{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3d54b332",
   "metadata": {},
   "source": [
    "# 6. Pathway Enrichment of LR loadings\n",
    "\n",
    "Cell-cell communication inference from single-cell data provides unprecedented insights into the molecular mechanisms underlying cell-cell communication. In particular, it enables the capture of CCC interactions at a systems level, which is not possible with traditional approaches. However, as the number of inferred interactions increases, the interpretation of the inferred cell-cell communication networks becomes more challenging. To this end, as done in other omics studies, we can perform pathway enrichment analysis to identify the general biological processes that are enriched in the inferred interactions. Pathway enrichment thus serves two purposes; it reduces the dimensionality of the inferred interactions, and also provides a biological summary of the inferred interactions.\n",
    "\n",
    "In this tutorial, we will show how to use the `cell2cell` and `liana` packages to perform classical gene set enrichment analysis using `GSEA` with `KEGG Pathways`, and also we will use perform a footprint enrichment analysis using a `multi-variate linear model` with the PROGENy pathway resource. We will use the [`GSEAPY`](https://gseapy.readthedocs.io/en/latest/) and [`decoupler-py`](https://decoupler-py.readthedocs.io/en/latest) packages to perform these analyses."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2ac22be8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import cell2cell as c2c\n",
    "import liana as li\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "import decoupler as dc\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import plotnine as p9\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "907c4925",
   "metadata": {},
   "outputs": [],
   "source": [
    "import logging\n",
    "logging.disable(logging.WARNING) # This is to avoid too many logs because of GSEApy\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eded8fe1",
   "metadata": {},
   "source": [
    "## Directories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c0a73062",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "../../data/tc2c-outputs/ already exists.\n"
     ]
    }
   ],
   "source": [
    "output_folder = '../../data/tc2c-outputs/'\n",
    "c2c.io.directories.create_directory(output_folder)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f5d0d38",
   "metadata": {},
   "source": [
    "## Load Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "973d1b0b",
   "metadata": {},
   "source": [
    "**Open the loadings obtained from the tensor factorization**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1657732b",
   "metadata": {},
   "outputs": [],
   "source": [
    "factors = c2c.io.load_tensor_factors(output_folder + 'Loadings.xlsx')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24b18655",
   "metadata": {},
   "source": [
    "**Load list of ligand-receptor pairs used as reference to [run LIANA](./02-Infer-Communication-Scores.ipynb)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e96e13f7",
   "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</th>\n",
       "      <th>receptor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LGALS9</td>\n",
       "      <td>PTPRC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LGALS9</td>\n",
       "      <td>MET</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LGALS9</td>\n",
       "      <td>CD44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LGALS9</td>\n",
       "      <td>LRP1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LGALS9</td>\n",
       "      <td>CD47</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ligand receptor\n",
       "0  LGALS9    PTPRC\n",
       "1  LGALS9      MET\n",
       "2  LGALS9     CD44\n",
       "3  LGALS9     LRP1\n",
       "4  LGALS9     CD47"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr_pairs = li.resource.select_resource('consensus')\n",
    "lr_pairs.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43090ea6",
   "metadata": {},
   "source": [
    "**Generate a list with the LR pair names**\n",
    "\n",
    "KEGG was originally designed to work with list of genes. Here we are using ligand-receptor pairs, so instead of using Gene Sets we need to build our LR-gene sets. This list is a first step to build the LR-gene sets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0550bdd3",
   "metadata": {},
   "outputs": [],
   "source": [
    "lr_list = ['^'.join(row) for idx, row in lr_pairs.iterrows()] "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3572fb9d",
   "metadata": {},
   "source": [
    "## Gene Set Enrichment Analysis\n",
    "\n",
    "In this case, we will run the PreRank method of GSEA since our LR pairs are pre-ranked based on their loadings in each factor obtained by Tensor-cell2cell."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa6385ac",
   "metadata": {},
   "source": [
    "### Generate LR-gene set for running GSEA\n",
    "\n",
    "`cell2cell` includes some resources associated with the Gene sets employed in GSEA. It includes annotations of GO Terms (BP), KEGG, and Reactome, for human and mouse."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97f8d4a6",
   "metadata": {},
   "source": [
    "**First, specify which organism and annotation DB to use**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c11ec2fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "organism = 'human' # For the COVID-19 data analyzed with LIANA+Tensor-cell2cell\n",
    "pathwaydb = 'KEGG' # KEGG Pathways"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77835f05",
   "metadata": {},
   "source": [
    "**Generate the LR-gene set that will be used for running GSEA**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2b9e896f",
   "metadata": {},
   "outputs": [],
   "source": [
    "lr_set = c2c.external.generate_lr_geneset(lr_list,\n",
    "                                          complex_sep='_',\n",
    "                                          lr_sep='^',\n",
    "                                          organism=organism,\n",
    "                                          pathwaydb=pathwaydb,\n",
    "                                          readable_name=True,\n",
    "                                          output_folder=output_folder\n",
    "                                          )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6ab0812",
   "metadata": {},
   "source": [
    "### Extract LR loadings for each factor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f43cd70c",
   "metadata": {},
   "outputs": [],
   "source": [
    "lr_loadings = factors['Ligand-Receptor Pairs']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a8b4d7d",
   "metadata": {},
   "source": [
    "### Run GSEA\n",
    "\n",
    "This external function implemented in `cell2cell` builds upon the function `gseapy.prerank()`.\n",
    "\n",
    "All outputs will be saved in the `output_folder`. Make sure that this path exist before running this function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "40220386",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "../../data/tc2c-outputs/ already exists.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████| 10/10 [00:08<00:00,  1.11it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 9.86 s, sys: 132 ms, total: 9.99 s\n",
      "Wall time: 9.05 s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "pvals, scores, gsea_df = c2c.external.run_gsea(loadings=lr_loadings, \n",
    "                                               lr_set=lr_set,\n",
    "                                               output_folder=output_folder,\n",
    "                                               weight=1,\n",
    "                                               min_size=15,\n",
    "                                               permutations=999,\n",
    "                                               processes=6,\n",
    "                                               random_state=6,\n",
    "                                               significance_threshold=0.05,\n",
    "                                              )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4654706",
   "metadata": {},
   "source": [
    "### Visualization\n",
    "\n",
    "We can use the `pvals` and `scores`outputs to visualize the results as a Dot Plot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "587f2ede",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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rRYsWaePGjdq3b58SEhK0adMmbdq0SePGjdPo0aPTfeZVerZu3aq33npLUvJgGl9++WWaj2BI+UiB999/334cZOTGe7Fuxnbd6QhcAADcoT799NNMX8VKj+1+nejoaCUmJqZ7BcDWHclisTicuNpO4jJzZSI7Vy+yIn/+/CpatKhOnDhhH4I9LSnn2+5PS8l2Nc7MEdpKlCiR7oN/b9SiRQt98MEHSkhIUGRkpP1E2zY6oa+vrxo2bJhquZiYGM2fP19SciD78ssv01xHWl1VM8MW2jJ6xlh6Vx/9/f11+vRpJSQkuC282rbpxoEnsqp27dqqXbu2pOQul2vXrtXUqVO1bt06HTlyRC+99JJmz56d5XqPHz+u559/XvHx8QoICNDIkSOVP3/+NMunfKRA7ty5Xd5PZm3X3YBBMwAAQLpsw7snJCRo9+7d6Zbdvn27JOnee+91uFfK9nDdo0ePpnuyHh0draNHj7ra5AyFh4dLkg4ePJjuIAm2QQckqWrVqqnm205ijxw5kumHFJvNz8/PPuDG/PnzlZiYqLi4OC1dulRS8gAKKT8bm0OHDtnv12rZsmWa9e/fvz/DrpjpsXXpzCikpvd4gAoVKkhKvpcr5X122XX27Fn7A71TPgjaVQEBAWrZsqUmTJigxo0bS0oe0CSrjz6Ii4vTgAEDdPbsWXl5eWnYsGEqWbJkusuUL1/eHm63bNmSrfanxV3bdbcgcAEAgHTVqVPH/nrGjBlplrPd43HjMpLsv4zbnhGVloiIiJvSTS3lw13Tus/typUr9nue7rvvPqfdpWzBLS4uzumogDnF1mXw/PnzWrNmjZYsWWIfDCKt7oS2bnpS+gNHTJkyxaW22e43O3jwoD3k3Oj8+fPpDjlvO8m/fPlyusO9Z5btsQdS8sAvZrB9B6SsDTZiGIZef/11+48db7/9tqpXr57hcoGBgfaRC+fOnWvalePsbtfdhMAFAADSFRYWpooVK0qSpk+frrVr16Yqc/nyZfuzfDw8PNSlSxeH+U2bNrU/q2rYsGFOB8U4dOiQRowY4e7mO9W0aVP7FYIff/zRaXs+//xz+9W4tJ6JlXI0QdvVvVtB48aN7d3NIiMj7SG3cOHCqlGjhtNl7rnnHvsVkVmzZjkNvsuWLdOvv/7qUtts609ISND//ve/VPMTEhL05ptvpjvIxaOPPmrv+vfZZ59p48aN6a5z06ZNDlcrb2T77Hx8fDIVZm60e/fudK/+GoZhD5AWi0XFixfPdN3ffvutFi9eLEnq0aOHHn/88Uwv279/f0nJ3UUHDRqU7lXF+Ph4/frrrw4DoZi5XXcT7uECAAAZ+vDDD/XYY48pISFBvXv3Vo8ePdSoUSPlyZNHu3fv1ujRo+1dAZ9++ulU94v4+Pho6NCheuWVV3ThwgU99thj6t27tz2wbNy4UWPHjpXValWpUqXS7Zp0+PBhbd682WGa7WHGsbGxqa541K9f3+F5QlLywBVvvfWW+vXrp5iYGHXp0kX9+/dXWFiYLl68qOnTp2vhwoWSkq9itWvXzmlbSpQoodDQUO3Zs0dr165Vx44dM9iTN4ePj4+aNWummTNnaunSpfaugq1atUpzkIWAgAA1aNBAK1as0KpVq/T000+rS5cuKlasmM6dO6dFixZp1qxZKlmypC5dupTtKyYNGjRQ8eLFdezYMX333Xe6cOGCmjZtKh8fH+3bt08TJ07Url27VKVKlTSfneXt7a1vv/1WPXr0UFxcnHr16qWWLVuqSZMmKlGihKxWq86cOaO///5bixcv1t69e/XWW2+lGTZtPyLUq1fP6fD/Gdm9e7eGDBmiSpUqqVGjRnrggQcUFBSkxMRERUVFaebMmVqzZo2k5DCc3sOlb2zXqFGjJCV37e3UqZP92VvOeHl5OVyJbdCggXr27KlffvlFGzduVMuWLfXEE08oPDxc/v7+iouL05EjR7Rp0yYtXrxYFy9eVPv27e0jDpq1XXcbAhcAAMhQ+fLlNWrUKL3wwguKiYnRuHHjNG7cuFTlunXrluYw1a1bt9bRo0f13XffKTo6Wl988YXD/Dx58ui7777T6NGjdejQoTSHmd68ebOGDBnidF50dHSqeb/88kuqwCUln4y+9957ev/993X27Fl98MEHqcqEhYVp+PDh8vT0dLo+SXr88cf1wQcfaOnSpbpy5cot82yiNm3aaObMmQ73W7Vt2zbdZd5991117dpVx48f159//pmqW1+xYsU0YsQI9enTJ9vt8vb21ueff67evXsrLi5O48eP1/jx4+3zPT09NXToUF28eDHdhxVXqVJFEydO1IsvvqgTJ04oMjLS/pwxZ9IaYCIqKsq+noz2T0Z27Njh0D3xRg8++KA++uijTNd34sQJ++t///03zeBvU7x4cftDoW2GDh0qPz8//fDDDzpz5oyGDRuW5vJ58+Z1eqy7e7vuNgQuAACQKfXq1dPixYs1YcIErVy5UkePHlV8fLyCgoIUHh6uJ554wqGLnTP9+/dX9erV9fPPP2vr1q26fPmygoODVatWLT3zzDMqW7asvv76a0nJo+mZ7fHHH7efuK9du1anT59Wnjx5VLZsWbVp00aPPfZYhs9latu2rb744gvFxcVp2bJlatWqlentzoxatWopODjYPijIfffdl+GDq4sWLaqZM2dqzJgxWrp0qY4fPy4fHx8VL15cTZo0Uc+ePVMNG54d1apV04wZMzRq1CitXbtWFy5ckL+/v6pWraonn3xSVatWTTcY2FSpUkWLFi3SzJkztXz5cu3atUsXLlyQh4eHAgMDVbZsWVWvXl3NmjVTmTJlnNYxb948GYahQoUKOdzblxWtW7dWwYIF9eeff2rHjh06deqUzp07p8TERBUsWFAVKlRQy5Yt073CaBaLxaLnn39e7dq105QpU7Ru3TpFRUXp8uXLyp07t4oWLary5curXr16atKkicMVvlt5u24nFiOnHqAAAABwg4SEBFWrVk1Xr15V//799eKLL+Z0kzLl3Xff1eTJk1WnTh39/PPPOd0cZJLVatUjjzyiQ4cO6ZVXXnHpyh2QFqIoAAC4ZSxZssQ+WIJthLXbwXPPPae8efPqzz//TLcbHG4tv//+uw4dOqSAgAB169Ytp5uDOxSBCwAA3DSHDx9Oc15UVJT9Qc1BQUGqV6/ezWqWy4KDg9WrVy9JumkjLcI1hmHYB6QYOHCg/flggLtxDxcAALhpHnnkET300ENq1KiR7rvvPuXNm1fnzp3T+vXrNWXKFPuw1a+//nqG907dap599ll7m2+lwTPg3OnTp9W8eXO1bt1aTzzxRE43B3cw7uECAAA3TWhoaLrzPTw89OKLL6pv3743qUUAYC4CFwAAuGmWL1+uP/74Q1u3btXZs2cVHR0tb29v+wN5u3XrluoZXgBwOyNwAQAAAIBJGDQDAAAAAExC4AIAAAAAkxC4AAAAAMAkBC4AAAAAMAmBCwAAAABMQuACAAAAAJMQuAAAAADAJAQuAAAAADAJgQsAAAAATELgAgAAAACTELgAAAAAwCQELgAAAAAwCYELAAAAAExC4AIAAAAAkxC4AAAAAMAkBC4AAAAAMAmBCwAAAABMQuACAAAAAJMQuAAAAADAJAQuAAAAADAJgQsAAAAATELgAgAAyMD69esVGhqqBQsW5HRTJJnTnmHDhik0NDRTZUNDQzVs2DC3rRu4k+XK6QYAAHA7iI+P13fffac5c+bo0qVLCg0N1Ysvvqi6detmuOypU6f08ccfa82aNbJarapZs6aGDh2qkiVL3oSWIy2ZDRe//PKLyS25u8TGxuqnn37Stm3btGPHDl28eFGffPKJOnTokKnlN27cqJ9++km7d+/W+fPnVaBAAZUrV04DBgxQeHh4qvJbtmzRF198oV27dil//vx65JFH9NJLLylfvnxuaxNuPVk9Tpxx199uAhcAAJkwePBgLVy4UD179lSpUqU0a9Ys9enTRxMmTFC1atXSXC42NlY9e/bU5cuX1bdvX3l5eWn8+PHq3r27Zs+erYCAgJu4FUjp888/d3g/Z84crVmzJtX0smXLav/+/TezaXe0CxcuaMSIESpWrJhCQ0O1YcOGLC1/6NAheXh46IknnlBQUJAuXbqkiIgIde/eXT/++KMeeughe9ndu3frySefVNmyZTV48GCdPHlS48aN06FDhzR27Fi3tQm3nqwcJ8649W+3AQAA0rVt2zYjJCTEGDt2rH3a1atXjSZNmhidO3dOd9nRo0cbISEhxrZt2+zT9u3bZ5QvX9746quvTGszsu69994zQkJCnM5bt26dERISYsyfPz9bdcfFxbnSNLe3x5nvv/8+ze2/UUhIiPH9999naz3Xrl0zTp8+bRiGYWzfvt0ICQkxZsyYka26bOLi4ow6deoYTz/9tMP0Z5991qhbt65x+fJl+7Rp06YZISEhxqpVq0xtE1I7d+6ccerUqRxbf1rHiTPu/NvNPVwAAGRgwYIF8vT0VOfOne3TfHx81KlTJ23dulUnTpxIc9mFCxeqUqVKCgsLs08rW7asateurfnz55vabrif1WrVDz/8oIceekiVKlVSr169dPjwYYcyPXr0UOvWrbVz505169ZNlStX1tdffy0puWvq999/r6ZNm6pixYpq0KCBPv/8c8XHxzvUsWbNGnXp0kXVqlXTgw8+qObNm9vryGp7JGn+/Pnq0KGDwsLCVLNmTb366qs6depUhtsbHx+vjz/+WLVq1dKDDz6ofv366eTJk07L7t+/X8ePH8+wTm9vbwUHB2dYLivy5MmjwMBAXb582T4tJiZGf/75p9q2bav8+fPbp7dr10558+Z1+P6Z0Sak9u+//6pRo0bq37+/li5dqsTExJu6fmfHSVrc+bebLoUAAGRg9+7dKlWqlMNJmyT7P8S7d+9W0aJFUy1ntVq1Z88edezYMdW8SpUqafXq1YqJiUlVL25dY8aMkcVi0dNPP62YmBiNHTtWr776qqZPn+5QLjo6Wr1791arVq3Utm1bFSxYUFarVf3799fmzZv1+OOPq2zZstq7d68mTJigQ4cOaeTIkZKST0r79u2r0NBQDRo0SN7e3jp8+LC2bNmSrfbMnDlTQ4YMUaVKlfTyyy/r3Llz+uWXX7RlyxbNnj1bBQoUSHN7/+///k8RERFq3bq1qlatqnXr1qlPnz5Oy7Zs2VI1atTQxIkTs7NrsywmJkbx8fG6cOGC5syZo71796pfv372+Xv27FFiYqIqVqzosJy3t7fKly+v3bt335R24rry5curf//+mjVrlgYMGKDg4GA9+uij6tixo0qVKuV0mYSEhEwFJEny9/eXh4fj9aSMjhNn3P23m8AFAEAGzpw54/TXb9u006dPO10uOjpa8fHxGS5L4Lp9XLt2TbNnz5a3t7ckqUCBAvroo4+0d+9ehYSE2MudOXNG7733np544gn7tDlz5ujPP//UxIkTHe77u//++/XOO+9oy5Ytqlq1qtasWaOEhASNGTNGgYGBLrUnISFBX375pUJCQvTrr7/Kx8dHkhQeHq6+fftq/PjxGjRokNO6//nnH0VERKhr16565513JEndunXTK6+8oj179mRj77nXCy+8oNWrV0uSvLy81LlzZw0YMMA+/8yZM5KkQoUKpVo2ODhYmzdvvjkNhV2BAgX0/PPP67nnntP69es1Y8YM/fLLLxo9erSqV6+uTp06qUWLFsqdO7d9mS1btqhnz56Zqn/p0qUqUaKEw7SMjhNn3P23m8AFAEAGrl69aj+hTcl28nr16lWny127dk2S0l3WVga3hw4dOjh8nrbgdPToUYfA5e3tnWqEuwULFqhs2bIqU6aMzp8/b59eq1YtSclDvVetWtV+xWnp0qXq2LFjql/ss9KenTt36ty5c3r++eftx5wkNWzYUGXKlNGKFSvSDFwrV66UlNxFMqVevXpp7ty5qcrf7BD26quv6umnn9aJEyc0e/ZsJSQkKDExMdX3Mq3vX1rfW5jPYrGoVq1aqlWrlt555x3NnTtXM2fO1BtvvKEPP/xQrVq10ssvvyw/Pz+VK1dOP//8c6bqdRaQMjpOnHH3324CFwAAGcidO3eqe2yk6//gpvw1NiXbP8zpLZveP/q49RQrVszhvS0cXbp0yWF64cKFU52sHT58WPv371ft2rWd1n3u3DlJyV3zpk+frjfffFNfffWVateuraZNm6pFixapwldG7bHdU1W6dOlU6ytTpky6V3mOHTsmDw8P3XPPPamWuxWUL1/e/rpt27bq0KGDhgwZou+//17S9e9lWt+/tL63uLny58+vJ554Qo8++qh++OEHjRo1SlOmTNETTzwhPz8/+fn5qU6dOtmuP6PjxBl3/+0mcAEAkIHg4GCnAwyk12VJSr6fwNvb214uK8vi1pTW1SbDMBzeOzuZt1qtCgkJ0ZAhQ5zWUaRIEfuyv/76q9avX68VK1Zo1apV+v333zV16lSNGzdOnp6eWW7Pnc7b21uNGzfW6NGjdfXqVeXOnTvdLr9nzpzhu3eL2L59u2bMmKHff/9dly5dUuXKldWpUyeVLVtWUnLouXjxYqbqCgwMdPh+3MjZceKMu/92E7gAAMhAuXLltH79+lQ3SW/btk2S4y+oKXl4eNi7dd1o+/btKlmyJPdv3UXuuece/fPPP6pdu7YsFku6ZT08PFS7dm3Vrl1bQ4YM0ahRo/TNN99o/fr1Wfq133YF7ODBg6murB08eDDVFbKUihcvLqvVqiNHjjhc1Tpw4ECm138zXb16VYZhKDY2Vrlz51ZISIhy5cqlnTt3qmXLlvZy8fHx2r17tx555JEcbO3d7dy5c5ozZ45mzpypf//9V/7+/nr00UfVqVMnh665krR161aX7uG60Y3HiTPu/tvNsPAAAGSgRYsWSkpK0tSpU+3T4uPjNXPmTFWuXNk+QuHx48dTPSC3efPm2rFjh3bs2GGfduDAAa1bt04tWrS4ORuAW8IjjzyiU6dOadq0aanmXb16VXFxcZKSb9i/kS3UO+vilJ6KFSuqYMGCmjJlisOyK1eu1P79+9WwYcM0l7U9GPbGUQcnTJjgtHxmh4XPitOnT2v//v1KSEiwT7N1vUzp0qVLWrRokYoWLaqCBQtKknx9fVW7dm1FREQoJibGXnbOnDmKi4vj+5cDTpw4oQEDBuihhx7S559/ruDgYH3zzTdatWqVhg4dmipsSbLfw5WZ/1Lew5XZ40Qy/283V7gAAMhA5cqV1aJFC3399dc6d+6c7r33Xs2aNUvHjh3TRx99ZC/3xhtvaMOGDQ6DB3Tt2lXTp09X37599fTTTytXrlwaP368ChYsqKeffjonNgc5pF27dpo/f77eeecd+wAZSUlJOnDggBYsWKCxY8eqUqVKGjFihDZt2qQGDRqoePHiOnfunCZNmqQiRYooPDw8S+v08vLSq6++qiFDhqh79+5q1aqVfVj44sWL68knn0xz2fLly6t169aaNGmSLl++rAcffFDr1q1z+pwvKWvDwv/vf//TpUuX7N39li9fbn++V48ePeTr6ytJ+vrrrzVr1iyHKxe9e/dW4cKFVblyZRUsWFDHjx/XzJkzdfr0aX3zzTcO63nppZf0xBNPqEePHnr88cd18uRJ/fzzz6pXr549UGa1Tci+I0eOaNeuXerTp486duyY4dUoSdm+hysrx4nZf7sJXAAAZMLnn3+ub7/9VhEREbp48aJCQ0M1atQoVa9ePd3l8ufPr4kTJ+rjjz/WDz/8IKvVqpo1a2rIkCEZDvmNO4uHh4dGjBih8ePHa86cOVq8eLHy5MmjEiVKqEePHvaBLRo3bqxjx45pxowZunDhggICAlSjRg0NHDgwWyf9HTp0UO7cuTVmzBh9+eWXyps3r5o0aaLXXnst3WdwSdLHH3+sgIAARUZGaunSpapZs6ZGjx6tBg0aZGsf2IwbN07Hjh2zv1+0aJEWLVokKXlgg/S2s2PHjpo3b57Gjx+vy5cvq0CBAqpcubK++uorh+H2JemBBx7Qzz//rC+//FKffPKJ8uXLp06dOunll192a5uQOZUrV9ayZcvSHXnTXbJynDjjzr/dFuNuu6sSAAAAAG4S7uECAAAAAJMQuAAAAADAJAQuAAAAADAJgQsAAAAATELgAgAAAACTELgAAAAAwCQELgAAAAAwCYELAAAAAExC4AIAAAAAkxC4AAAAAMAkBC4AAAAAMAmBCwAAAABMQuACAAAAAJMQuAAAAADAJAQuAAAAADBJrpxuAAAAAABkx4EDB7R27Vpt3rxZGzdu1MmTJxUfHy9vb28VKVJE1atXV3h4uGrXrq0yZcrkSBsthmEYObJmAAAAAMii+Ph4zZ49W8OGDdPq1aslSV5eXkpISEhVNuX0evXqaeDAgWrfvr28vb1vWnsJXAAAAABuCxEREerTp49OnTolT09PJSUlZXpZW/nChQtr9OjRatu2rYktvY57uAAAAADc0s6fP6/u3burXbt2OnPmjCRlKWylLH/mzBm1a9dO3bt31/nz593e1htxhQsAAADALWv//v1q2LChTpw4keWQlR5PT08VLVpUK1asUNmyZd1W740IXAAAAABuSfv27VOdOnV04cIFJSYmur3+XLlyKSAgQGvXrjUtdBG4AAAAANxyzp8/r8qVK+vkyZOmhC2bXLlyqWjRovrrr78UGBjo9vq5hwsAAADALWfQoEE6ceKEqWFLkhITE3X8+HENGjTIlPq5wgUAAADglhIREaF27drd9PXOmTPH7aMXErgAAACgPXv2aOPGjbp8+bJ8fX1Vo0YNhYSE5HSzcBeKj4/XPffcozNnzshqtd609Xp4eCg4OFhHjx6Vl5eX2+rN5baaAAAAcFsxDEOzZ8/Wd999p5UrV6aa37BhQw0aNEjt27eXxWLJgRbibjR79mydOnXqpq/XarXq1KlTmj17th577DG31csVLgAAgLtQQkKCnnnmGU2cODHNB8japvfo0UM//fSTW3/1B9JSv359rV271q1DwGeWp6en6tSpoz/++MNtdRK4AAAA7jKGYahXr1763//+p8ycClosFnXv3l0TJkzgShdMdeDAAVOfiZVZ+/fvV5kyZdxSF6MUAgAA3GVmz56tiRMnZipsSckBbeLEiZo9e7a5DcNdb+3atTndBEnSunXr3FYXgQsAAOAu8/3338vT0zNLy3h6emrYsGEmtQhItnnz5hzvuurl5aXNmze7rT4CFwAAwF1kz549WrFiRZbvj0lKStLy5cu1d+9ek1oGSBs3blRCQkKOtiEhIUEbN250W30ELgAA3Ix7XHArc/VEcsOGDW5qCZDayZMnc7oJktzbDgIXAABuZAtbhC7cqi5fvuzS8Xn58mU3tgZwFB8fn9NNkCRdu3bNbXURuAAAcCPbIAQMAoxbla+vr0vHp6+vrxtbAzjy9vbO6SZIknx8fNxWF4ELAAA3I2zhVlajRo0cXR5IT5EiRXK6CZLc2w4CFwAAwF0kJCREDRs2zNYohY0aNVJISIhJLQOk6tWr3xKjFFavXt1t9RG4AAAA7jKDBg3K1iiFAwcONKlFQLLw8PBbYpTC8PBwt9VH4AIAALjLtG/fXj169Mj04BkWi0U9e/ZU+/btzW0Y7nq1a9fO6SZIkmrVquW2ughcAAAAdxmLxaKffvpJ3bt3l6Q0uxfapvfo0UNjx45l9E2YrkyZMqpXr16Wu7y6i6enp+rXr68yZcq4rU4CFwAAwF3Iy8tLEyZM0MyZM1W/fn2nZR566CHNnDlT48ePz/H7anD3GDhwYJa7vLqLGV1nLQZDKQEAANz19u7dqw0bNujy5cvy9fVVjRo1GCADOSI+Pl733HOPzpw5I6vVetPW6+HhoeDgYB09etStPzAQuAAAAADcUiIiItSuXbscWW+bNm3cWiddCgEAAADcUtq2batu3brdtHu5PD091a1bN7eHLYkrXAAAAABuQefPn1flypV18uRJJSYmmrYeT09PFStWTH/99ZcCAwPdXj9XuAAAAADccgIDA7VixQoFBAQoV65cpqzD09NTgYGBWr58uSlhSyJwAQAAALhFlS1bVmvXrlXRokXd3r3QdmVr7dq1Klu2rFvrTonABQAAAOCWVbZsWf3111964oknJCWPJugK2/JPPPGE/vrrL1PDlsQ9XAAAAABuExEREerTp49OnTolT0/PLD2vy1a+cOHCGj16tNq2bWtiS6/jChcAAACA20Lbtm119OhRTZs2TXXq1LFPT+u5WSmn16lTR9OmTdPRo0dvWtiSuMIFAAAA4DZ14MABrVu3Tps3b9bGjRt18uRJXbt2TT4+PipSpIiqV6+u8PBw1apVS2XKlMmRNhK4AAAAAMAkdCkEAAAAAJMQuAAAAADAJAQuAAAAADAJgQsAAAAATELgAgAAAACTELgAAAAAwCQELgAAAAAwCYELAAAAAExC4AIAAAAAkxC4AAAAAMAkBC4AAAAAMAmBCwAAAABMQuACAAAAAJMQuAAAAADAJAQuAAAAADAJgQsAAAAATELgAgAAAACTELgAAAAAwCQELgAAAAAwCYELAAAAAExC4AIAAAAAkxC4AAAAAMAkBC4AAAAAMAmBCwAAAABMQuACAAAAAJPkyukGAAAAIHPWr1+vnj172t8/8sgj+vbbb9NdZvDgwZo1a5Ykac+ePQ7zhg0bpuHDh2epDSNGjFCTJk3SbN/s2bO1bds2nTx5UlevXlXu3LlVqFAhlSpVSmFhYapTp47CwsLk4cHv/rg7ELgAAABuUwsWLNCePXsUGhqao+2IjY3V66+/riVLljidd/DgQR08eFDLly/Xd999pzFjxuihhx7KgZYCNx+BCwAA4DZlGEa2rlI58/HHH6tSpUoZlitWrFiqaYMGDdLq1aslSffee68ee+wxVapUSX5+foqLi9Phw4e1ZcsWLVu2TOfOnXO5rcDthMAFAABwGwoICNCFCxe0ePFi7dq1SxUqVHCpvhIlSigkJCTLy61YscIeturVq6cffvhB3t7eDmXCw8PVoUMHJSUlacmSJSpatKhLbQVuJ3SeBQAAuA316NHDHmy+//77HGvH0qVL7a8HDx6cKmyl5OnpqebNm+v++++/GU0DbgkELgAAgNtQ0aJF1blzZ0nS8uXLtX379hxpx/Hjx+2v77333hxpA3ArI3ABAADcpvr06aPcuXNLkr777rscaYOXl5f99f79+3OkDcCtjMAFAABwmypUqJC6dOkiSVq9erU2bdp009vwwAMP2F+///77On/+/E1vA3ArY9AMAACA21jv3r01depUxcXF6fvvv9cvv/ySrXqioqIUEBCQbhkvLy+VLl3aYVqnTp30008/6cqVK9qyZYsaNWqkBg0aqEaNGgoLC1O5cuXSva8LuNMRuAAAAG5jBQsWVLdu3TRmzBitX79e69atU61atbJcz9ChQzMsU7x4cS1btsxhWtGiRfXNN9/o5ZdfVlxcnK5evaqFCxdq4cKFkiRvb2+FhYWpadOmat++vfz9/bPcNuB2RpdCAACA29wzzzyjfPnyScqZe7kaNWqk+fPnq0ePHqmuksXHx2vTpk365JNP1LRpU82ePfumtw/ISVzhAgAAuM0FBASoV69eGjlypLZs2aJVq1apfv36Warjl19+Uc2aNbPdhiJFiujNN9/U0KFDtXv3bv3111/6+++/tXnzZh06dEiSdOnSJb3xxhtKSkpSx44ds70u4HbCFS4AAIA7wFNPPaUCBQpIkoYNG5Zj7fDw8NADDzygbt266eOPP9bChQs1Y8YMhYeH28t89tlniomJybE2AjcTgQsAAOAOUKBAAT355JOSpG3btmn58uU526AUKlasqLFjx9qf03Xx4kWtXbs2h1sF3BwELgAAgDtEr1697INS5ORVLmfy5s2rVq1a2d8fPnw4B1sD3DwELgAAgDtE/vz59cwzz0iS/v77by1evDiHW+SoUKFC9tcWiyUHWwLcPAQuAACAO0j37t1VsGBBSdL3338vwzBMXV9W6t+5c6f9dcmSJc1oDnDLIXABAADcQfLmzavevXtLkvbu3auVK1eaur533nlHo0aNUnR0dLrl1qxZYx8SPm/evKpTp46p7QJuFQwLDwAAcIfp0qWLfvrpJ505c0YXLlzI1DJRUVGpnqHlTEBAgIKDg+3vo6OjNXXqVA0fPlwNGjRQ9erVFRISIn9/fyUmJurIkSNatmyZ5s+fL6vVKkl64YUXlD9//uxtHHCbIXABAADcYXLnzq1+/frpgw8+yPQyQ4cOzVS5nj176v/+7//s7233ZSUkJGjJkiVasmRJmsv6+Pho0KBB9tEUgbsBgQsAAOAO9Pjjj2vs2LE6ceKEqet588039fTTT2vVqlXatGmT/v33Xx0/flyxsbHKlSuXChQooPvuu081a9ZUu3btVLRoUVPbA9xqLIbZd1ICAAAAwF2KQTMAAAAAwCQELgAAAAAwCYELAAAAAExC4AIAAAAAkxC4AAAAAMAkBC4AAAAAMAmBCwAAAABMQuACAAAAAJMQuAAAAADAJLlyugEAANxuTp8+rV9++UXbtm3Tzp07FRcXp19++UU1a9bM1PLDhg3T8OHDU0339vbWjh073N1cl7m6vZJ06tQpffzxx1qzZo2sVqtq1qypoUOHqmTJkia2PHsuXbqkL774QosXL9bVq1dVqVIlDR48WA888ECGyw4ePFizZs1KNb106dJasGCBGc3NlPj4eH333XeaM2eOLl26pNDQUL344ouqW7duhsveTp+dTXa393b7btrExsbqp59+0rZt27Rjxw5dvHhRn3zyiTp06JCp5V055m82V7Z15syZGjJkiNN5q1evVnBwsLubK4nABQBAlh08eFBjxoxRqVKlFBoaqq1bt2arnnfffVd58+a1v/f09HRXE93K1e2NjY1Vz549dfnyZfXt21deXl4aP368unfvrtmzZysgIMCklmed1WpVnz59tGfPHj3zzDMKCAjQpEmT1KNHD82cOVOlSpXKsA5vb299+OGHDtN8fX1NanHmDB48WAsXLlTPnj1VqlQpzZo1S3369NGECRNUrVq1NJe7nT67lLK7vTa3y3fT5sKFCxoxYoSKFSum0NBQbdiwIdPLuuOYv5lc2VabQYMGqUSJEg7TChQo4K4mpmYAAIAsuXz5snHhwgXDMAxj/vz5RkhIiLFu3bpML//9998bISEhxrlz50xqoXu5ur2jR482QkJCjG3bttmn7du3zyhfvrzx1Vdfubu5Lpk3b54REhJizJ8/3z7t3LlzRrVq1YyXX345w+XfeOMNo0qVKmY2Mcu2bdtmhISEGGPHjrVPu3r1qtGkSROjc+fO6S57O312Nq5s7+323bS5du2acfr0acMwDGP79u1GSEiIMWPGjEwt6+oxf7O5sq0zZswwQkJCjO3bt5vZxFS4hwsAgCzKnz+//P393VJXTEyMDMNwS11mcXV7Fy5cqEqVKiksLMw+rWzZsqpdu7bmz5/vhha6z8KFCxUUFKRmzZrZpwUGBuqRRx7R0qVLFR8fn6l6kpKSFBMTY1Yzs2TBggXy9PRU586d7dN8fHzUqVMnbd26VSdOnEhz2dvps7NxZXtTuh2+mzbe3t7Z7g7nrmP+ZnFlW1OKiYlRUlKSG1qUMQIXAAA55OGHH1Z4eLiqVq2qV199VWfPns3pJrmd1WrVnj17VLFixVTzKlWqpCNHjtwywUSSdu/erQoVKsjDw/EUqVKlSrpy5YoOHjyYYR1XrlxReHi4wsPDVaNGDb333nuKjY01q8kZ2r17t0qVKqX8+fM7TLeFqN27dztd7nb77Gyyu70p3Q3fTRt3HPO3m549eyo8PFyVK1dWv379dOjQIVPXxz1cAADcZAUKFFD37t1VpUoVeXt7a9OmTZo0aZJ27NihGTNmpDpRvJ1FR0crPj7e6S/StmmnT5++Zbb5zJkzTu/xKVSokKTktoaGhqa5fHBwsJ599llVqFBBhmFo1apVmjRpkv755x9NnDhRuXLd/FOvM2fOZLj/nbndPjub7G6vdHd9N21cPeZvJ7lz51aHDh1Us2ZN5c+fXzt37tT48eP1xBNPaNasWSpatKgp6yVwAQDualarVQkJCZkq6+3tLYvF4vI6e/Xq5fC+efPmCgsL06uvvqpJkyapT58+Lq8jLTd7e69du2av60Y+Pj4OZdwtO9t69epVp221Tcuora+88orD+1atWqlUqVL65ptvtHDhQrVq1SqTrXeftLbJtv+vXr3qdLmc/Oxckd3tlXL2u5lTXD3mbyctW7ZUy5Yt7e+bNGmievXqqXv37vrhhx/0/vvvm7JeAhcA4K62ceNG9ezZM1Nlf//9d5UtW9aUdrRp00afffaZ/vzzT1NP6m729tpOcp3dB2I7kbOVcbfsbGvu3LmdttU2LTttffLJJ/Xdd9/pzz//zJHAldY22fZ/7ty5nS6Xk5+dK7K7vWm5Wd/NnGLGMX87qVatmipXrqy1a9eatg4CFwDgrlamTBl98sknmSpr62JjliJFiujixYumruNmb6+/v7+8vb115syZVPNs08zar9nZ1uDgYKdttXVDy05bc+fOLX9/f9M/27QEBwfr1KlTqaZntP9z8rNzRXa3Nz0347uZU8w45m83RYoUMfVeNQIXAOCuFhwcnOmHg5rJMAwdO3ZMFSpUMHU9N3t7PTw8FBISop07d6aat337dpUsWdK0+2Kys63lypXT5s2bZbVaHQYR2L59u/LkyaPSpUtnuR0xMTG6cOGCAgMDs7ysO5QrV07r169XTEyMw77etm2bJKl8+fJOl8vJz84V2d3etNys72ZOMeOYv90cPXrU1GfKMUohAAAmOn78uPbv3+8w7fz586nKTZo0SefPn1f9+vVvVtNM4Wx7mzdvrh07dmjHjh32aQcOHNC6devUokWLm93EdLVo0UJnz57VokWL7NPOnz+vBQsWqFGjRg73uhw5ckRHjhyxv7927ZrTUftGjhwpwzBy7LNt0aKFkpKSNHXqVPu0+Ph4zZw5U5UrV7YPFHC7f3Y2rmzvnfzdlJKvWu3fv9/h3sasHPO3E2fb6uzzXblypf7++29TP1+Lcbs8YAAAgFvIyJEjJUn79u3TvHnz1LFjR5UoUUKSNGDAAHu5Hj16aMOGDdqzZ499WuXKldWyZUuFhITI29tbW7Zs0bx581SuXDlNnjxZefLkubkbkwmubG9MTIweffRRxcbG6umnn1auXLk0fvx4JSUlac6cOTl25ceZpKQkde3aVXv37tUzzzyjgIAATZ48WcePH9dvv/2mMmXK2Ms2btxYkrRs2TJJUlRUlB599FG1atXKXm716tVauXKl6tevr9GjR6caevtmeeGFF7RkyRL16tVL9957r2bNmqUdO3Zo/Pjxql69uqTb/7NLKbvbezt+N23+97//6dKlSzp9+rQmT56sZs2a2a/m9ejRQ76+vho8eLBmzZqlpUuX2r+/WTnmbxXZ3VZbuYoVK8rX11e7du3SjBkzFBwcrN9++01BQUGmtJfABQBANqQ3THLKEzhnJ3Vvvvmm/QGs8fHxKlasmJo1a6Z+/frdkl20JNe2V5JOnjypjz/+WGvWrJHValXNmjU1ZMgQ3Xvvvaa1ObsuXryozz//XEuWLNG1a9dUqVIlvf7666pUqZJDuRsD16VLl/TBBx9o27ZtOn36tJKSknTvvfeqTZs2evrpp+Xl5XXTt8Xm2rVr+vbbbxUZGamLFy8qNDRUL7zwgsOv+nfCZ2eT3e29Hb+bNo0bN9axY8eczrOFDmchRMr8MX+ryO62fvPNN1q5cqWioqJ09epVBQcHq0GDBnr++edNC1sSgQsAAAAATMM9XAAAAABgEgIXAAAAAJiEwAUAAAAAJiFwAQAAAIBJCFwAAAAAYBICFwAAAACYhMAFAAAAACYhcAEA4KL4+HgNGzZM8fHxOd2Um4LtvXPdTdsqsb13ultlewlcAAC4KD4+XsOHD8/xf9RvFrb3znU3bavE9t7pbpXtJXABAAAAgEkIXAAAAABgEgIXAABukC9fvpxuwk3F9t657qZtldjeO92tsL0WwzCMnG4EAAC3istnT8g3qGhONwMAcIcgcAEAcINvOtyna7GX5GGRPCzJ0zw8Ury2SB4WI8Xr5OkWS