code/10.Phenotype-Enrichment.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cell2cell as c2c\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import tissue_enrichment_analysis as tea\n",
    "\n",
    "from collections import defaultdict\n",
    "from tqdm.auto import tqdm\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "output_folder = '../results/WormBase-Phenotype-Enrichment/'\n",
    "if not os.path.isdir(output_folder):\n",
    "    os.mkdir(output_folder)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Ligand-receptor pairs**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "ga_lrs = pd.read_csv('../data/PPI-Networks/Celegans-GA-BrayCurtis-Selected-LR-pairs.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(37, 11)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ga_lrs.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "bkg_lrs = pd.read_excel('../data/PPI-Networks/Celegans-Curated-LR-pairs.xlsx')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**RNA-seq data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Opening RNAseq datasets from /data/RNA-Seq/Celegans_RNASeqData_Cell.xlsx\n",
      "/data/RNA-Seq/Celegans_RNASeqData_Cell.xlsx was correctly loaded\n"
     ]
    }
   ],
   "source": [
    "rnaseq_data = c2c.io.load_rnaseq(rnaseq_file='../data/RNA-Seq/Celegans_RNASeqData_Cell.xlsx',\n",
    "                                 gene_column='symbol',\n",
    "                                 drop_nangenes=True,\n",
    "                                 log_transformation=False,\n",
    "                                 format='auto')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "excluded_cells = ['Distal_tip_cells',\n",
    "                  'Sex_myoblasts',\n",
    "                  'Socket_cells',\n",
    "                  'Vulval_precursors',\n",
    "                  'flp-1(+)_interneurons']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "rnaseq_data = rnaseq_data.drop(excluded_cells, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Metadata for cell types**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/data/RNA-Seq/Celegans_cell_metadata.tsv was correctly loaded\n"
     ]
    }
   ],
   "source": [
    "meta = c2c.io.load_metadata(metadata_file='../data/RNA-Seq/Celegans_cell_metadata.tsv',\n",
    "                            cell_labels=list(rnaseq_data.columns),\n",
    "                            index_col='#SampleID',\n",
    "                            format='auto')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Enrichment analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Dictionary for converting gene IDs from WormBase into symbol**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "mapper = dict(zip(bkg_lrs['Ligand_WB'].values.tolist(), bkg_lrs['Ligand_symbol'].values.tolist()))\n",
    "mapper.update((zip(bkg_lrs['Receptor_WB'].values.tolist(), bkg_lrs['Receptor_symbol'].values.tolist())))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "190"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(mapper)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Input genes**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "id_type = 'WB' # symbol or WB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "ga_genes = set(ga_lrs[['Ligand_' + id_type, 'Receptor_' + id_type]].values.flatten().tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "bkg_genes = set(bkg_lrs[['Ligand_' + id_type, 'Receptor_' + id_type]].values.flatten().tolist())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Fetch Phenotypes from WormBase using their tool (tea)**\n",
    "\n",
    "**Disclaimer:** This information could change depending on the updates from WormBase, altering the analyses that follow. This might cause that results reported in the paper are not consistent with this notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "phenotype = tea.fetch_dictionary('phenotype')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "phenotype.set_index('wbid', inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use only genes in our complete list of LR pairs (use them as background genes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "phenotype = phenotype.reindex(bkg_genes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Fisher Exact Test**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "n = len(ga_genes)\n",
    "N = len(bkg_genes)\n",
    "\n",
    "records = []\n",
    "for c in phenotype.columns:\n",
    "    phe_df = phenotype[c]\n",
    "    sam_phe_df = phe_df.reindex(ga_genes)\n",
    "    \n",
    "    test_n = sam_phe_df.sum()\n",
    "    test_N = phe_df.sum()\n",
    "    test = c2c.stats.enrichment.fisher_representation(n, test_n, N, test_N)\n",
    "    if N == 0:\n",
    "        exp = np.nan\n",
    "    else:\n",
    "        exp = n/N\n",
    "    if test_N == 0:\n",
    "        obs = np.nan\n",
    "    else:   \n",
    "        obs = test_n/test_N\n",
    "    test['fold'] = (exp, obs)\n",
    "    records.append((c, test['odds'][1], test['pval'][1], *test['fold']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "results = pd.DataFrame.from_records(records, \n",
    "                                    columns = ['Term', \n",
    "                                               'Enrichment Fold Change', \n",
    "                                               'P value', \n",
    "                                               'Expected', \n",
    "                                               'Observed'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Multiple test correction (adjust P-values with FDR Benjamini & Hochberg, and select FDR < 1%)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "fdr = 0.01\n",
    "results['Q value'] = c2c.stats.compute_fdrcorrection_asymmetric_matrix(results[['P value']], alpha=fdr)\n",
    "results['logQ'] = -results['Q value'].apply(np.log10)\n",
    "# remove np.infinites with np.nan\n",
    "results.logQ.replace([np.inf], np.nan, inplace=True)\n",
    "# remove np.nan with 70\n",
    "results.logQ.replace(np.nan, 70, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Term</th>\n",
       "      <th>Enrichment Fold Change</th>\n",
       "      <th>P value</th>\n",
       "      <th>Expected</th>\n",
       "      <th>Observed</th>\n",
       "      <th>Q value</th>\n",
       "      <th>logQ</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>208</th>\n",
       "      <td>organ system phenotype WBPhenotype:0002554</td>\n",
       "      <td>4.130435</td>\n",
       "      <td>0.000012</td>\n",
       "      <td>0.310526</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.002911</td>\n",
       "      <td>2.536017</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           Term  Enrichment Fold Change  \\\n",
       "208  organ system phenotype WBPhenotype:0002554                4.130435   \n",
       "\n",
       "      P value  Expected  Observed   Q value      logQ  \n",
       "208  0.000012  0.310526       0.5  0.002911  2.536017  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "significant_results = results.loc[results['Q value'] < fdr]\n",
    "significant_results.sort_values('Q value', ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "significant_results.sort_values('Q value', ascending=True).to_csv(output_folder + '/Phenotype-Enrichment-TEA-WormBase.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.8/site-packages/tissue_enrichment_analysis/hypergeometricTests.py:240: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df.sort_values(['Q value', 'Enrichment Fold Change'],\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f340b5883a0>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tea.plot_enrichment_results(significant_results, title='Phenotype', analysis='Phenotype', y='logQ', n_bars=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**GA selected genes involved**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "associated_genes = defaultdict(set)\n",
    "ga_associated_genes = defaultdict(set)\n",
    "for idx, row in significant_results.iterrows():\n",
    "    term = row['Term']\n",
    "    associated_genes[term] = set([mapper[g] for g in phenotype.loc[phenotype[term] > 0, term].index])\n",
    "    ga_associated_genes[term] = associated_genes[term] & set([mapper[g] for g in ga_genes])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5 is the fraction of genes associated to the enriched phenotype in the complete list of LR pairs and that were        selected by the genetic algorithm\n"
     ]
    }
   ],
   "source": [
    "fraction = len(ga_associated_genes[term]) / len(associated_genes[term])\n",
    "print('{} is the fraction of genes associated to the enriched phenotype in the complete list of LR pairs and that were \\\n",
    "       selected by the genetic algorithm'.format(np.round(fraction, 2)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualization of presence/absence given gene expression and selected genes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "expression = rnaseq_data.gt(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Colors from figure S14 in C elegans paper (cell type expression data)\n",
    "colors = {'Coelomocytes': (0.6509803921568628, 0.33725490196078434, 0.1568627450980392), # Brown\n",
    "          'Neurons': (0.596078431372549, 0.3058823529411765, 0.6392156862745098), # Purple\n",
    "          'Hypodermal/rectal cells': (0.30196078431372547, 0.6862745098039216, 0.2901960784313726), # Green\n",
    "          'Pharyngeal cells': (0.21568627450980393, 0.49411764705882355, 0.7215686274509804), # Blue\n",
    "          'Intestine': (1.0, 1.0, 0.2), # Yellow\n",
    "          'Body/muscular cells': (0.8941176470588236, 0.10196078431372549, 0.10980392156862745), # Red\n",
    "          'Excretory/Socket/Sheath cells': (1.0, 0.4980392156862745, 0.0), # Orange\n",
    "          'Sexual cells': (0.9686274509803922, 0.5058823529411764, 0.7490196078431373)} # Pink"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_567/3981993982.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  cell_colors['Groups'] = cell_colors['Groups'].map(colors)\n"
     ]
    }
   ],
   "source": [
    "cell_colors = meta[['Groups']]\n",
    "cell_colors['Groups'] = cell_colors['Groups'].map(colors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_567/3967940829.py:22: FutureWarning: Passing a set as an indexer is deprecated and will raise in a future version. Use a list instead.\n",
      "  cm = sns.clustermap(expression.loc[gene_list],\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAnYAAAWVCAYAAACHI5hbAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8GearUAAAgAElEQVR4nOzdeZhlVX3v//cnILYMShzijYK2JBhsQVutNHFAUTEOMahRfyIOIRHLn3HIjVe9JBqoqCRE2xiNExXDxSgOwQRtESUGbRuN0JTMtIAIRFq8IYoasdMo8L1/nN3hUFR1TWeo2uf9ep56+py11177u6uK8uPaZ++VqkKSJEkr3y8MuwBJkiT1hsFOkiSpJQx2kiRJLWGwkyRJagmDnSRJUksY7CRJklrCYCdJktQSBjtJkqSWMNhJkiS1hMFOkiSpJQx2kiRJLWGwkyRJagmDnSRJUksY7CRJklrCYCdJktQSBjtJkqSWMNhJkiS1hMFOkiSpJXYddgFqj7GxsWOAVcOuQ5rB9qmpqROGXYQk9ZvBTr20ampqamLYRUjTjY2NTQy7BkkahNZcik1yUpIbklw6y/YkeU+Sq5JcnOSRg65RkiSpn1oT7ICTgaftZPvTgf2br3HgAwOoSZIkaWBaE+yqahNw4066PAv4++o4B9g7yS8PpjpJkqT+a02wm4f7A9d1vd/atEmSJLXC/G6e+Fiqz3XMKS/iFXQuoe4wWVWTw6pHkiRpuVkxd8U2IW4pQe67wL5d7/dp2iRJklphlC7FbgBe2twd+xvAj6vqe8MuSpIkqVdWzIzdXJJ8HDgUuHeSrcBxwF0AquqDwBnAM4CrgG3A7w2nUkmSpP5oTbCrqhfOsb2AVw2oHEmSpIEbpUuxkiRJrWawkyRJagmDnSRJUksY7CRJklrCYCdJktQSBjtJkqSWMNhJkiS1hMFOkiSpJQx2kiRJLWGwkyRJagmDnSRJUksY7CRJklrCYCdJktQSBjtJkqSWMNhJkiS1hMFOkiSpJQx2kiRJLWGwkyRJagmDnSRJUksY7CRJklrCYCdJktQSBjtJkqSWMNhJkiS1hMFOkiSpJQx2kiRJLWGwkyRJagmDnSRJUksY7CRJklrCYCdJktQSBjtJkqSWMNhJkiS1hMFOkiSpJQx2kiRJLWGwkyRJagmDnSRJUksY7CRJklrCYCdJktQSBjtJkqSWMNhJkiS1xK7z6XTZ9Xftdx1zeuiwC5AkSVrmnLGTJElqCYOdJElSSxjsJEmSWqI1wS7J05JckeSqJMfMsP0BSb6c5IIkFyd5xjDqlCRJ6pdWBLskuwDvA54OrAFemGTNtG5vBv6hqh4BHAG8f7BVSpIk9Vcrgh2wDriqqq6uqp8BnwCeNa1PAXdvXt8DuH6A9UmSJPXdvB53sgLcH7iu6/1W4OBpfSaAf07yGmAP4LDBlCZJkjQYK2bGLsl4kqmur/EFDvFC4OSq2gd4BvCRJCvm/CVJkuayYmbsqmoSmJxl83eBfbve79O0dXsZ8LRmrK8nWQXcG7ihx6VKkiQNRVtmrM4D9k/yoCS70bk5YsO0Pt8BngyQ5CHAKuA/BlqlJElSH7Ui2FXVLcCrgTOBb9K5+/WyJG9JcnjT7X8BL09yEfBx4KiqquFULEmS1Hsr5lLsXKrqDOCMaW3Hdr3eAjx20HVJkiQNSitm7CRJkmSwkyRJag2DnSRJUksY7CRJklrCYCdJktQSBjtJkqSWMNhJkiS1hMFOkiSpJQx2kiRJLWGwkyRJagmDnSRJUksY7CRJklrCYCdJktQSBjtJkqSW2HXYBUjqGBsbOwZYNew6Wmr12NjYxLCLaKntU1NTJwy7CEkdBjtp+Vg1NTU1MewipIUwMEvLi5diJUmSWsJgJ0mS1BIGO0mSpJYw2EmSJLWEwU6SJKklDHaSJEktYbCTJElqCZ9jJ0krxDJ9iPVyfPizD03WyDLYSdLK4UOs52EZBk1pYLwUK0mS1BIGO0mSpJYw2EmSJLWEwU6SJKklDHaSJEktMa+7Yrd87679rmNODx12AZIkScucM3aSJEktYbCTJElqCYOdJElSSxjsJEmSWsJgJ0mS1BIGO0mSpJYw2EmSJLWEwU6SJKklDHaSJEktYbCTJElqCYOdJElSSxjsJEmSWmLXYRfQK0meBrwb2AX4UFWdMEOf/w+YAAq4qKqOHGiRGpixsbFjgFXDrmOBVo+NjU0Mu4gF2j41NXWn/9YkScPRimCXZBfgfcBTgK3AeUk2VNWWrj77A38MPLaqfpjkl4ZTrQZk1dTU1MSwi2i7FRhEJanV2nIpdh1wVVVdXVU/Az4BPGtan5cD76uqHwJU1Q0DrlGSJKmv2hLs7g9c1/V+a9PW7cHAg5N8Lck5zaVbSZKk1lgxl2KTjAPjXU2TVTW5gCF2BfYHDgX2ATYlOaiqftS7KiWpfVbgZ1ZX2udV/ayqembFBLsmxM0W5L4L7Nv1fp+mrdtW4Nyq+jlwTZIr6QS983pdqyS1jJ9Z7aMVFkK1zLXlUux5wP5JHpRkN+AIYMO0Pp+mM1tHknvTuTR79SCLlCRJ6qdWBLuqugV4NXAm8E3gH6rqsiRvSXJ40+1M4AdJtgBfBt5QVT8YTsWSJEm9t2Iuxc6lqs4AzpjWdmzX6wJe13xJkiS1Titm7CRJktSiGTtpVA35jsVh3n3onYSSNI3BTlr5RvKORe8klKQ781KsJElSSzhjJ0lqFR+o3Fd+BGKZM9hp2VriH+el/KH0D5e0so3kxxMGYQUF0JFlsNNyNpQ/zv7hkiStVH7GTpIkqSUMdpIkSS1hsJMkSWoJg50kSVJLGOwkSZJawrtiJUlawQb83L5BPnPPR08tgsFOkqSVrZXP7fPRU4vjpVhJkqSWMNhJkiS1hMFOkiSpJQx2kiRJLeHNE5IGqod38PXi7jzvupPUKgY7SYO2bO7g8647SW1jsJOWgSXOYi1l5soZK0lqkXkFu8uu363fdczp+cMuQOqvocxiOWMlSe3izROSJEktYbCTJElqCYOdJElSSxjsJEmSWsJgJ0mS1BIGO0mSpJYw2EmSJLWEDyiWtKL5cGdJup3BTtJK58OdJalhsJMkLUtLmI11JnYZW8DPdb4/R39mXQx2kqTlauCzsc7EDkRPf67+zO7ImyckSZJawmAnSZLUEgY7SZKkljDYSZIktYTBTpIkqSUMdpIkSS1hsJMkSWoJg50kSVJLGOwkSZJawmAnSZLUEq0JdkmeluSKJFclOWYn/Z6bpJKMDbI+SZKkfmvFWrFJdgHeBzwF2Aqcl2RDVW2Z1m8v4A+BcwdfpSRpWFx4XqOiFcEOWAdcVVVXAyT5BPAsYMu0fm8F/hJ4w2DLkyQNmQvPayS05VLs/YHrut5vbdr+W5JHAvtW1ecGWZgkSdKgrJhgl2Q8yVTX1/gC9v0F4K+A/9W/CiVJkoZrxVyKrapJYHKWzd8F9u16v0/TtsNewIHAxiQA/wPYkOTwqprqQ7mSJEkDt2Jm7OZwHrB/kgcl2Q04AtiwY2NV/biq7l1Vq6tqNXAOYKiTJEmtsmJm7Hamqm5J8mrgTGAX4KSquizJW4Cpqtqw8xEkqcO7JyWtZK0IdgBVdQZwxrS2Y2fpe+ggapK0Inn3pKQVqy2XYiVJkkZea2bsJElaqRbwEYCZzPdjAdP5MYEWMthJkjR8Pf0IwHz4MYF28lKsJElSSzhjp5HR47sdvYShFc87gKX2MdhplPTsUoeXMNQS3gEstYyXYiVJklrCYCdJktQSBjtJkqSWMNhJkiS1hMFOkiSpJQx2kiRJLTGvx51cdv1d+12HJEmSlsgZO0mSpJYw2EmSJLWEwU6SJKklDHaSJEktYbCTJElqCYOdJElSSxjsJEmSWsJgJ0mS1BIGO0mSpJYw2EmSJLWEwU6SJKklDHaSJEktYbCTJElqCYOdJElSSxjsJEmSWmLXYRcgSZJ6a2xs7Bhg1RzdVo+NjU3MY7jtU1NTJyy9Kg2CwU6SpPZZNTU1NdGLgeYZ/rRMeClWkiSpJQx2kiRJLWGwkyRJagmDnSRJUksY7CRJklrCYCdJktQSBjtJkqSWMNhJkiS1hMFOkiSpJQx2kiRJLeGSYlILzXOdSHCtSElqFYOd1E49WycSXCtSklYKL8VKkiS1RGuCXZKnJbkiyVVJjplh++uSbElycZKzkjxwGHVKkiT1SyuCXZJdgPcBTwfWAC9MsmZatwuAsap6GPAp4O2DrVKSJKm/WhHsgHXAVVV1dVX9DPgE8KzuDlX15ara1rw9B9hnwDVKkiT1VVuC3f2B67reb23aZvMy4PN9rUiSJGnAVsxdsUnGgfGupsmqmlzEOC8GxoAn9Ko2SZKk5WDFBLsmxM0W5L4L7Nv1fp+m7Q6SHAa8CXhCVd3c8yIlSZKGqC2XYs8D9k/yoCS7AUcAG7o7JHkEcCJweFXdMIQaJUmS+qoVwa6qbgFeDZwJfBP4h6q6LMlbkhzedHsHsCdwapILk2yYZThJkqQVacVcip1LVZ0BnDGt7diu14cNvChJkqQBasWMnSRJkgx2kiRJrWGwkyRJagmDnSRJUkvM6+aJy753137XIUmSpCVyxk6SJKklDHaSJEktYbCTJElqCYOdJElSSxjsJEmSWsJgJ0mS1BIGO0mSpJYw2EmSJLWEwU6SJKklDHaSJEktYbCTJElqCYOdJElSSxjsJEmSWsJgJ0mS1BIGO0mSpJYw2EmSJLWEwU6SJKklDHaSJEktYbCTJElqiVTVnJ3WPPhX5u7UZ1uu/HaGXYMkSdJytuuwC9Dyt/0tG3sa7Ffd9sSejXX42mf0bKx+uOGC1w67hIE5J0/r6Xgf/+YBvRvrhfv1bCyADc/+956Ot/0t63s63g9OfElPx+ul08f+YtglaES94jMvHokJIi/FSpIktYTBTpIkqSUMdpIkSS1hsJMkSWoJg50kSVJLGOwkSZJawmAnSZLUEgY7SZKkljDYSZIktYTBTpIkqSUMdpIkSS1hsJMkSWoJg50kSVJLGOwkSZJawmAnSZJGXpL7JvlYkquTfCPJ15M8Z9h1LZTBTpIkjbQkAT4NbKqq/arqUcARwD7T+u06jPoWojXBLsnTklyR5Kokx8yw/a5JPtlsPzfJ6sFXKUmSlqEnAT+rqg/uaKiqf6uqv0lyVJINSb4EnJXknkk+neTiJOckeRhAkokkr9+xf5JLk6xuvi5PckqSbyb5VJLdmz4nJNnSjLW+FyfSimCXZBfgfcDTgTXAC5OsmdbtZcAPq+pXgXcBfznYKiVJ0jL1UOD8nWx/JPC8qnoC8GfABVX1MOBPgL+fx/i/Bry/qh4C/CfwB0nuBTwHeGgz1tuWcgI7tCLYAeuAq6rq6qr6GfAJ4FnT+jwL+HDz+lPAk5upV0mS1HJJxpNMdX2N76Tv+5JclOS8pumLVXVj8/pxwEcAqupLwL2S3H2Ow19XVV9rXn+0GePHwHbg75L8DrBtkad2B8v+WvE83R+4ruv9VuDg2fpU1S1JfgzcC/j+QCqUJElDU1WTwOQsmy8DntvV91VJ7g1MNU0/ncchbuGOE2arug9/53LqliTrgCcDzwNeTeeS8JLMK9htufLbQ5/ZapJ1d7qebH5IkiRJS/El4M+TvLKqPtC07T5L37OBFwFvTXIo8P2q+s8k1wLPBEjySOBBXfs8IMmjq+rrwJHAV5PsCexeVWck+RpwdS9OZMXM2M2RtL8L7Nv1fp+mbaY+W5u7Wu4B/KDXdUqSpJWlqirJs4F3JXkj8B90Zun+N3C3ad0ngJOSXEzn8unvNu3/CLw0yWXAucCVXftcAbwqyUnAFuADdHLIZ5KsAgK8rhfnsmKC3RzOA/ZP8iA6Ae4IOom42wY63/yv05ny/FJVTZ8alSRJI6iqvkcnP8zk5K5+NwLPnmH//wJ+c3p78xSOW6rqxdM2baNzj0BPtSLYNdepXw2cCewCnFRVlyV5CzBVVRuAvwM+kuQq4EZm/+FJkiStSK0IdgBVdQZwxrS2Y7tebweeP+i6JEnS6Kqqa4EDB3W8tjzuRJIkaeQZ7CRJklrCYCdJktQSBjtJkqSWMNhJkiS1hMFOkiSpJQx2kiRJLWGwkyRJagmDnSRJUksY7CRJklrCYCdJktQSBjtJkqSWSFUNuwb10bZt25b8Ax5be1AvSpE0IFsmrh52CdKSrJnYr+djbrny2+n5oMuQM3aSJEktYbCTJElqCYOdJElSjyU5Psl1SW4a5HENdpIkSb33WWDdoA9qsJMkSSMtyR5JPpfkoiSXJnlBkk93bX9KktOa1y9LcmWSzUn+Nsl7Zxqzqs6pqu8N6hx2MNhJkqRR9zTg+qp6eFUdCHwBOCDJfZrtvweclOR+wJ8CvwE8FjhgKNXuxK7DLqANxsbGjgFWDbuOmWzatGnYJUiSNHRJxoHxrqbJqppsXl8CvDPJXwKnV9XZST4CvDjJ/wEeDbwUeCbwlaq6sRnzVODBAzuJeTDY9caqqampiWEXMZNt27YdN+waJEkatibETc6y7cokjwSeAbwtyVnAh+h8Tm47cGpV3ZLM/Ci8JLsA32jebqiqY3td/3wZ7CRJ0khrLrHeWFUfTfIj4Oiquj7J9cCbgcOarucBf53kF4GfAM8FLqmqW4G1w6h9OoOdJEkadQcB70hyG/Bz4JVN+ynAfarqmwBV9d0kfw5sBm4ELgd+PNOASd4OHAnsnmQr8KGqmujrWWCwkyRJI66qzgTOnGHT44C/ndb2saqaTLIrcBrw6TvvBlX1RuCNPS10HrwrVpIkaZok3wAeBnx02qaJJBcClwLXMEuwGxZn7Fpu8+Y9ejBK7xdj7pVeL3bej4WnJY2mXv89We5/73pZX6/PdTGq6lGztL9+0LUsxMgGux4/omR1j8aRJElatJENdvTwESVjY2M9GUeSJGkp/IydJElSSxjsJEmS+iTJAUm+nuTmJH3/fN4oX4qVJEnqtxuB1wLPHsTBnLGTJEkjL8lLk1yc5KIkH0lyTTr2TnJrksc3/TYl2T/JRJKTkmxMcnWS1840blXdUFXn0Xnwcd85YydJkkZakofSWTrsMVX1/ST3BD4GrAEeBJwPHJLkXGDfqvpWs27sAcATgb2AK5J8oKoGEuBmY7Drje3L9c7Y9euHXYEkScOXZBwY72qarKrJ5vWTgFOr6vsAVXVjkrOBx9MJdn8BvBz4Cp31Ynf4XFXdDNyc5AbgvsDW/p7JzhnsemBqauqEYdcwm40bc9ywa5AkadiaEDc5Z8fbbaKzZuz9gGOBNwCHAmd39bm56/WtwK5JXkUnBAI8o6quX2zNi+Fn7CRJ0qj7EvD8JPcCaC7FbgYeA9xWVduBC4FX0Al8s6qq91XV2uZroKEOnLGTJEkjrqouS3I88JUktwIXVNVRSa4Dzmm6nQ28ELhkIWMn+R/AFHB34LYk/xNYU1X/2bszuJ3BTpIkjbyq+jDw4Wlth3S9/hidGyp2vJ+Y1vfAWcb9v8A+vax1Z7wUK0mS1BIrdsZubGzsGGDVEoZY3aNSNERrJvYbdgkDtWXi6mGXMKtR+1n00nL+ufZar39PRul712u9/t718mfrz3XxVmywA1ZNTU1NLHbn5fp4EkmSpMXyUqwkSVJLGOwkSZK6NMuFvb5ZLmxshu1HJXnvLPsen+S6JDf1v9I7M9hJkiT1zmeBdcM6uMFOkiSNvCRvSnJlkq8Cv9a16SVJLkxyaZI5A1tVnVNV3+tfpTu3km+ekCRJWrIkjwKOANbSyUbnA99oNu9eVWuTPB44CZjxeXXLhcFuEXrwqJWBWb9+2BVIkjR8ScaB8a6myWb9WIBDgNOqalvTd0NXv48DVNWmJHdPsvdACl4kg93iLOlRK4O0cWOOG3YNkiQNWxPiJufsOMOus71Psgu3z+xtqKpjF1lez/gZO0mSNOo2Ac9OcrckewG/3bXtBQBJHgf8uKp+vGNDVd1aVWubr6GHOjDYSZKkEVdV5wOfBC4CPg+c17V5e5ILgA8CL5trrCRvT7IV2D3J1iQTfSh5Vl6KlSRJI6+qjgeOn9Y84yfVq+pk4ORZtr0ReGMva1sIZ+wkSZJawmAnSZLUEqN8KXb72NjYxCL3Xd3DOvpq3bqf9mCUg3owRn9smbi6p+Otmdivp+P1uj5p0PxvYvFG6Vxh9M53uRrZYDc1NXXCYvddQiCUJEnqGy/FSpIktYTBTpIkqc+SfCHJj5Kc3s/jGOwkSZL67x3AS/p9EIOdJEkaaUlWJ7k8yclJrkxySpLDknwtybeSrEuyR5KTkmxOckGSZzX77pJkfZJLk1yc5DUzHaOqzgJ+0u9zGdmbJyRJkrr8KvB84PfprDxxJPA44HDgT4AtwJeq6veT7A1sTvIvwEvpPC1jbVXdkuSewyh+B4Pd4izlUSkDtWnTpmGXIEnS0CUZB8a7miararLr/TVVdUnT9zLgrKqqJJfQCW77AIcneX3TfxXwAOAw4INVdQtAVd3Y3zPZOYPdIizlUSmDtm3btuOGXYMkScPWhLjJnXS5uev1bV3vb6OTl24FnltVV3TvlOROAyU5GDixeXtsVW1YZNkL5mfsJEmS5nYm8Jo0SS7JI5r2LwKvSLJr037Pqjq3qtY2XwMLdWCwkyRJmo+3AncBLm4u1b61af8Q8J2m/SI6n827kyRnA6cCT06yNclT+1Gkl2IlSdJIq6prgQO73h81y7ZXzLDvLcDrmq+dHeOQpVc6N2fsJEmSWsIZu5bbvHmPHozS20XAe2m5L1De6/rUTsv592TU/ptwIfvF6+XP1p/D4jljJ0mS1BJ9m7EbGxs7hs4zXvpldR/HliRJWnH6eSl21dTU1ES/Bl8pDwiWJEkaFC/FSpIk9UGSLyT5UZLTp7WfkuSKZn3Zk5LcpWn/xSSnNWvObk5y4Mwjz85gJ0mS1B/vAF4yQ/spwAHAQcDdgKOb9j8BLqyqh9FZg/bdCz2gwU6SJI20JKuTXNr1/vVJJpJsTPKXzezZlUkOabbvkmR9M+N2cZLXzDRuVZ0F/GSG9jOqAWymsw4twBrgS02fy4HVSe67kHMx2EmSJM1u16paB/xPYMf66+N0buJc28yunbKYgZtLsC8BvtA0XQT8TrNtHfBAbg998yt2MYVodgO4G3hB1q8fdgWSJA1fknE6gWyHyaqanMeu/9T8+w1ufyLHYcAHm1UnqKobF1nW+4FNVXV28/4E4N1JLgQuAS4Abl3IgAa73uvr3cALtXFjjpu7lyRJ7daEuNmC3C3c8Spm9wTNzc2/t7KT3JTkYODE5u2xVbVhZ/UkOQ64D13LlFXVfwK/12wPcA2woKc1G+wkSdKo+3fgl5LcC7gJeCa3Xx6dyReBVyT5clXdkuSeVXUusHY+B0tyNPBU4MlVdVtX+97Atqr6GZ0bKjY1YW/e/IydJEkaaVX1c+AtdG5k+CJw+Ry7fAj4DnBxkouAI2fqlORs4FTgyUm2Jnlqs+mDwH2Brye5MMmxTftDgEuTXAE8HfjDhZ6LM3aSJGnkVdV7gPfsZPv3aT5j13y27nXN187GPGSW9hnzV1V9HXjw/CqemTN2kiRJLdG6GbtlcFfq6iEeW0u0ZmK/YZegFWDLxII+yzxw/h5Lo6t1wY4h35XqGraSJGlYvBQrSZLUEgY7SZI08pLc1Px7vySfWuC+90ry5SQ3JXnvtG0vTHJJs/TYF5Lcu5d1T2ewkyRJalTV9VX1vAXuth34U+D13Y1JdgXeDTyxWXrsYuDVPSl0FgY7SZKkRpLVSS5tXh+V5J+ambZvJXn7TPtU1U+r6qt0At4dhmu+9mhWkrg7cH0/62/jzROSJEm9shZ4BJ2lxa5I8jdVdd18dqyqnyd5JZ11X38KfAt4Vd8qxWDXD9uX052x69cPuwJJkoYvyTgw3tU02awfO5ezqurHzRhbgAcC8wp2Se4CvJJOMLwa+Bvgj4G3LaD0BTHY9djU1NQJw66h28aNOW7YNUiSNGxNiJtPkJvu5q7XtwK7JnkOsON/X4+uqqlZ9l3bHPvbAEn+AThmETXMm8FOkiRpAarqNOC0eXT9LrAmyX2q6j+ApwDf7GdtBjtJkqQlSnItnZsjdkvybOA3q2pLkj8DNiX5OfBvwFH9rMNgJ0mSRl5V7dn8ey1wYPP6ZODkrj7P3Mn+q2dp/yDwwZ4VOgcfdyJJktQSBjtJkqSW8FJsy61b99MejHJQD8ZYGbZMXN3T8dZM7NfT8bQ89Prn2uvfu15a7r/Do/S9W87nCr2trx+/d1uO7PmQy5IzdpIkSS2xkmfsZnsQ8OoB1yFJkrQsrNhgN9uDgJfTqg+SJGnlSvJ84C3A/62qJy5w3wcAW4CJqhrYOlArNthJkiT12cuAl1fVVxex718Bn+9xPXMy2EmSpJGX5MXAa4HdgHOB64HHAX+XZANwGXA4sDvwK8BpVfXGWcZ6NnAN0Is7GBfEmyckSdJIS/IQ4AXAY6tqLZ01Ya8BpoAXVdUbmq5rm34HAS9Isu8MY+0J/G/gzwZR+3Stn7EbGxs7Blg17DqGZdOmTcMuQZKkoUsyDox3NU1W1WTz+snAo4DzkgDcDbhhhmHOqqofN+NtAR4IXDetzwTwrqq6qRlroFof7IBVU1NTE8MuYli2bdt23LBrkCRp2JoQNznL5gAfrqo/vkNjsnFav5u7Xt8K7JrkOcCO/609GjgYeF6StwN7A7cl2V5V713iKczLKAQ7SZKknTkL+EySd1XVDUnuCew1nx2r6jTgtK6mQ3a8SDIB3DSoUAcGO0mSNOKqakuSNwP/nOQXgJ8DrxpyWYtisJMkSSOvqj4JfHJa86Fd208GTu56/8x5jDnRk+IWwLtiJUmSWsIZO6nLcl/wXO20nH/vlvvC8722nH8Wo2TUfu96aVHBbp6PEFm9mLElSZK0OIudsZvzESKu2SpJkjRYfsZOkiSpJdr4Gbvt02YLVw+pDkmSNIKS7A6cSmdN2VuBz1bVMYM4duuC3dTU1And770kLEmShmB9VX05yW7AWUmeXlWf7/dBvRQrSZJGWpI9knwuyUVJLk3ygiSf7tr+lCSnNa9fluTKJJuT/G2SO60qUVXbqurLzeufAecD+/tQjCMAACAASURBVAziXAx2kiRp1D0NuL6qHl5VBwJfAA5Icp9m++8BJyW5H/CnwG8AjwUOmGvgJHsDv01n2bK+a92l2EGb56NfhmbTpk3DLkGSpKFLMg6MdzVNVtVk8/oS4J1J/hI4varOTvIR4MVJ/g/waOClwDOBr1TVjc2YpwIP3skxdwU+DrynqgbycD6D3dLN+eiXYdq2bdtxw65BkqRha0Lc5CzbrkzySOAZwNuSnAV8CPgssB04tapuSTLj2El2Ab7RvN1QVcc2ryeBb1XVX/fuTHbOYCdJkkZac4n1xqr6aJIfAUdX1fVJrgfeDBzWdD0P+Oskvwj8BHgucElV3QqsnTbm24B7AEcP6jzAYCdJknQQ8I4ktwE/B17ZtJ8C3KeqvglQVd9N8ufAZuBG4HLgx9MHS7IP8KZm+/nNTN97q+pD/T4Rg50kSRppVXUmcOYMmx4H/O20to9V1WTz+bnTgE9P36mqtgIzX7ftM++KlSRJmibJN4CHAR+dtmkiyYXApcA1zBDshmkUZuymr0TRa6v7OLYkDdWaif2GXcJObZkYyI2Gi7Kca9PcqupRs7S/ftC1LETrg930lSh6zZUtJEnScuGlWEmSpJYw2EmSpJGX5F97NM7xSa5LctO09nclubD5urJ5rErPtf5SrCRJ0lyq6jE9GuqzwHuBb00b/492vE7yGuARPTreHThjJ0mSRt6OGbYkhybZmORTSS5PckqaB9ElOSHJliQXJ1k/0zhVdU5VfW+Ow72QzlJjPeeMnSRJ0h09AngocD3wNeCxSb4JPAc4oKoqyd6LGTjJA4EHAV/qVbHdDHZL1+/HqSzJpk2bhl2CJElDl2QcGO9qmmzWj53J5uYhwzTPrFsNnENn3di/S3I6cPoiSzkC+FSzDFnPGeyWqN+PU1mqbdu2HTfsGiRJGrYmxM0W5Ka7uev1rcCuVXVLknXAk4HnAa9O8hTgG02/DVV17DzGPgJ41TzrWDCDnSRJ0hyS7AnsXlVnJPkacHUz67Z2AWMcAPwi8PU+lenNE5IkSfOwF3B6kouBrwKvm6lTkrcn2QrsnmRrkomuzUcAn6iq6leRzthJkqSRV1V7Nv9uBDZ2tb+6q9u6eYzzRuCNs2ybWEqN8+GMnSRJUksY7CRJklrCS7Ett3nzHj0YZb8ejCFJsGXi6mGXsFO9rG/NxGj97Vzu37stR/Z8yGXJGTtJkqSWMNhJkiS1hMFOkiRpAJLcvXkEynv7dQyDnSRJ0mC8FejrWp8GO0mSNNKSrE5yeZKTk1yZ5JQkhyX5WpJvJVmXZI8kJyXZnOSCJM9q9t0lyfoklya5OMlrZjnGo4D7Av/cz3PxrlhJkiT4VeD5wO8D5wFHAo8DDgf+BNgCfKmqfj/J3sDmJP8CvBRYDaxt1pO95/SBk/wC8E7gxcBh/TyJZRPsxsbGjgFWDbuOtlm/ftgVSJI0fEnGgfGupsmqmux6f01VXdL0vQw4q6oqySV0gts+wOFJXt/0XwU8gE5Q+2BV3QJQVTfOcPg/AM6oqq1Jenlad7Jsgh2wampqamLYRbTNxo05btg1SJI0bE2Im9xJl5u7Xt/W9f42OnnpVuC5VXVF904zBbUkBwMnNm+PBR4NHJLkD4A9gd2S3FRVxyziVHZqOQU7SZKk5epM4DVJXtPM5D2iqi4Avgi8IsmXd1yKrapzgbVd+27Y8SLJUcBYP0IdePOEJEnSfLwVuAtwcXOp9q1N+4eA7zTtF9H5bN7QOGMnSZJGWlVdCxzY9f6oWba9YoZ9bwFe13zN51gnAycvstQ5OWMnSZLUEs7YSZIGph+Lu6t9tkxcPewSVqx+BrvtY2NjEwvov7pPdUiSJI2EvgW7qampExbSf4EhUJIkSdP4GTtJkqSWMNhJkiR1STLRtcLETNvvk+TcZs3YQ6ZtOzvJhc3X9Uk+3f+Kb+fNE5IkSQvzZOCSqjp6+oaq+u+gl+Qfgc8MsjBn7CRJ0shL8qYkVyb5KvBrTdvLk5yX5KIk/5hk9yRrgbcDz2pm5e42y3h3B54EDHTGzmAnSZJGWpJHAUfQWQbsGcCvN5v+qap+vaoeDnwTeFlVXUhn/ddPVtXaqvqvWYZ9NnBWVf1nn8u/g+V0KXahj0fRPKxfP+wKJEkaviTjwHhX02RVTTavDwFOq6ptTd8da7semORtwN7AnnTWi52vF9JZbmyglk2wW+jjUTQ/GzfmuGHXIEnSsDUhbnLOjnd0MvDsqrooyVHAodM7JDkTuC8wteMzd0nuDawDnrOEkhfFS7GSJGnUbQKeneRuSfYCfrtp3wv4XpK7AC+aaceqempzSbb7RornAadX1fa+Vj2DZTNjJ0mSNAxVdX6STwIXATcA5zWb/hQ4F/iP5t+95jnkEcBQrkQa7CRJ0sirquOB42fY9IEZ+p5M5zLtbGMd2qu6FspLsZIkSS3hjF3LrVv30x6MclAPxlAvbJm4etglzGrNxH7DLmFgev1zWM7fu+V+rsu5vuX832s/LOffY4AtRw67gsFwxk6SJKklDHaSJEktYbCTJEnqkyQvSnJxkkuS/GuSh/fzeH7GTpIkqX+uAZ5QVT9M8nQ6D0k+uF8Hc8ZOkiSNvCQvbWbWLkrykSTXpGPvJLcmeXzTb1OS/ZNMJDkpycYkVyd57UzjVtW/VtUPm7fnAPv08zycsZMkSSMtyUOBNwOPqarvJ7kn8DFgDfAg4HzgkCTnAvtW1beSABwAPJHOg4uvSPKBqvr5Tg71MuDzfTwVg91cxsbGjgFWDbuOxdq0adOwS5AkaeiSjAPjXU2TzfqxAE8CTq2q7wNU1Y1JzgYeTyfY/QXwcuAr3L4qBcDnqupm4OYkN9BZM3brLMd/Ip1g97jendWdGezmtmpqampi2EUs1rZt244bdg2SJA1bE+Im5+x4u03AK4H7AccCbwAOBc7u6nNz1+tbgV2TvIpOCAR4RlVdn+RhwIeAp1fVDxZ3BvPjZ+wkSdKo+xLw/CT3AmguxW4GHgPcVlXbgQuBV9AJfLOqqvdV1drm6/okDwD+CXhJVV3Z17PAYCdJkkZcVV1GZ53YryS5CPir5hLrdXRueIDOTN1ewCULHP5Y4F7A+5NcmGSqR2XPyEuxkiRp5FXVh4EPT2s7pOv1x+jcULHj/cS0vgfOMu7RwNG9rHVnnLGTJElqCYOdJElSSyyrS7HL9NEiq4ddQNtsmbi6Z2OtmdivZ2ONGr93mg9/T5aPXv8sevm3WMvHsgp2LMNHi4yNjU0MuwZJkqT58FKsJElSSxjsJEnSyEty0xzb907yBzvZ/rokW5r1Zs9K8sCm/YFJzm8edXJZkv+/17V3M9hJkiTNbW9g1mAHXACMVdXDgE8Bb2/avwc8uqrWAgcDxyS5X7+KNNhJkiR1SfKGJOc1s29/1jSfAPxKM/P2jun7VNWXq2pb8/YcYJ+m/WfNw44B7kqfs9dyu3lCkiRpaJL8JrA/sA4IsCHJ44FjgAObmbe5vAz4fNeY+wKfA34VeENVXd/zwhsGu7ltX8l3xm7atNMl7SRJGglJxoHxrqbJqpqcoetvNl8XNO/3pBP0vjPP47wYGAOesKOtqq4DHtZcgv10kk9V1b8v/CzmZrCbw9TU1AnDrmEptm3bdtywa5AkadiaEDdTkJsuwF9U1Yl3aExWT3t/PPBbzdhrm7bDgDcBT+i6/Npdw/VJLgUOofM5vJ7zM3aSJEm3OxP4/SR7AiS5f5JfAn4C7LWjU1W9qarWdoW6RwAnAodX1Q07+iXZJ8ndmte/CDwOuKJfxTtjJ0mS1Kiqf07yEODrSQBuAl5cVd9O8rVmxu3zVfWGabu+g85l21Ob/b5TVYcDDwHemaTozAaur6pL+lW/wU6SJI28qtqz6/W7gXfP0OfInex/2CztXwQe1osa58NLsZIkSS3hjF3Lbd68Rw9GWb6LgI/aItbLeUH2Xv8senmuo/Z70svzHbWF55dzfcv5vzFY3t+7UeKMnSRJUksY7CRJklrCYCdJktQSBjtJkqQ+SPL4JOcnuSXJ8wZxTIOdJElSf3wHOAr42KAOaLCTJEkjL8mbklyZ5KtJPp7k9Uk2Jhlrtt87ybXN69VJzm5m485P8piZxqyqa6vqYuC2QZ2Hjzvps7GxsWOAVcM6/vr1wzqyJEnLR5JxYLyrabJZP5YkjwKOANbSyUbnA9/YyXA3AE+pqu1J9gc+Doz1pfAFMtj136qpqamJYR1848YcN6xjS5K0XDQhbnKWzYcAp1XVNoAkG+YY7i7Ae5OsBW4FHtyzQpfIYCdJkjSzW7j9Y2vdV9/+CPh34OHN9u0ASY4HfgugqtYOrszb+Rk7SZI06jYBz05ytyR7Ab/dtF8LPKp53X1X6z2A71XVbcBLgF0AqupNVbV2WKEODHaSJGnEVdX5wCeBi4DPA+c1m9YDr0xyAXDvrl3eD/xukouAA4CfzjRukl9PshV4PnBiksv6dAr/zUuxkiRp5FXV8cDxAEkmmrbLgYd1dXtz0/6tae3/e5YxzwP26UO5s3LGTpIkqSVGfsZuAI8jWd3Hsee0bt2Ms8MLdFAPxtBys2Xi6p6Ot2Ziv56Ot5yN0rn2Wq+/d73+PR4lfu9mV1UTw65hsUY+2NHnx5GMjY31bWxJkqRuXoqVJElqCYOdJEkaac0SYZfOs++hSU6fZdurk1yVpJLce6Y+/WawkyRJ6o2vAYcB/zasAgx2kiRJjST7JbkgycFJvt68/tckvzbXvlV1QVVdO4AyZ+XNE5IkSUAT3j4BHAVcAxxSVbckOQz4c+C5QyxvXgx2/bd9mHfGbtq0aViHliRp2UgyDox3NU1W1WTX+/sAnwF+p6q2JNkX+HCS/YEC7jK4ahfPYNdnU1NTJwzz+Nu2bTtumMeXJGk5aELc5E66/Bj4DvA4YAvwVuDLVfWcJKuBjdN3SHImcF9gqqqO7nHJi2KwkyRJgp8BzwHOTHITcA/gu822o2baoaqeOpjS5s+bJyRJkoCq+inwTOCPgAuBv0hyAfOcCEvy2iRb6awPe3GSD/Wt2Fk4YydJkkZacyfrgc3rHwG/3mz6s65ub262b2SGy7LNtvcA7+lTmfPijJ0kSVJLOGPXcps379GDUXq7aLcLqC/ecl60u9e19fL3ZM3Efj2tbzmfa68t5985SXfmjJ2k1jOcSBoVBjtJkqSWMNhJkiS1hMFOkiSpz5J8IcmPkpzez+MY7CRJkvrvHcBL+n0Qg50kSRp5SV6a5OIkFyX5SJLfTnJukguS/EuS+zb9JpJ8OMnZSf4tye8keXuSS5pZuRnXlK2qs4Cf9Ps8DHaSJGmkJXkonQcQP6mqHg78IfBV4Deq6hHAJ4A3du3yK8CTgMOBj9JZU/Yg4L+A3xpk7dP5HLseGBsbOwZYNew6ZrJ+/bArkCRp+JKMA+NdTZNVNdm8fhJwalV9H6CqbkxyEPDJJL8M7AZc07Xv56vq50kuAXYBvtC0XwKs7uNpzMlg1xurpqamJoZdxEw2bsxxw65BkqRha0Lc5Jwdb/c3wF9V1YYkhwITXdtubsa8LcnPq6qa9tuAXZMcDJzYtB1bVRuWVPwCGOwkSdKo+xJwWpK/qqofJLkncA/gu832313IYFV1LrC2xzXOi8FOkiSNtKq6LMnxwFeS3ApcQGeG7tQkP6QT/B60lGMkORs4ANgzyVbgZVV15tIqvzODnSRJGnlV9WHgw9OaPzNDv4lp7/ecbdu0focsrcL58a5YSZKklnDGTitarxd3XzOxX0/HG6XF5/3eLd4onas0H73+ewKw5cieD7ksOWMnSZLUEgY7SZKkljDYSZIkLUCStyQ5bJZtX0jyoySnD7ou8DN2kiRJC1JVx+5k8zuA3YFXDKicO3DGTpIkjbwkL06yOcmFSU5MskuSm5K8K8llSc5Kcp+m78lJnjfTOFV1FvCTgRbfxWAnSZJGWpKHAC8AHltVa4FbgRcBewBTVfVQ4CvAsl+m00uxy9jY2NgxwKqljLF+fY+KkSRpBUsyDox3NU0268cCPBl4FHBeEoC7ATfQWfv1k02fjwL/NJhqF89gt7ytmpqamljKABs3Ztn/vwtJkvqtCXGTs2wO8OGq+uM7NCZ/On2YadsPBk5s3h5bVRt6UetSeClWkiSNurOA5yX5JYAk90zyQDo5acdn6Y4Evtq9U1WdW1Vrm6+hhzpwxk6SJI24qtqS5M3APyf5BeDnwKuAnwLrmm030Pkc3k4lORs4ANgzyVbgZVV1Zv+qvyODnSRJGnlV9Ulu/zwdAEmoqtfN0PeonYxzSO+rmz8vxUqSJLWEwU6SJGkGVbXnsGtYKC/FDtAiHl+yeqnHXLfup0sdAjioB2OsDFsmrh52CSuW3zvNx5qJ/Xo6nr930h0Z7AZrQY8vGRsbm3dfSZIkL8VKkiS1hMFOkiSNvCQ39WncU5JckeTSJCcluUs/jrODwU6SJKl/TqHzXLuD6CxVdnQ/D2awkyRJaiTZM8lZSc5PckmSZzXtq5NcnuTkJFc2M3GHJflakm8lWTfTeFV1RjWAzcA+/azfYCdJknS77cBzquqRwBOBdyZJs+1XgXfSmYE7gM4yY48DXg/8yc4GbS7BvgT4Qp/qBrwrdrnbvtQ7Yzdt2tSjUiRJWrmSjAPjXU2TVTU5U1fgz5M8HrgNuD9w32bbNVV1STPeZcBZVVVJLmHuR5S9H9hUVWcv4TTmZLBbxqampk5Y6hjbtm07rhe1SJK0kjUhbqYgN92LgPsAj6qqnye5ltufQXtzV7/but7fRpOpkpxJJwhOVdXRTdtxzZivWOJpzMlgJ0mSdLt7ADc0oe6JwAMXsnNVPbX7fZKjgacCT66q23pX5sz8jJ0kSdLtTgHGmsurLwUuX+J4H6Qzg/f1JBcmOXapBe6MM3aSJGnk7VgXtqq+Dzx6lm4HdvU/quv1td3bpo070KzljJ0kSVJLOGOngRulRbtHacHzUTpXLZ4/V82HvyeL54ydJElSSxjsJEmSWsJgJ0mS1BIGO0mSpAVIclSS+82ybW2Srye5LMnFSV4wyNoMdpIkSQtzFDBjsAO2AS+tqocCTwP+OsnegyrMYCdJkkZaktVJLk9ySpJvJvlUkt2THJvkvCSXJplMx/OAMeCU5oHDd+seq6qurKpvNa+vB26gs5zYQBjsJEmS4NeA91fVQ4D/BP4AeG9V/XpVHQjcDXhmVX0KmAJeVFVrq+q/ZhswyTpgN+Db/S+/w+fYtcjY2Ngx3L5QMQCbNm0aUjWSJC0fScaB8a6myaqa7Hp/XVV9rXn9UeC1wDVJ3gjsDtwTuAz47DyP98vAR4DfHcQasTsY7Npl1dTU1ER3w7Zt244bUi2SJC0bTYib3FmXGd6/HxirquuSTDBt8gQgycHAic3bY6tqQ5K7A58D3lRV5yy5+AXwUqwkSRI8IMmONWKPBL7avP5+kj2B53X1/QmwF0BVndtckl3bhLrdgNOAv28u2w6UM3aSJOn/sXfn4ZaV5Z33vz9BZXJgcIgBLY0KImipx1IiGFASlZCIEU1EjSTi0YgSX5vLaItwHGhBsZMgAh5sX+xAGxTQ4ATaSqUEkaqDQFUxilDKoBKCBKGkZLj7j71Kdm32GWufofb+fq5rXXutZ1r34qDePmt4BNcAhyb5PHAlcBKwLbAa+AWwoq3tqcDJSX4D7NHxnN3rgZcC2yc5uCk7uKoum93wW0zsJEmS4L6qelNH2RHNtoGqOgs4q9sgVXUarWf05oW3YiVJkvqEM3Z9bvnyrXswytN6MIb63ZUj1893CJI0I1W1BthtvuPoBRO7BabbJ0umYVEPQ5EkSZsYE7uF5yGfLJmqoaGhGfWTJEn9wWfsJEmSukjyniRbTbPPFkmWJ7k8yRVJPjxb8XVjYidJktTde2itOjEd64CXVdVzgcXAK5O8uOeRjcNbsZIkaaAlWQScC1wCPJ/W0mHLgCcB5ye5rar2SXIXcArwJ7S+bfdXVfUf7WNVVQF3NYcPb7bOVS1mjTN2kiRJsDNwYlU9C7gTeARwC7BPVe3TtNkaGKuqZwP/DnRdtjPJZkkuA24FvlNVF8969A1n7PrLPZ0vUBx33DxFIknSApJkGBhuKxpt1o9d78aqurDZPw04rMswDwBntLU5u9u5qup+YHGSxwJfSbJbVa3eqAuYIhO7PjI2NnZMZ9nSpen6/yYkSRokTRI3OlGTSY679kmyE/C15vjkqjq57Zx3JDkfeCWtpclmnbdiJUmS4MlJ9mj2DwIuAH4NPKqtzcOAA9vbVNWNVbW42U5O8rhmpo4kWwJ/DFw9N5fgjJ0kSRLANcChST4PXAmcBPwWODfJLc1zdncDS5IcQev5ub/sMs7vAV9IshmtRPBLVfX1ObkCTOwkSZIA7quqN3WUfbrZfqeq3jvRIFW1Enhej2ObMm/FSpIk9Qln7CRJ0kCrqjXAblNot83sR7NxTOy0Sdt15Gk9He/KkesX9HiSJE3ExK6LoaGh9wNbTKPLolkKRZIkacpM7LrbYmxsbGSqjTs/CixJkjQffHlCkiRphpIcnOSEKbR7Y5KVSVYl+UGS57bVvTLJNUmuS/L+jYnHxE6SJGn23QD8UVXtDnyUZhWM5nt3nwFeBewKvCHJrjM9iYmdJEkaaEkWJbk6yalJrk1yepJ9k1yY5MdJljTbRUkubWbcdm4bYqckS5u2XZfyrKofVNWvmsMfAjs2+0uA66rq+qr6LfCvwKtnei0mdpIkSfB04FPALs12ELAncDjw32ktC7ZXVT0POBL4H219lwCvBZ4DvC7J0CTneivwrWb/94Eb2+puaspmxJcnNgEzeEv3d447rsfBSJK0CUoyDAy3FY1W1Wjb8Q1VtappewXw3aqqJKtoff3iMbSWCnsGUMDD2/p+p6r+s+l7Nq2EcGycOPahldjt2ZML62Bit2mY1lu67ZYu7T4lLEnSIGmSuNEJmqxr23+g7fgBWvnSR4Hzq+o1SRYBS9uH7zxdkkOBtzXH+1XVLUmeA3wOeNX6RBC4Gdipre+OTdmMmNhJkiRN7jE8mHAd3FH3x0m2A34DHAD8bVWN0XopAoAkTwbOBt5cVde29V0BPCPJU5vx/4rWbeAZ8Rk7SZKkyX0C+HiSS3noxNhy4CxgJXBWk9R1OhLYHjgxyWVJxgCq6j7gXcB5wFXAl6rqipkG6YydJEkaaJ1rxVbVwePUPbOt2xFN/anAqVM4xyHAIePUfRP45nRiHo8zdpIkSX3CGbs+t2TJ3T0YZfcejDE7rhy5fr5DkCRpwXDGTpIkqU+Y2EmSJPUJEztJkqQ+YWInSZLUJslIksMnqH9ckoubdWP3GqfN8Unumr0ou/PlCUmSpOl5ObCq+YTJQzRrxW47tyG1OGMnSZIGXpIPJrk2yQXAzk3Z25KsSHJ5krOSbJVkMa2PFb+6+dDwlh3jbAZ8EnjfnF8EJnaSJGnAJXkBraW8FgP7AS9sqs6uqhdW1XNprQrx1qq6jNYqEmdU1eKq+k3HcO8Czqmqn89R+BvwVqwkSep7SYaB4bai0aoabfb3Ar5SVWubtuc05bsl+RjwWGAbWst+TXSOJwGvA/buYejTYmInSZL6XpPEjU7acEOnAgdU1eVJDqZLwpbkPOAJwBjwFeDpwHVJALZKcl1VPX3mkU+PiZ0kSRp0y4BTk3ycVm70Z8BngUcBP0/ycOCNwM2dHavqFR1FT1y/k+SuuUzqwMROkiQNuKr6UZIzgMuBW4EVTdWHgIuB/2h+HzU/EU6diZ0kSRp4VXU0cHSXqpO6tD2V1m3aycbcZqMDmybfipUkSeoTJnaSJEl9wsROkiSpT5jYSZIk9QkTO0mSpB5K8pQkP2qWHLsiyTvm6ty+FStJktRbPwf2qKp1SbYBVic5p6pume0TO2MnSZIGWpKtk3wjyeVJVif5yyRHJlnRHI+mZfOmbO+m38eTPOQTKVX126pa1xw+kjnMt0zsJEnSoHslcEtVPbeqdgPOBU6oqhc2x1sC+1fVfcDBwElJ9m36fbjbgEl2SrISuBE4di5m68BbsX1laGjo/cAW7WXLli2bp2gkSVo4kgwDw21Fo836sQCrgE8lORb4elV9P8lrk7wP2ArYDrgC+FpVXZHkX4Cv07rd+ttu56uqG4HnJHkS8NUkZ1bVL2fp8n7HxG7hWbQRfbcYGxsbaS9Yu3btURsVjSRJfaBJ4kbHqbs2yfOB/YCPJfkucCgwVFU3Jhlhw4mT3YE7gMcDJHkRrbVlAY6sqnPaxr4lyWpgL+DM3l7VQ3krduFZNN8BSJI0SJpZtbVVdRrwSeD5TdVtzcsPB7a1/QtaM3gvBT6d5LFVdXFVLW62c5LsmGTLpv22wJ7ANXNxLc7YSZKkQbc78MkkDwD3An8HHACsBn4BrABIsgNwDPDyZibvBOCfgbd0jPcsWrd2CwhwXFWtmosLMbGTJEkDrarOA87rKB4DjujS/Jlt/Y4fZ7zvAM/pWYDT4K1YSZKkPmFiJ0mS1Ce8Fdvnli/fugejPK0HY0iSpNnmjJ0kSVKfMLGTJEnqEyZ2kiRp4CX5QQ/H+nySW5sPE88pEztJkjTwquoPezjcqbTWkZ1zJnaSJGngJbmr+d07ydIkZya5OsnpSdLUHZlkRZLVSUbXl3eqqmXA7XMY/u+Y2EmSJG3oecB7gF1pfRriJU35CVX1wqraDdgS2H+e4hvXJvu5k6Ghofez4YK8M7WoB2NIkqQFLMkwMNxWNFpVo+M0X15VNzX9LqOVK1wA7JPkfcBWtNaLvQL42qwFPQObbGIHbDE2NjaysYMMDQ1t9BiSJGlha5K48RK5Tuva9u8HNk+yBXAiMNSsEzsCbJFkJx5M7k6uqpN7FfNMbMqJnSRJ0lxZf5fwtiTbAAcCZ1bVjcDi+QtrQz5jJ0mSNImqugM4BVgNnAesGK9tki8CFwE7J7kpyVvnJkpnale9lwAAIABJREFU7CRJkqiqbZrfpcDStvJ3te0fARwxhbHe0PsIp8YZO0mSpD4x0xm7PWfppYNFszDmQFuy5O4ejLJ7D8aQJEmzbaaJ3ea9eCO1k2+oSpIkzZy3YiVJkvqEiZ0kSVKfMLGTJElqk2QkyeET1D8uycVJLk2yV0fdu5Jcl6SS7DD70W7IxE6SJGl6Xg6sqqrnVdX3O+ouBPYFfjr3YZnYSZIkkeSDSa5NcgGwc1P2tiQrklye5KwkWyVZDHwCeHWSy5Js2T5OVV1aVWvm/gpaTOwkSdJAS/IC4K9oLQ22H/DCpursqnphVT0XuAp4a1VdBhwJnFFVi6vqN/MS9DhM7CRJUt9LMpxkrG0bbqveC/hKVa2tqjuBc5ry3ZJ8P8kq4I3As+c67ulySbHeuGeK3+BbNMtxSJKkLqpqFBidZrdTgQOq6vIkBwN7dzZIch7wBGCsqg7ZyDA3moldD4yNjR0zlXZ+gFmSpAVpGXBqko/Tyo3+DPgs8Cjg50keTmvG7ubOjlX1irkMdDLeipUkSQOtqn4EnAFcDnwLWNFUfQi4mNabrldPZawkhyW5CdgRWJnkc72PeHzO2EmSpIFXVUcDR3epOqlL21Np3abtNs7xwPG9jG06nLGTJEnqE87YbaKGhobeD2zRUbxoHkKRJEkLhIndpmuLsbGxkfYCX86QJGmweStWkiSpT5jYSZIk9ViSRUlWT7Ht55PcOtX2EzGxkyRJml+nAq/sxUAmdpIkaeAl+WqSS5JcsX65sSR3JflkU/Z/kyxJsjTJ9Un+vGlzcJJ/a8p/nOSotmE3S3JK0//bSbbsdu6qWgbc3ovrMLGTJEmCv62qFwBDwGFJtge2Br5XVc8Gfg18DPhj4DXAR9r6LgFeCzwHeF2Soab8GcBnmv53NG1mlYmdJEnqe0mGk4y1bcMdTQ5LcjnwQ2AnWknZb4Fzm/pVwL9X1b3N/qK2vt+pqv+sqt8AZwN7NuU3VNVlzf4lzMFnyfzciSRJ6ntVNQqMdqtLsjewL7BHVa1NspTWt2Lvrapqmj0ArGvGeiBJew5VbGj98bq2svuBLZPsBHytKTu5qk6e2RV1Z2InSZIG3WOAXzVJ3S7Ai6fZ/4+TbAf8BjgA+NvxGlbVjcDiGUc6CW/FSpKkQXcusHmSq4BjaN2OnY7lwFnASuCsqhqbTuckXwQuAnZOclOSt07z/L/jjJ0kSRpoVbUOeFWXqm3a2ox09Nmm7fCmqjqgo34NsFvb8XETnP8N04t4fM7YSZIk9Qln7CRJkmaoqk6l9YHhBcHETpO6cuT6+Q5BkiRNgYndwnPf0NDQSEfZonmIQ5IkbWJM7BaeC8bGxkbaC7okepIkSQ/hyxOSJEk9lGSfJJe1bfckOWDynhvPGTtJkqQeqqrzaT5C3Hy4+Drg23NxbmfsJEnSQEuydZJvJLk8yeokf5nkyCQrmuPRtGzelO3d9Pt4kqMnGf5A4FtVtXbWLwQTO0mSpFcCt1TVc6tqN1orUZxQVS9sjrcE9q+q+4CDgZOS7Nv0+/AkY/8V8MXZC31DJnaSJKnvJRlOMta2DbdVr6K13uuxSfaqqv8C9klycZJVwMuAZwNU1RXAvwBfB/62qn47wTl/D9gdOG+2rquTz9hJkqS+V1WjwOg4ddcmeT6wH/CxJN8FDgWGqurGJCPAFm1ddgfuAB4PkORFwGebuiOr6pxm//XAV6rq3l5fz3hM7CRJ0kBL8iTg9qo6LckdwCFN1W1JtqH1nNyZTdu/ALYDXgp8PcmSqrqY5mWJDm8APjDrF9DGxE6SJA263YFPJnkAuBf4O+AAYDXwC2AFQJIdgGOAlzczeScA/wy8pXPAJIuAnYB/n4P4f8fETpIkDbSqOo+HPgc3BhzRpfkz2/odP8GYa4Df70V80+HLE5IkSX3CxE6SJKlPmNhJkiT1CRM7SZKkPmFiJ0mS1CdM7CRJkqYoyaIkq6fY9twkdyT5+mzHtZ6JnSRJ0uz4JPDmuTyhiZ0kSRpoSY5Jcmjb8UiSI5J8N8mPkqxK8uq2LpsnOT3JVUnOTLJVt3Gr6rvAr2c7/nYmdpIkadCdQWtd1/VeD3wBeE1VPR/YB/hUkjT1OwMnVtWzgDuBd85lsBNx5Yn+cs/Q0NBIe8GyZcvmKRRJkhaOJMPAcFvRaFWNAlTVpUke36wZ+zjgV7SWEvvHJC8FHqC1isQTmr43VtWFzf5pwGHAcXNwGZMysesjY2Njx3SWrV279qj5iEWSpIWkSeJGJ2jyZeBA4Im0ZvDeSCvJe0FV3ZtkDbDF+uE6h0/yIuCzzfGRVXVOr2KfDhM7SZKkVjJ3CrAD8Ee0bsfe2iR1+wBPaWv75CR7VNVFwEHABVV1MbB4roPu5DN2kiRp4FXVFcCjgJur6ufA6cBQklXAXwNXtzW/Bjg0yVXAtsBJ3cZM8n1aM4EvT3JTklfM5jWAM3aSJEkAVNXubfu3AXuM03SXKY63Vy/img5n7CRJkvqEM3Z9bvnyrTd6jL03PgxJkjQHnLGTJEnqEyZ2kiRJfcLETpIkDbQki5Ks7vGYL22WI7svyYG9HHsiJnaSJEm99zPgYOD/zOVJTewkSZJgsySnJLkiybeTbJnksCRXJlmZ5F8Bkowk+ZckFyX5cZK3dRusqtZU1Upay5HNGd+KlSRJgmcAb6iqtyX5EvBa4P3AU6tqXZLHtrV9DvBiYGvg0iTfqKpb5j7kh3LGTpIk9b0kw0nG2rbhjiY3VNVlzf4lwCJgJXB6kjcB97W1/beq+k3zEePzgSWzHf9UOWMnSZL6XlWNAqMTNFnXtn8/sCXwp8BLgT8DPphk/coU1Tl8kqOb9lTVvK0Z64ydJEnSQz0M2Kmqzgf+AXgMsE1T9+okWyTZntZ3/FdU1QeravF8JnXgjJ0kSVI3mwGnJXkMEOD4qrojCbRu0Z4P7AB8tNvzdUleCHwF2Bb4syQfrqpnz3bQJnaSJGmgVdUaYLe24+Mm6bKyqv56kjFXADtufHTT461YSZKkPuGMnSRJ0hRV1ch8xzARZ+wkSZL6hImdJElSnzCxkyRJ6hMmdpIkSV0keU+SrabZ511JrktSSXaYrdjGY2InSZLU3XuAaSV2wIXAvsBPex/O5HwrVpIkDbQki4Bzaa0R+3zgCmAZ8CTg/CS3VdU+SV4J/A9aHy++rape3jlWVV3ajDk3wXcwsZMkSYKdgbdW1YVJPg88ArgF2KeqbkvyOOAU4KVVdUOS7eYz2PF4K1aSJPW9JMNJxtq24Y4mN1bVhc3+acCeHfUvBpZV1Q0AVXX7LIc8I87YSZKkvldVo8DoRE0mOe4qyXnAE4CxqjpkhuH1jDN2kiRJ8OQkezT7BwEXAL8GHtWU/RB4aZKnAqy/FVtVr6iqxQshqQMTO0mSJIBrgEOTXAVsC5xEa4bv3CTnV9V/AMPA2UkuB87oNkiSw5LcBOwIrEzyubkJv8VbsZIkSXBfVb2po+zTzQZAVX0L+NZEg1TV8cDxvQ9vapyxkyRJ6hPO2EmSpIFWVWuA3eY7jl5wxk6SJKlPmNhJkiT1CRM7SZKkPmFiJ0mSNA1JDk7ypHHqnpLkR0kuS3JFknfMZWy+PCFJkjQ9BwOraa0l2+nnwB5VtS7JNsDqJOdUVbe2PeeMnSRJGmhJFiW5OsnpSa5KcmaSrZIcmWRFktVJRtNyIDAEnN7Mym3ZPlZV/baq1jWHj2SOcy0TO0mSJNgZOLGqngXcCbwTOKGqXlhVuwFbAvtX1ZnAGPDGZimx33QOlGSnJCuBG4Fj52q2DkzsJEnSAEgynGSsbRvuaHJjVV3Y7J8G7Ansk+TiJKuAlwHPnsq5qurGqnoO8HTgLUme0KvrmIyJnSRJ6ntVNVpVQ23baGeTLscnAgdW1e7AKcAWneMmeVFzS/ayJH/ecc5baD2Lt1fvrmRiJnaSJEnw5CR7NPsHARc0+7c1L0Ec2Nb218CjAKrq4uaW7OKqOifJjuufu0uyLa2Zv2vm5hJ8K1aSJAlaydehST4PXAmcBGxLa8btF8CKtranAicn+Q2tN2Dbn7N7FvCpJAUEOK6qVs1B/ICJnSRJEsB9VfWmjrIjmm0DVXUWcFa3QarqO8Bzeh/e1HgrVpIkqU84Y9fnliy5e+MH+erWGz+GJEkLVFWtAXab7zh6wRk7SZKkPmFiJ0mS1CdM7CRJknokybuSXJekkuww1+c3sZMkSZqhJJt1FF0I7Av8dB7CMbGTJEmDLckxSQ5tOx5JcniSZUm+keSaJCcneVhTf1eSTyW5HNijfayqurR5GWNemNhJkqRBdwbw+rbj1wO/BJYA7wZ2Bf4A+Iumfmvg4qp6blVdwAJiYidJkvpekuEkY23b8Pq6qroUeHySJyV5LvAr4EZgeVVdX1X3A1+ktTwYwP2M84Hi+eZ37CRJUt+rqlFgdIImX6a1HuwTac3gAVTnMM3vPU2yR5LzgCcAY1V1SO8inhkTu03DPUNDQyMdZYvmIQ5JkvrVGcApwA7AHwE7A0uSPJXWixB/SZfEsKpeMZdBTsbEbhMwNjZ2TGdZl0RPkiTNUFVdkeRRwM1V9fMkOwMrgBOApwPnA1+ZbJwkhwHvozXztzLJN+dyJs/ETpIkCaiq3TuK7qyq/bu022aCMY4Hju91bFPlyxOSJEl9whk7SZKkDlW1FFg6z2FMmzN2kiRJfcLETpIkqU94K3ZudftsSadFcxCHJEnqQyZ2c6jbZ0s6+RkTSZIWpiR7AScD9wJ7VNVv2uo+D+wP3FpVu43T/43APwABfg38XVVd3ssYvRUrSZI0iSSbAW8EPl5Vi9uTusapwCsnGeYG4I+az6p8lIlXwpgREztJkjTwknw1ySVJrli/jmySu5J8KsnlwAeA1wMfTXJ6Z/+qWgbcPtE5quoHVfWr5vCHwI69vQpvxUqSJAH8bVXdnmRLYEWSs4CtgYur6r8BJHk68PWqOrMH53sr8K0ejLMBEztJktT3mlm44bai0apqvxV6WJLXNPs7Ac8A7gfOmoVY9qGV2O3Z67FN7CRJUt9rkriuz7Ql2RvYl9YLEWuTLAW2AO6pqvu7tN8J+FpzeHJVnTzVOJI8B/gc8Kqq+s9pXcQUmNhJkqRB9xjgV01Stwvw4okaV9WNwOLpniTJk4GzgTdX1bUzinQSvjwhSZIG3bnA5kmuAo6h9WLDtCT5InARsHOSm5K8tSl/R5J3NM2OBLYHTkxyWZKx3oT/IGfsJEnSQKuqdcCrulRt09Hu4AnGeMM45Se37R8CHDKzKKfGGTtJkqQ+4Yxdn1u+fOuNHmPvjQ9DkiTNARO77uu3LpqHOCRJkjbKwCd23dZvdb1WSZK0KfIZO0mSpD5hYidJktRFkqVJhqbRfpckFyVZl+Tw2YxtPAN/K1aSJKlHbgcOAw6YrwCcsZMkSQMtyaIkVyc5PclVSc5MslVb/VOS/DjJDkkeluT7Sf6kc5yqurWqVgD3zukFtDGxkyRJgp2BE6vqWcCdwDvXV1TVT4FjgZOA/wZcWVXfnpcoJ2FiJ0mS+l6S4SRjbdtwR5Mbq+rCZv80YM/2yqr6HPBo4B3AvDw/NxU+YydJkvpeVY0CoxM1mei4uTW7Y3O4DfDrJIcCb2vK9quqW3oR68Zwxk6SJAmenGSPZv8g4IKO+mOB04EjgVMAquozVbW42eY9qQMTO0mSJIBrgEOTXAVsS+t5OgCS/BHwQuDYqjod+G2Sv+kcIMkTk9wEvBc4IslNSR49N+G3eCtWkiQJ7quqN3WU7d22/+L1O1X1F90GqKpf8ODt2nnhjJ0kSVKfcMauzy1ZcvfGD/LVrTd+DEmSFqiqWgPsNt9x9IIzdpIkSX3CxE6SJKlPmNhJkiTNkiSPbt6OPaGt7AVJViW5LsnxSdKr85nYSZIkzZ6PAss6yk6i9WHjZzTbK3t1MhM7SZI00JIsSrK67fjwJCNJliY5NsnyJNcm2aup3yzJcUlWJ1mZ5N3jjPsC4AnAt9vKfg94dFX9sKoK+N/AAb26Ft+KlSRJGt/mVbUkyX7AUcC+wDCwCFhcVfcl2a6zU5KHAZ8C3tT0We/3gZvajm9qynrCGTtJktT3kgwnGWvbhqfY9ezm9xJayRy0ErXPVtV9AFV1e5d+7wS+WVU3dambNc7YSZKkvldVo8DoONX3seFk1xZt++ua3/uZIG9K8iLgs83hkcAewF5J3glsAzwiyV3AP7Ph6hQ7AjdP8TImZWInSZIG3S+BxyfZHrgL2B84d4L23wHenuT89bdiq+piYHFbm3PW7yQ5GBiqqvc3x3cmeTFwMfDXwKd7dSHeipUkSQOtqu4FPgIsp5W0XT1Jl88BPwNWJrkcOGiap3xnM8Z1wE+Ab3VrlGS7JEua7SHP8XXjjJ0kSRp4VXU8cPwE9bfRPGPXPFv33mabytinAqe2HY8xwRJmSZ4KnAj8CbD+G3eV5NvAO6rqp+P1NbGTJElaIJI8HrgAeAD47zw4e/hM4F3AD5I8r6pu7dbfxE6SJGnh+ABwJ61n8u5ur0jyaVpv534A+P+6dfYZO0mSpIXjT4EPdyZ1AFV1DzDStOnKxE6SJGnh2Am4fIL6VU2brkzsJEmSFo57aX1XD4AkT0lyY1v9b4D/Gq+ziZ0kSRponWvF9njso5Pc2HyceCquAobajjcDHtt2/