completed successfully.\n", + "2025-11-26 09:51:28,457 - bioneuralnet.clustering.hybrid_louvain - INFO - Iteration 3: cluster size=32, Conductance=0.246 Correlation=-0.860 score=-0.307\n", + "2025-11-26 09:51:28,458 - bioneuralnet.clustering.hybrid_louvain - INFO - Hybrid Louvain completed after 4 iterations.\n", + "2025-11-26 09:51:28,460 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 09:51:28,461 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 09:51:28,462 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 09:51:28,462 - bioneuralnet.clustering.hybrid_louvain - INFO - Initializing HybridLouvain...\n", + "2025-11-26 09:51:28,462 - bioneuralnet.clustering.hybrid_louvain - INFO - Input G is a DataFrame; converting adjacency matrix to NetworkX graph.\n", + "2025-11-26 09:51:28,482 - bioneuralnet.clustering.hybrid_louvain - INFO - Initialized HybridLouvain with 900 graph nodes, 900 omics columns, 658 phenotype rows; max_iter=3, k3=0.2, k4=0.8, tune=False\n", + "2025-11-26 09:51:28,483 - bioneuralnet.clustering.hybrid_louvain - INFO - \n", + "Iteration 1/3: Running Correlated Louvain...\n", + "2025-11-26 09:51:28,498 - bioneuralnet.clustering.correlated_louvain - INFO - CorrelatedLouvain(k3=0.2, k4=0.8, nodes=900, edges=8930, features=900)\n", + "2025-11-26 09:51:28,500 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 09:51:28,501 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 09:51:28,502 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 09:51:28,503 - bioneuralnet.clustering.correlated_louvain - INFO - Running standard community detection...\n", + "2025-11-26 09:51:28,640 - bioneuralnet.clustering.correlated_louvain - INFO - Computed quality (supervised): Q = 0.7372, avg_corr = 0.4314, combined = 0.4926\n", + "2025-11-26 09:51:28,640 - bioneuralnet.clustering.correlated_louvain - INFO - Final quality: 0.4926\n", + "2025-11-26 09:51:28,641 - bioneuralnet.clustering.correlated_louvain - INFO - CorrelatedLouvain found 9 communities (nodes=900)\n", + "2025-11-26 09:51:28,715 - bioneuralnet.clustering.correlated_louvain - INFO - Computed quality (supervised): Q = 0.7372, avg_corr = 0.4314, combined = 0.4926\n", + "2025-11-26 09:51:28,716 - bioneuralnet.clustering.hybrid_louvain - INFO - Iteration 1: Louvain Quality = 0.4926\n", + "2025-11-26 09:51:28,776 - bioneuralnet.clustering.hybrid_louvain - INFO - Selected seed community of size 76 with correlation 0.8164\n", + "2025-11-26 09:51:28,777 - bioneuralnet.clustering.correlated_pagerank - INFO - Initialized PageRank with the following parameters:\n", + "2025-11-26 09:51:28,777 - bioneuralnet.clustering.correlated_pagerank - INFO - Graph: NetworkX Graph with 900 nodes and 8930 edges.\n", + "2025-11-26 09:51:28,778 - bioneuralnet.clustering.correlated_pagerank - INFO - Omics Data: DataFrame with shape (658, 900).\n", + "2025-11-26 09:51:28,778 - bioneuralnet.clustering.correlated_pagerank - INFO - Phenotype Data: Series with 658 samples.\n", + "2025-11-26 09:51:28,778 - bioneuralnet.clustering.correlated_pagerank - INFO - Alpha: 0.9\n", + "2025-11-26 09:51:28,779 - bioneuralnet.clustering.correlated_pagerank - INFO - Max Iterations: 100\n", + "2025-11-26 09:51:28,779 - bioneuralnet.clustering.correlated_pagerank - INFO - Tolerance: 1e-06\n", + "2025-11-26 09:51:28,779 - bioneuralnet.clustering.correlated_pagerank - INFO - K (Composite Score Weight): 0.5\n", + "2025-11-26 09:51:28,780 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 09:51:28,780 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 09:51:28,781 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 09:51:28,781 - bioneuralnet.clustering.correlated_pagerank - INFO - Initialized Correlated PageRank. device=cpu\n", + "2025-11-26 09:51:29,691 - bioneuralnet.clustering.correlated_pagerank - INFO - Generated personalization vector for seed nodes: ['ALDH1A1', 'C21orf131', 'ADAM28', 'C21orf129', 'TMEM71', 'C4orf26', 'SLC22A2', 'TINAG', 'ACSM5', 'CIDEC', 'C20orf118', 'RHCG_51458', 'ATP6V0A4_50617', 'SLC4A1_6521', 'KLK1_3816', 'ATP6V0D2_245972', 'CLDN8_9073', 'FOXI1_2299', 'DMRT2_10655', 'HEPACAM2_253012', 'CLCNKB_1188', 'ATP6V1B1_525', 'CFTR_1080', 'CKMT2_1160', 'GCGR_2642', 'SLC26A7_115111', 'CLCNKA_1187', 'FGF9_2254', 'BMPR1B_658', 'TMPRSS2_7113', 'DHRS2_10202', 'BSND_7809', 'AQP6_363', 'CWH43_80157', 'TRIM50_135892', 'C16orf89_146556', 'SLC9A2_6549', 'CALCA_796', 'OLIG1_116448', 'CLNK_116449', 'CYP11A1_1583', 'RANBP3L_202151', 'DIRAS1_148252', 'C1orf168_199920', 'INPP5J_27124', 'C15orf59_388135', 'CCBE1_147372', 'EPB41L4B_54566', 'GCOM1_145781', 'C9orf84_158401', 'ATP6V1G3_127124', 'VWA5B1_127731', 'LRRTM1_347730', 'KRTAP5_8_57830', 'ST8SIA5_29906', 'B4GALNT3_283358', 'CNTNAP5_129684', 'CEND1_51286', 'LOC643008_643008', 'CEL_1056', 'CR1L_1379', 'ASB5_140458', 'CTNNA2_1496', 'MARCH10_162333', 'SEZ6L_23544', 'hsa_mir_891a', 'hsa_mir_141', 'hsa_mir_187', 'hsa_mir_200c', 'hsa_mir_129_1', 'hsa_mir_129_2', 'hsa_mir_874', 'hsa_mir_206', 'hsa_mir_218_1', 'hsa_mir_618', 'hsa_mir_30c_1']\n", + "2025-11-26 09:51:29,704 - bioneuralnet.clustering.correlated_pagerank - INFO - PageRank computation completed.\n", + "2025-11-26 09:51:46,043 - bioneuralnet.clustering.correlated_pagerank - INFO - Sweep cut resulted in cluster of size 69 with conductance 0.109 and correlation 0.81.\n", + "2025-11-26 09:51:46,044 - bioneuralnet.clustering.correlated_pagerank - INFO - PageRank clustering completed successfully.\n", + "2025-11-26 09:51:46,044 - bioneuralnet.clustering.hybrid_louvain - INFO - Iteration 1: cluster size=69, Conductance=0.109 Correlation=0.810 score=-0.350\n", + "2025-11-26 09:51:46,046 - bioneuralnet.clustering.hybrid_louvain - INFO - \n", + "Iteration 2/3: Running Correlated Louvain...\n", + "2025-11-26 09:51:46,049 - bioneuralnet.clustering.correlated_louvain - INFO - CorrelatedLouvain(k3=0.2, k4=0.8, nodes=69, edges=585, features=900)\n", + "2025-11-26 09:51:46,050 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 09:51:46,051 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 09:51:46,052 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 09:51:46,052 - bioneuralnet.clustering.correlated_louvain - INFO - Running standard community detection...\n", + "2025-11-26 09:51:46,074 - bioneuralnet.clustering.correlated_louvain - INFO - Computed quality (supervised): Q = 0.3798, avg_corr = 0.7822, combined = 0.7017\n", + "2025-11-26 09:51:46,075 - bioneuralnet.clustering.correlated_louvain - INFO - Final quality: 0.7017\n", + "2025-11-26 09:51:46,075 - bioneuralnet.clustering.correlated_louvain - INFO - CorrelatedLouvain found 4 communities (nodes=69)\n", + "2025-11-26 09:51:46,093 - bioneuralnet.clustering.correlated_louvain - INFO - Computed quality (supervised): Q = 0.3798, avg_corr = 0.7822, combined = 0.7017\n", + "2025-11-26 09:51:46,094 - bioneuralnet.clustering.hybrid_louvain - INFO - Iteration 2: Louvain Quality = 0.7017\n", + "2025-11-26 09:51:46,114 - bioneuralnet.clustering.hybrid_louvain - INFO - Selected seed community of size 9 with correlation 0.8318\n", + "2025-11-26 09:51:46,116 - bioneuralnet.clustering.correlated_pagerank - INFO - Initialized PageRank with the following parameters:\n", + "2025-11-26 09:51:46,117 - bioneuralnet.clustering.correlated_pagerank - INFO - Graph: NetworkX Graph with 69 nodes and 585 edges.\n", + "2025-11-26 09:51:46,118 - bioneuralnet.clustering.correlated_pagerank - INFO - Omics Data: DataFrame with shape (658, 900).\n", + "2025-11-26 09:51:46,118 - bioneuralnet.clustering.correlated_pagerank - INFO - Phenotype Data: Series with 658 samples.\n", + "2025-11-26 09:51:46,119 - bioneuralnet.clustering.correlated_pagerank - INFO - Alpha: 0.9\n", + "2025-11-26 09:51:46,120 - bioneuralnet.clustering.correlated_pagerank - INFO - Max Iterations: 100\n", + "2025-11-26 09:51:46,121 - bioneuralnet.clustering.correlated_pagerank - INFO - Tolerance: 1e-06\n", + "2025-11-26 09:51:46,121 - bioneuralnet.clustering.correlated_pagerank - INFO - K (Composite Score Weight): 0.5\n", + "2025-11-26 09:51:46,122 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 09:51:46,124 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 09:51:46,125 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 09:51:46,126 - bioneuralnet.clustering.correlated_pagerank - INFO - Initialized Correlated PageRank. device=cpu\n", + "2025-11-26 09:51:46,159 - bioneuralnet.clustering.correlated_pagerank - INFO - Generated personalization vector for seed nodes: ['SEZ6L_23544', 'LRRTM1_347730', 'hsa_mir_187', 'KLK1_3816', 'CTNNA2_1496', 'CASS4', 'hsa_mir_618', 'CFTR_1080', 'ATP6V0D2_245972']\n", + "2025-11-26 09:51:46,162 - bioneuralnet.clustering.correlated_pagerank - INFO - PageRank computation completed.\n", + "2025-11-26 09:51:46,457 - bioneuralnet.clustering.correlated_pagerank - INFO - Sweep cut resulted in cluster of size 34 with conductance 0.211 and correlation 0.81.\n", + "2025-11-26 09:51:46,457 - bioneuralnet.clustering.correlated_pagerank - INFO - PageRank clustering completed successfully.\n", + "2025-11-26 09:51:46,457 - bioneuralnet.clustering.hybrid_louvain - INFO - Iteration 2: cluster size=34, Conductance=0.211 Correlation=0.810 score=-0.300\n", + "2025-11-26 09:51:46,459 - bioneuralnet.clustering.hybrid_louvain - INFO - \n", + "Iteration 3/3: Running Correlated Louvain...\n", + "2025-11-26 09:51:46,461 - bioneuralnet.clustering.correlated_louvain - INFO - CorrelatedLouvain(k3=0.2, k4=0.8, nodes=34, edges=235, features=900)\n", + "2025-11-26 09:51:46,462 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 09:51:46,462 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 09:51:46,463 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 09:51:46,463 - bioneuralnet.clustering.correlated_louvain - INFO - Running standard community detection...\n", + "2025-11-26 09:51:46,479 - bioneuralnet.clustering.correlated_louvain - INFO - Computed quality (supervised): Q = 0.3141, avg_corr = 0.7568, combined = 0.6683\n", + "2025-11-26 09:51:46,480 - bioneuralnet.clustering.correlated_louvain - INFO - Final quality: 0.6683\n", + "2025-11-26 09:51:46,480 - bioneuralnet.clustering.correlated_louvain - INFO - CorrelatedLouvain found 6 communities (nodes=34)\n", + "2025-11-26 09:51:46,493 - bioneuralnet.clustering.correlated_louvain - INFO - Computed quality (supervised): Q = 0.3141, avg_corr = 0.7568, combined = 0.6683\n", + "2025-11-26 09:51:46,493 - bioneuralnet.clustering.hybrid_louvain - INFO - Iteration 3: Louvain Quality = 0.6683\n", + "2025-11-26 09:51:46,505 - bioneuralnet.clustering.hybrid_louvain - INFO - Selected seed community of size 7 with correlation 0.8084\n", + "2025-11-26 09:51:46,506 - bioneuralnet.clustering.correlated_pagerank - INFO - Initialized PageRank with the following parameters:\n", + "2025-11-26 09:51:46,507 - bioneuralnet.clustering.correlated_pagerank - INFO - Graph: NetworkX Graph with 34 nodes and 235 edges.\n", + "2025-11-26 09:51:46,507 - bioneuralnet.clustering.correlated_pagerank - INFO - Omics Data: DataFrame with shape (658, 900).\n", + "2025-11-26 09:51:46,507 - bioneuralnet.clustering.correlated_pagerank - INFO - Phenotype Data: Series with 658 samples.\n", + "2025-11-26 09:51:46,508 - bioneuralnet.clustering.correlated_pagerank - INFO - Alpha: 0.9\n", + "2025-11-26 09:51:46,508 - bioneuralnet.clustering.correlated_pagerank - INFO - Max Iterations: 100\n", + "2025-11-26 09:51:46,508 - bioneuralnet.clustering.correlated_pagerank - INFO - Tolerance: 1e-06\n", + "2025-11-26 09:51:46,508 - bioneuralnet.clustering.correlated_pagerank - INFO - K (Composite Score Weight): 0.5\n", + "2025-11-26 09:51:46,509 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 09:51:46,510 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 09:51:46,510 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 09:51:46,510 - bioneuralnet.clustering.correlated_pagerank - INFO - Initialized Correlated PageRank. device=cpu\n", + "2025-11-26 09:51:46,532 - bioneuralnet.clustering.correlated_pagerank - INFO - Generated personalization vector for seed nodes: ['SEZ6L_23544', 'ST8SIA5_29906', 'LRRTM1_347730', 'CTNNA2_1496', 'hsa_mir_618', 'CFTR_1080', 'CR1L_1379']\n", + "2025-11-26 09:51:46,534 - bioneuralnet.clustering.correlated_pagerank - INFO - PageRank computation completed.\n", + "2025-11-26 09:51:46,625 - bioneuralnet.clustering.correlated_pagerank - INFO - Sweep cut resulted in cluster of size 16 with conductance 0.345 and correlation 0.83.\n", + "2025-11-26 09:51:46,625 - bioneuralnet.clustering.correlated_pagerank - INFO - PageRank clustering completed successfully.\n", + "2025-11-26 09:51:46,626 - bioneuralnet.clustering.hybrid_louvain - INFO - Iteration 3: cluster size=16, Conductance=0.345 Correlation=0.830 score=-0.242\n", + "2025-11-26 09:51:46,626 - bioneuralnet.clustering.hybrid_louvain - INFO - Hybrid Louvain completed after 4 iterations.\n" + ] + }, { "name": "stdout", "output_type": "stream", "text": [ "\n", - "Final Shapes for Modeling\n", - "Methylation (X1): (658, 2102)\n", - "RNA-Seq (X2): (658, 2284)\n", - "miRNA-Seq (X3): (658, 472)\n", - "Labels (Y): (658, 1)\n" + "Driver modules (one-vs-rest):\n", + " KIRP: 293 features\n", + " KIRC: 159 features\n", + " KICH: 69 features\n" ] } ], "source": [ - "X_meth_selected = X_meth[meth_inter3]\n", - "X_rna_selected = X_rna[rna_inter6]\n", + "from bioneuralnet.clustering import HybridLouvain\n", + "\n", + "labels = target[\"target\"] \n", + "unique_phenotypes = labels.unique()\n", + "multiclass_results = {}\n", + "summary = []\n", + "label_map = {0: \"KIRC\", 1: \"KIRP\", 2: \"KICH\"}\n", + "\n", + "for phenotype in unique_phenotypes:\n", + " y_binary = (labels == phenotype).astype(int)\n", + "\n", + " hl = HybridLouvain(\n", + " G=similarity_10,\n", + " B=omics_kipan,\n", + " Y=y_binary,\n", + " k3=0.2,\n", + " k4=0.8,\n", + " max_iter=3,\n", + " seed=SEED,\n", + " )\n", "\n", - "print(\"\\nFinal Shapes for Modeling\")\n", - "print(f\"Methylation (X1): {X_meth_selected.shape}\")\n", - "print(f\"RNA-Seq (X2): {X_rna_selected.shape}\")\n", - "print(f\"miRNA-Seq (X3): {X_mirna.shape}\")\n", - "print(f\"Labels (Y): {Y_labels.shape}\")" + " subnetworks = hl.run(as_dfs=True)\n", + "\n", + " name = label_map.get(phenotype, str(phenotype))\n", + "\n", + " if subnetworks:\n", + " df = subnetworks[0]\n", + " multiclass_results[name] = df\n", + " summary.append((name, df.shape[1]))\n", + " else:\n", + " summary.append((name, 0))\n", + "\n", + "print(\"\\nDriver modules (one-vs-rest):\")\n", + "for name, n_feats in summary:\n", + " print(f\" {name}: {n_feats} features\")\n", + "\n" ] }, { - "cell_type": "markdown", - "id": "f0d52489", + "cell_type": "code", + "execution_count": 50, + "id": "04f74f33", "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Omic Degree\n", + "Index \n", + "5 INPP5J_27124 12\n", + "26 SLC26A7_115111 9\n", + "12 HEPACAM2_253012 7\n", + "16 CLNK_116449 7\n", + "27 RHCG_51458 6\n", + "3 CLCNKB_1188 6\n", + "13 DMRT2_10655 6\n", + "18 LOC643008_643008 5\n", + "10 SLC4A1_6521 5\n", + "9 CLCNKA_1187 5\n", + "4 FGF9_2254 5\n", + "11 C9orf84_158401 4\n", + "8 ATP6V1B1_525 4\n", + "22 KRTAP5_8_57830 4\n", + "15 FOXI1_2299 3\n", + "23 ATP6V0A4_50617 3\n", + "21 ATP6V0D2_245972 1\n", + "24 TMPRSS2_7113 1\n", + "25 GCOM1_145781 1\n", + "1 hsa_mir_129_1 1\n", + "20 hsa_mir_200c 1\n", + "19 KLK1_3816 1\n", + "17 hsa_mir_891a 1\n", + "2 hsa_mir_129_2 1\n", + "7 EPB41L4B_54566 1\n", + "6 TRIM50_135892 1\n", + "14 hsa_mir_141 1\n" + ] + } + ], "source": [ - "## Data Availability\n", + "from bioneuralnet.metrics import louvain_to_adjacency, plot_network\n", "\n", - "To facilitate rapid experimentation and reproduction of our results, the fully processed and feature-selected dataset used in this analysis has been made available directly within the package.\n", + "kirp_df = multiclass_results[\"KICH\"]\n", + "kirp_adj = louvain_to_adjacency(kirp_df)\n", + "kirp_mapping = plot_network(kirp_adj, weight_threshold=0.76, show_labels=True, show_edge_weights=True)\n", + "print(kirp_mapping)" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "id": "66890c59", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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OmicDegreeSource
Index
5INPP5J_2712412RNA
26SLC26A7_1151119RNA
12HEPACAM2_2530127RNA
16CLNK_1164497RNA
27RHCG_514586RNA
3CLCNKB_11886RNA
13DMRT2_106556RNA
18LOC643008_6430085RNA
10SLC4A1_65215RNA
9CLCNKA_11875RNA
4FGF9_22545RNA
11C9orf84_1584014RNA
8ATP6V1B1_5254RNA
22KRTAP5_8_578304RNA
15FOXI1_22993RNA
23ATP6V0A4_506173RNA
21ATP6V0D2_2459721RNA
24TMPRSS2_71131RNA
25GCOM1_1457811RNA
1hsa_mir_129_11miRNA
20hsa_mir_200c1miRNA
19KLK1_38161RNA
17hsa_mir_891a1miRNA
2hsa_mir_129_21miRNA
7EPB41L4B_545661RNA
6TRIM50_1358921RNA
14hsa_mir_1411miRNA
\n", + "
" + ], + "text/plain": [ + " Omic Degree Source\n", + "Index \n", + "5 INPP5J_27124 12 RNA\n", + "26 SLC26A7_115111 9 RNA\n", + "12 HEPACAM2_253012 7 RNA\n", + "16 CLNK_116449 7 RNA\n", + "27 RHCG_51458 6 RNA\n", + "3 CLCNKB_1188 6 RNA\n", + "13 DMRT2_10655 6 RNA\n", + "18 LOC643008_643008 5 RNA\n", + "10 SLC4A1_6521 5 RNA\n", + "9 CLCNKA_1187 5 RNA\n", + "4 FGF9_2254 5 RNA\n", + "11 C9orf84_158401 4 RNA\n", + "8 ATP6V1B1_525 4 RNA\n", + "22 KRTAP5_8_57830 4 RNA\n", + "15 FOXI1_2299 3 RNA\n", + "23 ATP6V0A4_50617 3 RNA\n", + "21 ATP6V0D2_245972 1 RNA\n", + "24 TMPRSS2_7113 1 RNA\n", + "25 GCOM1_145781 1 RNA\n", + "1 hsa_mir_129_1 1 miRNA\n", + "20 hsa_mir_200c 1 miRNA\n", + "19 KLK1_3816 1 RNA\n", + "17 hsa_mir_891a 1 miRNA\n", + "2 hsa_mir_129_2 1 miRNA\n", + "7 EPB41L4B_54566 1 RNA\n", + "6 TRIM50_135892 1 RNA\n", + "14 hsa_mir_141 1 miRNA" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from bioneuralnet.metrics import find_omics_modality\n", "\n", - "Users can load this dataset, bypassing all preceding data acquisition, preprocessing, and feature selection steps. This allows users to proceed immediately from this step." + "# for those features in the KIRP module, we will identify their omics source\n", + "omics_df_list = [top_meth, top_rna, top_mirna]\n", + "source_names = [\"DNA_Methylation\", \"RNA\", \"miRNA\"]\n", + "\n", + "kirp_mapping_with_source = find_omics_modality(kirp_mapping, omics_df_list, source_names)\n", + "display(kirp_mapping_with_source)" ] }, { "cell_type": "code", "execution_count": null, - "id": "78e99139", + "id": "074f3225", + "metadata": {}, + "outputs": [], + "source": [ + "dna_feats = set(dna_meth.columns)\n", + "rna_feats = set(rna.columns)\n", + "mirna_feats = set(mirna.columns)\n", + "\n", + "def get_omics_type(feat):\n", + " if feat in dna_feats:\n", + " return \"DNA_Methylation\"\n", + " elif feat in rna_feats:\n", + " return \"RNA\"\n", + " elif feat in mirna_feats:\n", + " return \"miRNA\"\n", + " else:\n", + " return \"Unknown\"\n", + "\n", + "# kirp_mapping is the DF returned by plot_network\n", + "kirp_mapping[\"OmicsType\"] = kirp_mapping[\"Omic\"].map(get_omics_type)\n", + "\n", + "print(kirp_mapping)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "81315c47", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-11-26 01:05:57,049 - bioneuralnet.downstream_task.dpmon - INFO - Output directory set to: /home/vicente/Github/BioNeuralNet/dpmon_results_SAGE_FINAL/kipan/threshold_10\n", + "2025-11-26 01:05:57,050 - bioneuralnet.downstream_task.dpmon - INFO - Initialized DPMON with model: SAGE\n", + "2025-11-26 01:05:57,051 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 01:05:57,052 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 01:05:57,052 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 01:05:57,052 - bioneuralnet.downstream_task.dpmon - INFO - Random seed set to: 8183\n", + "2025-11-26 01:05:57,063 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:05:57,068 - bioneuralnet.downstream_task.dpmon - INFO - Running in Cross-Validation mode (cv=True) with 5 folds.\n", + "2025-11-26 01:05:57,071 - bioneuralnet.downstream_task.dpmon - INFO - CV Setup: Standard 5-fold split.\n", + "2025-11-26 01:05:57,073 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 1/5\n", + "2025-11-26 01:05:57,075 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:05:57,085 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:06:01,170 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:06:01,192 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (526, 901)\n", + "2025-11-26 01:06:01,195\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:06:11,311\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0313s.\n", + "2025-11-26 01:06:11,512 - bioneuralnet.downstream_task.dpmon - INFO - Fold 1 best config: {'gnn_layer_num': 2, 'gnn_hidden_dim': 512, 'lr': 0.0017134878267294578, 'weight_decay': 0.00037228516397726333, 'nn_hidden_dim1': 512, 'nn_hidden_dim2': 128, 'ae_encoding_dim': 4, 'num_epochs': 2048, 'gnn_dropout': 0.2, 'gnn_activation': 'elu', 'dim_reduction': 'mlp'}\n", + "2025-11-26 01:06:11,512 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 1 using train split only\n", + "2025-11-26 01:06:11,518 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:06:15,594 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:06:18,738 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 1 on test set\n", + "2025-11-26 01:06:18,755 - bioneuralnet.downstream_task.dpmon - INFO - Fold 1 results:\n", + "2025-11-26 01:06:18,755 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9470\n", + "2025-11-26 01:06:18,755 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9324\n", + "2025-11-26 01:06:18,756 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9470\n", + "2025-11-26 01:06:18,756 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9423\n", + "2025-11-26 01:06:18,756 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9960\n", + "2025-11-26 01:06:18,756 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9907\n", + "\n", + "2025-11-26 01:06:18,757 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 2/5\n", + "2025-11-26 01:06:18,758 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:06:18,763 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:06:22,834 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:06:22,844 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (526, 901)\n", + "2025-11-26 01:06:22,848\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:06:32,649\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0301s.\n", + "2025-11-26 01:06:32,793 - bioneuralnet.downstream_task.dpmon - INFO - Fold 2 best config: {'gnn_layer_num': 2, 'gnn_hidden_dim': 64, 'lr': 0.0007034204356990857, 'weight_decay': 5.358735876893429e-05, 'nn_hidden_dim1': 512, 'nn_hidden_dim2': 64, 'ae_encoding_dim': 8, 'num_epochs': 1024, 'gnn_dropout': 0.2, 'gnn_activation': 'relu', 'dim_reduction': 'ae'}\n", + "2025-11-26 01:06:32,794 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 2 using train split only\n", + "2025-11-26 01:06:32,799 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:06:36,856 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:06:37,956 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 2 on test set\n", + "2025-11-26 01:06:37,961 - bioneuralnet.downstream_task.dpmon - INFO - Fold 2 results:\n", + "2025-11-26 01:06:37,961 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9697\n", + "2025-11-26 01:06:37,961 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9487\n", + "2025-11-26 01:06:37,962 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9701\n", + "2025-11-26 01:06:37,962 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9579\n", + "2025-11-26 01:06:37,962 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9915\n", + "2025-11-26 01:06:37,962 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9353\n", + "\n", + "2025-11-26 01:06:37,963 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 3/5\n", + "2025-11-26 01:06:37,964 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:06:37,969 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:06:42,023 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:06:42,033 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (526, 901)\n", + "2025-11-26 01:06:42,036\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:06:51,939\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0300s.\n", + "2025-11-26 01:06:52,121 - bioneuralnet.downstream_task.dpmon - INFO - Fold 3 best config: {'gnn_layer_num': 3, 'gnn_hidden_dim': 128, 'lr': 0.0006980653261883734, 'weight_decay': 6.059927981082565e-05, 'nn_hidden_dim1': 256, 'nn_hidden_dim2': 128, 'ae_encoding_dim': 8, 'num_epochs': 2048, 'gnn_dropout': 0.0, 'gnn_activation': 'elu', 'dim_reduction': 'ae'}\n", + "2025-11-26 01:06:52,122 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 3 using train split only\n", + "2025-11-26 01:06:52,127 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:06:56,224 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:06:58,746 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 3 on test set\n", + "2025-11-26 01:06:58,751 - bioneuralnet.downstream_task.dpmon - INFO - Fold 3 results:\n", + "2025-11-26 01:06:58,751 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9545\n", + "2025-11-26 01:06:58,751 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9459\n", + "2025-11-26 01:06:58,751 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9544\n", + "2025-11-26 01:06:58,751 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9297\n", + "2025-11-26 01:06:58,751 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9966\n", + "2025-11-26 01:06:58,752 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9935\n", + "\n", + "2025-11-26 01:06:58,753 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 4/5\n", + "2025-11-26 01:06:58,754 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:06:58,761 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:07:02,888 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:07:02,900 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (527, 901)\n", + "2025-11-26 01:07:02,903\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:07:13,708\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0323s.\n", + "2025-11-26 01:07:13,923 - bioneuralnet.downstream_task.dpmon - INFO - Fold 4 best config: {'gnn_layer_num': 4, 'gnn_hidden_dim': 128, 'lr': 0.0008628455289617886, 'weight_decay': 0.005542594076906398, 'nn_hidden_dim1': 512, 'nn_hidden_dim2': 128, 'ae_encoding_dim': 16, 'num_epochs': 2048, 'gnn_dropout': 0.2, 'gnn_activation': 'elu', 'dim_reduction': 'linear'}\n", + "2025-11-26 01:07:13,924 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 4 using train split only\n", + "2025-11-26 01:07:13,928 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:07:18,066 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:07:21,166 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 4 on test set\n", + "2025-11-26 01:07:21,170 - bioneuralnet.downstream_task.dpmon - INFO - Fold 4 results:\n", + "2025-11-26 01:07:21,171 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.8931\n", + "2025-11-26 01:07:21,171 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.8940\n", + "2025-11-26 01:07:21,171 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.8929\n", + "2025-11-26 01:07:21,171 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9236\n", + "2025-11-26 01:07:21,171 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9934\n", + "2025-11-26 01:07:21,171 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9925\n", + "\n", + "2025-11-26 01:07:21,172 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 5/5\n", + "2025-11-26 01:07:21,174 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:07:21,183 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:07:25,311 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:07:25,322 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (527, 901)\n", + "2025-11-26 01:07:25,325\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:07:35,948\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0072s.\n", + "2025-11-26 01:07:36,170 - bioneuralnet.downstream_task.dpmon - INFO - Fold 5 best config: {'gnn_layer_num': 2, 'gnn_hidden_dim': 64, 'lr': 0.001685812648195555, 'weight_decay': 0.0023773937538678585, 'nn_hidden_dim1': 256, 'nn_hidden_dim2': 128, 'ae_encoding_dim': 8, 'num_epochs': 512, 'gnn_dropout': 0.2, 'gnn_activation': 'relu', 'dim_reduction': 'linear'}\n", + "2025-11-26 01:07:36,170 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 5 using train split only\n", + "2025-11-26 01:07:36,176 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:07:40,293 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:07:40,839 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 5 on test set\n", + "2025-11-26 01:07:40,844 - bioneuralnet.downstream_task.dpmon - INFO - Fold 5 results:\n", + "2025-11-26 01:07:40,844 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9618\n", + "2025-11-26 01:07:40,844 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9514\n", + "2025-11-26 01:07:40,844 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9617\n", + "2025-11-26 01:07:40,845 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9497\n", + "2025-11-26 01:07:40,845 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9969\n", + "2025-11-26 01:07:40,845 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9881\n", + "\n", + "2025-11-26 01:07:40,847 - bioneuralnet.downstream_task.dpmon - INFO - Successfully saved global best CV model state to: /home/vicente/Github/BioNeuralNet/dpmon_results_SAGE_FINAL/kipan/threshold_10/best_cv_model.pt\n", + "2025-11-26 01:07:40,848 - bioneuralnet.downstream_task.dpmon - INFO - Successfully saved global best CV model state to: /home/vicente/Github/BioNeuralNet/dpmon_results_SAGE_FINAL/kipan/threshold_10/best_cv_model_embds.pt\n", + "2025-11-26 01:07:40,848 - bioneuralnet.downstream_task.dpmon - INFO - Cross-Validation Results:\n", + "\n", + "2025-11-26 01:07:40,848 - bioneuralnet.downstream_task.dpmon - INFO - Avg Accuracy: 0.9452 +/- 0.0303\n", + "2025-11-26 01:07:40,848 - bioneuralnet.downstream_task.dpmon - INFO - Avg F1 Macro: 0.9345 +/- 0.0238\n", + "2025-11-26 01:07:40,848 - bioneuralnet.downstream_task.dpmon - INFO - Avg F1 Weighted: 0.9452 +/- 0.0305\n", + "2025-11-26 01:07:40,848 - bioneuralnet.downstream_task.dpmon - INFO - Avg Recall: 0.9406 +/- 0.0141\n", + "2025-11-26 01:07:40,848 - bioneuralnet.downstream_task.dpmon - INFO - Avg AUC: 0.9949 +/- 0.0024\n", + "2025-11-26 01:07:40,849 - bioneuralnet.downstream_task.dpmon - INFO - Avg AUPR: 0.9800 +/- 0.0251\n", + "2025-11-26 01:07:40,849 - bioneuralnet.downstream_task.dpmon - INFO - DPMON run completed.\n", + "2025-11-26 01:07:40,850 - bioneuralnet.downstream_task.dpmon - INFO - Output directory set to: /home/vicente/Github/BioNeuralNet/dpmon_results_SAGE_FINAL/kipan/correlation_10\n", + "2025-11-26 01:07:40,850 - bioneuralnet.downstream_task.dpmon - INFO - Initialized DPMON with model: SAGE\n", + "2025-11-26 01:07:40,850 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 01:07:40,850 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 01:07:40,850 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 01:07:40,850 - bioneuralnet.downstream_task.dpmon - INFO - Random seed set to: 8183\n", + "2025-11-26 01:07:40,855 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:07:40,856 - bioneuralnet.downstream_task.dpmon - INFO - Running in Cross-Validation mode (cv=True) with 5 folds.\n", + "2025-11-26 01:07:40,857 - bioneuralnet.downstream_task.dpmon - INFO - CV Setup: Standard 5-fold split.\n", + "2025-11-26 01:07:40,857 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 1/5\n", + "2025-11-26 01:07:40,858 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:07:40,865 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:07:44,953 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:07:44,964 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (526, 901)\n", + "2025-11-26 01:07:44,967\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:07:54,922\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0290s.\n", + "2025-11-26 01:07:55,109 - bioneuralnet.downstream_task.dpmon - INFO - Fold 1 best config: {'gnn_layer_num': 4, 'gnn_hidden_dim': 256, 'lr': 0.001257123304842143, 'weight_decay': 0.0003745702077936848, 'nn_hidden_dim1': 128, 'nn_hidden_dim2': 128, 'ae_encoding_dim': 8, 'num_epochs': 1024, 'gnn_dropout': 0.4, 'gnn_activation': 'relu', 'dim_reduction': 'ae'}\n", + "2025-11-26 01:07:55,109 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 1 using train split only\n", + "2025-11-26 01:07:55,114 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:07:59,376 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:08:00,931 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 1 on test set\n", + "2025-11-26 01:08:00,937 - bioneuralnet.downstream_task.dpmon - INFO - Fold 1 results:\n", + "2025-11-26 01:08:00,937 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9545\n", + "2025-11-26 01:08:00,937 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9482\n", + "2025-11-26 01:08:00,937 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9546\n", + "2025-11-26 01:08:00,938 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9679\n", + "2025-11-26 01:08:00,938 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9956\n", + "2025-11-26 01:08:00,938 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9900\n", + "\n", + "2025-11-26 01:08:00,939 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 2/5\n", + "2025-11-26 01:08:00,940 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:08:00,946 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:08:05,060 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:08:05,072 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (526, 901)\n", + "2025-11-26 01:08:05,075\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:08:15,288\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0300s.\n", + "2025-11-26 01:08:15,451 - bioneuralnet.downstream_task.dpmon - INFO - Fold 2 best config: {'gnn_layer_num': 2, 'gnn_hidden_dim': 64, 'lr': 0.0007034204356990857, 'weight_decay': 5.358735876893429e-05, 'nn_hidden_dim1': 512, 'nn_hidden_dim2': 64, 'ae_encoding_dim': 8, 'num_epochs': 1024, 'gnn_dropout': 0.2, 'gnn_activation': 'relu', 'dim_reduction': 'ae'}\n", + "2025-11-26 01:08:15,452 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 2 using train split only\n", + "2025-11-26 01:08:15,456 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:08:19,574 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:08:20,611 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 2 on test set\n", + "2025-11-26 01:08:20,616 - bioneuralnet.downstream_task.dpmon - INFO - Fold 2 results:\n", + "2025-11-26 01:08:20,616 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9697\n", + "2025-11-26 01:08:20,616 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9487\n", + "2025-11-26 01:08:20,616 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9701\n", + "2025-11-26 01:08:20,616 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9579\n", + "2025-11-26 01:08:20,617 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9927\n", + "2025-11-26 01:08:20,617 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9484\n", + "\n", + "2025-11-26 01:08:20,617 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 3/5\n", + "2025-11-26 01:08:20,619 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:08:20,624 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:08:24,748 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:08:24,759 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (526, 901)\n", + "2025-11-26 01:08:24,762\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:08:35,204\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0243s.\n", + "2025-11-26 01:08:35,374 - bioneuralnet.downstream_task.dpmon - INFO - Fold 3 best config: {'gnn_layer_num': 1, 'gnn_hidden_dim': 128, 'lr': 0.002302454339377372, 'weight_decay': 1.3521341174248578e-05, 'nn_hidden_dim1': 512, 'nn_hidden_dim2': 128, 'ae_encoding_dim': 4, 'num_epochs': 2048, 'gnn_dropout': 0.3, 'gnn_activation': 'relu', 'dim_reduction': 'ae'}\n", + "2025-11-26 01:08:35,375 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 3 using train split only\n", + "2025-11-26 01:08:35,380 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:08:39,498 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:08:41,284 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 3 on test set\n", + "2025-11-26 01:08:41,288 - bioneuralnet.downstream_task.dpmon - INFO - Fold 3 results:\n", + "2025-11-26 01:08:41,288 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9545\n", + "2025-11-26 01:08:41,288 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9478\n", + "2025-11-26 01:08:41,288 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9546\n", + "2025-11-26 01:08:41,289 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9482\n", + "2025-11-26 01:08:41,289 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9956\n", + "2025-11-26 01:08:41,289 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9939\n", + "\n", + "2025-11-26 01:08:41,289 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 4/5\n", + "2025-11-26 01:08:41,291 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:08:41,299 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:08:45,432 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:08:45,442 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (527, 901)\n", + "2025-11-26 01:08:45,445\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:08:55,587\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0224s.\n", + "2025-11-26 01:08:55,774 - bioneuralnet.downstream_task.dpmon - INFO - Fold 4 best config: {'gnn_layer_num': 4, 'gnn_hidden_dim': 128, 'lr': 0.0008628455289617886, 'weight_decay': 0.005542594076906398, 'nn_hidden_dim1': 512, 'nn_hidden_dim2': 128, 'ae_encoding_dim': 16, 'num_epochs': 2048, 'gnn_dropout': 0.2, 'gnn_activation': 'elu', 'dim_reduction': 'linear'}\n", + "2025-11-26 01:08:55,775 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 4 using train split only\n", + "2025-11-26 01:08:55,779 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:09:00,043 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:09:03,096 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 4 on test set\n", + "2025-11-26 01:09:03,101 - bioneuralnet.downstream_task.dpmon - INFO - Fold 4 results:\n", + "2025-11-26 01:09:03,101 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.8855\n", + "2025-11-26 01:09:03,101 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.8402\n", + "2025-11-26 01:09:03,101 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.8960\n", + "2025-11-26 01:09:03,102 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9137\n", + "2025-11-26 01:09:03,102 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9857\n", + "2025-11-26 01:09:03,102 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9669\n", + "\n", + "2025-11-26 01:09:03,103 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 5/5\n", + "2025-11-26 01:09:03,104 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:09:03,110 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:09:07,251 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:09:07,262 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (527, 901)\n", + "2025-11-26 01:09:07,265\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:09:17,331\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0317s.\n", + "2025-11-26 01:09:17,571 - bioneuralnet.downstream_task.dpmon - INFO - Fold 5 best config: {'gnn_layer_num': 1, 'gnn_hidden_dim': 64, 'lr': 0.001483781276337717, 'weight_decay': 2.5485016069955917e-05, 'nn_hidden_dim1': 128, 'nn_hidden_dim2': 128, 'ae_encoding_dim': 4, 'num_epochs': 1024, 'gnn_dropout': 0.2, 'gnn_activation': 'elu', 'dim_reduction': 'mlp'}\n", + "2025-11-26 01:09:17,571 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 5 using train split only\n", + "2025-11-26 01:09:17,577 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:09:21,713 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:09:22,694 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 5 on test set\n", + "2025-11-26 01:09:22,699 - bioneuralnet.downstream_task.dpmon - INFO - Fold 5 results:\n", + "2025-11-26 01:09:22,699 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9695\n", + "2025-11-26 01:09:22,699 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9572\n", + "2025-11-26 01:09:22,699 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9694\n", + "2025-11-26 01:09:22,699 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9558\n", + "2025-11-26 01:09:22,700 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9990\n", + "2025-11-26 01:09:22,700 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9971\n", + "\n", + "2025-11-26 01:09:22,702 - bioneuralnet.downstream_task.dpmon - INFO - Successfully saved global best CV model state to: /home/vicente/Github/BioNeuralNet/dpmon_results_SAGE_FINAL/kipan/correlation_10/best_cv_model.pt\n", + "2025-11-26 01:09:22,703 - bioneuralnet.downstream_task.dpmon - INFO - Successfully saved global best CV model state to: /home/vicente/Github/BioNeuralNet/dpmon_results_SAGE_FINAL/kipan/correlation_10/best_cv_model_embds.pt\n", + "2025-11-26 01:09:22,703 - bioneuralnet.downstream_task.dpmon - INFO - Cross-Validation Results:\n", + "\n", + "2025-11-26 01:09:22,703 - bioneuralnet.downstream_task.dpmon - INFO - Avg Accuracy: 0.9467 +/- 0.0351\n", + "2025-11-26 01:09:22,703 - bioneuralnet.downstream_task.dpmon - INFO - Avg F1 Macro: 0.9284 +/- 0.0495\n", + "2025-11-26 01:09:22,703 - bioneuralnet.downstream_task.dpmon - INFO - Avg F1 Weighted: 0.9489 +/- 0.0306\n", + "2025-11-26 01:09:22,704 - bioneuralnet.downstream_task.dpmon - INFO - Avg Recall: 0.9487 +/- 0.0208\n", + "2025-11-26 01:09:22,704 - bioneuralnet.downstream_task.dpmon - INFO - Avg AUC: 0.9937 +/- 0.0050\n", + "2025-11-26 01:09:22,704 - bioneuralnet.downstream_task.dpmon - INFO - Avg AUPR: 0.9793 +/- 0.0209\n", + "2025-11-26 01:09:22,705 - bioneuralnet.downstream_task.dpmon - INFO - DPMON run completed.\n", + "2025-11-26 01:09:22,705 - bioneuralnet.downstream_task.dpmon - INFO - Output directory set to: /home/vicente/Github/BioNeuralNet/dpmon_results_SAGE_FINAL/kipan/similarity_10\n", + "2025-11-26 01:09:22,705 - bioneuralnet.downstream_task.dpmon - INFO - Initialized DPMON with model: SAGE\n", + "2025-11-26 01:09:22,705 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 01:09:22,706 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 01:09:22,706 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 01:09:22,706 - bioneuralnet.downstream_task.dpmon - INFO - Random seed set to: 8183\n", + "2025-11-26 01:09:22,711 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:09:22,712 - bioneuralnet.downstream_task.dpmon - INFO - Running in Cross-Validation mode (cv=True) with 5 folds.\n", + "2025-11-26 01:09:22,713 - bioneuralnet.downstream_task.dpmon - INFO - CV Setup: Standard 5-fold split.\n", + "2025-11-26 01:09:22,714 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 1/5\n", + "2025-11-26 01:09:22,715 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:09:22,722 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:09:26,854 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:09:26,865 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (526, 901)\n", + "2025-11-26 01:09:26,868\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:09:37,243\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0253s.\n", + "2025-11-26 01:09:37,453 - bioneuralnet.downstream_task.dpmon - INFO - Fold 1 best config: {'gnn_layer_num': 2, 'gnn_hidden_dim': 512, 'lr': 0.0017134878267294578, 'weight_decay': 0.00037228516397726333, 'nn_hidden_dim1': 512, 'nn_hidden_dim2': 128, 'ae_encoding_dim': 4, 'num_epochs': 2048, 'gnn_dropout': 0.2, 'gnn_activation': 'elu', 'dim_reduction': 'mlp'}\n", + "2025-11-26 01:09:37,454 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 1 using train split only\n", + "2025-11-26 01:09:37,459 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:09:41,579 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:09:44,803 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 1 on test set\n", + "2025-11-26 01:09:44,820 - bioneuralnet.downstream_task.dpmon - INFO - Fold 1 results:\n", + "2025-11-26 01:09:44,821 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9470\n", + "2025-11-26 01:09:44,821 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9344\n", + "2025-11-26 01:09:44,821 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9476\n", + "2025-11-26 01:09:44,821 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9618\n", + "2025-11-26 01:09:44,821 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9899\n", + "2025-11-26 01:09:44,822 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9574\n", + "\n", + "2025-11-26 01:09:44,823 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 2/5\n", + "2025-11-26 01:09:44,824 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:09:44,830 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:09:48,915 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:09:48,926 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (526, 901)\n", + "2025-11-26 01:09:48,929\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:09:58,642\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0321s.\n", + "2025-11-26 01:09:58,800 - bioneuralnet.downstream_task.dpmon - INFO - Fold 2 best config: {'gnn_layer_num': 2, 'gnn_hidden_dim': 64, 'lr': 0.0007034204356990857, 'weight_decay': 5.358735876893429e-05, 'nn_hidden_dim1': 512, 'nn_hidden_dim2': 64, 'ae_encoding_dim': 8, 'num_epochs': 1024, 'gnn_dropout': 0.2, 'gnn_activation': 'relu', 'dim_reduction': 'ae'}\n", + "2025-11-26 01:09:58,801 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 2 using train split only\n", + "2025-11-26 01:09:58,805 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:10:03,027 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:10:04,075 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 2 on test set\n", + "2025-11-26 01:10:04,080 - bioneuralnet.downstream_task.dpmon - INFO - Fold 2 results:\n", + "2025-11-26 01:10:04,080 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9773\n", + "2025-11-26 01:10:04,080 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9648\n", + "2025-11-26 01:10:04,080 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9777\n", + "2025-11-26 01:10:04,080 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9835\n", + "2025-11-26 01:10:04,080 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9921\n", + "2025-11-26 01:10:04,080 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9552\n", + "\n", + "2025-11-26 01:10:04,081 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 3/5\n", + "2025-11-26 01:10:04,082 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:10:04,088 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:10:08,219 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:10:08,231 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (526, 901)\n", + "2025-11-26 01:10:08,234\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:10:18,090\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0282s.\n", + "2025-11-26 01:10:18,287 - bioneuralnet.downstream_task.dpmon - INFO - Fold 3 best config: {'gnn_layer_num': 3, 'gnn_hidden_dim': 128, 'lr': 0.0006980653261883734, 'weight_decay': 6.059927981082565e-05, 'nn_hidden_dim1': 256, 'nn_hidden_dim2': 128, 'ae_encoding_dim': 8, 'num_epochs': 2048, 'gnn_dropout': 0.0, 'gnn_activation': 'elu', 'dim_reduction': 'ae'}\n", + "2025-11-26 01:10:18,288 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 3 using train split only\n", + "2025-11-26 01:10:18,292 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:10:22,422 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:10:24,946 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 3 on test set\n", + "2025-11-26 01:10:24,951 - bioneuralnet.downstream_task.dpmon - INFO - Fold 3 results:\n", + "2025-11-26 01:10:24,951 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9621\n", + "2025-11-26 01:10:24,952 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9621\n", + "2025-11-26 01:10:24,952 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9622\n", + "2025-11-26 01:10:24,952 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9535\n", + "2025-11-26 01:10:24,953 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9952\n", + "2025-11-26 01:10:24,953 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9900\n", + "\n", + "2025-11-26 01:10:24,953 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 4/5\n", + "2025-11-26 01:10:24,956 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:10:24,963 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:10:29,116 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:10:29,127 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (527, 901)\n", + "2025-11-26 01:10:29,130\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:10:38,870\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0212s.\n", + "2025-11-26 01:10:39,028 - bioneuralnet.downstream_task.dpmon - INFO - Fold 4 best config: {'gnn_layer_num': 1, 'gnn_hidden_dim': 128, 'lr': 0.0019123736530165902, 'weight_decay': 0.0006168499043610002, 'nn_hidden_dim1': 256, 'nn_hidden_dim2': 128, 'ae_encoding_dim': 8, 'num_epochs': 1024, 'gnn_dropout': 0.4, 'gnn_activation': 'relu', 'dim_reduction': 'mlp'}\n", + "2025-11-26 01:10:39,029 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 4 using train split only\n", + "2025-11-26 01:10:39,033 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:10:43,160 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:10:44,156 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 4 on test set\n", + "2025-11-26 01:10:44,160 - bioneuralnet.downstream_task.dpmon - INFO - Fold 4 results:\n", + "2025-11-26 01:10:44,160 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9389\n", + "2025-11-26 01:10:44,160 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9449\n", + "2025-11-26 01:10:44,160 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9390\n", + "2025-11-26 01:10:44,161 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9554\n", + "2025-11-26 01:10:44,161 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9970\n", + "2025-11-26 01:10:44,161 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9949\n", + "\n", + "2025-11-26 01:10:44,161 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 5/5\n", + "2025-11-26 01:10:44,162 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:10:44,168 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:10:48,309 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:10:48,320 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (527, 901)\n", + "2025-11-26 01:10:48,324\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:10:58,591\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0247s.\n", + "2025-11-26 01:10:58,842 - bioneuralnet.downstream_task.dpmon - INFO - Fold 5 best config: {'gnn_layer_num': 4, 'gnn_hidden_dim': 64, 'lr': 0.0007025528058103543, 'weight_decay': 0.0018355753249756474, 'nn_hidden_dim1': 512, 'nn_hidden_dim2': 128, 'ae_encoding_dim': 4, 'num_epochs': 1024, 'gnn_dropout': 0.0, 'gnn_activation': 'relu', 'dim_reduction': 'mlp'}\n", + "2025-11-26 01:10:58,842 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 5 using train split only\n", + "2025-11-26 01:10:58,848 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:11:02,948 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:11:04,466 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 5 on test set\n", + "2025-11-26 01:11:04,471 - bioneuralnet.downstream_task.dpmon - INFO - Fold 5 results:\n", + "2025-11-26 01:11:04,472 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9618\n", + "2025-11-26 01:11:04,472 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9514\n", + "2025-11-26 01:11:04,472 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9617\n", + "2025-11-26 01:11:04,473 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9497\n", + "2025-11-26 01:11:04,473 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9979\n", + "2025-11-26 01:11:04,473 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9942\n", + "\n", + "2025-11-26 01:11:04,476 - bioneuralnet.downstream_task.dpmon - INFO - Successfully saved global best CV model state to: /home/vicente/Github/BioNeuralNet/dpmon_results_SAGE_FINAL/kipan/similarity_10/best_cv_model.pt\n", + "2025-11-26 01:11:04,476 - bioneuralnet.downstream_task.dpmon - INFO - Successfully saved global best CV model state to: /home/vicente/Github/BioNeuralNet/dpmon_results_SAGE_FINAL/kipan/similarity_10/best_cv_model_embds.pt\n", + "2025-11-26 01:11:04,476 - bioneuralnet.downstream_task.dpmon - INFO - Cross-Validation Results:\n", + "\n", + "2025-11-26 01:11:04,477 - bioneuralnet.downstream_task.dpmon - INFO - Avg Accuracy: 0.9574 +/- 0.0149\n", + "2025-11-26 01:11:04,477 - bioneuralnet.downstream_task.dpmon - INFO - Avg F1 Macro: 0.9515 +/- 0.0125\n", + "2025-11-26 01:11:04,477 - bioneuralnet.downstream_task.dpmon - INFO - Avg F1 Weighted: 0.9576 +/- 0.0149\n", + "2025-11-26 01:11:04,477 - bioneuralnet.downstream_task.dpmon - INFO - Avg Recall: 0.9608 +/- 0.0135\n", + "2025-11-26 01:11:04,478 - bioneuralnet.downstream_task.dpmon - INFO - Avg AUC: 0.9944 +/- 0.0034\n", + "2025-11-26 01:11:04,478 - bioneuralnet.downstream_task.dpmon - INFO - Avg AUPR: 0.9783 +/- 0.0202\n", + "2025-11-26 01:11:04,479 - bioneuralnet.downstream_task.dpmon - INFO - DPMON run completed.\n", + "2025-11-26 01:11:04,479 - bioneuralnet.downstream_task.dpmon - INFO - Output directory set to: /home/vicente/Github/BioNeuralNet/dpmon_results_SAGE_FINAL/kipan/gaussian_15\n", + "2025-11-26 01:11:04,479 - bioneuralnet.downstream_task.dpmon - INFO - Initialized DPMON with model: SAGE\n", + "2025-11-26 01:11:04,480 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 01:11:04,480 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 01:11:04,480 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 01:11:04,481 - bioneuralnet.downstream_task.dpmon - INFO - Random seed set to: 8183\n", + "2025-11-26 01:11:04,486 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:11:04,487 - bioneuralnet.downstream_task.dpmon - INFO - Running in Cross-Validation mode (cv=True) with 5 folds.\n", + "2025-11-26 01:11:04,488 - bioneuralnet.downstream_task.dpmon - INFO - CV Setup: Standard 5-fold split.\n", + "2025-11-26 01:11:04,488 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 1/5\n", + "2025-11-26 01:11:04,490 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:11:04,497 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:11:08,732 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:11:08,747 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (526, 901)\n", + "2025-11-26 01:11:08,750\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:11:19,358\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0249s.\n", + "2025-11-26 01:11:19,570 - bioneuralnet.downstream_task.dpmon - INFO - Fold 1 best config: {'gnn_layer_num': 4, 'gnn_hidden_dim': 256, 'lr': 0.001257123304842143, 'weight_decay': 0.0003745702077936848, 'nn_hidden_dim1': 128, 'nn_hidden_dim2': 128, 'ae_encoding_dim': 8, 'num_epochs': 1024, 'gnn_dropout': 0.4, 'gnn_activation': 'relu', 'dim_reduction': 'ae'}\n", + "2025-11-26 01:11:19,571 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 1 using train split only\n", + "2025-11-26 01:11:19,576 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:11:23,732 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:11:25,523 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 1 on test set\n", + "2025-11-26 01:11:25,533 - bioneuralnet.downstream_task.dpmon - INFO - Fold 1 results:\n", + "2025-11-26 01:11:25,534 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9545\n", + "2025-11-26 01:11:25,534 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9482\n", + "2025-11-26 01:11:25,534 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9546\n", + "2025-11-26 01:11:25,535 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9679\n", + "2025-11-26 01:11:25,535 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9943\n", + "2025-11-26 01:11:25,535 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9887\n", + "\n", + "2025-11-26 01:11:25,536 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 2/5\n", + "2025-11-26 01:11:25,538 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:11:25,543 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:11:29,653 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:11:29,667 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (526, 901)\n", + "2025-11-26 01:11:29,670\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:11:39,430\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0402s.\n", + "2025-11-26 01:11:39,573 - bioneuralnet.downstream_task.dpmon - INFO - Fold 2 best config: {'gnn_layer_num': 2, 'gnn_hidden_dim': 64, 'lr': 0.0007034204356990857, 'weight_decay': 5.358735876893429e-05, 'nn_hidden_dim1': 512, 'nn_hidden_dim2': 64, 'ae_encoding_dim': 8, 'num_epochs': 1024, 'gnn_dropout': 0.2, 'gnn_activation': 'relu', 'dim_reduction': 'ae'}\n", + "2025-11-26 01:11:39,574 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 2 using train split only\n", + "2025-11-26 01:11:39,579 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:11:43,701 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:11:44,838 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 2 on test set\n", + "2025-11-26 01:11:44,843 - bioneuralnet.downstream_task.dpmon - INFO - Fold 2 results:\n", + "2025-11-26 01:11:44,843 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9773\n", + "2025-11-26 01:11:44,843 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9648\n", + "2025-11-26 01:11:44,844 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9777\n", + "2025-11-26 01:11:44,844 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9835\n", + "2025-11-26 01:11:44,844 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9923\n", + "2025-11-26 01:11:44,844 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9447\n", + "\n", + "2025-11-26 01:11:44,845 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 3/5\n", + "2025-11-26 01:11:44,846 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:11:44,851 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:11:48,972 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:11:48,986 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (526, 901)\n", + "2025-11-26 01:11:48,989\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:11:59,032\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0306s.\n", + "2025-11-26 01:11:59,220 - bioneuralnet.downstream_task.dpmon - INFO - Fold 3 best config: {'gnn_layer_num': 3, 'gnn_hidden_dim': 128, 'lr': 0.0006980653261883734, 'weight_decay': 6.059927981082565e-05, 'nn_hidden_dim1': 256, 'nn_hidden_dim2': 128, 'ae_encoding_dim': 8, 'num_epochs': 2048, 'gnn_dropout': 0.0, 'gnn_activation': 'elu', 'dim_reduction': 'ae'}\n", + "2025-11-26 01:11:59,221 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 3 using train split only\n", + "2025-11-26 01:11:59,226 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:12:03,330 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:12:05,781 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 3 on test set\n", + "2025-11-26 01:12:05,785 - bioneuralnet.downstream_task.dpmon - INFO - Fold 3 results:\n", + "2025-11-26 01:12:05,786 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9697\n", + "2025-11-26 01:12:05,786 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9773\n", + "2025-11-26 01:12:05,786 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9697\n", + "2025-11-26 01:12:05,786 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9773\n", + "2025-11-26 01:12:05,786 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9936\n", + "2025-11-26 01:12:05,786 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9928\n", + "\n", + "2025-11-26 01:12:05,786 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 4/5\n", + "2025-11-26 01:12:05,788 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:12:05,795 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:12:09,897 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:12:09,910 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (527, 901)\n", + "2025-11-26 01:12:09,913\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:12:20,492\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0219s.\n", + "2025-11-26 01:12:20,669 - bioneuralnet.downstream_task.dpmon - INFO - Fold 4 best config: {'gnn_layer_num': 1, 'gnn_hidden_dim': 128, 'lr': 0.002226976717919125, 'weight_decay': 0.00016678702979522166, 'nn_hidden_dim1': 128, 'nn_hidden_dim2': 64, 'ae_encoding_dim': 16, 'num_epochs': 512, 'gnn_dropout': 0.3, 'gnn_activation': 'relu', 'dim_reduction': 'ae'}\n", + "2025-11-26 01:12:20,670 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 4 using train split only\n", + "2025-11-26 01:12:20,675 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:12:24,817 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:12:25,254 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 4 on test set\n", + "2025-11-26 01:12:25,258 - bioneuralnet.downstream_task.dpmon - INFO - Fold 4 results:\n", + "2025-11-26 01:12:25,258 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9389\n", + "2025-11-26 01:12:25,258 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9449\n", + "2025-11-26 01:12:25,259 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9390\n", + "2025-11-26 01:12:25,259 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9554\n", + "2025-11-26 01:12:25,259 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9934\n", + "2025-11-26 01:12:25,259 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9742\n", + "\n", + "2025-11-26 01:12:25,259 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 5/5\n", + "2025-11-26 01:12:25,261 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:12:25,266 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:12:29,434 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:12:29,448 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (527, 901)\n", + "2025-11-26 01:12:29,451\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:12:39,858\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0232s.\n", + "2025-11-26 01:12:40,073 - bioneuralnet.downstream_task.dpmon - INFO - Fold 5 best config: {'gnn_layer_num': 4, 'gnn_hidden_dim': 128, 'lr': 0.0013804780705269182, 'weight_decay': 0.0037674454696866636, 'nn_hidden_dim1': 128, 'nn_hidden_dim2': 64, 'ae_encoding_dim': 4, 'num_epochs': 1024, 'gnn_dropout': 0.0, 'gnn_activation': 'elu', 'dim_reduction': 'ae'}\n", + "2025-11-26 01:12:40,073 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 5 using train split only\n", + "2025-11-26 01:12:40,079 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:12:44,207 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:12:45,599 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 5 on test set\n", + "2025-11-26 01:12:45,604 - bioneuralnet.downstream_task.dpmon - INFO - Fold 5 results:\n", + "2025-11-26 01:12:45,604 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9695\n", + "2025-11-26 01:12:45,604 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9572\n", + "2025-11-26 01:12:45,604 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9694\n", + "2025-11-26 01:12:45,604 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9558\n", + "2025-11-26 01:12:45,605 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9978\n", + "2025-11-26 01:12:45,605 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9941\n", + "\n", + "2025-11-26 01:12:45,607 - bioneuralnet.downstream_task.dpmon - INFO - Successfully saved global best CV model state to: /home/vicente/Github/BioNeuralNet/dpmon_results_SAGE_FINAL/kipan/gaussian_15/best_cv_model.pt\n", + "2025-11-26 01:12:45,608 - bioneuralnet.downstream_task.dpmon - INFO - Successfully saved global best CV model state to: /home/vicente/Github/BioNeuralNet/dpmon_results_SAGE_FINAL/kipan/gaussian_15/best_cv_model_embds.pt\n", + "2025-11-26 01:12:45,608 - bioneuralnet.downstream_task.dpmon - INFO - Cross-Validation Results:\n", + "\n", + "2025-11-26 01:12:45,608 - bioneuralnet.downstream_task.dpmon - INFO - Avg Accuracy: 0.9620 +/- 0.0153\n", + "2025-11-26 01:12:45,608 - bioneuralnet.downstream_task.dpmon - INFO - Avg F1 Macro: 0.9585 +/- 0.0131\n", + "2025-11-26 01:12:45,608 - bioneuralnet.downstream_task.dpmon - INFO - Avg F1 Weighted: 0.9621 +/- 0.0154\n", + "2025-11-26 01:12:45,608 - bioneuralnet.downstream_task.dpmon - INFO - Avg Recall: 0.9680 +/- 0.0126\n", + "2025-11-26 01:12:45,608 - bioneuralnet.downstream_task.dpmon - INFO - Avg AUC: 0.9943 +/- 0.0021\n", + "2025-11-26 01:12:45,608 - bioneuralnet.downstream_task.dpmon - INFO - Avg AUPR: 0.9789 +/- 0.0207\n", + "2025-11-26 01:12:45,609 - bioneuralnet.downstream_task.dpmon - INFO - DPMON run completed.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Results for: threshold_10\n", + "Accuracy (Avg +/- Std): 0.9452 +/- 0.0303\n", + "F1 Macro (Avg +/- Std): 0.9345 +/- 0.0238\n", + "F1 Weighted (Avg +/- Std): 0.9452 +/- 0.0305\n", + "Recall: 0.9406 +/- 0.0141\n", + "AUC: 0.9949 +/- 0.0024\n", + "\n", + "Results for: correlation_10\n", + "Accuracy (Avg +/- Std): 0.9467 +/- 0.0351\n", + "F1 Macro (Avg +/- Std): 0.9284 +/- 0.0495\n", + "F1 Weighted (Avg +/- Std): 0.9489 +/- 0.0306\n", + "Recall: 0.9487 +/- 0.0208\n", + "AUC: 0.9937 +/- 0.0050\n", + "\n", + "Results for: similarity_10\n", + "Accuracy (Avg +/- Std): 0.9574 +/- 0.0149\n", + "F1 Macro (Avg +/- Std): 0.9515 +/- 0.0125\n", + "F1 Weighted (Avg +/- Std): 0.9576 +/- 0.0149\n", + "Recall: 0.9608 +/- 0.0135\n", + "AUC: 0.9944 +/- 0.0034\n", + "\n", + "Results for: gaussian_15\n", + "Accuracy (Avg +/- Std): 0.9620 +/- 0.0153\n", + "F1 Macro (Avg +/- Std): 0.9585 +/- 0.0131\n", + "F1 Weighted (Avg +/- Std): 0.9621 +/- 0.0154\n", + "Recall: 0.9680 +/- 0.0126\n", + "AUC: 0.9943 +/- 0.0021\n" + ] + } + ], + "source": [ + "output_dir_base = Path(\"/home/vicente/Github/BioNeuralNet/dpmon_results_SAGE_FINAL/kipan\")\n", + "all_results_SAGE = []\n", + "\n", + "for run_config in comparison_runs:\n", + " graph_name = run_config[\"name\"]\n", + " A_full = run_config[\"graph\"]\n", + " \n", + " current_output_dir = output_dir_base / graph_name\n", + " current_output_dir.mkdir(parents=True, exist_ok=True)\n", + "\n", + " dpmon_params_base = {\n", + " \"adjacency_matrix\": A_full,\n", + " \"omics_list\": omics_kipan,\n", + " \"phenotype_data\": target,\n", + " \"phenotype_col\": \"target\",\n", + " \"clinical_data\": clinical_for_model,\n", + " \"model\": 'SAGE',\n", + " \"tune\": True, \n", + " \"cv\": True, \n", + " \"n_folds\": 5,\n", + " \"repeat_num\": 1,\n", + " \"gpu\": True,\n", + " \"cuda\": 0,\n", + " \"seed\": SEED,\n", + " \"output_dir\": current_output_dir\n", + " }\n", + " \n", + " dpmon_tunned = DPMON(**dpmon_params_base)\n", + " predictions_df, metrics, embeddings = dpmon_tunned.run()\n", + "\n", + " all_results_SAGE.append({\n", + " \"graph\": graph_name,\n", + " \"predictions\": predictions_df,\n", + " \"metrics\": metrics,\n", + " \"embeddings\": embeddings\n", + " })\n", + "\n", + "for res in all_results_SAGE:\n", + " graph_name = res[\"graph\"]\n", + " graph_metrics = res[\"metrics\"]\n", + " \n", + " acc_row = graph_metrics.loc[graph_metrics['Metric'] == 'Accuracy'].iloc[0]\n", + " f1_macro_row = graph_metrics.loc[graph_metrics['Metric'] == 'F1 Macro'].iloc[0]\n", + " f1_weighted_row = graph_metrics.loc[graph_metrics['Metric'] == 'F1 Weighted'].iloc[0]\n", + " recall_row = graph_metrics.loc[graph_metrics['Metric'] == 'Recall'].iloc[0]\n", + " auc_row = graph_metrics.loc[graph_metrics['Metric'] == 'AUC'].iloc[0]\n", + " aupr_row = graph_metrics.loc[graph_metrics['Metric'] == 'AUPR'].iloc[0]\n", + " \n", + " acc_avg, acc_std = acc_row['Average'], acc_row['StdDev']\n", + " f1_macro_avg, f1_macro_std = f1_macro_row['Average'], f1_macro_row['StdDev']\n", + " f1_weighted_avg, f1_weighted_std = f1_weighted_row['Average'], f1_weighted_row['StdDev']\n", + " recall_avg, recall_std = recall_row['Average'], recall_row['StdDev']\n", + " auc_avg, auc_std = auc_row['Average'], auc_row['StdDev']\n", + " aupr_avg, aupr_std = aupr_row['Average'], aupr_row['StdDev']\n", + "\n", + " print(f\"\\nResults for: {graph_name}\")\n", + " print(f\"Accuracy (Avg +/- Std): {acc_avg:.4f} +/- {acc_std:.4f}\")\n", + " print(f\"F1 Macro (Avg +/- Std): {f1_macro_avg:.4f} +/- {f1_macro_std:.4f}\")\n", + " print(f\"F1 Weighted (Avg +/- Std): {f1_weighted_avg:.4f} +/- {f1_weighted_std:.4f}\")\n", + " print(f\"Recall: {recall_avg:.4f} +/- {recall_std:.4f}\")\n", + " print(f\"AUC: {auc_avg:.4f} +/- {auc_std:.4f}\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "80c83c6e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " 0 1 2 3 4\n", + "MGP 0.0 0.0 0.923418 0.000000 0.295637\n", + "ADH1C 0.0 0.0 0.521837 0.005931 0.421344\n", + "LOC440354 0.0 0.0 0.851917 0.000000 0.216582\n", + "C1orf110 0.0 0.0 0.625036 0.009498 0.256967\n", + "CLDN8 0.0 0.0 0.638092 0.000000 0.230229" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#This time we will select the gaussian_15 graph embeddings\n", + "res = all_results_SAGE[3] \n", + "tensor_data = res[\"embeddings\"]\n", + "\n", + "# .detach().cpu().numpy() to get the raw numbers out of pytorch\n", + "SAGE_embeddings_df = pd.DataFrame(\n", + " tensor_data.detach().cpu().numpy(),\n", + " index=omics_kipan.columns\n", + ")\n", + "\n", + "display(SAGE_embeddings_df.iloc[:5,:5])" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "1e1ed982", "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Plotting Feature Embeddings for graph: correlation_10\n" + ] + }, { "data": { + "image/png": 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", "text/plain": [ - "{'mirna': (658, 472),\n", - " 'target': (658, 1),\n", - " 'clinical': (658, 19),\n", - " 'rna': (658, 2284),\n", - " 'meth': (658, 2102)}" + "
" ] }, "metadata": {}, @@ -1175,35 +3182,989 @@ } ], "source": [ - "import bioneuralnet as bnn\n", + "from bioneuralnet.metrics import plot_embeddings\n", + "\n", + "# for a more interpretable embedding plot we will map the node labels to omics types\n", + "SAGE_node_labels = []\n", + "SAGE_embeddings_array = SAGE_embeddings_df.values\n", + "feature_names = SAGE_embeddings_df.index\n", + "\n", + "# using the sets from the orginal to serve as a loopup table\n", + "dna_feats = set(dna_meth.columns)\n", + "rna_feats = set(rna.columns)\n", + "mirna_feats = set(mirna.columns)\n", + "\n", + "for feat in feature_names:\n", + " if feat in dna_feats:\n", + " # 1 for DNA methylation\n", + " SAGE_node_labels.append(1)\n", + " elif feat in rna_feats:\n", + " # 2 for RNA \n", + " SAGE_node_labels.append(2)\n", + " elif feat in mirna_feats:\n", + " # and 3 for miRNA \n", + " SAGE_node_labels.append(3)\n", + " else:\n", + " SAGE_node_labels.append(0)\n", + "\n", + "# our plotting function needs an array to squeeze the embedings into 2Dspace\n", + "SAGE_node_labels = np.array(SAGE_node_labels)\n", + "\n", + "# feature(omics) embeddings colored by omics type.\n", + "print(f\"Plotting Feature Embeddings for graph: {res['graph']}\")\n", + "plot_embeddings(SAGE_embeddings_array, SAGE_node_labels, legend_labels=[\"DNA_Methylation\", \"RNA\", \"miRNA\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "9a15b24b", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-11-26 01:12:46,329 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 01:12:46,330 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 01:12:46,330 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 01:12:46,330 - bioneuralnet.clustering.hybrid_louvain - INFO - Initializing HybridLouvain...\n", + "2025-11-26 01:12:46,331 - bioneuralnet.clustering.hybrid_louvain - INFO - Input G is a DataFrame; converting adjacency matrix to NetworkX graph.\n", + "2025-11-26 01:12:46,343 - bioneuralnet.clustering.hybrid_louvain - INFO - Initialized HybridLouvain with 900 graph nodes, 900 omics columns, 658 phenotype rows; max_iter=3, k3=0.2, k4=0.8, tune=False\n", + "2025-11-26 01:12:46,343 - bioneuralnet.clustering.hybrid_louvain - INFO - \n", + "Iteration 1/3: Running Correlated Louvain...\n", + "2025-11-26 01:12:46,352 - bioneuralnet.clustering.correlated_louvain - INFO - CorrelatedLouvain(k3=0.2, k4=0.8, nodes=900, edges=8628, features=900)\n", + "2025-11-26 01:12:46,352 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 01:12:46,353 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 01:12:46,353 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 01:12:46,353 - bioneuralnet.clustering.correlated_louvain - INFO - Running standard community detection...\n", + "2025-11-26 01:12:46,464 - bioneuralnet.clustering.correlated_louvain - INFO - Computed quality (supervised): Q = 0.6936, avg_corr = 0.4879, combined = 0.5290\n", + "2025-11-26 01:12:46,464 - bioneuralnet.clustering.correlated_louvain - INFO - Final quality: 0.5290\n", + "2025-11-26 01:12:46,465 - bioneuralnet.clustering.correlated_louvain - INFO - CorrelatedLouvain found 11 communities (nodes=900)\n", + "2025-11-26 01:12:46,521 - bioneuralnet.clustering.correlated_louvain - INFO - Computed quality (supervised): Q = 0.6936, avg_corr = 0.4879, combined = 0.5290\n", + "2025-11-26 01:12:46,522 - bioneuralnet.clustering.hybrid_louvain - INFO - Iteration 1: Louvain Quality = 0.5290\n", + "2025-11-26 01:12:46,574 - bioneuralnet.clustering.hybrid_louvain - INFO - Selected seed community of size 108 with correlation -0.8495\n", + "2025-11-26 01:12:46,575 - bioneuralnet.clustering.correlated_pagerank - INFO - Initialized PageRank with the following parameters:\n", + "2025-11-26 01:12:46,575 - bioneuralnet.clustering.correlated_pagerank - INFO - Graph: NetworkX Graph with 900 nodes and 8628 edges.\n", + "2025-11-26 01:12:46,576 - bioneuralnet.clustering.correlated_pagerank - INFO - Omics Data: DataFrame with shape (658, 900).\n", + "2025-11-26 01:12:46,576 - bioneuralnet.clustering.correlated_pagerank - INFO - Phenotype Data: Series with 658 samples.\n", + "2025-11-26 01:12:46,576 - bioneuralnet.clustering.correlated_pagerank - INFO - Alpha: 0.9\n", + "2025-11-26 01:12:46,576 - bioneuralnet.clustering.correlated_pagerank - INFO - Max Iterations: 100\n", + "2025-11-26 01:12:46,577 - bioneuralnet.clustering.correlated_pagerank - INFO - Tolerance: 1e-06\n", + "2025-11-26 01:12:46,577 - bioneuralnet.clustering.correlated_pagerank - INFO - K (Composite Score Weight): 0.5\n", + "2025-11-26 01:12:46,577 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 01:12:46,578 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 01:12:46,578 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 01:12:46,579 - bioneuralnet.clustering.correlated_pagerank - INFO - Initialized Correlated PageRank. device=cpu\n", + "2025-11-26 01:12:47,133 - bioneuralnet.clustering.correlated_pagerank - INFO - Generated personalization vector for seed nodes: ['ALDH1A1', 'KRTAP3_3', 'TAS2R42', 'ADAM28', 'C21orf71', 'LOC645638', 'MIR196A1', 'ALDOB', 'TMEM71', 'HSD17B14', 'C4orf26', 'DGCR10', 'MIR942', 'BCAN', 'LOC100128288', 'ADM2', 'IFITM1', 'PSORS1C3', 'TMEM88', 'MIR26A1', 'ALDOC', 'C8orf80', 'MIR130A', 'MIR429', 'CCDC17', 'NDUFA4L2', 'ALOX12P2', 'ATP8B1', 'C14orf129', 'FOXS1', 'SLC10A6', 'C3orf24', 'GGTLC2', 'C10orf10', 'ART4', 'IL18BP', 'MIR554', 'DDR2', 'C15orf28', 'C7orf29', 'USHBP1', 'TMEM204', 'NME3', 'PTGER3_5733', 'HMGCS2_3158', 'SLPI_6590', 'CXCL6_6372', 'FOXJ1_2302', 'HGFAC_3083', 'UBE2QL1_134111', 'SORCS2_57537', 'BCHE_590', 'DACT2_168002', 'ELFN2_114794', 'C19orf21_126353', 'CCDC135_84229', 'CPNE7_27132', 'VAT1L_57687', 'AVPR1A_552', 'PRDM16_63976', 'ACTC1_70', 'ADAMTSL1_92949', 'PDE1C_5137', 'CPXM2_119587', 'SULT4A1_25830', 'ADCY1_107', 'CORO6_84940', 'ANGPT1_284', 'CLDN3_1365', 'CA8_767', 'RASGEF1C_255426', 'CCDC108_255101', 'BDKRB2_624', 'ACSM1_116285', 'DGKI_9162', 'C19orf51_352909', 'B3GALT5_10317', 'CLSTN2_64084', 'C1orf114_57821', 'MPPED2_744', 'RASGRF2_5924', 'SPARCL1_8404', 'C2orf62_375307', 'GPR116_221395', 'C6orf174_387104', 'AKR1C2_1646', 'ADORA1_134', 'C2orf89_129293', 'hsa_mir_31', 'hsa_mir_885', 'hsa_mir_577', 'hsa_mir_203', 'hsa_mir_135b', 'hsa_mir_582', 'hsa_mir_139', 'hsa_mir_335', 'hsa_mir_21', 'hsa_mir_100', 'hsa_mir_181d', 'hsa_mir_219_1', 'hsa_mir_3934', 'hsa_mir_181c', 'hsa_mir_625', 'hsa_mir_676', 'hsa_mir_34a', 'hsa_mir_1254', 'hsa_mir_3615', 'hsa_mir_3127']\n", + "2025-11-26 01:12:47,144 - bioneuralnet.clustering.correlated_pagerank - INFO - PageRank computation completed.\n", + "2025-11-26 01:13:01,127 - bioneuralnet.clustering.correlated_pagerank - INFO - Sweep cut resulted in cluster of size 857 with conductance 0.079 and correlation 0.83.\n", + "2025-11-26 01:13:01,127 - bioneuralnet.clustering.correlated_pagerank - INFO - PageRank clustering completed successfully.\n", + "2025-11-26 01:13:01,128 - bioneuralnet.clustering.hybrid_louvain - INFO - Iteration 1: cluster size=857, Conductance=0.079 Correlation=0.830 score=-0.375\n", + "2025-11-26 01:13:01,167 - bioneuralnet.clustering.hybrid_louvain - INFO - \n", + "Iteration 2/3: Running Correlated Louvain...\n", + "2025-11-26 01:13:01,196 - bioneuralnet.clustering.correlated_louvain - INFO - CorrelatedLouvain(k3=0.2, k4=0.8, nodes=857, edges=8206, features=900)\n", + "2025-11-26 01:13:01,198 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 01:13:01,199 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 01:13:01,199 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 01:13:01,200 - bioneuralnet.clustering.correlated_louvain - INFO - Running standard community detection...\n", + "2025-11-26 01:13:01,310 - bioneuralnet.clustering.correlated_louvain - INFO - Computed quality (supervised): Q = 0.6811, avg_corr = 0.5019, combined = 0.5378\n", + "2025-11-26 01:13:01,310 - bioneuralnet.clustering.correlated_louvain - INFO - Final quality: 0.5378\n", + "2025-11-26 01:13:01,311 - bioneuralnet.clustering.correlated_louvain - INFO - CorrelatedLouvain found 11 communities (nodes=857)\n", + "2025-11-26 01:13:01,369 - bioneuralnet.clustering.correlated_louvain - INFO - Computed quality (supervised): Q = 0.6811, avg_corr = 0.5019, combined = 0.5378\n", + "2025-11-26 01:13:01,369 - bioneuralnet.clustering.hybrid_louvain - INFO - Iteration 2: Louvain Quality = 0.5378\n", + "2025-11-26 01:13:01,423 - bioneuralnet.clustering.hybrid_louvain - INFO - Selected seed community of size 73 with correlation -0.8235\n", + "2025-11-26 01:13:01,423 - bioneuralnet.clustering.correlated_pagerank - INFO - Initialized PageRank with the following parameters:\n", + "2025-11-26 01:13:01,424 - bioneuralnet.clustering.correlated_pagerank - INFO - Graph: NetworkX Graph with 857 nodes and 8206 edges.\n", + "2025-11-26 01:13:01,425 - bioneuralnet.clustering.correlated_pagerank - INFO - Omics Data: DataFrame with shape (658, 900).\n", + "2025-11-26 01:13:01,425 - bioneuralnet.clustering.correlated_pagerank - INFO - Phenotype Data: Series with 658 samples.\n", + "2025-11-26 01:13:01,425 - bioneuralnet.clustering.correlated_pagerank - INFO - Alpha: 0.9\n", + "2025-11-26 01:13:01,426 - bioneuralnet.clustering.correlated_pagerank - INFO - Max Iterations: 100\n", + "2025-11-26 01:13:01,426 - bioneuralnet.clustering.correlated_pagerank - INFO - Tolerance: 1e-06\n", + "2025-11-26 01:13:01,426 - bioneuralnet.clustering.correlated_pagerank - INFO - K (Composite Score Weight): 0.5\n", + "2025-11-26 01:13:01,427 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 01:13:01,427 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 01:13:01,428 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 01:13:01,428 - bioneuralnet.clustering.correlated_pagerank - INFO - Initialized Correlated PageRank. device=cpu\n", + "2025-11-26 01:13:01,771 - bioneuralnet.clustering.correlated_pagerank - INFO - Generated personalization vector for seed nodes: ['ALDH1A1', 'ADAM28', 'LOC645638', 'TMEM71', 'C4orf26', 'C14orf129', 'GGTLC2', 'PTGER3_5733', 'HMGCS2_3158', 'SLPI_6590', 'CXCL6_6372', 'HGFAC_3083', 'UBE2QL1_134111', 'SORCS2_57537', 'BCHE_590', 'ELFN2_114794', 'C19orf21_126353', 'TFAP2A_7020', 'CCDC135_84229', 'CPNE7_27132', 'VAT1L_57687', 'AVPR1A_552', 'PRDM16_63976', 'ACTC1_70', 'ADAMTSL1_92949', 'PDE1C_5137', 'CPXM2_119587', 'KCNN3_3782', 'SULT4A1_25830', 'ADCY1_107', 'CORO6_84940', 'ANGPT1_284', 'CLDN3_1365', 'HOXD10_3236', 'CA8_767', 'RASGEF1C_255426', 'CCDC108_255101', 'BDKRB2_624', 'ACSM1_116285', 'DGKI_9162', 'C19orf51_352909', 'B3GALT5_10317', 'CLSTN2_64084', 'MPPED2_744', 'RASGRF2_5924', 'SPARCL1_8404', 'C2orf62_375307', 'GPR116_221395', 'C6orf174_387104', 'AKR1C2_1646', 'ADORA1_134', 'C2orf89_129293', 'hsa_mir_31', 'hsa_mir_577', 'hsa_mir_203', 'hsa_mir_135b', 'hsa_mir_582', 'hsa_mir_139', 'hsa_mir_211', 'hsa_mir_335', 'hsa_mir_21', 'hsa_mir_100', 'hsa_mir_10b', 'hsa_mir_181d', 'hsa_mir_219_1', 'hsa_mir_3934', 'hsa_mir_181c', 'hsa_mir_625', 'hsa_mir_676', 'hsa_mir_34a', 'hsa_mir_1254', 'hsa_mir_3615', 'hsa_mir_3127']\n", + "2025-11-26 01:13:01,782 - bioneuralnet.clustering.correlated_pagerank - INFO - PageRank computation completed.\n", + "2025-11-26 01:13:14,769 - bioneuralnet.clustering.correlated_pagerank - INFO - Sweep cut resulted in cluster of size 420 with conductance 0.112 and correlation 0.62.\n", + "2025-11-26 01:13:14,770 - bioneuralnet.clustering.correlated_pagerank - INFO - PageRank clustering completed successfully.\n", + "2025-11-26 01:13:14,770 - bioneuralnet.clustering.hybrid_louvain - INFO - Iteration 2: cluster size=420, Conductance=0.112 Correlation=0.620 score=-0.254\n", + "2025-11-26 01:13:14,782 - bioneuralnet.clustering.hybrid_louvain - INFO - \n", + "Iteration 3/3: Running Correlated Louvain...\n", + "2025-11-26 01:13:14,790 - bioneuralnet.clustering.correlated_louvain - INFO - CorrelatedLouvain(k3=0.2, k4=0.8, nodes=420, edges=3651, features=900)\n", + "2025-11-26 01:13:14,790 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 01:13:14,791 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 01:13:14,791 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 01:13:14,792 - bioneuralnet.clustering.correlated_louvain - INFO - Running standard community detection...\n", + "2025-11-26 01:13:14,891 - bioneuralnet.clustering.correlated_louvain - INFO - Computed quality (supervised): Q = 0.6275, avg_corr = 0.4688, combined = 0.5005\n", + "2025-11-26 01:13:14,892 - bioneuralnet.clustering.correlated_louvain - INFO - Final quality: 0.5005\n", + "2025-11-26 01:13:14,892 - bioneuralnet.clustering.correlated_louvain - INFO - CorrelatedLouvain found 8 communities (nodes=420)\n", + "2025-11-26 01:13:14,926 - bioneuralnet.clustering.correlated_louvain - INFO - Computed quality (supervised): Q = 0.6275, avg_corr = 0.4688, combined = 0.5005\n", + "2025-11-26 01:13:14,926 - bioneuralnet.clustering.hybrid_louvain - INFO - Iteration 3: Louvain Quality = 0.5005\n", + "2025-11-26 01:13:14,957 - bioneuralnet.clustering.hybrid_louvain - INFO - Selected seed community of size 88 with correlation -0.8538\n", + "2025-11-26 01:13:14,957 - bioneuralnet.clustering.correlated_pagerank - INFO - Initialized PageRank with the following parameters:\n", + "2025-11-26 01:13:14,958 - bioneuralnet.clustering.correlated_pagerank - INFO - Graph: NetworkX Graph with 420 nodes and 3651 edges.\n", + "2025-11-26 01:13:14,958 - bioneuralnet.clustering.correlated_pagerank - INFO - Omics Data: DataFrame with shape (658, 900).\n", + "2025-11-26 01:13:14,958 - bioneuralnet.clustering.correlated_pagerank - INFO - Phenotype Data: Series with 658 samples.\n", + "2025-11-26 01:13:14,958 - bioneuralnet.clustering.correlated_pagerank - INFO - Alpha: 0.9\n", + "2025-11-26 01:13:14,959 - bioneuralnet.clustering.correlated_pagerank - INFO - Max Iterations: 100\n", + "2025-11-26 01:13:14,959 - bioneuralnet.clustering.correlated_pagerank - INFO - Tolerance: 1e-06\n", + "2025-11-26 01:13:14,959 - bioneuralnet.clustering.correlated_pagerank - INFO - K (Composite Score Weight): 0.5\n", + "2025-11-26 01:13:14,960 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 01:13:14,960 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 01:13:14,961 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 01:13:14,961 - bioneuralnet.clustering.correlated_pagerank - INFO - Initialized Correlated PageRank. device=cpu\n", + "2025-11-26 01:13:15,438 - bioneuralnet.clustering.correlated_pagerank - INFO - Generated personalization vector for seed nodes: ['BCHE_590', 'ACSM1_116285', 'hsa_mir_181c', 'ALDH1A1', 'RASD2_23551', 'HSD17B14', 'ADAMTSL1_92949', 'hsa_mir_1180', 'VAT1L_57687', 'RASGRF2_5924', 'C19orf21_126353', 'CPXM2_119587', 'UBE2QL1_134111', 'PDE1C_5137', 'ELFN2_114794', 'hsa_mir_335', 'CXCL6_6372', 'PTGER3_5733', 'ADCY1_107', 'hsa_mir_219_1', 'hsa_mir_203', 'CCDC135_84229', 'hsa_mir_181d', 'hsa_mir_3615', 'ADAMTS5_11096', 'B3GALT5_10317', 'CLDN3_1365', 'HGFAC_3083', 'C2orf62_375307', 'DACT2_168002', 'SULT4A1_25830', 'hsa_mir_10b', 'hsa_mir_1254', 'FOXJ1_2302', 'hsa_mir_3934', 'DACH1_1602', 'TMEM71', 'AKR1C2_1646', 'hsa_mir_676', 'BDKRB2_624', 'hsa_mir_577', 'hsa_mir_139', 'AVPR1A_552', 'C19orf51_352909', 'C3orf24', 'hsa_mir_100', 'HOXD10_3236', 'RASGEF1C_255426', 'CA4_762', 'C14orf129', 'hsa_mir_625', 'ADORA1_134', 'SLPI_6590', 'BTNL9_153579', 'C6orf174_387104', 'C1orf114_57821', 'C2orf89_129293', 'SPARCL1_8404', 'CLSTN2_64084', 'hsa_mir_135b', 'C4orf26', 'DGKI_9162', 'SORCS2_57537', 'CDH17_1015', 'C2orf72_257407', 'ANGPT1_284', 'CORO6_84940', 'CCDC108_255101', 'BCAN', 'hsa_mir_21', 'GPR116_221395', 'CA8_767', 'hsa_mir_551b', 'CPNE7_27132', 'hsa_mir_31', 'PRDM16_63976', 'hsa_mir_211', 'HMGCS2_3158', 'PCDH17_27253', 'ACTC1_70', 'hsa_mir_582', 'GGTLC2', 'MPPED2_744', 'DDR2_4921', 'PDZD2_23037', 'KCNN3_3782', 'CNTN6_27255', 'LOC645638']\n", + "2025-11-26 01:13:15,444 - bioneuralnet.clustering.correlated_pagerank - INFO - PageRank computation completed.\n", + "2025-11-26 01:13:19,148 - bioneuralnet.clustering.correlated_pagerank - INFO - Sweep cut resulted in cluster of size 176 with conductance 0.225 and correlation -0.76.\n", + "2025-11-26 01:13:19,149 - bioneuralnet.clustering.correlated_pagerank - INFO - PageRank clustering completed successfully.\n", + "2025-11-26 01:13:19,149 - bioneuralnet.clustering.hybrid_louvain - INFO - Iteration 3: cluster size=176, Conductance=0.225 Correlation=-0.760 score=-0.267\n", + "2025-11-26 01:13:19,154 - bioneuralnet.clustering.hybrid_louvain - INFO - Hybrid Louvain completed after 4 iterations.\n", + "2025-11-26 01:13:19,158 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 01:13:19,159 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 01:13:19,160 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 01:13:19,160 - bioneuralnet.clustering.hybrid_louvain - INFO - Initializing HybridLouvain...\n", + "2025-11-26 01:13:19,160 - bioneuralnet.clustering.hybrid_louvain - INFO - Input G is a DataFrame; converting adjacency matrix to NetworkX graph.\n", + "2025-11-26 01:13:19,180 - bioneuralnet.clustering.hybrid_louvain - INFO - Initialized HybridLouvain with 900 graph nodes, 900 omics columns, 658 phenotype rows; max_iter=3, k3=0.2, k4=0.8, tune=False\n", + "2025-11-26 01:13:19,181 - bioneuralnet.clustering.hybrid_louvain - INFO - \n", + "Iteration 1/3: Running Correlated Louvain...\n", + "2025-11-26 01:13:19,196 - bioneuralnet.clustering.correlated_louvain - INFO - CorrelatedLouvain(k3=0.2, k4=0.8, nodes=900, edges=8628, features=900)\n", + "2025-11-26 01:13:19,197 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 01:13:19,198 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 01:13:19,198 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 01:13:19,198 - bioneuralnet.clustering.correlated_louvain - INFO - Running standard community detection...\n", + "2025-11-26 01:13:19,325 - bioneuralnet.clustering.correlated_louvain - INFO - Computed quality (supervised): Q = 0.6936, avg_corr = 0.4574, combined = 0.5046\n", + "2025-11-26 01:13:19,325 - bioneuralnet.clustering.correlated_louvain - INFO - Final quality: 0.5046\n", + "2025-11-26 01:13:19,326 - bioneuralnet.clustering.correlated_louvain - INFO - CorrelatedLouvain found 11 communities (nodes=900)\n", + "2025-11-26 01:13:19,384 - bioneuralnet.clustering.correlated_louvain - INFO - Computed quality (supervised): Q = 0.6936, avg_corr = 0.4574, combined = 0.5046\n", + "2025-11-26 01:13:19,384 - bioneuralnet.clustering.hybrid_louvain - INFO - Iteration 1: Louvain Quality = 0.5046\n", + "2025-11-26 01:13:19,438 - bioneuralnet.clustering.hybrid_louvain - INFO - Selected seed community of size 59 with correlation 0.8725\n", + "2025-11-26 01:13:19,438 - bioneuralnet.clustering.correlated_pagerank - INFO - Initialized PageRank with the following parameters:\n", + "2025-11-26 01:13:19,439 - bioneuralnet.clustering.correlated_pagerank - INFO - Graph: NetworkX Graph with 900 nodes and 8628 edges.\n", + "2025-11-26 01:13:19,439 - bioneuralnet.clustering.correlated_pagerank - INFO - Omics Data: DataFrame with shape (658, 900).\n", + "2025-11-26 01:13:19,439 - bioneuralnet.clustering.correlated_pagerank - INFO - Phenotype Data: Series with 658 samples.