{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "92092038",
   "metadata": {},
   "source": [
    "# Pre-processing"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "95cde18a",
   "metadata": {},
   "source": [
    "In this notebook, we will look at basic pre-processing of scRNA-seq data. We'll cover the topics of:  \n",
    "- QC \n",
    "- Data normalization "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "1c772fb6",
   "metadata": {},
   "source": [
    "# Library imports"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "0cc7ef24",
   "metadata": {},
   "source": [
    "Install all packages for the tutorial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "69e629d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy\n",
    "import os\n",
    "\n",
    "#single cell library\n",
    "import scanpy as sc\n",
    "\n",
    "#doublet detection\n",
    "import scrublet as scr\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2970f5d6",
   "metadata": {},
   "source": [
    "# Load data"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "cc3321af",
   "metadata": {},
   "source": [
    "10k Human peripheral blood mononuclear cells (PBMCs) of a healthy female donor aged 25-30 were obtained by 10x Genomics.\n",
    "\n",
    "The data is an mtx directory with an `mtx` file (*i.e.* count matrix), two `tsv` files with barcodes (*i.e.* cell indices) and features (*i.e.* gene symbols). `Scanpy` unpacks the files (if the files are in `gz` archive format) and creates an `anndata` object with the `read_10x_mtx` function.\n",
    "\n",
    "The data being used here was collected from: https://www.10xgenomics.com/datasets/10k-human-pbmcs-3-v3-1-chromium-x-with-intronic-reads-3-1-high"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "091ece7f",
   "metadata": {},
   "outputs": [],
   "source": [
    "input_path = '/Users/sara.jimenez/Documents/scWorkshop/data/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5b5e9b52",
   "metadata": {},
   "outputs": [],
   "source": [
    "adata = sc.read_10x_mtx(input_path + 'filtered_feature_bc_matrix/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1045dca3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AnnData object with n_obs × n_vars = 11984 × 36601\n",
       "    var: 'gene_ids', 'feature_types'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b652c992",
   "metadata": {},
   "outputs": [
    {
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "Empty DataFrame\n",
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       "\n",
       "[11984 rows x 0 columns]"
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     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata.obs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6fb3d253",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gene_ids</th>\n",
       "      <th>feature_types</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MIR1302-2HG</th>\n",
       "      <td>ENSG00000243485</td>\n",
       "      <td>Gene Expression</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FAM138A</th>\n",
       "      <td>ENSG00000237613</td>\n",
       "      <td>Gene Expression</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OR4F5</th>\n",
       "      <td>ENSG00000186092</td>\n",
       "      <td>Gene Expression</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AL627309.1</th>\n",
       "      <td>ENSG00000238009</td>\n",
       "      <td>Gene Expression</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AL627309.3</th>\n",
       "      <td>ENSG00000239945</td>\n",
       "      <td>Gene Expression</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>AC141272.1</th>\n",
       "      <td>ENSG00000277836</td>\n",
       "      <td>Gene Expression</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AC023491.2</th>\n",
       "      <td>ENSG00000278633</td>\n",
       "      <td>Gene Expression</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AC007325.1</th>\n",
       "      <td>ENSG00000276017</td>\n",
       "      <td>Gene Expression</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AC007325.4</th>\n",
       "      <td>ENSG00000278817</td>\n",
       "      <td>Gene Expression</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AC007325.2</th>\n",
       "      <td>ENSG00000277196</td>\n",
       "      <td>Gene Expression</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>36601 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    gene_ids    feature_types\n",
       "MIR1302-2HG  ENSG00000243485  Gene Expression\n",
       "FAM138A      ENSG00000237613  Gene Expression\n",
       "OR4F5        ENSG00000186092  Gene Expression\n",
       "AL627309.1   ENSG00000238009  Gene Expression\n",
       "AL627309.3   ENSG00000239945  Gene Expression\n",
       "...                      ...              ...\n",
       "AC141272.1   ENSG00000277836  Gene Expression\n",
       "AC023491.2   ENSG00000278633  Gene Expression\n",
       "AC007325.1   ENSG00000276017  Gene Expression\n",
       "AC007325.4   ENSG00000278817  Gene Expression\n",
       "AC007325.2   ENSG00000277196  Gene Expression\n",
       "\n",
       "[36601 rows x 2 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata.var"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "922fed76",
   "metadata": {},
   "source": [
    "# Basic pre-processing of data"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "d7237403",
   "metadata": {},
   "source": [
    "Data quality control can be split into cell QC and gene QC. Typical quality measures for assessing the quality of a cell include the number of molecule counts (UMIs), the number of expressed genes, and the fraction of counts that are mitochondrial. A high fraction of mitochondrial reads being picked up can indicate cell stress, as there is a low proportion of nuclear mRNA in the cell. It should be noted that high mitochondrial RNA fractions can also be biological signals indicating elevated respiration."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "e3dae771",
   "metadata": {},
   "source": [
    "We start with calculating the QC covariates: \n",
    "\n",
    "* total number of counts per cell\n",
    "* number of expressed genes per cell\n",
    "* fraction of mitochondrial reads per cell"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "dafbcf8d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n"
     ]
    }
   ],
   "source": [
    "sc.pp.calculate_qc_metrics(adata, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2b5529a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AnnData object with n_obs × n_vars = 11984 × 36601\n",
       "    obs: 'n_genes_by_counts', 'log1p_n_genes_by_counts', 'total_counts', 'log1p_total_counts', 'pct_counts_in_top_50_genes', 'pct_counts_in_top_100_genes', 'pct_counts_in_top_200_genes', 'pct_counts_in_top_500_genes'\n",
       "    var: 'gene_ids', 'feature_types', 'n_cells_by_counts', 'mean_counts', 'log1p_mean_counts', 'pct_dropout_by_counts', 'total_counts', 'log1p_total_counts'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2144434",
   "metadata": {},
   "source": [
    "## Visualize different metadata"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c695fb6",
   "metadata": {},
   "source": [
    "The following is the library size distribution. Library size is defined as the total mRNA count in each cell."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1848d0b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Frequency')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(adata.obs['log1p_total_counts'], 100);\n",
    "plt.xlabel('log1p library size', fontsize = 14)\n",
    "plt.ylabel('Frequency', fontsize = 14)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5829cfa5",
   "metadata": {},
   "source": [
    "The following is the distribution of number of genes expressed in each cell. It should be correlated with library size above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3f5dee32",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Frequency')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(adata.obs['log1p_n_genes_by_counts'], bins = 100);\n",
    "plt.xlabel('Log of num. genes per cell', fontsize = 14)\n",
    "plt.ylabel('Frequency', fontsize = 14)\n",
    "#plt.title('Histogram of number of genes expressed in each cell', fontsize = 14)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8e96d834",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Correlation = 0.951')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2400x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (8*3, 6*1))\n",
    "ax = fig.add_subplot(1, 3, 1)\n",
    "ax.hist(adata.obs['log1p_total_counts'], bins = 100);\n",
    "ax.set_xlabel('Log-library size', fontsize = 14)\n",
    "ax.set_ylabel('Frequency', fontsize = 14)\n",
    "ax.set_title('Histogram of log library size', fontsize = 14)\n",
    "\n",
    "ax = fig.add_subplot(1, 3, 2)\n",
    "ax.hist(adata.obs['log1p_n_genes_by_counts'], bins = 100);\n",
    "ax.set_xlabel('Log of num. genes per cell', fontsize = 14)\n",
    "ax.set_ylabel('Frequency', fontsize = 14)\n",
    "ax.set_title('Histogram of number of genes expressed in each cell', fontsize = 14)\n",
    "\n",
    "ax = fig.add_subplot(1, 3, 3)\n",
    "x = adata.obs['log1p_total_counts']\n",
    "y = adata.obs['log1p_n_genes_by_counts']\n",
    "ax.scatter(x, y, s = 5);\n",
    "ax.set_ylabel('Log of num. genes per cell', fontsize = 14)\n",
    "ax.set_xlabel('Log library size', fontsize = 14)\n",
    "corr_coef = np.corrcoef(x, y)[0, 1]\n",
    "ax.set_title('Correlation = ' + str(round(corr_coef, 3)), fontsize = 14)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ade8c05",
   "metadata": {},
   "source": [
    "## Visualize %MT"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9981a286",
   "metadata": {},
   "source": [
    "Compute the fraction of mitochondrial genes. Note: mitochondrial genes in human start with 'MT-'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3e1b99f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "mt_genes = adata.var_names[adata.var_names.str.startswith('MT-')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "af6d413a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['MT-ND1', 'MT-ND2', 'MT-CO1', 'MT-CO2', 'MT-ATP8', 'MT-ATP6', 'MT-CO3',\n",
       "       'MT-ND3', 'MT-ND4L', 'MT-ND4', 'MT-ND5', 'MT-ND6', 'MT-CYB'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mt_genes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "45ec29a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# compute the % of mito content by dividing the above result by library size\n",
    "row_sum_adata_mito = np.sum(adata[:, mt_genes].X.toarray(), axis = 1)\n",
    "adata.obs['mt_pct_content'] = row_sum_adata_mito/adata.obs['total_counts'] * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7dd07f87",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '% MT-content')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AAACSSH19vR599FH9/Oc/1zXXXON+/YsvvlBVVZW+/fbbGPYOkLIz/JuZ5G87AAAQOmJIALFGngAg2YU009lkMmny5MmaPHmyvvzyS61cuVJPPPGEHnvsMS1fvlwjR47UnDlzNGPGDGVmZoarz/DT0FvWxboLAAAAAXn00Ud10003qaWlRVJbvPnEE09IkhoaGlRYWKhly5Zp9uzZsewmktzo3CxZzemqtzd73a/NJMliTtfo3Kxodw0AgKREDAnACMgTACS7kGY6t3fEEUfopptuUm1trV577TXNmjVLn3zyiebNm6dBgwbpqquuUlVVVbguBwAAgARTXl6uefPmacSIEXruuedUUlLicfykk07SyJEj9eyzz8amg8B/paaYtLg4T1LbwFF7rueLi/OUmtLxKAAACDdiSABGQZ4AINmFrejcXkZGhvr06aMePXrI6XSqtbVVK1euVGFhoaZMmaKGhoZIXBYAAABx7L777tOQIUO0adMmXXDBBcrOzu7UZsSIEaqtrY1B7wBPRflWLZ1ZIIvZc2k8izldS2cWqCjfGtL5Wx1OVe7cq7U7dqty5161OrzNlQAAAMSQAIwkHHkCuQCAeBXS8trtff3111q9erUef/xxbd++XU6nU6effrquu+46TZ8+Xf/6179033336a9//at+8pOfaM2aNeG6NAAAABLAjh07dPnll6tv374+2xx11FHas2dPFHsF+FaUb9XEPIuq6hrVsL9Z2RltS+WFOnOhosam0vJa2ezN7tes5nQtLs4LuZgNAECiIYYEYDSh5AnkAgDiWchF5zfffFPLly/XX//6V3399dfq16+f5syZo5/85CcaNWqUu91pp52mv/zlL+rVq5eee+65UC8LAACABONwONSzZ88u2zQ0NCgtLS1KPQK6l5piUuGwgWE7X0WNTSVl1Z32gKu3N6ukrDoss6gBAEgkxJAAjCiYPIFcAEC8C6no7Fqaxul06pRTTtFPfvITXXbZZerXr5/P95x00klatWpVKJcFAABAAjrhhBP02muv+Tx++PBhbd68WSNGjIhir4DoaXU4VVpe22mQSZKcatsHrrS8VhPzLOwDBwDAfxFDAkgE5AIAEkFIezp/+umnuuqqq7R161Zt375dc+bM6bLgLEkzZszQpk2bQrksAAAAEtCMGTP09ttvq7S0tNOx1tZW/eIXv9Cnn36qK664Iga9AyKvqq7RYxm9jpySbPZmVdU1Rq9TAAAYHDEkgERALgAgEYQ009lmsykzMzOg9wwePFiDBw8O5bIAAABIQD/72c9UXl6uO+64Q6tWrVJ6erok6Uc/+pHeeust7dq1S5MmTdI111wT454CkdGw3/cgUzDtAABIBsSQABIBuQCARBDSTOe+ffuqqalJDofD63GHw6Gmpia1traGchkAAAAkgZ49e+qll17SLbfcor1796qmpkZOp1N/+9vf1NjYqAULFui5556TycRSYkhM2RnpYW0HAEAyIIYEkAjIBQAkgpCKzqWlpcrOztbevXu9Ht+7d69ycnL061//OpTLAAAAIEn06tVLv/71r/Xll1+qtrZWW7Zs0bvvvqu9e/dqyZIl6tWrV6y7CETM6NwsWc3p8jUkbpJkNadrdG5WNLsFAIDhEUMCiHfkAgASQUhF5+eff17jx4/XkUce6fX4kUceqQkTJmjt2rWhXAYAAABJ4LPPPlNTU5MkyWQy6cQTT9SZZ56p/Px8paamSpL279+vzz77LJbdBCImNcWkxcV5ktRpsMn1fHFxnlJTmKkFAIALMSSAREAuACARhFR0/vTTT3XiiSd22eaEE05QXV1dKJcBAABAEsjNzdWDDz7YZZuHHnpIubm5UeoREB6tDqcqd+7V2h27Vblzr1odTp9ti/KtWjqzQBaz57J5FnO6ls4sUFG+NdLdBQAgrhBDAkgUrlwgJ5NcAEB86hHKmw8dOqSUlK7r1iaTSc3NbG4PAACArjmdTjmdvotxrjZAPKmosam0vFY2+3c5kdWcrsXFeT4HjYryrZqYZ1FVXaMa9jcrO6NtGT1mNQAA0BkxJIDE4/nfLP4bBiBehDTT+dhjj9XGjRu7bLNx40buJAQAAEBY/Pvf/1ZGRkasuwH4paLGppKyao+CsyTV25tVUlatihqbz/empphUOGygLhp1lAqHDaTgDABACIghAcQDV/5Q39Ti8fqeppZu8wcAMIKQZjpffPHFuuOOO3Tbbbdp8eLF7n1SJKm1tVW33367duzYoUWLFoXcUQAAACSeO+64w+P5K6+84rVda2urPv/8cz399NMaM2ZMFHoGhKbV4VRpea28zUlwqm1fttLyWk3Ms1BQBgAgQMSQABIN+QOARGByhrA2w9dff63TTz9dH330kYYNG6Zzzz1XRx11lHbv3q1NmzZp586dGj58uN58803169cvnP2OuaamJpnNZtntdmVmZsa6O14NvWVd0O/ddfeUMPYEAACEWzzEIv5ov1WLyWTqdtmwQYMGac2aNTr99NMj3bWISJTfDd2r3LlXly5/s9t2T80eo8JhA6PQIwAAEicWSaYYMlF+MwBdI38AYFSBxCIhzXTu16+fNm/erJKSEq1Zs0affPKJ+1hKSoouueQSPfroowlXcAYAAEB4bNq0SVLbHlXjxo3TlVdeqVmzZnVql5qaqqysLJ144okeg4yAUTXsb+6+UQDtAADAd4ghASQa8gcAiSCkorMkHXnkkfrb3/6mPXv26K233pLdblf//v112mmnKTs7Oxx9BAAAQIL6wQ9+4P7z4sWLde6552rs2LEx7BHCrdXhVFVdoxr2Nys7I12jc7OSYjm47Iz0sLYDAADfIYYE4Eu85h/kDwASQchFZ5ecnBxNmcKSzAAAAAjO4sWLY90FhFlFjU2l5bWy2b+7G99qTtfi4jwV5Vtj2LPIG52bJas5XfX2Zq/7spkkWcxtg2AAACB4xJAAXOI5/yB/AJAIwlZ0BgAAAMKhvr5e27dv1759+9Ta2uq1zRVXXBHlXiFQFTU2lZRVdxowqbc3q6SsWktnFhh+4CcUqSkmLS7OU0lZtUySx/fgmmexuDgvLmZdAAAQD4ghgeQW7/kH+QOARBBy0bm2tlYPP/ywtm3b5jOoM5lM2rlzZ6iXAgAAQAJrbm7W7Nmz9fTTT8vhcHht43Q6ZTKZGDA0uFaHU6XltV7v0HeqbdCktLxWE/MsCT1oUpRv1dKZBZ1mW1jiZLYFAADxgBgSQKLkH+QPAOJdSEXnV199VUVFRWppaVGPHj2Uk5OjHj06n9Lp9PafewAAAOA7t9xyi1atWqXjjz9el156qY4++mivsSWMr6qu0WOQpCOnJJu9WVV1jSocNjCs1zbaHm5F+VZNzLMYqk8AACQSYkgA0co/opFrkD8AiGchRWC33HKLDh8+rMcff1yzZs1SampquPoFAACAJPPMM88oLy9P27dvV1paWqy7gxA07Pc94BNMO38ZdQ+31BRT2IvrAACgDTEkgGjkH9HMNcgfAMSrkIrO77zzjqZPn66