{ "cells": [ { "cell_type": "markdown", "id": "8d2f33b2", "metadata": {}, "source": [ "# High-resolution (sub-cellular) dataset\n", "Apr 2025\n", "\n", "For high-resolution (sub-cellular level) spatially resolved transcriptomics dataset.\n", "\n", "The dataset is available at:\n", "* Spatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball-patterned arrays\n", "* https://db.cngb.org/stomics/mosta/download/ " ] }, { "cell_type": "markdown", "id": "61d39eb2", "metadata": {}, "source": [ "## Part I. Infer the network" ] }, { "cell_type": "code", "execution_count": 1, "id": "62b3f371", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n" ] } ], "source": [ "# Load SpaGRN and dependencies\n", "import os\n", "import sys\n", "\n", "import argparse\n", "import pandas as pd\n", "import scanpy as sc\n", "from multiprocessing import cpu_count\n", "\n", "from spagrn.regulatory_network import InferNetwork as irn" ] }, { "cell_type": "markdown", "id": "20a24a38", "metadata": {}, "source": [ "### 1. Load in data\n", "**Essential data:**
\n", "Spatially resolved transcriptomics data: the matrix of gene expression for each single cell. Each column represents a gene, and each row represents a SRT sample. Values in the matrix are typically gene expression levels, either raw counts or normalized expression (raw counts prefered). " ] }, { "cell_type": "code", "execution_count": null, "id": "40a922f4", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "AnnData object with n_obs × n_vars = 50140 × 25879\n", " obs: 'annotation'\n", " var: 'Gene'\n", " uns: 'annotation_colors'\n", " obsm: 'spatial'\n", " layers: 'counts'\n" ] } ], "source": [ "fn = 'Mouse_brain_cell_bin.h5ad' #mouse_brain stereo_seq\n", "data = irn.read_file(fn)\n", "print(data)" ] }, { "cell_type": "markdown", "id": "1e158f75", "metadata": {}, "source": [ "#### Preprocess Data:\n", "Perform cleaning and quality control on the imported data before constructing the gene regulatory network.\n", "1. filter spots/cells with less than min_genes number of genes expressed;\n", "2. keep genes that have at least min_counts counts or are expressed in at least min_cells cells;\n", "3. keep spots/cells with no more than max_genes number of genes;" ] }, { "cell_type": "code", "execution_count": 4, "id": "75b0ee95", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "View of AnnData object with n_obs × n_vars = 13352 × 9788\n", " obs: 'annotation', 'n_genes', 'n_counts'\n", " var: 'Gene', 'n_cells', 'n_counts'\n", " uns: 'annotation_colors'\n", " obsm: 'spatial'\n", " layers: 'counts'\n" ] } ], "source": [ "data = irn.preprocess(data, min_genes=1000, min_cells=300, min_counts=10, max_gene_num=4000)\n", "print(data)" ] }, { "cell_type": "markdown", "id": "33f1dcde", "metadata": {}, "source": [ "#### Visualize Data" ] }, { "cell_type": "code", "execution_count": 12, "id": "b8de11dd", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/Oreo/anaconda3/envs/spagrn/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored\n", " cax = scatter(\n" ] }, { "data": { "image/png": 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RVMuA3sC1h7Zfv7u97IK/P//8kxo1auDv78+gQYP4+eefuZcIfdKkSURGRrJmzRpOnTrFvHnzcHJyAiAiIgKAjRs3EhMTw7Jl99u4adMmoqKi2LBhA+Hh4WXWdlE8+5etYFbv/ty5Zrgk1J3r0czq3b/Mg7+CTJo0iV69enH06FEGDhxIv379OHXqFKALvjp06IC1tTU7duxg165dWFlZ0bFjR32PMsCWLVs4f/48W7Zs4ZdffmHBggUGNzKGDh3K1atX2bJlC3/99Rfffvstt27d0u9/+eWXWbt2LTExMfpt4eHhpKWl0bdvX/22X375BScnJyIiIhg9ejQjR47k+eefp2nTphw6dIj27dszePBg0tLSANBoNHh5ebF06VIiIyOZPHky7777Ln/++WdZvZxC+wRITEzUAtrExMTybop4ihxOjtPOuHpcm56T/VjPeycrQ7s1PuaxnlMIIcpD2X5+52i1Wi+tVks+D4VWq/W+W670NW3aVDtr1iytVqvVZmdna52cnLRbtmzRarVa7XPPPad98cUX8zzu4sWLWkB7+PBhg+1DhgzRurq6ajMzM4t1nChb6pwc7WteVbUDMM37oTDTvuZdTavOKf332ZAhQ7RKpVJraWlp8Jg6dapWq9VqAe2IESMMjmnUqJF25MiRWq1Wq/3tt9+0/v7+Wo1Go9+fmZmpNTc3165bt05/jsqVK2tzHmj/888/r+3bt69Wq9Vqo6KitIA2IiJCv//UqVNaQPvVV1/pt9WsWVM7ffp0/fPnnntOO3ToUP3zVq1aaZs3b65/npOTo7W0tNQOHjxYvy0mJkYLaPfs2ZPvazJq1Chtr169DF6jbt265VteFI/0+IknQmRqAiPO7uFKRkqRysdlZzDi7B52Jt7Mt0ywlQPjvGphpny8q5a8en4vocfXse4xLyYvhBBPlx3k7ul7kBa4erdc6YqKiiIiIoL+/fsDoFKp6Nu3r35Y28iRI1m8eDHBwcGMHz+e3bt3F6neoKAgTExMSr29ouRO79iZq6fPgFbLnavXOL1jZ5mcv3Xr1hw5csTgMWLECP3+Jk2aGJRv0qSJvsfv6NGjnDt3Dmtra6ysrLCyssLBwYGMjAzOnz+vPyYwMBClUql/7u7uru/RO3XqFCqVinr16un316hRAzs7O4Pzvvzyy/rhzDdv3mTNmjW89NJLBmVq166t/79SqcTR0ZGgoCD9NldXVwCD3sS5c+dSr149nJ2dsbKy4ocffuDKlStFeOVEScg6fuKJsODWOb6/cQY/cxve8gostPymhBt8f+MMyepsmtu6PoYWFl13x0ok5WQTZKEbo8/pU/DB+zDoBXiuW/k2TgghKoyYwosUq1zRhYWFkZOTg4eHh36bVqvF1NSUOXPm0KlTJy5fvszq1avZsGEDbdq0YdSoUXzxxRcF1mtpaVnqbRWPJiHmRqmWKy5LS0t8fX1LdGxKSgr16tXLNScPwNnZWf9/Y2Njg30KhQKNpniJ71544QXeeecd9uzZw+7du6lSpQotWrQwKJPXeR7cprg77+beuRcvXsy4ceOYOXMmTZo0wdramhkzZrBv375itU0UnQR+4okwwasW1cysGehStUjlezpVYkH1ZrSxcy/WeY6nxlPVzApLpXHhhUuor3MV+jpXub8hPQ3S0iApqUzOdzs7g9V3rtPX2QdTI2XhBwghRIVQ1L/vxfscKExOTg6//vorM2fOpH379gb7unfvzqJFixgxYgTOzs4MGTKEIUOG0KJFC95++22++OILfY+e+jFkhBSPzs7drVTLlba9e/fywgsvGDwPCQkBoG7duixZsgQXFxdsbGxKVH+NGjXIycnh4MGDNGjQAND1eCckJBiUc3R0pHv37syfP589e/bw4osvluyCHrBr1y6aNm3Kq6++qt/2YE+lKH0S+IkngqOxGa+4+xe5vEphxBDX4t0h25Zwg9Dj6xjgXIWFNVoWt4klF1IPFv8Fj3CnN12dw1sXD9DK1tUwqAQ+vHyUOTGnSdfkFOs1FEKIJ1sLwAtdIhdtHvsVd/e3yGNfyYWHhxMfH8+wYcOwtbU12NerVy/CwsKIjo6mXr16BAYGkpmZSXh4OAEBAQC4uLhgbm7O2rVr8fLywszMLFc9D7pz5w5XrlzRLxcRFRUFoM/wKMpWjRbNcfDy5M71aNDm8T5TKHDw8qRGi+Zlcv7MzExu3DDsTVSpVPpkQUuXLqV+/fo0b96chQsXEhERoR9yPHDgQGbMmEG3bt346KOP8PLy4vLlyyxbtozx48fj5VV4VnJ/f386duzIK6+8wrx581CpVIwdOxZzc/NcZV9++WWeffZZ1Go1Q4YMeeRr9/Pz49dff2XdunVUqVKF3377jf379+sz44rSJ3P8xH9GVXNrmto408q2HD5IH3F4T2RaIvNioph9/VSufUNcqzHUtZosOyGEeMoogXup3R9OzXzv+ay75UpPWFgYbdu2zTNY69WrFwcOHEClUjFx4kRq165Ny5YtUSqVLF68GNB9af/666/5/vvv8fDwoFu3gof4//vvv4SEhOgXie/Xrx8hISF89913pXpdIm9GSiUvzJ6pe/JwCvC7z1+Y9QVGyrIZUbN27Vrc3d0NHs2b3w8yP/zwQxYvXkzt2rX59ddfWbRoETVr1gTAwsKC7du3U6lSJXr27ElAQADDhg0jIyOjWD2A8+fPx8PDg1atWtGzZ0+GDx+Oi0vurOZt27bF3d2dDh06GAyDLqlXXnmFnj170rdvXxo1akRcXJxB758ofQqtNq/bG49XUlIStra2JCYmlrirWlQsOVoN59KTqWGR/11QYeifuCvUtLDDz1x+R4QQT4bH8/m9DBiDYaIXb3RBX88yOqf4r9m/bAW/jnnLINGLg7cXL8z6ggY9u5dLmxQKBcuXL6d79/I5/8NSUlLw9PRk/vz59Owpv3sVkQz1FOXi/UuHmX7tBH8FhNLLqfJjP/+kS4fZkBDN6sC2OBibPvbzl0Q3x0rFKp+j1dD2+Hqcjc1YGhBaNo0SQogy1xPohi57Zwy6OX0tKO2ePvHf1qBnd+p1e47TO3aSEHMDO3c3arRoXmY9fRWJRqPh9u3bzJw5Ezs7O7p27VreTRIlJIGfKBchVg7UtLClmpl1uZx/b3IsB1PiiMvJfKTAL0WdjVUZJop5FFkaDcdS43FUVYzAVggh8qcEQsu7EeIpZ6RUUjO0VXk344lz5coVqlSpgpeXFwsWLEClkvChopKhnuKpdysrnTqHVvKMnZs+qUuKOpu47Ewqm1mVuN6fbpzhf2f38Lt/iyJnI33c4rIzMFYYYaOSdaOEEKVPPr+FEKLikOQu4qmnRku6JodUTY5+m5XS+JGCPgBrpTGWRiqslCriszP56PJRLhVxAfrHxdHYrNCgr9XRtdQ++C/ZxVzTRwghhBBCVBzSVyty+Tb6NBq0vOYRUOxju53czK3sDLbX7oix0eO9r6DRannrwn58zKwY41lTv93dxIKbjftioijd9txbr+/3W+dpdWwtx9MSuJ2TwdfVGpXqecrK0ZQ7mBkpicvJJCEnC02e6dKFEEIIIcTTQAI/YUCj1TLmQgRaLYxw90dVzGApKj2RuOxMcrQajB9zh3Jsdgazo0/hZWppEPgBZbqw+ZLYSxxPS2CoSzXeeOi8T6pUdTYNj6zCRmlMdKM+aNDK4u9CCCGEEE8xCfyEASOFgvDANmi1FDvoAzgY8iw5Wi3mSt1ba9WdazipTGlk41zaTc3F1cSczUEdcCxGspZ0dQ6n0hOpa+VY4vP+7NeMyLQEWtk9/vUBT6TGsyvpFv9zq47Rw+sPAZlpaXw35GV8Gzeky1tj9dstjFS85OqLvbHpY++ZFUIIIYQQj5984xO5dLD3pKODZ7GO2Z54g1fP7SVLo8H27pyyW1npPHtyEz1ObSnw2IScrBK39WGhdm4EWdoXufyYCxHUOxzOqjvXCi9813cxUfx+67z+ubOJWfGDvrQ0mDgefl1QvOMeMvbCfkac28uWhBt57o+PjmH/38vZu3ipwXaFQsE8vyZ86lP3kc4vhBBCCCEqBgn8RKmYE32aeTFRbEm8H4A4G5sxuVIdPqocnO9xG+KjcdyzmA8uHyn7RuYh1NaNpjbO1ChgUfS47AxOpsYDuiGSI8/t5ZWze/MsezkjBde9S3j9/D6D7bOvR/LFtRP3NyQnwckTcPTII7X/fe/aTPQOopmtS5773Xyr8VHETt78Z2me+4UQQgjx9JsyZQrBwcHl3QxRziTwE6Xiiyr1WVC9GV0dvfXbFAoFH1YO5mW36vkeZ6M0xl5lgnM5LaI+wKUqu+p0ploBgV+PyC3UPrSSqLRELJXGLKnRij8D8l7nJ0urIVmdbdCLqdVqGX/xIO9cPESWRq3b6OoG3/8EUz5+pPaH2rnxqU9dzAqYn1e1fj3sPTwe6TxCCPFftGfPHpRKJV26dCnyMZcuXUKhUHDkyJFCy2ZnZzNhwgSCgoKwtLTEw8ODF154gejo6Edotagohg4dikKhyPXo2LHjI9WrUChYsWJF6TTyMfDx8WHWrFnl3Yz/BJnjJ0pFJTMrhpj5Fvu4RjbO3G7SrwxaVDzJOdmYGhnx+vkI6ls76oPVg5EnCc4xwczODXcTcwD6OPvkW4+fuQ23G/fD/IFATKFQsLZWW3K0WkweDNDcJRgTQoiiUGshIgtuasDVCBqagDL3tOZSFxYWxujRowkLCyM6OhqPUryJlpWVRXp6OocOHWLSpEnUqVOH+Ph4xowZQ9euXTlw4ECpnUsUjUaj4UpMNMlpaVhbWFDJ3QOjMp4H37FjR+bPn2+wzdQ075vh2dnZGBsbl2l7HpSVlYWJiawD/DSRHj9R4V1IT+bHmDPkaEu2Dt2Mayew3fMH82+c4/sbZ/gm+jSg+wBYtX0rzgeiWFerXZEXQbdQqlA8lGiltZ077ewl0BNCiOJakw5NbkGfOzA6Qfdvk1u67WUpJSWFJUuWMHLkSLp06cKCBQv0++Lj4xk4cCDOzs6Ym5vj5+en//JepUoVAEJCQlAoFISGhgK63p3u3bszdepUPDw88Pf3x9bWlg0bNtCnTx/8/f1p3Lgxc+bM4eDBg1y5cqVsL1AYOHXhPLN//5Vf/l3Bso3r+eXfFcz+/VdOXThf+MGPwNTUFDc3N4OHvb0uV4FCoWDevHl07doVS0tLPvnkE3x9ffniiy8M6jhy5AgKhYJz587h4+MDQI8ePVAoFPrn9/z222/4+Phga2tLv379SE5O1u8LDQ3ltddeY+zYsTg5OdGhQwcAtm3bRsOGDTE1NcXd3Z133nmHnJz7ayNrNBo+//xzfH19MTU1pVKlSkydOhWAZ555htdee82gDbGxsZiYmLBp0yZCQ0O5fPkyb7zxhr7H856dO3fSokULzM3N8fb25vXXXyc1NfXRXvD/OAn8xBOtKMHc+IsHGX5uDyvjrhar7vW7drBp3x5slSZYKY3xt7Blfa12LK2hG8ZpZGREnw6d6NOhU65A7kl2KCWOWdcjSxwICyHEk2JNOrySADEP/Tm7odFtL8vg788//6RGjRr4+/szaNAgfv75Z7Ra3XqnkyZNIjIykjVr1nDq1CnmzZuHk5MTABEREQBs3LiRmJgYli1bpq9z06ZNREVFsWHDBsLDw/M8b2JiIgqFAjs7u7K7OGHg1IXz/LluDUmpKQbbk1JT+HPdmjIP/goyZcoUevTowfHjxxk2bBgvvfRSrh7C+fPn07JlS3x9fdm/f79+W0xMjP45wPnz51mxYgXh4eGEh4ezbds2pk2bZlDXL7/8gomJCbt27eK7777j+vXrdO7cmQYNGnD06FHmzZtHWFgYn3zyif6YiRMnMm3aNP3vxR9//IGrqysAL7/8Mn/88QeZmZn68r///juenp4888wzLFu2DC8vLz766CNiYmKIiYnRt7Vjx4706tWLY8eOsWTJEnbu3JkriBTFI0M9xRMrOjONwEP/0NbOnaUBofmWG+sZgJepBc/YuRdYX1RaImZGSiqbWaGOiiLi8CGMlEomNhzFcPe85yHWqFL1US6hXLx94QCbE28QZGFPG/sCXpOIffDdXBg9FkIku6cQ4smi1sIHSaDNY58WUABTkqC9WdkM+wwLC2PQoEGAbjheYmIi27ZtIzQ0lCtXrhASEkL9+vUBDHpVnJ11yxc5Ojri5maY8dnS0pKffvop3+FzGRkZTJgwgf79+2Njk//cc1F6NBoNa3fuKLDM2l078PepUibDPsPDw7GysjLY9u677/Luu+8CMGDAAF588UX9vqFDhzJ58mQiIiJo2LAh2dnZ/PHHH/pewHvvPzs7u1zvP41Gw4IFC7C2tgZg8ODBbNq0Sd87B+Dn58fnn3+uf/7ee+/h7e3NnDlzUCgU1KhRg+joaCZMmMDkyZNJTU1l9uzZzJkzhyFDhgBQrVo1mjdvDkDPnj157bXX+Oeff+jTpw8ACxYs0M9vdHBwQKlUYm1tbdDezz77jIEDBzJ27Fh9u77++mtatWrFvHnzMDMzK+Er/t8mgZ8od8PO7EKDlvnVmxtsVyhAhQKTQtYTbG7rSnNb1wLLpKizCT68EjulCTGN+6BUKRm2PwKj1m0qVG9eUXzsE0JoQgwt8sn0qRcTDTdvwo28l4IQQojyFJGVu6fvQVogWqMr16SU84NFRUURERHB8uXLAVCpVPTt25ewsDBCQ0MZOXIkvXr14tChQ7Rv357u3bvTtGnTQusNCgrKN+jLzs6mT58+aLVa5s2bV6rXI/J3JSY6V0/fw5JSUrgSE42Pp1epn79169a5ft4ODg76/9+7uXCPh4cHXbp04eeff6Zhw4asXLmSzMxMnn/++ULP5ePjow/6ANzd3bl165ZBmXr16hk8P3XqFE2aNDH4rtSsWTNSUlK4du0aN27cIDMzkzZt2uR5TjMzMwYPHszPP/9Mnz59OHToECdOnODff/8tsK1Hjx7l2LFjLFy4UL9Nq9Wi0Wi4ePEiAQEBhV6vyE0CP1FuDqXEsS3hBn/FXgKFgp/8NCgfCPLcTSy41bhvqQRmFkYqejtVxkl199tBNV/cF/yuiy6fMk1tXGhqU0jQB9CtBzRqAm6Pf+F5IYQozM0ijlYvarniCAsLIycnxyCZi1arxdTUlDlz5tCpUycuX77M6tWr2bBhA23atGHUqFG55l49zNLSMs/t94K+y5cvs3nzZunte4yS09JKtVxxWVpa4uubf3K8vN4zL7/8MoMHD+arr75i/vz59O3bFwsLi0LP9XBiGIVCgUZj+AuU33s0P+bm5oWWefnllwkODubatWvMnz+fZ555hsqVKxd4TEpKCq+88gqvv/56rn2VKlUqVhvFfTLHT5SKyNQEticWr+do3IUDvHnxAN/5NeFo3a4GQd89pdUbZ6RQ8Jt/C76q1vDByvMs+9nV41SK+IszaYmlcu6I5FgmXDxIck52qdRXqiToE0I8oVyL+A2lqOWKKicnh19//ZWZM2dy5MgR/ePo0aN4eHiwaNEiQDekbsiQIfz+++/MmjWLH374AUDfo6dWq4t0vntB39mzZ9m4cSOOjo6le0GiQNZFCJiKU+5x6Ny5M5aWlsybN4+1a9fy0ksvGew3NjYu8vuvMAEBAezZs0c/vxVg165dWFtb4+XlhZ+fH+bm5mzatCnfOoKCgqhfvz4//vgjf/zxR672mpiY5Gpv3bp1iYyMxNfXN9dDMo2WnPT4iVLR5eQmrmSmEt3oeVxNCr/7A/CJTwibE2Lo5VTZcJmDcnY5I4XozDQS1FmFFy6CGddO8tftyzS0dqKXU8F3uIQQQug0NAF3I10il7zm+SnQ7W9Yyt8Bw8PDiY+PZ9iwYdja2hrs69Wrl35ph3r16hEYGEhmZibh4eH6oWcuLi6Ym5uzdu1avLy8MDMzy1XPPdnZ2fTu3ZtDhw4RHh6OWq3mxt3h9w4ODvIF9zGo5O6BjaVVgcM9baysqFRGSzBlZmbqf+b3qFQqfbKgvCiVSoYOHcrEiRPx8/OjSZMmBvt9fHzYtGkTzZo1w9TUVJ8ltCReffVVZs2axejRo3nttdeIiorigw8+4M0338TIyAgzMzMmTJjA+PHjMTExoVmzZsTGxnLy5EmGDRumr+fll1/mtddew9LSkh49euRq7/bt2+nXrx+mpqY4OTkxYcIEGjduzGuvvcbLL7+MpaUlkZGRbNiwgTlz5pT4ev7rpMdPlIoxngG86u6PUzEWYv/j1gWuZ6YVO+jLyMkiW51TeMESmuvbiBuN+9DQ2rlU6vvUpy7fVGvIcw6lNzfg79uXef7UVmKzMkqtTiGEeJIoFfDh3RGPD4/PuPd8ik3pJ3YJCwujbdu2eQZrvXr14sCBA6hUKiZOnEjt2rVp2bIlSqWSxYsXA7ov7V9//TXff/89Hh4edOvWLd9zXb9+nX///Zdr164RHByMu7u7/rF79+7SvTCRJyMjIzo2b1FgmY7NWpTZen5r1641+Lm7u7vrE6MUZNiwYWRlZRkkfrln5syZbNiwAW9vb0JCQh6pfZ6enqxevZqIiAjq1KnDiBEjGDZsGO+//76+zKRJk3jrrbeYPHkyAQEB9O3bN9fcwf79+6NSqejfv3+uxCwfffQRly5dolq1avrkNLVr12bbtm2cOXOGFi1aEBISwuTJk0t1Lc3/IoX2wb7bcpKUlIStrS2JiYkyrv0xWHTrAjUt7Khj5VB44TKi1Wqx3bMIjVZLQtP+qApJ4HJPWnYG1X4YhJulA4eH/lDGrTT07YLOZGQmMfZ/2zAq5x7KXpFbWBZ3hXW12tLe3rNc2yKE+O96HJ/fa9J12T0fTPTiYaQL+joVbYCJEIU6deE8a3fuMOj5s7GyomOzFgRUrVaOLcvbjh07aNOmDVevXtUvnfAkuxfY7d+/n7p1JZN4eZGhnv8xR1PuMCBqByGWDhyq+1y5tUOhUHAw5Fm0Wm2Rgz4ApcIIBzNrnC3yHjZTXIk5WdzKzsDPvPAvLLduR5GZmYJak1No4Hdo5SoupibSrGcP3Io49LU45vk2Zrh79TyDvl9unmNeTBR/+Lekqrl1HkcLIUTF0clct2RDRJYukYvr3eGdZbGEg/jvCqhaDX+fKlyJiSY5LQ1rCwsquXuUWU9fSWVmZhIbG8uUKVN4/vnnn/igLzs7m7i4ON5//30aN24sQV85kx6//5hsjYbJlw/T0NqJHjLfjGeOrWNb4k1O1etG9UKCycysVDQaNeZmNhxIvs2hlDv8z80vVwIarVbLCybW5GjURO1ZxKGG3fX7TqUl4GJshqNx2a0/8/KZ3YTdPMvaWm3pIL2BQogyJJ/fQjxeCxYsYNiwYQQHB/Pvv//i6flkf85v3bqV1q1bU716df766y+CgoLKu0n/aRL4if+0j68cZcOdaIKs7PE3t+V1z6KtC9PoyCoikm+zL7hznnMBN/z0M99fP03Ii4N4r1JtAM6nJ1H9wAoa2zixq07nUr2OB6WrczibkURty/IbyiuE+G+Qz28hhKg4JPATRbY/+TbTr55gqk8I/qU01PJJcCsrHbd9f+JpYsHVRrkXQM3UqOlzahuBlnZ86qMborAhPprdSbd4r1LtIg9VTc7JpveprbSwdeH9SnVK9RqEEKI8yOe3EEJUHDLHr4I5kHybhJws2to//qxGy+Ou8HfcZVrYuuQZ+N25fp2b584T0KrlY2/bo3CJi2ezb0scbezy3J+Qk8Wa+Oucy0jSB37t7D1oV8yfgbXKmHVB7Ypc/lpmKr/cPM9Id38cipEtVQghhBBCiIc9WTNWRaG6nNxE+xMbiMt+vGn847MzuZqRyrveQYx098+1/1BKHC92fZZPQttz8dDhfOu5mJFMu+PrWX77crHboMnJ4eTIl7k447NiH5uvWzfhlZcJ/WI2QZa517lRazW8em4vPRwrsTmoQ+mdtwi+uh7J+5cPM//mucd6XiGEEEII8fSRHr8KZlKl2sRkpeOgerw9QHuTY/k99gLdHL3zXHfvTFoSJ9o1oI2nJ25+vvnWczjlDhsTYvAxsyp2cpnsuDhifl+AmXclqrw9sdjXkCdrG6gZCAE189ydoVGzLj4aO5VJkRemLy2jPQIwN1LygsuTl0ZaCCGEEEJULDLHTxSJVqvl79uXaWTjjLepZZ5lIlMT8LewQVnInLctCTEYoaClrWuujJgP0mi1BB9aSYo6mwMhz+JgbEri/giMHRywqJZ/cFnaLmekYGJkhLuJRd4FIk/C5o0weAjY2hVYV7ZGQ5ZWjaXSuPQbKoQQj5l8fgshRMUhQz1FkSgUCno7++Qb9AHUtLQrNOgD2Jp4k9Dj6wi7ebbQsjey07iYmcKkS4cAsG3Q8LEGfQCVzazyD/oA1qyCNavZdng3LY+u4WByXL5F2x5fj3fEX499qK4QQgghhPhvk8BPPHZNbZxpaO1EXSvHAssZKRTsqd2Zvk4+DHR9goc7DnkJxr7Jtsru7Ei6xe6kW/kWdTMxx9nYDOO7AfKVY8fZ8dvCx9XSp8q86NO8eWE/aq2mvJsihBBCCPHEk8BPPHYd7D2Z5F2bJkdWs6CAxCUnUuP54eZZvvVtTFMbl1I5d1JOFl9eO0lMVlqp1AeAkxO068DEynXYXrsjozxq5Ft0SUArour3wEZlAsBP/3uV714YxoUDB0uvPf8Rn187yazrkdzMkt5TIUTZ2LNnD0qlki5duhT5mEuXLqFQKDhy5EiRyk+ZMoUaNWpgaWmJvb09bdu2Zd++fSVssahobty4wZgxY/D19cXMzAxXV1eaNWvGvHnzSEu7/13Fx8cHhULB3r17DY4fO3YsoaGh+dZ/7/1472FtbU1gYCCjRo3i7NncI6+ysrKYMWMGdevWxdLSEltbW+rUqcP7779PdHR0qV23KB8S+P3HxGSl8cb5CKLSEsu1HVlaDdlaDZkadb5lZl0/xefXTvD37cusjLtKQk5WgXWmqrMLPe/PN8/x1sUDfH71RLHbXBhjIyNa2LpiVMC8xYd1f/8dnpswjkp1apd6e552/wY+w/pa7fAwLWAYrhDi6aAGDgLr7v6b/0dHqQoLC2P06NFs37691L/0ZmXpPtOqV6/OnDlzOH78ODt37sTHx4f27dsTGxtbqucThdOo1Zw5dJwDG7Zx5tBxNOqyfaNduHCBkJAQ1q9fz6effsrhw4fZs2cP48ePJzw8nI0bNxqUNzMzY8KECSU618aNG4mJieHo0aN8+umnnDp1ijp16rBp0yZ9mczMTNq1a8enn37K0KFD2b59O8ePH+frr7/m9u3bfPPNN490vaL8SXKXp8CK21cYfGYH8/2a0dvZp8Cys65H8saF/Yz1COCrag1LdL4vr51kb3IsC6o3x0JZ8sSwGRo1ZnlkCL3nUkYKy25fxlqpYvi5vYx096eDvQffxZzhe9/GVDKz0pfdlnCDNsfX816l2nxYOTjfOm9kpTPj2glecatO9adoEXohhCgPj+XzezMwE3hwFL0L8BbwTNmcEiAlJQV3d3cOHDjABx98QO3atXn33XcBiI+P57XXXmP9+vWkpKTg5eXFu+++y4svvpgraVmrVq3YunUrQ4cOJSEhgQYNGjB37lxMTU25ePFirvPee003btxImzZtyu4ChYEjW3ezdNYPJMTen6dv5+zI82OHExzatEzO2bFjR06ePMnp06extMydQ0Gr1erfTz4+PnTr1o3vvvuO5cuX07lzZ0DX43fkyBG2bt2a5zkuXbpElSpVOHz4MMHBwfrtGo2GNm3acPHiRc6fP49SqWTatGm89957HDhwgJCQkALbIyom6fF7CiSps0lR55BYhB6vYa5+fFW1ARO8g0p8vj9iL/LX7ctEP+JwyYKCPgAfMyve9Aqkvb0nPR0r0dupMv/GXWVt/HUOphgmUDEzUmKhVGFTSLZMNxNzZlZt8NiDPq1WS59TWxl0ekeZ1J+uhcQKNtXtYkYynU9sZM2da+XdFCHEk2ozMAHDoI+7zyfc3V9G/vzzT2rUqIG/vz+DBg3i559/5t698kmTJhEZGcmaNWs4deoU8+bNw8nJCYCIiAjgfg/LsmXL9HVu2rSJqKgoNmzYQHh4eK5zZmVl8cMPP+iH14nH48jW3fz43mcGQR9AQmwcP773GUe27i71c8bFxbF+/XpGjRqVZ9AH5AqyqlSpwogRI5g4cSIazaN96BsZGTFmzBguX77MwYO66SaLFi2iXbt2eQZ9ebVHVDwS+D0FXnCtRmKT/gxz8yu0rLXKmLGeNXF7hDXp/qnZmn3BXfA1fzy9s5XNrPi7ZmuesXPnq6oN2BzUPtcagI1snElqOoC3vAJL9+TZWUSl3GFp7KVHqiZHq2V9fDTr4q9TFp3svW5D01sVK/jbmxTLmvjr/Bt3Ne8CycmQk/N4GyWEeHKo0fX0FeRLymzYZ1hYGIMGDQJ0PTOJiYls27YNgCtXrhASEkL9+vXx8fGhbdu2PPfccwA4OzsD4OjoiJubGw4ODvo6LS0t+emnnwgMDCQw8P7nVXh4OFZWVpiZmfHVV1+xYcMGfSApypZGrWbprB8KLPPX7B9LfdjnuXPn0Gq1+Pv7G2x3cnLCysoKKyurPId1vv/++1y8eJGFCx89MVyNGrqcBJcuXQLgzJkzudrTo0cPfXuaNi2bnk/x+EjgV0G9eGYnlSL+IvZuYot7yUIeB09TSxpYl94H0snbF/l832IyCpnDB7rrbG3nnu9+jVZLUhHqKYo7N69ze+hAXgqfT5/T24hILvl8C2MjI07W68axel3L5I5ZFRVUVoLJk3oz7tYtuGN4J7WfcxXW12rH51Xq5y4fEw0vDIBpUx9TA4UQT5wj5O7pe9jNu+VKWVRUFBEREfTv3x8AlUpF3759CQsLA2DkyJEsXryY4OBgxo8fz+7dResRCgoKwsQk9+d169atOXLkCLt376Zjx4706dOHW7cKu3hRGs4djczV0/ew+Fu3OXc08rG0JyIigiNHjhAYGEhmZmau/c7OzowbN47Jkyfr54mW1L0b0QV9L/n22285cuQIL730kkGyGVExSeBXQd3OziQhJ4ssbf53oDRaLZkaNYtvXWRLQkyu/WfTkxh5dg8XM5Jz7YvPzmT61eNEZ5bdL/myZUuZM+crPtg2nwnbfiD8/J58y55PTypSnS+d2YXL3iVEpiY8cvuCL+4ktFdz3tofxeiYVGpbOhR+UAE8TS0LXg/wEcy1h9XOYP4kBn6ZmfDqcHj9NYPNCoWCdvYeWKvyGJ5ragaOjuDi+pgaKYR44twu5XLFEBYWRk5ODh4eHqhUKlQqFfPmzePvv/8mMTGRTp06cfnyZd544w2io6Np06YN48aNK7Te/Ib0WVpa4uvrS+PGjQkLC0OlUumDTFG2kuLulGq5ovL19UWhUBAVFWWwvWrVqvj6+mJunv/IrDfffJP09HS+/fbbR2rDqVOnAN0QUgA/P79c7XF3d8fX19eg51pUXBL4VVArarbmRqM+eBawoHrvU1tx3ruE/lHb6X96e679v908z3c3zvDHrdyTy8NunuWdS4f46rruDleGRs3S2EtFypxZVIcO7efo0cO8UbsHU5oNoXPVRpw7d5b0dMNg89vo0/geWM7c6NMF1pemzsFeZYqDsSnmyoLnDxZFU1s3vKsH0n36t3zd59VC5yQWJjbxDlXX/cjwdb8/ctvKnFYLCQmlU5exMdStBw2KkUzIwQF+WgDDR5ROG4QQFU9RB5aU8ojInJwcfv31V2bOnMmRI0f0j6NHj+Lh4cGiRYsAXc/LkCFD+P3335k1axY//KAbLnivR0/9CEMDNRpNnr09ovTZOBYtoClquaJydHSkXbt2zJkzh9TU1GIda2VlxaRJk5g6dSrJyblv3heFRqPh66+/pkqVKvo5ff3792fDhg0cPny4RHWKJ1/JUzKKx+ZkajxTrhxlUqXa+l4npcIIC2XBcbuN0hhLIxVvetakhvndZCaXgbvT497wrIm7iTkDXKrmOnawSzVuZ2cy3K06p9ISaHt8PdFZ6UyuVKfArJnFMW7cRNLS0vDw8KSZXwgnTx5n5sxpNGrUhFdeud875GtuTRUzK3zNrQ2O12q1rI6/TkMrJ5xNzAg8+A8ZGjX7gjsbZPwsqcUBrR65jgdlxsdxS6XgWEoS3U5u5suq9an2mOZJFkSj0WBk9NB76df5sPRP+GiqLmh7FEZG8O6kR6tDCPHfE4wue2dBIx5d75YrReHh4cTHxzNs2DBsbQ0TgfXq1YuwsDCio6OpV6+efjheeHg4AQEBALi4uGBubs7atWvx8vLCzMwsVz33pKamMnXqVLp27Yq7uzu3b99m7ty5XL9+neeff750L0zkybdOTeycHQsc7mnv4oRvnZqlfu5vv/2WZs2aUb9+faZMmULt2rUxMjJi//79nD59mnr18v/8HT58OF999RV//PEHjRo1KvRccXFx3Lhxg7S0NE6cOMGsWbOIiIhg1apVKO/eLH/jjTdYtWoVbdq04YMPPqBFixbY29tz5swZ1qxZoy8nKi7p8asA/r1zjb9uX+af/JJg5GOBf3NiGvdhSuVg+rlUgaVAb0B3sxJ7Y1NGetTA9oH5gVkaNRczknE1MWdalXpUNbfmTHoS0Vnp+JpZ093Ru9Suy87OHg8PT/1zDw9PAgNrUbu2YTap9vaeXGjQiw72ngbbV965yrMnNzHmQgSJOVkYKxTEZmcQfGglmnsJVEqSSOX6NcguvZ7Ne7x8/Iip0oozznb8e+cqG+NzD7993JJSUpgxP4xlG9cb7nBy1vW6WZd/YCqE+I9SoluyoSBv3i1XisLCwmjbtm2ewVqvXr04cOAAKpWKiRMnUrt2bVq2bIlSqWTx4sWAbj7g119/zffff4+HhwfdunXL91xKpZLTp0/Tq1cvqlevznPPPUdcXBw7duwwSP4iyo6RUsnzY4cXWKb3mP9hVAZBT7Vq1Th8+DBt27Zl4sSJ1KlTh/r16/PNN98wbtw4Pv7443yPNTY25uOPPyYjI6NI52rbti3u7u4EBQXxzjvvEBAQwLFjx2jdurW+jJmZGZs2bWLChAnMnz+f5s2bExAQwNixY2nWrBkrVqx41EsW5UzW8asA0tU5rLxzjWcdvB5p3TwigGnA20CTvIu8dGYXC26eY3vtjjS3vT+/6kRqPL5m1pg9yvnvUms1bIiP5ueb5/ikckiBSytotVq+uH6SGua2POfozbfRpwm7eZa3PWvx0tld1DC35aPKwRxKucMHV44QaGFHbUt7/qjRko9/+IyrRjDvhTdQmpgVrXEHD8Dk96DLc/Dqa4WXL4EmR1ZxMyuDU/W7YWr06K+nRqNh585t+PpWNwikiyIpJYV5SxbhV7kyPdu2f+S2CCH+W8ptHT9XdEFfGa7jJ/5b8lrHz97Fid5j/ldm6/gJ8bjJUM8KwFypok8hC7MXSUNgWcFFQiwd2G9hh8dDSUhuZ2dS93A4kyvV5j3v2o+UmfKNC/uZE30aLdDMxqXAwO98RjLjLx7E18ya5xy92Zscy6GUO1zPSCFTo6avYyWedfSmhoUt17JSGe9VS7/MxA+VnbhhpmKaOhsHihj4ubiATxXw1S2NcS0zlQkXDzLC3Z8WtvknGtmxYytr167i1VfH4OnpVeAp9gR3KVpbiujUqZMsWPATgYFBvPXWO8U61sbKivEvvSxr8wghnlzPAK3QZe+8jW5OXzCl3tMn/tuCQ5tSu0Ujzh2NJCnuDjaODvjWqVkmPX1ClBfp8RMF+jb6NJ9fO8HkSnUYfnYPWrSMdPdnZtUGmJYg2UlyTjb1DodzMzudKZXq8KpHDV09WVnw7RwIqAkdOhoc8/ONs/iaW9PS1o0MjZorGSlUv3aB9KM7Ma/ZAAIb53mus2mJJGdnUtfWpUTXnp6extKkGIac2clwt+p875d3N2l0Zhpb/l7Kpo3reeON8QQFldKiu3fiwMGx0GKZmZn888/fGAf4ke3mXDo3CR6jO9mZ2KtMJPgUogKSz28hhKg4ZI6fyMMp4EdAw7mMZK5kplLZ1IojdZ/D0diMHK0W291/8OaF/cWuOTY7gwsZydSytOMNr8D7weONGNiwDtasynXMS25+tLR1A8DMSImvuQ3vaFL5zc0d3Kvkey4/C9t8g77nT20l4MAKUvLJUvp1+J90mPUBdW4ls868Kp9ZVwIgRZ1N4MEV9D+tW8R3ZdxVPCOWEtmkFtOmfVl6Qd/6dTB4AISvLKDQASASU1NT+vQZwJTsGPqe3saJ1PjSacNjsC7+Ok57F/PB5SPl3RQhhBBCiKeaDPUUeRgLrAd8mFGlDeM8A/Ew1Q39vNW4L8dT41kYe6FEyxtUNbcmsl43nIwfGnpZqTJ88hm45b84+z03stKZHnueSqaWDHcofJ233Um36BW5lc+q1GWoqy8A0Vlp3MxOJ1ujyXO40I/WOZxoXotzaQn0+mQ6eFeCb78nS6PhemYadncT4riZmONtakk1cxtcSnPNOQcHsHfQrWWXpySgCUdSAnnn0gw+qhzM+961OZASRw390Nk4oPAew/JkrzLBydgMd5P81ysqLStuX+GHG2f43q8J3gUsgyKEEEII8TSSoZ4iD1uANcCHwKN/Ic/UqLmYkfJAQPLo1t65jovKhLrf/gBWVvDaGLKzs1i5cgW1atWhenV/fdl/4q7QPXILH1UOZlIlXY9clkZNllaDlTKPxcPv1r8l8QYfedTE9MuZUKUq9BsA6IarmhoZYfKI6/o9Gi3wOl9cq8bbF+35uHIw71d6sLdxPvAS8APwv3Jp4ZPmxTM7WXDzPMsDWtPdqVJ5N0eIp4J8fgshRMUhgZ8AICYrjUW3LjLMzc9geYfS8L+zu/npxlk21GpHW3uPEtWRqVHz8tnd1LG0Z5xXLd3GjAwY2BfMLeD3RZw4cYwvv5yeZ5KTm1npuBib5TuPTK3V8NnV4zS0dqK9ffEyY5anbI2GTQkxtLZze2jO5UpgCBAG9Hjk82g0GjQaDSpVxR0kkJiTxaGUOFrbFd6rLIQoGvn8FkKIikPm+FVA52KTGf/vYW4mp5danV9cO8lbFw8QduNsqdUZmZrApvgYGlk7EWLpgE8RF1XPyMggLOw79u7dpd92Iyudhbcu8MvN8/cLmpnBt9/D7DkABAQEMnDgEHr16c9PN85wPj0JgDt34og9c7bA5CFHU+OZdPkIEy8d0m3YvAm+/ALSH3iNr12Fb2bDjRtFfAXKnrGRER0dPPNItPMccIfSCPoAPv10CuPHjyUzM7NU6isPtioTCfqEEEII8Z8lgV8FNHfnWWZsPs2ig5dLrc4R7v686VmTAS5VS63OHqe20PbEetrZeXCo7nP6ZRbysvrONTqe2MCF9GSio6+za9cOtm/fot9f2cyKvcGdCQ9sY3igq5t+HpxSqaRNm/Ycs1Dwv7N7GHfxAABhYd/z5ZfTOXfuLHsW/8lQM1v2L1uhr+JaZiqxWRl859uYb6vdzRC6bg1s2qAL9u7ZvAnWrobtWx/pdamITExMMDGRzJtCCCGEEBVVxR239R/29jM18LA158VGpRek+ZnbMLNqgyKVPZwSx8GUOIa5+hUYCLzhWZOTqQl4mlrkW+aelXFXWRcfTUTybfpVrcbYsW/nWoy8obVzkdoXauvGGI8Aet6dx9WiRStsbe3w9PTk0uZtZGdmsnb1Shr07A7AkKidbE68wZ46nWlkc/ccb70NV6+CX/X7FffoBc7O3AqoyZJvvqRDhy4GcwmfZuPHv1/eTRBCCCGEEI9A5vj9R32zLYrN527yvya+dK5ZvHl3TY6sZm9yLHvqdKaxTdGCsXtytBr+jbtKa1s37I1N9duTc7KJSL7NM3ZuKBQK1FoNFzNSCuwlLImkpCTeeft1KlWpxjvvTAJg4a0LrLlznW99G2FThPmNW7du4tdff6Z9+0706zeoVNsnhBAViXx+C/HfERoaSnBwMLNmzQLAx8eHsWPHMnbs2HJtlyg6GepZAWxOiGHixYNkaNSPVE9mju74i3EpvL78ECuOX2fmltO5C54FFgBZedfzYeU6VDe3YWDUdpJz8l4HDyBNnUOaOsdg28JbF+h1aivvXT5ssIaetcqYNvbu+h7ESZeP4HdgOX/fLuFw1pmfw4A+ukXQH2BjY8PMWfMYN26ifttAl6r8XqNFkYI+gBYtQhk9+k26deuVd4GzZ3WL0ScklKztT6BUdTaZsTdh+qdw9MhjP79Go+HmTd3cygULfmLSpAmkpaU+9nYIIf5b9uzZg1KppEuXLkU+5tKlSygUCo4cOVLs840YMQKFQqH/Yi2ebkOHDqV79+4GzxUKBdOmTTMot2LFikKnWvj4+KBQKFAoFFhaWlK3bl2WLl1aFs0WFZgEfhXA1CvHmHbtBLuTbpW4js1nbmA5fimfbThJgmkmTZvbM6KlL9/2rp+78HfAXGB33nW1t/fEzdicpJxs1Og6jA8mx1H74L8svxuoabRaAg6uIODgCjQPdCqH2rrxvFNl1FoNtrsXsfbO9TzPEWRhh7+5DVXySwiTnAQH9kN+HdaZWZCTA5rc+83NzXNlpzxwIIIzZ6LyrushSqWSkJB6mJvns9TFmnBYtRIi9uXatfbOdW5llV5SnschVZ1Nlf3L+DD8V9i+DbZtKfygUrZ8+VImTnyLAwciuHXrBrdvx5KVlc+dCSHE00ethmNHYesW3b/qR7sRWlRhYWGMHj2a7du3Ex0dXap1P/w3bPny5ezduxcPj5JlvxaPTq1Ws/voVlZsXsTuo1tRP6b32YPMzMyYPn068fHxxT72o48+IiYmhsOHD9OgQQP69u3L7t35fJkrJwqFgkuXLpV3M/6zJPCrAGZXa8j3vk0ItXUrcR3GSiOMlUaYqpT8dOMsu11vUqeJDf6ueQzNGQ6MABrnX9/m2u252uh5/ULmp9MTOZ4Wz8EUXQ+bAvAytcDb1BKjB+5SVTaz4s+AUIItHbBQKrFQ5r0WXn+Xqpyu34ObWelsS8gji+YP38EH78OefP6gTXwPFi4BJ6f8LwI4eHA/Z89G8e23s5k37+sCyxbku+/m8M03X6HVamHgC/DaGAhtbVBm7Z3rdDq5kVHncweEAHz6MUz9SP/00qWLbN26ifIeja1SGOFhYs6J+sHw7iQYOuyxt8HT0xs3N3ecnZ15440JzJjxNXZ29vkfcPSIYWIeIUTFtWsnvPQCTBwPM6bp/n3pBd32MpSSksKSJUsYOXIkXbp0YcGCBfp98fHxDBw4EGdnZ8zNzfHz82P+/PkAVKlSBYCQkBAUCgWhoaHA/d6dqVOn4uHhgb///Tni169fZ/To0SxcuBBj47zXlxVla/WOZTQe5EOfca157bMB9BnXmsaDfFi9Y9ljbUfbtm1xc3Pjs88+K/ax1tbWuLm5Ub16debOnYu5uTkrV65k/fr1mJmZkfDQSKQxY8bwzDPPABAXF0f//v3x9PTEwsKCoKAgFi1aVOg5k5OT6d+/P5aWlnh6ejJ37txit1s8PhL4VQCVTa3YmniD326dL7xwPlpUcyF9Rh/ebF2DyZXq8KNfE4a6+uZd2B8YBpjlX59SYYTZA0sIDHSpyom6XfmwcjCgu6Ozq05n1tZqS6Mjqxhxdo/B8SM9apDcdCAtCwhmU9TZdDm5iWcjN5GdnWV4d7RFS2jSDPxrQE4WPDwMVqGAQj48L168wNy5s/jzz0X06tWH3r37EhNTsju6p0+fJCoqUnd30NEROnUGE8Oho/WsHOntVJkBzlVyV6DRcPLkCcaeO03EXl0w+/vv8/n115+5cKHkP/ei2Qc0B/L+EmVqpORI3a40XxPB+H/+ItPUNM9yZalx46Z8+ukXVK5cBWNjY6ysClga5NZNeHcCfPzh42ugEKJs7Nqpuyl2+7bh9tu3ddvLMPj7888/qVGjBv7+/gwaNIiff/5ZfyNu0qRJREZGsmbNGk6dOsW8efNwunujMSIiAoCNGzcSExPDsmX3A4dNmzYRFRXFhg0bCA8PB3RD2QcPHszbb79NYGBgmV2PyN/qHct45aPexNy+ZrD9xu3rvPJR78ca/CmVSj799FO++eYbrl27VvgB+VCpVBgbG5OVlUWbNm2ws7Pj77//1u9Xq9UsWbKEgQMHArqltOrVq8eqVas4ceIEw4cPZ/Dgwfr3c35mzJhBnTp1OHz4MO+88w5jxoxhw4YNJW63KFuS1bMCOJOexKLYi8RkpTMkv2CtGFxNzHnZTZetUq3V8PftK7S0dcXNJJ+hi0UUaJm7ByZVncOJ1AQKGpkenZmGi4kZKoXhfQgrpTHTfOphozJm0qR3yM7O5vPPZ6FUKqFhY90jIw1Wzgd7Fwgt3pp1Xl5ePPNMO/z9A2jQoBFL//yDsLDvse3SmtP+PiwLCMVcWbRfkY8+mlboAucXDh+m7ZETdBisy56aroXpydDCBNqYGZH00jCSFv5CYrJu/cEePfpw7lwUPj55BIoFiY+HPbvgmba6tQ4LtRvYhS7wa55vqezsbHJycoByzwdVMAdH6NwFKhfzdRNCPFnUavhhXsFlfvgOGjeBfEaPPIqwsDAGDdIl8OrYsSOJiYls27aN0NBQrly5QkhICPXr66ZL+Pj46I9zdtYlPXN0dMTNzfDmpqWlJT/99BMmD9wYnD59OiqVitdff73Ur0EUTq1W88G3Y9Dm8dmmRYsCBVPmjaVD02667x+PQY8ePQgODuaDDz4gLCys2MdnZWUxc+ZMEhMTeeaZZ1AqlfTr148//viDYcN0o3Y2bdpEQkICvXrp8hV4enoybtw4fR2jR49m3bp1/PnnnzRs2DDfczVr1ox33nkHgOrVq7Nr1y6++uor2rVrV+x2i7InPX4VQD1rRzbWas9v/vl/KS+pZbev0Pf0Nt6+eICjKXdQazUFltdotbkStgCknDzBjb//zLXd1cScCw16simofZ717Uq8hVfEUkady3v443jvWoxw98fW1g5bW7vck5uNlGBmQabZA0tG3I6BXasgOZ4rGSn5XouxsQmDBg2lQYNGAFRydsLTypy9RhlsSoghPqfoc8hsbGwLHnoI7Nq1nX379ujv4B3PhrBUmHc3R0mTNu2ZNXQ47VatgrNnCQysRbduvYr/QbNkEcz9BjauL1Lxc+kv0vXkn2xLGFxguYkTJzNjxmx+irvEnOhTBVe6aCH89ktRW1y6VCoY9To8+1z5nF8IUTpOnsjd0/ew27G6cqUsKiqKiIgI+vfvD+h6T/r27av/Ej5y5EgWL15McHAw48ePL/I8qqCgIIOg7+DBg8yePZsFCxbIOqnlZN+JHbl6+h6kRUt07FX2ndjxGFuluyHwyy+/cOpUIZ+3D5gwYQJWVlZYWFgwffp0pk2bpk9MNHDgQLZu3aqfq7pw4UK6dOmCnZ0doAuAP/74Y4KCgnBwcMDKyop169Zx5cqVAs/ZpEmTXM8fbHOnTp2wsrLSPwACAwP1z6WX+/GSHr8Koo29u8FzjVbLmxf2421qyVteJful2RAfzcb4aAY4V8Hd2JzgwyuZUqkOH9wdrvmwqLREWhxdQ7Imh1P1uuPzQOKVE/8bQvKRQ1j6+WNdu45++/7k2yTkZNHOPu/J6s7GplQzs8a/kGUbJk6cDMCtWzfJzMzE21u3Rh8mprxR3Z95MVHsSYkjxMoRrp+H6AtsNjOmTVoMYX5NecnNr9DXo1Hr9jTyrcIYc0sSLK3weGD9wYVpcCjxOv2sTGlgnf+8wT6ntnInJ4t1tdqifKgH88UXh3P9+lX92n8NjGG2LdR+YESozdUrcPYMXDgHfoW3OU8dO4FGoxsKWwTbEmNZeScNX/NoWtl55lvOyMgIjVbLmPO6YR8j3f1zXSOgS7izdInubn2/AYUOuRVCiDzduVO65YohLCyMnJwcg0QrWq0WU1NT5syZQ6dOnbh8+TKrV69mw4YNtGnThlGjRvHFF18UWK+lpaXB8x07dnDr1i0qVaqk36ZWq3nrrbeYNWuWJMF4DG7FxZRqudLSsmVLOnTowMSJExk6dGiRjnn77bcZOnQoVlZWuLq6GtxMaNCgAdWqVWPx4sWMHDmS5cuXG8xbnTFjBrNnz2bWrFkEBQVhaWnJ2LFjHzmR2k8//UR6+v2kdn5+fqxevRpPT933DZnT+nhJ4FdB3cnJ5Jvo07ibmOcK/FIzcxj99wHa+bvRv55PrmOTjhzGKrAWX1w7yfqEaDbUaoetyoT1CdHULyCo2Zd8m9icTCyNVCjv/i1RazWMOrcP+w/G8eLWg1jWCDA45tmTm4jNziC2cV9uZKXT5/Q2xnkG8uLdQKy6hS1nG/Qs0jVfy0ylw/rfCD18lm+yjDAaNhyCQzBXqjAxUmJ8LwgJbAhO7qSaKKl0MYnKpgXMB3uYtx9OwMOvwnt3Mrh8cSO/G5txs3HffA8PXubADZN0smpqMFcacfZsFC4urtja2mFvb4+9/f1eQYUCej68tn2/AdCwEfiWMOgD8KkCr75W5OJDXavhZmJOSxvXQssaKRSsqtUGrZa8gz7QXdi0GbrgU/6gCyFKysGhdMsVUU5ODr/++iszZ86kfXvD0Srdu3dn0aJFjBgxAmdnZ4YMGcKQIUNo0aIFb7/9Nl988YW+R68oGSEHDx5M27ZtDbZ16NCBwYMH8+KLL5beRYl8uTi6F16oGOVK07Rp0wgODjZIBFQQJycnfH3znxI0cOBAFi5ciJeXF0ZGRgbLlOzatYtu3brphzdrNBrOnDlDzZo1Czzn3r17cz0PCLj/XfBegPegypUrGwyPFo+PBH4VlJOxGdtqd9Bn1XzQyRuJzI+4yPm4lFyBX8yi3zkxbDA+497hm3feYXdyLM/YuWOkUHCkbtdcdWVp1Iy9sJ/6Vo686OpLJVNL/om7gl/EMn6Kt6RTpy78dOMs7nbmfPZZ7judkyvV4XLMVewVKvZlphKZlsjR1HxSFF8E1EA+f7NW3blGZGVnaqLEaNFaOHMGgkP41Kcun/rUvV9QZQKe1XgOeM7ZJ+/Kimm+kylhGbVpalHAPMg0eOefIFJNcjgwPA7v+DQ+++wjAgICefvtd4t2IqUS/Krrn3557SRfXY9kba22ec6hLA1KhRFdHLyKXL6Dff69gnrVi/YhVVK/3TyPqZGSPqX08xVCPIECa+kyMxc03NPJWVeuFIWHhxMfH8+wYcOwtbU12NerVy/CwsKIjo6mXr16BAYGkpmZSXh4uP7LrouLC+bm5qxduxYvLy/MzMxy1XOPo6Mjjo6OBtuMjY1xc3Mr8pd98Wga1WqBu5MXN25fz3OenwIF7s5eNKrV4rG3LSgoiIEDB/L11yXPOv6ggQMHMmXKFKZOnUrv3r0xfSBZm5+fH3/99Re7d+/G3t6eL7/8kps3bxYa+O3atYvPP/+c7t27s2HDBpYuXcqqVatKpb2i9Mkcvwqsua0rtfIIBhpWdiTcuiW/rG8MD31eWgUGYR1SD9uGTahuYctQV1+D5RYediEjhXkxUXwdfUqXltrODXOlCk1mFn+8OYHUU2fZE9yZjfnM4eu1+yjtWrTn/EeT6ezgxdn6Pfii6v21A1edvE7fBbu4mZQOQ9E98lkTfqirL7/4NWVeUAP46APo1Vu/75vtZzAbt4QtZ2/mey2PorWZguds7TiUcodUdT4NtIBFEy/w8ojd2KlMcHFxpW7d+jRsWMC6GIW4nJlKdFY6d4ox3zBvo4DWQOmvIajRaFiz+WOORf5T6nXnJVOjZsiZnQyOerzzLYQQj5lSCcNHFlxm+IhST+wSFhZG27Zt8wzWevXqxYEDB1CpVEycOJHatWvTsmVLlEolixcvBnTzAb/++mu+//57PDw86NatW6m2T5QupVLJh6/OBnRB3oPuPZ8yctZjS+zysI8++giNpuD8C0Xl6+tLw4YNOXbsmD6b5z3vv/8+devWpUOHDoSGhuLm5mawuHx+3nrrLQ4cOEBISAiffPIJX375JR06dCiV9orSp9CW9yJhQFJSEra2tiQmJmJjU/BcL1E4jUbLiQkJBO4xIWNGNJZN7g8bzMrO4uK16/hVroyRUd5x/96kWObfPMfHlYNxMTHnt7X/krH7IC++/x7XNFn8fOMsjSOiiN0ZwYAZn2FcQHr/pMOHOPG/F6j27ge49nw+1/4Bv+5m0aHLOFqasDmnDbWd7WBc7nr07tyATUvBwRXa9NFv/mLzKd4JP8rq4a1oX6NkwzEuXrzAnDlf0a1bT1q2bJ1rf+ixtWxLvMnWGq1p5Vwpjxpg5Nk9fHfjDPOrN8t/uYxi0Gi1xGVn4mxSlOycBakDnAUuA84FlkxT51D38Ep8zW0ID2xTaM03bp3mk1kBuDkH8P4bkY/YzqJZeOsCpgojekuPnxDl6rF8fu/aqcvu+WDPn5OzLuhrVvpJz8R/0+ody/jg2zEGiV48nL2ZMnIWnVsUbUqKEE86Ger5FJq+KZJ3nY/wodksGrc9SOOdB7AJ0Q2F3Lp/P3uOHqZLy1Dq5zM85vsbUSy4eZ7mNi4Mdq3G1RnfcnLzVpq0asXPVaz56nokXzStz1t9+uU6NkerMViWwSakLk0PnOD3W+eZf2wdv/o3x9P0/uT22T3rUt3Zmg/XnWBJu8vU7mJX8MU5uEGDtmBnOAtv3DMBjG5ZnVh1BlsSYmhtl3fwlx0fT+Rrw3F5thvu/QcZ7EtOTiI+/g6xsbF5HjvDoQbvLVvH7p2XaXU32cyD3rywnwMpccz3a8Ygl6oFX0cRGSkUpRD0AWwDUtEHfRoNZKSDhWWukmqtljs5WcRmZxSpZjeXGgzu/QsuTtULL1xKBpbS6yuEqACaNdct2XDyhC6Ri4ODbnhnOfXAiKdT5xY96dC0G/tO7OBWXAwuju40qtWi3Hr6hCgLEvg9oRYfusw3O87wy4DG+DpbF+vYul4OeF0z51hgNeqlZmHq7sF7lw5xOOUOsyrX4E5iAlXymGx7z9TKdWlp40rfu70pA76YxqltOwho1ZI31JnYq0y4nplKjY0rUO9TsmN0W9xszPk37go9IrfyrW8jXnE3nJuw+s51NifeIDIt0SDwc7YyY0qnIF5p6oubjRlqrYYrmalUMcvnmrOzYe9hCA4BO8OeK1OVkgEnt7Mj6RbLA0KZcOkQo9xr8Lrn/UnGKadOcmv5X6hTU3MFfrVrB/Pll3OwtbXL89S1HF1oa+FI9eo1DLbnaDVsSohhT1Isx1PjaWvvnmtNwvJnd/dx15zZsGkTzPoaqhgGUdYqYy436FWsa2hU94VSaaUQQuRJqYQHMkYLURaUSiVN64SWdzOEKDMS+D2htpy9ye6Lt4m8mVjswK9DgDtXA3qSmPMstneTv6w+tJ+TaQlY+jXhTp1qvHT9ML9aNdfvf5CHqYU+6yaAT0gwPiHBAHirVEyqVId6W1ay6bP27DG/Tcpw3bp+SoURRgpQ5jFn8DvfxozxCKCRTd7DDN1tdUlT3r14iM+vneCfmq3p6mg4nDL02FpITmbrb7/A8WMwdZp+3+3sDF45u4dAcxu8TMwxN1JxNj2Jk2kJBnXYN21OvdWbsKyR92TlgtbiMze3YPz49yEnG/auJRYVy4+dIaV5Xd6Ki2K8VyB/BbQyCGz11Gq4cF6XrfNJWKvJ1g6srCCfYbpFXbj+njNpifx66zxveQZib5z/0F8hhBBCCFE+JPB7Qs3qUZfhTX2p513yNNUPBnUbgtoRn5OFp6klS29fZmviDS5nplBbVbL6/w14BletGV2reGLirBsG0cXBi6xmg/NchNZGZUIjG2cuHDjIgjFv4da9Cx3796Oql7dBuUALO/zMbahkakV2dhb79u0hOLguFpZWnE9PBhVoXv4fRkGGd36PpsSzLO4KV+9k4KU0Af8WXG7YGzfj3Fk4HUKfKdE1A7BqJViYwe2zoFGyd+8u2vpUooOHB10dKuUO+tQ5cOcmbNgCC3+D116HTl3yrvtxGvKi7nFXlkZNYk52iYeVfnk9ku9vnMHL1JIR7pKJTgghhBDiSSOB3xPK3ERlEPRtOnOD91Yd45te9WhQybGAI2HCv0fQaLXM6Bai3+ZkbIaTse5L/ZIaLbmUmUJty5IHlZ5ulqT9k86m/XsIivbHx0M3dPRe0Dc3+jTHU+P5plojjB9IInNm917O795LgoMtlRo2yBX4veBajRdcq+muedN6Fi78hbZtOzJgwGBO1e8OgJEy99pwLiZmrAhojdPRvaBRg0KB90NB2IX0ZDxMLTAzKvp4/XPpSYTdOMubnoE4Z2TBt3PA2hrmfIMFRgyvHEz9+g0ZoMrnV+nkPog6BHaeumUaqhUt4Ytaq2FDfAwtbV2xKGbvW0n0Pb2N1Xeus6ZWW96/dJiR7v4MvvtzKIoJ3rWobGbFAOcqug3hK8HFRbcmoRBCCCGEKHcS+FUQuy7cZt/lOA5cuVNg4KfRaPlmxxkystXcSE7nt0FNc5VxMTHHxaSA9eiK6OKt6xyKiiRLnU3C0RM4envhXUu3mPzc6NOcSk/kHe8gfMzuL6DeftQIlgR7skqVxbjaIflVzf7k21SvXYdml1rS7G7WNqs8Aj6AiAXzaFTNAn9zG3qbZrA5qAMt7s5PO3hwP9bW1txxc6Tp0dUMcK7K7zWKvhbPnOjTzI4+hZuJOWM8a8Kb48DGFpzcsQQaOxWy6LmLl67HL6QV2R2780fsBdpmpuY9HPQBP8Sc4dXz+3jHqxafValX5PaWVHVzW06ZJXIjK509ybEEWNgWK/CrYmbNRO8g3ZNbt2DeHN36Ww0XllGLhRBCCCFEcUjg94RJPRNFwp5deAweiuKBnrJ329WkQw03GlYuuLfPyEjBypdb0mXtFpa5X+T15Oo0sHYq8BiOwIVPkvEZaIVRj6LPPwuoUpXuz7TFKiuHybXr4+5fnS9OH2P1nWvczsnk08ohBkEfgJFSSZyjNdfio8lS5n2ulXFX6Rq5GRdjM24Oe6XgRqjVVN64mZamjbGr7cW1zDRM775uSUlJzJ07CxsbG96cNoM6lg7Uty749ePDyeyxUPJ3z078cPMcfweE4mZifn9phjbtivTa6LlV1j2Av29dZMa1JVgrz9PT9GvAKt/DWtm60dHek87FWFj9fHoSUelJdHbwQqvVsizuCk2snfEwtSj02OlV6jH9boAZYGGLv3neiw0XiYsLvDZG928BotISuZmdTktbtyJXna0F4+JOkUxMhKhT0KDRkzG/Mg9XV+znzpHL1J7c0+D3XgghhBCitEjgV44yNGrC467S2cFLP5wvatwY4jauw8zLG8cHggyV0ohGProA7kZSOm42+ffYtfF3Y6yqOtOjT3A1M7XQwG9Z8mV6jdnKu3eCmErdIrffyMiIOv41UOfk0HHsaLxr6RKmXMtMIzY7A7s8EscA/BUQWuB8shoWNpgqjNCiRavVolAo2BgfjZXSmMb3ksNEALaAvxLXT79gm0IB3rpkMEkRe0lJP4FNq9b06tUHOzt7KplZcbjuc4Ve047UOFo+05BqcVfJ1KhZGXeVHUm3GOxSlfMZyWxNuMHrngEGGS8vpCfz4pldjPKoQR9nH+AYsB54Hbj/GrS1d6eq+VYaWm8E2gIDICtLl6K8dh2D1OQ1Le1YU6ttoe190KConexNjuVwyHNcy0yl96mt9HaqzNKA0GLVE2JVSHBcFJ06F1rk2ZObOJ+RzNWGvQvtAQWYlQyzU2CxIzTK+62Vtx/mwdYtMGmKLiV8Hu5o4K80eN4C7Msh7jry3p8kRl6ncp/G2NUserAvhBBCCFFUEviVo9nXI3nn0iE+rBTM5Mq6ZCWV33gby5qB2DVplucxCw9cYtDve/iyewhvhNbIswzAp1XrMsLTP1ePW14qB1vhG2lNzSZ2ABxMjuPV83sJsrBnYewFNgW1p6lN/r03SpWKwV/N0D8f7l6dLg6eeJpasvjQZRLSsxjR7H6WUBMjJcZGapJzsrFW5R6+aZOUTnyT/igUChQKBUk5WXQ4sREblTHxTfrDHeA1dEvSrQIqVdYfm5OTg9HHH2Kh0aCtW48uXboVev0PqjZxCi0vRvCipz/9nKvwQtQOjqXGcyMrgwmXDrIpIYbalvZUSswk6tJF2jdtzom0eLYn3aS6hc3dwG8isBoIAO4nctHNs/wa+Afortu46Hf4cwmMGg2dn9VtS02FTRugVagu+2YRjfLwp1aiHf7mNnibWvCCSzX6PcELnL/mUYPTaYm4FnHYsbEClOgexdKqNeTkQPX8k878nKoLKtO1MKZ4SXRLRZP5r5AUFSNBnxBCCCHKjAR+5ehZBy8OpMTRzfF+ghPH1m1wbN0m32PcbMxwszHDy67g4XtGCkW+Qd+s65H8dus8K2o+g7epJfWsHTnbqKd+/6GUOCKSb2NppCRHqyFHqynwXFqtlphFv2NbvyGWd79ce5pa0vXkJlamXIOjSvqFVMbOQtdNk6lRU/3AciyNVFxs2Mugrp07t/Hzzz/Qu3c/OtzNfmmjMmFypdo4qO4uE2AHDAYeml73yScfcPnyRXpXrYqTSkk9a5sC252Xr+Ivsz31Nm8ogzAzUvKrfws+r5KOj5kVH1cOJtTWlZa2rizdvZpzVy4T6OtHV69K7AvuTKCF3d1apgCNgLx+jgF3H3fVrQ9nzkD1GvDpx+DlDWbm8MvPuoWKh75U5LYPcqnGIBfdvDxzpYpf/JsX+/ofpzGeeS+pkZ9RVvCqZeGjNc+lJ+FkbHa/x7lho0KTzPQ1hxStrsevPDg19MWpYdES/wghhBDlITQ0lODgYGbNmgWAj48PY8eOZezYseXaruJQKBQsX76c7t27l3dTyoVMJilHgZb2LA0IpY5V3tk15+44g+cHKzh87Q4A3+48y/e7z3H2vWd5PrhSnscUxb7kWA6n3CE6M81g+7n0JLYn3uBlNz/2BXdmrGdNfvRtUugcrPgd2zj58gucHvuqfluOVsOa+OsADKhXSR/0AagUCmpa2BJkaZerLhcXV1xcXFE62uO4ZzFDonayM/Emu5NiaW13tx1GwGigj+Gxt2/fQq1W4zxkKPVmfl3k1+NBdazsCTC3pcofViQ/e4wLo0ZS2dQSEhNpkqbh/Up1MDFS0qVFK3q370gVT10PTUNrZyz1yWcaAJOBIiyNEFRbtx6hoyPs3QO7dkLr1tC1O7TvUKJreJoVFvRdykih/vnz1L+eRFLB9ysMeKtgig14FLs7UQjxNNuzZw9KpZIuXYq+DM+lS5dQKBQcOXKkSOWHDh2qH+Fy79GxY8cStlhUJEOHDjUIQO69F6ZNm2ZQbsWKFXkulfUgHx8f/fvH0tKSunXrsnTp0rJodpnSarX8+OOPNGnSBBsbG6ysrAgMDGTMmDGcO3eu1M83depUmjZtioWFBXZ2dqVe/5NGAr9SdDM5nYS0rFKr70p8GjFJ6cSl6ur84+Allh65yqU7qY9U789+zThTv0euxdS7R26h1bF1XMxIoaG1M4OidvDi2d3Mv3H2fqEDcOitOyzedVm/ybpeAzyH/Y/KrRtD9EUAVAojAs3tQAHjagcanEepMGJr7Y78G5i7R6x69RpMm/YlNf2qY5qjRgGsjb/O+oRotiXeLPC6Pv10Jp98MoO6dRsU7wV5wCCXakTW706dtQ5Y3KjGzSV/oUlLg4njYcTLkJAAgJ2NDYHVfPV/iM+mJ/HR5aOcjL/Oi6unc+jGmQLP0+jIKoIO/oP6Xm+qvT3MmQefTgdnF3hlJNxdIuOxiDwJ164+vvOVgFZbeBlHlSnO1pXJMnYkLo/A71Q2TEyEG2oIT4d+cXA1p/TbKoQoXWqNmq1XjrAochNbrxxBrVE/lvOGhYUxevRotm/fTnR0dKnWnZV1//tCx44diYmJ0T8WLVpUqucSRaPWqjmeuJXttxdxPHErau3jeZ89yMzMjOnTpxMfH1/sYz/66CNiYmI4fPgwDRo0oG/fvuzevbsMWllyCoWCS5cu5blPq9UyYMAAXn/9dTp37sz69euJjIwkLCwMMzMzPvnkk1JvT1ZWFs8//zwjR44sUvmtW7fi4+NT6u14XCTwKyVJGdn4fRJOo6/Wl1qdnz1bh5sf96Ctv66n68+hzdg9ph213O0eqV5zpQpf89zDIEe6+/Oymx9ed7NADtx8ilrzV3I09f4fn+wFGupud2DpL1f023pd2kvTvi3JrurOxZuXeOvCfvYl3eJX/+asCmxT/GQhqal4jxzJjb/3scC/Oe9XqsO6Wm0Z4V69wMMsLCzw8PAo3rkedq8n8UfI+iaWBju2o7S0hOAQXe+cRe6xgFqtli4nNvLBlSOMirnB0ixTfjlZ8PsgOSebFHUOBrFMpcq6nr8HnT8H49+Co0ce7boKcucOTBgHk98veR1nz8CP30Nycum16wELUsH3BuzILLictcqY/d4ObHZRUCWPgeyL0uD3NFibAVszYVcWnJLAT4gn2rIz2/H5fgCtF7/JgPCptF78Jj7fD2DZme1let6UlBSWLFnCyJEj6dKlCwsWLNDvi4+PZ+DAgTg7O2Nubo6fnx/z588HoEoV3XqmISEhKBQKQkNDgfu9O1OnTsXDwwN///vzjk1NTXFzc9M/7O3ty/TaRG574pYx/FAVJkU+w5dnBzIp8hmGH6rCnrhlj7Udbdu2xc3Njc8++6zYx1pbW+Pm5kb16tWZO3cu5ubmrFy5kvXr12NmZkbC3ZvX94wZM4ZnnnkGgLi4OPr374+npycWFhYEBQUV6QZEcnIy/fv3x9LSEk9PT+bOnVvsdt+zZMkSFi9ezJIlS5g0aRKNGzemUqVKNG7cmOnTp+t/xwD2799Pu3btcHJywtbWllatWnHo0CGD+s6ePUvLli0xMzOjZs2abNiwIdc5P/zwQ9544w2CgoJK3O6KRAK/UmKmMqJ+JQca+5RCRsS7jIwUOFvdHy7oYWtBkyp5Z+jckhBDQs6j9TaO8qjBj35NMbm7wLl29gJqL1hFfbUxF9J1X+iN3zZi/wtxvDL2/nykxJxsshWQ2bQLP9na8OX1SJocXs3YM3v0yxEkZWRzLraIQYFKBS6uKFx1Aa+ZkZL29p4oFYZv14sXL7B06SLS09PyqqVIUtTZ7E66dX9DApAEOIB5Ex+sAmvptg8fAR9/Cia500lmaNRcy0rDwkjFJfu6ODZ8nfebDC7wvMfqdeVM/R4G2UHzdOqULuvn8WPFuq5isbWFjp3g2cKznuZrxXJYsQwO7C+9dj0g596jCL1+dkbgm8/s5dFW8JkN9LWAD21guSO0L8KIXCFE+Vh2Zju9V0zhWnKswfbrybH0XjGlTIO/P//8kxo1auDv78+gQYP4+eef0d4dejBp0iQiIyNZs2YNp06dYt68eTg56T6fIyIiANi4cSMxMTEsW3Y/cNi0aRNRUVFs2LCB8PBw/fatW7fi4uKCv78/I0eOJC4ursyuS+S2J24Z0888T1zWNYPtcVnXmX7m+cca/CmVSj799FO++eYbrl27VvgB+VCpVBgbG5OVlUWbNm2ws7Pj77//1u9Xq9UsWbKEgQMHApCRkUG9evVYtWoVJ06cYPjw4QwePFj/fs7PjBkzqFOnDocPH+add95hzJgxeQZYRbFo0SL8/f3p2rVrnvsfHO6anJzMkCFD2LlzJ3v37sXPz4/OnTuTfPcGtEajoWfPnpiYmLBv3z6+++47JkyYUKJ2PU0kuUspMVEp2Twq/6QspelQShy2SmOq3e21C4+7ynORmxnkUpXf/Iu+OHlhnlscxpenIhh88xihGbfYUrsDVIYGow2D2xythjSNmsvm5oxxrMn+RUs56GJOzcD7Q0l7hO1g67lbRL7TGX/XQpKumJrCN98abPr48lFmRUeyomZrWtydc7h2bTj79+/D2dubEapYGlg7Fe/642+x+Mw+/pd1hxU1W9PNsRLMB9SAae7iGRo1a+5co6O9J+ZKFVy5DPHxmNcJ5mjdrpgojDiPAgUKnM1suZaQxv8WRzCscVV6PzQnU6UwgqIsKde5C/j4gH/+GVyLJSsLfv8V6tbT9WKCbhmJUa/nWXxLQgw9IrfwVdUGvOjml2cZAF4YAgEB0LxsEsq8bAkvWIDJIy7D56yEQfdWjlBA/eIsCyGEeKzUGjVjNs0lr/s9WnR/Qsdumks332YojUp/gm5YWBiDBg0CdEMxExMT2bZtG6GhoVy5coWQkBDq168PYDD0y9lZ99nn6OiIm5vhHHlLS0t++uknTB64idixY0d69uxJlSpVOH/+PO+++y6dOnXSzy8UZUutVfPTpbFQwDst7NIbNHTohlLxeH4ePXr0IDg4mA8++ICwsLBiH5+VlcXMmTNJTEzkmWeeQalU0q9fP/744w+GDRsG6G5CJCQk0KuXLsmep6cn48aN09cxevRo1q1bx59//knDhg3zPVezZs145513AKhevTq7du3iq6++ol27Yq57DJw5c8agJxxg7Nix/PTTTwDY2dnpg+F7PZX3/PDDD9jZ2bFt2zaeffZZNm7cyOnTp1m3bp1+NNinn35Kp06dit2up4n0+FUwt7MzaHRkFW2O3x9KGGLlQBcHL3o4ljzhS16OVfdgc51KNLBy5DWPGnAUiMpd7nxGMkoUuN7NpLj81QkcqPscc5renwwf6utCsypOuFibkXb+HOq04vXS/XjjDHdyslh78RTHXuhHYsQ+evbsQ//+g/EPqkNMVjrXMovZ83dgEy9fucRLZvY0PnkWvpsLp46DSV5//Dfz1fV99Dy1lZnXT+o2ffA+vDsB5n6Dn7kNlc2seMYMWpvB0thLhGwJZ+3pGP49cb147XqQkRHUCgLj3MtelMipSPh7Kfy5uEjFk9TZJKmzicspZIylqxs82xWMyy6SKnHQl5MDP/8EO7aVanuEEGVrx7XjuXr6HqQFribHsuPa8VI/d1RUFBEREfTv3x/Q9Z707dtX/yV85MiRLF68mODgYMaPH1/keVRBQUEGQR9Av3796Nq1K0FBQXTv3p3w8HD279/P1q1bS/WaRN4ik3bk6ukzpOV21lUik3Y8tjYBTJ8+nV9++YVTp04V+ZgJEyZgZWWFhYUF06dPZ9q0afrERAMHDmTr1q36uaoLFy6kS5cu+oQmarWajz/+mKCgIBwcHLCysmLdunVcuXIlv9MB0KRJk1zPH2xzp06dsLKy0j8AAgMD9c8DAw3zQDzsvffe48iRI0yePJmUlBT99ps3b/K///0PPz8/bG1tsbGxISUlRd/eU6dO4e3tbTAF6OG2FtWD7e/UqRNXrlwx2DZixIgS1VsepMevgrFXmfCCSzVcleY8+8M2utT0YGRzP8ID28BBSE/OgUq6eXwDT2/namYqG4Pa64dvFsebnjUJsrCjnb0HpulKeAWwAjYalnvJzY/I1HiCDv9LT8fKLAloRZV6d+cnaLSw+ypdPMxJ8rMj/sQBzrVujn23HjT44++HT8nVzFRcjM0wfai9i2u0ZHX8df63fh9n/lqCqbsH/tO/pF07Xeazaw2fx9Qo//sYp9ISiM5Mp429u37bLk8fHG3tCavXHt4ZBydPwsp/oWdvqFdf3yO2dNIzPP/xFjpY9yHC8RWedbi7/Ebb9rD4D7gdi+bu8B+ju8MQLmakcNspnfcH1WFc4N1lCz54H1DAhx8X4dUvI0G14fWxEFC0pRS6OVbiTpP+pKqzqX5gOX2cKvOJT92ybWNpu3ZNF+xWqQotWpV3a4QQRRSTUrThjkUtVxxhYWHk5OQYfGnUarWYmpoyZ84cOnXqxOXLl1m9ejUbNmygTZs2jBo1ii+++KLAei0tLQvcD1C1alWcnJw4d+4cbdo8npFE/2Xx2TGlWq60tGzZkg4dOjBx4kSGDh1apGPefvtthg4dipWVFa6urgZDIxs0aEC1atVYvHgxI0eOZPny5QbzVmfMmMHs2bOZNWsWQUFBWFpaMnbsWIMkRCXx008/kZ6ern/u5+fH6tWr8fTUJbAzfuDGtp+fH1FRhj0Mzs7OODs74+JiuJ70kCFDiIuLY/bs2VSuXBlTU1OaNGnyyO3Ny4MZevft28eECRMMbszY2BR/+bDyIoHfE+Z2dgbNjq6hrZ07c30b59qvVBgRVr0Zh67e4bPIdWSpNYxs7gfRoB6pwX/KCpSxCi406MmhlDvEZKWRqdEUP/DbB6bVlDzrdDfIsQAGALZAZjokxrHT1BizQ0o+tavL9ZqpRB3fQICFrWE9iRlw8AZfVr/BQqt4ksycCfLzIcvXi7paDTey0vE01X0QHki+TaMjq+nn7MPCGi0Nqmlq60qyJod6QXZMXbKQ0BadDfbntRD8g3pEbiEqPYmjIc8xLyaK3k6VaRd3BiMUZCsUKEaPhc2b4OgRtKtWol3+N1lL/sLM0oobZ8w4tNKMGq1as7zmA0MLBg6GLs+htTAn6NA/5Gi1nKrXHSOFgvHetejvUgXvu9eGRgP30hBrNLqevPJgZAQdijfMwU5lQnRmGpcyUjiTnlRGDSsKDbAJaAoU/uVJz8cH3nkPvGRxdCEqEvciJgYrarmiysnJ4ddff2XmzJm0b9/eYF/37t1ZtGgRI0aMwNnZmSFDhjBkyBBatGjB22+/zRdffKHv0VOrS5YR8tq1a8TFxeHu7l54YfHI7I2L9joXtVxpmjZtGsHBwbmGP+bHyckJX9/814QdOHAgCxcuxMvLCyMjI4NlSnbt2kW3bt30w5s1Gg1nzpyhZs2CbxTv3bs31/OAgPvrFd8L8B5UuXLlPDNj9u/fnwEDBvDPP//QrVu3As+7a9cuvv32Wzp31n0fvHr1Krdv39bvDwgI4OrVq8TExOh/lx5ua1E9+Jpeu3YNlUpV4Ov8JJPA7xHFpmRgplJy49PJKM0tqDpx0iPVl6bO4WpmKhczUgosV9fbgf1vtsfH4e4i7S5g1EOBp6kFKlPdOi77gjsz+fIRJl46yDfVGunv/Py89zyOlqZ0C8rni/BR4DWgMfDN3W0K4N40sF2bIPoiH9hbsl9rRtI7A/BcZ8mp+t1z12VvDs/68ZGZDw2Jo5ODJx8u/oVRHv68ffEgx7fEMy+rMb6v2OBibEaAhS11LB9Y1/DQQd1QvYaNiMvOJC4ni6y6DTEuxlor2RoNYz1rcjjxJu9v+4GVWrid05ifqzdDdXfNG7wrcbFvb+Y5BsO+5ZhapmEXf5m3LAN5bfEq1Dk5qPIabmlnhwIwQpFr3LQ+6ANdwPXdj/f/X1FotaBQUNPSjmsNe2OvymPy42OzABgGvAnMLN6hLVoWXkYI8URp4RWEl7Uz15Nj85x9pQC8rJ1p4VW62fjCw8OJj49n2LBh2Noa3szs1asXYWFhREdHU69ePQIDA8nMzCQ8PFz/ZdfFxQVzc3PWrl2Ll5cXZmZmueq5JyUlhQ8//JBevXrh5ubG+fPnGT9+PL6+vnToIGu5Pg41bVrgaOJFXNZ18p7np8DJxIuaNqWXQ6GogoKCGDhwIF9/XbK1iR82cOBApkyZwtSpU+nduzempvc/0/38/Pjrr7/YvXs39vb2fPnll9y8ebPQwG/Xrl18/vnndO/enQ0bNrB06VJWrVpVovb169ePZcuW0a9fPyZOnEiHDh1wdXXl8uXLLFmyxGDOq5+fH7/99hv169cnKSmJt99+G3Nzc/3+tm3bUr16dYYMGcKMGTNISkrivffey3XOK1eucOfOHa5cuYJardb37vn6+uqHpj5NKtA30CdPQloW1T5eScuZq7g8eyaXvvr8keusZGbF9YbPs6Jm60LL1q/kiJPV3V9aFSgmKtjTpjM76uh6dGxUJvx66zw/3ThL+t01j+LTshi2OIKhfxRw16Mq0BbonPfu9U7ObHH3ok3lQN5NCbofEN6VkpnN4N/38Nt+3Zp+VLOnqqcLr3sG4Gduw+81WtDExgU/M2tmhTfE92cbOKu79hP1ujHeu9b9yj78AD6eAtlZDHCpSmzjvoz2DKCoPrh8BMvdC6ll6kgvUwtWnlxPUMJ1XrcLYKBTVQa5VANgd9Itqu1fxt/KS3xlHMD+Dh149m5GUoVCkXfQ94BjdbsSWa+bfqhnnqytdY/SEHtLt4RCWfrjd+jVTX8eFxNzjMs1aG0GdACKvpCyEKLiUhopmd1mFJA7F9a957PajCr1xC5hYWG0bds2z2CtV69eHDhwAJVKxcSJE6lduzYtW7ZEqVSyeLFu7rRKpeLrr7/m+++/x8PDo8CeC6VSybFjx+jatSvVq1dn2LBh1KtXjx07dhh8KRdlR6lQ8rLPrLvP8n6nDfP56rEldnnYRx99hEaTx8K0JeDr60vDhg05duyYPpvnPe+//z5169alQ4cOhIaG4ubmZrC4fH7eeustDhw4QEhICJ988glffvlliW9aKBQKlixZwqxZs1i9ejVt2rTB39+fl156CW9vb3bu3KkvGxYWRnx8PHXr1mXw4MG8/vrrBsNBjYyMWL58Oenp6TRs2JCXX36ZqVOn5jrn5MmTCQkJ4YMPPiAlJYWQkBBCQkI4cOBAia7hSafQaouyLHLZSkpKwtbWlsTExAo1TjYzR02buZup5mTFbD8FRsYm2ISUwfyntGxYcx78HKC2S+HlH3A6LZFMjZo6Vvd70cL2nsfRwpTutYs/9C1Do8Zy10JMjIxIbzYozzIHrsTR4Mv1tKrmwtbRhcxPOAqcBXrB/Jtn2ZUUyxzfRpjd+yBfswqys6Fr92K3FeDjK0f548INXklqT92qWs5p1qHJdOZ/C88yukV1vu5VD4CLGcl0PbmZF118Gergi4Nl0T5wF96+xpgLB5hfrR7POXrrt59Ijcff3LbsAqXXRsClSxC2QJdY5RHdjjjHofGLCJnWD+fGd7N3/roA/v4Lpn8BNUopq2gRqTOz0Wq0qMwl7aYQT7LH8fm97Mx2xmyaa5DoxdvamVltRtGzuvTmi9KxJ24ZP10aa5DoxcnEm2E+X9HEsWc5tkyI0iOBX0UQkwJ/RkJVO3iu4EXMi+RzIBH4GDCC1Xd0f+Q6O3iRqs7GCAXmShW3stJ55vh6nnXwYlqVevrDv7h2AjMjJa955N/ztiEqhhouNnjb556L9dKZXWxPvElEcBccjO8HWA0Ph7M/JY4TdbsSaFl6i9fejNeyfI+C+n5Q3w8ibyTy/IKdvBlag2GNqz1S3TVjsklUa5msvMIr7rrx3stvX6bnqa284VmTL6s2KI1LyO3vpbo5g2+8lefagg+blay7bzkmnw7HkzNWcvhu4Bc44YH1c7KzSy+jaDH8GzCO7KR0ul+YhdL08Z9fCFE0j+vzW61Rs+PacWJS4nC3cqSFV1CZLOEg/tvUWjWRSTuIz47B3tidmjYtyq2nT4iyIHP8KgJ3K+gfCLalNOxjC5AMZILGTEvXk5sBSGk6gOoHlmNmpOR8g14kqbM5k57EybQEg8PHrawFe4CvAbu8T9HSz4VMtZq47AwcjQ1XyL6emcbN7HQytYYT3xfXaMWFjOS8g74VwE/ogtaiJaTUU1hm8E+lXZhaV6b+bXtqLl/CyYHPgXfhy19cykjBQWWCjSrv4KqtmTGxajX/c7w/ybe6uQ3Blg40snYqXkOLo9fzRS6ao4VvUnSB32grMMpjNGrAG51xrF8VlxYP9ewVIehLOHkNdUYWjvWqFrlNhTF3t0NpboJCKaPRhRC6YZ+hlYLLuxniKadUKAmyDS3vZghRZiTwewK8ELWDIynx7A7uhJUyny/aLrqes4ScLLI0alxMzPMuVxQLgGxd0GekUDDXtxFX4tMY+vteXGqaYX93XoEjplw90hs7XxN4cJmVSOAMul5Du7xP0fzoGk6kJpCpVbMpqD2t7e5nwwoPbEOGRp0rE2dVc2uqmufTJRUNd9JgdyZ00oKyGGu6RaUnsTb+OmZGRrwYdQzC/9UlWHllZIHHXUhPpvqBZTSwdmJPcN7zyr62B7h/N/BcepK+NzO/YZ4/3TjD+fRkPE9e5YsVS1n5/scE+RQtaLrXC/ucgxefPdALWxCVAv501AV+94K+5JxsrJQqfcIfI5USt9YFr6WTn/UtPiQnJYM+8T+isjQr/IAiaLf5/VKpp7yka2HQHfBTwbS8czoIIYQQQjxWcju9FL321wG6/ridbHXxJuGeS0/mUmYKGZrCUz83OBxO9QPLSVFnA/BrxEWmb4ws1vmynTX4xvyNauevbE24wSvu/iguKlly+CqDUvzYXLsDky8dptmqNbj+EYXpvP1wVXfsrUTImYauB65y/ufwMbPCwdgEF5VuUfcHGRsZFbr8Qi4j4LM/YKQb/JtRvENb2Lqyp05nfvZrBs1bgokpHDta6HHJ6mzUwNWUBDRZmSTnZBd6zISLB3n1/D5W3rmab5mPrxxj2rUTHL1+hSuxt7iVmFDka0nMpxe2MPVMoO7dH8PepFgc9i5i3MXSmbjsP7oDlXo3YlPnz7m5regLzT7N0rVwPBsOl/5yQkIIIYQQJSI9fqVozakYYpLSScvKwbYYSSk21+5AhkadK0B62MbvfqSytzGVvN0xvTvm/K1/DnM7NZNhjavdz/BZgOjENL7ccZprdmloAc3d1MUT2gRQy92W7neXeHAxMeOWZwZahw9QZGdC/xWc/92MlUehThVoXbvg8ywNCC20LcViBE5GN6irsaKxSf7pdbcn3uD3Wxf41KcuTrFx4OQMJib8dfsSFkYqWpnb4+Jghb2rE4WltqlubkMfYzsarNvMi7EpLHG24HDd5wiwsMv3mHFegVQxs6KdnUe+ZZYFtOZmdjodmrrzYe8BeDgaDgm9sfkkhyYsosE3Q+4nWrnLz9yGqw175/te0Wg0ZKQmYGHtkOd+AAulEhulCY53l2a4mplKyKGV9HSqxA9+TfM9Lj91PuxN1Nz1XHptATe2nMS1VQBn05PwMzec73Nbreuptf8P3G5yMIIdzmBRjJ5pIYQQQoiyJIFfKdoztl2xgz4AMyPl/SyW+cjJymLBq6/joVLxS2aSfoje3y81Jy41s0hBH8CPe84zc2MUozsEcNMnlRtZ6QBYmxnTr+79LrzXPAJ0yVtq2kBEGjQ0w94IXI3BI/+YQm9nYiyfXT3KZ1XqUtuy4AMSb94k8VYslYJq5VtGq9Xy/pn1qLVa/nYdTH6d1d/HnOGP2Iv0jU2jzdQZ0KYtGWPf5Jvo05gaKfGp2oC2Qzswu2pDXgfS1TmYGBmhVOSuz1ypYknlhnB7HW9b2+FgrMDcqOBfmSY2LjSxKTjzaj3r+4sNPxz0Adzed447By5w5+DFXIEfgGsBw3zX/PQWe//9hpenb6NyYDMApv21iDPXr/H9qDcwVqmobelAXJN++mPUWi2ZWjWp6pwC212Q6iPbYlfLG6cmfnwXE8X7u7Yw5scr+PZpTP9hXcnSQmgsmCnggGvBdcWpwfEpmEvv+hRcgxBCCCGeHhL4lSIX69KZ35QXlYkJY5f/iVJ1f14WQMtquYOM+OxMtGCQMRMgRZ3NYc9Yuj7nTqdANzqf3URsdgYDXAqYX/Z5Z9AAt8HhY+i/F/gZCuoum5QIv6U6cj01lbYJMYUGfl8814uLBw/x5dkTuFTNuy0KhYKf/ZqRo9WgyiNI0ze3Sj062nvSSmsBVauBmxtm69ayp3F7VCbG1LZ0oL29B16mlsRmZVD94HIaWjmxLqhd3hW6u8M33zIDmFHgVaBb7PzkXrBxgEr+eRZJSU/nty0beL55K5xs8p78VXP8c7i1rYVj/eInS7Fx8MDC1hlTi/u9bWEb1nLhZgxTB7+Eu4NjrmN8zKy43bgfJgW8rg/SaLVcz0ozWKBeYWSEaytdllc/MxuCEpXMHPl/9s46vqr6feDvG+vuDmDJGBvdo7tbGkEBBQEFFDAACUFECQOUEBABpbuRbjZysICx7u4bn98fdyzYKBXU7+++X6/7gvvJ55yde+95zlPu5Fqk00lRhJlcj3q6z7eA7SyAiZnwuSm8WTkh7AuRp4a9hdBFH0xfo3UxLzoVQ2dLJP9orUMtWrRo0aJFi5aq0Sp+/yHq9+z+3DFqIah5bTcCQXyjARUKij8szGV3ZgzNrG3pbO/MQd121DR8gcwTG4DvgL5Aa54Z2wcaq46BVMLKGo0Yblm19ethYQ5OuoboSmXU790Dc3s7TO2ebQoaZvf80gtOekZl41Z8D4sXwaZfqGswHVq1BsC5RGGRSySYynSwKHGbvJmXztaUKD50roWZXBdib8L9C9CgN5RY8XJVCupc30stI3N21mxTcfO8bAi9CsZmlRS/7amPWBEfStP7qXyx8WceJCWw+M2xVR6DVCbFusHLlZnYmBRJZGEOs/pOpUW/aRX6Ds/5grSc7CqVvsfoPcPiXJyZR8SaP6g2pBkG9ubMehTCvJib7PRtTS/ryplR21o40Hb023wecp40Qynmcl0kElj/ApZiSylYSgXWVaUefUHW5cOiHIhVwdSn5Ar6u4nde40/eizBb3oP6nzxxvMnaNGiRYsWLVq0vGa0j6ZfE1+fvEf9JYeJy8x/ofEKlZr9d+K4m5jJO79dITI1B4CrOak4XfqNHxPCUKsrl2CUqgT18vSoqzBCKpFUGONvZME6r6ZYyHS4k5dBJ0snXPWrjpc7mZnAm2FnSS6OgLZjocFt6IimnMJzSjV9bAqh9hJGW9tXmdnybFYSNa7s4J2IiwBs7NmAj6b1ZG7y/aeuGVuUh+/VXcx+FFLWGBcHS7+G2KcnUqF7T03x93qVM2Ba6OjxqGE/tvi2BOCr2DssiLnFnrSS9VJ3Q3oxZO0onaMUggxlMSmKosp7GZtBk87QqGOlrqMZ8ZzKSuIH00L6t+vAqHadSvuuhN9j6e7tqFTPT+7zND55FMyc6BvEFVe+vqrbO9LA888XYI9YfZLrUzdxd8l+QBNn6KZnhJOeIfFF+fwQf4+CKtxEPwtsyjKvxhUs1M+jpd5MbtgZ0N3g2p+Wt6s+DDCAns8wwCerYGAabHqxj+NzMXCyxLi6LabeDs8f/JoRQhC7P5jClOx/WhQtWrRo0aJFyz+I1uL3mrj4KJXrsekkZBfgZG743PErTocxZXcwrTxs+SMiGXtTfWZ18idFUUhCcQFXYlOZ/HUIE4O8WNg9sGxikYp9dx3BXJ+f5BG88/tVdo1uQTc/JwCiC/PZlxGHl6EZS6oqLh6SCErBGuMINqU8oItFCP1dfoTvTYCvKg2/FpPO9D+CyfQs4LfAllTTr2xiuZ+fBYB3iXXRUdcQP0NzAktcQBMUBagQPCrKfer5WJlwn7CCLEJL1gLgzCk4ehhsbGDIMPKLCjHUe+Ju38cHfHxYFHMLh3xDhj/DaviZawC1jSzoa11i0vQPAre1YDGLHKWCLFUxznpGxDTs99RSDTh7VNm8pHp9clUKfk15yJhhQ/G1KEv+MmXNKs7cvUU9Dy9a+Pk/Vb5n8ZtPS+KL80utmS/FmdNwMwRGjwH9svN3ID0WfyML3Ic0oyAxC69x7QAYblej9DyODb/Aj4lhSItV9I6RVa4D+NJI0RSe+PMWv2pyWGL+7DGPVHC+GMykMOT5H8cK5Kjhu1zoYQA1S5LTWtWtRq/IpX9G3FdO7N7rnOq5BPdBTWn+64R/WhwtWrRo0aJFyz+E1uL3mlg/uDHhH3ejvuvT3e3K087bji41HfmkvR/f9avPpCCN62BnS2cSGg3gXQcfTVZO8YTVz0gHhtaCPj4IoQk7UwvBycwE9qfH0tlSo3CcyEwkpiiPfWlPWMvOxsD5GBa71mGzdxB9rEcCq4AZZWMeAD2BzfD92XCOXUvmanQ6+9NiAUgpLmR8xEWuZqWi2qGm3vV91A/ZVyprdQMTbtXryXtOmpiwpdUboIsEJ0XVd+BKoeaLmNtIJRI2eDUr6+jRCyZOhl59+HDdj5i90ZOL9yqXtkhVFDI96jpTHlx55jn3MDBlqnMtDGUlz0NkLcFiPVCdLneOUePKDqILczGQyZ8ZZ1gVRjIdNni3IKHRANpZVMz4uWD4KOYNfZMmPi9Zmb4cjUxt6G39HB/cp7F3FxzYD4+iSpvOZyfT9c5xRoWdQ89SYxXOvh9faeo4By8mOPjg/OVZjgR9Tszuv1oiYh6QB9T9i+s8mwa6cNAalpTzdC5Sqyp/nqrgWBF8lwcrn/6c4l+FdSMPXPs3wn3wy2ds1aLl/ysjR45EIpEgkUjQ0dHBzs6O9u3bs3btWtTqqks2dezYEZlMxpUrz/6t0aLlMSNHjqRXr14V3kskEhYuXFhh3K5du57rPXPjxg169OiBra0t+vr6uLu7M3DgQJKTk58rR1RUFBKJhJCQkD9zGFr+Q2gtfq8JA105NaxfPOCotqMF+8doXBDbettX6LPTNcDO2YCchf2Qy6pQQCw0WR/HNPVgVKPqyGVSjM9tIl+tJLXxG7zj4E0jE2veDj/P4Yx4TtXuSJBZyR69vEElcDA05g1hDEMB7zHwWbn1M4EEIBbmvO2Pp7MxH3OVz2NuMti8OkdPHyXo2B5+a9eL+l90ZcS4GkhaUiHesDxypRSpQopknwSqMHjJJVL2+LVBCujJyl2yhobQsTMApoaGGOnro69bOaOqtY4+231blZYvqMBXh+F8JGsXB2FsaswAG/cqZWxsYkNOcho3Qu/iWqdhlWOeh1QiqTIjZ/Oa/jSv+XRL39YzJ9l+5TLfjp2ArdGfzHhSjkmRlwFYVqPkOCZPgUePwLvMWudvaMFw2xp0sXQi/dpDQpfsJ+1yJE5d6lRYq46xFSs8rIhuKyE8Ih2LwD+pfFbg9TyPqlWulGRCcT6+V3fR0sye3X5tnj4J6KQPn5hAh1eXy+lvxcDOjKDfJv3TYmjR8pdQqdWciUwhIbsAB1MDWtSwQfaKEyl16tSJdevWoVKpSEpK4tChQ0yaNIlt27axZ88e5PKy36Po6GjOnz/PhAkTWLt2LQ0aVOFR8y9ApVIhkUiQapNQVYlKDTfuQ1omWJlDgDdUdZv1KtHX12fRokWMHTsWCwuLF5qTkpJC27Zt6datG4cPH8bc3JyoqCj27NlDXl7e3ybbyJEjcXd3Z/bs2X/bmlpeM+JfQFZWlgBEVlbWPy3Kv5fcIiFORgmRmi9EWJoQt5Jeavr3cfdE2xuHxbrE8NK235MfiuH3zohMRVHVk9KFEEFCiDfLmlRqtdibGi3S4guEUGra1Gq1+PDBVbEk5raos/ig+GHwRCG6dBDpe3YL8Z0Q4vJzhFMLofxUJcSCis05ymLxe/JDUahSvtSxvhTv/iJym80TnP5ZGJ795ZlDvceNFHRvK6KSEkvbbuWmixH3zohzWYnPmFmGWq1+aRGbfTxN0L2t6HLx9kvPfRKVWi0cdq8Whmd/EUq1qvKASxeEGDNKLLh8RAwOPSWKVEqhVqtF5IbTIuNOzF/e/99KUlG+sL+wVQwJPf1Pi6JFy3+K1/H7vT0kWjjP2imY9Gvpy3nWTrE9JPqV7TlixAjRs2fPSu3Hjx8XgPjpp58qtM+ePVu88cYbIjQ0VJiZmYn8/Pxnrr9u3TphZmYmDh06JHx8fISRkZHo2LGjiI+PrzDup59+Ej4+PkJPT094e3uL7777rrTv5MmTAhAZGRmlbcHBwQIQDx8+rLDP7t27ha+vr5DJZOLhw4ciPT1dDBs2TJibmwsDAwPRqVMnERYW9tLy/S/xx2Uhek0SotmwslevSZr2V8WT19mIESNEt27dhI+Pj5g2bVpp+86dO8Wzbtl37twp5HK5UCgUTx2Tnp4uBg8eLKytrYW+vr7w8PAQa9euFUIIAVR4tWzZ8qnyzpo166WOUcu/C+0jn/8K4elwIwluJ8PRB3A8CooqJgO5FZ9JbpGiyumD4gzodlvNnKiQ0rZ+Nu6s926uyWBZjoJiJUIIsAD2AN+X9e1IfUT3uyf4IPsqlCSClEgkLKpWjw+c/Wjracfhhh0pmjYDiy5d4V3geQ8+JSD7XFrBmxRgQcwt+t87xcqEpyd9KU9+EWw/B7eiytoUajWRBRWTWjxKTiIjV5Msh28GYrRnMhu9m7PJu8Uz15/UvTfvdumBY7nsmBMiL7E+OZL+oaeeK9/etBh0z21kbWL4Cx3PY759byoBk+YT5Ov3UvOqIvTLvXzZ8zhHHzpUWbuQR48gNpaN+UlsSYkiRVGIRCKh+rAWmNd8eg2PsFXH2V93JjkPkgBNLcL8+Iy/LO/rwlbXgITGA/jF59nXgBYtWl4vO27E0G/dWWIzCyq0x2UW0G/dWXbceEZyr1dAmzZtCAgIYMeOsqRfQgjWrVvH0KFD8fHxwcPDg23btj13rfz8fL766is2btzI6dOniY6OZurUqaX9mzZt4rPPPmP+/PmEhoayYMECPv30U9avX/9SMufn57No0SJWr17NnTt3sLW1ZeTIkVy9epU9e/Zw4cIFhBB06dIFhUJRYd6z5Ptf4tQV+HgFpKRXbE9J17Sfeo3euzKZjAULFrBixQpiY2NfaI69vT1KpZKdO3dq7t+q4NNPP+Xu3bscPHiQ0NBQfvjhB6ytNbWEL1/WeAIdO3aMhISECte3lv8ttK6e/xVq2mj+9bQEZ1MoVoFeWQr+qVeusWRTGH0DXNj2ZvNK081vpjMpz5KW7QKfuU1YcjaBiw/RqIk5H7aqSWfLijf7TU1t6Wftxhs21Z6YeRooZnHPdkAdKhGdBUo1VH8xtwWAPlauhBdk08nCqcr+mFS4Egat/MHSBDLzNG26EgX+xIG7O1MeXmFF/D0O1WpHRwsnkjIy8H5nJD7OroQsWwW6crCUM5SypC+xqSmExcXSJqDicbzTpUclGea71WVG1DXG2ntpGm6EkLP7GLwxCBOvErkvX4Rjx/iudU2UQrx0ofRAO1tC7MrKYuy6eI7Np0+wYswECk9GYN2wBoZOL1ArATB0sULf3pwark/JPtlvADRpyiFrC1KVRTi9QLKY+EM3eLT1AhnBUeRFpxGjEJzs9Q013K3odOHzF5KrPCoBl4qhvi7o/vkcL/8zqJUq9gdMR26kR+fL8/5pcbRoeW2o1Gom7bxGVbexAk0KqMk7r9PT3+mVu32Wx8fHh5s3b5a+P3bsGPn5+XTsqMnoPHToUNasWcOwYcOeuY5CoWDlypXUqKH5/ZkwYQKff172nTlr1iyWLFlCnz59AKhWrRp3795l1apVjBgx4oXlVSgUfP/99wQEBAAQHh7Onj17OHfuHE2bamJ/N23ahIuLC7t27aJ///4vJN//Cio1LN307DHLNkHzeq/P7bN3794EBgYya9Ys1qxZ89zxjRs3ZubMmQwePJhx48bRsGFD2rRpw/Dhw7ErKZUVHR1NnTp1qF+/PgDu7u6l821sNPeYVlZW2NvbV1pfy/8OWovffwVdGUgkcPQhOJmAr3Vpl1Ko+Tr9DlipGaFwhx+BJ3WLnl5I+vhQx7bsA52rUjA2/AJ70qJL246e1sMnszVnjdPoceNoJTEc9Qz53bcVnSyfVMY6lbwKq5Z/b7jmpXjxkgX1Taz53bcV+9JjsT3zKyGDvoGDt0r7o5IgOgXi0uBefha5+lkMagmdb6yG8WPhwnkCjCypaWhGtWxjOAKm+oa0qOlPa/8AogpzmRh5iYeFORX2HfbNQtp+Oo1rEWHPlbGZmS2nAzozpCTLpfLH1ZhcOsqdN8uVez/1B5w7g15oKAABRi+u/FbFxpNH+e3sKS79cozTfb7hyoSfAcjITmfColF0mDGG4MiqrYrVBjejX8L32AX5Vr24RALOLrjqG1PXuHwios3AfKjiFux0v6UknbxL52vzMW9Zk96mdnz/x5e4D25WaWx5hBA82HCarNA4ABIf3mT7N6P4MSqKgenww38kecorRwiU+cUo8yqWEFmUDau050jL/zBnIlMqWfrKI4CYzHzORKa8PqHQfHeVT7Sxdu1aBg4cWBrzN2jQIM6dO0dkZOQz1zE0NCxVqgAcHBxKE3Hk5eURGRnJ6NGjMTY2Ln3Nmzfvues+ia6uLrVr1y59Hxoailwup1GjRqVtVlZWeHt7E1ryO/U8+f6XuHG/sqXvSZLTNeNeJ4sWLWL9+vUV/ibPYv78+SQmJrJy5Ur8/PxYuXIlPj4+3LqluW9655132LJlC4GBgXz44YecP3/+uWtu2rSpwvW3adMmFixYUKHtzJkzf+k4tbxetBa/fwv7b4KZATT3fPqYyAyIyYacItAv+9PJj0VR+MCXcyNtaf2xM4SCaC4IcU2ntpEFMomUS7p5dAk/zhxpABMcNTf913LSuHrpFifcojGvrUuQmT0Po/QwKramwboLODgqoPWbL3gAX6BR+p6S8aKlq8ZKqSODe6kaN1V9OzAEXJ69cpKigHShILu4CNLKgpSb+ICrTSF2VlFYXbyGXCIhq+lg8PWGUA9wcmK0vRuj7T1hJnAUDBbrcXTulwB8/ugGK+LvYSHXY45bYOm6I9p0wMHCEkcHZ4TQ6EIvzLvjeTD3Z2z6ltXpY8w70KoNQ91saFKYi5u+Mddz055QrMYD24HzwAIgEKg69f56Wye+bByE8+DOXLuSimt/zY/3sYt72XVsHXkyB/ZeaUF1ewciEuKp5+FV5TpKoWZtYgStzOzwMjSrckwZH7Ik5y0UIofpphULOTb8/k0UOYVY1a2GENBOX4KJoRE+71WuZwiQm5NHQlwipsnFnB+xErtWNWl/8hOCT2zk+tF1BLrWpkmryTSpIhfP30l6cBQXRv9IwJy+OHevXOfx34JUR07PsCVQzqKRq4Yf8sBYAmOrLsWpRct/noTspyt9f2bc30VoaCjVqmm8XtLT09m5cycKhYIffvihdIxKpWLt2rXMnz//qevo6OhUeC+RSErd9HJzNU91fvrppwoKGmhcAYHSBC3lXfvKu2o+xsDA4KXqqb6IfP9LpGX+veP+LoKCgujYsSMzZsxg5MiRLzTHysqK/v37079/fxYsWECdOnX46quvWL9+PZ07d+bRo0ccOHCAo0eP0rZtW8aPH89XX1Uu1fWYHj16VLj+PvroI5ycnJg4cWJpm5NT1V5ZWv6daBW/fwOZ+fDxTo3id+rDp4/rXAOyi8HmCfc7lUBXSGidJoPpQDj8ZBHO2OALfO4WyKeuAeSoFGSWLzx+BVrelfDZsUx6jdFjaVwoQWb2zBkPqRkSbIznoWtQloEyX6VETyrVxIXdAzx44uqpKmvg54AKmAO1ylwVOflIowRuswJTOex/YppKqbnJLYlBW+Rej5kutTH3V4KFISiLQSJDLpPhZjsC+I3htrvQlZa4L7Zqo3mVpycanTSwrOk9Rx/M5DoMs61Y229k247UbtGeujGJvGmpx4JfvgWpBMZP5HnI/XypvmVR6fs7IbcwNzbBqUFD+pe0+V7dRVhBNtEN+5ZzpcwAcoAYTmedpbr+bZz1qlb8jA8ewFihgKm6NF3/Tml791YDycrNRGrozKDW3Rj69RfsuHCWMwu/Kc0amh2WwLlh3+MzuTN3OrgwNuIC3Syd2evXttwOhWjSuLYFNMqbEFtYmdsINXKmmIBOuXuI6sODSv8vkcB3zzFofrPgWy6eucKXS2fj+0EXHNprZGs5YAa2LjXxDxpI/xfMmFko4LMsjVvogJesx5d5K5qM4ChSL0b8qxU/0Ch/5TGWwmZLMNS6wmr5H8bBtHIW5L8y7u/gxIkT3Lp1i/fffx/QWEScnZ3ZtWtXhXFHjhxhyZIlfP7556WK2stgZ2eHo6MjDx48YMiQIVWOeeyel5CQUJr98UXS8fv6+qJUKrl06VKpq2daWhr379+nZs0/X1bov4qV+d877u9k4cKFBAYG4u3t/dJzdXV1qVGjRoWsnjY2NowYMYIRI0bQokULpk2bxldffYVuSUZ0laqiV5aJiQkmJiYV3ltaWuLhUXXNYi3/frSK39+NEBp3TEMdaP4cU9ZjzA1hRheNUvMsDHQ0ryfpVAMmb4Ef98PGt6CnEwHZFtQxsqShicYltJ2FI2mNB2L+eM4nQJo5XRu7sibDklYlPt9GBpoXlFl1Eorz8bm6ixZmdux70Fajz40CynSOKhBorIBq4FNAzv38LKx19LDqXAMKVZAjB9snphUVwMGNYGkHQT0BkMRnYX4jBjrV0ih9+9eDoTG0HwQ0BR7yg2d94BlPnRqVvDSbAFlY6NgyyanqH7mdSfdJjL/M9WI/OHUSkMA7EypYXU7eDOHM3VvM6DcIHXnlj1JYTAxeBmYoisu+SFUqFZmXb2BpYYJNs/LazS9AITdyC2l5cwbNTU05E1BxPZVKpbmB+PIrUCrZmhVHqqKI8Y6aMgz6uvq81adMAe9UtwGZeXl4OJSdl6x78aRdjiTl7H1aDWjAVCc/eliVXaff5YK5NIYhhouBSzxW/H5JtmeiPIUWZg4VlL4/Q4MmdSksKMKphgvmS2qVthuaWFKvQ0ULc44aQhTQXLdqy+tDJWwugFDlyyt+1YcHYV7bFfNaL/g5/Zfxqi2iWrT807SoYYOzuQFxmQVVxvlJAGdzQ1rUsHkl+xcVFZGYmFihnMMXX3xBt27dGD58OABr1qyhX79+1KpVq8JcFxcXZsyYwaFDh+jateuf2n/OnDlMnDgRMzMzOnXqRFFREVevXiUjI4MPPvgADw8PXFxcmD17NvPnzycsLIwlS5Y8d11PT0969uzJ22