\n", + "2025-11-26 01:13:19,440 - bioneuralnet.clustering.correlated_pagerank - INFO - Alpha: 0.9\n", + "2025-11-26 01:13:19,440 - bioneuralnet.clustering.correlated_pagerank - INFO - Max Iterations: 100\n", + "2025-11-26 01:13:19,440 - bioneuralnet.clustering.correlated_pagerank - INFO - Tolerance: 1e-06\n", + "2025-11-26 01:13:19,440 - bioneuralnet.clustering.correlated_pagerank - INFO - K (Composite Score Weight): 0.5\n", + "2025-11-26 01:13:19,441 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 01:13:19,442 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 01:13:19,442 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 01:13:19,442 - bioneuralnet.clustering.correlated_pagerank - INFO - Initialized Correlated PageRank. device=cpu\n", + "2025-11-26 01:13:19,655 - bioneuralnet.clustering.correlated_pagerank - INFO - Generated personalization vector for seed nodes: ['BAAT', 'CAPG', 'CP', 'FABP7_2173', 'SLC6A3_6531', 'CP_1356', 'CA9_768', 'CKMT1B_1159', 'CYP2J2_1573', 'CREB3L3_84699', 'ANGPTL4_51129', 'MYEOV_26579', 'TJP3_27134', 'PCSK6_5046', 'C1QL1_10882', 'MCHR1_2847', 'C19orf46_163183', 'APOB_338', 'CKMT1A_548596', 'MAP7D2_256714', 'CCL20_6364', 'LOX_4015', 'C6orf168_84553', 'MAL_4118', 'LOC100131551_100131551', 'CNTN3_5067', 'PCOLCE2_26577', 'CDCA2_157313', 'C1orf116_79098', 'WFDC2_10406', 'ALDOC_230', 'ANO5_203859', 'PPFIA4_8497', 'STK33_65975', 'EPS8L1_54869', 'CBLC_23624', 'SEMA5B_54437', 'TMEM45A_55076', 'OGDHL_55753', 'EGLN3_112399', 'C4orf6_10141', 'FAM155B_27112', 'C7orf68_29923', 'B3GNT4_79369', 'hsa_mir_122', 'hsa_mir_599', 'hsa_mir_183', 'hsa_mir_96', 'hsa_mir_210', 'hsa_mir_182', 'hsa_mir_374c', 'hsa_mir_1271', 'hsa_mir_200b', 'hsa_mir_429', 'hsa_mir_875', 'hsa_mir_3941', 'hsa_mir_200a', 'hsa_mir_3678', 'hsa_mir_3912']\n", + "2025-11-26 01:13:19,666 - bioneuralnet.clustering.correlated_pagerank - INFO - PageRank computation completed.\n", + "2025-11-26 01:13:34,489 - bioneuralnet.clustering.correlated_pagerank - INFO - Sweep cut resulted in cluster of size 451 with conductance 0.087 and correlation 0.89.\n", + "2025-11-26 01:13:34,489 - bioneuralnet.clustering.correlated_pagerank - INFO - PageRank clustering completed successfully.\n", + "2025-11-26 01:13:34,490 - bioneuralnet.clustering.hybrid_louvain - INFO - Iteration 1: cluster size=451, Conductance=0.087 Correlation=0.890 score=-0.401\n", + "2025-11-26 01:13:34,504 - bioneuralnet.clustering.hybrid_louvain - INFO - \n", + "Iteration 2/3: Running Correlated Louvain...\n", + "2025-11-26 01:13:34,512 - bioneuralnet.clustering.correlated_louvain - INFO - CorrelatedLouvain(k3=0.2, k4=0.8, nodes=451, edges=3950, features=900)\n", + "2025-11-26 01:13:34,513 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 01:13:34,514 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 01:13:34,514 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 01:13:34,514 - bioneuralnet.clustering.correlated_louvain - INFO - Running standard community detection...\n", + "2025-11-26 01:13:34,616 - bioneuralnet.clustering.correlated_louvain - INFO - Computed quality (supervised): Q = 0.6249, avg_corr = 0.6253, combined = 0.6252\n", + "2025-11-26 01:13:34,616 - bioneuralnet.clustering.correlated_louvain - INFO - Final quality: 0.6252\n", + "2025-11-26 01:13:34,617 - bioneuralnet.clustering.correlated_louvain - INFO - CorrelatedLouvain found 10 communities (nodes=451)\n", + "2025-11-26 01:13:34,661 - bioneuralnet.clustering.correlated_louvain - INFO - Computed quality (supervised): Q = 0.6249, avg_corr = 0.6253, combined = 0.6252\n", + "2025-11-26 01:13:34,662 - bioneuralnet.clustering.hybrid_louvain - INFO - Iteration 2: Louvain Quality = 0.6252\n", + "2025-11-26 01:13:34,701 - bioneuralnet.clustering.hybrid_louvain - INFO - Selected seed community of size 57 with correlation 0.8714\n", + "2025-11-26 01:13:34,702 - bioneuralnet.clustering.correlated_pagerank - INFO - Initialized PageRank with the following parameters:\n", + "2025-11-26 01:13:34,702 - bioneuralnet.clustering.correlated_pagerank - INFO - Graph: NetworkX Graph with 451 nodes and 3950 edges.\n", + "2025-11-26 01:13:34,703 - bioneuralnet.clustering.correlated_pagerank - INFO - Omics Data: DataFrame with shape (658, 900).\n", + "2025-11-26 01:13:34,703 - bioneuralnet.clustering.correlated_pagerank - INFO - Phenotype Data: Series with 658 samples.\n", + "2025-11-26 01:13:34,703 - bioneuralnet.clustering.correlated_pagerank - INFO - Alpha: 0.9\n", + "2025-11-26 01:13:34,703 - bioneuralnet.clustering.correlated_pagerank - INFO - Max Iterations: 100\n", + "2025-11-26 01:13:34,704 - bioneuralnet.clustering.correlated_pagerank - INFO - Tolerance: 1e-06\n", + "2025-11-26 01:13:34,704 - bioneuralnet.clustering.correlated_pagerank - INFO - K (Composite Score Weight): 0.5\n", + "2025-11-26 01:13:34,704 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 01:13:34,705 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 01:13:34,706 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 01:13:34,706 - bioneuralnet.clustering.correlated_pagerank - INFO - Initialized Correlated PageRank. device=cpu\n", + "2025-11-26 01:13:34,936 - bioneuralnet.clustering.correlated_pagerank - INFO - Generated personalization vector for seed nodes: ['CAPG', 'CP', 'FABP7_2173', 'SLC6A3_6531', 'CP_1356', 'CA9_768', 'CKMT1B_1159', 'CYP2J2_1573', 'CREB3L3_84699', 'ANGPTL4_51129', 'MYEOV_26579', 'TJP3_27134', 'PCSK6_5046', 'C1QL1_10882', 'MCHR1_2847', 'C19orf46_163183', 'APOB_338', 'CKMT1A_548596', 'MAP7D2_256714', 'CCL20_6364', 'LOX_4015', 'C6orf168_84553', 'MAL_4118', 'LOC100131551_100131551', 'CNTN3_5067', 'PCOLCE2_26577', 'CDCA2_157313', 'C1orf116_79098', 'CST6_1474', 'WFDC2_10406', 'ALDOC_230', 'ANO5_203859', 'PPFIA4_8497', 'STK33_65975', 'IGF2BP2_10644', 'EPS8L1_54869', 'CBLC_23624', 'SEMA5B_54437', 'TMEM45A_55076', 'OGDHL_55753', 'EGLN3_112399', 'C4orf6_10141', 'FAM155B_27112', 'C7orf68_29923', 'B3GNT4_79369', 'hsa_mir_122', 'hsa_mir_183', 'hsa_mir_96', 'hsa_mir_210', 'hsa_mir_182', 'hsa_mir_374c', 'hsa_mir_1271', 'hsa_mir_200b', 'hsa_mir_429', 'hsa_mir_3941', 'hsa_mir_200a', 'hsa_mir_3678']\n", + "2025-11-26 01:13:34,942 - bioneuralnet.clustering.correlated_pagerank - INFO - PageRank computation completed.\n", + "2025-11-26 01:13:39,443 - bioneuralnet.clustering.correlated_pagerank - INFO - Sweep cut resulted in cluster of size 193 with conductance 0.221 and correlation 0.9.\n", + "2025-11-26 01:13:39,444 - bioneuralnet.clustering.correlated_pagerank - INFO - PageRank clustering completed successfully.\n", + "2025-11-26 01:13:39,445 - bioneuralnet.clustering.hybrid_louvain - INFO - Iteration 2: cluster size=193, Conductance=0.221 Correlation=0.900 score=-0.340\n", + "2025-11-26 01:13:39,451 - bioneuralnet.clustering.hybrid_louvain - INFO - \n", + "Iteration 3/3: Running Correlated Louvain...\n", + "2025-11-26 01:13:39,456 - bioneuralnet.clustering.correlated_louvain - INFO - CorrelatedLouvain(k3=0.2, k4=0.8, nodes=193, edges=1530, features=900)\n", + "2025-11-26 01:13:39,457 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 01:13:39,458 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 01:13:39,459 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 01:13:39,460 - bioneuralnet.clustering.correlated_louvain - INFO - Running standard community detection...\n", + "2025-11-26 01:13:39,506 - bioneuralnet.clustering.correlated_louvain - INFO - Computed quality (supervised): Q = 0.5349, avg_corr = 0.7495, combined = 0.7066\n", + "2025-11-26 01:13:39,507 - bioneuralnet.clustering.correlated_louvain - INFO - Final quality: 0.7066\n", + "2025-11-26 01:13:39,507 - bioneuralnet.clustering.correlated_louvain - INFO - CorrelatedLouvain found 7 communities (nodes=193)\n", + "2025-11-26 01:13:39,533 - bioneuralnet.clustering.correlated_louvain - INFO - Computed quality (supervised): Q = 0.5349, avg_corr = 0.7495, combined = 0.7066\n", + "2025-11-26 01:13:39,533 - bioneuralnet.clustering.hybrid_louvain - INFO - Iteration 3: Louvain Quality = 0.7066\n", + "2025-11-26 01:13:39,558 - bioneuralnet.clustering.hybrid_louvain - INFO - Selected seed community of size 58 with correlation 0.8576\n", + "2025-11-26 01:13:39,559 - bioneuralnet.clustering.correlated_pagerank - INFO - Initialized PageRank with the following parameters:\n", + "2025-11-26 01:13:39,559 - bioneuralnet.clustering.correlated_pagerank - INFO - Graph: NetworkX Graph with 193 nodes and 1530 edges.\n", + "2025-11-26 01:13:39,560 - bioneuralnet.clustering.correlated_pagerank - INFO - Omics Data: DataFrame with shape (658, 900).\n", + "2025-11-26 01:13:39,560 - bioneuralnet.clustering.correlated_pagerank - INFO - Phenotype Data: Series with 658 samples.\n", + "2025-11-26 01:13:39,560 - bioneuralnet.clustering.correlated_pagerank - INFO - Alpha: 0.9\n", + "2025-11-26 01:13:39,560 - bioneuralnet.clustering.correlated_pagerank - INFO - Max Iterations: 100\n", + "2025-11-26 01:13:39,561 - bioneuralnet.clustering.correlated_pagerank - INFO - Tolerance: 1e-06\n", + "2025-11-26 01:13:39,561 - bioneuralnet.clustering.correlated_pagerank - INFO - K (Composite Score Weight): 0.5\n", + "2025-11-26 01:13:39,561 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 01:13:39,562 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 01:13:39,563 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 01:13:39,563 - bioneuralnet.clustering.correlated_pagerank - INFO - Initialized Correlated PageRank. device=cpu\n", + "2025-11-26 01:13:39,832 - bioneuralnet.clustering.correlated_pagerank - INFO - Generated personalization vector for seed nodes: ['C4orf48_401115', 'FATE1_89885', 'BARX2_8538', 'FOLH1_2346', 'NGF_4803', 'hsa_mir_3678', 'C2orf72_257407', 'hsa_mir_503', 'OR51E1_143503', 'ANGPT2_285', 'CXCL10_3627', 'GABRE_2564', 'C6orf223_221416', 'ESM1_11082', 'CXCL9_4283', 'KLF14', 'hsa_mir_489', 'CNTD2_79935', 'CSPG4_1464', 'hsa_mir_224', 'CHL1_10752', 'CREB3L3_84699', 'FLT1_2321', 'COL5A3_50509', 'GPR4_2828', 'hsa_mir_126', 'hsa_let_7b', 'hsa_mir_561', 'BTNL9_153579', 'PCDH17_27253', 'COL21A1_81578', 'SLC35F1_222553', 'STC1_6781', 'ANXA3_306', 'APLN_8862', 'BNC1_646', 'LGI4_163175', 'hsa_mir_653', 'TRIM58', 'CAMK2B_816', 'CDH13_1012', 'CALCR_799', 'hsa_mir_3912', 'APOLD1_81575', 'CCNO_10309', 'KLHDC8A_55220', 'STK33_65975', 'RGS5_8490', 'LOC554202_554202', 'HMCN1_83872', 'C3orf70_285382', 'CD36_948', 'ADAMTS5_11096', 'COX4I2_84701', 'CXCL11_6373', 'TRPC6_7225', 'LCN2_3934', 'LOH3CR2A']\n", + "2025-11-26 01:13:39,836 - bioneuralnet.clustering.correlated_pagerank - INFO - PageRank computation completed.\n", + "2025-11-26 01:13:41,069 - bioneuralnet.clustering.correlated_pagerank - INFO - Sweep cut resulted in cluster of size 82 with conductance 0.135 and correlation 0.87.\n", + "2025-11-26 01:13:41,069 - bioneuralnet.clustering.correlated_pagerank - INFO - PageRank clustering completed successfully.\n", + "2025-11-26 01:13:41,070 - bioneuralnet.clustering.hybrid_louvain - INFO - Iteration 3: cluster size=82, Conductance=0.135 Correlation=0.870 score=-0.367\n", + "2025-11-26 01:13:41,072 - bioneuralnet.clustering.hybrid_louvain - INFO - Hybrid Louvain completed after 4 iterations.\n", + "2025-11-26 01:13:41,076 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 01:13:41,077 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 01:13:41,077 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 01:13:41,078 - bioneuralnet.clustering.hybrid_louvain - INFO - Initializing HybridLouvain...\n", + "2025-11-26 01:13:41,078 - bioneuralnet.clustering.hybrid_louvain - INFO - Input G is a DataFrame; converting adjacency matrix to NetworkX graph.\n", + "2025-11-26 01:13:41,100 - bioneuralnet.clustering.hybrid_louvain - INFO - Initialized HybridLouvain with 900 graph nodes, 900 omics columns, 658 phenotype rows; max_iter=3, k3=0.2, k4=0.8, tune=False\n", + "2025-11-26 01:13:41,100 - bioneuralnet.clustering.hybrid_louvain - INFO - \n", + "Iteration 1/3: Running Correlated Louvain...\n", + "2025-11-26 01:13:41,124 - bioneuralnet.clustering.correlated_louvain - INFO - CorrelatedLouvain(k3=0.2, k4=0.8, nodes=900, edges=8628, features=900)\n", + "2025-11-26 01:13:41,125 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 01:13:41,126 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 01:13:41,126 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 01:13:41,127 - bioneuralnet.clustering.correlated_louvain - INFO - Running standard community detection...\n", + "2025-11-26 01:13:41,261 - bioneuralnet.clustering.correlated_louvain - INFO - Computed quality (supervised): Q = 0.6936, avg_corr = 0.3912, combined = 0.4517\n", + "2025-11-26 01:13:41,262 - bioneuralnet.clustering.correlated_louvain - INFO - Final quality: 0.4517\n", + "2025-11-26 01:13:41,262 - bioneuralnet.clustering.correlated_louvain - INFO - CorrelatedLouvain found 11 communities (nodes=900)\n", + "2025-11-26 01:13:41,327 - bioneuralnet.clustering.correlated_louvain - INFO - Computed quality (supervised): Q = 0.6936, avg_corr = 0.3912, combined = 0.4517\n", + "2025-11-26 01:13:41,328 - bioneuralnet.clustering.hybrid_louvain - INFO - Iteration 1: Louvain Quality = 0.4517\n", + "2025-11-26 01:13:41,385 - bioneuralnet.clustering.hybrid_louvain - INFO - Selected seed community of size 81 with correlation 0.7998\n", + "2025-11-26 01:13:41,386 - bioneuralnet.clustering.correlated_pagerank - INFO - Initialized PageRank with the following parameters:\n", + "2025-11-26 01:13:41,387 - bioneuralnet.clustering.correlated_pagerank - INFO - Graph: NetworkX Graph with 900 nodes and 8628 edges.\n", + "2025-11-26 01:13:41,387 - bioneuralnet.clustering.correlated_pagerank - INFO - Omics Data: DataFrame with shape (658, 900).\n", + "2025-11-26 01:13:41,387 - bioneuralnet.clustering.correlated_pagerank - INFO - Phenotype Data: Series with 658 samples.\n", + "2025-11-26 01:13:41,388 - bioneuralnet.clustering.correlated_pagerank - INFO - Alpha: 0.9\n", + "2025-11-26 01:13:41,388 - bioneuralnet.clustering.correlated_pagerank - INFO - Max Iterations: 100\n", + "2025-11-26 01:13:41,388 - bioneuralnet.clustering.correlated_pagerank - INFO - Tolerance: 1e-06\n", + "2025-11-26 01:13:41,389 - bioneuralnet.clustering.correlated_pagerank - INFO - K (Composite Score Weight): 0.5\n", + "2025-11-26 01:13:41,389 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 01:13:41,390 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 01:13:41,391 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 01:13:41,391 - bioneuralnet.clustering.correlated_pagerank - INFO - Initialized Correlated PageRank. device=cpu\n", + "2025-11-26 01:13:41,824 - bioneuralnet.clustering.correlated_pagerank - INFO - Generated personalization vector for seed nodes: ['IL18', 'RHCG_51458', 'ATP6V0A4_50617', 'SLC4A1_6521', 'KLK1_3816', 'ATP6V0D2_245972', 'CLDN8_9073', 'FOXI1_2299', 'DMRT2_10655', 'HEPACAM2_253012', 'CLCNKB_1188', 'ATP6V1B1_525', 'CFTR_1080', 'GCGR_2642', 'SLC26A7_115111', 'CLCNKA_1187', 'FGF9_2254', 'BMPR1B_658', 'TMPRSS2_7113', 'BSND_7809', 'CD70_970', 'AQP6_363', 'CWH43_80157', 'TRIM50_135892', 'COL23A1_91522', 'C16orf89_146556', 'SLC9A2_6549', 'CALCA_796', 'OLIG1_116448', 'CLNK_116449', 'CYP11A1_1583', 'RANBP3L_202151', 'DIRAS1_148252', 'C1orf168_199920', 'INPP5J_27124', 'C15orf59_388135', 'CCBE1_147372', 'EPB41L4B_54566', 'GCOM1_145781', 'C9orf84_158401', 'ATP6V1G3_127124', 'DGCR5_26220', 'VWA5B1_127731', 'CHST9_83539', 'KRTAP5_8_57830', 'DEFB1_1672', 'ST8SIA5_29906', 'LOC643008_643008', 'CEL_1056', 'C6orf105_84830', 'CR1L_1379', 'ASB5_140458', 'MARCH10_162333', 'ANGPTL1_9068', 'hsa_mir_891a', 'hsa_mir_141', 'hsa_mir_187', 'hsa_mir_200c', 'hsa_mir_375', 'hsa_mir_592', 'hsa_mir_129_1', 'hsa_mir_129_2', 'hsa_mir_184', 'hsa_mir_218_2', 'hsa_mir_206', 'hsa_mir_33a', 'hsa_mir_362', 'hsa_mir_218_1', 'hsa_mir_188', 'hsa_mir_363', 'hsa_mir_20b', 'hsa_mir_651', 'hsa_mir_3200', 'hsa_mir_660', 'hsa_mir_1468', 'hsa_mir_501', 'hsa_let_7f_2', 'hsa_mir_500b', 'hsa_mir_30c_1', 'hsa_mir_532', 'hsa_mir_23c']\n", + "2025-11-26 01:13:41,836 - bioneuralnet.clustering.correlated_pagerank - INFO - PageRank computation completed.\n", + "2025-11-26 01:13:57,321 - bioneuralnet.clustering.correlated_pagerank - INFO - Sweep cut resulted in cluster of size 448 with conductance 0.116 and correlation -0.85.\n", + "2025-11-26 01:13:57,322 - bioneuralnet.clustering.correlated_pagerank - INFO - PageRank clustering completed successfully.\n", + "2025-11-26 01:13:57,323 - bioneuralnet.clustering.hybrid_louvain - INFO - Iteration 1: cluster size=448, Conductance=0.116 Correlation=-0.850 score=-0.367\n", + "2025-11-26 01:13:57,336 - bioneuralnet.clustering.hybrid_louvain - INFO - \n", + "Iteration 2/3: Running Correlated Louvain...\n", + "2025-11-26 01:13:57,345 - bioneuralnet.clustering.correlated_louvain - INFO - CorrelatedLouvain(k3=0.2, k4=0.8, nodes=448, edges=3766, features=900)\n", + "2025-11-26 01:13:57,345 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 01:13:57,346 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 01:13:57,347 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 01:13:57,347 - bioneuralnet.clustering.correlated_louvain - INFO - Running standard community detection...\n", + "2025-11-26 01:13:57,433 - bioneuralnet.clustering.correlated_louvain - INFO - Computed quality (supervised): Q = 0.6664, avg_corr = 0.5211, combined = 0.5502\n", + "2025-11-26 01:13:57,433 - bioneuralnet.clustering.correlated_louvain - INFO - Final quality: 0.5502\n", + "2025-11-26 01:13:57,433 - bioneuralnet.clustering.correlated_louvain - INFO - CorrelatedLouvain found 9 communities (nodes=448)\n", + "2025-11-26 01:13:57,476 - bioneuralnet.clustering.correlated_louvain - INFO - Computed quality (supervised): Q = 0.6664, avg_corr = 0.5211, combined = 0.5502\n", + "2025-11-26 01:13:57,476 - bioneuralnet.clustering.hybrid_louvain - INFO - Iteration 2: Louvain Quality = 0.5502\n", + "2025-11-26 01:13:57,514 - bioneuralnet.clustering.hybrid_louvain - INFO - Selected seed community of size 84 with correlation 0.8241\n", + "2025-11-26 01:13:57,514 - bioneuralnet.clustering.correlated_pagerank - INFO - Initialized PageRank with the following parameters:\n", + "2025-11-26 01:13:57,515 - bioneuralnet.clustering.correlated_pagerank - INFO - Graph: NetworkX Graph with 448 nodes and 3766 edges.\n", + "2025-11-26 01:13:57,515 - bioneuralnet.clustering.correlated_pagerank - INFO - Omics Data: DataFrame with shape (658, 900).\n", + "2025-11-26 01:13:57,516 - bioneuralnet.clustering.correlated_pagerank - INFO - Phenotype Data: Series with 658 samples.\n", + "2025-11-26 01:13:57,516 - bioneuralnet.clustering.correlated_pagerank - INFO - Alpha: 0.9\n", + "2025-11-26 01:13:57,516 - bioneuralnet.clustering.correlated_pagerank - INFO - Max Iterations: 100\n", + "2025-11-26 01:13:57,517 - bioneuralnet.clustering.correlated_pagerank - INFO - Tolerance: 1e-06\n", + "2025-11-26 01:13:57,517 - bioneuralnet.clustering.correlated_pagerank - INFO - K (Composite Score Weight): 0.5\n", + "2025-11-26 01:13:57,518 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 01:13:57,519 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 01:13:57,519 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 01:13:57,519 - bioneuralnet.clustering.correlated_pagerank - INFO - Initialized Correlated PageRank. device=cpu\n", + "2025-11-26 01:13:57,967 - bioneuralnet.clustering.correlated_pagerank - INFO - Generated personalization vector for seed nodes: ['ASB5_140458', 'C1orf168_199920', 'OLIG1_116448', 'FOXI1_2299', 'LOC643008_643008', 'hsa_mir_592', 'BMPR1B_658', 'CD70_970', 'hsa_mir_1258', 'FGF9_2254', 'C15orf59_388135', 'ATP6V1B1_525', 'C9orf84_158401', 'hsa_mir_218_2', 'GCGR_2642', 'GALNT14_79623', 'DHRS2_10202', 'CNTNAP5_129684', 'CFTR_1080', 'ATP6V0A4_50617', 'hsa_mir_1468', 'hsa_mir_129_1', 'LRRTM1_347730', 'TRIM50_135892', 'DGCR5_26220', 'DMRT2_10655', 'hsa_mir_651', 'ALPK2_115701', 'hsa_mir_874', 'ANGPTL1_9068', 'hsa_mir_891a', 'C3_718', 'VWA5B1_127731', 'CLDN8_9073', 'RANBP3L_202151', 'hsa_mir_129_2', 'CYP11A1_1583', 'hsa_mir_141', 'CHST9_83539', 'CR1L_1379', 'CEND1_51286', 'hsa_mir_206', 'hsa_mir_23c', 'C17orf55_284185', 'SLC9A2_6549', 'hsa_mir_218_1', 'IL18', 'hsa_mir_200c', 'CWH43_80157', 'CKMT2_1160', 'hsa_mir_618', 'DEFB1_1672', 'BSND_7809', 'SLC26A7_115111', 'ABCC3_8714', 'CCDC78_124093', 'hsa_mir_30c_1', 'CLNK_116449', 'CALCA_796', 'TMPRSS2_7113', 'RHCG_51458', 'CLCNKB_1188', 'DIRAS1_148252', 'ST8SIA5_29906', 'C6orf105_84830', 'hsa_mir_187', 'SLC4A1_6521', 'CLCNKA_1187', 'CEL_1056', 'AQP6_363', 'MARCH10_162333', 'ATP6V0D2_245972', 'C16orf89_146556', 'GCOM1_145781', 'SEZ6L_23544', 'INPP5J_27124', 'EPB41L4B_54566', 'HEPACAM2_253012', 'COL23A1_91522', 'CCBE1_147372', 'ATP6V1G3_127124', 'KLK1_3816', 'CTNNA2_1496', 'KRTAP5_8_57830']\n", + "2025-11-26 01:13:57,974 - bioneuralnet.clustering.correlated_pagerank - INFO - PageRank computation completed.\n", + "2025-11-26 01:14:02,424 - bioneuralnet.clustering.correlated_pagerank - INFO - Sweep cut resulted in cluster of size 211 with conductance 0.185 and correlation -0.85.\n", + "2025-11-26 01:14:02,425 - bioneuralnet.clustering.correlated_pagerank - INFO - PageRank clustering completed successfully.\n", + "2025-11-26 01:14:02,425 - bioneuralnet.clustering.hybrid_louvain - INFO - Iteration 2: cluster size=211, Conductance=0.185 Correlation=-0.850 score=-0.333\n", + "2025-11-26 01:14:02,431 - bioneuralnet.clustering.hybrid_louvain - INFO - \n", + "Iteration 3/3: Running Correlated Louvain...\n", + "2025-11-26 01:14:02,435 - bioneuralnet.clustering.correlated_louvain - INFO - CorrelatedLouvain(k3=0.2, k4=0.8, nodes=211, edges=1490, features=900)\n", + "2025-11-26 01:14:02,436 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 01:14:02,437 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 01:14:02,437 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 01:14:02,438 - bioneuralnet.clustering.correlated_louvain - INFO - Running standard community detection...\n", + "2025-11-26 01:14:02,491 - bioneuralnet.clustering.correlated_louvain - INFO - Computed quality (supervised): Q = 0.5780, avg_corr = 0.5743, combined = 0.5750\n", + "2025-11-26 01:14:02,491 - bioneuralnet.clustering.correlated_louvain - INFO - Final quality: 0.5750\n", + "2025-11-26 01:14:02,492 - bioneuralnet.clustering.correlated_louvain - INFO - CorrelatedLouvain found 10 communities (nodes=211)\n", + "2025-11-26 01:14:02,527 - bioneuralnet.clustering.correlated_louvain - INFO - Computed quality (supervised): Q = 0.5780, avg_corr = 0.5743, combined = 0.5750\n", + "2025-11-26 01:14:02,527 - bioneuralnet.clustering.hybrid_louvain - INFO - Iteration 3: Louvain Quality = 0.5750\n", + "2025-11-26 01:14:02,561 - bioneuralnet.clustering.hybrid_louvain - INFO - Selected seed community of size 34 with correlation -0.8628\n", + "2025-11-26 01:14:02,561 - bioneuralnet.clustering.correlated_pagerank - INFO - Initialized PageRank with the following parameters:\n", + "2025-11-26 01:14:02,562 - bioneuralnet.clustering.correlated_pagerank - INFO - Graph: NetworkX Graph with 211 nodes and 1490 edges.\n", + "2025-11-26 01:14:02,562 - bioneuralnet.clustering.correlated_pagerank - INFO - Omics Data: DataFrame with shape (658, 900).\n", + "2025-11-26 01:14:02,563 - bioneuralnet.clustering.correlated_pagerank - INFO - Phenotype Data: Series with 658 samples.\n", + "2025-11-26 01:14:02,563 - bioneuralnet.clustering.correlated_pagerank - INFO - Alpha: 0.9\n", + "2025-11-26 01:14:02,563 - bioneuralnet.clustering.correlated_pagerank - INFO - Max Iterations: 100\n", + "2025-11-26 01:14:02,563 - bioneuralnet.clustering.correlated_pagerank - INFO - Tolerance: 1e-06\n", + "2025-11-26 01:14:02,564 - bioneuralnet.clustering.correlated_pagerank - INFO - K (Composite Score Weight): 0.5\n", + "2025-11-26 01:14:02,564 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 01:14:02,565 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 01:14:02,566 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 01:14:02,566 - bioneuralnet.clustering.correlated_pagerank - INFO - Initialized Correlated PageRank. device=cpu\n", + "2025-11-26 01:14:02,682 - bioneuralnet.clustering.correlated_pagerank - INFO - Generated personalization vector for seed nodes: ['CEND1_51286', 'ST8SIA5_29906', 'LRRTM1_347730', 'BDKRB1_623', 'LOC440354', 'EPGN', 'hsa_mir_222', 'C17orf55_284185', 'hsa_mir_874', 'ANGPTL1', 'IL18', 'CKMT2_1160', 'hsa_mir_618', 'C1orf168', 'HOXA7', 'KCNRG', 'hsa_mir_1258', 'SEZ6L_23544', 'hsa_mir_130a', 'hsa_mir_221', 'HULC', 'GALNT14_79623', 'CYS1_192668', 'MIR744', 'CNTNAP5_129684', 'MIR618', 'CHST9_83539', 'CTNNA2_1496', 'C14orf53', 'hsa_mir_33a', 'BICC1_80114', 'ABP1_26', 'hsa_mir_766', 'CR1L_1379']\n", + "2025-11-26 01:14:02,685 - bioneuralnet.clustering.correlated_pagerank - INFO - PageRank computation completed.\n", + "2025-11-26 01:14:04,038 - bioneuralnet.clustering.correlated_pagerank - INFO - Sweep cut resulted in cluster of size 202 with conductance 0.058 and correlation -0.85.\n", + "2025-11-26 01:14:04,038 - bioneuralnet.clustering.correlated_pagerank - INFO - PageRank clustering completed successfully.\n", + "2025-11-26 01:14:04,039 - bioneuralnet.clustering.hybrid_louvain - INFO - Iteration 3: cluster size=202, Conductance=0.058 Correlation=-0.850 score=-0.396\n", + "2025-11-26 01:14:04,044 - bioneuralnet.clustering.hybrid_louvain - INFO - Hybrid Louvain completed after 4 iterations.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Driver modules (one-vs-rest):\n", + " 1: 857 features\n", + " 0: 451 features\n", + " 2: 448 features\n" + ] + } + ], + "source": [ + "from bioneuralnet.clustering import HybridLouvain\n", + "\n", + "labels = target[\"target\"]\n", + "unique_phenotypes = labels.unique()\n", + "\n", + "multiclass_results = {}\n", + "summary = []\n", + "\n", + "for phenotype in unique_phenotypes:\n", + " # one-vs-rest encoding\n", + " y_binary = (labels == phenotype).astype(int)\n", + "\n", + " hl = HybridLouvain(\n", + " G=correlation_10,\n", + " B=omics_kipan,\n", + " Y=y_binary,\n", + " k3=0.2,\n", + " k4=0.8,\n", + " max_iter=3,\n", + " seed=SEED,\n", + " )\n", "\n", - "tgca_kipan = bnn.datasets.DatasetLoader(\"kipan\")\n", - "display(tgca_kipan.shape)\n", + " subnetworks = hl.run(as_dfs=True)\n", "\n", - "# The dataset is returned as a dictionary. We extract each file independetly based on the name (Key).\n", - "dna_meth = tgca_kipan.data[\"meth\"]\n", - "rna = tgca_kipan.data[\"rna\"]\n", - "mirna = tgca_kipan.data[\"mirna\"]\n", - "clinical = tgca_kipan.data[\"clinical\"]\n", - "target = tgca_kipan.data[\"target\"]" + " if subnetworks:\n", + " df = subnetworks[0]\n", + " multiclass_results[phenotype] = df\n", + " summary.append((phenotype, df.shape[1]))\n", + " else:\n", + " summary.append((phenotype, 0))\n", + "\n", + "print(\"\\nDriver modules (one-vs-rest):\")\n", + "for pheno, n_feats in summary:\n", + " print(f\" {pheno}: {n_feats} features\")" ] }, { "cell_type": "code", - "execution_count": null, - "id": "39d9b227", + "execution_count": 14, + "id": "efbff52f", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "2025-11-08 15:17:58,736 - bioneuralnet.utils.preprocess - INFO - [Inf]: Replaced 0 infinite values\n", - "2025-11-08 15:17:58,737 - bioneuralnet.utils.preprocess - INFO - [NaN]: Replaced 2306 NaNs after median imputation\n", - "2025-11-08 15:17:58,737 - bioneuralnet.utils.preprocess - INFO - [Zero-Var]: 0 columns dropped due to zero variance\n", - "2025-11-08 15:17:58,826 - bioneuralnet.utils.preprocess - INFO - Selected top 7 features by RandomForest importance\n" + "2025-11-26 01:14:04,056 - bioneuralnet.downstream_task.dpmon - INFO - Output directory set to: /home/vicente/Github/BioNeuralNet/dpmon_results_GCN_FINAL/kipan/threshold_10\n", + "2025-11-26 01:14:04,057 - bioneuralnet.downstream_task.dpmon - INFO - Initialized DPMON with model: GCN\n", + "2025-11-26 01:14:04,057 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 01:14:04,058 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 01:14:04,058 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 01:14:04,059 - bioneuralnet.downstream_task.dpmon - INFO - Random seed set to: 8183\n", + "2025-11-26 01:14:04,064 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:14:04,067 - bioneuralnet.downstream_task.dpmon - INFO - Running in Cross-Validation mode (cv=True) with 5 folds.\n", + "2025-11-26 01:14:04,068 - bioneuralnet.downstream_task.dpmon - INFO - CV Setup: Standard 5-fold split.\n", + "2025-11-26 01:14:04,069 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 1/5\n", + "2025-11-26 01:14:04,071 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:14:04,081 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:14:08,191 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:14:08,215 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (526, 901)\n", + "2025-11-26 01:14:08,218\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:14:20,851\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0286s.\n", + "2025-11-26 01:14:21,044 - bioneuralnet.downstream_task.dpmon - INFO - Fold 1 best config: {'gnn_layer_num': 2, 'gnn_hidden_dim': 64, 'lr': 0.0006037895979188083, 'weight_decay': 0.004195106610298857, 'nn_hidden_dim1': 512, 'nn_hidden_dim2': 128, 'ae_encoding_dim': 16, 'num_epochs': 1024, 'gnn_dropout': 0.1, 'gnn_activation': 'elu', 'dim_reduction': 'ae'}\n", + "2025-11-26 01:14:21,045 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 1 using train split only\n", + "2025-11-26 01:14:21,050 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:14:25,269 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:14:26,773 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 1 on test set\n", + "2025-11-26 01:14:26,778 - bioneuralnet.downstream_task.dpmon - INFO - Fold 1 results:\n", + "2025-11-26 01:14:26,778 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9394\n", + "2025-11-26 01:14:26,779 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9287\n", + "2025-11-26 01:14:26,779 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9399\n", + "2025-11-26 01:14:26,779 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9566\n", + "2025-11-26 01:14:26,779 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9908\n", + "2025-11-26 01:14:26,780 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9839\n", + "\n", + "2025-11-26 01:14:26,780 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 2/5\n", + "2025-11-26 01:14:26,782 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:14:26,788 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:14:30,936 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:14:30,947 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (526, 901)\n", + "2025-11-26 01:14:30,951\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:14:41,518\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0283s.\n", + "2025-11-26 01:14:41,641 - bioneuralnet.downstream_task.dpmon - INFO - Fold 2 best config: {'gnn_layer_num': 2, 'gnn_hidden_dim': 64, 'lr': 0.0007034204356990857, 'weight_decay': 5.358735876893429e-05, 'nn_hidden_dim1': 512, 'nn_hidden_dim2': 64, 'ae_encoding_dim': 8, 'num_epochs': 1024, 'gnn_dropout': 0.2, 'gnn_activation': 'relu', 'dim_reduction': 'ae'}\n", + "2025-11-26 01:14:41,642 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 2 using train split only\n", + "2025-11-26 01:14:41,647 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:14:45,770 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:14:47,251 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 2 on test set\n", + "2025-11-26 01:14:47,256 - bioneuralnet.downstream_task.dpmon - INFO - Fold 2 results:\n", + "2025-11-26 01:14:47,257 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9773\n", + "2025-11-26 01:14:47,257 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9648\n", + "2025-11-26 01:14:47,257 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9777\n", + "2025-11-26 01:14:47,257 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9835\n", + "2025-11-26 01:14:47,257 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9928\n", + "2025-11-26 01:14:47,258 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9488\n", + "\n", + "2025-11-26 01:14:47,258 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 3/5\n", + "2025-11-26 01:14:47,260 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:14:47,265 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:14:51,373 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:14:51,385 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (526, 901)\n", + "2025-11-26 01:14:51,388\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:15:03,121\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0079s.\n", + "2025-11-26 01:15:03,293 - bioneuralnet.downstream_task.dpmon - INFO - Fold 3 best config: {'gnn_layer_num': 1, 'gnn_hidden_dim': 256, 'lr': 0.0016239332393902155, 'weight_decay': 1.2898372523486065e-05, 'nn_hidden_dim1': 512, 'nn_hidden_dim2': 128, 'ae_encoding_dim': 4, 'num_epochs': 512, 'gnn_dropout': 0.2, 'gnn_activation': 'elu', 'dim_reduction': 'ae'}\n", + "2025-11-26 01:15:03,293 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 3 using train split only\n", + "2025-11-26 01:15:03,299 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:15:07,440 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:15:08,042 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 3 on test set\n", + "2025-11-26 01:15:08,046 - bioneuralnet.downstream_task.dpmon - INFO - Fold 3 results:\n", + "2025-11-26 01:15:08,047 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9621\n", + "2025-11-26 01:15:08,047 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9621\n", + "2025-11-26 01:15:08,048 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9622\n", + "2025-11-26 01:15:08,048 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9535\n", + "2025-11-26 01:15:08,048 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9955\n", + "2025-11-26 01:15:08,048 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9937\n", + "\n", + "2025-11-26 01:15:08,049 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 4/5\n", + "2025-11-26 01:15:08,051 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:15:08,058 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:15:12,200 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:15:12,211 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (527, 901)\n", + "2025-11-26 01:15:12,214\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:15:23,504\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0155s.\n", + "2025-11-26 01:15:23,665 - bioneuralnet.downstream_task.dpmon - INFO - Fold 4 best config: {'gnn_layer_num': 4, 'gnn_hidden_dim': 128, 'lr': 0.0008628455289617886, 'weight_decay': 0.005542594076906398, 'nn_hidden_dim1': 512, 'nn_hidden_dim2': 128, 'ae_encoding_dim': 16, 'num_epochs': 2048, 'gnn_dropout': 0.2, 'gnn_activation': 'elu', 'dim_reduction': 'linear'}\n", + "2025-11-26 01:15:23,665 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 4 using train split only\n", + "2025-11-26 01:15:23,670 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:15:27,775 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:15:32,476 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 4 on test set\n", + "2025-11-26 01:15:32,482 - bioneuralnet.downstream_task.dpmon - INFO - Fold 4 results:\n", + "2025-11-26 01:15:32,482 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9389\n", + "2025-11-26 01:15:32,482 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9449\n", + "2025-11-26 01:15:32,482 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9390\n", + "2025-11-26 01:15:32,482 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9554\n", + "2025-11-26 01:15:32,482 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9940\n", + "2025-11-26 01:15:32,482 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9919\n", + "\n", + "2025-11-26 01:15:32,483 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 5/5\n", + "2025-11-26 01:15:32,485 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:15:32,490 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:15:36,751 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:15:36,762 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (527, 901)\n", + "2025-11-26 01:15:36,765\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:15:49,425\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0172s.\n", + "2025-11-26 01:15:49,636 - bioneuralnet.downstream_task.dpmon - INFO - Fold 5 best config: {'gnn_layer_num': 2, 'gnn_hidden_dim': 64, 'lr': 0.001685812648195555, 'weight_decay': 0.0023773937538678585, 'nn_hidden_dim1': 256, 'nn_hidden_dim2': 128, 'ae_encoding_dim': 8, 'num_epochs': 512, 'gnn_dropout': 0.2, 'gnn_activation': 'relu', 'dim_reduction': 'linear'}\n", + "2025-11-26 01:15:49,637 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 5 using train split only\n", + "2025-11-26 01:15:49,643 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:15:53,783 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:15:54,563 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 5 on test set\n", + "2025-11-26 01:15:54,568 - bioneuralnet.downstream_task.dpmon - INFO - Fold 5 results:\n", + "2025-11-26 01:15:54,568 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9695\n", + "2025-11-26 01:15:54,569 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9673\n", + "2025-11-26 01:15:54,569 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9694\n", + "2025-11-26 01:15:54,569 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9763\n", + "2025-11-26 01:15:54,569 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9986\n", + "2025-11-26 01:15:54,569 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9967\n", + "\n", + "2025-11-26 01:15:54,571 - bioneuralnet.downstream_task.dpmon - INFO - Successfully saved global best CV model state to: /home/vicente/Github/BioNeuralNet/dpmon_results_GCN_FINAL/kipan/threshold_10/best_cv_model.pt\n", + "2025-11-26 01:15:54,572 - bioneuralnet.downstream_task.dpmon - INFO - Successfully saved global best CV model state to: /home/vicente/Github/BioNeuralNet/dpmon_results_GCN_FINAL/kipan/threshold_10/best_cv_model_embds.pt\n", + "2025-11-26 01:15:54,572 - bioneuralnet.downstream_task.dpmon - INFO - Cross-Validation Results:\n", + "\n", + "2025-11-26 01:15:54,572 - bioneuralnet.downstream_task.dpmon - INFO - Avg Accuracy: 0.9574 +/- 0.0175\n", + "2025-11-26 01:15:54,572 - bioneuralnet.downstream_task.dpmon - INFO - Avg F1 Macro: 0.9536 +/- 0.0165\n", + "2025-11-26 01:15:54,572 - bioneuralnet.downstream_task.dpmon - INFO - Avg F1 Weighted: 0.9576 +/- 0.0175\n", + "2025-11-26 01:15:54,572 - bioneuralnet.downstream_task.dpmon - INFO - Avg Recall: 0.9651 +/- 0.0138\n", + "2025-11-26 01:15:54,572 - bioneuralnet.downstream_task.dpmon - INFO - Avg AUC: 0.9943 +/- 0.0029\n", + "2025-11-26 01:15:54,573 - bioneuralnet.downstream_task.dpmon - INFO - Avg AUPR: 0.9830 +/- 0.0197\n", + "2025-11-26 01:15:54,573 - bioneuralnet.downstream_task.dpmon - INFO - DPMON run completed.\n", + "2025-11-26 01:15:54,573 - bioneuralnet.downstream_task.dpmon - INFO - Output directory set to: /home/vicente/Github/BioNeuralNet/dpmon_results_GCN_FINAL/kipan/correlation_10\n", + "2025-11-26 01:15:54,574 - bioneuralnet.downstream_task.dpmon - INFO - Initialized DPMON with model: GCN\n", + "2025-11-26 01:15:54,574 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 01:15:54,574 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 01:15:54,574 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 01:15:54,575 - bioneuralnet.downstream_task.dpmon - INFO - Random seed set to: 8183\n", + "2025-11-26 01:15:54,579 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:15:54,581 - bioneuralnet.downstream_task.dpmon - INFO - Running in Cross-Validation mode (cv=True) with 5 folds.\n", + "2025-11-26 01:15:54,581 - bioneuralnet.downstream_task.dpmon - INFO - CV Setup: Standard 5-fold split.\n", + "2025-11-26 01:15:54,582 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 1/5\n", + "2025-11-26 01:15:54,583 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:15:54,591 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:15:58,722 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:15:58,734 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (526, 901)\n", + "2025-11-26 01:15:58,737\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:16:10,921\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0197s.\n", + "2025-11-26 01:16:11,103 - bioneuralnet.downstream_task.dpmon - INFO - Fold 1 best config: {'gnn_layer_num': 4, 'gnn_hidden_dim': 256, 'lr': 0.001257123304842143, 'weight_decay': 0.0003745702077936848, 'nn_hidden_dim1': 128, 'nn_hidden_dim2': 128, 'ae_encoding_dim': 8, 'num_epochs': 1024, 'gnn_dropout': 0.4, 'gnn_activation': 'relu', 'dim_reduction': 'ae'}\n", + "2025-11-26 01:16:11,104 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 1 using train split only\n", + "2025-11-26 01:16:11,109 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:16:15,261 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:16:18,073 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 1 on test set\n", + "2025-11-26 01:16:18,078 - bioneuralnet.downstream_task.dpmon - INFO - Fold 1 results:\n", + "2025-11-26 01:16:18,079 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9470\n", + "2025-11-26 01:16:18,079 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9273\n", + "2025-11-26 01:16:18,079 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9481\n", + "2025-11-26 01:16:18,079 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9627\n", + "2025-11-26 01:16:18,079 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9984\n", + "2025-11-26 01:16:18,080 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9982\n", + "\n", + "2025-11-26 01:16:18,081 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 2/5\n", + "2025-11-26 01:16:18,082 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:16:18,088 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:16:22,220 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:16:22,231 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (526, 901)\n", + "2025-11-26 01:16:22,234\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:16:34,076\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0191s.\n", + "2025-11-26 01:16:34,229 - bioneuralnet.downstream_task.dpmon - INFO - Fold 2 best config: {'gnn_layer_num': 4, 'gnn_hidden_dim': 64, 'lr': 0.00024165542558361394, 'weight_decay': 0.0002652189086272745, 'nn_hidden_dim1': 256, 'nn_hidden_dim2': 128, 'ae_encoding_dim': 8, 'num_epochs': 512, 'gnn_dropout': 0.1, 'gnn_activation': 'relu', 'dim_reduction': 'mlp'}\n", + "2025-11-26 01:16:34,229 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 2 using train split only\n", + "2025-11-26 01:16:34,235 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:16:38,349 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:16:39,516 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 2 on test set\n", + "2025-11-26 01:16:39,521 - bioneuralnet.downstream_task.dpmon - INFO - Fold 2 results:\n", + "2025-11-26 01:16:39,521 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9697\n", + "2025-11-26 01:16:39,521 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9487\n", + "2025-11-26 01:16:39,521 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9701\n", + "2025-11-26 01:16:39,522 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9579\n", + "2025-11-26 01:16:39,522 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9917\n", + "2025-11-26 01:16:39,522 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9461\n", + "\n", + "2025-11-26 01:16:39,522 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 3/5\n", + "2025-11-26 01:16:39,523 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:16:39,529 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:16:43,626 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:16:43,637 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (526, 901)\n", + "2025-11-26 01:16:43,639\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:16:54,182\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0281s.\n", + "2025-11-26 01:16:54,337 - bioneuralnet.downstream_task.dpmon - INFO - Fold 3 best config: {'gnn_layer_num': 1, 'gnn_hidden_dim': 128, 'lr': 0.002302454339377372, 'weight_decay': 1.3521341174248578e-05, 'nn_hidden_dim1': 512, 'nn_hidden_dim2': 128, 'ae_encoding_dim': 4, 'num_epochs': 2048, 'gnn_dropout': 0.3, 'gnn_activation': 'relu', 'dim_reduction': 'ae'}\n", + "2025-11-26 01:16:54,338 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 3 using train split only\n", + "2025-11-26 01:16:54,343 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:16:58,476 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:17:00,722 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 3 on test set\n", + "2025-11-26 01:17:00,727 - bioneuralnet.downstream_task.dpmon - INFO - Fold 3 results:\n", + "2025-11-26 01:17:00,727 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9621\n", + "2025-11-26 01:17:00,727 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9621\n", + "2025-11-26 01:17:00,727 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9622\n", + "2025-11-26 01:17:00,727 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9535\n", + "2025-11-26 01:17:00,728 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9975\n", + "2025-11-26 01:17:00,728 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9970\n", + "\n", + "2025-11-26 01:17:00,728 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 4/5\n", + "2025-11-26 01:17:00,730 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:17:00,737 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:17:04,871 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:17:04,883 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (527, 901)\n", + "2025-11-26 01:17:04,886\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:17:15,818\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0171s.\n", + "2025-11-26 01:17:15,993 - bioneuralnet.downstream_task.dpmon - INFO - Fold 4 best config: {'gnn_layer_num': 1, 'gnn_hidden_dim': 128, 'lr': 0.0019123736530165902, 'weight_decay': 0.0006168499043610002, 'nn_hidden_dim1': 256, 'nn_hidden_dim2': 128, 'ae_encoding_dim': 8, 'num_epochs': 1024, 'gnn_dropout': 0.4, 'gnn_activation': 'relu', 'dim_reduction': 'mlp'}\n", + "2025-11-26 01:17:15,994 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 4 using train split only\n", + "2025-11-26 01:17:15,999 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:17:20,122 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:17:21,350 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 4 on test set\n", + "2025-11-26 01:17:21,355 - bioneuralnet.downstream_task.dpmon - INFO - Fold 4 results:\n", + "2025-11-26 01:17:21,355 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9389\n", + "2025-11-26 01:17:21,355 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9449\n", + "2025-11-26 01:17:21,355 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9390\n", + "2025-11-26 01:17:21,355 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9554\n", + "2025-11-26 01:17:21,355 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9962\n", + "2025-11-26 01:17:21,356 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9940\n", + "\n", + "2025-11-26 01:17:21,356 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 5/5\n", + "2025-11-26 01:17:21,357 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:17:21,363 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:17:25,520 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:17:25,531 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (527, 901)\n", + "2025-11-26 01:17:25,534\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:17:38,250\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0174s.\n", + "2025-11-26 01:17:38,447 - bioneuralnet.downstream_task.dpmon - INFO - Fold 5 best config: {'gnn_layer_num': 2, 'gnn_hidden_dim': 64, 'lr': 0.001685812648195555, 'weight_decay': 0.0023773937538678585, 'nn_hidden_dim1': 256, 'nn_hidden_dim2': 128, 'ae_encoding_dim': 8, 'num_epochs': 512, 'gnn_dropout': 0.2, 'gnn_activation': 'relu', 'dim_reduction': 'linear'}\n", + "2025-11-26 01:17:38,447 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 5 using train split only\n", + "2025-11-26 01:17:38,453 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:17:42,581 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:17:43,369 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 5 on test set\n", + "2025-11-26 01:17:43,374 - bioneuralnet.downstream_task.dpmon - INFO - Fold 5 results:\n", + "2025-11-26 01:17:43,374 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9695\n", + "2025-11-26 01:17:43,374 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9572\n", + "2025-11-26 01:17:43,374 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9694\n", + "2025-11-26 01:17:43,375 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9558\n", + "2025-11-26 01:17:43,375 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9986\n", + "2025-11-26 01:17:43,375 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9967\n", + "\n", + "2025-11-26 01:17:43,378 - bioneuralnet.downstream_task.dpmon - INFO - Successfully saved global best CV model state to: /home/vicente/Github/BioNeuralNet/dpmon_results_GCN_FINAL/kipan/correlation_10/best_cv_model.pt\n", + "2025-11-26 01:17:43,378 - bioneuralnet.downstream_task.dpmon - INFO - Successfully saved global best CV model state to: /home/vicente/Github/BioNeuralNet/dpmon_results_GCN_FINAL/kipan/correlation_10/best_cv_model_embds.pt\n", + "2025-11-26 01:17:43,378 - bioneuralnet.downstream_task.dpmon - INFO - Cross-Validation Results:\n", + "\n", + "2025-11-26 01:17:43,378 - bioneuralnet.downstream_task.dpmon - INFO - Avg Accuracy: 0.9574 +/- 0.0139\n", + "2025-11-26 01:17:43,378 - bioneuralnet.downstream_task.dpmon - INFO - Avg F1 Macro: 0.9480 +/- 0.0134\n", + "2025-11-26 01:17:43,378 - bioneuralnet.downstream_task.dpmon - INFO - Avg F1 Weighted: 0.9578 +/- 0.0137\n", + "2025-11-26 01:17:43,379 - bioneuralnet.downstream_task.dpmon - INFO - Avg Recall: 0.9571 +/- 0.0035\n", + "2025-11-26 01:17:43,379 - bioneuralnet.downstream_task.dpmon - INFO - Avg AUC: 0.9965 +/- 0.0028\n", + "2025-11-26 01:17:43,379 - bioneuralnet.downstream_task.dpmon - INFO - Avg AUPR: 0.9864 +/- 0.0226\n", + "2025-11-26 01:17:43,379 - bioneuralnet.downstream_task.dpmon - INFO - DPMON run completed.\n", + "2025-11-26 01:17:43,380 - bioneuralnet.downstream_task.dpmon - INFO - Output directory set to: /home/vicente/Github/BioNeuralNet/dpmon_results_GCN_FINAL/kipan/similarity_10\n", + "2025-11-26 01:17:43,380 - bioneuralnet.downstream_task.dpmon - INFO - Initialized DPMON with model: GCN\n", + "2025-11-26 01:17:43,380 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 01:17:43,380 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 01:17:43,381 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 01:17:43,381 - bioneuralnet.downstream_task.dpmon - INFO - Random seed set to: 8183\n", + "2025-11-26 01:17:43,386 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:17:43,387 - bioneuralnet.downstream_task.dpmon - INFO - Running in Cross-Validation mode (cv=True) with 5 folds.\n", + "2025-11-26 01:17:43,388 - bioneuralnet.downstream_task.dpmon - INFO - CV Setup: Standard 5-fold split.\n", + "2025-11-26 01:17:43,388 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 1/5\n", + "2025-11-26 01:17:43,390 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:17:43,397 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:17:47,498 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:17:47,510 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (526, 901)\n", + "2025-11-26 01:17:47,513\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:18:00,506\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0235s.\n", + "2025-11-26 01:18:00,689 - bioneuralnet.downstream_task.dpmon - INFO - Fold 1 best config: {'gnn_layer_num': 2, 'gnn_hidden_dim': 64, 'lr': 0.0006037895979188083, 'weight_decay': 0.004195106610298857, 'nn_hidden_dim1': 512, 'nn_hidden_dim2': 128, 'ae_encoding_dim': 16, 'num_epochs': 1024, 'gnn_dropout': 0.1, 'gnn_activation': 'elu', 'dim_reduction': 'ae'}\n", + "2025-11-26 01:18:00,690 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 1 using train split only\n", + "2025-11-26 01:18:00,695 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:18:04,943 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:18:06,393 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 1 on test set\n", + "2025-11-26 01:18:06,398 - bioneuralnet.downstream_task.dpmon - INFO - Fold 1 results:\n", + "2025-11-26 01:18:06,398 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9621\n", + "2025-11-26 01:18:06,399 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9458\n", + "2025-11-26 01:18:06,399 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9629\n", + "2025-11-26 01:18:06,399 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9731\n", + "2025-11-26 01:18:06,399 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9831\n", + "2025-11-26 01:18:06,399 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9689\n", + "\n", + "2025-11-26 01:18:06,399 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 2/5\n", + "2025-11-26 01:18:06,401 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:18:06,406 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:18:10,545 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:18:10,557 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (526, 901)\n", + "2025-11-26 01:18:10,560\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:18:21,270\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0070s.\n", + "2025-11-26 01:18:21,414 - bioneuralnet.downstream_task.dpmon - INFO - Fold 2 best config: {'gnn_layer_num': 2, 'gnn_hidden_dim': 64, 'lr': 0.0007034204356990857, 'weight_decay': 5.358735876893429e-05, 'nn_hidden_dim1': 512, 'nn_hidden_dim2': 64, 'ae_encoding_dim': 8, 'num_epochs': 1024, 'gnn_dropout': 0.2, 'gnn_activation': 'relu', 'dim_reduction': 'ae'}\n", + "2025-11-26 01:18:21,415 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 2 using train split only\n", + "2025-11-26 01:18:21,420 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:18:25,548 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:18:27,026 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 2 on test set\n", + "2025-11-26 01:18:27,031 - bioneuralnet.downstream_task.dpmon - INFO - Fold 2 results:\n", + "2025-11-26 01:18:27,032 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9773\n", + "2025-11-26 01:18:27,032 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9648\n", + "2025-11-26 01:18:27,032 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9777\n", + "2025-11-26 01:18:27,032 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9835\n", + "2025-11-26 01:18:27,032 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9926\n", + "2025-11-26 01:18:27,032 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9360\n", + "\n", + "2025-11-26 01:18:27,033 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 3/5\n", + "2025-11-26 01:18:27,034 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:18:27,039 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:18:31,171 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:18:31,183 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (526, 901)\n", + "2025-11-26 01:18:31,186\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:18:41,965\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0160s.\n", + "2025-11-26 01:18:42,128 - bioneuralnet.downstream_task.dpmon - INFO - Fold 3 best config: {'gnn_layer_num': 3, 'gnn_hidden_dim': 128, 'lr': 0.0006980653261883734, 'weight_decay': 6.059927981082565e-05, 'nn_hidden_dim1': 256, 'nn_hidden_dim2': 128, 'ae_encoding_dim': 8, 'num_epochs': 2048, 'gnn_dropout': 0.0, 'gnn_activation': 'elu', 'dim_reduction': 'ae'}\n", + "2025-11-26 01:18:42,129 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 3 using train split only\n", + "2025-11-26 01:18:42,134 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:18:46,248 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:18:49,771 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 3 on test set\n", + "2025-11-26 01:18:49,776 - bioneuralnet.downstream_task.dpmon - INFO - Fold 3 results:\n", + "2025-11-26 01:18:49,777 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9470\n", + "2025-11-26 01:18:49,777 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9318\n", + "2025-11-26 01:18:49,777 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9468\n", + "2025-11-26 01:18:49,778 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9244\n", + "2025-11-26 01:18:49,778 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9966\n", + "2025-11-26 01:18:49,778 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9917\n", + "\n", + "2025-11-26 01:18:49,779 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 4/5\n", + "2025-11-26 01:18:49,781 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:18:49,788 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:18:53,915 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:18:53,926 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (527, 901)\n", + "2025-11-26 01:18:53,929\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:19:05,812\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0167s.\n", + "2025-11-26 01:19:05,972 - bioneuralnet.downstream_task.dpmon - INFO - Fold 4 best config: {'gnn_layer_num': 3, 'gnn_hidden_dim': 64, 'lr': 0.0013470053780947797, 'weight_decay': 0.00030758808386390933, 'nn_hidden_dim1': 256, 'nn_hidden_dim2': 128, 'ae_encoding_dim': 16, 'num_epochs': 1024, 'gnn_dropout': 0.4, 'gnn_activation': 'relu', 'dim_reduction': 'ae'}\n", + "2025-11-26 01:19:05,973 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 4 using train split only\n", + "2025-11-26 01:19:05,978 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:19:10,184 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:19:12,014 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 4 on test set\n", + "2025-11-26 01:19:12,019 - bioneuralnet.downstream_task.dpmon - INFO - Fold 4 results:\n", + "2025-11-26 01:19:12,019 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9389\n", + "2025-11-26 01:19:12,019 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9449\n", + "2025-11-26 01:19:12,019 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9390\n", + "2025-11-26 01:19:12,019 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9554\n", + "2025-11-26 01:19:12,020 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9963\n", + "2025-11-26 01:19:12,020 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9941\n", + "\n", + "2025-11-26 01:19:12,020 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 5/5\n", + "2025-11-26 01:19:12,021 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:19:12,027 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:19:16,138 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:19:16,150 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (527, 901)\n", + "2025-11-26 01:19:16,153\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:19:28,943\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0206s.\n", + "2025-11-26 01:19:29,171 - bioneuralnet.downstream_task.dpmon - INFO - Fold 5 best config: {'gnn_layer_num': 4, 'gnn_hidden_dim': 64, 'lr': 0.0007025528058103543, 'weight_decay': 0.0018355753249756474, 'nn_hidden_dim1': 512, 'nn_hidden_dim2': 128, 'ae_encoding_dim': 4, 'num_epochs': 1024, 'gnn_dropout': 0.0, 'gnn_activation': 'relu', 'dim_reduction': 'mlp'}\n", + "2025-11-26 01:19:29,172 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 5 using train split only\n", + "2025-11-26 01:19:29,177 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:19:33,346 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:19:35,544 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 5 on test set\n", + "2025-11-26 01:19:35,549 - bioneuralnet.downstream_task.dpmon - INFO - Fold 5 results:\n", + "2025-11-26 01:19:35,549 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9313\n", + "2025-11-26 01:19:35,550 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9080\n", + "2025-11-26 01:19:35,550 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9331\n", + "2025-11-26 01:19:35,550 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9473\n", + "2025-11-26 01:19:35,550 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9938\n", + "2025-11-26 01:19:35,550 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9915\n", + "\n", + "2025-11-26 01:19:35,553 - bioneuralnet.downstream_task.dpmon - INFO - Successfully saved global best CV model state to: /home/vicente/Github/BioNeuralNet/dpmon_results_GCN_FINAL/kipan/similarity_10/best_cv_model.pt\n", + "2025-11-26 01:19:35,553 - bioneuralnet.downstream_task.dpmon - INFO - Successfully saved global best CV model state to: /home/vicente/Github/BioNeuralNet/dpmon_results_GCN_FINAL/kipan/similarity_10/best_cv_model_embds.pt\n", + "2025-11-26 01:19:35,553 - bioneuralnet.downstream_task.dpmon - INFO - Cross-Validation Results:\n", + "\n", + "2025-11-26 01:19:35,553 - bioneuralnet.downstream_task.dpmon - INFO - Avg Accuracy: 0.9513 +/- 0.0185\n", + "2025-11-26 01:19:35,553 - bioneuralnet.downstream_task.dpmon - INFO - Avg F1 Macro: 0.9390 +/- 0.0210\n", + "2025-11-26 01:19:35,554 - bioneuralnet.downstream_task.dpmon - INFO - Avg F1 Weighted: 0.9519 +/- 0.0183\n", + "2025-11-26 01:19:35,554 - bioneuralnet.downstream_task.dpmon - INFO - Avg Recall: 0.9567 +/- 0.0230\n", + "2025-11-26 01:19:35,554 - bioneuralnet.downstream_task.dpmon - INFO - Avg AUC: 0.9925 +/- 0.0055\n", + "2025-11-26 01:19:35,554 - bioneuralnet.downstream_task.dpmon - INFO - Avg AUPR: 0.9764 +/- 0.0248\n", + "2025-11-26 01:19:35,555 - bioneuralnet.downstream_task.dpmon - INFO - DPMON run completed.\n", + "2025-11-26 01:19:35,555 - bioneuralnet.downstream_task.dpmon - INFO - Output directory set to: /home/vicente/Github/BioNeuralNet/dpmon_results_GCN_FINAL/kipan/gaussian_15\n", + "2025-11-26 01:19:35,555 - bioneuralnet.downstream_task.dpmon - INFO - Initialized DPMON with model: GCN\n", + "2025-11-26 01:19:35,555 - bioneuralnet.utils.reproducibility - INFO - Setting global seed for reproducibility to: 8183\n", + "2025-11-26 01:19:35,556 - bioneuralnet.utils.reproducibility - INFO - CUDA available. Applying seed to all GPU operations\n", + "2025-11-26 01:19:35,556 - bioneuralnet.utils.reproducibility - INFO - Seed setting complete\n", + "2025-11-26 01:19:35,556 - bioneuralnet.downstream_task.dpmon - INFO - Random seed set to: 8183\n", + "2025-11-26 01:19:35,561 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:19:35,562 - bioneuralnet.downstream_task.dpmon - INFO - Running in Cross-Validation mode (cv=True) with 5 folds.\n", + "2025-11-26 01:19:35,563 - bioneuralnet.downstream_task.dpmon - INFO - CV Setup: Standard 5-fold split.\n", + "2025-11-26 01:19:35,564 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 1/5\n", + "2025-11-26 01:19:35,566 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:19:35,573 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:19:39,710 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:19:39,725 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (526, 901)\n", + "2025-11-26 01:19:39,728\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:19:52,665\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0205s.\n", + "2025-11-26 01:19:52,846 - bioneuralnet.downstream_task.dpmon - INFO - Fold 1 best config: {'gnn_layer_num': 3, 'gnn_hidden_dim': 128, 'lr': 0.002995945702914729, 'weight_decay': 7.542726756559785e-05, 'nn_hidden_dim1': 256, 'nn_hidden_dim2': 32, 'ae_encoding_dim': 4, 'num_epochs': 2048, 'gnn_dropout': 0.0, 'gnn_activation': 'elu', 'dim_reduction': 'mlp'}\n", + "2025-11-26 01:19:52,847 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 1 using train split only\n", + "2025-11-26 01:19:52,851 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:19:56,959 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:20:00,808 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 1 on test set\n", + "2025-11-26 01:20:00,813 - bioneuralnet.downstream_task.dpmon - INFO - Fold 1 results:\n", + "2025-11-26 01:20:00,813 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9621\n", + "2025-11-26 01:20:00,814 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9538\n", + "2025-11-26 01:20:00,814 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9622\n", + "2025-11-26 01:20:00,814 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9740\n", + "2025-11-26 01:20:00,814 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9945\n", + "2025-11-26 01:20:00,814 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9854\n", + "\n", + "2025-11-26 01:20:00,815 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 2/5\n", + "2025-11-26 01:20:00,817 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:20:00,822 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:20:04,918 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:20:04,932 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (526, 901)\n", + "2025-11-26 01:20:04,935\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:20:15,891\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0178s.\n", + "2025-11-26 01:20:16,037 - bioneuralnet.downstream_task.dpmon - INFO - Fold 2 best config: {'gnn_layer_num': 4, 'gnn_hidden_dim': 64, 'lr': 0.00024165542558361394, 'weight_decay': 0.0002652189086272745, 'nn_hidden_dim1': 256, 'nn_hidden_dim2': 128, 'ae_encoding_dim': 8, 'num_epochs': 512, 'gnn_dropout': 0.1, 'gnn_activation': 'relu', 'dim_reduction': 'mlp'}\n", + "2025-11-26 01:20:16,037 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 2 using train split only\n", + "2025-11-26 01:20:16,042 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:20:20,158 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:20:21,370 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 2 on test set\n", + "2025-11-26 01:20:21,375 - bioneuralnet.downstream_task.dpmon - INFO - Fold 2 results:\n", + "2025-11-26 01:20:21,376 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9697\n", + "2025-11-26 01:20:21,376 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9487\n", + "2025-11-26 01:20:21,376 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9701\n", + "2025-11-26 01:20:21,376 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9579\n", + "2025-11-26 01:20:21,376 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9898\n", + "2025-11-26 01:20:21,377 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9414\n", + "\n", + "2025-11-26 01:20:21,377 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 3/5\n", + "2025-11-26 01:20:21,379 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:20:21,384 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:20:25,595 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:20:25,609 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (526, 901)\n", + "2025-11-26 01:20:25,612\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:20:36,485\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0081s.\n", + "2025-11-26 01:20:36,637 - bioneuralnet.downstream_task.dpmon - INFO - Fold 3 best config: {'gnn_layer_num': 3, 'gnn_hidden_dim': 128, 'lr': 0.0006980653261883734, 'weight_decay': 6.059927981082565e-05, 'nn_hidden_dim1': 256, 'nn_hidden_dim2': 128, 'ae_encoding_dim': 8, 'num_epochs': 2048, 'gnn_dropout': 0.0, 'gnn_activation': 'elu', 'dim_reduction': 'ae'}\n", + "2025-11-26 01:20:36,637 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 3 using train split only\n", + "2025-11-26 01:20:36,642 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:20:40,760 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:20:44,494 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 3 on test set\n", + "2025-11-26 01:20:44,499 - bioneuralnet.downstream_task.dpmon - INFO - Fold 3 results:\n", + "2025-11-26 01:20:44,499 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9470\n", + "2025-11-26 01:20:44,499 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9318\n", + "2025-11-26 01:20:44,499 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9468\n", + "2025-11-26 01:20:44,500 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9244\n", + "2025-11-26 01:20:44,500 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9970\n", + "2025-11-26 01:20:44,500 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9936\n", + "\n", + "2025-11-26 01:20:44,500 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 4/5\n", + "2025-11-26 01:20:44,502 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:20:44,509 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:20:48,666 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:20:48,680 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (527, 901)\n", + "2025-11-26 01:20:48,683\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:20:59,905\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0143s.\n", + "2025-11-26 01:21:00,060 - bioneuralnet.downstream_task.dpmon - INFO - Fold 4 best config: {'gnn_layer_num': 4, 'gnn_hidden_dim': 128, 'lr': 0.0008628455289617886, 'weight_decay': 0.005542594076906398, 'nn_hidden_dim1': 512, 'nn_hidden_dim2': 128, 'ae_encoding_dim': 16, 'num_epochs': 2048, 'gnn_dropout': 0.2, 'gnn_activation': 'elu', 'dim_reduction': 'linear'}\n", + "2025-11-26 01:21:00,061 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 4 using train split only\n", + "2025-11-26 01:21:00,066 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:21:04,216 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:21:09,152 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 4 on test set\n", + "2025-11-26 01:21:09,157 - bioneuralnet.downstream_task.dpmon - INFO - Fold 4 results:\n", + "2025-11-26 01:21:09,157 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9542\n", + "2025-11-26 01:21:09,157 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9398\n", + "2025-11-26 01:21:09,157 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9549\n", + "2025-11-26 01:21:09,157 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9659\n", + "2025-11-26 01:21:09,158 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9964\n", + "2025-11-26 01:21:09,158 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9957\n", + "\n", + "2025-11-26 01:21:09,158 - bioneuralnet.downstream_task.dpmon - INFO - Starting Fold 5/5\n", + "2025-11-26 01:21:09,160 - bioneuralnet.downstream_task.dpmon - INFO - Using GPU 0\n", + "2025-11-26 01:21:09,166 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:21:13,268 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:21:13,282 - bioneuralnet.downstream_task.dpmon - INFO - Starting hyperparameter tuning for dataset shape: (527, 901)\n", + "2025-11-26 01:21:13,285\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", + "2025-11-26 01:21:25,473\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/home/vicente/ray_results/tune_dp' in 0.0135s.\n", + "2025-11-26 01:21:25,665 - bioneuralnet.downstream_task.dpmon - INFO - Fold 5 best config: {'gnn_layer_num': 4, 'gnn_hidden_dim': 64, 'lr': 0.0007025528058103543, 'weight_decay': 0.0018355753249756474, 'nn_hidden_dim1': 512, 'nn_hidden_dim2': 128, 'ae_encoding_dim': 4, 'num_epochs': 1024, 'gnn_dropout': 0.0, 'gnn_activation': 'relu', 'dim_reduction': 'mlp'}\n", + "2025-11-26 01:21:25,665 - bioneuralnet.downstream_task.dpmon - INFO - Building graph features for Fold 5 using train split only\n", + "2025-11-26 01:21:25,671 - bioneuralnet.downstream_task.dpmon - INFO - Building graph with 900 common features.\n", + "2025-11-26 01:21:29,789 - bioneuralnet.downstream_task.dpmon - INFO - Built feature matrix with clinical correlations shape: (900, 7)\n", + "2025-11-26 01:21:32,020 - bioneuralnet.downstream_task.dpmon - INFO - Evaluating model for Fold 5 on test set\n", + "2025-11-26 01:21:32,026 - bioneuralnet.downstream_task.dpmon - INFO - Fold 5 results:\n", + "2025-11-26 01:21:32,026 - bioneuralnet.downstream_task.dpmon - INFO - Accuracy: 0.9618\n", + "2025-11-26 01:21:32,026 - bioneuralnet.downstream_task.dpmon - INFO - F1 Macro: 0.9432\n", + "2025-11-26 01:21:32,027 - bioneuralnet.downstream_task.dpmon - INFO - F1 Weighted: 0.9622\n", + "2025-11-26 01:21:32,027 - bioneuralnet.downstream_task.dpmon - INFO - Recall: 0.9506\n", + "2025-11-26 01:21:32,027 - bioneuralnet.downstream_task.dpmon - INFO - AUC: 0.9970\n", + "2025-11-26 01:21:32,027 - bioneuralnet.downstream_task.dpmon - INFO - AUPR: 0.9899\n", + "\n", + "2025-11-26 01:21:32,029 - bioneuralnet.downstream_task.dpmon - INFO - Successfully saved global best CV model state to: /home/vicente/Github/BioNeuralNet/dpmon_results_GCN_FINAL/kipan/gaussian_15/best_cv_model.pt\n", + "2025-11-26 01:21:32,030 - bioneuralnet.downstream_task.dpmon - INFO - Successfully saved global best CV model state to: /home/vicente/Github/BioNeuralNet/dpmon_results_GCN_FINAL/kipan/gaussian_15/best_cv_model_embds.pt\n", + "2025-11-26 01:21:32,030 - bioneuralnet.downstream_task.dpmon - INFO - Cross-Validation Results:\n", + "\n", + "2025-11-26 01:21:32,030 - bioneuralnet.downstream_task.dpmon - INFO - Avg Accuracy: 0.9590 +/- 0.0087\n", + "2025-11-26 01:21:32,030 - bioneuralnet.downstream_task.dpmon - INFO - Avg F1 Macro: 0.9434 +/- 0.0084\n", + "2025-11-26 01:21:32,030 - bioneuralnet.downstream_task.dpmon - INFO - Avg F1 Weighted: 0.9592 +/- 0.0088\n", + "2025-11-26 01:21:32,030 - bioneuralnet.downstream_task.dpmon - INFO - Avg Recall: 0.9546 +/- 0.0190\n", + "2025-11-26 01:21:32,031 - bioneuralnet.downstream_task.dpmon - INFO - Avg AUC: 0.9949 +/- 0.0030\n", + "2025-11-26 01:21:32,031 - bioneuralnet.downstream_task.dpmon - INFO - Avg AUPR: 0.9812 +/- 0.0226\n", + "2025-11-26 01:21:32,031 - bioneuralnet.downstream_task.dpmon - INFO - DPMON run completed.\n" ] }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Results for: threshold_10\n", + "Accuracy (Avg +/- Std): 0.9574 +/- 0.0175\n", + "F1 Macro (Avg +/- Std): 0.9536 +/- 0.0165\n", + "F1 Weighted (Avg +/- Std): 0.9576 +/- 0.0175\n", + "Recall: 0.9651 +/- 0.0138\n", + "AUC: 0.9943 +/- 0.0029\n", + "\n", + "Results for: correlation_10\n", + "Accuracy (Avg +/- Std): 0.9574 +/- 0.0139\n", + "F1 Macro (Avg +/- Std): 0.9480 +/- 0.0134\n", + "F1 Weighted (Avg +/- Std): 0.9578 +/- 0.0137\n", + "Recall: 0.9571 +/- 0.0035\n", + "AUC: 0.9965 +/- 0.0028\n", + "\n", + "Results for: similarity_10\n", + "Accuracy (Avg +/- Std): 0.9513 +/- 0.0185\n", + "F1 Macro (Avg +/- Std): 0.9390 +/- 0.0210\n", + "F1 Weighted (Avg +/- Std): 0.9519 +/- 0.0183\n", + "Recall: 0.9567 +/- 0.0230\n", + "AUC: 0.9925 +/- 0.0055\n", + "\n", + "Results for: gaussian_15\n", + "Accuracy (Avg +/- Std): 0.9590 +/- 0.0087\n", + "F1 Macro (Avg +/- Std): 0.9434 +/- 0.0084\n", + "F1 Weighted (Avg +/- Std): 0.9592 +/- 0.0088\n", + "Recall: 0.9546 +/- 0.0190\n", + "AUC: 0.9949 +/- 0.0030\n" + ] + } + ], + "source": [ + "output_dir_base = Path(\"/home/vicente/Github/BioNeuralNet/dpmon_results_GCN_FINAL/kipan\")\n", + "\n", + "all_results_GCN = []\n", + "\n", + "for run_config in comparison_runs:\n", + " graph_name = run_config[\"name\"]\n", + " A_full = run_config[\"graph\"]\n", + "\n", + " current_output_dir = output_dir_base / graph_name\n", + " current_output_dir.mkdir(parents=True, exist_ok=True)\n", + "\n", + " dpmon_params_base = {\n", + " \"adjacency_matrix\": A_full,\n", + " \"omics_list\": omics_kipan,\n", + " \"phenotype_data\": target,\n", + " \"phenotype_col\": \"target\",\n", + " \"clinical_data\": clinical_for_model,\n", + " \"model\": 'GCN',\n", + " \"tune\": True, \n", + " \"cv\": True, \n", + " \"n_folds\": 5,\n", + " \"repeat_num\": 1,\n", + " \"gpu\": True,\n", + " \"cuda\": 0,\n", + " \"seed\": SEED,\n", + " \"output_dir\": current_output_dir\n", + " }\n", + " \n", + " dpmon_tunned = DPMON(**dpmon_params_base)\n", + " predictions_df, metrics, embeddings = dpmon_tunned.run()\n", + "\n", + " all_results_GCN.append({\n", + " \"graph\": graph_name,\n", + " \"predictions\": predictions_df,\n", + " \"metrics\": metrics,\n", + " \"embeddings\": embeddings\n", + " })\n", + "\n", + "for res in all_results_GCN:\n", + " graph_name = res[\"graph\"]\n", + " graph_metrics = res[\"metrics\"]\n", + " \n", + " acc_row = graph_metrics.loc[graph_metrics['Metric'] == 'Accuracy'].iloc[0]\n", + " f1_macro_row = graph_metrics.loc[graph_metrics['Metric'] == 'F1 Macro'].iloc[0]\n", + " f1_weighted_row = graph_metrics.loc[graph_metrics['Metric'] == 'F1 Weighted'].iloc[0]\n", + " recall_row = graph_metrics.loc[graph_metrics['Metric'] == 'Recall'].iloc[0]\n", + " auc_row = graph_metrics.loc[graph_metrics['Metric'] == 'AUC'].iloc[0]\n", + " aupr_row = graph_metrics.loc[graph_metrics['Metric'] == 'AUPR'].iloc[0]\n", + " \n", + " acc_avg, acc_std = acc_row['Average'], acc_row['StdDev']\n", + " f1_macro_avg, f1_macro_std = f1_macro_row['Average'], f1_macro_row['StdDev']\n", + " f1_weighted_avg, f1_weighted_std = f1_weighted_row['Average'], f1_weighted_row['StdDev']\n", + " recall_avg, recall_std = recall_row['Average'], recall_row['StdDev']\n", + " auc_avg, auc_std = auc_row['Average'], auc_row['StdDev']\n", + " aupr_avg, aupr_std = aupr_row['Average'], aupr_row['StdDev']\n", + "\n", + " print(f\"\\nResults for: {graph_name}\")\n", + " print(f\"Accuracy (Avg +/- Std): {acc_avg:.4f} +/- {acc_std:.4f}\")\n", + " print(f\"F1 Macro (Avg +/- Std): {f1_macro_avg:.4f} +/- {f1_macro_std:.4f}\")\n", + " print(f\"F1 Weighted (Avg +/- Std): {f1_weighted_avg:.4f} +/- {f1_weighted_std:.4f}\")\n", + " print(f\"Recall: {recall_avg:.4f} +/- {recall_std:.4f}\")\n", + " print(f\"AUC: {auc_avg:.4f} +/- {auc_std:.4f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "d36f4ced", + "metadata": {}, + "outputs": [ { "data": { "text/html": [ @@ -1225,74 +4186,65 @@ " \n", " \n", " \n", - " histological_type_kidney clear cell renal carcinoma\n", - " histological_type_kidney papillary renal cell carcinoma\n", - " radiation_therapy_no\n", - " pathology_M_stage_mx\n", - " ethnicity_not hispanic or latino\n", - " \n", - " \n", - " Patient_ID\n", - " \n", - " \n", - " \n", - " \n", - " \n", + " 0\n", + " 1\n", + " 2\n", + " 3\n", + " 4\n", " \n", " \n", " \n", " \n", - " TCGA-2K-A9WE\n", - " False\n", - " True\n", - " True\n", - " False\n", - " True\n", + " MGP\n", + " 0.035549\n", + " 0.001088\n", + " 0.0\n", + " 0.0\n", + " 0.003548\n", " \n", " \n", - " TCGA-2Z-A9J1\n", - " False\n", - " True\n", - " True\n", - " True\n", - " True\n", + " ADH1C\n", + " 0.000000\n", + " 0.000253\n", + " 0.0\n", + " 0.0\n", + " 0.026202\n", " \n", " \n", - " TCGA-2Z-A9J2\n", - " False\n", - " True\n", - " True\n", - " True\n", - " True\n", + " LOC440354\n", + " 0.000000\n", + " 0.002382\n", + " 0.0\n", + " 0.0\n", + " 0.000000\n", + " \n", + " \n", + " C1orf110\n", + " 0.000000\n", + " 0.000261\n", + " 0.0\n", + " 0.0\n", + " 0.060484\n", + " \n", + " \n", + " CLDN8\n", + " 0.000000\n", + " 0.001527\n", + " 0.0\n", + " 0.0\n", + " 0.004444\n", " \n", " \n", "\n", "" ], "text/plain": [ - " histological_type_kidney clear cell renal carcinoma \\\n", - "Patient_ID \n", - "TCGA-2K-A9WE False \n", - "TCGA-2Z-A9J1 False \n", - "TCGA-2Z-A9J2 False \n", - "\n", - " histological_type_kidney papillary renal cell carcinoma \\\n", - "Patient_ID \n", - "TCGA-2K-A9WE True \n", - "TCGA-2Z-A9J1 True \n", - "TCGA-2Z-A9J2 True \n", - "\n", - " radiation_therapy_no pathology_M_stage_mx \\\n", - "Patient_ID \n", - "TCGA-2K-A9WE True False \n", - "TCGA-2Z-A9J1 True True \n", - "TCGA-2Z-A9J2 True True \n", - "\n", - " ethnicity_not hispanic or latino \n", - "Patient_ID \n", - "TCGA-2K-A9WE True \n", - "TCGA-2Z-A9J1 True \n", - "TCGA-2Z-A9J2 True " + " 0 1 2 3 4\n", + "MGP 0.035549 0.001088 0.0 0.0 0.003548\n", + "ADH1C 0.000000 0.000253 0.0 0.0 0.026202\n", + "LOC440354 0.000000 0.002382 0.0 0.0 0.000000\n", + "C1orf110 0.000000 0.000261 0.0 0.0 0.060484\n", + "CLDN8 0.000000 0.001527 0.0 0.0 0.004444" ] }, "metadata": {}, @@ -1300,476 +4252,75 @@ } ], "source": [ - "# BioNeuralNet provides a preprocessing function to handle clinical data\n", - "clinical = tgca_kipan.data[\"clinical\"]\n", - "\n", - "# for more details on the preprocessing functions, see `bioneuralnet.utils.preprocess``\n", - "clinical_preprocessed = bnn.utils.preprocess_clinical(\n", - " clinical, \n", - " target, \n", - " top_k=7, \n", - " scale=False, \n", - " ignore_columns=[ \"days_to_last_followup\", \"years_to_birth\", \"days_to_death\", \"date_of_initial_pathologic_diagnosis\"])\n", + "#This time we will select the gaussian_15 graph embeddings\n", + "res = all_results_GCN[3] \n", + "tensor_data = res[\"embeddings\"]\n", + "\n", + "# .detach().cpu().numpy() to get the raw numbers out of pytorch\n", + "GCN_embeddings_df = pd.DataFrame(\n", + " tensor_data.detach().cpu().numpy(),\n", + " index=omics_kipan.columns\n", + ")\n", "\n", - "display(clinical_preprocessed.iloc[:3,:5])" + "display(GCN_embeddings_df.iloc[:5,:5])" ] }, { "cell_type": "code", - "execution_count": 10, - "id": "d4d4acfa", + "execution_count": 16, + "id": "db08276e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Nan values in X_train_full: 0\n", - "Nan value in X_train_full after dropping: 0\n", - "X_train_full shape: (658, 4858)\n", - "\n", - "Network shape: (4858, 4858)\n" + "Plotting Feature Embeddings for graph: similarity_10\n" ] - } - ], - "source": [ - "import pandas as pd\n", - "\n", - "X_train_full = pd.concat([dna_meth, rna, mirna], axis=1)\n", - "\n", - "print(f\"Nan values in X_train_full: {X_train_full.isna().sum().sum()}\")\n", - "X_train_full = X_train_full.dropna()\n", - "print(f\"Nan value in X_train_full after dropping: {X_train_full.isna().sum().sum()}\")\n", - "\n", - "print(f\"X_train_full shape: {X_train_full.shape}\")\n", - "# building the graph using the similarity graph function with k=15\n", - "A_train = bnn.utils.gen_similarity_graph(X_train_full, k=15)\n", - "\n", - "print(f\"\\nNetwork shape: {A_train.shape}\")" - ] - }, - { - "cell_type": "markdown", - "id": "4c68ddb7", - "metadata": {}, - "source": [ - "## Reproducibility and Seeding\n", - "\n", - "To ensure our experimental results are fully reproducible, a single global seed is set at the beginning of the analysis.\n", - "\n", - "This utility function propagates the seed to all sources of randomness, including `random`, `numpy`, and `torch` (for both CPU and GPU).\n", - "\n", - "Critically, it also configures the PyTorch cuDNN backend to use deterministic algorithms." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "339c3c5d", - "metadata": {}, - "outputs": [ + }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "2025-11-08 16:34:44,524 - bioneuralnet.utils.data - INFO - Setting global seed for reproducibility to: 118\n", - "2025-11-08 16:34:44,525 - bioneuralnet.utils.data - INFO - CUDA available. Applying seed to all GPU operations\n", - "2025-11-08 16:34:44,525 - bioneuralnet.utils.data - INFO - Seed setting complete\n" - ] + "data": { + "image/png": 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nz2dYPiQkhAEDBjBgwADCw8MpV64cn3zyyWMl3U/q66+/ZtSoUQwePJj33nvvseqYN28eAI0aNXpgWT8/P3r37k3v3r1JSEigVq1ajBo1yiXpdjqdXLx40eVLlr+/V5YuXYrJZGLjxo0ue5fPmjXL5X758+fH6XRy8uRJypQpk2FM+fPnB1KTzge99zLD3ffLuXPnKFq0aNrx27dvExMTk25V+cxWvnx5fvjhh3RTQO5Oebg70iQsLIzo6GiKFy+ero6xY8cyduxY/vjjj3s+179asmQJycnJD/UeEUIIIauXCyGEeAw3btxw2aorLi6OuXPnUqZMmbTexQ4dOrB37142btyY7vqYmBjsdjsATZs2xW63880336Sddzgc6eYih4SEUKZMGebMmUNsbGza8c2bN3Py5MlMa9vdRGLatGkux/8ej8PhcIkDUhO97Nmzp9sq6mlYtGgRr7/+Ol27dmXSpEmPVcfWrVv56KOPCA0NpWvXrvct+/dV8T09PSlQoECGbf/r1lKqqjJ16lT0ej0vvPACkNrLqyiKy1ZXly9fZsWKFS71tGrVCo1Gw5gxY9Kt6n23R7hRo0Z4e3szduxYbDZbulju7sOdWZo2bQqQbpXzu7+DjFa9z0wtW7bEaDQya9Ysl2cyffp0IHXNA4DXX3+d5cuXu7y+++47IHW1/uXLl6cbOp+RI0eOMHjwYHx9fXnttdeyoEVCCPHvIz3dQgjxL7J+/XpOnz6d7ni1atUee55xRgoVKkTfvn05cOAA2bJlY+bMmdy+fdulZ/Kdd95h1apVNG/enF69elG+fHkSExM5duwYS5Ys4fLlywQEBPDiiy9SvXp1hg4dyuXLl9P2/P57Qgswbtw4mjVrRo0aNejTpw9RUVFMmTKF4sWLpy0o9aTKly9P27Zt+eKLL4iMjKRKlSrs2LEjrXf2bu9+fHw8OXPmpF27dpQuXRpPT09++eUXDhw4wMSJEzMlloe1f/9+evTogb+/Py+88AILFixwOZ/R7//ue8Vut3P79m22bt3K5s2byZMnD6tWrcJkMt33nsWKFaNOnTqUL18ePz8/Dh48yJIlS1wWTQMwmUxs2LCBnj17UrlyZdavX8/atWt5//3303phmzVrxqRJk2jcuDFdunQhPDycr7/+mgIFCnD06NG0ugoUKMDw4cP56KOPqFmzJm3atMFoNHLgwAGyZ8/OuHHj8Pb25ptvvqF79+6UK1eOTp06ERgYSFhYGGvXrqV69eqZur906dKl6dmzJ99//z0xMTHUrl07bVuuVq1aUbdu3Uy7V0aCg4MZPnw4I0aMoHHjxrRq1YojR47www8/0LlzZypWrAhAuXLlKFeunMu1d4eZFy9enFatWqWre+fOnaSkpOBwOIiMjGT37t2sWrUKs9nM8uXLMxy+L4QQIj1JuoUQ4l9kxIgRGR6fNWtWpibdBQsWZMqUKbzzzjucOXOG0NBQFi1a5DLc1N3dnR07djB27FgWL17M3Llz8fb2plChQowePTptoSiNRsOqVasYPHgw8+fPR1EUWrRowcSJEylbtqzLfRs3bszixYv54IMPGDZsGPnz52fWrFmsXLmS7du3Z1r75s6dS3BwMD/99BPLly+nfv36LFq0iMKFC6clo+7u7gwYMIBNmzaxbNkynE4nBQoUYNq0aS4rsT8NJ0+exGq1EhERQZ8+fdKdz+j3f/e9YjAY8PPzo2TJknzxxRf07t37oRbIev3111m1ahWbNm3CYrGQJ08ePv74Y9555x2Xclqtlg0bNvDqq6/yzjvv4OXlxciRI13eq/Xq1WPGjBl8+umnDB48mNDQUMaPH8/ly5ddkm5IXcAtNDSUKVOmMHz4cNzd3SlVqpTL/PUuXbqQPXt2Pv30Uz777DMsFgs5cuSgZs2a9O7d+8EP9BFNnz6dfPnyMXv27LRkdNiwYen2Is8qH3zwAb6+vkyZMoXBgwe7JOJP4quvvgJAr9fj4+ND0aJFGT16NP3797/nAolCCCHSU9THWTlGCCGE+I85fPgwZcuWZf78+Q8cei1S9erViyVLlmTaKAQhhBDieSRzuoUQQoi/SU5OTnfsiy++QKPRUKtWrWcQkRBCCCGeVzK8XAghhPibCRMm8Pvvv1O3bl10Oh3r169n/fr1vPTSS5myx7EQQggh/jsk6RZCCCH+plq1amzevJmPPvqIhIQEcufOzahRoxg+fPizDk0IIYQQzxmZ0y2EEEIIIYQQQmQRmdMthBBCCCGEEEJkEUm6hRBCCCGEEEKILPLcz+l2Op3cuHEDLy8vFEV51uEIIYQQQgghhPgPUFWV+Ph4smfPjkZz7/7s5z7pvnHjhqwkK4QQQgghhBDimbh69So5c+a85/nnPun28vICUhvq7e39jKMRQgghhBBCCPFfEBcXR65cudJy0nt57pPuu0PKvb29JekWQgghhBBCCPFUPWiasyykJoQQQgghhBBCZBFJuoUQQgghhBBCiCwiSbcQQgghhBBCCJFFnvs53UIIIYQQQojnm8PhwGazPeswhHCh1+vRarVPXI8k3UIIIYQQQohnQlVVbt26RUxMzLMORYgM+fj4EBwc/MDF0u5Hkm4hhBBCCCHEM3E34Q4KCsLd3f2JEhshMpOqqiQlJREeHg5ASEjIY9clSbcQQgghhBDiqXM4HGkJt7+//7MOR4h03NzcAAgPDycoKOixh5rLQmpCCCGEEEKIp+7uHG53d/dnHIkQ93b3/fkkaw5I0i2EEEIIIYR4ZmRIufgny4z3pyTdQgghhBBCCCFEFpGkWwghhBBCCCH+QWbPno2Pj8+zDkNkEkm6hRBCCCGEEOIRXb16lT59+pA9e3YMBgN58uThjTfeIDIy8onr7tixI2fPns2EKF316tULRVHu+cqbN2+m31NI0i2EEEIIIYQQj+TixYtUqFCBc+fO8dNPP3H+/Hm+/fZbtmzZQtWqVYmKinqi+t3c3AgKCsqkaP/05ZdfcvPmzbQXwKxZs9J+PnDgQKbfU0jSLYQQQgghhBCP5LXXXsNgMLBp0yZq165N7ty5adKkCb/88gvXr19n+PDhaWXz5s3Lxx9/TI8ePfD09CRPnjysWrWKiIgIWrZsiaenJ6VKleLgwYNp12Q0vHz16tVUrFgRk8lEQEAArVu3Tjs3bdo0ChYsiMlkIlu2bLRr1y7DuM1mM8HBwWkvAB8fH4KDg3n//ffp3bu3S3mbzUZQUBAzZswAoE6dOgwcOJCBAwdiNpsJCAjgww8/RFXVtGssFgtDhgwhR44ceHh4ULlyZbZv3/5Yz/nfQpJuIYQQQgghhHhIUVFRbNy4kQEDBqTt43xXcHAwXbt2ZdGiRS6J6OTJk6levTp//PEHzZo1o3v37vTo0YNu3bpx6NAh8ufPT48ePVyu+au1a9fSunVrmjZtyh9//MGWLVuoVKkSAAcPHuT1119nzJgxnDlzhg0bNlCrVq1Hble/fv3YsGFDWg84wJo1a0hKSqJjx45px+bMmYNOp2P//v18+eWXTJo0ienTp6edHzhwIHv37mXhwoUcPXqU9u3b07hxY86dO/fIMf1b6J51AEIIIYQQQgjxvDh37hyqqlK0aNEMzxctWpTo6GgiIiLShog3bdqUl19+GYARI0bwzTffULFiRdq3bw/Ae++9R9WqVbl9+3ZaD/RfffLJJ3Tq1InRo0enHStdujQAYWFheHh40Lx5c7y8vMiTJw9ly5Z95HZVq1aNwoULM2/ePN59910gdeh5+/bt8fT0TCuXK1cuJk+ejKIoFC5cmGPHjjF58mT69+9PWFgYs2bNIiwsjOzZswMwZMgQNmzYwKxZsxg7duwjx/VvID3dQgghhBBCCPGI7tUrnZFSpUql/f9s2bIBULJkyXTHwsPDM7z+8OHDvPDCCxmea9CgAXny5CFfvnx0796dBQsWkJSU9NCx/VW/fv2YNWsWALdv32b9+vX06dPHpUyVKlVc9q6uWrUq586dw+FwcOzYMRwOB4UKFcLT0zPttWPHDi5cuPBYMf0bSNIthBBCCCGEEA+pQIECKIrCqVOnMjx/6tQpfH19CQwMTDum1+vT/v/dhDWjY06nM8M6/z6M/a+8vLw4dOgQP/30EyEhIYwYMYLSpUsTExPz0G26q0ePHly8eJG9e/cyf/58QkNDqVmz5kNfn5CQgFar5ffff+fw4cNpr1OnTvHll18+cjz/FpJ0CyGEEEIIIcRD8vf3p0GDBkybNo3k5GSXc7du3WLBggV07NjRpTf4SZUqVYotW7bc87xOp6N+/fpMmDCBo0ePcvnyZbZu3frI9/H396dVq1bMmjWL2bNnp1tYDWDfvn0uP//2228ULFgQrVZL2bJlcTgchIeHU6BAAZdXRsPm/ytkTrcQQgghhBBCPIKpU6dSrVo1GjVqxMcff0xoaCgnTpzgnXfeIUeOHHzyySeZer+RI0fywgsvkD9/fjp16oTdbmfdunW89957rFmzhosXL1KrVi18fX1Zt24dTqeTwoULP9a9+vXrR/PmzXE4HPTs2TPd+bCwMN566y1efvllDh06xJQpU5g4cSIAhQoVomvXrvTo0YOJEydStmxZIiIi2LJlC6VKlaJZs2ZP9ByeV5J0CyGEEEIIIcQjKFiwIAcPHmTkyJF06NCBqKgogoODadWqFSNHjsTPzy9T71enTh0WL17MRx99xKeffoq3t3faCuU+Pj4sW7aMUaNGkZKSQsGCBfnpp58oXrz4Y92rfv36hISEULx48bTF0P6qR48eJCcnU6lSJbRaLW+88QYvvfRS2vlZs2bx8ccf8/bbb3P9+nUCAgKoUqUKzZs3f7zG/wso6qOsAPAPFBcXh9lsJjY2Fm9v72cdjhBCCCGEEOIhpKSkcOnSJUJDQzGZTM86HPF/CQkJ5MiRg1mzZtGmTRuXc3Xq1KFMmTJ88cUXzya4Z+B+79OHzUWlp1sIIYQQQggh/uOcTid37txh4sSJ+Pj40KJFi2cd0r+GJN1CCCGEEEII8R8XFhZGaGgoOXPmZPbs2eh0kipmFnmSQgghhMgyly9fZu7cuezcvZsUi4W8uXPTuVMnGjZsKB/ohBDiHyRv3rwP3Ht8+/btTyeYfxn5104IIYQQmc7pdPLxxx/z5ddTcRiMuBUpiMbDjdPHjrJq/ToK5c/PwgU/kj9//mcdqhBCCJGlJOkWQgghRKb76KOPmDR1Kn6N6hNYoxoavZ7kW7eJ3HcAh+rk6OkzVK9Zk182baJEiRLPOlwhhBAiy2iedQBCCCGE+He5ePEiX339NX6N6pOtbm1Uh4NLs+dwcdIE3E5vp37+azStpaDaI6hdqwrjxo3D6XQ+67CFEEKILCE93UIIIYTIVHPnzsVhMhJYoxpOm41L02fgEXOJsR8H0ai+Gb1eASDmThI/LYpkylfjSEhI4JNPPnnGkQshhBCZT3q6hRBCCJGpduzahVuRwmj0eu7s3Qc3LzB9Sg6aN/FJS7gBfPxM9O/uw6gh/sya+Q1Hjhx5hlELIYQQWUOSbiGEEEJkKovVisZoQFVVYn7bTdN67pQq4X7P8l3a+pMjGGbNmvUUoxRCCCGeDkm6hRBCCJGpcufIgfXGLaxR0djvhPNiU58My6lOJwpgMOho1cSLHds3PdU4hRBCPJrLly+jKAqHDx9+pnVkZj1PgyTdQgghhMhUXTp3xnIljKRr11EUMJu1GZZzWq3o9Xp0Oh0+PjqSkpKecqRCiH8Lh8PBiT1n2L1iPyf2nMHhcGTp/Xr16oWiKCiKgl6vJ1u2bDRo0ICZM2e6LAyZN29eFEXht99+c7l+8ODB1KlTJ129165dw2AwPNauDnfj+fu9LBYL/v7+KIrySPts9+rVi1atWj1yHFkho1hy5crFzZs3n4sdMCTpFkIIIUSmatKkCQVDQwnfuBmHEy5dsaQr40hOQbXZMRgMqKqTi5dTCAgIfAbRCiGed/vWHeK1CkN5v8knjOv2Je83+YTXKgxl37pDWXrfxo0bc/PmTS5fvsz69eupW7cub7zxBs2bN8dut6eVM5lMvPfeew9V5+zZs+nQoQNxcXHs27fvkWPKlStXuqk6y5cvx9PT85Hr+qfTarUEBwej0/3z1waXpFsIIYQQmUqv1/PTggUE6w3Y7bDw5yhwOsHpxJGUjC02DqfVAgokJCZy4dJtFq+KpknTls86dCHEc2bfukOM7/4V187dRGfU4e7ljs6o49q5m4zv/lWWJt5Go5Hg4GBy5MhBuXLleP/991m5ciXr169n9uzZaeVeeuklfvvtN9atW3ff+lRVZdasWXTv3p0uXbowY8aMR46pZ8+eLFy4kOTk5LRjM2fOpGfPnunKXr16lQ4dOuDj44Ofnx8tW7bk8uXLAIwaNYo5c+awcuXKtB70v/aSX7x4kbp16+Lu7k7p0qXZu3cvAImJiXh7e7NkyRKXe61YsQIPDw/i4+PTxeFwOOjbty+hoaG4ublRuHBhvvzyy7Tz94olo+HlO3bsoFKlShiNRkJCQhg6dKjLFyB16tTh9ddf591338XPz4/g4GBGjRr1KI/4sUjSLYQQQohMcenSJSZOnMi7777LggULGD1iBM0aNObgoSRmzbyBLT4ep82GxqBH5+GJzssL1ejOmMnRRERZWb5yBREREc+6GUKI54TD4WDW8J+wWuy4e7uh0+tQNAo6vQ53bzesFjuzPvgpy4ea/1W9evUoXbo0y5YtSzsWGhrKK6+8wrBhw1yGnv/dtm3bSEpKon79+nTr1o2FCxeSmJj4SPcvX748efPmZenSpQCEhYXx66+/0r17d5dyNpuNRo0a4eXlxc6dO9m9ezeenp40btwYq9XKkCFD6NChQ1pv/s2bN6lWrVra9cOHD2fIkCEcPnyYQoUK0blzZ+x2Ox4eHnTq1Cldb/usWbNo164dXl5e6WJ2Op3kzJmTxYsXc/LkSUaMGMH777/Pzz//DPDAWO66fv06TZs2pWLFihw5coRvvvmGGTNm8PHHH7uUmzNnDh4eHuzbt48JEyYwZswYNm/e/EjP+VH98/vihRBCCPGPFhkZyRuDB7Nu40acBgN6P1+cKSnYo2MoEBpK23bdmPD1Txw/baVb10BKlzZhd8D2bXHMnh/F/mNOQjp24eK2Xxnw2mss/v8HLSGEuJ/T+85z8+JtjO4GFEVxOacoCkZ3Azcv3Ob0vvMUr1b4qcVVpEgRjh496nLsgw8+YNasWSxYsCBdAnzXjBkz6NSpE1qtlhIlSpAvXz4WL15Mr169Hun+ffr0YebMmXTr1o3Zs2fTtGlTAgNdp+8sWrQIp9PJ9OnT057drFmz8PHxYfv27TRs2BA3NzcsFgvBwcHp7jFkyBCaNWsGwOjRoylevDjnz5+nSJEi9OvXj2rVqnHz5k1CQkIIDw9n3bp1/PLLLxnGq9frGT16dNrPoaGh7N27l59//pkOHTrg6el531jumjZtGrly5WLq1KkoikKRIkW4ceMG7733HiNGjECjSe1vLlWqFCNHjgSgYMGCTJ06lS1bttCgQYNHeMqPRpJuIYQQQjy2mJgYmrdswamr1whs0xLfMqXQ6PWoqkrSlTDCNm4mbOMGHLizYies3n4DvVZFdYLNqcGQO5Rc/ZrgVSA/ek8PtixayunTpylSpMizbpoQ4h8uJjwWh8OBUWvM8LxWq8XisBITHvtU41JVNd2XAIGBgQwZMoQRI0bQsWPHdNfExMSwbNkydu3alXasW7duzJgx45GT7m7dujF06FAuXrzI7Nmz+eqrr9KVOXLkCOfPn0/X85ySksKFCxceeI9SpUql/f+QkBAAwsPDKVKkCJUqVaJ48eLMmTOHoUOHMn/+fPLkyUOtWrXuWd/XX3/NzJkzCQsLIzk5GavVSpkyZR6yxalOnTpF1apVXZ599erVSUhI4Nq1a+TOnTtd7HfjDw8Pf6R7PSpJuoUQQgjx2MaNG8epK2Hkfe1lTIEBaccVRcEjbx7y9evN+RmzSTh8lBLvDsceG0fKnUgUjQaPXDlxyx6Sdo25RAnCV69nyZIlfPDBB8+iOUKI54hPkBmtVovD4UCnSZ/WOBwOtFoNPkHmpxrXqVOnCA0NTXf8rbfeYtq0aUybNi3duR9//JGUlBQqV66cdkxVVZxOJ2fPnqVQoUIPfX9/f3+aN29O3759SUlJoUmTJunmUickJFC+fHkWLFiQ7vq/94pnRK/Xp/3/u0nuX4fO9+vXj6+//pqhQ4cya9Ysevfune6LiLsWLlzIkCFDmDhxIlWrVsXLy4vPPvvssRaSexh/jf1u/Pcb9p8ZZE63EEIIIR5LXFwcCxYtxFytskvC/VeKVktAowag1ZJ89RpeBQsQWLUyAZUruiTcABqdFn2gPzdu3Hga4QshnnNFKhcgJF82LElWVFV1OaeqKpYkKyH5s1GkcoGnFtPWrVs5duwYbdu2TXfO09OTDz/8kE8++SRdEjxjxgzefvttDh8+nPY6cuQINWvWZObMmY8cR58+fdi+fTs9evRAq02/bWO5cuU4d+4cQUFBFChQwOVlNqd+SWEwGB57Pny3bt24cuUKX331FSdPnsxwIbe7du/eTbVq1RgwYABly5alQIEC6XrbHyaWokWLsnfvXpf3wu7du/Hy8iJnzpyP1Y7MIkm3EEIIIR7Lzp07iUtIxL9ShfuWMwYGYMydi+ijxx9YpzPFgslkcjmWkJDA0qVLmTZtGpMnT2bevHls27aNK1euPFH8Qojnm1arpfcnnTEYdSTFJWO32VGdKnabnaS4ZAxGHb0/7pxh0pkZLBYLt27d4vr16xw6dIixY8fSsmVLmjdvTo8ePTK85qWXXsJsNvPjjz+mHTt8+DCHDh2iX79+lChRwuXVuXNn5syZ47IC98No3LgxERERjBkzJsPzXbt2JSAggJYtW7Jz504uXbrE9u3bef3117l27RqQusf40aNHOXPmDHfu3MFmsz30/X19fWnTpg3vvPMODRs2vG/SW7BgQQ4ePMjGjRs5e/YsH374IQcOHHAp8zCxDBgwgKtXrzJo0CBOnz7NypUrGTlyJG+99VbafO5nRZJuIYQQQjyW+Ph4nKqK3tv7vuUMegNaby9Sbt9/zlzK7XCsN29Ro0YNAKxWK2PGjKFc2eK88koPhr//Jh98MISBr/WibetGVKpYmk6dOrBnz55Ma5MQ4vlSuWk53pv3OjkLhmC32EmKT8ZusZOzUAjvzXudyk3LZdm9N2zYQEhICHnz5qVx48Zs27aNr776ipUrV94z0dfr9Xz00UekpKSkHZsxYwbFihXLcC2L1q1bpy1E9igURSEgIACDwZDheXd3d3799Vdy585NmzZtKFq0aNpwdO///ze9f//+FC5cmAoVKhAYGMju3bsfKYa+fftitVrp06fPfcu9/PLLtGnTho4dO1K5cmUiIyMZMGCAS5mHiSVHjhysW7eO/fv3U7p0aV555RX69u37j5iupKh/H4vxnImLi8NsNhMbG5v2BhFCCCFE1tuwYQMdunUj/7tvYvT3v2/Z459Nxh4eTqlPRqPJ4EOgqqpcmf8T5vA7HDt8GEVR6N69C7/t2UTDOm5s3BZN7uxaenUyU6OSCYtVZf9hC4tXWzhxRsPESV9nuDiREOKfKyUlhUuXLhEaGppuhMujcjgcnN53npjwWHyCzBSpXCDLerjFw5k3bx5vvvkmN27cuGfy/zy43/v0YXPRLO3pbtGiBblz58ZkMhESEkL37t3TzdM6evQoNWvWxGQykStXLiZMmJCVIQkhhBAik9SqVQs/Hx/u7Dtw33LJN25CeARuGi1nv/mBm6fPEh4ezp2IOyQkJJASHUPYwsVYT57ms08/xWAwMGXKFPbu3sSXn2Rn++4Yqlc0snFRTnp3NlMwv5HiRYy0auLBrC/86djSyJC3B3Hs2LGn1HIhxD+NVquleLXCVG9VieLVCkvC/QwlJSVx4cIFPv30U15++eXnOuHOLFmadNetW5eff/6ZM2fOsHTpUi5cuEC7du3SzsfFxdGwYUPy5MnD77//zmeffcaoUaP4/vvvszIsIYQQQmQCd3d3enbtSvze/SRdz3jxM6fNxo2Vq/Hx9ASbk4TTZ7k+9Vtuz/2JiNXruDxrHsdHfYT16HF++OYbWrRogc1mY+6c6XRu7cX5Syk4HE6mjc+GyfTnxxZFUfD21ILiZOSQbOQIdvDDDz88raYLIcRTNXbsWDw9PTN8NWnS5FmH52LChAkUKVKE4OBghg0b9qzD+Ud4qsPLV61aRatWrbBYLOj1er755huGDx/OrVu30r4BGTp0KCtWrOD06dMPVacMLxdCCCGencTERFq1acOB48fxfaEO/hUroHN3Q3U6iTt1hogt21Bv3sKeZCGHkp/CHiUJt97guuUKFixoVS0Opw2LKZHZc2fTokULdu3aRYf2zdiwMB8vvXWeGpX1TBgRlOH9o2LsaDRGflyWwuffJnPq1AXc3NwyLGuz2YiMjEyb6yg9YUI8W5k5vPzfLioqiqioqAzPubm5kSNHjqcc0X9HZgwvf2r7dEdFRbFgwQKqVauWtjfa3r17qVWrlsuQg0aNGjF+/Hiio6Px9fV9WuEJIYQQ4jF4eHiwbMkS3nnnHRYtXcrttetTF1az2tDYrJQrXYZLcQlokgIo4VUeRVHI6RZKTrc/97BVVZWDsb8y8sORNGvWjMjISMBJnlxGrt20UKKI5z3vr9WAw+mkRFF3rJZoIiMj062Se+XKFWbMmMHPi+YTFxcDQEhIDrr36EvPnj3l84YQ4h/Pz88PPz+/Zx2GeExZvnr5e++9h4eHB/7+/oSFhbFy5cq0c7du3SJbtmwu5e/+fOvWrQzrs1gsxMXFubyEEEII8WzExcXx6aefsn7tBhxJVtT4ZCzXb6NJTKJVs+b079uXqDvRFHQvjqIoGdahKAoFPEpw+dIVtm7dioeHB6AhMsqO0aAhMdl5z/s71dTrExJT92/9+9zBXbt2Uf+FmixbPIXubR3MmZKdGV+EULdqFFO+HEnDhnW5ePFipj0PIYQQ4u8eOekeOnQoiqLc9/XXoeHvvPMOf/zxB5s2bUKr1dKjR490m9c/inHjxmE2m9NeuXLleuy6hBBCCPH4YmNjafFiS7776gd84oKp7dWMFwO60dCvDXnUwqxevJb33n0PN9zx1t+/N9lX748RE7///jtVqlTBw8PMktV3qFzeizWbEjO8xuFQsVpVjEYTazZFExpagMDAwLTzly5donevLpQvZWHvuiIMG5yLF2r50KiuLxNGhrJjZSHcdFfp0rk9SUlJmfpsHtf169c5ceIEYWFhT/R5SQghxD/HIyfdb7/9NqdOnbrvK1++fGnlAwICKFSoEA0aNGDhwoWsW7eO3377DYDg4GBu377tUv/dn4ODgzO8/7Bhw4iNjU17Xb169VGbIIQQQohM8O6773L89xNUdK9DEc9SuGs9URQFN60HhT1LUcmjLhG37mC12B6qPo2ixW634+npSfsO3Zi9MJbG9Xw5csLClp2uibcKqb3bioZLV52s35JEr979XXrTf/jhB9xNCfwwMR8eHunnb2cPNjLrq1CuXTvH8uXLn+hZPAmn08nixYtp1KARpUqUpma1WpQtXY66deqyYMEC7HZ7htc5HA5u3brF9evXsVqtTzlqIYQQD+uR53QHBga6fIv8KJzO1OFhFosFgKpVqzJ8+HBsNlvaPO/NmzdTuHDhe86vMhqNGI3Gx7q/EEIIITLHzZs3WbF0Bfn0RfHW+WRYxktnJliXi+vJl0m2JeGmd79nfSmOJJIdieTJkwdIHSn3644tTPr2DIULuPPqu+FMGBHAiw09UVVISHKQnKJy4qyWd0ZdoVDhcnTp0uXP+lJSWPzzfPp39c4w4b4rNI+JejXcmTd3Jl27dn28h/EEHA4HgwYO4qf5C/EhgGKG8rjpPbE4krj6+yUGvjKIDRs2MHPmzLTPSlFRUcyePZuZc+Zw4+ZNALy9venWqRP9+vUjb968T70dQggh7i3L5nTv27ePqVOncvjwYa5cSZ2j1blzZ/Lnz0/VqlUB6NKlCwaDgb59+3LixAkWLVrEl19+yVtvvZVVYQkhhBAiE6xcuRJbip2cptD7livuVR6H4uBC/P13JbmcfB5PsyctW7YEwNfXl2XLV1OoyAucPq8hJg76DL5FufqXeXPEbUZ/HkWnlyLpNeg2ufNW4aeFi/8/FzzVrVu3SExMoEqFB+9sUq2iJ+fPn32IVme+iRMn8uO8nyhuqEgl7zpkN+XBV+9PsCkXFcy1KG2syprlaxk5ciQAFy9epG79+oz+7DOiQrKRrVtnsvXsirN0Sb6eN4+adevy66+/PpO2CCGEyFiWrV7u7u7OsmXLGDlyJImJiYSEhNC4cWM++OCDtJ5qs9nMpk2beO211yhfvjwBAQGMGDGCl156KavCEkIIIUQmCA8Px6R1R68x3Lecu84DN707lx2nCbIEE2TMnq7MrZRrhDnO8ubLg122XAkKCuLnxUs5deoUS5Ys4dixY1y4cJ69f1gx+5gpVro44z7vQfXq1dMt0nb354eZF+1UVTSaLF9bNp3ExES++fpbcmkKkMOUJ+14siORa8mXSHYmo1U0+KvBzJw+iwEDBtC+Y0euJSeRf8hgDD7mtGvMRQrjeKEuV+b9SNeePdn+yy/kz5//qbdJCCFEelmWdJcsWZKtW7c+sFypUqXYuXNnVoUhhBBCiCzg7u6O3WlDVdV7rkoOqUmvm96N7PmDOXxxD/6WbOQ05sOkcU9NLi0XiVLCadW+JcOGDcuwjqJFi/Lhhx8+UnzZs2fH19efrTtjqVnFfN+yW3cmULx4pQzPHT58mD179mCxWMiePTtNmzbFy8vrkWKB1Kl1q1atYuHCRVwLu4bJzURgUACREXco4Z06AtDmtHI0bj/Xk6+gQYMJDxzYSSEJR7Kdvn37cu7SJfL9LeG+S2s0kLdHF85NmMwPP/zAp59++shxCvG8cjidHL59k8ikJPzd3SmTLQRtFn6Z1qtXL+bMmQOATqcjZ86ctG/fnjFjxqTt5awoCkajkTNnzqRNnQFo1aoVPj4+zJ4926XOvXv3UqNGDRo3bszatWuzLHbx9D21fbqFEEII8e9Rp04dxn40jgjrLYKMIfcsF2kLx6618uVXX3Lp0iVm/DCDYyf2oTqdKBoNJcoVZ/RLH9C5c2e02nvPvX5Uer2ezl16Mn/u5wzsa8PfT59huSMnEtlzIJlp3/R1OX7w4EGGDR/OwT8O4dTp0RoN2BMSMXt60KdHT4YPH55ue7J7OX78OJ07deHq5av4EICnxpsYZxKHbUdIsiZzWXeGgh4l2Ru9lXhrDPkpTpCSE52S+jEtXo3hkvMUW7Zuw1yuDKbAgHveS2Mw4F2xPD/+vIiRI0fi5ub2kE9MiOfXtssX+XzPLsJiY3GqTjSKhtxmM0Oq1aBu3nwPruAxNW7cmFmzZmGz2fj999/p2bMniqIwfvz4tDKKojBixIi0BP1+ZsyYwaBBg5gxYwY3btwge/b0I4PE80mSbiGEEEI8svLly1OqTEnO/34cf0MQWiV9wuxUnZxLPkaREkWoVasWtWvXpmfPnly8eJHY2Fh8fX3JmzfvfXvKn0T//v35edF8ur56gdlT8hEc5JokHz+VSJ83LlOiRAWaNWuWdnzXrl2079wZq78fwd264F20MIpGgzU2lsjf9jP52285efoUP85fkLa4GcCJEyfYsGEDsbGxmM1mmjVrhtFopFWLVqSE26nu0QhP3Z/D5+Pii3LedopzCSeJsNwi3hZLKaUqXoqPS5xeig+FlbJEKptRQrKhOlUUzb2fmWf+UG5s/5Xbt2/LomriX2/b5Yu8tXEdVocDk06HTqPD7nRyOSaatzauY1KjplmWeBuNxrQdl3LlykX9+vXZvHmzS9I9cOBAJk2axDvvvEOJEiXuWVdCQgKLFi3i4MGD3Lp1i9mzZ/P+++9nSdzi6ZOkWwghhBCPTFEUJk6aSIvmLTkYt4Oi7uXw1vuknY+3x3Ay8Q9sHilMnDwxLbFWFOWpzTXOli0bCxcto0vndlRtcoZmDTyoXN4Tu13ll1/j2LEnmeLFyzN/wcK05Dk5OZne/frhyBFC/t490Oj+/KhkMJsJadQAz3z52DRrLtOmTeONN97gwoULvD54MHv37UM1GdF5emGLj+OT8eMxe3kRfyuemuamGP42/91kMJFdyYviVLloPUUupWC6hBtARQUF9IoB1eEkOSUZd/d7rwSvOp0okGVfZgjxT+FwOvl8zy6sDgeeBkPae16v1aLTaEiwWpm4dze1cufN0qHmkDqiZc+ePS7DyAGqV6/O2bNnGTp0KGvWrLnn9T///DNFihShcOHCdOvWjcGDBzNs2DD5e/wvIUm3EEIIIR5L+fLlWbp8Cf369mdv2Ga8kswYVDesSgrxSgzZc2Vn/g+z0nYteRaKFSvGjl/3snDhQubPm8WqTRfRKBpKlSrLF1/2oWXLli5bka5cuZLbkXfI3+fPhFtVVZwWC6pTRWsy4lUwPx5lS/HDzJk0btyY5i1bEolKUOcOmIsXRdFqcdodRB89xtWVq1EcydiclnRJt8FoRKvV4u70AhQCyXgoqV21odFo8NX5EXP+Iim1qt836Y47dQZ/Pz8Zmir+9Q7fvklYbCwmnS7DxRRNOh1XYmI4fPsm5UNyZPr916xZg6enJ3a7HYvFgkajYerUqenKjRs3Lm0dq5o1a2ZY14wZM+jWrRuQOmw9NjaWHTt2UKdOnUyPWzx9knQLIYQQ4rFVqVKFPw4fYsOGDaxdu5bIyEj8/Pxo2rQpTZs2dRl+/ayYzWZefvllXn75ZZxOJ4qi3LP3aNmKFRjzhWL098NhsRJ18Hci9uwl+dZtAHTu7vhXqoBXkcJc+/0wvXr3JkqBfANeQef+5/xpjU6LZ/GiBHp7EzlvIYej91Pd54V09/P28iIi+iaKqkGvpn9WdtWGEydmL29ClUL8dnknyVevg59fhvHbEhJIOHSYV1555R/x7IXISpFJSThVJzpNximNVqPBabcTmZSUJfevW7cu33zzDYmJiUyePBmdTkfbtm3TlStWrBg9evRg6NCh7N69O935M2fOsH//fpYvXw6kLszWsWNHZsyYIUn3v4Qk3UIIIYR4Inq9nhdffJEXX3zxWYfyQA/aGizizh30vr7Y4uI49/1Mkm/dxq1IIfyrV0PR6bBcvUbEvgNE7N6LJsXCsRMnyNmnp0vCfZeqqmg93PGpXZuIJcuJt8fipXNdddxoMuHj6QPxKskkYVTdUFBQUXHiAMDb2ws3N3dC1Fzo47XcWbwUHz9fPHLldKnLGhNL2Ox5hPj40K9fvyd7UEI8B/zd3dEoGuxOJ/oMFmJ0OFMXVfO/z8iQJ+Hh4UGBAgUAmDlzJqVLl2bGjBn07ds3XdnRo0dTqFAhVqxYke7cjBkzsNvtLqNTVFXFaDQydepUzOb778Ag/vkk6RZCCCGE+D8fb29st29yfsYcrAkJhLz2EoZs2dLOe5Qohs8LdYhYuJjEI8dw8/LCXLSwSx1Ou4PY4ycI37OXxGs3QFVxKHZOxR+mvE+NdIvOZfPIgZKgEKncwlfrj1N1oCga3E0euLu7odWmflyzOFPQ6zXkCgjkytRvMeXLi2exoigaDYmXLpN88jQ5s2Vj8aKFaYs7CfFvViZbCLnNZi7HRKPTaFxGsKiqSordTqivH2Wy3XuHhcyi0Wh4//33eeutt+jSpUu6nQNy5crFwIEDef/9913WtbDb7cydO5eJEyfSsGFDl2tatWrFTz/9xCuvvJLl8YuslbUrCgghhBBCPEeaNG5M4olTJF29RmCn9i4J910agwG/1i3QB/jjREX5Sw+bLS6OM199zcV5C7DZ7XhVr4JXjaoY8+fjqnqNHdEbSHYkutQXa49Ca9ASYwxHMasEBWUjMDAQLy+vtITbqTo5kXCQgEB/du7YwazvvqNCUDDJv2wjceMv5Lc7+fzjj9mzcyfFihXL2ockxD+EVqNhSLUaGLRaEqxWbA4HTlXF5nCQYLVi0Gp5u2r1LF9E7a727duj1Wr5+uuvMzw/bNgwbty4wS+//JJ2bM2aNURHR9O3b19KlCjh8mrbti0zZsx4KrGLrCVJtxBCCCHE/3Xs2BHsdvQh2TDmzpVhGdXhQFHBXLki1sQkbAkJADhtNs7/MBNLXBzBL/UluF9vfOrUwrtWDQI7dyCkXx8SfRX2xG7D5rQCkOxI5FjyfipUKE/dBnU4lLyLc4knsDhTUu+lqkRYbrE/dhuJ7jF898N3+Pn50bZtW9atWcONq1e5ee0au3fupH///jIMVfzn1M2bj0mNmpLXxxerw0mi1YrV4STU1y9LtwvLiE6nY+DAgUyYMIHExMR05/38/HjvvfdISUlJOzZjxgzq16+f4d/dtm3bcvDgQY4ePZqlcYusp6iqqj7rIJ5EXFwcZrOZ2NhYvL29H3yBEEIIIf4RTp8+zdq1a9P+DW/SpAnFixd/1mGRO18+4gvlx1y3Nlqj8c+ebFXFabPhtFjQaXXoE+I5+9kX5GrXmuC6tbmzbz9Xfl5GyICXMIT8ObzbYbXiSEpGq2hwRsdz87sfKKQUQKNoualeIWf+HKxctYKgoCBGjBjB/LkLSE5Ixk3rjs1pw6GxU7RYET6d8Ok9Vz4W4nmUkpLCpUuXCA0NxWQyPVFdDqeTw7dvEpmUhL+7O2WyhTy1Hm7x73a/9+nD5qIyp1sIIYQQT9WVK1d4+6032LPnV8xeKtkC9URE2pg0cSwVK1Zl0uSvntpe3hkxe3tjN5nQOFXsiYkoGg0oCjidoKoYjSbMZjMpKSlotVqid+0loHJFInb/hqlwQZeEW3U6Ua1WDAY9iqJg8/XEWLQgp48cI1dIDl7p8RKvv/46gYGBAEyYMIFhw4axcuVKbty4gdFopFq1alSpUkX26xXiPrQaTZZsCyZEZpCkWwghhBBPTVhYGC1bNMHdeJtp47PR5AVfANZvieaL726we9cmypcvTqNGLRg06HWqV6/+1JPNksWLs+n4MXI1b4bFkoLVakVVQavV4GZyQ6tL7fmOO3MWX7MZT4OBC1O/JfH6DfzbtEytRFVx2u04LRY0KPj6+qLVarHb7WjKlSHy4iX27tuT4YJnvr6+9OrV6ym2WAghRFaSMRdCCCGEeGQ2m42wsDAuX77sMj/xQYYMeRM3wy1WzClAi8b+RETaaNrpBAPePY+3l5M3X/bhlR4eXDm/lg7tm9O5cwfi4+OzsCXp9e7VC9uNmyReuoTJZMLb2xuz2RtPT8+0hNthsRK37yCdO3Rg84YNVCtUGNVqxWmzYU9IwJ6QgDM5GYNOj7+/H9r/D1HX6XQYTCZ0Wi1Go/GptksIIcSzIT3dQgghhHhot2/fZvr06cyZPZ2bt26hqnY0ioKfXwDtO3TljTfeIGfOnBlee+7cOXbt3MaUcdkI8NcTF2+nU/8zWK021i7IQeniqXPlUixOBvWzc/SUgTc/3EyfPj356aef0emezseWevXqUbViRfYtWEiu3j1wz+k6ZNWRksLluQvwVJ289tpr5MuXj7WrV1OoaFGibofj4eaGRtFgNBkzjDnh8mV8/fxkLRohhPiPkJ5uIYQQQjyUU6dO0bBBHaZ89Ql37lwlJMjJq728+OBNM81fSGLhj19SoUIp5s+fn+H169evx8tTpVn91CHlcxeFc/1mCgu/z56WcAMYDRq0GoXqFU18PzE3u3dtYfPmzU+ljQBarZYF8+ZRvlBhLk/9hoszZnNn336ifj/E1WUrOfPxp9jOXyR/3lA++PBDPvnkE65cucIr/ftjO3UGN40GD0+PDBNuR0oKCYcO06tbt7TebyGEEP9uknQLIYQQ4oHi4uLo0rkdTvs1FGy83s+HP37Jw/gPghgywJ8vPsnGnjU56dAchrw9kFWrVmVYh7+vDoNBg8OhMu/ncFo29iRvLr1LOUUBrUbB6XRSo7I35UoZmD376e5V6+/vz9rVq/l+ylRKeXoRs3ItkUtWkPjbfuzxCaju7px02Nh2/SqffTON8pUqcf78efw9PbkyZwH25OR0dTosVq7M+xGzwUjPnj2fanuEEEI8OzK8XAghhBAPtHDhQiLvXMXLw0GbZp58+Ja/ywJnGkUhJFjPsDd8iI6N4aMxH9KsWTOX3lyz2UxktB2r1UlktJ3rt6xpC6n9laqCw6li/P92P01e8Gbyd/uyvpF/YzQa6dSpE506dSIxMZGOnTqx89Dv5OzUHp9SJdH8f36302rlzr4DLFi6lPo1anDo6FEufP4FXhXK4124EGgU4s+eI+7A73gBC+bOI1eujPcAF0II8e8jSbcQQgghHmjB/NkUL6zjyIlkBvYJznBFcY2i4O6mpVcnT7oPvMzWrVtp0KABAE6nEx8fH67fSKJApYMAxMXbWbMpgTLFjQQG/PmRxGJ14lRJW2jMZNRgs9mfQivvbfny5ezc9xu5XuqLZ2hel3Mag4GgmtXReXqyZdESvv/6a44cOcL8hQu5/utOADw9POjfvgMvv/zyM90OTQghxNMnSbcQQggh7ktVVS5evEC5kgqhufUULmC4Z1mDQaFoIT1BARoOHTpEgwYNSElJ4eWX+7N502rKldLTuJ6JQH8d+/9IZsX6BDbtSOSHicHUqOyO06mSkOjEoDeg16cOOz98LPGZ9gyrqsoPM2dgKlQwXcL9V75lShG1/VfWrV/P7FmzGD58ONeuXUNVVXLmzImHh8fTC1oIIcQ/hszpFkIIIcQDaTQa7A4Vo+EBe2arqX8YjRrs9tTe6UGDBrLr19XM/DIHvywtQd8ufrRs5Mm49wPYsjQHZUsY6f3GLQ6fSCEqxoHTqeDtbQbgTqSN1ZsS6dK1Vxa27v4iIyM5fvwEPmVL37ecoih4li7Jlu3bAHB3d6dQoUIULlxYEm4h/uO2b9+OoijExMQ861DEMyBJtxBCCCHuS1EUSpUqQ/gdOxcu24iMctyzrMXqJDJK5cZNG3nz5uXIkSOsXbOU8SNCaFjHF51Oh5+fPxqdAasNPNw1TBwdQFCAlsnfRaHR6PHz80en0xEXb6f/W5fw8AqiU6dOT7HFrpKTk1EBrZvbA8tq3dxISU5BVdWsD0wIkUZVHajWQ6gpm1P/VO/936lnoVq1aty8eROzOfULxbtJ+N1XYGAgTZs25dixYy7X9erVC0VR+PTTT12Or1ixIsNpPgBFihTBaDRy69atrGmMeGSSdAshhBDigXr07MPV6yoOJ/y0PC7DMg6HSnKKyrJ1KRhNZlq0aMGcOXPIEazQsrFfWjmtVoufrx8BAYG4ubmjKBo6tPBk49YkTp3Xcea8lS+/u0G91mc4c9GTuXMX4ufnl+E9nwY/Pz/0ej0pt8MfWDbl1i2CgoLu+WFYCJH5VMt21Mg2qNF9UWOGpP4Z2QbVsv1Zh5bGYDAQHJx+PYwzZ85w8+ZNNm7ciMVioVmzZlitVpcyJpOJ8ePHEx0d/cD77Nq1i+TkZNq1a8ecOXMytQ3i8UnSLYQQQogHatmyJaVKVcTu0PDZ11Fs253kct7uUImOdbB9j4XpCxLo1ftlvLy8OH7sEHVruKPVpk9CdTodPj4++PsHUK+mLxarSquel2jU8SJTZlqo26AP6zdsoXz58k+rmRny8PCgZfPmxO47cN8ebKfVSuLhY3Tp2PEpRifEf5tq2Y4a8w7YLwEGUDxT/7RfRo15J8sS7zp16jBo0CAGDx6Mr68v2bJl44cffiAxMZHevXvj5eVFgQIFWL9+PXDv4eVBQUEEBwdTrlw5Bg8ezNWrVzl9+rRLmfr16xMcHMy4ceMeGNeMGTPo0qUL3bt3Z+bMmZnWXvFkJOkWQgghxAMZDAbmL1hIyZJViY6Fjv1v0P21G6zeFM/u/YksWRXHa+9F8NbIOBo36cDQoUMBcDgcaDX37/XV6/X4+5txd/dg7NiJrF23jcNHTjFx4kRCQ0OfRvMeqH+/fqhR0dxYuyHDxFt1OAj7eSluQI8ePZ5+gEL8B6mqAzV+MqgWULxA0YOi/P9PT1CtqPGTs2yo+Zw5cwgICGD//v0MGjSIV199lfbt21OtWjUOHTpEw4YN6d69O0lJSQ+sKzY2loULFwKp/739K61Wy9ixY5kyZQrXrl27Zx3x8fEsXryYbt260aBBA2JjY9m5c+eTNVJkCkm6hRBCCPFQgoKCWLd+E3PmLiRP3pKs2Wyl66u3ebH7bV7/IJ6opDJMmvwd3333PTpd6gYp+QsUYf8fyQ+c47z3YDyKoqdx48aULVsWb2/vp9Gkh1axYkXGffQRCbv3cmn6bGJPncZps+GwWIk+fIQL077Hefos33/zjezBLcTTYjsCjjBQ3FKT7b9SFFBMqedtR7Lk9qVLl+aDDz6gYMGCDBs2DJPJREBAAP3796dgwYKMGDGCyMhIjh49es86cubMiaenJz4+Pvz444+0aNGCIkWKpCvXunVrypQpw8iRI+9Z18KFCylYsCDFixdHq9XSqVMnZsyYkSltFU9GtgwTQgghxEPT6/W0bduWtm3boqoqV65cITY2Fh8fH3Lnzp1uvmK3bt3p1HExu/fHU6Nyxom006ky66dIataqS548eZ5GMx7Lyy+/TM6cOfls0iSOzF3ANacTAL1GS41q1Rg67RuqVav2jKMU4j/EGQmqE5R7pTTa1PPOyCy5falSpf68k1aLv78/JUuWTDuWLVs2AMLDw+/5ReLOnTtxd3fnt99+Y+zYsXz77bf3vN/48eOpV68eQ4YMyfD8zJkz6datW9rP3bp1o3bt2kyZMgUvL69HapvIXJJ0CyGEEOKxKIpC3rx571umRo0alCtXhTeG7+fHb/NSuIC7y3mHQ+WDsVc4csLJop8HZ12wmaRZs2Y0bdqUI0eOcP78eTQaDcWLF6dw4cLPOjQh/ns0/qBoADugz6CAI/W8xj9Lbq/Xu95TURSXY3e/hHT+/wu6jISGhuLj40PhwoUJDw+nY8eO/PrrrxmWrVWrFo0aNWLYsGH06tXL5dzJkyf57bff2L9/P++9917acYfDwcKFC+nfv/+jNk9kIkm6hRBCCJFlNBoNs2bPo1OndjTueJjmDT14sZEv7m5ajp9OZP7iGMKua/h84lRq1KjxyPXHxMSwaNEifpz/I1evXsVkMlGzdk169+5N5cqVs2QVcUVRKFOmDGXKlMn0uoUQj0BfGrS5wX4Z0LkOMVdVUFNAlze13HPgtddeY9y4cSxfvpzWrVtnWObTTz+lTJky6b7omzFjBrVq1eLrr792OT5r1ixmzJghSfczJkm3EEIIIbJUYGAgK1euZf78+cydM4Pl6y4AoNcbaP5iJ6Z93/+xEth9+/bRtXM3IsMjCSAYX1127E4bqxas5eefFlOpakUKFixIcnIygYGBtGnThooVK8p2XkL8SyiKFrzeTF29XE0ATIAWcKQm3IoBxevN1HLPAXd3d/r378/IkSNp1apVhv+tKlmyJF27duWrr75KO2az2Zg3bx5jxoyhRIkSLuX79evHpEmTOHHiBMWLF8/yNoiMyUJqQgghhMhynp6evPLKK+zavY/fD51gz95DnDx1ga+//vqxEu4zZ87QoV1HbOEqtTybUc5cgwIexSjiVZqCxpJYLA5++fVX5vyymVUnjvHDsqU0bNaMeg0acPny5UxvnxDi2VCMdVB8Pkvt0cYKamLqn7q8KD6foRjrPNsAH9HAgQM5deoUixcvvmeZMWPGuAxZX7VqFZGRkRn2jhctWpSiRYvKgmrPmKI+aDnRf7i4uDjMZjOxsbH/uJVOhRBCiOdVYmIiy5cvZ+mSpYTfjsDL25NGjRvRpUuXtMWBMtuZM2dYvHgxN2/exGg0Ur16dZo3b47RaExX9uWXXmblj2uo7tUQnebPOZS3Uq6yL2EXxmKFMNephcbfTEBgAFqNhvhz57m1cg3BBiMb162TVcaFeMZSUlK4dOkSoaGhmEymJ6pLVR2pq5Q7I1PncOtLPzc93OKf7X7v04fNRSXpFkIIIYSLXbt20btnHyJuR+BLIG6KJ1ZnClFKOEYPPWM/HUvv3r0z7X6RkZEMeHUAv2zegsauxV31xKE4SFBjyRYSxKcTPnXpwQkPD6dUiVLkshYin8efW+s4VAebolagFAslW+u2KBoFi2rB3cMdb+/UlXtt8QlcnPINberWZcb06ZnWBiHEo8vMpFuIrJIZSbfM6RZCCCH+wxITE1m7di1Xr15Fr9djNpt5f+hw9PFu1PRsgrvWM62szWnldMIRhrw5BIPBQNeuXZ/4/nFxcbRs0ZIzR85R1FCOEK/caJTU2W8J9jjO3jxKvz79sdlsdOjQAYATJ06Qkmwh2CMnqurE6VTRKAo3LWGkaKzkqFMPRZNahwYNNps17X56L0/8alVn9dq13Lp1i+Dg4CdugxBCCHE/knQLIYQQ/0E2m40JEybww3fTiY2KxaAx4lAdxKXE4uX0obxvXdy0bi7X6DUGSnhVwBZr5c3Bb/HHH3+g1WrJnz8/7dq1w8/P75HjmDhxIqePnqWyRz08da69BJ46b8p6V+do/D7efvNtGjZsiI+PD3a7HYfdQXxcPPGWBFRAAc5zEsXfncQzp7B4e+NRqEjqLkJ/G9PnV6Ec4avXsmPHDjp27PjIMQshhBCPQhZSE0IIIf5j7HY7/fv35/NxE/GOCaSmZxPqeL1IBVMtFIdCDmcocdGxWCwpLtepqsrFpNPccFwnMjGe6atXMXPdWoZ8+AHFS5dixIgR2Gy2h44jKSmJeXPmE6LkSZdw36UoCkU8ypAQm8iiRYtQVZV169aRnJLMnZRwtOhIUhM4qhzgji4Sh9VCzKGDhK9bw5WvvyJ6+zY0f1sBWGsyoWi1JCYmPvrDE0IIIR6R9HQLIYQQ/zFz585l5dJVlDZVJZsxR9rxKHsEGrQEaXLiwE5sTCyBgYa0odpnEo5y2nYSzyoV8atYFs+QIMxmM/aERO78to8vv/2Wm7du8d2336LRPPh7/QMHDhAdGU0R9wr3LWfUmvBVA9m4YSNGo5HZM+bgqTMT4biOSXXjuPYgutCcBFZtiKFgXnSeXthjYkk4+Dtxu/eixMfj07cXijZ1USVrdAw4nAQEBDz+QxRCCCEekvR0CyGEEP8hqqry/bffE6AGuyTckLoQmQYtGkWDXtHjVFWSU5IBiLFFctpyAnP9egQ0aITObE4btq3z9CC4fj1Cunbk5+XLWLFixUPFkpCQgKo6MWoevICSQTERGxPL5xM+J9CZgzLmysSokRzV7MdQtADBXbrhUaAQigqqw4HOx4y5fl0Cu3Yi/tx5wn/dlVbXnd/24edj5oUXXni4h/aci4iI4OTJk1y6dMllmyEh/inkfSn+yTLj/Sk93UIIIcR/yKlTpzh79hxlTNXSnXPXeuDATrKaiJvigQYNKckpuLt7cCnpLFo/H3wqVQFUVFQ0Wtfv7n1KFCcqNJTpM2fSpk2bB8bi7++PotGQ6IjHoEm/LdhfJZOIU/Xj+tXrVHZ/AbPejxxuubnCVbwbvYCqTZ3XDQpOSwoaNzcURcGjUAGSS5UkYvdegmrXJOnqNWJ3/8bgl17Cw8Pj4R/cc2jr1q388MO37Ni+BXACCrlzh9KjZ1969eqFu7v7sw5R/McZDAY0Gg03btwgMDAQg8GA8rfpIEI8K6qqYrVaiYiIQKPRYDAYHrsuSbqFEEKILKSqKseOHSMyMhIPDw/KlCnzRP9wP6moqChUh4q70TPduWzGHBg0Bm44L5NfKY6CkvYN/03HDTxKVULRaHBgB1Tc3NzS1eFToRz7li4nMjISf3//+8ZSoUIFcuXORdiV8/jq7z3UO9EeTyyR5Mtfm2O/n8CsT12wzarY8ChQCL3ZjF39cy656lBxJiWjMRpQDQY8K5Tj9h+HubJoMcknTlOjQgWGDh36MI/ruTVx4kQmfv4xZUromDg6gIL53IiLd7Bi3U0mfDqMVSuXsXDREnx8fJ51qOI/TKPREBoays2bN7lx48azDkeIDLm7u5M7d+6HmjZ1L5J0CyGEEFnA4XAwd+5cvv3he86eP4/j/9taZQsMpGe3bgwaNAgvL6+nHpeXlxeKRiHFmYwHrvfXKjpCPYpwNv4YZtUfM35pHzLsTitaD09UnNhVO0ajEZ0u/ccIvbcXThXi4+MfmHTrdDr6v9yPke+PIsJyk0BjSLoyDtXO8cQDBGfPRunSpVmxaBWqqqIoChas6H39MChGVCW19/1uH5nFacGZYsFpsaDo9TitNpwnTvF6v/68//77GX5h8G+xdOlSJn7+EUMH+fFa3xAURUFFxWKxUKaEHx1autFr0G6aNm3Eli3b/9XPQvzzGQwGcufOnborgcPxrMMRwoVWq0Wn0z3xCAxJuoUQQohM5nA4eHXAABYtX45b8aJk79MTY2AA9oREog4eYsLXU9mweRMrly3H19f3qcZWvHhxcuXOydXLF/E3BJHsSCTCeguHaseoMZHfvQjxthhOphzEVw3EXwniVnwYTtWJJS4Kq2pBp9dj9jFnWL81OgaNojx0D+orr7zC3j17Wb96A7msBcjjXhB3rQdO1cktyzUuWk6heDuZNWceDocDVeMgyhaBvyEIvaojKTEBAOX//4PUwe+KAp5enmg1WpITkjCZjCxc8CP16tXLlOf4T6WqKlO+mkTDOiYG9ssOpG4PFxMTg8NpR69TKFZI4aOhPvR/6yBFixZk2rTvadq06TOOXPyXKYqCXq9Hr9c/61CEyBKSdAshhBCZ7Msvv2TR8uWEdO6AT6kSaccNPj6458xBcpWKHP1uJq8OGMDCn37KtPtaLBZWr17N8hUrCI+IwNvbi0YNGtKxY0fM5tQkWafT0bd/Xz4cNoKdkbFEW+/8v4c4tTdUrzGQxy0/7loPbnODcPU2Gp0bTp1KwuEjeNeqhtHogUbJeJhdzIGD1KlV86GTbr1ez5y5cxg/fjwzfpjJzshz6BR96qJuOoWqtavw8ScfU6ZMGZxOJ4WKFOLCiVP46QMJNuTgxOkTOJKT0Lr9OT/ZodpRFAV3dw80GoXI4yfIGZKdGjVqPPEz/qc7dOgQZ8+eYMyQ3ADY7Taio6PQalV8vHXo9alfTLRr7s3n02Kw22Po368HM2ctoFGjRs8ydCGE+NdSVFVVn3UQTyIuLg6z2UxsbCze3hnv8SmEEEI8LRaLheKlS5NSMD85W714z3JRfxzmzs/L2LtzJ4ULF37i++7fv5+effpw/dYtjKF50Pn54UhIIOX8RbxNJiZ99hkdOnQA4Pjx41StXA2SNORSChCs5Ean6EhWE7mhXiFMPYtDD+ZSJchWrw6mPLmxRkRybvJXeFauiHet6ri5ueNjNsNfRtzd2XeAO8tXsXDePBo3bvzIbUhMTGTDhg3cvHkTo9FI9erVKVasmEuZNWvW0LNbL7I5clPAoxi/RK/CVKkMAY2aoiipK7DbseHp5YWnpwfJN25y5ZvveXfgIN5///0nesbPg8WLF/PG6324eKAkBoOGqKgonE4rfr7adPuVv/ruLaJjdbi56Th62p/9Bw5LT6MQQjyCh81FpadbCCGEyESbN28m/M4d8vXqet9yPiVLErFmA4sWLWLEiBFPdM8jR47QpkMHLAH+5BvyBqbAwLRztvh4bq7dwCsDB6LX62nZsiWvvPQKXoqZou4VcKQ4cGBHVR1o0RFEDq7qLuJZvhTZ27fBy5z6IcKUKwe5WrXg6opV2OPj8SxXFn3hgnh4eJAScYeIXbuJ/+0AA/r1e+weUw8PD9q2bXvfMs2bN2fyV5MY8tY7RCRex18TwK39B3FYUvCsXgW9vx8eHh64Gw1EHvidiLXrKVu0GK+//vpjxfS8uTvP3mJ1otE4sdos+HilT7gBrDYVnU7h3YHZadD+Ahs2bODFF+/9RZEQQojHI0m3EEIIkYmuX7+OxmBwSXwzotFp0QUFcv369Se+5wcjRpDs5UH+fr3Q/K2nUu/lRa6O7bhis/PusGF4eXlx4vhJyrvVwt8QiNPhIDklGYfDiaLAHft1FL0J/xdeIDklBU9vLxRFISkpCaVoIXysjYjbuZvwI8e44+aG3mBAY7EQHBTEsFGjeO2117J8y58ePXpQtWpV5syZw+JFS3C/pSf52DFSTp3CI1dOEk0m7BF3UCwWWjRpwpSvvsLTM/1q7f9GpUuXBvRs2h5Dk3puKIDRmH4qQEKik937U+jXzZeihdwpWtDAr7/+Kkm3EEJkAUm6hRBCiExkMBhwOuw47Q40Ou39C9tsTzyc99SpU+zeu5fAju3SJdx3KYpCcOP6XPz8S7744gvcVA/89KlfCmi0Wjw8/kxIb0ffxL1EMfRunlidFiwWK1aLhaSkJDRo8SlRFt9iZYjcuYOYrVtRFBPuPm7Mnz2bKlWqPFFbHkXBggX5+OOP+fjjj1FVFZvNxpo1a9i7dy9Wq5WAgADKlClDUFAQCQkJaXPa/+3y5ctHzVr1+G7OdupVz4GiQEbfgcz9OZakZJUubVPfB95eGpKTk7MsLqfTyZEjR7hz5w4eHh6UL18eo/H+e7MLIcS/xeNvNiaEEEKIdKpUqYJOhdiTJ+9bzhIVheXqNapWrfpE99uzZw92RcFcovh9y5kCAzFkD+H8+fMYHe737I22qlZ03ua0lcAtKSkkJSWhU/ToFQMaNCgaLV5FS6DTm6hkroMpxZM+vfuSkpLyRG15XIqiYDAYaNOmDW+//TZeXl7MnD6Lnl170rhBE0qXLE2H9h3YsWPHM4nvaXv33WGcv6xn0PvXiYl14HD8uXyPw6Eyb3Esn06Jpm/XYLIHG7HbVS5ftREUFJTpsTgcDn744QdqVK9Ms6Z16dmjLe3aNqVc2eJ8/PHHxMXFZfo9hRDin0Z6uoUQQohMVLRoUapWrsxv237FXLTIPXufb/2yDT8fH1q3bv1E97NYLGj0+gf3qgOK0YjG7sCh+TM5tlqtWCwp/9/7WoMWLY7ERO6maRarBQ0atH/7yOBIiAdVxU3rTin3yuy6uoFp06ZRpEgRdDodJUuWJCQk/b7bWencuXO0btma22ERhGjyUMhUDq2iJcYWyd71B9jyS1s+nTCO/v37P9W4nrby5cszc9YC+vfrQe1W12nTzIMSRYzExDpZtTGBqzccdGsXyAdv5QJg47ZobkfwwPn0j8put/Pyyy+xccNSWjb24PMP8xCa20hktJ0lq+8wd/bnbNmykaVLV+Ln55ep9xZCiH8SSbqFEEKITDZm1ChebN2ay7PnkbNdGwy+Pmnn7MnJ3Nr0Cyl/HGbCxEm4u7vfu6KHEBwcjDMlBUtUNEa/jPf8tsXHE3P8JIkXL5G/YEGuqdeJs0Rji3dgs9vTdrhWAW/Vh5vHjmOuWxsMCk6HE71iSFdn/LGjeCpeeGq9uWW5hjXZxvvvDcdkNAFgMBlo2rwJ7777broVyLOC1Wqlc8fORIXFUc2zISatW9o5L52ZnKZQTiX8wdB3h1GoUCFq166d5TE9S/Xq1WPf/sN06dKFBcu2EuCbjNlbR80qvkz7LIiyJVOnFFy7YWHUhFtUrVqT4sXvP1riUX3xxRds2riU6ZNz0rDOn+/NbEEGRhTOTec2ybTve5yBA1/lxx8XZeq9hRDin0S2DBNCCCGywK5du+jZpw8R0dGYChbAEOCHPSGR5FNnMGk0jB0zhr59+z7xfRITEyleujSO0iXI0dR1my5bQgLXV64m6fhh9M4U3PROnBiJjU1BSXGjpFoND42Xy57bic54Dmh34F6rCoENX8Bms2FQjCh/mZGWfDWM2/PmU1JfGhUnJ+IO4a36kl2Xl7wB+VFVJ7cs1wiznUdn1rBo8UKqVav2xG29n2XLltGnR1+qeTTAS+eTYRlVVdkb9wtVGldg8ZLFWRrPP0VycjKdO7Xn6JFd9OzoTZe2geTJaeJOlI3FK+8w48do3D3zsWLlWoKDgzPtvikpKZQrW5z2zW2MfCf3Pcut2hDJgPdus3XbHooUKZJp9xdCiKfhYXNRmdMthBBCZIEaNWpw5NAhvp40iSpB2cgRGU1Jg5GR77zD0UOHMiXhhtRttvr16kXsrr3EnT2XdtwWH8/FqVPxuvY7I97wYveqHBzeXpATO4sz5j1/suW2cEy/g3g1Ju0aVVWxKhZ0dg1xu3YTsXottujotKHmjpQUYg/u5/ZPPxFEAJ5ab07EHSIXBSimVCBQG4xRY8KkdSeveyGqeTdEG2uge9fuREdHZ0p772XB/AX4KP73TLghde53LkN+tm3dxrVr17I0nn8KNzc3Fi5aQp9+Q1i0Sk/tlufJW/4oFRqc5osfUnihYS9Wr9mQqQk3pG6dFxMTQc+O958n3uQFXwL9YdEi6ekWQvx7SU+3EEII8Zyz2Wx079GD9Vu34lG2FP5VKnNrwwZ8o0+zYFoI2YNU9Fodfn5+2O12oqKiuHnTSZcB17l02YGPmg0depJJIJkkzHpfPLRehDkugVaLITAIrd6ILSICUmzk1uellHdFDsbsJCElnrJKTaxY8PT0TLc1l8WRwo6ENYz97BNeffXVx27j5cuX2bhxY9q/+40bNyZ37j97UMuXKY/1ooZiXmXvW0+8PZa9yZtZv2ndU11t/Z8gJSWFnTt3EhkZiaenJzVq1MDHxydL7vXtt98y6fOhnN5TIsPzTqeT5ORkkpOT6DP4Ott2O6hVswY9e/WlVatWmEymLIlLCCEy08PmojKnWwghhHjO6fV65s2dyzfffMP3M2Zw+atpOFJiGT0ygJzZwM3NHU8PTzQaDRaLBVTIk92ddwcEMfjD27ipbiiKBi9NdnK6heKvD0JFJTbuDrkK5OTMybME6nLjoy9CTt98uGndsThTuJ1ynQKUxIEdBXB3c0sXm1FrIoAQflrw02Ml3deuXeOdd99l0y+/YNdq0Lm7Y09M5P0PP6RJo0Z8NmECwcHB6PR6klXrA+tzqo60Z/ZfYzKZaNCgwVO5l9FoxGJxYrM50etdB1babDZiYqJxOh2YjAo2m0q1ikb8vY7x9lsvMX36tyxYsIhs2bI9lViFECKryfByIYQQIgPnzp1j69at7Nq1i9jY2GcdzgPp9Xpef/11Dv/+O/179SJboBud2uQkKDAQby9vNJrUf/JVVU1bOK1JHS/8fbT4GQKp7FuHMuYqBBiyoSgKGkWDUWuifv365MmbmyjnbRQ0GJXUvZUtjmRUwIQ7Dhx4enmi0Wa8grqXzuexhnNfvXqVRk2bsmH/Pvxat6DwyOEUfP9dCo0Yjk/LZqzZtZOGTZpw8+ZNqlavQqR6C6fqTFdPiiOZeHssKY5kbqZcxexjlvnDWaxKlSrY7Do2botxOe5wOIiOjkajcRLgryM2TuX4aSs9O2Zj7tcF2LioAFHhR+jatSNW64O/RBFCiOeB9HQLIYQQf7F27Vq+mTaFQ4f2A6m9oiaTJy1bdeCtt94iV65cj123qqocP36ca9euYTAYKFOmDP7+/pkUeSqdTofZbCZndjd8zOl7njUaDSoqKmA0aMgWpCP+Qvr9ta0OK3HJscyYPhOn3UmKPYWjln0cjz+At94PvWLArtqwaWx4eXnh4eFxz5jsTttjDRce9MYb3ExJJt9rL6P/y7A9rdFAQOVKeBcpzKWvv+PNt95i2NChzJs9j2spl8jtlh9VVbmecpmLKWeJckSSujy7CqqTFpWbP/Gq8eL+ihYtSqVK1Zg64zca1PbBaEz90icpKQlw4GvWoSjwxffReHnqaN00dcuwYoXdmfllXpp2OcTatWufeEs9IYT4J5CebiGEEOL/JkyYQO9endA49jJxlDsbFwawel42XuutY/uW2TRtUp9Tp049Vt3Lli2j7gsvUOuFenTs3p3WHTtSvHQpXnvtNS5fvpyp7fDw8CA6xo7DkX7ZFpPJCErqMGunUyU61oFOcR1q7XQ4OHvnOCm2FEIceajp0YSmgZ2o4dOYEF0eEpyxuOfUky04G8lusfdNuFVVJUK9Tu26j7ZF1+nTp/l11y4CG9V3Sbj/ymA2E9DgBTZv2YLZbKZzt86ctv7BlaRzHIzdxYHk30jM70NA+zZk69UDc5vm6AuGsnHbVga/+SZOZ/pe8X8yh8PBvn37WL16NVu2bCEuLu5Zh3RfI0d9xLlLBnoOusDV6xZUVSU5OQk3k4b4BCcffnqHn1cl8MFbuXFz+3OURKniHlSv5Mac2TOeYfRCCJF5pKdbCCGEAObPn88nH3/A269682pPMwZD6vfSFquTzq11tGyUjZffuUOPHp3ZvXs/BkP6vavvZdy4cYyfNAlDoQIE9+yGe84cOK02Yo4d56eNG9j4yy+sWLqUEiUyXnTqUdWvX59JEz9h665YGtT2cTmn0+kxGAxYLVb2H7ATHuGkolsOlzI3o29w2XGWEFMuSnpXTDse5B5CkHsIN1PCOHZ7P/Ua1mXbph3E22Px0pkzjOVayiUs2hT69OnzSG1Yu3YtqtGI+QHPxLdMKSJWr2P16tVMnjwZRVGY/sN0bFoNAe3b4F6o0P979p145Q0hd52axB09zuwfF5A7Vy7efvvtR4rrWXA4HEyfPp2ZM77l6tVLgBNQcHPzom27LgwZMoSgoPuvEv4slC1blgU/LqF//55Ua3aGWlVMBAXYSExysnVnMg6Hwsfv56FL28B019ap5smX0489g6iFECLzSU+3EEKI/7zY2FjefHMQ1SsZePc1P/z99Hh5avHy1OLvp8PHW4unh41xw324fu0ia9aseei6V69ezfhJk/Bt3IB8fXpiLlIYvacnRj9fstWuSb43BhJjMtC5a9fURc4yQZkyZShdugKff32LpCRHuvNmsxmLDSZND0frcMNL6wOAzWnlfPwJ/rDuQqfVUs5cPcP6Q0y5yUYuTp04RbFSRTiYtINwy03+uiGKQ3VwKeksp21/0LV7FypUqPBIbYiKikJn9kaj0+K0O7DGxGCNiUV1uLZHo9ej8/IkOjoaq9VKvnz5cCjgVas6xkL5cGAHDXh6eRIQEIBOp8OvXBm8q1dlyjff/H+4c8ZiYmL4/fff+f3337N8y7N7cTgcvPrqK3w05l0ql77Nyrl5ObmrGLtWF2RALz3r1/xA82YN/7FboFWrVo0DB47w2effEmcpx8yfEjh9TuX1/jnZv6kMvTplvFiaRqvwnG+wI4QQaaSnWwghxH/exIkTSU6Ko1+34LQe7rsUwGTUoCiQU3VQpbyBhQsX0KZNm4eq++tvvsEQmpdsdWpleF7n7kbOzh25+PkXrFq1ivbt2z9pcwD47PPJtG7djA79zzNySHYqlPFEUVITmcPHkxk1IYbjZxRUA2xLWIlBY8TqtGBzWFF1KnX8mmHSpp8Tfldet0Lsu76Fj8d+zPQfpvPbnj2YLO54qT44cRLDHVS9gz4v92bcuHEoivJI8Xt5eWGNieHa6rVE7j+I/f/Jsd7Tk4DKlQisXhW92Run3YE9IRFFUWj24ovsO3gQp16Pd9UqaD08wOHAabORkJCIVqPFzT21TQHVqnJh115Wr15Nx44dXe59+vRppk6dytLFy0hJTp3vbnIz0bptKwYNGkTRokUfqS2P6tq1a8ybN4/du3Zw8eJ5wq6G8dYrwQx/M2faSuDeXjrefCUHHVsF0K7PRfr17cX6DZsf+Tk/De7u7nTp0oU2bdpQskQhWjTW8PpL2e97zZ79CRQsWOopRSiEEFlLkm4hhBD/WtHR0SxatIhdO3eRlJRE9hzZad++PTVr1kxbzdvhcPDzogUYjQplit97sS+jQYNe56Rwfg2/HrjyUPe/cOEC+w8eJKhLh/uWMwUGYMoXyk8LF2Za0l2iRAmWLl3Nq6/0o3Wvs+TPoyMkWMut2w7OX7aTN29Btmz9noCAADZt2kRsbCy+vr6sXLGSo7+cxqS7/0Jj3jofHA4HycnJrF23lt9++4358+dz4dwFdDod5Su2o0ePHuTPn/+x4s+VKxeJEXdI3vMbnhXKYcoXCkDyuQvc2rWbO/v2U+ClPljuRKFYLGzesoVjly/hXbY08Tdvoff1Sa1Ip0NjMOBIsRAbF4dWq8VgNGD088Xg68OFCxdc7vvrr7/StXM3bLEOcujyEWQKASDCcotlc1eyasVq5i2YS926dR+rXfejqiqTJk1i8qRP8XC30aCWG/mzWzh7Uc/0+TfZsCWKWVMKUbTQn7+b7MFGPv0wO11e+Z39+/dTuXLlDOvdvXs3Cxcu5NrVyxiMJipVqkK3bt0IDg7O9Hbci8lkon2Hbvy47Fte7eXAwyPj1e4vXE5my85Exk/onrra/j/wiwQhhHgUknQLIYT4V5o1axYfDv+QxLgkfNQAtOjYpznIgrk/UqJkcebOn0toaCg3btzgTmQEOq1CUtL9F9YyGRUSEmwYjQ+3EvfNmzdxqE7csoc8sKwhezBhmTxEuFSpUuzc9Rs7d+5kzZo1xMbGkqegmTHjmlGrVq20Lx769u2bds2vv/6aOiT7AeyqDUVRMBqNKIpC1apVqVq1aqbEffv2bUaOGYMpfyh+7dug9/dPS7zcCxfCp24twucs4Nx3M9AZDRiB3w4cIFvPbtguXUb9+wJpioLWZMTudJCQmIifMXU+vupwoP3LNmfXr1+ne9ce6GJNVPaugVb582OSt96XvGohDsXtome3nuzau4vcuXNnSnvvmjRpEhM/H8Nbr/jxSs9gtFo70TFR+PvquHDJyusfhNOx32lWzS9G3tx/vgdrVPYmNJeGn3/+OV3SfePGDfr07sHRowcpGKqjVDEDSckOvvvmF778YgIDB73NO++8k/ZeyGovvfQSSxYvoO+bF5k+OR+ef0u812yK4uW3L5KQpPLeu4MZNfJ9mjZrRe/evSlXrtxTiVEIITKbJN1CCCH+dWbOnMmQN98h2JmbCp4lMWpTExRVVYmyRXDiyEFebNaCTb9sxG63o9NqMBo0rNmcQLHCxnvWa7OprN+aRMcuD7cSt9FoBBQcKQ+eq+1MseDmdu/h3I9Lo9FQu3Ztatd+uJirV6/OisUrSXEk33d4+Y2UKxhNBqpUqZJZoaaZPXs2EQnxFHh7MHEpKTgSk9CYjKn7gCsKWnd3Ajq159rnX2BJSkZV3NFm90cbkp2U6GhsERFYI+5gCAxAdTpxJiahqk4UgwGr1YLD7sBy+xb2uDhKlSrlct/E6CRqedV1Sbjv0ipaynpVZ0fsGmbPns2IESMyrc23bt3ii8njGfySL2+9mrqwXVKyBQXQ6RSKFDSy6LvsNO50jYnTrjPl0z9HEGg0CoUL6Lh584ZLnVFRUbRr2xK75QILv89N9UpeaV9exCfYmT7/NhO/GIvdbmf48OGZ1pb7yZs3L7Nm/0jvXl2p0vg0nVp7U7m8F1arg7GTr3HsVBIF8xno0TGY7MEmbty0smjljyxdsoC3hwx/Lha+E0KIv5OF1IQQQvyrREVF8cH7HxLszENJ74ppCTeAoij4G4Ko5FmX8LAIPv30U4KCgjCZ3ChS0J35S+K5HXHvXt4fl8URfsdJjx49HiqW4sWL42s2E33k6H3LOe12kk+dpnaNGg/XyCzUoUMHPM2enE08fs8yNqeVy7azNG7WmJw5c2bq/Z1OJ7PnzcOzTGlMZm/8/PzQazQ4k5KwJyRiT0zElpCAqtHgUaI4nnozHgYzbnlCMWpMmIuURGMyEbNtBzG/7uL65ClcHT+RaxMmc+2zyURv3kLCrZvc3vorubLnoEGDBkDqFzLz5swjm5ITvUZ/z/h0Gh3BmtzMnT03U7ccW7BgAUaDhZd7/jncW0FBBVRn6oJivj5aenc2s2ZTFHcibS7XxyeqmEyuX5JMnTqVqMhz/Dw9PzUqe7sM0/byTJ0TPuz1AL6eOolLly5lWlsepHr16mzZupNOXd/kpxU6er9+k24Dwjh1LoWxw7Ozb2MZBvXLTqsmZgb0CeTX1YV49zUfJn7+EbNnz35qcQohRGaRpFsIIcS/ysKFC0lOSKGQ5723mjJp3cipy8+Sn5disVh4sUU77kSBXq+lQ78bHDvl2jNttapMXxDDiAlRVKv+AoULF36oWNzd3eneuTMJB37HGhN7z3IRu/eiSU6hV69eD1VvVvL29mbMx6O5rQnjWNxBrE7XZxFni2Z//HY8Ak18+OGHmX7/uLg4wsPD8SyQDwCdXod/gD/+fv54uLvhZjCiqCpaRYtnnnxYVQv/z0wB0OoMmMtVJGHfAWI2bcGYJzcBndoT2K0TXpUrkXz2POc+/4LEw0cY+cEH6HSpPdrJycncuROJj97/gTH66P2JiYklISEh09q9d88u6lZ3w8vzzx52g8GAgkKK5c9VvFs08sRmd3Lo2J/3vh1uZd8hCzX+8qVNSkoKP/04hy5tzOTOee/RG/26ZcPX7GTOnDmZ1paHkStXLkaMGMGx42fY+9shsgUFMLBvMC/3zE5CQjwREeFERt1JfUXeoVcnTzq0cGPypPHYbLYH30AIIf5BZHi5EEKIf5Vt27bhowZg1Nx/3nVOU14uJpzkwIEDvPzyy6xc8TP1qus5dzGFJp2vUa6UkdLFjCQlq2zekcTV6zYMJjPz589/pHgGDRrE6nXruPzddHJ0bId7ntxpPY5Oq5WI3XuJ2vgLgwcMoECBAo/d7szUu3dvVFXlg/c/YEf8FXzUAPSKkSQSiFeiyZs/L/MXzKNgwYKZfm+NRpO6ZPzfepH1Bj16g56U5BSSk5LQK3pS/p+L+mh9CTt3HrVh6nZiiWdOoc8WhH+Hduj9fVF0etAoGPPkxrN8WaJWrkZ39brLvuh6vR5FAacz/RZrf+dUHWnXZBaLJRmPANe+EK1Wi9FoJCkpBZNRQaNR8PRIfe+kpPz5fL747gZ6vTft2rVLO3bixAliYyNp2Tj0vvc1GjU0ecGdnb9uAUZlWnselk6nY+/evSQmRtOjQ16ioiLRasHLU0Gv14IKFquT5JRE2r9oYOGKq6xfv54WLVo89ViFEOJxSU+3EEKIf5WkhNSE7H7sTjs2pxWH00FSUhIlSpTgy6++Zcuu1OSuU+sgdDojW3Za2bQ9hcho8A/IzoYNmwkICHikeIKCgli1fDnFQrIT9u10Lnz1NWGLl3J5wULOfjKB+F+28vagQYwcOfJJmp3p+vTpw7ETxxjz6WhKNyhGnqoh1G1Tg3k/zuXAwf0uCWtm8vLyIk/uPMSdPpvheavVioKCgobk8+fw0poJdS+EMzaehJMnSDxzGuudOwR27YwxRwiKTodqt6FaLDitVvQmI4X69kLj68O3336bVq9er6dU6dLctl9/YIy3rdcoXqJYps7BD8mem+NnrOn2pvb09MKpaoiOdWCzqxw/bQUgR4iR6Bg7H467wrwliYwY+THe3t5p1yUnJwMq3l4ZrxD+V2YvHcnJ996vPKudOHGC0Nx6vDySMBnB31eHu5sWvU5Br1fw9NDi76ujUH49vmY7hw4demaxCiHE45CebiGEEP8YKSkpXLp0CafTSc6cOTGbzY9cR/ac2TnCiQzPRdsiuZh4mhspV3CoDhyqnQ/e/4CIiAi6du3KipXrmTp1Cj+vWvv/Hk8tJpM3Xbp1YtCgQeTNm/ex2pU7d252bN3K9u3b+fGnn7gcFobR3ZNqr7xCjx49Mn0V7McVFhbG+fPn0Wg0FClShODgYAYOHMjAgQOfWgyKotCvd2+GjR6NpWF9jH6+LudTk1IFa2QkyafPUMhYDrPej5y63Fxbtw6Nnw/GfKEYgrOhoqI1pY54cCQlo3E68PXxRafX4V2pAouWLqVRo0aoqoq/vz+9+/Ri4P5BxNjuPcw81hZNJLcZ1T9zFx7r2LEj3bv9zKGjiZQv7Zl2XKfT4evrR0xMDJHRdr6fF4NOp2HazJts252CqrjzydhJ9OzZ06W+1K3AtJw+n0yeXPcf9XHqnIWQkMydm/8oVFXFbreh0aTuP57RDmEajYKPtxYFlaNH779GghBC/NMo6t+/Un3OxMXFYTabiY2NdfmGVwghxPPjxo0bfPvtt8z76SdiYmNQVTAZDbRp0ZIBAwY8Uq/qpk2b6Ni2E5Xc6rokTmFJ5zkcuw8TbgSTBxPuODUOkkyxRCq3KF2+NEuWLsbf35/bt28TFhaGRqOhYMGC//p/X/bu3ctXX01mx/Yt8P/twjQaAw0aNuOtt96mZMmSTzWe2NhY6r7wApcT4snTuwfGgD9/j/HxCcRcCSN60XLco+3U9mmMTqPHodr5PXY3YfbLeNevi7l2rf8PU1fBbkdRFHx9fDAYDTidKpFnznLl629xV/RoNBoUjYZcuXNis9uIuBpJabcq+BuypU0FSF35PpyjyfsoUbE4a9etydSebofDwQv1amFPOc3iGfnJFmRwOa+qKnMW3mDIqBvkzl2IgoUKUqfOC3Tq1Al//4y/IGjapCFm9yMs+Obe0wCuXE2hxotnmTjpezp16pQpbVFVlZSUFFRVxc3N7YH7bE+fPp333nmFfRtykS/PveefXwqzUqlxGCHZC3L8+KlMiVUIIZ7Ew+aiknQLIYR4pk6ePEnrdu24nZCAV/mymIsXRdFqSbh4idh9BzEkJTLz+x9o2rTpQ9XncDioWrkq10/fopJXXQwaIxGWm+yN2kIwuSmglETFiQ0b3t5euLt7EGeL5veknVSqXYHVa1Y/MEn4N1m6dCmD33iFYoVU+nQJoHolL5xO2Lorllk/RhJ2w8jMWQuoW7fuU43r0qVLtOvQgXNXLuNWuDCeBfODqhJ3+ixRR47i4XSjtk8j3LV/9gqrqsq66CUY6lTCq1oVVFKHjbu7ueHm7oZGo8HpdBIVGUXilTAiZs2nmkdt/A1BJDriCUs+zy31Gh7ebiTGJ+GpeuOrCQIgWo0gUYmjSrXKzJs/75GnGTyMy5cv07bNi9gs1+jRwYeWTfzw8tBy6lwycxfdYeP2ZHr1HsAnn3zyUO/RFStWMODVXnw0NJDenbOlOx+fYKfbgIuE3fDnt32HcHNz48iRI8yaNYs9u7eRkpJCcHB22nfoQocOHR448iQmJoYff/yReXNncuXKZUAlV67cdO/Rl65du+Lr65vhdceOHaNKlTK8O8CXD9++93P9YFwEcxfHozcEcunyg6cBCCFEVpOkWwghxD9efHw8VapX57bqJG+/3ug8PVzOO+0Own5aBOcvsm3zZooWLfpQ9Z45c4YWzVsQczOeXLoC3Ei5gs1qo7RSFQdOnDhwczNhNvukXRNhuclh2x5Wr1tF9erVM7OZ/1gnT56kcaO6tGmq5bNRoWi1romcxeLk5SEX2XPQwM5d+wgJCXmq8SUkJLB06VJmzp7NmbNnURSFYkWLEh8by6UTV6jsWQ8PnZfLNb/FbCMyu0JAzy54eHrg5eV6PjoqGovFSuKu30javpfGvq3R/WWLsHDLTQ6n7KZNx9bY7XaOHTkGQPGSxenZsyf/Y++uo6M62geOf+963AUL7u4uxSnu7k5xipXitAWKe7HgENzd2kJbihZ3CBLitpvNZu3+/kgJzS8QoC3Wdz7nvOe87M69d+Zm0+xzZ+Z5PvvsM5TK1++T/rtCQ0OZPXs227ZuwGiMJzk1u5J8+QvRq9cXtG7d+o0fCsmyzIQJE1i2dC6f13CgS1sfihZwItFkZ8/haFasjyI6zo2Nm7ZTpEgRRo8ezbq1y8nkDw3ruODilLw8/cBxI05O3qwMXEf58uVfeq27d+/SpnUzIsIf0qiOM1UrJgfop36LY+eBBDy9srBh41by5cuX5tjHjx9TsGBeHHUWZk/2oXXj1N/nZFlm9eZ4vv4ukkpl3bh804Wbt95fiTNBEIRXEUG3IAiC8NFbuXIlQ0aNIufIoWjc3V/axm61cff7WXRq0JA5c+a88bmDg4P59ttv2b51OzHRMeSlON6KDCiVSpycHHF0TB3gy7LMKf1BGrWvnyrB1uuEhoaybt06du3cQnR0FC4uLtSq3YDOnTt/NNnIX2XIkCGcOrma0/vyo1K9PJAzJNgoVesGPXp9xYgRI95zD194/nVFkiQiIiJo1LARd67dI6OUjQCHnGgUWow2A9f1F3kqPyVTj65kKFY01f5gq8VKZGQkiiQbz5YsI5spA0VcS6e51nX9BRI8Y7h6/QqOjo7va4ipxMfH88cff/w52+xPoUKF/tYKDFmW2bRpE4sXzePu3RuADZBQKnXUrtOAkSNHkSdPHsaMGcOa1QuYMtqPds18Uj2ACQs3M3BMMBevati95xAFChRIdY24uDhqVK+Mi0MIaxdlJ6N/6iXiYeFmOn5xn6g4P44d/xlPT89U71utVkqXKopaesSTZyaKFtTSqrELGXxVPA21snFHPNdvmene3p+zlxLx8q/J+vWb3vpeCIIg/NtE0C0IgiB89KpUq8Zt2Ua2Tu3TbRd67ATmU79y58YNnJyc0m37/23dupWuHbtSQVcbR5ULGo2a5M2+aV2O/x2/Eu6c/PFkqtfNZjNxcXE4OTmlCsJ27NjB4MH90KgSaVTHiayZtYRHWth10EBUjIKRo8YxcODAj3K5utlsJl/eHAzopmFQ74zpth3zTTCHfnLjwsWr76l3rxcTE8OMGTNYv3YDsTGxyHYZhVJBxkwZkBUQYtCTpXMHnLK+SFKn1+uJD48kdvtueBhGVbc6aWbKARKsBk4lHGDVukCaNGnyHkf17siyzIULF3j8+DEajYYSJUr8mWwt+QFVhfIl+XqoO707+b/0eKPRxudtb5MhoDq9e/cFIG/evGTKlInFixcz9dtRnN6XJ03A/VxYuJkK9W8xZNhkBg4cmOb9WbNmsWDeBCaN9Gf/0RhOnk6uay9JUKOyO13a+qFQQLs+j1i1egu1a9f+F+6KIAjCP/OmsajIXi4IgiB8MA+CH+JQ+fVLuZ2yZSXu6AlCQ0PJmTPnW13Dy8sLpUqFRqtFo9Sk29Yu21EqX/xpPHv2LMuXL+fA/l1YrRZAokKFynTu0g2tVkv/L3rSooGWSaPy4eL84rivh9pZsOIZ06aOx8nJiR49erxVn9+H6OhoTCYjhfK7v7ZtofyOrN4Sis1me6dLq9+Gh4cH33zzDaNHj+bMmTMYDAa8vLwoW7YsBoOBtu3b88uipehyZMe5YH4USiXRt++gv3gZnUVFOZeqLw24AZxUzqgkNSEhIe95VO+OJEnkzZuX6Oho9Ho9N2/exNPTE41Gw9q1a3FxttKppc8rj1epbLRpomP4xO2cPHEApVKJJKmpWasef1w6T8PaTq8MuAH8fDU0ruvE2jUrGDBgQJoHUd26dWPL5g0sCnzADzOysmxWLvQGG64uKrRaiWM/xTFwzBMqVa5JjRo1/rX7IgiC8D6IoFsQBEH4YFRKFbLN9tp2stWKRHL5pNex2+08efIEo9GIt7c3BQoUQKvVEJYUQnbHPK8+TrYTK0XQoHgtAObOncu0qRPJmVVi1EB3sgfoiIq2snXPL/TqeQylyoFKZRTMnJQNhSJ1AKHRKBjaNxNR0Va+nz6Ftm3bvvUM/bumVifvY04wvv7+GxJsqFXqj3LG3tnZOU0Q5uHhwZ5du9i7dy/LV67k/OFj2Gw21AoFzmY1VT3ro1W8uoyWXbZjs1vR6dIvtfWpiI2N5fvvv2fL5nUYDLGAHVDg5eVPh47dOHf2DFXL63BwePkDFZPJRFxcLJ9V1ODkKDFjQkY+q+TGydPxrFi/n9t3YvmyT7bX9qNqBTc2736CwWBIs9fe3d2doM3b6dC+NXVaX6dcSR3lSjpgscgc/SmBW/esVKlam2XLVnw0D34EQRDelOJDd0AQBEH431W6VCkSrt98bbu4a9fx8/UlY8ZXL4NOSkpi2bJlVKlcnnJli1H9s/IUKZyXIUMGUrxUcR5b7mKTra88PsQUjFVppkuXLgQFBTFt6gSG9XXj5K589OmcgTqfedCuuQ/bV+Vm+BdexMdF0rWtS5qA+6/6dvUnISGaHTt2vHaM74Msy5w8eZKOnTpRsEgRYuMMbN4ZQnx8PFZr2nsjyzK/no3nu7lPidfHkzmzL4UL5WXcuHHcu3fvA4zgzanVapo2bcq+PXt49uQJYSEhzJ8zB1ltx2q3pHvss6THKNQKKlSo8J56++5ERUXRuNHnbNuykO5tJU7tyc3tXwtxdGtOmtQxsmTRd5w7/ztazcu/ElqtVuLiYtFpwd9XhUol4eKsJKO/lnbNfdi1JjdqFRiNCdhsr/79Al5af/uvAgICOHb8J5b8sBaVQyWC9jiz64g7OfM1ZvOWvWzcGJQmWBcEQfgUiJluQRAE4YPp1qULhzp2QH/nHi65X75s3Bwbh+HiZQYPGZIyO/v/6fV62rdrzaVLp/m8hiNjB2fEw03J3Qcm1m09zqXLZhIVcD7+FMVcKqBRvFhmLssyYUlPuWG+SPO2zShYsCA9e3SmYW0dQ/pkeun1tBoF3p5KiuQHm92GUvHymbfMGbUUzKvj4sWLdOjQ4S3vzr/LarUyZMgQ1m7ahNLPF9ca1ZAfP+HQT79x4XIs+XIbcXVxTdmzbrfLfDXlIauCwsmcQUn/7l74+uh4+MjE5i3zCVy5hOnfz6Vt27YfdFxvQpIkJEmiUaNGjPEbw+2wyxRzrfDSmXubbOWB6QaValR8aabtT82gQf2Jjb7B3nW5yJHtxcx9vtyOTByZlYZ19NRqcYPfzmuRZTnNPTEajSgUMq4uak7+YgQgW5YX53F2VpE/twNnzifSukliukHxj7/EkylTZpydnV/ZRq1W06hRIxo1avR3hywIgvDREUG3IAiC8MHUrFmTzypV5uS6DWRs3waX3LlSfek3hYXzaM16svv7061bt1eeZ8CAfty+9Qs7VmWneOHnX+hlihTU0rC2I0vXRvDtnCgMihh+1O/Fh4y4qT2xyhbCrU9IUOhp0LQ+8+fP5+eff+bRo/vMn5LtldezWmUcdAokCRITE3F2enUQodGAxWLhzJkzXL9+HVmWyZMnDxUqVECheH8LziZOnMjqoCD8WzbDo0QxJEnCbjZzLyqcniMeM22sJ+WLxaFQKtBpdXw75wlrNoczfpgHrZt44eHpifRnAroR/e2Mn/aIYUP74+npSZ06dd7bOP4JnU7H1OlT6dWjN3/oz5DfqRha5YsA0mCN52rCORTuMGnypA/X0X/J3bt3OX7sEPO+9U8VcP9VqWIudGntTeCmcH78JZZqFV/U0paRMZkScXRQADKBG+MokNeJwgVSZ3Tv2NqPKTMfMaBHPEUKOfOyRIVh4WZ2HjAwZNjIj3KbgiAIwrskspcLgiAIH1R8fDydu3Th+M8/o/b3wyl/PiSlAuODYEx375E3Z042b9pE9uzZX3r8zZs3qf5ZBeZ960ez+l4AmM1JxMfrk5dM//lXbvikcHYdMlOtWk2ePQ0lJCQEjUZD5aqV6Nq1K9WqVePSpUt89dVX/PrLcZrU86BsSRdaN/HG0yP1DPvug1H0G3GX3WszkiObC+5/qff9V4YEG8U+u4pC5Y0xIQa1yg6Axaoge/Y8DB02kubNm/87NzIdYWFhFClRHIdqVfCvXi3Ve9aEBIJXr8EcfJecmWUqlXbAZlezJiiCoX3dGdTTHzc39zSBkt0u06HvHSLi83H06I+fVCAVFBTEsCHDSIg34iH7oJF0JJJAHFFkyJyB1WtWUaZMmQ/dzX9s2rRprAmcyvmj+dG8Yvk4QFh4ErnLXSJ7gI5tgXnIld0BSM6PEB4RjpuLxA+r45ixOIZF03PRqK5XquPj4q2UqX0JT3eJbasKvbxkWP/7RMW+vGSYIAjCp0pkLxcEQRA+Ca6urmzbupUTJ06wavVqzl64gM1mo1TOnHQZOoxGjRqlm9Bqw4YN+HhBw9rJM3RJSSZiYmKRZAmVpEahUCIBnZp5s/fQY3489hOt27fi93NnUhIyhYeH07xZY37//TT+PjYa1nbAajXx/YJYvl/whKF9M/FF9wwpgWWdzzzwcFOzYVs8Y4e9WE4ryzIXLidw624iANduxhMalkCd6lqG9AmgYpnktucuGVi65hED+vfg2bNn9O/f/13c2hQbN27EJMtkKV82zXsqJydy9O2D4f4DHh48xK0NN/D19cfVRU2/rtlwd3d46TkVCok+Xfxo1+cK586do3TptPWuP1atW7emTp06BAUFcejQYeLj4vH186FZs2Y0bNgQrfbVWbg/JaGhoWTPqk434Abw89WSNYszSRZ36rW5R7P6zjSu54mLk4JfzxnYud/A5etmvuyXOU3ADeDmqqJ7B38mz3hKpQa3aVTHiaoV3QD4+dc4dh1MwN0jMxs2bhUBtyAI/5NE0C0IgiB8cAqFgho1avytUkAPHtyneGE1arUCu91ObGwckqxArdCkWuRavKAOlQrsSRrWrVmHRqNh5syZmM1mWjRvjD7uJqvmZaJSWQdi46LxdFNiSJBZvCqWqfMeY7bIDO2bvMdbq1XQoZUv0+c9pkQRd9q1cOfAsRhmLXrCjTvJ+17tdpkEox0HnYJenfyoVPbFE/DSxV0oVcyZWYtD+PabcZQtW/adBq3Xr19HkzkzKoeXB9CSJOGSMwfOfXtzfcwEsmXLiZeLCR/vl7d/rlJZV9QqG1evXv2kgm5Izpbdu3dvevfu/aG78s7odDoMhtcvaLRaZcxmmZ69+6JQKFi/LpD12x4DMgaDEW9PBasX5KFGFfdXnuPcpUSqVatO/QaNWbN6OVv3BgOQOXMWhg3vQbt27UTALQjC/ywRdAuCIAifNKVShSUxObBITExEttvRKHQpAbcMWO1mTFYrduxkym7ExUXBpo3LOH7sIAULFScs9Cb71uf8c9+rjMqgIiHRhoeHijFDvHBwkJi95CnNG3iR9c8kUlq1hE3WMXxSFPOWR/HgkZHqlRxZ1d+P4kW0JCbKnLmQyNY9CXQfdIep47LRoYVvSr8lSWJIn4zsOhDPihUr3mnQKsvy61NHJ3cKSUpun15W9r80R6GQsNvt/0IvhTdhs9k4fvw4u3fvJjo6CmdnF2rVqvXSGfrKlSuzetUirt8yUiCv40vP9yzMzNjvgnkWamDmjO/QaDSULFWGXr37U7p0ac6dO8fkSSNwd3v1V8Zfz8bzy1kTixb3pUmTJvTt2zf5d1GWcXR0/KS2HgiCILwLomSYIAiC8EkrVqwYv51PIl5vxWQyIaFMSfglAxZ7ErJk5eylRLQaiWXfZ2JXYFYObsxI7apGDh7cRbPPtX9JNCXh7OxCklnGYLAhy9C3szsuThLrtkQgyzJbdkcy64dohg0bwYiRE7l5N4lOLV2YNcmbQgW0yLIKlUpB7WrObFiSgS6tXfhqykNu3Dam6rtCIdG2uTv79+3EbDa/dqw2mw2LJf1yVy+TN29eLE+fYktKSred4f4DFHaZggUL8se1JIyvqeF94XICSWaJXLlyvXWfhLd39epVKlcqR+dOLbj+x0a08jFCgrczcEA3SpYszJEjR1K1r127Nn5+mZm+IAS7Pe2M90+/xlG10WWO/BhNjw7uzJniy4ThriisZ5g8aRRTp35Dy5YtKVmyEp2+eMjhkzGpzmOzyew7Ek23wY+oWKkG9evXB5IfKDk6OuLk5CQCbkEQBETQLQiCIHzi2rZti8WqI3BDOHa7PSXghuTyT7Jkw8lRwaogPSUKOZInhxYJBZkzqGjTxAsHLdSsosb6lxrDOp0OVxc3jIkykdEWbHaZWtUc2bgjgtotbzFkbBgtWnbl66+/Jjg4mHy5nPlmTF68vXzw9vbBy8sbJFCpkktVTRjuja+XklWbwtP0P2c2HVarmdjY2JeOLzExkfXr11O71mdkyeJH1oAMlCldjDlz5hAREfFG96hNmzaorTaifj/3yjayLBP582ny5s7FyJEjSTCqCdoVme55l68LIyAgJ5UrV36jfgh/361bt2jRvBEuDg/Ysy47R7bmY/mc3Oxak5efduWhZKF4unVtz7Fjx1KOUalUTJ02i2M/W/li5AOePnvx0OX6LSOdvrhNsUJqjm/LwrRxOWndxIeubf3YujIPW5Zn5erlEwwY0I+16zZSpHhNug0KoUqjm4ya/JCRkx5SueFNen/5jDLl6hMYuOaVJf0EQRD+14mgWxAEQfik+fn50av3AGYsjiJoZzw2W/JSZ5nkoNtsgWETIrh8LYmB3bz/PCp5uXWiSUajkXB1kUg0JqY6r6OjI15eXmi1ThgTQaeVeBJiJSBnAzYF7WL27NnIssyunVto18wNjUaFWq1GpVQhSclFk57PCqpUEm2bubBjXyT/v2hIdIwVUODk5JRmbGFhYTRoUJcRw/vi73mD6WO9mDXJm0qlIpg3ZzzVqlXgwoULr71HmTNnpkO7dkQdPEzc9Rtp3pftdp4dOoL51m1GfjmcgIAAWrRsx5RZ4Zw8HZe2vSyzYHkIew4nMnjI8Pda+ux/1dixX+HvHcfm5bn+UhYvWY5sOpbPzknV8hLDvxyUnLX/T3Xq1OGHpas4+auW8p/fom2v2wwec5+G7W/g6S4xc6IfWTJ7pwmYy5d2Zc6UTJw8cZibN2+yadMWdu0+TMmy7bh0MzeXb+emXKWO7N13jDVr1qVbe1sQBOF/ndjTLQiCIHzyvvrqK4xGIxNnzuGHNTE0q+eGq7OCe4+NHDxuRJYlpo7OQGy8jSVro7DJVvLm1pA/nwsg8TTERtbMJlxcXFKdV6VS4+qqxtXVhdCIeKrXqENg4KqU9w0GA0lJJnJmd/t/PZLQarWYTEk4OiYH4DmzqTEm2jEa7Tg5KVNabt0TQ7lyFdIE3RaLhfbtWxMTcZnDm3ORP8+LPbmtGnszepCFboPv06F9Kw4fOUnmzJnTvUfTpk4lOjqanWvWE5UtK24liqF0ciIpPIK4s+chJpYp4yfQrFmz5PbTphMVFUmHfnupUl5Hi4aeeHmouB9sYsO2GG7csTPsy7G0adPmjX9OQjKLxcKBAwdYv34NDx/cRalUUqx4Wbp06ULp0qXTLMm+c+cOp34+wfzv/HBxfvlXN6VSYuSATNRpfY8jR45Qr169lPdq1qzJ4SMnOXbsGMePH+X2o3Bi408zor8fmTP5vXIJeM0q7uQICGXVqlUpyf4+tYR5giAIHwMRdAuCIAifPIVCwbfffku1atVo2rgp85bGo0BBkmyiayt3MmXQMG1RBBHRFrw9ldhliI6xk9EvFk8PFUG79JQrnXam+bknIWZOnE5k2vTUAaaDgwNIEjFx1jTHODg4EhNrwmRKzmAeE2dHkpIznz935MdYfjufxJIfeqQ5ft++fVy/doEDG3OmCrif8/JUs3ZhTio1uMWyZcuYOHFiuvdIo9GwcsUKmu3dy4rAQH7ZuRe7LKPVaGjdsCHdu3enVKlSKe21Wi2BgavZsWMHq1etYOBXZwEZhVJFrVoNmPhtDypVqpTuNYW0goOD6dC+Nffu3aBsCR2Nazlgttg5fDKIJjs2UqduYxYtWpz82frTjz/+iEZtpX5Nj3TPXTCfI3lzajhx4gR169bl2LFjrFq1khMnjiDb7SiUSmrVqkejxs24fPkstar5pLvnWqGQqFbRkVPnL/1bwxcEQfifJIJuQRAE4T+jdu3azF84n+FDh+Nm9iZK8ZCYOBvbD0bSqpEz3dv7kT+3GkmSuHrLwtI1sWzZredBsInPKhno0TE5u7jJZCcqxoJWo8BBJzFkbDDuHv40bdo01fW0Wi3ly1Vkx77fUmUmT35Pg4POkXi9EbtdZvs+PZXKuqJSSSQk2Ni0M5Ips8KpU7dJSgKqv1qzeiUVSjtQuMCrHwa4uqho08SVdZvWMHr06HTrmQMolUoaN25M48aNSUhIwGg04ubmhkajeWl7lUpFy5YtadmyJTExMSQkJODh4fHSpfDC60VFRdGyRRPU0iMOb86ZKqP410Nl9h+NYei4nfTpY2PVqjUpAbHRaMTZSfXaetsA7m4KEhISGD58OBvWL6dIATWTR7qTwU/DkxAzm3bsY8/u7YAVheL15cQUCinNlghBEATh7YigWxAEQfhP6datGzqdjnFfj8P02Ma+43qmjPKiT+fkJeCSJCEpFBTJr2XBd35kz6Ji/IwoRk6K4sRpCyDz61k9SWY7FqsMSCgULmzdthxHx7Qzzp27dKdP75Mc/TGWmlXd//KOhKubKwqFxJ5DkZz6LZEihVS07X2Hi1eSMCaqad2mO999NxWlUpnmvNevX2FAt9fvk61WyY1Fqx4TEhJCjhw53vg+OTk5vVXw7OHhgYdH+jOtQvqWLVtGXOwDjm3LTUb/1OW9FAqJBrU9UakkegzZw+nTp1NWEvj6+hIbbyUi0oKP96uTlVmtMg8fW0iw3OHa1d+YOTEDrZv4pGrTrZ0vc34IYdzURxz7OZLu7bO88nyyLHP6dyO58xT8B6MWBEEQROYTQRAE4T+nXbt2XL1+lTz58pE7h5qubVyRJAUKRfL//lrDu2tbV3IEaPDPmIO9h2O5cl1Pr04uzP/Oh1kTffi8hgtqtYXvvp1MTExMmmvVr1+f2nUa02vYY1YHhZOY+KLMVqLRzpY9iYz6xkDBwqXJX7gVbj6N6dXna878fomZM2e9cpb5TWcXU8YiZiM/ahaLhQ3rV9GigUuagPuv6nzmTt6cKgIDV6a8Vq9ePTQaFzZuTz9b/dGfYgkNl7lx4zJ9O3ukCbjheX34TOTMpmPl+gjM5leXhTv9u54bd6x06tT5DUYoCIIgvIqY6RYEQRD+kxQKBbExEQzt7Yk+QcZmt+PgoECl/LN+t9lOgtGOxSrRroUnU2bdpl83fyZ8mQFJSg5gNRo1HVqpuHojgXZ9ztGjexe2btuZah+sUqlk6dLljBjhwdffrWfa/HDKFNciyzJnL5nRG1S0aduDqVOnvVVJpTx58nPq9wv07Zp+u9O/x+Pk5ELGjBn/zm0S3pOHDx8SGRlGvZqvnlmG5KD485ourN/xS8prbm5utGjZnoWBS6lSwZVihdKugAgJTWLC9GdkzpyN0NB7dGsfkO51Jo4MoEPf2wwYfY/53+VKs3T9+i0jA0Y/oWTJ8mLvviAIwj8kgm5BEAThP0mv12OzWcmdywsnJw1GYwIJiVYUf8bLdhlUSjXu7i5k8DWhVMD4YVlwdEz7p7FQfidmT85E5wE/8fvvv1O2bNlU72s0GubMmcuQIUNZv349t27dREKiS7f8tG/fnixZ0g+0XqZjp64MHnSaW3eN5M2Vdlk7QEKCjQ3b42nZqk+qxFvCx+d5GS+d9vWLDHVaBWazJdVr48eP5+qVP2jc8Rca1namU2tfShd3JjrGyubdkSxbG4NGl5UKFapw5cJT/H1fvoLiucb1vMjo78TOAybO/XGTds3cKFLQCaPRxt7DsRz+0UiBAqVYtXqdKAknCILwD4mgWxAEQfhPcnJyAkkiItKKs7MnTk5OJCWZsFptSJKEWq1Go1Fjtdl4FmbCyVGBg0PavdXPfVbJjRwBz1i7dm2aoPu5rFmz8tVXX/0r/W/UqBHz5xWg26DbrF+cg2wBqZOk6Q1Wegy5j9HkQq9evf6VawrvToYMGVCpNFy6mkCJIunv1b94xUhAQIGUf0dGRrJ48WIePLiDMdHK+m0RrA4KB0lCpZCQFEr8/LPQvlM7wsLCeNOdBq6uapo075a8WmPdBhISQgCJPHnyMWlyD9q0aSMe5giCIPwLRNAtCIIg/CfpdDqqVavBlt3H6dzaF0mS0OnSBhBWi5Ud+wzUqOKOQpF++aTypR24cuf6u+x2Cp1Ox4aNW2jTujmfNb1F/VpO1K7mhkol8fsFA5t3xyPjweo1G8iWLdt76ZPw97m7u1OnbkPWbt5J59a+KJUv/6w9fZbE4R8T+ObbTgA8evSIli2aEBd7nzZNXGlYJy9ajZ0z5yNZtzWWc5eSKJzbgZw54lkwbwpGkwKllMTTZ0lkyvDqveO37hp5FmalSpUqNGzYkMmTk3MWaDQa3Nzc0i0lJgiCILwdsV5IEARB+M/q0qU7l65a2L4v6pVtNu2I5uZdCy0aer/2fDabjFL5/p5XZ8mShQMHj/LV19O5dCMT/UaG02tYGNv2a+nYeTjHT5yifPny760/r6PX6wkLCyMpKelDd+Wj1KdPX+4FS3z1TTA2W9rp6Ng4Kz2HPsDbOzPNmzfHZrPRuVM7lARzZEtuxn0ZQOECOrw9TDSq7ciBDZkZN8yLh09MtG3mzdnD+ejYwgF9gplFK5+k25cfVofi45ORunXrAqBWq/H19cXd3V0E3IIgCP8yMdMtCIIg/GfVqlWLlq06M/jrVTwINtG5tS/eXsnJzMLCzazcEMbCwBiQtMQbrOmey2qV+fGXRKrXLvo+up7C1dWV3r1706tXrz/3qdtwdXV9aZmxD8FqtbJz505Wr1rB+fO/AzJqtZb6DZrSvXt3SpYs+aG7+NEoWbIkM2ctYNjQ/ly8covOrT0pXdwFs9nO4ZOxrNsah9XuzcZNQTg7O3PkyBFu3brCnnXZU2atDXoDkmTDw12FQiExtI8Hv180sTjwGbvWFuD7Cdm5ct3IosAIcmRzols7v1RBtN0us3DFMzbvNvLd1O/eKrmfIAiC8PdI8ideYyQ+Ph43Nzfi4uJwdXX90N0RBEEQPjI2m43vvvuOFcsXYbclkD+PFlmGG3eSUKtd6PfFEC5fvkTw3f0c2JQX7SsSXW3bG8mgMeEcOfozBQv+O3WL7927x8aNG7l//z4qlYqiRYvSpk0bvLy8/pXzv0txcXFs3LiROXNm8/jxQ/Lm1NCrsx/ZA3TcvZ/I+m2xPHwMEydNpWfPnh+6ux+VM2fOsGjRAo4eOYAsJz/scXR0oUXL9vTt25eoqCjWr1/Ptq2byZ4llp2rc+Do6ACSRGREBC7OEo5/yT+w/5iBXsPCObGzMLlzOBAWnkS+CpdRq1Xkz62jdRN3/HzVPA0xE7QrjuAnMsO+HMOwYcM+1C0QBEH4T3jTWFQE3YIgCML/hNjYWLZu3crNmzeRJIkCBQrQvHlzXF1duXz5Mo0a1qFKOZn532XDxfnFQrDoGAu7D0YzcUYoteq0YvnyFf+4LwaDgcGDB7F/3w483GSKFdJgtsicvZiEjANf9B/Kl19++VFmjZZlmVmzZrFwwSwSEqIokEeBo4OCm3fMyeXXmvkwcWQASqXEd3OesHi1nqXL1tCgQYMP3fWPTlhYGI8fP0apVJIrVy6sViu9e3Xn1KkTZMkoERaRQLP6joz4whNZllBrNJiTkvD1VqXKPxAabqVU7WBWL8hLjSruALTrfZv4pJJkyZKFgwf3YDFb0Gq1NGjYjK5du1KiRIkPNGpBEIT/jjeNRd/Z8vKHDx8yefJkjh8/TmhoKBkzZqRDhw6MGTMGjeZFGYvLly/zxRdfcPbsWXx8fBgwYAAjRox4V90SBEEQ/sNu377N7t27iY6OxsnJidq1a1OqVCkkScLd3Z0ePXq89LgiRYqwMnA9vXp2oVStmzSv74wMnDgVy43biQAolWpu3bxGYGAg7du3T/W37G2YzWY6dmjLtas/M3OiH03qeaXMrsfEWlmxPpQ5s6dgMBiYNGnS37rGuzR+/HiWL5tL386uNK7rT46sGhwdFMTrbazfHs/0+eGER1r4YWYuvhqSmVv37jJ79nTq168v9gr/P35+fvj5+QFgNBpp0bwJTx9fYMWcjNSs4k6D9teQ7RI+XiqMiXb0hiRkWU5zH01Jz+vKv3jdzVWBPdGJH35YiizLJCUlodVqxc9AEAThA3hnQffNmzex2+388MMP5MqVi6tXr9KzZ08SEhKYMWMGkPxkoHbt2tSsWZMlS5Zw5coVunXrhru7uyh/IgiCILyxkJAQBg8awKlTJ3B3lcnoryYqxsrCBTMoULAIs2bNp0iRIumeo3r16pw6/Ttr1qxh7txZxMdFULKojpkTM5AvjwsJRonte4MZN3Ywe/fsYs3a9cllyd7SunXrOH/+J7auyEapYi6p3vNwV/HlF5nxcFczfvoCmjdvTtGi73cPeXrOnj3L8mULmDzKlxYNHElMNOCgS35g4OqipG9nD3Jl09B1UCi7DkbTrL4XPTr40q7PFS5cuCD2d6dj9erV3Lxxjn0bclIgb3Jd9pJFXNh7OAKrFZwclUgSxMXbMCXZU+47wIFjBjRqBYXyJX8eZVnm1l0rRUpmBPgzc78u7UUFQRCE9+KdrVurW7cugYGB1K5dmxw5ctCoUSO+/PJLtm/fntJm/fr1mM1mVq5cScGCBWnTpg0DBw5k1qxZ76pbgiAIwn9MaGgojRrW5eG9Eyyc6sfF4/k5vCUPZw/nZ/3izCjt12jWtAF//PHHa8/l7+9P5syZke0mpo3PxvEdxenRMRuVynpR5zNPfpiZk60rsnHl8kkGDx741n2VZZlVgcuo+5ljmoD7r7q08SWTP6xateqtr/EuBQYGki2LROfWvlitVtRqif8/cVqrqhMVyziwelMYABVKuwJWbt++/f47/Imw2+2sWb2CRnWcUgJugE6tfYmMtrN+ezwADjolKqWEIcGW0iYu3kbgxnga1fXCwz15LuXMeQO371tp2bIlkPy5O3/+PCtWrGDp0qUcPXoUqzX9xIGCIAjCv+e9bhaLi4vD09Mz5d+//vorVapUSbVEr06dOty6dYuYmJj32TVBEAThE/XVV6OxWx6zc00uGtfzQq1O/tOmUEhUreDGtpW5yZszkf5f9OZ1aUzsdjvz582iUR0H+nTO8NI2pYu7MHGEH/v27uDevXsvbfP48WOWLVvGjBkzWLp0KcHBwQA8efKEu3dv0bxh+onSlEqJJvVcOHH84OuG/14dPbKPFg1ck/cTp7NKuWUjF87/YSA2zppyz8Wy5ld7/PgxwcH3aVzPM9XreXI60KGFDxOmR7Fuaxw2m4xOJ2G1ypiS7Dx6YqFd32cYE2Fw7+RZ7fAIMyMnPSFfvsJUrFiR48ePU6vWZzRsUJPJE4cw7bvhdOrYnLJlihMYGPja3wlBEAThn3tvJcPu3r3L/PnzU5aWQ/LsRPbs2VO1e763KTQ0FA8PjzTnSUpKSlX/Mz4+/h31WBAEQfjYhYSEcPjQXqaM9sHf9+V7rB0dlXw9JCPNu93k1KlTVK5c+ZXn++WXXwgOvsu8ydnSvW6Tel58MzucDRs2MHbsWIKDg1m7di1Hjxzg1q3bGI16XF2UeHvpiIu3MnHCV9SoWZfu3XsCMh5ury/35eGhIiEh4bXt3hdZljEkJODtnRwYqlVqjEYTdllG8f8Cah/P5PHpDTbOXjIAqn8t4/t/kclkAmRcXdJ+LqZ8lQ2bDUZNiWDu0lhqVnVArZK4ftvMz78l4u2lZtX8POh0ChYHPmP5+mhQZGTHprXs3LmTgQN6Ub6UgnWLMlOlfPIDkyvXE1i5IYwxXw3m8ePHjBs37v0PWhAE4X/IWwfdo0aNYtq0aem2uXHjBvny5Uv599OnT6lbty4tW7b8x2VDvvvuOyZOnPiPziEIgiD8Nxw+fBilwkyz+unPHJcp4Uy2LEoOHDiQbtB99+5dlAobJYqkv1dbq1VQrJCGe/fuMX36dObOmY6DzkxioglvD4n+X7pTs4ojzk5qtFo39h+LZ84P+xkw4Dw2G9x7aEp3eTnAg+AkfHz8efToERs3buTu3bsoFAoKFSpEmzZt8PHxSff4f5skSXh6eBL8KPnBt4ODAwkJBhIT7Tg5pg4Wg59YkKTkfd5L14RTrFhpChcu/F77+ynx8fFBkpTcvJuY5nOhUkl8PzE7nVr7snpTGBt3RJJgVJMlIDvOLnHExsfTtMt9QEKjcaJhow6MHj0au93OkMFf0Ly+lpmTsqXKdl64gBOzp+SgcIEwxk2bTfny5alVq9Z7HrUgCML/jrcOuocNG0aXLl3SbZMjR46U/x8SEsJnn31GhQoVWLp0aap2/v7+hIWFpXrt+b/9/f1feu7Ro0czdOjQlH/Hx8eTJUuWtxmCIAiC8B8RHx+Pi7MSZ6f0Z44lScLfV0VcXFy67ZRKJXYZbDZQveYvpMUic+fGDQ4f2sGXfT05c0HPvQcWdq3JRAY/FVarTJzehskUS4uGXnxW0Y0mne/i4ODE+m3RtG7y6qBZb7CyfZ+eTFmyUb5cCVycbRQtoMYuw+xDm5jx/Tf07NWf0aNHo1S+ftb839K4SUu27lrMiAF21GolDg6OGBISUCoktDoFEskz4uu3xVOtohtTZj3mt/M21q4b9d76+Cny9PSkRs26rN1yiPbNfV66FL9wASe+HhbAjgMJjJ8wmYEDB2K1Wvntt98IDw/HwcGBsmXLpmzj+/bbb3HQJfLNV/lTBdx/1a2dHzsPxLJ8+Q8i6BYEQXiH3jro9vHxeeOn60+fPuWzzz6jZMmSBAYGpqk3Wr58ecaMGYPFYkGtVgNw5MgR8ubN+9Kl5QBarRatVvu23RYEQRD+g9zd3Yk3WInXW3F1efWfNFmWCQm1kr+oe7rnK1asGLKs4vipWGpXe/nfIYDYOCtnLpiI019n9EAvGtfzYsaip8ye7EMGv+R+qFQSHm5KomNsGAwG/HzdGTnAjz7Dn/L7BVi2NpSeHdM+YLZaZUZMDCYi0ozNdpnJo/xo1cgbxz9nk+PirazaGM7Mxd8TGxvL999//972S3fp0oVVgT8wZdZjJowIwMXFBbtsJ1afSPB1E0+fWTlxysjZSyY83CVsdgWz5yymevXq76V/n7KePXvTutV+Zi0OYWjfjGl+pmazncFfP0Slcadt27YAqFQqKlWq9NLz7dyxmab1XFI+N6/Svrknw8afICoqCi+v9FeMCIIgCH/PO0uk9vTpU6pVq0ZAQAAzZswgIiKC0NBQQkNDU9q0a9cOjUZD9+7duXbtGkFBQcydOzfVTLYgCIIgvEqdOnUAB7buiUq33enf9Tx6aqNBgwbptitcuDDFi5dhcWA4VuurE0yt3BBGghHcXKBXJ3/2HYnG2UmiUe3Uy9IVCglHR4mkJBM2u436tTzw8lBSslRFJs6IpP+o+1y4bECWZaxWmcMnY2jZ/TZb9ySg0ykInJeVLm38UgVObq4qBvXOyLSx/mxYv4Lffvvt9TfqX5I7d26++XYGKzYk0G3QXc5eNHD+skzPoVE06RxC7+FhrNumx2IBF7dszF+wlFatWr23/n3KKleuzFdjJjH7h1g69rvL8Z9jSUiwERtnJWhnBPXb3eKn3ySWLl31RpMfUVGRZM3y+kmK7AE6wE5UVPq/Q4IgCMLf984SqR05coS7d+9y9+5dMmfOnOq955ky3dzcOHz4MF988QUlS5bE29ubcePGiRrdgiAIwhvx8/Oj3ueNmb98CzWruBOQOW2QoTdYmTQzhHz5ClOuXLnXnnP0V2Np26YZg79+wNSxWVMtXbfZZNZsDmf2D9H4ZwigdOEIXF1UxMRZ8fVSotWmfZat1SqIN1ixmC3odDoy+KkpUaIE7dq1Y8H8Wezs+AC1yo7NLmO3qyhRoix580aTNWMwVSu4vbKfbZp6s2RVJKtWBVK+fPk3vGP/XJcuXfD09GTqd5Op2+YaVksSlcromDHRhzLFHNFoHfntvJnl60IZNLAPTk5O1KxZ873171PWv39/AgICmD9vFp36/wFYSU4Tr+az6rWYMXckxYsXf6NzOTk5ER2T9Np2UTHJ13B2dv4nXRcEQRDSIcmfeK2I+Ph43NzciIuLw9XV9UN3RxAEQXjPIiIiaNzocxIT7jK0jw9NP/fC0VGJ1Spz6EQMsxaH8SzCjR0795E/f/43OueePXsYMKA3GpWRJvWcyZFVR2yclR379TwOkenarS9Xr1wiwPcic7/NwewlT1m+NoRLx7OhVqdeFmyXZcIjrbi7uaNW6yhZ8wYdOo9ixIgR2Gw2Tp8+zf3791GpVBQtWpTMmTNTsEAu5n3r+9oEcfOXhTB/pZW79x797fv3d507d476n9egc2sHvh7ij1qtQqPRpiyLNpvt9B3+gJ/OqDh1+ncyZHh5CTYhLVmWuXz5Mg8ePECpVFKkSBGyZs36VucYPHgwp39cwy/786NUvnr7QfdB93gUnpujR38UZd0EQRDe0pvGou+1TrcgCIIg/Nt8fHzYtXs/RUs0ZNSUaIpVv0GlBrcoUu06vb8MxcWjLDt37X/jgBugYcOG/PLLOXr2HsPPZ/2YsTiJDTu1lKvUgX37j/HNN9+QOUs2rtwwI8sytat5EKeXOfJT2hJfVkvys22FUsnxU3FERsvUrl0bSE7cVqVKFbp06UKHDh0oXLjwn2XCZLw8Xr8YzcNDhTHR+EFqLa9YsYJc2VR8OyYnzs7OaLW6VEGbRqNgzjdZUUh61q1b99779ymTJImiRYvSpEkTGjZs+NYBN0DXrl15Gpq8FeJVfj0bz5GfjHTu3EME3IIgCO+QmOkWBEEQ/jMeP37Mnj17iI6OxtnZmdq1a1OgQIF3cq1Tp07RqmUDNi8PoEJpV5p0uk58vImdqzOlqrccG2/FalWi1XnQrMtdNI7FOXDwyCvPazAYyJcvBxOHu9G1rV+6fZg88xHb9jty+crNf21cb8JoNJI/Xw5GDnCiT+f0Z7BHT37I8V+9OHvuj/fUu/+miIgI4uLicHFxwc8v/c/Fc5MmTWLJ4pn07+ZBjw5+eHslJ61NTLSxdW8Uk2aEUbJ0Tdav35SS0FYQBEF4c28ai4qgWxAEQRD+BlmWqVO7OvExf7BlRU6MiXaadLpOBl8FXw32pFoFR0xJMrHxVq7eVDJjcQyPQ1zeaNa9a9cuPLq3l8Nb8r5yBjIpyU6pWjdo0XogEydOfBdDfKXHjx9TtkxRNv6QicrlXr3vHGB1UDhjp8Xy+HGYmE19S7Iss2fPHlauWMrvv/8K2AEFJUuWoUvXHjRt2jSlMkxCQgI7duxg29YgwsJCcHR0omq1WiQlJbF+3UrstgSKF9aiVsOVG2b0BhVNmrZixoyZODg4fNBxCoIgfKreNBZ9Z4nUBEEQBOG/TJIkVgaupUXzxtRpdZf2zV2ZMSE70+Y/oV2fUHy8FPh4KYmKVRAVoyZ/gcLs3PXDGy1z79GjJy1b7GbB8mcM6Jkxzft2u8y4aY+IN6jp3LnzuxheupJLd0rE623ptpNlmZjYRJJMJvr370+WLFlo3bo12bNnfz8d/YTZ7XaGDh3C5qDVVCitZd63vmTy1xAabiZo1wUG9O/GoUMHWbRoMefPn6dbtw7ExYbxWUVHSlTVEhMXyvo1l9EnqOjbbzDe3t5cvnwZu91O1wrZadu2LQEBAR96mIIgCP8TxEy3IAiCIPwDkZGRzJ07ly2b1xEfH4Ms27FaZVxdvciXvyClS5emfv36lCpV6q1memfOnMnMGZNpWNuBHh38KFHECVmGn3+LZ8mqME79bmPW7EW0bt36HY7u5WRZplrViuTJ+oAfZuZ8aRtjohF9fDzNuz3l0VOZMiXcuHnHTGy8gnqfN2b27Dni73Y6Zs6cyexZk5j7TUaafp42od6BYzH0HfGERo07cGD/booVTGL25Kxkzvgig39ioo3Fq0KZtSSGUaMnMXDgwPc5BEEQhP88sbxcEARBEN4jo9HI5cuXMZlM+Pn5kS9fvn+8nDooKIg5s78nOPguapUdWQarTUn+AoUZNWostWrV+pd6//YCAwMZN3Ywe9bmoEjB1PXJE4wJ6PXxnD5jZMBXkaxbnJfqld0xmezsOhjFxBlh5MxVli1bd+Do6PiBRvDxMhgMFCqYh5qV7QzsmYH8eRxfmoF81uKnfDM7jEL5tBwMyoeDg/IlZ4NvZj1m+QYzFy9dw9PT8113XxAE4X+GCLoFQRAE4T/Abrdz6tQp7t27hyRJFCpUiJIlS37w/dEmk4nmzRrz4N4ZZk7KRK2q7igUEjabjWfPwjnyYwJTZkdTvbIHP8zMhULxor9XrifQpPMDBg2ZwODBgz/cID4yFouFwMBAZs2axtMnD3F2UiBJEJBJR8dWvvTo4Ida/aLwzLPwRIpW/YPmDbxZ/H2uV543OsZCqVo3GTHqO/r16/c+hiIIgvA/QQTdgiAIgiC8U3FxcfTs0Y1Tp46TNbOCsiW0JCaaOfZzPAlGmeYNvPl+Qna02rQVSkdMfMCJXz058/tFVCqRYsZsNtO1ayd+/ukgtauqaFRHS6F8OkJCrWzeFc+ugwmUKeHK6gV50OmS72diYiKfNblOlQqeTB+f/j75Dn1vo3aux+rVq9/HcIT/ASaTiQMHDvDgwQPUajXFixenYsWKH/yBoCC8TyKRmiAIgiAI75SbmxtBm7dy4cIF1q5dy727Nzl79iwBmbTsWZ+bvLlevXS8ZSNvNmwP5vr16xQpUuQ99vrjNHHiRE7/fIA1CwIoUVhJYqIBHy8V2bKoqVDagdZNEun4xTPGTQ1m+oQXAbbFKqN7yUON/89BJ5FoSXqXQxD+R9jtdubOncvSHxYQFxeBj5cas9lOnF4mR468jB03iTp16nzobgrCR+X1/5UWBEEQBEF4BUmSKFmyJHPmzGHP3oNkyxZA47qe6QbcAN6eakBGr9e/n45+xGJjY9mwPpBBPb2oUt4NlVqFzS5jsb5YjFi+lAPD+nmwZXckkVEWAB49sfAg2JKqLvzL2GwyV29ayZxZZCsX/hlZlhkyZAgzvh9PywYWTu3Jw8Xj+bn6cwG2rshK1gwP6da1HVu2bPnQXRWEj4oIugVBEARBeGMWi4WdO3fSqmVzypQuSoXyJenXrx9nz55FlmW8vHwJfmJ+7XkePjYBCry80mbm/l+zc+dO7LYE2rfwAZJLsikVShKMNv66B7BtE1cUCplt+6IAWLouEhk1wY/Tn8E+fiqOxyF22rRp81b9SkpKYtu2bbRs0ZRyZYtTuVIZhg4dypUrV97qPMJ/x/bt29myeTXzvs3EhBEBZAvQAckP38qVcmH1gly0bqzjy2EDCQkJ+cC9FYSPhwi6BUEQBEF4Iw8fPqRa1Yr069sJm+kUTWrHUadKBJfOBdG4UW26detK3XoNOHTCmDIb+yobtkWRN29B8ubN+556//F68uQJmTJo8PZSAyAh4ezsgilJxmCw8Tz7jrubkmxZ1AQ/NjFr8VOCdhpp3qIdOw4YCdoZ8dJz339oYtTkp5QpU5HixYu/cZ9u3bpF5UrlGNC/K1h+oWGNaKqUfsZPx1dRp3ZVBg8ejMWS/s9Y+O8JXLmMKuW1Ly1jB6BQSEwYEYBWk8i6devec+8E4eMl9nQLgiAIgvBaERERtGzZBJ3yMUe25CJ/nhfLx8cMkdl3JIah43ZgTkpCrXVj5KRHLJmRPVW27ef2HYnm4AkjU6f1FkmXALVaTaLJhizLKffDwcEBWbaj1+tJNFlw0ClQKCBOb2PVpigUCkdGjZ7EgAEDGDnSm2Hjl7L/aBwdWnqTO7uOOL2N7XsjCdqlxz9DfpYuW/nG9zokJIRWLZvg7R7ByZ25yZXdIeW98cNltuyO5KtvAgGYM2fOv34/hI/T06dPuXDhdxZP90+3nbOTkkZ1nNi1cwsjRox4T70ThI+bmOkWBEEQBOG1li5dij4umE3LcqYKuCF5dqthHU/mfpOJEycO0L//UI6flmnZ4w5HfozFZkueqn0QbGLC9Ef0HfGUho3a0L59+w8xlI9OmTJlCIuwc+FyQqrXHR2d8PLyxsHBCVOSxNmLJu4HW6larRHHT5xm4MCBSJLEtGnTmDN3KaHR+ek6MIRKDe9Qv90Ddhx0oHPX4ezZexBfX9837s+8efPAHsbGH3KmCrgBVCqJts18+OYrfzYHreHatWv/yj34r5FlmaSkJD7xIkGpREdHA3YCMmtf2zZrZh0xMdHvvlOC8IkQJcMEQRAE4X+U0Whkz5493LhxA4D8+fNTtmxZDh06RGhoKDqdjooVK1K6dGlKlihMs3omJox4dTIuWZap3vQWWXPVpWzZ8qwKXEFw8F2cnSQ0GgXxehvu7r5079GXwYMHo1SmnwDsf4XdbqdihTLkCnhM4LzUNc2fk2WZXkPvcemGzyvLrMmyzJ07dwgPD8fBwYHChQuj0Wjeqi8Gg4FiRfPTp5OKoX0zvbKd1SpTtu51atbpwffff/9W1/gvu3z5MitXrmTvnu0YjUbUGjW1a9enS5euVKhQ4ZNe2fHw4UMqlC9B4LyM1Krqnm7bcVODOXLKizO/X3ovfROED0WUDBMEQRAE4aVkWWbhwoUsmD8TvT6KHFm1yLLMgvl6TCYbOq2afHmcSDDamTvHTsaM2YiIeEa9GjnSPa/NZqNaRSWzlwRx9MgeIHm5tLtHVipWqkqtWrWoXbs2Op3u/Qz0E6FQKJgw8Ru6dW3P0LEPmDAiAHe3F1/R4vVWpsx6zIHjZpb88M0r65pLkkSePHnIkyfP3+7L9evXMRr1r/1Zq1QSdT9z5rezp//2tf5rFi1axJTJY8nkL9OnkxtZMnkTGWVh866dtGyxjS5d+zJlyhQUik9zoWnWrFnJkyc/W3bdTzfoNpvt7DxgoFnLru+vc4LwkRNBtyAIgiD8D5FlmfHjx7N82Vy6t3OlR4e8ZM6oITo6muAnTmzepWftFj3VKzkyfngWfjunZ/z0R9wyGlEorK88r9liJjYmBrXShrubgp9250GjkTh70UDgxlC2bV1P0aJFRcD9CnXq1GHBwmUMGfIFew7foF4NRzL6aQgNt7D/WAI22ZHZcxbTqFGjd9oPs9kMyDjo3qD2t4Piz/ZCUFAQUyaPYVBPN4b2zYRS+WJGu08Xf9ZtjeCrbxbi5eXFsGHDPmBP/z5JkujcpQfjxg7h1Jl4KpV9+azevGUhRMcq6Ny583vuoSB8vD7NR22CIAiCIPwtp0+fZvmy+Uwe5cvEkVnJkkmLwWDAajVTOJ+OqV/7MmW0N8vXhfLLWT3lS7uydmFOlEqJX3+PfOkeVbtsJzYmBpVK5tY9C4XyORKQWYu/r4aGdTzZFpibPp1dGDd2BL/88ssHGPWnoWnTppw7d5lhw7/h/tMC7D3uye1HeRk0ZBJnz/5B69at33kfAgICACWXria8tu3FK4lkCcj+zvv0sbPZbHw//Rsa1dHx5RepA25IDlY7tvSlXxd3Fi2cTXx8/Afq6T/XoUMHKlWuTZcBwSxbG0q8/sWDuODHJkZNfsicpbGMGj2enDlzfsCeCsLHRezpFgRBEIT/IIPBQEREBCqViowZM6bsn+7WrSsP7+zhyNa8SJKELMtERITjoAMX5+Q2sixTo/kTcuVwZums3AC07XWT+w8NHNteAGfn1InUjMYE9Pp4jIkyVZs84duvs9GhRerEXbIsU6fVLTJn/5xVq9a8hzsg/F0tmjclKeE0O1fnTrUH2W6X+eWsntv3EgkNMzNnaSQrVm6gcePGH7C3H96RI0fo3KkF+zfkoEhBp1e2Cws3U7beLSZOmkPXrp/u0uvg4GA6duzIhfO/o1Ta8XBX4e2lISTUhqurDyNHjf2kxycIb+NNY1Ex0y0IgiAI/yGXL1+mf//+FCyQi4oVSlC2TFHKlC7GnDlziIyM5Mjh/bRq7J4STJnNZuyyPdVyYkmSaNXYhcMnY7Fak5/ND+2biTv3LYyd+gi7PfXz+sTERBJNdvqNDMfHS03Temlr+EqSRKfWnhw9coCoqKh3eAeEf6pvv/6c/8PKtHlPU1Y2bN0TSZWGl2nT8waTZgQzf/lTLBYzs2dNZ//+/a89p81mIz4+/j9Z2/v69et4uivTDbgB/Hw1FMij/WQzvtvtdqZNm0aliqV59vQyLRu70rqJG77eSh4EJ5EhQw4OHDwmAm5BeAmxp1sQBEEQ/iO2bdvGkMH9yORvZcQX7hQr5Ispyc7+o9HMmzOejRvWYrGayeD3IqP186BK8f8SiWf0V2G1yhgTbbi6qChd3IXypV1ZtUnPtdu36NzKk1LFnDGbZbbvjWTLbj0KhZJNy/Lh5KTAYrUg2+1IkgKVSoUkSRTK54jdHk5YWBheXmkDc+HjUKNGDcaO+4bJk8bwy7kEfL0V7D8Szec1HPnmK3/y5VIjSUpu3NWwfN1denTvwNRpc+jUqVOac128eJGVK1eyZ882zElJSAoFVatWp0uX7tSqVeuTzub9XHJ99TdrK0nJ7S0WCydOnODhw4eoVCpKlChBsWLF3mk//6lvv/2WRQu/Z1hfT3p0yIGLc3IYIcsyp87EM2zcYzq0b83efYfw8PD4wL0VhI+LCLoFQRAE4T/g3LlzDB7Ul+b1VUwfnwuV6kUU8Fkld77oZqJNr3sYE4yEhCWlvPc86LHbQPGXbwVPQy2oVBKODi+icRdnNeUrVMXPz49RUw4gy+EAGAxGShZxYNWC3Hh52ImMjMJqezGjqVQocXBwRG+wAtJbl7ES3r++ffuSN29evv12MkE7fmJYX3d6d3ZDqVCic3DAycmJqr5KqpR3Z9zUR3w1ehhlypQhX758Ked4ns07IBMM7eVG1izuREZb2Lb3OF06H6JJ03bMmzfvldnY/w5Zljl//jz3799HpVJRtGjRd763OG/evETF2Lhx25imhv1fRUVbuHbThKdfKGVKFyMs7AmODhIWix2LVUnRoqUY8/V4KlWq9E77+3fcvXuXRQtnM2awN327Zkj1niRJVC7nxublWuq1vc3ChQv5+uuvP1BPBeHjJJaXC4IgCMJ/wIIF88iTw8708dlTBdzPZQvQ8cPMbNjtMuu3RqXMcGs0GhSSgkSTPaWtLMsE7dBTt7pHyrkeP03i9FkTnTp1YfXqtVy4eI1du4+wb/8JWrXuRJJFhZNjInp9HCqlFQ83Jd6eKjzdVWg1dhIS9Gzd/QxvH3+yZs36fm6K8I9Ur16dnDlzU6ywG6MH58DbyxsfHx9cXVxR/rk0QpIkxn2ZBW9PG6tXr045dtu2bUyZPIaBPVw4tTcf/XtkpGEdT7q29WPv+rwsmpaBvXvWM3HixL/dP7PZzIMHD7h//z6JiYls27aNz6pVolHDmgwe1J3+X3SmcqXStGrVnPPnz//j+/EqNWvWxM8vM8vWhqbbbnVQGHF6O8eP7aFGxViObMnJ7d8Kcff3wqyalwmt4hJt2zR7o+X679uaNWvwdLfTvb3fK9tkC9DRurErGzesJikp6ZXtBOF/kQi6BUEQBOETFxYWxtEjB+jcxvOlAfdzRQs6UaSQI1dvJLJyQ/IstSRJODg4Yky0Y/lz//by9XHcfWilc+vkZGhWq8y4aY9xcfGmSZMmAPj5+VG6dGmKFy9Or169uH0vkV37o3BzVeLupkKrUaBSSmjUEq4uKuINMtv2xqPROKJWq9/tDRHemM1mIyoqiri4uDSZ6ZOSkti7dwctGjii1+uJjY0lOjqGhIQE7PYXD2nUagWtG7uyY3sQkLz3d+aMqXxeQ8vw/plQKNJ+JhvV9WJ4P2/WrF5GeHj4W/U5JCSEiRMnUrRIfipWKEGliiXJni0jnTu3x8v1NpuWBvDgXGFu/VKI+d/5ERvxI82a1ufo0aN/4w69nlqtZuCgL9m828iS1c9emuF/14Eops4LR6W0MWNCBqaPz54yK65UStSs6s7m5Xn4vIaK/l/0IiIi4p309XWuXLnCl19+Sb26NalT+zO++OILfvvtN3768Sif13RCo0k/dGjyuScxMRGf7L51QXhXRNAtCIIgCJ+44OBg7HYLZUu4vLbt59XdcHH1Yvz0cL7+NpiHj0w4OzuhUmn442oiIyaFM+H7KPp2yUC5Ui78dk5P+753OHFaZsHCpTg6pl0+W6RIEWRZzddTI9m+z4DF8iLokGWZMxcSad/nGe5uap48vs/du3f/1fELb+/JkydMnjyZIoXzUbhQHvLny0HFCmVYsmQJ8fHxyLLM7NmziY2JJqOfDY3aik4no1RaMBjiiYyMSDWbmT2rLiVR2qlTp3j48A69O/unu2e7YysflIpEgoKC3rjff/zxB7VrVSNowxzaNbEStCwLGxb706uDgqyZZC5f0yNJyQ8CnJyUNP3ciz3r8vBZRejdu+tbB/hvqkuXLgwYOJIps2Ko2fwWy9aGcvhkDGu3hNOo4y2+GPUMNzcfalR2pW0zn5eeQ6WSmDo2AGQ9GzZseCf9fJXExER69uxBndpVOXl0JQVy3KBInjv8cW4TzZrW5fr166jTeaD3nJuLCpAxmUzvvtOC8AkRe7oFQRAE4ROnUCQ/Q3+eaTw9VptMlswZ6NipB/PnzWBV0G2yZtYgy3Dnvp6kJBsOOjVHfjSy48BNQsNtZM+eh/UbZlK5cuWXnvPo0aNoNHbqfubFiEmRzFgUTfVKjmjUEuf+MHH9tplC+Z3YOjMXDdrfIygoiDFjxvyr90B4c6dPn6Zrl/YopVhaN3GldHF/LBY7R358ynffjGTtmpU0adqSObO/Q6uRsNnA1eXFV0abXUavtxEbG4OHhycajYaoGAtqjRqVSsXNmzdx0MmUKJJ+Nm9XFxXFCum4efPmG/U7Ojqajh1akzVTDGsX5sXdLblPMTExFMzrwZdfeNFzaCjdB93m8JbCBGTWAskB+OzJ2ShZ8wYbN25k0KBBf/POvZokSYwePZqqVauyYsVypszeh80WA5JElSqfMW16I0aOGET7Fv7pnsfNVUXD2k7s3LH5nfTzZWw2G7179+T0z3uY921GGtV5sWJGlmVOnIqjz5f3WLM5ka8GZ8bBQfnKc928mwgo8fdPf5yC8L9GBN2CIAiC8InLkycPOp0TR3+MTTeRk90uc+RkAkVKfE7fvn3p2rUr+/btSwl68uXLR8mSJdm/fz+hoaHodDoqV65MpUqV0p2xfPz4Ma4uKlbMzc3NO0bWbo7g4hUDVquNbAHOfDXElyrlXVEoJPLn0fD48eN//R4Ib+b+/ft06dyW4oWSWDYrX0oGakhe8j2sr4m2ve8yedJYenfy5s4DJdv2JdCmqVtKO6VCws1VRXSsFb1Bj6eHJ9v3xlG9ev23zkb+kpXYr7Rhwwb08c9YGZQvJeC22+0kmZNwdVbg6KBg2Ux/ytV7xOqgMMYOC0g51s1VRaM6TmzdsuGdBrMVKlSgQoUKJCUlER8fj7OzMw4ODly6dAmwkz1A+9pzZA/QcfyX97e8/NixYxw9spfV87NQo4p7qvckSaJ6ZXc2/JCNeq3vsCgwlGH9Mr3yXGs2R1KiRBly5MiR6vWHDx+ybt06rl+/hizbyZevAB06dHjnSe4E4WMhgm5BEARB+MS5urrSuEkr1mxeTec2vqlmJf/q0IlYHjy2M2t+ch1dnU5H8+bN07Tr16/fW11fo9GQlGTDapXJl9uRb8a8OlGaMVEmo/b1gYfwbixduhRnRwMrZufFySntjGW2AB0Lp/lTs/ktcuXQUaGMC1+MvMvRnxKoWeXFzLUkgZOjgth4M5t3hnPjjo1xk7sBkD9/fhJNEuf/MFCq2Ku3PMTFW/njmoka9fK/Ud83rF9FozpO+Hi/yAlgt9sAOWVm1tlJQZsmzmzcEcHoQVlS5TjIncOBAyfC3uha/5RWq8XH58UycmdnZ0AiKsb62mMjoy04O7u/u879P6tWraBYIXWagPuvShb1pG4NJ2YsekrvTn44O6f9b0zgxjB+/i2JxUv6prxmNpsZMWI4m4PW4e5qp2wJDZIEW4MO88OSuTRu0opZs2bj4ODwLoYmCB8NsadbEARBEP4DBg4cSILJnU797xMeYU71nizLHP85lsFfP6VGzc8pU6bMv3rtsmXLYrYoOfZzbLrtHj9N4tJV079+feHNGI1Gtm5ZT/vmbi8NuJ/LmVVJ1fIO7DsSTYPantSo7E6PoWEsXReLPsGW0i4pSSZwYxxDxz2lZatOVKlSBYCKFSuSLVtuflgd9tKkYs+t2xKBze5A69atX9t3q9XKw+CHlPn/eQv+nFmX7S+uU6aEA3HxNmJiUwe48XrbBwvucuTIQbZsudi6Jyrddmaznd2HDNSo+fl76hn8+sspGtR2TbeNJEk0b+BHQoKdWi1usWZzOFHRFhISbPxyNp7ew+4xdmoEvfsMoVGjRkDyKoS+ffuwc/sqvvnKi3NH8rNibm6Wz8nN2cP5mTHBh8MHN9KjRzesVismk4kTJ06wa9cuTp48+doM6BaLhdDQUEJDQ7FaX/8wQxA+JDHTLQiCIAj/AdmzZ2f9hi107tyWsnVv8XlNR4oVciIpyc6+o3qu3LBQ7bO6LFmy9K2XAL9OoUKFKFmyHAtWnKN6JTfU6pc/05+/PARnZy+aNm36r15fSC0xMZGdO3dy+vRpkpKSyJAhA61atUKr1WI0GqhYJiDd42VkKpR2YNk6PROmP+LMBT0xsVaGjQtnysxIShd3QKmAi1eTCIuwUe2zusycOTPlc6VQKPhy+Gj6f9GdafOeMrx/JpTKF585WZbZdSCa6Qsj6dptUKoZ4VdRKBRIkpSSYf85lVKJSqnClGRDq03+3D1vo/zLcwW7XWbXQT2VqzR8o3v4b1MoFHTq3J3vvhlFlzYGShZ1fmm7JatCiYxW0Llz5zc+t9Vq5bfffiM8PBwHBwfKlSuHh4fHGx0ryzJmixlnp/T33wO4uWpwdnYia66afP3dSb76JhSQASW5cuVn5qyBtGnTJuVzcPToUfbv28b44T5kyajh/GUDRQo44uKsQqNR0KapDxn9NbTrvY+OHTty6eLvxMVFAnZAgYeHD23bdWHo0KGpEjg+e/aMFStWsHHDamJikh9ieHn50K59F7p164af36vLmgnChyKCbkEQBEH4jyhZsiSnTv1OUFAQGzes4fCPwajVakqXqc2ar7pSvXr1lKRr/7ax4ybSskUjeg69z7SxAfj5alLeMyTYmL3kKRu2G/lu6rcvzYAu/DuCgoIYP240en0kxQtrcXaUOH/GyvJlCyhStCQ2m/21+6iVCiVWm0x4hIXdByPo2saFzyo5EhllY9naOE6dSSTJLNOoridHfjTRr98XqFSpv1I2a9aM8PBwJk0cw66DN2nb1I1sAVoio61s2R3LlRsWmjXvyLhx495oXAqFgiKFi3H0xyt0auX7l3eSS94ZDPE4WmTUaokjJxPIklGTsu8bYMP2CB49tbOwS5c3vJPpS0xM5Pjx4ymBbpUqVciYMWO6x3Tt2pUD+/fQod9pxn/pR5N6Xuh0yb+PYeFmlqwOZdm6eIYO+5rcuXO/tg82m40ffviBFcsX8+zZY8AGSGg0TjRu0opRo0aRIUOGlx5rMBg4fvw4UVFRODg4cPVGwmuvd+N2IjoHJ+bMmcuhQ4e4du0aDg4O1KlTh4oVK6Z6mCfLMpMnT0S2JzLp+0ckB+fg5KikeQNvBvXKiJ+vhkrlXChZBPbv28qwfhlp3SQXGfw0hISa2bQjglUrZ/DrLz8TtHkbzs7OXLx4kQ7tW2GzRtCqkSuVy2VEluGnX+NYtXIaG9avYsPGrRQuXPi14xGE90mS01v38wmIj4/Hzc2NuLg4XF3TXxojCIIgCMK7c/LkSXr36kpiYjQ1KjmQI6uWiCgLB44bMSVpGDtuCr169frQ3fzP2rBhA8OGfkHlskpKFHHGw11FnpwOlC/lwrGf4xjzbQj3HyYysIcPIwd6k5iYiN1uQ5Ik1BoNjg6OaDRqLBYLjTpc4+qNJK79nB1Xl9RL0U0mO72Hh3HohBFPrwAuX7mBRqN5aZ8uXbpEYGAgu3dtJSnJhKRQUL16bTp16kLNmjXfatXFpk2bGDa0Fwc35aJgvhcPbmRZJjo6GpvNTFy8nTqtnzK8fxb6ds2AIcHGmqBwps6PpG27Xnz//fd/7+b+yWw28/3337N82ULi46JQKGxYrWC1SeTLV4T5CxZSrly5Vx5vMBgYMmQQ+/buxN3VTsF8aszm5FUDarULw778ir59+772viRnHO/FoYNbadnQmY6tfMmdXUec3saOfVGs2BCDpMzIjp37yJr1RY6FxMREvvvuOzZtXI3BEINKCbFxSbg42/llXy4yZnBHqUy79cBmk6nU4AZJtizExoRjNifwfEba1dWLdu27MmLECHQ6HbIsM3HCBGbNmsLnNZ3o1taNgnm1GBLs7DlsYM3meFQqJVtX5sfdzcTmnRGMnx7NvbOlcHRMfe3L1xJo3esBtet24Ouvx1K9eiWyZ45lzYKcqR6qAETHWOj4xX2ehnlx/MQpvL29X/8DFYR/6E1jURF0C4IgCILwr4mPj2fbtm3s2L6FyMgwnJ1dqVX7czp06PDKWTfhn4uOjiZfvlw4ahMwW+xoNRKSBKYkmayZdQzqnYkyxZ2p0fwasXFmzhwIwMdLiUolIctgMslY7TJ6g4bFq6NYvDKCyuUc6NDSjeb1XXByTL1CIirGSpk6j8hboDoHDx5M9d7FixdZvXo1Fy/8htVqJVv2XLRu3Y6KFSvi6uqKWq3m7zCZTDRoUJeo8D9YOTcbRQu+WBJtt9u58Ec4PYc+IfiJjSb1PECS+OVsEsZENT16fsHYsWNfGlC+KbPZTPv2bTj10346tHSkTRMX8uTQYEiQ2XVAz6JVsdwPtjNv/g9069Yt3XM9ePCATZs28fDhQ1QqFSVLlqRFixZv/F123rx5fD99LMtmZaZ2teSl5AkJNg4cjyHkmRlTkp2N22Pw9i/K0aM/IkkSRqOR1q2ac+P6r/Ro70675j5kzqjl1l0j1ZtepVIZLTMn+ZHB3yfVfbLbZUZOesjSNRFk8NcxqKcPLRp64+Wp4tGTJDZsiyBwUxyFCldkU9BW9u7dy6CBPRneT0Ovju446FJ/dsIirLTqEQKSio1LfPj9YiL9R0fyx8nieHmm/WwsXxfKlNl6unTtzbo1s/lxV3Y83dWo1eo0KywiIi2U//wmg4ZMem8l14T/bSLoFgRBEARB+B/RsWNHdmzfQNPPnenT2Z1yJXUAXLicxLJ1sew9ksCX/TISGW1g7tIYKpd3YO38jHh5JgdXSWaZ0VPCCdqlByBvLg1qlcT120k4OigY2MOdXh3dsdvBaLKTZJZZHGhgyz5Hrly9jVqtJikpiYEDB7Bn9xYyZ5CoVdUJrVbB2UuJnP8jidy5C7J23SYCAtLfU56e8PBwOrRvw9Wr5yhfyoHqlZxBgl9+T+DkL0b8/LJSrnxloqIiUKlUFCtWgvbt27926febmDp1KnNnT+CHGV7UrJI8tr/OR0fFWGnc6SlXbtg4fuI0pUuX/lvXSUxM5Pz58xgMBry8vChRokSqINhisVC6VFHqVInnu7HZsFjsfL/gKWu3hGNIsOLpriTRJKM32EkwwsxZC+jduzdjxowhaONCgpZlo3jh1HvKj/8cS/dBd/D3VdCptQeN62VCqZA4fVZP4MZITp+Jx8tDzcld+cmUIW31gQuXDbTp9ZCWrfty5swpAvzuMHWMCw46cHFO+6Dj4hUTDTo8ZfZkb56F2Zi3LI6bv5Z8aT6IeL2V3GX/ICnJTufWjowe5JnynkatxdnZOdVKi2HjHnDqnDdnz/3xt+6/ILwNEXQLgiAIgiD8D7hx4wblyhajd0cnvvva56VLk2ctiWbm4hi+/cqLUVOicNApUChkmtRzpkQRLUtWx3LvoYXBvdxpXNeZTBnUODoqeRJiZsnqWFZtiqdnRzf6dHZHrVLj6OjEqd9NdB34jIuXbuDr60vPnj04dmQr30/ISOO6nqmSp125nkDfEY+wkZX9B47g5eX1t8drsVjYv38/q1et4Pr1K8iyTO7c+ejYqSuNGzdGp9P97XO/islkonChvDSqFc/EET44Orw8N8KZC4nUa/uEUqWrcvTo8be6RlxcHHPmzGHjhtXEx0cBMlarHUlS4+zsSkBAFipUrErevHkZNXIAR7bkJHcOB3oOucPJ07H06uhGx5auZM6oxmaTOX7ayIyF0Zy/AmvWBDFoYB/6dlYztO/L62xfvpbAyMkPOPWbHhcXhz/zP6goXKQk58/9xpqFAdSt/uoEbXOWPGXOciPmpCQ2LM5E8cISSUmJ+Hipedlq+c/bPcbDXcm9BxYqlPFg1uQcaRsB0+c/4ds5j5AkiRWz/Wj6efL3/aQkOwmJdqxWcHf3QPtnKcJteyMZNCaS+w+evJPPgiD81ZvGoiKRmiB8hCwWCwcPHuT8+fNYLBayZctGs2bN/tGXFEEQhE/JrVu3uH//PkqlkkKFCv0rM5X/VatWrSKDn4IR/T1SBdx2WcZkkrFY7HRt68LmXfHsOGBAqYRvxmQjItLChm3hBG6MR0Zm9Xw/alR2Ii4+uSyYBGTJqGHKaF98vFTMWBRD84aZKZw/uea02ZwISKhUKn777Tf279vOomkZaVQ37d+qwgWcCFqWg5ot7rBs2TJGjRr1t8erVqtp3LgxjRs3/tvneFs//vgjMTFhtGzkm2a59F+VKa6jUD4tv/76Cw8fPiRbtmxvdP6oqCiaN2tE6LPrtG/mSpPPs6FSJhD8xMTO/QZ2HnhKZFgEe3Ze44cwKyZTEjmz6Vi5IYzjp2JZOSf5Z/ecUilRq4oTxQpoGDAmir59u2O1GGnf/NU10YsUdGLv+oKUqnWdwsXq06VLF3LlysWqVasIf3aemunU8QZo28ybqfOvYjRa8PM1kpioQJZl4vVWXFyUKP5f5F0wr4ZdBxNINMFnldyZveQpMbFWXJyV1KrmTrFCzhz9MZZ5y57yZT9PFq+KQ6GQUgJ4nU6BVqsgLt5KbGws3j7eKBVKbH9WtXtXSSMF4e8QQbcgfGS2bNnC+EmTePLsGRpvLySVCnNkJGMnTqBLh45MmjTplQlrBEEQPnVHjhxh3txZnD9/BkiuvatQaKlZqx7Dhg0XWYn/H1mW2bljM22auPLX6URjoh1Dgg1ZllGpkgOVFg2dmfB9NJIk4+2pom0zHwb0zEDtllfJ5A8N/6zVrDfYSEqy4/RnUisJ+KKrB2u36NmwNYrvxibXyj58IpaAgKx4eHiwalUgObMqaFjHM00fn8uUQUuLBi6sXxfIsGHD/vbe7g8hIiICsJMnx8tnbZ+TJIk8OdVcvGLk+vXrbxx09+/fl+jI6+xem4PsARqioqJQKiBnVidqV3WmRcMEug8Oo0dHH7w94KtvnzDmm4f8clZP47pOqQLuv1KqJCYM96V68ycAqaoKvLS9Mrn/zs7O1KxZE0he0p89QI1KlV5yNxmdzoSD1orBYEelIHmZe5Ido9GO1Sbj6KBAp1WQ/MDGTkysncdPLeTO4UDf4XdxdZbw91URFWNj7tKnFC3oDMgUKaCle3tX9h8zcfyUkSb1XtRqlyRwdVESEWUl0ZiIWq1m/5EwEowmihTOS86ceWjXvhNNmzYVVROED0oE3YLwEVm1ahVDRoxAWzA/2Vs3xyGDPwDWhAQiz5xlcWAgwY8esWb16k/qy4ogCILJZGLXrl1sWL+GBw/uolQqKVa8NJ07d6VKlSooFApWrlzJ12O+pFxJFUtn+lO2hAtWq8yRn2JZuX4fjRsdY/WaTVSuXPmtrm0wGEhISMDNze0/t9zUYrGg1+vJmc0Vs0XGapUxW+zE6204OEg4OypRKpOTpRUpoMXRMfm1afOfUK6UC3qDjRu3ExnS+0UZLpVKwmyRsfxZggtArZZo9rkz2/fH8N3YbAQ/NrHncAIjRvVAoVDw+5lTtGrg8tqs2/VreRK46RH3798nb9687/Te/JscHR2R7RAXb8fdLf22sXF2ZCTsdvtL35dlmfPnz3Pw4EH0ej1ms5njxw+xZHpGcmV3IC4uDkmy4eGuQqFIvp/VKznRo70b67eG8/vhIiQkJPLt3HBUKpgx4eXLxW225J9hQGZnqldyYM+heEwme0qZsleJiZPJnMMh1djj4l8+lucMCQnExydgs4GDg4KjPxnp28UDjUaBTpv8eYyNsyFJNpAkEhPtHDqZiFKlQqGwseBbHz6v6YxGIyUvjT9lZOy0SC5eMRGQSU2jjs+w2RRs2qGnfzd38uR8sa9coZDQaSUMBj2PQywcPK6nXnUPKpVV8PvFPxgxvC9z58xg/YbNb1SKTRDeBbHuQhA+Ek+ePGHEV1/hWLYUWdu3SQm4AVROTvhXr0amLh3Yf/Qo69at+4A9FQRBeDv37t2jWtWKDBncE0f1Obq2stC2sZFH9/bRrm0T2rZtxYkTJ/h6zHB6d3Jmy4rcfF7TEy9PNX6+Gjq08GX/xryUK2Gje7eOf846ps9ut7N7926aN2tMntzZKF4sP7lzZaV792788ssvadqeOHGC7j16ULV6dWrWrs2YMWO4devWu7ol/xq1Wo1ao0ZvkFAqlMTpbegNNhwdFLg6K1Ptq46KtqFSSqxf5M/jpyYWrghFb7ABMr5eyfMwMiDbQZKUxMZZMVteBFveXkoMCTZu3DbSrs99MmbKQ4cOHYDk4D+9ZdfP6XQSIGOxWP7N2/DOVaxYEVlSs2N/Qrp1zh8/tfDL2US0Gg25cuVK8/7169epU7s6jRrWYtvmWVz8PZAN65fg4phIuRLJ98VkSvxzz33qBxgdWrgSr7dy7Kd42rf0xt1NgTHRTrYsaR/Cy0CC0QYo0DnoyB7gABLsOhiV7jhv3DZy/ZaZzz77LOW1qlWrcuOOmRu3jS89RpZljAkJ/PxbInZZ4vOaHgRujCcmNnmdt1ajwMdL/WcGfAlXFzd2HbQTF68gTw4H1i7MQNWKDij+km/tp1+NPH5qIUeAmoa1HWle35lC+VTYZZlynz9i7xF9qrEqlfDgkZmBX0WQO4cDP8zMSb9uGVg1Pxc/786Dk/YxrVs1JTw8PN3xC8K7IoJuQfhIrF27FrNCQcZ6dV85U+CaJzcO+fOybOUKPvEciB8No9HIo0ePCAsLe+WshCAIf19ERAStWjZBpwrmx125Wb84N4N6Z+TLLzJzeEte1izIzB8Xj9KzRzdyZ4cxQ7K89L+BOp2CBVOzYTHHsHHjxnSvabFY6N27F316d8Se9BszJnixal5Gxg514d7N3bRo3oAZM2YAEBoaSq06dWjSqhW7fvuVO1o117CzaP06yleuzOAhQz7qAFGSJKpXr8P2fXG4urlhscjIMjg5vriHspwcGG3ZY6BkES3lSjnRurELG7eH/7mEXCL4SfIYzWY7Vju4u7uhUGqIjrURHWPFkGDj/kMz8Xo7tVreRa3LS9Dm7SmJg7Jkycof1xJf298/rhlRKNSfXPk4Pz8/qlevy7L1cTx5Zn5pG1mWmTo/GrMZypevQL58+VK9f/36dZo2qY/dfIV1izJz9nB+DgbloVEdN4oU0KJUWImOjkaW5T+XYaeWNYsaV2cFIaFmvLxcqVnFEbsdQkKtqdrZ7DJ6vRWjScbF1QWFpCAqxoaHhzcLlkcQF29Nc25InhmfNj8EX99M1KtXL+X1unXr4ueXmWnzQ7DZ0n73MJlMGBKsLF0bR8UyrkwelY0ks0TrXiHcuvviXjk7KTEl2VmyKpzpC6PRapV8+UUG8uTywWKRiIq2Eh1r5eupEawKimPicC/OHMzCiP6eDO3rwZoFGfj9UFZKFtHStvczWvZ4yqqgOFZtjKXXsDAadgxBrVazfkk+XJxV3LhtZPTkh/QaeockcxL3799m5MiRWK0vH78gvEsi6BaEj8SuvXtxLFQApTb9/VYepUty49Zt7t27h9ls5sSJE2zbto3Dhw+j1+vTPVZ44eeff6Zq1ap4enmTK09eAgKykjt3HubPn4/BYPjQ3ROE/4xly5Zh0D9m4w85yJnNIdV7kiRRvbI7C6dmIjz8EaWK69LM7v2Vu5uKxnWd2ByU/mqfcePGcfjgFpbPzsy2wNy0aepDzaru9Ojgz7HteRk90INZM6ewbNkymjRvxoX798jSqxs5B/cnoEVTsrZuQe7RI/BsVJ/ADRsYMmTIR/2gs2vXbty4Y2P3QT0qlRqdNvke2u0yNpuM3S5z+KSRMxeSaNnIBYPRRvOGzkTFWAgJNVOmhDMbd8RjtcrE621o1Bq0Wi2enp64u3kgKTRExcgE7Tbg5Z2D+QsCOXL0JFmyZEnpQ5u2HTnyUwJPnyW9sp92u8y6LdHUql3/k0wMOnv2HKJidDTrEsLJ08ZUn4ngxxa+GBVG0E49SDqGDhuR6lhZlhk4sC9ZMujZvio31Sq6pXzWHR0UJJpkPD2USJIdkF+6b9xikUk0yWi1ClRKFR4ejkiSxNqtcUTFWImLtxITZyUyykqiCVxd3HB0cCQu3sqhE0a6dOlJjN6Tlt3v8vsFfar+33uYSI8h9zh+ysb3M+am2sKmVquZ/v0cjp+y0XvYfR4+MqXq16WrBnoMCSMsQmbiyAACMmvZtCwf8XqJGi0e07zbU8ZOjeDLCRFUb/aUb+dGU75CTVyclTSp54lWq8XHxxdXVzeehEis3aJn1ABPurd3x8lRiUKRXG8eIEeAhi0rMpI/j5YzF8xM+D6aCTOiOHTCSJniLhwMKkgGPzVDx96nVourHD4ZRYkiKmpU0lK6mJod29ZSo3oVgoOD/+nHQRDeitjTLQgfidi4ONSZX//kX+Pmht1uZ+7cuRw+dozQiHDssoyEhLurC+1atWbkyJF4eLy6rMf/ugEDBrBo6VIkjQbHggVQOjpijYom+P59hgz7ksCVgRw6fAgfH58P3VVB+KRZLBY2blhNq0Yu6SZwqlTWmcL5Ndy4lbx89d7DRG7fMyFJkC+XA9kCXuzDzp/Hkd2Hn77yXGFhYaxft5KRA7xfWt5IkiS+6J6BG3eMfPPNBKKTbOQcPACtd+ogUKFS4l2+LJJazfrNQXTp0oVSpUq97S14LypXrkyr1p35ckIgvTtq6djKGTdXBTKgN9gJ2hnPd3Nj+LymJ00+9yfBqEelAKtV5sJlA6WKuTBr8VNWboyleQMP3N09gOTkazqdDq1Wy/QFwch4cODgIbJmzZqmD82bN2fWzKn0/vIh6xfnxM019VdMu11m/LRH3LwL337f9/3cmH9ZlixZOHb8FLVqVaNx56fkyakmXy4NcfF2fr9oIskMSpUjs2fPT0lC9ty5c+e4fu0y6xdnxtkpdd3qcqVc2LI7gkdPrXh7KtAbbFiscqqtAQCHTiZgsUL5UsmJxK7dspAvfxF2H7pDm6b8meRNwtlZi4NDcskvWZaZNu8JdhwZMGAA7dq1o1fPrjTreoM8OVQEZFYQFSNz8UoSXl4ZCFy1gFq1aqUZe61atVixch1DBn9BpYa3KVNci7enkodPrFy6koDVauWn3YXIlzs5WVn+PI78vLcIB47FsHlXJL+cNaPRSkgKNe3adyV79uw8vPcTjs+T9UkSDg6O7DkchZuLgm5t3ZEkCUmRvGfbZn3xgMDdVUWvjm5MmR3DmYNF2LQ9mClzovn262w4OCgY8vUDdh2IZNpYb1o1cknJS5BktvPzGSPjpt2gVcum7Nt/GG9v73/4qRCENyOCbkH4SHi4uxMXE/vadqaISBITEli9OQjXMqXJ1q4VWi9PLHoD0ecusGT9ek789BN7d+0SQeNLTJo0iUVLl+JStgzeVaqjdHiRzdQSHU3Egb1cvnaNJo2b8POpn0XJEeE/ITIykidPnqBSqciZMycODg6vP+hf8OjRI6Kiwqn9WZZ02ykUCmpWdWTlBj0tu93g13N6kndqAkhUKe/G4N4ZKVPChYQEW7rJ0IKCglCrTLRvnj3da/bo4Mf6rddQ5yuaJuD+K88SxYg6dpKVK1d+tEG3JEnMnDkTDw8PZsz6jkWBMZQv5YBCAZeuJmFKkmnb1IfJo7OiVivQ6dRcuhpJoklm/PQIVColWp0nY6fFE/xUTfd2zuTIpkOWZX6/YGDxqlCO/mRh5qyFLw24AZydnVm7Log2rZtRvdktOrX0oE51d7QaBef+MBC4MYorN+x8N3U2ZcuWfc936O+7e/cujx49Qq1WU6hQIQoXLszDh0+ZMmUKa9as4saBCCTAwdGZ1m1a0adPH0qUKJHmPAcPHsTfV6JyubR1fBvX9WLSjEfMWhLNnMm+6A02jIm2VEvMTSY7C1bEUqaEM/nzOHLlegIXLptZuGgcSxYvoPvg84z70o/Gdb1SEqXdf2hizg8hbN9vYvr38/Dx8cHHx4eTP57m1KlT7Nixg6ioSAJyOtO9Ty0aNGiQbnWUOnXqcOHiVXbt2sXRo0cxGOLJW9CbGnUyM2/ud/z/HVpqtYJGdb1SSsg9fGSicqPbVKlSBb1eT0yslYQEG05/eQhx7g89n1VyxOHPOugSEnY7WK12TCY7Wq0CSYL6NZ0ZPz2Kid8/IWhXNGVKuFCskDMXrxjYuieSmRO9ad049b2WJCiUT8umpb7UbfOAJUuW8PXXX6f/ARCEf4kIugXhI9GscWOmzJ6NrbEJZTpfKJ/s3A1aDQE9u+GcPVvK61ovTzLUqYlnyeLcXryMfl98wZbNm9Mcb7FYePToERaLhYwZM6bsx/tfYDQamT5rFs4lS+Bb+3MkKXVArfb0xL9VW56uXsnZc+dTlqALwsfgecbjkydPkpiYiK+vL02aNMHPz++Vx5w/f56FCxdw5PA+bLbkvZUuLu60at2Rfv36vfN9tc/3Tmo16We0VioV6DQKomOsJJmTWDjVh0plHJFlmRO/JLJifRytetxkwdQc7D6kp0LFV9dnvn37NoXz63B1Sf8rTqF8OpydwP6aVUGSQoFTofz8eOrUK9vIsozJZMJut+Po6PjaDN7vglKpZPz48Xh5efH111/i6KjD0UFBny5etGrshYebHZMpgaQkCY1Gw+5DJgoWKsaKFWtwdHQkc+bM/PDDDyxZPI/VQbfx9lRhscjE6WWyZ8/DsuXjqV+/frp9KFKkCAcOHmPevHnMX7mJ7xfdJ/nhiYpqn9UkaOIAKlWq9F7uxz916NAhFi6Yy7lzv5Fcuk5Co3GkUeOWDBkyhClTpjB58mSio6NJTEzEy8sr3YdZ8fHx+HqrXrp9QqdTMGFEVoZ8fR9Hh0i6tnHBTVKSYLTh6Kgk+LGFkZMjuPvAytbAXISFmxkw+hE5cuSjYcOGlC5dmnbtWtNv5EW+/u4JeXNpMZuVXL+dhKenP3PnzaVly5Yp11MoFFSpUoUqVaq89X3R6XS0bt2a1q1bp7xmtVrZtnUji1c9Y9H0nC/9/MuyzIIVT0hMtDFi+GCsNhvxcYkE7XxGl3YZUfz599hikVO2RwCYLXbsdlBrtMTqzSgTbKhVEqYkG4kmO9v2JeLtHUAG3+RVMmuCwsmSUUmLBi5p+mCxJK8KzJxJl5zTYMNqhg8fjlarTdNWEP5tIugWhI9Ehw4dmDlnDk/27CNjk0YoJAVKpTK5QOqfoi9cwhQWjt/ndVMF3H+l9fbCp0E9jm3exq1bt1JKskRGRrJ8+XI2rF9FZGQYAGq1lnqfN6FPnz4UK1bsHY/ww1uxYgWJZjNZKlZKE3A/p1Cr8ahQkdCtm5k/f74IuoWPwrlz5xg1ahjXr13G0x1cXVQ8CzMzZfJYGjZqztSp09I8QNu6dStDBvcjZ1Y7E4Z7ULqYM2aLzOETsWzYuoDdu7ayKWg7+fPnf2f9zpAhA2q1lnN/JC9hfu75XtLnX86tVisXrpjw8VKyeVnGVDN8LRu60KSuMwPHhNF72F1sdgemTOv6ymtKkoT9DfZf2+12ZBkUqtd/FVJqtSSZ0ybP0uv1BAUFsXzlSu7cuweAv58f3Tp3pmPHjvj6+qY55l3r0qULCxfMxmozMGdKdkwmAyZTLHq9jFIhIcsyew4Z2bwrkt59ulCwYMGUY/v370/Pnj05dOgQ9+/fR6VSUbRoUSpWrPjGq36yZs3KzJkzGT9+PDdv3sRqtZI1a1YyZXp5WauP0cKFC/lmyteUL6Xmhxn+lCzqjMlk58CxGAI3ruPI4X0Ebd5BkSJF3nhvuru7O6HhVmy2tMvGAVo28sZikRnzzUNWbYqjdjUX3N1knoTY+O18Im6uaiaPzsrhE7Gs2xqHRpeJzVvWM3XqVFauWIzVaiB/bgdCw0z89IsBrdaRlq3aMWfOnNeWyZNlmQsXLrBv3z5iYmJwdXWlbt26lCtX7o0eIKlUKkaNHsfAAT3J6P+YkQMyo9G8+LwkJVmYuegBy9dFkSenEz06OKJUSsxYqGfGomeUKa4gXx5fVCoVWTJp+eNa8n5zWQaDwY5arcbL0xOr1YoxMRGb1cblGwmoVFrmzV/MxYsXWbtmIQ+CjZw5H8/nNZ3S3GNZBmOijE7ngEJS0KC2J8vWPeTOnTsUKlTojX6GgvBPSPLHnBnkDcTHx+Pm5kZcXNz/1Iyd8N9z8eJFvvzyS06eOoVDwfy4liuLLnMmHJ2cUFutRP12hohDR7EDhSZ8jdrt1Z93u9XGnW+n8WWv3owZM4aHDx/SsmUT4mMf0qKBC3VreKDTKjj/h4G1m2N4FKJkztzFNG/e/P0N+ANo1qwZB65eJkvn7kjp5JGUrVbuT/+O7Bkycufu7ffYQ0FI69dff6Vt2+YUymthaB9/qpR3RaGQiNdb2bonihkLIwjIVoLtO3bj7OwMwIULF2jcqC4tGqiZPj5bmi+gUdEW2va5R0x8Rn76+VecnJzeWf/79evHpXNBHNueG6s1iUSjEastuZSQSqnEwcGRh4+NVGt8j/Ff+tCqsSNurko0GkXKM0dZhpg4KyVrBePjV5SzZ8++MhhYsmQJ330zgvNH8+PpkbaU0nO/noujbuvruFarS5YmDdMdw8O1G8inUHHy+PGU1x4/fkzzVi25ee8+jgXy4ZI/H5JCgeH+AxL+uIyfmzubN278IA80T506RYcOLcmeSU+P9i58XtMJF2clwU8srN4cz/J1ceh0KixWF1at3kiNGjXeex8/VsePH6dD+xYM7uXKsH6Z0nzO4vVW2vW5R0i4L7/8ehZHR8dXnCm1P/74g3p1qxE4LxO1qrq/st3SNaGMmhxKufLliQgPJTw8EoNBj06nQJIUODu70bJVB/r378+UKVPYvWsdA7p70KmVL74+yUvDHz4ysXRNKGu2GBgydAzDhw9/5fXu3LnDgP59uXz5PP6+CjL6qwgLt/I01Ea+fIWZv2BJqgcz6Vm+fDnjx43C091Gi4YuZMqgJTTczLot4TwJMdOrkz/fT8iWck9Dw83Ua3MNq8XKkD6etG2Wld8vJtC+z002LPanQF4tdruEp6cXqv/3cKxj31uc/FXCbrciyxb0+kQK51ejN8h0aePCsL4vHobIMsTprSQlgZeXFyqVmhu3jdRqeZ+9+46/dDuAILypN41FRdAtCB+BoKAgBn4xEIVJjZPsRrgtlETJhMLZCVRK7Ho9Pp4eFCtUmF+vXSPvuNGvPee9RT/QtmJlZsyYQbWqFVHK99m0LCcZ/FLv17JaZUZMfMC2fVZ27jpAyZIl39UwP7iGDRty/OEDMrVpn27QDXB/2rdk8/Xlzt0776l3gpCWxWKhdKmi5M4WxdqFOVPNHj1347aRZl3v06rNACZPngxA7969uHF5O0e35UWlenlwGvzYRKWGt5n+/WLat2//zsZw5coV6tWrTp0qVsZ/6YGzswLtn+Mwm+08DbXSc1gYT5/ZuPJTcSzmeMwWMyqlhFoNyJBklrHLMH+5gd1HXLl2/dW/l9HR0ZQoXpAvuuoY1u/ls6uyLNNt0F2O/GgnycuXXIP7vzKIt8TFc2fqDGZ/9x3du3cHIDExkc9q1OB2ZARZu3dJsyfcakjg4aq1eCSa+PH4cTJmzPg37tw/M2jQIAJXLsJBBzqthFYrYUiw4+6qolt7f/p19afviAecuejI+QtX3umDl09J69YtMMT8yO61eV75mXj0JImKDW7x/YwltGvX7o3P3bBBPeKifmfH6tx4uKddYRESmkSjDncpXLwBq1evTXn9/v37hISEoNFoKFiwIE5OTuzZs4fevTqyaFqGlD3T/9+C5SFMnR/LwUMnKVKkSJr37927R6OGdfH1jGHMEP+UjOqyLHP6dz1TZoXw8IkzO3bue+PA++7du6xevZq9e7YRHRODUqEgPCKS1fNz0LR+2n6GhpvpP/Ieh0/G4uOtI1MGB65ejyNzRgXrF2chf17vNAH33KWPGDf1KblyOPLV4ExUKuPKnfuJdBlwm8goC5/XdCJwXgYkKXlJuTFRxm4HN3d3dNrkWf+gnREMGx/JhYvX8Pf3f6OxCcLLvGksKjIECcIHdubMGQb0G4BHkj+VXOtS0r0idTybUtGpGvmSspNN74ujVUXWjJlp0KABdrMZu9X22vPKpiR0Oh179+7l4cNbLJ2VNU3ADaBSSUwfn52cWe0sXrzoXQzxo5EnTx4sYeFYrenX3LVER2G3mMmXP1+67QThXZBlmbNnzzJw4EDKlyvJvXt3cHaUuXHn5TWQ8+dxpEtrdzYHrSUhIYHY2FgO7N9Fx1Yerwy4AbJm0VGjsiMb1q95V0NJvk7WrDg6urJlTzyte4WyZbeB2/fMXLtpZsGKOFr2eMalq0kM6plcHsjT0xNPDy/UagesNjU2uxqdzglvLx+KFfYgJiaGpKRXl6Xy9PSkV+8BzFkaxYZtEWlKfVksdqbMesyRHy0MHDQMOTyCkH0HX1oSzGYy8Wj9Jvw8PVPtid2xYwfX79wmoEvHlyZhUzk7ka1bJyISDKxcufIf3L2/x2w2c+L4Ifp39+fY9sKMG56VwX0ys/j73Jw7WpyhfTOh0ymZMjoLBkMk27dvf+99/BiFhITw808n6NTKK91l1QGZtVSv5MimjemXrvv/5s5bSLTei0YdbrNzfxRmc3LmscREG5t2RNC44z1U2mxMnz4j1XE5cuSgUqVKlClTJuXhyKrA5ZQrqX1lwA3Qt2sGMvlDYGDgS98fMXwoXm7RbF+Vi+qV3VP2m0uSRKWyrmxbmZusmQwMHvTFG5fMy5UrF5MnT+bipesEBz+jUKGifF7T/aUBN4C/r4atgfnp0MIHs9WF6rX7MXDwaFS6XLTuHcW3c0K4dNXA3QeJ7D4YRfOutxg39SlFCzrx+6EiNG/gjZ+vhkrl3Di9ryg1q3lw6ISRK9dNRMfYMCTIaDQOeHp5pQTcsiyzZnM01T6rKQJu4b0RQbcgfGDz589HneRAYZfSKYlEFJICP20m8rkUpZhbWcq51uDmtZsolUqUNhtx16+ne87EkGdYwsKpVKkSG9avoWIZB/LmevUSOJVKomMrDw4d3ENkZOS/Or6PSdeuXVEkGkm4dwuZV3+BiL14DsluZ8yYMe+xd4KQ/MS8TZuWNG5Um3O/rad8sRA6tnTi6g099dteo+uA2yQkpH3o1qapN3p9ND///DMhISFYrWZKFHn9zGXJIo4EB99/F0NJsXnzZkyJkWxalo+iBV35dk4M9ds/pUmXp6zfbqBxXR+cHRW4OEsYE40kJ6zS4Obmhpen1/+xd9bhUVzfH35nXbIbFywJ7l5ocXd3J1gpUENKlVL7FmhpKdCixd3dihZ3dw+SELfdbNbn90cgNI1AW6y/zvs8ffqwc+fOubPZmXvuPedz8PHxxWg0olAoMKe6kMllmWoIZ8fHH39Mj56D+PDrGOq3u8rUOQ9Yvi6W73++zxvNrjBzoYX/ffsDH3/8MeP+9z9SDx7m1vRZJJ49jyM5BVt8AjH7DnBz0hT0ySksXbQo0w7GnHlz0RYrhiYw55xthU6HR6UKzF24EIcj94W+Z82xY8eIi4uiW3t/ShXX0bdbIIPC8tCqiU+GsjVAvjxqar2uZfPmjS/UvleVBw8eAC7KlnxyyHjZkhoiI+/9pf4LFSrEpk3byRdSj3c+iaZc3UtUa3aFcnUvMfKreMpUaMHGTdtyFUcESEpK4vDhA3Ruk7sIoFwu0Km1kW1b12c5duXKFQ4f3s/wwQFZyrs9Qq+X8/F7QVy8eJZTp049/UD/wPnzZ2hQK6uo2Z/p0MoP0W1n0KBBfP311+zevZ+evUewYqOKlj3Cqdv2JkM+iubSDSMB/lp2rSmTJW0mMEDFgl+KUSCfmpFfx+FwqvEPCMDT0xOlIv2ZIYoi4ybd5+xFF4MGvf23xiQh8XeQhNQkJF4iUVFR/LZ1O4WVZTIc7uzwUvpisHix9/e9VK1cmSPbd6EKCUGhUaNSqTKtyItuNw9+20H+vHlp1qwZX37xCd3aPHkC8XolAy5XLPfu3ft/W7eyTJky1KxWnf2/7UDpH4DOJxCBzC9t842rpBw9SvkyZf5VZW0k/v3YbDZ6dO/CjWuHmTMpHw1re5GUnIhMsGPQy9m8M5WRX8UyYNh1Fk7NHDaeP68aEElKSqJgwfRSWXb7k3em7A4xS+jms2bB/Nk0q6+jaX1vmtb3JinZyf1IG3K5QMFgDRqNjNMXzOzca6FNUwseej2Q/S7j+m3JVKtW84miXjKZjO+++442bdowb95cfpi+BbvdjtFgpG27gYSFhWUIyA0cOJCCBQsycfJkDi9byQO3O70ElFpN51atGD58OMWKFcvU/6XLV9A3qPvEsRuLFSPq8DFiYmJeqJBYcnIy4CZfnpzLPz0iXx4FF278/11s/Ss8KpeVanE/oSWYU925ltfKidDQUFasXM3169f57bffSElJwdvbm+bNm+dYju3PpKSkACJ5AnNffIJ0R9RkTkIUxUxzhe3bt2PwcNOsQe6Oe53qngT6R7Jt27a/lX7mdovZRtzExjnYujuRhAQHer0coyG9bJjrod6Dj48Po0aNYuTIkVy6dAmbzYavry/NmjZgcB9/lMrsnwE6nZyFU4vTtMsFmna9xeA+eWnW0Bu1Ssapc+ml646fcTL6izF/S71dQuLvIjndEhIvkfDwcJwOJ77aJyvcGgUfft+zFxERU0IcN2fOxrtJI9SBAWh1Wjw8PHAkJRO5aSvu6zf5Yd48lEolCoUCm+3JEwi7I73N856Av2yWLl6cnos5dx76ypXwKFMWhVaPIzGBlDOnST17juCgPOzZs+ellP2R+O+yatUqTp06yPoFhahULl0QTSbIcLvSd6xaN/HA21NGt0EP2LorkVZNfDLOfRBtBwSMRiMFCxbE29uPbbsTeb1yzjtMoiiydZeJSpXrPrcxORwObty8zuBej0NLvTwVeHlmfs6EdQlk2Oc3OXXeQqO6YrZllfYeSubEGRszf+33VNcWBIEaNWpQo0YNRFHE6XTmuEPeqFEjGjVqxPXr1wkPD0epVFK6dGn8/f3/wmhfHTw9PQGBB9H2LPf6z0TFODF6+uTa5r9CsWLF8Pb2Z9P2BCqX98ixncslsmWnmVr1/r74aNGiRSlatOjfOtfLywuQcT8yq6L+n4mItONpNGZ5n5lMJrw9FTk6r4+QyQR8fRSYTKa/ZWuRIkU5cuI6fbul794nJTv5fOwdNm1PQBRFvL1kpJjcJJvcuN1qLBZLpvPVajUVK1YEID4+HosllVLFcw8JL1tKz6fDgvnkm0gmzExj3M/xD4/IqVatFgsWvkvDhg3/1ngkJP4uUni5hMRL5JGD6xZzz9EW3W4sFgtxsXEEWApQzaMeyvB4YmfOI3reYu4tX8Wln6dy/bsf0N6PYO6sWTRr1gyA8hWqsGNv6hPzsbbvScLDw5PChQs/m8G9ovj7+3Ng3z7e7dsP2bkLRM2Yyd2JE4iaNxfFjZu8PWAAVy5fkoQZJV448+b+SoNaugyHG0CtUWN3ijid6b/fWm/oqFpRw4Ll0ZnOXb4uDr3ei1q1aqFSqejWPYzl61OIjsl5Ur5zXzJXbjjp2/fpnNi/zVOkgnZo6UuhEC0DR0SzZWciLtfjk5xOkTWb43lz+D3q1W+e8Wz7KwiC8MSQdEh3hBo1akTdunVzdbhLlSxB6vWbT+wv5fp1vL29X3jpsCpVquDjE8jydbnvYD+ItrP3sJXmzVu+IMtebdRq9cPfjomIBznrBixfF0dkNPTr95x/OzlgNBqpVbsuS9cm5NrO4XCzcmMKrVp3zHLM29ubuAQnaWm5zz8cDjdR0Q68n1DPPid69urLb79buB9pIynZSfuwy/x+MJFP3vfm7J4QTu8K5fzeEMZ95kuRggIdOrTi5s3sf1uP6mmbs0mx+TM6rQwvbwOnTl1k5aotLF22gYOHTrJ6zXrJ4ZZ4KUhOt4TES6RYsWLoPfRE2SJybZeUnEys+wF5VMGUMJQnnzaExj5tqaqrQUAEqM/cx37pOtUqVuLC2bO0avW4/E2fPn25ftvJjr1JOfafkOhgyZpkOnXu+dTlT/7NeHl58eOPP3Lv9m12bdvGptVrOLxvP7FRUUyePDnjxS4h8aIwmUxcvHieVk28EEWRtLQ0EhISMJtMiCKkmJ0ZtadbNfHg6CkTbnf6v6/fSmP2kkQ6de6JwZC+sz1w4ED0HgXoOvAm129lFmATRZHf9iTyzsf3adCwBTVq1Hhu41IoFAQGBrJ7fzxJyUkkpyRjtVqzaCoolTKqVjKQZtUxaGQUNVpcYfjntxj8wQ2KVj1D3/fC8fYtQvfuPZ+brX+Ffn36knbtGtbomBzbOC1pmE+epm+vXk/l8D9L1Go13Xv0YcmaFM5fSs22jcsl8uX399Bqvf/fl4v8KwwaNAhPr4J0efMWp8+bMx1zONzMXx7Dp2Oi6NylN2XLln1JVkK/fm9y6pyDZWtjsz0uiiITpkUSHSsQFhaW5XiLFi1IsypYvy13x/23PUkkJAm0bt36b9nZsWNH/P1D6PvebT788jax8TbWzsvLwF5eeBrluEURm02kdVMjm5eWwM8zgSGDB2a7UeDh4UHZshXY+FvSE6+78bcU3ni9Bj4+PtSoUYM6depkpN5ISLwMJKdbQuIlYjQa6dSlI5Gu2zjc2e9IuVwuIq13sAtWinmUyfhcJsjIpwmhqlcd6vu0oIz+Nc6fvUBaWuYJdvXq1WnQsAXvfRrBzr1JWV5kEQ9s9Bh8E1EIYPDgwc9+kAkwdegAAQAASURBVK8wOp2O2rVr06xZM6pUqYJcLn/ZJkn8R3mkxq3VQFxcHMkpSQjY0WpEtGoBm00kMclJmtWFSglWq5sN2xKYvyyaDn1vkSdfWT7++OOM/gICAlixch12d0HqtbtOt7euM2FaBOMm3ad+u6v0HxrBG9VbMH36zCfmR/9dwsPDadG8Cbdu32b91iQiH5hx2tNISk4kLjY2kwJ5XLyDzTssfPLpaLZs3cPrNbqzaZeaZeuTsDvSha3cjtu8OaAHNapX4bfffnsuNj8t7dq1o2TRYtydtxBbXHyW487UVMLnzMdf7/HSdkOHDh1KseJV6TLwNnOXRmMyO4F0Z+zkWTNh79xg624nkyZPy1iskQA/Pz9WrlqHWleSVj1v06L7VUaNucMHX9ymSuPLfDYmjs5d3mT8+PEv1c7GjRvTs9dARn4VxRff3eHOPSuQroT+255Ewt65xuRZSYz6/JsM/YI/EhISQsNGzfhhSkyOu/qxcQ7GTY7mjTdqZtvH02AwGFiydCUxCb4sWxfHgJ5GCuRTYLO7Mae6iIt34nAKeHl54e+r4fMRQZw/fypH4bY+fQew+4CFU+fM2R4H2H8kmWOnrYT1eTm/PQmJ7JDqdEtIvGTCw8NpUK8B9liRSh7V0cgf7zSLosid5BuctRwhUJOP173r5ZhnbHfb+N20kW+//x96vZ7Dhw9jt9vJmzcvrVq1YvLkiezZvZVSxRQ0rW9Ao5Zx6pyFHfss+PnlZ9HiFU9dh1NCQuLZ4nA4KFY0hLBODob09cTLKM8kPmS1uUlKdiKK8OP0RKbPS0alFHC6BIqXKMemTZvJkydPln5tNhsbN25k8aL5hIffRKFQUKFiVfr06Uv16tWfm27B/fv3adWyCTpVFB++E8DXP97D0yCyaGoefH3kmM0u7A7w8vLGZJbR+52bPIj1Z+eufXh5edGjR1dOHNvJ8EF+dG3nh4+3ElEUOX0+lUkzH7D7gIspU2fRtm3b52L/03Dv3j3ad+rI1Vu30ZUqgaFkCQS5DPPN26SePUeA0ZMVS5dm5KM+D1wuF/v27ePKlSsAlCxZktq1a2cspJhMJj766EM2bliNWmUnOJ8Kc6qL+w9cBAcX4tsx42nQoMFzs+/fjMvlYvfu3SxZsoi7d26hVCqp/Fo1wsLCsgjr/R3cbjf79+/nzJkzOJ1OChUqRLNmzdBoNLmeJ4oip06d4urVq7jdbs6cOcPmTWtISIhBLnNjTrXjdqfrInh6+tC33yAGDhxI/vz5s/QVHR1N61bNsFtvM/Qtf9o390Wvl2O1ulm/LZ6JM2Kxu/KwfsNWgoOD/9F4p02bxldfDGff+rx4PMygkQkyNBotOr0Ohfxhup1bpFqzKzRtOYRvvvkmSz92u51OHdtx7cpBJnyTj0Z1Hpc6c7lENu9IYORXD6hcpTGLFi35f69TI/HyeVpfVHK6JSReAU6fPk23Lt2IjozBjyCMCh9cootYdySJ9jgEt0BTv84oZLm/PH5LWIXgIcOFgDq4AKiUOKNjwJxKnZo16dO7N+vXr+XE8cM4HA5CQwvRrXtvOnTogIdHzqIxEhISz59q1aqRGHeKUztD0Kgf7z673CKJiY/Dy2u3uU/7Fl50befLms1mFq0yUa58TZYuW4lWq31Z5meif/9+nDu1jk2Li+Lvp+T6rTS6v3WVhEQ7bZp6ULe6jtQ0F4eO29i2x4WHIS+Ll6ykTJkyTJ06le/GfsqS6cFUq5L1ve52iwwbFc6mnXDy1Hl8fF6eCJjJZGL58uXMmjOH6w/zUIMCA+kXFkavXr2eay738uXLmfDjOO7du43Hw+pw5lQICSnCiA8+pmPHx3m8Dx48YM2aNURGRqLRaKhevTr16tV7blEO/yVEUeTQoUNs27YNk8mEl5cXrVq1olKlSjkuam3evJlv//cl4eHX8TIKqFQyYuIceHr689agd3nvvfey/W62bdvGDz+M5dLFc8CjMnRKgvIEE3H/Dj5eTrq3N1LjdSMKhZL9h1NYujYFN74sWryCSpUqZekzOjqajz/+iB3bN6OQ2/HxVpCU7MRmV1CvfhPGjfs+W4f9rzJ58mRmTP2Sc3tL4nanC7fKZLJs71HHftfJE9KRKVOmZNtXSkoKb701gL2/7yC0gIxqr2kQRdh/JI2IKJEmTVvzyy9TM2qaQ7qzfuvWLZxOJ3nz5n2pzw2J/19ITreExL+MlJQUVqxYwcL5C7lz5y5KpYJqNaohk8nYuno7dQwtMl5OZmcKEdZw7G4bckFBkDo/ifY4TluP41OzGgWaN0Hl5QWA6HKRfOky0es3UyJfXrZu2vxQ+VRCQuJVwWKxUKpUUcwp0fTsaGT8F/4ZNWgTkpy4XCIeeoH3P4tlx14LmxYXonTJdKGvk2fNdB14m85dhzB27NiXOQwg3cGrWqU8X3/kRViXx05nYpKTxatiWLgihogoOy6XSKpFpEvXMMaNG4e/vz8ul4tqb1SmeqUYJnxTKMdrJCQ6eK3RFT78eCxDhgx5EcPKFVEUsVqtuFwu9Hr9c6988PPPPzN2zGhaNdYwsHcgFcqkOxenzqXy68JoNu2wMurzb1+Je/P/hejoaBYvXszGDatJTIzHw8NIhYpVOXH8MHfv3qRAXjkB/goiHzh5EOOmbNlK/DJlehaF8qVLlzJi+Ns0rK3knf5BVC7vgSAIhN+1Mm9ZDLOXJNOpc19++umnTH9H8+fP55OPh1H7DQUDewdS8/X0Oe/aLfEMGXmDN17TMHtiYQL8M6cKJKc46f3OTW7d9eL3vYdyFAiMiIhg69atJCUlYTQaady4MaGhoc/s/s2dO5cvR7/PpQOl0WpzTuUSRZG6ba5So+4Axo0bl2u7U6dOMX/+fK5fu4QgCJQoWZawsDDKly+f0S42NpYZM2awdMl8EhPjABG5XEXjJi0ZPHgIr7322jMbo8R/E8nplpD4f8Lu3bvp0LYjr6nroJPrOW06QrQzCkGnQeHpictiwZViwuWyo6tQlmL9+qBUZRXtscbGEf7LdIa9+SZffPHFSxiJhIRETpw9e5ZmTeswfJA3k2ZGUKqYin7dPan2mob4BAdnLthYtMrEjdsOfvrGn5qvG/Dzezx5njQjkp/n2Dhz9vJLfxcuXbqUD0YM5PLBMnjos06uRVHEZE5XH+7Y/waly/dg0qRJAJw5c4bmzeqxek5wruXOAAaPvMn92LJs3rL92Q/iFebkyZO0atmI4YM8GT44a+1vURQZ/0sEk2eZ2LJ1FxUqVHjxRv4/Y926dbz//mAUMgutGuvJn1dNdIyNFevjSEx2MbB3EN+NDkUQBNxukX2HU/jmxwdEx3uxcdNvGVVB7t69S80aVejaVsnYUSHZLs6s3hTH+59FMW36fNq0aQPAxYsXadyoDv26afnyw+BM54356R6LVkaxfWV+lAoBH19flIrMc4CERAevN73Cu+9/xdChQ5/fjcqFu3fvUu2NSnw/2pdu7XOuDHDyrJk2vcNZtHgN9evX/0fXvH37Np06tsFsukuXNkaa1vdGo0mv171wRSI3wgV++PFnunbt+o+uI/Hf5ml9USmuSELiFadu3boULlqYy6mn2Ze8g1hDGr4d2hI8dBj53hxIgXffw69HF1QhBbDevI09KirbfjT+fhiqVGL+4sVZxNYkJCReLk5nushV84beLJtZAi9PDcM+j6VSwzvUax/BqLHx5A1SsGp2PhrW1mcRROzW3g+73cTmzZtfhvmZMJvNaNSybB1uSC/fZTQoMBoUBPjKSE19rK6dkpICuMkbpHridfIGKUlKyl15+f8js2fPJrSAwNC38mZ7XBAERgzJR4G86buLEv+MHTt28M7bA2jdSODkzpJM+KYQwwfn5cN3jBzYlJ8PhnizZHUMi1alq4jLZAJ1a3iyZl4RfD0T+ejDDzL6WrhwITqtjdEjCuQYDdGhpR81qqqZM3tmxmezZs0iT4Cbz0dkdrjdbpFla2Pp1NpAviAlMpmYpc41gI+3krbNPFi0cM6zui1/meDgYBo0bMrkX2OJi3dk28ZudzNmYiQhIUWoW7fuP7qe3W6nR/fO6FQR7F5TjC9Gpi/klS+tp2+3QHasKk739mo+GPEuR48e/UfXkpB4GiSnW0LiFUcmk/HjhB+IF2OwGETyhPXCULoswiNxEBkoQwvgF9YDVd4gbi9aivgwX+rP+FSqSHxiYo6qoBISEi+H/PnzIwgKTp9PpVoVI0tnluDApnLUrOpJqWIqjmwNYf7PealcXoPDISKXZ9Z3CPBX4e2pIDo6OocrvDh8fX1Js4q51giH9B3Z23edeHh4kJSUhMvlerhLIPAgOvdzASKjHHh5/bfyMp1OJ5s3r6V7+8fiUdkhlwt0a+fJhvWrMvJnJf46oijyzdejqVlVxk//K4jRkP67s9sdOJwO/HwUjBrmR9e2Br6bdA+r9fG99jQqGD44gEOH9nLt2jUANm5YTbtmBnS63CtldG/vw/HjR4iKisLlcrF+3Uq6tffMJK4IkJziIiHJSdVKGgQBtBoZ1jQrkDWItWolA5GRkdjtT/5tPQucTidbt25l5MiRDBkyhC+//JLu3XtidQTSvs8NduxNwuVKt1MURU6cMdF14A1OX1QwafLUf6w3sHnzZsLDrzLjxxCCArIu4snlAt9+GkLxwiLTpmWfOy4h8SyRnG4JiX8BJUqUQOOhx1izOm6DBrtowyHasYs27KINBJCr1fg0a4ItPh7TtRvZ9qPQp++QZbcSLiEh8fIIDAykfoMmzFsen1F/OzRYQ7EiWtQqGfnypIeLOp0iNoeYRTDN4XBjSXO9EkJqDRs2RKs1smRN+s5fWpqL6Bg7Fosro01qqotRY+9w/lIKSxbPpVTJwpQtU5x169YREJA3U+1ht+jGYkklISGeuLhY4uPjuBeRxLbdqTRv0eaFj+9lkpqaisNuJzi/+oltQwqosdms0vP+H3Do0CFu3LjMuwOCMi1y2Gw25DJQqdKn0e/09yYpxcn6bZnLxzVv6I2H3s327ekpEImJCeTP++QojgL51ICbxMREUlNTsVrTKByaVdX8UZVLpzP9maGQC4iIGaKLf8TpFEHguesNAOzZs4eqVSrSv19XTh6ZR9Td1WxYM5l+fbvh7x+I2qMMfd+L4PUml2kXdo3ara/SNuwOMYkhLF++lqpVq/5jG5YsXkCNqlq0GjljfrpH/bbnqNroDE27XGDa3AckJDqQywXCuvqwc8fWV2LBUuL/N5KOvoTEv4CdO3fiBArXq41DELBZbbhFNzKZDI1Gg8WSht3lQlUgPwo/P5LOn8dYImtJE2tcHDJBwM/P78UPQkJCIlcGDRpC507b+PL7u3z5YTAymUCtN4wsWhnNxatWShRRk5TiQi5TZCkr9NueJNKscmrXrv2SrH+M0WikU+ee/DTjF34/mMypc2Ye+QC1q3nSppkPU+dEcuV6Gm2bedCuZRAqZXqe5fKlk4iNh1UbnXRuY6JiWRXJyUmIohu1SkCpEnA63Xzz4wNi4y3/uV1cnU6HIJMRm0N47h+JS3Aik8tfiYWYfysnT57E0yDwxmuZ9QVEMb0k1yP3NbSAklLFVJw+n0qXto/zlZVKGd6eCkwmEwAeHh7Exqc88boxcQ5AwGAwpH9/gkBSiitLO4OHnNACanbstdCmqYGH63U8tuwx239PplSpMiiVWTVfniW7d+8mrHdXar0uMH9SYUqXSC+D6nKJ7NqfxJffn8Phzs/8BSs4dOgQ8fHxlDEY+LZRo0zl7v4p4eE3yevvpEaLsxj0Ai0b6/H3lRN+z8n4X+4xYVoEv3xXmMrlPHC7Y7h//z6BgYHP5NoSEtkh7XRLSPwLSE5ORq5Wo9Dp0Gq1eHl74ePjg5eXFxqNBq1Wg+hygigiNxhwpVmz7SfhyDGKFiqUSdlTQkLi1aBGjRqMGTuBucssNOt2lSWrY/DzduGhF/j2p3iiY+04HG7kchl2x+MQUYvFxeRfY6hSpRolS5Z8iSNIRxRFRLcbk8lOQmIao4Z5M3tiAGM/88VkSmPQiBtcumZhzdy8zJlcgnbN/WjRyIfPRwRzdFsJGtZRYjK76DrwBpNmhJOW5sTPR4GXp4Kb4Q6GjY5jy840WjT0YuyY0SxduvRlD/mFoVQqqVOnPqs3JeXaThRFVmxIokGDJsjluYcyS+SM0+lErc5a1komE3C5RP64oaxSCTgcmXeYLRYXcQlOvL29AWjarA1rt5hxOHJfLFq+Lp5SpcuSL18+lEolNWrUZu3mpCztBEGgV5dANu9IJfyeA6vVjUqlymLvlesWdu230KfPgL8w+r+O3W5n6PtDqFtdxryfi2Q43JAezt24rjdr5xVBdN5j1aqV1KhRg9KlS1OwYEEMBsMz3YVPSEzmwNFkhvTx5OSOEL4fHcDIt32ZMi6QE9tDqFdDw1sjbnD0VPqCiFTPW+J5IzndEhL/Ary8vHDbbDgt2QugaTQa5DIZTosFV0oycp0uS5uk8xewXLjIoIEDpdqsEhKvKGFhYaxYuQEvvzoMHnmbJl1uEBXtYtOOVD75Np4UkxtRdJCYmEBqaiqXr1noPvgm4fd1fDvmu5dtPgBTp05l4cIZ/PS/YDYtLkzbZgYqlFbTpJ6OXycE8v1oP5QKgf3Hsk509Xo5syYUpkhBNckmGWMnJ1CvfQQNO97n9aZ3aNkzgivXXfz6U1GW/Vqczq21fP3VKKzW7Bca/z/St+8AzlxwsH5rfI5t1myO5+IVB71793lxhv0/JCQkhNh4B3fv2zJ9rtZocItgs6c7zyazm+u3HIT8Kex/7ZZ4rDYFLVu2BKB3797Exgv8POtBjtfcdziZXfvT6NPnzQwnNCysH8fP2NixNylL+27t/MifV03nNyO5dN2GTpv5/X/hciq9hoRTrHh5OnTo8JfvwV9hy5YtxMVFMmp4viz5548IDFDSt5uBpUvm0qN7O8Z/9xH/+3o4rVo2pGHDOuzatesf25GamkpKciLd2xn4+H1ftNrMcx4fbzlTvwukUlkV3/98H73eSJEiRf7xdSUkckOaeUtIPAeSkpK4ceMG9+/ffybhj40aNUKnUpFw4mS2xwVBwNvLG8fde9gfRCFXq3E/VEO2Rsdwb816Ihcvp0u79vTp0+cf2yMhIfH8qFatGinJSRQO1bNwajH2bSzHginFOH7GQcNOEQz5KIbR4+Jo3fMq9dtfJSI6iOUr1lKmTJmXbTpWq5Upv0ygTxcjfboG4enphb9/AEajF3q9EblCQYeWBt7t782cxTGYzM4sfWi1cnp18sTpsDB5TCE+GRpMnRq+tG4awNyfi3NwS3maNfBGEATeezMvycmxrF+//iWM9uXQsGFD2nfoyXufRfLzr5EkJT++h4lJTibNiGT46Ad06hz2j0su/ddp3rw5BoMvC1bEZPpcqVCiUqowm9243CIrN5qw2kQ6tXmcunUvwsaE6bE0btKC/PnzA1C8eHE++ng0E6Yn8sk34Zmc+RSTk+nzH9Dn3bvUq98yUxmr5s2b06RpO94acY+5S6NJS3P9wRaBti38uHrDQbs+MfQfeo9pcx8wbe4Durx5naZdb+LlV5alS1c991SDHTt2UKGMmqKFcr6OyWSmUR05KpXIyLf9uHakDDeOlWXxtPz4elyhd68urF69+h/ZsW7dOpQKN2Fdjdhs2c/BFAqBwWGeREbZqV2nEXq9/h9dU0LiSUixFBISz5BDhw4xffp0tm35DafTiSAIFC5ciP5v9qd3795P/cK7c+cOCxcuZOP6jSQnJ+Pj40PBkBAu79mHZ+mSqH19s5wjE91YDxzCS++B+eBhLh84hCCXI7hcBAUEMPKzz3j33XelUEMJiVecffv2cf78SVbNDsnIJS1XWk/jut6s25rAjt8TSUx2EX7fTkhISQ4eOvLc8zSfls2bN5OUFMuAno81JWQyWcazz2JJRaUW6NvNk+kLklm7JYHenQOy9FO1kga5HMqW0lOhjEeO1wsN1lCmhIZjx47RpUuXZz+gVxBBEJg4cSK+vr5MmDGDiTMvU66UGhE4d9GGiJYBbw5n1KhRL0Q06/8zWq2WAW++zcSfvqZ8aT2tmjxWy/f09CIhIZ4dv5v55sd4WjfxJU+gCpPZyepN8fw8Kw6NrjDffTc+U5/vvfceHh4ejP/+WxatvkrJomqUSoFrN+04nGq6dH2Tb78dkykKRCaTMWPGTD780JPR3y1m/JRYqlZMF2Q7dtqOyaygT99BVKtWnaVLFjJ59mUEQaBEiSpMmdqfFi1aoFI9WcDtn2I2m/HzyflvzmazkWoxkz+PAr1WhreX4uH4BOpU96TWG0Y++CKc4cPeoWrVqhQoUOBv2XHixAkqlNVRKMSD5BQrGElPE/hDG6dLpHxpNXqdjOLFi/+t60hI/BUkp1tC4hkxdepUPv9sNBqnjoKKkhiUXjjcdh5cvcMnH3zK6lWrWbFyBV5eXrn2M2vWLD79+DNcVjcBQj60Mm/iI83EiPexuNK48fM0Aps2xrtiBeRqFaLbTfKlK8Ru34nRamPj7t1otVrWr19PZGQkfn5+dOvWjdDQ0BdyHyQkJP4ZS5YspmRRBa9Xzuxs6nRyunfwp3uHdKGmrbsSeXP4bW7duvVcJo3h4eEsWrSIixfPI4oixYqVoEePHrle69q1a+TPoyI0OKvS8h8JClBQtKCS6zezT5l5VIdcIX+y06jTCi+sDNKrgkKh4KuvvuLdd99l+fLlXL16FYCmrUrQpUsXfLNZmJX4ewwbNoxbt24y5KOlrN6UQLf2vgTnUxMd62DZuhTWbUnEkiawaUcqB49fJjHJgVvU0KRpe8aMGUtAQOZFJUEQ6N+/P927d2f9+vWcOXMGp9NJy3YF6dSpU5b2j1CpVEycOInhw0ewePFirly5DEBY35L06NGD4OBgALp37/58b0gu+Pj4cPqYG1EUs13wsVhSUSoEkpJFHE4RX+/MbohMJvC/T4LZuvsyixYt4pNPPvlbdtjtdjRqAU9PL5KTk0hKsaKQu9Go021yOEXsdhGZTI7BoMPDI+eFPQmJZ4XkdEtIPAM2b97M559+Tj53YUoYymd62QRp8pPsSODE4X3079efVatX5bj7sHTpUj784CPyugtS3FgOhfD4J+pw2zlnOsb92FvErF5H7OatKI1GXKkWsNmoWrkyP/34I1FRUfw04ScOHTiMy5kegvbTDz/RtEVTPvjgA8qVK/d8b4aEhMQ/4k74DSqX1zxxl7JyOT3g5M6dO8/U6XY4HHz00YcsW7oAT4OLaq+pkckF1q/Zyaxff6F5i3ZMnvwzumy0IwRByCh5lh0KhRKb3YaHHtxuyGmIB49ZcLlyPv4Iq9XN1Rt2qtf9ezti/3b8/Px4++23X7YZ/6+Ry+X88ssUateuw+xZM+g/9CzgBgQKFizKt2O+oW7duuzdu5fk5GS8vb1p1qwZefLkybVfrVZL165dM4WRPw3BwcF/2xl93rRt25bly+Zy4oyZKhUzK7673W5sdjueBhlzlyah18mpX8srSx86nZz2zQ2sW7vib48zODiY3Tvs2GwiXl5e2O0OLBYLFqsd0e3OWNS7fddGXHwaUVFROJ1OSUxN4rki/XVJvPJEREQQGxuLXq+nUKFCr1x4tCiK/DD+RwxOH0oYy2c7UfZU+lBKXZk9u3/nzJkzVKxYMdNxm83GmjVreHvw2xhS/SigKYzoFOEPEaNKmYpKxhq4UlwY82sY+NZAEhIS0Ov1NGzYkHLlyjF//nyGDx2B3mGklLoyvppARNxE2yLYuWYPu3bsZvHSRdStW/c53xUJCYm/i0KhxG5/shaE3SECwjOdKLrdbt5+ewi/bV3BNx8H0K2dPxpNuvyLw+Fm3dYERo1dTZ8+SSxevCxLWHvp0qWZHO3k6g0LxYv80SlPrx2s1WpJSrZyI9zG9dt2BoZlddxTU10sXWtGrfFg7ZYESpfIOddy/bZ4kk0yOnfu/EzGLyGRHTKZjK5du9KlSxfCw8NJSEjAYDBQpEiRDGHSokWLvmQrXz61atWicOESfDb2BqtmF8FoePxsSte3Ebl83cavi5Lp3CYAD33287nQYDVxG2P/th1dunRh0sTvWbc1nq7t/FGpVIiiiM1mQxBEtBoZSqXAmpmJCLj5deZErl27wuzZc6XcbonnhuR0S7xU0tLSWL9+PfMWLODSlfQcpPJly9Gnd28A5sybx6EjR3A/DFUqGBLCgL596du373MRBImMjGT16tVERESgUqmoXr06DRs2zHVSe/78ec6eOUs5zRu57kwFqvNxzaRm0aJFmZzuTZs2MXzocCLuR2C3OSlGZdIsViyWNFRKJZ5eXhkLDYIgUERbihPheyldunQm5/nkyZN8MOwDgpzBlDZWzmRLiK4o+cVCnEo5QJ/efTh6/KhUj1JC4hWlUuXXWb/mOHa7G5UqZ73TrbsSUSq1lC1b9plde8+ePWzauJLp4/PRsrFPpmNKpYxOrf3In0dFpwE7WLNmTZY86qZNm+Lvn5cZ86OY8E0hXC4XljQLaWlp6ZPuh5vgU+ckoVbJaNM08zVSTE7eHH6L1DQjAwZ0YdbiGVSt5EHjut5ZbL1wOZWvxkfTtFk7KX1G4oUgCAIFCxakYMGCL9uUVxKZTMbMX+fSvl1LWvW4zrtv+tOqsQ9qtYy4BCezFiUzf3kKxYvo+fi9/Dn2k5Do/Ech36GhobRo2ZavflhNiaJaShdXk5SUiEoFnkYFMkFgw29mlqwxM2p4MJXK6xkwbDtvvz2YefMW/O3rSkjkhuR0S7w07ty5Q6euXbhy4wbaokXQ1agGosjhq9fZ3qcPLrsdY4ni+HfpgNrfD2eqhQcnT/PJV1+xfuNGVi5fjtFozPUaOeUV/Rmz2cwHI0eyeu1aHHIZSl9fRJuNSdOmEpwvP9+PHUuzZs2yPffWrVu4HC58tNnnYT1CJsgwuL25cf1GxmcbN26kX1g/PO1+5JGHkCDEYZSlTy7dohuHw0FCQgK+vj7IZOmOt7fSD6VVxcmTJzM53TNnzkRhV2dxuB8hF+RUMFTj94RNLFmyhGHDhj3xvkhISLx4evfuzexZU1iyJpY+XR8vjrndImlpbrRaGZY0N/OWJdCseWf8/f2f2bXnz59LmRJKWjTK6uQ+oloVI3Wqa5k/b3YWp1upVDJ8xMd88vH75A26S4/2KuRyEY1Ghkohw+UWmbkwmTlLU3C6YOCIWzRr4IVKJXD6XCqrNpmQK3yYv2AxVapUISEhngHDVtG8QQI9OvoRkl9DbLyDlRviWLXRTPESVZg4cdIzG7+EhMQ/o2TJkmzYuI3PP/+UoaN2M/LLSPQ6OSlmFyazneC8Kpb/WgJ9DrvcLpfI6k0p1G/Q7h/ZMWHCRLp3i6R9n0M0ra+gVWMtRUJVnDhjY8maFHbus9CuuR/vvpkHmUxg/BdOhny0kTNnzlChQoV/dG0JieyQnG6Jl0JycjIdOnXiVlIihYa9hybwscNqeL0KymvXiV+zHqfdhmfpUsgehjAaixXFUvM+R2bNZcjbb7No4cIsfZ86dYq5c+eycf0mUlNTMRqNdOjUnr59+1KyZMks7S0WCx07d+bI2bP4Nm+CT+WKyDXpIkCW+xFEbd9Fzz59mDV9Ou3aZX0JyGQyEEDkyeGgbtzIH+6ap6WlMfS9oXjZ/alorMF503Fkf6jiJxNkqFBhd9kwmcx4enoC6SvtMkGG0/m4TIzZbGb92g3kVxTJdZFBKVPhT14WL1wsOd0SEq8oRYsWpXuPfnw1/lc0aoF8QWoWroxh++9JOJ0ichkolXJcojcLRozIOE8URW7cuEF8fDwGg4ESJUr8pXQcURTZu3cXH7/r+cTFyvYtvHn/s5OkpKRkWfwMCwvj+vXrfP3DOBatTC//lT+vgphYF8vXmwi/56R35wA27Uhh5z4Hew6m15sODAxk4KD36NWrF0FBQQDMmDGTBQtqMmf2DLq9dZVHubSBgfl4+91hDB48ONvccgkJiZdH0aJFWbZsJbdu3eL3338nNTUVf39/kpOT+ebrDzl7KZXqVbLfNJm9OJrIaP5xeVODwcCKlWuYMGECY8d8yeqNySiVAiBQqpiO778oRJe2fshk6c+6Fo18yD8xhnnz5jFx4sR/dG0JieyQnG6Jl8LixYu5fucOhT54H7XPH8ILRUhNTUVdID+BfXrxYMoMEk+fwbdqlYwmugL5CWzdki2r1nLt2jWKFUsvTSOKImPGjGHC+J9QOtUEyQsQJNNhiTczb+oC5syay//GfMOgQYMy2TJlyhQOnz5FyFsD0BXIHO6ky5+Pgn16cnfZKt4dOpR69eplUR8vV64cSqWCaFsEBbSFchyzS3SSLMRTqVJ6aPm6deuIj02gpr4ZgiCgk3tgwYxDdKAUHuVJCshRYLVaMRgMyGQyLK5UrO60TOGUcXFxOOx2jIqcd6ce4an05n7k9Se2k5CQeHmMHTsWh8PBWyNmIAgOypRQMXSgJ/6+cu5GOFi5IZUHMamsXr2asmXLsnbtWg4e3EdcbHTGxDJfvmB69e7PwIED0WhyVxMHcLlcOBwOvIxPdtTTczVF0tLSso04stvt5MurpdYbHsxcmEiqxY1GLaNZA28mfhvAaxUMDOiZSvPut/hlyiyaN2+erY1yuZy+ffvSp08fLl26RHx8PB4eHpQtW/YflUmzWq0kJSWh0+meGDElISHx9yhUqBCFCj2eFzkcDnbv3kHYOzsYNTyQTq180enSnzdx8Q5mLYrmlzmJDBo8gvLly//j62u1WqpXr47BoGXhlGD0Ojm+PgoKh2YVqpTLBepU13L24tl/fF0JieyQnG6JF44oisyaOxdd2dKZHW7AZrfjcrmQazTIgwLRFC1C7KEjmZxuAK/y5Yjd8htLly7liy++ANJLdv0w7kcKy0tTyFgi0wO1uFiOq+ZzfPrxZ3h7e2eERDocDubMn49HpYpZHO5HCDIZeVs15/rY8Sxfvpy33nor0/HQ0FAaNGrA/i2HyasJQS5kP2G9k3YTQSXSs2dPAHbt2oVR9EavSM9byq8pyGXTGaLEuxQQCmecJxfkON3O9BIYGg3hlmt4+XjRsmXLjDYajQYEAZfoyPnGP8TpdqBWP3kCLiEh8fJQKpUULFgQvV7B58P96NBCC4KAXCZDq9Ux8h01P/96j6/HjMLlAoOHkJ5Og0CJIjq6t/fn4tU4Jvwwmh3bt7Jk6QoMBkOu11QoFPh4+3AthzJef+TGrTSUKnW2JRBTU1NZvWoJQ/r4MWxQPiBdhE2hEDI9l8uV1vN6JQ1Llyyiffv2uV5PEARKly79RLuy45FSsSAIHDt2jNmzZ7F1ywacTjsgULlyVfr0HUCbNm0k9WIJieeIUqlk3ryFjBz5AaPGLue7n2MpW1KFwyFy+rwNmVzPByO/eKaReI/SDEsU1ZI3SJ1rW7kMRPHJUYsSEn+HnBVaJCSeE4mJidy5cwdjqayh3i6XExEQHoZE6koUw3I/AtGd+SEoU8hR5gni9u3bQPokb/x3P5CXUArrS2ZZwZQJMkp4lMfHGciY/43JCM0+efIkD6Kj8a1SOVeblQYPtMWKsGnLlmyPf/TxR+Dh4lTKARzuzPViRVHkXtotbjjO03dA34wdarPJjAJVRjuNXEuwthB3uEqiGPeHHgRARBTdPLDe4777Jm8NHphJYTMwMJAiRYsQabub6zhEUSTaHUHturVybSchIfFyMZvNTJ3yE4PCfBjcN