code/13.Examine-GA-LR-Count.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cell2cell as c2c\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "import glob\n",
    "import json\n",
    "from collections import defaultdict\n",
    "\n",
    "import scipy\n",
    "import scipy.cluster.hierarchy as hc\n",
    "\n",
    "from sklearn.metrics import pairwise_distances\n",
    "\n",
    "from cci_distance_correlation import get_correlation\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analysis Setup\n",
    "\n",
    "Here, all inputs and parameters needed for the analysis are defined"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Location of files**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_folder = '../data/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "output_folder = '../results/CCI-GA-LRCount-LRs/'\n",
    "if not os.path.isdir(output_folder):\n",
    "    os.mkdir(output_folder)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "files = dict()\n",
    "files['rnaseq'] = data_folder + '/RNA-Seq/Celegans_RNASeqData_Cell.xlsx'\n",
    "files['metadata'] = data_folder + '/RNA-Seq/Celegans_cell_metadata.tsv'\n",
    "files['ppi'] = data_folder + '/PPI-Networks/Celegans-Curated-LR-pairs.xlsx'\n",
    "files['phenotype'] = data_folder + '/Digital-3D-Map/Celegans_Physical_Distances_Min.csv'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**RNA-seq data manipulation**\n",
    "\n",
    "- <font color=\"green\">'gene_col'</font> is the name of the column containing the list or names of genes in the dataset.\n",
    "- <font color=\"green\">'drop_nangenes'</font> is a boolean decision to remove genes with NaN values.\n",
    "- <font color=\"green\">'log_transform'</font> is a boolean decision to log-transform the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "rnaseq_setup = dict()\n",
    "rnaseq_setup['gene_col'] = 'gene_id'\n",
    "rnaseq_setup['drop_nangenes'] = True\n",
    "rnaseq_setup['log_transform'] = False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Metadata columns**\n",
    "\n",
    "- <font color=\"green\">'sample_col'</font> is the name of the column containing the sample or cell names in RNA-seq data (equivalent to the columns in rnaseq_data.\n",
    "- <font color=\"green\">'group_col'</font> is the name of the column containing the labels for the respective samples or cells in order to group/color them in downstream analyses."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "meta_setup = dict()\n",
    "meta_setup['sample_col'] = '#SampleID'\n",
    "meta_setup['group_col'] = 'Groups'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Columns for PPI network**\n",
    "\n",
    "- <font color=\"green\">'protein_cols'</font> is a list containing the names of the first and second interactors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "ppi_setup = dict()\n",
    "ppi_setup['protein_cols'] = ['Ligand_WB', 'Receptor_WB']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Cutoffs setup for binarizing gene expression**\n",
    "\n",
    "- <font color=\"green\">'type'</font> is the type of cutoff to use. \n",
    "    - 'constant_value'\n",
    "    - 'local_percentile'\n",
    "    - 'global_percentile'\n",
    "    - None\n",
    "- <font color=\"green\">'parameter'</font> is the corresponding parameter for the type of cutoff. If percentile is used, it should be a value between 0 and 1. E.g. a value of 0.75 represents the 75-th percentile, and the cutoff to binarize the gene expression will be that value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "cutoff_setup = dict()\n",
    "cutoff_setup['type'] = 'constant_value'\n",
    "cutoff_setup['parameter'] = 10 # TPM"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**General Analysis setups**\n",
    "\n",
    "- <font color=\"green\">'communication_score'</font> is the type of communication score used to detect active pathways between each pair of cell. It can be:\n",
    "    - 'expression_thresholding'\n",
    "    - 'expression_product'\n",
    "    - 'expression_correlation'\n",
    "    - 'differential_combinations'\n",
    "- <font color=\"green\">'cci_score'</font> is the scoring function to aggregate the communication scores. It can be:\n",
    "    - 'bray_curtis'\n",
    "    - 'jaccard'\n",
    "    - 'count'\n",
    "- <font color=\"green\">'cci_type'</font> is the type of interaction between two cells. If it is undirected, ligand and receptor are considered from both cells. If it is directed, ligands from one cell and receptors from the other are considered separately with respect to ligands from the second cell and receptor from the first one. So, it can be:\n",
    "    - 'undirected'\n",
    "    - 'directed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "analysis_setup = dict()\n",
    "analysis_setup['communication_score'] = 'expression_thresholding'\n",
    "analysis_setup['cci_score'] = 'count'\n",
    "analysis_setup['cci_type'] = 'undirected'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**RNA-seq**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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=files['rnaseq'],\n",
    "                                 gene_column=rnaseq_setup['gene_col'],\n",
    "                                 drop_nangenes=rnaseq_setup['drop_nangenes'],\n",
    "                                 log_transformation=rnaseq_setup['log_transform'],\n",
    "                                 format='auto')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Metadata for cell types**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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=files['metadata'],\n",
    "                            cell_labels=rnaseq_data.columns,\n",
    "                            format='auto')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**PPI network**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Opening PPI datasets from ../data//PPI-Networks/Celegans-Curated-LR-pairs.xlsx\n",
      "../data//PPI-Networks/Celegans-Curated-LR-pairs.xlsx was correctly loaded\n",
      "Removing bidirectionality of PPI network\n",
      "Simplying PPI network\n"
     ]
    }
   ],
   "source": [
    "ppi_data = c2c.io.load_ppi(ppi_file=files['ppi'],\n",
    "                           interaction_columns=ppi_setup['protein_cols'],\n",
    "                           rnaseq_genes=list(rnaseq_data.index),\n",
    "                           format='auto')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(245, 3)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ppi_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "symbol_lrs = pd.read_excel(files['ppi'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .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>LR Function</th>\n",
       "      <th>L Function</th>\n",
       "      <th>R Function</th>\n",
       "      <th>Ligand_desc_WormBase_versionWS277</th>\n",
       "      <th>Receptor_desc_WormBase_versionWS277</th>\n",
       "      <th>Pair_confidence</th>\n",
       "      <th>Location_type</th>\n",
       "      <th>Confidence_evidence</th>\n",
       "      <th>Location_evidence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>WBGene00000182</td>\n",
       "      <td>WBGene00004040</td>\n",
       "      <td>arf-1.2</td>\n",
       "      <td>pld-1</td>\n",
       "      <td>Cell adhesion</td>\n",
       "      <td>ADP-ribosylation factor 1</td>\n",
       "      <td>Phospholipase D2</td>\n",
       "      <td>Is an ortholog of human ARF1 (ADP ribosylation...</td>\n",
       "      <td>Is an ortholog of human PLD2 (phospholipase D2...</td>\n",
       "      <td>Putative based on data</td>\n",
       "      <td>Membrane-bound</td>\n",
       "      <td>Orthology, protein-protein interaction (STRING...</td>\n",
       "      <td>wormbase: arf-1 found in the endoplasmic retic...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>WBGene00000903</td>\n",
       "      <td>WBGene00004860</td>\n",
       "      <td>daf-7</td>\n",
       "      <td>sma-6</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",
       "      <td>Putative based on literature</td>\n",
       "      <td>Secreted</td>\n",
       "      <td>Orthology, protein-protein interaction (STRING...</td>\n",
       "      <td>wormbase; \" Located in extracellular space,\" h...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>WBGene00005647</td>\n",
       "      <td>WBGene00008779</td>\n",
       "      <td>srp-6</td>\n",
       "      <td>F14B4.1</td>\n",
       "      <td>Wnt signaling</td>\n",
       "      <td>Serpin family (Blocks wnt signaling)</td>\n",
       "      <td>Wnt-Receptor</td>\n",
       "      <td>Is an ortholog of human SERPINB10 (serpin fami...</td>\n",
       "      <td>Is predicted to have Wnt-activated receptor ac...</td>\n",
       "      <td>Putative based on literature</td>\n",
       "      <td>Secreted</td>\n",
       "      <td>Orthology, protein-protein interaction (STRING...</td>\n",
       "      <td>wormbase: srp-6 is found and active in the ext...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>WBGene00000074</td>\n",
       "      <td>WBGene00003930</td>\n",
       "      <td>adm-2</td>\n",
       "      <td>pat-3</td>\n",
       "      <td>Cell adhesion</td>\n",
       "      <td>ADAM</td>\n",
       "      <td>Integrin</td>\n",
       "      <td>Is an ortholog of several human genes includin...</td>\n",
       "      <td>Is an ortholog of human ITGB1 (integrin subuni...</td>\n",
       "      <td>Putative based on data</td>\n",
       "      <td>Membrane-bound</td>\n",
       "      <td>Orthology, protein-protein interaction (STRING...</td>\n",
       "      <td>wormbase: adm-2 is an integral component of th...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>WBGene00000074</td>\n",
       "      <td>WBGene00003929</td>\n",
       "      <td>adm-2</td>\n",
       "      <td>pat-2</td>\n",
       "      <td>Cell adhesion</td>\n",
       "      <td>ADAM</td>\n",
       "      <td>Integrin</td>\n",
       "      <td>Is an ortholog of several human genes includin...</td>\n",
       "      <td>Is an ortholog of human ITGA5 (integrin subuni...</td>\n",
       "      <td>Putative based on data</td>\n",
       "      <td>Membrane-bound</td>\n",
       "      <td>Orthology, protein-protein interaction (STRING...</td>\n",
       "      <td>wormbase: adm-2 is an integral component of th...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Ligand_WB     Receptor_WB Ligand_symbol Receptor_symbol  \\\n",
       "0  WBGene00000182  WBGene00004040       arf-1.2           pld-1   \n",
       "1  WBGene00000903  WBGene00004860         daf-7           sma-6   \n",
       "2  WBGene00005647  WBGene00008779         srp-6         F14B4.1   \n",
       "3  WBGene00000074  WBGene00003930         adm-2           pat-3   \n",
       "4  WBGene00000074  WBGene00003929         adm-2           pat-2   \n",
       "\n",
       "       LR Function                            L Function        R Function  \\\n",
       "0    Cell adhesion             ADP-ribosylation factor 1  Phospholipase D2   \n",
       "1  TGF-B signaling                                 GDF11            BMPRIB   \n",
       "2    Wnt signaling  Serpin family (Blocks wnt signaling)      Wnt-Receptor   \n",
       "3    Cell adhesion                                  ADAM          Integrin   \n",
       "4    Cell adhesion                                  ADAM          Integrin   \n",
       "\n",
       "                   Ligand_desc_WormBase_versionWS277  \\\n",
       "0  Is an ortholog of human ARF1 (ADP ribosylation...   \n",
       "1  Is an ortholog of human GDF11 (growth differen...   \n",
       "2  Is an ortholog of human SERPINB10 (serpin fami...   \n",
       "3  Is an ortholog of several human genes includin...   \n",
       "4  Is an ortholog of several human genes includin...   \n",
       "\n",
       "                 Receptor_desc_WormBase_versionWS277  \\\n",
       "0  Is an ortholog of human PLD2 (phospholipase D2...   \n",
       "1  Is an ortholog of human BMPR1A (bone morphogen...   \n",
       "2  Is predicted to have Wnt-activated receptor ac...   \n",
       "3  Is an ortholog of human ITGB1 (integrin subuni...   \n",
       "4  Is an ortholog of human ITGA5 (integrin subuni...   \n",
       "\n",
       "                Pair_confidence   Location_type  \\\n",
       "0        Putative based on data  Membrane-bound   \n",
       "1  Putative based on literature        Secreted   \n",
       "2  Putative based on literature        Secreted   \n",
       "3        Putative based on data  Membrane-bound   \n",
       "4        Putative based on data  Membrane-bound   \n",
       "\n",
       "                                 Confidence_evidence  \\\n",
       "0  Orthology, protein-protein interaction (STRING...   \n",
       "1  Orthology, protein-protein interaction (STRING...   \n",
       "2  Orthology, protein-protein interaction (STRING...   \n",
       "3  Orthology, protein-protein interaction (STRING...   \n",
       "4  Orthology, protein-protein interaction (STRING...   \n",
       "\n",
       "                                   Location_evidence  \n",
       "0  wormbase: arf-1 found in the endoplasmic retic...  \n",
       "1  wormbase; \" Located in extracellular space,\" h...  \n",
       "2  wormbase: srp-6 is found and active in the ext...  \n",
       "3  wormbase: adm-2 is an integral component of th...  \n",
       "4  wormbase: adm-2 is an integral component of th...  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "symbol_lrs.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "tmp = ppi_data.copy()\n",
    "tmp.columns = ['Ligand_WB', 'Receptor_WB', 'score']\n",
    "symbol_lrs = tmp.merge(symbol_lrs, how='inner').drop_duplicates().reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Phenotype**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "physical_distance = pd.read_csv(files['phenotype'], index_col=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cells to include"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "included_cells = sorted(list(set(rnaseq_data.columns) & set(physical_distance.columns)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "excluded_cells = sorted(list(set(rnaseq_data.columns) - set(included_cells)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Distal_tip_cells',\n",
       " 'Sex_myoblasts',\n",
       " 'Socket_cells',\n",
       " 'Vulval_precursors',\n",
       " 'flp-1(+)_interneurons']"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "excluded_cells"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Collect & Preprocess GA Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "../data//GA-LR-Count/0054565f-fa7c-4517-ae47-41f66391fd98.json\n",
      "../data//GA-LR-Count/040b576c-0e38-4d7d-8b05-5a4db7bbd5a3.json\n",
      "../data//GA-LR-Count/0493687c-9b0b-41dc-82ff-902f33a8534a.json\n",
      "../data//GA-LR-Count/0506a8e2-78a8-41b3-aef7-9415707ab8f0.json\n",
      "../data//GA-LR-Count/05dc8647-1387-491a-abad-af9495371c4c.json\n",
      "../data//GA-LR-Count/06ffa4cc-04ff-4548-86fc-7e1d32e5ef0d.json\n",
      "../data//GA-LR-Count/08d29508-9e23-4a5b-9f08-9697df7262c0.json\n",
      "../data//GA-LR-Count/0d9c927d-6670-40a9-a998-6a528203b205.json\n",
      "../data//GA-LR-Count/0da4272a-8cf1-4bc0-b8d0-e441242ce9d0.json\n",
      "../data//GA-LR-Count/1048a6ac-b437-4268-a9c5-22b41afd87c1.json\n",
      "../data//GA-LR-Count/12552e43-79f3-4e86-8a35-e98a1452200f.json\n",
      "../data//GA-LR-Count/14e6abea-4314-4076-a081-95424898cc8c.json\n",
      "../data//GA-LR-Count/16752b51-09b2-4aca-9dd5-f17b1fd802bf.json\n",
      "../data//GA-LR-Count/196a4924-fa6a-49a6-84d2-5f44bb85e024.json\n",
      "../data//GA-LR-Count/1ad72989-c229-4e93-8678-a9dcd7fd0d09.json\n",
      "../data//GA-LR-Count/1d01f168-b559-4aee-b919-a1097caea34f.json\n",
      "../data//GA-LR-Count/254655c1-a141-40f1-aa63-f739283d455b.json\n",
      "../data//GA-LR-Count/261d2523-2d02-4b83-9272-76ab3f0c75db.json\n",
      "../data//GA-LR-Count/26dece57-50ba-4a12-8208-cc6754edfcae.json\n",
      "../data//GA-LR-Count/2720cebb-cd64-4425-8302-ed45bfd708d8.json\n",
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      "../data//GA-LR-Count/2adb64f0-56a3-4499-9000-960ad898d4c2.json\n",
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      "../data//GA-LR-Count/30669d24-f140-4bef-a784-38d15907109d.json\n",
      "../data//GA-LR-Count/3299adf4-a503-4a61-96c1-fa7e456881e5.json\n",
      "../data//GA-LR-Count/3cac4129-b321-468a-ad77-50bacf8e1b05.json\n",
      "../data//GA-LR-Count/3e5f8ee5-0cab-4bb2-ae19-8a8f75c0b5fb.json\n",
      "../data//GA-LR-Count/3f00e820-9f19-42b1-9f7a-71a7d9792275.json\n",
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      "../data//GA-LR-Count/8eacd764-d193-4d3d-8e90-b685cf22bf59.json\n",
      "../data//GA-LR-Count/9086d69b-1b0a-4033-913d-fb56862917b4.json\n",
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      "../data//GA-LR-Count/9a10d5e3-8b50-420a-bd35-f2a11b509bcd.json\n",
      "../data//GA-LR-Count/9a697323-db0d-41d9-b186-8f6cdd77bac0.json\n",
      "../data//GA-LR-Count/9cf22791-a2be-4c0e-8152-a9b35c02b8f2.json\n",
      "../data//GA-LR-Count/9d8cb86e-5d42-49af-bb99-1771eead1f5f.json\n",
      "../data//GA-LR-Count/a1026152-b6fb-4649-a5f5-0b4a5b7d8008.json\n",
      "../data//GA-LR-Count/a234b721-69a0-4121-9c12-625b0147664c.json\n",
      "../data//GA-LR-Count/a3242780-570e-42e9-b82b-c85847003a94.json\n",
      "../data//GA-LR-Count/a4ec05c2-9dc4-4000-907e-ef010fb1ab20.json\n",
      "../data//GA-LR-Count/a531e0b0-3c00-4923-b68a-1d3919f0a470.json\n",
      "../data//GA-LR-Count/a5ba6888-45cc-4ea8-a786-bda1a83542fc.json\n",
      "../data//GA-LR-Count/a63ce6ef-75f8-4893-8d37-862c5f733184.json\n",
      "../data//GA-LR-Count/a85ed29e-0e40-4db8-9ebc-80f1e6c0c785.json\n",
      "../data//GA-LR-Count/a8d98cfd-a36d-48e7-a274-97567ba09e84.json\n",
      "../data//GA-LR-Count/ad0341ae-788e-4230-b566-2c9eb2c12ffb.json\n",
      "../data//GA-LR-Count/ad0b1faf-46d5-4a00-aeab-bd5c0abf5b80.json\n",
      "../data//GA-LR-Count/ae97aef0-653a-43f8-a207-36a2ef295532.json\n",
      "../data//GA-LR-Count/b000c548-d4d1-4e7d-b92b-c5ce390f4255.json\n",
      "../data//GA-LR-Count/b6643b7a-cc0d-4cc2-a013-0e90e43960dc.json\n",
      "../data//GA-LR-Count/b7e96a06-57ab-4c8f-acce-e341896c2456.json\n",
      "../data//GA-LR-Count/b9875478-f4d6-4c3d-94f1-fb324d219041.json\n",
      "../data//GA-LR-Count/bd149cc7-fba6-41ab-b467-ac80c7913bbb.json\n",
      "../data//GA-LR-Count/bd2ed679-01b1-4fc7-99ec-28d4ddd9d390.json\n",
      "../data//GA-LR-Count/be99be3c-7245-4714-ac86-f3c0bfc9eaf4.json\n",
      "../data//GA-LR-Count/beca3042-f4b1-4a8f-b6ef-cde57f605ccd.json\n",
      "../data//GA-LR-Count/c543859d-f956-4ed0-b25d-5bdedf5c2c1a.json\n",
      "../data//GA-LR-Count/c63067c6-f801-421a-8c1b-9fa8ee2150ef.json\n",
      "../data//GA-LR-Count/cb9c3ed7-4208-414f-8fe0-d1a6c41f2782.json\n",
      "../data//GA-LR-Count/cd0d72c4-130c-4b9f-a198-39c15965663e.json\n",
      "../data//GA-LR-Count/ce49a8ab-53b7-4da9-8cb5-8c110557a38e.json\n",
      "../data//GA-LR-Count/ce6770e6-e3e9-4ad1-8db6-2d6116a2a70f.json\n",
      "../data//GA-LR-Count/d284eb73-79c0-420a-8565-bd660c9245eb.json\n",
      "../data//GA-LR-Count/d74f4b60-a77c-4441-ad9e-62cdb7a12711.json\n",
      "../data//GA-LR-Count/e1bf45f8-40a9-4d82-a8ec-3cc2550baabd.json\n",
      "../data//GA-LR-Count/e26c63cf-c692-4dac-a199-02d1aab6b0b7.json\n",
      "../data//GA-LR-Count/e6c520d2-1538-4f12-aa4e-a2842c1df528.json\n",
      "../data//GA-LR-Count/ead89de4-6416-4dc4-9c3e-79c2c6d1b5f5.json\n",
      "../data//GA-LR-Count/ebe4d6af-0576-4a73-a264-091ce19b26c5.json\n",
      "../data//GA-LR-Count/ee546fb3-6553-4d68-84cd-6dcbe73b12bb.json\n",
      "../data//GA-LR-Count/f2e50296-934b-492f-8c9a-ab301097efab.json\n",
      "../data//GA-LR-Count/f3177daa-7405-4f09-927c-cce5fa95d624.json\n",
      "../data//GA-LR-Count/f78b3680-5a96-4bfc-b90f-f291621da6fd.json\n"
     ]
    }
   ],
   "source": [
    "all_files = sorted(glob.glob(data_folder + \"/GA-LR-Count/*.json\"))\n",
    "\n",
    "selected_ppis = []\n",
    "score_per_run = defaultdict(list)\n",
    "\n",
    "max_corr = 0.0\n",
    "max_array = []\n",
    "for filename in all_files:\n",
    "    print(filename)\n",
    "    with open(filename) as json_file:\n",
    "        data = json.load(json_file)\n",
    "        runs = sorted([int(r.lstrip('run')) for r in data.keys()])\n",
    "        selected_ppis.append(data['run{}'.format(runs[-1])]['ppi_data'])\n",
    "        for r in runs:\n",
    "            corr = data['run{}'.format(r)]['obj_fn']\n",
    "            score_per_run[r].append(corr)\n",
    "            if corr > max_corr:\n",
    "                max_corr = corr\n",
    "                max_array = data['run{}'.format(r)]['ppi_data']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Ratio of selection for each LR pair**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "ratio_selected_ppis = np.nansum(np.array(selected_ppis), axis=0) / len(selected_ppis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "ga_selected_ppi_data = ppi_data.copy()\n",
    "ga_selected_ppi_data['score'] = ratio_selected_ppis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**LR pairs selected in the list that led to maximal correlation**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "max_ga_selected = ppi_data.copy()\n",
    "max_ga_selected['score'] = np.array(max_array).astype(int)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Correlations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Correlation for basal PPI list**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "ref_distance_vector = scipy.spatial.distance.squareform(physical_distance.loc[included_cells, included_cells].values)\n",
    "\n",
    "bi_ppi_data = c2c.preprocessing.bidirectional_ppi_for_cci(ppi_data=ppi_data, verbose=False)\n",
    "\n",
    "interaction_space = c2c.core.InteractionSpace(rnaseq_data=rnaseq_data[included_cells],\n",
    "                                              ppi_data=bi_ppi_data,\n",
    "                                              gene_cutoffs=cutoff_setup,\n",
    "                                              communication_score=analysis_setup['communication_score'],\n",
    "                                              cci_score=analysis_setup['cci_score'],\n",
    "                                              cci_type=analysis_setup['cci_type'],\n",
    "                                              verbose=False)\n",
    "\n",
    "# Compute interactions\n",
    "interaction_space.compute_pairwise_cci_scores(use_ppi_score=True, verbose=False)\n",
    "\n",
    "# Distance matrix from analysis\n",
    "result_matrix = interaction_space.distance_matrix.loc[included_cells, included_cells]\n",
    "\n",
    "# Compute correlations\n",
    "result_distance_vector = scipy.spatial.distance.squareform(result_matrix)\n",
    "corr = scipy.stats.spearmanr(result_distance_vector, ref_distance_vector)\n",
    "base_result = abs(np.nan_to_num(corr[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "interaction_space.interaction_elements['cci_matrix'].loc[included_cells, included_cells].to_csv(output_folder + 'CCI-Celegans-CuratedLR-Counts.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Structure data for visualization**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_values = [base_result]\n",
