diff --git a/.gitignore b/.gitignore
index 7a6dc1e..a4aff76 100644
--- a/.gitignore
+++ b/.gitignore
@@ -28,6 +28,7 @@ TCGA-BRCA_Dataset_testing*.ipynb
 testsOLD
 network_construction_run.ipynb
 datasets_run.ipynb
+jupyter_execute
 # Other example data and tests not needed in the repo.
 
 Output**
diff --git a/README.md b/README.md
index bf07e9f..ab33058 100644
--- a/README.md
+++ b/README.md
@@ -17,9 +17,38 @@
 
 ![BioNeuralNet Workflow](assets/BioNeuralNet.png)
 
+
+## 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**](https://arxiv.org/abs/2507.20440).
+> *arXiv preprint arXiv:2507.20440* | [**DOI: 10.48550/arXiv.2507.20440**](https://doi.org/10.48550/arXiv.2507.20440).
+
+
+For your convenience, you can use the following BibTeX entry:
+
+<details>
+  <summary>BibTeX Citation</summary>
+  
+```bibtex
+@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}, 
+}
+```
+</details>
+
 ## Documentation
 
-**[BioNeuralNet Documentation & Examples](https://bioneuralnet.readthedocs.io/en/latest/)**
+For complete documentation, tutorials, and examples, please visit our Read the Docs site:
+**[bioneuralnet.readthedocs.io](https://bioneuralnet.readthedocs.io/en/latest/)**
 
 ## Table of Contents
 
@@ -38,12 +67,14 @@
 
 ## 1. Installation
 
-BioNeuralNet supports Python `3.10`, `3.11` and `3.12`.
+BioNeuralNet is available as a package on the Python Package Index (PyPI), making it easy to install and integrate into your workflows.
 
 ### 1.1. Install BioNeuralNet
 ```bash
 pip install bioneuralnet
 ```
+**PyPI Project Page:** [https://pypi.org/project/bioneuralnet/](https://pypi.org/project/bioneuralnet/)
+>**Requirements:** BioNeuralNet is tested and supported on Python versions `3.10`, `3.11`, and `3.12`. Functionality on other versions is not guaranteed.
 
 ## 1.2. Install PyTorch and PyTorch Geometric
 BioNeuralNet relies on PyTorch for GNN computations. Install PyTorch separately:
@@ -238,3 +269,30 @@ See the [LICENSE](LICENSE) file for details.
 <a id="2">[2]</a> 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](https://doi.org/10.1109/BIBM62325.2024.10822233)
 
 <a id="3">[3]</a> 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](https://doi.org/10.1093/bioinformatics/btat204)
+
+
+## 11. Citation
+
+If you use BioNeuralNet in your research, we kindly ask that you cite our paper:
+
+> Vicente Ramos, et al. (2025). 
+> [**BioNeuralNet: A Graph Neural Network based Multi-Omics Network Data Analysis Tool**](https://arxiv.org/abs/2507.20440). 
+> *arXiv preprint arXiv:2507.20440*.
+
+For your convenience, you can use the following BibTeX entry:
+
+<details>
+  <summary>BibTeX Citation</summary>
+  
+```bibtex
+@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}, 
+}
+```
+</details>
\ No newline at end of file
diff --git a/bioneuralnet/utils/__init__.py b/bioneuralnet/utils/__init__.py
index 4a278a1..f558e82 100644
--- a/bioneuralnet/utils/__init__.py
+++ b/bioneuralnet/utils/__init__.py
@@ -4,9 +4,4 @@
 from .preprocess import preprocess_clinical, clean_inf_nan, select_top_k_variance, select_top_k_correlation, select_top_randomforest, top_anova_f_features, prune_network, prune_network_by_quantile, network_remove_low_variance, network_remove_high_zero_fraction
 from .graph import gen_similarity_graph, gen_correlation_graph, gen_threshold_graph, gen_gaussian_knn_graph, gen_lasso_graph, gen_mst_graph, gen_snn_graph
 
-
-__all__ = ["get_logger", "rdata_to_df", "variance_summary", "zero_fraction_summary", "expression_summary", "correlation_summary",
-           "explore_data_stats", "preprocess_clinical", "clean_inf_nan", "select_top_k_variance", "select_top_k_correlation",
-           "select_top_randomforest", "top_anova_f_features", "prune_network", "prune_network_by_quantile", "network_remove_low_variance",
-           "network_remove_high_zero_fraction", "gen_similarity_graph", "gen_correlation_graph", "gen_threshold_graph",
-           "gen_gaussian_knn_graph", "gen_lasso_graph", "gen_mst_graph", "gen_snn_graph"]
+__all__ = ["get_logger", "rdata_to_df", "variance_summary", "zero_fraction_summary", "expression_summary", "correlation_summary", "explore_data_stats", "preprocess_clinical", "clean_inf_nan", "select_top_k_variance", "select_top_k_correlation", "select_top_randomforest", "top_anova_f_features", "prune_network", "prune_network_by_quantile", "network_remove_low_variance", "network_remove_high_zero_fraction", "gen_similarity_graph", "gen_correlation_graph", "gen_threshold_graph", "gen_gaussian_knn_graph", "gen_lasso_graph", "gen_mst_graph", "gen_snn_graph"]
diff --git a/bioneuralnet/utils/graph.py b/bioneuralnet/utils/graph.py
index 54a7418..5817740 100644
--- a/bioneuralnet/utils/graph.py
+++ b/bioneuralnet/utils/graph.py
@@ -88,7 +88,7 @@ def gen_correlation_graph(X: pd.DataFrame, k: int = 15,method: str = 'pearson',
     """
     Build a normalized k-nearest neighbors (kNN) correlation graph from feature vectors.  
 
-    The function computes pairwise `pearson` or `spearman` correlations, sparsifies the matrix by keeping `top-k`neighbours per node (or by applying a global threshold), optionally prunes edges to mutual neighbours, and can add self-loops.
+    The function computes pairwise `pearson` or `spearman` correlations, sparsifies the matrix by keeping `top-k` neighbours per node (or by applying a global threshold), optionally prunes edges to mutual neighbours, and can add self-loops.
 
     Args:
     