Gxan0npypcnJEmSeiBJgFTVA23FXwNOAH48xWHOAo5Nsv7beFt11B8C/Ot4nU3sJEmSYLMkpwB/SGuJr1fT+nDwpcBewNa0Von4ALA7cEZVHZFkEXAerVUkXgDsB/zuO3NV9UOAVs43JZ8Bns6GOdo1bftfopUsdmViJ0mSBM8A3lBVb0vyJeC1Tflvq2ooyd8D/0Yrebsd+EmSf2zr+5b1SdzGaN6GHU6yL/B8Wt+zu6StftzZOjCxkyRJAyDJMDDcVjRaVaNtxzdU1WXN/iU0q0zw4Jqvq4ArqurnzXjX03o79Q7gp71I6ppxt6Y1U/iHtNawfSJwd5KVwH5VdedE/X15QpIk9b2qGq2qobZttKPJurb9+3lw8mt9+QMdbR5oa3M3QJLNklzWbB+ZYagfAx5D63bsnsBa4HG0ZgmPm6yzM3aSJEk9UFX3A4s3cpjXAsNVtSbJ05px1yX5MPBNNpx1fAhn7CRJkmZJkk8kuQnYKslNSUYm6fJ4Hlwftt2vgC0nO58zdpIkaaBV1Rpgt7bjh9zyrKqlwNK2473bqndjHFX1PuB90wjnl8DvAWs6yt9C20sU41loid09Q0NDI1Nsu2gW45AkSZoPFwKvAC5qjh+Z5FLgqcDLJ+u8oBK7sbGxY6badhoJ4EBbvnzrjR5j740PQ5IkTc0HgSc1+3cC/wv4CfAvVfXLyTovqMROkiRpkFXVDcANzf5twN9Np7+JnSRJ0gKR5C0T1VfVFyaqN7GTJElaOD7dcbw58Eha39ZbB0yY2Pm5E0mSpDZJRpIcPkH945JcnOTSJHt11J2e5Jokq5N8PsnDp3Puqnp0x7YVsDOtN3JfPVl/EztJkqTpeTmwqqqeV1Xf76g7HdgF2J3Wd+cO2diTVdV1wAeAf56srYmdJEkaeEk+mOTaJBfQmiEjyduSrEhyeZKzkmyVZDHwCeDVzdJhG3w0uKq+WQ1gObBjj0JcR+uTJxPyGTtJkjTQkrwA+Ctay4FtDvyI1seAz66qU5o2HwPeWlWfTnIkMFRV75pgzIcDbwb+fpqx/FFnEa3PnxxOK1GckImdJEnqe0mG2XCd1dGqGm329wK+UlVrm7bnNOW7NQndY4FtgPOmccoTgWVdbtVO5nu0krlqK/strWfs3jFZZxM7SZLU95okbnTShhs6FTigqi5PcjBdvtmf5DzgCcBYVR3SlB0FPA54+wxCfVzH8QNVdcdUO5vYSZKkQbcMODXJx2nlRn8GfBZ4FPDz5rbqG4GbOztW1Svaj5McQmtJsJdX1QPTDaSqbp9++A/y5QlJkjTQqupHwBnA5cC3gBVN1YeAi2mt33r1FIc7mdYM3kXNyxVHTjeeJH+S5IIka5PcleTfk7xi8p7O2EmSJFFVRwNHd6k6qUvbU2ndpu02zkblVkn2Bb4G/J/m9yjg28CXkrypqr42UX9n7CRJkhaODwHHVNXfAF8G7m+SznfTSvIm5Ixdn1uy5O6NH+SrW2/8GJIkTdGuI0/r+ZhXHtTzIWfLC4B3dilfRuu5vwk5YydJkrRw3NdsnZ4C3DJZZxM7SZKkheNaYLe244cleTWtlzK+OFlnEztJkqQZSnJwkhOm0G7bJF9JsjLJ8iS7jdP0VGBJ2/EjgdNovUjx4cnO4zN2kiRJs++/A5dV1WuS7AJ8Bnh5Z6OqOrHtcA3w+1X1y6mexBk7SZI08JK8qZlJuyzJZ5Ns1nxD7pNJrkjyf5MsSbI0yfVJ/ryt+05N+Y+bVSe62ZXWcmFU1dXAoiRPmCimqnpgOkkdmNhJkqQBl+RZwF8CL6mqxcD9tFaa2Br4XlU9G/g18DHgj4HXAB9pG2IJ8FrgOcDrkgx1Oc3lwF8051tC62WIHXt9Ld6K3XTtOTQ0NDJZo2XLls1BKJIkLWxJhoHhtqLRZv1YaN0SfQGwIgnAlsCtwG+Bc5s2q4B1VXVvklXAoraxvlNV/9mc52xgT2CsI4RjgH9Oclkz1qW0EsieMrHbdG0+NjY2MlmjtWvXTvoxQ0mS+l2TxI2OUx3gC1X1gQ0Kk8OrqprDB4B1zVgPJGnPoYoNVZJDgbc1x/tV1S3A3zTjBrgBuH6m1zMeb8VKkqRB913gwCSPB0iyXZKnTKP/Hzd9tgQOAC6sqs9U1eJmuyXJY5M8oml/CLCsqu7s7WU4YydJkgZcVV2Z5Ajg20keBtwLHDqNIZYDZ9F6Zu60quq8DQvwLOALSQq4AnjrRobdlYmdJEkaeFV1BnBGR/E2bfUjHe23aX5PpfXtucnGvwh45kaGOSlvxUqSJPUJZ+wkSXOm14u7XznS82fPtQD4d505Z+wkSZL6hImdJElSnzCxkyRJ6hMmdpuuNfMdgCRJgy7JmiQ7dCnfJclFSdYlOXyu4vHliU3XmvkOQJIkjet24DBaHyyeM87YSZKkgZfkr5OsTHJ5kn9J8rgkZyVZ0Wwvadptn+TbSa5I8jlay5E9RFXdWlUraH3seM44YydJkgZakmcDRwB/WFW3JdkOOAH4x6q6IMmTgfNorR5xFHBBVX0kyZ8ySytIzJSJnSRJ6ntJhoHhtqLRqhpt9l8GfLmqbgOoqtuT7AvsmvxuQu7RSbYBXgr8RdPuG0l+NScXMEUmdpIkqe81SdzopA0f9DDgxVV1T3thW6JHR/mhwNuaw/2q6paZxLmxfMZOkiQNuu8Br0uyPUBzK/bbwLvXN0iyuNldBhzUlL0K2Bagqj5TVYubbV6SOnDGTpIkDbiquiLJ0cC/J7kfuJTWG62fSbKSVr60DHgH8GHgi0muAH4A/KzbmEmeCIwBjwYeSPIeYNequnM2r8XETpIkDbyq+gLwhY7iv+zS7j+BP5nCeL8AduxNdFPnrVhJkqQ+YWInSZLUJ7wVu+m6Z2hoaGSyRsuWLZuDUCRJ0kJgYreJGhsbO2Yq7dauXXvUbMciSZIWBm/FSpIk9QkTO0mSpB5J8o4kq5JcluSCJLvO5flN7CRJkmYoSedjbf+nqnavqsXAJ4D/OZfxmNhJkqSBlmRRkquTnJ7kqiRnJtkqyZFJViRZnWQ0zXpiSZYm+ackY8Dft4/V8QHirYGaw0sxsZMkSf0vyXCSsbZtuKPJzsCJVfUs4E7gncAJVfXCqtoN2BLYv639I6pqqKo+1eVchyb5Ca0Zu8Nm54q6M7GTJEl9r6pGm0Rs/Tba0eTGqrqw2T8N2BPYJ8nFSVYBLwOe3db+jAnO9Zmq+gPgH4AjengZkzKxkyRJeugt0wJOBA6sqt2BU4At2urvBkhydPOixGVdxvxX4IDZCHY8JnaSJEnw5CR7NPsHARc0+7cl2QY4sFunqvpgVS1uXpYgyTPaqv8U+PFsBdyNHyiWJEmCa4BDk3weuBI4CdgWWA38AlgxxXHelWRf4F7gV8BbZiHWcZnYSZIkwX1V9aaOsiPo8oxcVe093iBV9ffj1c0Fb8VKkiT1CWfs+tzy5Vtv9BjvHHlaDyJ50JUj1/d0vF7adYCudZD4d104/Genhaiq1gC7zXccveCMnSRJUp8wsZMkSeoT3oqVJEmaBUnuB1Y1hz+rqj+f7XOa2EmSJM2O36z/vt1c8VasJEkaaEkWJVnddnx4kpEkS5Mcm2R5kmuT7NXUb5bkuCSrk6xM8u75i35DzthJkiSNb/OqWpJkP+AoYF9gGFgELK6q+5JsN07fLZKMAfcBx1TVV2c92Nk+gaZtz6GhoZFeDXbccb0aSZKkTVeSYVoJ2XqjVTU6ha5nN7+X0ErmoJXcnVxV9wFU1e3j9H1KVd2c5GnA95KsqqqfTD/6qTOxW3g2HxsbG+nVYEuX5qhejSVJ0qaqSeLGS+TuY8PH07Zo21/X/N7PBHlTkhcBn20Oj6yqc6rq5ubc1ydZCjwPmNXEzmfsJEnSoPsl8Pgk2yd5JLD/JO2/A7w9yeYASbarqouranGznZNk22YskuwAvITWGrSzyhk7SZI00Krq3iQfAZYDNwNXT9Llc8AzgZVJ7gVOAU7oaPMs4LNJHqA1kXZMVZnYSZIkzbaqOh44foL622iesWuerXtvs43X/gfA7r2NcnLeipUkSeoTztj1uSVL7u7BKL39Pxy9XJC91wuKu0B5f1rof9de/mcCFv71Spo9zthJkiT1CRM7SZKkPmFiJ0mS1Cd8xk6SJGkWJLkfWNUc/qyq/rwpPx0YAu6l9YmVt1fVvb04pzN2kiRJs+M3bR8t/vO28tOBXWi9nbglcEivTmhiJ0mSBlqSRUlWtx0fnmQkydIkxyZZnuTaJHs19ZslOS6HzDFsAAAgAElEQVTJ6iQrk7x7Ouerqm9Wg9aM3Y69uhYTO0mSpPFtXlVLgPcA69dfH6b1seLFVfUcWjNw3WyRZCzJD5Mc0FmZ5OHAm4FzexZsrwaSJElaqJIM00rI1hutqtEpdD27+b2EZuUJYF/g5GYFCqrq9nH6PqWqbk7yNOB7SVZV1U/a6k8EllXV96d6HZMxsZMkSX2vSeLGS+TuY8O7mFu07a9rfu9ngrwpyYuAzzaHR1bVOVV1c3Pu65MsBZ4H/KRpfxTwOODt07uSiXkrVpIkDbpfAo9Psn2SRwL7T9L+O8Dbk2wOkGS7qrq47UWJc5Js24xFkh2AlwBXNseHAK8A3lBVD/TyQkzsFp418x2AJEmDpPnUyEdovcjwHeDqSbp8DvgZsDLJ5cBBXdo8Cxhr6s8HjqmqK5u6k4EnABcluSzJkT24DMBbsQvRmvkOQJKkQVNVxwPHT1B/G80zds2zde9ttvHa/4BxFluvqlnLv5yxkyRJ6hPO2PW55cu37sEoT+vBGJIkabaZ2G0ChoaG3s+Gb+hM2XHH9TgYSZK0YJnYbRq2GBsbG5lJx6VLc9TkrSRJUj/wGTtJkqQ2Sb6Z5LHT7PPYJGcmuTrJVUn2mK34JuKMnSRJUiNJgP1n8H25fwbOraoDkzwC2Kr30U3OGTtJkjTQkixKck2S/w2sBu5vPipMkg81dRck+WKSw7v0fwzwUuB/AVTVb6vqjrm8hvVM7CRJkuAZwIlV9WzgpwBJXgi8Fngu8CpgaJy+TwX+A/j/k1ya5HNJevFZimmbaWK3ppdBSJIkzaYkw0nG2rbhjiY/raofdpS9BPi3qrqnqn4NfG2c4TcHng+cVFXPA+4G3t/TC5iimT5jt6aXQUiSJM2mqhoFRidocvdUx0qyEw8meScDXwVuqqqLm7IzmafEzluxkiRJ3V0I/FmSLZJsA+wPUFU3VtXiZju5qn4B3Jhk56bfy4ErxxlzVvlWrCRJUhdVtSLJOcBK4JfAKuC/xmn+buD05o3Y64G/mZsoN2RiJ0mSBlpVrQF2azte1FZ9XFWNJNkKWAZcMs4YlzH+yxVzxsROkiRpfKNJdqW1tOcXqupH8x3QREzsJEmSxlFVB813DNNhYrfw3DM0NDTSUbZoHuKQNEeuHLl+vkMY164jT+vpeAv5WqV+YGK3wIyNjR3TWdYl0ZMkSXoIP3ciSZLUJ0zsJEmSOiQZ6bYu7CR9jk5yY5K7ZiuuyZjYSZIk9cbXgCXzGYCJnSRJGnhJ/jrJyiSXJ/mXjro/SHJukkuSfD/JLt3GqKofVtXP5ybi7nx5QpIkDbQkzwaOAP6wqm5Lsh1wWFuTUeAdVfXjJC8CTgReNg+hTsrEro8MDQ29n9YHFH/nuOPmKRhJkhaQJMPAcFvRaFWNNvsvA75cVbcBVNXtSdb32wb4Q+DL68uAR85J0DNgYtdfthgbGxtpL1i6NEfNUyySJC0YTRI3OmnDh3oYcEdVLW4vTLIZDy4vdk5VHbmRIfaEz9hJkqRB9z3gdUm2B2huxQJQVXcCNyR5XVOXJM+tqvuranGzLYikDkzsJEnSgKuqK4CjgX9PcjnwPzuavBF4a1N3BfDqbuMk+USSm4CtktyUZGQWw+7KW7GSJGngVdUXgC+MU3cD8MopjPE+4H09Dm1anLGTJEnqE87Yac65CLgkSbPDxG7TcM/Q0NDIFNotmuU4JEnSAmZitwkYGxs7Zirtppj8SZKkPuUzdpIkSX3CxE6SJGkWNOvL3pHk6x3lT01ycZLrkpyR5BG9OqeJnSRJ0uz4JPDmLuXHAv9YVU8HfgW8tVcnNLGTJEkDLcmiJKvbjg9PMpJkaZJjkyxPcm2SvZr6zZIcl2R1kpVJ3t1t3Kr6LvDrjnOF1tq0ZzZFXwAO6NW1+PKEJEnS+DavqiVJ9gOOAvYFhml9iWJxVd3XvgTZFGxPa+3Z+5rjm4Df71mwvRpIC8JDPoty3HHzFIkkSQtIkmFaCdl6o1U1OoWuZze/l/DgZ8X2BU5en5xV1e29inNjmdj1kW6fRVm6NEfNRyySJC0kTRI3XiJ3Hxs+nrZF2/665vd+JsibkrwI+GxzeGRVnTNO0/8EHptk8yYx3BG4eZLwp8xn7CRJ0qD7JfD4JNsneSSw/yTtvwO8PcnmAEm2q6qLq2pxs42X1FFVBZwPHNgUvQX4t42/hBYTO0mSNNCq6l7gI8ByWknb1ZN0+RzwM2BlksuBg7o1SvJ94MvAy5PclOQVTdU/AO9Nch2tZ+7+18ZfRYu3YiVJ0sCrquOB4yeov43mGbvmFup7m22iMfcap/x6YMlMY52IM3aSJEl9whk7qc2uI0+b7xA0C64cuX6+Q9hk+c9OU7XQ//vzyq43S/uPM3aSJEl9wsROkiSpT5jYSZKkgZfkrub3SUnOnKx9R9/tk5yf5K4kJ7SVPyrJZW3bbUn+qdext/MZO0mSpEZV3cKD35ibqnuADwG7Ndv6sX4NLF5/nOQSHlzJYlY4YydJktRIsijJ6mb/4CRnJzk3yY+TfKJbn6q6u6ouoJXgjTfuM4HHA9+flcAbzthJkiSNbzHwPFpLi12T5NNVdeMMxvkr4Ixm5YlZY2I3Q0NDQ+9nw7XkFqTjjpvvCCRJmn9JhoHhtqLRZv3YyXy3qv6rGeNK4CnATBO7N8+g37SY2M3cFmNjYyPzHcRkli7NUfMdgyRJ861J4qaSyHVa17Z/P7B5ktcA6//39ZCqGptogCTPBTavqktmcP5pMbGTJEmahqr6CvCVaXR5A/DFWQpnAyZ2kiRJGynJGuDRwCOSHAD8SVVd2VS/HthvLuIwsZMkSQOvqrZpftfQfLKkqk4FTm1rs/8E/RdNUDdn6635uRNJkqQ+YWInSZLUJ7wV2909Q0NDI5O0WTQHcWy0JUvu7sEou/dgjE3DlSPX93S8XUd6O/ve6/h6yWuduYV8rYOml39b/64z5z+7mTOx62JsbOyYydpMIfGTJEmaU96KlSRJ6hMmdpIkSRNIsneSr0+zzy5JLkqyLsnhsxVbJ2/FSpIk9d7twGHAAXN5UhM7SZI00JJsDXwJ2BHYDPgo8F/APwFrgQva2o4ATwae1vz+U1Ud3zlmVd0K3JrkT2c7/nYmdpIkadC9Erilqv4UIMljgNXAy4DrgDM62u8C7AM8CrgmyUlVde8cxjsuE7uZm8onUebdsmXL5jsESZLmXZJhYLitaLSqRpv9VcCnkhwLfB34NXBDVf246XtaR99vVNU6YF2SW4EnADfN9jVMhYndDE3lkygLwdq1a4+a7xgkSZpvTRI3Ok7dtUmeT2s9148B351kuHVt+/cDmyc5FHhbU7ZfVd2ykSHPiImdJEkaaEmeBNxeVacluQN4F7AoyR9U1U+AN0w2RlV9BvjMLIc6KRM7SZI06HYHPpnkAeBe4O+AHYBvJFkLfJ/W83RTluSJwBjwaOCBJO8Bdq2qO3saeQcTO0mSNNCq6jzgvC5Vu3RpO9JxvNs4Y/6C1lu2c8oPFEuSJPUJZ+z63PLlW/dglN4ueL6Q9Xpx915b6PEtZC7uPjML/d+5Qfpb9Fqv/7a9/FvMxr93Vx7U8yEXJGfsJPW9hZ6cSFKvmNhJkiT1CRM7SZKkPmFiJ0mSBl6Su5rfJyU5c5p9t09yfpK7kpzQVr5Vkm8kuTrJFUlmfXEDEztJkqRGVd1SVQdOs9s9wIeAw7vUHVdVuwDPA16S/D/27j1Ozro+///rkoOBBEQtWFR0QaQ2gkTYRq1AAS21iAeElopWUel6Rr4tWpRqVjwUJWCraHHRFhTqz6rEcqig30hcRCBOQkggIBSI4BdPSIHAmjQk1++PuVcmy84eZu7ZmZ25no/HPPY+vud9557Zfefzue/7oz9vNseJpLCLiIiIKEjqk3RTMX2CpIslXSHpdkmfHm8f24/Y/iHVAq92+Yjtq4rp/wVW0uJn26Wwi4iIiKhvAXAc1dEpjpO0RyNBJO0CvIrJx6FtSk8/x66/v/9UYE6782ilxYvbnUFERET7SRoABmoWDdkemsKuS20/WMRYCzwbuGea770t8DXgs7Zb+vDFni7sgDmVSmWw3Um00rJlWtTuHCIiItqtKOKmUsiNtbFmejOwraSjgdG/ryfarkwSYwi43fY/NfD+09LrhV1ERETEtNheAiyZyraSPg48CTixpUkVUthFRERENEnSOmBnYHtJrwWOAB4CTgNuBVZKAjjH9pdalUcKu4iIiOh5tucVP9cB+xbT5wPn12xz1AT799VZpZJSnJLcFRsRERHRJdJiF1Fj7WC5Nyv10uDzZf/blamTc4PyPydlHm+nfyd66TtWtk4/t9GYtNhFREREdIkUdhERERFdIoVdRERERAtJ+lNJKyStKX4e3qr3ms3X2G3o7+8fbDJGXwl5REREREzkPuBVtu+VtC9wJfCMVrzRrC3sKpXKGc3GKKEwjIiIiFlO0lzgP4BnAtsAHwM+RXUYsD8HHqU6HNk/AnsDZ9o+V9KhwOnA+mL5VcC7bG+pjW/7hprZm4EdJD3Rdu2oFqVIV2xERET0ulcA99re3/a+wBXF8rttLwCupvo8u2OBFwMfrdl3IfBeYD7wHOB1k7zXMcDKVhR1MItb7Dpdf3//qcCcduexeHG7M4iIiGg/SQNUW91GDRXjxwKsAc6S9CngMttXF6NEXFKzfp7t9cB6SRsl7VKsW277zuI9vgYcBHyzTg7Pp9oSeESJh7aVFHatM6dSqQy2O4lly7Ro8q0iIiK6W1HEDdVZd5ukA4AjgY9LWlqsGm1V21IzPTo/WkN5bDhJRwOjf39PtF2R9Eyq48u+yfYdzR1NfSnsIiIioqdJejpwv+0LJT0AnDiN3RdK2hP4KXAc1ZbAJVSLuNH4uwCXA6favqbE1B8n19hFREREr9sPWC5pFdWWto9PY98fA+cAtwB3UVPQ1XgP1ZsrPiJpVfHarcmcx5UWu4iIiOhptq+k+giSWn0168+nevPE6HwfQHEd3kO2j5ok/seZXrHYsLTYRURERHSJtNhFRERENMD2MmBZm9PYSq8XdmWMXlFPX4viTsvChY+UEGW/EmJEGdYO3tnuFGal+YN7lRov5yHi8fK96Aw9XdiVMXpFPRnVIiIiImZarrGLiIiI6BIp7CIiIiLGkLRMUn8x/aEJtnuepGuL0ShOmbkMx5fCLiIiImJidQs74H7gJKAjBvFMYRcRERE9TdJcSZdLulHSTZKOq1l3BrBD8VDhi8bua/tXtn8MbJrJnOtJYRcRERG97hXAvbb3t70vcMXoCtunAr+1vcD2G9qW4RT19F2xLdbKR6lM2fDwcLtTiIiIaDtJA8BAzaIh20PF9BrgLEmfAi6zfXUxqsSsk8KuRVr5KJXpGBkZWdTuHCIiItqtKOKG6qy7TdIBwJHAxyUtrRdH0ruBvylmj7R9b+nJNiGFXURERPQ0SU8H7rd9oaQHgBPHbLJJ0na2N9n+PPD5mc9yalLYRURERK/bDzhT0haqN0G8k63vch0CVktaOfY6O0m/D1SAnYEtkk4G5tt+aGZS31oKu4iIiOhptq8Erhyz+NCa9X8P/H2dfX8BPLNlyU1T7oqNiIiI6BJpsetyy5fPLSFKuQOolymDu3eOss9F2XJuY6Z1+neik+X72ri02EVE18sfiYjoFSnsIiIiIrpECruIiIiILpHCLiIiImIaJJ0sacc66/ok/bYYW3aVpHNnMrfcPBERERExRZK2AU4GLgRG6mx2h+0FM5fVY9JiFxERET1N0vslnVRMf0bS94vpwyVdJOlhSWdJuhE4DXg6cJWkq9qY9rhS2EVERESvuxo4uJjuB+ZJ2q5YNgzMBa63vb/t04F7gcNsH1Yn3p6SbpD0A0kH19mmJdIVOwX9/f2nAnPanUcjFi+efJuIiIhuJ2kAGKhZNGR7qJheARwoaWdgI7CSaoF3MHASsBn41hTf6ufAs2z/RtKBwLclPX+mhhhLYTc1cyqVymC7k2jEsmVa1O4cIiIi2q0o4obqrNsk6S7gBOBHwGrgMGBv4BZgg+3N4+0r6Whg9G/tibYrVItDbK+QdAewD9XxZFsuhV1EREREtTv2FOCtwBrgbGCFbUsau+16YCfgPttLgCWjKyTtCtxve7OkvYDnAjP2lPRcYxcRERFRLex2B661/UtgQ7FsPEPAFXVunjgEWC1pFfBN4B22729FwuNJi11ERET0PNtLge1q5vepmZ43ZtvPAZ+rE+dbTP16vNKlxS4iIiKiS8yaFrs235na16b37QidPIB6J+cGnZ9fdIZ8TjpHmedi/uBepcWC8j8nnZ5fNGbWFHa08c7U/v7+trxvRERExHSkKzYiIiKiS6Swi4iIiGghSU+VdFUxNNk5rXyv2dQVGxERETEbbQA+DOxbvFomLXYRERHR0yTNlXS5pBsl3STpOEnrJP2jpFWSKpIOkHSlpDskvaPY71BJw8W+P5F0rqTH1Va2H7H9Q6oFXkulsIuIiIhe9wrgXtv7294XuKJYfrftBVQfVHw+cCzwYuCjNfsuBN4LzAeeA7xuppIeT7pip2bDbL0zdvHidmcQERHRfpIGgIGaRUPF+LFQHULsLEmfAi6zfXUxjNglNevn2V4PrJe0UdIuxbrltu8s3uNrwEFUR5xoixR2U1CpVM5odw6NWrZMiybfKiIiorsVRdxQnXW3SToAOBL4uKSlxaqNxc8tNdOj86M1lMeGk3Q0MPr390TblWbzn6oUdhEREdHTJD0duN/2hZIeAE6cxu4LJe0J/BQ4jmpL4BJgSQtSnVQKu4iIiOh1+wFnStoCbALeydS7U38MnAPsDVxFnYJO0jpgZ2B7Sa8FjrC9tsm8HyeFXURERPQ021cCV45Z3Fez/nyqN0+MzvcBFNfhPWT7qCm8R99k25Qhd8VGREREdIm02EVEREQ0wPYyYFmb09hKCruY1PzBvUqNt3bwztJidXJu0Pn5lamT/+3KPg+9pOzz2smfYeitz0qnn4toTLpiIyIiIrpECruIiIiILpHCLiIiImIMSbtKul7SDZIOnsL2CyRdK+lmSaslHTcTeY6Va+wiIiIiHu9lwBrbU31Y8QjwJtu3Fw88XiHpStsPtC7Fx0thFxERET1P0puAU6gOEXY3sD+wg6R+qsODfbTYdAdge9t71u5v+7aa6Xsl/QrYFUhhFxERETFTJD0f+Afgj23fJ+kpwKuBftvvKTb7z2Lb/wB+MEm8hcD2wB2ty3p8Kezq6O/vPxWY0+48mrV4cbsziIiIaD9JA8BAzaIh20PF9OHAN2zfB2D7/mJUibExPgD81vbnJ3if3YGvAm+2vaWs/Keq1MKuxcVQX4vi1jOnUqkMzvB7lm7ZMi1qdw4RERHtVhRxQ5NuWIeklwN/ARxSzL8I+GKx+iO2L5G0M3A5cJrt65pMuSFlt9i1rBjq7+9vSdyIiIjoed8Hlkg62/Zviq7Y35H0bODzwJ/Z/i2A7euBBTXbbA8sAb5i+5szl/rW0hUbERERPc32zZI+AfxA0mbgBrYeKuwE4KnAt4su2nttHzkmzF9Sbc17qqQTRvezvaqFqT9OCruIiIjoebYvAC4Ys/j8Yt1Heeyu2Hr7Xwhc2JLkpiEPKI6IiIjoEmmxixlX5iDbGcS6cWUPdp5z0Tl66VyU/TkuU6efh/wO6E5dVdiVfFduX0lxIiIiImZEVxV2lHhXbu7CjYiIiNkm19hFREREdIkUdhEREREtIOlZkr4r6RZJayX1tfo9u60rNiIiIqJTfAX4hO3vSZoHtHyIsbTYRURERE+T1Cfpppr5UyQNSlom6VOSlku6TdLBxfptJC2WdJOk1ZLeO07M+cC2tr8HYPth2yOtPpa02EVERETUt63thZKOBBYBLwcGqD49Y4HtR8cOQVbYB3hA0sXAnsD/BU61vbmlybYy+Cy3oRvujF28uN0ZREREtJ+kAaoF2agh20NT2PXi4ucKHnsU2suBc20/CmD7/nH22xY4GHghcDfwdapDk315urlPRwq7OiqVyhntzqEMy5ZpUbtziIiIaLeiiKtXyD3K1pen1T4Td2PxczMT1E2SXgR8sZj9CPAzYJXtO4v13wZeTAq7iIiIiJb6JbCbpKcCDwNHAVdMsP33gLdLumq0K9b29cCC0Q0kbQPsImlX278GDgcqrTuEqtw8ERERET3N9ibgdGA51aLt1kl2+RLV7tXVkm4Ejh8n5mbgFGCppDWAgPPKzHs8abGLiIiInmf7s8BnJ1h/H8U1dsW1dX9bvCaK+T3gBeVlObm02EVERER0ibTYdbmFCx8pIcp+JcR4zNrBO0uN10vmD+7V7hRmpbI/c51+Hjo9v16R8xDtkBa7iIiIiC4xm1rspvJcub4ZyCMiIiKiI82awm4qz5XrhgcKR0RExMyT9LDteROs3wU43vYXJolzDPBN4I9st/zxJmOlKzYiIiJicrsA75poA0k7Ae8Drp+RjMaRwi4iIiKihqT3S/qxpNWSPlosPgN4jqRVks6ss+vHgE8BG2Yk0XHMmq7YiIiIiFaTdATwXGAh1YcKXyLpEOBUYF/bC+rsdwCwh+3LJb1/xhIeo+sLu/7+/lPZesy3njI8PNzuFCIiItpO0gAwULNoqBg/dqwjitcNxfw8qoXe3RPEfgJwNnBCKck2oesLO2BOpVIZbHcS7TIyMrKo3TlERES0W1HEjVfIjSXgH21/cauFUt+Y+U8Aryxm/wTYF1gmCeD3qbb0vXqmb6DINXYRERERj7kSeKukeQCSniFpN2A9sNPoRrZPs72geD1o+/ds99nuA64DZryogxR2EREREb9j+7vAvwPXSlpD9dElO9n+DXCNpJsmuHmi7XqhKzYiIiJiQrXPsLP9z8A/j7PN8VOMdWh5mU1PWuwiIiIiukQKu4iIiIguka7YmHHzB/cqLdbawTtLizUblHm8ZZ6HVujkc9vJuUFnn9uy/+3KPtZOPre9dKzRuLTYRURERHSJFHYRERERXaLbumI39Pf3D45Z1teGPCIiIiIeR9LOwFrg27bfU3b8rirsKpXKGWOXjVPoRURERLTLx4CWjfeZrtiIiIjoaZL6JN0q6XxJt0m6SNLLJV0j6XZJCyUNSjqlZp+biv1G971I0i2SvilpxzrvcyDwNOC7rTqWFHYRERERsDdwFvC84nU8cBBwCvChSfb9A+ALtv8QeAh419gNJD2hiH/K2HVl6qqu2Fbo7+8/FZjT7jwaNTzcstbeiIiIWUPSADBQs2jI9lDN/F221xTb3gwste1iWLE+YNUE4e+xfU0xfSFwErB4zDbvAv7L9s8kNXEkE0thN7k5lUplsN1JNGpkZGRRu3OIiIhot6KIG5pgk40101tq5rdQrZceZeueztpGH499O0kvAr5YzH8EeAlwsKR3AfOA7SU9bPvUaR3IJFLYRURERExuHXAUgKQDgD1r1j1L0ktsX0u1C/eHtq8HFtRsc8nohKQTgP6yizrINXYRERERU/Et4ClFN+17gNtq1v0EeLekW4AnA//ShvyAxlvsDqrzGJG+xlOJiIiImHm21wH71syfUGfdEWP3ldQHPGr7jdN4v/OB86ef6eQaLey2He+6szwzLiIiIqJ9co1dl1u+fG4JUTLwdKM6+Vg7OTcod8DzXjrWXtPp57aTdfLnbjad17Gtfe3WC4XdeMOMTUdfSXlEREREtFTXF3bjDTM2HelejoiIiNkid8VGREREdIkUdhERERHjkHRyvXFfJ9jny5JulLS6GDd2XqvyG08Ku4iIiIjxnQxMq7AD/o/t/W2/ALib6jPvZkzXX2MXERERMZHiWXRXACuAA4CbgWHg6cBVku6zfZikVwCfBLYB7rP9srGxbD9UxBSwA48fbqyl0mIXERERAX8AfMH2HwIPAdsD9wKHFUXdrsB5wDG29wf+ol4gSf8G/AJ4HvC5lmdeIy12k2v2cSlttXhxuzOIiIhoP0kDwEDNoiHbQzXz99i+ppi+EDhpTIgXA8O27wKwfX+997L9FknbUC3qjgP+rdn8pyqF3SSafVxKuy1bpkXtziEiIqLdiiJuaKJNJpkfl6QrgacBFdsn1rzfZkn/H/ABZrCwS1dsREREBDxL0kuK6eOBHwLrgZ2KZdcBh0jaE0DSUwBs/5ntBbZPVNXexXoBrwZuncmDSItdREREBPwEeLekfwXWAv8C/C9whaR7i+vsBoCLJT0B+BXwp2NiCLhA0s7F9I3AO2fsCEhhFxEREQHwqO03jln2OWpufrD9HeA79QLY3gK8tDXpTU26YiMiIiK6RFrsYsbNH9yr3SlEC6wdvLPdKdTVa5+5nIvO0MnnodO14nOy9vj662yvA/Yt/U3bIC12EREREV0ihV1EREREl0hhFxEREVEySQskXSvpZkmrJR1Xs+5wSSsl3STpAkmlXRqXwi4iIiKifCPAm2w/H3gF8E+SdikelXIB8Fe29wV+Cry5rDdNYRcRERE9TVKfpJtq5k+RNChpmaRPSVou6TZJBxfrt5G0uGhxWy3pvWNj2r7N9u3F9L1Un3u3K/BU4H9t31Zs+j3gmLKOJXfFRkRERNS3re2Fko4EFgEvpzrmbB+wwPajo6NQ1CNpIbA9cAfVocq2ldRvuwIcC+xRWrJlBeol/f39pwJz2p3HVCxe3O4MIiIi2q8YNWKgZtFQMX7sZC4ufq6gWsxBtbg71/ajALbvn+B9dwe+Cry5eIAxkv4K+IykJwLfBTZP41AmlMKuMXMqlcpgu5OYimXLtKjdOURERLRbUcTVK+QeZevL02obbzYWPzczQd0k6UXAF4vZj9i+pBha7HLgNNvX1eRyLTDarXsEsM80DmVCucYuIiIiet0vgd0kPbVoRTtqku2/B7x99G5WSU+xfb3tBcXrEknbA0uAr9j+Zu3OknYrfj4R+Hvg3LIOJIVdRERE9DTbm4DTgcpEXBQAACAASURBVOVUi7ZbJ9nlS8DdwGpJNwLjjWvxl8AhwAmSVhWvBcW690u6BVgNXGr7+2UcB6QrNiIiIgLbnwU+O8H6+yiusSuurfvb4lVv+wuBC+usez/w/ibSrSstdhERERFdIoVdRERERJfouK7YWfIokb52JxDRaeYP7tXuFCKijdYO3llarPw+aVzHFXbMgkeJ9Pf3D7Y7h4iIiIix0hUbERER0SVS2EVERETPk/RwzfSRxdiwz5b0RElfl/Tfkq6X1Fds0yfptzWPMjm3Zv91ktbUrPvjYvkVkh6QdNkEeZwg6dc1+544nePoxK7YiIiIiLaQ9DKqjz35M9s/lfQu4H9s710MBfYp4Lhi8ztsL6gT6rDiESm1zgR2BN4+SRpft/2eRvJPi11EREQEIOkQ4DzgKNt3FItfA1xQTH8TeJkkNRLf9lJgfdOJTiCFXURERAQ8Efg28FrbtSNPPAO4B373YOIHgacW6/aUdIOkH0g6eEy8q4qu1OsbyOUYSaslfVPSHtPZMV2xjdkwW+6MXby43RlERES0n6QBYKBm0ZDtoZr5TcCPgLcB75tCyJ8Dz7L9G0kHAt+W9HzbDxXrx+uKnYpLga/Z3ijp7VRbCw+f6s4p7BpQqVTOaHcOU7VsmRa1O4eIiIh2K4q4oQk22UJ1fNelkj5k+5PF8v8H7AH8TNK2wJOA39g2sLGIvULSHcA+QKXJPH9TM/sl4NPT2T9dsRERERGA7RHglcAbJL2tWHwJ8OZi+ljg+7YtaVdJ2wBI2gt4LtD0U5ol7V4z+2rglunsnxa7iIiIiILt+yW9AhiW9Gvgy8BXJf03cD/wV8WmhwCnS9pEtbXvHbbvnyi2pKuB5wHzJP0MeJvtKyWdDlRsXwKcJOnVwKPF+50wnfxT2EVERETPsz2vZvoeYM+a1X8xzvbfAr5VJ1ZfneVjb7AYXf6RmukPAh+cUtLjSFdsRERERJdIi11ElKLMAcCh3EHAy84tGtfJn5OydXJurdBrx9upyi7syngMSF8JeURERET0nFILuzIeAzJbng8XERER0WlyjV1EREREl0hhFxERETEOSadLevk4yw+VdFmdfd4j6b8lWdLvtT7LreXmiYiIiIhx1D6GZBquAS4DlpWbzdSkxS4iIiJ6mqQ+SbdIOk/SzZK+K2kHSedLOrbY5hWSbpW0EnhdvVi2b7C9bqZyHyuFXURERER1SLDP234+8ABwzOgKSXOA84BXAQcCv9+WDKegK7ti+/v7TwXmtDuPTrB4cbsziIiIaD9JA8BAzaIh20M183fZXlVMr2Drx689r1h/exHrwjGxOkZXFnbAnEqlMtjuJDrBsmVa1O4cIiIi2q0o4oYm2GRjzfRmYIepxJV0JfA0qmO9nth4huXo1sIuIiIioiy3An2SnmP7DuD1oyts/1n70nq8XGMXERERMQHbG6h2vV5e3Dzxq3rbSjpJ0s+AZwKrJX1phtIE0mIXERERPa64i3XfmvnHXaFu+wqq19pNFuuzwGfLzG860mIXERER0SU6scVuQwnjxfaVkEdXWLjwkRKi7FdCjIj2mT+4V7tTmFFrB+9sdwp19dq5iMZ08me403VcYVepVM5oNkYJhWFERETErJOu2IiIiIgukcIuIiIiYhokDUo6ZZzle0i6StLaYmiy99Wse4qk70m6vfj55FbklsIuIiIiohyPAn9nez7wYuDdkuYX604Fltp+LrC0mC9dCruIiIjoeZLeKGm5pFWSvihpG0lvk3Rbsfw8SedMFMP2z22vLKbXA7cAzyhWvwa4oJi+AHhtK44jhV1ERET0NEl/CBwHvNT2AqpDir0B+DDVlreXMoVn2I2J2Qe8ELi+WPQ02z8vpn9BdRiy0nXcXbElKeORKV1heHi43SlERES0naQBqqNHjBoqxo8FeBlwIPBjSVAdJ/aPgR/Yvr/Y/xvAPlN8r3nAt4CTbT80dr1tS3KjxzKRrizsynhkSrcYGRlZ1O4cIiIi2q0o4obqrBZwge0P/m6B9Frg6IliStoDuLSYPdf2uZK2o1rUXWT74prNfylpd9s/l7Q7EwxL1ox0xUZERESvWwocK2k3qN7BCtwA/ImkJ0vaFjhm7E6277G9oHidq2pz35eBW2yfPWbzS4A3F9NvBv6zFQeSwi4iIiJ6mu21wD8A35W0GvgesDvwSWA5cA2wDnhwklAvBf4aOLy4CWOVpCOLdWcAfyrpduDlxXzpurIrNiIiImI6bH8d+HrtMkk32R4qWuyWAN8uth2sE+OHVLt1x1v3G6rX8rVUWuwiIiIixjcoaRVwE3AXRWHXydJiF7NemYNFzx/cq/R4vaSTj7fXPidlxVw7eGdHn9de00uf4zLjNXqcth83ukSnkz39u237+/vPr1QqJ5SfTpRtZGSk6dup+xfsV0YqLVHmL6VWyB/EzpDPSXSDfI6bs/a2O8btIu02jXbFrisziYiIiIhoXq6xi4iIiOgSKewiIiIiWkjSIZJWSnpU0rFj1l0h6QFJl5XxXinsIiIiIlrrbuAE4N/HWXcm1WfflSKFXURERPQ8SX8r6abidbKkuZIul3Rjsey4Yrt1kv6xePhwRdIBkq6UdIekd4wX2/Y626uBLeOsWwqsL+s48riTiIiI6GmSDgTeAryI6gOGrwe2Ae61/cpimyfV7HK37QWSPgOcT3XEiTlUn3d37gym/jizvrDr7+8/leo/ZoxjeHi43SlERES0naQBYKBm0ZDtoWL6IGCJ7UeKbS8GNlEdAuxTwGW2r67Z95Li5xpgnu31wHpJGyXtYvuBlh7MBGZ9YQfMqVQqg+1OolONjIwsancOERER7VYUcUOTbri1A4AjgY9LWmr79GL5xuLnlprp0fltJX0CeGXxvgsaz3r6co1dRERE9LqrgddK2lHSXOBoYAUwYvtCqjc4HDDVYLZPs71gpos66I4Wu4iIiIiG2V4p6XxgebHoS8A8YLmkLVS7Zd/ZaHxJfwQsAZ4MvErSR20/v1h3NfA8YJ6knwFvs31lo++Vwi4iIiJ6nu2zgbPHLH5cgWW7r2b6fKo3Tzxu3Zh9fgw8s866g6eb60TSFRsRERHRJdJiFxEdqZMHPO/4wc5L/Lfr9GPtZGV/hnMuYirSYhcRERHRJVLYRURERHSJFHYRERERXaJjrrFrYgSJvpJTiYiIiKhL0iDwsO3F46z7V+Ao4Fe29x2zz98Avy4Wfcj2f5WdW8cUdjQ4gkR/f/+094mIiIhokfOBc4CvjLPuM+MVg2VKV2xERET0PElvlLRc0ipJX5S0jaS3SbqtWH6epHMmi2N7GLh/BlIeVye12LVVE13BHW14eLjdKURERLSdpAFgoGbRUDF+LJL+EDgOeKntTZK+ALwB+DDVocTWA98HbmwyjfdIehNQAf7O9v80Ge9xUtg9pqGu4E43MjKyqN05REREtFtRxA3VWf0y4EDgx5IAdgD+GPiB7fsBJH0D2KeJFP4F+Bjg4udZwFubiDeudMVGRERErxNwge0FxesPgMFJd5L2KLpuV0l6x0Tb2v6l7c22twDnAQtLyXyMFHYRERHR65YCx0raDUDSU4AbgD+R9GRJ2wLHjN3J9j01xeC5E72BpN1rZo8Gbiov/cekKzYiIiJ6mu21kv4B+K6kJwCbgHcDnwSWU70Z4lbgwcliSfoacCjwe5J+Biyy/WXg05IWUO2KXQe8vQWHksIuIiIiwvbXga/XLpN0k+2hosVuCfDtYtvBCeK8vs7yvy4v2/rSFRsRERExvkFJq6h2m95FUdh1sm5osdtQ0kOK+0qIERElmT+4V2mx1g7eWVqs2aDMf7toXM7D7Gf7lHbnMF2zvrCrVCpnlBEnI1hERETEbJeu2IiIiIgukcIuIiIiYhoknSxpxzrr/lTSCklrip+Hz2RuKewiIiIipkjSNsDJwLiFHXAf8Crb+wFvBr46U7lBCruIiIjocZLeL+mkYvozkr5fTB8u6SJJD0s6S9KNwGnA04GrJF01NpbtG2zfW8zeDOwg6YkzdCgp7CIiIqLnXQ0cXEz3A/MkbVcsGwbmAtfb3t/26cC9wGG2D5sk7jHAStsbW5T348z6u2JLVNZjUzrK8PBwu1OIiIhoO0kDwEDNoiHbQ8X0CuBASTsDG4GVVAu8g4GTgM3At6b5fs8HPgUc0WTq05LCrlDWY1M6zcjIyKJ25xAREdFuRRE3VGfdJkl3AScAPwJWA4cBewO3ABtsbx5vX0lHA6N/a0+0XZH0TKojVbzJ9h2lHsgkUthFREREVLtjTwHeCqwBzgZW2LaksduuB3YC7rO9hGoRB4CkXYDLgVNtXzMTidfKNXYRERER1cJud+Ba278ENhTLxjMEXDHezRPAe6i29H1E0qritVtLMh5Hoy12B7XgerS+kuNFRERETIntpcB2NfP71EzPG7Pt54DP1YnzceDjLUpzUo0WdttWKpXBMhPpxhsXIiIiImZSrrGLiFKsHbyz1HhlDqCewdhjqsr8HOdzF+2Qa+wiIiIiukQKu4iIiIgukcIuIiIiokuksIuIiIiYBkknS9qxzrqnSrqqGF/2nJnOLYVdRERExBRJ2gY4GRi3sKP6/LsPU33Y8YxLYRcRERE9TdL7JZ1UTH9G0veL6cMlXVS0vp0l6UbgNODpwFXjPaDY9iO2f0i1wJtxKewiIiKi110NHFxM9wPzJG1XLBsG5gLX297f9unAvcBhtg9rS7YT6Ojn2PX3958KzGl3HrPZ8PBwu1OIiIhoO0kDwEDNoiHbQ8X0CuBASTsDG4GVVAu8g4GTgM3At2Yw3YZ1dGEHzCl7hIteMzIysqjdOURERLRbUcQN1Vm3SdJdwAnAj4DVwGFUx3y9Bdhge/N4+0o6Ghj9W3ui7UrJqU9Lpxd2ERERETPhaqo3PLwVWAOcDaywbUljt10P7ATcZ3sJsGQmE51IrrGLiIiIqBZ2uwPX2v4l1Zsfrq6z7RBwxXg3TwBIWke1MDxB0s8kzW9BvuNKi11ERET0PNtLge1q5vepmZ43ZtvPAZ+bIFZfC1KckrTYRURERHSJtNhFRCnmD+7V7hQimpbPcePWDt5ZWqych8alxS4iIiKiS6Swi4iIiOgSKewiIiIipkHSyZLqjRU7us2ziqHIZnTM2E66xm5Df3//4JhlfW3IIyIiImJckrYBTgYuBEYm2PRs4DszklSNjinsKpXKGWOXjVPoRURERJRK0vuBjbY/K+kzwP62D5d0OPA24DXAF4GXUx1a7OnAVZLuG2+8WEmvBe4CHpmxgyikKzYiIiJ63dVUx4WF6hix8yRtVywbBuYC19ve3/bpwL3AYXWKunnA3wMfnZHMx+iYFrt26+/vPxWY0+48yjY8PNzuFCIiItpO0gAwULNoqBg/FmAFcKCknYGNwEqqBd7BwEnAZqotdVMxCHzG9sPjDEXWcinsHjOnUqkMtjuJso2MjCyafKuIiIjuVhRxQ3XWbZJ0F3AC8CNgNXAYsDdwC7DB9ubx9pV0NDD6t/ZE4EXAsZI+DewCbJG0wfY5JR5OXSnsIiIiIqrdsacAbwXWUL35YYVtj9Pyth7YCbjP9hJgSc260S5dJA0CD89UUQe5xi4iIiICqoXd7sC1tn8JbCiWjWcIuELSVTOV3FSlxS4iIiJ6nu2lwHY18/vUTM8bs+3ngM9NIeZgiSlOSVrsIiIiIrpECruIiIiILtHpXbHjjUbRKn0z9D4zavnyuSVE2auEGNFp1g7eWWq8+YO98znJv13nyLmI2FpHF3bjjUbRKhnlIiIiIma7dMVGREREdIkUdhEREREtJulQSask3SzpB616n0a7YteVmUREREREt5K0C/AF4BW275a0W6veq9EWu3VlJhERERHRLpLmSrpc0o2SbpJ0nKR1kv6xaGWrSDpA0pWS7pD0jmK/eZKWSlopaY2k19R5i+OBi23fDWD7V606lnTFRkRERK97BXCv7f1t7wtcUSy/2/YCqiNQnA8cC7wY+GixfgNwtO0DqI4te5bGGX8M2Ad4sqRlklZIelOrDqSj74qdYTP5aJUZs3hxuzOIiIhoP0kDwEDNoiHbQ8X0GqpF2aeAy2xfXdRnl9Ssn2d7PbBe0saie/UR4JOSDgG2AM8Angb8YszbbwscCLwM2AG4VtJ1tm8r+zhT2BVm8tEqM2nZMi1qdw4RERHtVhRxQ3XW3SbpAOBI4OOSlharNhY/t9RMj85vC7wB2BU40PYmSeuAOZLeDfxNse2RwM+A39h+BHhE0jCwP1B6YZeu2IiIiOhpkp4OjNi+EDgTOGCKuz4J+FVR1B0GPBvA9udtLyhe9wL/CRwkaVtJOwIvAm4p/0jSYhcRERGxH3CmpC3AJuCdwDensN9FwKWS1gAV4NbxNrJ9i6QrgNVUW/u+ZPumUjIfI4VdRERE9DTbVwJXjlncV7P+fKo3T4zO99Vs95IpvseZVFsDWypdsRERERFdIi12MatlwO7G5d+ucWX/22Ug+8b10rF2upyLzpAWu4iIiIgukcIuIiIiokuksIuIiIjoEinsIiIioudJerikOHtIukrSWkk3S3pfGXGnKjdPRERERJTnUeDvbK+UtBOwQtL3bK+diTdPi11EREREQdI8SUslrZS0RtJratZ9WNJPJP1Q0tcknTJ2f9s/t72ymF5PdYSJZ8xU/mmxi4iIiHjMBuBo2w9J+j3gOkmXAP3AMVTHeN0OWAmsmCiQpD7ghcD1rUy4VqOF3UH9/f2DZSYSrbF4cbsziIiIaD9JA8BAzaIh20PjbQp8UtIhVIf/egbwNOClwH/a3gBskHTpJO83D/gWcLLth8o4hqlotLDbtlKpDJaZSLTGsmVa1O4cIiIi2q0o4sYr5MZ6A7ArcKDtTZLWAXPqbSxpD2C0yDvX9rmStqNa1F1k++LmMp+eXGMXERER8ZgnAb8qirrDgGcXy68BXiVpTtEadxSA7XtsLyhe50oS8GXgFttnz3TyucYuIiIi4jEXAZdKWgNUgFsBbP+4uNZuNfBLYA3w4Dj7vxT4a2CNpFXFsg/Z/q+WZ04Ku4iIiAhszyt+3ge8pM5mi20PStoRGGacmyds/5DqdXptkcIuIiIiYmqGJM2nes3dBaOPNekkKey63MKFjzQdY+3g3BIyecz8wb1KjRcx2+U7ETE72D6+3TlMJjdPRERERHSJFHYRERERXSKFXURERESLSDpT0q2SVktaImmXVr5fCruIiIiIgqrKrI++B+xr+wXAbcAHS4z9OCnsIiIioqdJ6pP0E0lfAR4G7pB0vqTbJF0k6eWSrpF0u6SFxT5zJf2rpOWSbpD0mvFi2/6u7UeL2euAZ7byWFLYRURERMBzgS8Azwf2AM4Cnle8jgcOAk4BPlRsfxrwfdsLgcOAMyVN9hiJtwLfKT/1x+RxJ4X+/v5TmWAsuNlqeHi43SlERES0naQBYKBm0VAxfuyon9q+TlIfcJftNcV+NwNLbbsYjaKv2P4I4NWSTinm5wDPAm6p8/6nAY9SHdmiZVLYPWZOpVIZbHcSZRsZGVnU7hwiIiLarSjihibYpPbBrxtrprfUzG/hsdpJwDG2f1IbRNK/AS8E7rV9ZLHsBKpjy77Mths9hqlIV2xERETE9F0JvFeSACS9EMD2W2wvqCnqXgF8AHi17ZFWJ5XCLiIiImL6PgZsB6wuums/Vme7c4CdgO9JWiXp3FYm1WhX7Loyk4iIiIhoF9vrgH3HThfzJ9TZ7rfA26cQe+8yc51Moy1268pMIiIiIiKal67YiIiIiC7RMXfFdsDjRvra+N4ts3z5ZI/UmdyhzacRERNYO3hnqfHmD+5VaryImD06prCjzY8b6e/vb9t7R0RERJQhXbERERERXSKFXURERMQMkrRA0rWSbpa0WtJxZcXupK7YiIiIiF4wArzJ9u2Sng6skHSl7QeaDZwWu4iIiOhpkvok3SrpfEm3SbpI0sslXSPpdkkLJT1F0reLFrbrJL2g2HdQ0gWSrpb0U0mvk/RpSWskXSFpu7HvZ/s227cX0/cCvwJ2LeNYUthFREREwN7AWcDzitfxwEHAKcCHgI8CN9h+QTH/lZp9nwMcDrwauBC4yvZ+wG+BV070ppIWAtsDd5RxEOmKfcyGbrwzdvHidmcQERHRfpIGgIGaRUO2h2rm77K9ptj2ZmCpbUtaQ/WRaM8GjgGw/X1JT5W0c7Hvd2xvKrbdBriiWD66b72cdge+CrzZ9pZmjxFS2P1OpVI5o905tMKyZVrU7hwiIiLarSjihibYZGPN9Jaa+S1U66VNk+1re4ukTbZdu6+kFwFfLJZ9xPYlRVF4OXCa7eumdzT1pbCLiIiImNzVwBuAj0k6FLjP9kOSJt3R9vXAgtF5SdsDS4Cv2P5mmUmmsIuIiIiY3CDwr5JWU72r9c1NxPpL4BDgqZJOKJadYHtVUxmSwi4iIiJ6nO11wL418yfUWffacfYdHDM/r966muUXUr3JonS5KzYiIiKiS6TFLma1DJ4e0dvyOyBia40Wdq14NEhfyfEiIiIiekpDhV0rHg3Sjc+Qi4iIiJhJucYuIiIiokuksIuIiIgYRzF27LHT3OcTku6R9HCr8ppICruIiIiI8lwKLGzXm+eu2IiIiOh5kj4MvBH4NXAPsGLM+nXAfwB/DvwWON72f4+NMzo82FRGpGiFtNhFRERET5P0R8AxwP5UC7f+Ops+aHs/4Bzgn2YovWmZVS12/f39pwJz2p3HbLJ4cbsziIiIaD9JA8BAzaIh20PF9EuB/7S9Adgg6dI6Yb5W8/Mzrcm0ObOqsAPmVCqVwXYnMZssW6ZF7c4hIiKi3YoibmjSDScJUzstaRse67K9xPZHmozftHTFRkRERK+7BniVpDmS5gFH1dnuuJqf19rebHtB8Wp7UQezr8UuIiIiolS2fyzpEmA18EtgDfDgOJs+WdJqYCPw+vFiSfo0cDywo6SfAV+yPdiSxMeRwi4iIiICFtselLQjMAyssH3emG3OtP33EwWx/QHgA61KcjIp7CIiIiJgSNJ8qjdpXmB7ZbsTakQKuy63cOEjzQf59tzmY7TI/MG92p3CjFo7eGe7U6ir185FdIZ87hpX9u+T2X4ubB8/yfq+GUqlKZ1U2G3o7+8fnGSbvhnIIyIiImJW6pjCrlKpnDHZNlMo/CIiIiJ6Vh53EhERET1P0sPFz6dL+uY0910oaVXxulHS0a3JcnId02IXERER0W627wWOneZuNwH9th+VtDtwo6RLbT9afoYTS4tdREREREFSn6SbiukTJF0s6QpJtxfPqHsc2yM1Rdwcth6hYkalsIuIiIiobwHVkSb2A46TtMd4G0l6kaSbqT7c+B3taK2D2dcVO5U7Z6PG8PBwu1OIiIhoO0kDwEDNoqFi/NjJLLX9YBFjLfBs4J6xG9m+Hni+pD8ELpD0HdsbSkh9WmZVYTeVO2djayMjI4vanUNERES7FUXcVAq5sTbWTG8Gti1ujhj9+3qi7UrN+9xS3IixL1Bhhs2qwi4iIiKi3WwvAZaMzkvaE7inuHni2cDzgHXtyC2FXURERERzDgJOlbQJ2AK8y/Z97UgkhV1ERET0PNvzip/rqHajYvt84PyabY6qs+9Xga+2OsepyF2xEREREV0ihV1EREREt7CdV4+/gIFOijMb4nVybp0er5Nz6/R4nZxbp8fr5Nw6PV4n59aKeLP9lRa7gK2f69MJcWZDvE7OrdPjdXJunR6vk3Pr9HidnFunx+vk3FoRb1ZLYRcRERHRJVLYRURERHSJFHYBjT2Ju5VxZkO8Ts6t0+N1cm6dHq+Tc+v0eJ2cW6fH6+TcWhFvVlNx4WFEREREzHJpsYuIiIjoEinsIiIiIrpECruILiJpN0nPGn01EWdHSR+WdF4x/1xJ4w6lM4VYL5U0t5h+o6Szi0GyO4KkuZKeUEzvI+nVkrZrd15jSXqCpJ2bjFHaeS32V3FOP1LMP0vSwgZjPUfSE4vpQyWdJGmXJnIrNV4rlXFux8R7sqQXNLF/y74TJX2OZ825bYcUdj1E0usmejURdwdJf1BCfu+TtHPxx+LLklZKOqKJeJ8u4m0naamkX0t6Ywl57izpKaOvae57qaRL6r2ayOnVkm4H7gJ+AKwDvtNoPODfgI3AS4r5/wd8vMFY/wKMSNof+DvgDuArTeRWdrE4DMyR9Azgu8BfUzM2ZBM5Nv29kPTvxedtLnATsFbS+5sIWeZ5BfhCEev1xfx64PMNxvoWsFnS3lQvht8D+Pcmcis1Xtm/T8o+t5KWjf5uAlYC50k6u8FwpX4nWvA5Lvuz0lVS2PWWVxWvtwFfBt5QvL4EvLWRgJJeBawCrijmFzRRoLzV9kPAEcCTqf4yOaPBWABHFPGOolro7A0084vz7ZJ+AawGVhSvyjTDLAbOolqA/RY4r3g9TLXgadTHgBcDt9neE3gZcF0T8Z5j+9PAJgDbI4AajPWoq3dpvQY4x/bngZ2ayA3KLRZVHN/rgC/Y/gvg+c0kV+L3Yn7xGX4t1UJ9T6rfi0aVeV4BXmT73cCGIt7/ANs3GGuL7UeBo4HP2X4/sHsTuZUdr9TfJ5R/bp9UxHsd8BXbLwJe3mCssr8TZR9r2ee2q6Sw6yG232L7LcB2VL9ox9g+huoXttFm9kFgIfBA8R6rqH5pGzH6B+ZI4Ku2b6a5PzrbFj9fCXzD9oNNxAI4BdjXdp/tPYvXXtMJYPsHtn8AvNT2cbYvLV7HAwc3kdsm278BniDpCbavAvqbiPe/knYADNWuD6otPY1YL+mDwBuBy4sunma7dcosFiXpJVT/k3N5sWybJvMbpJzvxXZFF9hrgUtsb6I4Jw0q87wCbJK0TU28XYEtTcR6PfBm4LJiWTOfk7Ljlf37pOxzu62k3YG/5LHjbVTZ34myj7Xsc9tVUtj1pj1s/7xm/pdAo9djbRrnF1yjfGq8VQAAIABJREFUX9gVkr5LtbC7UtJONP5HAuAySbcCBwJLiz86G5qIdwcw0sT+teZK+l1RKGlPYG4T8R6QNI9qF8pFkv4ZeKSJeIuotjbtIekiYCnwgQZjHUe1eHib7V8AzwTObCI3eKxY/GuaLxZPBj4ILLF9c3Fermoyv7K+F+dSbR2aCwwX3c0PNZFXmecV4LPAEmA3SZ8Afgh8ssFYb6HarfsJ23cV34mvNpFb2fHK/n3yRco9t6cDVwL/bfvHxef49gZjvY9yvxNlH2vZ57ar5Dl2PUjSOcBzga8Vi46j+svgvQ3E+jLVPw6nAscAJwHb2X5HA7GeACwA7rT9gKSnAs+wvXq6sWpiPgV40PZmSTsCOxfFRSOxXkj1GqXrqWnlsH1SA7FeQfXakDuptko+G3i77SsbzG0u1T8yovq/7CcBFxWteA0p/v1fXMS8zvZ9jcYqm6TfB44Hfmz7alVvFDnUdlPX7pWljO9F8X041vZ/1CwTsE3RDdVobqWeV0nPo9r1L2Cp7VuaidfJyvx9Uif+ts2c29mkl451pqWw61GSjgYOKWaHbS9pMM6OwGlUr4sT1f8xfsz2lP8nK+mAidbbXjnNnA63/X3VuSHE9sXTiVcTdznVFok11LQk2r6gwXhPBJ5XzN5qu5kusVKUeS4krWf8VipVQ7nZO+OeDTzX9v8tPofb2F4/jf0vrZMfVBN8dRO5Nf29KOJUbDfTpT4ap+zv2IQ3Ddm+fzrxipgvpdqF/Wyq3Z6jn5NpXe7QqnhFzD8G+nisW5ZG/zNRfP+PGSfe6Q3G2xX4m3HiTfv6aUn7UL30ZGyswxvMrexjLf3cdpMUdj2q2T+KJecyURO/p/vLRNJHbS+S9G914jV6o8gNtl/YyL514jX9R0LSD20fNE4R1VDxVPa5aBVJfwMMAE+x/RxJzwXOtf2yacT4k4nWF9dCtpWkM4D7gK9T07U+3cKpBd+xu6h+3kavgR397DX8B7bo5vw/VG9K2lyTXEOtzi2I91XgOVRvihmN50Za7It4VwAPjpPfWQ3G+xFw9TjxvtVArBupXgYwNtaKBnMr+1hLPbfdJoVdDyrjj2JNrFL/Z9fJJH2S6nUil7J1V2wjrROl/pHoRK1o1amJvYrqzQnXjxbbktbY3q/RmGUq63tRFFBjdWXLhKTrXb2Ts1Pj3UL1prNS/mhKusn2vmXEKuKtsr2gpFgrbB9YRqwiXtnHWuq57TbbTr5JdKF3U/xRBLB9u6TdGoz1Dar/s/sSNf9zmo56Xaajmug63QV4E4//49po8TT6rK4P1qYHNPJHtp8S/ki0qniS9G6q1+g9UMw/GXi97S9MI8wKtm7V2So1Gvt3G7XR9v9WLzmrXq/DNG9OkLSmzj6jrU4NP+CVEr4XVJNo9A7zcZV0XmvjHQ18f/RGkeI7d6jtbzcQ7ipJZwIXs/V/nKbVTdzCeDcBvw/8fLINp+hHkvazvaakeJdJOtL2f5UQ61JJ76J6Y0xT/4ktlH2sZZ/brpIWux40+r+d0a7F4o/iykb+kJXxP7s6Xaajmuk6/RHVZ7mVck1cmSR9AzjJW9+d3EicsV1itZq5Pulx//svuyu6GZI+TfVRIm8C3gu8C1hr+7RpxJjwgca2f9pEfqW0eKj6iIh38tj1sMuAL7r6uIhG4pV6XsuMV6e7uOHu/xbFWwAsZ+tioqFrMSWtpfosvLuKeE39h6K4HOP/b+/M420dy////pyDzCSSiFBIpsisQmmOlCHRIKT4SRRNSqEBqaRSpmMIX/Mcmed5PikqJFF8yRT6yunz++O6l732Omvvs9f93NteZ+/7/Xrt19nPs866zv2cZ63nuZ5r+FxzAS+QdArJrGUtHSkehWMtem7HG9Wxm4CUuCm22fo28CjlnuyKIelW28MWjfdo75Pd9ucUT5e+SZQmRbNWakUUFVpld9ruWaQ0dXJuDSxpe19FB+trbN/YYH2TCKHtl5oTbB/ewF57zekcwCxNak5LfS8kHUHIuLQeRj4BTLO9fea6ip3X9P47O2/O/ZQSL8lQNZm5tZhDPVg0eaDoVybSsfYD1bGbgJS8KZZ8spO0MKGB9Vrb75O0PLC27SMz17YbMdHhXAo4nZIOaducnZB4uNX2Zhm2St8kijpPKc2xBKE/BbAj8KDtL2XYOpSImG5o+00p/Xeh7dVz1pZs7mr74BntG6GtYjWnbTaLfC8k3WF75Rnt68FesfOa7B1FPCS2xoj9P+CVtj+dYWs+QmevFZ28AtjHmULApe0lmwsDrc/tjbYfzbWV7K3MgDD5VbbvaGhvY9qiu7azhIpLR4qTzWLHOhrndjxRHbsJSMmbYkkknU/oxH3D9sopRXxb7tN/qif6LnHjaX3Qs9MJXezPD/yP7feWsNdwLUWdp+T8f5aBkUQXAUfY7rlerBU5bU/RNXFO2m127MtNAfZtI4akW4HNbd+btpcCTs2NRJc8r8neXMA3O+ztZ7tncWxJpxF1bO3RyZVtZ82xHgV7WxDC2pcTD8RvA/awfWqmvV0JeZJWDfGmwGG2Dxn6XcPa+wHhdB6fdm0F3Gz7a0O/a0hbpSPFpY+16Lkdb1THbgJS4qaoUdCKk3ST7dU7HIDsTi9J9wFreJSEddNT7e9s9zzoXdJawCHAm4jZmpOBZ3PqYZK94s5Tm+0FgMWcKRQt6QZgHUJMeFWF3taFmU7YVoQw8XqEtEOLeYj5kTmd3SVrTot+LyS9k3jYaRey3tYxMq4RTc9rF3uTgbkcM0Fz3t+tXq/J97+0vTuAjVpRuvQ5vrhB9PROIiPxbNqeC7iuQd3ZncAqtv+bticTD8Y5n+PSkeLSx1r03I43alfsBKLtprikBg8knwfoNT35DuBS4ENdXjMDT2a98KxCFb9V/7MWoX2Uy58pNwIMDRa0nQQsT3Q/5vAz4GPp/W8l6h2XabC8kjM7kXQ5sDFxjbgFeFTStbZ3yzDXOXZqM2CvzKVdS3QlLgi0a2A9A+Q6KFdI+jowh6SNiJrTczJtFf1e2L4kpYZbDw/3uIGQdeHziqQTgM8Rnb83AfNKOth2zsi45yWtZ/vqZHtd4PmcdY2SvUkdqdfHaTaWUwzumJ6W9jVhfgau5fM1sDNN0tIdkeLs7m7KH2vpczuuqI7dxKLYTdH23unPbYutDnYHzgaWlnQNsBDhBOTyLHC7olGh0QiwxA/bfn8ReMD233IXZ/vPkianNNgUSbcxWEqlF0o6TwDz2X5a0vbAsQ7B5yzHyfbxkm5hYOzUh505dioVWz8g6USi6P+JHDsdfJWoOZ1K1Jz9hpApyVnf3unXfWwPqrNTzLPsCUmbAxfYvlPSXsCqkvZzvqxDsfOaWD7Z2xo4n/i/vIW8WcCfA45N9VMATxBD3nMpbe8CSb9l8CjGJtIiU4AbJLWm/nwYyKonTnwPuC1d70TUn30109aXCUmRQZHiBmsrfaylz+24ojp2E4i2m+LWwMNO440UXYCLEeK7I0LS7jP4t36Usb5bFU0FyxIXk3uaFOsCZ6afUrzf9lfad0jav3PfCHlO0myE43kA4XBnP/2XdJ4Ss0haBNiCGI2VTYq83mX752l7Xklr2r6hgdlXAzelGrSjiAag3LqSOYCjnBqIUuRzDppFe08DOuvgTiUGyPfCN22fImk94tz+EDgUyBVnLXZeE7OmkoQPAz+z/R9JPZ+H9H/+iVRbOy9Abkp3lOyJeHhanSgDgKgRyx3FOImQYrq8zd62tm9rYO+/xAzgVl3tV5wxxzb9361MzBNvHCkehWMtem7HI7XGbgIi6WZgHdsvpO3ZgGt6KbSXtPdwr9v+Tsa6iomnpi//xbY36PW9w9jsVps4ndzDCG0tQchhzEqMxpkP+IXtPzdY32RgYQaLMf8109bmRFH81bZ3SqmYA21/NMPWbcCqLccrXehvzm0AaLMrorN7WyKdfTJwZCt91IOd64F32f5X2p6bqAFcJ2NNywFvBg4A9mh7aV6i0L4nWZG2ur/vA1Ntn6BmunPFzmuy9wXgK8AdwAeAxYFf237bsG/sbut622vlrONlsle0oabJeRzCXpG5wsnWjbbXKGEr2St9rEXP7XijOnYTkCEKT4sU2jdhiHU1uYldAnzEDVvgJX2eqLtamqjbazEPcK3trZvYL4GkXYj2/0cYqF9xbnFySYY4r1kOcRfbKxOO3XuBy4iIxUW292y4vqxCbEmbENGrjYmyghbPEB3U1/Zo71zgIWAjIgL4PCGzMabf1eGQNIvtFzPedyiwKFF32j4XN3fyTGl7xxBRyZty3t/F3g+B64DTG0Sb2+0VmSucbP2YeOjstJVVAjAKx1r03I43qmM3AZF0EXCI7bPT9ibEFIScjsIlCZHj1zM4UtSz0K7Ki6eeBbyFkGBo//L3VGOX6jheCXyfwTUrz/R60dTQY6xaa8vtEvszsKYLDcFWTAOZbp3OmAIi6XQiDXNo2rUTsIHtDzdY365Ew8ljRD3cmSkNOAn4k+2le7B1DbBL66YlaTXiBr52g/Wtbfu63Pe32ZmTcFqnOkb/LQKsaPvCTHvFzmuy961u+23vk2Gr2wQaN1hbaXt3E9MTHiCuJ6UmRbwI/LvNXm5nfElN0dJTO0ofa9FzO96oNXYTk88Bx0v6OXGR/xtxk8zhTKII9hwadGEmLgBOktQunnpBA3unk9edO4gU8XtK0sHAP50mEmTWin2w6XqG4EGadRB30i5sOjuhO/Vwpq3PEfVJexGft0sILbUmLEBEYwcp19v+r6Re/4+/CJwi6WHihvMaojC+CZtKuouIsF0ArATsZvvXIzWQHmxutb1ca59jBF2TMXQlzyu0PTAlex8EchtjSjZiFbcHvKekMdvzFLZXbK5wyRKWZK/0sZY+t+OKGrGbwKRaIlq1RZk2brCdW8jdaauoeGqyOQewuO17CqxvVGrFGq6p1cTyZqLQ+TwGdwD33MQyxL8ziajL6rnubDRoa8h4yckG3tSjk91ub1YGF4o3adp5KZUraVPC2dkduLLXFGqKOu+SWys5AvtFz6ukVxCNLOtnvLd0NLG0vcW77W9Qx/r2bvttX5lpr+TIw2KR2GSv9LEWPbfjjRqxm4Co7Oiug1MjxYUMdih6rsVwCGv+Mv00RtKHiC7C2YAlJa1CyFDkzmNVe31Iig5lfYdSaqLzwvQUcDPwJdv3jdBU60n4r+lntvRDF/tNeCPRidovHMrgrtN/ddk3YpIj97sC62oxa/rzA8Aptp+KXo+eeSVwl6QbGVxOUGqmcOnzOifRYZ9D6WhiaXvnEd8pJXtLAvcQD1U5tDfXzE5MP7kFyB1k39789tLIQ6Bnx46CkdhE6WMtfW7HFdWxm5gcTRrdlbb/SBTJ5jh2KxLjXDZkIBVr8r+wJfk2cQG5HMD27akLMJf7Uhdge63YSB2wTn5CpMBPIG4UHyOaM1ryHeuPxIhT97GkzW0PEktOHZBZtDmeSn/+g+h+7BeKOdmjxDmpJut54PMKweh/Z9j5ZslFlT6vHTWjkwntyayoju3TOmyfCFydu7ZRsDeoI1bSqsQ1INfeIBFrSa8jrgu59nbpsDc/8D+Zttp1TlvND79tsLbSx1r03I43aip2AqKCo7tS0f7yTtIp/USrJb7jOLO7MSW9mqgV25CBWrEvOmMQeLcu5Lb0Xc8dyuouxTLdvrFAAyLMJW0Wb8gojWJk11O2pylGKM3jDF2xfkYh29PiReARZ3TEDmF7WeA822/oR3vJZjEJFEVI9y7byxeylz3ysIutVxIjAUudi9LHWvzczsz00xNu5eWj5Oiu3xFjbHp2bl4G7pL0cWCyYizTF4jpG1kkB+5jhdb2nGKoeGuA+GYMRHRG/LQl6X3A+4FFJf207aV5iRttNpJWYvpu55xmlD8phnZPsf37Jmtqo70hA+BiGjRkFDzWlr05CWdz8bSu1xI1fOcO974udtpT9rMRKd7smcLJZrFjtf1Auum/LtlbWFJWKcYoRBNL22sXZZ9EpP2z03+SDmHwiMJViIh9rr1uIw9PzrRVLBKb7JU+1n7PKIwpNWI3AUkphEOAFQjHbCFgM2cMA1fMnlyJmBPZXmOXI3eyDFGLsQSDbzq5LfZzEunmdxMXgN8C+zpN3Mhc36HAwrZXSDfIjW3vl2FrKeBgYG3iwnQ9IVT8ELCa0wzEEdhZmbhI7gO0Fzw/A1zmzJFbko4izutdtKXYc4qTJc1DOMTbEhf1owhNt75Qiy95rG02TyJqiD6ZPitzEpqH2UPKU5RjE2At21mjokofq6R9gU8D9zJw43bud7af0WBR9heJST2nNbietI/AehH4i+1rGqzvHR32skcelo7Elj7WyvBUx26CkuqRGo/u6riYvITtKzJs3UE0TtxC28Bo27fkrK00kq4gHM9ftaV2f2d7hbFdWZzPUimwZO/3pdIkHXbfQdQVzk9EK/d1xrQNSYsRDyfrpl1XAbvm3MhG41iVpgB0lAEUEQFXM9Huoscq6R5CV69xKUZyXLcGlrS9b+pCfY3tG/vBXpvdOW03GTfXbqtY136ytwTwRtsXJ9uzOHWOZ9haL9maImlBopSgm1beSO2VVCgYlXM7Xqip2AmEpI8M8dIyKX3SUzpGobP1K7fpbDXkRduHzvivDU9HSmI6GnQUzmn7Rg3ubizmTOUg6WTbWxDDv7u1/+dOd7hO0vIlUqfpc/IBImL3euAg4HjgbcQQ9WUyzE4hHMRWg8g2ad9GGbaKHWsbL6QbWavcYWnaItojpeM7O4kYnZYVIUqUPtaSpRi/IKKIGwL7ElHn0xjc7Tlm9iStTTSYzQ0snqLlO9rOaqBQ4a59STsQaf8FiEasxYgH5Rzh+b2Jz9qyxPdqNuDXDDxI9WqvtEJB6c/KuKI6dhOLDw3zmulRzDcVhd8jaXE30NlKReYQnYQ7AWcwOK3b60icH6Y/P0KIzbZEYbciRm7l8li6Qbdu1pvRTCy2BLumP0sLHx9LOAH/IM5FE5X9PxHjvg704JFap2oIfasRsJDtdvX5oyV9MdNWyWNt8W1CmPh1ko4nbog5oqrt39lW+m+TBusqfazfJx4qfkfDUgxicsqqCr1IbD+hmGOdS2l7PyFEis9O9u5o8PmF7l37TUSGd072bkj2/qRo+MphU2Jqz63J1sOppCKXb1P2WEuf23FFdewmEB4dte4SOlu3MFAIC4M1jwz0JFHSSgNLOsiDh2KfI+nmXmx1sDNwGLCcpIeA+4l0wJjhmETQKmJ/DXHxNNHB1qQD80hCxmYqDSaKpGjd0R5C2NQ9jndr43FJ2wAnpu2tgNxxakWOtR3bF0q6hZhdKyJN/FiGndLf2dLHegywfyF7/0mfl9aD00INbZa2h+0HOyL2Tbq9/+Pp9Q2b1Eb9n+0XWvZSuU2uvRdsu5UFUHR1N6H0sRY/t+OJ6thNQFRWoLixzpbTKBxJs3cWIkuavYHpuSQt5ST2m54Qsy9Qyc670kVuEvAc0RTwwLBvbEODO+u6/RtZkyIkbU80T1xKOBKHSNrH9lE59oD/dZol3IQU1f0gDTrqhuAzRI3dj4mL+7XkRcSg0LG2I+kSx+zl87rs68XOMYRT+GTafiVwUIPGjtLH+pztn874r42InxLR+ldL+i7RKb7X8G95We09KGkdwAopkV1pJtpbtGsfuELS14E5JG1EdGWfk2nrZMVox/lTivczwOEN1lb6WEuf23FFbZ6YgEg6nyRQbHvl9GR3mzP1mDoKducEJucU7KqwFpuk9xIRtvsIZ2cJ4LPucYC6YlzVzsCiwFmEtMbOwJeAO22PODWmwZ110+EkONwrqYh9HduPp+1XEV2YWRpWkn5B1E6dw+AUW8+yGJJ+TMh0nMTgqG623EFJCh/r7MT0hcsIkelWiGJe4IJe61HVpVGi274e7BU71mTvR8nO2TScPJPsLUfUhAm4xHYTx6movdRAcDAx8lDEtJ1dW9+5DHulu/YnAdt12DvCmTf55By+ZMv2RTl2kq2ix5psFv2sjCeqYzcBUVmB4pcKdm0vnZ7GftlLZCKlEBclauE+zuCb4S+bNGcoZle23n+37ZwC9rOAJ4DriAvJqxlIr92eu7aSSLoWWL/VnZjqTS535gxQxSzGTpwTKZJ02RC2siUxUvR1F6bXY8uR2Sl5rLsCXyR06x5i4LP8NHC47Z/1aO8O4rw+kbYXAK5o8BBW7FiTvaLnNqXXFmbwOW1Sv1vUXqV/qOd2aKpjNwFRaM99FLgoFaCuBexvu6t0yQxs3U4q2G1zEntSY1doHH2a6MJqr4F7hqjPyo0mzAp8HmgVOF9OdPH2JO3SfjzpYvJ3om2/ydPm7MTT9ZuJWYdAowHlxxLj3c4iUpObAHemn+wUb7+SHJ4j6ajtcobMzmggaRfbhxSw80ng60BrXNzmwHdtH9fUdr8haRdgb6LBaRoNGztGwd5CwA5M/zCR+51dBvhyF3u5TvG6RJNCSwe0dbw9j1FUdGPvz8BDbMtWljD2KBxr0XM73qiO3QREZQWKb7C9Ziv6l9K6t+Z8wSR91B0zAJsg6QgiBXhM2vUJYJrt7Xu0Mygd3CQ93GbjFOBuIkK5D9GE8Qfbuw77xqHtjUqKtwSS5iMuwi0H+wpC6iB32slLn7tC6yvqZLfZXYfpb2Q9D2RPNbCtG+ClLivL0jcoxhOumZvafBnsXUvoJXbqbGZds1RYt1Mxm3i3LvZ6Pv70f/ehUunNUTjWoud2vFGbJyYgtm9VCMU2FiimYMGu7dMkfYDpb7C5hfere7Ag7KXpAtMrK0tqTUkQcaxP0+wp9g22N5e0ie1jJJ1A3DSyaDluKiieWpCjiAeILdL2J4gaz6F0FUfCwcmZvZDmtV3HEU72e2hzshusDUnHEVpitzNwIzMhN9IrCxBjxKZIWkjSkm4gFNvHPEj+aMOXw96ctkuOrSqi29nGU7bPL2TrkcI1a6WPtfS5HVdUx24CImlzopD7Lkl7AatK2i/zpvhVItoxlai1O8/2EZnr+iVReL4BcATR6dRESXyapKVt35vsL0WGPIHtyQ3WMBQtR/pJSSsQsw5zNaeKi6cWZmnbH23b/k5K4TdhRcJB3JC20VgMRLZ6oaiTnXgrsHxu4XoLTS8UOysNhGL7nPuAyyWdx2BnPbeMoLS9cyW93/ZvMt/fSSndzhaXSTqQ0CNt+rBzs2Is3pkUaLKh/LGWPrfjiurYTUy+afsUxciYdxKCvocCI05tSdoEWMz2z4HDUxPFQsBqkp60ferwFrqyju2VJN1p+zuSDgKaPIHuQVzs2rtiR0PLL4fDFNIV3yQ6Cudm8KzXXikinqrRkWN5XtJ6TvNvUy3Q8xl22tkcWMoFRllR2MlO/I4Qx24qYF1EKLb0edXQU2xa9nIcgL+mn9nST1NK29sV+Lqk/yM+M43qzoDW/NRGup1ttK7f7dqduQ878xJyTu/usJXr2JU+1tLndlxRHbuJSStq9QGiU+88Sb0Ost+T0HBrMRuwGuGgTCHmgPZK62b/nKTXEoKzi2TYAcD2JalLtyX5cU9OV+xo0BbVvIL8i1unzRLiqU3U5Yfic8CxqdYOosP4U8P8/ZFQcpRVaScbYEHg9wrh7iYTGUoJxZY+r0Wn2EDZOtDU5LSM7WIC4raL/h866XcWtLdBQVtFH4BH4VjHrGZ4ZqA6dhOThxTikxsB+ydJkEk92pjN9oNt21ensPo/G9x8zpU0P3AgEaEwkZLNIhXF7wSsl2xdJemXTbpZS6GyItFQSDx1lC6YTzv0EudN/8bTajZOCMKpu1vSTTQcZTUaTjbRnViCIkKxpc9r6Rt/aRzC2EtImq1QVLdSmWmoXbETEIVY5HuBqY55gosAK7oH4V5Jf7b9hiFeu9f20g3X+Apg9oadkycTkimtWbEfB+a3vfnQ73p5UHmR6NLiqcU6Rbt1EUu6xfZqOWtL7+8qzeMMuZNRcLKLoAi/LkboMJYSii1yXkcpZV8UhQTQm4gobLsw9pivrVIZTWrEbgKSuiZPB5D0WduH0Xst0A2SdrA9KHogaUcyGx6Sw/klQiNuB0mLS3qb7XNz7AEr2F6+bfsySf0iFbGg7ZMlfQ3A9ouSsudOOuaQlpxb27hTVKEM/2Zgvo6arHlpcypysH1FcshWT7tutJ2blj2a5GSn7T8SUzJ6duwkPUP3GZg912OlFOxvkrOf7cx1UKoDuGhaMqVOv2D7xwXN3pt+JjE6JQaVMULSq6rUydBUx67yOWLsVq/sBpypmP/X6rpaDXgF8OHMtUwhdI7WTtsPEcKsuY7drZLWsn09gKQ1GSyAPJY8qxj71aqdWov+at8v0Sm6LPBBIm3aXpP1DCH0mo2kLYiU/eXw0mzcPTKbdoo52aXrsIjP8Oq2bypkr0gH8CikdqdJ2oqY/VvKZhEJIMW0j+H+nazOTkmbErqET6Xt+YkpI2dm2tsZON6D5wpvZfsXPdgYrVnWpxMPSufb/u+M/v4IuD511k9JNmvqsY3q2FU0478yPSk6so6kDYmoDITUyaUN1rK07S3TBR7bz6mjG6BHVgOuldQaM7M4cI+kqYy9SvnuRIpoaUnXkESix3A9nTTuFLV9FnCWpLVtX1d4fd8gdAofhZemAlxMXtNOPzvZawLbSPoLkU5sqrBfWmanpLjzNZJ+RqGZwionAXQL8dkQcQ15Iv0+P9GZmVsvurftM1obtp9UyNtkOXbADkmloGXviVSXOWLHjtGLbP6CUCT4qUKcfYrtexrYW4YoO/lMsnkyMaXoj82XOvNTHbvKh9LF+UO2T5nh3+4gOXJNnLl2XpA0BwM32KVpK4zP4L1FVjUKuKxI9GhQslP0cUmXAAvbXkHSSsDGtnvtxG5nUkfq9XF6bwBq0c9O9nsK22ud170YOK/fbGCvpLhza1Z1uyB5rlwHFJIAanV0SjocOMNJx07S+8jPTkD3z2trdn2qAAAgAElEQVSTe/JkSWpFr1J6uycpkNHqNrV9MXBx6ozfKv3+INEI9Oter33pGC8CLpK0AVFHvZNCgP6ro/AgOVNRmycmKOlL/x7iS/Zu4CrbY3ozU0yu2AtYnij+Xxf4tO3LM+0dBBzpPhzBpO46YE8RDS0jrhWbSYrYryD0q37lgXnCv7O9QgObBwIrASemXVsCdzpzMkBqXulLJ1sxArDV2X1NbgQr2ZpuakW3fT3Ya40SvNOhQTkrcS1ZK3eNpVDHuMO07w4PnkbTi73pZmB329eDvaOAJ4FWlG1nYAHbn860dyCh1fmrtGtH4EHbX8qwVXzMXoqKb0MIiz8MHE98rle0vX4DW48QkdmziYeDU1xYXmVmo0bsJhgpSvRx4P1Ek8O6wJJNalBKYfsiSbcCaxE32F1TU0AufyDEk2chajFObNJlW5jtiFrCy9L2+kTKZ0lJ+3jkQ95HJXVSuFN0Tts3dmTVX2yyPtt7SPooAxMYDmtPa40ESasTN75/pLq61YCPAg9I+nZu7VRJJH2LEGNu6cJNkXRKg2jnaUDnnONTibKFHIqldlV+pnARCaA2HlZM6ml12W9NOCi57EJES09K2xcRzl0uXyGcuc+32cuViyo6Zk/SGcSD03FEdqjVrHeSpJy65+uSrQ/b/lvb/psVE4wmNrbrzwT5Af4GXEs85cyT9t0/1uvqWOOiwDrExf3twNsL2FwW+AHwAHACsEEfHOdvidRka3vhtG8B4Hd9sL7zidmud6TtWYhoYq6tpYFb0/ZmRMHzWB/jrUSEhPRZe5hw7PYFTh3r9aV13UPI/rS25yAiir3aWS4d273EjN7Wz6eBuxqsb3vglcA7iDFPjxJ1bDm2TgO+Q2gJLkU4eac3WNuCRFTokbSuX7fOd6a9BQhJodvSZ+cnTez18w8hvQQRBYcYZXd9pq1JwF4F1zYZOGis/4/6+adG7CYWpxI1IVsSc1TPors0w5ggaX9ibXcxeP7nlQ1sTiZuassBjwF3ALtL2tH2x4Z98+jyOtuPtG0/mvb9U1LPacBRSJ2UlGPZmei8Xk7SQ8D9ZEqzqKCcCDDZA1G5LYmo32nAaWo+y7YUDxPnsyWq/QqiW7xXRqVD2WXFnUvPFF7WHZMnFOPsrskxlj4ru0qay/azM3zDEEj6ie0vSjqHLp9l9yiyLelk21u0msK62MtptCkWibX93xRdb1JT225vWorEVoagOnYTiHQx2Y1I+20FHEAo2m9JdLT+ayzXRzidy7rQ2C9JPyZuYpcA37Pd0tfbX1KTjqwSXC7pXELOBSKacrliaseTGfaKpk4o1CmaHOudbL8rHdsk28/kLspl5UQmS5rF9ovEzOTPtr3WL9fGp4C7JF1EnIuNgBsl/RTA9hdGYsSj1KEs6V7gekIy5SrbdzUwV3qm8CFMn3butm9EJGfiCJp32bbKLH6Ys44u7Jr+/GAhe1B+zN4lybk73Sns1pDbJZ1NXD/bO6hzZ9mOK2rzxAQm1Z20GijeY3vBMV7P+cDmpRxMSdsCJ3d7upY0n8ew3k5RcNZeI3YNcFruRa90EXsq2D8EWIGYy7oQsJntOzNsXZ+7jhnYXQ94o+0piskb87iHJgBJ3yBqTR8jZCxWtW1JbwCOsb3usAZeBiQNO1PX9jE92juGqF1t1zo7KDeyq5gQsybwNuKzvCyRvts0w9bKwLHAoJnCvX7mFDIn6wBfZLAu3rzAps5vnriBKCM424WagCYKKdI+FzG/+nnyIuzt9qZ02e0GGYpxRb88lVZeBiRtAizmAa2jqxkIr+82NqsCSYcQ0YjniCexSxg8/3NEUYlO0g1/0XTDmKVt/5Vj6dSlNZhIjefornWjqD6Zy8qx3Fb66Vqh9/XWtL4phKzDrxlwlGeI7e+mz9oiwIVtTvUkorB9zOnVcRsBK7WcumT/CUlvaWBvGvHZm0aUTzyafnIoNVN4NiLCNAuDm4uepqGMje0HO5qAsqfFDJE6fYoQUd/PPU5WGKJMoWXvS7bv68FW0TF7hSPtuM9nFY811bGbWOwJtNeVvYK4Oc5F3BxH2olZmlZX1C0kzakSSPoBcby/Z+AC3KhmrxQKuZP9CedLNHyCpXDqRNPLsSwjqWc5lsTshM5cux6ZGej0zGFT4C2kqSe2H5bU883DaSpJx