rr746XP2BQQy9ZV2n13bdPSUGPQEAAMli3759uuyyyxgsTADZGelhbeePeN/DDQAABIcYEkCk8w9yDQDwT0oob+7bt6+ys7PD1RcAAAAksRNOOEF79uyJdTcQBqNzs2Q1p8vXAnAmtc0KGJ2bFZbrdbeHm9S2h1urg21/AABINMSQACKZf5BrAID/Qio6n3/++XrttdfC1RcAAAAksV/+8pdau3atPvnkk1h3BSFKTTFpcXGeJHUa+HE9X1ycF7Z9yQLZww0AACQWYkgAkcw/yDUAwH8hLa9933336eyzz9bPf/5z3X333erTp0+4+gUAAIAkc/TRR2vy5MkaPXq05s+fr4KCAmVmZnptO3bs2Cj3DoEqyrdq6cyCTvueWSKw71ms9pAGAACxRwwJQIpc/kGuAQD+C6noPH36dPXr10+PPPKIVqxYoeOPP95rUGcymbRhw4ZQLgUAAIAEd84558hkMsnpdOr222+XyeT7LvTW1tYo9gzBKsq3amKeRVV1jWrY36zsjLYl7cI1w9klFntIAwAAYyCGBOASifyDXAMA/BdS0fmVV15x//nrr79WdXW113ZdBXsAAACAJN12223EjQkoNcWkwmEDI3oN1x5u9fZmr3utmdQ2wyFce0gDAADjIIYE0F648w9yDQDwX0hFZ4fDEa5+AAAAIMndfvvtse4CDKzV4fQ5Y8G1h1tJWbVMksdgUCT2kAYAAMZBDAkgEtrnH9NPH6z7X/6YXAMAuhFS0RkAAAAAIq2ixtZpbzZrh73ZormHNAAAAIDE5S3/6N+npyRp3zeH3K+RawCAp7AVnb/++mt99NFHOnDggL7//e+H67QAAABIMgcOHNCzzz6rHTt2qKmpSZmZmRo1apSmTp2qvn37xrp7iLKKGptKyqo7LWVXb29WSVm1ls4s8Cg8R2MPaQAAYDzEkADCwVf+Yf9vsfmGCcdr6BF9yDUAwIuQi867du3S9ddfrxdeeEEOh0Mmk0mHDx+WJL3++uuaPXu2Hn30UZ1zzjmhXgoAAAAJ7u9//7vmzJmjffv2yen8Ls03mUzq37+/li9frosvvjiGPUQ0tTqcKi2v9bp3mlNty9mVltdqYp7FY6ntSO8hDQAAjIUYEkA4+JN/PL3tM21ZMI5iMwB4kRLKmz/77DONGTNGL7zwgi666CIVFhZ6BHZnnHGGvvzySz311FMhdxQAAACJ7Y033tD06dN14MABXXvttVq9erU2bdqkp556SrNnz9Y333yj6dOnq7KyMtZdRZRU1TV6LGnXkVOSzd6sqrrG6HUKAAAYCjEkgHAh/wCA0IQ003nx4sX66quv9Oqrr+rMM89UaWmpRwDXo0cPff/739frr78eckcBAACQ2H7zm98oLS1Nr7/+uk4++WSPYz/+8Y/105/+VGeeeaZ+85vfqLy8PEa9RDQ17Pc94BNMOwAAkHiIIQGEC/kHAIQmpJnOL730kn74wx/qzDPP9NnmmGOO0e7du0O5DAAAAJJAZWWlfvzjH3caLHQZOXKkfvSjH+mNN96Ics8QK9kZ6WFtBwAAEg8xJIBwIf8AgNCENNO5sbFRQ4cO7bKN0+lUS0tLKJcBAABAEvjmm2+Uk5PTZZucnBx98803UeoRwqnV4VRVXaMa9jcrOyNdo3Ozut0HbXRulqzmdNXbm73uq2aSZDG3nQsAACQnYkgAgegqLyH/AIDQhFR0zsnJ0ccff9xlm/fee09DhgwJ5TIAAABIAkOHDtX69ev1m9/8xmebDRs2dHvTI4ynosam0vJaj/3RrOZ0LS7OU1G+1ef7UlNMWlycp5Kyapkkj4EfV7l6cXFet8XrYARTJAcAANFHDAnAX93lJdHMP8g3ACSikJbXnjhxop5//nm9++67Xo+/9tpr2rhxo84///xQLgMAAIAk8KMf/Ujbt2/XrFmz9J///MfjmM1m05VXXqnt27frxz/+cYx6iGBU1NhUUlbtMbAjSfX2ZpWUVauixtbl+4vyrVo6s0AWs+cSdhZzupbOLOiyaB1Kn8++Z6MuXf6mrn96hy5d/qbOvmdjt30FAADRRwwJwB/+5iXRyD/INwAkKpPT6fS2UoRfdu3apVGjRkmSfvnLX+qDDz7Q6tWr9fzzz+uNN97Q73//e/Xt21fvvPOOrNbwDwbFUlNTk8xms+x2uzIzM2PdHa+G3rIurOfbdfeUsJ4PAAAELx5ikUB98803Ovfcc7Vt2zb16tVLxx57rHJycrRnzx598sknOnjwoEaPHq1Nmzapd+/ese5uUBLxd+tKq8Ops+/Z2Glgx8W1PN2WBeO6vas/WjMBXINRHZMk15UiUehmlgMAIFoSMRZJ9BgyEX8zINqCyUsiFaNHI98gvwAQToHEIiEtrz106FC99NJLmj59uhYtWiSTySSn06kLLrhATqdTQ4YM0d/+9reEKzgDAAAg/Pr06aPNmzfrnnvu0Z///GfV1taqtrZWkvS9731Ps2bN0s0336y0tLQY9xT+qqpr9DmwI7UtV2ezN6uqrlGFwwZ2ea7UFFO3bULV6nCqtLzW6/5tTrUNBJWW12piniVsgzbBLj0OAADaEEMC6E4weUkk8o9o5BvkFwBiKaSisySdccYZ+vjjj1VeXq6tW7eqsbFRmZmZOuOMM3TRRRepV69e4egnAAAAkkBaWppuu+023Xbbbdq/f7+ampqUmZmpjIyMWHcNQWjY73tgJ5h2kRbOIrk/fM1ycC3xF6nlwwEASDTEkAC6YpS8JNL5BvkFgFgLuegsST169NAPf/hD/fCHPwzH6QAAAABlZGQwUBjnsjPSu28UQLtIi+ZgVCxmVQMAkAyIIQF0ZJS8JJL5BvkFACNIiXUHAAAAAEl6/fXXdeONN6q+vt7rcZvNphtvvFFvvvlmlHuGYI3OzZLVnC5fQxomtS31Njo3K5rd8imag1GBzHIAAAC+EUMC6I5R8pJI5hvkFwCMIKSZznfccYdf7UwmkxYtWhTKpQAAAJDgfv/73+vdd9/V73//e6/HrVarnn/+ee3evVt/+ctfotw7BCM1xaTFxXkqKauWSfK469414LO4OM8wd9q7BqPq7c1eZwhIUlbfnjr1mAEhX8soS/wBABDviCEBdMcoeYk/+Ub/3j3lcDrV6nAG1B/yCwBGEFLR+fbbb+/yuMlkktPppOgMAACAbm3btk3jx4/vss3YsWO1fv36KPUI4VCUb9XSmQUqLa/1uPPeYk7X4uI8Q+0p1tVglEvjgUP6wX2bQu67UZb4AwAg3hFDAvCHEfISf/KNfd8e0ozHt8oaYL/ILwAYQUhF502bNnl93W63q7q6Wg899JAmTJiguXPnhnIZAAAAJIGGhgYdddRRXbaxWCxqaGiIUo8QLkX5Vk3Ms6iqrlEN+5uVndG2dJ1RZji352swqr16e7NKyqq1dGZB0INT3c1yMKltAMwoS48DAGBUxJAA/GWEvMSffEMKPOcgvwBgBCEVnX/wgx/4PHbhhRdqxowZKigo0LRp00K5DAAAAJJA//799dlnn3XZ5v/+7//Ur1+/KPUoObU6nBEZhElNMalw2MAw9DDyivKtGndijsYs2aDGAwc7HXeqbdCmtLxWE/MsQX0/RlniDwCAeEcMCSSnYPMWI+QlruL3mzv3au7qau379lCnNoHmHOQXAIwgpKJzd4477jj98Ic/1N13360f//jHkbwUAAAA4tyYMWO0Zs0aff755xo8eHCn45999pmeffZZjRs3Lga9Sw4VNbZOd9wHuqxbotj+f195LTi7OCXZ7M2qqmsMetDKCEv8AQAQ74ghgeSTCHlLaopJKSkmrwVnl0BzDvILALEW0aKzJGVnZ+vDDz+M9GUAAAAQ52688UaVl5frrLPO0l133aWJEyfKarXKZrPpn//8p2699VZ9++23uummm2Ld1YRUUWNTSVl1p6XYwrGUtFEEMhuiYb/vpe6CaeeLEZb4AwAgnhFDAsklUfKWVodTr3/yhV9tA8k5yC8AxFJEi84tLS2qqKhQ//79I3kZAAAAJICxY8fq97//vW666SZdddVVkiSTySSns204ISUlRQ8++KDGjh0by24mpFaHU6XltV73/grHUtJGEOhsiOyMdL/O62+7rhhhiT8AAOIVMSSQPBIlb/GWm3Ql0JyD/AJArIRUdP7zn//s9fXDhw9r9+7devrpp/XBBx/o5z//eSiXAQAAQJK4/vrrde6552rZsmXatm2b7Ha7+vfvr9GjR+u6665Tfn5+rLuYkKrqGrsc8AjHUtKxFMxsiNG5WbKa01Vvb/Y6qGVS2zJ1o3OzItVtAADgJ2JIIDkkQt7iKzfxhpwDQLwJqeh85ZVXymTqfMeQ605Ck8mkSy+9VHfffbff59y8ebPuu+8+bd++XTabTWvWrNHUqVM9rrly5UqP90yePFkVFRXu542NjfrZz36m8vJypaSkaNq0aXrwwQfVr18/d5t3331Xc+fO1bZt23TkkUfqZz/7mW6++Wa/+wkAAIDIGDlypB599NFYdyOpRGsp6VgIdjZEaopJi4vzVFJWLdN/27q4Wi0uzjP0DAoAAJIJMSSQ+OI9b+kqN+mInANAPAqp6Pzkk096fT0lJUUDBgzQqaeeKqs1sP0TDhw4oJNPPllXX321Lr74Yq9tioqKPK6dlpbmcXzGjBmy2Wxav369Dh06pKuuukpz5szR6tWrJUlNTU2aNGmSJkyYoGXLlum9997T1Vdfrf79+2vOnDkB9RcAAACId9FcSjraQpkNUZRv1dKZBZ2WvrN0sSw3AAAAgMiI97ylu9ykPXIOAPEopKLzrFmzwtUPt/POO0/nnXdel23S0tJksVi8Hnv//fdVUVGhbdu26bTTTpMk/eEPf9D555+v3/72txo0aJBWrVqlgwcP6k9/+pN69eqlk046STt27NDvf/97is4AAAAGUVpaqjvvvFOHDx+OdVcSXqyWkm51OFVV16iG/c3Kzmg7f7jv4g91NkRRvlUT8ywR7ycAAAgPYkggcUUybzFSbjLv3GN1w8TjyTkAxJ2Qis6x8sorryg7O1sDBgzQuHHjdNddd2ngwLZZCZWVlerfv7+74CxJEyZMUEpKirZu3aof/vCHqqys1NixY9WrVy93m8mTJ+uee+7RV199pQEDBnS6ZktLi1paWtzPm5qaIvgJAQAAIH23bQsiq/1S0r6Ee1m3ihpbpxnE1gjczR+O2RCpKSbD7gkHAAA6I4YEElNqikmLpgzXT1e/3elYKMtRGy03OevYIyg4A4hLIRWdN2/eHPR7x44dG9T7ioqKdPHFFys3N1c7d+7U//zP/+i8885TZWWlUlNTVV9fr+zsbI/39OjRQ1lZWaqvr5ck1dfXKzc316NNTk6O+5i3ovOSJUtUWloaVJ8BAAAAoyvKt2rO2Fwtf61OjnbjtCkmafb3c8M62FJRY1NJWXWn2Qn19maVlFVr6cyCsF0vVrO4AQAAAIRXRY1Nd6573+uxYJejJjcBgPAJqeh8zjnnyGQK7o6b1tbWoN43ffp0959HjBihkSNHatiwYXrllVc0fvz4oM7pj4ULF+rGG290P29qatLgwYMjdj0AAAAgmipqbHpsc12nwQ+nU3psc51OGTIgLIMtrQ6nSstrvQ6yONU20FJaXquJeZaw3N3ffha36b/XcAllNgQAAACA6PFVHHZZNCXwgjO5CQCEV0hF59tuu01bt27VSy+9pOOOO05nnXWWcnJytGfPHr3xxhv66KOPNHnyZI0ZMyZc/e3ke9/7no444gh98sknGj9+vCwWixoaGjzaHD58WI2Nje59oC0Wi/bs2ePRxvXc117RaWlpSktLi8AnAAAASF5XX321pk6dqgsvvLDTMafTydKIURLNwZaqukaPZeu8Xc9mb1ZVXWPYlrQuyrdq6cyCTkvmBTsbAgAAxBYxJJBcuspXpLZ85c51tZqcH1i+Qm4CAOEVUtF5/Pjxuvvuu/XYY4/pmmuu8Zj17HQ6tXz5cl1//fX61a9+pbPPPjvkznrz73//W3v37pXV2vYf48LCQu3bt0/bt2/XqaeeKknauHGjHA6HzjjjDHebX/3qVzp06JB69uwpSVq/fr1OOOEEr0trAwAAIDJWrFihoUOHeh0wvOGGG3TVVVfFoFfJJ5qDLQ37fV8nmHb+Ksq3amKeRVV1jWrY36zsjLZl65hFAABA/CGGBJJLpPIVchMACK+Qis6LFi3SlClTdO2113Y6ZjKZNGfOHFVUVGjRokXatGmTX+f8+uuv9cknn7if19XVaceOHcrKylJWVpZKS0s1bdo0WSwW7dy5UzfffLOOPfZYTZ48WZI0fPhwFRUVafbs2Vq2bJkOHTqkefPmafr06Ro0aJAk6bLLLlNpaamuueYaLViwQDU1NXrwwQd1//33h/J1AAAAIIzMZrPMZnOsu5EUojnYkp2RHtZ2gUhNMYVthkKgWh1OBpUAAIgCYkgg8UQqX0m03IScA0CshVR03r59u66//vou2wwfPlwPPfSQ3+d86623dO6557qfu/ZRnjVrlpYuXap3331XK1eu1L59+zRo0CBNmjRJd955p8fS16tWrdK8efM0fvx4paSkaNq0aR59MJvN+uc//6m5c+fq1FNP1RFHHKHbbrtNc+bM8bufAAAAQKKI5mDL6NwsWc3pqrc3e10ez6S2peVG52aFfC2jqKixdVo+z8ryeQAAAIBfIpWvJFJuQs4BwAhCKjr36tVLb7/9dpdt3n77bfXq1cvvc55zzjld7rvy0ksvdXuOrKwsrV69uss2I0eO1GuvveZ3vwAAAIBEFc3BltQUkxYX56mkrFomyeN6rnvwFxfnJcwd+RU1NpWUVXf6XuvtzSopq9bSmQUMAgEAAABdiFS+kii5CTkHAKMIqeg8adIkPfPMM7r77rt14403ehSXDx48qN/97nd66aWX9OMf/zjkjgIAACAxPfvss9q1a5ff7U0mk5544onIdSiOBbucWrQHW4ryrVo6s6DTnfiWBLsTv9XhVGl5rdeBMafavtvS8lpNzLMYfiALAACjIYYEEpe3vCZS+Uq85ybkHACMxOTsalpxN/79739rzJgxstlsys7O1mmnnabs7Gw1NDTorbfeUkNDgwYNGqTKykodffTR4ex3zDU1NclsNstutyszMzPW3fFq6C3rwnq+XXdPCev5AABA8OIhFvFHSkpKwO8xmUxqbW2NQG8iL5K/WziWU4v2kmyR3HPMCPuZVe7cq0uXv9ltu6dmj4nZXtMAgORCDBl/MWSi/GaAv7rKSSRFLF+JZv4QzmuRcwCItEBikZBmOh999NF66623dMstt+iZZ57RunXfFTnT09N1+eWX6+6775bFYgnlMgAAAEhgV155pWbNmhXrbsS1cC2nVpRv1cQ8S9QGW1JTTBEZ+DDKfmYN+5u7bxRAOwAA8B1iSCDx+JPXbFkwLiL5SqRyk47CnauQcwAwkpCKzpJksVi0YsUKLV++XB9++KHsdrvMZrOOP/74gPZyBgAAQHIaOnSofvCDH8S6G3Er3MupRWuwJVD+zgYw0n5m2RnpYW0HAAC+QwwJJJZA8hoj5ivt+cpdIpGrkHMAMJKQi84uPXv2VH5+frhOBwAAAMAPVXWNHnfJd+SUZLM3q6qu0fCDM774OxvAaPuZjc7NktWcrnp7s9c+mdS2V9zo3KyI9wUAAAAwskTJa3zlLoum5OnOdeHPVcg5ABhJ4BugeFFfX69HH31UP//5z3Xttde6X//iiy9UVVWlb7/9NhyXAQAAANBBoi+n5poN0HEAyjUboKLG5n4tkIGqVodTlTv3au2O3arcuVetDm9DNKFJTTG5957rOGzker64OC/qe00DAAAARpMIeU1XuctPV3d+vb32uUpHXeUu5BwAjCTkmc6PPvqobrrpJrW0tEiSTCaTHn/8cUlSQ0ODCgsLtWzZMs2ePTvUSwEAAADoIJGXUwt05rK/A1Av19brxmd2RGXP56J8q5bOLOg028ESgz2mAQAAAKOK97ymu9zFXx1zGn9WfSLnAGAUIRWdy8vLNW/ePJ122mm67bbb9OKLL2rZsmXu4yeddJJGjhypZ599lqIzAAAAOnnyySc1atSoWHcjriXycmqBLrHn7wDUE6/v6vRaJPd8Lsq3amKexa89qQEAQPeIIYHEE+95TXe5i7/a5zSB7AFNzgHACEIqOt93330aMmSINm3apL59+2r79u2d2owYMUKvvfZaKJcBAABAgpo1a1asuxD3XMuplZRVyyTPu+jjfTm1QJfY82egymSSvK2kHek9n1NTTIbeew4AgHhCDAkknnjPa0Jd9rtjUT3QVZ8kcg4AsRdS0XnHjh26/PLL1bdvX59tjjrqKO3ZsyeUywAAACBBjRs3LuD3mEwmbdiwIQK9iV+JupxaoEvsdTdQ5ZTk7GJtu44zpwEAgDERQwKJKZ7zmkCW/fanqB7oqk8AYAQhFZ0dDod69uzZZZuGhgalpaWFchkAAAAkqFdeeUUmk0nOriqBHZhMxryzPdYScTm1YJbY62qg6rx8i/7kZWntjkKdpQAAACKLGBJIXPGa1/ibuyyaMlx3rnu/26J6oKs+AYARhFR0PuGEE7pcOvvw4cPavHmzRowYEcplAAAAkMB69Oih888/X1dffbVGjx4d6+7EtURbTi3YJfZ8DVRV1TX6VXQOZJYCAACIDWJIIHHFY17jb+5SlG/V5Hxrt0X1QFd9AgAjSAnlzTNmzNDbb7+t0tLSTsdaW1v1i1/8Qp9++qmuuOKKUC4DAACABPXCCy/owgsv1IsvvqipU6dq0qRJevrpp9WjRw/l5OT4fETa3XffLZPJpPnz57tfa25u1ty5czVw4ED169dP06ZNYxuZKHDNXLaYPQdTLOZ0LZ1Z4HOJPddA1UWjjlLhsIFKTTG5Zx/4miNhkmTtMHMayafV4VTlzr1au2O3KnfuVau3TcABADFFDAnAiPzNXbzlKh2RuxgTuQLQNZMzkHVoOjh06JAmTZqkzZs3a9iwYUpPT9e//vUvTZs2TW+99ZZ27dqlSZMm6cUXX0y4JWyamppkNptlt9uVmZkZ6+54NfSWdWE93667p4T1fAAAIHjxEIsEYu/evfrf//1fPfnkk3rvvffUq1cvFRcX6+qrr1ZRUVFUY8lt27bpRz/6kTIzM3XuuefqgQcekCSVlJRo3bp1WrFihcxms+bNm6eUlBS9/vrrfp870X63aGp1OMOyxF5FjU0lZdWSvM8+6KqQjcRXUWPrtDS7NQ72EAQAfyVaLJIMMWSi/WZAMiB3SUzkCkhWgcQiIRWdJengwYMqLS3VsmXL9NVXX7lfz8zMVElJiUpLS9WrV69QLmFI8RDwUXQGACBxxUMsEqxt27bpT3/6k/7yl79o3759OuqoozRr1izNnz9fRxxxRESv/fXXX6ugoECPPvqo7rrrLo0aNUoPPPCA7Ha7jjzySK1evVqXXHKJJOmDDz7Q8OHDVVlZqTFjxvh1/kT+3eJJog4WhGtwK1m5BvU6JsgM6gFIJIkciyRqDJnIvxmA7hk1d0m23INcAcksqkVnF6fTqQ8//FCNjY3KzMzU8OHDlZqaGo5TG1I8BHwUnQEASFzxEIuEqqWlRX//+9+1YMEC/ec//9GaNWt04YUXRvSas2bNUlZWlu6//36dc8457gHDjRs3avz48frqq6/Uv39/d/tjjjlG8+fP1w033ODzM7S0tLifNzU1afDgwQn9u8WLRBskMepgVLxodTh19j0bPb6/9kxqWxZxy4Jxcf33BACIISMjnDEk8SOAjoyWuyRb7kGugGQXSPzYI5QLfe9739N5552nRx55RCaTSSeeeGIopwMAAAAkSfX19Vq5cqWefPJJ7d69W+np6crKiuxeVU8//bSqq6u1bds2r/3p1auXx2ChJOXk5Ki+vt7nOZcsWaLS0tJwdxVh4NpHLRH4uuu+3t6skrJq7rr3Q1Vdo89BJKltOUObvVlVdY0J8/cGABJRIsSQxI8AOjJS7pKMuQe5AuC/lFDe/OWXX3KHHQAAAMLi8OHDWrNmjYqLizVkyBAtXLhQ/fr10yOPPCKbzaazzz47Ytf+/PPPdf3112vVqlVKT08P23kXLlwou93ufnz++edhOzcgtd11X1pe22nQR/pu37fS8lq1OsKywFXCatjvexApmHYAgOhJtBiS+BGAUSVr7kGuAPgvpJnOI0eO1EcffRSuvgAAACAJ1dTU6E9/+pNWrVqlL774QgMHDtRPf/pTXX311Ro5cmRU+rB9+3Y1NDSooKDA/Vpra6s2b96shx9+WC+99JIOHjyoffv2ecxU2bNnjywWi8/zpqWlKS0tLZJdR4IJdOk87roPj+wM/woF/rYDAEReosaQxI8AjKJjbuJwOJMy9yBXAPwXUtF5wYIFmjZtmjZt2qRzzz03XH0CAABAkhg9erS2b9+ulJQUTZgwQVdffbWmTp2qnj17RrUf48eP13vvvefx2lVXXaUTTzxRCxYs0ODBg9WzZ09t2LBB06ZNkyR9+OGH+uyzz1RYWBjVviJxBbM3Gnfdh8fo3CxZzemqtzd7nbnh2qdtdG5kl2gFAPiHGBIAIstbbtK/t3//jU203INcAfBfSEXnr776SpMmTdKkSZM0depUnX766crJyZHJ1PlO/CuuuCKUSwEAACABvfXWW+rZs6cmT56so48+Wps2bdKmTZu6fI/JZNIjjzwS1n5kZGQoPz/f47W+fftq4MCB7tevueYa3XjjjcrKylJmZqZ+9rOfqbCwUGPGjAlrX5Ccgt0bjbvuwyM1xaTFxXkqKauWSfL4HVzZ7eLivC5nnQMAoocYEgAix1dusu/bQ369P9FyD3IFwH8hFZ2vvPJKmUwmOZ1O/f3vf9ff//53SfIoOjudTplMJorOAAAA8OrQoUN6/vnn/W4fiQFDf9x///1KSUnRtGnT1NLSosmTJ+vRRx+Nej+QeLrbG82ktr3RJuZZOg1kcNd9+BTlW7V0ZkGnGR2WbmabAwBigxgSAMKvq9ykO4mce5ArAP4JuOjc1NSk9PR09erVS08++WQk+gQAAIAk0d2MlFh65ZVXPJ6np6frkUceiclgJRJbKPsyc9d9eBXlWzUxzxLQvtoAgOgjhgSAyOguN/ElGXIPcgWgewEXnQcMGKDbb79dixYt0qxZsyRJW7du1datW/Xzn/887B0EAABA4vrBD34Q6y4AMRfqvszcdR9eqSmmTsV9AICxEEMCQGT4m5v0793TY7ntZMk9yBWArgVcdHY6nXI6PRdXqKio0B133EHRGQAAAAACFI59mbnrHgAAAECo/M1NHplRoBSTidwDgIeQ9nQGAAAAAIQmXPsyc9c9AAAAgFD4m5uM+d5AiswAOkmJdQcAAAAAING0Opyq3LlXa3fsVuXOvWp1eBuyaePal1n6bi80l2TYGw0AAABAbHTMWySRmwAIGjOdAQAAACCMKmpsnfZXtnazxxn7MgMAAACIpq7yFnITAMGg6AwAAAAAYVJRY1NJWXWnpehs9mZdV1atRy87ReePHOT1vezLDAAAACCSWh1OVdU16uXaej3x+q5Ox+vtzSopq9bSmQXasmAcuQmAgARVdC4rK9Obb77pfv7JJ59Iks4//3yv7U0mk9atWxfMpQAAAAAgLrQ6nCotr/W695nLvKfe1sMy6fyR3mcHsC8zAAAAgEjwNrO5I1cuU1peq4l5FnITAAEJquj8ySefuAvN7VVUVHhtbzJx9wsAAACAxOOaKdCwv1lf7m/pcgBHkhxO6aerq7UspYBl6QAAAABERPs8JTsjXV8dOKi5qzuvyOSLzd6shzd+rOsnHB/RfgJILAEXnevq6iLRDwAAAKCT9evX64MPPpDJZNLw4cM1fvz4WHcJcPNnpoAvC//xnibmWVieDgCACCCGBJDMvOUpKSb5XXB2uf/lj3WCJYObZQH4LeCi8zHHHBOJfgAAAABun3zyiS6++GLV1NS4XzOZTBo5cqTWrFmjoUOHxq5zgHzv3eyvr745pDc/3auzjj0irP0CACCZEUMCSHa+8hRHkImLa5ltbpYF4I+UWHcAAAAA6GjOnDlKTU3Vli1bdODAATU2NurPf/6zdu7cqeuuuy7W3UOS82fvZn9U7twblv4AAIA2xJAAklm48pT2bPZmVdU1hvGMABJZUHs6AwAAAOFQWVmpwsLCTq9v2bJF69at05lnnilJ6t27t2bMmKGtW7fq8ccfj3Y3AQ9VdY1BLandWTiHgwAASB7EkADQWfjyFE8N+8N/TgCJiZnOAAAAiJnvf//7+tnPfqavv/7a4/WBAwfqrbfe8njN4XBox44dGjhwYDS7CHQSrkGXwu+xtDYAAMEghgSAziJVHM7OSI/IeQEkHmY6AwAAIGZeeOEFlZSUaO3atVq6dKmmTJkiSbr++uv1q1/9Sq+++qpGjRqllpYW/fOf/9T777+v++67L8a9RrILx6BL/z49NWYYg98AAASDGBIAOgt3cdgkyWJO1+jcrLCeF0DiYqYzAAAAYmbSpEmqqanRJZdcoqlTp2r69On64osvdMstt6isrEx2u12PPfaYVq5cqf79++vpp5/WTTfdFOtuI8mNzs2S1Zwuk4/jJkkD+vTs8hx3XzxCqSm+zuC/VodTlTv3au2O3arcuVetDpbsBgAkPmJIAOisuzxFkjqmIK68peN7XM8XF+f5zFvIRQB0ZHI6nfyXIAhNTU0ym82y2+3KzMyMdXe8GnrLurCeb9fdU8J6PgAAELx4iEUCtW3bNl177bXavXu3fvvb3+rKK6+MdZfCLhF/t2RVUWNTSVm1JM+dmV3DMUtnFkiSbn/uX6pvanEft2Sm6fYLT1JRvjUsfSgtr/XYt81qTtfi4rywnB8AkHgSMRZJ9BgyEX8zAJHTXZ7yyGWnaEDfNDXsb1Z2Rtss5vW19QHnFeQiQPIIJBah6BykeAj4KDoDAJC44iEWCUZra6vuuece3XXXXTrzzDO1fPly5ebmxrpbYZOov1uy8megpdXhVFVdo8egTjhmOLsGkzomc+2L3gz2AAA6StRYJJFjyET9zQBETjAF4UDyFnIRILlQdI6CeAj4KDoDAJC44iEWCcVHH32kOXPmaNu2bbr99tt10003KSUl/neGSfTfLRlFqqjc3TXPvmejxyBSe66917YsGBfxvgAA4kuixyKJGEMm+m8GIDIilaeQiwDJJ5BYpEeU+gQAAAB4tXv3bq1atUqfffaZhgwZohkzZuj444/XK6+8oscee0wLFizQ008/rccff1ynnHJKrLsLeEhNMalw2MCoXrOqrtHnII/Utoyezd6sqrrGsPUtFsV1AAC6QgwJAL5FKk95c+fesOci5BpA4qDoDAAAgJh54403VFRUpG+++UZHHHGEvvzyS91111365z//qTFjxmjOnDkqLi7W3LlzdcYZZ2j+/Pm64447lJ6eHuuuAzGzvrber3YN+30PBgW6fB77tQEAjIQYEgCir6LGplv+/p5fbbvKRaTv8pGXa+u1ZsduNR445D5GrgHEr/heXwYAAABx7Re/+IWOPPJIffrpp6qvr9enn36qI488Ur/4xS/cbaxWq/7xj3/o6aef1qpVqzRixIgY9hiIrYoam/70+i6/2mZneB9Yr6ix6ex7NurS5W/q+qd36NLlb+rsezaqosbmtW1JWXWn2Qz19maVlFV7fQ8AAJFGDAkA0eXKC/Z9e6j7xvKdi7jO5cpHnnh9l0fBWSLXAOIZRWcAAADETE1NjS6++GINGTJEkjRkyBBdfPHFevfddzu1vfjii1VbW6tx48ZFu5uAIbQ6nCotr/WrrdXcNnu5o0CKyK7rOb2c3/VaaXmtWh3eWgAAEDnEkAAQPV3lBR2Z5DsXkXznI+2RawDxi6IzAAAAYuaoo45SVVWVx2tVVVU66qijvLY3m8364x//GI2uwUBaHU5V7tyrtTt2q3Ln3qQdeOhuL+f2FhfndVouO9AiciB7RwMAEE3EkADiVTzmNoHkIZL3XEQKrHhNrgHEJ/Z0BgAAQMzceOON+slPfqK8vDyNGjVK77zzjt5//30tW7Ys1l2DQbCf8He62xfN5Zqzhnr9bgIpIhcOG+j39fxtFy2B7FcNAIhPxJAA4lG85jb+xvv9e/fU3dNG+PwsgRavA7l2uJBLAKGh6AwAAICYmT17tgYMGKDHH39c77zzjgYPHqzS0lJdcsklse4awiDUhN219FrHO+FdS0EvnVlg6MGZcOtqX7T2JuRZvL4eaBHZ3+v52y4a4nUgDwAQGGJIALEWaK4Tz7mNv/H+IzMKdNaxR/g8HkwBOZq5BrkEEDqKzgAAAIipSy65hAHCBBRqwt7dUtAmtS0FPTHPkjR3no/OzZLVnK56e7PX78UkydLF/mmBFpFDvV60xfNAHgAgcMSQAGIl0Fwn3nMbf/OCMd8b2OV5AikgRzvXIJcAwoM9nQEAAACElSth77h0mithr6ixdXsO9hPuLDXFpMXFeZLaBmHacz137Z/mba8412CRr2Esk9oGy1wDO4FcL9YC3a8aAAAACEYwuU685zbhyAtaHU45HE71793T7+tGK9cglwDCh6IzAAAAgLAJV8Ier/sJR1pRvlVLZxbIYvacJWAxp7vvvq+osensezbq0uVv6vqnd+jS5W/q7Hs2an1tfcCDRf5czwjifSAPAAAAxhdsrpMIuU0oeYErP5nxxFbt+/ZQt9eyRjnXIJcAwofltQEAAACETSAJe+Ew38uvxeN+wtFSlG/VxDyL1z3k/FkWbunMgk7LAVq6WA6wq+sZRSIM5AEAAMDYgs11EiW3CSYv8JWfdDSwby9dNGqQJuZZop5rkEsA4UPRGQAAAEDYhCthj7f9hKMtNcXUqWjv715xWxaMC3iwyNv1jCRRBvIAAABgXMHmOomU2wSSF3SVn7j0791Tj8wo0JjvDYzZTa3kEkD4sLw2AAAAgLAJV8IeT/sJG0UgMy9cg0UXjTpKhcNiN8ATLoHuVw0AAAAEKthcJ1lzm+7yE0na9+0hpZhMMf3s5BJA+FB0BgAAABA24UzY42U/YaOI1LJwrQ6nKnfu1dodu1W5c2+3+3HHQrIO5AEAACB6Qsl1kjG3CTU/iVYeQi4BhA/LawMAAAAIG1fCXlJWLZPksZRaMAl7POwnbBSRWBauosbWaf9naxf7P8eSayAvkP2qAQAAAH+FmuskW24TSn4S7TyEXAIID4rOAAAAAMIq3Am70fcTNopw7xVXUWNTSVl1p3PV25tVUlZtyBkZyTaQBwAAgOgKNddJptwm2PwkVnkIuQQQOorOAAAAAMKOhD36wjnLvNXhVGl5rdfBIed/z1daXquJeRbD/abJNJAHAACA6CPX8U8w+Ums8xByCSA07OkMAAAAICJcCftFo45S4bCBDMJEQbj2iquqa/SYudGRU5LN3qyqusZQugsAAADEJXId/wSan5CHAPGNmc4AAAAADKvV4WQGQYDCMfOiYb/vgZ5g2gEAAADwLtFznkDyE/IQIL5RdAYAAACSUDwMbFTU2DrtlWYNcl/oZBPqsnDZGendNwqgHQAAAGAkRsmHkiXn8Tc/IQ8B4htFZwAAACDJxMPARkWNTSVl1Z328qq3N6ukrDqgpaIRuNG5WbKa01Vvb/a6n5pJbUvijc7NinbXAAAAgJAYJR8i5+mMPASIb4bb03nz5s0qLi7WoEGDZDKZ9Oyzz3ocdzqduu2222S1WtW7d29NmDBBH3/8sUebxsZGzZgxQ5mZmerfv7+uueYaff311x5t3n33XX3/+99Xenq6Bg8erHvvvTfSHw0AAACIOdfARsd9slwDGxU1thj17DutDqdKy2u9DjK4Xistr1Wrw1sLhENqikmLi/MktQ3stOd6vrg4z3Cz4wEAAICuGCUfIufxjjwEiG+GKzofOHBAJ598sh555BGvx++991499NBDWrZsmbZu3aq+fftq8uTJam7+7v8kZsyYoX/9619av369nn/+eW3evFlz5sxxH29qatKkSZN0zDHHaPv27brvvvt0++2367HHHov45wMAAABiJV4GNqrqGjsNArXnlGSzN6uqrjF6nUpCRflWLZ1ZIIvZc+k6izk9KWdduLQ6nKrcuVdrd+xW5c69Mf/3AgAAAP8YKR8i5/EtEfIQcgYkK8Mtr33eeefpvPPO83rM6XTqgQce0K233qqLLrpIkvTnP/9ZOTk5evbZZzV9+nS9//77qqio0LZt23TaaadJkv7whz/o/PPP129/+1sNGjRIq1at0sGDB/WnP/1JvXr10kknnaQdO3bo97//vUdxGgAAAEgkgQxshLIfcDDa76n28Z79fr2nYb/vz4LwKMq3amKexRD73RmBUZZiBAAAQOBinQ+R8/gvnvMQcgYkM8MVnbtSV1en+vp6TZgwwf2a2WzWGWecocrKSk2fPl2VlZXq37+/u+AsSRMmTFBKSoq2bt2qH/7wh6qsrNTYsWPVq1cvd5vJkyfrnnvu0VdffaUBAwZ0unZLS4taWlrcz5uamiL0KQEAAIDI8HfAItoDG96Scn9kZ6R338ig2g84GX0AJTXFFPWbEIyIPfcAAADiWyzzoUTIeaKdw8RjHkLOgGQXV0Xn+vp6SVJOTo7H6zk5Oe5j9fX1ys7O9jjeo0cPZWVlebTJzc3tdA7XMW9F5yVLlqi0tDQ8HwQAAACIAX8HLKI5sOErKe+KSW1Lq43OzYpUtyKKO9/jT3dLMZrUthTjxDyLYW8eAAAASHaxyocSIechh+keOQNgwD2djWrhwoWy2+3ux+effx7rLgEAAAABGZ2bJas5Xb7SW5PaBg6iNbDRVVLui6vvi4vz4jJRdw04dZzh4LrzvaLGFqOeeWIPMk/suQcAABD/YpEPJULOE4kcJhHzDXIGIM5mOlssFknSnj17ZLV+d/fMnj17NGrUKHebhoYGj/cdPnxYjY2N7vdbLBbt2bPHo43ruatNR2lpaUpLSwvL5wAAAABiITXFpMXFeSopq5ZJ8hj4iMXARndJuTeWOL6b3oh3vntbIm99bT2zGDow6tL0AAAA8F8s8qF4z3kikcN0N2s6nrYiao+cAYizonNubq4sFos2bNjgLjI3NTVp69atKikpkSQVFhZq37592r59u0499VRJ0saNG+VwOHTGGWe42/zqV7/SoUOH1LNnT0nS+vXrdcIJJ3hdWhsAAABIFEX5Vi2dWdApyY/FwIa/yfa8c4/VcTn94mrAoaNWh1MrXq/z+873aOxd5m2wp3+fntr3zaFObevtzbqurFo3TDhOQ4/oG9e/RTCMuDQ9/BOvg5YAACAyop0PxXPOE4kcprs9j+eMzdVz79g8c5TePXXVWUM1b9xxhvhefEn0nIG4Gv4wXNH566+/1ieffOJ+XldXpx07digrK0tDhgzR/Pnzddddd+m4445Tbm6uFi1apEGDBmnq1KmSpOHDh6uoqEizZ8/WsmXLdOjQIc2bN0/Tp0/XoEGDJEmXXXaZSktLdc0112jBggWqqanRgw8+qPvvvz8WHxkAAACIqol5FmWk91Tlzr2SnCr83hEaM2xg1BNGf5Pts449IipF2ED5m3R7K+52JRp3vvsa7PFWcJa+mwVy/8sfu1+zmtO1aEqeBvTtlfADD6Nzs2TJTFd9k/ffxmh77qENew8CAJDcfMXrRflWTcyzRKWAZoScJ5hiYSRymO5mTUvSHzfXdTq279tDuv/lj/XkG7t01Zm5GnpEH0PmHomcMxBXw1+GKzq/9dZbOvfcc93Pb7zxRknSrFmztGLFCt188806cOCA5syZo3379unss89WRUWF0tO/+4/3qlWrNG/ePI0fP14pKSmaNm2aHnroIfdxs9msf/7zn5o7d65OPfVUHXHEEbrttts0Z86c6H1QAAAAIAa8JYt/r94dk2TRtadavb3Z68CDEZJyXwM0/ibdvoq7XYn0ne/B7Cvnjc3erJ+urvZ4LVEHHtbX1qv5cKvXY0bbcw9tuptFs3RmQcL9PQUAAN/pLl5PTTFF5cbWWOc83r6HrL69NHXUIE3Ms3gt3EYqhwlmqfH29n1zSPe//JH7udFyj0TNGYirEQiT0+mM/x3aY6CpqUlms1l2u12ZmZmx7o5XQ29ZF9bz7bp7SljPBwAAghcPsQg6i/Xv5itZdKW8sUgWXX2SvO+pFssE1tdA1YUnW/XY5rpuv8dWh1Nn37PR74EV14DTlgXjIjoQUblzry5d/mZEzm2E3y3cuht0G9Cnp5ZcPCJhPm8i6O7fXrT+rQFGFOtYBIHjNwMCZ7S8J1Y5jz/F446F20jmMGt37Nb1T+/wr/N+XlsyRu6RqDkDcTWkwGKRlCj1CQAAAEAM+bOUWWl5rVod0b0n1bWnmsXseWe8xZwe84JzSVl1p+TaZm/WH70UnKXO32Mgd/JH8873SC7fHcu/S5Hgz6zwtB4pmphniVqf0L3u/u2133sQAAAkFiPmPbHIefxd3cj239mqFTU2SYHNRg40hwn3ik5GyT0SOWcgrkagDLe8NgAAAIDwCyRZjPb+ydHcU80foSw/3f57DKS4a4ni0nCRXr47ln+Xws2fQbf6ppaE+KyJxN9/e9HYPx0AAESXUfOeaOc8gS5lXVpeq4l5lojmMN0tNR4MI+QeiZwzEFcjUBSdAQAAgCRg9GQxWnuq+SPUvcYkuQeS/LFoynBdeVZupwEnX/tJhyoSgz3eJMLAg9H/3cA7f//tRfoGDAAAEH1Gjt+imfME8vnaF25DzWF8ceU25+db9MTruzodN0kh5SaxjMeN/HcuVMTVCBRFZwAAACAJkCz6LxyDAa4icVfFXdf+V94GaypqbLr9uVrVN33XF0tmum6/MPTZ0KkpJi0uzlNJWXWnwR3X8/59emrfN4dCuk4i/F3i30188vff3ujcrGh3DQAARBjxW5tgPl/D/mZdMHJQwDlMdzfLVtTYVFpe63Fjb4pJar8itsWcrgtPtuqPm+sC7rcU298zkf/OEVcjUOzpDAAAACQBV7Lo6z50kyQryaKk0AYD2n+PruKu6/WO7STv+59V1Nh0XVm1R8FZkuqbmnVdu/3WQtHVvnLLZhZo+60T9dTsMXpw+ijdMOF4mbx8Bl8S6e8S/27iU7D/9gAAQPwjfmvT3ffgTXZGesBxVEWNTWffs1GXLn9T1z+9Q5cuf1Nn37PRnbNU1NhUUlbdaSUp538rmFefNVRPzR6jLQvGaeH5eVo2s0D9+/T0u89G+D0T+e8ccTUCRdEZAAAASAIki/7zd4DGn++xq+Lu0pkFnWYttzqcuuUf73V53YX/eE+tjtAXxi7Kt2rLgnHu4rJrsKco3+pe+u+iUUfp+gnH6ZHLCjSgb69uz5lof5f4dxO/Av23BwAAEgPxW5uuvoeOOhZF/Y2jfBWU6+3NKimr1gvv/kel5bVeZ8g6/3vdF2vqPWZGF+Vbtf3WibphwvHq37vr4rNRfs9E/ztHXI1AmJxOZyS38UpYTU1NMpvNstvtyszMjHV3vBp6y7qwnm/X3VPCej4AABC8eIhF0JkRfjdvS5tZzelaXBz6ss2JxDWAInVeflqS5ozN1XPv2Pz+Hv3dn/n1T77UjMe3dtu/VdeeobOOPSKgzxQsb39nsvr21LSCo/T8u/VJ8XeJfzfxK1J7owPxygixCALDbwYEh/itjbfvoT1XVOTrhlhfcVSrw6mz79nY5XkH9O2pxgPdb9nz1OwxXve6fuHd/+jWtTU+z2G03zPR/84RVyevQGIR9nQGAAAAkkhRvlUT8ywki91w3c3dcdDA0m7Q4Oai4X5/j66Zw92p3LnXr/5V7twblaKzq/je8U7lrw4c0uOv7dIjl52iAX3TEv7vEv9uYifUwS1//+0BAIDEQvzWpv338HJtvdbs2O1RxLV0URTtKo6qqmv0WXCW2m7c9afgLLXtJd1RRY1Nc1e/7XWWtCTdMOE4zRt3nKF+z3j+O+dPzE1cDX9QdAYAAACSDMmif7obNIjM9+jvQlSRX7Cq1eHsdjm8O9e9ry0LxgU0kBKvd8hH8t9NvH4nkZbos0UAAEBkkfe0cX0PhcMG6n+m5IUl7vRWKA5Wdobnss1d5SFSWx7y9LbPNW/ccX5fI1rxdqh/52KRFxBzI5woOgMAAACAD9EeqCr83hF6eNNOv9pFmj+zF2z2ZlXVNfr9HTGg0RnfiXe+Ztm79ghk/zgAAIDAhSu/6Vgo9iWrby99deCg1wKySW0zrV17SbuEOw+Jl3g7Fv0k5ka4pcS6AwAAAACQKFodTlXu3Ku1O3arcudetToCm5E8ZthA9e/Ts8s2/fv01JgoFML9nb3gbzvXgEbHASTXgEZFjS3gPsY7vhPvuptlL0ml5bUB//sCAABA4LzlOKNzs2Q1p8vXHFyT2gqmd12U737e8bgkLS7O6zSTN5x5SLzE27HoJzE3IoGZzgAAAAAMId6XGA7HnempKSbdffEIXVdW7bPN3RePiMr34u/sBX/a+bNUd2l5rSbmWeLqNw8F34lvkZhlDwAAEA+MlhN1leMsLs5TSVm1TPLc/Kd9Qbko36qlKQWdztHVXtLhykPiJd6OVT+JuREJFJ0BAAAAxJxRlzzzd9AnnMuSFeVbtWxmgW5/7l+qb2pxv27JTNPtF54Ute/DNXuh3t4c0HJ43jCg0RnfiW/hnmUPAAAQD6KZE/mT5/iT4yyd2X1BuSjfqol5Fr+L6eHKQ+Il3o5VP4m5EQkUnQEAAADElFH3kfJ30CcSd6YHOjDjr0BmTqSmmPyaveBPnxjQ6IzvxLdwzrIHAACIB9HMifzJc/zNcbYsGOdX3hLoXtLTTx+i+1/+qNPrgeQh8RJvx6qfxNyIBIrOAAAAAGLGqEueBTLoE6k70wMdmOmOt8Gl/r176qqzhmreuOO8fr9F+Va/Zi90hwGNzvhOfAvnLHsAAACji2ZO5G+eE2iOE668xVvO0l4geUi8xNux6icxNyIhJdYdAAAAAJC8AhnMiJbuBn2ktkGfVkfbs3i4g941uNTxu9737SHd//LHOvWu9aqosXl9b1G+VVsWjNNTs8fowemj9NTsMdqyYFxAMy1cAxq+hshMaptdkUwDGnwnvrlm2Uvq9P0EOsseAADA6KKVEwWS58Qix/GVs7TndHrrvXfxEm/Hqp/E3IgEis4AAAAAYsaIBdtAB32Mfgd9V4NLLvu+OaSSsmqPwnOrw6nKnXu1dsduVdU1anRuli4adZQKhw3sNPDQvm3lzr3ugrxLNAc0uuuLUTDI0zXXLHuL2fPfjcWcHrMl9wEAACIhWjlRIHlOtHMcf3IWSdrT1NJl3tI+/g9XvB3p/CKWeQExN8KN5bUBAAAAxIwRC7aBDvoYfVmy7gaXXJz6btm+9bX1fu1nLfm/93W4luruir99MYpofCfxLFJ7mwMAABhJtHKiQPKcC0YOimqOE0jO0n658e7yllDj7WjlF7HMC4i5EU4UnQEAAADEjBELtoEO+rjuTC8pq5ZJ8vgcvu5Mb3U4I5LUeztvIDMibPZmPbzxEz3w8kd+7WcdyN7XUmQHNALti1EwyNO1cO9tDgAAYDTRyokCyXO6y3Gcks7Lt7hXRAo1dg0kZ3HNyPY3bwk23o52fhHLvICYG+FC0RkAAABAzARTsI20YAZ9Arkz3dvd8ll9e+qui/J1/shB3fbPV8Ha1134008fEtDnf/L1Op/7vLWfVaD//tmftu1/v/YDGuEqvne3P52vvhgFgzwAAADJK1o5UaB5jq8cx+VPr+/Sn17f1e3MX39i/mBmcfubt6SmmDrF264ls331KVb5BXkB4h1FZwAAAAAxZbQlhoMd9PHnznRfd8s3Hjikn65+Wz/59z4tPD/PZ998FZYvPNmqxzZ3HnSptzfrgZc/Uv8+PbXvm0N+ff593/pu13E/a3/3hPM2cBLOpeoC2Z+OQRwAAAAYTTRyomDynPY5zuOv7dSGD77olHPYupj562/M7yqI+7PEtou/eUvH+N+fPpFfAMGh6Ay/Db1lXafXdt09JQY9AQAAQKIx2hLDwQ76dHVneld3y7v8cXOdTj56gM4f2fn8vgrWNnuz/ri5zuv5XHfh+/MtmiSZe/fscvDGJZDl77y1DfdSdYHuww0AAAAYTTRyomDynNQUk746cFAbPvjC53md6jzzN5CY31UQv66s2q/P0btnir495Oi2Xcf4398+kV8AwaHoDAAAAMAQjLaUWLgHfbq7W95l0doaTc63+L28W3eckr765pBumHCcnnxjl9cZz64rXXXWUN3/8sfdnnPXlwc0Ote/36rjUnmRWKou0H24AQAAACOKRk4UaJ7T6nDq1rU13Z63/czfYGL+onyrLik4Sn+r3t3ttU4fmqXNH3/Zbbv28X8gfSK/AIKTEusOAAAAAEC0uPbuWrtjtyp37lWro+syrmvQ56JRR6lw2MCQZhn4exf83gMH3ctXu/hbsO7K0CP6avutE3XDhOPVv3dPj2MWc7qWzizQvHHHyWpO73Zm9P0vf6yvDrR02daktmXq2u99LQW2VJ2/XMvxBdoXAAAAIFm0z4Wq6ho1OjfLrzynqq5RjQcO+nUNV84TbMx/1rFH+HWdoUf0CTj+D6RP5BdAcJjpDAAAACAphHMP4WAEchd8xwJ1OJZty85IV2qKSddPOE7zxh3rc2aDa5+3rpgk3bnufS2akqe5qwPb+zoSS9UFuw83AAAAkAxCyYUCictdOU+wMb/F3Nuv9617t153XpQfUC4SSJ/IL4DgMNMZAAAAgKTAZwHHE9feXR3vbHft3VVRY4t4H0bnZimrb8/uG6pzgTqUZdu83YXf1Qzuonyr5k84vstzumYBDOjbS0tnFshi9uyfa+a0twGsSC1V59qfLpC+AAAAAMGIp9wp1FzI37g8q29Pd84RbMzvb86098DBgHORQPtEfgEEjpnOAAAAgKQlS5boH//4hz744AP17t1bZ555pu655x6dcMIJ7jbNzc266aab9PTT/7+9+46Pos7/B/7a9EIKIcIGhCRAEEIoglIEQSAIggrCw1NED4HjLMGjeCqoCBwqqN87UEFQVPCn0qwIKEgTBEMRBAk1YEJNaGmQkLrz+yPOumV2d2Z2tmVfz8eDh2Z2duazM1s+9f1egYqKCgwYMADvvfceGjVq5MGSa8PTq4BdyRU5hNUIDNDh1SFpeHrZb3b3kwrTJoZ3yy8ut5vXWatZ+EnxEbL2u3StHEM6NlGUE87Ra9GhtiNHTag6LfNw1xgEzfJ5ExFR3eTv9Ucif+VLbSct2kJi/d1Rup9Xh6QZj6G2zh8YoMMDHZvgo525jl6a4raImjJp1b5g24L8BVc6ExEREREB2LZtGzIyMrBr1y5s3LgRVVVVuPvuu1FaWmrcZ9KkSVizZg2++OILbNu2DRcuXMCwYcM8WGpteMMqYFdyRQ5htQa1b4wneiXbfFwH6QFiMbybuI/lc3QAnuiVrNksfKWrAJTmvn749qY2O3qAv66BmhUkWuThXp+Vh55vbMGIxbswYcUBjFi8Cz3f2OLznwUiItKWP9cfifyVr7WdtGgLiW0Re7XqJ3olY1D7xlbPAaTbL4DtibHpqXo7Z/qLmraIo3bItMFtsCenwKz94Wz7gm0L8idc6UxEREREBGD9+vVmfy9duhQNGzbEvn370KtXLxQXF+Ojjz7CsmXL0LdvXwDAkiVL0KZNG+zatQvdunXzRLGd5i2rgF3JFTmEAfWz1acOSkWHm+vj5dVZKCitNG53tDpCDO9muapCb/K85we20WQWvkEQEBsejKIbVZL7qF2NLLUqxJTpa3HlChJ7907sSLT8TIgdia4MpccVEEREvsVf649E/soX205atYVstUWiw4Lw2gPtcF+HxrKfo3dQp5cT5SkuMhj5JeXIPHVVVp1ZTjvk/g4JmLXuqOr2h1RdfuORfMm2RV5xOZ78bD/ee6QTBrX3rtXxRM7goDMRERERkYTi4mIAQFxc7aDavn37UFVVhfT0dOM+rVu3RrNmzZCZmSnZaVhRUYGKigrj3yUlJS4utXJKZr53b9HAfQXTkCtyCDs7IDogTY+Y8GBk/nEFQO3M+W7NHc+adxTeTZyFr5ajzhhAfchuW4O5oknprTC+b0sEBuhcOvBr7971T9V7rCPRl8I0EhGRNH+pPxL5K2faTp6aXKhlW2hgWgIMBuHPybO1k1NLyqvx+vdHERyok6yzqglPLa6Sfuqz/Vbpg0QFpVWYtPIAAMd1ZjntkJSG9ZCxTH37Q6our48OQ3l1jd30SOOX78d83Gq2SpzIl3HQuY5ImrLO00UgIiIiqjMMBgMmTpyIHj16IC0tDQCQn5+PkJAQxMbGmu3bqFEj5OfnSx5n9uzZmDlzpquL6xRXrQL2JlrnEHZ2QFSqQ+Kr/edkDy46O7Bsr1z2OmNEjlYmSLG3KgSovQcr9p7B+L4tXbqCxNG9m5jeyiOTMDy5upqIiLThT/VHIn+ltu3kycmFWraF1mflIWPZb4rrrGraL7ZWSUuxd3657RBBEFS3P2zW5Uscv18MAvD0st+wKEB60J7I1zCnMxERERGRhYyMDGRlZWHFihVOHWfq1KkoLi42/jt79qxGJdSOK1YBextn8olZcjQgCtR2SNjKPexsDjhHOY7V5EB29LpEkaGByLirBf7vwQ7ob5FnzdF5lawKcVUObjn3bskvObKOpeUkDGffU0RE5B38qf5I5K/UtJ08nQNaq7aQ1nVWW+0H0+0x4SHY9lwffP6Prnj6rhaoFxooeSzhz39S55fbtsgvqXC4j1T7Q047Sg7W96mu4EpnIiIiIiIT48ePx9q1a7F9+3bcfPPNxu16vR6VlZUoKioyW61y8eJF6PV6iSMBoaGhCA0NdXWRnaL1KmBvpTafmCVnQ+o5s4LX0QoJZ1ZQOHpdAFBaUYMFP53Cgp9OyT6vGErvB5mdaT9k5eHyNdsdPqaUDvzu+uOqw3tXVCadw9qSlpMw/CHEPRFRXedv9Ucif6W07eQtOaC1aAtpWWe11X64v0MCvjuYZ7Y9NiIYgLx6utT5tZwsKnUsOe0oOVjfp7qCg85ERERERAAEQcAzzzyDb775Bj/99BOSk5PNHu/cuTOCg4OxefNmDB8+HABw/PhxnDlzBt27d/dEkTVhL1+W2ty93kpNPjFLzoQjd6ajxlH45X/2SsYH23NUh2dW2hkj57xPfrYfsRHBsgdyAeD/ZZ6Wva/SHNxTvjoka9/I0ECUVUjnXnPFJAx/CHFPRFRX+Wv9kchfKW07edPkQmfbQlrVWW21a/KKy/H+duuoQ0raEgCw8Ui+2bXUcrKo5bFqDAJ2nryi2fFZ36e6gIPORERERESoDYm4bNkyrF69GlFRUcY8ezExMQgPD0dMTAzGjh2LyZMnIy4uDtHR0XjmmWfQvXt3dOvWzcOld45Wq4B9gbP5kJ0JR662o0bOConFP1sP/Jo+7mgFhdLOGDnnBZR3EsmVoEEObltKK2okt7tqEoY/hLgnIqqr/Ln+SOSvlLSdvG1yoTNtIS3qrFqForZn9YELeGnwX/V1cXW6M6uRpSaeSq3Wdhbr+1QXcNCZiIiIiAjAwoULAQB33XWX2fYlS5bg8ccfBwDMnTsXAQEBGD58OCoqKjBgwAC89957bi6pa2ixCtgfOBOOXG1HjZwVEoKdnhs5KygcvS4153UlJTm4Z3ynTceWqyZhOPOeqjEI/MwSEXmQv9cfifyV3LZTXZpcqEVaJq1CUdtztbTSrN0TGKDDtMGpeHrZflXHk5p4qnRSq5xzOBtNie0C8hYcdCYiIiIiQm14REfCwsKwYMECLFiwwA0lcj9nVwH7A2fCkavtqNFq5YO949h7Xd5mbI8k2QO/87dkI79E/fWLiwzGtHvbQh/tuo4bte8pZ3J4ExGRNlh/JPJfctpOWgzUegst0jK5a0W35XnqR4aoPlZcZAheeyDNWL/WerW2FtGU2C4gbxLg6QIQERERERH5EjGknj7GfEWCPibMbu5ksaMG+KtzQWSrs6HGIODKtQpNyu1oBYWt1+Uqd6bEq3peeqpe1n7rs/Iwd1O2qnOICkqroI8OQ/cWDVy6UkDpe0pcXWG5UkTMtb0+K89lZSUiIiIiedTU/72Z2naQyF0rui3P48xg98uD25i9Lq1Xa8u9drawXUDehiudiYiIiIiIFFIbjlxJDji5ecJ0AHQ6wGBjur2jFRSWodi2PdcH+04XIr/4BmatO4rC0kqbKzPsndeRu1rdhJ+zr8jeX+5KkBqDgF2nrmLKV4fUFcyCu1ZkyH1Pycnx7SiHNxERERG5h5L6vy9wJi2TmpQ+SthqLzgz2K2PCTf7W8u2wbTBbfB4j2TVdXa2C8gbcdCZiIiIiIhIBbXhyOV01MjNEyY+Y9ydyfhgew4AZaHu7IVie6DTzQgPCbQZclsAcG87Pdb8nq8oJLfYGfRY9yR8uCNHVqeT3JUgcgfqlXBnjj057yk5Ob4d5fAmIiIiIvdxZqDWG6ltB7k6pY8A4OHbm2Lt7xfMrrGawW5bA9i5V8qcLqd4bGcGnAG2C8g7Mbw2ERERERGRm4kdNUM6NrEK3awkT5gYjm3qoFTJUHdxkSEY3SMJMeEhqLFYkiwnFJujkNtrfs9HbEQwYiKCzbbH/vm3vTCCIUEBNsMN2nqd9laC2Ho9aulQOwDvbTn25K6u2Hgk38UlISIiIiK57NX//YnSlD46HRATLm/tZERIIOZuysaEFQcwYvEu9HxjC9Zn5dkNcy55zj//aznhdX1WHuZtOiGrLI5oEVZdbrvAXZGbiACudCYiIiIiIvIqcvOEWYZjM11BselIPr45cB5XSyvx8c5cfLwz17iCeWBagqJQbOJx5285ibkSnSzFZVUAgEnprZAUH2FcVbDxSL7DMIID0xKw4JFb8fLqLBSUVhn3axQVgjtTbkJEaBAS4yLwWPckhATZnjOtZKBeLgHemWNP7srrj3fmoktynEdDNlqGbvflFT1EREREpA2xffHClwfx5f7zdvcVBOC9kZ0BAchYth9FN6ps7ltWWWP2tziZVpy8KhXmPDIkENABpRV/Pbd+ZDBeHZJmVo9W296oFxqE6xXVZtssJ+yqJbddoCZyE+vxpBYHnYmIiIiI6ig2FH2T3Jno8VGhVvczMECH4hu1A82WHSJ5xeV48rP9WPRoJ8SEhygOxbbklxyb++oArNh7Bjte6Cs5CG4vjPisdUfNBpzrhQahokYw64D6cEeO3Zx3cgfqLcWGB6HoRrXjHd1EzmdWDA8oJ9e3J3O42Qvd7mu5C4mIiEgetj9Iro1H8h0OOIuuXK9Aw6gwuwPOUmxNprV8j27Iyv9zEmwlAKCgtAqz1h1FQIDOWG9V096oFxpoNeAM1E7aNR0Ml8vy89WxaSwCdIDBzkh4gA7onFhfUblZjydncNCZiIiIiKgOYkPRdzkzY13ODPwpXx/C9PvayjqHOAA+f8tJFJXZ7uSxlS/MXr43W3mrpTpmLFcp2CqnEgPbNsKuPwpsPu7uQVu5n1kxPOCTn+23ezxP5nCzdW8d3UciIiLyXWx/kFxim0Wu3Culqs9lWSe2bJ+sz8pDxjLH9VY17Y2qGulWmeVguJy2htTnKy4y2O6AM1A7IL3vdKHs9gDr8eQs5nQmIiIiIqpj5OTqJe8lrmS11fVgL9ewnBn4RWVV2Jl9RVZZGkaFocYgYMlO6VXOluR0xtQYBOw8eQVTvjokOzyduN/MNUesclOL5VSqXmiQ3dUSYgfVrj+uKj62Uko/swPTEjC2R5KsY7s7h5uj0O2A7ftIREREvontD1JC6arhuZuynRp4BqTrxJXVBrz4jXSbxLLeGh8ZqvicFdUGm4+ZtjUyT13F6gPnkXnqqmQd2dbnyzRalD1y2wOsx5MWOOhMRERERFSHsKHo+8SVrACsBp7Fv23lGpbbobDxaD700fIGtvfkFMgOZXflWoXd99b6rDz0fGMLRn64W1V4PHGVgqUuyXHQR8vvCIqLCMb3Wfmy9s343LUdpWo/s+mpelnHj68X6rAjS0uOOhHF+7h0Z47bykRERESuw/YHKaV0UqQOwPI9Z+y2XxwRJ9OK9eK3N2Wj2+zNdgduzdofCk8sd/eMz/djxOJdmLDiAEYs3oWeb2wxa3uozSVtSu4EXbn1eMv2mOl1Zd2eGF6biIiIiKgOUdJQdHfIXZJvYFoCFj7aySqEmt5OiMIag4Ar1ypkHb/4RjXG9GiOeZtOQAeYdWJYDmwr6RSate6ozfzLtkK1KSVVnsAAHUZ0aYa5m7JlHaPATqhwS0U31OVck0vtZ1ZcEZ9fXC55TXUAYiOC8eyqA8gv+et94eowl3LfL7PWHXVbmYiIiMh12P4gpZRGKRIA5JdUYFJ6K8n2iyOx4cEoLK1Ezze2KM7LDKiLHCS3fJYTcS3DWKvJJW0qQAcUlsprI8p9nab7Maw+WeJKZyIiIiKiOkRNQ5G808C0BOx4oS+Wj+uGtx/uiOXjumHHC30lG+/iCmLTgTxHkuIjsPDRTtDHmHf6xEQEY2J6K/T/cyWt0k4hqTCKWszQF9kqT1J8pAZHlyZA+QoduTP+1X5mHa2IFwAUllWZDTgDrg9zqSbUOUNvEhER+S62P0gpcfKkUrbaL47cmdIAGcusw1PLdeVaBbIvXpO9f3Cg2vXY1tEBnP3cGAQgY9lvsurZcuvx4n4Mq09SuNKZiIiIiKgOUdpQ9DU1BgF7cgpw6Vo5GkbVhn+WCjNdVwQG6ByuCFG7grhhVBi6t2iA/ql6zN+SjSU7c1F0owpFZVWYu+kEVuw9g+n3pcKgMDyagNpBz5lrjqB/qh6BATqnZ+iL6kcES+ayFl+PK1mu0LH3XlQy49+Zz6y9FfE3qmpQJLGiW+r+aMnRCmwpri4TERERuU5db3/4Km9uNwUG6HB/hwS8vz1H0fNM2y97cgrw4+E8LPnltN3nRIYGYm9uoerJrzpA0cReAKiqcW6qrWl0ALmfG50OEOycVk49W04kJf2fKZgchdVn3d5/cdCZiIiIiKgOUdJQ9DUM3WVNzQpiy/fAxiP5mLcp2+oY+cXlePKz/YiNCFZcLsswilqtbCksq8LGI/lW97vGIMBgEBAbHqw4V7QS+cU3ANh/LwKQnARgGSpP7AjMLylHXGSwzXxyjj6zA9MSjB1vYqeiwSBg