TME_Classification.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The current notebook contains a Tumor Microenvironment comprehensive analysis pipeline.\n",
    "\n",
    "**Data preparation:**\n",
    "\n",
    "The analysis begins by reading the provided expression matrix data and performing normalization if necessary. If you intend to utilize public data and retrieve it from GEO, please refer to the [GEO_data_retrieval.ipynb](GEO_data_retrieval.ipynb) notebook for detailed instructions on data retrieval and subsequent steps.\n",
    "\n",
    "**Quality check:**\n",
    "\n",
    "The data preparation is followed by the Quality Check (QC), which is essential for ensuring the integrity and reliability of the data. This step involves checking for batch effects,outliers, and assessing the distribution of gene expression data to determine the quality of the dataset.\n",
    "\n",
    "**TME classification:**\n",
    "\n",
    "Next, the Tumor Microenvironment (TME) classification section is using the processed gene expression data from TCGA. During this step each sample is classified into one of the TME subtypes.\n",
    "\n",
    "**de novo clusterization:**\n",
    "\n",
    "We also have an additional section which is de novo clusterisation. It is an optional step recommended for experienced users only. This section involves the formation of a reference *signatures* cohort based on a reference *gene expression* cohort provided by the user. The resulting cohort can then be used in place of the default TCGA cohort in the TME classification section. However, it is advised to use the default cohort to ensure accurate and unbiased results during classification."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Import of python base packages"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you have opened the current file in view-only mode but want to use the code, please go back to our repositories Setup section and follow the [instructions](https://github.com/BostonGene/MFP). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-02T13:25:16.958768Z",
     "start_time": "2018-08-02T13:25:14.882714Z"
    }
   },
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%matplotlib inline\n",
    "%config IPCompleter.use_jedi = False\n",
    "\n",
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import pathlib\n",
    "import subprocess\n",
    "import logging\n",
    "import csv\n",
    "import matplotlib.pyplot as plt\n",
    "import umap\n",
    "import matplotlib.pyplot as plt\n",
    "from IPython.display import SVG\n",
    "from tqdm import tqdm_notebook\n",
    "\n",
    "%config InlineBackend.figure_format = 'png'\n",
    "plt.rcParams['pdf.fonttype'] = 'truetype'\n",
    "plt.rcParams['svg.fonttype'] = 'none'\n",
    "plt.rcParams['figure.dpi'] = 120"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from portraits.plotting import *\n",
    "from portraits.mapping import get_gs_for_probes_from_3col,get_expressions_for_gs\n",
    "from portraits.utils import read_gene_sets, ssgsea_formula, median_scale\n",
    "from portraits.classification import KNeighborsClusterClassifier\n",
    "from portraits.clustering import clustering_profile_metrics, clustering_profile_metrics_plot,clustering_select_best_tr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext rpy2.ipython"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you already have RNA-Seq data insert your data path in EXPRESSION_MATRIX and skip to the QC section."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#Constants\n",
    "\n",
    "EXPRESSION_MATRIX = 'Test/expressions.tsv'\n",
    "TCGA_SIGNATURES = 'Cohorts/Pan_TCGA/signatures.tsv'\n",
    "TCGA_COHORTS_ANNOTATION = 'Cohorts/Pan_TCGA/annotation.tsv'\n",
    "CLASSIFIED_SAMPLES = 'classified_samples.tsv'\n",
    "GENE_SIGNATURES = 'signatures/gene_signatures.gmt'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data preparation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you have your own RNA-SEQ data change the EXPRESSION_MATRIX value into your expression matrix file path.If you want to retriev your data from GEO go to the [GEO_data_retrieval.ipynb](GEO_data_retrieval.ipynb) notebook and after finishing the RNA-Seq data generation change the EXPRESSION_MATRIX into that file path."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
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       "      <td>2.223272</td>\n",
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       "      <td>2.223272</td>\n",
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       "      <th>A2M</th>\n",
       "      <td>11.726740</td>\n",
       "      <td>11.031260</td>\n",
       "      <td>11.897860</td>\n",
       "      <td>10.843689</td>\n",
       "      <td>12.949128</td>\n",
       "      <td>12.603719</td>\n",
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       "      <td>13.931410</td>\n",
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       "          GSM872328  GSM872329  GSM872330  GSM872331  GSM872332  GSM872333  \\\n",
       "A1BG       3.078552   2.987851   2.987851   3.165811   2.728912   3.655134   \n",
       "A1BG-AS1   5.749913   4.904272   4.388127   4.360712   4.244502   4.356337   \n",
       "A1CF       2.223272   2.223272   2.223272   2.223272   2.223272   2.223272   \n",
       "A2M       11.726740  11.031260  11.897860  10.843689  12.949128  12.603719   \n",
       "A2M-AS1    6.443771   6.102404   6.606300   5.389406   7.313690   7.771913   \n",
       "\n",
       "          GSM872334  GSM872335  GSM872336  GSM872337  ...  GSM872383  \\\n",
       "A1BG       2.987851   2.987851   3.576304   6.004672  ...   4.336052   \n",
       "A1BG-AS1   4.494508   4.478265   4.595821   5.300090  ...   4.310518   \n",
       "A1CF       2.223272   2.223272   2.223272   2.223272  ...   2.223272   \n",
       "A2M       13.162092  11.998046  12.926885  12.836179  ...  12.034147   \n",
       "A2M-AS1    7.293835   6.468055   7.778143   7.140280  ...   8.238855   \n",
       "\n",
       "          GSM872384  GSM872385  GSM872386  GSM872387  GSM872388  GSM872389  \\\n",
       "A1BG       3.016967   2.987851   3.565415   3.567615   2.987851   2.987851   \n",
       "A1BG-AS1   4.597341   4.636256   4.597341   4.606501   4.597341   4.597341   \n",
       "A1CF       2.223272   2.223272   2.223272   2.223272   2.223272   2.223272   \n",
       "A2M       12.973168  12.724960  10.293074  13.931410  13.758179  12.476536   \n",
       "A2M-AS1    7.263452   7.986488   4.551769   8.104151   8.618934   6.642779   \n",
       "\n",
       "          GSM872390  GSM872391  GSM872392  \n",
       "A1BG       2.899146   3.058037   2.987851  \n",
       "A1BG-AS1   4.639346   4.412341   4.742684  \n",
       "A1CF       2.223272   2.223272   2.223272  \n",
       "A2M       10.576425  12.909873  12.791583  \n",
       "A2M-AS1    5.118838   7.025310   8.441181  \n",
       "\n",
       "[5 rows x 65 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "annotated_expression = pd.read_csv(EXPRESSION_MATRIX, sep='\\t', index_col=0)\n",
    "annotated_expression.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check if expression matrix is normalized if not log2 transform it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          GSM872328  GSM872329  GSM872330  GSM872331  GSM872332  GSM872333  \\\n",
      "A1BG       3.078552   2.987851   2.987851   3.165811   2.728912   3.655134   \n",
      "A1BG-AS1   5.749913   4.904272   4.388127   4.360712   4.244502   4.356337   \n",
      "A1CF       2.223272   2.223272   2.223272   2.223272   2.223272   2.223272   \n",
      "A2M       11.726740  11.031260  11.897860  10.843689  12.949128  12.603719   \n",
      "A2M-AS1    6.443771   6.102404   6.606300   5.389406   7.313690   7.771913   \n",
      "\n",
      "          GSM872334  GSM872335  GSM872336  GSM872337  ...  GSM872383  \\\n",
      "A1BG       2.987851   2.987851   3.576304   6.004672  ...   4.336052   \n",
      "A1BG-AS1   4.494508   4.478265   4.595821   5.300090  ...   4.310518   \n",
      "A1CF       2.223272   2.223272   2.223272   2.223272  ...   2.223272   \n",
      "A2M       13.162092  11.998046  12.926885  12.836179  ...  12.034147   \n",
      "A2M-AS1    7.293835   6.468055   7.778143   7.140280  ...   8.238855   \n",
      "\n",
      "          GSM872384  GSM872385  GSM872386  GSM872387  GSM872388  GSM872389  \\\n",
      "A1BG       3.016967   2.987851   3.565415   3.567615   2.987851   2.987851   \n",
      "A1BG-AS1   4.597341   4.636256   4.597341   4.606501   4.597341   4.597341   \n",
      "A1CF       2.223272   2.223272   2.223272   2.223272   2.223272   2.223272   \n",
      "A2M       12.973168  12.724960  10.293074  13.931410  13.758179  12.476536   \n",
      "A2M-AS1    7.263452   7.986488   4.551769   8.104151   8.618934   6.642779   \n",
      "\n",
      "          GSM872390  GSM872391  GSM872392  \n",
      "A1BG       2.899146   3.058037   2.987851  \n",
      "A1BG-AS1   4.639346   4.412341   4.742684  \n",
      "A1CF       2.223272   2.223272   2.223272  \n",
      "A2M       10.576425  12.909873  12.791583  \n",
      "A2M-AS1    5.118838   7.025310   8.441181  \n",
      "\n",
      "[5 rows x 65 columns]\n"
     ]
    }
   ],
   "source": [
    "if all(0<=sample<=18 for sample in annotated_expression.mean()):\n",
    "    print(annotated_expression.head())\n",
    "else:\n",
    "    annotated_expression = pd.log2(1+annotated_expression)\n",
    "    print(annotated_expression.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Quality check (QC)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Quality Check (QC) section identifies batches, outliers, and assesses the expression data distribution and after each check it saves the generated plot in the Plots folder for your further use.\n",
    "\n",
    "We will provide examples of both bad and good datasets, which will help you differentiate them visually and understand the types of possible data quality issues that may affect downstream analyses."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creating a Plots direcotry to save all of the generated plots."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.mkdir('Plots')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Batch detection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This section is responsible for identifying any potential batch effects in the analyzed data using UMAP and PCA analyses. The results of these analyses are plotted to visualize any potential batches in the data.\n",
    "\n",
    "If you are not familiar with batch effects follow [this link.](https://towardsdatascience.com/batcheffectsc71c886ca9c5) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Batche detection by UMAP"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is an example of a sample check results for the presence of batch effects using the UMAP analysis. Two plots have been generated to visualize possible analysis results. The plot on the right shows an example of a dataset with no identified issues, whereas the plot on the left exhibits potential batch effects.\n",
    "\n",
    "You can see that the plot on the left shows two distinct clusters of data points that are grouped quite far from each other. This is indicative of the presence of a potential batch effect.\n",
    "\n",
    "If your data plot resembles the one on the left, remove any identified batch effects to ensure accurate and reliable results.\n",
    "\n",
    "[Here are the instructions for batch effect removal](https://github.com/chichaumiau/removeBatcheffect)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Umap Example](plots/umap_examples.svg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 768x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate umap_plot for the input data\n",
    "fig_umap, axes_umap = plt.subplots()\n",
    "\n",
    "umap_plot(annotated_expression.T, legend = 'Out',ax = axes_umap)\n",
    "\n",
    "# Saving the generated umap plot into a picture.\n",
    "fig_umap.savefig('Plots/umap_batches.svg')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Batch detection by PCA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To further enhance the significance of our analysis, we additionally perform the PCA analysis and plot the results to ensure that our data is free from any batch effects.\n",
    "\n",
    "Below is an example of a sample check results for the presence of batch effects. Again, two plots have been generated to visualize possible analysis results.  Batch effects in the plot on the left can be suggested from the presence of two distinct groups of data points that appear far from each other. In contrast, a normal dataset appears as a single group of data points that are relatively close to each other, as reflected in the PCA plot.\n",
    "\n",
    "This step is essential to ensure that (a) the data used is free from any systematic variation that could confound the analyses and (b) any observed effects are biologically meaningful."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Pca batches example not colored](plots/pca_batches_example_colored.svg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 768x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate the pca plot for the annotated_expressions\n",
    "\n",
    "fig_pca_batches, ax_pca_batches = plt.subplots()\n",
    "\n",