    "std_values = [0.0]\n",
    "\n",
    "for k in sorted(list(score_per_run.keys())):\n",
    "    v = score_per_run[k]\n",
    "    mean_values.append(np.nanmean(v))\n",
    "    std_values.append(np.nanstd(v))\n",
    "    \n",
    "run_values = list(range(len(mean_values)))\n",
    "\n",
    "data = np.array(list(zip(run_values, mean_values, std_values)))\n",
    "df = pd.DataFrame(data, columns=['X', 'MEAN', 'STD'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Plot results**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6721706035688216"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.2501454403268008,\n",
       " 0.5345809294948457,\n",
       " 0.6412647602852268,\n",
       " 0.6470163842328283,\n",
       " 0.6607398011642972]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.0,\n",
       " 0.008344552520165114,\n",
       " 0.007751672863764261,\n",
       " 0.009735091355071243,\n",
       " 0.011408683308477338]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "std_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with sns.axes_style(\"darkgrid\"):\n",
    "    fig=plt.plot('X', 'MEAN', data=df, linestyle='', marker='o', markersize=8, color='b', label='Mean')\n",
    "    \n",
    "    mean_precision = np.array(mean_values)\n",
    "    std_precision = np.array(std_values)\n",
    "    precision_upper = np.minimum(mean_precision + std_precision, 1)\n",
    "    precision_lower = np.maximum(mean_precision - std_precision, 0)\n",
    "\n",
    "    plt.fill_between(run_values, precision_lower, precision_upper, color='steelblue', alpha=.2, \n",
    "                     label='$\\pm$ 1 std')\n",
    "        \n",
    "    plt.xticks(run_values)\n",
    "    plt.tick_params(axis='both', which='major', labelsize=12)\n",
    "    plt.ylim(0,1)\n",
    "    \n",
    "    plt.xlabel('Iterations of Genetic Algorithm', fontsize=16)\n",
    "    plt.ylabel('Abs(Spearman score)', fontsize=16)\n",
    "    \n",
    "    plt.legend(loc='center left', bbox_to_anchor=(1.05, 0.5),\n",
    "          ncol=1, fancybox=True, shadow=False, fontsize=12)\n",
    "    \n",
    "    plt.savefig(output_folder + '/GA2-Runs.svg',\n",
    "                dpi=300,\n",
    "                bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Distributions of PPIs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/earmingol/opt/anaconda3/envs/c2c_dev/lib/python3.7/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "threshold = np.percentile(ga_selected_ppi_data['score'], 90)\n",
    "\n",
    "ax = sns.distplot(ga_selected_ppi_data['score'],\n",
    "                  bins=50,\n",
    "                  kde=False,\n",
    "                  label='All Fractions',\n",
    "                  hist_kws={'alpha' : 0.2})\n",
    "\n",
    "ax = sns.distplot(ga_selected_ppi_data.loc[ga_selected_ppi_data['score'].values > 0.]['score'],\n",
    "                  bins=50,\n",
    "                  kde=False,\n",
    "                  ax=ax,\n",
    "                  label='Non-Zero Fractions',\n",
    "                  hist_kws={'alpha' : 0.2})\n",
    "\n",
    "ax.legend(loc='center left', bbox_to_anchor=(1.05, 0.5),\n",
    "          ncol=1, fancybox=True, shadow=False, fontsize=12)\n",
    "\n",
    "plt.axvline(x=threshold, c='r', ls='--')\n",
    "plt.tick_params(axis='both', which='major', labelsize=12)\n",
    "plt.xlim(0,1)\n",
    "\n",
    "plt.xlabel('Fraction of GA-Selected Times', fontsize=16)\n",
    "plt.ylabel('Frequency', fontsize=16)\n",
    "plt.savefig(output_folder + '/GA2-Distribution.svg',\n",
    "                dpi=300,\n",
    "                bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3979999999999998"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "threshold"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conserved LR pairs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import pairwise_distances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "lrs = symbol_lrs.apply(lambda row: row['Ligand_symbol'] + '_' + row['Receptor_symbol'], axis=1)\n",
    "lrs2 = symbol_lrs.apply(lambda row: row['Ligand_symbol'] + ' / ' + row['Receptor_symbol'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr_dict = pd.DataFrame()\n",
    "lr_dict['symbol'] = symbol_lrs.apply(lambda row: row['Ligand_symbol'] + '_' + row['Receptor_symbol'], axis=1)\n",
    "lr_dict['WB'] = symbol_lrs.apply(lambda row: row['Ligand_WB'] + '_' + row['Receptor_WB'], axis=1)\n",
    "lr_dict.set_index('symbol', inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Presence**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(np.array(selected_ppis), columns=lrs).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>arf-1.2_pld-1</th>\n",
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       "</table>\n",
       "<p>5 rows × 245 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   arf-1.2_pld-1  daf-7_sma-6  srp-6_F14B4.1  adm-2_pat-3  adm-2_pat-2  \\\n",
       "0              0            0              0            0            0   \n",
       "1              0            1              0            0            0   \n",
       "2              0            1              0            0            0   \n",
       "3              0            0              0            0            0   \n",
       "4              0            0              0            0            0   \n",
       "\n",
       "   let-756_sdn-1  let-756_ver-3  let-756_ver-1  srp-2_F14B4.1  srp-1_try-1  \\\n",
       "0              0              0              1              0            0   \n",
       "1              0              0              1              0            0   \n",
       "2              0              0              1              0            0   \n",
       "3              0              0              1              0            0   \n",
       "4              0              0              1              0            0   \n",
       "\n",
       "   ...  emb-9_ddr-2  mec-1_mec-4  mec-5_mec-4  mec-9_mec-4  mec-1_mec-10  \\\n",
       "0  ...            0            0            0            0             0   \n",
       "1  ...            0            0            0            0             0   \n",
       "2  ...            0            0            1            0             0   \n",
       "3  ...            0            0            0            0             0   \n",
       "4  ...            0            0            0            0             0   \n",
       "\n",
       "   mec-5_mec-10  mec-9_mec-10  shn-1_ced-1  col-119_daf-4  col-19_daf-4  \n",
       "0             1             0            0              0             0  \n",
       "1             1             0            0              1             0  \n",
       "2             1             0            0              0             0  \n",
       "3             1             0            0              0             0  \n",
       "4             1             0            0              0             0  \n",
       "\n",
       "[5 rows x 245 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/earmingol/opt/anaconda3/envs/c2c_dev/lib/python3.7/site-packages/seaborn/matrix.py:654: UserWarning: Clustering large matrix with scipy. Installing `fastcluster` may give better performance.\n",
      "  warnings.warn(msg)\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1152x648 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.clustermap(df,\n",
    "               metric='jaccard',\n",
    "               method='complete',\n",
    "               col_cluster=True,\n",
    "               row_cluster=True,\n",
    "               cmap='Blues',\n",
    "               figsize=(16, 9)\n",
    "              )\n",
    "plt.savefig(output_folder + '/GA2-LRs-Runs.svg',\n",
    "                dpi=300,\n",
    "                bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Co-occurrence**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "co = pd.DataFrame(np.zeros((len(lrs), len(lrs))), columns=lrs, index=lrs)\n",
    "for i, lr in enumerate(lrs):\n",
    "    v1 = df[lr]\n",
    "    for j, lr2 in enumerate(lrs[i:]):\n",
    "        v2 = df[lr2]\n",
    "        if sum(v1.values | v2.values) == 0:\n",
    "            val = 0.0\n",
    "        else:\n",
    "            val = sum(v1.values & v2.values) / sum(v1.values | v2.values)\n",
    "        \n",
    "        co.at[lr, lr2] = val\n",
    "        co.at[lr2, lr] = val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>arf-1.2_pld-1</th>\n",
       "      <th>daf-7_sma-6</th>\n",
       "      <th>srp-6_F14B4.1</th>\n",
       "      <th>adm-2_pat-3</th>\n",
       "      <th>adm-2_pat-2</th>\n",
       "      <th>let-756_sdn-1</th>\n",
       "      <th>let-756_ver-3</th>\n",
       "      <th>let-756_ver-1</th>\n",
       "      <th>srp-2_F14B4.1</th>\n",
       "      <th>srp-1_try-1</th>\n",
       "      <th>...</th>\n",
       "      <th>emb-9_ddr-2</th>\n",
       "      <th>mec-1_mec-4</th>\n",
       "      <th>mec-5_mec-4</th>\n",
       "      <th>mec-9_mec-4</th>\n",
       "      <th>mec-1_mec-10</th>\n",
       "      <th>mec-5_mec-10</th>\n",
       "      <th>mec-9_mec-10</th>\n",
       "      <th>shn-1_ced-1</th>\n",
       "      <th>col-119_daf-4</th>\n",
       "      <th>col-19_daf-4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>arf-1.2_pld-1</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.119048</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.138298</td>\n",
       "      <td>0.187500</td>\n",
       "      <td>0.161290</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.160000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.130435</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.038462</td>\n",
       "      <td>0.115385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>daf-7_sma-6</th>\n",
       "      <td>0.119048</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.057143</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.081081</td>\n",
       "      <td>0.163265</td>\n",
       "      <td>...</td>\n",
       "      <td>0.052632</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.215385</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.315789</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>0.162791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>srp-6_F14B4.1</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>adm-2_pat-3</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.057143</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.021053</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>...</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.021739</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.055556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>adm-2_pat-2</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 245 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               arf-1.2_pld-1  daf-7_sma-6  srp-6_F14B4.1  adm-2_pat-3  \\\n",
       "arf-1.2_pld-1       1.000000     0.119048            0.0     0.000000   \n",
       "daf-7_sma-6         0.119048     1.000000            0.0     0.057143   \n",
       "srp-6_F14B4.1       0.000000     0.000000            0.0     0.000000   \n",
       "adm-2_pat-3         0.000000     0.057143            0.0     1.000000   \n",
       "adm-2_pat-2         0.000000     0.000000            0.0     0.000000   \n",
       "\n",
       "               adm-2_pat-2  let-756_sdn-1  let-756_ver-3  let-756_ver-1  \\\n",
       "arf-1.2_pld-1          0.0            0.0            0.0       0.138298   \n",
       "daf-7_sma-6            0.0            0.0            0.0       0.333333   \n",
       "srp-6_F14B4.1          0.0            0.0            0.0       0.000000   \n",
       "adm-2_pat-3            0.0            0.0            0.0       0.021053   \n",
       "adm-2_pat-2            0.0            0.0            0.0       0.000000   \n",
       "\n",
       "               srp-2_F14B4.1  srp-1_try-1  ...  emb-9_ddr-2  mec-1_mec-4  \\\n",
       "arf-1.2_pld-1       0.187500     0.161290  ...     0.000000          0.0   \n",
       "daf-7_sma-6         0.081081     0.163265  ...     0.052632          0.0   \n",
       "srp-6_F14B4.1       0.000000     0.000000  ...     0.000000          0.0   \n",
       "adm-2_pat-3         0.000000     0.083333  ...     0.125000          0.0   \n",
       "adm-2_pat-2         0.000000     0.000000  ...     0.000000          0.0   \n",
       "\n",
       "               mec-5_mec-4  mec-9_mec-4  mec-1_mec-10  mec-5_mec-10  \\\n",
       "arf-1.2_pld-1     0.160000          0.0           0.0      0.130435   \n",
       "daf-7_sma-6       0.215385          0.0           0.0      0.315789   \n",
       "srp-6_F14B4.1     0.000000          0.0           0.0      0.000000   \n",
       "adm-2_pat-3       0.043478          0.0           0.0      0.021739   \n",
       "adm-2_pat-2       0.000000          0.0           0.0      0.000000   \n",
       "\n",
       "               mec-9_mec-10  shn-1_ced-1  col-119_daf-4  col-19_daf-4  \n",
       "arf-1.2_pld-1           0.0          0.0       0.038462      0.115385  \n",
       "daf-7_sma-6             0.0          0.0       0.066667      0.162791  \n",
       "srp-6_F14B4.1           0.0          0.0       0.000000      0.000000  \n",
       "adm-2_pat-3             0.0          0.0       0.000000      0.055556  \n",
       "adm-2_pat-2             0.0          0.0       0.000000      0.000000  \n",
       "\n",
       "[5 rows x 245 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "co.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "col_bins = 10\n",
    "data = co.loc[(co != 0).any(), (co != 0).T.any()]\n",
    "colors = ga_selected_ppi_data['score']\n",
    "colors.index = ga_selected_ppi_data.apply(lambda row: row['A'] + '_' + row['B'], axis=1)\n",
    "colors = colors.loc[lr_dict.loc[co.columns, 'WB']]\n",
    "colors.index = co.columns\n",
    "colors_df = colors.copy()\n",
    "colors_df.index = lrs2\n",
    "colors = colors.apply(lambda x: plt.get_cmap(\"YlOrRd\",col_bins)(x))\n",
    "colors.name = 'Presence in Optimal Lists'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "colors_df.to_frame().to_csv(output_folder + '/Celegans-GA2-Run-Ratios.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "smp-2 / plx-1      0.99\n",
       "cwn-1 / mig-1      0.99\n",
       "lin-44 / mig-1     0.95\n",
       "let-756 / ver-1    0.94\n",
       "mec-5 / mec-10     0.91\n",
       "                   ... \n",
       "egl-17 / egl-15    0.00\n",
       "kal-1 / egl-15     0.00\n",
       "kal-1 / sax-7      0.00\n",
       "unc-52 / egl-15    0.00\n",
       "unc-52 / pat-2     0.00\n",
       "Name: score, Length: 245, dtype: float64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "colors_df.sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "colors_export = colors.to_frame()\n",
    "colors_export.columns = ['Color']\n",
    "colors_export.index = colors_df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "colors_export.to_csv(output_folder + '/Celegans-GA2-Run-Ratios-Color.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(111, 111)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "c_input = data\n",
    "D = pairwise_distances(c_input)\n",
    "linkage = hc.linkage(D, method='ward', optimal_ordering=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(111, 111)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "D.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x72 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.palplot(sns.color_palette(\"YlOrRd\", col_bins))\n",
    "for i, text in enumerate(sns.color_palette(\"YlOrRd\", col_bins + 1)):\n",
    "    plt.text(i-0.5, 0.6, i/col_bins, rotation=0, horizontalalignment='center', verticalalignment='top',fontsize=14)\n",
    "plt.title('Fraction of presence in GA-optimal solutions', fontsize=18)\n",
    "plt.tick_params(axis='both', which='major', bottom=False, left=False,labelsize=16)\n",
    "plt.savefig(output_folder + '/GA2-Fraction-Colors.svg',\n",
    "                dpi=300,\n",
    "                bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "co_clusters = sns.clustermap(c_input,\n",
    "                             method='ward',\n",
    "                             cmap='Blues',\n",
    "                             col_linkage=linkage,\n",
    "                             row_linkage=linkage,\n",
    "                             figsize=(15, 15),\n",
    "                             col_colors=colors\n",
    "                             )\n",
    "# Title\n",
    "co_clusters.ax_col_dendrogram.set_title('Co-Ocurrence of LRs in Optimal Lists', fontsize=16)\n",
    "\n",
    "# Color bar label\n",
    "cbar = co_clusters.ax_heatmap.collections[0].colorbar\n",
    "cbar.ax.set_ylabel('Co-Occurrence', fontsize=14)\n",
    "cbar.ax.yaxis.set_label_position(\"left\")\n",
    "\n",
    "plt.savefig(output_folder + '/GA2-Co-Ocurrence.svg',\n",
    "                dpi=300,\n",
    "                bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "clusters = c2c.clustering.get_clusters_from_linkage(co_clusters.dendrogram_col.linkage, 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Export Selected LRs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Selected by Co-occurrence**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "records = []\n",
    "selected_cluster = np.argmin([len(c) for c in clusters.values()]) + 1\n",
    "\n",
    "for idx, row in lr_dict.loc[co_clusters.data.index[clusters[selected_cluster]]].iterrows():\n",
    "    records.append((*row['WB'].split('_'), *idx.split('_')))\n",
    "    \n",
    "ga_export = pd.DataFrame.from_records(records, \n",
    "                                      columns=['Ligand_WB', 'Receptor_WB', 'Ligand_symbol', 'Receptor_symbol'])\n",
    "\n",
    "ga_export = ga_export.assign(score=1.0)\n",
    "ga_export = ga_export.merge(symbol_lrs, how='inner')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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_WormBase_versionWS277</th>\n",
       "      <th>Receptor_desc_WormBase_versionWS277</th>\n",
       "      <th>Pair_confidence</th>\n",
       "      <th>Location_type</th>\n",
       "      <th>Confidence_evidence</th>\n",
       "      <th>Location_evidence</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",
       "      <td>Putative based on literature</td>\n",
       "      <td>Secreted</td>\n",
       "      <td>Orthology, protein-protein interaction (STRING...</td>\n",
       "      <td>wormbase; \" Located in extracellular space,\" h...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>WBGene00002881</td>\n",
       "      <td>WBGene00006894</td>\n",
       "      <td>let-756</td>\n",
       "      <td>ver-1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7</td>\n",
       "      <td>Canonical RTK-Ras-ERK signaling</td>\n",
       "      <td>FGF</td>\n",
       "      <td>fms-related tyrosine kinase 1</td>\n",
       "      <td>Is an ortholog of human FGF16 (fibroblast grow...</td>\n",
       "      <td>Is an ortholog of human FLT1 (fms related rece...</td>\n",
       "      <td>Putative based on literature</td>\n",
       "      <td>Secreted</td>\n",
       "      <td>Orthology, protein-protein interaction (STRING...</td>\n",
       "      <td>wormbase: let-756 is found in the cytoplasm;ex...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>WBGene00006620</td>\n",
       "      <td>WBGene00020504</td>\n",
       "      <td>svh-1</td>\n",
       "      <td>svh-2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11</td>\n",
       "      <td>Cell adhesion</td>\n",
       "      <td>Plasminogen-like (HGFL)</td>\n",
       "      <td>HGF receptor</td>\n",
       "      <td>Is an ortholog of human TMPRSS15 (transmembran...</td>\n",
       "      <td>Is an ortholog of human MERTK (MER proto-oncog...</td>\n",
       "      <td>Literature supported</td>\n",
       "      <td>Secreted</td>\n",
       "      <td>DOIs: 10.1038/nn.3052; 10.1016/j.celrep.2014.1...</td>\n",
       "      <td>wormbase: svh-1 is found in the extracellular ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>WBGene00003029</td>\n",
       "      <td>WBGene00003238</td>\n",
       "      <td>lin-44</td>\n",
       "      <td>mig-1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>25</td>\n",
       "      <td>Wnt signaling</td>\n",
       "      <td>Wnt</td>\n",
       "      <td>Frizzled</td>\n",
       "      <td>Is an ortholog of human WNT10A (Wnt family mem...</td>\n",
       "      <td>Is predicted to have Wnt-activated receptor ac...</td>\n",
       "      <td>Literature supported</td>\n",
       "      <td>Secreted</td>\n",
       "      <td>WormBook, DOIs: 10.1016/j.devcel.2006.02.010; ...</td>\n",
       "      <td>wormbase: lin-44 is said to be located in the ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>WBGene00002280</td>\n",
       "      <td>WBGene00003930</td>\n",
       "      <td>let-2</td>\n",
       "      <td>pat-3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>26</td>\n",
       "      <td>Cell adhesion</td>\n",
       "      <td>Collagen</td>\n",
       "      <td>Integrin</td>\n",
       "      <td>Is an ortholog of human COL4A5 (collagen type ...</td>\n",
       "      <td>Is an ortholog of human ITGB1 (integrin subuni...</td>\n",
       "      <td>Literature supported</td>\n",
       "      <td>ECM-component</td>\n",
       "      <td>10.1083/jcb.201903124</td>\n",
       "      <td>Wormbase: let-2 is located in the extracellula...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Ligand_WB     Receptor_WB Ligand_symbol Receptor_symbol  score  index  \\\n",
       "0  WBGene00000903  WBGene00004860         daf-7           sma-6    1.0      1   \n",
       "1  WBGene00002881  WBGene00006894       let-756           ver-1    1.0      7   \n",
       "2  WBGene00006620  WBGene00020504         svh-1           svh-2    1.0     11   \n",
       "3  WBGene00003029  WBGene00003238        lin-44           mig-1    1.0     25   \n",
       "4  WBGene00002280  WBGene00003930         let-2           pat-3    1.0     26   \n",
       "\n",
       "                       LR Function               L Function  \\\n",
       "0                  TGF-B signaling                    GDF11   \n",
       "1  Canonical RTK-Ras-ERK signaling                      FGF   \n",
       "2                    Cell adhesion  Plasminogen-like (HGFL)   \n",
       "3                    Wnt signaling                      Wnt   \n",
       "4                    Cell adhesion                 Collagen   \n",
       "\n",
       "                      R Function  \\\n",
       "0                         BMPRIB   \n",
       "1  fms-related tyrosine kinase 1   \n",
       "2                   HGF receptor   \n",
       "3                       Frizzled   \n",
       "4                       Integrin   \n",
       "\n",
       "                   Ligand_desc_WormBase_versionWS277  \\\n",
       "0  Is an ortholog of human GDF11 (growth differen...   \n",
       "1  Is an ortholog of human FGF16 (fibroblast grow...   \n",
       "2  Is an ortholog of human TMPRSS15 (transmembran...   \n",
       "3  Is an ortholog of human WNT10A (Wnt family mem...   \n",