5Ee7bb4OV4a5FFdgP/XZfkWhDxl6k4iIyDfV5faHr/L2dlONQcB3B+WvgrV8DwUG6FB8o9LhgLP4XMvIP0p4MjvxpWvluLd9YxkpdYJQWFZt8zhy69n26vGWKZgyT11lWH2SxPDaRERERER1iKOQu8BfDUU55IYIdjWG7pKmZgWxgL/eA45mqAOQXC0rlzjYrNXKFnHGvOn7UAwtPvKj3S4dcAaAgtJKh+/FKV8fsns9Z645gu9/ry3ziMW7MGnlAbsDzoD1Z9bycwkA3Vs0wJCOTdC9RQNcUZi3TevPubgCW2noQ9MyERERkW/Quv1RF3iyDeUL7SalbRjT9gvw18RbOa5X1KgpoldoGBUmK6VOZbW899eFohsO35e26vH6mDDj5FmAYfXJNq50JiIiIiKqY+yF3FUyu13tDHm1odxsPY+hu2zLL1HeiJ+UnmK8f1qFvbZFHGxWE3JZijhj/n8/HkdcZAjOFpZhqYwVDlqJjQhxapBeLP/Ty/bLOp/UZ1bO51JJmEtXrYSxXIF95VqFrNCEtsruzSEiiYiI/J1W7Y+6QG7dSqu6jelx4iNDMeM77283KR2InNCvJWLCQ7D6wHljVB9XtmG8gU4HdE6sD8D25ysmIhhFZVUorZQ3sP7vLw+aheC2VeeXiqRk+f6U297Ivngdmaeusu7uRzjoTERERERUB8lpKNpjK0+wZYhgqeepGcCy97yY8BCG7pKwPisPs9YeVvy8pPhI4/+7cuZ5gA4o/HPFrdqQy7Ys+OmU0+VTo6is0qUdXDoAcZEheHlwG+hjwq0+s3I/l3LDXBaWViBj2W+yPudqOkZNc5LXGAR8uCNHVehNbw8RSURERM63P+oCuXU1reo2Usexx1vaTUqjIH3yy2m8vfmk8e+IkEBFz4+LDEFhaaVHQ2UrJQjAwp9OYkJ6KwDWn6/4yFA8+8VBAPIjPVnmfLbXtjetx4vMJjjUC4U+OhQXSyrsXtf5W09i/taTrLv7EYbXJiIiIiKqo8SGohhy1zQcmb2wWnJCLluGOAbUh3Jz9LxNR/JlvV5/Ct0lXjNbYZntMe3k0SrstRSDAGQs+814322FakuICcMTvZKRoCIUszslxIQhrl6oS88hALhaWgl9TLjZZxZQ9rmUE+Zy2uBUvPhtlqzjiSHMRyzehQkrDmDE4l3o+cYWReEZ1Ybe9IUQkURERFTLVvujLhPbVt/sP4cXv3Fct/r+d23qNrbqSHJ4ut0kTpCUyzKFTpnMlb1A7UTY4Z2aALCug3q7JTtzzdrcpp+vgACdqqhXpuy17S1ZtgdGfrgb5dUG4wp6R1h39x8cdCYiIiIi8iNyBo8chVw2nSEvUjtQLed53xw4L+u1uXIA1ZvYu2b26FA7cGq6klTs8HFVB4wA8/s+MC0BO17oi+XjuuHthzti+bhu2PFCX0wdlIodL/TF5//oitjwYBeVxjnTBrdBwXV5uZKdtfPkZavPitLPpaN8bNmXrskKBT5/y0nNBn3l5ogTqf1eISIiInIH07bVpFUHUVBaaXNfsW718mp5k/7sUdseEHm63SRORnTHILBBAD78OQf/7JVsVQe1xxsGqItuVOHjHX9IThbXauKAVNvekq0JDsV/tiViIhy331h39x8Mr01ERERE5CfkhnuT24A13U/JgJhpmC45zysorbIbEs1eWN66SE0eZlsrSZ0Jex0THoQaA3C9otrufpb3XSpUm7i9R8t4zBneDk99Vpvz2JXdEZGhgWh5Uz0cPFfscN9+rW/CrHVH3ZY7bv7WU/hq/3mzEHRqPpe2wlwCwJSvDsk63pKdOZrmBTQtU37xDRSUViKuXihiwkOMq7RFar9XiIiIiFzNVtvKETkD047qNmraA4B3tZvEyYhTvj5kdyKkVr47mIdtz/XBvtOF2HnyCuZvPWl3f28ZFn3t+2PG/zcNUR2vcQQmW20NOZNAw4MDsWBsJ2T+cQXzt9pOg8S6u3/gSmciIiIiIj+gZMWg3JnvpvupGRBT8ryhHRsDUBaWt65SM6vd1kpSwPbqU0fG90lxOOAsUlJmteVRol5oEEoramQNOAPA5mOX3TbgLLJcSazmcwmYh+HrkhyHPTkFmLvxhFWYQlvs7SdnZYSUwAAdim9U4s0NxzFr3VFMWikddUHt9woRERGRFmylJXJ2pbEjjuo2auo+3thu6p+qx4IRnXBPWiNEhprnadYy+pFYZ/00M/fPa+ctQ8rKmLUPNH4JttoaciY45BWX49fTBUhpFCXrXKy7121c6UxERERE5AeUrBgUQy7nF5fLXlmsdkAs90qprOf1T9WjS3IcZq45YvY69Cazvf2FmnB4gmC/V8Jy9emsdUcdriyPrxci+/w/n7gMAMaVto46ugamJaBv60b45JccbDh8Eb+eLpR9LjnkDpZ7kuVKYjWfS1Prs/KsPj+ORIQEysqZp7TjSG7UBbXfK0RERETOkqo7iStNY8JDVEUeqh8ZjIJSxxP/HNVt1NR9vK3dJHV94yKD8UDHJkhP1cMgCBj54W5Nzzlr3VFNj+dupu2D5wfcoskxHbUh5Nbz527KxqT0FFn7su5et3HQmYiIiIjIDyhZMWgv5LKtGfJqBsTWZ+Vh7qZsu+UxfV5ggE4yVLC3zNR3F0fXWsrFkgqzwTwppmGvw0MCHd7/mHD5g85f7j+PL/fX5uaODQ/C43ckIfmmejbv4fqsPLeF2vOU2PBgh6uNLUPQTb8vFU/+GXpcal9bK1fUhn8clJaAL/efc7ifko4jR1EXtBxoJyIiIlLD0QS5MT2SFB1PrJ29OiQNs9YddbpuI6eO1Cg6FP/9W0dcuV7hde0mW9e3sLQKH+/Mxe3Jceifqlfc5vEHYvvAXph2pabflwoAyDx11aqdraSev3zPGeijw3CxhHV3f8bw2kREREREfkBuYzE+MhSZp66iotqAiekpaBRtnivKVphmcaAakBcCu7LagBe/kZdT1vR5pqGCu7do4DUdJ+5k71rbYhlC3RFbIa5N77/Y2aVU0Y1qzNt8EhNW1IZUvv21TfjPmsPGkIXrs/Lw5Gf76/SAMwAsGNkJ96TpZe3rTAg6teEfYyOC8fqwdtBH277HOtSu+FHScaQk6oLS7xUiIiKqG2yFtXbXuR2lJfrmwHlFxxTr0IPaN5ZVtwFg9/XLqSPNuL8terSM97p2k9y0TwAcvsbYCO1CcPuac4VlTh8jQAdM6JeCvTkFuP21jRixeJexjSamvemcWB9xkfImG+eXVGBEl2YAWHf3Z1zpTERERETkB+TMho+JCMazXxxEfolJ+OroMExKb4Wk+AiHM+TFgUpHIbDXZ+XhxW+yZIWWm5jeymtCwHkTW9faHstVs3LOYbqyPL5eKCAAV0orkHnqKrokxxlXxDvTDVhQWomPd+bi4525aBQV6hOhr50VGxEMQ42A3TJzITeMCjN20NliukLY9DMqJw+blIduuxlbjl1EebV0eG21HUem3y/2iAPtA9MSsOCRTnh5dZbZig4lISJrDIJkhARb24mIiMhz7IW1dke7QM4EuYLSKsRFhthMRwPUhoqedm9b6KPN6xiO2kwA0PONLQ5fv9y2l7dRMgGxf6oeE9NbYcnOHLMIQeJrNG2r5F4pdRhFqy5Ztc9xNCJHDAIwb7P0NcsvLseTn+2XFZ3JVFJ8hKz3JevndRcHnYmIiIiI/ICjkNkC8OfKUvMG5cWScszbdAILH+2kaqDSsqGoNMxvUnyEzD21544GrzPnML3Wm47kY8XesyjVIP+uVJk2HsnHv784KNn5teCRW/Hcl7/LOrcjF69VOH0MX1BUVoXHluyRtW9cZDAMBgFzN56Q3UFn+llVu0p61a/n8P72HJuPx0YEY/awdooGfTcdyceqfWdlnV+MzrA+Kw+z1h0xG3COiwzGtMFtZJ3bVsf1/R0S8N3BPI91aBMREZE1R2Gt7aWK0YrcutPQjo2xZGeuzXQ0rz9gu55kq8208Ui+otfvqO0ll9btHnvHk3t9Nx7Jx+RVB8zqarHhwRjdIwnj+6YYByj/okNsRHCdj5YkKq1wvu1lj3hllQw4A7V1+O4tGjjsE2D9vO7yuUHnGTNmYObMmWbbbrnlFhw7dgwAUF5ejmeffRYrVqxARUUFBgwYgPfeew+NGjUy7n/mzBk89dRT2Lp1K+rVq4dRo0Zh9uzZCAryuctBRERERCSbrdnwjaJDUV5tkGygW+ZYldP5YJob2JSaML9KckhpyR0rLLQ4R2CADsU3alcKy72u9q6pVJlsdd4YZ79HBGsy4EzSyqsMGPnRbtn7W3bkqf0MFTrosAsNCkD/VMfhwaXeU/aY5nqzl+8vY9lvWBigs/tZsfX8vOJyyQF1d3ZoExERkTlHYZeVtknUklt36p+qR5fkONUrjS3bTGpfv622l1xat3scHU/u9f14Z67VtuIbVZi3KRu36KMAQFEdk1zLMl+zrfcl6+d1n0+OsrZt2xabNm0y/m06WDxp0iSsW7cOX3zxBWJiYjB+/HgMGzYMO3fuBADU1NRg8ODB0Ov1+OWXX5CXl4e///3vCA4Oxuuvv+7210JERERE5E5Ss+ENgoCRH9oe1FIalllKjUHA0p05qgae3M0dKyy0OoeSgXxH11TMpWzJ1moBwcHjpI0yhQP6uVfM87uJofW17pDLL6lw+J2gNLKBZS5DZzqe1UxycWeHNhEREZlTEnbZmUFWwP5KXDlpicQ6dWCATpOVxoB7X79I63aPnOP1T9Xbvb5Aba5hqTTeYl1t6teHHE6QJPebNjjV7meB9XP/4JODzkFBQdDrrWdUFxcX46OPPsKyZcvQt29fAMCSJUvQpk0b7Nq1C926dcOPP/6II0eOYNOmTWjUqBE6duyIWbNm4YUXXsCMGTMQEiIvKToRERERka+ynHW8+sB5Wc9TG6ZX6UpHkdJcsVpwxwoLLc4hdpTtPHlF0XW1dU1rDAKmfH1I9nHIe83bdAK36OsZOwjF0PpSEwqcdelaud18bEo7laAD/nlnMgamJSDz1FWnOl7V5rJ2RYcuEREROSa3raG2TSJytBLXUVoiwLxO7exKY6C2Lr7z5BVZ++48eUWTMNhat3uUHM9R2iepAWfTY/nCgPO4O5Ow9vd8v1iJrQOQntoQs9bZXzHP+rl/CPB0AdTIzs5G48aN0bx5c4wcORJnzpwBAOzbtw9VVVVIT0837tu6dWs0a9YMmZmZAIDMzEy0a9fOLNz2gAEDUFJSgsOHD9s8Z0VFBUpKSsz+ERERERHVBXJDnKkJ0yvOdlfSuIyLDPZY+CwlKww8dY71WXno+cYWjFi8C/O3npR1zoiQQMlrWmMQkHnqKsYv288Vy3XIzDVHzHLcDUxLwKT0VpqfZ+ORi8b34oQVBzBi8S70fGML1mflqepUEgTgg+05WJ+V53THs7Md0s4+3xHxs7f6wHlknrpqkZOQiIjI/7iyTSKy1TYRV+Kuz8oD8FdaIn2M+bn0MWGat1PEur3cev38rSet6l1qaN3uUXI8e9d3bI8kWefzdl/tP48tz96F8X1aerooLicA2HjkksPPlTvq56xje57PrXTu2rUrli5diltuuQV5eXmYOXMm7rzzTmRlZSE/Px8hISGIjY01e06jRo2Qn58PAMjPzzcbcBYfFx+zZfbs2Va5pImIiIiI6gIlIeSUULPSsUFkCDKn9kNIkGfmx7pjhYUz51AarlgkFaJZ7Qp08m6WKwHElcjN4sJRPyJY05Uha3+37uQUO5fGONFhOHPNEfzfgx1k7Wur49nZfPCuzCfvjpzxREREvsZVbRKR0pW9UmmJnFlZLEVt3V7kTPofrds9SvezdX3nb8mWdRxvV1BahS6vb8LYHsmeLorHWH6uXF0/Zx3bO/jcoPM999xj/P/27duja9euSExMxKpVqxAeHu6y806dOhWTJ082/l1SUoKmTZu67HxERERERO6iNIScXGpWOs4akuaxAWfAPSss1J5DVbhiE6adaM52cMl1X3s9fs6+iqIbXEXtbj8ezseenKtYsjPXrddf7Fz6RmbYfqnn5xWXAwKgjw5FfkmF5H6OOp4ddVzbonU+ecvw44WllchY5tqc8URERL7IVW0SkZqcyVqEzrbF2bo94Fz6H63bPWr2s7y+NQYBy/eckXUcX3CtvBrzNmfbzFHtD0w/V87Wzzsn1kfmqauSk0C0zk9O6vncoLOl2NhYtGrVCidPnkT//v1RWVmJoqIis9XOFy9eNOaA1uv12LNnj9kxLl68aHzMltDQUISGhmr/AoiIiIiIvIAY4sxyZnD9yGC8OiRNVQNNzWrg+pEhip+jJVevsHDmHGpzYIlMG/vOdnDJtTe3EHteSsfenAJk/nEF5wtv4JsDF9xwZlryS67Hzi2gdnVHXGQICkorVR1j87GLKK82SD7mqONZHOi9J02Pj3fmWnVc26JFh7YpqdUWATrpsmiVM56IiMiX2WqT6DVYraj1il3LiWVKV0E7W7cXqc13q3W7R4vj7ckpsDnh0Jf564CzqR/+DLE9bXAqMpZZTyyxRfxE3d8hAb3f2iq5irl/ql7T/OTkHJ8fdL5+/TpOnTqFxx57DJ07d0ZwcDA2b96M4cOHAwCOHz+OM2fOoHv37gCA7t2747XXXsOlS5fQsGFDAMDGjRsRHR2N1NRUj70OIiIiIiJPG5iWAIMBeHl1lnGgqKC0CrPWHUXAnyHmlFCzGlirPKpqO4FcvcLCmXNocW0uXSvXrINLjvySCuw7XYgeKfHokRKPymoDVh+8wI4XLye3E8iRW5vGYPOxy6qe+/HOXJuPxUQEY86wdpLfSVIDvTpdbb5oUUJMGO7vkIDvDuZp3qFtWg6p1Rb23vtqO42JiIjqEleFtdZyxa5UfSPuz8m6g9o3lnUeuXX7u1Mb4ccjFzU7nkjrdo8Wx9OqLUje5/9lnsb/yzyNhJgw/LNXslU93F79/P4OCfhge47NVcwT01MURzEg1/G5Qed///vfuO+++5CYmIgLFy5g+vTpCAwMxIgRIxATE4OxY8di8uTJiIuLQ3R0NJ555hl0794d3bp1AwDcfffdSE1NxWOPPYY333wT+fn5ePnll5GRkcGVzERERETk19Zn5Wka9lVN+Cwt8qg6m8vJlSssnDmHFtemYVSY2ztzTM+373QhB5x9QGRoEN4c3h4vfvM7im5Uqz7O5mOXkdY4ClkXrmlYOiA8OBD9U60jlTka6H28eyKaxkUgLjIE+phwPHt3a+zNLUDmqaswCAbEhoXg0PliZJ0vRvfm8ejWooGqDm5nw2V6e4ersyu7iIiIHHFFWGutVvbaqm8UlFbh6WW/4YlzRZg6yPHiNrl1+67JcbIGndW0FbRu9zh7PC3aO0rVjwhGYRlTAblLfnE5Ptieg3/1S0F1jQGnr5Yi60IJqmsMOHX5OtZP6IUjeSXGembnxPro/dZWu6uYl9iZrGpKTR2b9V7lfG7Q+dy5cxgxYgSuXr2Km266CT179sSuXbtw0003AQDmzp2LgIAADB8+HBUVFRgwYADee+894/MDAwOxdu1aPPXUU+jevTsiIyMxatQo/Oc///HUSyIiIiIi8jh7gyRqQ1LZm+1uSas8qlrlcnLVCgtnzqE2B5Yo4c/ruyenwLmCK2TaeeSuwTR2HjmnusaAVb+ecWrAWaT1gDMgvVJBzkDv0szTZn/HRgQDAIok3ivzt55CREggnujVAk/d1QL7ThfK/i5wNpqAJzpc5XJ2Ug8REZGnaLESV0594/3tOehwc30Mam//d1HuIPhj3ZPw4Y4cl6X/0brd48zxnG3vqHFHiwZYdyjfTWcj8b6+vTnb6rFzReXo8J8f0fymCGycdBcCA3TIPHXV4Srmohvy2n1K69is96qjEwSB88xVKCkpQUxMDIqLixEdHe3p4iBpyjqPnDd3zmCPnJeIiMjfeVtdhOTxhvtma6Zu5qmrGLF4l8PnLx/XTfGqA6nGmpSxPZKQnqpX3clRYxDQ840tNs8jdsjseKGvz85OFgfVAeXhj997pBMGtU8wXidXd+ZIXW+57zNnJMSEYdtzfbDvdCEWbz+JLcevuPR85BlvP9wRQzo2Mf7tjveWyLKzyfJ7Nb+kHJNWHlB8XHvfUd6wysLWpB6xFEqjYZBneENdhJThPaO6Tu1vnNrnOTOQJLe+0SAyBHteSndYHlt1e8vfVrn71QW26hvkXwJ0wPwRt2Lf6UJ8JGMlc2x4MIpvVNmdmKGkH4D1XnNK6iI+t9KZiIiIiIjUsdfBUlFtkHUMNStVTWe7bzqSj28OnEdB6V+zkQN0teFvP9qZi4925qqePexodWFdyOVkK2SdHPUjQwDIW4E++o5ErNp3DqUVNU6V13S1SI1BQHWNQbN8wbZ0TqyPkKAAFJZWcMC5DouvF4rMU1eNHc0/Hs5z27lNIycAkMzpqJS9FVZyO8ddOTDtimgYREREageAnRk4dmYlrty20NXSSlltDrnhqN2R/sdbDExLwMT0Vpi76YSni0IeZBCAp5f9Jnv/9DYN8eX+85KPCQAevr2Z7GOx3uscDjoTEREREfkBR2GnJ6anyDqO2rCvYl627i0a4IV72uDTzFxsz76MbSeuWOX4VZtDWm4nkLfnS3XEsqMs++I1zN96yuHzTF+3rY4rsbPueP51pwac9dGhmHF/WwxMq11Z/e7mbHy44w9cd3IQW461v+dhUFoeXl6d5fJzkWfEhgfh2VUHkF9S4ZHzi51NU74+hOIy6xUVppNq5LLVaSwnZUD/VD3mbzmJJTtzzMILahn+zxsm9XjDam8iItKO2rQ4WqTTsZcz2t7vjZK2kNw2h9xBcHek//EWSfERni4C+RCdDjYHnEVzN53Akp05GN0jGeP7tnQqVY5lvZd1VHMcdCYiIiIiquPkzNRdvucM9NFhuFjimlxhIjmhttXOHpbbCeTN+VLlMu0oyzx1Vdags+XrttVxBQBTvtqoumyT0lsZG/Lf/34Bk1YdlL2SXivTVmepGvgj31B0oxrQIN+0MwRI54JWY1J6Csb3TZEMqe3ou3vK14cw5atDkrnsxM73BY90Qv3IEKc6wjw9qYc59YiI6ha1KwldvQLR0e9Nl+Q4xEUGy6pnKmlz2BsEV7Ofr3N1e83VkZfIveQmEC66UVU7+PxLDuYMa2dVhxQHj3/IkhdB6dK1ctZRJXDQmYiIiIiojpMzUze/pAKT0lth3qYTVo1we2FflVCSn0vNqrkuyXFIiAmzmatYq4Fzb+PM65bquMo8dVVyAMsRy8b17O+P4P3tOYqPo4WrpZUeOS+RGp9k5uKpu6xXXMj57rY38C1+H4xfvt8sooSajjBXTeqRszJEixVtRETkXdRG0HBl5A25vzevDklzGPY3oQ62OdzJUfvGWREhgSitdH0EJvJORWVVePKz/XjPZGJm7pUyLN9zBvkl8idQ5l4pw7xNJ1hHtcBBZyIiIiKiOk7uyrOk+AiX5QqztyrBHiWr5uzlKtZq4Nwbaf265V7ziOBATOqfgvh6odDHhJsNFn3/e57HBpyJfE1BaRW6vr4Jsy1WXGi1atjZFAY1BgEGg4DY8GCbE1LUTOqRWhkSFxmMBzo2QXqq3ngs5tQjIqp71EbQcFXkDSUrqAe1b4wnzhXZrOvqUDfbHO5k2r5xhTIOOBOsJ2bKpQPQKDoUy/ecYR1VAgediYiIiIjqOCUr1Lq3aKAoV5jc/EWOViU4W3aRrVzFWgycK+XO3E5avm6517ysqgavfX/MuGpSfG01BsEr8inXjwhGoUbhj4lcrfDPFRej70jE3W1rw3e6KrSk2DnmqCOsxiBg/pZsLNmZazf6gZrJLbZWkxWUVuGjnbn4aGcuEmLC8PDtTT2eS5qIiLSnNoKGqyJvKF1BPXVQKjrcXB8vr85CgUmEHa3C6talHLFqX4ut9o0WGFqbAOuJmXIJAEZ0aYa5m7Lt7uOvdVQOOhMRERER1XFKwy/LzRWmJH+R0tUGpmVS2lFhK1exOztq3J3bqcYgICY8BM8PbI2C6xWIiwyxWn0sl9JwdparJvfkFJh1vnmCTgd0bx6H77MuerQcREot+eU0lvxyGlFhgejUtD7CggNQXuWanOj2OsLWZ+VhyteHZOWtVjq5RW7ki/zicrudeaZclUuaiIhcQ216GFel05H7O7Lz5BVj/XpQ+wQMSNO+zVGXcsQ6+1pM23WLt5/EluNXXFlcItl+P1ckaz9/rKNy0JmIiIiIqI5zRdhppTk2law2MC3TxiP5qjoqpAbO5Qxea7GqwN35R+115qjp9LL3fpFiGT7MGxrWggAOOJNPu1Zeg23Zru9YNc1bJ37/bTqSj4925jp8bmx4MBaM7IRuzRvI/q6pMQhYujNH1oolJYtPXLUqnIiIXENt+8RV6XTk/o7M33oSX+0/Z2yLyJ2sK5c72xG22j1arbLW6rUEBuhQWFqJn05wwJm8x+Zjl2Xt5491VA46ExERERH5gYFpCVjwyK1/hoD7a+WamvDLSnKeiR0USlbPimUCoFmni5xZ9lqsKlBzbZzhqo4pMZyd3JWOpuHD/LFhTeSrCq5XAJD+/nOk6EYVIEB2x7SacziidkUbERG5l9RAptr0MK5Ip6OkrZJXXI4nP9uPRRpPJHVnO8JWu+f+Dgn47mCe06ustXwt67Py8PQy1+R2JnIVf66jctCZiIiIiMgPrM/Kw6x1R80GnOMiQzBtsPKOGaU5z0QP394MczedsPm8vq1vwrg7WxgbZj3f2KJZR4WjgVlA2QC3ZcdZ58T62He6EDtPXnZb/lFXd0z1T9VjxndHAMjPi3zpWjnubd8YCTFhmudeIyLtrc/Kx7nCMiz55bSq52cs22+W79lWx7St72EltFzRRkRE7uNoYqeatDhap9NRGukHAKZ8fcjpAWDTNsWVaxWy2hG7/riKHi3jVZ/T1m9yXnE53t+eY7W/VHvI0Wpote1FS2J7h8jXCPDfOioHnYmIiIiI6jhbHQuFpZXIWLYfCwOUzdKXGz5Z3E/u6ratxy7jb7c1RWCADpmnrmraUWFvYHbGd4cB6GQP3kq9ngAdYFAwmqJFCGolnTldkuMUd8rtySkwC70rR8OoMLNOO2cGmIjI9faeLsTe04Wqn2864AzY7piWk8PZnnqhgQgKDDCLvODMijYiInIPuVF51EzG1Dq0ta0V1LYUlVVh/paTmJCeoup8aiOAZHy+H3OGt1P1+6fmN9myPSSV/kgfHYYRXZohKT4CDaPCZLchHLWJHLV3iLxVREigp4vgMRx0JiIiIiKqw1yxGlZu+OSGUWGKV7cpzQvsbEeFACC/pMLuMUwHb4tvVEq+HiUDzoA2uZ3kXqNNR/IxedUBxWHylAyMW4YPEzvtXvjqEIpvyF8pTUS+Tep3RYsO4+sVNQBqMCk9BUnxkU6vaDOlVe5KIiIy5+60M1oQV1DP3Xgc87eecrj/kl9yML5vS8XldyYCSNGNKtVpdNT+JovtoflbsjFvU7b1JIKScrOIVnGRwbKO66hNpMVEXSJPKKuskRUxrS7WOznoTERERERUh2kV2syUo5xn4gBk58T66P3WVtmdKWryAruzoyK/+Abe3HDc6fCwWuV2knuNPtqZa7VNTs5npQPjluHDBqYlICo0GCM/2q3oOETk28Tv8rkbj6NHy5uQX3xDk+PqAKzYexY7XuirunPOsqOvsLQCs9YddSp3pT90HhIRqeGKdog7BAbo0KPlTbIGnYvKqhSXX4sIIIC6AXtn20bvb/tDVrlNUzpJkdsm0mKiLpGnCKgNwx8eGIgTl69j58nL2HemENfKa4z7qMmZ7u046ExEREREVIdptWLYlL2cZ6Y5NvedLlQ1k15uXuAEN3dUFJRWOrVaT+v8o3IG/3U2wn6Lm8TOKgBWgyaOji/SR4diRJdEVFQbkHnqqtmAS7cWDZjfmchPzd96CvO3nkJUmDZdT+LgxNKdOYiPCkXDqNrJTftOF8oa8JUbxlTOpBx7x6yLnYdERGq4oh3iLoWllbJzOystvxYRQNQO2DvbNiqrqnG8kwV77UVHbSK57REib1VUVoVRn+y1+biSeqev4KAzEREREVEdptWKYUu2cp6Z5thcfeC8omOaliUwQIf7OyTg/e05Nve7v0OCsaPC1kozLToqxJn4cfVCVR6hlpL8o3JWzjka/BcACA5edF5xOSau2I9fTxdJDprYOr7o3vYJ+DW3wCycnvjc/ql67MkpwKA0veRqayLyD9fKqzU93qx1R43/H2AxscbWgK+SMKZyQ77KzVNKROSvXNUOcbX1WXnIWCY/9LXS8ms5yK7kWDUGAQZBQGx4MIrcmP6mfmQICkorjX8raROZtneI6iJvTTXgDA46ExERERHVYXJDYasJ9yzmPLM1OKq0A8a0LDUGAd8dzLO7/3cH8/D8wDbYeCTf7kozRwOnjsoE1M7EjwkPUfjsv4zv0wKT+t8iqxGpZOXcwLQE/LNXMhb/nGM1wHxr0xj8drbY4fnW/J5vtc100ERqckFCTBju75CAD7bnSA64PPnZfsRGBKOojPmcich1LCM55P35/TMpPQXj+6YgMECnKoypoxVkvpinlIjI3VzZDnEVJb8Zasuv5SC73GPJjfbhCtMGt4E+JlxxGgpxEm5FtQH3ttdLtlmI6gJvTTWgFgediYiIiIjqMLmhsNV2igcG6Gw2jJSsMrYsS+apqw47RfKKyzF/y0nM23TC4UozqYFTOeIiQ/DaA2kYmJaAymqD1ao6uXq0vEn2gLOSlXPrs/IkB34FQNaAsy2mgybbnuuDqNBgZP5xBUDt/b49Kc5mvm5xGwecichT5m7KxvI9ZzHj/toJQ2o72XeevCzZSe6reUqJiNzJ1e0QV1Aa+lpN+eW0kWLDg1FVY0Bppe1w1rERwbIGvOVG+xAnla789Zym9Xh9TLji30JPDpITecqla+WyIp55Ow46k1OSpqyz2pY7Z7AHSkJEREREtsgJhQ3IC+mshJwQ2bbKIjdU3JKd1gOugPVKM8tV2dkXr2H+1lMOj//y4DbGMu07XahqwFlO7mlA+co5Nav3lBAHTbrN3mwWEu+r/efw8O3N2AlERF4tv6R21XOHm2NUH8P0d8I04oSzeUrrQociEZEcctsharjiu1Tu93tsRDDmDGunqvz2BuNFcsJfy3mlctoLsRHBWDCiE7r9OTC8+kAeAOcHndWuBFeSEoOoLsm5XIqeb2xxGPHM2+uRHHQmIiIiIvIDjkJhKwnpLJe4CteW+9rrkZ6ql2woyQ0VZ69DxnKlmemq7MxTV2UNOutjwo3/ryb/mg7yV0DIXTm369RVBATosPPkFbcM/JoOOAO1q65NczgTEXmzg+fUR30wZRpxwpk8pa74vSUi8maO2iFquOq7VO73+4IRndAjJV71eZyJxCQqLKtyGFFDzsrtorIqBATojNGm8kucb184Wslua9DM1ZNqibzZvM3ZVtssI575Qj2Sg85ERERERH7CVihspSGd5ZDTYfDr6SLMe7iTZEeEnBxwMeHBslYBSA0Wq8kxpzT/mtj465+qR+apqw472eQOamcs2y/rdbsKO4GIyB9Zph1Qk6fUFb+3RES+wF5KHqVc+V0qt43QTYPXIg7G7/rjKjI+V1e/d9R+UBqZQ80kWylSK9nFgeaNR/Lx7YELZhNbxXaTMykxiOoi0/qnwSAgY9lvXl+PDPB0AYiIiIiIyHMchXQGahs4NQrjSsuZVS+uQpYihp0DrEPHiX+P7pEkqyxSg8Vyjm85M1/shLK1JkMHoEFkCOb+rQOWj+uGHS/0BQD0fGMLRizehQkrDmDE4l3o+cYWrM/KM3tujUHAlWsVsl6PJweciYj8mRhxYt/pQkW/ITUGATuzr2DKV4c0/70lIvInrmq7iNS0EZwRGKBDgE6nun7vaFKs0sgcSifZArXXJSEmDJ+P7Yq3H+5obAeZDn6tz8oztok+3plrFUkp789Bs01H8hWfn6iuE+ufL6/O8ol6JAediYiIiIj8mNyQzrYGh21xNt8l8FfYOX2MeeeHPiYMCx/thPF9UxwOAtvLp+zo+JazhOV0Qr32QBoe6HQzurdogI1H8vHUZ/utrq84E1kceBY7YWatO2rjlWjHezI9ERF5l8iQQNzaNFbWvvkl5bJ/Q8Tv+JEf7ZadEoKIiKS5qu1iSmkbwVlqVxc3iAxB58T6dveRM2nWtL3kaH+p5wO1A/E9UuIxpGMTY1ojkbgy3dGEZAHAyl/Pyjwzkf8pKPWNeiTDaxMRERER+TEtBoelOJPv0pSjHHDT70vFU5/thw7mYZ/lrkRQmmPOVv41yxByjlZhOAqRpSUdgLjIENzXIQHfHcyzCmV3b/sEfLQjB14wKZqIyGNKK2vw29kiWfvOWnsY4cEBDn9DbIWAtUer0KZERHWRq9oullyRh9oWNauLAeBqaSV6v7XVbi5XcdKs3PaSvf2lSIXRNqU0R/P1ihqEBwegvMrAlD5EKnhDPZKDzkREREREdYSYJ0tJx4hWg8OWxFnyjma0F1qEVpNiLwec3EFgtce3dU5HnVByV2HYCpFlKVZm/mpb57paWomlv5wGAMRFBuOBjk2QnqpHl+Q47MkpwOKfc1Qdm4jIHxWUVuHJz/Zj0Z8r3qR+Q5R2tIvUDj4QEfkDLdsujtpOWuahtsdRHml75ORyVdpesrV/QkwYpg1ORf3IENntTTkplyzdqDJIbhfP8q9+KVj6Sy6KmXKIyIo31CM56ExEREREVAesz8qT7BhwNPDqqJNDh9oOCVshqm0JDNBh2uA2eHrZb3b3m7XuCAak6Z1aNeDOlQgie51QNQYBO09elnUceyGyRNMGt8G18irM23zS4b71QgNxvaLG7j6FpVX4eGcubv/zGrlyNvRj3ZohOjwYC7aectk5iIg8ZcrXh9A/Vfo3TE1He5yMUKlERP5Mq7aL2raTKzhajSwAiAwNRKlEHd80gpKt3yNAXXQnW/ubDtbvySmwexwt2xmmg+QBOh3mbjqh2bGJfJ3afhtXYE5nIiIiIiIfZytPlmXuYCly8hQ7ClFtS/3IUIf7aJV3SBwElsoj5k5i7s75Gg6yxkWGYMVex/nNEmLCsH/a3Vg+rhvmPtQRcZHBkvuJHVkz1xxBjUFQPBu6QWSI7H07JcZhcv9bEBshXRYiIl9WVFaF+VukJwSp6WgvKK3Erf/5EW9vykYNcx4QEVnRou3iTNvJVezlkZ6UniI54CySm8tVaXtJan+xrTNi8S5MWHEAIxbvQs83tti8Zs6uuoyLDMbchzpi+bhu2PFCX+OEgKT4CKeOS1TXCADu75DgsX4QUxx0JiIiIiLyYY5yBwN/DS7aYq+Tw16oNkfclXPN3WoMAjJPXcXqA+eReeqq8dra6sCSooP8wduC0krkl1Q43O/h25shJCgA3Vs0gD46zO4qatPOKXHFiJzm6aT0FGRO7WdzQNuSPjoMgQE6zBnWTtb+RES+ZuFPJ1FZbR0KVG1He2llDeZuOoHOr270yMAHEZG3c6btokXbyVUGpiVgxwt9sXxcN7z98F8DrUnxkbKe7+o2ldLB+hqDAINBQGy4+smnBaVV0EeHWQ2Se0MIYSJv8/72HK+oOzK8NhERERGRD5ObO3hPToHdnGSuCFHtqnzRnmQrFN+0wW0wa91RWXnYxCs6a0gaZq074jA8YFw9xyvGAfMZ/0oG/MUVI09+tt9huVfsPYvxfVPw6pA0h6HTE2LC0DmxPjJPXUVFtQGT0lvh4505zL9GRHVKebUBnWb9iP97sIPZQEdhaaVTxy0qM88bTUREf1HbdtGq7eQqUil8vKFN5Wiw3jLEt1SbSS2pdk2X5DjEhgejiO0KIjNTvz6Evq0bISTIc+uNOehMREREROTDtFxNbC9PsRquyhftaqZ5ykw7sMTZ/ZavJb+43OEArCmzfGQBsJm/DagNDxgTLm9FtGlHk9LOqYFpCZiUnoK5m7Jt7mvaCTeofWM8ca4I72/PkdxXh9rwXr3f2mrW2VQ/gk1QIqp7rlfUmA0Q1xgEzFp3RJNjO8rTSUTkr9S0XXwxEpM3tKmUDNYX36iUbDOpJdWuCQzQYXSPZOZ1JrJQWFaFbrM34/UH0jw2aZEtfiIiIiIiH+YNM98tmQ7aPnx7M8zbdMLuoKo3dKSLZd50JB/fHDhvFpq6diVzKmatsx+KT47xfVpiUv9Wxtcshge0XAlgOjBdYxAUdzSp6ZxSGrpv6qBUdLi5Pl5enYUCkxV9CTFhuL9DAj7YnmN17sKyarvH1gFoFB0KQIeLJdJlJyLyVuIAsaPOeSU8ueKOiKiu8ca2kxTLSbDTBqciY5n9iapq21S2JtyakjsIn19SjjfXH7Nbh48ND8bjdyRh+Z7TuHjNdlQQR4Pp4/u2xJJfclBUxtXORKYKSmsnfjiTKs0ZHHQmIiIiIvJh3jDz3ZRUKLXYiNo8XqYdAqaDqlqT03HiqMymalcy2w89LVePlvFWZXEUHlAMf+1oRbTpcdU8R00n3KD2CRiQZl72zon10futrQ4HjG2V65V72yL70nWuXCAinyMOEGu9Qs6bVtwREfkyb2s7SbGVzuefvZLx3cE8mxNVHZFqI208ki95Lstj5l4plVX2gusVDiddFd2oQtfmDfBMvxTM33JSss4vZzA9MECHOcPaOUwRROSvPBUth4POREREREQ+TM3goqvYCj9dXFYFAcCk9BQkxUdqki/aXhnkdJw4KrMpLVbbOurAchQe0NaK6JjwYIzukYT+qXrZz7HVOaW2E86y7Jmnrspa4Vc/MsRshbT+zxXSs9Zpk/+NiMgTxM58LXl6xR0RUV3hTW0nKfbS+XywPQcLHrkV9SNDFeWxFo8rNTFYapVwfnG52SrJGoOA5XvOODxHQkwY4iLlpQXKL76BPTkFSIqPwKT0FCzfcwb5JRXGx+UOpg9MS8AiifYOkb/zZH56DjoTEREREfk4pYOLrlBjEDBzje3w0zoAK/aexY4X+rqsE8deJ41UeCl7ZXaGqzqwxBXR87ecxJKdOSi6UYWiG1WYuykbK/aelbzXjlZRm9KqE07uirxpg9tAHxNuLFdhaQUylv1m837c116P7dlXUHzDfohuIiJPEr9n7U3ikcsbVtwREdU13tB2kiKnPTVr3VHF7SlbbSRbYanFc5mmjDAdELbl4dubQR8TLqtMs9YdNZ98Gh2GSemtkBQfoXiCsml7Z+ORfHy8M1fW84j8gSei5XDQmYiIiIioDnA0uKg05LRSjvJXunqmrZxOGsvwUlrm3BQHBsTcz67qwNp4JB/zNp2QPbAOOF5FbUqLTji5K/L0MeHGctUYBPR8Y4vdwZk1v+fLOi4RkSeYDhDbm8Sj5HiAZ1fcERHVVQPTEtC3dSN8mpmL0wVlSIyLwGPdkxASFOCxMrmiPaV2kq3pueQOWiXFR8iedGU64AwAF0vKMW/TCSx8tJOqtqLY3uneogG6JMfJWvkcoAMMWs8+JvIynoiWw0FnIiIiIqI6wtbgotKQ02rI7Yxw1UxbNZ00astibxXwwDTrHMdaDfBXVhvw4jeHFA2sq6FkdbQUNWG6tZwAQESkVudmMdh3pljx86QGiG1N4pHbye3pFXdERHWZVPvowx05Hv3edUV7ytk6tpKUEQ2jwlRPujJty/Rt3Qj7TheqbkuZtmXyi2/gyvVKFJXVDnLHRgQjPioM+ugwdE6sj32nC7HpSD5W/HoWpRU1ss9B5A3sfcY8GS2Hg85ERERERHWY0pDTainpjHAFNZ00Ssry10rmNpi17qjdVcBKVhbLtT4rDy9+k4WCUukweIC2q8mdeQ1qwnRrORkhISYML93TBq+ssX+9iMj39WgRh8pqAXtPFzp9rNiIYKx6sgc2ZOVj/PL9dgeGLb/bbA0QS03iKSytRMay/QCsvx8FAGN6JKF/ql7ziCRERFTLXe0jpVzRnnK2ji0nZYTl4JatSVdxkcGy2jLdZm82WwmtZrK03LaMuDr6xcGpeHdzNuZtzpZ9DiJ3iAgOwOgeydDpdOiaHIcAnQ5XSisc1ikBz0XL4aAzEREREVEdpSbktFpqVrdqSU0njdKcm3+tZE5waahyS7Y6xmzxRN4mS0rDdGsxGeHv3RNxT1qC8X4EBenw1GfWjXAiqjtubVYfk/rfgs6vbrSZm1KuOcPaITBAh0HtEzAft+LpZb9Z7SN+0y94pBPqR4bI+h2Q6vheGOB9uUSJiPyBO9tHSrmiPaW2ji03ZYStwS2pSVf5JeWYtPKAw3Nbht52x2SAwAAdJvZvhdYJUZjx3WFZOayJ3OF/D3W0+773xjolB52JiIiIiOoouSGnl+7MQXxUqFMDqGo6I7SkppNGbvg3y9n1rljJbIuaPGyeyNskRUmYbqUTAKTck5Zgdl9sDXzLpY8OxSv3puLl1VwxTaQ1tXmOLXVvHo/AAB3mDGsne3KOZXhrfXQoZtzf1qxjblD7xlgUoHNZJ56zaQyIiEgdV+RNNlVjEFR/t7uiPaWmjq0kZYS930XLNlPmqauyy23KnZMBxN/nXaeuImPZfhTdkG4D6FAbIaWiugZllQaXlYf8V2xEMOYMa+ewzumNdUoOOhMRERER1VFyV7zOWnfU+P/O5HpW0xmhFbWdNLbK3CAyBEM6NvZ4iFMledg8mbfJFrkD9I7un71OMnuv27IRnnulDMv3nEF+if1rqgOMg1DpqXqrMH+WYiOCERYUaHbcyJBAVNUYUFnDddbk38bdmYy1v+dJ/i4AwJSvD6lapRwbEYxuf36/2PouT4gJw7TBqWarksUcjo465lzdiefOCUxERFTLFXmTRVJ5opW2rbRuT8mpY8dGBJv9DitJGaHkd9GZSaZaphFyJDBAhx4p8ZgzvJ1k1CTx1c4e1g59Wzdy2E4gsicuMhgvDUpFQWklim5UQofaSZXdWjRQNGHFm+qUOkEQ2AJWoaSkBDExMSguLkZ0dLSni4OkKes8XQSj3DmDPV0EIiKiOs/b6iIkj7vvW+apqxixeJei54jNGmfClzkzw9/Z82w8kq+qs0dNmd3xOlcfOI8JKw7I2lcH5+6bN5DqrIuLDMbwTk3w4c+5AKQ7fZS8btP7lnul9M9B6L9C6Em9X8QQ5/bOL9UJV2MQ0G32JkUrpVvr6+FY/nXZ+xPZExESgODAQBTbWKnjSqafJXvflzUGAbv+uIrMU1dhEAyoHxGK+KhQnLlairmbbOdWXCTxuXfX748vYx3S9/CeEWlHbvto+bhuigZxbKXDUdu2svw9kzthyl75bLWRtJxg5eh32FadWq63H+6IIR2baF4uW+RMJHD2NVHdFRcZguGdmmDxzzlWj2nR7+JOSuoiHHRWydsqfBx0JiIi8i/eVhchedx932oMAnq+sUXxbHJx5eiOF/oCgFd24Lur40TN+bVsNMrtGIuLDMbrDzgOv+ULvv8978+Q1n+tGEiICcP9HRLw3cE8za+53E4otfdcSU5u8bM3bXAb/GftUYcrsqnuSm/TEJuOXrL5uOWqJFsWPdoJABTlhXdGXGQIhmoYJWJ9Vp5VbkWpcNgkH+uQvof3jEg7jtpHpu0gub9h4jFtRSeydUxX10GlyunKNpLcctqaZCpnkqbSyQBKymWLnOsmdQ5bdDrA1oicGLYbAAol6nmRIYEoraxxeA7yDH10KEZ0aYak+Eiz94q7+g5ciYPObuBtFT5vGnSWwoFoIiIibXlbXcSfLFiwAG+99Rby8/PRoUMHvPvuu+jSpYus53rivjkz83pSegpW7D3rdY0jrVcSePP55UwcaBAZgsyp/RASFKDJOT3J0bVd8EgnszC57p4EoeUqCXuWj+uGLslxsvPJSXVK2RIWHIDyKuW558KCdAgLDrJZlroqLCgAI7o2w7UbVdh49JLZimHx+7C62oCp3x7CtfK/OgEt8wZbiosMxpCOTbD6wAWrCRbid+z3v1+wyiluOblmw+E8rNh71uqeWuaBs9XZNW1wG7z4bZaq8NaieqGBeOi2pkh3UToCrl7WFuuQnqO2Dsl7RqQtORFslNTl1ayeVjJA68l2j1xKyym1krv3W1s1nQygplzOcBRNKTY8GOltGuLL/ecdHuvzf3RFgE6H/OIbKCitRFy9UOija69Trze3yp6YGqAD6kcE46qCqEveIlAHBAcGoLz6rzpuZEggqgwCKquVtWUc1csBIDosECXlygb0I4IDMKn/LYivFwJ9TLjdOqqv12c56OwG3lbh46AzERGRf/G2uoi/WLlyJf7+979j0aJF6Nq1K+bNm4cvvvgCx48fR8OGDR0+31P3TemAlz2e7uBQu5LAl8+vdceYt/L0vXW1GoOAuRtPYP7Wkw73NQ0dKOf+A8CUr35H0Y1qm8cUr9+25/oYwzPmXinDvE0nrI4t+leflmjRqJ6xYwSojXyw6Ug+vjlwXlHYcDXqRwTjka7NANR21MXXC0XDqDDszS3A0l9ybQ6AhwTqcF+Hxpg9rD22HLto9f0XERIIQRBww2Sg1rIzql5oEP7RMxnP9EsxCwNtL0S06WOFpRXIWPYbAPufW0cdUHI6qEzDUwOCzTxwto61PisPT/75HrNHzP04sV9L1Pb12T4XeS/WIT3DmTok7xmR9rRceSg3HY5Yv5M7EOordWOtyql1m8fT10+q3rX29wuK3itSHE0qjwwNxMMWkwFrI8cc0SSKUu08Zx2qHY3iSggLDoDBIKCyRvq5wYE63No0Fs/0TcEdLeMBWEddA4Bdp64i848rEAQgNiIEF4rKsOSX01bHk5q4HBcegmMXr+FMQRkAAR1vjkXj+hE2U3XZI5Xqpa7ioLMbeFuFj4PORERE/sXb6iL+omvXrrj99tsxf/58AIDBYEDTpk3xzDPPYMqUKQ6f78n7ZtrovXKtArPWHVV9LE92cLgqD5u3n78uhORyxNP31h3UvkY597/GIOCdzdl4e7N1Dlx7nXVq31vid4rUAHRCTBheuqcNLl4rx+mCMjStH47W+mj8dPySrMFqOZ2Lpt9p8ZGhgA64cr1C9sAtAE1zJUrxpc+tVGek5UC8t5adlGEd0jOcqUPynhG5hlYrD5XU77okx8keCN2TU+ATdWMt6/Ba1p28sW2hVZmkrlNsRDBG35GM8X1bSr6Pxff7B9tPYevxy3bPHxcZjP/c1xYNosKQX1KOgusViIv8azUvAMzfchIfbD9lN9z3xH4tcXtSA1wprbCqg+eXlOPKtXIUlVVBp9Ohe4sG6NZc/URGrcPQbzySj28tIhI5c1xfp6QuEuSmMhERERER+bTKykrs27cPU6dONW4LCAhAeno6MjMzJZ9TUVGBioq/QmqVlJS4vJy2BAbojA3XGoOAD3fkKM71LBIA5BWXY09Ogds7OC5dkzfrWO5+vnL+gWkJbslV7Umevrfu0CU5DgkxYQ5DB4odMiI59z8wQIdJ/VuhTUKUVYeL3k7HiNr3lvid0r1FA7w4OFXW8+9sdZPVvoWltZNg5JbX8vxy2NrXcpvW32e+9LmVKqsrBuKJ/JHSOqQ31R+J6jIldQl7lNTv9uQU2F1FadrO8pW6sZbl1LLu5I3XT21bwJKa62Rad5dK4RIXGYwHOjaRnS5lQnoKxvdtiflbsrFkp3kEIkeDsq7oQ9DqvWN6nV76s90iNfDOOrFtHHQmIiIiIpLhypUrqKmpQaNGjcy2N2rUCMeOHZN8zuzZszFz5kx3FE+RwAAdpt+Xiqc+228Mlyqy/NseT3RwNIwK03Q/Xzq/Vh1j3srT99YdHH32AGD6famSnRhy778znVBqOTsAPCAtwScGZtXwpc+tVFl9pexE3kxpHdJb649EJE1J/U7JQKiv1I21LqdWdSdvvH7OtAWkjqX2Og1q31iT+ndggA4T0lthfN8Ur6jLa13v9qV6vDcJ8HQBiIiIiIjqqqlTp6K4uNj47+zZs54uktHAtAQsfLQT9DHmjWx9TBgmpbeSdQxPdHCIs8NtNWF1qJ1Z7Wh2uK+evy7zl2tr77OnVX5usYNkSMcm6O4D+XZ9rbxERK7kzfVHIpImt36nZCDUV+rG3lpOby2XO9oCcmhZ/2ZdnkxxpTMRERERkQzx8fEIDAzExYsXzbZfvHgRer1e8jmhoaEIDQ11R/FUsbUiEgBW7D3jdNgvV9Bydrgvnr8u86dr60thl4mIyDlK65DeXn8kImly6ndKwiv7St3YW8vpreUC2Baguo0rnYmIiIiIZAgJCUHnzp2xefNm4zaDwYDNmzeje/fuHiyZc6RmJYsNdABWM8M93UAHPD873NPnr8v86dpyRQARkX+oq3VIIrLmqH6ntJ3lK3Vjby2nt5YLYFuA6i6dIAhyU7aRiZKSEsTExKC4uBjR0dGeLg6SpqzzdBHsyp0z2NNFICIiqlO8rS7iL1auXIlRo0bh/fffR5cuXTBv3jysWrUKx44ds8rTJ8XX7tv6rDzMXHMEecV/5R5LiAnD9PtSvaKDo8YgeHR2uKfPX5fx2hIRuYav1UXqCmfqkLxnRHWP0naWr9SNvbWc3louIl+hpC7C8NpERERERDI99NBDuHz5Ml555RXk5+ejY8eOWL9+vawBZ1/k7WG/xNnh/nr+uozXloiI6hJ/q0MSkX1K21m+Ujf21nJ6a7mI6iKudFbJ22YZcqUzERGRf/G2ugjJw/tGREREnsS6iO/hPSMiIiJPUlIXYU5nIiIiIiIiIiIiIiIiIiJSjeG1yS2kVmJz9TMRERERERERERERERGR7+NKZyIiIiIiIiIiIiIiIiIiUo0rnX2Qt+dvJiIiIiIiIiIiIiIiIiL/wZXORERERERERERERERERESkGgediYiIiIiIiIiIiIiIiIhINQ46ExERERERERERERERERGRahx0JiIiIiIiIiIiIiIiIiIi1YI8XQAiU0lT1llty50z2AMlISIiIiIiIiIiIiIiIiI5OOhMHiM1wExEREREREREREREREREvsXvw2svWLAASUlJCAsLQ9euXbFnzx5PF4mIiIiIiIiIiIiIiIiIyGf49UrnlStXYvLkyVi0aBG6du2KefPmYcCAATh+/DgaNmzo6eLRn+SuiGYYbiIiIiIiIiIiIiIiIiL38+tB5//9738YN24cRo8eDQBYtGgR1q1bh48//hhTpkzxcOlIKWfCdUsNWDO/NBEREREREREREREREZFjfjvoXFlZiX379mHq1KnGbQEBAUhPT0dmZqbV/hUVFaioqDD+XVxcDAAoKSlxaTnTpm9w6fGpVrNJX2i6n6/JmjlA1n5y34/OHE/uc53hzHnryjUga+64F3XlHJ7izOevLn52xTqIIAhuPS85R7xfrq5DEhEREUlhHdL3sP5IREREnqSk/qgT/LSWeeHCBTRp0gS//PILunfvbtz+/PPPY9u2bdi9e7fZ/jNmzMDMmTPdXUwiIiIiu86ePYubb77Z08Ugmc6dO4emTZt6uhhERETk51iH9B2sPxIREZE3kFN/9NuVzkpNnToVkydPNv5tMBhQUFCABg0aQKfTueScJSUlaNq0Kc6ePYvo6GiXnIPU4/3xXrw33ov3xrvx/ngvqXsjCAKuXbuGxo0be7h0pETjxo1x9uxZREVF4dq1a/zMeRl+D3of3hPvxPvifXhPvI+33hPWIX2Paf3RVX2QWvHW931dx+vuGbzu7sdr7hm87p7hTdddSf3Rbwed4+PjERgYiIsXL5ptv3jxIvR6vdX+oaGhCA0NNdsWGxvryiIaRUdHe/xNRbbx/ngv3hvvxXvj3Xh/vJflvYmJifFgaUiNgIAA46xQsdOQnznvw3vifXhPvBPvi/fhPfE+3nhPWIf0Lab1R1/hje97f8Dr7hm87u7Ha+4ZvO6e4S3XXW79McDF5fBaISEh6Ny5MzZv3mzcZjAYsHnzZrNw20REREREREREREREREREZJvfrnQGgMmTJ2PUqFG47bbb0KVLF8ybNw+lpaUYPXq0p4tGREREREREREREREREROQT/HrQ+aGHHsLly5fxyiuvID8/Hx07dsT69evRqFEjTxcNQG1I7+nTp1uF9SbvwPvjvXhvvBfvjXfj/fFevDd1E++r9+E98T68J96J98X78J54H94T8kd833sGr7tn8Lq7H6+5Z/C6e4avXnedIAiCpwtBRERERERERERERERERES+yW9zOhMRERERERERERERERERkfM46ExERERERERERERERERERKpx0JmIiIiIiIiIiIiIiIiIiFTjoDMREREREREREREREREREanGQWcvtmDBAiQlJSEsLAxdu3bFnj17PF0kvzN79mzcfvvtiIqKQsOGDTF06FAcP37cbJ/y8nJkZGSgQYMGqFevHoYPH46LFy96qMT+a86cOdDpdJg4caJxG++NZ50/fx6PPvooGjRogPDwcLRr1w6//vqr8XFBEPDKK68gISEB4eHhSE9PR3Z2tgdL7B9qamowbdo0JCcnIzw8HC1atMCsWbMgCIJxH94b99m+fTvuu+8+NG7cGDqdDt9++63Z43LuRUFBAUaOHIno6GjExsZi7NixuH79uhtfBSnl6PuR3C8pKQk6nc7qX0ZGhqeL5rfk/F6R+127dg0TJ05EYmIiwsPDcccdd2Dv3r2eLpZf0aLuQNpydE++/vpr3H333WjQoAF0Oh0OHDjgkXISuZNUHw1pb8aMGVb119atW3u6WHUe23Pux/aaZ7BN5hm+3ubioLOXWrlyJSZPnozp06dj//796NChAwYMGIBLly55umh+Zdu2bcjIyMCuXbuwceNGVFVV4e6770Zpaalxn0mTJmHNmjX44osvsG3bNly4cAHDhg3zYKn9z969e/H++++jffv2Ztt5bzynsLAQPXr0QHBwMH744QccOXIE//3vf1G/fn3jPm+++SbeeecdLFq0CLt370ZkZCQGDBiA8vJyD5a87nvjjTewcOFCzJ8/H0ePHsUbb7yBN998E++++65xH94b9yktLUWHDh2wYMECycfl3IuRI0fi8OHD2LhxI9auXYvt27fjn//8p7teAikk5/uR3G/v3r3Iy8sz/tu4cSMA4MEHH/RwyfyXnN8rcr9//OMf2LhxIz799FMcOnQId999N9LT03H+/HlPF81vaFF3IG05uielpaXo2bMn3njjDTeXjMgzbPXRkGu0bdvWrB67Y8cOTxepTmN7zjPYXvMMtsk8w+fbXAJ5pS5duggZGRnGv2tqaoTGjRsLs2fP9mCp6NKlSwIAYdu2bYIgCEJRUZEQHBwsfPHFF8Z9jh49KgAQMjMzPVVMv3Lt2jUhJSVF2Lhxo9C7d29hwoQJgiDw3njaCy+8IPTs2dPm4waDQdDr9cJbb71l3FZUVCSEhoYKy5cvd0cR/dbgwYOFMWPGmG0bNmyYMHLkSEEQeG88CYDwzTffGP+Wcy+OHDkiABD27t1r3OeHH34QdDqdcP78ebeVneRz9P1I3mHChAlCixYtBIPB4Omi+C1Hv1fkfmVlZUJgYKCwdu1as+2dOnUSXnrpJQ+Vyr+pqTuQa1neE1M5OTkCAOG3335za5mI3MlWHw25xvTp04UOHTp4uhh+he0578D2mnuwTeZ+daHNxZXOXqiyshL79u1Denq6cVtAQADS09ORmZnpwZJRcXExACAuLg4AsG/fPlRVVZndq9atW6NZs2a8V26SkZGBwYMHm90DgPfG07777jvcdtttePDBB9GwYUPceuutWLx4sfHxnJwc5Ofnm92fmJgYdO3alffHxe644w5s3rwZJ06cAAAcPHgQO3bswD333AOA98abyLkXmZmZiI2NxW233WbcJz09HQEBAdi9e7fby0yOOfp+JM+rrKzEZ599hjFjxkCn03m6OH7L0e8VuV91dTVqamoQFhZmtj08PJyrqrwE63FE5Gm2+mjIdbKzs9G4cWM0b94cI0eOxJkzZzxdpDqN7TnPY3vNfdgmc7+60OYK8nQByNqVK1dQU1ODRo0amW1v1KgRjh075qFSkcFgwMSJE9GjRw+kpaUBAPLz8xESEoLY2FizfRs1aoT8/HwPlNK/rFixAvv375fMacB741l//PEHFi5ciMmTJ+PFF1/E3r178a9//QshISEYNWqU8R5Ifc/x/rjWlClTUFJSgtatWyMwMBA1NTV47bXXMHLkSADgvfEicu5Ffn4+GjZsaPZ4UFAQ4uLieL+8lKPvR/K8b7/9FkVFRXj88cc9XRS/5uj3itwvKioK3bt3x6xZs9CmTRs0atQIy5cvR2ZmJlq2bOnp4hFYjyMiz7LXR0Ou0bVrVyxduhS33HIL8vLyMHPmTNx5553IyspCVFSUp4tXJ7E953lsr7kP22TuVxfaXBx0JpIpIyMDWVlZPjOjpK47e/YsJkyYgI0bN1rN/CHPMxgMuO222/D6668DAG699VZkZWVh0aJFrIR72KpVq/D5559j2bJlaNu2LQ4cOICJEyeicePGvDdEbsDvR+/30Ucf4Z577kHjxo09XRS/xt8r7/Tpp59izJgxaNKkCQIDA9GpUyeMGDEC+/bt83TRiIjIg9hH4xmmqw3bt2+Prl27IjExEatWrcLYsWM9WLK6i+05z2N7zX3YJvMMX29zMby2F4qPj0dgYCAuXrxotv3ixYvQ6/UeKpV/Gz9+PNauXYutW7fi5ptvNm7X6/WorKxEUVGR2f68V663b98+XLp0CZ06dUJQUBCCgoKwbds2vPPOOwgKCkKjRo14bzwoISEBqampZtvatGljDPMk3gN+z7nfc889hylTpuDhhx9Gu3bt8Nhjj2HSpEmYPXs2AN4bbyLnXuj1ely6dMns8erqahQUFPB+eSlH34/kWadPn8amTZvwj3/8w9NF8XuOfq/IM1q0aIFt27bh+vXrOHv2LPbs2YOqqio0b97c00UjsB5HRJ7jqI+mpqbG00X0C7GxsWjVqhVOnjzp6aLUWWzPeRbba+7FNpln+Hqbi4POXigkJASdO3fG5s2bjdsMBgM2b96M7t27e7Bk/kcQBIwfPx7ffPMNtmzZguTkZLPHO3fujODgYLN7dfz4cZw5c4b3ysX69euHQ4cO4cCBA8Z/t912G0aOHGn8f94bz+nRoweOHz9utu3EiRNITEwEACQnJ0Ov15vdn5KSEuzevZv3x8XKysoQEGD+8x8YGAiDwQCA98abyLkX3bt3R1FRkdlsxy1btsBgMKBr165uLzM55uj7kTxryZIlaNiwIQYPHuzpovg9R79X5FmRkZFISEhAYWEhNmzYgCFDhni6SATW44jIcxz10QQGBnq6iH7h+vXrOHXqFBISEjxdlDqL7TnPYnvNvdgm8yxfbXMxvLaXmjx5MkaNGoXbbrsNXbp0wbx581BaWorRo0d7umh+JSMjA8uWLcPq1asRFRVlzIMVExOD8PBwxMTEYOzYsZg8eTLi4uIQHR2NZ555Bt27d0e3bt08XPq6LSoqyphbWxQZGYkGDRoYt/PeeM6kSZNwxx134PXXX8ff/vY37NmzBx988AE++OADAIBOp8PEiRPx6quvIiUlBcnJyZg2bRoaN26MoUOHerbwddx9992H1157Dc2aNUPbtm3x22+/4X//+x/GjBkDgPfG3a5fv242Cz0nJwcHDhxAXFwcmjVr5vBetGnTBgMHDsS4ceOwaNEiVFVVYfz48Xj44YcZaspLOfp+JM8xGAxYsmQJRo0ahaAgNpM8zdHvFXnGhg0bIAgCbrnlFpw8eRLPPfccWrduzXaqGzlbdyDtObonBQUFOHPmDC5cuAAAxsEKvV7PFehUZ8jpoyHt/fvf/8Z9992HxMREXLhwAdOnT0dgYCBGjBjh6aLVWWzPeQ7ba+7HNpln+HybSyCv9e677wrNmjUTQkJChC5dugi7du3ydJH8DgDJf0uWLDHuc+PGDeHpp58W6tevL0RERAgPPPCAkJeX57lC+7HevXsLEyZMMP7Ne+NZa9asEdLS0oTQ0FChdevWwgcffGD2uMFgEKZNmyY0atRICA0NFfr16yccP37cQ6X1HyUlJcKECROEZs2aCWFhYULz5s2Fl156SaioqDDuw3vjPlu3bpX8nRk1apQgCPLuxdWrV4URI0YI9erVE6Kjo4XRo0cL165d88CrIbkcfT+SZ2zYsEEAwO87LyHn94rcb+XKlULz5s2FkJAQQa/XCxkZGUJRUZGni+VXtKg7kLYc3ZMlS5ZIPj59+nSPlpvI1Sz7aEh7Dz30kJCQkCCEhIQITZo0ER566CHh5MmTni5Wncf2nGewveZ+bJN5hq+3uXSCIAjuGd4mIiIiIiIiIiIiIiIiIqK6hjmdiYiIiIiIiIiIiIiIiIhINQ46ExERERERERERERERERGRahx0JiIiIiIiIiIiIiIiIiIi1TjoTEREREREREREREREREREqnHQmYiIiIiIiIiIiIiIiIiIVOOgMxERERERERERERERERERqcZBZyIiIiIiIiIiIiIiIiIiUo2DzkREREREREQK6HQ63HXXXWbbZsyYAZ1Oh59++sm4LTc3FzqdDo8//rhby+fNHn/8ceh0OuTm5nq6KEREREREXkmqzsy2BfkCDjoTkccsXrwYrVu3RlRUFLp3746dO3dK7vfjjz8iMDDQ5uO2/PTTT9DpdNDpdLjtttts7vfDDz8Y9xM7D5cuXWrcJuefr/zYi9dkxowZHjm/eF2XLl3qkfMTERGRdxM7UgYOHOjpohARERH5NdbLiIhIqSBPF4CI/NOXX36Jf/7zn+jRowcGDx6Mr7/+GgMGDMDRo0fRtGlT435lZWV48skn8cQTT6BHjx6qzhUUFIR9+/bh999/R/v27a0e/+ijjxAUFITq6mrjto4dO2L69Olm++Xm5uKTTz5Bhw4dMHToULPHOnbsqKpsRERERFQ3jB8/Hg8//DCaNWvm6aJ4tdmzZ2PKlClo0qSJp4tCREREREREGuKgMxF5xOLFi3HLLbdg+/btCAgIwL/+9S80b94cn3/+OaZMmWLc7+WXX0ZlZSXmzJmj+lwDBgzADz/8gI8//hjz5s0ze+zKlStYs2YNBg0ahO+++864vWPHjlYDyT/99BM++eQTdOzY0WMrhYmIiIjIO8XHxyM+Pt7TxfB6CQkJSEhI8HQxiIiIiIiISGMMr01EHnH27Fl07NgRAQG1X0OJiYmIj4/HmTNnjPvs3bsX77zzDt577z1ER0erPtfNN9+M/v374/PPP0dlZaXZY5999hkqKysxZswY1ceXa/v27Rg6dCgaNWqE0NBQNG3aFMOGDcOOHTvM9istLcX06dPRunVrhIWFIS4uDoMHD5YML26aO3DZsmXo2LEjwsPDkZCQgAkTJuDGjRtm+/bp0wcAMHPmTLPw4Kb5QSorK/G///0PnTp1QmRkJKKionDnnXeaDcqLxPwiOTk5eOedd9C6dWuEhoYiMTERM2fOhMFgMNt39OjRAIDRo0ebnZ+IiIhIjdOnT2Ps2LFo0qQJQkJCcPPNN2Ps2LFmdUpTv//+OwYNGoSoqCjExMRg0KBByMrK0iTPsFROZ1OHDx/G4MGDERsbi3r16uHuu+/Gvn37rPa76667oNPpUF5ejpdffhktWrRAcHCwcdLjiRMn8Pzzz6NTp05o0KABwsLC0KpVK0yZMgXXr19XdLxHH30UOp0Oe/bskSzzK6+8Ap1Oh+XLlzt8/dnZ2Rg9ejSSk5MRGhqKuLg4dOjQARMnToQgCMb9pK51UlKS3VQ2lhM+L126hEmTJqFly5YIDQ1FfHw8hg8fjqysLIflJCIiItfwRL3MNMftyZMn8cADD6B+/fqIjIxEeno6Dh48aPUc0/R6lpKSkpCUlGS2TSzPH3/8gf/7v/9Dq1atEB4ejtTUVKxYsQJAbV/aSy+9hKSkJISFhaF9+/b44YcfHJZfjurqasyePRstWrRAWFgYWrZsidmzZ+OPP/6wmfJPSV1JfM3Xr1/HhAkT0LhxY4SGhqJ9+/b48ssvJcukpO+wuLgYr7zyClJTU1GvXj1ER0ejZcuWGDVqFE6fPu3w9ZumCtyxYwfuuusuREVFITY2FsOHD8fJkycln6fmGhQVFWH8+PFo2rQpgoKCZKUHFAQBS5YswZ133onY2FhEREQgJSUFTzzxhNV7/9q1a5g+fTratm2L8PBwxMbGYsCAAVZ9w0S+jCudicgjmjZtioMHD8JgMCAgIABnzpzBlStXjOEIq6ur8Y9//AMPPPAA7r//fqfPN2bMGGzYsAFr1qzB8OHDjds//vhjtG3bFl27dnX6HPa8/fbbmDRpEsLDw/HAAw+gWbNmOH/+PHbs2IEvv/wSPXv2BACUl5ejb9++2LNnDzp16oSJEyfi4sWLWLlyJTZs2IDly5fjwQcftDr+/PnzsX79egwZMgR9+/bF+vXr8c477+DKlSv4/PPPAdR2OIohwnv37m1WwY6NjQUAVFRUYODAgfjpp5/QsWNHjB07FlVVVVi3bh2GDBmCd999F+PHj7c6/3PPPYdt27bh3nvvxYABA/Dtt99ixowZqKysxGuvvQYAGDp0KIqKirB69WoMGTKEIcmJiIjIKSdOnEDPnj1x+fJl3HfffWjbti2ysrLw8ccfY82aNdixYwdatWpl3P/gwYO48847UVpaimHDhiElJQW//vorevbsiQ4dOri0rH/88Qd69OiBTp064amnnsLp06fxxRdfoFevXtiyZYtkXXT48OE4ePAgBg4ciNjYWCQnJwMAvv76a3z00Ufo06cP7rrrLhgMBuzatQtvvPEGtm3bhu3btyM4OFjW8fr164fPP/8cH374Ibp06WK2f01NDZYsWYIGDRpg2LBhdl/fhQsX0KVLF5SWlmLw4MF46KGHUFpaiuzsbLz33nv4v//7PwQF2e5+mDhxIoqKiqy2L1++HCdOnEBERIRx26lTp3DXXXfh3LlzuPvuuzF06FBcunQJX331FTZs2IDNmze7vG5PRERE5jxdL8vNzUW3bt3Qtm1bjBkzBqdOncLq1avRp08fHD16FI0aNXL6NU6ePBm7d+/Gfffdh8DAQKxYsQKPPPII6tevj3fffRdHjhzB4MGDUV5ejmXLlmHIkCE4evQoWrRo4dR5x4wZg08//RTNmzdHRkYGKioqMHfuXGRmZkrur6auVFVVhbvvvhuFhYUYPnw4ysrKsGLFCvztb3/D+vXrcffddxv3VdJ3KAgCBgwYgN27d6NHjx4YOHAgAgICcPr0aXz33Xd47LHHkJiYKOs67Nq1C7Nnz8bAgQPxzDPP4PDhw/jmm2/w888/Y9euXWjevLlT16CiogJ9+/bF9evXcf/99yMoKMjh+8ZgMOChhx7Cl19+iSZNmmDEiBGIjo5Gbm4uVq1ahXvuucfY111QUIBevXrh8OHD6NGjB5588kmUlJQY36dffPGFVTpHIp8kEBF5wKpVqwQAwp133ik8++yzQnJyshAZGSmcOXNGEARBeO2114T69esLeXl5qs+xdetWAYDwxBNPCBUVFUKDBg2EQYMGGR/fs2ePAED473//K+Tl5QkAhN69ezs83qhRoxSV48CBA0JAQIDQuHFjIScnx+wxg8EgnD9/3vj3zJkzBQDCyJEjBYPBYNy+f/9+ISQkRIiNjRVKSkqM26dPny4AEGJiYoRjx44Zt5eVlQmtWrUSAgICzI4vvobp06dLlvXFF18UAAjTpk0zO39JSYlw2223CSEhIWbHGzVqlABASE5OFi5cuGDcfvnyZSE2NlaIiooSKioqjNuXLFkiABCWLFni+MIRERGR38nJyREACAMGDHC4b58+fQQAwvvvv2+2fcGCBQIAoW/fvmbbe/bsKQAQPv/8c7Pt06ZNEwAIAKzqarZI1RvFetnWrVutXg8AYcqUKWb7r1+/XgAgtGvXzmx77969BQBCx44dhatXr1qd+9y5c2b1K5FYj/zss88UHS81NVWIiooSrl+/brZ97dq1AgBh4sSJktfA1DvvvCMAEObNm2f1mOU5xfqjo2u9fPlyQafTCV27dhXKysqM2++44w4hMDBQWL9+vdn+x48fF6KioqyuJxEREanjC/Uy07rWnDlzzB57+eWXBQDC7Nmzzbbb6/9LTEwUEhMTzbaJdZdWrVoJly5dMm7fvXu3AECIjY0VevbsaVaXWrlypQBAeOaZZxy+Bns2bdpkrMeVlpYat1+4cEFo1KiRZD+l0rpSYmKiAEAYMmSIWR1TPLfl/VfSd/j7778LAIShQ4davbby8nLh2rVrDq+B2JcJQFi0aJHZY4sWLRIACPfee68m12DAgAFm9U5H3n33XQGA0K9fP6vnlZWVmdWDH3nkEQGAsHjxYrP9Ll68KDRt2lS46aabhBs3bhi3S9WZxfe70r5pIndieG0i8ogHH3wQCxcuxMWLF7Fo0SI0atQIGzZsQNOmTXHixAnMmjULb731FvR6PebMmQO9Xo/g4GD06dMH2dnZis8XEhKCkSNHYsOGDbhw4QKA2lXOwcHBeOyxx7R+eWbef/99GAwGvPrqq1YhenQ6HRo3bmz8+5NPPkFwcDDmzJljFnb61ltvxahRo1BUVIRvv/3W6hwTJkzALbfcYvw7PDwcI0aMgMFgkAzbKMVgMGDhwoVo0aKFMfy2KCoqCq+88goqKyvx9ddfWz132rRpZrn54uPjMWTIEFy7dg3Hjx+XdX4iIiIiuc6cOYOtW7ciNTUV48aNM3vsySefROvWrbFlyxacPXsWQG24xx07dqBDhw545JFHzPZ/4YUXUL9+fZeWNzY2Fi+99JLZtgEDBqBfv344dOiQZH1t5syZiIuLs9ouhqy0JK4o2bRpk2QZbB3viSeewLVr14zhIUUffvghAFhdX3vCw8Ottkmd05HMzEyMHj0aTZs2xerVq43H/e233/DLL79g1KhRGDBggNlzWrVqhXHjxuHQoUMMs01ERORG3lAvS05OxnPPPWe2bezYsQBq0/dp4aWXXsJNN91k/LtLly5o3rw5ioqK8NprryEyMtL42PDhwxEcHCwZ3luJzz77DEBtyhPTyC9iWj1LztSV5s6da1bH7NevHxITE82un9q+Q6k6YmhoKOrVq+foEliV39S4ceOQkpKCdevW4fLly05fgzfffFOyrLa89957CAwMxMKFC62eFx4ebqwHX7lyBStXrkTfvn3xj3/8w2y/hg0b4rnnnsPly5dt1uOJfAnDaxORxzz55JN48sknzbYJgoB//vOf6NatG8aMGYPly5fjxRdfxH/+8x/cfvvteOGFF/DAAw/g999/N+aDlmvMmDF455138Mknn2DSpElYsWIF7r33Xtx0003Iz89X/TqKioowb948q+1i7jkxR55pKBopJSUl+OOPP9CmTRvcfPPNVo/36dMHixcvxoEDB6wGyjt37my1v3gMqVCFUo4fP47CwkI0btwYM2fOtHpcrLwdO3bM6jEtzk9EREQk14EDBwAAvXv3NuvsAoCAgAD06tULx44dw4EDB4xpXQCgR48eVseKjIxEx44dsXXrVpeV99Zbb5XsVLvzzjuxefNm/Pbbb1b1Kctw1yLhz7xxS5cuRVZWFoqLi2EwGIyPixMsLdk63t///ndMmTIFixcvNnbOXrx4EWvXrsUdd9yB1NRUh6/vvvvuw9SpU5GRkYHNmzdj4MCB6N27t1mYQ7lyc3MxdOhQBAcHY+3atWZhDXft2mUsn2WeZ+CveuqxY8eQlpam+NxERESknDfUyzp27GjVT6h1v5RUmriEhAT88ccfVo8FBgaiYcOGNutlconXSkzLZ0rq+qmtK5mmcjF18803m4XxVtp32KZNG7Rv3x7Lly/HuXPnMHToUNx1112S98uRHj16WD0nICAAPXr0QHZ2Ng4ePIj09HTV1yAsLAzt2rWTXZ7r16/j6NGjaNmyJVJSUuzuu3fvXtTU1KCiokKyTOICq2PHjuHee++VXQYib8RBZyLyKosXL8bu3btx6NAh6HQ6vP3220hPT8fLL78MAKhXrx569uyJjRs3Ws1Wc6RDhw7o1KkTlixZgmbNmqGoqAhjxoxxusxFRUWSFS2xElFcXAydTme2ElhKSUkJANjMFyI+X9zPVHR0tNU2MW9eTU2N3fOKCgoKAACHDx/G4cOHbe5XWlrqkvMTERERyaW03iT+t2HDhpL7a5Hnzx5bxxe3FxcXy37Ov/71L8yfPx9NmzbF/fffj4SEBISGhgKoXc1cUVGhqAyxsbH429/+hk8++QRZWVlIS0vD0qVLUV1dLXuVc1JSEnbt2oUZM2bg+++/x6pVqwAArVu3xn/+8x88+OCDso5TXFyMwYMH4+rVq1izZo1Vx59YX123bh3WrVtn8zhS9VUiIiJyDW+ol7mjX8reOWw9VlVV5dQ5S0pKEBAQgPj4eKvHpK6T2rpSTEyM5H5BQUFmkxuV9h0GBQVhy5YtmDFjBr766is8++yzAICbbroJ48ePx0svvYTAwECbxzEltz6t9ho0bNjQatKEPeL5mjRp4nBfsUw7d+7Ezp07ZZeJyBcxvDYReY28vDw8//zzmD59Olq2bAmgdgad6WzBW2+9FYD0als5xo4di+zsbLzwwgto3Lgx7rnnHqfLnZSUBEEQrP6JYmNjIQgC8vLy7B5HrKBevHhR8nFxNbZURVYL4nGHDx8u+XrEf0uWLHHJ+YmIiIjkUlpvEv976dIlyf1tHUcrto4vbpfq6JPq9Lp06RIWLFiA9u3b49ixY1i6dClmz56NGTNmWEUQknM8kfjcxYsXAwA++ugjREdH429/+5vdY5pKS0vDl19+iYKCAmRmZuKVV15Bfn4+HnroIbuda6Lq6mo8+OCDOHLkCObNmydZTxfv47vvvmu3vjpq1CjZ5SYiIiLn+Fq9TKfTobq6WvIxqYmAnhQdHQ2DwYArV65YPSZ1nVxdV1LThVRlfAAAC8RJREFUd9igQQO8++67OH/+PI4cOYL58+cjLi4O06dPx5tvvin73HLr02qvgZIBZ9PznT9/3uG+YpmeffZZu2WaPn26ojIQeSMOOhOR18jIyEBSUhL+/e9/m203Xa0h/r/SioDokUceQVhYGM6fP4+///3vsmfTOUMMZfjjjz/a3S86OhrNmzfHyZMnJSssP/30EwDpcD5yia9XapZnmzZtEB0djV9//dXpmZhqzk9EREQkl1gf2r59u9lkP6A2/PT27dvN9uvQoQMA4JdffrE6VllZmdP59hz57bffcP36davtP//8M4C/JlY68scff0AQBKSnp5vl9TM9lhrdunVD+/bt8dlnn+HHH39EdnY2Ro4caXUOOYKDg9GtWzfMnDkT77zzDgRBwNq1ax0+b/z48di4cSOeeeYZY35qS127dgUAszCPRERE5Fm+Vi+rX7++ZL9bbm6u16WIE6+V1AQ+qevn6rqSM32HOp0Obdq0QUZGBjZu3AgA+O6772Q/f+fOnWarroHaHNO//PILdDqd8Vq5q75Yr149pKamIicnxxge25bbb78dOp2OdVjyCxx0JiKv8PXXX+O7777Dhx9+aAxNA9RWZn788UfjDMTvv//euF2N2NhYbNiwAd988w0mTZrkfMFlePLJJxEYGIiXX34Zp0+fNntMEASz/C6jRo1CVVUVpk6dalZR//3337F06VLExMRg6NChqssSFxcHADh79qzVY0FBQXjqqadw+vRp/Pvf/5asPGZlZdmciers+YmIiIjkatasGfr06YPDhw/j448/Nnvsgw8+wNGjR9G3b180bdoUAJCYmIgePXrgwIEDWLlypdn+b731ljHknasUFRXhtddeM9u2YcMGbN68GWlpaVb5nG1JTEwEUNvJaNrpdu7cOUydOtWpMj7xxBMoKCjA6NGjAUB2aG0A2Ldvn2QKGHHlSVhYmN3n//e//8X777+PQYMGYe7cuTb369KlC7p27Yrly5db3UegtuNx27ZtsstNREREzvO1etntt9+O3NxcszpDZWUlJk+e7NLzArUrqY8dO+YwGqJo5MiRAID//Oc/uHHjhnF7fn4+3n77bav9XV1XUtp3mJubi9zcXKt95NYRTZ04ccIYlUe0ePFinDhxAoMHD8ZNN90EwL31xYyMDNTU1ODpp582uz8AUF5ebnwv6/V6/O1vf8Mvv/yCt956y2pyBgDs3r0bZWVlmpSLyJOY05mIPK64uBjjx4/HhAkTcNttt5k9NmHCBDz88MPo06cPOnbsiKVLl6Jdu3bo16+f6vP16tXL2SIr0q5dO8ybNw//+te/0LZtWwwdOhSJiYnIz8/H9u3bMXjwYMybNw8A8Pzzz2PdunX49NNPcfToUfTr1w+XLl3CypUrUV1djcWLFyMqKkp1WVq3bo3GjRtjxYoVCA0Nxc033wydTodnnnkGMTExmDlzJvbv34933nkH69atQ69evdCwYUOcP38ehw4dwsGDB5GZmWkz744j3bt3R3h4OObNm4fCwkJjhVDM2U1EREQEAIcOHcLjjz8u+Vjr1q0xZcoULFy4ED179sS4ceOwZs0apKam4vDhw/juu+9w0003YeHChWbPe/fdd9GrVy+MHDkSX331FVq2bIn9+/dj165d6NWrF7Zv346AANfMy77zzjuxcOFC7N69G926dUNubi6++OILhIeH48MPP5R9nISEBAwfPhxfffUVbrvtNvTr1w8XL17E2rVr0a9fP5w6dUp1GR999FE8//zzuHDhAjp37ix79TUAfPrpp3j//ffRq1cvtGjRAtHR0Thy5Ai+//57xMXFGQeypeTn5+P5559HQEAAWrdujVmzZlntc9ddd+Guu+4CACxfvhx9+vTBww8/jHnz5qFTp04IDw/HmTNnkJmZicuXL6O8vFzx6yciIiJpda1eNnnyZPz4448YNGgQRowYgYiICGzcuBGxsbHG/NOu8s0332D06NEYNWoUli5d6nD/9PR0PPLII1i2bBnatWuHoUOHoqKiAqtWrULXrl2xZs0aq+vk6rqSkr7DAwcOYNiwYejSpQtSU1Oh1+tx/vx5fPvttwgICFC0IGjAgAH417/+he+//x5t27bF4cOHsWbNGsTHx1sNwLurvvjUU09h27ZtWLVqFVJSUnD//fcjOjoaZ86cwYYNG/DRRx8ZFw+99957OH78OJ5//nl8+umn6N69O2JjY3H27Fn8+uuvyM7ORl5enqpIQ0ReRSAi8rBx48YJycnJQmlpqeTjb775ptC4cWMhNDRUSE9PF06dOiXruFu3bhUACE888YTDffPy8gQAQu/evR0eb9SoUbLOL/X8e++9V4iLixNCQkKEm2++WRg+fLiwc+dOs/2uX78uTJs2TWjVqpUQEhIixMbGCvfcc4/w888/Wx1z+vTpAgBh69atVo8tWbJEACAsWbLEbPuuXbuE3r17C1FRUQIAAYCQk5NjfLy6ulp4//33hR49egjR0dFCaGio0KxZM2HgwIHCwoULhevXrxv3HTVqlNXzHZVt3bp1wu233y6Eh4cbz09EREQkCIKQk5NjrB/Y+mdaX8vNzRVGjx4tJCQkCEFBQUJCQoIwevRoITc3V/L4v/32mzBgwAChXr16QlRUlHDPPfcIhw4dEu69914BgFBYWCirnFL1Rqm6j/h6Ro0aJWRlZQmDBg0SoqOjhcjISCE9PV349ddfrY7du3dvu/Wja9euCc8++6yQlJQkhIaGCikpKcKsWbOEyspKyXI5Op6pRx99VAAgLFq0SNb+ol27dglPPPGEkJaWJsTGxgrh4eFCSkqKMH78eOH06dNm+1rWH+Xc8+nTp5sdo6CgQHj55ZeFtLQ0ITw8XKhXr56QkpIiPPLII8LXX3+tqOxEREQkzRfqZaZ1LSm2+vq++OILoV27dkJISIig1+uFZ555Rrh27ZqQmJgoJCYmmu1rr+/LXj1L6lhiX52SvsWqqiph1qxZQnJyshASEiI0b95ceP3114Xdu3cLAIQJEyZYPUdJXUmqnI5en9y+w7NnzwpTpkwRunXrJjRs2FAICQkRmjVrJgwbNkzIzMyU9frF/tjp06cLP//8s9C7d28hMjJSiI6OFh544AEhOztb8nlaXQNHDAaD8OGHHwrdunUTIiMjhYiICCElJUV48sknhTNnzpjtW1ZWJrz55ptC586dhcjISCE8PFxITk4Whg4dKvy///f/hKqqKuO+Uu87R+93Im+gEwSJtfxEREREREREfqCmpgYtWrTAjRs3jKH+/FW7du2Qk5ODCxcuIDo62tPFISIiIj/Depl8H374IcaNG4f33nsPTz31lKeL4zI//fQT+vTpg+nTp2PGjBmeLg4ROcCczkRERERERFTnVVdX48qVK1bb58yZg9OnTxtD3/mrH374AVlZWRg5ciQHnImIiMilWC+TLz8/3yoH8Pnz5/Hqq68iMDAQ9957r4dKRkRkjTmdiYiIiIiIqM67fv06mjRpgv79+6NVq1aoqqrC7t27sXfvXiQkJPjtyomFCxfi7Nmz+PDDDxEWFoYpU6Z4ukhERERUx7FeJt+cOXOwbt063HnnnWjYsCHOnDmDtWvX4tq1a5gxYwaaNm3q6SISERlx0JmIiIiIiIjqvIiICIwdOxZbtmzB9u3bUV5ejoSEBDzxxBOYNm0aEhISPF1Ej3jjjTdw7tw53HLLLfj444+RnJzs6SIRERFRHcd6mXwDBw7EkSNHsG7dOhQWFiIsLAzt27fH008/jUceecTTxSMiMsOczkREREREREREREREREREpBpzOhMRERERERERERERERERkWocdCYiIiIiIiIiIiIiIiIiItU46ExERERERERERERERERERKpx0JmIiIiIiIiIiIiIiIiIiFTjoDMREREREREREREREREREanGQWciIiIiIiIiIiIiIiIiIlKNg85ERERERERERERERERERKQaB52JiIiIiIiIiIiIiIiIiEg1DjoTEREREREREREREREREZFq/x9yOUJeD+a1LAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 2400x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (8*3, 6*1))\n",
    "ax = fig.add_subplot(1, 3, 1)\n",
    "ax.hist(adata.obs['mt_pct_content'], 100);\n",
    "ax.set_xlabel('% MT-content', fontsize = 14)\n",
    "ax.set_ylabel('Frequency', fontsize = 14)\n",
    "\n",
    "ax = fig.add_subplot(1, 3, 2)\n",
    "ax.scatter(adata.obs['log1p_total_counts'], adata.obs['mt_pct_content']);\n",
    "ax.set_xlabel('Log library size', fontsize = 14)\n",
    "ax.set_ylabel('% MT-content', fontsize = 14)\n",
    "\n",
    "ax = fig.add_subplot(1, 3, 3)\n",
    "ax.scatter(adata.obs['log1p_n_genes_by_counts'], adata.obs['mt_pct_content']);\n",
    "ax.set_xlabel('Log num. genes per cell', fontsize = 14)\n",
    "ax.set_ylabel('% MT-content', fontsize = 14)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb8be986",
   "metadata": {},
   "source": [
    "As you can see from the left plot, there are only very few cells with substantial MT content. And from the figure on the right, you can see that those cells with high MT-content are also the ones with low number of genes expressed (or library size). As such, we argue to remove these high MT-cells.\n",
    "\n",
    "The following code removes cells with MT-content greater than 30%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "67b9b6f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "adata_clean = adata[adata.obs['mt_pct_content'] < 30, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b819eca0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "View of AnnData object with n_obs × n_vars = 11908 × 36601\n",
       "    obs: 'n_genes_by_counts', 'log1p_n_genes_by_counts', 'total_counts', 'log1p_total_counts', 'pct_counts_in_top_50_genes', 'pct_counts_in_top_100_genes', 'pct_counts_in_top_200_genes', 'pct_counts_in_top_500_genes', 'mt_pct_content'\n",
       "    var: 'gene_ids', 'feature_types', 'n_cells_by_counts', 'mean_counts', 'log1p_mean_counts', 'pct_dropout_by_counts', 'total_counts', 'log1p_total_counts'"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata_clean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "8a90dbfa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Frequency')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# just double check: there must be no cell with MT > 30\n",
    "plt.hist(adata_clean.obs['mt_pct_content'], 100);\n",
    "plt.xlabel('% MT-content', fontsize = 14)\n",
    "plt.ylabel('Frequency', fontsize = 14)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3430a5b",
   "metadata": {},
   "source": [
    "NOTE: For the sake of exposition, we will continue with original `adata` (i.e. will not use `adata_clean`)."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "1660f166",
   "metadata": {},
   "source": [
    "## Visualize gene metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "28f51337",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Histogram of number of cells each gene is expressed in')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(adata.var['n_cells_by_counts'], bins = 100);\n",
    "plt.xlabel('Number of cells a gene is expressed in', fontsize = 14)\n",
    "plt.ylabel('Frequency', fontsize = 14)\n",
    "plt.title('Histogram of number of cells each gene is expressed in', fontsize = 14)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e11724a",
   "metadata": {},
   "source": [