    "pca_plot(annotated_expression.T, legend = 'out',ax = ax_pca_batches)\n",
    "\n",
    "# Save the generated pca plot into a picture.\n",
    "fig_pca_batches.savefig('Plots/pca_batches.svg')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Outlier detection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The purpose of this section is to identify outliers in the annotated_expression data through PCA analysis and the generation of corresponding plots.\n",
    "\n",
    "Below is an illustrative example of data outliers.\n",
    "In the plot on the left, two points lie far away from the main group of data points, indicating the presence of outliers in the dataset. In contrast, the data points in the plot on the right lie relatively close to each other, with no outliers detected.\n",
    "\n",
    "To get detailed information on data outliers, follow this [link](https://www.geeksforgeeks.org/detect-and-remove-the-outliers-using-python/)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![PCA outliers example](plots/pca_outliers_example.svg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The code cell below generates a PCA plot of the transposed annotated_expression data. Transposing the data allows the sample IDs to be located in the row positions, which facilitates dimension reduction based on samples. This plot can help identify any possible data outliers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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2dm8zfPhwhIaGqmyLjY1FSEhIYUInMogSFeRjjjUlyda2QIny+o+JiIhIVwqUIM+cObPAJ5DJZJg2bVqBn6dLDg4OAICMjAy1fenp6SptHBwc8tUuLx4eHvDw8JAUL5GhObrJJ+RF/6S+r0Y/9h4TEZF5KVCC/OWXXxb4BMaYICuGRyiGWuQWHx8PV1dXZa+xt7c3jhw5AiGEyjALxXN9fHz0EDGR4bVfBshk8jHH2ZnynuMa/YB2Sw0dGRERkXYVqIpFTk5Ogb+ys7N1FXuh+fr6wt3dHefOnVPbFxUVhcDAQOXjwMBApKam4vr16yrtzpw5o9xPZAls7IGOq4DP4oDBUfLvHVexxBsREZkfSWXeTFnXrl3x22+/4cGDB8pthw8fxs2bN1XGDHfq1AlFihTB8uXLlduEEPjxxx/h6+uL4OBgvcZNZGiOboBvPQ6rICIi86WVSXrPnj3DoUOHcO/ePQBAuXLl0KJFC5QsWVIbhy+wpUuX4sWLF8oKE7/++qtyQZNPP/0ULi4umDx5MrZu3YpmzZph1KhRSElJwfz581G9enUMHDhQeaxSpUph9OjRmD9/PrKyslCvXj3s2rULx48fx4YNG7hICBEREZGZkQkhhJQDfPnll5g3bx4yMzOR+1C2traYMGFCoSb2SVWuXDncv39f4767d+8ql8e+du0aPvvsM/z999+wtbVF+/btsXDhQnh6eqo8JycnB/PmzcOKFSsQHx+PypUrY9KkSejTp0+h4rt27RoCAgJw9epV+Pv7F+oYRERERJZK17mUpB7kr776CjNnzkT79u0xcuRI+Pn5AQBu3LiBpUuXYvbs2ShSpIjeJ+kperLfxd/fH3/88cc721lZWWHSpEmYNGmSxMiIiIiIyNhJ6kH29fVF3bp1VZabzq1Dhw44f/78OxfTsDTsQSYiIiIqPF3nUpIm6SUlJaFNmzZ57m/Xrh1evnwp5RRERERERHolKUH+4IMPlOXONDlz5gw++OADKacgIiIiItIrSQnyjz/+iFOnTmHMmDGIjY1V1j6OjY3F6NGjcfr0afz444/aipWIiIiISOckTdKrUaMGcnJy8P333+P777+HlZU8387JyQEA2NnZoUaNGirPkclkSEpKknJaIiIiIiKdkZQgd+3aVWX5ZSIiIiIiUycpQV6zZo2WwiAiIiIiMg6SxiDnp0JccnKylFMQEREREemVpAS5UaNGuH37dp77f//9d9b5JSIiIiKTIilBvn37NmrWrImlS5eqbH/58iUGDx6M9u3bw8fHR1KARERERET6JClBjomJQYcOHRAREYEWLVrg/v37OHToEKpXr47169dj1qxZOHXqlLZiJSIiIiLSOUmT9EqUKIFffvkFXbt2xYgRI1CtWjWkp6ejVq1a+PXXX1G9enVtxUlEREREpBeSepAVvL294ezsjLS0NAghEBgYiAoVKmjj0EREREREeiUpQc7IyMC4cePQtGlTFC1aFGfPnsVXX32F9evXo2bNmvjrr7+0FScRERERkV5ISpADAwOxePFiTJgwAWfPnkWdOnUwZcoUREVFoVixYmjevDnGjBmjrViJiIiIiHRO8hCLEydOYPbs2ShSpIhyW40aNRAVFYUpU6Zg+fLlUk9BRERERKQ3kibpXbx4Efb29poPbGODGTNmICQkRMopiIiIiIj0SlIPsiI5zsjIwKlTp7B7924kJiaqtKlVq5aUUxBZhNREIC5K/p2IiIgMS/IQi++//x7e3t5o2LAhunTpgsuXLwMAEhMT4ebmhp9++klykETmKisN2B0OLPQBVtWXf98dDrxON3RkRERElktSghwZGYnRo0ejTZs2WL16NYQQyn1ubm5o3rw5Nm3aJDlIInO1byQQ/ROQkyV/nJMlf7xvpGHjIiIismSSEuSFCxeiU6dO2LhxIzp06KC2v06dOrh27ZqUUxCZrdRE4PI6zfsur+NwCyIiIkORlCDHxsaibdu2ee53dXXF06dPpZyCyGw9v/O/nuM3ZWcCz+/m/1gcw0xERKQ9kqpYFC9eXG1SXm4xMTHw8vKScgois1WiAmBVRHOSbG0LlCj/7mNkpcmHY1xeJz+OVRGgRj+g/TLARnOBGSIiInoHST3I7dq1w8qVK/HixQu1fdeuXcP//d//oWPHjlJOQWS2HN3kyawmNfrJ978LxzATERFpn6QEedasWcjOzkZAQACmTp0KmUyGtWvXom/fvqhbty48PDzwxRdfaCtWIrPTfhlQK1zeYwzIv9cKB9otffdzOYaZiIhINyQNsfDx8cH58+cxefJkbN68GUIIrFu3DkWLFkWvXr0wd+5cuLnloxuMyELZ2AMdVwEt58rHHJcon7+eYyB/Y5jzeywiIiL6H0kJMgB4eHhg1apVWLVqFZ48eYKcnBy4u7vDykpyiWUinUlNlCeYJSoYRxLp6FbwOLQxhpmIiIjUSU6Qc3N3d9fm4Yi0zpwmtSnGMEdrWIsnv2OYiYiISJ1WE2QiY6eY1KagmNQmk8mHOpia9svksV9eJx9WYW0rT47zM4aZiIiINGOCTBbjXZPaWs41vV5XKWOYiYiISDMmyGQxzHlSW2HGMBMREZFmnElHFkMxqU0TTmojIiIiBSbIZDG0sTAHERERmT/JCXJycjLmzp2L1q1bo1atWoiKigIAPHv2DIsWLUJsbKzkIIm0RcrCHERERGQZJI1BfvjwIZo0aYIHDx6gcuXK+Oeff5CSkgIAcHV1xYoVK3D//n0sXrxYK8ESScVJbURERPQukhLk8ePH4+XLl4iOjoaHhwc8PDxU9oeEhOC3336TFCCRLnBSGxGRZTK2haLIOElKkA8cOIAxY8agWrVqePr0qdr+ChUq4MGDB1JOQUQmhr98iMgYmdNCUaR7khLktLS0t66e9/LlSymHJyITwl8+RGTMzG2hKNItSZP0qlWrhr/++ivP/bt27UKtWrWknIKITITil4+i1rTil8++kYaNi4joXQtFpSbqNx4yfpIS5NGjR2PTpk2YN28ekpKSAAA5OTmIjY1Fv379cOrUKYwZM0YrgRKR8eIvHyIyZvlZKIooN0lDLPr27Yv79+9j6tSpmDJlCgCgTZs2EELAysoKc+bMQUhIiDbiJCIjZs6rFBKR6VMsFKXpPsWFokgTyUtNT5kyBf369cP27dsRGxuLnJwcVKxYEV26dEGFChW0ESMRGTn+8iEiY6ZYKCr3GGQFLhRFmkhOkAGgTJkyHEpBZMH4y4eIjF37ZfIJeZfXyT/ZsraV35+4UBRpImkM8oULF7B8+fI89y9fvhzR0dFSTkFEJoKrFBKRMVMsFPVZHDA4Sv694ypW2SHNJPUgT5kyBQ4ODhg+fLjG/X/++Sf27dvHxUKILABXKSQiU8CFoig/JPUgnz9/Ho0aNcpzf6NGjXDu3DkppyAiE+PoBvjW4y8gIiIyXZIS5JcvX8LGJu9OaCsrK2X5NyIiIiIiUyApQa5cuTIOHDiQ5/79+/ezkgURERERmRRJCXJ4eDj27t2Lzz77DC9evFBuf/HiBcaMGYP9+/cjPDxcaoxEZCFSE4G4KC4sQkREhiVpkl5ERASio6Px3Xff4fvvv4ePjw8A4NGjR8jJyUG/fv1Y/o2I3ikrTb4k9eV18lrKVkXk5ZfaL+MMcyIi0j9JCbJMJkNkZCT69++P7du3486dOwCATp06oWvXrmjatKk2YiQiM7dvpGoN5Zws+WOZTF4Zg4iISJ+0slBIs2bN0KxZM20ciogsTGqivOdYk8vr5GXjWBGDiIj0SdIYZCIiqZ7f0bxENSBf7er5Xf3GQ0REJClBFkJgxYoVCAoKgpubG6ytrdW+3lYGjoioRAX5mGNNrG3lC44QERHpk6TsdcKECVi0aBECAwPRt29flChRQltxEZGFcHSTT8jLPQZZoUY/Dq8gIiL9k5Qgr127Fl27dsWWLVu0FQ8RWaD2y+QT8i6vkw+rsLaVJ8ftlho6MiIiskSSEuS0tDS0bNlSW7EQkYWysZdXq2g5Vz7muER59hwTEZHhSBqD3KJFC5w9e1ZbsRCRhXN0A3zrMTkmIiLDkpQgL1++HKdPn8acOXPw9OlTbcVERERERGQwkhLk9957D3fu3MG0adPg4eEBJycnFCtWTOXLxcVFW7ESEREREemcpDHIXbt2hUwm01YsREREREQGJylBXrNmjZbCICIiIiIyDlxJj4iIiIgoF60sc/fw4UNcvHgRSUlJyMnJUdvfv39/bZyGSO9SE+VLIZeowMoKRERElkJSgpyeno4BAwZg+/btyMnJgUwmgxACAFTGJjNBJlOTlQbsGylfuCInS74Uco1+8gUtbOwNHR0RERHpkqQhFpMnT8aOHTswe/ZsHD16FEIIrF27FgcOHEDbtm1Rs2ZNXLp0SVuxEunNvpHypY9zsuSPc7Lkj/eNNGxcREREpHuSEuRt27Zh4MCBmDhxIvz9/QEAvr6+aNmyJX777TcUL14cy5Yt00qgRPqSmijvOdbk8jr5fiIiIjJfkhLkhIQEBAUFAQAcHBwAAK9evVLu79q1K3bs2CHlFER69/zO/3qO35SdKV8KmYiIiMyXpATZ09NTuYKeo6MjSpQogRs3bij3JycnIz09XVqERHpWooJ8zLEm1rZAifL6jYeIyJykJgJxUfw0joybpEl69evXx99//42JEycCADp06ID58+fD29sbOTk5+Pbbb/H+++9rJVAifXF0k0/Ii/5JfV+NfqxmQURUGJz8TKZEUg9yREQEKlSogIyMDADAV199heLFi6Nfv34YMGAAXFxc8P3332slUCJ9ar8MqBUu7zEG5N9rhQPtlho2LiIiU8XJz2RKZEJRl01LcnJycOXKFVhbW6NKlSqwsdFKqWWzcu3aNQQEBODq1avKyY1knFIT5WOOS5RnzzERUWGlJgILfTTP77C2BT6L4z2WCkbXuZTWV9KzsrJCzZo1ERAQYNTJ8dGjRyGTyTR+nT59WqXtyZMn0bBhQzg6OsLLywsRERFISUkxUOSkT45ugG893riJiKTg5GcyNQXKYP/66y8AQOPGjVUev4uivTGKiIhAvXr1VLZVqlRJ+e/o6Gi0aNECVatWxaJFi/Dw4UMsWLAAt27dwu+//67vcImIiEyOYvJzXj3InPxMxqZACXLTpk0hk8mQlpYGW1tb5eO8CCEgk8mQnZ0tOVBdadSoEbp165bn/smTJ6NEiRI4evQoihUrBgAoV64chgwZggMHDqBVq1b6CpWIiMgkcfIzmZoCJchHjhwBANja2qo8NnUvX76Eg4OD2pCQ5ORkHDx4EGPGjFEmx4B86ewxY8Zgy5YtTJCJiIjyof0yQCaTV7HIzpT3HNfox8nPZJwKlCA3adJE+W8hBGrVqgVbW1vY25tufZaBAwciJSUF1tbWaNSoEebPn4+6desCAK5cuYLXr18rHyvY2toiMDAQFy9efOfxExIS8OTJE5VtsbGx2nsBREREJsDGHui4Cmg5l5OfyfgVehZdZmYmXF1dMWfOHEyYMEGbMemFra0tunbtinbt2sHNzQ0xMTFYsGABGjVqhJMnT6JWrVqIj48HAHh7e6s939vbG8ePH3/neZYvX44ZM2ZoPX4iIiJT5OjGxJiMX6ETZDs7O3h5ecHOzk6b8ehNcHAwgoODlY87duyIbt26oUaNGpg0aRL279+PtLQ0AND4Gu3t7ZX732b48OEIDQ1V2RYbG4uQkBBpL4DIwFIT5TPTS1TgLzsiIjIvkuqwhYWF4eeff8awYcOU45JNWaVKldCpUyfs2LED2dnZcHBwAADlQii5paenK/e/jYeHBzw8PLQeK5GhcDUsIiIyd5IS5OrVq2PXrl3w9/dHWFgYypUrpzFp7NKli5TT6FXp0qWRmZmJV69eKYdWKIZa5BYfHw8fHx99h0dkcIrVsBQUq2HJZPLxhURERKZOUoLcq1cv5b+nTZumsY2xl3l70507d2Bvbw9nZ2flYifnzp1D9+7dlW0yMzMRHR2tso3IEqQmynuONbm8Tj75hsMtiIjI1ElKkE25zNuTJ0/g7u6usu3SpUvYs2cP2rZtCysrK7i4uKBly5ZYv349pk2bhqJFiwIA1q1bh5SUFLWxxUTmLj+rYTFBJiIiUycpQc5d9s3U9OjRAw4ODggODoaHhwdiYmKwcuVKODo6Yu7cucp2s2fPRnBwMJo0aYKhQ4fi4cOHWLhwIVq1aoU2bdoY8BWQPnFCmhxXwyIiU8B7NkklKUE2ZSEhIdiwYQMWLVqE5ORkuLu7o0uXLpg+fbrKUtO1a9fGoUOHMHHiRIwZMwZFixZFeHg4vv76awNGT/rCCWmquBoWERkz3rNJWyQnyP/99x9Wr16NCxcuICkpCTk5OSr7ZTIZDh8+LPU0WhcREYGIiIh8tW3YsCFOnDih44jIGHFCmjquhkVExor3bNIWSQny5cuX0bRpU6SlpeG9997DlStXUK1aNbx48QJxcXGoWLEiSpcura1YifSKE9I042pYRGSMeM8mbbKS8uTPP/8czs7OuHHjBg4dOgQhBBYvXowHDx5g8+bNeP78ucp4XiJTkp8JaZbM0Q3wrcdfOERkHHjPJm2SlCCfOHECH3/8McqUKQMrK/mhFEMsQkND0adPH4wfP156lEQGoJiQpgknpBEg77GKi5J/JyLD4j2btElSgpyTkwNPT08AQPHixWFtbY1nz54p91evXh3nz5+XFiGRgSgmpGnCCWmWLSsN2B0OLPQBVtWXf98dDrxON3RkRJaL92zSJkkJcvny5XH3rvwzCysrK5QvXx6HDh1S7j958iSKFy8uKUAiQ2q/DKgVLu99AOTfa4Vb9oQ09pr+byKQ4uNcxUSgfSMNGxeRpeM9m7RF0iS9Vq1aYevWrZg9ezYAYNiwYRg7dizu3LkDIQSOHj2KsWPHaiVQIkPghLT/YfkkOU4EIjJevGeTtkhKkKdMmYJevXohKysLRYoUwejRo/Hq1Sts374d1tbWmDZtGiZPnqytWIkMxtGNN1mWT5LjaoJExo/3bJJKUoJcokQJ1KlTR/lYJpNh6tSpmDp1quTAiMh4sNf0f7iaIBGR+ZM0Bnn58uV48uSJtmIhIiPF8kn/w4lARETmT1KCPHLkSPj6+uLDDz/E6tWrVSpYEJH5YPkkVZwIRERk3iQlyP/88w+mTp2K+Ph4DBkyBN7e3mjXrh3WrVuH5ORkbcVIRAbGXlNViolAn8UBg6Pk3zuusqzJikRE5kxSguzn54cvvvgCV69exZUrVzBhwgTcuXMHAwYMgKenJ0JCQrBp0yZtxUpEBsReU3VcTZCIyDzJhBBC2we9dOkSNm3ahOXLl+PVq1d4/fq1tk9h0q5du4aAgABcvXoV/v7+hg6HqEBSEw1TPik1UT4WukQFJqRERJZO17mUpCoWmly+fBlbtmzBtm3b8PLlSzg4OGj7FERkQPoun8T6y0REpG+ShlgoxMTEYPr06ahatSpq1aqFhQsXolq1ali/fj0eP36sjVMQkYXiqnVERKRvknqQv/rqK2zZsgUxMTGwtrZGixYt8PnnnyMkJAQuLi7aipGILBTrLxMRkSFISpBnzpyJJk2aICIiAl26dEHJkiW1FRcREVetIyIig5CUIMfFxcHDw0NbsRARqeCqdUREZAiSxiAzOSbKv9REIC5K/p3yh/WXiYjIELRexYKIVLEKgzTtlwEymfz6ZWfKe45r9LPs+stERKRbTJCJdExRhUFBUYVBJpOvvkZvp1i1ruVcw9RfJiIiy6OVMm9EpNm7qjAUZLiFpQ/R4Kp1RESkL+xBJtIhbVRh4BANIiIi/dJJD/KTJ0+ggxWsiUyOogqDJvmtwsCFMoiIiPRLawlySkoKwsLC4ODgAC8vL9jb26N37954+vSptk5BZHKkVmHQ5hANIiIiyh+tJcjDhg3D3bt38fvvv+P69evYvHkzTp8+jcGDB2vrFEQmqf0yoFa4vMcYkH+vFZ6/Kgz5GaJBRERE2lXgMcibNm1Cz5491bYfOXIEe/bsQe3atQEAfn5+ePToESZPniw9SiITJqUKAxfKICIi0r8C9yCPGzcOjRo1wsWLF1W2+/n5Yd26dcjKkv8mf/HiBXbs2IHKlStrJ1IiE1eYKgxcKIOIiEj/Cpwg37hxAw0bNkTDhg0xZMgQPHnyBACwdOlS7NmzB8WLF0epUqXg6emJGzduYMWKFVoPmsiSSBmiQURERAVX4CEWTk5O+PrrrzF48GCMHTsWlStXxtSpUzF69GjcuHEDp06dQnx8PDw9PdGgQQPY2trqIm4ii8GFMsgcpSbKx9iXqMCfZyIyPoWug1yxYkXs2rULBw8exJgxY7By5UosXLgQHTp00GZ8RPT/OboxkSDTx7reRGQKJFex+PDDD3Hp0iWMHDkSYWFhaNWqFa5fv66N2IiIyMywrjcRmYICJ8ivX7/GvHnz8MEHH6BWrVr45JNPkJCQgIiICNy4cQMVK1ZE7dq1ERERgefPn+siZiIiMkGs601EpqLACfL48ePx9ddfo1WrVhg0aBD+/vtvtGnTBjk5OXBzc8MPP/yA06dP48qVK6hcuTKWL1+ui7iJiMjEsK43EZmKAo9B3rhxI6ZMmYLx48cDAJo2bYrAwEBcu3YN1atXBwDUrFkTR44cwdatWzFhwgQMHz5cu1ETEZHJYV1vIjIVBe5BLlKkCFJTU5WPU1NTIYRAkSJF1NqGhoZyPDIREQFgXW8iMh0F7kEeMmQIvv76azx48AAlSpTAxo0b0ahRI1SpUkVje3t7TksmIiK59ssAmUw+5jg7U95zXKMf63oTkXEpcII8ffp0VKxYEXv37kViYiKGDx+OUaNG6SI2IiIyM6zrTUSmoFB1kPv27Yu+fftqOxYiInqDuS6owbreRGTMCr1QCABkZWXh5s2bePToEdLS0uDg4AAfHx/4+flpHJNMRET5wwU1iIgMp1AJ8u3btzF9+nTs3r1bOWFPCAGZTAYAcHR0RMeOHfHll1+icuXK2ouWiMhCKBbUUFAsqCGTyYcoEBGR7hQ4Qb548SKaNm0Ka2tr9OnTB0FBQfD29oa9vT3S09MRHx+P06dPY9u2bdi7dy+OHDmCWrVq6SJ2IiKz9K4FNVrO5fAEIiJdKnCCPHbsWJQpUwZHjhyBm5vmO/SgQYMwZ84cNGvWDOPGjcPhw4clB0pEZCnys6AGE2QiIt0pcB3kqKgoDB8+PM/kWMHNzQ3Dhw9HVFRUoYMjIrJEigU1NOGCGkTSpCYCcVFc2pzersAJsqOjI54+fZqvtomJiXBwcChwUETGhjdU0icuqEGkfVlpwO5wYKEPsKq+/PvucOB1uqEjI2NU4CEWISEh+Oabb+Dv74/OnTvn2W7Hjh1YsGABevToISlAIkNiJQEyFC6oQaRdnPhKBVHgBHn+/Pm4du0aunbtCi8vL9SuXRve3t6ws7NDRkYG4uPjceHCBTx+/Bj169fH/PnzdRE3kV7whkqGwgU1iLSHE1+poAqcILu4uODvv//G1q1bsX37dly8eBHHjh1T1kH29vZGw4YN0a1bN3Tr1g1WVgUexUFkFN51Qw0eD2Qkmd8CDmRcuKAG0dvlZzEdTnylgipUHWSZTIbu3buje/fu2o6HyGi864b6QwCQ85rDLiyFua5oR2SqCjIETjHxVdM9nRNfSRN27xLl4W2VBAB5cgz8b9jFvpH6iYv0ixN7iIyTYgicIul9272YE1+poHSaIF+9ehU///yzLk9BpDNvu6Fqcnkdq1yYo4L8EiYi/XjXEDhN9+L2y4Ba4fIeY0D+vVY4J76SZjpNkHfv3o2BAwfq8hREOvXmDdXqLYOSFOPYyHwU5pcwEelefsYUv0kx8fWzOGBwlPx7x1UcGkeaFWoMMpGleLOSgF0x4IfqHMdmKTixh8g4SRlTzImvlB8FTpAHDRqU77aXLl0q6OGJjFLuG2qNfqql3xQ4js38cGIPkXFSDIHjvZh0pcAJ8po1a1CkSBHY2tq+s21WVh5dL0QmjAs4WA7+EiYyXrwXky4VOEEuXbo0AgICsHfv3ne2nTVrFqZPn16owIiMFRdwsCz8JUxknHgvJl0qcIJcv359HDlyJF9tZTJZgQMiMhUcx2YZ+EuYyLjxXky6UOAqFs2aNYOzszMePHjwzrY1a9ZE//79CxUYEZExcXQDfOvxFzERkSUocII8bNgw3L17F6VLl35n248++giRkZGFCoyIyJilJgJxUSz1RkRkjljmjciATHn5YlOOXYqCLG9LRESmiQkykQGYcpJlyrFrg2JlPQXFynoymXysMhERmT6drqRHRJqZ8vLFphy7VFxZj4jIMjBBJtIzU06yTDl2bSjM8rZERGR6mCAT6ZkpJ1mmHLs2KFbW04Qr6xERmQ8myER6ZspJlinHrg2KlfU04cp6RETmQ1KCPGjQIJw5cybP/VFRURg0aJCUUxCZHVNOskw5dm1pvwyoFS7/gwCQf68VzpX1iIjMiaQEec2aNbh9+3ae++/evYu1a9dKOQWRWTLlJMuUY9cGxcp6n8UBg6Pk3zuusowKHkRElkKnZd4ePXoEBwcHXZ6CyCSZ8vLFphx7YeRV75nL2xIRma8CJ8i7d+/G7t27lY9XrlyJQ4cOqbV78eIFDh06hHr16kmLkMiMmXKSZcqx54el13smIrJkBU6QY2JisHXrVgCATCbDmTNncP78eZU2MpkMTk5OaNy4MRYtWqSdSImI9IgLghARWa4Cj0GeNGkSXr58iZcvX0IIgdWrVysfK76Sk5MRHx+P3377DX5+frqIm4hIZyy93jMRkaWTNEkvJycHvXv31lYsRi0jIwMTJ06Ej48PHBwcUL9+fRw8eNDQYRGRDlh6vWciIkuntUl6KSkpeP78OYQQavvKlCmjrdMYTFhYGLZt24bRo0ejcuXKWLNmDdq1a4cjR46gYcOGhg6PiLRIUe9ZU5JsCfWeicj05DWhmApHUoKcnp6OGTNmYPXq1Xj69Gme7bKzs6WcxuCioqKwadMmzJ8/H+PGjQMA9O/fHwEBAZgwYQJOnjxp4AiJSJsU9Z5zj0FWsJR6z0RkGjihWDckJcjDhw/H2rVrERISgkaNGqFEiRLaisuobNu2DdbW1hg6dKhym729PcLDwzF58mQ8ePAApUuXNmCERKRt7ZfJJ+RdXicfVmFtK/+lYyn1nonINHBCsW5ISpB37NiBwYMHY8WKFdqKxyhdvHgRfn5+KFasmMr2oKAgAEB0dDQTZCIzY2n1nonI9LxrQnHLubxvFZakBFkmk6F27draisVoxcfHw9vbW227YtujR4/yfG5CQgKePHmisi02Nla7ARKRzph7vWciMl35mVDM+1fhSEqQO3XqhEOHDuHjjz/WVjxGKS0tDXZ2dmrb7e3tlfvzsnz5csyYMUNnsREREZFl4oRi3ZFU5m3atGm4c+cOhg4divPnz+PJkyd49uyZ2pepc3BwQEZGhtr29PR05f68DB8+HFevXlX52rVrl65CJSIiIguhmFCsCScUSyOpB7ly5coA5GN0V69enWc7U69i4e3tjbi4OLXt8fHxAAAfH588n+vh4QEPDw+dxUZERETaY2rl0jihWDckJchffPEFZDKZtmIxWoGBgThy5AiSk5NVJuqdOXNGuZ8sj6ndRImIKG+mWi6NE4p1Q1KC/OWXX2opDOPWrVs3LFiwACtXrlTWQc7IyEBkZCTq16/PChYWxlRvokRElDdTL5fGCcXapbWV9AAgKSkJzs7OsLa21uZhDa5+/foIDQ3FpEmTkJCQgEqVKmHt2rW4d+/eW4eWkHky9ZsoERGpYrk0epOkSXoAcO7cObRp0waOjo4oWbIkjh07BgBITExEp06dcPToUamnMAo///wzRo8ejXXr1iEiIgJZWVn47bff0LhxY0OHRnr0rptoaqL+44mL0v95iYh0Sd/3tvyUS6N3M6ffSZJ6kE+ePInmzZvD19cXffv2xapV/+s+c3NzQ1JSElasWIGmTZtKjdPg7O3tMX/+fMyfP9/QoZABGUvNSQ7zICJzZKh7G8ulSWOOv5Mk9SBPnjwZVatWRUxMDObMmaO2v1mzZsqJbETmQHET1USfN1HFMA/FzVwxzGPfSP2cn4hIFwx1b2O5NGnM8XeSpAT57NmzGDhwIOzs7DRWs/D19cV///0n5RRERsUYbqLGNsyDiEgbDH1va78MqBUu7+wA5N9rhbNc2rsY+n3TFUlDLIoUKYKcnJw898fFxcHZ2VnKKYiMjqFrThrLMA8iIm0y9L2N5dIKx9Dvm65ISpDff/99bNu2DaNHj1bb9+rVK0RGRqJJkyZSTkEWyNjrCxv6JsqxckRkjozl3sZyaQVjLO+btkkaYjFjxgycO3cO7du3x++//w4AuHTpElatWoU6dergyZMnmDZtmlYCJfOXlQbsDgcW+gCr6su/7w4HXqcbOjLNHN0A33r6v5EawzAPIiJt473NNJnr+yapB7l+/frYt28fhg0bhv79+wMAxo4dCwCoWLEi9u3bhxo1akiPkiwC6wvnn6GHeRAR6QLvbabJHN83mRBCaONA0dHRuHXrFnJyclCxYkXUqVPHIpahLoxr164hICAAV69ehb+/v6HDMQqpifIe47w+ovksznT/CtWl1ESOlSMi88N7m2nS5/um61xKayvpBQYGIjAwUFuHIwtjroP8dY1j5YjIHPHeZprM6X3TSoIcExODO3fu4Pnz59DUIa0YfkGUF3Md5E9ERGQujH0SvTZJSpBv376Nvn37IioqSmNiDAAymYwJMr2TYpB/7jHICqY8yP9NlnRzISIi82COK+W9i6QE+eOPP8aVK1fw3XffoVGjRihRooS24iILZI6D/BUs8eZCRETmwRIn0UtKkE+cOIHJkyfj008/1VY8ZMEMXV9Ylyzx5kJERKbvXSvltZxrPr+rc5NUB9nNzQ0uLi7aioUIgOHqC+uKuS7DSURE5i8/k+jNkaQE+ZNPPsH69euRnZ2trXiIzI6l3lyIiMj0KSbRa2LOk+glDbHw8/NDdnY2atasiUGDBqF06dKwtrZWa9elSxcppyEy6cltrNBBRESmylIm0b9JUoLco0cP5b/HjRunsY1MJmMPMxWaOUxus9Sbi66Z8h9NRESmxJwn0edFUoJ85MgRbcVBpJG5TG6zxJuLrpjDH01ERKbEnCfR50VSgtykSRNtxUGkxpxmzlrizUVXzOWPJiIiU2NOK+W9i9aWmo6JicH9+/cBAGXLlkW1atW0dWiyUOa4/LQl3Vx0wZz+aCIiIuMlOUHevXs3PvvsM9y7d09le/ny5bFo0SJ07NhR6inIQnFyG73JHP9oIiIi4yOpzNu+ffvQtWtXAMCcOXOwc+dO7Ny5E3PmzIEQAl26dMH+/fu1EihZHsXkNk04uc0yWWq5ISIi0i+ZEEIU9skNGjRARkYGjh8/DicnJ5V9r169QsOGDWFvb49Tp05JDtScXLt2DQEBAbh69Sr8/f0NHY5Re53+vwlZb05uM6YJWayooD+7wzVXBKkVzjHIRESWQte5lKQhFpcvX8acOXPUkmMAcHJyQlhYGCZPnizlFGThjH1yGysq6B8rglB+8I9WIpJCUoJsb2+PZ8+e5bn/2bNnsLdnlkDSGevkNlZU0D9j/6OJDIt/tBKRNkgag9y8eXMsXrxY4xCKM2fO4Pvvv0fLli2lnILIaL2rokJqon7jsTSOboBvPSbHpErxR6tiMqfij9Z9Iw0bFxGZFkk9yN988w0aNGiAhg0bIigoCO+99x4A4MaNG4iKioKHhwfmzZunlUCJjA0rKhAZF5YBJCJtkdSDXL58eVy+fBkRERF4/vw5Nm/ejM2bN+P58+cYNWoULl26hHLlymkpVCLjwooKRMYlP3+0knSpiUBclOV8SmZpr5fkJNdB9vDwwLfffotvv/1WG/EQmQxFGTpNFRVYho5I/1g7XbcsbXy3pb1eUqW1lfQSEhKUi4WUK1cOHh4e2jo0kdFiRQUi48E/WnXL0iYlW9rrJVWShlgAwOHDh1G3bl14e3ujQYMGaNCgAby9vVG3bl0cOnRIGzESGS1FRYXP4oDBUfLvHVexd4HIUNovk9fEtraVP7a2lT/mH63SWNqkZEt7vaROUg/yzp07ERoaCk9PT0yYMAF+fn4A5JP01q1bh7Zt22LLli3o3LmzVoIlMlbGWoaOyNKwDKBuWNqkZEt7vaROUoI8depUBAQE4Pjx4yhatKjKvsmTJ6Nhw4aYOnUqE2QiItIr/tGqXZY2vtvSXi+pkzTE4s6dOxg4cKBacgwAxYoVQ3h4OO7e5bRhIiIiU6YY362JOY7vtrTXS+ok9SBXqVIFCQkJee5//PixctgFERERmS5Lm5Rsaa+XVEleKKRnz54ICgpCp06dVPbt3LkTK1aswObNmyUFSERERIZnaeO7Le31kipJCfKSJUvg7u6OLl26wMfHB5UqVQIAxMbG4tGjR/Dz88P333+P77//XvkcmUyG3bt3S4uaiIiIDMLcxnenJson5ZWooPl1mdvrpfyRlCBfvnwZMpkMZcqUAQBlHWQbGxuUKVMG6enpuHLlispzZDKZlFMSERERScaFQOhtJCXIioSYiIiIyJRwIRB6G8kLhRARvU1qIhAXxcL6RGQ8uBAIvYtWlprOyspCXFwcnj9/DiGE2v7atWtr4zREZEL48SURGSsuBELvIilBfvHiBcaNG4cNGzYgMzNTbb8QAjKZDNnZ2VJOQ0QmiB9fEpGx4kIg9C6SEuSwsDD8+uuv6NmzJ+rXrw8XFxdtxUVEJuxdH1+2nMveGSIyHMVCILn/iFfgQiAESEyQDxw4gIiICHz77bfaioeIzAA/viQiY8eFQOhtJCXIJUuWVNY+JiJS4MeXRGTsuBAIvY2kKhZDhw7Fpk2bkJOTo614iMgMKD6+1IQfXxKRMXF0A3zr8b5EqiT1IE+bNg0ZGRmoW7cu+vXrh1KlSsHa2lqtXZcuXaSchohMED++JCIiUyUpQY6Li8Off/6J6OhoREdHa2zDKhZElokfXxIRkamSlCAPGjQIFy5cwKRJk1jFgog0cnRjYkwFk5oon+hZogJ/dojIMCQlyH///TcmTpyIGTNmaCseIiKyUFxchoiMhaQE2cvLC66urtqKhYiILBgXlyEiYyGpisXYsWOxatUqpKSkaCseIiKyQO9aXCY1Ub/xEJFlk9SDnJ6ejiJFiqBSpUro3r07SpcurVbFQiaTYcyYMZKCJCIi88bFZYjImEhKkMeNG6f899Klmms3MUEmc8MJRETax8VliMiYSEqQ7969q604iIweJxAR6Y5icZncY5AVuLgMEembpAS5bNmy2oqDyOhxAhGRbnFxGSIyFpISZIVXr17h2LFjuH//PgB54tykSRM4OTlp4/BkhCxtmMG7JhC1nGsZ14FIl7i4DBEZC8kJ8pIlSzB16lSkpKRACKHcXrRoUcyePRsjR46UegoyIpY6zIATiIj0h4vLEJGhSSrz9vPPP2PUqFEICAjAxo0blUtO//LLL6hevTpGjRqFdevy6HYjk6QYZqBIFhXDDPaZ+d9BiglEmnACERERkXmRidzdvgUUGBiI4sWL4/Dhw2rl3bKzs9GiRQu8ePEC0dHRUuM0K9euXUNAQACuXr0Kf39/Q4eTb6mJwEKfvGeZfxZnvL0+2hgSsjtc8wSiWuEcg0xERKRPus6lJPUg37hxA6GhoWrJMQBYW1sjNDQUN27ckHIKMiL5GWZgbLLS5IntQh9gVX35993hwOv0gh+r/TJ5MmxtK39sbSt/zAlERERE5kXSGGQXFxfcu3cvz/337t1DsWLFpJyCjIgp1inVZuUJTiAiIiKyDJJ6kNu3b48lS5Zg06ZNavs2b96MpUuXokOHDlJOQUZEUadUE2OsU6qrpWsd3QDfesb3eomIiEg7JPUgz507F6dOnUKfPn0wduxYVK5cGQBw69Yt/Pfff6hSpQrmzp2rlUDJOJhSnVJWniAiIqLCkJQgu7u748KFC1ixYgV+//13ZR3k6tWrY+LEiRg6dCjs7c249peZyc9ENlMaZmCKQ0KIiIjI8CTXQba3t8eoUaMwatQobcRDBlCY2samUKeUS9cSEVkOS1vAinRL0hjkZ8+e4fLly3nuv3LlCp4/fy7lFKQH5lzbmJUniIjMmzarFREpSOpBHjNmDG7cuIHTp09r3P/xxx+jatWqWL16tZTTkA6Z+xLKpjQkhIiICk6b1YqIFCT1IP/555/o2LFjnvs7dOiAQ4cOSTkF6Zgp1jYuDFaeICIyP7qqVkQkKUF+8uQJ3NzyzjhKliyJhIQEKacgHeMSykREZKospZOH9E9Sguzt7Y2LFy/muf/8+fNwd3eXcgrSMVOrbUxERKTATh7SFUkJckhICFavXo09e/ao7du9ezciIyPRuXNnKacgPeBENiIiMkXs5CFdkTRJ78svv8ShQ4fQuXNn1KxZEwEBAQCAq1ev4tKlS6hatSpmzJihlUBJdyxxIhvLARERmQdTWsCKTIekHmQXFxecPn0aU6dORVZWFrZt24Zt27YhKysL06ZNw5kzZ1C8eHEthapda9asgUwm0/j133//qbXfs2cPateuDXt7e5QpUwbTp0/H69evDRC57ljCRDaWAyIiMi+KTp7P4oDBUfLvHVflXcefKD8kLxTi5OSEGTNmmGxP8cyZM1G+vOogpTeT+t9//x0hISFo2rQplixZgitXrmDWrFlISEjADz/8oMdoSSqWAyIiMk+msIAVmQ7JCbKpa9u2LerWrfvWNuPGjUONGjVw4MAB2NjIL1mxYsUwZ84cjBo1ClWqVNFHqCSRudd8JiIiIu2QNMTCXLx8+RLZ2dka98XExCAmJgZDhw5VJscAMHz4cAghsG3bNn2FSRKxHBAZWmoiEBfF2qxERMbO4nuQmzVrhpSUFNja2qJ169ZYuHAhKleurNyvKGP3Zi+zj48PSpUq9dYydwCQkJCAJ0+eqGyLjY3VUvRUEIpyQJqSZJYDIl3KSpMP77m8Tv7zZ1VEPomo/TKOkyQiMkYWmyA7OjoiLCwMzZo1Q7FixXD+/HksWrQIwcHBuHDhAkqXLg0AiI+PByCv+fwmb29vPHr06K3nWb58ucmOzzY3inJAuccgK7AcEOkSx74TEZkWs0iQc3JykJmZma+2dnZ2kMlk6N69O7p3767cHhISgtatW6Nx48aYPXs2fvzxRwBAWlqa8nlvsre3R3Jy8lvPN3z4cISGhqpsi42NRUhISL7iJe1iOSDSN459JyIyPWaRIP/1119o1qxZvtpev349z0l1DRs2RP369XHo0CHlNgcHBwBARkaGWvv09HTl/rx4eHjAw8MjX7GR7llizWcyrPyOfWddbiIi4yEpQX716hUcHR0hk8k07n/y5AmuX7+Oxo0bSznNO1WpUgWRkZH5aqtpqERupUuXxo0bN9Tax8fHK4ddKMTHxyMoKKiA0ZIxYDkg0pe3jX23KgKc+Q64tpVjk4mIjEmhEuS1a9fiiy++wMOHD+Ho6Ihu3brh66+/hpeXl0q7AwcOoH///nlWiNAWLy8vhIWFaeVYd+7cgbu7u/JxYGAgAODcuXMqyfCjR4/w8OFDDB06VCvnJSLz9Lax78XLA1c2/u8xxyYTERmHApd5O3z4MAYOHAgnJyeMGTMGHTt2xObNmxEQEIBjx47pIkadeLOyBADs27cP58+fR5s2bZTb/P39UaVKFaxcuVIl0f/hhx8gk8nQrVs3vcRLRKar/TKgVrh8zDsg/169D/Aij9KCl9eZbik4lrIjInNQ4B7kWbNmoU6dOjhx4gRsbeV3+9jYWISGhqJNmzZYu3atyuQ3YxUcHIxatWqhbt26cHFxwYULF/DTTz+hdOnSmDx5skrb+fPno2PHjmjVqhV69uyJq1evYunSpRg8eDCqVq1qoFdABZGayDGeZDiaxr4/vwNc2aC5vWJssin9rLKUHRGZkwInyFevXsUXX3yhTI4BoFKlSjh58iR69OiB3r17IyEhASNHjtRqoNrWo0cP7N27FwcOHEBqaiq8vb0xZMgQTJ8+HZ6eniptP/roI+zYsQMzZszAp59+Cnd3d0yePBlffPGFgaKn/OIvbTImb459N6e63CxlR0TmpMAJcnZ2tsqKcgoODg7YtWsXBg8ejFGjRiEhIQHvvfeeVoLUhVmzZmHWrFn5bh8SEsLSbCaIv7TNl6l/KmBOdblZyo6IzE2BE+TKlSvjxIkTGDZsmNo+Kysr/PTTTyhZsiRmzZoFPz8/rQRJVBj8pW2ezOlTAXOpy52fUnb8v0ZEpqTACXK7du0wf/58PHv2DK6urhrbzJ8/Hx4eHpg4cWKeJeCIdI2/tM2TOX0qYC51ubmMOxGZmwInyOHh4XBzc8Pjx4/zTJABYPz48ahUqRIuX74sKUCiwuIvbfNjrp8KmHpdbnMaLkJEBBQiQS5VqhRGjBiRr7adO3dG586dCxwUkTbo4pe2qY97NXX8VMB4mctwESIiwEyWmibKi7Z+aZvTuFdTxk8FjJe5DBchIgKYIJOZ09YvbXMa92rK+FG+8TP14SJE+sRPJY0XE2SyCFJ+aZvruFdTxY/yicjU8VNJ48cEmegdOO7VuPCjfCIydfxU0vhZGToAImOnGPeqCce9Go6jG+Bbj8kxEZmWd30qmZqo33hIM50lyFlZWdi1axe6deumq1MQ6YVi3KsmHPdKREQFkZ9PJcnwtD7E4tixY9iwYQO2b9+O58+fw8nJSdunINI7jnvlZBIiIm1gNR7ToJUE+fLly9iwYQN++eUXxMXFwcvLC926dUPHjh3RokULbZyCyKAsedwrJ5MQEWkPq/GYhkInyP/++y82btyIDRs2ICYmBu7u7mjWrBm2bNmCJUuWoEuXLtqMk8goWGIJK04mMS7syTdOfF+oIPippPErcIK8YsUKbNiwASdOnICLiwu6dOmCb7/9Fs2bN8edO3ewefNmXcRJRAbAEnfGgz35xonvCxWGJX8qaSoKnCAPGzYM5cuXx/bt29G+fXsUKfK/6f0ymUyrwRGRYbHEnfFgT75x4vtCUljip5KmosBVLOrWrYu7d+9i2LBhGDt2LE6dOqWLuIjICLDEnXFgWSjjxPeFyHwVOEGOiorCzZs3MXToUOzfvx8ffPABypUrh88//xzR0dE6CJGIDIUl7owDy0IZJ74vROarUHWQK1WqhBkzZuDmzZs4deoUOnTogMjISISGhkImk2Hbtm04efIkhBDajpfMXGoiEBfFnhdj0n4ZUCtc3mMMyL/XCudkEn1iT75x4vtCZL4kLxRSv359LFmyBI8ePcJvv/2GXr164ddff0WjRo3g6emJgQMHaiNOMnNZacDucGChD7Cqvvz77nDgdbqhIyPFZJLP4oDBUfLvHVdxApI+sSffOPF9ITJfWltJz9raGu3atcP69evx+PFjrF27FnXq1MHGjRu1dQoyY4qJLoqPKxUTXfaNNGxc9D9c2tmw2JNvnPi+EJknmdDxOIgnT57A3d1dl6cwOdeuXUNAQACuXr0Kf39/Q4djcKmJ8h7jvFYV+iyOSRmRQmoiy0IZI74vRPql61yqwD3ISUlJaNOmDebMmfPWdrNnz0a7du3g4OBQ6ODIMnCiC1H+sSffOPF9ITIvBU6Qly5dipMnT2LIkCFvbTdkyBCcOHECy5YtK3RwZBk40YWIiIiMSYET5J07d6Jnz57vHDbh4eGBXr16Yfv27YUOjiwDJ7oQERGRMSlwgvzPP/+gbt26+Wpbu3ZtXL9+vcBBkeXhRBciIiIyFgVearqgc/pycnIKegqyQFyXnoiIiIxFgRPkMmXK4Pz58/lqe/78eZQpU6bAQZHl4rr0REREZGgFHmLRvn17rF+/Hrdu3Xpru1u3bmH9+vVo3759oYMjIiIiItK3AifIEyZMgKOjI5o0aYLNmzfj9evXKvtfv36NzZs3o1mzZnB0dMT48eO1FiwRERERka4VeIiFh4cH9u3bh86dO6N3795wcHCAn58fihYtipcvX+LmzZtIS0uDl5cX9u7dC09PT13ETURERESkEwVOkAGgXr16uHbtGn788Ufs2bMH//zzD5KTk1GsWDHUrFkTHTp0wCeffILixYtrOVwiIiIiIt0qVIIMAC4uLpg4cSImTpyozXiIiIiIiAyqUAlyeno6du/ejbt378LNzQ3t27eHt7e3tmMjIiIiItK7AifICQkJCA4Oxt27d5U1kR0dHbFr1y60bNlS6wESEREREelTgatYfPXVV7h37x7GjBmD3377Dd999x0cHBzw8ccf6yI+IiIiIiK9KnAP8oEDB9C/f38sWLBAuc3T0xO9e/fGjRs38N5772k1QCIiIiIifSpwD/K///6Lhg0bqmxr2LAhhBB4/Pix1gIjIiIiIjKEAifIGRkZsLe3V9mmePzmoiFERERERKamUFUs7t27hwsXLigfJyUlAZAvL62p9nHt2rULFx0RERERkZ7JhKIURT5ZWVlBJpOpbRdCqG1XbMvOzpYWpZm5du0aAgICcPXqVfj7+xs6HCIiIiKToutcqsA9yJGRkVoPgoiIiIjIWBQ4QR4wYIAu4iAiIiIiMgoFnqRHRERERGTOmCATEREREeXCBJmIiIiIKBcmyEREREREuTBBJiIiIiLKhQkyEREREVEuTJCJiIiIiHJhgkxERERElAsTZCIiIiKiXJggExERERHlwgSZiIiIiCgXJshERERERLkwQSYiIiIiyoUJMhERERFRLkyQiYiIiIhyYYJMRERERJQLE2QiIiIiolyYIBMRERER5cIEmYiIiIgoFybIRERERES5MEE2c6mJQFyU/DsRERERvZuNoQMg3chKA/aNBC6vA3KyAKsiQI1+QPtlgI29oaMjIiIiMl5MkM3UvpFA9E//e5yTJX8skwEdVxkuLiIiIiJjxyEWZig1Ud5zrMnldRxuQURERPQ2TJDN0PM78h5jTbIzged39RsPERERkSlhgmyGSlSQjznWxNoWKFFev/EQERERmRImyGbI0U0+IU+TGv3k+4mIiIhIM07SM1Ptl8kn5F1eJx9WYW0rT47bLTV0ZERERETGjQmymbKxl1eraDlXPua4RHn2HBMRERHlBxNkM+foxsSYiIiIqCDMagxyfHw8Pv/8czRr1gxFixaFTCbD0aNH82x/8uRJNGzYEI6OjvDy8kJERARSUlLU2mVkZGDixInw8fGBg4MD6tevj4MHD+rwlRARERGRoZhVgnzjxg3MmzcPcXFxqF69+lvbRkdHo0WLFkhNTcWiRYswePBgrFy5EqGhoWptw8LCsGjRIvTp0weLFy+GtbU12rVrh7///ltXL4WIiIiIDMSshljUqVMHT58+haurK7Zt26Yx2VWYPHkySpQogaNHj6JYsWIAgHLlymHIkCE4cOAAWrVqBQCIiorCpk2bMH/+fIwbNw4A0L9/fwQEBGDChAk4efKk7l8YEREREemNWfUgFy1aFK6uru9sl5ycjIMHD6Jv377K5BiQJ77Ozs7YsmWLctu2bdtgbW2NoUOHKrfZ29sjPDwcp06dwoMHD7T7IoiIiIjIoMyqBzm/rly5gtevX6Nu3boq221tbREYGIiLFy8qt128eBF+fn4qiTQABAUFAZAP1ShdunSe50pISMCTJ09UtsXGxkp9CURERESkIxaZIMfHxwMAvL291fZ5e3vj+PHjKm3zagcAjx49euu5li9fjhkzZkgJl4iIiIj0yGgT5JycHGRmZuarrZ2dHWQyWb6PnZaWpnzem+zt7ZX7FW3zapf7WHkZPny42ljo2NhYhISE5DteIiIiItIfo02Q//rrLzRr1ixfba9fv44qVark+9gODg4A5OXb3pSenq7cr2ibV7vcx8qLh4cHPDw88h0bERERERmW0SbIVapUQWRkZL7aahoCkZ/2iqEWucXHx8PHx0elbVxcnMZ2AFTaEhEREZHpM9oE2cvLC2FhYTo5dkBAAGxsbHDu3Dl0795duT0zMxPR0dEq2wIDA3HkyBEkJyerTNQ7c+aMcj8RERERmQ+zKvOWXy4uLmjZsiXWr1+Ply9fKrevW7cOKSkpKmOGu3XrhuzsbKxcuVK5LSMjA5GRkahfv/5bK1gQERERkekx2h7kwpo1axYA4Nq1awDkSa9ixbupU6cq282ePRvBwcFo0qQJhg4diocPH2LhwoVo1aoV2rRpo2xXv359hIaGYtKkSUhISEClSpWwdu1a3Lt3D6tXr9bjKyMiIiIifZAJIYShg9Cmt1WzePOl/v3335g4cSIuXLiAokWLonv37vj6669RtGhRlXbp6emYNm0a1q9fj+fPn6NGjRr46quv0Lp160LFeO3aNQQEBODq1avw9/cv1DGIiIiILJWucymzS5BNwYULF1CnTh3s2rULlSpVMnQ4RERERCZFUTL3/PnzqF27ttaPb3ZDLEyBYnlq1kImIiIiKrwHDx7oJEFmD7IBvHjxAseOHUPp0qU1LkJiKIq/xtiz/T+8Jup4TdTxmqjjNVHHa6IZr4s6XhN1b16TjIwMPHjwAE2aNEHx4sW1fj72IBtA8eLF0alTJ0OHkadKlSpxbPQbeE3U8Zqo4zVRx2uijtdEM14Xdbwm6nJfE130HCtYZJk3IiIiIqK8MEEmIiIiIsqFCTIRERERUS5MkEnJ3d0d06dPh7u7u6FDMRq8Jup4TdTxmqjjNVHHa6IZr4s6XhN1+r4mrGJBRERERJQLe5CJiIiIiHJhgkxERERElAsTZCIiIiKiXJggExERERHlwgSZiIiIiCgXJsiEpk2bQiaTafwqUqSIStty5cppbPfJJ58YKHrdWLNmTZ7X5L///lNrv2fPHtSuXRv29vYoU6YMpk+fjtevXxsgct05fPgwBg0aBD8/Pzg6OqJChQoYPHgw4uPj1drm9TPVpk0bA0SuOxkZGZg4cSJ8fHzg4OCA+vXr4+DBg4YOSy/Onj2LkSNHwt/fH05OTihTpgy6d++OmzdvqrQLCwvT+LNQpUoVA0WuO0ePHs3zvnH69GmVtidPnkTDhg3h6OgILy8vREREICUlxUCR605e77/iKy4uDoB53zNSUlIwffp0tGnTBq6urpDJZFizZo3GttevX0ebNm3g7OwMV1dX9OvXD0+ePFFrl5OTg2+++Qbly5eHvb09atSogV9++UXHr0R78nNNcnJysGbNGnTs2BGlS5eGk5MTAgICMGvWLKSnp6sdM6+fsblz5xYqRptCPYvMypQpUzB48GCVba9evcInn3yCVq1aqbUPDAzE2LFjVbb5+fnpNEZDmTlzJsqXL6+yrXjx4iqPf//9d4SEhKBp06ZYsmQJrly5glmzZiEhIQE//PCDHqPVrYkTJ+LZs2cIDQ1F5cqVcefOHSxduhS//fYboqOj4eXlpdK+VKlS+Prrr1W2+fj46DNknQsLC8O2bdswevRoVK5cGWvWrEG7du1w5MgRNGzY0NDh6dS8efNw4sQJhIaGokaNGvjvv/+wdOlS1K5dG6dPn0ZAQICyrZ2dHVatWqXyfBcXF32HrDcRERGoV6+eyrZKlSop/x0dHY0WLVqgatWqWLRoER4+fIgFCxbg1q1b+P333/Udrk59/PHHaNmypco2IQQ++eQTlCtXDr6+vsrt5nrPSExMxMyZM1GmTBnUrFkTR48e1dju4cOHaNy4MVxcXDBnzhykpKRgwYIFuHLlCqKiomBra6tsO2XKFMydOxdDhgxBvXr1sHv3bvTu3RsymQw9e/bU0ysrvPxck9TUVAwcOBDvv/8+PvnkE3h4eODUqVOYPn06Dh8+jD///BMymUzlOR9++CH69++vsq1WrVqFC1IQabBu3ToBQGzYsEFle9myZUX79u0NFJX+REZGCgDi7Nmz72xbrVo1UbNmTZGVlaXcNmXKFCGTycT169d1GaZeHTt2TGRnZ6ttAyCmTJmisr1JkybC399fn+Hp3ZkzZwQAMX/+fOW2tLQ0UbFiRdGgQQMDRqYfJ06cEBkZGSrbbt68Kezs7ESfPn2U2wYMGCCcnJz0HZ5BHDlyRAAQW7dufWu7tm3bCm9vb5GUlKTc9n//938CgPjjjz90HabBHT9+XAAQs2fPVm4z53tGenq6iI+PF0IIcfbsWQFAREZGqrUbNmyYcHBwEPfv31duO3jwoAAgVqxYodz28OFDUaRIETFixAjltpycHNGoUSNRqlQp8fr1a929GC3JzzXJyMgQJ06cUHvujBkzBABx8OBBle0AVK6JVBxiQRpt3LgRTk5O6NSpk8b9mZmZePXqlZ6jMoyXL18iOztb476YmBjExMRg6NChsLH53wcyw4cPhxAC27Zt01eYOte4cWNYWVmpbXN1dcX169c1Puf169dm+bExAGzbtg3W1tYYOnSocpu9vT3Cw8Nx6tQpPHjwwIDR6V5wcLBKjxYAVK5cGf7+/hp/HrKzs5GcnKyv8Azu5cuXGodZJScn4+DBg+jbty+KFSum3N6/f384Oztjy5Yt+gzTIDZu3AiZTIbevXur7TPHe4adnZ3aJ2yabN++HR999BHKlCmj3NayZUv4+fmp/Fzs3r0bWVlZGD58uHKbTCbDsGHD8PDhQ5w6dUq7L0AH8nNNbG1tERwcrLa9c+fOAJDn7520tDSNQzAKigkyqXny5AkOHjyIkJAQODk5qe3/888/4ejoCGdnZ5QrVw6LFy82QJT60axZMxQrVgyOjo7o2LEjbt26pbL/4sWLAIC6deuqbPfx8UGpUqWU+81VSkoKUlJS4Obmprbv5s2bcHJyQtGiReHl5YVp06YhKyvLAFHqxsWLF+Hn56eS5ABAUFAQAPnH6JZGCIHHjx+r/TykpqaiWLFicHFxgaurK0aMGGF2SVBuAwcORLFixWBvb49mzZrh3Llzyn1XrlzB69ev1e4Ztra2CAwMNPt7RlZWFrZs2YLg4GCUK1dOZZ+53zPeJi4uDgkJCWo/F4D8npL75+LixYtwcnJC1apV1dop9pszxTwgTb931qxZAycnJzg4OKBatWrYuHFjoc/DMcikZvPmzXj9+jX69Omjtq9GjRpo2LAh3nvvPTx9+hRr1qzB6NGj8ejRI8ybN88A0eqGo6MjwsLClAny+fPnsWjRIgQHB+PChQsoXbo0ACgnqHl7e6sdw9vbG48ePdJr3Pr23XffITMzEz169FDZXrFiRTRr1gzVq1fHq1evsG3bNsyaNQs3b97E5s2bDRStdsXHx+f5vgMw+/dekw0bNiAuLg4zZ85UbvP29saECRNQu3Zt5OTkYP/+/Vi+fDkuXbqEo0ePqnzyYupsbW3RtWtXtGvXDm5uboiJicGCBQvQqFEjnDx5ErVq1XrnPeP48eP6Dluv/vjjDzx9+lTt94sl3DPe5l0/F8+ePUNGRgbs7OwQHx8PT09PtfG3lnLv+eabb1CsWDG0bdtWZXtwcDC6d++O8uXL49GjR1i2bBn69OmDpKQkDBs