3I5KbkLiadD+dSvHcpbDKdlbmxD8jSLZJ9mSTHfIst/bzwsye+vtz35tcVD18voehnLDfXY2+hhcVivxUP2emzdZJLX6/nfdE/7aw8PT1k8U0+3WDwdyl+fn1yjxdNxvsWS67/XHo51pyzj6aQei6csHrn+a7Cn9F/55Mb/N93DM3me/bVtusf16RZPe3mLxfP6B5FiukP9N9YpSUq5/lwObTEsKepL8dpQyumW69MdyifXkzzNVqfF/tqwpNwOjxTl067TMJKnG4Yh21liytfWlNOtxn/zJdsJZbrLWZ2XsTopb1jlWI+tfqshq229VkNWw3A+PeVr2RuRdhl7PXKYfr3t/722GvaGWa2O063219Y0Xqcon5RGGcMqI8l5PdeXNRzKO9TjZFkjyZBhtV5fNun6a9v868tdn28kGTJSlLW3K2Xbk1K0N2U9KdaZsn4jybGNhpF0fbv/O0CSp9teJ9lfG1arrNbk8oZhldVWv2GV1VbGVl86dTiuJynV67z5c2tx1AS5Sy631QQAwB3iWuwlxcddznLg8nAIUJI1owDlQuCynR8bHhb9d34sI0XgMizJ75OnS0aKwOVhT1OyV27xuB7ilOK1xcNyPVlZrocv3bBeh7Dx32vDw1PytE3P5bSMUgYv2zTrDfNThqO06jDSmp4iENnKWDyl/06sZKQIR4anZHEy3cNTkvX6svZgY5U9Zlis18vIKlns8eC/+ddP0pMn2157Xi+Tsnyq1/YPIcU0i+N7iyVF2ZTrT+t1yrpcdz0cuet1WkEsG4HLhTJS6mBlZCZwpRGaUgaVtAJaWoErM8s6Xe8Nwcl54Eo9/8bpDoErxWvHwGVNY50ZBK40ApLj6xsDl7PwlbX6nAUuW13uwj1cAAAAAGASAhcAAAAAmITABQAAAAAmIXABAAAAgEkIXAAAAABgEgIXAAAAAJiEwAUAAAAAJiFwAQAAAIBJCFwAAAAAYBICFwAAAACYhMAFAAAAACYhcAEAAACASQhcAAAAAGASAhcAAAAAmITABQAAAAAmIXABAAAAgEkIXAAAAABgklw53QAAAG41PvkKSJI8LMn/SZKHR4rXFsnDYqQqY7Ekl5OSf9H0dCif+rVDeYvhtMyNbUieZpHs0ywp5ltk+e+NhyX5/fW2J7+2eOh6GV0vY7mhHnsbPSwO67V46Ho9ul7G4uH532tPx9eeTqZbHMskV+55fQM9PZPf26ZbcqX4EFKWT/Ha00k9Fk/JI1eK157XG2+b7uGZPM/ZumzTU9WTQf031pm8USnqy5XitYeu71RPx9dKOd3ipPwNr+2/o1uU4tNJ57V7WTKo0pLOap1OtljsW2RIMizXXyvFdNuEVNOdvJblejuNG1/b6rFYZPnvjfHf/y0ehgzrf6+thmQkv7Zar0+3Wg1Z7a8lq0eK1/bpluvlPSwO0+2vDYuMJCfTUy6b5JGivDVFGausSSle/zfdSDJkWK3/LWvImnT9tW3+9eWuzzeSDBkpytrblZRinUnWFNNT1JNinSnrN5Ic22gYSfbtMAxrium210n214bVKqs1ubxhWGW11W9YZbWVsdWXTh2O60lK9Tpv/txyJ4thGEbGxQAAAAAAWUWXQgAAAAAwCYELAAAAAExC4AIAAAAAkxC4AAAAAMAkBC4AAAAAMAmBCwAAAABMQuACAAAAAJMQuAAAAADAJAQuAAAAADAJgQsAAAAATELgAgAAAACTELgAAAAAwCQELgAAAAAwCYELAAAAAExC4AIAAAAAkxC4AAAAAMAkBC4AAAAAMAmBCwAAAABMQuACAAAAAJMQuAAAAADAJAQuAAAAADBJrpxuAAAAt5pz5y7LMLK3rMUiFSzo61IdSBv711zsX3Oxf83njn1sq8NdCFwAANzAMOTyyZA76kDa2L/mYv+ai/1rvltpH9OlEAAAAABMQuACAAAAAJMQuAAAAADAJAQuAAAAADAJgQsAAAAATELgAgAAAACTELgAAAAAwCQELgAAAAAwCYELAAAAAExC4AIAAAAAkxC4AAAAAMAkBC4AAAAAMAmBCwAAAABMQuACAAAAAJMQuAAAAADAJAQuAAAAADAJgQsAAAAATELgAgAAAACT5MrpBgAAcKcwDEPRZy/pWkyc4q4kyK9gAVkslpxu1h3DMAxdvhCnpCuJir1yTfn987J/3cgwDF08d1nxsVcUdyVBBQJ92b9ulHz8XpH1apJi4q4pv38e9q+bGYahS+djlRgXr9gr8fINyHdL7GMCFwAALkhKTNKfCzZr/uQV+nvDXsVcjLXPy++XTw/UCFHLro1Uu3lVeebyzMGW3p6SEq3a+sderZqzXf9ui1Lc5av2eXl9c+v+yiX0ULvKqvLQ/fLMRcedrEpKTNK6xdu0eOpq7d68XzEX4+zz8vvlVfnwsmr2RD3VbFKZ4zcbkhKt2rb6gNZE/q39O44r7vI1+7y8vj4qW6mY6rV5QGH1ynD8ZlNSYpI2r/hHK2dt0d6/jij20vW/EfkK5FZIlXvUsEO4qjYIzbFj2GIYhpEjawYA4BZ19uxlZeZfx7/W7NLnA3/Qqaiz8vD0kDXJmqqMbXrhEkF6Y3h/Va5TwYQW35l2bzqsse9G6tyJS7J4WGRYU38otukFixZQ7/faqFz4vTnQ0tvT9rV79M3LP+v0sXPy8LTImpR6/9qmFypeUC9/85Qq1QrNgZbenvZsOaqfP1ik8ycvZ3j8Bhbx1VNvNVdo1RI50NLb166NBzXqzZk6ezxaHh4WWZ3sY9v0oGL+6v9hB5WvXjrDei0WKSjI123tJHABAHCDjAKXYRia+OUMTfhiRpr/yN/Iw8NDVqtVvV7rqB6vdrwlurncqgzD0JwxqzX7x1VpnqjeyFaufd/6ate7Hvs3HYZhaPK3kfr1m8gsHL/J5bq91EZdXmzD/k2HYRiaO269Iseuy/Lx2+bZWmr9dE32bwYMw9DMUcs1Y+TyTO9j2zHccUAjdejXKN197O7ARZdCk/366696//33FRYWpunTpzstExqa/GvRG2+8oaefftph3syZMzVkyBD99ttvCggI0MMPP5yp9S5dulTHjh1Tz5499d1336lFixYO848ePaqff/5Za9as0cmTJyVJxYsXV82aNdW5c2eVK1fOXnbYsGEaPnx4mutavXq1goODFRUVZW/f999/r+bNmzuUs9Wzdu1aBQYGplmfbZttvL29VaxYMdWtW1cDBgxQUFCQJGn9+vXq2bOnvZyHh4f8/f1VvXp1vfDCCypbtqzT+pcvX67//e9/2rlzp+Li4lSsWDE1btxYffr0UUBAgNNl1q9fr4kTJ2rr1q26ePGifH19VblyZXXo0EHNmjWTJIftd+aVV15Rnz59JElWq1URERH69ddfdfjwYSUkJKhQoUKqXLmyunbtqipVqtiXi4qK0ogRI7Rx40adOnVKBQoUUKlSpVSzZk0NGjTIXq5Hjx66cOGC5s6da5/WuHFjHTt2TN27d9dbb72VapvccXxkZOXKlerTp4+Cg4P1xx9/yMMjdZeJrLbT2THi5+en0NBQNWjQQB06dFD+/PkzbNuNx1CuXLlUpEgRhYeHa+DAgSpZsqSk1J+txWJRgQIFFBYWpueee04PPvigQ72Z/c5I0vnz5zVy5EitXr1ax48fV758+ez7esCAAcqXL58kafDgwZo1a5bT+ry9vbVjx45U2zRjxgxVrFjRoezgwYO1cOFCbd26NdV+TEvx4sW1bNky+/tOnTppx44deuedd9S1a9dU683Inj17HP62VapUyWH+5s2bNW7cOG3dulWXLl1SoUKFVK9ePfXr10/FihVzKGvb1wULFtTSpUuVJ08eh/mNGzfW/fffrx9//DFTbcssW9iSlKmT1eRyyVe/bMv1fK2TW9t0J7GFLUmZOpFKWc62XPs+9c1p3B3AFrakrBy/yeVsy3V9qa05jbsD2MKWlPXj17Zcm2dqmdO4O4QtbEmZ38e2Y9i2XMf+jc1pnBMELpNFRkaqePHi2r59uw4fPqx77027q8NPP/2kLl26pDphsAkMDNTnn3/uMO3nn3/WyZMnU500BQYG6tixY07rWb58uV566SV5enqqTZs2KleunDw8PHTgwAEtWrRIkydP1tKlS1W8eHGH5d59913lzZs3VX0FChRINW3EiBFq1qyZS7/QDBo0SCVKlFB8fLw2b96syZMna+XKlZo7d67DPurRo4cqVaqkxMRE7dmzR1OmTNH69es1d+5c+0mtzWeffaZx48apXLlyevbZZ+Xv76+///5b//vf/zRv3jyNHz9eZcqUcVjm+++/14gRI1SqVCl17txZxYoVU3R0tFauXKmBAwfqyy+/VJs2bezlW7durYceeijV9lSocL0b0Ycffqhff/1VDz/8sNq0aSNPT08dPHhQq1atUsmSJe2B6/Dhw+rUqZN8fHzUsWNHlShRQqdPn9auXbs0ZswYh8CVnmnTpqlPnz4qXLhwhmWze3ykJSIiQsWLF9exY8e0bt061alTxy3tlK4fI4mJiTp79qw2bNigjz/+WOPHj9fIkSMzHQxTHkO7du3S1KlTtXLlSkVERDi0xfbZWq1WHTp0SJMmTVLPnj3122+/2X84SSmj70x0dLQ6duyomJgYdezYUWXKlFF0dLT27NmjyZMnq0uXLvbAJSUHqw8//DBVfZ6ezvukDx8+XKNGjUpzu6tXr57qb8qbb76psLAwPf744/ZpKdtw6NAh7dixQ8WLF1dkZKQ9cJUtWzZVXV9//bXy5s2rfv36pdmGG02cOFEfffSRSpYsqe7duys4OFgHDhzQ9OnT9fvvv2v06NGqWrVqquXOnTunyZMnp/rRygx/rdllD03ZNeGLGapctwLdC53YvemwPTRl1+wfV6lc+D10L3Ri+9o99tCUXb9+E6lKtUPpXujEni1H7aEpuyLHrlPIgyXoXpiGXRsP2kNTds0YuVwVqpXOVPdCdyBwmejo0aPaunWrhg8frrfffluRkZF6/vnnnZYtX768du/erSlTpuipp55yWiZv3rxq166dw7Tff/9dly5dSjU9LUeOHNHLL7+sYsWKafz48SpUqJDD/FdffVWTJk1yehWiefPm6V6ZunFbFi9ebL/6kx0PPfSQ/Zfvxx57TP7+/vr555+1dOlStW7d2l6uWrVqDldoSpcurXfffVezZ89W79697dPnzp2rcePGqWXLlvryyy/tJ6mPPfaYOnTooJ49e+qFF17QrFmzlCtX8ldjwYIFGjFihJo3b66vvvpKXl5e9vqeffZZrVq1SomJiQ7trlChQrqfx9mzZzVp0iQ9/vjj+uCDDxzmGYah8+fP29+PHz9ecXFxmj17dqqAc+7cufR34H/uv/9+HTx4UGPGjNGbb76ZbllXjg9n4uLitGzZMr388suaOXOmIiMj0wxcWWmnTcpjRJL69u2rtWvXql+/fhowYIB+//135c6dO8N6Uh5DHTt2VKlSpfThhx9q9uzZ6tu3r73cjZ9teHi4evfurcmTJ+vdd99NVW9G35nffvtNx48f1+TJk1OFiJiYGIfjTUq+ApfZ73r58uW1fPly/f3333rggQeclilZsqT9Kp7Nu+++q5IlS6a5noiICBUsWFCDBw/WoEGDFBUVpRIlSigoKCjVMmPGjFFAQECm27x582Z9/PHHCg8P19ixYx1+WOnSpYu6dOmiQYMGad68efLz80u1vT/99JO6du2aqc88u5ISk/T5wB8y3Q0rLR4eHvrs+R80ccO3DESQQlKiVWPfjcx0F6G0WDwsGvNOpD6fPYCBCFJISkzSNy//7Ibj16KvX/pZY1d9xPGbQlKiVT9/sMgtx+/PHyzUR9Of4vi9QVJikka9OdPlfezhYdEPb87UN/NevCnHMJ+iiSIjI+Xn56cGDRqoefPmioxM+xelqlWrqlatWho7dqyuXr2aZjlXjR07VnFxcfrkk09SnUxLySd0PXv2VNGiRbO9jpYtW6pUqVIaMWKE3HmLYK1ayZfXo6Ki0i1XrVo1ScmBN6Xhw4fLz89PH3zwQaorAmFhYXr22We1d+9eLVy40D79u+++k7+/vz7++ONUJ7+SVL9+fTVq1ChL2xEVFSXDMJz+Sm+xWFSwYEH7+yNHjqhw4cJOryalLJee4sWLq127dpo2bZpOnTqVbll3Hx+LFy/W1atX1aJFC7Vs2VKLFi3StWvXnJbNSjvTU7t2bQ0YMEDHjh1TREREtupw9VjLrCNHjsjT09OhC6lN/vz55ePjk616Jal79+7y8/PTsGHDsl2HM3PnzlXz5s3VsGFD+fr6OnRhddXIkSNlsVj06aefprrSf8899+i1117TmTNnNHXq1FTLPvfcczp79qwmT57stvY48+eCzToVddalk1UpuXvhqaizWrtwi5tadmfY+sdenTtxyaUTKSm5i9G5E5f01x//uqlld4Z1i7fp9LFzbjh+DZ0+dk7rl2xzU8vuDNtWH9D5k5fdcvyeP3lZ21cfcFPL7hybV/yjs8ejXd7HVquhs8ejtWXlHje1LH0ELhNFRkaqadOm8vb2VuvWrXXo0CFt3749zfIDBw40/YRh+fLluvfee1W5cuUsL3vx4kWdP3/e4b9Lly6lKufp6an+/fvrn3/+0eLFi93RbEnJJ6eS5O/vn245W1fKlF0dDx06pIMHD+rhhx9O896e9u3bS0reR7ZlDhw4kO4yzly5ciXVfjp//rz9SpjtHpQFCxboypUr6dZVvHhxnTx5UmvXrs30+p3p37+/kpKSNGbMmHTLuXJ8OBMZGamaNWsqODhYrVq1UmxsrMO9QNltZ0ZsV1RWr16dreVdOdZSyug7U7x4cSUlJWnOnDmZbpuzYysmJiZVufz586tXr172q1zusG3bNh0+fFitWrWSt7e3mjZtmu4PSVlx5coVrVu3TuHh4amuutm0bNlS3t7e9u9oSuHh4TflR6v5k1fIw9M9/3R6eHpo/uQVbqnrTrFqznZZPNwzWICHh0WrIggEKS2euloenm7av54eWjx1jVvqulOsifzbrcfvmrnu+dt9J1k5a4s83LiPV8y6OT96EbhMsnPnTh04cECtWrWSlHwyUKRIkXRPTqpVq6aaNWvqp59+MuWEISYmRqdPn9b999+fat6lS5ccTuCcrb9FixaqXbu2w38p7/NIqU2bNi5f5YqJidH58+d18uRJ/f777xoxYoRy586d6opSbGyszp8/r9OnT2vVqlX6+OOPZbFYHLoz7tu3T5Kc3mdjU6JECeXPn18HDiT/orR//35JUkhISJbaPWzYsFT7qXbt2tq5c6ckqVChQmrfvr1WrFihBg0a6Pnnn9e4cePs60upR48e8vLy0pNPPqn27dvro48+0pIlSzIMajcqWbKk2rZtq2nTpun06dNOy7h6fNzo3LlzWrt2rf07UKxYMVWpUiXd70Bm2pkZRYoUka+vb6avPKU8hlauXKmPPvoo1TEkXQ/TZ8+e1aZNm+z3Tt44QIxNRt+Zjh07KjAwUIMHD9Yjjzyid955R3PnztXly5ed1hcXF+f02HrhhReclu/Zs6f8/PzSHcAjKyIiIlS0aFGFh4dLklq1aqV9+/Zp9+7dLtd9+PBhJSYmpnvfnbe3t0qXLm3/jt7o+eef19mzZzVlyhSX2+OMYRj6e8Nep0O/Z4c1yaq/N+xxa0+A25lhGPp3W5TLv1zbWK3/1cf+lZS8f3dv3u906PfssCZZtWvzfvbvfwzD0P4dx916/O7fcZz9m4JhGNr71xGXr9DaWK3J9d2Mfcw9XCaJjIxUUFCQatasKSm5q1jLli0VERGhwYMHp3mT+8CBA9W9e3dNmTJFTz75pFvbZPsV3NlN/D169NA///xjf//666/rmWeecSgzbNiwVFd60hrgw3aV64033tCSJUvUtGnTLLf3xu0vXry4vvzyy1QDKgwdOtThvW1wkbCwMPu02NjkB5GmvPnfmXz58tn3k+3/GS1zo86dO6ca9U+S7rvvPvvrTz75RGFhYZoxY4YWL16sxYsX67PPPlOtWrX0+eef27fx/vvv1+zZszVy5EitWLFCu3fv1i+//KK8efNqyJAhaQZeZwYMGKCIiAiNHj3a6T1Srh4fN5o3b16q0NK6dWt9+umnunjxYqp7cDLbzszKmzev/XPPiLNj6NNPP001et6wYcMcuujlzZtXgwcPdvp528qn950JCgrSnDlzNGLECC1ZskRTpkzRlClT5OXlpf79+2vAgAEOA8/4+Pg4HQQjrdE1fX191bNnTw0bNky7du1yGLglqxITE/X777+rffv29jbVqlVLBQsWVEREhMqXL5/tuqXMf99SfkdvVL16ddWsWVNjx47VE088kal7ueLj4xUfH+8wLX/+/HI23k/02UsODzV2h8vRsbp0/rL8g5xfJb2bXL4Q5/BQY3eIvXRVMRfjVCAga3/H70QXz112eKixO8REx+ryhRj5FXTf8Nm3q8sXrjg81NgdYi9dU+zFK/INSP3v8t3o0vlYh4cau0PsxSuKiY5TgUDHvxHuHpWfwGWCpKQkzZs3TzVr1nS4ByQsLEzjxo3T2rVrVa9ePafL3njC4E62E5m4uNR/cN9//33Fxsbq7Nmzeu2115wuX61atUwNmmHTpk0bjRw5UiNGjFCTJk2y3N63335bpUuXlqenp4KCglS6dGmngzU899xzqlatmuLi4rR48WLNmzcvVTnbtmd0Ah4bG2u/N8p2opzZk3abe++9N92R+KTkG+a7deumbt266cKFC9qyZYumTJmiP/74Qy+99JImTZpkL1u6dGl98cUXSkpK0r59+7RixQqNHTtWb731lkqUKJHhumxSXj2yDU+fkqvHx40iIiIUFham6OhoRUdHS0oe2CAhIUELFixQ586ds9XOzIqLi8v0fW62Y8jDw0MBAQEqW7asfeCUlGxh+tq1a1q3bp0mTpyopKSkNOvNzHemUKFCeu+99/Tuu+/q0KFDWr16tcaMGaPvv/9ehQoV0mOPPWYv6+npmenP26ZXr16aMGGChg0bph9++CFLy6a0Zs0anT9/XmFhYTp8+LB9es2aNTVv3jy99tprmR5MxZnMft9iY2PTDWVZ/dHqxx9/dLgCmC9fPm3ZskUFnZxAXotx78mqTZ7cudz6vJfbVdKVxIwLZUO+3D7sX0nxsVnrGZFZHL/JrFfT/rfAFfnycPzaJMbFZ1woG/Lm9jJ9HxO4TLBu3TqdOXNG8+bN07x581LNj4yMTDNwScndYnr06KEpU6akeW9Idvj6+io4OFj//pv6JmLbPTsZDRKQFbarXIMHD9bSpUuzvHxYWFiqKwzOhISE2E9CmzRpoitXruitt95SeHi4fXAH2zO59uxJ++bIY8eOKSYmxl7WNjz83r17s9z2rLA9X+3hhx9Wjx49tGHDBh07dizVQBmenp4KDQ1VaGioqlSpop49e6Y76p8z/fv3V0REhMaMGZMqBLvz+LANHS7J6UiVkZGRaQaujNqZGSdPntTly5d1zz33ZKp8ymMoPSnDdKNGjeTh4aGvvvpKNWvWzNSxmh6LxaLSpUurdOnSatiwoZo1a6aIiAiHwJUdvr6+6tWrl/0qV3bZBiB58cUXnc7fsGGDfbCR7LjnnnuUK1eudL+j8fHxOnjwYKpni6VUvXp11ahRI9M/WvXt29fpyLDnzqV+8HHclYQM68uOK1cTdfas826kd5PYK+69OmCv9+o19q84fs0WE2fS8XuF49cm9oo5gSvuakKqfWyxyOkPb9lF4DJBZGSkChYsqLfffjvVPFv3sffeey/N7i41atSwnzAMGDDArW1r2LChpk+fru3btzt0uTNL27Zt9cMPP2j48OFq3PjmPGDu1Vdf1ZIlS/TDDz/o/fffl5R8lahUqVJaunSpYmJinA6CMXv2bEmy3yNmO/ldunRphr+qu0vFihW1YcMGnTlzJt3nXNlOOLN6n9M999yjtm3baurUqU4HxnDX8REZGSkvLy99/vnnqa56bN68WRMnTtTx48dTPcQ2s+3MiG0QivR+2HCH/v37a/r06fr222/1008/ua3ekiVLqkCBAjpz5oxb6rNd5Ro+fHi2fsSxDe/fsmVLp/erffjhh4qMjHQpcOXNm1c1a9bUunXrnP7gICU/BiM+Pj7DkUEHDhxo/9EqI97e3vL29k413TCUKnD5FSyg/H753Nqt0Nc/nwoE+qZa190ov39e5fXN7dZuhfkK5FZ+v7zsX0kFAn2V3y+vW7sV5vfPJ9+A/OxfSfn98yivr49buxXmK+CjfH552L//8Q3Ip3wFcru1W2E+vzzK72/+3wgGzXCzq1evatGiRWrYsKFatGiR6r9u3bplOFKblHzCcObMGU2bNs2t7Xv22WeVJ08eDR06VGfPnk013903Dtqucu3evTvDbXaXe+65R82aNdOsWbMcTlife+45Xbx4Ue+8806qbmA7d+7U2LFjFRIS4nBFZtCgQYqOjtabb76Z6nlbUvIoeM5GTEvPmTNn7IN4pBQfH6+1a9fKw8PDfmVm06ZNSkhI/avkypUrJSWHwqzq37+/EhMTNXbs2FTz3HV8REZGKjw8XC1btkz1HXj22WclKcPhxNNrZ3rWrl2rkSNHqkSJEmrbtm2Wls2qAgUKqHPnzlq9enW2Bo7Ytm2b0y6c27dvV3R0dLY+X2dsV7mWLl2arXYuXrxYcXFx6tatm9O/a40aNdKiRYtS3QuVVf3795dhGBo8eHCqgVmOHj2qL7/8UsHBweleHZUcf7RK6zEE2WGxWPRAjRC3jlL4QI1Qlx4QfyexWCy6v3IJt47ydn/lEuzf/1gsFpUPL+vWUQorhJdl//7HYrGobKVibj1+y1Yqxv5NwWKxKKTKPW4dpTCkyj03ZR9zhcvNli1bptjY2DSv5lSpUkWBgYGKiIhQy5Yt06zHdsKwYcMGt7avVKlS+vLLL/XKK6+oRYsWatOmjcqVKyfDMBQVFaW5c+fKw8NDRYoUSbXswoULnQ6oULduXQUFBaW5Ttu9XO4YySyznnnmGc2fP18TJkzQq6++Kin5atuOHTv0yy+/aP/+/WrTpo0KFCigXbt2acaMGfL399d3333n8Lytli1bas+ePRo1apR27dql1q1bq1ixYoqOjtaqVau0du1affXVVw7r3rVrl9Nhvu+55x49+OCDOnnypB577DHVqlVLtWvXVlBQkM6dO6d58+bpn3/+Ua9evez3/YwZM0Z///23mjZtah9hcdeuXZo9e7b8/f3Vq1evLO8b29WjWbNmpZrnyvFhYxs6vFu3bk7nFy5cWBUqVFBkZGS692il106bP/74QwcOHFBSUpLOnj2r9evXa82aNSpWrJh++OEHl55jlVk9e/bUhAkTNHr0aH3zzTcO8zL6zsyZM0eRkZFq0qSJKlasKC8vL+3fv18zZsyQj4+P+vXr57BcYmJimkPIN23a1Om6UrZz/Pjx+ueff9It50xkZKT8/f314IMPOp3fuHFjTZs2TStWrHDpYefVq1fXG2+8oU8++URt27bVo48+quDgYB04cEDTp0+X1WrV6NGj0xxwJaXnn39ePXv2zHZb0vJIl4Zav3irW+qyJln1SJeGbqnrTlG/XZi2rU79g1R2WK2G6rd1zyMu7hRNO9fTxmU73FKXNcmqpp3ruqWuO0XdNg9ox58H3VKX1WqobmvnD62/mzV4tKq2/uGeWz2sVkMNH039TFQzELjcLCIiQj4+Pqpb1/kfIQ8PDzVs2FCRkZG6cOFCmqOLSeadMDRp0kSRkZEaN26c1qxZoxkzZshisahYsWJq0KCBunTp4nRo5nfffddpfb/88ku6gStXrlzq37+/fQjtm6FSpUqqUaOGJk+erL59+8rXN7kf7v/93/+pZs2amjRpkn788UdduXJFRYsWVbdu3dS7d2+nAxy89NJLqlWrliZOnKjJkyfr4sWLKlCggCpXrqyRI0fq4Ycfdig/d+5cp1dvHn30UT344IMqXbq0hg4dqpUrV2rSpEk6d+6cvL29FRISog8//FCdOnWyL9O3b1/NnTtXGzduVGRkpK5evWp/ptWAAQPSfF5RRmz3SDkb8CG7x4eNbdj39LqQNm7cWMOGDdM///yTbl3ptVOSvv/+e0mSl5eX/P39FRISoqFDh6pDhw5ZenaaKwoXLqw2bdpozpw5OnLkiMN9Yxl9Zzp37qzcuXNr3bp1WrZsmWJiYhQQEKC6deuqb9++qUYVjI+P1+uvv+60zqVLl6YbpAoUKKBevXpleYj4lMP7pzW6au3atZUnTx5FRES4FLik5NFJK1asqHHjxmnChAmKiYlRcHCwWrRooX79+qXb1TalmjVrmvKjVZ0W4SpcIkhnjrv28FgPDw8FFwtU7eY35x/728WDD4WoYNECOn/KtYfHWjwsCizsqyoPpX7Mxd2sVtPKKlS8oM6eOO/i8WtRUNFA1WxCoE2pcr0yCiziqwunY1w+fgMK5VdYvTJubN2dIbxhOQUV89e5kxdd2sceHhYFFvFT1QZpPy7InSwGA/wDAODg7NnUg2bY/LVml1559AOX1/H17LdUuU72h+q/U+3edFif9f3V5XoGj+6mcuH3uqFFd5bta/doSOcvXa7n02mvqlKtm3OyejvZs+Wovnpuhsv1vDKik0KrlnBDi+48uzYe1IdPj3O5nrfGPa3y1Z133bdY5NaRC7mHCwCALKhSt4J6vdbRpTp6vdaRsJWG8tXuVfu+9V2qo33f+oStNITVDlW3l9q4VEe3l9oQttIQWrWk2jyb/QGEJKnNs7UIW+moUL20Og5If/CkjHQc0CjNsGUGAhcAAFnU49WO9tCV2eeP2cr1eq2jerzqWmC707XrXc8eujI7CIGtXPu+9dWut7kjlN7uurzYxh66MjsAga1ct5faqMuLrgW2O13rp2vaQ1dWj982z9ZS66drmta2O0WHfo3soSurx3DHAY3UoZ9rgS2r6FIIAMAN0utSmNK2P3fps+d/0Kmos/Lw9JA1yZqqjG164RJBemN4f65sZcE/mw9rzDuROnfikjw8LE7vO7JNL1i0gHq/14YrW1mwY90eff3Szzp97FyGx2+h4gX18jdPcWUrC/ZsidLPHyzU+ZOXMzx+A4v46qm3mnNlK4t2bzyoH96cqbPHozPcx0HF/NX/ww6ZurLl7i6FBC4AAG6Q2cAlSUmJSVq7cIt+n7Rcuzbu1eXo68/p8vXPpwdqhOqRrg1Vu1lVeeZyPvAI0paUaNVff/yrP+Zs077tUQ7P4MlXILfur1xC9dtVVpX698szFx13siopMUnrl2zToimrtXvLAcWkOH7z++dThfCyavZEPdV4OIzjNxuSEq3avvqAVkf+rQM7jyv20vVHVeQr4KOylYqpbpsHFFa3DMdvNiUlJmnLyj1aMXOz9m47qtiLV+zz8vnlUUiVe9Tw0aqq2iA008cwgQsAAJNlJXClZBiGLp2/rDy5c+nK1UQVCPTlOTpuZBiGYi7GKV9uH8Vevab8fnnZv25kGIYuX4ixH7++AfnZv25kGIZiL15Rvjw+ir1yTfn88rB/3cwwDMVExylvbi/FXU1Qfv/s/Y0gcAEAYLLsBi7p+j/UrtSBtLF/zcX+NRf713zu2MeMUggAAAAAtwkCFwAAAACYhMAFAAAAACYhcAEAAACASQhcAAAAAGASAhcAAAAAmITABQAAAAAmIXABAAAAgEkIXAAAAABgEgIXAAAAAJiEwAUAAAAAJiFwAQAAAIBJCFwAAAAAYBICFwAAAACYhMAFAAAAACYhcAEAAACASQhcAAAAAGASAhcAAAAAmITABQAAAAAmIXABAAAAgEkIXAAAAABgEgIXAAAAAJiEwAUAAAAAJiFwAQAAAIBJCFwAAAAAYBICFwAAAACYhMAFAAAAACYhcAEAAACASQhcAAAAAGASAhcAAAAAmITABQAAAAAmIXABAAAAgEkIXAAAAABgEgIXAAAAAJiEwAUAAAAAJiFwAQAAAIBJCFwAAAAAYBICFwAAAACYhMAFAAAAACYhcAEAAACASQhcAAAAAGASAhcAAAAAmITABQAAAAAmIXABAAAAgEkIXAAAAABgEgIXAAAAAJiEwAUAAAAAJiFwAQAAAIBJCFwAAAAAYBICFwAAAACYhMAFAAAAACYhcAEAAACASQhcAAAAAGASAhcAAAAAmITABQAAAAAmIXABAAAAgEkIXAAAAABgEgIXAAAAAJiEwAUAAAAAJiFwAQAAAIBJCFwAAAAAYBICFwAAAACYhMAFAAAAACYhcAEAAACASQhcAAAAAGASAhcAAAAAmITABQAAAAAmIXABAAAAgEkIXAAAAABgEgIXAAAAAJiEwAUAAAAAJiFwAQAAAIBJCFwAAAAAYBICFwAAAACYhMAFAAAAACYhcAEAAACASQhcAAAAAGASAhcAAAAAmITABQAAAAAmIXABAAAAgEkIXAAAAABgEgIXAAAAAJiEwAUAAAAAJiFwAQAAAIBJCFwAAAAAYBICFwAAAACYhMAFAAAAACYhcAEAAACASQhcAAAAAGASAhcAAAAAmITABQAAAAAmIXABAAAAgEkIXAAAAABgEgIXAAAAAJiEwAUAAAAAJiFwAQAAAIBJCFwAAAAAYBICFwAAAACYhMAFAAAAACYhcAEAAACASQhcAAAAAGASAhcAAAAAmITABQAAAAAmIXABAAAAgEkIXAAAAABgEgIXAAAAAJiEwAUAAAAAJiFwAQAAAIBJCFwAAAAAYBICFwAAAACYhMAFAAAAACYhcAEAAACASQhcAAAAAGASAhcAAAAAmITABQAAAAAmIXABAAAAgEkIXAAAAABgEgIXAAAAAJiEwAUAAAAAJiFwAQAAAIBJCFwAAAAAYBICFwAAAACYhMAFAAAAACYhcAEAAACASQhcAAAAAGASAhcAAAAAmITABQAAAAAmIXABAAAAgEkIXAAAAABgEgIXAAAAAJiEwAUAAAAAJiFwAQAAAIBJCFwAAAAAYBICFwAAAACYhMAFAAAAACYhcAEAAACASQhcAAAAAGASAhcAAAAAmITABQAAAAAmIXABAAAAgEkIXAAAAABgEgIXAAAAAJiEwAUAAAAAJiFwAQAAAIBJCFwAAAAAYBICFwAAAACYhMAFAAAAACYhcAEAAACASQhcAAAAAGASAhcAAAAAmITABQAAAAAmIXABAAAAgEkIXAAAAABgEgIXAAAAAJiEwAUAAAAAJiFwAQAAAIBJCFwAAAAAYBICFwAAAACYhMAFAAAAACYhcAEAAACASQhcAAAAAGASAhcAAAAAmITABQAAAAAmIXABAAAAgEkIXAAAAABgEgIXAAAAAJiEwAUAAAAAJiFwAQAAAIBJCFwAAAAAYBICFwAAAACYhMAFAAAAACYhcAEAAACASQhcAAAAAGASAhcAAAAAmITABQAAAAAmIXABAAAAgEkIXAAAAABgEgIXAAAAAJiEwAUAAAAAJiFwAQAAAIBJCFwAAAAAYBICFwAAAACYhMAFAAAAACYhcAEAAACASQhcAAAAAGASAhcAAAAAmITABQAAAAAmIXABAAAAgElymb2CZcuWaf78+bpw4YJKlCihxx57TA888IDZqwUAAACAHOdS4Fq3bp1eeukl+fj4KCIiQgUKFHCY/+233+rHH390mPbbb7/pww8/VPv27V1ZNQAAAADc8lzqUrhy5UpduHBBlSpVShW2/vnnH/34448yDEOGYahAgQIyDEOJiYl65513FBUV5VLDAQAAAOBW51Lg2rJliywWi2rXrp1q3uTJk2UYhvz8/DRz5kytX79e06dPl5+fn+Lj4zVlyhRXVg0AAAAAtzyXAteZM2ckSffff3+qeStWrJDFYlG3bt1UoUIFSVKlSpXUvXt3GYahP//805VVAwAAAMAtz6V7uM6fPy9J8vX1dZh+5MgRnTp1ShaLRU2bNnWYV61aNUnS0aNHXVk1AAAA3MgwDCXGnFWccU4JMVZ55guSxWLJ6WYBtz2XApdhGJKkmJgYh+mbNm2SlBzEypcv7zDP399fknTlyhVXVg0AAAAXGUmJiv57gc6un6TYQxuVdCXaPs8zj7/ylaquoJrd5P9Ac1k8TR/cGrgjufTNCQoK0vHjx7V//377lStJWr16tSSpatWqqZaJi4uTJPn5+bmyagAAALjg8r41OjR5oOIvHJUsnpKR5DA/6Uq0Lv2zTJd2L5Z3QEmV6jJMvvfVzaHWArcvlwJXlSpVdOzYMU2ePFlt27ZVnjx5dPToUS1dulQWi0V16tRJtcyhQ4ckJYc1AID066+/6v3331dYWJimT5+ean5oaKj9tcViUVBQkEJCQtS3b1/VrFnToWxCQoImT56sOXPm6MCBA5KkMmXKqF27durSpYu8vLwcyjdu3FjHjh2zvw8MDFTp0qX11FNPqWnTppo5c6aGDBmS4TYUL15cy5Yts7/fvHmzxo0bp61bt+rSpUsqVKiQ6tWrp379+qlYsWKplt+0aZNGjRqlPXv2KDo6WgULFlS5cuXUqlUrtWnTxmFfdOvWTW+//bZ92vnz5zVy5EitXr1ax48fV758+VS8eHHVrFlTAwYMUL58+TJsP3A3MQxDJxZ9qRMLP5cs/93Of0PYul44eXp89DHtHdleRZu/rqLNXqWrIZAFLgWuTp06ad68edqzZ49at26tBx54QBs3btS1a9eUJ08etW7dOtUytu6GpUqVcmXVAHDHiIyMVPHixbV9+3YdPnxY9957b6oydevWVbt27WQYhqKiojR58mT16tVLP/74oxo0aCApuQdB3759tWHDBjVq1EgdOnSQxWLRqlWr9NFHH2nx4sX68ccflTdvXoe6y5cvr6eeekqSdPr0aU2dOlXPP/+83n33XdWrV0+ff/65Q/k333xTYWFhevzxx+3TUoaaiRMn6qOPPlLJkiXVvXt3BQcH68CBA5o+fbp+//13jR492qEHxPz58/XSSy+pfPny6tmzp/z8/BQVFaWNGzdq2rRpDoHrRtHR0erYsaNiYmLUsWNHlSlTRtHR0dqzZ48mT56sLl26ELiAG9jDliQZ1swt9F8523LFmr9mRtOAO5JLgat27drq2bOnfvnlFx07dkzHjx+339f12muvKTAw0KH8tWvX7Fe/qlev7sqqAeCOcPToUW3dulXDhw/X22+/rcjISD3//POpypUqVUrt2rWzv2/atKnatm2rX375xR64Pv30U23YsEFvvfWWunfvbi/btWtX+1W0zz77TO+9955D3YULF3aou3379mrWrJnGjx+vLl26qGTJkg7l3333XZUsWdJhGZvNmzfr448/Vnh4uMaOHas8efLY53Xp0kVdunTRoEGDNG/ePHvX8uHDh+u+++7T1KlT5e3t7VDfuXPn0t1/v/32m44fP67Jkyen6sYeExOT6ooecLe7vG/N9bCVTScWfi7fsnXoXpgN164m6Pzpy7oWnyBvH/4+3S1cvvtx6NChql27thYsWKCzZ88qODhY7dq1c/psrqVLlyp//vzKnz+/GjVq5OqqAeC2FxkZKT8/PzVo0EDNmzdPM3DdKDQ0VAEBAfaHyJ88eVK//fabatWq5RC2bLp166aFCxfqt99+U//+/VWkSJE06w4ODlaZMmW0Z8+eLG/PyJEjZbFY9OmnnzqELUm655579Nprr+mNN97Q1KlT1adPH0nJI9u2bt06VdiSpIIFC6a7viNHjsjT01NVqlRJNS9//vxZbr+rLpyJ0cbFu5VwJVFeeXKpRrPy8g+6+e0AnDGSEnVo8sDkboSZvbLljMVDhyYPVMWhGxhII5P2bo3S4slbtH31ARmGZLFIYfXKqGmXqgp5sERONw8mc8u3pFGjRpkKUC1btlTLli3dsUoAuCNERkaqadOm8vb2VuvWrTV58mRt375dYWFh6S538eJFXbp0yd798I8//lBSUpLat2+f5jLt27fX+vXrtWrVKj322GNplktISNDJkyfto8pm1pUrV7Ru3TqFh4enuipm07JlS7311ltavny5PXAVK1ZMa9eu1cmTJ9MNgs4UL15cSUlJmjNnjh599NEsLetO1iSrpg1boaVTtsiQ5OFhkdVqaPr3K/XwE1X1+MCG8vB06dGXgMui/16QPECGqwyr4i8cVfTfCxUQ1sr1+u5wK2Zs06Qvl8vD06L/OoLJMKQdfx7UtlUH1PXVRmrYsXLONhKm4q8/AOSQnTt36sCBA2rVKvmEJTw8XEWKFFFkZGSqsteuXdP58+d1/vx5bd++XS+88IKSkpLUokULSdK+ffskSeXKlUtzfbZ5+/fvd5iemJhor/uff/7RG2+8obNnz9rrzqzDhw8rMTEx3TZ4e3urdOnS9gE9JKl37946ceKEmjRpop49e+q7777Tpk2bZLVm/At8x44dFRgYqMGDB+uRRx7RO++8o7lz5+ry5ctZarurpg1bocWTNstqNWRYDSUlWmVYDVmthhZP2qzpw1be1PYAzpxdPyl5NEJ3sHjq3IZJ7qnrDrZ3a5QmfblckmRNMhzm2d5P+nK5/v3rWKplcefgOjAA5JDIyEgFBQXZRxq0WCxq2bKlIiIiNHjwYHl6Xj8x+u233/Tbb7/Z3/v4+Oipp55Sr169JEmxsbGSlO4AEbZ5Nz47cfXq1Q7dwD09PdWuXTu9+uqrWdoeW70ZDVKRL18+hzZ06tRJhQsX1vjx47V+/XqtX79eI0eOVMmSJfX55587fcSITVBQkObMmaMRI0ZoyZIlmjJliqZMmSIvLy/1799fAwYMMH00tQtnYrR0ypZ0yyyZslnNu1eneyFyjGEYij20Me3RCLNcYZJiDm2QYRiMWJiOxZO3yMPTkipspeThadGiyZt1f5XiN7FluJncGrhiYmIUFRWl2NjYTP0yycAZAO5WSUlJmjdvnmrWrGm/D0uSwsLCNG7cOK1du1b16tWzT3/44YfVvXt3WSwW5cuXT/fdd5/DaIO2kGMLXs6kFcoqV66sF198URaLRblz51bZsmVVoECBLG+T7Z6p9Npgm39jG+rXr6/69evrypUr+vvvv/X7779rypQp6tevn+bPn5/uvVyFChXSe++9p3fffVeHDh3S6tWrNWbMGH3//fcqVKhQut0n4+PjFR8fn2o7snL+uHHxbqV9KpXMkLRx8T9q1rVaBiWREYtFmjtjh2o3LJXTTbmtJMacdXiosTskxUUrKe6cvPLzqB9nrl1NsN+zlR5rkqHtqw4o/lqCfHIzkIarLBbpo8Gz1e+1h12qw53cErimTZumSZMmae/evfZRCjNisVi0a9cud6weAG4769at05kzZzRv3jzNmzcv1fzIyEiHwFWkSBGnzza0KVu2rCRpz549Kl++vNMytkEw7rvvPofpAQEB6dadWffcc49y5cqV7mAb8fHxOnjwoCpWrOh0fp48eVStWjVVq1ZNAQEBGj58uP74449M3Z9lsVhUunRplS5dWg0bNlSzZs0UERGRbuD68ccfNXz4cPv7fPnyacuWLSpY0DfD9dkkXEmUh4dFSda0//3z9LQoPi5BQUGZrxfOzZ2xQ5K0dsUhte5YKYdbc/uIM9If8TO7/PJZlJfj2qnzpy9nGLZsDEPK4+2lQPalyz4aPFuSNOqLpfq/T9vnaFtsXApcSUlJGjhwoJYvT+6bmtmwBQB3u8jISBUsWNDhAb42ixcv1uLFi/Xee+8pd+7cmarvoYcekqenp+bMmZPmwBmzZ89Wrly5VL9+fVeanqa8efOqZs2aWrdunY4dO6bixVN3j/n9998VHx+fqYGWbKHszJkzWW5LyZIlVaBAgQyX7du3r/0ZZCmdO5f5EyWvPLlkTSdsSVJSkiHvvF46e/bm3lt2J6rdsJTWrjik2g1LsT+zICHGhVEJ03Ex1lCchc/BmWvxCbJYlKm/JRaLdCU+gWPaDfq99rBGfbFU/V57ONv702JRln54y4hLgWvKlClatmyZpOR+9B06dNADDzwgPz8/eXgwHgcAOHP16lUtWrRILVq0cDowRaFChTR37lwtW7Ys0yO7Fi1aVB06dND06dM1adIkde3a1WH+5MmTtW7dOnXu3DnLIwFmRf/+/bV27VoNHjxYY8aMcQiMR48e1Zdffqng4GB17tzZPn3t2rVOHyWycmXyQBOlS5dOc33btm3T/fffn+phztu3b1d0dHS6939JyYN4OBuO3jAyd5IkSdWbltf071em263QIql603KZrhPpa92xks6ezXwohuSZL0ieefzd2q3QM6+/PPMW5HNIg7ePl8LqldGOPw9meA9Xpbql5e3jxb50k//7tP0t9TfCpcA1e/ZsScndU3799Vf7QywBAGlbtmyZYmNj1bhxY6fzq1SposDAQEVERGTpURpDhgzRgQMH9N5772nVqlX2K1mrV6/W0qVLVaNGDQ0ePNgt25CW6tWr64033tAnn3yitm3b6tFHH1VwcLAOHDig6dOny2q1avTo0Q7/XgwYMEAlSpRQo0aNVLJkSV25ckV//vmnli9frkqVKqV7NWzOnDmKjIxUkyZNVLFiRXl5eWn//v2aMWOGfHx81K9fP1O3V5ICgvPr4SeqavGkzWmWadIlnAEzkKMsFovylaquS/8sc8/AGRZP5S9VgwEzMtC0S1VtW3Ug3TLWJEPNuoTfpBYhJ7gUuPbv3y+LxaIBAwYQtgAgkyIiIuTj46O6des6ne/h4aGGDRsqMjJSFy5cyHS9+fLl0/jx4zVp0iRFREToiy++kGEYKlOmjIYOHaquXbvKy8v8G7KffPJJVaxYUePGjdOECRMUExOj4OBgtWjRQv369UvV1fDDDz/U0qVLNX/+fJ0+fVqGYahkyZLq16+fevfurVy50v6nqnPnzsqdO7fWrVunZcuWKSYmRgEBAapbt6769u2rChUqmL25kqTHBzaURRYtmbJZhpLv2UpKMmRRcth67PkGN6UdQHqCanbVpd2L3VOZkaSCNbpmXO4uF/JgCXV9tZH9OVwpr3TZ3nd9tREjFN7hLIYLN15VrVpVV65c0cyZM9O8SRsAgNtNdruiRJ+N0cbF/yg+LkHeeb1UvWk5rmy5mcUiBQX53lLdhW4XRlKidn5cQ/HRxyTDhXu6LB7y9i+uikM3yOLJE4Yy49+/jmnR5M3avip51EKLRQqrX0bNuoQTttzMHX8jbHW4i0vfklKlSmn37t2Kjo52U3MAALh9+QflV7Ou1QgEuCVZPHOpVJdh2juyvWsVGVaV6jKMsJUF91cprvurFFf8tQTl8fbSlfgEefswBPzdwqWRLVq2bCnDMLRixQo3NQcAAABm8b2vroo2f92lOoo2f12+9znvEo30+eT2UmAhX563dZdxKXD17NlT5cqV0+TJk7Vp0yZ3tQkAAAAmKdrs1euhy5LJU8H/yhVt/rqKNnvVpJYBdyaXApe3t7d++uknPfDAA3rqqaf0+eefa/fu3bp27Zq72gcAAAA3slgsKtb8NYUMmC1v///uH7J4plE4ebq3f3GFDJitYs1fY2RCIItcGjQj5UAZhmFk6QtosVi0a9eu7K4aAADTuONma+7hMgf7172MpERF/71QZ9f/qtjDG5UUF22f55nXX/lL1VBQzW7yq9CMe7bcgOPXfHfcoBk3ZjUXshsAAABuMotnLgWEtVJAWCsZhqGkuHPyy2fRxVhDnnkLcjULcAOXAtfzzz/vrnYAAAAgB1ksFnnlD1LeIF/FWbgCA7gLgQsAAAAATOLSoBkAAAAAgLQRuAAAAADAJG4dbiYhIUG7du3S3r17dfHiRUmSn5+fQkJCVKFCBXl58ZA3AAAAAHcPtwSuK1euaOTIkZo2bZouXbrktEyBAgXUuXNn9e/fX3ny5HHHagEAAADgluZyl8Ljx4+rffv2Gjt2rC5evCjDMJz+d/HiRY0ZM0bt27fXyZMn3dF2AAAAALiluXSFKyEhQb1799bhw4clSWXKlFGHDh1UuXJlBQUFSZLOnj2r7du3a9asWdq3b58OHz6s3r17a9asWcqViwfoAQAAALhzuZR4pk+frv3798tisahv374aOHCgPD09HcqULl1a1atX11NPPaVhw4bphx9+0L59+zR9+nR16dLFpcYDAAAAwK3MpS6FCxYskMViUZMmTfTiiy+mClsOK/Lw0AsvvKCmTZvKMAwtWLDAlVUDAAAAwC3PpcD177//SpI6duyY6WU6deokSdq7d68rqwYAAACAW55Lgevy5cuSpEKFCmV6meDgYElSTEyMK6sGAAAAgFueS4HL399fkhQVFZXpZWxl/fz8XFk1AAAAANzyXApcFSpUkGEY+vXXXzO9zKRJk2SxWFShQgVXVg0AAAAAtzyXAlerVq0kSRs2bNCQIUMUFxeXZtkrV67o//7v/7Ru3TpJUuvWrV1ZNQAAAADc8lwaFr5t27aaMmWKtm7dqtmzZ+uPP/7QI488orCwMBUsWFAWi8X+HK758+fr/PnzkqSqVauqbdu2btkAAAAAALhVWQzDMFyp4OLFi+rbt6/++uuv5AotFqflbKupUqWKfvzxR+7hAgDcss6evazs/utosUhBQb4u1YG0sX/Nxf41F/vXfO7Yx7Y63MWlK1xS8uAXkyZN0uTJkzVp0iTt37/fabmyZcuqa9eu6tKlizw8XOrJCAAAAAC3BZcDl5T8UONu3bqpW7duOn36tP79919dvHhRUnIgu//++7M0dDwAAAAA3AncErhSKlSoEOEKAAAAAOTiKIUAAAAAgLQRuAAAAADAJJnqUjh8+HD76+eff97p9OxIWRcAAAAA3GkyNSx8uXLl7MO979692+n07EhZFwAAtwqGhb91sX/Nxf41F/vXfLf1sPBp5TIXH+MFAAAAAHesTAWuf/75J0vTAQAAAAAMmgEAAAAApiFwAQAAAIBJXApcPXv2VK9evXTs2LFML3Pq1Cn7cgAAAABwJ8v0oBnObNiwQRaLRVeuXMn0MteuXbMvBwAAAAB3MroUAgAAAIBJbnrgiouLkyT5+Pjc7FUDAAAAwE110wPXH3/8IUkqXLjwzV41AAAAANxUWbqHa8iQIU6nf/vtt/L1Tf9pzPHx8Tp69Kh27Nghi8WiGjVqZGXVAAAAAHDbyVLgmjVrVqrBLgzD0NKlSzO1vGEYkiQ/Pz/16dMnK6sGAAAAgNtOlgJXsWLFHN4fP35cFotFwcHBypUr7aosFot8fHwUHBysBx98UF26dKFLIQAAAIA7XpYC17JlyxzelytXTpI0btw43Xfffe5rFQAAAADcAVx6Dlf16tUlSXny5HFLYwAAAADgTuJS4Jo4caK72gEAAAAAdxwefAwAAAAAJiFwAQAAAIBJXOpSaBMfH6/IyEgtWbJEe/bs0YULF3T16tV0l7FYLNq1a5c7Vg8AAAAAtySXA9fBgwf13HPP6eDBg/bnbAEAAAAAXAxccXFx6t27t6KiouTh4aGHH35YgYGBmjZtmiwWi/r376+LFy9q586d2rZtmywWi6pUqaK6deu6q/0AAAAAcMtyKXBNmTJFUVFR8vT01NixY1W7dm39+++/mjZtmiRp0KBB9rK7du3S66+/rm3btqlVq1bq3r27ay0HAAAAgFucS4NmLF++XBaLRS1atFDt2rXTLVuhQgX98ssvCgwM1KeffqqdO3e6smoAAAAAuOW5FLj27dsnSWratKnT+Var1eF9YGCgnnrqKSUmJurXX391ZdUAAAAAcMtzKXBdvnxZklSsWDH7NG9vb/vruLi4VMtUrVpVkrRhwwZXVg0AAAAAtzyXAlfu3LklJQ/xbuPr62t/feLEiTSXPXv2rCurBgAAAIBbnkuBq0SJEpKk06dP26cFBgbKz89PkrRly5ZUy/z999+SJC8vL1dWDQAAAAC3PJcCV8WKFSUp1QAYtWvXlmEY+umnnxQdHW2ffvToUY0ePVoWi0Xly5d3ZdUAAAAAcMtzKXDVqVNHhmFo2bJlDtN79OghKTlgNW/eXIMGDVLv3r3Vrl07+9Wwxx9/3JVVAwAAAMAtz6XA1ahRI1WvXl358uXTkSNH7NPDw8P13HPPyTAMXbx4UYsXL9bq1avtg2h06NBBbdq0ca3lAAAAAHCLc+nBx3ny5NHEiROdzhs4cKCqVaum6dOn699//9X/t3ff4VHUaxvH701ISKMHQarUhBY6gnAoASmho9ioCgoiggUV1BcVURA9cKRYUBFQpKgQklAE6SCgVOm9g0gILb3t+wcnewjZJLvZHZKN38915dLMzs48+2QS9t75zW9SUlJUsWJF9ejRQx06dHBktwAAAADgEhwKXNlp1qxZtjdEBgAAAID8yqEhhQAAAACAzDkUuIKDg9WuXTudOXPG5udcvHhRbdu2Vbt27RzZNQAAAADkeQ4NKbx48aJMJpOSkpJsfk5ycrIuXLiQ7mbJAAAAAJAfMaQQAAAAAAxyzwPXrVu3JEleXl73etcAAAAAcE/d88AVFhYmSSpbtuy93jUAAAAA3FN2XcPVv39/q8vHjBkjb2/vLJ+bmJio8+fP6+rVqzKZTGrevLk9uwYAAAAAl2NX4Pr9999lMplkNpsty8xms/bt22fXTsuXL68hQ4bY9RwAAAAAcDV2Ba7GjRun+/6PP/6QyWRSrVq1sjzDZTKZVLBgQZUsWVL169dX586d5ePjk7OKAQAAAMBF2BW4vvvuu3TfBwYGSpImTpyoqlWrOq8qAAAAAMgHHLoPV48ePWQymVS4cGFn1QMAAAAA+YZDgWvixInOqgMAAAAA8h2HAldmkpOTdfPmTUlS4cKFVaCAIbsBAAAAgDzNaUnoxIkT+uGHH/Tbb7/pzJkzlpkMTSaTKlasqObNm+