    "We see that there are a lot of genes that are expressed in 0 cells. We can zoom in the plot to verify and we will remove them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "d4ca5f0e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 1000.0)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ixYt2c8rtPlJO7O0bZp79qkGDBvLz85OPj4+qVKmixx57THv27Mn1snNTg508eVIVK1ZUqVKl9Ouvv9rcP7t5mc/j//73v+ratavKlSunEiVKqEWLFlq9enWWPDJfC4cPH9aMGTNUp04deXp62lxOLC/7Xbndl/rzzz81cOBAVa9e3fqeULt2bfXt21fJyck2y8xtfZ/p+PHj6tq1q8qUKaOSJUuqdevWN3TkpL3H/0afjwCKNupH6kfqx/W5Wgb14/VRP/6D+vF/qB+diAGczJEjR4wkExUVZY4dO2Y8PT1NtWrVzOXLl60xcXFxRpKJiYmxuW9oaKgJDQ21u9zWrVuba18S48ePN5LMgw8+aPz8/EzPnj3NgAEDTIUKFYwk8+6775p//etfJjAw0PTu3dv069fPlC5d2kgyGzZssFlWTEyMkWTuv/9+U6ZMGdO3b18zbNgwU7NmTSPJdO7cOUtOs2fPNhaLxZQuXdr07NnTDBs2zLRp08ZIMnfeeadJTU21xq5bt85IMvfee69xd3c3kZGRZvjw4ebxxx+/7phu2rTJlChRwri5uZkuXbqYUaNGWcejatWq5q+//jLGGHP69Gkzfvx467yYmBgzfvx4M378eHPkyJEc13HhwgXTokULI8lUr17dPPvss2bYsGHmwQcfNCVKlDC7du2yxv7666+mYsWKRpKJjIw0Q4cONT169DAlSpQwPj4+ZsuWLdd97DLHY/z48dZpv//+u/H39zfu7u6mU6dOZuTIkaZ///4mKirKuLu7m7S0tOuO1ccff2x8fHzMAw88YAYMGGCGDh1q7r77biPJ3HbbbebMmTPXXYYxxly5csW0bdvWSDLh4eFmxIgR5qmnnjKlSpUy999/f5bcjTFmy5Ytxs/Pz7i5uZlOnTqZ4cOHm0cffdS4ubmZgIAA89tvv9nESzINGjQw1apVM40bNzaDBg0y3bp1M66ursbDw8Ps27fPJv7kyZOmbt26RpJp0aKFGTRokHnyySdN2bJljZubm/nqq69ytW3z5s0zksy8efOs03bt2mU8PT1NiRIlTNeuXc2oUaNM3759TatWrUzlypVztdxLly5Zn/8NGzY0zz77rOnbt6+pVKmSkWTefPNNa2x6err1cbk6761btxp3d3dTp04dc+HCBev0zNf6/fffb0qUKGF69eplRo4caRo3bmwkmebNm9u8x2S+D7Vo0cL4+vqaFi1amCFDhpiYmBjz+++/G2OMGTVqlJFkKlSoYJ588kkzePBg06RJEyPJPPLIIzbbNmLECOvrLTY21owaNcr06NHDhIWFmeeff94aN3v2bCPJBAcHmz59+pjRo0ebXr16mbp165ru3bvbLPObb74xnp6extvb23Tp0sUMHz7cREdHW1+Dp06dsomfNGmSkWQCAwNN//79zeDBg03lypXNfffdZySZ1q1b5+pxsvf4G5P35yOAW8PVdd31xMfHm/Lly1vfr0eNGmV9jypfvryJj4+3id+9e7cpVaqUcXFxMY888ogZPXq0uffee42fn59p1aqVkXTd+uVmZNZ+cXFxpl+/fkaSef/9921ioqKisuSR3fuoMfbrG2P+eY+tX7++qVKlimnevLkZMmSIdWxuu+02s2/fPuPv72/uueceM3ToUOvnadu2bW2Wk/l43HXXXcbPz8+0bdvWjBo1ynTt2tW4ubmZEiVKZKnD8lo/ZNZtHTt2NBUqVDC9e/c2Q4cONfPnz89xPDMyMsyDDz5oJJkaNWqYoUOHmr59+1pr71dffdUa+9VXX1k/20NDQ631qr0xvdbnn39uPDw8jLu7u3n44YfN6NGjTe/evU29evXMgw8+aBObl8/67B47e3VsbuuCnNSsWdOEh4ebmJgYM2rUKNO7d2/r6+eNN97I1TKMMWbFihXGzc3NeHp6mp49e5pRo0aZZs2amcqVK5sGDRpkyT0jI8N07drVWm8888wz5tlnnzW1atUykszQoUNt4jMfp86dOxsfHx/TrVs3M3jwYFO7dm0jyXTr1i1LTnnZR8qJvX3Dxx57zPp6GjhwoBkxYoTp2rWrCQoKMu+++26ulpuXGmzNmjXGxcXFNG3a1KbWzHyuX/u6yMytcuXKpnHjxmbkyJHmySefND4+PsbV1TXL6y3zfahjx46mTJkypkePHmbEiBHmlVdeMcbkbb8rt/tS58+fN2FhYcZisZioqCgzfPhwM3DgQPPAAw+YEiVKmF9++cW6zLzU98YY88cff1j3xaOioszo0aNNp06djIeHh/X9dN26dbl6nOw9/jfyfARQ+KgfqR+pH6kfqR+pH6kfkVc0w+F0ri16hw0bluWNLr+b4WXKlLFpNCYkJBgPDw/j5+dnatSoYU6cOGGdt2XLFmuRfbXMD5Ly5cub48ePW6enpqZaC+rPP//cOv3AgQPGzc3NNGjQwPz99982y5o6daqRZP0QMuZ/BZK9Qjkn6enppmrVqkaSWb58uc284cOHG0nmySeftDsuuf2gMMaYoUOHGkmmR48e5sqVKzbzzpw5Y86ePWv9+8477zSurq5Z8omPjzelSpUy4eHhNtNz2wx/4403jCTz+uuvZ8nv5MmTudqOpKQkm1wzffDBB0aSeeGFF3K1nPfee89IMh06dLAZjwMHDhgvL68suV++fNlUqVLFlCpVyvz44482y9q0aZNxdXU19913n830zOfDv/71L5Oenp5l3c8884xNfLdu3YykLIVaUlKSqVSpkilfvry5ePHidbfN3k7YkCFDjCTz9ddfZ4m/9vmdneeee85IMmPHjjUZGRnW6SkpKaZJkybGw8PD2og2xpj//ve/pmzZsqZMmTLmv//9r0lJSTFVq1Y1np6eZs+ePTbLznxOe3h42MzLyMiwjsvVr7fM9yFJZty4cVlyXblypfV96ty5czbL69u3b5bXe5kyZUxISIg5f/58lmVd/dxs1KiR8fDwMElJSVnirh7Hv//+2/j6+poKFSqYo0eP2sR9/PHHRpLp37+/ddovv/xi3NzcTIUKFWyWnZycbP3BTn40w/PyfARwa8jLl5mZPyJ7++23babPmjXLSDJ33323zfSWLVsaSWbBggU208eOHWt9TyqsLzMTExNNyZIlTcWKFW0+S/Pzy0xJZvDgwTbTM79E9ff3t6l/MjIyTMeOHY0ks3PnTuv0qz/fRo0aZbOs5cuXG0lZ6rC81g+ZdVvDhg1zXX8Z879aq3Xr1jZfVh07dsyUK1fOuLm52f1hYG4/v4wxJjEx0fj4+BgfH58s9ZYxxqaGz+tnfV6+zMxtXZCTa8fCGGPOnj1rwsPDjZ+fn91lX+vKlSsmNDTUWCwWs2nTJpt5PXv2tD5XrvbOO+8YSaZXr142X86lpqZaf/C5Y8cO6/TMGszPz88cOnTIOv3ChQumRo0axsXFxaa+y+s+Uk6u3Tc8c+aMsVgspnHjxln2V65cuWJOnz593WXmtQYz5n9fio8YMcIY87/3tK5du2ZZfuaYd+vWzaYe3rNnj/Hw8DDly5e3+cFn5vtQxYoVzbFjx7IsLy/7Xbndl/r222+NJDNo0KAscWfPnjWXLl2y/p3X+j5ze67d53r77betY5MfX2bm9vkIwDGoH6kfc4v68R/Uj9SP1I/Uj6AZDid0bdF76tQp4+/vbwICAqxNyvxuhk+cODFLfOZRpx988EGWebfddluWo12ze+M15p9mpiSbZuaAAQOMJLNx48Ys8enp6aZ8+fKmcePG1mmZBVKjRo3sbl92Nm7caG3KXuvs2bOmTJkyxsvLy6ZozGszPC0tzZQqVcr4+fllORr1Wj/++KPdBnymzKbq1UeR5rUZfu1OUH7IyMgwvr6+pk2bNrmKz/wFm71CuU+fPlly//LLL40kM2nSJLvLe/jhh42Li4tJTk62TpNkfHx8sjTv09LSjJubm81z5a+//jKurq5ZdgQzZY7d4sWLr7ttOTXDV6xYcd3725Oenm5Kly5tqlatalPoZMosmq799d/XX39tJJk2bdqY//u//zOSzMyZM7PcP/M5/dRTT2WZd/ToUePq6mrq1atnnZb5PhQUFGT316MPPPCAkWS3aMwslK8+G0SZMmVMlSpVbIo7exo1amR8fHyu+zp69dVXjSTz4YcfZruccuXKWf+eOHGikWRmzJiRJfajjz7Kt2Z4bp+PAG4duf0y89ixY0aSqVOnTpbPgfT0dOtRAwkJCcaYf967pX/OSHGtc+fOWY8GKawvM40xZty4cUaSeemll6wx+fllZsmSJbN8QZVZ59n7/Pzwww+NZPsjyszHw9/f3+6P/+655x6bL6NupH7IrNu++eYbu/fJTmbtvXXr1izzpkyZYrdOyuuXmS+99JKR7P/Q7Vp5/azP65eZuakLbsSMGTOMJLN+/frrxq5fv95IMg888ECWeQkJCcbV1TVL7vXr1zc+Pj42X6hl2rt3r5Fsj+7JrMHsjXnmvG+//dY6La/7SDm5dt8wOTnZSP8coWav3syNvNZgxvzzo9emTZsaFxcX88Ybbxhvb29TpUoVm7o+kyTj6uqa5YtSY4zp3bt3li/RM9+H7NW/ed3vyu2+VGZdPnr06Bzj8lrfp6amGi8vLxMQEJDlB7rp6emmevXq+fZlZm6fjwAcg/qR+jG3qB/zB/Xj/1A/Uj9SPzovNwFOrnTp0ho1apRGjRqlV155JVfXi8mrhg0bZpkWHByc47ytW7faXdZdd92VZVpERITc3Ny0a9cu67QtW7ZIklasWKE1a9ZkuY+7u7sOHTqUZXrTpk3trjc7meu0dw2ckiVLqkmTJlq5cqXi4+MVHh6ep2VnOnTokM6ePat27dqpdOnSOcZmbndSUpLdxzJzmw8dOqR69erlKY/7779fo0ePVmxsrNasWaP27durdevWuu222/K0nC+//FJvv/22fvzxR50+fdrmmj5//PFHrpaxZ88e+fj46Pbbb88yr0WLFnrnnXdspmWOS3x8vN1xSUxMVEZGhn7++Wc1adLEOr1GjRoqWbKkTaybm5sCAwN15swZ67Tt27crPT1dqampdpf/yy+/SPpn3O+7775cbePVHnvsMb3++ut66KGH9Pjjj+vee+9Vq1atVKFChVzdPz4+XqdPn1ZISIgmTpyYZf5ff/1lze9qDz74oPr27as5c+ZIkjp27KgBAwZkux57r8/Q0FBVqlRJBw4c0OXLl+Xh4WGd16BBA5u/M23ZskU+Pj52r20kSd7e3ja5dunSRbNnz1a9evXUpUsXtW3bVhEREfL29ra5X5cuXTRixAjVq1dP3bp1U9u2bdWyZUv5+vpmWb8kbd26Vb/99luW9V+6dEl///23/v77b5UrV856DSN7229v2o3K7fMRAK61e/duSVLr1q2zXGPOxcVFrVq10qFDh7R7925VqlTJ+r7WokWLLMvy8fFRw4YNtW7dulyt+/XXX8/yHvXEE09kuVbY9QwbNkxvvfWWpk2bpqeffvq6NVFeVa9eXSVKlLCZllmv1q9fP8u4Zc6zV7vcfvvtWd6vpX8+E9asWaNdu3apcePGN1U/3HHHHbnfOP1Ts5YoUcLu/dq2bSvpf8+TG7Vt2zZJUmRk5HVj8/pZnxe5rQtycuLECU2bNk3fffedjh07luVadbmpWTNfRy1btswyr1KlSqpcubKOHDlinXbhwgXt27dPISEheumll7LcJy0tTVLWek2SGjdunGVaxYoVJcnm9Xej+0i54evrq44dO2rZsmVq1KiRHn30UbVp00ZNmzaVu7t7rpaR1xosM+ePP/5YDRs21IABA+Tq6qoFCxZkqe8yVa5cWaGhoVmm33XXXZo7d6527dqlzp0728yz97rJ635XbvelWrVqpeDgYE2bNk179uzRfffdp9atW6t27do270N5re/j4+N16dIl3X333fLy8rKJdXFxUYsWLazvOTcrt89HAEUb9eP1UT9SP16N+jHvqB9tUT/aon4sWmiGo1gYMGCA/v3vf2vGjBn617/+le/Lt/dB4ubmluO8K1eu2F1WYGBglmmurq4qW7askpOTrdNOnTolSZoyZUqecrW3/JykpKTkeL/MQjcz7kZkblduGp+Z27106VItXbo027jz58/nOY8qVapoy5YtmjBhgpYtW6ZPP/1UklSrVi1NmjRJjz766HWXMWPGDA0bNkzly5dXZGSkKlasaC0sX3/9daWmpuYql5SUFFWqVMnuPHuPRea4LFiwIMflXjsu2RVBbm5uNk38zOVv3rxZmzdvzvXyc6tZs2Zav369XnzxRS1cuFDz5s2T9M+PN1566SXrTkh2MvM7cOCADhw4kKf8HnroIWszvH///jmuJ7vXQWBgoI4ePaqzZ8+qbNmy140/deqUrly5Yrcws5frzJkzFRYWpnnz5umFF17QCy+8IC8vLz322GOaMWOGtdgdNmyYypYtq7feekszZszQK6+8Ijc3N0VHR+u1115TWFiYdf2SNGvWrBy39/z58ypXrpz1NRoQEGB32/NLbp+PAHCtvNYrmf/ae1/LaTn2vP766zp27JjNtDZt2uT5y8xSpUpp7NixGjBggKZOnarp06fn6f7XcyP1qvS/L5iultPnofS/2u5m6ocbqVmzq53yo16V8l6z5uWzPi9yWxfklFvTpk2VkJCgFi1aqF27dvL395erq6t2796tb775Jlc1a25eR1d/mXn69GkZY/T777/neVxyeo7aq1nzuo+UW5999pm1Xn3++eetufXq1UsvvvhilobBtfJag2W67bbb1KBBA23evFmNGzfWnXfeme19c/v6vN598rrfldt9KT8/P23ZskXjxo3T4sWLtWzZMkn/fAE+atQo6/56Xuv7nOrV7LbxRuX2+QigaKN+vD7qR+rHq3Ojfrwx1I/2c5WoH6kfixYXRycA5Advb29NnDhR586dy/GD08XFJdsmtb03/YKQlJSUZVp6erpOnjwpPz8/67TMN9CUlBSZfy5pYPd2rWt/tXk9meuxl5f0zxHHV8fdCH9/f0nS77//nut83nzzzRy3OyYm5oZyqVevnj7//HOdOnVKcXFxGjdunBITE/X444/nWIRL0pUrVzR58mQFBwdr//79WrBggV566SVNmDBB48eP1+XLl3Odh6+vr/XXatey91hkjsvixYtzHJfWrVvnOgd7yx86dGiOyx8/fvwNLV/659eG3333nU6fPq1169ZpyJAh2rdvn6Kjo3X48OFc5de5c+cc88tssmc6c+aMnn76afn4+MjLy0vPPvuszp49m+16snsdJCUlyWKxqFSpUjbTs3u9+fr6qmzZsjnmenXx7+bmpmHDhunAgQP6/ffftXDhQt1111368MMP1b17d5v1Pfnkk9q+fbv++usvffXVV3r44Yf1zTff6L777rMWV5njtW/fvhxzyPxVaOZ7z4kTJ3I9JgBQmPJar2T+a+99Lafl2HP06NEs75/2zqiTG3379lXVqlX15ptv6vjx43ZjXFz+2UWzV7M6sl69enrm58bN1A83UrNm93jmR70q5b1mzctnfV7kti7Izty5c5WQkKDJkyfr+++/15tvvqnJkydrwoQJat68ea7zyOvrKDO+cePGOY5Lbo+qyymnvO4j5VaJEiX0wgsv6PDhwzp8+LDmzp2rmjVraubMmRo8eHCu88ttDZbp1Vdf1ebNm1W2bFlt27ZNs2fPznYduX19Xs3e6+1G9rtyuy9VuXJlzZ8/X3/99Zd27dqll156SRkZGYqNjdXHH39ss/7c1vc51as5jQuAWxf14z+oH6kfqR+pH6kfqR9BMxzFSExMjOrWrat3331Xv/76q92Y0qVL68SJE1mKw/Pnz+fbKTGuZ9OmTVmmxcXF6cqVKzanzG7WrJmk/51+pKBkrnP9+vVZ5p0/f147duyQt7e3atasecPrqFmzpnx9fbV9+3adPn06x9jM7Y6Li7vh9eWGu7u7mjdvrokTJ+qNN96QMUZLlizJ8T5///23kpOTFRERkeUXZTt27Mhy+qCcNGjQQOfPn7d7OqYffvghy7SCHpemTZvKYrEU+LhL//x4pU2bNpoxY4aee+45Xbx4UatWrcrxPrVr15avr6927Nhh9xfI2enTp48SEhI0c+ZMvfzyy/rtt98UGxubbby91+exY8d0/Phx1a1b1+4p0e1p1qyZTp48eUPvKyEhIeratauWL1+uatWqafXq1XafW2XLllWnTp30ySef6O6779bBgwet7315fb40aNBAkv3ttzcNAApb5mVpNm7cmOWLCmOMNm7caBOX+b5m7zP1woUL1tP3FTZ3d3e98MILunTpksaNG2c3JvP0l/a+ULv6kjoFadeuXTp37lyW6ZmfCZn1Y2HWD7fffrsuXLhgPRXl1TLrWHuXL8qLzFMBrly58rqxN/NZnxe5rQuulnl6xQcffDDLvLx8rme+juz9YPS///2vEhISbKaVKlVKtWvX1k8//VRgpwIsrH0kSQoLC9OTTz6pDRs2qGTJkvr222+ve58bqdl37dql5557TjVr1tS+ffsUFhZm/TLbnoSEhCxHG0pZX58FkWum3O5Lubi4qGHDhhoxYoT1S8zMccxrfV+jRg15eXlpx44dunTpks28jIwMu+/3AG5t1I//oH6kfqR+pH6kfqR+BM1wFCOurq568cUXlZaWlu11w5s2baq0tDSbU00bYzR69OgbPg1NXs2cOVP//e9/rX9fvnzZegqVJ554wjr9X//6l9zc3PTss89mKRSkf454zY+CtkWLFqpataq+++47rV692mbeCy+8oJMnT6pr1665bgLa4+bmpmeeeUbJyckaOHBgllODJCcnWwvmO+64Q82aNdPHH3+sTz75JMuyMjIytGHDhhvKY+fOnXZPf5T5K7Brrx1yrYCAAHl7e+vHH3/UhQsXrNNPnz6tZ599Nk+5ZP4qc8yYMcrIyLBOP3TokD744IMs8Q8++KAqV66sV1991brDdrW0tDR9//33ecrhakFBQXrsscf0ww8/6OWXX7b7i8itW7fabHdexMXFZSk6pNyPvZubm/r166djx45p2LBhdgue/fv32/zSb+7cufrss8/06KOPqnfv3urfv7/uu+8+ffTRR1q4cKHd9Xz44Yfau3ev9W9jjJ577jmlp6fbvD6vJ/O65E8++aROnjyZZX5iYqJ++uknSVJqaqrd4uv8+fM6d+6c3N3drb/yXr9+fZbHJi0tzXqaoMxx7NWrl0qVKqXnn3/ebiF84cIFm52Abt26ydXVVa+++qrNGKakpOiFF17I9XYDQEGpXLmy2rZtqwMHDmS5xt4777yjn376SXfffbf1NIihoaFq0aKFdu/enaWeePnll63vm47w+OOPq3Hjxvrwww/1888/Z5nfuHFjWSwWLVq0yOaz85dfftHMmTMLJcczZ85kOY1g5jX26tWrZ70eWkHXD1fLPLpg9OjRNnXA8ePH9eqrr8rNzS1XR71cbx0lS5bUjBkz7P5g8eovmPPyWZ8XeakLspN51Mi1teHChQutpxzMjZYtW6py5cpavHhxli+8xo4da/d0fwMGDNCFCxf09NNP292/OnLkiI4ePZrrHK5VkPtIf/31l/bv359l+unTp5WamnrdelXKew12/vx5de3aVZL08ccfKzg4WAsXLlRaWpq6du1qt35OT0/Xc889Z/N627t3rz766COVL19eHTt2zNX25nW/K7f7UgcOHLB7lM21cXmt7z09PfXYY4/pxIkTmjFjhk3ce++9Z/f9FMCtjfqR+pH6kfpRon6UqB+pHyFxzXAUMw888IBatmyZbVOwf//+mjdvnp566imtWrVK5cuX16ZNm3TmzBk1aNCgUH7l2bx5czVo0ECPP/64fHx8tHjxYsXHx+vhhx9W586drXH16tXT7Nmz1a9fP9WsWVMdO3ZU1apVdfbsWR0+fFgbNmzQE088Yb0W8o1ycXHR/PnzFRUVpY4dO+rRRx9VaGio4uLitH79elWtWlXTpk272c3WpEmTtGXLFn300UfasmWLOnToIE9PTx0+fFjLly/X999/b/015scff6y2bduqS5cuev3119WoUSN5e3srISFBcXFx+uuvv+x+sF/PRx99pLffflutWrVS1apV5evrq4MHD2rZsmUqU6aMevXqleP9XVxc9K9//UszZsxQgwYNdP/99yslJUXfffedQkNDFRISkutcevXqpY8++khLly7V7bffrg4dOujUqVNatGiR7r33Xi1evNimUPX09NTnn3+uDh06qHXr1rr77rsVHh4ui8WiY8eOadOmTSpbtqwOHTqU53HJNHv2bMXHx2vEiBH66KOPFBERIX9/fx0/flw7duzQL7/8oj///PO617qx56WXXtK6devUqlUrhYWFycvLSz/++KPWrFmj2267TQ899NB1lzFx4kT9+OOPeuONN7R06VK1atVKAQEB+v3337Vv3z7t2bNHcXFxCggI0M8//6yBAweqUqVKeuedd6zLeP/991W/fn3169dPERER1mtsZ4qKilJERIS6dOmi8uXLa82aNdqxY4eaN2+epx88tG/fXmPHjtXkyZNVrVo1tW/fXqGhoTp58qR+/fVXbdq0SS+88IJq166tixcvqkWLFqpRo4YaN26sypUr69y5c1qyZIkSExM1bNgweXp6SpI6deokX19fNW/eXKGhoUpLS9OqVat08OBBPfLII9adl/Lly+vjjz/Wo48+qgYNGqh9+/aqVauWUlNTdfToUW3YsEF33nmnli9fLkmqVq2axo0bp/Hjx6t+/fp67LHH5Obmpi+++EL169dXfHx8rrcdAG7Evn37sv3RUa1atTRq1Ci99dZbatmypZ5++mktXrxYderU0YEDB/Ttt9+qfPnyeuutt2zu9+abb6pVq1bq3r27vvjiC1WrVk0//vijtmzZolatWmnjxo3X/VKoIFgsFk2bNk333nuv3dMgZh7JsXDhQjVu3Fjt27fXiRMn9NVXX6l9+/b64osvCjzHu+66S2+99Za2bt2q5s2b6+jRo/rss8/k7e2t9957zya2IOuHq/Xo0UNffvmlvvnmG9WvX1/33Xefzp8/r08++USnTp3SjBkzdNttt93UOgICAvThhx+qS5cuuuOOO/TAAw+oZs2a+vvvv7V161ZVqVJFX3/9taS8fdbnRV7qguz06NFDL730kp599lmtW7dOoaGh2rNnj9asWaOHH35YX375Za5ycXV11Zw5c/TAAw/o7rvv1uOPP67g4GBt2LBBv//+uxo0aGDzI0JJeuaZZ7RlyxZ98MEH2rx5s9q1a6eQkBAlJSXp0KFD2rp1qxYuXJjna6ZmKsh9pN9//1233367GjRooPr166tChQo6efKkvvnmG6WlpWnYsGHXXUZea7CBAwcqPj5er7zyivWInObNm2v8+PEaO3ashg8frjfffNNmHfXr19f333+vpk2bql27dvrrr7/0ySef6MqVK3rnnXfk7e2d623Oy35XbvelVq1apeHDh1ufx2XLltXhw4f17bffysvLy+YsTXmp7yVp2rRpWrNmjcaMGaPvv/9et99+u3766SctW7ZMkZGRuToqD0DxQf34P9SP9lE/Uj9K1I8S9SP1IyRJBnAyR44cMZJMVFSU3fmbN282kowkExMTk2X+2rVrTbNmzYynp6cpW7as6dGjh0lKSjKtW7c2174kxo8fbySZdevWZVlOTEyMkWSOHDmSZZ69ZWXG//bbb2batGmmWrVqxsPDw4SGhpoJEyaY1NRUu9uzbds206VLFxMSEmLc3d1NuXLlTKNGjcyoUaPMTz/9ZI1bt26dkWTGjx9vdznXs3fvXvPII4+YcuXKGXd3dxMaGmoGDhxo/vrrryyxOY1LTi5dumReeeUV07BhQ+Pt7W1Klixp6tSpY4YOHWpOnz5tE3vq1CkzZswYU69ePWts9erVTbdu3cyXX35pE2tvvO2Nx5YtW8wzzzxj6tWrZ/z9/Y23t7epXr266d+/vzl27FiutuHy5ctmypQppnr16sbT09NUrlzZDB061Jw9e9aEhoaa0NDQXI/HuXPnzNChQ01ISIjx9PQ0derUMe+88475/PPPjSTz2muvZbnPf//7XzNw4EDr+n19fU3t2rXNU089ZdasWWMTK8m0bt3a7rqzy/XChQtm+vTppnHjxsbHx8d4e3ubsLAw06lTJ/Phhx+atLS0627XvHnzjCQzb94867Tly5ebnj17mpo1a5pSpUpZH/vnnnvO7nMsO1euXDFvv/22adGihfH19bU+Bu3btzdvvfWWOXfunElNTTWNGjUyLi4uZsOGDVmWsXLlSmOxWEzz5s2t23P1c/rdd981devWNZ6eniY4ONgMHDjQpKSk2Cwj833I3nvM1VatWmXuv/9+U758eePu7m6CgoJMRESEmTx5sklISDDG/POceumll0xkZKSpWLGi8fDwMIGBgaZVq1Zm4cKFJiMjw7q82bNnmwceeMCEhoYaLy8vU7ZsWXPHHXeYt956y1y+fDnL+g8dOmR69+5tQkNDjYeHhyldurQJDw83AwYMMNu2bcsS/+6775o6deoYDw8PU7FiRTNs2DBz4cKFHJ9L17L3+BtzY89HAMVf5vtpTrer3zuOHj1qevXqZYKDg42bm5sJDg42vXr1MkePHrW7/F27dpmoqChTsmRJU6pUKdOhQwezb98+c9999xlJWeqP/JRZ+8XFxdmdHxkZad3Ga+vJCxcumAEDBpjAwEDj6elp6tevbxYsWJBtvZfde2xOn1f2lnV1/P79+03Hjh2Nr6+v8fHxMe3atTM7duywuy15qR/s1W25lZaWZl555RUTHh5uPD09TalSpUzr1q3NN998Yzc+L59fV9u1a5d57LHHTGBgoHF3dzfBwcGmQ4cOZsmSJVlic/NZb0z2tfq145GXuiAnu3fvNpGRkaZ06dLWcVq9enW2n9M5Wbt2rWnZsqXx9vY2ZcqUMY8++qhJSEgw9erVM35+fnbv88knn5h27dqZ0qVLG3d3d1OhQgXTpk0bM2PGDJvaL6f9ipxyze0+Uk6urT9Onz5tJkyYYFq1amWCg4ONh4eHCQkJMe3btzffffddrpaZKTc1WGbNf++992Z5XNPT002rVq2MJLN48WLr9Mzn9PHjx83jjz9uypQpY7y8vExERIRZuXJlljxy2mfNlNv9rtzuSx08eNAMHDjQ3H777aZs2bLG09PT3HbbbSYmJsYcOHAgy/pzU99f7dixY+bxxx83/v7+pkSJEuauu+4yGzZsyPM+qr3680afjwAKF/Uj9WNeUD9SP2aifqR+pH68tVmMsXMuEgCAw4wZM0ZTpkzRsmXL1KFDB0enc0uYMGGCJk6cqHXr1qlNmzaOTgcAUIDS09NVtWpVXbx40e6p2ABc39mzZxUYGKjw8HBt3brV0encMiwWi1q3bm29zikAoHBQPwI3j/rRMagfgX9wzXAAcJA///wzy7SDBw/qjTfekL+/P01ZAABuwpUrV/T3339nmT5t2jQdO3ZMnTp1KvykACdz/vx5nT171mZaenq6hg8frosXL/I6AgAUK9SPwM2jfgRQFHHNcABwkH79+uno0aO64447VLp0af32229avHix0tLSNHfu3DxdrwUAANg6d+6cKlSooHvvvVc1atRQWlqatm7dqu3btys4OFgTJkxwdIpAkffLL7+oZcuWioqK0m233aazZ89q06ZNOnjwoOrWrasBAwY4OkUAAPIN9SNw86gfARRFNMMBwEEeffRRzZkzR19++aWSk5NVsmRJtW7dWkOHDlVUVJSj0wMAwKmVKFFCvXv31tq1a7Vx40ZdunRJwcHBeuaZZzR27FgFBwc7OkWgyKtQoYIeffRRbdiwQcuXL9eVK1dUuXJlDRs2TM8//7x8fHwcnSIAAPmG+hG4edSPAIoirhkOAAAAAAAAAAAAACh2uGY4AAAAAAAAAAAAAKDYoRkOAAAAAAAAAAAAACh2aIYDAAAAAAAAAAAAAIodmuEAAAAAAAAAAAAAgGKHZjgAAAAAAAAAAAAAoNihGQ4AAAAAAAAAAAAAKHZohgMAAAAAAAAAAAAAih2a4QAAAAAAAAAAAACAYuf/AbSIsBEUKp+HAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 2400x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (8*3, 6*1))\n",
    "ax = fig.add_subplot(1, 3, 1)\n",
    "ax.hist(adata.var['n_cells_by_counts'], bins = 100);\n",
    "ax.set_xlabel('Number of cells a gene is expressed in', fontsize = 14)\n",
    "ax.set_ylabel('Frequency', fontsize = 14)\n",
    "ax.set_title('Histogram of number of cells each gene is expressed in', fontsize = 14)\n",
    "\n",
    "ax = fig.add_subplot(1, 3, 2)\n",
    "ax.hist(np.log(adata.var['n_cells_by_counts'] + 1), bins = 100);\n",
    "ax.set_xlabel('Log - Number of cells a gene is expressed in', fontsize = 14)\n",
    "ax.set_ylabel('Frequency', fontsize = 14)\n",
    "ax.set_title('Histogram of log of number of cells each gene is expressed in', fontsize = 14)\n",
    "\n",
    "ax = fig.add_subplot(1, 3, 3)\n",
    "ax.hist(np.log(adata.var['n_cells_by_counts'] + 1), bins = 100);\n",
    "ax.set_xlabel('Log - Number of cells a gene is expressed in', fontsize = 14)\n",
    "ax.set_ylabel('Frequency, ylim trimmed', fontsize = 14)\n",
    "ax.set_title('Histogram of log of number of cells each gene is expressed in', fontsize = 14)\n",
    "ax.set_ylim([0, 1000])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8de22c9c",
   "metadata": {},
   "source": [
    "Remove lowly expressed genes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "e1433a2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "sc.pp.filter_genes(adata, min_cells = np.exp(4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "847f2f13",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AnnData object with n_obs × n_vars = 11984 × 17698\n",