2rOAn0tpgDTIK2dnZIi0tLV9fOTk5Go/RoEED4e7urjKmNi85OTmidevWwsbGRjx48EDbL0crtHFNhJCPm5PJZOLjjz9Wbps5c6YAIB4/fqzWvlGjRqJmzZq6eEmSaeOaHDt2TNjY2Iju3bvn65xDhgwRAMSpU6e0+VIMpkKFCqJt27Zq22/fvi0AiG+//Vb/QRnQ9evXRbFixUSDBg3eOQZy9uzZAoD45Zdf9BSd4dy6dUs4ODiI1q1bCyGE+PnnnwUAcebMGbW2/fr1Ey4uLnqOUL969eolihQpIhITE9/Z1tzuGULkPd72r7/+EgDE5s2b1Z4zbdo0AUA8f/5cCCFE8+bNRdWqVdXaZWdnCwBi1KhROohcd942LvtNinvH8uXL39k2IyNDBAQEiOLFi4vU1NQCx8UhFmbmr7/+goODQ76+bty4ofb8O3fu4NSpU+jRo0e+enZkMhnGjBmD169f5zkz19CkXhOFhg0bon79+jh06JBym4ODAwB5ua83paenK/cbG6nX5J9//kHnzp0REBCgVp0gL4rKJ7mvnylzcHDI831X7LcU//33H9q3bw8XFxfl2Oy3GTNmDKysrMzmZ+FtKlWqhE6dOuHIkSPIzs422XuGNqSkpGD37t1o3bo1SpYs+c725nbPeJt3/VzkbmOp957Nmzdj6tSpCA8Pz1ePsK2tLUaOHIkXL17g/PnzBT6f+Xy2RQCAKlWqIDIyMl9tNX2Uoxivo2l4RV4Uww2ePXuW7+fok9Rrklvp0qVVEkZF+/j4eOV1UIiPj1eOCTM2Uq7JgwcP0KpVK7i4uGDfvn0oWrRovo5j7D8nBeXt7a2s4Zqb4qNScyhPlR9JSUlo27YtXrx4gePHj+frdTs4OKBkyZJm87PwLqVLl1ZObM59z3hTfHy8Wf/c7Nq1C6mpqfn+/WJu94y3edfPhaurK+zs7JRtjxw5AiGEyjALc773HDx4EP3790f79u3x448/5vt5Un6GmCCbGS8vL4SFhRX6+Rs3bkTFihXx/vvv5/s5d+7cAQC4u7sX+ry6JPWa5Hbnzh2V1xkYGAgAOHfunEoy/OjRIzx8+FClwoExKew1efr0KVq1aoWMjAwcPnz4nX9Q5GbsPycFFRgYiCNHjiA5OVllot6ZM2eU+81deno6OnTogJs3b+LQoUOoVq1avp738uVLJCYmms3PwrvcuXMH9vb2cHZ2RkBAAGxsbHDu3Dl0795d2SYzMxPR0dEq28zNhg0b4OzsjI4dO+arvbndM97G19cX7u7uKhM6FaKiolTuJ4GBgVi1ahWuX7+u8n/OXO89Z86cQefOnVG3bl1s2bKlQPMWJP0MFXhQBpmtCxcuCABi2rRpGvc/ffpUbWxhZmam+OCDD4Stra2ypqE5SEhIUNu2d+9eAUBERESobK9SpYqoWbOmyrWZOnWqkMlkIiYmRuex6ktKSooICgoSRYsWFefOncuzXVJSkkhPT1fZlpOTI3r06CEAiPPnz+s6VL04ffq0Wh3k9PR0UalSJVG/fn0DRqYfr1+/Fh07dhQ2NjZi7969GtukpaWJ5ORkte3jx48XAMSOHTt0HaZeabpvREdHiyJFioiOHTsqt7Vp00Z4e3urXJtVq1YJAOL333/XS6z6lpCQIGxsbES/fv3U9lnKPUOIt4+3/eSTT4SDg4P4999/ldsOHTokAIgffvhBue3Bgwd51kH29fU1iTrIub3tmsTExIiSJUsKf39/8ezZszyPoen/XnJysqhYsaJwc3NTq9meH+xBJqUNGzYAyHt4xZ49ezBr1ix069YN5cuXx7Nnz7Bx40ZcvXoVc+bMyVedR1MRHByMWrVqoW7dunBxccGFCxfw008/oXTp0pg8ebJK2/nz56Njx45o1aoVevbsiatXr2Lp0qUYPHiwWhkeU9anTx9ERUVh0KBBuH79ukoNSmdnZ4SEhAAALly4gF69eqFXr16oVKkS0tLSsHPnTpw4cQJDhw5F7dq1DfQKtKt+/foIDQ3FpEmTkJCQgEqVKmHt2rW4d+8eVq9ebejwdG7s2LHYs2cPOnTogGfPnmH9+vUq+/v27Yv//vsPtWrVQq9evZRLS//xxx/Yt28f2rRpk2eddVPVo0cPODg4IDg4GB4eHoiJicHKlSvh6Oiostzt7NmzERwcjCZNmmDo0KF4+PAhFi5ciFatWpnF0sqavK06kiXcM5YuXYoXL14oK0z8+uuvePjwIQDg008/hYuLCyZPnoytW7eiWbNmGDVqFFJSUjB//nxUr14dAwcOVB6rVKlSGD16NObPn4+srCzUq1cPu3btwvHjx7Fhw4Z3zgEwFu+6JlZWVmjdujWeP3+O8ePHY+/evSrPr1ixIho0aAAAWLZsGXbt2oUOHTqgTJkyiI+Px08//YR///0X69atU6vZni8FTqnJLGVnZwtfX19Ru3btPNucO3dOdOjQQfj6+gpbW1vh7OwsGjZsKLZs2aLHSPVjypQpIjAwULi4uIgiRYqIMmXKiGHDhon//vtPY/udO3eKwMBAYWdnJ0qVKiWmTp0qMjMz9Ry1bpUtW1YA0PhVtmxZZbs7d+6I0NBQUa5cOWFvby8cHR1FnTp1xI8//vjWKiGmKC0tTYwbN054eXkJOzs7Ua9ePbF//35Dh6UXTZo0yfPnQfGr5fnz56Jv376iUqVKwtHRUdjZ2Ql/f38xZ84cs/v/IYQQixcvFkFBQcLV1VXY2NgIb29v0bdvX3Hr1i21tsePHxfBwcHC3t5euLu7ixEjRmjsbTcX77//vvDw8NDYu2kJ94y33T/v3r2rbHf16lXRqlUr4ejoKIoXLy769Omj8fdOdna2mDNnjihbtqywtbUV/v7+Yv369Xp8RdK965rcvXv3rfeYAQMGKI914MAB8eGHHwovLy9RpEgRUbx4cdGqVStx+PDhQscnE0KIgqfVRERERETmiWXeiIiIiIhyYYJMRERERJQLE2QiIiIiolyYIBMRERER5cIEmYiIiIgoFybIRERERES5MEEmIiIiIsqFCTIRERERUS5MkImIiIiIcmGCTERERESUCxNkIiIyK0ePHoVMJsPRo0cNHYrJkslk+PLLLwv8PENf+8LGTfQmJshEOrRmzRrIZDLll729Pfz8/DBy5Eg8fvxYrf3jx48xbtw4VKlSBY6OjnByckKdOnUwa9YsvHjxQuM5goKCIJPJ8MMPPxQoth9++AGhoaEoU6YMZDIZwsLCCvEKSRfmzJmDXbt25bt9Yd/LIUOGQCaT4aOPPlLZLoTAjBkz4OvrCw8PD4wePRqZmZkqbVJSUuDr64uNGzfmO04iIlNhY+gAiCzBzJkzUb58eaSnp+Pvv//GDz/8gH379uHq1atwdHQEAJw9exbt2rVDSkoK+vbtizp16gAAzp07h7lz5+Kvv/7CgQMHVI5769YtnD17FuXKlcOGDRswbNiwfMc0b948vHz5EkFBQYiPj9feiyXJ5syZg27duiEkJCRf7QvzXp47dw5r1qyBvb292r4NGzZgzpw5mDhxIpycnDB79mx4enpi0qRJyjazZ89GuXLl0Lt373ydT58aN26MtLQ02NraGjoUi8NrT+aCCTKRHrRt2xZ169YFAAwePBglS5bEokWLsHv3bvTq1QsvXrxA586dYW1tjYsXL6JKlSoqz589ezb+7//+T+2469evh4eHBxYuXIhu3brh3r17KFeuXL5iOnbsmLLH0dnZWfJrJMMp6HsphEBERAT69++Pw4cPq+3/7bff0KdPH8ycORMAkJaWhj179igT5Nu3b2Px4sX466+/tPtCJEpPT4etrS2srKw0Jv6ke7z2ZC44xILIAJo3bw4AuHv3LgBgxYoViIuLw6JFi9SSYwDw9PTE1KlT1bZv3LgR3bp1w0cffQQXF5cCfdxdtmxZyGSyQr4C4MWLFxgzZgzKlSsHOzs7lCpVCv3790diYqKyTUJCAsLDw+Hp6Ql7e3vUrFkTa9euVTnOvXv3IJPJsGDBAixbtgwVKlSAo6MjWrVqhQcPHkAIga+++gqlSpWCg4MDOnXqhGfPnqkco1y5cvjoo49w4MABBAYGwt7eHtWqVcOOHTvU4r5z5w5CQ0Ph6uoKR0dHvP/++9i7d69KG8U4yi1btmD27NkoVaoU7O3t0aJFC8TGxqod88yZM2jTpg1cXFzg6OiIJk2a4MSJEyptvvzyS8hkMsTGxiIsLAzFixeHi4sLBg4ciNTUVGU7mUyGV69eYe3atcqhOe8aMlHQ93LdunW4evUqZs+erXF/WloaSpQooXzs6uqqEuPYsWPRs2dP5R997/L48WPY2NhgxowZavtu3LgBmUyGpUuXAgCePXuGcePGoXr16nB2dkaxYsXQtm1bXLp0SeV5ivdo06ZNmDp1Knx9feHo6Ijk5GSN42CPHz+uHIZiZ2eH0qVLY8yYMUhLS1M5blhYGJydnREXF4eQkBA4OzvD3d0d48aNQ3Z2tkrbnJwcLF68GNWrV4e9vT3c3d3Rpk0bnDt3TqXd+vXrUadOHTg4OMDV1RU9e/bEgwcP8nXt4uLiMGjQIHh6esLOzg7+/v746aeflPvT0tJQpUoVVKlSReW1PHv2DN7e3ggODlbGrXhtd+7cQevWreHk5AQfHx/MnDkTQoi3xnH//n0MHz4c7733HhwcHFCyZEmEhobi3r17Ku00XfumTZsiICAAMTExaNasGRwdHeHr64tvvvlG7TwZGRmYPn06KlWqpHyfJkyYgIyMDLV2Y8aMgbu7O4oWLYqOHTvi4cOH+bqmRPnBHmQiA7h9+zYAoGTJkgCAPXv2wMHBAd26dcv3Mc6cOYPY2FhERkbC1tYWXbp0wYYNGzB58mSdxJxbSkoKGjVqhOvXr2PQoEGoXbs2EhMTsWfPHjx8+BBubm5IS0tD06ZNERsbi5EjR6J8+fLYunUrwsLC8OLFC4waNUrlmBs2bEBmZiY+/fRTPHv2DN988w26d++O5s2b4+jRo5g4cSJiY2OxZMkSjBs3TiVJAOTDTXr06IFPPvkEAwYMQGRkJEJDQ7F//358+OGHAOSJWnBwMFJTUxEREYGSJUti7dq16NixI7Zt24bOnTurHHPu3LmwsrLCuHHjkJSUhG+++QZ9+vTBmTNnlG3+/PNPtG3bFnXq1MH06dNhZWWFyMhING/eHMePH0dQUJDKMbt3747y5cvj66+/xoULF7Bq1Sp4eHhg3rx5AOTJ6+DBgxEUFIShQ4cCACpWrKidNw7Ay5cvMXHiREyePBleXl4a29SrVw/Lly9HaGgonJycsGLFCgQHBwMADh48iD///BM3b97M9zk9PT3RpEkTbNmyBdOnT1fZt3nzZlhbWyM0NBSA/A+YXbt2ITQ0FOXLl8fjx4+xYsUKNGnSBDExMfDx8VF5/ldffQVbW1uMGzcOGRkZeX60v3XrVqSmpmLYsGEoWbIkoqKisGTJEjx8+BBbt25VaZudnY3WrVujfv36WLBgAQ4dOoSFCxeiYsWKKsOYwsPDsWbNGrRt2xaDBw/G69evcfz4cZw+fVr5x8Ps2bMxbdo0dO/eHYMHD8aTJ0+wZMkSNG7cGBcvXkTx4sXzvG6PHz/G+++/D5lMhpEjR8Ld3R2///47wsPDkZycjNGjR8PBwQFr167FBx98gClTpmDRokUAgBEjRiApKQlr1qyBtbW1ymtr06YN3n//fXzzzTfYv38/pk+fjtevXys/MdDk7NmzOHnyJHr27IlSpUrh3r17+OGHH9C0aVPExMQoh4rl5fnz52jTpg26dOmC7t27Y9u2bZg4cSKqV6+Otm3bApD/wdGxY0f8/fffGDp0KKpWrYorV67g22+/xc2bN1XG5Q8ePBjr169H7969ERwcjD///BPt27d/awxEBSKISGciIyMFAHHo0CHx5MkT8eDBA7Fp0yZRsmRJ4eDgIB4+fCiEEKJEiRKiZs2aBTr2yJEjRenSpUVOTo4QQogDBw4IAOLixYsFjtPJyUkMGDAg3+2/+OILAUDs2LFDbZ8inu+++04AEOvXr1fuy8zMFA0aNBDOzs4iOTlZCCHE3bt3BQDh7u4uXrx4oWw7adIkAUDUrFlTZGVlKbf36tVL2NraivT0dOW2smXLCgBi+/btym1JSUnC29tb1KpVS7lt9OjRAoA4fvy4ctvLly9F+fLlRbly5UR2drYQQogjR44IAKJq1aoiIyND2Xbx4sUCgLhy5YrytVauXFm0bt1a+bqFECI1NVWUL19efPjhh8pt06dPFwDEoEGDVK5X586dRcmSJVW2FfT9KMhzx40bJ8qXL6+8fmXLlhXt27dXaZOcnCwaNmwoAAgAwt/fXzx8+FBkZWWJatWqiblz5xY4rhUrVqhcO4Vq1aqJ5s2bKx+np6cr3weFu3fvCjs7OzFz5kzlNsV7VKFCBZGamqrSXrHvyJEjym1vthFCiK+//lrIZDJx//595bYBAwYIACrnEkKIWrVqiTp16igf//nnnwKAiIiIUDuu4mfh3r17wtraWsyePVtl/5UrV4SNjY3a9jeFh4cLb29vkZiYqLK9Z8+ewsXFReU1TZo0SVhZWYm//vpLbN26VQAQ3333ncrzFK/t008/VYm1ffv2wtbWVjx58kS5HYCYPn268rGm63fq1CkBQPz888/KbZqufZMmTdTaZWRkCC8vL9G1a1fltnXr1gkrKyuV/59CCPHjjz8KAOLEiRNCCCGio6MFADF8+HCVdr1791aLm6iwOMSCSA9atmwJd3d3lC5dGj179oSzszN27twJX19fAEBycjKKFi2a7+O9fv0amzdvRo8ePZQfrTdv3hweHh7YsGGDTl5Dbtu3b0fNmjXVelwBKOPZt28fvLy80KtXL+W+IkWKICIiAikpKTh27JjK80JDQ+Hi4qJ8XL9+fQBA3759YWNjo7I9MzMTcXFxKs/38fFRiadYsWLo378/Ll68iP/++08ZU1BQEBo2bKhs5+zsjKFDh+LevXuIiYlROebAgQNVeiQbNWoEQN7LCQDR0dG4desWevfujadPnyIxMRGJiYl49eoVWrRogb/++gs5OTkqx/zkk09UHjdq1AhPnz5FcnKy2rXUtps3b2Lx4sWYP38+7Ozs8mxXtGhRHDt2DNeuXUN0dDSio6Ph6+uL5cuXKz/aVnxc7uvri759+74z/i5dusDGxgabN29Wbrt69SpiYmLQo0cP5TY7OztYWcl/NWVnZ+Pp06dwdnbGe++9hwsXLqgdd8CAAXBwcHjna8/d5tWrV0hMTERwcDCEELh48aJae03vk+J9B+T/B2QymVqPOPC//wM7duxATk4OunfvrvzZSExMhJeXFypXrowjR47kGa8QAtu3b0eHDh0ghFB5fuvWrZGUlKRyPb788kv4+/tjwIABGD58OJo0aYKIiAiNxx45cqRKrCNHjkRmZiYOHTqUZzy5r19WVhaePn2KSpUqoXjx4hrflzc5Ozujb9++yse2trYICgpSuaZbt25F1apVUaVKFZXXqxiSprhe+/btAwC11zd69Oh3xkGUXxxiQaQHy5Ytg5+fH2xsbODp6Yn33ntPmQQA8mTu5cuX+T7egQMH8OTJEwQFBamMiW3WrBl++eUXzJs3T+X42nb79m107dr1rW3u37+PypUrq8VRtWpV5f7cypQpo/JYkSyXLl1a4/bnz5+rbK9UqZLaOFw/Pz8A8nHOXl5euH//vjLxziumgICAPGNSjMtVnPvWrVsA5ElaXpKSklTG877tmMWKFcvzONowatQoBAcHv/O9A+STrapVq6Z8nJiYiC+//BI//fSTsjTcRx99hPnz5+Ozzz7Dp59+qja+PDc3Nze0aNECW7ZswVdffQVAPrzCxsYGXbp0UbZTjOtdvnw57t69qzLuVzEkKbfy5cvn67X/+++/+OKLL7Bnzx61n52kpCSVx4rxxLmVKFFC5Xm3b9+Gj48PXF1d8zznrVu3IIRA5cqVNe4vUqRIns998uQJXrx4gZUrV2LlypUa2yQkJCj/bWtri59++gn16tWDvb09IiMjNY5Lt7KyQoUKFVS25f5/kpe0tDR8/fXXiIyMRFxcnMqY5TevnyalSpVSi6dEiRK4fPmy8vGtW7dw/fp1tWuvoHi99+/fh5WVldrQo/fee++dcRDlFxNkIj0ICgp664SmKlWqIDo6GpmZmfkqj6ToJe7evbvG/ceOHUOzZs0KF6yB5B4nmZ/t4h2TirThXedW9A7Pnz8fgYGBGtu+WVXCUK/nzz//xP79+7Fjxw6VROj169dIS0vDvXv34OrqmmeSPm3aNNSuXRshISE4fvw44uPj8c0338De3h4zZsxAmzZtEBkZ+dY/zHr27ImBAwciOjoagYGB2LJlC1q0aAE3Nzdlmzlz5mDatGkYNGgQvvrqK7i6usLKygqjR49W640HkK/e4+zsbHz44Yd49uwZJk6ciCpVqsDJyQlxcXEICwtTO25e71FB5eTkQCaT4ffff9d4zLdVHFHE1Ldv3zz/AKtRo4bK4z/++AOAvJrHrVu38v3HQ358+umniIyMxOjRo9GgQQO4uLhAJpOhZ8+eGt+XN+Xn5z4nJwfVq1dXjqN+05t/LBPpEhNkIiPQoUMHnDp1Ctu3b1cZkqDJq1evsHv3bvTo0UPjpL6IiAhs2LBBpwlyxYoVcfXq1be2KVu2LC5fvoycnByVpOmff/5R7tem2NhYCCFUeqkUE8kUpe/Kli2LGzduqD23sDEperCKFSuGli1bFiZsjaRUF8nLv//+CwAqvbUKcXFxKF++PL799luNH1NfunQJP/30E86fPw8AePToEUqUKKEs5+Xj44PMzEw8efIEnp6eecYQEhKCjz/+WDnM4ubNmyq1lQFg27ZtaNasGVavXq2y/cWLFyqJdEFcuXIFN2/exNq1a9G/f3/l9oMHDxbqeID8vf/jjz/w7NmzPHuRK1asCCEEypcvr+ylzS9FdYbs7Ox8/WxdvnwZM2fOVP4BMnjwYFy5ckVl2BIgT0Lv3LmjEs+b/0802bZtGwYMGICFCxcqt6Wnp+e5gFFhVKxYEZcuXUKLFi3e+n+gbNmyyMnJwe3bt1V6jTX93yYqLI5BJjICn3zyCby9vTF27FiN1QESEhIwa9YsAMDOnTvx6tUrjBgxAt26dVP7+uijj7B9+3a1skja1LVrV1y6dAk7d+5U26foEWrXrh3+++8/lTGnr1+/xpIlS+Ds7IwmTZpoNaZHjx6pxJOcnIyff/4ZgYGBymoN7dq1Q1RUFE6dOqVs9+rVK6xcuRLlypVTGVKQH3Xq1EHFihWxYMECpKSkqO1/8uRJoV6Lk5OTVhMPQD5GfefOnWpf7u7uqFu3Lnbu3IkOHTpofO6oUaMwePBg5fATT09PPHnyRFlu7/r167CxsXlnAlu8eHG0bt0aW7ZswaZNm2Bra6u2GIq1tbVab/rWrVvVxpwXhKL3MvdxhRBYvHhxoY/ZtWtX5YqDb1Kcp0uXLrC2tsaMGTPUXpMQAk+fPn1rzF27dsX27ds1/jGa+2crKysLYWFh8PHxweLFi7FmzRo8fvwYY8aM0XhsRUk9RRxLly5FkSJF0KJFi7fG8+ZrWLJkiVrpOym6d++OuLg4jTXf09LS8OrVKwBQVr34/vvvVdp89913WouFiD3IREagRIkS2LlzJ9q1a4fAwECVlfQuXLiAX375BQ0aNAAgH15RsmRJZdmtN3Xs2BH/93//h71792rsLVT49ddflbVls7KycPnyZWUS3rFjR7WPb3MbP348tm3bhtDQUAwaNAh16tTBs2fPsGfPHvz444+oWbMmhg4dihUrViAsLAznz59HuXLlsG3bNpw4cQLfffddgSYl5oefnx/Cw8Nx9uxZeHp64qeffsLjx48RGRmpbPP555/jl19+Qdu2bREREQFXV1esXbsWd+/exfbt2ws8btvKygqrVq1C27Zt4e/vj4EDB8LX1xdxcXE4cuQIihUrhl9//bXAr6VOnTo4dOgQFi1aBB8fH5QvX17j2GmF/LyXZcqUURv/DMgnNnl6eua5at/WrVtx+fJlbN++XbmtQYMG8PT0RGhoKLp06YIFCxYok8F36dGjB/r27Yvly5ejdevWamXOPvroI2VPaHBwMK5cuYINGzaojZstiCpVqqBixYoYN24c4uLiUKxYMWzfvl1tLHJBNGvWDP369cP333+PW7duoU2bNsjJycHx48fRrFkzjBw5EhUrVsSsWbMwadIk3Lt3DyEhIShatCju3r2LnTt3YujQoRg3blye55g7dy6OHDmC+vXrY8iQIahWrRqePXuGCxcu4NChQ8o/UGbNmoXo6GgcPnwYRYsWRY0aNfDFF19g6tSp6NatG9q1a6c8pr29Pfbv348BAwagfv36+P3337F3715Mnjw5z7G/gPx9WbduHVxcXFCtWjWcOnUKhw4d0jguvLD69euHLVu24JNPPsGRI0fwwQcfIDs7G//88w+2bNmCP/74A3Xr1kVgYCB69eqF5cuXIykpCcHBwTh8+LDGGuVEhabXmhlEFkZR5u3s2bP5av/o0SMxZswY4efnJ+zt7YWjo6OoU6eOmD17tkhKShKPHz8WNjY2ol+/fnkeIzU1VTg6OorOnTu/9VyKkk+aviIjI98Z69OnT8XIkSOFr6+vsLW1FaVKlRIDBgxQKUn1+PFjMXDgQOHm5iZsbW1F9erV1Y6tKPM2f/58le2KclFbt25V2a7pmipKlf3xxx+iRo0aws7OTlSpUkXtuUIIcfv2bdGtWzdRvHhxYW9vL4KCgsRvv/2Wr3MrYn3zNVy8eFF06dJFlCxZUtjZ2YmyZcuK7t27i8OHDyvbKMq85S6llfv13L17V7ntn3/+EY0bNxYODg4CwDtLvkl5LzWVeVNITU0VZcuWFd9//73avrNnz4ratWuLokWLig4dOoiEhIS3nkchOTlZ+bpylwBUSE9PF2PHjhXe3t7CwcFBfPDBB+LUqVOiSZMmokmTJsp2eb1HufflLjUWExMjWrZsKZydnYWbm5sYMmSIuHTpkto1GjBggHByclI7puL9y+3169di/vz5okqVKsLW1la4u7uLtm3bivPnz6u02759u2jYsKFwcnISTk5OokqVKmLEiBHixo0b77xejx8/FiNGjBClS5cWRYoUEV5eXqJFixZi5cqVQgghzp8/L2xsbFRKtyliq1evnvDx8RHPnz9XeW23b98WrVq1Eo6OjsLT01NMnz5drbQe3iiX9vz5c+X/ZWdnZ9G6dWvxzz//iLJly6r8fOZV5s3f31/ttQ0YMECULVtWZVtmZqaYN2+e8Pf3F3Z2dqJEiRKiTp06YsaMGSIpKUnZLi0tTURERIiSJUsKJycn0aFDB/HgwQOWeSOtkQmhh5kuREQ6VK5cOQQEBOC3334zdChERissLAzbtm3TOByIiFRxDDIRERERUS5MkImIiIiIcmGCTERERESUC8cgExERERHlwh5kIiIiIqJcmCATEREREeXCBJmIiIiIKBcmyEREREREuTBBJiIiIiLKhQkyEREREVEuTJCJiIiIiHJhgkxERERElAsTZCIiIiKiXJggExERERHl8v8AxNtFzu1DW3sAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 768x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate pca plot for annotated_expression to find outliers\n",
    "\n",
    "fig_pca_outliers,ax_pca_outliers = plt.subplots()\n",
    "\n",
    "pca_plot(annotated_expression.T, legend= None, ax= ax_pca_outliers)\n",
    "\n",
    "# Save the generated pca plot into a picture.\n",
    "fig.savefig('Plots/pca_outliers.svg')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If the pca_plot of your data suggests the analyzed data contain outliers, remove the outlying points from your dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-02T14:25:38.970277Z",
     "start_time": "2018-08-02T14:25:38.958537Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check for repetitive sample ids \n",
    "annotated_expression.columns.value_counts().max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data distribution check"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data distribution check\n",
    "\n",
    "This is the final section of the QC, where data quality is checked based on its distribution.\n",
    "\n",
    "As with the previous sections, an example of two gene expression datasets is provided below, with one exhibiting a good-quality dataset and the other exhibiting a poor-quality dataset, as indicated by their respective distribution plots.\n",
    "\n",
    "In the plot on the right, the expression values start off very low and then rise before dropping down. This pattern suggests potential RNA degradation, which can compromise the reliability and accuracy of downstream analyses. In contrast, the distribution plot on the left shows good-quality gene expression data.\n",
    "Deviations from such distributions may indicate gene degradation, should be carefully investigated and, if necessary, corrected to ensure high-quality data.\n",
    "\n",