77xLHI6SdIrbT8BL01VaHIfeBqYCvwIOLxXZ6SD04io6dNt+3ru2HXMC75C0tG2H2iwnk5Kd9meT1yXTkjbHwPmJB7KjmZwLeRI+AnRIHcCcT35GKlpCTiKKMEZKUdTaMxeC0kbM9DxfLnzR0UiaTEio9B6kLuKiET/beh3TRyqYzexmM32g23bV6cL8eOp/mlM6BaVSE7K63JSf21sSsGavcIcQLT9N7kxvEThInYoJ8cyWk/XL6S0aasGcMw+vzMZBwHXKdT/IaRUvtvA3laEFtlOwPaSrgWutH3JSA1o9GYKv0LSYcDrGRyxzxU8/hzRFbso0cByIXHcubzLdnu931RJt9peVdI2GfY27kgzHybpdttfkfT1Hm0VnWWdHrJXJ7qUIZpQ1rX9tUyTUwgHdvO0vU3at1HuGscT1bGbWLyyfcP2/2vbXOhlXst0SLoc2Jj4XN4CPCrpGtvDivAOw31Em34/OnaPlHLqoHzqhDgHb3Lq3E32jyXqqa6kh+hu6bquxMmSfkU0/+xAjBY6vIG9CYHtYxW6YS3n5iNuIODd1pSxHPA+oq5tT0KWZaSM1kzhU4BfEg0P2U5JG0W7bInmnTVaTV0KXcXJ6bUcncfnFDOUW+UdmzHQTd1r7W7pMXvvB1ZxmhObrgm3AbmO3UK22+vsjpb0xQbrG1dUx25icYOkHWwPugFK2pEQKx5r5kt1NdsDx9reW1JOsf6o1OwV5mZJJxFzIdvXlpuePJqyqZOSciyl67qw/UPFpJKnCcfgW7YvamKzn9AQUhgt3KMkRgcLAM+mGtSF1GzyxGnAyoQ+3pXAJ4EberExWh27wIu2Dy1or2iXLREVnyJp7rT9DLBdij5/P8Pe1kRE8RfEZ+d6Ys7wHMD/G+6NXRiNMXvzAy15ofmG+4sj4PEU1WxNntmKKPeoUB27icZuwJmSPk6qTSLqV15Bs5mHpZhF0iKEMO43ZvSXh2FUavYKMy/heL67bV+TurOiqRPKyrGUrutqcSfx2YXQJxxPtKQwPgK8hoFpB1sRgrtZdGk6mZUem046+D4hZlsiIvZIcmjXIr4L1wG79VL038E5knYCzmDww1NPE0XaumwX0uARfvMyEGHrCYUM0Ntsr6iYuEFHQ9fJGfZ2sj1UXd7Vvdgr3DwF6XMi6bJk7+3AVxvY+wzhVLe6nq8BakNFosqdTEAkbUjUtECozl86lutpIWlzovj/ats7SVoKONCDRUvHBZJe1bDQvNPe5YTzdVGq0VkL2N/2OzLtFZNjkfRJ4OsMOImbE7qCx+asLdncAjgQuJy4UbwN2MN2qS7jvkDSzbbfOqN9Pdi7ndR00ibZcafzRGyLIul6Ym5qKwrzMWAX22tm2usWhbTtnmpQk4OzPlFj1z6u6hngHNt/ylzfjbbXyHnvEPaKyQp1aZ6CjFnWHTYXIersAG60/Y/c9VWGpzp2lcoYIOlPwO1E1OT8HIepw14x3bnRQNKbgQ3S5qVN6rqSvTuAjVo3GUkLARc7U6OsX5H0B+ADrahVkv/4je03Zdq70fYabUX6cwHX9YljN52DKemOfjmnkpYo2WWrEFCflSiZaJcBunXINw1v71CisaOxrJCk8xiieYqY39uTgkKqRbzd9rMphboqcHDu/6ekA4hJFs8DFwArEdHdXw/7xglCdewqlTEgRcTeRaQUVidSL0c3kdqQNAuFUicqKMciabv2Jo6UNtrL9ncarG+q7RXbticRc21XHOZtMx2S3kuMY7uPOAdLENMOfptp78vAG4nuwe8Tn78TbB9SZsX5KEYKPgH8D5GK3ZJo+DoQRp5ClbSh7UuHiDpl17Gmh4c9mX5sX1aXbUpLdlletr1ior2Sfgt8skvz1FZE13NPosypVnplwgGbQtT+btEgo3C77VUkbUo03uye1tUXDwFjTa2xq1TGgBShuwi4SNIGRJ3TTikS9dVei8i73MSa6M5BWTmWdyrGCW1HFO4fTciyNOGCdPNppe22BH7T0GbfYfsCSW8kZh0D3O1M+Z70MHFSslWk6UTS6cRN+vxWx2MDtkh/7tix/2OEozfSFOo7gEvprgPXpI71eOL/74NEWvZTwP9m2sL2BjP+Wz3ZK1ljVnSWNdHIYoVI/s9tHylpuwbra/kuHwBOsf2UBgtHT2hqxK4ybpH0VqIJYwniQtCKOvVD2ulVhPbSJ4hi+COJRo9ViAtVT4r7o5A6ucYFR2op5hH/nEgRfdx2lkSEYtzXwravSc7seumlJ4Hjbd9bZMF9hGKSyPIMjhJl1Sd2RjoLrO1dRNH6WkQKcIrtsZ7DPCpIusX2au0pY0k32V59Ru8dwl5RiSJJywCHEt+PFSStRGjb7Zdh6xfEmL325qm/AXsA5/bqlEq6gkiZbks0TjxKgwi7Qhfvw0Qqdg2i4/bc3HrM8UZ17CpjTken2XTY/lGm3XuIC9FUYtxRy15JNfosJP2R0IKb4g61dElfsb1/j/ZKp04OJroxG8uxpIjTMcR5eBMxCWR3289l2DoX+JrtqR37VyQaMnpV6+9rUhfr+oRj9xtCK+5q21nSEwr9sJ/ZvqnYIsPufMRn7RvAg4Sm4K97KQeQNCeRUlvc9mfT52ZZN5hQUJJWc0L6rv0UeBg41fbSmfbOJ0kUOUapzUJ0GOc6O1cQ17tfueEs25LNU8nea4CPAzfZvkrS4sD6DRuoFgCesj0tfXbmrQ0ZQU3FVvqB9lFQOwK/KmT3f233ndxJqjE7x/a+3V7v1alLlE6dlJRjOQfY2fYl6YaxO3ATA53ZvbBwp1MHYHuqpNdn2Ot3NiNqk26zvW1y2JsUiK8JbC3pASJ62jiK3RF9vo1IWa5HpCrX78HUFCLKvE7afoiIGPWFYwfslxzYLxGNSvPSbMZ2aYmiOW3f2JGSzBE6Lj7LOjlcP2rb/ivx4NmE5YDXJ4e4RVOb44Lq2FXGnPYiekkfblJU38Heko4AOgWKm8wobUx6wlxnxn+zJ0rqzgF82eXkWNZwmv+ZbhgHKfTKcph/mNd6mXYws/C87f9KelHSvCSHvYG99xRaFwCSziBq9Y4jajL/nl46STHhoheWtr2lpK0AbD+nzMKp1Eyzlu1rc97fjbbI4VMMdHg3ofR0h8ckLd1mbzPg78O/pTslm6dGyd5xxBzc2xk8B7w6dlTHrtJ/lKwN2JZ4qpuVgVRs0+Hzpbhd0tkUkCZI7Mzg1MmxDKROcm5C1ys0z7LlWCTtafsAxzSRzW2f0vbypwltu165Wd2np2xPRHvGGzdLmp9Ibd4C/IsQ7u0JSbcQIrXnEwPY/z2Dt4yUn9ru1t2Je9fae0ExJaHlmCxN5jjA5Az/nNDsa0Trc6yBiTad/1buJJvS0x12Jjqol5P0EHA/EUnNoegs61Gw91Zg+dzU8Hin1thV+golfa1Ctu6xvWwJW6UpKU0wGqRISSM5lvZz2Xlec89zSkWeAbzAgCP3VmA2YNPxXGOTUs3zOkObMKWr1gPeSzj6jwO/JZz2niV2unRhDyKzFnMjYC+invBC4iHl07Yv79VWsvdDwgk+vYkDIOlDts+R9Klur9s+poHtQRJFwAIdJRU5NucCJtl+poGN0s1Tpe2dAnyhLUJcaaM6dpUxR9JU4klYRHj9z62XaFD/k5ynA91QDHdmoHSqo8N2S45lLmJ014jkWCTd1lbE/dLv3bYz19QqCu+b6SmjgaRFGejsBsD2lQ1tvpZw8t4LvAG43vZOPby/9WDyaqImrvX/vwFwre0PZq7rVUSHrdKaHsuxk2w9Q3xmpxHdk8W+EyVJEdmPEs0Fb7L92kw7r0h2Xs/gz8o+GbaKNU+Nkr3LCAWBGzvsNZmhPG6oqdhKP5B1ExgBaxEpz/uJL38/yZ3MTui6dYqd5kbsiqY6NL0cyy60ybEQMiozwkP83m27J1L6r2sKcDyhEO3dkugkbq8lauTY2X4YOAo4KtWjrd3j+7dN67uISIn9PW0vQugU9oxCbPZS2+el7flTze2ZOfZszzPjv9XT+i4CNrf9ZNp+JfA/tnuuW0wp500IZ+4tRAPZh2l2Xs8iavRuITOF3UbpWdal7X07830Tghqxq/QFkj5MRA6mOlNVv4vNJbrtd3/InZwC3E1c2PcBtgb+YHvXTHulUx2N5VhSh1+r83IO4sJO2p7d9qyl1jteUUj2rORMUeI2O+cwjDOdG+mQ9Ae3jTdLTuJdzhh5pjRNoGNfdmQ3lRNsDSxpe19JrwMWsX1jpr0i65N0AjHb+EJiysalwJ/do3ZlF7tZ0iZD2Co6y3o0SNf3N9q+OMmdTG6Sfh5P1IhdZcxRiGG+GbgW2FfSGh5CCqQXWg6cpFfTFhXrE95ge3NJm9g+Jl3sr2pg72ZJJ1FGd66IHIvtyb3+25XpuI9o/mkagflhgbV04xJNPwHk4kxbk7rsa3KP+gXRNLUhsC/RePJzBgbR98o0SYsnqY6WY5ETGVmeGJ32B+JhbpqkEhGWayWt6C5yQBk0bp5qRwXFk5O9HYDPEpNsliZm5P4SeGeTdY4XqmNX6QfeDqzsAaHJq4gLcSMkbQwcBLyWkIlYgriY5uinlaalLfekYrLAP4h6pVyKpTo8OnIslTyeI8oJOiV7eurEtN10hNtQdv9fSqG+Pe06zPYZmeZulvQjwvmC6PJs0um8pu1VJd2W1vqEpNka2PsGcLVCCFhE1O2zvRpxzDhdjhB0vljSY8A8khZu2DixHvDpQqUnyzDQPPVTSU1nWR9OEk8mFnVnepjNcuyIz8YawA3J3p/SA3yF6thV+oMXbE+DZtpVXdiXqLO72PZbUsF9bvt/aQ5LNTrfJGrX5ga+1cBeSd05KC/HUsnj7PRTBMU0h+8z/Yiykc5h7ca1hBCuiWL2XHYhvg8nJVsXETfwXP6Tos8t+ZSFaJtA0yuOub2rEtcUgC/mNnfYvhvYm9DaXI00lUHS32znPlS9L/N93dZXdJY1BcWTE/9n+4WWvdRdXOvKEtWxq/QDy0lqSTiI0HW6k+bNDv+x/bikSZIm2b5M0k+KrLghto9Iv17ByIebD0fR1Alx03+cSGO16BcNwAlDStPPRkRQAO5xD2O6ujCFcCh+THSwbkv3FOiIkLQFcCBwOfF9PUTSHrZ7nlhg+1ngq5LmSr835aeENM6rJX2X0Ijbq6HNdRiITkKBqRi2bwFukfRlIgrYE5LmdQiAF6svK9Q81U4x8eTEFZK+DsyhkMnZiZhwU6E2T1T6gKGaHFrkNjtIupjoNPsB8CoiHbt6gyfiYpSUJkj2GuvOVfoPSesTc3b/QjhOrwM+lSt3ooFB9lOdZpK29mXauwPYyPajaXshIkK+coatdYAjgLltLy5pZWDHXmRYuthcjqi7EnBJk65xxeD51YmRaRCp1Jts9yS0nTrityTq7M4B9iQcunuBfXuNAko61/YHUwq2JRvVwjnR2BLNUx3vWYoQT16HOO77gW1s/6XXtSV7kwhVgXcTx/tb4IgCD7TjgurYVcacVHx9ARFpurug3bkI/apJRHfcfMDx/dDtJekCBqQJXpoPafugArazdOc6bJSWY6lkoJgY8XHb96TtZYATGzhi1xK1WKcS3ZgPAT9wppB3u4OYticBdzhjkL2kG4io2tluOMS+zeZkYGEGPzz9NdPWncAqtv/bZvu2XjMKqV7tP8T385XA7wgHb71kf7Tkn0ZEOq4DbH9pFGw3Fk/uYnMBYDFnCHePV2oqttIPfIoQSv12unHdQDh6FzdJydh+tq0l/phWS3yRFTdnMdvvLWVsFFInxxFyLO+hTY6l1HorI2bWllMHYPuPkprIxOwKzAl8gahB3ZD4/uVyQZeu2N/kGrP9YEcd1rSh/u6MkLQLkXZ+JNkREdFqomM5P/DP9Pt8mTaWT52hswB/s/2OtP+CFAHtiVT3NyS2b+3F3mg0T0nalSgDeAY4PK35q7YvzLR3ObAx4cPcAjwq6VrbuxVa8kxNdewqY45jDNTRwNHpiX9NohB4T0nPAxfaPqBXu33eEl9SmgBidNJxwIc7Uic3S/plhr3SciyVPG6WdAQRgYVwsG/ONWb7pvTrv4j6ukbY3kMx9WS9tKtJV+yDyaFwcl53pdnDxK7AsgUj9N8HblNMPRBRa/fVDDsvANh+UdLDHa/lOLLDRfnN4DrZkVK6eeoztg+W9B6iLOYTxPUqy7ED5nPMoN4eONb23m112hOe6thV+oqU5rgu/XxL0oJE1CiHvmuJ18D4tFmAbSXdR0NpAhXSneugtBxLJY/PE5/jlrzJVYQ+WxYpIr4H048oy7n5t957OnB6+q42caI+BxxMPIA9RNz0m3TFPkiUOxTB9okpUtTSwfuK82YTLybpp8R3vvU7aXvRjHVtkLGGGVG6eaoVhn0/4Yjd1VD9YBbFlJMtCBmaShvVsav0DZKWJG5gnTed3Pl//dgSX7x+ZjRSJ5SXY6lk4Jg48aP0U4JTiKj14TRLc65FNCX9k0jpHgcsCEyS9EnbF/RobzJwsO2tc9fUZmv39Ot9wOWSzmOwBmDW/6WkdYHbbZ8taRsio3BwRnPXHm2/d0Zfs6OxKcr5eQa6di8HfpXTRe00Mq4gt0i6kCgJ+ZqkeWggPUOUh/wWuNr2Tak5408F1jkuqM0Tlb4h1ZccCUyl7UvvTHFVSQcATwKfJGrOdgJ+b3vMnvBSU8LnSOPTgCNtN9Fzatk9lHjar7pz44i2CG87TxEOwH69phmbdMB22LkZ+DpRZ3YY8D7b16cu1BOdMQZM0tXAhrZfaLi2vYd52Q06z+8EViZq9KYQ16ot2mrkGpGuDR+yfUrm+48gppQck3Z9Aphme/vMtRRrnkolNqsA99l+MtUEL1obHkaH6thV+gZJN9hes6C9vmuJV4z9+g+RUnsf8IAz58N22J3SZbcbXIiLyrFU8kgPJ9OAE9KujxHND/8A1rP9oR7tfZuQ/TmDwVGsfw71niHsvDQ3VdPPi82a7yrpWOBNRIS4/eEkN8K2eaeT1G1fD/ZudUyy+BbwkO0jW/ty7CWbk4lSk62I69RVtjfLtHVHp8xMt30jtFV0lnXl5aU6dpW+QdLHgTcStTXtN52eurqGsN0XLfEarB82C3BjkxvDaDGaciyVkdPNcWhzMAZJjYzQ3v1ddts9ap21r6tzjbnOzlCRNtvf6dXWUOto4ogpRoldQDSdvJ1wkHOlXd5BOE3vJ6Z1rAssZfu5nLUlm7cCm9u+N20vBZyaeS5uc0zrudP2SinNe5XttWb45sqYU2vsKv3EikT6YEMGUrG5XV392hL/Ur1L6oorYrR06oTCciyVbCZLWsP2jQCSVmdAsqfnFL7tXmVvhmJlSU8TkfA50u+k7dmHftuwa8ty4DqR9D7CYVq0rTEBYp5yk7KHLQlnbDvb/5C0ODF1o9f1/Q34K3AoMQrwGUn3N3HqEnsAl6WGLBG1yrm1crV5aiamOnaVfmJz4qm1UY1NG/3YEr9yx01wjrYbpG3Pm2m3tO5caTmWSh7bA0dJmjttPwNspxB6/f5IjUja0PalSZpkOnqtxbTdL3qQ3XiYeJDbOP3Z4hmgyUPdM0SDx7TUXbwcA/p9vXAqMRFnS2CapLMo0NRl+xLFLOCW2PQ9qfkmhyLNUylTMiS9lgC02a2lIsNQU7GVvkHSmcBnncYTFbA3lahbOQb4RuqeujNHUqTfKZU66ZBjeSPRWdhIjqXSHEnzAdjOku+Q9J30YFO0FrOfSc7w69Pmn23/u6G9W4jRX68ErgFuAl7I6eRNUh/rE7V17yeaULYDfmP7Xw3WuA7TOzvH5tprigaPOVucGCcmQuj5r7kR5FoqMjw1YlfpJ+YH7pZ0E4Nr7HLlTiZSS3yp1MmYjjOqdCfXoWt7f6t+bR/bg+rskszQuCHVrn6PSEP+lXAkXpec2m/kyH+0TNt+TtJ2wC9sH6CMSREQnjRwGZE6nZWYvPMxQqNwwazFSccRQuy3M+DsGOjZsSsVEWs5bpIOB86w/Zu0/T4iaplLLRUZhhqxq/QNqaB4OnLlTiYSKd18GgNSDHMD37Ld09SJ0ZJjqfQHQzQUFJFAaYqkhYAdmN6Z6CmaKOnHwDzAbk4zSSXNC/wQeD63s1PSbYRk0o+JOru7chpYZvBvzGH7+cz3/oEYV9b4pl46Itbt/6nJ/52kw4BDaqlId2rErtI3dDpwktYjUhWNHbumsgT9ju0j0q9XAD11OHZwDIPlWJYnRjNVZmIU+nJvBubrqLObl8xmh1HgLOJzdzENxJOJqPMy7Q5OqrX9PFGHmvt53hX4GhF5uitlAC7r1YikTYiI08/T9g3AQunlPYkavBx+B7wG+Hvm+9spHRF7WNJeDB6N1zlOrRfWAz6dUr21VKSD6thV+gpJbyE6zzYH7ieiUEVMF7LTlxQsJl6+TY7lSEKKoTJGSNoZON72k2n7lcBWtnsdK7Ys4fDMD7Rr3z1DRMn6gTltf6WAHXeLWqWmh+xolu0rgSvbtu9jYNRbL+xJpF1bvIIYUzYXEW3vybGTdA6Rcp0H+L2kG2leylK6eWorYG9CP9HE/+NWDey9r8SixivVsauMOanDbKv08xhwElEmUHIG4nkFbfUjZzGQOsnthINRkmOpZLNDK7IDYPsJSTvQ47xY22cBZ0la2/Z1pRdZiHMlvb9Vh9WA3yvGmg2qLVOMAbs712hKFe/J9JJCvcoxzWb7wbbtqx0TRB5P3c698sOM93RFozDLGl7qft1V0ly2n53hG2Zs74GU0Xmj7Snp3Mw9o/dNFGqNXWXMkfRfIqWevR8AAAzlSURBVAWzne0/p333uUfR1ImMpN/ZXqGAnWkMqP4LmAN4juZyLJUM0o12pVYESjGp4E7bb860dwCwH/A8Iba7ElGL9uth3ziKSHqGgc7JuQhH4j9kfuYkLUoMq3+eAbmTtxKf5U1tP5S5zguJh84vE3WonwL+t9coo6Q/237DEK/da3vpHu29AVjY9jUd+9cD/u4kWDxCW0sM97p7n4vbsrsOcAQwt+3FJa0M7Gh7p0x7exPndFnby0h6LXCK7XVz7I03Jo31AioV4CNEXchlkg6X9E4KpE4lfUTSnyQ9JelpSc+0aciNN66V1LiI2/Zk2/Omn3lsz9L2e3XqXn4uAE6S9M70vTgx7cvl3bafJtKyfyGaZPYY9h2jTOuzlf6cZHuOJp852w85RhPuQxzjX4hu4DVynbrEq2wfCfzH9hWpqSNHPP2GFHUdhKQdySt9+AnQ7br2VHqtFx4BNiU+E+8lRqc90PrJWFuLHxMam48D2L6DmN6Ry6aETuGzyd7DRCq6Qk3FVvoA22cCZ6Y0xCbAF4FXKwbbn2H7wkzTBxBDtZsI9fY1o5U6qfQNXwF2BD6fti8iIh+5zJr+/AAR4XiqX9LtkjYFLnWSdpE0P7B+uj70jO1LgUsLLrFVpvB3SR8giv+HFeAdgt2I693Hgda4xNWIWrscCZCFu9XC2Z4q6fU92hq15inbD3Z81po0yLxg262aycwU9rilOnaVviHVXpwAnJCKxDcnbmy5jt0j49mpS1TduXGM7f8So6cOLWTyHEl3E2nKz6fapEbCvQXZ2/YZrQ3bT6aUW5ZjNwrsl4SivwQcQnQU9zzJwiHAvo6kDYl6PYDzkiOaw/zDvDZHj7ZGq3nqwZSOddLt25Vmk3FOlvQrYP4U/fwMcHiBdY4Lao1dZdwi6WCi/f9MBneJ9TQ+qZ+punPjE0kn296iLSI7iCaRWMWYp6dSl+hcwDy2/9FguUVQl6kwpXXixiOSTiQinYd37N8e2Mj2lj3YGiQLVUomStKCwMHAu4hswoXAF5w5UizZ3IiYLCTgt7YvarrO8UJ17CrjFk2A8UmSTmJw6uQBZwqwVvoHSYvY/vtQxewNitjnBHYHFrf9WaXZorbPbbDcIkg6CngSaHUB7wwsYPvTY7aomQBJCxMyIi8wuFlkNqJZZMRO+2g1T0lat0tzx3T7KmWojl2lMhPTHtFQjFK6scQTdqU/kLR/Z9dlt3092DuJuPl/0vYKydG71vYqBZbbiBQ9/CYR1YGoJ9yvhDzGREDSBkCrM/6uBqnd4nSL/DWJBrZ1UrfzFHAz8CWHxuCEpdbYVcYtkhYjamFaLfBXAbva/tvYrao4VXdufLMRUWfazvu67BspS9veUtJWAI7Zp33xoUkO3FfHeh0zK7YvI2MSxmgiaW1gHWAhSbu3vTQvMLmB6Z8AfyNqskUIPi9NNKMcBazfwPZMT3XsKuOZKcQXf/O0vU3at9GYrag8K7dJuAiYI21X3bmZGMX4q52ApSTd2fbSPECT9NULkuYgRTskLU0zQetiSLqM7vWEOZIixehwSKbD9o9errXMhMxGCAfPwmA5kqeBzRrY3dj2ym3bh0m63fZXJH29gd1xQXXsKuOZhWy319kdLemLY7aaUcB2k6feSv9yAnA+8H0GR7GeaVJwDnyb0MF7naTjiWj2tg3sleTLbb/PTozI64dGoKqPlolj/vcVko5uqIPXyXOStmBg/NpmDHR3T/j6slpjVxm3SLqEiNCdmHZtBWxr+51jt6pKpXckvZrBY6z+2sDWq4C1iKju9bYfa77C0UHSjbbXGOt1VJpRcBxby95SRJft2oQjdz0hPfMQsJrtq5uueWamRuwq45nPEDV2Pya+/NfSP9GJSmWGSPoQ8CPgtcCjwBKE/lfuSLFL0oPNeV32jSlJhqXFJEK0d74xWs50JGmh7ZjeORk3XfajyPHEOLYP0jaOrYG9p2x/aIjXJrRTB9Wxq4xjUuh/47FeR6XSgP2I6NrFtt+SOh+36dVIckrmBBZM4t+thol5gUVLLbYhtzAwM/ZF4H7CkeoXjgPuJkZj7QNsTTOR3YnEq2wfKWnXtvTsTQ3sXS/pdqJR4gLX1OMgqmNXGXdI+tYwL9v2vi/bYiqVZvzH9uOSJkmaZPsySb3O/4QYS/ZFIvJ3CwOO3dPAzwqttRG2lxzrNcyAN9jeXNImto+RdALRaV+ZMaXGsbVYhpDF+QxwiKSTgaNt/7HZMscH1bGrjEe66V7NRTz9vwqojl1lZuFJSXMDVwLHS3qU7p/vYbF9MHCwpF1sH1J6kSVIo6Y+z8Bw+MuBX9n+z5BvenlpreNJSSsA/wBePYbrmZkoMo6tRYrQXQRclKLYvwZ2knQH8FXb1xVY80xLbZ6ojGskzUPMJdwOOBk4KM1qrFT6niTa+28iwrY1UXN2vO3HG9hcB3g9bQ/2to9tttLmSDoCmJUYRA/wCWCa7e3HblUDpBFdpwErEU1ZcwPfsv3LMV3YBCQ1AG1DfEYeAY4EzgZWAU6ZCaK/o0p17CrjklSIvTtxMzwGONj2E2O7qkolD0nzMtgRy5I8kXQcIeR6OzBtwJy/0HiRmUiaJYlr39GhTUa3fZWZB0l72j5A0iF01yjM+txJ+iNR8zilU3Be0lds75+14HFCTcVWxh2SDgQ+AhwGrGj7X2O8pEolC0k7At8honb/JQlPA0tlmnwrsHyfFZvfCKwKTJO0tO174SVJi2nDvvNlRNIrCG291zPYyd5nrNY0E9BqLrm5sN2VbT/fvkPSgrYfm+hOHdSIXWUcIum/hJr+iwx+SqzTGCozFZL+BKxdSmtO0inAF2z/vYS9Eki6LXX8bggcDbTmfL6e0J3sizFZki4g5pHeQpvDafugMVvUBCVNY/ms7evT9keB79teZmxX1h/UiF1l3GF70livoVIpxL3AcwXtLQj8XtKNtI0Ssz2WskDtc0R/xcAM0WnAW+if+aeL2X7vWC9iZkLSOQwzCaLB525r4ChJlxOd3q8CxnT0XD9RHbtKpVLpX74GXCvpBgY7Yrk1cd8usajCTCYaEdSxv3O+6FhzraQVbU8d64XMRPxwNIzanirpu0Sd3TPA2ztr7SYyNRVbqVQqfUqKrF0NTCVq7ACwfcyQb5rJkHSr7VXHeh0zQtLvgTcQwsn/x0Bpx0pjurAJiKQjiSagbQlNu4OBQ2z/fEwX1ifUiF2lUqn0L7Pa3n3Gf214JD1D95RYP9Sddkbq+pX3jfUCZjYknWx7C0lT6V7vnOsUTwW2T01A90takxi9V6FG7CqVSqVvkfQ94C/AOQxOxWbJnfQjkhaYmY5H0qsZPCv2r2O4nL5G0iK2/y5piW6vp7GPOXZnJ6KnAH+2/e/cNY5HqmNXqVQqfYqk+7vstu1cuZNKJpI2Bg4iivUfBZYA/mD7zWO6sD5G0huAhW1f07F/XeAfLWmbHuzNAnyPSMH+lYj8vY4QjP5GH00pGVNq92ClUqn0KbaX7PJTnbqxYV9gLeCPabLBO4Hrx3ZJfc9PiHnEnTydXuuVA4kZs0vZXi3VZi4NzM8oNWrMjNSIXaVSqfQpM8H81AmDpJttvzXNI32L7f/WyRjDI+km26sP8dpU2yv2aO9PwDKdAtuSJgN3235j/mrHD7V5olKpVPqXQ4n5qb9I259I+/pifuoE40lJcwNXAsdLehR4dozX1O/MP8xrc2TYc7epKbanSapRqkRNxVYqlUr/srrtT9m+NP1sC3SNgFRGnU0IsejdgAsI8egPjemK+p+bJe3QuVPS9sQEj175vaRPdrG3DXB3hr1xSU3FViqVSp8i6VZg8475qafODLpv44VhGgDWA/7eawPARELSwsAZwAsMOHJvBWYDNrX9jx7tLQqcDjzfYW+OZO+hEuue2amOXaVSqfQpkt5JdPzdR3QALkEfzU+dCEg6F/ha58QJSSsC37Ndo3YzQNIGwApp8y7blza0tyHQ6kb+ve1Lmtgbb1THrlKpVPoYSa8Alk2b99j+v+H+fqUspRsAKpXRptbYVSqVSp8haXVJrwFIjtwqhNzGgZIWGNPFTTxKNwBUKqNKdewqlUql//gVUZeEpLcDPwCOBZ4CDhvDdU1ESjcAVCqjSk3FViqVSp/Rro8m6efA/9r+dtq+3fYqY7m+iUTpBoBKZbSpOnaVSqXSf0yWNIvtF4kJB59te61et19GbD8CrNPRAHBe0waASmW0qBeISqVS6T9OBK6Q9Bgh7XAVvCS98dRYLmyikjqRazdype+pqdhKpVLpQyStBSwCXGj72bRvGWBu27eO6eIqlUrfUh27SqVSqVQqlXFC7YqtVCqVSqVSGSdUx65SqVQqlUplnFAdu0qlUqlUKpVxQnXsKpVKpVKpVMYJ1bGrVCqVSqVSGSf8f2fry+szhK9OAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 648x1440 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gene_list = associated_genes[term]\n",