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deleted file mode 100644
index f1e5fac..0000000
--- a/docs/jupyter_execute/Quick_Start.ipynb
+++ /dev/null
@@ -1,947 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Quick Start Guide\n",
-    "This notebook demonstrates the core functionality of the BioNeuralNet package. It covers data loading, network generation, network embedding via GNNs, subject representation, downstream disease prediction, evaluation metrics, clustering, and use of external tools.\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from bioneuralnet.downstream_task import SubjectRepresentation\n",
-    "from bioneuralnet.downstream_task import DPMON\n",
-    "from bioneuralnet.clustering import CorrelatedPageRank\n",
-    "from bioneuralnet.clustering import CorrelatedLouvain\n",
-    "from bioneuralnet.clustering import HybridLouvain\n",
-    "\n",
-    "from bioneuralnet.utils import get_logger\n",
-    "from bioneuralnet.utils import rdata_to_df\n",
-    "from bioneuralnet.utils import variance_summary\n",
-    "from bioneuralnet.utils import zero_fraction_summary\n",
-    "from bioneuralnet.utils import expression_summary\n",
-    "from bioneuralnet.utils import correlation_summary\n",
-    "from bioneuralnet.utils import explore_data_stats\n",
-    "from bioneuralnet.utils import preprocess_clinical\n",
-    "from bioneuralnet.utils import clean_inf_nan\n",
-    "from bioneuralnet.utils import select_top_k_variance\n",
-    "from bioneuralnet.utils import select_top_k_correlation\n",
-    "from bioneuralnet.utils import select_top_randomforest\n",
-    "from bioneuralnet.utils import top_anova_f_features\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",
-    "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_gaussian_knn_graph\n",
-    "from bioneuralnet.utils import gen_lasso_graph\n",
-    "from bioneuralnet.utils import gen_mst_graph\n",
-    "from bioneuralnet.utils import gen_snn_graph\n",
-    "\n",
-    "from bioneuralnet.metrics import omics_correlation\n",
-    "from bioneuralnet.metrics import cluster_correlation\n",
-    "from bioneuralnet.metrics import louvain_to_adjacency\n",
-    "from bioneuralnet.metrics import evaluate_rf\n",
-    "from bioneuralnet.metrics import plot_performance_three\n",
-    "from bioneuralnet.metrics import plot_variance_distribution\n",
-    "from bioneuralnet.metrics import plot_variance_by_feature\n",
-    "from bioneuralnet.metrics import plot_performance\n",
-    "from bioneuralnet.metrics import plot_embeddings\n",
-    "from bioneuralnet.metrics import plot_network\n",
-    "from bioneuralnet.metrics import compare_clusters\n",
-    "\n",
-    "from bioneuralnet.datasets import DatasetLoader\n",
-    "from bioneuralnet.external_tools import SmCCNet\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Load Demo Dataset\n",
-    "\n",
-    "- BioNeuralNet includes built-in demo datasets via `DatasetLoader`, allowing you to explore and test the framework without preparing your own data.\n",
-    "- Each omics dataset includes 358 samples, pre-aligned across phenotype and clinical data.\n",
-    "- 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": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "from bioneuralnet.datasets import DatasetLoader\n",
-    "\n",
-    "Example = DatasetLoader(\"example1\")\n",
-    "omics1 = Example.data[\"X1\"]\n",
-    "omics2= Example.data[\"X2\"]\n",
-    "phenotype = Example.data[\"Y\"]\n",
-    "clinical = Example.data[\"clinical_data\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "             Gene_1     Gene_2  ...   Gene_499   Gene_500\n",
-      "Samp_1    22.485701  40.353720  ...  13.400950  12.769172\n",
-      "Samp_2    37.058850  34.052233  ...  12.066379  12.583460\n",
-      "Samp_3    20.530767  31.669623  ...  12.891962  12.760553\n",
-      "Samp_4    33.186888  38.480880  ...  12.810732  12.972879\n",
-      "Samp_5    28.961981  41.060494  ...  12.479124  12.156407\n",
-      "...             ...        ...  ...        ...        ...\n",
-      "Samp_354  24.520652  28.595409  ...  13.644383  13.018032\n",
-      "Samp_355  31.252789  28.988087  ...  12.947672  13.161434\n",
-      "Samp_356  24.894826  25.944887  ...  12.129990  13.844271\n",
-      "Samp_357  17.034337  38.574705  ...  12.943670  13.996352\n",
-      "Samp_358  20.839167  27.099788  ...  13.257230  13.178058\n",
-      "\n",
-      "[358 rows x 500 columns]\n",
-      "              Mir_1      Mir_2  ...     Mir_99    Mir_100\n",
-      "Samp_1    15.223913  17.545826  ...  11.422531  10.862970\n",
-      "Samp_2    16.306965  16.672830  ...  12.413667  10.719110\n",
-      "Samp_3    16.545119  16.735005  ...  11.072915  11.418794\n",
-      "Samp_4    13.986899  16.207432  ...  10.121957  11.039089\n",
-      "Samp_5    16.338332  17.393869  ...  12.206151  10.724849\n",
-      "...             ...        ...  ...        ...        ...\n",
-      "Samp_354  15.065065  16.079830  ...  11.102427  11.993050\n",
-      "Samp_355  15.997576  15.448951  ...  11.708466  10.654141\n",
-      "Samp_356  15.206862  14.395378  ...  10.830833  10.983455\n",
-      "Samp_357  14.474129  15.482863  ...  11.491449  11.684467\n",
-      "Samp_358  15.094188  16.047304  ...  11.551237  11.221372\n",
-      "\n",
-      "[358 rows x 100 columns]\n",
-      "           phenotype\n",
-      "Samp_1    235.067423\n",
-      "Samp_2    253.544991\n",
-      "Samp_3    234.204994\n",
-      "Samp_4    281.035429\n",
-      "Samp_5    245.447781\n",
-      "...              ...\n",
-      "Samp_354  236.120451\n",
-      "Samp_355  222.572359\n",
-      "Samp_356  268.472285\n",
-      "Samp_357  235.808167\n",
-      "Samp_358  213.886123\n",
-      "\n",
-      "[358 rows x 1 columns]\n",
-      "           Age  Gender  ...  Emphysema  Asthma\n",
-      "PatientID               ...                   \n",
-      "Samp_1      78       0  ...          1       0\n",
-      "Samp_2      68       1  ...          0       0\n",
-      "Samp_3      54       1  ...          1       1\n",
-      "Samp_4      47       1  ...          0       1\n",
-      "Samp_5      60       1  ...          1       1\n",
-      "...        ...     ...  ...        ...     ...\n",
-      "Samp_354    71       0  ...          0       1\n",
-      "Samp_355    62       1  ...          1       1\n",
-      "Samp_356    61       0  ...          0       0\n",
-      "Samp_357    64       0  ...          0       1\n",
-      "Samp_358    61       1  ...          0       0\n",
-      "\n",
-      "[358 rows x 6 columns]\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(omics1)\n",
-    "print(omics2)\n",
-    "print(phenotype)\n",
-    "print(clinical)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Generating an Omics Network\n",
-    "\n",
-    "BioNeuralNet supports a variety of graph construction techniques to model relationships between biological features. These graphs serve as the foundation for applying Graph Neural Networks in downstream tasks.\n",
-    "\n",
-    "Supported graph types include:\n",
-    "\n",
-    "- **Cosine similarity / RBF kernel**\n",
-    "- **Pearson or Spearman correlation**\n",
-    "- **Soft-thresholding (WGCNA-style)**\n",
-    "- **Gaussian k-NN**\n",
-    "- **Mutual information**\n",
-    "- **Graphical Lasso (sparse inverse covariance)**\n",
-    "- **Minimum Spanning Tree (MST)**\n",
-    "- **Shared Nearest Neighbor (SNN)**\n",
-    "\n",
-    "Each method is available through the `utils.graph` module.\n",
-    "More details can be found in the [utils documentation](https://bioneuralnet.readthedocs.io/en/latest/utils.html)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Example: SmCCNet 2.0\n",
-    "\n",
-    "In this section, we'll construct a multi-omics networks using **SmCCNet 2.0**, an R-based method.\n",
-    "\n",
-    "A Python wrapper is available under `bioneuralnet.external_tools.smccnet` to streamline usage. This requires R and the SmCCNet package.\n",
-    "\n",
-    "For setup instructions and usage examples, see the [external tools guide](https://bioneuralnet.readthedocs.io/en/latest/external_tools/index.html).\n",
-    "\n",
-    "**Resources**\n",
-    "\n",
-    "* CRAN: [SmCCNet on CRAN](https://cran.r-project.org/web/packages/SmCCNet/)\n",
-    "* GitHub: [KechrisLab/SmCCNet](https://github.com/KechrisLab/SmCCNet)\n",
-    "* Paper: [BMC Bioinformatics (2024)](https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-024-05900-9)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from bioneuralnet.external_tools import SmCCNet\n",
-    "\n",
-    "smccnet = SmCCNet(\n",
-    "    phenotype_df=phenotype,\n",
-    "    omics_dfs=[omics1, omics2],\n",
-    "    data_types=[\"genes\", \"mirna\"],\n",
-    "    subSampNum=1000,\n",
-    ")\n",
-    "global_network, clusters = smccnet.run()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Global network shape: (600, 600)\n",
-      "Number of SmCCnet clusters: 3\n",
-      "          Gene_1    Gene_2  ...  Mir_99  Mir_100\n",
-      "Gene_1  0.000000  0.158521  ...       0        0\n",
-      "Gene_2  0.158521  0.000000  ...       0        0\n",
-      "Gene_3  0.000000  0.000000  ...       0        0\n",
-      "Gene_4  0.000000  0.000000  ...       0        0\n",
-      "Gene_5  0.039205  0.035508  ...       0        0\n",
-      "\n",
-      "[5 rows x 600 columns]\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"Global network shape:\", global_network.shape)\n",
-    "print(\"Number of SmCCnet clusters:\", len(clusters))\n",
-    "print(global_network.head())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## GNN-Based Network Embedding\n",
-    "\n",
-    "Once a graph has been constructed, BioNeuralNet enables low-dimensional embedding of omics data using the `GNNEmbedding` module. This component applies Graph Neural Networks (e.g., GCN or GAT) to integrate omics, clinical, and phenotype data into a unified graph framework.\n",
-    "\n",
-    "The output is a compact embedding for each subject that captures both feature and network structure, ideal for downstream tasks like classification, clustering, or visualization.\n",
-    "\n",
-    "To use:\n",
-    "\n",
-    "1. Concatenate omics datasets into a single feature matrix\n",
-    "2. Provide the graph and metadata to `GNNEmbedding`\n",
-    "3. Call `.fit()` to train the model and `.embed()` to extract embeddings as a DataFrame\n",
-    "\n",
-    "These embeddings form the foundation for advanced machine learning on complex biological systems.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from bioneuralnet.network_embedding import GNNEmbedding\n",
-    "\n",
-    "merged_omics = pd.concat([omics1, omics2], axis=1)\n",
-    "\n",
-    "gnn = GNNEmbedding(\n",
-    "    adjacency_matrix=global_network,\n",
-    "    omics_data=merged_omics,\n",
-    "    phenotype_data=phenotype,\n",
-    "    clinical_data=clinical,\n",
-    "    phenotype_col=\"phenotype\",\n",
-    "    tune=True,\n",
-    "    gpu=True,\n",
-    ")\n",
-    "gnn.fit()\n",
-    "embeddings = gnn.embed(as_df=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "         Embed_1   Embed_2  ...  Embed_15  Embed_16\n",
-      "Gene_1  2.291541 -0.006752  ...  0.865972  1.384877\n",
-      "Gene_2  1.301154 -0.011410  ...  1.459566  1.621363\n",
-      "Gene_3  0.004610  0.534755  ...  0.156256  0.479289\n",
-      "Gene_4  0.158565 -0.005850  ...  0.611468  0.844665\n",
-      "Gene_5  0.323387 -0.003033  ...  0.789242  1.205275\n",
-      "\n",
-      "[5 rows x 16 columns]\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(embeddings.head())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Embeddings visualization\n",
-    "\n",
-    "We projected the 16‑dimensional node embeddings into 2‑D and observed three clear regions plus one broad cloud.\n",
-    "The class buckets (after binning the continuous phenotype into four equal‑frequency groups) contain:\n",
-    "\n",
-    "| Group  | Samples |\n",
-    "| ------ | ------- |\n",
-    "| 0      | 38      |\n",
-    "| 1      | 158     |\n",
-    "| 2      | 141     |\n",
-    "| 3      | 21      |\n",
-    "\n",
-    "**Visual observations**\n",
-    "\n",
-    "- **Middle‑left cloud**\n",
-    "\n",
-    "    - Largest and most diffuse region\n",
-    "    - Contains the majority of points\n",
-    "    - Most likely accounting for group 1 and 2\n",
-    "\n",
-    "- **Top‑right hand oval**\n",
-    "\n",
-    "    - Compact cluster on the far right\n",
-    "    - Roughly forty points\n",
-    "    - Could be group 0 or 3\n",
-    "\n",
-    "- **Lower-right hand cluster**\n",
-    "\n",
-    "    - Compact group far below the main cloud\n",
-    "    - Also around forty points\n",
-    "    - Could be group 0 or 3\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2025-05-27 12:27:04,683 - bioneuralnet.network_embedding.gnn_embedding - INFO - Preparing node labels.\n",
-      "2025-05-27 12:27:04,736 - bioneuralnet.network_embedding.gnn_embedding - INFO - Node labels prepared successfully and saved to /tmp/tmpd6libqyo/labels_600_0527_12_27_04.txt.\n"
-     ]
-    }
-   ],
-   "source": [
-    "from bioneuralnet.metrics import plot_embeddings\n",
-    "\n",
-    "# Using our embeddings instance we get the necessary labels for the graph.\n",
-    "node_labels = gnn._prepare_node_labels()\n",
-    "embeddings_array = embeddings.values  "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "embeddings_plot = plot_embeddings(embeddings_array, node_labels)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Subject Representation\n",
-    "\n",
-    "We enrich the original omics data by combining it with our 16D graph-based embeddings.\n",
-    "\n",
-    "- `SubjectRepresentation` merges embeddings, omics, and phenotype data.\n",
-    "- This produces a compact, information-rich input for modeling.\n",
-    "- For details, see [GNN Embeddings for Multi-Omics](https://bioneuralnet.readthedocs.io/en/latest/gnns.html)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from bioneuralnet.downstream_task import SubjectRepresentation\n",
-    "\n",
-    "graph_embed = SubjectRepresentation(\n",
-    "    omics_data=merged_omics,\n",
-    "    embeddings=embeddings,\n",
-    "    phenotype_data=phenotype,\n",
-    "    phenotype_col=\"phenotype\",\n",
-    "    tune=True,\n",
-    ")\n",
-    "enhanced_omics = graph_embed.run()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Before graph embedding:\n",
-      "           Gene_1     Gene_2  ...     Mir_99    Mir_100\n",
-      "Samp_1  22.485701  40.353720  ...  11.422531  10.862970\n",
-      "Samp_2  37.058850  34.052233  ...  12.413667  10.719110\n",
-      "Samp_3  20.530767  31.669623  ...  11.072915  11.418794\n",
-      "Samp_4  33.186888  38.480880  ...  10.121957  11.039089\n",
-      "Samp_5  28.961981  41.060494  ...  12.206151  10.724849\n",
-      "\n",
-      "[5 rows x 600 columns]\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"Before graph embedding:\")\n",
-    "print(merged_omics.head())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "After graph embedding:\n",
-      "           Gene_1     Gene_2  ...     Mir_99   Mir_100\n",
-      "Samp_1  30.124834  51.637941  ...  14.502229 -4.488456\n",
-      "Samp_2  49.648960  43.574352  ...  15.760591 -4.429015\n",
-      "Samp_3  27.505743  40.525486  ...  14.058350 -4.718116\n",
-      "Samp_4  44.461565  49.241394  ...  12.850998 -4.561226\n",
-      "Samp_5  38.801318  52.542352  ...  15.497125 -4.431386\n",
-      "\n",
-      "[5 rows x 600 columns]\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"After graph embedding:\")\n",
-    "print(enhanced_omics.head())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Disease Classification with DPMON\n",
-    "\n",
-    "DPMON extends the GNN pipeline for multi-class disease prediction by:\n",
-    "\n",
-    "- Fusing omics features with graph-derived node embeddings\n",
-    "- Applying a classification head (e.g., softmax + cross-entropy) for phenotype prediction\n",
-    "- Supporting full end-to-end training.\n",
-    "\n",
-    "In this example, we discretize the continuous phenotype into 4 equally populated classes using `pd.cut`, enabling multi-class classification."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from bioneuralnet.datasets import DatasetLoader\n",
-    "import numpy as np\n",
-    "\n",
-    "Example = DatasetLoader(\"example1\")\n",
-    "omics1 = Example.data[\"X1\"]\n",
-    "omics2 = Example.data[\"X2\"]\n",
-    "phenotype = Example.data[\"Y\"]\n",
-    "clinical = Example.data[\"clinical_data\"]\n",
-    "\n",
-    "min_val = phenotype[\"phenotype\"].min()\n",
-    "max_val = phenotype[\"phenotype\"].max()\n",
-    "\n",
-    "# linspace creates an array of evenly spaced values\n",
-    "bins = np.linspace(min_val, max_val, 5)\n",
-    "\n",
-    "phenotype[\"phenotype\"] = pd.cut(phenotype[\"phenotype\"], bins=bins, labels=[0, 1, 2, 3], include_lowest=True)\n",
-    "count_values = phenotype[\"phenotype\"].value_counts(sort=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "         phenotype\n",
-      "Samp_1           1\n",
-      "Samp_2           2\n",
-      "Samp_3           1\n",
-      "Samp_4           3\n",
-      "Samp_5           2\n",
-      "...            ...\n",
-      "Samp_354         1\n",
-      "Samp_355         1\n",
-      "Samp_356         2\n",
-      "Samp_357         1\n",
-      "Samp_358         1\n",
-      "\n",
-      "[358 rows x 1 columns]\n",
-      "phenotype\n",
-      "0     38\n",
-      "1    158\n",
-      "2    141\n",
-      "3     21\n",
-      "Name: count, dtype: int64\n"
-     ]
-    }
-   ],
-   "source": [
-    "# After binning\n",
-    "print(phenotype)\n",
-    "print(count_values)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## DPMON Evaluation Example\n",
-    "\n",
-    "In this example, we evaluate **DPMON** over 3 independent runs to assess classification performance.\n",
-    "\n",
-    "For each run:\n",
-    "\n",
-    "- A new `DPMON` instance is initialized with the same omics, phenotype, clinical data, and global network.\n",
-    "- `repeat_num = 3` runs internal training three times with different seeds.\n",
-    "- We call `.run()` to generate predictions and extract:\n",
-    "\n",
-    "  - **Accuracy**\n",
-    "  - **F1 (Weighted)**\n",
-    "  - **F1 (Macro)**\n",
-    "\n",
-    "Afterward, we compute the mean and standard deviation of each metric to enable fair comparison with other models like Random Forest, using consistent evaluation across runs."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from bioneuralnet.downstream_task import DPMON\n",
-    "from sklearn.metrics import accuracy_score, f1_score\n",
-    "import numpy as np\n",
-    "\n",
-    "acc_scores = []\n",
-    "f1w_scores = []\n",
-    "f1m_scores = []\n",
-    "\n",
-    "for i in range(3):\n",
-    "    print(f\"DPMON run {i+1}\")\n",
-    "    \n",
-    "    dpmon = DPMON(\n",
-    "        adjacency_matrix=global_network,\n",
-    "        omics_list=[omics1, omics2],\n",
-    "        phenotype_data=phenotype,\n",
-    "        clinical_data=clinical,\n",
-    "        repeat_num=5,\n",
-    "        tune=True,\n",
-    "        gpu=True,\n",
-    "        cuda=0,\n",
-    "        output_dir=\"dpmon_output\"\n",
-    "    )\n",
-    "    \n",
-    "    predictions_df = dpmon.run()\n",
-    "    y_true = predictions_df[0][\"Actual\"]\n",
-    "    y_pred = predictions_df[0][\"Predicted\"]\n",
-    "\n",
-    "    acc = accuracy_score(y_true, y_pred)\n",
-    "    f1w = f1_score(y_true, y_pred, average=\"weighted\")\n",
-    "    f1m = f1_score(y_true, y_pred, average=\"macro\")\n",
-    "\n",
-    "    acc_scores.append(acc)\n",
-    "    f1w_scores.append(f1w)\n",
-    "    f1m_scores.append(f1m)\n",
-    "\n",
-    "# get the mean and std in tuple form\n",
-    "dpmon_acc_tuple = (np.mean(acc_scores), np.std(acc_scores))\n",
-    "dpmon_f1w_tuple = (np.mean(f1w_scores), np.std(f1w_scores))\n",
-    "dpmon_f1m_tuple = (np.mean(f1m_scores), np.std(f1m_scores))\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Performance Comparison\n",
-    "\n",
-    "We compare **DPMON** to a baseline Random Forest using raw omics data across 5 runs.\n",
-    "\n",
-    "- Metrics: **Accuracy**, **F1-Weighted**, **F1-Macro**\n",
-    "- Bars show mean performance, error bars indicate standard deviation\n",
-    "\n",
-    "DPMON consistently outperforms the baseline across all metrics."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2025-05-27 12:41:52,230 - bioneuralnet.metrics.plot - INFO - Plotting multiple metrics: ['Accuracy', 'F1-Weighted', 'F1-Macro']\n"
-     ]
-    },
-    {
-     "data": {
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-      "text/plain": [
-       "<Figure size 1500x500 with 3 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from bioneuralnet.metrics import evaluate_rf, plot_multiple_metrics\n",
-    "\n",
-    "# raw omics evaluation\n",
-    "X_raw = merged_omics.values\n",
-    "y_global = phenotype.values\n",
-    "rf_acc, rd_f1w, rf_f1m = evaluate_rf(X_raw, y_global, n_estimators=100, runs=5, mode=\"classification\")\n",
-    "\n",
-    "# metrics dictionary\n",
-    "metrics = {\n",
-    "    \"Accuracy\": {\"Raw\": rf_acc,\"DPMON\": dpmon_acc_tuple},\n",
-    "    \"F1-Weighted\": {\"Raw\": rd_f1w,\"DPMON\": dpmon_f1w_tuple},\n",
-    "    \"F1-Macro\": {\"Raw\": rf_f1m, \"DPMON\": dpmon_f1m_tuple}\n",
-    "}\n",
-    "\n",
-    "plot_multiple_metrics(metrics)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Phenotype-Aware Clustering\n",
-    "\n",
-    "BioNeuralNet includes phenotype-guided clustering tools like **HybridLouvain**, which extend standard graph methods (e.g., Louvain) to incorporate phenotype correlation. This allows detection of biologically meaningful modules.\n",
-    "\n",
-    "- Accepts any network and omics matrix as input\n",
-    "- Optimizes modularity while aligning clusters to phenotype signal\n",
-    "- Can be compared with external clustering (e.g., SmCCNet)\n",
-    "\n",
-    "For details, see the [Correlated Clustering documentation](https://bioneuralnet.readthedocs.io/en/latest/clustering.html)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from bioneuralnet.clustering import  HybridLouvain\n",
-    "import networkx as nx\n",
-    "\n",
-    "merged_omics = pd.concat([omics1, omics2], axis=1)\n",
-    "G_network = nx.from_pandas_adjacency(global_network)\n",
-    "\n",
-    "hybrid = HybridLouvain(\n",
-    "    G=G_network,\n",
-    "    B=merged_omics,\n",
-    "    Y=phenotype,\n",
-    "    tune=True,\n",
-    ")\n",
-    "hybrid_result = hybrid.run(as_dfs=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Number of clusters: 2\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x500 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Cluster</th>\n",
-       "      <th>Louvain Size</th>\n",
-       "      <th>Louvain Correlation</th>\n",
-       "      <th>SMCCNET Size</th>\n",
-       "      <th>SMCCNET Correlation</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Cluster_1</td>\n",
-       "      <td>35</td>\n",
-       "      <td>0.821929</td>\n",
-       "      <td>65</td>\n",
-       "      <td>0.641804</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Cluster_2</td>\n",
-       "      <td>5</td>\n",
-       "      <td>0.800173</td>\n",
-       "      <td>40</td>\n",
-       "      <td>0.067307</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "     Cluster  Louvain Size  Louvain Correlation  SMCCNET Size  SMCCNET Correlation\n",
-       "0  Cluster_1            35             0.821929            65             0.641804\n",
-       "1  Cluster_2             5             0.800173            40             0.067307"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Lets compare hytbrid louvain with the SmCCNet clusters\n",
-    "print(\"Number of clusters:\", len(hybrid_result))\n",
-    "\n",
-    "compare_clusters(hybrid_result, clusters, phenotype, merged_omics)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Network Visualization\n",
-    "\n",
-    "BioNeuralNet supports flexible visualization of omics networks, including but not limited to clustering results like Hybrid Louvain.\n",
-    "\n",
-    "You can:\n",
-    "\n",
-    "- Visualize **any adjacency matrix** or feature-level graph\n",
-    "- **Toggle node labels** and **edge weights** to suit your analysis\n",
-    "- Apply a **threshold** to filter weaker edges and focus on key structure\n",
-    "\n",
-    "This makes it easy to explore network topology, inspect connectivity patterns, and interpret biological relationships across genes, samples, or other entities.\n",
-    "\n",
-    "For details, see the [documentation](https://bioneuralnet.readthedocs.io/en/latest/metrics.html)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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WrVpF+/btmThxIk2bNqV69eoA+rXPnrXG2bPW+VuyZAkA//zzDw0bNjQ6fuXKlQzX/CxJSUl88sknXLx4kbZt2+rXO3teXYsXLzY5YjMz64I39+tNCCGEEOJDID+OFEII8V5ycnKib9++APTp08dknxw5clC0aFFiYmLYtGlThq6fFu5otdpn9vnoo49wc3Pj9OnTLFiwAEhdzy1NWsA2ffp0rly5Qu7cuQ0WoIf/rym3adMmg00X0vz777+cPHkSMzOzV9pUQKVSMXbsWH755Rfu3r1LlSpVOHny5Etfr1mzZjg6OnLlyhXWrVvHihUrgIxNLX0WCwsLmjZtql9b7Mk604KgZ214kLbL59MePXoEgLe3t9Gxc+fOvdK7eJKiKLRv3569e/cSFBRktO5dRuravHkz4eHhJs9Lz69PU6pUqYKZmRknT57k1KlTRsfv37+v/72SFnQKIYQQQgjTJHQTQgjx3urRowd58uTh8OHDHDx40GSfYcOGAam7aa5du9bouKIoHD58mC1bthi058qVC0gNZJ5FpVIRFBSEoihMmDABS0tLg2AsODgYlUrF33//DZieWlqpUiXKli1LQkIC3bp102/KAKkbQaRNS2zZsqV+5N6rGDp0KH/88QcPHz6kevXqz3xvL2Jra6ufktm5c2cSEhIIDAzM8BpsEydONLl5wYMHDzh27BhgGEgFBQVhZmbG5s2bDTaWUBSFv/76i+XLl5u8T0BAAAATJkwwmIJ6//592rdvn+Hw6ll69+7NkiVLCAwMZOXKlS/cHTatrqd3z7106RJffPHFM89Lz69PU/LkyUOzZs1QFIVu3boZbHoQFxdH165dSUxMpEKFClSoUCFD1xZCCCGE+NBI6CaEEOK9ZWVlxZAhQwAMwqonNWjQgHHjxvHo0SMaNmxIgQIFqF+/Pm3atOHjjz/G09OTcuXKsWPHDoPzmjRpAqSOomvQoAGfffYZXbp0MdrlNC1IS0xMpGLFitja2uqPubm5Ubx4cf3C8s9az23BggV4e3uzevVq8uXLR7Nmzfj000/x9fXl6NGjlChRQh/cZYbevXszadIkHj9+TM2aNY2ePb3SRrWlbULxMqPcpkyZgr+/Pz4+PjRs2JC2bdtSq1YtfHx8uHPnDkFBQQbTQXPnzs3XX3+NTqcjODiY6tWr06RJEwoUKMAPP/zATz/9ZPI+ffv2xdLSkqlTp1KwYEFatGhBnTp18PX1JSkpiUaNGr3EGzB0+/ZtRo0aBaTu5tmzZ0/9LqRPfk2bNk1/zsCBA1GpVPTv35+iRYvSqlUrgoODCQwMxMfH55nB16effoqZmRl//fUXNWvWpHPnznTp0oU1a9a8sM4JEyZQrFgxDh8+jK+vL40aNaJZs2bky5ePdevWkS9fPubPn//K70MIIYQQ4n0noZsQQoj3Wrt27QgMDHxun549e/Lvv//StWtXVCoV27dvZ9WqVVy7do2PPvqIv/76i549exqcU69ePaZOnUqRIkXYsWMHM2bMYPr06Vy+fNmg35NBmqlQLa1NpVIRHBxssj4fHx9OnDjBzz//jJubG+vWrWPr1q34+voyYsQI9u3bh4uLS7reR3p98cUXzJ07l6SkJOrVq8e6desyfI3SpUvr372lpSVt27bN8DWGDx9O9+7dcXZ25tChQyxdupTz589TtmxZZs+ezaZNm7CwMFyidsyYMYwaNQo/Pz8OHDjArl27KFSoEIcOHdJPSX1a2bJlOXbsGA0bNiQuLo41a9Zw7do1vv76aw4ePIijo2OGa39aSkqK/r+3bt3K7NmzTX7t27dP369x48bs3r2b4OBg7t+/z5o1awgLC2PQoEFs3LjxmSPlihYtyvLlyylfvjyHDx9m1qxZTJ8+nRMnTrywTjc3Nw4cOMBvv/1Gvnz52LJlC+vWrSNbtmz07duX48ePp3tXXCGEEEKID5lKeVu2uxJCCCGEEEIIId4RWq2WoYP707BWeQILF8zqcjJdzQbt2LP/KEmPLurbdu87zMcNO/BLnx70/+nr13Zvv2JBAFw+9XKj7cXLGzpiPMP+mMCWNbOpWqlsVpfzzvl11FTqf9qSUqVKATLSTQghhBBCCCGEeCfdvHUHK1f/535FPY7O6jLRarXMnr+chs27kse/EvbZA3H3LkWF4KYMHD6WkNt3s6y2tHfYpYfpJSjepLTvWfHy9Q1GyKd5EPoQK1d/ajZolwXVvR1Cw8L5ps8QKtVoTu6CFXHwDMSncFVqfdqRVWu3YGpcWZcePz3398jrZPHiLkIIIYQQQgghhHhb+eTLQ+tmDUwes7ayesPVGAq5fZembXpw+uxFsntkI7haBXLl9CQuLoGTp88zcuxUxvw9gxP715Lfx3i37g/RhUtXmbNgJZ3aNc3qUt46d+7eZ/6i1ZQpVYyG9Wrg6uLEw4ePWL95Jy069KRzu2ZMGjfU5LlfdWuPs5PDG61XQjchhBBCCCGEEOId5psvz2ud7vmyYmJiqd+0C5ev3OC7rz9jUN9vsLKyNOhz9XoIfX4ZQVyc6U2vPjQe7m7EJyQw7Pe/adWsAdbWWRuavm2KFvEn9MYRzM3NDdpjYmKpVLMFM+Yu5esv2lMooIDRuV93b0/ePLneVKmATC8VQgghhBBCCCHea7v3HcbK1Z+hI8YbHXud0yvH/D2Dy1du0Lp5Q34b3NsocAPI7+PNigWTCCjo+9xrpU0RvHnrjtGxoSPGY+Xqz+59hw3aV67ZTI36bcnlVwFHr6LkLVSZ2o06sXLNZgDmLFhBweKpm1rNXbjKYMrhk9dSFIVZ85ZTrXYrsuUpiXPO4pQPasKsecufW8ucBSsoW60xzjmLp3tKqLOzI99+2Yk79x7w9z9z0nUOQHhEJN///Ct+xYNx8Awkl18FWnf6lnPnL5vsf/vOfdp1+Q5Pn7K45i5Bjfpt2Xvg6HPvsffAURq1+oIc+cvh4BlIoVK1GDh8LPHxCUZ9X/TuX5ZarTYK3AAcHOz5OLgSAFdv3Hqle2QmGekmhBBCCCGEEEKITDd7/goA+vb+8oV9LS2NA7lX8c+MhfT8YTBenu40rFcDN1dnQkPDOXriDKvXb6NRw1oUCwzgq27t+fufORQt4k/Duv/fSd47T04gNXDr0PUHFi9fT35fb1o0rY+lpZrtOw/QrWc/Lly6yu9DfzS6/+jxM9i97zAN6gRRo3pFzM3TP+ap11edmTJzESPHTqVz+2a4ujg/t//D8EdUqdWS6zduUbVSGZo3rsvNkLusWLOZjVt3s27ZNCqWK6nvf/9BGFVrteTu/VBqBlXio2KFuHj5OnUbd37m5gn/zFjIN72H4OzkSL3a1XDP5saJk2cZMWoyu/ceZsua2frvYXrefZouPX5i7sJVTP37V9q3bpzud/S0xMQkdu09jEqlopB/fpN9NmzeRWxsHJaWlvj7+RJUtVym/7p7moRuQgghhBBCCCHEO+zajVsmR7F9HFyZsqWLv/mCSF3L7c69B+TK4UkB37xv/P4z5y7F0lLNkd2r8HB3MzgW8SgSgGKBAXzd3YG//5lDsUB/k1N0Z8xZyuLl6+nQujETxgxGrVYDkJycTMuO3zB2wkxaNKlHieJFDM7be+Ao+7YupkihjO9sa29vR9/eX/Jtn6H8Pvofk6Hek/oN+pPrN27Rp1dXhvb/Tt++cetuPm3Rjc+/6svZIxsxM0sN/voPHc3d+6EM7vctP33/hb7/tFmL6fHdQKPrX7h4le9+Gk5g4YJsWjUTN1cX/bGRY6fwy5DRTJgyj15fdQbS9+5fVdjDCP6ZvgCdTiEsPILNW/dw++59funT45lrA/b6cZjBZy9Pd6aM/5WPgytnSk2myPRSIYQQQgghhBDiHXb9xi2G/THB6OvwsVNZVlNoaDgAOXNkz7Ia1Go1arXxWKMnQ6MXmTRtPnZ2towbOUAfuEHqyLwhv3wLwOLl643O+6x9s5cK3NJ06dAcXx9vJk9fwO0795/ZLzk5mcUr1uPm6szP33c3OFanZlWCq1Xg2vUQDhw+oe+/dOVGPNzd+LZHJ4P+nds3I7+vcWA1ddZitFotY37/xejdfd+zC+7ZXFmywvAdpPfdD+3/HacObeCT+jWf+YymPHwYwbA/JvDrnxOZNmsxD8LC+W1wb3758SujvpXKl2bBjLFcPb2Tx/dOce7YZgb+3JOoxzE0afMlx/89k6F7Z4SMdBNCCCGEEEIIId5hNYMqsW7ZtKwu463SrFE9+g4aSYmKDWjRpD5VK5elYtmSODrap/sa8fEJnD1/mRyeHvw5bqrRcY1WC8ClK9eNjpUuUfTliyc1tBrc9xvadvmOwb+NY9qEESb7Xbpyg8TEJKpWKoutrY3R8WqVy7J91wFOnblApfKluHw1tX+1yuWMNmkwMzOjQpkSXL0WYtB+5L/wduuOfezcfdBErRZcunJD/zkj797L0wMvT48Xv5CnFC7kR9Kji6SkpHD77n2WLF/PwOFjOXT0JAtmjMHC4v9xV8e2TQzOze/jTd/eX5LDKzvdevZj+MiJrFgwKcM1pIeEbkIIIYQQQgghhMhU2bNnA+De/bAsuf93X3fGzdWZKTMXMnbCTMb8PQMLCwvqfFyVkcN/Jp/3i3exjIyKRlEU7t4PZdgfE57Zz9RGAh4ebiZ6ZkzTRnUYM2EG8xev4dsencjm5mrUJzomFoDs7qbv55ndHYCYmDgAHken9vfIZnyt1LqzGbU9inoMwIhRk9NVd2a8+/QyNzcnb55c9OnVDXNzC/oOGsn0OUvp1rnVC89t1+pTvukzhIP/jQJ8HWR6qRBCCCGEEEII8R5LW8tLm5JidCz6vxAms3nnzklOr+zcvnufK9duvvL1zFT/PYPW+Bkem3gGlUpFx7ZNOLB9GXevHGDJ3L/5tH5N1m7YTqOWX5Bi4l08zdHBDoASxQuT9OjiM7+2rDHeZVSlUmX0EU1eY/jAH9DpdPQbPPoZNaaOHgt9GGHyeGhY6jRfh/+exem/0WZh4Y9M9g/7r7/hPVLPDQ859tz38GTdr/ruX0bNoIoA7Nl3JF39zc3NcXZyJM5EaJpZJHQTQgghhBBCCCHeYy7OjgDcuxdqdOzk6fOv7b5p0/pGjHrx1L3k5OTnHndOe4b7xs9w6szzn8HN1YVP6tVg/owxVKtSjguXrnL1euoUSnMzcwBSUnRG5zk42OPv58vFy9eJehz9wmd4HapXKUfNoEps2rqbfQeOGR0vWCAf1tZWHP/3jMkRd7v/C6CKBQYAUMA3tf+Jk2dJTEwy6KvT6Th49F+ja5QuWQzgpdYIfN67z2xpoypNrSVnyq0793gQ+lC/U+3rIKGbEEIIIYQQQgjxHvPLnw8HezvWbdrBo8gofXtoWDi/pXPK4Mvo9VVn/ArkY96i1fQfOpqkJONg7UbIHZq27cGFS9eee61SHwUCMHfBSoP2Fas3sWf/UaP+u/cdRlEUgzaNRkNkZOpUybT1zFycHVGpVNy5a3qzgh7d2hEfn0D3b/oTFxdvsv6bt+48t/ZXNXzg96hUKvoPG2N0zNLSkhaN6xEeEckfY6YYHNu8bS9bd+zD18ebCmVLAGBlZUnTT2sT9jCCsRNmGvSfMWcpV67eNLrHF51bYWFhQa+fhnHrzj2j41GPow3C2/S+e4D7D8K4ePk6j6NjXvAWUp0+exGNRmPU/igyigH/vZ/aNavo2x+EPuSuibA56nE0n/f4GYCWTeqn694vQ9Z0E0IIIYQQQggh3mOWlpZ82bUtv4/+h3LVGlO/TjCxsXGs37yTyhVKc/3GrddyXwcHe9Ytm0bTNj34Y8wU5ixYSY3qFciZw5P4+EROnTnPgcP/YmFhzoghfZ57rQZ1g/HJl4c5C1dy+94DigcGcPHyNXbtPUztmlXZtHW3Qf9mbb/C0cGeMqWKkSd3DjQaLdt3HeDCpas0blgL79ypo5vs7e0o9VEgew8co9MXfcjv442ZmRmtWzTEO3dOPu/YgiPHTjJ34SoOHjlBUNUKeHl6EBYWzqUr1zly/DRzpvxJ3jyZt07Z04oFBtCyaX0WLl1r8vjwQT+w58BRfhs1iYNH/6VMyaKE3LrL8tWbsbW1Yerfv+qnGAMMG/A9O3cfYuDwsew/dJziRQO4ePk6m7bupkb1imzbud/g+oUL+fHXyAF8/cNgAsvUoXaNKvjky0NMbBw3bt5m74GjtGvViAmjBwPpf/cA/YeOZu7CVUz9+1fat278wnfx16RZbNi8i/JlS5A7lxc21