       "    obs: 'n_genes_by_counts', 'log1p_n_genes_by_counts', 'total_counts', 'log1p_total_counts', 'pct_counts_in_top_50_genes', 'pct_counts_in_top_100_genes', 'pct_counts_in_top_200_genes', 'pct_counts_in_top_500_genes', 'mt_pct_content'\n",
       "    var: 'gene_ids', 'feature_types', 'n_cells_by_counts', 'mean_counts', 'log1p_mean_counts', 'pct_dropout_by_counts', 'total_counts', 'log1p_total_counts', 'n_cells'"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eadc9fb2",
   "metadata": {},
   "source": [
    "# Other QC metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09633af3",
   "metadata": {},
   "source": [
    "## Ribosomal RNA content"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "f7d783a9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['RPL22', 'RPL11', 'RPS6KA1', 'RPS8', 'RPL5', 'RPS27', 'RPS6KC1', 'RPS7',\n",
      "       'RPS27A', 'RPL31', 'RPL37A', 'RPL32', 'RPL15', 'RPSA', 'RPL14', 'RPL29',\n",
      "       'RPL24', 'RPL22L1', 'RPL39L', 'RPL35A', 'RPL9', 'RPL34-AS1', 'RPL34',\n",
      "       'RPS3A', 'RPL37', 'RPS23', 'RPS14', 'RPL26L1', 'RPS18', 'RPS10-NUDT3',\n",
      "       'RPS10', 'RPL10A', 'RPL7L1', 'RPS12', 'RPS6KA2', 'RPS20', 'RPL7',\n",
      "       'RPL30', 'RPL8', 'RPS6', 'RPL35', 'RPL12', 'RPL7A', 'RPS24', 'RPLP2',\n",
      "       'RPL27A', 'RPS13', 'RPS6KA4', 'RPS6KB2', 'RPS6KB2-AS1', 'RPS3', 'RPS25',\n",
      "       'RPS26', 'RPL41', 'RPL6', 'RPLP0', 'RPL21', 'RPS29', 'RPL36AL',\n",
      "       'RPS6KL1', 'RPS6KA5', 'RPS27L', 'RPL4', 'RPLP1', 'RPS17', 'RPS2',\n",
      "       'RPS15A', 'RPL13', 'RPL26', 'RPL23A', 'RPL23', 'RPL19', 'RPL27',\n",
      "       'RPS6KB1', 'RPL38', 'RPL17', 'RPS15', 'RPL36', 'RPS28', 'RPL18A',\n",
      "       'RPS16', 'RPS19', 'RPL18', 'RPL13A', 'RPS11', 'RPS9', 'RPL28', 'RPS5',\n",
      "       'RPS21', 'RPL3', 'RPS19BP1', 'RPS6KA3', 'RPS4X', 'RPS6KA6', 'RPL36A',\n",
      "       'RPL39', 'RPL10'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "# Identify ribo-genes\n",
    "ribo_genes = adata.var_names[adata.var_names.str.startswith(('RPS', 'RPL'))]\n",
    "print(ribo_genes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "576c684c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# now we need the row sum of the subsetted data to compute % ribo content\n",
    "row_sum_adata_ribo = np.sum(adata[:, ribo_genes].X.toarray(), axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "cafdbe3a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# compute the % of ribo content by dividing the above result by library size\n",
    "adata.obs['ribo_pct_content'] = row_sum_adata_ribo/adata.obs['total_counts'] * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "ff718eb6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '% Ribo-content')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 2400x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (8*3, 6*1))\n",
    "ax = fig.add_subplot(1, 3, 1)\n",
    "ax.hist(adata.obs['ribo_pct_content'], 100);\n",
    "ax.set_xlabel('% Ribo-content', fontsize = 14)\n",
    "ax.set_ylabel('Frequency', fontsize = 14)\n",
    "\n",
    "ax = fig.add_subplot(1, 3, 2)\n",
    "ax.scatter(adata.obs['log1p_total_counts'], adata.obs['ribo_pct_content']);\n",
    "ax.set_xlabel('Log library size', fontsize = 14)\n",
    "ax.set_ylabel('% Ribo-content', fontsize = 14)\n",
    "\n",
    "ax = fig.add_subplot(1, 3, 3)\n",
    "ax.scatter(adata.obs['log1p_n_genes_by_counts'], adata.obs['ribo_pct_content']);\n",
    "ax.set_xlabel('Log num. genes per cell', fontsize = 14)\n",
    "ax.set_ylabel('% Ribo-content', fontsize = 14)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6195a170",
   "metadata": {},
   "source": [
    "## (Optional but often recommended) Remove ribosomal genes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59461d9b",
   "metadata": {},
   "source": [
    "Many publications often remove ribosomal genes from the beginning of their analysis because there are very limited publications which highlight their important application beisdes cell homeostasis. Here is an example of code of how you can remove ribosomal genes from your data: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "e4a72993",
   "metadata": {},
   "outputs": [],
   "source": [
    "adata_clean = adata[:, ~adata.var_names.str.startswith(('RPS', 'RPL'))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "ec3cd1f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AnnData object with n_obs × n_vars = 11984 × 17698\n",
       "    obs: 'n_genes_by_counts', 'log1p_n_genes_by_counts', 'total_counts', 'log1p_total_counts', 'pct_counts_in_top_50_genes', 'pct_counts_in_top_100_genes', 'pct_counts_in_top_200_genes', 'pct_counts_in_top_500_genes', 'mt_pct_content', 'ribo_pct_content'\n",
       "    var: 'gene_ids', 'feature_types', 'n_cells_by_counts', 'mean_counts', 'log1p_mean_counts', 'pct_dropout_by_counts', 'total_counts', 'log1p_total_counts', 'n_cells'"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "f85707bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "View of AnnData object with n_obs × n_vars = 11984 × 17601\n",
       "    obs: 'n_genes_by_counts', 'log1p_n_genes_by_counts', 'total_counts', 'log1p_total_counts', 'pct_counts_in_top_50_genes', 'pct_counts_in_top_100_genes', 'pct_counts_in_top_200_genes', 'pct_counts_in_top_500_genes', 'mt_pct_content', 'ribo_pct_content'\n",
       "    var: 'gene_ids', 'feature_types', 'n_cells_by_counts', 'mean_counts', 'log1p_mean_counts', 'pct_dropout_by_counts', 'total_counts', 'log1p_total_counts', 'n_cells'"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata_clean"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "d15f5feb",
   "metadata": {},
   "source": [
    "NOTE: For the sake of exposition, we will continue with original `adata` (i.e. will not use `adata_clean`)."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "a1e1e774",
   "metadata": {},
   "source": [
    "## Doublet score"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "af53a4a6",
   "metadata": {},
   "source": [
    "Let us estimate the amount of doublets in the dataset. Here, we use the tool `scrublet` that simulates doublet gene expression profiles based on the data. We apply it for each sample separately."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "8ffb8490",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Preprocessing...\n",
      "Simulating doublets...\n",
      "Embedding transcriptomes using PCA...\n",
      "Calculating doublet scores...\n",
      "Automatically set threshold at doublet score = 0.14\n",
      "Detected doublet rate = 2.1%\n",
      "Estimated detectable doublet fraction = 35.0%\n",
      "Overall doublet rate:\n",
      "\tExpected   = 2.0%\n",
      "\tEstimated  = 5.9%\n",
      "Elapsed time: 43.7 seconds\n"
     ]
    }
   ],
   "source": [
    "# filtering/preprocessing parameters:\n",
    "min_counts = 2\n",
    "min_cells = 3\n",
    "vscore_percentile = 85\n",
    "n_pc = 50\n",
    "\n",
    "# doublet detector parameters:\n",
    "expected_doublet_rate = 0.02 \n",
    "sim_doublet_ratio = 3\n",
    "n_neighbors = 15\n",
    "\n",
    "\n",
    "\n",
    "scrub = scr.Scrublet(counts_matrix = adata.X,  \n",
    "                     n_neighbors = n_neighbors,\n",
    "                     sim_doublet_ratio = sim_doublet_ratio,\n",
    "                     expected_doublet_rate = expected_doublet_rate)\n",
    "\n",
    "doublet_scores, predicted_doublets = scrub.scrub_doublets( \n",
    "                    min_counts = min_counts, \n",
    "                    min_cells = min_cells, \n",
    "                    n_prin_comps = n_pc,\n",
    "                    use_approx_neighbors = True, \n",
    "                    get_doublet_neighbor_parents = False)\n",
    "\n",
    "adata.obs['doublet_score'] = doublet_scores\n",
    "adata.obs['doublet'] = predicted_doublets"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "6fbed403",
   "metadata": {},
   "source": [
    "**Tasks:** Plot the doublet score as a histogram and as violin plot. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dcb453d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "rcParams['figure.figsize']=(6,6)\n",
    "sb.distplot() #histogram of the doublet score\n",
    "plt.show()\n",
    "\n",
    "rcParams['figure.figsize']=(15,7)\n",
    "sc.pl.violin() #violin plot of the doublet score"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "d7ded014",
   "metadata": {},
   "source": [
    "### Filtering doublets  \n",
    "\n",
    "Scrublet proposed a different threshold than we would choose based upon the histogram plot of the doublet scores.\n",
    "\n",
    "**Tasks:** Decide on a threshold to filter doublets. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "54e50a1b",
   "metadata": {},
   "outputs": [],
   "source": [
    "thr = #add threshold\n",
    "ix_filt = adata.obs['doublet_score']<=thr\n",
    "\n",
    "adata = adata[ix_filt].copy()\n",
    "print('Number of cells after doublet filter: {:d}'.format(adata.n_obs))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc2d7d96",
   "metadata": {},
   "source": [
    "# Data Normalization"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d718520a",
   "metadata": {},
   "source": [
    "One of the most common normalization methods is median library size normalization followed by log-transformation. Median library size normalization is inspired from normalization techniques in bulk data (Transcript Per Million or TPM normalization). This approach aims to normalize the library size of all cells to the same value, which is usually set to be the median library size in the data. The assumption here is that all cells in a sample should have the same total number of RNA counts. Obviously this is not necessarily an adequate assumption and in many circumstances is false (for example, a sample with cells of different sizes or at different stages of activation can have different total RNA). However, this is still the most common kind of normalization being used.\n",
    "\n",
    "The normalization step is followed by a log transformation of the data. This step is done primarily to normalize for the differences in gene expression scales. Of note, scRNA-seq data is massively undersampled, which means many of the entries are 0 thus taking log of the data can be problematic. As such, it is typical to add a non-zero pseudocount (typically 0.1 or 1) to the data. It is generally wise to ensure that zeros remain as zeros post log-transformation. So, it is common to add 1 to the data before taking log of the data: `adata.X = np.log(adata.X + 1)` or adding and subtracting 0.1: `adata.X = np.log(adata.X + 0.1) - np.log(0.1)`. Either way, zeros before log remain as zeros.\n",
    "\n",
    "Before we apply this normalization step, one very important note on Scanpy's data structure: The main data on which processing is to be done is stored in `adata.X`. So if we normalize the data in place then the original raw data will be replaced by the new normalized counts and we will lose the original counts. To avoid this from happening, we will store the original raw counts in adata.layers as follows. It is wise to save original counts for later, as we may require them for downstream analysis (e.g. count based factor analysis methods)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "d4858ab1",
   "metadata": {},
   "outputs": [],
   "source": [
    "adata.layers['raw_data'] = adata.X.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "187fc9d3",
   "metadata": {},
   "source": [
    "Now that we have made a copy of the original counts, we can go ahead and apply Scanpy's normalization function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "a7dc0680",
   "metadata": {},
   "outputs": [],
   "source": [
    "sc.pp.normalize_total(adata, inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67033f57",
   "metadata": {},
   "source": [
    "We can now log transform the data as follows (Note - we did not save normalized data in layers as raw_data because we can always recompute it if needed):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "29d604f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "sc.pp.log1p(adata)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f1d2f3a",
   "metadata": {},
   "source": [
    "# Save results for future"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "aacb5713",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AnnData object with n_obs × n_vars = 11984 × 17698\n",
       "    obs: 'n_genes_by_counts', 'log1p_n_genes_by_counts', 'total_counts', 'log1p_total_counts', 'pct_counts_in_top_50_genes', 'pct_counts_in_top_100_genes', 'pct_counts_in_top_200_genes', 'pct_counts_in_top_500_genes', 'mt_pct_content', 'ribo_pct_content', 'doublet_score', 'doublet'\n",
       "    var: 'gene_ids', 'feature_types', 'n_cells_by_counts', 'mean_counts', 'log1p_mean_counts', 'pct_dropout_by_counts', 'total_counts', 'log1p_total_counts', 'n_cells'\n",
       "    uns: 'log1p'\n",
       "    layers: 'raw_data'"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "fc957bdc",
   "metadata": {},
   "outputs": [],
   "source": [
    "adata.write_h5ad(input_path + 'processed/processed_data.h5ad')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "faa365ee",
   "metadata": {},
   "source": [
    "# Loading saved data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "3f461370",
   "metadata": {},
   "outputs": [],
   "source": [
    "adata = sc.read_h5ad(input_path + 'processed/processed_data.h5ad')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "0ce17d82",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AnnData object with n_obs × n_vars = 11984 × 17698\n",
       "    obs: 'n_genes_by_counts', 'log1p_n_genes_by_counts', 'total_counts', 'log1p_total_counts', 'pct_counts_in_top_50_genes', 'pct_counts_in_top_100_genes', 'pct_counts_in_top_200_genes', 'pct_counts_in_top_500_genes', 'mt_pct_content', 'ribo_pct_content', 'doublet_score', 'doublet'\n",
       "    var: 'gene_ids', 'feature_types', 'n_cells_by_counts', 'mean_counts', 'log1p_mean_counts', 'pct_dropout_by_counts', 'total_counts', 'log1p_total_counts', 'n_cells'\n",
       "    uns: 'log1p'\n",
       "    layers: 'raw_data'"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1513c64a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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