       "4  Is an ortholog of human COL4A5 (collagen type ...   \n",
       "\n",
       "                 Receptor_desc_WormBase_versionWS277  \\\n",
       "0  Is an ortholog of human BMPR1A (bone morphogen...   \n",
       "1  Is an ortholog of human FLT1 (fms related rece...   \n",
       "2  Is an ortholog of human MERTK (MER proto-oncog...   \n",
       "3  Is predicted to have Wnt-activated receptor ac...   \n",
       "4  Is an ortholog of human ITGB1 (integrin subuni...   \n",
       "\n",
       "                Pair_confidence  Location_type  \\\n",
       "0  Putative based on literature       Secreted   \n",
       "1  Putative based on literature       Secreted   \n",
       "2          Literature supported       Secreted   \n",
       "3          Literature supported       Secreted   \n",
       "4          Literature supported  ECM-component   \n",
       "\n",
       "                                 Confidence_evidence  \\\n",
       "0  Orthology, protein-protein interaction (STRING...   \n",
       "1  Orthology, protein-protein interaction (STRING...   \n",
       "2  DOIs: 10.1038/nn.3052; 10.1016/j.celrep.2014.1...   \n",
       "3  WormBook, DOIs: 10.1016/j.devcel.2006.02.010; ...   \n",
       "4                              10.1083/jcb.201903124   \n",
       "\n",
       "                                   Location_evidence  \n",
       "0  wormbase; \" Located in extracellular space,\" h...  \n",
       "1  wormbase: let-756 is found in the cytoplasm;ex...  \n",
       "2  wormbase: svh-1 is found in the extracellular ...  \n",
       "3  wormbase: lin-44 is said to be located in the ...  \n",
       "4  Wormbase: let-2 is located in the extracellula...  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ga_export.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "ga_export.to_csv(data_folder + '/PPI-Networks/Celegans-GA-LRCount-Selected-LR-pairs.csv', index=False)\n",
    "ga_export.to_csv(output_folder + '/Celegans-GA-LRCount-Selected-LR-pairs.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(28, 15)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ga_export.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analyze functions in lists selected across all GA runs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Generate function metadata**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "functions = symbol_lrs['LR Function'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "function_colors = c2c.plotting.get_colors_from_labels(functions, cmap='tab20')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "func_color_dict = dict()\n",
    "row_cols = []\n",
    "row_functions = []\n",
    "for idx, row in symbol_lrs.iterrows():\n",
    "    name = str(row['Ligand_symbol']) + '_' + str(row['Receptor_symbol'])\n",
    "    func = str(row['LR Function'])\n",
    "    func_color_dict[name] = function_colors[func]\n",
    "    row_cols.append((name, function_colors[func]))\n",
    "    row_functions.append((name, func))\n",
    "row_cols = pd.DataFrame.from_records(row_cols, columns=['LR', 'Function']).set_index('LR')\n",
    "row_functions = pd.DataFrame.from_records(row_functions, columns=['LR', 'Function']).set_index('LR')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "func_mapper = row_functions.to_dict()['Function']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Compute fraction of each function in each GA-selected list**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "func_count = df.T\n",
    "func_count.index = df.columns.map(func_mapper)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "func_count = func_count.reset_index().groupby('index').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "func_frac = func_count / func_count.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "sorted_order = func_frac.T.median().sort_values(ascending=False).index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Wnt signaling', 'Cell migration', 'Cell adhesion',\n",
       "       'Hedgehog signaling', 'Insulin signaling', 'Notch signaling',\n",
       "       'Mechanosensory mechanism', 'Canonical RTK-Ras-ERK signaling',\n",
       "       'TGF-B signaling', 'Trypsin-like proteolysis', 'Stress response',\n",
       "       'Receptor-Mediated-Endocytosis', 'PCP pathway', 'Apoptosis',\n",
       "       'Neuronal communication', 'Lipid homeostasis', 'Immune response',\n",
       "       'Epidermal development', 'Eph receptor signaling', 'Longevity'],\n",
       "      dtype='object', name='index')"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted_order"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Plot Fraction for each Function**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JsbGxXs/ZZs6cqf79++uvv/5yGb9jxw7t2LFD06dP18cff5ylc1QAgeNrBYvLjDE103tIuszR0BhzZUZtU9sDAPzkfsczdz1nDZUngNzFXdqAb5WlcrP6FLzjPQD+lwxx5swZbdiwwWWa427MG264QWFhYbruuutUtGhR/f777zp+/LhLW0fShTFGN998c7rr+/DDDzVlyhQ9+eST2rRpk44dO6YNGzaoQ4cOkqSPP/5Y33zzjcs8SUlJLokLv//+u5KSkpyPLVu2+Ly906dPdy5r4MCBWrdunQ4dOqSEhAT9+uuvmjZtmrp06aLQ0FCflylJQ4YMcS63R48e+vnnn3Xs2DHFxMSoX79++uijjzK929HhzjvvVPny5TV79mzFx8fr4MGDmjx5skqUKKGkpCQ9+uijXuerWrWqBg0apC+//FIxMTE6fPiw9uzZo2XLlumBBx6QlHKR56uvvsrStmVF7969dfr0aU2dOlX79u3TkSNH9Omnn6py5cq6cOGC+vfv7/Wu0XHjxjmTK9q3b6/vv/9eCQkJ2rp1q55//nmtWLFCb7zxRo7FDQAIvvr166tGjRqSpHnz5umxxx7TH3/84fP8U6ZMUVJSkmrWTLkcNXToUJfzhaSkJK9JoEuWLNE777yje++9V2vWrHH+/vTo0UOStGfPHrVv31779+9Xw4YN9dFHHykuLk6JiYnasGGD+vXrJ0l64403NGXKFJdlx8bGavjw4ZKkjh076ttvv9XevXt1/Phx7dixQ1988YX69eun0qVLu8z3xhtvaP369QoNDdXw4cMVExOjo0eP6siRI/rll180YcIEtW7dOltVnQYNGqR9+/bprbfe0h9//KGjR49q2bJluvzyyyVJY8eO1ZAhQ3Tp0iX9/e9/18qVK3XkyBEdPnxYX3zxha6++molJiaqe/fuOnDgQMDj79mzp0qVKqWZM2cqPj5e+/bt0+TJk1WhQgWdPn1ad955pw4dOuQx36hRo5zJFR07dnQ5p3jhhRcUGhqqTZs2qVu3bhlWsnj99df17bffasSIEYqNjdWxY8f0/fffq1mzZs5tdFRvczh+/LgGDBigS5cu6brrrtO8efO0e/duHT9+XHFxcRbqsnMAACAASURBVFqwYIGefPJJVapUyWN9X375pXr37q2//vpLnTp10qJFixQfH6+jR49qyZIlioyM1NmzZ9WzZ09t2rQp3bgB5BxfK1hMybyJbOpznA/tqJwBD79e0TXYIQSF+z9uG0YP8GjjeQG9fY7E0vLQSi9jG+XIugor9wv6DYMUB4Dc58v3PXKWt4ulvA95j/fEwZw59wEAuEp7oWH16tW68cYbXYYlObsGKV68uJo3b64ff/xRP/zwg7p27erRtmHDhl7/aeywe/duvf3223r22Wed4ypWrKivvvpKV111lfbv368PPvhAXbp0cU4vXbq0SpQo4RwuWbKkx4UIXy1cuFCSdNddd2n8+PEu0ypVqqQmTZqob9++WVrm3r17NXZsyjnHAw88oI8//tg5rWLFinr//fdVokQJj/Wl59KlS/r5559d9mN0dLQuXLigJ554QuvXr9eWLVvUqJHr3+7uVUEcatasqXbt2ikiIkKjR4/WW2+9pe7du2dpG3117Ngx/fLLL6pTp45z3P33369SpUqpW7duOnjwoJYsWeJSxeLMmTN6+eWXJaUcj4sWLXJWUqlUqZLefPNNXXbZZS7HDACg4DHG6O2333YmBU6aNEmTJk1SRESEWrRooRYtWqh169a67rrrvF6YL1asmIoVK+acVrRoUZ/OFw4ePKg+ffo4u/SS5PIbPHDgQB0/flwtWrTQ8uXLVbJkSee0ChUq6P3331f16tX1j3/8Qy+99JL69OnjPG/59ttvlZycrKpVq+qbb75xqRRWvnx51alTx2s1Asf5ypNPPqmRI0e6TKtSpYqaNm2qxx57LNNty2y7lyxZoltuucU5ztEl24EDB/T8889Lkt555x0NHjzYZd4777xTHTp0UIsWLbR161aXpIZAxX/s2DFt3LjR5ZwiOjpaTZs2VcuWLXX8+HG99tprmjBhgnN6fHy8XnvtNUlSp06dNH/+fGfSbKVKlfTGG28oPDxcjz32mDZs2KCpU6emm7i6e/duzZkzR/fee69zXGRkpBYsWKDatWvrzJkz+uijj/TWW285p69atUqnT59WaGioFi9e7FKVpXz58goPD1fnzp091nX27Fn1799fkvT4449r3LhxLtM7dOigtm3bqmPHjlqxYoWGDh3qUTUNQM7ztYKF8eHhT1sAQB5GtQcEC1Va/EeVGwAA4K/w8HBdeeWVkjy7/nAMp61I4Ui2SNv2woULWrdunaTMuwcJDw/XM8884zG+RIkSzn9g//zzz1ndDJ85yjU77s4MhI8//liXLl1SSEiIyz/Z0xo1apTLBZmMvPzyy16TVHr16uV87c8+csy/du3aLPfZ7qsnn3zS5UKIw+233+68yOAe+1dffeUs//3mm2+6XHxyGDRokPOOZABAwdWzZ0/Nnj3b5Xc6Li5Os2fP1rPPPqsWLVqodu3amjRpkpKTkwOyzrCwsHR/v3fu3Om8kD1+/Ph0f8tffPFFlSpVSgkJCVq8eLFzvOO8o3Llyl5/39KTE+cr7rp06eKSXJHWlClTdO7cOTVu3NjreZuUkgA7dOhQSdKnn34qa61zWiDif/zxx72eU7Ro0UIPPvigpJQuNS5evOicNnPmTGdVirFjx3qtSDZgwAD97W9/kyRNnTo13fXffPPNLskVDlWrVtWtt94qyfOcxrHdpUqVUvny5TPcvrQ++eQTHT16VBUrVtTbb7/ttU1YWJgzWWXRokUe1eQA5LzMEiz2pj72+PDwte3eQG8EAADIGyivDgAAkL85Eih++OEH57gTJ07ot99+U0hIiEtVC0eChaP7EEn65ZdfnBfsM0uw6NChg0JCvP9rql69epKkw4cP+7EVvnH08z19+nTNmjVL58+fz/Yy16xZI0lq2rSps7S5u7Jly6pt27Y+Le+2227zOr58+fKqUqWKpPT30a5du/T888/r+uuvV4UKFRQaGipjjIwxaty4saSUf/7v3LnTp1iyKr3YQ0JCnBdJ3GN37L/KlSvrpptu8jp/aGioS8UUAEDBdd9992nXrl367LPP1Lt3b9WuXdtlelxcnB577DHde++9zgva2dG0aVNVrVrV67Rly5bJWquKFSuqYcOGOnXqlNfHpUuX1KBBA0nS+vXrnfM7zjt+//13DRs2zOeL4o753n77bS1YsCBgySRpeauk4LB06VJJKd12nT59Ot3tdlTTOn78uMu5RSDi91bZw+Guu+6SJJ08eVK//fabc7wjAfiaa65xnld640ic2Lhxo06fPu21TXrnNFL656zXXHONjDE6efKk+vfvr/j4+HSXkZZjf0dGRurixYvp7m/HMWat9ejaD0DOy7CrDmttRC7FgQKOktzIy8av/NBjXHTEc0GIBNmVW93pFFQ9hrqeFvyaTjvAobB27wXAN1NuO+UxLjoIcTjQVRvgm8jISH3yySfO/qkbNmyoNWvWKDk5Wddcc43KlSvnbNuyZUtJ0oYNG3Tu3DkVK1bMJdkiswSL6tWrpzvNcVfo2bNns7M5GXr66ac1ffp07du3T7169dKAAQMUGRmpyMhItW/fXtdff32W+zPfs2ePJOmqq67KsN1VV13lUzlnf/fRZ599pr59+/q0/xwVIwLNn9gd+y+jCyFS5vsXAFBwFCtWTD169FCPHj0kpVzAX7VqlT7++GPNmTNHycnJ+uKLLzRmzJhsdyFVq1atdKdt25byf8fExESVKVPGp+UdPXrU+bpdu3bq1q2bvv76a73++ut66623nF2dtGnTRu3atVOxYsU8ljFixAjNmzdPR44cUZcuXVSpUiW1adNGkZGR6tChg5o0aZLFrfTky3aPGzfOo7uK9Bw9elR169YNWPyOZILMpu3Zs8eZ0OE4p3DvRs1d2qTTAwcOeD3H8Oecpk6dOho4cKDGjx+vadOmacaMGWratKkiIyPVpk0bdejQwWu3NY79/eWXX/p1nAHIHb52EQIAAIA8gAohAAAgJ6VNinDc+eetexBJuuyyy1SvXj2dO3fO2S2Io23NmjUz7cbBW6nm3FS+fHmtW7dOAwYMULly5ZSUlKQFCxZo6NChuuGGG1S3bl19/PHHWVqm487HUqVKZdjOl37gJd/2Udoy3FJKCfM+ffro7Nmzqlu3riZNmqSNGzfq8OHDOnnypJKSklzu8ExbTjuQ/Ik90PsPAFDwVKhQQd27d9enn36quXPnOpMhJ06cmO1llyhRIt1p/iQknjt3zmX4888/1+jRoxUeHq6LFy9qzZo1euONN9S5c2dVq1ZNr7zyikdFrVq1amnDhg168MEHVaJECR07dkxz587V008/rauvvlrXXnutvv322yzHllZObncg4s/ovCDtOcGpU6c8Xmd2zpA2iSEpKclrG3/OaaSUrkkmT56shg0bKjk5WRs2bNC7776ru+66S5dddpmeeOIJj3UG4jgDkPNIsAAAAAAAAICklLv4KlasKOl/XX84nh1dgqTl3k2Io2uRzKpX5BXVqlXTxIkTlZCQoLVr12rMmDHq2rWrihYtql27dunBBx/M0gUbxwWA9EpMO6S9ABBo06dP1/nz51WuXDmtWbNGjz76qK699lpddtllKlOmjEqXLu3skzyvyQv7DwCQf9xxxx26/fbbJUm7d+/OsapM0v8u1NesWVPWWp8eM2bMcFlG0aJFNWTIEMXFxWnr1q2aNm2a+vTpo4oVK+rEiRMaOXKkevXq5bHuunXraubMmUpMTNTKlSs1evRotW/fXiEhIdq0aZM6deqk+fPn5+h2v/zyyz5vt3tXaNmNP6PzgrTnBGmTKRyvMztnSDvd14oRvjLGKDo6Wlu2bNHu3bs1a9YsRUdH6/LLL9fZs2c1fvx4dezY0aXbFEfcffr08Xl/R0VFBTRuAJnLsIsQwJeuE9xLykvBLStP6V/AN3xWcpYvXSO5vwdS3n4f8lp3T3mpS5hAvZfu+5jutPIv93Moup4CvMtv5yP57bcb8IcxRq1atdLXX3+t1atX6/z5887+w90rWEgpCRYzZszQ6tWrFRsbq4SEBEn5J8HCISwsTDfccINuuOEGDRo0SAcOHFCHDh0UGxurkSNHasCAAT51FxIeHq7Nmzdr+/btGbbLbHp2bN68WVJKKfIqVap4bZO2gkVeEh4eLkn6448/MmyXk/sPAJC/NG7cWN98840k6cyZMy7dmQVS7dq1JUnx8fE6e/ZshlUffNGgQQM1aNDA2aVXVFSUZs+erTlz5ji7aXNXvHhxtW7dWq1bt9aQIUO0detWtW3bVkeOHNFrr72mrl0D35Vr7dq19csvv2jXrl3ZXpa/8cfGxqpFixbpTnNwnEdIUkREhDZt2qStW7dmGNPvv/8uKaVKxRVXXJHVTfJZRESEIiIi9Pe//13jx4/X888/rzFjxuinn37S4sWL1blzZ0kp+zsmJiYg+xtAzqGCBQAAAAAAAJwciRS7d+/WvHnz9Ndff6lGjRpeu/xwVLBYs2aNvv/+e+f4nEywKFKkiPP1pUuXcmQdV1xxhaKjoyVJhw8f1okTJ3yar2XLlpKkjRs36sCBA17bJCUlacWKFQGJ0xtHmeiM9k1Wuz7JLY7956go4k1ycrLzQhoAAPv375eUcn5QuXJll2mOc4ZAnC906NBBknThwgXNmzcv28tLq0SJEho6dKhzOG3SQEYaNmyoBx54IEvzZFXHjh0lSQsWLAh4BSlf489ofzumlS1bVk2aNHGOd5zPbt68WTt27Eh3/s8//1yS1LRp00y7KAuUsLAwvfzyy87htNvu2N9r167V3r17cyUeAFlHBQsAWeJescTfaiW+3K2YWRtf7iDkLkMA8E9++/6kikj25LcqAoVVYT0+AeS+tMkRb7zxhiTv1SuklLsvK1asqMTERE2ePFmSVKlSJa93XQZKpUqVnK/j4+NVp04dv5YTGxurBg0apDt9586dklLKeftaMvqBBx7QsGHDlJycrBdeeEEzZ870aPPSSy/pzJkzfsXsi1q1aklKSXpJTEx0dvni8Nlnn2nhwoU5tv7s6Natm8qWLauTJ09qyJAh+u677xQW5vp/iH//+9/as2dPkCIEAOSGnTt3avr06XrmmWc8fsfSiomJ0dy5cyVJbdu2dUnClFLOGXbs2KH4+Phsx9SwYUN17txZCxcu1ODBg3XTTTcpIiIi3fZxcXGqXr26ihUrJimlOlOdOnUUEuL9vmfHeYcjbgdfz1fSzhNIAwcO1JgxY5SYmKgBAwZoxowZCg0NTbf99u3bddVVVzmHAxH/+PHj1a9fP49zvnXr1mnWrFmSpF69ermcM/Tq1UsvvviiLly4oEGDBumrr77y2PdTpkxRTEyMJKlfv37prt8fu3fv1hVXXKGiRYt6nZ7e+927d28NHz5cCQkJ6tu3r+bPn59htZRt27apfv36gQscgE+oYAEACLqBTbd5PFBwbR0z1uMBIO/buPCQywMAUHA1b97c+Y/cjRs3SvpfpQp3xhiXqg1SSjKGL91p+Ktu3boqW7asJOnNN9/Ujh07dO7cOV28eDFLd6h27txZN910k7M88+HDh5WQkKCYmBi98MILmjBhgiTpnnvu8bjIn57w8HA9/vjjkqRZs2apZ8+e2rBhgxITE7V582Y98sgjGjt2rLPMeE647777JEnHjh1T586dtXz5ch09elSxsbF66aWX1Lt37xxNgMmOUqVKacSIEZKklStXqnPnzlq9erUSExO1bds2DR06VM8991yO7j8AQPCdPXtWo0aN0hVXXKEHHnhAH330kbZs2aJjx44pISFB69ev18svv6zWrVvrr7/+UmhoqPP3I61mzZpJkr788kstXbpUJ0+e1MWLF3Xx4kVZa7Mc16RJk1S5cmXFx8erefPmGjVqlDZt2qTExEQdPXpUmzZt0tSpU9W9e3fVrVtXSUlJznlHjRqlevXq6aWXXtLSpUu1f/9+HT9+XNu3b9eECRPUv39/SVLNmjV10003Oedr1KiROnbsqMmTJ2vjxo06evSojhw5op9++kn9+vXT/PnzJUk9e/bM8vb44sorr9S//vUvSdLMmTPVsmVLffrpp4qLi9OJEye0f/9+rVixQiNHjlSjRo30zDPPuMwfiPgrVaqkNm3aaNasWTp06JAOHDig//znP+rUqZMuXryo8uXLa/jw4S7zVKtWzTnum2++UZcuXbR69WodO3ZM27Zt04svvqiBAwdKSjn3DXSCxQcffKDw8HA9/fTTWrBggfbs2aMTJ05o165d+uCDD3T33XdLksqUKePsHkRKOReaNm2ajDFatmyZmjVrpqlTp2rHjh06ceKE4uPjtWbNGr3zzjtq0aKF7rnnnoDGDcA3VLAAAACFVn6r0gAAAJAbihYtqhtuuMGlG4v0KlhIKckXjn+OSznbPYiUUlZ5wIABevPNNzV//nyXdYeHhysuLs6n5VhrtXbt2nS7opBS/uE+dmzWEoLfeustbdu2TYsXL9Znn32mzz77zGX6gw8+qDp16mjkyJE+J25kxS233KJ+/fpp6tSpWrdundq3b+8yvX79+po2bZrLxZu8ZNCgQfrtt980bdo0fffdd/ruu+9cprdt21Y9e/bUo48+muEdtACA/KtYsWIqWrSo/vrrL3366af69NNP021brlw5vf/++86Ez7QeffRRvf/++0pISHB28eGwfPlytW3bNktxhYeHa+XKlbrrrru0fft2vfTSS3rppZe8tg0NDfX4ndq1a5dGjRqlUaNGeZ2ncuXKmjNnjkslDmut19/DtG677TaPBINAciQiPPPMM1q3bp2zWw9v3KtVBCL+Tz75RLfddpt69erlMa1UqVKaN2+eqlWr5jFt2LBhOnTokCZOnKhFixZp0aJFHm3+9re/6euvv/aofhIIhw4d0rvvvqt3333X6/SSJUvq448/VpUqVVzGd+vWTZ9//rmioqIUGxurhx9+ON11NG3aNKAxA/ANCRaAF97vnm/vZRwQWONXfugxLjriuSBEAhQM7p8pPk9AweCt8k1OdZdBtxwACqvIyEhngkWZMmV09dVXp9vWvbpFTidYSNLrr7+u6tWra9asWYqNjdWpU6eyfCfqokWL9O2332rp0qXavn274uPjdfr0aVWqVEnXXHONevTooaioqCwnQRQrVkwLFizQlClTNHXqVMXGxio0NFSNGjXSww8/rH79+mnw4MGS5HPXI1n13nvvqUWLFvrPf/6jLVu2KDQ0VLVq1dI999yjwYMHKyEhIUfWGwjGGE2dOlUdO3bUxIkTFRMTI2ut6tSpo969e+upp57SpEmTJOXc/gMABFe9evV09OhRLVy4UCtWrNAvv/yiXbt26cSJEwoLC1PFihXVqFEj3XrrrYqKivK4QO1wzTXXaMWKFXrrrbf0008/KSEhQRcvXsxWbI0aNdKvv/6qmTNnau7cufrll1907NgxhYSEqFq1amrSpIluueUW3XfffapQoYJzvjfffFO33HKLlixZos2bNys+Pl6JiYkqXbq06tevr9tvv10DBw706Cpjw4YNWrJkiZYvX65du3YpPj5e58+fV5UqVdSsWTP16tVL9913X45WD5NSkiy6d++uiRMnasmSJdq1a5dOnjypUqVKKSIiQtdff71uv/12de3aNeDx33jjjVq/fr1Gjx6t7777TocPH1blypV122236aWXXkq3slVISIgmTJige+65R5MmTdKaNWt09OhRlS5dWk2aNNH999+vRx55JEeSKwYNGqTGjRvru+++0/r16xUfH6+jR4+qRIkSqlOnjjp27KgnnnhCV155pdf57777brVp00aTJ0/WwoULFRsbqz///FPFixfXlVdeqWbNmqlTp0668847Ax47gMyRYAEAAAAEARfuASB3xL3RJdgh5EsjR47UyJEjfWobGRmZpeQGXypMREVFKSoqKt3pISEheuqpp/TUU0/5vF53DRo0UIMGDfTkk09mab6IiIhMtzckJEQDBgzQgAHef98d/W7XqFHDY1pm2+6Q0X40xuiRRx7RI4884nV66dKlM9yGjJY9YsQIr2XYpZTqEr4cC2mro6SnZ8+e6ZYLz2j/AUBeNviz+Zk3giSpbNmyuv/++3X//fdnazktW7bUvHnzMmzjy+9SWkWLFtVDDz2khx7yrIyanqpVq6p3797q3bt3ltbVrFkzNWvWTC+88EKW5suML+cz7q688kqNHj1ao0eP9nmeQMVfr149TZs2za9527dv71HRyxe+7J/0zovKly+v++67z9l1mz8qVaqkYcOGadiwYX4vA0DOIMECKCDcy9xT4h4FDV05AED+1+piSLBDAPINz6p6VNQDCorTp09r+fLlklK6IEHWWGv1zTffSGL/AQAAAMh9JFgAforekeg5MiLXwwAAIN8jSdB/vnRrRvdT8EePoa5/Kv4apDh8xfcIgLzk3LlzunTpkkqWLOl1+pAhQ3Ty5ElJytZdjQVZYmKiKlas6HXav/71L+3YsUMS+w8AAABA7stygoUx5uXUlzuttbMCHA8AIBO52e88AAAAACBr4uPj1bJlSz366KPq3LmzatWqpeTkZP32228aN26cs0x5jx491Lhx4yBHmzfVqVNH/fv3V/fu3XXVVVcpJCRE27dv17Rp0zR16lRJKf2xd+7cOciRAgAAAChs/KlgMUKSlTQ0sKEAAAAAAAAA+V98fLxeeeUVvfLKK16nt2rVSlOmTMnlqPKPEydO6O2339bbb7/tdXrDhg01e/ZshYTQ/RgAAACA3OVPgsUJSeUk7Q5wLAAAAECe515JiCpCANKiuxIA1atX17Rp07R48WJt2rRJhw8f1qlTp1ShQgVde+21euCBB9S7d2+FhoYGO9Q865NPPtGiRYu0fv16HT58WCdOnFC5cuXUpEkT3XXXXYqOjlbx4sWDHSYAAACAQsifBIt9SkmwKBvgWAAAecz4lR+6DEdHPBekSFDYcAHbfwW1G6GG4+8MdgjIZ7jIDQAIlmLFiqlv377q27dvsEPJt3r27KmePXsGOwwAABAkUVFRioqKCnYYAOCVP3X05ksyktoHOBYAAAAAAAAAAAAAAIA8yZ8EiwlK6SakhzGmVYDjAQAAAAAAAAAAAAAAyHOy3EWItfagMaanpDmSFhhjXpQ0zVp7NuDRAYWAL+Wrc6tUfkEtK59bfOlOI3pHouuIiBwMCACAfIjzkZxH9ykAAAAAAACAf7KcYGGMmZb6crOkVpLGSnrDGLNR0gFJmSVaWGttv6yuFwAAAACQMV8SPgEAAAAAAAD4J8sJFpKiJNnU147nUkpJtvAVCRYAAPiAC2Xpy63qPu53ekvc7Q0AAAAAAAAAQGEU4ud8xu3hbVx6jwLFGFPDGPOmMWaLMeaUMeaEMWajMeZlY0yFAK2jsjFmmDFmtTHmmDHmgjHmpDFmkzFmrDGmUSDWAwAAAAAAAAAAAAAAvPOngkWtgEeRTxljOkn6RFJ5t0nXpj4eMcbcYa3dkI113CLpM0mV3CaVkXRN6mOAMWaotfYdf9cDAAVBblU0AJBq9UTX4Q6jgxMHgDzJvQqTRCUmAAAAAAAA5G9ZTrCw1u7JiUDyG2PMNZI+V0r3KGckvSlpqVL26R2SnpR0haT5xpjm1tqDfqyjlqSvJJVMHfWNpBmS9kiqKqmTpOjUdb5tjNlrrZ2djc0CAAAAAAAAAAAAAABe+FPBAineVUpyxSVJna2136eZttIY84ukjyRVk/QPSZ4duGdusP6XXPEva+1gt+nzjTFLJc1NHR4uiQQL5AvudzTm1N2M0TsSPUdG5MiqACDf8OWu8sJ65/n3i1xP2RoGKQ4AAAAAAAAAQN5DgoUfjDHNJbVLHZzhllwhSbLWzjTG9JXUXlIfY8wQa+2RLK6qpWNxkl711sBa+4UxJkYpXZI0McaUsdYm+bqCgU23uY1pn8UQkV8U1gtlgZJbCSGB4n6BUMq5i4S/XtE1h5YMAAAAAAAAAAAA5B0BSbAwxlSWdIOk6pLKSEqSdFDSOmttQiDWkcfcneb11AzaTVNKxkKopO6S3s/ieoqmPh+z1p7MoN0OpSRYpJ0HCBoSd4DcTXLJLb5sU0Hc7tzUY6jrqdmvQYoDAAAAAAAAAAB4ylaChTGms6Shklpl0GaVpDestYuys6485ubU5zOSfs6g3XK3ebKaYLFNUmNJlYwxZTNIsqiT+nzMWnssi+uAF1vHjPUY1zB6QBAiAQAAhRXVp9KXm8lcdBsDAAAAAAAAwMGvBAtjjJE0SVJ/x6gMmkdKijTG/MdaW1CuUDdKff7DWnsxvUbW2oPGmCSlVPVolF67DExWSrUMI2m4JI//qBtjuktqmjo4wY91AMhnqBCQPfmtuxfAXX672Jvf4gVyAhWA8gfeA8BTXFycZs6cqW+//Va7du3S0aNHVbRoUVWtWlXNmzdX165ddc8996hkyZIBXe+KFSvUrl1Kz6y7d+9WRESEy/SIiAjt2bNHr7zyikaMGBHQdQdK27ZttXLlSv3f//2fZsyYEexwckXKvwul6dOnKyoqKrjBAAAKhBEjRujVVz17Tw8NDVX58uXVoEEDderUSdHR0apSpUoQIkSgpD3/k6Rbb71VixcvznAea61q1aqlPXv2OMfFx8erWrVqORZneqKiovTBBx+oTZs2WrFihcu0GTNmqG/fvpJSYi7IMtoPvnKcU/oqWPs0P/xNUpDktc9RiJ/zjZH0iFIu/BuldFExSdLTSkm6eFrSREl/pGnziDFmTHYDDjZjTDFJlVMH9/swy77U5yuzui5r7RJJ/0gdfNYYM88Yc48x5npjTBdjzFhJn6dOXyDpzayuAwAAAAAAIK0LFy5o8ODBql+/voYPH65Vq1bpwIEDOn/+vE6dOqWdO3dq9uzZ6tOnj8LDwzVt2rRgh4wcsmLFChljZIxRXFxcsMMBAECSdOnSJR07dkw//PCDhg8frsaNG2vt2rXBDitPBbPgDAAAIABJREFUiYiIkDEm3174Xbp0qQ4fPpxhm1WrVrkkVxRGcXFxznM1fxMaAIljKauyXMHCGNNM0hOSrKTjkqKttf/NoP3dSqnEUFnSE8aYD621G/2MNy8ok+b1KR/aO9qU9mdl1trhxpjlkoZIuiP1kdYfkl6X9JG19pI/6wAAAAAAoKD6Ym3G/5jNb+66sWqOLv/MmTPq1q2bli1bJkmqW7euBg4cqHbt2unyyy/XhQsXtG/fPi1evFjTp09XXFycxo4dq4ce8qwCAwAAXP0wtWAVoW7Vb2Curev3339XzZo1JUlnz57Vtm3b9K9//UtffPGFjh49qjvvvFM7duxQ6dJ+XYpBHlK2bFmdPHlSn3zyiQYNGpRuu5kzZ7q0R8Hy4IMPavLkycEOA/DKny5C+iulIsU5SbdYazdl1NhaO9cYs0PST5KKKqXyRX7uKqREmtfnfWh/zst8PjPGVJPUV9LN6TSpK6mPpO2S1mSwnEeUsu+dJyEAkFdsHTPWY1zD6Pz8U5HLVk90He4wOjhx+IgS7DmL/Vu4+PL9+esVXXMrnHyHbqPSl9lxU1i/a9yPGYnjBgXPwIEDnckV0dHRGjdunIoUKeLS5vLLL9cNN9ygIUOG6J133tHcuXODEWqeVhjv+soLpXoBAAVXyZIlnckTpUuXVpUqVXTzzTfr7rvv1hdffKHDhw9r1qxZio6ODnKkyK67775bM2bM0KxZs9JNsDh37pw+/zylwPs999yj6dOn52aIWRIVFUX3aX4ICwsjYQpOee1z5E8XIW2VUr3iw8ySKxystZslfaCUxIy2fqwzLzmb5nVRH9oX8zKfT4wxDSX9LKmXpL+UUjkkPHW9VSTdKylWUjtJy40xPdJb1v+zd9/xNd3/A8dfR2RKSIxIzcRordKiqBkjOggltvqKlB++ihpfjerQVo3SUrRUiwRpVW2KmBEj9ia2GLEyjFiZ5/dH3NPc3HuTe29ihPfz8biP3Nzz+XzO59yRc3LP+7zfqqrOUlW1tqqqtaUWmRBCCCGEEEIIITLbtGkTQUFBAPj6+jJz5kyD4IqM7Ozs+Oyzz5g3zzD4SAghhBDiaRgxYoR2f/v27c9wJiK3dOrUCTs7O/bt28epU6eMtvnnn3+4desWZcqUoVGjRk95hkKIl501ARYlH/+0dE+1I1P/vCohw31zQqd0bcwpJ5LZPKAU6cEZjVRVna6q6iVVVZNVVY19XJqlHulBFnbAXEVRnmyuUCGEEEIIIYQQQryQJk2aBEC+fPmYOtUwS5IpVapUMfq4qqr89ddftGnThhIlSmBnZ0eRIkVo3rw5wcHBpKWl5cq8LREUFKTVFga4fPkyAwYMwMvLC0dHR7y8vBgyZAjx8fFan4cPHzJx4kTeeOMNXFxccHNzo02bNhw5csTkery9vVEUJcurrPbt20eHDh0oXrw4jo6OVKxYkeHDhxMbG5ttDWTdsqCgIFJSUpgyZQp16tTBzc0NRVFYvny51vbs2bP88MMP+Pj48Morr2BnZ0ehQoWoUaMGn376KdeuXTM6P0VRaNq0qfa7l5eXtl5FUfD09DQ5J1MOHDiAv78/np6eODg44OrqSp06dRg3bhz379832S9zHflFixbRpEkT3NzccHJy4s0332Tq1Kmkpkr1XCGEeNm89tpr2v2bN28abRMfH8/o0aN56623cHNzw8HBAU9PT/z9/bPcn2fs/+233/L2229TtGhRrX+zZs0YP348N24YL0mXnJzMrFmz8PHxoXjx4tjZ2VG8eHF8fX1ZuXKlyfWNHj1ab1978OBBunTpQsmSJXFwcMDLy4vBgwcTExNj0Nff3x9FUbh48SIAX3/9td7+29SxRW7to4OCgmjSpAlFixZFURSmTJlisq8pbm5uvPfeewCEhIQYbaMrD9KtWzftuC47kZGR/Pe//+W1117D2dmZAgUKULVqVT799FOT7x2dpKQkJk6cSPXq1XFycqJo0aI0b96cZcuWZbvezMefmUVHRzNjxgxat25N6dKlsbe3x9nZmcqVKzNgwADOnj1rtJ+npydeXl7a702bNjV4rZ+n5+FpyXwcvmHDBt577z2KFSuGg4MDlStX5ptvvuHhw6yvi7958yaDBg3Cy8sLBwcHSpYsSdeuXTl48KDZc7H2c6Vz5coVPv30U2rWrImbmxuOjo5UqFCB9957j2nTpmmlca5evUr+/PlRFIXg4OAsx7x8+TI2NjZ6ba19Ly1ZsgRfX188PDywt7fH3d2dd955hwULFmSZ3S4hIYHvvvuOunXr4urqiq2tLe7u7lSrVo0ePXqwYMECg+P67D5H1oyZE9aUCLF5/DPFwn669jZZtnrOqaqaqChKLFCU9OCH7OjaXLZkPYqi1ABqP/41RFXV4ybmc1dRlO+A+YAT0AX4yZJ1PQ0vawphkTdI6nQhhBBCiBeflIQRImsJCQls2LABgGbNmhmcPLdUXFwc7du3Jzw8XO/x+Ph4Nm/ezObNmwkJCWHp0qXPLPXvgQMHaNmyJXFxcdpjUVFRTJkyhc2bN7N161ZSU1N5//332bNnj17fVatWsWXLFrZu3UrNmjUtXndwcDAfffSR3pd8ukCIRYsWZfvFqM6jR49o2rSpySt279y5Q8WKFQ0eT05O5siRIxw5coTZs2ezevVq6tWrZ/F2WGLcuHGMGjVK78vWxMRE9u7dy969e5k5cyahoaFUqlQpy3H69+9vUI/70KFDDB48mN27d5s8ESOEEOLFlHG/4ubmZrB8w4YNdOrUidu3b+s9fvHiRYKDg5k/fz7Tp0+nf3/j5YrXrl1Lt27djPa/ePEiW7Zs4dGjR1qAgU5UVBStW7fm+HH9Uzs3b95k9erVrF69mh49ejBnzhzy5zd9qm7FihV07tyZxMRE7bGoqCimTp3KwoUL2bJli8lgV3Plxj5aVVU6derE33//naO56Hz44YesWLGCP/74g2+++UZv2e3bt1mzZo3Wbu/evdmON2HCBEaNGmVwgvXEiROcOHGC33//nVWrVlG/fn2Dvnfv3qVly5bs3r1be+zhw4faMW1gYKA1m6ipVq2awfsrKSmJkydPcvLkSebOncvChQtp06ZNjtYDz/fz8CSMHz+ezz77TO+9ffLkSb766is2bdrEpk2bjH7+jh07RrNmzfSCmK5evcrChQtZtmwZf/31V7brzunnau7cufTv31/vsw9w7tw5zp07x7p163BxccHf358SJUrQsmVL1q5dS3BwMD179jQ5r3nz5pGWloazszMdOnTIdjuMefToEV27dtUL6gaIiYlh/fr1rF+/nqCgIJYtW4aLi4tem+joaBo3bsz58+cN+sbExHD8+HEWLFhA69atcXV1NWs+T2LM7FiTweL645+W/veoa288lC9vOfH4Z0VFUUzu+RRFKQEUzNTHXBnjD/Zn0zbj8qz/CxVCCJErpm+dp3cTQgghhBAiL9u1a5f2RWtO0ywnJyfz/vvvEx4ejpubGxMnTuTEiRPEx8dz+vRpxo0bh6OjIxs2bKBPnz65MX2r+Pn54e7urtVtv3jxIl999RUAR44cYcKECfTu3ZszZ87w888/c/HiRW7evMmCBQsoWLAg9+7dM3kyJisHDhzQgiu8vLz4+++/uXHjBpcvX2bGjBncu3eP3r17mzXWmDFj2LVrF5999hnHjx8nNjaWiIgIqlWrprWpW7cukyZNIiwsjFOnThEXF0dkZCRz586lSpUqxMXF0bFjR4Or6BISErSTFwDHjx8nISFBu504Yf5XXfPnz9e+3K5Vqxbr1q3j5s2bnDt3jvHjx+Po6MilS5d45513uHPnjslx5s2bx6+//sqgQYM4fPgwcXFx7N+/nxYtWgDwxx9/8M8//5g9LyGEEHlfxhISlSvrX9a5b98+Wrduze3bt6lXrx5Lly7lypUrxMXFsWPHDtq2bUtaWhoDBgxg3bp1BmPv2LGDNm3acPv2bYoXL85PP/3EqVOniI+P59y5cyxcuBA/Pz+DE7R37tyhWbNmHD9+nFKlSjFjxgzOnDlDfHw8x44dY8SIEdjY2DB//ny++OILk9t2584d/P398fLyYuXKldy8eZPz588zfvx4HBwcuHnzJr6+vnpX4v/6668kJCRQpkwZAEaOHKm3/05ISNA71sutffTs2bP5+++/6devHwcOHCA2NpaDBw/i7e1tsk9WWrduTaFChTh37hwRERF6yxYtWkRiYiI1atSgatWq2Y41depUAgMDSU1NpVu3bmzdupWbN29y48YNli1bxuuvv058fDxt2rQhOjraoP9HH32kBRX06dOHw4cPExsby44dO3j33XcZP348W7dutWo7AapWrcq3337Lxo0bOXHiBLGxsZw+fZq///6bevXq8fDhQz788EOuXLmi1+/EiRN6ATxr1qwxeK3z0vOQ27Zu3cpnn31G165d2bNnD3FxcRw/fpxu3boBEB4ezq+//mrQ7/79+/j6+hITE4OjoyMTJkzg/Pnz3Lhxg+XLl1O2bFn8/f2z/Dzk9HP1119/ERAQQGJiIuXKlWP27NmcP3+e+Ph4Tp06RVBQEC1bttTL5hAQkH6he1hYmJbBxhhdeccOHTpQoEABwPL30oABA7Tgiq5du7J3715iY2M5dOiQ9n/Mpk2b6NGjh8H6R4wYwfnz53FycuKHH37gxIkTxMXFcePGDXbv3s2kSZN48803Tc7fmCcxZnasyWCxA/ACAhRFmaSqarYBE4qieAABgMq/pULysu1AY9IzRrwFRJho552pjyUyZggxXfDUcLmlmUWEEEIIIYR4KiInG6abr9zX8hNTQrwMMn9e5LMinrQLFy5o97PLIJCdn376iT179lC4cGEiIiJ49dVXtWVubm4EBgZSu3ZtfHx8WLhwIUOGDKFOnTo5Wqc1FEVhx44dele7jh49mvPnzzN//nwmTZqEoihERERQq1YtrU337t15+PAhffr0Yc+ePZw8edKi5+zTTz8lNTWVIkWKsG3bNkqW/Leabr9+/XjzzTdp0KCBWWNFR0czZ84cevXqpT1WpEgR7X6hQoXYtWuXQb/ChQtTqVIlOnbsyBtvvMHZs2f5888/9QI7nJ2dcXR01H53cnKyKtvIo0ePGDp0KABvvPEG4eHhODk5AVCsWDE+/fRTXn/9dVq1asWlS5cYN24c48ePNzrWhQsXmDhxIsOHD9fblpUrV/Lqq69y5coVgoODadWqlcXzFEIIkTd9//33ANja2vLRRx9pj6uqSq9evUhKSqJt27YsWbIEG5t/E6zXr1+f5cuX85///If58+czfPhw3nnnHe2EZVpaGgEBAaSkpFCyZEkiIiIoXbq01t/NzY1y5crRuXNnUlL0T8uMGjWKCxcuUK5cOSIiInB3d9frN2HCBF599VV69+7NDz/8wMcff6x3PKBz+/ZtSpcuzfbt27X9u27fWbFiRfz8/Dh//jzTp0/nf/9Lz9Bnb2+Pvb29th12dnYm99+5uY+Ojo7myy+/5Ouvv9Yey3hMYikHBwf8/PyYM2cOISEhvP3229oyXXmQDz/8MNtxoqOjGTFiBJBeDm/YsGF6yz/44ANatGhBnTp1iIyM5LvvvuOXX37Rlu/atYvFixcDMGTIEH788UdtWf369Vm9ejWtWrUiNDTU6m01lomsSJEiVKxYkXbt2tG0aVO2bdvGzJkzGTNmjNbGyclJe70AHB0dTb7WeeF5yCwlJYV79+5l2cbOzg47Ozujy6KiohgwYADTp0/XHitcuDALFizg9OnT7Nu3j+DgYAYMGKDXb9q0aURFRQEYZA5p27Ytb7/9NjVr1jQahAI5/1wlJCRoQdw1atQgLCxML+uCm5sbr776Kj179tT729OmTRuKFi1KbGws8+bNMxq8FRERwenTpwH0Shla8l7av38/c+bMAdIDbWbNmqUtK1KkCL/99huFCxfm+++/Z8WKFaxbt453331Xa7N27VogPcPHoEGD9MZ2d3enTp06Bu/P7DyJMbNjTQaL+Y9/ugKbFUXJMveQoiiVgA2A7r/VF+Ey36UZ7n9kslV6UAlAKmC6oJZxGfOYZHfpSBMT/YQQQgghhBAiT5OsUUI8Hbdu3dLuFypUKEdjTZs2DYDAwEC94IqMWrRoQbNmzYD0rAPPwhdffGE0lXjnzp2B9C91u3XrphdckbGN7sRF5vIhWbl69SqbNm0CYNiwYUZPptStW5cuXbqYNV61atX0gissVaBAAdq1awegzSu3rVq1itjYWAAmTpyo9+Wtzvvvv699eT1nzhyTNZvLli2rfWGdkaOjo5bi2Jw04UIIIfKeBw8ecO/ePe7du0dMTAw7duzAz8+P5cuXY2Njw7x58yhV6t+q7lu2bOHYsWPY2NgwY8YMveCKjL777jsgPVPT4cOHtcdDQ0O1E5GTJ0/WC67ILGMGi3v37jF37lwgvTxBxuCKjAICAihfvjzJyclZltX4/PPPjQYqtG/fnsaNGwMQFBRksn9WcnMfXaRIEUaNGmXVPEzp3r07kH41v+5E8sWLF9m+fTv58uWja9eu2Y7x66+/kpiYSNWqVY0eQ0B6UOnIkSOB9BPqGbdRV7bNxcWFb7/91qCvjY0NkydPtmzDLGBjY6MdF+bkWC0vPg8hISG4uLhkedMFjRhToEABxo4da/C4oihacM6hQ4dITk7WW67b1qZNmxoty+Lu7p7lez2nn6sFCxZo/5v99ttvWZa0yPi3x87