    "If your data plot shows distribution of a good quality data, you can proceed with the analyses. If it resembles the one on the right, then your data is probably abnormal and might have RNA degradation. In the case of RNA degradation,  do not proceed with the analyses and choose a new dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Data distribution example](plots/distribution_example.svg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The code snippet provided generates a visualization of the distribution of annotated expression values. This type of data visualization is useful for identifying trends and patterns in gene expression data and can aid in the quality control and preprocessing of the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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UAAAAmA4hFQAAAKZDSAUAAIDpEFIBAABgOoRUAAAAmA4hFQAAAKZDSAUAAIDpEFIBAABgOoRUAAAAmA4hFQAAAKZDSAUAAIDpEFIBAABgOoRUAAAAmA4hFQAAAKbjk5B65ZVX6s0331RVVZUvTgcAAIBuzich9cCBA/rhD3+oPn366JZbbtFHH30kwzB8cWoAAAB0Qz4JqXv37tXWrVv14x//WGvXrtWUKVPUv39//fKXv9SOHTt8cQkAAAB0Iz4bk3r++efrhRdeUHZ2tv75z39q8uTJeuWVV3TeeecpKSlJTz31lA4fPuyrywEAAKAL8/nEKavVqilTpmjlypXKzMzUjBkz9O233+pXv/qVBg0apO9///v68MMPfX1ZAAAAdCHtMrt/06ZN+vnPf67ExET9/e9/995JfeaZZ1RQUKBrrrlGDz/8cHtcGgAAAF1AgK9O9O233+r111/XX//6V2VmZio2Nla33HKLfvSjHyk5Odm7391336158+bpD3/4gx5//HFfXR4AAABdiE/upCYnJ2vUqFF6/vnndcEFF2jNmjXKzs7WM8880yigelx66aUqKio67XkdDoceeOABxcfHKyQkRCkpKVq3bl2r67v88stlsVh0xx13tPpYAAAAdDyf3EmNjo7W0qVLNXPmTEVGRp52/2uvvVYHDx487X633nqrVq9erXvuuUdDhw7Vq6++qmnTpmn9+vW66KKLWlTbP/7xD23ZsqVF+wIAAMAcfHIn9bXXXtPNN9980oBaVVWlzMxM759DQ0M1cODAU55z27ZtWrVqlX77298qNTVV8+bN0yeffKKBAwdqwYIFLaqrurpa9913nx544IGW/zAAAADwO5+E1MGDB+udd9456fb3339fgwcPbtU5V69eLZvNpnnz5nnb7Ha75s6dqy1btigrK+u053jqqafkcrl0//33t+raAAAA8C+fdPef7ulStbW1slpbl4e3b9+uYcOGNbk7O378eEnSjh07lJCQcNLjMzMz9bvf/U5/+ctfFBIS0qpr5+fnq6CgoFFbenp6q84BAACAtmtzSC0tLVVxcbH3z8eOHWvUpe9RXFysVatWKS4urlXnz83NbfYYT1tOTs4pj7/vvvs0ZswY3Xjjja26riQtWbJEjz32WKuPAwAAgG+0OaQ+99xz3iWkLBaL7rnnHt1zzz3N7msYhhYvXtyq81dVVSk4OLhJu91u924/mfXr1+vtt9/W1q1bW3VNj/nz52vmzJmN2tLT0zV9+vQ2nQ8AAACt0+aQesUVVyg8PFyGYWjBggW66aabNHbs2Eb7WCwWhYWF6bzzztO4ceNadf6QkBA5HI4m7dXV1d7tzamrq9Ndd92lH/3oRzr//PNbdU2P2NhYxcbGtulYAAAAnLk2h9QJEyZowoQJkqSKigpdf/31GjVqlM8Ki4uLU3Z2dpP23NxcSVJ8fHyzx7322mvau3evXnnlFWVkZDTaVlZWpoyMDMXGxio0NNRntQIAAMC3fDK7/5FHHvFpQJXcDwhIS0tTaWlpo3ZPF35zDwmQ3BOmamtr9b3vfU+DBw/2fknuADt48GCtXbvWp7UCAADAt9p0J/Xxxx+XxWLRr3/9a1mt1hY93tRisWjRokUtvsaMGTP09NNPa+nSpd4lpBwOh5YvX66UlBTvzP7MzExVVlZqxIgRkqQbb7yx2QB73XXXadq0afrpT3+qlJSUFtcBAACAjtemkProo4/KYrHogQceUFBQkB599NHTHtPakJqSkqKZM2dq4cKFys/PV2JiolasWKGMjAwtW7bMu9+cOXO0ceNG7zJYI0aM8AbWEw0ePJjJTwAAAJ1Am0Kqy+U65Z995bXXXtOiRYu0cuVKFRUVafTo0VqzZo0mTpzYLtcDAACAOfhkMf/2YrfblZqaqtTU1JPus2HDhhad63QPHAAAAIB5tFtIrays1KpVq+RwODRt2jQNHDiwvS4FAACALsYnIXXu3LnaunWrdu/eLUmqqanRBRdc4P1zVFSUPvnkE40ZM8YXlwMAAEAX55MlqNavX6/rr7/e++c333xTu3fv1htvvKHdu3erb9++PGYUAAAALeaTkHrkyBENGjTI++d3331X48aN00033aRzzjlHP/3pT9v8iFIAAAB0Pz4JqWFhYSouLpbkfizphg0bNGXKFO/2iIgIlZSU+OJSAAAA6AZ8MiZ17Nix+tOf/qRLL71U77//vsrKynT11Vd7t+/fv199+vTxxaUAAADQDfgkpD7xxBOaMmWKxo0bJ8MwNGPGDI0fP967/Z133tH3vvc9X1wKAAAA3YBPQuq4ceO0Z88ebd68WdHR0Zo0aZJ3W3FxsebPn9+oDQAAADgVn62T2rt3b1177bVN2qOjo3X33Xf76jIAAADoBny6mH9ZWZkOHTqkoqKiZp/wxONMAQAA0BI+CanHjh3THXfcobfffltOp7PJdsMwZLFYmt0GAAAAnMgnIfWnP/2pPvjgA9111126+OKL1aNHD1+cFgAAAN2UT0Lq2rVrde+99+qpp57yxekAAADQzflkMf/Q0NBGT5wCAAAAzoRPQuoPf/hDvfPOO744FQAAAOCb7v4ZM2Zo48aNmjp1qubNm6eEhATZbLYm+40dO9YXlwMAAEAX55OQetFFF3lfr1u3rsl2ZvcDAACgNXwSUpcvX+6L0wAAAACSfBRSb7nlFl+cBgAAAJDko4lTDeXm5mrnzp2qqKjw9akBAADQTfgspL733nsaMWKE+vfvr7Fjx2rr1q2SpKNHj2rMmDHM/gcAAECL+SSkfvDBB7r++uvVq1cvPfLIIzIMw7utV69e6tevn1599VVfXAoAAADdgE9C6uOPP66JEydq06ZN+sUvftFk+4QJE7R9+3ZfXAoAAADdgE9C6u7du/WDH/zgpNv79Omj/Px8X1wKAAAA3YDPHot6qolSBw4cUExMjC8uBQAAgG7AJyH10ksv1YoVK1RXV9dk25EjR/SnP/1JV1xxhS8uBQAAgG7AJyH1iSee0OHDh3X++efrlVdekcVi0X/+8x899NBDGjVqlAzD0COPPOKLSwEAAKAb8ElIHT58uDZt2qSYmBgtWrRIhmEoNTVVTz75pEaNGqX//ve/GjRokC8uBQAAgG7AJ0+ckqSRI0fqo48+UlFRkdLT0+VyuTRkyBD17t3bV5cAAABAN3HGIdXhcOj111/X2rVrtX//fpWVlSkiIkKJiYmaOnWqbr75ZgUFBfmiVgAAAHQTZxRSd+3apWuvvVaHDh2SYRiKiopSeHi48vPz9dVXX+nvf/+7nnjiCb3//vs6++yzfVUzAAAAurg2j0ktLy/XNddco7y8PD3xxBPKyspSUVFRo++LFy9WTk6Orr766lMuUQUAAAA01OaQunz5cmVmZurDDz/Ur371K/Xr16/R9n79+mnhwoX64IMPdPDgQR6LCgAAgBZrc0j98MMPdcUVV+iSSy455X6TJ0/W5Zdfrg8++KCtlwIAAEA30+aQumvXrtMGVI/Jkydr165dbb0UAAAAupk2h9TCwkL17du3Rfv26dNHhYWFbb0UAAAAupk2h1SHw6HAwMAW7RsQEKCampq2XgoAAADdzBktQZWRkaGvvvrqtPsdPHjwTC4DAACAbuaMQuqiRYu0aNGi0+5nGIYsFsuZXAoAAADdSJtD6vLly31ZBwAAAODV5pB6yy23+LIOAAAAwKvNE6cAAACA9kJIBQAAgOkQUgEAAGA6hFQAAACYjqlDqsPh0AMPPKD4+HiFhIQoJSVF69atO+1x77zzjqZMmaL4+HgFBwerf//+mjFjhnbv3t0BVQMAAOBMmTqk3nrrrXr22Wc1e/ZsvfDCC7LZbJo2bZo2bdp0yuN27dqlHj166O6779aSJUt0++23a/v27Ro/frx27tzZQdUDAACgrc5oMf/2tG3bNq1atUqpqam6//77JUlz5sxRUlKSFixYoM2bN5/02IcffrhJ22233ab+/fvrj3/8o15++eV2qxsAAABnzrR3UlevXi2bzaZ58+Z52+x2u+bOnastW7YoKyurVeeLjY1VaGioiouLfVwpAAAAfM20IXX79u0aNmyYIiMjG7WPHz9ekrRjx47TnqO4uFgFBQXatWuXbrvtNpWWluqyyy5rj3IBAADgQ6bt7s/NzVVcXFyTdk9bTk7Oac9xwQUXaO/evZKk8PBwPfTQQ5o7d+5pj8vPz1dBQUGjtvT09JaUDQAAAB8wbUitqqpScHBwk3a73e7dfjrLly9XaWmpDhw4oOXLl6uqqkpOp1NW66lvIC9ZskSPPfZY2woHAADAGTNtSA0JCZHD4WjSXl1d7d1+OhMmTPC+vvHGG3X22WdLkp5++ulTHjd//nzNnDmzUVt6erqmT59+2msCAADgzJk2pMbFxSk7O7tJe25uriQpPj6+Vefr0aOHJk+erDfeeOO0ITU2NlaxsbGtOj8AAAB8x7QTp5KTk5WWlqbS0tJG7Vu3bvVub62qqiqVlJT4ojwAAAC0I9OG1BkzZsjpdGrp0qXeNofDoeXLlyslJUUJCQmSpMzMTO3Zs6fRsfn5+U3Ol5GRoY8//ljjxo1r38LRSFWNU7uyS7T3SKmyCitV4ajzd0kAAKATMG13f0pKimbOnKmFCxcqPz9fiYmJWrFihTIyMrRs2TLvfnPmzNHGjRtlGIa3bdSoUbrsssuUnJysHj16aN++fVq2bJlqa2v1u9/9zh8/TrdU63Tpz5sOKLek2ttms1r04wsHaUjvcD9WBgAAzM60IVWSXnvtNS1atEgrV65UUVGRRo8erTVr1mjixImnPO7222/Xhx9+qH//+98qKytTbGysrrjiCj344IMaNWpUB1WPtd8caRRQJcnpMrT6y8O667Khsgfa/FQZAAAwO4vR8BYkTuqbb75RUlKSdu/erZEjR/q7HNN75L1vtGJLhiSpf48Q/c+oOO3NK9f6ve6hGOMG9tD1Y/s3e+zNKQM6qkwAANBBWpulTDsmFZ1XQZlDq786LEkKCrBq1rgEDYgJ0/fPjlVirLub/4tDRdpzpPRUpwEAAN0YIRU+98cN+70TpK49N14x4e6HMlgsFl0/pp+CA9wfu3e2Z6umzuW3OgEAgHkRUuFTdU6X3t/pXt92cK8wJSdEN9oeHRqkq0a5H21bVl2nb3O5mwoAAJoipMKnNu8/pqPlNZKkMQnRslgsTfZJTohWSP2kqe2ZRR1aHwAA6BwIqfCpd3e476IGWC1K6hfV7D4BNqvOrb/Dmp5frpKq2o4qDwAAdBKEVPhMVY1T/9l9RJI0vG/EKZeYGjsgWpJkSNrB3VQAAHACQip85qPv8lRR45SkJmNRT9QvOkSxEe4JVV9lFouV0AAAQEOEVPjMe/Vd/ZH2AA3vE3HKfS0Wi8YO6CFJKih36HBRVbvXBwAAOg9CKnyiqKJGG/YWSJKmjYpTgO30H63kAdHyTKv6ii5/AADQACEVPvHf9KOqc7m77K8+N75Fx0TaAzW0j3tx/29ySuWiyx8AANQjpMInth44JkkKDrBq3KAeLT5uRN9ISVK5o055pdXtUhsAAOh8CKnwia0HCyVJYwf0UHDAyWf1n2ho/WNSJWlfXrnP6wIAAJ0TIRVn7Gi5Q+n57oCZMqRnq46NCQ9Wz7AgSfKeAwAAgJCKM/Z5/V1USUoZHNPq4xPr76ZmHKtQTZ3LZ3UBAIDOi5CKM+bp6g+yWTWmfpH+1vB0+de5DGUcq/BlaQAAoJMipOKMfVY/aerchKhTPmXqZM7qHS5r/VpUdPkDAACJkIozVFxZo715ZZLa1tUvSfZAmxJ6hEqS9uWX+aw2AADQeRFScUY+zyiSZ3nT8YNbN2mqocT69VLzSh06UsJSVAAAdHeEVJwRz/qoNqtF5w1s+fqoJxoae/wxqv+3r+CM6wIAAJ0bIRVnZFuGe9LUqH5RCgsOaPN5+vcIkT3Q/XH0jHEFAADdFyEVbVZV49Q3OaWSpJQz6OqXJKvFooE9wyRJXx4qOuPaAABA50ZIRZt9d6RUTpd7QOq5CdFnfL6BMe7JU4eOVSq/jHGpAAB0Z4RUtNk32SXe10nxUWd8voExYd7XX2ZwNxUAgO6MkIo2253t7uqPsAcooWfIGZ+vf48Q2SzuBVM/J6QCANCtEVLRZrtz3HdSk+KjZKkPl2ci0GZVfLRdkvTlocLT7A0AALoyQiraxFHnVFr9Iv5J/SJ9dl5Pl/83OaWqrKnz2XkBAEDnQkhFm6QdKVet0z1pKqnfmY9H9RhUP3mqzmVoR1axz84LAAA6F0Iq2sTT1S/5NqQOYPIUAAAQIRVttLt+Zn9YkE2DGwTLMxUeHKAhvdzn+5z1UgEA6LYIqWiT3fWL+J8THymr9cwnTTXkebzq9kNF3nVYAQBA90JIRavVOl36LtcdUkf6YH3UE50/yP30qjJHnfYeKfP5+QEAgPkRUtFq6fnlqqlzSZJG+XA8qsfY+jupkrQ9iy5/AAC6I0IqWm13dvtMmvIY0itMEfYASdKOzGKfnx8AAJgfIRWt9k39eNTgAKvO6u27SVMeVqtFyQnRksQyVAAAdFOEVLTat/UhdURcpAJs7fMR8oTU9IJylVXXtss1AACAeRFS0SqGYWhv/ZOmzu4b0W7X8YRUw5C+Plxy6p0BAECXQ0hFq+SXOVRS5b6zOaxP+4dUiS5/AAC6I0IqWqXhklDD2/FOakx4sBJ6hkiStjN5CgCAboeQilZJyzseUtvzTqokJSe4l6LakVUsw2BRfwAAuhNCKlrFcye1Z1iQeoUHteu1PF3+R8sdOlxU1a7XAgAA5kJIRat47qQO6xMui8W3j0M90ZgB0d7XjEsFAKB7IaSixVwuQ2l55ZKk4e3c1S9J58RFKtDmDsKEVAAAuhdCKlosq6hSVbVOSdKwdpw05WEPtOmcuEhJhFQAALobQiparOHM/hEdEFKl4+NSd2eXqNbp6pBrAgAA/yOkosUazuwf2gHd/ZKUXD8u1VHn0p7cslPvDAAAugxCKlpsb/141PgouyLtgR1yTc8yVJK0I6uoQ64JAAD8z9Qh1eFw6IEHHlB8fLxCQkKUkpKidevWnfa4f/zjH5o1a5aGDBmi0NBQDR8+XPfdd5+Ki4vbv+guLK2+u78jxqN6DIoJVXSoOxBvZ1wqAADdhqlD6q233qpnn31Ws2fP1gsvvCCbzaZp06Zp06ZNpzxu3rx5+u677/TDH/5QL774oqZOnaqXXnpJEyZMUFUV6222RU2dS/sLOm5mv4fFYtG5/aMlMXkKAIDuJMDfBZzMtm3btGrVKqWmpur++++XJM2ZM0dJSUlasGCBNm/efNJjV69erUsuuaRR23nnnadbbrlFb7zxhm677bb2LL1LOni0QnUu91Of2vtJUydKTojWxrQCHSioUEllraJCO2aoAQAA8B/T3kldvXq1bDab5s2b522z2+2aO3eutmzZoqysrJMee2JAlaTrrrtOkvTdd9/5vNbuYG+DSVPDO7C7Xzo+eUqSdh4u7tBrAwAA/zDtndTt27dr2LBhioyMbNQ+fvx4SdKOHTuUkJDQ4vMdOXJEktSrV6/T7pufn6+CgoJGbenp6S2+Vle0rz6kWixSYmx4h147ub67X3J3+U8c1rtDrw8AADqeaUNqbm6u4uLimrR72nJyclp1vv/93/+VzWbTjBkzTrvvkiVL9Nhjj7Xq/F1der57PGpCj1DZA20deu0eYUEaFBOqjGOVjEsFAKCbMG1IraqqUnBwcJN2u93u3d5Sb775ppYtW6YFCxZo6NChp91//vz5mjlzZqO29PR0TZ8+vcXX7Gr21YfUoR18F9UjOSFaGccqtT2zSIZhyGKx+KUOAADQMUwbUkNCQuRwOJq0V1dXe7e3xH//+1/NnTtXU6ZM0RNPPNGiY2JjYxUbG9vyYru4WqdLGUcrJEmJffwXUt/dkaOiylplFlZqYEyYX+oAAAAdw7QTp+Li4pSbm9uk3dMWHx9/2nPs3LlT11xzjZKSkrR69WoFBJg2k5vaoWPHZ/Yn9vZPSB0zoOGi/sV+qQEAAHQc04bU5ORkpaWlqbS0tFH71q1bvdtPZf/+/Zo6dapiY2P1z3/+U+Hh/glXXYFnPKrUcY9DPdHZcZEKCnB/XLdnFvulBgAA0HFMG1JnzJghp9OppUuXetscDoeWL1+ulJQU78z+zMxM7dmzp9GxR44c0RVXXCGr1ar//Oc/6t2b2eBnYl/e8ZB6Vm//dLMHBVg1Mt690gN3UgEA6PpM2/+dkpKimTNnauHChcrPz1diYqJWrFihjIwMLVu2zLvfnDlztHHjRhmG4W2bOnWqDhw4oAULFmjTpk2NnlDVp08fXX755R36s3R2nklTcVF2Rdj9t5B+ckK0tmcW69ucUjnqnAoO6NhVBgAAQMcxbUiVpNdee02LFi3SypUrVVRUpNGjR2vNmjWaOHHiKY/buXOnJOmpp55qsm3SpEmE1FbydPd39PqoJ0pOiJYk1Thd+i63zPtnAADQ9Zg6pNrtdqWmpio1NfWk+2zYsKFJW8O7qjgzTpeh/QXmCKljEhpMnsosIqQCANCFmXZMKswhu6hKjjqXJGlorH8mTXkk9AxRz7AgSYxLBQCgqyOk4pT25Zd5X/v7TqrFYvHePSWkAgDQtRFScUr7Gi4/5eeQKh0fl5pxrFJFFTX+LQYAALQbQipOyTNpqld4kHrUd7X7U8NxqDsOF/utDgAA0L4IqTglz53Us/z0pKkTndswpLKoPwAAXRYhFSdlGIb214fUoX3MEVKjQgI1pP6BAtsZlwoAQJdFSMVJHSmtVrmjTpKUaJI7qdLxLv+dWcUsNwYAQBdFSMVJpTecNNXHv8tPNTSmPqSWVNXq4NEK/xYDAADaBSEVJ7Uvz1wz+z3GDDi+qP92xqUCANAlEVJxUp5JUxH2APWOCPZzNccN7xuhkECbJOmLQ0V+rgYAALQHQipOyjtpKjZcFovFz9UcF2izasyAaEnS5xmF/i0GAAC0C0IqTsrztCl/P2mqOecP6inJPW62kEX9AQDocgipaNaxcoeKKmslSUNjzTNpymP84J7e119wNxUAgC6HkIpmNXwcqhnvpI4ZEC2b1T0EgS5/AAC6HkIqmmX2kBoaFKCk+EhJ0rYMJk8BANDVEFLRLM+kqZBAm/pFh/i5muZ5xqV+k12iypo6P1cDAAB8iZCKZnkmTZ0VGyar1Twz+xs6v35cap3LYL1UAAC6GEIqmpXuXX7KfJOmPDx3UiVp20HGpQIA0JUQUtFESVWt8kodksw5HtWjZ1iQtz4mTwEA0LUQUtFEusknTTXkuZu6PbNYtU6Xn6sBAAC+QkhFE/sbhNShJg+p4wf3kCRV1Tr19eFi/xYDAAB8hpCKJjyTpoJsVg3oGernak7twrN6eV9/mn7Mj5UAAABfIqSiCU93/+BeYQqwmfsj0ifS7r3bu2nfUT9XAwAAfMXcCQR+4VnI3+zjUT2+l+i+m/pVZpEqHKyXCgBAV0BIRSMVjjodLqqSJA3t0zlC6kX1IbXOZbAUFQAAXQQhFY00fBzq8D7mXSO1oQvOipGt/oEDm9Lp8gcAoCsgpKKRtCNl3tfD+naOkBoeHKAxCdGSGJcKAEBXQUhFI2l5x2f2DzT5zP6GPONS9+aVKb+s2s/VAACAM0VIRSN760PqWbHhpp/Z39DFQ48vRbWZpagAAOj0Ok8KQYfw3Ekd1kkmTXmcmxCtsCCbJMalAgDQFRBS4VVSWau8UockaVgnmTTlEWiz6oIhMZKk/0srkMtl+LkiAABwJgip8ErLbzBpqpOFVEm6ZESsJCm/zKGvs0v8XA0AADgThFR4ebr6pc6z/FRDl5/dx/t63bdH/FgJAAA4U4RUeHmWnwoJtKl/jxA/V9N6faPsGt0/SpK07ts8P1cDAADOBCEVXml57oX8h/YJl7V+cfzOxnM3NS2vXIeOVfi5GgAA0FaEVHgdn9nf+br6Pa4Y2df7mrupAAB0XoRUSJKOljt0rKJGUudbfqqhYX3CNaD+IQRrvyGkAgDQWRFSIanxpKnOfCfVYrHo8nPcXf5fHCpUYX3wBgAAnQshFZKOT5qSOndIleQNqS5D+vg77qYCANAZEVIhSdpbP2kqIjhAcVF2P1dzZsYN7KEeoYGSpDVf5/q5GgAA0BaEVEiSvs0tlSSNiIuQxdI5Z/Z7BNisump0nCTpv/sKlF9a7eeKAABAaxFSIafL0N4j7pB6Tlykn6vxjevH9pfk7vJ/b0eOn6sBAACtRUiFDh6tUHWtS5J0TnzXCKljEqI1uFeYJOntrw77uRoAANBahFR4u/ol6ewucifVYrHohrH9JEl7jpTp25zS0xwBAADMhJAKfVcfUm1WS6ef2d/Q9DH9vK//wd1UAAA6lQB/F3AqDodDDz/8sFauXKmioiKNHj1aixcv1uWXX37K4/bu3auXX35ZW7du1VdffSWHw6GDBw9q0KBBHVN4J+O5y3hW7zDZA21+rkZ6c2tmm4+9OWWA93X/HqG6YEhPfXagUO/uyNGvrhyhABv/LgMAoDMw9d/Yt956q5599lnNnj1bL7zwgmw2m6ZNm6ZNmzad8rgtW7boxRdfVFlZmc4+++wOqrbz8nT3d5Wu/oY8E6iOljv0yZ58P1cDAABayrQhddu2bVq1apV++9vfKjU1VfPmzdMnn3yigQMHasGCBac89pprrlFxcbF27dql2bNnd1DFnVNBmUMFZQ5JXWdmf0PTRsUpItjdYbBs00E/VwMAAFrKtCF19erVstlsmjdvnrfNbrdr7ty52rJli7Kysk56bM+ePRUR0XXGVran7xpMmuoqM/sbCg8O0E31QwC2HizUrsMlfq4IAAC0hGlD6vbt2zVs2DBFRjYOTuPHj5ck7dixww9VdT1dcWb/iW65cJBsVvcDCv686YCfqwEAAC1h2olTubm5iouLa9LuacvJab8F2vPz81VQUNCoLT09vd2u50+eSVOxEcHqFR7s52raR7/oEF01Kk7v78zRmq9z9cDUEYqPDvF3WQAA4BRMeye1qqpKwcFNQ5Pdbvduby9LlixRUlJSo6/p06e32/X8ydPd3xW7+hu67eLBktxP11qxOcO/xQAAgNMybUgNCQmRw+Fo0l5dXe3d3l7mz5+v3bt3N/p699132+16/lJd69T+gnJJXber32N0/2iNH9xTknuJq8KKGj9XBAAATsW03f1xcXHKzs5u0p6bmytJio+Pb7drx8bGKjY2tt3ObxZ7j5TJZbhfd8WZ/Se6/ZKztO1gococdXrx43169JqR/i4JAACchGnvpCYnJystLU2lpY0fZ7l161bvdpyZXdnHZ7p39e5+SbpkWG9deFaMJOn1zw7p4NEKP1cEAABOxrQhdcaMGXI6nVq6dKm3zeFwaPny5UpJSVFCQoIkKTMzU3v27PFXmZ3ajqxiSVKkPUCDY8L8W0wHsFgsenDa2bJYpDqXod/96zt/lwQAAE7CtN39KSkpmjlzphYuXKj8/HwlJiZqxYoVysjI0LJly7z7zZkzRxs3bpRhGN62kpIS/f73v5ckffrpp5Kkl156SdHR0YqOjtYdd9zRsT+MSe2sD6nnJkTLWr9EU1eX1C9K143pp398la3/fJOnbQcLvWNVAQCAeZg2pErSa6+9pkWLFmnlypUqKirS6NGjtWbNGk2cOPGUxxUVFWnRokWN2p555hlJ0sCBAwmpksqqa5VeP2kqOSHav8V0sPuvGK4Pv86Vo86lRe/u1nt3fE/2QJu/ywIAAA2Ytrtfci83lZqaqtzcXFVXV2vbtm2aMmVKo302bNjQ6C6qJA0aNEiGYTT7lZGR0YE/gXntOlwiz9t2bv9ov9bS0eKjQzT/kkRJ0t68Mj39n71+rggAAJzI1CEV7WfH4WLv63O72Z1USZp/6Vk6t3+UJOnPmw5qc/pRP1cEAAAaIqR2UzsyiyW5n8bUO6JrPmnqVAJtVj03K1kh9d389/19p0oqa/1cFQAA8DD1mFS0n531d1K723jUhob0DtevrzpbD727W7kl1fr561/q1Z+cr+AAxqei7VwuQ0fLHcopqVZxZY3Kquvqv2pVVl2nyhqn6lwu1bkM1TldqnMachqGbFaLgmxWBXq+AiyyB9gUGRKoSHtA/fdARYYE1H8PVERwQLeZ9Aig+yGkdkNHSqqVV+p+mte5CVF+rsa/ZqcM0KfpR/Wv3Ue05cAx3f/3r/XCrGT+4kezDMNQUWWt/rLpoEqqalVcVauSylqVVNWopKpWJVW1Kq2qk/OEcfLtxSLJHmhTaJDnK8D7OqTBa0/7TSkD1CM0UCGBNlksfMYBmBshtRvakVXkfZ2c0MOPlfifxWLRc7OSlV+2VV8eKtIHO3PUJyJYv77qbP4S74bKqmuVU1ytnJIq5RZXK7ekSjn133NL3N+ra11tPr9FUlCAVVaLRTarRVaL6r9b5DIMOV2G6lzu7y7DUK3z1GHXkFRV61RVrVPHWvBsipfWp0uSgmxWRYcGqkdokMKCbQoLDlB4cIDCggMUFuT+84lt2w4WKjjAqqBAm4Jt1vrXVgVYWzZq7OaUAS3aDwA8CKnd0I4s95OmbFaLkvp1/SdNnY490KY/zxmnG17erAMFFfrzpoOqrHXq8WtGKsDGsO2uwDAMlVTV6kipuxchr7RaR0pOCKHF1Spz1LX63FaLFBkSqKj6r2jva3cAtAfWfwVYFRRgbdU/flyGIUetS1W1TlXXh9Fq72uXqmrcwwcqa5yqqnGq0vPnWqdq6k4epmucLuWXOZRf5mj1z3sizzCF4EB3cA0OsMkeaFWEvfEwhV2HS9S/R4iiQwP5ByCAFiGkdkOeRfyH9YlQaBAfAUnqERakFT8erxuXfqbs4iq9uTVTeSXV+v3NY3iPTOrNrZmSpJo6l0qra91fVe6xn6VVtSqtrqtvc48FrXO1vgveYpFiI4IVFxWi+Gi74qJCFBdl1/6CCm8YDbcHyNpOoctqsSgkyKaQoNaPk65zulRZ66wPsXWqqnFqVL8oFVXWqriyRkWVNSqurFVFTZ3KHU5VOOqOf9U45Wzh++V0GapyuQP0qaz87JAkKcIeoIExoRoYE6aBPUM1qFeYhsaGa2ifCIUH8/8agOP4jdDNOF2GvvZOmup641E9waWt/jH/Qv14+ef6NrdUH+/J1/Q/fKpnf5CspH5d770yu+papwrK3Hc980odyi+r/15arbyyaqXllausuvaMut9Dg2zH73yGBjW4CxqoqFD3HUBbM+OTR3WCz0OAzapIm1WR9kBv243jW9blbhiGHHUulTvqVOlwqtxRp3e3Z8tR55Kjzn2X1lH/VVPnrP/u8m6vqnGqrLpO5Y46nRh1y6rrtDu7VLuzS5tct190iIb2CdewPhE6Jy5SSf0iNbhXeLP/DQB0fYTUbuabnBJV1LjveIwZ0L3HozanT6Rdf/v5BM1/4yv9X1qB0vLKNf0Pn+qOyYmaf0miggLo/j9ThmGotKpOh4srlV3kHuvZOIhWK7/MoeIzWBIs0GY5PgPefnw2fGSD1xH2AAV2s+EcZ/KPuEG9wlp9jNNlqNxRp9L6SWVFlTU6VlGjwooaHSt3qKSqVg1v2GYXVym7uEob9hZ42wJtFo3uH62k+EiN6h+tsQOiNbhXGEMGgG6AkNrNfHbgmPf1hCExfqzEvMKDA/SXW8ZpyYb9evHjfapzGXr+o316+6vDuvf7w3Rtcj/u7JyCYRgqKHcou8gdOBp+P1z/urwNYz8l9/hPz1jHiIbLMZ0QSO2BrRv7ifZhs1q8d6YTmtnudBk6VuFQfqlDeWXV7u+l1Tpa7vCG11qnoS8PFenLQ0WS3EMGeoQGauyAHho7sIfGDuihMQOiebQx0AURUruZLfvdIbVfdIgSeob6uRrzCrBZdddlQzV5RKz+3992KC2vXFmFVfp/f9upP6xP148uGKjrxvZXVEjg6U/WxTjqnMorcSi7uEq5JQ1CaPHxEHqqSTvN8YTPCE/4PPF7iPt7aJCt3cZ/ouPZrBbFRtgVG2FXko4PoahzuZRf6v6M5RRXyVHn0ne5pXLUf66KKmv18Z58fbwnX5J7tYIxA6I14awYXXhWLyUnRNPrAXQBhNRupM7p0ucZ7uWnJpzFXdSWSOoXpTV3Xqy3vsjSix/vU0GZQ/sLKvToB9/qd//eo8vP6avLz+mjScN6d4nA6qhzqrCiRnmlDuXWB8/ckmrl1IeFnJJqFbRhRnhwgFU9QoMUHRro/goJ8i6BFB0aqLDg9pt8hM4nwGpVfHSI4qNDvG1Ol6G80modKqxUVmGlDh2rUFH9kJAap0tbDxZq68FCPf/RPgXaLBoUE6bhfSP0yynDNTCm9UMVAPgfIbUb+San1NvNegFd/S0WFGDVjy4YqBlj+2vlZxla+dkhZRW618v8YGeOPtiZowCrRSPjIzWmvutxaGyEhvQO81sXpGEYqqhx1s9yd896P/66VkWVtTpW4dDRshodq3DoWHmNCsodKqtuWzd8aJDNGzi9YbRBEKX7HWfKZrV4g6tnqFJZda0OHavUgaPlOlBQ4V1Sq9ZpaF9+ufbll2vN17ka0itMl46I1aXDY3X+4B48VQ7oJAip3ciWBuNRLxjS04+VdE4hQTbNm3iWbrtoiDalH9VbX2Rp494ClTvcyxvtPFyinYdL9Opm9/5Wi3siVkx4kHqFB6tXeLBiwoMUExak4ABb/eMvLd7HYBoyVOc0VOt0yekyVNvgsZm1Lpd3vUzPWphVDdfHrK1fH9Ph3t6aJYROxx5Yf1erfvkld1Bwf//yUJGiQ4LoWoVfRNgDldQvyrv6Rll1rQ4erdD+gnKl5ZWrpMp9p/XA0Qod2HRQyzYdVFiQTd9L7KXJI2J1+Tl9FBMe7M8fAcApEFK7Ec+kqYSeIerfg/GozWnN7OfvndVLKYN76uDRCu3LK1dljXtpnRqne9ycy1D9U4qq26vcMxIadPxJQ+ENnjAUHhygCHuAd2H6kKCmj9B0uqSswirFRtj9VD3QVIQ9UKP7R2t0/2gZhqG8UodCgmxavydfX2YWyely9zCs/TZPa7/N04Pv7NL5g3pqalJfTRnZt9HwAgD+R0jtJmqdLn1+sFCSdMFguvp9JcBq1dDYCA2NjdDNKQPkqHNqX1659heUKz2/XDnF7pnKDbvWT/eoy1Px3HkNCrAqqP57oO346yCbVYEBVtkDrQoJPP60I/frxm2sUICuzGKxqG+U+x9R08f005SRfbUvv0x7j5QpLa9MFTVOuQx5x7I+9sG36t8jRCPjozQyPlK96u+w8jhXwH8Iqd3E7uzj66Myaar9BAfYGnU/nsgw3OtG1tZ369fUuVTrdOndHTmySLJZLLJaGz/XvXEbwRJoi5Agm/cuq8swlHmsUt/klOib3FLvmryH65dJ+883R9QnMlgj46M0ZkC0RvSNYEw14AeE1G7iswOF3tdMmmofZ7JQet9Ius2BjmK1WDSoV5gG9QrTtFFxyimu1jc5JdqdU6qj5e7JV3mlDuWV5uuTPfkaFBOqaaPiNG1UnEbGRxJYgQ5CSO0mPk0/KkkaGBPKuCsAqGexWNSvR4j69QjRFSP7Kr+0Wt/kluqb7BLl1I8nzzhWqSUb9mvJhv0aFBOqq0a7A+s5cQRWoD0RUruB0upabT3onjR18dBefq4GAMwrNtKu2Ei7Lh0eq8KKGgUHWPXP3bnanlksyR1Y/7B+v/6wfr8G9wrTtFF9CaxAOyGkdgMb9hZ4J+tccU5fP1cDAJ1Dz7Ag3ZwyQD+dOESHiyr1r11H9OGuXO3IKpYkHTxa0SiwXlU/JODsOMawAr5ASO0G1n2bJ0mKCA5gPCoAtELDseZhwQH6wbgEXX5OH+3OLtGu7BIdLqqS5A6sL61P10vr09UrPEhJ/aL0wNQRTLoCzgAhtYurqXNpQ/3zrScN782i6wBwhnqEBuniob118dDeKqqo0e6cxoH1aHmNNuwt0Ia9BRrSK0xXjY7Ttcn9lBgb7ufKgc6FkNrFfXbgmMrqH4V6+Tl9/FwNAHQtPcKOB9bCihrtzi7R7pzjgfXA0Qr9/pN0/f6TdI3uH6Xpyf10TXK8dx1WACdHSO3iPF39gTaLLh0R6+dqAKDr6hkWpInDemvisOOBdVd2ibKL3YH168Ml+vpwiRZ/+K0SY8M1JqGHzo6LbLaHi4cIAITULs0wDG9IvWBIjCLtgX6uCAC6h4aB9WiZQzsOF2tHVrEKK2rkMqS0vHKl5ZUrKMCqpPhIjRnQQ4N7hfHADqABQmoXtiu7REdK3ev80dUPAP7RKyJY3z+7jy4bEavMwkptzyrWrsMlqqp1qqbOpa8yi/VVZrGiQwI1ZkC0xg7o4e+SAVMgpHZhH+zM8b7+/tmEVADwJ4vFooExYRoYE6b/GRWntLwybc8q1p4jZXK6DBVX1Wr93gKt31ugDWkFmnFef101Oo5eMHRbhNQuylHn1NtfZUuSUgb35ClTAGAiATarzomP0jnxUaqsqdPXh0v0VWaRd8LVl4eK9OWhIj36/je6YmRf3TC2ny4e2ls2K8MB0H0QUruodd/mqbCiRpJ003gG4AOAWYUGudewvmBIjPJLq/VVZrH25pUqr9QhR51LH+zM0Qc7c9QnMljTx/TTjLH9NbRPhL/LBtodIbWLWrUtS5IUFRKoqUk8ZQoAOoPYSLumJvXV8h+fr03pR/X2l4f1n2+OyFHnUl6pQ69sPKBXNh7Q6P5RmnFef109Ol49woL8XTbQLgipXVDmsUptSj8qSbpuTD/ZA21+rggA0Bo2q0WThvXWpGG9VVpdq39+navVXx7WF4eKJB1fzuo3a77VZSP6aMZ5/TVpeG8F2nhgC7oOQmoX9NYXxx/jR1c/AHQ+DR/H6nH92P6aNKy3vsos1vasIhVX1qrWaejf3xzRv785orDgACX3j9Lo/tFaMHU4j2NFp0dI7WLqnC79/YvDkqQxA6I1vC/jlgCgq4gJD9bl5/TRZWfH6uDRCm3PLNLu7FLVOF2qcNTp0/3H9On+Y3p/Z46uTOqraaPjNCYhmsCKTomQ2sX846ts5Zc5JEk3nc9dVADoiqwWi87qHa6zeofr6nOd+ia7VF9lFunA0QpJUnZxlf686aD+vOmg4qPsunJUnKaN6qsxCT1kZYUAdBKE1C7EUefUCx/vkyT1jbTrmuR4P1cEAGhvwQE2jR3YQ2MH9lBJVa2+ySlRfqlDnx8qlGFIOSXVWrbpoJZtOqi4KLsuP6ePJo+I1QVDYpizAFMjpHYhq7ZleZ8RfedlifzyAYBuJiokUBee1Us3pwxQXmm1/r37iP65K1fbMtyBNbekWq9tOaTXthxSSKBN30vspcvOjtWlw2PVN8ru7/KBRgipXURVjVMvrU+XJA3oGaofjEvwc0UAAH/xTLwKtFl1bXI/TR4Rq29ySvVNTokOHq2Qy5Cqap366Ls8ffRdniQpNiJYZ8WGa+73BuuCs2IUHkxEgH/xCewiVmzJUEH9WNR7vj+UZUgAAF4R9kDvAwOqa53al1+uvUdKtfdImSpqnJKk/DKH8ssc2rL/mAKsFiUnRGv84J46f1BPjR3YQ1EhPJ4VHYuQ2gVkFVbqpU/cd1GHxobr2uR+fq4IAGBW9kCbRvWL0qh+UXIZhrKLqrQvv0zp+RXKLHTfZa1zGfriUFH9uqz7ZbFIw/tEaNygHho7oIdG94/S4F7hPKYV7YqQ2snVOV26560dKnfUSZJ+fdXZ/NIAALSI1WJRQs9QJfQM1eQR7gm4g2LCtCn9qD5NP6q9eWUyDMkwpD1HyrTnSJle/8w9lCA0yKak+CiN6u8OvMP6RGhI7zDmQ8BnCKmd3B/W79eX9U8gmTNhoC4ZHuvnigAAnVVwgE2XjojVpSPcf5eUVtfqq0NF+iKjSJ9nFGpHVrEcdS5JUmWNU9syCrUto9B7vNUiDYwJ09DYcA3rE6GhfcI1NJbwirYhpHZiX2QU6sVP3EtOJcaG68FpZ/u5IgBAZ9fc067io0N0bXI/XTU6TvmlDmUXVym7qErZxVU6UlItp2FIklyGdPBohQ4erdDab/O8x1stUr8eIRoUE6aBMaEaFBOmAT1DNaiX+zsBFs0hpHZSO7KK9eNXP5fTZSjQZtELNybzPzkAoF0FWK2Kjw5RfHSIzh/kbqtzuuonXVUrr9ShoACr9uWV6VBhpeqzq1yGlFVYpazCKv13X9PzxkXZveF1YEyYBsWEamB9oA1jlYFui//yndCOrGL9aNlWlVW7x6Eunp6kkfFRfq4KANAdBdiOB1ePS4fHqtbpUkF9eM0vdehYRY0KK2p0tNzhHTLgkVtSrdySan12oPDE06t3RPDx0NozVAN7ub8PiglTVCgrDnRlhNROZs3XOVr49i6V1U+UevK6UZrF408BACYT2Ex4lSTDMFRZ49SxihodKz8eXj2vK+uXxPIoKHOooMyhzzOKmlwjJNCmmPAg9QwL0qRhvb13XwfGhKp3eLAsFiYSd2amDqkOh0MPP/ywVq5cqaKiIo0ePVqLFy/W5Zdfftpjs7Ozde+992rt2rVyuVy69NJL9dxzz2nIkCEdULnvlVTWatF7u/X+zhxv25PXjdLNKQRUAEDnYbFYFBYcoLDgAA3oGdpke1WNU8cqGoZXd4AtrKjx3qDx7lvr1OGiKh0uqtLXh0sabQsNsrnHvXqD6/EAGxcVwko4nYCpQ+qtt96q1atX65577tHQoUP16quvatq0aVq/fr0uuuiikx5XXl6uSy+9VCUlJXrwwQcVGBio5557TpMmTdKOHTsUExPTgT/Fmckvq9aKzRl6/bNMlVTVSpJ6hAbqt9eP0tSkOD9XBwCAb4UE2dQ/KFT9ezQNsI46pze4FlbUuO/GVrgDbElVrXcMrORefcCzbNaJgmxW9e8ZcnwCV0yoBtSH17gou6JCArkLawKmDanbtm3TqlWrlJqaqvvvv1+SNGfOHCUlJWnBggXavHnzSY9dsmSJ9u3bp23btun888+XJF155ZVKSkrSM888oyeffLJDfoa2OlJSrfV78/Xxd/n6v30FqmkwdueyEbH67Q2jFBvBM5YBAN1LcICtPkiGNNlW53SpqLLWG1qPlR8PsEUVtd4VCCSpxunSgYIKHSioaPY69kCr4qJC1CcyWHFRIeobZVffSLt3aIHnq0doEE94bEemDamrV6+WzWbTvHnzvG12u11z587Vgw8+qKysLCUkNP98+tWrV+v888/3BlRJGjFihC677DL97W9/M3VIfejdXd6FkhsaMyBaP5t4lqaM7MO/7gAAOEGAzareEcHqHRHcZJvLMFRSWXv8zmt5zfHhBBUO1TqNRvtX17q8S2mdTqQ9QDHhweoZFqQIu3sYQ0T9cIaGr8PtAbIHWBUYYFWwzaqgAPdXoOf1Cd9tVossFskii6wW94MXLBZ1qwxg2pC6fft2DRs2TJGRkY3ax48fL0nasWNHsyHV5XLp66+/1k9+8pMm28aPH6+1a9eqrKxMERERJ712fn6+CgoKGrV9++23kqT09PRW/yytEVyWo5qCQ5Kk6NBAjRvUU9OS4pTUL1IWyzF9++2xdr2+rxw+kOvvEgAAaMQuqZ+kfmGSwtxthhGoipo6lVbVqsxRp4rqOvd3h1Nl1bWqcNSp3FEnl9H8OY/Wf3UUd3D1hFZPcJWsavC6vr01lv5onGLCmwZ8X/JkKIfD0aL9TRtSc3NzFRfXdMylpy0nJ6fJNkkqLCyUw+E47bHDhw8/6bWXLFmixx57rNlt06dPP13pPpMr6TtJKzvsigAAoDua9IeOu1ZWVpbGjh172v1MG1KrqqoUHNw00dvtdu/2kx0nqU3HesyfP18zZ85s1FZaWqq0tDSNGjWq2XN3J+np6Zo+fbreffddJSYm+rsc0+B9aR7vy8nx3jSP96V5vC8nx3vTPLO9Lw6HQ1lZWZo0aVKL9jdtSA0JCWn2dnB1dbV3+8mOk5q/lXy6Yz1iY2MVGxvbpH3ChAmnLrqbSUxM1MiRI/1dhunwvjSP9+XkeG+ax/vSPN6Xk+O9aZ6Z3peW3EH1MO2UtLi4OOXmNh3X6GmLj49v9riePXsqODi4TccCAADAHEwbUpOTk5WWlqbS0tJG7Vu3bvVub47VatWoUaP0xRdfNNm2detWDRky5JSTpgAAAOB/pg2pM2bMkNPp1NKlS71tDodDy5cvV0pKindmf2Zmpvbs2dPk2M8//7xRUN27d68++eSTJmNNAQAAYD6mHZOakpKimTNnauHChcrPz1diYqJWrFihjIwMLVu2zLvfnDlztHHjRhkNFumdP3++/vSnP+mqq67S/fffr8DAQD377LPq06eP7rvvPn/8OF1K79699cgjj6h3797+LsVUeF+ax/tycrw3zeN9aR7vy8nx3jSvs78vFqNhujOZ6upqLVq0SK+//rqKioo0evRo/eY3v9GUKVO8+1xyySVNQqokHT58WPfee6/Wrl0rl8ulSy65RM8995wpZrcBAADg1EwdUgEAANA9mXZMKgAAALovQioAAABMh5AKAAAA0yGkAgAAwHQIqWiRzz//XHfccYdGjhypsLAwDRgwQD/4wQ+Ulpbm79JM54knnpDFYlFSUpK/S/G7r776Stdcc4169uyp0NBQJSUl6cUXX/R3WX63b98+3Xjjjerfv79CQ0M1YsQIPf7446qsrPR3aR2ivLxcjzzyiKZOnaqePXvKYrHo1VdfbXbf7777TlOnTlV4eLh69uypH/3oRyooKOjYgjtQS94bl8ulV199Vddcc40SEhIUFhampKQkLV682Pv4766mNZ8Zj9raWp1zzjmyWCx6+umnO6bQDtaa98XlcumPf/yjkpOTFRISopiYGE2ePFk7d+7s2KJbwbTrpMJc/vd//1effvqpZs6cqdGjR+vIkSN66aWXNHbsWH322WcEsnqHDx/Wk08+qbCwMH+X4ndr167V1VdfrTFjxmjRokUKDw/X/v37dfjwYX+X5ldZWVkaP368oqKidMcdd6hnz57asmWLHnnkEX355Zd67733/F1iuzt69Kgef/xxDRgwQOeee642bNjQ7H6HDx/WxIkTFRUVpSeffFLl5eV6+umntWvXLm3btk1BQUEdW3gHaMl7U1lZqR//+Me64IIL9POf/1yxsbHez9DHH3+sTz75RBaLpeOLb0ct/cw09Pvf/16ZmZntX5wfteZ9+clPfqI33nhDc+bM0R133KGKigpt375d+fn5HVdwaxlAC3z66aeGw+Fo1JaWlmYEBwcbs2fP9lNV5jNr1ixj8uTJxqRJk4yRI0f6uxy/KSkpMfr06WNcd911htPp9Hc5pvLEE08Ykozdu3c3ap8zZ44hySgsLPRTZR2nurrayM3NNQzDMD7//HNDkrF8+fIm+91+++1GSEiIcejQIW/bunXrDEnGK6+80lHldqiWvDcOh8P49NNPmxz72GOPGZKMdevWdUSpHaqlnxmPvLw8Iyoqynj88ccNSUZqamoHVdqxWvq+vPXWW4Yk4x//+EcHV3hm6O5Hi1x44YVN7loMHTpUI0eO1Hfffeenqszl//7v/7R69Wo9//zz/i7F7958803l5eXpiSeekNVqVUVFhVwul7/LMoXS0lJJUp8+fRq1x8XFyWq1dsm7gycKDg5W3759T7vf22+/rf/5n//RgAEDvG3f//73NWzYMP3tb39rzxL9piXvTVBQkC688MIm7dddd50kdcnfyS39zHj86le/0vDhw/XDH/6wHavyv5a+L88++6zGjx+v6667Ti6XSxUVFR1Q3ZkjpKLNDMNQXl6eevXq5e9S/M7pdOrOO+/UbbfdplGjRvm7HL/76KOPFBkZqezsbA0fPlzh4eGKjIzU7bff3mXHzLXUJZdcIkmaO3euduzYoaysLL311lv64x//qLvuuouhIvWys7OVn5+vcePGNdk2fvx4bd++3Q9VmduRI0ckqdv/Tt62bZtWrFih559/vssNe2iL0tJSbdu2Teeff74efPBBRUVFKTw8XEOGDDH9P/YYk4o2e+ONN5Sdna3HH3/c36X43csvv6xDhw7po48+8ncpprBv3z7V1dXp2muv1dy5c/Xb3/5WGzZs0O9//3sVFxfrr3/9q79L9JupU6fqN7/5jZ588km9//773vZf//rXWrx4sR8rM5fc3FxJ7jvMJ4qLi1NhYaEcDoeCg4M7ujTTeuqppxQZGakrr7zS36X4jWEYuvPOOzVr1ixNmDBBGRkZ/i7J7/bv3y/DMLRq1SoFBAToqaeeUlRUlF544QXdeOONioyM1NSpU/1dZrMIqWiTPXv26Be/+IUmTJigW265xd/l+NWxY8f08MMPa9GiRerdu7e/yzGF8vJyVVZW6uc//7l3Nv/111+vmpoavfLKK3r88cc1dOhQP1fpP4MGDdLEiRN1ww03KCYmRh9++KGefPJJ9e3bV3fccYe/yzOFqqoqSWo2hNrtdu8+hFS3J598Uh999JGWLFmi6Ohof5fjN6+++qp27dql1atX+7sU0ygvL5fk/rvqs88+U0pKiiTpmmuu0eDBg7V48WJCKrqOI0eO6KqrrlJUVJRWr14tm83m75L86qGHHlLPnj115513+rsU0wgJCZEk3XTTTY3ab775Zr3yyivasmVLtw2pq1at0rx585SWlqb+/ftLcgd4l8ulBx54QDfddJNiYmL8XKX/eT5DDoejyTbPkBHPPt3dW2+9pYceekhz587V7bff7u9y/Ka0tFQLFy7UL3/5SyUkJPi7HNPw/H8yePBgb0CVpPDwcF199dV6/fXXVVdXp4AA80VCxqSiVUpKSnTllVequLhY//73vxUfH+/vkvxq3759Wrp0qe666y7l5OQoIyNDGRkZqq6uVm1trTIyMlRYWOjvMjuc53Nx4uSg2NhYSVJRUVGH12QWS5Ys0ZgxY7wB1eOaa65RZWUlYy3rebr5Pd3+DeXm5qpnz57cRZW0bt06zZkzR1dddZVefvllf5fjV08//bRqamo0a9Ys7+9iz5J3RUVFysjIUE1NjZ+r7Hgn+30suX8n19bWmnYiFSEVLVZdXa2rr75aaWlpWrNmjc455xx/l+R32dnZcrlcuuuuuzR48GDv19atW5WWlqbBgwd3yzG75513niT3+9NQTk6OJHXrYRF5eXlyOp1N2mtrayVJdXV1HV2SKfXr10+9e/fWF1980WTbtm3blJyc3PFFmczWrVt13XXXady4cfrb3/5myjthHSkzM1NFRUUaOXKk93fxxRdfLMk9HGLw4MH69ttv/Vxlx4uPj1ffvn2b/D6W3L+T7Xa7IiIi/FDZ6XXvTzRazOl0atasWdqyZYvee+89TZgwwd8lmUJSUpLeeeedJu0PPfSQysrK9MILL+iss87yQ2X+9YMf/EC/+93vtGzZMk2ePNnb/uc//1kBAQHeGe7d0bBhw7R27VqlpaVp2LBh3va//vWvslqtGj16tB+rM5cbbrhBK1asUFZWlrf79uOPP1ZaWpruvfdeP1fnX999952uuuoqDRo0SGvWrGHog6S77rpL06dPb9SWn5+vn/3sZ7r11lt17bXXavDgwf4pzs9mzZqlF154QevWrdPll18uyf0ggPfee0+TJ0+W1WrOe5aEVLTIfffdp/fff19XX321CgsL9frrrzfa3tXXojuZXr16NfmlKMm7Vmpz27qDMWPG6Cc/+Yn+8pe/qK6uTpMmTdKGDRv097//XQsXLuzWw0R++ctf6l//+pcuvvhi3XHHHYqJidGaNWv0r3/9S7fddlu3eW9eeuklFRcXe++uf/DBB96u2TvvvFNRUVF68MEH9fe//12XXnqp7r77bpWXlys1NVWjRo3Sj3/8Y3+W365O995YrVZNmTJFRUVF+uUvf6kPP/yw0fFnnXVWl7yRcLr3ZezYsRo7dmyjYzyz+0eOHNllfx+35P+lhQsX6m9/+5tuuOEG/b//9/8UFRWll19+WbW1tXryySf9Wf6p+flhAugkJk2aZEg66Rca6+5PnDIMw6ipqTEeffRRY+DAgUZgYKCRmJhoPPfcc/4uyxS2bt1qXHnllUbfvn2NwMBAY9iwYcYTTzxh1NbW+ru0DjNw4MCT/j45ePCgd7/du3cbV1xxhREaGmpER0cbs2fPNo4cOeK/wjvA6d6bgwcPnvL38S233OLvH6FdtPQz05DnveqqT5wyjJa/L/v37zeuu+46IzIy0ggJCTEmT55sbNu2zX+Ft4DFMAyjHTMwAAAA0GrmHIQAAACAbo2QCgAAANMhpAIAAMB0CKkAAAAwHUIqAAAATIeQCgAAANMhpAIAAMB0CKkAAAAwHUIqAAAATIeQCgAAANMhpAIAAMB0CKkAAAAwHUIqAAAATIeQCgAAANMhpAIAAMB0/j98jHly9huJYgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 768x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generating distribution plot\n",
    "\n",
    "fig_dist, ax_dist = plt.subplots()\n",
    "\n",
    "sns.distplot(annotated_expression.mean(axis=1), kde = True)\n",
    "\n",
    "#Save the generated distribution plot into a picture\n",
    "fig_dist.savefig('Plots/distribution.svg')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you have successfully passed all of the QC tests, you can move to the TME classification step. If there are any data issues, fix them before proceeding to the next step as they can affect the results of the analyses."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GSM872328</th>\n",
       "      <th>GSM872329</th>\n",
       "      <th>GSM872330</th>\n",
       "      <th>GSM872331</th>\n",
       "      <th>GSM872332</th>\n",
       "      <th>GSM872333</th>\n",
       "      <th>GSM872334</th>\n",
       "      <th>GSM872335</th>\n",
       "      <th>GSM872336</th>\n",
       "      <th>GSM872337</th>\n",
       "      <th>...</th>\n",
       "      <th>GSM872383</th>\n",
       "      <th>GSM872384</th>\n",
       "      <th>GSM872385</th>\n",
       "      <th>GSM872386</th>\n",
       "      <th>GSM872387</th>\n",
       "      <th>GSM872388</th>\n",
       "      <th>GSM872389</th>\n",
       "      <th>GSM872390</th>\n",
       "      <th>GSM872391</th>\n",
       "      <th>GSM872392</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A1BG</th>\n",
       "      <td>3.078552</td>\n",
       "      <td>2.987851</td>\n",
       "      <td>2.987851</td>\n",
       "      <td>3.165811</td>\n",
       "      <td>2.728912</td>\n",
       "      <td>3.655134</td>\n",
       "      <td>2.987851</td>\n",