tbcun2PjVt3ExcXT5NPatPiiRDt0pXr1G38GeXLFCe/T16yZXPhzt0HbNm+l4hHUVSrUo7ve3bJ0PcjIyR0E0IIIYQQQggh3nOD+n6DpVrNrHnLmTprEd55cvLzD92pVzuIlWu3vLb7eufOyYHtS1mwZA3LVm1i6479PIp8jLWVJfl9vfm+52d83rEluXN5Pfc6NjbWbFw5g979RrBz90GOHDtFmVLF2L5uLhs27zIK3YYO+I4t2/dy7MRp1m/eiZ2tDT558zB+1CA6PbWb5YzJv9O73wg2bN7F4+gYFEWhQrkSeOfOiUqlYtqEEdSuUZXpc5ayYfMuYuPi8cjmSn5fb0YM6UNQtQqZ/t6eNqjfNyxfvYnkZONRXu7ZXNm3dQm/jpzIuo3b2X/wOE6O9jSsG8wvfXpQuJCfQX8vTw92bV5I34F/snXHPvYdPEaJYoXZsGIGu/YcMgrdAD7r0JxigQGMmziTfQePsX7zLpwc7cmdy4ue3TvQtmUjfd+MvPuMat38E3Q6hSPHT7F772ESEpNwc3WmUvlStGv5Kc0a1zXo75M3D+1bNeLYv2dYs2Ebj6NjsbezJbCwHy2a1qdzu2aYm5u/Uk3Po1KeHvMnhBBCCCGEEEKI59JqtQwd3J+GtcoTWLhgVpcjhHgL/DpqKvU/bUmpUqUAWdNNCCGEEEIIIYQQQohMJ6GbEEIIIYQQQgghhBCZTEI3IYQQQgghhBBCCCEymYRuQgghhBBCCCGEEEJkMgndhBBCCCGEEEIIIYTIZBK6CSGEEEIIIYQQQgiRySR0E0IIIYQQQgghhIGaDdph5epv0LZ732GsXP0ZOmL8a723X7Eg/IoFvdZ7vO9Mff/Em2eR1QUIIYQQQgghhBAi427eukPB4jWe2yf0xhGcnRzfUEWmabVa5i9ezfLVmzl5+jyPIh9jY21Fgfx5qRlUic7tm+GdO2eW1Jb2Dtu1+pRpE0ZkSQ1p0hOSJT26+AYqeTv5FQsi5PY9k8eqVCzN1rVzjdovXLrGiFGT2LnnEJFRj/H0cKdB3WB++bEHri7ORv2f9z14mV8jEroJIYQQQgghhBDvMJ98eWjdrIHJY9ZWVm+4GkMht+/StE0PTp+9SHaPbARXq0CunJ7ExSVw8vR5Ro6dypi/Z3Bi/1ry+3hnaa1vAzdXZ7p3aZPVZby1nBwd+PqL9kbt3nmMQ9vDR09Sp3FnEhISaVA3GJ+8uTl15iITpsxly4697N60EDdXF+Nr5c5Bu1aNjNqLBgZkuF4J3YQQQgghhBBCiHeYb7489P/p66wuw0hMTCz1m3bh8pUbfPf1Zwzq+w1WVpYGfa5eD6HPLyOIi4vPoirfLm5uLm/l9/Jt4ezkkO730/3b/sTFxbNs/kQa1Pn/dOVRf02n76CRDBg2lgmjBxud550nZ6Z9D2RNNyGEEEIIIYQQ4j32vLXYbt66g5WrP116/JTp9x3z9wwuX7lB6+YN+W1wb6PADSC/jzcrFkwioKDvc6/VpcdPWLn6c/PWHaNjQ0eMx8rVn937Dhu0r1yzmRr125LLrwKOXkXJW6gytRt1YuWazQDMWbBCPz137sJVWLn667+evJaiKMyat5xqtVuRLU9JnHMWp3xQE2bNW/7cWuYsWEHZao1xzlmcmg3avfiFvYT9h45To35bXHJ9hJdvWdp07sXtO/ef2T88IpLu3/Ynl18FnHMWp0JwU1av28qcBSuwcvVnzoIVRuecOXeJtp99h3dAZeyzB1KgaBDf/jiUiEeRRn137T1Eg2afk7dQZRw8A8ldsCJBddswbdbiTH3uF7l24xbnLlyhVIlAg8ANoNdXnXBzdWbBkjWvPeyVkW5CCCGEEEIIIYTIdLPnpwY4fXt/+cK+lpbGgdyr+GfGQnr+MBgvT3ca1quBm6szoaHhHD1xhtXrt9GoYS2KBQbwVbf2/P3PHIoW8adh3WD9+WnTFRVFoUPXH1i8fD35fb1p0bQ+lpZqtu88QLee/bhw6Sq/D/3R6P6jx89g977DNKgTRI3qFTE3z/wxTzt2H6Rh866Ymalo1qgOXp4e7NxziOp1WuPsbLyOX2xsHDXqt+PCpauUL/MRlSqU4u69UNp2+Y6aQZVM3mPtxh206fwtZmZmNKgTRK6cXly4dJVJU+ezdcc+9m1dgouzEwAbtuyicavuODs50qBOEJ6e7oSHR3L63EUWLFlDl44t9NcdOmI8w/6YwC99emRoVFlSsoY5C1Zw/0EYDg72lPookDKlihn1Cw0NByBvnlxGx8zMzMidKwcnT5/n8LFTBFUtb3A86nEM02YtJuJRJC4uzlQo+xFFChVMd41PktBNCCGEEEIIIYR4h127ccvkKLaPgytTtnTxN18QqWu53bn3gFw5PCngm/eN33/m3KVYWqo5snsVHu5uBsfSRmgVCwzg6+4O/P3PHIoF+psMf2bMWcri5evp0LoxE8YMRq1WA5CcnEzLjt8wdsJMWjSpR4niRQzO23vgKPu2Ls5wWBMREfnM3WELFvCheZN6AOh0Or7sNQCtVsv29fOoWK4kkBoSduzWm0XL1hmd/+e4aVy4dJXPOjRn4pgh+vZ2rRpRp1En41oeRdL5iz5kc3Vh56YFBptdLFm+nnaff8/g3/5i7O/9AZg9bzmKorBlzWyKFvE3ulZmeBD6kM+/6mvQVqpEIHOmjsI3Xx59m5ubM4DJkZE6nY7bd1I3ZLhy7aZR6Hb67EV6fDfQoO3j4MpMnzjC6NfSi0joJoQQQgghhBBCvMOu37jFsD8mGLU7OTlmWeiWNtIoZ47sWXJ/ALVajVptHHuYWjz/WSZNm4+dnS3jRg7QB26QOjJvyC/fsn7TThYvX28Uun3WvtlLjY6KeBRl8nsJ0KBusD5023/oODdu3qZe7er6wA1ApVIxpH8vlq7cSEpKisH5C5auwdJSzcCfexq0B1UtT43qFdm2c79B+7xFq4mOiWXsH/2Ndpdt3qQeo/+eztIVG/ShWxpra+PNO55+590/b0uzxvXI5pb+70X71o2pWL4UhQMKYG9ny5VrNxk3cRbzF6+mzqcdOb5vDQ4O9gD45c9Hvry5OXbiDBu27KLux9X01/lr0mwiHkUB8PhxtME9vu3RiUYNPqZA/rxYqtWcu3CFX/+cxOZte2jU6gv2bF6Eubl5umuW0E0IIYQQQgghhHiH1QyqxLpl07K6jLdKs0b16DtoJCUqNqBFk/pUrVyWimVL4uhon+5rxMcncPb8ZXJ4evDnuKlGxzVaLQCXrlw3Ola6RNGXqtuvQD7OHN74wn6nz14EoGK5UkbHvHPnJFdOT0Ju3dW3RUfHEnLrLgEF85PdI5vRORXKljAK3Y4cO5X6v8dPcf3GLaNzEhOTCY+IJDwikmxuLjRvXI9V67ZS5eOWtGhaj6Aq5alYvpTJYC2bm0uGAjeAX378yuBzscAAZkz6HYD5i1czfc5Svu2ROmJPpVLx18gBNG79JU1af0nDusH45MvD6bMX2bZzP0UK+XH2/GXMzAyn/T49VbhcmY9YtWgytT7pwJ79R1m7YTufNvg43TVL6CaEEEIIIYQQQohMlT17arBz735Yltz/u6874+bqzJSZCxk7YSZj/p6BhYUFdT6uysjhP5PP23itr6dFRkWjKAp374c+c/QZpIZzT/PwyNg0xIyKjo5NvY+7q8nj2d2zGYZuMc/vb6reR5GPAZg8bcFza4mLjyebmwtNPq3NUssJ/DVxJlNnLmbytAWoVCqqVi7LH0N/pFhgwIsf7CV06dCC+YtXc/DwCX3oBqlTQnesn8evf05k197DbNy6m8IBBVgy92927jnI2fOXcc9m+n08yczMjM7tm7Nn/1EOHD4hoZsQQgghhBBCCCFSpY3m0T413RD+H95kNu/cOcnplZ3bd+9z5drNV17XzUz13zNojZ/hsYlnUKlUdGzbhI5tmxDxKJJ9B4+zZPl6lq3ayNVrIRzft/qF0wQdHewAKFG8MAd3GO9U+jwqlSpD/TMqbcRe2MNHJo+HPgw37O/w/P5hYRHG9/jv+U/sW0PhQn7pqqth3WAa1g0mJiaWA4f/ZdW6Lcyat5wGzT7n9OENODsZb/DwqtLWb4szEX6WKVWMVYv+MWr/e/JsAEp+VMTomMl7uD77Hs+T+dtnCCGEEEIIIYQQ4q3h8t9OlvfuhRodO3n6/Gu7b8e2TQAYMWrSC/smJyc/93jabpz37hs/w6kzz38GN1cXPqlXg/kzxlCtSjkuXLrK1eshAJibpQZvKSk6o/McHOzx9/Pl4uXrRD219ldWS9uoYP+hY0bHQm7f5c7dBwZtjo72eOfJybUbIYQ9NA7YDh7516it9H+7gh46ejLD9Tk42FOrRmUmjR1K+1aNCA0L109XzWxHj58G/r/j7IuE3L7L/kMnCCiYP93r7qXdI28675FGQjchhBBCCCGEEOI95pc/Hw72dqzbtINHkVH69tCwcH4bNfm13bfXV53xK5CPeYtW03/oaJKSjIO1GyF3aNq2BxcuXXvutUp9FAjA3AUrDdpXrN7Env1Hjfrv3ncYRVEM2jQaDZH/TZlMW+zfxdkRlUrFnbv3Td63R7d2xMcn0P2b/sTFxZus39QOma9bxXIlyeudiw2bd7H/0HF9u6IoDBg6xmgTBYBWzRqQnKxhyFO7o+7ed5itO/YZ9e/QujEO9nYMHD6W8xeuGB2Pj0/g8BOB3N4DR03eNyw8NeR7coOF8IhILl6+TnhE+nY1vXj5uslpvBcvX6ff4FEAtGxa3+BYbGyc0a+Bx9ExdP7iR1JSUhg64DuDY2fPX0Kj0Rjd4+DhE/z51zTUajWNP6mdrnrTyPRSIYQQQgghhBDiPWZpacmXXdvy++h/KFetMfXrBBMbG8f6zTupXKG0yUXyM4ODgz3rlk2jaZse/DFmCnMWrKRG9QrkzOFJfHwip86c58Dhf7GwMGfEkD7PvVaD/xbCn7NwJbfvPaB4YAAXL19j197D1K5ZlU1bdxv0b9b2Kxwd7ClTqhh5cudAo9GyfdcBLly6SuOGtfS7cdrb21Hqo0D2HjhGpy/6kN/HGzMzM1q3aIh37px83rEFR46dZO7CVRw8coKgqhXw8vQgLCycS1euc+T4aeZM+ZO8eV68Rlx6REREMvSpUOxJn3dqiWd2d8zMzJg4ZgiftOhGnUadaNaoDl6eHuzac5gHoQ8JLFyQM+cuGZz7Q88urFyzhakzF3H+whUqli/J3XuhLFu1kXq1q7N+006DjQXcs7kyZ9ooWnf6llJVPuXj4EoULOBDUnIyIbfusnf/UcqV+Ui/icd3Pw3n/oMwKpQriXfuHKhUKg4cOsHRE6cpW6qYwS6rk6bOY9gfE/ilTw/6//T1C9/L0hXrGTdxFpUqlCJP7pzY2dpw5dpNNm3dg0ajoU+vrlSuUNrgnDUbtjNg6BiqVSmLl6cHDx8+Yt2mHTwMf8Sgvt/QoE6QQf+xE2aycctuKpQrSa6cnqgtLDh/8Srbdu5HpVIxbuQAfPPleWGtT5LQTQghhBBCCCGEeM8N6vsNlmo1s+YtZ+qsRXjnycnPP3SnXu0gVq7d8tru6507Jwe2L2XBkjUsW7WJrTv28yjyMdZWluT39eb7np/xeceW5M7l9dzr2NhYs3HlDHr3G8HO3Qc5cuwUZUoVY/u6uWzYvMsodBs64Du2bN/LsROnWb95J3a2NvjkzcP4UYPo9N+01zQzJv9O734j2LB5F4+jY1AUhQrlSuCdOycqlYppE0ZQu0ZVps9ZyobNu4iNi8cjmyv5fb0ZMaQPQdUqZNr7ingU9dxNGxrWq4FndncAgqtVYNOqmQwaPpblqzdjY21F9SrlWTBrLJ91/9HoXAcHe7avn0f/IaNZu3E7x0+epZB/fuZOHcX1m7dZv2knDg6Gu7vW/bgah3etYMz4GezYfYDtuw5gZ2tLzhzZad+6Ma2bN9T37fNtV1at28qJU+fYumMfagsLvPPkZPigH/iic6sXrqH3PFUrl+Xi5eucPHOe/QePE5+QSDY3F2rXrEK3zq2oGVTJ6JwihfwILFKQbTv3Ex4RhZNjagj7zZcdqVa5nFH/BnWCiXocw+mzF9m+6wDJyRo8PbLRvHFdvv6iA6VLZnxHWpXy9Fg7IYQQQgghhBBCPJdWq2Xo4P40rFWewMLpWxdKiLdVx269Wbh0LScPriegoG9Wl/PO+nXUVOp/2pJSpUoBsqabEEIIIYQQQgghxAfh/oMwo7Y9+4+wZMUG/Arkk8Atk8n0UiGEEEIIIYQQQogPwCctumFjbUXRwADsbG24cOkaW7bvxdzcnDEjfsnq8t47EroJIYQQQgghhBBCfADatvyURcvWsnTFBmJi43B2cqBe7er0+bYrZUoVy+ry3jsSugkhhBBCCCGEEEJ8AHp270DP7h2yuowPhqzpJoQQQgghhBBCCCFEJpPQTQghhBBCCCGEEEKITCahmxBCCCGEEEII8R7bve8wVq7+DB0x3qC9ZoN2WLn6Z1FVmc+vWBB+xYKyuowMed++B8KQhG5CCCGEEEIIIcQ76OatO1i5+j/3K+pxdIauOWfBCqxc/ZmzYMVrqvrtkBZEWrn682WvASb7LFm+3mRY+SE5cuwUn335Ix9VaICnT1kcvYoSUPJj2nTuxfF/zxj1T0xMone/3wiu15a8hSrj6FWUPP6VqFa7FbPnL0ej0WTBU2Qd2UhBCCGEEEIIIYR4h/nky0PrZg1MHrO2sqJ0iaKcOrSBbG4ub7iyd8Ps+Sv45suOFCzgk9WlvHX2HTzG9l0HKFOqONWqlMXWxoYbN2+zbtMOlq/exPSJI2jT4hN9/9i4eKbMXETpEoHUqVmNbNlciIyKZsu2PXT9uh9LVmxg7dKpmJl9GGPAJHQTQgghhBBCCCHeYb758tD/p6+f28ffTwIlU3zy5eH6jVsMGDqGxXM+3BFtz/Ll52357uvPjNrPnb9MhRrN+GnAH7Ru3hCVSgWAq4sTD28exdLS0qC/VqulbuPObNu5n03b9lD342pvovws92FEi0IIIYQQQgghxAfqWWu6Pa1Lj5/4/Ku+AHz+VV+DaapPiomJZchvf1G8fH2cchTDI29p6jX5jP2HjhtdM23NssTEJAYOH4t/iZrYeRQxqOVGyB2+6PkL+QOr4+AZiHdAZbr0+ImQ23dN1rlmw3YqBDfFKUcxchesSPdv+hMZ9TijrwWAoKrlqVKxNKvWbeXIsVPpPu/c+cu07vQtufwq4OAZiF/xYL7/+VciHkWa7L//0HFq1G+LS66P8PItS5vOvbh95/4zr68oCrPmLada7VZky1MS55zFKR/UhFnzlhv1TUxMYszfMyhV+RPcvUvhkusj/IoF0brTt5w+ezHdz2SKtbWVyfbChfzw9/Ml7GEE0TGx+nYzMzOjwA3AwsKChvVqAHDt+q1XquldIiPdhBBCCCGEEEIIQYO6NYh6HMPaDdtpUDeYYkWMF/h/FBlFcL12nL94hQplS1CjektiYmJZu3E7HzfswIKZY/nkv3DlSS069OTM2Yt8HFwZJycH8nrnAlLXDKvftAtx8QnUrVWN/D7ehNy6y8Kl69i8bS+7Ny/CJ29u/XXmLVrFZ1/+hKODPa2bN8TZyZENm3dRp1EnkjUaLNXqDD/38IE/UPnjFvQdNJJt6+a9sP/+Q8ep37QLyckaGjf8GO88OTl89CR//zOHDVt2sXfLYoOpvDt2H6Rh866Ymalo1qgOXp4e7NxziOp1WuPs7Gh0fUVR6ND1BxYvX09+X29aNK2PpaWa7TsP0K1nPy5cusrvQ3/U9//sy59YtmojgYUL0r51Y6ysLLlz9z679x3h2IkzFH3i++hXLIiQ2/e4dHIbefPkyvC7SnPtxi0uX71B7pxeODk6vLC/Tqdjy/Z9ABQOKPDS933XSOgmhBAiXRRFISQkhLNnzxIZGYmFhQXe3t4EBgbi4PDiv2iFEEIIIcTrce3GLZOj2D4OrkzZ0sXTfZ1P6tXg8eNo1m7YTsO6wbRv3dioT68fh3H+4hUmjR1K5/bN9O1DH35HhaCm9Og1gFrBlY1GSN1/EMaxfatxdXHWt2k0Gtp2+Q6dTsf+bUsoXrSQ/tj+Q8ep2aA93/88nJULJwMQHR1Lrx+HYWdny/7tS/HLnw+AIb98S51Gnbj/4CHeuXOk+3nTlClVjMYNa7FizWbWb95JvVrVn9lXp9PRpcfPxMcnsHbpVD4Orqw/9vPAkYweP51+g/7kn/HD9f2/7DUArVbL9vXzqFiuJJD6b+uO3XqzaNk6o3vMmLOUxcvX06F1YyaMGYz6vyAxOTmZlh2/YeyEmbRoUo8SxYvwODqG5as3UaJ4YfZtXYK5ubn+OikpKcTExmX4fZhy9PhpNm3djUar5dbte6zbuAOA8aMHmeyfnJzM76P/QVEUIiKj2Ln7EJeuXKdD68YEVS2fKTW9CyR0E0II8VxhYWHMnTuX+fPn8+DBfVA0gC71oMoClUpNpUqV6NixI7Vq1fpgFkUVQgghhHhbXL9xi2F/TDBqd3JyzFDo9iLhEZEsXbmRalXKGQRuAB7ubvT6ujPf/TSc7bsPGAVX/X/62iBwA9iweRcht+4y8OeeBoEbQMVyJWlQJ4g1G7YTHR2Lo6M9azZsIzomli+7ttUHbgBqtZrBv/QiqG6bl362If17sWbDdvoPGU2dmlWf+W/aA4dPcP3GLWrVqGIQuAH06/0ls+YtY9HydYwfNRBLS0v2HzrOjZu3qVe7uj5wA1CpVAzp34ulKzeSkpJicJ1J0+ZjZ2fLuJED9IEbgKWlJUN++Zb1m3ayePl6ShQvggoViqJgbWVlVLO5uTnOToYj6TaumoVGoyWnV/YMvZ+jJ04b/BrL7pGN6RNHUDOoksn+yckag/4qlYpeX3Vm2IDvMnTfd52EbkIIIUxSFIUlS5YwcOBAoh+HgS4eS7WWQvktyO5mhkarcPlmCnce6Ni7ez179+6kfPnKjB49Gm9v76wuXwghhBDig1EzqBLrlk177fc5duIMKSkpJCclmxxZd/V6CACXLl83Ct1Klwg06n/4vzXULl+9YfJ6oWHh6HQ6rly7QcmPAvXrk1UqV8qob7nSxbGwePmIo4BvXjq1a8rUmYuYt2iVyVF+ACdPnwegSqUyRsfs7e0oUbwI23bu5/LVGxQpVFBfc0UTNXvnzkmunJ6E3Pr/2nXx8QmcPX+ZHJ4e/DluqtE5Gq0WgEtXrgPg6GhP7ZpV2bR1N2WrNabJJ7WoUrEMpUoEGgR2aXzz5XnRqzDpy8/b8uXnbUlISOTq9RDGTZxJw+ZdGT7we5MbLdjb25H06CI6nY5798NYv3knA4aO4fDRk6xePAVHR/uXquNdI6GbEEIIIzqdjp9++ol582ZDymMC/RS+bGNHnSpWWFqqDPrevp/C3FUJTF8aycH926hVqxZz586ldOnSWVS9EEIIIYR4HdI2Kzhw+AQHDp94Zr/4+ASjtuwe2YyvF5l6vYVL1z73vnH/XS86OnXBfnd3V6M+5ubmuLk6P/c6L/JLnx4sWLKGIb+Np3njeib7xMSkTtfM7u5m8riXp/t/tcYZ1OxhoubU62QzCN0io6JRFIW790NNjl5M8+Q7XjhzLL+P+YdFy9YxYNhYABwd7GnfujFD+/fC1tbmmdfJKBsbawILF2TahBE8DI+k3+BR1AquTOFCfib7m5mZkSunJ906tyKbqwutO3/LiNGT+XXQD5lW09tMQjchhBBGBg8ezLy5szAjkp++sOWLVrZYWKhM9s3tZU7f7va0aWjNV4OjOX7+Fm3atGH16tUEBAS84cqFEEIIIcTr4uBgB8C3PToZLOSfHiqV8b8l0663YuGk566jliZtdNTDh4+MjqWkpBDxKIqcXh4ZqutJntnd+aZ7R379cyITpswjd07PZ9Yc+jDC5DUehIb/V6udQc1hJmpOvU64wWfH/65fonhhDu4w3qnUFFtbGwb3+5bB/b7lRsgddu89zNRZi/j7nzkkJCYyccyQdF0no2pUr8imrbvZd+j4M0M3g/5BFQHYs+/Ia6nnbSQL7wghhDCwe/dupk79B23yIwrnN2PaknisizygSN2Hzz3v3/Na1u1MIi4ukdjo+/Ts2RONRvOGqhZCCCGEEJkhbSH+lBSd0bFSHwWiUqk4fPRkptyrTMliAOm+XtounPsOHTM6dujoSbT/Tb18Fd993Rn3bK6MHDuFqOgYo+Npa8+ZCo7i4uI5cfIsNjbW+jXn0mreb6LmkNt3uXP3gUGbg4M9/n6+XLx8najH0RmuP593Ljq2bcK2tXOxt7dl/aadGb5Get1/EAaAOp3Teu/d/6+/+sMZ/yWhmxBCCL2UlBR+/PFH0MVQtYya81e15Pe2oFD+5//FmJCo0OvXaLJnMyOHhxkuDomcO3uSmTNnvqHKhRBCCCFEZnBxcQLgzt37Rsc8s7vT9NM6HDzyL6P+mo6iKEZ9jhw7ZXJ6qSkN6gaTJ1cOxk2cxd4DR42OazQa9h86btDf0cGe2fNXcPnqDYN+g4aPTdc9X8TBwZ6fvv+CyKjHjPl7htHxCmVL4JMvD5u37WH7rgMGx34bNYmIR1G0aFwPS0tLIHVDiLzeudiweZfBsyiKwoChY4w2UQDo0a0d8fEJdP+mP3Fx8UbHb4Tc4eatOwA8DH/EufOXjfpERj0mKUmDlZWlQfu1G7e4ePl6un84fvzfMybbT525wNSZi1Cr1QRV+/9upBcuXjX5/Y+PT6DPLyMAqF2zSrru/T74cOJFIYQQL7R9+3Zu3bqOi0MyM0e4YW+X+rOZjn2iOHb22X8x/zY5ljxe5uTLZc6xsxp+7GrHj3/GMnPmTLp06SI7mgohhBBCvCPKlS6OjY014yfPITIqGvdsqWuR/fxDdwD++nMAl6/eoO+gkSxYspqypYvj7OTInbv3OX7yLFevhRByYW+61hGzsrJk4axxNGz+OTXqt6NalXIUCfBDpVJx68499h88hqurM2cObwTAydGB0SP60aXHz1QMbkazxnVxcnRgw+Zd2NhY6ddTe1VdO7Vk/OQ5XL9xy+iYmZkZ0yb8Rv2mXfikRTeafFKLPLlzcPjoSXbvO4JPvjwMG/i9Qf+JY4bwSYtu1GnUiWaN6uDl6cGuPYd5EPqQwMIFOXPuksE9Pu/YgiPHTjJ34SoOHjlBUNUKeHl6EBYWzqUr1zly/DRzpvxJ3jy5uHc/lDJVG1G0iD+BhQuSw8uDiEdRrNu4A41GQ6+vOhtcu86nHQm5fY9LJ7eRN0+uF76LVh2/wdzCghLFCpM7lxfJyRouX73B9l0HUBSFUb/1NbjOslUbGTdxFhXKlcQ7T04cHey5dz+Uzdv2EPEoikrlS9Gze8cMfkfeXRK6CSGE0Fu2bBnoEmhV31ofuL3ItRAto2bEcWCxG2Nmpi4Y26SWDcMnxRIScp0TJ05QqpTxbk1CCCGEEOLt4+rizMJZ4xj2+9/MmLuUhIRE4P+hm6uLM7s3LWTi1PksW7WBRcvWodPpyO6RjaJF/On7w5dkc3NJ9/1KlQjk6J7VjB4/nU3bdnPw8AmsLC3J4ZWdhnVr0LyJ4YYG7Vo1wtHRgRGjJjFv0SqcHB2oXzuIXwf/QNmqjTLlHVhaWjLkl29p/7npxf4rlivJ3i2LGP7HRLbt3M/j6FhyeLrzVbf2/PxDd6PnD65WgU2rZjJo+FiWr96MjbUV1auUZ8GssXzW3XhtPJVKxbQJI6hdoyrT5yxlw+ZdxMbF45HNlfy+3owY0oegahUA8M6Tk/4/fsWuvYfYsfsAEY+iyObmQvGihfiqW3tq1aj8Su+iT69ubNiyiyPHTrF+8050Oh2e2d1p2bQ+3bu0oUypYgb969aqxr0HYRw6cpLDR08SGxePk6M9gYUL0qxRXTq2bfJKu8y+a1SKqfGgQgghPkhlypThTsgplo13oEKJ/w9FTxvpdnaD8U8P63/+iNxe5kwa4mTQr2OfKLYctGbwkN/5/PPP3+RjCCGEEEK8dlqtlqGD+9OwVnkCCxfM6nKEEG+BX0dNpf6nLfWDDmS+jxBCCADi4+O5c+cOoKVwgfT99Gnt9kQO/JvM0G8djI4V8bMARcOlS5dMnCmEEEIIIYQQ77cPZ0yfEEKI50pKStL/t72t8ZbuAAkJiSQkxGNhYYE2xZxvhscyuKcD2VyNf4aTeg3F4LpCCCGEEEII8aGQ0E0IIQQANjZpi92qeByj4OpsHLxpNBqSk1O/xs9TQIFW9W2Iik7dUj5Zo6DTQVS0jvAoHaDC1tb2zT2EEEIIIYQQQrwlJHQTQggBgLW1NXnz5uXmtUecvayhShkroz5arVb/31dDFG7cAfeyoUb9XEqGUrqoBagc8Pf3f611CyGEEEIIIcTbSNZ0E0IIoVeiRAlQWbJ5n+kpoU+Gbl+1VbFhqh0757nqv2pVtiJvLnPWTXEhNFwHKsvUawohhBBCCCHEB0ZGugkhhNBr3rw5K1YsZdmmCHq0sePAiWQAQu6lEB2rY8321A2vyxWHAt4qXFyssbL6/y6ns5YncOeBitv3U9CkWFEwIICiRYtmxaMIIYQQQgghRJaSkW5CCCH0KlWqRMGChYiJt6bvqGia9YyiWc8odh1O5vZ9HZ8PUPh8gMLlm6n9LSyMf3aj0SqMmhEPKnu6dOmCSmV6UwYhhBBCCPFm7N53GCtXf4aOGG/QXrNBO6xc35+lQPyKBeFXLChL7m3l6k/NBu2y5N7i7SWhmxBCCD0zMzP+/PNPzCyc2LI/hanDnVCueKFc8SL2pBP395pxf68ZFT5SYWamwszM8K+Rcf0d8c5hTlyiLWXKVqBVq1ZZ9CRCCCGEEO+/m7fuYOXq/9yvqMfRGbrmnAUrsHL1Z86CFa+p6rdDWhD5vC8J0Qw1bN4VK1d/HL1Mz2QZ/Otf1G7UCd8i1XDKUYwc+ctRPqgJ4ybOIj4+4Q1X+3aQ6aVCCCEMlCxZkt69+/D7778yYNwjbt1L4ecv7A3Wc4PUUW5PDmI7fVHD10OiuXJLTTYPb8aNG2cUygkhhBBCiMznky8PrZs1MHnM2sqK0iWKcurQBrK5ubzhyt5+JYoXpu7H1Uwe886T880W8xabPnsJW3fsw9raCkVRTPaZNG0++X28qVG9Iu7ursTExrFn31H6/DKCeYtWsXvTQmxtbd5w5VlLQjchhBBGevbsSXJyMmPGjGLasmi27IugRR2FmhUUsrmkJm0WFhZotQqnLmqZtzqBZZuSSMGe7F55WbBgAd7e3ln8FEIIIYQQHwbffHno/9PXz+3j7+fzhqp5t5QoXuSF7+5Dd/PWHX7s/zvffNmRFas38SAs3HS/83uwtrYyau/0RR8WLFnD7AUr6N6lzesu960iQxCEEEIYUalU9O7dm7lz5+OZw59boY4MnZhCcCeFjz/T0a6PjkY9kvCr+ZAG3R6zeKMZKSo3Gn7Sgm3bthEQEJDVjyCEEEIIIf7zrDXdntalx098/lVfAD7/qq/BVMsnxcTEMuS3vyhevj5OOYrhkbc09Zp8xv5Dx42umbZuXGJiEgOHj8W/RE3sPIoY1HIj5A5f9PyF/IHVcfAMxDugMl16/ETI7bsm61yzYTsVgpvilKMYuQtWpPs3/YmMepzR1/JSZsxZykcVGuDoVRTfItX4eeBIEhOTntn/zLlLNGzeFbc8JXD3LkXD5l05d/4yXXr8hJWrPzdv3TE6Z82G7dT6tCPZ85XB0asoH1VowOjx00lJSTHop9PpmDFnKRVrNMPTpyxOOYrhU7gqjVp9we59hzPleRVFodvX/fD0dGfgzz2f29dU4AbQ+JPaAFy7fitTanqXyEg3IYQQzxQcHMzevXuZNm0affr0QRurJSoaLt9UcHS0w0JtjZ2jA7Vq1aJTp06ULFkyq0sWQgghhBAvqUHdGkQ9jmHthu00qBtMsSLGmyw8iowiuF47zl+8QoWyJahRvSUxMbGs3bidjxt2YMHMsXxSr4bReS069OTM2Yt8HFwZJycH8nrnAuDIsVPUb9qFuPgE6taqRn4fb0Ju3WXh0nVs3raX3ZsX4ZM3t/468xat4rMvf8LRwZ7WzRvi7OTIhs27qNOoE8kaDZZq9Wt7P7+OnMjg3/4iu0c2OrdvhtrCgmUrN3Lx8jWT/U+fvUhQ3TbExSfwaf2a5Pf15vi/Z6letw1FixQ0ec4vQ0YxcuxUcnpl59P6NXF0tGf/oeP8PHAkR4+fZuGscU/0Hc2ov6bhky8PLZrWw8Hejnv3w9h/6Dg7dh+kaqWy+r41G7Rjz/6jbFkz26D9RSZMmcue/UfZvm4eNjbW6T7vSRu37AKgcECBlzr/XSahmxBCiOeys7MjMDAQNzc3dDodWq0WRVGYNHkKxYoVw8fHR9ZuE0IIIYTIQtdu3DI5iu3j4MqULV083df5pF4NHj+OZu2G7TSsG0z71o2N+vT6cRjnL15h0tihdG7fTN8+9OF3VAhqSo9eA6gVXNlo1NP9B2Ec27caVxdnfZtGo6Ftl+/Q6XTs37aE4kUL6Y/tP3Scmg3a8/3Pw1m5cDIA0dGx9PpxGHZ2tuzfvhS//PkAGPLLt9Rp1In7Dx7inTtHup8X4MTJs88cAfjk+7t6PYThIyeS0ys7h3atwMPdDYD+P35NxZrNTJ7/bZ+hxMTGMXvKSFo2/f+ae4N//Ytf/5xo1H/bzv2MHDuVmkGVWDz7L+zsbIHU0WZf/zCYqTMXsXLNZho1rAXAzLlLyeHlwfG9q43WSnsUGZWh92DKlWs36T90DD26tqNCuRLpPu/Pv6aREJ9A1OMYDh45wfF/z1KjekXatvzklWt610joJoQQ4oUuXrwIpO5uamlpiZubG02aNMniqoQQQgghBMD1G7cY9scEo3YnJ8cMhW4vEh4RydKVG6lWpZxB4Abg4e5Gr687891Pw9m++wD1alU3ON7/p68NAjeADZt3EXLrLgN/7mkQuAFULFeSBnWCWLNhO9HRsTg62rNmwzaiY2L5smtbfeAGoFarGfxLL4LqZny9sBMnz3Hi5DmTx558f4uXrUOr1dLzy476wA3A0dGen7/vTqcv+hicG3L7LvsPHadoEX+DwA3gh2+6MGnafKMpsZOmzQdg4tgh+sANUpd+GT7we6bNWszi5ev1oVvas5ubmxvV/vS7nj7pd+LjE8mTy+sZb8KQTqejy5c/4ZndnSG/fJuuc9KM/msaEY+i9J9bN2/I+D8Hon6NoxDfVhK6CSGEeKHLly8bfC5Y0PRweCGEEEII8ebVDKrEumXTXvt9jp04Q0pKCslJySZHh129HgLApcvXjUK30iUCjfofPnYKgMtXb5i8XmhYODqdjivXblDyo0BOn039QXClcqWM+pYrXRwLi4xHHF06tmDC6MEv7Ke/d3nje1csb7zEyumzlwAoX9Z4hJidnS3FAv3Ztddw3bUjx05hZ2fL7HnLTdZgY2PNpSs39J+bNa7HP9MX8FHFBjRvVJeqlctSrnRxk9NA8+TK2AjA0eOnc/jYKbasmZ3hHUfvXT0EwIPQh+zae4h+g0ZRqWYL1i2bRq6cnhm61rtOQjchhBAvlDbSLY2EbkIIIYQQH560kVkHDp/gwOETz+wXH59g1JbdI5vx9SJTr7dw6drn3jfuv+tFR8cC4O7uatTH3NwcN1fn517nVTyOefa9s7sbP1vMf/09shn3BwxGy6V5FPkYrVZrctRimrj4eP1/j/6tL3nz5GTOgpX8NmoSv42ahLW1FU0/rc3vQ38im5vL8x/qGS5fvcGQEePp9lkrqlQs81LXAPDM7k7Lpg3I75OXijWa8WP/35k/Y8xLX+9dJKGbEEKI59LpdEYj3fz9jRfVFUIIIYQQ7zcHBzsAvu3Rid+H/pihc1Uq1TOvt2LhJKORcaY4OtoD8PDhI6NjKSkpRDyKIqeXR4bqSi8nh//f2zt3ToNjoQ/Djfo7/Nc/LNy4VoCwhxFGbY4OdqhUKv1IsRexsLDgu68/47uvP+Pe/VD2HjjK7PkrmLdoNQ9Cw1m/fHq6rvO0C5eukZSUzORpC5g8bYHJPmk72obeOIKzk+Nzr1eqRCAuzk7s2X/kpep5l0noJoQQ4rlu3bpFYmKiQZuMdBNCCCGEeD+lrQ+WkqIzOlbqo0BUKhWHj57MlHuVKVkMgMNHT6YrdCv6326q+w4do8mntQ2OHTp6Eq1Wmyl1Peveq9ZtZd/BY5R6aqrs/oPHTfRP/ffyoSP/Gh2Lj0/QT1d9UumSxdi8bQ9Xrt2kgG/eDNWXwys7LZrUp1mjuhQpU4cduw+SkJD4UjuO5s2Tk05tm5o8tnTVBhISkmjfqhEAVpaWL7xebGwcj6Nj8MrunuFa3nWy3ZwQQojnunTpklGbhG5CCCGEEO8nFxcnAO7cvW90zDO7O00/rcPBI/8y6q/pKIpi1OfIsVMmp5ea0qBuMHly5WDcxFnsPXDU6LhGo2H/oeMG/R0d7Jk9fwWXr94w6Ddo+Nh03fNltWhaH3Nzc/6aOMtglFp0dCy/jZpk1N87d04qlC3BqTMXWLpig8Gx0eOn8yjysdE5Pbq2A6Db1/2IeBRpdPxB6EMuXLoGQFJSMgdNTPGNi4snLi4etdoCM7P/Rz637tzj4uXr6freFAsMYPJfw0x+ubk4Y2Fhrv+cFupdv3nb5I6pGo2GH/r+hk6no1bNKi+89/tGRroJIYR4rqdDN09PT5ycnLKoGiGEEEII8TqlLcQ/fvIcIqOicf9vTbKff+gOwF9/DuDy1Rv0HTSSBUtWU7Z0cZydHLlz9z7HT57l6rUQQi7sTdfi+1ZWliycNY6GzT+nRv12VKtSjiIBfqhUKm7ducf+g8dwdXXmzOGNADg5OjB6RD+69PiZisHNaNa4Lk6ODmzYvAsbGyu8PDM+kurEybMmN3EAsLa2ove3XQHI7+NNv95fMmTEeEpV/oQmn9bGwtycVWu3UqSwH5ef2OAgzZjffyG4fls6dOvNyrVb8PXJw7+nznPk2CkqVyjF3gPHDIKxWjUq0/eHL/n1z4kUKlmLj4MrkSd3Th49iuLajRD2HTzO4H7fEFDQl4TERKrVaU2B/HkpUawwuXPlIDYujo2bd/Eg9CG9vuqMldX/R6F91v1H9uw/ypY1s6laqWyG39OL7DtwlK++H0SFciXJ550LN1dnQkPD2bH7IHfuPcDfz5chv/TK9Pu+7SR0E0II8VyyiYIQQgghxIfD1cWZhbPGMez3v5kxdykJCanLjKSFbq4uzuzetJCJU+ezbNUGFi1bh06nI7tHNooW8afvD19maAH/UiUCObpnNaPHT2fTtt0cPHwCK0tLcnhlp2HdGjRvUs+gf7tWjXB0dGDEqEnMW7QKJ0cH6tcO4tfBP1C2aqMMP++Jk+c4cfKcyWNOjg760A2gX58eeHl68Nek2UybtRiPbG40a1yXgT/3xDlncaPzixctxI718+k3eBSbt+9BtV1FhXIl2blhPr8MHQ2A439rv6UZ2LcnlSqUYsKUuezcc4ioxzG4uTqTN09O+v/4FS2bNgDAztaG4YN+YOfug+w/eJyw8C24ODvhlz8vQwd8R/PG9YzqeZ3Kly3BZx2as//gcU6fuUDU4xgc7O3w9/Phy67t6N6ldYZ3QX0fqBRT40GFEEKI/wQHB3PhwgX9527dujFw4MAsrEgIIYQQIutptVqGDu5Pw1rlCSwsP5QU6ZeSkkJAiZokJCZx+9L+rC5HZKJfR02l/qctKVWqFCBrugkhhHgOjUbD1atXDdpkpJsQQgghhBAvptVqCY8wXptt5NiphNy+R4O6wVlQlXiTZHqpEEKIZ7p58yYajcagTUI3IYQQQgghXiw2Lp58hasQXK0CBXzzotFqOXr8NMdOnMHL053+P36V1SWK10xCNyGEEM9kaudSPz+/LKhECCGEEEKId4utjTUd2zZl195D7DtwjMSkZLyyu9OlYwv6/vAlXp4eWV2ieM0kdBNCCPFMT4duuXPnxs7OLouqEUIIIYQQ4t1haWnJ+D9lLeQPmazpJoQQ4pme3rnU398/iyoRQgghhBBCiHeLhG5CCCGe6emRbjK1VAghhBBCCCHSR0I3IYQQJiUlJXHjxg2DNhnpJoQQQgghhBDpI6GbEEIIk65du0ZKSopBm4RuQgghhBBCCJE+EroJIYQw6emppWZmZuTPnz+LqhFCCCGEEEKId4uEbkIIIUx6ehOFvHnzYmVllUXVCCGEEEIIIcS7RUI3IYQQJj090q1gwYJZVIkQQgghhBBCvHskdBNCCGHS06GbrOcmhBBCCCGEEOknoZsQQggj8fHxhISEGLTJSDchhBBCCCGESD8J3YQQQhi5cuWKUZuEbkIIIYQQQgiRfhZZXYAQ4tmio6M5f/48UVFRmJubkz17dvz9/bG0tMzq0sR77ulNFNRqNT4+PllUjRBCCCGEEEK8eyR0E+ItEx4ezsKFC1m6dClXr14FtKDoUg+qzFGrrSlZsiRt27alfv36EsCJ1+Ly5csGn319fVGr1VlUjRBCCCGEEEK8eyR0E+ItodVqmTx5Mn/++SfJSdGgJICiIbenGR5uZuh0cONOClExcOhAOIcO7mX48DyMHDmSoKCgrC5fvGeeHunm5+eXRZUIIYQQQgghxLtJQjch3gIPHz6kU6dOnDh+GHTRFA9Q0aGRDR9XcsLF6f9LLyqKQsjdFFZuTWTOyiju342jbdvWdOr0GUOGDMHc3DwLn0K8T54O3WTnUiGEEEIIIYTIGNlIQYgsFh4eTpMmTThxfB+OtlGM7WfL+qkutKhnYxC4AahUKvLmsqBXJ3sOLMnG583MUekimDljCr169UKn02XRU4j3SXR0NPfv3zdok00UhBBCCCGEECJjJHQTIgvpdDq6d+/O1SunyZEtno3TXGhe1waVSvXCc22sVQz+xoF/hjhgoYpi2bJFTJo06Q1ULd53T6/nBjLSTQghhBBCCCEySkI3IbLQ7Nmz2b9/NzbqOBaPdSZfbuMZ37NXxPNRw4dYF75PtjIPqPPZIxISFf3x+kHW/Pa9PaQ85o8/fjcZmAiREU9PLbWysiJPnjxZVI0QQgghhBBCvJskdBMiiyQmJvLHH39ASjS/fGmHr7dx4DZ8YgxfD4mmRT0bNs9w5Z8hTuTLZU5KimLQr3VDa4LLm6NJiuL3339/U48g3lOXLl0y+FygQAFZL1AIIYQQQgghMkg2UhAii6xevZrHUQ/J7amjfSMbo+OXrmsZND6WNZNdqFPVWt/epLZxX5VKxS9f2rP9YCRbtmzm/v37eHl5vdb6xfvr6dBNppYKIYQQQgghRMbJSDchssiqVatASaDtJzaYmxuv4TZzeTz5cpkbBG7PU9DHgnLFzEnRxrN27dpMrlZ8SJ4O3WQTBSGEEEIIIYTIOAndhMgCiqJw6tQpUDRULWNpss+hkxoC/dQMmxCDR9lQLAPuU65pGHuPxpGSokNRjM+pUtoKFE3qtYV4CRERETx8+NCgTUI3IYQQQghjZmZmgIqUlJSsLkUI8RbQ6XTodDqDpXlkeqkQWSA0NJSoqEgszFPw9zH92/BBeArHz2k4fUnDhEGOaJKiGTsnhXqfR3NgYQwebuZYWFigVltgYaHGwsKCIn4WoMQZLYQvRHo9PcoNZHqpEEIIIYQpZmZmWNvYEB0Tm9WlCCHeAtExsaAyw8bm/0tCyUg3IbJAbGwsoGBro8LS0nhqKYBOB7FxCv8M0vFxhWSCysHsESoUBWYsV9DpdCQnJxMXF8/jx4+JiIhASYlEo0nm7NmzTJ48mb179xIREfFmH068054O3ezs7MiZM2cWVSOEEEII8Xbz8fXj8pWQrC5DCPEWuHzlBhZqG/Lmzatvk5FuQmQBC4vU33oaTepUU5XKOHhzdlTh6gSF8qtISEhAp1NwcTSjSAGFSzdNXzf5v+tFRkYyZMgQfXv27NkpXLgwhQoV0v+vj4+P7EgpjJhaz83Ur08hhBBCCAFFihRh8emjXL95G5+8ubO6HCFEFklMTOLEyfP45PfD2vr/67JL6CZEFvDy8sLCwpKEJLj7QEcuL8PwS6co5M+t4+p/PzRTFHgy90hKMn3da7chJUVlFKaFhoYSGhrKjh079G1WVlb4+/tTuHBhfRAXEBCAo6NjpjyjeDc9Hbr5+fllUSVCCCGEEG+/ggULUsC/KEtXbqF2jQoEFPTF0tL0ms1CiPePoijcvRfKlu37iNda0Cy4hsFxCd2EyAJWVlYULFiQc2cecuK8xiB0UxSIfhxNcHmFhevh7BWFYv4W2NvbExah4eyVWLq1MD0z/MxlBW2KCrVa/cIakpKSOHXqlNGmC7lz56ZQoUIUKVKEQoUKUahQIXLnzv3fQrHifaYoitF6gLKemxBCCCHEs1lYWNCqVWuWLFnMui2H2LTtADm83LCxspLZAkK855I1Gh5GRBEdm4yjsxsdOrbHy8vLoI9KUUztgSiEeN2GDRvGxAl/ElQmiXmjXPTtcXHxxMTEoNMp1PtCISoahn3ngIOdBb9NjuVKiJaz693xyGaGVqNFq039evQ4maD2ydx5YIajk0um/oTN3t6egIAAg1Fx/v7+BgtEindfaGgoH330kUHbokWLqFKlShZVJIQQQgjx7oiMjOTcuXPcv3+fxMREFEWX1SUJIV4jtdoSZ2dn/P398fb2NjlQRUI3IbLIzZs3qVChPCrdQ7bOdKFQATXJyclERkaS9rsyIkph+D9qNuzWkKxRqFzKkjF9HSlUwHgk28T5cQybpCFn7sIMHDiQCxcucO7cOc6dO8fdu3czvX4zMzPy5ctnEMQVLlyY7Nmzy0/13lF79uyhZcuWBm3//vsv2bNnz6KKhBBCCCGEEOLdJaGbEFmoW7durF2zmCK+CayZ7Ex09CN0uv//RMzOzg4HB/sXXuf6LS3BHR6RlOLKmDHjadGihcHxx48fc/78ec6fP8+5c+c4f/48Fy9eJDk5OdOfycXFxSCEK1y4MAUKFEjXlFeRtaZMmcKgQYP0n52cnDh//ryEqEIIIYQQQgjxEiR0EyILhYWFUa1aNaIibtAwSMOA7jrMzFIDDktLS1xcXHhR3vEoSkfjHpFcvmVDlaq1WbhwYbpCEq1Wy7Vr1wyCuHPnzvHw4cPMeDQDarWa/Pnz60O4tFDO1dU10+8lXt7333/PwoUL9Z/Lli3LypUrs7AiIYQQQgghhHh3SegmRBbbsmULTZs0xtIijtqVoX93Fa7OFri5ub5w84LzVzR8MSCaq7ct8cxRgHXr1pEjR45Xqufhw4cGQdz58+e5cuUKKSkpr3RdU7Jnz64P4NI2bsiXL5/R7qvizahfvz4nTpzQf27fvj0jRozIwoqEEEIIIYQQ4t0lu5cKkcVCQ0OxUFsSFZ3Aul0KJy8o9OlqTbM6KmysTZ9zPyyFGcvi+WdRIlrFDq+cvixZsuSVAzcAd3d3qlatStWqVfVtSUlJXL582SCMO3v2LNHR0a90r9DQUEJDQ9mxY4e+zdraGn9/f4PpqQEBATg4OLzSvcTzKYrCpUuXDNoKFiyYRdUIIYQQQgghxLtPRroJkYWOHz9O48aN0Wg0aDQaoqOjcbC3xFKtxdFOQ6WSlhQtqCZ7NjNSUuDGHS2nLmo5+K+GFMUazOyoU6c+I0aMwN3d/Y3WrigK9+7dM5qeevPmTV7HHyt58uQxCOIKFSpE7ty5Zb2xTHLnzh3KlClj0LZs2TIqVKiQRRUJIYQQQgghxLtNQjchskhYWBi1atUiNDRU39a2bVt8fHyYPXs2ISHXQUkGRQukba5gDio1qCwpX74CXbt25eOPP36rgqe4uDguXrxoEMRduHCB+Pj4TL+Xg4MDAQEBBkGcv78/1tbPGCIonmnbtm20b9/eoO3MmTO4ubllUUVCCCGEEEII8W6T0E2ILKDRaGjWrBlHjhzRt5UuXZply5ahVqvR6XQcPXqUf//9l7NnzxIVFYW5uTmenp4ULVqUcuXK4evrm4VPkDE6nY6QkBCDIO78+fPcvXs30+9lZmaGj4+Pwai4woUL4+Hh8VaFk2+bCRMmMHz4cP1nd3d3Tp06lYUVCSGEEEIIIcS7TUI3IbJAv379mDlzpv5z9uzZ2bRpE9mzZ8/Cqt68qKgo/WYNaUHcxYsX0Wg0mX4vV1dXoyAuf/78qNXqTL/XuyIlJYU7d+4QGxvLb7/9xtatWzEzM0OlUlGpUiWWLFmS1SUKIYQQQgghxDtLQjch3rClS5fyzTff6D+r1WqWL19OqVKlsrCqt4dGo+HatWtGYdzDhw8z/V5qtZoCBQrop6amhXEuLi6Zfq+3RUhICAsXLuTQoUOcPXuW+PhYQCEqMopkjRZQoVarqVy5MnPmzMHT0zOrSxZCCCGEEEKId5KEbkK8QWfOnKFhw4YkJSXp23777Tc6dOiQhVW9G8LCwoyCuKtXr5KSkpLp9/L09DQK4vLmzYu5uXmm3+tNOXXqFCNHjmTnzh0ougRQkkDRYGWp4GSvQqvVEhUDScmgTQFzC1tsbF2oXbsOvXv3xs/PL6sfQQghhBBCCCHeKRK6CfGGPHr0iFq1ahmsY9aiRQtGjx4ta429pKSkJC5dumS0g2p0dHSm38vGxgZ/f38KFSpEoUKFKFKkCAEBAdjb22f6vTJTcnIyo0aNYuLECaRoHoOSQLUyaj6pYU3xADX5vc1RFB3h4eFotArXb8OZy7DtkJojp3WgskFt5UyfPj/SrVs3LCwssvqRhBBCCCGEEOKdIKGbEG+AVquldevW7Nu3T99WrFgxVq1ahZWVVRZW9v5RFIW7d+8aBXE3b958Lffz9vY2COIKFSpErly53oogNSIigjZt2nD61DHQRfNJsJofu9qRN5dhcJaYmERUVJRBm4eHB5dvpDB8UizbD6aAmSOVKldnxowZb33QKIQQQgghhBBvAwndhHgDhg0bxsSJE/WfXV1d2bx5Mzlz5szCqj4ssbGxXLx4UR/EpX0lJCRk+r0cHR0JCAgwmJ5asGBBrK2tM/1ez/Lo0SMaN27M5UsncXWI448+DtStZvr+sbFxxMbG6j+bm5vj7p4NSA0xl25M5JcxscQmOlCyVEUWLVqEnZ3dG3kOIYQQQgghhHhXSegmxGu2Zs0avvjiC/1nc3NzFi1aRMWKFbOwKgGpu3eGhIQYBHHnzp3j3r17mX4vMzMzfH199UFc2v96eHhk+qi4lJQUmjRpwpHDu/F0jWXpXy74ej97WmhU1GMSExP1n62srHBxcTboc/qihhbfRPE43oHadT5l+vTpb8VoPiGEEEIIIYR4W0noJsRrdPHiRerXr098fLy+beDAgXTr1i0LqxIvEhUVZRTEXbp0CY1Gk+n3cnNzMwri8ufPj1qtfulr/vPPPwwe9Av21lGsn+pCgbzPX4ctPDwCrVar/2xnZ4eDg/EU0hPnNDT6MhKN4sb48RNp0qTJS9cohBBCCCGEEO87Cd2EeE2io6OpXbu2wVpin3zyCRMnTpQRQu8gjUbD1atXjXZQDQ8Pz/R7qdVq/Pz89EFcWhjn4uLywnNv3rxJ9erVSIq/z8g+trT5xOa5/RUFwsJCefJvAicnJ2xsTE9F/Wt2HCOmJOHkmo+9e/eSLVu2DDyZEEIIIYQQQnw4JHQT4jXQ6XR06NCB7du369sCAgJYu3Yttra2WViZyGxhYWEGGzacP3+ea9eukZKSkun38vLyMgri8ubNi7m5ub5Pv379mDljApU+SmLxOGdUKhVLNyYwb3UCx89qiIxWKOBtTs/2dnRqakNKSgrh4REG99n3rz3NekZTuIAFZze4GxzTahXqfPaIc9ft6N2nP7169cr05xRCCCGEEEKI94GEbkK8Bn/++SejR4/Wf3Z0dGTTpk3kzZs364oSb0xiYiKXLl0yCOLOnz9PdHR0pt/LxsZGv2mDr68vw4cPR5v0gMVjHalc2hKA8s3CyZvTnE9rWuPuasbW/Un8MTWOAV/Z8+PnaqKiHuuvl5CkENTRjIREhWwuZkahG8DKLYn0GByPZ85CHDlyBAuL509fFUIIIYQQQogPkYRuQmSyLVu20LFjR/1nlUrFvHnzqF69etYVJbKcoijcvXuXc+fOGQRxT04/flUJCQkkxD+maEGFtZMtsLK0xMLCgqgYM7w8LDEzMydtZnPXX6JYvD6R27vtDNYcHDkDjp21IF8uc46d1ZgM3ZKTFUo2CicixoUFC5ZQrVq1THsGIYQQQgghhHhfyPAEITLRtWvX+Prrrw3a+vTpI4GbQKVSkStXLnLlykWtWrX07bGxsVy4cMEgiLtw4QIJCQkZvodGo8FSDdVKq1B0Ov2OpBYqePgwtQZzc3NUKhV+3ilExyqEPozF3laFSqXi5l2FyQsVDixxZMzMuGfex9JSRbWylizfmsyJEyckdBNCCCGEEEIIEyR0EyKTxMbG0rlzZ2JiYvRttWvXNgrhhHiSvb09pUuXpnTp0vq2lJQUQkJCOHv2rMHGDffv33/utbRaLbZWCgG+KkDRb47w5IDmtLXmDpwAT3ews0k9rlJB/3EKreqrKRbw4p1TixZUs3yLltOnT2f8oYUQQgghhBDiAyChmxCZQFEUevXqxZUrV/Rt+fPnZ9y4cZiZmWVhZeJdZG5ujo+PDz4+PjRs2FDfHhkZqR8Rd/r0aU6cOMGVK1dISkoiJSWFpKQkHGzBN7eCTvfs6x85Dat3wIAv/9+2eZ/CsbMwvr+O+IQE4PkrDwT4WoCSyLVr117xaYUQQgghhBDi/SShmxCZYMKECaxfv17/2d7enhkzZuDg4JCFVYn3QXR0NFeuXOHq1atcuXJF/3Xr1i10Oh1qtRqVSoVWqyUhIQGVSsHW5tnXuxcG3QdDhY/gsyapbYlJMHA8fN8JnB0Uoh9Hk5iooNOp0GpTsLAwN7qOvW3qaLqXmQYrhBBCCCGEEB8CCd2EeEW7d+9mxIgRBm3jxo0jf/78WVSReNcoikJYWJg+UHsyYAsNDX3h+RYWFlhYWPw3qjIFRVFhZmY8xfRxDLTrAy6OMHUIpA3CnLYMzFTQqKaKxzGpJyRrFLRahWs3HuLkaImzkx1WVlb6jRi0Kan91OoXT0UVQgghhBBCiA+RhG5CvIJbt27xxRdfoHtiLl/Pnj2pU6dOFlYl3lYpKSncuXOHy5cvGwVs0dHRr3x9MzMzdLoU7j+E3F4AKn1IlpgMHX5WiI6DNRPB0f7/5129BTfuQpEGxlNK/esqjPg+mQ6fajA3N8PGxhYbGxvuhupAZY6bm9sr1y2EEEIIIYQQ7yMJ3YR4SQkJCXTu3JnHjx/r26pVq0bv3r2zsCrxNkhOTub69esG00GvXLnCtWvXSEpKem33tbCwQJui5cI1KFP0/+0ajY6u/eFKCKwcD17u/z+mUsFXbaBtQzUpKSn6TRfGz1O4dhvG/qzCJ3dq35QUHbGxscTFxXLkpDkpKWqKFCny2p5HCCEyQlEU9u/fz4YNGzh16hSXLl0iISEBtVqNj48PxYoVIygoiNq1a8soXSGEEEK8ERK6CfESFEWhd+/enD9/Xt/m7e3NxIkTMTc3Xv9KvJ9iYmKM1lpLW28tbZfQN0mtVqPRJHLyokIHVCiKgqIo/Dwath1M3TghJg6On0vtb2amoniABUX91djY2GBpaUliYiLx8fEs3pDE/YdQ4SOV0X0UBY6e0RATo2XVqlXkz5+f5s2b4+jo+IafWAghUm3YsIERI0Zw9eol0CUCGlA0gEKyVsXFc6FcPH+UxYvm4ZE9Jz169KBz587yd7YQQgghXiuVkjasQQiRbtOnT6d///76z9bW1qxbt45ChQplYVXidVAUhfDwcKNg7cqVKzx48CCryzOg0WiIinyEh5uODf+Au2tqe9kWcOcZpd7Y6U7eXMY/f2nf+xHHTiezc44ZT/81ce2WQqOvFR49NsPNLRvm5uZYW1vTqFEjOnbsSGBgYGY/mhBCmBQdHU2fPn1Ys2YV6KKxs06mUU1rKpa0pHABCxztVSQkKly6nsKR08ks3ZTIw0fmYOZAqdLlGT9+PN7e3ln9GEIIIYR4T0noJkQGHTp0iGbNmhmMZJo4cSKffvpp1hUlXplOp+Pu3bv6QO3JddeenEL8NrOzs+P27dtYWybxTTvo3koF/H+kmq2tLfb2dv9tuJA+Op1CYmIC8fEJaLVaAH79R8fsVSo0KdY4OzsbnVOiRAnat29Pw4YNsba2ftXHEkIIk6KiomjevDlnzxzDnBi+bGPDV21tcbB/9p9xGo3CovUJDJ0QR2yCLR6e+Vi6dCkFChR4g5ULIYQQ4kMhoZsQGXD//n1q1apFeHi4vq1r164MGjQo64oSGaLRaLhx44bJ9dYSEhKyurx0yZkzJwUKFNB/ubm5sXbtWlavXk1CQgJxsY/xctexdJyK3J4qLK0scXBwQG3x8isKKApoNMkcPxNH068TCYs0w8nJBUtLy2ee4+zsTKtWrWjXrh158+Z96XsLIcTTtFotTZs25cjh3bg5xjL7D2dKFE7/Om13H6TQvk8UF65b4pXTj61bt+Lq6voaKxZCCCHEh0hCNyHSKTk5mcaNG3PixAl9W4UKFVi0aBEWrxBmiNcjLi7O5HprISEh+hFbbzNzc3Py5s1rEK75+fnh6+uLnZ0dAElJSUybNo1x48YRGxsLpE6HjYqKQm2eRNUysGScM9bW1vpdTF+FRqNQp8sjzl21Jq9PIZKSktI9xbZatWp07NiR4OBgWUNJCPHKJkyYwPBhg3CwieKv/o6s3JLIoZPJnL2ixd/HgrMb3A36V2sTwe4jyUbXKVHYgvsR9nzyaUsmTZr0psoXQgghxAdCQjch0ql3797Mnz9f/9nLy4vNmzeTLVu2LKxKREREmFxv7d69e1ldWrpYW1uTP39+g3CtQIEC5MuX75m76ymKwoYNGxg6dCi3bt0yOm5jY0NMTDQWqhg6NbFgWC8HVK+YuqWkKPQYFM2anQqu2XzYtWsXzs7ObN26lVmzZrF37950XSdHjhy0a9eO1q1b4+7u/uIThBDiKffv36d8+fIkJ9xnTF9brK1UfDX4MWWLWXL5phadDpOhmzZF4c8fDTd8MTODJl89JkXlxqJFS6hSpcqbfBQhhBBCvOckdBMiHebPn0/v3r31ny0tLVm1ahXFixfPuqI+IIqicO/ePZPh2qNHj7K6vHRxdnY2CtYKFChAzpw5M7TG2tmzZxkwYACHDh0yOmZmZkb79u354Ycf2Lt3L19+2R1FG0HrBmp+/c4BS8uXC97i4nX0+jWadTt1qK09mDNnLlWrVjXoc/36debMmcOiRYuIjo5+4TXVajV169alQ4cOlC1b9pVDQSHEh2PkyJGMGf0bZYrEs3KiC4qSuhszQMc+URw7qzEZutnbqlg31XgKab/R0cxcoaJmrcbMnj37jTyDEEIIIT4MEroJ8QInTpygUaNGaDQafdvo0aNp2bJlFlb1ftJoNISEhBgFa1evXiU+Pj6ry0sXT09P/VTQp9dde5VgKSwsjBEjRrB48WKj3UQBqlSpwqBBg/D399e3LVq0iO+//w4lJZKCeXWM7edIsYD0r3kEcOBEMt/9Gs2tB2rUVm78888Uateu/cz+CQkJrF69mlmzZnH69Ol03aNgwYJ06NCBJk2a4ODgkKH6hBAfFkVRKFGiBKH3LvDPEFsaBBtu1vIyodu1EC2VWz9CZeHBiRP/kj179tf6DEIIIYT4cEjoJsRzPHz4kFq1ahmsW9W+fXtGjBiRhVW9+xISEkyut3bz5k2DcPNtZWZmhre3t9Gotfz582d6aJSUlMTUqVMZN24ccXFxRsd9fHwYNGgQwcHBJkO9rVu38v333xP+8DZmSix1qqrp0MiWiiXVzwwBU1IUdh5KZvbKBLYf1ICZAzlz+fLXX39Rvnz5dNd+8uRJZs+ezapVq0hKSnphfzs7O5o0aUKHDh0ICAhI932EEB+OW7duUa5cWdSqh1zZ6m40gvfJ0C0lRYe5eepI4mptIjh+ToNOl/pnXNlilgz91p4qZawAqNEhgvPXHZg+Yy516tR5488lhBBCiPeThG5CPINGo6F58+YcPnxY31ayZElWrFjxzLW2hKGoqCiDUO3y5ctcuXKFO3fuZHVp6WJlZYWvr69RuObj4/PcXTszw4vWbXN0dOT777+nY8eOL/z1+OjRI/r168fq1atASQBdPO6uCkULqilSwAIXJzMURSEiSuH0JQ2nL2qJijEDMxtQ2dCuXXv69++Pvb39Sz1LVFQUixcvZvbs2dy8eTNd55QuXZqOHTtSr1691/6uhRDvjvXr1/N5lw4E5o9h80w3o+Pte0dy7Ewye+er0Wg0uLm5kZiUyIh/NOTNpaZgPkvuP0zhz+lxnLqoYfd8N8p/ZMl3v0azaKMF33z7Mz/++GMWPJkQQggh3kcSugnxDP3792f69On6zx4eHmzevFmmnTxFURQePHhgcr218PDwrC4vXRwdHU2ut5YrV64s2Wnzeeu2mZub0759e77//ntcXY2nST3PxYsXmTNnDkuXLiUuLhoUDShaQPdfDzNQWYBKjZOTK61ataJ9+/bkzZv3lZ8JQKfTsW/fPmbNmsWWLVvQ6XQvPMfNzY1WrVrRrl07cufOnSl1CCHeXbNnz+bnn3pRt3Ii0351RlFAq9WSlJREYmIiPQYnc+oS7JqTOsLNxsaGhIQE/fkqlQoLCwuSNOaUb5FAgK8FG6a5MW52HCOnK7Rt350//vgjqx5PCCGEEO8Zi6wuQIi30fLlyw0CNwsLC6ZMmfJBB25arZZbt26ZXG8tNjY2q8tLl+zZsxtMBU37bw8Pj7diIf/0rNs2ePBgChYs+FLX9/f359dff2XAgAGcO3eO06dPc+HCBWJjY1GpVDg4OFC4cGGKFi1KQEBApo8wMzMzo0qVKlSpUoV79+4xf/585s+fT1hY2DPPiYiI4O+//2bChAnUqFGD9u3bU61atSwJQ4UQWS/tz0adTkdMTCyJSYmkaFOe2f/pqe2KoqDRaDBDQ1BZhXW7NISFhREXB4mJZhw6dIg1a9ZQqFAh8uXLJ3/WCCGEEOKVyEg3IZ5y9uxZGjRoYPAP9eHDh9OpU6csrOrNSUxM5Nq1a0bh2vXr19+Z9dby5MljtNZagQIFcHR0zOryTHrVddveZRqNho0bNzJnzhwOHDiQrnPy5MlD+/btadmyZYZH+wkh3k06nY5jx44xevRo1q5ZRtmiKSz403jn52+G6wxGuimKgrm5ucmRtT+P1rFuF5xZY8bgCTrmrVGhMrfXT6W3srLC39+fgIAAChcuTEBAAIUKFcLZ2fl1PqoQQggh3iMSugnxhMjISGrXrs3t27f1bc2bN2fMmDHvXdgRHR1tFKxdvnyZ27dvmxxl9bZRq9UGo9WeXG/Nysoqq8tLF0VRWL9+PUOHDjX4NZcmI+u2vQ8uX77M7NmzWbZsGTExMS/sr1aradiwIR07dqREiRLv3e9RIT50Wq2WgwcPsmHDBjZu3EhYWBharZZHjyLwzKbj4CIVagvD3/dPhm7m5uZYW1tja2uLSqVCq9Wg0WrRarQ8jtFQoWUyAT4w9w8zWnyn4/ApFbZ2zlhbWz+jolReXl4UKlTI4CtfvnxYWMgEEiGEEEIYktBNiP+kpKTQpk0b9uzZo28LDAxk9erVL/wH+NtKURTCwsJMrrf2vCl9bxN7e3sKFCiAn5+fQbiWO3fud3raz5kzZxg4cGCmr9v2PoiLi2PlypXMmjWL8+fPp+ucQoUK0bFjRxo1aoSdnd1rrlB8SCIjIzl9+jQhISFotVrs7OwICAjA399fNvl4DZKTk9mzZw/r169n8+bNREVFGRxXFIXw8HCc7LX89YuKoLIq4hMVdhxMPT5rFYTcg5E/OmBubka1MpZcvJ7CyGmxNKppTd5c5twL1TFqRiznrmjZOc8FN6cUqrWLJTrOivz5C/Dw4cMM121lZUXBggX1o+HSvlxcXF79pQghhBDinSWhm3ivaTQaLl++zL1790hJScHOzo6CBQvi4eFh1Hf48OFMmDBB/9nV1ZVNmzaRK1euN1nyS0lJSeH27dsm11uLjo7O6vLSxd3d3eRmBtmzZ3+vRjC9aN22qlWrMmjQoJdet+19oigKJ06cYNasWaxZsyZd05sdHBxo1qwZHTp0oECBAm+gSvE+io+PZ+XKlcyePZuzZ8+mbjpC2rphKlBZYGlpQ506dejYsSNlypR5r/6cetMSEhLYuXMn69evZ9u2bS8c6RobG0uKNpagsgpThqi4H25Bycam/3zYOc+VXJ7mfDU4mlMXNURE6bCzUVHhI0sGfm1PmWKWDB4fwz+LoXpwQ+bPn8/jx4+5cOECFy5c4Ny5c/r/TkxMzPCzeXp6GoRwAQEB+Pr6yqg4IYQQ4gMhoZt478THx7Nq1SoWLVrEqVOn0GgSQTH8P0uenl7UqFGDjh07UqhQIdatW0fXrl311zAzM2PRokVUqlQpax7iGZKTk02ut3bt2jWSk5OzurwXUqlU5M6d22S45uTklNXlvVZJSUlMmTKFv/7664Nbty0zREREsHjxYubMmcOtW7fSdU6FChVo3749derU+SCm54rMsXPnTr7//nse3L8FSjwoyeTLZYZfXgss1Soio3WcvawlKgZQ2YCZLTVr1uKPP/74oDfbyaiYmBi2bdvGhg0b2LFjh8EOoy+SkpJCTEw0jnYaJg6yp3Et25eu48JVDbU/i0KjuDJnznxq1KjxzHuGhIToQ7jz589z/vx57ty5k+F7Wlpa4ufnZ7BOXKFChT7Ikc1CCCHE+05CN/He0Ol0zJ07l99++43ox+GgJICSjKO9Qr5cFliYQ2S0jht3UlAUCzCzBpUNH31UkrNnzxqMounfvz/du3fPsmeJiYkxOSX01q1bJheDftuo1Wp8fHxMrrdmY2OT1eW9UelZt+2HH36gQ4cOEgylQ0pKCrt27WLOnDls27YtXesPenh40KZNG9q2bYuXl9cbqFK8i3Q6HcOGDWPy5ImgiyZXdi2fNbWhWR0bXJ0NF+xXFIWzl7XMXpnA0o2JaHT2ODl7MWPGDMqXL59FT/D2i4yMZMuWLaxfv57du3dnaHMelUpFmTJlqFevHnXr1mXhwoWM+nMEznZRrJvigk+ejI8ci47R0ahHJBdu2FC7TmOmT5+e4R96REdHG4RwaaPiMhIipsmePbt+NFxaIOfr6yt/NwghhBDvMAndxHshLCyMr776in37dkFKNN45dLT/1IY6Va3wzmlu8I/ouHgdx89qWLA2kQ27k3gcA3EJqdPSbGxsaNiwIZMmTXrto40UReHhw4cmw7XQ0NDXeu/MYmdnZ7CZQdq6a3ny5JGpM6Rv3bYffvhB1vx5Sbdv32bu3LksXLiQiIiIF/Y3MzPj448/pmPHjlSqVAkzM+OdD8WHSVEUfvnlF2bOnAopkXze3JqfutljY/3ivwcuXtPyzbDHnLlijpWNBwsXLqJcuXJvoOp3Q1hYGJs2bWL9+vUcOHCAlJSUF5/0H3NzcypUqEC9evWoXbu2wdIQGo2GTz75hJP/HiC7SxwLRjsTkD/94VRYRAodf3zMyYsWZPPwYdu2bSaXnngZOp2OkJAQfRCX9mXqBy8volar8fPzM9q4wc3NLVNqFUIIIcTrJaGbeOfdu3ePpk2bcvPGeWzUcfTrbkfHJjaYmT3//ywpCpw+/4ifRydx5DQ8jjUjXz5fTp48ia3ty09VeZpOpzO53tqVK1femfXW3NzcTE4J9fLykqmQJsi6bW9WcnIy69evZ9asWRw9ejRd5+TLl4/27dvTokULnJ2dX2+B4q23ZMkSvv32a1S6R4z+2Z4W9TI2IjcxSaFL3yh2HFbh5u7Lrl27PuhQ5O7du2zYsIENGzZw5MiRDO2IrVarqVatGnXr1uXjjz9+7g8lwsPDadq0KZcvnUJtFsP3ne3o2tIWa6tn/72UkqKwamsi/cfGEhVrg4tbbpYuXUqhQoUy9IwvIzo6mkuXLhlMUb1w4QLx8fEZvpaHh4fBOnGFChUif/78MipOCCGEeMtI6CbeaXFxcdStW5crl06SxzOBBaOd0z3FJDY2jtjYWHQ6hcmLYPIiBZU6G+PGjadly5YZrkWj0XD9+nWT6629zOLLWSFXrlwmwzUZiZU+L1q3zdfXl0GDBhEUFCRh5Wty4cIFZs+ezbJly9L1f2StrKz49NNP6dChA8WLF3/9BYq3TmhoKFWrViU68ia1Kpnx6HHqaOjIaIUC3ub0bG9Hp6Y2qFQqbt7Rkq+66Z0trSyhQglLLt60peEnLZg8efIbfpKsdfPmTdavX8/69es5efJkhs61sbEhKCiIevXqERwcjIODQ7rPjYyM5JtvvmHbts2gi8bZXkuLetZU+MiSIn4WONipSEyCi9e1HD2TzKJ1idx+oAIzRwoXKc6kSZPInz9/Bp828+h0Om7duqUP4NICuZCQkAxfS61WU6BAAaONG9zd3V9D5UIIIYRIDwndxDutX79+zJwxGU/XaNb+40pOT/N0nZeUlERkZJRB28yV1oyfp8XWITc7duwgT548Js+NjY3l6tWrRuFaSEhIhqbNZBULCwvy5ctnFKz5+vpm6gi/D4miKKxbt45hw4bJum0ZoNPpXtsUz5iYGJYtW8acOXO4dOlSus4pVqwYHTt2pGHDhh/c2oMfskGDBjHln3EU80sg8rGOfLnM+bSmNe6uZmzdn8QfU+MY8JU9A792IClJ4d/zhuuQKUDtzo8IKm/J4J4O1Ps8ihSVG9u37yQgICBrHuoNUBSFy5cv64O2CxcuZOh8BwcHatSoQb169ahevfor/Z5TFIVly5bxxx9/cPduCOgSQUkGtKR+h1SABagsQGWNk7M73bp1o0ePHm/tn8mxsbFcvHjRYHrqhQsXTP5A50Xc3d0N1okrXLjwezsqLiEhgTt37qDRaLC1tSV37tyYm6fv34ZCCCHE6yChm3hnHT16lE8+aQApESwe64SXhxl/Tovj0Mn/sXff4VFUXQCHf7MtvSf03hHpvYOIQCKdAAkQAgoKonQQRawoIE1s8ImSUAKEDibSpffepPcWSO/bZr4/IgsxARJISLvv8+SBmZ2ZPZNkNztn7j3HwJlLJqqU03AmNPXdXYNB4dOZsSxam0hMHFQpD58MlujQygFbW1t6DIviwCkrWrb25Mcff0x3Sui9e/dy6Iwzx8bGJlW9tUdfZcqUyZcftHPK6dOnmTRpEgcPHkzzmKjb9lh8fDzr1q1j//79nDx5khs3bmAymSxNN2rWrEnTpk3p2LEj1tbWWfa8iqJw8OBBAgICCA0NxWQyPXcfR0dHevfujZ+fH+XKlcuyWITcJykpidq1axMbdY3F0+2pUVmLu2vqRPDgidEsD0km6mjhdMsW7Diop3XfSILnOOPdwYb3JsawYaeGfn7vM3Xq1Fd1Kq+EoiicPn2akJAQQkNDuXLlSqb2d3FxoV27dnh5edG8eXN0Ol2Wxmc2m9m+fTt//vknp06d4tKlS5bmQ6VKlaJGjRq0adMmzybWH5WreHJE3Llz57h+/Xqmj6XRaCyj4p5MyGVVXbtX6fr16yxatIi///6bS5cuYTYbeZRstbGxo3r16nTs2BFvb28cHR1zOlxBEAShgBFJNyHPGjhwIBtDV9Crg8KsTx1ZtzWZYV/G0LCmjovXTcgyaZJuQyZFs3BtEh8PkihfCpaHKmzcDTuXOFG9Ely5acBzUDIPIlU4ObnkieSUi4tLulNCixUrJgrFZ6OwsDCmTJlCcHBwuvWKWrVqxRdffEGlSpVyILrcIyYmhunTp7N8+XLi4yJB0YNiBMw8HoGiBkkLkhVOzh707duXESNGYGdnl6WxPHjwgKVLl7Jo0SLu3r2boX2aN2+Ov78/bdu2Fc1B8qGtW7fi18+HEoViOLDCLd2k2q9LEhj6RSyxxwvjYJ/2PfVRUi7sQGGsrST2HDHQc3gcrh4VOX36dJ6fSi7LMseOHbMk2jLbDKBQoUJ06NABLy8vGjVq9EpfR2azGYPBgFarzdev34SEBMuouCcTcvHx8Zk+lru7u6VG3KOvihUrZnmCNCuEh4czceJE1q9fl9KxXtYDRhztwVonERuvkGyQ/v37YoONrTMffvhhrh7hKAiCIOQ/Iukm5En37t2jfv16yMYH7FjsQqWyGmRZsVww+Y+L5sgZoyXppihw866e8m0i+WIYvNNdsiRK3hwApYpBwHcpF1Pjpsus2SKhSLa56o5osWLF0k2uFeRi3TlB1G3LuC1btjBu3DjC7t8AOZ5yJaFrW2tqVdVSuawGG2uJhCSFC1dNHDtrZPXmR7WW7ClZqgIzZ86kadOmWR6XyWRi+/btBAQEsGPHjgztU6RIEfr27UufPn0oXLhwlsck5IwZM2YwY/rXeL9l5IfPnFCUlNFcT375jY1h9xEjl7Y4o7PSkRCfgCRJ2NnZYTZDkSZhdGxtTcA0ZwD0eoWKbR9iwoPDh49QvHjxnD3JF2AymTh48KClGUJmO2oXL14cLy8vvLy8qFu3rrgBlAMURXnqqLjMfvTXaDRUqFAhTeOGQoUK5djfue3bt/Phhx8SFXkHSYnnjUY6entZU6+6lsLuKdNJzWaFq7fM7DliYNG6JM5fVUDlRI2adfntt98oWbJkjsQuCIIgFCwi6SbkSUFBQYwZ/SH1Xktg/TzXNI//N+mm1xtYGRpB33GwcxFU+Ldcm0ql4sufZRashosbJXRaif0nFAZ9phATr3nlxYfVajWlS5emYsWKVKpUKVW9NXt7+1cai5CaqNuWOfPmzePLLz8HOZbyJU18M8KBFg10z7xAM5sVtu4z8NnsOG6HaVFpnJkxYya9evXKtjgfTUtaunQp0dHRz91eo9HQoUMH/Pz8aNKkSYFPrOYkRVEwGo3Ex8eTkJDSGCcxMTHVckJCQqr//3ebw4cPkxh/n0/fh36dpDTJiIOnFLp9qPD5BxKDe0o4OTkRGxuDooBKJfH3IR29Ryax6Q9X3mpuZdmvjV8E/1xzZPGS5bzxxhuv+lvzQoxGI7t37yYkJIRNmzYRGRmZqf3Lli1rSbTVqFFDvDZyqYSEBC5cuGAZFfeoXlxcXFymj+Xq6ppqRNyjUXFWVlbP3/kl/PXXX7z33mBMhnBeKy8z+1NHXq/07L+7iqKwZnMyn86MJybBliLFyrN27dqn1u8VBEEQhKySf8faC/naqVOnQDHSoEbGkhuyLJOsT/m/1b+7PBoVp9OC3gA376Uk42pWBq0GFEXGbDZnSwFea2trypcvn2bUWrly5UTCJhc6deoUn3/+uajblkELFixISbiZIxnQXcdnH7hhbfX8C3C1WqJdcyua1tHyyYw4Vm4KZ9SoEZYOo9mhTJkyfPbZZ4wdO5b169ezcOFCjh079tTtTSYTGzZsYMOGDVSsWJH+/fvTo0ePXDUqNreSZZmkpKRUia8nE2P/TZQ9LVn25DYZqdH3LNHR0VhpFOxtUkZEP+nuA4X3P1doWhve7ZGyLjEx0bKdLCsEbUjCwxUa1TQhKzpU/yaaHOwkQMlQB92clJyczI4dOwgJCWHLli3ExsZmav8qVarg5eWFp6cnVapUEYm2PMDOzo46depQp04dyzpFUbhz506qhg1nz57l2rVrzxwVFxkZyZ49e9izZ49lnVqtpkKFCmkaN2TVqLhz587x/vvvYdI/pGtbNbM/dUarff5xJUmiWzsbGtfW0Wt4NJdvXaZv375s3rw5S+uICoIgCMJ/iaSbkCedP38eMPF6ped/UFIUiI+Po2yJlOXj/0CpYhIptaTg2NmU9dH/XmvY2kiUKqYQGctLJ92cnJzSnRJaokQJMd0mDxB12zLv3LlzfP75JDBHMWqANWPezfwITXs7FT985oiDfRwLVkUxZswYateuTenSpbMh4hTW1tb07NmTnj17curUKQIDA1mzZg3JyclP3efSpUtMnDiRyZMn061bN/z9/alWrVq2xfiqGY3GF0qOPW2blIRV7htcr4DlpswjMXEKfcYquDjC/G8ky00ao9HAo78dCYkKm/dCn46QmBhPcnIidvZ22NrYPr7Jk80jfl5EfHw827dvJyQkhG3btmU6MVizZk1Lok00GskfJEmiRIkSlChRgrfeesuyPikpifPnz6eZovqs5KzZbObChQtcuHCBtWvXWta7uLikGRVXqVKlTL1GjEYjw4cPx6iPokENMJkU6ncLf2rzrDFTYvlrp56b98xIQOVyGkYPtGPFj860HxjJ5UtnmTZtGpMmTcpwDIIgCIKQWSLplkspioLJZMJsNqPT6USC5j/i4+NBkXF2zMjdTXB2dqFq+Yc0rAHfzoMSRaBcSYXlobD/5OPtHnGyB0lSMnyBWKRIkXSTa+7u7uLOfx6k1+uZN28ec+bMSfeCtEKFCpa6bcJjjy6ITIZoOrRQM/qdtI0QLt8wPbfLMKRcBH49woELV6PZd+Iho0ePZsWKFa/k9VSjRg1mzJjBpEmTWLFiBQEBAVy9evWp2yclJbFkyRKWLFlC3bp16d+/Px07dnylCRdFUUhOTs5QcuxZ0zCfXDYYDK8s/pyiVqsxm+HyzZSmHpIkoTeC38cKcQmwcb4VHm4qJEnCYDBYOmEChO5KSdZ1ezPld1KWZeJi44iNSeDCVRnQ5JqkVExMDJs2bSIkJISdO3dm6mcrSRL16tWzJNpKlCiRjZEKuYmNjQ21a9emdu3alnWKonD37t10R8U9+fr4r6ioKPbu3cvevXst69RqNeXKlUuTjCtSpEi67/VLly7l7JkTuDgk0+0tOz6dGUfDmjpkBdJ76vgEhUE9balSXo0kSazcmITPyGiWzHDm+/GO+I2L5X//m4efnx9lypR5qe+VIAiCIDyNSLrlImFhYQQHB7N//35OnTplqadiZWVFtWrVqF27Nt7e3tSoUSOHI815KV3IJDIys0hRsCROZn8C738OHYekJNNKFIFR/SW+/0Oh0BP9CEyPGis+QaVSUapUqTSJtQoVKoipZfnEo7ptX3/9Nbdv307zuJOTE2PGjMHPz09MA05HaGgoZ8+cxNk+mSljXNO9aDp7yUTIjuRnXig9olJJzJjgwBv9oti3bzd79uyhefPm2XgGqTk5OfHuu+/yzjvvsHfvXgIDA9m4cSNms/mp+xw9epSjR4/y+eef4+PjQ79+/dIdoWc2m7MsOfbo61kXvPmRVqvFzs4Oe3t7y7+2trbpLj9a9+jr0fKBAwf4bOJ4zl2Np1AhV2QZug6N4vJNA7uD3HmtYsrrXFFSkqsJCfGYzSnf5zVbFcoUhzrVUv+en7tiJiFRITYxmsOHD1O6dOkc6ZwZHh7Oxo0bCQkJYe/evZmaiqtWq2ncuDGenp506NBBNA8RLCRJonjx4hQvXpy2bdta1iclJXHx4kVLMu7RV0xMzFOPZTabuXTpEpcuXWLdunWW9c7OzqmScFWrVqVSpUoEBASAksAIfzv6drbBr6st8LiO73/N/dop1XK75lacu2wiYHUimwPcaNVAw44jiSxevJiJEye+5HdGEARBENInGinkAhEREXz11VesWbMGkzEeFD0oRuDRhZ3q33bnKS3P69Spx9dff53qzmNB4+/vz+aNK/nyQw2DetmmffyJRgpGo4nIyEhkWbaMXLt1D5INUKGUxLzl8L9ghRNrH48mbN5H5n6EDe+9P5QWLVpY6q3lxqlCBYmiKMTFxaHX69HpdDg4OGTZKNBTp04xadIkDh06lOYxUbctY7p27crB/VsYPUDF6HfSn1b6rC7DT/PpzFgWrJbo4NWT33//Pcvjfh5FUTAYDCQkJHD16lWCg4NZt24dERERlg6Xj95f0vtydXXFw8MDKysrS4LsWdNW8ytbW9t0E1/pJcv+u016+2RF4jsqKoratWtjSLpDyP+c+XVpAr8tT2LGBAea1Nal2rb2a1p0OkhMTOLG7XhqdjYzrC+Mfzf1e9DkuTKBayXMig1OTk6UL1+esWPH8vbbb2f7qPX79+8TGhpKSEgIBw8ezFQiVqvV0qJFC7y8vHjrrbdwdU3bpEgQMkNRFO7du5dqRNw///zDlStXMn2TwGw2ExMTRRE3E/uW2+PmokWj0aJSqRgwPmN/SwA6vRdJbLzCjiVubNmjp//4eDyKVOHEiRNiZoIgCIKQLcRItxy2ZcsWRo4cSWTEHZDjqfe6mq5tran9mi3lSqqRJHgYKXPqgonNe/SE7Ajn2JEddOz4Nh98MIxx48ZlS6H/3K5GjRps3rSWUxfS3tn8L61Wg6OjY6rOhCWLpvxrNKtYFirj390KFxc71GoNt+4pJCRH4eZeiO+++06MaMpht2/fJjg4mCNHjjwxAjRlKpiDgwPVq1endu3a9OjRg8qVK2f6+M+r29a6dWs+//xzUbftOe7evcvBgwdQS8n06eT21O1UKilNwfrn8etiy4JVUWzevJn4+PjndvKVZZnExMTndq7MTLfL/44SepRoS0pKeu5UvXv37nHv3j3UajU2NjbY2Njk+pIBarU6Q8mx540ke7SNra1trvxb5eLiQufOnVmxPIDZgQnsPJRSjG30d2k7OV7724MyJTTY2dmy/ZCMyRxH97fUPDks+t5DhQ1/Q7JewtHJBoArV67w/vvvU7VqVcaNG8dbb72VpRf3N27cIDQ0lNDQUI4ePZqpfa2trWndujVeXl68+eabYtS2kKUkSaJYsWIUK1aMN99807I+OTmZixcvpkrEnT179pkdpPV6PSrJRK0qCioSiIpKWa9SqdAbZGQZEpOS0Go0aDQay2tMURTMZohPVNiwPZnNe/Qsnu4MQIv6OjRqMw8fPuDu3bsUL148u74VgiAIQgEmkm45KDg4mFGjRiKbIqlaTub78c7UqZY2weNgr6JcKQ1d2lrzMMLMlz/Fs3rzQ36cM5Nbt27x448/5sqLmexUr149kHRs35+AXq9gZSWRmKQQuiNl9MiNu2Zi42VW/pUEQMsGOuzs7PhxYTyO9lC8sMStezBvuRkbazWffeCMlVXKRfDGXQkg6ahRo4ZIuOWgs2dTChxv3boFRU4CxfCfEaAQFx3Ovj232Ld3Kz///BONGzdh1KhRNG3a9LnHF3XbstaJEydAMVKlnJoiHum/H5llmeSkZJKSkrCxedwERVH4d1SY/G8yK/VIsZJFFAq7yVy7G8VHH32Ei4vLMxNqr6JjpCRJWFtbY21tjclkIikpiaSkpGfWgTSbzZZ4rayssLGxQavVZkkCxsrK6rmJr4wkxx4tW1lZFZhRH0OGDGHt2jVs2Xufnz93outbz2/QM6yfPcP62SPLiiXBK8syX/6kEBkjoVJr0/z9+OeffxgwYAA1a9Zk3LhxtGrV6oW/x5cuXSIkJISQkBDOnj2bqX3t7Ox488038fLy4o033sDWNu1ocUHITtbW1tSoUSNVuRRFUQgLC0szPfXKlSuYzWZMJhMaNVQtn/o1I8sysjkl6RYb87jBg52dHQ4O9mzbZ6Ctf0q5Fo0GfprkSI8OKQlxKyuJymXVnL1q5OzZsyLpJgiCIGQLkXTLIbt27UpJuBnD6dNRy7ejM9by3MNNzU+fO/Fmk2SGfxPN2jUr8PDw4Msvv3wFUeceTZs2pXjx0ty5Gcu6bcn09LThQYQZ74+iU233aPnvxa60bOCAgp4ZC4zce5jSlc6zJYx/V0GrMQMqZFlh4dokkBzx8fF55eclpBTj/+GHH5gz5wdMhhhQEmlWV4tXKytqVrGlcjkN1lZgNMKVmyZOnk8ZBbp5Tzz7927Ce/9e/Pz8mThxYrojojJSt23s2LH069dPJF0z4cyZM4CJGlVSf88UBQwGA0lJSU9MqVRISJAxGBVMJjNhYWHPPX7lMjIXryeyevXqXJck0Gg0ODg4YG9vT3Jycroj456kKAp6vR6DwYCdnR0lS5akXLlyODs7v9A0S1tb2xypGZZfVKlShREjRvL9tG8ZMyWaEkVU1K+he/6OpIzctLe3w8bGhk9mRLHnmIHEZBWuro5PTaidPHmSPn36UL9+fcaPH0+TJk2e+zyKonDu3DlLou3SpUuZOkcnJyfat2+Pp6cnLVq0EKUShFxHkiSKFClCkSJFUt3s0uv1XLx4kVGjRnH86E6KeCioVDx3eqqiKMTGxlGrqo7Dq92IiVPYuEvPh1/HotFIvOOd8nfEw1UFV5VnjrITBEEQhJchPqXngNjYWEaOHIlsiqJnBw3Txjuk+nDeqk8EOw+lP11p6Sxner9tQ5e21mg1MGhiNL/99j/atWuXoQ/u+YVaraZfv35M+e5Lpv8eh2dLK8qU0KBcKvrM/T79wI0hPlEYjamnpUZHR+Pm5kbg6mRu3FXh6OJOly5dsvEMhPTExsbi7+/Pgf27QI7Fq5WGjwe7Ur502rcqnQ6qVtBStYKW3m/bcO+BmR8CE1i4NoKFgb9x4MABli1bRpEiRSz7PK9uW//+/Rk9erSo25ZBycnJhIeHEx4ezrFjxzAZ9Xi4pNTdM5nMGI1GzGZzuqO/ZNmELGd8imURD1BJz7/Qyg6ZLdhva2tLWFgYe/bsYd++fZjNZiRJSvX1pIiICJKTk/H29sbPz48qVaq88nMs6IYNG8bhw4fZ8fcmeo+I4ZuR9vR+2zpDI9GiY2UmTI9j3TbQWHnQ9c2WHD58+LnTjg8fPkyPHj1o1qwZ48ePp27duqkel2WZ48eP89dffxESEsKNGzcydU7u7u506NABLy8vGjduLG4iCHmSlZUV1atXp3Llypw9vR8rKzUeHnbIsozJZMJkMqJWJyBJMpKUcqMnpcRAAiBhbW2mXnVnANo0scJkhlHfxuLfzQa1WuLfvigFbsaIIAiC8OqIpFsO+OGHH7h39xplipn4drRbmg/1v3zhSGx86ovU2YEJrNqUzJtNHt+d9mptTd9OBhZviOXjjz9mx44dub5OUFZ65513CAoK4ub103z5Yzzff/z8WjSSJOHs7ExkZGSqDoSyLHPibCSTf5VB7crHH3+MjY1NdoYv/EdiYiJ9+vTh6JG9ONjE8f14Bzq1ef40r0eKFlIzZawjb7c28OFXMVw8f5xu3bqxbt06ZFl+bt22L774gooVK2blKeU5ZrOZ6OhoSyItIiKChw8fWpb/+5WQkGDZNzY2FhV6kpMV4uP1Garblpk+PpmZhZeRgv1PGzmWlQX7R44cSVRUFMuWLWPRokVcv379qdsmJCQQEBBAQEAADRs2xN/fH09PT5EoeUW0Wi2///47gwYNYvu2TYyeEsuaLcl80MeW5vV1luYfT4qNk1mxMZkfFybwIMoKldadGd9Px8fHh3v37jFnzhyWLFny3K6he/bsYc+ePbRp04bRo0eTlJRESEgIoaGh3L9/P1PnUbRoUTw9PfHy8qJ+/foikSDkG0WLFgVJzbXbZiQJ1GoVarUOKysdOp0RtdpIoUIeGAx6IiOjgJTXrF5vSNXAp+7rGmYHKDyMlCnioebqLTNI6pTjC4IgCEI2EN1LX7GkpCRq165NbNQ1Aqfa07ZZxqZ4lGv9gKrlNYTMT91NLC5epl63cOKS3Vi2bAUtWrTIjrBzrb179+Lt3R3MkYwfZMNwf7sM7feoo+mjX/97DxUGfaZw+ZaWN9t2Ijg4uEAlMHOD4cOHsyJ4MU62saz40ZnXK714suH2PTPdh0VxK8wWj8KliY+PJykpKc12BaFuW1JSkiVxFhERYUmYPZlMe7Q+IiLihUaSmUwmYmJi0KoN+HeFT9579vYpCTSJEd8qnLwAuxarkSRVmtFgKtWj/6sYPjmZ9ds1dOzci86dO+e5gv2yLLNr1y4CAgLYunVrhr7P7u7u+Pr60q9fP1Fr6BUxm83MmzePadOmYdBHg5xIEXeFOtW0VCqjQaeViIqVOXPRxPFzRpKNOpBsqVDxNWbPnk2dOnVSHe/mzZvMmjWLFStWPPVn/qg7rl6vt3RmtrOzy/CU4TJlylgSbbVq1SowtfiEgmXDhg28N3gAr5ePY3NA6mY9/+2EHRkZicHweEaDo6MjtrYpN1Lf/yyGpX8mEXG4MNGxCjU6hoO6EOfPnxeNRARBEIRsIZJur9iaNWv4YOggShWOZV+wW7p3z//r7/3xvOEXx8LvHenXJW1SaeLMOP5YDZ269GHu3LnZEXau9uuvv/L111+AOYo+HXV8/qE99nbPT5jp9XqioqI5cFLhsx8Urt+V0Bs0DB8+nO+//z77AxcstmzZQv/+/VApEaz60YmGtTJWT+lpFAXOX07Ac1Asdx+osLF1SFUHLC/XbTObzURFRT119NnDhw9TJdeyq6mAoihoNBqSkpIsReQlDDSrA4umSZbE2rMMnyxz+pKKcxsLP/f5GnuHc+O+M8uDV9G8efOsOYkccufOHRYvXsySJUsIDw9/7vYqlYo333yT/v3707JlS3FD4BW4fv068+fPZ8WKFcTFRaU0cVHMpHQqVYGkAUlL5cpV6d+/Pz4+Ps+sk3blyhVmzJjBunXrLA1CDAYDycnJ6PX6dEd9WltbY29vn24SuVKlSnh5eeHl5UXVqlVFok3I98LCwqhbtw6y8QE7FrtQooja0jzr5yWJXLlpYuaElKRZ/eoyV67H8c1chY6tJcqW1KBS2/Pn38nMD07iu9EOjH/Pnv8tS+SLnwxUr9mcTZs25eTpCYIgCPmYSLq9Yp9++ikLfv+R93opfP6hw3O3j42NY8TkeJb/BafWgr2dGq1Wa7kDLkkSB0+a6TNGj5VtMT7++GM0Go1lm0f/6nS6dNc/+e+jr2dtl1s/2Kck3r4EOY4ShY2MfdeOTm9YY2X19HgvXjMxJzCG4L8MxCdKmGUtzs7OqNVqpk2bRt++fV/hGRRcRqORxo0bc/f2OYb0VvPZsJTXxeUbJqbPT+DACQNnLpmoUk5juYsNKVO7Zi5IIHSnnovXTFjpJBrU0PLlcFvKFE3EYDCyLEThm7kK0XFq3N3d0Wq1+Pv7M3r0aJydnXPojNNKSEhIM+osvZFoDx8+TDVCM6tZW1vj4eGBu7t7ul9ubm48fPiQ7du3s2XLFuLj4y37mkwmIiMjKOIus3WBhLND+q+9xGSF7ftT/h+4TuLGHZj5ScqFUssGOjzc0iYYbt0z07BHBKgLce7cuVz1s3sZRqOR0NBQAgMDOXDgQIb2KVOmDH379qV37964uro+fwfhpSQmJnLixAlOnTrFzZs3MRqN2NnZUbVqVWrWrEnlypUz/HcxISGBwMBA5syZw+XLlzP8OraxscHOzo5atWrh5eWFp6cnFSpUeJnTEoQ8aeDAgWwMXYHv2wrD+tpStvXDdLfbttCFQs4xfPaDzJGz8DASnBwkqpTTMmqgHZ3ftMZgUGjVN4Lr9xyZ9v1s8ZlPEARByDYi6faKdezYkaOHt/HL59Z0afv8elX37j2keicTzevCz5NSP/ZoClZ8okIzX4XwaBXu7h7ZOgpCo9FkSfIuq5KATx7n9OnTzJw5k7D7N1GRjKuTidYNdVSvoqFsCQ1arURMrMLpi0YOnzJy5IwZJGviEmQURcLe3t7yvdNoNAQFBdGsWbNs+14KKVKmjLyDh3MUB1e6Y/1vonTd1mSGfRlDw5o6Ll43IcukSrqduWikrX8k7/SwpUV9HYnJZqb9FseJf2Q2/iZRqYyELCv4jFY4fEbi9er1WLJkySup22YymZ46Gu2/I9HCw8PTnfqaFSRJws3NLVXSzN3dPVVi7cl1T+sKGh0dzapVqwgKCuKff/5JdxtFUYiMjMTW2sDHgyT6d0k/EXHngYp63dOvcfX3YldaNUw7WujbX+P5aYlMi1Zvs2zZsgyefd5y/vx5Fi1axIoVK1IlM59Gp9PRuXNn/Pz8qFOnTq69IVLQxcbGsmXLFkJCQvj777/R6/VAyntEfHy8ZflptFotVlZW2Nvb4+fnx/Dhw1M1hxGEguTgwYN07doZzBGs+smJxrWfPio+Ojqa5OTHr6+UWp6PZ4tMmRfPnEVG3AtVZP/+/djZZaw8iSAIgiBklki6vWJNmjTh+pWjrPvVgfo1nj+Fbsna+/QdqxDwHbR9SnNSSZJo2U/h2h0VLi5uGa4Dkx/JskxSUhJJSUlImNFqFDQaUKtSfs0VBUxmCZMZjCYVtrZ2uLi4EB0dbZmG9+jiVaPR0KBBAxwdHXM0mfisf/PDNDNvb2/27v6LEX4qxg22t6x/svDxf+u1ACQkykiShI01JCQkkpCQQHyCTH1vha5vwuSRKd+b9dslJv2opkz52uzbt++FvmeKolhGoz2vuUB4eDhRUVHZNhrN1tb2qSPR/vvl4uLywvXNZFlm3759BAUFERoa+txOjJBSPy4pMYZKZRRW/yhhb5vy85MkCSsrK2xsbNDpdJlqivAwwkwL30hiklz444+FtG/f/oXOJ6+Ij49n9erVBAYGPjXB+V+vv/46/v7+dOnS5amJU+HViYiIYNOmTYSGhrJ79+403bKfZDQaiY+PT/X60ul0WFlZYWVlleb1q9Pp8Pf3Z9iwYbi7u2fbOQhCbjV27FiWLP6dom5xrJ/rSvEi6f+NS07WEx0dbVnWaNS4ubkjSbBlj56BE2IxS27Mn/8Hnp6eryh6QRAEoSASSbdX7FHSbe0vDjSo+eykm9ks0/PDMP4+BMdXg/YZubSW/eDabbCxTSkqnh+SMS/jUb0co9GIyWRClmUURUGSJEvSSqfTWS5oZFlO09EUUlrIu7q65trvp1qtzrUjDzNyHLPZTLVq1TAb7rE/2JXSxdP/JU8v6aYooNcnExcXn+rn5jlYpmghWPCtJuWutmRDNc+H6M3u7N27n7JlywIpF7uRkZHPbS7waN3zRqS8KJVKZRltlt5ItEfrPTw8cHNzy/akSlhYGMuXL2fp0qXcuHEjU/sqikJERAR21iZ831b48iMdNjY22NhYv3Cyc9CnMYTuUlOteiP++uuvAnNTQVEUjhw5QkBAAH/++eczEzePODo64u3tTf/+/cX0w1csLCyM0NBQQkND2b9/f6Yakmi1WipXrkx4eDh3797N0GvFxsaGd999lyFDhuSb6daCkBFxcXF4eXlx+eJJihdK4o/vnKheOW1tVkWBhw8fIMuPL3NcXV1Ys8XMuGnxGGVnevv0Y+bMma8yfEEQBKEAEkm3V6xz584cPriFnz6zols7m2du+zA8nrJvxNGtLUwZ/fTt4hKgRT94EMG/Hf9UWFtbY2trW2AuULNCSk2qtPWydDodzs7OYvpWNjAajcTERFKxlMy2gMfdK4En/oVhX5s5eV5hT5AOJNBqtJhMxlTdyQBi4hRqd1MY6qvh6xEOKIqCLMv0+DCB/Sc1vFatNjqdzjIaLbvY2dlZkmRPJs8eJdOeXP+ojmBOMhqNbN++naCgILZt2/ZCHUwfUalUxMREYW+jZ9o4e/p1efEk4U+LEvh2bhIaq8Js3LiJ11577YWPlZeFh4ezbNkyFi5cyO3btzO0T7NmzfDz86Ndu3Z5rllIXnHr1i1CQ0MJCQnh6NGjmRrdamVlRatWrfDy8uKtt97C0dERRVH4+++/mTp1KqdPn87QcRwcHBg8eDCDBw/GweH5dWIFIT+4d+8e3t7eXL1yFjXxDO1jw+Betri5pE5Yx8bGkpiYUr7h0g2FX5eq2XFYApUTnTp358cffxTvj4IgCEK2E0m3V2zixIn8MX8O7/ZQ+GrE0z8gKwrMX/aAwZPMrPkRGtZM/diTDp2CdyZCVKxkuXh/9GPV6XRYW1tjZWUlkkYZYDAY0k3G2NjY4ODgIL6HWSwxMRGDPpYOzRV+nvT00R3DJ8ucvAA7FqoA5d/fb+nf/z/ebtx0WLMVdi+WKFbo8c9q8lyZgDUSktoee3v7/x7+udRqNW5ubpYRZ8+a0unm5oaNzbMT6rnFtWvXWLp0KcHBwTx48OCljlW3bl18fX3p2LEjv/zyC7NnTwdzFBOH2vK+j22GOjU/YjIpTP89gTkLk0HtwpdffsOgQYNeKr78wGw2s337dhYuXMj27dszlOQpXLgwffv2pU+fPqIWWBa4cuUKISEhhIaGcurUqUzta2try5tvvomnpydt2rR5ag0pRVHYtGkT06ZN4/z58xk6trOzMx988AEDBgwQU4yFAiEyMpIJEyawYcNakOPRqvW0a66jbjUtVcppsNJJhEcZOXIqjgMnFY6dA71BhbNLcUaPGcMHH3yQ4ze8BEEQhIJBJN1esfXr1/P+e+9QzD2GgyvdUKvTvxDV6w10fj+CM5fg8ArpmcmeyXNlAtdKmBUbnJyc0t2mUKFC9OrVC29vb1xcXDAajZapl0/712AwvNTjj54jq57nv1M/s0tSUhKxsbFp1tvb24tCu1ksISEBszGO3p4K34x4ftLt70AJRVFQqVRpRmMF/wUjp8APn0j07JD69fLrUoVZgSBji6NjSqdMBweHDI1E8/DwwMnJKddOMc6s5ORkQkNDCQoKYt++fS91LGdnZ7y9vfHx8aFKlSqW9Yqi8MUXX/Dbb3PBHE2jmhJTxjpSqezzR96eOm9k3LQ4Tl0A1M6MHTuekSNHvlSc+dGNGzdYtGgRS5cuzdCoTbVaTbt27fD396dp06biBkIGKYrC+fPnCQkJISQkhAsXLmRqf0dHR9q1a4eXlxctWrTA2vr5DZQekWWZDRs28P3333P16tUM7ePu7s5HH31Ev379sLJK25REEPKbv/76izlz5nDy5HFQ9KAYQXnUrEciMcmAwahgMErodFb89NNPDBw4MEdjFgRBEAoWkXR7xfR6PXXq1CEq/DLzJ9vh2Sr9D+CR0TJFm4QxxEfLhMHyU0c0xMYrtHtX4U6YCkcnF3S6Z9eJ02g0dOjQgf79+9O4ceM8deGlKMorSe4ZjUa2bt3KoUOHLM/7SIsWLShSpMhLPY/JlH7nxoIos0m37QEpy5IkWX4ukiSx/SD4f6zwkZ/EJ+9pUalUqb5+CDTywyLo2NmXL7/8Ejc3t0xd/OYHZ8+eJSgoiFWrVqWbVM6MFi1a4OvrS/v27Z/6nqMoCgsXLuTrr78mMf4hKAk0q6ul21vW1KqqpUJpNRqNhNGocPG6iePnjKz4K5nDp82gssPRqQjffvst3bp1e6lY8zu9Xs+ff/5JQEAAR48ezdA+5cuXx8/Pj549ez71Rk1BpigKJ0+etCTarl+/nqn93dzcaN++PV5eXjRt2vSlp6+ZTCZWr17NjBkzuHXrVob2KVKkCCNGjMDHx0dMnxMKhBMnTrBjxw5OnTrF9evXMRqN2NraEh8fz9mzZy2NSTp16sTcuXNzOlxBEAShABFJtxwwZcoU5vzwPcXc4/h7kSsO9s8eQfOoI2diYlKa0V4Tf5BZuUkiSa/F1dU1U0m0SpUq0b9/f3r06CFqwfyH2Wzm3XffZdOmTanW29jYsG7dOl5//fUXPvazkofZlUTMjufJiuRhUlIS+uQY3mys8L+vMpd0s7Kyws7OlsOnFdoNjKa3lzW/f+ec7v6jv4tlaaiGESM/Ydy4cS8dd14RGxvL2rVrCQoKyvRUuP8qUqQIPj4+9OrVi1KlSmV4v5s3b/LFF1+wefMmZHOiZSSCJMloNRJGk4KiqEHSgmSFRmtPx44dmTRpEoULF36pmAuas2fPEhgYyKpVq0hKSnru9tbW1nTp0gV/f39q1KjxCiLMvcxmM0eOHLFMHb17926m9i9cuDCenp54eXnRsGHDbJm2ZjQaWbZsGbNmzeL+/fsZ2qdkyZKMGjWK7t27ixqvQoF0+fJlWrRoYVm2srLi1KlT4nOvIAiC8MqIpFsOSExMpE2bNty4dopOreHnLxyfOs30SYoCBoOexMSklNENfyt8MlshKkaFs4vrC9/NtrOzo0ePHvTv3z/VFLGCLiEhgS5dunD27NlU64sUKcJff/1V4BMCGUkePi+5d/nyZb78YhIeznEcXeOAJD2uWfjorcloNPL+pCTL9FJIqY3k5OTIuUtGmvtG0KS2jjW/uKDRpP86ats/grNXHZj/+0I8PT1fyfcnpyiKwuHDhwkKCmL9+vUkJye/8LE0Gg1t27bF19eXVq1avVQi4fbt2yxdupT9+/dz+vRpEhLiLY85OjpRo0YNmjRpgo+PT4F/bb2s2NhYVq1aRUBAAJcuXcrQPrVr18bPz4/OnTtn+ShQRVE4deoUx48f59SpU9y/fx+TyYS1tTXly5enRo0a1K9fnxIlSmTp8z6P0WjkwIEDhISEsHHjxkzXNSxVqhReXl54enpSu3btVzYFXa/Xs2jRIubMmUN4eHiG9ilXrhxjxoyhU6dO+WaqvCBkVIcOHTh58qRledasWfTq1SsHIxIEQRAKEpF0yyEHDhygZ09vTPqHdG2rZvrHjthYZ2yUmqIoLF6XyMffxxMRI6HT2b5Qcfj0NGrUCH9/fzp06CCmpJDSIcvT05OwsLBU62vWrMnq1avzTMH83MpoNFKhQgWMyXfZvtCFKuUfj8RITFJYuyWexMQEFqxWuH4HvhwmYW1jQ7vm9ihA3S7hKAos/N4Z2ydeP472Eq9VTPn9jYmTqe4VjgkPDh06/Mov7F+V8PBwVqxYQVBQEFeuXHmpY5UrVw5fX1+8vb3x8PDIoggfk2WZhw8fYjQasbKywt3dPU9Ndc8rFEVh//79LFy4kNDQ0AyNTnVycsLHxwc/Pz/KlCnzUs8fGxvL8uXLCQwM5OrVS//WWjICMvCoGYrm31GOWlq1ao2/vz9vvvlmtiWGDAYDu3btIiQkhE2bNhEdHZ2p/StUqICXlxdeXl5Uq1YtR39vExMTWbBgAT///HOGz6NKlSqMHTuW9u3bi9ecUGD8/vvvfPbZZ5blZs2aERwcnIMRCYIgCAWJSLrloPXr1zNs2AeYDBGULW5i6lhHmtbVPvOD8O17Zj6bHcemPSZQO+Pt7UOjRo1YtGgRJ06cyLLYChUqRJ8+fejbty9FixbNsuPmRSdPnqRr165pRgy9/fbbzJ07V4waeEn+/v5s3riKd3uQqqPvhatJVGkXne4+fy92BaB138h0H2/ZQMeOJW4A/LY8kc9/NFC1WmO2bt2ary40zWYzO3fuJCgoiM2bN7/UlF9ra2s6duyIr68vDRo0yFffJwHCwsIICgpi8eLF3Lt3L0P7tGzZEn9/f9q0aZPpqYlbtmxh7NixPAi7BUoidtZGGtTQUqOylrIl1ei0EvGJMv9cMXHyHxPH/zGhYAMqWxo3bsaMGTNeOun3SFJSEtu3byckJIStW7cSHx///J2eUK1aNcvU0UqVKmVJTFkpLi6O3377jXnz5hEXF5ehfWrUqMG4ceNo3bq1eK0L+d7Dhw+pU6eOpUSLJEkcOXKkwH++FQRBEF4NkXTLYbt372b48OHcv3cd5Hiqlpfo3s6aGpW1lCupRq2GBxEyp86b2LJXz9Z9BmRs0Fq5MHr0GIYNG2ZJ+pw4cYLAwEDWrl2LXq/PkvhEx7sUf/75J4MHD06zfvjw4YwfPz4HIso//v77b/r06Y2jTQQHV7rj5KBCrzcQHR2dqomFvb099vaZ6x5rNCq07BPB9XuOTJk6Ez8/v6wOP0fcunWL5cuXs3Tp0gwnUJ6mevXq+Pr60rVrV0tnVyH/MplMbN26lYCAAHbt2pWhfYoVK0bfvn3x9fWlUKFCz9zWaDQyYcIEgoIWgxxL2eIm3uttS7e3rLG3e/oNiht3TCxam8SCVckkGWyxsXNn+vTpdO3aNVPn90hcXBxbt24lJCSE7du3Z3qadZ06dfD09MTT0zPLkn/ZLTo6ml9//ZX58+dnqKYfQL169Rg/fjxNmzbN5ugEIWf17duX7du3W5YnTpzI0KFDczAiQRAEoaAQSbdcIDY2lilTprBs2TKSk2IetzznUdMElWX6DZINzZo158svv6Rq1arpHi8qKoply5axcOFCbty4kWVxVqhQgf79++Pt7V0gL87nzJnDlClT0l3fo0ePHIgof5BlmdatW3PpwlF6tleYOtYmTcLNzs4Oe3t7MpvznfF7PDMWGHFxK8fBgwezbBp2TjAYDGzcuJGgoCB279791I7GGeHo6Ej37t3x8fF5qaYgQt527do1Fi5cyLJly4iJiXnu9hqNBk9PT/r370+jRo3S3IQxGo289957bPxrPSpieL+3NWPetcfaKuMv3Bt3TIz6No79JxRQOzN9+kx8fX0ztG9UVBSbN28mJCSEnTt3YjQaM/y8kiTRsGFDvLy86NChA8WKFcvwvrlNeHg4P/30EwEBARgMhgzt07RpU8aPH0+9evWyOTpByBlr165NlWSrWrUq27Zty8GIBEEQhIJCJN1ykZiYGFatWsWBAwc4efIk9+/fx2w24+TkxOuvv07t2rXp0aMHFSpUyNDxZFlm586dBAQEsHXr1pe6SH+SjY0N3bt3x9/fn9deey1LjpkXKIrC8OHDWblyZar1Wq2WFStW0KBBgxyKLO87cuQInTt3QjY+4PuxCm82fvzYiybcjp4x0nVoNCZcmTv3Nzp16pS1Qb8iFy5cYOnSpaxYsYKoqKiXOlbjxo3x9fXFy8sry4vlC3lXUlIS69evJzAwMMNlCtLrfj1+/HgWLfwDnTqK+ZOdeLOp1QvFI8sKk2bH88cqA5LGjYULF9GmTZt0t33w4AEbN24kJCSEffv2penw/SwajYamTZvi6elJ+/bts6V+YU66f/8+c+bMYcmSJRlOQL7xxhuMGzeuwHezFfKfpKQkatSoQUJCgmXd1q1bC9TnWEEQBCFniKRbAXHr1i0WLlxIUFDQS1+4P6l+/fr4+/vj5eWFTqfLsuPmVgaDAW9vbw4fPpxqvaurKyEhIZQuXTqHIsv7hgwZQsCC/+HmLDPnU4kmtSVsbW1xcHDIdMLt9AUjvYZHE53gwNsdezBv3rw8NTU6ISGBDRs2sGTJEo4ePfpSx/Lw8KBnz574+PhQrly5LIpQyK9OnjxJYGAga9asyVCZAltbW7p3707lypWZOPETJDmCgCmOtG32Ygm3RxRFYezUOIL+lClctDI7duzAyckJgDt37hAaGkpISAiHDx/O1A0lnU5Hy5Yt8fLy4q233sLZ2fml4swLbt26xaxZs1ixYkWGk5IdOnRgzJgxTx1RLwh50YgRI1I1UBgyZEiqBguCIAiCkB1E0q2A0ev1bNiwgcDAwJe+mH+Su7s7vr6+9OvXj+LFi2fZcXOjiIgIvLy8uHnzZqr1FStWZMOGDQVy6u3LOn78OL169eL27dvIcjJuTgqjBlgx3N8FjSbjyTJFUQgOTeaz2fHEJztQp24Tli9fjp1d5mrB5QRFUThx4gRBQUGsXbs21d34zFKpVLRp0wYfHx/atGkjOhELmRYdHU1wcDCBgYFcu3btmdvKskxERARO9jLv9dbxzSiXTCfK05OsV2jrH8GV2/Z08OxKnTp1CAkJyXTTIBsbG9q0aYOXlxdt2rTJ09PMX8bVq1eZMWMGa9euzVCiUpIkOnfuzJgxY0TCXsgXdu/eTa9evSzLhQsX5siRI6jV6hyMShAEQcjvRNKtADt9+jQBAQGsWbMm00Wmn0alUtG2bVsGDBhAs2bN8m1nz4sXL9KxY8c0neJatmzJokWLMt3pryA7ffo03t7exMbGoigKsbGxqFUK9rYydV6DcYPsaFZPh0r19Kt4RVE4dtbErAXxbD8gg8qRJk1bsmDBAsvUt9wqKiqKVatWsXTpUv7555+XOlapUqXw8fGhV69eFClSJIsiFAoyWZbZu3cvAQEBbNq0CVmW02yTmJiIPjmW18orrJwjYWejZuMeDSs3yRw7ayIqVqFiaTUf+dkxoIeNZdRpYpLC1z/HsTwkmfvhZkoUUePfzZZxg+xQqyVMZhP7jybiPTyRiGgJNzf3DF8cOzo60rZtWzw9PWnVqhU2NjZZ+n3Jy86fP8/06dMJDQ3N0PYqlQpvb29GjhxJqVKlsjk6Qcg+ZrOZevXqERYWZlm3fPlymjdvnoNRCYIgCPmdSLoJxMTEsHz5cgICArh+/XqWHbds2bL4+/vTs2dPy7Sg/GTHjh3069cvzXSdAQMGMHny5ByKKm/5559/6N69O9HR0ZZ1PXr0oHHjxnzxxRfExYaBnEiZ4uDVyooalbVUKa/B2krCYFC4ctPMyfNGtuzVc+aSDJItOmtnxo0bz+DBg3Nt8lOWZfbt28eSJUsIDQ3NVMH3/9JqtXh6etKnTx+aNGmSbxPdQs67d+8eS5YsYfHixTx48ABISXhHRkZiZ23g0/cl+nRMSai9/b5MySLw9htaihexZsdBme/nJzBpmD2ff5iSCB/4cTSrNiXz7WgHXqugYd8xI5/PiWNkfx3jBymYTCnvrQM/ldl5SEKttX/mKDUXFxfat2+Pl5cXzZs3FyM8n+P06dN8//33bN26NUPbazQafH19GT58OEWLFs3m6AQhe3z99df8+uuvlmVvb29++OGHHIxIEARByO9E0k2wkGWZ3bt3ExAQwJYtW9Id0fAirK2t6dq1KwMGDMh3nRIDAgL45JNP0qyfPHkyAwYMyIGI8o4LFy7QvXt3IiMjLeu6dOnCjz/+iFqt5t69e/z000+sXLmSuNgIUAyAERQz8OhtSwOSBiQdOisHunTpwgcffEDFihVz4pSe6/79+yxfvpylS5emmZ6cWVWqVMHX15fu3bvj4uKSRREKwvMZjUY2btzIwoUL2blzJ1FRERR1l9kWIOFgl5J0i4hWcHN+PDpVrVYzbjqs3mwi6mhhABxqhTHmHTs+HWKNXp9McrKeD74ycugUHFj+OHm8cbfC6KkKcYla3N3dU8VSuHBhPD096dChA40aNcq1ifbc7OjRo0ydOpU9e/ZkaHudTkf//v0ZNmxYvms+IeR/586d480337Qs29nZcfLkSWxtbXMwKkEQBCE/E0k3IV137txh0aJFLFmyhIiIiCw7bt26denfvz8dO3bEyurlCm3nFhMnTuSPP/5ItU6lUrFo0SJat26dQ1HlbleuXKFbt248fPjQss7Ly4tff/01zUVzQkICISEhHD16lJMnT3L9+nX0ej06nY4SJUpQo0YNatWqRadOnXJl8sloNLJt2zaWLl3Ktm3bXiqZbWdnR5cuXfD19aVWrVp5qjmEkD/Nnj2bLz+fQJNayfw++dmjLAPXKHw8U+HmDhucHLR4NIpl4hAVg7wfbzNxtsyWfXAw+PGxomIVWvkphEep8PDwoFSpUnh5eeHl5UWdOnXE6M4ssnfvXqZNm5amUdDT2NjYMHDgQIYOHZor33sF4WnatGmTqpzDL7/8QpcuXXIuIEEQBCFfE0k34ZkMBgMhISEEBARk+IN4Rri6uuLj44Ofnx8lS5bMsuPmBJPJhJ+fHzt27Ei13t7envXr11OlSpWcCSyXun79Ol27dk1VU6Vdu3b873//y1fTwa5du0ZQUBDBwcGpkosvom7duvTp04eOHTvmiaYQQsExceJE/pg/h0HeMuMHaUlKSsRoNKW77dCvZA6cgGOrJWRZYfwM2H0E5n4hUbE0HD0Hgz9TGNFf4v3eqRPKnoMVrt6x5Yc5P+Pn5ycSztlEURR27NjB1KlTOXXqVIb2sbe357333mPQoEGikZCQJ/zyyy988803luU33niDxYsX52BEgiAIQn4mkm5Chp09e5bAwEBWrVpFUlJSlhxTkiTefPNN/P39admyZZ4dsRAbG0unTp24ePFiqvUlS5YkJCQkzZSogurmzZt069aNu3fvWta1adOG33//HZ1Ol4ORZY3k5GRCQkIICgpi//79L3UsFxcXvL298fHxoXLlylkUoSBkrcGDB/Pn+iAmj9AwoIctipIyujMpKYnk5GRLl8yDpxS6fajw+VB41zvlvV+WYdx0haA/Hx/vw77wyXspfwc0Gg3W1tZYWVvRZ1Qce47b8suvv4sRKa+Aoihs3ryZadOmZbjBi5OTEx988AEDBw4UU/WEXO3evXvUq1fP8v6kVqs5duyYmC4tCIIgZIu8meEQckS1atWYNm0ax48f5+uvv6Z8+fIvfUxFUdiyZQt9+vShWbNmzJ07N1VR/bzC0dGRhQsX4urqmmr9rVu3GDhwIHq9Pociyz3u3LmDt7d3qoRbixYtmD9/fp5PuJ05c4ZPP/2UWrVq8eGHH75wwk2SJFq2bMm8efM4fvw4X3zxhUi4CbmayWQCFB41FZUk0Om0ODk54uHhjoODA2ERKt7/XKFJbRjY/fG+38xV2LYfZoyXWP2jxMT3JX5fCb+vSqnd5u7uhr29HVqNBs2/x3+ZpiNCxkmSRLt27diyZQtz587N0N/7mJgYvv32Wxo1asRvv/0m/u4JuVbRokVp2rSpZdlsNrNu3bocjEgQBEHIz8RIN+GFKYrC3r17CQgIYNOmTWm6eL4oKysrunTpgr+/PzVr1sySY74qhw8fpkePHmkuDLt168aPP/5YYKdE3b9/n27duqXqjtu0aVMWLlyIjY1NzgX2EmJjY1mzZg1BQUGcPn36pY5VtGhRfHx86NWrV56fbi0ULEOHDmXt6oVMHKJmaJ/UU59lWeFuWDxt/eORgDU/gcO/m5y/Cm0GQMB30PENK6ysrLG2tmbKvAS+/jmehwcL42D/+L5gx8GRHP3Hnv/9FsDbb7/9Cs9QgJTk6urVq5k5c2aGm8AUKVKE4cOH4+vrm69KBwj5w/Llyxk5cqRluWbNmvz11185GJEgCIKQX4mRbsILkySJZs2aMX/+fA4dOsTIkSOzZGi+Xq9n+fLldOjQAU9PT4KDg0lOTs6CiLNf/fr1mTlzZpr1q1evLrAt6R88eECPHj1SJdwaNmxIYGBgnku4KYrCwYMHGT58OLVq1WLChAkvnHDTaDR4eXmxePFiDh06xJgxY0TCTchzKlasCJKGf648ruOmKJCQkMitOw/p9kE8sfGwZLqEk4MKlUqFVqvl8s2UGxCvV5RwcHDEzs4WtVpF7de06A1w+/7jmzhms8L5qyaQNLm2M3F+p9Fo6NmzJ7t372batGkULVr0ufvcv3+fCRMm0KxZM5YvX/7vqEhByB08PT1TNfQ6efIkly9fzsGIBEEQhPxKjHQTspTRaOSvv/4iICCAAwcOZNlxnZ2dLY0XSpcunWXHzS7Tpk1j9uzZadbPnTuXTp06vfqAckh4eDjdu3fn0qVLlnV169Zl6dKl2Nvb52BkmfPw4UNWrFhBUFAQV69efaljlS9fHl9fX3r06CHqxwh53rZt2+jXtzflisewe6k7ycnJxMfHo9ebGPipwpEzsOYnicplJbQ6LQ72Dmi1WjbvekD7d80s+Fai85vWuLg4A/D1T3F8Piee+JNFsLVJScxdvGaiVd8obOxLcfHiRdSP5rIKOUav17N48WLmzJmT4UYxZcuWZcyYMXTq1En8DIVc4f3332f9+vWW5eHDhzN+/PgcjOjFmEwmtmzZwo4dOzh16hT3799HURSKFClCjRo1aNmyJW+99ZYYcSoIgpBDRNJNyDbnz58nMDCQlStXkpCQkCXHlCSJ1q1b4+/vT+vWrXPtB3dZlhkyZAgbNmxItd7Kyoo1a9ZQq1atnAnsFYqMjMTb2ztVEe5atWqxbNmyPNHhzmw2s2PHDoKCgtiyZctLjdKwtramU6dO+Pr6Ur9+/QI7zVjIf6Kjo6lZsyaGpDssmylRuYwMwJhpMks2wOcfSDSqpcbGxhadTosE1H5Ni9GYTDOfaO4+gLHvSNSs6sjRszJf/xyHb0cb5n/rbHmOqfPi+WGRTItWb7Ns2bKcOVEhXYmJiQQEBPDTTz9luB5r5cqVGTt2LB06dBDvhUKO2rp1K35+fpblkiVLsn///jzT1EtRFIKCgpg+fTphYXdATgaMoPw7UlhSA1qQrClcpDgjR46kb9++eeb8BEEQ8guRdBOyXVxcHCtXriQwMDBNd8+XUapUKfz8/Ojdu3eaBga5QVJSEt27d+fEiROp1hcqVIjQ0FCKFSuWM4G9AtHR0Xh7e3P27FnLutdff50VK1bg5OSUg5E9361bt1i2bBnLli3j3r17L3WsGjVq4OvrS5cuXfJEolEQMuvEiRP06NGDB/ev4uOl8OWHKRdz9b1lbt9Pf59rf3tQuriGsxce8t08E7uOQEQUlCymxudtG8YPtsfGOiUZYzQq1O0aTniMC//77Q9Rzy2XiouLY/78+cydO5e4uLgM7VO9enXGjRvHG2+8IZJvQo4wGo3UqVOHiIgIy7o1a9bQsGHDHIwqY6KjoxkyZAg7d24DcxzuLia6vWVNvde1lCmRckP6xh0zR88YWbU5mYeRalA70qxZK+bOnZsrPzcLgiDkVyLpJrwyiqKwf/9+AgMD+euvv7KsvotOp6NTp074+/tTu3btXPXhPSwsDE9PzzTJm9dee41169ZhZ2f3lD3zrtjYWHr16sXJkyct66pWrcrKlStxcXHJwciezmAwsHHjRoKCgti9ezcv87bo6OhI9+7d8fX1pVq1alkYpSDkHteuXWPq1KmsX78eg8FATEwUHi4yS2dIVC2vws7ODltb22e+HycmJhEbG2tZdnN3Q6vRpNpm1oJ4vv/dSOGiVTh06JCYHpXLRUdHM3fuXObPn09iYmKG9qlbty7jx4+nWbNm2RydIKQ1ceJE/vjjD8ty3759mTZtWg5G9HzR0dF0796df84dw0oTx8eD7RjQ3RadLv33W6NRIXBNEt/+Gk+yyYHKVWqxatUqkXgTBEF4RUTSTcgRYWFhLF68mMWLFxMWFpZlx61evToDBgygc+fOuaZI/9mzZ+ncuXOaC5C33nqL33//PddOkX0R8fHx+Pj4cPToUcu6SpUqsXLlStzd3XMwsvRduHCBoKAgVq5cSVRU1Esdq0mTJvj6+uLp6Ym1tXUWRSgIucuDBw+YPXs2ixcvttw4URSFmJgYVFIy9aqp2LTAHSur509fUpSUuo+POl9bW1vj7Px4JOw/l420fycao+LKzz/PpWvXrtlzUkKWCw8P5+effyYgIAC9Xp+hfZo0acL48eOpX79+NkcnCI8dP34cLy8vy7KjoyMnT55M1WQhN1EUBT8/P7Zt/ZNCzvEsneVM1QopNyP+3J7MpB/iOHvJRCE3FQN72DJpmD1q9eP6mL1HRHE/0p4WLduxdOnSXHWjWhAEIb8SSTchRxmNRjZt2kRgYCB79+7NsuM6OjrSu3dv+vfvT9myZbPsuC9q8+bNDBgwIM0IqiFDhvDZZ5/lUFRZKzExEV9fXw4dOmRZV65cOVavXk2hQoVyMLLUEhISWL9+PUFBQamSgy+iUKFC9OzZEx8fn1zxeyYI2SUuLo558+Yxd+7cNDcQJEmiY8eO7Pj7b2JjbtGptcRPnzui0Tz/Yu6/o93c3d3RaNTcDTPTeUgUdx7a0b5DV37//XdxcZgHhYWFMWfOHBYvXozRaMzQPq1bt2bcuHHUrFkzm6MThJQkVvPmzVM1SZo/fz6enp45GNXTBQcHM2LEh2ilCDb+7mJJuB04bqBp7wh83ramXxdbzl4yMnFWHEP72DH948flLS5cNdH+nUj0ZlemT5+Nr69vTp2KIAhCgSGSbkKucfHiRQIDA1mxYgXx8fFZdtyWLVsyYMAA2rRpk6OjyubOnctXX32VZv306dPz/IeepKQk/Pz8UiVOy5Qpw+rVqylSpEgORpZCURSOHz9OUFAQ69ate6nGHiqVijZt2tCnTx9at24tprsJ+ZrRaGTRokXMmjUrVd2jR9q2bcuECROoUqUKO3fuxM+vH8bkh7RrJjF7oiNODs8e8ZYy2u0hZnNKAwYbGxtuhdkw4OMY7jywoVyF6qxbtw43N7dsOT/h1bh9+zazZs0iODjYMrLxedq3b8/YsWOpWrVqNkcnFHSzZs3i+++/tyx36NCB33//PQcjSp8syzRp0oSb108yYbCOD/0elyhpPzCSh5Fmjq593BV9xu/xTJgRx61dhSjs/vjz79ygBL76xUDxkq9z4MCBfDXjQhAEITcSSTch10lISGDVqlUEBgam6nz5sooXL46fnx8+Pj45MtVRURTGjh1LUFBQqvUajYalS5fStGnTVx5TVtDr9fTv359du3ZZ1pUsWZLVq1dTvHjxHIwMoqKiWLVqFUFBQZw/f/6ljlW6dGl8fHzo2bNnrkgkCkJ2kmWZ9evXM2XKFG7evJnm8bp16zJx4sQ0Bcc3bdrE4MGDMOojKeyqZ+pYR9o20z1zlFpCQiJxcXEk6xUWrIYFa1SYFAfKla9GcHBwvm46U9Bcu3aNGTNmsGbNmgzVzpQkiU6dOjFmzBjKly//CiIUCqIbN27QuHFjy7JWq+XkyZM4OzvnXFDp+Pvvv+nTpzeONhEcX+dhaTgDULhRGAN72PDdmMej2s5cNFLdK5zAaU74dbW1rNfrFep0CScqwYXAwCW0bdv2lZ6HIAhCQSN6Rgu5jp2dHX5+fmzdupU1a9bQuXNnNP8prv0i7ty5w3fffUfdunUZNmwYR44ceamC+ZklSRLfffddmuSayWTi3XffTTW1Ia8wGAy8++67qRJuxYoVY8WKFTmWcJNlmd27d/P+++9Tq1YtJk2a9MIJN61WS5cuXQgODmbv3r189NFHIuEm5Hu7du2iffv2DB06NE3CrUKFCvzxxx+sX78+3Q5/7dq1Y82atZSrUIuwKEf8P47jjX6RLFiZyPXbpjTvuQaDwqUbGmYHwlvvKPy4WCE6zoq3O3qzfv16kXDLZ8qWLctPP/3E9u3bU9XRehpFUVi3bh0tW7ZkxIgR3Lhx4xVEKRQ0pUuXTlVL0Gg0smHDhhyMKH27d+8GOZlOb1inSrgBJOsVrP7TSOHR8j9XUjcus7KS6NLWCmR9qs9vgiAIQvYQI92EPOHBgwcEBQWxaNGiNJ1AX0a1atXw9/ena9eu2NraPn+HLBAdHY2XlxfXrl1Ltb5s2bKEhITkujurT2M0Ghk0aBCbN2+2rCtcuDBr1qyhTJkyrzyee/fusXz5cpYuXcqtW7de6lhVq1bF19eXbt265dqOq4KQ1U6dOsU333zDnj170jxWuHBhxo4dS8+ePTN0EyQ5OZnp06ezYMECkhKjQUkCxYijvUK5kho0akhIUrh8w4zRpMJoUhEbZ0RBjZOTE8eOHaNkyZLZcJZCbnLmzBm+//57tmzZkqHtNRoNPj4+jBgxgqJFi2ZzdEJBsnDhQj7++GPLcv369Vm3bl0ORpRWjx492LcnlFkTrOjllbpZWL0uD3F3lVjxgzWSJGFvb8eitYn4jY1hcC9b5n3jlGr7VRuT+PAbPfUbts115ykIgpDfiKSbkKeYTCa2bNlCQEBAyh2/LOLo6EjPnj3p37//K5nCcvXqVby8vIiJiUm1vmnTpgQFBeX6OmEmk4khQ4YQEhJiWefh4cHq1atf6RQgo9HI1q1bWbp0Kdu3b0eW5Rc+lp2dHV27dsXX15eaNWuKou1CgXH9+nWmTJnC+vXr0zzm6OjIsGHDeOedd16oI3RsbCwrVqxg9erVnDlzBqMxGZRHNb0kkDQ4OTlTp04d9u7di16vR5Ik+vXrx9SpU1/yzIS84ujRo0ybNi3Df9d1Oh1+fn4MGzYsVzXqEfKu6Ohoatasmarhx/79+yldunQORpVaixYtuHzhECt/tKdRLS1GoxGDwYDBYGDhWgOjpih89ZFEL08VYZHO9Bkdxd0HMu962zL369RJtyOnDXR6P44y5euyb9++HDojQRCEgkEk3YQ868qVKwQGBhIcHJyq+93LatasGQMGDKBt27ZZMq31afbu3YuPjw8mU+ph/z4+PkyfPj3XJn3MZjPDhg1LdWfUzc2NVatWUalSpVcSw9WrVwkKCmLFihU8fPjwpY5Vr149+vTpw9tvv42dnd3zdxCEfOLhw4fMnj2bRYsWpXkf0mq1vPPOO3z44YdZNtrTaDRy4cIF7t27h8lkwtramvLly1OyZEkkSeLHH3/ku+++szz//v37xfTSAmbfvn1MmzYtVRfsZ7GxsWHAgAF88MEHYlSy8NIGDhzIxo0bLctjx45l5MiRORjRY/Hx8TRt2pT7d86y4FsVdauZefIKTpYVvvgppS6myQw6LXz+oQOzAxL4oK8tn3/okOp4h04a6DJUJN0EQRBeBZF0E/K8xMRE1qxZw4IFCzh37lyWHbdo0aL069cPX1/fbLuTHhQUxJgxY9KsnzRpEu+//362POfLMJvNjBw5kpUrV1rWOTs7s2rVqmzvMJeUlERISAhBQUEcOHDgpY7l6upKz5498fHxoWLFilkUoSDkDfHx8cybN49ff/2VxMTEVI9JkoS3tzdjxoyhRIkSrzSuuLg4GjRoYBkBPGDAACZPnvxKYxBynqIo7Ny5k6lTp3Ly5MkM7WNvb8/gwYMZPHgwjo6Oz99BENIRGhrKu+++a1kuV64cu3fvzpGboHFxcRw6dIj9+/ezf/9+Tp06RXh4ONZaPV8Phx7t0o8pNl7hzgMVr1V0RKXSUqjRAzb+4Uq75laptlseksTI7/Q0aeaZ6jOdIAiCkPVE0k3INxRF4dixYyxYsIANGzakmiLwMrRaLZ6envj7+9OgQYMs//D19ddf8+uvv6ZaJ0kSCxYs4K233srS53oZsiwzZswYli1bZlnn6OjIihUrqF69erY975kzZwgKCmL16tUvNaJRkiRatmyJr68v7dq1y/VTeAUhqxmNRhYvXszMmTOJiIhI8/ibb77JhAkTsj2B/iwzZ85k+vTpQMoUwoMHD1K4cOEci0fIOYqisGXLFqZOnZrhTuZOTk4MHTqUgQMHipHLQqbp9Xpq1qyZ6rNGaGgotWrVyvbnjo2NtSTZ9u3bx+nTp9OUzIiPj0cxx9PLU+Gb4Y974alUKnQ6HTqdFp1Oh1qtQZJg0uw4Fq9L4tJWD9Tq1J9dx0yJJehPNe8PHc2kSZOy/fwEQRAKMpF0E/Kl8PBwli5dysKFC7lz506WHbdq1ar079+fbt26YW9vnyXHNJvNvPPOO6kaEgDY2tqybt06qlWrliXP8zIUReHjjz9m0aJFlnUODg4sX748Wz6MxsbGsnr1apYuXcrp06df6ljFihXDx8eHXr16vfKRO4KQG8iyzIYNG5gyZUq63R/r1KnDp59+SuPGjXMgutRiY2OpX78+cXFxAAwePJgvvvgiZ4MScpQsy/z55598//33XLlyJUP7uLm5MWzYMPr374+1tXU2RyjkJ2PHjmXJkiWW5YEDB/LNN99k+fPExsZy8OBB9u3bx/79+zlz5sxz69IaDAZiY6IoVkhm1xIb3F2t/k2yqTl8ysDOQwZqVdWSpFdYvy2ZRWuT+Ot3V95onHqUW3yCTJ0u4cTr3QgOXkWzZs2y/PwEQRCEx0TSTcjXzGYz27ZtIyAggB07dmTZce3t7fH29qZ///5ZUscsISGBzp07p5keW7RoUUJDQ3N0pIeiKHz22Wf88ccflnV2dnYsW7aMunXrZunzHDx4kKCgIDZs2IBer3/hY2k0Gtq1a4evry8tWrRArVZnWZyCkJfs3r2bb775Jt3kdbly5fjkk0/o0KFDrqohOW3aNGbPng2AtbU1hw4dwt3dPWeDEnKcyWRizZo1zJgxg5s3b2Zon8KFCzN8+HB8fX3R6XTZHKGQHxw8eJCuXbtalt3c3Dh27NhLj46Pjo7m4MGDlpFsZ8+eJaOXYIUKFaJx48Y0atSIX3/9lZvXT/GBr5pPhz6u03binJH3J8Vw9nJKfc6GNbV8PcKBxrXT/t5P+188sxeaKFehTo5NnxUEQShIRNJNKDCuXbvGwoULWbp0aZY2XmjSpAn+/v4vPWXx7t27eHp68uDBg1Tra9WqxapVq16oc+DLUhSFr776innz5lnW2djYEBQURMOGDbPkOR48eMDKlSsJCgri6tWrL3WsChUq4OvrS48ePcRFulCgnT59msmTJ7Nr1640jxUuXJjRo0fTu3fvbG0W86KioqJo0KABCQkJAAwdOpSJEyfmcFRCbmE0Glm+fDmzZs3i3r17GdqnRIkSjBo1ih49euTK33kh95BlmUaNGnH79m3LuoULF/Lmm29m6jjR0dEcOHDAMpLt3LlzGU6yFS5cmMaNG9OkSROaNGlC2bJlLYmxTZs2MWBAf1RKBCvnONEonaTasxw5baDr0BjMkiv/+9/vvP3225naXxAEQcg8kXQTCpykpCTWrVvHggULXnrq4pMKFy5M37596du37wuPTDt+/DjdunVLM8rr7bffZu7cuahUqqfsmfUUReG7777jp59+sqyzsrJi8eLFNG3a9KWObTKZ2LFjB0FBQWzdujVN58TMsLGxoVOnTvj6+lKvXj1xx1Yo0G7cuMHUqVNZu3ZtmsccHBz44IMPePfdd7G1tX31wWXC5MmT+fnnn4GUqfaHDh3C1dU1h6MSchO9Xs+SJUv44YcfMtzFukyZMowZM4bOnTuLEdDCU02dOpUffvjBsty5c+c0tXf/KzIykgMHDlhGsmW0DiFAkSJFaNKkiSXRVqZMmWd+lvnwww9ZtTIIB5tYAqY6pTuaLT2HThrwGxdDbKIjnTp7M3fu3AzHKAiCILw4kXQTCixFUTh+/DiBgYGsW7cOg8GQJcfVaDR06NCB/v3707hx40wngdavX59u59IRI0Ywbty4LIkxI2bMmMGMGTMsyzqdjsDAQFq2bPnCx7x58ybLli1j2bJl3L9//6Xiq1WrFj4+PnTu3Fl0qxMKvPDwcGbPns2iRYvSNJHRarUMGDCAjz76KM8krsLDw2nYsCFJSUkADB8+nPHjx+dwVEJulJSUREBAAD/99BNRUVEZ2qdSpUqMHTuWDh06vNKbWULecOnSpVSfdaysrDh16hQODo+nc0ZERFhGsh04cCBTSbaiRYtaRrE1btyY0qVLZ+qzYlJSEn379mX/vh2oiGVwT2s+6m+Hs2P6v8sxcTI/Lkxg7rJkZByp36AZS5cuzfU3XwRBEPILkXQTBFLuUC5btozAwEBu3bqVZcetVKkS/fv3p0ePHqk+rD3P7NmzmTZtWpr1P/74I927d8+y+J7mhx9+YOrUqZZlrVbLH3/8QZs2bTJ9LIPBwMaNG1myZAm7d+9+qbgcHR3p0aMHvr6+vPbaay91LEHIDxISEpg3bx6//vqrZTrmI5Ik0b17d8aOHUvJkiVzKMIX9+WXX1qmtjs4OHDo0CGcnJxyOCoht4qPj2f+/PnMnTs3wyUkXn/9dcaNG0ebNm3EKGkhlfbt23Pq1CnL8pdffkmRIkUsibYLFy5k+FjFixe3JNgaN25MqVKlXvr3LSkpiQkTJhAcvAzkOKy0eto1s6JedS1liquRJLh+x8yR00Y27daTbLQClQPduvVg2rRpIuEmCILwComkmyA8wWw28/fffxMYGMj27dszXH/jeezs7OjRowf9+/enSpUqz91eURQ+/PBDVq9enWq9Vqtl5cqV1K9fP0viSs8vv/ySqlOXRqPht99+o127dpk6zvnz51m6dCkrVqwgOjr6pWJq2rQpvr6+dOjQQXSiEwRS6lotWbKEmTNnEh4enubxN954g08++SRPJ6fDwsJo1KiRZbr96NGjGT16dA5HJeR2MTExzJ07l99++43ExMQM7VO3bl3GjRtHs2bNRPJNAGDWrFl8/fXXGI1GDAYDKpUKFxeXDO1bsmRJS4KtSZMm2XrTY8uWLUybNo2zZ0+DkgyKCRRzyoOSGiQNSNa89trrjB07NtOf5QRBEISXJ5JugvAUN27cYNGiRQQFBb100uhJDRs2ZMCAAXTo0OGZjRf0ej3e3t4cOXIk1XpXV1dCQkIoXbr0U/eNiIggLi4OSOm06ubmlqELifnz5zNp0iTLslqtZu7cuXh5eT13X0gZabB+/XqCgoI4duxYhvZ5msKFC9OzZ098fHwoU6bMSx1LEPILWZb5888/mTJlCtevX0/zeK1atfj0009fuu5ibjFx4kRL52RHR0cOHz6cqVHDQsEVERHBzz//zIIFCzLcDbtx48aMHz+eBg0aZHN0Qm4TFhaWqibbhQsX0tzQcHd3T7cWYKlSpSwJtsaNG1OiRIlXFTaQcqP22LFj7Ny5k5MnTxIWFoaiKBQpUoQaNWrQsmVL6tatKxLKgiAIOUQk3QThOZKTk1m/fj2BgYEcP348y45bqFAh+vTpQ9++fSlatGi624SHh+Pl5ZVmymulSpVYv369pZZZREQEwcHB7N+/n1OnTvHgQRjw6KUt4eFRiBo1atC4cWO8vb3x8PBI81wBAQF88sknlmWVSsVPP/1Ely5dnnkejz7sBQUFsW7dugyPLEiPWq2mTZs29OnTh9atW4suc4LwhD179jB58mROnjyZ5rGyZcsyYcIEvLy88tWF1b1792jUqJGlTt3HH3/MRx99lMNRCXlJWFgYc+bMYfHixWnqHT5Nq1atGDduHLVq1cre4IQcExYWZkmw7d+/nytXrqTZJioqKlW9X3t7e+zs7ChTpgyNGjWyJNmKFy/+KkMXBEEQ8hiRdBOETDh58iQBAQGsXbs2w3fOn0etVtOuXTv8/f1p2rRpmgvmCxcu0LFjR+Lj41Otb9WqFV9++SVz5sxh/fr1GA1xoOhBMSFJJmytU46TmKygKJp/pxhYodU54OnpyejRo6lQoQIAQUFBjBkzxnJsSZKYPXs23t7eT407MjKSVatWERQUlKnaJukpU6YMPj4+9OzZ84U7vwpCfnX27FkmT57Mjh070jzm4eHB6NGj8fHxeebI2bzs448/ZuHChQC4uLhw6NAh7OzscjgqIa+5ffs2s2fPZvny5ZjN5gzt065dO8aNG0fVqlWzOTohu92/f9+SYNu3bx/Xrl177j7JycnExMSgVqvR6XSUK1eOTZs2PfVGqSAIgiCkRyTdBOEFREdHs2zZMhYuXJjuFK8XVb58efz9/fH29k7VkfPvv/+mX79+yLIMpIwuS0pKQpLAxkoBJZFaVdV0bWtN7dc0VKuoxebfpFuyXuHsJRMn/jGydksyR8+aQbJFZ+3CuHHjcHV1ZfTo0anq182YMQMfH5808cmyzJ49ewgKCuKvv/7K8KiB9Oh0Ory8vPD19aVx48aig5yQL5jNZlQqVZaMNrt58ybTpk1LU9sRUkZcfPDBBwwaNCjfF8S+ffs2TZo0wWQyASlTTocOHZrDUQl51fXr15kxYwarV6/OcN3WTp06MWbMGMuNKiH3u3v3bqqRbJn5rFauXDmaNGlCnTp1+OSTT0hOTrY8tm3bNpGEFQRBEDJFJN0E4SXIsszOnTsJCAhg69atWdZ4wcbGhu7du+Pv728phL5gwQI+/fRTZFkmJiYG2WzAwU6mZQMtn33gRK3XMjbK5fQFI9/NjWfHIRmDyZqERANOTk6WpNeUKVPw8/NLtc+9e/dYtmwZy5Yte+nurlWrVqVv37507doVZ2fnlzqWIOQkRVHYt28fGzZs4OTJk/zzzz8YDAYkSaJ06dLUqFGDVq1a0blzZ2xsbDJ83IiICH744QcCAwPTJLa1Wi3+/v589NFHuLm5ZfUp5VqjR49m6dKlQEpdpYMHD2bqeyoI/3Xx4kWmT5/On3/+maHtVSoV3bt3Z9SoUc+sqSrkjDt37lgSbPv37+fGjRsZ3rdChQqWmmyNGjVKNeJ++PDhrFixwrI8ZMgQPvvssyyNXRAEQcjfRNJNELLIrVu3LI0XIiMjs+y49evXx9/fHy8vLyZOnMicOXNQSQYKuSqMHiDh3R5cXFyxstJl+JiKorBobRyf/5DAgwgJs6LD2dmZyZMn88477wAp3RG3bt1KUFAQf//9t2WU3Yuwt7ena9eu+Pr6UqNGjXxVc0oomLZs2cI333zDpUvnQU4GDCld4yy1FDUgaUGywtHJnUGDBvHhhx+i0z39dZqYmMj//vc/fvnllzTTySVJolu3bowdO5ZSpUpl23nlVtevX6d58+aWaYFffvklgwYNyuGohPzg7NmzTJs2jS1btmRoe41GQ+/evRkxYgTFihXL5uiEp7l165YlwbZ//35u3ryZ4X0rVqxoqcfWqFEjChUq9NRtd+3aRe/evS3LhQsX5siRI+k2VBAEQRCE9IikmyBkMYPBwIYNGwgICODo0aNZdlw3NzesrKy4eP4UhdyM/DJJokbllOSVSiXh6uqa4cYDycl6YmKiOXdZ4b3PFW6HSVSvUZ8DBw5w9epVli5dSnBwcJrOXZlVv359fH196dixY76fAicUDAkJCXz88cesWrUC5FhsrQx0e8ua5vV0VK+swdlRhd6gcOGqiSNnjASHJnPzngQqB6pUrcHPP/+cZmqS0WgkKCiImTNn8vDhwzTP2bp1az755BOqVav2qk4zV3pyxEnhwoU5cOAAVlZWORyVkF8cO3aMadOmsWvXrgxtr9Vq8fPz48MPP3xm0kZ4eYqipEqy7du3j9u3b2d4/8qVK9O4cWNLki29ZlJPYzabqVevHmFhYZZ1wcHBNGvWLFPnIAiCIBRcIukmCNno9OnTBAYGsnr16lQ1QV5EUlISCfGxFHKV+f1biZqVAR6PGFOr1bi5uT63Npperyc6OsYyFfbMJYWBn0okJNtTrnwl7ty581Jxurm50bNnT3x8fET9GyFfiYuLw9fXl6NH9qEilvd6WTPC3w4H+6e/5mRZ4c+/9Xw6M46IGGscnYsTFBREnTp1UBSFP//8kylTpqRb1LtmzZp8+umn4uLuX1euXKFly5aWUbeTJ09mwIABORyVkN/s37+fadOmcfDgwQxtb21tzcCBAxk6dCiurq7ZHF3BoCgKN2/eTFWTLTOfTapUqZIqyebu7v5S8Xz11VfMnTvXstyzZ09mz579UscUBEEQCg6RdBOEVyAmJobg4GACAgIy1DHrv2RZJiIiHEc7MyP6SwzyliwXnpIkWaZr6nRaXFxcedrsTb3eQHR0dKracxqNhj9WGZk6XyE2Xo2rq1ump01IkkSrVq3w9fXlrbfeyrddFIWCS5Zl+vbty46/N+JkG0fAVCca1sr4lO6IKJl3Ponm0Gk1Ti6l+Pbbb/ntt984ceJEmm3LlCnDhAkTePvtt8VU7P8YOnQoa9euBaBo0aLs37//mVN2BeFFKIrCrl27mDZtGsePH8/QPnZ2dgwePJj33nsvVSMk4fkUReHGjRupuoveu3cvw/tXrVo1VU22rE5+njt3jjfffNOybGdnx6lTp0RdSUEQBCFDRNJNEF6hR90/AwIC2Lx5c4brpMXHx2M2xlPvdYUl30toNBKgcOWmwtzlcOwcXLgGFUrBwRV2ODo6pkq8RcfKfDojhlWbkomOgyLu4NcFhvROSdiZzQr9P1bYf1JCrbHH3t4+Q3EVL16c3r1707t3b4oXL575b4gg5BGBgYFMmDAWxRhB8cIqbtyViY2XKV5YTZe21nz+oT1ODo9HvG3YlszE2XFcuGqiVDE1E96zp6enNT2GRXHgpEKyQY2zs3OqpJqHhwejRo3C19dXJK6f4sKFC7zxxhuWGwfTpk2jb9++ORyVkF8pisLWrVuZOnUq586dy9A+jo6ODBkyhHfeeSfDf0sLGkVRuH79Ovv27bMk2u7fv5+hfSVJomrVqqlqsrm4uGR7vG3atOH8+fOWdb/88gtdunTJ1ucVBEEQ8geRdBOEHHL37l0WLVrEkiVLnlk7TVEUwsPDcbQ3MXO8RLtmjy/S/9ol8+lsqP0aXLsFsgLbA8DW1gYnJ2ckCRISZZr0DAfMDOkN7i5w9RbEJ8JQ38dJgm0HFIZPVoiJ1+Du7v7UETZarZb27dvj4+ND8+bNRTFhId+LioqiQYMGJMTexKulGhcnFQ1ranFzUXHmookvfoyjzmtaNgekdBPdc8RAq74RvOttSy8va7bvNzD513gCvtNRpawR7xEK98NV2Nk7YW1tjZ2dHUOHDmXw4MHY2dnl8NnmfoMHD7Z0nCxZsiR79uwRSUohW8myTEhICN9//z2XL1/O0D6urq4MGzYMf39/rK2tX+h5IyMjOXjwIKdOneKff/4hISEBSUqp4Vq9enVq1KhBgwYNcn1tQ0VRuHr1aqruok/WSHsWSZKoVq2aZSRbw4YNc6Tz+S+//MI333xjWW7Tpg2LFi165XEIgiAIeY9IuglCDjMajYSEhBAQEMChQ4fSPK7X64mPi6ZCKZmN8yW0msfJMFlWkKSUD7QjvoNTF1KSbpBS483Gxoavf05mxUYTW/8A239nQjw5JfURk0mhw2CFSzdU2No5pblIqFixIr6+vvTo0QM3N7cs/R4IQm7266+/8vVXE3mtXDybF7iiUqV+7fy2PJHBE2O4s6cQxQqraTcggvhEhb3L3ZFlmfj4BPzHJ3DmksKuxSrmLlOYvRD0RivGjBnD8OHDX7rmUEHy36les2bNolevXjkYkVBQmM1m1qxZw4wZM7hx40aG9ilcuDAfffQRffr0yfBU6KNHj7JgwQI2bNiA0ZAAmEAx8rg7surf7shaXF0L4ePjQ//+/SlRosQLnVdWUxSFy5cvW6aKHjhwgAcPHmRoX5VKRbVq1WjSpAlNmjShQYMGODk5ZXPEz3f37l3q169vGWWrVqs5duxYppoyCIIgCAWTSLoJQi7yzz//EBAQwKpVq0hMTARSppbK5nh8vRS++ij9gu2KIjP829RJt0dqdwX/bjC8X8pyegm3RybPlQlYI4HKDgcHB2xsbOjUqRN9+vShbt26or6UUCC1aNGCyxcPM32cNb6d0tbwWb0pie7Dorn2twdFPdQ41L7P1LEODPKWSExMQJYVNu1R8J+gcChYwsYa2g8Co+LO7t17qFSpUg6cVd42YMAANm3aBKTUwNu1a1eGuzcLwssyGo0EBwcza9Ys7t69m6F9ihcvzqhRo+jRo8dTR2bGxMQwadIkVqxYDnIiKElULquiTjUtr1fU4OKkQlHg7gMzp86bOHTKQFiEClS2WFk7MX78xwwaNOiVj0BXFIVLly5ZEmz79u3LcPdzlUpF9erVLY0PGjZsmGtr4nl7e7N3717L8tdff80777yTgxEJgiAIeYFIuglCLhQbG8vKlSsJCAjg8OHDWGv1fDEMenk+Pen10WQ5TdLt1j1o1BumjIZt+2HnYbC1Bs+W8NWHEna2qY+3ZqvCJ7MUrG2LMH36dDp16oSDg0M2naUg5H7R0dG89tprYH7APxvdLXXbzGYFownOXTYx8ONoShdXs26uK2cvGXndM5ylM1S0avD4OFdvKTT1VQj+QUuXts74jopj7wlbZsz8CR8fnxw6u7zr1KlTtG/f3rL8448/0r179xyMSCiIDAYDS5Ys4YcffsjwSK4yZcowevRounTpkio5dvz4cQYOHEjY/RtIShze7XUM6G5LzapPnzptMils229g3tIEDpxUQOVI/QZN+OOPP7J1RLosy1y8eNEyVXT//v1ERERkaF+VSkWNGjUsNdnq16+fa5Ns/7V8+XJGjhxpWa5VqxahoaE5GJEgCIKQF4ikmyDkYoqiUK1aNcLDLhA4RaF21acn3YZPljn5n6Tb0bPQaSjY2YBnC/BuL3H1Nnw7T+GNhvDrF49HzqlUKi7e0OI93ESR4tUy3LFNEPKzPXv20NO7G6WLRLN/xeMpoCWahXEnLKURSvsWVqz80RlbGxWh2x/y9vsm/pwrUbfa49drbIKGyu0NLJnhjG8nG77+KY5fl0n4DxzGt99++8rPKz/o168f27ZtA6B8+fLs2LFD1JgUckRSUhIBAQH89NNPREVFZWifihUrMnbsWDw9PTl69Cg+Pj4kxt+jXAkjsz91pF71jHflVRSFpRuS+eLHeOKT7ahYqSarVq3Ksmnrsixz4cKFVEm2yMjIDO2rVqupWbMmjRo1okmTJtSvXz/P3syLi4ujRo0a6PV6y7rdu3dTvnz5HIxKEARByO3EXAxByMUkSUKj0aDRqClZ3BF7eyOJiYkZ7noq/5tSL1cS5kxMSbA1rwcaNYyZpvDxYIWKZaywsbHB2tqa+GQTkhRFUlJSdp2SIOQpKaM3zJQokjqZEzrflYQkhbOXTHzzSzwdB0cROt/x3ymOJst2arUaBwd71Fod8LhweMrxTBmegiWkNWLECEvS7cqVK4SEhNCpU6ccjkooiGxsbBgyZAj9+vVj/vz5zJ07l9jY2Gfuc+nSJQYPHky5cuW4ceMGZmMEzeooLJjiip1t+qUknkaSJHw72dCwppaew6O4dPEk/fr1Y/369S/UZESWZc6fP5+qJltGk4kajYaaNWtaGh/Uq1cv33RxdXBwoF27dqxfv96ybtWqVYwbNy4HoxIEQRByO5F0E4Rc7vHIDQl7ezvs7OzQ65NJTEzCYDCk2V6SJEuhX+d/P+c2rZN6m5YNVICZexGONKxja1lvMqc8jxgtIgjPVqNKyoVs49o66lfXUqtTOIvXRFCxTMrjcQkSjo4O2NjYIklw635KIs7VWdRFzCp169alRYsW7Nq1C0hpqPD222+jUmUuYSEIWcXe3p4RI0YwYMAA5s2bx2+//UZCQsJTt1cUhSNHjqDTGGhcS+J/X6eMmH1R5UtrWDHHhbcHR3HyxGHmzJnD6NGjn7ufLMv8888/lu6iBw4cIDo6OkPPqdFoqFWrlmW6aL169fJ1F+bu3bunSbqNGTNGvO8IgiAITyWSboKQy7m6uhJ2V83dMJnXK4EkgbW1NdbW1hhNJpISk1KNTHvcKEGhbEkFK92j9aDTWWFra0OiQQ08xGhKnQC4+8AMqHB1dX1l5ycIuVlKXSQ1t++bn7pNjSoatBq4dgfaNgWtBm6HWWFr+zihff5KStKtSrmUP7spx1OJTsAvadSoUZak24ULF9i4cSOenp45HJVQ0Dk5OTFu3DjeeecdfvnlF/74449UUxIfSUpKQpGNuDopTB4JyUnRyGYd9vb26HSZH6EGUK6Uhu/GODDk81h++GE2Xl5eVKlSJdU2ZrOZc+fOpRrJ9ryReY9otVpLkq1JkybUrVs31XtdfteqVStcXV0t02tv3brFkSNHaNCgwXP2FARBEAoqcVtGEHK56tWrg6Th1AVjmse0Gg2Ojg54eHig1Wkt3UXVajVarRadVkWLerD3mIS7uwcuLs5YWVmxdV/KCLk6r6X+UH/qvAkkLa+//nr2n5gg5AGvv/46SBpu3JWJjk1/WvfBE0aMJihdDKx0Ek1qw9ptqV+vy0OTqFpeQ5kSKUm3UxdSXms1atTI9nPIzxo0aECTJk0sy7NmzUKUqhVyCzc3Nz777DMOHDjAwIEDU031VBSFxMRE7G1lhvtJlCic8vfbYDAQGRlJVFQURqOR0B3JtPSNwKNBGFav3aNc6weM+jaWmLinl5no1MaKt5pqMBlimTdvHmazmVOnTjF37lz69+9PtWrVaNeuHV988QWbN29+ZsJNq9XSsGFDRo4cSXBwMOfPn2fdunWMHz+e5s2bF6iEG6R8Pzp37pxq3cqVK3MoGkEQBCEvECPdBCGXq1GjBsHLtRw5k7bOWmKSQuiOZADuhEkkJEnsOGyHrCg0rgU2OhOjB0p0GqLgNzaG/l1tuXTDxITpcfTpZE350qnfAo6eNYJkJRIBgvAvZ2dnKlSowOWLUYTu0LNyYxL1quuoUVmDjbXEyfNGvp+fQLUKEu2bp+wz0l+i+0cmhn4eQ09Pa/4+YCBoQzLLf3AG4EGEmYMnjSBpqVevXs6dXD4xcuRI9u3bB8DZs2fZunUrbdu2zeGoBOGxwoUL88033zBkyBBmz57NsmXLSExMRMKMmzN0fTPtPnq9Ab0+klt31dSrruEjP1vcXFScuWjiix/jOHPRyOaAp42UlRjia8XG3bHMn/8bGzZsIDExMUOxarVa6tata5kuWrduXaytrV/43POj7t27s2DBAsvy+vXr+eabb9DpMt78QhAEQSg4RPdSQcjlrl69SrNmTZHkhxxY4UbJoo/rrV2/baJs64fp7rclwInXy8cBsPuIwtTf1Zy5aMLFSUWfTjZMHumAldXj6aX3H5qp3y0Cs+TB9u1/p5mOIggF1a+//srXX03ktXLxtGmsIzg0mSs3zcgKlCmupttb1vh3ScbWOmXkiVqtZv8peybOiuPCVROliqmZ8J49A71TRoTM+D2eGQvM1K3/Bhs2bMjJU8sXFEWhS5cuHD58GICaNWsSGhpqGfkrCLnN9evX6dSpEzeunWFwTxg94Pm/q9bW1tjb26PRqPlteSKDJ8ZwZ08hihVWoyhgMhkxGIwYDAYMBgOyLNNrpMLhMxLWNk7Y2Nike1ydTke9evVo3LgxjRs3pk6dOiLJ9hyKotCsWTOuXbtmWTd9+nQ8PDy4f/8+JpMJGxsbypcvT7Vq1QrcaEBBEAQhNTHSTRByuXLlytG8eQt27wxh4ZpEPh3qYHmsTAkNyqWi6e6nKBAekYjZZKZ5PYkOrWyxt396cePF65IwK9Y0atxYJNwE4Qm9e/dmxowZnLscj+/bao6v90j1uKIohIU9HkWi0Wjo1MaaTm3SXrheu2Xi58WJoHLlnXfeyfbYCwJJkhg1ahQ+Pj4AnDx5kh07dtC6descjkwQ0lemTBkcHR2xtdHSor4EpC0f8V/JyckkJydjY2ODi1PKNNXomERsdGYMBkOaadWSJNGoFpw4D0aj0ZJ0s7Kyon79+jRq1IgmTZpQu3ZtrKyssvoU8zVJkujevTtTpkwhKSmJ5ORkBg16FydHO+BR/U8JJDUqlY7q1avTp08funbtmq+bTAiCIAjpEzXdBCEPGDBgAKjsmL8iiWu3TBnaR5LA2urxRX96RZwfuXnXzNylSaCyxd/f/2XDFYR8xcXFhYkTJ4Laicm/JnDqfOoLZJMpdZOFp3X/TUiU+eDLWJKNdjRv3jpNXSDhxbVo0YLatWtblmfOnClquwm5Vnx8PFevXkWSzDSp64ybm9tzE19ms0KyXubA8QQ+/yGat5qCi0MCycnJyLIMpP19f608aDTg6OjIuHHjWLNmDRcuXCA4OJhRo0bRqFEjkXB7ARERERw/fpzIyAgUcwJO9iac7JKoXCqWdk2S8WqRTKt6iRR2jkI23ufk8Z2MGzuCunXr8vvvv//78xIEQRAKCjG9VBDyAEVR6N27N7t3bqT+63pW/+yCWv386SgGg9HSYQvAw8MDtTp1rl2WFXp+FM2+EzoaN32TFStWoFKJfLwgPEmWZfr06cPOHZtwso0jcJoTDWqm1O9JTtYTHR1t2dbR0SHNdKLIaJmBE6I5dFqNk0sptmzZQokSJV7lKeR7W7duxc/Pz7IcHBxMs2bNcjAiQUjf9evXadKkEXa6cC5tLWRZbzQaiY+PR683pNmndjeZ+/9Wk2jdAP73FdimP2MUSQJJUnHusoreo2VUWg9GjhxJ4cKFKVy4MIUKFbL8K+qQZc6mTZsYM2YMEeG3MRtjaFpHwbudRMOaUKSQE7b/+aHcf2hm3dZkAtckcf2OBGonGjVqxpw5c8TfAEEQhAJCJN0EIY+4ffs2b7zxBvExN/HrouG7MQ7PrVmkKPDw4UPLXdX/JgMURWHS7Hh+X2nExr4427Zto0yZMtl5GoKQZ8XFxeHr68vRI/tQEcv7va0Z3t8OiSTi4+Mt27m4uGBllXIhK8sKf/6t59OZcUTEWOPoXJygoCDq1KmTU6eRbymKQvv27Tl9+jQAjRo1YvXq1TkclSCkdfnyZVq0aIajTTjnNxVK87jBkJJ8MxhSkm+yLHPuCiQlw4Vr8MMiKFUUls0AtTolyQaS5d9Hzl9V8B6hEB2nwcPDI83zQEqzmP8m4tJLzolpkbBw4UImTBiPYo6majmZb0dZUbpIguVxnU6Lq6truvvKssKitUl8/XMCiXpbChctx4oVK6hQocKrCl8QBEHIISLpJgh5yJ9//sl77w1GMUXg21HLd6Md0GqfnXiLiYklKSml86lOp8PV1QUAo1Hh8zlxBKw2gtqFn376hW7dumX7OQhCXpaQkMCECRNYuTIY5DhsrfR4tVRR5zUTVcuBowM4Orpx+YbM0bNGgkOTuXFXApUDVarW4Oeff6Zq1ao5fRr51saNGxk4cKBledWqVTRu3DgHIxKEtG7fvk2DBvXRSg+5ss0DjSbt33FFSXm/iYuLs0yVliQJRVE4exneegd++0ri7dZP/wxw+LTCgE8UYhO0uLu7v1TM9vb2aRJz6SXoHB0d82UTk9WrVzNs2FAwR9G/q5avhjugVqfc2HzyUsrdwx3NU0oMQEo5j/7jorlwXUeRYhX5888/KVas2Ks4BUEQBCGHiKSbIOQxy5cvZ/ToUcimSKqWk5n9qSPVK2ufur1erycqKhpIuRvu4VGI81dMDP8mlrOXVUgaZ6ZMmUa/fv1e0RkIQt63ZcsWvvnmGy5dOk9iQiQqyYTm3xEnKTXdNCBpQbLC0cmdQYMG8eGHH4qpXNlMlmXatm3LP//8A0CzZs0IDg7O4agEITWz2UzlypVJjLvB9oUuVCmftq9ZYmISsbGx/y4pKApoNGp0Oh2yrFC4SRLjB6kY1kd6av3CJRsUvv4VDCZrnJ2ds++EnmBlZUWhQoUoVKgQRYoUeerIOVdX1zxTyuLGjRu88cYbJMXfYVBPLV98ZG9JLEZFRaeqmWtvb//MplWQUm6g2wdRXLxpTbPm7Vi+fHm+TFQKgiAIKUTSTRDyoC1btjBq1Cgiwm+jJp6Ob+jw72ZD/RraNB/cFAUePHiALMucughrt+kI2WHGpNjh4lqc6dOn06FDhxw6E0HIuxRFYf/+/XTq1In4+HhMJhNqtRp3d3dKly5NjRo1aNWqFZ07d7Z0DhSy34YNG3jvvfcsy+vXr6devXo5GJEgpNW1a1cO7t/ErAlW9PJ6/P6gKCmNFhISElJtr9NpcXZ2QaWSOHDcQOOeESz/wRnvDjYoioIsy8iyGbNZ/vf/MqOnJBH8l4Srewl0Ot0TSbycp9FocHd3T3fE3JPJOnd3dzSatEnJV0WWZby9vdm/dytNahkInuOMSvX4c9Z/a3pqNBrc3Nx4Xg7t2i0TbfwiSTa7MmXK9FT1KAVBEIT8RSTdBCGPioiI4NNPP2X9+rUgJ4GSRPHCUKuqhuqVtLg5q5AkiIyROXI6kVPnTdwOA5NZi7WtKx06eDFlypSn1nkRBOH5oqKiqFatmmW5ffv2/P7772LUQg6SZZnWrVtz6dIlAFq3bs2SJUtyOCpBSG3q1Kn8MHsKLeoms2x2StkHRVGIiYklOTnZst3AT2Xqvq6lQU17bK0lTp438v38BAq5qTi8yh2dLv33moREmdqdw4nXu7FixWqaNm1KUlISDx484MGDB4SFhVm+Hi0/+vfJBkw5TZIk3NzcnltzrnDhwtnSiXXbtm306+eLjSaS7YtcKF08dQIwpXbuA5b+KTPiu7SXVOMH2zFlrGO6x/5teSKfz0nG1aMCx44dEyOhBUEQ8imRdBOEPO706dMEBgayevVqkpPjQTGCYgIetaRXYTQpxMYmYZYl7Ozs2LRpE40aNcrJsAUhXzh69CgdO3a0LA8bNoxPPvkkByMSANasWcMHH3xgWQ4NDaVWrVo5F5Ag/MfNmzdp3LgRiukBe5a6UrqEiuioaIxGY6rt5gVrWLtV5spNM7ICZYqr6faWNWPescPR4enTMxeuSeTjGXrKVajLrl27MjWV02g08vDhw3QTcg8ePOD+/fs8ePAgVaOm3MDJyem5iblChQphb2+f4WP6+fmxdfMa3usFn3/okO42MTGxBKxKZMR3CkHTJQq5W2P3b9Oq4kXUlCyafo03k0mhYY9w7kU489PP80RdXUEQhHxKJN0EIZ+Ij4/nxIkTnDp1inPnzhEfH4+iKDg4OFC2bFmmTZuGJEmoVCoCAgJ46623cjpkQcjzgoODGTFihGV51qxZ9OrVK+cCEoCUmlktWrTg2rVrALRt25bAwMAcjkoQUuvfvz9bNq3hjYZGZo43pUpgSRI4Ojq+0NT06FiZVn0ieBDtxJdffcegQYOyMmwLs9lMREREuom5J/8NCwtLk0zMSba2tqkSc09L0CUnJ1O3bh0U0wP2LnOlbMn0p7kaDEZ+XRzBiO8UzmyQ8HBV4+Hh8dwppgCzFyQw7XcTTZp1YOXKlVl8pkJeoygKd+/eJTIyEpVKRcmSJXF0TH+kpCAIeUfOFUkQBCFL2dvb06xZM5o1a5bu40eOHGHnzp0A/PXXXyLpJghZ4OrVq6mWy5cvn0ORCE9Sq9UMHz7ckhDdsmULZ86c4fXXX8/ZwAThCZ988gmbN21i0+4w1taV6PRGSpZGpZJwcnLGyurFpht+NjuOB1FWlK9YNVubJKnVakvThGe9tlKmzcZYRsg9LTH34MEDEhMTsy3eRxITE7l+/TrXr19/5nYmk4mE+Cjqvy7j4hBPbKwalUqFSq1Crfr3/yoVWq323zpvKeMYZFnGYDBk6OfX8Q0rps1P5NixY5hMphytXyfkDFmW2bNnD4sWLWLfvn1ERUXyeLaKmjJlyvDWW2/h5+dHuXLlcjJUQRBekN2bHdMAAMRhSURBVHhnF4QCon379pak2+bNm8WHO0HIAv9NuokPxLlH165dmTlzJjdv3gRg9uzZzJ8/H0i5mH7w4MG/F8Yp3RZTus4Kwqtz+vRp9AYDiQkqvv5VpqgHNKqlwdnFGe0L/n3+cWECqzYZUWk9mD17NtbW1lkcdeZJkoSzszPOzs5UqVLlmdvGx8enm5D77/KraAqRnJyMSjJTtbxCcrL+qdspioLJnJJwa+WnEBmjULJIBO942zL2XRs0GjWSJD3x9XjfsiXV2NsqxCcncvny5ed+f4T85eLFi4wcOZLjx4+AnAiKHo1axt1FwmSG8CiF61ci+d+8c/zvf/Po18+Pzz77LFNTpAVByHlieqkgFBBhYWHUrl3bsrxq1SoaN26cgxEJQt7Xpk0b/vnnHyClntC5c+dEE4VcJCgoiDFjxgApU+H69u3LpUuXOHPmDMnJSaSMTJGwtbXj9ddfp2nTpvj6+lK8ePEcjVvI3xRFYebMmcyYMcMyCkyRkynkBnM+c6Lzm5mfUmo0Kkz7LZ6fl+hB7cKkSV/y/vvvZ0P0uUNycnKqphD/HTH36P8REREv/BzR0dHoNMl8NgT6dEz7vq4oCo8uo3YcguPnoPZrKVODN++Fheugfxf4dmTqenqpE3ASfcaY2HdCQ5OmrahSpQr29vbY2tpib2+PnZ1dqq/01tna2oq/O3nQ2rVrGT78I4z6aOysE+nlaUO3dta8XlFjaZASHStz6KSRxeuT2LrPCCoHSpWuxNKlSylbtmwOn4EgCBklkm6CUIC8/fbbHDt2DIBBgwbx5Zdf5nBEgpB3ybJM+fLl0etTRkDUqVOHP//8M4ejEp5kNBqpW7culy9fxmDQY2+nwdZGDYoRjVrG2koiWa9gMqtA0oJkhUptS4cOnnzxxRci+SZkOaPRyJgxY1ixYoVlnaIo2NrakpgYgyTH0rmNlq+G2+PhlrHRl6cvGBn1bSxnL6tA7cT48RMYPnx4dp1CnmI0GgkPD3/mlNawsDAePnyI2WxOtW90dDRWmmQmj4RubVMntRRF5mlXUI+aVnz1s8z/VsDRlRKF3Z+eFBs8SWbLPglrG6cXquEnSRK2trbPTc49WvdkQu9p67RarUjkZaMNGzbw/vvvoZgieaORxPSPHSni8ezX+96jBkZ+G8vtMCsKF63Ahg0bKFGixCuKWBCElyHmlglCAdKhQwdL0u2vv/7iiy++EB+qBOEF3bt3z5JwAzG1NLdRFIWgoCDCwu6jViXj5qTQtI6BXm87Uvd1e8qWUKNWS5jNCldumjl+zsiqTcnsORpHyJ/B7Ny5k6+++orevXvn9KkI+URMTAzvvvsue/fuTbX+3Xff5dNPP2X27Nn8/PNPrNseQ+jOCDq+YUUvTxtqVtGk6lSqKAph4TIHThhZvC6JfcdNoLLDxb043333HZ06dXrVp5ZrabVaihYtStGiRZ+5nSzLREZGpurOOnv2bC5fPAZIaLUSsmzGZDLzvPEKiqIgSSk1+n5dpnDmMhR2f/r2hn97TLzo5zFFUUhISCAhIeGF9k+PRqPJUHLuvyPynjZKz9bWVpQ0+detW7cYPHgwUZEP0GoU1myBMxcj+cjPjgE9bCy/BwaDwmez41i0LomoGJnqlbV8PNiOHxclcuH6VT766CNWrlyZqc7EgiDkDPHuJwgFSPv27Zk8eTIAt2/f5ty5/7N353Ey138Ax1/fuXZn7wPrinXfhCJF7jM3RYTkKIqIotjcRa7kRwmFnEW7znXkKJQUudd9JhZ7ze7O7s71/f2xmWy72I3dWfb9fDz2wXy+n8/M+7Nm18x7Pp/35wSVKlVycVRCPJqknlvupaoqY8eOZf78L9ASR/2nYdTrCiWKKhiNNnx9PZ19tVqFsiV0lC2ho8sLRk6es/HuFBMHjl/mnXfe5sKFC4wcOVI+oBAP5PLly7zyyiucPXvW2aYoCuPGjaNv374AjBw5kpYtWzJq1CgOHvyd77cl8f3WeMBGcBEt/j4aHCpcu2HnRrT69+pMI1qDB23atGXcuHHkz5/fRTN8tGk0GvLly0e+fPmoWLEiAMePH+fyxQiuRTlwd9eSmJjg3BKaSs1gtVtqg15/e4tgCjqtFp1O49yOeue2VFVVufwX2B1KrqorabPZMJlMD7V2nru7e6a2y2a2zWg0PpK/lz/44AOiom5SIACmjvAhKJ+WbXtT6Dc6jivX7YwZ5A3AkEkmloQlMWmoN+VKavl6TRIvD43l+7n+vD0hnn379rB8+XJeeeUVF89ICHE/knQTIg8pVaoUpUuXdr7o37x5syTdhPiPzp07l+a2JN1yj2nTpjF//udgj2H0QA96tHMnMSEBgKSkZDw9vdDpMn6DW76UjrDP/flsSSJTF0Qze/aneHl5MWjQoJycgniMHDx4kFdffZVbt24524xGI3PnzqV58+Zp+larVo0NGzZw+PBhFi9ezO7du7l69SoXr9m4eO3vEw0VLRq9njJlytCyZUt69Ohx35VcIuuqVKmC1aZy4Ggi8fEZJXfSHopwuw3Abnew4UcDWi3UfyaAfPnS/r5R1dSE21+RNmLjYwgIyMeyZcuw2WwkJCQ4V67d/rpXm9lsJiEhAYfD8e9gcpXk5GSSk5MfqM7enRRFeaAttBmt0tPr9Q8ltrs5c+YM27f/QKCfyk/LAylZLPWteKM6bkTFOpjxVSIhb3px7YaDL1eZmfmBD4N6pn5I1LyeG9Xa3OLLlWbe6+fJ2P8l8MUXX9C9e/dHMvkoRF4iSTch8piWLVsye/ZsADZt2sSwYcNcHJEQj6Z/r3QrVaqUiyIRd9q3bx8zZ84AewyfvOfJK+08UFWVJLPZ+aY0MTERX1+fu96HVqswtLcX3p4aPpwVw5Qpk6lXrx5PPvlkDs1CPC42bdrEm2++mWYrev78+VmyZAnVqlW767hq1aoxY8YMAKKiojh58iSJiYloNBr8/f2pUKECHh4e2R5/XnXmzBm++eYbYmMTOXJKJToOAnzvntjo+o6DujUVKpQErU7L9n06Fn5n5u1enhnW6lKU1KTRj/ttoDFQpUoVGjdu/J/jVVWVlJSUdIm4jJJz92ozm83OvyclJf3neHKCqqokJCSQ8PcHKg+DXq9Pl8TL7BbajFbkeXh4pFnBuHLlSlCTaPm8mzPhdlv1Cnrmr0oi0axy5JQVux2a1XVzXlcUhWZ13fjfN4l8PdmXaQsTOH/+DPv376d27doP7XsghHj4JOkmRB7TokULZ9ItIiKCS5cuUbx4cRdHJcSj599JNzlJzPWSk5N55513wGHi5dZuvNLOg7OXbExbkMjPBx2cOOegdDH48ZskPL080d3xZmjhd2amfJnA5b/slCupY9JQb/q+5MHB41bCtscxZMgQtm3blu0rIcTjQVVVvvzyS8aPH5+mBli5cuX45ptvslQAPTAwkOeeey47whT/EhcXx7Rp01i0aBF2ux2tTk+i2cK6HfBqh7uPK10cVmxUuXYTHA4bZUvAp6N8GNTz7olRVVX5+nszKF507tz5geJWFAV3d3fc3d3Jl+8eBeSywG63Yzab0yTiMkrO3W9V3p23rVbrQ4ktu1itVmJjY4mNjX1o92k0Gp1JuLNnz6La4qn/tI7Y2Lg0p9ju+jWJwgUUdFoLpoTU75ObIe19uRkgxQI3o1Ua1Daw4UcLBw4ckKSbELmcJN2EyGOqVatGUFAQkZGRAGzZsoX+/fu7OCohHj13Jt0KFSokq05ygbVr13LxwhmCAiyMeSsQgONnbGzclUytanps9hQcjtStXebERHx8Ule7rdyQRL9RcYwa4EWjOgZWbUymw5sx7F4eyKR3vNn9exSnT50gPDxcitSL+7LZbHz44YcsWrQoTXvdunVZsGCB83kncg+bzcbSpUuZOnUqMTExznaj0UiS2cKy9SqdmoG3Z/rVboqiMHOUN54enmg0md/m98NeCyfOqrh5+NClS5eHMo+HSavV4u3tjbe390O7T6vVmulkXWZX6d3vYAtXS0pKIikpiVu3bhETE0OAr4PST1hJTrY5+/x6RGX1FpUxbyrExcVRMDD1ebT/iJXgov+8Xd93KDUZFx3noGo5PRt22Th+/HjOTkgIkWWSdBMij9FoNLRs2dL5ZiA8PFySbkJkkdVq5fLly87bsrU0d1i0aBGoZvq+5OE87bFNIzfaNQkC4JV3bvH7sdQ3Lbdru2m1GsZ8Fk/XF9yZMDT1zWXDZ9w4csrK+DkJbFoQQK8ORmYsMrN48WJJuol7SkxMZMCAAfzwww9p2rt06cInn3wiKyVzod27dzNmzBhOnjyZ7pq7uzuJiYlcuW5j+tcqY99S0l338vZKs2o2M+LiHbz3iQk0PvTp0wc/P78HmcIjQ6/X4+fn99Dmq6oqycnJmdoum9m25OTkhxJbRrGmnmwLAX7/tP91Q+WNMSrPVYe+fy94rFhaQ72ntIyYGs8ThbSUDdbx9RozP+63AKmVAwP9NICN+Pj4bIlXCPHwSNJNiDyoRYsWzqTbb7/9xq1btx7adgQh8oJLly6lKVothyi43pUrVzh8+BA6TQpdX/hnZcadK0+0Oi2QmnRTVZXExERuxRo5fcHOlHfTrj7q+oKRd6eYSElReaWdkZmLovjll5/l96W4q8jISHr06MGxY8fStI8YMYLBgwdLsfNc5uLFi4wbN44tW7bctY+iKPj4+BAXG8P321SeqqTSuqGCTq/Dx9sHgyHrSVSrVWXwBBOR0W6ULF2B4cOHP8g08jRFUTAajRiNxod2cq/dbncm8P7LFtqM2my2f1a1oULy3yUe4+JVur+r4u8DCyYqzv+vNIrC4k98eentWJ59KfXgieJFtHz4phdjPkugUAENJ87aAAWD4V97UIUQuY4k3YTIg+rUqYOPjw8mkwmHw8EPP/xA165dXR2WEI8MObk09zl8+DCoViqU1hLor8mwj4KCRvPPtaSkJI6fTX0pVL5k2pdEFUrpsFjhwp92ypfSUfIJDef+tHHkyBEaNWqUfRMRj6SIiAheeeUVrl275mzT6/XMmjWL9u3buy4wkU58fDyzZs1i/vz5maoxZjAYcDd6EBdv5sPZDtzcjbzS3jeDk0vvLyVF5a3xcWzbq2IwBjJr1izc3d3/wyxEdtFqtfj4+DzUbeAWi4XExERatmzJpfN/cD3anRLFdHQcbCLBbGfnEm8KFVBQVQcOh4pWqyEwUMdv3+fj4p82zEkq5UrqmPFVIoUKaCheREfEOTMoOllpL8QjIONXpUKIx5per6dJkybO2+Hh4S6MRohHjyTdcp8TJ04ANqqUu/fKkzuTbqqq8ue11K05vt5p30H7/31SYXRc6orGquX0gNTPEent2rWLdu3apUm4+fn58e2330rCLRex2+2sWLGCunXrMnfu3CwV9ff396fmU7VRNfkYMc3C2xPiiIt33H/gHf44bqVZ72g2/gh69/zMn7+AmjVrZnUa4hFkMBjw9/endu3aKBo39h6w0+PdRE5dsLPlq0BKl/DE0zP1RFQfH288PT2dY4OL6qhYRo/FCgtXm+n7ogcOh8qeAxZAf89TkIUQuYMk3YTIo1q0aOH8+48//khiYqILoxHi0fLvk0vlk2bXi4uLA9VBgYB7v7RRHQ5UVcXhcKAo4LDZAYiKjiY+PgHb37f/rUCgBlRH6uMI8belS5fSo0cPEhISnG3BwcFs2LBBThTMRfbv30+rVq0YNmwYN2/ezNLYpk2bsmvXLvbs2cOQocPR6AuweqtCvZejmLYggWs3Mv6dAamJ/f2HLQwcE0ebN2I5c8WD/EFlWLZsOU2bNn3QaYlHTNu2bUFj5H9LzWzYmcKoAV6YElT2/WFxfqWkpB4M8b9vEvkmzMyuX1NYtMZM7c63cDcojOjvya5fLVz6S8HbJ4AGDRq4dlJCiPuS7aVC5FENGzbEYDBgsViwWCzs3LmT1q1buzosIR4JdybddDodRYsWdWE04k73OsjOoarYHQ4UJTUx53Co+P5d/i3WZCefX2r9Hb1ez7XI1MLoAb6aNPcrdbkEgMPh4OOPP2bOnDlp2mvWrMmiRYsIDAx0UWTiTlevXmXChAmsW7cuy2PLlCnDuHHj0iQ1RowYQZMmTRgyZAjnzp5kxqIkPl0URbmSWqqW01PyCS06LSQmqZw4a+NQhJXrtxRQjKDxpkOHTkycOBF/f/+HOEvxqGjQoAHBwaXY/2tq4nfYx+kPQbiwMz/BRXWkWFTGfpbIn9ftBPpr6NjMnQlDvNFpFcZ+lgAaT7p27SonpwvxCJCkmxB5lKenJ/Xr12fbtm0AbN68WZJuQmTSnUm3YsWKyYmEuUC+fPlA0XA1MuMtY6oKVovFeVtRFBwOB6WKpd4+ewlK//13q9XKkZMWDHrw944nOdnIn9ftoOgICAjI7qmIXC45OZkhQ4akS+S0adNGanTlEmazmTlz5jB37lxSUlKyNNbX15d3332XHj16ZPi7vWbNmuzYsYPw8HAWLVrEvn2/EHHBRsQFK6i3V70poLiD4oW7pxcdOnTg1VdfpUqVKg9hduJRpdVqmThxIq+80g3FEcWCSd60rJ/x74thfbwY1scrTZvDoTJ8cjxnr2gpULAYQ4cOzYmwhRAPSJJuQuRhLVq0cCbdfvjhB6xWqyQPhLiP+Ph4bty44bwtW0tzh8qVKwN6jpxKyvB6YmJCmhNnIbW+W5lgHaWLW9mwC1rU++fa2h0qdWuC6rAQG2vh4HEHCYn/JOvurA0n8o6oqCheffVVDhw4kKb9zTff5P3335fnhYupqkpoaCgTJ07k+vXrWRqr1Wrp2bMnw4cPv+9KNL1eT9u2bWnbti3Xr1/n8OHDHD58mOvXr2Oz2XB3d6d06dJUrVqVKlWqpKnRJfK2Ro0a8eqrr7Ho6/m8HhLLpHccvNLOeN9V1PEJDt6fHs/3W21o9PmYPn06fn5+ORO0EOKBSNJNiDysadOmaDQaHA4HJpOJX375heeff97VYQmRq124cCHNbUm65Q7VqlUDRceZS3auXLPzRKHU7aHmJJV1PySSkJDAn5EQnwgbdqbuFX22hkK50n6Mf9tC92GxlCvpRp0nVb7bbOGPExD6v9Q3QRf+VLl6HczmFMaOHcvChQvp2LEjnTt3pkyZMi6bs8hZ58+f55VXXuHixYvONq1Wy+TJk+nevbvrAhMAHDp0iJCQkHQJ0cyoV68e48aNo3z58lkeW7BgQQoWLEjz5s2zPFbkTePHjycuLo7Q0O8YMTWO9TtSGNzTk+dq6tMl38xJKmHbkpnxdSJ/3dSj0efj009n0bhxYxdFL4TIKkm6CZGH5cuXj6effppff/0VSN1iKkk3Ie7t34coyMmluUOBAgWoV+95dv+4kaVrk3j/jdRtOddv2nj5nbR1c/p9mJp02/KVJxXLaXm5jRFzssrkeQlMW2inXAkdK2e68WwNOxaLhe82qyRbFAwGNzQaDVevXmX27NnMnj2batWq0blzZ9q1a5e6xVU8ln799Vd69+5NbGyss83Ly4svv/xSCpm7WGRkJB9//DHffvttlscGBwczduxYmjZtKvUaRY7R6XTMnj2bqlWrMnnyx+z5I5Y9B0wEBapULa+naEENViucuWTj6Ckb5hQ9KN4UL1GWGTNmUKdOHVdPQQiRBYqq3qvksBDicTdv3jzGjRsHpH5a+/vvv8v2GCHuYfr06UyfPt15e/Xq1Tz77LMujEjcFh4eTp8+vfAxxrBraQBB+bTExcWRnJycrq/RaMTX1+e+93nhioXGPWO4dlOLh6c3bm5uGfbTarU0bNiQzp0706xZM6nr9RgJDQ1lyJAhWK3/1AssVKgQS5cupUKFCi6MLG9LSUlh3rx5fPbZZ5jN5iyN9fLyYsiQIfTt2xeDwZBNEQpxfxcvXmT+/Pl8++23JCbGgWoD7IACaEHRU7x4CXr27EmvXr3k4AQhHkGSdBMij7t06VKaT8w2btxI9erVXRiRELnbm2++SWhoqPP2H3/8QVBQkAsjErfZ7Xbatm3LHwd20/gZG/PGu2EymdL10+q0BAYEotHce2WLw6Hy0uBYfj5k4JlnGzNq1Ci+//571q5dS3R09F3HeXt788ILL/Diiy9Su3Zt+SDjEaWqKp999hlTpkxJ0165cmWWLFlCwYIFXRRZ3qaqKps2bWL8+PFcuXIlS2MVReHll19mxIgR5M+fP5siFCLrkpKSOHr0KEePHiU6OhqNRkPRokWpVq0aZcuWlf9HhHiESdJNCEHjxo2JiIgAYNCgQbz//vsujkiI3Ktly5YcPnwYAA8PD86cOSPbknKR06dP07RpEyxJ1+nZzsaQnqT591EU8PcPwGC496ExqqoyemY8X6+xYfQqwvbt2wkODgZSTzfdtWsX3333HVu3bsVyx6mo/1akSBE6depE586dKV269EOZo8h+VquV9957j1WrVqVpb9KkCZ9//rkUxneREydOEBISwi+//JLlsbVr12b8+PFygqgQQogcJUk3IQTTpk1jxowZAJQpU4Yff/zRxREJkTupqkq5cuVISEgAUle8bN261cVRiX9bunQp/fv1wdNooXNzeLePgod7auLNy8sLL697J0ziExx8MCOeNVtsKLoA5sz5nPbt22fY12QysX79elavXu2sj3k3Uv/t0WAymejbty979uxJ0/7qq68yfvx4dDopiZzToqKimDJlCsuXL093CvH9FClShJCQENq0aSMfkAghhMhxsk5VCEHLli2dfz9z5gznzp1zYTRC5F43b950JtxADlHIrf78808MbkZiTRpWboKXhqj8+JuKVqu/5wolm01l485kGvaIZs1W0OjzMWPGp3dNuAH4+PjQvXt3QkND2bdvH++9995dnxeHDx8mJCSE6tWr06tXL9atW5dhvTnhOn/++Sft2rVLk3BTFIUxY8YwadIkSbjlMKvVyrx583juuedYunRplhJuRqOR9957j927d9O2bVtJuAkhhHAJWekmhEBVVWrXrs2ff/4JwKhRo3jzzTddHJUQuc++ffvo2LGj8/bQoUN59913XRiR+LdffvmFzp07o6oqKSkpxMfHo9Pa8DSqlCymp0NTd6pX1FOuhBaju4I5SeXkeRsHj1sJ3ZbCXzcU0HhRPPi/nxKnqip//PEHa9asISwsjJiYmLv29fb2pnXr1nTu3Fnqv7nY4cOH6dWrFzdu3HC2ubm5MWfOHFq1auXCyPIeVVXZvn07Y8eOTXdidGZ06tSJDz74gEKFCmVDdEIIIUTmSdJNCAHAhx9+yIIFCwCoWbMm69evd3FEQuQ+y5cvZ/jw4c7bs2fPplOnTi6MSNwpLi6ORo0ace3aNWebw+EgMTERX19fHPZkUFP+Ph3OBqiAAooO0IPihn9AAXr06MHgwYMfyilxVquVnTt3snr1arZs2ZLmBMx/K1q0qLP+W6lSpR74sUXmbdmyhYEDB5KUlORsy5cvH4sWLaJGjRoujCzvOXPmDGPGjGHXrl1ZHlu9enXGjx9PzZo1H35gQgghxH8gSTchBJC6OuTO5IGcyChEehMnTmTu3LnO25s2beLJJ590XUDCSVVVXn/9dTZs2JDuWteuXZk0aRLr16/nl19+4ciRI1y4cAGLxYKbmxslS5akatWqPPfcc7zwwgu4ubllS4y3679999137N+//559n3zySWf9t8DAwGyJR6RasGABY8aM4c6XxKVLl2bp0qUUK1bMhZHlLXFxcUybNo1FixZht9uzNDYoKIhRo0bRsWNHWS0qhBAiV5GkmxACAJvNRrVq1ZzboCZPnkzPnj1dHJUQuUvv3r3ZsmWL8/bJkyfx8fFxYUTitpUrV/LOO++kay9RogRbt27NdadNXr58mTVr1rB69WouXLhw1346nY6GDRvSqVMnmjdvnm0JwbzIbrczduxYFi5cmKb92WefZeHChfj6+roosrzFZrOxdOlSpk6des+t2BkxGAwMGDCAt956K9f9jAshhBAgSTchxB2GDh3KqlWrAGjQoAHLly93cURC5C7169fnzJkzQOrWsyNHjrg4IgFw/vx5mjVrhtlsTtOu0+lYt25drl6NmNX6b23atKFz587UqlVLVvQ8ALPZzMCBA9OdPty5c2emT5+OXq93UWR5y+7duxkzZgwnT57M8tjWrVszevRoWY0ohBAiV5OkmxDCaevWrbz66qsA6PV6jh49Kqt4hPib3W6nZMmSzppctWrVIiwszLVBCaxWK23atMkwAfqoHQpjtVrZsWMHq1evZuvWrVL/LZvcuHGDnj17pnvODBs2jHfeeUdOucwBFy9eZNy4cWlWDmdWxYoVmTBhwn865EQIIYTIaZJ0E0I4JScnU6lSJWch6blz59K+fXvXBiVELnHp0qU0b/K6du3KjBkzXBiRAJg0aRJz5sxJ1/7cc8+xatWqR3Y1WFxcHOvXr2f16tWZqv/24osv0q5dOwICAnIowkfTyZMn6dGjB1evXnW26fV6pk+fTufOnV0YWd4QHx/PrFmzmD9//j2TyhkJDAxk5MiRdO3aFa1Wm00RCiGEEA/Xo/lKVAiRLdzd3WnQoIHz9ubNm10XjBC5zPnz59PcltVFrrd37940B1vc5uvry2efffbIJtwgdQ6vvPIKYWFh/PLLLwwfPpzg4OAM+x46dIhRo0bx5JNP0qtXL9avX09KSkrOBvwI2L17N+3atUuTcPPx8WHFihWScMtmdrudFStWULduXebOnZulhJter+eNN95g7969dO/eXRJuQgghHimP7qtRIUS2aNmypfPv27dvlzduQvzt30m3kiVLuigSARATE8OgQYPIaMH+9OnTKVSokAuiyh7FixfnnXfeYe/evaxfv55XX30VPz+/dP1sNhvbtm3j9ddfp1q1agwfPpxff/0Vh8OR80HnMitXrqR79+7Ex8c724oVK8b69et59tlnXRjZ42///v20atWKYcOGcfPmzSyNbdq0KTt37uTDDz+UchdCCCEeSZJ0E0Kk0aRJE+enyImJiezZs8fFEQmRO5w7dy7NbUm6uY6qqgwfPpzr16+nu9a9e3datWrlgqiyn6Io1KxZk48++ojDhw/z9ddf88ILL2RY9N9kMrF8+XI6dOhAnTp1+OSTT9IljvMCVVWZMmUK77zzDjabzdleo0YNNmzYQJkyZVwY3ePt6tWrvPHGG7Rv356jR49maWzZsmVZsWIFixcvlt+1QgghHmlS000Ikc5LL73kTLZ169aNadOmuTgiIVyvS5cu7N69G0hNfpw/fx43NzcXR5U3LVu2jHfffTdde6lSpdiyZQseHh4uiMp14uLiWLduHatXr+a33367Z9/q1avTuXPnPFH/zWKxMGTIkHQHnrRq1YrZs2djNBpdE9hjzmw2M3fuXObMmZPl1fK+vr68++679OjRQ06QFUII8ViQpJsQIp2vv/6aUaNGAZAvXz7++OMPqaEi8rynn37aWQuqWLFi7Nu3z8UR5U1nz56lefPmzgNfbtPr9WzYsIEqVaq4KLLc4dKlS6xZs4bVq1dz8eLFu/bT6XQ0atSIzp0707Rp08cugRwTE0Pv3r3THUIxYMAARo0a9UjX+8utVFUlNDSUiRMnZrgK9V60Wi09e/Zk+PDh+Pv7Z1OEQgghRM6TpJsQIp2//vqLp556ynl77dq1PP300y6MSAjXSk5OTrPFqUGDBixfvtyFEeVNFouF1q1bc+zYsXTXQkJCGDBggAuiyp1UVeXgwYOsXr2atWvXEhsbe9e+Pj4+tGnThs6dO1OrVi0URcm5QLPBxYsX6d69OxcuXHC2aTQaJk2aRK9evVwY2ePr0KFDhISEcODAgSyPff755xk3bhzlypXLhsiEEEII15KP+YQQ6RQuXJhq1ao5b4eHh7swGiFc78437yD13FxlypQpGSbc6tWrx+uvv+6CiHKv2/XfPv74Yw4fPsxXX31Fq1at7lr/bdmyZc76b1OnTk33nH9U/P7777zwwgtp4vfw8GDx4sWScMsGkZGRDBkyhFatWmU54RYcHMyiRYtYsWKFJNyEEEI8tmSlmxAiQ7NmzWLKlClA6gvjvXv3PvKrH4TIKlVVuXHjBuvXr+eDDz5Aq9WiKAqTJk2id+/erg4vT9m9ezddunRJ1+7v78+OHTsICgpyQVSPntjYWNavX893333H77//fs++NWrUoFOnTjlW/01VVSIjIzl69Cg3btzAbrfj7e1NhQoVKF26NDqd7p7j161bx+DBg7FYLM62oKAgvvnmGypXrpzd4ecpKSkpzJs3j88++wyz2ZylsV5eXgwZMoS+fftiMBiyKUIhhBAid5CkmxAiQ6dOnaJhw4bO2zt27KB8+fIujEiInJGSksK6desICwvj0KFDxMREYzYnkpiQiApotTpeeeUVQkJCCA4OdnW4eUJ0dDSNGzcmMjIy3bWvv/6a5s2buyCqR9/FixdZs2YNa9ascWn9t6tXr/LNN9/w3Xffce3aX6DaAPvfVxVQ9Li7e9CsWTN69erFM888k+ZDIFVVmTt3LpMmTUpzvxUqVGDp0qUUKlToocabl6mqyqZNmxg/fjxXrlzJ0lhFUXj55ZcZMWIE+fPnz6YIhRBCiNxFkm5CiAypqspzzz3H6dOnSUlJoXjx4litVhITE9FqtRQsWJCqVatSs2ZNOnbsSIECBVwdshAPxOFwsHTpUqZMmUJM9A1Qk0C1oNXYMbqppFhUklPAageDmw8arQetW7dh4sSJ8vzPRqqq0rt3b7Zu3ZruWs+ePZk8ebILonq83Fn/LSwsjLi4uLv29fHxoW3btnTu3Jmnn376gVZAJyUl8fHHH/PVVwtx2M3gSEKjWClbQkexQlq0WoiKdXD8jI3EJAUUI2iM1KxZi5kzZ1K6dGmsVisffPABy5YtS3PfDRs25IsvvsDb2/s/xyfSOnHiBCEhIfzyyy9ZHlu7dm0mTJggKw6FEELkOZJ0E0Kk43A4WLlyJe+//z7X/voTNzcVg17BzaAFHIACaEDRg2JAp/eiVatWjBw5Ulb+iEdSdHQ0b7zxBnv27AK7iSJBDl5pa6R+LQMVSulISIzBkmLh2k04dkZh688Gdv5qRVW88PMvzKeffkqzZs1cPY3H0pIlSxg5cmS69jJlyrB582aMRqMLonp8WSwWtm/fzurVq/nhhx+wWq137VusWDE6depE586dKVGiRJYe5/jx4/Tr14+LF06Bw0Tdmjp6tjfS+Fk3jO5pE3kOh8qx0zaWrUti9eZkkiweGNwDGDFiBD/99BM//vhjmv49evRg0qRJ992OKjInKiqKKVOmsHz5chwOR5bGFilShJCQENq0aSMlKoQQQuRJknQTQqRx+fJlhg0bxt69P+KwmfB0S6FFPaheQaHOUwHk89dis8GFP+0cOWUl/McUDhy3g+KBu0cg77//Pn369EGjkXNaxKMhKiqKTp06cfrUYdx18XwwwIvenYxotf+8Qbxx46bzzaZOpyNfvkAizloZMsnE0TMaNLoA/ve/ObRv395Fs3g8nT59mubNm5OSkpKmXa/Xs2nTJipVquSiyPKG2NhY1q1bx+rVq+9b/61mzZrO+m/+/v737Hvo0CG6du2KKfZPCuVLYdpIHxo+k7ktq39F2nl3iomdv9qJMYHBYMTT09N5ffTo0QwYMEASPA+B1Wrlq6++YubMmZhMpiyNNRqNDBo0iDfeeAN3d/dsilAIIYTI/STpJoRwOnz4MN26dSMm6k+MBjPD+3rQ4tlEjO6pvya8fbzx9PBIN+7YaSvj/5fAngMO0PrSseNLfPrpp7LKQOR6drudDh068PtvP1EwIJGVn/pTtkTa563DkXqYwm3u7m74+fkBYLWqjJgaz8qNNnRu+QkNDaNmzZo5OYXHlsVioVWrVpw4cSLdtbFjx9K/f38XRJV3Zbb+m16vT1P/7d+F8q9evUrTpk2Jjb5ErSp2lnzih4931j6ksVisTP0ymllLHMTEa/Dy8sHX15fZs2fTpk2b/zI9cQdVVdm+fTtjx47l/PnzWR7fqVMnRo0aRcGCBbMhOiGEEOLRIkk3IQSQenBCu3btMMVeoWpZO1+M9yG4qA6TyYTZnASAwWAgICDjFQwOh8ri75MY81kiNtWPF1/qxqeffiqrDUSuNnfuXCZOGAOOaCqW0nHyvI0Yk0qZ4loG9/Skd2cjVquN6Oho1m5XWbdT5VCEwl83VKaO8GZ4Xy8cDpUBH5pYv0uhVJnqbNu2TVZ2PARjx47lyy+/TNdev359li1bJqtpXURVVQ4cOMDq1atZu3Ztpuq/vfjiizz11FMAdOvWjR93baJqmRTW/M8PT4+s/TumpKQQGxuHqqp8vkJlznKVeLOB77//nhdeeOGB5ibgzJkzjBkzhl27dmV5bPXq1Rk/frx88CCEEELcQZJuQggsFgstWrTg5InfqFnRyoqZfnh5pr4RSkmxEBMTA4CiQP78BdBo7p5I27I7hb4fmLArAcyc+RldunTJkTkIkVXR0dHUrFmTFPNfeBnt1Kysp31Td/IHaNi2N4VP5ify4VtevNdXT1xcHP1CHFy8CrWfNLDwO4sz6QYQF++gQfcoImN8GR0ynoEDB7p4do+2Xbt20a1bt3TtAQEBbN++naCgIBdEJf7tdv237777ju3bt9+z/lvx4sUpW7YsmzdvxKiPZfviAA6esLJ0bRIHjlnTJbvv/MAm1uTgw0/j+W5zEjFxKgXzwasdFPq9CH1Gw6HTnjRv0ZHFixfnxLQfS3FxcUybNo1FixZht9vvP+AOQUFBjBo1io4dO0oyXAghhPgXSboJIfjkk0/4dOYnBPrEsWtpIDEmB9MWJLLvkIVjZ2yULga7lqS+kPb19SUySkeJhjczvC83A0x/34dJn6fg7VecXbt2UahQoZycjhCZkrrKLYQqpRNYOs2P/IHaNNf7j45l1cZkLv/oSZLZjMOhotEoBAQE4FbpVpqkG8DKDUm883EyxUpUY+/evWi12n8/pMiEW7du0bhxY27eTP87ZvHixTRt2tQFUYn7iYmJcdZ/O3DgQLrrqqoSHR2Nh7uFYb21vPOaN417JRJcRJthsnvMoNRTRxPNDp7rEoVGcfB6F5X8AXDuCiQkwtu93LgV50XDHjGomvzs2bOXkiVL5vTUH2k2m42lS5cydepU5wdsmWUwGBgwYABvvfVWmrp6QgghhPiHFFwSIo+LjY3l888/B4eJj4d5E+ivYc8BCxt3JVO7mgGHClbrP596p6QkUyi/L798G5jmflSgxWvRNKpj4PWuHmzalcIfp27w+eefM378+ByelRD3991334Fq5tWOHukSbgDVK+iZvyoJU7wdvRbnCk+dLuNkWrsm7oybHc/lyxfYv38/derUydb4H0eqqjJs2LAME269e/eWhFsu5u/vT69evejVq5ez/tvq1au5dOkSkJrccThseBmhUzMHJpOJhROhcJAOd3cNbm5uNKrjRlSsgxlfJRLyphcajcLH8xKIi7ez/WvwMKb+DD5bHdzd3fH19SUgABrXMfDDviSWLFnC2LFjXfhdeLTs3r2bMWPGcPLkySyPbd26NaNHj6ZYsWLZEJkQQgjx+JA14ELkcatWrSIl2USl0govNEw9Pa5NIzeu7A5i9f/8qVFRn2Y7aUqKBYMBnqluSPOVYlExJah0a2NEp1N4t58nOMx8++23mM1mV01PiAwlJCRw+vRpUK00fS7jUxP3HLBQJEiD0f2fpLNGo6AoGf/XaXRXeP5pN1At/PHHH9kS9+Nu0aJFbNu2LV17uXLlCAkJcUFE4r8IDg5m2LBh/Pzzz6xdu5YePXqgKApuepUGtcHfJ/X/lABfSE5OITY2lps3b2IymahSVoMpQSXRrGK3O5i/KpEurf5JuAF4eXni6+vL7R2oXV9wB0cyP/74oyum+8i5ePEivXv3pkuXLllOuFWqVIk1a9bw5ZdfSsJNCCGEyARJugmRx33//fegJtGr4z81dP5ds01RNM5rqqqSkmJJdz/L1yfh46XQplFqAfnnnzYQXETFFBfF9u3bs3kWQmRNREQEqmqlUH6FfAHp/yvc87uFlRuTGdbHC7vtdtJNRVVTfwbupmp5HahWjh49mk2RP75OnjzJuHHj0rUbDAbmzp0rh1M8ghRF4emnn2bKlCm0aNECT08DT1XWZ3jAjsPhwGxOYue+RArlB9WRyB/HbnEjKjU512ukg+KNHFR8QWXoxzYSzQ7n2JqV9YCNM2fOyIc89xAfH8/EiROpX78+W7ZsydLYwMBApk6dyubNm2UVrxBCCJEFknQTIg9LSUkhIiICVAsNa2e82uc2g8GQZtydrFaVNVuS6dDUHXe3fxJ39WsZQLVw+PDhhx+8EA8gNjYWVAdB+dJvFf3zmp0uQ2Jo+IyBQT2MuLkZ0Oq0OBwqDoeD6Ojou95vgUANoKbev8i0lJQUBg4ciMWSPqEfEhJChQoVXBCVeJjOnj2LVgNPVfUhf/78+Pj4oNfr0/T59YjK2u3wRleFhIQErt9MTXiPn6Pi560Q9rkPHw3z5rvwZPqN+ufU1KB8WgL9FBwOK+fOncvReT0K7HY7K1asoG7dusydO/eeB178m16v54033mDv3r10795dalUKIYQQWSQ13YTIw06ePInNlkKAr0LhoHvn4N3c3JzJtpSUZFTVx7m1J/ynFKJjU7eW3qlqOT1g4ciRI9kRvhD/WepKGwWHI217rMlBy77RBPppWPM/f3Q6Db6+fkTHRDtP5bPZbABYrbZ093v7/uSNadZMnDgxw21ujRo14rXXXnNBROJhS0pKAhz4eCloNAoeHkY8PIzYbHaSk5M5d8nMG2NsPFcdXut4ezWpBnBQqhgsm5kPnS71ZatOp9BvVByT3rFRslhqm7enQlS8+vfjiNv2799PSEjIf1p927RpU8aOHUuJEiWyITIhhBAib5CkmxB52K1bt0B1UCRIk2a7j8OhYrPZsNlsWK1W7HY78fHxOBwOFAXs9tST6ry8vNDr9Sxbl0RQPg2NnzWkuf8iQRpQ7URFReX01IS4p0KFCoGi4eJVG6qqoigKSckqrftHExfv4Jdv8+HrnZpkM5vNWC3pV4YkJydjMsXj7e3tTEBf+NMOaClYsGAOzubRtn37dhYuXJiuPV++fMycOTPDrYji0ZO6qk3h3z9KOp0Wm8PIK++ZyeevZcFEBxpNatLNN/UAUxo/6+FMuEHqwQkAx8/8k3S7nQO/s19edvXqVSZMmMC6deuyPLZs2bKMGzeO+vXrZ0NkQgghRN4ir0yEyMPsdjsOhx1VVUlISExNstmsd9SwApvN4axjpSgKGo0GVVWxWCxER0eTYtWxfoeFvi95oNX+uxbcP3XghMhNypYti8FgxJSgcOGKnWKFtbw0OIaIczZ2Lw+kSMF/VqqlrsaxZbiCxmw2Y7Va8fPzQ6vVcCjCCoqRqlWr5uR0Hlk3b95k6NChGV779NNPyZ8/fw5HJLJL0aJF+fPyMc5esv1dgy3VncnujV9o8fFSgdT/O0oW0+BmcKSrM3pbsiX1/5ZEs4O/bthBo6Vo0aLZPpfczGw2M3fuXObMmZOuFMT9+Pr68u6779KjR490W3+FEEII8d9I0k2IPCI+Pp6TJ09y4sQJ59ehQ4eIiYnj2g0HCQn2e47XaDTo9DpQwW63Yben7qNbt91KUjK0qpdCYqIZo9HofIN0I8oOigZfX99sn58QWaHX63n66afZu/sWoduSOXXBxoadKUx/3xtTgsq+P/6pLVa9oh5fXx/OXtHwx7FE+DuJHHFeZcNOMBotNH02imSrNz8ftILiyzPPPOOqqT0yHA4HQ4YMSV1x+y99+vShUaNGLohKZJdq1aqx7+ftHD5ppcsLqaUIbDbVmexe/7me/AH/bNnWaDTkCwigWd04tv+cNnm0bW/qz2eNiqmJoWOnbaiqjoIFC1GgQIEcmlHuoqoqoaGhTJw4kevXr2dprFarpWfPngwfPhx/f/9silAIIYTImyTpJsRjxm63c/nyZY4fP05ERIQzwXblypV0fR0OBzY73IiGWzEq+fwVFEVBp9Oi0+nR6XQY3JLR6WwUKPDPihNVTd1aZzYnEvqDheAi8GR5lfj4eBISEjAajXh4eHDklA3QU6lSpRz8DgiROa+88gp79+xi6dpYouNSk8jDPo5P1+/CzvwEF9WxfofKuNn/FIH7bjN8t1mlaEFo/IyDeStiSU7RU6dubcqWLZtj83hUffXVV+zcuTNde4UKFRg9erQLIhLZ6emnn2beFwbCf4xl3GAVvV5h4Ng4NuxMYeIQPTFxVg78fTaCgsLzz/ih02kZM8ibZ1+6Rfd3YujVwYMzl2y8Py2e7m3dKVU89WXsuh3JoBioVauWC2foOocOHSIkJIQDBw5keezzzz/PuHHjKFeuXDZEJoQQQghJugnxCIuLiyMiIiJNcu3kyZOZLiRdqFAhVFVFtUdz/LwbHZt7oNPqSEpW2bQrGYA/r6uYElRWh6feZ/1aBvIHajEa3Yk369l94AZv99QBqckIVVUxm80kJiby468KNruXbLUTuVKrVq0oUjSYq1eO06eTlglDve/Zf+xgb8YO9sbhUImLi0uzdev0RZXFoSqxJhsJCQmYTCZ8fHyyewqPrIiICCZMmJCu3c3NjTlz5uDmdu/TlMWjp2nTpuTLX4jIyHjCf0qhbWN3tu5JXbE2+tN/10xUubBTwcsTalbWs2lBACOnxdP2jWj8fTX07+rBpL9/XhMSHXwXngyaQLp165bDs3KtyMhIPv74Y7799tssjw0ODmbs2LE0bdpU6iYKIYQQ2UhRpdiSELmezWbj4sWLzsTa7STb1atXMzXeYDBQvnx5KlSoQMWKFZ1/BgQEMHXqVGbO+Ig6VZNZMyd1W8nFP22UaHgzw/vauTSABrVT3xDPWZrIW+NMnAjPR5lgDWazmaSkJFRV5fAplR7vqUTHaWnQoAFvvfUWL7zwgtSJEbnKrl276NatK9ijmDfemzaN3TM1TlUhMTGRhIQEYuNVXvtA5chpBYfqjq+vL8HBwSxYsEBWeWYgOTmZFi1acPr06XTXJk2aRO/evV0QlcgJn3zyCZ/OnEKxoHi2LwkANYn4+IQ0fXx9fTEaM/dzCDB6Rjxffe+gZOnq/PTTT85Thh9nKSkpzJs3j88++wyz2ZylsV5eXgwdOpQ+ffpgMBjuP0AIIYQQD0SSbkLkMjExMWkSaydOnODUqVOZLohcuHBhKlas6PyqUKECJUqUuOuJbteuXaNWraexWyIJneNL7Scf7EW4w6GSaE6kz/sJhP8EquLhXPFTqFAh+vTpQ/fu3aXOm8g1Ro8ezVcL56Ello+He9K9rTHTKz9OnU+i7wdxHDkF5mQtAQEBaLWphzC4ubkxefJkunTpkp3hP3JGjRrF119/na69adOmLFq0SFbdPMbi4+Np1KgRV6+coGtLO+++Zknz7+3l7YWXp2em72/P7xZeejsOtIGsWLHqsT9tU1VVNm3axPjx4zMsGXEviqLw8ssvM2LECDmgRAghhMhBknQTwkWsVivnz58nIiIiTf21zBZAdnd3p0KFCulWr/2XZNbw4cNZvuwrggslsH1JIEb3B3vTuzo8icETE7E6fClbtiwXLlxIc93Dw4MuXbrQr18/goODH+ixhHhQdrudd955h+++WwH2OBo9oyHkTW/Klbx7BYZEs4NvwpL4ZH4iSRYPzMkKbm5uGSa3u3fvzsSJE2XLJLBt2zZ69eqVrr1AgQJs376dwMBAF0QlctJPP/1Ex47tURwxvNlN4fUuqQkhDw8PvL29yWzO9cAxK93eiSU+yYdu3Xszbdq07A3cxU6cOEFISAi//PJLlsfWrl2bCRMmULly5WyITAghhBD3Ikk3IXJAVFRUmq2hx48f5/Tp01it/65jk7EnnnjCmVS7/VW8eHHnipoHZTKZaNiwIdeuRtC6Acwd64tO998Sb4cjrHQeFEtiih8j3w9h0KBB7Nu3j3nz5rFt2zbu/JWjKArNmjXj9ddfp3bt2rLCRbiMw+Hgiy++4JNPpmBJjgE1iWeqaXn+aTeqlNMR4KvBYlU5c8nGweM2NuxMJiFJDxpv6tZtwJQpU5g/fz6LFi3K8P6rVKnC/PnzKVasWM5OLBeJjIykcePGREdHp7u2YsWKx36Vkkh15MgRGjduTGxsNH7eDlo3gLGDjBQr6pephJvDofJNWBLj/5dIktWHZ+rUZ/ny5bi7Z35L6qMkKiqKKVOmsHz5chwOx/0H3KFIkSJ8+OGHtG7dWv5/FUIIIVxEkm5CPERWq5WzZ886E2y3k2w3btzI1HgPD490K9fKly+fIwXZf/75Z15+uSvW5Ju0qKfw6SgffLyzVhvnx19T6B9iIj7Jm7r1mrB8+fI0K38uXLjAggULWLlyZbrDHqpWrUr//v1p06aN1H0TLnPmzBkmT57M1q1bsNvMoFpTv/j7v0pFB4oeFDdKlCjNm2++SdeuXZ11pNasWcO7775LcnJyuvv29fVlzpw5NGrUKAdnlDs4HA66d+/Ojz/+mO5a//79GTt2bM4HJXLcpUuXaNOmDbdu3Uo9cCchHm8vhWKFFN7q4clLLd3v+v+O3a6y61cLc5Ymsu+wClpfGjRowoIFC/Dw8MjhmWQ/q9XKV199xcyZMzGZTFkaazQaGTRoEG+88cZjm4wUQgghHhWSdBPZTlVVIiMjuX79OoqiUKhQIQoUKODqsB6IqqrcvHkz3cEGZ8+ezfTqteDg4DSr1ypUqECxYsVcWgR669at9OvXF2tKFAUDLUwe7kPTuob7fkIeE+fg4y8SWLouBTQ+1H7meb755hu8vLwy7B8bG8vSpUtZuHAhkZGRaa4VLFiQPn368Morr0jdN+Ey165dY/369Rw+fJiIiAji4+PR6/U88cQTVKtWjeeff55nn302w5/XiIgI+vbtm25bNaSu7hw6dChDhw59aCtVHwXz5s1j3Lhx6dorVqzIpk2bpKB7HhAVFUWbNm24ePGis61QoUK4ublx8cJpUM0YDRZqV9NTtbye4oW1aDQQHevg6Gkb+49Y+euGAhojRo9ARo0axauvvvrYHZygqirbt29n7NixnD9/PsvjO3XqxKhRoyhYsGA2RCeEEEKIrJKkm8gWqqry+++/s2TJEn766Sdu3rwB3N4WoSEoqCANGzakV69eVKtWzZWh3ldKSgpnzpxJszU0IiKCqKioTI339vZOt3qtXLlyd01Iudrvv//O4MGDuXjhFDjiKVUMurU2UquagUpldLi7KaiqyrUbDo6csrL5pxTCfkjBYnMDjTevvvoaISEhGI3G+z6W1Wpl/fr1zJs3j6NHj6a5ZjQa6dq1K3379qVEiRLZNV0hsoXJZGLo0KGEh4dneL1+/frMmTOHgICAHI4s5x0/fpxWrVql+0DC3d2dLVu2UKZMGRdFJnKK2Wymc+fOHDp0yNlWsGBBNmzYQGBgICtXrmTRokWcOhUBqgVUG2D/u6eSuroUPT6+Abz88su89tprPPHEEy6YSfY6c+YMY8aMYdeuXVkeW716dcaPH0/NmjUffmBCCCGE+M8k6SYeuqtXrzJ8+HB+/HEnOMygpqDV2CgQmPppdOQtBw5VB4o7aIy0aNGKyZMnu3z12+0Vef/eGnr27Fnsdvt9xyuKQokSJZzJtUqVKlGhQgWKFi36yNVSSUpKYvr06SxevJjEhNT6Vqg2FMWG0U3BZgeLFUAPigE0RipXrsqYMWN47rnnsvx4qqry66+/Mm/ePLZu3Zqu7lvTpk15/fXXeeaZZx6576XIu1RV5YsvvuCjjz7K8HdI4cKFmT9/PtWrV3dBdDkjKSmJ5s2bc/bs2XTXJk+eTM+ePV0QlchJNpuN3r17s337dmebj48PoaGhVKhQwdmmqirHjh3j4MGDHDlyhJs3b2K32/H29qZixYpUrVqV2rVrZ+oDnUdNXFwc06ZNY9GiRZl6vXGnoKAgRo0aRceOHR+7VX9CCCHE40CSbuKh2r17N3369CHBFIlBl0jHZu50aeVOtQp63N1SkyXmJJU/TlhZuTGJtT+kYFO98PMvzJIlS3jqqadyJM7k5GROnTrl3BZ6+ys2NjZT4318fNIcalChQgXKlSv32NWVSUhIYM2aNWzbto0jR45w69ZNUmtbKWi1OsqWLUvNmjXp2rUr1atXfygJsXvVfatSpQr9+/enbdu2UvdNPDJ++eUX3njjDW7evJnuml6vZ8KECfTo0eOxTCiPGDGCb775Jl178+bN+eqrrx7LOYt/qKrKsGHDWLlypbNNr9ezYsUKnn32WRdGljvYbDaWLl3K1KlTiYmJydJYg8HAgAEDeOutt/D09MymCIUQQgjxoCTpJh6affv20bVrFyzJN6lZ0cGno3woVVx3zzERZ60MnmDi+Dkdnt6FWbNmDVWrVn1oMamqyl9//ZWm7tqJEyc4f/58pk4B02g0lCpVKt3JoYUKFcpzbxZVVSUqKoqEhAR0Oh358uXL1gLNcXFxzrpv169fT3MtKCjIWffNz88v22IQ4mGJjIzk9ddfZ//+/Rle79y5M1OmTHmsVvFs3ryZ1157LV17UFAQ27dvzxNba/O6qVOnMnPmTOdtRVH4/PPPadu2rQujyh12797NmDFjOHnyZJbHtmnThtGjRz+WW2yFEEKIx40k3cRDYTKZqF+/PpHXTtPsWZUvJ/piMCh8F57E0rVJHDhmJcakUqa4lsE9Pend2ehMWpmTVF4dEcueg1qCS1Zl+/bt/+mNp9ls5uTJk87k2u0/M3vql7+/v3NL6O3Va2XLlpWTv1zMarWyYcMG5s2bx5EjR9JcMxqNdOnShb59+1KyZEkXRShE5litVj7++GO++OKLDK9XqFCBBQsWPBY1DK9fv06jRo0yXD28atUq6tWrl/NBiRz1zTffMGLEiDRt48ePp2/fvi6KKHe4ePEi48aNY8uWLVkeW6lSJcaPH0+dOnWyITIhhBBCZAdJuomHYuTIkSxZ/CUlCifww+JAjO6pCbU6L94iuIiW9k3dyR+gYdveFD6Zn8iHb3kxZpC3c7wp3kGDV6K4Hu3DW4OG8cEHH9z1sVRV5cqVK+m2hl68eJHMPJ11Oh2lS5dOt3qtQIECeW712qNEVVX279/PvHnz2LJlS4Z13/r370+dOnXk31Hkahs2bGDo0KEkJiamu+bt7c2sWbNo0aKFCyJ7OBwOB127dmXPnj3prg0YMICQkBAXRCVy0pYtW+jTp0+aFeUDBw5k9OjRLozKteLj45k1axbz58/P9CnntwUGBjJy5Ei6du2ap049FkIIIR4HknQTDywmJoYaNWqQYv6T7z7z5bmaBue1W9EO8gWkLezbf3QsqzYmE3MgCI3mn+TI5p+See39RHz8S3Dw4EE8PDxISEjg1KlTzhNDb69gS0hIyFRsgYGBVKxYMc0KtjJlymAwGO4/WORaFy9edNZ9M5vNaa5VrlyZ/v37065dO6n7JnKtc+fO0adPH06fPp3h9YEDBzJy5Eh0untv0c+N5s6dy8SJE9O1V6lShQ0bNsjP5WPu999/58UXXyQlJcXZ1rFjRz777LM8Wejfbrfz7bffMnny5AzrOt6LXq+nT58+DBkyBB8fn2yKUAghhBDZSZJu4oF9/fXXjPpgOJVLJbDl64D7rjL6fFkiA8eaMP0RhLdX6gtwVQWr1Ua9l6M5fcmN6jVqk5SUxKVLlzIVg16vp0yZMulODs2fP/8Dz0/kXnFxcSxbtoyFCxdy7dq1NNeCgoJ47bXX6NGjh9R9E7lSYmIi7777LmFhYRlef/bZZ5k7d67LT3bOiqNHj9K6det0K3mMRiNbt26lVKlSLopM5ISzZ8/Stm3bNNuK69Wrx9KlS/NksnX//v2EhIRw9OjRLI9t2rQpY8eOfSy2mwshhBB5mSTdxAMbOHAgYd9/w4i+Gt5+9f4naHUbGsNPv1k4vdUbm82G1WrDZrOhqiozFql8sRIceNz1U92goKA0W0MrVKhA6dKl8+QLepHKarWyceNG5s2bx+HDh9NcMxqNvPTSS/Tr10/qvolcR1VVFi1axNixYzPcchYUFMS8efOoVauWC6LLGrPZTLNmzTh//ny6a1OnTqV79+4uiErklMjISNq0acOff/7pbKtUqRLff/893t7e9xj5+Ll69SoTJkxg3bp1WR5btmxZxo0bR/369bMhMiGEEELkNEm6iQfWoEEDTkfsY9l0Txo+43bXfjabjfBd0bR/086YNxX6v5R+Rdzm3SrvTFYxp7hRsGBBypUr50ys3f4zMDAwO6cjHmGqqvLbb78xb948Nm/enK7G3+26b88++6zUfRO5yoEDB+jXr1+6k3oBtFotISEh9OvXL1c/b4cPH87y5cvTtbds2ZIFCxbk6tjFgzGZTHTs2JETJ04425544gnWr1//SK3UfFBms5m5c+cyZ86cNNtrM8PX15d3332XHj16yIeIQgghxGNEkm7igdWsWZNrfx5j80Ifqpa/+wvFy3/ZqN3pJmWKw8oZSpp6blqtFp1Ox29HoecIG8GlarBnz55Hsp6RyB0uXrzIwoULWbFiRbq6b5UqVeL111+Xum8iV4mKimLAgAEZHkAA0KZNG6ZPn46Xl1cOR3Z/GzdupF+/funaCxYsyPbt2/H393dBVCInWK1WunfvnuZ56+/vz7p16/LMdmJVVQkNDWXixIkZJs7vRavV0rNnT4YPHy4/J0IIIcRjKO9VtBUPnZtb6uq2pOS7529jTQ5e6BdDgK/C4ikGPD098PbxJiAggAIFCpA/fz78/f1QNEY0Wi2+vr6ScBMPJDg4mAkTJnDw4EFGjx5NoUKFnNeOHz/O4MGDqVWrFp999hkxMTEujFSIVIGBgaxYsYLBgwdneH39+vW0atXqrocvuMq1a9cYPnx4unZFUZg9e7YkEh5jDoeDt99+O03Czd3dnSVLluSZhNuhQ4do27Ytb731VpYTbs8//zw//PADkyZNkp8TIYQQ4jElSTfxwEqVKgWKjhNnbRleT0pWad0/mrh4B1u+zk+J4oH4+vrg6eGBwaBPs+It9T50lC5dOoeiF487Hx8fBg4cyL59+/j888+pVq2a81pkZCSTJ0+mZs2ajBw5knPnzrkwUiFSV72MHDmSxYsXZ1jX8uzZs7Rq1Yq1a9e6ILr07HY7gwYNIi4uLt21gQMH8txzz7kgKpFTJk6cmOYgEI1Gw7x586hZs6brgsohkZGRDBkyhFatWnHgwIEsjQ0ODmbRokWsWLGCcuXKZVOEQgghhMgNJOkmHtiTTz4JioEf91vSXbPZVF4aHEPEORubFwZQtJD2nvf1028WUPRpEiNCPAx6vZ527dqxadMmwsLCaNWqlbPGVHJyMkuWLKFevXr07NmTPXv2pKsHJ0ROatq0KVu2bKFSpUrprpnNZgYMGEBISEiGhy/kpM8//5yff/45XXu1atV47733XBCRyClffvklX3zxRZq2Tz75hKZNm7ooopyRkpLCZ599xnPPPce3336bpbHe3t6EhISwa9cumjVrJnUOhRBCiDxAarqJB3b69GkaNKiPxnGTfd8Fpkms9R8dy/xVSUx/35tnqxvSjKteUY+b2z8vOE+dt9GwRwwafQH27/+NwoUL59gcRN50u+7bypUrSUxMTHOtYsWKvP7667Rv317qvgmXSU5O5v3332fVqlUZXq9ZsyZffvllmu3TOeX2tjqbLe0qZw8PD7Zt20aJEiVyPCaRM9atW8cbb7yRpm3YsGEMGzbMRRFlP1VV2bRpE+PHj+fKlStZGqsoCi+//DIjRowgf/782RShEEIIIXIjSbqJh+Kll15iz0+bafasja+n+Do/vQ1ucINLV+0ZjrmwMz/BRVPrtjkcKi8OiuWXwwZatX6RBQsW5FjsQphMJpYtW8bChQv566+/0lwrUKAAvXv3pkePHgQEBLgoQpHXrVixgvfffx+LJf2K4sDAQD7//HPq1q2bY/EkJibStGlTLl68mO7ajBkz6Nq1a47FInLW3r176datW5pVlt27d+eTTz55bFdunThxgpCQEH755Zcsj61duzYTJkygcuXK2RCZEEIIIXI7SbqJhyIiIoIWLZpjTb7O2EEe9O/qkaXx0xcmMP2rFDy8n2D79u0UL148myIV4u6sVivh4eHMmzePP/74I801d3d3XnzxRfr16yc1B4VLHDlyhH79+mW4ykaj0TBy5EgGDhyIRpP9lSOGDh2a4eq7Nm3a8MUXXzy2yZe8LiIigvbt2xMfH+9sa9q0KQsXLnwsDz+KiopiypQpLF++HIfDkaWxRYsWJSQkhNatW8vPgxBCCJGHSdJNPDRffPEF48ePAXs0Q3oZGdrbE73+3i80U1JUPp6XwJerUkAbwLRpM+jWrVsORSxExlRV5cCBA8ybN4/w8PB0b7aaNGlC//79ee655+TNlMhRsbGxDBo0iO3bt2d4vVmzZnz22WcZHsJwL5GRkRw9epQbN25gsVgwGAwULlyYKlWqEBgYmKZvRlsLAQoXLsz27dvx9fXN0mOLR8PVq1dp3bo1kZGRzrYaNWrw3XffYTQaXRjZw2e1Wvnqq6+YOXMmJpMpS2ONRiODBg3ijTfewN3dPZsiFEIIIcSjQpJu4qFRVZXJkycze/anYDdRqbSDt3t50ryeW7rkm8WisnFXCp8uSuTMZS1ofBg1KoQ333zTNcELcReXL19m4cKFLF++PF3dtwoVKjjrvhkMhrvcgxAPl8Ph4LPPPmPq1KkZHvgRHBzM/PnzMzyE4U6HDh1iyZIl7Ny5k8jI66DagDvLAWhB0VOkSBGaNm1Kz5498fb2pnHjxukSEYqisHr1aurUqfMQZihym9jYWNq1a8eZM2ecbSVLlmTdunWP1bZ7VVXZvn07Y8eO5fz581ke36lTJ0aNGkXBggWzITohhBBCPIok6SYeurCwMD744ANiY66DIxEfLzvVyusJLqJFVeHCn3aOnLISn6gDjQf5CzzBJ598QvPmzV0duhB3ZTKZWL58OQsXLuTq1atprhUoUIBXX32Vnj17PlZvQEXu9uOPPzJw4EBiYmLSXXNzc2PKlCm89NJL6a79/PPPTJgwgcOH/wBHEqjJaBQbpYvrKF5Ei0EPKRY4f8XG+St2QAcaIyjugAar1ZrucJHBgwczcuTIbJqpcKXk5GReeuklfv/9d2dbgQIFWLduHcWKFXNhZA/XmTNnGDNmDLt27cry2Bo1ajBu3Dhq1qz58AMTQgghxCNNkm4iW9y8eZOvvvqKZcuWcetW5L9WUGhB0REUVJgePXrw2muv4efn58Johcg8m83Gpk2b+PLLLzl48GCaa25ubs66b2XKlHFRhCIvuXr1Kv37909Xg/C2V155hQkTJuDm5kZiYiIfffQRX3+9EBwJ6LXJtG3sRpdWRmpU0uNhTL9V2hTv4MBxK8vXJxH+YwrxiSqJSQoeHp54enqiKArVq1cnLCxMTvl9DNntdvr168fmzZudbV5eXnz//fePzcEAcXFxTJs2jUWLFmG3Z3zw090EBQUxatQoOnbsmCO1FIUQQgjx6JGkm8hWVquVkydPcvjwYSIjI1EUhUKFClGtWjXKli37WBZeFnnH7bpvmzZtSlf3rXHjxvTv35+6detK3TeRrSwWC2PGjGHx4sUZXq9atSoTJkxg2LBhnD1zHBwmerRz471+XgT6Zy5RYLFYOXU2iulfq2z8ERLMCiqpdd927NhBcHDwQ5yRyA1UVeWDDz5I87zS6XQsW7aMevXquTCyVElJSSQlJaHT6fD29s7y71mbzcbSpUuZOnVqhqtF78VgMDBgwADeeustPD09szRWCCGEEHmLJN2EEOIB3a77tmLFChISEtJcq1ChAv3796dDhw4Pte6b1Wrljz/+4PDhw5w4cYKEhAS0Wi358+enatWq1KhRg1KlSj20xxO53+rVq3nvvfdITk5O02632zGZTPh4aShSwMKs0b7Uezrzz0WHQyUqKsq5Cmj7Lyrj56pcjVQILlmR3bt34+/v/1DnIlxv1qxZTJkyJU3b//73Pzp27OiSeMxmM2FhYezYsYMjR47w559/Oq/5+PhQpUoVatWqRZcuXe677XX37t2MGTOGkydPZjmONm3aMHr0aJ544oksjxVCCCFE3iNJNyGEeEhMJhMrVqxgwYIF6eq+5c+f31n37d+nQWZFZGQkS5YsYdmyZdy4cQ1Ua+oXt3+Vp27fRtFTo8ZT9OrVi/bt28vWvzwiIiKCvn37cuHCBSB1tVJ0dDQGnZXyJVWWTfeiTAlvsrIoKC7ORFJSUpq281dU3hgLt0y+PF3reb7//nu0Wu1DnIlwpVWrVjF06NA0baNHj2bgwIE5HovZbGbGjBksXboUU9wtUJP//p1nJ+3vPT0oBhSNkSZNmhISEkLp0qXT3NfFixcZN24cW7ZsyXIclSpVYvz48XJYiBBCCCGyRJJuQgjxkNlsNsLDw/nyyy85cOBAmmtubm507tyZfv36UbZs2Uzfp6qqrFixgrFjx5IQfwscZgJ87TxVWU+VcjoCfDU4VLh0NfWgkj9O2LDa3UDxoFLlJ5k1axYVK1Z82FMVuZDJZGLo0KGEh4cTHx+PzZpIqSdUlkxRKFxAwc3NDV9fXzSa+2fekpKSiYuLS9eu1Wq5FedL+4GxxCf5MTpknEsSMuLh27FjB7169UpT36xv376MGzcux7fK79+/nyFDhnDxwilwJBBcRKXrC0ZqVtZTqYwOHy8FixXOXbZxOMLG+h3J/PibFRQPDO4BjBgxgtdff53ExERmzZrF/PnzsVqtWYohMDCQkSNH0rVrV0ksCyGEECLLJOkmhBDZ6MCBA3z55Zds3LgxXd23Ro0a0b9/f+rVq3fPN7MpKSkMHDiQ8PD1YDdRrTwM7OZBi+fd0OszHnczys6KDcl8scJMbII7OoMfU6Z8wssvv/xQ5ydyp9v1uKZN+wR/HwezRyvUf/qf54pWq8XPz/eeKyDtdjtRUVE4HOlfJgQEBGAw6Fm5IYl3PjZjMBbkhx+2p1tZJB4thw4dolOnTmlWNrZp04bPP/88xw8KWLduHW+99SY2SzSF81v56B1vmjxnuG+y+PxlG6NnxrNrvwM0vlR7siZXr17l1q1bWXp8vV5P3759efvtt/Hx8XmQqQghhBAiD5OkmxBC5IArV66wcOFCli9ffte6b+3bt8fNzS3NNYvFQq9evfhx11YM2jhG9POkf1cPtNrMrTi5EWVnxCfxbNljB60/U6ZMpUePHg9tXiL36tq1Kzu3r6N1/WQmDUn/fFEUBW9vb4xGY7rtpqoKMTHRWCzpVwV5eXni5eX1dz+VHsNj2fGrgQ6dXmHOnDnZMheR/S5evEibNm2IiopyttWpU4fly5en+72U3X744Qd6934Vu+UWbRtp+eQ9b3y8M5/0U1WVRWsSGDUjkeg40Ok9spQ4a9q0KWPHjqVEiRL/JXwhhBBCCCdJugkhRA6Kj4931n27sxA4QL58+Xj11Vfp1auXs+7bqFGj+PqreXgY4vhmmi91qmf9MAZVVZnwvwS+WGlB0QXy/feh1K5d+6HMR+RO58+fp27d51AcN9mzwh9fz/g0CbQLf6p8vlLl4HE4eQHKl9RxbFN+5/WEhESWrY1n3U6VP07AtZvw4UCFwT0NBAQEpEnSHTlppUWfWPTuhTlw4AD58uXLyamKh+DWrVu0bduWixcvOtvKly9PWFhYjq/yunnzJg0aNCAm6jwvNtcwc5RPprZC32a3O4iPjyc5OZkfflYZPlUlOlaDl7cv7u7u9xxbtmxZxo0bR/369R90GkIIIYQQAOTsXgEhhMjjvL296d+/Pz///DNffvklNWvWdF67desW06ZNo2bNmgwfPpyVK1fy9dcLwRHHvAk+/ynhBqkrmkLe8qJzcx2qPY6hQ4emK4wvHi/ffPMNqEk0edZAiSf0+PsH4Onp4bx+6gJs/wWCi0DZ4qlbSW221BpeFouVhIQENuxSufwXNHn2n/v19fNNtyquank91StosVriWbVqVU5MTzxEiYmJ9OjRI03CrVChQixbtswl2ypHjRpFTPRVKpZy8MyTBjoMjKFo3Ug8q17nyTY3+eo7M3d+XtygexRKmWvOL135SPyfNnPmkkqTZxVef0nB2zM1EffvLf63+fr6MmnSJH744QdJuAkhhBDioZKVbkII4WIHDhxg/vz5bNiwwfmm8Papk14ednp31FOrmjvL1iVx4JiVGJNKmeJaBvf0pHdno7Me3PDJJsJ/TOHyNTsKUK6kjmGvedK1tREAU7yDhj2iuBblw7vvjU53OqF4fDRp0oQTx/Yyb5yRNo3/Wd2TnJxCXFwcdrvDuXro7UkODp+Cn5Zq8fb2ISEhAbvdjsOhOvsUqufgo3eMvD/AL8PHW7TGzAczrTzfoA0rV67M9vmJh8NqtfLqq6+yc+dOZ5uPjw9r166lXLlyOR5PREQEjRs3QqveZPNX/vT9II7gIlraN3Unf4CGbXtT+GR+Ih++5cWYQd5AatLNYrUTMoA0SbVKpcHdTcFqU+k2TOXACQWd3su5NRpSaxv27NmT4cOH4+/vn+PzFUIIIcTjT+fqAIQQIq+rWbMmNWvW5MqVK3z11VcsX76cqKgoVIcNH08Hr7/koNvwFEoU1TFtpA8FAlPffPYbHceV63bnm8+ERJV+L3lQvpQWRVFYvTmJl4fG4nBAt7ZGfLw1fPCGF4MmJLJkyRLeeuutexbSF4+mlJQUTp8+DaqVGpXSrlRyd3dDpwskNjYWm82W5prDoRIbGwukro7895Y+vf7uLxlqVNKDaubIkSOoqprjp1yKrFNVlXfffTdNws1gMLBkyRKXJNzgnxWaLZ43UKmMnvXzAsgX8M+mjEZ13IiKdTDjq0RC3vTCbrdhtVrxdFepXkEDpH/e6XUKfTrDiSkqpsQkPD09URSF559/nnHjxrlsrkIIIYTIGyTpJoQQucQTTzzBmDFjeOedd2jTpg2HDu6mdQPw9lRY9LFKoJ8djSYBDw8P6tf2SvPmU6NR+GKCb5r7a17PjRNnbSz63ky3tqmr3do0cmfs7AQiI6+yc+dOmjVr5oKZiuwUERGBzZZCgK9C4aD0VSR0Oi0BAQGYTCaSk5Od7aqqOrft3Zk4u31qpcVqxWw240xsKKD8/ffihVV0WjuRkddYtGgRQUFBaDQatNrUBLBWq0Wj0aT5+ndbVvrceZ//fozbt2+PERmbMmUK3377rfO2oijMnTuXWrVquSQeVVVZu3YtOJLo2T71g4Q7E263Va+gZ/6qJP66bkKnSSYzGzYa14ECgZBgdhAYGMjMmTNp0qSJJIeFEEIIke0k6SaEELmMt7c3JpMJD6OOzi3d0RtSCPRLLYLvcDhISEggMTGR8iW0mBJUEhId+HhrM7yvQD8NpoR/3pQaDAqt6rvxzboU9u/f/9gm3VRVxeFwYLfb/65XZkvzd4fDkaYto3536/tf+mQljgeNNTo6mtiYKMo94eDmzVvO78cd3x1u31RVFfWO79m/v4epf6Zu2UtJTsFkstz1e+7v4+D6eRPvvfderlpBeb/E3H9N7GUl+ZcTCcasPMaOHTtSV5X9TVEUXnvtNTw9PdmzZ889H/O/zuPfbbfv83bi68qVK8TERKPX2ald7e7Pnz0HLBQJ0mB0t2P9++n4yyEo2dSBwwHVK8B7fRXqPPlPQk2vU3iqMtyIcaN///40bdo0W55rQgghhBD/Jkk3IYTIZW7evMn169dQFBtPVTHi6eGJxZK6yiglJRlVTU2I7PndQsH8kJwUjZvBF71ej6o6sNkgwayyYWcyW/eksPgTH2w2mzPRUqmMgsNhYdeuXTRu3Pi+iaR7JYcySjb9l0TSgyap/h2r3W537T+iC6WkpOBwONBp1bsWjr9NURQU7r1SyOH4Z/VbRtv3btPrUq/mtlKxDofjvt+HvCQ5OZm4uLg0bZ6enixdupSlS5e6JCatVktKSgrxpijqVHMQE3Pz7wM7/nm+KQrsO6yycqOdcYM02G12VFXlmWrQuTmUfAIibyl8vlKly1CV72fDU5VTxxuNRp6uqmH7ryonT550yRyFEEIIkTdJ0k0IIXKZK1euAHYKF9Di6ZG6vcpg0GMw+GK3e5GYaGbHLwms3QEfDgSbzUZ0dDSKovDT76lvOAF0Wpg0VKF+zQRu3Upw3n8BPxWbVeWnn36iU6dOrpiiyAHJKaTZPndnEiPN3zUqiqKi0Wjum5y6fV1RlHRb81IsoIJs2cvFLBYLJpMpTZu7uzuenp4uiiiV3W7HarWiKCr5Am5vdQbuSAj/dUOlf4jKc9XhtY6pCWWNRsPw1+5MsKs0qQMNesHMxSrfzXLD29sbvV5HoQJJgMVZt1AIIYQQIidI0k0IIXIZqzV1K6khgx1WVquN85eTGDAWnq0Off6VM6tREcLnK8QnwM5fVUZ/qqLTQrfW/yRCDPrUpEtuW5GUVYqioNPp0Gg06HQ6tFotWq02w7Z/t9+rz52373Z/97rvrPa5XxxZjfXChQs0atSAqzduERiYH53u3kkwd7dYtForQUH5uXnzxt+JtdTvbephCyqgoij863AFFUXRoNfrSUrRcSvWjLe3L7NmzcLDw+PvE1Adzq9/386o7fZtVVWdf7fb7RnevrN/Zvpk9TH+Sxz3mqur2Ww2YmNj0/zcGwwGfHx8cn2iNC5epfu7Kv4+sGDiP4d8aDQK/17UanSHxs/Axh//Ofzjzl91uX2uQgghhHi8SNJNCCFyGXd3d0Ah0fzPO0WHQyU+3sT1G0m8PCz1zefCicrfCZXUVSGKouDnq+PpKqlvKhs9Cyo2xv7PTo/2BnTavwvi2+0oio3AwEA6dOjw0BJJd0sO/ddE0r2SVLfrRIn0ypYti5eXDwlx0Zy5aKNC6czVV/tn9ZuCVqshX75AHA4HKSkpQKzz2p1UVcVisfD70RSsVtC7pSZxGjZs6PLVU7nN3RKOD5rYy0yfW7du8eGHHzq3XauqSvHixRk+fPjf29IfPIn577aMkpj36nP58mX2/7qHmzFW3NwMzkRZUrJKr5EW4hNh83wDgf6pz0EVFbvNhqIoaRKJqaswU28nJppJTDSj02m5eEWD6tDi5+eXo//uQgghhMjbJOkmhBC5TMmSJQEtN6JVomIceHnYMJniSDDb6TFCxZQAG75Q8PG6nQBRnCvXvL28cXMzOO/ruafMfLEyDrvqS37/1MMW/ow0o9PbaNq8OXPmzMn5CYpspdFoqFKlCr/svcovf1gzTLqZk1Q27Uo9ufTSX3ZMCQ5WhycRZ3JQu6pKgUA4ccbKibM255gLf7mxY78OvdZK/adtzlpvAAeOgdUG5hQT/fv3x2AwUL9+fVq1akXTpk0JCAjI/onncq46TdVkMtGhQwcSEhJwc3MDIDg4mLVr15I/f/4cj+durly5Qu3atTh/5Saenn4YDAo2m0qvkTGcuQy7l+ejYpl/nsuqmrp6LykpicTERCA14WZOUtn2MzxZ/p/7ttnsHDphxZSgsHPnTr744gvatWtHoUKFcnqaQgghhMhjJOkmhBC5jLe3N6VKleLc6Wh274/jmWoWbDaV1z9UOXsJQv+nUCh/+i1Sd54EeNue3634eCnk8//nzf6BY1ZQ9FSrVi3b5yJco2XLlvzy8y6Wrkugd2djuufFjSg7Lw6OTdN2+/aazxQKBMK34cmMm/1PLcBvwpL5JgyKF9FyYWcBLBYLKSnJxCckE/qDgxSLBg9PdyC1dti2bdvYtm0bGo2GZ555hpYtW9KiRQuKFCmSnVMXd7BYLLz22mtEREQ42wIDA1m+fHmuSrgBFC1alICAQKJvRvPLHxbq13Zj4Ng4NuxMYfr73pgSVPb98c/pudUr6tl/xMHUBVbaNfHmiYJw+WoKn32Tws1omD/+n+e8xapy4ARYbQo3btxg/PjxTJgwgWeeeYb27dvTunVr/P39XTFtIYQQQjzmFPVRL+ojhBCPoV69ehG6ZhltGtiZPlLD8E8cLFsPY95UeLpK2r6Vy8DVm+6Mn2PnpZZGgotqnaeXLvg2iY+HeTPidS8A4uIdPNn2Fin2fKxbt4GnnnrKBbMT2c1kMvHkk0+SnPgna/7nQ53qhvsPIvXkXLs9tUB9gQKZS8qs35FM/9EJqJoAgoODuXz58j37V61alRYtWtCyZUvKli0rNbayicPhYODAgaxbt87ZZjQaWb16NdWrV3dhZHc3atQovv5qDi2es/DVZD+CG9zg0tWMTyK+sDM/Nju8Nc7E4ZNWomIdeBoVnq1u4P033Khc2kZKSgqqqrLxR5X3pqqYEnXky5cv3XNOp9PRoEEDOnToQLNmzWRrtBBCCCEeGkm6CSFELpKYmMikSZNYsGAB0dFRFAhwsHauQsdBKn9ez3jMyS1++PkYGDLRxC+HrFy/acfXW0P5kjreec2Tdk3cnX3nLE1k0hdWKlZ+lm3btknC4zH23nvvsfSb+VQsaSZ8QQB6/f3/rbOadEs0O2jcM5rLkd68PeQ93nvvPU6ePEl4eDjh4eEcP378nuNLlChBy5YtadmyJdWrV5c6fQ+JqqqMHTuW+fPnO9u0Wi2LFy+mUaNGLozs3k6fPk2DBvXROG6xcb4f1Spkrh7h3ThUlYSEZFr1NfHHCdDqPfHy8rrnGKPRSLNmzejQoQMNGzZEr3+wGIQQQgiRt0nSTQghcolff/2VIUOGcOnSJQBiYmLQa1NoUEvli7HKv06OTH1z6O3tna79bi7+aaNxzxiSbP7MnDmbLl26PPQ5iNzj5s2bNGjQgJhb5xneR887r9072ZA65hZ2ux2NRqFAgQL37T9qhomvv1cp8kRFduzYgbe3d5rrly9fZvPmzYSHh7N///57npgbFBTkXAFXp04dSXY8gM8//5wJEyakaZs5c+Yj8TM/YMAA1oatpHywmc0LAzAYHuyDgcnzEvjsGyv+gSUZMmQIW7du5eeff87U6c2+vr688MILdOjQgWeeeQatVvtAsQghhBAi75GkmxBCuFhycjKTJ09m/vz5ad4I2mw2oqOj8fO2M/w1hVc7pL751Go1+Pj4pjkw4X6SklVeGhzDgRNuPFevGatWrZJVRXnA2rVrGTCgP1o1mq8+9qFpXbd79r+ddFMUhaCgeyfdvt2UxJBJCaANZNWq76hXr949+9+6dYutW7cSHh7OTz/9hNVqvWtfHx8fmjZtSsuWLWnQoAEeHh73vG/xj9DQUN588800be+99x5DhgxxTUBZFB0dTf369Ym6eY52jRRmf+jz9ynNWbf2h2QGjo1H1QSycOHXtGzZEoDIyEjWrl1LWFgYhw4dytR9BQUF0bZtWzp06EC1atVklbAQQgghMkWSbkII4UIHDx7k7bff5ty5cxleN5vNmBPj8fNxMKKvQp8Xjfj4+GR6dRukbgF87f04dh9Q8PZ9gq1bt1K8ePGHNQWRi6mqyjvvvMOqlUvRa2KZN8GbFs+737V/ZpNuqzYmMWxyAg7Fn4EDBzN69OgsxRUfH8/OnTvZtGkTO3bsICEh4a593dzcaNCgAS1btqRZs2b4+fll6bHykj179tC9e/c0Cc2ePXvy8ccfP1JJoh9//JEePV7BlnKTFvU0zPjABz+fzH9I4HCofL0miTGfJeLAj1d79+Ojjz7KsO/FixcJDQ0lNDSUs2fPZur+g4OD6dChA+3bt6dMmTKZjksIIYQQeY8k3YQQwgUsFgvTpk1j7ty5OByOu/ZTVZWEhASsliT8fRVeaKDn42He5A/M3DanX/6w8M5HJi5d0+PhVYjly5dTq1athzUN8Qiw2WwMGDCAjRvCUNRY+r7ozsjXvTC6p0/C3Lx1C7vt7kk3U7yDsbMTWLkxBbT+vPLKq0yZMuWBEjoWi4Xdu3cTHh7Oli1biIqKumtfrVZLnTp1aNmyJc2bN6dw4cL/+XEfN8ePH6dDhw5pEpgtWrRg/vz5j+S2yPDwcN5443WsKdEEBaQw7m1vXmjghlZ77+fayXM2Qj6NZ+9BB2h9efnlHkydOvW+K3tVVeXEiROEhoYSFhbGX3/9lak4K1WqRIcOHWjXrp2czCuEEEKIdCTpJoQQOezIkSO8/fbbnDp1KlP927RpQ6lSpfjf/2Zjs8Ti45lM1xfceaWdkVLFtOkSHlaryu7fLSwOTWLbXitovClcpCRffvklNWrUyI4piVzOZrMREhLC4sVfg8NEcGEb7/b14oUGbmlqZt26dQtbBkm3pGSVsG3JTF2QwPUoA4rWl0GDBjNixIiHuoLKbrfz+++/Ow9iuHLlyj37V6tWzXkQQ15ecXTlyhXatGnDjRs3nG1PPfUU3377Le7ud1/ZmNs5VwKfjQBHPEWCVF5q6U7NynoqldHh46XBalU5c8nOkZNW1u9IZt9hOygeGD3zMWrUKHr37p3l56jD4eD3338nNDSU9evXEx0dnalxtWrVokOHDrRu3ZrAwMD/MmUhhBBCPGYk6SaEEDnEarUya9YsZs2ahd1uv29/f39/Pv74Y9q2bQvAsWPHGDJkCCdOHAWHGdQkAv0UqpTVEeCnwW6Hy3/ZOX7WhsWqBY0RFCPdunXnww8/xMfHJ7unKHK5nTt3Mnz4cK79dREciQT62enU3J2alfRULa/DwxCL3W5HVVWSrPk4csrGb0ctrNmSjClRD4onwSXK8umnn2b7iklVVYmIiCA8PJxNmzYRERFxz/6lSpVyJuCefPLJR2o75YOIiYmhbdu2abaoly5dmnXr1j0WW3GTk5OZPXs2X3/9NbExN0FNBtUK2IDbL2F1oOhAcUOr86BFi5aMGjWK4ODgB358q9XK7t27CQsLIzw8nMTExPuO0Wq11K9fn/bt29OiRYv7npgqhBBCiMeXJN2EECIHREREMHjwYI4fP56p/s2bN2fKlCnpTpB0OBzs2rWLRYsWsXPnTux2y99vQG//KteCosPPL4AXX3yRnj17UqpUqYc7GfFIM5lMLFiwgKVLl3L9+p/gSAasoNpQHTZ0OrDZQNHoQNEDOtAYKVasBD179qR3794YjcYcj/vixYvOk1B///33e54+WbBgQWcCrnbt2o/tSahJSUm89NJLHDhwwNkWFBTE+vXrKVq0qAsje/iSk5NZv349u3bt4siRI5w/f975HMiXLx9Vq1bl6aefpkuXLhQsWDBbYkhKSuKHH34gNDSU7du33/MwkNvc3Nxo1qwZHTp0oFGjRhgMmT8ARwghhBCPPkm6CSFENrLZbMydO5fp06dn6g2aj48PEydOpFOnTvddqZOcnMyJEyc4ceIECQkJaLVaChQoQNWqVSlevLicTiruyWazsW3bNnbu3MmRI0eIiIggMvI6NpsNgCeeKEalSpWoWrUqTZo0oUGDBrnmOXXjxg3nSah79uy558+Wr68vzZo1o2XLltSvX98lCcPsYLPZ6Nu3L1u3bnW2eXt7ExYWRoUKFVwYWc6w2WwkJydjMBhcksgymUxs2rSJ0NBQ9u7de8/anLf5+PjQsmVLOnbsyLPPPvtI1toTQgghRNZI0k0IIbLJmTNnGDJkCH/88Uem+jds2JDp06dn2yoNIe7FarXSuHFjZ63BK1euPBKrckwmEzt27CA8PJzt27djNpvv2tfd3Z2GDRvSsmVLmjZtiq+vbw5GmjFVVYmMjOTo0aPcupV6eqynpyflypWjTJkyGa7SU1WVESNGsHTpUmebXq9n2bJl1K1bNyfDF6QmgdevX09YWFiaVYf3kj9/ftq2bUv79u2pUaNGntkOLYQQQuQ1knQTQoiHzG63M3/+fCZPnozFYrlvfy8vL8aOHcvLL78sb7yESzVr1oxjx44BcPnyZXQ6nYsjypqUlBR++uknwsPD2bp16z0L4Ot0OudJqC1atMjxZPfp06dZvHgxGzdu5MaNSFBtwO1ajwooegwGd5555hl69OhBs2bNnAm4GTNmMG3atDT39/nnn9OuXbscnYNI79KlS6xdu5bQ0NBMH5ZTrFgx2rdvT4cOHShXrlw2RyiEEEKInCRJNyGEeIguXrzI22+/zW+//Zap/nXr1mXGjBmPXf0l8Whq0aIFR44cAVKTB49yLTSbzcZvv/3mPAn16tWr9+xfo0YNZx24kiVLZltcV69e5f333+eHH7aBmgSOJLQaO2VLaClaUItGgbh4ByfO2TAlKKC4gcaDggWLMmHCBEwmE8OGDUtzn2PHjqV///7ZFrP4byIiIggNDSUsLIw///wzU2MqVKhA+/btad++PU888UQ2RyiEEEKI7CZJNyGEeAgcDgeLFy9m4sSJJCUl3be/0Whk9OjR9OrVK9fUyRKiVatWHDp0CIALFy7g5ubm2oAeElVVOXbsmPMghpMnT96zf9myZZ0r4KpWrfrQVqCuXLmSDz/8kARTJBoSaV7PQI/2RmpXM2B0T/sYDofK+St2Vm9OZtm6JKLi9FhsbiQnW/Hy8nL+3nj99dcZM2bMQ4lPZA9VVTlw4AChoaGsW7eOqKioTI2rWbMmHTt2pHXr1uTPnz+boxRCCCFEdpCkmxBCPKArV64wbNgw9uzZk6n+tWrV4tNPPyU4ODh7AxMii9q0aeOsSXXu3LnH5tCBf7tw4YJzBdz9anAVLlyYFi1aOE9C/S9bblVVZdq0acycOQ3scTxVGWZ+4EOp4pm7r5QUlWkLTMxZZiYuXsHm0OPv70/Hjh353//+J4n7R4jNZmPv3r2EhoayadMmEhIS7jtGo9FQr1492rdvT8uWLfHx8cmBSIUQQgjxMEjSTQgh/iNVVVm+fDljx44lMTHxvv3d3Nx4//336dOnj5xaJ3Kltm3b8vvvvwOpNce8vLxcHFH2i4yMZMuWLYSHh7N3717n6a0Z8ff3d56E+vzzz+Pu7p6px/jf//7HRx9NAHs0w/sYebuXJ1pt5lfP2Wx2oqOjOXLKztuTVC79peDpnZ+IiAgCAgIyfT8id0lOTmb79u2EhYWxbdu2TNUANRgMNGnShA4dOtC4ceNMPweFEEII4RqSdBNCiP/g2rVrDB8+nJ07d2aqf40aNfj0008pXbp0NkcmxH/Xvn179u/fD8DJkyfz3Ioak8nEDz/8QHh4ODt27LjnVnEPDw/nSahNmjS56/fqwIEDtGvXFof1JmPeMvL6y55ZisludxAdHY3dnnrIwqW/VF77AKIT/OjZqx9TpkzJ0v2J3MlkMrF582bCwsLYvXu389/7Xry8vGjVqhXt27enbt26j9zBJ0IIIUReIEk3IYTIAlVVWb16NSEhIZhMpvv21+v1vPvuu7zxxhvyhkjkeh07dmTfvn1AahF4X19fF0fkOsnJyc6TULds2UJsbOxd++p0Op577jlatmxJ8+bNCQoKAlJPU23SpAnnzvxBp2Yqsz/M2vfT4VCJiYnBarU627RaLScvetFlSDxoA1m16jvq1av3n+Yocqdbt26xfv16QkNDnStP7ycwMJC2bdvSoUMHatasKSdhCyGEELmEJN2EECKTbty4wXvvvcfWrVsz1b9y5cp89tlnlC9fPpsjE+Lh6Ny5Mz///DMAx48fx9/f38UR5Q42m41ff/2V8PBwNm/ezF9//XXXvoqiOE9CNZvNzJg+mQJ+cexaFoifj4bvwpNYujaJA8esxJhUyhTXMrinJ707G1EUBVO8gxlfJ7JpVwqnLlgx6KF6BXi/v0KlMloCAgLQ6bSMmmHi6+9VqlSry+bNmyXJ8pi6cuUKa9euJTQ0lIiIiEyNKVq0KO3ataNjx46UL19enhtCCCGEC0nSTQghMmHdunWMHDnynqtdbtPpdLz99tsMHjwYvV6f/cEJ8ZC89NJLzgNBjh49SmBgoIsjyn1UVeXIkSPOgxjOnDlz137R0dF4edgYP1hP/64+6HQ6nn3pFsFFtLRv6k7+AA3b9qbwyfxEPnzLizGDvDl22krTV6N5pY2GmpXspFjg85UqR07BL9/6Ua1C6uEWMXEOqre7hcWRjw0bNlGjRo2c/DYIFzh16hRhYWGEhoZy+fLlTI0pW7YsHTp0oH379hQvXjybIxRCCCHEv0nSTQgh7iEqKooPPviA9evXZ6p/+fLlmTVrFlWqVMnmyIR4+Lp27cpPP/0EwKFDhyhQoICLI8r9zp07x+bNmwkPD+fgwYPOdqvVSmxsNEUKONj2lYK3p4JWqyXBbKBIISN6vZ7bC5D6j45l1cZkYg4EkZSskpCQiOowO+8r0axS6yXo1taYZovqkIlxfLtZx8vd+zJ9+vQcm7NwLVVV+eOPPwgNDWXdunXcvHkzU+Nq1KhB+/btadOmjXMLtBBCCCGyl5wxL4QQdxEeHk7Dhg0zlXDTaDQMGjSIzZs3S8JNPLI0mn9eFshncplTqlQp3nzzTTZs2MDBgwf56KOPqFevHna7HYNe5dnq4O2Zml2z2+0Y3ZKIjo7m5s2bxMWZSElJ4ckKekwJKolmFYXkNAk3gEIFfSldXMdfkY407W0auYOawm+//ZZj8xWud3sL84QJEzh48CCrVq2ia9eu9z345ODBg3z44YfUrFmTl156iRUrVhAXF5dDUQshhBB5kyTdhBDiX+Li4njrrbfo06cPt27dum//UqVKsW7dOt5//30MBkMORChE9rgz6eZwOO7RU2SkYMGCvPrqq6xatYqOHTvi6eFGtfL6DGtqORwOkpKSiImJZfveeAoXUFAdiZhM8Wn6eXl5YrG5ceyMjQql0h7GUrWcDrBx7tw5EhISsnNqIpfSarXUq1ePGTNmcOTIEb766ivatGmDm5vbXcc4HA727NnDsGHDqFq1Kr1792bdunX3PK1XCCGEEP+NJN2EEOIO27dvp0GDBnz//ff37asoCq+//jrbtm2TekrisSBJt4fnzz//RKeFGpW9KVAgP35+fhiNxjTfY4BfjzgI267y+ksqCQkJaVYYGo1GPD29eO8TE4oCb7zskWZs/kAtgX4Kqmrj4sWLOTEtkYsZDAZatGjBvHnzOHr0KLNnz6ZRo0Zotdq7jrFarWzZsoU33niDqlWrMmjQILZv357mxFwhhBBC/He6+3cRQojHn8lkYuzYsaxcuTJT/YODg5k5cya1a9fO5siEyDmSdHt4kpOTARWju4KiKBjcDGg0GnQ6HSkpKVitFq5cc/DGGHi2OrzWKXWcoig4HA7c3d3x8fFh0Roz81clsWiKL0ULpU+eeBoVouJVUlJScnaCIlfz8vKiU6dOdOrUiaioKDZs2EBYWBi//vrrXcckJiayZs0a1qxZg7+/P23atKFDhw48/fTT6ZLFQgghhMgcSboJIfK83bt3M3ToUP76669M9e/duzejRo3Cw8Pj/p2FeIRI0u3hSElJISkpCbvdTkxcIrduxWOz2dL0iYtX6TEC/H1gwQS4/a1XVRVFSU3Ubf4pmf4hcYS86UWvjhn/vrFYVUBBp5OXdCJjgYGB9OrVi169enH16lXWrl1LaGgox48fv+uYmJgYlixZwpIlSyhcuDDt27enffv2VKpUKcPt0kIIIYTImJxeKoTIsxITE5k4cSKLFy/OVP8iRYowc+ZM6tatm82RCeEaffv2ZdOmTQDs3buXEiVKuDii3M9qtRIREcHhw4edX6dOneLWrVsYdMmMeRNefiFtkiIpRaXLUJWrkbBxnoYnCunR6/WkpKRgt9sBOHBc5aWhKi+3NrLgI78MHzsh0UG55jdRNQU4fPgI+fPnz+7pisfImTNnCAsLIzQ0NNPbk0uXLk379u3p0KGD/H4QQgghMkGSbkKIPGnfvn0MHTqUS5cuZap/t27dGDNmDN7e3tkcmRCu079/fzZs2ACkrgAtVaqUiyPKXaxWK2fOnEmTYDtx4kSG9a8SEhJw2BN4+QWVCYNTl7GlLhDS0ut9B78dcbDjGz+qlnfn9sIhm81OVFQUJ8876PCWytNVFDbMD0Kvz3hl0a+HLHR4M56CRSpx8ODBbJq1eNypqsrhw4cJCwtj7dq1REZGZmpctWrVaN++Pe3ataNgwYLZHKUQQgjxaJK9CEKIPCU5OZmPP/6YBQsWkJnPHIKCgpg+fTqNGjXKgeiEcC3ZXvoPu93O2bNnncm1I0eOcOzYsUzXTjMYDCSZdfx62I7R6IXRaECv1/F6SBxbdicx/X1vkpI1/HrI4hxTvaKexGR3ug1PxN0N+r0Ie36Px+juDoCPl0LFMnpn/x37LKAYePLJJx/q3EXeoigKTz75JE8++SQhISHs27eP0NBQNm7cSFxc3F3H3f7ZGD9+PHXq1KFDhw688MIL+Pn55VzwQgghRC4nK92EEHnGgQMHePvttzl//nym+r/44ouMHz8eX1/fbI5MiNxh4MCBhIWFAbBz507KlSvn2oByiMPh4OLFixw6dMiZSDh27BhmszlT4xVFoVSpUlSrVs35VbZsWerVq8etyNPMn+jBCw1TE2fBDW5w6ao9w/u5sDM/56/YaNwzJsPr9WsZ2LUsEACLReWpjre4ZfJnwYJFtGrV6j/MXIi7s1qt7Nq1i++//56tW7eSlJR03zF6vZ4GDRrQoUMHmjVrJrVPhRBC5Hmy0k0I8dizWCxMnTqVzz//PFOrd/Lnz88nn3xC8+bNcyA6IXKPvLDSTVVVLl++7Fy9dvvP+Pj4TN9HcHBwmgRb5cqVM9x63q1bNz6b9QmzvzHTvJ4bOp3CxV0F7n3fRXWYj/inWWFkNBrx9fVJ02/ZuiRuxWgJKlyEZs2aZTp2ITJLr9fTtGlTmjZtSmJiIlu3biUsLIydO3emOxjkNqvVyrZt29i2bRtGo5HmzZvToUMHGjRogF6vz3CMEEII8TiTlW5CiMfakSNHePvttzl16lSm+rdt25aPPvqIgICAbI5MiNxn0KBBrF69GoDt27dTsWJFF0f0YFRV5a+//kpTg+3w4cP33DL3b0888USaBFuVKlUyvfo1MjKS+vXrY4q5yKgBbrz5imcm44bo6Og0teICAwOcSYsr1+w0fCUas9WfSZOm0Lt370zPR4gHFRMTw8aNGwkNDWXfvn2ZKtXg6+tL69at6dChA88880yaBP+DiI+P59dff+Xo0aNcu3YNVVUJCgqiSpUq1K5dW7a6CiGEcDlJugkhHktWq5VZs2Yxa9Ys52mA9+Lv78/kyZNp06ZNDkQnRO4RERHB6tWrOXDgALt27XKu+CpZsiS1atWidu3adOnShaCgIBdHen+RkZHpEmxRUVGZHl+oUKE0CbaqVas+cAJ+1apVDB06GL0SxcpP/ahT3ZCpcVarLU3ser2egIAAzEkOurwdy8EIN555tjGrV69+aAkMIbLq2rVrrF27lrCwMI4cOZKpMUFBQbRv35727dtTtWpVFCXjg0Lu5cqVK8yePZs1a9aQZDYBVlD//r9e0QJ6DG6etGvXjkGDBlG6dOksP4YQQgjxMEjSTQjx2ImIiGDw4MEcP348U/1btGjBlClTyJ8/fzZHJkTucfz4cT788EN++eVnUJNAtWC3pQCp20o1Gh2KxgCKGzq9F+3atePDDz/MNT8nN2/edG4Pvb1FNLOnLkLqNvInn3ySqlWrOhNsBQrce+vnf6GqKv369WPTxlA83Ux8PdmXuk9lLvEWF2dKU0dL0fowYEwS+45o8fUvRnh4OMHBwQ89ZiH+i/PnzxMaGkpoaGima6eWKFGCDh060L59+0wlxlRV5ZtvvmH8+PGYE2+Bw0xwEahZWU+JoloUBS7/5eDAMStnLztA8cDg7sd7743gjTfekAS1EEKIHCdJNyHEY8NmszFnzhxmzJiRZlvW3fj4+DBp0iQ6duz4nz5pF+JR5HA4mDVrFjNnzsBmiUWrJNHieQPN6rpRonAKPp7JOFQwmX05fkYl7IdkfjtqB40n/gFFmDp1ao4X7Y+JiUmTYDt8+DB//fVXpscHBASkWcFWrVo1goKCcuznPjk5mV69erH7px/QYGJgNyPD+3hiMNz78R0OB7du3cLhUPn5D5Xxc1RuxHjg7VeUFStWUKNGjRyJX4isUFWVY8eOERYWRmhoKNevX8/UuMqVK9OhQwfatWtH4cKFM7zfcePG8eWXn4M9jtrV4L1+XjzzpD7dz7Kqqvxxwsb0hQns/NUBWl9efPFlZsyYgVarfSjzFEIIITJDkm5CiMfCmTNnePvttzl06FCm+jdq1Ihp06ZRsGDB7A1MiFzE4XDw7rvvsmLFN2CPpeXzOiYO9aZQgdQ3oSaTCbM5dWVVQEAABkNqDbFDJ6wMn2LixDkNitaPqVOn061bt2yJ0WQyceTIkTRJtsuXL2d6vI+PT7oEW5EiRVyeWE9JSeHdd99l9epvwRFHcGE7fV704MUW7vh4Z7z6xuFQ2bYnnq9XJ7LjV0hIVAjMX4Tw8HAqV66cwzMQIuscDgf79+8nNDSU9evXExsbm6lxzzzzDO3bt6d169bOLd5z585l4sSxYI9h9EAP3njZA43m3j/XqqqybF0S709LxI4fAwYOJiQk5AFnJYQQQmSeJN2EEI80u93Ol19+yZQpU7BYLPft7+Xlxbhx4+jatavL34QLkdM++eQTPp05DQ0xTH3Pi66t3dP8HJhM8ZjNZiBt0g3AalUZ81k8i763ougCWbx4CU2aNHmgeBISEjh27FiaFWwXLlzI9HgvLy/n9tDbW0SLFy+eq3+2N2/ezIgRI7h54yqoZtz0FqqU1VG1nJ6iBTVotQqmBAdHT9k4FGHlRrRCotlBYpKK0eiBr68vu3btkhpV4pFjtVr58ccfCQsLY/Pmzc7fNfei0+moX78+Tz31FNOnTyPBdI38ARAdq2JKcFAkSEv7pu6MGeSFr7cGu11l+sJENuxM4cQ5Kw4HVCuvp1ldA7O/SUbR5ef770OpXbt2DsxYCCGEkKSbEOIRduHCBYYMGcJvv/2Wqf5169ZlxowZFC1aNJsjEyL3OXjwIG3btsFhvcnI/u6cOGtn3yELx87YKF9Sx7FN+dMk3dyN/kyZn8yqjclcv2WnaEEtvToYiYlzsHyjSoGCZdm1a1emTwdMSkri+PHjaRJsZ8+ezdTJhwBGo5EqVaqkWcFWokSJR7JGU0JCAmvWrGHRokWcOhUBqg1UK6n19FRAAUUPig4vLz+eeeYZNm/ejE6nA6Bhw4YsXbo0VycXhbgXs9nMDz/8wPfff8/OnTvvWxIiJiYGgy6FiqWgTnUDz9Y0ks9fw7HTNsbOjqdGRT1bFwWSkOjgiedv8GpHD5o8a0CrhS9Xmlm3I4X2Tdz5+ZCWqk/WJTw8XH5+hBBC5AhJugkhHjkOh4NFixYxceJEkpOT79vfaDQSEhJCz549H8k36EI8KFVVad68OceO/EzHpipNn3PjrXFx1K5m4PRFGw4H6ZJuI2foCd1m4aNh3lQsreOXP6x8OCueYa958vMfFs796UXv1wYwadKkdI+XkpLCiRMn0hxycOrUKRwOR6bidXNzo3LlymlWsZUuXfqxq8Wkqirnzp1zbqeNiorCbrfj4eFB+fLlqVq1KlWqVMFoNNKnTx/Cw8OdYxcvXkzTpk1dGL0QD0dcXBwbN24kLCyMvXv3pkvE22w2oqOjyOfnYP3nCk8UUtBoFNzc3HF3d2dxqI3XQ+K4uqcAQfk0mBJU/H3/+b/eblep3OomxQtrOXnBjsWRjw0bNklNRCGEEDlCkm5CiEfKlStXeOedd9i7d2+m+teuXZuZM2fKCX8iT/vtt99o164N7tpb/B6aDz8fxVkL6dX3Yvn9mDVN0s3hUCnTQuXdvl6MHeztvJ9e78Wy53cLX0/2pevQeDx9ivPrr79y9erVNCvYTp48ic1my1Rser2eihUrplnBVqZMGfR6/f0H5yGXL1/m+eefd26jDw4OZteuXRgMmTsJVYhHQWRkJOvWrSM0NNRZozUxMRGbJZ5mz6l8Pjb9B2fhP8Froxyc2upPmWB3MlrA1uXtGM5eslGnuoHvf9Az8M3hjB49OptnI4QQQoDO1QEIIURmqKrKsmXLGDduHImJifft7+bmxvvvv0/fvn1ldZvI85YvXw4OMx1auhPgd/efh9tvVlUVbDbw9U777tXHS0FVVZ6qbKdEETuHT13i/+3deZxOdeP/8fe5ttmvmTFjSzSSrczI1kqRpYXkjhstaCGUhLKF3KGksZaiUiktJFuLRJFS8itki4SsMWH29drO74/pO91zI1NdM9csr+fj4fG4z+d8ruP9eTzuh2be1+ecU69evSIXPzabTfXr1y9UsDVo0IDiqAhq1aqlhx56SDNmzJAkHTx4UC+//LIGDRoU4GSA/1StWlX9+vVTv379dPDgQS1fvlzPPvussnwZat7oj3+PvF5Tbo+096A07XVTN7aUnCFpyshwy+mMKHRNj8fUtz+41aq5Xdc0cWjpGpe2b99ewisDAFRUlG4ASr3jx4/r0Ucf1RdffFGk+U2bNtWsWbNUp06d4g0GlBHff/+9ZLrUsXXYeWbm/1JrtRrq1cWh2Quy1LyRVKemT99udWnBMreG9DGUnp6hNlea2rHXo9zc3LOWZhaLRfXq1St4wUHjxo116aWXKjg4uBhWWDE89NBDWrhwoY4fPy5Jmjlzprp168ZbmFEuxcXFaciQIVq1apW2bTmty+pZZbV65PV61eLfpo6fzJ/X5krpxSfy/+06279Fz76SpWNJXg29Nzp/wMz+Sy9sAQDgn6B0A1BqmaapxYsX64knnlB6evp559vtdg0fPlwDBgwoeOA4UNFlZmZq//79kjxq3KAot2ya8vlMPfmQSzk5pq67M6PgzMN3SwN65v9y27COZLeZysxxyzAM1alTp9AOtssuu0yhoaHFs6gKKjQ0VOPHj9eAAQMk5T+M/qmnntLzzz8f4GRA8fF4PDIMQ9GRIYqNDZLb7db7z2cpJc2lPft9mvmmqd6jTC2eaVFQUFChz67ZkKfxz2XoiYfC1ayRXTv3uiWZRb79HQCAf4rfSgGUSr/99puGDx+uNWvWFGl+fHy8Zs2apQYNGhRzMqBsSUlJkeSTwy7FRBflVmtDkqmn5pr6fKM0baSh2hdKW3ZJ0+abioow9XBvm+IutMpud+uiKhdp8+bNioiIOO+V8c/deuutmj9/vr799ltJ0pIlS9S7d2+1aNEiwMmA4hEVFSUZFp045ZNhSA6HXdc0i5JpSu2udenKy7PU6s5crfnGpt5d//jcll1udR2UojtvDdETD+f/+5R0yifJUuS3LgMA8E/xoCMApYppmlqxYoVat25dpMLNZrPpscce00cffUThBpyFcbanip9DRES4qlator2HDM1ZKD073FCv22xqc1WIRg2I0Mh+IXr2VVPBwZXkjAiXxWJVREQEhVsJMgxDkyZNKvSsyrFjx8rr9QYwFVB8LrvsMkl2bf/JXWjcMKSgIIeubR4lu106evKPnbz7Dnl08/3JuqapQ/OeiiwY37bHLRl2NWrUqKTiAwAqOEo3AKXG6dOn1b9/fw0cOFCpqannnd+wYUOtXLlSw4YN402HwDlER0fLMKxyuaWTp89fzBiGoSMnQiRJ118Vo8qVYxUVFamwsDC1SAhWnks6esKrI8e9kmFRTExMcS8B/+PSSy9Vr169Co537NihhQsXBjARUHyuvPJKyQjSx+vy5HabZ5zf9INbbrd0Sa38G3iO/+ZVh3uSVesCq95/Pkp2e/4XD6ZpasVneZIRpCuuuKJE1wAAqLgo3QCUCp988olat26tjz766LxzLRaLBg8erE8++YRvq4HzCAsL+/2lIjZt/6lozzGqVzu/dPthd+GSbvNOtwxDuqjG/13Lrvj4eD8nRlGMGDFCkZF/7OCZPHlykZ59CZQ17du3V5WqF+i3ZKuu7XFaT8/J1Edrc/X5N3ma/lqm/vVQihIa2NSlXbByck3dfH+yTqX49MRD4dq516Nvt7r07VaX5r6TrZ8PmQoNi1KXLl0CvSwAQAXBM90ABFRqaqrGjh2rpUuXFmn+JZdcolmzZqlJkybFnAwoP1q0aKF9e7fpo3V5antNkLJzTK38IleSdOhXr9IzfXr/kxxJ0vVXONQ83q7m8Xb1H5empFM+XXKRVZu2uTX5pUzd1y1EwUHSh2tzJcPJs8QCJDo6WiNHjtTjjz8uSUpOTtbUqVM1YcKEACcD/Mtut+uBBx7QpInj9cuxZL37YY6eeckrnynF1bCqX/dQPXZ/mBwOQwePerRtT/6XC50HpBS6jsMuxcRUUp8+fbglHgBQYgzTNM/cpw0AJeCzzz7T8OHDlZSUdN65hmGof//+GjFihIKDg0sgHVB+bN68Wbfe2lFB1lP6bkmsMrN9qt3m5FnnrnurklpfGaQTJ70aNzNDa7526bfTXtWsbtUdnUI08oFwffuDS3c9mqHwyDht2bJF4eHhJbwiSPlvdbzxxhu1e/duSZLVatVnn32m+vXrBzgZ4F8ej0edOnXS9h++UcOL8/TerOgivhhGysj0qdfwVP2/HTbFXZygzz//XCEhIcWcGACAfJRuAEpcenq6xo8fr0WLFhVpflxcnGbOnMkzWIC/yTRN3XLLLdq2dYM6t/Fp7sTI83/oHHJyTbXtfVoHj4erb79B7KwKsI0bN6pr1z9e2diyZUstWrToL71AAygLDh48qNtuu00nkw7ogsp5mj46QtddEfSnn/l+h0tDnkrXgaN2OaNqasmSJb+/mAEAgJJB6QagRH355ZcaNmyYfv311yLNv/feezVmzBiFhoYWczKgfNu+fbs6drxFXtdvmjoyTHd2/us7PUzT1LCnM7ToE5+qXdBAX3zxhZxOZzGkxV8xYMAAffDBBwXHr776qm6++eYAJgKKx/79+9WrVy8d/OUnyZeuqy+36Y5OwWoeb9dFNaySpKMnfNr6o1uLPs7Ruk1uyeJU1WoX6Y033lBCQkKAVwAAqGgo3QCUiKysLE2cOFFvvvlmkeZfeOGFmj59ulq2bFnMyYCKY/r06Zo69RkZvhQ9NTRMfW4PKfKOKJfL1JjpGXr7Q7cs9li99dbbat26dfEGRpEcO3ZMrVq1Um5u/nP6atasqfXr13MrPsql7OxsPf3003rjjfnyerIlM1cy3TIMnwxJPtOQDLtkBMuwhKh79x76z3/+U+jFIwAAlBRKNwDFbuPGjRo6dKgOHz5cpPl33XWXnnjiCR50DPiZaZoaPXq03nzzNcmbqnbX2DRpaIRqXWD90899