5SAgEAC/APw9fVDp9OSmmqme3s1PTt6kDdIxpWDodw9VZh5k4PQalx88+NdWjTyZs28UK5dPcyIEU83ie3YqTsr1puw2XKeeLrdIotXJ9GyZXvU6qyT2Hv37pGWlkqtNx7vICuVsmxD1utU03PlyoWnsu2vkJKSwuzZs6lfrxYhwXkIDc1LmdKlaNG8ARfPruaT9z2YNzkvP33jj1Zxinff6UffvmHYbLZnbouExIvg0eLSq45Wq+WXX6Zw5Ohp+g34BN887chXsBOffT6e02cuMXz48KfS4nlaihQpAig4eMyUaztRFDl4LI2ixf7e4p6ExJOQnG6JF47L5Up3rBVZd4QFHj5oH7085PIsLxKXy4XNasNps5Gamgqkh2onJSRRSJ/Vkc/oWxAorCvF3fB77N69GwCTyYRbFFF6Pjm8UGE0kpScnO2xSpUqsWjJQlzeVn43beJMyhFupl7hqvkc+01bueI6Tc++Pfj2228zzvEP8Mcms2QaXxljFfzUQVwQj3LJfYJ4MRqTmEwCsZxNO8J5+1E6de/IyJEjs4xtwJv9iReiSHEk5jiGB7Z7pMlM9OvX74njlZCQeHmsW7cOiyWRAT2zVhlIS7OSZrVgNMgZ0tcTk9nNnoNpKBQCTevrWTMnH3VraBjy4Q0KBmsYPSKQzZvWER4e/sTrhoWFYbao+fibO7hcWSfxoijy7U/3uHNfoH///v94nM/DT7h27Rr169Xkqy+HUzT4Jl+ONNCiAdy/f5VBvVWsmePHgJ7+NKzjRafWfiybWZRFU4M5uH8TI0d+8OwNknhluXTpEh999BEVKlemWKlS1K5Xj2nTppGYmPN79FXi5MmTvPvuu5QoXpD8+QMoW6Y4n332Gdevv/opZAUKFODDDz9k+vTpTJkyhYEDB2YbOfNPCQ4Opk7dBsxZEpftM+0Rew4kc/OOi94Pq+dISDxrJKdb4oXj7e2Nt5cX5lu3sxyTA+5UCy6rFQBb+B3Uvr4IMhkOh4PExCRiY2OJi4gg5dp1tmzaSvNmzdm4cSMeMiM6ee71Fb2UPqgENRcupO+seHl5IRME7IlJT7TbkZSEj7c3KSkpzJ07l4EDBxIWFsYnn3zC6dOnqV+/PsdPHufzbz7DWExNtD6cVL8EWndvweZtm5g8eXKmHMR27dphEcwkOh7taos4bA6KCxUoJC9JqmDioniMk+7fuSKcIrRKPqbOnML06dOzDYHs0aMHFatU5IRlH9G2iEzOvFt0czftJhdtx2nboS21a9d+4nglJCReHpcvX6ZYIU024ZAiFksqapWARi1QrJCKPIEKrt54vEOrVAp8/7k/NpubFevjaNvMB6OHixUrVjzxugULFmTipGms3mynfd9rbN6RgNXqxm53s2tfEt3fus6MBWa+/uY7KlWqlG0fBQoUQKfzYN/hlCdeb+9hMyVLPruSZ3FxcXTt0h5PfSQHNhZj2vhChHUJ4PylVNo19+CzYb6IopOkxMRMz8i6NTwZ/UEgq1ct5c6dO8/MHolXE7fbzZdffknNunWZuWI5cfnyYCtXmmu4+ejLL6j42mvs3bv3ZZuZI6Io8vXXX9OqZSNOHFnCgO7w7SfedG5pZeO6qdSrW525c+e+bDNfGd57bxiXrgmM/PI2DkfWKJ7zl1IZOiqCatXq8Prrr78ECyX+C0jJSxL/mJiYGOLj49HpdAQHBz8xLEihUBDWsycTfv0VV6MGyNRqUi5dIfbgYVKuXsMtpqvTaosUIu36TfK3ao7NZicpMRFEUKDEfPwsSoeMcrqqXNx/jTjnA9Q8XZ6yW3STnJyM1WqlYsWKFMiXj/ijx9GHBOd4jj05Gev1G/gWL0mZUmUxJZkwit7IRDkWuZkZ02ZSo2Z1Zs2exbBhw54qH6lu3boUL1GMS5dOUkmsSWqKBZfLjYBAAPnxd+cjhkhucI6WrVuwZs2aXO+tTqdj5aoV9O3Tl/17D6C2aTGKPoiIJAmxuBROuvXuyoSfJjzT0C0JCYkXh9PpwuF04G2UPyp5jUzIumPs76egcV0d67bE82avIIoXUXH3bu7pJ49o164dfn5+/DB+HG99cBi49/CIgjJlKjJ7zsgcSygC6PV6OnTszuLVvzKgZyAnz5rZtjuJFJMTb08FLRr7UL2KgbMXUzl22sb0GWF/+T7kxLx58zCl3GfzwmIEBqSn7xw5YeJmeBrffZ4XlVKGl6dAfKIDq9WKVqvNOLdTK1/GTY5hyZIlfPLJJ8/MJolXj++//56fpkzBt3kT/KpXQ/aHyDuH2cy95avo1qsXm9evp2LFii/R0uyZNGkS06f9yJcj/enXPTBDgRvgw3fdfPvTfT77dAS+vr60bt36JVr6alCtWjUmTprOsKFD2H/kCj06eFK6pB6LxcWGbUns2JdKuXLVmDV7njQ/knhuCOK/JQkkB1JSUvD09CQ5OVlSIH2BiKLIb7/9xsxff2Xv/v24RBGZIFCsSBEG9OtHr169ss31e8S9e/eoVbcuZh9v5AYPEk6dRpUvL/qKFZAbDDgSEjAfO4H9QRRB9euiqvEGMlGGwi0n+cRxEnfsopSqDMU9yiKKImcTj3HTepEaPo0J0mQVRHOLbh5Y73I97RJxrhh0ej06rZaWzZtjNBiYs2QJ+fuHYSicVX1cdLkIX7gU17XruNIcBIkhFNOXRiPXZfQdY4vksu0UBUuFsO23bXh7P1lFHODKlSs0adSExIgU8lOIICEEhaDAKlp4IN4hgtvIZTLUBhWLli6iadOmT/XdHD9+nIULF3L18lXkCjmVKleiV69elChR4qnskpCQeLksWLCAzz59l6PbihMU8Fj7wW63k5AYj5+3Apkcrt+00qBjBL+MDaRts8xCaeMmx7NuaxpHfqtAi+7XKFG2Bz/99NNfsuPy5ctcvHgRURQpVqwY5cqVe6pJ6e3bt6lbpzpWaxwyAYqEKgkKVBDxwEn4PQcF8qpJMcvIH1yZLVu3/y0l8ujoaFasWMGNGzeQyWSUKVOGyZN+oFldE2NGhWa0m788htHjbhN+olCG7YlJTtwo8fXxzdRnryHXURtbMGfOnL9sj0TuxMXFsXTpUnbs2oXJbCZPYCBt27ShWLFiuN1u8ubNm1Eq7nkSHR1NuUoV0dSsTp7GDbNt43Y4uPnLdOqVKsWqFSufu01/hZSUFCpWKEWfznI+G14g2zaiKDJg6A2uhgdz4ODR9NKmEly+fJlZs2axds1yrFYLIFCiZCnCwgbQpUuXp6ryICHxZ57WF5V2uiX+Mo/Cmn765ReUwfnx6dAWjb8fztRUwk+eYcRnn7J+40aWLl6Mh4dHtn0UKFCAxQsW0KJ1axKtafi2b4tHpQrp/TudqPLnQ1emFKknTxO9Yxeae/fQ+AdhuXAJd7KJ4uqSFNOXAdLzmct5vkaU7R4XTCeyON1u0c2J5P1EuCNRFSyAb7nq+OTNgz0+nnWHDuKOjSMkX17uzF2Ad93a+L5eFaXBA1EUMd+4SfSuPQj3I3DZHOQVC1LGmFl0TSbICNLkx6Dw5MilXfz00098/fXXT3UvS5QoQeEihTny4Ci33Ze5JV5GLspx4UIpU1JIV4Li+nKcNh3kg+Ef0KBBgydOTgVBoGrVqlStWvWpbJCQkHj1aNeuHd98PYo5i6P5dNjjifUjp9EtisgRmL0kBR8vOc0bZH3Wxie68NDLuXvfxrlLVnr3/+vPhJIlS1KyZM5aGTmRlJSEy+2ibAkVI9/2pmpFDSqVLF2s6GgaX0+I50GMi1/nfINcLufgwYPcvHkTmUxG+fLlKVs255Bzh8PBZ599yrKlC1AqrJQqrsbthhXL00hOTuOND0IgXTkEAOFhJIDbDY/KlqvVAiazI1M7SBeJk3a6nj1z587l088/x+J0oClWFJlOx6kL51ixdi24XGh1OlRKJQ3q1WPI4MHUrVv3udmyZMkSrG6RArVq5NhGplTiW7sGu1et49atW5nKXr1sVq9ejcNhYkDP4jm2EQSBwX2DaBt2nf3791OnTp0XaOGrS8mSJfnxxx8ZN24cCQkJqNVqPD09pd+8xAtBcrol/jLz589nwi+/4NeyGf41q2c65lmqJKnhd9g3Zz7vDxvG7F9/zbGfsmXLotFoUFV7HX3JErgtFgBkMhkeeh1ajRZb/bo44hMwHz2O6kYcoYq8hHrWwqj0ytSXTC4nRFWES7bT3E27SbD2ccmtc6bjRMqi8evUEVWREAxGQ4byt3+tGkTv2sOdHbupW6MGxw8e4fqu31F5eeKy2RAtaZQuWYKKHTqyZN4yihtyngjqFQbyyEJYtGAxH3/8MTqd7on38tKlS5w9fY7XPGthVPgQZ4/CJTpRy7UEqvKhkKX/RIvrynH43k62bdtGq1atntivhITEvxuDwcCgwe8z4cevKRiioWs7PwRBQKFUIJfJSUtzMW99KotXmxj5ts/D2tyPsVjcbN6RSs9OQfwyOxKDwY82bdq8ENtFUWT4sHcpX0pk2cwyiG4baWkW0h4qolcq78GqOb50e+s+w4a+h1IpJzz8OjKZC1EEUVRQoUIVPvr4syzOgsvlYuDAAezeuY6P3/Oje/uCD2uGw9kLZhp0uIDdnobJZM4okVamhA63G/YfTaNu9UfP5fSqEH/EZHZy/IydIe9I6sXPCrvdzvTp0xn99dfoX69C0aaNENRqEhISUbtdGJJTSNy0FXt0ND71arP76hV2du7M/778kiFDhjwXm06ePImqYCiKP6QWZIdnmTI8WLGaU6dOvVJO9/nz5yldXJ2RPpETlct7YDQIXLhwQXK6/4RSqSQwMKtIpYTE80RyuiX+Ei6XiwmTJqErXzaLw/0IfWgIAS2bs3btej775JMcX1arV6/G4nBQtEFdFAYDblf6hEwml/No0VGhVGB8rTJpJ85QWleBfJqQHG3zNwShEpVctJ8gwh5OPlUoAgK3bdcxNm+IqkgIer0+U6ktQRAIalgf64No7kVGcv70aTZv3kxkZCQqlYpq1arxxhtv0KJZC7xFf5Sy3F9y+bUFOZJwk1OnTlGzZs1c2wLs378f0QmBuvzIBBl6RZFs2xmV3uitRg4cOCA53RIS/xGGDRtGTEwMI7+aydxlCXRt60Wgv5Jb4WksWhnL3UgXg8MC6dpWQ3KKE6NBnpHb+fPsRFLMIonJTpautTBm7LdPtRD4tLhcLnbt2sXhw4ex2WzkzZuXDh06kCdPHo4cOcLVqxdYNjMYvU4JKPHw0D8ULntcq7tQiIKVG87Sua0fP30ZwmsVPHC5YM/BZKbPO02P7h35ZcqvtG3bNuO6a9as4bdt65j/cwEa1PbKZFOp4joC/JRcuGyj5utmNBoNSqWSimX1lCquZ/r8JGq/oUUmE7A73MjlCv64y71oZSw2u4oePXpkO2abzYbT6USn00k7Y0/g+vXrzJo1i8ULlxAVH4O+6mtoalXHJoqkJSbicruR6/UoDAYC+/biwfRZpIbfpfA7g3iwbQefffklhQsXpkmTJs/cNofTmW31lD8jyOWIYvrf+quEKIoZERu5IQgCcpnwytkvIfFfRXK6Jf4S+/bt4879+wS3aZFrO++K5Ynd+htLlixh1KhR2ba5evUq6gB/lA/zH+Q5vARV/n7IDR6YbNmX63qE0+1Eq9Px408/sH7devbvO4AlzYKoVeJdpRIe3t455pn7167BzWm/cv78+WzLRZjNZtSyJ+f6qAQ1olskLS3tiW0BrFYrCpkCmfDkfCuFqHjqfiUkJP79yGQyxo0bR9OmTZk3bw5f/vAbbpfr4cKkD1qNidIlDej1OixpJmLjnSQmu5izJIU5S1NQKpUs32Bn9Bdj6dOnzzOza9u2bXw+6iMiIsIJLaBEp5URfs/Od+O+plXrDvj5+ZMnUE6Nqn/MMRcyOar7jySzbXcCH73rzfsD86DXp4fHKxTQqI4X9Wt6MmL0bd55+012797NhfOnsFrTuHPnHiEFoNprBv6MUimjcxt/Vm+MoU9XTywWS0bo6Mh38tPvvWt8OiaOL0b4YrOJGAw6XG4X1jQr67cl8s2EGNq07ZlpB8xqtbJ69Wrmzf2VixcvACK+vn506x5GWFgY+fLle2b39f8L27dvp29YPxxmJ2qnHplChXeN2ohOSEkxASJyvR7hYZ6xTK3Gs1Z14tduwB6fQJ6mjUi7c4dJP//8XJzugqGh7Dh+DNHtzrAhOyx376GQyQgJyXmx/2VQuHBh1q+1kWJyZkR5ZMeV6xYSk10P61RLSEi8bCRlBYm/xO3btxFlAvoCWcXK/ohMqUSZN0+udWHdbjdPo+KnUqvSFc2foPkXabtD4SKF6d27N2vXreVW+E06dGyPV7Ei+AYF5SrspgsuAApFRm1Lt9tNdHQ0ERER2Gw2gvIEkSqacr2+w+1IL9WFmGk3PTeCgoKwu21YXZZc27lFNxbML0Rk5lUmLS2N06dPc/ToUe7fv/+yzZGQeO4IgkC9evWYP38ht29HcPHSjfT/X7xCnXpteP+zGGq2us37nyXTb1gCddpEMnNhKgEBoQwdNprDh08xaNCgZ2bP+vXr6d+vJyULR7FlSSEObCrB9pXFOLWrJF996MmuHUtZvHghvt7yXHeDZy+KplRxFQN6eOLO5tkul8Mn7/sikMTyZb/yWplwmtaO5fWKadwKT6VGi3OcOmfOcl6/7oE4XQLDR8eSkPj4udqojhfjvyzI4tUmKjQIZ9KvySxbG8sPP9+gUccrvP3hPex2FxvWr6Bli6acP3+euLg42rRuwUcfDiGP7xV+/MqXX8YG0KaxhQXzvqde3RocOHDg2dzY/ydcvHiRPr37ojEZqG1ogUqmQh0SgtrghVJQIUMGMjn86W9DVyZdo8V86xaCIOBbvRpHjh17LvWmu3fvjmgyk3zxUq7t4g4eoljhwq+cPkrnzp1xubUsXhWba7vZi6MJCMhHo0aNXpBlEhISuSHtdEv8JeRyOaJbfOIKMQAud5Z60larlTVr1jB71myOHTuGRXQReeMWxqAAtFodcvnjPkVRxGq1YY+OxWUykSxLQBSzF7lJdiQSL0TxwYAxGSqdRqMxXcjtKfX5RdGN1Wpl6tSpzJ41h/Db4SCCTq+jfMVyJLhjSHWa0Csy77CYnMncTL3E/bRw7G47ghy6delG957deeuttwgNDc3xms2aNcPgaeCO+QbFPcrl2O6B9S5uhYuOHTs+3WD+nxEdHc3UqVNZvmwBKSmJDz+VU7NWXQYNepv69eu/VPskJF4ESqUyozKCUqlk/vxFXL9+neXLl3Pv3j0ClEq69HyNjh075ihi+U9ITk5m+LB3aNtMxaRvC2YqU+Shl9OnayCVynpQv/0VbiPH4XCjVGZ9T6SYnOzan8Q3H/sgimSrrGwym0G00LWtgd37bYz7PBSny0lcnBKz2c2IL+PoMegq6xeWoljhx7m5wfnVzJlUlM4DrtCg4z36d3dSvaoBpwvOXUxFoVSTbFYzfX4qDkcier2MxnW9GDc6iGqvGdh9IJkJ007Qtk1z8ubLjynpKluXFqZ0iceh+W2b+zLyHSeDRt6mT1g3tmzdRbFixZ7x3f53MmXKFNxmqGisjlyQ4xJdyNSPvx8REUGuQIRM73NBqQCZDLfDCYA+JBin282dO3coWrToM7WxXLlyNKhTh51rN6D29UWbN0+WNjH7DmC9eJlhkye/csrfAQEB9OjZj+9/mUKRQloa1fHK0mbu0miWrjXz9Tdf/a3KABISEs8eyemWyBaz2cz69eu5fv06MpmM0qVL07JlSypWrIhCEEi5fBXP0jkr2jrMZmx371K+X/+Mz+Li4ujSuSsnj53EhwBKKipyLu0UpmMnkdWrhSXVgpe3FyqlCnOqGUuqBbfbTcL+g+ASuW+7Tbw9hnLG18mvDQXSX9rRtggu2U5S6fWK9OrVK5MdZcqUYcnatTgtFhS55DOab95CcLqYOWMmEeGR+At5Ka2qglyQk2iO59jvJ7HZbRxN3kNt7+YZAmextgccS9yLXFSQj0J4CJ54aPUkpyQw+5e5rFi2ghWrVlC2bFm2bNnCsqXLuBN+F41WQ526tenduze9+/Ri2qTp+Nvz4KPyz2JbqtPENfs5GjSr/5+c2N2+fZuOHdqQlnqPnu2NtGhcEJ1WxvnLFuYt20fPHnsY9fn/npvojoTEq0zRokVzTOF5Em63m7Nnz5KQkICHhwcVK1ZEpcpZt2L58uU4HSmMHlEik8P9R8qV1tOptSdLVsexZWcCLRr5olBkbpuQ6EQUoUBeJSCgUWdO3XG5XFhSU9HrZJQoqmbF+lSAh2k4AnmDlCyakodm3e7zw5T7zJyQ2SmrVsVI57Z+LFljZ/FaOdPmRwIQGJiH994fxr1799i6eR4rfi1BmZKZ87Ob1POm9htGGnS4zPlzJ9i6vGQmh/sRBg8FsyYUom7bq0ydOpWJEyfmeN/+KyQlJbF29TryKwojF9LTxbRyHTFRMX9aqE9fCf+j0+2IiQW3G6WXJwBupwMBnpvD+OvMmbTv2JFTU2agK1cGn0oVkOu0WKNjSTx6HMedu3zw/vt07dqVxMREli1bxuatW0lOScHf15e2bdrQvn3757K49TR89dVXREdH0fe91dSoGkvnNt4E+CkJv2djyepEzl928tag4fTv3//JnUlISLwQpDrdEplwu91MmDCByVOnkGQyo/LzBVHEHhdPgJ8fn374IUuXL+d0XAyF3xqA8Gc1DzH9dfpg6284jp3g0rlzeHt743Q6adG8BacPnaWSriaeSh8ArpkvcNF+Hu9mTdBWLIsgB5VKjc1qReYWMB05RsruvVTQvIbcruRm2hXiicJPHYhR4Y1JSMAms1K7bi3mzJ2Dj49PJnPi4uIoU6E86hrVydO4QbZjFkWR23PmY7t8FbVdTRWPungoMv8tuUU3J5MOcM92Cy+VN0W1ZfBU+LI/fhsG0ZtilAcBtFoNnp5eADjcdk6Y9iF4u/D09ORu+D088cFD8MQpOoknClRuBg0ZxPlz59m/5wBBhBCiLYJO4YHNZeWe9RYR7lsULVWYDZs24O+f1Sn//4zD4aBe3ZoIrhssn1U4U71iSP/uxv8SweRZKcxfsFwKo5OQeAqcTiezZ89m3txfuXPnBuAGZPj5BdG9Rx/eeeedbJ2Jdm1b4aU9zuxJOeeIiqLIwhUxDBxxE5VSwEMvw9tLQdtmfvTu4k/xIjqiY+xUrH+a70f70rKxH15eXpn6MJvNWCwm/HwVTJqRyK+LTVw6mF6qMTExAbfbjq+3grnLkvlyfDxHf6vwp1rmbl5rdJn2nd7l888/Jy4uDplMhq+vL2lpaVSsUIpBvRUMH5xzPnbrnpcwma1sW14KtTpnPY9fZkXy00wrZ89d+c/PQc6dO0fdWvWooq6LlzK9/nmiPY69pu34d+uKvkhRHKIdl+BGrk9fyJA9nEPEb9qC7dIVyoz6BJlCTvSevaT9vo+LZ8/h6+ub4zX/CampqcyaNYvZ8+ZxL+I+opguqxecLx8OhwOL1Yrd4SA5MRGUCvSlSqH0NOJITMR6/SZ+Xl7MnjmTevXqPRf7noTL5WL9+vXMnfMrJ08eA0QEQUb9Bk3o27e/FAEmIfGCkOp0S/xlHjx4QO/evdl/7BieNasTUqsGen8/AKyxccT8vo/hH31Ej86dOXvuHHeWriB/h7YoNFqsNhsWiwVbWhrmk6dJ3rGLBrVqkZycjLe3Nzt37uTY4eO8pq2T4XADFNWXxu62cmPLNswnTqApXxqZXodosmA5fQ5XfCKlNGUJ1RcDPeTxyM/p5CM8EO9Q8rWilCtfn169elG5cuVsw879/Px4Z9Bgxk+ehMrLiE+V1zK1cztdRGzaTNrlq4hWBxUM9bI43JC+w1LZqyapSSY8gjRcSzyLKdGMzC2nsFAauVyG7k/K6EqZipK6iuyO2IAl2sbrxvp4qXwyctlcoovblqtMmTSFd4e9S/Ua1Zk7ey5HHuzE7XYjyAQ8vTx5s2d/Pvzww4yw0v8S27dv59atK2xbViiLww08FEjKx5GTFqZN/VlyuiUknoDD4eDNN/uza+d62jbV8+PoEEKD1cTGOVi5IY5ZM8exe9d2Vq5ai6enZ6ZzU1KSKBma886jyyXy0Ve3Wbo2lvKl1bRv7oGPl4wHMU7WbI5l4cpoRg0vgCiC1QabdqTRvWPW563dYUelFECENVvM1Kvx2A6dTk9ikg1LmouWDfV8Pi6OU+fMNG+Y/l4RRZFvfrxHkklBWFgYSqWSPHkehw/v2bMHiyWZru1yrnEMEBVtp3kjLVarLVenu051T8b9nEB4eDjlyuWcIvRf4FEYtviHnC4vpS++Mj/it25F1csfhZcRl9uK2+FEpkyfglouXcF87CR5mzREppDjTEsj+cgxurdt99wcbgC9Xs/777/P22+/zc2bNzly5Aiff/kltx9EoitdCkWAP2nJybguX8WVmISHQk6+1i2RKeTYk5KIWLOBbr16snblKqpVq/bc7MwJuVxO+/btad++PYmJiZhMJry8vP7ziz8SEq8qktMtQXJyMiM//JAVK1disljwadcabdnSmFxOrPHxGI1GNP5+BHdqT5S3F0tXruTLUaMYN34817/9HnmxogieBlxWK2kXLuFKMaELLsCRixepVqsWs6ZPZ9HCRRhEzyzh04IgUNpQmSBHAcLjrxG+bQcgoJZpyKcsQEHPKngr/TLaK5RKKvq8QbIpjiZNm/Dxxx8/cXyffPIJSUlJzF4wn4T9hzBULI/SwwNrXBzmU2dQWK2UKlaMiAsxKG0a4lPi00tyyGRotBo0Gi2CICATZASri3DPfJXN2zbTplVbvEwB+On9UatV/LH0DIDFlcqJxAOoRA1FHOWxJjqIEWLQarXo9DrkcgVF9KUgFab9Mo3jp44zbNgwDh48SEJCAgaDgWrVqmXUmn1ZPAqGeRklcpYuXUTl8mrKlMxZmE4QBPp09WXIRwcIDw/PNYdeQuK/zvfff8+eXeuZO6kA9Wt5ZXweFKCibCk93dpb6NT/FEOHvsfcufMznevt7cf9BzkLW42ddJ8VG+IYP9qferU0eHv7YE2zYrNZ6dfNkwnTE/ngi3B0OiOvVanDwePHOHYqlTf+rEQuiggyWLrWxO27Tn746rGauFqtRq/zIMVsxukUcbvB4RARRZFjp8xMmxfFzn0Ovvt+EoULF85iY1JSEjKZSJ7A3Ms/iojIhPToL4mnIzQ0FA+DBzGmyIz3tiAIvGasycHknUTOmYXhtdfQlC6BW5OGmJqK5cw5Us+dx7tcWYIa1MOWkMj9JcvxEmQMGzbshditUChwu92M+vJLbP6+FO3xNgq9B7GxsShkAj7NmmA5f4G41esRFApCu3VG5eVFSO8e3Jr+K5+MGsWenTtfahk5b2/v/+TCvITEv4lXSx1C4oWTkpJCq7ZtWLFlM05PI5oihfCs/gYKDw9kWi0Ol5uEhAQcdgcAgfXrgtHArVu3OHX8OCVCQkg5dhzTwSPYr1zHp2wZSn04jFIjh1F81EeIRQrRb+CbHD1yFG95YLY2CIKAnyqQUoaKyJ0yijvL0dSzHZU8q2dyuB+hkCnxF/KyZtWapxqjXC5n/PjxbFyzlrbVa+DYf4jEdRtRnb/E4G7d2b/ndyLvP0CVpsFsNuNyuBCdIg67k+TkFGJjY3HY7QD4KQOxWe2YzWacDgcBurwPVdEzv2yTHPH8HruJZGc8eSmIVtCjRIUgyrFYLMTHxWN/2GdBXTFEu8DChQtRqVTUq1ePDh060Lhx45fmcKekpDB79mxq1ahFnsA85A3KS5NGTVi+fDlWq/WF2XHv7m0qlnlyqbYKZTwAF/fu3Xv+RklI/EtJTU1lwfxfGdjLK5PD/UdKFtMx+oMgftu2iVu3bmU61qJla/YctBAVY89yXmycg9mLohgxyIuWjXUo5ArUajVeXl74+fnj4+vF1x+H0KKRF8WKFWPLli3UqNmIsHfvsGxtLDZbunN7K9zK/BXJ9HkviqGfx9CptS9VK2UOdTcYPDAaPDlzwUGqxc3w0fcpVfMiHfrd4ea9EGb+uiCLvscjjEYjbrdAXLwz13tVoqiO/UesCDnkrj9i/5EUVCrtK1dW6mXg4eFBtx5diXTfweF+/Deileuo5dWYQvb8WPYeIWraTKJ++pnoGbMxnziFR0gwmoAAwucv4ub3E/BzOFmzcmW2iybPix9+/JFUlZKQPr1Renhgs1lxuV3I1WoEQUBfriw+LZsRf+IkaVFRAMgUcvwb1OXs+XOcOnXqhdn6Z6xWK8uXL6dzpw7UrvU6jRvV4+uvv861eoyEhMSLR3K6/+OMGTOGc9dvkL9vb2yJiXhUrpR+QBCQKZUo9DpEmYyk5CQQQZDJMFSqyLpNm0hMTOT6rVsU7NOLSt9/S7kvPiWkcwd0D+uWypRKgrt2xunjQ3xiAk9aA7a77QiCgFbQPXHFWCf3ICEh4anHKQgCNWvWZPasWdy/c4eoiAhuXrvGmDFj2L9/P9FRUYCAWtCgFFQoBCVKQYVKUIMbEhITcTodiLgfi7sIAm6y7oLY3FaOJOxB7lYgR0kA+ZAhQybIUAgKVIIGRIGkxERcLhdyQYEPARzY/+xLz4iiSHx8PPfv38diyb0s2SOuXbtGrZq1+eD9kUSdiyfEUYIC9mJcPxzOW/0H0bhRY6IeTjqeNwqFEqvtyTtNNnt6G0mlVUIiZ7Zt24bZnECvzgG5tmvT1Acvo5sVK1Zk+rxjx45otT6MGnMPpzOzHMyK9XHI5SLdOxhIs4podTqEh099uVyOTqvDw8ODt/vn49atq5w+fZp58xZSv2FnPvgyjrK1L1C4ygkqNzzN2EkxHD+Tvri3dVcS/5twD7v9j88BAZ1Ox/ptDgoVKs7wD8bw/rAxLF+xkf0HjtCyZcscx1anTh3Uag9WrI/L9R7UreHJiTNWLl5x5dgmLc3FghWJtGnbOUso/n+VwYMHY/DTccK0D5vr8QKtWqahrLEKZTQVkDmcKAUBrUqFWhCQP4hGfuYsFY1eTJkwgRNHj1KhQoUXZnN0dDQbN2/Gu/obyNXpERB2uwNBJsukW+NRqQIyvZ64Q0czPjMWL4aoUnP48OEXZu8fOXv2LNXeqMywoW8iOA9Qv9oDShe+xtJFE6herTLjxo3jXy7dJCHx/wbJ6f4Pk5KSwqJlS/GsUQ2VZ3oOkOLPuUCCgFyjweVyYbPZAFB6GTGbTMyfPx9Rr8e7QvkcryFTyPGpWQ2r00GMMyJXe5SCElF04xDsyP8s0PYnrC4LRuPfm+QIgpBRyiwlJYWvv/wGvcxAMvFZXk4CAkpBBSKYTCaibZHoPPSUL1+eQoUKEmPPWiv6btpNHG47wUK60rgceZYFB6WgQhRF0h46wnJBgc1q+1vjyQ673c6CBQuoW6cuxYsWp3yZChQKLcxbb73FiRMncjwvLi6O9u06EHcrgZoezahkrEmorhgFdcWp4lmHarqGXD19nc6dOr+QHe8qVWuwY68FhyN3x3vz9gS0WgOlSpV67jZJSPxbiYiIwNtTQf686lzbqdUyihZSEhkZmelzg8HAz7/MYPteF50HXOPA0eSMZ+bJs2bKllThdoNSpUavyz4lpEZVAyqlm4sXL6LT6ZgxYyazZi/EbFEQ6K9g0rf5uHygFGd2F2Hnynz07uzB3CVR9H77WibHe86SaLbutvLpZ1/w7rvvMmTIEGrVqvXE8k7e3t60aduZmQsTuHMv+2eYw+Fmx94UZHIt730axeVrWRcszakuBo64RXySTqqc8AdCQ0NZuXolmiAFe82bOJ18mHDLNW6lXmFn/DpOWA4i8/fGv2lj8nTrTEDbVoi+PiQlJVOqRAm6d++eSRvlRXDhwgWsDgeeZUohut2kXLtO3I5dJO3YTfK+AziTkgEQ5HK0xYqSeu/xe1+QyZCplBnzoxfJjRs36NK5HXkDYti/oRhLZxRl9AfB/Ph1QU7uLMVH73gyedJYfvjhhxdu26uI2+3mwoULHDx4kAsXLkipIxIvHCmn+z/Mnj17SDGnUvT1Ksgeru46k5OztBPkcpDJsdqsqDVqHEnJeBgMnLtwAXXhgsgUuTvIxhLFkauUJFsTSbTH4a3KGjIOoJJpEGQCifJoBKFsjv25RBcxYgTvtP3nE52VK1diTjZT2vAaJ5IOkEgsPmTdBZKjIM1q4b5wk15de+Dp6Un/N/vz8YhPMDtTMomv3bHcwI88eAieIIKZZDSCNkufMuRY0tLwMHhgFpOpEvpsRHhMJhPdu3XnwO8H8SGAEurKKBUqTPYkNizexOqVq/lu/HfZlhKZN28eD+5GUdOjCRp51jI5RqU3lXQ1OXx6J+vXr6dLly7PxOacCAsLY8H8mSxfH0fPjtnvziUkOli4Kom27fpJAjISErmgUqmw2ty4XCJyee7RRKlpYqbyYSkpKSxZsoQF82djt9vYtS+NHXsTMXqoyBuk5dpNE7WrqdBq9RgMhlyjlQRByHDWHQ4HX3zxKZXKyVk2sywe+vT3icvlAkQG9FDweiU1/YfG8OX4u9R6w5OFK+PYd9jGoMHD6dy581+6B1FRUbRr1469v++iXZ8bfDYsiFaNfVCpZIiiyIkzZr7/5QEnzspYsHApkyb9SJMup2hYW0fT+l5o1AKnz6eyfH0KLreRufMWUbx47qJs/zUqVarEkWNHWLp0KfPmzif8zmUcTgepcht+9eoS3KYNcuXjeUNA3dokHD/J7IUL8fb25vPPP3+h9qb/rUFq+B0itm7HFheHzGBAptGQev4CiTt2oytdEr82rRDkskzOmiM5BafJTL58OSvhPy+++24cPp7JLJ5WFKMh83Reo5Hx7pt5cThFJk0aT+/evQkMzD7F7/87DoeD2bNn8+ucOdwKv41bFJEJAoVCC/Jmv370799fipKTeCFITvd/mKSkJERBQPlwl9tYrCjmU2cwVKmctbFMwO0WEd1uTCfP0LtVK65cv87T6IYICCjkCgoWDeHMrUNUltXGqPDK1MbpdnDadBC9UU+KPYFY2wP81Xmy7e+a+TyCGnr37v1Xh5yFo0ePYhS9yasJwV99gyu2U5TiNbyEzAsDLpxcFk8h1wm88847AHTv3p15c+dx4sI+Kuqq46n0wS26sThN5KUgOlGPHg8ecBc/IetYZMhwuh0k2OIxC8l07979H48HYMjgIRz+/SivaetkEq4LUOehkFiCS+bTfDjiI/Lly0fTpk0fj9HlYt6ceQSQL1uH+xFGpTfe+DNn1pzn7nSXLFmSrt368PnY2cgEgc5t/DLV/L19x8qgkeE43QEMHTo0y/l/rAMrIfFf54033sCSJrD7QDKN6njl2O5WuJVLV20MeucNAMLDw+napQMPIm/QsrGeEW/lRSbA/iNJrNqYTFSsjBo163Pt+mHkcn2Ov7l0pzYFqy29vjjAjh07iLh/mzk/Fs5wuCE9JN3Hx5fU1FTKlpTRroWen2dFMX95KpUrv870GUNo1arVU/++d+zYwcyZ0zh4YC/gQhTBLQoMGHqPPIFRhBRQkZzi5m6Ek9DQoixZOpEaNWrQsGFDVq1axbx5vzJ89CVAxNvbh569P6BPnz7kz5//qa7/X8PHx4e3336bt99+G7fbTY1atbgpugnt0C7LdyYIAr5VX8OenMyUGdMZPHgwfn7ZL84/DwoWLIjodHJr/mLUhQsR2LYVqgL5EUUR0W4n9dwFknfu5sGc+eBwYCzw+DuPO3IMTw89zZs3f2H2QnpI/LatG/hypE8Wh/uPDOgZyPT5V1i0aBEjRox4gRa+GthsNnr27sX2339HW7o0fr26o/T0xJViIuL0GT76YjS79uxm0YKFD/V5JCSeH5LT/R/GaDSCKOIwmVAaDPjXrM7NOfMxnzqNR6WKmRu7RWRKgahde8Bkol+/fsyfP59j69Yhut0IuYT0mW7cQCGTMWPmDD768COOnNqJrxBIoDI/MkFGoiOOKPc99N46Vs1fyaxfZ7F1428UcpYkWFsElSx9t8XsTOFG6iVi5RH875tvKFSo0D++BzabDZkoRxAEqnjV5ljiXs7Zj2AUvfEjDzJkmEgilkjsgo1PB3+ScV2DwcDqNavp0qkLR87uwkvwxUvwx4ULJzYcgoMC8iJcdZ3lvvsm+WVZRWHsoo2r1lOUKF2chg0b/uPxXLhwgS2btlBS/VoWpXhIn9yU8qiIOSWJH3+YkMnpjouL48GDKMqoqj7xOv7KPJw/f/6FOLXfffcdbrebD79ewE/TY2nWQI9eJ+fspTT2HU4jT55QVqxcToECBQA4ceIEc+fOZcf2TZhTU/Hy9KRN286EhYVRokSJ52qrhMSrTIUKFShf/jV+nnWGutWNKJVZn9uiKDJxRiTe3oG0atUKs9lMt64dURDO3vXFCM7/eGLappkvn7zvIOzdW9y6fZ3UNAVL18YyoGdQpj7tdnt6SUmblV9mxWAypTFu7P+IihrA9u3bqVBGRekSWRf6ZDIZBoMBDw8P+vfQsGzdbcb/8DM9e/b8S+P+7rvvmDRxHK9VUDLpW3+KFdKSbHKxbks8qze7EGW+FC/TAl9fX+rWrZspTF2r1dKrVy969epFWloaDocDDw+PJ4axSzzm6NGjXL52jXwD+uT6vvCvUY3rv+9n2bJlGYvbL4L8+fPjFkXURQoR2LtHRh632+0GlQpDlcqogwsQPXMObrMZv07tATBdv0HS3v28/9ZbLzzK6uTJk7hcVlo0DM21ndGgoE41NUePHgKe7HTfv3+fHTt2kJKSgpeXF02bNv1X75CPHj2a337fi0+XjghBgTjcIg4AL0/0jeqjL1uG7ctW8MUXXzBu3LiXba7E/3Mkp/s/TN26dTHotMQfP0lQ/bp4liqJ3xtViVu7EXt0LMY3qqLw9kJ0uXDExmI5ex77uQuM/uQTypUrR69evZizcAFJ5y/iXT77cHDR7Sb+wGFq16jBG2+8wdZtW1mxYgWzf53N5ctnQASfAG+G9nmPsLAwChQoQM2aNfnss89YtGARt02X0cuMiLgxu1PwD/Rj4hc/PZNdbkh/2VrkJtyiG6VMRTWfBkTZ7hNuucYd+1VE3GjlHoSqixIh3qZ27dqZzs+bNy+7f9/N5s2bmTd3HpcvXkblVJJkT6C4V1mC1IG4TU6umy+S6I4lD6HoMeLGSZR4j2jZPQqGhLBk2ZKMPPM/Exsby9KlS9m6ZQPJyYl4efnQomVbunXrho+PT6a2ixcvRuFSkUdXIMcxC4JAiLoYp04c5fz585Qtm/7dPQqZexonWkB4YflQSqWSiRMn0q9fP+bNm8fvR/fjcNjJl68cEyf1pHXr1mg0GkRR5KuvvmLmjMmEFhAY1MuIv58Pd+/bWLlxBgvm/8rX32QfVi8h8V/hy6/+R6eOrWna5TI1X/fA06igWGEtTep5kWpx893k+6zZYmXipImo1WqWLFnC/fvX2Lsus8P9CF8fJQunFKZ6iysUKVKWMRPPUTBYQ4PaXkC6YrrJnIJcBvNXpPDb72m82cufBzFnGfr+myhVBlrUz30qIggCxQobkMuFv7wbtWLFCiZNHMtnQ30Z3DdzxFHN143072Gh68DbRD24z+TJk3PtS6vVotVmTRWSyJ2LFy/ilsvwKJz7QrlCp0OVPx8XL158QZals379epDL8KpfF7fDkaEpIxNkuHEjiiJKfz/0FcphOnQEURS5s2wllnMXaFKvHqNGjXqh9gIPq5+I6HS5p/cB6HVy4mNyzzmPjIzk008/ZueOrchlNowGBckmJ5+P0tKseRu+/XbMC40+eBbEx8czd8ECVK9XgYBAZG45ckEOCIi4cTlduP180VR7g/mLFvHhhx9mmVM9a0RR5MSJEyxZsoR7d8NRqlS89lpVevToQVBQ0JM7kPhXIznd/2G8vb3p3L4D89aswbtcWdR+vgR3aIfK05PovfsxHT6CwssLt8OBKzGRQsEhfDp+PH369AHSd02aNWrMljXrUXoa8QjNXDJFdLm4t2Y9PIhixOSfAdDpdPTp04ewsDAsFgtOpxODwZBp10ClUjF+/Hg+/PBDVq5cSXh4OEqlkgoVKtC6detnGgLUtWtXpk+ZTrQtgjyaAsgEGXk1weTVBKeP4eFO7tmUoxQIzk+dOnWy9KFUKmnbti1t27YFYPny5bzVfxBWmQU1GkoZKqJx67hlucpF8Rjp5cVEnDgJCPBnyrQpFCxYMFv7Vq5cyciR7yOIqTStryNvBRX3H0Qy/rtj/DB+DBMnTaVNmzYZ7a9evYbB7Y1MyH0Xxk8VhDPFxc2bNzOcbj8/P7y8PImPjyZQnXt+WoIzhiIli7zQ0O1y5coxYcKEHI9PmjSJmTN+4qsPA+jbLQDZH0r9fPC2m3GT7/P5qJH4+PjQrl27F2GyhMQrhSiKXL58GY1Gy+nz8dyPTEUuF4iLd6FQCDhdCnR6T8b/8HNGrvSihXNpUldPaHDOpfu8vRR0bGlg3W/R1K7bmrB3N1C9SiytmuhQq6zExLhYtSmVG7cdfPxufoYOSn++7D+STKseV3kQk3M6yyPiEhyAgIeHxxPb/nG8v/z8E03rabI43I8oWUzH91/kpf/QPZw5c+aFqmb/VxBFMUPJ/kkIMuGFq21v3rIFXZHC+IQEk5KSgtPpQqZSZkTwiXYHosOBrmxpUg4c4upPPyNXKPDx9ibQ35/Jkyez+/ffuXrtGoJMRsXy5egb1ofGjRvnuJj+T0lPa5Bz/lIq1arkvMsuiiLnLtkpVT44xzb379+ndatmyIlg7Gd+tGvui04nJ8XkZNXGeH6etZLWrU6zYePWf5XjvXbtWpIsFgLLlIYEE/akZJDJUAcGItfpkQlyXLhQlSxB3N79rFu3jn79+j03e2JiYnhzQF+OHz9EaAEZ5UursFrdTJvyGxN/+o4hbw/jo48+emIUjSiKnDp1ikOHDmG1WsmTJw+tWrWSKij8C3huTve3337L5s2bOXPmDCqViqSkpCxt7t69y+DBg9mzZw8eHh6EhYUxduzY5/aQksjK6NGjOXzsKNem/Ypf00Z4VyhHnsYN8a9di6gdO0k8dhJ5aipfjvqcESNGZBLWAZgxfTrde/Zk7/RZaIoWxrN8OeQaDWmRDzCdOIkiLY0pkyZRq1atTOcJgvBEhVJ/f//nrgpbtmxZ6tSrw/4dhzAoPDMJokG6nRFp4URxlzHvfPtUYhtt27Zl+rQZnDpxgIq6GsjSFOjSPCknvIFNSMMkJnFbvIJSpsKdItCrR2/WrFtN5cqZc+k3btzI++8NoksbDZ+PKImX5+PfRWKSky++u8uQwf3RarU0btwYAJkgIJL7hMXhthNuuY7VbuG7cd+yadMm2rdvT+PGjekV1ospP06lqLsMSpkq2/MtLjNxYhQf93918sNSUlL45ecfGRTmRf8eWUPhlEoZo4YX4O79m3w37hvatGkjhYdK/Ccwm82sWbOGY8eOceLECS5ePEP7FkZGvlOW0PwCVquVqzfSWLQqmZUbrYSFvUmPHj2A9Mnd1WtX6NXe64nXqV3dk7nLIvnuu+852r4Dc+fO4p2PfkMud2PwkNO0vg9jRwVkchBqveFJ13Y+rNoYx71ICwXy5ux8r9oQj0ZjoHr16k899hMnTnDjxmW+/ShnhwOgYW0v8ueJYsmSJZLT/RwoVqwYuFxY7t5DH5Lzd+GyWrHdj6Rol6Iv0Lr0kqByoxGdTodCocBisWC12uDhu1Qul6PUaBA9jellU0uWwKdsGdJiopk2Zw6i6MZYsgSeVSqB283vV66xPaw31apUZfHChfj6+j5zmytXrkzhwiWYv/xOrk734RMmrt1y8r/vctaMGfr+O6jkEaydV4TAgMfvfaNBQb/ugTSo5Um7Pjf48MORzJkz95mO43ly9OhRUCpJWLcR2/0/KM7L5eiLl8C7ei1Uvr4odR6g1z3XuuYpKSl06tiWlKTLLPglH3VreGZsDJjMTmYviubHn8fhcDgYPXp0jv0cO3aMUZ99zIULpzEaQK+TERvvZPTnH9Gla2+++OILNJqcF0glXi7Pzbu12+106tSJatWqMXv27CzHXS4XLVq0ICgoiEOHDvHgwQN69+6NUqlkzJgxz8ssiT/h4+PDpvUbGDpsGNvWrCd201aU3l64LWm4UlIoW7Qok376iddffz3b8w0GA2tWrWL16tXMmjOH02vWI4oiOq2WPh06MGDAgFe+jNPMX2fStk1bjpzbRRDB5NcWRCVTYXaauGe7QRxR9O7bi0GDBj1Vf2q1muUrltG1SzcOHt6OxuaBrxCIDDnJxJFALB5KA29410ct03A8cR89e/Ti9JlTGQ9Lp9PJl198StN6Sn74KjTLjrK3l4KJ3xYkKeUGX4z+lIYNGyKTyShfoTy//7YPl+hELmT9ed9Nu8kV61EUGjstGqnJk+cet++EM6D/SvLnL8S3Y75nwbyFnIw7QGVDzSyOt9Vl4ZT5AMGFgunUqdPfvOPPnjVr1mC3p/Bmz5xVhAVBYFCfINr0vsXevXupV6/eC7QwZ+Li4ti0aRMxMTFotVrq1KlDuXLPRsle4r/N3LlzGTvmSyyWRMqXUuJntJEvUGTH70mIbpg0phA+PjqqVYVqVfNRpuQDvp04hZYtW+b4zH8Scrmctm3bEhgYyNEj+/n1x3zUr+WJVpt9GOz/PglhyZpY/vfjXab/UDzb6JmIBzZmL0mkbbu+f2k3J30S7aJKhdx3x+VygYpllYSH33rqviWenpo1a1I4NJTIfQfQ98rZ+Ys/dgK1KD4zUdGnxcfbG1dUenk8lUqFSqXC7XbjdrsREHA4nSQlJeG2pCFTKsnToC76kGCu/PQzygB/fDt3QGE0YDAY0jcTGtbHfDucowuX0r1nTzZt2PDM1bEFQWDwkPf4YMQQFqyIoXfnrNU97kfaGDH6PmXKVKRGjRrZ9nP58mUOHdrH9PFBmRzuPxJSQMOwQf58NmYTERERL0Wp/e+wZ88enCYTyrxB+HXumC6O53KSduUq5qPHiVg4jzydu6LKmweX3U5ERO5lbf8J06dPJzLiEluWFqZwaOYUFYOHgqGD8qHRyvjfhEl06dIl24oI+/fvp2ePTpQt6crkuMfGOVi6JpbJs6Zy/doVFi9ZJonCvaI8t62er776imHDhmWErv6Z7du3c+nSJRYtWpQeptysGd988w1Tpkx5mKsi8aLw9/dn8aJFnDh6lNHDh/Nmq9YM69uXTWvXcvjgwSdOvpRKJV27dmXn9u3cvn6dqxcvcvvGDSZMmPDKO9yQHla9ZesWhn88FGeQmWPW3exP3cpZ5yHylPPjl2k/M3HixEw7o3a7nX379rFhwwb27NlDWlpapj4DAgLY9ttWggsWwCpPJVIWzn3ZDUSVSEWvatT1a4FeYUAhU1JOX5UH9x+k55U9ZOfOnTx4cJf3B+bJMYRbJhN4f2Ae7ty5wd69ewHSBYZUbu6mZZ083ku7xSXbQbp0ULNrVT5mTijAz2OLsGlxcX5bXhhfz/u8+85bfDd+HDJfN/vNW7hkOkW0LYIo633OpRzjgHkbPqFerFy14pUqz3XhwgVKF1fnOGkAEEU3Fcpo8TQILzxnMDvMZjNDhw6lcqUyfPH5+6xY8j1Tfh5N0yZ1aNG8CWfPnn3ZJkr8i5k2bRqffTqc9s1cHNlanIVT8zBnYhCnd4cy+Vt/TpxJocubV0hNdWWcM6hPEIVDZMyZMwdIn9gXL16C/YdNT7ze3oPJ+PkFZOREXr9+HYXCTbOG3jk63AB+vkoqlzOwcoOZIR/e4mb442epyyXy255E2ve5id5QiE8++eQv3YNHUXM2+5P1J2x2UYqye07I5XI++uADrBcv82D7rmzDx5MvXiZu23Z69+xJnjzZpwI8L1q2aIHt9h1scfEZn8lkMhQKBXKFHLPZBHI5louXkOu0GIoUJu7QEewpKQT27YUmfz4ElQqz2YzoTh+bR8FQ8vfpyeHjx9m0adNzsbtbt2706/8On34bQ++3b7BjbxL3ImxcumphzE/3aNL5OoKiIHPnLcpxHrFhwwZ8vESa1PPK9Vrtm/uiUtqf21ieNQcOHOBuZCTGWjXwD+uJrkwpFJ5GlD4+GKtXI2jwQJRB/kStXoH1zh1cySnPLXTe4XCweNFcOrUyZHG4/0j/7oH4+4osWLAgy7G0tDQGDepH9Soiq+cUpX4tr4ydcn8/Je8NzMuS6aGcOL6bX3755bmMQ+Kf89LeMIcPH6Zs2bKZVBGbNGnC4MGDuXjxIhUrVsz2PJvNhs32WBAiJSXludv6X6FgwYIMHz78H/XxKjlifwWj0chnn33GyJEjOXv2LBaLBX9/f0qWLJnpZWW1Wpk4cSJzZ88lJioWt9uNTCbg4+dLWN/eDBs2DIPBAKRPOqMeRFPFqw4BOZQ/s7osOEUHercny5ctzyjBdezYMYLzKSlbKvcQ/Erl9AT6yzl27Bj16tWjYMGC9Ozdk/mzFqC2qsmjDkYQBJyik6vWY3Ruq+eLET6IMjFTbmTpEjqWzihMy+7XWL9+LfsO7GXOnDksmLeAyKRwAPIUCOLT/p8QFhb2t15OJpOJDRs2cOvWLQRBoHz58jRt2vSZ7AC4XK5MOdyPEEWRtLQ00tIsOJxOAGw2C5s3b6Zly5aEhob+42v/HVJTU+nUsR23bh7no3d96do2FC9PBS6XyK79SUyYdpL27VqwYuX6LGkHEhJP4v79+3z77WgGhxn5bHgBRERiohPx0AuoVQLtWxgoUURF694RzFgQxfDB6TtXgiDQtZ0n30/dgNM5BYVCQa9e/fh81PuE37XmmNedkOhg1SYT/d98J8NxfbRI+TTpuXmDVKj0VTh48g4b21ynXCk1RoOMm7cdPIgRqVq1BtOm/4q/f9aKDLmRPo9QsW13Ep1a5/zMMpmdHDxm463Br/2l/iWeni5duhAZGck3Y8diPncez6qvofbzxWlOJenUGezh4bRv2YqxLyHSsU2bNoz64gsi1m0ktE8vZIrHi0R2ux2n04kzOQXz8ZMEVq+GIJcTe/gourJlUHh7AyBTqXDa7aRZ09Dp0tMk9MEF0BQqyKw5c56LjoggCHzzzTdUrFiRmTOm0ve904ALEDAavenc9T3ee++9XN/XCQkJ5A1SZlvJ4I/o9XL8fZXEx8fn2u5VYeq0aajy58NYuyY4HCDP7O7INBp8O3Yg8qfJxP22DcEtUqlSpediy5UrV4iNfUDb5iG5tlMqZbRoqGff3h3At5mOrV+/nqTEaMZ8WizH76pqJQPd2hlZuGA27733nlR7/BXkpTndUVFRWcoQPPp3VFRUjueNHTuWr7766rnaJvHfRaVSUaVKlWyPWSwWOnfqzKF9R8hDMG9oy6GV67G607gXf5PJ439mz+7fWb9hHUajkejoaNwuNwZN1oWIaFsEt1KvEGt7gAi4RRc7tu/gl19+oW/fvtjtdnTaJweiCIKAViPD4XBkfDZ+/HjSLGmsXL6KW/bLBMoLkOxIRFSmMTDMB+QiXp5eWfLzjQYFQ/r5M/Kr7bjdY/niiy/49NNPiY+PRxAE/Pz8MlRd/woul4vx48czbcp0UhJT0Mr0iIikianky5+XUaNH0a1bt7/c7x8pWrQo69ZYSUp2ZuS+u91uEhMTcDodqFUCOoOMqzftpJhcXDy/n/r1avLrrPk0aNDgH1377/Ddd99x4/pxVs8pSJmSjxdW5HKBxnW9qfW6kR6DbzLwzb4cOXpSenlK/CUWLlyIXutg2KC8QPrikwjI/7AwVaq4mo6tDCxeFcO7A/JkTOTy51XjsCdhsVgwGo106tSJmTOm0GvILRZNK0hIgcyOd2ycgz7v3UKjDcoQ2QQoXbo0breCvYeSqV/LK0dbTWYnx8/YGfJOM95++202btzI/v37sVqtNC8bQKdOnf52ukVoaCh16jZg5oJdtG7ig1qd/TN13tIYrDZVRi67xPNh2LBhVKtWjVmzZ7Nx82aSnE4EQaD6G28wYNTntGzZ8m+9Y/4pGo2GWTNm0KVHd27NnE1go/p4FCmMIAhYTSZMJ05jPnQEXWAAQY0b4DSZsScl4VnycQiwIJMhyOU4HM5MfXuUKsGpnXueW3lNQRDo0KED7du3T1/oj4pCo9FQtmzZp1La9/DwID7R9UT7HA43SSnOjE2FV5mYmBi279qJT5NGCDIZosOJW7AiU6nhD2NUGA1oihbGcuY8Bo32L+lF/BUeRUJ6Gp/schkN8iyRkwAbN66nRlVtlufvn+nW3o/5K25z9OhRatas+fcMlnhu/KXw8o8//hhBEHL978qVK8/LVgA++eQTkpOTM/67d+/ec72ehMQjPv/8cw7tPcJrmtqUMb6GUemNUqbCoPCklKESVbR1OX/ifEa0gE6nQxAEHO7M6RJXzOc4krCHNFsaRSlPRaEmpYUqeNuCGP3xF7Rq2RpfX19u37WRnOLMzpQM4uIdRDywZ8qxUiqVTJ8xnTXrV9OgXV1i9feIdN2mcgU1xYp64ufnn6PQRpumPoiijUOHDmX0FRQURGBg4N+aDImiyPvvv893//sez+QAank0p5ahGbUNzamm/T/2zjpAirKP45+Z7b3Y6yLu4I7uBmnpVGnpRkBERQxQwcAAFcWkWxqkS7obpOsODrjO3dvenXn/ODy99w4wAGs/99/O7DPPzM7NPL/6/lrguCUw8rnn+fbbbwt899y5c/zwww+sW7eO69ev3/c43bp1Q5J1LFqRkntcZDKzMpEkJwH+SvwMSnRakcUrjRSN0PDT7go0quti8KA+fyjV/OLFi4wbN44O7VvTvl0rRo8ezfHjx3+T6q7ZbGbZ0gUMfNYvn8H9a3Q6Be+/UZTExJts3779d8/Pw3+bH7dvpm0zr7x2QoKQqx3tlvLfn107+JCc6uDClV8WecmpDkSFIm/B7uXlxfdLViArStLk6asMH3udlevTWL0xnVcmxFGv7SUSUoL4fsnKfI70qlWrUrFiNaYvSEGS7v1/sWhFKnZHrsGr0Wjo0qULX3zxBdOnT+e999770/oGr702jth4DYNejCU9w5lvm8slM3dJMpO/TmfYcy889rTm/yJ169Zl1syZ3IyN5eLZs9y4do31a9fy1FNP/SUG9880btyYNStWUsbLizuz53N10sdcm/I5NyZ/RtbmrfhVKEep54ag0GiQ5bvtNRWFGVH573VBoXgs7TUFQaB06dI0atSI2rVr/+bWdi1atCAx2c2Bo/cvIdm8IxNTjkiLFi0exnQfKUlJSbjcEoaoKERBQBREZIcLt9mMZLMhOexINhtusxlVUDCiKNLx6Y4UK3bvVqt/htznosilqwWN6f/nynU7YWEFa+YzM9IoGv5go71YEQ0gkZmZ+Qdm6uFR87si3WPGjMnnyS6MkiXv34fxZ8LCwjh69Gi+z5KTk/O23QuNRuMRCPDw2ElPT+f7RUsooSyLv7rwVC1flT8xqoqsXbOOCRNuUaVKFQICA7idHkd5VW4K2m1rHJdNPxFFWYoJ0XmeZTVaIryKI2mcHD+2F51eiyTrWPpDGsP63vv/YcnqVESFV167sp8RBIEmTZrQpEkTAAYMGIDLvBEf7/t7qXU6BWqVUKin9Y8wb9485s2ZT5SyDJH6GHSKX9SJfVV+VDc8wUXTad4a/zbNmjWjdOnSbN26lU+mfMrJ4ydwOXPrTZVqJU2ebMKrr44tVGMgKCiI3n0G8cm3XxFTUkfT+nqcTgcBBgUqZW4LmrlLjSxZY2Liq5H4+qr4dnJJmnW+zFdffVWo0V8YNpuNl156kbU/LCc0GBrV1SKKAkcOHWfF8gU0aPAkM2bOxs/P755j7N27F5Mpg2c73Vv0DaB8GT2Vy6tYt24dbdu2/U3z8/Dfxel0smXLFk6ePMnly1fw1TrJyHQS4K9CILe/tc1mR68nr3lTYECukZNzt65blmWW/ZBF8+Zt82VXREVFsWXrDpYvX878ebNYv+0aABEREYx+6VV69epVIIVVEARefW0c/fp25/X3bvD+G5Go1b/4+WVZZtWGdD76Mo3+A14okPn2sKhatSrzFyxlyOB+1GxxibbN9ZQuqcOY42LdlhwSU6D/gJGMGzfukRz/v4zZbGbVqlXMXTCfK1evIYoCVSpVZmD//rRv3/6R/eZ/lHr16rF/z14OHTrE/v37sdlspKamMm/xYsKaNUFx11mt0HshqtVYb9xEE5O75hUA3BLK/zPELTduEhkZ+Vjba/4e6tSpQ9mylfjwi0usnO1VqP6C0eRi6ncp1K3bgLJly/4Fs/x9qNVqBAEkpwOfoECM2dkoBAUCAm7XLxoWChTgdKJQKnjrrbce2XwiIyOpWbMui1aeoEOre/cBT0pxsG1PDn36VmPhwoUolUqqV69OmTJl8PYxkJp+/yAMQHJqbmvFf2qp57+d32V0BwcH/+6aqntRr149Jk2aREpKCiEhuaqL27dvx9fX9x8hvuXhv8W6deuwmW0U94m+735FtCW4lnOe1atXM3r0aPr278OXn3xNpKsUeoU3V3LOEUgoxcWYvO+4ZCcCoNfpEBXelNdU5/CBw7Tt0IZPvl5P1Qpe1KlR0Fjef8TI5zPSebbXSPzv1pXdi7CwMHZucyNJcqG1zz8Td9OKw3l/x9dv4dSpU3z++Wf8sGY5CoWbBM5yy3gef6EIMfqKBKp/UVot412ZJFM88+fPJzo6mrEvv4q3y4+KmjoE6kIBmRR7Ake2HuepfU8xZ96cQo3QiRMnkpKSzOCXVlC9EjzdWkeJKDW3bjv5frWJc5fsDOsXntdSTKMR6dvNnw+nrSEjY1KeANS9kCSJ4cOHsWfXD3z6Thid2gXmpeRKksyOfdm8/NZOej7bndVr1t4zmyC3faJM0Yh7i779TPEiSjIz/xk1dB7+OpYvX84HkyaSknKHqGIqioTaOHTcRs3mp+nZOZgJY4uj0+vJzLRhtUp5pSuxNx2AQFBA7lJg6Zo0Ll5189a7BXvV+vr6MnjwYAYPHozdbkeSJLRa7X2NiebNmzP18295Zcwotuy8SLenfCkZqSUr28WqDdlcvu6ma7f+TJgw4ZFcl59p1KgRh4+cZOnSpaxcsYT9x5LQ6Xxo0rwr/fv3v6fgq4c/zvXr1+navTvXbt5EV7Y0Xk0aIksSRy9fZe/QodSsWpWl33+ft/77uyAIAk888UReqrHT6eTHXbtI3XeQ4l07YbPayDYa0ZYrS86JU/g2rI+gViE5nciylM9Z5TQaMZ87z4CJD7ckUpZlXC7XQyk7EgSBqZ9/Radn2tNtyDXefCmC2tW9EQQBSZLZf8TIe58mkpLhx6z5nz6E2T96oqOjCQsJJevMWYpFlwRkTEYTkiwjIgK5rVXdkgvrpUs806FDblu7R8jQYSMYOqQ3sxcnF9rW1Gx28VSfS5hMTubN/QaFQiI3QUJF3boNqFipKvPmbic5xXFfwdgVa9MwGIKoXbv2ozsZD3+YR1bTHR8fT0ZGBvHx8bjdbk6fPg1ATEwM3t7etGzZkvLly9OnTx8mT55MUlISb775JiNHjvREsj387UhKSkKn0KMW739vKkUlOkFPYmIiAC+88AKbNm7i6IXdFFNGY3JlU0kod3dvGZfswo0bHx8fxLupdSGaIqhMGoKCgqlesxk9hu7k6TZ6uj8dTHioijtJDpauTmPdNjP1G7Ri4sSJD5x/ly5dmD/vO3YfKFhfKcsyNpsNi9XCN3OSyc62Mv7tt7l+/To9e/b83T1Gt23bxpDBfSlZ3MmkN/xoWNMHhajg8EkLC1Ykc/x8AhXdDSiiiwJAFERChKIsWbwUk9FIuBRFed9q+RbzRXRRhGuLc9p0kKGDh3L46GGKFi2a77gqlYrp02ewfn0HBg/qy8GjaWi1IoIATzbw47UXomjaIP+5N6rny7ufphEXF/dAo3vnzp1s3bKWOV8UpWWT/E4OURRo0diPxd+q6NDnMEuWLGHAgAEFrvOVK1dISEjA5ZJISnEQEXb/+ykp1U1oUb/77uPh38nFixc5cOAANpuN0NBQWrduXWg95dy5cxk/7mWebqPl+UHRlC2lx2Ixc/NWJtt2W/h8egq3ExzM+jwGvc4LU44Zt1tGrxNZvMpIuVI6DD4KPvz8Ft/My6J3n2E0btw4b/yrV6+ydOlS4uPjUSqV1KxZk65du/7mSEq3bt2oWbMm8+fPZ8WqpaRnpKPVaGn6ZEfe/XAADRo0eCxRQH9/f4YPH87w4cMf+bH+66Snp/NMly7csVmJfuVFNEG/eoc0aYT51m1OzV9Ej5492bJpUwF9kb8TKpWKF0eN4tU3x5MYGoJQKgYRBQE163L73AXSV6wmoPPT4HKBLJCVlUVAYCA47Nycv4iIoGB69OjxUOZy4sQJ5syZw5bN67BacwXb2rZ7mgEDBtxTfPi3UKVKFVauWscLo4bTeeAlooopCAlSkpDk4naiRLnylfhh7UxKlXq8/dP/KCqVin69ezNp6lSyatdAHRCAn78fLpcLu92OLMuIogL7xUvo7A7Gjh37yOfUvn17ho8Yw4TJn3LkRA79nw2meiVvjCYXm3dkMnHKbbKynfTsHMigXuFUreiFyyWzZWcm380/yLy5R3BLWt799DZfflii0ODJpasWFq7Mpnffl39zeYGHx4sg/5YixD9A//79mT9/foHPd+3alZfyevPmTYYPH87u3bvx8vKiX79+fPTRR7+rbYfRaMRgMJCdne1Jp/DwyPjss8/4cMLHNPXpeN8FoizL7MvZzHNjhuRFb5KTkxnQbwB79+7DbnXwBC0RBQWSICEIAj7e3ui98tf2Hs/aR9VW5VmydAkzZ85k/ryZ3L59E5AAkcjIaPr1H8ygQYN+k7dblmU6tG9DatJRVs2NzjP2csXGMnE4nRw+aee5V1ORoiqiNhiwnD1PiJ8fSxcv/s0K2vHx8TRuVI9mDSS+mFSM7OwMVKgRhdzImsslM35yEivX2njCpx0+Sj8AYs2XuOg4hY9ooKFvm3teY5fkYk/OBsaMe+m+7YPq1a3Ok/VSGdYvDIOvAh/vwp8pF69YaNE1lvUbdjzwHPv06UXKnW1sWlL6vvfA0Jevc+1WSXbt3p+rHO9ysXDhQubOmcG1a5eQZYmcHDOhwUrGPl+U/j1CClUjvX7DSuOnrvLlV3Pp3Lnzfefm4d/D2bNneevNNzh69CAatRu9TkGW0YVe78ezPfszfvz4PMd0fHw8TzxRg4E9dEwYWyzvvpRkibTUVFQqmVNnHfR/IYn3x0XRt1swOTlmzBYzW3eaePntNCLC9WSbQKMxMOy5FxgzZgyiKGI0GnnhhZFs27qRQH+ZCmVU2B1w4ic7KpUPY14Zx/Dhw3+3wZzb8eGRdSv18Dfg008/5d1PPiH6lRdR+xXeV91y+w43vvyWOd99R5cuXR7zDH8fsizz2muv8dmXX6IpXhxDtRoo/QOwXL9G5v69KHx88KlZHa/IaJxuG874W7jOXyTEy4uVy5b9aV0CWZb56KOP+HLaFKKKCXTtYCAsVEVCkoPla43cSpAZ88p4Xn755T/lwJIkiQMHDrBlyxays7Px8/OjQ4cO1K5d+2+bHl8Y33//PR9OnszFS5cQ/f0IeLoDmiIRqFRqfHy8USmUZBw/Qeq6jfTu0oWvvvrqsZyfLMssW7aMaV98yvkL53C7nEhSrsilgEyjer7Ur2MAGcJC1TzVOoCgQBVOp8SQl2PZuV/C7XbRsomaV0ZEUKFsbrme1epm7ZYMJk1NIbxoFdasWf+PELz7N/FbbdFHZnQ/LjxGt4fHwbFjx2jVvDVVVE/cs/0XQKYzjWO23axYvZzmzZvnfS7LMp999hlvvv4WTyhbohTVqNUqdFodQiEL0KOZe6jToRpLly0FchXAz58/n/cirFChwu9euN65c4dnnm6PzXKDAT386dwxAGQT1+OsrN5iY+naHISSFYnq1w9RqcCZk0P8/MX4mHLY9eOPREbev90FwPvvv8/iBZ9wbFs5tFpISUlFiQqF8EudmNMp06RLHEJqaSr75qZAnTee5Ir1LJW8axHjde/ykixnOsez9uPS2mjRogVVq1Wld+/eBVp/DRjQn1txG9m67P4G8rfzEpn8lZnTZy4+MEW/ZIkijHlOy/AB9xdb2rAtg+fGJvPT2csYDAYGDx7Izh3rafOkjh7PBFEkXM212EyWr01nxz4rDeoYmDutVD5lZZdLZtCL1zl9wcDxEz95sn/+I5w4cYJuXZ+mRFELo4aE0vpJP1QqkcRkB4tXpvDtvExq1WnOzJlzOH/+PDNnzuTA3lWc2lkenS6/Y8lut5OVlYlSCWMmpHIrAX5cVZFrcTbmLU1m7vcZFC1ejubNW1C2bFk6deqU9w61WCx06fwMcdeP8O7rYXRoGZBXj52c4uC7+UnMXGRkzCtvMWbMmMd+nTz8fXG73VSuVo3MiDCKd+10331jZ8yhRlAwmzdufEyz++MsXbqUQf0H4ysEkiGl5xZxy+AleKFBQzbZuHEjyRIybp4fPpyxY8fmEzn9o0yfPp13Jr7G+BcDGdYvLF+U0+2W+Xp2IpO/zmDSB1MLZFj9GkmSOHz4MLGxsSgUCqpWrUq5cuXuuf8/kcmTJ/PBlCloK1XAr2plErdsw5KQiCo0BFVEBLLTiXwzHpXdQZ+ePflkypTH2h3k8uXLPNujM2mpN2jfwosnantjsVjZfySHTTvMOJ0QHaUiPUMCBDq1D+S91yOx2SVqtrhE+459OHhgN0lJt4iOUuHtJRIX78SUI9KyVQc+//wLDIbCHV0eHh2/1Rb9y1qGefDwT6JmzZpUqlKRayfOEaQOzYvc/hpJlrhiPkuJUiVo2rRpvm0/t/X48P0PsQo5FNPdW3DQKTkxihlUr/FLz0iFQvGnveVFihRhw8atuR7zOcv46MuL2O02ZIUK0TcA3ybtCG3aJK9Hqcrbm8iBfbn+yedMnz6dD35D/9SlSxbQ7SnfPNVklVqFy+7KZ3SrVAI9nvZl2nfXkeTcvrjJ8i0USgW+ysINX6fk4ETWfpLtCahlNT5Of05tOc/urXv5/LPPGTBoAB988EHey7NXr970enYlO/am8kQtb1RqFSpl/her1epm4fIsOnR89oEGtyzL2B0OvL3v3zcdwNtLAeSm7E+bNo3dO9cx/8viNKn/y4swOkpDraoqDp/IYeTrKbz14U0mTyyBLMuc/MnMlK8TOXwC5s3/1mNw/we4du0aq1ev5rNPp+BvsPD2KyWpX8c3z2EUHqrmlZFFqVzeix5DN1C2bEkE3JhMOfTr7o3ZnIkse6HX6/hZJk2j0eDvH0BOjom2zfUMfimFkjXP4nQJBAaG8cb4sYwaNapQ593s2bO5cP4waxeUpFL5/Pd8aIiaCWOL4+tzh08/+YBnnnnmNwuoevj3k5aWRmJSEqHNmz5wX+9yZTj9CNtpPUz27NlDgDKEJwzNsbttOGQbSkGNVtTd7VLixCaZcUsSh8zbqVOnzkMxuK1WK59PnUy/bj6FOnwVCoEXhkYQf8fO1M8+plevXoWm6/8cYY2Lu4IguMkNt6moWbMur70+nvr16//puf7V7Nu3jw8/+QT/Vs0JbZpbImMoVxbj5SukHT6KPTEJR0YmosXCmh9+yBcYeRxkZGTwbI/O+PsksWFhecJC1FitVrKyZDq11fHWywEMGJ2MywV71hZj049mpk5P5/oNG0tnlKVtcy8uXviJo8dOs23bNg4cOIDdbqd5m3A6d+5cIPjg4e+HJ8fLg4ffgCAIfDz5Y1zedo4b92J25W+vYXWbOWU8gEVnZMqnkwttfRIVFUWzFs244biCW3YX2P4zN6xXUGhFevfu/dDPIyQkhM8++4xTpy9Qtnwt3P7FKDJkJKXeGE94i2Z5BvfPKHU6fGrWYPGypVgslvuObbfbycjIyEt5AvDS65GRCpxvuRgNbpy4ZCeXc35CUrnQajQ4Zcf/D4tbdnE4cyfp9lTKCTWoKTSllKIKNf0a0sS3PSXkCsz+bg4vvvgikiQxY8YMXnjpJYxmB8PG3ODg0VTS09JJz0jH4cgdP8fsZuiYWFIzdDz//PMPvG6CIBAeFs7Fy/e/BpCbsq5SaVAqlSxaOJsXhgTkM7gBRFEkICCA+rX9GN7fwHfzk2je5RINO1zmqb43iE8syqLFK3jyyScfeDwPf5y/OtErMTGRHj260qhhbWZOf4/iRXJQq9z0GHqJVl3Pc+a8OW/f5BQH730aT4CfTN+uAtuWl6BohIqoYiqUSjcmUzbZ2UZ+3bJIrVYTEBBI6ZggdDotffq9wKzZSzhx8iyjR48u1OB2u90smD+Lp9t4FTC4f82IAeH4GyQWLFjwUK+Jh382ee2xFA9eXgqi+FjaaT0MrFYrSjk3TqVRaPFR+qFT6POcBSpRhY/SDz91AKJQeK/lP8L69esxGlMZ0uf+Su/D+oWRlpbA5s2bC2ybMmUKL704jPLRt1g1J5KbJysTd7wSMz4NQ3Ic49kendj4D8g2eBAzZs5EERpCSJNGeZ8JooihXFmiB/Sl/NiXqPzuW6gD/Nm/f/9jn9/ChQvJyrzFom9KEhaiRpZljEYjGo2An5+C6Cg1i74OIyHJxbbdZp7r58eS78I5dzGHL2clEBOlIS0tGaVSSdu2bZk0aRKffPIJY8aM8Rjc/xA8RrcHD7+RunXrsmzFUlRhAvvNWzicvZNT2Yc4krWLvTmbkILsLFg0n2bNmt1zjNffeB3BR+KEcT92yZZvmyRL3LBcIc59kZGjRhAREfHIzsVgMJCclkZA7Zr4li5VwNjOt2+5MhiNJm7evHnfMZVKJYIoYLZIuNwuTCYTFosVQRRxyg6ckjPPyLFYJSQZzhqPc1u4zjvvvUPlKpVJtBc8xk3rdTId6VQS6hAshCMJEhpNridfISgpqS9DOVV1vl+4hN69ezN23Dgyw0Mp+eKLGP1L0X1EGsPHpbN8XSYLl8czblIstVpe5OgZLXPnff+bW6B0696b1ZtyMOUUbNshSTJ7DmYzYNQV3vzgJhaLmebNGpOVlUH7FoVH0UVRgb+/P4N6ReHjrSIlI4ywoo34ePKXHDx0jEaNGhX6PQ9/jrNnz/LSSy9RrmxJihYNoXy5aF599VUuXLjwWOeRnJxMx45tuHZpB199GMq+tcXYtLgIJ7ZHsvibcJRKF10HXuT0uRxkWea5sdewWl1sXlqU0UMMRJdQEuivIjnVjZ+vEoOPApvNgtlsAWQcDjtZWVmkpqZw5VoGDoeLiIgImjVrdl/hqitXrnDnTjxdOhbeGvFnNBqRjq28+XH7pod8ZTz8kwkKCsJgMJATG/fAfc2xcUSXLPm3j3JDbq9lq2h+oKPO7DKBID80Vfbz589TMlJDVPHCu2H8TKmSOopFqDl//ny+z3fv3s3Uzz5g3OhApn8aTZ0aPoiigEol0rZ5AKvnlqbNkwqeHzmUxMREZFlm//79vPDCC3Tr1pk+fXrz3Xff3e268ffFZDKxeds2DLVq3Pd+ElUqvKtVYdnKlY9xdrkO3oULZvN0G+889XGrzYosS3h7iXltHIsXVdGmmZ5FK4wAVK+spcfTPixemUJahhO9/sHZdh7+vniMbg8e7sOVK1dYs2YNq1ev5vz58zRq1IgzZ08zffZ31O9Ym5L1i1C3Qw2mffMFZ8+fpXXr1vcdr1q1any/dDEEOthj2sip7INczTnHBdNJ9ho3cU0+x7CRQ3nzzTcf+blJkgS/ZbEjisjAwYMHee211xg5ciRvvfUWx48fz7cAUSgU1KxZh1UbUkhLTcOcY8Zpd4GUewyX7MQu2bBLdjbszMIlu1EVlZj29Rc8//zzDB4ymAxSyHCk5o0pyzI3zFcIJBQfwQ9JdiMjo9fr802xiDYKwSmyav06Qjo/RWT3LvhElyR6+HMEde7FgVvhvPZRDqPfzuCr2Wl06zGK3bsP/i7Dtk+fPrglH14cfxOH45fojNXqZuALV+n13CWu38jhxWF+THglkNpV0lEqnHQecIkTZ3IKGVHGbDaDbKRIGMTfvMLhQ9t57dXR9OjRlTNnzvzmuXl4MLIs8+mnn9KqZWP27ZrPgO4SH4zzp09nN9u3zKR5s4Z89dVXj20+48ePw2W/ydqFMTzdNhClUkahyFXCb/yEnlWzIyhbSskLb8Ry4kwOx06Z+PDNIKKjcsskJEmibfMA1m01Y7ZIaLUiOq2A2WImMzOTjMwMXC4bWq3M2s0mvL3gvXdfo2mTBsTF3dsgMpvNgEyA34OrzwL8lZjNhd3bHv6rqFQqevfoQc7xk7jt9nvu58jKxnrxMgP69XuMs/vjdO7cGYdoI9WReN/9blivEhgceF/n++8hKysLt8uJxWLBZrPd1+gXRaHA9lmzZlC5vJLhAwpvA6pUCkyeUBylwsxXX31FixZN6Na1PWeOf4+/7gCSdRsfTnqNalUrMHv27IdyTo+CzMxMJElCE5zrLHRZraTsO0Dcwu+JnbeQ2+s2YE1MAkATFERaWtpjzXQyGo0kJNyh8RO/ZL3ZbTY0agGFIv/v1qS+nms3HDiduZ/17ORLWoaTJWsykFEzYsQI3n77bU6dOvWXZ2t5+H14aro9eCiEAwcO8PGUKew/eBCnlJsarRQV1KpenbFjxtC9e3e6d+/+h8Zu3LgxJ06dYOnSpSz5fimJCQnodHqebdGN/v37U7Vq1Yd4JvemXJkyHIq78cD9si5dwWI2M+bFV9DjjQoNNix8Pe0bqtWoysxZM/Hz82Px4sXs2b0fSTJx9KSe2tX0iIKIQsh9zEiyG6fs4NwVO3uPOBkx8gU++uijvDrs7t27s3zZcg7t3k9ZqTrhmmLYJRsmVzbFhBjcsgsXLnQ6LWp1/jpnQRBwSRKqqCgCa9fK+1xUqQisXZPA2jWRXG5cRhPXpnxGeHj47663Cw8PZ/qMuQwe1Id2Pa8wsGcgTev7MnbiDfYfzebz94JpUEeHr8GAWqUmK1vDS0O9GP9ROr2eu8gP80tTprQvwl0FnuxsIzabBa0296X78vAwXhtVlE07Mvl2zm6eefowixavyOsX6+HPMWfOHD795D1eez6AEQPDUSh+cTi9PDyCL2Yk8MGkt/D396dXr16PdC5JSUls3bKeiWODCA/NjXoIgsivM211OpE3Xwqk04AEPv8ugWIRSp5soM/bRxREencN5tt5CXzweTrvvxGETqvAbHEiS278fRWoNSLbdpnZe9jKx2+XpGZVb4a8FEu3rk+zecsOgoIKRrNzPxOJi7dTtpS+wPZfc/2GjaCgBwssevhvMWjQIOYvXszNBYuJ7NsbhSZ/ZoXTlMPNeQspGhr6h9+jj5tatWpRo1YNzh4+ha/SD62i4P9Gmj2JO1Isrwwe86dbNl27do133pnAhvXrELBwLTadkGAloiCi0+vx9vLOF9G9ectG/B17vn7TGRkZ7Nq5jclvB943+uvjraRjaz3ffD2VcqV1rJhVnLo1ffK+k5buZNrMBN568xUkSWLIkCF/6tweBV5eXgjk3lvJu/aQsPVHJLcLTfFiCEol2cfjSN6zD0O5sujCwvDy9v5LMix+fUhJklEqc9cvsiQjy4XHQSLCFDidMukZNsxpN0k8l4pNsPDVtK+pXac2M2ZOp3jx4o/vJDz8YTzq5R7+NUiSxL59+5g7bx4HDh3C4XQQWaw4fXr1olu3br9Z0XHt2rUMee45pNAQAhs+gW/ZMiCI5Fy7Ttr+A7hvxPPZ5Mn079//0Z7QI2bNmjX0GzKEqNEj0UcUrshtM1u4+OFkNGlW6vo3w0+Z+/KWZZlURyKX7WdQByhQKlSkJqbibfPHosjGO9jMpHGBNHlCi1IpohY0yDLsPmzilfeSCAgqw+EjxwuIhBmNRkYMH8HmjZtRutRo3V4k2OKpKNTGBz8EnYzeS49W4YVazF3Iud0ucpwmduRsJKD7M5Ro3PC+531j8VLKyAJ7du36Q9ft1KlTTJ36KTt+3ILDYcVms/HlB8G0bxmAl5cXdrsNi8V6t7JWQqUUaPPsHapV0vLBuDD8/f1xOBxkG7Mw+CiIvemkVffbzPisFG2b5/YKt9kk+r9wnXNXfDh69DTe3t5/aK4ecrHZbFSvVoH2ze189FbUPfd7cXwse4/6cez4mUeqaLtgwQLeHP88Z/eUz2tnZ7FYMJmyCQpQ5jkEZFmm0VO3yDJCraoq5nweTo7ZjcUKQcHBiILIopUpvP7uDdo00zO0j4Gi4Qr8/RRkG2UWrjDy1ewsWjb159spMSgUAonJDp7sdIWBg9/gtddeK3R+bdu0xE//E4u+jbnnOaRnOKnZ4iKvj/vY0/vaQwH2799Pz759MUlufGrWwCemJLIkkX3xEuZTPxHu58fqFSv+UerZt27dol3b9iTFJVNMGUNRbQnUogaz28gNyzUS5Ru0aNOcxd8v/lPPjwsXLtDpmfYE+WUzsGcAH3x+i/7dfXh1VABWq4TFKqFWa/Dz888zHCd8fJOVG1WcPHU+z+C/cuUKTRrXYfXc4tSufv8WUl/MiGPCx0lcPlQjL/35/5k4OZ75y52cOHm2UIfdX03L1q05eOki9qwsfOrVwbdhfZR3W2fJbjeW8xfJ2LAJKSeHft17MGvWrMc2N0mSqFG9Ei0aZue9gzIzMwAHfgYlsiTd7R8uMOqNFM5csLNvXSRut8yps1aad72Nr7M49QNb5o4nS6Q6ErlkO01Q8QC2bt/yUIT7PPwxfqst6kkv91AomZmZXL9+neTk5H9E+orVaqX/gAE81aULG44dxVGlImK9OlxBYsz48dSqW5cTJ048cJwbN24wbORIFOXLEj1iKP5Vq6DQalFo1BgqlKPk0EHo69Tilddf4/Tp04/+xB4h7dq1o2K5ctxesBh7WnqB7ZLdwfV5C3CnZ1HXvzn+qqC8F7wgCIRoIqikq0PCrQSyb+VQXWhEKbEy1eQmSKlhjHotnfa9knhvahoTP0+gda+bDBmTQlaaFpVSl1dXajKZmDt3Lk8/9TRtWrclMzOT50Y+R/eBXan0ZFkUKgVJinjOqA9z2L2HncbNbMlYxeH0XcSlXCM1NY2UzGRkZBR+flgt1l9rSRVAExREcmrqvXd4ANWqVWPBgkUcOXqaBg1bUCbGhx6dIgkICMBms2G2WBG0GpQ+PgiiAoVCoF93A9t2W0jPcpCRkYHZnINWLaDVCHw5K5PQYDUtm/xS+63Vinz2bnGM2SmsXr36D8/VQy7r168nKyuV5/oVnmL5M8P6hZGScoetW7c+0vkYjUa89fn7x2t1WgRBJMfszrt9BUEgPFSJ2w1mi4zLLWOxSmi1urwOCr27hPD1x9H8dN5Fmx63adb5Nk92uk3t1vHMWGhkcJ9wvv44Os+QDw9V06W9D4sXzcXpdBY6v0GDh7H7oI1lPxT+f+J0Srz2bjwqjR89evR4eBfGw7+GBg0asGfHDkb06o1w6gyJcxaQNG8RXtfieHXECHbv2PGPMrgBihUrxrbtW+k+oCsJmlj25KxnW9ZKDlm2I4VbGDfhjT9tcLvdbgYN7EvxCCMbvi9F/2dDGdYvnG/nZ7FqgwkfbwV+BgUOhx2zObfGfNHKFGZ/b2TEyJfyRdi9vLwAgSxjQR2SXyNJEmnpNvx8Ffc0uAFeHBaBKFhYsmTJHz6/R0mHdu2wJCbi07ABAW1b5xncAIJCgVfligT36YksCHevzeNDFEV69xnI6o05pKXnPnc1Gi0Oh4zklhFEEUEQuJ3gZMN2M8+08yYzy0VqupOlP5jApaGO/y/CqqIgEqopQh3vJ0mNT2PihImP9Xw8/DE86eUe8pBlme3btzNj1kx27dmLJMuIQLWqVRk8cCBdunRBqfz73TKyLDPy+edZu20b4b17YKhQPl/akDPbSPyiJXTt0YMft269b3ub+fPnYxMESnfthFCIArkgCBTp2I6rl68we/Zsvvzyy0dyTo8DtVrN8iVL6NS1K5enTkNfoQKGiuURFApybt4k+8hxLCmpVNXVwl8VWOgYd2w3UMkayrirIssCIgqUgpLycm1M9iwSr9xgZVwGOXIW/ooIanrXwO62ceH6cZKTkzl37hxDBw8lMy0Lf4LRCnqS5HQO7j2M3kfL+LfGc/j4MdJs2fhUrUZo6XIIKiXWhDukHj9BYsoRysgV0eGNhIRksZKdnY3dYcdg8Cs0VctlNuP9EF64RYsWJTMjmVZNfVCrVTidTqxWK6JWi6jOXXSJGi02q4W6NTQ4nDI3khT4ersRkAGB8R+ksWG7ha8+ikapzD/ZiDANTzbQs3rVcvr27fun5/tf5ty5c0RHPViMqFxpPRGhSs6dO0f79u0f6hycTic//vgjV69e5cSJE6Rm2MnKduJnuHuvCCK+vgaysrOQs114eylQKCAhyUVkMQ0Hj+Vw5bqdkCBNgcyHp9oE0r5lAEUrH6VoERUN6wZQIlJHx1YB+PoUfGa3bubP3KW3iI+PJzo6usD2Tp06cejQIcZMmMnx0zkM7BlKudJ6XC6Z7Xuy+HZeCmcvKpg1e/YD2+15+O9SokQJJk2axFtvvUVycjKiKBIWFvZY+yI/bMLCwvjyyy+ZOHEiBw4cwGKxEBQURKNGje4rUPhb+fHHH7l58yqbvi+Z97/70nMRJKc4GDMhhYUrjHR/ygdfH5FbCWY2/ujm7EUX/QeMLNCJIyIiglKlyrJ6Q1w+p+7/Y7c7WLclh2aN7v+/7GdQ0rS+lr17dzFq1Kg/fa4Pm4yMDNS+vvjUqIbkcCCoVHlrQVmSkRwOFAZfDDVr8OOuXTidzsd6L/bt25cF82fRe/g1Zn5elEB/BSBgynFj8FWSkibRf3QKgQFKOrcPRJIFLl3NYdkaK5GqiijFgs9yrUJHpKo069au47333yMs7P6OZQ9/LX8/C8rDY8XtdmMymVAqlXz00Ud8NX06qqJFCHi6A+rAAFwmE2dPnGLYqFFs3LSJ2bNmPZQXy8Pk5MmTrFm/jtBuXfCrWKHAdpXBN7ff9NQvmTZtGp9//vk9x/p+2TK8q1dBvM+DWBBFfGvVYOWaNXzyySf/6D7KRYsW5cetW1m0aBGz5s7l5pLlyIBWo8FbkrAJCm44r5OWlUKkNppQTZG8CJtbdhNvuUa4EIlCUOX2Wr07riAI+OKPr+APbvhJOoQgKghUh5BmTwJXrkLq8yNGobf40tC7Lbpf1cjZJRsXTacY/dLLaMJDCe3bE40hEKWgRJZlVOHh+FarQfr2rVw+ehKD0wfBLWO/eAXvEjHYrDYUChM+PvlT6iSXC/PZ87R5SOI9LpcLlSr3rC0WC4hCnsENIKiUKNAhSU6sVolvZqVSpZyK5GQHO/ZZcbkFPn47iqfbFu7UiCmhYf2OW0yfPp3ExEQ0Gg1PPPEEDRs2LLTdk4fCkSQJpeK31e8plQ+3jZEsy0yfPp1pX3xCRkYygf5K3G4Zu81OnVZn+OjtEjxz9/fXarX44YfJZCQt08Wpn+xcuGLH36AgK1ti3tIcPno7vNDf3m6XcDhkWjXxZczIyLxjHz9tYtGKVK5ctyIIUL60nupVcp1O94p0C4LAlClTiImJYfp3X7JkzXU0anC7ZVxuJTVq1GHZ8gnUrVv3oV0nD/9etFotkZH/rtr/wMBAOnbs+NDHXb16NRXLqqhc4RfHsCgKfDwhihZN/Zm3JJk3JqXjlmRMJon6DZqwcNE4nnzyyQI1yoIg0K//YCa8/RInzuRQo0rhZUpL1qQTF+/iy48erLhu8FWScsv8wP3+Crbt2IF/jWrofX1zW7bZ7XD3WSm7JQQBfLy88alXl9szZnPp0iUqVar02OYXGxtLQHAE+w9do07LCzzTzpva1TQgyxw5aWfzDgtBASqWzChPYICaRctv8eG0FIScEEr73nueRbUluGw8w+7duz2ZR39zPEb3f5QbN24wZ84cFi1ZQnZ2NjabDYfbTdgzTxHRpGG+h7d/1SoYL11h7YLFhIwbx6effPIXzrwg8+fPRzAY8K9y74eSUqfDUKcWy1ev4p133im0vtvlcpGeno5/wwcLV+lCQzE6HGRlZREaev/+mX93fH19GTFiBM899xxJSUm8MW4c6zZtxK5Wo69bExRqMm7fJvHWfgxWX+r6Nkav8MbmtuCQHPgLIbnGtlBQOfVn/AnhlvMaAEZ3Ngqlks8/+xy1WUcNQ8M8Q/5nNKKWYHUE193X8XuqA/7FimIympBkCVEWERFBFPBv0RzbnXgybyZQQlOKWz+dw1C9JorQICwWS67YjPjLvZy8aw8Ku41+D8noLhldhqOnbgDgcDgQCskEEVQqzlwVcKFk/zV/9pzMxpllYli/UF4dVYywe6Tzmc1uftiUzvUbSXz0wasUi1CTY5H4cpqbqKhSTPpgMk2bNn0o5/FvJyYmhvnz7CSnOO6bPhl/286tBHuh0d8/QnJyMj179uT4sT30eMabnp38KF1Sg1qj4fRZC7MXpzPy1WtkZLoY1Cv3OaLVatFoNKRnWJjyzXV8DSEMHDICWZb57tvPMPgm8MLQ8Hyp6Tfibbz8djySrOL85VyHQbbRxbAx19h/JJuoYiqeqKVFkmDPwQzmL0vG4VTh5+d3z7kLgsBzzz3HoEGD2L17N/Hx8SiVSmrWrEmFCgWdmx48ePjzpKel5HUo+DWCINCisR8tGvthsbjJyHJSp/UlBg8eel+l9F69erFu7Rp6j9jHe6+H8VTrAFSq3Pet0eRi7vcpTPk6FQQVet2DHblX4xwE30MD5q8mJycHVXAgBoMv3t5eWK1W3K67Qrh6JTqdDlEUsVlzNVdyOzU8HrZs2UK/QQNxBwUR3rMXjswsluw6ysI1mUhOJ7JbQqFQEhSoYeRrt4i/4yA13Y5s0dEssDlK8d6BIJWoRiEoyckpvJNEUlISixYtYs3qZaSnp+Hl5UXzFu3o37//P67E45+Ox+j+D7J9+3b6DRqEBfCpUY3AIuHcXLYSfflyiOVKk5GRgb+/f75oim/Z0gS1as7C7xfz6tixfytD8+jJk+jKlkZ4QOTPUKEcN7bv5PLly9SuXbvAdoVCgUqlwmW1PvCYbpsVQRD+tELp3wlBEJgwcSI/bNlMSOenUZWKwWKxoRE0gIAtMYHUNWs4kLGTRn4tke9WnuZqcYNWrcZmt6OkkAUDADKyLHPHFUvdhnXYv+cAVXRPFDC4fybefh1t8UiUwUEoFAr8/P0xGrNxupx5EXVE8K5Vi+yETZTxroTZlEPS4kX4t2qFukxJbDYbOr0Op9FI8p59mA4c5q3XXiMm5t4CUb+HXr36MKD/ek7+lEPR8MKlR91umcUrs/GqUJmSgwdhvnWH65M/RK8T72lw22wS3YdcIjnVzruvhdKvR1F8fZR3I5c5TP3uJn37dGfmrAUPbFPnAZ555hnefWc885el8Oqoovfcb97SZHx8Hk4E6/Lly7Rr25Lk5NtMeiOQoX39AAGnU8JqtVO+tIJP3wlh6vQM3ng/juqVvahWyTsvhfuzb5NJTA1ky5aNlC9fPjfDQ6Vi8scfMHV6MjElVMREaXFKAgeO2AgKiuDFl4YzZ/bnXL9h5aU347geZ2bO52E0b6RHvOt8cjpllv5g5K2Ps3j55dEsWLD4vmVDKpWKFi1a/Onr4cGDhwfj5e1DRmauoSjJd8W1BDFfIESvV2DKcSMI4gNrkzUaDQsXfc8LL4zixTfX8f5nyVSrpMblkjl8wo7TpWX4yNdYu3Ylc75PpEQxO2537vFVKjV6vR61Ro2AwPlLFk7+5GDW6G6P7gL8CUJCgklOSwNy13P3EiC1p6ahEASCg4Mfy7wSExMZNHQoQqkYSj7bHVGZW7oot26Z21ZPlknavZfsHbupVqsToaGhPBMezvnz5/lh0XqUwv1T4G1uC27ZRUBAQIFt69at44UXnkOlsNCxlRclI7WkZ5pYs2kmCxfMYtQLr/D666//JUru/0U8+Yn/Mc6ePUvfgQNwRxan9LixFGnfBlGhRHI6MTSsj9LLC6fLRVZmVgExqoDaNbEDS5cu/Sumfk9cLmeh9df/j6BQAjIuV+GiIoIg0KRRI0xnzj5wrKxTP1GtSpV/lWL+kSNHWPnDGkK7dCKwZg10ej0gI5EbOdOGRxDWqzcWjZNY8yV0Cj0qQUWGnIIgCHj75LbgcMqOAmNnkY6P0sAF0ymsQg4+Pj7ITpkg1b2dNzlyDtpixRAQcDqcaLUadHcFpFSiGpWoRiNq8S4ahSCK2CUb9QxNCbMHkrF6LYlffcvNeQu4/u1Mrn74CfKpn/hg4kTGjBnz0K5Zs2bNKFe+CiNejedOkhv57mLlZ9xumQ+mJHL6gpvgJk0AsCUng6hlxfocrsUV7uD5anYCZ87nMOfzMEYMLJZX2ycIArWq+bDwm1K0aqLghVHPYTKZHtr5/Fvx9fVl8JCRfDUnkx82FRQNBFj2QyozF2Uz7LkX/rQzzWw206tnVyzmZOpU1zJyoD9qlYhaJeClVxAYoER7tyrlpWGh+BtEmne+RIP2l6nS5AJDXk5E71uLH9Zuonz58pjNZkaNep5vvp6Kv59IudJarDY3m3ZksfeggwEDR3H02GnGjh1LUFBROvW/wqmzJhZ9E07LJl55Brcsg8XqpmUTL+Z8EcXuXVseuWicBw8efjuNGzdh32EzFy4lk5KcQmpKKskpyWRlZeFw/PJuXbk+HbXai/r16z9wTB8fH+bOncfuPYfp1HU0srolKu+2jBr9DseO/0SnTp1ISU5l6Zp0tu/OxksPeh1Ikp3MrAyyMjPJyHLw8oR4ihePoWXLlvnGl2WZo0ePMnLkSBo2aULDJk0YPnw4hw8ffqxCvN06d8F66QpOo/G++6UfPkq1KlXuq+/zMFm4cCFmt5tiXTvlGdyQW6qo1OlQ6vUUadMKTdEiuNxuJk2axPPPP0+vXr2wCxbSnSn3Hf+m9Rq+fr40b9483+e7d+9mxPBBtGsGx7eXZcrEEgwfEM6bLxfj8OayjH/Rjy+nffSP1ib6p+Exuv9jfDFtGnYvLyJ798irW7alpSHqdKhDQxAUCkSdDofTgd2R33hS6nSow8KIjY39K6Z+T8rElMJ+I/6B+5njbqASFURFRd1zn4EDBuBKSCTr7Ll77pMTG4ft2nUGDxz4R6b7t2XevHkI/v55afoqlQqVWoVLduZFtVUGA15Vq3DDGYuASKi2CInyTZQaJUplbrqqLMg4JDtuOdcAtcg5pMlJmCUTVxzncIgSqzdswOi2sCNrA9fMF/L2/TUCArIkAb9KWxdyo+YKlChQIiAiu6W8/VWimjp+jWnm1w61yU4pUUnnmrX47IMPuHj2LCNHjnyoHl2lUsnChUtR60rRqX8y705O5uAhIydPm1m0JJ0O3eOYv9JCaOdu+MREI8syWYeP0qJFc4oWr0zXQbFs3pGJy/XLwiQh0c4XMxLo3N6bJ2oHoijEoaRQCLz7ejGs1gxWrlz50M7n38yrr77KM5368vwbiXTqf5VlP6Sy91A2S1an0rHPZcZMSKZnr6GMHj36Tx9rzZo1JCTEIctuenXxLaTWEnx9lChE0GoV9OsRhpeXD206vMDQ4W+zbfte1m/YTLly5bBarfTo3oVtWxYz4RUDp3aWY9/6ipzZXZ2dayrQsomWeXOns337dry9vVm0eDm3EySeqKklpqQKh1PG6ZKxWN2kZ7qw2cHHx5emDQOoWVXD/Plz/vT5evDg4c/jdDrZuWsXmdl2ps5IQ9BoEPU6BI0GmyO380VOTg5JKQ5mf5/JU093+11ChqVLl2bixIksWLCQefPm8eKLL+JyuejRvRNlY+w81TqQse+mMWFyOtdvOPHxzn33LFyeRvNO57mT5MfceYvyZcYYjUa69ehOy3btWPLjdmK9tMR6aVm2cwetO3SgS9euZGdnP/RrVRjdunUjwMeHWyvWILkKrikA0o+fxH71GkMHD35s0d3vly1DX7kiCu29hTwFQcBQuyYbt2zBeNdpUL9+fSpUqsAl6ykcUsFgBkC2M4Pb0nV69+2VLwgkyzKT3n+H2tUEPn+/RL5yJACVSmT4gHCe6+fHF59Pzjumh0eLJ738P0RqairrNm7Av1ULxF89NAVByOsRKAgColKJJCqwWi1oNP+X/ipJfzsBpz69e7OhT2/M8bfwKl6s0H1kSSLz4GFaNGtGRETEPcdq1qwZnTp2ZNXSFThNOQTWqpHnnJBcbrLO/ETy2vU0a9iQzp07P5Lz+avYd/Ag3hUr5EvT9zP4kZ6RjsNtR4kSUVDgVbYcyYePcsF0knQ5GVnt5iqn8XbWx+l0oVIpcTlduCQHOZKVCxzHJTixqhX4VqhEcP26yF5eZCenYDt3iXNnz5KYdZt6hqb56pb8hQASLl/Bq2E9JLMbhyyhVKmQ70bfxbs+Q8u1KyhkER/lL3X6LtmFSqPigw8+eOSpsREREWzctI3vvvuOSZMmsmhVPKJahSwq8apQhagRDfGKLI4syyRu2Y77TgKjP/2MKlWqMPy5IQx5eTfhIYlUKKvEaoN9h8xYrBI9O4XeN3UwLERN0/p6Nm1az4ABAx7pOf4bUCgUTJs2jVatWjFv7mzGTNhLbjqPSMNGTZgzdzCtWrV6KAux7xfP54maWg4es1O8SOGpgYIAOp1AjtlGZDENkmxh9OjRbN68ma1bt7Jz505q1arFvn37OH/uECvnRFG1Yv50yaoVvZk7LZpRb8Tx0ksjady4McWKFUOjVdOxjS9WK5gtrryspVzNfDCasjGbc2jVRMMHX+xF+hs+1z14+K8xbvx41m3bSkDzVizfvAdJSGHksBCKFVWDWo3LamPLzlQmf2VCqY5i3Lhxf/qY3377LUgpLJleBoOvgukLkpizOJmlP9zJ20eSIMcsMHvOpHw1wE6nk959+7L3+HHC+jyLoVzZvPWDLMsYL15ix/LV9OjZk7Vr1jxyEV6DwcDcWbPo0bs317+ZTlDjhhgqlEdUKrAmJJJ64BA5x08yqE8funV7fCnyycnJ6Co/WAdDGxJChttNeno6vr65ztpZc2bRvm17Dif/SAl1GSK0kSgEJXa3jXjrNW66r1KtTlXGjx+fb6xTp05x/vxpFnxVJK9dZGEM7RPKrMWXWb58OYMHD/7T5+rh/niM7v8QsbGx2J0uIkrlr2fVFy2CbLdjvxmPNipXYVRQKnA686dhO7ON2BMSqFy58mOb82+hefPmVKlYkXOLlxE5dACawPxK0LIkcWv1WkhNY9T/tdT4f0RR5Ltvv8V7rDeLliwhffsONCWiQBBw3LyFYDbzdNu2fPP11//otieF4XA48ilvAyiUCgIDAzEaTdjtNpCduJQSLlwkq2/Rp1cvWrduTa9ne7M1dRWhQlEMciASEhmkkEoCCo2ATudLkZ7dCaheFcht3+HQ6fCKLImrWg2Sv/+e06bD1DQ0zDt2EW0kccnbSfz6G7hbEqDQ6tCVL4t39ero/UNw2+yYjp+guDISlZj7QpdlmeuW80RFR/Lkk0/yODAYDLz22mu0bNmSpzt3JtPlxL9ubQKqV0NUKcn86SwZB4/guhnPpIkT8+a1fMVqzp49y9KlS7l9+zYGHxXdn/Vi+bI5xJQ0APc3AMNDlJy8WHi6tIeCCIJA+/btad++PdnZ2WRnZ+Pn5/fQy0Ti4q4zsIc3B49lk5p+7x65KpWIjIuERAd2u4uaNSphsWQSHKjCZpP40ChhtzsZ3NtQwOD+GVEUmPBKUTb+eJkVK1bQsWNHREEgOMib4GA/LFYLOSYTgiCj14m5avsy2B0SCtFBdnYWsbGxD03nwIMHD7+fxMRE5i9cSFCrFoQ0bkhGsaKsXruatVviqFZJg5+vwNU4F3FxNlQqHUuXfMLu3buRZZnSpUtTrVq13+0wtFgsrFi+iAHdDfj75ZoDw/uHM6R3GIeOG0lIcqDViNSu7k3fkXFs3bqF7t27531/7dq17N6/n+LPDca7RFS+sQVBwFC+HMoBfTjw7UzWrFmT77v34ubNmyxcuJCNW7aQk5NDcHAQXTt1pkePHr8pqt+kSRPWr1nDuDff5OD8RcRKUq6H0+0m0M+P8WPHMnbs2Mdaw6zV6XBbLPk+kyUJ09VrZJ7+CZfZjEKjQentBbKcr7ypTJkybNm2hfHjxvPj9h+5aDyFUlDhlB14+XjRv2df3nnnnQL166dPn0atctOkfkHR4F8TEqymWkUNp0+ffmjn6+HeeIxuD3hHl0QbFET2vgNoIovf82GUvHcfBi+vv12EV6lUsmTRYp7p0pnLU7/Cq0ol/KpUQlSpsNy6TfaRY4hZ2Xw5dSpPPPFgZXK1Ws20L77gxdGjWbhwIWfPnUNGpmz9hvTu3ftfq/YYWbw4Z28nFPhcoVBgMPhitapwOJzY09PRa7UcPHSAUqVKMXLkSGSXTIAmmHRXMknSLQD0Ci8ilMW57b6JoXpV/KtVzRtTEAX0ej1msxl1kXD8W7TgzroNlHPl4KX0xuTK5qT5MIKXDn2FcujLlkVUq7HduEHO8ZPk/HSOgNatMB89gTLbRilDrhfZKTm5mHOKLFUaU9+bU2hq9qOkWrVq7Nm5k6lTp7J89Wridu1BlkEpitSrU4fRkz4oUA9XqVKlfG1LDh48yIrl87mT6CDA//6OncQUF37+hbcb83B/DAZDoV0M/gwZGRls2rSJnJwcXC4Vtap5s2JdDt063sOol2WcTonPvk1GqRDo382Pvt3LEBGmQZZlZixIZtykOFo1UWKz2dDeIz0xJFjNkw10bNywlr59+6LT6Tl/yULTBt7kmExo1GDwVeXT+dNoRG4nuFAqZfr368WOnXv/0e0PPXj4J7NkyRIcgkBgnVoABNSohl+lCmSePsO5K9eQUp2oInzwiYCUnbvp3KMH8t1/aJVCpFKFCrz2yljatWtX6Pgmk4mVK1eyYeNGMrKyCPD3o0a16hiNWTRvHJVvX6VSoGHd/M/GFo29WbnpeL7PZs+diza6ZAGD+9d4RRZHGxPNzNmz72t0y7LM119/zcT338elUqKvUB5lWAipaem8NnECH06ezOwZM35T5lpcXBznz5xF6RTwcRtQiipkwJpl5IupXxAZGflYW2u1fPJJVu7dg9yiGYIgYE1IJHbhYmwpqSiDglAG+CNlZmG7GY9WoeDixYv5+m1HR0ezdNlS4uLi2LVrF2azmaCgINq0aXPPDhROpxOlUszT87gfajX31Dry8HDxGN3/IWJiYtCqVRgvX0Eb+ks/RkEUKdKuDdfnLyRj/Sb827REdrlQ3U0FkiWJlL37MR04xPtvT/hbiocVKVKEbZu3MGfOHGbPm0fCydOAjFqlpnP79gwbOpQaNWr8rjFLlizJhAkTHsl8/4707tmTF14diyMzC7W/HwCSJGMymbDZrEiyDLJM5qEj4HDw4Ucf0a5tW5YuXkZFTS2K6KKQZAmn5EAQBFSCmkR7PHcsCWirVMJms+bz4Hr7+OByu7Db7GjKl0b+UUOc5RJhmqIcNO5CjgigWJ+eqA2+2O12EBWoixfDu25t0patJHX5CgQZiqmiuG27gdVtJo1EVHolX34+7ZH0UP0tREVF8cUXXzBx4kTOnz+Pw+GgePHivzmSWKtWLYKDw1n2QxqVyt87vTwpxcGuAxbefa/Dw5q6hz9ITk4Ob7/9NqtXLUGSLFgsNjZsExnUy5fxHxjZvDOHNk8WjFTbHTKvv5eOw+Fi0XcxtGn2i5quIAiUiNSg04oUK6IkOzsLtTrknmng4SEqbp3NRKVS8fQz3fl+1Vz6dPFCFHN76/6/L9VikVi5Pod+3UNYsf4iGzZs+Ns5VD14+K9w5coV1EUi8tX9imo1gbVrEVg71xBPP3qcG8tWoo4uSVCTRgRXqYwggOnadS7u3U+fAQOY8uGHDBo0KN/YmzZtYsSoUWSYjGhjYlAafHHdvsWWH3egcOUgy877zk2SJSTJyc2bN2lYvyEGPwOt27Tm6LFj+D/V/r7fdbvcqEtHs2fZKsJDw/Hx8aVt+zYMGDCAKlWq5O03e/Zs3nznHXwbNyCsWVPEX6WiO3NyuL3qB3r378eaFSvzBU9+FnG7evUqoiiSkZHBxLcmEuSKoK6hel4GHIBTcnAx5xQjhz+PXq9/bGuE/v37s3zNajJOnMSrWDEufz0d0c+X0MED0BQvllvi6XZju3ET64FD9Ojdi+XfL6Fx48b5xilRogQlSpT4TccsWbIkFqvMhcsWypfR33M/q9XN2YsOBjzx28b18OcQ5McpLfgIMBqNGAwGsrOz/5bG4N+NYcOGsWLXTmJefiGvVvln0o4cJX7lGlCp0Jcvh6FIEWSrlZwzP4HRxJjRoxk3btzfvrWAy+Xi9u3bOBwOwsLCPPfFb8RkMlG7Xj1SlQqiBvdH1GjJyMjA5XYhqNUICiVZ27ZjOnSEkAb1sVy8BNlGNFYNjQxtC70vrpkvcF68TPjLo1Cqc1PVf40sg8VixmK2kDh/Ee7rN1AolTgVAiVfeRG/okUQEHA4HFisFhwOB7IMostF0nczqRFTCrvVTnZ2Nv7+/jzT+Rl69+5937r9fwJTpkzh268nsWJ2FNUqFTTW3G6ZEa/GsuewhhMnz+Lj4/MXzNID5BrcXbs8Q+z1Y4waFEiPZ4I4ddZMv+cvM/uzEJb8kMPuAxZeGeFP7y4G/Ay52RcJSU6mzcrgqzlGuj/lz+wvyhYY+8gJE50HXGDdggiKhCvx9va9Z51//1FXsbieYOWqNVy8eJGWLRrTuqmDKW8H5anf/4zTKTPy9WR27LOy64fKvPpuPA65Fj+s3fDwL5AHDx4eyPDhw1l57CjRw4cUut2WnMKFT6air1YV3yaN8PMzoNf/YkzJskzChs1YDx3hx61bqVq1KgA7duygR+9eKEqXIuKp9qh/ld1jSUjg+uSPefcVAyMGRaJRF8x0sdvtZGVlMfjlRPbvgyKqUtglK+kkY3JZKN63N6F1axU6Z6vVSna2Eculy2QsW00VfQ1ckosk6RYulYOXxrzI+PHjMZvNlK9cGXe5MhTr9FShY0kuN7HfzaRGWDhbN28GcgUrp3w8hUsXL+N2upFkCbvDRhDh1PZvjEZTMDNIlmVOGPfjFaXm5OkT922X+LCQZZkXX3yReUuX4NLpkBQKwocNRtRpQZaRXC4kmw21UoWfwZe4OQuIcLo4eezYH87Wczqd1K5Vlab1svjknXsb1ItXpvD6+xkcPHSCyMjIP3qK/3l+qy3qiXT/xxg9ejQbtmzhxoLviezdA8Wv0gmD6tRGodVyc/5ipDNncVyLxcfHm84dOjJgwIC/XS33vVAqlfdVKPdQOD4+PixdvJhOXbty/bNpqKtUQlkiEoW3N47rcZiOHMVx+w7FOz1NcP16OJ9swrlPP0c22e/piBHI9eCKsoDT6USS5HzpToIAXl5eeOm9yFSpaNqxPbE3bpDorce/2C/9lNUaNer/E/Vz1K3NrbMXuHz+/CMXaHncjBo1in17d9Fj6AFeGRFM96eD/q9PdyIHjsHMWd95DO6/mA8//JDr146xcnYJKpbLNYibNjBQv7aBl95O44tJwQT6i0z5JpOp07OoUFaN0ylz8ic7JrOESinSpb0vTpcTlTK/I7R6ZS9Cg9UsX5fD6y/4Y7NZCzW6c7MerLz7Xm7kply5crw85g3eHP8K8bedDO7lR4M6OmQZdu63MHdJNtfiXHz3aQzFi2qoV8OLOcuuPPqL5cHDfxRZljl16hQLFizg6pULAJQtV4l+/fpRuXJlKlasyPdr1uCyWFDqC0YmUw8cQtDpcjMR7fYCxqIgCES0a83VCxeZPXs2X375JZIk8eobb0BkcSJ7P5tPJBVAHxGBd7WaLF59iu5PG4kID+LXOiJ2h53MzExib7g4dMRJRX0jiupyDTib28rmjFVkxsViqFoZrTa/wW6z2cnOykZEgZycgVbUEa0vjyAIlJYrEWe5zKcff4qvry8Gg4GsHBMxTza55/UTlQqCmzbm6KIlnDlzhn379vH2+An4uYOppmtAoFcISfbbHLHtIoISZGVmYfAzoNXmb/8oCAIx+gocvbmTHTt20KpVqwf+dr+FuLg4FixYwIo1a8jIyMDX15cObdrQv39/KlSowKefforD4WDGnDkEdH46N3vAYgG3G2QZjUaLn8GAIAqEt2nJja+/Y8eOHQXK0X4rKpWKESNfZMLbr1C1Ugq9u4QU2OfYKRPvfppCh47dPQb3Y8IjV/ofo3z58iyePx9NYhJXP5jC7R/Wk3HiJGmHjxI7cw6J3y+naYMGxMfGknjrFlcuXGTq1Kn/GIPbw5+jSpUq7Ni2je4tW5G+eSvJM+aSOO0b0letQavTUWrYYILr1wNA5euLX/OmmLGQ7cwodDx/VRDYHdhu3ASZe/bstGdm4kpK5plnniEpORltVCRWqxWrxYrD7ijQMx7AJzqa7OxsUlNTH9r5/13QarUsWbqCdh36M+lzE9WbXaLJU5ep1fISz/S/SXxSJPMXLKV169Z/9VT/05hMJpYvW8iQ3v55BjfkipvN+jyGSuV9GDA6mTPnHXR7yptqlby4fM3JnoMWLFaZJvUNqFUQHOgiPT2NrKysfP8jKpVIn64hLF9r4sw5O5JU8B/B5ZJ568Nb6HQBdOnSJe/z1q1bo9PpkWQVo99MpUaLm9RseZPX3kujSLgXq+aVo1XTXGEitySjUHiWAx48PAqMRiO9ez9L+3bNOLx/IWUiz1O6+Hn27JhL61aN6d+/L+3bt0enVJB64FChY6SfOIV39WrIbjdKpbJQR7MgivjWqsGylSvZvn07n332GVeuXSOsRbMCBvfPhDRtwpVbAm99mERycgYZGelkZKRjMpkwZhtJTnbz4vg01G4D4dried/TKnSU0pTFcvInstPTC7yjc0wmBEQUboGcU6cprorKc86Lgki0VzmKCNF8MvlTDh06hKZIEdR+99fY8C1XBrcgsHz5cia8NZFilKKmb0OC1KEIgkCWMx2toCdAEYKASHZ2Nm53wdZhfqoAtIKOY8eO3fd4v5WFCxdS+4knmDp7FlnFi6Bt2ghz6WhmrVpJw6ZNmTJlCgqFgpo1a6I3GAiuXg2NQoFGVKDX6QkKDMLf3w/hbkBCX6woyqAgduzY8afmNXjwYPoPGMnr76XQecBVVm9M59TZHH7ck8Vzr1yn6+CbVK7ShM8+m/owLoOH34An0v0fpEmTJhzat48FCxYwf9Ei0o4eRwBq1qjBoG/H8tRTT/3rIocefjtRUVHUrVuXxatWUaRPTxRqFeqAADSBAfl3FEBfKoYsX2/u2G5gUAUUGMtfFYRB9CP70EECI7vcU9QjedceAgwGoqOjycrOQp9thKxfensqFAq8vL3yp9NJuS/Tf2urIy8vLz7//AvGjRt/t+9zAhqNhieeeIJGjRr9a8/7n0SuqE0Wz3YqU2Cbj7eSJTPKsOdgNvOXpjBvSQZOl4hSAWNGFOWN0UW4fsNOmx7ncLoEDD4KjCYrWdky/n5+/BxxGj4gnP1HjfR5PolRgwMY3Nsff7/crIfDx018PiOJwydg9pwZ+bIeoqKiCAwM5MkGMP2TGK7G2RCAsqV0FI3IH5X6cW8OlSo/WGTSgwcPvw+n00m/vr24eGEfMz6NoFVT/7wWTm63zMbtGbz67jpefdXCc4OHMPXrr1EbDATUqpFnpEouF26bFYWfL7hceN9DPMty+w7ZP/2EPTuFvn0652aX2Z0kbdlCSLNm+JQqqCuiNhgQff1ZtjaBk2dj6dvVl8oVNVgsMjv2mlm1wYLT5Ett72YohPypziX0ZbiedZW0tRsw9OuN1iv3/exwOHC5XCglBakb14PRQgm/gs/IaH1Z9mbFcvnyZQTVg80RQRQRlUp2796NxqWjjE+lfFl2ua1Ec+eoEtQ4ZBtWiwXvQrLBREGBw1F47+vfw7p16xg9Zgy6WjUo0aFtvrJNuXVLknfv5f3Jk/Hx8cHhcKDUafG9q5lzP0S9juPHj9OyRUtux99Go9XSuGkj+vfvn1c68CAEQWDSpEnUr1+fObNn8MK4/YAECMTElGHiO0Po3bu3Z73/GPEY3f9RihYtyrhx43jjjTew2Wwolcp/XQssD3+c9PR01D7e+FW4v1K73tsb0c8XW6Kt0O2CIFBBV5W917Zj3LyV4J7d86XOSU4nST/uxHLsBH0HDaJLp67IdhnH9RtoauUaARIybrcLY7YRl8uVVy9jvHSZ0JAQgoODCz32v4WQkBCGDRv2V0/DQyFkZGSgVEBEWOGq36Io0LSBH00b+NGg/U9ci7OzY1UFKpTN/R8oX0ZBkTANK9aZqFdThyhAptGG3e7IUxLXaES+mRxNuSfO8Mk3Vr6Ze5Ei4WrMFjep6RLR0WVZtPjjAqI7Op2Ort168/3qrxnWL4wWjf0KneOBo0Z+uuBg/uv9H9p18eDBQy6rV6/m6NG9rJkXRc2q+Y0/hUKgY+tAAvxV9Bi6nWef7c2gPn2Yu2ghGfsO4Fu9KkovL2xpaUg2G26TGV9fX7RaLfLdrJefo6PGS1e4vWAOZYq66D0+iGfaFcVqs7DvcDaLf4jjxKzphHbuTmDtmnnHd1ksxE3/jqJe2Qx7M4Tzl51Mm2XE6ZJyu24oICtbpogiDC9lQcPVS+lDHe8GHDi7k+vTviakSSN8S5fCYrWSc+481uNnkBJTqeX9BN7KgnWuWoUeH9EPh8OBMykZyeksoDX0a2zJKUh2OxfOXSBGUalAWZte4YUNCw7ZjlrQIKLAarUWMLrtkg2r20zx4sX5M0iSxDvvv4+ydAxFn+lYYD6CQkFYs6Y4s4189MknTBg/HrfVijMnB5V34S0gARxWG8bbdzh49hIR6ih8FAacsoulsctZOG8R/Qb2ZfLkyQVKDLKysrh27RqyLFOiRAmCgoIQBIF27drRrl07kpOTSU9Px8vLi+LFC3YqcjqdJCcnc+rUKTZt2sSlC5cAqFy1Mn379qV69ep/e02nvzseo/s/jiAI+RSlPXiA3Ciry2pDcrkQ7yM0otPqkCwWMhxGJFlCFPJHX2VZJs2ZjJdag+pGPFcnTUZXoRyawECcOTlYzp5H6XQy/tVX+e6b6ahMOqp6lebk9WPYk1PQhIYiIiAKatyyC4vZgkqlRul0kHPqJ1586aXHIoTiwUNh+Pj44HLLZGa58vrcFoYsy1yLs9G2mXeewQ25i+6+3UP55Otb9OrsS/UqWlRKCYvFkmd0y7LMlzMT8fHxY/OWnRw5ciQv66FevXrUr1//nguh5557jjWrl9F7xHVmTy1BaEj+iMbRkyaee+UWtWs3emw97T14+C8xf95smjyhK2Bw/5oGdXypW0PD/HmzWb1mHZ06deK7GTPYtHkzDqcTvV5HdFQUadeuIzVtTGpKKu67mV4KhQK1y82dhfNo+YSCKW8H461TEhigw2yWaNPMSdv2wUyaksSC1cvRRYSjL1oEgJQ9+9BZk5m/OJIIg4NBvXxwT9SQmOzE7rDg5+1g/WYbb354jWxn2UKz2cK0xQi0BRKi9yJpw2bS3OtxOJw4rDaKqSIp5dOMAPW9HeOCLFCqVCkuXr1K5qkz+ZwC/0/qgUMEBwaQmZReqBFfRBvFeeNJkuR4igulEBDyrtOvibdeR+utpVOnTvc81m9h7969XIuLo/hzg+9rjIY0acT1o8dxOp14abSkHz1O2P/Vr7ucLiwWC3aHHdPFSzizsimrKU85n2oohdx3iyRX5ZY1lrkz56FSqfj444+BXOX7L7/8khVr1mC12wHQqlR0bN+eUc8/n1ceGhoaSmhoaIH5JScnM2fOHObMn0/8rVu4XC4EWcAPAwZlABdPXGHhvEW0adea6TOmF+gJ7uG341mtevDgoQDNmzdH8fbbZP10joDqVe+5n+32bZQWC24tHDHuJEpThhBNBAIC6Y5k4myXMSrTmTx5Ml26dGHJkiWsWLWK1IuX8fXxod2QIfTt25ddu3aRkZpBQ++2qEUNsfYrJC9bSmj3HmjuviQUghK37MaYmIjxh/UUDQqif//+j+eCePjPcu7cOebOncuP2zdiMpkICAjgqae70adPHxo3boxK5cXydWkM6xt2zzGOnjRhNrvp2KpgT/XBvUP5cW8mPYcn8sboANo8qQdyF07X4qxMm5HI6k02PvxoKhUqVKBChQq/ee5FixZlydJV9OndnbptLtO2uRc1q3phd0hs25XD0VM2atVqwLz5Cx97T3sPHv7tWCwWTp8+yafvFPy//386tPJj/IeHMBqNbNq0ib07d+OyOFChxJ5tJcloxuxyoDx+Au8y5VAKuRFhySWRtP8gPiobH74dhVrhyCvD0uq0mHJMCJKL8a+Gs+9wLKn79hP5bHcklxvjsUP07+BD0RAB2QFarQ5RFPHxVmKxyBizHXRpa+CbeVlcT7hEKWVFJElCEAQ0Gg0ajQan5MIlOmnYoAFOp5Pbt29jMpk4deI05byq4qvyu+c5OyUHOWRTrVo1EAR+2LgZXXgY+l+JqP5MxqnTmI4e54VXXmHKx5/glAqmhqtFDZH6GG6Yr+Ij++GLfwFjONWexA3XJYb2H0JAQH4ngsVi4YcffmDp8uXcSUhAr9fTrEkT+vbtS8mSJQsc78yZM4g6HfrI+0fMNQH+aMJCuX79Os927cqc5cvwLVMafZEIkHNr/i1WCwgibquV7J17UOj0XHNc407Gber5NsagCkAURCL1MUgWN7NnzGbo0KEkJSXRo3dvzAoFhiYNCS1TGkQB07VYVu3dw/pNG5k3a/Y99V/Onz9P527dSMzKQlm2NH5P1EEpqLDfjCfn1Gls5iTq6hvjkG1sWbeNvrZ+LFu21JMZ+wfxtAzz4MFDoXTu0oVd585S8vnnUBaSDSG53MTNnE2UQsWUjz9m8seTOXLoCC6nG2RQqBRUqlKRV8a+QocO9+8l3aZ1Wy7vj6WWoREANreFQ8bdZJGNLqYkuuhSIApY429ivnCe6MhI1q1ZQ5kyBevEPHh4GMiyzMcff8y0LyYTHgKd2vkQHKQi/radVRtM5Fg0fDx5KgcOHODA3iVsWBxdaJq5yyXTY8gVtu/JZO3CsjSp71dgH4vFzevv3eCHzekolVAsQolS6cXl63YCA8N56+336Nat2x8+l+zsbJYtW8biRfO4cTMOpUJB9eq16dtvAK1bt/YsoDx4eARkZmZSoXwMMz4NpW3zglHiX7N6YzqjxqVQoXw1Th09QzExmuK6GHQKPQ6XnYupP3FdvoCsVxHYqhW+larm9bJOnP45fVo5eWOUAa1Gg7+/P8JdTYjs7GwsNisKvZ65izKY9JWJYq+8hjsri+QZX7NsVjiVYkCv1WH4VTsxSZJITU1BlBRM+SaDefNdVHQ2yu1IcvdPoRDJUCZzwXESnUaHt2hAJalxCDZSrcmEE0nNwAb3fL5cN1/kluoqP507k1sO0707h0+dRF+pIgG1aqDy9saelk764aPYr16l37M9+fzzz2nZohXXj9yktqFJgTHdsptjWXtJsSXgLwdTVFOCIEMINreFW7ZY0oUkWrRpzsJFC/PVMp84cYJeffuSkJKMNiYGTVgIbqsVy4VLKOx2xox+kTfeeCOfEf/ZZ5/x/pfTKP3m6w+8F2K/+o4+TzbjnXfe4elOnTh+/jyG+vXQVCiHQ5nr8DSfu4Bx3wGQoUivfiC5SflhDcLtNBobWuWl+LtlN3uMG+g7rDer1q7FHOhPVL8+KP6vw4vkchP//VKE2Bvs372b6OjofNszMzOp36gRyciE9e6B2elCJWgQ72psu202kletQI69w5N+bTG7czhh28v8RfN46qnCW7v9FoxGI8uXL2ftD6vIzEzDx8dAy1bt6NmzZ6GR+H8CnpZhHjx4uCeyLHPp0iUyMzPx8fGhfPnyBSJdH3/0Ea3atiXu25mEdWiHd0zJvBeO+dZtkjZuRpGUwlerVlGnTh2aNGnC+fPnOX/+PLIsExMT85trgJISE/EWf3lQaRV6Gvm1IsF2g7hr18i6vA2Q0YleqGX45ssvPQb3XWRZRpZlj7DaQ+bbb79l2hcfMm50EEP7hqFU/nIfj3tR4t1P4nllzPNM+eRLjh09SOtuV2jXwodK5fWUidFTvbIXZ86b+fjLRI6eEfD2MXDxirVQo1uvVzDtw2heH12Ufs9f5fwVNQMHDmbUyzVp3779nxa6MRgMDB06lKFDh/6pcTx48PDb8fHxwcvbmwuXLQ80ui9ctiC5JU4ePkUtr6b4/SqV22lzEioUI4QiHLXsJG3DBrJ37UETVRwZAUdKCqWjAhFkGY1ak2dwA/j45gp4Oc1mYiJFcDrITkjAmZyM5HKgE+2oVd4FDAVRFNFqdVjMFry8BGQk1MIvTkUZmSxnKpecp1GKSup6NcNPlRvRl2WZc8Jxruac52KaN+WDq6L8v3aIKfYErrsuMHBQ/zxD64fVq5k5cyaz580jftZcJBkUgkDVypUZ9tXXdOvWDUEQGDxkEMOPjiDNnkSQ5pcMoyxnBvHWa7hxo1AoSXbfwiRmoLQqEUSBmLLRvDrsI/r375/PEXD58mU6d+uG2eBL9NiX84nGSk4nKfsO8PHUz1CpVIwdOzZvW3R0NC5TDrbUVLT30ZZxWazYk5KIjo7G29ubH1av5oMPPmDuwoXc3rARUa0BZARRRBMTTfCTLVD55DpAwnr05M6smVzJOUc1Q27nGIWgwJ9gNm7YSKbFQqkXRxYwuCG3zVrxZ7tx9aNPmDNnDpMmTcq3fcmSJdxOSabUa69gdDoR7/79jEKrJbRzV25/9SVxliuU96mGwRbIrJmz/rDRvX37dkaMGIzdlkXzhjpqVlCTknabr6Yd4/Opk3l/0mT69Onzh8b+J+CJdHvw8B/C7XazYMECZnw3g8uXryC7ZQRRIDKqOIOHDmbw4MF5taSQ+zIaPHQoZy9cQOHvj+hnQMrJwZWaSonikXz71VfUq1fvT8+rwRMNSPvJRBVDnUK3//yYynSmccKxl937dv2n29i53W62b9/OnNlzOLD/AA6Hk4iIcHr37U2fPn0IC7t3qrOHB2MymaherQLPPg0TXy08dVCWZQa8cI0zFwMJDgnh+LFDCIILAZBkUCoVSJKSmJjSfPLpNJYvX86JI0vYu65snnrx/2M0uajZ4hLDR77FmDFjHt0JevDg4ZFz48YN+vXrR0riMXaviUKnU6PT6Qo4uG02iZotL3D7jkRJsQJlvCvl256amgJuAaWgIkNO4ax0mGL6aFyyAxlIl+OZMCaAPl19EFUiQUFBed+1WCxkG40gCuzYncNzr6bgUOpBBrXbwmfvBFGujBcatTcRYZp8nQ0yMzOxWW28PCGFTZskKkhPoEGHAxtJ8i2S5HhkJDQKHS1DOuXTdJFlmZ+Mx7huvoCfIpAY3/LoFd7YJRt37HFkCWm0e6otc+bMKeBUdLvdXLp0iZycHPzuKrU7HA5CQ0MJCQnB6XTSq2cvfty8kzKqyoRoinDKdIhkdxKiny+qYkWQ3C7c8bfQCQLPdOzISy+9RPny5Qt1TvcfMIB1B/YTPXokCq220N8yceuPWPcf4Oyp03lOArvdTqWqVTGXjKJYp1+MUGe2kcwzP+E0mhA1alwmE+4zZzl76nQ+4dexY8fyzbx5hLRpiVKnQ1MiihyXC7Wgzec4yTx8kJztu2nl/zRqMff3OZN9hDvKm/jUq0PxLs8UOuefSdi8FeHUT1y7dCmfs6FmnTrc8fai+LNdSU5ORokKRSGx2LQd23EcPEnrgE7EW68RK14gKSXxd5ckHThwgGd7dKJZA5EP3yxGSPAvv7vR5OLjaXeYv9zMF9Om07Vr19819l+NJ9LtAchVM1y6dClrf1hHelo6fv4G2ndoT8+ePfM9mD0UxOFwcPbsWaxWK2FhYcTEFGy38U/C6XQyZMgQ1q5aRxBhVNU8gU7jhU2ycvt6LG+9/jZbt2xl6bKleTVhZcqUYe/u3Rw6dIh169aRmZmJt7c3rVu35sknn3xodaAtWrXgy5++xi27UAgFH0s/R8tv224QXjSMcuXur6r+b8ZisTBo4CC2bNqKj2QgXFkSpaAi+0YGH73zMV9/+TXzFsyjSZMmf/VU/7GsXr0amy2bYf3unU0hCAIDe/rTsfdlfPUJLJkRQ4M6euw2O4dP5DB/aSZ7DrsYNHg4jRo1wmAw8MOaZbz14U3eHxdZoH2e3S7xwrgbIPrSu3fvR3yGHjx4+COkpaWxZMkSfli3jqzsbPwMBp7q0IFnn302z6Cy2+288soYVq9agkbtICPTwdsfJ/D2mADM5hx0Oj0+vj4ICDidEi+9FUdGpgBuiPTKv86QJQnJLaEk11jyJxid4IVaVFPDtz4AR7J3sHlnKn27GXC5XMjIuSJibjdGkxFRrULUatl+MBNVcCjFnnoGQRCIX7GSNyalotWkAwKCIFCvpg9D+4XzZAMf7HY76ekCO/bacUtqTssH8ualEbWES1EEy2GckQ6RZL9FhDYyb7sgCFT2rYVaVHPZeoZL7lOIUu4xqtapxpCh79G5c+c8IdSLFy9y7do1RFGkQoUKFCtWjJkzZzJ39lwSEhJBBlEh0rxFM4aPGM78BfN58cUXWblsJScyDoK3F/7tnkIbE42gVOCl16NXqUg9dJiV69cRGBjI5MmTgV9qt7dt28bthAT27NmDT93aGM1mlE4nOp2ugEBrcKP6XN1/gEWLFuU5RDUaDS+MHMm4iRNJLxqBoWJFbq1eQ+aZsyCKKHy8cZstuI1GihcpSmJiYj6j+8Tp0/hWq0Jo44ZArphaTloaEhIKfllbeZcpS9aPO8l2ZhCsCUeWZYxk4nK58Cpe7IH3rL54MVL2HiArKyvv+C6Xixs3buD3VHt+ib0W7gzWFY8k58BhnJIDpaBClmWcTufvWv/JssyECeOpXkniu0+iUanyOz98fZS8P644ppw43pk4no4dO+YLAP1b8Bjd/2I2bNjAiOdGYMrKIYBQdKIXGdJtJhx6h48//JipX0z9U3WC/1ays7P5+uuvmT93PqnJqch3H/bValRj+Ijn6NSp0z+ybcLHH3/M2pXrqaKtR6imSN7n3vgSpA4lw5HKgV37ePXVV/nqq68wGo0kJCSgVCqpVasWTzzx6Pr49uvXjy+/+Ipr5osFvPw/Y3JlkcItXhvw6n+6BnXEiBFs27CdqtonCNFE5NvmlKpyOuMQvZ7tzdbtW6hYseJfNMt/NmfOnKFCGQ3hofdO63a5XZQu4STQX0HXjgG0auoPgJdeQ9sWvrRpHs4HU2/zzsRx1KxZkxo1ajB5yheMfWUU5y5fYeCzQdSr6YNbktmxL5u536dz846GOXPnF6hrW7lyJR98MInY6xdxu92o1RrqPdGEDz744D+d8eHBw+NkzZo1jHzhBcxOJ7pyZVAVCeNOtpHjH0zioylTmDZ1Kk8//TSDBw/kwL4NfDA+lK4dgti8M5MXx1/n5m0XvTr7ULWCC7XGycmzErMXp3M1Tkmnzs+yfOEKtIr7d5MRBAGtrMcu/dKmM1JThlNnb7PviJkn6vxiqFisFmRAodFw+aqNLTstBLZug1dkMeK++45AZTZdevjydGtvggIVXL7mZN4yIwNGXWbUkFC6tFPx2vsZYNfTMrgjZrcJu9tGjttIijWRTCENI5mILpFb1rh8RvfPcy3jXZlE+SYvvPI8vXr1wtvbm5CQkLx9tm/fzmeffsbRw0dxOd0IQm5atEIpYrc4iVBEUk1TH6WowujK4sCmw2zf+iPvvD+R6dOnU6xYMSZ9+ilhA/qgiQhDqVTdzSbINerCnmyCQqdj1rx59OjRg6SkJJ4fPZq0rEzEIkVwa1QoixXFeOoM5tg4Ap9qjzk8DK1Gi8FgyGvJptTp0ESX5PCRI/nOceTIkdy6dYvvZs3i5tKVoFFjaNkcr8oVQVQgOx1It+5gPHSEth07snbVKmrUqAHkBkJEr19+b6VKiUqtwuVwoRBEfjaChbsOAAkJgAxnChbBhFajRXI6H3jfyk4ngkC+jIK8NayU23VGEARkSQKhoCEtS1Led7JdGQSGBvxug/jEiRNcOH+GRd8ULWBw/3pOo4eFs3rTVTZs2EDnzp1/1zH+CXiM7n8pO3bsYGD/gRjswVT3boJG8UvKjEOyczHnFMOHjUCr1dKxY8e/cKaPj8TERJYvX058fDwqlYqaNWvSoUOHfA+PlJQUnn7qaS6fu0oYxampq4haUGNyZRN75DqDjw3hxIkTTJo06R9leOfk5DBz+iyKidH5DO5fE6AOJtpVge8XfY8x28j2bT9it9kRBAgOCabfgH4MGTLkkWRIREVF8cb413n/nUm4TU6ivcqjEXPvWUmWSLbf4aL9JBWrV2D48OEP/fj/FE6fPs36H9ZTTlOzgMENoBLVVPdtwAHjVj777DPmzJnzF8zyn48kSTyoE53VYkUUQa8XKaxISxAE3nixKJt3XmL27NnUqFGDnj17UrRoUb6c9jnPv7EbuAMIiKKaFi3bM+3bMfmMaJfLRbNmzThxfD+RxZS8MNgbfz8FsTcdrN64lXp1tzDmlXG8++67D/P0PXjw8H9s2bKFwc89h7pSBUo91R6l/pfWfy6LhYR1Gxk2YgSnTp1ix48bWPRNcZrUz63LfaZtIGHBKr6YkcBr76XjckvY7TIajRctWrbjw09e5sKFC3y/YCku2ZXXIgpAEEVEhYjklhDvRj+dOPNlhIVoIvCzFWPU+Hg+GB9Mr66gVILNZgeFkv2Hc3jt7SSk4OIE1KxB3HffUlSXwbx5JYkIkpHsNjRqgZLF1XRo6cWXs7L44IskZswXyE7XUsOrORqFFjduzpiPkS1noywairpoWVwuN67Ll7htikebfYRKvrXypZmLgohKyDX2/l8BfM6cOYwd8yreTj8qamoTqAtFQiI2/TLxzus4BSeh/kXy6rb9VIEU05bksvksE96cQGRkJD+sX4+hWhVCy5e9528XVKcWmXv28c6773Lg8CGUZcsQ1rMbDrUKFEpklwt3Tg4ZGzaRtnwVIf37YDMISJmZ+Af8ooAuqJTYHflV0wVB4KOPPiI2Npb1u3YS2LM7quAgkGVUooje3x9deARStSrEzZzLoKFDOX7kCEqlkhKRkVw8fSrfeF5e3mQ5Mn91HwjYExMRZBm9whuzy8RZ61Fq1K6OxkvH8XMXCK5//xK/7LPnKR1TKl/as0KhoGLFCly8dJmgenXQ6fRYzBaU5B7zF2TM164gynDWeIxEZzzDOgy95/r3Z3X7/99+9OhRvL1kGtW7fxlwdJSOcqU0HDlyxGN0e/hnIEkS414fh95qoKqhXoHeyWpRQ2WfOpw07mfc6+No06bNvzpyaLFYeP2111m2ZBkuu4SX4IOEm++k6YSEBTPx3Yn07NkTWZYZOGAg187GUterWb4+kF5KH8K0Rblpuca3X35H6dKl/1HtqjZu3Eh2ZjZVvBvcdz8BEWO2iU3LtxKlK42/OggJicTEW3z6wWcsW7qcNT+spkSJEg99ji+//DIqlYqPPviY26ZYfOUARFnEosjBpbDTpGUTZsycgY/Pvfud/tuZP38+SpeGcP29U8oUgoJiqhg2rttIcnLyP1YN9HFz+fJlzp07hyRJqNVqLl6xYzS58PUp/DVptVpJSHKRmeWmRGThdYCiKNCrsx+Tv16D7bPP0Gq1NGrUiEaNGhEbG8v169cRRZFy5coREVHQidKxY0fOnNrPx28FMaS3Xz4xt3dfdfPy2yl8+skkwsLCGDFixMO5EB48eMiH2+3mjTffRBFTkuLduyD8X12wUq+nWLfOxFmtfPvtVzRvqM0zuH+mXi1f6tXy5Ua8jSuxVt547xbRZVqwePESILeHskqtIMF6g+L6/Cnmer0ekykHJUpyZBNmjJTXVM3bLgoiZb2qsSvrFmPfzebruRdpUl+H3Wbl4Ek7126BOjKaEn37YbkZjzvpFpNnFaVokZ8jnwJ2mxWHww0CdH/Gmz2HLGzZaaG+bxMC1MHYJRsHsndg81cR9nR/1OHhed91tGiK5ex54rbtQDJKeYJfAC7JiU2yFnDWHz9+nNdeeY1wdyTlfX8RXHU4HPi7QwkSinCZU5zI2s+TwR3RKXKdHIIgUMarEtnGdKZ8PIXYuDhC+/a87+8niCJelSuxdet2fKtWpki3LqRnZiBoNIgKBS6XC1VIMCH9epM8cw5Z234ktH8fHGYLVosVvZceWZZxJSQRVbFKgfFNJhOHjh0ltFlTQiuUR7obPRYVv3I+qNWEd2xH3NffsXPnTlq2bEnvXr1Yu2kj5vhbeWniWq0GH18fTEYTkiwhIpJ9/Bg++HDDcoVkblOqfDSLFi/i0KFD7B86lJy4G3iXiCr03K1JSVgvXGLIRx8VMIQHDxjIiJdewpqUhD4oCKvFglN2ohJUgIAbF7bsDExnf0LprSEh0IkzXcWcBQtIS0/ny2nT8uqYlyxZwpz584mLi8stLahUkUEDBvLMM8+g1Wqx2+1oNSIyEjk5Vmw2G5IsIQgiWq0GvU6fl66u0wk4f0ME/5+Ix+j+F3LgwAGuXr5KdX2jAgb3zwiCQIy+Aodv7WD79u20bdv2Mc/y8eBwOOj5bE/27dhPtKoCRb1LohJzHQw5LiPXks7z/IhRWCwWqlWrxsH9h6isrZvP4P41kfoYMrKT+erLr+nbt+8/RjE6Pj4ejajLe3EVRqYjjbPGo4RRnAqqGvh5+eVtC1KHYnWX59j1PXTv2p19B/Y99HobQRB44YUX6NOnD0uWLOH48eM4HA6KFy9Ojx49PGm0wOmTp/EXgu/5f/0zoZoIrpjPcOnSJY/R/QAOHjzIlMkfcuTIASD3Re92i9hsNhatSGTEwIIODhkZSZZYvMpEUICKlk387jl+TEkdTmcm2dnZaH8l0lOyZMlCe7/+zOXLl9mzZxuvj/JneH//AtsNvgq+mxJGQvJt3n9vgsfo9uDhEbFr1y5ib96k+IihBQzunxFEkaCG9Yk7cYSOre6dDRZVXEtUcS3Xb9iY/PVBZFlGEASKFy9OyzYt2bF2N2FSsTzBLCAvCml324jjIlqFjlB1EdLsScRZr5LsTsQuWVFrNXTt1gtZljh//RrHT5zAovUnamhPvEuWQBAE0o8epXxpNdWq/LIWEBQigiDi5+8HMggCDOuvYce+qyTY4gnWhHPdfBGL1kVErwGo/PwAGUmSclOelSJ+NWqiVGq4uW49JZyl85TMb9tuoNSKBTIqZ86YidKhyWdwQ64zU0BAKSopI1fliPwjN63XKOv9y/tfEAQiNaU4c+YgklpRaEvT/0dWKnA6nYS3aYnVbgNBRPFzurUoIjudiDodvo0akr5iNc70DERvbywWS67T4+o13OkZPPvsswXG3r17N8YcMzF1aiGK4j3XhfpiRVGFhbF+/XpatmxJs2bNqFiuHBeXLCdq2CDUd0XjvLy8UCpVmHNySNu1G8u1K6BQIxex89rAsQwdOhQ/Pz86dOhAo3r12D9/MRE9uuJTplS+a5kTe4M73y+laoUKdO/ePd9cYmNjuXjxIpLDwaWvviPk2W7oIyKw2204ZAkBEXtGGumr1qD08iJ0UH8Ueh2+Xl5YLl5i3eatxD/zDJ9Mnky/gQO5lZyEvkJ5DB3aIrvd/HTpCsNeGMU3079jxdJlFC1alJRUGz+dSyQsVImgVCIolEiSTI7ZjNlsxtfHF0HQcC3OSZMWhWdk/tPxGN3/Qo4ePYpSVhOgyhVMMLmySLLdxik7UYsawjXF8FL6YFAFoLd5c+TIkX+t0T1nzhz27txHdV1DAtUh+bZ5K32p4luXCzknGf/6eJ7u/DRqt5YQr4IRp18TqSvN8at7OHToEPXr13+U039oqFQqJNmV94IvjGvmC2hlL6KF8igKeWnoFHqqedXj4KXtj7Text/f32NA3ANJyn0ZPoiflU+lu7VYHgpnw4YNDH9uIJXLy3w7OYxmDQ0oFAKHT5gY+Vos735yh5KRKlo3y68GLwBrNppYsNzIxNciUavv/ZtkG12AgO43LAx/zVtvvYW3Hp7r63fPfVQqgRED/Og1PInVq1fTqVOn33UMDx48PJijR4+i8PVBX6zofffTBQcjigLeXg9uChTgp8TpdOZGWe9mGk6aNInjR1txJHEXZbVVCVKHIggCoiii8BW5kHmCNDmJMEURDmRuJ400lCFB6CrWxUuvQ2G2sGT9OvSCyMzvvuPChQu8P3Uq+iIRee99KSudqnXUCOTW6sqAZHcAMm63G51OhyiI1KqqQKtVcsd8g+KOaG46Y/GqUfWuwQ0ggABu2Ylwt9WUT6XKZO/bR5zpCtUM9chxGYl1XqBjpw4UKfKLEWU2m1n7wzqKKksVWI+4Xa6895dSUBEiF+GW5Xo+oxtyy+EkmwyyhC0lBa+o/PXk/481KRlRrUYXFkZOaiqCSpnrXQAUGjVuqw1J4UBfoRwZaxTYrsfiXbMGLqsFS0oqiSvXULtGDerWrVtg7MzMTGRBQG0wFNj2/wi+vmRkZuYeV6Hg+4WLeKpTJ2I//wqfGtXxq1IJhUaD5U4CmYeOIMXfYszo0QwYMICYmJh8WakqlYrFixbRf8AAds1biDIkGF3pGEDAGhuHMyGR2tWrs3jhQry8vPK+N336dMZPmIBLrcK7dg2yz10gccYcNCVLoI0ugSCK2GLjsF69jtLfj6Be3dH5++Ht7YVKpUJXuyb6YkU5+dW3tO3QAXdQIDGvjcl3/sH162FNSOTc3AV0e7YHo58fhcnsYOVGMy++UCTv2gMga5DsdoxGI1t3uzHlKP61elMeo/tfwOXLl1mwYAEHDxzC6XRiNufgcrvIcRn5yXyMVHcKgk6LQqvDbTFzPus0ocoIqvrURoHiX5vGIUkSs2bOJkgOL2Bw/0xuqlJlEk3xHDx4EC/Z94G12n6qQCSLxO3btx/FtB8JtWrVwi26SXemEKQuGPm0SzaS7LeIksuSLiST6nRhz8r1OPso/fBV+hGiicBH6YcfQcyft+BfWW/zd6dc+XJcPb3xvs4TgDRHMkql4r6R1P86t27d4vnnh9KuuYJpH5TMl7rd+AkDJ36sTP12Z+n5XBxtm2fT45lgggNV3Lxt5/vVGew7lI2fQc2wvvfPJFizKZNq1Wr+7paWP/30E3VraAkMKPw1LQPIMq2aeqFR5woSeYxuDx4ePk6nE1GtfuDaQKHXISMQF++4734A1+Ns+BkM+YyoqKgoNmzawOBBgzl95gAauw6NpMMlOskhm7CYULq2eo7ly1eQnmPCv2M7fCtXxsvLC51OhyAKuDu05dby1QwcMoRZ06ejE0XurN9EsS65iuWyIGK3SUhSrmNAdruQXS5EBEzZRsw5Zvz9/bHZJdQqNZHlYzhyaRdW2Y7f3a4huZk+bly4kAUZAXDJLhSiEn35cqQcOsNV83niXVcpU6kUU6ZMyXfuaWlpOB1OfJV+BS+MIOQ+3O5eai98SZYKrrUk2Y0oiFStUpVzh48RUKvmPX8fl8WK+ew5NP65x5NlOZ/RJ6rVuSrxNjuyUgEKBa6sLBxJSeScOEnGxSuUKV6M+XPnFnoMX19fkGWcJhOqB5S/yTk5GH71LoiMjGT7li18++23zF+0iFsHDuWK3wkCTRo2ZPjHk2nRosU9xzMYDKxauZIDBw4wb/58Tv/0EwDlKleh3yef/o+9uw6zomwfOP6dOX22znawdHd3IyiohLQ0AiIGBi8GBr6vidgFKt0lDQZI59KdC0tsd5w+M/P7Y2FxfxugIkvM57q81DNzZp6Z3T1n7ifuu0CVmQULFvD622/h3bI54Y90QtTpkN1ukvcfJHH7TjJ+34jsciMAlkc64dOwPl5+fvj4+uaLk03hYQj+FjISk6j11FD0vgWv2xQRTunhQzj81Xe8PuENCAxn2sJUGjTMoU2rP+0vCIhGI8dO25n4SRxdHh1EmTKFl+q816lB9z3M4/EwYcIEpv80A41Hi78SgkbQkO5OIcuZye/ulejDwghq2wuvKlURNBpktxvr6ZMkbdnK1ozfQePJ1wN5Pzlz5gwXoi9Qz1j8aLRW1BFEOInxCZiVm/dUSooH4J5aB9+iRQuq1ajK+aMnCNAVnJ5s9eTglt1c1J7DJbjQizJaUcbPR8ApwMUsAb3Dhwr6Olg0gUSfO19CV/JgGzxkMEsWLSHZlUCI4dqaOkW5tjZKQBBEZEXmsus8Dz36EGXLFt/7/yCbO3cuRr2NT9+tni/gvs5g0LD7lzrUa3+Mnft0bNmVCMiASKvW7Xj1tUZ8/dXHrP09nW6dAws9x86oLLbusvPFl0/95fZJkoROV7BdiqLk/QMgCgqCCL/8sp5NmzbRoUOHv3wulUpVtNKlS+NKS8OTY0Xr7VXkfrLLhYSORSuzeWGUUqAs4HVut8yS1Vn07PVcgW2VK1dmy9bcMp2rVq0iJSUFb29vHnroIbp06UJCQgILly6hdK8ehLRpVSAI1BgMlHmyL9FTfmD6jBl89dlnjHnhBS5mZxPaoR1KcBgbd8TwlkPCIEooLhcaNOhEfW7gKLlIT09n3QYPOr2ZZT8vY+LEicxfuhRJq+BU7LknEsBgMOLt7YXNZsNhdyApHmS9QI6URaLpEoP6DeDdd9/Nq7V9ndFoRBDAoxTsnNDrdLj/lKzMgztf+azr4p1XMRj1vPD884wcM4bETVsIe6h9wZ+JR+LK4qUYBREEAUWSEEUR6f/NAtMYjQgaDe6UNOQcK5lbtpO1fRe4Xbwy9kVef/11AgICCv15tmvXDh+zidR9Bwjr0K7QfQDscfG4Y+Po0qVLvteDg4N55513ePXVVzl9+jQul4vw8HBKl755OTAAURRp3bo1rVu3LnY/l8vFf99/H2Pd2kQ81iXvd8ejKFC+HKHlyiGiJWnlcuxnzhHYtCWyoGCz2ZBkCYvFPy/wlj0SzvQMzHVrg7HopYbmiHB05csSc/wklcc+S8qOnTzzylEe72SmZ3cLpcJ1pKZ5WLM+k2VrcshIh/btH7ql674XqUH3Pez1119n+g8zqKytQ1mfSnmBlKLI/Ja4EmeYicDB/TF5+edN1xF1Onxq18VUtjyxs2YgZOXQu3fvkryMYkmSxB9//MGMmTPZu28fkixRvmw5hg4eTO/evYsdPcrJyUGRFYyawpMc5WbFvsolezSpzkTcLidW7NhcVsz6or9Y451X0Btys5/fKwRB4P0P3qd/3/4czt5NTe+GednBAaKtJ5EMItrIEMypsXTv7MPQJwOpWU0PHg8nT1pZ+HMOy9fuxugOIURXcI3pX+VyuVi/fj1z58wl+nw0Wq2WJs2aMHz4cBo1KrrX+kHWokULWrZuyZ4tUdSVm6N1GXA4HLkj34BGqyFGPIPTbOPFl14s6ebe1ZYtXUCvx3wwm4uuNarTibz8TCjvfZ7Fjp37kCSJgIAAAgMDURSFmJiLvPjmYpJTPQzoGYTJlHssl0tmxfpU3v44kdZtHvlbI9BlypTh4NFL17Ic5/4tKEruOkqE3IEaQRDYd9iJy6XgbUpl0MDe/O+9Txg5cuTfuykqlaqAHj168ObEd0iJ2ldsUJUatR9vLy8ux3r49LtYxj9fqsD3mCwrvP3xZdIzdUUmYxUEgRYtWhRapnPevHl4tDqCmjUt8jtS1GoIat2SbYt/5pNJk5g7cyZvv/suZ7+dilMQsDscLF2cyMBevmjQohV1uU+IgoBO0JOaaefHOUk0avwwv/76K1WrVsWk16HJysSnUgUEQcRg0OeNoPr5+eHjk1vXOystnVo1a7D5jz/w9y/8OSEkJISq1aoSd+wS4cb8I5omswmr1YqkeBDRkEwcQYb8s4k8socr7vM89sRj9OrVi5iYGN77+GPsl68S1KoFXuXLoUgeMk+cInX7TnRpaXw6aRKvTphA5olTmCqUJzsnB8WYf8aYqNNhP3UKrclE+SEDSPx1A62rVM2r710Uf39/+vbsxazly/GvWxtDYMFOWNkjEb9mPZEREXTu3LnQ4xiNRurVq1fsuf6J9evXE5+URIVB/fOuW1EUMtIzQBHQC3pAwKdZE2ynTmOPvoBX5SqIiDgdTmw2a940dU92NpLdgaFCeRS5+OUUxooVyTh2Aq+KFXD5+5O+J4TlOw+w/Jc4REFBUUAx+6Cv1xpT9AVOnjz5r92DkqYG3feo48ePM3PaLKpo61LOXDnftgxPOnatnaAOXRFNJjyKO69kw3Wirxc+bVqQ8+vvuP5fCQRZltm7dy+xsbHo9XqaNGlCWFj+NY13QlZWFsOGD+ePbdvQlYrAu0lDNFotp2Mu89Lrr/HZl1+yZOFCatasWej7/f39EUUBm2TF5/9NY7J6stmbvplsTxY+WAhSIpAUN/Fc5njqQap51bkW0Of/UvPIHmJcZ3m428P33PSXDh06MGPWDMaMHsPWzHUEEYpR9CLDnUqcHItPi+Z4DkcxdqSF50eH3Pgy0mipUVPgf5X1VCpr5YOv42hUrmAGz7/i4sWL9OvTj7Onz+FHIH5iAA5klp9dxaL5i+nesxtTpkzJl3hKlfswNnvObFq2aMW2M78QSBghQgQadOSQSbzrEnZyaFy38b/65X0/SEpOonJFy033q1LRhCSlodPp8k3XFwSBb7/9jgkT/Hh38kw+m5JM0wYGNCLsO+wiNV2ga7e+fPnlV39rVsyECRPo1vVhlq7JYlBvv9y1l4qSF2xf//OcNi8TjUZk3++1+fS7ON55+1WqVatGq1bFVypQqVS3JiAggKEDBzFl1iy8SpfGp3LFAvtkR18g7Y/NPD10GOXLl+f9997k1DkHIweF0LyRD4oCW3dl8sOcJHbtk/js8++oVKlSIWcr3pGjR9GXK4PGoC92P9/q1YmTJY4fP07Pnj3p0qULjzzyCFFb92MQQpj0dSomvZ6+j1n488S3+AQPL/43iVPn7Jy7uoWNu/cgajW4JJm4pSsQnC7COrQrkFBOFEVEuwPPhRjGff55kQE35H5+jRg1gnFj/0OGOzUv6RqARqPFbDZjtdnIUBKwkk0dc5O87Q7JzuGc3egsGsaPHw/AuHHjKFu2LJ9/9SUnp8/KHcUWQCdqaN+mDW+8/jqNGjVizbp1bFuzjjJPj0QQQLI70JqMeVPNHZevkLV9J8HNm+DOyERJSWXM58/c0s/l7bffZtfePZz7/ieCunTCv24dRJ0ORVHIuXCRpN82oklM4odFi0psluShQ4fQBwViDL2x3NLhcORW7BAMXH/eNYSFo/H2wpkQfy3o1iCiwWa14WX2AuH6jCs5L+dAsa59V2VmZOLxSAQ0bobQsDmupCQkuw1Rr0cfFoZH9JB45hypqan/0h0oeWrQfRdyOp3Ex8cDEBERka+g/XWzZ89G49FRxrfgh3+84xIaP1/MFSshISEpHjSCBgEBBQVJkZCR8KtXG9f2nfzyyy88/fTTKIrCrFmz+HbKFKIvXkRSZAQEjDodjz/6KBPeeONvfUn8HbIsM/ypp9i0dy+RI4biU/lP520LrowMLs+ZT88+fdi0YUOhU+QrVqxIrTq1uHIwOl9taodkZ1faRhRJob7QGh/BD6fiwMfHhwA5mNPZhxGtGirK1fC33JhOZJOsHM3Zi84i8uZbb/6r1/9vefzxx2l+tDmLFy9m+c/LSU1JxZ1ow8uvLBrJQ6kIkWdHBRfoQRf1eiSPm6H9vVm6PqPIaXO3Ijk5mR7dnyDpQirNzZ3w1VnytimKQpzzMquWrkGWZWbNmqWOeP8/u3fvJjE+kRBjOE7JyRnPESC3VFgpczn8dUGcOX6Ml156iR9//LGEW3v3MhmNZGV5brpfRmbRidB0Oh2TJ0/mhRdeYP78+Zw4cRyPItO7XzUGDhz4jz4vH3roIcqUrciEDy9SqbyeJg2MoCgI4o2A+7sZGSxZnU3/niEYDBomvBzJtr1n+fHHqWrQrVLdRv/973+JvniBDTPnYKpVk4AmDdFbLLgyM0mL2o/t2Ak6tmnN+++/j8FgICIigi8+/4R+o04iilLuaJ6ipWbNesyaPaHYdbrFkWUZNEXPzrlOuFauSpIkIDcojrsaRzlTFap41eZ49j7e+ugMU2Zl8OhDZrzMGk6dd/LHditWu4RQqiwRT3TDUr0qgiCQdTWO2M2biV27HmdyCmX798n33eyx27myYDERISE3ndmTnZ1NmzZtaNSsIQf2bKeGoSGhhlJ5szXNPmauui9yxnkEjVZDsjOeDHcqOVIGKSQQGBLAvAWLqX5tjTlA79696dWrF/v37+fixYtotVrq1q1LxYo3npF/nDqVx7p25fyUH/Bu3AgqVUDxeCNbrVgPHSVn336MwUFIOTZS9v7Cy889V+So9P8XGBjIutVrePGll/j151Ukr/kFnb8FyW5HzsyiauVKfLVsWaGJ2O4Uj8eDoM0f9jkcDgTEfAlaNWhAEJGs1huvCVrckhOX241er0Pn64MAuK5cRXeTa3LEXALAlpKCyS83s78syujCgjEgcj0qFx1uXMkpXLp06TZc7d1JDbrvIpcvX+bHH39k/twFZGVmAgKWAAtDhg5m5MiR+QLLbVu2EySEFVo6yCW70Pr65k79UQRkRcKluPLGbDUaDT5mH8xeZtK9vXIzLyoK48eP56fZszDVrkXkMyMxl4pAdrpIP3KUldu3sWXbNlb+/PMdKd20bds2/ti6lfBhg/IH3NfoLRbKPTWM6M+/YurUqbz33nsF9hEEgVFPj+KFMWOJd1wh3Ji7PibadgqX5KSh0BaDYMKtuBCE3IfqqkJtEBROZR8hznaR0kp5DFojVjmbNJIICQth3oIF1KhR41+/B/+WwMBAnn32WZ599lkcDgdlypcnsHlTUn5dS5+nfBE1AvkymVwjaHXIspOBvQL4aloULper0A6hm/nhhx+IvRBHC++HC5QwEwSBUsayCAisWbmW3bt3FzrF7kGlKAqfTPoEX08ATfzbAeBSnMiKjF40oBFyH8ZEu8jPS5czfvx4KleuXPQBH2Ct2zzEyl/W8NyI8GI7dlasT6d6jZoEFjJl8LoyZcrwxhtv3PY2bt68jUYN69F1cCyPdTTTp6s3gQEazl90M2dJFnsPOmjSwJfvJ+WOwAuCwNC+Abz23q8kJSURElJ4AkmVSvXXGAwGFsybz7Rp0/hpxgwuTJuFgoKAQIVy5Rg5cSKjRo3KG8Xs0aMH3bt3JyoqirNnzyIIAjVq1KB+/fr/qCO5cqVKbIiKQvZIiNqig++c6AtoBTFf0ClLudUvREGkjm9TyrorE5NwjvkL4pEVN1rFTLbTgal+PSxdO+MTcmPGm29kBJqePUiICCd55Vo0BgNhD7VH9rjJOHaCzN1RWASBhUuW5MuUfZ2iKGzcuJHp06bzx8Y/kCUFBDD5GjmYuQMvpze+BKIoMpliKrJJotcTPfEP8Gfblu1YHVmUiizFq4Nepm/fvvgVkilcEAQaN25M48aNC70noaGh/LJuHR999BGLf15GxqbNuFxuZEkCWUar00JyKmFmb16aPJmhQ4f+pZ9VcHAwC+bP58KFC6xYsYKUlBTMZjPt27enZcuWJT6AUL58eZzJybhzctB5ewPXfyf+3xKIHCtSVhb2S5dQJAlBoylQESU7+gK4PdgOHSGjXVsMPt4Y9LlruwVRzOsYdqak4jofjVYQsB48gqZd/nXnAuQucRC05Bw7huB0c/L4SRwOx30501FQrmdjuUdlZWXlFWf/q9lh7yZRUVH07/sk2Sk5hItlCdKHAQrJrnji5csEhFlYsmxJ3pTR+nXrI13UUd2nXoFjncg+yAVTHJHPv4AiKrgUF95eXuj0OjQaDTqtDgSQ3W7O/O9DPn77Hfz8/Bjz4lhCevYgsEnBtcoeu52Yn2YSjsCBffv+VrD1VwwdNoy1+/dR6aXni/2gil3/K5ojxzl17FihI1GyLDN69GiWLfyZSLECpU0V2ZH6G8FyJOWFakiKB0WQsVgsGP70B57tzmRb5nosgRbCwkIJCw+jX/9+9OzZs9AvlHtVUlIS1WrVIrh/H2Lnz+bL/wXzWBffa6mR81M8bhSHk8Mn9Iwal8TRY2cICiq6HmlhnE4ntWvURp/sQ02fhkXupygKO7J/5fH+Xfjxp+JHa2VZZtu2bcyePZtjR44BUKtOLYYNG0abNm3umVrqt+LgwYN0bN+JuroWNxKpFUJSJLZmreW5/4zh3XffvXMNvIds376dfn278c1HYTzxaOEB9f7D2fQcHsNHH3/L4MGD73ALc2VlZTF27FgWL5qLKCqIIsgyBAZoGdrPwoujAtFoNJhMJvR6PcdO2ujy5EV+/W2bWttepfoXyLLMiRMnyMzMxM/Pjxo1auTLEP1vOnXqFC3btiWob08C6tcrdB9FUbg4YzbVDEa2btqc9wzVvVt3jmw6SVO/ggnHAM7kHOUUZ4l44Vkw6QgOCSkwq83pcBL903TsZ85i8vJGAExGA7269+CVV17JF+T/uT1vvvkmU7/7AS/Zl1La8pg0XjhkG3HuGDLkFMpXLk+piFK5I9T16jJkyJB/dWZlZmYmO3bsICcnh8zMTLRaLYIgUL58eVq3bn3Hfp53UlpaGjXr1sHQqgVhHTtcey0dj9ONTriRDC1t62ZytubWkNeWj8TSoiXGCuXxCG68NVqSd+8mdfM2BIcrt1RapfIE9uyOYDBcm24uYDSa0LvdXJ01l1IGIydPnEDR6wnq3wdzzRq5mfQVBcXtRnG7cV2JJW3hMkLc/mRqUvnve+8SERFBnTp18s1ouFvdaiyqjnTfBeLi4niy35NIKdDap0tuFslrgg3hVJJrsT9hK/379mfHrh0EBQVRpmwZjkYXnmwgzBDJ2cxTZMecQ18ud3TXarWCTcBoNOLt5YVWpyX9yFE0HolHHnmEwcOGYqxerdCAG0BrMhHZrzcXPv+atWvXFpg+dOTIEebMmcOJU6cAqFm9OkOHDv3bD337DhzAq1rVm5fvqlGdK9t2EhMTU+gfpiiKTJkyhUqVKvHDlB/ZnvQLDsmBRQjAjQutTouvjx96Q/7siz46P8oZqmAIFdh/cP/fuoZ7gbe3N6Ig4LZaEUQNqekeRFHMzYysKHmxd95PQRRJSfeAIPytzocLFy6QkpJKA0PxMwUEQSBICGf3rt3F7peens7gQYPZuX0XZsUbfyF3ZG/D2U2sWr6alq1bMGfunCKzjt5rzp07h+SRCPIqvkyVRtDgo1g4c+bMHWrZvadVq1Y80XMAr7w9j9Q0NwN7BeclQnO7ZdZuSGfCB/E0btyWfv36lVg7fX19mTVrFkeP7KNRrRQeamsiPFimSkUdep2AILhxu93YHXZ0Wj05Vg0g3FPVFVSqe4koitSuXbtEzl29enUe69yZNSvXYvD3L1CfWlEU4n/dgPt8NP+ZNj3fM9SQoUMYsWkkWe6MfMu6rrvqvoy5fg0w6jAajYUuIzMYDYS3bU3i1Ti+++orSpUqRe3atYudCfTdd9/x/TdTqKatR1nv/DOvypgqEuu4xInz++jVqxdvv/32X7wjf4+fnx+PPfbYHTnX3SIgIIARQ4fx9U8/YYoIx69GdYwGA1lOZ96sjZzTp8jauYuqhmqEGCI4duUAyQsXoRh1CDoNOF14HE6ChWDqW1qwL3sbaeejSZj6E14N62MoWxYkD6lnzmE7epzI4CAiK1TkdGws+vBwUpetwHb8BN4N6qOxWJBycrAeOIj1yDEMLpFsJRObw8Y7EyYiCiJavZZmLZoy4c0JtGxZfCWie4EadN8FZs2aRWZyNm18Hs0XcF+nF/U09GnNtrh1zJ8/nxdffJEBAwewddMzWD3ZeGnz18dzSDYkj5uMTZsJHTgQrcGMKAhIiozD7sDpcOCj05H6xxY+wb/mAAEAAElEQVQ6duiA1Wrl5OnThA8fUtis4jzG0BCM5cuxaMmSvKA7JyeHZ8aMYd1vvyH4+GCoUA6AqJUrmTF3Lo898ghTp0zB+9pUllslyTLCLa1byt3H4yl6baZWq+W1117jhRde4JtvvuF/E9/DR+dLgDEAXTEj9hpBi8tl+0vtvteYzWbat2vLloOHMVetwcp1Jxk6MDD3i1rIP+lIdnswGg0sXZ1I69btCp1ZcDPXf07Xp0EXRyNosLuKriHvdDrp368/B3cdpr6xFUH60HwZOVNciezdtpf+/fqzZu0aDIaiy1rcK/Kur7CpCP+PolDo8hNVLkEQ+PLLL/H29ua/n07n86kpNG9oQKsV2H/ERWIyPNK5O998892/PrPnVjRv0Y69O+cw8T9e+HjrMJtFxLzfh9yM6VnZLpatzsA/IOiO5d9QqVR31vfffUf6gAFs/2E6xqpVsDSoi9ZkwpGYRMbefSgpqXzw7n/p1q1bvvc9/vjjVKtRlUMnd9LYux1mTf6Oc6fiRG/xAYFiO9UNgYEgCNSsWfOmCTsdDgdffv4VEZSjrLnwpU6ljGXJ9mTyw5QfefHFF+/pWat3u4kTJ3Ll6lVWzllAWpXK+DVsgEv24MjMwXbkOI5z0ZTWlKaqdx1EQaSdvgux9hgO23ZRuXw5zp46R0PvNkQay7E7czM5ZonwroNwxl4l+9ARcnZHAaC3+OFVKoL0pGS279xJUI+u6CtUxHnsNJkH95M8f1Fem3T+/hjLlcN25iy+koXyYg0qWaqgCJDojOXotpP02teLaTOn8fjjj5fUrbst1KC7hMmyzJxZcwgVIgsNuGVFJtmVm0RC49bzxedf8vTTT9O9e3fef+8DDsfsorFPO/RibkCR7cngYOYuguUwMq+mkjR/IQHtO2AuXwGtqEUjQea5U8Rv3kwZH18++uAD5s+fj9VmJ1sUsSUmIooiZrMJk8mMRpP/oV0XEkx8QgIAbrebIUOH8see3YT164WlTu28jJaKLJNx9Bhrl69i6PBhLFqw8C+NvFSpVIm9F2Nuul/OxRiMBv0t1TM0m80MGTKEyZMm48JebMANkCVlUKPs/f/gOmrESDYMHIhPy+ac3OFh5ZoMnuiWP/Oo7HKBLLNpu5ODR93MmPn3ShJFRESg1WkLZCwtTKaURpUK5Yvcvnz5cvbuiqKpuUOBYwmCQLAhjAZiK/bu3sTy5ct58skn/1ab7ya1atVCq9OS5IzLy1FQGI/sIVtMp1btWnewdbfHyZMnWbx4MVeuXMmrnnCz8oB/l06nY9KkSTz33HPMmzeP48ePYnNJPNq1CoMGDbqrprU99NBDzJk9lc077QzomX89owAY9CI5Ng9L12RRo1ZDdaRbpbpP+fj48POyZSxcuJBpM2dycuFSFECn1dK9SxdGjRxJ8+bNC7zPYDCwaMkiej7Ri13nfyNYKUWYMRIRDenuFJyyDY3Vip/Fkru+uQju7GwEQcDHx6fIfa5bv349KUkptPIqvsRqOVNlLmed5eeff2b48OE3Pa7q79HpdMyYPp1HFi/mx2nTOLpoCU6XC4fdjlExUMtUm0peNREEAUmRiHdc5qzrKPUb1cdkMpGuzaa0qTxprmSSpASCu/bFq0pVvKtUJaBNOyS7FRduvAMD8PXz5fSX3+K8dJny9euSlZ2DT726+NSthzstDdlhR9Qb0ARacGSn4bwQQ6AcSpg5AlHMHZiJMJYhzBDJ4azdjB41mn0H9hEREVHCd/HvU4PuEhATE8PcuXM5eOAgToeTS5cvUc1QP7fW7p+mAsXaYzhhP4ING6K3F4oB0pITqV67NmOffZaFixbQ64le7Iz/jUhNBcKNZTibcwKNrKWqUA+rlE1M3BmSFyxE9PVB4+WFlJ2NnGNFkSW69uvPf8aP5/c//sgtsaDIiEYDiiSTk2PFarXhb7Gg/1NpCsnhwGTKXfu8evVqft+0Cf/2bZDdHmyXr2AuWya3nI0o4l+vLlpvbzb8NJO1a9fyxBNP3PI9Gjp4MFufeQZ7QiKmsMKn0soeiYw9UfR9vCsWi+WWjhseHs5DHR9i1/ooIo0Vipy+bvVkk04Sgwa/e8ttvld16tSJYQMHMn3ePITACCa8d5XYOBdP9g0k0CIiu1zEx9r49Pts1m6wUrlyLWJjY/PWs/0VgYGBPPp4Fzb8vIkySm5teUmRyPFkIisyRo0Zk8aM1ZNDGkkMHvJOkceaPm0GAYQUG7xbdIEEEMJPP/50XwTdNWrUoGnzJhzbdjpfttf/77L9PIJeYdCgQXe4hX9fWloazz33DFu3bCQ0GGpU0ZLhgHfXLeSD99/h9TcmMnLkyH8lGU2ZMmWYMGHCbT/u7XTw4EGMBi1vfpiKIAj0ftwHne7GvTh41MHzbySi1YpcijmH0+m8L2Z3qFSqggwGA8OGDWPo0KEkJSVht9sJDAy8aSBcrlw5/ti0kZkzZzJj2gyOxe0FFIxmEw2q1+XU+QvoNcXPkErff5AqFStSvnzRneLXnTt3DqNoLjAj8/8zakx4ib6cP3/+pscsKR6Ph40bN3L48GEkSaJixYp07dr1nsvzo9FoGDBgAE8++SRXr14lKyuLPXv2MHnSZC7EnSIpOxYNWuzkIGklHu7aiffee49mTZpTVV8PgEv282gC/DFXujF7QdBo0Hr7guLG6XIhiCI+TRuRc+EigtWGVqvF7XGhE/To/7QcwaU40fr4oTGacDmdmM1eKIpCujsFh2xDI2ip5lWPXdm/M2/ePF599dU7fctuGzWR2h3kdruZMGECM6fPBJeInxKIgECiLRaX4CTEEEEjS2v0ooEY21kO2/djrFUdS7MWGMMjkBQPjswUOHOOrN17eGrgIF566SW+/PJLli5ehjXbSo4thzJUoaKhGl5e3uj1etLdKSQ4r+bV644wliXGdo547SUEby/C+/fm4twFeDVrgv+15AqKoiDZ7QiSRGBAIFqdFsnp5NwHk3j9hbG0bduWHj17kpKRgfinoNwUHk54x/b417tRx/nCD9NpFBLK+rVrb/leOZ1OWrVpQ3RmBuVGDUf//4Jq2SNxedESOHee39f/8pfWjm/dupUnuj1BmLsclU210Om0aDQ3+p9cspN92VuxlPVh3/6ovzWN+l4jSRKTJk3i26lTSU1LQ4sLk1GhZhUdickSV2I96HQC1SubMZmNRF90otF6M2z4aCZMmIBWe+v9d/v27eOxzo9hcYZgEE1ccl3AiTO3VqasEKQJxqnYCK8Yyt6oPZjN5gLHcLlclAovRSWlDmVMBRO3/NllezTnhaNcjbt6XwQh27Zto/cTvfF3hVLbp0m+qfrXS66dcO5j5JgRTJ48uQRbemuOHTvGwoULmT9/NkZdOu+/EUHXzsHodbnXlZjk4tvp8cxclM2EN9/n+eefL+EWl4z27VrSsEYMbo/C0tUphAZr6NjGhF4ncOCok6MnnVSrZObVsZE89WIsa9dtokGDBiXdbJVKdZfyeDwkJCTg8XgICQkhPj6eJi1a4NWmJRGdHy70PTkXYrgybQafvv8Bo0aNuuk5Jk+ezKfvfUE7n5tPC96Z/RvDxg7m/fff/8vX8m9btmwZ7773HlfiYtH4+iJqtbjS0vH39eW50aMZN27cPZ98zeVysXbtWnbu3InT6SQ8PJy+fftSuXJlYmJiaFivIfW0LQkyhLEt4zcc9UoT8mjXAseRFA+S4CE0LJS0uHiiP55M5adH4FOtGmnpaUgeKbf2t6ABFNyyC1ESiP3sS2rI1dDp9EQ7z2IlB0UQQAGtImKUDZSqHMqxE8fu/M25CTWR2l1GURRefvllFsxeSCVtLcr4VkQj5N7+ZHcSKZ5ELjpPsjttE/X8mnHEdgDv5k0I7Phw3siOjIQxKIigalVJi4xg5vz5dOjQgS+//JJ3332X7du3M2jAYEppIwkw3+hFCtAHE6APztceraglx2Gn0rDB+FWvRmCjhqRE7cenUUO0Fj8EQUBrMuGxWsmxWrFY/EjctBWdrBAeHk73Xr3I9vEmqEsnvGrWRNCIOGIukbVrDxfmLqBUWjphHdoB4Fu3NlGr1+F2u295yqPBYGDp4sX06NWLC599jbleHbyqVsEtS9gvXyF7/0F0Njs/fv/9Xwq4jx07xowZM3B73Jy1HyPRHku4UBZ/fRBGs4EUKZFY6QKWMF8WL1n0QATckNvzOWHCBJ5//nl+/vlnjh49ypkzZ7hw4QJJKTGMHhLEsyNKUaFsbgCcnOJm7tIkvvrxM+LiYvn++ym3nCW8cePGvP3u27zyn/FgMuLTvD5+1Wsg6vQ4EuJIO3AA99VMnukxqtCAG3K/HBTlWj3Jm12boEVRct9zPwTdbdq0YepPU3luzPNsyVpDmFgGH40vLsVFgnQFu5hD3wF9+PDDD0u6qcWKi4tj9NOj2bVjF067Cx8/B/O+jqBiOYX4hGR27FH4eV06sXEu9HqBCmUE3vvf23Tv3v2WlpPcbxwOOxY/LRNeLs2owWHMWZLEwaM5SB6FMpFmZn5Tjg6t/Lh81QkoOByOkm6ySqW6i2m1WiIjI/P+v2LFirwzYQIT338fyWYjpF1bDAG5S80kp5O0/QdJ+fV3OrRsxdChQ2/pHLVq1cKl2Ml0p+On8y9yP6snhxw5i1q17r4lUdOnT+c/b7yBvkY1yvbqhvla+V5XegbJO3fz/uTJXL58mW+++abEy4L9E3q9np49exZaZ93Pzw9BFLHJN2p3F5VaRkFGuJaET6fVgqzgyshEo9UQGBiI3WbHZrPhkVx573FEXwCXmzRNCgmuRLzq1SasXgN0QcHIdjs5p06QuXcvZ6LPc/nyZcqUKXNbr/1OUUe675Bdu3bxeJeuVNc0INKUf0qOzWYlKysbFw6OKDux6INI87JT+oWxiNeCVAUZl+LE188Pszk3ELwwdRpNwiNYu3o1kFsCoWL5SlSjPqVM5Yptz+bUtWQFi9R9ewKCRsSdnc2Zr75DQiGgRzeM5cshCAKy04UnIwP5+Emyd+7m5eee48fp05EqlMf4UDs03l55bYTczoWMTVvI2rKNKs+OxqdiBdIOHSF16XJizp+/pTVAf5aamsrkyZP5+rvvcLpcgICoCJgUA+gU/AMtTJo8if79+9/0WNu2bWNA/4HIWVBaVwkRkRjbOdLcSchKbu1BXz8fhj41lBdffJGyZcve5Ij3t+PHj/Nwp7aMf9aPF0cXvoZm3YY0Rv8nlu+nzKZHjx63dFxJkmjf8SGOXL5M4JN9kU3GvA9vQRQxGo1k79yFdfsuFs2dyyOPPFLgGIqiULVyVfRJxZceAziRfQBncBZnzp25r8qHXbx4kdmzZ7NowSJSU1PR6XQ81Okhhg8fTvv27e/qL//ExEQeebgzCdGJVNLX5rRzL0MG6HhzbCinztt5dkIsKeke2rUwU6eGAZtdYd1GK2ejXdSt14zNm7c8cGuWn+jRFW/9PmZ/W3yeifUb03h6XAI7dx24pemfKpVKdZ2iKMyYMYP3PvqItMxMDBHhoNHgTkpC55Ho27s3kz/5pMgO8f/P7XZTv259nJcVGvgVnX36WNY+HIFZnDh1/K4a7Lhw4QJNWrTA2LQRpbo+Vuj3atqhIyQuXsbsn3665eege1GvXr3Z9/tBmvt25GhWFDHmREo/98L/S3qs4FSceHmZ8fH1ISVqPzGz5+Ffrw4VRg6/cf+U3JxWkiyREp9IyrxFaK+k49C6Cer1BN7VCla4cWSlkTh/Hs2qVmPzxo131TOOOtJ9l5k1cxZG2Uwpr3IFtplMJmw2G4LHRABhJMpx+NZpnhfMysi4ldzyVtfXUwNYmjRi99LlJCYmEhoaiq+vL9WqVSXh6JWbBt1ZShZeVRsjXFu7o/PxofKzo7kwaw5JM+egCw5CFxaG7HBiP30GP7OJ9995h5ycHGyKTOW+vUjJzESRJPjTw68gCFg6tMN+8jRJ23bgU7EC9vh4fH18cLvdTJs2jVPXyopVq1aNPn36FLse22q1smrFKvxkCxW9auCrtWDUmNGJOuySjTPpR3jumdxa3sWV9UlKSmLo4KHoskw09G2dNyW3jLkiVk82Vk82MfZzZAkpDBw48IEPuAFmzpxJeIjCcyOKrgf9WKcAWjROZdbMabf8ZbN582aOHD9BmWdG4lWuLLIkI0kSCOTVyvTt8ggXLl/lq2++KTToFgSBwUMH89Wkb6gq10ErFh6AeWQPCfIVXhz2wn0VcAOUL1+ed999l3fffRePx4NGo7mrvoQKEx8fz+LFi5kxYwbnT0VTxVwHvahHEh083jGQK3EuRo6PpUwpDT/PDKdcaR2iRkQA3no5kDlLMpjw4T5effVVvvjii5K+nDuqT98nGf+frVyJdVK6VNEzNuYsSaVhw6ZqwK1SPeAkSWLTpk3MmTOHUydyn7vqN6jP0GFDadmyZaHfF4IgMGLECPr378+KFSs4ePAgHo+HsmXL0q9fv3wj47dCp9Px1jtv8ezo5ziVfTgvK/Z1siJzwXaaeOESH73+4V0VcAPMnj0bj15PRJdHivx+Dahfl4yo/fw0ffp9HXSPHDmCzRs3cdkeTVlTJS5knifn1El8al0vn6fgUdwgKJjMJmSPRPrO3bRs1pRjJ09xeeFSIh7rjM7PFwQQNSKu1FRSf16O8/JVRI0ZrwZ1Cw24QUH0MRPaoxuHFy9j165d92QJsfvrKfQutumPTYSIpYr4kBPx9w9Ao9XgTzCS4kH09cGjuHEpTtyKE61Oi79/QL736wP8kVHIyMi4dhyBEaNGkEoime70ItuS4kpEwlMge7chwJ9qL4+lyjNP41umDBqbHa3bjVaRmTNjJi+88AKLli7Fq25tNEYDZpMJxe3OrU2U73oEvBs3JPPEKdzZ2WTvO0i50qWpVa8er7z5JnP+2MicPzYy7q03qVGnNh9++GFu0FWI9997n7TYTJr6tqeUqSw+Oj901wIsk8ZMXd9mBHnCeW38a2RnZxd5zQsWLCAzNYt6Ps0LlKvy0voQYoygsaUNereJH3/4scjjPEh++3U1vbv6otUWH8j16+FPVNRuUlNTb+m48xcsQBceirls7vQgUSOi0+vQ6XR5v9+CIBDYohl7oqKKTKwydOhQzH5GDmXvQlIK/v5IisSh7J2Y/Iy3PBXuXnW9s+Ju5XQ6eeWVl2nSuC7ffj2REEs0zZoIxGkOsT/nDxQkLH4afpifitEI878Po3xpHaDkfb5otQJ9uvny7vhAFi+andd596Do0aMHgYERvDDhEjnWwj8vp81LYMdeJ6OeHnOHW6dSqe4mSUlJPNr5Ufr26sfmldtxngfHeVi3+Fe6PdaN3r37kJWVVeT7vby8GDRoEJ9//jlff/0148aN+8sB93X9+/fn/Y/eI057kW3Z6ziVfZhLtvOcyTnK9uz1xAineW3Cq4wePfrvXu6/5udVq/CuVzvfjM7CWJo0ZM/evSQmJt6hlt15nTt3ZsTTIzjtPsQV+wVCxTBS16/HFn0+b3BQQsLXxxdBkrm8YBG6jAw+/+xzpk2dijb6Auc+/pQL02dzafEyoqf8xIXPvsI3Mxu9KOIUXPjWL3zmoqRIKCgE1qiOJjCAJUuW3OGrvz3UoPsOcTic6IRiakJrNAQFBmLxtgACHms2siih02vxs1gIDAwoUL7LnZmFgJBvKkP//v1p0KQBB2zbSHYm8OfVA4qikOC4yhH7bkqFh+O+dIX/v7pAEAR8Klek3IB+VHn2aQIaNcDH24eGDXP/EJKSkjCGhABgujbNXXI4CgTeuuAgFEXm0uJluNPT2X/sGNqmjaj8xn+o+MIYKr4whspvvIquWRMmffEF48aNK9CWxMREVq9cTRldZQyikcIIgkBV77pkpmWxbNmyIu/v/LnzCSYir7RaUccqpa3AqpWriw3gHxRZWdmEhtx8Cm9YiB6Qb/meRV+8iKF05E2DRHPZ0nhkmUuXLhW6vUyZMsyaOwunj5UdWb8SbT1FlieDbE8G0dbT7Mj6FaePldlzZ92z63/uBx6PhxEjhrPi5xm8M86PnWvLM/ubMJZMLcvW5eXo19OIR5Y4dsbKL5uzGdrXF39L/uRw10mSwhOP+hEanDsC8SAxm83MnDWfM9FedO53hpkLE0lKdpGd42HH3ixGvhTNu5NTGPPsuAL1eVUq1YPDarXSp3dfDu0+QkNDG1r6PkwNnwbU9GlAK5/O1NE1Z+tv2xg0cBBut/uOtOnZZ59l+85tDB0zGGtgGtHicbL8k+n3VB82btrA66+/fld2HGdmZhRI5FsYvcWSbxDsfiQIApMmTeK/H7yLIziLTCEF0e4kbsE8rs6eTvbBgyjRF0jb8AfnPvwE8eIlZk+fQcOGDenZsycnjh7ly48/pl3pMtTR6OhcoyYzf/iB0ydOUL1mdSRBRhMcwJ8XiyvXRs89uDGbzegNerRhocTGxZXcjfgH1Onld0h4eBhZZzKK30kQcGrsGHU6lPPRBPfphagtOlFUxv4DNKhXj7CwsLzXTCYTS5YuZvCgwezeuQOz0xsLwSgopCtJODV2Oj6au+7zySFDyDkfjU/lwtcIyh6JjN17ebxz57xzGE1GPHY7kDu6ZvHzIyMjE49kQ9DrEDQaBASk7BxkpxP36bMIWi1hvXoQ2CR/nUadjzfhj3RCHxjArPnz6dq1Kw899FDe9l27dmG3OYj0LVfsbTNpzPgq/mzbtq3I+o5xcXFE6G5ec9tP589Fl4uUlJS/vP78fmOx+BEbd/NkTFfjnIB4y+XDdDodsttz0/0UtwdBoNi1ux06dGDDH7/z9ddfs2rFKqLtJwAwmgz06N+dF154gZo1a95Su1T/jqVLl7J50zrmTylDm+Z+2Gw2MjNBQCAkUMtHr4ezbY+V2UvTcbpkOrfPXSt4+ryLeT9ncfyUC1mB8qW1dHnIi7YtQ+jU1sz+fbtK+MruvIYNG7Jm7W988skk3p28hrc/TiT3AUVLlSo1+OLLsfTt27ekm6lSqUrQokWLOHrwKM28OuKrteTbJggCoYZSaAUd27ZsZf369XTv3v2OtKt69ep88sknfPLJJwVK5N6tfH19ySxmRsB17sxMBIS/XEb1XiOKImPHjuXpp59m3bp1nDt3jujoaE6fPcu5vfuwejwEBwczZswYhgwZki/pqa+vL0899RRPPfVUgeOOf3U8Q0aNwuW0IpkUBHJ/NxRkEAS8vLzw8c59JpcdDkzGwgfi7nZq0H2HPDnwSd5/50PcsgudWPiIt6IoxHou8sgjD7Nt104S/9hM+CMdC9038+QpHOeiGfXttwU+uAIDA1m7bi07duxg9uzZnDx+CkEQaFu/K8OGDaNJkyYoikLzJk3Ys2gppUcMwxyRf92u7PFwZfEytBkZjH3hhbzXO7bvwPLt21A6dsitx42AKIpIkgfZ5uHa3wk5hw5j0hto3qwp+69eJaBx0cmuAho2IH3nbmbOmpUv6Lbb7SiKgla4+WirBh02q63I7QaDEY/t5j26HtkNgnBfZLn+px7v2otlq6fy6gsyOl3Rk2IWrkinVet2+PsXnZn0z5o1bszBefOQPVLxnUrHjmMyGG6azbRGjRpMnTqVDz74gAsXLgBQoUIFAgOLrt+tujMURWHmjJ/o0MpEm+a5DyPX19Zff+gSBIGXRgYz/v04TGYRUQMvvZ3E8vU5BAdqaNfCjKiBqIMOlq/PoUl9O5UqGHG7XcWd+r5VpUoVpk2bTkJCAocOHcLtdhMZGUn9+vXviYdYlUr171EUhWk/TiOIsAIB958F6kPwcwQy7adpdyzo/rO7/bNKkiS2bNmCRoHkXXsxNG6E3mTEbDKj0xd8Jk3fd5DGDRvmGwS7nxmNRnr16pXvNUVRkCTpL5WQva5du3ZYvL3RXI3Fq35dPB4JAdDqdJhNJsRrM33d2Tk4L16i5bCCgfu9QA2675CBAwfy5edfcjhjNw18WxVYV6woCqdzjuDQ2nj11VdpFxXFxPffx52VRWj7thiCcgMIj9VKyp4o0v7YQs/HH6d3796Fnk8URdq0aUObNm0K3S4IAvPmzKFPv37s/+Z7zNWr4Vu7Fhq9HtvVq2TtO4jB7Wba1B/yppYDDBs6lGUrV5B24CDmmjXIzMwCjQatlxcIIoos4bxyFce5aLSKwrYdOwnr2b3YD1hBEPBt2IB1a9Yzfvx4ftnwO9YcK3q9HrfkItWVRLCh6A8yRVFwCFZCw0KL3KdNu9Zs+HkzlZWaxbYlznmJitUqPDAfnMUZOnQos2f9yORvY3njpcKngy9ZlcL+wy6mTR/xl4475aefSN0bRXDL5oXu47HbydgdxcAeTxAQEJBvm8PhQKvVFvhgDwwMVAPtu0xiYiLHjx/h+ck3/jb1Bn1uR53iQUvuw0ufx/34YV4KZy86efa1JKJj3HzydhC9u3qj0wkoCsiywrY9TsZNTGbvwRzaP9S6pC7rrhAWFkaXLl1KuhkqleoukpGRwblz56hmKL6qB0CothT7ovbdM6POd0piYiKDBg5mf9R+9B4jkieL9K078WvTErvNjsFoxHKttC5AxvGTOKOjGfHduBJueckSBOFvBdyQ+33W9bHHWLFtK0GNGqItYqZpwh+b8DYYbqli0d1IXdN9h4SGhjJz9kwc3jnsytrAZXs0LtmJW3aT6IwlKmsLsWI0H3z0Ps2bN+fFF1/ki08+wXTxEtGTvyD686+J/uo7zn34CY5tO3h+xAh++vFHNJqb1ykuSmBgIOvWrOGrSZ9QCYHUpT+TOG8hyr6DjO7Xj22bNtG1a/7C9y1atGDIkwNIWLaSqxs2oSgyWrMJQasFFGwnT5O8YDE+lSpQqk9PHE4nHq0Wl8uFLBdenU5RFLLPnCUrO5ufli0lo1wZaNaYrNKlkEwGtmdtINZR+LpegDR3MnbRWmQHBMDw4cOxiznEO68UuU+WO50U4hkxasR9l+n676hatSrvTPyA72dl8fzrFzhx+sZMgktXHPx38mXGTYxnwMCRf+nhv0qVKjw1ZAgp634lZe8+FFnOt92Zls6l6bPxF0VeeeUVIDfr9aRJk6hRuzaRZcsSVqoUnR55hMWLF+N0Om/PBT9gFEXhwIEDvP766wwdOpTRo0cze/ZscnJybts5co8lExx4Y2RAFERMZhOSIuWV6hMEgaF9AnC74cARJ1+8F0y/Hj5oNAK5vx4CGo2GDq28mPNNGB5JJigo5La1U6VSqe4HLpcLRQENN3821KBBkqQiE9k+iLKzs+ndqzdH9hyloaENbS2PUcfcEOu2XWT8sgElLQunw0FGRgburGzif99I/PxF9OnxRLHPoKqbe3PCBAIEkZgfp2OPi8+3zWOzcXX1Wqx79vG/d94pturR3Uyt032HHTx4kE8mfcLGDRtxuzyggFanoV6D+oz7zys8+uij+fZ3OBysXr2aI0eOIEkS5cuXp3fv3rd9RE9RFDIzM3G5XPj7+xe7jtbj8dCpUye27dqFxuKHsXw5EEVcV64iZWdjqVGD8N49sLvdRE98H0vH9vg2b4ogCBiNJsxmEy6XC6fDiawoZO3eQ+rGzVjat6XiY48i6m58WWSmpBK7fBWOwydo6dOeEEP+etFOycHenM1UqluOrdu3FhksK4rCyJEjWb54JdV0dYk0VcgrW6EoCsmueI479lGrUU3WrluDl5fXP7+p94nFixfzyaT3iY+/TFiIFq1GIDbBhY9PEM+MGcvYsWP/cieF2+1m/PjxzF6wAMHXB3PN6mgMRuyxsTjOnqdUaCgL582jXr167Nixg0FDh5LhsONVry5eZUsjezxkHTuJ4/x5mjVsxKIFCwqMiKuKFh8fz+inR7B//25KR4hUraQhO0dh32EnZnMAE9/9gIEDB/7j86SlpVGrZmU++28Q/XoE572uKDJpaem4XW40ghYNGj76NonvZmZQp5aG3xaXwttLRBBEBOHGVERJUkjPlHjtvRROx1Ri584otYNMpVKprnG73VSqUBlLVijVvOsWu+/RrCh0ZWWOHj96h1p39/v+++9589W3aO7VCR/tjfXZMbZznLIfxSG60Ab4I4ugsVnxM3vx9FNP8eabbxb73Ky6NSdPnmTQkCFEx8SgKx2JLjgIye7Aee4cJq2O/73zDk8//XRJN7OAW41F1aC7hMTExHD8+HEkSaJixYrUrFn8tOe7TaOmTbliNmIIDMB2NRZFljGGBBPUtAmKnx9Z2bnTzpNmz8eTlUWpV8aCoiC7XCDLuWvBEZEdTmK/n4JXg3pY2rUhICAAveHGmndFgfS0NK7OnodwLpbWfp3w0Vlwyy6uOi5yxXOeoNIBrFm3hgoVKhTbZpfLxSsvv8LCBYvQeLRYlGBERDKFVJyinZatWzBr9ix1inIhPB4PGzdu5NixY3m/s48//vg/rql5+PBhZs2axbZdO3E4nJQtXZoB/fvTs2dPvLy8OHv2LA898giOkCDKDh6A9v+dz3rlKldmzqF5rdqsXb36H838eFCkpKTQ9fHOeJwX+PDNCNq38kMUcz974hKcfDE1joUrbHz08Ze3pdRa3769sGVuY/XcqvleV5TcjPd2ux2rTaJDz1iSUyVef8GfkYN80etFzCYRnTZ3ernDKWN3yAiChgPHNIx8OZF9+49RqlSpf9xGlUqlul+88cYbTP9mJm18HyuwlPE6t+xiS/Za3vzvG4wb92BPi75OlmUaN2pC1lk7pY0VsUnZCIKIRReIr9aCpEjEOy6T4U7jivM81etV4/cNv9/3ydPuNLfbze+//86SpUuJT0jAy2ymfbt2DBgwgKCgoJJuXqHUoFv1rypfuRJKk0aEdWiX73WXy0VaWhqCXo/GaCR7/0GSFy3Bv8sjWDq0Q5YkZLsDZBmDYCRr/35S/vidsDFPo/PxAQECAgLyJapQFEg6fIQr02dhEnSIiCCC2ctE9ye689Zbb/2l+pGnTp1i3rx5HDxwELfbQ9VqVRg8eDBNmza9pzo+HgQvvvgic9etpeIrL6IxFJ6AMPt8NFenzWL5okX5EvGpCvfGG2+wesVU1i+sROlSBRMGKorCWx9eZuEqiQMHjv3jTqjffvuN4cP688GEYIb2Kzgl3OOReO1/F5m71Iogirz/eiBD+gZis1lxulxcLx8iCiImkxmzl5moA1b6jLzCjp0HbtrZplKpVA+Sc+fO0bZ1O7xy/Kjn2yJvVt91HtnDgeztCIESe6J2ExpadD6cB0l8fDyVK1XBoIjIGidarYKigMct4CuEUsVUn0B97nfYeetJkr2vcPlq0UsfVQ+OW41F1URqqr/Fx8eHtELqMlutVhA1aK6l89d4eSGIIlmbt+JOScW3RTP04WFIVhv2hDjSd+9AFxaKwceCVtDiUpxkZWcTGHhjqrAgQEjd2qT6+fL0oME0btwYk8lE8+bNCQ4OLtCGm6levToffPDB37941R2RmZnJkuXL8W3TssiAG8C7YgV04aHMmj1bDbpvIjs7m2VL5/P0IEuhATfkTuUe92wEC1ecZvHixTz77LP/6JwPP/wwT414jjc//Ibjp6yMGBhKtcpmFEVh9/5spsxMZPNON99+9xPffP0Zp8/FYTAYMBgMuesNZQmB3AQt1zvFTpyxodXq79peb5VKpSoplStXZvrMaTw1bAQ7s38jUluBIH0YiqKQ5Ioj1nMRvUXLwgUL1ID7TyZPnowo2Oj6mC+De5WiTjUjHklh004r0xems//wBuop7Qg1lEIjaPB47kyNc9X9Qw26VX9Lt0cf47u5c5Ef64J4LVuhLMk4nS5E442H+ZxDh/EqWxZL08YkbtxEwtHjiGYziseDnGNF6wFNQGBeWTCNoMPtcuF2u/OtjxFEEUHUULlyZfr161dku6Kjo5kzZw5Re6PweCQqVqrAoEGDaNmypTqKfY+5cOECdoeD4KqVi91PEARMlStx9PjxO9Sye9f+/fuxWjPp+Vjx9zTAX0eHViY2b974j4NuQRB47733KFu2LN99+wULV0Tj5yPikRSsNqhUqTozZk6kc+fOJCQk8PWXE3l7nAeLnxaNRlNgyYAsK8xbmk7nLj3V2U13OafTycWLF/F4PERERKh5F1SqO6RLly788tt6vv3mW9asXss5+zEAjCYjfQb34vnnn6dq1ao3OcqD45dffmHRwhlM/E8gg3oGoBVyn2v1okDndj481NKbl96N47eN22irfYJMdxqlKqhLm1R/jRp0q/6W3LJPP5L4x5a8WuKSLAEKwrWHZHv0Beynz1Dmie4YatVErFABT0wcruQkbNHRKFlXqWCuwrn4i8huN6JOhwYRD+B25Q+67XHx4HZRvnz5Qtvjdrt58803mfHTDAS3BgtBiGg4secUSxYsoXGzJsyZO1vt1b0n3byzREDgHl8pc0dYrVZAIcD/5h/9/hYNCRcLzmb5OwRBYNSoUQwbNoyNGzdy4cIFRFGkbt26NG/ePK9DbMCAAXz/3Ze88MZFpn1REYMh/7RIRVF477MrRF+Cz76++5KpqHIlJiby448/snDBbDIyUgEFjUbPI527MmbMs/nKUKpUqn9HvXr1mDZ9GklJScTExCAIApUqVcLf37+km3bXmfbTDzSpr+epgaHYbXY0gjbfk4dOJ/Dha2Fs3X2Ri7YzJBHH88PeLanmqu5RatCt+lsqV67MOxPezK0lnp1NaPu2aK6NOsl2O9Yjx8jYuAnfKpUJbNqEbGsOgkaLV6XKeFWqjOxwIF1KoaypEucyTpFz4ji+9eqTG2AJKOQPoJJ27CIyPIL27dsXaIuiKLmZsKfPobK2NmV8K+UlD1EUhRRXIod2RdGzR09++e2XB2507PLly8yZM4ffN27EarMRGhJC39696d27N97e3iXdvCKVL18eo0FP9rnzmEtFFLuvPfoCLWvUvEMtu3flLscQOX/RQcO6xf/so2PcBIfc3nr1Op2u2PJyISEhTJs+h2FDn6RzvzMMHxDIw+0saDUCew5kM2NBClGHPHzw4Wc0btz4trZNdXtER0fTp3d3bNar9O/hS+cOZTAYRA4ezWHO4lX06L6GTz/7ptgZSyqV6vYJCQkhJEQtsViU+Ph4du/ezjcfhWA2m7Hb7XhkF1pRny/w9vPV8HgnLxYtP0pIeARPPvlkibVZdW9Sg27V3zZ27Fi8vb15/+OPid5/AH1YGDanE3dKCgICgY0aULpnd0StBo2ouRZIKyiygu3kKUqJQXhpfSilK0Pshg3ogoIwRpYCFDTijSmlKbv3Yjt4iP9+8CE6nY7Lly+TmJiIwWCgWrVqHDt2jLmz5lJNV58ypor52igIAsGGMBpr2rH72EamTZuWV/v5fqcoCl9//TXvffQRHq0WU41qaP3DuJqcwo7x43nvww+ZO2sWLVq0KOmmFspisdCrew8Wbvid4JbNEYsox5ETcwlXbCzDP5l8h1t472nUqBGRkeWYvyyp2KD7bLSdqEMOpky983VH27Rpw6rVv/L555/y1kfrmfBBwrUtWpo0acHceS+ra/fvUk6nk4ED+uJrjmfd3CqEhtzIxVC3phdD+obw+nsxjHvleSpUqKB2nKhUqhKXnJwMSFQsa0Sn0+UlxHLLTjSCBuFazXMZiTKlBdBKzFswT80povrL1Ozlqn/MZrOxatUqDh06xM6dOzl44gSVXnwOrzKl8/bxeCRSkpPRCjpsx0+RumIlbXw6EaAPxiN72Ju5hWQhBUP1Kpjr1CQoshSu1DTS9+7Dfekyz44aRevWrZny/RR27diFJMkIgkBIaDABgQFEH4uhre9jxa7bPpK5F20ZiUNHDj0Q9RS///57JkyciG+71oR1aIeov/EA7MrI5OriZegSk1i/ejV16xZfz7OknDp1io6dO+OOjKDswCcLJFSzxydwefosGlWpyq/r16slw27BlClT+PCDN5j2RSSd2loKbM+xSgx45jxxSWHsjTpYon8rcXFxnDhxAlmWqVChApUrF78WXVWyli5dyotjR7BlZWUqlS+8nKAkKTzc5wzlq3RlxoyZd7iFKpVKld/58+dp07oxC6ZG0qZ5bvkvl8uF1WrF6XTmltABEAS+npbFL