uJJ57gWi8AAAAA/xhOCVz//ve/NWvWLKWmpqabMl66PW38qVOndPr0ac2fP1+DBg3SK6+84ozdAgAAAECe5nDgev/99/XDDz9YglaVKlUUFBSkkiVLSpKuXLmiffv26fjx40pJSdFXX32l2NhYvf32247uGgAAAADyNIcC186dOzVv3jyZTCZVrVpV48aNU4MGDayuu3v3br3zzjs6evSo5s2bp5CQkEzXBQAAAID8wM2RJy9cuFCSVK5cOc2fPz/LAFW/fn3NmzdP5cuXlyQtWLDAkV0DAAAAQJ7nUODasWOHTCaTnnvuORUqVCjb9QsVKqRnn31WZrNZO3bscGTXAAAAAJDnORS4IiMjJUk1atSw+Tk1a9aUJF29etWRXQMAAABAnudQ4CpYsKAkKS4uzubnpK3r6enpyK4BAAAAIM9zKHCVK1dOkrR27Vqbn5O2btq1XAAAAACQXzkUuFq2bCmz2azvv/9eW7duzXb9bdu26fvvv5fJZFLLli0d2TUAAAAA5HkOBa4BAwbIz89PycnJevbZZzVu3DgdOHBAqamplnVSU1N14MABjRs3ToMHD1ZSUpL8/Pw0YMAAh4sHAAAAgLzMoftwFS9eXP/5z3/0/PPPKykpSfPnz9f8+fPl4eGhIkWKyGQy6fr160pKSpIkmc1meXh46NNPP1WxYsWc8gIAAAAAIK9y6AyXJLVo0UKLFi1S7dq1ZTabZTablZiYqCtXrujvv/9WYmKiZXnt2rX1448/6qGHHnJG7QAAAACQpzl0hitNjRo19NNPP+nPP//U1q1bdfToUd24cUOSVKRIEVWvXl3NmjVTUFCQM3YHAAAAAC7BKYErTVBQEKEKAAAAAP7L4SGFAAAAAADrCFwAAAAAYBCnDSlMTU3V8ePHde7cOcXExKSbGj4zPXr0cNbuAQAAACDPcThwxcfH6/PPP9eiRYt0/fp1m59nMpkIXAAAAADyNYcCV3x8vAYMGKA///xTZrPZWTUBAAAAQL7gUOCaPXu29u7dK0mqVq2a+vbtq1q1aqlIkSJyc+PyMAAAAAD/bA4FruXLl0uS6tevrzlz5sjT09MpRQEAAABAfuDQaahz587JZDJp8ODBhC0AAAAAuItDgcvDw0OSVKZMGacUAwAAAAD5iUOBq3LlypKkK1euOKUYAAAAAMhPHApcPXv2lNls1sqVK51VDwAAAADkGw4Frscee0xNmzbV0qVLFRER4ayaAAAAACBfcGiWwkuXLun//u//9Pbbb+u1117T6tWr1bVrV1WqVEne3t7ZPp9rvwAAAADkZw4FruDgYJlMJkmS2WzWqlWrtGrVKpueazKZdPDgQUd2DwAAAAB5mkOBS7odtKz9PwAAAAD80zkUuCZMmOCsOgAAAAAg33EocPXs2dNZdQAAAABAvuPQLIUAAAAAgMwRuAAAAADAIAQuAAAAADCIw7MUSlJycrLWr1+vnTt36ty5c4qJiVFKSkqWzzGZTJozZ44zdg8AAAAAeZLDgWvHjh16/fXXdenSJcuyrKaHN5lMMpvNlvt3AQAAAEB+5VDgOnHihJ599lnFx8fLbDbLw8NDDzzwgIoUKUKgAgAAAPCP51Dg+vLLLxUXFyd3d3e9+OKL6tevn3x9fZ1VGwAAAAC4NIcC17Zt22QymdS/f38NHTrUWTUBAAAAQL7g0CyF165dkyS1a9fOKcUAAAAAQH7iUOAqXry4JMnLy8spxQAAAABAfuJQ4GrYsKEk6dixY04pBgAAAADyE4cC18CBA+Xu7q65c+cqOTnZWTUBAAAAQL7gUOAKCgrSmDFjdPjwYQ0fPlxRUVHOqgsAAAAAXJ5DsxROnz5d0u3gtX79egUHB+uhhx5S5cqVbbqua/jw4Y7sHgAAAADyNJPZbDbn9MmBgYHpbnBsNpvtuuHxoUOHcrprAAAMExl5Szn919Fkkvz9Czm0DWSO/hqL/hqL/hrPGT1O24azOHSGS7odsrL6HgAAAAD+qRwKXIcPH3ZWHQAAAACQ7zg0aQYAAAAAIHO5FriuXbuWW7sGAAAAgHvingaupKQk/fLLL3r++efVsmXLe7lrAAAAALjnHJ40wxa7d+9WaGioVq5cqZs3b96LXQIAAABArjMscF24cEGhoaEKCwvT2bNnJf1vBkM3Nzc1bNjQqF0DAAAAQJ7g1MAVHR2tFStWaOnSpdq1a5fMZrMlZBUoUECNGzdW+/bt1b59e5UoUcKZuwYAAACAPMfhwJWamqpNmzYpNDRU69atU0JCgqT/nc0ymUwaMGCAhgwZomLFijm6OwAAAABwGTkOXIcOHVJoaKiWLVumq1evSvpfyKpWrZq6d++uTz75RJIUFBRE2AIAAADwj2NX4Lpy5YrCwsK0dOlSHTt2TNL/QtZ9992nLl26qFu3bgoMDJQkS+ACAAAAgH8imwPXoEGDtG3bNqWmplpClq+vr9q3b69u3bqpadOmMplMhhUKAAAAAK7G5sC1ZcuW208oUEAtWrRQ165d1a5dOxUsWNCw4gAAAADAldl142OTySR3d3cVLFhQBQsWlLu7u1F1AQAAAIDLs/kMV+PGjbVjxw4lJCRo1apVWrVqlQoXLqyOHTuqW7du3FcLAAAAAO5i8xmu7777TmvWrNGIESNUsWJFmc1m3bhxQ4sWLVLfvn3Vrl07ffrppzp58qSR9QIAAACAy7BrSGGZMmU0bNgwrVy5UgsXLtQTTzyhwoULy2w26/z58/riiy/UuXNnPfroo5ozZ45RNQMAAACAS8jxfbjq1q2runXr6q233tL69esVGhqqDRs2KDk5Wfv379eBAwcssxbu3r1bTZs2VfHixZ1WOAAAAADkdSZz2hzvTnD9+nVFREQoNDRU+/fvv72D/4Yud3d3NWzYUO3bt1f79u1VsmRJZ+0WAACnioy8pZz+62gySf7+hRzaBjJHf41Ff41Ff43njB6nbcNpNTkzcN3pxIkTCg0NVUREhC5dunR7Z/8NXyaTSfXr19e8efOM2DUAAA4hcOVd9NdY9NdY9Nd4eTFw2XUNlz2qVKmiV199VWvXrtW3336rHj16yNvbW2azWampqdq1a5dRuwYAAACAPMGwwJXGZDKpWbNmmjhxorZs2aKPPvpIzZo1s5ztAgAAAID8KseTZuSEt7e3unfvru7du+vy5cv3ctcAAAAAcM8ZfoYrM6VKlcqtXQMAAADAPZFrgQsAAAAA8jsCFwAAAAAYhMAFAAAAAAYhcAEAAACAQQhcAAAAAGAQAhcAAAAAGITABQAAAAAGIXABAAAAgEEIXAAAAABgkALO3NitW7e0Zs2aLNfx8vJSx44dnblbAAAAAMiT7Apc69at08cffyxJGjZsmLp06ZLu8b/++kujR4+WyWTKcjteXl5q3bq1fZUCAAAAgIuxa0jhlClTdPLkSRUvXjxD2Lqb2WzO9Gvy5MkOFQ0AAAAArsDmM1z79u3T0aNHZTKZ9Morr2S7/oQJEzIsu3HjhiZOnKhjx45p7969qlu3rn3VAgAAAIALsTlwrV69WpJUu3ZtNWjQINv1e/bsaXX5+vXrtX37dq1Zs4bABQAAACBfs3lI4f79+2UymdSqVSuHdti2bVuZzWb9+eefDm0HAAAAAPI6mwPXiRMnJN0+w+WIqlWrSpJOnz7t0HYAAAAAIK+zeUjhzZs3JUklSpTIdB0vLy8FBARkOUth0aJFJd2+ngsAAAAA8jObA1dKSoqk27MPZqZ8+fJaunRplttJTk5Otz0AAAAAyK9sHlKYdmbq2rVrDu0wKioq3fYAAAAAIL+yOXCVLl1akrRnzx6Hdpg2WUba9gAAAAAgv7I5cDVu3Fhms1m//PJLjndmNpu1cuVKmUwmNWrUKMfbAQAAAABXYHPgatu2rSTp1KlTWrBgQY529uOPP1pmO0zbHgAAAADkVzYHrgYNGljOcn3wwQd2n+n69ddfNW7cOMvZrYYNG9pdLAAAAAC4EpsDlyS988478vX1VXJysl566SW99NJL2rlzZ5bP2bVrl1555RW9+OKLSk5Olo+Pj8aOHetQ0QAAAADgCmyeFl66fdPiKVOmaOTIkYqLi9Mvv/yiX375Rb6+vgoMDFTx4sXl7e2tuLg4RUVF6fDhw4qJiZF0+/otLy8vTZ48WdWqVTPkxQAAAABAXmJX4JKkli1bauHChXr11Vd17NgxSVJ0dLTVM1133rOrWrVq+vjjjxUYGOhAuQAAAADgOuwOXJJUvXp1hYeH69dff9XSpUv1xx9/6Pr16xnWK1q0qBo3bqzu3burXbt2jtYKAAAAAC4lR4ErTbt27SxB6vLly7p27ZpiYmLk6+urYsWKqVSpUk4pEgAAAABckUOB606lSpUiYAEAAADAHeyapRAAAAAAYDsCFwAAAAAYxK4hhZcuXVJERIQkKSgoSA8++GC6x48dO6Zu3bpluQ0vLy8tX75c999/v52lAgAAAIBrsesM15QpUzR58mR98803qlSpktV1zGZzll9xcXH697//7ZTiAQAAACAvs/kMV2RkpJYtWyZJGjlypO67775M1zWZTGrUqFGG5bGxsTpw4IBWrlypMWPGqESJEjkoGQAAAABcg82B65dfflFKSor8/f31yCOPZLv+d999l2FZSkqK2rdvr4sXL2r58uXq16+ffdUCAAAAgAuxeUjhjh07ZDKZFBwcLE9PzxztzN3dXZ07d5bZbNauXbtytA0AAAAAcBU2B67Dhw9LUoaJMuwVFBQkSTp06JBD2wEAAACAvM7mwHX16lVJcvjmxmnXfl27ds2h7QAAAABAXmfzNVyxsbGSJF9f30zXqVixokJDQ7PcTtpwxJiYGFt3DQAAAAAuyebA5evrq5s3b+rGjRuZruPp6anAwMAst5P2/KyCGwAAAADkBzYPKUybwv3kyZMO7fDUqVOSpOLFizu0HQAAAADI62wOXLVr15bZbNbmzZsd2uHGjRtlMplUu3Zth7YDAAAAAHmdzYHroYcekiStX79eR48ezdHOjh07pvXr10uSmjdvnqNtAAAAAICrsDlwderUSSVLlpTZbNZLL72kmzdv2rWjmzdv6qWXXlJqaqpKlCihkJAQu4sFAAAAAFdic+AqWLCgRowYIbPZrFOnTqlHjx7asGGDTc/dtGmTevbsqRMnTshkMmnkyJE5vnkyAAAAALgKm2cplKTevXtr3759WrRokS5duqShQ4eqQoUKatGihWrWrKnixYvL29tbcXFxioqK0qFDh7Rp0yadPXvWso1HH31UvXv3dvoLAQAAAIC8xq7AJUnvvfeeSpQooS+++EJms1lnz57VDz/8kOVzzGazTCaThgwZopEjR+a4WAAAAABwJTYPKUyTNiRw4cKFatu2rUwmk8xmc6ZfJpNJbdu21cKFC/Xyyy/Lzc3uXQIAAACAS7L7DFeaoKAgzZgxQ7du3dKOHTt05MgRXbt2TTExMfL19VXRokUVGBioRo0aqVChQs6sGQAAAABcQo4DV5pChQqpTZs2atOmjTPqAQAAAIB8g/F9AAAAAGCQXAlc0dHRio6Ozo1dAwAAAMA9Y3Pg6tmzp3r16qVz585ZffzWrVsKDQ1VaGholts5duyYGjVqpCZNmthVKAAAAAC4Gpuv4Tp06JBMJpMSEhKsPv7XX39p9OjRcnNzU48ePbLdntlstrlIAAAAAHBFDk+acTeCFADgn8psNutmVIySYxMVE5eoQsV8ZTKZcrssAHmA2WxW9PV4pSakKiY2Qb5FvPj78A/h9MAFAMA/SUpyinauP6wNS3bp6J6zirkZb3nMt7CXqteroNa9GqpBqwC5F3DPxUoB3GspKanat+W0tq04rFMHLiv21v9GivkUKqhKtUqpWUgN1X6ootzdmcsuvyJwAQCQQwf/OKUv3l6syIvX5eZmUmpq+lEeMTfjtXfzMe3eeFT+ZYrq+fG9VKNxpVyqFrAuPjZRO9Yd17aVRxT11y0lJqbI09NdxUsXUtOOAWrUpqq8fDxzu0yXc2zPBX0/cZ2iLkfL5GaS+a6/D7G3EnTw93M6sO2sipfyU9/RwapWr0wuVZs/xMUkKuryLV09d1NxiUkqXqqQvH1z/9glcAEAYCez2azFX6zTz5+tk8nt9pCgu8NWmrTlUX/d0PvPzNIjw9qo19A2DCVCrkuIS9KyOTu0ffVRJSempH8sPlmXzlzTki+2KXzWH3rw4erqPKCRCnp75FK1rsNsNmvF3J1aMXuH5e/D3WHLsu5/l1+7EqOpL4ep08BG6tS/IX8f7HT26BVtDj+onWuPKyU51bLcvYCbGgZXVYuuNVWheslcq49zl7gnzp49q7Fjx6pt27aqU6eOGjRooCeeeEJz5sxRfPz/ht8EBwcrICDA6tegQYMs602bNk0BAQGKiorKdJ/bt29XQECAVq5c6VDtjz76qAICAvTDDz9YfXzx4sXp6qxTp45atGihQYMGae7cuVZvgZBd/cHBwRoyZEi6ZZn1JSAgQGPHjk237tq1a9W3b181a9ZMdevWVdu2bTVy5Eht3LjRss758+cVEBCgb775JsP+L168qLFjxyo4OFi1a9dWs2bNNGzYMO3cuTPDuml9DggI0P79+zM8Pnr0aNWvX9/q67TWk8DAQF26dCnD49HR0QoKClJAQIDGjRuX4fFr167po48+UocOHVSnTh01adJEgwYN0rp16zKsm9Vrv5Otx2OaQ4cOadSoUWrVqpVq166tJk2aaODAgfr555+VkpJi076/+eYbBQQE6Pz585Zl/fr1U5cuXbKsNa1/mX1duXLFsm5UVJTGjx+vjh07KigoSM2aNdOjjz6qjz/+WDExMVnuJ42tv9NpUlJS1KJFCwUEBGjDhg1ZvoacHAPOeE32SAtbUuZvpO6WFrx+/mydFn+R8bgE7qVb1+I07fUI/bbicIawZfHfQzs5MUW/rTis6a8v061rcfeuSBeVFrYk2/8+pK23YvYOrZib8d9aWBcfm6gv316pyS+GaseaY+nCliSlJKdqx5pjmvxiqGb+30rFxybmSp2c4YLh1q9fr5EjR8rT01Pdu3dX9erVlZSUpJ07d+rjjz/W8ePH9f7771vWr1Gjhp5++ukM27nvvvvuZdmSpNOnT2vfvn0qW7aswsPD9dRTT2W67ogRI1SuXDklJycrMjJSv//+uz788EPNnj1bn332mQIDAx2up3nz5urevXuG5ZUq/W+I0jfffKNJkyapSZMmGjJkiLy8vHTmzBlt3bpVy5cvV8uWLbPcx86dO/Xcc89Jknr37q0qVaooMjJSS5YsUZ8+ffTWW2+pX79+Vp87ffp0ffHFFw68QsnT01MRERF69tln0y1ftWpVps85efKkBg4cqKioKPXq1Ut16tTRzZs3FR4erqFDh+qZZ57RG2+8kaN6bD0ef/zxR73zzjsqUaKEunfvrooVKyomJkbbtm3TW2+9pStXrmjo0KE5qsEe7777rnx8fDIsL1y4sCTp+vXreuSRRxQdHa1HHnlElStX1vXr13XkyBHNnz9fTz75pHx9fbPch72/05K0bds2XblyxfK71KpVq0y3b+8x4IzXZI+Df5yyhK2c+vmzdarZqBLDC5ErEuKS9OXYlbp89rpdgeCvs9f05diVenFSF850ZeLYnguWsJVTK2bvULW6ZRhemI342ERNGxWhi6duf3idmpLJKIP/Lj+047ymjYrQi590uedDZAlcMNS5c+f08ssvq0yZMpozZ066N6l9+vTRmTNntH79+nTPKVWqlNVQkRvCwsJUokQJjR49WiNGjND58+dVrlw5q+u2bNlSderUsXw/ZMgQbd26VUOHDtWwYcO0fPlyeXl5OVTPAw88kGVvkpOT9dlnn6l58+aaNWtWhsevXr2a5fZv3LihkSNHysvLS/Pnz1eFChUsjz399NMaNGiQPvzwQ9WqVUsNGjRI99waNWpo3bp1OnDggGrVqmXnK/ufVq1aadmyZRnebEdERKh169b65Zdf0i1PSkrSyJEjdfPmTc2bN09169a1PDZw4ECNGjVKs2bNUp06dRQSEmJ3PbYcj3v27NE777yjevXqaebMmfLz80tXw759+3Ts2DG7950THTp0UPHixTN9/KefftLFixc1f/78DD/D6OhoeXhk/SYqJ7/T0u3fpVq1aqlHjx6aMmWKYmNjrQZDyf5jwNHXZI+U5BR98fZiq9dj2MPNzaTP316sKcteYiIN3HPL5uzQX3aErTSpqWb9dfa6ls3ZoV5DmxlUnetKSUnV9xPXOfz3weRm0vcT12rsvKeYSCMLcz5cq4unouz60ODiqSjNnbBWz73f0eDq0uOnCEN9/fXXio2N1QcffGD1DFXFihU1YMCAXKjMNhEREerQoYNat26tQoUKKSIiwq7npw3Fu3DhgsLCwgyq8n+uXbum6OjoDG8605QoUSLL5y9cuFBXrlzRa6+9li5sSZKXl5cmTpwok8mkGTNmZHhu3759VaRIEU2bNi3nL0BSly5ddOjQIZ04ccKy7MqVK9q2bZvVYXWrVq3S0aNH9eyzz6YLW5Lk7u6ucePGqXDhwg7XlZXp06fLZDLpk08+SRe20tSpU0e9evUybP/2OHv2rNzd3VWvXr0Mj/n5+algwYJZPj8nv9Px8fFavXq1QkJC1KlTJ8XHx2vNmjWZ7sPeY8DR12SPnesPK/Ki/W9U75aaalbkxevateGIkyoDbBMfm6jtq4/m+Bg2p5r1++qjuTY0Ky/bt+W0oi5HO/z3wZxqVtTlaO3/7YyTKst/zh65okN/nLO71+ZUsw7+fk5nj17JfmUnsvsM15gxY+Tt7Z1heVzc/8b09u/fP9Pn37ke8r9169apfPnymQYAa5KTk61e2+Tj4+PwGSJ77N27V2fOnNGHH34oT09PPfzww5Yhavbo3r27Jk+erM2bN+uxxx5L99iNGzesPic1NdXq8oSEBKu98fPzk6enp0qUKCEvLy/LNVxFixa1q9a1a9eqYMGCmZ4JKl++vBo2bKjt27crPj4+3c/Dz89PAwYM0NSpUx06y9W4cWOVLl1aERERGjlypCRp+fLl8vHxUevWra3WLCnTG64XKlRIbdu21ZIlS3TmzBlVrFjRrnqyOx7j4uK0bds2NWrUSGXK5P7wD2vHVIECBSxDCsuWLauUlBQtXbpUPXv2tHv7OfmdXrt2rWJjY9W5c2eVLFlSTZo0UXh4uLp27Wp1fXuPAUdfkz02LNlldTbCnHBzM2n9kl1q3LamEyoDbLNj3fHMr9myUVJiinauO6HmnWs4qar8YduKww6f3UpjcjNp24rDqvsvhh1bsznioNzcTZkOI8yKm7tJW8IPqsKrmQ9tdza7A5e1i+LTpM2o8scff+S8IuQb0dHRunz5stq2bWvX8zZv3qxmzTIOVXj11Vct1xbdC2FhYbr//vvVsGFDSVLnzp31888/69ChQ6pRw/Z/ZEqXLq1ChQrp3LlzGR7r2DHzU9oBAQEZlv3000/66aefMiyfPHmyOnfuLDc3Nw0aNEgzZsxQmzZt1KhRIzVs2FD/+te/bApAJ06cUKVKleTpmfnY5oCAAP3+++86c+ZMhhr79++vOXPmaPr06fr888+z3V9mQkJCtGzZMsub7fDwcD388MNW6zpx4oQKFSqksmXLZrq9tOvnTpw4YXfgyu54PHPmjJKSklS9enW7tmsUa8dUpUqVLJPHPPLII5o9e7ZGjx6tmTNnqkmTJmrcuLFatWqlQoUKZbntnP5Oh4WFqX79+rr//vsl3f5deu+99xQVFZXp8Ed7jgFHXpM9zGazju4565SwJd0+y3V0z1mZzWZmJLNTQnxSbpfgsratdMJZVZO0deVhAtcdzGazTh247JSwJd0+E3Ny/1/8fbAiLiZRO9cez1HYkm5f07Vj7XH1HNpMXvdoyni7ApfZ7JyDCP8MabPz2Xuxet26dfXSSy9lWG7vG2VHJCcna/ny5erRo4flD13Tpk1VokQJhYWF2RW4pNtnQ6zNlDZt2jSrQ9Bee+01q9tp27at+vbtm2H5nW/2R4wYocqVK+uHH37Q5s2btXHjRk2ZMkU1a9bUJ598oipVqmRaZ0xMTLY/r7THrc2+WKhQIfXv31/Tpk3TwYMHVbNmzj6579q1q2bNmqU///xTRYoU0b59+/TKK68YUnN2sjsec3qcG8XaMXXnqAR/f38tXbpUM2bM0K+//qoFCxZowYIF8vDw0PPPP69hw4Zl+o97Tl7rtWvXtHnzZo0ZM8ayrH379ho3bpxWrFihPn36WH2ePceAI68pMTFRiYnph0b5+fnJ2uo3o2LS3dTYGWJuxCn6eqwKF88bx48rWDJ7uw7uPK+aDcup58AHc7sclxP11y3HN2LW7XtLkQMsoq/Hp7upsTPE3kpQzM14FSqacWTZP1nU5VsZZiO0V0pyqqIu31LZKtYvtXD2sW1z4Dp8+LBz94x8L+1Nn71TMhcrVkwPPfSQESXZbMuWLYqKilJQUJDOnPnfGOoHH3xQy5Yt02uvvSY3N9svgYyNjbV6/VSjRo2sfsKf2TUnpUuXtqk3Xbp0UZcuXRQdHa29e/dq8eLFioiI0NChQxUREZHp9n19fbP9eaU9ntmb7gEDBmjOnDmaNm1ajs9y1axZU5UrV1ZERIQKFy6skiVLqmnTppnWfO3aNYdqzkp2x2NOj/Ps5PQTzcyOqTvdd999eu+99/Tuu+/q9OnT2rx5s7766itNnTpV9913n3r37m31eTl5rcuXL1dSUpJq1KiR7ncpKChI4eHhmQYue44BR17Tl19+qenTp1u+9/X11a5du1SiRMYzY8kGXbPi4+Uhf3/nnYnLzxLik3Rw5+1bJhzceV79h7dSQS9my7NHooPDCS3bSUjmuL1DaoJjASAzvt4F6fNdrp676ZTteHneu7+9zFIIw/j5+em+++67Z7OzOVPaBBfWzmxI0u+//57lm787/fXXX7p161aGSSjuBT8/PzVv3lzNmzeXh4eHlixZor1796pJkyZW169SpYoOHjyoxMTETIcVHjlyRB4eHnrggQesPl6oUCENGDDAcpYrp7p06aL58+fL19dXnTp1yjTgVqlSRYcOHdLFixczvYbqyJHbQ2iqVq2a43oyU7FiRRUoUEBHjx61af20sGvtXlXS/65zdeZED5kxmUyqVKmSKlWqpNatW6t9+/YKCwvLMnDZ+zsdHh4uSXryySetPn7u3DmVL1/e6mO2HgN3svc1DRkyxOq0/1ev3tLdgzpi4owJXLHxSYqMdMJZh3+Img3LWc5w3YqO161o5551zO88Pd2VEJ/s+HYKFuC4vUNMrHPPblm2G5dAn+8Sl+icIcXxiZn/7TWZZPWDt5zKtVkKV6xYkVu7xj3Upk0bnT17Vrt3787tUmwWGxurtWvXKiQkRJ9++mmGr5IlS1reRNpi6dKlkqQWLVoYVbJNateuLUnpboB7t9atWyshISHT38/z589r586devDBB7OcwGTAgAEqXLhwujMH9uratauuXLmi06dPZzq5QlrNkhQaGmr18ejoaK1Zs0aVK1c2ZFiqt7e3mjZtqh07dli9We/dihcvLm9vb506dcrq46dOnZK3t7eKFSvm7FKzVL58eRUuXDjL40Oy73f63Llz2r17t/r27Zvh92jKlCny8PDI8nfJ1mPAkdfk6ekpPz+/dF+SZDZn/CpUzFe+hZ07cY9vEW/5FfWxuj++rH/1HPigJn7bRz0HPpjrtbjiV/HSTngTaZKKl/LL9deSl758i3jJp5BzPyjzKVRQvoW9cv215bWv4qUKyb2AYxHGvYCbipcqlOV+nMnmat977z2n7XTJkiUaNWqU07aHvGvw4MHy8fHR22+/rcjIyAyPnz17VnPmzMmFyjK3evVqxcbGqk+fPurYsWOGrzZt2mjVqlUZrvuwZuvWrfrss89Urlw5devWzfDa4+LiMn0jvHHjRknpb5J8t8cff1wlSpTQxx9/nGGSj4SEBI0ZM0Zms1kvvPBClnWkneVas2aNDh06ZOeruK1ChQp688039eqrryooKCjT9Tp06KCqVavqq6++0r59+9I9lpqaqnfeeUc3btzQ8OHDc1SHLV544QWZzWa9/vrrVofb7d+/X0uWLJF0e6r65s2ba926dbp48WK69S5evKh169apefPmcnc35t5Me/fuVWxsbIblf/75p65fv57l8SHZ9zudFqYGDx6c4fcoJCTEMlthZmw9Bhx9TbYymUyqXq+C3NycM7jfze329rgg3n4MI8y5ph0zTshkN7PUrGOg49vJR0wmkyrVKiWTk/4+mNxMqly7NH8frPD29VTD4Kpyc89Zb9zcTWoUXPWeTZgh2TGkcP78+SpYsKBGjx7t0A4XLFjg1PCGvK1ChQr65JNP9PLLLyskJETdu3dX9erVlZiYqN27d