       "      <td>2.987851</td>\n",
       "      <td>3.576304</td>\n",
       "      <td>6.004672</td>\n",
       "      <td>...</td>\n",
       "      <td>4.336052</td>\n",
       "      <td>3.016967</td>\n",
       "      <td>2.987851</td>\n",
       "      <td>3.565415</td>\n",
       "      <td>3.567615</td>\n",
       "      <td>2.987851</td>\n",
       "      <td>2.987851</td>\n",
       "      <td>2.899146</td>\n",
       "      <td>3.058037</td>\n",
       "      <td>2.987851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A1BG-AS1</th>\n",
       "      <td>5.749913</td>\n",
       "      <td>4.904272</td>\n",
       "      <td>4.388127</td>\n",
       "      <td>4.360712</td>\n",
       "      <td>4.244502</td>\n",
       "      <td>4.356337</td>\n",
       "      <td>4.494508</td>\n",
       "      <td>4.478265</td>\n",
       "      <td>4.595821</td>\n",
       "      <td>5.300090</td>\n",
       "      <td>...</td>\n",
       "      <td>4.310518</td>\n",
       "      <td>4.597341</td>\n",
       "      <td>4.636256</td>\n",
       "      <td>4.597341</td>\n",
       "      <td>4.606501</td>\n",
       "      <td>4.597341</td>\n",
       "      <td>4.597341</td>\n",
       "      <td>4.639346</td>\n",
       "      <td>4.412341</td>\n",
       "      <td>4.742684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A1CF</th>\n",
       "      <td>2.223272</td>\n",
       "      <td>2.223272</td>\n",
       "      <td>2.223272</td>\n",
       "      <td>2.223272</td>\n",
       "      <td>2.223272</td>\n",
       "      <td>2.223272</td>\n",
       "      <td>2.223272</td>\n",
       "      <td>2.223272</td>\n",
       "      <td>2.223272</td>\n",
       "      <td>2.223272</td>\n",
       "      <td>...</td>\n",
       "      <td>2.223272</td>\n",
       "      <td>2.223272</td>\n",
       "      <td>2.223272</td>\n",
       "      <td>2.223272</td>\n",
       "      <td>2.223272</td>\n",
       "      <td>2.223272</td>\n",
       "      <td>2.223272</td>\n",
       "      <td>2.223272</td>\n",
       "      <td>2.223272</td>\n",
       "      <td>2.223272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A2M</th>\n",
       "      <td>11.726740</td>\n",
       "      <td>11.031260</td>\n",
       "      <td>11.897860</td>\n",
       "      <td>10.843689</td>\n",
       "      <td>12.949128</td>\n",
       "      <td>12.603719</td>\n",
       "      <td>13.162092</td>\n",
       "      <td>11.998046</td>\n",
       "      <td>12.926885</td>\n",
       "      <td>12.836179</td>\n",
       "      <td>...</td>\n",
       "      <td>12.034147</td>\n",
       "      <td>12.973168</td>\n",
       "      <td>12.724960</td>\n",
       "      <td>10.293074</td>\n",
       "      <td>13.931410</td>\n",
       "      <td>13.758179</td>\n",
       "      <td>12.476536</td>\n",
       "      <td>10.576425</td>\n",
       "      <td>12.909873</td>\n",
       "      <td>12.791583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A2M-AS1</th>\n",
       "      <td>6.443771</td>\n",
       "      <td>6.102404</td>\n",
       "      <td>6.606300</td>\n",
       "      <td>5.389406</td>\n",
       "      <td>7.313690</td>\n",
       "      <td>7.771913</td>\n",
       "      <td>7.293835</td>\n",
       "      <td>6.468055</td>\n",
       "      <td>7.778143</td>\n",
       "      <td>7.140280</td>\n",
       "      <td>...</td>\n",
       "      <td>8.238855</td>\n",
       "      <td>7.263452</td>\n",
       "      <td>7.986488</td>\n",
       "      <td>4.551769</td>\n",
       "      <td>8.104151</td>\n",
       "      <td>8.618934</td>\n",
       "      <td>6.642779</td>\n",
       "      <td>5.118838</td>\n",
       "      <td>7.025310</td>\n",
       "      <td>8.441181</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 65 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          GSM872328  GSM872329  GSM872330  GSM872331  GSM872332  GSM872333  \\\n",
       "A1BG       3.078552   2.987851   2.987851   3.165811   2.728912   3.655134   \n",
       "A1BG-AS1   5.749913   4.904272   4.388127   4.360712   4.244502   4.356337   \n",
       "A1CF       2.223272   2.223272   2.223272   2.223272   2.223272   2.223272   \n",
       "A2M       11.726740  11.031260  11.897860  10.843689  12.949128  12.603719   \n",
       "A2M-AS1    6.443771   6.102404   6.606300   5.389406   7.313690   7.771913   \n",
       "\n",
       "          GSM872334  GSM872335  GSM872336  GSM872337  ...  GSM872383  \\\n",
       "A1BG       2.987851   2.987851   3.576304   6.004672  ...   4.336052   \n",
       "A1BG-AS1   4.494508   4.478265   4.595821   5.300090  ...   4.310518   \n",
       "A1CF       2.223272   2.223272   2.223272   2.223272  ...   2.223272   \n",
       "A2M       13.162092  11.998046  12.926885  12.836179  ...  12.034147   \n",
       "A2M-AS1    7.293835   6.468055   7.778143   7.140280  ...   8.238855   \n",
       "\n",
       "          GSM872384  GSM872385  GSM872386  GSM872387  GSM872388  GSM872389  \\\n",
       "A1BG       3.016967   2.987851   3.565415   3.567615   2.987851   2.987851   \n",
       "A1BG-AS1   4.597341   4.636256   4.597341   4.606501   4.597341   4.597341   \n",
       "A1CF       2.223272   2.223272   2.223272   2.223272   2.223272   2.223272   \n",
       "A2M       12.973168  12.724960  10.293074  13.931410  13.758179  12.476536   \n",
       "A2M-AS1    7.263452   7.986488   4.551769   8.104151   8.618934   6.642779   \n",
       "\n",
       "          GSM872390  GSM872391  GSM872392  \n",
       "A1BG       2.899146   3.058037   2.987851  \n",
       "A1BG-AS1   4.639346   4.412341   4.742684  \n",
       "A1CF       2.223272   2.223272   2.223272  \n",
       "A2M       10.576425  12.909873  12.791583  \n",
       "A2M-AS1    5.118838   7.025310   8.441181  \n",
       "\n",
       "[5 rows x 65 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Print the dataframe to know how it looks in table form \n",
    "annotated_expression.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "# TME classification"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The classification section determines each sample subtype against the reference TCGA cohort. The TCGA cohort was split into clusters using KNeighborsClusterClassifier. The resulting clusters are stored as a MODEL variable, with each cluster belonging to a different subtype. Then the samples EXPRESSION_MATRIX and GENE_SIGNATURES files are read and passed to the ssgsea_formula function to determine each signature (columns) score for every sample (rows)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Load Reference Cohort with known TME labels and gene expression values\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the TCGA cohort"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reference signatures provided for 8024 samples\n",
      "Reference annotation provided for 8024 samples\n"
     ]
    }
   ],
   "source": [
    "TCGA_signature_scores_scaled = pd.read_csv(TCGA_SIGNATURES, sep='\\t', index_col=0).T  # Signatures in rows\n",
    "print(f'Reference signatures provided for {len(TCGA_signature_scores_scaled)} samples')\n",
    "\n",
    "TCGA_annotation = pd.read_csv(TCGA_COHORTS_ANNOTATION, sep='\\t', index_col=0)  # Contains MFP cluster labels in MFP column\n",
    "print(f'Reference annotation provided for {len(TCGA_signature_scores_scaled)} samples')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded 29 signatures\n",
      "Classifying cohort, N=22836 samples\n"
     ]
    }
   ],
   "source": [
    "# Fit the model\n",
    "MODEL = KNeighborsClusterClassifier(norm=False, scale=False, clip=2, k=35).fit(TCGA_signature_scores_scaled,\n",
    "                                                                               TCGA_annotation.MFP)\n",
    "\n",
    "# Read signatures\n",
    "gene_signatures = read_gene_sets(GENE_SIGNATURES)  # GMT format like in MSIGdb\n",
    "print(f'Loaded {len(gene_signatures)} signatures')\n",
    "\n",
    "# Read expressions\n",
    "gene_expressions = pd.read_csv(EXPRESSION_MATRIX, sep='\\t', index_col=0)  # log2+1 transformed; Genes should appear to be in rows\n",
    "\n",
    "print(f'Classifying cohort, N={len(gene_expressions)} samples')\n",
    "\n",
    "if gene_expressions.max().max() > 35:\n",
    "    print('Performing log2+1 transformation')\n",
    "    gene_expressions = np.log2(1+gene_expressions)\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Classify the input cohort and give the output .tsv file with the TME subtype for each sample"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The codeblock establishes to which subtype each sample belongs and then prints out the number of samples that have the given subtype. The CLASSIFIED_SAMPLES file is given as an output. It lists the subtypes of all analyzed samples.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calc signature scores\n",
    "signature_scores = ssgsea_formula(gene_expressions, gene_signatures)\n",
    "\n",
    "# Scale signatures\n",
    "signature_scores_scaled = median_scale(signature_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted labels count:\n",
      "D       31\n",
      "IE      18\n",
      "IE/F     8\n",
      "F        8\n",
      "Name: TME, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Predict clusters\n",
    "classified_samples = MODEL.predict(signature_scores_scaled[MODEL.X.columns]).rename('TME')\n",
    "\n",
    "#Output the predicted clusters\n",
    "print('Predicted labels count:')\n",
    "print(classified_samples.value_counts())\n",
    "\n",
    "# Output the classified samples table\n",
    "classified_samples.to_csv(CLASSIFIED_SAMPLES, sep='\\t', index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visalize classified data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The code provided below generates several plots to make the data visualisation easier and to help you understand the differences betweeen signatures."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "classified_samples = pd.read_csv(CLASSIFIED_SAMPLES, sep='\\t', index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 768x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# genere distribution plots for already scaled signature values\n",
    "\n",
    "fig, ax = plt.subplots(1, 1)\n",
    "for i in signature_scores_scaled.columns[:5]:\n",
    "    sns.distplot(signature_scores_scaled[i], label=i, ax=ax, kde=True)\n",
    "ax.legend()\n",
    "\n",
    "# Savie the generated distribution plots into a picture.\n",
    "fig.savefig('Plots/scaled_data_distribution.svg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 768x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tme_palette = {\"IE\": '#0000CC', \"IE/F\": '#009999', \"F\": '#CC0033', \"D\": '#FF9900'}\n",
    "\n",
    "fig_pie_plot, ax_pie_plot = plt.subplots()\n",
    "\n",
    "vector_pie_plot(classified_samples.squeeze(),palette=tme_palette, ax= ax_pie_plot)\n",
    "\n",
    "# Save the generated pie plot into a picture.\n",
    "fig_pie_plot.savefig('Plots/classified_samples_percentage.svg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x1008 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "af = axis_matras([.4, 8], x_len=15)\n",
    "ax=next(af)\n",
    "\n",
    "order = [\"IE\",\"IE/F\",\"F\", \"D\"]\n",
    "\n",
    "ordered_classified_samples = pd.read_csv(CLASSIFIED_SAMPLES, sep='\\t')\n",
    "\n",
    "ordered_classified_samples['TME'] = pd.Categorical(ordered_classified_samples['TME'], categories=order, ordered=True)\n",
    "\n",
    "ordered_classified_samples = ordered_classified_samples.sort_values('TME')\n",
    "\n",
    "classified_samples = pd.Series(classified_samples.squeeze())\n",
    "\n",
    "line_palette_annotation_plot(classified_samples[ordered_classified_samples.iloc[:,0]], ax=ax, palette=tme_palette)\n",
    "\n",
    "ax = next(af)\n",
    "sns.heatmap(signature_scores_scaled.loc[ ordered_classified_samples.iloc[:,0],gene_signatures].clip(-2, 2).T,\n",
    "            yticklabels=True, xticklabels=False, cbar=False, cmap=plt.cm.coolwarm,ax=ax)\n",
    "plt.tight_layout(pad=.1)\n",
    "\n",
    "# Save the generated heatmap plot into a picture.\n",
    "ax.figure.savefig('Plots/signatures_heatmap.svg')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "# de novo clusterization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This section helps to create your own reference cohort for further classification."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculate signature scores of reference gene expression values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Insert the path to your reference cohort file into the REFERENCE_EXPRESSION."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#constants\n",
    "REFERENCE_EXPRESSION = EXPRESSION_MATRIX\n",
    "DENOVO_REFRENCE_COHORT = 'samples_subtyping_by_tme.tsv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MHCI</th>\n",
       "      <th>MHCII</th>\n",
       "      <th>Coactivation_molecules</th>\n",
       "      <th>Effector_cells</th>\n",
       "      <th>T_cell_traffic</th>\n",
       "      <th>NK_cells</th>\n",
       "      <th>T_cells</th>\n",
       "      <th>B_cells</th>\n",
       "      <th>M1_signatures</th>\n",
       "      <th>Th1_signature</th>\n",
       "      <th>...</th>\n",
       "      <th>Macrophage_DC_traffic</th>\n",