OTvvM6LJUZKbbtnLlyml/PyylC65wcnJi4sSJRtt888032t++2bNn6y3TZU0sUaKEVes35kmMmR2LM1ioqrpBUZTVQGvSy1EcVBRlLbAeOA3cBwoAFQEfoNXj9ajAalVVN+TS3J8ZVVX3K4oSRnqGCn9FUYJVVd2WsY2iKN2B5o9/naeq6s1Myz0B3eUhW1VV9c60mkPAFaAU0F5RlJaqqq7PPBdFUbwA3Sc5DXjqeRCPlmz9XK2r79l4/Qc8n8xchBDme5p/J8TLI3xdgN7vlU20E0II8fySY3chXjxnzpzh0qVLADRp0iTLq86qV6/O5s2b2bdv39Oanp6WLVsafbxcuXLafR8fH6NtXFxcKFasGDdv3uT69etG2xiza9cu7QtUX19fk+3atm1LSEhItuO99957Zq13zZo1zJs3j71793L9+nUePHhg0EZ3Eim36a7KdHV1pXnz5ibbdezYkZUrVxITE8OpU6eMZgVp0aIF+fIZv16sYsWKANy48SJUJxZCCJGZqVIQXl5e/PPPPwblQXQno19//XVcXFxMHpO4ublRrFgxYmJi2LdvH2+88QYAmzdvBtJPOuuCEc0RERGh7WcbNWqU5bFQjRo1OHfuXJbHQm3btjW5rF27doSHhxMZGcmtW7eMBo5mJTf30c2aNTOZScBa3t7elCxZkujoaEJDQ2nVqhUhISGoqkrTpk2NBqpmpnsfNGvWzKAcWkZVqqRfS37r1i3OnTtHhQoVANi5cyeQfgyiK6eQWeXKlXnttdf0ytVYaseOHfz+++9EREQQHR3N/fv3DYJZcnKslleeh9xUr149ChYsaHSZ7rgxOTmZW7duacEA8fHxnDx5EkjP6mFKu3bt+O9//2t0WU4/V7q/Pa+99hpvvfVWVptoICAggJ9++omzZ8+yfft2GjZsqC1LTEzkr7/+AqBnz5565UUsodu+Fi1amAzMt7e3x9fXl9mzZ7Ntm97pcy2rx6hRo/Dw8NCbo7WexJjZsaZECEBXYB3QgPTyFL6Pb8boXqHtj/u9KAYDO0kPJlmnKMp4YBPpz2nbx8sBrgOfWzq4qqppiqIEAgsAG+AfRVF+A1YB14BCpAd4DObf7CCzVVV9Mv8NPw3bf9H/vcW4ZzOP55CcRMwbJIhACJGVTiP1D7uOPqN5vMwy709B9qnPI3mdhBDi2cj4hXxW9YSzk/EL1bp165rVJyYmxur15YSHh4fRxzOWxTDVJmO7jHXPs5OxHrKp7B7ZLcvIy8sry+UpKSl0796dRYsWZTtWTl73rOi2uVKlSll+kZvxxNmlS5eMnrx55ZVXTPbXXR1oyeshhBAi77tw4QKBgYEsXrwYW9t/q6nrjkkOHTqEi4uLWWNlPCY5d+4ckH7COeNV4tnJeCyU1X7L1HozcnNzo3jx4ib76faVqqpy6dIliwMscnMfbeqY5MGDB6SlpRldll3pMV2WikmTJhESEqIFWMC/2S2yo3s9pk2bpmVZy05MTIwWWKArFZFdObhKlSpZHVgwdOhQs7I/5MYx+rN6HhITEw0yReg4OjoazTDTs2dPq7OzgHnHjaB/7JjxWD2rbfXw8KBQoUJGX5Ocfq50f3tq1Khhsq8p1atXp1atWuzfv5/g4GC9QIMVK1Zw+/ZtFEWhZ8+eFo+to9s+XTCOKbrtu3HjBklJSVoA1vjx42nSpAmnT5+mUaNGlChRgiZNmtC4cWN8fHwoX768xXN6EmNmx5oSIaiqep/0shQjgRukB1GYul0HPgW8VVU1DI/Po1RVPQJ0AG4DTsA3wA5gKzCU9KCIaKC1qqpXrVxHyOOxkkkP3OgPrAEOAmHAaP4NrggBPrZqY4QQQgghxFMXvi5A7yaEEEI8Sxm/FNddtWUNa774TUxMtHp9OWEqVbilbUylyjZGd8Vg/vz5s7zKM7sTDjoZg0GMGT9+vBZc4efnx4oVKzh79ixxcXEkJCSQkJBAYGAggEH9+Nyiu3I3u23KePIrISHBaBtzXg8hhBAvpgsXLqCqKqqqEhcXR2hoqHZ198qVK/nqq6/02uf0mES3LzI3OCO31puRqUwBOhn3rVllyjAlN/fRpo5JqlSpYrK8gzl0pRxWrFhBeHg4J06cwMHBQSsNlp2cvh66YzdLXgtLLFiwQAuuaNq0KYsWLSIyMpLY2FjtWG3GjBnAv2UQrPGsn4e+ffuafB9kznCQW8w9bsx4LJ8xu4e125rTz5W1f3t0AgLSv2NctGiRXvCIrjyIt7c3ZcuWtWpsyPn2vf322+zatQtfX19sbW25evUqf/75J/3796dChQo0btzY4pJ/T2LM7FibwQJVVdOACYqi/AjUB+oArwAuQALpWRb2ADtVVTUelpTHqaq6TlGU14FBpJdMKQOkkl76YxkwVVXVW1kMYc46JiuKsgr4P6ApUB4oCDwELgO7gCBVVcOtGT9y8lS93yv37Z+T6QohhBBCCCGEECIPqlevHjY2NqSmpuboS86MX7SdP38+2wwLLxvdF7UpKSl6V3JlZs1JEmN+/fVXALp162ay5MiTzvige09kt00Zl1v7hbIQQoiXQ+HChWnZsiX169enTp06REZG8v3339OrVy8t9b9u/9O4cWO2bt1q8Tp0+yJTAQWm6NarKAopKSkmS1uZI6tSDqC/77TmBH9e2EfXqFGDatWqcezYMXr16gWkl1kzVfohM2dnZ27fvs2XX37J119/bfH6CxQowN27dy16LSwxc+ZMABo2bMjGjRuNvl8ePXpk1dgZPe/Pw/MiY1CFtdua08+VtX97dLp168awYcO4e/cuy5Yto1u3bly/fp3Q0FAA7XNkLWdnZ+7cuZOjvxs1a9Zk5cqV3Lt3j4iICHbu3Mm6devYtWsX27Zto3HjxuzYsYOaNWuaPa8nMWZWrA6w0HkcPLH18e2lo6rqFWDE45sl/aL4t3xKdm3PWjq+EE/K0yyDMX3rPL3f+3r+L8vlxtoIIYR4Ocg+QbwMpGycEOJJcnFxoUWLFoSGhrJ582YuXrxo1ZVN5cqV0+5LgIWhjM/p6dOnqVatmtF2OamxrRMfH8+VK1cA6NSpk8l2x44dy/G6suLp6QmkZ0ZRVdVkquTjx49r93NyVZ0QQoiXh7OzMzNnzqRJkyakpqby9ddfs2DBAuDfY5Lz589bNbauNMKJEydISUkxu0yIbr2qqnLhwoUcpaa/desWN2/exN3d3ehyXdYxRVEoU6aMxeM/jX20rrRETnTv3p2RI0dqr6W55UEg/fU4cOCA1e8DT09Pjhw5km2GN2szwB05cgSADh06mAzGyY1jtWf9PAQFBeWo3MfTkvH9ffLkSVq2bGm03fXr101mBcnp56pChQocPHhQe29YytXVlXbt2vHnn38SHBysBVqnpqbi4uKCn5+fVePqeHp6cvjwYSIjI7Nsp9s+Dw8Pk0Hlzs7O+Pj44OPjw1dffcX27dtp2bIlDx8+ZMKECfz1118Wz+9JjGmM9aFzQuRh07fO07sJkZdkfv/Ke1gIIYR4vsm+WwiRVwwfPhyAtLQ0Bg4caHa/EydOaPerVauGh4cHgFaaQvyrXr162pesq1evNtlu5cqVOV5XxrTOplJKR0dHZ3lVb8Za9tampdbVfr59+zabN2822W7x4sUAFCtWjFdffdWqdQkhhHj5NG7cmHfffReAhQsXcu7cOQB8fHwAuHLlChERERaP27x5cyD9Kuzly5dbNB/dycTcOBbKat26ZZUrV8bNzU1vmW4fntX+O6/so7t166YdPxUuXJj333/f7L6698GaNWusyq5Qv359ADZu3Ggyo0FkZCSnTp2yeGz493jN1Ov04MGDLN8D5h6rPe/Pw/OicOHCVKpUCTDvs2dMTj9Xur89J0+eZN++feZPPgNdmZCNGzcSHR2tlQfp2LEjTk5ORvuY+17Sbd/GjRu5e/eu0TaJiYmsWrVKr705GjZsqAW15KRs5ZMeE6wIsFAUJU1RlBRFUdpY2O8dRVFSFUV5MgUdhRBCiFzysp4EO1qytd5NCCGEEEK8XFq0aEGPHj0AWLVqFf369SM52XTV16SkJMaOHct//vMf7TFFURgyZAgAs2fPZs2aNVmu8+7du1y7di0XZp83lChRgmbNmgHwww8/GN32ffv28eeff+Z4XcWKFdPSHOu+4MwoNTWVfv36kZJi+qu6IkWKaPetfZ18fX0pVqwYACNGjDBakiQ0NFT7ojogIMAoIUA3AAAgAElEQVTklX5CCCGEMV999RWQvm8bP348AC1btqRq1aoA/N///R/x8fFZjpH5xLCPjw+vvfYaAEOHDiU6Otpk34z70kKFCvHRRx8BMHbsWPbv35/lem/evMmtW6YrzY8ZM4a4uDiDx5cuXaoFSfr7+xss1+3Ds9p/55V9dJkyZTh9+jSRkZEcOnRI70RwdgYMGICdnR3x8fH0798/24DRzFnEevbsCaSXa/jiiy8M2qempjJ06FCz55OZLtubqcDb4cOHG339ddzc3LTXJKvX+nl/Hp4num3dsmWL0aDnmJgYxowZY7J/Tj9X3bt31wKm/u///s9kpgzA5HF88+bNKVu2LGlpaQwdOpSjR48Cxv9W6Jj7XtIFb9y/f5/AwECjbUaPHs2NGzcAtL+Huj66DHvGpKWlceHCBUD//5CsPIkxzWFtBgtr/4IqOegrhBBCCCGEEEIIIZ6gGTNm0KRJEwB+/fVXqlSpwk8//cSRI0eIjY3l2rVr7N69m2+//ZbXXnuNUaNGGXyx98knn1C/fn1SU1Np06YN/fv3Z9u2bdoJhNOnT7N48WI++ugjSpcuzY4dO57Fpj4z48ePx8bGhtjYWBo1asSSJUu4efMm0dHRzJo1i3feeSdXSmTkz5+fdu3aAelpmYcNG0ZkZCRxcXGEhYXxzjvvsHr1aipXNl10qkKFClqN8wkTJnD27FkSExNJSUkxO6OFvb09P/zwAwAHDhzA29ub9evXExsby4ULF5g4cSLt27cH0k+gjBw5MiebLYQQ4iVUr1497QrlefPmER0dTb58+QgODsbBwYFjx45Ro0YNpk6dSmRkJLdu3eLGjRvs3buXn3/+mWbNmlG7dm29MfPly8fs2bPJnz8/ly9fpnbt2kybNo0zZ85w+/ZtoqKiWLp0KZ07d9aCOnTGjh1LxYoVuXfvHo0aNSIwMJA9e/YQGxtLXFwcJ06cICQkhC5dulC2bFkt60Zmrq6uJCQk0KhRI1atWkVMTAxRUVF8//33WpmMcuXKMWDAAIO+NWvWBGDFihVs2rSJu3fvkpKSQkpKCqqqAnlrH12hQgUqVapE6dKlLepXunRpfvzxRwAWLFhA/fr1WbhwIVFRUdy+fZsrV64QFhbGN998Q5UqVQyCBOrVq0eHDh0AmDx5Mn379uXo0aPEx8cTERGBr68v69at08pCWKpjx45A+sn8Hj16cOjQIeLi4tizZw+dO3dmxowZWR6rOTk5aRkXpk+fztGjR3n48KH2WueV58GYlJQU7t27l+0tLS0t19YJMHDgQG07unTpwsSJE4mKiiImJoYVK1bQsGFD7t+/j6urq9H+Of1c6UofARw8eJDatWsTFBSkvVZnzpwhJCSE999/n5CQEKNzUBRFC6bQZdKpUKECjRo1Mrnd5r6XatasqQVZzJgxgw8//JD9+/cTHx/PkSNH6Nu3r/Y3sU2bNlqGIUgPTvHy8qJdu3YEBwdz/Phx4uLiuHbtGlu3bqV9+/ZaaZQuXbqYnGtGT2JMc5hXNEoIIYQQQgghnhPGsgv19fzfM5jJ8zUXIYTIDQUKFGD9+vX873//Y8aMGZw9e5ZPPvnEZPvixYszbNgwvcfs7Oz4559/+PDDD/nnn3+YOXOm9iWhMaZq8r6oateuzaxZs+jTpw/nzp3TvqzWKVWqFLNmzaJFixYAZtd8N2bChAls3bqVy5cv8+OPP2pfrOsMHjwYV1dXvv76a6P98+fPT//+/ZkwYQKrV6/Wu7qybNmyZtdV79GjB1euXGHUqFHs2bOHd955x6BNmTJlWLduHYUKFTJ/A4UQQojHvvrqK9avX09SUhITJ05kypQp1KpVi/Xr19OpUyeuXLnC4MGDTfbPXGIDoEGDBixfvpyuXbty/fp1Bg0aZLRv5hPgrq6ubNmyBT8/P3bv3s2ECROYMGGCyXWbyshQqFAhpkyZQufOnWnTxjCpvLu7O6tWrTKa8r9fv378/vvvxMbGascUOlu2bMHb2xt4OfbRugCUoUOHsmfPHrp27Wqyre4Ec0azZ8/m8uXL7N69m1mzZjFr1iy95SNGjODGjRtmHxdl9Omnn7Jq1SoOHz7MggULWLBggd7y9u3b06pVK70sAJkNGjSI/v37s3v3bqpXr663TBdMA8/382BMSEiIyQCCjA4ePMgbb7yRK+uE9P+HVq5cSfPmzYmJiWHEiBGMGDFCW25nZ8eiRYsYPHgwt2/fNjpGTj9XnTp14u7duwwYMICzZ8/Sq1cvo+vp1KmTye3o1asX33zzjfYe0GXmyIq576Wff/6Z+Ph4li9fbvJ1at68ucH7GdIDZ5YvX55lmRV/f3/69OmT7Xyf5JjZsTaDhTVcHv80zIUihBBCCJFB+LoAvZswX+YSNy9TmRshMpLPgRBCWM/Ozo6ffvqJU6dO8fXXX9OwYUNeeeUV7OzscHZ2pkKFCnTu3JkFCxYQFRWllRXJyNXVldWrV7N+/Xq6d++Ol5cXjo6O2Nra4uHhQZMmTfj88885ePCg0RMGL7qAgAB27dpFu3btKFasGPb29pQrV45PPvmE/fv34+7urrV1cXHJYqSslShRgr179/Lf//6X0qVLY2tri7u7Oy1btmTp0qVMmTIl2zHGjh3LlClTeOutt3BxcbE6NfjIkSPZu3cv//nPfyhbtiz29vYULFiQ2rVrM3bsWI4fP57lFZpCCCFEVurXr68FEvz222/ExsYC0KhRI86ePcuUKVNo1qwZ7u7u5M+fHycnJypWrIifnx8zZ87k7NmzRsdt1aoV586d4/PPP+fNN9+kUKFCODg44OXlhY+PD7/88gsDBw406FeyZEl27tzJ33//jZ+fH6VKlcLe3h57e3tKliyJj48PY8eO5fTp09SoUcPkdn3wwQfs3LmTjh074uHhgZ2dHZ6engwcOJCjR49SpUoVo/2qV69OWFgYbdu2xcPDI8uAzZdhH607UR0YGEitWrVwc3PDxsaGggULUr16dT766COWLFnCwoULDfoWLFiQ8PBwJkyYwOuvv46DgwOFCxfG29ubRYsWZRk8kx1nZ2e2bdtGYGAg5cuXx9bWlsKFC9OwYUNmz57N4sWLyZcv61O5/fr1Y968eTRq1AhXV9cs2z+vz8Pz5vXXX+fYsWN8/PHHlC1bFjs7Ozw8POjYsSM7d+6kbdu22Y6R089V7969OXXqFJ988glVq1bF2dkZJycnKlSogK+vL8HBwQaB2hmVLVtWK02YL18+vbKOppj7XnJwcGDZsmUsXryYVq1aUbx4cWxtbSlWrBg+Pj7Mnz+fDRs2GPwfU6ZMGbZv385XX31F06ZNKVeuHE5OTtjb2+Pp6Unnzp0JDQ1l7ty5Zv/P8STGNMfTzGDR/PHP609xnUIIIYQQQgjx0jhasvWznoIQz5129Yo/6ynkWV5eXnz55Zd8+eWXVo/h4+ODj4+PRX28vb31rpDKLCdXxfn7+2dZexjA09Mzy/WbM4+wsLBs+7/11lssXbrU6LKdO3dq90uWLGmw3Jz56RQvXpyff/6Zn3/+2ejy0aNHM3r0aJP98+XLx+DBg7O86tfcOdWqVYvg4OBs22VmzmtuzmsrhBDPWoOPDEs5CNOy20dltmHDBqOPFyhQwKx9mSnFihXj22+/5dtvv7WoX758+ejQoUOWJ0HNUatWLS3NvyXq16+f5RXdmdfxpPbR5sju+C8r5h4DlC5dmnHjxjFu3DiL12FnZ2eQySCjoKAggoKCrJqfi4tLlvMyZ/t69OhhNOjZmGf1PJjL2veBjjnH4ea839zd3Zk2bRrTpk0zutyc9761nysdT09PJk+ebHV/BwcHAJo2bUqZMmXM6mPJe8nPzw8/Pz+z55MvXz4aNGhAgwYNzO6jY+pzkJMxcyLLAAtFUZoATUws7qIoSnY5VxSgAFATaAqoQISlkxTPjnxBK6wROXmq3u+V+/Z/RjMRQoinQ0oECCGEEEKI3LRq1Sog/cqzokWLPuPZCCGEEEIIIfKS69evExoaCmCyxIiwXnYZLLwBY5cpKEBnC9elACnA1OwaCiGEEEKIl0fmABUJTjGfsRI6BskFt/9i2LGF5VcpCPE8k78jQoi8Jj4+nsKFCxtdtmvXLubPnw9Ax44dn+a0hBBCCCGEEC+AGTNmkJKSgpubm0VZJoR5zCkRYqogiaWFSg4Co1RV3WthPyGEEEIIIYQQQgghXhidOnXCw8ODLl268MYbb1CgQAGio6NZvnw5EyZMIDk5GVdXV4YMGfKspyqEEEIIIYTIA1JSUkhMTGTDhg1MmjQJgI8//lgrFSJyT3YBFkFAWIbfFWAz6aU+vgB2ZNM/DbgHXFBV9bZ1UxRCCCFEXiGlMoQQQgghhMheSkoKISEhhISEGF3u4uLCkiVLKFGixFOemRBCCCGEECIvsrW11fvdy8uLESNGPKPZvNiyDLBQVfUicDHjY4qiJa44pqrq1ic0L/GUZE4rbZBSWgghhBBCCCGEEELkqu+++44lS5awbds2rl69SmxsLA4ODnh5efHuu+/yySef4OHh8aynKYQQQgghhMhj3N3dadasGRMmTMDZ2flZT+eFZE6JkMyaPv55LDcnIoQQQgghhBBCCCHEy6BBgwY0aNDgWU9DCCGEEM+h0aNHM3r06Gc9DSFEHqOq6rOewkvD4gALyVohhHgZHS3Z+llPwSJ5rUxD5mw68Pxn1MmN90Ree52eN5mfvyf13MnrJKyR1/Yb5hjw5qlMjzR7JvMQ4nlnzv6p00j9f8WPPtEZCSGEEEIIIYQQQojcYk0GCwNKet2QKkAJwBm4B1wFTqgSLiOEEEIIIYQQQgghhBBCCCGEEEKIPC5HARaKotQChgK+QAEjTe4rirICmKKq6v6crEsIIYQQQgghhBBCCCGEEEIIIYQQ4lnJZ21HRVG+B3YBXUjPWqEYuTkD3YBdiqJMyPFshRBCCCGEEEIIIYQQQgghhBBCCCGeAasyWCiKMhPoQ3oQBcAtYAdwFrhPejaLCkADwA2wAYYrilJIVdV+OZ20EDn1ItZFFy+P3Hr/mlMfXAghhBBCCCGEEEIIIYQQQgiRzuIAC0VR3gX+D1CBBGAEMEdV1WQjbW2BXsD3QEGgj6IoS1VVXZ+jWQshhLm2/2L4WItxT38eQgjxHJFAQ9Ne1uCzl3W7hRBCCCGEEEIIIYQQwhLWZLDQZaBIAXxUVd1jquHjoItZiqIcArY9Xt8AQAIshHjMnBMaT+ukR/i6AIPHKj+RNQkhhBBCCCGEEEIIIYQQQgghRN5iTYBFXdKzV8zLKrgiI1VV9yiKMg/46HF/IUQukytPhRBCCPG8k2BOIYQQQgghhBBCCCFEXmZNgIXr45/hFvYLJz3AopAV6xRCCCFEDkgQlhBCCCGEEEIIIYQQQgghRM7ks6LP9cc/Uy3sp2t/PctWQgghhBBCCCGEEEIIIYQQQgghhBDPGWsyWEQAZYDawB8W9Hvr8c+dVqxTiKcqt9JXHy3ZOueTEUIIYTX5OyzEi+lpfrYlA5AQQgghhBBCCCGEEELHmgCLn4FOQG9FUaarqno+uw6KopQjvTxIKjDdinUKIYQQ2eo0Un+3dtTKceSkvBBCCCGEEEIIIYQQQgghhMjM4hIhqqruAD4HnIGtiqK0yKq9oijNgS1AAeAzVVUjrJmoEEIIIYQQQgghhHh6oqKiGDNmDI0bN6ZUqVLY29vj4uJChQoV6Ny5M/Pnz+fBgwe5vt6wsDAURUFRFKKiogyWe3p6oigKo0ePzvV154aoqCht/mFhYQbLdcuCgoKe+txywtvbG0VR8Pf3f9ZTEUII8RIYPXq0ts/MeLO1tcXd3Z2mTZsyZcoU7t27Z9Z4oaGh9O/fn2rVqlG0aFHs7OwoWrQob731FoMHDyYiwvipK39/f6PzsLOz45VXXuHdd99lzpw5JCUl5ebmP3eyOz4TQoiXicUZLBRF+Q9wFVgBtAVCFUU5DmwCzgIPACegAtAMqPa46wrgxuP+RqmqOs/UMpG3ZU6tDJJeWQghhBBCCCGEeB4lJycTGBjI9OnTDU4WJCUlce/ePc6dO8eiRYsYOnQoEyZMICDAsNSmEEIIIURuS0lJISYmhrCwMMLCwpg+fTpr166lYsWKRtufOnWKgIAAdu40rF4fFxdHXFwc+/btY+rUqTRo0IDffvuNypWzLxienJzM9evXuX79OqGhofzyyy+sXbuWYsWK5Xgbn6agoCB69eoFgKqqz3g2QgiRN1hTIiQI0P2VVQEFqPr4ZozyuF3bxzdTVEACLIQQQgghspA5aFECFp++8HX6J5Cy/9pFCCGeDClrZqbRhZ71DHLX6DtPdPgHDx7g6+vL5s2bAahQoQIDBgygadOmlChRguTkZC5fvkxoaChz584lKiqKqVOnSoCFEEIIYYYrgdue9RRyVanxjZ7Keo4fP06ZMmWA9MCGM2fO8P3337NkyRLOnTtH27ZtOXLkCPnz65/yioiIoFWrVty6dQuADz74gG7dulGzZk3c3Ny4desWJ06cYNmyZYSEhLBjxw5CQ0NNBlgkJCRo95OSkjhy5Ahff/01YWFh7N+/n+7du7N+/fon9CwIIYR4XlgTYAHpQRNZ/Z5deyHyvu2/GD7WYtzTn4cQQgiRx8kJQus9b1nCJADoBZL5WDfTce7z9t4TQuSeAQMGaMEVffv2Zdq0adja2uq1KVGiBHXr1iUwMJBJkyaxdOnSZzFV8ZQZK3cihBBCPA1OTk44Oztrv9epU4fFixfj4+PDxo0biYyMZOnSpXTq1Elrc+PGDdq3b8+tW7coUKAAf//9N++9957euIULF6Z8+fL4+vryxRdf0Lt37yznkXEOkF4+q0GDBlSvXp2TJ0+yYcMG9uzZQ506dXJhq4UQQjyvrAmw6JXrsxBCCCGEEEIIIYQQz9SmTZsICgoCwNfXl5kzZ2bZ3s7Ojs8++4wPPvjgKcxOCCGEEELf559/zsaNG4H045iMARbDhw/n+vXrAAQHBxsEV2Tm5eVFaGgo58+ft2gOtra2BAYG4u/vr81DAiyEEOLFls/SDqqqBj+p25PYQCGEEEIIIYQQQgiRvUmTJgGQL18+pk6dana/KlWqGH1cVVX++usv2rRpQ4kSJbCzs6NIkSI0b96c4OBg0tLScmXeljpy5AhjxoyhcePGFCtWDFtbW9zc3Khbty5jxozh9u3b2Y5x8OBBOnXqRPHixXF0dKRChQoMHTqUmzdvWjSXtLQ0Zs6cSZ06dShYsCAuLi7Ur1+f+fPnZ9s3OTmZWbNm4ePjQ/HixbGzs6N48eL4+vqycuXKLPseOnSIgIAAKlasiJOTE46OjpQpU4Z69eoRGBjIvn37DPp4e3ujKIp2AsmY69ev87///Y9q1arh4uJCgQIFqFSpEoMGDeLixYsm+/n7+6MoCt7e3gDs3buXjh078sorr+Dg4ED58uUZNmyYluJdCCGEAKha9d/K9VeuXNG7v3DhQgCaNWuGn5+fWePlz5+fV199NdfmYQ5PT08URWH06NGoqsrs2bN5++23cXV1xcXFhXr16vH777+jqqrJMaw5tomKikJRFHr1+veaakVR9G66/bIxd+/eZdSoUbz22ms4OjpSpEgRfH192bVrl9H2Hh4eKIrCzz//bLDs0aNHODg4oCgK9vb2PHz40KDNzJkzURQFd3d3vedCVVV2797NZ599Rt26dXFzc8PW1pYiRYrQuHFjpk6danS8q1evkj9/fhRFITg469OTly9fxsbGxqy2QoiXg7UlQsTLwpwyGM9bqYxs0hmLJ09q0wsD8rm0mpROMO1FfG5exG16acnfPSGEEHlMQkICGzZsANJPRHh6euZovLi4ONq3b094eLje4/Hx8WzevJnNmzcTEhLC0qVLDdJtP0mHDx/mjTfeMHj89u3b7Nmzhz179jB79mzWr19PxYoVjY4REhKCv78/KSkp2mPnzp1j8uTJLFq0yOwv3lNSUmjbti2rV6/WezwiIoKIiAhOnjzJd999Z7RvVFQUrVu35vjx43qP37x5k9WrV7N69Wp69OjBnDlzDOrRz58/n169epGamqr3+OXLl7l8+TK7d+/m5MmTLF++3Kzt0Nm4cSPt27fXq08PcOrUKU6dOsVvv/3GggULsj3JtWDBAgICAkhOTtYeO3/+PD/++CNr164lIiKCQoUKWTQ3IYQQLyYbGxvtfsaT7qtWrdL20wEBAQb9ntY8LNWtWzctMERn9+7d7N69mzVr1rBo0SKD/XpuHNtYKjo6Gh8fH86ePas99ujRI1avXk1oaCjLly/n/fff1+vTpEkTFi1aRFhYGAMGDNBbtmvXLhITEwFISkpi586dNG/eXK+NrlRZ48aNURRFe3zlypVGs6nFx8ezbds2tm3bxty5c1m/fj3FihXTlpcoUYKWLVuydu1agoOD6dmzp8ntnTdvHmlpaTg7O9OhQ4dsnh0hxMvA4gwWQgghhBBCCCGEEOLFsmvXLu2Ee6NGjXI0VnJyMu+//z7h4eG4ubkxceJETpw4QXx8PKdPn2bcuHE4OjqyYcMG+vTpkxvTN5uiKLRo0YLp06ezfft2zp07R2xsLEePHmXq1KmUKVOGqKgounTpYvQEyeHDh7XgijJlyrBw4UKuX7/OxYsXmTZtGgkJCdnWb9cZO3Ys69evZ/To0Zw8eZK4uDjCw8OpWbMmAOPHj+fo0aMG/e7cuUOzZs04fvw4pUqVYsaMGZw5c4b4+HiOHTvGiBEjsLGxYf78+XzxxRd6fW/dukX//v1JTU2ldu3aLF++nAsXLnDr1i2ioqJYs2YNgwYNokiRIhY9r6dOnaJt27YkJCRQvHhx5syZw5UrV7h27Rp//PEHZcqU4dGjR3Tp0oW9e/eaHOfMmTP07t2bZs2asXXrVmJjYzlz5gyffPIJAJGRkYwZM8aiuQkhhHhxRUZGavdLlCih3d++fbt2P6fHNTmZhyWCg4NZuHAh/v7+HDx4kNjYWHbt2kXbtm0BWLZsmcF+Haw/tilbtiwJCQl6JeESEhL0bmvXrjU61x49enD//n1mz57N5cuXuXnzJgsXLqRo0aIkJyfTp08fvUBJSA+wANi6davBeLrHbG1tgX+DKYy10Y2jkz9/fj744AN+//13du7cSVRUFDExMRw6dIgxY8ZQpEgRDh06RN++fQ3G1AXfhIWFZZlpa968eQB06NCBAgUKmGwnhHh55EoGC0VRbIFygBtgp6pqeDZdhBBCCCGEEOKlYE52mulb5xk81tfzf09iOkIIYdSFCxe0+5UqVcrRWD/99BN79uyhcOHCRERE6KXadnNzIzAwkNq1a+Pj48PChQsZMmTIU6tVXr16dS1TR0ZFihShWrVqdOzYkSpVqnDgwAE2bdpEixYt9Np9+umnpKSk4OrqSnh4OGXLltWWffzxx9SoUSPLdNoZXbhwgb///lvvSshGjRqxZs0aypUrx4MHD5g/fz7ff/+9Xr9Ro0Zx4cIFypUrR0REBO7u7toyNzc3JkyYwKuvvkrv3r354Ycf+PjjjylZsiQA27Zt4/79+9jY2BAaGkrhwoW1vq6urpQtWzbbGvXGDB8+nAcPHuDs7Ex4eLjea961a1caNGhArVq1iI2NZeDAgSbTh1+9epU2bdqwbNky8uVLvy6sSJEiTJ48mcuXL7NkyRLmzZvHxIkTLZ6jEEKIF8/48eO1+xn3v7rjGl0JrCcpLS1Nb79k7nFAZlFRUfTv359ffvk3I2aRIkVYtmwZH3zwAStXrmTSpEkMHDhQL4jD2mMbRVFwdnbG3t5e62NuVrG4uDgOHDhA+fLltcc6d+5MgQIF8PX15erVq2zYsEEvi4XueYmJieH48eN6ZVV0ARX+/v789ttvBgEWp06d4vr163rj6LRq1YpWrVoZzLFo0aLUqFGDdu3aUaNGDZYvX86ZM2f0sni0adOGokWLEhsby7x584wGsERERHD69GltfkIIATnMYKEoSktFUdYBd4ATwA5gs5F2/1UUZZaiKF/lZH1CCCGEEOIltf0X/ZsQQgghctWtW7e0+zktvzBt2jQAAgMDTdYxb9GiBc2aNQPgjz/+yNH6cpOHh4d24mHTpk16y65du6adwBgyZIhecIVOo0aNzE4d3bBhQ6NtixcvTsuWLQEMsj3cu3ePuXPnAuknlTIGV2QUEBBA+fLlSU5O5u+//9Ye12UpKVCgAK6urmbNMzs3btxgzZo1AAwePNjoa16mTBk+++wzID3VeebSJhn9+OOPWnBFRj169ADSy6BkdZWpEEKIF1tKSgqRkZEEBASwatUqID0bQ8eOHbU2uuOaJ1lSKikpiQMHDtC2bVsOHToEwNtvv03Dhg2tGs/R0ZFx4wzLiyqKwuTJk1EUhZSUFEJCQiwaN6tjG2sNGjRIL7hC5/3339eCNzMfw1SpUkUr0ZExgCIxMZFdu3ahKAqjRo3CxsaGPXv28PDhQ62NLnuFLmjEElWqVOHNN99EVVU2b9Y/fWlnZ0f37t2Bf7NUZKYr/VauXDkaN25s0bqFEC8uqwIsFEXJryjKHGAt4AM4AEqGW2bXgN7Al4qieFo1UyGEEEIIIYQQWZNgJCHEM3bmzBkuXboEpKdwvnfvnslb9erVAdi3b99TnWNaWhp//vknH3zwAWXKlMHR0RFFUbSbLiBBd7Wizq5du0hLSwMwWutbp127dmbN45133jG5THd15Y0bN/Qej4iI4MGDB0B6MIep5/b+/fvUqFED0H9+q1evjqIo3L17lz59+nDt2jWz5pqVnTt3as+Ln5+fyXYZT3xt27bNaJvy5csbPWED6F1xmvl5EUII8WLz8vLS9tO2trZUqVJFCzj08PBg+fLlepkYnpSMxwv29vbUqlWL1atXA/Dqq6+ycOFCq8f29vY2GRBSrlw57bhpx44dBsutPbaxlqljmHz58mn7cWP7al15j4wBFrt3702SCWIAACAASURBVObRo0dUr16dsmXLUrNmTZKSkti5c6fWRte+cePGKIrhKcikpCRmzZrFu+++S4kSJbC3t9fbfl2wh7Ht15UJOXv2rF5pGUgP/vjrr78A6Nmzp9F1CyFeTtaWCJkB+D++fwdYA9gD7U20XwncAlyBtsBPVq5XCPGCOHrhUs7HMCPdthBCCOvI31ghnj+Zy4hICREhRG5yc3PT7t+5c8fqcU6dOqXdr1u3rll9YmJirF6fpRISEmjdujXh4dlXt838PERFRWn3syqjYm6JlVdeecXkMicnJwC9qzdB//nNqn9GGZ/f8uXLM2DAAKZPn86cOXMICgrizTffpFGjRjRp0oQWLVqYnR5cJ2M2iSpVqphsV6pUKQoVKsSdO3e0IJzMzHlOwPB5EUII8XKxt7enSpUqtGnThoEDB1KkSBG95brjmpwc05jDycmJGjVq4OfnR79+/ShQoIDVY2V3/FCpUiUOHz5skMUpJ8c21rLmGAbSAywWL16sZaSAf4MndKU/vL292bt3L2FhYTRv3hz4N4OFLkAjo2vXruHj45NldiwdY9tfvXp1atWqxf79+wkODtbLQLJixQpu376Noij07Nkz2/GFEC8PizNYKIrSAPgIUIFQwEtV1e7AfFN9VFVNBTaQnt1CcugIIYQQQgghhBBCPEe8vLy0+ydPnrR6HGu+uE9MTLR6fZYaMmQI4eHhKIpC7969WbduHRcuXCA+Pp6EhAQSEhLo1q0bkJ6CPKP79+8DkD9/fuzs7Eyuw9wABRsbm2zbqKqq93tuPL9Tp05l5syZVK5cmbS0NPbv38+UKVNo164d7u7uDBw4kISEBLPHv3fvHpD+vGR39bDuuTE1vjnPCRg+L0IIIV5sx48f1/bTDx8+5NGjRxw4cIDRo0cbBFfAv8c1Dx8+NBnUZw3dHBISEkhMTOT+/fvs3LmTYcOG5Si4Asi2v24fqtvv6uTk2MZa1hzDwL9BFDExMVpAhC54Qrcsc5aLM2fOcPXqVb02GfXo0YPjx49ja2vLsGHD2LJlC5cuXeLWrVva9jdo0AAwvf26LBaLFi3SCwzRlQfx9vY2WhpOCPHysiaDRe/HP6MBP1VVH5jZ7yDQCahqxTqFEEK8wPqejTd80POpT+OFlteyEchV2tbLa6+1yDsiJ0/V+71y3/7PaCZCCCGehHr16mFjY0NqaqrJ8g3myBhccP78eb3AjWft/v37LFiwAICRI0fy3XffmWxnjO7ER0pKCklJSSaDLDKf+MhNuudXV4c9Xz7Lq/8qikLfvn3p27cvUVFR7Ny5k/DwcFatWsXVq1eZPn06e/fuZefOnWaNr5tTSkoKiYmJWQZZ6J4bFxcXi+cthBDi5eXk5GRRhqWGDRvyxx9/AOllqbp3754r87A0y5MlTB1/6Oj2oRnnkNNjm6etatWqFC1alNjYWMLCwqhYsSIREREoikLjxunXZjdq1AgbGxv27NnDw4cPtUALNzc3Xn/9db3xzp49y6ZNmwD4+eef6dOnj9H1Znds1q1bN4YNG8bdu3dZtmwZ3bp14/r164SGhgLQq1evnGy2EOIFZPl/YdCI9OwVQRYEV0B6QAaAefkLhRBCCCGesOlb5xnchBBCCCFeRi4uLrRo0QKAzZs3G6SfNle5cuW0++fPn8+VueWWU6dOadkcOnXqZLLdsWPHjD7u6emp3c8qy0dOMoBkR/f8qqrKhQsXcjyep6cn3bp1Y+bMmVy8eJEhQ4YA6fXQdScVzBlDJzIy0mS76OhoLQOHXAUqhBDiSfL19SV//vTri+fMmfOMZ2Oe7I4fdMsz7kNzemzztGUMpAgLC9OCKKpXr07hwoUBKFiwIG+++SZJSUns3LlTy3DRuHFjg8DPI0eOaPdNbX9ycjKnT5/Ocl6urq60a9cO+DdrRUhICKmpqbi4uODn52fF1gohXmTWZLDQBUhY+hdZl1fHwYp1ZktRlJJAFcDN3HWoqipnUUSeIFcj5xHbf9H/vcU469oIIYR4IiRbTt5gLNBJstjkLoPPguczmYYQ4jk0fPhwQkNDSUtLY+DAgaxcudKsfidOnKBKlSoAVKtWDQ8PD65fv86iRYu02tnPg4ylMlJTU4222bNnD+fOnTO6rF69eiiKgqqqLF++nOrVqxttt3z58pxP1oTGjRtjZ2dHUlISixYtYuTIkbk2dv78+fnyyy+ZPHkykH4i57333su239tvv02+fPlIS0tjyZIlvPHGG0bbLV68WLufsb65EEIIkdtKlSpF586dCQkJYfPmzSxZssSsk+QpKSlcuHCBihUrPoVZ6gsLC+POnTsUKlTIYNn58+e1YAJduQvI+bENgK2trd4Y5pbrslaTJk1YunQpW7dupUaNGoBh6Q9vb2/27dtHWFiYFmChKx2SkTnbv2zZMr2yH6YEBATw559/snHjRqKjo7VAi44dO+Lk5GTWtgkhXh7WZLDQ/ZWytK/b45+WF4vMgqIo/oqiHAMuAeuAP4G5ZtzyRtiieCb6no03uAnxNBwt2drgJoQQ4uUk+4TnQ3avgbxOQogXSYsWLejRowcAq1atol+/fiQnJ5tsn5SU9P/s3Xd4FNX+x/H3IUBCTWgCIpCINBEE4YpSI4KAFKWoKJYAv3tRlGJHLESUJihSruAVEMSCqKD0qmChiiCxAEYIglISMBCkSJLz+2OzazbZTTabDp/X8+wz2ZlTvjMJO8POd85hzJgx3H///a51xhjXKAizZs1i+fLlGfZ56tQpDh8+nAPRZy71SAtLlixJt/3MmTMMGjTIa/2qVatyyy23ADBp0iSPo3x89dVXfPTRR9kP1ovg4GAGDBgAwJgxY9i+fXuG5Y8dO8aff/7per9//37+/vtvr+VT34DxNKe9J5UrV6ZLly4ATJ48mejo6HRlDh065Bq2vHnz5jRooBmERUQkd02cOJHLLrsMgAceeIAVK1ZkWD4mJoaOHTuybNmyvAgvnbNnz3pMnLTW8thjj2GtpWjRom7TnWT32gbcz/d5cU3mTKaIjY1l5syZbuucnMkU8+bN49ChQx7LAG5T0Xna/2PHjvHUU0/5FNfNN99MzZo1SU5O5rHHHiMqKgqAiIgIn+qLyKXFnwSLIynLq7JYr1nK8jc/+vTIGDMbmAXUB4wfLxERERERERERSTF9+nTXl9pvvvkmV199NZMnT2bXrl3ExcVx+PBhtmzZwksvvUTdunV59tlnSUxMdGtj2LBhtGjRgqSkJLp3785DDz3EV1995brZv3fvXj7++GMGDBhA9erV+eabb/Jk36pWreoaOWHMmDGMGTOG6OhoYmNjWb58Oa1atWLHjh3UrVvXaxvjxo2jaNGixMfH06ZNGxYsWMDRo0c5ePAg06ZNo2vXrtSoUSNX92PMmDHUrl2b06dP07p1a4YPH87WrVuJi4vj+PHj/PTTT7z33nv06dOHmjVruiVNzJ07l5o1a/Loo4+yfPlyDhw4QHx8PPv27WPu3Ln07NkTcEwZ48voFU4TJkygRIkSJCQk0KZNG+bOncsff/zB0aNH+fDDD2nVqhWxsbEULVqUKVOm5PgxERERSatKlSp88sknhISE8Ndff3HrrbfSs2dPPvnkE/bv38+ff/7Jvn37WLJkCQMGDKBu3bp8/vnn+RZvaGgo06dPp1+/fnz//fecOHGCrVu30qNHDz777DPAMdrY5Zdf7qqTE9c2jRs3dk29ERkZycGDB7lw4QKJiYleR4XIjoYNG7qmAzlw4IDbtCFOrVu3JiAgwJXMGhIS4hrtIrVmzZq5pkwZMmQI06dP58CBAxw5coQPP/yQG2+8kdjYWJ+mJjPGuJIpFixYAMBVV11F69at/d5XEbl4+TNFyDc4kit6Ai/7UsEYUwa4E7DABj/69NRmHyAiTVyfA78D5z3VERHxKO20HaCpO0RERERE5JJUqlQpVq9ezZNPPsn06dOJjo5m2LBhXstXrlyZxx9/3G1d8eLFWbZsGffeey/Lli1jxowZzJgxw2sbxYsXz7H4MzN9+nRat25NfHw8zz77LM8++6xrmzGGiRMnsmvXLvbs2eOxfuPGjZkzZw4RERH89ttv3HXXXW7bL7/8cmbOnEn79u1zbR9CQkL44osv6NWrF1u2bGH8+PGMHz/ea/nUQ38DHDlyhNdff53XX3/dY/mSJUvy/vvvU6lSJZ9jqlu3Lp999hm9evXi8OHDHp/2DAoK4t133+X666/3uV0REZHsaNWqFRs3bqR///5s3ryZRYsWsWjRIq/lw8PDufXWW/Mwwn/cf//97Nmzhzlz5jBnzpx022+//XZeeumldOuze21TpUoV7rjjDj788ENmzZrFrFmzXNvatm3L+vXrs71vqTkTKpxTqjVq1MiVcOEUHBxM48aNXSN1tW7d2pUEklrRokWZOXMmXbt25dSpU+lG6yhevDjvvPOOK/EiM/369WPUqFFYawHHyCciIp74k2AxH3gAuNYY84i1dlpGhY0xBvgfEIIjwSL9pMr++b+U5d9AH2tt7k1wKSJSyOTp8ORpE1SUnCIiIiKSIU0lIwVd8eLFmTx5MsOGDWPevHmsWbOGX3/9lePHj1O8eHGqVKlC06ZN6datG7169SIoKChdGyEhISxdupQ1a9Ywd+5cNm7cyJEjR0hMTKRChQrUrVuX1q1b06tXLxo3bpxn+3bNNdfw7bff8uKLL7JmzRqOHz9OxYoVad68OUOHDiU8PDzToaD79u3L1VdfzZgxY9iwYQOnTp3i8ssvp2vXrowYMYJz587l+n5Uq1aNjRs3snDhQubPn8+WLVuIjY0FoGLFilx99dXcdNNN9O7d220e+WHDhtGgQQPWrl3Lt99+y+HDh4mNjaVEiRLUqlWLDh06MHjwYKpXr57lmDp06MCePXt47bXXXKNjWGupXr06t9xyC48//rhPT5CKiIjkpPr167Np0yZWrlzJokWL+Oqrrzhy5AgJCQmULVuWWrVq0aJFC+655558TQI0xvDBBx9w0003MXv2bHbv3k1SUhINGjTg3//+NwMGDMBxu81dTlzbzJkzh/r167Nw4UKio6M5c+ZMLu2lQ9u2bV0JFp6m/nCudyZYOEdX86R9+/Zs2rSJl156iQ0bNnD69GkqV65M27Zteeyxx2jSpAnTp0/3Ka6aNWvSrl071q1bR5EiRdymwRMRSS3LCRbW2lXGmC+Am4DXjTE1gNfSljPGBABtgJFAaxzJFR9aa7/PXsgujVPafFvJFSL6klYufgOjT6RfGZrnYYiIiIjkiS9X9nd7Xz+f4rgoRJ7M7wgKrbCwMF544QVeeOEFv9vo0KEDHTp0yFKd8PBw15ODnsTExPgdD0CtWrV45x3vz/94e3I0tSZNmvDRRx953Z5R/Bltc4qMjCQyMjLDMkWKFKF379707t070/acQkJCuOOOO7jjjjt8ruPkyxOsVatWZcKECUyYMCFLbftyzENDQ306diIiBdkV4zTdgK98ORdmRadOnejUqVOW6/lyjsopxhgGDhzIwIEDs1Qvu9c2QUFBjBw5kpEjR3otk9n1mZMv1wvDhg3LcIQ0gIkTJzJx4sRM2wLHddnChQuzFZOTM3H4pptuyvVp30Sk8PJnBAuAu4DNwJXA48BjQLxzozFmP3AZ4HyEwQC7gP/4HWl6JVOWX+ZgmyIiIiIiIiIiIiIiIiJyCTly5AirVq0CHNOFiIh441eChbU2zhjzL2A2cBuOBIryOEaUAKiRss7pE6C/tfZ0NmJN6w8gLE0/IiJyCdNILtmk6V5ERERERERERETkEjR9+nQSExMpV64cvXr1yu9wRKQA83cEC6y1fwI9jDFNgPtwTAMSCgQDp4HfgQ3AO9bardkPNZ11wP/hmCrk/VxoXwoi3fwTkYuIEkJEUuj8ftHQ55pkmf79i4iIiIiISD5JTEzk/PnzrFmzxjUlySOPPOKaKkRExBO/EyycrLU7gB05EEtWvQ7cD/Q3xoy31h7PhxhERPJe2hsRoJsRWaCbfyJ5a2D0CfcVofkShohIpqZtSD9n8cDQJ/MhEhEREREREckLxYoVc3sfFhbGU089lU/RiEhhke0Ei/xirf3ZGPMQMBNYZYzpba2NyeewRERELmlKYBEREREREREREZHC5LLLLqNdu3aMHz+e0qVL53c4IlLAFdoEC2PM/UAy8DFwJ7DbGLMU2AzEpWzLkLU2/SNKIiIiIiIiIiIiIiIiIrksJiYmv0O4pFlr8zsEESmEspxgYYwJBJ4CDLDCWrvNhzr/AjrjSHoYZ61NzGq/HswBnJ98FigO9Eh5+cICSrAQERERERERERERERERERGRTPkzgkUv4EXgAvCWj3UOAs+m9Pcz8Ikf/XpiMnkvIiIiIiIiIiIiIiIiIiIikm3+JFh0SVl+Ya097EsFa+0RY8w6oBPQnZxJsOiXA22ISFZ9/Yb7+/Zj0xWZtsF9cJiBoU/61VVUta5+1ZOco99BBjL5t5D23wH4/2+hoLgY96nA8eEzNlf6yc2+8og+r0QuXoXt33eexptX5w0REREREREREQH8S7BoimN6jQ1ZrPcljgSLZn70mY61dm5OtCMiIiIiIiIiIiIiIiIiIiKSmSJ+1LkiZRmTxXoHUpbV/ehTREREREREREREREREREREJN/4k2DhHPUiKYv1klOWgX70KSIiIiIiIiIiIiIiIiIiIpJv/Jki5DhQBaiZxXo1UpZ/+tGnz4wxpXHEVwZIAI5Ya0/nZp8iIiIiIiIiIiIiIiIiIiJycfMnweInoCrQDZiQhXrdU5Z7/egzQ8aYysDDQE+gHmBSbbbGmN3Ax8B0a+3RnO6/MJu24R239wNDn8ynSCS3RVXrmt8hFG5fv+H+vv3Y/IkjB+lvQkQkf+k6TKTw0fWTiIiIiIiIiMilzZ8Ei9XAzUBLY0xva+3HmVUwxtwBtAQssNKPPjNq+25gOo4RK8A9ucL5vj7wPDDMGPOgtXZ+TsYgmbtkv4i8CG/Ki4gUKvocFrk4pf23Dfr3LZm6ZP9PIiIiIiIiIiIiOcafBIu3gGdxJDTMNcYEW2tneStsjPk/YHLK29PAm3706a3t+4C3cSRRGBwJHD/jGCXjNFAaqI1jVIsiQFngPWNMgLX2vZyKQyQ7fPqiN49uEKZ9khb0NK2IiFxEdFNexEHJZyIiIiIiIiIiIn7JcoKFtTbeGDMUR2JDEPA/Y8xwYCmO5AZnYkN9oCtwJf8kPzxqrT2eE4EbY6oAb+BInEhO+fkVa+1BD2WvAJ4CBqWUn2GMWWetPZITsYiISDbppuclZWD0ifQrQ/M8DBHJoi9X9nd7Xz+f4hARERERERERERHJL/6MYIG1dq4xpiIwDgjAkUQxxEtxAyQBT1trZ/sVpWcPA6VwJG70t9amf/T+n3gPAUOMMduAuUBJHMkWL+RgPCIiIiIiIiIiIiIiIiIiInKRKuJvRWvtq0AbYC3/TNHh6bUKaGWtfS3b0brrhCO5YkVGyRVpYp4HLEuJq3MOxyMiIiIiIiIiUmhFRkZijMEYQ0xMTKblIyIiXOXzS3h4OMYYIiIi8i0Gb2JiYlzHZ/369fkdToGyfv36LP2tFUbO/ZszZ066bc5/O+Hh4Xkel4hIYdauXTvX5+uIESPyO5xLnq51RORS5dcIFk7W2k3ALSmjWbQCrgDKAqeAQ8DX1tq4bEfpWVjKcnEW6y0GuqSqL5cizTstcunSv38REclrPkyFFVWtax4FIyIiIiIiUvgcPHjQ7Sb++++/z+jRo/M10TMvhIaGcuDAAUaOHElkZGR+hyMiImQzwcIpJYni05xoKwtKpyz/zGK9+DT1RURECi4lhHiXR8dGNz1FREQKubXP5HcEOUvXgyIiIoXWuRVR+R1Cjgrq3DDP+nrvvfew1rreHzhwgC+//JK2bdvmWQwiIiKQQwkW+eQ4UAW4Kov1nOWP52w4IiIiIiJ5SwlAkl8K2t9e1P7f8jsEERHJgvDwcLebZJeaOXPmeJw6REREvHv33XcBaNmyJT/++CPx8fG8++67SrDIR6GhoZf0+VxELl1FcqIRY0wxY0xdY8wNxpg2OdGmD3YCBogwxhTzpUJKuQcAm1JfREREpFCJqtbV7SUiIiIiIiIicjHbsWMHP/74IwD9+/end+/eAHz00UecO3cuP0MTEZFLULYSLIwxtxhjVgIngZ+Ab4DPPZQbZIz5nzFmZHb6S2NRyvIqYJ4xJjCTWIsDc4A6Kas+ycFYRESkIPj6DfdXfrcjIiJyCUubEKakMJFL15YtW4iIiODKK6+kZMmSlC1bluuuu46XX36ZhISEDOsmJCTw3HPPUbduXYKCgqhcuTLdunVzm4M9M2vXrqVz585UqFCBUqVK0aBBA0aNGsWZM2dYv349xhiMMcTExOTKPjidP3+e8ePH06hRI0qVKkVISAg333wzK1asyLRudHQ0gwYNok6dOpQsWZIyZcrQqFEjRowYQVxcXKb1FyxYQNu2bQkJCaFMmTJcd911TJ48mcTERObMmeM6Bv74+++/mTp1Km3atKFChQoUK1aMihUrUr9+fXr37s3MmTM5c+aMWx1fjnvq41WyZEkqVapE+/btWbx4MQAREREYYwgPD09XNzIyEmMMoaGhAOzZs4d+/fpxxRVXEBgYSPXq1fn3v//NoUOHvO5XXFwcc+fOpXfv3oSFhREUFETJkiWpVasW/fr1Y8eOHX4dr8xiT/v7+OOPPxgyZAhhYWEEBgZSpUoV7r77bn7++ecM+0hOTubNN9/k+uuvp3Tp0pQrV46WLVsyd+5cIP0xEhEpyJyjVwQFBdGrVy/69u0LwMmTJ1myZInXeqGhoRhjiIyMxFrLrFmzuPHGG13nwxtuuIGZM2dmOgrDyZMnGTVqFE2bNiU4OJgSJUpw5ZVXMmDAAFfihydpP2t37NhBnz59qFatGkFBQYSFhTF06FBiY2PT1XWeKw4cOADAiy++6Do/OF+eroe+++47IiIiCA0NJSgoiJCQEK6//nrGjh3LX3/95TXW5ORk3nnnHW655RYqV65MsWLFKFeuHHXq1KFr165MnTqV48fdB4aPiYnJMBZ/2hQRKQz8miLEGFMU+B+O0SDAMZJERg4D0wBrjJlrrY3xp9803gaeAGoDdwD/MsZMAdYAe621iSlx1gY6AIOBK3GMXrEHmJsDMYiIiBQOaRNFCvjc3boJJyLZpc8REZH8l5yczLBhw5g6dWq6bTt27GDHjh3MmjWLlStXUrdu3XRlfv/9d8LDw4mOjnatO3bsGEuXLmX58uVMmzYt0xhefvllnn/+ebd1P/30EyNHjuTjjz9m1KhRuboPTgkJCbRp04atW7e6rf/888/5/PPPefPNN/nPf/7jse68efMYMGAAFy5ccFsfFRVFVFQUM2bMYPHixbRq1SpdXWstAwcO5K233vIY++LFi+nTp4/XuDOTkJBAu3bt+Pbbb93WHz9+nOPHj7N7924++eQTmjVrRuPGjX1u9+TJk7Rv396t3bNnz7Ju3TrWrVvHc88953Nba9eupUePHpw+fdq17tChQ8ycOZNly5axadMmatasma5ehw4d2Lkz/QC4+/btY9++fcybN49p06bx4IMP+hxLVu3atYsOHTpw7Ngx17qjR48yf/58li1bxhdffEHTpk3T1fv777/p0aMHy5cvd1u/ceNGNm7cyLp167jyyitzLW4RkZyUlJTE+++/D0DXrl0JDg6mbdu2VK9enYMHD/Luu+9yxx13ZNrOPffcw/z5893WbdmyhS1btrB8+XIWLFhA0aLpb5nt3LmTzp07c+TIEbf1+/fvZ//+/cydO5dJkyYxePDgDPv/7LPPuOuuuzh//rxrXUxMDFOmTGH+/Pl88cUXXH311ZnuR0bGjh3Ls88+65Ywcv78ebZt28a2bduYMWMGq1atol69em71EhMTue2229KdN+Lj44mPj+eXX35h2bJlVK9endtvv92nWHKjTRGRgsLfESymAxE4EitOAR8ACzMovxj4M+Xn2/zs0421NgnoDhxPiSMUeA2IAs4bY84D54EfgEk4kisMEAd0T6kvIiKFRdpRJTSyhIhIoTMw+kS6l4h4Nm3DO24vkcLoiSeeYOrUqQQEBPDII4+wZcsW4uLiOHToEPPmzaNmzZrExMTQtWtXt5vf4Ehs6NWrF9HR0QQEBDB8+HB2795NbGwsa9eupVmzZgwdOpRffvnFa/+LFy92JVc0btyYlStXEhsby6+//srYsWP55ZdfePzxx3NtH1IbOnQou3fvZtKkSezbt4+4uDiWL1/uusn96KOPut1Ed9qwYQMRERFcuHCBq666io8++ogjR47w22+/MWPGDMqXL8+ff/5Jly5dXE+4pjZ16lRXckW7du348ssviYuL4+eff+app55i/fr1jBs3LsNjkJFx48bx7bffEhAQwPPPP8/OnTuJjY3l2LFjfPfdd/z3v/+lTZs2WR4do1+/fq7kikGDBhEVFcXx48fZvHkzt912Gy+//DJffvllpu3Ex8dz5513Ur9+fVasWMGxY8c4cOAAY8aMoUiRIhw+fJgnnnjCY93Q0FBGjBjBihUriIqKIjY2ln379rFs2TI6depEUlISgwcP5rvvvsvSvmXF7bffTkhICAsWLODw4cP88ccfzJgxgxIlSpCQkOA1uWP48OGuG1p33nkn27Zt4/jx4+zcuZMBAwYwb9485s2bl2txi4jkpLVr17qSG+69914AjDHcc889AKxYsSLTURDmzp3L/PnziYiIYMeOHcTFxbnOKQCLFi1Kl5AJEBsbS6dOnThy5AhlypRh0qRJ7N+/n2PHjrFkyRKuueYakpKSGDJkCIsWLUpX3+nkyZNEREQQFhbG4sWLOXbsGPv27WPcuHEEBQVx7NgxunXrxtmzZ1113nzzTRISEqhRowYAzzzzDAkJCW6v1q1bu8rPmzePZy3zPAAAIABJREFUESNGYK2ladOmrFy5kmPHjvHrr78ybtw4SpQowW+//UbHjh05efKkW3xvv/2267zx8MMPs3XrVo4cOUJcXBxRUVHMnj2bLl26EBAQkOFxzu02RUQKiiyPYGGMaQkMwDESxGrgbmttvDHmNqCnpzrW2iRjzBrgTqANMNn/kN3a3WuMaQLMBDqm2VzMQ5UVwH+stb/nRP8ikssye+Le0w32Av5UvoiIFGyX6qgHl+p+Fzb6PYlIXjpz5kyGCQPgeDLRm23btjFp0iQA5s+f75or3enee++lXbt2NGnShOjoaN544w2eeuop1/YFCxawZcsWAF577TWGDBni2nbzzTfzxRdf0KJFC77//nuvMTz55JMA1KlThw0bNlC2bFkAKlasyPDhw6ldu3a6uHJyH1I7ePAgX331FTfccINrXefOnfn0009p1KgRZ86c4eOPP2bQoEFu9QYPHkxycjJXXHEF33zzDZdddplr28CBA2nevDk33HADp06d4umnn3Z7MvfMmTO88MILALRu3ZqVK1dSrJjj67IKFSowfvx4LrvsMq8JBr5wTm8yZMiQdKOBVKpUiSZNmqTbp8x88803rptUw4cPZ+zYf/6f37x5cxYtWsRtt92W4ZDwTidPnqRZs2Z8+eWXBAUFudY/88wzHD9+nFdffZXPPvuMkydPEhwc7FbX042yihUrEhYWxq233so999zDBx98wGuvveYauj6nJSUlsW3bNipUqOBaN3DgQC5cuMDgwYP59ttv+emnn9yeeP7tt9+YMmUKAHfffbfrqW+A8uXLM3PmTEqUKOHTCDAiIgWB8zO2fPny3Hrrra719957L+PHj+fChQt8+OGHGZ5vYmJieOihh3jjjX++T65QoQKLFi3i9ttvZ/HixUycOJHBgwdz+eWXu8qMGjWKo0ePUqRIEZYtW+aW0NC1a1datWpF8+bN2bt3L0OGDKFbt24eR8GIj4+nevXqfP31167P9EqVKvH0009Tu3ZtevXqxb59+5g2bZrr+iUwMJDAwEBXkmLx4sUpXbq0x/07d+4cjz32GOBIKv3yyy8pWbKkWz8NGzakS5cu/Pbbb4wdO9YtwdJ5Pu/Ro0e680OFChW45ppr6Nevn9fj60lutCkiUlD4M4LF/6Usfwd6WWvjfaznnJiwgR99emWt/d1a2xm4FngRRxLFTuDXlOWKlPXXWmu7KLlCRCSFRoMQEREREREvGjRoQJkyZTJ8vffee17rO6fUuPXWW70mMVx++eU88sgjAG43gcHxpClArVq1XGVSK1mypNuN97Q2b97M3r17Acf8587kitR69epFy5Ytc20fUuvTp49bcoVTw4YNXVNnbNu2zW3btm3biIqKAmDkyJFuyRVOjRs3ZuDAgQB88sknxMf/8zXd4sWLXU+ojh8/3pVckdqwYcNcT8b6IynJMUBs6ptR2eUcWSE4ONiVIJKaMYZXX33V5/acTwendd999wFw4cIFj1OBZMb5FPW6deuyXNdXL7zwgltyRdq+If3fzfvvv09SUhJFihThlVde8dju6NGjXTfeREQKsr/++suV8HbnnXe6ncuuueYarr32WoBMR+UpUaKEx+sGYwyTJk3CGENiYqLbtU1iYiLvvOMYSe7uu+92S65wCgkJcSUqHDp0iFWrVnmN4bnnnvP4md6zZ0/atGkDwJw5czLcD2+WLFlCXFwcABMmTPD4GX/rrbfSvXt3AGbPnu02jUhunM9zo00RkYLCnwSL1jhGr5hjrT2ThXrOxIaqfvSZKWttlLX2xZQkiuustbVTll1S1kflRr8iIgVSHk6nsajTXreXiIiIyCXNl2swJbrKJcB507ldu3acPn3a66tBA8dzOFFRUfz9998AWGvZtGkTAN27d6dIEc9fX91yyy1ebxJv3LgRcNw46dKli9c4nUOD5/Q+pNWxY9qBV/9Ru3ZtAI4ePeq2/uuvv3b93LOnx0FjAVzzzicmJrJ582bXeucxqFixIjfeeKPHugEBAXTt6v8ISc7kkAkTJrB8+XKSk5P9bsvJGXf79u0pUaKExzK1a9f2aZ76wMBA2rZt67UNp7TH3mnXrl08/PDDNGrUiLJly1KkSBGMMW5/V0eOHCEhISHTWPzh7e8mJCSESpUqeYzdefyaNGnCFVdc4bF+2bJlCQ8Pz7lARURyyaJFi/jrr78A6Nu3b7rtznWbN28mOjraazvh4eHpRipyuvLKK2nUqBHgGEXJKSoqilOnTgGOpExvunbt6jpfffXVV17LZXTN0aNHDwB+/vln/vzzT6/lvHFeM4SEhHDzzTd7Lee8ZoiNjWXPnj2u9c7z+dtvv817773n9XomK3KjTRGRgsKfBAtngsQPWaznnDwqfcq4iIiIiIiIiIgUGPv378dam+HrgQce8Fj39OnT/PHHHwA88cQTGY6C4bxhkZyc7Jo/PT4+3jXyQr169bzGGBAQ4HaTPLUDBw4AUKVKFY+jVzjVqVMnV/YhrapVvT9v5EwSST3vetp9KF++vNf6zgQPcEwPkba+t2Pk5O0Y+MI5OsixY8fo0qULl112Gb169eL111/nhx+y+tWhgzPuzOLyJe5KlSp5HKodcEvOSXvswTE1zXXXXccbb7xBVFQUCQkJbk/7ppZ2Lvuckp2/m5w4fiIi+c05MkVYWJjHUafuueceVyJmRtM1ZXQ9kXq78zM07c8ZJfUVK1bMda5NfR5OrVy5clSuXDnT/q21XtvIiDPWevXquaYU8cTbNcOjjz5K9erVOXPmDPfeey8VK1akS5cujBs3jq1bt3o9/2UkN9oUESko/EmwSPKzbrmUZe78j0NERETyXx6OniIiIiIiBZO/N5vPnz8P4HpSFaBUqVIZ1vE2F7mzDX/rZ3cf0goICMi0btobDadPnwa8x+hUpkwZ18+pR1LI7jHwRVhYGNu3b6dv376UKFGC48ePs3DhQh599FHX9CerV6/OUps5Gbcvxx3SH/uvv/6axx9/nKSkJJo0acLcuXP54YcfiI2NJSEhgYSEBJYtW+Yqn5iY6FM/WeXP301e/N5FRPLC4cOHXaNJtWrViu+//56dO3e6vWJjY2nYsCGQcYKFr5+JznNv2p99PRd7G9EoK5/Jqfv1VXavGUJCQti6dSsPPfQQwcHBJCQksHz5cp555hmaN2/OVVddleFUaJ7kRpsiIgWF5xTujB0BagFXZbFes5Rl1tPvCjBjzBXAYKAbUANIBPYDi4Cp1tqsj+fkva/aQH+gM1AdKAUcA2KA9cACa61/jwd44Wm4/x452YGIyEUqqpr/w+wWVBfjPuUUHRsRERGRf6T+cn/27Nn069cvS/VT34RInWzhibebEM42/K2f3X3ICZ5u9niSenvqGyfZPQa+uuqqq3j33Xc5d+4cW7duZePGjaxZs4b169fz/fff06lTJxYvXuzzVCSlSpXi1KlTuR53RmbMmAE4ho3fuHEjQUHpB+QtqEOd59XvXUQkt33wwQckJTme9503b55rNAtvfv31VzZt2uRxWixfPxNTn/+zkvTg3J76POxP/2n79VV2rxnAMWLWG2+8wZQpU9i+fTubNm1i3bp1rF69mn379tG3b1/i4+MZNGiQz3HlRpsiIgWBPyNYfAMYwPvkj2kYY8oAdwIW2JCVzowxs1Nes7ys9/c1y1ufWYitExAFPAXUx5HwEAw0Bl4EoowxTXOgH2OMeQHHtCzDgWuB8kAgjkSL1sDzwP9lty8RERERERERkewIDg52TWmxb9++LNcPCQlxzZO+e/dur+WSkpL45ZdfPG6rWbMmAEeOHPH6NCnA3r3pHyyB7O9DTggNDQUc+5DRfOw//vij62fnfqf+2dsxcvJ2DLIqKCiINm3aMHz4cNatW8cPP/zAZZddhrWWl156yed2nHFnFldOxe3Jrl27AOjevbvH5ArA7ylQcltBOH4iIjkhs4SKrNTJ6Hoi9fbU51HneRjg559/9lo3MTHR9Zmaun5qf/75J8eOHcu0f2MMNWrUyDBWT5yx7t69O8OpN7xdM6RWtGhRmjdvzrBhw1iyZAn79u1zTWEyatQov6b2yI02RUTykz8JFvNTltcaYx7JrLBxTPj0PyAkZdU7WewvAnggZelpvb+vtO1liTGmEfAxjv06A4wEWgHhwCQcU6lUA5YaYy7PTl/Af3EkbBQHvgeGAW2BJkAHHAkem4DkbPYjIiIiIgWFptwpHPR7EhHxqEOHDgB8/PHHJCdn7esKY4zr6dMlS5Z4rb9mzRrOnDnjcVuLFi0Ax/QJqadySGvx4sVet2VnH3JCq1atXD8vXLjQa7mPP/4Y+OfmhZPzGMTFxbF582aPdZOTkzM8PtlRv3597r77biDzG1upOeNet24d586d81gmOjqan376KftBeuGc6sX55HRa1lrmz5/vcVt+cx6/HTt28Pvvv3ssk5CQwPr16/MwKhGRrPnxxx/ZuXMnAOPGjcNam+HrrrvuAmDBggVcuHAhXXvr16/3Ov3Xvn37XIl1LVu2dK1v2LChK+Hzk08+8RrrsmXLOHv2LOB+7k7r008/zXRb/fr1KVeunNu2YsWKAd7PSan7jY+P5/PPP/daznnNUKlSJerUqeO1XGrVqlVj4MCBABw9epT4+Hif6uV1myIieSnLCRbW2lXAFzhGsXjdGPOKMaZK2nLGmABjzE0pZZ2jV3xorf3ejzhNBuv9fWXX6zhGrEgCOltrR1lrv7HWbrDWPsY/CRxVgJf97cQY8wDwUMrbCcB11trJ1tovrbU7rbVrrbUTrLUtcIxuISIiIiIiIiKSrx599FHAcWP9ueeey7BsUlISv/76q9u6Bx54AHDcSJ82bVq6OmfOnGH4cO9fgzjn9gZ48cUXPQ6ZvWjRIr7++utc24fsatasmWte+RdffJG4uLh0ZXbt2sX06dMB6NWrFyEhIa5t3bp1o2zZsgAMHz6cxMTEdPUnT57MgQMH/I4xs8QJ5zGpUKGCz23ee++9gOMm0csvp/9KzVrLk08+mYUosy4sLAyA1atXe5wK5JVXXnF7CrggufvuuylSpAjJyck8/fTTHss899xzXpOTREQKAudIFMYY+vTpk2l5Z0Lf8ePHWb58ebrtZ8+e5Zlnnkm33lrLY489hrWWokWL0rdvX9e2gIAA7r//fsAxXcnGjRvT1T916pTrs/aKK66gY8eOXmN8+eWXOX78eLr1CxcuZMMGx8DvERER6bY7z6GHDx/22na3bt2oVKkSAE899ZQr4SO1VatWuRI5+vfvj+PZaAdfz+fFixf3Og1KWrnRpohIQeHPCBYAdwH7Uuo/DhwCXFNuGGP2A6eAtTimrzA4ptL4jx99haW8rvSy3t9X2vZ8ljLtx00pb+dYa79MW8Za+y7gTBW83xhzmR/9lAZeS3m70lr7lLXW6yMT1tqCOfmjiIiIyEVkYPQJt5eIiIik17x5c5544gkAxo4dS8eOHVm8eDGHDh3i5MmT/Pbbb6xevZrhw4dTq1YtJk2a5Fb/zjvvdI3G8NhjjzFixAh++eUXjh8/zrp167jpppv46aefuPxyz4OGGmN45ZVXAMcX/G3btmX16tUcP36cmJgYXnnlFfr27cuVV3r/eii7+5ATpk6dSpEiRTh48CAtW7Zk0aJFHDt2jEOHDvHWW2/Rrl07zp8/T9myZRk3bpxb3VKlShEZGQnAhg0b6Ny5M19//TUnTpxgz549PPPMMzz55JMZHoPMXH311XTo0IEZM2awY8cOYmNjOXbsGFu2bGHAgAEsXboUwKebY06tWrWie/fuAIwePZrBgwfz448/cuLECbZu3UrPnj359NNPXUkQueGOO+4AYM+ePdx2221s2bKFuLg4du3axcMPP8zw4cOpX79+rvWfHTVr1uSRRxyDDr/33nv06dOH7du3c+LECXbt2sV//vMfpkyZkq3fu4hIbrLW8v777wNw4403ep3KIrXOnTu7kgw9TRMSGhrK9OnT6devH99//73rnNKjRw8+++wzAJ544ol01xXPP/88lStXJikpic6dOzNlyhQOHDhAbGwsy5cvp3Xr1uzZsweAKVOmULRoUY/xhYSEkJCQQOvWrVmyZAmxsbFu1yMAV155JQ8//HC6utdddx0An332GevWrePUqVMkJiaSmJjomlojMDCQV199FYDvvvuO8PBwVq9eTVxcHPv372fChAn07NkTgBo1aqRLNuncuTM33ngjkyZNYsuWLRw9epS4uDh27tzJ008/zX//+1/AkczpbR/Tyo02RUQKCr8+tay1ccaYfwGzgdtwJFCUxzFKBUAN3EeJ+ATob61N/7hA5n15TKP3tj6P9Ez18yyvpRzHpx0QAHQHZmaxn3twHFcA3yerFPFTVLWu+R2CiIiIiIiIXCTGjx9PYGAgY8eOZfXq1axevdpr2eLFi7u9L1KkCJ988gnh4eFER0czduxYxo4d69pujGHatGksWLCAP/74w2ObPXr0IDIyksjISL777rt0T5Vec801vPjii/Tq1QvA45f72dmHnNC2bVvmzJnDgAED2Lt3r+vmSGrlypVj8eLFbnPFOw0bNowffviB2bNns3btWtauXeu2PTw8nD59+vDggw8SEBCQ5fistR7bTa1jx448//zzWWp3zpw53HzzzezYsYNp06alG8VkxIgRHDp0iP379+fKTZmIiAgWLFjA6tWrWblyJStXrnTb3qJFC0aMGEHXrgXze5RXXnmFPXv2sGrVKj788EM+/PBDt+19+/alVq1ajBo1Sje1RKTAWb9+PQcPHgT+GZkiM8WLF6dnz57Mnj2bpUuXEh8f7zaq0/3338+ePXuYM2cOc+bMSVf/9ttv56WX0t+CqVSpEitXrqRz584cOXKEoUOHMnToULcyAQEBTJo0iR49eniNLzg4mNdff5277rrLlUSY2mWXXcaSJUsoWbJkum0PPvggM2fOJC4ujvbt27tt++KLLwgPDwfgvvvu49ChQzz77LNs3brV42gaNWrUYOXKla6pT5ystWzevNnrlGIATZs2ZcqUKV63p5UbbYqIFBT+jmCBtfZPa20PoCmO6TK2AydwTJlxEvgReAO4wVp7h7U2IQfiLSicE2mdAbZlUO4LD3Wy4q6U5XFrrWv8KWNMRWPMVcaYEC/1RERERERERETyVZEiRXj55Zf58ccfGTJkCNdccw1ly5YlICCAcuXK0axZMx5++GFWrlzJxIkT09WvVq0a3333HSNGjKBOnToEBgZSqVIlbr31VtasWcOgQYMyjWHkyJGsWrWKjh07EhISQokSJahXrx7PP/88mzZtcs1rDngcnjq7+5AT7rvvPn788UcefPBBrrrqKkqUKEGpUqVo2LAhzzzzDHv37vU657sxhlmzZvHBBx/QunVrypQpQ+nSpbn22muZOHEia9ascU2B4c/w3Nu3b2fcuHF07NiR2rVrU7p0aYoXL061atXo1q0bH374IStWrKBEiRJZardcuXJs2rSJMWPG0KBBA4KCgihfvjw33XQTCxcuZPTo0a5pX3JjWPGAgACWLl3K2LFjufrqqwkMDCQ4OJhmzZrx6quvsn79ekqVKpXj/eaUwMBAli9fzhtvvEHTpk0pVaoUZcuW5YYbbmDmzJm8++67uXr8RESywzkCRUBAgGtEIV84kzHOnz/PRx995LbNGMMHH3zAjBkzuP766ylbtiylSpXi+uuv56233mLhwoVeE84aN27M7t27iYyMpEmTJpQpU4bAwEDCwsLo378/O3fuZPDgwZnGd/vtt7Nx40buuOMOqlSpQvHixQkNDWXw4MFERUVx9dVXe6zXqFEj1q9fz2233UaVKlUyTIx75pln2LZtG/fffz81a9YkMDCQsmXL0qxZM8aMGcOPP/7ocQSmlStXMnnyZLp37069evUIDg6maNGiVK5cmQ4dOvDWW2+xefNmKlasmOl+5mabIiIFRbZTlK21O4AdORBLlhhj2qT8+IO11uexmVOSEhoBeJraw0fOM90v1tr0E1imsNb+YYxJAMqkquNrnEWAf6W83WUcE2INAoYBV6Uq9zPwP+ANTREiIiKSNRq5R0SkEPj6Dff37cd6Lifijf5mfOYc7cFX3p4CTatevXpMnjzZr5jKlCnD6NGjGT16tMft69evz7SNW265hVtuucXjNuf832XKlKFs2bJe2/BnH0JDQ11Dd2fEl+NYu3Ztpk+fnqX+U+vTp4/XaTqcx+CKK67IcrvXXXcd1113nWv+eV+Fh4dnemwCAwN55pln0g1j7pRR3L7+LWcUQ7FixRg+fDjDhw/3uD2zfchoW0a/84iICCIiIrzWdYqJiclwe5EiRXjooYd46KGHPG7Pzu9dRPwX1LlhfodQ4M2ePZvZs2dnuV779u0z/Ow1xjBw4EAGDhyY5baDg4MZOXIkI0eOzHLd1Jo2bcqCBQuyXK9FixZ8+umnPvcxd+7cLLVfr1496tWrx5AhQ7JUL6NrHX/bFBEpDPwewcIfxpirjTEfZl7SJ+txjBCR1ZEhmqfU/dyfTo0xgYAzpe6QD1UOpiyrZ7Gr6jgSM8AxMsjHwDRSJVekqA9MAtYaY4IRERERERERERGfLFmyBHAkCjiebbm0WGtZtmwZ4LghU1gcOHCAqKgooHDFXVD89ddffPGFY+BdHT8RERERkazJkwSLVIkVu4DeedGnD/z9X3PqcfNO+1DeWaZ0Fvspn+rnLkBPYD+O4xcMlAJu5p8pSloDM7PYh4iIiIiIiIjIRevECe+Dnn7yySd8/rnj+ZusDEFe2GR0DF577TWio6OBgnUMEhISSEz0PGhsUlISQ4cOJTk5mcDAQI9z2V/qzp8/z5kzZ7xuHz58OKdOnQIK1u9dRERERKQwyPYUIRkxxtQHRuJICjApr8zHRsxdzsQKf+NIPWmkL1NynPdQzxepJ3IMAmKBltbaw6nWf26MCQc2Aw2B3saYf1lrt5GGMeY/wH8AatSokcVQROSSpSG5C4VFnfa6ve+RT3FIAZH23y3o3+7FrKD9vnXeuGhkem4paH97IiJetGzZkvDwcHr37k2DBg0oXrw4+/fvZ/78+bz++usAhIWF+TQlQ2FVq1Yt/v3vf9O9e3fq1KlDkSJF2Lt3L7Nnz2bWrFkA3HDDDXTu3DmfI/3H9u3b+fe//82DDz5I+/btqVGjBufOnWP79u1MmDCBL790zPo7dOhQypcvn0lrl57Dhw/TokULHnzwQTp37kxYWBjJycn88MMPTJ061TXM/J133kmDBg3yOVoRERERkcLFpwQLY0xpYDDQHaiD44b/b8BG4FVr7U9pylcHRgP38E9iBcCfwNQcidx/zuk9/vKz/tlUPxf3oXygh3q+OJfm/StpkisAsNaeMcY8CyxOWdWHf0a1SF3uf8D/AJo1a5bfSS4iIiIiIiIiIrnu7NmzzJgxgxkzZnjcXrVqVT799FNKlSrlcfvFID4+ngkTJjBhwgSP2+vXr8+CBQsoUiRPZxLOVHR0NE888YTX7T179mTUqFF5GFHhcvjwYUaOHMnIkSM9bm/ZsiVvvvlmHkclIiIiIlL4ZZpgYYypA6wCnMMeOJMl6qS87jPG9LHWLkwp/3/AJKBkqrJ/AK8Bb1pr/U1syDZjTCDwQMrbGD+bSUj1sy/TfjjL+DKdiLd+AFZkUHYtkIjj9/mvLPYjIiIiIiIiInJRmj59OosXL2bTpk0cOXKEEydOULp0aWrXrk23bt0YPHgwwcHB+R1mrvrggw9YuXIl3377LUePHiU+Pp7g4GCuueYaevTowcCBAwkKCsrvMN00adKE//73v6xatYqff/6Zo0ePcu7cOSpWrMi//vUvHnjgAXr00Nh93lStWpXZs2ezatUqvv/+e44ePcrp06cpV64cjRs35u677+a+++4jICAgv0MVERERESl0MkywMMYUAz4GanranKqN91MSMe4DRqXaFg28Asy11l7wN0hjzAP8kxiR1svGmGGZNYFjyo16KUsLrPMnFmvteWNMHI6RMK7woYqzzMEsdnUIR5zOY+m1vrX2bEpMVYBKWexHREREJL3CNvx/YYsXiKrWNb9D+Iem9hARkYtU586dC9TUF/mhT58+9OnTJ7/DyJLg4GAGDRrEoEGD8juUQikwMJB+/frRr1+//A5FRCRfxcTE5Gv/kZGRREZG5msMIiKS8zIbwaIncA2OG/3HgJeAr4DzQAPgCeBGoBjwJtAeR0LAQeBZ4H1rbXIOxBkKhKfEkZpJicNXzmSFY8DEbMTzE9AGqG2MKWqtTfTYmTGXA2VT1fGZtfYvY0wMEJayKrOUcuf2pKz0IyIiIuKvRZ32ur3XM4QiIiIiIiIiIiIicjHzJcEC4C+glbX211Tb9hpjlgBfAC2BW1LWLwQeyKWpQIyP69KyOKbo2I9j5IpXrbV/ZCOOr3EkWJTEMSXHJi/lwtPUyaov+SfBohbwradCxphgHCNqAPzuRz8iIiIiInIRUOKTiIiIiIiIiIhI7imSyfYmOJIT3kuTXAFAysgNo1PeGuAAcFdOJ1dYa1+01hZJ/XJuAm5Pu83DK8BaG2ytbWytfTybyRXgSCJxGpBBuf4pyyRgsR/9fJTq514ZlOvBP4kmX/rRj4iIiIiIiIiIiIiIiIiIiGQgswSLy1KWWzIoszllaYE51tq8nKLCl9Ercpy1djuwPuVthDGmddoyxpi+wM0pb9+x1h5Lsz3UGGNTXuvT1k+xAvg+5eehxpgmHvqpxj9JLueBt7OyLyIiIiIiIiIiIiIiIiIiIpK5zKYIKZOy/NNbAWttvDGuPIfonAjKF6lGscgvQ4GNQClgpTFmHI7pR4oCt6VsBzgCPOdPB9baZGPMQzimYSkBbDDGTATWAolAc2A4cHlKlWdzYHQOERERERERERERERERERERSSOzBAuDY2QK62N7p7MXTuFhrd1ljOkNfACEAKNbFwRoAAAgAElEQVRSXqn9DtyWnaQHa+0mY8ydwDtAMPBiysutGPCitfZVf/sRERERyRVfv+H+vv3Y/IlDRCQTUdW65ncIIiIiIiIiIiJSwGWWYFFgGWMCgHtwJIF8a639yYc6VwPNgGRr7bvZjcFau9IY0xAYAnQFagBJwH5gETDFWut19I8s9LPYGNMAGJyqn6LAHzhGt5hqrd2V3X6kkNMNLBERESno0l6vgK5ZRLxQwoeIiIiIiIiISMFTaBMsgC7AXCAZqOdjnfPAbMAYY2KttauyG4S19hDwVMorK/VicCSH+Fr+dxzTgQzPSj/iJ335L/lFf3siIiIFi5JYRUREREREREREJIWvCRb/M8a8nkPlrLW2lo/9ZuS2lOVGa220LxWstb8aY74G2gA9gWwnWIiIiIiIiIiIiIiIiIiIiMjFz9cEi0qZbLc+ljOpymbX9SltfZ7Fep/jSLBonkNxiIiIiIiIiIiIiIiIiIiIyEXOlwQLn6exyGM1U5a/ZLHerynL0JwL5dK2qNPedOt65FFfnvrxpYzkLs0XLWnp36UXPkwJU6COnaaw8S6Hjk2B+n2LiEh6OheKiIiIiIiIiFzSMkuwCMuTKPwTmLI8n8V6f6csS+ZgLCIiIiIiIiIiIiIiIiIiInIRK5LRRmvtgdx45VDsJ1KWl2exnrP8yRyKQ0RERERERESk0IuMjMQYgzGGmJiY/A4nx6xfvz7D/QoNDcUYQ2RkZJ7H5q+YmBjXPq1fvz6/w8kVGf1enH+roaGheR6XiIjkjdTXJcYYduzYkWH5iIgIjDHUq1cvjyIUTzK77pJLQ2G/VgsPD8cYQ0RERH6HIgWUL1OEFFTRwGVAJ2BKFup1TFnuz/GIJFsu2WHR0w4znJ9DDGvI42zRFDYikpGo/b/ldwgihYKmGst9uh4REREREZHCJjIyks8++yy/wxCRQswYA8Dbb7+t5AnJlsKcYLEWaAl0NMa0ttZ+lVkFY0xbHAkZFlidy/GJFCxKnrikpL1xArp5IiIiIlJQKMklbzWc2zC/Q8hRUQ9E5XcIIiIi4qfCNFqSL/JyfxYvXsz27dtp2rRpnvUpIiLiSY4mWBhjqgM3AVhr38nJtj34H/A0EAgsMsbcZa1dl0Fs7YH5KW//Bt7M5fhERERERHJVQUuo001jEREpjArj8NWhoaFYa/M7jHwTGRl50d2kFBERz4oVK0blypU5dOgQkZGRLFmyJL9DEpFMFPZrtYt1Cj7JOUVyuL3rgDnA7BxuNx1r7WFgFGCAcsBqY8w6Y8xQY8wtxpgWKcuhxph1wCqgPI7RK1621h7M7RhFRERERERERERERETEP0WKFGHEiBEALF26lG3btuVzRCIicqnL6QQLJ5NL7bqx1o4D/pvSnwHCgdeAFcBXKcvXUtY7Y5pmrR2dF/GJiHeLOu11e4mIiBQaX7/h/hIRySe6ppa8ZozBGMOcOXNITk5mxowZXH/99ZQtW5YyZcrQokUL5s2bl2EbO3fupH///tSuXZuSJUtSokQJatSowQ033MDw4cP59ttvM+zXm8jISIwxhIaGZnm/QkNDMcZ4fMouIiICYwzh4eEAbNu2jTvuuIOqVasSFBRErVq1ePzxx/nzzz+z3K9TcnIy77zzDrfccguVK1emWLFilCtXjjp16tC1a1emTp3K8ePH3erExMS4jou3J+ySk5N58803uf766yldujTlypWjZcuWzJ07F8j4mM2ZM8fVPsAff/zBkCFDCAsLIzAwkCpVqnD33Xfz888/e92vhIQEPvroI+677z7q1q1LyZIlCQoKokaNGtx1113ZejIwo9jXr1/vij0mJoZTp07x7LPPUrduXUqUKEGFChXo1q0bmzdvzrSfBQsW0LZtW0JCQihTpgzXXXcdkydPJjExMd0xEhGR3DNgwACqV68OZH9aklWrVnHXXXdRo0YNgoKCXOfHKVOm8Pfff3us4+tnfkbXFOHh4RhjiIiIAOCzzz6jU6dOVKlShYCAAIYNG+ZWPjo6mkGDBlGnTh1KlixJmTJlaNSoESNGjCAuLs5rDGn7WbNmDZ07d6ZSpUoEBQVRv359Ro0axdmzZ722ER0dzauvvkqHDh2oWrUqxYsXJzg4mGuvvZann36aw4cPZ3gccsKJEyd46aWXuPHGG6lYsSJBQUGEhobSrl07xo0bx9GjRz3WO3LkCE8++STXXHMNZcqUoVSpUtSrV48hQ4Zw4MABr/2lvebbtGkTPXv2pGrVqpQsWZKGDRsyZcoUkpKSXHUOHTrE0KFDueqqqyhRogTVqlXjoYce8vr7SXv9du7cOUaPHk3Dhg0pVaoUVapUoXfv3vz0009u9RYsWEB4eDgVK1akVKlS3HjjjSxcuNDrvqT9G/Ak7fVSRnGeP3+e8ePH06hRI0qVKkVISAg333wzK1as8Nq+r9fma9asoW/fvoSFhVGiRAnKly9Po0aNiIiIYOXKlenKx8XFMXfuXHr37k1YWBhBQUGULFmSWrVq0a9fP3bs2JHhMXHq16+fax89HQdfjmFO/a3lxv8vJPfl6BQh+cFaO9gY8xXwIlA3g6K7gRestR/nTWQiIiIiIiIiIhefxMREbrvtNpYuXeq2ftOmTWzatIndu3czenT6Z1vmzZtHv3793L6YBjh48CAHDx5ky5Yt7N69m08//TRX4/fXu+++S//+/blw4YJr3b59+3jttddYsWIFmzZtIjg4OEttOo/l8uXL3dbHx8cTHx/PL7/8wrJly6hevTq33367z+3+/fff9OjRI127GzduZOPGjaxbt44rr7zSp7Z27dpFhw4dOHbsmGvd0aNHmT9/PsuWLeOLL76gadOm6erdf//9Hn+Xzt/3ggULGDFihMe/lZzy+++/06FDB6Kjo13rzp07x9KlS1m1ahWffvopt956a7p61loGDhzIW2+95bZ+x44d7Nixg8WLF9OnT59ci1tERNwVL16cZ599lgcffJDly5ezdetWrr/++iy1cfbsWe6//34+/tj9FtH58+dd58e3336bFStWUKVKlZwMP50nn3ySiRMnet0+b948BgwY4HbNARAVFUVUVBQzZsxg8eLFtGrVKsN+xo0bx4gRI9ymFdu9ezcjR45k3bp1rFu3jqJF3W8Tnjx5ktq1a6dr68KFC+zatYtdu3Yxa9Ysli5dyg033ODL7mbZihUruOeee/6fvfsOi+JqGzj8GwRBiiBiSYKC0dh7rBG7JjEajWLBgtEYxfLaU9T4ivraYzRRoxgbGnvD3lERuzESjR0RFcVCU6xRme8P3P1Y2F12YQE1z31dey3MnHPmzDAwh51nnkNCQoLO8mvXrnHt2jX27dvH06dP0wSy7NmzhzZt2pCYmKiz/OLFi1y8eJF58+axdOlSvL29jW5/4cKF9OrVS2fM+vfffzNw4EBOnjzJ4sWLOXnyJJ999pnO+OjWrVsEBASwf/9+jh07Rt68eQ1u4/79+3z00Uc6AQGPHz9m3bp17Nmzh3379lG5cmV69erF/PnzdeoePXoUb29v5s6dS69evYzuS2YlJiZSr149jh8/rrN879697N27N8N9ePToEV27dk0TKPL06VPi4+M5c+YMixcvTjMlXtOmTQkLC0vTXkREBBEREfz+++/MmjWL3r17m90nc1jqXMuK/y9E9siqDBbZSlXV1UBZoAYwhOSsFfNfvQ8BqquqWlaCK3JQ6ic95WlPIYQQQgghRHaS/0eEsJgJEyawa9cuRo8ezYULF4iNjeXAgQNUrVoVSP4w/8yZMzp14uPj6dOnDy9fvqRatWps2LCBq1evEh8fT2RkJNu2bWPAgAHkz58/J3YpXZcvX+brr7+mUaNGhISEEBMTw+XLl7VPm54/f55x48aZ3e6iRYu0QRD9+vXj+PHj3L59m5iYGM6cOcPChQtp3rw5uXLlMqvdYcOGadtt3749J06cIDY2lrCwMHr06MHvv/+ebrYRjS+++AIXFxdWr15NdHS09uZBnjx5SExMNPgBdqFChRg0aBAbN24kLCyMO3fucO3aNfbu3UvHjh2B5HNp06ZNZu2bOXx9fXn06BELFizgxo0b3L17l5UrV+Lm5sbz58/p2bNnmptXADNnztQGVzRq1IgDBw4QExPD+fPn+e6779i/fz+TJk3Ksn4LIYRI66uvvsLDwwMAf39/s+t37tyZtWvXYmdnx8iRIwkLCyM2NpbIyEhmz56Nq6srYWFhtG3bNk0wqCXt3r2bqVOn0rZtWw4fPqy9vrRv3x6AkJAQunXrxvPnzylRogRr1qzh9u3bXL9+nYCAAFxdXYmPj6d58+ZGn5IPCQlhxIgRdOzYkePHjxMbG8vZs2fp1KkTAAcOHGDu3Ll669asWZOpU6eyf/9+Ll68SGxsLOfPn2fRokWULVuW2NhY2rVrx6NHjyx+fA4dOkTLli1JSEigUKFC/PLLL1y8eJG4uDiuXLnCypUr8fb2ThMYcvHiRVq1akViYiKFChVi4cKFREVFER0dzfLlyylatChPnz7Fx8fH6DQzly9fpk+fPnz66accOnSImJgY/v77b9q0aQPAkiVLWLNmDd7e3ri5ubFhwwbtGGfUqFEABoONUxo0aBBXr15l1qxZXLt2jbt377Js2TKcnZ25f/8+/fr1Y8aMGSxcuJBvv/2Ws2fPEhsbS0hICGXLlgVgyJAhRrOZWMLAgQO5cOEC06dPJyIigpiYGLZt26YN1B08eLBOkIkpVFWlffv22uCKNm3aEBwczO3bt7l79y5Hjhxh7NixerNfeHp6MmLECLZv386ZM2e4d+8eERERbN26lU8//ZSXL1/Sv39//vzzT51627dv1wmGCAgIIDExUeel+fuSHkuea1nx/4XIHm98BgsNNTmM6Y9XLyGEEEK8rvTd1GoyMfv7IYQQQgghMuTq1ausWbOGtm3bapfVrVtX+2Hr48eP+f3335kyZYp2fWhoKI8ePSJXrlzs3LkTV1dX7ToXFxc8PDxo1qxZtu6HOW7dukXLli0JCgrCyir5eaX8+fMzffp0bty4wbp161iyZAk//vijWe1qUiu3bt2aWbNm6azLnz8/5cuXp3v37ma1ef36dWbMmAFAx44dWb58uXadq6sr8+fPJ0+ePGm2Z8jLly85ceKETvCLn58fz58/p3///vzxxx+cO3dO+2G/RkBAgN72ihYtSsOGDfH09GTixIlMmTKFli1bmrWPpoqNjeXPP/+kePHi2mUdOnTAwcGBzz//nFu3brF7926dLBaPHz/W3iCpW7cuO3bswMbGBkj+mUyePJmCBQvyzTffZEmfhRBC6GdjY8PIkSPp2bMnO3bs4OjRoyZnUFi7di1BQUHkzp2b4OBgPvroI+06V1dX+vTpQ926dalevTqHDh1i3bp12oAHS7t16xZdu3bVTtkF6Fxj+/fvT1JSEu7u7hw6dIiCBQtq1/n5+VGzZk1q1arFgwcP+P7771m5cqXe7URGRtKvXz+d672rqytLly7l0qVL/PHHHyxevJh+/frp1HN2dtY7jZarqyulS5emXbt2VK5cmfDwcFasWMHXX3+d4WORWlJSEl999RUvXrzgvffe48iRI9qpYQDy5cvH+++/T4cOHXjx4oVO3W+++YbHjx/j6OjIgQMHKFmypHZdx44dqVOnDh9++CExMTH079/f4FRht27donXr1qxbt047pUT+/PlZuXIlpUuXJiIigk6dOlG0aFEOHz6sk11gzJgxXLlyhWXLlrF48WImT55scF9v3rzJoUOHqF69unZZp06dePr0KT169ODIkSOcOHGCn3/+mf79+2vL1KtXj6CgIEqXLs2jR49Yu3ZtlmZruHHjBqGhoTq/a82aNWPDhg1UrFiRx48fs3btWvr27Wtym8uWLdMGAo8aNYoxY8borC9QoAC1atVi+PDhaeoGBQWlWebm5kaxYsX47LPP6NSpEytWrGDatGksXbpUWyZPnjw6dWxtbXF0dDS5zylZ8lzLiv8vRPZ4KzJYCCGEEEIIIcQbTTK+5Tz5GQhhMi8vL53gCo1ChQrx8ccfA6R5WkvzJKhm3uY30bRp07Qffqbk6+sLwN27d40+SaqP5ri8++67me/gK8uXL+fly5dYWVnpBLmkNH78eOzt7U1qb9SoUXozi3Tp0kX7tbGn8wzR1D969CiPHz82u74pBgwYoBNcofHZZ59pg3xS933Tpk3cv38fgMmTJ2uDK1IaNGgQRYsWzYIeCyGEMObLL7+kWLFigHlZLDSBh19//bVOcEVK5cuX12Z3SBmcaGnW1tYGr88nTpzQZgHz9/fXCa7QqFy5Mn5+fgCsW7cuzTQaGg4ODkyYMCHNckVRtNfgsLAwvZmcjHFwcKB169YABAcHm1U3PTt37uTSpUsATJ8+XSe4IrWUGSzu3LmjvWE/cOBAnRveGkWLFmXEiBEAHDt2jLNnzxpse+rUqdrgCg0bGxttFosXL17w3//+V+/UDZopxO7cucP169cNbqNjx446wRUa7dq10267SJEi/Oc//0lTpmTJktrMcamn7rA0Hx8fvYFMFSpUoHLlyoD548CZM2cCyb9zxn6PU2cpMYXm3Lb0ualh6XMtK/6/ENlDAiyEEEK8NYI+vaTzEub5Vx4/uZkmhBBZR/7GCvHW+uSTTwyu08zZfefOHZ3lFStWRFEUHjx4QM+ePYmOjs7SPlpa8eLF9d6oB3TmKU+93+nRfDC9aNEili1bxj///JPxTr5y+PBhAKpUqYK7u7veMnnz5qVBgwYmtWfo5+3i4kKBAgUAw/sdERHBd999R/Xq1cmXLx+5cuVCURQURaFcuXJAcpDJlStXTOqLuQz13crKSvvzTN13zfFzc3Ojdu3aeuvnypWLFi1aWLCnQgghTKHJYgGwa9cu7d9sYx4/fqx9grxBgwY8fPjQ4KtChQoA/PFH1iVKr1KlCoUKFdK77uDBg9qvNTfz9WnXrh2QfKPf0NPxtWrVIm/evHrXacYuz58/Jz4+Xm+Zbdu24ePjQ/HixXFwcNBevxVF0T5RrwmGsJS9e/cC4OjoqA3iMMXhw4dJSkoCwNvb22A5zXGD5Oxq+pQoUUI7/UVqKZc3bdpUb5mU48Xbt28b7IsmKDk1Jycn7fiqcePGaQI9Um/H2DYsISPjfmMePHig/f3q3Lmz3uCC9Jw+fZp+/fpRsWJF8ubNi5WVlfbcbN68OZB8XFJOCWIpljzXsur/C5E9LD1FyAVgTLqlsoCiKFZAcSAfYGdKHVVVD2Rpp4QQQgghhBBCCCHeMu+8847BdZqsCE+ePNFZXrx4cW2a6oULFxIYGEiVKlWoW7cu9evXp0mTJhlO05sdTNlnSLvf6Rk8eDCLFi3ixo0bdOnSRZuivG7dujRq1Ijq1asb/GDdEM1TbvqeqEupZMmS2ifwjMnIzxtg1apVdO/e3aRjoskYYWkZ6bvm+KX8YFuf9I6vEEKIrNG1a1fGjx9PREQE/v7+7N6922j5iIgIbZYGU6f9uHfvXqb7aYgmA4c+mmtQ4cKFdaZTS00TpAgYzJKQ0bHLixcv6Ny5M6tXrzZYX8PS129NwGXZsmXNyl6Q8gn/1FOWpeTu7o6zszP37983eNwKFy5ssH7KaSYMlUtZxtgYyJTtmFLG3LGnuTI6DjQkMjJSG6BQqVIls/szbdo0vvvuO20WOGPu37+Pk5OT2dswxpLnWlb9fyGyh0UzWKiqelFV1TGqqmZbkIWiKA0URdkCPCQ5wOMIsM+E197s6qMQQgghhMicf2WGFSHEG0n+Xol/g1y5cqVbRlXVNMtmzJhBQEAAZcqUISkpiZMnT/Lzzz/TunVrChYsSP/+/bPkSTNLMGWfQf9+G+Pi4sLx48fp06cPzs7OJCYmsm3bNoYPH07NmjUpUaKE2WnKHz16BCSn8DbG1ICWjPy8r1y5QteuXXny5AklSpRgzpw5nDp1ijt37vDgwQMSExP5+++/teVTz6NuKRnpu6WPnxBCCMuytrbmv//9LwB79uzRyfqgT0aCACyRUcqQlDfgU3v48CGQ/jUm5U1jQ2OnjI5dJk2apA2u8Pb2ZuPGjYSHhxMbG0tiYiKJiYkMGzYMsPz1W7Mv5t4U1xw3a2trbG1tjZbVHNvMHreMjofNqZ/ZbViCpfuQ8rib+3M+ePAgQ4cO5eXLl1SpUoXFixfz999/c+/ePe25uXXrVm35rBhf5sS5ltU/Y5Exls5gka0URfEHRmm+zcm+CCGEEEIIIYQQQgjDFEXBz88PPz8/IiMjOXz4MAcOHGDz5s3cunWLWbNmceLECQ4fPmx2uuCsukGfHQoXLszs2bOZMWMGJ0+e5MiRIwQHB7Nr1y4iIiLo3LkzCQkJ9O3b16T2NIEBmkABQzQfEGeFRYsW8c8//+Ds7Mzhw4e1qa5TMnfO9+zyOhw/IYQQxnXp0oXx48cTHh7OqFGjtFNL6JMyWGHv3r00bNjQ7O2Zmk0qs+MRTV/Tu8akXG/pJ/Tnzp0LQKdOnVi2bJneMln1RL1mX8wNuNUctxcvXvDs2TOjN741x87Sx+11Ysr5mlNjZ1OCgwwJCAgAkqdqOXz4MHZ2aSczyMrgKJBzTfw/i2awyE6KonwC+JMcWKEA14HfgUkkT1OS3mts9vda/NvI02tCCCGEEEIIIURanp6edOrUiYCAAK5du8bgwYMBOHbsGDt37tQpq/nw1NiH+dHR0VnX2WxibW1NzZo1GTRoEJs3byYiIoLSpUsDMHbsWJOfXvPw8ADSnxfd0vOmp3T69GkAGjZsqDe4AtDJYPE60Ry/y5cvGy2XlcdPCCGEcSmzWOzbt4+QkBCDZT08PLSBmxERERnaXsobuYbGI8+fPycmJiZD7Wt4enoCcPv2beLj4w2WO3v2rPZrzXXLEuLi4oiKigKMT6eSVdfwEiVKAHDu3DmzAgA0xw3g/PnzBsvdvHlTm9HEksftdfM6j509PT21mRs040VTacq3bNlSb3AFZP34Us41oWEwwEJRlHpZ9bJQ3//z6j0JGAIUU1X1S1VVR2imKUnvZaF+CCGEEOJtdnB22pcQ6ZAgSyGEEMJ01tbWjBo1Svv9hQsXdNZr5p82dMM7KSmJ4ODgrOtgDnnvvffw8/MD4M6dOyQkJJhU76OPPgLg1KlT3Lx5U2+ZxMRE9u/fb5F+6vPs2TMAo/Njmzv1SXbRHL+YmBiOHj2qt0xSUpJOCmohhBDZr3PnznzwwQcA+Pv7Gyzn4uJC9erVAbRTX5hLMxYBw+OR0NBQ7fUvo7y8vLRfr1+/3mC5tWvXAv8fnGkpKftv6Bp+8+ZNowEtmdG4cWMg+cn/DRs2mFyvdu3a2iCadevWGSynOW6ge6zfNumNnQF2796dXd3RkTdvXu3v47Jly8ya/iK98aWqqqxcudJoG9bW1kbbSI+ca0LDWAaL/cC+LHgZztVknhqACqxSVfVnVSahEUIIId4eEtAghMgsE/6OnHmvhc5LCCFE1rl69arRlL1XrlzRfp0/f36ddZoPYdetW6f3xsUvv/zCtWvXLNTT7JU6mCQ1zXHJnTu3yemFO3bsiJWVFUlJSXz//fd6y4wcOZLHjx+b11kzFCtWDIDDhw8TFxeXZv2qVavYvn17lm0/Mz7//HPy5s0LwLBhw/Q+Qfsmn3NCCPG2yJUrlzZAMyQkhNDQUINlhwwZAsCuXbv47bffjLb79OnTNH/jK1eujI2NDYDeaTOePXvG8OHDzeq/PtWqVaNChQoAjBkzRm9GjNOnTzNnzhwAvL29cXFxyfR2NQoUKKCdKmvz5s1p1r98+ZLevXtn2fQSTZs2pVSpUkDyz8xQoCjoTnFRqFAhmjdvDiRfo8PDw9OUj4qKYvz48QDUrFmTcuXKWbLrrxXN2DksLEwn24nG8ePH0w1EyEr/+U/y8/Nnzpxh3LhxBsulPs8048tdu3bp/b9iypQpevc3Jc3/GRnN4CHnmtBIb4oQJYteluD86n2HhdoTQgghhBBCCCGEEFlg8eLFeHh4MHjwYLZt28a1a9