2vlypUZ7k90+fJly1mhO/n6+qpdu3bpls2ePTvDmRY3N7d0U7evWrVKJ0+ezLC9nj17WmZNu1N4eLiKFi2q+vXrW31NwcHBWrRokdavX6/27dtblm/cuFEnT55USkqKIiMjtX37dm3ZskVlypTR559/7pQhYqdPn7baG39/fzVv3lxxcXF64oknVK9ePf3rX/9S6dKldevWLf3666/asWOH2rVrl+VEFsWKFdPUqVP13HPPqWfPnurdu7eqVKmiyMhIy7Tqb731lk1Tgvfv31+zZ8/W4cOHM73BbXZsuUebp6enpk6dqgEDBuipp55Sr169VLt2bd26dUsRERE6cOCAnnnmGXXu3DnDc7du3aqEhIzDQNq1a2eZiMSW47FBgwYaO3as3nvvPXXq1Endu3dXxYoVFRMTo99//11r165NNzz1lVde0WOPPaaePXvq8ccfV9myZXXhwgUtXLhQJpPJ6uQQUVFR+uyzzzIsvzvM//LLL1b73bx5c8vkEuHh4WrXrp1q164tDw8PnThxQj///LMKFiyY7W0P7PmdDg8PV40aNaz+nkm3f5fef//9LG8jYMsx4Ohrskerng20e6Ntw0ezk5pqVuuetk+vDzhDozZVFT7rD4emhvfwdFfDNplPwPRP1bRToA5sO+uUbZlTzWraiVCbmRZda+r3VTn7W5yaYlaLbvf2dhx2XcM1Z84ceXh46NVXX83Rzr799ltNmjRJZrM5y2mnkb+0bdtWYWFh+uabb7RmzRrNnz9fnp6eCggI0OjRozPcm+rQoUN6/fXXM2ynbNmyGQLXl19+mWE9d3f3dG+wli1bZrWuJk2aZHgjePXqVW3dulWdO3fO9AxDs2bN5O3trbCwsHSBa+rUqZIkDw8PFS1aVNWrV9ebb76pXr16WZ2JMCe2bNmiLVu2WH0tzZs3V+HChTV+/HitX79eixcv1pUrV+Tu7q5KlSrp9ddfV79+/bLdR6NGjRQWFqYvv/xSK1eu1JUrV+Tn56f69evrgw8+UKNGjWyqtXDhwhowYIBDwwptVaVKFYWFhWnmzJlau3atFi9eLC8vL9WuXVuff/65goODrT5v06ZN2rRpU4blZcuWtQQuW4/HJ554QnXq1NGsWbMUGhqqa9euycfHRzVr1tSECRPShaIqVapo0aJFmj59un766SfduHFDRYoUUfPmzfXCCy9YnUny6tWr+vTTTzMsb9asWbptv/vuu1Zf69y5c+Xv76/HH39cXl5e2rZtm9auXavo6GgVK1ZMzZs315AhQ2yaWdKW3+kDBw7o5MmTGjZsWKbbadOmjd5//32FhYXl+L5tkpzymmzVsHWg/MsU1dW/bjg0/bObm0nFSxdRg1ZOONsA2MHLx1MPPlxdv604nKNj2ORmUpOHq8vLh/dxd6vT/AEVL+Wna1diHPr7YHIzqVhJX9V+qKITq8tfKlQvqRqNy+vwzvN29drkZlKNRuVUvlpJA6uzsl+zjXO9h4SE6OTJkzKZTHr++ec1YsQIu3b02Wefadq0aTKbzfLy8tLUqVPVsmXLHBUNAICRIiMzTpqR5uAfpzT+mVkO7+P/Zj2jGo25qam9TCbJ379Qlj8jZC0hLknTXo/Q5bPX7bqvnJubSaUrFNPwSZ1V0JthndYc23NBU1+2/TrvzIyY0k3V6lmfCAq3xccmatqoCF08FWVT6DK5mVSmUnG9+EmXbD8wSPs74yw2X8M1Z84cVaxYUWazWZ9//rld0z1PnjzZErZ8fHz0xRdfELYAAC6pZuNKemRYG4e28ciwNoQt5JqC3h4aMq6jSlUoavM1R6b/hq3nxnUgbGWhWr2y6jTQtpEgmek0sBFhywZePp568ZMuqtGonCRlek1X2vIajcppxL+75srZWZvPcEm3r2Xo27evzp07J5PJpFdffVWDBw/O8jkffPCBvv/+e5nNZhUqVEhffvmlTdd/AACQW7I7e2I2m7X4i3X6+bN1cnMz2XSWIG29R4a1Ua+hbbgYPoc4w+U8CXFJWjZnh35ffVRJiSmSSdKdPf3v9x6e7mrycHV1HtCIsGUDs9msFXN3asXsHTK5mWw++2JONavTwEbq1L8hfx/sdPboFW0JP6gda4+nuymyewE3NQququZda6pCdduHETr7DJddgUu6PYtW3759dfHiRZlMJo0ZM0b9+/e3uu7YsWP1448/ymw2q2jRovr6668tU1MDAJBX2fpm/tAfp/T524sVefF6psErbbl/maJ6fnwvzmw5iMDlfPGxidq57oS2rjysqMvRSkxIlmfBAipeyk/NOgaqYZsqXLOVA8f2XNT3E9cq6nJ0psErbXnxUn7qOzqYM1sOiotJ1LXLt+Tl6aH4xCQVL1UoR7MR5nrgkm7fW6V///66dOmSTCaTxo4dm+6mlqmpqRo9erTCw8NlNpvl7++vb775RgEBXBwMAMj77Hkzn5Kcol0bjmj94p06uvecYm7EWR7zLeKt6vUqqHXPBmrQKkDuBYyZ7v+fhMBlLPrrXCkpqdr/2xltXX5Ipw5cVuyt/82M61OooCrXLq2mIYGq3ayi3N1z7fa4+YozjuE8Ebgk6cyZM+rXr5/+/vtvubm5ady4cXr00UeVlJSkV199VatXr5bZbFapUqX07bffqnLlyk4rGgAAI+X0H2qz2azo67Hy8fJQbHyS/Ir6MDTIyQgExqK/xjGbzYq5GS9f74KKiUuQb2Ev/j4YIC8GLrumhb9TxYoVNXv2bPXv31+RkZEaO3asUlJStG7dOq1fv17S7WmTZ8+erfLlyzurXgAA8iyTyaTCxX15wwogA5PJpEJFvfn78A/k0LnLypUra/bs2SpWrJhSU1P17rvvasOGDZJuB7J58+YRtgAAAAD8Yzk8WLRq1aqaPXu2ihYtKrPZLLPZrGrVqmnevHkqXbq0M2oEAAAAAJdk85DC6dOnZ/l4o0aN9Ouvv8rNzU1NmjTRggULst3m8OHDbd09AAAAALgcmyfNCAwMdPqFfYcOHXLq9gAAcAZnXGzNNRrGoL/Gor/Gor/Gc/lJM3I4oaFVzMoCAAAAIL+zOXDNnTvXyDoAAAAAIN+xOXA1adLEyDoAAAAAIN/hltYAAAAAYBACFwAAAAAYhMAFAAAAAAYhcAEAAACAQQhcAAAAAGAQAhcAAAAAGITABQAAAAAGIXABAAAAgEEIXAAAAABgEAIXAAAAABiEwAUAAAAABiFwAQAAAIBBCFwAAAAAYBACFwAAAAAYhMAFAAAAAAYhcAEAAACAQQhcAAAAAGAQAhcAAAAAGITABQAAAAAGIXABAAAAgEEIXAAAAABgEAIXAAAAABiEwAUAAAAABiFwAQAAAIBBCFwAAAAAYBACFwAAAAAYhMAFAAAAAAYhcAEAAACAQQhcAAAAAGAQAhcAAAAAGITABQAAAAAGIXABAAAAgEEIXAAAAABgEAIXAAAAABiEwAUAAAAABiFwAQAAAIBBCFwAAAAAYBACFwAAAAAYhMAFAAAAAAYhcAEAAACAQQhcAAAAAGAQAhcAAAAAGITABQAAAAAGIXABAAAAgEEIXAAAAABgEAIXAAAAABiEwAUAAAAABiFwAQAAAIBBCFwAAAAAYBACFwAAAAAYhMAFAAAAAAYhcAEAAACAQQhcAAAAAGAQAhcAAAAAGITABQAAAAAGIXABAAAAgEEIXAAAAABgEAIXAAAAABiEwAUAAAAABiFwAQAAAIBBCFwAAAAAYBACFwAAAAAYhMAFAAAAAAYhcAEAAACAQQhcAAAAAGAQAhcAAAAAGITABQAAAAAGIXABAAAAgEEIXAAAAABgEAIXAAAAABiEwAUAAAAABiFwAQAAAIBBCFwAAAAAYBACFwAAAAAYhMAFAAAAAAYhcAEAAACAQQhcAAAAAGAQAhcAAAAAGITABQAAAAAGIXABAAAAgEEIXAAAAABgEAIXAAAAABiEwAUAAAAABiFwAQAAAIBBCFwAAAAAYBACFwAAAAAYhMAFAAAAAAYhcAEAAACAQQhcAAAAAGAQAhcAAAAAGITABQAAAAAGIXABAAAAgEEIXAAAAABgEAIXAAAAABiEwAUAAAAABiFwAQAAAIBBCFwAAAAAYBACFwAAAAAYhMAFAAAAAAYhcAEAAACAQQhcAAAAAGAQAhcAAAAAGITABQAAAAAGIXABAAAAgEEIXAAAAABgEAIXAAAAABiEwAUAAAAABiFwAQAAAIBBCFwAAAAAYBACFwAAAAAYhMAFAAAAAAYhcAEAAACAQQhcAAAAAGAQAhcAAAAAGITABQAAAAAGIXABAAAAgEEIXAAAAABgEAIXAAAAABiEwAUAAAAABiFwAQAAAIBBCFwAAAAAYBACFwAAAAAYhMAFAAAAAAYhcAEAAACAQQhcAAAAAGAQAhcAAAAAGITABQAAAAAGIXABAAAAgEEIXAAAAABgEAIXAAAAABiEwAUAAAAABiFwAQAAAIBBCFwAAAAAYBACFwAAAAAYhMAFAAAAAAYhcAEAAACAQQhcAAAAAGAQAhcAAAAAGITABQAAAAAGIXABAAAAgEEIXAAAAABgEAIXAAAAABiEwAUAAAAABiFwAQAAAIBBCFwAAAAAYBACFwAAAAAYhMAFAAAAAAYhcAEAAACAQQhcAAAAAGAQAhcAAAAAGITABQAAAAAGIXABAAAAgEEIXAAAAABgEAIXAAAAABiEwAUAAAAABiFwAQAAAIBBCFwAAAAAYBACFwAAAAAYhMAFAAAAAAYhcAEAAACAQQhcAAAAAGAQAhcAAAAAGITABQAAAAAGIXABAAAAgEEIXAAAAABgEAIXAAAAABiEwAUAAAAABiFwAQAAAIBBCFwAAAAAYBACFwAAAAAYhMAFAAAAAAYhcAEAAACAQQhcAAAAAGAQAhcAAAAAGITABQAAAAAGIXABAAAAgEEIXAAAAABgEAIXAAAAABiEwAUAAAAABiFwAQAAAIBBCFwAAAAAYBACFwAAAAAYhMAFAAAAAAYhcAEAAACAQQhcAAAAAGAQAhcAAAAAGITABQAAAAAGKZDbBQAAACD3xEUnaMvyfVq/eI8iL1xXQnySCnp5yL9sUbXuVU/NQ+rI269gbpcJuCyT2Ww253YRAADkJZGRt5TTfx1NJsnfv5BD20Dm6K/zxMcm6sdp67Rx6V4lJSZLd/fTdPs/Hp4F1LJ7XfV+sY28fDzveZ35Ccev8ZzR47RtOItLDilcvHixAgICVKdOHV2+fDnD4/369VOXLl2sPjclJUUtWrRQQECANmzYkGGb2X0FBwdLkkaPHq369etnWmP9+vU1evRoy/fbt29Pt50aNWqoWbNmGjFihE6cOJHpdjZs2KCAgAC1aNFCqampVtcJDg7WkCFDMt1GZtJe8759+9Itv3Xrlh599FHVqVNHGzdulCRNmzZNAQEBioqKsqyXXQ+y2sedzp8/n2XPZ86caVm3X79+6R4LCgpS165dNXv27Ez7Y01KSop+/vln9evXT02aNFHt2rUVHBysMWPGpKs1u+Niz549lnUDAgI0bty4LPeb1bGZlTuPn6VLl1pd54knnlBAQECG7QcHB2da/6BBgyzrpf2M077q1q2r1q1ba+jQofr555+VmJiYYZ/ZHQOPPvqoAgIC9MMPP1h9Ldl9Sfb/DNK+atasqSZNmqhXr14aP368jh8/blOv02R3jNy8eVMtWrTQE088IWufW+3Zs0eBgYEaMWKEza/3/PnzludfvHhRY8eOVXBwsGrXrq1mzZpp2LBh2rlzZ7r9ZPXzvfNr8eLFlufExsZqxowZ6tq1q+rWrauGDRvqqaeeUmhoaLrXsmXLFgUEBGj69OkZXt+5c+dUt25djRgxwrIsq+Nh9erVGjx4sB588EHVrl1bLVq00MiRI7V161bLOmnHxsqVKyXJ5r7NnDlTAQEB2rRpk9V9P/vss2rYsKHVfy+Af6obV6P1wTNzte6nXUpKsBK2pNvLzFJSQrLW/bRLHzwzVzeuRt/rUgGX59JDChMTEzVz5kz93//9n83P2bZtm65cuaKyZcsqPDxcrVq1kiQ1btxYkyZNSrfu22+/raCgID322GOWZb6+vg7V3K9fP9WpU0fJyck6cuSIFixYoO3btysiIkIlS5bMsH5YWJjKli2rCxcuaNu2bXrooYcc2n92oqOj9cwzz+jIkSOaPn26WrZsaej+7tSlSxer+6tZs2a670uXLq1XXnlFknTt2jVFRERowoQJunbtml5++eVs9xMfH6/hw4dr06ZNaty4sYYMGaIiRYrowoULWrFihZYsWaL169erdOnSlueMGDFC5cqVy7CtChUq2PsyHVKwYEFFRESoe/fu6ZafP39eu3fvVsGC1od81KhRQ08//XSG5ffdd1+GZe+++658fHyUmJioy5cva/PmzXrzzTc1Z84cffnll7r//vttqvX06dPat2+f5XftqaeekiRVqVIlw+/a5MmT5ePjo6FDh2a6PVt/Bs2bN1f37t1lNpsVHR2tw4cPKzQ0VPPnz9eoUaOs9uFuth4jb775pl5++WUtWrRIjz/+uOX5ycnJeuedd1SmTBlNnDhRbdu2Tbf9b7/9Vn/99ZfGjBmTbnnx4sUlSTt37tRzzz0nSerdu7eqVKmiyMhILVmyRH369NFbb72lfv36SZLefPNNxcTEWLaxceNGRUREaMyYMSpWrJhleYMGDSRJkZGRGjhwoE6cOKGQkBD17dtXCQkJWrVqld544w1t2LBBn3zyidzd3dW8eXN16dJFX375pTp37qxKlSpZtvfee++pQIECeuutt7Lspdls1ptvvqnFixerZs2aevrpp+Xv768rV65o9erVGjhwoObPn2+p7053HydLly7Vli1bMixv3ry5wsPD9d577ykiIkJeXl6Wx1asWKGNGzdq7NixKlWqVJa1Av8U8bGJ+njYfF08FanUVNtOAaSmmnXh5BV98sICvTWrP2e6kOfERSdo24rD2r3huBJik1TQx0P1W1VV006BuT4k1qUDV40aNbRo0SI999xzNv9DGhYWplq1aqlHjx6aMmWKYmNj5ePjo/Lly6t8+fLp1n333XdVvnz5DG9uHdGoUSN17NjR8n2lSpX07rvvKjQ0VM8++2y6dWNjY7V27Vq98sorWrx4scLDww0NXNHR0Ro0aJAOHTqk6dOnW8LovVKzZk2bel2oUKF06z355JPq1KmTvvvuO40YMULu7u5ZPn/SpEnatGmTxowZo4EDB6Z7bPjw4Zo9e3aG57Rs2VJ16tSx6XUYqVWrVlq7dq2ioqIsb84lKSIiQv7+/qpYsaJu3ryZ4XmlSpWy+Tju0KFDum0PHz5cYWFheuONNzRy5EgtWrTIpu2EhYWpRIkSGj16tEaMGKHz58+rXLly8vf3z1DLV199pWLFimVZo60/gwceeCDDdl599VU9//zzmjhxoipXrpztsW3rMRISEqIlS5bo3//+t9q2bSt/f39J0ty5c3X48GHNnDlTPj4+GepZvny5bt68afX13rhxQyNHjpSXl5fmz5+fLlA+/fTTGjRokD788EPVqlVLDRo0ULt27dI9PzIyUhEREWrXrp3VgPrGG2/oxIkTmj59erog2L9/f3300UeaNWuWatSoYQl8b775pjZt2qR33nlHc+fOlSQtW7ZMmzZt0ttvv53t395Zs2Zp8eLFGjBggMaMGSOTyWR57Pnnn1doaKgKFLD+T9Hd/dm7d6+2bNlitW/vv/++nnzySX322WeWD2Sio6P14Ycfql69enryySezrNNZrl+J1o41R5Ucn6ICXu5q3C5ARfwd+6AOcLYfp63TxZO2h600qSlmXThxRT9OW6d+b3QwqDrAfttWHNK8j9cqKTEl3dnao3suaPHnm9XntWA17VQj1+pzySGFaYYMGaLU1FR99dVXNq0fHx+v1atXKyQkRJ06dVJ8fLzWrFljcJVZa9SokaTbw3Putnr1asXHx6tjx44KCQnRqlWrlJCQYEgdMTExGjx4sA4cOKBp06apdevWhuzHCAULFlTt2rUVExOjq1evZrnuX3/9pYULF6p58+YZ3khLkru7uwYNGpTu7FZe0rZtW3l6elqGXKWJiIhQp06dsg2bOdWtWzf17t3b8obXFhEREerQoYNat26tQoUKKSIiwpDabFGsWDFNnjxZBQoU0Oeff57luvYeI++8844SExM1YcIESdKlS5c0bdo0hYSE5OhDi4ULF+rKlSt67bXXMpy98/Ly0sSJE2UymTRjxgy7t71nzx5t3rxZPXv2zHDWTbodTB944AF9/fXXio+PlySVKFFCo0aN0vbt27VkyRLdvHlTEyZMUJ06ddSnT58s9xcfH6+ZM2eqcuXKeuONN9KFrTQ9evRQUFCQ3a/lbvXq1dMTTzyhWbNmWYaP/uc//1FUVJTGjRsnNzdj/7lLTUnVj1M36s1e3+rnGVsU/u12/Txji8b0nKUfp25UaortQ54BI8VFJ2jj0r12h600qalmbVq6V3HRxrwfAey1bcUhzR6/WkkJKRmHxpqlpIQUzR6/WttWHMqV+iQXD1zlypVT9+7dtWjRIpvG5q9du1axsbHq3LmzSpYsqSZNmig8PPweVJq5CxcuSJIKFy6c4bHw8HA9+OCDKlmypDp37qyYmBitXbvW6TXExcXp2Wef1f79+/Xpp5+qTZs2Tt+HrXVERUVl+EpOTs72uRcuXJDJZLLaxztt3LhRycnJ6tatm121RUdHZ6jr2rVrdm3DGby8vBQcHKxly5ZZlh0+fFjHjh3L8tqw5ORkq71Ne1Nti7Sebd68Odt19+7dqzNnzqhz587y9PTUww8/7PDvmqM/gzJlyqhx48bau3evoqMzvwbB3mOkXLlyevHFFxUREaEtW7Zo/PjxKlCggN58802ba7vT2rVrVbBgQYWEhFh9vHz58mrYsKG2b99u189PktatWyfpdsixpkCBAurSpYtu3LihXbt2WZb37t1bDRo00EcffaT33nvP5hCzc+dOXb9+XV26dDHsw4A7vfrqqypWrJjGjh2r/fv364cfftAzzzxjuR7QSD/P2Kw1C/coNdUsc6pZKcmpMqealZpq1pqFe7T4M9s+qACMtmX5vtsTZDggMTFZvy3f76SKgJyLi07QvI9te2887+O1ufZBgUsHLun2kJSUlBSbznKFhYWpfv36lmtQOnfurC1btqSbCMJoMTExioqK0t9//61Nmzbpww8/lMlkUvv27dOtd/XqVW3dulWdO3eWdPvNYr169QwJiKNHj9aff/6p//znP1Y/9b5Xpk2bpmbNmmX42r8//R/1lJQUyxvukydPatKkSdq/f79atWqV7toNa9ImKLH3DdjAgQMz1HUvr2+7U9euXbVz505dunRJ0u3junz58qpXr16mz9m8ebPV3qYNEbNF9erVJVk/G3u3sLAw3X///WrYsKGk279rx48f16FDOf90yRk/g2rVqik1NTXd5BR3y8kxMmDAANWoUUOvvPKKfv31V40aNcrqNZm2OHHihCpVqiRPz8yvjwgICFBSUpLOnDlj17bTzvwEBgZmuk7aY3dO5mMymTRu3DhFR0crIiJCffv2zXBtpTU5/X3LKT8/P7399tvauXOnBg0apDJlyuiFF14wfL/Xr0Rr3Y97s1xn7aI9uhEZk+U6wL2w/ufd1ifIsNO6xbsd3wjgoG0rDt8eRmiDpMQUbVt52OCKrHPpa7ik25/2duvWzXItl7VJAKTbkyts3rw53UXq7du317hx47RixYpsh8Y4y92fehcvXlyTJk3KMKRm2bJlGYJYly5dNHHiRN24cUNFihRxWk2RkZHy9PS0eTIEozz++OPprm9LU7Vq1XTfnzx5Us2aNUu3LDg4WB988EG2+0g7s2Hv5Cdjx45NN2GAJMOHKGWmefPmKlKkiJYtW6ZBgwZp+fLl2V6fVbduXb300ksZllesWNHm/fr4+EhSugkarElOTtby5cvVo0cPyxCypk2bqkSJEgoLC1ONGjkbQ+2Mn4EtryEnx0iBAgU0btw49e7dW/Xq1Us30Y69YmJist132uNZnanLbNt3Pt+ebfv5+cnDw0NJSUlq3ry5TfvL6e+bIzp06KBWrVppw4YN+vjjj7P9ECYxMTHDDJx+fn6yMvoxUzvWHM32/av5v+u1eyLrmV2RvVU/7tH2Ncf0YNtqat+7Xm6X43IiL95wfCNmKfLCdbt+T3CbySS9/cJCvTzO+igG2Gf3huO2f4BglnZvOKHg3nWzXdXZx7bLBy5JGjZsmMLCwjRz5ky9/fbbVtdZvny5kpKSVKNGjXSfCgcFBSk8PNyQwGXteoUXXnhBjRo1UmxsrFavXq1ly5ZZfdMYFhamoKAgXb9+XdevX5d0e5KQpKQkrVy5Mt2MaI4aN26cJkyYoMGDB2vevHmqXLmy07Ztj4oVK9o0KUjZsmU1fvx4paam6uzZs/riiy907dq1TGfou5Ofn5+k7EPD3YKCgvLEpBmS5OHhoY4dOyoiIkJBQUG6dOmSunbtmuVzihUr5vCEK7GxsZKyf/OcdtY4KCgo3e/agw8+qGXLlum1117LUVh1xs/AltfgyDEiSbVq1bL6u28rX1/fbPdtS3DKbNtpz89s+G1m2x43bpxMJpPKli2rjz76SA899JA8PDyy3F9Oe+moOnXqaMOGDapdu3a263755Zfppr339fXVrl27VKKE7fdfSY5PkZubSSlZXBPj5mZSUlyyU+/r8k+1fc0xpSSnavuaY3rq+X/ldjkuJyE+ySnbSYxP4njOgbdfWChJmjJ2ucbPcN57uX+qhFj7jueEmMRcOW7zReC6+yyXNWlD8TKbqercuXMZZinMiqenpxITE2U2mzO8uTKbzUpISLA6JKh69eqWN77t2rVTXFyc/u///k8NGza0nGFKm05bUoahhmmvxZmBq0qVKvrqq680YMAAPfPMM5o/f36un+3Kio+PT7rw0KBBA/Xq1UtTpkzJNHCnSQuTR44cyfGZlryga9euWrBggaZNm6bAwMAMZwGNcPToUUnZT4UfFhYmSVbPqEnS77//rqZNmzq1NlsdO3ZM7u7uVmfvS5Pbx0iVKlV08OBBJSYmZjqs8MiRI/Lw8NADDzxg97Z//fVXHTlyRI0bN85021L6M8urVq3S2rVr9eabb+qBBx7Qc889p2+++SbLafyl9L28ezbFvGLIkCFWbxVw9artN8ws4OWe7QQEqSlmeXgXUGTkrZyUiTs82Laa5QwX/bRfQS8PxcdmvK+ivTy9POh/Drw8LkRTxi7Xy+NC6J8TFPTJ+oO/DOv7etrUd5NJdn3wlp18Ebik29dyhYWFWb2W69y5c9q9e7f69u2b4U1GamqqXn/9dYWHh2vYsGE2769s2bJKTk7W2bNnMwzLOnPmjFJSUlS2bNlstzNq1Cj9+uuv+vzzzy03zQ0PD5eHh4cmTZqU4UzAzp079d133+nixYsqU6aMzfVmJygoSJ999pmee+45Pf300/rhhx/STQ2elwUGBqpbt25asGCBnnnmmSz70rJlS7m7uys8PDzTiQNcQcOGDVWmTBn9/vvvGjVq1D3ZZ1qQ+te/Mv9EOe1WBiEhIerQIeOUwePHj1d4eHiuBK6LFy/qjz/+UL169SxnXqzJ7WOkdevW2r17t1asWGF1qOj58+e1c+dONWvWLNvhcta2/eWXXyo0NNRq4EpJSVF4eLiKFCliuS9WdHS0xo8fr1q1aqlv375yd3dXhw4d9Pnnn6tz585ZflDVsGFDy/DXoUOH3pOJM+zl6elpNdiazbI5cDVqW12LZ2zJclSLyXR7PVu3icy1711PTz3/L0VG2h6K8T/+ZYro/PErjm3EJPmXLUr/c2j8jMc5fp2kfquqOrrngm3DCk1S/VZVcqXvLj9pRpoKFSqoW7dulimV75R2dmvw4MHq2LFjuq+QkJAczVaYdrH+999/n+GxefPmpVsnu7rbt2+vJUuWWOoODw9Xw4YNFRISkqHewYMHS5IhU2w3a9ZMkydP1tmzZzV48GC7rw/JTYMHD1ZycrK+/fbbLNe7//771bt3b23evFnfffddhsdTU1M1a9Ys/fXXX0aV6hQmk0lvvfWWhg8f7tT7xGUmPDxcP/74o+rXr5/h+rk7rV69WrGxserTp0+GY7djx45q06aNVq1aleGaGaNdv35dr7zyilJSUrI9K5Pbx8jjjz+uEiVK6OOPP84wQUlCQoLGjBkjs9mco8kgGjRooIceekiLFy+2zFh4pylTpuj06dMaPHiwJcz95z//0ZUrV/Tee+9ZAtNbb70lNzc3vf/++1nuz9vbW4MHD9aJEyf0ySefyGzlX7mlS5fqzz//tPu15CVFS/qpTTbXBAQ/Xo/7cSFPaP1IfckJ16e06cX1iMh9TTsFysPTtg/zPDzd1bRj5pNGGSnfnOGSpKFDh2rp0qU6deqUqlWrZlkeHh6uGjVqZDpMLjg4WO+//74OHDigWrVq2bSvGjVqqHfv3po7d67OnDljGeL222+/acOGDerdu3eWM4HdadCgQVqxYoXmzJmjhx9+WGfOnMn0mrJSpUqpZs2aCg8PTzd88syZM/rss88yrF+zZk277qn18MMP6/3339ebb76p559/Xl9//XWW10YlJSVZ3W+RIkXSvYaff/5ZmzZtyrBe//79Lf9/8OBBLV26NMM6FSpUUP36Wf9hr1q1qlq1aqWffvpJw4YNU7FixTJdd/To0Tp37pzGjx+vVatWqU2bNipcuLAuXbqklStX6uTJk5bZIdNs3LhRJ0+ezLCtBg0apPuEf//+/Vb70aRJE8s916KioqyuU65cObumq2/Xrp3Nw7QuX75stbe+vr4ZtvHLL7/Ix8dHSUlJunz5sjZv3qxdu3YpMDBQn376aZb7CQ8PV9GiRTP9eQUHB2vRokVav3691eGyWbH1Z3D69GktXbpUZrNZMTExOnz4sFauXKnY2FiNHj3apg9CcnKMOEuxYsU0depUPffcc+rZs6d69+6tKlWqKDIyUkuWLNGZM2f01ltvWc5A2eujjz7SwIEDNWzYMHXp0kWNGjVSYmKiVq1apd9//10hISEaNGiQJFmmVu/Tp0+66+dKlSqlkSNHasKECfrll1+sns1MM3jwYB0/flyzZs3S9u3b1aFDB/n7+ysyMlK//vqr/vzzTy1YsCBHryUveeSFFjKZTFq7aI/Mun3NVmqKWSbT7bDV63nbJhoBjNY8pI4W/metkhJyPjW8p2cBPRSS/TWSgNG8/Qqqz2vBmj1+dbbr9nktWN5+2V/vb4R8FbgqVqyobt26acmSJZZlBw4c0MmTJ7McLtimTRu9//77CgsLszlwSbcvIq9evbp+/vlnTZ48WZJUqVIlvf3223ZNwlGnTh01adJE8+fPt9y4Nzg4ONP1g4ODNW3aNB0+fNgS6k6dOmX1zfCjjz5q902MH3nkEd24cUMfffSRRo4cme6C8rslJSVZ3W+FChXS9WD+/PlWn9+rVy/L/0dERFg9c9ezZ89sA5d0O7iuX79e33//vV588cVM1/P29tZXX32lxYsXKzQ0VJ999pni4+N133336cEHH9Qnn3yiUqVKpXvO1KlTrW5rwoQJ6d7s7927V3v3ZpweeuTIkZbAdfXqVas9a9asmd33B7PVoUOH9Prrr2dYXrZs2QyB691335V0+4bSxYoVU40aNfThhx+qa9euWU5VfuetDDIbOtasWTN5e3srLCzM7sBl689gy5Yt2rJli9zc3OTn56dy5cqpR48eevzxx22+1i0nx4gzNWrUSGFhYfryyy+1cuVKXblyRX5+fqpfv74++OADy7GUE/fdd59+/PFHffvtt1q5cqVWrVold3d3BQQEaOLEiZbZJVNSUjR27FiVKFHC6vV4/fr1U2hoqD788EO1aNEi0wk83NzcNGnSJLVt21aLFi3SrFmzFB0drWLFiqlx48Z67bXXbPr9zuvc3N306Iv/0sNPNtCONUeVFJcsD+8CatS2Ome2kKd4+xVUy+51te6nXTm6+bGbm0n/6l431964Andr2un29dbzPl57e4r4Ow9r0+0zW31eC7aslxtMZmtjPAAA+Adz5PoKk0ny9y/ENRoGob+Oi49N1AfPzNWFk1eUmmJ7E93cTSpbuaTemtVfXj6ZfwCHzHH8GicuOkHbVh7W7g0nlBCTqIK+nqrfqoqadgy0+wOCtJ+TsxC4AAC4C4Er76K/znHjarQ+eWGBLpy4YtOZLjc3k8pWKalRM55QkRKZTzyErHH8Gs8ZPXZ24Mo3k2YAAADANkVK+OmtWf3V5tEG8ixY4PZEGndPpvHfZZ4FC6jNow301qz+hC0gB/LVNVwAAACwjZePp/q90UGPvtBavy3fr3WLdyvywnUlxifJ08tD/mWLqk2v+noopDbXbAEOIHABAAD8g3n7FVTbxxqq7WMNGfIGGIAhhQAAAABgEAIXAAAAABiEwAUAAAAABiFwAQAAAIBBCFwAAAAAYBACFwAAAAAYhMAFAAAAAAYhcAEAAACAQQhcAAAAAGAQAhcAAAAAGITABQAAAAAGIXABAAAAgEEIXAAAAABgEAIXAAAAABiEwAUAAAAABiFwAQAAAIBBCFwAAAAAYBACFwAAAAAYhMAFAAAAAAYhcAEAAACAQQhcAAAAAGCQArldAAAAeY3J5PhzHdkGMkd/jUV/jUV/jeeMHjv752Mym81m524SAAAAACAxpBAAAKeKjo5WgwYNFB0dndul5Ev011j011j013h5sccELgAAnCwmJia3S8jX6K+x6K+x6K/x8lqPCVwAAAAAYBACFwAAAAAYhMAFAIATeXp6avjw4fL09MztUvIl+mss+mss+mu8vNhjZikEAAAAAINwhgsAAAAADELgAgAAAACDELgAAAAAwCAELgAAAAAwCIELAAAAAAxC4AIAAC7j9OnT2rdvn+Li4nK7lHzpxo0bmjBhgo4dO5bbpeQ7ZrNZMTExuV1GvnHr1i3Fx8enW3bjxg1t3bpVmzdv1pUrV3KpsowIXAAA2On48eN6/fXX9cgjj2jw4MFasmSJrN1lJSwsTDVq1MiFCl3bjz/+qJCQELVo0UKvv/66bt26patXr+qxxx5Tp06d9Nhjj+mhhx7SnDlzcrvUfCc6Olpz587VuXPncrsUl/Tnn3/q+vXr6ZYdPnxYzz77rOrWratGjRqpXr16Gj58uE6ePJk7Rbq4+Ph4vfDCC2rSpIkaNmyojz76SJI0b948tW7dWs8884yeffZZtW7dWmPHjlVqamouV8x9uAAAsMvp06fVs2dPpaSkqGrVqrp69aouX76sBg0a6NNPP1XJkiUt64aFhemNN97QoUOHcrFi17Ju3To9//zzCgwMVOnSpbVp0yY9/PDDSk1NVUxMjDp06KD4+HiFhobq0KFD+vzzz9W6devcLttldO3aNcvHk5OTderUKZUpU0a+vr4ymUwKCwu7R9W5vho1amjSpEmWPu/fv199+/aVJLVt21alS5fWuXPntHbtWvn6+uqnn35S+fLlc7NklzNjxgxNmzZNPXr0kL+/vxYsWKB+/frpiy++UI8ePdS2bVslJSUpIiJCa9eu1csvv6znnnsuV2sukKt7BwDAxfznP/+Rr6+v5s2bp4oVK0qSli5dqvfff1+PP/64vv76a1WuXDmXq3Rd33zzjRo3bqy5c+fKZDJp9uzZmjRpklq2bKlvvvnGst5TTz2lbt266bvvviNw2eHYsWPy8fFRrVq1rD6emJgoSfL19VXRokXvYWX5w93nMSZNmiRvb28tXLhQFSpUsCw/fPiwnnzySc2YMUMTJ06812W6tIiICPXq1UsffvihJKlmzZp69dVX9cgjj2j8+PGW9Tp16qRnn31WS5YsyfXAxZBCAADssHfvXvXt29cStiSpe/fuWrhwodzc3PTUU0/pzz//zMUKXdvx48fVvn17mUwmSbfPCqSmpqpTp07p1itQoIC6du2q/fv350aZLmvkyJEym80qUKCAxo4dq++++y7d17///W9J0ksvvWRZhpxJSUnRzp079cwzz6QLW5IUGBio3r1767fffsul6lzXpUuXVL9+fcv3DRo0kNlstvrBS5s2bXT+/Pl7WJ11BC4AAOxw/fp1+fv7Z1hepUoVLViwQKVLl9aAAQO0adOmXKjO9cXHx8vLy8vyvZ+fnyTpvvvuy7Cuv78/kxDY6fnnn9fKlStVtGhR9ezZU++99166a47Sgi4cFx8fbxl6bE21atUUFRV1j6tyfUWKFEl3zKb9/93XzqUtywtnaglcAADYoWzZsjpy5IjVx/z9/fX999+rZs2alje2sI+/v7/+/vtvy/deXl56/PHHVbp06QzrXr58OU+8mXI1pUqV0pQpU/Ttt99q165dat++vWbPnq3k5OTcLi1f2L9/v1atWqUtW7bI19dX165ds7re1atXLR8owHYNGjTQggULdOLECV2/fl1Tp06Vl5eXfvnll3R/O86cOaN58+apZs2auVjtbVzDBQCAHZo0aaKVK1fqjTfeUIECGf8Z9fPz07fffquRI0dq7dq1nDGwU61atbRnzx7L997e3nrvvfesrrtjxw4FBATco8ryn8aNG2vJkiWaP3++pk6dqvnz56t///4csw6aM2dOuhk0N2zYoF69emVYb/fu3emGJsM2L730kh599FF16dJF0u3r5kaOHKmyZcuqffv2qlmzplJTU3Xw4EGlpqZq+PDhuVwxgQsAALv07NlTkZGR2r9/v+rVq2d1HU9PT82YMUMTJkzQ4cOH722BLu7FF1/UhQsXsl0vKipKfn5+CgkJuQdV5V9ubm7q06ePOnfurClTpuiDDz7I7ZJc2ty5czMs8/DwyLAsKipK8fHx2c4aiYwqVqyo8PBwhYaGKiYmRk2aNNG//vUvSZK7u7vmz5+vqKgoPfTQQxo8eLDq1KmTyxUzLTwAAAD+68yZM7p8+bKqVaumYsWK5XY5QL5A4AIAAAAAgzBpBgAAAAAYhMAFAAAAAAYhcAEAAACAQQhcAAAAAGAQAhcAADkQFxen4cOHKywsLLdLyZfor7Hor7Hor/FcqccELgAAcsDb21u//fab4uPjc7uUfIn+Gov+Gov+Gs+VekzgAgAghxo2bKjdu3fndhn5Fv01Fv01Fv01nqv0mMAFAEAOjR07Vjt37tSUKVP0119/5XY5+Q79NRb9NRb9NZ6r9JgbHwMAkEP169dXSkqKkpKSJEnu7u7y9PRMt47JZNLOnTtzozyXR3+NRX+NRX+N5yo9LpCrewcAwIV16NBBJpMpt8vIt+ivseivseiv8Vylx5zhAgAAAACDcA0XAAAAABiEIYUAADggOjpas2fP1vr163Xx4kVJUpkyZdS6dWsNHDhQfn5+uVyha6O/xqK/xqK/xnOFHjOkEACAHLp8+bL69Omj8+fPq3LlyqpcubIk6dSpUzpx4oTKly+vefPm6b777svlSl0T/TUW/TUW/TWey/TYDAAAcmTUqFHmunXrmtevX5/hsfXr15vr1atnfv3113OhsvyB/hqL/hqL/hrPVXrMNVwAAOTQpk2bNGDAALVq1SrDY61atVK/fv20YcOGXKgsf6C/xqK/xqK/xnOVHhO4AADIobi4OJUoUSLTx/39/RUXF3cPK8pf6K+x6K+x6K/xXKXHBC4AAHKoSpUqWrZsmRITEzM8lpSUpGXLlqlKlSq5UFn+QH+NRX+NRX+N5yo9ZtIMAAByaMWKFXr55ZcVEBCgp556Sg888ICk2xdsL1iwQEeOHNGUKVPUsWPH3C3URdFfY9FfY9Ff47lKjwlcAAA4YPHixfr3v/+tq1evymQySZLMZrNKlCihUaNGqWfPnrlcoWujv8aiv8aiv8ZzhR4TuAAAcFBycrL279+f7h4wtWvXVoEC3O7SGeivseivseiv8fJ6jwlcAADkUGhoqBo1aqRy5cpZffz8+fPasWOHevTocW8Lyyfor7Hor7Hor/FcpcdMmgEAQA6NGTNGu3fvzvTxP//8U2PGjLmHFeUv9NdY9NdY9Nd4rtJjAhcAADmU3SCR2NhYubu736Nq8h/6ayz6ayz6azxX6XHeGNgIAICLOHz4sA4fPmz5fseOHUpJScmw3s2bN7VgwQJVqlTpXpbn8uivseivseiv8Vyxx1zDBQCAHaZPn67p06dLkkwmU5afsBYuXFgfffSR2rRpc6/Kc3n011j011j013iu2GMCFwAAdvj777/1999/y2w2q3fv3hoxYoRatmyZbh2TySRvb29VqFAhz8yS5Sror7Hor7Hor/FcsccELgAAcuj3339X1apVVbx48dwuJV+iv8aiv8aiv8ZzlR4zaQYAADlUvXp1/f3335k+fuTIEd24ceMeVpS/0F9j0V9j0V/juUqPOcMFAEAOvfHGGzp16pQWLVpk9fEnnnhClStX1ocffniPK8sf6K+x6K+x6K/xXKXHnOECACCHtm3bpuDg4Ewfb9OmjbZu3XoPK8pf6K+x6K+x6K/xXKXHBC4AAHIoKipKxYoVy/TxokWL6urVq/ewovyF/hqL/hqL/hrPVXpM4AIAIIdKliypgwcPZvr4gQMH8vzF3HkZ/TUW/TUW/TWeq/SYwAUAQA61a9dOP//8s9asWZPhsV9//VWLFy9Wu3btcqGy/IH+Gov+Gov+Gs9VesykGQAA5NCtW7f01FNP6fjx4woMDFS1atUkSceOHdPhw4dVpUoV/fDDDypcuHAuV+qa6K+x6K+x6K/xXKXHBC4AABwQGxurr7/+WqtXr9bZs2clSRUqVFD79u01aNAg+fj45HKFro3+Gov+Gov+Gs8VekzgAgAAAACDcA0XAAAAABikQG4XAACAK0tISNAvv/yigwcP6tatW0pNTU33uMlkyvWbbroy+mss+mss+ms8V+gxgQsAgBy6cOGC+vfvrwsXLqhw4cK6deuWihQpolu3biklJUXFihXLE9cPuCr6ayz6ayz6azxX6TFDCgEAyKFJkyYpOjpaixYt0sqVK2U2mzVlyhTt3r1bo0aNkpeXl7755pvcLtNl0V9j0V9j0V/juUqPCVwAAOTQtm3b9OSTTyooKEhubv/7J9XT01ODBw9W06ZNc30oiyujv8aiv8aiv8ZzlR4TuAAAyKH4+HiVLVtWkuTn5yeTyaRbt25ZHq9fv7527tyZW+W5PPprLPprLPprPFfpMYELAIAcuv/++3X58mVJUoECBVSqVCnt2bPH8vjx48dVsGDBXKrO9dFfY9FfY9Ff47lKj5k0AwCAHGratKnWrFmj4cOHS5J69uypmTNn6ubNm0pNTVVYWJi6d++ey1W6LvprLPprLPprPFfpMTc+BgAghy5evKh9+/apTZs28vT0VEJCgsaNG6dVq1bJzc1Nbdq00dtvvy0/P7/cLtUl0V9j0V9j0V/juUqPCVwAANhh8uTJCgkJUWBgYG6Xki/RX2PRX2PRX+O5Yo+5hgsAADvMnDlTx44ds3x/7do11ahRQ1u3bs3FqvIP+mss+mss+ms8V+wxgQsAAAcxWMRY9NdY9NdY9Nd4eb3HBC4AAAAAMAiBCwAAAAAMwrTwAADY6cKFCzpw4IAkWW6yeebMGRUuXNjq+rVq1bpnteUH9NdY9NdY9Nd4rtZjZikEAMAOgYGBMplM6ZaZzeYMy+5cfujQoXtVnsujv8aiv8aiv8ZzxR5zhgsAADtMmDAht0vI1+ivseivseiv8Vyxx5zhAgAAAACDMGkGAAAAABiEwAUAAAAABiFwAQAAAIBBCFwAAAAAYBACFwAAAAAYhMAFAAAAAAYhcAEAAACAQQhcAAAAAGCQ/wcfAcMizp2DxwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 500x1200 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with sns.axes_style(\"darkgrid\"):\n",
    "    dotplot = c2c.plotting.pval_plot.generate_dot_plot(pval_df=pvals,\n",
    "                                                       score_df=scores,\n",
    "                                                       significance=0.05,\n",
    "                                                       xlabel='',\n",
    "                                                       ylabel='{} Annotations'.format(pathwaydb),\n",
    "                                                       cbar_title='NES',\n",
    "                                                       cmap='PuOr',\n",
    "                                                       figsize=(5, 12),\n",
    "                                                       label_size=20,\n",
    "                                                       title_size=20,\n",
    "                                                       tick_size=12,\n",
    "                                                       filename=output_folder + '/GSEA-Dotplot.pdf'\n",
    "                                                      )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8eb5cea3",
   "metadata": {},
   "source": [
    "**Similarly, we can use gsea_df to easily see the Enriched Pathways**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "136e6817",
   "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>Factor</th>\n",
       "      <th>Term</th>\n",
       "      <th>NES</th>\n",
       "      <th>P-value</th>\n",
       "      <th>Adj. P-value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Factor 2</td>\n",
       "      <td>ANTIGEN PROCESSING AND PRESENTATION</td>\n",
       "      <td>1.622506</td>\n",
       "      <td>0.001000</td>\n",
       "      <td>0.016266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Factor 2</td>\n",
       "      <td>CELL ADHESION MOLECULES CAMS</td>\n",
       "      <td>1.428369</td>\n",
       "      <td>0.001000</td>\n",
       "      <td>0.016266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Factor 3</td>\n",
       "      <td>ANTIGEN PROCESSING AND PRESENTATION</td>\n",
       "      <td>1.370556</td>\n",
       "      <td>0.001001</td>\n",
       "      <td>0.016266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Factor 4</td>\n",
       "      <td>ANTIGEN PROCESSING AND PRESENTATION</td>\n",
       "      <td>1.556417</td>\n",
       "      <td>0.001000</td>\n",
       "      <td>0.016266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Factor 4</td>\n",
       "      <td>NATURAL KILLER CELL MEDIATED CYTOTOXICITY</td>\n",
       "      <td>1.374844</td>\n",
       "      <td>0.002002</td>\n",
       "      <td>0.026026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Factor 4</td>\n",
       "      <td>CELL ADHESION MOLECULES CAMS</td>\n",
       "      <td>1.220381</td>\n",
       "      <td>0.002002</td>\n",
       "      <td>0.026026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>Factor 5</td>\n",
       "      <td>ANTIGEN PROCESSING AND PRESENTATION</td>\n",
       "      <td>1.320129</td>\n",
       "      <td>0.001000</td>\n",
       "      <td>0.016266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>Factor 5</td>\n",
       "      <td>CELL ADHESION MOLECULES CAMS</td>\n",
       "      <td>1.277904</td>\n",
       "      <td>0.001000</td>\n",
       "      <td>0.016266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>Factor 8</td>\n",
       "      <td>ANTIGEN PROCESSING AND PRESENTATION</td>\n",
       "      <td>1.424629</td>\n",
       "      <td>0.001000</td>\n",
       "      <td>0.016266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>Factor 8</td>\n",
       "      <td>CELL ADHESION MOLECULES CAMS</td>\n",
       "      <td>1.243899</td>\n",
       "      <td>0.003003</td>\n",
       "      <td>0.035490</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Factor                                       Term       NES   P-value  \\\n",
       "13  Factor 2        ANTIGEN PROCESSING AND PRESENTATION  1.622506  0.001000   \n",
       "14  Factor 2               CELL ADHESION MOLECULES CAMS  1.428369  0.001000   \n",
       "26  Factor 3        ANTIGEN PROCESSING AND PRESENTATION  1.370556  0.001001   \n",
       "39  Factor 4        ANTIGEN PROCESSING AND PRESENTATION  1.556417  0.001000   \n",
       "40  Factor 4  NATURAL KILLER CELL MEDIATED CYTOTOXICITY  1.374844  0.002002   \n",
       "42  Factor 4               CELL ADHESION MOLECULES CAMS  1.220381  0.002002   \n",
       "54  Factor 5        ANTIGEN PROCESSING AND PRESENTATION  1.320129  0.001000   \n",
       "55  Factor 5               CELL ADHESION MOLECULES CAMS  1.277904  0.001000   \n",
       "91  Factor 8        ANTIGEN PROCESSING AND PRESENTATION  1.424629  0.001000   \n",
       "93  Factor 8               CELL ADHESION MOLECULES CAMS  1.243899  0.003003   \n",