1sCOHL0dAm2WHW3UUuGqUpEamoqnR55hJisTEoN6IdX6UgUSSLz5CkS9u7HEReHOy6BEE0YLf075Y1oS4rE8az9RLtOozMa0BsMaASBli1a8PTIkRw/fpxPPpqMj2ShtKESvlo/3LKbWMclzluPU1qsTIPgZohi0UFXijOBQ56d7InaTZUqVe7ULSkRGRkZ1KhTB7F+HSK7PV7oPrLbTfQ3U2hXvQY/L1t2h1t46zZv3szg4cPJkSV8GtTHXLY0ittD5vET2E+fpUGtWixZtEidPneLJEni6adH8ftvPzO4ty9D+4dQqbwRh0Nm9W9pTJmZQmKqL4uXrKBevXol3VzVPeSJHl3RC1Es/KH4zpHZi5N4++N0Dh85pY4WqVSqEiVJEi1bNKZJnQS+/KBCgW2S5AEEFEWk+aNnqFrjYfr164efnx8tW7bEeK1aj+rBpZYMU5WIwMBAVi5fTp9+/Tj59XeI/v7Y09ORXC70oaFogwIR9HqSr8bxW/oK6pobYdJ4cdlxnmQhjn59+vDuf98lMTGRpKQkfH19OXPmDJ98NJnyQnUq+dXId74AfTBXbNEIskBGegYBxdQU1oo6UHKnQN7vFi9eTI7TSeX27YrcR9TpCGzbms3LVnD+/HkqVap0x9r3V7Rv356dW7cya9Ys5syfT8quPSAIVK9WlZGffELfvn0xm80l3cx7hkaj4YcffuTrr2sxc8YPzFp8Hq1GwSMpCIKBdu0f5seZE6lWrVpJN1V1j4mJiWZAj5v/LTaq640sJ3HlyhU16FapVCVKo9EwZOhIJn30BiMGWqldwyvfNo1Gg6zIfDftMucvZHP+ym+s/2MjoiAQ5B/A8CFDeOWVV9TnENVNqUG36raRZZm1a9fy0/TpnI+ORpZkrFeuIppMBHTqSFDrFphNZpxOJxmXLpO+8Q92n9mCl85IpcqVeWn0+7Rr145PP/2UVStWYbc5ALDb7WjR4WPxK/S8Zq03DrcNl9uNy+VCry+8pnOWOx2NVvNAZDCPiopCX6Y0Op/ik2VZ6tQibvEy9u/ff9cG3QBly5Zl4sSJvPXWW2RnZ6PT6TCbzWoZuL9Jq9Xyyiuv8Pzzz7Nlyxbi4uIwGo00a9aMcuXKlXTzVPcorVaL0ynfdD+nK3efB2GZj0qluvsNHz6ctWtW0v/pKD58M5xHO/qj0+VWz0jLcPHNjzF8+WMamjKVKD90IHo/P1zpGaRG7efT779j244dLF+27K5OTqsqeWrQrbotPB4PL4wdy4KlS9GXK0tAr+7YETCnZ2A7fpyMzVvQ2e349uqBTu+NV80aBFcsz6UZswn3yOzdt4cjR47waOfHsKc6idRWJsxcCtkjc9l6kUTlKnvSNlPbrzEVzPlrS5Y2VeSE+wBllCrY7fZCg25FUbjivkCnrh0fiGnILrcb4U8PtIqi4HK5URQZQRDR63UIgoCg1SKIAi6XqwRbe+s0Gg0Wi6Wkm3Hf0Ov1PPzwwyXdDNV9on6Dpvy+ZTlvvKQU2yH226Z0fH39qVixYpH7qFQq1Z1iMplYtHgZzz33DM+9/ishQYnUqaHF7Yatu7LIyPLg17o9ZXo/gSDmBuPG0BBKdX0UW/26RP04gwkTJvD111+X8JWo7mbizXdRqW7uo48+Yv6yZYT170PFZ0biW7cO2sgIfJo0JHz0SAJ6dCN5TxSJm7YAIAhgMJmI6NKZ2Ph4NmzYwJP9nkROFWjh8zAVvarhpfVBj4FAwqgntqQUFTiWuY8UV2K+c5c2VUAn6jmnHMXlcRRom6IonMk5il2bwzNjnrkTt6PElSldGk9CIrLbQ3Z2NslJyaSnpZGRnkF6WhrJSclkZ+dguxqLiEBkZGRJN1mlUt3jhg0bzvkYiV83ZRS5T0qqm4UrsujbbzAmU+HJ1lQqlepO8/X1Ze7cBfyxaSfdnngeDI+g6DuQZdMT0rMPZfv2ygu4/8wcWYqAju1Z8vPPJCUllUDLVfcKNehW/WOZmZlM+ekn/Nu1wb9eHQBcThcIAqI2dzKFT8P6+DRvQsKWbch/GlX1Kl8W0c+PH374gZTEVOp5N0cn/mnKoSCAkPuvCkJ1vPDhgvVUvvPrRT1N/NuSKaRxyLOTi7Yz2CUbTtlBguMqUVmbuSqe570P/0ebNm3+/RtyF+jXrx9KdjZXo/ZhzbEiyAJ6wYBeMKITDAiygDUnh6ubthAZHv7A3BeVSvXvad68OZ0e7sqLb8by66Z0/n+e1pjLDgY8E41GF8YzzzwYHaAqlereUr16df73v/8xZ85cWrVqhdbHm+AWzYp9T0Cjhtg9HlatWnWHWqm6F6nTy1X/2M8//4zV6aRSi6Z5rymKci1gvjHF0KdpE7J3R5F+9DiBjRoAuXW0NSYjhw4eIogwjJr8iSh0Oi0CudnNNYKWcKUs0Y4TOGUHBvFGxkg/bQBeRjOVapTn0qUznM05CoBGK9K4ZWNefOlFunTp8i/ehbtLnTp18Pf1JXbNOsIHDUUbdGNKvYCAKOixHT9C1r4D1G/WHK1W/ShQqVT/jCAITJnyA2PGwMiX11ClQiKdO3hj0IscPGZj0w4b4eHlWLxkCRERESXdXJVKpSpWfHw8On9/NAZDsftpzSZ0Fj8SExOL3U/1YFOftFX/WHR0NPrgIHQ+PnmviRoRZAVFubG2TxcYgMbPF2dSct5+stuNOz0D2WYnSFu6wLFFUYPBaMTpcKJBi7fgh6IoOCV7vqA7xn4WrUnL8hXL0ev1HD16FI/HQ7ly5ahevfq/ePV3F0VRiIqKYsqUKcReuooMxM2Yjk/9+vjUrI3GZMKdlkbWoYPYT58lSAnkxPGTJCUlPRBr3VWq+4EkSRw4cIDU1FS8vb1p1KjRXTNV22w2M2vWHHbu3MmsWTNZsnYPHo+HsmXrM/nTwfTo0UPN8qtSqe4JJpMJ2eHI9yxbGEWWkRwOtXyYqlhq0K36xwRBADn/NEKjwUgW2SguN4LhT4nNZBnEGx9c6UeOIbpc+Fv8kVI8hR7f29sbl9OJW3EikbuPKOTW45YVmRj7OS5IJ3np5RcpXTo3cO/UqdPtvMR7QkZGBk8Nf4otm7YiOrWESqURgKSsODJ27iRrzx5EQYOggJ9ooYapCWH6SLbmrGXlypU8/fTTJX0JKpWqELGxsSxZsoQLFy5w8uRJLl86j9WaASgoCnh7W+jXfwjjx4/Hz6/wKg93kiAItGrVilatWpV0U1Qqlepva926NZ9+9RXWmEt4ly9X5H7Z56NRbHb1M09VLDXoVv1jtWvXxvnDVJypqRiu1ckWNSImkxG7w4Gg1SBoNLjiE5CyszGXyp1W6ExJJeWX3+nSqRM+3t6snr+OqkqdAr2JWq0W/4AA0tPTSfBcAUHhsi0aCQ8pJCBp3Tw7dgxvvfXWHb/2u4XT6eTJ/k+yb8cB6hibYZJ9sLvt6AUDVZX6JEqxnPMcxlfvR32/5vhq/fPus1FjJiEhoYSvQKVS/X92u51XXx3PiuWLMRldOJxOHHYPTzzqTa/HfSkdqSMl1cPaDTnMnvkJc+fOYOHCZbRo0aKkm65SqVT3vNatW1O5QgVift+I18jhCBpNgX1kj0TShk3UrlGDpk2bFnIUlSqXmkhN9Y9169aNIP8AkrZuz/e6r48vOq0WyWbDY3eQuX0HOl9fzJGlSNi4iYvf/UDl8DC++vJLhg8fjlNjJ85xqdBz6HQ6jBY9qbp4AsMDyA5IxlBR4KnnhrJj13Y++OADNIV8GD4olixZwp4de2lobk2ooRTin2YTCIJAmBhJLbEpme50rFJOXsCtKAoe2a1OiVKp7jJut5uhQwexfu1c/vuqH/26eyMis/incL75MJgm9fVYfKBWNT3/HR/EmrkR6MUUHnmkAytXrizp5qtU97XU1FT27NnDrl271HW89zFRFPls8mTE2Dhi5szHmZqWb7sjOYWYWXPQJiUzedKkYqegq1TqSLfqHzOZTLwxfjzjJ0wgwc+P0PZtEUQRQRQI8A8gKzOThA0bydl3AJ1Oz/mPP8PbbGZ4r168/fbbBAQEEBQURN/+fVg8fwmS4iHSVAFRyO0TkmWJY9n7iXadAa1IpsOBKDhxeNxkZGTg7e19S+3MzMxk8eLF7N+/H7fbTZkyZejfv/89v+ZbURSm/TSNAEKw6HJnGuj1enKw5tXlBvAXgvFV/ImxnSXCWAaAVFciHtGtTolSqe4ys2fPZs+ujcz6JpQKZRQ6fZXMiIE+tG6a20Hm4y2i0ShYbTLeZg11ahj55J1gnnopgTHPjCAyMpJGjRqV8FWoVPeX06dP8+WXX7JyxVIcThsooNMbeOyxbrzwwos0bNiwpJuous3atGnDgjlzeXrMGKInf46hXDk0fr5IGZk4Yy4RFhzMtAULaNas+AznKpWg/P+aHveYrKws/Pz8yMzMxNfXt6Sb88BSFIXPPvuMDz/5BHy88a5fD52fL670dHIOHUWwWunXqxfNmzfHz8+Ptm3bYrFY8h3D5XLxn//8h/lzFqCRtPgrIQiIXHFF49BK+DZqQKl2bTGXCkey20k/eJj0nbsJNZpYu2oVlSpVKrJt33//PR9MmkSO04mhTCSCVoc7IQHBauORjh2Z8v33Bdpzr8jKyqJCuYpUpR6RpvJ5r6ekJCO7FXTijTX1sfJFLgoneTx0AAoKezM3U7pOGDt27VB7aFWqu4SiKLRq2ZRKpc/z8Vv+bNhm5fX3Uti1tjSlS2lRFLj+zZ2aLmEyavDx1uB2KzR65BJWm0jrtr2YPXtuyV6ISnUf2bFjB31698DtygRBwajP/c50uhQUBATBixkz59K1a9cSbqnq32C321m1ahVr160jPSODAH9/uj7+ON26dVNnCz7gbjUWVYNu1W117NgxZsyYwer168jOysbPYqF3jx4MHTqUatWqYbfbWbt2LTExMWg0GurXr0/btm0RxRsrHU6dOsXs2bPZF7Wfy5cvcTkpkcjBAwiuXw/+X1zozskh5ofpVLb4s3P79kKnmH/22Wf87+OP8WnZjNB2bfKyrMseiYxjx0lavZYGVaqyZtUqvLy8/s3b869ISkqiRtUa1BSbEGaMzHvd5XKSnpaOgIhW0CEgkCBf4RxHaB/UldO2w9i9slm2fCktW7YswStQqVR/Fh0dTeNGtfjxU38e6+TNjAWZfDMtnRPbywHXyjEqCrIMOTYZtwuCAnUA9BkZR0amyJkLOvYfOEZYWFiJXotKdT9ITEykbp2a2O3pPPqQF6OHWGjV1IQgwJ4DDn6am8Gq33IQRC92795H1apVS7rJKpXqDrnVWFSdXq66rWrXrs0XX3zBF198ke91SZL47LPP+O6b70lLScMgmlAUGbfgomLlirz734k8/vjjAFSvXp2PP/4Yj8dD/UaNCKzQlOAG9Qo9n87bm4jeT3Bqyk9s2bKFhx56KN/2ixcv8uEnn2Dp0Jbwhzvm2yZqNQTUr4spNISD3//IDz/8wCuvvHL7bsYd4u/vj8lsIjMnjTBuBN16vQGLvz+ZmRm4ZCeiIpKlpCELMrtsvxMUGsSCafMfyIBbURT279/PihUrSE5OxsvLiw4dOtClSxd0Ol1JN0/1gLt69Soej4vSEVoMehG9XsDhVHC7FXS6a32PgoAgKIiCgPynvvMcq0yZSBOnzju4cOGCGnSrVLfBZ599RnZOOmNHWHj/jaB8M8OaNzLRrKGRyt+k8dHXaXzwwQfMmTOnBFurUqnuRmoiNdW/SpIk3G43L730Eu9P/ABzmj+tvLrQzudx2vl0pZGhLWmnsxg6eBgLFy7M9949e/ZwOTaWwBbFZ4M0lymNLjSEJUuWFNg2Z84cJIOe0PZti3y/KSIcc93azJg9G4+n8LJldzOdTkff/n2Jly4hK3K+bQaDgeDgEPz8/BANAsmaOKrVrsrUaVM4dvwo7du3L6FWl5yYmBg6PtSRRzp2ZsY3s9mwYAvLpq9kyMCh1KtTjw0bNpR0E1UPuD179iDLkJQiAdCikQmXW+H3rbZ8+wkCSH8q13jhkotjp5zUq+UFCA90ckmV6naaM2cWZSO1vDs+qNClWIIg8NrzAVSrpGPVquW4XK4SaKVKpbqbqUG36razWq3MmjWLtq3bEhYSRmBAID98/yOVlNrU8K6HlzY38ZkgCATog2nk14YQTyQvj32ZK1eu5B0nISEBWZExhYUWez5BENCEBBFfSAbRXzZswFyzBuJNRi8DGtYnNi6OM2fO/I0rLnkjR45EMMPR7L0FAm9BENAb9cRoThEQamHdunX0798fk8lUQq0tOVevXuWxLo9xMuoMdXUtaOPzKE0t7Wnp+zDNzZ2wX/Ew8MlBauCtKlFWqxWzScvCFdkAVK9ioFE9I99Oz8DpVK6t6c79t9OhoBFBVmQ+n5qOxVeDzW4lKyuHQQP70Ktnd1avXo3b7S7hq1Kp7k0ej4fs7DT69/BBpys694koCgzq7YvbbScpKekOtlClUt0L1KBbdVtdvXqVjh068vLzrxB7OImKcm00Dj0WORizw4+UlJQCo8mCIFDDuwEeu8y8efPyXjeZTAgISDb7Tc+r2J2YCwkirTk5aG9hnbbWywuZ3Ifde1H16tX5fup3ZBiS2J21gSv2C9glG3bJyiXbOXZl/Y7dO5sZs2YQGRl58wPepyZOnEjKlTSaencgxBCeb8TCV2uhoW9rvB0Wxj4/Vh2pUJUYvV6Pn5+eLbvsrFifG3i/My6I8xddDB2bSMyV3N/N7BwZWYGsbIlX/5vMivXZPPeUL4tXZtKwrhcvjdKhuHbzzOjBPP5YZ5KTk0vyslSqe5LH40FRFMqXufnSo/JldAjkJt1SqVSqP1ODbtVtY7PZ6NOrDxdPXKKF18M09G1NiCEcu2SjlFgOvWBAlhTS09OQ5fyjsVpRS7AQwfJly/Nea9q0KWajgbRDh4s9rzs7B8eFi7RrW3AKeWhICK6UlJu23ZGcgkYQCA4OvrWLvQv17NmTlWtW0uLRppxVDrPNuo5t1vVcEE/QsWd71v+6jk6dOpV0M0tMQkICa1etpayuCgZN4ZlGRUGkmlc94mMTWL9+/R1uoUqVq2HDhuRYBTq0sjD2zSQmfpKCn4/A7G9COXXWRYvHrtJzeDxvfZTKuInJtO1xlZ/X5TBujIVN2+1YbTD104qMGR7OshlVWDu/PEkJhxg0sD9Op7OkL0+luqfo9Xp0Wh3JqdJN901K8SCIIhEREXegZSqV6l6iJlJT3TY///wzp06cpoXXw3hrc7P3ueTcBzwDuaPWekGPU3Jit9nw+n/1tc0ab1JT4/L+PygoiF7de7Dgl/UENGyAzqfwetwJG/7A22Cgf//+Bbb17d2bqLffxp2dnZe1vDBpe/bSqEEDypcvX+Q+94LmzZvTvHlzLl26xPnz5xEEgWrVqqkPAMCWLVtw2J1E+hb/M/bR+uGNHxs3bqRHjx53pnEq1Z906tSJ8PAyGAzJjHs2kp/mJjBtfgYB/hpcThmnS2b3/tyRNIdTodsjXnh7i3w2JYMcq8KX71egSsUbM3/q1fJm1tdl6fLkPlavXk2fPn1K6tJUqnuOKIo0bNSMn9fuZcxQPwyGwnMluD0Ky9bkUK1qzXuyEopKpfp3qSPdqttmxvQZBBKKj9Yv7zWtkFsj2sX10RUBDRpsNluB9zskG76+fvlee+ONNwgzexHzwzRyYi7x5wp37uxsrixfiW3ffj74738LrbPdr18/Ary9ubp0BbKn8F7q1Kj9OM9fYPSoUX/xiu9eZcuW5aGHHqJDhw5qwH2N1WpFRMxXt7woOkVPTk7OHWiVSlWQVqtl4rsf8MsmNwlJbtbMK89HbwYxcqCFl0YHsOSnUvR/wgenC4x6gU077Ow/5KRtcxOyIvPGBxfpNugkV+NujGrXruFFm+Ym5syeUYJXplLdm954401OnpVZvCobh1Pmz7V2FcDlkln9axZRh1y8+tqEkmqmSqW6i6kj3arbQlEUTp44STld9XyvmzVe+OosJLqvECTklq4REfHIbmRZzqvPLSkSSUosY3qMzvf+yMhI1q1ezaChQzgxdRrakGC0IcHIDgfOCzF4Gwx88clkhg0bVmi7/Pz8mDV9Ov0HDSL6u6kEtW2NX62aCBoRe1wcyTt2Yz14mNHDh9OzZ8/bf2NUd42goCAUQcEuWTFpih6FUBQFh2gjKCjoDrZOpcqve/fuWK3f8vprr7Dg5wt0fdhIudI6Yi5LfD41HbcHJr0dTN9u3piMoNeLZGRKbNtjZ8wwC+s2WOk59BRr5tcgNCS3o6lTW1/enXwQRVEKzcCsUqkK17FjR/r0HcKr780hPkmiX3cfAvw1CEBGpsTy9TlM+iaDRzr3pFevXiXdXJVKdRcSlD8PHd6DbrUguerfJcsy4aHhlPPUoJy5cr5tMbZzHM3cS02hCQFCCLIi4cFNcEgwopg7TetU9mHitBfZE7WbSpUqFXr8bdu2sWTJEuITEjCZTLRt04b+/fvj5+dXYP//b//+/fz3vffYuXs3bllG0IgIskzpiFKMfe45Ro0apT6E3udycnKoWb0WlqxQqnnXLXK/FFcih1zbWbN+zQNZw1x1d0lISKDjQx3IyYomMECLxVdLl44BPNIWSkVo8PPRXOvAFMixylRvFcP3k8JoUt/IYwNjadXUwlcfVgRg3rIk3vggnStXEvM6PFUq1a1xu91MnPgOM2dMRad10KC2DkGAg8fcOJx6BgwYxqRPJqPX33w2lUqlun/caiyqjnSrbgtRFKlSpQrxRxMpR/6gu4ypIonOq5x07KesUoVgItBotIiiiNWTzTnrCZI1sbz33v8KDbivH79du3a0a9fub7WvUaNGrFm1ilOnTnHw4EFcLhdlypShbdu2aLXqn8GDwNvbmyHDBvP9l1MIdUfirwsssI9LdnLacYhadWvRokWLEmilSpVfWFgYDz/SmV3bZrNjbTVEUcBut5GVlYmPlya3WDe5MzT2HnQAuRmUw0K0jBzoyyffpjFxfBkC/HVEHcihfLnyasCtUv0NOp2ODz/8iLFjX2T+/PmcOnUKRVF4tm0VBgwYQOnSpUu6iSqV6i6mjnSrbpvp06fznxfH08q7M2ZN/qRnkiJxPGs/l+3nkWQJiz4AnUFHjpxFUGgQb739ZpFTxFWq28Vut9OrZy/2bo+itKYSZUyVMGnMeGQPsY4YYtxn8Av3Zu36tVSuXPnmB1Sp7oADBw7Q9fGOzPgqgofb+ZORkY4sOwmw5HYYyrKMLMsMeT6R9EyZ9QsiEQSB5BQP9Tte4vtPKtG0gQ/Nupzh9QkfM2bMmBK+IpVKpVKp7g+3GouqQbfqtsnOzqZN67Yknk+mkXdbTBpzvu0u2cnerC14zHb69O2DxWKhbt26dOvWDaOx8BJOKtXtZrPZ+OCDD5g7ex6ZGZloBR2S7EFn1NL50c689957lCtXrqSbqVLlURSFfn17cfL4Zhb+UJ6IUAcCLix+N4LuSd+k8e2MDH78LJTHOuZWapAkhbINLzDxP2VY8Usm8cnBbN22u9CkkyrV/SQuLo558+axdctG7HYbIaHh9O7dj65du2IwGEq6eSqV6j6iBt2qEnH+/Hl69ezNlQtXCCaCEEMEAiIprgQSlasEhvmzaMkiGjRoUNJN/cusVitbtmwhLS0NHx8f2rRpQ0BAQEk3S/U3ZWdns2HDBpKTk/Hy8qJt27bq9EDVXSs9PZ0n+/fh1Ml9dGqrpXN7A5EROk6dczFvaRanzrkYO8qf4f19MBlF9DqB6Esu2na/itlsxM9SioWLfqZWrVolfSkq1b9GURS++eYbPvnkPcxGNw+3NePnq+HsBRc7o+yEhpZm5qz51KtXr6SbWqLUZIoq1e2jBt2qEpOens68efOYPm0GV69cASA4OJhhTw1j6NChhIWFlXAL/xqbzcakSZOYM2su6anpKLICAnj5mOnVpxdvv/02ISEhJd1MlUp1n7PZbMyZM4dvv/mCmJjzeJlFdDqBjm0sjBwcRpP6Zmw2G3aHHUWR+XxKOj/MyeGVcW8wevRotXyg6r73/fff8/57Exg70sKzT4Xj7XWjpvaFGAcvvX2J8zE+rF7zK1WqVCnBlt55sbGxzJ07l/mLFpGYmIjBaKB9m7Y8NXw4bdu2VYNwlepvUoNuVYlTFIWsrCwkScJisfwryXvsdjsrV65k1cpVpKSkYrH40eXRLvTp0yfv9+Hy5ctcuXIFvV5P9erV8fb2vslRb7BarfTu1ZuonfsoJVSgrLkSJtELl+zkiuMCVzzniaxcivW/rLvnOhNUKtW9ye1206xpI4y6yyyYWpHSpfIvz1EUhd37Mhn47GVGPf0qb7311m07d1ZWFkePHsXpdBIZGUnVqlVv27FVqn8iNTWVBvVrMWKAnrdeKXzWUnaOh0f7n6VKzW7MnDnrzjawBP3666889fTT2GQJr7p1MIaHIdntZB8+ipSUTJ8nnuD7775Dp9OVdFNVqnuOGnSr7nvbt2/nqWFPkZyYgkUJwiiYcSp2MoQUfP19GDV6FAf2H2D71u1IHhkE8PXzZcCgJ3nhhRduadRn/PjxzJgyk0bmtlgKyXZtk6xE5WyiRcdmrFi54t+4zPvOsWPH2L17N06nk4iICDp37oyXV9F1s2+XhIQEVq9enTedvEOHDtSpU+dfP69K9W84deoUT/R4jCBLBs+NCKZ750CMRpGrcU7mL0vmx7npNGr8EPMXLLotJYxiY2P5/PPPWbliMXZ79rVXNdSt25Axz75At27d/vE5VKriJCYmcuTIETweD2XLlqVGjRr5Rme/++47Jk+awIGN1fG3FF2VZM6SJN76KJ29UYcpVarUnWh6iYqKiqLbE09A5YqU7tsLzZ/WtCuKQsaRY8Qv+ZnhTz7JV19+WXINVanuUWrQrbqv7d27lx7dnsBo9aamVyO8tDdGrx2SjSMZUVx1XcRfH0gVc238dUFIioc45xXi5IuERAaxfOXyYkdp0tPTqVWjNqG2MlT2LnodZJzjMiel/WzftY0aNWrc1uu8nxw4cIB33p7AoUNRmIwyRoOGjCwP3t4BDB4yktdee+1f6WXPyMjg1VdfZdXyVbgdHowaEy7JhaKVaNy0MZ9+9qm6zlV1Tzp37hzvvvsOmzf9jii6MOhF7A4FH58ABgwczuuvv35bkkadO3eO3r26IZLIkD4WHn/EHy+zhmOnbMxelMKWXQ7Gvvg6r7/++m24KpUqv9OnTzP5009ZvWYNOVYrkiQhAKUjI3nt1VcZNWoUbrebhx/uRIjfEb6bFI4oihgNRgwGQ4Fp0xmZHmq1OcGUqfPo3r17yVzUHdSrT2+2nD5NxefHIGo1he6TvGsP6WvWs3/PHipUqHCHW6hS3dvUOt2q+5aiKLz+2htocww08muDKOSfti5KGsq4quJUHNglKxHGsnn7+Or8KS9VYd+VLfTv9yR7o/YUOQr0yy+/YMu2Uca78Nrh14UZIjmTfYTly5ffNOhWFIWDBw8yd+5cTp05gyAK1KtdhyFDhtzXAfuOHTsYNLAPNap4mP5lBB3bWNBoBGLjncxdkswPP0zmzJlTzJgx67YG3pmZmXTr2o2Th05TQVedSJ/y6EQ9siKT5Izj2M7jPNblcVatWfnAJ9ZR3XsqV67M/PkLiYmJYfv27djtdkJCQujUqdNtmz3idrsZNKgfvl7xzPgijAB/EVF0YjSa6NjWj05tLUydHc/7n0+iRo0a6oi36rbat28fvfr1I9VmRfBYMZsl6tUyYNDDiZOXefHFZ/j6qy9xu2ViYqLp282E2+EBFOw2OxqNBovFku973suc+zzgdDrzXrPb7WRmZmI2m2/rAE5GRgZ2ux1/f/8SqdJy8eJFNm/dRlDvHkUG3ACBjRuSuuEP5s6dy8SJE+9gC1WqB4cadKvuOQcPHuTIoSPUNTUvEHBD7jpsURApL1TnoLSdROdVwo1l8rYbNEbqejVn1/nfWbt2LT179iz0PElJSehEPQZN8V+UoiBiwkxSUlKx+2VlZTFy1Cg2bN6E4OeHoVxZQGHv4kX8OGMGvZ94gq+/+uq+K59ms9l4+ulhNG8kM/Oryuj1N35mpcINvP5iJM0b+zD0hbX89NNPPPvss7ft3P/73/84eeg0Tbza46P1y3tdFETCjJEE6cPYl76ZkSNGsTdqDxpN0Q8lKtXdqly5cv9ambv33nuPM6eOsnpuOP4WDwLgcUO6w45Wo8XPYuGZoeFs35PD1CnfqkG36rbJyspiwODBpHncaN3ZDO/vy/AhQYSF5nbMOp0y69en8v7ks6SnaAjRlOHk6QR0gg5BEJAVGY/kJi0tjYDAAPS63MD72CkboCEiIoI9e/Ywffo0fv1lDZLkBgQaNWrKsOEj6d69+9/6TnC73SxbtoxZM6dx7NhhQEGnM/DY408wcuTIO1q95dixY7hlGd/q1YvdT9TpMFQoz5GjR+9Qy1SqB8/tz2z1N3z33XeUK1cOo9FI06ZNiYqKKukmqe5iO3fuRJQ1BOvDC2yTJAmX04UGLd6CHya8SHElFtjPR+uHRQhk3tx5RZ7HbDbjkd3IilzwPIqHy/ZotqX/xtrUpcS54vjlt99YuXIlbre7wP5Op5P+Awbw+66dhA7sT+VXX6Fs/z6U7d+Xyq+PJ7BXdxavXs3IUaOQJOkv3pG724oVK8jMSGLS22XyBdx/1raFHz0f9WLWzB9v2/VnZmayaMFiSmsq5Qu4/0wraqlmrk/0uWg2b958W86rUt0vfvnlFz7/bBLNGhlo1shMgEWLxU9LYICWQIsWQZBIT0vD4/EwtF8Qhw/v58yZMyXdbNV9YtmyZSQkJ0NOOi+MsvDG+PC8gBvAYBDp3j2Qmd+EY/aRMGpMRF/0sOegDcjtXNWJBgRFICszC8hdTTlncRKlS5dn37599HziUc6eWMlbL/sw+5sIPv9fMAbxAM8/N5ynnhqGy+X6S222Wq08+WRf/jPuGUIsJ/jmoxBmfxPBq8+ZObx/MV0ff5iZM2fetnt0M9dXkArizTOTC6KIJBd83lGpVLdHiQfdixcv5pVXXmHixIkcPHiQunXr8sgjj9x01FD14HI6nWgFbaHlLWRJQoG8EXAtWiSl8CDOR7BwKeZSkedp27Ytgk4g3nkl3+tWTzab09dzwBGFtXIAXg+3wffhDqR6mRkyciQPd+5MYmL+QH/p0qXs3LuX0k8NxVKrJsKfMrmLWg2BjRoSMbAfa379lY0bN97qrbgnrFq1nDbNTERGFL+2dGDvEK5ejeHQoUP/6HzZ2dkcPHiQH374geysbMqYKha7v0UbiFnxYe3atf/ovCrV/cTtdvPaqy/jb4GGdU1oNfk/b3U6AX8/DaKokJ2dRe3qXoBEXFxcibRXdf+Zv3Ahio83QRYYNTy44A6KAgrUrOVD9y5m0uVYzEogEz5KIi4ht/NbADSCFo/Hg8vtZtmaFJattVK/QVMmf/I/xo3xY9OKqowaHMZDbSz07R7E4p8qM+fb0mzdvJrXXnv1L7X5+eef4+jhzSybXo7Z31biiUcDeaiNhTHDw9m2uhqjBpl5c8I4fvvtt39+g25BpUqV0IoiOdEXi91PkSRcly5TtXLlO9IulepBVOJB9+eff86oUaMYPnw4NWrUYOrUqZjNZmbMmFHSTVPdpcLDw3HKDhySveDGvEBcQVIk7FgxacyFHkdSJHS6orP6Vq1aldZtWnHBeRK3nPsF7pJd7MrajD1QT6nRzxDetx9eTRpiadGMKs+Optzzz3D4QjT9nnwSh8OR2xJF4acZ0zFWrYxXmcLLmAD4Va+GrlQE02ZMv7UbcY9IS02mXOn89zk7x8OK9an8NDeBBT8nExvvpEwpAyCTmpr6t84TExPD+PHjqVGtJg+168i7E/+L0+bEke3A7S56tMIu27A5rCxZPI/q1SrQpHFd3nzzTc6ePfu32qFS3Q/Wr19PSkoc5UobsNkKH/0SRQEvs4jT5SIjK/dv7HZkSlepAGLj4xGdOTz+sBdGQ8HH1etZgAVRpGsnb1yilSqm+iRdNdP9qct8OS2ZcxedJKfK7D1gZ/QrF3jprXj69R/O0SP7eayjgZefKVVoB36H1hbeHhfC0iXzuXr16i2199SpU/z262o+fDOcpg19CmwXRYG3x5WmVVMdX37x6V+6F39XzZo1adygASk7dlJc3uSM4ydRsnMYPHjwHWmXSvUgKtGg2+VyceDAATp27Jj3miiKdOzYkd27d5dgy1R3s65du+Ll68Vle3SBbVqtFlEQkBSJZCUWCQ+RxvIF9pMVmVQSaNm6RbHn+ujjjzAE6ojK3ky6O5VL9nNYtQ7CnhyILigAt+JCERR8/XxBAHPpSEoPH8LBo0dZtWoVkDvN+fiJE/jVu3l5Kt+6tdmxc1exX473Gm9vX5JTczstbDaJtz+6RKOOh3nh9fN88s1lXvvfBZp3OcIz/zmPR+Iv1VG/7vDhw3Ts0JHZU+YSmFOKpsaHqKrPvd859hzSUtNwOAp20lyxX2B71kq8grLo/bjAc8MEOrdNZ83K72nXtjlffPHFffWzUKlu1ZYtW6hVzUiH1hZ+32LD6Sw88DYaRAQUVv+SgsnkQ+3ate9wS1X3K6PBgOJx428pYl21cuM/AvxFRCF3VLuZTxeMGdX4fpqdLgMv07p7DEOeT2Tnfi8mf/o9TzzRk5iY84waHFrs+ft2C8LL7GbhwoW31N558+YRGgxdH/Yvch9BEBgxMJgjR/Zz7NixWzruP/X0yJHYTp3h1KdfErv+V7JOn0X50zRy66XLJC5fSeeOHdW/X5XqX1SiidRSUlKQJInQ0PwffKGhoZw+fbrQ9zidznwZJ7Oysv7VNqruPn5+fgweOogfvvmRQFcIgfqQvG2CIGAym0jOSeQCpwgzlsZLW7DH+ar9Ah6Nm2HDhhV7rurVq7N67SqGD3uKqLObsDkdmBrWRfEz4VKciKKIv8WCVqslOzsHu82GrBGRI8J59vnnkWWZli1boijkq41ZFI3RgNvtRpIktNr7I8/hw488yicfb+dqnINnX73A6XNWRg/xY0BPX8JDtVhtMsvXZfPlj+k47Ar+/kU/sBQmKyuLJ/s9iTNJopVPZ3Ri7kibUWPiTM4x0pQkwihNZkYm2kAt2mvZ0RMcVznh2MmTvcz857kQIiIC85LYvfmyzDfT4pn8yX/x8fFh5MiRt/emqFR3Obvdjp8vDO4Twk9zE5i5KItnhloK7CcIkJouMXdpBr16P6uW7lTdNu1at+bsjFNcuVowTwqQO3dcAcXt4UqshCwLGEQjBo2R2r6NqS7XJ8OdglN2csyzl5dfeZWBAwcyY8YMdFqZhnWL7+D18tJQr5aBc+fO3VJ7z507Q5P6enS64sezWjXxATycO3fuXw1yHQ4HEydOZM6C+Sg6Hc7sbBJ37CL+t43oLX4ENGqIOzUV++mztGralB+mTv3X2qJSqe6C6eV/1UcffYSfn1/eP6VLFz1dV3X/mjhxIm07tuGgYztHs6LIcKfhkp1keTK4pJzluBCFR3BR0Zy/DJesyFyyneeM5wiDhg68pS+82rVrszdqD9NnTUNj0OBdoyoGkwGLxUJwSDAgkJKSgjUnB2QBLTq8qlQlMyOLZ0aOYcRTIzDo9djjE256LltcPCEhIfdNwA3Qv39/EL3oO+IMp89ZWfJTOOPGBBAemnuNXmaRJ5/wZcHUMGpWNTB27Ji/NLq8ZMkSEmITaeDdMi/gBjCIRkoZy3KVaCQ8gIDVlptgR1EUzjoO0LGdgbdf8cfbR4fBeKNTRKcTefmZCDq21jP+P6/QsWNbunR+iHHjxnFUze6qusfEx8fz9ddfM27cOF577TWWLFmSt/ylKCEhIVyI8VC2tIFRg8P48Ks0ps7OKDDifeyUg6deTMTp9uell176F69C9aAZPnw4KBrWb8gmPcNTYLsgCKDIyB43K9ZZ8RYC8dLc6GTXilqCDGG4FSdGsyGvUsn16eS38jUjy0qh088LIwgCt5KH7Po+t3rcv8PlcjFw0CCmzpmNoU0rakx8k1pvvUGF8a8QPGQAQoA/8b/8RlBGFt98+ikrfv5Z7TBTqf5lJRp0BwUFodFoCiSdSkxMJCwsrND3vPHGG2RmZub9c+XKlUL3U93fjEYji5cs5s13JyBGuolybGJz9ir22DbiDMlm9AujqFG7OvudW9iT+Qcnsg9yLGsfW7PWcU45ypARg/n001tfU6XRaGjTpg0mkxkfPz8sFj+MJiOSRyIjPR1kAb1gRCfo0aBFFHVoBR1NzR04vOcoRp2erKj9KMVk5pacLqyHjjLoySdvxy26awQEBDBx4gccO21ncB9valW7EdwqCtgdMmkZHoICjHz0ThlOnjjC3r17b/n4c2fPJYgwjIWs3a/p0xC91sBhZSepSgI2uxVFkUlxJWAX0hnazxtBAxY/CwI3HoDSMzz0GXGa37ekU660i1qVTlOjwmm2/jGTzo+0ZcSIp7BdC+BVqruV3W7nxRfH0qRxXb7+8h1OHpnL/t0zeOnFkTSoX5O5c+cW+d6ePXsSn6SwaUcmb48rzYiBYbz/RRqNHr7MK+8k8fbHyXQfGstDva9y+rzM8hVriYiIuINXp7rf1a1bl+FDhpCd5eG/71/F7cofJSseD7Ldzi9/5LBxq50y+moFAtlsTwbR7pN079md8PDciie1atXC7dGwM6r4mZKZWR4OHHFSs2bNW2pv7dp12RnlxOEoPvL+Y3sGoKNGjRrF7vdP/Pjjj2zcvp3I4UMI69AOrbcXOr0Oi7+FMg3qU/3Z0QQ1b0pWdjbdu3fHcAsz8VQq1T9TosNper2ehg0b8scff9CjRw8AZFnmjz/+4Pnnny/0PQaDQf1wUAG5vz/jxo1j7NixREVFkZ6ejq+vL02aNMFoNOJ2u1m/fj3z5s3nYvRFDAYzj7UaxLBhw275S/TP/P398bdYsF6IwVIz98vSarWiKGAQ9PCnoM1x9TJmjTf++iBqK03Zn7QVjUnHlRWrKd2ze77s5QCyR+LyoqWYRfG+TGRisVjwNuvp0cWPlHQPOo2AIILHoyArYDAY8fP1o3VTgXKl41m+fDnNmjW7pWNfunSJQG1kodsMGiOtAh7mcOZuzjqPIsoiAVmBZLsz8QtUaNrICz8/v3zJnxwOmUFjznAl1s7c78KoW1OPXm/G19cPj0dh7e9pvPq/5Ywe7WTWrDlqbW/VXcntdjN06CAO7NvIxP+E0KdbID7euV/5MZcdfDMtntdeHYvNZmP06NEF3l+vXj0aNmzGOx/vpVY1MxPHl2FI3xDmLk1iz/5s3G7p/9i778Aoir6B49+9fsml90IavYTepPcqHeld0EdRXxXEhqhgQyxYaNJ7RzpI7733EkoIpPfk+t3u+0c0EpMAKk3dz/PP4+3c7MzmuNvfzsxv0GpVqJQaXnj1dapUqfK4uyj7D/juu+9IS0tjzdrlpKRcZegAb+rXcUWJyLUbFlasMzJ/WTZWmxqTkIvZaUSncMEsmrhlvsYd53UqVi/PhAkTkCSJI0eOcPnyZbx9Apg8K4EGddwQhKLHn+YtTcYp6vNmaz2Avn37MnnStyxZncKgXkWvF3c4JGYsTKVu3QaULVv2L1+Xe3E6nfw0cyYuVaJxKxlV6LggCKjUakI7d+Tq5xNYunRpkd8BMpns4Xric1jffPNNBg4cSM2aNalduzYTJ07EaDTmTSuSyR6AWq2mfv36Rb7eqVMnOnXq9FDOo1KpGNC3L9/OmIGzZTMEjQaLxYISJXcH3A5jLqaz56mozQvsfTUBuFk9KVUxgnMnTnEtMQmfBs9gKFUSRJHsS1dI338QdWYms2fO+lcumUhNTcXVVU2lCkFYrBbsNhuSBGqNAr1OX2A6fUQJJampqQ9ct0qlQixmWzjIW9td17sZt0wxnLYcomP/9sTGxnLz2n78/Hy5+28HsPjnFM5dMrJ+QQjR5bWkZTjypyGqVAKd2/ng7qZkwCvr2bZtG61bt/5T10ImexzmzJnDoYPbWDItgro1C+a1iAjT8fXYSLw94xg3djRt2rQhPDy8UB1Tp82gc+f2tO8Tw7B+XnR71ofRb5bg5i0rC1cmM3dpNvXqt2DMmDGPq1uy/xiVSsWSJUtYufI53ntvFC++GYdeC1qNEotNib9/CO+89zZpaWksXriY2MwriKKIQqHAw8uDF/oP5Z133mHHjh188/V4YmIuolA4MZsd3Lhh480PLjPunQgMBn3+OSVJYsnPqYz7Jomw8NIMHNAHrVZHvfqN6NevX7EzOiIjI+nZqz8fT5iNn4+adi28Coy8Wywib318k9PnBZYsffuRXbPTp09z6/ZtQtvf+7dJ7WZAX7Y0q9eulYNumewxeOJBd8+ePUlJSWHMmDEkJiZStWpVNm/eXCi5mkz2NBg8eDCz5s3j5ryFhPTuiSRJKITfRzqdZjPJK5ajtSvxUwSTnZ0FgJvdk6yMLH5evpyvv/2WvUtXkvDrwi61Ukmr5s0ZOWIENWrUeCL9etRcXV2x2kRMJhFXVz16nb7YsumZEqWDXfP/Oysri+TkZLRaLaGhoSj+MEvgmfrPsGP1HkpLle65Ri7DkUZUZBQ//PADGzdu5H8v7uP6TStREbr8MpIksWBZMq2auBBdXosoSjidElpdwdHsZg09qRadxOzZM+SgW/bUEUWRObOn0665S6GA+25vvhTMwlUXmT9/PqNHjy50PCQkhPXrf+HTTz9lwuTlfDoxBRABJZ6evgwZOpKRI0fmJyCUyR6Vbt260a1bN06dOsXx48ex2WyEh4fTokWL/JlK77//Prt37yY7OxsPDw8aN26Mm5sbkydP5pNx79OysZbP3gmjbg03JAnGfBHL5NkJbNh6lr7dfSlbyo30DAcLlqdz9lIuOq2SQJ87VCiZQq5RZMZP+/j+uwm88eY7vPnmm0X+3nz55QRyc3N5ceQKKpZLoXNbdzzdVVy5ZmbZmmxMFj2TJs8ocqDgYcnKykKUJDRenvctq/b0ID0t45G1RSaT/U6Q/uH74fz25ZqVlSUngZA9Fvv376d3//5kOewoKpTDNbwUCglM16+Re/IUqlw75RxVcMUdxa+jqDekyyQob/LW2yP58MMPiY2NJSYmBkEQKF++PGFhYU+4V49WfHw8tWtV4ZN3vRjQw7/YcleumWnW9SpTps4nMDCQqVOnsnH9Jux2OwICEVERPD90CAMHDsTVNS8w37FjB906d6eK5hkCtCFF1mt05HLQtIX3PnqXESNGYLVaqV6tEp1bWxj37u8jfOkZdio3Psnk8f50bG3AaHKSa5Tw9fNDqSgYeP80L5HPfzBy82b8I02II5P9WVevXqVxo9osnBJK43oe9yz77ribHDgZwt59986jkJ6ezr59+8jNzcXX15dGjRrJwbbsqZCdnc2WLVtIS0vD1dWVpk2bEhKS91tw9OhROnVszf8Nc2fk8MJ7cl+OMTH4taucu2jGYHBDpdZgt9koEwU/fB5O5Yq/ZzjPNTr5aV4i30xN580RHzBy5Mgi2yOKIjt37mTu3Nns2bMDm82Gr48vz/XoS//+/YmIiPhb/XU6nezYsYMNGzaQmZmJh4cHbdu2pUWLFqhUKo4fP07zNm0o8b+huEYUnsFytxvzFlHd4MamDRse+PySJLF//34WLVrE9dhYdBoNdevUoX///vnXXSb7L3nQWPSJj3TLZP809evXZ/f27Xz//ff8MHkKuexHIShQo6GEFIyPPQgXwYBKUAECkiSRQwYGwZ1JEyeTkpzClKlTKFWq1JPuymMTHBxMixZt+WHGGurXFHAzSEiASqXERe+CTqdDFOHz7+7g5xfCtWvX6Nu7Lw6rE0FSoBJUuKs9Sb2UwfujRrNs6XJWrFyOr68vTZo0oU271mxZvw1RqkGgtkSBG6tMezqnTQeJKhvJkCFDgLzcEC/+71XGf/EBVSql0r2DLwDWXxP1uLoI2GwiuUYRnc6lUMANYHBVYrfbkaQHz24rkz0OeVtpSvj7qu9b1t9PTU5O1n3LeXt707Fjx4fQOpns4cjNzWXcuHEsXLqErFwjSo0Gp92GTqmiXZs2fDJuHDNmTKdkuFBkwA1QtpQLu1ZHU6vVRTp2eRmTycSu7XNZMas0fn/492NwVfLmS3lB5cRvx9OnT58ip5orFAqaN29O8+bNkSQJURQfWu6PEydO8PJLw7h1K4bypdUEByo4f1Ni+bI5hIREMGnydKpXr06JkBDSjh6/Z9DtyDVivnSJDh9+9MDnj4+Pp//AgRw/dQqlny+akGBEYw67fvyBrydO5P9eeYX33nuv0Iw0mUwmB90y2V8SGRnJt99+S05ODqsXraOWoTEaUUtmRhYqQYVS+P2fVhZpGMnhGfdmOCQHy5Ysp0PHDjz77LNPsAePV3Z2NvGJSVy4YqTX/+L44C1/atfQYxdFMrOyiD2XzpQ5RnYekHi2Q2PeHvk2SkmFvxCKK244JAeptnjMmAjUhnD+2AUGDxrM2nVrUSgUzJw1kxdffJENazdy1XoOH0UgAgJZUhrZZFCpckUWL11cYA/wV155hRs3bvD66NksX5tB327eBPrn3WQdP22hUjkNGo222KeWFy6bCAwIlG8uZE8dHx8fQEFcvJXyZQpn9b9b3B0rXl5y1nHZP0tubi5dunXj6LmzeDSoT5k6tVB7uOO0Wkk/cYq1u/ZyqHVrcrNSGP1G3tpqp9OJ2WzGbDYjik4EQUCj1eKid6FHRzcWrliE1Wrl9WGehQLuu70wIJCf5ucty3j77XuvzRYE4aEF3GfOnOG57p0oX9rC5M8jqVrp91H4sxeMfDThDj17dGb5irUMHTyYMZ9/Rm6tGhiKCLwlUeTOug24aXX0fsAdU9LT0+nUpQvX0lIJGToIQ8mo/AcZTquVlH0HmPDddzgcDj766KOH0meZ7N9Enl4uk/0NFy5coGXzVmhzXCkpVsRudaIRfs+unytlcVY6jJvGnQberREEgYPZ26jZshqrVq18gi1/fBwOB926d2f38WP4tW9D+t49OJLuEFlCSVSYitR0J6fPGJEkNR9//Bnvv/s+BqsXFRQ1C1xLSZJIkm5zlTP4aYPI1WSw8ZcN+VnOf8tMO2fOHI4dOYbT6aRUmVIMGDCA1q1bo1YXvomSJIk1a9Ywa+ZPHDt2GHCSnW0hMkxg77pyuLm5FDk6kmt0UrPlRZ4f9u59b7pkssdNkiRatWxKkM8l5vxQ/Iya7BwHNVpc5JXXPuKNN954jC2Uyf6eUaNG8dPCBYS9+DwuRUxptufmcu2HKYhJcayYVZoGdfVkZWYiiaBA8WvGcgmn5AQBNu6w8vbYbLRa2LeuNBFh9146Mfzta9xJrcbadRsfUQ8LkiSJtm1aItlPsWp2afT6woG81Sry3NCrGK1l2bR5Gz169WL3kcP4tGiGd+2aKLU6EARMcXEkbduBM+Y6M6ZOpUuXLg/Uhk8++YSvpk4l8rWX0fp4F9FIiN++g/RNWxj34Ye0a9fukWVol8meJg8ai8pBt0z2N+3YsYOB/QeRlpBGIOG4C96IkpNUEkgnGU+1N3W9m6FV5P2I3zLFEKM4y81bN/PXJf+bbdq0iV4D+hP6/GDcSpdEkiRyr98g4+RpHLm5KDQaXMJKkLp5KyW8vbl9IYHqQiPUCk2R9d0Rb3CN87jp3ekxqBuTJk16KO1MSEggLS2N+Ph4nh/Sjx4d1XzxQQRKZcGg22YTeXHkdfYdUbN332F5b2LZU2nJkiWMePN/TPkymGdbFb5BFkWJEWNusvoXkSNHTsnJS2VPNVEUuXLlCrm5uahUKtp36ojqmToEtWxe7HsyTp8lbvpUpn0VRvOGShSSApVCjXDXjhUSIEoOZixJ5YsfjBgMak7vLI+P972XZrw99iZnrpTmly07H1YX7+nkyZO0b9eMOd+H0KKxZ7Hl9h3OptcLt1j182aqVKnCy8OHs3zVKqySiNLDA8luR8rJISQwiB8mTnzgGXdWq5UKlStjLVea0I6F32M2mck1GnFYrcT/MAXBaMTg5kaDevX44P33qVWr1l/tukz21JPXdMtkj0mzZs3YtWcnlSpW4rbtOpIYg4CAp9qbqq51CdVHFJhurlHoEEWJ3Nzc/0TQPWvObDShobiVLgnkTbdzKxlVaP9Qc2Iil7fvpiQVUCuKv+EJFMKIla4g2SVirsQ8tHYGBQURFBSEr68vw195k2+/+YLzly8ztJ8f9Wu54RQlduzLYvaidG7EqZk5a44ccMueWj169GD37l0Mf2cxp84ZGdDDn7BQLZIkcehYDj/OTGLPIScTvvqenTt3Ehsbi0qlonr16jRu3FheNiF7KthsNmbOnMnMGbO4fu06kijhcNqxCBJlqkTnRc3FpNTwjK7ILbWOZWtSadEgEJVCU6ioAChQsm6zGZtNRJIELlwx0bDuvRMQXrxqJSj48W3vuWPHDrw8oGmDe7erfm03AvwUbN++nYyMDH7ZuhWFqwsGP18UanXeGvOcXFJT05g+Y0Z+hve75ebmcvLkSSwWCwEBAURHR3P+/HnS0tMpUa1qoXPm5ORgNBpBpUbl7oahWhUct27h16oFB/bsp0OXziyYM5cWLVo8zEsik/3jyEG3TPY3Xbt2jTFjxuCwO/OepAsaRETMThM5jiz+eEdgcuaiVqvw8Lj3j+e/xbHjJ3CtU/O+5Qxly5K8YzeuFL/FEYBSUOIl+ZEppqB4SGvlAHbt2sW0aZPZvWs74AScHDvlZP+RWFxdlCgUAgqFlpat2jNx0ptUqVLloZ1bJnvYFAoFP/44iaioksycMZlp8y7j56PGahXJypEoWaocrdtU4KMP38VkyiDQT4PNLvL1VyIREaX5YMxY2rZt+6S7IfsPM5lM9O7Vm7079+FLEFW19dEqdMQYzxOrisfoFBGzsvJ+S4sIvAWFApVfALsPxnLkhI0GNbWFCwEbtudw8YoDtaDF4ObFvKWp9wy6z100cuKMjZmvP9ha6IfBZDLh4a4qNPPqjwRBwNtTxbVr1/hhymQoVYpyvbqj+MPyqtzrN9k9dwHDXniBxYsWIQgCycnJfPvtt3mJ6XJykCRQKgQqlCtHy2bNESUR5R92LLBarBiNRhQ6HYpft25TaLVIdgdeVavgUakSsfMXMmTYME4eO/ZrvgmZ7L9JDrplsr/h0qVLdGjfgZwkExXVNXGzeaFXuGKRzCRIsdwwXibLnk5d72YoBSWSJHHHeZO2Xdr+Z7bbsTscaB4kOFbmjawpEHBKzgKzA/5IgQIbVqIrV+Lo0aNYrVZCQ0P/8lYsefu4jqZqJSXfjPUjurwLJrPIul/SWfxzFhptAB+MGUujRo3k0W3ZP4ZSqeStt95i+PDhbNy4kdjYWNRqNVWrVmXBgvls2riMlwZ6MaBnWYID80bBT5wx8sOMWzw/pB8Tv5tCjx49nnQ3ZP9RI0eOZN+O/dTQN8Jb45f/urfGj1v22yjsEmbMKFUqDIaiZ40JSiVmM7z0dhIfvO6kQ0t39Lq835oco5Pl67KYMCUdf6EUVsFGQEAAm3acY+GKZPp2L7y9ZVq6nddHxxERUYaWLVs+mo4XwdfXl6QUO0ajE1fX4n9PLRaRO4l2skxnsHt4ULJ3DxSqInbfiIogsHtnflm0lBMnTuDv70+HTp24mZqKR91aRFatglKvw5yYRMzBI5z54QfsZjOm+Hh0fr759RhNRlAq8wNuAFtSMupfBxUUKiUlenTj6mcTWLJkCcOHD3+IV0Um+2eR13TLZH+RKIrUq1uPuAvx1HZrhkJUkJaaigIlKiHvqXKWlM5Z6RAlDeWo4FadGOMFbnKJNetX06BBgyfcg8ejUZMmXJGcRAzoe89y8b9s487qdVQWauHh8CmQRO1ukiRxSNyGWZFDWFgAFouRvDmGSurUqc9LL79Kq1atHrh9W7ZsYdDAXrw21J23Xim8rczteCs9h11D71aRrVt3PbRMtDLZk7J48WJGjvgfP30dStvmXoWOi6LE22NvsmK9yKHDJwgKCnoCrZT9l92+fZvqVaoTKVYgwqVMgWMmZy5bM9bi1akDLpUrICkk/Pz9Cn13m+ITiPn6OySTjRLaCJKcN/D2hppVtYgiHDlhJSdbIFhZlopuNTietY/az1YlqmQUc2ZPpm0zPQN7+VGhjJ5cY95D2NmL03ESyIqVaylduvRjux537tyhTu2qfPqeF/2fK/ww4DfL1qTy+gdJWJ1KfLp0xLdObew5OeReu45os6N2d8dQqiQKlRJJFLk64Vv6tm7DmfPnOHv7NhEvDkXjWXiUP+3ocWIXLELn50eF0e8A4HSKpKQko9Dr80fS7ekZxH/7A+E9uuJbp3b++2MXLaOUw8ne3bsf8pWRyZ68B41F5UVbMtlftGvXLi5fukIFfQ00Cg0qlQo3dzecOLGLNiRJxEPwJpgIbhivcDxzH9fFC4x6963/TMANMGjAAMwXL2PLyCy2jOhwkHP8JCGBgaQpE5CQsEu2IsvGizewq7OIDFfzYn81W5dHsW9daSaPDwD7YQYN7Ml33333wO2b9ON31K2hKjLgBggN1vLD52FcuniGHTt2PHC9MtnTSJIkZs2cRvOG+iIDbgCFQmDMyBJo1GYWLlz4mFsok8GyZctw2iRCdZGFjrkoDQSogsjavx8sdkRRxGqxFigjOZ0kbtxMkL8/Op2OEtpSNHTrjCG7Esd2+HNiVwA+5io0cu9KtHtekq9cIZNSpUvx6aef8vU3U4iJi6TXC3FUbnKReu2v8M00M42aDWTDxq2PNeAGCAkJoU3bjnw1KYVrN81FlomNszD+h2QqV66BUwKdvz835i/i7LjPuT5/ETeXLufq9Jmc+/QLErftAElCVzKKvQcOcPrsOYK6dyky4AbwqVUDjxrVMSUkkrR7LwCi6ATypvEDiBYLqStWoTa44lW1aoH3a/19SUhKekhXQyb7Z5Knl8v+Mex2OwcPHiQ1NRVXV1eeeeaZJzq7YfXq1bhIBrzUv0+1cnFxRaFQkpuTg81pQ5DAl0BixSs4fc18N24i/fv3f2JtfhK6d+/O1xMnEjt3AZFDB6P6wzRA0eHk1uJlaK1W3hs9mvffeZ80IR5fazBWyYLi1/9JQLaYwTX1KaIralk7vxIB/r+PhkeE6ejQ2puJ0+IZ/8VHlCtXjtatW9+zbTExMRw9epCpEwKLDLh/U72ygcoVNCxatOCxTimUyR622NhYzp8/w5sT7z167WZQ0aGVK+vWrmTkyJGPqXUyWZ64uDhccUNVTFLNaENN9qRtIXH+fNxaNsVx157V5vgEEjZuhtg4ZixcyOj3RnPj7GVqeTSmglu1IutLsNzCqXLQt29fBEGgd+/e9OrVi1OnTpGQkIBWq6V69ep4eRX9oOpxmDDhKzp3ukin/hd5cYAXvbr44eerJj3DztLVqUydm46bRyleemk4B4YM4dqsuUgqFZ6tWuBauRIKvR57cgo5R45xZ/NWjHG3Ubm6kpKUhNLPF0NU4Qccdwto1IDck6dJWbsB0/UbeNatjajXIzmdmC9dIefQESSTmdIvDEGpLbj7iMNkwsPV5VFeHpnsqScH3bKnnt1u54cffmDG7NncSUxElEQEBNwNrvR+rgdvv/02vr6+96/oIUtJTkErFt7HWafTodPpsNmsOOwODJIBrVXLR2M/YsCAAY+9nU+au7s7yxYvpluPHsR8PRG3GtXwqFABQakg9/oNso4cQ2MyM3v6dNq2bYvT6WTM+x+SIaTgL4agsmtxiDZSpUSSFXfQ6wVmfVe2QMD9G0EQeP3FYA4cNTJ50vf3Dbpv3boFOKlRxXDPcgDVo3UcPnP9r14GmeypkJGRAYiUCC56+cbdQoM0bNuXft9ykiRx5MgR5s+fz4XLlxEEgWqVKzNw4ECio6MfQqtl/zUqlQpREIs97qpyo6FHS44k7yVlwSJMQYFo/f2RTCbsCYmEBgczadEimjRpwoi3RjB08DAu556ljGulQr/ZGbZULtlO0q5zW8qXL5//uiAIVKtWjWrVig7UHzcvLy/WrN3Ixx9/zMTpSxn/4wU0agGbXUKjMdCxU38+/PBDjEYjVosZlZcnQS8MQXnXLimaoEB8Oj2LvmxpUhYtQ61WEejmjjMw4J4PngFcgoNQadS8OGgwh48e5ezcheSaTEgCKDVaPCtVILhNK3QBBae/iw4nxjPn6Svnh5D9x8lBt+ypZrfb6T9gAJt27EBfrQp+7Vqj9vZCtFgwnb/AjGVL2bl7N+vXriUwMPCxts3gZsBB0VOgATQaLRqNFqtoQelQ/qdzDlSsWJEdW7cybdo05i1axJ39B5Ek0Gm19OncmZdeeomKFSsCMHz4cCpXrsy0adP4ZdMWnGonggBh4WEEajwJ8YujYrnig2RBEBjY05f/vXWQ69evExUVVWxZ9a/r0KzW4m/ufmO1iahU8lem7J8t73tIQWKyjQpl7z3ylJRix93d755lMjMzGTRkCLv27kXh7YUuMgJJkjj58ypmzptH144d+fGHH9Dr9Q+xF7J/uxo1ajBdnIHRkYOrqugdLQwqdyq5VOe4ZQ+dGzZCo9FgMBho0aIFrVu3zv9+79q1K7du3WLcR5+QkhNPqCoSg8oDu2gj3nqTNCGJ+o3rMXnK5MfZxb/E09OTb7/9lg8//JAdO3aQmZmJu7s7zZo1w9vbG4AzZ86gUKrwbNsapUvR/8ZdypbBpUo0uQcOofL0wpKSgiSK+VPFi+K02hAQaNKkCRMmTOD06dNMnTqV+UuXEPb8QDzKlS3yfan7DyAYjQwcOPDvXwCZ7B9MTqQme6p9+umnfD5xIp7dO6MKDQFBAEEBkgiShMJkIX3xUhpUimb92rWPtW1Llizhf0NfooFrm2JvCgCuGS9yR3uNs+fPPJER+aeNxWIhNjYWURQJCQm557/btLQ0kpOT0el0hIWF0bRJPZrUSeCjUWH3PEfcHSvPtLvCkqXraNSoUbHl0tPTqV6tIm+97MJLg4ufbmu3i9RufYlnO73Ep59+WuCYKIrynsayfwxJkmjcqB7lIm8y9auSxZYzm53UaHmRfgNGMnr06CLLWCwWOnbuzJELFwh6rgvu5crm37RLTicZp8+S9PMa2jdtxvx58+QkhLIHZjabqVQxGlWyC1U96hZZRpREjmTtJKxqMHv27bnvSO2+fft+f5jrcIAgUK5cWZ4f9jx9+/ZFq73/7I9/guHDh7Nox3bc+/ZCUKtR6XX8dqMvSRKSzYZos2FNTCJlxhzUej12swl9UBChHdvjGV2pyHqT9x3A+Ms2zpw8SUBAAJD3d+rUpQtHzp3Dv3MHPKMr5WdLd5hMJO/ZR+bOPYx49VU+/PDDx9F9meyxe9BYVB62kT21zGYzk3/6CXXVyqjCwlDotHn7MgsCSBKiw4EoKNA1bsjW5Stp3rw5kZGRdOjQgXbt2uU/5X5UOnXqxOj3RnMp5TTV3eoX+YNvcZq45bhKt95d5ID7VzqdjrJli34i/kc+Pj4F9vVUqzWYLfcflf6tzP0+A97e3nTo2J05SxbSt7sf7m5FfyUu/jmVlDQYMGAAkiSxf/9+Zs+exa6dWzFbzPh4+9Cla08GDRp0z5F1mexJEwSBwUNeYMwHb7DvcDYN6hS+QZAkia8m3yHXpL5nDorFixdz6MQJIoa/iEtoSMHzKJV4V6+KUq9jw9yFbNu27b7LPWSy3+j1ej78aAyvv/oGF3JOUMZQGdVd20jaRBvnc49i1ucw9pOx9w24ARo0aECDBg3IysoiNTUVvV5PUFDQA733nyQhMRFNgD9eXp5kZmZhz8lFUKtAoUCy25GcThRqDbrwMBQaDSHdu2BSKsg5coxrc+YT0fM5fGrXLFCnw2QmY98Buj/7bH7ADXl/p+VLl/LSyy+zedlKUjZsRhUcCE4ntpu30KuUfPD224wYMeJxXwaZ7KkjD8/InlpbtmwhOTUV1+pVUbq6oFCp8gJuAEFAUKkQ9Do0URFIrq4cirnK6uPH6P/881StUYPDhw8/0vbp9Xomfj+RLHUKJ7L3kevIzj8mSRLJ1ngO5+4kIMKfMWPGPNK2/FfUqduQrbtN2O33Drw3bE1Hr3fLn7J+L2+++Sa5Zi/6vnSN2/EFM+A6nRJLfk7hw/FJ9Oo9iJIlS/LGG2/Q47kOXL+8lteHaRg/2pvnnrWwavkPNGn8DEuXLv1bfZTJHrV+/frRsFFrBr4Sy9S5CWRlO/KPxdww88boG0ybl8NHH31OeHh4kXVIksTMObNxKV+2UMB9N4/y5dCEBDNz9uyH3g/Zv9uAAQP4bPynJGni2J29jtPZh7mce4aTWQfYnbMOk1sWM2bNoGnTpn+qXg8PD0qWLElwcPC/KuC22WxcvHgRm82G02xGrVKjEAQQJSSrHclsyQu4dToUWg2ixYKEhFKjwbd8OXw6d8SlahVil6/Emp6RX685MYmbM2bhrVDy7jvvFDqvh4cHixYuZP/u3bzSrx9tSpbm2fIV+eT99zl36jRvvfWWPBtMJkOeXi57io0aNYpvpk0j7KP38wLuu0iShCiKIAhIdjspcxeid3Wh1LAhmBMTiV+9HnVSMut+/pkaNWo80nZu2LCBN19/k6SEZNwED5SiGovChE1hoWbtmsyYOZ2wsHtPh5Y9mEuXLtGsaT0+edeHQb0CiiyTlm6n5XNXadV2KF9++eUD1Xv69GkGDuhNauptmjVwoXIFPblGJxu3GbmdINKj50AmTJjAuHHjmD3rOyZ8GMRzHX3yb9gkJMwmBx+Oj2PJGitz5y2lRYsWD63fMtnDZrVaGT36fZYtXYBSYSYqQovdJnH1hhUfnyDee/8jevfuXez709PTKV2uHL49u+FVtco9z5W8ey/mHbuJj4v7VwU5ssfj1q1bzJs3jw3rNpCdnY2vry/dnutG79698fO7d86B/4LU1FSmTZvG3AULSElLxWq2YJNEgl99GYWrKxpBhwBYJSuSUoHy1/wK2QcOkbl1GxGvv4pvaAjGXCNZ6ekkfD8JrbcXHuXKYktKxnrzFhElSrBo/nwqVSp66rlM9l/2oLGoHHTLnlodO3Zk444dhI8dkzc16i6iU0QCBEXeVPOEH6eiUasoP+L1vON2O9emTqeqXwDbtmx55Dd6NpuN9evXs3PnTsxmM/7+/nTr1o3q1avLN5kP2ahRo1i8aBofj/Knbzc/1Orfn6BfuWZm+NuxpGb6sWHjVkJDQx+4XqPRyM8//8zSJQu5c+cWWq2WOnUbMWjQIKpWrUpSUhI1a0Qzargbw5/PW//tFJ2YTWZMJhOiKCKKEv8blcyVm35s376bEiVKFDiHyWTi7NmzWCwWAgMDKVOmjPz5kD1RKSkprFixgtjYWFQqFTVq1KB9+/ZoNJp7vi8hIYGKVaoQMKAPHuXLFVvOmp7BreUrsZw/Q3hYCdzc3GjTthMDBw6Ul2LIZH/TzZs36dy1KzeTkzFUr4JndDSSKHJl8lT0VaLxbNEcjUKLQlBiFc0o9HoElQqn0Uji1Blog4Px7tgOfz8/FEoFTqfIzWUrsJ88TeXKlQkJCqJXz56PZcmeTPZPJQfdsn+8/v37s2jFCvz69MRQtXL+67+NcguCAIKAPS2d+Anf4F2tKiWH/J4dM+vCRRLnL2L75l+oXr36k+iC7BFwOBy89957LJg/gwA/aN3EBRcXJWcuWDhw1EyJEqWYN3/xA68bf1Dffvstk374mGNby+HupsLusJORnoHoFFGgRCHkBf+7D+Uy+I14PD2CWL12NTVr1iQ1NZXvv/+eZUvnk52dAUiAkujoarzw4st07dpVDr5l/yhWq5XI0qXRNHiGwOZFT+9NO3KUxFUr8NBa6dzahbKlvEhMtrF6Uy6Z2UreHz2Wl19++TG3XCb7d7Db7TRo3JiraalEvPA8Gk+P/GNxO3aRtG4jrtWr4la3FjqDN3ZsCHo99jsJpK9Zj2gyEdxvIJKHDncPd1x+zXSetHM3HDrKjZiYJ9U1mewfRU6kJvvHCwsLQ6VWkbVnHy7ly6L4NbNo/nMiQUCSJLK270RyihhKFszE616uLAlaLXv37pWD7qfcb3/TBwk8VSoVX375JYMHD2bevHkcOrgHm81KSGg4kyb3e6BROoDbt2+zZMkSYmJiUCgUREdH07Nnz/xtV/7o/Pnz1Kqqwd1NhSiKZKRnIDklNIK2QLsb13ZHr0nGkm6j53M9Wbp8KcNffoGcrBv07+ZO53ZRuBmUXLxqZv6y87z6yvOcOnWKsWMfLBmQTPY00Gq19OjalXnr1xHQpBGCUok1LZ2Mk6ewZ+dgTc/Acuk0A3u48/oQHwL83DEY8rb6G/2myLdT4/lk3PsYDAYGDBjwhHsjk/3zbNq0iYtXrhDxf8MLBNwA7jWqY7Paydyxk9xjJ9BHRSLotDjSM7Anp6Dx8SW4dz80nl5YJQui+Pv4mz07G2/34ndkkclkf40cdMueWu3bt+f7yZOxpWeQNGcBPp2eRRP4+zpeZ3YOmdt2YDx1GoVahfddo+EAgkKBUqfDbDY/7qbLHkBubi7Lly9n9ty5XLl6FUEQqBwdzZBBg+jUqRM6ne6e7y9fvjyff/75nz6v1Wpl1FujWLxwMaJNwlXyACQWs5RxH3/C/73xWpGJX/K2Bsv7/2azGdHpzFsr94dAWaHIy/cXrivLjeRL9OnTA2/3TH5ZVoqQoN+3pAkN1tKysSdzliQx+vMfqFChwj3X0MpkT5uhQ4eyaNlSYpesQLKZMV08h4dBIjRIxcULRto01vPuy64oFIoC+3RrtQre+b9QUtPtfPH5WHr06HHff+8ymayg+QsWoA0PwyW48HaXCkHAULUyHuWjSd28iewTJ1ALaiQvA0HP9UIfEfnr9n4SIOUlXANEhwPjmXMM6lf8rgUymeyvkYNu2VPrmWeeoWrlypy9cwdbdjYJk6aiCQ1F5e2N05iL5foNBIUChVaLoUxpjJKIkJWFVqtFq9XhNJuwZ2cX2N5C9nS4du0a3Xr04HrcLVzKl8O1VXMk4OSlKwx7ZTg/TJ7M8iVLCAoqfu/sv8LhcDB40GA2r/uFUupKlDCURKXI+xq0ihZumC7zxSfjycrK4rPPPivw3lKlSjF/jg2LRcRkMqFAWeTI9PEzZux2AQ+dN242LxITrjN7YoUCAffdBvUK4ODRXKZM/p5evXrJo92yf4xKlSox/tPPGD78RQL9BT4a7U/b1l6cOGVi+BtG3njRE61GwO6QcDodhfbpHj4kiCWrr7Bu3Tqee+65J9QLmeyf6dqNG+jCShR5TKvVYjQaEXQavBo2wnzuAqVUpYkxxoBC8WvADU6c+eUBkrbvQmG20K9fP/bs2cPcefM4d/ECgqCgaqVKDBw4kLp168q/UzLZXyDn8Jc9tQRB4KepU/EWBHQGAwGNG+Li4Y6QlYVks6EvVxZBq0Xh4oJbk8ZYzTYsJguZGZmkpCSTdOAQLmo1HTp0eNJdkd0lPT2dLt27E2c2UXLkG0QM6Itfg3r4N6hH1NBBRL42nPO343iuV6+HPkthxYoVbFq/maq6ekS6lM0PuAG0Ch3lDFUoo6rMtMnTOHbsWIH39unTh6wcJcvXpeB0OvPXcN9NkiTmrchAJ3rgo/bHJlooV1pN7Wqu92xX/x6+XLp0ljFjxrBo0SJiY2MfTodlsj/BZDIRGxtLfHw8Tqfzgd5z6dIlQoN1LJ1egk7NtagdJk6fyMTHS0ntajq8PFVo1QJZWVn8MYVMZLiOsiW1nDp16hH0Rib7d1OqlEhOR5HHNBoNKpUKh+TILxOoLUGAEEDy0qVkHjmMw2LGITnQ6rQ4srK4teJnsnbuZuTrrzNy1Cg6duvGmkMHuePtRZyXO8t37aRtx44816MH2dnZRZ5XJpMVTx7plj3Vypcvz4a1a3lz5EgOHTwCWi0qVxfMySnYjUZ0JaMI6NQFjcEd+G37JhHTndskbd5Ch6bN8PX1fbKdkBUwb948YhMTKDnyjULr0AD0wUGEDR7Ame9+5Oeff6ZPnz4P5bySJDH9pxl4SX74aYsfQQ/XlyY2+yqzZ8+mZs2a+a9HRETQtVsvPp6wAHdXA/WqF1zzJkkSk+elsWGbmYramgiCgENhpnJ5bbGjApIkkWvMJcjPhM1q5tsJE1EqlWi0alq1acVHH39E6dKlH0r/ZbLiXLx4kWnTprFm9TKsVgsAISEl6D/geQYOHIiHR+F/p5CXPGbF8oW8PMiHGlUCcTgdOB1OtForGrUxf2TbzaAkNcOB1WotNI1crRaw2+2PtoMy2b/QM7Vqs2DTRqQO7fNHrvMJeftnp6enk33lAgpRwFXpTrCmBNm5GaRt2kjazm1oAvzRazTYk5Px8fTiw08+YfnKlRy7fIngIQNwK13q960xJYnsi5fYtnQF/fr3Z+WKFXJGc5nsT5BHumVPvfLly7Npwwb2bN/OB2+8wWt9+lIxIgKlCGJiKrmnTmPPzEJyOrGnp5O+cyep85egNjo5f/a8fEP3FBFFkZlz5uBaObrIgPs3+qBAdKVLM2vO7Id27tTUVE6fOkWwJvye5QRBIFBZgk0bNhc69tVXX1O3XluGvJ7MC2/dYd3WbPYeMTJ/ZQbPDrjF11MyiVRWJ1QfCYDNacNilVAoCgfdkiSRmZmJMScXiwkUKKnu2oAWHl0pRWW2r91Fm1ZtOH/+/MO5ADJZEdauXUub1k3Zv3serw/TseSnUOZ8H0yDmql8+/UHtG3Tgr1793Lo0CHOnTuHw/H7yNru3bsxm7Pp1SVvr2SVUoVWq6VkpCtJqQ7i7uR996pUAmqVgMViKXDurGwHMTesREREPLb+ymT/FgMHDkTKyiLj9Jkij6s1atx1OkwnT+EULWxOWcaJzP0oHAo87B6ocu04Y2/jabEyfuw4Lpw5g16v58jJk4Q9PxD3MqULPDAWBAGPCuUJHdifXfv3s379+sfVVZnsX0Ee6Zb9Y0RHRxMdHc2dO3f48bsfqexaG5M9h1s795O1e09eIQk0kopSmihCPMM5nLiTjRs30qlTpyfbeBmQN7X8Tnw8/k0a3LesW7kynNm05dcEZn//+aDRaEQSQaO6f8ImrUKHyWQs9LpOp2PevAW8/rovM6dPY+feZAQBkBT4KsKo5VIWH01eDgGz04gNO/uOCOTkOnEzFPy6NRqNWC1WVIKWHXuzEB0qvFx8UAkqwvQlCdKW4EjKLgb2H8ihI4dQqeSva9nDdfLkSV4ZPowOrZR8M7ZcgT3vmzX0IDw0gXFfn6J9++YYXPWAguDgvBHwF1544dcppiKB/gVHu9q38OLD8SrmLs1i9Jt5M42UCpAksUC5RatScIp6eT23TPYXVK9enW6dOrN85WoEQcCzSuUCQbI1LZ07CxbjrdaQq9HhafcnUl0WV6U7Op0OpUpJsjWeS0knmf7TDLp27cqMWbPQlyuDS0hIsec1REWgi4pkxqxZdOnS5XF0VSb7V5Dv4mT/OLt378ZqsRHhXgqVQk15sSqptiTskg2NoMVXG4hKyPtoG8webN++XQ66nxKimHfTLSiU9ykJglKJJEmF1oH+Vd7e3iiUCozOHPwIvGfZXEcOPoFFL0v4bcuykydOcuX0NSrpa+Gt9iuwPtzoyOGEcR+RJaPIykpi+vwk3nzp95sYSZLyk7GZjBJzlmbhqwhHp3TJL6NWaKjoUpMjV3ewfft2Wrdu/TevgExW0KRJPxJZwsk3Y0sWCLidTonX37/O6k2ptG/pQruWLlQq50tOroIV61KZ+M2HbN2yiQEDhwAK4hNtBRIFurgoGdIngO+nx1O5oo6OrQ04RVCqfj/HnoNZTPgxlZ69XsDPz+9xdlsm+1cQBIFJP/6IJEmsXLKClC3bca1YHoVag/n2bSxXYggNCCDLIRIolKCqb71CS50CdaG4q704FLONsWPHcv7iRXy6db7vud0qVeDIxl9wOp2FEiRC3h7iGzZsYMH8OVy5chFBgIqVqjJgwGBatGghP0SW/SfJn3rZP47RaEQpqFAp8kZX1AoNQbqiM3iqRDW5ubmPs3kPLCEhgYULF3L02DEcTgeR4RH06dOHatWq/Wszg3p7e+Pl5UXOtWt4VCx/z7K5164TFRVV5A/6X+Hu7k6rNi3ZvWYf4VKpYq+xU3KQJMXxaq/hxdbl4uLC8hXL6fFcD06e3oe3xR9fdSAgkO5IIk1KIrJ0JCtWLmfFihV88+2nqFUCw/oHotMpsNlsiE4n8XcE3v40gVs3FdR1q1LoPJ5qb1xN7qxatUoOumUPVWpqKr9sXsdHb3kVCLgBfpyRwJrNaUz6IoCOrQ2kZzgQFE4qlPWgTg03+j9npNeLh1i/3g9XV0+W/JzKiJcLjoy98b8Qbt228vLbSSxZnU27Fq6UKakhKyeTpT+nsWW3iabNnuWTTz59nN2Wyf5VtFotM6ZPZ9jQocyePZsDhw9js9spHx5Ov2+/xWQy8fab71DD0LTY3zwXpSuhipKsWvkzkiii0Nx/nbZCq0GSpCKD7jt37tC3Tw+uXDlLvVp6Bj3ngijBzv07GDJ4M9WrP8PceQvx8fF5KNdAJvunkINu2T+Or68vThyYnSb0d40M/pEkSVgV5qduFEUURT777DO+nzQJqyCgiYxAoVax89QpZs6bS+MGDZk5ffq/8gdJpVIxqF8/vv5pGo6WzVHdtXfv3WyZWZjOX2DI2HEP9fwvvPACmzds5prpIqVcKxQ6LkkS53OOo9IrGDhw4D3rCg4OZtv2baxevZpZM2dz7uxZAEpXKs3o59+me/fuGAwG3nrrLZxOJ+O//4qpc9Np08wVvU7k9PkcDh6xo7S7UtPQFIPKvcjz6CRXkhKT/n7nZbK7xMbG4nTaqFujYEJAq1VkxoJEBvV0p2NrAwBqjYDF8vta7soVXflwZAAjPtxI5y49mbloMe1belGu9O/fx0qlwMRPo6hXy51RY2+yYasZNzczoKB8hUp89vlQ+vTpI494yWR/kyAI1K1bl7p16xY61r9ff9wl73veKwGU0EdxPecCOg8XTLfv4Bld6Z7lTbfv4Ofnh0ajKfB6dnY2PXt0xW65yi9LS1Gx3O/nffMlOHIihxdGHKZ/v96sXrO+0Ptlsn8z+ddO9kTZ7Xbi4uKw2+0EBQXh7l448EhPTyc5ORmdTkeJEiVo0aIFnl6exGbHUM5Qudi6U21JWBXmp27N0ZgxY/h+2jS8WzQlvEE9lL9m85VEkayLl9i9cg3dnnuO9WvXYjAYnnBrH74hQ4Ywe948YucsIGJw//z+/8aek0Ps7HmUCAikV69eD/XcjRs3ZuTbI/ny8wlkZ2UQ6VIOL7UPkiSRakvkuvkSudpMJv344wMld9LpdPTq1YtevXrlT4P/42iCIAi8++679O7dm3nz5rFv704SkxK4ejmNivo6RHiWQSkUP5rvwIabu1uxx2Wyv+K3PAlOZ8HlG1t2ZZKRZWdQr7u+iyXgD5/rzm19GPd1Mn5+foSUqEz3IWcY8bIv3Tv44GZQIYoSuw9ksXBVOmqND4sWTyMiIgI3NzfCw8P/tbN5ZLKnSU5ODmq09y2nUeSVqV+3LjuPHUds2RxFMQ/EnBYLxpNneGV44dlgCxcuJO7WJXb+XJqIsML5U2pXd2POD+E82/cQa9eupXv37n+yRzLZP5ecvVz2RKSlpTF+/HgqV6tGjbp1qduwAWUqlOell17i9OnTABw4cIABAwdStmIFnmnYkGq1a1O9Vi3mzJlDj17PcdsZQ4Y9tcj6raKFi5aTVK4aTZ06dR5n1+7p3LlzTP7pJ3yfbUtgi2YFAk5BocCzYgXCXhjMyYsXmDlz5hNs6aMTEhLCkoULMWRlc3X819xZv5GsS5fJunCRuFVruDZhIgGCghVLlxa7VdHf8c477/DD5O8xlNRw1LKTrVmr2Jq1klP2A4TXCGbx0kX07NnzT9crCMI9A4mIiAjGjBnDlq072b37IN7efoiI9wy4zU4TWUIazZs3/9PtkcnupVSpUuj1Brbtzizw+u14K+4GBVHheSNQEmC1SahVBaecarUKypfRkJKSwqpVa2nUtAcfTciiWrOLNHj2MtWaXaT/8DtYxYqsWLmODh06EB0dTUREhBxwy2SPiZ+/HxahcFLQPzI6c1AoFHTp0gWdU+TW4mWIDmehcqLdTuyipbir1QwYMKDAMUmSmD9vJh1auRYZcP+maiUDDevqmTvn33mPI5MVR5AeVpaiJyQ7OxsPDw+ysrKKHCWVPX1u3rxJ527duJmYiGvVynhGV0RQqzHG3iLr0BHURiNdOnRk2apVCH6+eNatjUtwEE6LlYxTpzGdPU90mTJo1WpOHTlNqKIk4frS6JUu2EU7dyw3uGm/gneIBxs2bSAqKuqx9k+SJE6ePMncuXPZv3c/NpuN8MgI+vXry8GDB5m7dg1l3hmJcI+1yreWrsQ3JZUTR4/+a6dfxsXFMWvWLOYuWEBmVhYAAf7+DB4wgEGDBuHv7/9Iz+90OtmzZw8xMTEolUoqVapErVq1HltA8Prrr7NoxhLqGloUOfVPkiRO5xzC6pXD2fNncXOTR7tlD9eIESPYuXU2O34ug7tb3vfM9PmJfPHdLa4cjESpFLBaRTKynXh7eaPRaHE4HJjNZhwOBz2GxWFxRrNkyRJKlChBYmIia9asITk5GRcXF5o0aUL16tXlIFsme0I2btxI3559qaNvjofau9hyp7IOkeWaTO26dYi9dYsrMTFoggLxbVAP97JlkCSR7EtXyDx4GJ3ZwsK5c2natGmBOtLT06lUsTTTvgqgfcvizwUwZ0kSH36Zza24RPn7QfaP96CxqBx0yx4rm81Gg8aNiElPJ+KFIWg8PQscl5xOrk6dTtaFiwS2bkVoh3aFvpDNiUncmj6b2uXLU7N6dRbMW0hWRiYKlDglJ1q9hnbPtmPs2LGEhYU9xt7lTZd//fXXWbJwCWqnFl8hCCUqssUMMkjBJjjwbNKIsM4d7llPztUY4mfN5fC+/ZQpU+Yxtf7JsNvtpKSkIAgCfn5+/9qHDH+UlJRE61ZtSLyWRDldNfw1wfmfdaMjh8vGM6Srk5g2fao8Be8vys3NZcOGDdy+fRuNRkOdOnWKfbCSmprK0qVLOXv2LJIkUbp0aXr37k3IPbbO+ae7efMmrVo2pVRELrMmRuLvp+Hk2Vw69D3PnO8DaVhXT1a2E7Vai6enF1nZWVgsFhSCREqak2Zdb6NSa1AoXOjTdzCfffYZavX9kzDJZLLHw+FwUKtmbVJjMqjl1hi1ovAa6nPZx7hkPYfGYMCtYnlUri6Yb90h98YNQEKrd0GpVKBTa+jUoQP/99prVKxYsVA9ycnJVK1SjtnfB9Oysec927VoZQqjxqURF5f00JKlymRPyoPGov+Nu1vZY5ednU1cXBwA4eHh+WuTN2zYwKWrMUS+/kqhgBvytokSHQ50pUphaFh4ewsAfWAAQT26cnj2fD4aM4Z3332XrVu3kpqaiqurK02aNCE4OPiR9q84b775JovmLqa8phohLpEohN9XcOQ6stmSuQarJBa7zcZvlC56JAnMZvPjaPYTpVarn9jf60kKCAhg/YZ1PD9kKEcPH0Jj1aETXXAKDnLIxMfPm+lf/0TXrl2fdFP/UWw2G2vWrOHjsWOJuX4dp8OJBhUalRZBBdFVKvHpZ5/SoEHeXvFOp5PPP/+c6T/9iEIwU6OKFkGA6TtsTPx2PN2f68P48V+i1d5/XeQ/hdlsZsWKFcyZM52srAx2H8ilVO0Mosu70LWDN96ear6ekk7ZUgG4uurw8PAkMzMTm82Cu0GJXqfg22mZeHup2bO2Emt/yeCTb34iOzuLqVOnySNXMtlTQqVSMXfeHDp37MzB1G1EasoRrAtHKSgxOXM5nXWYO+JtPOo/Q2TnjqgNrvnvtSSnELd4KarUdL6ZMIFWrVrdMzGtl5cX7u4eHD+Ve9+g+9ipHMJKhMkBt+w/RR7p/pdISUlh8+bNpKWlYTAYaN68OZGRkY+9HVeuXGHy5MksW7UK068Bo8HVhV7dn+Pll1/mzZEjORB3i6j/DS3y/eaERC589S0+z3VFFxGBv79ffsKfu0mSRMzX39GrWXMmTZr0SPv0oM6ePUuThk0po6hCmL5kkWW2Z6zHWimYEr173PPzmn78BGkrVnPu9GmCgoIeVZNlTwFJkjhx4gQrVqwgMTERvV5Po0aN6NSpE/pisrvLinbp0iV69e3DxStXUJYIRRsYDHYHlouXEXOMBCmDcWLHbjAzb8E8WrZsyahRo1i86CfefNGbQb0D8PTIexZtNDpZtjaVT75JpmHjDsyaNedfMQsjKSmJPn16cPnSSVo0cqF1Uw80aoFDxzJZuiaD9Awnehd37DYzndoY+PKjSAwuTrKyM/HyUOJ0wMSfMpg8J5Nx70YwuHcAAOt+SeelUfHMX7BSzkEgkz1lLl26xJgPxrBj2w4cNidKhQqHaMfktOJepyYl+/VBoSj8sMxptXHtxyk0q1SJFcuW3/c8H3zwAatX/sihTeXQ64sOqNMz7NRqdYk3Rozjtdde+9t9k8meNHl6+X9EZmYmI0eOZM3qNdjMdnRKPTbRhlIt0Lxlc8Z/Of6BsjA/DHv37qV3//6YlErc69TErXQpALIvXSH76DHcBQUKQUCsXoWg1i2L7s/Zc1ybM5+Qt94EJHx8fIqdrhi7dAXRChVbNm9+VF36U0aMGMGCnxbRyK19gRHuu53PPs5F+wU8WzcnoGxZ3EqXRCjiocK1SdNoGBnFqpUrH3WzZbJ/hbi4OFq0bk2i04GhbWtc/IJQkHfTJzkcZJ08TubWbZRUlMKGGYeXmekzpzNwQA++HONPn25Fj+Ds2p9Fv5dv8cOPs+jWrdsjabvdbic2NjZ/FwfPImYBPazzPNu+DcmJJ1gwOZLyZQrmEsjOcTDszeucvqDn/dFjGTfuA+zWLJrUUxAeqsBoktiw1UiuSeLd10vw0qDfHwhKkkTbXlcILNGaefMWPJL2y2Syv+f69evs3bsXi8XCpUuXmDF/PqXeHoHW26vY96QfP0HqitUcPXiQkiWLHlD4zc2bN2nerAH1azmYOiEKna7g/U12joOBr1zn6k139uw9hK+v70Ppl0z2JMnTy//lJElixYoVvPS/l8hMz0KBAoWgxFXjTjl93v7Duzfuo/XJ1mzcvPG+X5R/V1xcHH0HDsQWFECpAX1R3LX3omtYCfwb1efmnAVkX7iIT27xbREUv94ki85fg9HipylKTicqTfEZMh+3QwcO40NgkQF3hi2VC8ZTpDiTQSmQuW0n2bv2ovXxJqBJI3zr1UUSJRxOJ6l79mKPu83/vhj/BHohk/0zff311ySbTXj174NCo88PuAEElQrPWnVAkri2eSuN3FtwLH0Pn3wyjlIRSnp3Lf7Gr0l9DxrW1TFn9oyHHnSnp6czY8YMZs+bR2JyEpIEGrWaDu3a8fJLL1GjRo2Her5ffvmFs2ePsW5B4YAbwN1NxayJJWnc6TLnzp3j6NHTzJ07l9GjRxEcoCA0SEv/HoH0e86fEiEFp9sLgkCX9h588cN2JEmSp5jLZE+hqKio/OSyL7/8MtoSofcMuAE8K0eTsHwVBw4cuO+9ZEREBDNmzuf5If1o3OkS/Z/zpH4dd0QRdu7LZOHKLKx2LxYuWioH3LL/HHnLsH8gURQZNWoUA/sOREpXUV6oQVVFfcoJ1RBtcDLrIMm2eOoYmpGTYGLY0GE86gkNc+bMIdtuI7x/nwIB92+UOh3hA/si6HRkHj9ZbHv0IcEIggLT+YsIggKVqujpSaLDgTXmGjWrV3+o/fg77HYbiiK2f0q2JrA3exuZQUp8unch/K1RBI8agW//3iiCAold+TNXFy8l9tgxrs6YxZ2f14LNzrZt27h169YT6IlM9s+SlZXFslWrMNSuCRpNsduwuVergWBwIcEahzf+nD51jO4d3O8bIHbv4MXx40fIzMws8HpaWhqTJk2iYePGlCpXjio1qjNq1CguXLhw3zbHxcXRqk0bPps4kezwEgQPGUiJ/w3FrXULVh/YT+v27Vm8eHGh9zkcDkRRvG/9RZk/fw61qmqpFm0otoyrq5J+3T1YtXIxarWa3r174+5m4IcvSrFtVTTvvl6iUMD9Gy8PFXabHYfD8ZfaJ5PJHh+r1YrwALkqFGo1glKJ1Wp9oHqbNm3Kps07qN94AN/+ZKFDv1g6DYhl+kKR9h1fYvMvOx76A0WZ7J9AHul+yl2/fp25c+eyedMacnJy8PT0wsc3iN3b9xDhqECQIgyl8Puf0V8IIVVK5KL5ODqFnrK6Kpw6cZDjx49Ts2bNR9JGp9PJ3AULMFSvivIeX+AqvR7PmtVJ3b2X7AuX8KhYvlAZjacH7mVLk3PgEL7VqhV7M5x2+CiC2VJon8gnKbJkJAcvHSvwmk20cjR3H+pyJQns2h1BpcKJA6VkRx8ehiYwEHVgABkbNiHsOYi7ypNoTVUA5kyZx+pVq1m1ehWVKlV6Aj2SQd5Drv3797NhwwYyMzPx8PCgbdu2NGrUqMh8A7LH78yZMxjNJkIrVsQICMXMkFGo1ejLliH19C18FL7YHXa8PO//M+jjpQYkjEZj/tTv7du3M3joUDJNJlwqVUBXrTJpJhPTly9jxpw5/N/w4XzwwQdFfkbsdjs9+/TmemYGkW+8WmCkyTUiHN96dYlbtYZX33iD8PBwSpUqxcKFC1m0cA534u+gUCioVrUGAwc9T8eOHdEU8aCzKJcunmNAd9f7lmtUz4OvpsQSFxdHVFQUao2WmOvm+yZHunrNjKenh5zBXCb7BwgKCsKxcyeSKBa5zO03lpQUcDj/VH6ZsmXLMnHiRMaOHcvt27cRBIHw8HBcXArPsJHJ/ivkoPspNnnyZD79ZAxeHk46tTEQ6K/h1p0Elqw6g1OUkJAKBNy/8RUCCZNKc9N0ldKuldBYtaxevfqRBd05OTlkZGbgGx5+37JuUZFk7jtA/JLl0KcH7uXKFgisbRmZODIycKakkrJyFS59e6E2/D4qI4kiGSdPkbphM8/360epUqX+VFsvXrzIjRs3UKvVREdHExgY+KfeXxxJkqhXrx7r127gmvEiIboIdEo9cebr2NUSoe2fRVCpAAmn5ESj1eJmMGCzpuFerTq2SzHorqfT1Kt9/vWIFMtyNHE3vXr05tCRg/kZ4GWPz7lz53hh2AtcOn8JreSCRtJhEyz8NGU6pcuW5qfp06hateqTbuZ/ntVqRZJAqdWA3Y6EVOzCFIVGg4iIVbKg1Wi5eev+ozfXYy0olKr8gPvYsWP0HTgAKSKcMj26oXL9PZAV27Uldf8BvvnxR3Q6HW+//Xah+rZs2cL5S5cIf+UltF5e2Gw2RKeIIAioNRoUCgUlunbi2u07fPzxx8TevIrdlkaXdgaqRftgtYn8svMEr726n9mznmHBwiV4ed17imiePz/jSavV0r59FxauXMqLAwOLTLYEYLOJLFubTbfur/zpc8hkssevR48eTPrpJ7IuXMKzUoViy6UeOIS/ry8tWrT40+dwd3enQoXi65bJ/kvkoPspNXfuXD4Z9z6vPu/B6y8Go9XmPYU0W8y80E/Dd1Ozmbf0HDqbC35C4aePQUI4t6Sr3LHcRIsL6enpj6yteVs+CEi/TSmU8m6CzWYzDqcTQRDQaNS46F2QnA5cXV1p1rAhW+cuJCXAH9eKFVCoVZhuxWG+dJkSgUG889NPfDh2LDGfT8ClYgV0QYE4rVZyz5xDysigXYuWlCpVim+//RZ/f3/at29/z+RDmzZt4ruJ33H08FGcjrypmVqdhnYd2jFq1CjKly886v6gVq5cyffffc/Z0+dw2BycthzmfPYJgvVhZImZ6KPLonI1ABIOyY6EiKurK0aTEQEBjaDBvVoNUmNWYnIacVXlBdcahZZqhnrsvbWJVatWPVWj+v8FFy5coEP7DtjTJKrrG+Gt9kMQBByinSvGc5w7fYG69etTpXJlOnfsSP/+/QkNDX3Szf5PCg0NRalQYEtKRunni9PpLHKpB4AtPh5PSUe6kETNWvVYtvYob70iotEUPdIjSRKLVmbQqlUHXH8Nrsd9+il2b29K9u+L4g9LYBQqJf6NG+K0WPn2h+8ZMmRIoW125i1YgCY0FMnLk5SUFJxOZ/4xQRDQ6/UYDAYMlSqwd80qWjf1YubE8vmZ1QEG9Qrg5NlcBr16hEED+7Hq57X33X6nbLlK7D20jzf+d89i7D2YjU7nkv95Hjp0KM/+vJTPJ97mvTdCC81AEkWJ9z+LJStHw6BBg+5duUwmeypUrlyZhvXqsXfNOvSBAWh9fQqVybpwkZzDR3n9rVH/qm0TZbInQZ4b+RSyWCyM/2IsvTq78PZrofkBN4DT4cRVr+TDNwJo01zPbdX5ItdHawQtrriR68jGjhU3N7dH1l6DwUDZMqXJvnARp8NJaloqGZkZWBx2nIKAAwmjyURKagppJ09TpXJlli5ezM/LltG2eg3Up8/CoaNUUGv5/ssJHNq/n/79+3Ps8GE+H/MhUXYn0qEjuFy8TIuq1ahUphy/bPiF0aPG8MWHX/LKi69SsXwlRo4cidFoLNS+yZMn0693Py7uj6GSsg5N3DrQ2NCeCGd5Ni/fSuuWbTh48OBf6vvnn3/OsCEvEHcikSrqerTx6U4dVXPCKE2KKYkMZzoqH2+cOLBJVpw4cXd3R61WYbFYUaICBNQ+vqAQsIoF9+V2URrwIYD58+b/pfbJ/ro3Xn8Ta5qT2m5N8dH4IwgCGbZUtmWs47LjEqqyUehq1+KyQmD8jz9SrVZNJk6c+MjzJ8gKK1euHDWqViX9wCH0Li6IOBEpvO7ZmpiANe4OSCI6g5Zx48aRma3moy9vFft3mzQzgUsxIs8/n7fN4ZUrV9i7fz++jRsUCrjv5tewPmans8h12VdjYpAC/MnJzkFyglrQohF0aAQtCkmJyWQmLS0N4+3bhAYpmf5NGJ4eKkRJxOl0Ikp5fasWbWDKhBIcPbqfnTt33vc69e8/iCMnLZw6l1tsGZPJyYIVmXTp2iv/d6N69ep8PPYLpszNYcDwGHYfyMLhkLDbRbbsyqDH0KssWW3hq69/eORJO2Uy2cMzfdo0SgcEcuPHKcRv/AVLcgoOoxHjzVhilywnfv5iurRrz+uvv/6kmyqT/ePJI91PoXXr1pGZmcIrz5cptowgKHi+txdbdsWT4UjFm8Lb3YhIWEUzZpWRVq1aPbL2CoLA0MFDeP3tt0m6chWFrzdKV1eEu0ddJAnzrTiyz1/gRkAg77zzDm3btmXunDnFrov19vbm5Zdf5uWXXwbg1KlTdO7YGVu6SEVtTQJdS6AQFFidFm6ZYpg9dQ4Xzl9gxcoV+euG9u3bxwfvjyFELEk59yoFRmgiXMoQKkZxPHMP/fr04/jJ439qq54NGzYw4YuviBTKU8rj9+lTQb5a3I0e+BmDOSztxG7KxS7aUKpUuBsM6F30OOwOkCSEXzOdixYzSBKqIpYLuCu9ib0R+8Dtkv19p0+f5tiRo1TS1kGtyFufmm3P5EDODoSIYEKf7YDayxsRJ3bJhqfBQPr+A3z46aeo1WqGDx/+hHvw3/PaK68w4PnnyT1yFHWVythtVlSoUQhKBAQcuTkkr1mN0ilh1Gfxw9ffU6dOHcZ/OZGRI14h9nYM/xvkT/3a7ggCnDhjZMaCJNZtMTNi5AfUr18fgJMnT2J3OvGoeO8pkyoXPZrwME6cOFHoWGpaGg6DC2pBUyDLOgioBAVKVFhM2ZgvnmX4YDc0Gifp6enYbLb8klqtFhcXF56paSC6vJp58+bcd/pnmzZtqFixOkNfP8WCKZGUK11wfWVOroMXRtwg22jgpZdeKnBs2LBhBAUF8e23X9L3pbPAb8nSVFSrVpsFC9+hadOm9zy/TCZ7PERRfKCcIwEBAWzesIFvvvmGBUsWc2PPXiQJFIJARFgYL3z0ES+88AIqlRwuyGR/l/yv6Cl08uRJypXSEhFWeDsstTpvXbCESLWKOry9FOSY0vEWCgbdZsmIiRwUIkSViqJJkyaPtM09e/bkw48/JnHhYvz790EVdtc6bEnCcvMWKUuXo3Rz49bteGZPms/0qTMoXaYUU3+aSvX7ZCF3OBwMHjQEMV3gGbfmqBS/J+rRKnWUNlTCzx7E4X27+fLLL/noo48AmDplKnq7a6GA+zcqhYqqhnrsTlnP0qVLefHFFx+4z5MnTcZd9KKUe8Gbb9EpYjFbUEoq/B2BJJ+/iGfTpoiCSFZWFg6nA71e/9vVASD3/Dn0ggsGlUeh84iSE7VGTkz0OG3btg2FU4W/S3D+axeNpxD9PAnp2Ts/Q7+CvG3tHEBwm1ZIDgfjPv+c3r174+3t/WQa/x/VsWNH3h05ks+/+gr11WvoqkTj8PRAstkxX75C7rETiNk5hIeG8uWEL/O3/+rTpw/e3t6M/+ITer94HrXKiUIhYLUJhIWV5OtvRtK7d+/88zgcDiQo+FCxOEoVNru9wEtWq5Xs9AwsFitCszZFzjcTECAzF8FupX4tD3JychGUShQ6HSgEJFHEardjzcjAxdWFZg1cWbb+1H2bo1arWbBwCX16P0eL7idoWk9Nk/o6tBqB0+dtrN5sQan2Zc7chUXmy3j22Wdp3749J06c4OrVqwiCQIUKFYiOjr7/tZDJZI/U2bNnmT17NqtX5SXedXNzo2PnDgwaNOieuUe8vb355JNPePfddzl69ChGoxE/Pz9q1Khx3yUrMpnswclB91PI4XCgVhedrEaj1aJUKfPKKDRoVAJ2HIhSwTWMt8VriIITtYeSyVMmPfIvTkmSEJFQAEnTZ6ENK4GuZBSSJGG+eg1b3G20QUEEdOpKwqxZ+FkDCdFGcOnSKTp16Mza9WuoVq1asfVv27aNm9duUtulWYGA+26eah+CLZHMmzOfUaNGYTKZ+GXzFkqqK91zSyCtUocvQSxasOiBg+6bN29y6OBhKmgKbnvhdDpIz0gHMW9NdoRUjsTsHeQeP45v3YY4JCfG3Lwp8AqlAqfTiT0lDeOZs1TQlCu0x7ckSaRI8bSv3+aB2iV7OIxGIxqFJv/vYXLmkuiIx+uZDn/YEi8vT/ZvU5MDmjbmyoHDLFmyJH+Ghuzxefvtt6lQoQKTp07l8Jr12J1OHA4HGqWSmuXKMWzYMPr161cou3abNm1o3bo1R48e5fz584iiSKlSpWjYsGGh0aLw8HBUCgXGW3EYIopPHik6nDjiE4hsUnD0d+fOnThtDkRLFsbLlzCUL3rEXLLakZxOUIBSr0O4q80CgEaDaLNhNJpwOOwPvKzB1dUVH18/TGaRLbuMbNqeA0LeyJbNrqBMGU9CQkKKfb8gCNSoUUPe8kcme4p89913jPt4HEqbmkBlGP6KcCwZJpbMXMb8uQt49/13GDFixD3vhVxdXR/5AI1M9l8mB91PoYiICFYss5KV7cDDveCfSEDAYHAjKyuLm/EmUtKdhCgN2EU7gpR343WHm9zmGqElQlmybAl16tR55G0+d+4cuSYzZV75H9a0dFIPHMJ07AROUUTt40NAl264liyNoFSiL1+O1OPXiNbUoo66KYezdvD6/73Ort27iv1BWLNmDa6SG57qe48ehutLsS91M7t37yY8PBynw4m71vO+7XdTeXL79p0H7m9CQgKiU8RdUzBjcG5uLpKYt6YewFVww9PuSebWHShsIh616qDUqTDmGtFq1GRcOE/2pm14OlyJ9ChX+DzWOCwKE4MGD3rgtsn+Ph8fHyyiBYfkQCWoSLelICoFXMv+8W+Ut4vAb9utqFxd0UZFcuDAATnofkI6dOhAhw4diImJIT4+Ho1GQ6VKle6b/V8QBGrXrk3t2rXvWa5evXqUiozk9t4D9wy6s86dQzAa6du3b4HXExMTUaAkRBlA/Lp1CEolLqXLFPjuc+Rkk759G6JD4thZO5VrFv2gUaHRgCiyc18iJUve/3teFEWGDRvCqRPbWTo9ihaNPLBYRBxOCYOrkktXzbzwZiw9e3Rl0+Zt8mwNmewfYN68eXw8ZixhlKa0e6UCD+9LS5W4ZrzAp2M/w8vLi+eff/4JtlQm+2+Tg+6nUPfu3Rn/xVgWrUrhpUGFM5Pr9XokSeTbqbFYbE4cOju5Qjq51hwSpTgcCivPD3qeSZMe/Qj3b/LWGkooXfR4hUbjVSUah91BamoqKkGD8q51i4JanZ/kSCmoKK2L5tTpAxw5cqTYBwRpaWloJH2Rx+7mojQgiiLp6emUKVMGQQCHaL/v+xyiHZ2u8HT+4uh0OgTAIf1etyiKWCyWX5Oj/c4NLyx2M7m79pN14CDayHAklQLbnTs4MjJRiUoquDfLXzsMeSOn8dZbXLAep0O3Z6lXr94Dt0329z377LN89MFHxFtiCdOXzPu8Cnn7PN/NSV7W6bs/OwqNGutda29lT0apUqX+9JaCD0KhUPDG//0fL7/+Osl79uHfqEGhMqY7d0havY42zZsX2i5Hr9eDAFXc6iDlHCZh6TJUAf64VCiPoFJhS0jAfPESCgdYnQoW/Wykbx+xQELNu1285uTIKTOvvnr/HRh27NjB7l1bmD8plKYNPH9tz+/fzeXLuLBkehTNul5l1qxZjBw5ssD7RVHk9OnTpKenYzAYqFat2gPvES6TyR4+u93O559+jq8zmLIelQsdVwgKShsqYco28sVnX9C3b98/da8jk8keHjl7+VPI39+fHj378dWkVPYczCqyzLY9Fhb+bOeZ+k0gxEqCy3WkEhaGvTaEw8cOM3Xq1Me6Fic4OBiloMB8Jz7/Nas1b/9b5R8+ZvakJPTC7wG0ryYQtahh69atxdbv4eGBnfsHMmanEafoYO/evWzevBkvby/uWG7e8z15U7jv0Lhpo/vW/5sKFSrg6+dboG673f5rApLfr7soiaQJiYRqI6juqEe4MQzD2UykUzGoU01EqKJAEtmfuZX9GVu4kHOSc9nH2ZuzkQuOY3Tu0ZFp06bdc0qY7OELDw+n7bNtuWY7j9GRi17hiiCCNSkxv4yEiENyoNVpUf265CM7M4vs6zfYumUL9erW45tvviEpKekJ9kT2KPTp04eRr71GxsZfuDb5J9KPn8R0+w7ZV64Su3gZsZN/olaFikybOrXQe5955hk0WjVJ1tvU9mhEfUMz/FM1WHcdxrR1L9oLCVTRVCNIGYQqwJ+biUpGvncbi7VwNvabsVZefTsBh6R5oB0q5s2bQ6VyaprUL5w74jchQVq6tTewYP4s7L+uR3c4HPz000/Ur1eb9u2a0r9fF7p0bk2N6tF8/vnn5OYWnw1dJpM9Ops2bSIhPpFSrvd+6FbSpTwpSamsX7/+MbVMJpP9kTzS/ZT69NPPSEiIp9/LG2jZyIVeXX3w91UTd8fK4p/T2H3AQtduA/nuu++eikQXpUqVonbNmpw4cBjP6EoAiJL064rX3wNGW2oqlhs3idbVzX9NEATUgqbI7b5+07JlS5YvXoHRkYOrqvDNpSRJXDdd5rz5FDaFyJL161i8dg12owm7zYq70Zso17JF1n3bch2r0sKQIUMeuL9arZb+g/rz3Zc/EOEo++ve2nlrKoW7+hsv3cSODU+rHxp0RCrypidfFk9hU1uo5dWIys467M/cQpaQhlcJd1z1eprW7cLgwYOpXr26HHA/AEmSuHr1av4IXLly5f52ttWvv/6aSxfac+TSDkooS6NHT/bJ42iDg3FKTpw4UKoUeLh7kJtrJDc3B8v1WOzJ6YSpS5F2LodPzn3G1xO+Zsq0KXTs2PEh9Vb2pAmCwAcffECtWrWY9tNP7FnxM05JQvg14+/QD8YwePDg/F0U7hYWFkbrdq3ZvnoXQbow/LSB+GkDC5SRJIlrxoso1XpCeg5i88J5nOhwnZ6d3ahcSY/NJrF9Vw4btxtxeASh8bIVWqdelOPHDjGsj9t9v1PatvBi3vLb3Llzh5CQEF54YSjbtq6hU2sXvh4TTkSYlpRUO8vXpjJz+nh2bN/C8hU//6ndH2Qy2d937lxeElY3lec9yxlU7rgoDJw/f57u3bs/nsbJZLIC5KD7KaXRaJg9ey6LFi1i7pwZDH7tPCACCqpVq8X3PwyjW7duT1VA9srLL9Nv8GCSdu4moGljFArFryte84Jvp8lEypqfcZVcCNKF5b/PKTmxShZ8fHyKrbtTp06MDvyAC4knqOHesMCaJUmSOJdznBjHFfS1KhNU/xn8y5RGkiRyrsQQ98tWTlw8hNmZSwW334NYp+TkljmGGMc5+g3u+6cTAw0fPpx1a9Zx9NIuKulr4aHIW98tSiISIvHSTW5yiVB1FHqHK2pBk9/eXLLwVOatl9QqtTTwas2enPUMGjKQESNG/Kl2/JeJosjixYv5adpPnDtzDlHMC3zCwkswZOgQhg0bdlem+D/Hz8+PjZs38sEHH/DzytXYLLlYT55AVSIY18qV0Ol0uLu5YbZYyM3JQczMJWvDFvyUflRxr4MgCNhFG+dyjzJ0yFCWrVgmJ6n5l2nTpg1t2rQhMTGRlJQUXFxciIiIuO+D0A8//JCD+w9yJGUXlVxq4nFXrgqL08xl42msKjNClh19UCBRb4wgZf8BflxwBNGaDYDK2xevlq3Qlwjl9k8zqVix4n3b63A40Gjv/5BWq8n7frXb7Xz11Vds37aamd+WoEVjz/wygf4aoiu40ruriR5DT/F///cKc+cuuG/dMpns4RFFMX/r0fsRBAFRLDxjRiaTPR6C9KApT59S2dnZeHh4kJWVhbu7+5NuziMhSRKxsbHk5OTg5eVFaGjok25SscaPH8/nX32FpmQUnnVrYdbrUNgkzJevknP0GOosK/Xdm+Gu/j0B2R3zTS5KJzh05CClS5cutu6tW7fSr08/XMwelHGpnJ9ULd5yi0PGvXi0b4VHrRr4+HgXyDjsdDiIWbiE7ENH8FH54KXwRUQkk1QktZPBQwfz2WefPdBI0R/Fx8fTr28/Th4/hYtkQGnW4HA6yRbScOKgpKECvqZgFJISlZBXf5aUxmnpIM94N8df+/ua/dPZh1GHiZw9f/ZPt+O/yOl08uorr7J4wRK8JX9K6EriqnTDKpqJs9wghTvUbVCHpcuWPtDU23tJTk7ml19+Ye7cuRw4dgxDxQr41quL1s+P5Pg7mM5dxHz8NK4WFfU9WqBX/j7CKUoiR7J2ElUrnB07tz9VD8pkT8758+fp17c/N6/dwCB5oscVu2QjU0jF4OHK2E/G8vGnn+IoX5bQzh2AvIzoTpMRQalE+esoeuyipfimZXDq+PH7zu5o0bwxkcFXmPZ1yXuWmzIngS9/NHLk6GkaN6pL3y4i779Zotjyy9em8sYHyezdd5SSJe9dt0wme3iWLl3Ki8//j4aGtrgoi08WaXaa2Ju7ke+nfEf//v0fYwtlsn+/B41F5aBb9tCtXbuW73/8keOnTmEym3HYHWgkFSXUEZRxjf51KnYes9PE4dwdNGxTnxUrlt+37h07dvDG/73BrZtx6HFFhYZkawKKyFBChw7G08MDhbLwU1/RZuPi2M+pEBaGp7snarWamrVrMmDAAKKiooC8UZ1ffvmFPXv2YDabCQgIoHv37pQrVzireIG6RZH9+/ezcOFCjhw6wvnz5/FVBlLdsz56wYXk5GRUqFEISiySiTPSQbRqHY192hUIwOLM17gqnOFOwh05OdED+Oabbxg35hMqaWsRrCucRTrDnsZx0x669OzEjJkz/tI5EhIS2LZtG9nZ2Xh5edGyZUu2bdvGlGnTOHvhPFabDavFig4dEeqSlDZURKsonKQm2RrPaftBtu7YIm+1JMtnt9vZunUrq1atIikhCYObgWbNm9GjRw88PDyYPHky7344Bu82rfBv1CA/Sz6A5HSSsGU7OXv2MuX7H+jVq9d9zzdr1iw++vANDmwoQ3Cgtpg2iTTtcplqtXrRokULhr88kAMbyhIWWnR5AKtVpGbLi/QbOIp33333z18ImUz2l5hMJipViMYl3ZOKbsX/tlzIOUm2RwrnLpx94IfQd+7cYevWreTk5ODp6UmbNm3w8/N7WE2Xyf415KBb9sRdvHiRCxcuMPr90STHpRKpLkcJfUk0Cg120cZtyw1u2i8TXDKITZs3EhRUOFN7URwOB9u3b2fXrl2kp6ezYPFiAnp2x7/uvbf6iVu1Bt+EJE6fOFHo2C+//MKIN0ZwO+4OroI7KkmFGSNOpYOmzZswecpk/P39H6h9n3/+ORO++Ap3pxehmijsmXnTudJIJJFbaFU66nm3KPRUOib3PJcdp+nUqRN2h53IyEh69+5N1apVH+i8/yUWi4VKFaPRJrtTyb34G41bphhihLMcPXGUiIiIB64/ISGB90ePZv3GjVicThQaDaLViotWQ/fOXRg7diyJiYmMHj2a/ZsO0sCrLSqh+FFGSZLYlr2KLyeOZ+jQoX+mq7L/MEmS+PTTT/nm++/B0wO3alVQe3hgS88g58RJFLlGxn74IcOHD3+g+rKzs2nUsC5BfiksnFIST4+Cn1mHQ2LkhzdYvdnJho3b2L17N5N/+IBze+8/db3b4KuERD7Hjz/++Jf6KpPJ/ppvv/2WsWPGUk5dnTB94Zkmt803uGA/zrsfvMOoUaPuW198fDzvvfceG9dvxG5xoFKosYs29K46unTrwrhx4+65HFAm+6+Rg27ZUyMtLe3XdbE/YzZaUAlqHKIdrYuWjp068MmnnxAQEPCX6r569Sq169cjZNjzGKIi7lk2ec8+rLv2cic2tsDrGzZsYNCAQbhZvSnrUjl/6rsoiSRY47hiPU1khXA2bd70wPvWrl+/nimTp3DowGFyc3KQRNAp9YS5lKKUa4UCo6GSJHEp9zQXc06hVqoJ0ISgEJTkClnYVVaaNG3M9BnT5R+5u6xdu5YBfQZS37U1BlXx/+6dkoNdOet5a/QI3n777Qeq+86dO7Rt35647Cy8mzTCu0Y1lDodDpOJtKPHydi1l/JhJdiwdh2jR49mw/wt1HVvVmx9vz1gOpN7mKo1qtK8eXN69OhBlSpV/nS/Zf9Nx48fZ86cOazZsAGT0YibuzvdOnVi8ODBD7SW+25nz56lV8+uqBRp9O3mTovGnmjUCg6fyGHe0gxuxCn5/ItvMBqNfPfdV9yKvU6pSC31a7szoKc/ZovI0ZO52GwSIcEaOrb2xsNdRZueV6hUrR/ffPPNI7oKMpmsKKIo8s477zBj2kxcne6EqCPRK12wOE3ccdwkV8hkwJABfPPNNwWW3hXl9u3btG3TjuSbKUSoyxGqi0ClUGMTbcSZrxPruExwVCD9+vdDFEW8vb1p3749wcHBj6m3MtnTRw66ZU+dlJQUtm7dSkZGBh4eHrRo0YLAwMD7v/EeEhISqFilCn69n8OrcvQ9y97ZuBnXy1e5dO58/mtGo5HKlSqjSNVSza1+kettjY4cDhm38/zwwYwfP/5Pte/GjRtMmzaNSd9Npp5bq/x16Hc7n3OcmNwLhBBFWY9KuLnkbecjSiLJ1jtcsJ6gQvXyrN+w7m+vTf63mDRpEh++8zHN3Tvft+yh7O10GNj2gUfgOnXpwt6zZ4l8+QXUHoW/UywpqcROmU731q0pXaoUX479isbuzxY50n3DdJnz5tPYlSKq8BD07m5IKalIObk0ql+f6dOmPfAMCpkM8m6w73fjfD9xcXFMmTKF5csWYDTmJWZTKjW0btOBBg0aMv6LcZiMqbRuqiMsxInTCeu3GrkcY0MUJSJKqHF1UZCQ5ESlUtCuhRcrN+Ty/Q+zeO655x5GN2Uy2Z8gSRJbt25lxowZ7Ny+E9EpISgEmjZrwpDnh9CmTZsHyifSuVNnDm0/Qh1DM3TKgrsv5DqyOZ59gBQxCbXBgKuXF47cXDRAx2efZfwXX+Dr6/uIeiiTPb3koFv2nyBJEk2aN+OSzUrk4AHFl3M6ufLFVwzp0pWvv/46//UFCxbwyv9evW8Sksu5Z0h3S+D8xXN/OvDNzc2lTes2XDl9jWou9fBU/z5inW3PZEfqOsIpTbimdN5I+h9+GLMdmRw27WD0x+/x5ptv/qlz/1tNmzaNd0e8Twv3Lve9kTiQvZXuQzvz7bff3rfeixcvUr9xY/x6PYdXleIf4qTsP0jOpi1sWLuWdq3bUVKqRLhLwSSA14wXOWs5iaFOLQzP1EbpYchbDyeJZF24RNLqtZQNDmbzho3yVkuyJ8JoNHL9+nUcDgclSpQgJSWFDs+2ololGz9+HoGfr5q09DREpw2HQ2TTdhMfTkijT1d3xn/gT3KqgwUrsvl6SjoWq5642wkYDMV/j8pkskcvJyeH7Oxs3Nzc/tR98cWLF2nwTEMqKGsUypOS48hkb9Y2nH7ueDRqgKZsFP6BgUhWC3c2bCZt/0FUgkBoaCiVK1Zk0MCBtG7d+i8lqJXJ/mkeNBb9e4/LZbInTBAEXnh+KJYrV8m5GlNsueQ9+xByjQwaNKjA61t+2YKH5H3PgBughD6K7Mxs9u/f/6fbaDAYWLFyBRVqlOOweQcHs7dxOfcMl3LPsD9zCypJRQlNSby8vAoF3ADuKk/8CWH2zNk4HI4/ff4nRZIkjEYjubm5POxnezVr1gSlRLIt/p7lTM5ccqRMatWq9UD1rlmzBkmvx6NihXuW865RDaskcfr0abr16MZV+1nSbSn5xy1OE+fNp3Cr/wxerVogGPS4uroiCCAoFHhWqkD4i0O5FHuL77777oHaJpM9bK6urkRHR1OtWjV8fX2ZMOFLAn1NzP4uCj/fvJtlg6sBh0NCp1MwuLcHY9/2YeHKbK7H2vD1VjK0rweTx/uj14ksXLgQSZK4ePEie/bs4fjx49hstifcS5nsv8XNzY2QkJA/PRC1Zs0alE4lgdqCOxVIksTxnAOIgV4EDxqMW4VKCAoFpvR0rs2cQ+qhw6hCg9HWq0NmyUh2X4+h76BBNG/VkoSEhIfZNZnsH00OumX/eD169KBN8xbcnruA5H0HcFos+cfsWdncWbeR9F+2MuL//o/o6IKjl1lZWWgonG36j7QKfd6+3zk5f6mNgYGBbN22hQWL51OrZTXsQTk4gnPQuKoI1Ufi6+NbIDPxH4XoIrhzO56rV6/+pfM/TpmZmUyaNImaNWoRXiKCiLBIqlWtznfffUdaWtpDOUf16tWpWr0K180XEaXi9x29mnseb19vOnfu/ED1pqWlofb0QKG6917GSp0OlcFAamoq33zzDQ2a1eeYZQ8nsvaTZL3DpdwzOHUqXJ+phUOy4+Lqgqtrwal6Oj9f3GpWZ97ChVju+szKZE9CYmIiW7dsYEhfb1xcfv/8OxwOFAoBg6sCUZTo1ckNTw8l0xdkkpLmwGyRaNk0kC7t3Jkw4QuaNW1I82b16NWzAx2ebU6N6tF89tlnZGdnFzhfTk4Os2fPpnHDxgQHBhMSFEr7du1ZsWKFHKjLZE9AWloaOsEFxR/2/c6wp5IpZuLdogVKnQ4BARxObs1biCk5hYDBAwh++UW8WjbHpW5tIl8cRtgr/+PcrTi69ejxl++bZLJ/Gznolv3jqdVq5s2Zw6CevcjZvIWrn03g+pTpXPtxKjFffIV06gzjPhjDe++9V+i9vn6+WATTfc9hcuagUAgPnEituHY+++yzrFy1kvOXznP+4nkCAgLQq12Ae0+R1ggaJEnCbDb/5fM/DjExMTRu1ITRb48h96qVslSlHNUwX3fy8ftjadywMRcvXvzb5xEEgbHjxmJzNXEyZz9WsWDQ6hAdXMg5QbLyNh9+PAYXF5diairIYDDgMBrvOzIvOpw4zWYMBgMuLi4sX7GcL7/5Au/yBs46DnHDfgVtuVJo3Q14enkWO+LgXaMaqRkZnCgio75M9jidOXMGm83EnXgrPYdeolP/C7z0Vgxbd6WhUoFKpURQKNBqFTStr+fMBTsGgxt+fn7otHqSU+2kJN8iPPAKCyaHcnBjGTYsiqRzaxOzZ35Jp07t8x+6XblyhQb1G/LmqyO4cyqFUGtpgs1RnN97haGDhtGmVRuSk5Of8BWRyf5b3NzcsGEt9PsXb4lF6emBPjzi11ckjJcuY74Tj3+/3uiiIgEQ1GokwGK14loilLChgzh/5QqLFy9+rP2QyZ5WctAt+1fQarV8N3Eip4+fYMyIEfSo+wx9Gzdh4vjxXDhzhtdee63Itb+dOnUiR8gk2555z/pjTTH4B/rTsGHDv9S+rKwsVq1axcyZM1m+fDmpqakABAYFYnRm3+fdkOPIQqlUPNVJt7KysujWtTsp19NoaGhDVfdnCNVHEqKPoKp7XRoa2pFxK4fuXbvn9//vaNCgAfMWzMPuaWJ3znqOZ+3jYs4pTmUdYnfOepI0cXw2/lMGDhz4wHU2b94cMSub3Os37lku69x5FHY7LVq0AECj0TBs2DAOHj7I0RNHqRhdCTcfH7x9vNHpip9JoXJ1RZIkTKb7P/iRyR4VSZJYvXo1ublmZi1KxM3gICoMrt3MYfg78XQZGM+NWBsKQUChUKBRKxAEFa6uBhQKJdPmJrL/SBbfjvNjylfhNKnvQYkQLVUquvLx2+FsWFSStORzvPS/YaSkpNC1czdSr6fTwNCGGu4NiHQpS5RrOeq4N6WOS3POHbtAzx695BFvmewxatGiBTbBSpotqcDrNsmK0tMjfzaeEyfGU6fRl4xCWyI0v5wgCAgCSGLe7DOdvx/6CuWYMWvWQ19iJpP9ExW/saxM9g8UHBz8p5KNtW3blhLhJTh/8xi13JsUmYE63ZZCArG8NWQkGo3mT7UnOzubsWPHsnj5MrKNJhQqJaLDgatOR7dOnWnbvi2fHv0Mi9OMTqkvtp44+3XqNalHaGhosWWetMWLF3Pr+i0aGNqiV7oWOq5T6qlpaMTeuI0sWLCA119//W+fs1WrVpw6c4olS5awcvlKUlNT8XfzYNCzvenXrx8lSpS4fyV3qV+/PhXKleXKpi24vvg8iiKSwDjMZlK27aBBvXqUK1euwDGFQkFUVBRRERHcuX3rvuezJKegFIS8BGsy2RMyadIkVq1cwPDnPXjjBS8C/PI+95Ikcey0mZEfpfDc0HjWzQ8lKEDJ8dMWKpXP22XBbheZsSCRXp3daNPMtdDUVIBSkXo+fS+IF0fu4osvviAhLpEGhtaFsiMDeKq9qe7SgEPHtrNu3Tq6dev2aDsvk8kAeOaZZ6hQsTxXzpzFS+OL8tf7IbWgwZmTlhc4C+Bw2rAlJuL9bLsC75ckCUnKy5r+G49KFbm2dAWZmZl5eWtksv8wOXu57D9v/fr1DBn8PGKuRCldRUroS6IQFFicJmLNMdxyXqVhswYsW74MrVb7wPVmZWXRoXMnzlyNwbNhPXxq10Tt7p6/33Pmnn2Ee/uQkZqGIl1DDfdGKIXCa4mvGy9xTTrP/EXzaN++/cPs+kNVq2ZtEs4n4a7yxiE50Cq0hOgi8PjDNmmnsw+hCRc4ffbUA21h8qjFx8ezZs0aUlNTcXFxISgoiLfefRebrzeB7drgEh6G02gi9chRss5dwHzrFnqFkiWLFtG6desi61yyZAkvvPoKJd96A+099le/OX8xETY7B/fvfyquhey/JykpiVo1K/Nifx0v9HdBEOx4earyF7yIkkRSso1n+ybQuJ6eLu3c6P2/BJbNLE+9Wu5s3Z3J4Fcvs2p2EJFhOnz9/PLWfP6BwyFRt81F4uKV+FpDqex+7+SGh7N2UqFhaTZu2vgIei2TyYpy8uRJOrTviJCtppy+Cl5qX1Jtiew37sR/YH9UoYEgidz+eiLenTviVqNa/ntFmw3RYsXPzxelMu9eJvP8BZIXLOHSuXNP9Uw9mezveNBYVB7plv1n7du3j2+/m8jO3XuwOR3YlFaOmg5wMvcgrhoDouDEzcONlwb9jw8++OBPBdwAb7/zDmdiYgh/aRj6wID811UuLgQ0bohndCVuTvmJqqVKcuXiFQ5kbSFCW4ZAbRhKQUmGPZVY8xVSFYm8+dYbT3XAvXv3bk6dOwMoyfAAhYsLjsxUrmRfwlfpR3W3erj8OvrtpwniYvwJcnJynuiDsoyMDEaOHMna1WuxmmyoBDUiTgS1QJmypckxm4idMh2HUgGmTFy1EvWqadGHKbkU42DwoN60bvMs3333Y6F+dOrUiQ/HjuX2slVEPj8QRREzJDJOncF8/gIvfvmlHHDLnpiFCxeiVlkY/nwkGrWDzKwMjEYnrq5KBPI2VPD3VTGgpxvfTM1k+z4z1aINPFMzb+vE2/FWlAoIC1Hh4uJSZMANoFIJVCir5PylHCq6B+e/bhUt3DbfIMeRBYCH2otQXSS+qiBOnzrzyPsvk8l+V61aNdasW81LL77EsSu70VtcUElaJKeT1B1bCRrQD29/P5I9PbHG3c4PuiVRRLTa0Gq1+QE3gDH2Fu7u7vIot0yGHHTL/qMWLVrEq2+8gRAUgG/3LhiiIpGcTjLOnidt/0HIzWXEa2/ywgsvsGvXLt59911sNhuhoaH07NmTqKioe9aflJTEz2vW4N2qeYGA+25aby/82rXm9LJVzJ4+nUULF7F963bOZx8HQKlUUKp8aT5+YzS9e/d+6NfgYdm/fz89+/RBUzIK7yZN0QeVQBAEJKcT07UY0rduZW/6Fhp5tkavdOFpmFqTmZlJuzbtOHvqHCIKHAonIIIEKovAxVOXcfMx0LR+ffbt/YXhL3oxuJcPQYEGNBo1drvE2l/S+eCLtfTtk8LyFT8XWLut1+tZMHcuXXv0IOaHKfg0bohXlWgElQpLYhIpBw5hPHacPs8996fWnMtkD9v+fbtp1kCPu5sKUOHmdCcnNxurTUKvU6BWCYiiQPOGLnw0IR1XFxUzJ5ZGEASycxwcPJqD0SwCGlxcCi8ruZvJnJcyUkBAlEQu5p7kjuMSWhcnFcprEUWJC1evEJN9HIMYgKh1Po5LIJPJ7qLVamnQqAEJaclkZGSgUVupUKost+LjyV6xCn2bVvjWqUXCjl3YWzRDUKuQrDYQRUTRicViQafVITrs5B4/ySv9B8j7dctkyNPLZf9BJ06coHW7dqirV6VEl46FtupyWm3cnDMP4dZtvNx15OamE11eh4te4HKMjYwsxa8jnD8U+5mbNWsWb7z3LqVHv4NKX/xabdHh4Oqn43n3lVcZNWoUN2/e5MSJE9jtdsLDw6lduzaKe2wl9qQ5HA6q1axJokaFe6cOSJICtVBwVNeRm0P8rFkE53pQ07Mhp7IPoYsQOHXmyU0vf+2115g2dTqSRoOhahXcqlZD7eGJ02gk59wZco+fQMw1otU6+GJMGC8NCiqyntPnjXQecJ133vuCl156qdDxc+fO8fG4sWzfuQub04mgVKKQRIIDAnn5xRd5+eWXC4wKyGSPW/t2rSgXcZ4JH0Xmv2a1WTEZTdhsVqRfH5Pl5krUbnMLQdBQoawLer2Cm7ds5JoUWC1mZk4Mp3M732LPk5Rso06bS5jMWoJsEVgkE5nqa/zfME96dfTEyyNvDCA5zcGClRlMmpOGu2cQ5y9cQKfTyTftMtkjJkkSX3/9NZ99+SWSwRVDtSpo3N2xZmSQe/I0gtGEj7c3qRkZOLQaTGnp6CLD8enaGaVOjxIlIiISImqVkqx1G1Hfjmffrl1ERkbevwEy2T+UPL1cJivGTz/9hNPDnag/BNySJGG6FUfKwcPk3IpDmZNCl1aevPlyBKWj3AABq1Vk7S/pfDxhDc91v82qn9fi6lp4dCc5ORmVwe2eATeAQqVC5eVFSkoKABEREURERDzM7j5SW7Zs4dad24S/+jK4uZGdnY2EiHDXxggqgxse9esRv3ELmbY0UqQ7fDh0zBMLuDMzM5kzew6iVk1Qrz7o77reShcXfJo2xy26MnFTJxPoL9C/u2exdVWp6EqHVq7MmzuTF198sdADkkqVKrF86TKuX7/O4cOHsVqthISE0KRJEzmIkD0V/AOCuRRzssBrWo0WrUaLKDpxOkUEQeBGnAkXFwNvv/Mx6enp2Gw2ujwXSrdu3Xj1lZeZPGs3bZp5o9MV/ZDwx5kJqDXuDO7dm59+mI5CbeaHjwNo27TgDYq/j4r/G+aNn7/I6C/iiYwIQaPV0KBBYwYOHEKbNm3kB1Uy2UNks9mIj49n4cKFfDnxW7xaNCOgaWOEu/6diW1bE7/pF9IOHKZ7p04sXrgYN7sWS0wcqT/Nxa1mDVyiSoKgwBR7jaxjR1EZTaxdtUoOuGWyX8lBt+w/JScnh9Xr1+HZrEmBgNtptXFz4WIyz19A4eaG0pjJsAHejHzJEwETael2vDy90GoVPNfRl4plXeg04DiTJ0/mrbfeKnQeV1dXnBYz0q+jm8WRJAnRZCoycP8n2LJlC+rAQFxCgpFECaPJiN1hQy1oCgTehorRpP/yC0dydhMSFUy/fv2eWJvXr1+P0WrFq1mzAgH33TS+fujddXRsLeFwWIDiH550fdaHVRuvcePGDUqWLFlkmaioqPsuSZDJ/i6Hw8H27dtZsGAeMVcvolAoqBRdnYEDB/HMM88U+aCrR4+ePD/kZ85eMBJdoeD3kEKhRKHI+/6atzSFqKiyvPrqq4UeLr0/egxdu7Rn0GvX+PrjMEKCfs9/YTQ6+X56PLOX5PDx2Am0bNmSqVMmU7OKijZN3Qr3QXLgkGz07Gxg8c+5eHoq6NXFm5837mXY0G20at2JadOm/+kcGzKZrKDExESmT5/OvDnzSEtNI9tixPBMbVxq10SU4O47F4VKRciz7biZkcWylSvxVvjzjH8LjM4crhn/n737Do+iahs4/JvZvtlN7wGS0EKXjlQpUqUqUhRQBHuvqK/ltetnwY4KSK8qIAgISpHeS+gQQggJpJftdeb7IxDNm0Iv6txeXpfunpk5M0l255lzzvMcIn31eopXry1pK4uECEG4tMJFVxBRKP7Jbtx5qwrFVZCbm4vb48VQLa70NVmSSJ02g+JjKYQPuQNTi2YEB6t45KFoVAEBqIwGvF4fBYWFpbUmGyQZGdLfzKyZU/B6veWO07VrV0SPl+IDh6rsj/1EGv6iYrp163ZlT/QasVqtCAElZX8EUSAkJBRRLeKR3XhlD358+PEh6UQktYAhRMcPP80nrIqM3lfbhg0bQK0iqGnzKtsJokBQoAqv11dlu9BgNSBjt9uvYC8ViouTlZVF717dGX3vEAqyf6NP53x6dMzhUPICBt/Rh7vuGorVai23Xffu3UlIqMszr52ioLD8ZxnAj0vyWLLSwdj7H65wuUvTpk2ZOesH9h8107bPEe55LIU3P0rn6VdSadH9EBOmO/jPK+8wduxYIiIiMBjU3N7XjEdy45W8+GU/ftmPR3Ljkz0YDQKR4VpGDA7kyDEno4ZEsGhaXaZ+Xp0/1vzMc889e8Wvn0Lxb3LgwAG63NKFzz74Al1uEDFSIoJKQ1DLNjjsTvLy8/C4PWW2EQSBsI7tsDudBKvCEQQBkzqQm4La0CtkEJ0De9E5sCe9Qm+nY2hPtLKeGTNmXKczVChuPErQrfhX0Wq1CIDkdpe+VnzwEJYjR4kYdicBjRvhSzlCr65GzGYVCCCo1aiMRrxeL06ns3S7wf3DyMk5TXJy+Qy7DRs2pE2rVuT+vhr/X471V5LPT/aK30iqXZv27dtf8XO9FkJDQ5GKiksfRqjVKsLCwjEHBiKoBXx48eFFctnRIPDue+9Qv37969pnt9uNaDCgMpcfZfsrIcBMeub5EzkdP+kCVEqtbcV1Y7VaGTrkdgrz9vLz9ESWzKzLf56pzqvP1mDNoiSmfF6N3TtXMmbMvfh8ZR8iqdVqpkydSU5BGL2GHuObaWfIyvFgs/vZtsvKYy+m8tQrZxg6bHSVSf/atWvHtm17eO/9r7B5WvH7xigOp9VjzP0vsXXrHh599FEEQcBms6FWq0hMCMFoMiKL0p+fE4KETisQaFYjCAIR4So8Xhmfr+Tz5dZbgnljXBQ//TiH1NTUq3pNFYp/quLiYoYMHoI100kHcy8ampsjIaGLiEQfEoFW0IEkUFhUiN9X9jtQGxONaDKV26dG1BKsCSVYE4ZW1CIKImYphEMHyw88WCwWDhw4wMGDB7HZbFftPBWKG40SdCv+VWJiYoivUYOi5P2lr+Vu3Iy2WjUMdWqXvOB2ERZasvKidDKmSkRQq3E4HHA2sVBEmAaQK/3S+PjDDzG53Jz4bjL2tJP8NWehI/M0aZOnoMrK4bPx42/oZGlV6d+/P/78Auwn0kpfE0WBgAAjERHhREVFERUVBcdTCQ0OYtCgQdevs2c1bNgQfD58voofhpyjTWrEkpU27M4qmzHrxwJuvrk9MTEVJ1tTKK62GTNmcDLtAHO/q0mLm8reEAuCQPdbgvnu4xpsWP87v/32W7ntk5KS+GXpStq0H84HX9hp2f0w9drt5/bRJ9lzKI533v2UTz4Zf948DCaTiZEjR7Jw0RI2bNzOipWrGTduHHFxf84sCgoKQlSpyTzjI9AcSGRkJBEREYSEhCAIYDSqSo9zKtNHgFFEo/nz8/HOfuGEBEnMnDnzci6ZQvGvNWfOHE6fOkMLUwd04rmqGzKcvQ8RENAIWmSJs/c8ZQmicElVSI4ePcpTTz1F/aQGdGzXiQ5tO9KgXgPGjRtHWlraJZ+PQvF38fe801coLpFKpWLs6NE49u3HlZ0DgC31BMZGDf5sZDRx8pQXZMrcZIoaNT6fD0kq+bpJO+UGxEqnSjdo0IAlCxeSqDOQ/s0kUj75nOOTp5Iy/gtOfvE1UR4fP82bx80333zVzvdqa9++PQ3qJZH18y/4nOWjU0EQcGVlU7R+E8PvHEJQUNB16GVZPXr0QCVJ2E+kICNV0kpGHRdDscXPmx8V4PVW3G7q3Gy27HQzZuyDV6/DCkUVJEli+rRJ9OsRQM0EfaXtOrQJpHkTLVOmTKrw/fj4eL766it27trP91PmM+GbmSz6eSUbNm5l9OjRVyzxoV6vp3v3PsxdWLJcRxAEVCoVklTyN6bTltyW+Hwy8xZZ6dcjtMz2Op3IzS20HD588Ir0R6H4t5k6ZRrhxKBXGUtfC1CZ8ebl4T8bZAsIqFDhcDr5a40jf1ExfqsNv1z1siu/7MciFtCgYcm91bp16+jW5VbmTJpPlKMGLXW30FJ7C6HWWKZ8PY0ut3Rl27ZtV/5kFYobiBJ0K/51Ro0aReOkepyc+D2WYynIPj+i9myZK1lGW68hq9Y5yMv3wl9vNM/+97kR61k/5lKrVlLJyGklmjRpwtbNm1k4bx6jevbitrr1uKtrN+ZMm87uHTv+ttPKzxFFkcnfTSTQ7SH1q28p2L0H6ez0VZ/DQfYf6zn57SSa1q3La6+9dp17W6JJkya0a9MG2+atuL0O/Pig9Lm9jIQft+TGtnsXQSFRrN4ocsd9x1j2ewE+n4wsy+zZb+PJl1N55b0cxt7/BH369Lmep6T4F8vNzSU9PY0+3UPO27bPrYHs2L6lyjbh4eH06tWLAQMGXLWShWPGjOXQMT/fzciu8H1Zlvl4QgGns/3cOzyq3PvXq/KBQvF3J8syJ1JTCdWUXQ5VzZCI6JWw7NlV+pooiMiSVPpADKBgy1b0Wi02qZCqKg5nutKQ1H5GjhxJeno6I+4eibpYT4fAXtQxNSJEE06INpwkUxM6mHoh5QsMHzqc7OyKPxMUin8CJehW/OuYzWYW/PADrZLqcXryNCSfD1f6KfxOJz6bHUPd2rhFA2++n43X++eXiuz3gyAgiiIr1xay9HcH94158Lw3gKIo0rVrV8aPH8/UqVP5/PPP6dOnD2r1P6N4QP369fl16VI6N2hI3vwFHHn9bY6+/QFH33of5+q1jBp0O4sXLaqyduG19uEHH2Cy2SlctBhXYS5u2YVHduGW3bhshRStWIGYmsZ333zDvPmLELWteODZLBJbJpPYYh99705j695I3n3vM9544w0lCFBcNx5PSbIjQyWluv5KrxPxen1V3ixfCx06dOCxx5/nrY/zeObVVA4ddZR+Hm7f7eSRcdl8MbmI/zxdnUb1y2ZU93gktux0k5R0fXNDKBR/V6IoIv/PBHGdqCdRU5viP9ZhP3oE+PNR9Llvt/xtO7Bu3saYe+7BpXeQbN2KJJefBZbjPs0Rzx7uHDqYOnXqMGXKFByFTpqb26MWyt/3aEQtzc0dKMotVpaNKP7RBPl6f/tepgstSK5Q/C9Jkli/fj3Pv/ACe44epdpTj6E3mzEYDTiOp5I57XtaNxK4/94wOrY3g9NObq7IwuVOJs8upmevwXz77XdKzdi/OHbsGKtWrcJutxMeHs5tt91GeHj49e5WhTZu3Mi9Y8ZwJjcXdY3qqENC8NtteI+fIDgggPEffcSdd95Z2v7gwYPs3r0br9dLYmIiHTp0UH72iuvO5XLRoH4tnhij44kHYqts+8yrqWxLjmXT5h3XqHeVk2W55CHkZx+RnZ1BWIgKu91BYbGX2okGnn2kGkMGlP/smLMgl+ffyGX9hu2VluhTKBSV69OrDwc3HKNNUJcyr0uyxE7LRjL8GehrJWJo0hB1oAmjLFO0fSfeUxncf++9fPDBB/z444888diTSE6ZGCEeszoIr+why3cKC4X06d+byZMno9FoSKqThD4vmIbmqiuG7LVsRVNDYt+BfVfz9BWKK+5CY1El6Fb862VnZ9O6bVucUZEk3HM3okYDlKz1zvrlFzwZaRg1PjQqGa9PS0hIFKPve5BnnnnmHzNa/W/ldDpZvHgxPy1cSG5eHsGBgfTu1YshQ4YQHBx8vbunUFyQp59+mvVrprFxab0yScf+qqDQS6seh3nmubd5/PHHr3EPK+f1evn99985evQoR48eZd7cGYy5K5C3XopHrS47g2TtxmLGPp1Orz538fXXE65TjxWKv7cffviB+0c/QBtjV4I1ZXPSyLJMpiuNFOch8vw56AwGDHo9nTt2ZOyYMfTq1at0ZtfRo0eZOnUqc2bNwWq1IQgC7Tu0474x93HbbbehUqkoKCigbu26NBBaEqOvUWW/TjmPc0xIJvNMJtpzS/4Uir8BJehWKC7CH3/8wV2jRuHU6whpdzOB9ZMQRBXWYynkrl6LPyeXoXfeSb9+/ejZsyd6feUJixSKfyqXy8XPP//MsmVLsRQXEhQcym239aV///7odLrr3b1/rUOHDtGrZxf6dhf55M2EcoG3ze5n9BPHOXjMzPoNW2/Y2ScAs2bNYtwLTxEV7ueuO4KonWjAYvWxcFkRW3a66XZrXyZOnKx8BisUl8jj8dC7Vx/2bdtPU2M7QjRlPw9sPgu77BuISAxj+oxpxMfHExJSec4ISZJwOBzodDo0ZwctzrFYLNRMqEU9mhFnSCjznixLyLKM1+PF4XRwypXKMZJp2LAho+4dxYgRI0qqnygUNzgl6Fb8q/j9fmRZvqyR5wMHDjD+009Z/MsvuH0l2cvVokj3bt148oknaNu27RXssULx97JmzRoee+wBCguyadtST0ykmswsH1t3uQgLi+HrCZPo2LHj9e7mv9bixYt57NH7qVnDzz3DQmnXyozPJ7N6fTHT5xdisQcyY+Z8Wrdufb27el4HDx5k8uTJLFo4H6fTAYLAzW3acc+9Y+jbt6+yrEOhuEy5ubncNfwutm/dQRBhhKuiASj05VJADnWSajP/x/kkJiZe1nFkWabdze3IO1BM86AOyLKE0+nE4XDg8/nP5peQERA5Iu/GpXYQratGLpkEhpmZMWsGHTp0uAJnrFBcPUrQrfjHc7lcLFiwgO+nTmFv8j5kWSY+Pp7Ro0Zx1113ERoaev6dVCA7O5ujR48iSRI1a9akevXqV7jnCsXfy4YNG7hr+B3c0lbgjReqkVDjz1HG1DQXr35wik3bBebNX/S3LoH3d7dr1y6+/PILVq74BUnyAqDTGRkwcAiPPfYYtWvXvs49vDh+vx+bzYZer1dmUigUV5jX62X58uV8P/l7kvcmI0kSdZOSGH3fvQwcOBCDwXBFjjNp0iSef+oFbjZ0w2Px4/P5EBER5JIZOTISVrmQvWymrqkxjQJb4JU87LZuxBfkZuXvK6hfX0mcqLhxKUG34h8tOzubIcOGsXv/Pgx162Kqn4SoErGlpuHYd4CYsDDmz5lDkyZNrndXFYq/NUmS6NSxLdFhJ5g1oXaFa4a9XokhY49hcSaxes16JZv7dZaTk8PJkycRRZHatWsTFBR0vbukUCj+pWw2Gz2792TftgPUlZsSIkYgIJSWIrPKhRxkJ148qAQVt0T0wawOwif72GhZwaCR/ZkwQcnhoLhxKUG34h/L4/HQo1cv9qadoMboURhiY8q877VaOTl1BqFuL2tXrSI2tuqMvhdLlmV27drF7t278fl8JCQk0K1bt3Jrma4nWZbZsGEDy5cvp7i4mODgYPr06UO7du3+VgGRx+Nh6dKlTJ82nWNHj6FWq2nRqgWjR4+mffv2f6tz+btav349Q4f046fv42nTwlx5uy3FDH/wFAsW/qqMdisUCsUNwOPxsGzZMv744w+cTidRUVEMHjyYxo0bV7mdz+dj5cqVrF69GrvdTkREBLfffjtNmza9pH5MnTqV+8c8gCirCCaMIMKQZZki8rBQiJlg6gstSGYT0cY4mgaVLOc7bj9Epi6lNKP5iRMnAKhVq9Ylz2ZUKK40JehW/GMtWLCAex94gIQnHsYYF1dhG5/dTsqHn/LsAw/w6quvXrFjr1u3jv++/l/27NqL7JMRRRV+fMRVi+XxJx/nwQfPX7f7aktOTuahBx7i8MHD6GQjWvR4cOEWnTRs1IBvJ35LgwYNrmsfL0RaWhrDhgzj8MEjJV/SqnAk2U++nIVLtNOjVw8mTZ6EyWS63l39R3vnnXdY8MMnbFtRr8rfbVmWadb1ECPvfYnnn3/+GvZQoVAoFP9r2bJlPPv0s5zJzMIkBKGW1TgFOz6Vh3Yd2/Pdd98SExNTbrs1a9bwxONPknHyFEYCUaPBhR2vysPNN7fh24nfXvSyu0EDB7Hz92TidbVJcxyjyJUPQABBxIoJhBKJKIikS8c4JaTQM/IONKIWm8/CeutyunbvwpbNW3C7PADoDXoG3TGQxx9//IaZem61WsnIyEAURWrUqHHFpucrbnwXGosq9Y4U192+ffuYMmUKv69chcNhJyYmhqHDhzJ8+HDCwsLKtf9+6lT0NRMrDbgB1AEBmFo0ZerMmYwbN+6KlJ9YsmQJY0ePRecM4CZDWyICYhAEAYu3iBOnjvDi8y9x4sQJ3n///esWeO/bt4/+ffvjK4Dmhk6EaiIQBAFZlsn35nB47x769unL0uVLb5gvqooUFBRw+6A7OHMsi7bGWwnU/Jk5VZZlst2Z/LZ0FWPuG8OcuXMQxYrLJCkun9PpJMisOu/vtCAImE0qnE7nNeqZQqFQKCqyZMkS7rvnPgI9YbQL6IFZXbLERJIlst2ZbFuzndv69OXXFcuJjIws3W716tUMH3oXRmcgbYzdCNKElm6X4z7Nrg176dP7Nlas/PWiZhEe3H+QMFUksfp4YvXx5OXlIftk1ELZGYLBQjhp8hGcfjsaUUueJxuHy8GmlVuJ19UlwhCNDOS6z7Bg+s8sXrSEWXNmcsstt1z+RbtEhw8fZsKECcxfsACXywUCBAUGMXL4cB588EGqVat23fqmuLEod6qK60aSJF555RU6d+zCrO/mIKdrCMyPJHtvIa+9+DotmrVg9erV5bZL3r+PgLrnTwgUVC+JgsJCTp8+fdl9zc7O5uEHHybQFU6boK5E6mJLg5BATTA3BbWhvqY5302YyJIlSy77eJdClmWeePxJvAUybcxdCdNGlvZREATCtVG0MXfBnefj2WeevS59vFCTJ0/mZEo6LU2dywTcUHIu0fpqNNa3ZuWvv7FmzZrr1Mt/h+joaNIzPdjt/irbWaw+zmR7lRIvCoVCcR3Z7XaeeuIpgj0RtAjsWBpwA4iCSIy+Oq1NXTmVksG7775b+p7X6+WxRx7D6AykZWCn0oD73HbR+mq0MXUhOy2X/77+34vqkyAI/HVarVqtRkKq+jx8VvYWbyWcGDoG9aRmQD3M6mAC1cHUCqhPh8CeaCwGRo24h8zMzIvqz5Xy+++/07VHD2Ys/QVdp/ZUe2gscfePwX9TI76cPo2u3buTnJx8XfqmuPEoQbfiunnvvff46rOvqCk0pFPgbTQwN6eOqRHNgtpxi7kvYoGOu4ePYOfOnWW2k2UZLmhk88qNNs+aNQuHxUVjcytEoeJj1zDUIkgO5btvv7tix70Yu3btYu/uvdTVN0YtVjyJRSNqqaVryNbNW9m/f/817uGF8Xq9TP1+KpFUw6gKqLRdpDaWAMnM1KlTr13n/oUGDRqEy63hp6X5Vbb7YXE+Pr+OgQMHXpuOKRQKhaKchQsXkp9bQD1T00pnKBlVAVQXa/HDvB8pKioCSqajn844Q/2AppXe5+hVRuLVdVj882Kys7MvuE+NmzahQPqzvdFoQEZGkssG3gVyDmpBjVFlJtVxBFEWaaRviVpVfraiSlDTzNweW6Gd6dOnX3BfrpTjx49zz5gx+GtUo/ZzTxHdtTMBCfGYaiYQ16cXtZ57igKdhiHDh5deY8W/mxJ0K66L7OxsPv/0C+LFetQ0JpX7gNeKOloEdkS0a3j33ffKvFc/qR6O46nnPYY1JYVAs5no6OjL7u+P838knBg0YtXT1Ktpa7Fl05br8tR15cqVaCQtEdrya7T+KkoXh+hXs3LlymvUs4uTmZlJ1pksYnRVT8kSBIEIVSybN26+Rj37d4qLi6PPbQP5vy9yOXzMUWGbA4cdfDwhl37971BGuhUKheI6+u233wiSwzCqqs53Us1QE7vVzoYNG0q3MxGEWR1c5XbVDTVx2l2sXbv2gvs0evS9WCmkwJMLgFarQ6vV4MODfHYM3Cd7ySKd6saaCMBx+0GihGoEmiqvvqARNUQJ1Zg+dTrXOkXVxIkTcYgi8SOGI1aQSFdtNBI/agSn83KZO3fuNe2b4sakBN2K62L27Nn4nD5qGpMqbSMKIgnauqxdvZaUlJTS10ffcw/Oo8dwZedUuq3f5cK6Yzcjhg1Dr9dX2u5C5ebmEqCqPHPzOQEqM5Ikk59f9ajg1WCz2dCIuvOuvRUFsSRBic12jXp2cbxeLzIgCios3kL2WbazIX8lG/JXkmzZRrG3oLStKKjw+aqe9qy4fB9++BGx1W9i0L2pvPfpKU6cdOF2S6SmuXjnk1PcPjqVhMSWvP/+B9e7qwqFQvGvZrPa0HD+PDY6UY8sydjt9pLtbDbU0vmrsGhELSLiRd1D9OjRg9ZtW7PXuZmis9/hwcEhqDVqPLIbh2Rjn7QVn+BFQGCtZSl+/EQFxJ63MkyIJpzc3DzcbvcF9+dyud1uZs2bR2Cr5hUG3OdoggIxNmzA1OswEq+48SiJ1BTXxa5duwiUQs87chytr87+4u3s2bOH2rVL1nEPHDiQ8Z9/xrEp0wm+uTWyJCFqtQTWqY0+KhK/y0XatJkEqlTcf//9V6S/JpMZT875P9A9kgtB4Lpk1A4NDcXld+CX/agEVaXtfLIPt+SqMEndjSA6OhqtVsueoi3YfBa06AgmDBA47UnnhP0oMfpqNA9qT7Evn8TEhOvd5X+8oKAgFi5cwocffsiMeTP4asoxQAJEAgPDGDHqaZ577jkCAipfDqBQKBSKqy8iMgKXsPO87ex+K4JKKC29FRYWhkflQpblKh/eO/0OZEG6qHsItVrNrNmzGDZ0GFu3riKECCI1cYgGgRyyOO09iU/wYDQGYDHncdfgocyYOoNCKZf0wlTssh1REAkTw0k01CVEE17aR7/sQxBLjnGt5ObmYrfbiY6PP2/bgIR4Tvz623mvq+KfTwm6FdeF1+NFqGTN0F+JiCCX1IwsfU0Uadu6DXsnTcLy0yJEowF8fmS/H02gGbXfT5jJzJyZMwkODiYjI4OgoCDM5vOPVFemT9/eTPx8MpLcrNK1TgAZrhPUblCHhISESz7Wperfvz/vvv0eZ1zpVDMkVtrutOskogb69u17DXt34QwGA+YgM6dyT5EkNiOC2NJrLskSeZzhmGsfm6VVOFRW/nPvC9e5x/8OZrOZN998kxdffJGNGzdSXFxMUFAQHTp0UEqjKBQKxQ1iwIABzJs1j2JvQZlkaP/rpCOFyKgIOnXqVLrd9xOnUODNJUwbWfl2zhSCQoLp1q3bRfUrPDycpcuWsmjRIr6f9D27du0GZGLrxPLO6Lfo3bs3wcHBhIaGcuLECb6fNo3jUirGOnXRxTZE9no5ffgwp/J+I05dneaB7VAJKrK8GbRo3+KaBt3njiX7zz/TTvb5UKnVSsCtUIJuxfVRs1ZN1oh/IMlSlUFsoTcXlUYsDWLdbjfDhg9n9eZNRPbuScBNjfFq1PjcHpxHj2HdtAW11cbI4cN5+6232bplG7Iko1KJdO/VnbFjx9KlS5eL7u+oUaP4dsK3pNgPUtfUqMI2BZ5c8oQzvHD/U9elhFWdOnXoemtX1i1fT5g2CoPKWK6Nw2/juOcAvQb2Iv4CntBWJSMjgyNHjgBQt27di67bWZlly5aRfTqbemIzQs7W7jxHFEQiiUOLjt3uDUTHRnHHHXdckeNeDr/fz9q1a5k7Zy5paSfR63W0bdeWkSNHXrHrcqMwGo107979endDoVAoFBXo3r07ibVrcuDYTlqZO6MRy09/zvfkcIY0nh/zHDqdDoAOHTrQoGF9Du3bRRt11wpnIhZ5C8jwp/DQqAcvaSBDp9MxdOhQhg4dit/vR5KkctPHT58+zaDBg1FFRxHVtzfG0OiSARgg9JYu2A8fInPxEoTiTdQ01qOIPMaMfbeiw101ERERxMXGkn/gIEENqy6/aj10hJubNbtGPVPcyAT5WmceuMIutCC54sayb98+OnfsTANVS2L1lQd/O4vXE94giE1bNiGKIh988AHvjv+E6mNGY6qZUNpOkkoyYEoeD0e++gZnygnitNWI0yaiEw04/FYyvSdwqG089cyTvPrqqxf91PGjjz7inTffJVZOpHZAffRng1qf7CPTmcYxbzIdurbnhx9/uCJ1wS/FmTNn6N2rD6ePnyFRU484fSIaUYNX8pDhOkGa9wjxSdVZunxpmdqcF2PHjh18/Mkn/LZqFZ6zT3k1okj3bt149plnaNWq1WWdw6CBg9j5ezLNdO0pLioCBFSoEAUVIOOX/Uj4OSjvJKldTdZvWH9Zx7tc6enpjLh7BPv27scomTERhF/2USTkIejgyaef4OWXX1ZqiSsUCoXimkhOTmZAv4G48r3U1NYjRl8DlaDC6Xdw0nmMU/4Ubrm1E3PnzS0NuqGk5nTfPn2x57hI1CYRq49HJahx+Z2kO4+T7j9Gy3bNWbBwwVVbTvTyyy8zYdYsEp9+DKvHg9fjQyNozgbeJfdt1gP7yFuwEKOgpV3ntiz6edEl3XfJssyuXbvYtm0bXq+X6tWr06tXrwuavfXpp5/y+gfvU/OZJ9GFhlTYxp52kpMTJjJj8mQGDBhw0f1T/D1caCyqBN3/ctdzjcnQIUNZtWwNLY2dKpwClWI/yAnpIF9M+IJWrVrhcDi4Y8gQ3El1qDawX4X7tFisFJ1II3fiVG42diRWX6P0PVmWOeE4Qoq0n4/Gf8iYMWMuqr+yLDNhwgTef/cDrEVWzEIwgixiFywIGplBgwcxfvz4676uNTs7m5deeolfFi/F6/KiEbV4JA96g5YBgwbw9jtvExERcUn7Xrp0KWMeeABvSDCh7W/GXLcuANajxyjYtAVVfgGTvvmG/v37X9L+fT4fMVGx1JQakGCsi9frxWG343K5Smt8ioKAwWggX8wmVbWfU5mnrkiyvEuRk5NDj+49yTqeQ2ND6zLrzHySjxOOw5yQDvPMC0/z7LPPkpWVhUqlIjb2/MlhFAqFQqG4VIcOHeKVV17lj9Vr8XslVIIar+whOCSYe++7h5dffrlMwH3O8ePH+c/L/+H3lb+XbueTvJiCTNw1Yjivv/76VbvPsdls1G/cGLl5U6J7dEMQBIqLivF4PAgIpSPefsnHmW++I1ZnYPfuXQQHB1/0sTZu3Mirr7zKnl17wS+gFtV4JDeh4aE89MiDPPPMM1VOWS8qKuLWHj1ILSqk+j13Y/ifSjnW46lkzpxL28aNWbxokfKd/w+mBN2KSh06dIipU6fy46JFFBcVYTKb6de7N6NHj6Z58+bXrB9FRUXcOfhOdm7dRQSxxOoSSrJq+4rJ8KZiFYpo0641J0+cJDs7pyQAk7zEP/EIIQkJaLRlP8Akv0Rubi6irCJn+kwiMgVaBXcsd9w9xZvR1JDZvXf3JX0IWiwWfvrpJ3bt2oXX6yUhIYFhw4Zdl3XcVTl9+jQrV64s/Rvp1avXZZVzSktLo23Hjsi1EqkxfCiiumyyNtnvJ33uD8hHU9iwdm1p4ruL4XA4qFEtniSallmXLksSPr8fQQCVqmRt1GlXOofkHaSkplzSF+6V8Oqrr/LNp9/SNqBHhdP5AfZbdpDqPURwUDAetwcQiIqO5N777uW+++4jPDz82nZaoVAoFP8aKSkprF+/HqfTSVRUFD179rygZK8nTpxg7dq12O12IiIi6NWrF0FBlZfvulyZmZm88cYbfD9jBpFjR6ONikQQBQwGAxq1BrfbjdfnBQQ0GjXFq9YSl1/Ijq1bL/pYv/32GyPvHoXGbqCWoT6R2lgEQcDus5HmPEqmlModw27n22+/RaWqPDHtyZMnGTJ8OIeOHkVfqybGmgnIkoT9yDG8mZm0b3Mzs2bMICSk4pFwxT+DEnQrKvTtt9/y8muvIRmNmJs3RRsSjNdixbp7DxRbeOaJJ/jPf/5zzUa/HQ4HU6ZMYfLE7zmZloYsg6gSaN2mNSnHUsjPLiSKasToa5DnyeKg9wBxLzwNGhVBgUEYjH9OAbLb7VgtVrSCnpwlPxOQnEWHkPJrTy3eQjY7f2fO/Nn07t37mpznP8Hrr7/OZ1OmUPfl5ystkSH5fBx790MeGTGCd95556KPIcsytWvWxpgfSgNz1WugDtuSKQw8w4m01Cq/FK8Wh8NBw/qNMBeGU9/ctMI2Z1yn2FG0HlESqaGrTYy5OpIske3OIJsMqiXGsXDRAmrUqIEsy8qTcIVCoVD86+zdu5c777iT7Mxc7LKbuIcfQhsejiRL+PEjiBASElJmCnnm0uWEncxgz87zZ2r/q+LiYm5qfBOqAj3NAztUmFcoy3WKZM9Wxn/xCffee2+V+0tJSeHTTz9l7bp15OXlYQoMpGWzZoy+9166det2TRO8Ka6PC41FlUWG/yLz58/nxVdfwdjuZuq8+ByxfXoS3rYNMT1vpc4LzxDU81Y+/OxTJkyYcM36ZDQaefTRR9m+cxvbdm5j3cY/2JO8h+KiYixZdtoFdKdxYCvCtVEEqUNLphc5fYiyimJLyZSjc3w+PwIiAgKS3Y6GigOYQE0IGrQcP378ip3H3/zZ1XnJssyM2bMxt2haZU1KELj8OgABAABJREFUUa3G3LI5s+bNK11nfzEEQWD43cPJktLxy5VnBZVkiTP+NIbfNey6BNxQkpegqLCIuEpyEhR7C9lZtJ5QOZIWQhdiSSBcG0WkLobGga1ob+zB6aPZNL2pGdGR0cRGx9KiWUu++OKL61LnXaFQKBSKa62goIChdw7FmeWlnflWVLKI90wWIirUggatoANJoLCwCP9fsoW7T2VSK7HySi2VmT9/PsUFFhqZWlWayDdaX50wovnum+8qvb9LT09n5KhR3Ny+PdPnzyejoAC7x4PdbicmOpq2bdsqAbeiDCXo/pfw+Xy8/d57aBvUJ/a2XuWmBguiSFTnTphvbsP/ffIJdrv9mvZPpVJRq1YtGjVqxN69ezmw/yA3GdtiVP05BSpcG4VaErHt349G0CDIQpl+nhub91qKcaWmEaWLq/BYsiwjI192YqsdO3bw6KOPUrdOPHFxETSoX5uXXnqJw4cPX9Z+b0QOh4NiiwVDXOx52xpiY7BaLdhstks61ujRo1EHqEi2bkWSywfukiyxz7oNwcB5n0BfTW63G1mWUVeQGRbguP0gGllHPaE5KkHkr9/bkuTHUeQk0dMAl81FiDuK2nITbMfcvP7Sf+nQrgPJycnX6EwUCoVCobhwhw4d4qWXXuLm1m1p2qQZfW/ry6xZs3A4HBe9r9mzZ5NzOpcWpo4EakKIUsdg2bED+eyDewEBjaBFlqTS/TuzsnCdSGPkiBEXfJwDBw7w/PPP8/yLL+LwethmWUea4xg+yVth+xq6Whw+fIRDhw6Ve+/EiRP07NOHpZs3ETzgNpL++wr13niFuq++iK5Te6b88AP9Bw3CYrFc9PVQ/HMpQfe/xJo1aziZcYqoLrdUOXU8olMHCoqLWbRo0bXr3P+YMWMGgXIIwf+TXE0jaqmujse6dRveomJUghq3243fX/LBrNaokSQ/BWtWofWrqKZPKLdvl9/BXssWLK5iFi5cyGuvvXbRQbIsy7zxxhv079eD3dvn8tBIkfdfCeXuQV6W//It3bp24D//+Q8fffQRb7/9NpMnTyYvL++Sr8eNQKvVIgCSy33etpLHjYBQYYKWC1GrVi0mfPs1xbpctlh+J9OZhkdy45E8ZLpOssWyinxtFl989Tn161ddquNqiomJQaVSYfEWlnvPK3k47TpJDPGIgohMSdk6AGSZwsJCfD4fQWIo4UIMdr+VGoZaNA1qS0dTH2yZLu68407OnDlzjc9KoVAoFIqK+f1+Xn75ZTq068jkL6ZgOeRCStNw4I9jPPrgY7Rs0Yrdu3df1D6nfj+VCGLRqUoSotY21MeXcZr81b+XCbxVqHA6nHiLLWTMnEtSrVr06dPngvr84osv0rFLFyb99CPCTY0xdW6HIymM3Z6d/Fa4mEJP+Xs0o8qE7JcoKCgo87osy9z/4INk+7wkPvYQ4W1ao9KVTHvXmExEd+1M/ENj2XPkCK+//vpFXQvFP5sy7+FfYt++fYgBAcjBweTl5eH3SwiCgE6nxWg0lq4l1YWGoIuK5MCBA9etrylHUwgSy2czB6hvakpe8UrOzJhOaI/uqGvVwO/3oVJpodhCweIluJIP0cbcqcwIpCzLHLLt4bj9EJIkESpGkrLpJHu37OOrL77mtr59+PKrLy8oL8Bnn33Gt998whsvRDJ6eCSi+OdDjCfud/L2xyf57NP3UUlmArRmXH4Hr7z8KsPuGso777yD0Vhxwq0bmUajoXWrVuxI3kd42zZVti3eu58WzZtfctAN0K9fP0IXhPLp+E/5Y+069lt9yJQ8WOnQtT3PPf8cHTuWT5J3LdWpU4eWrVpwdPNxovVla3E7fDb8skQQociyhIRUWoLE5Xbh9frQCjoEBAIJ5bQvtXRbvcpAS3Mn1p9ZzuTJk3nllVeu6XkpFAqFQlGRN954gwlffENtdWMSAuuUmZ5t99nYe2ozg28fzIrfVlxQMlWfz8fJkyeprW1S+lqELpqb/C3Zu3kr7pMnMbdshS4mBr/PjfXIIfIPH6VGeDjz5sy5oDJhr732GhO+/57wvr0Jv7k1BYVF+L0SGkGLt7iY3J8XsunkGjoF9cCs/jNRnFtyIYhiuaRzu3btYuee3UTfMwJNJQnpDDHRBN/Sgbk//shrr72mJFJTAErQ/a9x5MgR7A4HVpsNQaMGrQZZlnG63DidToxGI4HmwJI52qJYZt3MtaZWa3DIFY+o6lR6OgTdyg7rRnLn/QBBRmzVqyF4vHjOZGHUapFUKtySq0w5tH2W7ZxwHKU6tYgUqhEeHIFer0eSJU67TrJs4XKG5Q1jwcIFVZafslgsfPnFxzx8TzBj7i6bCdztdmO1FvPk2GAyMn2sWqmhQ0AvvLKHdOdxpk+cwdEjR/lpwU/XrcTV5Rh7331seOABrMdTMdeqWWEb24k0XCnHGfP1swB4PB6WLl3KunXrcDqdREdHc+edd9KwYcPy29pszJ8/n++nTeXw4SMAJCXV5bkXnqV27doYDAaSkpKoU6fOBfdZkiQKCwvx+/2EhoZe8fVVjzz2CKO33Ueq/TA1A+rh9/ux22wUOAuRZQkffjyyBwS5dF2Yw+FAPJt7AEBGKv3vc7SijmixOlOnTGPcuHFKgjWFQqFQXFdpaWlM+HICNVUNqGlMKvd+gNpEK/MtbMpdyYcffsi3335b4X4kqWTQRxAERFFEEAQkqew9Z4KxLiZ1ECnZB8n+eQmyKCDJErLs47mnn+bpp5++oGosaWlpfDNxImG9uhPRvi0AOr0Om9eGjIwmKIjoocPJnDyRI8X7aBnUoXTbU85U4uJjadSoUZl9Llq0CCEwkMC6Vd+LhLVuybGVq/j1118ZPnz4efuq+Oe7qtPL161bR79+/YiNLUnF/79TlmVZ5rXXXiMmJgaDwcCtt97KsWPHrmaX/pV27tzJvB9/xG93ILldqAwGVDodKr0etSkAUa/H4XBgtVnxWm24s7MvqdzTxcrIyODDDz/koYce4uGHH+brr7+moKCANm1bU0B2het5AfQqIx2Cu1NTUxu908PAps0Z1b0H0ydN4lRaGs+98Cyp8gHWW5dxxJbMQcsujtsPkUBd4oREwoLCSoNeURCpZkikubEjWzZsZcaMGVX2+aeffsLjsTB2RNkPe1mWKSoqQpBFtCo99w0Lwy1ayfWcQSvqqB3QgBbGTmxev5XPPvvsylzAa6xfv35069SJzGmzKEzeVzrtC0pKehXt20/G1Jl0bt+eQYMGsWTJEho3bco999/PjN9/Y8GuHXw2bRodunRhwKBBZGdnl26flpZG565defrFF0kRILj/bQT3v43josh748fz3v/9Hw0bNrzggLu4uJgvv/ySFs1bklQniQZJDahXtx5vvvkmp06dumLXZMCAATzz/NMcl/aztXANx3OPYHEUo5bUCAjkcQYEGQGR4mILVqsFr9dXWmsUoIBsgjVh5fYdqY2lML9QmWKuUCgUiutu5syZyB6BBGPdSttoRC3V1LVZvHAxubm5pa8XFRXxzTff0Kb1zURHRhMTFUOP7j354YcfaNykCbne8t9z4doobg7uQs+Qgdxi6k6sGEO92nXp3LkzO3bs4MCBA+dNYjt9+nT8Oh3h7W4ufc1gMIIg4JO9gIyo02Fu1ZrTvgxcfmdJf70F5AgZ3Df2vnIP6/Py8lCHhCBUkhfI6/VSXGyh2OXGLUuMHz+e33777boOZiluDFe1ZNjy5cvZuHEjLVq04Pbbb2fhwoUMHDiw9P0PPviA9957j2nTppGYmMirr77Kvn37OHjw4AWPBColw87vjsGDWXPoIM7CIvSNGhDWr/waGL/bjez24N+TjG/LNg4mJ1+R6TBer5elS5cyfdp0jh45ikqlolmLZtjsdtauW4dPo0YTGwOSjPtUBgFaDXcOup0Z02bSUNWSOENChfv1SB42Wlcw8sG7+Pjjj8u9v2PHDqZMmcKyX5aTnZ2F2qOlfUAPjEZTpaOdu4o3ElrfxOatmytNsvbss89yYM8Mls0p+6XjcDooLipGJ+gRBAFZlmnVO5UQe3PqBPw5qrvPsh051sXefXsvaFrUjcZqtfLAgw/y6++/IQQFoa+VCAi4Uk8gFxXRo1s3Jn77HWvXrmX02LGo69UlplcP9FGRQEkt7+IDh8hespTaEZEsX7oUrVZL527dSLMUEz/mHnRhZQNQd34B6d9Po4bJzNpVq85bJzQtLY3Btw8m9egJIoglUheHiEi+N4csKR1zmIlZc2bSrl27K3JNZFlm3rx5PDD2QZx2JypBBQj48aGRtbQSu6IT9PhlHz58CIAKNSpBTZGcR7K8hTYhnYnWVyuz31x3Fnt9m9i1dyfx8RVnSFcoFAqF4lroe1tfDq89QYvg9lW2c/rtrLMt44eF8+nevTsHDhxgyOAhnD51hghiCNVEIiGT6z1NIbnE1Yjl1MkM2gV0J0hT8dLCLFcGWyyr0Zr0CKqSeziVKNL8ppt46okn6NevX4Xb9enbl11OOwl3DS3zusPhwFJsOZshXY3PYiXjiy+42dAer+zlqCeZVu1bsGDhgnJLAl944QW+X/oLtZ95oszrsixTXFyMy+UCQUSWZTL/72P0gYGoXC6aNW7C7JkziY09f0Jaxd/LhcaiV3V6ee/evSutgyzLMp9++imvvPIKAwYMAEqeSEVFRbFo0SKGDRt2Nbv2r3H8+HHWrFtH+J234y0uJuOXZWijIjG3blmmnajVYk3ej/X3VYx78qkrEnCfPHmSYUOHcWj/YYIII1gVjl/yMe/Ij3j1AuG9epLQqSNqQ8naX5/NTu7GTUydM4fExBocTNmJIAjE6GqUSf7m9NvZbdtEUJSJJ554osJjt2zZkpYtW8JX0KBeA9SnTQSag6vsb5w+gX3HtpKRkUGNGjUqbCNJEipV+UR0bpe7ZMrw2X4KgoBKJZR7ClvDUJutZ1axbds2OnToUG4/Nzqz2czsWbPYvXs306dPJ/nAAUCmce8+jBo1iubNm2O323ni6afRNKxP/F1DyzwNFlQqgps0wlAtlmNffMMHH3xA7dq1SUlPp9azT6ILK/+FqwsLpcaYezn+0Xhmz57Nww8/XGn/nE4nQwYPIePoGdqbepbJfh+tr0ZdqTG78tdz17C7WbtuDQkJCZd9TQRBwGQyISDQJuQWREGFgIhBDGBL0Sr2+7fRkJboBSPS2fXdEhJWOZ+D8k7CtZFE6sp/Ced6zhAUGkR0dHSFx7VYLPz0008kJycjSRI1a9Zk6NChlbZXKBQKheJSeTzesw+VqyaebeP1esnOzmbw7YOxZDroZOqDXvVnAJtI3ZLSmunrUWlU7LSvp1lAB0L+Z+ZXuvM4220b0cREENGtKyGNGyJo1NhPnGTfps2MGnMfb7zyarn7QavVSmZmJi6vm/xtOwhMqosmqCQgMhqNCIKA1WLFI7mRVH4k2c8exxZ0Bg0Dh/Zn/PjxFebgufXWW/l2yvfYT2UQUP3Ph+XFRcW43G5EgwFRrca6aw+CIFLn8YfxWW0kz5nPoMGDWbl8+XkHDxT/TNdtTfeJEyfIysri1ltvLX0tKCiINm3asHnzZiXovkL27t2LV/IT1LA+olaLp7CInCVLse3cjalFM9QhIfitFmy79uJMSaFmbNwVSdxUUFDA7YPuIPPoGdoabyVQUxLEpzuP4/cLRA8djjo+FqvTQYheBwKoTQHE9OyO2mQifckyOnZtz4Y/NnLcc5AIIRZRUGHxF1JANjHVYpg7f84FjQA6HA7CKknM9lcaQYssy1WWvKhTpw6LFrgotvgICvzzz0eSyq7LPXbCTUGRn1hN2SdeRlVA6TrjvytBEGjevDnNmzev8P2ffvqJAquF2n37VDr9ShcaSlDbNsz5YT5R4REENKxfYcD9Z/sQAho1ZPLUqTz00EOVZuBftGgRRw8fo11AjzIB9zkaUUMLc0fWFy5n4sSJvPPOOxdwxuc3b948ggglzlC2ZmjbkG5sKVzDdv8awuQozATjk73kk41DsBKmjaJVSOdytUK9kocsOZ2HRz1YLiGdLMt8+eWX/N8nH2OxO9DGxoAo4vlhPu+8/z4j776b99599285k0KhUCgUN6bEmgkc2HKoTL6cihR58xHVIjVq1GDixInkZubTwdwLnVh+BmuQJoTmxg5ssf9OdM0otqWtJsgRSpg6umSJlvcMGd5TGJLqUPfBsWjPJiQFCGpYn8AG9chauYr/vv02TZo0oXPnztjtdt59912mz55FTl4+flHAPu8HBFFFcKMGVBvQF21wMAaDAb1ej8vlovBUBmq1igceHMtjjz1W5QP5bt26kVi9BpnLfiVxzGhEtQqP24PL7SoJuDUa/A4Hlj/WE1Q/CV1YGLqwMOLvv4/Dn37J999/z9NPP31ZPwvF39N1KxmWlZUFUC4RQlRUVOl7FXG73VgsljL/Kirn8/kAEFRqBEGg2qD+1B47GoPZTMGSpeRMn0n+wsVo1SpMiQk0bdbsiiSbmjJlCmlH02hluqU04JZlmVTXUQx1ahOQUAuNoMXtduP2lE2aFt62DUJICNWrV+eX5Uu4bWgvfHE2bOG5JLaJY/yXn7B1+xaaNGlS0aHLiYyMxOY7/++JzW9BVImEh4dX2ubOO+9Ekg3M/CG3zOuCKPDXMe0ZPxWi8gcQpSt5Cmr3WUm1H+aQdQ8+n/e865D+zn77/Xd0iQlog6t+khvWqgUWm52jKSmYkipfI3aOOakuJ9LSqnwoMnXKVEKJLJOB9H+pRQ3RQg1mzZhdMg3sCjh18hQBQvkpRYGaELqE96VxUEu8GjfpwjFOCIfxmVwYtUZuCrwZrVg2OPZKHnZaN2AOMzFmzJhy+3z33Xd55c03kG9qTO0Xn6PW4w9T69EHqfPyC5h6dGPijBncN2YMPp8PWZbZs2cPK1as4I8//lA+LxUKhUJxSYYPH45DsFHgza2y3UlXCk2b3UTdunWZPnU6UUL1CgPuc4I1oYSI4VSvVo3ps6bRrFtjioLPUBh8GnOCEb3ZTN3R95QJuM8RBIHoHt0QY6KZ8O232O12bh88mC+nTEFs1YI6L4+j2gvPUG3cswT37oEl7SSHP/sKd35B6fZ6vR7X3n10aNuWDz/88Lwz4FQqFV9+/jmq02c4MXkKRaknsFgsyAjIkoTzWArZk6aAx0v1gf1Lt9OFhxFwU2MmT51aem+u+Hf522Uvf++993jjjTeudzf+NmrWrIlKELGfSMNcpxaCIBBUvx5B9evhd7nwOZyIOh3OjAyOfzOJPwoKqdeoEYnx8YwaMYKBAweWljq6UD6fjymTpxApVCsz2uiWXBT5Cwlv3AUAERUCAg6Ho8xoniCKmJs2YdnKlXz11VeXvfZ26PChvPv6e3glDxqx8tG/DG8q3Xp3rTLojoiIYMTIMXz09ZfUStTTq2vJAwW9To/bVYwkScxeVMycBVZqadrg9NtJtu0g23cGtGrQqfEjcP/DD7NixQreeecdgoODL+v8bjQWqxVVQMB526lNAUiyjHCep+alzo6aV/XA4tDBw0SqK14a8FcR2mgyLcfJysq6IlPMDUYjPqniGxGNqCXRmESiMQmbz8JGxwq+mTiBjz/8mM1HfiNCiCFSe3bduSeHbPkUQRFmZs+dXW4mR3JyMh9/9hmhvXoQ1blTmffUBgORnTqgj4xkydQZPPXUU+zauYvDB48g+SUQwBxoZvjdw3jmmWeUaegKhUKhuGCdOnWiafOm7N+xjVaqzhXOJjtmP0CxKo8nnvyArKws8vMKuElXPtP5/wpXxXBg/wH69+9P//5/Bqqdu3YlLyIEbVDZh9qSJOFyukqSkwlgatGM339Zzrhx49iavJf4B8dgrBYHMrgL8vH6/Jhbt8TYoD7Zk6dyYuYckp54BIDsVWvwpqbyxKTJF3YvAnTo0IGH73+A9z/6kIJ9+1FHRqIymfAVFOArLEIXFkrdRx9CF152qnxwk0Zk7tpDRkbGFbn3UPy9XLeR7nM3fH/NYHzu/6u6GXzppZcoLi4u/fdKZiL+J2rRogUN6iWRt2FTufdUej0aUwDpc3/g6ISJCMFBaFq3wtmwHjsL8nnwicdp064dhw8fvqhjZmZmcuZ0FtG6srWL/bIPBFAZ/gzIRFR4Pd5y+1CbAnDY7Rd13MqMGDECY5CBZOvWSjOip9gPYldZeODBB867v//+97/06HUnY5/O4M4xx5j/cx5bd7n56Rcbg+4/yesf5hMrNCZCG8O64pXkhbgJGzSAas8+Q8zTj1H7P+MIuLULs5Yspu+AARQVFV2R87xRRIaH4ys4//R5d34+KlEomYmQknre9raU48TGxBBQRUAvCAIy559FcKXnGXS6pSMFQg4+qeqn15muk5gDzfTp04ffV//OO//3FuY6eg7Luzgob8cXbePpl55k/cb1tGlTvh76999/D2YzkZ06IMsyfr+ELJU9m8B6dRHiq/Ptd5PI2VdIU017Opv70d7YkzBbHFMnTKf7rT1IS0u7kpdAoVAoFP9goigyc9YMqifFsdn+O/stOyn05mH1FZPpOsmW4lWkyYd58T/jGDhwIFJphZMLCWQFJKn8N3PmmTPoY/6MCWRZxmKxkJObi8Vqxe5yYnc68QQYKbJamDl3DsGdOpQE3GcPHRwcgkoU8dvtCFoNwT1vxZ52kjMrV5E64TuKV63hlXEvlgn2z+err77i0y+/JLB5c2oMvh19jeqoAs3oayZibNgAr81Gwc5d5a+hVgvIeDyeCz6W4p/juo10JyYmEh0dzapVq2jatClQkhho69atVSZK0ul05dY4KionCALPPPkUYx56iONTpiN5vfhsdkSdlqD69bAeO471eCpht/cnqFFDQkP/XFfrzssnfcZsBg0ezKqVKy8442LJtBkZ1f+sU9WKOgQZvIUFGP7yhK+iIMmdl0fY2RHn7Oxsfv75Z7KystDr9XTo0IG2bdte8BPJqKgoJk6eyL2jRrO5+HcS9UlE6aohIlLgzSXNeZQCMYtx/xlHt27dzrs/jUbDt99+x5Il/Zjy/USeeW0zIOPz+bHZJALlMKJN1dhp24Q/0kzMyFEIBh0+vGi0GoJCQxEiIwhMqsv+bybyyiuv8OWXX17QufwdDBw4kB8WLcJ5+gyG2JhK2+Vt3kpMZBT333cfb370Id7e3dFUkvXRa7Vh3bOXIcOGc/DgQerVq4dKVT6hS5OmTdi39iC1aVBlH3M8pwkJD71iWURHjhzJJx+N57jjEEmmxhW2cfmdnJZOMGr4iNLslo888ggPP/wwFosFv99PUFBQhed1ztIVv2Jo0pBiiwWXyw2yDAJoNVqMRiN6vR6H04k2KQn7vsM0NLUoTVyjE/UkmRoT76/F1rQ1jLx7JH+s/6PSTP0KhUKhUPxVXFwcK35bwddff830KdPZkfcHsiwjqkTad2nHw488XJpAOTo6GnOgmfyibCJ0ZQfTZFmmyJtPljsDn+wl25NB3XrlS9XqdbrSZWCyXFJ6zO32IOi0iFpt6X2gVxCR/RJWh5O4JmXraqtUImFhoTjsDhzOkjwogtFA7tLl9OrZk4ff+4AePXpc8DVITk7mtTfewNy5I7G9SrZT5eXjA9TGkpmhxRs2kbXiNwLr1MFcp1bpto6MTLQa7QXVGFf881zVuy2bzcaePXvYs2cPUJI8bc+ePaSnpyMIAk899RRvv/02ixcvZt++fYwaNYrY2NgyZcUUl69GjRrotVoKdu3GZXcgREbg12rJWPorhXv2oK9Xl8CGDQgOLpuxXBceRsLY0ZyxWJgwYcIFHy8qKgqdXk+hN7/M6xpRS5Q6BuvuXaVThGUkVKqyz34krxfb7mQG9evHY48/TpNmzXj+tdf4fPYs3p/wNX0GDKB9p45s3LjxgvvUq1cvFi1eSLMujTno38lvRT+xovgHdrnXEVrPxNfffc24ceMueH+iKDJgwAAW/fwLhw6nsm17MsdTM1jyyy/ENYhhq3M1ef5czLd2xm8Q8Qk+9AYDoSGhpV8S+qhIQjp34sdFC8nLy7vgY9/oevbsSXz16mQu+Bm/211hG+vxVGw7dnHfPfdw7733EhceQdr30/FabWUbymDNy+Pw19/iLCxiyqSpdGzXiRbNW/LFF1+UW5N9332jKZLzKfqf372/8khusqR07hk96oolG6tWrRovvPg8J6UjHLEl45XKzt4o8uazzbaWyOoRPPvss2XeEwSBoKAgQkNDqwy4AXKyc3Aj4Ha6UclqNIIWNRq8Hh9FRUUUFRVht9tQB5gQBRGfXH7kXa8y0tjQmv37DrBu3brLP3mFQqFQ/GuEhobyyiuvsO/gPlb/sYpff1vOrj07WbxkcZmKRXq9nrtH3sUZ+SS+v3wnFnsL+aPoV/6wruSYLp2ToUVYjD72HT7EHYMHk5mZWdq26y23YN9/sGSttMOB2+1GNBpQ6XRlBl7sBw6iDghANBpwyHLJkqq/EEURk9lEREQE4RERmOPiGHz77SxcsOCiAm4omXEmB5qJ6fFnImijwYDs8yGfHd0PbN8WTWQEuRv/nGUqSxLFW7fT/7bblOzl/1JXtU732rVr6dKlS7nX77nnHqZOnYosy7z++ut89913FBUV0aFDB77++mvq1j1/UqVzlDrdVUtOTqZP//64w8OI6N8Xv1GP2+0BZHx2B9YNm7Dv3UfCsMGEt25V4T4yl/2Kau9+DiYnV1g+oSKPP/44P0xZQMfA3mUyM+e4z7DJtobgHt0JatMGj+wiMCiwdL+yJHHqp0V49+6jXt267EtJIfTWLoS2aoHaYECWZWwpqWT/tgp1djZzZ86ic+fOF3VNDh06RHJyMj6fj8TERG6++eYrOtonSRL33HMPC9euocZTj6PSqNHrDahU5Y/hczg4+tZ7fPXxJ4wcOfKK9eFqS01NZeHCheTn52M0GunatWuZ2Qe7d+9mwB13YNPrCO96C8GNGyGq1XiKisjbvI3iDZu4tUMH5syejVar5cCBAwweOpTTBQUENG1CYN3agEDevv0U7diF6PRyk7ElEboY3JKLDGcqucJpbu7Qhnnz52E2mwHweDz07NGLAzsO0sLYsTSJ3zkuv5Ndtg0YY7Ss+WPNFa2XKcsyH330ER9+8BF+l0SwHI5a0OAQLFjlYqLjorhzyJ1ERkbSqFEjbrnllov6vZs7dy4j7rkXc7u2RN7ak/+dsifhxyt7kGUZ94EjFC78hd6ht1eYwEaWZTZaVtBvRB+++eabyz11hUKhUCjKSUtLo3OnLlCgorm5Iw6/lQ2WVcixYYTc0hlDzUR8gg9BkFGfySLv19+JCwhgxfLlxMbGsnv3brr27EnYgL7ItWriF4TS0eRzvLl5nJnwHeaEBCzHjxP7xKMEhoQQYKp8KVrK+C+4t1dvPvroo4s6H1mWia9ZE6F1izJBtyTJ5OXlIgkCqrPlyCybtlC04jeavvsmgkrFqYWL8ezaw4qlS2nRosXFXUjFDe1CY9GrGnRfC0rQXbWBgwax/ugRaj320Nm1JCVBoeSXKCgsRFKJFC1bgevwUZq8/nJpm7+yp5/i1NffsX71aho3rnjq7P/av38/3bt1x2wP46bAm8sE3oesezjsPYi+SQPMrVtQrUkTQMaacpzcteuR0k5ya+fOrFi/jhoPjv1zbc5fSD4/adNmEJhfyL49ey462dvV9thjjzF/yyZqPnz+NeJH33qP159+hieffPIa9Ozy5OTk8MSTT7Jy1SokrRZNcBB+hwPJaqNRg/p89sn40i+TgwcP8p9XXuGPDRvwCaDSaPG7XIQEBTJ65CheeumlMktFcnJymD59Ot9Pm0Z2djYutxu300V1dTxNAluXqe8JUOjNZ6djHbcPG8jESRNLX8/OzmbInUPZu2svIUQSqYkpWUrgyyVHPk10XCTzf5x/wb/LF+v06dPMnDmT9evW43A4cbtdnDpzmkKLBbUpANkvIblc1EpI4NWXX2bQoEHn3aff76d1qzac2H8Sh1mg+uNPIGo05dr5ZC9e2UvBjHmEZHppF1z5condxZuo0zGepcuXXtb5KhQKhUIBJUHptm3bWLZsWen9efXq1Xnzjbew5dtxez3I1SKIGnE3aNVI+FGpVYSGhKJSq/AWW0j96lv6tm/PjOnTkWWZZ599lokzpqPt0A5z2zaoz97vybKM63gq+Qt+Rms0En/XUA6P/5yQ23oT2KQx4f+TxOwcR+ZpTn7xNXOmTadPnz4XdX5ut5u4GjUIub0/YS3LBs5ej5eCwkJkQNBqcKWkkjdnHtUH9seyZy9yVg5fjB/PXXfddUnXVnHjutBY9G+XvfzvTpIkCgoKkCSJ0NDQK1KeqzJHjx5l3caNhA+5o0wwLYoioigiOZ248/LRJcZj27WHgt17CW9TfrRbPNtHr7d8wrPKNGrUiG+++4YH73+QDZYVVNPUJEwTgYQMMuhlFZ79B7AcS+HwDwuRZQlRkmnSsCHjvv+eJ555BlPrlhUG3CV9UhE3oB/HPxrP4sWLGTp06EVenavLbDbjt9nPW89S8njwOZ2lI7U3sry8PG7r14+UnGwiBg8kuEljRI3m7OyD4xxe8Tv9Bg1i0Y8/0rp1axo0aMDCBQs4duwYGzZswOl0EhUVRc+ePTGZymc9jYyM5LnnnuPpp58mKyuLDu07YvAH0Tio4hkYIZow6moas2jBIl559ZXSTN9RUVGsWPkrixYtYtLESSTvTUaWZRLqJvDkmLcYNmxYmdwFV1psbCwvvPACL7zwAm+88QaffPkFAS2akdixPYbo6JJa8OmnyFyzjvsefJCCgoIKS4P91YYNG0hNSaWRqRU7bJvIX/M74d17lfvdUgkqinbvwHXyJNGGlth8FowqU7k64FASoBuMN9bDKoVCoVD8PR08eJCHH3qYfXv3o5V0aNHjxoVXdJNUry5xreNYtHQp4d06IWkFVCowG80YjIbSWV+aoEDCut3C8sVLOXXqFNWrV+f//u//yM3NZda8eVg3bUFfpzaCWo3nVAa+vHwCEmpQ696RaAIDCUyqi2XDJgLq1ALKB92y30/W8hVUj4276GnlUDI4IEkSzoJCvF4vmr88/NZoNYSFhWG323C5XHjz8vA7XRQtW0H3W2/lia8fo3379pd8fRV/f0rQfY3k5+czbdo0pkyewpnTZwAICw9j1L2jGD169BWd5nrOpk2b8AHBjcsmlXDl5pH1+2ryd+1G9vsBAcnrIePnJWgCzQTVr1emve1kOhqVimrVql3U8QcMGECNGjWY8PUEFv+8mOOufSAImIPNPDbyUR544AFSU1M5fvw4KpWKRo0a0apVK9auXUteQT4JrVpWuX9deBi6xASW/PLLDRd0d+/ena8nTcJxMp2AhPhK2xXu2YsG4YISuF1v//3vf0nJyiLh0QfRhf0ZtAqCgLlObQIS4jkxaQoPPPQQO7dvL12fXKdOHerUqXPBx1GpVOzcuZPCvEIaBLSusm2sIYFj1v3MmzePF154ofR1vV7PsGHDGDZsWMnMDkm6qg+4KrJmzRo+/epLwvr2IbLDn2XvBEEgIL4GCffcTeYvyxj3n//QoUMHkpIqL6ty7NgxBEkgRl+Dm2Qvu7dsw19sIejmtuirVUcQBDy5ORTt3IZl+3Zkj4dkzw4EYSdGVQAJxjokGJPQiCU3CF7JQ5GQR4eOHa76dVAoFArFP9uhQ4fo26cv7jw/TfXtCNdGl1QTkWVyPVkcPbCXw0cOY4qJpnrLFgiiUPIwuIIxiZCmTcn5eSkrVqxg7NixZGVlcfhQSRUdXbVq+AuKkCU/mohwgrt1ITipLmpzyehi9YH92f/u/5E9fTbGYXdiqplY+nDamZXFmaUr4GQ6X8yafVH3BAcOHOD999/n16W/YrNZcGzeiqpRQzRaLSZTAHp9yTIutVpFUFAQZrOZ4qMpdO7YkenTpl30/bPin0kJuq+Bo0ePcsftg8lMO00kcTTQtUJAIC8rm/HvfcqUyVOY98O8K77Gw+12I6rViOo/kzPZT2Vw7NtJoNEQ1K0L+qS6iBo1zuOp2LfvImXSFKoP6Edkp5KbcVmSKNq8ldt69iQyMrLSY8myTE5ODk6nk9DQ0NLpFc2aNeO7id/x3vvvcerUKVQqFTVr1iwt+xQfH0/Lli3xer0EBQUhCALFxcVIsow2JPi856gOCiS/oOAyrtLV0blzZ+okJnJy2QoSH7ivdLbAX3ltNvLXrKPXrbeWq8d8o8nLy+PHRQsJ7nJLmYD7r0SNhpi+fTjx1Tf8/vvv9OzZ85KPd+rUKdSCBpO66iUjakFNAGbS09MrbXNuZse1NnHSJFTR0US0b1vh+4IgENu7F8f27mPatGm8++67le5LFMXSHP/xxtpoRS0Hj+wl+8g0BL0eQRDwOR1IPi86n44a1CdYDMePjxx/JoetyWS40mgXcis6lZ4U+0G0Rm3pNDdJkvD7/WWe2isUCoVCcSGefPxJXHk+bjZ3K324CyXfc5G6GEI0YazI+wkhRodKXXXCUJVOiybASHFxMQUFBQwaMIj0w5mYxUAEtZbYu88Nssj4ZT9OpxMEgaCgQNQmE3q9jghRRebEKajCQhFDQ5DtdjyZZ4iLjuarmbMqzDdVmY0bNzJsyHD8xTK1NI3RmLVsy9mAa+9BVE0bU1RYhMlsxvSXNeRFe/chncli3IcfKQG3opQSdF9lFouFOwcPIT+tiI6mXmXWpUbrq1FXaszOnHUMvXMo6zasu6Ij3rGxsUgeT8kU8vAw/C4XxydPRRUWStSoEYgGPZJfQkbGWC+JgEYNsa7bwKmfl2CIicZUqyanFvyMkF/Ao488UuExPB4Pc+fOZdL337P/4EFkQK1ScVuvXjxw//20a1cywhcWFkZY2J9TfZxOJ/Pnz2fS999z8PBhkGXMgYHcPXQo9evXRxQEvEXFqKIqD/QBfEUWQpOu/CyByyWKIl9/+SUD77iDE999T3Sfnhjja5Q8+ZUkLEeOkv3LciI1Wt6rIti6UaxevRq7y0W1VlU/GDJWr4YmKorly5dfVtCtVquRZOm80/MBJKQbLli0Wq2sXLWKoNvKTwH/K1GtwtTsJuYvWFBl0N2wYUMEFeR7sgnXRROjr0G0rjp5nmyKfQUUefJJd6VSk3rEEI8oCoCARtASIkRQTa7FPu8WthauJUIbzSkhhddeepUNGzYwecoUtu/YgSRJxMTEcM+IEYwcOVIpaaJQKBSK89qzZw87d+ykka5NmYD7rzSilnB1FGcKC/G6PWh0lVcO8bvdeO0OgoOD+eabbzhxNI225u6ccWWwb/8eHI1vwphYExBQCWqQwelwYNDryFr8C2adni0bN3Ls2LHShK8mk4kePXrQu3fvi7pfKC4uZtSIUaiKdbQJ7IhKUCPLEvHORNKW/oq/qJiAls2xARqNGtHjJW/TForWrmPUsGGXNIVd8c+lBN1X2bx580hPTaeDqXe5RFAAWlFLS3Mn1uUsY8qUKfznP/+5Ysfu3r07EaGh5G7aTLX+fSnYuRuvzUbcA2MQDXqQZQRRQPKUlDkQZAjq2hnnseOk/7iwJMlVfgFfjB9PmzZtyu3fZrNx94gRrN24EX29JCKG34naYMCVk8sv27axeOlS3nj1VR5//PEy2+Xl5TFk2DB2JCdjqFeXsMGDELUaHOkZfD1zJnq/H6NOT/72HcT1LZvkwu/z4/a4kWUZX1ExrhNp9H2mbAmmG0Xr1q1Z9NNPPPr44xz9ZhLq0BBEUwD+omJkq5VWzVvw3Tff3PCj3FDy8EhUa1AHVJ4N9BzBbKK4uPiyjteyZUtQyeR4ThOlq3hdP4DDb8MqF9GqVcXrvq+X4uJiJElCdwFrx3WhoRQVFlb5gKF169Y0aFif1OTDhGmjEAQBQRCI0EUTro1iXf5ywoUoqlELWZQIDQmhuLgYj9+NiIAOPTXlBuz3bMNtsPPCuOf5ffVq1m/ejL5mIubePRA1GvLTT/HWJ5/w1TffMHPaNGX9mUKhUCiqtHLlSlR+DZHGqgdAagc0JKNwBQWHDhPVtEml7Qp27UEnCHTt2pVePXoRRXWMKhOJxrrkFp8ha948gjp1JPCmZqgCAhARcZ8+Q+rPS1ClZ/DtV18RFRVFVFQUHTpc3hKqefPmUZBbSCfTbagENQ6HA7vNRrQ/AT8SmX9sxrJ5C6rIMHLVKlQWCyadjucef5yXX375vIMGin8XJei+yqZ8P5VwYjCqKg9WNKKWKKE606ZMY9y4cRe0zuTw4cNMnz6dVX+sxeV0ERsTw13DhnH77beXTt3W6/U8MGYM737yMcV1apO/fSf6unUQA834/f6SHUkystuNWlSB5Edyegm4qTEFP//CbbffzrPPPFNpQPPY44/zx47tVH9oLKa/rFs216lNeLubyVq5itfefJO4uDhuv/32ksNJEiPvuYfdKcdIePxhjLExpdsFN2qI1L0r6XPmY9u9F2HzNkKaN8MYG4Pf58diteJ2u5GRQZLIX/AzLpuNTZs2cdttt92Q2etbt27N1s2b+eOPP/jtt9+wWq2EhoYyYMAAmjVr9rf5QA4KCkLyefHabGgqSIJ2jizLSMUWgoODL+t4zZs3p2nzm0jZdogIbUyFicAAjtkOEBoeysCBAy/pODt37mTKlCn8umwFDoediIgIhgwbwsiRI6lRo8Yl999sNiMIAl6b9bxtvVYrASZTlb8LgiDw8isvM/LuUey37qChuUXpNbH4CinyFlCf5kiCH1NAABqtlvCIcNwuN06XE79fIpwoguQQBt7Rj63btrFxzx5qPHx/mb/dsNYt8d3Wi/QZcxg2YgSrVqy4qBKOCoVCofh3sVgsaEVdpd/T54RpI1EjkrdiJWFJdUqzkP+Vp6iYgtVrGdC7Nw6Hg9ycPFroGwAgCiKtgjpx0LqLtFXrKP5jHeqgICSfD29xETqNmjnz55WpFX65Zs+aQzjR6FUGrFYrdrsdFSp0op5aNCReqku28xTZaRkUkMOQoUOYMGECISEh59+54l/n2i90/BeRJImUY8cI01Q9RRogXBtFQX4BBedZnyxJEq+99hrtOnViwpzZZAQHUZgYz86iAh595hmat2rFzp07S9s/88wzDOjVm9PTZ5ckRIuOQpakkmDb40FyOOBsoqkAYwDhYeFEJNXFZDbz0osvVhpwHzlyhMVLlxLR77YyN+0AyCXTznWtmuOtFsewu+6iUYNGvP/++/z4449s2raN2GFDygTc54gaDTXuGoY2OgqzRsPJbyeRtW4juWdO4/Z6EHRa/PkF5C9YjOdkOmHtb2bGwoX0HzgQi8Vy3ut8PYiiSJcuXXj33Xf54osveOONN2jevPnfJuAGuPXWWzEZDORv24HX48Xj8eD3+cu1c6SfwpebR9++fS/reIIg8OZbb+IJcLDbuhG35Crzvk/ycdC6ixxVBq/999ULrh9/jiRJvPzyy/To1pOfpi0iID+MOHcdvGki49/7jDatbmbBggWX3P+goCDatmlD8Y7dZd+QS8p/+Xw+JElCliRsu5PpdwE3CX369OHTz8eTpzvNH9ZfOGjdTbojhWP2A/hlH0bMGI1GTKZzmfAFdHo9wcEhZ5d3hBOmieLA/oOs3bCB2OF3lv/bBdQGA/H3jMCuUvHll19e8jVQKBQKxT9faGgoLsmJJEtVtpOQMKh16IqtpH79HUXJ+88m8wXJ6yV/2w5OfPUN8SGhfPD++yWDLLKMWvhzIEolqGgc2IqeIQO5SdOMRGskdZzViFfFEx9X7YoG3ABZp89gUgXjcbux2+2oUaMWNAhnM8CpBQ1xYk3qijehRsPKX1eSn59/Rfug+OdQRrqvMkEQSkZmz0subV+Vd955h8++mUBY756Et2tbJkmau6CQjLnzuX3IEFYuW0ZSUhIajYbvJ0+mZ8+erFm/HsnuQLI7SrdRoUItavDLPqxWKyqVClGSEUUBbQU1u8+ZPXs2stFISNObyp6FLFNUVIzb5UJAIKhlK3IOHcWe5uXDtz/GJTvR1KiGqXbNSvctqlWEtG9L8fIVDOjZi7k//ICk1aCNiEByu/FbLOjCw6n9wH0E1q2D8/QZ9nw7mf/+97988sknVV4/xaXxer3UiI1j968r8UZFojk7bVqr1WIMCECv1+F3e8hasoy6NWvSuXPnyz5mhw4dmDZjGvePuZ8/in4hnGgMogm35CKfLES9wDtvvc2999570fv+4IMP+PqLCdRV30SCuU6Zvzuf1Iz9tu08dP9DhISEXFTClb8ae999rL9/LEX7DxDUsAFOhxOHw4HP5ytpIIBr7z68eXmMHj36gvY5cuRI2rRpw7Rp05g/dz5ZRWnIOhm1R01QYBBmfdWzPSTZz5ms06ijIjHXrTyjvEqnJejmVvywcAFvvvnmZc9cUCgUCsU/U79+/XjnrXc540onzpBQabvTrpOotSrmzZ7Nx+PHs2XOfLJ0WtQBAfisNlReL727d+eTjz8mKioKWZZRqUWKfQUEasqOHGtFHYnGP2dhbS/+gxrxld9XXiq9QY9X9uBwOBDOrSGvgA8voqACj8C0adN46623rnhfFH9/ykj3VSSKIg0bNSTXe+a8bbPdp4mOia6yfvCpU6f47KuvCOnejchOHcoE3AC60BAS7rsHq0rk/z788M99Z2ezb88+woVwPIeOoZbVaAUtOkGPRtAiIKAW1IiI2Gw2CvfuJSoiktq1a1fal6PHjqGpFluuD8XFFtwuF2pBi1bQE1A9EUGlIlpfjc7mvvh9AkJsDD6vr8rrYa5TG69fokvnzuj1esJbtSSkXl0iWjSnzoNjaTjuWQLPBg2G2BiCb+nA3B9/oKioqMr9Ki7eyZMn6dG9JykHUtFY/eRPn4trzwFEj4zX46MwP5+sHTtJnfAdhqJiJn77bWm5MICCggKmTJnCO++8w8cff8zGjRuR5Qt5EAU9e/ZkT/Ie3vm/t4lvHQs1XEQ0CeKZl59i5+4dPPzwwxd9Pnl5eXz+6RckiEkkGuuWe9ClFtU0MbfB4DLz9ltvX3Bf/1f//v25c+AgTs+aR+qSpRRl5yD5ZNSCFsHmxrJ6A7lLluF1uNiyZcsF77du3bq88847HDt+jKycLPbu24sp0ESBlFPldn7ZR5GQh1eWMNSpdd4HfIFJdXG63Bw/fvyC+6ZQKBSKf5c6derQuWtnjnn24/I7Kmzj9Ds47jlA957d6datG8t++YX1a9bw2tPP8MRdd/PWiy+yc9s2Zs+aRXR0NADR0dF07daVU57UKr+HHX4bBeQwbPhQfv31V+4eMYLW7drRrlMnnnrqKfbu3XvJ59alWxdypExcLheqKsYpc+RMDCojMaoEfpz/4yUfT/HPpox0X2X3jbmPx7Y/jtVXjFkdVGEbl99BDpm8MvblMsHK/5o8eTJOWSakQX3y8/MRRRG9Xo/+bMkgAJVeT0j7tixZupSsrCyio6OZM2cOkkemibkFG/PX4Dx0GHPDxv+z95IneM78XCw7dvP4U09VmeFRrVYjS2U/BH1eHy6nE7WgQUXJeciyDHLJOR6y7cWPD1mWyS8owGQyYTQYEFXln/2c+4Bdt24d6rBQ4ofcgVBF2aew1i05tnIVK1asuOFqdv+d+f1+7r7rbrKO59De1BNREEm2buf0kl8pXPk7apMZn9OOz2GjSaOGTF88h8aNS363nE4nr732GjPnzsHu8aINDsLvciO//z4N6iXxf++9f0FJTkJDQ3nkkUd4pJIM+hdr7ty5uO1uEs2V18UWBZFEQz1279rC3r17adq06UUfRxRFJnz9NRs3bODo8t9Qrd6ALjoaWZLwZmWh9os00TXFL/t59T+vUb16dfr3739Rx1CpVMTFxdGrT09WLVxLNTmh0ifxJ53HQSsREhKCpaLiqOVOQECGS37ooFAoFIp/h88//4zevfqw5cQqEjX1idMnoBbV+CQvma40Ur2Hia0VzfhPx5du07BhQxo2bFjlfh959BFW/TaYI7ZkkkxNyj0s9koe9to2ExEVwcTJk9l/+BCauDj08dWRfX4OL1nCtNmzuXPQIL74/POSBMEXYfTo0UydPI1sMqhGrQrb2GUrOWRS19gInagnrSDjoo6h+PdQgu6r7Pbbb+frr75m1771NA/oWC7wdvjt7LKtJy4hllGjRlW6n8WLF/Phhx8hNqyHTxYQPBI+/LhdbqyileDgYLRnSzAEN2lE7pJl7Nmzh169enH48GECpCDCtdFUV8dzaskvyD4/5kaNEf4S5HtOZ5G78EdiAgJ44IEHqjyvJo0bs2TV7/hdLlR6fcm5OB2AUBpwA9hTjiF5PBzwJ6Mym1CZQ3GlnURSiVhtNux2G0FBQejP7uMcy5GjGHTakvU8IcFVBtwAGpMJUacjLy+vynaKi7N69WoO7DtIK0NnDGez77cK6ojTbyfTdRKPzYVKUJMrniE0KJRGjRoBJTXih991F2u2bCGk6y3EtW6F2hSALMvYjqdy7PfV3DF0CDOnTqN79+7X9JySk5MxyyFoxaq/fCO0MUhO6ZKDboADBw6QcyaH1gEd8MhubGeKAYEgbXOq6RPQiCW/41ZLEeM/+ZR+/frh9XrJzMwsLeF1IevVx40bx9rVf7CjeD1NzW3RiX/+PcmyzClXKim+fTzwyP1kZGayYl/yefdpPXYcvVZDQkLCJZ27QqFQKP4d4uLi+HXFcsaNG8evy37lqHUvGlGLR/Kg0anofUdvPvjgg9JR7AvVuXNn3nr3TV59+VWKLPnE62oTrAkvWSrlPkWGLxVDqBaNUceh7CxqPPIgATWql24vSxKFu/cyb+HP+Hw+Jk+adFH5dJo0acKo0SOZ8Pk3yMhUo2bpg21Zlikkl6PyXkyaQGoa65HqOExAQOXJZhX/bkrQfZUZjUZ++PEH7hx8J5v2rSSUSCI0MQiI5HuzyeMM1ROr88OP8wkPD69wH8uXL2fMvWPAq0Kt1qEV/gwWZCS8kpfCwkJCQ0PRaDUIGg0yJcnM4M914oIg0CywLYJlC+mLl1D0x1oMSUkIKhXujAw8GaeR/R4ef+/dKqe5A9x999383ycfk7d5K1FdbgHA6/UhIsLZUTTZ7yd/5QpktUhQl86E3NwOV0YGp+fNwnMqg4C6dZDcboqKigkJEUqfQEpeL8VbtjGs/wCMRiN+m/2819nv9iB5PJiqyKytuHAWi4WioiKmTJlKgBRIiLbs76ZBFUDtgAal/x/kCmXPrq0cOnSIBg0a8NVXX7Fm8yaqjx2NKTGhtJ0gCJhr18KUmEDa9Fk88PDD7Nuz56r/3CRJwuVyYTAYkCSpNAlKVYSz/0hS1clhqjJr1iw0fh3VzImVZnYVBIEEfV1279zIo48+yopVqygoLARkTAEBDL9zCA888ECVyz0aNWrEnHmzuWfkPfyR+wsRxGBSB+OTvOTImXhVLu4ZM4p33nmHNWvW8Mtdd2E7kVbmZ/NXks9P0eat3Nm3X6WfSwqFQqFQnBMbG8uMGTNIT09nxYoVWCwWgoKC6NWrF9WqVbvk/T7yyCPUqlWLzz/7nC2btuJ3+0EAg9HAnSNux2QyMWH6NOIffxRdaEhJiqSzX/GCKBLaohmCSmTB3B8Zu3kzbdu2paCgAJ/PV3Lf/D+zOo8ePcq0adP4beXvOB0OomOiCYkIJjX3IJmkEixHICJipRAnDsK0EbQM7oRKUJMlpTOk/+DLuIqKfzIl6L4G4uLi+H3V7yxYsIDvJ3/Pgf0HkGWZWkm1eWHMUwwdOrTSclder5fnn30BsyeUII2aMxmny9TzFRDRClo8sgeLxUJAQAD5Bw7hcjh44/U32b59O5GRkdjEIrySB42opXlQO2p565PmOErBjhQkZMKFAII1DTmpOUrHjh3Pe06xsbE8OGYsX3z3HeqAAEJbtSjzvuTxkLN0Cd7CfILbtSO0QycA9PHx6GvEU/DTItQjhqOvUR2f5MBqtaHT6fC7XJycORejx8Pjjz9Oeno6E6dNxXEqA2P1yj+0C/fsQSsKdOvW7UJ/LIoKrF27lkmTJrFy+Ur8fgmb3UqMnIBL50SvL1/e45xgTRh+t0RGRgZ16tRh8tSpBDRrWmlQJ6hUxA3qT8r7H/PTTz9xzz33XPFzkWWZ9evXM3nyZFYsX4HP50ev11E9vjoWuRC/7Kt0KjZAoTcPVFCrVsVTyi7EsaPHMEnB5y2lokaDze1g+sIFhLRpTWy9JBAFbMdTmfjDfGbPn8+sadO45ZZbKt1Hhw4d2LFrB3PnzmX2rDlkZmRiMOi5o+tARo8eTYsWLRDO1j5t1awZO2fNpcbYezH8z8iD5PWSPmc+WrudRx999JLPXaFQKBT/PjVq1OD++++/ovvs2bMnPXv25NixY5w8eRKNRkOjRo1IT0+nW48eCA3qYfH7IDcXlUqFwWjEaDQgnp0lGXxTE3J/W824ceNwO92kHk9FBkwmE3eNGM6YMWOoVasW7777LuM/Ho/gVRFBLBrByImTGdh8JYM/YdooJMGPX/YSoY4h3lCbEE04giBw3H4Yr8pzScldFf8Ogvw3X7B37klacXHxDVmnGUpu/vPy8nC5XISGhmIwGEqyMlaxfvucZcuWcffQu7lJ25ZTzhOkSWlEjByOMT4BUVCjOjuy7JN9+GQPCAL5Py5CPnSCaup4CoRs/CovXq+XutqbyoxO/m8ftxWvpUazGP5Y/8cFTb/x+Xw899xzTJs9CzEsDHXdOvhVKqTcAqy7diM5XaAWiR46nICkeqX79LhsZP/4A97TZ9DVTMRQPwkkCVWRBee+fQTp9MycOpVOnTrh8/lo0bo1p9UiiWNGl0vcBuC12Tjx5Tfc1rYdM6ZPP2+/FeXJssy7777LJx9+gsFnIk6TSIDKzJ7iLRh9ZmqJjdDrdWezWJf/3bD7bGxwLGfh4gUYDAZ69etH9UcfJKCKByUAqZOn0jG2Gj/9eGUTj0iSxLhx45j83fcYJBMxqhroRD0Ov40M33EKnYU01reibkijSvexq3gDgXWNbN+xrfSL+2INvmMwu389QMvgyh9keSQPqwqX4I0LotaDYzCFhZU9F4+HtBmz0Z3JYt3qNSQmJl5SX/4qJyeHO4bcya69yagiwjHExqANCwVZxrZrDwZJZsrEifTs2fOyj6VQKBQKxZX2888/M/a+sRTYrESPGokxoeS70S/7kfCjUqsJDQ1BpVIh+SWOL/qZ4lV/UFNbiyhtNVSCmkJvHmf8J1GbVNzWrw8/zP2RWqqG1DTWK/Ow3OmzsTr/F7ySh/oBzUg0JaEVS5Z0uvwOjjsOkykf5+nnn+a11167LtdDcf1caCyqjHRfRW63mzlz5jB54mQOHTyELINWp6X/wH488MADtGjR4rz72LBhA36PxBbPOmSDDlE0ULB0Oaq7h6EOCsKHgEbQ4JO9CAg4tu3GeziFFgE3U91QE7/sJ81xlP3e7Rzy7UaLjurGmmWCakmWOGDdhVVbyEv/+fyC17uo1WrGjx/P0KFDmTJ1Kr8sW0bu6dMIKjWCWo06NBhJ8pH98wK04RGEdemGsWZN0GmIu+duhPQMcjdtpmjZr/idTqIiInn5iScZOXIksbGxpcf46vPPGTxsKCcmTyGmT6/SEW9ZkrAcPUbOkmVEanW88/bbl/BTUgBMnz6djz74mFpiQ2oG/vmAJNYQT5rtGKIs4nK5sVisFX6gnHGnYzAauOmmm9i2bRuSLKMNCT7vcdXBweSdpzb9pfi///s/Jk6YRH1tc6oHlP19ry03ZI13CYecuzGpA4k11yizrSzLHHccIk/M4o3nvrzkgBugWfNmrF3xBz7ZV6bW6F+dch7HqfYSfeftGIKCy70varXEj7iLlA8+ZvLkybz9dklGda/XW2VZv3OOHDlCeno6arWaxo0bEx4ezs6dO/G4PSDLuHJycebkIrndqASBHl278umnn1Y5nV2hUCgUiutl9+7dPHj/gxicgaiwIxVZ8eUXogkNQyOokJHw+DwUFhQSFh5GYWEhsqhCJ+hpFti+dD9RujjqyI3YbdnI1MlTSTTWr3BwyqA20SPidlbmLeCoN5l061FMQhAIMlapGHOwmf8+/1+eeOKJa3kZFH8zStB9lVgsFoYNHcbmDVsII4r62hZoBC0WdxGLZy1lwQ8L+L+P/++89XnXrVuHQ/QS2rUbQS1bI7ndnJ4zk9xpMzHe1ARD3dp4NBq8ufk4dyXjST1JPV0DqhtK6hX6vX7CvNFU99XhOAfYXryOI9Zk4rSJmPRm7H4rWfIp0EmM/+QTevfufVHnKQgCbdu2pXXr1kgPPcT0efMwtm5JcLNWiEEmfKKELysHy7oNZP00n7B+fdHXq405MBRtiwhCWzRDliSOvPkuL78wjscff7zcMTp06MCPc+fx6BNPkPbVN6gjIlAFBOArLES2WGjRtCkTv/2OGjVqVNBDxfn4fD4+/vBjIqRYapnrl3kvwVCH47aDZHGKWOJxOhyYTAGI4l8S8EluMnzHGTJyMKGhoQQGBiIIAt5iC5rzrNX2FRcTGhVzRc+nqKiILz77knhVXWoYyk8NFwWRW8L6sDL3J7bZ1xInxROnS0Ar6rH7LGT4TuBUWRn38gsMHz78svoyYsQIPv34U046UqgVUK/CNifcKRgaJ2EMC0NVwUwOKKmbbW7ZnElTpnDs6DHW/bEOn89HYGAgw+4axr333kudOmXrbi9fvpxPP/+c7Tt34ju7Lt2o09Gwfn127N6Nrm4dEh6+H1OtmsgyODIzyduwifVbtzJ37lxeeeWVyzp3hUKhUCiuhi8+/wKXxYNXAB9+cpb/giCKaMPDCWzWgsCbmqFRafH63NhsNrxeL/6sHAIEE263G5VKRK0uWcutElQkqW4iV87GK7lLj+GTfZx2naTQk4eERIDKRL2AJqRykKeeeZLCwkJEUaRRo0YMHjwYs9l8vS6H4m9CCbqvkgceeIBt63fQytiFEM2f00UjdDHUlOtx0LaL5555nmrVqlWavTktLY09+/YR2K0zIW3bAwKiVkvcyHso2roFa/JebFu3I3k9CH6ZYH8oDY2tSTCXjFA57HYsVisCAnFCTU7LaZi1wTj9Ng65d6HyqYiOjuG+u+/h3nvvpV69ioOCCzF79mx+WLSIhHtHIVeLxevxIMl+BAn0NRPQJcST/9NC8pYuJanRs6WZ1gHceflIThfx8fGV7r9Dhw7s2r6dVatW8dtvv2Gz2QgLC2PQoEE0b978orJRKspas2YNp05m0NrYtdx7AWozNQPqk2o/hCALhBGN0+kszc5p81nYa99CULSZZ599FoCWLVsSExlJ/vYdGOMqL4HlLbbgOnacvveNvaLn88MPP+CwOkg0VV4STC1qaBPSjc22lZgT9RzK2InslxFVKnr06879D9xP586dL7sv8fHxjH1wLN98+S0ap4bq+rKj7h6/m2J/ISE1bq4ymZwsA5ER5OblsW7pRuI0ddCKWmz5FiZ+PplJ303m8y8/Ky2X98UXX/DaW2+hSYgn8u5hBNSohuTzUbh3HxtWr0XyeqnevSum+JIHVYIApurVMA0fQk5cLB9//hnt27enS5cul30NFAqFQqG4UrKyspgzdy4+USSgQW0iG/dCDDIje73Yd+0hb9VvOFJTiR5UUmrWYXfgs9hwHjxCtLsWhZ5CoGQmZUBAAAaDAb9PIoYETrmP4ZZcnHGlc8C5F4/oQxsdhaBW4805Ck43kuyjTp06l/1QXvHvowTdV8GePXv47dffaKBrVSbgPkcQBBqYmmO1FPPJx59UGnTPnDkT0WTC3LI5fvyozv64VAYjYZ27EtqhE668bAq3b8Gz4wD1aU6suWTU0O12YbFaUaFCLWhAgDApGq/golfUnRR7C9lhX0fjJo147733zhu0ut1uVq9eTVZWFgaDgXbt2pWOLMuyzLeTJqGvn0ToTY2QZRmn04XDbsfj9SJ5vQgaDSF9e+M6fBR78n7MXTuX7jt3wyaiIyLo1atXlX1QqVT06NGDHj16VNlOcXFSUlJQoSJYU3HG+gbmZshIpNoPclI+QqQjjgDJhE0qpog8qidUZ+68OaWlpbRaLaNHjeK9zz7F0aJ5hQnwZEkic8lSggMCuPPOO6/o+Rw8eBCzEIxOpa+yXZg2AoM6gLEPjGXIkCHY7XZCQ0PPrlu/fHl5eWzfvp2WLVvSo88Jfvv1N9IsR4hUVUMjaLD6isiRT4MgY9Dr0Wg1le7LarXgdjpRoaK1uTN61Z9lxJLkJuy3buexhx8jPLwkocvrb71FYOdORPfoVuZvO6Blc6JqJpC/4GdSv59Gw5eeLy35d05Ex/ZYdu9h4qRJStCtUCgUihvKa6+9hlclEjVkGKZadZDw45HcCFothjq1cR5LIW/2PPJW/07IrV1xO2wULFyMxqsmVkhAI2iQZBm/z0dxcTFerxdZhiBCOSlLHLEmk+pLIaBlcyJvbocmJAQAye3Guj+ZvFUr+eTT8dxxxx0XtMRLoThHCbqvglmzZqH26Yg2Vp5EShAEEnR12LZlGwcPHqRBg/JrSNauW0dAg3oYTCZcLheCIJ4tyXV2H2o12uhozDe3JnfPAXxiSRsAu82OiFgScJ+lQo1bLplmGqQJoZGhJZs2bGb79u20bt26wn56vV7Gjx/PpG8nkZOTC5KALEto9Rp69O7Bf//7X2RZ5sChQ0SPuqv03IxGA0ajgaKiIpwuN6JajcZkwtCgHoW79xJ9NujO37EL27YdvPjaa8qH13UiiiKyLJfJil/mfUGkcWArEox12FC4Ek+4jbBoE3XjGzP8ruH079+/tNzbOY8//jir165l68QphPW8ldAWzUqDO3v6KbJ/W42UeoKJEydenQSIFzjx4VzpsKioqCt26PT0dD744AMW/LgQp91Zcl1FgejYaKKioziTcZpCt5vo6CgeHvEqvyxbSvLRFOhScWZyv9+Pw+HAnXICo2hCK5YNkkVBpLG5NVuLV/PBex9gDglEFRdLdI9ugIDL5cLpdOL3+fH6fIgmE+FDBnN6/OcU7NpDRLuby14TQSC4dStW/rIci8VywyaoVCgUCsW/S3FxMYuWLCGwU3uMZyuLiKjQCFq8Xg9+vx99YjyBt3TAsnY9slaNfe8eKLDRXGqPRii5zxQFAREtfrnk+1Wr1SALErIskeo5hvmWjoR1LvvQWdTpMLdojhAZwuGfFjJlyhQefPDBa30JFH9jStB9FRw5fIRAOeS8ZYLCtFH4LH5SUlIqDLqdbheiOYDAoCB8fj9erxsVqrNljgRkZCTZh3A2WNUbSgIfn8+Hx+tFQ9mRMxvFBKj+nMIaqY1F5zIwc+bMCoNur9fL6HtHs2zxcmJIoL2xOSZ1ID7Jx2n3SVb9vJYtm3rx9rtvI8kyugpqewcFBSFJRXgcDiS1BlVQEPYjRznyxQRsJ9KQ3W6qx8URGBiI3W4nICDgvNdXcWU1atQIWZTI9+YQrq08+NQIOlRakf++8fp5y4EYjUZ+nD+f5194gZ8WLSTv15VoQ0ORPB58hUUk1qjB/02fflVmLSQlJWGVinFLLnRi5aPdRd4CfHhJSqp8GvrFOnr0KAP6DaAgs4hq6lpUMyWgFrRYfIWcPH2M5NPJjLx3BJ9+9mlpgrbq1asz+sEHsZ/KqDDbu9PhxFdsxZl8gAbaehV+rpTU+k5i+7bNyBqRiCF34Pf7KSwswu/zISCWPGCQZRAExEAz+rp1yN++s1zQDaALD0OSJAoLC5WgW6FQKBQ3hIULF+Ly+wludhOS7C/9PlQJagQEfJIPyenCUL8+Rb+vpnjVakRJxEQgZjG43P5UggpJ9pd8X5JbkvXcZCakfYcKj++XfQQkxCM3bsSk77/n/vvvv6xkq4p/F+U35SoQRRGZ81dik5FBoNI/2OqxcXiyshFFgbDQUAJMAciijEd245FdeGU3qEDKy0eW/RhUJQGr3+8r6YfwZ1Imq1yElSLi/5JYShAEzISQciylwuN/9dVXLF28jJt0bWkU2AKTuuTmWy2qqWGoRTtzd9y5ft588y0EwGuxltuHIAiEhIQQGBiIyu/Dtns33uJi7FnZBLZuSWTvntijI3n8uedo2aYNe/fuPe91U1xZ7dq1I6l+EqmOQ1RVQfCE4whGs/GCp4ObzWa+mTCBvTt38c7L/+H+/gN44u4R/Dh7Nju3bbtqywSGDBmCwaQnzXGsynapjkNUj69O167l17JfCr/fz8i7R1KcaaetuTu1AxqgVxlRi2pCtRE0C2xHQ01LZkydydSpU0u369evH62bNydj6gzsaSfL7deelU3enPkYvVoSjHUrPX6kLhavx4fX70cXHU1BQSGSz49G0KEVdCVP+AWBko8dAU10FK5KMsd7bTYEqHKduUKhUCgU11J6ejra0BBM4eH48Ze51xZRoRV06EQ9BlMw2uBQkCQaB7bAJTgoIq/CfapR4/F5yBEykNQCxkYNEDXll3v5ZV9JQrWAAEJbtSDlxAlSUiq+f1YoKqKMdF8FNzW9iY2rNuOX/aiEymtxZ7sz0WjUNGzYsML3hw8bxrIxY3BmZWGIjsZsNmMymfB6vEiyhCiKaDRaUhYtQQCy3acJ1ITw59xaGRBwyy4Oy7sx/z979xkYRbU2cPw/s303vRNKgNAJvTeliHSQ3rsNu9fert4rei1XvSh2ehOR3nvvvUNIh5Bed7N9d+b9EI3EJIC+NHV+fnLn7JkzQ7LZZ845z6P2I0xXudQ5JNlbksHxWm63mxnfzSCcqoTpIssdn0bUEmNsycErWwmODCPv2HF8a5fNFi0IxTOfWavX4i00EzF0MJEd26O+5kPNmZdP6qIfGThkCFs3baJmzZoV3rc7TZIkrly5gsPhICwsjMCf9/f8VQiCwFtvv8XY0eM4azlKbVMMaY4ULruScMgOREFEJ2mxioW8/cI/f/ee58jISJ566qnbM/hyBAUF8diUR/nfx9PwsftS2VC91HFZlomzniNXlc7nr36OWn1rPga3bdvGpYtxtDZ2qXCGvbKhOlmuq3z91TdMmDABURTRarUsXrSIUWPGcOjr79FGVcNUrw6CIGBNTKLgzDlEp0T7wH5oRV25/f6WtbAAOSgYraArWUIPoEKF1+0GrRbZ6UIWRXJzc1GpVOj1enQ6PYIABUdP0LJFC4KDy+akUCgUCoXiblCpVMgeDyaTCYfdgVtyoRG0pf7OCYjIePF63GgELbWMDcl2ZnDBdZyGtMJfKL0q04OHi/IJ9EFaPE4vmHS4ZdfP398FZCS8shf554DbYDSAjw+yLGOxlJ1sUigqosx03wbjxo1D1ni5Yk+ssI0kS6Q4L9H1ga7UqFGj3Da9e/cmOiqKqz/8hMdqBYoDJK1Oi16vR6vVkrNvP97EZIYPHUIyFzlUuJ0cbzpO2YZZKiBJushxeTeoZNoEdim1NNUreygQcmjRsnmZcx88eJCrqWlUN9Quc+xa/pogfAggPCQE2+mz5c7UAdjSM8jef5DAHt2pfF/HUgE3gC4okKjJ4ymQJT799NPrnvNOsVqtfPXVV7Rv15J2bZvRpXNbYhrWZvz4sezdu/duD++W6t27N9O++B/pYgrr8pZw0n0CR8MI1F1aIbdtQJ6/C4+6ODmY1+u928O9oTfffJMxE0dz3nOU/YVbSLZdIt1xhXjrefaYN3BZvMQb/3yDMWPG3LJzLlmyBJPsR0A5yROvFWWoQ0JcPEePHi15LSQkhHVr1rBg9mw6Va+BePgYHDxCM/9AHurVC5PGiOGa5GnlyXFlotFqCA4MJP/kKVSoSn0RgeKldEgSXqcT27nz6KpUwe1047S7KMgvIDsri7Qjx8g/c5Zz58/To1cvZs+erXyxUCgUCsVd17JlSzyFhTjTMwgMCkRQUbLy0yO78chuXLIDa0YqUmEh/pogvF4PDbTNMYo+nJL2cUraz1UpiXQphUvSKQ7JWykQcnj19Vdp0qQJos0OKnDLrpJ+NVo1AYEB+PoVlwVz5ucjCoLyYFrxuyhB920QHR3N6HGjueQ+RZojpcySXY/k5oRlH5KPm5dfebnCfjQaDYsWLCBYkkn433Qyd+zCmZuHp8iKOfYSSbPnkbt2A889+STz589n3sK5NOhUhwvycU6q9nFM3kW6kEJVUw3uC+6JSV26hmCKPQG0EuPGjStz7uzsbCSvhEl9g/2csowBEwH+AdzXti2XZ84le98BvM5fax06snNInDkHldFIpU4dUKnKn/1XGwz4t23N0pUryM3Nvf55b7O8vDweGtCXjz54jdaNM5g3PZKVc6vx/htBXE3ZyLCh/fnqq6/u6hhvtWbNmqH3M+HToC6Rz04h8KG++HVsS6W+PYl56zUihwzk61kzmTp16t0e6g2pVCo+//xzFi9dTIc+bUgUz3FePkKaLoH+o/qwfuO6khJnt8rV1KsYuXGdTl+1P16PREZGRqnXNRoN/fr1Y/myZSTGxZEUH8+mDRv48MMPEbQyqfbkCvuUZZlE2wWialTD38eHouMncRXk4pHdpZbfCYiIqLAePYYnNw9jvXqAgFbQgtNNweEjXF24GHVgAELLZpwssvDsKy/TvFUrjhw58kdvjUKhUCgU/2/dunUjqkpVMrftQK1WExoSip+/H6JGhSR6kUUJjU6D68QpAv39MbsLyMzJxGV1U09qTi2hEV7ZS4J8jjjOYFblEaILJzAwgAYNGhAeEkLR8ZNI584TYDAQHBxMaGgoQcFB6K+p9JF38AjNmjS5bqlbheK3BPl6mzj/BMxmM/7+/hQWFt5TCX/cbjdPPvEkS5csxSD5EK6qikbQYPYUkiWnYvDTMXP2THr06HHDvlJSUvjkk0/4acVyrHYHACpRoFGDhjz1xBMMHTq0VNbphIQE9u7dy0svvITe7ktzvw6llrvKskyqI5EL7hM8MuVhPvroozLn3LRpE8MHj+A+n94le8V/4fF4sNlsOOzFmZkvyMcxVFPz5ZdfsnbdOpYuX45bpUIbForsduPMyMTjcmFq2Zzo0SOue63O3FwSP/qM1cuWcf/95Wdzvt1kWWbI4IeIi93ND9/WoH4dY5nj//3yKtO+L+T7GQvo06fPXRnnrTZ23DjWHTlMrWeeQKwgk3zmjl1Ytm7nxJGjVK1a9Q6P8I9zOp1YrVZ8fX3RlLNX61YYPHgIJzaeoVXA9X5uZQpsBewqWkutOtGEhobRvkM7xo8fT506Fe/XfuKJJ1g870ea6tsTqqtUukdZ5pT5EEnuS2iNBsTAAGy5uYg+PgT164M2MhKNqEEtqPG6XFhOnyR382ZwuECvQ1s5AkFU40xPQ/Z68WnciNC+vQgJDwPAVVDIlR+WYMjLZ9O6ddSvX/9W3C6FQqFQKH631atXM+HhhzG0bE5kn56lyl56nU7S1m2k6OBhNAgU5pmJEupQU/w1WbEke3HLbkRRwC/Ijz2WDeiD9dicTmSdDofHg9dmQ9RoCG7ZnKoP9Sv1nSj/5GkyFi/hu+lfMnz48Dt67Yp7083GokrQfRvJssyuXbuYNWsWO7fvwul0EB4ezuixoxk7diyRkeXvlYbioD0vLw+NRkNgYCCCIJCXl8fJkydxuVxERkbSqFGj69bX3rt3L+PGjCM/p5BQKuGrDsAtuciSruJSOxgzbjSffPJJuUFIYWEhDevHEGqtSh2fmJLX7TYbZrMZABE1btnJEXk7Bq0RQS8zcsxIXn75ZZYvX16c8EKrpWXLlkybPp0kHwNVBz103XvmLjQT9/5HLF+8mAceeOAGd/j2OHr0KP37dWf255Xpfn9AuW1kWWb0lHgKrI3YuGnrTfUrSdI9m+UyNTWVpi1bENCvDyFtyy8fB+B1uoh7/0NeefIpXnvttTs4wtvDarVy7NgxbDYboaGhNGvW7A//G33++ee8/fq/6OzbD61Y9qFFcTbxfC6740iWY6lpqgsI5AtZeNUeJj8yiffff7/cPeYOh4OJEyayYd1GAgimkiYKraCl0FPAZXccZo8FfWQEUSOGYqhenYzERPKWr8aVlYUmLBRNpQgEr4wjNg7ZYiPSU5Uo6nBC3oNX5cUh2zHWq0dYv/4IviY8sovgkBA0muKxeJ1OEqZ9yYAOHZk9a9Yfuj8KhUKhUNwKCxcu5B8vv4wTMDSsjzYgAFdhIfaz59DKEOznjznVRogYQWzRGapSi6pCLdTCL39fZQqkPGKFE9g0Nvzq1yP8wW741orG6XKRm5aO7ew5zHv341OtGrUfm4y70Ez2/gOY9x9izLBhTP/ii3v2O53izrrZWFRJpHYbCYJA586d6dy5M0CFdZCvFR8fz6xZs1i0YBEWSxGCADWjo3n4kcmMGjWKrl27IssyBw4cYMrjUzh75hwI0KhxDOPHj6dly5Z4vV60Wi0dO3bk6PGjLF68mIULFpGeloZer2dgl/5MnDiRVq1aVTgef39/hg4fwoLvF1HVWxODyojT6aDQbEaFCrWgQZZlEohHLWroHNiXbFc6i+b9QEBAAO+9916p/tatX8/FY0fLPde1rCmXUYviXV2ys2DBAqpXFenWyb/CNoIgMGlkKBOeOca5c+cqTIZ39uxZZs2axYo1a7CYzRhNJvr37s3EiRNp0aLF7bqE3+3kyZO4PF78Y8qWrruWSqdFF12TIzfxb3kvy83N5dNPP2XB4sUUmAuR5eK6nbVr1uTxRx9l4sSJv+uPaXx8PMnJydgcRZy0H6Smqj56gx6DwYhKpUKSJPLy8nB47GTIV6hurENT/3bAz/kd7HF8//UMvF4vn3zySZn+9Xo98xfMZ/Xq1cyaOYv9+w7gcNrxuD3FnysBvgQMH4I3JARJljCGhKAeNwZvylUKT53EfvES3oICgjwh1BE64SP6USSbceEkQluZDF0+EUOG/fw0X8YDuFzOkqBbpdMR2KEdazesJz09nUqVKpUZo0KhUCgUd8Lo0aPp0qULCxYsYMXq1eSnXSQyIICHnnmWRo0aMWbkWJro2xOqjUAURGKLTpMmJxEkh6NGgw0L+XI2HlEisHEToidPRFQXb33U6/WEVI7E4ueHtnIk2fN/4Oxr/0StUhESFMTbr77Kc889pwTcit9Nmem+h2zevJmJ4yfhLvJQSYgiQBOMhJcMZyo5pBPTtCGzZs/i5ZdeZteO3ei8RgKFEGRZJsNzBatsRVSrissZBAUxZuRIxo8fT/Xq1cs9n81mY+XKlSxcsJDkpGR0Oh0dOnVgwoQJNG/enKysLHo82JOMhCzq65shmjVIXhmNoMUh20iWY8niKs0C2lHt51Jk8dbzXNXEc+rsKcLDf635vGXLFoaMGkW1xx/GVL38gFqWZRK/nUnLsHA2rFt3y+/vzRrQvw9R4Sf433s1kKSfy7oJAvwmKZXZ4qFBx/N88+0C+vfvX6af6dOn8/a//43s64NP86ZoAwNxF5qxnDgJBYW88OyzvP766zd8EHMnrFq1ijGTJ1HvX2+VWqpVnuRFP9LS5Mva1avv0OhurYyMDPoNGEBcWhp+bVoS1LwZKqMRZ3Y2OQcOYT9zjhGDB/PVl19WmH/gWtOnT+edf/4LwSWi9mjJdWZRWahJZaqjFjX4+/njcrvJsqaRyAVklUSn4J4Yf7NtI8UWR5x8hl17dxITE1PB2Yq3m/TvN4DUxFTCqEyyOwlT144EdOiAFw+yIGM0GLHabMUFDJCRHE7Sv/gKLHYM+FBJqEaOnI6gAb3ahDUmnPABA0vO4ZQd+PiaSpUMc1ssxE39kB/nz6dXr16/+74rFAqFQnG7vfvuu0z/6Cvu9+1b8v3K7rWRYosj25WOV/aiVxnRiXoSpARqvfwPAqtWKbcvl8tF6orVCOfOM/1/0+jXr1+pvd0KBSgz3X86Z86cYfzYCZis/rTza1+q1FikPgqLp5AjJ3bRrk07cIk01rclzBSJV/ZyxLwbq+BCW70G+gZ10BhN2C0WPpsxg29nzOD7b74ps+/4/PnzDB82givJVwgiDF9VIC7Zw0/xy1k4fxEjRg7nf9P+x7r1a5k0cTIH9+9FcoMP/ngFD2by0Ygamvu1o+o1tb+jDLVILrrIokWLeP7550te79q1K00aNuTs4p+IenQSuqDSJRtkWSZ90xY8KZd59t27m6jL6/VitbnIyspGkiSgODGX0WjEYDCUPN10u4ufV5W3HHjRokW8+a9/4de5E5W6d0O4JniLeKALWbv38tH//kdQUBBTpky5A1d1fdWqVUMtilgvX8GvTsUZ62VJwpV6leief86gS5ZlJk6eTHxONjWenoIu+NefQ42vDz41a5B/+gyLf1hCo5iYG5Y6mzNnDv98422qyrWo7ReDiEiC7QIXLCfJlK/g7w1GzFdhE8xYZQv+mkBaB3YrE3ADVDVEk2i+wJw5c/jvf/9b7vncbjcjR4wkMzGb9j49sHrMJHgT8a0fg0pQo0KFW3YXB9zICIioUCPq9Zjq1Uc8loSv5E+SfAGVoKJLQD9OFR1GUP/681mceE1GFEs/cBDUauSfx6BQKBQKxb3IbDajE/SlJjQMKiP1fJtQjyYlrx3I346+Zg001ymDqtVqiejYnpQTp6hUqZIScCv+X5S1EfeI6dOng1WgmV+Hcmt7+6r9iVBVpTDfTIy6NeG6ygiCwHHzfrLUeYSPHkXlsRMIbNEGXb06BHfvRp3XX0KKrsnkRx8tlXn48uXLDBwwkPxkMx1NPWnlfz/1fBpTz6cJdbSNCHKHM3f2PB599FEqV67Mxk0beO6FZ3GrHegMOnwNfjT1b8uDoYNLBdxQXLvbVw7gwoULpV5XqVQsWrCAmoFBJE37ktSVa7Amp2BPSyf36DESpn9D0a69TH37bXr27Hl7bvJNOH78OAcOHWXrbjN2r4Bo0CMa9HhFAbPFQm5uLh6vB4DNOwsQBC2NGjUq1Yfb7ea9Dz5A3ziGyJ4Plgq4AQRRJLzzffi0aclHn36KzWa7Y9dXkaZNm9KoQUNy9u6/bjvLpXikvHxGjx59h0Z2ax0/fpyDRw4TMbB/qYD7WoGNG2Fq0Zyvv/vuugGm3W7n3X9PJcxbhXq+TVAJKgRBoJapAQ+GDqKeXxPUehV5YgZmOZ/G/q3pHNIXnwoqAoiCSIhQiT27Ki5Ht2nTJs6fvUBTYzuMKhNe2QMC16xOEIr3rMkyoqxCK+oQBRUyMqJOjywIVBfqUVtoDECR14xJNOFKvVpSZcErexAEAb2+dE1w25VU1KLwp0qgp1AoFIq/l4CAAByyHUmWrtvOiQt1gP8Nl4lrgwKRZJn8/PxbOUzF35ASdN8D8vLyWLViNVXU0aXqaP9WtjOdYMLRuQwAFLjzSPOmEtS3D8YaNQEQUSEgYrPZELVaqo0chjsokE+uqX09bdo08jMKaeV7Pya1L5IscdFyis1ZyzlcsJur9hQ8Tg8L5i2kf7/+JCcnU716dfRaI60D7qdlQCeijLWweS2cMR9hf95WDuRt47zlOFaPBWShTJk0gCpVqrBpwwZefuJJjPGJXPlmBimff0XeslV0qVuXZT/+yJNPPnmL7+7NS0tLY9jIkUhhYeRZNazcZEfQaBA0GlQGA2ofE15ZJj8vH7vDw8yFuXR7oGeZIGTLli2kpqUR3vm+654v7P5O5Bbks2rVqtt5WTdFEASee+YZXJfiydi+s9x/P0dmFulLl9O+TRtatWp1F0b5//fjjz8i+PvjV7fiTOEAoe3bkpqexq5duypss3r1anKzcqllKrsPXqfSU9vUkPZBD9DItxUiIoFiyHV/vwHUggbnNeX2fmvhgoX4EYi/Jujn8xgQJBlXTnZJG69cXEddhRqB4mBeJYp4cnIwoEdAoLJYA3+CSbLGUs1QC09WDvaUZCQkvHhKrej4Re6+AzRq0JCmTZte9xoUCoVCobhb+vXrh1flJsuZdt12Hq8Tr6UI3W8eMP+Wu7AQUSjOdaRQ/H8oy8vvAUlJSTgdTkL04RW28cgeCt15RAsxuD3FM60ptjjEAD986pYu4aNChdvtKk4OpVYR1L4tW1asJiUlhcDAQJYs/olIsQZaUYckSxwu2EW2I41KVKeSEIUBE17BS7qczN5NB3jwgQf517v/QlRBnjsbf3Ugxwv3k+64ghYdfgQhI5PkjCOu6DyoZGrVqlVqTF6vl507d7J9+3ZsNhujR4ygWbNmVKlShUqVKt0TiZlmzZpFjrWIWq+8SNr6Dbz/6V5MRpF+vQMQRQFEEZXRQE6amWfeiCPpio7Ppr9Upp9z586h8vPDEFl8TbIsI0kSgiCUCmR0QUHowsI4d+7cHbvG6xk0aBCJiYm899GHWC/EEti2FfpKlfDa7eSfOIX15Gka1anD3Nmz74l96H9EWloaqrAwhBs82dZXikD6uX1FTp48iY/oh0l9/drcYbpIZCDfmUOQIfS6bS1SATFR9So8HncpHj/h1xn6AHUwvoIv5pPHMURVB4qDbpHi1RUyxZkIXDk5OFMu09i3LWqbGrfHSRiViXOdpqnQDj/Bn6zVKwkZMxxTeDi+fqWvKXPHLpyxcTz7zTd/2n97hUKhUPz1NW7cmDZt23Bi72mCtKFoxbJBtd1rxY0Dd1IyXksRqoCKA+rcI8cICQqmdeuKK7soFDdDCbrvAb8EYsV7Kcv3yzKZX75MAxTKBeija5ZZvvxrvq/ir9x+9eqQKUnExsYSEBBAkaWIGH3x7Gyc9SzZjjQaCq0JFH4NCNSoqUQUYVQmPvMUn336P2rXrU3C+Qu4vS7yXbnUFZoSSmTJ7J1XLg7UEzznOHXqVEmJrH379vHMc88Rn5yMOigQldGIOz8f4Us7nTp04Osvv/z/3cBbwO12M2fBAnyaN0NtNFDloQFccbt58e3DfD0rj349fDGZROITHKxaV4jTqWLNmhU0adKkTF+SJCGIAm63u7ieucNRMnOsVqtL9oYLggBC+asC7pYXX3yRJk2a8PU337Br6Uq8sozX60WjUqH2ysRfiKV7t+6MGDWCcePGXbfs3b1Ir9cju1w3bCe53CDL6HQVPwH3eDwIN7FYyKT2Ra1Sc9WTTE25XoVBq9VjIZ9sxox5t8K+NBoNNtle8v+CIBCtr8uJM8ew1K6Db4OY4qXk14zL63CQvXYNRklPpCEKQQ9FRUUU2fR4JA87LWsQRRGhyEP+vIXIrVuibd4MlV6HPT2T/IOH8Vy+zGsvvsjgwYNveL0KhUKhUNxNX379Jb169OJA2laitQ2opK+GSlDhkT2k2ZNJcJ+nep3q5FsKSVuznqjRw8t9GG9PS6foyDEee3yKsp9b8f+mBN33gOjoaIwmI1n29JJlo7+lFtSoRQ0WqYBKmuKAuaJYTZaLg92SL/c/B8WyLBfvUZVBJaiQZIlk6yUiqFYq4P7teRsaW3Hk0g6eef5pPjn3CS6nh2ZiB/yF4FJtBSBUqIxBZ2Lj2o3s2LEDnU7HkBHD8VaqRLXHH8YYVQ1BEJA8XgrPn2fPuo307tuXTRs2lMp2fqfl5uaSl5dHeJ3iGXpBgIiePbA1aEDaqVN8sSgeye1A7eePULMmuuQUOnXqVG5ftWvXxp6TS2Z8AurgIAStFlGlAlnG4/ZgNpux2+34qNQ4s7LLrAq427p370737t25fPkyr732GmtXr0PrNBChropW1mFJMvPx1E/4YtoXfDfjuzJJ+u5l7dq1Y8nq1bgLzWj8K84wWXDqNFqVmrZt21bYpkaNGlglMy7JVW5d7l9YPRbUGhVWsZBY6xnqmhqVCbxdkpOT1gNUrV6VAQMGVNhXm3atWXx+CZIslTzsijLUJt+dS/KKldiTEjE0jUEMi0ByuSiKvYj50CHEHAtt/LoVv0cAXz8/rNpCTE4j//noP9SuXZvGjRszZ84cZs+bR+rhY8iAShDo2L49T0x9767mWlAoFAqF4mbVqFGDjZs38sILL7Jz+04uWk6iFbU4vU5UWoGeA3vy3//+l6NHjzJh8mSSZs8n4sFuGH/OYu51usg/cZKcDZtpXr9BqcTACsUfpZQMu0c899xz/DBjCR19e6AWNeW2OZS/kyx7Gg8EP4RBZ+RE4QFS/QqoMuWJkid0MjIu2YHJx4Svb/ES0bzjJ8n5aTnHDh1CpVLRvElz6grN0Io6DuRtp7lwHz5C2Xvnlp1odVoCAgPZW7iJfmN6sXPHLvIvWagvtkCFCgEBGZDwICGj1+vx9/djv3kL9/XvwKmzZ8ky6KgxaUJJDcRruQoKSZr+NcN79OSbb775f9/Hy5cv8+OPP5KYmIharaZp06YMHTr0hj8bGRkZNGjcmOAhD+HMziHn4GHcFgtQnKQquFULwjp2QBcSTPbe/di2bCftypVyZy23bt1Kz7598WnflpCH+hVH8NeQvV68NhvWfQfRnj3Prm3bCAwMJCgo6J5auvvWW28x/bMvqadtRlV9zVJj80huTlsOYzHksnzVctq3b38XR3rzzGYzDRo3Rm5Yj6qDHiq3jdfhIGH6N/Rs3oJFCxdW2FdWVhZNYpoQ6Youd1/3L85ZjmEPLuTJp5/gvX+/h48nkGq6WgRqgotXhzguk+pNJCDCjxWrVlRY8x3gxIkTPNDlAeqJzaliqFHyuizLJNpiSXDGYpEtyAKIiKgkgUrqSOr7NMVXXXr53DHzHiIaBbNn355S/7Zut5vY2FgcDgfh4eFK4jSFQqFQ/GnFxcWxefNmzGYzgYGB9O7dm2rVqpUc37JlCy++8grJV66gDgpE1Olw5+Wh9ngY0Lcfn3366Z86vlDcfjcbiypB9z0iPj6eB7p2R85T0dy3Q5k9KHmubI5ad+OU7ERqqtPMtz2Fnjx2mTcTMmwoPnXrATJu2YUsyISEhKBSq5AliYQvv+H+WnVYtnQpAIMHD+HIpmNU1tTgROFBOgq9yyR4kpFwyS4CAwPQ6fQcK9hLvc41OXjgELW8jQjwhuJwOEqm27VaLUajEd3Py2/iredJFi7gVgnUePbJkv3N5cnavRfb1h2cPnGCsLCwP3T/7HY7L7z4IkuWLsWj0aCtFIHs9eJMvYpRo6Zpo8bkm83YbDYiIsIZPmQow4YNK/mZ8Xg81KlXj9TcHNBoMDVtgqFOLQRRxJGUQtGx4wheL9ETx5OzZx8NjSa2b9lS7lgGDR7M5kMHcRYVEdSvDz6tWpQJpi3HT5Lz41KMggqtVocgQM2aNZn08CRGjx5d8sDkbklLS6Np42ZU9dSuMKCUZImD5m007RzDmrVryhw3m80sX76cpKQk1Go1TZo0oVevXmg05T9UulNmz57NP155GVPb1kR0fwC10VByzJGVzdUlyzBZiti4di316lW8vxrgpZdeYsbXs2imb0+orvTPeKE7nyRbLKmeRB6d8giffvopmzdv5ovPv+DAvoN4vV4EBIw+RoYOH8Lzzz9PVFT5Nex/Icsyjz/2OD8tWkoDXUsiddVK/WxZPWb2FWzF7C4gSh9NjF8rDCojbslFqiOJK85knDiRZQmHZOHd997lxRdf/AN3UaFQKBSKvwav18u2bdvYu3cvTqeTSpUqMXjwYOWhs+KmKEH3n9DBgwcZPXI0+dmFhFGZAE0wEl6y3FcpEHLp0Kk9EydN5Oknn0awaqiirkmqK5k8XREhI4ahqRwOAgQEBqDT6ZA8Xq4sW4H79FlWLVtGhw4dANi5cyeDBw7BxxlApiONtsIDaIVf96rIPwfvKrWKkJBgQOBgwXZa9WrKjm07aaRqQ7iucnFLSS7Zm3yty7Z4TtoPYKodTa3nrl/r2GOzE/uvqcz48iuGDx/+u++b2+1mxMiRbN2/j5DePQlq3gyVTossy6SuWE3Gzl2IBgMBzZpi8PfHlZ2N7UIsIf7+zJs9m44dO2KxWKhTvz75GjXhk8ej+U2WSsnpJPuHJbhSrqCRvHz7xfRyy2YlJSXRok0bgocOxJZ8max9+9FWqoRPy+aoAvzxWiwUHTuBM+UKyDJ+TgON/Vrhlb1kulLJltNo0Lg+y5YvIyIi4nffi1vl448/5oN/fcT9vn3RVLDyAiDNkcJ57zH2HthD/frFCf08Hg/vv/8+386cSZHdjjY4qLi+d24elSMiePvNNxkxYsSdupRyzZgxgzffeQe714O+Th1URgPunBycSSlUjYxkwdy5NGvW7Ib9uFwuJk6YyPo1GwiSw6iiq4HNayXBcRGzbEZWiajVKgw6PY0bNuSlF16gX79+xMfHc+XKFTQaDTExMQRcp07obzmdTp588kmW/7QCnddAqBiJSlBR6M0jjywqV42ke4/uzJ01j8rUIEAdzEnbEVxqL4Y6dVAHBeK2F+GKjcNHpeLtN9/kqaeeuqdWWSgUCoVCoVD8GdxsLKrs6b6HtG3blgOHDrBw4ULmzp5LwtUzqFQqWnVoxeSHJ9OnTx80Gg01a9Zk2rRpbFi3AZfHhddqJ33uHHzq1SW4RTPsOTnkXU3HfPQYWqeTb6ZPLwm4ATp37syHH3/ASy+8jNfuIZ0rVBNqgSzjxYuEF1GlIjAwEBCwe22YhTwefPBB9u3Zj8VZ+HPQLSCI5X9RN3sK0el0CD4+N7xutdGAoFZTVFT0h+7bggUL2Lp7N1UenoBvdM2S1zO2bCNr336CevXA1LI5eL2EBIeg1qhxF5q5smQZw0aNYv3q1Rw/fpwip5OwMSMQVGpkSSqVVEPU6QgZNpTU/36Kv8nEwIEDyx1LfHw8HknCLzqaoGZN8atfl+x9B8hfs74kUZ62ahVCHuqPM+kymuPJP99LiNRXo8hj5ujpXYwYPpItWzfftVnhU6dO4ScFXjfgBgjXVeF04SHOnDlD/fr1kSSJKU88wZKVKwno3Ila7dqg+fkDyJ6RQeb2XUx55mkKCwt57LHH7sSllOvhhx+mf//+/PDDD2zZto0iq5WIWnUY9tob9O3bF6224j3a19JqtcydN5cffviBGd/P4Pixvdg8LnQ1qxPevg/BjRqiNxgoSkzk4p59jJs8mQ/efZfHHnvsD+/l1+l0fP/99zzyyCPMnj2bfXv2YXe5qVU9irHj3mTQoEEYjUaio6OZ+u/3uFB4BkO9ukT07oXoYwQBDHoDJr2ezO07ef3tt0lNTeXpp5+mSpUqf2hMCoVCoVAoFIqKKTPd9zCv11s6IdpvpKenk5CQgNvt5tKlS/y0bBknTp0CWcZoNDJy2DAmTpxYMgP5W3v37mXSxElcjU+nsdAeraBDFEWMRgNGowlRFJFlmVOWgzgDLZw5d4ZXXnmF5fNWcZ9f7wrH5ZU97DSvpU7jWsTm51HrhWevO4vmNpu59N5HzP3uOwYNGvS77pEsy3S8/z4SBKg+7teZZ7eliDPvvo9vh3YEdu9WnMSsyIrRoC/5OZHcbhK++JouDRuSmZlFnCBTZcQw8vLzizOQa9QIKjUgI3k8yB4PRXv2YbwYR8KlS+UGZtu2bWPg8OHUeu1FtNfMXnodDrwOB0UOFy5ZRifoyN6wDu2JFO4PKJ2gKt+dyxH7dhb+uJDevXv/rvtxq4waOYqDq4/RKuD+67aTZInNBUv5dtY3jBgxgiVLlvDIk09QafQIAhrFlGkvyzJp6zZgP3CYQ/v2ER0dfbsu4Y5LSkqidfv2iA3qUXXoYNSa0s80ZVkmbf1GrHsPsHn9elq2bHnbx9S7b1/2JcRTaeJYBJUalVpVnDkfAavVis1uJ3/LViz7D+Hv60uXzp156skn6dKly20fm0KhUCgUv5ckSezZs4fZs2dz5OAR3B430bVqMW78WB566CEMBsONO1EobqGbjUVvXO9GcdeoVKrrBquVKlWiY8eOdOnShccee4ytmzeTkpDApQsXSIyL46OPPqow4Abo2LEju/fsplrdqiToT4Ofh9CQEHx8fBFFEZu3iJOWA+So0/novx/h6+vLww8/jFfr4nzRiXJLXUmyxGnLYTQmNc888wzenFysKZeve505B4/g72PigQceuPmb87MrV65w4WIsgS2al3o998hREAT8OrQrfkEQEDRqHA5nSRtRoyH4/o5s37mLi5cuYapRHbVGTUhIML6+PoiShORwIDmcqAF/Pz/CYmKw2mxkZWWVO54GDRqgU6spPH+x1OsqvR61nx9uZFSokL0S9rgEAoTAMn0EaoLxJZA5c+b87vtxq0TXiqZILCwpVVeRXFcWKo2qJHieMWsWulrR5QbcUFziqlKP7ni0WubNm3fLx303zZs3D7dGTdTQQWUCbii+9sjePRGCApkxY8ZtH09sbCwHDx8m4oEuBAQH4x/gj4+PD7Isk5Obi9VuA40a/66dEY0GNI0asDshnsHDh98wqaHT6SQrKwur1Xrbr0OhUCgUCigudzlq5CgG9hvIlmU7UKf7YMoOIXZvAlMeeYJ2bdsTGxt7t4epUJRLCbr/Ynx8fAgODr7pZckRERFs3LSBmDb1OSMdYrdlA4fzd3KgYCt7ijbgCbbxzXdfM2zYMACaN2/Oh//9kHQxmcPmHWQ6r+KVPXgkN2mOFA6at1Goy+bb779h2LBh1K1dm4yVa/A6HOWe356eQeG+A4wePuIPrVSwWq3IsozaZCz9elIKuupRqIzXvF5OTeyARjG4JS9ejwdZ+rkWuihiMpkIDQ0lPDyM8PBwQkJCMBqN8HMQqvptbfSfVapUid49e5K//yCS213qmNfrRZZlREFF0cXzSIVmqhtrl9tPoBDKxd8E7nfS6NGj8ao8pDlSKmwjyzLJjlgaNKxPy5YtSU9P5+jx4wS2bFFue8krYbPasDocaOvXZdGPP95TNcpvxOPxsG3bNmbOnMmcOXM4depUqeM/LFmCT7MmiNf53RMEAb/WLVmxZg12u73CdrfC4cOHcUsS/jG/PgCRZcjPL0BCRmUyodLr0QQEoIuqBjLUfPIxTB3b8fo//8n27dvL9HngwAEmT55MVNUoGtRtQPVq1Rk0cBBr165Fkq7/gEahUCgUij9KkiQeefgRtqzbRmNtOzr4Pkg9nybU8Ymhlf/9dDT1JDshl8EDB5OZmXm3h6tQlKEE3QqioqLYtn0b6zetY8zjI+k4uA29xnRn+jdfcPb8WYYOHVqq/aRJk1i4eAHRratzxnOQLYXL2WpewXnvMZp1acTyVcvp378/oigy6/vv8XM4SfjyW/JPnkLyeAHw2Gxk7txN8jff07ROHd54440/NPagoCBEQcCZm1vqddnrRfht8CMV1y+/lqBWI8tQrUoVimIvlelfEIRSOeLMF2OJCA8nNLT8uuYAL/zjH+itNpLnLcLzm8BKlmVs8XHkrltPZXXVCuuyy8hlxnon1atXj34D+nLRdZI8V3aZ47Isc8l6hgJVDv948R8kJiby4YcfYrPbcKlV2Gx2ZKk4oJYlmcLCQrKyszGbzdiKbHh1OhKTkuh8f2cOHTp0py/vd5FlmdmzZ9O2TXPGjhnE1H//g3+++Qy9enamV8/u7N27F6/XS05ODvrwG2ff14eF4na7KSgouK3jdrvdCCqxVKk+p9OBx+tBZTCUylkgaDTIHk/JbLyqciRffvVVyXFZlvn444/p26sfG5ZspoqrDg3F1kRLjTi69SRjR41jyuNTcP/mQZNCoVAoFLfCnj172LR+EzG6VoTrKpdZCWpS+9LS534yrmTx7bff3qVRKhQVUxKpKYDi4LJt27a0bdv2ptr37NmTHj16cPbsWS5duoQoijRo0IC6deuWatewYUPWr1nDK6+9xp4fl5GxbCUqvR6P1YZRo2b8oMFMnTr1D+/HDw8Pp1OHDuw7fIyga5aYa4ODKDp7riQhmizLyG4PBh9TqffbLl9BJYoMHjSIj6dNw3b1KsbKlcs9l9tspujEaZ57/nnU6op/dRo3bsyiefMYN3Eice9/hKlxIwyVI/E6HGQdPIznagaVVVVo7t+u3PfLskyunEm3ptffT327Tf9yOrm5uezdtYsgeziVdVFoRB1FnkJSPYk4VDZeee1lVq5axcOPP45Xo8Ht8WLNzUMKCcZiseDjY8LhcOB2uVGhQSUU13bHYscomEg8fpmH+g/kp2VL6Nix41293vLIssw///lPZs74nGH9TUwcWZNGDUx4vTLb9xby9eyTjBwxiK++nolWp8Nru/HstddmRxC47fvOqlatiiBJ2NPSS0r22W12BJUK4ZqVGrLXizsjE/9mTYDiz4LAdm3YsXQFly9fplq1aixcuJD/vPsfqov1qeXboNSXnShqke64zJJFPxEaFsrUqVNv63UpFAqF4u9nzpw5GGTfkuSz5dGrDEQI1Zg7ex6vvPIKOp2uwrYKxZ2mJFJT3DGxsbFs374dm81GSEgIffr0ISQk5P/d77p16xg9YQLBA/oS2q4NANYrqVz83xeEjByGqX694uXtHg8hIaGoVL/O8CUvXEykxcreXbvo3bcvp5ISqTpxbJnA25mXz+U584lQqdm5bdt1Z7p/kZmZyYIFC5i/aBFpaWlodTp8DAYykzPp7N8frar8DNnZznROuPaxfNWyu57QyuVysWjRImbNmMX5c+eRZRmVWk2vPj0ZO3YsH/33vxw+d5bQvr0JbNqYS19+g1erJWzsKCSnC8nlAllGK+oRf15YI3k8XPl8GtGuqjTwbcYR8y70ldWcOn3ynvsDuX79eh6ePIr3Xg9j/PCys9gej8wzryeycadAq9Yd2X7uLLWef/q6uRgSZ8yhiZ8/WzZtuqkxXL58mfnz57N1xw5sdhuVIyoxfNgwBgwYgF6vr/B9brebpi1akB8ZQbWhxQkKs7NzkFQiqmveZz1zlpwly2jwwnMlwbkjO5ukT6axYdVqWrduTYtmLbAleWju36HccwHEWc+Rrkvk9NnTN/X7oVAoFH82brcblUp1V1ei/V01rN8QdZoP9XyaXLddniubY67d7D+0j3r16t2h0Sn+zpREaop7Tt26dZkyZQovvPAC48ePvyUBN0Dv3r154pFHyFm1hpQfl2JPS8dUtQq+taLJW7kGe8plZLcHfz//UgF39oFD2M+c5Zknn8RoNPLjDz/QtGY0yZ9/TeJ3s8javZfsvftJnreQhI8/pbJWx/KffrrpgCI8PJwXXniBk8eOkZmWRmpyMuvXrSMgLIDjRXtwSc4y78lzZXPacYj7Onfi/vvv7kw3FJfEmjBhArv27OLcxXMcPXGU+MQ45s6dy9mzZzl08iTVHplIcMvmiGo1oR3a4YhPwHbhIiqDHkFbvMT/lxBUlmXyd++EIhvVjbURBZGGphZkXM1g7dq1f3icp06d4p133uHJJ5/k5ZdfZsOGDXg8nv/39c+c8R1tmuvKDbgB1GqBD96KQiVYCQ0JwZuVTf7J0xX2Z4lPwBmfwMOTJt3w3LIs8+mnn9K8dWs+/vprLgoyV4OD2Hv1Co889STNWrbk+PHjFb5fo9Hw5OOPYz12grxjJwCKt0pc85jVlZlJ3toN+NWtUxJwA0huDwICWq2WHTt2cDn5CjWNFSdlBIgy1MZpdbFkyZIbXptCoVD8WaSlpfHhhx/SrGlDoqpVomrVcAb078OKFSuULTV3kMfjKXl4fz2CIJa0VyjuJcrycsWfniAIvPfee9SoUYP/ffEFyZ9/iaDV4XW7kMwWsmfOJbB1C3xatcJuNODIyiL/4BFciUk89ehjjB8/HigOkjeuX8+aNWuYOXs2p7bvQpIkateqxaQPP2LIkCH4+vr+4TEC1KxZkyVLf2Tk8JHsylpLqFCZQHUIEl6yPFcpIJcO97Vn7ry599STdEEQCA8PL/l/j8fDzDlzMDVpVGpVQFDzZpgvxJLz4zJcndIxtmyBKIp4ZS/e7GwKDu7HfuosjQ3N8VEXPw30UfvhRyBr165l8ODBv2tcqampPDHlUY4ePUBEqED1ahryCyQWzP+WSpWq8cmnX9C5c+fr9pGcnMy8efNYt3EjRUVFhIaEMGzIELp27cqBA3v4/P3r79P281XTv4cPR04dZdTQoSz8aRkei4XgNq1R6bTIsozTbqfg5Cly126kR+fON1Ua7/PPP+df//kPAV3uJ6zzfah0v66McObkkvrjUgYNHcrGdesqfJr/+OOPE3vpEnMWLqDw9Bn0MTF4fU14gKKTp7GePIUhNIQao0eUel/B6TP4+fpQr1495s6diwo1ARXkH/iFVtTiIwRw6VLZ3AgKhULxZ7R7924mTx6LSCGD+/jSuGEINruXDduO8uQTe5k3937mzlugrLS8A6Kjo7mYnnDDdvmubLQ6LZUr2CqoUNwtStCt+EsQBIFHHnmECRMmsGPHDhITE1Gr1dSuXZsjR44we948Ur+biQyoBIE2rVrx+Otv0L9//1JLgXU6HUOGDGHIkCFA8Wzj9ZYK/xGtWrXiwKEDLFy4kHlz5pFw5SxqtYqWHVoy+eHJ9OnT56azz98KdrudlStXsviHxVxNvYrRaKRz186MHz++3DrabrebxYsXk5SSQuUHu5a6R4IoUn3UcLSBgWTu3Ufh7n2ofH3A7UEutGJAT3NjG6oZSverkw3k5eb9rnGnp6czoH9vtKqrzJoWSbdOAahUxeM4e8HKf6alMXbMMObOW0zXrl3LvF+WZb744gv+/f77eDQajDEN0FSpRG5OLq/+618Yp05F8rioUe3GS96jq+vZsCObadOm4efnx4w5c8jduh2xciQer4QjJQXJXIRaEEmIS2DWrFlMnjy5wtwAOTk5/Ofjj/Hr1J5KPcqW0tOFBFN98ngSPv+K/3zwAXMrKC8niiL/++wz2rVty9fffcvJn5ZRZLMhqFVoAwKI6Hwf4Z3vK7Xc3G0pwnLkOFNGjcLHx+fnf1v5Jn8Xbv3vi0KhUNwNFy5cYML4kbRr4eGrj+rh6/Pr5/WEEeEcPm5hwjO7eeSRSSxe/JPy2XebjZswjsf3TqHIYy55aP9bkiyR6klkwPD+BAaWLcmqUNxNyp5uxd+C2+0mNjYWu91OaGgo1atXv9tDKuH1ehFF8a78wT5x4gSjR40h7UoagYTiI/rhltzkkoGs9fLcP57j9ddfRxRFnE4n06dPZ+acOaRcuYLD6SBiyqPoQkMwGoyYfEylrsFWUEjagUPYTp5BSkmnlV9HInRVEIWyM/gHC7fReXBH5s6de9Njf+yxxzh8YAnrF9UmPKzs/niPR2bSswmciQ3k6LHTZR5kfP/997z0+uv4de5ERLcupUp9eYqspCxajP3scX6aWZseXa+/FWLqp1dYtdmXY8fPAnDmzBn69+tP2tV0DIKJEE04UYbaCILAFUcCWVxlwOD+zJgxo9zA+4svvuDN96ZS+41XUBuNZY7/IvvAIcxrN3Dy2LEbPtWXZZm4uDjeeOMNNmzbRsTgAQS3alkqqZojM4srCxcTLqrYumkTkZGR7Nq1i4f6DaSl7n6CtBVvrXB6HewqWsuHn37Ao48+et2xKBQKxb3uscce48yJpWxdWhe9vvyVZ1t3FTDhmVSWLltP+/bt7/AI/17sdjvt23UgMz6HVj73o1eVTkYqyRKnzYcoNGSzYfMGmjVrdpdGqvi7udlYVJnpVvwtaDQaYq6pV3wvqajm9+0WFxfH4IFDcOdIdDT1xKT+dem8V/aSZIvlkw8/RaVS8dxzzzF85Eh2HTyIT4umVOlyH4lzFyDZ7chqNUVWK06Xk6DAIASxOPA2+Pvh27wprpQrGNOcROqrlTsOq8dCIXn07t37pseemZnJhvUrefP54HIDbijeb/3G85F0GxzPhg0b6N+/f8kxi8XCu/95H1PrlkT2fBCP24OtqAhJkhAEEb1OR/WJ4zn36nkWr8i8btDtdkusWG/mwV6/LtF+b+p7FOXY6BY0AF+1f6n2wdowMp1XWbV0NQ0afMrLL79cps8DBw+irVnzugE3QGCTxmStXMORI0duGHQLgkCdOnVYtGgRL774IvN/+IG87bswNKiHqNbgvHoVR2Iy0VFRLF60iMjISAA6depEdO1oEi9eIFATUuHDoSR7LEZfI8OGDbvuOBQKheJel5OT8/PfmMAKA26Abvf5U6t6OnPnzlGC7tvMYDCw5KcfGThgEPsubyJCrEqEriqiIJLvyiHVk4hs8PD1d18rAbfinnTvbBpVKBR31IcffIgtx0Er3/tLBdwAKkFFLVMDosS6fPbJ//jHCy+w69Ahqj4ykaqDHiKgcQzGypWwHj+JSq9HZTLi9ngoNJtL+hAEAa3Xi+3CBULU5e+LlmSJ89bjhIaHMmDAgJse+86dO/F4bAwbcP0Z6Lq1jDRuoGPz5s2lXl++fDkFRUWEdO5EXl4eOTk5FFms2K12rEVWcnNzKSgsxKdFa9ZutnDoWEGF55ixMJPMbKEkN8DFixfZsnkrdbVNygTcvwjXVaayWJNvv/4Ou71smTGny4movfEWA1GrBWRcLhcpKSnMnTuXr776iiVLlmC+5t/iWhqNhmnTprFz61YmDHiIyrkFhFy5SoeqUcz57jsOHThQqvSfKIq88ebrFGiyuVB0AkmWSvUnyzIptnhSvJd4+tmnCAgIuOG4FQqF4l4WFxeHx+Ogc4fyP8N/IQgCnTuYuHD+1B0a2d9b7dq12bZjK8++8jSOUDPHXXs46tzFFW0c/Uf3YeOWjQwcOPBuD1OhKJcy061Q/A1lZmayZvUaojT10IjlzxQD1DTWI8Ucy8IffiCodw98qkcBxV80wjp1IPnHpVhPn8HUuBGiTofD4cDr9UGlUiF5vORv3IIGSPMm42PzpbK+OmpRgyzL5LmzibOfxWm08sOMRdctf/VbVqsVrUbAz/fGH2FhIQIWi6XUa0ePHkUbGUmRx4vXK6EWNKhQUZxnXUZCwuN2Y+jUnowD+xn1eBLvvV6Fgb2D0emKn1Xm5Lr5bl4GX80p4IknX6JBgwYA/PTTT6g8air5lj+z/4vqhtrszd7Ili1bSs3CA1SrUhX36dM33EdtT01FkOG7779nytNP45ZlRK0GyeHEz8fE6OEj+Oc//4nJZCrz3saNG/Ppp5/e8P4BDBw4kJycHF57+TWu5qUQIVTFqPJBUnnIkK7gUNl49IlHyp21VygUij+bX3Zeijex7UsUf22vuP3Cw8N56623ePnll0lOTsbj8VC5cmXlga/inqcE3QrF39CxY8dw2l1U8rl+YKgRNWg8OmxqGyGtW5U6FtSqJZaERHJ+WoHj8hV8W7VENBiwW614LqeSs2MX6pwcfly0iFWrVrF65Rriis5iEIx4cOPEQe36tfnk09l06tTpd40/ODgYlxsyslxEVLC8HH6ehb0i0aJtcKnX3W43LknC65XQCjoErv1iJSCiQiOIuNUg6wxUqdaQF9+5yHufZdGovgaXG46dciKqTLz08js899xzJe++evUqBtmn3L3r1zKpfVELaq5evVrm2IgRI5i9YAGWuHj86tSusI+sPftxOZ0cS04i5KG+BDZtgqjV4jabyTl0hG8XLODk6dMsX7q03MD7ZmVnZ3Py5EkQweo1c8l7BlmWEUWBuvXq8vF/v6Nnz55KIiGFQvGXEB0djShq2XPITM3qFT8QlmWZPQdt1K7T8A6OTgHFiW+vXZWlUNzrlKBbofgbcrvdyLKMSrjxfnJJllD5+qD2KR20CYJA1PCh6MPCyNq9l6JDRwABlSBg0Gho07Il73zzLW3btqV///5ceecKK1asIDMzE4PBQKdOnejUqdMfKo3WtWtXjEZ/Fi7N5oUnKt7LfOREEXFJHt794KFSr/v5+WG/ehWVFwR1+YGigICUW4jX4eShhwYxaNAgFi1aRHJyMiqVitf7NWP48OFlnq7rdDokwXvDa5BkCa/kRacrmx29devWtGnZksNLV6B77GF0wWXLdeUePkLekaPoAvyJfuaJUvu/NX5+VOreDf96dTn43Szef/993nvvvRuOqTxZWVn07d2X5NjLVFfVp2pITbSiDrvXSqo9mcspcbz773dp06YNQUHXLyumUCgUfwbh4eF0f7APcxevYdSgEDSa8v9O7T9i4UKch7f+Pf4Oj1ChUPzZKEG3QvE3VKVKFVRqFQXuPEJ1ERW2k2UZl+AEd/nl0wRRJKJbF8Lu74T5/EWuLF7CA23bMXXqVBo1alSqbdWqVXnmmWfKnCM1NZUdO3ZgtVoJDg6mR48eN6xE4Ovry/AR4/hu/pc82DmARg3KzuKaLR7e/OAqtWrVp2PHjqWOBQYGIlms2C7E4teoSYXnKTp+AtErk5eXR40aNXjjjTeuOy6Adu3aMXfmPKweS5m98tfKcKYiakTatm1b5pggCMydPZt+AwYQ9/lX+LZqTmDzZqj0ehyZmeQdPIz1/EVEWaba2FEVJlwzVq1CQMd2zFu0kFdfffUP1Zl/9tlnSb54mTY+XTGqfEpeN6hM1PZpSCVPVQ6f2cFLL73EzJkzf3f/CoVCcS965plneWjARp55PYlp79VAqy0deJ+PtfHUq1do0aId9913310apUKh+LNQEqkpFH9DzZs3p36DeqQ4Ll23Xa47C9QSKreboviECtuJajWaAH/UksyTTz5ZJuAuT0pKCmPHjqVZk+Y8/fgzvPHSWzw84REa1o/h5Zdfpqio6Lrvf/3116ldpzVDH07ii+/TyMl1A+B0Sixdk0O/0XGkpvvx7Xezysym63Q6tIKa/E2bcaSnldu/5dwZLEeO4iP4YLVab3g9vxgwYADBYcFcsp4tZ5+fjCRJeCQ3iY4LtOvQtmQv+G9FRESwacMGnnv4YdRnL5Dy+VckfPhfLn8/G++lePSiiCokGE3lylxvO2Fw29YUWorYunXrTV/DLxITE9mycQu1tDGlAu5r+aj9qKmpz5pVa8pdKq9QKBR/Rs2aNePrb2axaSe07XWRj6ensnF7PivX5/LI8wn0GplARGRz5sxd8IdWbCkUir8X5VNCofgbEgSBZ557hjwxi0RbbLltrJ4iztqP0LJ1S5rExJC1dQeSp/xl07IkkbllOzWqVaNbt243PH9iYiI9H+zJlhXbiJZi6OY3kAf8BtLZtx8hRZWZ+dVsBj006LqBt8lkYslPyxkw8BH+972D5g9coNF956nf4RzPvZlNZFQX1q7bTP369cu8NzAwEJ1aS4DDSMbcuWStW409JRlXdjbWuEukL1lM7opVRInV0WjUvytBi8Fg4P3/vEeuOp2zliO4JCderweLxUxWZhZXMlPYnbmJXHcWrVq3Kjd7+S+CgoJ45513OH/6NE8/9hgaCdRuAd+iQNyFEmJgIIWFheRkZ+N2u8vtQ+vvj6BWk5ube9PX8IuVK1eCRyRSH3XddlX0NfA4JFavXv27z6FQKBT3qt69e7N12x769J/CzB/g4eczeOq1LBKvRvPu1GmsXLWW4ODgG3ekUCj+9pTl5QrF39SwYcOIjY3lf59MI7swjWq6WvipA3DLbq46ksmQLlOzXnUWLFxAUlISg4cPI3neAqoMHIA2MKCkH3ehmaur1yElJvHx/Pk3rDsuyzKTJz1MYVoRbX0fQCf+mqRGrzJQx6cR4e7KHDm4i6lTp/LBBx9U2JePjw8ff/wxb7zxBhs3biQrKwuTyUSXLl2oWbNmhe/r0aMHWoOWqp4oIpFIOhFP5vFTIAggywSIgcQYWqMV9OR60ujXr9/N31hg+PDhuFwuXnnpFbYVrMLH6Y9G1uLAQQE5qEQVwaowPvv4f+zds5cfl/x43f3QS5cu5evpXxPurUZd/8ZoRR3HC/eRbnWgFXS4vW7y8vIJDgpCrSn9se51OJA8Hnx8yp+pvp7c3Fz0gvGGe//Voga9yvCHAnuFQqG4l9WqVYv33nuPd955h8LCQrRaLb6+vkriSIVC8bsoQbdC8TclCAJvvfUWTZo04euvvubIoSNILgkECAoJ4pkJT/HUU08RFBREZGQki+bN5+HHHiP+w0/Q1aqJxt8fj8WCIy6eQB9fZsyYQffu3W943sOHD3PqxCma6tqXCriv5a8JoqqqFgvnL+L111+/4R7vgIAARowYcdPXHhkZSd/+fVi7ZD1tfLoSbaxPkacQj+xBK+rwUfvhklwcsmynaasmNG/e/Kb7/sXYsWOJjIxk4EMDMbvzMYo+6FR6munb/Vw6TU2hO49jB/cwbuw4Vq9ZXe4SRYvFwpuvv0Wopwoxfi1LvuiF66qQkrYfd3YumtAQ3JITS5GFwMDAUu/PO3ocg0ZD586df/c1GI1G3LLzhqXLJFnCLTkxVrC3XKFQKP7sNBoNISEhd3sYCoXiT0oJuhWKvzFBEBgwYAADBgwgISGB9PR0DAYDDRs2LFM3u2vXrpw5eZIVK1awavVqcvLyCIqsTJ/JjzBkyJAbBsa/WLlyJVpJT4i24gRuAFGGaJIKLrBp0yaGDh36h6+xIh988AGnT57m0KXtRGsaEGmojlpQI8kSVx0pJDjP4RNu4Jtvv/nDMxrz5s3DR/anQ1iPckuI+WuCaKRvzb49+9i3bx+dOnVCkiT27t3L0qVLSc/IID0tjeysbDr7tyk1jkq6qhhsOvJ2biN80FBUKjVOpxOvx4tKXTwz7bZYyNu9j4G9exMRcf37XZ5u3brxyUefkuPKvG7CvSxnGpLKe1NbCxQKhUKhUCj+bpQ93QrF31BeXh4XLlwgISEBj8cDFNcl7dixIy1atCgTcP/CZDIxZswYflqyhAVz59KqRUumfTaNurXrUiOqJo88/Aj79u0rJ4HYr3Jzc9FLxhsGsnqVEbWoIScn549f6HWEhYWxbsM6eg56kHjxDDvMq9ltWc8OyyouSMdo3bUFGzZtoE6dOn+o/4yMDDas20BVTa3r1uwO0UZglH2YO3cuiYmJ3NelC/0HD2bh1i3szkzndJEZlxp2mTdx2f5rMjtREGlmaoPzQjwZy37Ck5ULMrjcLmRJovBiLElff08lo5F//+tff+ga2rRpQ0yjhsQ5zuCVy9/P75E8xDvP0bJVS5o0qTgTvEKhUCgUCsXflTLTrbhn2O128vLy0Ol0BAcHK/ulboMDBw7w9TffsGHzJtweL4IgULlSJSaNH8/DDz9807PV69at49GHH8VhcREmVKGqui5uh4t1P25k2U/LGTN+NJ9++ilqddmPGJPJhEd03fAcHsmNV/pje5FvVnh4OPPmzePy5cusW7eOvLw8fH19efDBB6lXr97/q+/4+HjcTg+hxuvPMAuCQAAhHD92gr4DBpDpcVPl0UmYalRHEAQK8guwZudStPcAx48dAqCaIbp4/LrKtOU+Tlw6TNql7xHDgygMDESwWJDNZlo0bcaM776jSpUqf+gaBEHgs2mfMaDfAA6bd1Lf2JQATXHSIFmWyXfncNF+EnWgwCefffKHzqFQKBT3GrPZzPr168nIyECv13PfffeVqTQhyzJHjhxh9uzZHDywC4fDQWRkFYYNH83w4cN/VwJOhULx1yfI15uS+hMwm834+/tTWFh40wGD4t5y8uRJvv/+e5avWoXT5UIA6tevz8MTJzJixIgKZ10Vv8/MmTN56bXXEEJDCGjbGmPlSCSXi/yTp7GeOkOD6JqsWLrshsuQDxw4wMABA/G1BdHYtw1qUVNyTJZlUh1JXHAfZ8rTj/P++++Xef/atWsZM3Is7QwP4KcJLHP8Fym2OBLFcxw/dfwPB4130759++jbsx/tjQ/io77+Z9MZ81HyTGk4jQZqPvMkGt9fHzSYzWZsVjtatORsXI/z6Bl6BD6ERtSWtJFkiUTbRc7ajvJA9240bdqUQYMG0bx581vy8OrIkSM8+shjJCcmY5R90UhaXKIDu2Cldp1afD/ze2WWW6FQ/Ok5HA7+/e9/M3/uAiyFFrSiDq/kATW0adea/3zwH5o0aYLb7eb5559n+bKF1Kgq0vdBX3x9VJy9aGPDNhtGUwiz5yykbdu2d/uSFArFbXazsagSdCvuqvnz5/OPl17C6+eHf+sWGCpVwutwUHDqNI6Ll2jfqhWLFy3C39//bg/1T23btm0MHTkSU/u2RPbtVSYQc2TnkPL9LFpE12Lzxo3XrTk6oP8Ajm8/TVu/bhUum06wXuCyKrbcgNntdtOsSXOsl5209Luv3D6ckoODlq10H9iN+fPn/4Ervvuys7Np1LARVd11qWmsW2E7WZbZVbiWIpWV8IH9CbuvY6njbpeb3NxcNIIWyWIj9YvPaaJtRo3f9HnGfAQ50sHps6fRaDTcal6vl+3bt7N+/XoKCwsJCAigb9++dO7cWalRq1Ao/vScTifDhw1n97Y9VFPVJspQC73KiCRLZDpTSXCcRwyA5SuX8cMPP7Dsp5l8/E4lBvUJRhR//ZuanePmqdeSOXlOx+o1m8otW6lQKP46lKBbcc/bsWMHQ0aMQN+yOVUe6ofwmy/u1iupXJk5l+7t2rHkxx+V5eb/D/0GDODAlctEP/FohffRkpDI1e9ns2rpUu6///5Sx/Lz89m6dSuxsbF89J+PaKRtSxVj9QrP55E87LSs4aU3X+DVV18tc3znzp2MGDYCg82PusYm+P884y3LMtmuDGKdJ/GP9GHj5o1ERV2/RvS97NFHHmXND+vp4NejwrJbGY5Ujtn3IOvV1Hv7DTTlLKfPy83D5XKjFbRkLF5MUIKdNoGdr+njCqddh3j73X/y3HPP3Z6LUSgUir+wjz76iA/e/ZDm+k4Ea8PKHPfKHo6Yd6GNUGG15vD+GyGMG1a2HYDN5qXHsEs0aDKQ77+fcbuHrlAo7qKbjUWV6QnFXfPJZ58hVqlcbsANYKpahYghA9m6cwcnTpy4CyP8a0hISGDfgQMEd2h73QcXPjVroAoLZf6CBSWvFRQU8Nxzz9GwfgwPT3iED6Z+iN3mQDRrKSwsRJKkcvtSCSq0bgOrV69m2bJlJCUllToeEhLC/V3uJ0vMYGvhGlZnL2J7zhp2mddxyr2fui1qs27Duj91wA3w7HPPovITOGbei1tylzme68rinPMIMY0bIggiaoOh3H4CAgJQq1U4PTbcDhvZrnQO5G3nYN529udv4ZTzIIOHD+Lpp5++3ZekUCgUfzkul4tZM2YRQVS5ATeASlDT0NSCK5cvE+DnYcRDFZcPMxpVTBodxMYNq8nMzLxdw1YoFH8iStCtuCsuXbrE/oMHCerQrtyA+xf+Deoh+Pszb968Ozi6v5akpCQ8koSpRvXrthMEAV1UNeISijNk5+fn06dXHxbMWESEozpd/PrT2NgalaBCLatx2O3k5eWWCrxlWSbZFse2/DVkC9kcPXOaCY89Sos2bRg2fDhHjx7l+eef576uXdl85DD+nToQ+mA3tPVrYtYWIfjBtzO+Ye26NQiCwOXLl3G7ywarfxYNGzZkwaL5eAMd7LKs5Yz5CFfsCSTZLnHQvJ2jjl106NqeN954A5Uo4MjMKrcfUSWizsoi49uZuK5eRaoeRl5dExkhDjLIJCQylBdeeAGVqvzZdIVCoVBUbP/+/WSmZxFlqHXddr7qADSiyH1tNWi11/8K3aNLAF6vg7Nnz97KoSoUij8pJXu54q6Ij4/HI0n41qp53XaCKKKrHsXFuLg7NLK/HrVajQDInvJLPl1L9nhKMo6/9NJLXDoTTxtT15JEYCaVHyBQJBQSIITg9jgxFxYSEBiILMucsxwn3nMJQ6OGhLZoTGB0TUxGAwVnzrFt9142PPAAkiBQachAglu1QLgmSHRkZpEyfxGPP/kkWkGNy+kEBCIqhTNh0gQmTZpEcHDwbbhDt1fnzp05cHA/8+fPZ/7c+cRlnUYQBdp2bMukyZPo06cPAJHhEeQcPETVQQ+V6aPwwkUS58xHF12TkOGDCapeHZVajSgIOK+kkrF6Lf0HDmTT+vXUrHn93ymFQqFQlJaXl4ckSRhVN66WoRJEVDfx7VmjLg7KfynLqVAo/t6UmW7FXfFL4iVZunFKAVmSUCmJmv6wmJgYtBoNuadO43Q6K/wCIHm8OOLiad2iBWlpaaxeuYaamvqlMm8HakLw0wRwVU4CGVRocDqdeL0e0p2XiXfHEti7JyED+qGrHInRxweVTkdwy+ZEPf4IUtXKuIGAJo1KBdwAQoA/voMGYJFlpAKBpuoONFa3RUrV8sG/P6Jrl24kJibezlt121SuXJlXX32VcxfOcTX9KhlZGaxes5qHHnoIjUaDRqPh0cmTKTp6nMILF0u9V/J4SVmyDF10TYIG9MUQEY7JZEKv16HVafGtVZMajz9MruTljTffvEtXqFAoFPc+WZY5cOAAjz36GK1atKZl85aMGT2G2NhYBFHAKdlv2IfkUXH8lJMbpUQ6eqoIUFO9evVbM3iFQvGnpkQyiruiQYMGaEQR828CjN+SPF6ciUk0bdz4Do3sr+XMmTO88MILFBUUkLljN7kZmeRk55CXl4fTWbpWdt6x4whWG+PGjWP16tV4HB4q62uUaiMIAnV8GpFHFilyLCIisiyTY83iovUM2uga+DZvhkd2o9frUWt+nQ5weNwE9euDLEDekWOl+vW4PRTkF6A2mPBv2ZoiwYq/JogwXSSN/VrRwdSD7MRchg0djt1+4y9F9zKtVltutu+nn36a/j16kjp3AYlLlpKXmITdZiPv2HFc+fn4dWiPSqUurv36m635aqOR4C73s3nbNpKTk+/IdSgUCsWfSVFREaNGjqJPz76s+WE99jgvznjYtWofH773ES63k8v2hOv2Yfda8aq8xCZ42LY7C5vNVu6DbFmWmbUomxYt2lC3bsXVKxQKxd+HEnQr7opq1arRvVs38vbuR7rOsuf84ycQrDbGjh17B0f317B792569ejNlmXbqa1pjCrPSv7S1WB14nZ6yM/Pw2azI0sSecdPkr16LWNGjKB+/frk5OSgVxnRiGVLT1XWR9HQrzkpchz7pI0cFney17mVXCkHfdOGuCQnKrWqTJk3l8uNOiAAQ/165B0vnRjParMiy6ARtJjq1MMjeDF7CkqOG1RGmpk6EB8bz6pVq27L/brbTp06RVZ2Nl67g9xde0n49HPOvvImibPmog4OxrdqZYKDg0qW//9WYNPGuGWZnTt33tmBKxQKxT3O4/EwYfwENq/dSiN1Gzr69qShbwsa+DanvX93Whk6gySSYL1Akbuw3D4kycv+/G248GBzSjz16mVOncslJ6f4QfYv+UdkWeaDaakcOOrh6Weev4NXqVAo7mXKnm7FXfPSiy+yc0B/Uhb8QLURQ1Dp9SXHZFmm8NwFslatZdSQIbjdbl588UXOnDqDJMs0aFifsWPH0qJFC6WUWDmysrIYN2YcGrOBFn6dUAkqQp0RHEjYQeoXX2CoVw9NRBiFLheeuARURUWMGDyY//73vwCYTCbckhNJlsqtox1lqEWKI4ECdRGGRg0whIdRuG0nuiqVETRqvF4vFosFP/9fl6bLsgyigCY0BEfq1V9fl2QcdgcqVICAqFEjA5JcOjO6j9qPIMKYM3suI0aMuC337W7Zt28fQ0eOxBngT7XJE/CtV4eihETsaelkbN2BymQqXs4vVpwoTdRqEbVabDbbHRy5QqFQ3Pu2bNnCts3baabvSKguoszxIG0oHf0fZEfeWvYXbqWhqQWV9NVK/v4VuvLYX7gVm8ZN8H33EdKsGekrljHk0TQG9DTS434DJn0RVzPULFxu4Xysl7ff+Q8PPvjgnb5UhUJxj1KCbsVd06JFC+bPnsPEhx/m0vsf4dO0MfpKlZAcDiynz+BJz6Rvjx7YrFY6d+qM2qslQA4BBM4eOsf8OQvo2r0rs2bNVGq0/8bChQsx5xVxv29fVIIKt+TikvUMXpcbg0uL92QcLuECEhKy5KFGjeo8/9xzaLVaALp27crUf00ly5lGhL5Kmf5PWA5SZHATOXo8YkQIersdy87d4PGgNpmQXC6sdhsqtRqTyQjwc8krD5LdjqjR4PF48HolJMmLLMslX26cGekIkoxRZSpz3gB1CHGX/lpJ9Ww2GxMmT8ZTKYLoiWMRNcWrC/zr18O/fj3chWZyT5/Bbrej0WpL7udvuQvNSA4HISEVl7FRKBSKv6NZM2fhK/uXG3D/IlAbQjVDTfI12VyQjxNrOYUBE1485LtzcGsEosaPIbRFMwAMTz5J9t79/Lh9P4tW5iG7XUhemYceGso7U5+kY8eOd+ryFArFn4ASdCvuqu7du3P4wAHmz5/Pgh9+IOPYSTRaLQ926sT4j8cxe9ZsNq7cSD1tcyKNUSWBmSzLZDpT2blxFyOGj2DFyhXodLq7fDX3joXzFxIqRKIVtUiyxOGCXRQ4c2kstiNQ+Dkok8Ere7Bi5nJGLEMGDWHbjm2Eh4fTpEkTWrZuydn95wjRRqAWf/2oKPKYSXOnEty3P6qIMFQaEb/Qamj8/LCeOoO+ehSiVovs9WK1WjEajQgCGAwG7Lm52E6fw1S/LjnZOSV9SrKMBw9qwHz0KCHqUExq3zLXJcsSKtVfa1fMypUryczNIXryuJKA+1pBzZuStW8/zitXsGk0mIzGMnu6AXIOHcHPx0TPnj3vwKgVCoXiz+PA/oOEq6Nu2K6KrgYFUg5r1q1m586dZGZmotPpWLl6NeZK4SUBN4DaYKBS925EdO2MMzcPZ24uqbPn0bt335sOuCVJYu/evZw7dw5Zlqlbty6dO3dWyj8qFH9Bf61vr4o/pcjISF555RXOnDxJVno6aZcvs2jhQjweD5vWb6axvh1VDDVKLXMWBIEIfVWaGztyYM9Bli5dehev4N6TlpaGvzoQgCxnGjnOTOoLLX4NuH8mIKLDSAtjJzKuZPH999+XHPvk009QB8IRyw7yXTklmVov2xMQfAxo69UCQcbfzx9RrSK0bRusp07j+rnWtKjVIkleXC4nADqdHuvR47jz8tDVqoVa0KIVdGgFHWrUeCUvObt34ExKobahQbnXlSNl0LxF81t+v+6mZcuXo4+ORhcUVO5xY1Q1TFWrUrB5G67CwnLrlluvpFKwZx9jRoxUVn0oFArFb3g8blTCjeeZREENMlStWpXXX3+dadOmMXr0aLJycwlu27rc9wgqFfqwUPzr18NQpw5Lly27qTGtWLGC1u3a0X/wYF59911ef+89Bo0YQYvWrfnhhx9+1/UpFIp7nxJ0K+4p1+7PnjVrNr4EEKarVGH7QE0IgYTy/Xczbli+4+9Ep9PjkYqDs2R7HL74EyiEltOy+J7pVQYihKrMnT0Xl6s4q3lMTAyr1qwivG4IR5w72WvZxKGCHSTaL6CqFIZKpyEoKAiNtnh2Nuz+juhDQsicPQ/b+YsgCICA1+PFY7OTum4DBVu2o/JAwfI1FO7agyszC3dePo4LseQu+BHzzt2oPQIh2rJLAHNcmRRRyMRJE2/LPbtbsnJy0ASXH3BD8e9EjbEjETweMmfPI3v/ATw/Z3B3FRSSvmkLV76bRbumTXnrrbfu1LAVCoXi/y09PZ2PP/6Y1u3aUateXZq3asXbb79NQsL1s4j/XlWrVaPQnXvDdgXuXPQGfaltOjk5OUiyjO4mtu5oQoLIyM6+Ybvvv/+eSY89xmWtmiqPP0y9f79FvX+/RbUnHiXD35cpzzzDp59+esN+FArFn4eyvFxxT5IkiX179lJVVeeGbStpq3H2zMnixF3KLB8Ane7vyLYVO6klN6TAlUME5S+r8+JFrVYhiirCdVU4kbuXy5cvU6tWLQCaNm3KwcMH2blzJ2vXrqWgoIBjx46RKkBoaGipZc4qvZ7ajz9M0vxFZP/wIyp/f9ShIRQKIlJqKk5LEX6SkY7BPUiwX+Dy7oOY9+xFBgRJxt8bQFVvA5KFi6Q7LlPF8Gu5slxXFqfsB+jYuQMPPPDA7bx1d5yfry8ec8F12+iCg6kxchjx07/BsnEL+Rs2IajV4PES4OfLkxMn8sYbb2A0lr/fW6FQKO41y5Yt46lnn8UmSRgbNURXvSqZFgufz57N9G++4V9vvcVTTz11S841bvxY/vnaO1g8hWQ4U8l0pOKW3egEPZGGKKroqyMKKtK8yYwaMRyDwVDyXpPJhCAIeKxFaAP8r3MW8BZZ8fctuzXqWufOnePVN9/Ap0NbIvv0KjXZYKpWFdPoEWRs3c7UDz6gQ4cOtGnT5v938QqF4p6gBN2Ke5LX68Xr9aIup2TVb6kFDUgyDodDCbp/NmnSJNauWkuG8woyMkI5i1pkZCQkfIzFXxBERJApU3NUpVLRrVs3unXrBsCsWbN47tVXcZvNaPxL32+Njw91pjyK9fIV0tZvxHLmHJ0feIDgBg1Z+tMywjVVkASJRn6tqC81w+zJR0LCIJrQoSc/P49cdwanzYdwSk5kJLK9aVjIp8P9HZi/YP5fbq9brx492Dt1Kh6rFbWpbPK4X5gvxVEtKortmzdz4MABrFYrQUFBdOvWDd9rvuTFx8ezevVq8vLy8PHxoXv37jRv3lzJ8q9QKO4Zmzdv5tEnnkDXOIY6D/UrVb1E6tubjK07ePNf/8JkMjFx4v9/ddPIkSOZ+u+pbMlagVrQEEw4PhhxYOe06xDnLcfx1wQh+sGjjz5a6r3NmjUjNDiYvKMnMFauXOE5PHY7tguxVOrVi88//xy1Wk3r1q3LVFmZOXMmso8Pkb16VPi5HN6tC5aTp5kxc6YSdCsUfxGC/Cdfk2s2m/H396ewsFAJuP5i6tetj5hmIMav5XXbxRadIdcnlcTkRDTlJKL6O5JlmcmTJ7NyySpkp4DB60OM+Ot+NEmW8OBCrSleIi4IAvHW86TpEomNu3jd3yWz2UyDxo2hcQxVBvQtt43k8ZLwzfdEeLyIskBKUgoOm6M4sBcEQnURNPBtToCm9LJqWZY4nX+EdFUyfv7+qNUqmrdsweTJk3jwwQcrrFH9Z5abm0tMkyaIjWKoMnhAuV/C7BmZpHz1HS9OmcKbb75Zbj/p6ek888yzbN+6HcEjYhCNOCUHHtFN0+ZN+PyLz4mJibndl6NQKBTXJcsy7Tp0IEH2UnPSeASx/J2OKT8tRzx/kS8//xwfHx+aNm36h6szLFu2jMkTJuNnDaEqtdALRkSh+AGuXSoiTj5DnpDJ+x++z0svvVTm/VOnTuW/X39N9ScfQx8eVua45PEQO+1LPCnx+PnqCQnS4HBKWG0C9Rs04p133qNTp07IskxUdDRCy+ZU6nH9VVuZu/Zg37aTy0lJJZVFFArFvedmY9G/3jdYxV/G6LGj+eyDadSTmpbKnn0tSZZI9yYzZsSoWxpwy3LxzLnH42H16tUsXrKEtPR0jEYj3Tp3Zvz48dSoUePGHd0lgiDw9ddfo9fpmTd3PlaPBbNUgEEwISMhI6PVagkMCEAQBCRZ4qo3iSFDB93w4ZWfnx9vvPIKr/7zn6Qb9IR3ub9U1m2P1UrqspU4EhNJcctUFqvTWtcZl11CLWjIJYNUZwL7XJtpG9SFYG34NeMWEdQCjRo3Zu++Pbft/txLgoOD+fiDD3jmH//gsttFRPdu6EKCgeKHFwVnzpC1Zj2NatfimWeeKbePjIwMevXsTXpCBvW0zajkWw2VoEKWZbJcacQeOUvf3n1Zs24NjRo1upOXp1AoFKXs27ePi3FxVH5kYoUBd96JkxQlJmHLymTUhAmo1WqMOh0D+/fnxRdfJDo6ulR7WZaxWCyIoliyHPwXLpeL1199nVC5Mo1DW2Oz2bDbHXjk4lVdakFDjK41J717mTVzFn5+fsTExNC+ffuSfp555hk2bdnC2W9nEta/N/4xMYjq4qDdejmVuC++wCgV8djEAEYPDqZ2LT80Gi17DliYPvMio0YO5rvv59K1a9fiVUrXyePxC11wMIVuN2azWSkFqVD8BSgz3Yp71pUrV2jXph16sy/N/DqUyl4OxQH3acthCvRZ7Ny9g/r16wPFf3wPHjzInDlz2LFtBw6Hg4iICEaNGcXo0aOL9yJXID4+ntmzZ7N40WJyc/MoctoQtVp869fDGFkJr8OO7dxFVC4nLz3/D1555ZV7ftnu8ePH6d2rD65cDw1VrTFpfDAaDWg0xU/OJVnipHk/RaYCtm7fQsOGDW/YpyzLfPrpp7z/0Ud4dTpMjRqiNplw5uRgO3cBjSzjtNhpYmhLFUMNZFkmOysLQRZRCxq8spdz8mFsYhHdQx8q2UYgyRK7LGuZ/NRE/vOf/9zW+3KvWbJkCa+99RbZubnoKkeCVosnOxvBZqfHAw/w5fTpBFWQ4XzixIms+2kDbX0ewKAqu6/bI3k4ZNlOVKPK7N2/957/mVUoFH9dX3zxBW99+CF133mj3M+itA2bSd+6DX3tWhgbNyKgRnVMej0Fp8+Qv/8QgYLAiqVLadKkCVlZWcyfP5/ZM2eTkZEJQI2aNZg0eSKjRo3C39+f5cuXM2ncZDqYeuCjLv6eKMsSHo8XWZZIdSWR4jmHS7Rg8pHR6fQ4nCqio+vxyqtv0rdv8YquvLw8HpsyhW07diCbjKjDQnGkZ+DMuIpRL/DNf8Po0dmExwtuj4xapSYgIBBQ8dSriWzbp+bw4ZPENGmCvnMnwrvcf937lHPwMAWr13ElObnUHnOFQnFvudlYVAm6Ffe0TZs2MWHcRASrmihNLcJ0kYBAjiuDy654nDorX37zJUOHDgXA7Xbz/PPPs2j+D+i8BsJVVVALWiyefLJJwzfQhznz5tClS5cy51q+fDlPPP4kXrtEgBTKFU8y6hpVCezXG5W/Lzq9noAAf2SPh6xde8jfup1/vvoaL7zwwh2+K79fXFwcgwYOJj05g3ChChG6KoiCijx3Nle9SQhGmRmzvqdPnz6/q9+EhATmzZvH+k2bsBQVERYSwrAhQ1i0cBGZZ/Jo6X9fSVuLxYzNakMr6AABh2zjiLydJv5tiDLWBiDJFksC59h3YC/16tW7lbfgT8HhcLB69WoOHDiA0+kkMjKSYcOGUadOxQkF09LSaNq4GTW89alurLhdjiuTE649rF63+qZryCoUCsWt9tlnn/HutP9R563XyhzLO3WGpLkL8H+wG/6dOuC12TBodfj/nD/EY7eTPGMOYV6JWd9/z/ix48lJzyVMqEKwNhyX10GSPZZCuQCDyUDPHj2QvF72bzhER/+eZc533nKCVPkMQ/ubGDs4kOho8PH14exFma/nZLBtj5sPPvwf48aNK3nPhQsXWLJkCdu2bePM6UME+sP97fXM/OzXSitut4zZ4sUriQQHB1FQKNOy+wVeee0DDh85wtojh4l+7qnrPgBN+PJb7qsZzTKlJKpCcU9Tgm7FX8axY8f47NPP2LRhE27Xz8vBNCru79qZf/zjeTp06FDS9qWXXmLmN7Oor2lOZX310kvMJBenLAdw+lpZt2EtTZs2LTm2d+9eBg0YRKArnEa+rTlauIesIBeVJ00urjeNF7fswmAw4P9z9tK0jZtx7j/EmRMnCAsru8frXpOVlcWMGTOYO3suOdk5yDLoDXqGDBvMY489dlMz3DfjwoULdGzXkcbqdj8/JCkmSV5yc/OQvBIaQYOAyFnpMKIW2gZ1I8UeT7znDI8+8QgffvjhLRnL38GcOXN4/ql/0MV3AJrrJB6UZZk9lg1MemY877333h0coUKhUPxqxYoVjH/kEaJfev7XrTReCUuRhZQZsxE0GsImjgNZRrLa0Go1BAUGIYjFf8+dubnEf/QpPmot6iI9LXw7oRV0xFnPcdFxBkmvQV87GnRq3JlZeDOzwCnxQGC/kplugCxnOiccW3jrH0FMGFq8isgpOfDx88HH5IMsy/zzg8vMXeJg564DJVU9oHjWu3nzGHp3lVi5Po9508Pp2rF0IkxJksnN96DRGAgICGDKSwmkZDTgjTffof+gQYQMGkBw6/Jz1uSfPkPGoiUsmjPndz8MVygUd5ayp1vxl9GiRQsWLFxAamoqFy5cAKBWrVpl9lQnJSUxe+ZsaqljSpWb+oVW1NLcryP7zVv4+KOPWbhoYcmxjz78CJ3TRGO/NjgkOxmedALb9kH8OXmJiAq1oMFut2PyMaFWqwm7vxNxe/ezaNEinnvuudt3A26RsLAwXn/9dV588UVSU1PxeDxUqlSpVObrWyE7OxuvV8JHX/qDRxRVBAUFkp9fgMvjQkRAh4FM9xV2mdcia7w8/tRjvPvuu7d0PH91hYWFaETtdQNuKN7nr0VHYWHhHRpZ8R+iZcuWcfbsWSRJonbt2gwfPpzg4OA7NgaFQnFv6dWrF6FBQWTv20+VAf2QvBK5ebk4s3NxpaYRPGwwAgKSxwOyjNvlJi8vrzjppyigCw5GXT2K3NMX6BM8DK2o41LRWc67zuB3fwf827RDpdcjI+GSncg5uWQuX8nerK3cF9ADo6o4OE52XKRRAy3jhwQCxQ8mZWRUYvFebUEQeOuFqqzedIG5c+eW+tu0ePFikKz071GFletziapS9vNXFAWMBpEiqwOv5KV6VR0nzufQsWNHJo0bx4z583DlFxDaoR1qn+IxeWx2cg4eIn/LdoYPGkSvXr1u9z+HQqG4Q5SgW/GnUaVKFapUqVLh8fnz54NLpJpfrQrbqAQVUdrabN64mcuXL1OtWjViY2PZt2c/DXQtEAWRfHcOkgpMdUsvb1ahwoMHu82Or58vaoMBbc0aHDx06JZd452g1WqpWbPmbevfaDQiCAIuyYlR5VPqmEqlJiQkGKfTid1ux+two/fV8fjTjzJ+/PhSMwmKmxMQEIBbcuGWXGjEijPcyrKMEwf+/tevM/t7OZ1O1q5dy44dO7BarYSGhjJgwAAOHDjAtM8+p6iwCB/BH0EQKJIKefdfU3n40cm8/fbbf8ls9AqF4vr0ej1PTZnCO/95n7xqVRGiovB6JASrEwBtRDiy14vsdKIS1KgENW63E7OleDYJGYTgIFSCBq2ow+61cdFxBr/7OxJ0f+eS8wiIxf9FhBM6egTZM+cTW3SaZv7tcEtu8rypvNg/uGRFnEf2IIoier2upA+tVmRYfz+WrFhSKujet28PndrqqFypuG1Wjpfo6uVdq4jF6sHtcpOV48bX1w9BEPjoo48ICgpi+jdfE7d7D9rISBDAlZaOQVTx1COP8M477yBWkGhOoVD8+SjfeBR/GcePHSdADkElXL+Oc7iuMhcsxzhz5gzVqlXj4sWLeNweQg3F+7Ek2QuAUCYbuoCIgPuaOtaiRoPL5bql1/Fn16hRI8LCQ0nNSCZAU96MpoBOp0elUWEVCnnplRd57bWye/sUN6dHjx5o9RpSHUnUMNatsF2OKxOX6ChJCnQrbNiwgeeeeY7M9Cx8CUAlq3EIVr6Y9gWSR6a2sSHNfe5D/3NyN5fkJNkWx/TPviQrM4uvv/la+VKpUPwNPfvssyQnJzNrwQKkyEr4NW2G4JaQJQmvxYKo1yMiohE0gIAKNQ67o3hllgxehxPtzw8ZL9vjkfUaAtq2K3MeERUerwdTcDDW1s24vGUvDaXmeGQ3gihTrXLx33mv7EXCg8nog/CbpK1R1XTk5echy3JJgO6w2wgPUFGvtoHKETp+Wm2hXcuyyc5EAZDBUuRi3RYrkx4pXiquUql44403mDJlCj/++CMXLlxAlmXq1KnD8OHD/xRb1hQKxe+jfNtR/GV4vd4yfyzLIyIiy8Xty2NUmRBkGWdGegU9FKdBkCUJd3oGUdWq/dEh/yXpdDrGTRhHBpcp8pgrbBdnPYdKLzJmzJg7OLq/noiICPo91I9kdyw2b1G5bdySm1jHKRrGNKBt27a35LwbNmxg3JjxeNIFOph60N6/O20CutBQ3wrJLVNDrk+Yoyoarpk1EnXU8YkhRteaJYt/Yt26dbdkLAqF4s9FFEX+97//MaBXL9yxCeQtWUHOipXILhe2s+fRoEEraIHiIFclqEtKeUpeL/bzF/GneNVOtjsTfd06iDpdhefz8/fHr1EMbtHD8YL92DxFSDJk57txSy48shudXo9Go8FqtWK1WnG5nIBMTo4b088ruH4RHlGZ2Hg3oghjhoaxckMRJ844So7LgCTLuFwSkiTx8fRUrqYXkZaWRnJyckm7oKAgpkyZwueff84XX3zB008/rQTcCsVflBJ0K/4yatWuhUXI50a5AXNdWag1qpIl1nXq1EGtUZPjKi43EqQJwwcfzMeO/OadxRWuNeriJ+OWuHikvHxGjBhxy68FIDc3l+nTp9OmfXuqVI8ium4dJk2ezJ49e254jXfb008/Tf1GdTli3UmG4wqSLJUcc3jtnDUfI5V43vznG1StWvUujvSv4f3336danSocKtrOZXtCSf1ZSZZId1zhkGUbuhAV337/7S0pF+Z0OnnumefwcwbTwq9TqeREyfZLGPGlqhCNJBXXzv2tSH01/ORAZs2c9f8ei0Kh+HMSBIHw8HDCtGG0U3elmdyOKlIU9uNn8JqL+CXgBhAQAAFJksg7eBjJYinZTuMVvCX5V35LwotGrUYUBYIjItBoNXgCbRx37cXtlVi2oQBBLaDT63C73RTk52MxW7CYLeTl5pGZmc3ilXk80L10MrNhw4ZxIc7D0ZNFPDI2gkYNfBj5eDoLlhZitXqRpOJZ+9gEJ//+NJ9vf7ChrRfDvFUr6di5Mzt37rxNd1WhUNyrlKBb8ZcxZswYXKKDTOfVCtvIskyKM47mLVsQExMDQP369WnTrjXJztiS5WO19PWxnzmP+cSxkvd68QIyBoMBZ04u6UtX0LZVK1q3bn3Lr2Xfvn20atuWN6ZO5bLJgPb+TniaNmHVgf30GzSIRx599J5e1u7n58eKVSvo8EA7znoPs9uynsP5uzhYsJ3dReso8MnkvQ/f4+mnn77bQ/1LCAsLY92GdfR4qDvxnGanZQ17ijaw07Kac97DNOkUw7oN62jQoMEtOd+aNWvITM+inqlJqSBelmUyHKmEUwVBEFGhwulwIEllV5VEaqqzZ/fecoNyhULx1+dwONixYweF1kJku4zR60uUVAetRSZj/nxsV1KQZQkoTnAmuV3k7N5HzvqNdGrfngIhB6fkwIABV2Zmmf5lJCQkDMbi7S2OjAz0Wh3Lli9jw+b1vPjyKxw7BYeOu3E6HOAFjaBDJ+rRino0go4lK82cjy0iNDS0VN/33XcfdevG8OLbqZgtHhZ+U5cuHQN57b1cWvZIYeD4qzw47CqdH7rKonVewgePpO6Tj1Pn1RdxRVZizITxXLp06U7cZoVCcY9QSoYp/jJkWWbwoMHs2bKPFsb78NcEljl+segUV8UE5i+aT+/evUuO7dixg6GDhxHqrkyMb0sEBM5ajpLgiUdXpxa+zZoiBPujRsYTl0DR8ZPUqVqVNStXEh4efkuv4/z58zzYuzfO8FCiRo0oyWr6yzUUnDpDxk/LGDNkKF9On35Lz307nDp1isWLF5OSkkJmZiZGo5H69evToEEDBg0apPze3mLJycmsW7eO3NxcfHx86N69O40aNbql53jiiSdYM28D7f27l3pdkiXWZiyiNo2JEIu3XTglBwEB/ugNpfc7ZjqvcsZ7iHMXzhIREXFLx6dQKO5tkiQxceJEli9ZgcvhopnYCT+h+G+2Q7ZxRjhCkboIbaUItJGRSB431ksXkaw2fHQGRo8Zxbat27GlO6mqieaY4zCRjzyMLrz4s6Q4c7kLtVpFcEhxsrTk+T9Q3eXmwL59CIKA1+ulb5/eHD2yjVeeDGFIr0B8fYpzwuTkeVi4Ip8vZxcg2AMR/WT2HdhL7dq1S64hOTmZgQ/1ASmNSSODGNAriMwcK9/NSWP/MScJKR70NWtR+4nHUV2z9F1yu4n/+DPG9x/AZ599dgfvukKhuB2UOt2Kv6X8/HyGDhnKsUMnCCWCSH111IIGizufZEccFrmAmCYxdOjQgUGDBtGhQ4eSRE6LFy/m2aefBbtIJVUUvip/slxpXHYl4RLcqDQatBoNIiBKYNDriapenXHjxzJy5EgCAwOvP7ibNPnhh1mxdw+1nn2ywiVzuYePkLtiDft27aJ+/fq35Ly30+zZs/nvx++Tn59JzSgdahUkXnah0fgycdLjvPrqq0om6z+R8ePHs/unA7QO7Fzm2PrMH6kkRVFNqIMbFy7JSYh/MEZT6Uz2SbZYUtQXSUpJwvjzTJRCofh72LFjB4MGDCZG3ZrzRSfQeLTECG0Qf87LIssyuVIm6aTgUDmwymZ0go42gZ3JdWVyRUqgSs1I7DY7WWnZONwuhMhQwkePQjDokJBQq9UEBgaiUqvI2LqdrLUbGDV8OEOGDKFr165otVpGjRrF2qVrEdUefHxlGtXX4vHKnD7vwmVXUU0TQy1jQ3YXreOxZx9h6tSpeL1eRFFEEATS09P58MMPWbVyCU5nEQ6HA7dXRhNWiYAO9xHasT1COckiM7Zux7XvILHnzt3ysp0KheLOUoJuxd+WzWZj3rx5zJwxi8T4RNxuN3aHHVEQ8NcEYxJ8cKrsOAQrDRs1ZPbc2SWlqs6fP8/MmTP56cefsNlsCIJAq9at6PZAN9auWcvRI8fwFwIJU1dBJagpdOeSTRoh4cH88OMPtGzZ8v819szMTBo1bYpvz+6EdmxfYTvJ4yXug4+ZMnIU77///v/rnLfbtGnT+PCDtxnxkIkpEyOIrl4845mZ5WL+T1l8MTOfvv1G8uWXXymZrP8kXnjhBRZ99yOdfHuV2SN+LH8vqfYkVGodbsENgF7QU1Nfm+qGOuhUemRZZp9lEw8M6sLcuXPvxiUoFIq7aOzYsWxfuZsOvg+S68rkQP52/OUgagoNMAl+yLKMJEkUkEsS53GJTjoGP1iygq3IY+awbQdde3WmQ8cOfP3V18QlJSIG+OPftg0hLZtj8vcn/8xZMtasQTbn4WNUYzTqcLkFgoMjGDV6Ap/+91NqSg0J11Xhij2BIq8ZAQE/dSBVDTXRisUz1GcLj5Glu0JISDA5ObnodFq6de/GpEmTuO+++ygoKODIkSOMnzQJ6tel2rDB5Qbbv7Amp5D67Qz279r9p3hwrlAoKqYE3Yq/PUmSmDdvHv949h/4e0Jo6NMCk7r4ibIsy+S5szlvP45fpJGNmzdSvXr1kvd6PB4sFgt6vR69Xs/YMWPZsGoTjfVtCNNFljqP0+vgRNE+xGCZrdu3EB0d/YfHvG3bNgYOH06tV19EGxhw3bYpPy6lkahm88aNf/h8t9uFCxfo1rUj/3jcj39MqVxumzWb8pjy8lW+/mYeAwYMuMMj/Htzu91kZ2cjiiIhISE3vdpg79699O/Tn2baToRof91ekefKZr95B04D+DZrhrFajeKMw8mJ2E6dQedS0d6vC9nOdJI4z8o1K+nUqdPtujyFQnGPqhFVk8CCCGr7FOdWyXamc7xwPw6vHR/80chanNgpwowXD52CehCuL/035LI9gXhOc/TEUaKiooiLi+Ozzz5jxZrVWO0OPB4PuIpo0kDHU5PDGdQ3HJ1ORXySnTk/ZDFvSSG5eS7amh4gTB9Z3jCB4r3nl/LPEiefpq5PY3zVAbhlFxney9hFKyNGD2fatGloNBrqNGiAs1FDKj3Y7brXX5SUzNXvZnJwz17q1q241KNCobj33WwsqkwrKf6y3G437099nyBvOK387y8JuKE4a2qwNow2vl0oSDPz9ttvl3rvL8vSDAYDhw8fZv3aDTTUtSwTcBe688l0XSVMXZnCrEI+//zz/9eYJak4y7eguvGvpqBSlaoZfi+aM2cO4aESTz9c6f/YO+/4KMrtDz8z22sqaUAKoXcIvTepIkoH6WDvXa8Vr/dn71cslNAEpCpFQar03mtogZDes9m+M/P7IxCNSSiKXsV9Pv4hO++8ZTY7M+e853xPpW369wqmXUs9iTOm/okz+2dz8eJFXn31VerUqkujBo1pUK8hTRo14d133yU7O/ua57dv355GTRpxwrkft1xSJscp2dlp+wmxRlWqP/ww1m6d0NaMwVi7JlV69aHqgw/ii7CwMf97zkhHefKZJ/0Gtx8/N5nc3FzWrl3LqlWrOHjw4F+u0oWiKDgcDlwuJ2pRU/p5FV0kt1W5i5ZBHTFrLMjImIUA4oS6qNGUqZBwhSh9DF6XjwkTJjBs+HAmT55MrVq12Lh2HfNnzSIy2EKPToGsXdyIEYOi0OlK8rVrxhl4418xfPV+NfQ6Hymuc5XO1+NxU1hQgBcPepWJhtYWxBhrUtNUn/aWXtRXJbBgzje8/PLLADRt3AhH0ulrXoeik6ewWqxE+0uO+vHzj8GfROnnlmX58uVkZWTT3tSr0jJJWlFHjKYOP6z6gdTUVKpWLb8bO3PmTHSSgQhTtdLPst3pnHAcJk/ORVGV9K0oEl98NZWWLVv+5trTNWrUQC2KFJ87T1DTJpW2UxQF94WL1OnUucLjBQUFfPPNN+zcuROXy0W1atUYPnw4zZs3vyklo66XH77/lmH9rWg0V3ciDLsziMde3EFubi4hISF/0uz+mezcuZMRw0ZSnGcnQqhOjLYeCgqZl9J4a/LbzEqczZJli6+6+yIIAjMSZ9CvTz92pK0jVlObYl8RXoNI1cGDQadBxoeCgg8vPsULJg1BQ+4i8/OvuHPQAP71r3/9iav24+fW5sKFC7zzzjusXLEEr9dx+VMVtWvX54EHH2Xo0KF/6r3/12RlZTFnzhy+nptIRkY6tuIiTklFKEB1fQ00ogZREInSx2D1BWP3OdAKOi7JZ1GJKnSivkx/HtnN3qKt2CQnO5NOYfK6UXwSK9av56333qVHl644nYV8+O/aGI0Vv+r27h7MwNstLFxyHkVpX+H1KS4uBgVyySBCV63MMUEQqGqIxSW7mDF1Bg8//DDjx45jzZjRFJ9PxhwXW+G4PqcT2579PDhqFIZfCUzm5eVx5MgRvF4v0dHR1K5d+/ovsh8/fv7S+I1uP7csGzZswEIgZvXVRUqq6WM5WXSALVu2VFhze9+e/QQL4aUP5FRnMnsdO9DEVqdK6+4Y42uCIOC4dIGC3Tt58LFHycvL49FHH73hOcfHx9O+bVt2bNtJYJPGlb4k2U6fQcrOYfTo0WU+VxSFr776in9P/jf2IgdWJRgVajaKm5n+1QzatGtN4szEm664XhmFhUVEhJuv2S4yXAvIFBUV+Y3uP5CUlBRGDBuJnCvS0dKntM4tQLiuKi6pIXsv/sSQwUPZum3LVcOk4uPjWf3jal599VW+X/kDee5CLC3bIukEBFHGbDRhNpvxeLz4fCW53eqgQHwtmnPq9On/qQHgx8+txIkTJxgyZAAGTQ7PPhzE7bdVxWgQOHbKxexvzvHE4/dx7NgxJk+e/D/53R04cIBRdw/F7cpmYD8zrRPCKLZb+HFDHms37iSl6CQtLd0xqkqeFSVzVFAUhXQuEKWPLrMr7pO9bC/YQKHJQ2jfwYQ0bYw1oORe5XM6yd6yjSXLltC7s4G4GH1FUyplzLBwFn53mjP249QyNyhzzOfz4vF4ySYVJw7ijBUbwDGGmpy3neDrr7/mqaeeok2LluyeO59qY0dhiq5epq3XVsyFWXMJ1um47777Sj8/f/4877//PksWLcXpcIICKrWK1m1b8cijj9CnT5/rvt5+/Pj5a+I3uv3ccqSkpDBr1izmfT0Pvd1KpjMTjVaD0WhEr9cDZV861KIGERGHw1Fhf7IsIV5+UXFIxeyz78TQvBFV+vYvI5RijI5BVS0c38HDvPbGG7Ro0YJ27SoXQ6uMJx57jG3Dh5O6fBVV+/ctJ8bizMgkfeFS2rZqVa7/zz//nJdeeJlIOZYW5gboVCUvHIqikOVJY++W/fTv15/VP64mODj4hud2owQGBpCW7rxmu0tpHkAkICDgD5/TP5np06djyy2mk6VvGYP7CnqVgebmjmw9/wOLFi1i4sSJV+0vNjaWWbNmcfjwYdp37kRQ3ToEBAWi0+lKX+51Oi063c9jmWvGc2bF90iShEpVEu5pt9v59ttvOXToEJIkER8fz5AhQ8rVxvXjx09ZvF4vY8eMJDI0j5mfVEerceP1FeL1KtSpIfDea8G0beHg1Xc/oXHjxgwePPg3jeNyuVi+fDmLly4lIzMTi9lMr9tuY8SIEVf9naampnL3yCHExxSS+HEdgoNKjGdJlujZScOFCT7ufzqLvRfX0z6gHypBjVarQ1ZsnFGO4BKc1DDWLdPnOccpCtTFRNw9FqFKIHrDz4a12mAgsmcPCrZvoXqUB5/Pi1qtoTLq1rKi1apJchxGEARiDDVL7412dzEX5dOkCxeoYapLsLbidapFNVY5iKNHj6JWq5k3dy7DR45k12dfoo+vgaV+XQS1GnvyBRxHjhEeFMSCBQtKQ8sPHz7MwDsHYsuyU00dT6SxOipBRb43l+Nbkhi1YxSvvv7qb3LkV8S5c+fYs2cPbreb6tWr07FjR3/1ED9+/gT8Od1+binWr19P+zbt+eitj9G49LhwICoqfB6JgoJC8vLyURS5zDkOqRhFUAgNDa2wzzp161Cg5AKQ7DiNYtQS2qu8MSwrMoIoENWzB2KVUKZOm1bmuKIoHDhwgBdeeIGxY8dy3333MWvWrJLwtV/QrVs33n/7bZy79nDmg0/I2rqd4nPnKTxxkgvzF5L86RQaxcYwe+bMMrsW6enpTH71daoqNWhoTSg1uKFk5yBcV5VWpi6cO5nMxx9/fOMX9zfQ7/aBLFllw+uVr9puwbI82rbt+Kc4Av6peL1e5syaS6QYXaHBfQWjykQoESROT7zuvqOiotBoSoxrvV5/1d00RVEQBAFBEFAUhS+//JIGTZrwwOOPk/jD98xZv44X/v06jZo25fnnn8fr9d7QOv34+Sfx/fffcynlLG88H4AoFCOKPgItIkEBKixmAcnnpt9tIp3bqvjyi//+phzvPXv20DQhgUkPPsimc2c5Y9Cxt6iQl9/8Pxo1a0ZiYuX3iunTp4Ocy+z/xpca3AAqUUVQUCAx0Wo+eSsEtzqP845TuCQHWb5UTgh7ucQ5GllbEKT9+dksKzLnPWcwNWmIWCUYtVqNtoLSmprQKuQXyTgcJU5fn8+H3W6nuLgYu8OOJEkA5BdI6HR67hjYn1TtGTbZVrKzaD07itaxzbWGFM5Qx9yIhpaEa1wlAVkqec6FhISwasUKEr/8koTQKhSvWUfB8lVEFdr4z0svsWPrVpo3bw6UVFsZMXwk7iyJ9pZe1DTVx6S2oFcZidRXp5W1K9WVWkx+ZTLr16+/ru+rMg4fPsyQwUNoldCKeyfcxyMPPMrAAYNo3jSBr7766i+X/+/Hz63GH+raevPNN1m6dCknT57EYDDQrl073n777TK5gi6Xi6eeeooFCxbgdrvp1asXU6ZM+dPCX/3cOhw5coTRd49BX2yms6U7Bb48tuWuxSYUEiSEIisyHo+HgoLCMjW1LzjOEBQSRI8ePSrsd8zYMXy/4gfyPTmkeJMxNW+MqPm151xBwodep0elVhHQqgWrVq/GZrNhsVjIzMxkwqRJbN+1E8FqRR0WhuJ2M3/ZUl5+7TUG3H47BQUF5GTnEhgYQO8+vVm6cCFz5s5l+apV5Pu8CAjExsQw6ZVXGTNmDGZz2bDtuXPn4nP6qGVpWOk1MqktRIrRzJk1l+eff75cPtnNQpIkCgsLueuuu5iZ+CXvfZbK849Vq9AYW7oql90HPHw1ddIfMhc/JeTk5FBQUEB17bWVcoM1YZw5c7LUQL5m++BgoiIjyTtxisCGDa7atvjkKerXr4coirz77ru88c47mFu1IL5LJ3TBJb9Ln8NJ7q7dTEmcQWpaGokzZvh3Yvz4qYCFCxfQrJFAdFUIClCh1ZZ1BhsMYLdL3NlHx8Mv7OXkyZM3VKLq6NGjDBw6FFdIEPHPPIEu9Of0H5/DSfqPa3nyuWdRq9Xl0p08Hg/z581i5F1WAqzlf79arY7gkGAa6ux0apfPmo27Oes7jkqjokmnxhw5fJRM9yVCvGFYr5QKk4pwKHZC6tUBQSkNK/815rr1WbvhNFk5dnySD4/bgyKUOKAVWcFGSXWSRd8VYbUGM23aNGw2GwsWLODs2bOIoojZbOazTz4jRBt21fugpEgUqwqoVbvWL9amZdCgQQwaNKi0/NmVyJ5fsmzZMlIvptLB1KdCZ6ggCNQ2NSLPlsWUKZ/TvfvVVdErY8eOHQwZNBS5EOromhEZUB0RFYW+fJKTT/HsU89x8uRJ3n//fX/qjx8/fxB/6FvMTz/9xEMPPUTLli3x+Xz861//omfPnhw/fhyTyQTAE088wapVq1i0aBEBAQE8/PDDDBw4kG3btv2RU/NzC/LxRx+jFAs0t3ZAJagI0YQRoAnijPcITWiHVtChUTS43W68Xg8ajZZcTxaX5LM8PvExjEZjhf12796dxs0ac2j/DlySk8CQX++IK3gVL4pA6d+1PjyMfJ+PvLw8vF4vt99xB2eys4gYNRJrvTqlu+T27GxSvl/DlK++IpAAQjTheJTT/LByNcGhQfx3yn/54P33ycrKQqfTUbVq1UprWa9ft55gJRyNWHkoHUA1fRw7885x4MCB3xT+fjVSU1NJTExkzqw55ObmAgIms5kPv8whNcPDQxMiqVe75DpfSnMzc0EWX87OZ+iwCfTr1++mzsVPWa783VzPboaCgiiKeL1e7HY7JpOpwt2kX/Y9YexYJr/7Lt6ePdBU8iLsyszCeeo0kz78kOPHj/Pmu+8S1KMrET26lWmnNhoI79oZfUQ4y2d9zZIlSxg2bNgNrNaPn38GyedP06KhiMVc3uCGkmQqs0lF43pafD43ly5duqrRrSgKxcXFaLVadDodr05+DYfRQPzEcYi/ugeojQaqDehPisfLS6+9xsCBA0ufgQAZGRkUFubTsW3F5SIBNGoNgYGB9O7uYO8RGzNnzic+Pp64uDh2797NxAmT2HFhHWZHADrFQLFciISEymQgKDio0vtSSOuWnFy1nM+m5/HcY6GIBj3CZWe5IsvIXi9JZ218NSedtu3vQq/XYzQaeeyxx8pci80/beb8gVOEaiMqNUZTXcnIaqlSAVVBECo0uAHmfT2PYMIwqSvXPhEEgeqaeH7asKlSwderUVxczJhRY1AX6WgR0AmV8POrf6AmmKYBbUlxniNx6kxatWpVobaNHz9+fj9/aHj56tWrGTduHA0aNKBJkybMnDmTixcvsm/fPgAKCwuZPn06H3zwAd26dSMhIYHExES2b9/Ozp07/8ip+bnFyMzMZMXylVTXxKMSSh5ugiDQMrATikrmgLKFVPk8MnKJEmlxNsdt+9nv3EKn7h157rnnKu1brVaz4Jv51GgYiw8vXqcNGQkZCZ/ixa24kQWZwIAANNqSh7rP7kAAjEYj77//PqfT0oi9/x4CGtQrNbi9Hi/FkkxQz54EtG2HAxf1zc1oHdiVTua+iLl6xo0Zx65du6hZsybVq1ev1OAGsNmKrxo2fAWNqEORFZzOa+da3wg7d+6kY/tOfPjWx6gzzNQXWlJXaI4u34LTLjBvSSE9Bp+mVc8TtO93inb9TvH1UoEnnnyFDz744B/lXfd6vaxdu5bp06cza9Ysjh079oePGRoaSmRkBFmetGu2zXBdQlSJRFeLplZ8LapXrc7o0aPZtGlTpUb72LFjiQ4LI3n6TNx5+eWOOzMyuThjNg3q1GbgwIHMnDkTxWwivGvFCvwAAfXqoqsZz7QZM65/oX78/IPIyyvA4ZQx6K/+OifJIEkyp09XXM4qOTmZyZMnU7NOHWJr1qRqdDTtO3VkzZofCe7QrpzBfQVBEIi4rTv5RUUsXbr0N69DEER0Wh09evQgLi4OgFatWrH/wD7mzp/D7aN6k9CvEbcN6obRYMDo9V7VEag2m3HLaqbPt/HGJ4Vk5pUY0bIk45MUftrhYuwTOWTlCyxbvpzut91GampqubU98+wzFKlzOVFccfm1HE8mpzyHGDj4LmrWrHnD6065mIJZvLaWiVUThCTJpKen3/AYS5cuJTszh8bm1mUM7is4pGK0og69z1SyeeEPM/fj5w/hT43XKywsBCjN29y3bx9er7dMWG/dunWJjo5mx44dtGnTplwfbrcbt9td+u+ioqI/eNZ+/g6cPn0aj8tDFWPZetAmtYWOIb04bjvAedcJzirHSh4oboWqYVV5ctITPPPMM+h0uqv2HxkZyZof19C7Tx8OHDuGuW2r0rxUg0GP0WhCo/n555S//wCNGjXCYDAwd8ECrK1boAspm69cVFQEioBW0BLcoRMp+w9y0XWWWqYGGFQmmlnasbdoC08/+TT7Duy7ZnhtVFQkqYcPXrWNrMgkO5Jwep28OnkyX3z5Jd27dWPYsGFlQu5vlOTk5FJV7M6WfmWM/6r6GGr7GrOneBNWq4UBg0YhiiLx8fH079+/zM7IrY4sy3z55Zd89ulnpKakgiKiKDIqTYlK7WuTX6NVq1Z/yNgqlYrxE8fzf6++iUtqiF5VcWRHRsEl0l0pWHKt1NDXx6gy4vQ62PDtZlYt/577H7qPN954o5wDKCQkhCWLFjF42DDOvvshxvp1MdeMR5Flik+dxpmURMO6dVm04BuMRiPLv/8ec5PGCJXsAF0hqEUz9n2zhIyMDCIiIm7a9fDj51bAYLSy7qeLuFwyBkPlhvf36x3IMhU6bteuXcu4SZOwyzKWhGaEVq+K7PFwYv8hnD4f2dt3EtCwAWpjxelI2qBAtNWrsX379jIh5hEREQQEBLFlRxGd2l7dsNyys5g6dRqX+1yj0XD77bdz++23l352x513smPXHoKbNy3ZtfZ4EDWaMveSopNJKD4fhg6d+HrlIb759iwJTXRYTCLHkrykZsqI0XFY+tak8McNHDh7ljsHDmTNDz+U0Rbp378//3nrP7z0wkvk2NKpqorDog7AI7tJ81ygQMihe69uv1knRavV4VR812wnyT4EQbiqo6EyFi1cRDBhGFRln7V5nmySHEfJ9KUjiwIKMln7Mxlw5528+sorJCRcK4/djx8/N8KfZnTLsszjjz9O+/btadiwJOc0IyMDrVZLYGBgmbbh4eFkZGRU2M+bb77J5MmT/+jp+vmbIlB+t9SoMtMisCMuyUG2J4Nkx2kCYozsP7j/hnKarVYr7737Lv0GDEB9MYXgFgmIKrHcDm3xufO4kk4z6cOP2LdvHwVFhcQlNC/Txuv14vV60QhaQEBlMKKvXYvM42nUMpXkxIqCSG1jI3Ynb2DdunX07t37qvMbPGQwa1evo9CbT4E3hwx3KhISWkFLNX0setHI7uKt2LChi4vhrE5D0qUU1rz6Cq//3//x9n/+Uy4n73qZOnUqxXl2Olr6VhjeblKbSTB3ZHvaj9SuXfsfGSqsKArPPPMM07+aQYQSTRtjD6yaIGRFJtOdyuHNxxnQ/07mzpvzm/P2rsXYsWOZmTiLPcmbSTB3KC3Rc4VMWxr7HFsxqEx0DepfpkxPnFKHi84zTPn0c8LCwnj88cfL9V+7dm22/vQTixYtYsbMmSSt/AFBEGjQoAGTPvqYu+66qzSNw2azobNevZwfgMZqRUHBZrP5jW4/fn5FrVq1WH3mENO+LuSRSRU7TjOzfcxZWIQgqsppgRw+fJgxE8Yjx8ZSe+TQMjvahoYN0J1MIm/pt5ybOYdaD9xT5nknSRKSTyqJYddqymyIQEle8/ARY1gw/yMeuceH1VLxK+fpc042bnPw/gcTrmvND9x3HxtHjuTE+x/jys5G9noRBBFrnVpUad8Wc62aXPxmEeoAK7FDBuJz9SNly1Z2pqaBTUFV3UrgHU3RVY1Cdrsp3LgZa8vmnN6xmylTpvDSSy+VGe/++++nRYsWTJ06le+WLcfr9SIIAs3aNGXSPf9m4MCBaMrpvFwfnbp05OvT85EVGVGo3GmS5r5ASJXgMppI10t6Wjomsey9Ns11gb32HYhRYQS3ugNDTBwKMrbzSWw+fJR+A+4gceo0f6kyP35uIn+a0f3QQw9x9OhRtm7d+rv6eeGFF3jyySdL/11UVET16tWvcoaffwI1a9ZEo1OT7cnAVEldbr3KSDV9HMneU3Ts1PE3iYi1bduWsXffTeK8echOJ6FtW6PSl6iEyz4f+QcOkbV8FT06dWbYsGGsXbsWRQH1r/LFPR4PAOIvMjxUBgM+ynq8AzXBGFwmtm7dek2je8CAATz91DOsz1qJoNGgqxGDymjCl5tLStoWJJ8XbUQYYXeNp0qtmhgu71p4i4tJX7OOR596ClEUufvuu2/omrjdbubNnU+kEHPVfHKLOoAgqjBj+ox/pNG9ePFiZkxNpL6mOdUN8UBJ5IHNV4hO1NPY1JqT9oNMHD+Rg4cPlnNG3gxCQ0NZsnQxQ4cMY+vZ1YQQTrAmHAWFHG86qY4LaEUdXUL7lTG4oSTUMsZYi2JbER9/+An33HNPhVEKVquViRMnMnHiRGS5RM23ot21oKAgCioIQ/817rw8REH4XZEYfvzcqrRt25Z1P37HW5/mIUkK94wOxGT8Wb/hwBE3j7+UhccrYDQYSjc9rvDRxx/jMZmIHzUc8VfRVCpRhTYqgpCBA8j+egHFZ85hqRWPx+PBbrfjdnsABUWSKUq+wEGPjx07drBv3z6WLl5Kbm4uOp2OvEKR0Q+eYeanNQkKLDvG+Qsuxj1ynho16jNgwIDrWnNKSgqSJOHOz8fcuhXasCrITif2g4c5/dV01FodotuNqUYcKrUau+zE3Lwp6k4d4FdOclGnQ2U0IggC5uZNmTl3Ls888wxqtZq8vDygJDqzRYsWtGjRgo8++oiCggIMBsNNuUePHz+eWTNmk+I8S4yxRIjNJTmwS8UICJjVAbgkO8nu01QVI2nTqg0mk4kePXswduzY0lD8q2Eym7HJuaX/tvkK2Vu8A13TBoT1618aISAjY2rciOBOHUlbvJSJ993H9s2biY2N/d3r9OPHz59kdD/88MOsXLmSzZs3U61atdLPIyIiLqtJF5S5eWVmZla6o6HT6a4ZCuznn0dERAR9+vVh7ZINRCvxlXqMsz0ZuAQ7Y8aO+U3jCILAe++9h9ls5qvp08nb8BPa2GgEQcRz6RKi08XgAQP45KOP0Gg0hIWFIQoCrpwczOafDZSSnCmBX9YM9+bmYhH05cZUo8Hlcl1zbuvWrcPmcmBo2oCAzp3QBYQgCioEoDj9Armrf8RXUIjBZCjjcNCYzVQfOIAUSeKFl19mwIAB5XZDfonP52P9+vXMmj2bw0eP4vZ4SMtIo66uyjW99SHqcE6eOHXNtdxqKIrCF59/SaAcSnVDPD7Zy1nHSZI9Z3AqzpIXQUUhUAikOKuABQsWcP/99/8hc6lduzZbtm5m8eLFJE5P5PTpUyUl5WLCyTyppqO1N4ZKQs8B4ox12ZL7Pd999x0jR4686lhX0yAYctddfDhtGnK/3hVUA/iZgt376NS+Q6Ul/fz4+SczYsQI3n/v/2jawMcHXxbwxexCurY3YDaJHDnh4ehJNzXjDMTFaFBUTWnSpEnpuVlZWaz8/nsC+/QsZ3ADaHU6VKKIEB2Npkoo2Tt2oq5WtSRVUBQR9ToEtRrH8ZPIxXaSsrPo2LUrRkFLuKo6BtFEoezCIcms31JMQo8jDL8ziNYJFnySwrqfCvlhg4Pq1eswb/6i63KEf/vttzz30kuEdO9CUNfO2B1OJMmHAhga1sd5/CSFK7+nbu3aJBcVoigKHo8bQa0pZ3ADyF4vstOJqNdjrVeXC9t28Oyzz7Jp449kZ2cBClWqhDPy7nGMGzeO8PDwm1r1o1GjRoybOJYZXyVSVFSIXS4m05eOIgIoICt4PS7UghpvGkgqDTmKjf8ensJ/P/mMZ59/hmeeeeaqmig9e93Gh/s/wif7UItqkh1JYDUS1vf2MiH5kuJDVIlo9Hqihw8h6f/eYdasWbz66qs3bb1+/PyT+UOF1BRF4eGHH2bZsmVs2LChnEcuISEBjUZTpvbgqVOnuHjxIm3btv0jp+bnFuSxxx5DMfk4WLQdSZHKHS/w5nLUtZsOnTvQvn373zyOWq3mjTfe4ND+/bz85JP0qVWHnnE1eGLCRHZt28b0qVNLdwCbNWtGjdhYcrfvKtOHKKoo0YguESzx5ObgTr5ANV1smXaS4sOhFBMZWTZX/dd4PB6eevZZ9A3qUWvcGIzhVfAJXjyKC5fiQhUeRti40agDrRSs/4lfR+ELgkBEzx4UFNtYvHhxpePk5OTQr39/ho66mx+PHaWoVjyu+nURY6pz3HuMzQVrcEmOSs9XUCp677klKSoqYtasWbz88ss89thj7N61i2raODyym60F6zihnERsUZ/wcWOJuu8+QocOwVkzBDcSb7751h8qZmOxWBg/fjybt24mLSON1PRUhgwdQoA2CKsm8KrnGlUmTCoLJ0+e/F1zGDt2LBqfj9QV31e61tzde/BeTGHSxIm/ayw/fm5VwsPDuefehzlwVOK5R6sxfkQEmTkiJ04rRFcz8dUHtWjWyMzRkwLPPf9iGePszJkzuH1eLL8odfVLBAFMRhP4vOjiYnGkZ1BYWIigUaM2GRG1Wnz5BeR/vxpzzXiCJ4xFX7MmkiJS39ycepamNA1oQ4/AO6ku1CMrW2HhSnjo+SweezGH42djeOXV91m9Zv11RSzKssz/vfUW2tq1iOrXB6PJRJXQUIKDgwkMCCAwIJDoTh2JvL0vFy5dwpdfQPGZcyX3l0qeO44jx1B8PgIb1MdTUIjPWcjK5Yn061bM9I8imP5RZMn/T32THt07ceTIkd/0PV2Nt99+m663deGU5xiZoW4s/XsSdt8EQu8Zh7FHJzRhVdBoDMQb61PP0pQm1tZ0sfanulSLN99465r55KNHj0bQCZxxHENWZC56kzE3a4bwC0eLgoyMhNFgRBBA1GiwNG/GnHnz/MJqfvzcJP7Qne6HHnqIefPm8d1332GxWErztAMCAjAYDAQEBDBx4kSefPJJgoODsVqtPPLII7Rt27ZCETU/fq5G8+bNmZ44nUkT7uEn20oihRgCNcH4FB8ZnhTyyKJN+9bMmj3rpihlR0ZG8vTTT1+1jUql4v577uGZl16k4GhDAhvWB0Cv12ErEko8y16FnFWrMMh6IvXRZc5PdV0AjcygQYNKP5NlGbvdjk6nKxVVWblyJelZWdQYPQKNTkuQTovkk/B4vXg8bhwOJ2qrhYCOHchd9h2u7Bz0VcruHGoDA9DGRLN161bGjRtXbi0ul4thI0aw/3QS1e+diLlGiRNNUUCT0AxPSjq5S79lR+FGOgb2Qi2Wv73k+jKIjonm3XffxWazERQUxO23306tWhW/9P0dkSSJt956i8+nfoXN4UQbGorP58Uh+9jv2IXOrsNukokcPQFNYCDFx45QeGQ/ckEuCAK68FAuZaQzdepU7r333j98vr/1t/B7f0OxsbF88M47PPbUU5wvKCCsWxdMMdEIgoArK5vsrdux7drD/RMm0Ldv3981lh8/two2m42UlBQEQSA6OhqTycSLL75IUVERb348nfgYFQP7BRIYqOZcspNnXkvD7tTx0cdf0K1bt2sP8CtMJhNen5cCrw93Tg7uS6noq1fFk5WN/eBhivfuQ2uxENi/D4JGR8Sdg0j55BMuOs9S21wSyq4WNTQJaI1SpKAILg4fOYjJZEKv19/QfWT79u0knT1LtXsn/nyeQDlxsdA2rclfv4lQi4XMH9YQOHQgSgXjSHY7hZs2Y61TG1GvI+2bebRoouWbabUIr/LzbnavrkE8+YCX0Q+d4+6RQ9iwcetvjrwpKipi8eLFzJw9m+SLF1CpVNStVZude/YQ2r4dIX174ZUlFKXkWaIJq0JoQhsyFi1k5/mf6B50OzpRj0pQUcvUgMKCPP71rxf58JNP8Pp8REVGcvfw4dx9992EhYUBEB0dzYsv/4vJr7yOx+bGq3gICA+/PCMFCRmf4kWtUZdJGTJUjSJ3xy6cTmelJVX9+PFz/fyhRvfnn38OQJcuXcp8npiYWPpS/+GHHyKKIoMGDcLtdtOrVy+mTJnyR07Lzy1M37592bJtM4mJiXw9Zx5ptnOAQKMWjfj3Pa8waNAg9PryIdx/JBMnTmT3nj0s+noBtpYJhLZrgz48DJ1aTe7+g9h37kHILKStpWtpuTMAm6+AM56j9L2rL7GxsZw/f57ExEQWfjOXoqJCQKB58xaMHTeRn376CW1UJPqwKqXnq9QqDGoVKAoOwYkgCJgaNST32xXYkk6XM7oBBK2u0lJiy5YtY8/BA8Q+8gDGX9QJFQQwGA3IVcMJHzGC9KnTSHGdI85Yu8z5ac4LpHpSyDmay5GLyajNZryFRfz7zTfp1rkzH3/00Q3XH/2rIcsyjz76KHMXLSKgUwdqtmuDJsCKz+sj/fRp7Lv2U7B3D5a4RgiiQGbi56id+fTqZCShqQnJB+s229hbIPPc888waNAgQkJC/pS516tXD6dsp9hXhFldcZ1tALuvGLtko379+r97zNGjRxMYGMjkN97gzBfTEPR6BLUKn62YiCpVeOG113jooYf+UeXk/PipiFOnTvH555+zcOlSHJfv0VaLmbuHDee+++7j3XffZfDgwSQmJvLFnDXYHQVUCQ1l5Oh7GT16dIV5uTVr1kSn1lT6PABAgABrAGkpl5BtxWTPmYegLnlOqfQGqrRqQXDnjhQ5S0KgRYMOY6MGXNh/htr8nD8uCAK1TA3YlrmGTZs2MXjw4Bu+BidPnkRRqTDFxZSUvXQ5cTqdSJKEQImyt9FoRGM0oK1ejZY14tmxezc58xaia9cGU4N6iCoViiThTDpN/pp1CD4f0YPuJHfnboxKMV++H1vG4L5CcJCGWZ/WoG3fk8yZM4cnnnjihud/9OhRho0cyaWMDAz16mDo0A7F52PT2vVIGg2hPbtjCQhAEAUkn0R2djZqQYOoUxN+1yBSPv2EFOc5aprqIykSBwq3c0m+hBgeiqdpIwxWK2lpGbz2zjt89OmnzJoxo/T9+7HHHkOr1fLG6//B5/bi9TjwKO7L0XYKWp2OwMCSsa8gu90I8JtF4vz48VOWP9Tovp6QFL1ez2effcZnn332R07Fzz+I+Ph43njjDV577TVsNhsajeaqOcp/NCqVii+/+IIG9evzxdSpnNuxC68i4/N4kL0+8Eno0ZPpTsWkMuNRPKQ4z5GmJFO/WV0+/uRjVq1axUMPTsJicjFqoJWG9cKwO2RW/HiAxx6diN4QhBJcsXEmqkQEQJEkBI0aQaNG9nrLtVNkGSk7h8i27SrsZ3riDPS1apUxuK9gNJpwOpwooYHo69bh/InTxBpqlRpLac6L7Cj+CW21KKoN6E9AvToIKhWyz0fBkaNsWL2O3n37suaHH4iKivrtF/t/zMqVK5m7cCERwwcT1PTn3Em1Wo0+NBRNr+4IVhNFm7cipZ6nUayXT96Jo3rVKzs1CuPHhLBvfzEPP5nCgDv6sX7DT3+KjkXfvn0JjwzjbMYJmlhbV9runOMkwaHB3HHHHTdl3P79+9OvXz+2bNnCoUOHkCSJGjVq0Lt3b79+h59bGrvdTmZmJiqViqioqEqNm02bNnH32LE41WqsHdoSUjMeFIWiU0l8sWA+3yxezKIFC2jTps0NRQmGhYVxe9++fLdtKyGtW1aY1w1QlJQEBQUYTSZCB9yOLjgYlU6LMToalU6L3W4Hp6tUGFQXEUmhfKhcP2a1FbNoZe/evb/J6L7yTun1eikoKECSZQS1GkGjQVEUnB43TpcTvd4AKISEhLDi22954KGH2D13PvmBgWiCApGKipCKizHFRBM3ciLa4GByN6xlZC8jMdUqdziGhmi4q6+ZuXNm8Pjjj9+QMzA1NZWBQ4aQp1UT/9yTaC/rGEluD+k/rsPapjVeWaKgoICgoCA8l5/RIpcdHCYTxob1uXDoLDVN9TlctJtLYjpV7hqEpk4N9AYdgUElffr69+Xi/G8YOWYMq1eupHHjxgiCwIMPPsjIkSNp2749aadPE9SyOSqVCoNBX+5vT3K5KNh3gITmzf1Gtx8/N4k/NKfbj5//JWq1mqCgoP+pwX0FlUrFE088wSsvvohKUtA6FKopVWlpbEtTfTusBHPcdoCV2QvYal9NcXAuDz5xPytXreTs2bM8cP8EeneF3Wvq8a8nqnNH7xBGDKzCvC9qsWxmHEjZOE4nIUvlc9m1Wh2iqEL2ePEVFKJ4PGis5V8sbKfPIOfnM3To0HLHvF4vBw8dxtqw4t1NlUokKDgIQQXa2nHk+3JJdp7mguMMuws2sbN4I9pqkdR77GECG9YvFW8R1WqCmzUl7sF7uVRczDPPPvs7r/T/lqnTp6ONjSljcAMggNFoREHB0r4NgkpFkNrG1E+q/cLghituyhYtLHz6ZhUOH95DWGgY9erU4/XXXyc5OfkPm7tOp+OZ554hU0jhjP14Oaepoiicc5winfM89cyTN1VMSBRFOnfuzKOPPsoTTzzBgAEDsNvtbNmyhY0bN3L+/PmbNpYfP/9rTpw4wRNPPEGtevVIaNOGZi1b0rhZM9555x1ycnLKtE1OTmb0+PF4q0ZR85nHiejeFVNMNKbYGCJ73Ub8U49RZDEzbORIsrOzb3gujz/2GFq7nQtzFyBfrqrxS+wXU0j/ZjHtWrVGrdGgCw4isGF9LLVqotKV3LtKbhU/C4Mql0t4VYSIqrR6x41St25dFK+XzCPHkAUBtdmM2mhEpdOh0utRm0yIBgOOggJsZ89Tp04dmjRpwrYtW5j+xRcY3W6k9AwMVUKpOXE8NSeNx5WTw7lpifiKiujYNvCakXDtW1lJT08rcTTcAJ9//jlZDjuxE8eVGtwAvmIbss+HLroaot6A2+O+fH1K9qB/adbrIqOwS3ZsvgIueM8T3Ls35nr1EQWhVB8GQG00EDvmbtxmE+++916ZeQQGBvLaK6+gpFxCW1iI1WopNaoVSSJv337OfPpfjr/4LxynjnBo/04efPBB9u3bd0Pr9ePHT3n8RrcfP38SW7Zs4fFHnyBCjqZXyBBaB3Ul1lSHWkH16BzZmw6BPTFpzDRv2Zyjx4/w+uuvY7Va+fjjD6kVJ/Hxf+LQ6cr/ZFs2s/Dxf6qjkpxk/1S+JF+JGI4RxeuhaOduVDodAQ3qlWnjKSgkY9lyWjZvTsuWLcv14fOVqMOK6l8onfokHHYHxcV2HA4HKlFFaGgoRosFBIUT3n0cce/CE+hA1KiJvrM/GnP5ElMAmgAroT26sGbtWi5cuHCDV/avQV5eHtt37iSwRbMKjxtNxhI1b0VBq5IY1M9EaMiVHYSfRfUQSqIOmjXS06KxAa1Xh5hm5JN3/kvb1u1Yvnz5H7aGiRMn8uwLz5DMSbbYfuC0/RiXnOc5Yz/OxoIVHHXsRq3V8J9//x8N6jXk5Zdf5uzZszd1DmfPnuXhhx+mQb2G3NH3Du66YyAtmrdkwB0DWLdu3U0dy4+fP5vVq1fTrWdPZi1fjrZ9G6ImjSNi3CiKYqrzfx9/TLfbbuPcuXOl7WfMmIFNkogZNaJClX+1wUDs2FFkFxUyb968G55P48aNmT0jEdWFiyS9+S6pK74n/+Ahcnfv4dy0mVyY8iVtGzVm3ty5xNeoQcGh8kJiKtUVYdCSEoH2EycIVVUp184re3EoNqKjo8sdux7at2+PQaejaPceRIMB4dfVEQQBUaPBcew4Xrudpk2bXv5YYOzYsezdvZsxQ4eizcomddZckib/Hxmz5lJHq8dkMqHVXjv1TJKvDHX9u9wOh4O5C+ZjbZGA+ldlFq84oBWvF1GjBlGFw+lApSqpPCL/wphWfD5EQSTZcRrRasbSqDElEmjK5e/gZ0SNhqB2bfjhxzWkpqaWOXbXXXfRqU0bUmbOoeDYcRRZRvZ6OZ84k6yFc2kTeYn3X7Iy+9OqvPREEAf3fkP/23syderU616zHz9+yuM3uv34+ZN4/7330btNNLa0qqCslkCYMYrGplYcOnCIU6dKymqlpqayYf0aJt4dgkZT+c+1T49QasbqSF/1A56CwnLHTSYTSmYWRZu3ojabcWVmIblcuPPySF+7nvOfTCHaYiVx+vQKXyb0ej3hYWHYL6Qg+STy8/PJzs6mqKiIYlsxRYVFZGVnU1hYROGuPYgqFYJehy4ynEIBfIrChYVLyN66vdK0k6CmTfCKAmvWrLn+i/oXoqioCFlR0AYEVHhcEARMZjNSfgGi5KVTW8NlU/vnPQpBABRQfBICcFtHM7LKRUNrAl2st2OxB3PPhHvYurW8c+VmIAgCgwcP5pXJL9O8UxNSNWc5qRzgtHQYu1SESWUhwhtDVXdNhFQdX3z4FW1btyMxMfGmjH/gwAF69ujJoplLiXLXoJ2xFx1NfaknJnBgw1GGDR5e+uJXWFjItGnTeOKJJ3j88ceZMmVKuV1CP37+Shw/fpzx90xCqRFLzWefIKJHNyzxNbDWqU31gQOo8eSjpDodDB0+HJfLhc/nY878+VgSmpXuKleE2mzC1KgRM+fMueYcLly4wBtvvEHbDh1o0Lgxnbt14+TJk6z89luemDgJ7fGT5CxcSv63K2lssTL1v5/x3bJlBAcHM2n8eJzHTuDMyCzTp16vQxDFkmob587ivZRGnL52ubEvuc4j6sQKo6muh8LCQjxuN86TSRSu34Qiy+Xa2I8ep3DTZjQGPatWrSpzrGbNmnz15ZccOXCQJfPnM/3zz5k9fQazZ86kTu26bNxadM05bNxaSGxsjRsSFjt//jwFhUUE1K9b7pjGakUbFITjeEk1CEGtxuPxotVoUalVSIrv57WdOEGIKpR8OR99zXgElQoJGVAqjDwKqF8Pj0/i2LFjZT7XarXM+/prerRpS+ac+Zx+90NOvPUOYvJhEt8P5qu3QxnSP4Q7b6/GgxOi2Ly8Lg+MNfPqK8+xcuXK6163Hz9+yvKn1On24+efztmzZ9myeSv1dAlX9ZCH66qRZDvM7NmzadasGUlJSSiKl05tK88zAxBFgR6dA0lZUMz5T6cQ0L4tIQnNUVvMuLNzyNm5G9vuvTSsVQufJJEy5SskRUZAwGIyMX7QIJ577jnCSxVNyyIIAmNHjeI/H31EdotmoNGhFjSoUAEloW2SIpG5ajVFp5IIateG6j26oQ8Po6iwiMLUVOwHDnFx2Xd4i4uJ6t2z/Bq0WtRGY0kN2L8hAQEBiIKAJ78A2ePBmZ6B7POhDQxAd1kMzWg0UIACioJGI1wunyZc/k9AvvwSqXi9iIiXHS0lJrlKUNPE2oadRet55+136NChw02d/6ZNm3j/vffZvm0HkrckTUGtURFXI5bTJ09TR9+Umqb6ZRxGdZWmnLAd5OknniEwMJC77rrrN49vs9kYOXwknmyZ9paeaMSfjQyjykSULpoTxQd47pnn2b9/P8u/X4XN5UYXVVJOz7NgAa//33+4Z/wEXnnlFX8eop+/HJ9//jluvZ6aI4dVXBc7MIDqY0eR9MHHfPfdd3Tr1o3CwkLC4mKu2bcpLpqUQ4fx+XyoK8nN/uKLL3h58mS8KhXGhvVRR4aRm5fPS//5Dxadji+nTOHll1/Gbrej1WrLaSqMGjWKed8s4Mi0RKJGDMVcIw5BEBAEAaNeT/b+AxSuWE24GE6Yrqw2R54nm7PeowwbMfQ363YcO3YMWRSI6N6VzJ824zh6DHNCM9TBwUh2O/aDh/FcukRQk8ao9Hp27tlTYT/p6eksW7aMZUu+xX1ZLExn0HH+XAHPPOQkNrri1JmUVDcrf7TzwosTr/ocT0lJ4cyZMwiCQN26dZEup30JFXwvgihSpW1r0n5ch7dzB8Qr6XBCibO8qLAISfHhTr5Q4swwdiTJdQxEEeWy6rhWp63wfndlF12qIO3MarWyaOFC9u7dyyeffMJ3337Dq6+E0qVdEEaj4XJ/JWsURYF/PUxhmMgAAHudSURBVFGNk2fO8OGH79CvXz+/uKUfP78Bv9Htx8+fQFJSEpJXItRSsVF7BVEQsSohnDxx4zWQRVGkcaNGNG7SgoVLl3B6zTpkRUElCFQJDeXhxx/nySefRBRFdu/eTXZ2NkajkdatW2OtIMf714wZM4bJb7xB9pLviBw0DJX259uHgID7/AVsO3cR2LMHoZ3aow8KKjkmCmhCQgi9sz+akGDS167HWrcO5tiSF0nZ68WefAFvkQ1XQcFfIgf/txAUFETrFi3YtHwlKd+tRHL9rAJvrhFHeJdOBDaoj7VKGLk+mf1H3LRqJ5Ym7SmKAoqM7PaAJKEWdOw95MQg/PzdiIJIrK4227Zs5+TJk9StW37n5Lcwd+5cHn/0CYweC/V1CYRYwlEUhXR3CgcP7CCCGGKCa5WL0FAJKhpYmuMqcvD6a69zxx13lAtzvF4WL15MRmomHc19yhjcVxAEgbqmpiTnnmbG3LmE9+pBrQ7t0FgsAPgcDnJ27OKTL78kMyuLLz7/vCSc34+fvwBFRUUs/vZbAjp3qFSwDEAfVgV9zZrMnD2bnj17lohgen3l2vmcTvL3HcB+MQVFkvAWFyPIcqV/87NmzeKFl1/G3KEtcT17IP6izJbPbufS0uWMnzSJRQsW0Llz5wr7MJvNLP5mIaPHjmXX1ERU4WEY4mJRZBln0mkc6RkoHh8qjYpsTzpGlRm35OSS+zzZpNGha3veu5xjfOTIEebPn8/5c+dRa9QkJCQwYsSISh2/cNl4VKBKh3YENWlE1tbt5G/YhHLZqLTWrkX18WMIqF+PS8tX4nO6y/WxZMkSHrr/IXCqqKaOJ1ATgqxIXLIlc8mXzeAJJ/hmWh3iY8uGgSdfdDH24fNERMYzcuTICue3c+dOPvnkQzZtXAeUfGeiqKVzl9tQCVB87jzGauWFSEPbtSF3zz4yE+cQPOB2zNWrAWDQG3A6nBQeP0H+tysxuXW4fV4EQcCZfB635EKj0xD4ixzxKyiSRMaGjbjtdt597z3mzJ3LbT16MHjwYCyX75mCINCyZUtq165N9aomxg2PrTCF7Urbe0aHMfL+I+zfv5+EhISKvyQ/fvxUit/o9nNLckXdVKvVYrVab6pXtqCggFWrVpGZmYnBYKBz587XLJ9UOv61Bf2vnAFA7dq1EQQNm3cUMfyu8jlyV5BlhS07nbRsm8DHH3/Ma6+9xtatW7Hb7YSEhNCpU6cyuxbt2lWsUH41MjMzUSsCntMXSf3ySywtEjDGxoEg4kw+T+76dairhBLQshVutwdZkhFVInqdHrvdjixJWDu0o3jfAbK3bscQGUHG2g3k7NqNz+FAkWQUn5f3P/qI4uJiHnnkkT+9vNvv4cKFC5xMSsLr9mBp3RJTk0aIWi2ejAxsu/ZydsYsonr1RJF8+CSRBUuKGD3EhsGkAUFAliQUr6+kRIugJTXdx/rNDmpompYZJ0xblcNFuzh+/PhNMboPHjzIk489SZi3Gg2tLcr8VlSIiKioShyFBQWEVglFFMsa1YIgEG+sz57zG9m4cSM9evT4TfOY9/U8ggnHoKo47x8gz5uFS/AS0Ls3ET17lDEw1EYjEd27oqsSyjfzFjLgjjvo16/fb5qLHz83m9TUVFwuF6Fxsddsa4yL4fT+gwQGBhIXF8elY8cJatoYKHHOZf20hfQ1a5G8PnTVqoJajTslBcHhZNI99/DxRx+VGlZQklP82htvYEhoStXby9e8V5tMxNw9jLNfTefl117lpw0bK31mhoWF8cOqVWzevJnZc+ZwIikJtUpFsz59GT16NCdOnGDql1M5cmwnskdBEATiasXx2KR/M3HiRJxOJ2PHjGXDug2oJS0m2YqMzKplP/DW/73N408+xrPPPluh8yAmJgaVKGK/cKFEgDMmmtjhQ5DcbkSttowzw33xEjWbltXX2LdvHw/e9yCBrjAaW1uXcSJW0UVS3RPHzqQ1tO1zjAG9Q+jUzoIgwObtNtZsslO1ai0WfLO4Qif10qVLeezR+6lXS+aD16vQvpUFWYaN2wpJnLcGxesie8MmQtu1LaONAiV5+bXuv4ekKV+SOTURZ8147DXicNrt2E8l4cvKQeUDh6Kw37cZCQklRwVp6YQkNCtT5gvAkXKJs7Pm4srKwhBTneOKxJGzp1m59kde+/e/+eDddxk0aFBp+6SkU7Roqq3U4L5Cu5ZWwEdSUpLf6Pbj5zfgN7r93FKcOXOG6dOn8/U331BcXIwgQN06dZg0fgLDhw//XYrLDoeD1157ja/nzMNhc6AVdfhkL6ihbfs2vPnWmzRq1KjCc+vVq4daqyLLk0p1Q3ylY8iKTAHZNGlaYixUrVqVbt17Mf3rNQy6vfK87h83FXDhksx/x44FSnZd+/fv/5vXWhHLly/HIJhoH9CRs8UnuLTuJwrZAIAgCwj4CGiWgEbU4lFcuNyukpqpWg0ajQavy4XKZMKc0Iz89ZtwZWfjzMzG3KI5pqaNEdRqRKcL54mTvPH++/y0ZQsLFyy4ody5/xWSJDFy9GgKBIh99EE8GnXJS6BWi6ZKKMaGDSjcuJlLK1dhUGt44bnnSZzxBa+8lc//vRiCVgOCXBLurxV15ORKPPSvdASvhWrW2DJjXXkZlivIZ/wtTJs2DZVHQwNL+dSHAl8eJixYxADciguHw1lhJEKQJgSNS8vhw4d/s9GdciEFqyqowmOyIpPlTuOgbSeq8FCMjRridLowGY1l5H0VBUx16iBGRvDmW2/RsWPH64ri8OPnj0YUxVKRxGuhSBKiqEIQBCaNH8+zr76KKzsHfZVQMtZuIG3Nj1jatMbasT1qq6Vkp7uwECH5IktXryZ9+HCWLVlS6rT87rvvyC3IJ77b+ErHFESRsK6dOZI4h71791YoqHn69Glmz57Nnv37kXw+asXH895bb9GuXbvSe0fLli0ZPXo0p0+fJj8/H4vFQp06dVCpVNjtdgYNHMzh3Ueor2tJhLFaqeHrlT2cc5zk7f+8g8vl4rXXXis3fmxsLJ07dGDrtp0ENW1SEtquUqH+1TPCnnwBb1o6o997v8znn376KSqXrpzBfYVQbQRtLX3YbvuR/cdCWPNTFoqiUKd2XV7/94Qyu8S/5MSJEzz+2AMM6qfm3dfiUKl+vimNGRrG8DtDuefJMyxZmcH52XOJGzOqnOENChqViqiYaBrWqcuPa9ficXmJUEVR29qLYE0YdsmGS3bilTzsLt5MxpJlWKMiMFUtCddXZAVnRiZJX0xFDAwgfOI4wuvWKXW4ewoLSV+1mkn334/L5WLkyJGl6QHXU+L3Sht/aLkfP78Nf+ydn1uGVatW0aFLF75YMB8SmhI2ajghQwZyVoDHnnuOfv37/2ahJafTyZDBQ5g2ZQZhjmg6mfvRxdKfbtY7qa9qwYGfDtOvz+2VltWIiYmhx209uOA5g6xU/tKV5rqApPYxZsyY0s8ef/xJziSrefRf53G7y5+754CNJ19JpUvXnjRv3vw3re96yMvLQ4cBqyaQZgFt6R10F12sveli7UWPoP6IghpNQBDC5dIxsvzzQzwgIABRAcluRzSb8Tns2NPSCb17GNZOJXlsGqORKrVrUX3gAKLvGc+WvXt4+eWX/7D13EzWr1/P0RPHqTpiKCHVqmExW8Drw1dcjK+4GKnYjimhKYYaNYivUYPXX3+dGYlfs22PjoHjcliwzMexU7D7gJM3/5tF77svkHRSQwtzN9Ri2Vy9HE8GKrWKmjVr/u55O51OlixaSpQqrsKXUAUF4fJjQoUKp9NZrs0VRMTf5QjQ6rT4lPJhtHmebNZnL2dn3kaKFXtJBIEgYisqIjs7G6/Hi6KAvdhOdnY2efl5qOrWZvuuXdRt2IBnnnnmN5VS8uPnZhIbG0tQYBCFx09cs6395CkSLitv33333dSvGc/FaTPJP3KUtDU/EtC1M8H9epcY3D4fksOBzmAgon1bqk8ax/a9e5kxY0Zpf3v37kUXGYkuJPiq41pq1UTWaNi7d2+Zz71eL088+SSt27fn09mzOOh2chSZbzasp9+dd9L39tvL/MYEQaB27dq0bt2a+vXrl6aczJgxgwO7D5Bg7ESUPrrMPUcjaqljbky8qiGffvxfjh8/XuEcH33kEaTUNFJXfl+hoejOzSN1/iKaNGxAt27dSj/Pysri+5U/UF1To8J73RVCtGGEqiOJr1mPixczSEnJZMPGLYwfP75CgxtKHJdVgr28/UpsGYP7ClqtyGdvxxMZbsB19Bhn3vuQjA2bsJ09R1HSaS4u+ZZz739CtNnCow8/guT1ojglOul70SawKyHacARBwKy2EqoNJ9JQna6BfZELbZz58FNOz5zDhe07uLB3L6e+moai0xEyZDAh8fGlBrfP58MlCOi6dcEXF8PYiRNo074dM2bMoFat2uza78HhKJ/7/Us2bS8C1DRo0OCq7fz48VMxfqPbzy3BgQMHmHjvvVArnlrPP01U754ENqhPcLOmxI65m7hHHmDf6SR69OzJjBkzWL16NW53+Xyvynj//ffZuWUXLYydqG1uiF5VsmMuCiJR+mjaWHtAgYrx4ybg9Xor7OPpZ55GNnk4aNteoXGR5U7jhOcAdw2+s0y4ekJCAlM+n87qjdCq1wn+78MUlq/OZf7SbEbcd5q7xp2nXv1OfPHF1D/UA202m/HiKX3R0YhaAjXBBGpCMKiMCAhIxTZKYugVxF/MRa1WExwSjAoBb24uiiRj7dYFTUQ4iiKDIpeEyF3u2xQbQ3C3Lnz9zQJyc3P/sDXdLObNn48mKgpTdHUQwGw2ERZWhcCAAMwGI2ajkaCgIKp270pKaiqnTp3itttuY/WajXTqNo5PprsZ/VA2ox7KYPocB1ZbI9pZ+mFRB5YZR1EUkl1JNG3ehCZNmlQ8mRsgLy8Pj9uD9VfjXMGssuLAhlfxlAi9VSDIA2D3lezA1KhR4zfPpWPnjuQoaWVepPO9uezIW49a0tBUaI8KNRqjBQ1aNIIOWVLIy8sjLy8PW3ExikpEZTKhCQ1FZdAjNmvK1IXf0KtvX9LT03/z3Pz4+b3odDrGjByJbe8BvMXFlbaznT6DNy2DCeNLdqUtFgtLFi6iftWqJH81AzQazK1bIrnd+Ox2JIcDjVpDUFBQSYWE6OoYGzZgWmJiqRPM5/PBVfLIryCIIqJaXeYZpigKjz/+ODO+/pqg/n2p/a9niR0xlJihg6j59ONETRjDzuPHGDhkMDabrdK+JUli+rQZVCGKAE3FES0AccbaqHxqZs2aVeHxrl278u6bb+LYvoszH/2XnB27cKRcwnb2HClLv+P8x58RHxLCvDlzy+hLXLx4Ea/HS7Am7JrXIVAM4UzSaURRvKYuhNfrZdnSbxg5KOCqFUbMJhXDBgRSPSqM0X374d68jbRpiWQkziEwJZXubdtSmJvPS8+/zOqVPxLgDcFbLJGdlYXdXv7vxaoJoqq6Oj6Hg8I9+8iav4jsufPxZmZhbdcGUa/F7nDg8/pwudzk5ubicDoRtFoCb+sOGi2nCgp44oXn+f6HH7AVi8xbWrlzUpYVvpyVRdOmLSuN6PPjx8/V8Rvdfm4JPv7kE7wBVqKHDy0nUuPz+XAZDBh69uDI8eM8+uBjDB8ygsYNG/Pee+9VaiRfweVykTh9JpFCLEGa0ArbqAU19Y0JXEy+yI8//lhhmxYtWjBj5gwc5iJ+KlrJ0aJ9XHSe5ZzjFDuK1nHQvZ3e/Xvy6aeflju3X79+bNy0jbsGP8LcpSoefC6LZ17Pxe5pxiefzmDhoiV/eBhtr169cAsO8rzlH8yiIBKhjqT44MFSh8KvlW89Hg8+yYfj0BEEtQprqxaoDUbUZjMqkwmfLJOXl4d8uRBqSKsWOLw+vvvuuz90XTeDc8nJ6KqVVeQVBAGDwYDZYsZsMaPX6zFHV8cny6SkpABQp04dPvjgA44cTWLb9v306TsQUTAQposqdexcQVEUjhcfwKbO5+lnnr4pDhadTocggE+p+DdQzRAHAqQrF64sqsJ25xynqBIeSt++5fNFr5fx48fjVrm45DoPlKz3cOEu9IqJRkIbAsRg1KjxFRYiCAIiIlpBiyIreDweRKMBlcFQUkYnPx9BpSKqT0/iHn6A5Lw8Jt1772+emx8/N4P77ruPCKuV5Gkz8eQXlDtuO3OW1K+/oUv79mV2aatWrcrGdeuIjo7G0rgRoqIgeH3o1GqCAoMICQ4uYxwGt2zO+QvJnD59GoDo6Gi8WdlIbs9V5+fOyUVyOMrU0d69ezfzFi0kfNAAqrRrU+b5KggC1tq1iLlnAodPnmLmzJmV9n3u3DlSLlykqj7uqnMQBZEqVOXH1WsrbTNx4kRWfvstfZsnULDyBy5+9iVp0xKxXkjhxccf58fVq6lWrVrZfkWxRJSO6wjvR75uQci8vDxcLgcN61WuRXGFhvWMFBQW8MEHH3D6xAn27tjJ/t27GT5kCBvWbsSYF0g7Y09ERSBEiEQr6BAUEZutGJutbDkzr+whR8pBFRFOg8mv0OT1V6jarw8qoxFLywTUZjMykJuXR0FBAYpKXfKs1evQRYSji6mOPjKC2Ece5GR6GjpDAP/5MJvlq3PLRRB4PDLPvZ7Mrv0STz/z/HVdFz9+/JTHn9Pt529PZmYmq374gcB+vcvkSXkKC8nevpPcw0eRPR5UBiNqs5UQRwANjQkkZyTxn9f+j0OHDpGYmFhpmZUtW7aQm5NLe2OLq84jQBOExRHAd999V6mAU58+fdi+cxuzZ8/m6zlfcyb3IqJKpGO3jkyYOIFevXpV+rCvcTksefLkyaUlXbTaymu33mzat29PnXp1OHXsEK01XVEJZa9XnKE2aWmbKDp8kOBWCah+8V3IsozNZsNx5BjenFw0Vaqg/kV+vaBWIxiNSHY7tuJiAgKsqE0mtIEBpKWl/Wlr/K3otFpkz9WdN1Ci1C4IlPveTCYT8fHxzJw5k1GjRrNx7UaCnGFEaaPRiFpsvkLSfMl4tS7efuet32Xc/pKQkBDq1qtL+uGLROnLlyXSiXpijbU4b09Cq+iJ1FdDURRsvgI8ige1oKHQm0eacp7XH5v8u4TvEhISGHH3cObNmo+syJjVVgq8eTQQWqISVCiKTIhUheyDBwlu3wFUJUaGAghaTWl5HEVRKN63n8AG9RE1GnTBQYQPuJ3tc+dx8OBBml4O2/Xj588mMjKSZYsXM3jYMM688z6GunUxxUYjSxL24yfxXLpE53btmT1rVrnngEajQaVWYwkJISzs6ru1apMJRYHiyzvqQ4cO5c133yFv/wGqtG1d6XnZ23YQGhSEx+Ph888/R6/Xs2bNGoSgIIKaNa30PH14GMZGJbvrDz74YIXPMIfDgaIoaCuoTPBrtKIOpyPvqm3atWtHu3btyMnJISMjA41GQ40aNSotFVirVi3MVjMZtksEaCoPs1cUhRw5gztaXp8IY4lzWbhmaDaA3SGjVqkRRRGz2YzZbGb37t18/MEnxIsNiDfVK00/K0nSElALGgRFwG53oNPp0GpLnNkXnWfxaCTChg9GFxyIKIqo9XoEjQbx8jVQGQ34Lv8NaAz6Mk5TUadD9ngwREYQNWIoKV/NoEu7Tjz43BY+S8xhYL8AAq0qzpxzsXB5EQVFGj74cEoZZ5AfP35uDL/R7edvz9mzZ/FIPiy1fhYoy9qyjUvLV6GIIoZ6dUp2UvPykC5dItVbSLxUl4bWFlRxR7Lq2++Z0nIKjz76aIX95+XlocgKRtW1S1npFCM5OVcPh46JieHll1/m5Zdfxuv1olKpbqi0kSAI/5OyWqIo8tnnn3HH7QPYVbSROobGBGvCEAQBWZGRFRmtLJC/6nu0bjemzh3QBgQAUJSRSf6mzdj37sdULQqPy42iKGV2awVRRNBqcbmcWCwlqrGSy/23UDBv16YNexITkX2+q5YDyj98BKNeX2l4nslkYuHCb1i8eDHTpk7j0MGDKJKCVqflzhEDmDRp0k1VjRUEgYn3TOTJR56iwJtLoCakXJsGlgTyPTkc9+zlkucMUpYPl+wCRUFGBhE6denIpEmTfvdcPvroI9RqNXNnfY27yINKUWElCA9uFBSqamLJKthJ3uafCO7SDVmQSmqcqzUlqQmCQNHW7Xizsqky8Oea4dZ6dci0Wlm0aJHf6PbzP6VevXrs3LaNxYsXM3P2bM5t2YZapaZLQnMmvvkWPXr0qNQBHBYaStJ1pNu4c3IRBYHQ0JLIrOrVqzP4roEsWP4dxsgITLHlHWx5Bw+Tu3kLRlHNvRPuQy2okWQJm+QgqHtXfJKM5irPqaBmTbg4cy4pKSnExsaWn3tYGKIoYvMVXtXoBSiWComKuHp5zSuEhoaWrvNqWCwWRtw9gsQps4mT61Zq/Ge4L+FWORk3ftx1jR8QEEDduvVZseYMA/qUv3/+khU/FtKmbccyz70ZM2aglfTUsJRUohAFEZPaSr4vhwhKIg5UghpJkXA4HKVG93n3GfSN6qINDCrNUdcEWFG8Hrx5+WiCS9INBLUaxVc2nU2RZbw5uZjrhuLKykYTEIAmIpzAoGDmfr2UWbMSeevTdXg9XgIDAxg0+GHGjh17U3RE/Pj5J+M3uv387Sl5gAml5biyd+wi5dvlmNu2xtq5Iyr9zzuqnuwcClasYsepn+ggdCdcV5UwVzWmfjmVBx98sMKXHbPZjCAIuGUXBtXVlbQ9uLBaKxZbqYjKvPJ/VZo3b853K77loQceYv/JLehcRjRocePEq/LQtlNbWrVuyZz58zizYye6yzsyhRcvIssyVW/viyEqkrMzZuG5lIqu+q9CADUafG43Xq8Hz8UUFIfjN5U3+7MZM2YM//38c3K27ySsU4cK2/gcTgp37mbMwEEEBVWe06jRaBgxYgTDhw+nsLAQp9NJYGDg71LevxojRoxg4TcL2bN1Cw10LQjXVf1ZIV2RueQ6j1NVTGh4CAU5hQRJYcQK9dALRhSNRK6Yyf5dBxk2dBgLvlmAyXTtMMvK0Gg0fPzxx9x///3ce++9HNlxHJVKRKVWYzAY0Oki8NndHNm6HW9BAZaWLRCjqoAo4E7PoHjnbuwHDxHZo1sZJ5wgiqhCQ8nIyPjd18uPn9+LxWJh/PjxjB9fuZp4RQwdPJgX33gDn92O+iq/s7xde2iZkEBMzM/G9fvvvcel1FS2fDUDY8MGBLdsjtpiwZObR+6u3RQePgpuL5G6WsSZa2NUmZEVmZV5C5FFkby8XIKDgtFoK35mqXQ6QKlUbDEyMpIOnTqwf/2RkrSVSnDLLnJI54kRD13fRbkBHnroIZYuXsrezJ9oZm5f5nmuKApZnjSOefbSp39v2rRpc119CoLA2HGTeOnFRzl6wl5pmPmWnYXsPehm6rQJZcZc8d0KIlWxZQzxWGMtjhXtx6U40Qsl930VKtwu92Vnp0KxVIQ1Lhqj0VBawSGgfj1UegPFe/YS1Ou2klcilQq8vpKIoMv9O06ewpudQ05BATm7dpesQ1Sx0baJ/376Kd26dUNRFHw+39/uHcWPn78yfqPbz9+eWrVqYdBqKTp5Ck1AAKkrv8eU0Iyg3j3L5SapTAaqDLyL7K+/4VjqATpqexJjrMXulA3s3LmTDh3KG0wdO3bEEmAhpfgstc2VC4jYfTaKhHz69Olz09f4VyIhIYHtO7ezbds21qxZg81mIzg4mDvuuIOmTZsiCALPP/88y5Yt49SpUwCsXLWKjCArkbd1R5FldEFBFGz8ibBRIxB+uXtyxdjz+chct4EG9erStm3b/8Uyb4j4+HjumzSJz6ZNQxBFQtq0LpPq4M7JJWXeAkI1Wp544onr6lMQBAIDAwkMDPyDZl2CXq9nwTcLmDB+AhvXb+KUR49VCQYUCoQcJJWP2g1rcfr4GVoHdCVMG1UifgdkeFMRXKD3Glm7Zh3Dhg3j22+/rXSn7nqpV68eI0aM4NiBVwiwBJRRcI831UMn6jlx7DCZR4+h6DWIOi2yy402IIDoQXcRWkEIreL+e0RN+PFTGcOHD+ft997j0pJvibl7eGlKxS/J3bsP99lz3PvFs2U+N5lMLFm0iOnTpzMtMZHzM2YhK6ASBEKCgnB4FVpaehCm/1mbQhREzKIFb3YeyAL5BQVUqVKlQmkHR1o6apWKiIiISud/3/33cfemUZyzn6SGqW6545Iicdi2i4BgKyNGjLiBK3N9REdHs3jpYoYPHcGW1O8JESIIUAUjKxLZUhp20Uavvj35aupXN6SZMWzYMBbMn8vI+/fy0RtV6dohoPR8SVL4fl0+z0xOo1PnXvTq1av0PLfbjdvtwSCWdeZXN8Rz1n6CY9JuGtIanaC/XBUEZEXBrbjwKT4EQcBo+PlcUaulSttWZPy0FX3NePQ1yjs3PNnZ5C5bAaJIYJ9e6KKikL0e7IeOkHnoCD379GbZ4iVERESwf/9+Fi9eTGZmBgaDkY4dOzJw4MC/RRlPP37+igjK9RTn+wtTVFREQEAAhYWF/nqs/2Duv/9+Fm5YT1CHtlxcupyqTz6KGGC9rIZd8rBSJB+y04lW0OE4dZqchYvoau2NSWVlXdFSZs5N5K677qqw/6eeeopZX86hjak7JnX5nWxZkdlftA0hzMuRY0f+sF3JvyuTJk3i2z27qfnogwAUHj/B2RmzMdSvS1DfXqgv/3YVScKdlo5ryzZUF1NY8s3CCh0hf0V8Ph8vvfQSX82YgWIyYaxfF1GnxZ2WgevMWWKqVmX+11/TsGHD//VUK0RRFPbv38/s2bNJOpWEIIjExsUQEhLCZ//9DKMrgISADuhVBnLcGewr3IZLcmIhAC16nIodGwXUqV+H2XNm/e7ydZcuXaJ5k+bEyQ2INdaqcL4pzrPstm3F0KQBUR3aE9SgXoWGiDsnl7PvfchXn/6X4cOH/655+fHzv2T16tWMnTABKSKcKl06Ya1XB0EUcaZnkL1tB8V79nHP2LG89957lRqOkiSRlJREcXExVquVQXcNwndRRZOA8s6qM/bjHJWOUu3RR5AMKgIDA9EbyjqvFEXhzIef0q9FS2ZXojp+hTfeeIP33/mAUDmCGENtgjShyMhkuFJIdp9CsUjMW/A1Xbp0+c3X6FoUFRWxePFiZiXO4sKFC2jUGtq0b8P48ePp0qXLDaV7XSE/P5+JE8ayc+dmasaqad/KgCQr/LTdSUqazG09b2fKlC/KRAIpikK1qGqEO2KoZS77XLD5CtiRtwG35KIKUQQSgoyMz+gii1ScuDA3b0qNsaPKnCf7JM5On0nRmbOYWzTH2Kg+Kqu1JLR//0GKtmxH8fmo+uSjaIICS8/zFRejKraTt2AxtcPCCLQaOXJ4P7HVRWrVUFNYJLPnoBuLJZR/v/E2Q4YMueFr5MfPrcr12qJ+o9vPLcGJEyfo0bs3+R43QnAwkfdPQpblkrxhBBRJQnY5EZXLiseSzIV336GxugkRumpsKf6eb5YsKOOF/iV5eXn06dWH8ycuUFvbmMhf1Bgt9OZxynEYu76QOV/PrrSPfzKrV69m2OhRxDz8AMZqVQEoOHyU5AULkTxe9DXjUVkteHNz8Zw5R/XISKZ+8QXdu3f/H8/8xjlx4gSzZs1i09ateNxuqlerxsjhwxkwYMDfZqc1OTmZF55/gbU/rsXt8CK5JRShJAc/RBtGjjuTAEKoIdTHLPx8382Vs0jTnEFTRcXK71fQuHHj3zWPiRMnsvyblbQydcWsLn9/t/tsrMv/DlW1cOo/9RiqCq6voihcnL8QY0oqRw8d8jvE/Pzt2bp1K5P//W/2HtiPhICoVqN4PERFRPDIgw9y//33X7fhuH37dvr1vp1W+q4Vajq4ZRfr8lcg1qxO0KAB6C0mgn5hrCmKQtqq1di37WDFsmW0b9/+quMpisL8+fP56IOPOJN0BskngwBqrZou3brwr3+9cFN1K34riqKwc+dOfvjhB4qKiggMDKRfv360aNGiUmeGoijs2LGDOXPmcDrpGIIg0KBhM8aOHVsaBfZrHnzwQZbO/o5O1r7ljrtlFxccZ0h2JGHzFaLRaoiPr8H4ieMRBIHX3nqL+KcfL1d/XfZJZK7fSNbW7Xjy80AQETUaZJ8XUaen6pOPog74+X4qe33ITgfBwcE4kk5z4csvaFTXyDuvRtOhtRVRLJnXpTQ3732WyuKVLj786HOGDRtW6fXzer0UFRVhMBj8O+N+bnn8RreffxybN2+m7+23o1SvRtjYUSCKKIqM4pNAkhAR0Qja0jCtC++9SwOlLrIik21K4diJowRcFv6qiKysLB566GE2rNuAyqdCjwlJ8OFQiqkeW533P3iP22677c9a7t8Kn89Hm3btSHY6iL13ImpjieEjuVzk7d1PwZFjuHJy8WRmcPew4Xz22We/KzfYz2/n3Llz9O3dl4I0GzW0dQmQQnEWOxEQyVQukaycRMJHG6EnOrGsketV3GgMGo6xh4Zt67Jm7ZrfNZe8vDz6396fU4dPE62qSbShJjqVHrfsIsV5jou+00TGhVNQbMMRYKXqoDsxREX+PJ/CItJW/4j74GG+mjKFwYMH/675+PHzV+LgwYPs378fr9dLTEwM3bt3v+Ec3BUrVjBqxGi6W+5EU4m4WI4nkx22TchVrAS0bUXVNq0QVCrs55PJ3bYD6UIKb/7739x3333XPa4sy+zcuZMLFy6gVqtp1qzZX0ao69ixY9z/4IMcOX4cMSAAldWCVFiEUlREk8aN+XLKFOrUqXNTxtq7dy+9evQmlrrUNNWvsM15+ylOy4eZM28Offv2Ldm1ttno0q0b521FRI0egU9vwOv1gAJqtRq1z0f6nHkYiotx2B34AgNw5uQQctcALAnNSvtWfD4kpxOtRktwcBAXFizEfG47y+fEUKdmlXJzURSFp145z8r1avbvP1ruvfvQoUNMnz6dxcuW4Xa7QRBo3bIlkyZM4I477vjdqUd+/PwV8Rvdfv6RPPfcc/x39mzCHroPRQBZLtnp1ogaVKi4Emruycsl9fPPaapL4LzvBKMmjeTDDz+8rjFOnTrFkiVLyMrKwmAw0KlTJ2677Tb/w+QanDhxgv533kmeIhPUoR1BzZqi0utwZmSSs30n9n0HGDJgAF9+8cV110j1c3NRFIUe3XtwYk8Src3d0Io6bEVF2O12NOiQkbEpBRxnL8GEUU9VNoTco7gxmgzYNUUclXax4acNNGnS5HfNqaCggMmTJ7NwwSKKi4oREZGRMVlMDBk2mFdffZWUlBRGjxvHhZQUNNWroQ4JRrY7cJ09R4DRyHtvv33VXRk/fv6pbNiwgYEDBtHe2KvCaJIrFHrz2Za/Fq/Oh/ZyRIlaFGndsiVPPPYYPXv2/LOm/Idy/Phx+vbvT7HRQHi/3phrxiMIAoosYzt9hsxVqwnweFm9ahW1a9e+KWO++eabvPPmu4Qr1Yk31iv9Huw+G+cdp0jlPA8/9hD//ve/y+yGHzt2jI5dulDkdGKsVxdDjXgQBJwXknEcOYpOFFm1fDlqtZrBw4aRnZdH5GMPoa1SovYue70okoROqyUoMAjJ4SDpP//m+fv0TBwRQJXQ8kY3QGaWh1a9T/L6vz8uIwaYmJjIMy88j2yxYG2ZgD48DMnhpPDAIdznztO3Z08SZ8z420R8+fFzvfiNbj//SJKSkmjToQOhg+8isFkTiopsOB0OBAREQYVKUCMikrP+R5zb9mJRm4isGc6aH1cTHn59JUr8/HbOnz/Pv994g5Xff4/T60FRQCUKVIuM4v577qm0xqufP4e9e/fSs3svmmjaEqaLwuFwUFRUhCIraAQNMiWl4TK5xAVO0UroXrrbraDgUdwEBgai1WnZaFvO868+S2xsLHPnzePCxYtodVo6tGnL+PHjbzi3vaCggPXr15fe67t3715GBd7r9bJmzRqWLl1KRlYWFrOZ23r0YOjQof5ngx8/lWC322lQryHWgirUszSttJ1LcrK5eBX/evUFmjZtiiRJxMXF3bQd378CiqLQo2dPDqanUeOBeypMV/E5nZz77Eva1qzFqhUrbtq406dP55233iErIxujygQIOKRigkODefLpJ3jooYfKGNySJDFyxEjWrlpHKFHkyjnYZRsARtFEuDqKXDmd2HrR/LjuR7Zt28bgkSMJnTQOVXBJOLpWq8FoNKLT6RGEkpKWWV/PYNOyakSEqKhylVJsd9+fhNrUkzlz5gIlKWQjx4zB2KYlVfv3KyuQChQlnSZ19jxG3HknX3z++U25bn78/FXwG91+/rHcPWoU361fT8CwwairhCCIqpLSGbKEIiu4ks6Qt/RbtJJCh44dSZw5g6pVq/6vp/2PIj09nZ07d+JyuYiIiKBDhw7+0iR/AV555RW+/HAanS398Ljd5BcUoEKFrEiAgKAIyMgICOxkLfE0JEqIRgF8ihdZkFGrVRgNRnb61qEJ0mJ3udDXiENXtUQl13H8FEJxMWPvvpt33nnH/7378fM/5sUXX+TLj6dWKhSqKAqHbbuwW/M5evzqaVh/Z/bv30/33r2IGHM3AfXKq6tfIf/wEbLnL2Lrpk3Ur19xSPhvwe12s2rVKo4fPw5A7dq16d+/f4U6FGvXrmXooGE01bYjTFeiOC8pEgAqocRxbffZ2G5fyytvvMS4ceOo16gh+s4dCevSuSTm71cp5rl795G7eC77f6xKgNlw1e/54efPklnQiiVLv0NRFLr26MGxYhs17hlfac57zq495H27gj07dhAfH19hGz9+/o5cry3qj4f1c8vRrGlTFi5eTNasuZhbNsfcrCkqkwlvbi5Fu/bgOHwEg1rDipXf0aVLlxsqDeLn5hAZGVmpUryf/x15eXnoMSAIArbiklButaBBRoVXcaMgAApqtKjR4MODrCjISMiCXPKyJwlkFKWTpy7AZIwh/qH70If9HKao3CGRu2cfM+Z9jSAIfPDBB/+7Bf8JuN1ucnNz0Wq1hISE+O83fv5yPP3002xYt4HdxzZSR9eECF31UqHQYl8RSfYj5GkymfLBZ7eswQ3w448/gtGEtc7Vw8YD6tcnU6tlzZo1N9Xo1ul0DBw4kIEDB16zbeKMREyytdTghp+N7SuY1BbChChmTJvBww8/zJC7BjJn1SpC27aucBdfY7YgSQrJyR7atgoud/yXnE32EVurpDzcwYMHOXTkMBFjR131/hbcvCm5q9fy9ddf88orr1xzjX783GrceF0EP37+wly6dIm333+fsNu6E9m9C+7jJ8n4chqpH3xM1qy5kJtHVK+eqExG9u/f738B9uPnF5jNZry48Xjc+Hw+VJf9sqJQIkKoCAoKCl48+PAiIODFgyRIaAQNGkGLWtCQzAlUgVaChgxEHVz25U1QqQht04rQ/v2YOXcuJ06c+F8s9Q/n2LFjPP7449SIjadRg8bUrVWX9m3bM336dBwOxx86ttPpZMGCBYwbN4677hrIPffcw4oVK/B6vX/ouH7+ngQFBfHdiu/o2LM9x6V9bLatYmfhBrYX/cg2+xoI9/DVtC9veV0Em82G2mwqFxr9a0S1CrXZRHFxMXC5QsLFi5w4cYKsrKw/Y6rs3LGLMFXUNdtF6qJJvZRGeno6jz32GAGCQPKM2XhttjLtFEVB9njwuiW+Xe24agTSoWN2jpzwlApTJiUl4ZNkLNcQwhM1GjTR1Thx8uR1rNCPn1sP/063n1uKOXPm4FWJxPbphUqnJeK2HjguXUJ2uVFbTBiiohAEAcnlYsasWTz66KP+8FY/fi5z22238eWUr8h1ZSEomjJlh0RBhV7Q45bdZCmXUJAJIKRELwERlVDyOCmU88lX5xHYohuiwYjDbscaUD7cKqRlC/LWb2LWrFm89dZbf9oa/wwWL17MQw88jOASiRRjCdQE41O8ZBxL4enHnuHrufNYtHghISHlSzT9XtasWcNDDzxEdmYOViUILXo8ootFCxZTPaY6iTNn0KJFi5s+rp+/N2FhYSxZuoTjx4+zaNEi0tPT0el0tG/fnjvuuOMfIX4VHByMt7AQ2SchqivXFpG9XnxFNiwWCzNmzGBa4gxOnkpCQUEUBLp07MS999xTrnxocnJyaVpVVFQUXbt2/c3vH16vF1G49iu8KKgABa/XS3x8PIsXLGDEqFGcfvNdjA3qY6xWFdnrpfjocXwZmdSr25hl359hQJ8iOrcrH9VQWOTj2ckpBAaGcfHiRRYuXEhBQcHlo9eRraqA6N/s8PMPxZ/T7eeWolPXrpxWi8QMH3LVdvbkC6R8MY1Na9fStGnTP2dyfvz8xZEkidat2pB2LIt4dyMMqvL1VT2Ki33yZlw4iKcBIYSjFfTIgkyGcpGLymk8Opnq9z+AKjQYWZAICwtDEMu/aF1cvIx4p5stmzb9Ieux2+2sXLmSc+fOoVaradq0Kd26dftDxfp27NjBgNsHEOQOp7G1dWmY7hWKvPnsdWymdeeWrFi54qZG26xYsYK7R9yN16MgI4MgYBANxGrjCdZU4YzrGAT4WPnDyt9dQ92Pn1uNU6dO0bZjR6oMG0RQ08qrLuTu3kvu0u9o0bw5uw4cwFC/LkHNm6E2GXHl5FCwaw++lFQef/hhXnnlFU6cOMHk1yazfu16vG5fyW9ehMioCO574D4eeeSRG74ndWzfkYxDeTQPuHpd9DP246TpznH6bBJmsxmA/Px8vvnmG+Z8/TUXUlLQajW0b9OW8ePG0b59eyZMGMdPG1cwaoiFUYNDqB1vxu0WWPZ9Lp9MTefkGTeK2ohKp0WRJDSiCrvNRvVRIwht06rSucgeD0n/9w7P3P8AL7744g2t14+fvzL+nG4//0jsdjvqqhHXbKc2m1FQSsPD/PjxAyqViimff0a/Prdz3L2XeLkBAUJwSckcRSGPLM4px5GRCSWSCyRxlmOolJJyfGpBTRV9JBlKBqJaiyiokBQfPklCI5Z/3IhaDe5CW/mJ/E4kSeL9999nyn8/pyCvAIPKhKxIeBQ3MTVieG3yq9x55503fVyAjz/6GI3bUKHBDWDVBNFI34rtW7axbds2OnTocFPGTUlJYfjIu3ErMuZG9TDWqgWiCtelFE4eOoymOImWlg6cKjzE8889z/c/fH9TxvXj51ahTp06dO3UiY0/rMUcF4emgggdT0EBOWvXExIYyK4jR6h+7wTMcbGlx02xMYS0SCBr63Y++O9/URSFxOkz8eRL1NY2ISowBhEVNqmQ5LTTTH7pdY4dO8YXN1gqc9SYUTz/1Au4JAf6CpyjALIikyqdZ8jQQaUGN5SkE9x///3cf//9ZdvLMlOnTuXIoWPk5sl88lU2n8/IQqMRABGPV8AtiwR36ExEj67ogoPxOZ3k7zuA88d1JM9fiLlGXBkNj1+Su3svKo+Xu++++7rX6cfPrYQ/p9vPLUVYlSp4cvOu2c6VnY0oiIRepSSGHz//RFq1asWKVcvxmhwcULawV9nIIXk7u5X1HFP2oNfoaaxuQz2xOdHUQhQE6lmb0iKwAz3DBtLY0hJBBndG+i96rTigyn0pjbiY6Js6f1mWefTRR3nz9bewFITS0dyXTua+dLH0p5WhK7azLiaOm8Ts2bOvu09FUZBl+ZrtUlJSWLd2HTHamhUa3FcI1UagV0zMmT3nuudwNdxuN31uvx2vyUjVe+8jfOBgLI2aYGnQkCq9+lDtkUcgNoI9tq1UU8exa8dujh07dlPG9uPnVuLjjz6iqsnEuc++JGfXbiS3BwDJ5SJ7+07Of/YlEUYTuQUFhPXvU8bg/iVhHdphaNyQt997DzlPoJ2lB9UN8agENYIgYFUH0tjakka61ixesITExMQbmufw4cMJjwpjf/E2vLKn3HFZkTls2wUGiXvvvfea/cmyzOOPP87zT7+AO1mhk7UPvQNH01DTjQBXTYqLRByKhrhHHyZm6CB0l7U61AYDVTq0o+ZjD6EOCuTEBx8jeX3l+i84eozs71czeuRIYmMrvmZ+/Nzq+I1uP7cUgwcOxHkyCW9h0VXb5e3aQ6P69W+pGqN+/Nws2rRpw5yv52AwGVBrNFgMAVQzxdEppDcdQ3pj0QSQo6RzUThNDWM9apsbUdUQi0bUYlJbCFVVoWjfXiS5JJRSpSq/y+1MS8d94SKjRt7cXY9ly5Yxb858GuhaUt/S7HLN2xICNSEkWDsQLlXnmSefITk5udJ+nE4n8+bNo0f3HkSGRxIRFkHbNu346quvKCqq+P5y9uxZfF6JEG34VecoCAKBhHLi+M0RkVuxYgVnzp+nytDB6EPDyh1XGYyEDxmGz6zBJhWBT+Cnn366KWP78XMrERUVxSsvvUSIABnz5nD8mac4/MSTHP3XqxSu/IE7u3Tlzv79UfR6gpo1LXOuIit4PB7cbjc+rw9ryxZ4fD6qamNRixXnbkfqq1NFieTLL766LsfeFQICApj/zXy0VUS22lZzqvgIhd48inwFJDuS2G77kUJ9Np9/+TmNGjW6Zn9z5sxhTuJcGmha0DSgLYGaEDSihmqGOJoGtEWl1mNp1xav1UpFSammkBCqDboLn62Yk5PfIPX71eTu3U/Wlm2c+WQKGXMXMKT/Hbz7zjvXvUY/fm41/Ea3n1uKIUOGEGK1krJ4KbKvvLcVIP/gIVwnk7jvnnv86uV+/FTCgAED+OiTD8HswybkoxG1OGQ7yc4kjiq7OKHsI0QbRkNreVGumoZ6eM5fIH/rZnR6HeKv8rm9hUVcmvcNdeLj6d27902d97Sp0wiQQ6iqj6nwuCAI1LM0Q3IpzJ07t8I26enp9LytJw/e+xDndqUQ7a1LnNSAnKOFPPfk87Rv14GTFSjwXhGeux6lFAWljFDd7yFx1ix08XFow8ob3FdQ6fWYmjXlovc8KkGF0+m8KWP78XOrYLPZGD58CI89eg+x1Yp4+ckoXn06nL6d9Vi0HmKrhfPySy+RnZ2NJioS8bIImizJFBXZyMrOIi83j/y8fHJycvBYzAgaTWn97MqINtTi7OmzHDhw4Ibm26xZM9ZvXM/YB0aTa05ll2sDO53rOK86Qc/B3Vm1etV1leZUFIXPP/ucUCWCqobYcscz3al4RYmgZglIkoTb5aqwn5AG9bBWr0bD2Dg0h4+Rv/Q7iteso3v9Biz8+mumfvWVX7jWzz8af063n1sKq9VK4rRpDB81irNTviK0S0cC6tdHVKtwZmSSs30Htt17GT1sOCNGjPhfT9ePn780EyZMoGXLlkyfPp0li5ZwyelCVAm07dmWfftkstPTWZ/9HQaViUh9NNUNNdCKWkK1EViKTeT/tAUxMxO5XVuM1ashezzkHz6Cbc9+qgUF8c38+Tf1JSw9PZ09u/ZQV5dw1XZqQU2YUJWli5fy0ksvlTnmcrkYOmQoSQfP0tZ0G1Z1YOmxWGrhlOzsS97C4IFD2PjTBqpU+Tl/sU6dOuj0OrI8qcSpK4+ikRWZfLLo1bzLb1rnrzl6/Bim5k1RUCgJ5a/YmWiMq4FtyzZckkxISAjnzp3D6/USGRnpFyL1849GkiQmThzH4YPrmTulOp3bWcs45c+cdzLp8WSGDrmTBg2bweVdacknkZeXhyTJqFBdrpUtoCDj9blRZJk07wXijHUqdPK7JReSLOH1eklLSyMh4er3rl8TGxvLu+++y8svv8yZM2eQZZno6GjCruKA+zWHDh0i6dRpmukrFmVzyy4EtRqNNQiP4sbpcqI3lFezF0QRdWgI9erVY/v06Xg8HjQazU1zLvrx83fHb3T7ueXo0qULK5Yt47XXX2fH/EWkAaJKheL1EhUeznMvv8LDDz/sfxD48XMdNGrUiI8++ogPPvgAm83Gp59+ygfvf4DL4cKqBGHwmfH6PBx17+VY0T7C9VEUi4UYQ/W88OR/2L5jBxsXL0NSFAQgMCCAR8aO5eGHHyY8/Oph2DdKQUEBsqxgVJuu2dagMpGXn1bu82+//ZYjB4/S1tijjMH9y/NamDuz9eJqZs+ezVNPPVV6LDw8nP533M6qb1YTrcSXllH7NenuFDwqN2PHjr3+xV2DEueFgoRUWl+9IiRZQlYJvPfuf/jXC09cPldHn753cv/99/urOfj5R7Jhwwa2blnP/C+r07FN+VJZNeMMLJgaT9e7kigsrIH74kV8xXYKXE5kSUYr6BB+4ewSUOE9n4Lglcn2ZpLiOke0Ib70eK4nizOOE2T40pAFBUnx8sDDD3Po0CHuvffeGzKaoWTDoXnz5r9p7VlZWciSjNlQft1Q4qRUJAnZ40HQCMhS5WHwitOF0WhEEAR0Ot1vmo8fP7cqfqPbzy1Jy5YtWbViBcePH2f37t14vV6qVatGjx49/OFNfvz8BlauXMmzL7zA6bNnEdUaBK2GIsGGpMhUk2sQJ9UlWTlJqvsCI0eN4PXXXycmJoanKBEYS01NRaPRUK9ePYzGitV2fy8WiwVBFHDJjqu2c0p2Up0XkNQunnvuORISEkprEc+YnkgwYVg1QZWer1cZCBOqkjg9kccff7yM6vATTz7B6h/WsK9oK80s7dCI2jLnZrnTOOHez+0D+900A7d+3XrsO5dMULOmuN0eREFEqCB7rOjEMVSCm5BguOM2F316VEevE9l3qJg5CxdxR/9lfPjRFAYNGnRT5uXHz9+FWbMSadJAU6HBfYWIMC2Db7ewbM1RjBotaRs3oW2RgEbQljG4ARRZpmjXLqySFYNg5GzxCarrayAIAhccpzno2IMqKpyghD6oI8KQZC9cSuW9L75g4ZIlfLd0KXFxcX/0sgEwGAwIgoBX8aDHUO54qC4C0aFQfPwYhib1K03Lc2Vm4bmUSrdu3f7oKfvx87fEX6fbjx8/fvxcla+++ornXnoRX7VqWFq0wBxdAxBwpVykcPdOXCeSaKRvRpy+Frttm4lsEMqOXTv+dM0ERVHo3q07yXtSaRnQudxxn+LjsG03KZ5kJL0KY7WqaAQBT0YmVUJCeO2ll3j26Wep7qlNnPHqIosZrksclXdz/OSxcjv2mzdvZsyoMdjyiqkiVCVQHYxP8ZHpu0SxWEivPj2ZkTjjqs6HzMxMFi1aRHJyMmq1moSEBPr3749eXz6sc+HChUx68EFiHn0Qp1aLz+dDhRqVoEJAQEHBYy8i/cP3aFBLzXdzGlI1suwulM+n8Ozk8yxZ5ePb73644TBXP37+ztSvF8+9dys8em/UVdtt3VXE8HtTGDf+Ad7/9FMs3boQnNAG4RdikbLbTfaPP+A6cJS6viaIisBx9tE5tA8+xce2ovWY2rYipPttIIJHcWMymbBYLXgKC7nw1QxqBASybcsWtFrtVWZzcyguLqZh/UYEFIRR29wISfGhEtRlKjDsKthEVqCTkHEjCQgLw2wuG02kyDLJs+cRkJPL0UOH/pR5+/HzV+F6bVG/0e3Hjx8/firl0KFDdOt5G9pWLdG2aoFW1CP+YhdVURTyNm3AtmU7XQJ6ISk+9ru3sPz75TetBvWNMH/+fB689yGa6NoSrqta+rmkSOwo3ECuuhBr9y7oG9YlrGoUarUad04uGes34jp4CMnppp66ObHG2lcdJ9OdyhFpF8dOHCUiIqL88cxM5s6dy5xZc0hLS0Oj0dC+Q3smTJzAbbfdVmlNXpfLxfMvvMC8bxbgVkAXVgVFknD/wjEwevToMue43W669ujOyYwMqk8Yg1evx+lwcuXxLrvcZM6cg74onS0rG9KwrqXCsX0+hZ5DThFf9w6mT59x1fX78XMrUad2LI9OVPHA+Mirttu938bA8RfZuGknffv05UJqKpqgEEyNGqIyGPHm5+E4chTR6aW5uS3BchVyCnPYq2ykWWA7MjyXyI2AqHHjkUXwKV7UahXBIcGlKW/OtHTOf/wZs6dN48477/wTVg9jxoxh4YLFIIgogIhAlLoascbahGrDsftsrMtfAZHB1BgxDEvNGqVOVVd2Dunfr0ZKOs2cxJn06dPnT5mzHz9/Fa7XFvWHl/vx48ePn0qZMWMGitVKUNdOOB2uMgY3lKiBB3fuiuPYcc7bkmhqbYPGrWPHjh3/E6N76NChrF69muVLVhDrrUuMsSY6Uc95xymyhTyqjBiOplokFqsFtbrkEagLDSFm2GBSjUYyf1xHhpx6TaM7x5NJYGggISEhFR4PDw/nqaee4qmnnkKW5TIaErIss23btlIF9Dp16tCuXTskSWLUmNGs3bKFkJ49iG6ZgNpQEu55xTHw6FNPYrfbuf/++0v70+l0LJy/gEFDh3Liw/9irF8XS726+GQFR3Iy9kOH0Tnt9OgUXKnBDaBWC4wZFsSr76wkJyeH0NDQ67vofvz8zYmJiWPfofIVCX7NvkPFaDQ6oqKiqF+/Pp50BWOxmYyt+3EqPnSijjqaWsQE1cSgKolkMSlGKFI44tyNR5YISuiPR/CCoqDVagkMDCxzfzBERaKLi2XO3Ll/uNGtKAqTJ09m+Q/fI0RVwdi4ETprEFKRjYyDB7mUtZ4a6poIiOh0akJ1etKmz0QVGoIYFITidOJJuUR4aCif+A1uP36uit/o9uPHjx8/FSJJEouXLcPavg2CULnwoCCKmJs15dL6LTSlDSpBjdvt/hNn+jMqlYpp06YxufpkZkxL5ILtFAbBTLYnG32zhuiiq2KxWDAYy+cuRvbqQc627aTlJbO/cDsG0Ui4ripBmtAyofIeycVF7xnizDG0btkaQRBo0qwJ48aNo0OHDuVEGn/572+//ZY3/+8tTp9MQpFL+hREhVp1a9OyVQvWbvqJahPHYqkZX6aPUseAycRLr71G7969iY2NBcDr9bJ//37iY2MpLigk49gJ0g+UhHiGhYXx5BNP8vXcGbROuPZ30qqZBUnKJiUlxW90+/nHMPLusbz80mNcSnNTLapiATCvV2bu4gLuGDASi8VC+w7t2b5pJ60snWlaSR1ugDyyCA4NYfTYUXzyxRdY4qLRm4wYDPpKNWb00dU4c+7cTVnb1ZgyZQoffvZfQm7vS3Cb1hQWFuD1eBEQMLZshm3vPpLW/IhFZ+Dtt9/i3nvvZfv27SxdupScnByMRiPd/9WdO+644//bu+/4Ksv7/+Ov++xzsncCJOwpIEORIFgRRS0igiCOOqgLHHWVorZqrfUr1f60ah1QFVur4Aa3CAoOQAHZyt4rBDJOcpKzr98fqdFIQFRiGO+njzwe5r6v3PfnPtfjkLzPdd/XpYnTRH6Apm8WEZF6BQIBgqEg7swMbHYb5n//1ceZnkGMGIFYBdWxAE2bNq233S/B6XTy17/+leVfLeP+f/yNsy48A7vbQXZhb7Kys+sN3BG/n03P/Rdb0E92NqS0XkcoYznzq9/l0/K3KI3sAaAyUs77xa8SDFWxZ10ZkfV2wuss3n95JkPOOodhQ4dRXl5eb10TJ07k8ssup3hFKT3cJ3Fa8jBOSx5GD/dJ7FpRyoR//QtH2zZ7Be7vyjv9VKJOB8899xwAy5Yt4/jjenHJRZfy6RvziG62kRxIwxG147Y5+MMttzBu3Di8Xi+RSN2+q7n9vO62cKRmZuJv7gIQORoMHz6czMx8Lr9pA3tKInvtj0Ti3HTHRrYXObn66qsB+M1vfoPdY2N91ap9HjccD7E1uo7h553LBRdcgNfjITHBR3Jy0n4ndTXRaIO/B6uqqvj7P/5BYu9eZPftg8NhJyMjg/SMdDw+Ly6Pi8x+fUjvdyLpOZmMGjUKm81G3759efDBB/nPf/7Dk08+yYgRIxS4RQ6AfquKiEi9vF4vFhbRQBUpHi8VFZXETAxHPUthxaqqsIDt1RvxJnp/sWcR9yc9PZ0rrriCbt26MeW110jITKe+ud0iFZWsf/xxchx7+Ms92Qzs58HrsRENx/jiyxCPPl3O54vfIZ0CtoU24jJuTkzpT7bv20mXjDEUh3fy6cw5XHjBhUydNrXOH9VLlizh9ltvp0m8FR1TutUZOc9wZeOy3Gwp34yjfVsikcg+/yC3OZ34unTmjbff5vzzz+ecs88hVByrWeLsOzOuh+JBVlcuY+zNY3E6nRzb7Ximz3qdsddFqa6uprq6mng8Dha4nC58Ph9uj5sPZpWRmJhCq1atDkIPiBweEhMTeWHyy4w8bygnn7OaC4Ymc9rJqTidFl98WclzL5WydaeTfz72Lzp37gxAs2bNuPn3N/G3e+/HqoSWCR3q/NtYES1jSeBzUvOSGTt2LBkZGSQnJVK2bAW+/XwoaeJxqr5eReHA0xv0mt988032lJXSul/dx4BcLledidA8A/qz/u//4K233tLKBiI/g0a6RUSkXk6nk5N/dRL+RUuw2W14vR5iRIiz9zqtlcuWkmKlsim+hosv/Q1paftecuuXlpaWht2yCO8prXf/tjfeJNPazfNPFTD49EQSvA4yMzJJTkmizwk+nn44i+OPs7GLDTjsDvqmD6wTuKHm2fZsdx7dvSfy2cdzeOutt+rsf+aZZ3BEXHRIPLbeWd2jRLBbduwJiVRV7X/JM2dyEuV+P3/5y1+oKg7RK+nkvZY4c9s8dE46jqxYM/50+58YPnwEX68J8uobm6msCmAcdmweD5bbTTgWo7SsjA0bd/PfV8oZcd5vSEj44bXORY4kHTt25P3pHzF85O/472s2hl62mbMu2sR9j1TSted5vPnWdAYPHlznZ/7whz8w9rbfs8Wxltn+t1hUPpdl/gXMK5/JnMAHZLVOZ+obU2nevDmJiYn85vwLqJi/kGhlYJ91lC5ZhikrY9SoUQ16vV9//TWuzEzcGen7befJysSVkV47B4WI/DQK3SIisk9X/PZywlu3Ubp0GcnJybhcLiImRMSEiRPHYKhYuYLgli1UWX76n96fu+++u7HLrqNVq1Z0PqYTJV/M32tfpKKSquWLueqyNJo1cWKiUbxeDzabjcTERLKysyjIz+H/7miN02lIs2eS4t73H6nprixSrQyefurp2m2xWIxXXnqFbNOMSDhCNLL37asuq+b2TOOvJFgd/P5d33WES0pJSkzgvbffo7mz3V5rgX/DsizaJXTBX1rBZ599RqA6zu/v2cOcxQbL7cZyObG5XNgTfOzyOxh1wxZ27oozZswY1q5dy6effsrChQsJBoP7LkbkCJKXl8fdd9/N4iVf8+FHc/lgxqcsWbqSJ554gq5du+7V3mazcfvttzN/4RfcMO568nvlkNbZR+Gg4/n3888yf8EXdOzYsbb9NddcQ7YvgQ1PTSK0Z0+dYxljKF28lKJXX2fY2UPo0aNHg1/vATus1zkSOTTo9nIREdmngQMHMnLYMF6c8grhklIyTjieoNtNdVUVoSo/gSXLKJ/5ESkJPu688w7GjBlzyK3RalkWo6+8ijE33EDp0mWkde1Su8//9UocJsyQs1KJBYNYWHi93z7zbWFh2ex075xA21YOtn1ds/2b28n3hIuImRheu4+mnhZ47F6yHc2Y/8V84vE4xcXFjB8/nl1Fu0ijKaXVNaPtDoeDhISE2nP57Imk2tIILFmCu11LjDH1jojHgkGqlq+g04BTWbtiDU2Tm+/32r12H8kmjRcmT8adX0A4OZErbvqKY9rt5vRTEnG7bSxZVs0Hs6sIWR5i4SgjzjuPVWvXEjNxLCzS09K49KKLuP7660lP3/+omMiRwOPx0KFDhwNu36JFC+644w7uuOOO/bZr1qwZU199lfPOP591DzyEt107PM2aEI9GCSz/inhJKeeefTaP/fOf9b7/D6aOHTsS3r2bUEkJ7v28r0O79xAuKaF9+/YNWo/IkU6hW0RE9slms/HYP/9Jeloaz/z735TMnIWrWVPAENy0BXssxsXnnccTTzyBx+Np7HL36fzzz2f2xx8zZfLLVG3aQmaf3rgz0olWV+NxQ6I9DJHY/5bvqbuGdtzECQaDNMmx8/WyUpaWf06J2UbYVklutp0En41tOyOs9S8k296KVEcm8XicDRs2MGzouWxbvx2Lmono3JabuDHEolHKy8uJRCIkJydjWRatPO1YsGoeVStXQc7ea3+beJxtU9/EZ7PTq1cv3njtTez1PF//ffG4Ycv27TS74Dwyeh1Hxeq1rJ0zh6//s4F4LIYzPZOUQSfgbNua9Q8/zpIN6ym4aCTe3FxiwSCli5fw0L/+xZtvv80bU6fSpEmTHzyniNSvY8eOfD53LlOnTuW5559nw4qVuJxOfn1yf0aNGsUJJ5zQ4IEbYPDgwWT86Y8Uf/wZzc4ZvM92uz7+lKyMDM4666wGr0nkSGaZmulLD1sHuiC5iIj8PDt37uSFF15g1aqa2Xrbt2/PhRdeSG7u3gHxUBSLxXjwwQd5fOJE9pSV4khMJFjuxxXz88V7Lclvlvq9WXgNlZUBAoEA8ViMIZfuYMnSOE5PlAH9vFx7WQY9OydisywqKmO8/l45D04spaLESdPWBWRmZrDqy7X0SuzP4vJ5VIeq6G77dtKimIkRJUJKcjJenw9jDO/sfpGwx5BzSv/aDwaMMVSu38Cuj2YTX7+RiY8/TnJyMucNO4/evtNIdqTu85qNMcwofZ0qZ4hOd/0JV2pKve2qq6spLy+n7IMPMbv30PGm6+vsD5WUsGnC03Rv2YoZ06f/IqFARBrWo48+yp/uvpvMsweR2ad3nfe1MYbiTz5jzzvvMf4v9zBmzJhGrFTk0HWgWVQj3SIickByc3O5+eabG7uMn8xutzN27Fiuu+463nnnHTZt2kQkEuHhf9zPB5/EGXNZ3cDt9/sJBKqw42Dlyhhr18VxOSzOOT2Zv92RiWWPEzNRLMtJUqKdS4anc+wxHs6/ZjMpqcksXbyMnt5+eO0+Wvra8XloFsVmO1lWzUix3bITNzECVQG8Ph87QptxuO1cMHI4M2bNYt1nc3AkJWEiUUwwSPu2bbjnuecYOHAgkUiE3KZ5bNq2hi7Jx+/zmovDO4jYwjgcTthPTg4EAuBwYDkc1PdZvDs9nbwRw1j41LN89tln9O3bt56jiMjh5LrrrqOoqIjHJk6g7PP5pPTsjjMlhXB5Of4FXxLfvYdbrv8do0ePbuxSRQ57Ct0iInJU8Xq9dZa+2bZtG09Meo4zT0mjRUHNLfKhUJiqqmqclpNwyMZ9/9yBFXPh8QW57bos3HYHMWLETBSbsWG37BigY3s7FwxNZvK0r/DGk0h3ZgOQ425KM28LVlYvImiqyLOa47CcOHBQFQmwyr+UTWY15180ksefeJxgMFj7wYDD4aBnz56ceOKJtSNRTqeTq0dfxd13/IWsYB65nmZ7XWd1LMDXwS/p3r0by1evpGLNOjKO23typkg4QjQaxfJ4CK7fQErLFvW+bomtW2HPyuSFF15Q6BY5AliWxT333MMZZ5zB0888wzvvvUc0FsXpcHLOmWfy21GjOPHEExu7TJEjgkK3iIgc1f785z/z5cLPGXrZasZem805Z6YTClURj8InC4M88vQeli4FD0n07WcnO9OBAWzYif8veBtMzai3zeKSkU34z8urcUXTakOyZVl0T+mDy+ZmY9VqNps1JJlUAErNbnw2L2OuHc3dd9+NzWbD5/MxfPjw/dZ93XXXsWzZMl576XV2hppS4G1DkiOFcDzE1uAGtsU20KxNUya/OJkbb76JDz+bS3qPbli2uguXxOIxDBBat4FoSQmZF46s93yWZeFsksemLVt+7ksuIocIy7Lo27cvffv2JRKJUFVVhc/nw+l0NnZpIkcUhW4RETmqpaenM3Xa24wd+3vG3fMWf36giARvkAo/lJRCosmgV2JvVlTPpWmuE5vNwhiDMWDDRtREMZbB6/OSkJCAw2mwWRCh7tJgNstGl+TjaZtwDJur1+GPlhEzMWwxePSxR/jNb37zo+p2OBxMnDiR448/nglPTGDBhtnEq+JYNovklCRGXXgpv//978nOzuaWm25m1tChbHnpVZoNH4bN8e1kccZAePtO9rw2lYSWLfEW5O/znPFIBHfKoTU7vYgcHE6nk5SU+ud9EJGfR6FbRESOepmZmUya9CxbtmzhxRdf5M477iLHakavxGNIdWRgWRb2Kjfbd1ZjWRaWZWEAY2zYsMjKysJurwmyO4oCWDYH1ZafcDyM63vraHvsPtol1ixbtj6wkqDLz2mnnfaT6rbb7YwePZorr7yS+fPnU1xcjM/n4/jjj68zoUvv3r2Z8NhjjLn+etb87e8kH9cTT5M8AmVllC1eSvXq1dhTU0kedAa7i3fj8XpISkrC9p1R8VgwSGjNOgrPOvsn1SoiInK0UugWERH5n/z8fG688Ub+3wP/j/RQFmnOzNp9Oc4CPvpsO0W7I+RkOr8zL5mF3f5tOJ3y+m5yc5uyu7iStYEVdErqXuccxhhKI7upjPpZG1rBkCGDycnJ+Vl12+12evfuvd82w4YNo2PHjkyaNInnp0xhw+7dxKJRvFYCqSaZKsuBy+bFGDvVVUEi4Qjp6enY/ndtuz75DLcxP3pEXkRE5Ghn++EmIiIiRw+Hw8HgswezI7apzkzezTwtiYXc3PfPYmIxgwFiJorX6+GbqcEXLatkytQKLr3sKm7/021sZR1fVywmEo9gjGFD1Spmlr7J7IoPmB+cS4VVzbsfTOfGG29kx44dDX5tHTt25P7772fwr39NouXjlORBDMocwQlpJ2PfU0nRlCmYskpclotoNEa5v5x4JELRR7MpmzmLm373O/Ly8hq8ThERkSOJ1ukWERH5nnnz5jHojLNoQQfaJHSq3b4juIWl1bPpf5KLqy9JocsxDjIzM6gO2nhx6m7+/ngxHTudyEsvv4bb7eaRRx7h//56H5GqCLEwBBzV+Lp2JqFbVzy5OSS5PfiXL6d87hc0SUri7TfeoEWLFg16bUVFRRzb5Vjyw+1oldChdntpZA+f+2dTbQvhadMaZ3Ym0VA1bNyEIxTmpt/9jttvv73OLeciIiJHswPNogrdIiIi9Rg/fjx/+7/7yY43o5WvA8nOVAA2BFaxMjgf4wjRPN9HeqqHLdvDxOIefv3rIbRt156PPvyAiooyUlMz6HdSfz799FPemTGDjKFDSD62M16vF7fbw/8mNyfi97NhwtN0zM7mk1mzGzTYPv300/z+hj/QP2kwzu89bx41UbYHN7E5tJ4qU00gVs6vTurLk08+SevWrRusJhERkcPRgWZRPdMtIiJSj3HjxpGTk8MDf3uAuds+wB2sWcM7GK8mMy+P/qecTEFBAfF4nCZNmmCz2bj33rt4/70XGXiyl+wOTrZu38gj//iE0vIwiW060PzkfvWey5mcTJMRw1j+5FPMnj2b/v37N9h17dq1C4/ds1fgBnBYDgq8rSnw1gTsORUf0KNHDwVuERGRn0H3iImIiNTDsixGjRrFkmVL+O/k5xhxybk0aZ0LlqFkdwmvvzKVf0/6D5Zl4fV6uevOcQw702LBBx154oE23D2uOU8/3IZP3mzNoAEuIlvW4l+9Zp/nS2hegCMnmxdffLFBr8vn8xGJR4ib+H7bGWOImDA+n69B6xERETnSKXSLiIjsh9PppLi4mCnPT6F4TSmdnMdR6D2V49y/IqEkjUf//hjXX3c1vx7g5IE/tyAtte5NZFkZNv52ZxYnHe+k6M032NdTXZZl4czJZuv27Q16PaeccgrGHmNXaNt+25VEiglZ1Q066i4iInI0UOgWERHZj08++YSxN48lJ9qcPsmnUeBtTbIzjTRnJp2SetDGeQyYCJeN3NeIsIXDYXH1bzOI7tqOf+06QqEQoVCIWCxWp2U8GMLr8TTo9XTp0oVevXuxNvQV0Xi03jZxE2dN9TLatm9L3759G7QeERGRI51Ct4iIyH48+sijeCNJHJPUA8uy9tpfES2nbQsPLQtshEKhvfa7XE4sC47r6iI1GYqWLae0pJTSklKKi4spLS0lEo4QraoitG49J/Wr/7nvg+n+B+7HngZfVHxEWWRPnX3+SCnz/bMJJ1Tz8CP/0GzlIiIiP5MmUhMREdmHbdu28eHMD2nn6lZv4IaaUeEEnw0bNqqqq/B8b6TaZrPjdDoJR8J43BaVMXBZNW3ixAgHw5SESgjO/Ryvw8EFF1zQ4NfVpUsXpr0xlct/ewVfrP0IX1UirriXiC1EpVVOs4Jm/HfiJAoLCxu8FhERkSOdQreIiMg+bNmyhVg0Tpo3c59tvHYfGzZHqK4GmyO21/5oNEokHKF4d4TinUHsBRYWNQHejgNCEfbM/QT/p59x1+23k5m573P9WPF4nI8//pgZM2YQCATIzMxk6NChdOrUie7duzN/wRd8+OGHvPnmm5SUlJCcnMyZZ57JGWecgdPpPGh1iIiIHM0UukVERPbhm+AZM3uH6W8087ZkQ/li3ppRycihe6/RWVVVBQamvR0kGITI7E8IbtiAMzuLWDBEcPUabKEoXpxEo/U/Y/1TzJs3j99d9zvWrF6LBx9OXARNFQ8+8BAn9juRJ558nKZNm3Laaadx2mmnHbTzioiISF2W2dc0qoeJA12QXERE5MeqrKykU4djSK/Io31i1322W1j+McHk9Ux+soATe+d+Z4+hqGgXny8IMvr3xaSHO5LpymVLcD3VBLFjI8uRS3NvG9ZXfU1VRilr16/d563sB2rOnDmcO3Q4roCXdt6upDkzsSyLuIlTFNrK6tBSsltm8f7098jLy/tZ5xIRETlaHWgW1Ui3iIjIPiQmJnLhby7gmceepUW8HW7bt89rB6IVVETLMECCLZVtJXDxdbsYOSTIeedkkpPlZPO2IM/8t5ip71SRFMnnmOSe2CwbeZ78vc6V4khnR/kmAoEAiYmJP7nmaDTKmNHX4Ar46JV8Mjbr24nQas5dQJozk7kbZnDnHXfyr6f+9ZPPJSIiIj9MI90iIiL7sW3bNk495VT82wJ0S+hDOB5iVeUydoVq1tM2Jk6MGM0KmnHW4EF89ulHFBVtAwxgUV4eINtqxXEp/eoE4O/bVLWWdbZlbN+5/Wc9T/3+++9z/vAL6OU9hVRn+j7bra9axWbHKpYuX0JOTs5PPp+IiMjRSiPdIiIiB0HTpk15beprnD/yAmatfotIKEIiKbSxOpNMOliGalclu/ds56XJL/HwPx+mbdu2+P1+0tLSuHXcrayes2G/gRtgZ2Qzx/c7/icF7ng8zvz589m5cyfPPvssbuPbb+AGyPe0ZLV/CTNnzuTCCy/80ecUERGRA6PQLSIi8gM6duzISy+/SOEJfUiLZtPG6ozdsuN0ufD5fDidTtqYjiyt+IKbbriJj2Z/xHHHHQfA5VdczlVzrqYkXEy6K6ve4+8J76LM2sMVV17xo+oyxvDss8/yr4mPsXHjWiBGKBQmGIvzZfmntE/oRoKj/lvVnTYXDpuTioqKH3VOERER+XH2/7G7iIiIAPDiiy9ii9rpnX0yOdm5ZGZlkZKSUjsybbNsdE3qham2eOaZZ2p/bujQoZzUvx+Lqj9lR3AL332qK27ibA9uZnH1HPqf2p/BgwcfcD3GGG6++Sb+ePuNdO+0ndefbc6qOZ2Z83Yr7vx9Ku4mm5hX+Q7+aFm9Px+KBYnGI6Slpf20F0REREQOiJ7pFhEROQDt27bHWZTIMUk999tuVeVSSpK2s27DutpA7vf7ufLKK/ngvRm44h5STc1a3GXWbsK2IGf8+nQmTJxAUlISUHO7+LJlyygtLSUpKYmuXbvuddv5s88+y+233cA//prH8MHfru0dDoco2VNCpd/BqBu3s26ll34pZ+91e/uayuUU+Taz4uvlpKam/tyXR0RE5KijZ7pFREQOknA4TElJCW0de886/n3JjjS2BtZSXl5OZmZNGE5OTmbKlCl8+eWX/Oc//2H5shUAnNG1P5dccgndu3fHsiyi0SiTJk1iwlNPsXb9eowxWJZFk9xcfnvppVxzzTX4fD7i8Tj/mvg4Z5/uY/jgTIwxBENBYtGa9cRtdjuJyXHuuz2bc0ZtpSi4FYfNVbNUmakiHo/hj5dw8YW/UeAWERFpYArdIiIi9TDGMG/ePF588UU2bdxERWUlW+0byXLl4bF79/lzURMBy8LtdtfZblkWPXv2pGfP+kfKI5EIl19xBdPefRdv5040veIy3BkZRCoqKFmwkHv+39+ZPmMGr7z0EmvWrGHDhtU88KcCKgOVBAJVxONxLJsFpmZGdYA2bSy6dXEy74tPiDksHFlZ2HOziYeDhNeX8vobb3Dsscdy9dVX/+y1wUVERKR+Ct0iInLUWrp0Kc8//zxfrVyJzWajW9euXHzxxSQkJHDpJZex4IsFuGNeEkwy6bEcdoW38UHoddoldqFdQud6g+rO8BaO7XVs7a3iB+r+++9n2nvv0eSSC0np2KF2uystlYSCfKpOOJ7PJz7DTbfcwrBzzgHiNM2LUlERxuZy4nB5wVZzC7mJxYiHwsQjEdq2sTNnaYi0Cy7Cnd8Mm82Gx+vBa7Oz66NZ3HrHHcTjca655pqf81KKiIjIPih0i4jIUaeiooLRY8bwzvTpWEmJuJoXYIxh9rOTeOTxx3HZ7JhK6ObtQ2ZCLpZlEQoFKS4ppshsZWXFEgyGDold6xy3JFxMCbv4v6vu/lH1BAIBJj7zDMl9C+sE7u/yNW1K1q/PYOq0NxjQvz+xaIyiXQEKWiZhueo+723Z7dh9XkxljMqAwVPQnMxjOmGzWXg8Hmz/C+fNzj6LbTYbd997L+eee67W6xYREWkAmr1cRESOKuFwmAsvuoi3Z80ie+Rw2o67hRYXnU/Liy6gxS034hpwMnsCldgjLpJJrx3Ndrs9JHgSaWq1JJ82rK5YSiBaCdTMQr4juJlF1Z/S7+S+DBs27EfV9M4771DqLyersPd+26V3P5aY08m6desIReJMn129V+CuZQwV/hgzPwvhadOOxMQEfD5fbeD+Rs6A/gTjcV544YUfVbOIiIgcGIVuERE5qrz00kt8PG8u+aMuJq1bVyy7nUgkwu7duynz+/Ec04mMoUPZbe1hfclqdu/eTTQaASA1NRWvx0MO+WAs5pXOZFH5XD6peIflsfmcetYAnn/heVwu14+qadu2bTgSEnClpe63nc3lwpmdxbZt2wiGLf7zSiXFuyP1tjWxGM9MLqMi5MB9TKd9HtPh9eJp15YPZ836UTWLiIjIgVHoFhGRo4Yxhn898zSedm1JaNEcgGgkSklJCbFoDDs1o8aJbTvgaprHLrYTi8Yo2VNCNBrBsixSUlPJycom19kMEmO0OrEpF1wxkpkfzWDy5Mk/aflKl8tFPBLFxOM/fA3hMPF4HLfXy87KBC4bvZkly6rqrP/t98d46LFdPDGpHHefk7Al+vZ7TLvXTaCq6kfXLSIiIj9Mz3SLiMhRo6ysjOUrVpAx4tzabeX+coiD03JjiIOpmWk84ZhjKNvyIS7jJmxC+Mv9pGdkAOBwOPG5E8lpk85777/3s+s64YQTsCJh/KvXkNKh/T7bBYt2Ed5ZRO/evXl16lSSTz2V9V98znmjtnBMexed2jmpDMSZNSdIRbVFdcxFVvOCH5yZPLJrN007d/nZ1yEiIiJ700i3iIgcNYLBIMaA3VOznFckEiESjuCwnFhYQE04NRhsHg9x4hhjcOAgHIkQjXx7K3fA+GnSNO+g1NWjRw+6delK8cxZxP+31vb3hf0VbJz8Ej6Xi+zsbHr26EFg1Vra3nILeZddxVpnF6YuyWXGhua4+w2i3R/vwNskj8CChbjdbqqrq6kKVFFdXU38OyPq1Tt2Etq8hfNGjDgo1yIiIiJ1aaRbRESOGmlpabhdLoI7i0jp2IFQKARY2P73GbQNCwsbcaKEi4tx4cayLCzsWCZCKBTC4XQSiFZQRjEjzx95UOqyLIt777mHYeeNYNNzz9Ns2Dk4U2puUw+XlbNt6htULlmI04pi3E6uuvIiHE4v/j1+SpeuIL1bl3pnPXdlZFC+dDl72rTB26kDFhYGg2VZeD0evHYH2156lVYFBZxxxhkH5VpERESkLoVuERE5ang8HoYNGcLkDz4g+1f9MHHzv7Htb26/tnDYHISCASoXL6Yg1vQ794RZxI0hEo+wNPA5TfKbMmTIkINSVzwep0+fPvz32X9zxdVXs3b833EW5BMsLSW6fTP5eTZuviGF4YMzKMhPY/PWCM+/uosnn42z+emnCA4aRFbfPjgTEwkW76b40zmUf/01VZu3YlmwZ9pbJG7bTnKv43Dm5hCtrKT4s7kEPl9ArsfD89Om4HTuYxZ0ERER+Vks892ZVw5Dfr+flJQUysvLf9LkNSIicnRZtGgRp515Bu7je5I24BQqKytxWx5qby2PRdn55lQqFy8mJ9qUAlsbkkihygSo8pRTZNuCJ8PFq6+/Qo8ePQ7onNFolBkzZvD+++9TWVlJWloaXbt2ZcmSJbw6bRr+8nJ8CQmcdcYZjBgxgokTJ/LqtGlYROnQ2sF/nmhKVrodE41is1mkpqTidrv5fKGf4b9dg7/KhXE5icejWMEA6SnQJNdBqT9OUamNuCeJiLETqzNZmoUtFmPQwIG89uqrB/+FFhEROcIdaBZV6BYRkaPOv//9b24aOxYrvxmOzp1IKGiJDQdV69bi/+JzYlt2UOBswe7wLgLRCoyJEydOSmoyZw05i9tvv5127dod0LnmzJnD6GuvZdPWLThzcrASEqjauJFgWRnOtDQy+/TGk5lBxF9BxZdLCO3YQdyySOl1HFWLPmfSP/PoW5hUczBjiFUHIRYlPT0dl9PF3x7ZylMvRLEsOy7HbsZem8HggWmEwwEst5uFyyM88kQxc5fGyTx9EI6EBGxOB4ktW1K+ciVl095m0YIF5OfnN+ArLiIicuRR6BYREdmPGTNm8P8eeogPZ88mGotjt2xYMch25NDe14UMVzbGGLYHN7Os6nO69TqWyVMm06RJkwM+x5w5cxh23nnEmuSSN+hMfM2asmfBQjZOfomkEwtJKuyN3W4jPT0dh8NBNBBgyZ/uJqFnd5wOB7lF85k+tRU2W93Zx2OBAE67g4z0dLbtCNG9/1ISEpzMmtqB5vkeKgOVVFRW4khMAgvC4Tijb9jCZyu9tLv1dmwOe81xQmHW3Duev952O9ddd91BfX1FRESOdAeaRTV7uYiIHJVOPfVU3n37bWa8+y4tmjTBG3fS3t2J45P7keHKJhIPs6F6NavDS2jTqQ0vvfzSjwrc8Xic3914I7G8HFpePgpfs6aYeJzt707H1/kY0k4/DUdyEnHA768AoOTLxeCwk9zvRIKlpbRu4dgrcAPYXC4i4TDRaIS8HCcuZ5wTe3lpnu+pPbdl2WofVXe5bPzhpmyoKMX/1de1x7G7XTiSk9m9e/dPfh1FRERk/xS6RUTkqNavXz/mzp3D+Zecxy73Zj70T6v92uJYxdALh/Due++Qk5Pzo447a9Ys1mzYQO4ZA2tHlv1fryJcVkZy3z41s6JbFja3m3A4RDQaJbBxE+6CfJwZ6eB0UOaP13tsy+nEAOFIhDJ/NaFwnL4nfPsJu82y4Hs3snVo56VLRxdly1fUbjPxOLGqahISEn7UtYmIiMiB0+zlIiJy1MvOzmbCxAn85Z6/MH36dMrLy0lJSWHgwIE/Omx/Y+bMmTjS0/A1L6jdVrV9OzZfAu6m346Y2xwO4liEQ2FMLIblcGDZbHhatmLx9GVs2hyieYG7/pMYeOv9MoIh+PWp6bWbXW43VAYw0SiW49tf9XnZNlbu+HYyNf+q1VBdxYABA37SNYqIiMgPU+gWERH5n5ycHC6++OKDcqxAIIDN58OyvnN7uDFY379d3LLAqlmOzJWeRvmCDZhYDF/H9pTMTuThx3fx9/9rVvc281jNCHigCp54tgQsGx73t/tdTicOp4NoKITd7qi9zXzrjhh2X82odjwcZtf0mfQ4thvdu3c/KNcsIiIie9Pt5SIiIg0gMzOTaFkZJhar3ebJziZWWUl4V3HtNmMMxsSx2WxkHH8c8UCAwLIVYLPIOGsIb8wIMu5PW9myNVz7M9FQiOUrw1w0ZjNlFemkp6Uz5fXvPpdtkZKcghU3xKqqMLEYy7+q4qvVEVK7diawaTPrJz6Dz1/Bg3//e90PBkREROSgUugWERFpAEOGDIFAFeVfr6zdltK5E87ERCrmzqvdFg+HsSwLj8eNNy+XlI4dKHnjbaLFxWQf14MmF1zCtNkWA4du4LKrN3LzHzYy7OJN/Oaa3QSjrZn2xruMPP9iJvynhDXrq2uP63Q6SU9Pww5U7qnk3vHbCMds7Hp3Opuf+BctnC6mvfYaxx577C/5soiIiBx1FLpFREQaQJcuXTixd292vfUukXI/UPP8ds4pJ1O54Ev8874gHo1iwmG8Hg82W82v5NSuXYhXVlDy3GS2vjoVZ3Iyza8ajafXScxa7OSVN6sIxzswecobfPzJXDp06MCdd95JXrMuDLtsPZMmF1FRGa05n83Joq8cjB5bytIVNk47aQCXnflrXpsyhc/nzqVHjx6N9vqIiIgcLbROt4iISAPZunUrZw4axNYKP+m/6kf6cT2wud1sfuV1ij/9DEd2NsnH9SAjP59oZSVlC74kvHkLw4cMoUP79jz73HNs27EDA9gti25duzL6qqsYMWLEXreEl5WVMW7cH3jn7ak4HSFys12UlUcp80PXrj25/4EH6dq1a+O8ECIiIkegA82iCt0iIiINaMeOHfzpjjt48+23qY5GsTudxMIhXDY72ZmZFO/ZQ9wYLMvipBNP5LejRjFo0CBsNhuRSISVK1cSCATIysqiVatWP/j89c6dO3njjTcoLi7G5/PRv39/unXr9stcrIiIyFFEoVtEROQQsnPnTj788EP8fj/p6ekMHDiQ1NRUAoEA5eXlJCYm/uDvscrKSl577TXmzp1LKBQiLy+PkSNHagRbRESkESh0i4iIHEH++9//8se77qLU78eV3wzL7SJaVAyVlZx04on8a8IEsrOzG7tMERGRo8aBZlGt0y0iInKImzRpEjeP+wPe7t1oc9oAXGmpAJhYjPKvV/Lx1DcZfM45vPf226SlpTVusSIiIlKHZi8XERE5hBUXF3P7HXfgO/448kcMqw3cAJbdTmrnY2gx+kpWbd7MQw891HiFioiISL0UukVERA5hkydPpioWJe+MgfucRM2dmUFSr548N/kFqqqqfuEKRUREZH8UukVERA5h02fMwNOuLQ6fd7/t0nv2oKSsjIULF/5ClYmIiMiBUOgWERE5hFVUVmL3+X6wnSMhAWMgEAj8AlWJiIjIgVLoFhEROYTlZGcT2bPnB9uFindjsyyysrJ+gapERETkQDVo6H7iiSfo2rUrycnJJCcnU1hYyLvvvlu7PxgMcu2115KRkUFiYiLnnnsuRUVFDVmSiIjIYWX4sGGE1m8ktHv/wXvPvC9o3bIl3bt3/4UqExERkQPRoKG7WbNmjB8/noULF7JgwQJOOeUUhgwZwooVKwC46aabePPNN3n55ZeZPXs227dvZ9iwYQ1ZkoiIyGFlyJAhNM3NZevLrxILhettU7b8K6qWLePqK67AZtNNbCIiIocSyxhjfskTpqen88ADDzB8+HCysrJ44YUXGD58OAArV66kY8eOzJ07l969ex/Q8Q50QXIREZHD1YIFCxg6YgQBr4fMk/uR2rULNqeT4K5iij+bQ+UXCxl+9tlMnDABu93e2OWKiIgcFQ40izp+qYJisRgvv/wygUCAwsJCFi5cSCQS4dRTT61t06FDBwoKCvYbukOhEKFQqPZ7v9/f4LWLiIg0puOOO4733nqLu+7+Mx++MpUdL72GZbdDLEZedja33HorN9xwgwK3iIjIIajBQ/eyZcsoLCwkGAySmJjI66+/TqdOnVi8eDEul4vU1NQ67XNycti5c+c+j3ffffdx9913N3DVIiIih5ZjjjmGV156mXXr1jFv3jxCoRBNmjThlFNOweVyNXZ5IiIisg8NHrrbt2/P4sWLKS8v55VXXuHSSy9l9uzZP/l4t912GzfffHPt936/n/z8/INRqoiIyCGvdevWtG7durHLEBERkQPU4KHb5XLRpk0bAHr27Mn8+fN5+OGHGTlyJOFwmLKysjqj3UVFReTm5u7zeG63G7fb3dBli4iIiIiIiPxsv/gUp/F4nFAoRM+ePXE6ncycObN236pVq9i8eTOFhYW/dFkiIiIiIiIiB12DjnTfdtttnHnmmRQUFFBRUcELL7zArFmzeP/990lJSeHyyy/n5ptvJj09neTkZK6//noKCwsPeOZyERERERERkUNZg4buXbt2cckll7Bjxw5SUlLo2rUr77//PqeddhoADz30EDabjXPPPZdQKMTpp5/O448/3pAliYiIiIiIiPxifvF1ug82rdMtIiIiIiIiv7QDzaK/+DPdIiIiIiIiIkcLhW4RERERERGRBqLQLSIiIiIiItJAFLpFREREREREGohCt4iIiIiIiEgDUegWERERERERaSAK3SIiIiIiIiINRKFbREREREREpIEodIuIiIiIiIg0EIVuERERERERkQai0C0iIiIiIiLSQBS6RURERERERBqIQreIiIiIiIhIA1HoFhEREREREWkgCt0iIiIiIiIiDUShW0RERERERKSBKHSLiIiIiIiINBCFbhEREREREZEGotAtIiIiIiIi0kAcjV3Az2WMAcDv9zdyJSIiIiIiInK0+CaDfpNJ9+WwD90VFRUA5OfnN3IlIiIiIiIicrSpqKggJSVln/st80Ox/BAXj8fZvn07SUlJWJbV2OUINZ/45Ofns2XLFpKTkxu7HPkJ1IeHP/Xh4U39d/hTHx7+1IeHP/Xh4e1w6D9jDBUVFTRp0gSbbd9Pbh/2I902m41mzZo1dhlSj+Tk5EP2DSIHRn14+FMfHt7Uf4c/9eHhT314+FMfHt4O9f7b3wj3NzSRmoiIiIiIiEgDUegWERERERERaSAK3XLQud1u7rrrLtxud2OXIj+R+vDwpz48vKn/Dn/qw8Of+vDwpz48vB1J/XfYT6QmIiIiIiIicqjSSLeIiIiIiIhIA1HoFhEREREREWkgCt0iIiIiIiIiDUShW0RERERERKSBKHTLQXX22WdTUFCAx+MhLy+Piy++mO3bt9dps3TpUvr164fH4yE/P5/777+/kaqV79u4cSOXX345LVu2xOv10rp1a+666y7C4XCddurDQ9u9995Lnz598Pl8pKam1ttm8+bNDBo0CJ/PR3Z2NmPHjiUajf6yhco+PfbYY7Ro0QKPx8MJJ5zAF1980dglyT58/PHHDB48mCZNmmBZFlOnTq2z3xjDnXfeSV5eHl6vl1NPPZU1a9Y0TrGyl/vuu4/jjz+epKQksrOzOeecc1i1alWdNsFgkGuvvZaMjAwSExM599xzKSoqaqSK5fueeOIJunbtSnJyMsnJyRQWFvLuu+/W7lf/HV7Gjx+PZVnceOONtduOhD5U6JaDqn///rz00kusWrWKV199lXXr1jF8+PDa/X6/n4EDB9K8eXMWLlzIAw88wJ///GcmTpzYiFXLN1auXEk8HmfChAmsWLGChx56iCeffJLbb7+9to368NAXDocZMWIEY8aMqXd/LBZj0KBBhMNh5syZw7///W+effZZ7rzzzl+4UqnPiy++yM0338xdd93Fl19+ybHHHsvpp5/Orl27Grs0qUcgEODYY4/lscceq3f//fffzyOPPMKTTz7J559/TkJCAqeffjrBYPAXrlTqM3v2bK699lrmzZvHBx98QCQSYeDAgQQCgdo2N910E2+++SYvv/wys2fPZvv27QwbNqwRq5bvatasGePHj2fhwoUsWLCAU045hSFDhrBixQpA/Xc4mT9/PhMmTKBr1651th8RfWhEGtC0adOMZVkmHA4bY4x5/PHHTVpamgmFQrVtxo0bZ9q3b99YJcoPuP/++03Lli1rv1cfHj4mTZpkUlJS9tr+zjvvGJvNZnbu3Fm77YknnjDJycl1+lUaR69evcy1115b+30sFjNNmjQx9913XyNWJQcCMK+//nrt9/F43OTm5poHHnigdltZWZlxu91m8uTJjVCh/JBdu3YZwMyePdsYU9NfTqfTvPzyy7Vtvv76awOYuXPnNlaZ8gPS0tLMU089pf47jFRUVJi2bduaDz74wPzqV78yN9xwgzHmyHkPaqRbGkxJSQnPP/88ffr0wel0AjB37lxOOukkXC5XbbvTTz+dVatWUVpa2lilyn6Ul5eTnp5e+7368PA3d+5cunTpQk5OTu22008/Hb/fXzsyII0jHA6zcOFCTj311NptNpuNU089lblz5zZiZfJTbNiwgZ07d9bpz5SUFE444QT15yGqvLwcoPb33sKFC4lEInX6sEOHDhQUFKgPD0GxWIwpU6YQCAQoLCxU/x1Grr32WgYNGlSnr+DIeQ8qdMtBN27cOBISEsjIyGDz5s1Mmzatdt/OnTvr/KEP1H6/c+fOX7RO+WFr167l0Ucf5eqrr67dpj48/KkPD127d+8mFovV2z/qm8PPN32m/jw8xONxbrzxRk488UQ6d+4M1PShy+Xaa34M9eGhZdmyZSQmJuJ2uxk9ejSvv/46nTp1Uv8dJqZMmcKXX37Jfffdt9e+I6UPFbrlB916661YlrXfr5UrV9a2Hzt2LIsWLWL69OnY7XYuueQSjDGNeAXyY/sQYNu2bZxxxhmMGDGCK6+8spEql2/8lD4UEZEDd+2117J8+XKmTJnS2KXIj9S+fXsWL17M559/zpgxY7j00kv56quvGrssOQBbtmzhhhtu4Pnnn8fj8TR2OQ3G0dgFyKHvlltu4bLLLttvm1atWtX+f2ZmJpmZmbRr146OHTuSn5/PvHnzKCwsJDc3d6/ZBr/5Pjc396DXLjV+bB9u376d/v3706dPn70mSFMfNo4f24f7k5ubu9ds2OrDQ0NmZiZ2u73e95j65vDzTZ8VFRWRl5dXu72oqIhu3bo1UlVSn+uuu4633nqLjz/+mGbNmtVuz83NJRwOU1ZWVmekTe/JQ4vL5aJNmzYA9OzZk/nz5/Pwww8zcuRI9d8hbuHChezatYsePXrUbovFYnz88cf885//5P333z8i+lChW35QVlYWWVlZP+ln4/E4AKFQCIDCwkL++Mc/EolEap/z/uCDD2jfvj1paWkHp2DZy4/pw23bttG/f3969uzJpEmTsNnq3hCjPmwcP+d9+H2FhYXce++97Nq1i+zsbKCmD5OTk+nUqdNBOYf8NC6Xi549ezJz5kzOOeccoObf0ZkzZ3Ldddc1bnHyo7Vs2ZLc3FxmzpxZG7L9fn/taJw0PmMM119/Pa+//jqzZs2iZcuWdfb37NkTp9PJzJkzOffccwFYtWoVmzdvprCwsDFKlgMQj8cJhULqv8PAgAEDWLZsWZ1to0aNokOHDowbN478/Pwjow8beyY3OXLMmzfPPProo2bRokVm48aNZubMmaZPnz6mdevWJhgMGmNqZiDMyckxF198sVm+fLmZMmWK8fl8ZsKECY1cvRhjzNatW02bNm3MgAEDzNatW82OHTtqv76hPjz0bdq0ySxatMjcfffdJjEx0SxatMgsWrTIVFRUGGOMiUajpnPnzmbgwIFm8eLF5r333jNZWVnmtttua+TKxRhjpkyZYtxut3n22WfNV199Za666iqTmppaZ7Z5OXRUVFTUvscA8+CDD5pFixaZTZs2GWOMGT9+vElNTTXTpk0zS5cuNUOGDDEtW7Y01dXVjVy5GGPMmDFjTEpKipk1a1ad33lVVVW1bUaPHm0KCgrMhx9+aBYsWGAKCwtNYWFhI1Yt33Xrrbea2bNnmw0bNpilS5eaW2+91ViWZaZPn26MUf8djr47e7kxR0YfKnTLQbN06VLTv39/k56ebtxut2nRooUZPXq02bp1a512S5YsMX379jVut9s0bdrUjB8/vpEqlu+bNGmSAer9+i714aHt0ksvrbcPP/roo9o2GzduNGeeeabxer0mMzPT3HLLLSYSiTRe0VLHo48+agoKCozL5TK9evUy8+bNa+ySZB8++uijet9vl156qTGmZtmwO+64w+Tk5Bi3220GDBhgVq1a1bhFS619/c6bNGlSbZvq6mpzzTXXmLS0NOPz+czQoUPrfBgtjeu3v/2tad68uXG5XCYrK8sMGDCgNnAbo/47HH0/dB8JfWgZoxmuRERERERERBqCZi8XERERERERaSAK3SIiIiIiIiINRKFbREREREREpIEodIuIiIiIiIg0EIVuERERERERkQai0C0iIiIiIiLSQBS6RURERERERBqIQreIiIiIiIhIA1HoFhEREREREWkgCt0iIiIiIiIiDUShW0RERERERKSBKHSLiIiIiIiINJD/D07ciuj/QKI8AAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "import bioneuralnet as bnn\n", - "\n", - "SEED = 118\n", - "bnn.utils.set_seed(SEED)" - ] - }, - { - "cell_type": "markdown", - "id": "38315975", - "metadata": {}, - "source": [ - "## Hyperparameter Tuning\n", - "\n", - "- First step on our benchmark analysis is dedicated to **hyperparameter tuning** for:\n", - " - **SAGE:** GraphSAGE (Graph Sample and Aggregate)\n", - " - **GCN:** Graph Convolutional Network\n", - " - **GAT:** Graph Attention Network\n", - " \n", - "- We use `tune=True` and `repeat_num=5` within **DPMON** (our classification framework) to let the framework automatically search for the best architectural and training parameters.\n", - "\n", - "- After the tuning phase, we will manually set the best-performing parameters for each model.\n", - "- We then use these optimal configurations in the final cross-validation to rigorously test each model performance and stability." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c05cb625", - "metadata": {}, - "outputs": [], - "source": [ - "from pathlib import Path\n", - "\n", - "# We rename the target to phenotype so we keep it consistent with DPMON expectations.\n", - "target = target.rename(columns={\"target\": \"phenotype\"})\n", - "\n", - "dpmon_sage_tune = bnn.downstream_task.DPMON(\n", - " adjacency_matrix=A_train,\n", - " omics_list=[dna_meth, rna, mirna],\n", - " phenotype_data=target,\n", - " clinical_data=clinical_preprocessed,\n", - " model=\"SAGE\",\n", - " tune=True,\n", - " repeat_num=5,\n", - " gpu=True, cuda=0,\n", - " output_dir=Path(\"/home/vicente/Github/BioNeuralNet/dpmon_tuning/KIPAN_SAGE\"),\n", - ")\n", - "\n", - "dpmon_sage_tune.run()\n", - "\n", - "dpmon_gcn_tune = bnn.downstream_task.DPMON(\n", - " adjacency_matrix=A_train,\n", - " omics_list=[dna_meth, rna, mirna],\n", - " phenotype_data=target,\n", - " clinical_data=clinical_preprocessed,\n", - " model=\"GCN\",\n", - " tune=True,\n", - " repeat_num=5,\n", - " gpu=True, cuda=0,\n", - " output_dir=Path(\"/home/vicente/Github/BioNeuralNet/dpmon_tuning/KIPAN_GCN\"),\n", - ")\n", - "\n", - "dpmon_gcn_tune.run()\n", - "\n", - "\n", - "dpmon_gat_tune = bnn.downstream_task.DPMON(\n", - " adjacency_matrix=A_train,\n", - " omics_list=[dna_meth, rna, mirna],\n", - " phenotype_data=target,\n", - " clinical_data=clinical_preprocessed,\n", - " model=\"GAT\",\n", - " tune=True,\n", - " repeat_num=5,\n", - " gpu=True, cuda=0,\n", - " output_dir=Path(\"/home/vicente/Github/BioNeuralNet/dpmon_tuning/KIPAN_GAT\"),\n", - ")\n", - "\n", - "dpmon_gcn_tune.run()\n", - "\n", - "# Due to the extensive length, the terminal output has been omitted. To see the full terminal output for the results below please visit: https://github.com/UCD-BDLab/BioNeuralNet/TCGA-Notebooks/TCGA-KIPAN.ipynb" - ] - }, - { - "cell_type": "markdown", - "id": "9e9023fe", - "metadata": {}, - "source": [ - "## Analysis of Hyperparameter Optimization\n", - "\n", - "**Hyperparameter tuning using `SEED = 118`**\n", - "\n", - "### Tuning Search Space\n", - "\n", - "**DPMON** parameter search space is extensive, offering over 12,000 discrete combinations plus a continuous space for learning rates. By sampling **50 trials** from this space, we are effectively probing it to find the distinct, optimal architecture best suited for each GNN (SAGE, GCN, and GAT).\n", - "\n", - "\n", - "| Parameter | SAGE (GraphSAGE) | GCN (Graph Convolutional) | GAT (Graph Attention) |\n", - "| :--- | :--- | :--- | :--- |\n", - "| **gnn\\_layer\\_num** | 2 | 2 | 16 |\n", - "| **gnn\\_hidden\\_dim** | 128 | 16 | 4 |\n", - "| **lr (Learning Rate)** | 0.005197 | 0.005345 | 0.003226 |\n", - "| **weight\\_decay** | 0.046079 | 0.003940 | 0.046863 |\n", - "| **nn\\_hidden\\_dim1** | 16 | 64 | 16 |\n", - "| **nn\\_hidden\\_dim2** | 32 | 16 | 128 |\n", - "| **num\\_epochs** | 4096 | 512 | 512 |\n", - "\n", - "\n", - "### SAGE Model Configuration\n", - "The optimal SAGE configuration was **shallow (2 layers) and wide (128 hidden dim)**. This confirms a preference for a high-dimensional feature representation over recursive depth. It also required the longest training time at 4,096 epochs.\n", - "\n", - "### GCN Model Configuration\n", - "In a major shift, the GCN model favored a classic, shallow architecture: **2 layers and 16 hidden dim**. This highly efficient configuration converged in just 512 epochs and achieved a perfect (1.0) tuning accuracy, indicating it found a strong, generalizable pattern quickly.\n", - "\n", - "### GAT Model Configuration\n", - "The GAT configuration favored a **deep (16 layers) and narrow (4 hidden dim)** structure. It was also highly efficient, converging in 512 epoch*, suggesting GATs attention mechanism works well even when stacked deeply with low-dimensional features." - ] - }, - { - "cell_type": "markdown", - "id": "165b9714", - "metadata": {}, - "source": [ - "---" - ] - }, - { - "cell_type": "markdown", - "id": "54cec9f0", - "metadata": {}, - "source": [ - "## Cross-Validation Results with 5 folds\n", - "\n", - "| Model | Avg. Accuracy | Avg. F1 Weighted | Avg. F1 Macro |\n", - "| :--- | :--- | :--- | :--- |\n", - "| **GCN** | **0.9525** +/- 0.0267 | **0.9563** +/- 0.0241 | 0.9232 +/- 0.0410 |\n", - "| **GAT** | 0.9506 +/- 0.0759 | 0.9528 +/- 0.0718 | **0.9349** +/- 0.0954 |\n", - "| **SAGE** | 0.9206 +/- 0.0581 | 0.9119 +/- 0.0695 | 0.8695 +/- 0.1418 |\n", - "\n", - "The cross-validation results for the KIPAN dataset show a new performance ranking. GCN emerged as the top model, followed closely by GAT, with SAGE also performing strongly.\n", - "\n", - "### 1. GAT Performance\n", - "The GAT model was a top contender, achieving an average accuracy of 0.9506. Its deep-and-narrow architecture (16 layers, 4 hidden dim) proved highly effective on this dataset. Its stability was significantly improved, though its variance was the highest of the three.\n", - "\n", - "- Avg. Accuracy: 0.9506 (+/- 0.0759)\n", - "- Avg. F1 Weighted: 0.9528 (+/- 0.0718)\n", - "\n", - "### 2. GCN Performance\n", - "GCN was the **top-performing and most stable model**. Its shallow-and-narrow architecture (2 layers, 16 hidden dim) translated perfectly from its 1.0 tuning accuracy. Most importantly, it had an **extremely low standard deviation** (+/- 0.0267), indicating its performance was highly reliable across all folds.\n", - "\n", - "- Avg. Accuracy: 0.9525 (+/- 0.0267)\n", - "- Avg. F1 Weighted: 0.9563 (+/- 0.0241)\n", - "\n", - "### 3. SAGE Performance\n", - "SAGE delivered a strong and reliable performance, with its average accuracy exceeding 0.92. Its shallow-and-wide architecture (2 layers, 128 hidden dim) was effective and showed good stability with a low standard deviation (+/- 0.0581), second only to GCN.\n", - "\n", - "- Avg. Accuracy: 0.9206 (+/- 0.0581)\n", - "- Avg. F1 Weighted: 0.9119 (+/- 0.0695)\n", - "\n", - "### Summary\n", - "In summary, all three models performed exceptionally well on the KIPAN dataset, with GCN taking the top spot. The most significant finding was the **dramatic increase in stability** across all models in comparison to the **GBMLGG** dataset.\n", - "\n", - "**These results are shown below**" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4906eda5", - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import numpy as np\n", - "from sklearn.model_selection import StratifiedKFold\n", - "from sklearn.metrics import f1_score, accuracy_score\n", - "from bioneuralnet.downstream_task import DPMON\n", - "\n", - "X_meth_full = dna_meth\n", - "X_rna_full = rna\n", - "X_mirna_full = mirna\n", - "C_full = clinical_preprocessed\n", - "Y_full = target\n", - "A_full = A_train\n", - "\n", - "patient_indices = np.arange(len(Y_full))\n", - "y_target_classes = Y_full.squeeze()\n", - "\n", - "sage_params = {\n", - " 'layer_num': 2,\n", - " 'gnn_hidden_dim': 128,\n", - " 'lr': 0.005197038211089667,\n", - " 'weight_decay': 0.04607928341516355,\n", - " 'nn_hidden_dim1': 16,\n", - " 'nn_hidden_dim2': 32,\n", - " 'num_epochs': 4096\n", - "}\n", - "\n", - "N_FOLDS = 5\n", - "skf = StratifiedKFold(n_splits=N_FOLDS, shuffle=True, random_state=SEED) \n", - "output_dir_base_sage = Path(\"/home/vicente/Github/BioNeuralNet/dpmon_cv_results_SAGE_FINAL/kipan\")\n", - "\n", - "accuracy_scores_sage = []\n", - "f1_macro_scores_sage = []\n", - "f1_weighted_scores_sage = []\n", - "\n", - "for fold_num, (train_index, test_index) in enumerate(skf.split(patient_indices, y_target_classes)):\n", - " print(f\"\\nRunning DPMON SAGE Fold {fold_num + 1}/{N_FOLDS}\")\n", - "\n", - " X_train_omics = [X_meth_full.iloc[train_index], X_rna_full.iloc[train_index], X_mirna_full.iloc[train_index]]\n", - " Y_train = Y_full.iloc[train_index]\n", - " C_train = C_full.iloc[train_index]\n", - " A_train_fold = A_full.iloc[train_index, train_index] \n", - " \n", - " dpmon_fold = DPMON(\n", - " adjacency_matrix=A_train_fold, omics_list=X_train_omics, phenotype_data=Y_train,\n", - " clinical_data=C_train, repeat_num=1, tune=False, gpu=True, cuda=0,\n", - " output_dir=output_dir_base_sage / f\"fold_{fold_num + 1}\",\n", - " **sage_params\n", - " )\n", - "\n", - " predictions_df, _ = dpmon_fold.run() \n", - " actual = predictions_df[\"Actual\"]\n", - " pred = predictions_df[\"Predicted\"]\n", - "\n", - " test_f1_macro = f1_score(actual, pred, average='macro')\n", - " test_f1_weighted = f1_score(actual, pred, average='weighted')\n", - " test_acc = accuracy_score(actual, pred)\n", - "\n", - " accuracy_scores_sage.append(test_acc)\n", - " f1_macro_scores_sage.append(test_f1_macro)\n", - " f1_weighted_scores_sage.append(test_f1_weighted)\n", - " \n", - " print(f\"SAGE Fold {fold_num + 1} | Accuracy: {test_acc:.4f} | F1-Macro: {test_f1_macro:.4f}\")\n", - "\n", - "mean_acc_sage = np.mean(accuracy_scores_sage)\n", - "std_acc_sage = np.std(accuracy_scores_sage)\n", - "\n", - "mean_f1_macro_sage = np.mean(f1_macro_scores_sage)\n", - "std_f1_macro_sage = np.std(f1_macro_scores_sage)\n", - "\n", - "mean_f1_weighted_sage = np.mean(f1_weighted_scores_sage)\n", - "std_f1_weighted_sage = np.std(f1_weighted_scores_sage)\n", - "\n", - "print(\"\\nClassification with SAGE and 5-FOLD Cross-Validation\")\n", - "print(f\"Avg. Accuracy: {mean_acc_sage:.4f} +/- {std_acc_sage:.4f}\")\n", - "print(f\"Avg. F1 Weighted: {mean_f1_weighted_sage:.4f} +/- {std_f1_weighted_sage:.4f}\")\n", - "print(f\"Avg. F1 Macro: {mean_f1_macro_sage:.4f} +/- {std_f1_macro_sage:.4f}\")\n", - "\n", - "# Due to the extensive length, the terminal output has been omitted. To see the full terminal output for the results below please visit: https://github.com/UCD-BDLab/BioNeuralNet/TCGA-Notebooks/TCGA-KIPAN.ipynb" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "81315c47", - "metadata": {}, - "outputs": [], - "source": [ - "output_dir_base_gcn = Path(\"/home/vicente/Github/BioNeuralNet/dpmon_cv_results_GCN_FINAL/kipan\")\n", - "\n", - "gcn_params = {\n", - " 'layer_num': 2,\n", - " 'gnn_hidden_dim': 16,\n", - " 'lr': 0.005345517248048608,\n", - " 'weight_decay': 0.003940908881257014,\n", - " 'nn_hidden_dim1': 64,\n", - " 'nn_hidden_dim2': 16,\n", - " 'num_epochs': 512\n", - "}\n", - "\n", - "skf_gcn = StratifiedKFold(n_splits=N_FOLDS, shuffle=True, random_state=SEED) \n", - "\n", - "accuracy_scores_gcn = []\n", - "f1_macro_scores_gcn = []\n", - "f1_weighted_scores_gcn = []\n", - "\n", - "for fold_num, (train_index, test_index) in enumerate(skf_gcn.split(patient_indices, y_target_classes)):\n", - " print(f\"\\nDPMON GCN Fold {fold_num + 1}/{N_FOLDS}\")\n", - "\n", - " X_train_omics = [X_meth_full.iloc[train_index], X_rna_full.iloc[train_index], X_mirna_full.iloc[train_index]]\n", - " Y_train = Y_full.iloc[train_index]\n", - " C_train = C_full.iloc[train_index]\n", - " A_train_fold = A_full.iloc[train_index, train_index] \n", - " \n", - " dpmon_fold = DPMON(\n", - " adjacency_matrix=A_train_fold, omics_list=X_train_omics, phenotype_data=Y_train,\n", - " clinical_data=C_train, repeat_num=1, tune=False, gpu=True, cuda=0,\n", - " output_dir=output_dir_base_gcn / f\"fold_{fold_num + 1}\",\n", - " **gcn_params\n", - " )\n", - "\n", - " predictions_df, _ = dpmon_fold.run() \n", - " actual = predictions_df[\"Actual\"]\n", - " pred = predictions_df[\"Predicted\"]\n", - "\n", - " test_acc = accuracy_score(actual, pred)\n", - " test_f1 = f1_score(actual, pred, average='macro')\n", - " test_f1w = f1_score(actual, pred, average='weighted')\n", - "\n", - " accuracy_scores_gcn.append(test_acc)\n", - " f1_macro_scores_gcn.append(test_f1)\n", - " f1_weighted_scores_gcn.append(test_f1w)\n", - " \n", - " print(f\"GCN Fold {fold_num + 1} | Accuracy: {test_acc:.4f} | F1-Macro: {test_f1:.4f}\")\n", - "\n", - "mean_acc_gcn = np.mean(accuracy_scores_gcn)\n", - "std_acc_gcn = np.std(accuracy_scores_gcn)\n", - "\n", - "mean_f1_macro_gcn = np.mean(f1_macro_scores_gcn)\n", - "std_f1_macro_gcn = np.std(f1_macro_scores_gcn)\n", - "\n", - "mean_f1_weighted_gcn = np.mean(f1_weighted_scores_gcn)\n", - "std_f1_weighted_gcn = np.std(f1_weighted_scores_gcn)\n", - "\n", - "print(\"Classification with GCN and 5-FOLD Cross-Validation\")\n", - "print(f\"Avg. Accuracy: {mean_acc_gcn:.4f} +/- {std_acc_gcn:.4f}\")\n", - "print(f\"Avg. F1 Weighted: {mean_f1_weighted_gcn:.4f} +/- {std_f1_weighted_gcn:.4f}\")\n", - "print(f\"Avg. F1 Macro: {mean_f1_macro_gcn:.4f} +/- {std_f1_macro_gcn:.4f}\")\n", - "\n", - "# Due to the extensive length, the terminal output has been omitted. To see the full terminal output for the results below please visit: https://github.com/UCD-BDLab/BioNeuralNet/TCGA-Notebooks/TCGA-KIPAN.ipynb" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "efbff52f", - "metadata": {}, - "outputs": [], - "source": [ - "output_dir_base_gat = Path(\"/home/vicente/Github/BioNeuralNet/dpmon_cv_results_GAT_FINAL/kipan\")\n", - "\n", - "gat_params = {\n", - " 'layer_num': 16,\n", - " 'gnn_hidden_dim': 4,\n", - " 'lr': 0.003226646486974539,\n", - " 'weight_decay': 0.04686394640851084,\n", - " 'nn_hidden_dim1': 16,\n", - " 'nn_hidden_dim2': 128,\n", - " 'num_epochs': 512\n", - "}\n", - "\n", - "skf_gat = StratifiedKFold(n_splits=N_FOLDS, shuffle=True, random_state=SEED) \n", - "\n", - "accuracy_scores_gat = []\n", - "f1_macro_scores_gat = []\n", - "f1_weighted_scores_gat = []\n", - "\n", - "for fold_num, (train_index, test_index) in enumerate(skf_gat.split(patient_indices, y_target_classes)):\n", - " print(f\"\\nDPMON GAT Fold {fold_num + 1}/{N_FOLDS}\")\n", - "\n", - " X_train_omics = [X_meth_full.iloc[train_index], X_rna_full.iloc[train_index], X_mirna_full.iloc[train_index]]\n", - " Y_train = Y_full.iloc[train_index]\n", - " C_train = C_full.iloc[train_index]\n", - " A_train_fold = A_full.iloc[train_index, train_index] \n", - " \n", - " dpmon_fold = DPMON(\n", - " adjacency_matrix=A_train_fold, omics_list=X_train_omics, phenotype_data=Y_train,\n", - " clinical_data=C_train, repeat_num=1, tune=False, gpu=True, cuda=0,\n", - " output_dir=output_dir_base_gat / f\"fold_{fold_num + 1}\",\n", - " **gat_params\n", - " )\n", - "\n", - " predictions_df, _ = dpmon_fold.run() \n", - " actual = predictions_df[\"Actual\"]\n", - " pred = predictions_df[\"Predicted\"]\n", - "\n", - " test_acc = accuracy_score(actual, pred)\n", - " test_f1 = f1_score(actual, pred, average='macro')\n", - " test_f1w = f1_score(actual, pred, average='weighted')\n", - "\n", - " accuracy_scores_gat.append(test_acc)\n", - " f1_macro_scores_gat.append(test_f1)\n", - " f1_weighted_scores_gat.append(test_f1w)\n", - " \n", - " print(f\"GAT Fold {fold_num + 1} | Accuracy: {test_acc:.4f} | F1-Macro: {test_f1:.4f}\")\n", - "\n", - "mean_acc_gat = np.mean(accuracy_scores_gat)\n", - "std_acc_gat = np.std(accuracy_scores_gat)\n", - "\n", - "mean_f1_macro_gat = np.mean(f1_macro_scores_gat)\n", - "std_f1_macro_gat = np.std(f1_macro_scores_gat)\n", - "\n", - "mean_f1_weighted_gat = np.mean(f1_weighted_scores_gat)\n", - "std_f1_weighted_gat = np.std(f1_weighted_scores_gat)\n", - "\n", - "print(\"Classification with GAT and 5-FOLD Cross-Validation\")\n", - "print(f\"Avg. Accuracy: {mean_acc_gat:.4f} +/- {std_acc_gat:.4f}\")\n", - "print(f\"Avg. F1 Weighted: {mean_f1_weighted_gat:.4f} +/- {std_f1_weighted_gat:.4f}\")\n", - "print(f\"Avg. F1 Macro: {mean_f1_macro_gat:.4f} +/- {std_f1_macro_gat:.4f}\")\n", - "\n", - "# Due to the extensive length, the terminal output has been omitted. To see the full terminal output for the results below please visit: https://github.com/UCD-BDLab/BioNeuralNet/TCGA-Notebooks/TCGA-KIPAN.ipynb" + "from bioneuralnet.metrics import plot_embeddings\n", + "\n", + "# for a more interpretable embedding plot we will map the node labels to omics types\n", + "GCN_node_labels = []\n", + "GCN_embeddings_array = GCN_embeddings_df.values\n", + "feature_names = GCN_embeddings_df.index\n", + "\n", + "# using the sets from the orginal to serve as a loopup table\n", + "dna_feats = set(dna_meth.columns)\n", + "rna_feats = set(rna.columns)\n", + "mirna_feats = set(mirna.columns)\n", + "\n", + "for feat in feature_names:\n", + " if feat in dna_feats:\n", + " # 1 for DNA methylation\n", + " GCN_node_labels.append(1)\n", + " elif feat in rna_feats:\n", + " # 2 for RNA \n", + " GCN_node_labels.append(2)\n", + " elif feat in mirna_feats:\n", + " # and 3 for miRNA \n", + " GCN_node_labels.append(3)\n", + " else:\n", + " GCN_node_labels.append(0)\n", + "\n", + "# our plotting function needs an array to squeeze the embedings into 2Dspace\n", + "GCN_node_labels = np.array(GCN_node_labels)\n", + "\n", + "# feature(omics) embeddings colored by omics type.\n", + "print(f\"Plotting Feature Embeddings for graph: {res['graph']}\")\n", + "plot_embeddings(GCN_embeddings_array, GCN_node_labels, legend_labels=[\"DNA_Methylation\", \"RNA\", \"miRNA\"])" ] } ], diff --git a/docs/source/notebooks/TCGA-LGG.ipynb b/docs/source/notebooks/TCGA-LGG.ipynb new file mode 100644 index 0000000..7a37802 --- /dev/null +++ b/docs/source/notebooks/TCGA-LGG.ipynb @@ -0,0 +1,940 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "b42de981", + "metadata": {}, + "source": [ + "# TCGA-LGG Analysis: *Vital Status* classification\n", + "\n", + "- **Cohort**: Focuses on the TCGA-LGG dataset for Lower-Grade Glioma (LGG).\n", + "- **Goal**: Classification\n", + "- **Prediction Target**: Predict `Vital Status` based on their omics profiles.\n", + "\n", + "**Data Source:** Broad Institute FireHose (http://firebrowse.org/?cohort=LGG)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "027dc20c", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "from pathlib import Path\n", + "root = Path(\"/home/vicente/Github/BioNeuralNet/LGG\")\n", + "\n", + "mirna_raw = pd.read_csv(root/\"LGG.miRseq_RPKM_log2.txt\", sep=\"\\t\",index_col=0,low_memory=False) \n", + "rna_raw = pd.read_csv(root / \"LGG.uncv2.mRNAseq_RSEM_normalized_log2.txt\", sep=\"\\t\",index_col=0,low_memory=False)\n", + "meth_raw = pd.read_csv(root/\"LGG.meth.by_mean.data.txt\", sep='\\t',index_col=0,low_memory=False)\n", + "clinical_raw = pd.read_csv(root / \"LGG.clin.merged.picked.txt\",sep=\"\\t\", index_col=0, low_memory=False)\n", + "\n", + "# display shapes and first few rows-columns of each file\n", + "display(mirna_raw.iloc[:3,:5])\n", + "display(mirna_raw.shape)\n", + "\n", + "display(rna_raw.iloc[:3,:5])\n", + "display(meth_raw.shape)\n", + "\n", + "display(meth_raw.iloc[:3,:5])\n", + "display(meth_raw.shape)\n", + "\n", + "display(clinical_raw.iloc[:3,:5])\n", + "display(clinical_raw.shape)" + ] + }, + { + "cell_type": "markdown", + "id": "cbacccaf", + "metadata": {}, + "source": [ + "## Data Processing Summary\n", + "\n", + "1. **Transpose Data:** All raw data (miRNA, RNA, etc.) is flipped so rows represent patients and columns represent features.\n", + "2. **Standardize Patient IDs:** Patient IDs in all tables are cleaned to the 12-character TCGA format (e.g., `TCGA-AB-1234`) for matching.\n", + "3. **Handle Duplicates:** Duplicate patient rows are averaged in the omics data. The first entry is kept for duplicate patients in the clinical data.\n", + "4. **Find Common Patients:** The script identifies the list of patients that exist in *all* datasets.\n", + "5. **Subset Data:** All data tables are filtered down to *only* this common list of patients, ensuring alignment.\n", + "6. **Extract Target:** The `vital_status` column is pulled from the processed clinical data to be used as the prediction target (y-variable)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "820583f3", + "metadata": {}, + "outputs": [], + "source": [ + "mirna = mirna_raw.T\n", + "rna = rna_raw.T\n", + "meth = meth_raw.T\n", + "clinical = clinical_raw.T\n", + "\n", + "print(f\"miRNA (samples, features): {mirna.shape}\")\n", + "print(f\"RNA (samples, features): {rna.shape}\")\n", + "print(f\"Methylation (samples, features): {meth.shape}\")\n", + "print(f\"Clinical (samples, features): {clinical.shape}\")\n", + "\n", + "def trim_barcode(idx):\n", + " return idx.to_series().str.slice(0, 12)\n", + "\n", + "# standarized patient IDs across all files\n", + "meth.index = trim_barcode(meth.index)\n", + "rna.index = trim_barcode(rna.index)\n", + "mirna.index = trim_barcode(mirna.index)\n", + "clinical.index = clinical.index.str.upper()\n", + "clinical.index.name = \"Patient_ID\"\n", + "\n", + "# convert all data to numeric, coercing errors to NaN\n", + "meth = meth.apply(pd.to_numeric, errors='coerce')\n", + "rna = rna.apply(pd.to_numeric, errors='coerce')\n", + "mirna = mirna.apply(pd.to_numeric, errors='coerce')\n", + "\n", + "# for any duplicate columns in the omics data, we average their values\n", + "meth = meth.groupby(meth.index).mean()\n", + "rna = rna.groupby(rna.index).mean()\n", + "mirna = mirna.groupby(mirna.index).mean()\n", + "\n", + "# for any duplicate rows in the clinical data, we keep the first occurrence\n", + "clinical = clinical[~clinical.index.duplicated(keep='first')]\n", + "\n", + "print(f\"\\nMethylation shape: {meth.shape}\")\n", + "print(f\"RNA shape: {rna.shape}\")\n", + "print(f\"miRNA shape: {mirna.shape}\")\n", + "print(f\"Clinical shape: {clinical.shape}\")\n", + "\n", + "for df in [meth, rna, mirna]:\n", + " df.columns = df.columns.str.replace(r\"\\?\", \"unknown_\", regex=True)\n", + " df.columns = df.columns.str.replace(r\"\\|\", \"_\", regex=True)\n", + " df.columns = df.columns.str.replace(\"-\", \"_\", regex=False)\n", + " df.columns = df.columns.str.replace(r\"_+\", \"_\", regex=True)\n", + " df.columns = df.columns.str.strip(\"_\")\n", + " \n", + " df.fillna(df.mean(), inplace=True)\n", + "\n", + "# to see which pateints are common across all data files\n", + "common_patients = sorted(list(set(meth.index)&set(rna.index)&set(mirna.index)&set(clinical.index)))\n", + "\n", + "print(f\"\\nFound: {len(common_patients)} patients across all data types.\")\n", + "\n", + "# subset to only common patients\n", + "meth_processed = meth.loc[common_patients]\n", + "rna_processed= rna.loc[common_patients]\n", + "mirna_processed = mirna.loc[common_patients]\n", + "clinical_processed = clinical.loc[common_patients]\n", + "\n", + "# extract target labels from clinical data\n", + "targets = clinical_processed['vital_status']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d9c562ba", + "metadata": {}, + "outputs": [], + "source": [ + "display(mirna_processed.iloc[:3,:5])\n", + "display(mirna_processed.shape)\n", + "\n", + "display(rna_processed.iloc[:3,:5])\n", + "display(rna_processed.shape)\n", + "\n", + "display(meth_processed.iloc[:3,:5])\n", + "display(meth_processed.shape)\n", + "\n", + "display(clinical_processed.iloc[:3,:5])\n", + "display(clinical_processed.shape)\n", + "\n", + "display(targets.value_counts())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "edc9e7f8", + "metadata": {}, + "outputs": [], + "source": [ + "import bioneuralnet as bnn\n", + "\n", + "# drop unwanted columns from clinical data\n", + "clinical_processed.drop(columns=[\"Composite Element REF\"], errors=\"ignore\", inplace=True)\n", + "\n", + "# we transform the methylation beta values to M-values and drop unwanted columns\n", + "meth_m = meth_processed.drop(columns=[\"Composite Element REF\"], errors=\"ignore\")\n", + "\n", + "# convert beta values to M-values using bioneuralnet utility with small epsilon to avoid log(0)\n", + "meth_m = bnn.utils.beta_to_m(meth_m, eps=1e-6) \n", + "\n", + "# lastly we turn the target labels into numerical classes\n", + "mapping = {\"astrocytoma\": 0, \"oligodendroglioma\": 1, \"oligoastrocytoma\": 2}\n", + "target_labels = targets.map(mapping).to_frame(name=\"target\")\n", + "\n", + "# as a safety check we align the indices once more\n", + "X_meth = meth_m.loc[common_patients]\n", + "X_rna = rna_processed.loc[common_patients]\n", + "X_mirna = mirna_processed.loc[common_patients]\n", + "Y_labels = target_labels.loc[common_patients]\n", + "clinical_final = clinical_processed.loc[common_patients]\n", + "\n", + "print(f\"\\nDNA_Methylation shape: {X_meth.shape}\")\n", + "print(f\"RNA shape: {X_rna.shape}\")\n", + "print(f\"miRNA shape: {X_mirna.shape}\")\n", + "print(f\"Clinical shape: {clinical_final.shape}\")\n", + "print(Y_labels.value_counts())" + ] + }, + { + "cell_type": "markdown", + "id": "0643ec13", + "metadata": {}, + "source": [ + "## Feature Selection Methodology\n", + "\n", + "### Supported Methods and Interpretation\n", + "\n", + "**BioNeuralNet** provides three techniques for feature selection, allowing for different views of the data's statistical profile:\n", + "\n", + "- **Variance Thresholding:** Identifies features with the **highest overall variance** across all samples.\n", + "\n", + "- **ANOVA F-test:** Pinpoints features that best **distinguish between the target classes** (KIRC, KIRP, and KICH).\n", + "\n", + "- **Random Forest Importance:** Assesses **feature utility** based on its contribution to a predictive non-linear model.\n", + "\n", + "### LGG Cohort Selection Strategy\n", + "\n", + "A dimensionality reduction step was essential for managing the high-feature-count omics data:\n", + "\n", + "- **High-Feature Datasets:** Both DNA Methylation (20,114) and RNA (18,328) required significant feature reduction.\n", + "\n", + "- **Filtering Process:** The **top 6,000 features** were initially extracted from the Methylation and RNA datasets using all three methods.\n", + "\n", + "- **Final Set:** A consensus set was built by finding the intersection of features selected by the ANOVA F-test and Random Forest Importance, ensuring both statistical relevance and model-based utility.\n", + "\n", + "- **Low-Feature Datasets:** The miRNA data (548 features) was passed through **without selection**, as its feature count was already manageable." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "978e9281", + "metadata": {}, + "outputs": [], + "source": [ + "import bioneuralnet as bnn\n", + "\n", + "# feature selection\n", + "meth_highvar = bnn.utils.select_top_k_variance(X_meth, k=6000)\n", + "rna_highvar = bnn.utils.select_top_k_variance(X_rna, k=6000)\n", + "\n", + "meth_af = bnn.utils.top_anova_f_features(X_meth, Y_labels, max_features=6000)\n", + "rna_af = bnn.utils.top_anova_f_features(X_rna, Y_labels, max_features=6000)\n", + "\n", + "meth_rf = bnn.utils.select_top_randomforest(X_meth, Y_labels, top_k=6000)\n", + "rna_rf = bnn.utils.select_top_randomforest(X_rna, Y_labels, top_k=6000)\n", + "\n", + "meth_var_set = set(meth_highvar.columns)\n", + "meth_anova_set = set(meth_af.columns)\n", + "meth_rf_set = set(meth_rf.columns)\n", + "\n", + "rna_var_set = set(rna_highvar.columns)\n", + "rna_anova_set = set(rna_af.columns)\n", + "rna_rf_set = set(rna_rf.columns)\n", + "\n", + "meth_inter1 = list(meth_anova_set & meth_var_set)\n", + "meth_inter2 = list(meth_rf_set & meth_var_set)\n", + "meth_inter3 = list(meth_anova_set & meth_rf_set)\n", + "meth_all_three = list(meth_anova_set & meth_var_set & meth_rf_set)\n", + "\n", + "rna_inter4 = list(rna_anova_set & rna_var_set)\n", + "rna_inter5 = list(rna_rf_set & rna_var_set)\n", + "rna_inter6 = list(rna_anova_set & rna_rf_set)\n", + "rna_all_three = list(rna_anova_set & rna_var_set & rna_rf_set)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "467d9ff1", + "metadata": {}, + "outputs": [], + "source": [ + "print(\"FROM THE 6000 Methylation feature selection:\\n\")\n", + "print(f\"Anova-F & variance selection share: {len(meth_inter1)} features\")\n", + "print(f\"Random Forest & variance selection share: {len(meth_inter2)} features\")\n", + "print(f\"Anova-F & Random Forest share: {len(meth_inter3)} features\")\n", + "print(f\"All three methods agree on: {len(meth_all_three)} features\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6fb1e10b", + "metadata": {}, + "outputs": [], + "source": [ + "print(\"\\nFROM THE 6000 RNA feature selection:\\n\")\n", + "print(f\"Anova-F & variance selection share: {len(rna_inter4)} features\")\n", + "print(f\"Random Forest & variance selection share: {len(rna_inter5)} features\")\n", + "print(f\"Anova-F & Random Forest share: {len(rna_inter6)} features\")\n", + "print(f\"All three methods agree on: {len(rna_all_three)} features\")" + ] + }, + { + "cell_type": "markdown", + "id": "be2ce6f8", + "metadata": {}, + "source": [ + "## Feature Selection Summary: ANOVA-RF Intersection\n", + "\n", + "The chosen strategy for feature selection is based on the **overlap** between features identified by the **ANOVA F-test** and **Random Forest Importance**. This approach offers comprehensive filtering by balancing class-based relevance (ANOVA) with non-linear model importance (Random Forest). The resulting feature sets are considered the most robust for downstream analysis.\n", + "\n", + "### Feature Overlap Results\n", + "\n", + "The following table details the number of features resulting from the intersection of different selection methods for each omics data type.\n", + "\n", + "| Omics Data Type | ANOVA-F & Variance | RF & Variance | ANOVA-F & Random Forest (Selected) | All Three Agree |\n", + "| :--- | :--- | :--- | :--- | :--- |\n", + "| **Methylation** | 2,704 features | 1,768 features | **1,823 features** | 809 features |\n", + "| **RNA** | 2,183 features | 1,977 features | **2,127 features** | 763 features |" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a1934672", + "metadata": {}, + "outputs": [], + "source": [ + "X_meth_selected = X_meth[meth_inter3]\n", + "X_rna_selected = X_rna[rna_inter6]\n", + "\n", + "print(\"\\nFinal Shapes for Modeling\")\n", + "print(f\"Methylation (X1): {X_meth_selected.shape}\")\n", + "print(f\"RNA-Seq (X2): {X_rna_selected.shape}\")\n", + "print(f\"miRNA-Seq (X3): {X_mirna.shape}\")\n", + "print(f\"Labels (Y): {Y_labels.shape}\")" + ] + }, + { + "cell_type": "markdown", + "id": "9348ca2a", + "metadata": {}, + "source": [ + "## Data Availability\n", + "\n", + "To facilitate rapid experimentation and reproduction of our results, the fully processed and feature-selected dataset used in this analysis has been made available directly within the package.\n", + "\n", + "Users can load this dataset, bypassing all preceding data acquisition, preprocessing, and feature selection steps. This allows users to proceed immediately from this step." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "08ec1120", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "from pathlib import Path\n", + "import bioneuralnet as bnn\n", + "\n", + "tgca_lgg = bnn.datasets.DatasetLoader(\"lgg\")\n", + "display(tgca_lgg.shape)\n", + "\n", + "# The dataset is returned as a dictionary. We extract each file independetly based on the name( Key).\n", + "dna_meth = tgca_lgg[\"meth\"]\n", + "rna = tgca_lgg[\"rna\"]\n", + "mirna = tgca_lgg[\"mirna\"]\n", + "clinical = tgca_lgg[\"clinical\"]\n", + "target = tgca_lgg[\"target\"]\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "95296b47", + "metadata": {}, + "outputs": [], + "source": [ + "dna_meth = bnn.utils.select_top_k_variance(dna_meth, k=500)\n", + "rna = bnn.utils.select_top_k_variance(rna, k=500)\n", + "mirna = bnn.utils.select_top_k_variance(mirna, k=500)\n", + "\n", + "print(dna_meth.shape)\n", + "print(rna.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "09c76e4f", + "metadata": {}, + "outputs": [], + "source": [ + "from bioneuralnet.utils import preprocess_clinical\n", + "clinical_for_model = preprocess_clinical(\n", + " X=clinical, \n", + " top_k=10,\n", + " scale=False,\n", + " ignore_columns=[\n", + " # target-related or outcome-related or irrelevant columns\n", + " \"days_to_last_followup\", \n", + " \"days_to_death\",\n", + " \"date_of_initial_pathologic_diagnosis\",\n", + " \"histological_type\"\n", + " ]\n", + ")\n", + "display(clinical_for_model.iloc[:5,:5])" + ] + }, + { + "cell_type": "markdown", + "id": "61a2b14e", + "metadata": {}, + "source": [ + "## Reproducibility and Seeding\n", + "\n", + "To ensure our experimental results are fully reproducible, a single global seed is set at the beginning of the analysis.\n", + "\n", + "This utility function propagates the seed to all sources of randomness, including `random`, `numpy`, and `torch` (for both CPU and GPU). Critically, it also configures the PyTorch cuDNN backend to use deterministic algorithms." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3f28d80e", + "metadata": {}, + "outputs": [], + "source": [ + "import bioneuralnet as bnn\n", + "\n", + "SEED = 1883\n", + "bnn.utils.set_seed(SEED)" + ] + }, + { + "cell_type": "markdown", + "id": "88084af0", + "metadata": {}, + "source": [ + "## Classification using DPMON: Training and Evaluation" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "54f23044", + "metadata": {}, + "outputs": [], + "source": [ + "from bioneuralnet.utils import find_optimal_graph\n", + "\n", + "omics_lgg = pd.concat([mirna, dna_meth, rna], axis=1)\n", + "\n", + "optimal_graph, best_params, results_df = find_optimal_graph(\n", + " omics_data=omics_lgg,\n", + " y_labels=target,\n", + " methods=['correlation','threshold'],\n", + " seed=SEED,\n", + " verbose=False,\n", + " trials=10,\n", + " omics_list=[mirna, dna_meth, rna],\n", + " centrality_mode=\"eigenvector\",\n", + ")\n", + "display(optimal_graph.iloc[:5,:5])\n", + "display(best_params)\n", + "\n", + "results_df.sort_values(\"score\", ascending=False, inplace=True)\n", + "display(results_df)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7adc097f", + "metadata": {}, + "outputs": [], + "source": [ + "# Graph analysis: The optimal gaph uses a proxy to evaluate its properties, but for the final output\n", + "# the graph is built without the target variable. The search only helps to find the best graph parameters..\n", + "from bioneuralnet.utils import graph_analysis\n", + "\n", + "graph_analysis(optimal_graph, graph_name=\"Optimal\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8bbf7958", + "metadata": {}, + "outputs": [], + "source": [ + "from pathlib import Path\n", + "from bioneuralnet.downstream_task import DPMON\n", + "\n", + "output_dir_base = Path(\"/home/vicente/Github/BioNeuralNet/dpmon_results_GAT_FINAL/lgg\")\n", + "\n", + "current_output_dir = output_dir_base / \"Gat_best_graph\"\n", + "current_output_dir.mkdir(parents=True, exist_ok=True)\n", + "\n", + "dpmon_params_base = {\n", + " \"adjacency_matrix\": optimal_graph,\n", + " \"omics_list\": omics_lgg,\n", + " \"phenotype_data\": target,\n", + " \"phenotype_col\": \"target\",\n", + " \"clinical_data\": clinical_for_model,\n", + " \"model\": 'GAT',\n", + " \"tune\": True, \n", + " \"cv\": True, \n", + " \"n_folds\": 5,\n", + " 'repeat_num': 1,\n", + " \"gpu\": True,\n", + " \"cuda\": 0,\n", + " \"seed\": SEED,\n", + " \"output_dir\": current_output_dir\n", + "}\n", + "\n", + "dpmon_tunned = DPMON(**dpmon_params_base)\n", + "predictions_df, metrics, embeddings = dpmon_tunned.run()\n", + "\n", + "graph_metrics = metrics\n", + "\n", + "acc_row = graph_metrics.loc[graph_metrics['Metric'] == 'Accuracy'].iloc[0]\n", + "f1_macro_row = graph_metrics.loc[graph_metrics['Metric'] == 'F1 Macro'].iloc[0]\n", + "f1_weighted_row = graph_metrics.loc[graph_metrics['Metric'] == 'F1 Weighted'].iloc[0]\n", + "recall_row = graph_metrics.loc[graph_metrics['Metric'] == 'Recall'].iloc[0]\n", + "auc_row = graph_metrics.loc[graph_metrics['Metric'] == 'AUC'].iloc[0]\n", + "aupr_row = graph_metrics.loc[graph_metrics['Metric'] == 'AUPR'].iloc[0]\n", + "\n", + "acc_avg, acc_std = acc_row['Average'], acc_row['StdDev']\n", + "f1_macro_avg, f1_macro_std = f1_macro_row['Average'], f1_macro_row['StdDev']\n", + "f1_weighted_avg, f1_weighted_std = f1_weighted_row['Average'], f1_weighted_row['StdDev']\n", + "recall_avg, recall_std = recall_row['Average'], recall_row['StdDev']\n", + "auc_avg, auc_std = auc_row['Average'], auc_row['StdDev']\n", + "aupr_avg, aupr_std = aupr_row['Average'], aupr_row['StdDev']\n", + "\n", + "print(f\"Accuracy (Avg +/- Std): {acc_avg:.4f} +/- {acc_std:.4f}\")\n", + "print(f\"F1 Macro (Avg +/- Std): {f1_macro_avg:.4f} +/- {f1_macro_std:.4f}\")\n", + "print(f\"F1 Weighted (Avg +/- Std): {f1_weighted_avg:.4f} +/- {f1_weighted_std:.4f}\")\n", + "print(f\"Recall: {recall_avg:.4f} +/- {recall_std:.4f}\")\n", + "print(f\"AUC: {auc_avg:.4f} +/- {auc_std:.4f}\")\n", + "print(f\"AUPR: {aupr_avg:.4f} +/- {aupr_std:.4f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "313822d2", + "metadata": {}, + "outputs": [], + "source": [ + "from bioneuralnet.downstream_task import DPMON\n", + "\n", + "\n", + "output_dir_base = Path(\"/home/vicente/Github/BioNeuralNet/dpmon_results_GCN_FINAL/lgg\")\n", + "\n", + "current_output_dir = output_dir_base / \"Gcn_best_graph\"\n", + "current_output_dir.mkdir(parents=True, exist_ok=True)\n", + "\n", + "dpmon_params_base = {\n", + " \"adjacency_matrix\": optimal_graph,\n", + " \"omics_list\": omics_lgg,\n", + " \"phenotype_data\": target,\n", + " \"phenotype_col\": \"target\",\n", + " \"clinical_data\": clinical_for_model,\n", + " \"model\": 'GCN',\n", + " \"tune\": True, \n", + " \"cv\": True, \n", + " \"n_folds\": 5,\n", + " 'repeat_num': 1,\n", + " \"gpu\": True,\n", + " \"cuda\": 0,\n", + " \"seed\": SEED,\n", + " \"output_dir\": current_output_dir\n", + "}\n", + "\n", + "dpmon_tunned = DPMON(**dpmon_params_base)\n", + "predictions_df, metrics, embeddings = dpmon_tunned.run()\n", + "graph_metrics = metrics\n", + "\n", + "acc_row = graph_metrics.loc[graph_metrics['Metric'] == 'Accuracy'].iloc[0]\n", + "f1_macro_row = graph_metrics.loc[graph_metrics['Metric'] == 'F1 Macro'].iloc[0]\n", + "f1_weighted_row = graph_metrics.loc[graph_metrics['Metric'] == 'F1 Weighted'].iloc[0]\n", + "recall_row = graph_metrics.loc[graph_metrics['Metric'] == 'Recall'].iloc[0]\n", + "auc_row = graph_metrics.loc[graph_metrics['Metric'] == 'AUC'].iloc[0]\n", + "aupr_row = graph_metrics.loc[graph_metrics['Metric'] == 'AUPR'].iloc[0]\n", + "\n", + "acc_avg, acc_std = acc_row['Average'], acc_row['StdDev']\n", + "f1_macro_avg, f1_macro_std = f1_macro_row['Average'], f1_macro_row['StdDev']\n", + "f1_weighted_avg, f1_weighted_std = f1_weighted_row['Average'], f1_weighted_row['StdDev']\n", + "recall_avg, recall_std = recall_row['Average'], recall_row['StdDev']\n", + "auc_avg, auc_std = auc_row['Average'], auc_row['StdDev']\n", + "aupr_avg, aupr_std = aupr_row['Average'], aupr_row['StdDev']\n", + "\n", + "print(f\"Accuracy (Avg +/- Std): {acc_avg:.4f} +/- {acc_std:.4f}\")\n", + "print(f\"F1 Macro (Avg +/- Std): {f1_macro_avg:.4f} +/- {f1_macro_std:.4f}\")\n", + "print(f\"F1 Weighted (Avg +/- Std): {f1_weighted_avg:.4f} +/- {f1_weighted_std:.4f}\")\n", + "print(f\"Recall: {recall_avg:.4f} +/- {recall_std:.4f}\")\n", + "print(f\"AUC: {auc_avg:.4f} +/- {auc_std:.4f}\")\n", + "print(f\"AUPR: {aupr_avg:.4f} +/- {aupr_std:.4f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9a77d244", + "metadata": {}, + "outputs": [], + "source": [ + "from bioneuralnet.downstream_task import DPMON\n", + "output_dir_base = Path(\"/home/vicente/Github/BioNeuralNet/dpmon_results_GIN_FINAL/lgg\")\n", + "\n", + "\n", + "current_output_dir = output_dir_base / \"gin_best_graph\"\n", + "current_output_dir.mkdir(parents=True, exist_ok=True)\n", + "\n", + "dpmon_params_base = {\n", + " \"adjacency_matrix\": optimal_graph,\n", + " \"omics_list\": omics_lgg,\n", + " \"phenotype_data\": target,\n", + " \"phenotype_col\": \"target\",\n", + " \"clinical_data\": clinical_for_model,\n", + " \"model\": 'GIN',\n", + " \"tune\": True, \n", + " \"cv\": True, \n", + " \"n_folds\": 5,\n", + " 'repeat_num': 1,\n", + " \"gpu\": True,\n", + " \"cuda\": 0,\n", + " \"seed\": SEED,\n", + " \"output_dir\": current_output_dir\n", + "}\n", + "\n", + "dpmon_tunned = DPMON(**dpmon_params_base)\n", + "predictions_df, metrics, embeddings = dpmon_tunned.run()\n", + "graph_metrics = metrics\n", + "\n", + "acc_row = graph_metrics.loc[graph_metrics['Metric'] == 'Accuracy'].iloc[0]\n", + "f1_macro_row = graph_metrics.loc[graph_metrics['Metric'] == 'F1 Macro'].iloc[0]\n", + "f1_weighted_row = graph_metrics.loc[graph_metrics['Metric'] == 'F1 Weighted'].iloc[0]\n", + "recall_row = graph_metrics.loc[graph_metrics['Metric'] == 'Recall'].iloc[0]\n", + "auc_row = graph_metrics.loc[graph_metrics['Metric'] == 'AUC'].iloc[0]\n", + "aupr_row = graph_metrics.loc[graph_metrics['Metric'] == 'AUPR'].iloc[0]\n", + "\n", + "acc_avg, acc_std = acc_row['Average'], acc_row['StdDev']\n", + "f1_macro_avg, f1_macro_std = f1_macro_row['Average'], f1_macro_row['StdDev']\n", + "f1_weighted_avg, f1_weighted_std = f1_weighted_row['Average'], f1_weighted_row['StdDev']\n", + "recall_avg, recall_std = recall_row['Average'], recall_row['StdDev']\n", + "auc_avg, auc_std = auc_row['Average'], auc_row['StdDev']\n", + "aupr_avg, aupr_std = aupr_row['Average'], aupr_row['StdDev']\n", + "\n", + "print(f\"Accuracy (Avg +/- Std): {acc_avg:.4f} +/- {acc_std:.4f}\")\n", + "print(f\"F1 Macro (Avg +/- Std): {f1_macro_avg:.4f} +/- {f1_macro_std:.4f}\")\n", + "print(f\"F1 Weighted (Avg +/- Std): {f1_weighted_avg:.4f} +/- {f1_weighted_std:.4f}\")\n", + "print(f\"Recall: {recall_avg:.4f} +/- {recall_std:.4f}\")\n", + "print(f\"AUC: {auc_avg:.4f} +/- {auc_std:.4f}\")\n", + "print(f\"AUPR: {aupr_avg:.4f} +/- {aupr_std:.4f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c8b344bd", + "metadata": {}, + "outputs": [], + "source": [ + "from bioneuralnet.downstream_task import DPMON\n", + "output_dir_base = Path(\"/home/vicente/Github/BioNeuralNet/dpmon_results_SAGE_FINAL/lgg\")\n", + "\n", + "current_output_dir = output_dir_base / \"sage_best_graph\"\n", + "current_output_dir.mkdir(parents=True, exist_ok=True)\n", + "\n", + "dpmon_params_base = {\n", + " \"adjacency_matrix\": optimal_graph,\n", + " \"omics_list\": omics_lgg,\n", + " \"phenotype_data\": target,\n", + " \"phenotype_col\": \"target\",\n", + " \"clinical_data\": clinical_for_model,\n", + " \"model\": 'SAGE',\n", + " \"tune\": True, \n", + " \"cv\": True, \n", + " \"n_folds\": 5,\n", + " 'repeat_num': 1,\n", + " \"gpu\": True,\n", + " \"cuda\": 0,\n", + " \"seed\": SEED,\n", + " \"output_dir\": current_output_dir\n", + "}\n", + "\n", + "dpmon_tunned = DPMON(**dpmon_params_base)\n", + "predictions_df, metrics, embeddings = dpmon_tunned.run()\n", + "graph_metrics = metrics\n", + "\n", + "acc_row = graph_metrics.loc[graph_metrics['Metric'] == 'Accuracy'].iloc[0]\n", + "f1_macro_row = graph_metrics.loc[graph_metrics['Metric'] == 'F1 Macro'].iloc[0]\n", + "f1_weighted_row = graph_metrics.loc[graph_metrics['Metric'] == 'F1 Weighted'].iloc[0]\n", + "recall_row = graph_metrics.loc[graph_metrics['Metric'] == 'Recall'].iloc[0]\n", + "auc_row = graph_metrics.loc[graph_metrics['Metric'] == 'AUC'].iloc[0]\n", + "aupr_row = graph_metrics.loc[graph_metrics['Metric'] == 'AUPR'].iloc[0]\n", + "\n", + "acc_avg, acc_std = acc_row['Average'], acc_row['StdDev']\n", + "f1_macro_avg, f1_macro_std = f1_macro_row['Average'], f1_macro_row['StdDev']\n", + "f1_weighted_avg, f1_weighted_std = f1_weighted_row['Average'], f1_weighted_row['StdDev']\n", + "recall_avg, recall_std = recall_row['Average'], recall_row['StdDev']\n", + "auc_avg, auc_std = auc_row['Average'], auc_row['StdDev']\n", + "aupr_avg, aupr_std = aupr_row['Average'], aupr_row['StdDev']\n", + "\n", + "print(f\"Accuracy (Avg +/- Std): {acc_avg:.4f} +/- {acc_std:.4f}\")\n", + "print(f\"F1 Macro (Avg +/- Std): {f1_macro_avg:.4f} +/- {f1_macro_std:.4f}\")\n", + "print(f\"F1 Weighted (Avg +/- Std): {f1_weighted_avg:.4f} +/- {f1_weighted_std:.4f}\")\n", + "print(f\"Recall: {recall_avg:.4f} +/- {recall_std:.4f}\")\n", + "print(f\"AUC: {auc_avg:.4f} +/- {auc_std:.4f}\")\n", + "print(f\"AUPR: {aupr_avg:.4f} +/- {aupr_std:.4f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "00140d16", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.model_selection import StratifiedKFold, ParameterSampler, RepeatedStratifiedKFold\n", + "from sklearn.metrics import accuracy_score, f1_score, recall_score, roc_auc_score, average_precision_score\n", + "from sklearn.base import clone\n", + "from sklearn.preprocessing import StandardScaler, label_binarize\n", + "from sklearn.pipeline import Pipeline\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.neural_network import MLPClassifier\n", + "from scipy.stats import loguniform, randint\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "from sklearn.tree import DecisionTreeClassifier\n", + "from xgboost import XGBClassifier\n", + "from sklearn.svm import SVC\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "X = pd.concat([dna_meth, rna, mirna, clinical_for_model], axis=1)\n", + "y = target['target']\n", + "print(f\"Successfully created X matrix with shape: {X.shape}\")\n", + "print(f\"Successfully created y vector with shape: {y.shape}\")\n", + "\n", + "\n", + "pipe_lr = Pipeline([\n", + " ('scaler', StandardScaler()),\n", + " ('model', LogisticRegression(solver='lbfgs', max_iter=1000, penalty=None, random_state=SEED))\n", + "])\n", + "\n", + "pipe_mlp = Pipeline([\n", + " ('scaler', StandardScaler()),\n", + " ('model', MLPClassifier(max_iter=500, early_stopping=True, n_iter_no_change=10, random_state=SEED))\n", + "])\n", + "\n", + "pipe_xgb = Pipeline([\n", + " ('scaler', StandardScaler()),\n", + " ('model', XGBClassifier(eval_metric='logloss', tree_method='hist', max_bin=128, random_state=SEED))\n", + "])\n", + "pipe_rf = Pipeline([\n", + " ('scaler', StandardScaler()),\n", + " ('model', RandomForestClassifier(random_state=SEED))\n", + "])\n", + "\n", + "pipe_svm = Pipeline([\n", + " ('scaler', StandardScaler()),\n", + " ('model', SVC(probability=True, random_state=SEED))\n", + "])\n", + "\n", + "pipe_dt = Pipeline([\n", + " ('scaler', StandardScaler()),\n", + " ('model', DecisionTreeClassifier(random_state=SEED))\n", + "])\n", + "\n", + "params_lr = {'model__penalty': ['l2'], 'model__C': loguniform(1e-4, 1e2)}\n", + "\n", + "params_mlp = {\n", + " 'model__hidden_layer_sizes': [(100,), (100, 50), (50, 50)],\n", + " 'model__activation': ['relu', 'tanh'],\n", + " 'model__alpha': loguniform(1e-5, 1e-1),\n", + " 'model__learning_rate_init': loguniform(1e-4, 1e-2)\n", + "}\n", + "params_xgb = {\n", + " 'model__n_estimators': randint(50, 200),\n", + " 'model__learning_rate': loguniform(0.01, 0.3),\n", + " 'model__max_depth': randint(3, 7),\n", + " 'model__subsample': [0.8, 1.0], \n", + " 'model__colsample_bytree': [0.8, 1.0]\n", + "}\n", + "params_rf = {\n", + " 'model__n_estimators': randint(100, 300),\n", + " 'model__max_depth': [10, 20, 30, None],\n", + " 'model__min_samples_split': randint(2, 10),\n", + " 'model__min_samples_leaf': randint(1, 5),\n", + " 'model__max_features': ['sqrt', 'log2']\n", + "}\n", + "params_svm = {\n", + " 'model__C': loguniform(1e-2, 1e3),\n", + " 'model__kernel': ['rbf', 'linear'],\n", + " 'model__gamma': ['scale', 'auto']\n", + "}\n", + "\n", + "params_dt = {\n", + " 'model__max_depth': randint(3, 15),\n", + " 'model__min_samples_split': randint(2, 20),\n", + " 'model__criterion': ['gini', 'entropy']\n", + "}\n", + "\n", + "\n", + "models_to_tune = {\n", + " \"LogisticRegression\": (pipe_lr, params_lr),\n", + " \"SVM\": (pipe_svm, params_svm),\n", + " \"MLP\": (pipe_mlp, params_mlp),\n", + " \"XGBoost\": (pipe_xgb, params_xgb),\n", + " \"RandomForest\": (pipe_rf, params_rf),\n", + " \"DecisionTree\": (pipe_dt, params_dt),\n", + "}\n", + "\n", + "all_results = {\n", + " \"LogisticRegression\": {\"acc\": [], \"f1_w\": [], \"f1_m\": [], \"recall\": [], \"auc\": [], \"aupr\": []},\n", + " \"MLP\": {\"acc\": [], \"f1_w\": [], \"f1_m\": [], \"recall\": [], \"auc\": [], \"aupr\": []},\n", + " \"XGBoost\": {\"acc\": [], \"f1_w\": [], \"f1_m\": [], \"recall\": [], \"auc\": [], \"aupr\": []},\n", + " \"RandomForest\": {\"acc\": [], \"f1_w\": [], \"f1_m\": [], \"recall\": [], \"auc\": [], \"aupr\": []},\n", + " \"SVM\": {\"acc\": [], \"f1_w\": [], \"f1_m\": [], \"recall\": [], \"auc\": [], \"aupr\": []},\n", + " \"DecisionTree\": {\"acc\": [], \"f1_w\": [], \"f1_m\": [], \"recall\": [], \"auc\": [], \"aupr\": []},\n", + "}\n", + "\n", + "\n", + "for model_name, (pipeline, param_dist) in models_to_tune.items():\n", + " print(f\"Evaluating model with nested CV: {model_name}\")\n", + " \n", + " outer_cv = RepeatedStratifiedKFold(n_splits=5, n_repeats=1, random_state=SEED)\n", + " \n", + " # inner folds are for finding the best hyperparameters\n", + " for fold_idx, (train_idx, test_idx) in enumerate(outer_cv.split(X, y), start=1):\n", + " X_train_outer, X_test_outer = X.iloc[train_idx], X.iloc[test_idx]\n", + " y_train_outer, y_test_outer = y.iloc[train_idx], y.iloc[test_idx]\n", + " inner_cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=SEED)\n", + " \n", + " best_score_fold = -np.inf\n", + " best_params_fold = None\n", + " # May or may not want to set a seed here. A fix seed = same hyperparamters each fold.\n", + " # No seed = different hyperparameters each fold. This adds more randomness, and may yield better generalization.\n", + " param_sampler = list(ParameterSampler(param_dist, n_iter=20))\n", + " \n", + " for params in param_sampler:\n", + " inner_scores = []\n", + " \n", + " for inner_train_idx, inner_val_idx in inner_cv.split(X_train_outer, y_train_outer):\n", + " X_train_inner = X_train_outer.iloc[inner_train_idx]\n", + " X_val_inner = X_train_outer.iloc[inner_val_idx]\n", + " y_train_inner = y_train_outer.iloc[inner_train_idx]\n", + " y_val_inner = y_train_outer.iloc[inner_val_idx]\n", + " \n", + " inner_pipeline = clone(pipeline)\n", + " inner_pipeline.set_params(**params)\n", + " inner_pipeline.fit(X_train_inner, y_train_inner)\n", + " \n", + " y_val_pred = inner_pipeline.predict(X_val_inner)\n", + " score = f1_score(y_val_inner, y_val_pred, average='weighted', zero_division=0)\n", + " inner_scores.append(score)\n", + " \n", + " mean_score = np.mean(inner_scores)\n", + " if mean_score > best_score_fold:\n", + " best_score_fold = mean_score\n", + " best_params_fold = params\n", + " \n", + " print(f\"Outer fold {fold_idx}: best params (inner CV F1-W={best_score_fold:.4f})\")\n", + " print(f\"{best_params_fold}\")\n", + " \n", + " final_pipeline = clone(pipeline)\n", + " final_pipeline.set_params(**best_params_fold)\n", + " final_pipeline.fit(X_train_outer, y_train_outer)\n", + " \n", + " preds = final_pipeline.predict(X_test_outer)\n", + " \n", + " if hasattr(final_pipeline, \"predict_proba\"):\n", + " proba = final_pipeline.predict_proba(X_test_outer)\n", + " else:\n", + " proba = None\n", + " \n", + " acc = accuracy_score(y_test_outer, preds)\n", + " f1_w = f1_score(y_test_outer, preds, average='weighted', zero_division=0)\n", + " f1_m = f1_score(y_test_outer, preds, average='macro', zero_division=0)\n", + " recall = recall_score(y_test_outer, preds, average='macro', zero_division=0)\n", + " \n", + " auc = np.nan\n", + " aupr = np.nan\n", + " \n", + " if proba is not None:\n", + " try:\n", + " if len(np.unique(y)) == 2:\n", + " auc = roc_auc_score(y_test_outer, proba[:, 1])\n", + " aupr = average_precision_score(y_test_outer, proba[:, 1])\n", + " else:\n", + " auc = roc_auc_score(y_test_outer, proba, multi_class='ovr', average='macro')\n", + " y_test_bin = label_binarize(y_test_outer, classes=np.unique(y))\n", + " aupr = average_precision_score(y_test_bin, proba, average='weighted')\n", + " except Exception:\n", + " auc = np.nan\n", + " aupr = np.nan\n", + "\n", + " print(f\"Fold {fold_idx} results: Acc={acc:.4f}, F1-W={f1_w:.4f}, \"\n", + " f\"F1-M={f1_m:.4f}, Recall={recall:.4f}, AUC={auc:.4f}, AUPR={aupr:.4f}\")\n", + " \n", + " all_results[model_name][\"acc\"].append(acc)\n", + " all_results[model_name][\"f1_w\"].append(f1_w)\n", + " all_results[model_name][\"f1_m\"].append(f1_m)\n", + " all_results[model_name][\"recall\"].append(recall)\n", + " all_results[model_name][\"auc\"].append(auc)\n", + " all_results[model_name][\"aupr\"].append(aupr)\n", + "\n", + "print(\"\\nFINAL BASELINE RESULTS\\n\")\n", + "for model_name, metrics in all_results.items():\n", + " avg_acc = np.mean(metrics[\"acc\"])\n", + " std_acc = np.std(metrics[\"acc\"])\n", + " avg_f1_w = np.mean(metrics[\"f1_w\"])\n", + " std_f1_w = np.std(metrics[\"f1_w\"])\n", + " avg_f1_m = np.mean(metrics[\"f1_m\"])\n", + " std_f1_m = np.std(metrics[\"f1_m\"])\n", + " avg_recall = np.mean(metrics[\"recall\"])\n", + " std_recall = np.std(metrics[\"recall\"])\n", + " avg_auc = np.nanmean(metrics[\"auc\"])\n", + " std_auc = np.nanstd(metrics[\"auc\"])\n", + " avg_aupr = np.nanmean(metrics[\"aupr\"])\n", + " std_aupr = np.nanstd(metrics[\"aupr\"])\n", + " \n", + " print(f\"\\n{model_name}:\")\n", + " print(f\"Accuracy: {avg_acc:.4f} +/- {std_acc:.4f}\")\n", + " print(f\"F1 Weighted: {avg_f1_w:.4f} +/- {std_f1_w:.4f}\")\n", + " print(f\"F1 Macro: {avg_f1_m:.4f} +/- {std_f1_m:.4f}\")\n", + " print(f\"Recall: {avg_recall:.4f} +/- {std_recall:.4f}\")\n", + " print(f\"AUC: {avg_auc:.4f} +/- {std_auc:.4f}\")\n", + " print(f\"AUPR: {avg_aupr:.4f} +/- {std_aupr:.4f}\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".enviroment", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/source/notebooks/TCGA-PAAD.ipynb b/docs/source/notebooks/TCGA-PAAD.ipynb new file mode 100644 index 0000000..1b1b002 --- /dev/null +++ b/docs/source/notebooks/TCGA-PAAD.ipynb @@ -0,0 +1,1058 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "dc820e6e", + "metadata": {}, + "source": [ + "# TCGA-PAAD Analysis: *Overall Survival* classification\n", + "\n", + "- **Cohort**: Focuses on the **TCGA-PAAD** (Pancreatic Adenocarcinoma) dataset, a vital cohort for studying pancreatic cancer.\n", + "\n", + "- **Goal**: Perform survival prediction using a multi-omics profile.\n", + "- **Prediction Target**: Predict **Overall Survival (OS)** based on the patient's omics data (RNA, Methylation, CNV, and clinical features).\n", + "\n", + "**Data Sources:**\n", + "- Omics Data and Target: [https://xenabrowser.net/datapages/](https://xenabrowser.net/datapages/)\n", + "- Clinical Data: [Broad Institute FireHose](`http://firebrowse.org/?cohort=PAAD`)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ec5a7a8a", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "from pathlib import Path\n", + "root = Path(\"/home/vicente/Github/BioNeuralNet/PAAN\")\n", + "\n", + "cnv_gistic_raw = pd.read_csv(root/\"Gistic2_CopyNumber_Gistic2_all_thresholded_by_genes.txt\", sep=\"\\t\",index_col=0,low_memory=False) \n", + "rna_raw = pd.read_csv(root / \"HiSeqV2.txt\", sep=\"\\t\",index_col=0,low_memory=False)\n", + "meth_raw = pd.read_csv(root/\"HumanMethylation450.txt\", sep='\\t',index_col=0,low_memory=False)\n", + "clinical_raw = pd.read_csv(root / \"PAAD.clin.merged.picked.txt\",sep=\"\\t\", index_col=0, low_memory=False)\n", + "target = pd.read_csv(root / \"survival_PAAD_survival.txt\", sep=\"\\t\", index_col=0, low_memory=False)\n", + "probe_map_meth = pd.read_csv( root / \"probeMap_illuminaMethyl450_hg19_GPL16304_TCGAlegacy.txt\", sep=\"\\t\", index_col=0, low_memory=False)\n", + "\n", + "# display all shapes and first few rows of each dataset\n", + "display(cnv_gistic_raw.iloc[:5,:5])\n", + "display(cnv_gistic_raw.shape)\n", + "\n", + "display(rna_raw.iloc[:5,:5])\n", + "display(rna_raw.shape)\n", + "\n", + "display(meth_raw.iloc[:5,:5])\n", + "display(meth_raw.shape)\n", + "display(probe_map_meth.iloc[:5,:5])\n", + "display(probe_map_meth.shape)\n", + "\n", + "display(clinical_raw.iloc[:5,:5])\n", + "display(clinical_raw.shape)\n", + "\n", + "display(target.iloc[:5,:5])\n", + "display(target.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "85ddcf20", + "metadata": {}, + "outputs": [], + "source": [ + "import bioneuralnet as bnn\n", + "cnv_gistic_processed = cnv_gistic_raw.apply(pd.to_numeric, errors=\"coerce\")\n", + "\n", + "# collapse GISTIC to -1 / 0 / +1 per gene\n", + "cnv_signed = cnv_gistic_processed.copy()\n", + "cnv_signed[cnv_signed > 0] = 1\n", + "cnv_signed[cnv_signed < 0] = -1\n", + "\n", + "cnv_signed_T = cnv_signed.T \n", + "rna_numeric = rna_raw.apply(pd.to_numeric, errors=\"coerce\")\n", + "rna_T = rna_numeric.T\n", + "\n", + "# make numeric (beta values)\n", + "meth_numeric = meth_raw.apply(pd.to_numeric, errors=\"coerce\")\n", + "\n", + "# build gene map from probe_map_meth\n", + "gene_map = probe_map_meth[['gene']].copy()\n", + "gene_map = gene_map.dropna(subset=['gene'])\n", + "gene_map = gene_map[gene_map['gene'] != '.']\n", + "\n", + "gene_map['gene'] = gene_map['gene'].str.split(',')\n", + "gene_map_exploded = gene_map.explode('gene')\n", + "\n", + "meth_with_gene = meth_numeric.join(gene_map_exploded['gene'], how='inner')\n", + "\n", + "data_cols = meth_with_gene.columns.drop('gene')\n", + "meth_gene_beta = (\n", + " meth_with_gene\n", + " .groupby('gene')[data_cols]\n", + " .mean()\n", + " .T\n", + ")\n", + "\n", + "meth_gene_m = bnn.utils.beta_to_m(meth_gene_beta, eps=1e-8)\n", + "\n", + "clinical = clinical_raw.copy()\n", + "clinical = clinical.T\n", + "clinical.index = clinical.index.str.upper().str.slice(0, 12)\n", + "clinical.index.name = \"Patient_ID\"\n", + "clinical = clinical[~clinical.index.duplicated(keep='first')]\n", + "clinical = clinical.drop(columns=[\"Hybridization REF\", \"Composite Element REF\"], errors=\"ignore\")\n", + "\n", + "outcomes = target.copy()\n", + "outcomes = outcomes.set_index('_PATIENT')\n", + "outcomes.index = outcomes.index.str.upper().str.slice(0, 12)\n", + "outcomes.index.name = \"Patient_ID\"\n", + "outcomes = outcomes[~outcomes.index.duplicated(keep='first')]\n", + "\n", + "cnv_signed_T.index = cnv_signed_T.index.str.upper().str.slice(0, 12)\n", + "rna_T.index = rna_T.index.str.upper().str.slice(0, 12)\n", + "meth_gene_m.index = meth_gene_m.index.str.upper().str.slice(0, 12)\n", + "\n", + "cnv_signed_T = cnv_signed_T.groupby(cnv_signed_T.index).mean()\n", + "rna_T = rna_T.groupby(rna_T.index).mean()\n", + "meth_gene_m = meth_gene_m.groupby(meth_gene_m.index).mean()\n", + "\n", + "common_patients = sorted(\n", + " set(cnv_signed_T.index)\n", + " & set(rna_T.index)\n", + " & set(meth_gene_m.index)\n", + " & set(outcomes.index)\n", + ")\n", + "X_cnv = cnv_signed_T.loc[common_patients]\n", + "X_rna = rna_T.loc[common_patients]\n", + "X_meth = meth_gene_m.loc[common_patients]\n", + "Y_labels = outcomes.loc[common_patients, \"OS\"]\n", + "clinical = clinical.loc[common_patients]\n", + "\n", + "def clean_cols(df, prefix):\n", + " cols = df.columns\n", + " cols = cols.str.replace(r\"\\?\", \"unknown_\", regex=True)\n", + " cols = cols.str.replace(r\"\\|\", \"_\", regex=True)\n", + " cols = cols.str.replace(\"-\", \"_\", regex=False)\n", + " cols = cols.str.replace(r\"_+\", \"_\", regex=True)\n", + " cols = cols.str.strip(\"_\")\n", + " df.columns = cols\n", + " return df.add_prefix(prefix)\n", + "\n", + "X_cnv = clean_cols(X_cnv, \"cnv_\")\n", + "X_rna = clean_cols(X_rna, \"rna_\")\n", + "X_meth = clean_cols(X_meth, \"meth_\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "81713f21", + "metadata": {}, + "outputs": [], + "source": [ + "display(X_cnv.iloc[:5,:5])\n", + "display(X_cnv.shape)\n", + "\n", + "display(X_rna.iloc[:5,:5])\n", + "display(X_rna.shape)\n", + "\n", + "display(X_meth.iloc[:5,:5])\n", + "display(X_meth.shape)\n", + "\n", + "display(clinical.iloc[:5,:5])\n", + "display(clinical.shape)" + ] + }, + { + "cell_type": "markdown", + "id": "7767b027", + "metadata": {}, + "source": [ + "## Feature Selection Methodology\n", + "\n", + "### Supported Methods and Interpretation\n", + "\n", + "**BioNeuralNet** provides three techniques for feature selection, allowing for different views of the data's statistical profile:\n", + "\n", + "- **Variance Thresholding:** Identifies features with the **highest overall variance** across all samples.\n", + "\n", + "- **ANOVA F-test:** Pinpoints features that best **distinguish between the target classes** (e.g., Alive vs. Deceased).\n", + "\n", + "- **Random Forest Importance:** Assesses **feature utility** based on its contribution to a predictive non-linear model.\n", + "\n", + "### PAAD Cohort Selection Strategy\n", + "\n", + "A dimensionality reduction step was essential for managing the high-feature-count omics data:\n", + "\n", + "- **High-Feature Datasets:** DNA Methylation (34,013), RNA (20,530), CNV Amplification (24,776), and CNV Deletion (24,776) all required significant feature reduction.\n", + "\n", + "- **Filtering Process:** As an example strategy, the **top 6,000 features** could be extracted from each high-feature omics dataset using all three methods.\n", + "\n", + "- **Final Set:** A consensus set could be built for each omics type by finding the intersection of features selected by the ANOVA F-test and Random Forest Importance, ensuring both statistical relevance and model-based utility.\n", + "\n", + "- **Low-Feature Datasets:** The **Clinical** data (19 features) was passed through **without selection**, as its feature count was already manageable." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "be3941c5", + "metadata": {}, + "outputs": [], + "source": [ + "import bioneuralnet as bnn\n", + "\n", + "print(f\"METH before: {X_meth.shape}\")\n", + "X_meth = bnn.impute_omics_knn(X_meth, n_neighbors=5)\n", + "print(f\"METH after: {X_meth.shape}\\n\")\n", + "\n", + "print(f\"RNA before: {X_rna.shape}\")\n", + "X_rna = bnn.impute_omics_knn(X_rna, n_neighbors=5)\n", + "print(f\"RNA after: {X_rna.shape}\\n\")\n", + "\n", + "print(f\"CNV before: {X_cnv.shape}\")\n", + "X_cnv = bnn.impute_omics_knn(X_cnv, n_neighbors=5)\n", + "print(f\"CNV after: {X_cnv.shape}\\n\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8a96f822", + "metadata": {}, + "outputs": [], + "source": [ + "import bioneuralnet as bnn\n", + "\n", + "meth_highvar = bnn.utils.select_top_k_variance(X_meth, k=6000)\n", + "meth_af = bnn.utils.top_anova_f_features(X_meth, Y_labels, max_features=6000)\n", + "meth_rf = bnn.utils.select_top_randomforest(X_meth, Y_labels, top_k=6000)\n", + "\n", + "rna_highvar = bnn.utils.select_top_k_variance(X_rna, k=6000)\n", + "rna_af = bnn.utils.top_anova_f_features(X_rna, Y_labels, max_features=6000)\n", + "rna_rf = bnn.utils.select_top_randomforest(X_rna, Y_labels, top_k=6000)\n", + "\n", + "cnv_highvar = bnn.utils.select_top_k_variance(X_cnv, k=6000)\n", + "cnv_af = bnn.utils.top_anova_f_features(X_cnv, Y_labels, max_features=6000)\n", + "cnv_rf = bnn.utils.select_top_randomforest(X_cnv, Y_labels, top_k=6000)\n", + "\n", + "meth_var_set = set(meth_highvar.columns)\n", + "meth_anova_set = set(meth_af.columns)\n", + "meth_rf_set = set(meth_rf.columns)\n", + "\n", + "rna_var_set = set(rna_highvar.columns)\n", + "rna_anova_set = set(rna_af.columns)\n", + "rna_rf_set = set(rna_rf.columns)\n", + "\n", + "cnv_var_set = set(cnv_highvar.columns)\n", + "cnv_anova_set = set(cnv_af.columns)\n", + "cnv_rf_set = set(cnv_rf.columns)\n", + "\n", + "meth_inter1 = list(meth_anova_set & meth_var_set)\n", + "meth_inter2 = list(meth_rf_set & meth_var_set)\n", + "meth_inter3 = list(meth_anova_set & meth_rf_set)\n", + "meth_all_three = list(meth_anova_set & meth_var_set & meth_rf_set)\n", + "\n", + "rna_inter1 = list(rna_anova_set & rna_var_set)\n", + "rna_inter2 = list(rna_rf_set & rna_var_set)\n", + "rna_inter3 = list(rna_anova_set & rna_rf_set)\n", + "rna_all_three = list(rna_anova_set & rna_var_set & rna_rf_set)\n", + "\n", + "cnv_inter1 = list(cnv_anova_set & cnv_var_set)\n", + "cnv_inter2 = list(cnv_rf_set & cnv_var_set)\n", + "cnv_inter3 = list(cnv_anova_set & cnv_rf_set)\n", + "cnv_all_three = list(cnv_anova_set & cnv_var_set & cnv_rf_set)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f6d3d1c8", + "metadata": {}, + "outputs": [], + "source": [ + "print(\"FROM THE 6000 Methylation feature selection:\\n\")\n", + "print(f\"Anova-F & variance selection share: {len(meth_inter1)} features\")\n", + "print(f\"Random Forest & variance selection share: {len(meth_inter2)} features\")\n", + "print(f\"Anova-F & Random Forest share: {len(meth_inter3)} features\")\n", + "print(f\"All three methods agree on: {len(meth_all_three)} features\")\n", + "\n", + "print(\"\\nFROM THE 6000 RNA feature selection:\\n\")\n", + "print(f\"Anova-F & variance selection share: {len(rna_inter1)} features\")\n", + "print(f\"Random Forest & variance selection share: {len(rna_inter2)} features\")\n", + "print(f\"Anova-F & Random Forest share: {len(rna_inter3)} features\")\n", + "print(f\"All three methods agree on: {len(rna_all_three)} features\")\n", + "\n", + "print(\"\\nFROM THE 6000 CNV feature selection:\\n\")\n", + "print(f\"Anova-F & variance selection share: {len(cnv_inter1)} features\")\n", + "print(f\"Random Forest & variance selection share: {len(cnv_inter2)} features\")\n", + "print(f\"Anova-F & Random Forest share: {len(cnv_inter3)} features\")\n", + "print(f\"All three methods agree on: {len(cnv_all_three)} features\")" + ] + }, + { + "cell_type": "markdown", + "id": "1d61f83a", + "metadata": {}, + "source": [ + "## Feature Selection Summary: ANOVA-RF Intersection\n", + "\n", + "The final set of features was determined by the **intersection** of those highlighted by the **ANOVA F-test** and **Random Forest Importance**. This methodology provides a balanced filter, capturing features with both high class-separability (ANOVA) and significant predictive value in a non-linear model (Random Forest). The resulting feature pool is considered highly relevant for the subsequent modeling tasks.\n", + "\n", + "### Feature Overlap Results\n", + "\n", + "The table below quantifies the shared features identified by the different selection techniques for each omics type.\n", + "\n", + "| Omics Data Type | ANOVA-F & Variance | RF & Variance | ANOVA-F & Random Forest (Selected) | All Three Agree |\n", + "| :--- | :--- | :--- | :--- | :--- |\n", + "| **DNA Methylation** | 1,410 features | 1,076 features | **1,152 features** | 280 features |\n", + "| **RNA** | 1,910 features | 1,815 features | **1,910 features** | 589 features |\n", + "| **CNV** | 1,884 features | 1,516 features | **1,035 features** | 477 features |" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "11a83aea", + "metadata": {}, + "outputs": [], + "source": [ + "# Subset each omics dataframe using the selected feature lists\n", + "X_meth_selected = X_meth[meth_inter3]\n", + "X_rna_selected = X_rna[rna_inter3]\n", + "X_cnv_selected = X_cnv[cnv_inter3]\n", + "\n", + "print(\"\\nFinal Shapes for Modeling\")\n", + "print(f\"Methylation (X_meth_selected): {X_meth_selected.shape}\")\n", + "print(f\"RNA-Seq (X_rna_selected): {X_rna_selected.shape}\")\n", + "print(f\"CNV (X_cnv_selected): {X_cnv_selected.shape}\")\n", + "print(f\"Clinical (clinical_selected): {clinical.shape}\")\n", + "print(f\"Labels (Y_labels): {Y_labels.shape}\")" + ] + }, + { + "cell_type": "markdown", + "id": "9706f2e3", + "metadata": {}, + "source": [ + "## Data Availability\n", + "\n", + "To facilitate rapid experimentation and reproduction of our results, the fully processed and feature-selected dataset used in this analysis has been made available directly within the package.\n", + "\n", + "Users can load this dataset, bypassing all preceding data acquisition, preprocessing, and feature selection steps. This allows users to proceed immediately from this step." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b20ce715", + "metadata": {}, + "outputs": [], + "source": [ + "# for training puroses we will do variance selection to reduce the number of features\n", + "import bioneuralnet as bnn\n", + "\n", + "tgca_paad = bnn.datasets.DatasetLoader(\"paad\")\n", + "display(tgca_paad.shape)\n", + "\n", + "# The dataset is returned as a dictionary. We extract each file independetly based on the name (Key).\n", + "cnv = tgca_paad[\"cnv\"]\n", + "clinical = tgca_paad[\"clinical\"]\n", + "target = tgca_paad[\"target\"]\n", + "dna_meth = tgca_paad[\"meth\"]\n", + "rna = tgca_paad[\"rna\"]\n", + "\n", + "display(cnv.iloc[:3,:5])\n", + "display(dna_meth.iloc[:3,:5])\n", + "display(rna.iloc[:3,:5])\n", + "display(clinical.iloc[:3,:5])\n", + "display(target.iloc[:2,:5])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0bca75bf", + "metadata": {}, + "outputs": [], + "source": [ + "# Variance selection\n", + "cnv = bnn.utils.select_top_k_variance(cnv, k=500)\n", + "dna_meth = bnn.utils.select_top_k_variance(dna_meth, k=500)\n", + "rna = bnn.utils.select_top_k_variance(rna, k=500)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4cbd23b0", + "metadata": {}, + "outputs": [], + "source": [ + "from bioneuralnet.utils import preprocess_clinical\n", + "\n", + "dataleak_columns = [\n", + " \"vital_status\",\n", + " \"days_to_death\",\n", + " \"days_to_last_followup\",\n", + " \"date_of_initial_pathologic_diagnosis\",\n", + " ]\n", + "\n", + "clinical_for_model = preprocess_clinical(\n", + " clinical, \n", + " top_k=10, \n", + " scale=False, \n", + " ignore_columns=dataleak_columns,\n", + " nan_threshold=0.6)\n", + "\n", + "display(clinical_for_model.iloc[:5,:5])" + ] + }, + { + "cell_type": "markdown", + "id": "03ecd4a7", + "metadata": {}, + "source": [ + "## Reproducibility and Seeding\n", + "\n", + "To ensure our experimental results are fully reproducible, a single global seed is set at the beginning of the analysis.\n", + "\n", + "This utility function propagates the seed to all sources of randomness, including `random`, `numpy`, and `torch` (for both CPU and GPU). Critically, it also configures the PyTorch cuDNN backend to use deterministic algorithms.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2b8428d6", + "metadata": {}, + "outputs": [], + "source": [ + "import bioneuralnet as bnn\n", + "import pandas as pd\n", + "\n", + "SEED = 1883\n", + "bnn.utils.set_seed(SEED)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c25ce1a8", + "metadata": {}, + "outputs": [], + "source": [ + "from bioneuralnet.utils import find_optimal_graph\n", + "\n", + "omics_paad = pd.concat([cnv, dna_meth, rna], axis=1)\n", + "\n", + "optimal_graph, best_params, results_df = find_optimal_graph(\n", + " omics_data=omics_paad,\n", + " y_labels=target,\n", + " methods=['threshold', 'correlation','similarity','gaussian'],\n", + " seed=SEED,\n", + " verbose=False,\n", + " trials=30,\n", + " omics_list=[cnv, dna_meth, rna],\n", + " centrality_mode=\"eigenvector\",\n", + ")\n", + "display(optimal_graph.iloc[:5,:5])\n", + "display(best_params)\n", + "\n", + "results_df.sort_values(\"score\", ascending=False, inplace=True)\n", + "display(results_df)\n", + "## {'k': 20,'metric': 'cosine','mutual': False, 'linkage_mode': 'eigen_corr', 'epsilon': 0.0001}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ccaf3cd7", + "metadata": {}, + "outputs": [], + "source": [ + "# Graph analysis: The optimal gaph uses a proxy to evaluate its properties, but for the final output\n", + "# the graph is built without the target variable. The search only helps to find the best graph parameters..\n", + "from bioneuralnet.utils import graph_analysis\n", + "\n", + "graph_analysis(optimal_graph, graph_name=\"Optimal\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5c9f3a3a", + "metadata": {}, + "outputs": [], + "source": [ + "from pathlib import Path\n", + "from bioneuralnet.downstream_task import DPMON\n", + "\n", + "output_dir_base = Path(\"/home/vicente/Github/BioNeuralNet/dpmon_results_GAT_FINAL/paad\")\n", + "\n", + "current_output_dir = output_dir_base / \"Gat_best_graph\"\n", + "current_output_dir.mkdir(parents=True, exist_ok=True)\n", + "\n", + "dpmon_params_base = {\n", + " \"adjacency_matrix\": optimal_graph,\n", + " \"omics_list\": omics_paad,\n", + " \"phenotype_data\": target,\n", + " \"phenotype_col\": \"target\",\n", + " \"clinical_data\": clinical_for_model,\n", + " \"model\": 'GAT',\n", + " \"tune\": True, \n", + " \"cv\": True, \n", + " \"n_folds\": 5,\n", + " 'repeat_num': 1,\n", + " \"gpu\": True,\n", + " \"cuda\": 0,\n", + " \"seed\": SEED,\n", + " \"output_dir\": current_output_dir\n", + "}\n", + "\n", + "dpmon_tunned = DPMON(**dpmon_params_base)\n", + "predictions_df, metrics, embeddings = dpmon_tunned.run()\n", + "\n", + "graph_metrics = metrics\n", + "\n", + "acc_row = graph_metrics.loc[graph_metrics['Metric'] == 'Accuracy'].iloc[0]\n", + "f1_macro_row = graph_metrics.loc[graph_metrics['Metric'] == 'F1 Macro'].iloc[0]\n", + "f1_weighted_row = graph_metrics.loc[graph_metrics['Metric'] == 'F1 Weighted'].iloc[0]\n", + "recall_row = graph_metrics.loc[graph_metrics['Metric'] == 'Recall'].iloc[0]\n", + "auc_row = graph_metrics.loc[graph_metrics['Metric'] == 'AUC'].iloc[0]\n", + "aupr_row = graph_metrics.loc[graph_metrics['Metric'] == 'AUPR'].iloc[0]\n", + "\n", + "acc_avg, acc_std = acc_row['Average'], acc_row['StdDev']\n", + "f1_macro_avg, f1_macro_std = f1_macro_row['Average'], f1_macro_row['StdDev']\n", + "f1_weighted_avg, f1_weighted_std = f1_weighted_row['Average'], f1_weighted_row['StdDev']\n", + "recall_avg, recall_std = recall_row['Average'], recall_row['StdDev']\n", + "auc_avg, auc_std = auc_row['Average'], auc_row['StdDev']\n", + "aupr_avg, aupr_std = aupr_row['Average'], aupr_row['StdDev']\n", + "\n", + "print(f\"Accuracy (Avg +/- Std): {acc_avg:.4f} +/- {acc_std:.4f}\")\n", + "print(f\"F1 Macro (Avg +/- Std): {f1_macro_avg:.4f} +/- {f1_macro_std:.4f}\")\n", + "print(f\"F1 Weighted (Avg +/- Std): {f1_weighted_avg:.4f} +/- {f1_weighted_std:.4f}\")\n", + "print(f\"Recall: {recall_avg:.4f} +/- {recall_std:.4f}\")\n", + "print(f\"AUC: {auc_avg:.4f} +/- {auc_std:.4f}\")\n", + "print(f\"AUPR: {aupr_avg:.4f} +/- {aupr_std:.4f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5a9e86d6", + "metadata": {}, + "outputs": [], + "source": [ + "from bioneuralnet.downstream_task import DPMON\n", + "\n", + "\n", + "output_dir_base = Path(\"/home/vicente/Github/BioNeuralNet/dpmon_results_GCN_FINAL/paad\")\n", + "\n", + "current_output_dir = output_dir_base / \"Gcn_best_graph\"\n", + "current_output_dir.mkdir(parents=True, exist_ok=True)\n", + "\n", + "dpmon_params_base = {\n", + " \"adjacency_matrix\": optimal_graph,\n", + " \"omics_list\": omics_paad,\n", + " \"phenotype_data\": target,\n", + " \"phenotype_col\": \"target\",\n", + " \"clinical_data\": clinical_for_model,\n", + " \"model\": 'GCN',\n", + " \"tune\": True, \n", + " \"cv\": True, \n", + " \"n_folds\": 5,\n", + " 'repeat_num': 1,\n", + " \"gpu\": True,\n", + " \"cuda\": 0,\n", + " \"seed\": SEED,\n", + " \"output_dir\": current_output_dir\n", + "}\n", + "\n", + "dpmon_tunned = DPMON(**dpmon_params_base)\n", + "predictions_df, metrics, embeddings = dpmon_tunned.run()\n", + "graph_metrics = metrics\n", + "\n", + "acc_row = graph_metrics.loc[graph_metrics['Metric'] == 'Accuracy'].iloc[0]\n", + "f1_macro_row = graph_metrics.loc[graph_metrics['Metric'] == 'F1 Macro'].iloc[0]\n", + "f1_weighted_row = graph_metrics.loc[graph_metrics['Metric'] == 'F1 Weighted'].iloc[0]\n", + "recall_row = graph_metrics.loc[graph_metrics['Metric'] == 'Recall'].iloc[0]\n", + "auc_row = graph_metrics.loc[graph_metrics['Metric'] == 'AUC'].iloc[0]\n", + "aupr_row = graph_metrics.loc[graph_metrics['Metric'] == 'AUPR'].iloc[0]\n", + "\n", + "acc_avg, acc_std = acc_row['Average'], acc_row['StdDev']\n", + "f1_macro_avg, f1_macro_std = f1_macro_row['Average'], f1_macro_row['StdDev']\n", + "f1_weighted_avg, f1_weighted_std = f1_weighted_row['Average'], f1_weighted_row['StdDev']\n", + "recall_avg, recall_std = recall_row['Average'], recall_row['StdDev']\n", + "auc_avg, auc_std = auc_row['Average'], auc_row['StdDev']\n", + "aupr_avg, aupr_std = aupr_row['Average'], aupr_row['StdDev']\n", + "\n", + "print(f\"Accuracy (Avg +/- Std): {acc_avg:.4f} +/- {acc_std:.4f}\")\n", + "print(f\"F1 Macro (Avg +/- Std): {f1_macro_avg:.4f} +/- {f1_macro_std:.4f}\")\n", + "print(f\"F1 Weighted (Avg +/- Std): {f1_weighted_avg:.4f} +/- {f1_weighted_std:.4f}\")\n", + "print(f\"Recall: {recall_avg:.4f} +/- {recall_std:.4f}\")\n", + "print(f\"AUC: {auc_avg:.4f} +/- {auc_std:.4f}\")\n", + "print(f\"AUPR: {aupr_avg:.4f} +/- {aupr_std:.4f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6c2c8381", + "metadata": {}, + "outputs": [], + "source": [ + "from bioneuralnet.downstream_task import DPMON\n", + "output_dir_base = Path(\"/home/vicente/Github/BioNeuralNet/dpmon_results_GIN_FINAL/paad\")\n", + "\n", + "\n", + "current_output_dir = output_dir_base / \"gin_best_graph\"\n", + "current_output_dir.mkdir(parents=True, exist_ok=True)\n", + "\n", + "dpmon_params_base = {\n", + " \"adjacency_matrix\": optimal_graph,\n", + " \"omics_list\": omics_paad,\n", + " \"phenotype_data\": target,\n", + " \"phenotype_col\": \"target\",\n", + " \"clinical_data\": clinical_for_model,\n", + " \"model\": 'GIN',\n", + " \"tune\": True, \n", + " \"cv\": True, \n", + " \"n_folds\": 5,\n", + " 'repeat_num': 1,\n", + " \"gpu\": True,\n", + " \"cuda\": 0,\n", + " \"seed\": SEED,\n", + " \"output_dir\": current_output_dir\n", + "}\n", + "\n", + "dpmon_tunned = DPMON(**dpmon_params_base)\n", + "predictions_df, metrics, embeddings = dpmon_tunned.run()\n", + "graph_metrics = metrics\n", + "\n", + "acc_row = graph_metrics.loc[graph_metrics['Metric'] == 'Accuracy'].iloc[0]\n", + "f1_macro_row = graph_metrics.loc[graph_metrics['Metric'] == 'F1 Macro'].iloc[0]\n", + "f1_weighted_row = graph_metrics.loc[graph_metrics['Metric'] == 'F1 Weighted'].iloc[0]\n", + "recall_row = graph_metrics.loc[graph_metrics['Metric'] == 'Recall'].iloc[0]\n", + "auc_row = graph_metrics.loc[graph_metrics['Metric'] == 'AUC'].iloc[0]\n", + "aupr_row = graph_metrics.loc[graph_metrics['Metric'] == 'AUPR'].iloc[0]\n", + "\n", + "acc_avg, acc_std = acc_row['Average'], acc_row['StdDev']\n", + "f1_macro_avg, f1_macro_std = f1_macro_row['Average'], f1_macro_row['StdDev']\n", + "f1_weighted_avg, f1_weighted_std = f1_weighted_row['Average'], f1_weighted_row['StdDev']\n", + "recall_avg, recall_std = recall_row['Average'], recall_row['StdDev']\n", + "auc_avg, auc_std = auc_row['Average'], auc_row['StdDev']\n", + "aupr_avg, aupr_std = aupr_row['Average'], aupr_row['StdDev']\n", + "\n", + "print(f\"Accuracy (Avg +/- Std): {acc_avg:.4f} +/- {acc_std:.4f}\")\n", + "print(f\"F1 Macro (Avg +/- Std): {f1_macro_avg:.4f} +/- {f1_macro_std:.4f}\")\n", + "print(f\"F1 Weighted (Avg +/- Std): {f1_weighted_avg:.4f} +/- {f1_weighted_std:.4f}\")\n", + "print(f\"Recall: {recall_avg:.4f} +/- {recall_std:.4f}\")\n", + "print(f\"AUC: {auc_avg:.4f} +/- {auc_std:.4f}\")\n", + "print(f\"AUPR: {aupr_avg:.4f} +/- {aupr_std:.4f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f96dcd75", + "metadata": {}, + "outputs": [], + "source": [ + "from bioneuralnet.downstream_task import DPMON\n", + "\n", + "output_dir_base = Path(\"/home/vicente/Github/BioNeuralNet/dpmon_results_SAGE_FINAL/paad\")\n", + "\n", + "current_output_dir = output_dir_base / \"sage_best_graph\"\n", + "current_output_dir.mkdir(parents=True, exist_ok=True)\n", + "\n", + "dpmon_params_base = {\n", + " \"adjacency_matrix\": optimal_graph,\n", + " \"omics_list\": omics_paad,\n", + " \"phenotype_data\": target,\n", + " \"phenotype_col\": \"target\",\n", + " \"clinical_data\": clinical_for_model,\n", + " \"model\": 'SAGE',\n", + " \"tune\": True, \n", + " \"cv\": True, \n", + " \"n_folds\": 5,\n", + " 'repeat_num': 1,\n", + " \"gpu\": True,\n", + " \"cuda\": 0,\n", + " \"seed\": SEED,\n", + " \"output_dir\": current_output_dir\n", + "}\n", + "\n", + "dpmon_tunned = DPMON(**dpmon_params_base)\n", + "predictions_df, metrics, embeddings = dpmon_tunned.run()\n", + "graph_metrics = metrics\n", + "\n", + "acc_row = graph_metrics.loc[graph_metrics['Metric'] == 'Accuracy'].iloc[0]\n", + "f1_macro_row = graph_metrics.loc[graph_metrics['Metric'] == 'F1 Macro'].iloc[0]\n", + "f1_weighted_row = graph_metrics.loc[graph_metrics['Metric'] == 'F1 Weighted'].iloc[0]\n", + "recall_row = graph_metrics.loc[graph_metrics['Metric'] == 'Recall'].iloc[0]\n", + "auc_row = graph_metrics.loc[graph_metrics['Metric'] == 'AUC'].iloc[0]\n", + "aupr_row = graph_metrics.loc[graph_metrics['Metric'] == 'AUPR'].iloc[0]\n", + "\n", + "acc_avg, acc_std = acc_row['Average'], acc_row['StdDev']\n", + "f1_macro_avg, f1_macro_std = f1_macro_row['Average'], f1_macro_row['StdDev']\n", + "f1_weighted_avg, f1_weighted_std = f1_weighted_row['Average'], f1_weighted_row['StdDev']\n", + "recall_avg, recall_std = recall_row['Average'], recall_row['StdDev']\n", + "auc_avg, auc_std = auc_row['Average'], auc_row['StdDev']\n", + "aupr_avg, aupr_std = aupr_row['Average'], aupr_row['StdDev']\n", + "\n", + "print(f\"Accuracy (Avg +/- Std): {acc_avg:.4f} +/- {acc_std:.4f}\")\n", + "print(f\"F1 Macro (Avg +/- Std): {f1_macro_avg:.4f} +/- {f1_macro_std:.4f}\")\n", + "print(f\"F1 Weighted (Avg +/- Std): {f1_weighted_avg:.4f} +/- {f1_weighted_std:.4f}\")\n", + "print(f\"Recall: {recall_avg:.4f} +/- {recall_std:.4f}\")\n", + "print(f\"AUC: {auc_avg:.4f} +/- {auc_std:.4f}\")\n", + "print(f\"AUPR: {aupr_avg:.4f} +/- {aupr_std:.4f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6d8f2d74", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.model_selection import StratifiedKFold, ParameterSampler, RepeatedStratifiedKFold\n", + "from sklearn.metrics import accuracy_score, f1_score, recall_score, roc_auc_score, average_precision_score\n", + "from sklearn.base import clone\n", + "from sklearn.preprocessing import StandardScaler, label_binarize\n", + "from sklearn.pipeline import Pipeline\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.neural_network import MLPClassifier\n", + "from scipy.stats import loguniform, randint\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "from sklearn.tree import DecisionTreeClassifier\n", + "from xgboost import XGBClassifier\n", + "from sklearn.svm import SVC\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "X = pd.concat([dna_meth, rna, cnv, clinical_for_model], axis=1)\n", + "y = target['target']\n", + "print(f\"Successfully created X matrix with shape: {X.shape}\")\n", + "print(f\"Successfully created y vector with shape: {y.shape}\")\n", + "\n", + "pipe_lr = Pipeline([\n", + " ('scaler', StandardScaler()),\n", + " ('model', LogisticRegression(solver='lbfgs', max_iter=1000, penalty=None, random_state=SEED))\n", + "])\n", + "\n", + "pipe_mlp = Pipeline([\n", + " ('scaler', StandardScaler()),\n", + " ('model', MLPClassifier(max_iter=500, early_stopping=True, n_iter_no_change=10, random_state=SEED))\n", + "])\n", + "\n", + "pipe_xgb = Pipeline([\n", + " ('scaler', StandardScaler()),\n", + " ('model', XGBClassifier(eval_metric='logloss', tree_method='hist', max_bin=128, random_state=SEED))\n", + "])\n", + "pipe_rf = Pipeline([\n", + " ('scaler', StandardScaler()),\n", + " ('model', RandomForestClassifier(random_state=SEED))\n", + "])\n", + "\n", + "pipe_svm = Pipeline([\n", + " ('scaler', StandardScaler()),\n", + " ('model', SVC(probability=True, random_state=SEED))\n", + "])\n", + "\n", + "pipe_dt = Pipeline([\n", + " ('scaler', StandardScaler()),\n", + " ('model', DecisionTreeClassifier(random_state=SEED))\n", + "])\n", + "\n", + "params_lr = {'model__penalty': ['l2'], 'model__C': loguniform(1e-4, 1e2)}\n", + "\n", + "params_mlp = {\n", + " 'model__hidden_layer_sizes': [(100,), (100, 50), (50, 50)],\n", + " 'model__activation': ['relu', 'tanh'],\n", + " 'model__alpha': loguniform(1e-5, 1e-1),\n", + " 'model__learning_rate_init': loguniform(1e-4, 1e-2)\n", + "}\n", + "params_xgb = {\n", + " 'model__n_estimators': randint(50, 200),\n", + " 'model__learning_rate': loguniform(0.01, 0.3),\n", + " 'model__max_depth': randint(3, 7),\n", + " 'model__subsample': [0.8, 1.0], \n", + " 'model__colsample_bytree': [0.8, 1.0]\n", + "}\n", + "params_rf = {\n", + " 'model__n_estimators': randint(100, 300),\n", + " 'model__max_depth': [10, 20, 30, None],\n", + " 'model__min_samples_split': randint(2, 10),\n", + " 'model__min_samples_leaf': randint(1, 5),\n", + " 'model__max_features': ['sqrt', 'log2']\n", + "}\n", + "params_svm = {\n", + " 'model__C': loguniform(1e-2, 1e3),\n", + " 'model__kernel': ['rbf', 'linear'],\n", + " 'model__gamma': ['scale', 'auto']\n", + "}\n", + "\n", + "params_dt = {\n", + " 'model__max_depth': randint(3, 15),\n", + " 'model__min_samples_split': randint(2, 20),\n", + " 'model__criterion': ['gini', 'entropy']\n", + "}\n", + "\n", + "models_to_tune = {\n", + " \"LogisticRegression\": (pipe_lr, params_lr),\n", + " \"SVM\": (pipe_svm, params_svm),\n", + " \"MLP\": (pipe_mlp, params_mlp),\n", + " \"XGBoost\": (pipe_xgb, params_xgb),\n", + " \"RandomForest\": (pipe_rf, params_rf),\n", + " \"DecisionTree\": (pipe_dt, params_dt),\n", + "}\n", + "\n", + "all_results = {\n", + " \"LogisticRegression\": {\"acc\": [], \"f1_w\": [], \"f1_m\": [], \"recall\": [], \"auc\": [], \"aupr\": []},\n", + " \"MLP\": {\"acc\": [], \"f1_w\": [], \"f1_m\": [], \"recall\": [], \"auc\": [], \"aupr\": []},\n", + " \"XGBoost\": {\"acc\": [], \"f1_w\": [], \"f1_m\": [], \"recall\": [], \"auc\": [], \"aupr\": []},\n", + " \"RandomForest\": {\"acc\": [], \"f1_w\": [], \"f1_m\": [], \"recall\": [], \"auc\": [], \"aupr\": []},\n", + " \"SVM\": {\"acc\": [], \"f1_w\": [], \"f1_m\": [], \"recall\": [], \"auc\": [], \"aupr\": []},\n", + " \"DecisionTree\": {\"acc\": [], \"f1_w\": [], \"f1_m\": [], \"recall\": [], \"auc\": [], \"aupr\": []},\n", + "}\n", + "\n", + "\n", + "for model_name, (pipeline, param_dist) in models_to_tune.items():\n", + " print(f\"Evaluating model with nested CV: {model_name}\")\n", + " \n", + " outer_cv = RepeatedStratifiedKFold(n_splits=5, n_repeats=1, random_state=SEED)\n", + " \n", + " # inner folds are for finding the best hyperparameters\n", + " for fold_idx, (train_idx, test_idx) in enumerate(outer_cv.split(X, y), start=1):\n", + " X_train_outer, X_test_outer = X.iloc[train_idx], X.iloc[test_idx]\n", + " y_train_outer, y_test_outer = y.iloc[train_idx], y.iloc[test_idx]\n", + " inner_cv = StratifiedKFold(n_splits=5, shuffle=True)\n", + " \n", + " best_score_fold = -np.inf\n", + " best_params_fold = None\n", + " # May or may not want to set a seed here. A fix seed = same hyperparamters each fold.\n", + " # No seed = different hyperparameters each fold. This adds more randomness, and may yield better generalization.\n", + " param_sampler = list(ParameterSampler(param_dist, n_iter=20))\n", + " \n", + " for params in param_sampler:\n", + " inner_scores = []\n", + " \n", + " for inner_train_idx, inner_val_idx in inner_cv.split(X_train_outer, y_train_outer):\n", + " X_train_inner = X_train_outer.iloc[inner_train_idx]\n", + " X_val_inner = X_train_outer.iloc[inner_val_idx]\n", + " y_train_inner = y_train_outer.iloc[inner_train_idx]\n", + " y_val_inner = y_train_outer.iloc[inner_val_idx]\n", + " \n", + " inner_pipeline = clone(pipeline)\n", + " inner_pipeline.set_params(**params)\n", + " inner_pipeline.fit(X_train_inner, y_train_inner)\n", + " \n", + " y_val_pred = inner_pipeline.predict(X_val_inner)\n", + " score = f1_score(y_val_inner, y_val_pred, average='weighted', zero_division=0)\n", + " inner_scores.append(score)\n", + " \n", + " mean_score = np.mean(inner_scores)\n", + " if mean_score > best_score_fold:\n", + " best_score_fold = mean_score\n", + " best_params_fold = params\n", + " \n", + " print(f\"Outer fold {fold_idx}: best params (inner CV F1-W={best_score_fold:.4f})\")\n", + " print(f\"{best_params_fold}\")\n", + " \n", + " final_pipeline = clone(pipeline)\n", + " final_pipeline.set_params(**best_params_fold)\n", + " final_pipeline.fit(X_train_outer, y_train_outer)\n", + " \n", + " preds = final_pipeline.predict(X_test_outer)\n", + " \n", + " if hasattr(final_pipeline, \"predict_proba\"):\n", + " proba = final_pipeline.predict_proba(X_test_outer)\n", + " else:\n", + " proba = None\n", + " \n", + " acc = accuracy_score(y_test_outer, preds)\n", + " f1_w = f1_score(y_test_outer, preds, average='weighted', zero_division=0)\n", + " f1_m = f1_score(y_test_outer, preds, average='macro', zero_division=0)\n", + " recall = recall_score(y_test_outer, preds, average='macro', zero_division=0)\n", + " \n", + " auc = np.nan\n", + " aupr = np.nan\n", + " \n", + " if proba is not None:\n", + " try:\n", + " if len(np.unique(y)) == 2:\n", + " auc = roc_auc_score(y_test_outer, proba[:, 1])\n", + " aupr = average_precision_score(y_test_outer, proba[:, 1])\n", + " else:\n", + " auc = roc_auc_score(y_test_outer, proba, multi_class='ovr', average='macro')\n", + " y_test_bin = label_binarize(y_test_outer, classes=np.unique(y))\n", + " aupr = average_precision_score(y_test_bin, proba, average='weighted')\n", + " except Exception:\n", + " auc = np.nan\n", + " aupr = np.nan\n", + "\n", + " print(f\"Fold {fold_idx} results: Acc={acc:.4f}, F1-W={f1_w:.4f}, \"\n", + " f\"F1-M={f1_m:.4f}, Recall={recall:.4f}, AUC={auc:.4f}, AUPR={aupr:.4f}\")\n", + " \n", + " all_results[model_name][\"acc\"].append(acc)\n", + " all_results[model_name][\"f1_w\"].append(f1_w)\n", + " all_results[model_name][\"f1_m\"].append(f1_m)\n", + " all_results[model_name][\"recall\"].append(recall)\n", + " all_results[model_name][\"auc\"].append(auc)\n", + " all_results[model_name][\"aupr\"].append(aupr)\n", + "\n", + "print(\"\\nFINAL BASELINE RESULTS\\n\")\n", + "for model_name, metrics in all_results.items():\n", + " avg_acc = np.mean(metrics[\"acc\"])\n", + " std_acc = np.std(metrics[\"acc\"])\n", + " avg_f1_w = np.mean(metrics[\"f1_w\"])\n", + " std_f1_w = np.std(metrics[\"f1_w\"])\n", + " avg_f1_m = np.mean(metrics[\"f1_m\"])\n", + " std_f1_m = np.std(metrics[\"f1_m\"])\n", + " avg_recall = np.mean(metrics[\"recall\"])\n", + " std_recall = np.std(metrics[\"recall\"])\n", + " avg_auc = np.nanmean(metrics[\"auc\"])\n", + " std_auc = np.nanstd(metrics[\"auc\"])\n", + " avg_aupr = np.nanmean(metrics[\"aupr\"])\n", + " std_aupr = np.nanstd(metrics[\"aupr\"])\n", + " \n", + " print(f\"\\n{model_name}:\")\n", + " print(f\"Accuracy: {avg_acc:.4f} +/- {std_acc:.4f}\")\n", + " print(f\"F1 Weighted: {avg_f1_w:.4f} +/- {std_f1_w:.4f}\")\n", + " print(f\"F1 Macro: {avg_f1_m:.4f} +/- {std_f1_m:.4f}\")\n", + " print(f\"Recall: {avg_recall:.4f} +/- {std_recall:.4f}\")\n", + " print(f\"AUC: {avg_auc:.4f} +/- {std_auc:.4f}\")\n", + " print(f\"AUPR: {avg_aupr:.4f} +/- {std_aupr:.4f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c265a190", + "metadata": {}, + "outputs": [], + "source": [ + "import bioneuralnet as bnn\n", + "# plotting function, the values below were enter manually\n", + "\n", + "baseline_data = {\n", + " \"Accuracy\": {\n", + " \"GIN\": (0.7005, 0.0583),\n", + " \"GraphSAGE\": (0.7005, 0.0934),\n", + " \"GAT\": (0.6894, 0.0646),\n", + " \"GCN\": (0.6608, 0.0469),\n", + " \"LogReg\": (0.7062, 0.0560),\n", + " \"SVM\": (0.6778, 0.0620),\n", + " \"XGBoost\": (0.6779, 0.0721),\n", + " \"MLP\": (0.6710, 0.0667),\n", + " \"Random Forest\": (0.6665, 0.0830),\n", + " \"Decision Tree\": (0.5715, 0.0654),\n", + " },\n", + " \"F1 Weighted\": {\n", + " \"GIN\": (0.6973, 0.0601),\n", + " \"GraphSAGE\": (0.6925, 0.0984),\n", + " \"GAT\": (0.6774, 0.0701),\n", + " \"GCN\": (0.6515, 0.0487),\n", + " \"LogReg\": (0.7025, 0.0586),\n", + " \"SVM\": (0.6740, 0.0630),\n", + " \"XGBoost\": (0.6715, 0.0740),\n", + " \"MLP\": (0.6678, 0.0690),\n", + " \"Random Forest\": (0.6592, 0.0870),\n", + " \"Decision Tree\": (0.5663, 0.0659),\n", + " },\n", + " \"F1 Macro\": {\n", + " \"GIN\": (0.6966, 0.0602),\n", + " \"GraphSAGE\": (0.6924, 0.0911),\n", + " \"GAT\": (0.6762, 0.0629),\n", + " \"GCN\": (0.6505, 0.0435),\n", + " \"LogReg\": (0.7008, 0.0597),\n", + " \"SVM\": (0.6725, 0.0637),\n", + " \"XGBoost\": (0.6693, 0.0751),\n", + " \"MLP\": (0.6662, 0.0697),\n", + " \"Random Forest\": (0.6568, 0.0883),\n", + " \"Decision Tree\": (0.5648, 0.0668),\n", + " },\n", + "}\n", + "\n", + "baseline_data_cont = {\n", + " \"Recall\": {\n", + " \"GIN\": (0.7002, 0.0589),\n", + " \"GraphSAGE\": (0.7032, 0.0734),\n", + " \"GAT\": (0.6883, 0.0553),\n", + " \"GCN\": (0.6607, 0.0427),\n", + " \"LogReg\": (0.7032, 0.0585),\n", + " \"SVM\": (0.6755, 0.0631),\n", + " \"XGBoost\": (0.6737, 0.0736),\n", + " \"MLP\": (0.6686, 0.0682),\n", + " \"Random Forest\": (0.6620, 0.0846),\n", + " \"Decision Tree\": (0.5700, 0.0671),\n", + " },\n", + " \"AUC\": {\n", + " \"GIN\": (0.7471, 0.0573),\n", + " \"GraphSAGE\": (0.7482, 0.0772),\n", + " \"GAT\": (0.7418, 0.0784),\n", + " \"GCN\": (0.7390, 0.0779),\n", + " \"LogReg\": (0.7905, 0.0590),\n", + " \"SVM\": (0.7602, 0.0604),\n", + " \"XGBoost\": (0.7454, 0.0852),\n", + " \"MLP\": (0.7363, 0.0720),\n", + " \"Random Forest\": (0.7509, 0.0813),\n", + " \"Decision Tree\": (0.5802, 0.0747),\n", + " },\n", + " \"AUPR\": {\n", + " \"GIN\": (0.7634, 0.0743),\n", + " \"GraphSAGE\": (0.7812, 0.0722),\n", + " \"GAT\": (0.7570, 0.0872),\n", + " \"GCN\": (0.7661, 0.0739),\n", + " \"LogReg\": (0.8042, 0.0689),\n", + " \"SVM\": (0.7824, 0.0574),\n", + " \"XGBoost\": (0.7624, 0.0836),\n", + " \"MLP\": (0.7533, 0.0824),\n", + " \"Random Forest\": (0.7770, 0.0734),\n", + " \"Decision Tree\": (0.5869, 0.0559),\n", + " },\n", + "}\n", + "\n", + "bnn.metrics.plot_multiple_metrics(\n", + " baseline_data,\n", + " title_map={\n", + " \"Accuracy\": \"GNNs vs Baselines: Accuracy\",\n", + " \"F1 Weighted\": \"GNNs vs Baselines: F1 Weighted\",\n", + " \"F1 Macro\": \"GNNs vs Baselines: F1 Macro\",\n", + " }\n", + ")\n", + "\n", + "bnn.metrics.plot_multiple_metrics(\n", + " baseline_data_cont,\n", + " title_map={\n", + " \"Recall\": \"GNNs vs Baselines: Recall\",\n", + " \"AUC\": \"GNNs vs Baselines: AUC\",\n", + " \"AUPR\": \"GNNs vs Baselines: AUPR\",\n", + " }\n", + ")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".enviroment", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/source/notebooks/datasets.ipynb b/docs/source/notebooks/datasets.ipynb deleted file mode 100644 index 703b2a1..0000000 --- a/docs/source/notebooks/datasets.ipynb +++ /dev/null @@ -1,3612 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Datasets Guide\n", - "\n", - "- The `DatasetLoader` class provides a simple interface to access example multi-omics datasets included in this package.\n", - "\n", - " - Each dataset is loaded as a collection of **pandas DataFrames**, with table names as keys and the corresponding data as values.\n", - "\n", - " - Users can explore the structure of any dataset via the `.shape` property, which returns a mapping from table name to `(rows, columns)`. \n", - "\n", - "In BioNeuralNet `rows` reprecent subjects/patients and `columns` represent omics.\n", - "\n", - "**Five datasets are available out-of-the-box:**\n", - "\n", - "1. **`brca`**:\n", - "\n", - " - Breast Cancer cohort dataset from The Cancer Genome Atlas (TCGA).\n", - " - Provides comprehensive omics DataFrames: `rna`, `mirna`, `meth`, `pam50`, `clinical`.\n", - " - Full dataset description available at: [https://bioneuralnet.readthedocs.io/TCGA-BRCA](https://bioneuralnet.readthedocs.io/en/latest/notebooks/TCGA-BRCA.html)\n", - "\n", - "2. **`lgg`**:\n", - "\n", - " - Brain Cancer (GBM + LGG) cohort dataset from The Cancer Genome Atlas (TCGA).\n", - " - Provides the processed, feature-selected omics DataFrames: `rna`, `mirna`, `meth`, `clinical`, and `target` (histological subtype).\n", - " - Full dataset description available at: [https://bioneuralnet.readthedocs.io/TCGA-GBMLGG](https://bioneuralnet.readthedocs.io/en/latest/notebooks/TCGA-GBMLGG.html)\n", - "\n", - "3. **`kipan`**:\n", - "\n", - " - Kidney Cancer (KIRC + KIRP + KICH) cohort dataset from The Cancer Genome Atlas (TCGA).\n", - " - Provides the processed, feature-selected omics DataFrames: `rna`, `mirna`, `meth`, `clinical`, and `target` (histological subtype).\n", - " - Full dataset description available at: [https://bioneuralnet.readthedocs.io/TCGA-KIPAN](https://bioneuralnet.readthedocs.io/en/latest/notebooks/TCGA-KIPAN.html)\n", - "\n", - "4. **`example`**:\n", - "\n", - " - Synthetic dataset designed for testing and demonstration.\n", - " - Contains small DataFrames: `X1`, `X2`, `Y`, `clinical_data`\n", - " - Useful for quick checks of package functionality.\n", - "\n", - "5. **`monet`**:\n", - "\n", - " - Multi-omics benchmark dataset from the **Multi-Omics NETwork Analysis Workshop (MONET)**.\n", - " - Includes multiple DataFrames: `gene_data`, `mirna_data`, `phenotype`, `rppa_data`, `clinical_data`\n", - " - Workshop details: [https://coloradosph.cuanschutz.edu/research-and-practice/centers-programs/cida/learning/multi-omics-network-analysis-workshop](https://coloradosph.cuanschutz.edu/research-and-practice/centers-programs/cida/learning/multi-omics-network-analysis-workshop)" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "brca shapes:\n", - "\n", - "mirna: 769 x 503\n", - "pam50: 769 x 1\n", - "clinical: 769 x 118\n", - "rna: 769 x 1687\n", - "meth: 769 x 1605\n", - "\n", - "\n", - "lgg shapes:\n", - "\n", - "mirna: 511 x 548\n", - "target: 511 x 1\n", - "clinical: 511 x 13\n", - "rna: 511 x 2127\n", - "meth: 511 x 1823\n", - "\n", - "\n", - "kipan shapes:\n", - "\n", - "mirna: 658 x 472\n", - "target: 658 x 1\n", - "clinical: 658 x 19\n", - "rna: 658 x 2284\n", - "meth: 658 x 2102\n", - "\n", - "\n", - "example shapes:\n", - "\n", - "X1: 358 x 500\n", - "X2: 358 x 100\n", - "Y: 358 x 1\n", - "clinical_data: 358 x 6\n", - "\n", - "\n", - "monet shapes:\n", - "\n", - "gene_data: 107 x 5039\n", - "mirna_data: 107 x 789\n", - "phenotype: 106 x 1\n", - "rppa_data: 107 x 175\n", - "clinical_data: 107 x 5\n", - "\n", - "\n" - ] - } - ], - "source": [ - "from bioneuralnet.datasets import DatasetLoader\n", - "import pandas as pd\n", - "\n", - "for name in [\"brca\", \"lgg\", \"kipan\", \"example\", \"monet\"]:\n", - " ds = DatasetLoader(name)\n", - " print(f\"{name} shapes:\\n\")\n", - " for tbl, (rows, cols) in ds.shape.items():\n", - " print(f\"{tbl}: {rows} x {cols}\")\n", - " print(\"\\n\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## TCGA-GBMLGG" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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LASS4IQGAP1LOC150776PDK1EDEM3
patient
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TCGA-CS-4944-1.596503-0.578981-3.306971-1.621030-1.671006
..................
TCGA-WY-A85A-1.474161-0.777362-3.467333-1.753156-1.824650
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ZFYVE20_64145CSRP2_1466SUPT5H_6829USP6NL_9712LTC4S_4056
patient
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hsa_let_7a_1hsa_let_7a_2hsa_let_7a_3hsa_let_7bhsa_let_7c
patient
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TCGA-CS-494111.80980812.81581511.82006113.04785311.955006
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TCGA-CS-494411.79452212.78545411.81668813.30893713.326909
..................
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years_to_birthvital_statusdays_to_deathdays_to_last_followuptumor_tissue_site
Patient_ID
TCGA-CS-493831.00NaN3574.0central nervous system
TCGA-CS-494167.01234.0NaNcentral nervous system
TCGA-CS-494244.011335.0NaNcentral nervous system
TCGA-CS-494337.011106.0NaNcentral nervous system
TCGA-CS-494450.00NaN1828.0central nervous system
..................
TCGA-WY-A85A20.00NaN1320.0central nervous system
TCGA-WY-A85B24.00NaN1393.0central nervous system
TCGA-WY-A85C36.00NaN1426.0central nervous system
TCGA-WY-A85D60.00NaN1147.0central nervous system
TCGA-WY-A85E48.00NaN633.0central nervous system
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C19orf22C15orf41APOL5ADCY10MIR762
patient
TCGA-2K-A9WE0.362406-0.2286361.8236981.240816-2.290156
TCGA-2Z-A9J10.107703-0.631482-0.1452290.943370-3.210824
TCGA-2Z-A9J20.416326-0.1428911.0355231.129828-1.963174
TCGA-2Z-A9J30.565581-0.4815170.6231151.191905-1.830991
TCGA-2Z-A9J50.404795-0.4117961.2402551.217618-2.055250
..................
TCGA-Y8-A8980.438930-0.3012261.3591561.288669-1.917179
TCGA-Y8-A8RY0.439501-0.4783841.0865091.580170-2.041550
TCGA-Y8-A8RZ0.590624-0.5979970.7432821.490705-1.910828
TCGA-Y8-A8S00.622166-0.6096771.0170051.429859-1.775636
TCGA-Y8-A8S10.400045-0.2742811.6096461.219365-2.337758
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ARAP3_64411GPRC5C_55890RASGEF1C_255426PLAC9_219348C9orf169_375791
patient
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TCGA-2Z-A9J35.05732512.8577345.9767072.2341955.348059
TCGA-2Z-A9J55.85895612.2552145.5074844.6938994.703483
..................
TCGA-Y8-A8985.92491212.7911615.3148604.3399504.861171
TCGA-Y8-A8RY7.61795912.3140352.4991164.4028124.720237
TCGA-Y8-A8RZ5.81799913.0756353.0725690.9851363.359451
TCGA-Y8-A8S04.69107112.5611554.7127662.7341354.308310
TCGA-Y8-A8S18.82291212.3329560.5146027.1511983.404045
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658 rows × 5 columns

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hsa_let_7a_1hsa_let_7a_2hsa_let_7a_3hsa_let_7bhsa_let_7c
patient
TCGA-2K-A9WE12.93349913.93302512.93852812.86196911.474055
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TCGA-2Z-A9J312.55741013.54891312.56400312.08928611.283549
TCGA-2Z-A9J512.81276713.81316612.82092212.83872711.498841
..................
TCGA-Y8-A89811.89997512.87299711.89977012.51889210.930260
TCGA-Y8-A8RY12.14729813.14968112.15264411.78000810.966539
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TCGA-Y8-A8S112.40457413.40556812.41669511.44864410.276151
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658 rows × 5 columns

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target
patient
TCGA-2K-A9WE1
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TCGA-2Z-A9J21
TCGA-2Z-A9J31
TCGA-2Z-A9J51
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years_to_birthvital_statusdays_to_deathdays_to_last_followuptumor_tissue_site
Patient_ID
TCGA-2K-A9WE53.00NaN214.0kidney
TCGA-2Z-A9J171.00NaN2298.0kidney
TCGA-2Z-A9J271.00NaN1795.0kidney
TCGA-2Z-A9J367.011771.0NaNkidney
TCGA-2Z-A9J580.00NaN3050.0kidney
..................
TCGA-Y8-A89869.00NaN475.0kidney
TCGA-Y8-A8RY63.00NaN769.0kidney
TCGA-Y8-A8RZ55.00NaN205.0kidney
TCGA-Y8-A8S058.00NaN183.0kidney
TCGA-Y8-A8S161.00NaN404.0kidney
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658 rows × 5 columns

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" - ], - "text/plain": [ - " years_to_birth vital_status days_to_death \\\n", - "Patient_ID \n", - "TCGA-2K-A9WE 53.0 0 NaN \n", - "TCGA-2Z-A9J1 71.0 0 NaN \n", - "TCGA-2Z-A9J2 71.0 0 NaN \n", - "TCGA-2Z-A9J3 67.0 1 1771.0 \n", - "TCGA-2Z-A9J5 80.0 0 NaN \n", - "... ... ... ... \n", - "TCGA-Y8-A898 69.0 0 NaN \n", - "TCGA-Y8-A8RY 63.0 0 NaN \n", - "TCGA-Y8-A8RZ 55.0 0 NaN \n", - "TCGA-Y8-A8S0 58.0 0 NaN \n", - "TCGA-Y8-A8S1 61.0 0 NaN \n", - "\n", - " days_to_last_followup tumor_tissue_site \n", - "Patient_ID \n", - "TCGA-2K-A9WE 214.0 kidney \n", - "TCGA-2Z-A9J1 2298.0 kidney \n", - "TCGA-2Z-A9J2 1795.0 kidney \n", - "TCGA-2Z-A9J3 NaN kidney \n", - "TCGA-2Z-A9J5 3050.0 kidney \n", - "... ... ... \n", - "TCGA-Y8-A898 475.0 kidney \n", - "TCGA-Y8-A8RY 769.0 kidney \n", - "TCGA-Y8-A8RZ 205.0 kidney \n", - "TCGA-Y8-A8S0 183.0 kidney \n", - "TCGA-Y8-A8S1 404.0 kidney \n", - "\n", - "[658 rows x 5 columns]" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from bioneuralnet.datasets import DatasetLoader\n", - "\n", - "kipan_data = DatasetLoader(\"kipan\")\n", - "\n", - "dna_meth = kipan_data.data[\"meth\"]\n", - "rna = kipan_data.data[\"rna\"]\n", - "mirna = kipan_data.data[\"mirna\"]\n", - "target = kipan_data.data[\"target\"]\n", - "clinical = kipan_data.data[\"clinical\"]\n", - "\n", - "display(dna_meth.iloc[:, :5])\n", - "display(rna.iloc[:, :5])\n", - "display(mirna.iloc[:, :5])\n", - "display(target.head())\n", - "display(clinical.iloc[:, :5])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## TCGA-BRCA" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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OSR1_130497ARRB1_408RGS22_26166SPNS2_124976ZNF680_340252
patient
TCGA-3C-AAAU3.4174349.5938119.4419517.3378548.413068
TCGA-3C-AALI3.7060447.9444461.9284278.7238337.950801
TCGA-3C-AALJ3.7654608.1939572.1804987.5739808.224889
TCGA-3C-AALK7.4581018.4530013.5341368.5813248.922770
TCGA-4H-AAAK6.9670128.7069184.0152667.3242678.314234
..................
TCGA-WT-AB447.7754467.1797172.8739129.9249908.199838
TCGA-XX-A8997.59768611.7923235.51315310.0708088.398323
TCGA-XX-A89A7.5436889.4840335.8127968.8648859.226318
TCGA-Z7-A8R58.1020619.6488893.16346710.4133568.210767
TCGA-Z7-A8R64.9689209.4500456.5269146.9257188.394437
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769 rows × 5 columns

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hsa_let_7a_1hsa_let_7a_2hsa_let_7a_3hsa_let_7bhsa_let_7c
patient
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TCGA-3C-AALI12.91806913.92230012.91319414.5126579.646536
TCGA-3C-AALJ13.01203314.01000213.02848313.4196129.312455
TCGA-3C-AALK13.14469714.14172113.15128114.66719611.511431
TCGA-4H-AAAK13.41168414.41351813.42048114.43854811.693927
..................
TCGA-WT-AB4413.37571514.36667113.36982714.51402411.926315
TCGA-XX-A89914.03615515.03634114.04331314.33950312.361761
TCGA-XX-A89A13.67956914.68485513.69146314.19820712.684212
TCGA-Z7-A8R512.96208813.96635012.98489714.32066011.980246
TCGA-Z7-A8R613.34971114.34990113.37844714.11721210.378041
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769 rows × 5 columns

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" - ], - "text/plain": [ - " hsa_let_7a_1 hsa_let_7a_2 hsa_let_7a_3 hsa_let_7b hsa_let_7c\n", - "patient \n", - "TCGA-3C-AAAU 13.129765 14.117933 13.147714 14.595135 8.414890\n", - "TCGA-3C-AALI 12.918069 13.922300 12.913194 14.512657 9.646536\n", - "TCGA-3C-AALJ 13.012033 14.010002 13.028483 13.419612 9.312455\n", - "TCGA-3C-AALK 13.144697 14.141721 13.151281 14.667196 11.511431\n", - "TCGA-4H-AAAK 13.411684 14.413518 13.420481 14.438548 11.693927\n", - "... ... ... ... ... ...\n", - "TCGA-WT-AB44 13.375715 14.366671 13.369827 14.514024 11.926315\n", - "TCGA-XX-A899 14.036155 15.036341 14.043313 14.339503 12.361761\n", - "TCGA-XX-A89A 13.679569 14.684855 13.691463 14.198207 12.684212\n", - "TCGA-Z7-A8R5 12.962088 13.966350 12.984897 14.320660 11.980246\n", - "TCGA-Z7-A8R6 13.349711 14.349901 13.378447 14.117212 10.378041\n", - "\n", - "[769 rows x 5 columns]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
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OR4N5KIAA0196RTF1C19orf59ZNF655
patient
TCGA-3C-AAAU-3.017241-2.467556-1.5926110.441655-2.574662
TCGA-3C-AALI-2.474721-2.396231-1.366665-0.325130-2.190856
TCGA-3C-AALJ-0.962452-2.455018-1.6584740.677827-2.224305
TCGA-3C-AALK0.328606-2.314768-1.730472-0.494891-2.318108
TCGA-4H-AAAK-2.592710-2.444838-1.546156-0.459881-2.313006
..................
TCGA-WT-AB440.807513-2.489575-1.408843-0.803121-2.185531
TCGA-XX-A899-1.632685-2.256371-1.707611-0.587766-2.238850
TCGA-XX-A89A-1.559722-2.290714-1.717666-0.772229-2.366888
TCGA-Z7-A8R5-0.490567-2.065821-1.456703-0.927756-2.037512
TCGA-Z7-A8R6-3.939185-2.817846-1.914555-1.111444-2.334904
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769 rows × 5 columns

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" - ], - "text/plain": [ - " OR4N5 KIAA0196 RTF1 C19orf59 ZNF655\n", - "patient \n", - "TCGA-3C-AAAU -3.017241 -2.467556 -1.592611 0.441655 -2.574662\n", - "TCGA-3C-AALI -2.474721 -2.396231 -1.366665 -0.325130 -2.190856\n", - "TCGA-3C-AALJ -0.962452 -2.455018 -1.658474 0.677827 -2.224305\n", - "TCGA-3C-AALK 0.328606 -2.314768 -1.730472 -0.494891 -2.318108\n", - "TCGA-4H-AAAK -2.592710 -2.444838 -1.546156 -0.459881 -2.313006\n", - "... ... ... ... ... ...\n", - "TCGA-WT-AB44 0.807513 -2.489575 -1.408843 -0.803121 -2.185531\n", - "TCGA-XX-A899 -1.632685 -2.256371 -1.707611 -0.587766 -2.238850\n", - "TCGA-XX-A89A -1.559722 -2.290714 -1.717666 -0.772229 -2.366888\n", - "TCGA-Z7-A8R5 -0.490567 -2.065821 -1.456703 -0.927756 -2.037512\n", - "TCGA-Z7-A8R6 -3.939185 -2.817846 -1.914555 -1.111444 -2.334904\n", - "\n", - "[769 rows x 5 columns]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
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pam50
patient
TCGA-3C-AAAU3
TCGA-3C-AALI2
TCGA-3C-AALJ4
TCGA-3C-AALK3
TCGA-4H-AAAK3
......
TCGA-WT-AB443
TCGA-XX-A8993
TCGA-XX-A89A3
TCGA-Z7-A8R53
TCGA-Z7-A8R64
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769 rows × 1 columns

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" - ], - "text/plain": [ - " pam50\n", - "patient \n", - "TCGA-3C-AAAU 3\n", - "TCGA-3C-AALI 2\n", - "TCGA-3C-AALJ 4\n", - "TCGA-3C-AALK 3\n", - "TCGA-4H-AAAK 3\n", - "... ...\n", - "TCGA-WT-AB44 3\n", - "TCGA-XX-A899 3\n", - "TCGA-XX-A89A 3\n", - "TCGA-Z7-A8R5 3\n", - "TCGA-Z7-A8R6 4\n", - "\n", - "[769 rows x 1 columns]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
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synchronous_malignancyajcc_pathologic_stagedays_to_diagnosislateralitycreated_datetime
patient
TCGA-3C-AAAUNoStage X0.0LeftNaN
TCGA-3C-AALINoStage IIB0.0RightNaN
TCGA-3C-AALJNoStage IIB0.0RightNaN
TCGA-3C-AALKNoStage IA0.0RightNaN
TCGA-4H-AAAKNoStage IIIA0.0LeftNaN
..................
TCGA-WT-AB44NoStage IA0.0LeftNaN
TCGA-XX-A899NoStage IIIA0.0RightNaN
TCGA-XX-A89ANoStage IIB0.0LeftNaN
TCGA-Z7-A8R5NoStage IIIA0.0LeftNaN
TCGA-Z7-A8R6NoStage I0.0LeftNaN
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769 rows × 5 columns

\n", - "
" - ], - "text/plain": [ - " synchronous_malignancy ajcc_pathologic_stage days_to_diagnosis \\\n", - "patient \n", - "TCGA-3C-AAAU No Stage X 0.0 \n", - "TCGA-3C-AALI No Stage IIB 0.0 \n", - "TCGA-3C-AALJ No Stage IIB 0.0 \n", - "TCGA-3C-AALK No Stage IA 0.0 \n", - "TCGA-4H-AAAK No Stage IIIA 0.0 \n", - "... ... ... ... \n", - "TCGA-WT-AB44 No Stage IA 0.0 \n", - "TCGA-XX-A899 No Stage IIIA 0.0 \n", - "TCGA-XX-A89A No Stage IIB 0.0 \n", - "TCGA-Z7-A8R5 No Stage IIIA 0.0 \n", - "TCGA-Z7-A8R6 No Stage I 0.0 \n", - "\n", - " laterality created_datetime \n", - "patient \n", - "TCGA-3C-AAAU Left NaN \n", - "TCGA-3C-AALI Right NaN \n", - "TCGA-3C-AALJ Right NaN \n", - "TCGA-3C-AALK Right NaN \n", - "TCGA-4H-AAAK Left NaN \n", - "... ... ... \n", - "TCGA-WT-AB44 Left NaN \n", - "TCGA-XX-A899 Right NaN \n", - "TCGA-XX-A89A Left NaN \n", - "TCGA-Z7-A8R5 Left NaN \n", - "TCGA-Z7-A8R6 Left NaN \n", - "\n", - "[769 rows x 5 columns]" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from bioneuralnet.datasets import DatasetLoader\n", - "\n", - "brca = DatasetLoader(\"brca\")\n", - "rna = brca.data[\"rna\"]\n", - "mirna = brca.data[\"mirna\"]\n", - "meth = brca.data[\"meth\"]\n", - "pam50 = brca.data[\"pam50\"]\n", - "clinical = brca.data[\"clinical\"]\n", - "\n", - "display(rna.iloc[:, :5])\n", - "display(mirna.iloc[:, :5])\n", - "display(meth.iloc[:, :5])\n", - "display(pam50.iloc[:, :5])\n", - "display(clinical.iloc[:, :5])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Example 1: Synthetic dataset" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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Gene_1Gene_2Gene_3Gene_4Gene_5
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Samp_237.05885034.05223333.48702023.53146126.754628
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Samp_528.96198141.06049428.49495618.37449530.815238
..................
Samp_35424.52065228.59540931.29966632.09537933.659730
Samp_35531.25278928.98808729.57419531.18928832.098841
Samp_35624.89482625.94488730.85264126.70515830.102546
Samp_35717.03433738.57470525.09520137.06244235.417758
Samp_35820.83916727.09978831.03845319.41085931.818995
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Mir_1Mir_2Mir_3Mir_4Mir_5
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Samp_35515.99757615.44895115.35556616.50175211.701778
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phenotype
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AgeGenderBMIChronic_BronchitisEmphysema
PatientID
Samp_178031.211
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..................
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" - ], - "text/plain": [ - " Age Gender BMI Chronic_Bronchitis Emphysema\n", - "PatientID \n", - "Samp_1 78 0 31.2 1 1\n", - "Samp_2 68 1 19.2 1 0\n", - "Samp_3 54 1 19.3 0 1\n", - "Samp_4 47 1 36.2 0 0\n", - "Samp_5 60 1 26.2 0 1\n", - "... ... ... ... ... ...\n", - "Samp_354 71 0 23.0 1 0\n", - "Samp_355 62 1 25.5 0 1\n", - "Samp_356 61 0 21.1 1 0\n", - "Samp_357 64 0 37.6 0 0\n", - "Samp_358 61 1 31.3 1 0\n", - "\n", - "[358 rows x 5 columns]" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from bioneuralnet.datasets import DatasetLoader\n", - "\n", - "Example = DatasetLoader(\"example\")\n", - "omics1 = Example.data[\"X1\"]\n", - "omics2= Example.data[\"X2\"]\n", - "phenotype = Example.data[\"Y\"]\n", - "clinical = Example.data[\"clinical_data\"]\n", - "\n", - "display(omics1.iloc[:, :5])\n", - "display(omics2.iloc[:, :5])\n", - "display(phenotype.iloc[:, :5])\n", - "display(clinical.iloc[:, :5])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Monet: Set from the **Multi-Omics NETwork Analysis Workshop (MONET)**, Univ. of Colorado Anschutz " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " overall_survival status years_to_birth race \\\n", - "0 3015 0 37 blackorafricanamerican \n", - "1 2348 1 73 white \n", - "2 3011 0 41 asian \n", - "3 3283 0 67 white \n", - "4 1873 0 42 white \n", - ".. ... ... ... ... \n", - "102 2329 0 63 white \n", - "103 1004 1 74 white \n", - "104 984 0 46 white \n", - "105 867 0 44 white \n", - "106 1133 0 53 white \n", - "\n", - " radiation_therapy \n", - "0 yes \n", - "1 yes \n", - "2 yes \n", - "3 no \n", - "4 no \n", - ".. ... \n", - "102 yes \n", - "103 yes \n", - "104 yes \n", - "105 no \n", - "106 yes \n", - "\n", - "[107 rows x 5 columns]" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from bioneuralnet.datasets import DatasetLoader\n", - "\n", - "monet = DatasetLoader(\"monet\")\n", - "gene = monet.data[\"gene_data\"]\n", - "mirna = monet.data[\"mirna_data\"]\n", - "phenotype = monet.data[\"phenotype\"]\n", - "rppa = monet.data[\"rppa_data\"]\n", - "clinical = monet.data[\"clinical_data\"]\n", - "\n", - "display(gene.iloc[:, :5])\n", - "display(mirna.iloc[:, :5])\n", - "display(phenotype.iloc[:, :5])\n", - "display(rppa.iloc[:, :5])\n", - "display(clinical.iloc[:, :5])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Generating an Omics Network\n", - "At its core, BioNeuralNet leverages Graph Neural Networks to power downstream applications via learned embeddings. It supports a range of standard graph construction techniques for omics and other biological entities:\n", - "\n", - "- **Cosine similarity / RBF kernel graphs** (`gen_similarity_graph`) \n", - "- **Pearson / Spearman correlation graphs** (`gen_correlation_graph`) \n", - "- **Soft-threshold (WGCNA-style) graphs** (`gen_threshold_graph`) \n", - "- **Gaussian k-NN graphs** (`gen_gaussian_knn_graph`) \n", - "- **Mutual information graphs** (`gen_mutual_info_graph`) \n", - "- **Graphical Lasso (sparse inverse covariance) graphs** (`gen_lasso_graph`) \n", - "- **Minimum Spanning Tree (MST) graphs** (`gen_mst_graph`) \n", - "- **Shared Nearest Neighbor (SNN) graphs** (`gen_snn_graph`)\n", - "\n", - "For more details on all of these utilities, see the [utils documentation](https://bioneuralnet.readthedocs.io/en/latest/utils.html). \n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": ".enviroment", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.3" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/docs/source/notebooks/index.rst b/docs/source/notebooks/index.rst index 9a9fd82..77becb9 100644 --- a/docs/source/notebooks/index.rst +++ b/docs/source/notebooks/index.rst @@ -1,21 +1,17 @@ Notebooks ========= -This collection of demonstration notebooks establishes a robust and reproducible benchmark for multi-omics classification using the **BioNeuralNet framework**. +This collection of demonstration notebooks provides a reproducible benchmarkfor multi-omics classification and subgraph detection using the **BioNeuralNet framework**. -These workflows provide a comprehensive, step-by-step guide to: +Each workflow walks through: -* **Feature Selection** and data preprocessing. -* **Hyperparameter Optimization** for Graph Neural Networks (GNNs). -* **Downstream Tasks** including disease prediction with DPMON. +* **Feature selection** and data preprocessing. +* **Network construction** (similarity graphs and phenotype-aware networks). +* **Hyperparameter optimization** for Graph Neural Networks (GNNs). +* **Downstream tasks**, including disease prediction with DPMON. +* **Subgraph detection and biomarker modules** in selected cohorts. -This documentation also includes dedicated guides for the framework's supporting utilities: - -* **Dataset Loading**: Access the 5 included example datasets (TCGA-BRCA, TCGA-GBMLGG, TCGA-KIPAN, etc.). -* **Network Construction**: Demonstrates techniques for building omics networks. - -.. note:: - The **TCGA-BRCA** notebook reflects an older version of the framework. The **TCGA-GBMLGG** and **TCGA-KIPAN** notebooks demonstrate the latest, end-to-end workflow. +The TCGA notebooks showcase the full end-to-end pipeline on multiple cancers (BRCA, LGG, KIPAN, PAAD). The biomarker notebook focuses on phenotype-associated subgraphs and driver modules derived from these analyses. The following notebooks are included in this guide: @@ -23,7 +19,7 @@ The following notebooks are included in this guide: :maxdepth: 1 TCGA-BRCA.ipynb - TCGA-GBMLGG.ipynb + TCGA-LGG.ipynb TCGA-KIPAN.ipynb - datasets.ipynb - network_construction.ipynb \ No newline at end of file + TCGA-PAAD.ipynb + TCGA-Biomarkers.ipynb diff --git a/docs/source/notebooks/network_construction.ipynb b/docs/source/notebooks/network_construction.ipynb deleted file mode 100644 index 7f5b1a6..0000000 --- a/docs/source/notebooks/network_construction.ipynb +++ /dev/null @@ -1,1231 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Network Construction Guide\n", - "\n", - "This notebook covers the first step in a BioNeuralNet workflow: converting a multi-omics data table (subjects in rows, features in columns) into a multi-omics network that can be fed directly into BioNeuralNet's Graph Neural Network module for embedding generation.\n", - "\n", - "**Why start with networks?**\n", - "\n", - "- Networks capture complex relationships and hidden structure in high-dimensional omics data better than raw tables.\n", - "- BioNeuralNet includes several network construction methods (similarity, correlation, soft thresholding, Gaussian k-NN, Lasso, MST, SNN, SmCCNet) allowing you to select the best method for your data and analysis goals.\n", - "- The output from each method is a `D × D` adjacency matrix (`D` = number of features), ready for embedding and further downstream tasks.\n", - "\n", - "In this notebook we will:\n", - "\n", - "1. Install `BioNeuralNet` and its core dependencies\n", - "2. Load a sample multi-omics dataset\n", - "3. Constructing a multi-omics networks\n", - " - Perform basic checks (shape, sparsity) and visualize the networks\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 1. Install `BioNeuralNet` and its core dependencies\n", - "\n", - "A detailed installation guide is availble [here](https://bioneuralnet.readthedocs.io/en/latest/installation.html).\n", - "\n", - "```bash\n", - "!pip install bioneuralnet\n", - "!pip install torch\n", - "!pip install torch-geometric\n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Network Construction\n", - "from bioneuralnet.utils import gen_similarity_graph\n", - "from bioneuralnet.utils import gen_correlation_graph\n", - "from bioneuralnet.utils import gen_threshold_graph\n", - "from bioneuralnet.utils import gen_mst_graph\n", - "from bioneuralnet.utils import gen_snn_graph\n", - "from bioneuralnet.external_tools import SmCCNet\n", - "\n", - "# Network Pruning Utilities\n", - "from bioneuralnet.utils import prune_network\n", - "from bioneuralnet.utils import prune_network_by_quantile\n", - "from bioneuralnet.utils import network_remove_low_variance\n", - "from bioneuralnet.utils import network_remove_high_zero_fraction\n", - "\n", - "# Network Visualization\n", - "from bioneuralnet.metrics import plot_network" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2. Load a sample multi-omics dataset\n", - "Full description of the `TCGA-BRCA`(Breast Cancer) dataset and preprocessing details available [here](https://bioneuralnet.readthedocs.io/en/latest/TCGA-BRCA_Dataset.html)." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'mirna': (769, 503), 'pam50': (769, 1), 'clinical': (769, 118), 'rna': (769, 6000), 'meth': (769, 6000)}\n" - ] - }, - { - "data": { - "text/html": [ - "
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pam50
patient
TCGA-3C-AAAU3
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" - ], - "text/plain": [ - " pam50\n", - "patient \n", - "TCGA-3C-AAAU 3\n", - "TCGA-3C-AALI 2\n", - "TCGA-3C-AALJ 4\n", - "TCGA-3C-AALK 3\n", - "TCGA-4H-AAAK 3" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import time\n", - "import pandas as pd\n", - "from bioneuralnet.datasets import DatasetLoader\n", - "\n", - "tgca_brca = DatasetLoader(\"brca\")\n", - "brca_meth = tgca_brca.data[\"meth\"]\n", - "brca_rna = tgca_brca.data[\"rna\"]\n", - "brca_mirna = tgca_brca.data[\"mirna\"]\n", - "brca_clinical = tgca_brca.data[\"clinical\"]\n", - "brca_pam50 = tgca_brca.data[\"pam50\"]\n", - "print(tgca_brca.shape)\n", - "\n", - "# Display the first few rows of the datasets\n", - "display(brca_meth.iloc[:5,:5])\n", - "display(brca_rna.iloc[:5,:5])\n", - "display(brca_mirna.iloc[:5,:5])\n", - "display(brca_clinical.iloc[:5,:5])\n", - "display(brca_pam50.iloc[:5,:5])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3. Constructing multi-omics networks" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2025-08-07 00:16:27,557 - bioneuralnet.utils.preprocess - INFO - [Inf]: Replaced 0 infinite values\n", - "2025-08-07 00:16:27,558 - bioneuralnet.utils.preprocess - INFO - [NaN]: Replaced 0 NaNs after median imputation\n", - "2025-08-07 00:16:27,558 - bioneuralnet.utils.preprocess - INFO - [Zero-Var]: 0 columns dropped due to zero variance\n", - "2025-08-07 00:16:27,561 - bioneuralnet.utils.preprocess - INFO - Selected top 1000 features by variance\n", - "2025-08-07 00:16:27,712 - bioneuralnet.utils.preprocess - INFO - [Inf]: Replaced 0 infinite values\n", - "2025-08-07 00:16:27,712 - bioneuralnet.utils.preprocess - INFO - [NaN]: Replaced 0 NaNs after median imputation\n", - "2025-08-07 00:16:27,712 - bioneuralnet.utils.preprocess - INFO - [Zero-Var]: 0 columns dropped due to zero variance\n", - "2025-08-07 00:16:27,715 - bioneuralnet.utils.preprocess - INFO - Selected top 1000 features by variance\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "similarity_cosine shape: (2503, 2503)\n" - ] - }, - { - "data": { - "image/png": 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", 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OmicDegree
Index
59hsa_mir_7673
24hsa_mir_105_23
23hsa_mir_105_13
12SNORD803
19MAGEA6_41053
49hsa_mir_516a_13
17CDC20B_1669792
2SNORD4A2
41hsa_mir_449a2
42hsa_mir_449b2
\n", - "
" - ], - "text/plain": [ - " Omic Degree\n", - "Index \n", - "59 hsa_mir_767 3\n", - "24 hsa_mir_105_2 3\n", - "23 hsa_mir_105_1 3\n", - "12 SNORD80 3\n", - "19 MAGEA6_4105 3\n", - "49 hsa_mir_516a_1 3\n", - "17 CDC20B_166979 2\n", - "2 SNORD4A 2\n", - "41 hsa_mir_449a 2\n", - "42 hsa_mir_449b 2" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Combine each omics modality into a single multi-omics dataframe\n", - "# Let's select the top 1000 featurs from RNA and Methylation. (6000 -> 500 features).\n", - "from bioneuralnet.utils import select_top_k_variance\n", - "\n", - "brca_meth = select_top_k_variance(brca_meth, k=1000)\n", - "brca_rna = select_top_k_variance(brca_rna, k=1000)\n", - "\n", - "merged_brca = pd.concat([brca_meth, brca_rna, brca_mirna], axis=1)\n", - "\n", - "# Build cosine-similarity network\n", - "sim_network_brca = gen_similarity_graph(merged_brca, k=15, metric=\"cosine\")\n", - "print(\"similarity_cosine shape:\", sim_network_brca.shape)\n", - "nodes_df = plot_network(sim_network_brca, weight_threshold=0.0895, show_labels=True, layout=\"spring\")\n", - "display(nodes_df.head(10))\n", - "\n", - "\n", - "# node_table is a DataFrame with node IDs (\"Omics\") and their degrees, you can save it and the network for later analysis:\n", - "# similarity_network.to_csv(\"brca_similarity_network.csv\")\n", - "# node_table.to_csv(\"brca_network_node_degrees.csv\", index=False)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "correlation_spearman shape: (2503, 2503)\n" - ] - }, - { - "data": { - "image/png": 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OmicDegree
Index
12SNAR_C24
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" - ], - "text/plain": [ - " Omic Degree\n", - "Index \n", - "12 SNAR_C2 4" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
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OmicDegree
Index
48hsa_mir_194_12
\n", - "
" - ], - "text/plain": [ - " Omic Degree\n", - "Index \n", - "48 hsa_mir_194_1 2" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Correlation Network\n", - "corr_network_brca = gen_correlation_graph(merged_brca, k=8, method=\"spearman\")\n", - "print(\"correlation_spearman shape:\", corr_network_brca.shape)\n", - "nodes_df = plot_network(corr_network_brca, weight_threshold=0.1998, show_edge_weights=True, show_labels=True, layout=\"kamada\")\n", - "\n", - "# We can use the label to see the number of edges at this omics/node\n", - "display(nodes_df.loc[[12]])\n", - "display(nodes_df.loc[[48]])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "soft_threshold shape: (2503, 2503)\n" - ] - }, - { - "data": { - "image/png": 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", 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OmicDegree
Index
524ROPN1_5476319
589SFRP1_642212
507PGR_524111
1341hsa_mir_36479
502CYP4Z2P_1637209
688CLDN19_1494618
449PIP_53048
537NEK10_1521108
1368hsa_mir_3797
1062MFAP4_42397
\n", - "
" - ], - "text/plain": [ - " Omic Degree\n", - "Index \n", - "524 ROPN1_54763 19\n", - "589 SFRP1_6422 12\n", - "507 PGR_5241 11\n", - "1341 hsa_mir_3647 9\n", - "502 CYP4Z2P_163720 9\n", - "688 CLDN19_149461 8\n", - "449 PIP_5304 8\n", - "537 NEK10_152110 8\n", - "1368 hsa_mir_379 7\n", - "1062 MFAP4_4239 7" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Soft-threshold Network\n", - "soft_network_brca = gen_threshold_graph(merged_brca, b=6, k=10)\n", - "print(\"soft_threshold shape:\", soft_network_brca.shape)\n", - "nodes_map = plot_network(soft_network_brca, weight_threshold=0.15, show_labels=False)\n", - "display(nodes_map.head())\n", - "\n", - "# At times a dense graph may be difficult to visualize, but the network plot still provides useful information.\n", - "\"\"\"\n", - "Omic Degree\n", - "ROPN1_54763 19\n", - "SFRP1_6422 12\n", - "PGR_5241 11\n", - "hsa_mir_3647 9\n", - "CYP4Z2P_163720 9\n", - "\"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(2503, 2503)\n" - ] - }, - { - "data": { - "image/png": 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99HGC6.310
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" - ], - "text/plain": [ - " Omic Degree\n", - "Index \n", - "47 REP15 21\n", - "115 MIR1280 20\n", - "147 ASF1A 14\n", - "64 MIR198 10\n", - "99 HGC6.3 10" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Minimum Spanning Tree\n", - "mst_network_brca = gen_mst_graph(merged_brca)\n", - "print(mst_network_brca.shape)\n", - "nodes_df = plot_network(mst_network_brca, weight_threshold=0.95, show_labels=False)\n", - "display(nodes_df.head())" - ] - }, - { - "cell_type": "code", - "execution_count": 861, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(2503, 2503)\n" - ] - }, - { - "data": { - "image/png": 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" - ], - "text/plain": [ - " Omic Degree\n", - "Index \n", - "37 ROPN1_54763 8\n", - "50 hsa_mir_301b 6\n", - "22 FAM74A3 3\n", - "15 OR2J3 3\n", - "5 LOC100130331 3" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Shared Nearest Neighbor\n", - "snn_network_brca = gen_snn_graph(merged_brca, k=30)\n", - "print(snn_network_brca.shape)\n", - "nodes_df = plot_network(snn_network_brca, weight_threshold=0.3005, show_edge_weights=False)\n", - "display(nodes_df.head())" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": ".enviroment", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.3" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/docs/source/quick_start_bio.rst b/docs/source/quick_start_bio.rst new file mode 100644 index 0000000..906f91b --- /dev/null +++ b/docs/source/quick_start_bio.rst @@ -0,0 +1,2 @@ +Quick Start for Biologist +========================= \ No newline at end of file diff --git a/docs/source/subgraph.rst b/docs/source/subgraph.rst new file mode 100644 index 0000000..7276014 --- /dev/null +++ b/docs/source/subgraph.rst @@ -0,0 +1,100 @@ +Subgraph Detection & Biomarkers +=============================== + +BioNeuralNet supports **phenotype-aware subgraph detection**, enabling users to extract small, interpretable modules of omics features that are tightly linked to clinical labels. These methods go beyond purely structural clustering by explicitly incorporating feature-phenotype correlations, making them well-suited for biomarker discovery. + +For a step-by-step example on the TCGA-KIPAN cohort, see +:doc:`TCGA-KIPAN `. + +Method Overview +--------------- + +BioNeuralNet implements three complementary, phenotype-aware network methods: + +- **Correlated PageRank**: + Ranks nodes using a PageRank-like iteration with a **phenotype-informed personalization vector**. Features that are both central and highly correlated with the phenotype receive higher scores. + + .. math:: + + \mathbf{r} \;=\; \alpha\,\mathbf{M}\,\mathbf{r} + (1 - \alpha)\,\mathbf{p} + + - :math:`\mathbf{M}`: normalized adjacency (transition probability matrix). + - :math:`\mathbf{p}`: personalization vector built from feature-phenotype correlations. + +- **Correlated Louvain**: + Modifies Louvain community detection to balance **structural modularity** and **phenotype enrichment**, optimizing + + .. math:: + + Q^* \;=\; k_L\,Q \;+\; (1 - k_L)\,\overline{\lvert \rho \rvert} + + - :math:`Q`: Newman-Girvan modularity. + - :math:`\overline{\lvert \rho \rvert}`: mean absolute Pearson correlation between cluster features and the phenotype. + - :math:`k_L`: user-defined trade-off between structure and phenotype relevance. + +- **Hybrid Louvain**: + Iteratively combines Correlated Louvain (for initial modules) with Correlated PageRank (to refine and expand phenotype-enriched clusters), yielding compact driver modules suitable for biomarker interpretation. + +Graph Construction in Case Studies +---------------------------------- + +In the KIPAN and ROSMAP examples, the networks used for subgraph detection were constructed as follows: + +- Start from the **ANOVA-Random Forest intersection** for each omics type. +- Apply a variance filter to retain the most variable features: + - Selected methylation features: **300** (reduced from 771) + - Selected RNA features: **300** (reduced from 2054) + - Selected miRNA features: **300** (reduced from 310) +- Concatenate the three omics blocks, yielding **900 features** for graph + construction (matrix of shape :math:`900 \times 900`). +- Build a **similarity network** using a :math:`k`-nearest-neighbor graph with + :math:`k = 10`, and then apply the correlated clustering methods above. + +Case Study 1: TCGA-KIPAN KICH-Associated Module +----------------------------------------------- + +As an example, we applied **Hybrid Louvain** to the TCGA-KIPAN multi-omics network using a KICH-versus-rest label. One resulting driver module is a KICH-associated cluster of omics features most strongly linked to the KICH label. + +.. figure:: _static/kipan_net.png + :align: center + :alt: KICH-associated Hybrid Louvain module + + Figure S2b. KICH-associated driver module from Hybrid Louvain. The full module contains 69 omics features; the visualization displays only the strongest associations (Pearson correlation threshold :math:`r \geq 0.76` in the original data), yielding 27 filtered nodes and 62 strong edges. + +Within the filtered subgraph, a small set of hub features dominate the local connectivity pattern (e.g., INPP5J, SLC26A7, HEPACAM2, CLNK, DMRT2), forming a compact KICH-associated module. This pattern is consistent with a Chromophobe RCC origin in intercalated cells and highlights a small set of candidate biomarkers and regulators for further validation. + +- **SLC26A7** (KICH marker): Schrader et al., *Urol Oncol*, 2016. +- **DMRT2** (lineage regulator): Picard et al., *PNAS*, 2025. +- **INPP5J** (pathway regulator): annotated in *The Human Protein Atlas (HPA)*. + +Case Study 2: ROSMAP NCI-versus-Rest Submodules +----------------------------------------------- + +We also applied Hybrid Louvain to a similarity network built from ROSMAP multi-omics data, using an **NCI-versus-rest** clinical label (NCI = a non-impaired cognitive status category in the original study). Because ROSMAP is a protected dataset, we only report graph-level summaries and omit detailed clinical or feature annotations. + +Two nested submodules were examined: + +- A **50-feature submodule** (stronger connectivity) +- A **25-feature core submodule** (more stringent pruning) + +First ROSMAP Submodule (50 Features) +------------------------------------ + +.. figure:: _static/rosmap_net1.png + :align: center + :alt: ROSMAP NCI submodule with 50 features + + Cluster 1. ROSMAP NCI-versus-rest submodule with 50 features. The full submodule contains 50 nodes and 1,225 edges (average degree 49.0). After applying an edge-weight threshold (:math:`r \geq 0.7`), 30 nodes and 45 edges remain, forming a single connected component with an average clustering coefficient of 0.347. + +Second ROSMAP Submodule (25 Features) +------------------------------------- + +.. figure:: _static/rosmap_net2.png + :align: center + :alt: ROSMAP NCI core submodule with 25 features + + Cluster 2. Core ROSMAP NCI-versus-rest submodule with 25 features. The full submodule contains 25 nodes and 300 edges (average degree 24.0). After thresholding (:math:`r \geq 0.6`), 22 nodes and 34 edges remain, again forming a single connected component with an average clustering coefficient of 0.393. + +Together, these ROSMAP subgraphs demonstrate that the same Hybrid Louvain pipeline used in KIPAN generalizes to an independent cohort, yielding compact, phenotype-associated modules even when only graph-level summaries can be shared. + +AD Knowledge Portal. Available from: https://adknowledgeportal.org/. Data generated from postmortem brain tissue provided by the Religious Orders Study and Rush Memory and Aging Project (ROSMAP) cohort at Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago. Supported by Cure Alzheimer’s Fund and NIH grants AG058002, AG062377, NS110453, NS115064, AG062335, AG074003, NS127187, MH119509, HG008155, RF1AG062377, RF1AG054321, R01AG054012, and GM087237. diff --git a/docs/source/user_api.rst b/docs/source/user_api.rst index da9d0f1..ef2cd77 100644 --- a/docs/source/user_api.rst +++ b/docs/source/user_api.rst @@ -1,35 +1,79 @@ User API ======== -The **User API** lists BioNeuralNet's key classes, methods, and utilities. +The **User API** lists BioNeuralNet's key classes, methods, and utilities and summarizes the main entry points exposed at the top-level ``bioneuralnet`` namespace. + +Top-level Imports +----------------- + +After installation, the most common pattern is: + +.. code-block:: python + + import bioneuralnet as bnn + + print(bnn.__version__) + + # Core entry points + from bioneuralnet import ( + GNNEmbedding, + SubjectRepresentation, + DPMON, + SmCCNet, + DatasetLoader, + CorrelatedPageRank, + CorrelatedLouvain, + HybridLouvain, + load_example, + load_monet, + load_brca, + load_lgg, + load_kipan, + load_paad, + set_seed, + get_logger, + ) + + +Architecture Overview +--------------------- + +The figure below summarizes the main BioNeuralNet modules and public objects. + +.. figure:: _static/bioneuralnet_api.png + :align: center + :alt: BioNeuralNet module and API overview + + BioNeuralNet 1.2.0: core modules (utils, network\_embedding, clustering, downstream\_task, datasets, metrics, external\_tools) and their key user-facing functions and classes. + +Module Reference +---------------- + +The following submodules are documented via autosummary: .. autosummary:: :toctree: _autosummary :recursive: - bioneuralnet.network_embedding - bioneuralnet.clustering - bioneuralnet.downstream_task - bioneuralnet.utils - bioneuralnet.datasets - bioneuralnet.metrics - bioneuralnet.external_tools + bioneuralnet . Executables ----------- -Certain classes expose a high-level ``run()`` method to perform end-to-end workflows: +Several classes expose a high-level ``run()`` method to perform end-to-end workflows: -- **SubjectRepresentation** for integrating embeddings into subject-level data -- **CorrelatedLouvain** or **HybridLouvain** for clustering -- **DPMON** for disease prediction +- :class:`bioneuralnet.downstream_task.SubjectRepresentation` for integrating embeddings into subject-level representations. +- :class:`bioneuralnet.clustering.CorrelatedLouvain` and :class:`bioneuralnet.clustering.HybridLouvain` for phenotype-aware clustering and subgraph detection. +- :class:`bioneuralnet.downstream_task.DPMON` for disease prediction using multi-omics networks. +- :class:`bioneuralnet.external_tools.SmCCNet` as a wrapper around external network-inference tools. -**Usage Pattern**: +Usage pattern: 1. **Instantiate** the class with the relevant data (omics, adjacency, phenotype, etc.). -2. **Call** the `run()` method to perform the pipeline. +2. **Call** the :py:meth:`run()` method to execute the pipeline. -Example: +Example +------- .. code-block:: python @@ -40,14 +84,16 @@ Example: omics_list=omics_list, phenotype_data=phenotype_data, clinical_data=clinical_data, - model='GAT' + model="GAT", ) predictions, avg_accuracy = dpmon.run() print("Disease phenotype predictions:\n", predictions) + print("Average accuracy:", avg_accuracy) -**Methods**: +Run Methods +----------- -Below are references to the ``run()`` methods: +Direct links to the main ``run()`` methods: .. automethod:: bioneuralnet.external_tools.SmCCNet.run :no-index: diff --git a/docs/source/utils.rst b/docs/source/utils.rst index 1e2f20a..668a1ad 100644 --- a/docs/source/utils.rst +++ b/docs/source/utils.rst @@ -1,23 +1,37 @@ Utils ===== -The `utils` module provides a collection of supporting functions for data preparation, logging, graph construction, and exploratory analysis. -These utilities streamline common preprocessing workflows and enable efficient manipulation of omics and clinical datasets. +The ``utils`` module provides a collection of supporting functions for data preparation, logging, graph construction, and exploratory analysis. These utilities streamline common preprocessing workflows and enable efficient manipulation of omics and clinical datasets. + +Limitations and Best Practices +------------------------------ + +To ensure robust results, consider the following guidelines when using BioNeuralNet's utilities: + +* **Suitable Use Cases:** These tools are optimized for tabular multi-omics data (rows as samples, columns as features). They work best when samples are consistently labeled across different omics layers. +* **Feature Selection:** For high-dimensional data (e.g., >20,000 features), we strongly recommend applying feature selection (e.g., ``select_top_k_variance`` or ``select_top_randomforest``) prior to network construction to reduce noise and computational load. +* **Network Construction:** Different graph construction methods capture different biological signals. + + * Use **Correlation** (Pearson/Spearman) for capturing linear co-expression patterns (e.g., WGCNA-style analysis). + * Use **Similarity** (Cosine/RBF) for capturing broader geometric relationships in the data space. + * Use **KNN** (k-Nearest Neighbors) to ensure sparse, connected topologies suitable for GNNs. + +* **Sparse Data:** For datasets with high dropout rates (e.g., single-cell data or metabolomics), use ``clean_inf_nan`` and imputation tools (``impute_omics``) before running sensitive metrics like correlation. RData Conversion ---------------- -- :func:`bioneuralnet.utils.rdata_to_df` converts an RData file to a CSV and loads it into a pandas DataFrame. +- :func:`bioneuralnet.utils.rdata_convert.rdata_to_df` converts an RData file to a CSV and loads it into a pandas DataFrame, facilitating interoperability with R-based bioinformatics pipelines. Logging ------- -- :func:`bioneuralnet.utils.get_logger` configures and returns a logger writing to ``bioneuralnet.log`` at the project root. +- :func:`bioneuralnet.utils.logger.get_logger` configures and returns a standardized logger writing to ``bioneuralnet.log`` at the project root, ensuring reproducible tracking of analysis steps. Graph Generation ---------------- -This section details utility functions for generating networks from omics data matrices. All methods are industry-standard and open in the literature. +This section details utility functions for generating networks from omics data matrices. All methods are based on established literature. .. rubric:: Methods and Examples @@ -33,245 +47,226 @@ This section details utility functions for generating networks from omics data m .. math:: S_{ij} = \exp\bigl(-\|x_i - x_j\|^2 /(2\sigma^2)\bigr) - Sparsify by keeping top-\(k\) neighbors per node, optionally mutual. + Sparsifies by keeping the top-\(k\) neighbors per node (optionally mutual). .. code-block:: python - from bioneuralnet.utils.graphs import gen_similarity_graph + from bioneuralnet.utils import gen_similarity_graph A = gen_similarity_graph(X, k=15, metric='cosine', mutual=True) - Reference: Hastie et al., 2009 [Hastie2009_] + Reference: Hastie et al., 2009 [Hastie2009_] 2. **Pearson/Spearman Co-expression Graph** - Compute correlation: + Computes correlation: .. math:: C_{ij} = \mathrm{corr}(x_i, x_j) - Then sparsify by top-\(k\) or threshold. + Sparsifies by keeping the top-\(k\) correlations or applying a hard threshold. .. code-block:: python - from bioneuralnet.utils.graphs import gen_correlation_graph + from bioneuralnet.utils import gen_correlation_graph A = gen_correlation_graph(X, k=15, method='pearson') - Reference: Langfelder & Horvath, 2008 [Langfelder2008_] + Reference: Langfelder & Horvath, 2008 [Langfelder2008_] 3. **Soft-Threshold Graph** - Soft-threshold absolute correlation, similar to WGCNA: + Applies a power function to absolute correlations, similar to WGCNA, to emphasize strong connections: .. math:: W_{ij} = |C_{ij}|^\beta - Then top-\(k\) selection and normalization. + Followed by optional top-\(k\) selection. .. code-block:: python - from bioneuralnet.utils.graphs import gen_threshold_graph + from bioneuralnet.utils import gen_threshold_graph A = gen_threshold_graph(X, b=6.0, k=15) - Reference: Langfelder & Horvath, 2008 [Langfelder2008_] + Reference: Langfelder & Horvath, 2008 [Langfelder2008_] 4. **Gaussian k-NN Graph** - Gaussian kernel sparsified by k-nearest neighbors: + Constructs a Gaussian kernel graph sparsified by k-nearest neighbors: .. math:: - S_{ij} = \exp\bigl(-\|x_i - x_j\|^2 /(2\sigma^2)\bigr),\quad W = \text{Top}_k(S) + S_{ij} = \exp\bigl(-\|x_i - x_j\|^2 /(2\sigma^2)\bigr),\quad W = \text{Top}_k(S) .. code-block:: python - from bioneuralnet.utils.graphs import gen_gaussian_knn_graph + from bioneuralnet.utils import gen_gaussian_knn_graph A = gen_gaussian_knn_graph(X, k=15, sigma=None) - Credit: adapts common practice from spectral clustering (Ng et al., 2002). + Credit: Adapts common practice from spectral clustering (Ng et al., 2002). -5. **Mutual Information Graph** +5. **LASSO Graph (Graphical Lasso)** - Estimate pairwise mutual information: + Estimates the sparse inverse covariance matrix (precision matrix) to infer conditional independence: - .. math:: - \mathrm{MI}_{ij} = I(x_i; x_j) + .. code-block:: python + + from bioneuralnet.utils import gen_lasso_graph + A = gen_lasso_graph(X, alpha=0.01) + + Reference: Friedman et al., 2008 [Friedman2008_] + +6. **Shared Nearest Neighbor (SNN) Graph** + + Constructs a graph based on the overlap of neighborhoods between nodes, robust to varying densities: .. code-block:: python - from bioneuralnet.utils.graphs import gen_mutual_info_graph - A = gen_mutual_info_graph(X, k=15) + from bioneuralnet.utils import gen_snn_graph + A = gen_snn_graph(X, k=15) + +7. **Minimum Spanning Tree (MST) Graph** + + Constructs a backbone structure of the data using a Minimum Spanning Tree: - Reference: Margolin et al., 2006 [Margolin2006_] + .. code-block:: python + + from bioneuralnet.utils import gen_mst_graph + A = gen_mst_graph(X, metric='euclidean') Preprocessing Utilities ----------------------- -A collection of data-cleaning and feature-selection functions for clinical and omics datasets. +A collection of data-cleaning, imputation, and feature-selection functions for clinical and omics datasets. **Clinical Preprocessing** -- :func:`bioneuralnet.utils.preprocess.preprocess_clinical` splits numeric and categorical features; replaces Inf/NaN; optionally scales numeric data (RobustScaler); encodes categoricals; drops zero-variance; and selects top-k features by RandomForest importance. +- :func:`bioneuralnet.utils.preprocess.preprocess_clinical` splits numeric and categorical features; replaces Inf/NaN; optionally scales numeric data (RobustScaler); encodes categoricals; drops zero-variance columns; and selects top-k features by RandomForest importance. **Example**: .. code-block:: python - from bioneuralnet.utils.preprocess import preprocess_clinical + from bioneuralnet.utils import preprocess_clinical df_top = preprocess_clinical(X, y, top_k=10, scale=True) -- :func:`bioneuralnet.utils.preprocess.clean_inf_nan` replaces Inf with NaN, imputes medians, drops zero-variance columns, and logs counts. +- :func:`bioneuralnet.utils.preprocess.clean_inf_nan` replaces Inf with NaN, imputes missing values (median), drops zero-variance columns, and logs data statistics. **Example**: - .. code-block:: python + .. code-block:: python - from bioneuralnet.utils.preprocess import clean_inf_nan + from bioneuralnet.utils import clean_inf_nan df_clean = clean_inf_nan(df) +**Imputation & Normalization** + +- :func:`bioneuralnet.utils.preprocess.impute_omics`: Simple imputation (mean/median/zero) for tabular data. +- :func:`bioneuralnet.utils.preprocess.impute_omics_knn`: Advanced KNN-based imputation for estimating missing values based on similar samples. +- :func:`bioneuralnet.utils.preprocess.normalize_omics`: Standardizes omics data (z-score or min-max scaling). +- :func:`bioneuralnet.utils.preprocess.beta_to_m`: Converts Beta values (DNA methylation) to M-values for better statistical properties. + **Variance-Based Selection** -- :func:`bioneuralnet.utils.preprocess.select_top_k_variance` cleans data, then picks the top-k numeric features by variance. +- :func:`bioneuralnet.utils.preprocess.select_top_k_variance` cleans data, then retains the top-k numeric features with the highest variance. **Example**: - .. code-block:: python + .. code-block:: python - from bioneuralnet.utils.preprocess import select_top_k_variance + from bioneuralnet.utils import select_top_k_variance df_var = select_top_k_variance(df, k=500) **Correlation-Based Selection** - :func:`bioneuralnet.utils.preprocess.select_top_k_correlation`: - - - **Supervised:** if you pass `y`, selects features by absolute Pearson correlation with `y`. - - **Unsupervised:** if `y=None`, picks features with the lowest average inter-feature correlation (redundancy reduction). + * **Supervised:** If ``y`` is provided, selects features with the highest absolute Pearson correlation to the target. + * **Unsupervised:** If ``y=None``, selects features with the *lowest* average inter-feature correlation (redundancy reduction). **Example**: - .. code-block:: python + .. code-block:: python - from bioneuralnet.utils.preprocess import select_top_k_correlation - df_sup = select_top_k_correlation(X, y, top_k=100) # supervised + from bioneuralnet.utils import select_top_k_correlation + df_sup = select_top_k_correlation(X, y, top_k=100) # supervised df_unsup = select_top_k_correlation(X, top_k=100) # unsupervised **RandomForest Feature Importance** -- :func:`bioneuralnet.utils.preprocess.select_top_randomforest` fits RandomForest (classification or regression), and returns the top-k features by importance. +- :func:`bioneuralnet.utils.preprocess.select_top_randomforest` fits a RandomForest model (classification or regression) and returns the top-k features ranked by importance. **Example**: - .. code-block:: python + .. code-block:: python - from bioneuralnet.utils.preprocess import select_top_randomforest + from bioneuralnet.utils import select_top_randomforest df_rf = select_top_randomforest(X, y, top_k=200) **ANOVA F-Test Selection** -- :func:`bioneuralnet.utils.preprocess.top_anova_f_features` runs ANOVA F-test (classification or regression), applies FDR correction, selects all significant features, and pads with next-best to reach `max_features`. +- :func:`bioneuralnet.utils.preprocess.top_anova_f_features` runs an ANOVA F-test, applies FDR correction, selects significant features, and optionally pads the selection to reach ``max_features``. **Example**: - .. code-block:: python + .. code-block:: python - from bioneuralnet.utils.preprocess import top_anova_f_features + from bioneuralnet.utils import top_anova_f_features df_anova = top_anova_f_features(X, y, max_features=100, alpha=0.05) **Network Pruning** -- :func:`bioneuralnet.utils.preprocess.prune_network` prunes edges below a weight threshold, removes isolates, and logs before/after stats. +- :func:`bioneuralnet.utils.preprocess.prune_network` prunes edges below a weight threshold, removes isolated nodes, and logs network statistics. **Example**: - .. code-block:: python + .. code-block:: python - from bioneuralnet.utils.preprocess import prune_network + from bioneuralnet.utils import prune_network pruned = prune_network(adj_df, weight_threshold=0.1) -- :func:`bioneuralnet.utils.preprocess.prune_network_by_quantile` uses a quantile cutoff on edge weights, prunes accordingly, removes isolates, and logs stats. +- :func:`bioneuralnet.utils.preprocess.prune_network_by_quantile` uses a quantile cutoff on edge weights to prune the network. **Example**: - .. code-block:: python + .. code-block:: python - from bioneuralnet.utils.preprocess import prune_network_by_quantile + from bioneuralnet.utils import prune_network_by_quantile pruned_q = prune_network_by_quantile(adj_df, quantile=0.75) -- :func:`bioneuralnet.utils.preprocess.network_remove_low_variance` drops rows/columns in the adjacency matrix whose variance falls below a threshold. +- :func:`bioneuralnet.utils.preprocess.network_remove_low_variance` drops nodes (rows/cols) from the adjacency matrix whose variance falls below a threshold. +- :func:`bioneuralnet.utils.preprocess.network_remove_high_zero_fraction` drops nodes where the fraction of zero-weight edges exceeds a threshold (e.g., disconnected nodes). - **Example**: - .. code-block:: python +Graph Analysis Tools +-------------------- - from bioneuralnet.utils.preprocess import network_remove_low_variance - filtered = network_remove_low_variance(adj_df, threshold=1e-5) +Functions for analyzing and repairing graph structures. -- :func:`bioneuralnet.utils.preprocess.network_remove_high_zero_fraction` removes rows/columns where the fraction of zero weights exceeds a threshold (default 0.95). - - **Example**: - - .. code-block:: python - - from bioneuralnet.utils.preprocess import network_remove_high_zero_fraction - filtered_z = network_remove_high_zero_fraction(adj_df, threshold=0.95) +- :func:`bioneuralnet.utils.graph_tools.graph_analysis`: Computes comprehensive graph metrics (density, sparsity, connectivity, connected components) and logs a summary. +- :func:`bioneuralnet.utils.graph_tools.repair_graph_connectivity`: Analyzes graph connectivity and, if fragmented, repairs it by adding minimum spanning tree (MST) edges or connecting components to the giant component. +- :func:`bioneuralnet.utils.graph_tools.find_optimal_graph`: Iteratively tests different graph construction parameters (e.g., k-NN `k` values) to maximize a target metric like connectivity or community structure. Data Summary Utilities ---------------------- - :func:`bioneuralnet.utils.data.variance_summary` computes summary statistics for column variances. - - **Example**: - - .. code-block:: python - - from bioneuralnet.utils.data import variance_summary - stats = variance_summary(df, low_var_threshold=1e-4) - -- :func:`bioneuralnet.utils.data.zero_fraction_summary` computes statistics for the fraction of zeros per column. - - **Example**: - - .. code-block:: python - - from bioneuralnet.utils.data import zero_fraction_summary - stats = zero_fraction_summary(df, high_zero_threshold=0.5) - +- :func:`bioneuralnet.utils.data.zero_fraction_summary` computes statistics for the fraction of zeros per column (sparsity). - :func:`bioneuralnet.utils.data.expression_summary` computes summary of mean expression values across features. +- :func:`bioneuralnet.utils.data.correlation_summary` computes statistics of each feature's maximum pairwise correlation. +- :func:`bioneuralnet.utils.data.explore_data_stats` prints a comprehensive summary (variance, sparsity, expression, correlation) to standard output. **Example**: - .. code-block:: python - - from bioneuralnet.utils.data import expression_summary - stats = expression_summary(df) - -- :func:`bioneuralnet.utils.data.correlation_summary` computes statistics of each feature's maximum pairwise correlation (excluding self). - - **Example**: - - .. code-block:: python - - from bioneuralnet.utils.data import correlation_summary - stats = correlation_summary(df) - -- :func:`bioneuralnet.utils.data.explore_data_stats` prints an overall summary (variance, zero fraction, expression, correlation) to stdout. - - **Example**: - - .. code-block:: python + .. code-block:: python - from bioneuralnet.utils.data import explore_data_stats + from bioneuralnet.utils import explore_data_stats explore_data_stats(df, name="MyOmicsData") References ---------- -.. [Langfelder2008_] Langfelder, P., & Horvath, S. (2008). WGCNA: an R package for weighted correlation network analysis. *BMC Bioinformatics*, 9, 559. - -.. [Margolin2006_] Margolin, A. A., Nemenman, I., Basso, K., Wiggins, C., Stolovitzky, G., Dalla Favera, R., & Califano, A. (2006). ARACNE: an algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context. *BMC Bioinformatics*, 7(Suppl 1), S7. - -.. [Faith2007_] Faith, J. J., Hayete, B., Thaden, J. T., Mogno, I., Wierzbowski, J., Cottarel, G., ... & Gardner, T. S. (2007). Large-scale mapping and validation of *Escherichia coli* transcriptional regulation from a compendium of expression profiles. *PLoS Biology*, 5(1), e8. +.. [Langfelder2008] Langfelder, P., & Horvath, S. (2008). WGCNA: an R package for weighted correlation network analysis. *BMC Bioinformatics*, 9, 559. -.. [Hastie2009_] Hastie, T., Tibshirani, R., & Friedman, J. (2009). *The Elements of Statistical Learning* (2nd ed.). Springer. +.. [Margolin2006] Margolin, A. A., Nemenman, I., Basso, K., Wiggins, C., Stolovitzky, G., Dalla Favera, R., & Califano, A. (2006). ARACNE: an algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context. *BMC Bioinformatics*, 7(Suppl 1), S7. -.. [Friedman2008_] Friedman, J., Hastie, T., & Tibshirani, R. (2008). Sparse inverse covariance estimation with the graphical lasso. *Biostatistics*, 9(3), 432-441. +.. [Hastie2009] Hastie, T., Tibshirani, R., & Friedman, J. (2009). *The Elements of Statistical Learning* (2nd ed.). Springer. +.. [Friedman2008] Friedman, J., Hastie, T., & Tibshirani, R. (2008). Sparse inverse covariance estimation with the graphical lasso. *Biostatistics*, 9(3), 432-441. diff --git a/setup.cfg b/setup.cfg index fa72bd5..a100ef3 100644 --- a/setup.cfg +++ b/setup.cfg @@ -3,7 +3,7 @@ name = bioneuralnet version = 1.2.0 author = Vicente Ramos author_email = vicente.ramos@ucdenver.edu -description = A comprehensive framework for integrating multi-omics data with neural network embeddings. +description = A Graph Neural Network based Multi-Omics Network Data Analysis Tool long_description = file: README.md long_description_content_type = text/markdown url = https://github.com/UCD-BDLab/BioNeuralNet @@ -19,18 +19,18 @@ project_urls = [options] packages = find: include_package_data = True -python_requires = >=3.9 +python_requires = >=3.10 install_requires = - pandas - numpy - scipy - matplotlib - scikit-learn - statsmodels - networkx - python-louvain - pydantic - ray[tune] + pandas>=2.2.3 + numpy>=1.26.4 + scipy>=1.13.1 + matplotlib>=3.10.3 + scikit-learn>=1.6.1 + statsmodels>=0.14.4 + networkx>=3.4.2 + python-louvain>=0.16 + pydantic>=2.5 + ray[tune,train]>=2.46.0 [options.packages.find] where = .