dISEggIiKCxYsX06ZNGyB5juJmzZrp1P3yyy8BuH79Oq1ateLUqVPEx8dz5swZBg8ezNChQ3n//fezfZ8soVmzZtSuXZvp06dz7Ngx7ty5Q0xMDGFhYXz//ff8+uuvQPINFM0Td+nx8PDQfnC9bNkyfHx8OHnyJHFxcZw+fZpevXoxY8aMLD1m7dq1AyA2NpZmzZqxb98+7t27x4ULFxg5ciS+vr6UKVMmy7afGQ4ODowePRpIvmHXrFkzDh48SFxcHBcvXmT48OF8++23b+w5J4QQb5OOHTtqb8gbm/6jffv22muTn58fPj4+7N69m+joaO14ZMuWLQwYMAAPDw/WrFmjU9/Z2ZlWrVoB8NNPPzF58mSioqKIiYlh586d1K9fn/DwcJydndNs21wzZ87EysqKGzduUKdOHYKCgrh79y5RUVHMmzePRo0a8ezZM/LmzcukSZMyvb2UrK2tad26NQCBgYEMHTqU8+fPExsby/79+/nkk0/YsmVLll3DraysWLBgAdbW1ty4cYNq1aoxc+ZMLl++TEJCApGRkaxfv54OHTqk2fcff/yRPHnykJiYSL169Vi8eDG3bt3izp07rFq1Ci8vL+7du4e1tTUzZszIkv6/Ltq1a4e9vT2qqvLFF1+wc+dO4uLiiIyM5JdffuHjjz+mSJEiOda/Tp068dlnnwEwatQo2rdvz759+7h79y4xMTGcOHGCiRMnUrJkSZ16mt/hixcv0qpVK44dO0ZMTAynT5+mX79+DBs2LN1zs2rVqkDy/ybHjh3j0aNHvHjxwqygITnXBICx/8zSm0KjGtD81dcJwEEgHHgEOAAlAC/AheRME1uBk5npbCq3gSKA4ccfhBBCCCGEEEIIIcRr4fbt2/z888/8/PPPetfb29uzfPlyChQooLO8efPmdOrUieXLl4lI0QwAACAASURBVLNz50527typs75fv364ubkxZsybNxusqqocPXrU4FQUAB9++KHZH85OmTKFixcvsnPnTlatWsWqVat01nfu3JnixYszduxYkwM3zNG4cWN69OjBggULOH78OI0aNdJZX6pUKRYuXEjt2rUtvm1LGDRoEH///TcLFy5kz5497NmzR2d9gwYN8PHxoXfv3tp5xIUQQmQ/TRaLzp07p1v2999/x8nJiYULF+q9NqaUO3fuNMumTp3KoUOHiI6OZtiwYQwbNky7zsnJiQ0bNvDVV19x//79jO3MK/Xr1ycwMJAePXpw6dIlbRBqSvny5WPTpk14enpmalv6TJ48mZCQEG7cuMG0adOYNm2azvqBAwfi4uKSZeOuOnXqsGHDBjp27Mjt27cZMGCA3nKpb6SXKlWKjRs34u3tTXR0NN26dUtTx87OjqVLl1KjRo2s6Ppro0CBAkyfPh0/Pz/Cw8P59NNPddbXqlWLH374gc8//zxH+qcoCqtXr6ZTp05s2rSJNWvWpAlq0qdbt26sXr2aXbt2sWPHDnbs0H3+/qOPPmLEiBG0aGE4Q2n//v3Zvn074eHh1KpVS2fd1atXTfqdknNNgJEMFqqqjjH0IjmQoinJwRT/AQqrqtpSVdUhqqr+99V7S6Aw0A94+Kr8pVf1LeHQq3fJrSKEEEKIfw+ZPkUIIYQQb6BBgwaxevVqevXqRdWqVXnnnXewtrbGycmJypUr8+2333LhwgWDH4guWbKEGTNmULlyZfLkyYOzszP16tVj9erVzJo1K5v3xnJ27NjBL7/8QsuWLSldujTOzs5YW1tTqFAhmjZtyrx58zh69Chubm5mtWtra8u2bduYPXs2H374IQ4ODuTNm5datWoxf/58li5dysOHDwFMnnrEXPPmzWPu3Ll8+OGH5MmTB0dHRypUqMDo0aP5448/dOazf90oisKCBQtYsWIFdevWxcnJCUdHRypVqsTUqVPZvXu3NjV1Vh0/IYQQpvHx8TEpo4KtrS0LFizg2LFjfP3115QsWRJHR0esra1xc3Ojdu3aDB06lIMHD+q9qe/h4cHx48fp2bMn7u7u2NjY8N577/Hll19y8uTJNMGEmeHr68vZs2fp3bs3JUqUIE+ePDg4OFChQgWGDx/OpUuX8PLystj2Unr33Xc5ceIEffv2pUiRItjY2FCwYEE+/vhj1q9fbzBQ1pKaN2/OlStXGDlyJFWqVMHZ2Rk7OzuKFStG06ZNmT17Nv37909Tr2nTply8eJFvvvmGsmXL4uDggL29PaVKlaJ///5cuHABb2/vLO//66BXr15s27aNRo0a4ezsTJ48eShfvjyTJk0iJCQER0fHHO2fg4MDGzduZPPmzXh7e/Pee++RO3du8ufPT8WKFenbt2+aaX9y5crFli1bmDhxImXLlsXW1hZnZ2eqVavGTz/9xP79+7VT3BjSrFkztmzZwscff4ybm1uGA2XlXBNmh6grilIWmEfyVB8fq6p6xFBZVVX/AeYoihIGhADzFEU5paqq8QkmTTMT6AB8qSjKRFVVn1igTSHeWEGfXtL5vnUO9UMIIYQQwpDU4xWQMYsQBqUOomsyMWf68RY48+WZnO7CG2P06NHaqRH0UVU1w224uLjQrl07bWpfc+XKlYv+/fvr/TDd2HYhOduAsb5HRkYaXBcYGEhgYKDRvnl6epp0bPQpXbo0pUuXNvh0Zma2aWVlRZ8+fejTp4/e9VeuXAHA3d09zbpu3brpfRovNWPHTlEUevXqRa9evfSud3R0zPDPJTM/b439+/enW8bHxwcfHx+964wdPyGEMMTYdVboSm9comFlZcW5c+dMbrdGjRoZfqrc3d2d3377zeB6Y9cuU647KX3wwQfMmTPHrDqmbie9a2WhQoX49ddftVOVpWaJ63B6ChQowP/+9z/+97//mVXvnXfe4ccff+THH380q54pYz5TxkfGxmimjhmNnUcapvS3WbNmaabe0zD2czK1n8b6YOrvb4sWLYxmnEjNxsYmTQaZlEw5/5o3b07z5s2NljHl9ygrz7XM/H8hskdGcgAOAOyARcaCK1JSVfWIoii/A92BgYD+/+zMoKrqUUVR/IH/AesVRfFRVTVzuZeEEJYhHwYLoT+zgPwuCCGEEEIIIV4Djx49Yt++fUDyFCTCPKqqsnXrVkCOnxBCCCGEEP82GQmwaAKoJGekMEcIyQEWTTKwzTQURakHhAIrgI7AZUVRlgBHgRggKb02VFU9YIm+CJHVsis7hTxVKoQQQogcJwFqQgghRKY9e/aMly9fYm9vr3f9sGHDePDgAUCGs4q87eLi4nB1ddW7btq0aYSHhwNy/IQQQgghhPi3yUiAxbuv3p+bWU9T/l2jpUy3n+RAD169uwGDzaivkrH9F0IIIYQQ4o0hAZRCCCHEv090dDQfffQRvXv3plmzZhQrVoykpCT+/vtvZs6cyYYNGwBo37495cqVy+Hevp6KFy9Oz549admyJSVLlsTKyopLly6xcOFCFixYAECtWrUMpt4WQgghhBBCvJ0yEmDwCLAFqpGcPcJU1V69P87ANg1R0vleCGFh2ZVNw1JMuakkN56EEP828nfPCJnmSrzO5PwUQghhhujoaPz9/fH399e7vk6dOsydOzebe/XmSEhIMDqvdpkyZVi9ejVWVlbZ3DMhhBBCCCFETspIgMVfQCOgh6Iov6iqej29CoqiFAV6kJw14q8MbFOfMRZqR/wLvWlBAkKkJDdGM0d+/4XIXvI7J4QQQgiR/d555x0WLlzIzp07+euvv7hz5w4PHz4kX758VK5cmY4dO+Lr60uuXLlyuquvrRUrVrBjxw7++OMP7ty5Q0JCAs7OzpQvX57WrVvj5+eHnZ1dTndTCCGEEEIIkc0yEmARSHKAhRMQoiiKr6qqBw0VVhSlDrAEyEtygMWiDGwzDVVVJcBCCCGEEEIIIYQQQohUbG1t6d69O927d8/prryxfHx88PHxyeluCCGEEEIIIV4zZgdYqKq6VFEUX6ApUJTkIIuTQDAQTvIUIPZACZIDMaqlqL5bVdVlme61EEIIIYQQQgghhBBCCCGEEEIIIUQ2ykgGC4AvgDXAZ6++//DVSx/l1fs2oH0GtyeEEEIIIYQQQgghhBBCCCGEEEIIkWOsMlJJVdUnqqq2ALoCYSQHURh6hQFdVFVtoarqY4v0WgghhBBCCCGEEEIIIYQQQgghhBAiG2U0gwWQPF0IsFRRFE+gOvAu4Ag8BG4Bx1VVvZbJPoosdOa9FjndBSGEEEIIIYQQQgghhBBCCCGEEOK1l6kACw1VVSOBSEu0ZSpFUV5msglVVVWL7L8QQgghhBBCCCGEEEIIIYQQQggh3m5vcoCBktMdEEIIIYQQQgghhBBCCCGEEEIIIcS/w5scYHEAUNMpYwW4ASWBXK/KnwISs7Zr4k0X9OmlNMta50A/xL+PnHtvjtQ/q9f95/Sm9dcUb+M+WYL8Hcl6Fjv3Ds7W/b7JxIy2lC1mhSzR+d7P89sc6snrRX7nMkf+lgshhBBCCCGEEEKIN0mmAywURXkPKAvkA+xMqaOq6pL0S6XbRgNTyyqK4gL0A0YC9kAHVVWvZLYPQgghhBBCCCGEEEIIIYQQQgghhPh3yHCAhaIo3YBvgDJmVlWBTAdYmLVBVU0AxiuKcgrYDGxSFKW6qqqPs7MfQgghhBBCCCGEEEIIIYQQQgghhHgzWWWkkqIoC4EFJAdXKBl45QhVVbcBm4DSJGe0EEIIIYQQQgghhBBCCCGEEEIIIYRIl9kZLBRF8QG6pVh0CNgL3ASeWaZbWWo70AroAPyYw30RQgghhBBCCCGEEEIIIYQQQgghxBsgI1OEfP3q/R/AR1XVDRbsT3aIe/VePEd7IYQQQgghhBBCCCGEEEIIIYQQQog3RkamCKkMqMCiNzC4AuD9V+82OdoLIYQQ4i0U9OmlNC8hhBBCCPFmGD16NIqiaF+nTp0yWr5bt24oikLp0qWzqYeW5enpiaIojB49Oqe7oiMwMFD7M3gbRUZGavdv//79adY3aNAARVHo1q1btvdNCCHE6yPluCQyMtKsupp6gYGBFu9XetcxU2j2zdPTM0e2L/69XtfxrxBvmoxksLB/9X7Akh3JDoqiuAC9SQ4QuZrD3RFCCCGEEEIIkY7UwXqts2k7WbktId4Uo0ePZuPGjTmy7f3799OwYUMArl69mqEbEEIIIYQQQmSnbt26sXjxYurXry8BMEK8xTISYHELKAa8EWH0iqJYA+8CjYDhgCfJARZvYvYNIYQQQgghhBBCmGFu5I853QWL8vP8Ntu2tWnTJk6ePMmHH36YbdsUQggh3mZHjx7N6S5YVK1atXK6C0IIIUS2y0iARTDwNclThSy3bHdMpyjKy0xUvwZMtVRf3mgHZ+t+32RizvTjDSRPuGW99J5WlJ+BEEK8HSzxdLpcEzInuzIEiLeH/M4J8XazsbGhUKFCREVFMXr0aDZv3pzTXfpX6dat2796egx52lMIIURmqaqaZW17enpmaftCZCVzp9sRQuiXkQCLn4GuwFeKokxWVTXWwn0yVUYzaIQCX6qqet+SnRFCCCGEMEZuYOc8uSEshBBCmMbKyooRI0bQt29ftmzZwokTJ6hevXpOd0sIIYQQQgghhMhxVuZWUFX1PNAHcAF2KoriaeE+meoAEGLCaxewGhgN1FJVtb6qqpE50F8h/jWCPr2k8xJCvB1S/27L7/f/k2MjhBBCiLdNjx49KFKkCACjR4/OcDtPnz5l+vTp1KlTB1dXV2xtbSlSpAidOnXi0KFDeusoikLDhg213xcrVgxFUbQvT09PvfWOHz9Ojx49KFmyJI6OjuTNm5dy5crh4+PDmjVrePnSeDLU1atXU79+ffLly4e9vT1VqlRhxowZ6dYzJjExkfHjx1OzZk1cXFywsbGhYMGClC9fHl9fX5YuXZqm/cDAQO2+GvLgwQNGjBhBqVKlsLOzo3DhwrRq1YrQ0FAAGjRogKIoejNhdOvWDUVRaNCgAQAnTpygXbt2vPPOO9jZ2VG8eHGGDh1KfHy8we3fvHmTOXPm0KJFC4oUKYKtrS2Ojo6UKVOGfv36ER4ebv7BesVY30ePHq1zDly8eJHu3bvj7u6uPbd69uxJVFSU0W08e/aMyZMnU7FiRezt7SlQoABNmjRh06ZNQNpjJIQQ4s2iuY4GBgamWZf6OhMUFESjRo1wc3PD3t6eSpUqMXXqVJ4/f6637cjISG37hrIuqarKvHnzqFmzJk5OTri4uFC7dm0WLFhg8ewXKa9pDg4OuLi40LhxY7Zv355u3fDwcPr27UvJkiWxt7fHycmJihUrMmLECGJiYgzWS30Mt2/fzieffEKBAgVwcnKievXqLF26VKfOhQsX+Oqrr/D09MTOzo5ixYoxbNgwHj16lG4/d+7cSYcOHShatCh2dnbky5ePOnXqMGPGDP755x+jdTMyFtWIiIigf//+lC1bFgcHB2xtbXF3d+fDDz9k4MCB7Nu3T1tWM35bvHgxACEhITrjV0VRdMbUqqpy7NgxRowYQc2aNcmXLx82Njbkz5+fevXqMWPGDJ48eWK0fyEhIfj4+GiPqaOjI8WKFaNevXqMGTOGCxcupKnj6emZpi+ZbVOIfyOzM1goitIVSALWAu2BC4qibAGOAjGv1hmlquoSc7erp40GmW1DCCHEm0OePBdCCCGEECL75M6dmx9++IHevXuzbds2jh8/To0aNcxq49q1a3zyySdcvHhRZ3lUVBQrVqxgxYoVfPfdd0yePDlTfX3x4gUDBw5k9uzZadadO3eOc+fOsWrVKq5evWowOKNPnz4EBAToLAsLC2PgwIEcO3aMZcuWmd2vmzdvUq9ePSIiInSW37t3j3v37nH27FmWLl1KixYtcHFxMbndGzduUL9+fa5evapddufOHTZt2sTWrVv1HgdDli5dyldffaVzEykiIoJp06axfft2jhw5grOzc5p65cuXJyEhQWfZP//8w4ULF7hw4QKLFi1i5cqVtGzZ0uS+mGvPnj20bt2ahw8fapdFRUUxf/58tm7dypEjR/Dw8EhT7/79+zRp0oQ//vhDu+zJkycEBwcTHBzMyJEjs6zPQgghXi/Dhg1LMw45ffo03377LevXr2fXrl04Ojqa1ebz589p27atNmhP4+jRoxw9epTg4GBKliyZ6b5DciBnvXr1OH78uM7yvXv3snfvXubOnUuvXr301v3999/p0aNHmkCSM2fOcObMGQICAti0aRNeXl5G+zB27Fj8/f11lv3xxx/4+vpy+fJlxowZw44dO2jbtq1OMEVkZCSTJ0/m8OHD7N27F2vrtLcrnzx5QteuXVm7dq3O8mfPnnH48GEOHz7MokWL2L59O4ULF05TPzNj0eDgYD7//PM0QQ43b97k5s2b/Pnnn4SEhBAWFmb0+BiyadMmvvjiizTL4+LiCA0NJTQ0lEWLFrFr1y4KFCiQptyECRP44YcfdJY9e/aMR48eERkZSWhoKPHx8fz8888m9ykr2hTibWV2BgsgEFgEtANUIDfJ97gmAwterTP2WpjZTgshhBBCCCGEEEKIrPXVV19pb1Cn/uA8PU+fPqV58+ZcvHiR3LlzM2rUKC5evMi9e/fYu3cvdevWBWDKlClMnz5dp25iYiLbtm3Tfn/27FkSExO1r3PnzumUHzBggDaooEGDBmzZsoVbt24RExPDyZMnmTp1KuXKlTPY1yVLljB37lwGDBjAX3/9RWxsLCdPnqRJkyYALF++nK1bt5q1/wDfffcdERER2Nvb89NPP3Hu3DliY2O5c+cOx44dY+rUqVSpUsWsNl++fEmbNm24evUq1tbWjBw5kkuXLhETE8O+ffuoVasW/fv3NymDxOXLl/n6669p1KgRISEhxMTEcPnyZQYNGgTA+fPnGTdunN665cqV43//+x979uzh3LlzxMTEcOnSJdasWUOtWrV48uQJXbp0STeTREYlJCTQvn17ypQpw/bt27l79y7Xrl1jwoQJWFlZER0dzTfffKO3bvfu3bXBFX379uXMmTPExsZy9OhRWrVqxbhx4zhw4ECW9FsIIcTrIyQkhMmTJ9O8eXOOHDlCTEwMYWFh9OjRA4AjR47Qs2dPs9sdMWKENriidevWHDt2jJiYGP788098fX1ZsWKFNstBZg0cOJALFy4wffp0IiIiiImJYdu2bbz//vsADB48mLt376apFxISQrdu3Xj+/DklSpRgzZo13L59m+vXrxMQEICrqyvx8fE0b96ca9euGdx+SEgI/v7+dOvWjZMnTxIbG8uJEye047xx48Zps09UqlSJPXv2cO/ePS5fvoyfnx8AoaGhzJs3T2/7nTt3Zu3atdjZ2TFy5EjCwsKIjY0lMjKS2bNn4+rqSlhYGG3btk2TESwzY9GkpCS6d+/OkydPKFGiBMuWLSM8PJy4uDhu3LjBnj17GD58OO7u7to6Xbp0ITExkc6dOwPg5eWlM35NTExkxIgR2vLW1tZ88cUXzJ8/n8OHDxMZGcm9e/cICwtj3Lhx5M+fn7CwMO1xSunChQv897//BaBp06bs2rWL69evEx8fT3h4OEFBQfTo0cOs4KCsaFOIt5nZGSxeSZ2j0HDOQgtQFGX9qy8nqqp6Iiu3JYQQQgghhBBCCCHAxsaGkSNH0rNnT3bs2MHRo0epVauWSXV//fVXzp49C8CiRYvo1KmTdl3Dhg2pU6cOjRs35uDBg/zwww98+eWXuLq6AuDo6EiePHm05e3t7Q1+mHvw4EHmzJkDJE/rsHDhQp2pNfLnz0/VqlUZMmQISUn6k65evXqVH3/8UeeGvKurK5s2baJkyZJERUWxePFimjdvbtK+a2hSc0+cOJEBAwborCtYsCA1atRg6NChZrW5cuVKbXDArFmzdD50b9CgAcHBwXh5eelkZzDk1q1btGzZkqCgIKyskp/Byp8/P9OnT+fGjRusW7eOJUuW8OOPP6ape/DgwTTL8ufPzwcffEDr1q1p2LAhoaGhBAQEGAzSyIz79+9TrVo1Dhw4gJ2dnXb58OHDiY2N5aeffmLjxo3cv39fJwPHoUOHCAoKApKfWp44caJ2Xc2aNQkKCqJVq1Zs3rzZ4n0WQgjxeomMjKR58+Zs2rRJ5zo4f/587O3tmTlzJitXrmTIkCFUr17dpDajoqK0N+vbtGnD2rVrteOS/Pnzs2TJEmxtbZk/f75F9uHGjRuEhobqjM+aNWvGhg0bqFixIo8fP2bt2rX07dtXp17//v1JSkrC3d2dQ4cOUbBgQe06Pz8/atasSa1atXjw4AHff/89K1eu1Lv9yMhIBg0apBOgoBlDeXh48ODBA1q0aEH16tXZt28fuXPnBsDNzY2AgADOnj3LwYMHWbx4MX369NFpe+3atQQFBZE7d26Cg4P56KOPdLbRp08f6tatS/Xq1Tl06BDr1q2jffv22jKZGYv+/fff3LhxA4B169ZRsWJFbd18+fLh7u5O48aNdfprbW2No6OjNhNHrly5jAYjNG/eXO/Y0s3NjUqVKtG6dWsqVarEhg0buHz5Mh988IG2zK5du0hKSqJQoUJs3boVGxsb7ToXFxeKFy+uNzuGMVnRphBvs4xksOieyddXGdjmF0Ar4B1DBRRF2asoSrCiKB8ZKiOEEEIIIYQQQgghTPfll19SrFgxwLwsFgsXJicwrVOnjs4H2hq5c+dmxowZQHL65xUrVmSofzNnzgSSP4z+9ddfdYIrUlIUhVy5culd5+HhwZAhQ9Isz5MnD23btgXgxAnzn/fRPEn57rvvml3XkN9//x2AUqVK6U35bWtrqxM0kJ5p06Zpbyql5OvrC6DNDGGOXLly4ePjAySn184qkyZN0gmu0ND0/fnz52nSdmuOn7OzM6NGjUpTV1EUfvrppyzorRBCiNeRoevg+PHjtTfHAwMDTW5v2bJlvHz5EkVRmDZtmt5xyZQpU/RevzLCx8dHb/BrhQoVqFy5MpB2DHPixAnOnDkDJI/tUgZXaFSuXFkbxLlu3bo004JpODg4MHbs2DTLXVxc+Pjjj4HkqdwmTJigDa5IqUOHDgCcOnWKFy9e6KzTjBO//vprneCKlMqXL68dZy5fvlxnXWbGoimzYVhyHGeOsmXLUqVKFVRVZe/evTrrNP1zc3PTCYTIjKxoU4i3mdkBFqqqLs7sKyt2BGjw6uWWRe0LIcS/TtCnl3ReQmQXOfeEENni4GzdlxBCiDQ0WSwg+cm2w4cPp1snLi5OO42Ht7e3wXJVqlShePHiQHJ66IzQfODs7e2Nvb19htpo0qSJ3psrgPZpwTt37pjdrubGxg8//KA344O5VFXl6NGjALRo0cJgMEmjRo1MSt9cvHhx7fFPLeVTkob2/dChQ3Tv3p3SpUvj5OSElZUViqKgKAr9+vUD4NKlrBnL29raUr9+fb3rjPVdc/42adJEJ0tK6vply5a1UE+FEEK8rsqUKUPJkiX1rnNyctJmKDh06JDJbWquM5UrV9ZOs5Zavnz5DF7DzPXJJ58YXGdoDJNyTNKmTRuD9du1awckB0hoxh+p1a5dGycnJ73rNNOU2NnZ4eXlpbeMZhzyzz//EBcXp13++PFj7TYbNGjAw4cPDb4qVKgAoJO9K7Nj0VKlSmmDYLp3786VK1cMtpEZ//zzD7/99huffvop7777Lra2ttqxlKIo2uCY1OMpzRjz7Nmz/PDDD8THx2e6L1nRphBvs4xksBBCCCGEEEIIIYQQ/xJdu3bVfkhuShaL69eva79O70Z1uXLl0tQx1YMHD4iJiQGgUqVKZtfXeOcdgwlTtUEbT548MbvdSZMmYWNjw6VLl6hbty7vvfcenTp1IiAgIEMf1CckJHD//n0AgzeEAKysrChRokS67Zmy36B/34cMGYKXlxeBgYFcvHiRhw8foqpqmnKa/lpagQIFtCm4UzPWd002DmPHz5T1Qggh3nylS5c2ab05mZwiIyPNajuzMjKG0exP4cKFtVNi6KMZo4HhcVrhwoUN1tcEMrq5uRm8ZqcMdkzZz4iICJ4/fw5A+/btcXJyMvgaPHgwAPfu3dPb34yMRe3t7bVTnG3ZsoUSJUpQvnx5+vTpw8qVK4mNjTXapimio6OpWrUqfn5+7Ny5k+joaP755x+9ZVOPpxo2bMjnn38OwIQJEyhYsCB16tRh+PDh7Nixg2fPnpndn6xoU4i3mQRYCCGEEOKtJZkwxJss9fkr53DOkJ+BEEIkzyn93//+F4A9e/akm43h4cOH2q/Ty6SgeeoxMTHR7H6lrGPo6UlTGJo6JLNq167N0aNH+fzzz7GxseHWrVusWLGCPn36UKJECerVq2fW1COPHj3Sfu3g4GC0rCkZLEzd79SBE0uXLtXOtd6wYUNWr17N+fPniYmJITExkcTERObMmQPopti2pIz2XXMMLXH8hBBCvNlMvRakHNekJ7uvM6ZcD1NfCzX7Y+oYDQyP00zZfkau2RkJ0EwZnGCJsejQoUNZvXo11apVA5IzOwQEBNCxY0feeecdunTpwu3bt83up4avry9nz57FxsaGoUOHsm/fPq5fv058fLx2PFWnTh2ANNOnAKxdu5aJEyfi4eHBixcvOHz4MJMmTaJZs2YULlwYf39/gwEbhmRFm0K8rSTAQgghhBBCCCGEEEIY1aVLF21WhFGjRhktm/KD7PRuSmjWZyRAwpQP/nNa1apV2bRpE3FxcezatYvRo0dr50oPDQ2lXr16/Pnnnya1lfJmTcpgC33MuRlkroCAAAC8vLzYs2cP7dq1o3Tp0uTPnx9HR0ccHR15+vRplm0/MzTHMCePnxBCiNeDqdcCc4Ih3oTrjKmBIynXZyaQNSNSHvO9e/eiqqpJL331MzMWbdeuHSdOnCA6Opq1a9cycOBA3n//fZ4/f86yZcuobs3LgwAAIABJREFUU6dOhsag4eHhBAcHA/Drr78ydepUGjRoQJEiRXBxcdGOp4z1PXfu3AwbNozIyEjOnz/PwoUL6dq1K66uriQkJDB27Fi6dOliVr+yok0h3lYWC7BQFMVJUZT3FEUpmt7LUtsUQgghhBBCCCGEEFkvZRaLffv2ERISYrBs0aJFURQFgPPnzxtt9+zZswAG5yk3Jm/evBQoUACA06dPm10/Ozk6OtK0aVP8/f05cuQIoaGh5MmTh6dPnzJ58mST2nBxccHZ2RlIOxd3SklJSYSHh1uk3/pojnXbtm2xstL/0eLff/+dZdvPDM15Zuz4mbJeCCHEm+/ChQsmrTdnjOLp6WlW2zlB08fbt28THx9vsJxmjAYZG6dlhoeHh3aMERERYXZ9S49FCxcujLe3Nz///DPh4eFMmzZN27elS5ea3b+U49b27dvrLfP8+XOTxyOlS5eme/fuLF68mKioKG2ba9asSXf/s7NNId4mGQ6wUBQll6Iovoqi7FIUJQFIAK4DV9N5mf/XUAghhBBCCCGEEELkqM6dO/PBBx8A4O/vb7Ccq6urdr7rdevWGSz3119/aQMBvLy8dNbZ2NhovzY2zUTjxo2120k9x/jrzMvLi48//hgw/SaLoija7Bdbt241WG7fvn1Z+mSsZg5uQz+Xx48fs2HDhizbfmZ89NFHAAQHBxvMshEeHs65c+eys1tCCCFywPnz5w3ewE5MTNRmGNBM02AKzXUmLCyMa9eu6S0THx9vNFA1q6Ucc61fv95gubVr1wLJQbY1a9bM8n6l5OLiQvXq1QFYvXq12fUtMRY1RFEUBg8erA16TT2O04xhjY1fNWMpY+WCgoIyNLbNkycPw4cP135viWCerGhTiDddhgIsFEV5BzgEBAKNgbyAYsZLCCGEeG0FfXpJ5yWEEEII86W+nso1VYg3X65cubTTg4SEhBAaGmqw7FdffQXAwYMHWbVqVZr1z58/Z8CAAQDY29vTsWNHnfX58+fXfh0dHW1wO//5z38AuHfvHv37908zz7iGqqpGP+i2tEePHhEVFWVwfVJSElevXgV09zU9mrTMFy5cYP78+WnWP3v2jBEjRpjZW/MUK1YMgC1btuhd/8033xAbG5ulfcgozfFLSEhg3Lhxadarqsq3336b3d0SQgiRQ4YOHUpSUlKa5T/88IM2WLFbt24mt9e5c2dy5cqFqqoMHTpU77jk+++/z9GptKpVq0aFChUAGDNmDDExMWnKnD59mjlz5gDg7e2Ni4tLtvYRYMiQIQDs2rWL3377zWjZp0+fpgloycxY9ObNm0anebl79652apDU4zjN98bGr5qxFMDmzZv1tv/dd98ZrH/58mW9563GlStX0vQnPVnRphBvM7MDLBRFsQI2ATVIDpaIBFa+Wq0C+4C1wAngeYrlu4DFwJJM9LegCdOOGCwjU5UIIYQQQgghspsEGggh3iYdO3akVKlSgPGUzX379tU+Odi1a1fGjh1LeHg4sbGxhISE0LRpUw4cOADAuHHjcHV11alfokQJ8ubNC8DkyZMJDw/n2bNnvHjxQidQok6dOvTp0weABQsW0KRJE7Zt28bt27eJi4vjr7/+4pdffqFSpUrcuHHDcgciHffu3aNYsWK0bt2axYsXc/bsWWJjY4mOjiYkJIQ2bdpo00P7+PiY3G7Hjh2pWrUqkHyM/f390xzXsLAw3nvvvSzZL0iejxySM2X4+voSFhZGbGwsx48fp0OHDsyZM4cyZcpk2fYzw8vLi5YtWwIwfvx4+vfvz9mzZ4mLi+P48eO0adOGDRs26Nz4EEIIkfNOnTrF0aNHjb6MTXehj4eHB1u2bKFVq1YcO3aMuLg4zpw5Q8+ePZk5cyaQfI3WZFIwhbu7O4MHDwaSMyd4e3tz4sQJ4uLiCAsLo2vXrsybN087TUdOmTlzJlZWVty4cYM6deoQFBTE3bt3iYqKYt68eTRq1Ihnz56RN29eJk2alCN9bN++vXbM4efnh4+PD7t37yY6OpqEhAQiIiLYsmULAwYMwMPDgzVr1ujUz8xYdPfu3bi7u+Pn58eGDRu4cuUKCQkJXLt2jXXr1tGkSROSkpKwtrbG29tbZ7uacdqVK1eYO3cu9+7d48WLF7x48UIbwFCtWjXtlCQDBgxgzpw5XLt2jdu3b7Nq1Spq167NvXv3DE5bMn78eD744ANGjhxJcHAwUVFRxMfHc+nSJX799Vd69uwJJE+VUrt2bZOOd1a0KcTbzDoDdToCH5IcNPEL8I2qqkmKomj+G/xFVdVNAIqiFABGAP2BCsAPqqqezER/5xpZp5pQJnX5jOy/EEIIITJB343F1jnQDyGEEEIIYT5NFovOnTsbLWdnZ8e2bdv45JNPuHjxIv7+/nqnFfn2228ZNGhQmuXW1tb06dOHyZMns2XLFp1sCR4eHkRGRmq/nzFjBi9fvuS3335j79697N27N+M7aEEvXrxgw4YNRqfL6Natm/YDa1PkypWL9evXU79+fa5du8bYsWMZO3asdr2VlRWzZ89m+fLl3Lx5E2try3/09f3337N582b++usvli5dmmbu8TZt2tC8eXN69Ohh8W1bQmBgII0bN+bUqVPMmjWLWbNm6awfMWIEUVFRXL16NUuOnxBCCPO1adMm3TJBQUF88cUXJrfZoEEDChUqxJQpU/RmZapdu3a6mRP0mTBhApcuXWLTpk0EBQURFBSks75Dhw6ULl2aMWPGmN22pdSvX5/AwEB69OjBpUuX9B7ffPnysWnTphwNBvn9999xcnJi4cKFrFq1Sm8mCo3cuXPrfJ/ZsWhCQgK//fabwXPA2tqa2bNnU65cOZ3ln3/+OZ6enkRGRtK7d2969+6tXefv78/o0aOxtrZm/vz5tGjRggcPHtC3b980+7JkyRJt4IU+ERERjB8/nvHjx+td7+bmxpo1a3Sm3UtPVrQpxNsqI1OEtH31fhP4TlVVgzljVFW9p6rqYKAv8A6wXlGUfBnYpoYp047IVCWvIXlqTwghhBBCCCGEeDv4+PiYlKHAw8ODsLAwpk2bRu3atXFxcSF37ty4u7vj4+PDwYMHmTJlCoqi/yOaCRMm8PPPP1O9enWcnJwMlrO2tmbu3LmEhobSpUsXPDw8sLOzw8XFhXLlytGtWze2b99O0aLZl8y0aNGiHDx4EH9/fxo2bMj777+Pvb09tra2eHp60qFDB3bu3MmiRYsM7pchHh4e/PXXX3z//feUKFECW1tbChQoQIsWLQgODsbPz0+b1tzJycni++bo6EhoaCjDhg2jePHi2NjY4OrqipeXFwsWLGDt2rVYWWVoVuJskS9fPo4cOcKECRMoV64cdnZ2uLq60rBhQ9avX8/48eOz9PgJIYR4fUyePJnVq1dTv3598uXLR548eahYsSI//vgjISEhGboO2NjYsGHDBn777Tdq1KiBg4MDefPmpUaNGgQEBLBixYos2BPz+fr6cvbsWXr37k2JEiXIkycPDg4OVKhQgeHDh3Pp0iW8vLxytI+2trYsWLCAY8eO8fXXX1OyZEkcHR2xtrbGzc2N2rVrM3ToUA4ePKid7iOljI5F27dvz+bNmxkwYAA1atTA3d0dGxsbHBwcKFu2LH379uX06dN6g2Tz5MlDaGgoPXv25P3338fW1lbvvjVp0oQjR47QunVrXF1dyZ07N0WKFKFLly4cPXqUDh06GDwukydPZsmSJfj6+lKpUiUKFiyItbU1Li4u1KxZkzFjxnDhwgVq1Khh8rHOijaFeJtlJAxbk71iqaqqL/SsT/MflKqqvymK0gmoC/QBJpi5zev8f4YKIYQQQgghhBBCCJP4eX6b0114Y4wePZrRo0enW87Kyopz586Z1KadnR2DBw/Wpss2h5WVFQMHDmTgwIEmlffy8jLrRkDKLBiGdOvWzay51zWsrKyoU6cOderUMbuuKdt0dnZm0qRJBtN2a6ZvcXd3T7MuMDCQwMBAo+17enrqnTdew8nJiYkTJzJx4kS9643tQ3pt79+/3+A6U89RY+1D8g2b4cOHM3z4cL3rNfOM6zt+QgiRGbVq1crpLrwxTP2br09614GU2rVrp52KwhTpXccAFEWhZ8+eBrNUZWbfTNk+mHa9/+CDD5gzZ47ZfTB2rdYwZR8bNGhg0r7UqFEjwzf2MzIWtbe3p0WLFrRo0SJD23R3dzcp+0mVKlVYv369wfWGjnOhQoXw9fXF19fX7L4ZGv9mpk0h/o0yEmDh9ur9aqrlSSRnhbAzUG8dUA/4AjMDLFRV9TSnvBBCCCGEyByZykUIIYQQ4s0UGhpKQkICAB9++GEO9+bNc+3aNc6cOQPI8RNCCCGEEEKklZF8fZpcOXGplie+ei9koN6dV++eGdimEEIIIYQQQgghhBD/evHx8Qaf9nzy5AnffPMNAIULF87x1N6vo8TERF680JeUF16+fMnAgQNJSkrC1taWli1bZnPvhBBCCCGEEK+7jARY3H31njfV8luv3ssbqKfJqZe6nhBCCCGEEEIIkWFBn17SeQkhxNts48aNVK1alYCAAM6fP098fDw3btxg9erV1KpVi+PHjwMwatQorK0zkrz27Xby5EnKlCnDTz/9xF9//UV8fDzR0dFs2bKFRo0asXHjRgAGDhyIq6trDvdWCCGEEEKI/2PvzuOiqv4/jr8uyiouKIrmhlsu5ZppZpqaprlGmmsqWmlman6t1Moycy1t0UozUxFNk1Q0zV1xJSt/mfuKmLgjmFsuwP39ATMxwLAMIC7v5+NxH8Pce865nzsz985l7ueeI3cbR/7L2k98skT5JPP/BCoDbQ3DGGia5r+WBYZhGIBl4J6zjgQqIpKTkv5Qr27y/zM4+A+b535PtMqhSERERERERB4Mu3btol+/fnaXv/HGG7z22mt3MKJ7y9GjR609faTkhRdeYNSoUXcwIhERERERuVc4kmCxDWgOPJlk/iKgK+ANLDYMYzAQBpQDPgaqAyaw3uFoRe5DunAvIiIi8p+Ueh/Q+ZGIiMh/WrRowcSJE1m7di1Hjhzh/Pnz3L59Gx8fH5588kn69u1Lo0aNcjrMu1bNmjX5+uuvWb16NQcOHODcuXPcuHEDb29vHn/8cXr27Imfn84+REREREQkZY4kWPwCjAKeNAyjiGmaliFDgoH/A2oBzwL7Uqh7A/jEkUBFREREREREREQedEWLFmXIkCEMGTIkp0O5J+XPn5/XX3+d119/PadDERGRHBASEpLTIYiIyD0uwwkWpmn+n2EYIwF3oCRwPmG+aRhGO2AN8UOFJHUN6Gaa5iHHwxURERERERERERERERERERG58xzpwQLTNFMchNA0zVOGYVQHugBNAR/gOvA7MMs0zbOOBioiIiIiIiIiIiIiIiIiIiKSUxxKsEiNaZoxQGDCJA+AwcF/JJvn90SrHIhEROTusaTF4WTzko7im54ykvP0PomIiIiIiIiIiIiICIBTTgcgIiIiIiIiIiIiIiIiIiIicrfL8h4sRERERO4V6p1C5N6kfVdERERERERERERyghIsRERERERERO4ySROJlEQkIiIiIiIiIpLzMpxgYRjGBgfXFQdcAaKA3cBG0zR3O9gWhmF8kPDnMdM05znaTmYZhlECGAC0AUoBMcBxYAkwxTTN6GxYpxOwDXjCMs80TSOr1yMiIiIiIiIiIiIiIiIiIiLxHOnBohFgZsXKDcPYAbzmYKLFyIQ4hmdFLI4wDKMFMB8okGRRjYSpj2EY7UzT3JnFq+5PouQKERERERERERERERERERERyV5ODtYzEqbEf6c22Sv3BLDDMIxGDsRwKeHxuAN1M80wjGrAT8QnV1wHPgSeIj4B5XMgFigOLDcM46EsXG9JYAzxySUXsqpdERERERERERERERERERERsS/DPViYpulkGEY+4DvgReAIMBUIAcKAa0AeoCzxyQb9gArAwoS/nYE6wBvAs4ArsNAwjLKmaV7NQCgngfxAvoxuQxb5gvjtjAWeM01zc6JlmwzD+D8gECgKjAZ6Z9F6vwHyAjOIf12fzqJ2RURERERE7ltLWhy2ee6XQ3GIiIiIiIiIiMi9K8M9WBiG4QQEAx2Iv8j/qGmaX5imucs0zcumacYmPO4yTfML4FFgJvHJGIuAC6ZpLjdNswXwSUKzhYA+GQxlOfG9YDTJ6DZklmEYjwGNE57OTpJcAYBpmnOBDQlPexiGUSQL1tsJaE18zxVDM9ueiIiIiIjcXwYH/2EziYiIiIiIiIiISNZxZIiQl4nvmWI38JppmrdTK5ywvA+wl/jeFhL35DCc+B4wAJ7LYBxfEz9MSEfDMOpnsG5mvZDo7+9TKTcz4TEX0DYzKzQMwwv4MuHpENM0ozLTnoiIiIiIiIiIiIiIiIiIiKSfIwkWLwEmEGiaZlx6KiSUm0N8jxM9Es03gaCE+VUyEoRpmqeBzsB14BfDMPobhuGekTYy4amEx+vA76mU25hCHUdNBHyAjaZpBmayLXkALWlxONkkIiIitvRdKSIiD7KRI0diGIZ1GjduXJp1wsLCbOpUqlTpDkSafpa4Zs+endOhiEgCy7HG19c3p0Nx2P2wDSJ3u8TnJeHh4TkdjoiIiFVuB+pY/lP+O4P1LOWT/qd9IOGxYEYaMwzD0jvEbqA+MBkYbxjGn8Ap4N80mjBN03w5I+tMxJIMcsQ0zZhUVnDaMIwrQF4ymECSmGEYjYnv+eMm8Jqj7YiIiIiIiIiIpNe8efMYPnx4qmXmzp17h6IRERERERERyXmOJFjkTXgsksF6hZPUt7iR8Hgrg+35E9+TBoke8xCfbJFeGU6wMAzDFfBOeBqRjioniU+uKJnRdSWszw2YnvB0nGmaupVSREREREREJJ0OVKqc0yFkqcoHD6RdKJNy586Ni4sL+/btY9euXdSoUcNu2Xnz5gGQL18+Ll++nO2xiYiI3MvWbyiX0yFkqWeaHMvpEERERO44R4YIOZXw2CGD9V5MeDydZL6l54qLDsRiJJlSmmdvclTiBJGr6ShvKePp4Po+BMoDh4HxDrYhIiIiIiIiIpIuuXLlom3btsB/CRQp+e233zh8+DCenp40b978ToUnIpLjRo4ciWmaGrZARERE5AHkSA8Wa4kfpuJpwzDeNk3z07QqGIbxNvA08T1NrE6yuGrC49kMxlEmg+Wzinuiv9PT68bNFOqli2EY1YC3Ep72M03zZmrl02irD9AHoFSpUo42IyL3kSUtbDvE8cuhOCB5LJCz8YiISHJ7irfO6RCy3N30XSgicrd56aWXWLBgAfPnz2fChAk4OSW/R8cyPIifn1+Ky0VERERERETuN4789zsR+Dfh7/GGYSw3DOPZhKEsrAzDcEuYv4L/el64kVA/sRbEJ178npEgTNM8kdkp45sO/LftAC7pKO+aQr00GYbhBMwgPgkm0DTNDRmpn5RpmtNN06xtmmbtwoULp11BRERERERERB5YzZs3x9vbm1OnThESEpJseUxMDD/++CMA3bp1S3e7q1evplOnTpQqVQo3Nze8vLyoX78+kydP5tattO9jWbt2Ld26daNMmTK4u7tTsGBBqlWrhr+/P6tWrUq1blxcHNOmTaNOnTrky5ePvHnz8uSTTxIYGGi3jmma7Nixg3fffZe6devi5eWFs7MzhQoVomHDhkyePJl//7X/k4+/vz+GYdCoUSMAfv/9d1588UWKFSuGm5sb5cqVY8iQIURHR6cae2xsLDNnzqRp06YULlwYFxcXihUrhp+fH8uXL0+17rlz5xg2bBg1atQgX7581rrVq1enT58+BAcH26177do1Jk2aRIMGDfD29sbV1ZXixYvTsWNHtmzZkq3bfeXKFcaMGUPdunUpUKAAzs7OFClShEcffZTu3bszd+5cYmNjU6x79uxZ3n77bR599FHy5s1Lnjx5qFSpEgMHDuTECfs/CSaNe/PmzbRv357ixYuTO3dunn/+eX788UcMw8DJySnN3gvmzp1rLXv8+PFUyyYWEhKCYRgYhkF4eDjR0dEMHTqUhx9+GA8PD0qWLEnv3r05efKktU5sbCzTp0+3vl758uXjmWeeYdOmTWmuLyoqipEjR/L444/j5eWFm5sbvr6++Pv7s3v37nTV//jjj6lXrx7e3t7W+k2aNGH8+PGcO3cu1fqHDh2iV69elChRAldXV0qWLMmrr75KRIT90ZkjIyMJCAigQ4cOlClTBjc3Nzw8PChXrhy9evXizz//tFs3PDzc+vqGhIRw8+ZNJkyYQLVq1ciTJw8FChTgmWeeYeXKlXbbGDlyJIZh4Ovrm+JyR/a7pJ+/0NBQXnjhBYoVK4aHhwdVq1Zl8uTJNp/7iIgIBg0aRPny5XF3d6d48eL069ePyMhIu7GL3C8s+/Hs2bOJi4vjq6++onbt2uTLlw9vb29atGhBaGioTZ21a9fSsmVLihYtiru7OzVq1OC7776zu46k+/r+/fvp0aMHJUqUwN3dnYcffpiPPvqI69evW+tERUUxYsQIKleujIeHB4ULF6Zbt252vzOSHpPsSXqMSC1OR46rFgcOHOD111+nYsWKeHp6kidPHh555BGGDh3K+fPn06xvT6NGjTAMA39/fwCWLl1KixYtKFq0KLly5eLNN9+0Ke/oOQjApk2b6Ny5M76+vri5ueHp6UmZMmVo2LAhH330EQcPHkxWJ/Hn6fbt20yaNImaNWuSN29eChQoQOPGjVm8eHGa25lV5yB3+tzp9u3bTJ8+nWbNmuHj44OLiws+Pj60adOGZcuWpbndIg+aDPdgYZpmmGEYrwBziE/QeC5hijMM4yxwHfAAivJfAocBxAK9TdO0/jdhGEZDoCwQB9wre+iVRH+nZ9gPS5n0DCeS2CDgcSAKGJLBuiIiIiIiIiIiDsudOzedOnXi66+/Zt68eTRp0sRm+Zo1azh//jw+Pj40bdqU+fPnp9rev//+S48ePfjpp59s5t+8eZPt27ezfft2Zs2axcqVKylatGiy+teuXaNHjx7Jfti+ceMG0dHR7Nmzh4CAAEzTTHH9MTExtGvXLlkyQmhoKKGhoRw8eJAxY8Ykq7ds2TKef/75ZPOjoqLYsmULW7ZsYdasWaxZs4a0bmiZO3cuvXv35vbt29Z5YWFhfPbZZ6xcuZLQ0FDy58+frF50dDStW7dm+/btNvPPnj1LcHAwwcHBdOvWjVmzZuHs7GxTZs+ePTRu3JiLFy8mq3v27Fl2797NwoULuXTpUrL17tq1izZt2iS7GHP69GmCgoIICgpi2LBhjBs3Lsu3+9SpUzRs2JCwsDCb+RcuXODChQvs27ePuXPn0rp1awoUKGBTZt26dbzwwgtcuXLFZv6hQ4c4dOgQ3333HXPnzqV9+/apxj1lyhTefPNN4uLibOY///zzeHl5ER0dzZw5c/jggw/sthEQEADA008/TZkyjnXG+/fff9OzZ0+bC3MRERHMmjWLtWvXsm3bNooUKUKHDh1YsWKFTd0NGzawefNmli5dSsuWLVNsf+3atXTs2DHZZ+DEiRMEBAQQGBjIV199Rb9+/VKsv3LlSrp27Zpi/RMnTrBx40Zu3LjByJEjU6y/bt06/Pz8uHr1v59OIyIimDFjBitWrCA0NJTSpUsnq9esWTN27dqVbH5YWBhhYWHWuF977bUU12tx5coVGjZsyG+//WYzf8OGDWzYsIFvv/2WPn36pNpGUpnZ7yxmzpxJnz59bC6E7d27l0GDBrFz504CAgLYuXMnLVu2tLngefr0aaZNm0ZISAg7duwgX758GYpd5F50+/ZtWrZsyerVtp2nr169mo0bN7Js2TKaN2/Oxx9/nOyY/ddff9GnTx+OHz/O2LFjU13PqlWraN++vU0yxZEjRxg5ciRbt27ll19+ISIigmeffZajR49ay/z777/88MMPbNiwgR07dmR7D+OOHlcBJkyYwHvvvZfsIvz+/fvZv38/M2bM4Oeff+bJJ5/MVIxvv/02EycmvRf7P5k5Bxk7dizvvfeezbybN29y7do1wsPD2bJlC9HR0XzxxRcprvvWrVs0bdqUzZs328wPCQkhJCSEAQMGMHny5BTrZtU5yJ0+dwoPD6d169bs27fPZv758+dZvnw5y5cvp3v37sycOZPcuR0ZGEHk/uNQ/42mac4nvueJ48QnTxhALqA4UD7hMVeiZceB5qZp/piknc2maeY2TdPZNM31Dm/FHZQwTIclBbhEOqpYypxMtVRywxIeNwLPGIbROekEFLEUTjT/hQyuR0REREREREQkGUvPFIsWLeLGjRs2yyzDg3Tu3JlcuXKlq62ffvoJNzc33n//fXbt2sXFixcJDw/nm2++oWDBguzatYsOHTok+1HfNE06duxoTa544YUXWL9+PWfPnuX8+fOEhoYyatQou3eSQ/yP7WvWrGHkyJEcPHiQixcvsnnzZmrVqgXA+PHj2bNnT7J6ll4LZsyYwfbt2wkPD+fChQvs2rWL0aNHU6hQIXbt2kXfvn1T3f4jR47wyiuv0KRJEzZt2kRkZCRHjhyx3q154MABRo8enayeZdu3b9+OYRgMGDCA3bt3ExkZSWhoqDX5Y968ebz11lvJ6r/22mtcvHiRIkWKMH36dA4fPkxUVBSnT59my5YtfPzxxzz88MPJ6p04cYImTZoQERFB5cqVCQwMJDw8nKioKHbu3MnLL79sfd2+/fbbLN/ud955h7CwMDw8PJg0aRL79+/n4sWLnDt3jh07djBx4kRq1qyZrN6hQ4do164dV65cwcfHh5kzZxIREcGZM2f44YcfKFWqFDdu3KBz5878/rv9znQPHjzI4MGDadiwIevXr+fChQscO3aMgQMH4urqSteuXQGYM2eO3aSeiIgINmyI75DWcreuI3r27Mnt27cJDAzk9OnTnD59mq+++gpnZ2ciIiIYOnQow4cPZ/369YwdO5aakqDYAAAgAElEQVQjR44QGRnJzz//zEMPPURMTAx9+vSxuUhj8ccff9C6dWsuXbrEE088weLFi4mIiODixYts27aNdu3aERcXR//+/VPsIWbbtm20bduWS5cu4ePjw5dffsmhQ4eIiori2LFjLFiwgPbt29u9GHPp0iU6duxI5cqVWblyJefPn+fEiROMHTsWJycnzpw5k+LnGsDX15d3332XlStXsmfPHi5cuEBYWBgrVqygRYsWxMbGMmDAAP7v//4v1dd30KBBHDx4kM8//5ywsDAiIyP55ZdfKFu2LACDBw/O8B3bju53FkeOHKFfv360aNGCbdu2ERkZyd69e3nhhfiffefMmUNQUBDt27fH29ub4OBgzp07x4kTJ6wXj+0ljYncj8aOHUtISAhjxoyxOQaWKFGCW7du0adPH4KCgvjggw/o1asXO3fu5OLFi/zxxx889dRTQHxiQdKLy4ldunSJrl27Ur16ddatW8eFCxc4cuSINYlr3bp1TJ8+nY4dO3Ljxg0CAwM5deoUZ86cYcqUKTg7O1t7NshOmTmuTp48mWHDhhEbG0vXrl3ZtGkT58+f59y5cyxZsoSqVasSFRVF27ZtOXXqlMMxrl27lokTJ9KhQwe2b99OZGQkBw4coGPHjkDmzkEOHjzIiBEjgPhEvDVr1vD3338THR3N0aNHWbJkCS+//DKenvbvnR43bhxbt25