       "      <th>Th2_signature</th>\n",
       "      <th>Protumor_cytokines</th>\n",
       "      <th>CAF</th>\n",
       "      <th>Matrix</th>\n",
       "      <th>Matrix_remodeling</th>\n",
       "      <th>Angiogenesis</th>\n",
       "      <th>Endothelium</th>\n",
       "      <th>Proliferation_rate</th>\n",
       "      <th>EMT_signature</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>GSM872328</th>\n",
       "      <td>1.343932</td>\n",
       "      <td>-1.927966</td>\n",
       "      <td>-0.709875</td>\n",
       "      <td>-0.624693</td>\n",
       "      <td>-0.223812</td>\n",
       "      <td>-0.336393</td>\n",
       "      <td>-1.245419</td>\n",
       "      <td>-0.217499</td>\n",
       "      <td>-0.253581</td>\n",
       "      <td>-0.228317</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.187442</td>\n",
       "      <td>0.519710</td>\n",
       "      <td>-1.172032</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.316514</td>\n",
       "      <td>-1.326077</td>\n",
       "      <td>-2.399462</td>\n",
       "      <td>-0.497626</td>\n",
       "      <td>-0.671340</td>\n",
       "      <td>0.940784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GSM872329</th>\n",
       "      <td>-1.631422</td>\n",
       "      <td>-2.525349</td>\n",
       "      <td>-0.932813</td>\n",
       "      <td>-1.382324</td>\n",
       "      <td>-1.451595</td>\n",
       "      <td>-1.640942</td>\n",
       "      <td>-1.186805</td>\n",
       "      <td>-0.235935</td>\n",
       "      <td>-0.663517</td>\n",
       "      <td>-0.366576</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.948353</td>\n",
       "      <td>0.181062</td>\n",
       "      <td>-1.507739</td>\n",
       "      <td>-0.649172</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.408041</td>\n",
       "      <td>-1.699175</td>\n",
       "      <td>-0.740179</td>\n",
       "      <td>1.942653</td>\n",
       "      <td>-0.986939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GSM872330</th>\n",
       "      <td>-1.475856</td>\n",
       "      <td>-0.841059</td>\n",
       "      <td>-0.996619</td>\n",
       "      <td>-1.044256</td>\n",
       "      <td>-1.520137</td>\n",
       "      <td>-0.329958</td>\n",
       "      <td>-0.859033</td>\n",
       "      <td>0.156090</td>\n",
       "      <td>-0.485315</td>\n",
       "      <td>-1.054475</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.306083</td>\n",
       "      <td>0.140916</td>\n",
       "      <td>-1.032607</td>\n",
       "      <td>-0.087223</td>\n",
       "      <td>-0.969004</td>\n",
       "      <td>-0.109842</td>\n",
       "      <td>0.541437</td>\n",
       "      <td>-0.295640</td>\n",
       "      <td>0.501525</td>\n",
       "      <td>0.183189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GSM872331</th>\n",
       "      <td>-2.327941</td>\n",
       "      <td>-2.106570</td>\n",
       "      <td>-1.020332</td>\n",
       "      <td>-1.292678</td>\n",
       "      <td>-2.004865</td>\n",
       "      <td>-0.939926</td>\n",
       "      <td>-1.051379</td>\n",
       "      <td>-0.069391</td>\n",
       "      <td>-1.008595</td>\n",
       "      <td>-0.969880</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.908347</td>\n",
       "      <td>-0.866855</td>\n",
       "      <td>-0.614442</td>\n",
       "      <td>-0.003902</td>\n",
       "      <td>-0.390023</td>\n",
       "      <td>-1.701606</td>\n",
       "      <td>-0.491603</td>\n",
       "      <td>0.012649</td>\n",
       "      <td>-1.286683</td>\n",
       "      <td>0.303761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GSM872332</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.908612</td>\n",
       "      <td>1.908271</td>\n",
       "      <td>0.401430</td>\n",
       "      <td>0.331190</td>\n",
       "      <td>0.119207</td>\n",
       "      <td>1.286086</td>\n",
       "      <td>3.315214</td>\n",
       "      <td>1.645273</td>\n",
       "      <td>0.547043</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.005509</td>\n",
       "      <td>-0.395391</td>\n",
       "      <td>0.387733</td>\n",
       "      <td>-0.050035</td>\n",
       "      <td>-0.653643</td>\n",
       "      <td>-0.261586</td>\n",
       "      <td>1.845939</td>\n",
       "      <td>2.865235</td>\n",
       "      <td>-1.060149</td>\n",
       "      <td>0.919687</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               MHCI     MHCII  Coactivation_molecules  Effector_cells  \\\n",
       "GSM872328  1.343932 -1.927966               -0.709875       -0.624693   \n",
       "GSM872329 -1.631422 -2.525349               -0.932813       -1.382324   \n",
       "GSM872330 -1.475856 -0.841059               -0.996619       -1.044256   \n",
       "GSM872331 -2.327941 -2.106570               -1.020332       -1.292678   \n",
       "GSM872332  0.000000  0.908612                1.908271        0.401430   \n",
       "\n",
       "           T_cell_traffic  NK_cells   T_cells   B_cells  M1_signatures  \\\n",
       "GSM872328       -0.223812 -0.336393 -1.245419 -0.217499      -0.253581   \n",
       "GSM872329       -1.451595 -1.640942 -1.186805 -0.235935      -0.663517   \n",
       "GSM872330       -1.520137 -0.329958 -0.859033  0.156090      -0.485315   \n",
       "GSM872331       -2.004865 -0.939926 -1.051379 -0.069391      -1.008595   \n",
       "GSM872332        0.331190  0.119207  1.286086  3.315214       1.645273   \n",
       "\n",
       "           Th1_signature  ...  Macrophage_DC_traffic  Th2_signature  \\\n",
       "GSM872328      -0.228317  ...              -0.187442       0.519710   \n",
       "GSM872329      -0.366576  ...              -0.948353       0.181062   \n",
       "GSM872330      -1.054475  ...              -1.306083       0.140916   \n",
       "GSM872331      -0.969880  ...              -1.908347      -0.866855   \n",
       "GSM872332       0.547043  ...              -0.005509      -0.395391   \n",
       "\n",
       "           Protumor_cytokines       CAF    Matrix  Matrix_remodeling  \\\n",
       "GSM872328           -1.172032  0.000000 -0.316514          -1.326077   \n",
       "GSM872329           -1.507739 -0.649172  0.000000          -1.408041   \n",
       "GSM872330           -1.032607 -0.087223 -0.969004          -0.109842   \n",
       "GSM872331           -0.614442 -0.003902 -0.390023          -1.701606   \n",
       "GSM872332            0.387733 -0.050035 -0.653643          -0.261586   \n",
       "\n",
       "           Angiogenesis  Endothelium  Proliferation_rate  EMT_signature  \n",
       "GSM872328     -2.399462    -0.497626           -0.671340       0.940784  \n",
       "GSM872329     -1.699175    -0.740179            1.942653      -0.986939  \n",
       "GSM872330      0.541437    -0.295640            0.501525       0.183189  \n",
       "GSM872331     -0.491603     0.012649           -1.286683       0.303761  \n",
       "GSM872332      1.845939     2.865235           -1.060149       0.919687  \n",
       "\n",
       "[5 rows x 29 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Example script\n",
    "\n",
    "# Read signatures\n",
    "gmt = read_gene_sets(GENE_SIGNATURES)  # GMT format like in MSIGdb\n",
    "\n",
    "\n",
    "# Read expressions\n",
    "exp = pd.read_csv(REFERENCE_EXPRESSION, sep='\\t', index_col = 0)  # log2+1 transformed; Genes in columns\n",
    "\n",
    "# Calc signature scores\n",
    "signature_scores = ssgsea_formula(exp, gmt)\n",
    "\n",
    "# Scale signatures\n",
    "signature_scores_scaled = median_scale(signature_scores)\n",
    "\n",
    "signature_scores_scaled.head()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize clusters according to the selected threshold and statistical scores of correlation between data values\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The clusterization is done using the clustering_profile_metrics function from the clustering.py library. The calculations are done using Davies Bouldin, Calinski Harabasz, Silouhette scores to get the correlation between clusters and each ssGSEA score data point.\n",
    "\n",
    "For more information on the mentioned calculations and correlation scores, follow [this link](https://medium.com/@haataa/how-to-measure-clustering-performances-when-there-are-no-ground-truth-db027e9a871c)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check the clustering within a range of 30% to 65% similarity\n",
    "- \\>65% - means no correlation between data points in the graph\n",
    "- <30% - means a false positive correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 35/35 [00:01<00:00, 23.57it/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Cluster %'>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 960x1440 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "clustering_metrics = clustering_profile_metrics(signature_scores_scaled, threshold_mm=(.3, .65), step=.01)\n",
    "\n",
    "# Visualize the partitions\n",
    "clustering_profile_metrics_plot(clustering_metrics)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The plot above shows the count of clusters and their size for each threshold."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Classify each cluster/sample into a TME subtype "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def detect_type(clustering_metrics: pd.DataFrame,scores: pd.DataFrame) -> pd.Series:\n",
    "\n",
    "    clusters_number = clustering_metrics.loc['N'] \n",
    "\n",
    "    # samples_clustering is a pd.Series with patients as indexes and their clusters as values\n",
    "    samples_clustering = clustering_metrics.loc['perc']\n",
    "    cluster_means = pd.DataFrame({cluster: scores.loc[samples_clustering[samples_clustering == cluster].index].mean()\n",
    "                                  for cluster in range(1, clusters_number + 1)})\n",
    "\n",
    "    deltas = (cluster_means.loc[['Angiogenesis', 'Endothelium', 'CAF', 'Matrix', 'Matrix_remodeling']].mean() -\n",
    "              cluster_means.loc[['MHCII', 'Antitumor_cytokines', 'Coactivation_molecules',\n",
    "                          'B_cells', 'NK_cells', 'Checkpoint_inhibition',\n",
    "                          'Effector_cells', 'T_cells', 'Th1_signature',\n",
    "                          'T_cell_traffic', 'MHCI']].mean()).sort_values()\n",
    "\n",
    "    mapper = {}\n",
    "    mapper[deltas.index[-1]] = 'F'  # The cluster with the highest fibrose value is classified as Fibrotic\n",
    "    mapper[deltas.index[0]] = 'IE'  # Thecluster with the highest level of immune cells is classified as \n",
    "                                    # Immune enriched, non-fibrotic\n",
    "\n",
    "    # Remove detected clusters\n",
    "    cluster_means.pop(deltas.index[-1])\n",
    "    cluster_means.pop(deltas.index[0])\n",
    "\n",
    "    deltas = (cluster_means.loc[['Angiogenesis', 'Endothelium', 'CAF', 'Matrix', 'Matrix_remodeling',\n",
    "                          'Protumor_cytokines', 'Neutrophil_signature', 'Granulocyte_traffic',\n",
    "                          'Macrophages', 'Macrophage_DC_traffic', 'MDSC_traffic', 'MDSC',\n",
    "                          'Th2_signature', 'T_reg_traffic', 'Treg', 'M1_signatures', 'MHCII',\n",
    "                          'Antitumor_cytokines', 'Coactivation_molecules', 'B_cells', 'NK_cells',\n",
    "                          'Checkpoint_inhibition', 'Effector_cells', 'T_cells', 'Th1_signature',\n",
    "                          'T_cell_traffic', 'MHCI', 'EMT_signature']].mean() -\n",
    "              cluster_means.loc['Proliferation_rate']).sort_values()\n",
    "\n",
    "    mapper[deltas.index[0]] = 'D'  # The cluster with the highest proliferation value \n",
    "                                   # is classified as (Immune) Desert\n",
    "\n",
    "    mapper[deltas.index[-1]] = 'IE/F'  # Every sample not falling into the above-mentioned \n",
    "                                       # categories is classified as Immune enriched - fibrotic\n",
    "\n",
    "    return samples_clustering.map(mapper).rename('MFP')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GSM872328      IE\n",
      "GSM872335       F\n",
      "GSM872359      IE\n",
      "GSM872378      IE\n",
      "GSM872329    IE/F\n",
      "             ... \n",
      "GSM872341       F\n",
      "GSM872372       F\n",
      "GSM872373       F\n",
      "GSM872379      IE\n",
      "GSM872349      IE\n",
      "Name: MFP, Length: 65, dtype: object\n"
     ]
    }
   ],
   "source": [
    "# Select the best threshold using one or more metrics\n",
    "best_threshold = '0.31'\n",
    "\n",
    "# Detect cluster types\n",
    "samples_subtyping_by_tme = detect_type(clustering_metrics.loc[best_threshold], signature_scores_scaled)\n",
    "\n",
    "# Output the data into console\n",
    "print(samples_subtyping_by_tme)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you want to use your reference cohort change the REFERENCE_COHORT value into DENOVO_REFRENCE_COHORT in **TME classification** section."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Output the clusters\n",
    "samples_subtyping_by_tme.to_csv(DENOVO_REFRENCE_COHORT, sep='\\t', index=True)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "QA3.10-common",
   "language": "python",
   "name": "qa3.10-common"
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