t92l4VMytPeQRRZbJU2fPkPdu3cvodQoihkzZigxMbHgePjw4Ro6dGgAEwHF6/jx43rrrbe0bt06/fjjj3K58iRJdrtD9evX13XXXae7775bcXFxgQ0KAKjQKN0AFJucnBxNnjxZ8+bNK9L8atWqadq0aWrTpk0xJwMqLtM0NWfOHE2Z8ozceakylK121zjU4dogJTSwqVqsRT5TOnjUq217PFq2Jlfb9nglS7hiK9fUtGnT1L59+0AvA/8jNzdX119/vY4cOSJJCg4O1oYNG3TBBRcEOBlQ/Nxut1JSUmSapqKjo+VwOAIdCQAASZRuAIrJ5s2bNXjwYP3yyy9Fmv/vf/9bEydO5PlQQAnZs2eP/vOf/+jLL9dLZo5kuvP/yPf7DOvvt2g5ZHdEqGvXrho7dqwqVaoUyNj4EytXrlTfvn0Ljjt37qy5c+cGMBEAAEDFRukGwK/y8vKUmJiouXPnyufznXd+5cqVlZiYqA4dOpRAOgD/a//+/Vq8eLG2bNminTt3KjU1VYZhqGrVqmrcuLGuvPJK/fvf/1ZMTEygo+I8TNNUjx49tGHDhoKxJUuW6Oqrrw5gKgAAgIqL0g2A32zfvl2DBw/W3r17izT/tttu09NPP63o6OhiTgagqP7vx4KivmABpctPP/2kdu3ayev1SpIaNmyoTz/9VDabLcDJAAAAKh5LoAMAKPvcbrcSExPVsWPHIhVulSpV0ksvvaQ5c+ZQuAGljGEYFG5lWP369XXPPfcUHO/evVtvv/124AIBAABUYOx0A/CP7N69W4MHD9auXbuKNP/mm2/WM888o8qVKxdzMgComNLS0nTttdcqOTlZkhQVFaWvv/6aLzkAAABKGDvdAPwtHo9Hs2bN0k033VSkws3pdGr27NmaN28ehRsAFKPIyEiNHj264Dg1NVWJiYkBTAQAAFAxsdMNwF/2888/65FHHtEPP/xQpPlt27bV1KlTVbVq1eINBgCQJHm9Xt1yyy3asWOHJMlisWjNmjVq2LBhgJMBAABUHOx0A1BkXq9Xc+bMUfv27YtUuIWHh2v69Ol68803KdwAoARZrVZNmjSp4Njn82ns2LHiu1YAAICSQ+kGoEh++eUX/etf/9LEiRPlcrnOO79Vq1Zat26devbsyUPZASAAWrRoodtvv73geOPGjfrwww8DmAgAAKBi4fZSAH/K5/Pp9ddf11NPPaXc3Nzzzg8NDdW4cePUu3dvyjYACLATJ06oZcuWys7OliRdcMEF+uqrrxQSEhLgZAAAAOUfO90AnNPhw4fVvXt3jRs3rkiF21VXXaXPP/9cffr0oXADgFKgWrVqGjJkSMHxr7/+qhdeeCFwgQAAACoQdroBOINpmnrrrbc0YcIEZWVlnXd+UFCQHn/8cd1///2yWOjyAaA0cblcat26tQ4ePChJcjgc+vLLL1WrVq3ABgMAACjn+O0YQCG//vqr7rzzTo0cObJIhVuzZs302WefqV+/fhRuAFAKORwOPfnkkwXHLpdLEyZMCGAiAACAioHfkAFIyt/dtmjRIrVp00br168/73y73a4xY8Zo+fLlqlOnTgkkBAD8Xe3atVObNm0KjleuXKkNGzYEMBEAAED5x+2lAJSUlKQRI0ZozZo1RZofHx+vWbNmqUGDBsWcDADgL/v371ebNm3k8XgkSfXr19fq1atlt9sDnAwAAKB8onQDyoH9+/dr06ZN2r59u44fPy7TNBUVFaXLLrtMzZo1U9OmTc9666dpmlqxYoVGjx6ttLS08/49NptNQ4YM0cMPP8wvaQBQBk2YMEFz584tOJ44caLuv//+ACYCAAAovyjdgDLKNE2tXr1aL7/8sjZu/EYyXZLpluT9fYZFMuySYVft2pfonnvuUZ8+feRwOCRJp0+f1qhRo/Txxx8X6e9r2LChZs2apUaNGhXPggAAxS4jI0MtW7bUyZMnJUlOp1Nff/21YmJiApwMAACg/KF0A8qgkydPauTIkVq16mPJlyWrkaurm9iVUN+m2hfaZLFISad82rHXrQ2bXcrItktGmOo3iNfMmTN17NgxjRw5UqdPnz7v32W1WjVo0CANGzaM3W0AUA4sWrRIQ4cOLTi+6667lJiYGMBEAAAA5ROlG1DG7Nu3T927d9eJ4wdkt2TpgR7Buq9bqKpXsZ51fla2T0tX5ypxXpZOpgQpK8eQzWZXcHDwef+uunXratasWbr88sv9vAoAQKD4fD7deuut2rp1qyTJMAytWrVK8fHxAU4GAABQvlC6AWXIsWPH1KlTJyUd36e6tVyaO8GphpcUbffZ8d9y9OjTafp0g6m0TIsiI6MUFBR01rmGYWjAgAEaMWLEOecAAMquLVu2qFOnTgXHzZs314oVK2QYRgBTAQAAlC9nPlkdQKnk8/k0dOhQJZ04oPpxLi17MbpIhZvPZyotLV2GL12Jw6UubSVnuE/p6eny+XxnzI+Li9Py5cs1btw4CjcAKKeaNm2qHj16FBx///33WrZsWQATAQAAlD+UbkAZsXDhQm3Y8IWCbVnqdlOw7h2VqgtbJiks4YQuv/WkXlucrf/duDr3nXTVbXdCVa/JUtt7fFr7rfSfhw1dVkcKsnuVkZFRaP59992nzz77TC1atCjJpQEAAmD06NEKDw8vOJ44caKysrICmAgAAKB8oXQDygCfz6cXXnhB8mVoeN8wvbUiR6HBhqaNdurDl6J18/VB6jc2TRNmZ/4+39Rri5P14H+y1PkG6e1EQ80aSfeNMbX9J2nCYENhIaby8nLl9XpVs2ZNLV68WJMmTVJoaGiAVwsAKAlVqlTRsGHDCo6TkpL03HPPBTARAABA+cIz3YAy4KuvvlKPHt0UEXxaW5bHKidXiq1UuDN/YGyqFn2cq6SN0crIyNDVPdxKqC+9OP6PebcO9MkZLr2daNGA8T6t2WioWfOWWrlyZaHdDgCAisHtduuGG27Q/v37JUl2u13r169XXFxcYIMBAACUA+x0A8qA9evXS2auOrUJVlio5YzCTZKaNLQrPdPU0V9TdOCIR/uPSLe2KfxA7NvaGtqwWcpzmerS3qKIMLvCw8Mp3ACggrLb7ZowYULBsdvt1vjx4wOYCAAAoPygdAPKgO3bt0umR80uO/eLEzZsdqlGVYsiwizadyh/7JKLCs+pe5Hkcku/pQTruisqyWLxadeuXXK73cWYHgBQmrVp00bt27cvOF6zZo3WrVsXwEQAAADlgy3QAQCc3+HDhyXTo3q1Q856fsP3Li38OFeTHrHLapVSM/LfShr5PxvYop0WSV65vaGqXdMmh92Uy52nEydOqGbNmsW8CgBAafXkk0/qiy++KPgS5oknntDatWuVl5enHTt2aOfOnUpNTZVhGKpatari4+N16aWXyuFwBDg5AABA6UXpBpQBHo9HkmS3GWecO3TMre6Dk3VtE6lPF488HkNWa3659t+Cg4MVFRUi6bQkyTAMOeySK++P6wMAKqa4uDj1799fs2fPliTt3r1bbdu21aFDh+R250imR5Lv99kWybDL6YxWt27ddO+996pOnToByw4AAFBacXspUAaEhYVJMpSa7isY83p9OnwsXTfdd0pREabmTTJkseSXcpUirZKk9CzJYrEoKipKUVGRSsswfz9vUV6eqewcU5Lx+/UBABXZI488opiYGKWlpSktLUXbtm6QO/dXXRCTqpuuzdW9//Koz20eXd8sR9FhyUpPOaDXXp2tNm1aa+rUqTyqAAAA4H+w0w0oAxo2bKiff9qsHXs9atXCoaysLJ1OyVL3IT6lZ0ofzTXkDDdksVgUFh6mZvEOSSd1+LhdVzePltWS36/vOeCRwy5dXNOqXT975DOtiomJVeXKlQO7QABAwB05ckRZWVmSmaOYKFNd2kp9/mXX1c2izpjr85n66juXXnkvW2u/zdb0ac9o3bp1WrBggSpVqlTy4QEAAEohdroBZUDjxo1lyq61G7N16tRJpaVl6oFxPu07JL0z1VCNqlZFRIQrtnKswkJDVecim+rVtmrll5aCwk2SFq3MUdtrguRwGPrqe5dkONS4cWMZxpm3rQIAKo59+/apW7duys3+TQn1pAVTDP1nkEW1a+SddQebxWLo+iuD9Na0aL00IVxRYWnauuUb9ejRQ+np6QFYAQAAQOlD6QaUcjk5OUpOTtbp5Ex9vdmjA0d8GjXd1JpvpEd6G/KaIdp31Kkde+36fz+4lZeXfwvpfx6O0Dsf5mr8rAx9sSlPA59I06Ztbo17KFxer6kFK3IkI0S33XZbgFcIAAgkt9ut/v37K/nUISXU8+jDlyspvt4fX8akp2fINM/9+VvbBmvF3GjFRmZq187NGjNmTAmkBgAAKP0M0/yzH6MABIrL5dLbb7+t5557TklJSUpNTZXNkqubW5n65gfp6Imzf+6XdZUVd2H+neOvLs7WMy9l6vCvXtW/2Kanh0Wo0w3BenVxtsbNzFV0bB1t2bJFQUFBJbcwAECpMnXqVE2f9ozslhTVqGrVzwe9Ss/0qVqsdFMr6dF7DdWsEaWQkGAlvpKpdz7M0S9HvXJ78h9X0L9nqB66O1Rbdnl028BU+YwYvfHGArVv3z7QSwMAAAgoSjeglPF4PHr//fc1ffp0HT16tGDc7XYrNTVFlSJNzRwToTtuDf1b1//5oEc33pusXG8lTZ6cqD59+vgrOgCgjMnIyFCTJk2UnXFYPTvaZbMaurKxXdGRhjZtTVXia6bi60mLZ9oUGxurcTMzFBFmUaO6NgUHGfp8Y56mvJylSUMj9PjAcE2cnaE5C32Kb9xSq1at4vEFAACgQqN0A0oJn8+nDz/8UImJiTpw4EChcxaLRf/+97/ldDr1yssvym5J1UsTI3TTdcF/6e/45YhH3R5O0fHT4WrZqr0WLlwoi4W7zAGgonr99dc15vHHVLdmpr54u1KhkiwrO1tz3krX8ERTW5cZqnNRuCIiws+4xl3DUvTdDrf2rqmi5FSfmnY5JZcvVh99tFJNmzYtyeUAAACUKvy2DQSYaZpavXq12rdvr4EDB55RuHXu3Fnr16/XjBkz9MQTT+i2Lt3k9kWp75gMTZydody88/fmpmlq0cc5urlvio6fDtMldeM1Z84cCjcAqOA+/fRTyczVXZ1DztiVFhoSqthKVkmS2y1lZ2fJ4/GecY2YKItcv79roVKURR1bB0lmbv61AQAAKjBboAMAFZVpmtqwYYOmTJmiLVu2nHG+ffv2GjlypC699NKCMavVqueff15Op1MLFryhOQvTtfyzU+rdJVS33xisC6tZCv3SlJru0+oNeZq/NEc/7DYlS5SaNrtS8+fPV0xMTImsEwBQOpmmqW3btkmmW1dd/scjC7xeU26P9OM+j2bMN3VjS6lmdUOmmX87alRUlLxeUzm5pr78zqU3l+do/MN/7IC7srFdyz5za/v27YFYFgAAQKnB7aVAAHz33Xd65plntHHjxjPOtWrVSiNGjFCzZs3+9Bpr1qzRiBEjlHTiqGRmS2aeop2Gal9olcUiJZ3y6cgJn2TYJSNEjiCnhg8frv79+8tmo28HgIru1KlTSkiIl7y/6eC6KnI48r+0ubBlko4l+SRJN10XpHmTLLIaeQWf+y0lVI07ZxYcj30wXBOHRhQcb97p1q3901WjVoK+++67EloNAABA6cNv3kAJ2rFjh6ZMmaK1a9eeca558+YaNWqUrrnmmiJdq3379vr222/14YcfasGCBdq6datSsvKUsuf3W38Mi2S1qU6dOurevbvuuOMOxcbG+nM5AIAyLC8vv0hz2I2Cwk2SVs6rpKwcU7t+9mjSi5m6e7hFbz8rORw2maZPkWFZ+nqhUy6PTV9959IzL2fJYpGefCS/eAsNliRTubm5AVgVAABA6UHpBpSAvXv3KjExUR9//PEZ5xo1aqSRI0fqhhtu+MtveQsKClK3bt3UrVs35eXlaffu3UpKSpLP51NkZKQaNWokp9Ppr2UAAMqRoKAgSZLLbcrlMguKt4QGdknS1U0cahFv1+WdT+mzb4PV4RpX/ucchurWylFMTIxaXxkkZ7hFjz6TroF3hqpaZauycyXJUHDwX3vZDwAAQHlD6QYUo4MHD2r69OlasmSJ/vdO7rp162r48OG65ZZb/PJCg6CgIF1++eX/+DoAgIohJiZGkZFRSks+rT0HPAVl239LaGCT3S4dOV74v1Ner1eZmZlyOiPUrJFdXq908KhX1Spbtetnt2TYdMkll5TUUgAAAEolSjegGBw/flwzZszQwoUL5fF4Cp2rVauWHn30Ud1+++2yWq0BSggAqOgMw1Djxo315ReHtWmb+6yl26Yf3HK7pbq1HQoJMZSTk1NwLjs7WyEhwdqw2SXDkGrXzP9v2qZtbsmwKyEhocTWAgAAUBpRugF+dOrUKT3//PN644035HK5Cp2rVq2ahg4dqp49e8puP/MXGwAAStqNN96oL9ev0VsrMvXJ+lw1j3coob5NIcGGtu1xK3FelhIa2NSlXbCychzq/GCWurY3FHeh5PFI325L1svv+dS/Z6iqxlqVnOrTx1/kSUaEOnToEOjlAQAABBRvLwX8IC0tTXPmzNG8efOUnZ1d6FxMTIwefvhh9e7dm+fbAABKlfT0dDVt2lTZGYfV7hqbdvzk1v7DXvlMKa6GVbd3CNZj94fJGWFRXp6pvmOS9fVml46flEKCpLga0gM9QtSvZ6SsVkOTXsjQi+/6FN+4pVatWvWXn1UKAABQnlC6Af9AZmam5s2bp7lz5yo9Pb3QOafTqYEDB6pv374KCwsLUEIAAP5cYmKiZkyfokoRaVq3oJIqx5z70QemKaWmpha8+VTKv001JiZG2/b4dNvAVPmMGM2f/yY73QAAQIVH6Qb8Dbm5uXrjjTf0/PPPKzk5udC50NBQ9e3bVwMHDlRkZGSAEgIAUDQul0s33XST9vz4nRLqubVoVpQiI879gh+P16vTp04XekHQ0SSb7hvj06m0CN3e9U7Nnj27JKIDAACUapRuwF/gdrv17rvvaubMmTpx4kShcw6HQ/fcc48GDRqk2NjYACUEAOCv27dvn7p06aLkUwdVp6ZLz42NVJPLzv380aysbGVkZEiSVn9tatIcUymZobq8yTVasmSJnE5nSUUHAAAotSjdgCLwer1aunSppk2bpsOHDxc6Z7PZdMcdd2jIkCGqXr16gBICAPDP7NmzR3fccYeSThyURZnqcUuQ7usWosvqnlm+eb2mPl57Sm8s8+jL76WMLEN2R5i2bdumiy++OADpAQAASh9KN+BP+Hw+ffzxx0pMTNS+ffsKnTMMQ127dtWwYcMUFxcXmIAAAPhRSkqKxo4dq2XLlki+bMnM0QVVDCXUt6l6FYtMU/rliFfbf/IoOU3KyPIqN89QaGiYwsLC1LNnT82YMSPQywAAACgVKN2AszBNU59//rmeffZZ7dy584zzHTt21PDhw1WvXr0ApAMAoHht2rRJr7/+ulauXCmPJ1cyPZK8kgxJFsmwKyIiSlWqVNFPP/0km81W8Nn33ntPLVu2DFR0AACAUoPSDfgfX3/9taZMmaLvv//+jHNt27bViBEjFB8fH4BkAACUrIyMDO3cuVM7duxQamqqLBaLqlSpooSEBF166aVyu91q3bq1jh07VvCZ2rVra+3atQoKCgpgcgAAgMCjdAN+t3nzZk2ZMkUbNmw449w111yjkSNHqkWLFgFIBgBA6bVmzRr16dOn0NiQIUM0YsSIACUCAAAoHSjdUOH9+OOPmjJlitasWXPGuSZNmmjUqFFq2bKlDMMIQDoAAEq//v3768MPPyw4ttlsWr16tRo0aBDAVAAAAIFF6YYKa9++fZo6dao++OCDM841bNhQI0eOVPv27SnbAAA4j6SkJF1//fVKT08vGGvWrJlWrFghi8USwGQAAACBw09BqHCOHDmioUOHqnXr1mcUbhdffLHmzp2rNWvWqEOHDhRuAAAUQdWqVTV27NhCY5s3b9aCBQsClAgAACDw2OmGCiMpKUmzZs3S22+/LbfbXejchRdeqEcffVRdu3Yt9AY2AABQND6fT//617/03XffFYxFREToyy+/VNWqVQOYDAAAIDAo3VDuJScna/bs2Xr99deVl5dX6FyVKlX0yCOP6K677pLD4QhQQgAAyoe9e/eqffv2hb7c6tixo1555ZUApgIAAAgMSjeUW+np6XrppZf08ssvKysrq9C5qKgoDRo0SPfee69CQkIClBAAgPJn6tSpmj59eqGx119/XTfeeGOAEgEAAAQGpRvKnezsbL322mt64YUXlJaWVuhcRESE+vfvr379+ikiIiJACQEAKL/y8vLUrl077d+/v2CsevXqWr9+vcLDwwOYDAAAoGRRuqHcyMvL04IFC/Tcc8/p1KlThc4FBwfr/vvv14MPPqjo6OgAJQQAoGLYuHGjunbtWmjs/vvv18SJEwOUCAAAoORRuqHEeTweffbZZ/rss8+0bds2HTt2TKZpqnLlyoqPj1erVq3UuXNnhYaGFul6brdb7733nmbMmKFff/210Dm73a5evXpp8ODBqlKlSnEsBwAAnMWjjz6qd999t+DYMAx99NFHatKkSQBTAQAAlBxKN5QY0zS1ePFiPfPMMzpx4qjky5Xklkzv7zMskmGXjCA5I2M1YMAAPfTQQ7Lb7We9ntfr1YoVKzR16lQdPHiw0Dmr1aru3btr2LBhqlGjRrGuCwAAnCk1NVXXXXddod3nDRs21KpVq87533YAAIDyhNINJSIjI0ODBg3SmjWrJF+6YiI96nZTsFrE21Wnlk0Wi3T0hFdbdrm15NNcHTxmSBanGsU30auvvqqaNWsWXMs0Ta1atUrPPvusfvrpp0J/j2EY6tKlix577DHVrl27pJcJAAD+y/Lly/Xggw8WGhszZoweeuihACUCAAAoOZRuKHaZmZnq2bOntmz+RnZLuh67P0z9e4bK4TDOOt/nM7V8Ta7GzshUamaIqteoq2XLlqlmzZr64osvNGXKFG3fvv2Mz910000aPny4GjZsWNxLAgAARWCapu6++26tW7euYCw4OFhr165VXFxc4IIBAACUAEo3FLsHH3xQy5ctVFRYht6dEaXGDc+8pSQzy6cGN57UsSSfvlsao+bxDp046VX3wanadyRYF9aqr2rVqun7778/47PXX3+9Ro4cqcsvv7wEVgMAAP6Kw4cPq02bNsrJySkYu+666/Tuu+/KMM7+BRwAAEB5YAl0AJRvn376qZYvXyqLMvRmYuRZCzdJmvhCpjzewmPVKlv11rRwRYRkaPu277V+/fpC56+44gotXbpU7777LoUbAAClVK1atTR8+PBCY19++aWWLl0aoEQAAAAlg9INxcY0TU2dOlXyZWhAz2A1j3ecdd6e/R698Ha2nhwcXjDmdnuUmpoqhyVVI/pKEWGmsrKyZJqmEhIS9M4772jZsmW66qqrSmo5AADgb+rbt68aNWpUaGz8+PFKTk4OUCIAAIDiR+mGYvPDDz9o164dCrLn6aG7w8457+EJaRpwR6jqX2yTJGVkZOr06dPKzc2TJN3cSoqrIQUHGerVq5c++eQTtW7dmltSAAAoI2w2mxITE2Wx/PGjZ3JysiZOnBjAVAAAAMWL0g3F5quvvpLMPLW/1qHoyLP/X+39T3K0Y69HYwaGKjMzW5KU53IVmmN32NTtphA5I4KUkZFB2QYAQBnUuHFj9e3bt9DYokWLtGHDhgAlAgAAKF6Ubig2O3bskEy3mpzjOW7ZOaaGTU7Xfx4Olis3WS5XXqHzVqtVTqdTsTGxuvLyEMn05F8TAACUScOHD1eNGjUKjY0YMUK5ubkBSgQAAFB8KN1QbE6ePCnJp5rVrWc9P+nFDFWNter+f4dJ+uMluhbDoghnhGJjYxUaGiLDkGpVt0ry6tSpUyWSHQAA+F9YWJgmT55caOzgwYOaOXNmYAIBAAAUI0o3FJv/uw3UZ5557tAxj6a9mqUnB4crK8eiXHewsnPz59uCnDLNYP33XaRenyRxWykAAGVdu3bt1Llz50JjL774ovbs2ROgRAAAAMWD0g3FJv/2EasOHPaece6Xo1653FLHfimKbpakuDbZ6jXCJ0lq2ytF7foUfpvZgSNeSdYzbkkBAABlz4QJE+R0OguOPR6Phg8fLp/PF8BUAAAA/kXphmITHx8vGXZ9t8N1xrnLG9q17q1Khf7MeDz/h++5E5x68cnIQvO/2+6SDJsSEhJKJDsAACg+VapU0bhx4wqNbd68WW+++WaAEgEAAPgfpRuKzQ033CAZQVr//9w6dqLwbrcop0Wtrwwq9OfyS22SpGaN7Gp62R8vX3C7TS1elSsZIfnXBAAAZd4dd9yhK664otDY008/rRMnTgQoEQAAgH9RuqHY1K1bV9de21I+hejZVzL/9nXmvZetk8lWVal6gW666SY/JgQAAIFisViUmJgou/2PL9oyMzM1duzYAKYCAADwH0o3FKtRo0bJsEZo8SqXPvw890/ntr4ySObP1dU83lEwtutnt6a8kiVZIzVixIhCP5gDAICyrW7duho8eHChsZUrV2rVqlUBSgQAAOA/lG4oVs2aNdPAgQ9KligNmpChj9b+efH233740a2eQ1Ll8jrVpk073XHHHcWYFAAABMLDDz+sOnXqFBp7/PHHlZGREaBEAAAA/kHphmI3atQodbr1X3L7ovTAuAwNmZSmX5POfKPp/0nP8OnZlzPVeUCqTqdHKD6hhebMmSPDMEowNQAAKAkOh0OJiYmFxk6cOKFnnnkmQIkAAAD8wzBN0wx0CJR/Ho9HkydP1ty5c2R6M2RRjtpc5VDzRnZdcpFVFot05LhPW39069Ov8pTrdkiWCHXqdJumTp0qp9MZ6CUAAIBi9Nhjj+mdd94pODYMQx9++KGaNm0awFQAAAB/H6UbStT333+vZ555Rt9887Vk5kqmR5Ln97NWybBJRrAaNrxMQ4cOVadOnQIZFwAAlJC0tDRdd911OnnyZMFYw4YNtWrVKp7pCgAAyiRKNwTE3r179fnnn2v79u06duyYTNNUbGysEhIS1LJlSzVv3pzbSQEAqGA++OADDRgwoNDY448/rkGDBgUoEQAAwN9H6QYAAIBSwTRN9erVS2vXri0YCwoK0rp16xQXFxe4YAAAAH8DpRsAAABKjSNHjqh169bKyckpGGvVqpUWLlzILngAAFCm8PZSAAAAlBo1a9bUiBEjCo199dVXWrJkSYASAQAA/D3sdAMAAECp4vF41LFjR+3YsaNgLDo6Wl999ZUqVaoUwGQAAABFx043AAAAlCo2m02JiYmyWP74UTUlJUVPPvlkAFMBAAD8NZRuAAAAKHUSEhLUr1+/QmOLFy/Whg0bApQIAADgr+H2UgAAAJRKWVlZatOmjY4ePVowFhcXp7Vr1yo4ODiAyQAAAM6PnW4AAAAolcLCwjR58uRCYwcPHtSMGTMClAgAAKDoKN0AAABQarVt21adO3cuNDZnzhzt3r07QIkAAACKhttLAQAAUKr99ttvuu6665Senl4w1rRpU61YsUJWqzWAyQAAAM6NnW4AAAAo1apUqaInnnii0NiWLVv05ptvBigRAADA+bHTDQAAAKWez+dT165dtWnTpoKx8PBwrV+/XtWrV5ckpaWl6cCBA3K73QoJCdEll1yikJCQQEUGAAAVHKUbAAAAyoSff/5Z7dq1k9vtLhi7+uqr1aBBA61du1aHDh2S5JVkSjJksdhVr149dezYUXfddZeqVasWqOgAAKAConQDAABAmTFt2jRNmzZNXq9XGRkZcrnyVCkqWDarV5JH1WINhQQbSs80dTrVlAy7ZITIagvVvffep1GjRik0NDTQywAAABUApRsAAADKDJfLpWbNmmnPnt0KcvgUEWrqhqsM9ekapSsbOxQZ8ccji5NOefX1ZpcWrMjRpm1eyeJUXO36eu2119SgQYMArgIAAFQElG4AAAAoMz755BPdc08fufNOKqGeNGGwoXpxhkJDQ+V0Rpzzc19sytOjk9N1/FSwIqMv1NKlS9WwYcMSTA4AACoaSjcAAACUCbt379ZNN90od+5v6nS9V+Me9MpuMwrOV6pUSQ6H/ZyfT033qddjqdr8o03VLqindevWKTIysiSiAwCACshy/ikAAABAYLndbg0ZMkTuvBTdcJWhF56MUZDDWmjOex+n6squpxRx+QlVvyZJ3Qen6MBhT8H5KKdFb0+L0sUXunTi+EGNHz++pJcBAAAqEEo3AAAAlHpLly7Vju1bFBmWq2mjnbLbLYVuJ/1mq6k+ozyqGycteyFaM8c4tW2PWx3uTVZO7h83djgjLJo5xinDzNB77y3Uzp07A7AaAABQEVC6AQAAoFQzTVOvvfaaZGZrUK9QVY3N3+EWFBSsoKAgSdLyz01dWFWaOtyr1lfa1KNjiOZOiNT+w159v8Nd6HrN4x26ra1D8uVo/vz5Jb0cAABQQVC6AQAAoFQ7cOCAduzYJrs1T3d0CikYNwzJ6YyQYRjyeKTw0Pzx9PR0maYK3mR6tkcY9/lXiGTmaPny5fJ4PGecBwAA+Kco3QAAAFCqbd26VTLdanKpTZWiCv/4arVaFR4eru43G9p7UJq/zNSp5Dzt3pelx6dlqMmlNl3bzHHGNVsk2BUW4lN2dqb27dtXQisBAAAVCaUbAAAASrUff/xRMj2Kr3f2N5OGhoaqVXOHXnvK0FNzTdW/2dRlt6Qr6ZRXn7xaSVarccZnLBZD8fVskunWrl27insJAACgAqJ0AwAAQKmWlpYmyafY6LP/6GoY0q79IXp4kqm7bpWWzbbp1UlW+XxSx34phV6k8N9iK1kkmUpPTy++8AAAoMKyBToAAAAA8GdstvwfWT3es5dnkjRscpauv8KmKcODlJOTI0lqkWBT49vytGB5jh7oGXrGZ9y/v1/BarX6PzQAAKjw2OkGAACAUq1GjRqSYdP+w95zzvlxn1vN44MVFhYmiyX/dtLK0W7FRBnaf/jsL0rIv55VF154YXHEBgAAFRylGwAAAEq1hIQESTZt/dF91jeRStJFF1i15Ue3bDarnM5ISdKRE6ZOp5q6sOqZn0nP8Gn/EY9k2H6/PgAAgH9RugEAAKBUa9q0qYKCw3XoV2nzTvdZ5wy4I0zL1+TpkYlp2rBZWrXBod4jTcVGS+2vyZPXW3iX3JJPc2UqSPXq1VdsbGxJLAMAAFQwlG4AAAAo1ZxOp7p06SIZIZrzTvZZ5wzuE6o5Tzr1xf9zqcuDKRo93aU6tSxa8pyhqAhTqalp+r9Ncm63qdeWZEtGiHr37l1yCwEAABWKYZ5rjz4AAABQSuzevVsdOrSX1/WbXn06XDdfH3zez3i9Pp0+fVo+n0+SFBoaKqczQlPnZWr6fLcqxdbRN998I6fTWdzxAQBABcRONwAAAJR6DRs21IMPPiRZnHp0cob27D/7yxH+m9VqUVRUZMFxdna2PlqboVlv5EgWp55++mkKNwAAUGwo3QAAAFAmPProo2ra7CqlZoWr66AUbfjedd7POBwORUSEyzRNLV1jqv+4TLm84erR40517ty5BFIDAICKittLAQAAUGakpqbqzjvv1A9bN0m+dN3dOUiDeoWp1gXWc35m+x63Jr2QonWbvErPtKhSTBXt3r1bUVFRJRccAABUOJRuAAAAKFOysrL01FNPaf781yRflgzlqGUzu1rE29XgYpuCgwxlZJnaudetb7a6tW2PV6YRopTUPAUFBSs0NFRdunTRiy++KMMwAr0cAABQTlG6AQAAoEzauHGjnnvuOa1f/4Vk5kmmW5JXMk3JMCTZJMMuuyNcHTt21M0336xBgwbJ7XZLkp566inde++9gVwCAAAoxyjdAAAAUKb98ssvWrdunbZv364DBw7I5XIpNDRU9evXV0JCgtq1a6fKlStLkhYsWKCRI0dKkux2u5YtW6amTZsGMj4AACinKN0AAABQYZimqUceeUTvv/++JOmCCy7QmjVrFB0dHeBkAACgvKF0AwAAQIWSnZ2tjh076qeffpIktWnTRgsWLJDFYglwMgAAUJ7wkwUAAAAqlNDQUM2bN09hYWGSpHXr1mnWrFkBTgUAAMobSjcAAABUOHXq1NG0adMKjqdOnaovv/wygIkAAEB5Q+kGAACACqlz5866//77JeU/6+3BBx/U8ePHA5wKAACUFzzTDQAAABWW2+3W7bffrs2bN0uSmjVrpqVLl8putwc4GQAAKOvY6QYAAIAKy26366WXXip4e+nmzZs1adKkAKcCAADlAaUbAAAAKrQLLrhAL7zwggzDkCS98sor+uijjwKcCgAAlHWUbgAAAKjwWrdurWHDhhUcDxs2TAcOHAhgIgAAUNbxTDcAAABAktfr1d13363169dLkho2bKiPPvpIISEhAU4GAADKIna6AQAAAJKsVqtmz56t6tWrS5J2796tUaNGie+oAQDA30HpBgAAAPwuJiZGL7/8smw2myRp8eLFeueddwKcCgAAlEWUbgAAAMB/adasmcaPH19wPGbMGO3YsSOAiQAAQFlE6QYAAAD8j/vuu0+dO3eWJLlcLvXr109paWkBTgUAAMoSXqQAAAAAnEVmZqZuvvlm7d+/X5LUoUMHvfbaa7JY+N4aAACcHz8xAAAAAGcRHh6uV155RcHBwZKk1atXa+7cuQFOBQAAygpKNwAAAOAcGjRooGeffbbgePLkydq4cWMAEwEAgLKC0g0AAAD4E926dVOvXr0kSV6vVwMHDlRSUlKAUwEAgNKOZ7oBAAAA55GXl6fOnTsXvMX06quv1qJFi2Sz2QKcDAAAlFbsdAMAAADOIygoSK+88oqcTqckaePGjYVuOwUAAPhflG4AAABAEdSqVUvPP/98wfHs2bO1evXqACYCAAClGaUbAAAAUETt27fXoEGDCo4HDx6sQ4cOBTARAAAorXimGwAAAPAXeDwe9ezZU998840kKT4+Xh988IGCgoICnAwAAJQm7HQDAAAA/gKbzaYXX3xRVapUkSTt2LFD48aNC3AqAABQ2lC6AQAAAH9RlSpVNHfuXFmtVknSW2+9pcWLFwc4FQAAKE0o3QAAAIC/4aqrrtLo0aMLjkeOHKndu3cHMBEAAChNeKYbAAAA8DeZpqn77rtPn376qSSpdu3aWrVqlSIiIgKcDAAABBo73QAAAIC/yTAMzZo1SxdddJEk6ZdfftFjjz0mvtcGAADsdAMAAAD+oZ07d6pTp05yuVySpAkTJqhv374F591ut37++WcdO3ZMpmkqJiZGl156qUJCQgIVGQAAFDNKNwAAAMAP3nnnHT322GOS8t9wunTpUmVnZ2v+/Plat26d8vJyJNOTP9mwymKxq2nTpurdu7c6d+4sh8MRwPQAAMDfKN0AAAAAPzBNU0OHDtV7770nr9crl8ul4CCbDOVKZq7CQ01dXNMmm1U6esKr35JNyQiSjFBdXKe+Zs6cqebNmwd6GQAAwE8o3QAAAAA/ycnJUatWrbRt2w8KdvhUKUq6+7Yw3d05WPUvtsliMQrmHjvh1furcvX6kmz9lhIkw+rU009PVp8+fQK3AAAA4DeUbgAAAICfbN++XbfeeqsyUo+oeSNTkx4xdGm9CIWHh53zM2kZPo2dkaEln3oka7RmzJilHj16lGBqAABQHCjdAAAAAD/Izc1Vu3bt9OOu72U1XPL5TGVmS9VipS7tgzVhSKQiIyySpDUb8vT6kmxt2ubWgSNePXhXqKpXtujFd1wKDquhtWvXKi4uLrALAgAA/4gt0AEAAACA8uCFF17Qgf17FBHqVpd2IWoR71NoUJ72HJCmvZ6rH/f5tOaNGEnSqq/ytG2PR9df4VByWq4MQ3p8YLi2/5SqDVtOafTo0Xr33XcDvCIAAPBPsNMNAAAA+IdcLpeaNWum07/t1dwJYercNlimKSWnJMvtcuutD0wNTzR19KsqqlHNKp/PLHi+W1zr39SpTZBmj4/UoWMeXdsjWT5LZa1f/6Xq1q0b4JUBAIC/yxLoAAAAAEBZt27dOp0+dULVYn265fogSZJhSFGRUbJYLIqOzJ+XnJopSYVeqPDfLqphU4eWDsnM0XvvvVci2QEAQPGgdAMAAAD+oc2bN0tyqd01QbLZ8gs1r9eU22PowLEwzZhv6saWUuWoXOXm5v7ptW5sFSSZLm3durUEkgMAgOLCM90AAACAf2jPnj2S6VGjevaCsYuu/03HknySpPbXWPXiE/n/Oz09XQ5H0Dl3uzWqZ5PMLO3evbv4gwMAgGLDTjcAAADgH8rOzpZkKsr5x4/XK+dV0jfvxeiVpyK195B0z2hDpmkoMjLqnIWbJEWGWySZv18TAACUVex0AwAAAP6h4OBgSYYyMv94R1lCg/xdb1c3cahFvF2Xdz6lr7ZEqntHx59eKyPLJ8lQUFBQMSYGAADFjZ1uAAAAwD9Ur149STbt2uc+6/mEBjbZ7dKBo77zXmvXzx7JsKl+/fp+TgkAAEoSpRsAAADwD11++eWS4dC6b13y+cwzzm/6wS23W7q4pvW811r7rUsy7GrSpEkxJAUAACWF20sBAACAf6hdu3ZyRsbo0K/puu7O07rl+mAl1LcpJNjQtj1uJc7LUkIDm7q0C5YkHTrm0Xfb83fFZeeY2n/Yq/c/yVFqhk8ff5EnGRHq2rVrIJcEAAD+IcM0zTO/igMAAADwl0yYMEFz58yU3UhTVKRFvxzxymdKcTWsur1DsB67P0zOiPwbTeYvyda9o9LOep3qVcPV/Io2+uCDD0oyPgAA8DNKNwAAAMAP0tPT1aZNGx0/tked20izxztls537LaX/a87bWZr4Yo7swdW0atWnatiwYTGmBQAAxY1nugEAAAB+4HQ69dxzz8keFKMP1vp076g0/Xbae97P5eWZevL5DE18MUeyRmv06Mcp3AAAKAfY6QYAAAD40erVq/XAA/3kyk1WVHiuHugRqjs7B6tKTOGXKGTnmFq+JlcvvpOlA0dtksWpRx8drmHDhskwir5DDgAAlE6UbgAAAICf7d69W4888oh27tgqmdmSmae4GhZdcpFNFkM6muTV3l+88vjskhGqKlVr6tlnn1WHDh0CHR0AAPgJpRsAAABQDNxut1asWKE33nhDmzdvlkyPJM/vZy2SYVdcXG317t1bd955p5xOZyDjAgAAP6N0AwAAAIpZSkqKduzYoaNHj8rn86ly5cqKj49X9erVuZUUAIByitINAAAAAAAA8DPeXgoAAAAAAAD4GaUbAAAAAAAA4GeUbgAAAAAAAICfUboBAAAAAAAAfkbpBgAAAAAAAPgZpRsAAAAAAADgZ5RuAAAAAAAAgJ9RugEAAAAAAAB+RukGAAAAAAAA+BmlGwAAAAAAAOBnlG4AAAAAAACAn1G6AQAAAAAAAH5G6QYAAAAAAAD4GaUbAAAAAAAA4GeUbgAAAAAAAICfUboBAAAAAAAAfkbpBgAAAAAAAPgZpRsAAAAAAADgZ5RuAAAAAAAAgJ9RugEAAAAAAAB+RukGAAAAAAAA+BmlGwAAAAAAAOBnlG4AAAAAAACAn1G6AQAAAAAAAH5G6QYAAAAAAAD4GaUbAAAAAAAA4GeUbgAAAAAAAICfUboBAAAAAAAAfkbpBgAAAAAAAPgZpRsAAAAAAADgZ5RuAAAAAAAAgJ9RugEAAAAAAAB+RukGAAAAAAAA+BmlGwAAAAAAAOBnlG4AAAAAAACAn1G6AQAAAAAAAH5G6QYAAAAAAAD4GaUbAAAAAAAA4GeUbgAAAAAAAICfUboBAAAAAAAAfkbpBgAAAAAAAPgZpRsAAAAAAADgZ5RuAAAAAAAAgJ9RugEAAAAAAAB+RukGAAAAAAAA+BmlGwAAAAAAAOBnlG4AAAAAAACAn1G6AQAAAAAAAH5G6QYAAAAAAAD4GaUbAAAAAAAA4GeUbgAAAAAAAICf/X/wuLP/tzz1BAAAAABJRU5ErkJggg==",
-      "text/plain": [
-       "<Figure size 1400x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "           Omic  Degree\n",
-      "Index                  \n",
-      "11     Gene_411       6\n",
-      "16       Gene_7       5\n",
-      "24     Gene_174       5\n",
-      "3        Gene_1       4\n",
-      "28     Gene_114       4\n"
-     ]
-    }
-   ],
-   "source": [
-    "from bioneuralnet.metrics import plot_network\n",
-    "from bioneuralnet.metrics import louvain_to_adjacency\n",
-    "\n",
-    "cluster1 = hybrid_result[0]\n",
-    "cluster2 = hybrid_result[1]\n",
-    "\n",
-    "# Convert Louvain clusters into adjacency matrices\n",
-    "louvain_adj1 = louvain_to_adjacency(cluster1)\n",
-    "louvain_adj2 = louvain_to_adjacency(cluster2)\n",
-    "\n",
-    "# Plot using the converted adjacency matrices\n",
-    "\n",
-    "cluster1_mapping = plot_network(louvain_adj1, weight_threshold=0.1, show_labels=True, show_edge_weights=False)\n",
-    "print(cluster1_mapping.head())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1400x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "           Omic  Degree\n",
-      "Index                  \n",
-      "1        Gene_7       4\n",
-      "2        Gene_6       4\n",
-      "3        Gene_1       4\n",
-      "4      Gene_446       4\n",
-      "5       Gene_53       4\n"
-     ]
-    }
-   ],
-   "source": [
-    "cluster2_mapping = plot_network(louvain_adj2, weight_threshold=0.01, show_labels=True, show_edge_weights=True)\n",
-    "print(cluster2_mapping.head())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "BioNeuralNet version: 1.1.1\n"
-     ]
-    }
-   ],
-   "source": [
-    "import bioneuralnet\n",
-    "print(f\"BioNeuralNet version: {bioneuralnet.__version__}\")"
-   ]
-  }
- ],
- "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
-}
\ No newline at end of file
diff --git a/docs/jupyter_execute/TCGA-BRCA_Dataset.ipynb b/docs/jupyter_execute/TCGA-BRCA_Dataset.ipynb
deleted file mode 100644
index d3e6011..0000000
--- a/docs/jupyter_execute/TCGA-BRCA_Dataset.ipynb
+++ /dev/null
@@ -1,1354 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "d182cc95",
-   "metadata": {},
-   "source": [
-    "# TCGA-BRCA Demo\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",
-    "- **Note: Omics matrices are features × samples; clinical matrices are samples × fields.**\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 × 20,107\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<br>LumB: 209<br>Basal: 192<br>Her2: 82<br>Normal: 40 | Raw FireHose & TCGAbiolinks files       |\n",
-    "| **Patient-Level Intersection** | 769 × 101   | 769 × 20,107 | 769 × 1,046 | 769 × 20,531   | LumA: 419<br>LumB: 140<br>Basal: 130<br>Her2: 46<br>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": "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,
-   "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"
-     ]
-    }
-   ],
-   "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",
-    "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())"
-   ]
-  },
-  {
-   "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": "markdown",
-   "id": "3505b6b8",
-   "metadata": {},
-   "source": [
-    "## Preprocessing: Phase 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"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "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"
-     ]
-    }
-   ],
-   "source": [
-    "import pandas as pd\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",
-    "\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",
-    "\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",
-    "\n",
-    "meth = meth.T\n",
-    "rna = rna.T\n",
-    "mirna = mirna.T\n",
-    "\n",
-    "print(\"\\nAfter tranpose\")\n",
-    "print(f\"meth: {meth.shape}\")\n",
-    "print(f\"rna: {rna.shape}\")\n",
-    "print(f\"mirna: {mirna.shape}\")\n",
-    "\n",
-    "def trim(idx):\n",
-    "    return idx.to_series().str.extract(r'(^TCGA-\\w\\w-\\w\\w\\w\\w)')[0]\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",
-    "\n",
-    "idx1 = clinical_biolinks.index\n",
-    "idx2 = clinical_firehose.index\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",
-    "\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",
-    "\n",
-    "clinical = pd.concat([clinical_biolinks, clinical_firehose], axis=1)\n",
-    "\n",
-    "print(f\"Combined Clinical shape {clinical.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",
-    "\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",
-    "\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\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "32ba4b2c",
-   "metadata": {},
-   "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)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "b841497a",
-   "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"
-     ]
-    }
-   ],
-   "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",
-    "\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",
-    "\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",
-    "\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",
-    "\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}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9d8dac23",
-   "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."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "4f35bd67",
-   "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"
-     ]
-    }
-   ],
-   "source": [
-    "print(meth.head())\n",
-    "print(rna.head())\n",
-    "print(mirna.head())\n",
-    "print(clinical.head())\n",
-    "print(pam50.value_counts())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "17f7d599",
-   "metadata": {},
-   "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"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "5bb6450e",
-   "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"
-     ]
-    }
-   ],
-   "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())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "54fae854",
-   "metadata": {},
-   "source": [
-    "## Save & Load\n",
-    "\n",
-    "Our data is clean and consistently structured across all modalities.\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",
-    "\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",
-    "\n",
-    "**Saving files:**  \n",
-    "Set a common patient index across all datasets and saved each one as a `.csv` file.\n",
-    "\n",
-    "**Verifying saved files:**  \n",
-    "Loaded each `.csv` and printed the head to confirm successful read/write with preserved structure and content.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "2f0714e8",
-   "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())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "09265512",
-   "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."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "fa70dcca",
-   "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"
-     ]
-    }
-   ],
-   "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",
-    "\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"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "cc981cdb",
-   "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"
-     ]
-    }
-   ],
-   "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\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "da639dd6",
-   "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\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "1c371389",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "out_dir = Path(\"/home/vicente/Github/BioNeuralNet/TCGA_BRCA_DATA/ANOVA\")\n",
-    "\n",
-    "rna_af.to_csv(out_dir / \"rna_anova.csv\")\n",
-    "meth_af.to_csv(out_dir / \"meth_anova.csv\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c2168980",
-   "metadata": {},
-   "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."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "2d0340f6",
-   "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"
-     ]
-    }
-   ],
-   "source": [
-    "from bioneuralnet.datasets import DatasetLoader\n",
-    "\n",
-    "tgca_brca = DatasetLoader(\"brca\")\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"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0ddf042e",
-   "metadata": {},
-   "source": [
-    "## Feature Selection: Phase 2\n",
-    "\n",
-    "We used `preprocess_clinical` to reduce the clinical dataset to the top 10 most informative features based on random forest importance.\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",
-    "\n",
-    "**Result:**\n",
-    "- Clinical data reduced to 10 features across the 769 patients  "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "338dc995",
-   "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())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "89cb8500",
-   "metadata": {},
-   "source": [
-    "## Graph Construction\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)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "b4646135",
-   "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"
-     ]
-    }
-   ],
-   "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",
-    "\n",
-    "print(f\"\\nNetwork shape: {A_train.shape}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "376f8a5b",
-   "metadata": {},
-   "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",
-    "\n",
-    "DPMON is an end-to-end optimized pipeline that fuses multi-omics data and network structure for disease prediction using GNNs.  \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)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "43396d92",
-   "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"
-     ]
-    }
-   ],
-   "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",
-    "\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}\")"
-   ]
-  }
- ],
- "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
-}
\ No newline at end of file
diff --git a/docs/requirements.txt b/docs/requirements.txt
index a34533e..ce36eae 100644
--- a/docs/requirements.txt
+++ b/docs/requirements.txt
@@ -6,3 +6,4 @@ pandoc
 nbsphinx
 myst_nb
 furo
+sphinx-copybutton
diff --git a/docs/source/conf.py b/docs/source/conf.py
index 3f3ca7b..34836ce 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -20,6 +20,7 @@
     "sphinx.ext.intersphinx",
     "nbsphinx",
     "myst_nb",
+    'sphinx_copybutton',
 ]
 nb_execution_mode = "off"
 myst_enable_extensions = ["colon_fence"]
diff --git a/docs/source/datasets.ipynb b/docs/source/datasets.ipynb
index c94a360..683d3ec 100644
--- a/docs/source/datasets.ipynb
+++ b/docs/source/datasets.ipynb
@@ -6,25 +6,24 @@
    "source": [
     "# Datasets Guide\n",
     "\n",
-    "\n",
-    "- The `DatasetLoader` class provides a simple interface to access example multi-omics datasets included in this package.  \n",
-    "- Each dataset is loaded as a collection of **pandas DataFrames**, with table names as keys and the corresponding data as values.  \n",
-    "- Users can explore the structure of any dataset via the `.shape` property, which returns a mapping from table name to `(rows, columns)`.  \n",
+    "- The `DatasetLoader` class provides a simple interface to access example multi-omics datasets included in this package.\n",
+    "- Each dataset is loaded as a collection of **pandas DataFrames**, with table names as keys and the corresponding data as values.\n",
+    "- Users can explore the structure of any dataset via the `.shape` property, which returns a mapping from table name to `(rows, columns)`.\n",
     "- Three datasets are available out-of-the-box:\n",
     "\n",
-    "    1. **Example1**:\n",
-    "        - Synthetic dataset designed for testing and demonstration  \n",
-    "        - Contains small DataFrames: `X1`, `X2`, `Y`, `clinical_data`  \n",
+    "    1. **example1**:\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",
     "    2. **monet**:  \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",
+    "        - 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>\n",
     "\n",
-    "    3. **brca**  :\n",
-    "        - Breast cancer cohort from TCGA (BRCA project)  \n",
-    "        - Provides comprehensive omics DataFrames: `rna`, `mirna`, `meth`, `pam50`, `clinical`  \n",
+    "    3. **brca**:\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/en/latest/TCGA-BRCA_Dataset.html>\n",
     "\n"
    ]
@@ -83,7 +82,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Example 1: Synthetic dataset"
+    "## Example 1: Synthetic dataset."
    ]
   },
   {
@@ -640,7 +639,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Monet: Set from the **Multi-Omics NETwork Analysis Workshop (MONET)**, Univ. of Colorado Anschutz  "
+    "## Monet: Set from the **Multi-Omics NETwork Analysis Workshop (MONET)**, Univ. of Colorado Anschutz."
    ]
   },
   {
@@ -1345,7 +1344,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### BRCA: Breast cancer cohort dataset."
+    "## BRCA: Breast Cancer dataset from The Cancer Genome Atlas."
    ]
   },
   {
diff --git a/docs/source/index.rst b/docs/source/index.rst
index 64beca2..210e1fb 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -46,6 +46,31 @@ Get started quickly with these end-to-end examples demonstrating the BioNeuralNe
 