lyJAh7N+/n8jISDZu3Ej9+vWB+ATMlM59suoc5E6fO/3zzz80adKEffv2UaJECaZOncqRI0eIiopi7969vPPOO+TKlYvAwEDraysiDvRgYWGa5nrDMCoB7RKm2sBDQB7gGnAa+ANYCiw1TTMm8+HaZxiGC/AEUBnwAlxM0xyVTavbDzQEKhiGkdvethmG8RCQL1GdjLAMLdI+YUqL5VaRf4C0+ykSEREREREREUlFvXr1KFeuHMeOHWP58uV06NABgKtXr7J06VIAXnrppTTb+emnn1iyZAkuLi6sX7/e5q7HggUL0q9fPxo0aMDjjz/Otm3bWLRokfVHdohPHvjll18A+OCDD/joo49s2i9cuDBPPPEEw4cPtxvD8ePHCQoKsm4DQIMGDawXUq9fv05gYCCffPKJTb1WrVrRqlWrZO15e3tTvXp1/Pz8qF69OsHBwRw5coQKFSqkuP7Tp0/Ttm1blixZgpNT/P1OhQoV4vPPP+fkyZMsWrSIOXPm8Omnn9rUW7x4MevWrQNg9OjRvPvuu9ZlhQoVYvHixXTp0oUff/yRKVOm8Nprr1G5cmUALl++bO314rvvvqNt27Y2bRcrVoynnnqK999/P1m8/fv3Jzo6mjp16rBx40Y8PDysy7y8vJgxYwbFihVj9OjRvP/++/To0QN3d/cs227L0Azjxo1j4MCBNsuKFClCnTp1GDIkeYevb731FtevX8fT05PNmzfbXMTu0qUL9evX57HHHiMyMpIBAwbw66+/JmsD4od3aNKkCatXr7YmB3h7e1svuvfu3Zuvv/6aY8eOsXXrVho0aJCsjcDAQOLi4vD09LT53GXUpUuX+PPPP20SiPr378+5c+f4+OOPWbhwIQDBwcG0adPGWqZ169YEBATQrFkzTp06xfr162nRooV1uWma9OrVi1u3btGuXTsWLVpkkyz15JNPEhwcTI8ePQgMDOStt96iefPmGIYBxA+507t3b2JiYihevDihoaGULFnSWt/Ly4uyZcvSqVMnYmJS/ln4n3/+oXbt2mzevBk3t/9Gnx4+fDgXL15k0qRJLF26lH/++SfZnbpLlixJ1p63tzdlypShZcuWdO3alfnz5/PZZ59ZE8JScvLkSbZs2cITTzxhnffcc88RHBxMtWrVuH79Oj/99BOvv/663TYSy8x+Z3H69Gn8/PxYtGiR9fUuVKgQCxYsoFKlSoSFhdG1a1dKlSrF9u3bbV6bjz76iGPHjjFv3jwCAgKYMGFCuuIWuZeFh4ezZMkSmx6nWrdujYeHB8888wx///03Xbt25X//+x+TJk2ylilYsCBLly6lTJkyXL58mcDAQMaPH5/SKvjnn3+oW7cuGzduxNU1/tKNt7c3U6dOZf/+/WzevJk333yTvHnzsmvXLpteKt544w3Onj3LmDFjWLJkCZcvX8623mUcPa6eOnWKd955B4CJEycm+459/vnnadq0KXXq1OHAgQOMGTOGb775xqEYT58+TY8ePay9PEH8Mc4iM+cga9asIS4uDh8fH1asWGHTs1eBAgUoV65cij2TJRYeHs6ECROsrwfED2+yfv16GjRowO+//867775Lz549bV7jrDoHudPnTu+99x7Hjx+nbNmyhIaGUqSI9b5uvLy8mDBhAg8//DCvvPIKkyZN4o033qB48eKpvoYiDwKHerCwME0zxjTNRaZp9jBNs4ppmgUSeqMokPC8R8LybEuuMAzDzTCMscA54nt7+AYYA3yYQtkJhmEcNgwjs71lbE149CB+GA97GqVQR0RERERERETknmC5Uz/xBcrFixdz/fp1KlasSO3atdNsw9KN8iuvvGK3S+lHH33Uuq4ffvjBZtmUKVOsZT78MNnPPVapdVn81FNPpXiR28fHh2effRYg1bsJ7alSpQo1a9bENE1rbwX2fPbZZ9YfyhPr3r07gPUu08RmzpwJQOnSpRk6dGiyuoZh8OWXX+Ls7IxpmtbygE1PIA899FC6t+nYsWPWhJavvvrK5sJGYu+++y558uQhMjIyWbfsiTmy3ZbYMxL3uXPnrHEPGjQoxR4CSpUqZU1S2bFjR6p3K3/yySd2P1O1atWievXqADYXiBKzzH/xxRfJkydPurcjqYEDB6bYO0unTp2A+ESHBg0a2CRXWDzzzDN4e3sDJBsCY+PGjezdu5dcuXIxdepUuz3RWHpB2LdvH3/99Zd1/urVqzl8+DAAn3/+uU1yRVKp7Zvjx4+3uUBlYfl83L59O8WhQNJiSf5avz71n4E7d+5sk1xhUbVqVWrUqAFk7Njg6H6X1MSJE63JFRbOzs7WXixiYmIYMWJEisMKde7cGYjfJ/7++2+HYxC5VzRs2DDFi+aNGze2Dt/l5uaWLEET4pMsmjVrBiQ/TiY1btw4a3JFYpak0JiYGAYOHJjiECCW/dLRY1pGOHJc/fbbb7l58yaPPPII//vf/1Js19PT05rMumDBArs9OKUld+7cyRJaLTJ7DmI5Bnt7eycbNi29SpYsmWIigqurqzVpLSoqip9//tm6LKvPQe7UudPVq1eZNWsWEP+5SZxckVjv3r0pV64ct2/fJigoKN3ti9zPMpVgkdMMw/ABfgeGAvn5b0gSw06VxcQPYdLIMIw6mVh14h4iXk6lXO+Ex1hgWUZWkJCkYqQ2AZsSlbfML5BKsyIiIiIichda0uJwsklE5G5guUi5cuVKoqOjgf+SLSxDiKTm+vXr1jv0GjVqxNWrV+1OVatWBeKHLbC4fPmy9Xm3bt1S/LE5PZo3b253maXXiXPnzqW4/NatW0yfPp0WLVrw0EMP4erqimEY1sly8dVysTkl5cqVo1y5cqmuP2kMpmmybds2ANq1a2f3AriPjw8NGzYEYMuWLdb5Xl5e1os8AwYMsLk4npoNGzZgmiYFCxakcuXKdt+v2NhYKlWqBNi+Z5ndbsB6Yfu9995j69b03bO0fft24uLiAFId2/zFF1+0/p349UqscOHCPPbYY6mur3fv+J/9goKCuH79us2yHTt2cOjQISBzw4MA1gSgpCy9aQDWi4NJGYZhLXf27FmbZZbEg6pVq5I3b16777OXl5f1AmXi99mSUOTp6Ymfn59D2+bq6srTTz+d4rLUPh8Wu3fvpn///lSrVo18+fLh5ORk3S8tPc+cPXuWK1eu2I0hM8eGlDi63yVWvnx5m/c3sfS874n3uaTvu8j9yN5x0jAMypQpA8ATTzxhd1gIyz6T2v7i5uaWYm9FcHftl44eVy3fCU2aNOHatWt2vxOqVKkCQHR0NMeOHXMoxpo1a+Lj45Pissyeg1jOH/bt28d7771nPXfNiNatW9s952rUqBFeXl4A1nM0yNpzkDt57hQaGmo9h2nQoIHd1/vatWvWxFJ753wiD5p7NsHCiE/hDQYeIT6hYgvQF7A7LIhpmjuA4wlPWzq6btM0dwIhCU/9DcNI9s1qGEY34JmEp3NM0zyfZLmvYRhmwhSStL6IiIiIiIiISE57+OGHqV27Nrdu3SIoKIgzZ85YL6ymJ8EiLCyM27dvA/F3eObNm9fuNHjwYAAuXLhgrR8eHm79wdryw64jihUrZneZ5e7If//9N9myM2fOUKtWLfr27cvq1as5c+YMt27dSrGdf/75J1PrTxrDP//8Y23TckHDnkceeQQg2d3qkyZNwjAMfv31V2rUqEG5cuXo3bs3s2fP5vTp0ym2ZUkMiIqKSvX9yps3Lzt37gRs37PMbjfE30Xp7OzM4cOHadCgAcWLF6dr165MmzbN7gWdxHdypvZ6lShRwnrXv727+y0X5FLTrVs3XFxcuHz5crLhKiy9V5QtW9buBbn0Klq0aIrzEw/JYq9M4nJJX2PL+7xr164032fL+5v4fba8D1WqVEm1h4rUFC5c2G7d1D4fEH93b61atfjmm2/Ys2cPV65csXs3dWb3zZTWnxpH9rvE0vN+plYucZmMxi5yL0rPPuPIcTIxb29vu8eru2m/dPS4avlOmDJlSqrfB4l7LrP33Z+W1L5jM3sO0rhxY2uPTmPHjqVIkSLUr1+f4cOHs2rVKm7evJlmfJbEjZQYhmHtnSLxeUdWnoPcyXMny+ttWW9qr/fixfH3nTv6vovcb+7ZBAugC1AXMIExpmk+bZrmd8CfadRbT3xCRr1Mrn8QcA3IBawyDGOEYRhPGobR0DCMSYClf8CzgP1B9URERERERERE7mKWXizmzp3L/PnziY2NpV69enbvsE4stQub9iROYEh853nevHkz3JaFvTsRE0vp4mz37t3Zt28fzs7ODBkyhI0bN/L3338THR3NlStXuHLlCvXr1wfiuwbPzPqTxnD16lXr3/buurWwvDZJ79Tv0KED69ato1GjRjg5OREWFsasWbPo1asXJUuWpHXr1hw5csSmjiPvmb0LFo5sN0C9evX49ddfadOmDc7Ozpw+fZr58+fTr18/ypcvT8OGDZMN22B5vXLnzp1iF+6JWV5Pez0bJL4QZk+hQoVo164dYDtMyM2bN1mwYAEAPXv2TDbMQ0al5zV05POd2ffZ8tpl934JyWPfunUrQ4YMITY2lpo1axIQEMDevXu5cOGCdb9csWKFtXxm982MdoPvyH6X0ZjSW87RLvxF7iXZdZzMaP2sWE9mOXpczcrv/rSk9h2bFXH89NNPjBs3jtKlSxMTE8P27dsZP348zz33HEWLFuXDDz+0mywLpDmsl+UcIvF5Wlaeg9zJc6c7+b6L3G/sphcbhvGB5W/TNEelNN9RidvLhE4JjztN0xyRgXp7Eh7tp6Glg2mauw3D6ADMBwoQ33NG0u06BbQzTTPt1GQRERF54A0Otu1mz++JVjkUiYiIiMh/OnfuzJAhQ9i6dSsnT54E/ku6SEvixIANGzbQuHHjDK078cXb1Lr5zw5Hjx61dpn99ddf8+qrr6ZYLvEP7Fkp8WuX1josy1O62N2kSROaNGlCVFQU27dvZ9u2baxYsYI9e/awYsUKQkND2bVrFyVLlrRZb6lSpZKN730n1apVi2XLlnH16lVCQ0PZvn07q1at4tdff2XLli00bNiQbdu2UatWLZu4Y2JiuHnzZqoXOFJ7vTKid+/eBAUFsX79eiIiIihRogQ///wz0dHRGIZBz549M9V+drK8Xg0bNmTTpk1plE7OXlLPnTBt2jQgvoeQ7du34+bmlqxMahfP7oSM7nci8uBKbyJeaslimeXp6cmlS5f44IMP+Oijj7JtPemJAzJ3DuLi4sKwYcMYNmwYBw8eJDQ0lJCQEJYvX05UVBSjRo3iwIEDLFy4MMX6165dS7V9yzlE4vO0nDgHSYmj506GYRATE+PwUHwiD6LU9paRwIcJk735jk5Z4THie69YkMF6lv5rvDMbgGmaq4CqwKfAAeJ7tLgM/EX861Q1YTgREREREREREZF7ko+PD02bNsU0TcLDw3F2dqZjx47pqlu6dGnrj7VhYWEZXrevr6/1Tr7du3dnuH5mJF6fve29ffs2hw8fzpb158+fnwIFCgBw4MCBVMvu27cPiH+97SlYsCCtW7dm3Lhx7N69m4ULF+Lk5ERUVBRfffWVtZylZ5IzZ87cFcMLeHp60qxZMz788ENCQ0PZsmUL7u7u3LhxgwkTJljL+fr6Wv9O7fU6deqU9Y7N1F6v9Hj22WcpUaIEcXFxBAYGAv/1ZtG4ceNMt5+dLO+zI/slQPny5QHYv39/tl70S4ll32zbtm2KyRUAe/fuvZMh2ZXe/U5EHlyJj2Opfe+eOXMm22LI7HdCVseRVecglSpVolevXgQEBBAREWE9nwsKCrJ7rnDw4EG77ZmmaT3vS/wdnxPnIKlJ77mT5fU2TZPjx49nWzwi96O00pHspc4ZmZiyiiVBIqNpbLEJj1mSimWaZoRpmu+YplnFNE1P0zTzm6ZZwzTNj0zTjE6lXrhpmkbC1MjBdTeytOHwBoiIiIiIiIiIpCFxjxXNmzfH2zt9960UKFCAxx9/HMDunYKpyZcvn7X+vHnz7mh394m7QI6NjU2xzJIlS7ItCcEwDOvwI0uXLrUbw/nz59m8eTMATz31VLrbf/HFF6latSpgezGhadOmQHzySHBwsEOxZ6ennnqKZ599FrCNu169etZknkWLFtmt/9NPP9m0lRlOTk7WXioCAgI4d+4cq1atAsDf3z9TbWe3Zs2aARAREUFoaGiG6z/zzDNA/J24d/pzYtk37e0Tpmlah2m529jb70TkwVWwYEGcnZ0B7A4fdPnyZXbs2JFtMVi+E3755Zds65krPbLzHMTd3Z3hw4dbn9s7Bi9fvtzu98umTZuIjo6/7Gc5R4OcOQfJCHvnTg0bNsTFxQVw7Dxd5EGWWpJB44SpiZ35jk5J23OUpZ8ejwzWK5bwGJVFcYiI3HUGB/+RbBIREREREXFUx44dOXDgAAcOHGD27NkZqvu///0PgDVr1jB9+vRUy964cSNZl9BvvPEGAHv27GH06NF262b1XfRlypSx/v3zzz8nW37+/HneeeedLF1nUr179wYgPDyciRMnpljmzTff5NatWxiGYS0PEBkZycWLF+22fePGDU6dOgVAoUKFrPMrV67Mc889B8CQIUMIDw9PNcbw8PAsHY/72rVrRERE2F0eFxdnvcsycdw+Pj60ahU/xN6XX37J0aNHk9WNiIhgzJgxANStW5dHHnkk0/H26tULwzA4dOgQAwcOJCYmhrx589K+fftMt52dnn32Wev29+nTh6io1H8qPXTokM3zZs2aUbFiRSB+H7d8llKSXfvmmjVrUhwK5JNPPrH26nKnObrficiDy9nZmRo1agDwww8/pJhMOmLEiDSHrsiM/v374+LiQlRUFP369bObYGCRXb13ZfYc5MiRI8TFxdktf+zYMevf9o7BJ0+eZNKkScnm37x503reV7BgQdq0aWNdllPnIBaOnjvlz5+fl19+GYCxY8eyc2fqHfKfP3/emmAi8qCzm2BhmuYmy2RvvqNTFsX+d8Jj9QzWa5jweCjVUiIiIiIiIiIiAsSPZ12pUiUqVaqU4YuCHTt25MUXXwSgb9++dO7cmbVr13LmzBkuXbpEWFgYy5cvZ+DAgZQuXZqgoCCb+l27dqVly5YAfPDBB3Ts2JGNGzdy/vx5IiMj+f333xk3bhwPP/xw1mxsgtq1a1u7bx44cCBTp07lxIkTnD17lh9//JF69epx4cKFbO3i2c/Pz9pTwPDhw3nzzTfZt28fUVFR/Pbbb7Rv35758+cDMGDAACpXrmytu3fvXkqWLMlLL73EggULOHToENHR0URERLBy5UqeffZZIiMjAejUqZPNeqdOnYq3tzdnzpzhscceY8yYMfz1119ERUVx4cIF/vrrL77//nvatm1L+fLluXLlSpZt84ULFyhTpgx+fn4EBASwb98+Ll68yJkzZ9i0aRMvvPCCdYiIzp0729T99NNPcXd358qVKzRs2JCAgABOnz7NuXPn+PHHH3nqqae4cOECuXPnZvLkyVkSb7ly5WjYMP7nRsvdnx07dsTDI6P3hN1ZTk5OBAQE4Obmxt69e6levTqTJ0/mwIEDREdHc+7cOX7//Xe+/vprmjRpQu3atZPV//7778mdOzcnT56kdu3aTJkyhSNHjnDp0iXCw8NZvHgxnTp1Yvz48Vkau+V4cujQIdq1a8eOHTuIjIxk9+7d9O/fn2HDhtnsC3dSZvY7EXlwWXpD2rFjBz169LAei//44w+6d+/OlClTbBI/s1rJkiX57LPPAJg7dy5PPvkkCxYsIDw8nEuXLhEREUFISAijRo2iSpUq1uTZ7JCZc5AxY8ZQoUIF3n//fdavX09ERATR0dEcPnyYr7/+mldffRWAUqVKUa9evRTX7+vry/Dhw3n77bc5ePAgFy9eZNOmTTRt2pTff/8diE9GSDpEVU6cg1hk5txp7NixVKhQgatXr9KgQQOGDRvGb7/9Zk0Y3L9/P/PmzaNz586ULl3aJklF5EGWO6cDyIQNxCdXdDYM433TNG+kVcEwjPJAW8AE1mdzfCIiIiIiIllmSQvbu4T8cigOERFHBAYGkjdvXmbOnMmPP/7Ijz/+aLespatiC8MwWLhwIV27dmXZsmUEBQUlS8LIDrlz52bGjBm0bt2ay5cv8/rrryeLc86cOdbEi+xg2fY2bdqwfft2vvzyS7788stk5bp27ZpiDxf//vsv8+bNY968eXbbf//992nevLnN/NKlS7Np0yb8/Pw4fPgw77//Pu+//36KbeTKlYtcuXI5sHX2xcTEEBwcnGr34P7+/tYLJRYVK1Zk6dKltG/fnjNnzqQ4TIebmxtz586lTp06WRZv79692bTpv3vK7vbhQSwee+wx1qxZQ8eOHYmIiGDQoEF2y3p5eSWbV79+fYKDg+nSpQtnz55l4MCBKdbN6mQHf39/Fi5cyJo1a1i1apV1WBaLJ598knfffZfWrVtn6XrTy9H9TkQeXH379mXhwoVs3ryZuXPnMnfuXOsywzCYMGEC+/bts/ZCkB369+8PxPdK9Ntvv9GlSxe7ZStVqpRtcWT2HCQsLIwxY8ZYe4tIytvbm6CgIOuwLEkNGzaMefPmMXHixBTPrd544w369u2bbH5OnYNYOHruVKBAATZu3Ej79u3ZsWMHEyZMYMKECXbbsPe6iTxoUhsiJEsZhtHbMIwNhmFkVWLD90Ac8UN+TEvH+n2An4hPKrkJzMiiOEREREREREREJBWurq58//337Nixg1deeYWHH34YT09PcufOjbe3N/Xq1WPIkCFs3bo1xYu0efLkYenSpfz888+0b9+e4sWL4+LiQqFChahWrRqvv/46W7ZsyfK4mzZtSmhoKH5+fhQsWBAXFxfr3em//vrrHbkDvWDBgmzevJkZM2bQpEkTChUqhLOzM0WLFqVdu3b8/PPPzJs3L9kP3k8++SRr165l6NCh1K9fn9KlS+Pq6oq7uzsVKlTA39+f0NBQRo0aleJ6q1Spwp49e/j+++9p1aoVxYoVw8XFBTc3N3x9fWndujWff/45J06cSPHiu6NKlSrF1q1b+fDDD2ncuDFly5bFw8MDV1dXfH196dSpE6tXr2bWrFkYhpGsfrNmzTh06BBvvfUWVapUIU+ePHh4eFCxYkUGDBjAwYMHs3z4jg4dOpA3b14Aypcvf0fHVc+sBg0acPToUb744guaNGlCkSJFyJ07Nx4eHlSoUIH27dszbdq0FLs7B2jVqhXHjh3j/fffp2bNmuTPnx83NzfKlClDs2bN+OabbxgwYECWxpwrVy6WL1/OuHHjqFKlCq6uruTPn5/atWszadIkQkJCyJMnT5auM70yu9+JyIMpd+7crFq1ilGjRlG5cmVcXV0pVKgQLVq0YO3atbz99tt3JI7+/ftz9OhRhg0bxmOPPYaXlxe5cuUiX758VKtWjZdffplFixaxYMGCbI3D0XOQCRMmMGfOHLp370716tWt32kFChSgbt26fPTRRxw8eDDVBAdXV1fWr1/P+PHjqVatGnny5CFfvnw0atSIRYsWMWXKFLt1c+IcBDJ/7lS8eHG2b99OUFAQ7du3p0SJEri6uuLq6krx4sVp1qwZY8eO5fDhw1SvntFBBUTuT3eyB4syQCPie4/INNM09xuG8TUwAOhuGEZF4AugoKWMYRilgJLAs8DrCctMYLRpmueyIg4RERERERERuXtVPnggp0O4Z4wcOZKRI0c6VHf27NnMnj07zXJ16tTJ1F17rVu3ztBd6SmNY55UWttds2ZNFi9ebHd5SEiI3WXpeV18fX3TjDNXrly8/PLL1nGy08PFxYWmTZvStGnTdNdJqY3evXvTu3fvDNXLzHY7OTlRv3596tevn6F1JlasWDE+/fRTPv300wzVS+/nOKncuXNbE1ws3bxnRqNGjdL12U1PmdQ+nxZ58uRh0KBBqfZgkZrChQvz8ccf8/HHH6erfHqPNaltn7OzM8OGDWPYsGEpLk/tNUzPPgepfx7sbUNm9rv0fP78/f3T7CElvdsnOeOZJupeP73SOlZk1TEwtfWk53iVVcdsd3d3RowYwYgRI1Jc7sgxKaMxQPxwIePGjWPcuHFpls2I9LwXiTlyDuLj40P37t3p3r17BqOz5ezszNChQxk6dGiG62bnOUh2njs5OTnRoUMHOnTo4HAbIg+Se3mIEID/EZ9A8TxQB/ghYb7lCJO4vyRLWlaAaZpj70x4IiJyJw0O/sPmud8TrXIoEpEHgz2xo24AACAASURBVPY5ERERERFYtmwZUVFRODk50aNHj5wOR0REREREstEdGyIkO5imGWua5gvAYOAC8UkU9qZIYIBpmr1yKFwRERERERERERG5z1i6C2/evDmlSpXK4WhERERERCQ73es9WABgmuaXhmFMA5oDDQBfID9wFTgFbAJ+MU3zeo4FKSIiIpIFlrQ4bPPcL4fiEEmJejURERGRB4FpmsTGxnL58mW+/vprNm/eDMBbb72Vw5GJiIiIiEh2uy8SLABM07wJLEuY5D6lH+1F5EGj456IiIiIiMjdZdOmTTRu3NhmXocOHWjSpEkORSQiIiIiInfKfZNgISIiIiIiIiIiInKnODk5UapUKTp37syIESNyOhwREREREbkDlGAhIiIiIiIiIiIikk6NGjXCNM2cDkNERESyiL7XRSQj7psEC8MwmhE/DPnjwEOAJ3AVOA38Diw2TXNdzkUoIiIiIiIiIiIiIiIiIiIi96p7PsHCMIxawEygauLZCY95gaJALaCvYRh/AS+bpvnnnY1SREREROTetsTzTZvnfjkUh4iIiIiIiIiIiEhOSTXBwjCMUlm4rvxZ2BYAhmE8BywCXPkvqQLgEnANyAMUSDS/BrDNMIwXTNNcldXxiGSXwcF/2Dz3e6JVDkUiIiIiIvc6nVuKiIiIiIiIiIg4Jq0eLMKBu3LgIcMwSgALALeEWb8CnwEbTNOMSlSuINAEeBN4MqH8AsMwHjFN89SdjVoeNPrxWkRERERERERERERERETk/pDeIUKMtIvccW8RPwSICXxqmuawlAolJFv8BPxkGMZYYFhCvbeAwXcoVhERERERG0taHLZ5riE3RO5+Sfdb0L4rIiIiIiIiIvIgSSvB4m/u0h4sgOeIj22HveSKpEzTfNcwjKeBekArlGAhIiIiIiIiIiIiIiIiIiIi6ZBqgoVpmr53KA5HlEx4nJ/BevOJT7AokbXhiIiIiIiIiIiIiIiIiIiIyP3KKacDyISrCY9nM1jvXJL6IiIiIiIiIiIiIiIiIiIiIqm6lxMsDiU8ls5gvVJJ6ouIiIiIiIiIiIiIiIiIiIikKtUhQu5yPwD1gR6GYXxummZsWhUMw8gF9ARMYF42x3dfGBz8R7J5fk+0yoFI5H6T9LOlz5XI3U/fCSL3r/vxe3lJi8M2z/1yKA6R7HQ/7rsiIiIiIiIiIneze7kHi+nAduBRYJZhGC6pFTYMwxmYmVB+O/BdtkcoIiIiIiIiIiIiIiIiIiIi94V7NsEioceK1kAw0A3YbxjGYMMwqhuGkceIlyfh+WBgP/ASsARolZ4eL0RERERERERERERERERERETgHk6wMAwjFrgItAMMoCwwEfg/4DIQk/D4fwnzyyWUex6INgwj1s4UkwObIyIiIiIiIiJyVwkPD2f06NE0bNiQEiVK4OrqSt68eSlfvjydOnUiMDCQ69ev53SYd6WRI0diGAa+vr45HYoNwzAwDIPZs2dnuO7s2bOt9RNPuXLlwsvLi8cff5xhw4Zx4sQJm3qNGjVKsV56p5CQEABCQkLSFX9cXBy9e/e2lu3YsSMxMWn/3Ofv75/i+l1cXPDx8aFJkyZ88cUXXL16NcOv3d3A3vtnb/L390/Whr2ynp6elCtXji5durBmzZo0Y0ltHYnNnDkTJycnDMOgSpUqnD171sGtT5/w8PBkn7v7ja+vL4ZhMHLkyGTL7tbjljy4LJ/J9E4pfa4zy/Ld0KhRI4fbyMx374PkThyDE59LhIeHZ8s6ckpWfFZFJP3u2QQL4pMlLFNK85JO6SmTtKyIiIiIiIiIyAPl9u3bDBkyhIoVKzJixAi2bNnCqVOnuHXrFlevXuXYsWMsXLiQHj16ULp0aWbOnJnTIUsOiouL49KlS/zxxx9MmDCBRx55hKVLl+ZYLC+//DKzZs0CoFOnTsyfP5/cuXM73Obt27c5f/48GzduZPDgwVSrVo0jR45kVcj3hWvXrhEWFsaCBQto3rw5PXv2JC4uLlNtzpgxg1deeQXTNKlSpQobN26kaNGiWRSxiIiISNZLLZFR7i+O/3eR8zYDZk4HISIiIv8ZHPxHsnl+T7TKgUhEREREEnw7NacjyFp9+2Vr89evX6dNmzZs2LABgPLly9O/f38aN27MQw89xO3btzl58iSrV69m1qxZhIeHM3nyZHr37p2tccnd5ZdffqFBgwYA3Lp1iyNHjjBz5ky+++47rl27RpcuXTh48CClSpVi5cqVxMamPFJv3rx5AejWrRvTpk1LsYy7u3u6YoqLi6NXr17MmTMHgC5duhAYGEiuXLkyunlcuXLF5u/9+/czatQoNm/ezPHjx2nfvj27du3CyenevHct8ftnj7Ozs91lSd+viIgItmzZwvDhw7l48SJz5szh0Ucf5e2333Yovu+++46+fftimiaPPPIIGzZsoEiRIg61JXK3OXr0s5wOIUuVL/+/O7Keffv2UapUqVTLuLi43JFYRERE7tkEC9M0G+V0DCIikj5JL7rrgruIiIiIyN2pf//+1uSKvn37MmXKlGQXWh966CHq1q3LsGHDmDhxIosXL86JUO9qI0eOvK/vXHN3d8fT09P6vG7dutStW5c8efLw+eef8++//zJ16lTGjRuXrgSJ3Llz27SXUXFxcfj7+xMYGAjEJwAEBAQ4lFwB2MTi6elJsWLFaNy4MXXq1GHnzp3s2bOHtWvX0rx5c4djzklJ37+MSvp+VapUiUqVKlG7dm1q165NXFwcn332mUMJFtOnT+e1117DNE2qVq3K+vXrKVy4sMOxZoSvry+m+eDez3e/H7fk3ubh4ZGp45ajZs+eraE9REQkmXs2wUJERETiKYFF5MGlXmNE4um7UCRrrF+/3noRoU2bNnZ7FLBwcXHh3Xff5fnnn78D0cm94J133uHzzz8HYOvWrXdknbGxsfj7+zN37lwAXnrpJQICArK8dwknJyfeeustunTpAsRv372aYJFdatasSbNmzVi9ejVnz57l2LFjlCtXLt31p02bxuuvv45pmlSrVo3169fj7e2djRGLiIiIiGSc3f80DMOoljDlvZMBiYiI5LTBwX/YTA+KB3W7RURERCTexIkTgfgLyZMnT053vSpVqiSbt3v3bkaPHk3Dhg0pXLgwzs7OeHl5UbduXUaPHs2lS5fstjdy5EgMw8DX1xeAQ4cO0atXL0qUKIGrqyslS5bk1VdfJSIiIs3YFi1aRJs2bShatCiurq4UKVKE5s2bM3fuXLt3qoeHh2MYBoZhEBISws2bN5kwYQLVqlUjT548FChQgGeeeYaVK1emexvsWbt2Ld26daNMmTK4u7tTsGBBqlWrhr+/P6tWrUpWPjIykoCAADp06ECZMmVwc3PDw8ODcuXK0atXL/788880X5PsVLRoUfLlywfA+fPns319sbGx9OzZ05pc0aNHj2xJrrCoWLGi9W9725eZzz7Arl276N27NxUqVMDDwwN3d3dKlSrFE088wbBhw/jjj7v7f7X0vEYpmTp1qjW5onr16mzYsCFTyRVxcXHMmTOHZ599Fh8fH+v78PDDD9O6dWumTJnCxYsXbeok3ffttfvtt99Sp04dPD098fLyon79+gQEBACp7/uzZ8+2tg9w+vRpBg4cSJkyZXB1daVo0aJ06dKFAwcO2N2uK1euEBQURPfu3alYsSIeHh64ublRqlQpOnXqZDfu9Egt9pCQEGvs4eHhXL58mffee4+KFSvi7u5OoUKFaNOmDb/++mua61m4cCFPP/00BQoUIG/evNSqVYsvv/ySmJiYZK+RSGYk/dyeP3+ewYMHU65cOdzc3PDx8aFTp07s2rXLbhv+/v4YhkGjRo3slgkLC6NXr14UL14cNzc3SpcuzauvvsqxY8fSHevJkyd5++23qVatGvnz58fd3Z0KFSrQv39/jh8/breer68vhmFYe5+ZPXs2Tz/9NN7e3hiGwRdffJHidoSGhvLCCy9QrFgxPDw8qFq1KpMnT7YZ0isiIoJBgwZRvnx53N3dKV68OP369SMyMtJuPKdOnWLq1Km0bt2akiVL4urqiqenJ5UrV6Z///4cPXo03a+JozZs2ECrVq0oVKgQHh4eVKlShQ8//JCrV6+mu40dO3bg7+9P2bJl8fDwIF++fNSqVYvRo0fbDCNm8fbbb2MYBj4+PnaHRbMYOnQohmFQpEgRYmJiki2Piopi5MiRPP7443h5eeHm5oavry/+/v7s3r073duQkn/++YdRo0bx2GOPWT9nZcuW5eWXX2bfvn126yX9fvjzzz/p3Lmz9TNfpkwZBg0axIULF+y2YdkXZ8+eTVxcHF999RW1a9cmX758eHt706JFC0JDQ23qrF27lpYtW1K0aFHc3d2pUaMG3333XZrbefv2baZPn06zZs3w8fHBxcUFHx8f2rRpw7Jly9K9nRn5H8Syj504cQKAjz76yLrNaZ1byL0ptR4sdgEm4AdYP3GGYXyQ8OcC0zQPZ2NsIpIBumtP7ne6S1tE5N6nY7mIyN3rypUrrF27FoAmTZqkmRiQmr/++osaNWokm3/p0iV+++03fvvtN77//nvWrFlDhQoVUm1r3bp1+Pn52fwoHhERwYwZM1ixYgWhoaGULl06Wb0bN27QpUsXgoODbeZfuHCBNWvWsGbNGmbPns2SJUvIm9f+vUVXrlyhYcOG/PbbbzbzN2zYwIYNG/j222/p06dPqtuQkmvXrtGjR49kw6vcuHGD6Oho9uzZQ0BAQLIkkGbNmqV4ISgsLIywsDACAwP56quveO211zIcU1axxOzl5ZWt64mNjaVHjx788MMPQPwP299//322JVcANu9HStuX2c9+YGAgvXr1SnZx5uTJk5w8eZIdO3Zw8ODBZJ/ru0lar1FKvvnmG9544w1M06RmzZqsW7eOggULOhxDTEwM7dq145dffrGZf+nSJS5dusSRI0dYsWIFJUuWzFAPPLdu3cLPzy9Zu9u3b2f79u2sX7+esmXLpqut3bt306xZM5sklHPnzrFgwQJWrFjBxo0beeyxx5LV69GjR4rvv+UzsnDhQt59913GjBmT7u3KqFOnTtGsWTObC6U3btxg+fLlrF69muDgYFq2bJmsnmma9O3bN9nFsT///JM///yTZcuW0blz52yLWx5sx44do2fPnpw6dco67/z58yxcuJDFixcTGBjo0Odvw4YNtGnThuvXr1vn/f3338yYMYOgoCBWr16dZhtz587l1Vdf5caNGzbzjx49ytGjR5k1axY//PBDqscr0zTp2LEjQUFBaa5v5syZ9OnTx+a7Zu/evQwaNIidO3cSEBDAzp07admypc0x6vTp00ybNo2QkBB27NhhTahM7NFHH02WSHjr1i0OHjzIwYMHmTVrFgsWLKBt27ZpxumI8ePHM3z4cJt5Bw4cYNSoUfz00098/PHHqdaPi4vjzTffZMqUKcmWWY5V33//PatWrbJJKOzWrRsTJ07k/PnzrFu3zm4PV6ZpMn/+fAA6depE7ty2l2jXrl1Lx44dk72GJ06cIOD/2bvz+Bju/4Hjr0lEhBDETRFXpUVdFVVR0qTEfYsjKlWU0OrlaqmqUi11lKLqKuqss+4j+TrirrhalCSOipwSCXEkmd8f6c4vm+xudjeHqPfz8diH2PnMZz47M5n5ZD/veX+WL9f6eUOHDjX5OQwJDg7G29ubO3fu6L0fGhpKaGgoy5cvZ+bMmYwYMcJkPVu2bKFXr148evRIey8sLIw5c+awZs0aAgICDAZf6zx58oS2bdtm+t3YvXs3AQEBbN26ldatW/PVV18xYcIEvTJnz55l8ODBhIaGMmXKFIP1h4WF0b59+0wBI5GRkfz+++/8/vvv+Pr6smTJkkz7Pz1r/wYRzw9r/uKYCHwB1M7ZpgghhBBCCCGEEP99kjVK5EfHjh3Tvmh3d3fPVl2KouDp6cncuXM5fPgw165dIzo6mvPnzzNnzhwqV65MWFgYPj4+RrNIQNpgaM+ePXF1dWXnzp1ERkZy/fp1pkyZgo2NDeHh4XzyyScG1/X399cGIXv37s3JkyeJjo4mODiYd999F0ibEsXX19fkZ/nggw+4dOkSM2fOJCQkhOjoaHbs2KENon744YcWZ2rQDYLogiu6du3K/v37uXPnDpGRkRw9epRJkyYZDHKpWrUq48aNY+fOnZw/f56oqChCQkLYvn07bdq0ISUlhREjRvDHH39Y1KacEh4erj3Z6erqmmvbSUlJwdfXVwuueOedd3I9uALSnmTUMfT5snPu3717l6FDh5KSkkLjxo3ZvHkzoaGh3L17l7CwMHbs2MH777+Ps7Nzrn7G7NLto8KFC5s18DBv3jz8/f1RVZWGDRtmO7gCYOnSpVoQhL+/PydOnODOnTvasViyZAnt2rXD1tbWonrHjBmj1duzZ09OnjxJTEwMwcHBDBw4kBUrVrBixQqz6urcuTPFixdn3bp1hIeHawOXDg4OJCQkGA2SKlu2LCNHjmTLli0EBwcTERHB9evXOXDggDZ9zZQpU0w+oZtdvr6+3L9/n8WLF3Pz5k0iIyNZs2YNpUqV4smTJwwaNIgnT55kWu+HH37Qgis8PDw4ePAg0dHR/PXXX4waNYrAwEC++eabXGu3eL69++67JCYmMm/ePG7cuMGdO3dYvXo1lSpVIjk5mf79+3PhwgWL6rx9+zZdu3blwYMHlCxZkoULF3Lr1i1u377NL7/8goODQ5ZBG1u2bMHX15eHDx/Spk0bdu3aRXh4OFFRUezduxd3d3eSkpLw8fHh7NmzRutZvHgx69ev57333uOPP/4gOjqaM2fOZMq88ffffzN06FDatGnDkSNHiI6O5sKFC3Tt2hWAX375hfXr19OtWzdKlSrF5s2bteuMbrD70qVLRoO4Xn75Zb766iv27dvHn3/+SXR0NFeuXGH9+vU0bdqUpKQk+vXrZ1YWMktt375dC66oU6cO27dvJzIykqtXrzJp0iSuXr3Kxx9/bLKOTz75hB9++AFbW1uGDx/O8ePHiY6O5tatW6xYsYIqVapoA/jpB97r16+vBRXo+iaGHDx4kJs3bwJpQRnpnTp1ivbt2xMXF0fTpk3ZuHEjt27dIiYmhiNHjtCpUydSU1Px9/c3mOXMlKioKNq0acOdO3coWrQoM2fOJDQ0lMjISLZt20adOnVISUnh/fffZ9OmTUbriY+PZ8CAAbi4uLB161YiIyMJCQnhm2++oVChQkRGRtKhQweSkpKM1jFlyhQCAwP5+uuv+fvvv4mOjmbbtm1UqlSJx48fM3jwYNavX8+ECRPw8/Pj9OnTxMTEcOrUKZo3bw7AtGnTDGbciI+Px8PDg4sXL1KpUiXmz5/P33//TWxsLBcuXGDUqFHY2tqyYsUKxo8fb7SN1vwNsnDhQhISEqhcuTIAY8eOJSEhQe+V3b+xRP5iKoNFKqAAdnnUFiGEEEIIIYQQQgjxFKRPP127dvaeqalXr56WDSM9Z2dn6tSpQ48ePXjppZf4448/2L9/P56engbriY+Pp3Hjxhw8eJBChQpp748dO5aYmBhmzJjBli1biI+Px8nJSVt++vRplixZAsCgQYP46aef9NqwaNEiSpYsybfffsuWLVvYtWsXbdq0MdiGmzdvcujQIZo2baq95+3tzebNm6lXrx4PHjxgw4YNDBs2zOz9s2rVKm2QdsKECXz55Zd6y0uXLk3Tpk0zPYEJGPzSu1SpUri4uNC2bVv69OnD6tWr+f7777VpM/LSt99+q/2cm1k0JkyYwO3bt4G0QbOffvop16cUSE1NZcaMGUDaedSjR49MZbJz7h86dIj79+9ja2vL7t279YIMihcvTpUqVfD29s6Rz5KUlJRlqvTChQtbHLBy5swZ9u3bB6RlWnBwcDBZfs+ePdrUGo0aNWLv3r05kvlEN31Ply5dmDt3rt4y3bHw8/OzqM4bN25oUyf17t1bbwCtZMmS/Pzzzzg4OGTanjEpKSmcPHlSL2BmyJAhPHnyhBEjRnDq1Cn+/PPPTE8BL1iwwGB9lStXplWrVlStWpWpU6fy7bff5toT4jExMfzxxx9Ur15de69Xr14UKVKEDh06cPv2bS2tu86DBw+0wVl3d3d27dqFnV3asIOzszPTpk2jTJkyRoPmhHjw4EGW1y1HR0ejy3T389dee017z8fHBzc3N+rXr8+9e/cYPXo027dvN7tNkyZNIj4+Hjs7O/bs2aOXdcbX1xc3NzcaNmxodP2kpCQGDRoEwPDhwzNlTfD09KRly5Z4eXkRGBjI2LFjM2XQ0fnnn38y9SkMBeTdvn2bLl268Ntvv2n3TWdnZ9asWUPt2rUJCQmhT58+VK5cmaCgIL3+1Zdffsm1a9dYtWoVy5cvZ9q0aZnqP3z4cKb3nJ2dqVmzJl26dKFVq1YcOnSIBQsWMHnyZKP7xhq660e1atU4dOgQxYsXB9L6VePHj6datWr069fP6PonT55k5syZAKxZs4bu3bvrLe/Xrx8eHh40aNCAq1ev8uOPPzJq1Chted++ffnss8/YtGkTCxcu1Ou76ujuHdWrV9frW6qqip+fH48fP6ZTp0789ttvekGAzZo1Y/PmzfTv358VK1bwySef0Lp1a7P7PpMmTSIiIgIbGxu2b9+uN9Dfvn17mjdvjpubG1euXOH999+nQ4cOBrM7xMXF8cILL3D48GHt/CpdujSjR4+mZs2adOvWjZCQEObOncunn35qsC1hYWFs2rRJLyNL+/btKVy4MG+++SY3btygT58+fPTRR1q/C9LutVu2bMHFxYV79+6xYsWKTEF5n332GaGhoVSrVo2jR49SpkwZbVmJEiWYNm0atWrV4t1332XGjBkMHz6cihUrZmqjNX+D2NvbY29vrx2TggULmrwmiWefqV6yLgeN5DgRQgghhBBCCCGE+A+7e/eu9nP6L9NzQ7ly5bSB5f3795ssq3siLiNd5oknT55kmjJDF1xRuHBhvvvuO4P1Tpo0SfvSdfHixUa37+Pjo/cFuE7dunW1qSBOnjxp8jNkpBtAqVOnDl988YXRcqbSFhujGzjIar9ml26APjExkdjYWE6cOMGQIUOYPXs2kBZo4ebmlmvb1wVXODo68tVXX+V4cIXusyUmJhIeHs7+/ftp1aoVp0+fpnDhwvz2228ULlzY4npNnfu6DDJFihTRBoVyS9u2bSlatKjJl6l53pOTk/X20eXLl1m0aBFvvfUWqampuLm56QXbGBMeHq79PHHixBybVka3LytUqJAj9UHaoFhKSgo2NjZGP9vXX39t9nkxYcIEg4Of6Qf/LL22pF//2LFjelMW5KT3339fL7hCp23btlpgUMa2b926lfj4eCDtyWNdcEV6I0eO1J78FSKjl19+OcvrVsZpFdLr2bOnXnCFjouLCyNHjgRg165dREREmNWe5ORkbbC8X79+Bqf0qVWrFv7+/kbrWL16NVFRUZQsWdJof6VAgQJMmjRJa1/6/lp6zs7OfPbZZ2a1ffr06Znum3Z2dloWi+TkZMaPH2+wP6jLyBEREcGNGzfM2p6Ora2ttn5O91N0U2gBfPHFFwbvo3379qVJkyZG69D1z9q2bZspuEKnQoUKDB8+HMicqaJPnz4oikJCQgLbtm3LtO6TJ0/YsGGDVja9gIAALly4gK2tLfPnzzeaYUmXOeTixYsmM5qkl5yczC+//AKkBQgayqJQvHhxLVjh1q1bJqe2+fzzzw3ev7p27UqLFi0AWLZsmdH1W7RoYXC6m1atWlG6dGkAChUqlCkAGdKCLLy8vAAyTd+XmJjI0qVLgbS/H9IHV6T3zjvvUL16dZ48eWJySh1r/gYRzxdTARYXSctgMVxRlFcVRcnY6zGew1EIIYRVMqaLlpTRQgghhBBCiGdRamoqq1evpnPnzlSuXBkHBwcURdFeui80r1y5YrQOe3t73njjDYPLatasqf2ccTBE9/Skp6en0WARe3t7OnToAKRlDjDG2Bza6dtg7mAMwL179zh1Ku3vvL59+1o1pcW5c+fw9/enXr16FCtWDBsbG22/tmvXDoA7d+5oU3XkhvQD9M7Ozri5ufHTTz9RokQJjhw5YvSpxZzyyiuvAGlfprdu3drogJO10g/YVahQAU9PTw4ePEjjxo25cuWK0fMSrD/369Wrh6Io3Lt3j0GDBukFH+Q3q1at0ttHtWvXZvDgwURHRzN+/HgOHTpE0aJFs6yndu3aFCxYEEgboMypqW10wU9Lly5l1apVPH78ONt1BgUFAdCgQQMqVapksEyxYsUypeM3xti1pXjx4toAk7FrS0hICKNGjeLVV1+lRIkS2NraaufXyy+/DKQFmVy7ds2stljKWNttbGy0wIuMbdftv1KlShkc5Ia0wdf27dvnYEuF+H+GBnR1unTpAqRdv48ePWpWfefPn9fus+bUbYguyMDd3T1T4Fr6ly6zmKqqnD592mBdHh4e2vXUlBo1amjTnGWU/n3dIHZG6YOr7ty5Y7DMkSNH8PPzo3bt2hQtWlSvn6ILODHV/7OG7hqjKIrJ7D3mHA8PDw+jxyIxMVG7zp4/f17v/lK1alWaNWsGpN0nM9q5cyexsbFA5ulBdNuuW7cuRYsWNbrtEiVKaPcIXX8yK+fPn+fevXsAdOvWzWi59u3ba5mnTPWNO3XqZHSZbv/+9ddfRvtmb731lsH3FUXBxcUFgKZNmxrN/qA7BzOef0ePHtUCC93d3Y3uw/v372v9SGP70Nq/QcTzxVQo/BrAnbQMFseA9FFtCrDZyuhwVVVVy0PwxX9exoHkLk3bPaWW5K389LkNDeY/L8chJ5hzLPPT8RZCiOeN3OeeDXKchBDi6Uj/5LjuKWNrJSQk0L59ew4ePJhlWVPbKl26tNEsDumfEs84z/P169cBMqXWz0j3BXlERASPHz82ODBRvnx5o+vr2mBqnumMg6yKjgAAIABJREFUwsLCSE1NBf4/SMAS33//PaNGjdKe0DclPj7erEHunBQbG8tHH33Enj17KFasWK5tZ+TIkQQHBzN79mzOnTtH27Zt2bt3b6Yv41NSUoweHzs7O+zt7S3a7qlTp5g8eTLz5883uDw753716tXx9/dn7ty5LFmyhGXLltGgQQPc3d1544038PT0NDjYYCxlvo2NjdFsCgEBAWYHAlhqxowZtGrVilatWmVZ1s3NjcmTJ9OrVy/i4+Np3bo1Bw8exNXVNVtt+PDDD1m6dCk3b96kX79+DB06FHd3d9zd3fHw8ODVV1+1OOuJ7rpSq1Ytk+Vq1aplNIV/etZeW9auXYufn59Z153sXsuNsabtuv2XfmDKkKz2r3h+hYaGUrVqVavXNzX9WfplunM1K2FhYRbXndHly5cB2LJli9n366ioKIPv6wams1KuXDmjy9JP62SsXPoyhq5DH330kTbNhik5fX3SHY9y5cqZzAJl7HgkJiZq2bE++eQTs6YrSk1NJSYmRu+a2LdvX44cOcLOnTuJi4vTa4su6KJRo0a8+OKLenXpzoXg4OBsnwsZpT+nTfWN7ezsqFmzJufOnTOanaREiRKULVvWaB3pg4Fu3LhhMDOVOeegOWUynn+6fQim71PpGduH1v4NIp4vpsLkFwI7SAumSP/Syfi+JS8hhBBCCCGEECJLkt1LiLyR/ot5XYpla3344YccPHgQRVF499132bVrF6GhocTGxpKQkEBCQoKWGjk5OdloPcbSI2ekqvpJVnUDzlnNe5z+C2xj2R7MaUPG7ZuSfjuWBj8cPnyYjz/+mJSUFBo0aMDy5cu5cOECUVFR2n5NP3e8qX2bXQEBAaiqiqqqxMfHc/jwYe2JxOPHj/Pee+/l2rZ1Zs6cycCBA4G06RA6derEw4cP9croMikYeg0ZMsRo3brPpqoqkZGRbNiwQRv4XbBgAT/99JPB9bJ77s+ZM4cFCxbg6upKamoqp0+fZtasWXTp0oUyZcowYsSITOeqsc+XVYBRdrz99tva/nn8+DHXrl3jiy++wM7OjgcPHtC9e3ezM3B069aNxYsXoygK0dHReHl5ERoamq32FS9enBMnTjB06FCcnJxISEhgx44djB07Fjc3N2rUqJEptXtW7t+/D6RN4WKKufOtW3NtuXbtGv379ycpKYkaNWowf/58zpw5Q0REBPfu3SMhIYELFy5o5XPrGmBN23N6/wlhKVPnXqFChbTz2ljQWka6czqruk2d09YEGTx69Mjg++kDH0wxt29lze/5ypUrteCKVq1asW7dOv766y+io6O1e6AuQNGcQFFLZPcaY23AR8bj0bNnT+zs7Hj8+LE2HQigN21IxuwV1m7f2LmQUfpz2ty+sbF+sSX719jvkjnnljXnX07uQ2v/BhHPF6OZJFRVTVUUpQPQGWgLvADYA2+QNj3In0B0XjRSCCGEENkj2VOEEEIIIYQpTZs2xdbWlpSUFJNpgbNy//59Vq5cCcDYsWO1uaINlcstjo6OxMfHZzlIkn55XmV6MCeow5gFCxYAaSm8g4KCDM4LnRNTIViqWLFivP766+zYsQMvLy8CAgJYvXo1gwYNMiuLgbUUReGnn34iMTGRtWvXcuDAAXr27MnGjRuNPnVojdKlS9OtWzeaNWtG/fr1iYyMZNSoUfTo0UPvycycOPcVRWHIkCEMGTKEsLAwgoKCOHjwINu2beP27dvMnTuXkydPEhQUZNX0MrnBzs6OatWqMXHiRCpXrszAgQOJjY1lzJgxLF++3Kw63n77bRITExk+fDj//PMPnp6eHDp0iAoVKljdrnLlyvHjjz8yZ84cTp8+zdGjR9m/fz979uwhJCSEvn37EhcXx7Bhw8yqTzeolNW1y9zBWWssXbqUx48f4+TkRFBQkJYmPr0nT57k2vazIz/sP/F8M3XuPXz4UBvwNzfIJ/1As6m6TZ3Tum3179/f7OtlfqbrpzRv3px9+/YZvE9lDITMKdm9xqQ/7kuWLMHPz8+qdjg7O9O6dWt+//13fv31V959910ANm3aRFJSEjY2Nvj4+BjdfosWLfjf//5n1baNMSfoIeNyY/1iS/ZvXgfM6banKArJycn5pp8k/rtMnmFqmk2qqg5SVbWNqqrp/yr6TFXVVta8cvkzCSGEEEIIIYQQQggLFC1aFE9PTwAOHDhgdorsjC5fvqw9DdazZ0+j5dI/ZZ3TdCnE//rrL5PlLl68CKQNxJozb3lOqFq1qvZU3Llz5yxaV1e+Y8eOBoMrIHf3a1ZsbW1ZtGgRdnZ2AIwfPz7Xt2ljY8OKFSto3749ANu2bcPX11ebhqVly5Z62SjSv5YtW2bRtsqXL8/06dOBtKckZ8yYobc8p8/9qlWr0qdPHxYsWMD169f58MMPgbQMIbt379bKGft86dPX55V33nkHDw8PIC0Vevp03Vnx9/dnypQpAISEhODl5UV0dPaf7StQoABubm6MHDmSbdu2ERISoqUwnzRpktlPn1apUgWAK1eumCyX1fLs0F0DWrVqZTC4Ap7uNcAU3f77+++/TZbLzf0nnm+msnOlX6Y7V7OSfroSc+vOqFq1akDaNe+/QHeN6t69u9HB7dy6RumOx507d0xmMjB2PJycnChZsiSQ/eOhy1Dxv//9T5t2RJc1ycPDw+D0Fbl5LqQ/V031jZOTk7VrsLHfg7t37xIZGWm0Dt3+VRSFypUrW9Fa6+n2oaqq2c6EJYQ5JIRHCPHMypgu2lDKaHPKCCGEyOxZu34+a+0VQggh8iPdfNOpqamMGDHC7PX+/PNP7ef0qXaNpX8+ceIE165ds7KVWWvevDkA+/bt4969ewbLPHr0SEvVrCufF4oVK8arr74KpA1AW5JaWLdvje1XVVVZs2ZN9huZDdWrV9em7Thy5AiBgYG5vk07OzvWr1+vZctYs2ZNrk1R0rdvX23qjblz5+oN4uTmuV+gQAEmTJig/T+70/jkJl2QREpKClOnTrVo3bFjxzJmzBgg7brSpk0bo7/D1qpYsaI2PUxERARxcXFmrdesWTMAzpw5wz///GOwTEJCQq6e81ldAwCLpz7JK7r9Fx0dzbFjxwyWSU1N1ZvmSIictHnz5iyX2djY8Nprr5lVX926dbWn/M2p2xAvLy8gbZqrGzdumLXd/Cyra9SDBw9M7o/s0F1jVFVly5YtRsuZczw2bNigBWpao2PHjjg6OpKamsrq1auJjIxk3759gOHpQdJv+9atWxw9etTqbRtSt25dnJycAPjtt9+Mltu+fTtJSUmA6b6xOee7q6urXpavvNCiRQstYHrdunV5uu30dIHGOT0Njsh/rAmwaAV4AEdyuC3iKdjU5oreSwghhBBCCCGEEM8nT09PfH19gbRMAO+9957JdPOPHz9mypQp9O/fX3sv/VNyugCG9B48eGB2Sn5rvfPOO0BaGmPdYG1GEydOJCIiAkALCMgrw4cPB+D8+fNMnjzZaLnk5GS9/7u4uACwZ88eg1OBfPvtt1pWjqdp7Nix2pfLxqbJyGmFChVi69atuLm5AbBo0SItYCgn2djYaJk54uPjmTt3rrYsu+d+aGioySle0gdmODs7W9LsPOXm5kbr1q2BtCAiS7PhTJ06FX9/fwBOnz5N+/btefDggUV1ZBWAotuXBQsWNHt6oN69e2NjY0NqaiqjR482WObzzz+3uK2W0F0DgoKCiI2NzbR87dq17Ny5M9e2nx0dOnSgWLFiAIwZMybT9Q1g9uzZVmdPEiIr69atMzhwHRoayqxZswBo06YNZcuWNau+AgUK0KdPHwBWrlzJ6dOnM5W5cuWK3n0iI19fX0qVKkVycjJ+fn7a4LYxlmQFehp016jff//d4PJPPvmEmJiYXNm2m5sbL774IgBffvmlweC5X3/9lePHjxutQ5cp6tKlS3z++ecmt5eSkmI0YLJw4cJ06dJF2+batWtJSUmhUKFCdO3a1eA6b731Fi+//DIAgwcPNniNT8+Sc8HW1lbrq69evZqgoKBMZe7du6fd2ypVqqTdxw2ZPHmyweO4ceNGbXqTAQMGmN2+nOLk5KT16adMmWLwdzK9yMhI7t69m+Pt0PXRwsPDc7xukb9YHGChqur//n3lzpVQCCGEEEIIIYQQQjwV8+fP54033gBg4cKFvPTSS8yePZtz584RHR1NeHg4x48f56uvvuLFF1/ks88+0xsoK1++vPbU25QpU5gyZQpXr14lKiqKHTt20Lx5c86cOaN9CZ4bGjZsqAVZzJ8/n379+nH69GliY2M5d+4cQ4YM4ZtvvgHSnjJs06ZNrrXFkD59+tC2bVsAJkyYQM+ePQkICCAyMpLo6GhOnjzJ1KlTqVWrlt56PXr0ANK+VO/UqRPHjx8nOjqac+fO4e/vz5gxY3B1dc3Tz2JI5cqVtbnL9+3bx8mTJ/Nku46OjuzcuZN69eoBMGPGDL788ssc307Pnj21KSZmz56tDahn99xfvnw5VapU4cMPP2THjh1cv36duLg4QkJCWL58uTYoU7RoUby9vbP1GZKSkkhMTDT5ymqgz5QvvvgCSAsS+vbbby1e/4cffuDtt98G4NChQ3Tt2tVk8ElG3t7evPbaa8ycOZPjx48TERFBdHQ0wcHBjB49mnnz5gHQrVs3ChQoYFadVapU0YKjVq1ahY+Pj951ZfDgwcyZM0dLUZ4bdNeAmJgYvL29CQgIICoqShsM9PX1zRfXAEOKFCnCxIkTgbS0+d7e3hw+fJjY2FguX77M2LFj+fTTT3N1/4ln24MHD7K8bj18+NDo+i+88AJt27Zl/vz53Lp1i4iICNauXcsbb7xBfHw8dnZ2TJs2zaI2TZgwAScnJ548ecJbb73FokWLuH37Nnfu3GHlypW0bNnS6HQ+kPZ7sWTJEhRF4cCBAzRs2JDFixdz9epV4uLiCA8PJygoiOnTp9OkSRO6detmUfvymu4aFRAQgK+vL8HBwcTExHDixAl69erF/Pnzc/UapZu6KyQkhBYtWrBz506io6MJCQlh8uTJ+Pn56QVDZuTm5qYFZ06dOpXWrVuzdetWbt26RXx8PDdu3GDPnj2MGTOG6tWrM3PmTKN16TJV/PHHH1q70geaZWRjY8Py5cspVKgQFy5c4JVXXmHOnDn89ddf3L17l4iICE6ePMm8efPw8PCgcePGFu2b8ePHU7ZsWVJSUvD29mbOnDlcv35d66O4u7trQRtz5swxem8sXrw4CQkJuLu7s23bNqKioggLC+Pbb7/VPnO1atW0QMm8NmXKFGrWrEliYiLu7u6MGTOGEydOEB0dTUxMDH/++ad2D69SpUquZNRr2LAhAFu2bGH//v3cu3eP5ORkkpOTLcpcJ/I/83qQWVAURQFeAsoDRYEE4DbwlypnjMiHMqYN79K03VNqiRBCCCHMIfduIcSzQq5X4llXpEgR9uzZw6effsr8+fO5evUqI0eONFq+bNmyfPzxx3rvzZ8/H3d3d+Li4vjss8/47LPPtGWKojB9+nTOnTuXq09izps3j9jYWDZv3syqVatYtWpVpjJvvvkmK1euzLU2GKMoCuvWraNPnz5s3bqV9evXs379+izXGzBgAOvWrWPPnj3s2rWLXbt26S1v1qwZ48aNo3379rnVdLONGzeOpUuX8uTJE77++utcSwmeUYkSJdi7dy/u7u5cuXKFiRMn4uTkZPIctpQui0Xfvn2Jiorip59+0urP7rl/584dZs2apT1NnVHhwoX59ddfTQ7YmUMX4GPKK6+8QnBwsFX1v/baa3h5ebF3716WLFnC+PHjKVeunNnrK4rC4sWLSUxM5LfffmP37t307t2bdevWYWtrm+X6qqpy7Ngxo1NRADRq1Ig5c+aY3SZIyxJz+fJldu/ezdq1a1m7dq3e8r59+1K9enUmTZpkduCGJd58800GDhzI4sWLOXHiBB4eHnrLX3zxRZYsWWL2FAd5beTIkVy4cIElS5awb98+LWW+TsuWLfHx8eG9994z6ziL54vu6X5TOnXqZPR+8/PPP9O/f3+GDRuWKZtQgQIF+OWXX6hTp45FbapQoQIbN26kQ4cOxMbGMnjwYL3lTk5ObNy40eTvZIcOHdiwYQMDBgzg0qVLvPvuu0bLNmjQwKL25bXRo0ezbds2zp49y8qVKzP1sbp27Uq7du1yLXNYu3btmDp1KmPHjuX8+fOZ7nWurq5MnjzZZKDKtGnTsLe3Z+rUqezZs4c9e/YYLaubjsIQT09PypYtS0REhJaZx9j0IDqNGjViz5499OzZk1u3bvHBBx8YLWvp9BulS5dm165deHt7c+fOHT744INM9dva2jJz5kwt+4YhTk5OzJo1i169etGxY8dMy8uUKcO2bdsoXLiwRe3LKcWLFycgIIBu3bpx/Phxpk2bZjJwSpdxLSe99957/Pzzz0RHR+Pp6am3LCAggJYtW+b4NsXTYc0UIRpFUeoqivILcBc4B+wGNvz773ngrqIoyxRFqZvtlgohhBBCCCGEEEKIXFewYEFmz57N5cuX+fLLL2nevDnly5enYMGCODo6UqNGDXr16sXKlSsJCwvTphXRqVOnDqdOncLX15dy5cphZ2dH+fLl6dy5MwcOHOCjjz7K9c9QqFAhNm3axIYNG2jXrh1ly5bFzs6O0qVL4+XlxYoVK9i7d6/Z0wPktCJFirBlyxa2bdtGt27dqFixIgULFsTZ2Zl69eoxbNgwDh06pLeOra0tv//+O1OnTuWll17C3t4eJycnGjduzIwZMwgMDKRIkSJP5fNkVKVKFS0DwdatW/N06pIyZcqwb98+qlSpAsBHH33E4sWLc3QbPj4+WiaKGTNmaNkVsnPujxw5knXr1jF48GAaNmxI+fLlKVCgAEWLFqV+/fp8+umnXLp0KV8E0JhDl8Xi4cOHfP/99xavb2try6+//qplmNm4cSPvvPOOWU9/7tq1i9mzZ9OxY0dq166Nk5MTBQoUoGzZsnh5ebFo0SKOHTtGqVKlLGqTvb09O3bs4Mcff6RRo0YUKVKEYsWK0bRpU37++WdWrlxJYmIiQK5dWxYtWsTChQtp1KgRDg4OODo6UrduXSZOnMipU6csCmTJa7rAmdWrV+Pu7k7RokVxdHTklVdeYfr06ezdu1f7XXpa12bx31W9enVOnz7NiBEjcHFxwd7enjJlytCjRw9OnjyJj4+PVfV6eHhw7tw5BgwYQIUKFShYsCAvvPACfn5+nDx5kqZNm2ZZR9euXQkNDWXy5Mm8/vrrODs7U6BAARwdHXF1daVv376sWLGCgwcPWtXGvOLo6MihQ4e0DA92dnaULFmS5s2bs3jxYjZs2ICNTbaGJLM0ZswY9u/fj7e3NyVKlMDBwYEXX3yRcePGcfz4cUqWLGlyfRsbGyZPnszFixd5//33qVOnDsWKFcPW1pYSJUrQuHFj/P392bVrF9OnTzdaj62tLb169dL+X6JECbOyT7m7u3P16lVmzZqFh4cHZcqUoUCBAhQuXJiaNWvSrVs3FixYwNWrV83fKf+qX78+ly5dYuLEiTRo0ICiRYtib2+Pi4sL77zzDsHBwYwYMSLLejp37kxQUBA9evSgXLlyFCxYkKpVqzJixAjOnz/PSy+9ZHHbclLFihUJCgpi/fr1dOvWjUqVKmFvb4+9vT0VK1bEy8uLKVOmcOXKFV555ZUc3369evUIDAykU6dOlCtXLlcCLkX+YPWRVRRlDPDlv3UoRooVA3yB3oqiTFBV1bIcS0JYQZ4Ye/rkGAgh8kJ+utZkbAvIte95l5/OTyGEeO4NGfq0W/DMcnFxYcKECUyYMMHidatXr84vv/xidPmyZctYtmyZwWUTJ07UUsmbYs5Aa7du3SxOqV21alWz6s6Jz9C+fXuLBszt7OwYM2YMY8aMMbi8ZcuWJtuenUSzAwYMsGhO7UWLFrFo0SKTZSxpT1afLb0XXniBsLAws+sG08czIxsbGy5dumRwmbXnfvHixenRo4eWYj2nWXr8DLHkeL3++utGy5tbT8GCBdm5c6fZ29SpXbs2tWvX5v3337doPXN+921sbBg6dChDhxq+t+jSjVeqVCnTMnOPgalzV1EUBg8enOlJeR1HR0eTn8FU3aauW+b+/gUGBmZZxsfHx+hgtqn9919Ro0buBxn+V5h7LzVXuXLlmDNnjkXZa8y5N1SvXp2lS5caXW7O746zs3OmzEfmMPdeZ87nMOcaldV1smjRokydOpWpU6davA1z+19Z8fDwyJThR8fca1nt2rWZPXt2ttoxe/Zsq+ooUqSIwQwTWTHnGDs5OfHFF19oQZDWatSoEevWrbNonZy6h5hzXbCxsaF79+50797dzNaZXzdk/VmaNWuWZ9nbxNNjVYCFoiijgSmASlpwRQJwGLgCJAKOQE2gOWlBFnbAFEVRVFVVLZ94TwghhBBCCCGEEEIIIYTIp+7fv09AQACQNvgkLKOqKtu3bwdk/wkhhBAif7M4wEJRlBrApH//+wgYD/yoqmqSgbKFgGHAV4AD8JWiKL+pqnrN+iYLIYQQQgghhBBCCCGEEHnn0aNHpKSkGJ1bfsyYMdy7dw8g1zKRPOtiY2ONpuj//vvvtbT3sv+EEEIIkZ9Zk8FiKGkZKVKAjqqq7jNWUFXVh8D3iqKcBXb/u72hwCdWbFcIIYQQQgghRB7IapobmRpJCCGEEM+b8PBwmjVrxnvvvYe3tzcuLi6kpqZy4cIFfvjhBy0deM+ePXn55Zefcmvzp+rVqzNo0CA6duxIrVq1sLGx4cqVKyxZsoTFixcD0LRpU7y9vZ9yS4UQQgghjLMmwMKTtKlB1psKrkhPVdX9iqKsBXoDb1mxTSGEEEIIIfKFrAaehRBCCCGEEP9N4eHhJuevf/3111m4cGEet+rZERcXx3fffcd3331ncLmrqyvr1q3DxsYmj1smhBBCCGE+awIsKv/7714L19tHWoDFC1ZsUwghhBBCCCGEEEIIIYR4KsqXL8+SJUvYvXs3Z8+eJSIigsTEREqUKEH9+vXp3bs3vr6+2NraPu2m5lurV69m165dnDp1ioiICOLi4nBycqJOnTp06dKFIUOGUKhQoafdTCGEEEIIk6wJsND1cB5YuJ6uvL0V2xRCCCGEEEIIIYQQQgghngp7e3v8/Pzw8/N72k15Zvn4+ODj4/O0myGeEy1btkRV1afdDCGeeRMnTmTixIlPuxlC5CvWBFhEApUASyeSe+nff6Os2KYQQgghhBBCiGdIxul0QKbUEUIIIYQQQgghhBDPNmsCLE6QNs3HO4qifKeqakJWKyiKUhR4B1CBk1ZsU4g8JV8GCyGEEEIIIYQQQgghhBBCCCGESM/GinXW//tveWCboiilTRVWFKUUsAWo+O9ba6zYphBCCCGEEEIIIYQQQgghhBBCCCHEU2NxBgtVVdcpijISaAq4A1cURVkO7AGuAPeBIkBNwAt4GyhOWvaKY6qqbsihtgshhMhlGbO5SCYXkVfk3LOeZGESwnxyrRFCCCGEEEIIIYQQQljCmilCADoDh0gLoigGjPj3ZYjy779XgC5Wbk8IIYQQQgghhHhuSACQEEIIIYQQQgghRP5jzRQhqKoaCTQCFgKPSQuiMPZ6BMwHGv+7nhBCCCGEEEIIIYQQQgghhBBCCCHEM8XaDBaoqpoIDFUUZTzQDmgClAeKAglAOHAC2K6qanQOtFUIIYQQ4pmVn55ElmlEhBBCCCGEEEIIIYQQQgjLWR1gofNv8MTyf19CCCFymQyMZk9+GuQW4nkgv3NCiGeF9LGEEEIIIYQQQgghRFasmiJECCGEEEIIIYQQQgghhBBCCCGEEOJ5IgEWQgghhBBCCCGEEEIIIYQQQgghhBBZkAALIYQQQgghhBBCCCGEEEIIIYQQQogsFHjaDRBCiGdBxjm5ZT7unCVznj9f5HgL8WySe6HIDXJeCSGEEEIIIYQQQohniWSwEEIIIYQQQgghhHiOKYqSrZcxN27cYOrUqXh6elK5cmUKFy6Mg4MDFSpU4M0332TcuHH88ccfRtdftmyZ2W2YOHFijn1uGxsbihcvjpubG5MmTSImJsbiurMrMDBQa09YWFiebz8v6D7fsmXLMi0bMGAAiqLQsmXLPG+XEEKI/GHixIkW9Ues6QtkpWXLliiKwoABA3K8biEykv6f9P/Es0MCLIQQQgghhBBCCCFEjnn8+DEff/wxNWvWZNy4cezfv5+bN2+SlJTEw4cPCQ8P58CBA0ydOpVGjRrRrFkzk4EWeU1VVeLj4zlx4gRffPEFL7/8cr5qnxBCCCHE88TUoLwQQjwNMkWIEEIIIYQQQggh/rPmvXfgaTchR/kv8MjxOhMSEgy+f+jQIdq2bQvAggUL6Nu3b5Z1JSYm0q5dOw4ePAhAtWrVGDp0KC1btqRChQrY2dlx584djh49ym+//caePXs4evQoGzdupGHDhkbr3bFjB+7u7kaXFyxYMMu2GdO3b18WLFig/f/Ro0eEhYWxfPlyfvzxRyIiIujcuTOXL1/GwcHB6u0IIYQQ5QKCn3YTctSdVvXzZDsXL16kcuXKJstkpy8ghBBCWEICLIQQz72Mc3+DzP8txPNCfv+FEOL5kfGaL9d7If6fo6OjwffTBxPY29sbLZfekCFDtOCKYcOGMWvWLOzs7PTKlC5dmrp16zJ48GCCgoIYMmRIlvU6ODiYtX1rFChQQK9uR0dHnJ2dadSoEYULF2batGncvHmTTZs20adPn1xpQ0YtW7ZEVdU82VZ+tGzZMnlKVQghhKZw4cK51g8QIr+Q/p/0/8SzQwIshLCSDMoJIYQQ4lkgfRYhhBB5Zffu3fz6668AdO3alXnz5mW5TrNmzTh27Fi+nYLDz8+PadOmAXDq1Kk8C7AQQgghhBBCCJE/2TztBgghhBD/RR9uPpXpJYQQQgghxH/Zd999B4CtrS0LGxzXAAAgAElEQVSzZ882e70iRYqYnP7jaUqfjjwpKcmqOh4/fswPP/xAixYtcHZ2xs7OjlKlSuHq6kr37t35+eefefDggd46gYGB2nzjYWFhBut99OgR06ZNo169ehQuXJjSpUvj6enJ1q1bARgwYACKotCyZctM606cOBFFUahatSoAly9fxs/Pj0qVKmFvb88LL7zAoEGDuHXrltHPFR0dzfLly+nevTsuLi4UKlSIwoULU716dfz8/Dhz5oxV+yurti9btkzbNwC3b9/m/fffx8XFBXt7e8qVK0fv3r3566+/TG4jNTWVhQsX0qRJExwdHSlRogSvv/46y5cvBzLvIyGEEM+WjPfSyMhIPvzwQ6pXr06hQoUoW7YsvXr1IjjY/Glb9u7di7e3N6VLl6ZQoUK4uroyadIkq/sIgNbGZcuWkZyczKxZs2jSpAklSpRAURQ2b96sV/7mzZt8+umn1KtXDycnJxwcHKhZsyb+/v6EhoZmub1bt24xevRoGjZsSIkSJXBwcKBGjRp4e3vzww8/cO/ePYPr3b9/nxkzZuDu7k6pUqWwt7enYsWK9OzZk0OHDhndXsZ7emBgIB06dKBs2bI4ODhQu3Ztxo8fT2JiYqZ1W7Zsqd3vIS3wVbe/TPWTDhw4QM+ePbV+jbOzMy1atGDevHk8efLEaFstPRamSP/PctL/E88SyWAhhBBCCCGEEEIIIbIlISGBwMBAALy8vKhUqdLTbVAOuXnzpvazNZ8pISEBDw8PTp3SD7iOiYkhJiaGS5cu8dtvv9G4cWPq1zd/Hvv4+Hg8PT316k1KSmL//v3s37+fzz//3Oy69u3bR5cuXfQGNm7dusXPP//M9u3bOXr0KFWqVMm0npeXl8FBqZCQEEJCQlixYgVz587lvffeM7stljp37hxeXl5ERkZq70VERLBmzRq2b99OQEAAjRo1yrTe48eP6dKlCzt27NB7PygoiKCgIPbv30+1atVyrd1CCCHy1rVr13j77bf5559/tPciIyNZt24dGzduZMWKFfj4+Jis45tvvmHcuHF6UzhcunSJL774Qrv/Fihg/ZDbw4cPadWqFYcPHzZaZuXKlQwaNIiHDx/qvX/16lWuXr3K0qVL+fXXX+ncubPB9ZcuXcrQoUN59OiR3vvXrl3j2rVr7Nq1i6JFizJgwAC95cHBwXTo0CHTwPvt27dZv34969evZ8yYMUydOtXkZ/zxxx8ZMWIEqamp2nuXL19m8uTJrFu3jsDAQMqXL2+yDlNSU1MZMWIEP/74o977sbGxHDp0iEOHDrFo0SJ27txpcjvmHAtTpP8n/T/x3ycZLIQQQgghhBBCCCFEthw9epSUlBQAXn/99afcmpyTfh5oT09Pi9f/5ptvOHXqFLa2towfP57g4GCioqKIjIzkjz/+YN68ebRo0ULv6Uxz+Pn5aV+uDxs2jPPnzxMTE8OxY8fo1KkTkydP5uDBg1nWExcXR8+ePXF1dWXnzp1ERkZy/fp1pkyZgo2NDeHh4XzyyScG161atSrjxo1j586dnD9/nqioKEJCQti+fTtt2rQhJSWFESNG5Or0L507d6Z48eKsW7eO8PBwbt++zYIFC3BwcCAhIcHol/tjxozRvlzv2bMnJ0+eJCYmhuDgYAYOHMiKFStYsWJFrrVbCCFE3nr33XdJTExk3rx53Lhxgzt37rB69WoqVapEcnIy/fv358KFC0bX/9///se4cePo3bs3J06cICYmhosXL2pThx08eJCFCxdmq42TJ0/m2LFjjBs3josXLxIdHc3Ro0epU6cOAFu2bMHX15eHDx/Spk0bdu3aRXh4OFFRUezduxd3d3eSkpLw8fHh7Nmzmepfu3Yt77zzDo8ePaJatWosXryYkJAQYmNjuXz5MsuWLeOtt97K1Ce5fv06Hh4e3Lp1C1dXV1asWEFYWBixsbGcPn2agQMHAml9HlP74O+//2bkyJE0adKE/fv3ExUVxaVLlxg9ejQ2NjZcuXKF7t276wWw7Ny5k4SEBO3/CxYsICEhQe+VPgjg66+/1oIrvLy8OHjwINHR0fz111+MHj0aW1tbzp49S4cOHUxmssjqWGRF+n/S/xP/fdnKYKEoSjngTeAloARQyIzVVFVVB2Znu0IIIYQQ5sg4NUuXpu2eUkuEsJyhqYXkHM57WV1H5DgJIUSa9GmMX3zxxRyvPykpyWDqaIACBQpQqJA5X0kZlpycrFf3o0ePuH79uvYEHkCvXr1wc3OzuO6dO3cC8P777zNp0iS9ZaVLl6ZBgwYMGzbMojqPHDnCpk2bADI9Lerm5samTZvo1KkT27Zty7Ku+Ph4GjduzMGDB/X24dixY4mJiWHGjBls2bKF+Ph4nJyc9NbVtSG9UqVK4eLiQtu2benTpw+rV6/m+++/Z+XKlRZ9RnOlpKRw8uRJnJ2dtfeGDBnCkydPGDFiBKdOneLPP//kpZde0pbfuHGDOXPmANC7d29+/fVXbVnJkiX5+eefcXBw0I69EEKIp+/BgwdG+wE6jo6ORpfdvHmTQ4cO8dprr2nv+fj44ObmRv369bl37x6jR49m+/btBtcPCwvD399f795QsmRJVq5cyZUrVzh16hTLly/H39/fwk/2//755x+WLFmCn5+f9p7u/paUlMSgQYMAGD58OD/88IPeup6enrRs2RIvLy8CAwMZO3as3lP6CQkJDB06FIBXXnmFwMBAihcvri0vUaIEtWrV4u233yY5OVmvbn9/f+7evUuTJk0ICAigcOHCeuv9/PPPlC9fnsmTJ/P555/Tv39/HBwcMn2+27dv07BhQwICArQ+R6lSpfjmm28oU6YMH3/8MUFBQWzYsIEePXoAZKrH3t7e6HEODw/nq6++AqBNmzb8/vvv2Nraavvxm2++oUqVKgwbNozTp0+zePFiowPxpo6FOaT/J/0/8d9nVQYLRVGKK4qyDLgB/AKMAYYAb5vxGpDdRgshhBBCCCGEEEKI/OPu3bvaz8WKFTNa7tGjRyQmJhp8mdK2bVuKFi1q8JVVSu+srFq1Sq++UqVK0ahRI2bNmkXFihWZPn06q1atsqpuXVaPChUqZKuN6emerHNycmLChAmZliuKwowZM8yu75tvvjEYoOLr6wvAkydPLJqfXqdfv34A7N+/3+J1zTVhwgSDAx66bQOcPHlSb9mvv/5KSkoKNjY2fPvttwbr/frrr/UGkIQQQjxdL7/8stF+gO4VFxdndP2ePXvqBVfouLi4MHLkSAB27dpFRESEwfWLFCnClClTMr2vKIp2zwkODjaZFSErderU0RvQT2/16tVERUVRsmRJvvvuO4NlChQooA3m79q1S69vtnLlSu3/ixYt0guuMFSPzrVr17RAjblz5xq9N44bN44iRYoQHR3N7t27jdY9depUg32OkSNH4uLiAuhnD7PEypUrtf0/Z84cLbgivaFDh/LKK68AsHjxYqN1mToW5pD+n/T/xH+fxQEWiqI4AAcAX9IyYCgWvoQQQgghhBBCCCHEc2jgwIFGB0byo5s3b7Jjxw6OHz9u1fq6ebW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\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "composition = func_frac.loc[sorted_order,:].T#.sort_values(by=list(sorted_order)[:5], ascending=False)\n",
    "composition = composition.T\n",
    "composition['Complete'] = symbol_lrs.groupby('LR Function').count()['index'] / len(symbol_lrs)\n",
    "composition[['Complete'] + list(composition.columns)[:-1]].T.plot(kind='bar', stacked=True, cmap='tab20', figsize=(16, 9))\n",
    "plt.legend(loc='center left', bbox_to_anchor=(1.05, 0.5),\n",
    "              ncol=2, fancybox=True, shadow=True, fontsize=26)\n",
    "plt.ylim((0,1))\n",
    "\n",
    "plt.xlabel('List of Ligand-Receptor Pairs from each Run\\nof the Genetic Algorithm', fontsize=30)\n",
    "plt.ylabel('Fraction of Ligand-Receptor Pairs\\n per Function', fontsize=30)\n",
    "\n",
    "plt.tick_params(axis='y', labelsize=28)\n",
    "plt.xticks([])\n",
    "\n",
    "plt.savefig(output_folder + '/Functional-Composition-GA2-Runs.svg',\n",
    "                dpi=300,\n",
    "                bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Presence in all GA runs**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "index\n",
       "Apoptosis                          False\n",
       "Canonical RTK-Ras-ERK signaling    False\n",
       "Cell adhesion                      False\n",
       "Cell migration                      True\n",
       "Eph receptor signaling             False\n",
       "Epidermal development              False\n",
       "Hedgehog signaling                 False\n",
       "Immune response                    False\n",
       "Insulin signaling                  False\n",
       "Lipid homeostasis                  False\n",
       "Longevity                          False\n",
       "Mechanosensory mechanism           False\n",
       "Neuronal communication             False\n",
       "Notch signaling                    False\n",
       "PCP pathway                        False\n",
       "Receptor-Mediated-Endocytosis      False\n",
       "Stress response                    False\n",
       "TGF-B signaling                    False\n",
       "Trypsin-like proteolysis           False\n",
       "Wnt signaling                       True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(func_frac.T != 0).all()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Presence in at least one GA run**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "index\n",
       "Apoptosis                          False\n",
       "Canonical RTK-Ras-ERK signaling     True\n",
       "Cell adhesion                       True\n",
       "Cell migration                      True\n",
       "Eph receptor signaling              True\n",
       "Epidermal development              False\n",
       "Hedgehog signaling                  True\n",
       "Immune response                     True\n",
       "Insulin signaling                   True\n",
       "Lipid homeostasis                  False\n",
       "Longevity                          False\n",
       "Mechanosensory mechanism            True\n",
       "Neuronal communication              True\n",
       "Notch signaling                     True\n",
       "PCP pathway                        False\n",
       "Receptor-Mediated-Endocytosis       True\n",
       "Stress response                     True\n",
       "TGF-B signaling                     True\n",
       "Trypsin-like proteolysis            True\n",
       "Wnt signaling                       True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(func_frac.T != 0).any()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Wilcoxon test to detect functions that change proportions across GA runs**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "wilcoxon_functions = pd.DataFrame(columns=['P-val'])\n",
    "for idx, row in func_frac.iterrows():\n",
    "    wilcoxon_functions.at[idx, 'Greater P-val'] = scipy.stats.wilcoxon(row.values-composition.loc[idx, 'Complete'],\n",
    "                                                                       alternative='greater')[1]\n",
    "    wilcoxon_functions.at[idx, 'Less P-val'] = scipy.stats.wilcoxon(row.values-composition.loc[idx, 'Complete'],\n",
    "                                                                       alternative='less')[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**FDR correction**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.stats.multitest import multipletests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "wilcoxon_functions['Greater Q-val'] = multipletests(wilcoxon_functions['Greater P-val'].values.flatten(), alpha=0.01, method='fdr_bh')[1]\n",
    "wilcoxon_functions['Less Q-val'] = multipletests(wilcoxon_functions['Less P-val'].values.flatten(), alpha=0.01, method='fdr_bh')[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Functions that increased their fraction across the GA-selected lists**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Apoptosis                          False\n",
       "Canonical RTK-Ras-ERK signaling     True\n",
       "Cell adhesion                      False\n",
       "Cell migration                      True\n",
       "Eph receptor signaling             False\n",
       "Epidermal development              False\n",
       "Hedgehog signaling                  True\n",
       "Immune response                    False\n",
       "Insulin signaling                  False\n",
       "Lipid homeostasis                  False\n",
       "Longevity                          False\n",
       "Mechanosensory mechanism            True\n",
       "Neuronal communication             False\n",
       "Notch signaling                    False\n",
       "PCP pathway                        False\n",
       "Receptor-Mediated-Endocytosis      False\n",
       "Stress response                    False\n",
       "TGF-B signaling                    False\n",
       "Trypsin-like proteolysis           False\n",
       "Wnt signaling                       True\n",
       "Name: Greater Q-val, dtype: bool"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wilcoxon_functions['Greater Q-val'] < 0.01"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Functions that decreased their fraction across the GA-selected lists**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Apoptosis                           True\n",
       "Canonical RTK-Ras-ERK signaling    False\n",
       "Cell adhesion                       True\n",
       "Cell migration                     False\n",
       "Eph receptor signaling              True\n",
       "Epidermal development               True\n",
       "Hedgehog signaling                 False\n",
       "Immune response                     True\n",
       "Insulin signaling                   True\n",
       "Lipid homeostasis                   True\n",
       "Longevity                           True\n",
       "Mechanosensory mechanism           False\n",
       "Neuronal communication              True\n",
       "Notch signaling                     True\n",
       "PCP pathway                         True\n",
       "Receptor-Mediated-Endocytosis       True\n",
       "Stress response                     True\n",
       "TGF-B signaling                     True\n",
       "Trypsin-like proteolysis           False\n",
       "Wnt signaling                      False\n",
       "Name: Less Q-val, dtype: bool"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wilcoxon_functions['Less Q-val'] < 0.01"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Functions that neither increased or decreased their fraction in the GA-selected lists**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Apoptosis                          False\n",
       "Canonical RTK-Ras-ERK signaling    False\n",
       "Cell adhesion                      False\n",
       "Cell migration                     False\n",
       "Eph receptor signaling             False\n",
       "Epidermal development              False\n",
       "Hedgehog signaling                 False\n",
       "Immune response                    False\n",
       "Insulin signaling                  False\n",
       "Lipid homeostasis                  False\n",
       "Longevity                          False\n",
       "Mechanosensory mechanism           False\n",
       "Neuronal communication             False\n",
       "Notch signaling                    False\n",
       "PCP pathway                        False\n",
       "Receptor-Mediated-Endocytosis      False\n",
       "Stress response                    False\n",
       "TGF-B signaling                    False\n",
       "Trypsin-like proteolysis            True\n",
       "Wnt signaling                      False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "~((wilcoxon_functions['Greater Q-val'] < 0.01) | (wilcoxon_functions['Less Q-val'] < 0.01))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "index\n",
       "Wnt signaling                      1.201757e-16\n",
       "Cell migration                     4.074349e-17\n",
       "Cell adhesion                      8.984027e-14\n",
       "Hedgehog signaling                 1.201757e-16\n",
       "Insulin signaling                  4.050678e-16\n",
       "Notch signaling                    3.534783e-08\n",
       "Mechanosensory mechanism           7.196871e-16\n",
       "Canonical RTK-Ras-ERK signaling    8.581293e-09\n",
       "TGF-B signaling                    1.776953e-16\n",
       "Trypsin-like proteolysis           5.404550e-02\n",
       "Stress response                    2.220223e-07\n",
       "Receptor-Mediated-Endocytosis      2.529128e-09\n",
       "PCP pathway                        3.047941e-23\n",
       "Apoptosis                          3.047941e-23\n",
       "Neuronal communication             2.006827e-21\n",
       "Lipid homeostasis                  3.047941e-23\n",
       "Immune response                    1.216211e-07\n",
       "Epidermal development              3.047941e-23\n",
       "Eph receptor signaling             5.124455e-09\n",
       "Longevity                          3.047941e-23\n",
       "dtype: float64"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wilcoxon_functions.loc[sorted_order, ['Greater Q-val', 'Less Q-val']].min(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Apoptosis\n",
      "Canonical RTK-Ras-ERK signaling\n",
      "Cell adhesion\n",
      "Cell migration\n",
      "Eph receptor signaling\n",
      "Epidermal development\n",
      "Hedgehog signaling\n",
      "Immune response\n",
      "Insulin signaling\n",
      "Lipid homeostasis\n",
      "Longevity\n",
      "Mechanosensory mechanism\n",
      "Neuronal communication\n",
      "Notch signaling\n",
      "PCP pathway\n",
      "Receptor-Mediated-Endocytosis\n",
      "Stress response\n",
      "TGF-B signaling\n",
      "Trypsin-like proteolysis\n",
      "Wnt signaling\n"
     ]
    }
   ],
   "source": [
    "print(*wilcoxon_functions.index, sep='\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "wilcoxon_functions.loc[sorted_order, ['Greater Q-val', 'Less Q-val']].min(axis=1).to_csv(output_folder + 'Wilcoxon_Functions.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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