       "\n",
       "    Adj. P-value  \n",
       "13      0.016266  \n",
       "14      0.016266  \n",
       "26      0.016266  \n",
       "39      0.016266  \n",
       "40      0.026026  \n",
       "42      0.026026  \n",
       "54      0.016266  \n",
       "55      0.016266  \n",
       "91      0.016266  \n",
       "93      0.035490  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gsea_df.loc[(gsea_df['Adj. P-value'] < 0.05) & (gsea_df['NES'] > 0.)]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0f19de0",
   "metadata": {},
   "source": [
    "**And Depleted Pathways**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "94dc00a9",
   "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>Factor</th>\n",
       "      <th>Term</th>\n",
       "      <th>NES</th>\n",
       "      <th>P-value</th>\n",
       "      <th>Adj. P-value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Factor 4</td>\n",
       "      <td>APOPTOSIS</td>\n",
       "      <td>-1.260816</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.016266</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Factor       Term       NES  P-value  Adj. P-value\n",
       "41  Factor 4  APOPTOSIS -1.260816    0.001      0.016266"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gsea_df.loc[(gsea_df['Adj. P-value'] < 0.05) & (gsea_df['NES'] < 0.)]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56fd5694",
   "metadata": {},
   "source": [
    "## Footprint Enrichment"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad5bb159",
   "metadata": {},
   "source": [
    "Footprint enrichment analysis build upon classic geneset enrichment analysis, as instead of considering the genes involved in a biological activity, they consider the genes affected by the activity, or in other words the genes that change downstream of said activity [(Dugourd and Saez-Rodriguez, 2019)](https://www.sciencedirect.com/science/article/pii/S2452310019300149). This is particularly useful when working with transcriptomics data, as it allows to identify the genes that are regulated by a biological process, instead of the genes that are part of the process. In this case, we will use the PROGENy pathway resource to perform footprint enrichment analysis. PROGENy was built in a data-driven manner using perturbation and cancer lineage data [(Schubert et al, 2019)](https://www.nature.com/articles/s41467-017-02391-6#Sec8), as a consequence it also assigns different importances or weights to each gene in its pathway genesets.\n",
    "\n",
    "To this end, we need an enrichment method that can take weights into account, and here we will use multi-variate linear models from the `decoupler-py` package to perform this analysis [(Badia-i-Mompel et al., 2022)](https://academic.oup.com/bioinformaticsadvances/article/2/1/vbac016/6544613)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca5209d4",
   "metadata": {},
   "source": [
    "###  load the PROGENy pathway resource:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "68972dfc",
   "metadata": {},
   "outputs": [],
   "source": [
    "net = dc.get_progeny(organism='human', top=5000)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bac3fcc0",
   "metadata": {},
   "source": [
    "Now we will use the PROGENy genesets to assign pathways and weights to the ligand-receptor pairs in liana's consensus resource. Specifically, we will use the weighted bipartite networks from PROGENy, where the weight represents the importance of the genes to a given geneset, and assign a weight to each ligand-receptor interaction, based on the mean of the weights of the proteins in that ligand-receptor interaction. We keep only ligand-receptor weights only if all the protein in the ligand-receptor interaction are present for a given pathway, and are sign-coherent."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "25731683",
   "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>source</th>\n",
       "      <th>interaction</th>\n",
       "      <th>weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>JAK-STAT</td>\n",
       "      <td>LGALS9^PTPRC</td>\n",
       "      <td>1.307807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>Androgen</td>\n",
       "      <td>SEMA4D^MET</td>\n",
       "      <td>-0.831693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>Androgen</td>\n",
       "      <td>HGF^MET</td>\n",
       "      <td>-1.288956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>Androgen</td>\n",
       "      <td>TIMP3^MET</td>\n",
       "      <td>-1.122612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>NFkB</td>\n",
       "      <td>SELE^CD44</td>\n",
       "      <td>3.332552</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       source   interaction    weight\n",
       "1    JAK-STAT  LGALS9^PTPRC  1.307807\n",
       "74   Androgen    SEMA4D^MET -0.831693\n",
       "90   Androgen       HGF^MET -1.288956\n",
       "106  Androgen     TIMP3^MET -1.122612\n",
       "151      NFkB     SELE^CD44  3.332552"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr_progeny = li.rs.generate_lr_geneset(lr_pairs, net, lr_sep=\"^\")\n",
    "lr_progeny.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d7909a0",
   "metadata": {},
   "source": [
    "### Run footprint enrichment analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "536c29e1",
   "metadata": {},
   "source": [
    "We will now use the `decoupler-py` package to perform the analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "358b2ddb",
   "metadata": {},
   "outputs": [],
   "source": [
    "estimate, pvals =  dc.run_mlm(lr_loadings.transpose(), \n",
    "                              lr_progeny, \n",
    "                              source=\"source\", \n",
    "                              target=\"interaction\", \n",
    "                              use_raw=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ae118e0",
   "metadata": {},
   "source": [
    "### Visualize Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "88645855",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.clustermap(estimate, xticklabels=estimate.columns, cmap='coolwarm', z_score=4)\n",
    "plt.savefig(output_folder + '/PROGENy.pdf', dpi=300, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69a55f24",
   "metadata": {},
   "source": [
    "Let's zoom in on **Factor 5**, which is highly correlated with COVID-19 severity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c84985d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "selected_factor = 'Factor 5'\n",
    "\n",
    "dc.plot_barplot(estimate,\n",
    "                selected_factor,\n",
    "                vertical=True, \n",
    "                cmap='coolwarm', \n",
    "                save=output_folder + '/PROGENy-{}.pdf'.format(selected_factor.replace(' ', '-')))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d94507d7",
   "metadata": {},
   "source": [
    "From the plots above, we can see that the JAK-STAT pathway is active and highly active in Factor 5. Let's see which ligand-receptor pairs are enriched in this pathway."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "fce94e3b",
   "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>interaction</th>\n",
       "      <th>Factor</th>\n",
       "      <th>Loadings</th>\n",
       "      <th>source</th>\n",
       "      <th>weight</th>\n",
       "      <th>sign</th>\n",
       "      <th>relevant_interactions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>818</th>\n",
       "      <td>B2M^LILRB2</td>\n",
       "      <td>Factor 5</td>\n",
       "      <td>1.086240e-01</td>\n",
       "      <td>JAK-STAT</td>\n",
       "      <td>1.428233</td>\n",
       "      <td>positive</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1024</th>\n",
       "      <td>CCL18^CCR1</td>\n",
       "      <td>Factor 5</td>\n",
       "      <td>2.542308e-03</td>\n",
       "      <td>JAK-STAT</td>\n",
       "      <td>1.096684</td>\n",
       "      <td>positive</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1058</th>\n",
       "      <td>CCL2^CCR1</td>\n",
       "      <td>Factor 5</td>\n",
       "      <td>7.912275e-02</td>\n",
       "      <td>JAK-STAT</td>\n",
       "      <td>2.103921</td>\n",
       "      <td>positive</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1138</th>\n",
       "      <td>CCL7^CCR1</td>\n",
       "      <td>Factor 5</td>\n",
       "      <td>2.964358e-02</td>\n",
       "      <td>JAK-STAT</td>\n",
       "      <td>1.145112</td>\n",
       "      <td>positive</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1184</th>\n",
       "      <td>CCL7^CXCR3</td>\n",
       "      <td>Factor 5</td>\n",
       "      <td>7.615716e-11</td>\n",
       "      <td>JAK-STAT</td>\n",
       "      <td>0.682378</td>\n",
       "      <td>positive</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     interaction    Factor      Loadings    source    weight      sign  \\\n",
       "818   B2M^LILRB2  Factor 5  1.086240e-01  JAK-STAT  1.428233  positive   \n",
       "1024  CCL18^CCR1  Factor 5  2.542308e-03  JAK-STAT  1.096684  positive   \n",
       "1058   CCL2^CCR1  Factor 5  7.912275e-02  JAK-STAT  2.103921  positive   \n",
       "1138   CCL7^CCR1  Factor 5  2.964358e-02  JAK-STAT  1.145112  positive   \n",
       "1184  CCL7^CXCR3  Factor 5  7.615716e-11  JAK-STAT  0.682378  positive   \n",
       "\n",
       "     relevant_interactions  \n",
       "818                   None  \n",
       "1024                  None  \n",
       "1058                  None  \n",
       "1138                  None  \n",
       "1184                  None  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pathway = 'JAK-STAT'\n",
    "# loadings to long format\n",
    "lr_loadings_long = lr_loadings.reset_index().melt(id_vars='index', var_name=\"Factor\", value_name=\"Loadings\").rename(columns={'index':'interaction'})\n",
    "# join progeny weights and keep only NFkB pathway\n",
    "lr_loadings_long = lr_loadings_long.merge(lr_progeny, on='interaction').query(\"source == '{}' and Factor == '{}'\".format(pathway, selected_factor))\n",
    "# add sign to the weights\n",
    "lr_loadings_long['sign'] = lr_loadings_long['weight'].apply(lambda x: 'positive' if x > 0 else 'negative')\n",
    "# keep only relevant interactions for labels\n",
    "lr_loadings_long['relevant_interactions'] = lr_loadings_long.apply(lambda x: x['interaction'] if (x['Loadings'] > 0.025) and (x['weight'] > 2.5) else None, axis=1)\n",
    "lr_loadings_long.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "593583eb",
   "metadata": {},
   "source": [
    "**Plot the loadings for Factor 5 and weights of the JAK-STAT pathway**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c72b3e73",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 480,
       "width": 640
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure Size: (640 x 480)>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(p9.ggplot(lr_loadings_long,\n",
    "           p9.aes(x='weight', y='Loadings')) +\n",
    " p9.geom_smooth(method='lm') + \n",
    " p9.geom_point(p9.aes(colour='sign')) +\n",
    " p9.theme_bw() +\n",
    " p9.theme(legend_position='none') +\n",
    " p9.labs(title=\"{} | {}\".format(pathway, selected_factor), x=\"PROGENy Weights\", y=\"Loadings\") +\n",
    " p9.scale_colour_manual(values=[\"royalblue\", \"red\"]) +\n",
    " p9.geom_label(p9.aes(label='relevant_interactions'), size=8, nudge_y=0.01, nudge_x=0.01) + \n",
    " p9.xlim(-1, 8.5)\n",
    " )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "acb3c5e7",
   "metadata": {},
   "source": [
    "Here, we can see that for example the `CCL8 -> CCR1` is an interaction found with high importances in the JAK-STAT pathway, and high loadings. This interaction is proinflammatory (https://doi.org/10.1016%2Fj.cytogfr.2020.12.005), which could be one reason why this factor is highly correlated with the disease severity."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:ccc_protocols]",
   "language": "python",
   "name": "conda-env-ccc_protocols-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.0"
  },
  "vscode": {
   "interpreter": {
    "hash": "a89d9df9e41c144bbb86b791904f32fb0efeb7b488a88d676a8bce57017c9696"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}