    "\n",
    "colors = dict()\n",
    "for g in gene_list:\n",
    "    if (g in ga_associated_genes[term]):\n",
    "        colors[g] = 'blue'\n",
    "    else:\n",
    "        colors[g] = 'gray'\n",
    "        \n",
    "colors2 = dict()\n",
    "for g in gene_list:\n",
    "    if (g in ga_associated_genes[term]):\n",
    "        colors2[g] = 'y'\n",
    "    else:\n",
    "        colors2[g] = 'whitesmoke'\n",
    "\n",
    "color_df = pd.DataFrame(columns=['Phenotype'])\n",
    "    \n",
    "for k, v in colors2.items():\n",
    "    color_df.at[k, 'GA-selected'] = v\n",
    "\n",
    "cm = sns.clustermap(expression.loc[gene_list],\n",
    "                    cmap=sns.dark_palette('orange'),\n",
    "                    col_cluster=True,\n",
    "                    row_cluster=True,\n",
    "                    row_colors=color_df[['GA-selected']],\n",
    "                    col_colors=cell_colors,\n",
    "                    figsize=(9, 20),\n",
    "                    metric='jaccard',\n",
    "                    method='complete',\n",
    "                   )\n",
    "\n",
    "_ = cm.ax_heatmap.set_xticklabels(cm.data2d.columns.str.replace('_', ' '))\n",
    "cm.ax_heatmap.set_ylabel('Gene', fontsize=14, rotation=-90)\n",
    "\n",
    "plt.savefig(output_folder + '/Phenotype-Association.svg',\n",
    "                dpi=300,\n",
    "                bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Ligand_WB</th>\n",
       "      <th>Receptor_WB</th>\n",
       "      <th>Ligand_symbol</th>\n",
       "      <th>Receptor_symbol</th>\n",
       "      <th>score</th>\n",
       "      <th>index</th>\n",
       "      <th>LR Function</th>\n",
       "      <th>L Function</th>\n",
       "      <th>R Function</th>\n",
       "      <th>Ligand_desc</th>\n",
       "      <th>Receptor_desc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>WBGene00000903</td>\n",
       "      <td>WBGene00004860</td>\n",
       "      <td>daf-7</td>\n",
       "      <td>sma-6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>TGF-B signaling</td>\n",
       "      <td>GDF11</td>\n",
       "      <td>BMPRIB</td>\n",
       "      <td>Is an ortholog of human GDF11 (growth differen...</td>\n",
       "      <td>Is an ortholog of human BMPR1A (bone morphogen...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>WBGene00003738</td>\n",
       "      <td>WBGene00004215</td>\n",
       "      <td>nid-1</td>\n",
       "      <td>ptp-3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>46</td>\n",
       "      <td>Cell adhesion</td>\n",
       "      <td>Nidogen</td>\n",
       "      <td>LAR-RPTP</td>\n",
       "      <td>Is an ortholog of human NID1 (nidogen 1) and N...</td>\n",
       "      <td>Is an ortholog of human PTPRD (protein tyrosin...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>WBGene00001328</td>\n",
       "      <td>WBGene00003930</td>\n",
       "      <td>epi-1</td>\n",
       "      <td>pat-3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>54</td>\n",
       "      <td>Cell adhesion</td>\n",
       "      <td>Laminin</td>\n",
       "      <td>Integrin</td>\n",
       "      <td>Is an ortholog of human LAMA5 (laminin subunit...</td>\n",
       "      <td>Is an ortholog of human ITGB1 (integrin subuni...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>WBGene00000857</td>\n",
       "      <td>WBGene00003006</td>\n",
       "      <td>cwn-1</td>\n",
       "      <td>lin-17</td>\n",
       "      <td>1.0</td>\n",
       "      <td>57</td>\n",
       "      <td>Wnt signaling</td>\n",
       "      <td>Wnt</td>\n",
       "      <td>Frizzled</td>\n",
       "      <td>Is an ortholog of human WNT4 (Wnt family membe...</td>\n",
       "      <td>Is an ortholog of human FZD4 (frizzled class r...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>WBGene00006750</td>\n",
       "      <td>WBGene00006765</td>\n",
       "      <td>unc-10</td>\n",
       "      <td>unc-29</td>\n",
       "      <td>1.0</td>\n",
       "      <td>62</td>\n",
       "      <td>Neuronal communication</td>\n",
       "      <td>Organization of presynaptic sites</td>\n",
       "      <td>Cholinergic Receptor</td>\n",
       "      <td>Is an ortholog of human RIMS1 (regulating syna...</td>\n",
       "      <td>Is an ortholog of several human genes includin...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>WBGene00003395</td>\n",
       "      <td>WBGene00003007</td>\n",
       "      <td>mom-2</td>\n",
       "      <td>lin-18</td>\n",
       "      <td>1.0</td>\n",
       "      <td>101</td>\n",
       "      <td>Wnt signaling</td>\n",
       "      <td>Wnt</td>\n",
       "      <td>Ryk/Derailed</td>\n",
       "      <td>Exhibits receptor tyrosine kinase binding acti...</td>\n",
       "      <td>Is an ortholog of human RYK (receptor like tyr...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>WBGene00004264</td>\n",
       "      <td>WBGene00004210</td>\n",
       "      <td>qua-1</td>\n",
       "      <td>ptc-3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>148</td>\n",
       "      <td>Hedgehog signaling</td>\n",
       "      <td>IHH</td>\n",
       "      <td>PTCH2</td>\n",
       "      <td>Is an ortholog of human DHH (desert hedgehog s...</td>\n",
       "      <td>Is an ortholog of human PTCH1 (patched 1) and ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>WBGene00004890</td>\n",
       "      <td>WBGene00004047</td>\n",
       "      <td>smp-2</td>\n",
       "      <td>plx-1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>155</td>\n",
       "      <td>Cell migration</td>\n",
       "      <td>Semaphorin</td>\n",
       "      <td>Plexin (semaphorin receptor)</td>\n",
       "      <td>Is an ortholog of several human genes includin...</td>\n",
       "      <td>Is an ortholog of human PLXNA2 (plexin A2); PL...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>WBGene00004889</td>\n",
       "      <td>WBGene00004047</td>\n",
       "      <td>smp-1</td>\n",
       "      <td>plx-1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>156</td>\n",
       "      <td>Cell migration</td>\n",
       "      <td>Semaphorin</td>\n",
       "      <td>Plexin (semaphorin receptor)</td>\n",
       "      <td>Is an ortholog of human SEMA6A (semaphorin 6A)...</td>\n",
       "      <td>Is an ortholog of human PLXNA2 (plexin A2); PL...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>WBGene00006951</td>\n",
       "      <td>WBGene00004208</td>\n",
       "      <td>wrt-5</td>\n",
       "      <td>ptc-1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>168</td>\n",
       "      <td>Hedgehog signaling</td>\n",
       "      <td>DHH</td>\n",
       "      <td>PTCH1</td>\n",
       "      <td>Is predicted to have signaling receptor bindin...</td>\n",
       "      <td>Is an ortholog of human PTCH1 (patched 1) and ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>WBGene00002246</td>\n",
       "      <td>WBGene00001609</td>\n",
       "      <td>lag-2</td>\n",
       "      <td>glp-1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>208</td>\n",
       "      <td>Notch signaling</td>\n",
       "      <td>DLL</td>\n",
       "      <td>Notch</td>\n",
       "      <td>Exhibits Notch binding activity. Is involved i...</td>\n",
       "      <td>Is an ortholog of human NOTCH1 (notch receptor...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>WBGene00006852</td>\n",
       "      <td>WBGene00006745</td>\n",
       "      <td>unc-129</td>\n",
       "      <td>unc-5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>226</td>\n",
       "      <td>Cell migration</td>\n",
       "      <td>BMP non-canonical</td>\n",
       "      <td>Immunoglobulin</td>\n",
       "      <td>Is an ortholog of human BMP3 (bone morphogenet...</td>\n",
       "      <td>Is an ortholog of human UNC5A (unc-5 netrin re...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Ligand_WB     Receptor_WB Ligand_symbol Receptor_symbol  score  \\\n",
       "0   WBGene00000903  WBGene00004860         daf-7           sma-6    1.0   \n",
       "8   WBGene00003738  WBGene00004215         nid-1           ptp-3    1.0   \n",
       "10  WBGene00001328  WBGene00003930         epi-1           pat-3    1.0   \n",
       "11  WBGene00000857  WBGene00003006         cwn-1          lin-17    1.0   \n",
       "13  WBGene00006750  WBGene00006765        unc-10          unc-29    1.0   \n",
       "16  WBGene00003395  WBGene00003007         mom-2          lin-18    1.0   \n",
       "22  WBGene00004264  WBGene00004210         qua-1           ptc-3    1.0   \n",
       "23  WBGene00004890  WBGene00004047         smp-2           plx-1    1.0   \n",
       "24  WBGene00004889  WBGene00004047         smp-1           plx-1    1.0   \n",
       "26  WBGene00006951  WBGene00004208         wrt-5           ptc-1    1.0   \n",
       "28  WBGene00002246  WBGene00001609         lag-2           glp-1    1.0   \n",
       "30  WBGene00006852  WBGene00006745       unc-129           unc-5    1.0   \n",
       "\n",
       "    index             LR Function                         L Function  \\\n",
       "0       1         TGF-B signaling                              GDF11   \n",
       "8      46           Cell adhesion                            Nidogen   \n",
       "10     54           Cell adhesion                            Laminin   \n",
       "11     57           Wnt signaling                                Wnt   \n",
       "13     62  Neuronal communication  Organization of presynaptic sites   \n",
       "16    101           Wnt signaling                                Wnt   \n",
       "22    148      Hedgehog signaling                                IHH   \n",
       "23    155          Cell migration                         Semaphorin   \n",
       "24    156          Cell migration                         Semaphorin   \n",
       "26    168      Hedgehog signaling                                DHH   \n",
       "28    208         Notch signaling                                DLL   \n",
       "30    226          Cell migration                  BMP non-canonical   \n",
       "\n",
       "                      R Function  \\\n",
       "0                         BMPRIB   \n",
       "8                       LAR-RPTP   \n",
       "10                      Integrin   \n",
       "11                      Frizzled   \n",
       "13          Cholinergic Receptor   \n",
       "16                  Ryk/Derailed   \n",
       "22                         PTCH2   \n",
       "23  Plexin (semaphorin receptor)   \n",
       "24  Plexin (semaphorin receptor)   \n",
       "26                         PTCH1   \n",
       "28                         Notch   \n",
       "30                Immunoglobulin   \n",
       "\n",
       "                                          Ligand_desc  \\\n",
       "0   Is an ortholog of human GDF11 (growth differen...   \n",
       "8   Is an ortholog of human NID1 (nidogen 1) and N...   \n",
       "10  Is an ortholog of human LAMA5 (laminin subunit...   \n",
       "11  Is an ortholog of human WNT4 (Wnt family membe...   \n",
       "13  Is an ortholog of human RIMS1 (regulating syna...   \n",
       "16  Exhibits receptor tyrosine kinase binding acti...   \n",
       "22  Is an ortholog of human DHH (desert hedgehog s...   \n",
       "23  Is an ortholog of several human genes includin...   \n",
       "24  Is an ortholog of human SEMA6A (semaphorin 6A)...   \n",
       "26  Is predicted to have signaling receptor bindin...   \n",
       "28  Exhibits Notch binding activity. Is involved i...   \n",
       "30  Is an ortholog of human BMP3 (bone morphogenet...   \n",
       "\n",
       "                                        Receptor_desc  \n",
       "0   Is an ortholog of human BMPR1A (bone morphogen...  \n",
       "8   Is an ortholog of human PTPRD (protein tyrosin...  \n",
       "10  Is an ortholog of human ITGB1 (integrin subuni...  \n",
       "11  Is an ortholog of human FZD4 (frizzled class r...  \n",
       "13  Is an ortholog of several human genes includin...  \n",
       "16  Is an ortholog of human RYK (receptor like tyr...  \n",
       "22  Is an ortholog of human PTCH1 (patched 1) and ...  \n",
       "23  Is an ortholog of human PLXNA2 (plexin A2); PL...  \n",
       "24  Is an ortholog of human PLXNA2 (plexin A2); PL...  \n",
       "26  Is an ortholog of human PTCH1 (patched 1) and ...  \n",
       "28  Is an ortholog of human NOTCH1 (notch receptor...  \n",
       "30  Is an ortholog of human UNC5A (unc-5 netrin re...  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ga_lrs.loc[(ga_lrs.Ligand_symbol.isin(gene_list)) & (ga_lrs.Receptor_symbol.isin(gene_list))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}