    `View BioNeuralNet Workflow. <https://bioneuralnet.readthedocs.io/en/latest/_images/BioNeuralNet.png>`_
 
+
+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 <https://arxiv.org/abs/2507.20440>`_.
+   *arXiv preprint arXiv:2507.20440* | `DOI: 10.48550/arXiv.2507.20440 <https://doi.org/10.48550/arXiv.2507.20440>`_.
+
+For your convenience, you can use the following BibTeX entry:
+
+.. code-block:: bibtex
+
+   @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}
+   }
+
 What is BioNeuralNet?
 ---------------------
 
@@ -181,13 +206,13 @@ We welcome contributions to BioNeuralNet! If you have ideas for new features, im
    - Run the test suite and and pre-commit hooks before opening a Pull Request(PR).
    - A new PR should pass all tests and adhere to the project's coding standards.
 
-.. code-block:: bash
-   
-   git clone https://github.com/UCD-BDLab/BioNeuralNet.git
-   cd BioNeuralNet
-   pip install -r requirements-dev.txt
-   pre-commit install
-   pytest --cov=bioneuralnet
+   .. code-block:: bash
+      
+      git clone https://github.com/UCD-BDLab/BioNeuralNet.git
+      cd BioNeuralNet
+      pip install -r requirements-dev.txt
+      pre-commit install
+      pytest --cov=bioneuralnet
 
 
 .. toctree::
diff --git a/requirements-dev.txt b/requirements-dev.txt
index 846dfd5..fa6935e 100644
--- a/requirements-dev.txt
+++ b/requirements-dev.txt
@@ -14,6 +14,7 @@ nbconvert
 ipykernel
 myst_nb
 furo
+sphinx-copybutton
 
 # code quality and formatting
 flake8