+/zapVqzAxMWH69Ok4OTnRs2fPPyXrf5kAb7CxfLa7p62lZtzrxt/fnyFDhrB8+fJnjtu3bx9btmzhjTfewMvLCyEEe/fu5cCBA6xbtw6Azz77jHr16uHn50dRURH79u3D11fjAWZra4uBgQGHDh3C2dkZfX19zMye5wmk5b+INsbv76ZYDWFpGnfGl2FgA+jwF7I2Nq4O9d3B0pj8Q1E0yjfget3udCyXGMX8zmXY/ROkJcIUYLIe8m/fYFSPDlQ3eHqNQR2JFDO5LpZyXfAEfKiy3l5FJMCpkpecqMJcal7ZhdPZLcxSRoKPlUb5HPPkNAnIdUCnXKbRRQdh7ioYPoycsPuEouK6urikcxJwmWcqfSWkKgqZ/SiE6MJhaPxLI0r71EIQX5QPwGdRwWxKuEsLU1s+cXSBZd/Biu8qKH0An25ax6xf13M57F6V+9VwdGRd6DXO5ySVtinUKoov38H01A10peWfAksBQ2oYmPCGjTvDbctfC5f5ZEMv5L074rvxG05YGoKXN+9EXGRC5CXSFRVjvx7zdseuHJ+3GHuLssQvLj3q0SV4AfWWDMFYpsPi6vVpYaYJ/M5Rw6IcWJDtAazjcTrXHKWC4WFnmRF2gsAnyiQWpmST+/ApMR9FRXDmNBRWjPvs1KM985d+hrnF839U5ufA4HTY95TQUV8d2GgJK8yfvsbCbGiUBLFV5NSxDHRHKn/5p/H/SyQcvcXpAcv+U1lYtfz/QCaVsqy3xhr/5HOfx++X9q77yhK7HDp0CAcHB9zd3enUqRMnT55k+fLl7N69G5lMxrVr17hx4wZ9+/atNNfMzIy2bdu+UGKOp/HWW2+xevVq1q1bh7+/Py1btuTnn38udTPV0dFh8+bN3Lt3j9q1a7No0SLmzXuxBFDr1q2jXr16dOvWjSZNmiCE4MCBA5XcO/8/IJPC5KqNqqVMGvL66/k95vPPP0etVj9zTM2aNTE0NGTKlCkEBgbSuHFjfvvtN1avXl2aZEhXV5cZM2ZQu3ZtgoKCkMlkbNmyBQC5XM7y5ctZtWoVjo6O/y8fAPx/QSL+BQ7b2dnZmJmZkZWVhekT8UP/RvqvO4tKCHaMekrq97R8kMvA7PU/kl/5fT6bLxqwvGMCAUMcK3aGXoXwEDBvCg72mgyhZ9B4GfaAiJQclGqBj92f/xtkK4vpffckLc3s+cytzGSVt/UiHQpvcNFdj0H33fkluZVG8QMoUGiS1zztW/V8BKxbA/HXyfxoOm7yBOoaW3GydtUxZE/jq9jbTHt4jSlOqSystpr3IhbS3aoejXVM6bdtLSedDTlUvys/JYSzOz2akDrd8XtGIpZrEWGM2BJMvtqAn7ubIRVKmtctc5lM/OMu+jammPuVZUY9lhFP+xuHaGPuwPHAzi8ouQsQi+PI5iQ0bMUnXfow170Oh9LjSFcWMdi2+kudh6opAvQ4XghGEmj8xKW7NjEcU5kO/WwcgTLtb2/NaeQ8SKZP7Ar0rZ+4brZuhg0/w7AR8MbgPyXV8UJYnw/zTcHlT/onTMyA/YVwwBq8/yX3NIpiBZHhD/Hxqzr+8nVyYdQqItedosXWibgNaPxPi6PlP8br+P3ecSOGSTuvVUj04mJuyNLedekT8N+02Gv593Hqiia7Z3nLn62lRulr+byyVFq0/EfQunq+JEIITkUmoxYClVpd9ZNGq5f0OXtB1CU6uvQZQdqmrvoYhgj06tigPiPI3qzA7EOQbDsJzTygxRDYcAuiYjWK3xwgC2gBDb4+TJFSTfqCvujrlFhBtgM2QNBTt6xAiqKQU1lJFAt1BcXPKLuYc98nkmJkhIV6MJS4TKgSMpD1XQmNqsM3A6tetKkHyNvAbTPMPasRa9McHYmEDUmRNDG1wdPgxW42+po6kuuiYLSDFwfTB7Ay8QRpygguBT/i5M59mAXVx66JAb/6tCBdWYy97rPjRup5eGFpGk1SShajP+tKcXEh93ZnY6BvSH58BsfazCfb0Yhf9vXiXEBnJBIJzUxt+cgtgI4Wjs9cuyJzUasjublsCncppLGJxq2pcmbVP8stoCHwFm31V1Q5YpS9J7CWLPUUHql+o7ZOewAcOtbGKCwBHdPK1/zBJm24b+zARD+3F3AtEMBAwAiNtVFDW33NKyciEYW9OTrGLxj8V45vzOFzAeZVCHE8I4GjmfHMdgtEX/r6LH8/r9rErq37mDZrMq3aVy6/olapybwVjWWg+yuXpe7iwTj3rIdz97qvfC8tWv4MfQJc6OnvxJnIFBKyC3AwNaBFDZvXWsJBy/8+LRtoSjbcuK9J5GJlrnHv/KcsfVq0vAq0it9LIpFIuP1RFwTitf/o1AneS65Kwb16vdF5yt6Du0kZ3A1Ayu13M6l1xZwLK+/Q5NBFiEiGlcM0MYHmJSadT4B0wAJGNqxOfrESPXnJ2qnAQsCSF1L8itUq/shK4oR/B/yftJS9HQS962Jz8BbUzQMXNaF3rtMlOoxgfQnmZs+5obdzgdwsMLHARK7DsYx4RoSdpYO5I4f92z97rlLBzehoGkydwPA27XCZUAdnXUN2+ramsakNBRbeZOTmMLFbb6oZmCGTyiopfRHR9wi+d4l+7YcTXpBNnlpJHWMrfn6rIYEh+3B0GkVfEzsM9A3JvB1DXnQq3uPbs0QviZjifFbkQj9DcJTJWVjtaYlE7gBeQJlZSqjVwHCkUinWZi+sfwNw6vYNPByccLKyrtCuVgtm7r+Bt60pbzaqjuZrQA94XoIEHT7IXM+RovbssoJ6ulD/m6fXqfpC346Hde3o+fsKqvXrA47PUlSLgUNUlRU2804s+2t/hEPH2rQ58NFzZNSgVqkpTMzE0MkSmQTMn/KsZH7MTU5mJdLRwpHW5k+pbfgKqBVYk/t3w6nu4VZl/+0Fu7j52TYa/fQ2nm+1rtSfcj6MjBuP8BzX7k9l6yuPnpUJLj3r/6U1tGh51cikUlp52v3TYmj5H0cmhbpPqXakRcv/AlrF709ga/LyVoeXJjqL8XHXuKKfz7mALuhIpcgA9UuUF8gcU8xYw8v0HOHE3WYdcferhiGAtxWwFjgJrVaisbLAN239ICwNdZEKib4crIGP0Vj8XoBtqY94K/w8w2yrs8G7CjdYa2NO96iGuBVFy8NR6JqFkeLlwKyf2rCsRsNnL+7mo3mV0MTUhomOvnR9ntUr8RGc3YerfXUsjI2JTkkmcNJYds6YTS97V80YewO+Hv0OXuNGYKinT/CyH9F54hHfzBXvcj7kJM527vQtTCJVUURy44HoymToSqR4tnmDiV6a7Gin+nxDTngi9a5/iuXlY4y2aMDiXAnpapj91LC2nUAf4ANAE5xfUFTEwVofIZfJ6B761Uvd4F+PDKfVzCm0rFWbPxZ8XaEvLiufRcdDqWZlVKL4+QKZz1wvRw36kmG00RcUCHB7AePYUnN4dOw41Q7vQ1W9GrJnKn56aBRfzVdSUVoOOmaGZKZGEx17AatGHtg2f/HI+utTfuHe8sO0PTIdh3ZPD0hdXqMhF7JTaGn2egvWNmnRkCYtnn7NW9WvjkWgGxa1q3Zjuzx+HRkhj7Bu4vlarIJatGjRokWLlv8+WgP2vxBVTiFi531mhOjzqCiP/HuJ8Ms1mulbEVWUy/HMhCrnfRV7m+Y3DpJSrMmEEVDLgi19Ingz8yx+5vd5u7h89dE1aDJJliU44WoCkX88xPzTHYzbelnT1gt4dh1uSM2HiHQ6WjjyroM3Na7F0bf9UCLuP6gwTC0E7W4dpZ36JkqpPTXii8mu0/P5Sh+Qn5VFQU5ZoXUjmQ7LajSkg8UzlIn0JDizByRgbmFJ4obfsTe35MbDSOLSKyff0dPRpVgtwWDab3x1oqxg6uxf1/Mw35wRPSYQ6NOQIeYudNGzwlSmg7OeEalN3mB1idIHUHNaN3wmd2ZXeDA/7N9N5rmjjDeCkUaVtiyHJxAAaAIJCoqK6DTrI8KTE1BKXiwM9/eUKOoH7+NOXgaeDk4MbNGKYa3aszoxjHHhFyhWa7KLulgYsfftILa/WdnFEODyhHUca78AVaEmgU6CChokw6gMGGIo4VcrsH4Bxa+uLvRu1YR++e9iO6KIxMTn1dpyARzIuBXNdod3uTBqFTuXjub3ZUPw/qEtKSZGLL+bSuELnA5TbweMq9lgYG/+zHG1jCx428Hrme7T/wROnQPpGvwF1g2rTpld98vBBH7xBha1XV+zZFq0aNGiRYuW/ypai9+/kN2RyTzMyiRSWUwde2skFx6AVJeAa6nUrGmGq17VGsQfWUmcz07mTFYSJ7ISmOLkRw19E4Lz0jGT6dDc1BbSC+BWMjRYD4bRaJSNEurYoZeVz4yCPKYmAom5YP8C1aGPPICUfKyG1OI7j8b8fCyS6wolqnK1Y0ATmzjfvQ7q7HzktgkwZCxS4ydMYLfReP2VCzdSFhczxbMWwkAf1bH1zHerg+1z4u8A0NUHQ1OoXhN8NQrVjxPe59OBQ/F4wvokIpIInfwFJyXQaeUfFSx+J2/dIDgul80fT8dAz4Abm3ZxNOQawV97VFkc3fNtTQ1Bz7xcTAwNeSOoNU/mPalMLSCk9F2/RXM4G3qHxMFOXPhydiVrn0oNyZlgb1FmAb6Qk8K13DTu5mfhZ+POlmma7DkB1/dwMy+DD5xqlhZq7+b3dIU56Y9Qch8ko8gtRKavi74ErKVg/2ceExkaIrWxQipNfmFLtY6JAQaOFhi5WtOo03gsHWqQczyNuCm/83BAJGfWj6P9c4zuXu+0x+ud57gAV0HqpQiSToXi+0GXf3W2T4f2/qX1D7Vo0aJFixYtWl4EbVbPfyFpeUWM3HSRw/cT6dPKCcvcRGbcN+BQgANDu9bGQFdOTqEClVpgbqjJsKhQqbkSl4altS4/JYbxddxdTgWbUk/PnBGBhXRz92SknQecegQhSRDkCnWecG+7lwqHHyDsjJCk5kNfH3B4epmHUsLSIDEPmjlrHORzsskbOpRia1ss1qyuMDRHqWDfvbv02vYQg1FB4GpZca0gNIrfH5SGe6lVKua36chNCtk272226JrQx7MYHcsBgJT7l8L5Lu4ek1x9qVH/KUVFU5I1GSa7dgefig78EfGx1BkzmgFFtqw58DNCLq2gaGXm5pKanVWqLP54aB/7L11gqm8dGnTphL5xReU4tyCJ6JQQarp25Gr4fdzt7LE2fbl6ODM3rOHAtcsc/XwRNmbmlfov3IODV5NoXVuH1rU157BYrSI0P4sA44rnNCw/i0dFebR/wYQyRem5KPOKMHKxAuCn7d9w6NwufvxsG1bmZX6/UYW5LI8P5X2nmrg85WHEY9RqgVT656xqD+MiGN23FU2iPHHdtIqRbbzQK7dUogomZkJvAxj0F/MqfRU0CfWtJIbv+BT71n++vIoit5CjredhWceNxj++/deE0qLlX4z291uLFi1a/jtoXT3/BItPhFJ70QFiMvJYezGShcfuPnXsjdx0nC79xtK4p495EisjPfaOaUnxkoFs6d6c7wf1Y1EtO8acus/6Kw8BCFx8CM/5+yhUaNz3Pt5/g2ZfH+PGvUw+cq7FeuNaBK29idEPp9k2445G6UvPA32VRunzs6FIraq4sa0ROBojaegI79Z/MaUvvxhWHYe4BJBJ2Xs7jq9CI+nfuQlTqkhc8Xn0DQZnhDBP+QiO3qm83tvAm1TI8SGVyfj01DFil83ALjEFy3b9CO4zCNjGb2f/YHrsFVbYpPPVxp2oVZVr3cSmpnDx118YL8tiwr2zlfqlUikyXTlydxsCp75L848mV+g3NzbG0dqOyFSNq+mYTt14z86dlQOHsmPOfJQqFTceRpaOPxLSGA+Hzuy/8jENpoynzqRxtP54CoXFxTxJsTqchKIv0ZRTKGPB8NGELFtVpdIHYGZYwJc7RzPs6/GlbbpSWSWlT5FbiP7B+zgmJ7P0l7nkF+Q9uVQl9CyNS5U+gDPXj3H59hkSUmMrjFuTGM43cXdZnxTx5BKV+LNKH8DNsKuEmcQRX0/BmNYe6EngajGcLjllUUq4UO79nyU5MYWT6jhC6uli28Ln+ROegSq/iOzQODJvxvw1obRo0aJFixYtWv4mtK6ef4Lg2AzuJGaTlFPIh3tDSM8vZmxTDywMdSuNzVAWk1hcSGzR82+4n8VbjWugVAt61tLUhPN3MCOjoBgdmeaGupGbNXWcLfC1N+V2fiY+Hq4wp6emBl6tEre+Gdvh0kOY/TYnspJpJzvCas+mDLSohq5cio6lAfSvCcDGpEiMZHL6WFeddbCUxCw4HgqpuWT0rEOPzaegowL8HHGSVTa/GMg07nNhrVyhaaPSdrVShep0GDoWxTAsoNI8gFWeTbhk7IhFiwDMGxeAogFL93xDsuI2X44zpUuQHOkTSVmikhKZ+OO3HLxyCZ0pQ5DJdVguRIWYrur2jmRs34dSpaLGmGFVKmjDfrnArYQrHHlHB3fL8fi1aUXd7l2p17MbszevZ/5vv7Lh/Y8Y1ro9zlY2KNUx+Lr406W+IORBJJfu3yO3sAB93YrXyN2MqQRa7eFCvBlNHMdW2jcnMonYvdfxHNsWuYFmbvT2yzjVdaNzvXrYmT+9ziDA7fm7uLNwDw+6qPmxeCvVnDzp2fqNZ855ku9mbiY+JQZv94oWsImOvljIdTUPFV4hPVoNxMbSnkCvBkhKstmOSIdcAXfsNDUHD1iDexWemapiJUKpQm74/JqatvY2jBw3hN827uSreSv4cPbkPy2zvq0ZvR4uQ25Ucd+86FSSTt/DfVDTSteqFi1atGjRokXLK0X8C8jKyhKAyMrK+qdFeSEKFUoRlZYrhBDizU0XhXTyr+L4/YSnjk8pLhAqtbpi4+77QmwPFeLJ9id4mJojuqz6Qxy4E/dCsmUqioT09HpheX5zxY7wJCFafSlE/x+EaFQglE3UQvfUBvHN/TvC+INNot2oOaIgN1eI5DyRl5UnOP2zMDi78YX2FMHRQiRq/nafH70l6p/ZJ96+f07cyc0QCqWqwtAMRZH4+OE1cTcvo0L7qf5LRbhTB3He3E7kX71VxSY/CyE+EkIoNW+3/y5Et04i5sgh8cetVUIImRDinQozioqLhcmA7sJkQDfx9ool4kJSjLiXl/nMQ4nMyRC5RYWV2r88flecjqgnhEAIsV8IsUgIcVIIIcTui+dEwMQx4lpEWOUFcwpFRnqmiE1NqXK/HRGHxZWwKeJ0VGyFdrVaLdRqtTg79DuxkUEicuMZIYQQCSdui40MEsc6fvHM43hMyuUI8Uefr8X1oyfF8k3zRX5B3gvN+yvEJceIjOz0V7rHulwhvsnW/P/akXVibn8LEXnjZKVx+wKmi99txwpFXuW/aVWkpaSL/h2Hi/kfL/4bpS3jVN9vxEYGiehdV17J+lq0vG7+a7/fWrRo0fL/Ga3F70+gJ5fhZqmJaartaIahrhwDnaefSmudKjJRpOSBSmjqM8iqdoPLK1LSe+0ZQuIycTE3pHPN58domcl1menij7XOExaOlBzIyIcu/tBZHxlQFDSMKbuu43T1KLanN7PbooiBrj0xtDRgXftmGEnLHdOxu7DlCszuAc7lrEyFCvhoG9iYwC9v8WluLT41rgVecDM+A5PZ25jQ3JPFXQNALTDX1WWeuyZzS3BuGr3unuQTl9rUdbEitiAGRXEyRYrsKirKzUOTgXQc4A5yOUhlONva4VyrI9AXqGj90pHL6dGwCeoidxYNH4TFc8JPgnPTaBC8nwE27vzqo6mYF5WUSI95n/Jmu46cC67DtTvJ1KvxgBZ+HwH1gSv0aNSUHo2aVl4wPQ96rMDcxwHz1SOe6NwMHKCT89fsvCinR8MyF01VkYK9vlPRszah0Y9vITfSw7GzxgpqWa8aHm+1xqlbnWcfTAnWDWpQkJBJ6ojfeSdsCXL9v1aKJOViOIVJWU+t+3Z3/THWz/ycm22zOLjhxl/aqzxpVx+QHZ5ItUGa81w+Q2peVgoFuRmEbTxMTMR1mm0aX2rhM3AwR6jVSF7QumZpbcHmfWuRvaLELl7vtkfXyhjboL/mSqpFixYtWrS8LK1atSIwMJClS5f+06Jo+YfQ+hr9RSa38iFnUX+aVLN+/uDyDPWH4bU1yVCAnTdjqPvVIe7eT4ZHWQCk5hVxKyELLxsTvuwRWHmN3CLEyLWE/XCwQvNc9zpMcqpZcWyTGnBoMnzQQRND96ameVj9atTt3BGfLl2p36cH1LAAT0tG2nnQ38a9bP75SLgaBWGJGtmio/l28HDCL10CXRnoySGyCD4/Dp8lASBBglSiyeZZuOE66jXBUKQEQC3UqA/e5Ifv71NT8jn1ljjQIu46zW6FY964CiWK34DdQIlMPXrBrr0Q8Pi8WAFSzt29zdjvviE1OwuJRMLMXjOIvjeIuSvLVtp35SKuowZxJPhqhR2sdfTxMjDFz9C8tC3h7gNuR0cR/CACX+eRHL8xnGp2/clVLeZC9kzEE7mRQmMe0WrmB+y6eI6d1y6itjIC+6o0zrXAL2w//wNDlizgq52/lfZcjkrlrjKHUySRFZtK+KrjXJuzm037ITHPkMY/vf3cgts7b8bQcsUxwlNykOnrINOTg/TFPu5FGbmknA+rsu9M/2Wc6vU1+fEZVfZHbrmGf7wzzc0aVdkPUCCgbxp8mvVC4gBwfsQPnBv8LVn34ir1JZnZ0bTPavK25BCz+yoFiZmlfW0OfkS3m4uQ6em88F6vSukDsG/jR+NVb6Fn8QLZcrVo0fKPMXLkSCQSSaVXp06diI+Px8LCguXLl1eYc+nSJXR0dDhy5Mg/JLWW/xojR46kV69eFd5LJBIWLlxYYdyuXbueWsu3uLgYa2vrSnMeM3fuXOzs7FAoFOzYsYO5c+f+bfJr+e+htfj9U+hVPPWXH6URHJuBzblYyFHC4Fq42Rhx68PO2BjrY6qvg1KlJqtQgdXjuKHsAhbbZvNRLRmrE8MZbeZJFaayMuwrZ5YMdLbg1+lvwPRycV95xbD4GATVgNQcaOMD0zpCj0AI1BSUvn30OBc2/4axpSWee5dq5u2/Ceqz4JoF9MHf0Zychf3JSE4mYcMVTPSM0RFKDp4+w9b9v7HhuA31AqMh7QT8oEI2qCYGG28QH3GYVUOb8lnX2sikUlKKC3n/QS5DbevRqXzukiq+BFdt/o2NN87TSseOQWMG42wPLepCs3IGsodJCcSkpvAoOanCXJefN3D3URTM6qxpSM+jybTjRDq3x3HCZPR0dOnZWFPUcPC9hmxOecjumjH0sCqrpXY1IoxTt2+iVKk5F3qbOYNHYOJoyZ678cyt4UjzUkPsOuA29Tx8aewUi2W5ENB9Eckc9jfELiuRh9mpmHo7EGlXhx+3QvgjmP3u0/7AZew9domCYyc57W3B6BOfPHNs6uUIFDmFWDesgY6JARdH/0TMziu0/+NT7FqWZUA9e/04ijec8DdqhYGDeZVrffvlGJTDovllYNVxmgC5arhRDAWV8/AAGovn3cX7cOhYG+sGNQCoPacfGTceYeLpgLKguDTeMSL6HpO/HIGD0opJsR1ovuU9TKrbPfN4n0StVJF08i62LX2R6Wq/ErVo+TeiVqlJPnOPgoRMDBzMsW3h88rjZDt16sS6desqtOnp6WFhYcGKFSsYO3YsnTt3xtPTk4KCAkaMGMFbb71Fhw4dXqlcWl4dKpWKOzdCSU/LxNLKHL8AX2Sy11vaR19fn0WLFjF27FgsLJ4dxw+gq6vL0KFDWbduHdOnT6/QJ4Tg559/Zvjw4ejo6GBpafmUVV4ciUTCw4cPcXd3/8traXn9aO9yXhM7b8bgZ2+Gl23V/oafd6nNe8am2ERlg6clmGs0BN9yytrQXy6w82YsIdM6adodzan5die8Mu7iFWuqKbY+AnjnTwioUIFcSnBeOh+dP8ncs+E0OnEXEjLgvTYwugUEuhCXmY+TuSHNhw9Fz9gY//ZtYTmwA1jpC+/nQivv0mWlErBQG/KOdwaHjJL5MMGMz5YsQAoYK+0oqNUMA7/18M4BmNYO5f1HmChlfHXkDjZmhtxJzKJlkDWbUh6gFGo6WVasP7fjRgwHQxP4pFtNVqeGM86vBW1OpNJPODBy/lVu3pfw8xx7aldz4kFiPDcePuC9br3p0bApbrZPKAi3bkBcHBQUgJ4emOhDCy+quVuBji5xaan8eHg/73buwWCbaiiEmoYmmvIGSqWStJR0hrZqh7utPYZ6eizctoWejZrSZttuOo4qxk/eGPgO6A44A864mOdhfSeTC9F/MPFtjTvox+398NZ7E9PUeJp2boH+4E7kF0DxAWjVADhzClzdwM290p8xOzyB2/N34+9aTFJ2ErHHjyLa1yUxIx07cwukT1j9Mu/EcqjRZwC4DWhMi60Tce5ZD1VhMaY+FV2LJ305nKS0eK5ujtM8ebwXCmbmUFQIkRHQtj39q1sQ6WqBzTPux/Ql0FIP2jwl30riiTvc+PR3Us6F0ebgRxrZ+jXCrV8jItac5OLbq2m+eQLuA5tQ3dmLqSM+x6HAgoDeHrgNaAxA0qlQ1EoVDm1rPV2QEu4tP8T1KZsImNsfw+m9OVoII4zAoIqHqwpU6PDvre/3uinKyCUj5NFfKn2hRcvziN5xmauTNpAfm17aZuhsSf1lw3Ht0/CV7aunp4e9vX2VfUOHDmXHjh2MHDmSM2fOMGPGDBQKBYsXL35l8mh5tZw7dZEfl64jNSWttM3axooxk9+kWcvGr02Odu3aERERwRdffMGXX375QnNGjx7NsmXLOHv2LM2bNy9tP3XqFA8ePGD06NFAZVdPd3d3Ro8ezd27d9mzZw/m5ubMnDmT8ePHV7WNlv8BtIrfa+BqdBp91p6libs15ydXXVRaRybFMU8F2cXQxQF0Kt9culsa4WxuiIl+mdtaN08fuuEDd9FY+x57kBWr4PhDcDUDP5tKawHMehTC9tRHnLBuiO3gnyHImxPjvTlqkEdQ92o0NKnBjX03uG2iz1Dg65P3mLI7mBaubvT09mXKgH4aeYsAJSDXgRFPuGneTEb6xyOEnxmF+inUMpGybuI0JBIZ11QB2NexwlmVAGfT4VYs+Y3NORB6jO/q+rP1WjiHHmUzsK4rO3xb0cTUttIxfH8unONhSRjWhOVZoWQ18GH5+tlQ3Ya4CfEYCAfikqOpXQ26zf2E0JhofprwPl396nP53FUaNqvPyhGjCTlwmC/OncTC3Ay1qSlTVn+Pu609k74ZWLbXgT0s+P1XdGQyPhk4lG5WLqV9K79Zw8HdR/li+Wxa1NUU1v59+mfAPTa3/5ri/OZYWMUADyvIb2xixOdLPsHQqMxUa6grZ3i7euwP3MbeCXvp9XAZhiYGvNUXiAiHhQvAywu+WUHC8dtY1HZF30bzQCF0yQEerD9N0w+7ctnMhMvnrrLr9Cn6fj2fqb368+WbY1AWFJN6KQK7IB+MXK1w7BRAdngC9m01N+81RgRRY0RQqTw5D5IwsDNjzrvLiE+Oxt7aUVMXcer74OwCRkYaJdDJhbd8nh+7FqaEI0Ual88h5WL1hFoNEgn2bWtRd/Fg7KtQ2orJJab+KSKivXCnCVKplMlDPwW1GpKzIKcAYWLA8Y4LEUoVb+T//Fwrnn1rP+zb1cK+XS1m58CeQnCQQc8nrOcniGAzNxhPEwJ5sZqI/+tcfncdj7ZcoM3Bj3Ds9HQrrxYtf5boHZc53W8pPFFxOD8undP9lhK0bfIrVf6excqVK6lVqxZDhgzh999/58SJExgba924/4ucO3WRBR9/Vak9NSWNBR9/xcz5U1+b8ieTyViwYAGDBw9m4sSJODs7P3eOv78/DRo0YO3atRUUv3Xr1tG0aVN8nvHbvHjxYmbOnMmcOXM4fPgwkyZNwsvLi/btq75f1fLfRqv4vQZqOZgzvrknLT0qKy6PuZqTSlIDXbo2rw3mVSfgWNg9kPdbeXM8LIkBga7Iy7u51AT2F4MEQJe7oUnUDEuHfIVG8UvPhlM3oV1DUOuCGdzKy+B+fhYZWTnY6shAX85ER1/8jcxp1cye9AIldS9GYHsrlqEDGmBrJMNUms0ZnQeEJaXwweX6SC7GQY8aMN4CpCrgiVgqKwPIzuOna8b8OP5LLIzfhbazefdOB36PTeSNMFP2WrTEvKaMlQvMMGnjw9XQACTz5jIzC95cvIFWHlW77t3ITadjR1vGN/OkdU07bOP1GWZbHfQ1P7xbFzgQlVhAXR93APxc3AiNicZY35BvFnzLnRuhfPPTQooLi1AWF6M2NABbW1IzM1i2dydOVtZM6tGndL/xXXqgK5czpmPXSrK4V3fFxd0ZK+sn3SgEHQIzABvgEeBaaW6dBrUBUKvVFSxyRu7WIJUgLXkIsGTn78QlJ7KkVx8kNWuSePIOx9stwLlnPZw3v026sojcqBQA7OrX4PM3G5GZkY2OnSku1jZUs7Nna3Q0nyts6TV/P28+SMZjVCsar34bfXtzpDIpy0fOY3P8DX75cQk13F2J2XOVUz2/pvpIPbqtCwI+0AhnbgFt22msjs4ucPsW1KhRKvu5ewlY6sjwrVH5mq+nC5sswbPct4+qWMlenynoWhjR5doCak7tVvkPDhg0NEP1UzGJq++S3TMBUy8HTUexEnILQKVCoa+H5bhuODvolSh9ArgC1ANk5D5KQd/aBLmR5nNmWcedtkdmAPCuEtzl0LYKa6QcGTKkyLWh0aW49mmAMrcQ84DK1/VfIfNOLDdnbaPWJ72wDHT/W9fW8t9BrVJzddKGSkofoGmTwNXJG3HuWf+VuH3u27evkiI3c+ZMZs6cCYCtrS1z585l3LhxvPPOOwQFBVW1jJZ/OSqVih+XrnvmmB+XraNx8wavze2zd+/eBAYGMmvWLNasWfNCc0aPHs3UqVNZvnw5xsbG5OTksG3btkqxqE/SrFmzUhdRLy8vzp07xzfffKNV/P5H0d7BvAaO3E/AVF+HXv6Vn9pEFGSzLO4ufUP/oFvYH8QYqKpYoYzpe28wZOMFfguJrtihUEGPFdD3B9RCMPBICC0T4rgbaA3BiTD5E1g9F2Ycho7AbdhcoxnRC5PwnrwHTk6DWT3QkUrpYOGErlSGlZEex95pzYExLUGhIuTsXrIjtiMJUJHimUlMbBxqQxmTikJZvHI9dPwGsgsqiKWSC3IexmCub4xFcQ2gmFzVHH5Mv8Mpo1ss0DnE1tQoNmdGQxd/4iTFfJ0by+bhfXFo1owBdUpuKKPT4avDkJQNh2/DurOMjbjAh/HXcKymj7lcl49da+OqfwjQZJO0NJdS12cI0AJQsvXDT3m0ZhNvBLWma++OdOzeFtdqLry3ZSMrU2KxKnmqZmtuwYl5iznw2fySg1BDfjGOVtbMGjQc2ydq52XnQ4FdZyZ/sRQnV40lSKjVpF17gBA+QBawgqqUvsds+XkbvdsOJvT2/dK2Vrum8Gj5m5yO0SRSWbp3B8sO7iV1wEBo1gJzfxdc+jTAZHADOt4+SpOQgzh/2Y/6S4fh2qs+yeFxXB/4A4ql53i05lf8C8PZ9us8sg31kbXwxbqxB4kn77DD5T2ufbARgEOxoVwyL+b0Tc05zHmgiYP0/3Q/MIpbp2dphNPRgfenQp9+0LARjHpL0wYkZBVwrNVcztWaRlFGbunxFKXnoizQ1EgM0tNY1SoglT43AU01/yCa+UyFMBlJf9wFIC8mjZTrUeBoBXYWzJlzm87LlFy2eex++D3QCFhIVmgc3wxZRe8zsTxUlq17oM5M9vhMxVeqZpoJGD8hxoMNp0l1XsLcC35YHEgg8udTz5Tz/wtu/RvTeu80DB2eH4fyMsTtDyZ6+2Vid1/7W9fV8t8i+cy9Cu6dlRCQH5NG8pl7r2T/1q1bExISUuE1bty40n6VSsXPP/+MoaEhFy9eRKlUPmM1Lf9W7twIreDeWRWpyWncuRH6miTSsGjRItavX09o6IvtO2jQIFQqFb/9pkkWt3XrVqRSKQMHDnzmvCZNmlR6X37Pzp07Y2xsXPoC8PPzK33v56d19f8vobX4vQY+P3yHazHp9A90oY5zRYvQp4+C2ZISxTh7L6x09HDSLSt6Hpmaw+8hMUxo4cmuW7Hk5yuYaW+DaX1o6/WEFUwm1ZRZ0NOh9c3DxDTJpEtOTbyrWcHKy2DqDnk5oHbUJMA0Aj1dHRy8XFHoSlked5d25g4EGJfJdz8/i0iTbOotu0D62RgWFqhIbV2XJg4BnN9zDteNB0kb1Zhvs5KwryVl2kmD0iyljxmaGcLuNyXcMLbA03I9yQ9bcnPlPlYrzuI7cSpr8pIwkkvZ4K1xTXDRM2KfX1scdQ3w6GlVttCOa/DLRbAyhs2XIDmHj7YPZLu+CXWMHst8F+gP+AM3S9puo1G8FEilBrjaaM5by3bNadnusTtEHnLdLCjnvtfKPxC+OwG/bIHaLnAjBra/wx1z8DKYhI70DnAGMCQ9B+LSwMRA4OOiCQy7+9V+gj/aTP3lI/CZ0AHiUxFyGZNzw7HXNcAgUoej9x5R6JVFL08/rEseaQsheCNVkCokfKPKZvy2q3jbmnBvZjcOzf6CjNwcbNLTYeN69N8Ygvn6EdQP2Ye/zII37Ryo4VkdeW0PjXvKohUY2UnwS9coX3bu/jQ/spYZaeep+5nGopYpwNjdGlNPTRxL419+4rbaAA9LQVH6e3iN/YH06+NRG8/j6Iax3D67Gf+gOVVc5WXYGOthUsMOWVJmaTbNwuQsdtV4H4varnQ8N7vSHJmunJ7hXz81a1l5mn87nJzJ+ZjXagDAqT7fkH71AT3uL0GZX0Sdm2doU9eZevUeXxcNgIZAE/SsTYgc0pqQAA8uFkO1km9AoVZr3EVFVaYFyI1KJT8unYLELC6NWU1Rag5OXeuUuthq+XvxmdgR4+q2OHUJ/KdF0fIPUpCQ+beOe1mMjIzw8PB4av9XX33FgwcPuHr1Ki1btmTBggV89tlnr0QWLa+O9LTMv3Xc30VQUBAdO3ZkxowZjBw58rnjTU1N6devH+vWrWPUqFGsW7eOAQMG/GX349WrV1NQUPZQ39PTkwMHDuDkpMm5oKPz4lmztfzzaBW/18DqgQ25lZBZSekDmOpUCzc9Y2a6+GMq163Q9/mBe2y4FomtsR4Td1xniJ4hY6xtWOZvDyZPBCBJJfDzKACMbh/DXFeXTzv4IZNKoX11iEuCCYPAx6HcJAl8M5Cj6bFMvXOc9uYOfOpSm2Xx95jiXJO50Tc5mBHHL7mZ+OerUDia8nOfIYgYBfn1ahJ39Q4XdQOZXSShTzMd6FD5qb+tjgEWunoYeDvCPvhp4VbunjtFF91iPO+b82OmBeydCIaaYK+1Rw9iYmBIneYtgfXAWgpU60kYXER1h8bQuR7UdubO/TBOqDPZlPKQkOtZBA/sio7MC5I+gq1OMDAb7EyBEEDJVzv3EpkQz7dj33vCVSMYaAoogEjATdO8/XdUF44glA4UGkgxNtVnV2Eyva9fZKK9BUs9IpBICgFD3O2gU70kWs8cR31PP/Z+Og+r+tXRDXBGVtuR1EvhWJjpI/R1+C7+HmYSGZ7XrbgUdgz8/LkRcZXUkSOo264RI5ct5uGA9zGxtsPDzYSve9Wh+uUIrn6wkbqLBiHVkcOqH+DgAXBzx6x9W/ytjGjpaIDvqWtsvHudN3tNwNLKgr6De2JnY0Wzvp0A8KzbgUkr71T4+5j7OdPrwbLS904mhpjlgIlUgkQuRSIBy0AbzGzHUK1WNbz9jHnwy1ncBzbWyFIFcpmUD87NIlEFD1KzUf14Atf+jTCpYYeptwMrcmFtHmyxBO9yvxcvovQB6BgvxzLwC8AImEb14S0w9bTH0MWK61N+oXDfeX5YOQivgMfXY0PgEgD6NvDd2GZcKtYkmHlM1xsLQQgkT7E4+n/aG4+3W5datgoTM7VK3wuizC9iX+3pmHjY0fbQ9OdPAGT6urj1e3pJEC3/P3ha9uA/O+7v5M6dO8yaNYtff/0VX19ffvjhBwYNGkSvXr2oXbv2a5dHy5/H0sr8bx33d7Jw4UICAwPx9vZ+/mA07p6tWrVi3759nD9//oWSDV28eLHSe1/fsozejxW88ri5uWmzev5H0bp6vgYCnS0Y1qBa5Y59N6i3J5yF1ephKtdl45WHrLv0ANDoaTGn6tPLuBN9A1z4bWQzunWrCf62UKvqZC2POVCrHVEN+2EsK7mr9rSF+T2fUPrKaGPuwBzXQHJUClrdOsz2tEdsiwjlI2tvpiQa0XFge6J2jMZg3/skf7SNr2Z9TfztuzjO6M3yi7Yc22iD96fH4ERld5tldgHEKergfF4f5qppbzOOoCFv0HrHbizrNtWUmChJvpGTn8+Gk59w4NpkIIP8hJ8Jnp7J0NsH8LifwvWu28BUn33qBGptWUTUpWAMMuQ8iM4jviAfkMP2jgwQMgJu7iNfpUST7cac7/bvYdWRA0RnVnQbit6yDShEXWgBlCt3ceUykoxo6tpdYEUzCRz5AE97W7ylhlz4TsWqZZ8CliSdDuV0v6UYZk1CLdJQqjQxdlH1bRm4wo/JVkncWrCHnUHzSbgZx4QsfdLX7KCtXQobh72B1aHLZGzcy73YaK5GhHPxfihpi96j2+nNGEglvN/KB9kvZ7j3zUFyIkvKTwwcBBMmQvuOuOsb86avAzlmWay6upE5K98vjRUc9e4wOvfqwNXPfiPh2C0AClTKkvNSNSNlxRy4eIVaqmJ0TZch0y+i5geachAeddsT++0Nzg/7nsu/nONiVGqVa6hLjGY9UqFLkSHnP/6N032/oWvIFzRZO5ZUFWSqIadYScaNR0A+8AdVB/JUxRA0hSj7knk7hsRjt/F5vzPy+BjMHNWY+mbj2LmsTlFWaBzXpvxCYbKmcKCRFNrog6ycnimRSJ6q9D3uf6z0ufZugNc7mtgHoVaTcPQWitzCF5T9zxN/6AZ3Fu+tVDvy345QqVHmFqJ4wg1ci5bnYdvCB0Nny5LY9SqQgKGLFbYtnp9U6s9QVFREYmJihVdqaipKpZIRI0bQp08f+vTRxIH37duXvn37MnLkSK3L538MvwBfrG2snjnG2tYKvwDfZ455Ffj7+zNkyJDnxuk9JigoCA8PD4YPH46Pjw9Nm1ZVF7ki586d48svvyQsLIzvvvuO33//nUmTJv1V0bX8S9Fa/P5BUr46jCSvCOvugQgTPUZtvoRKCIbWd0dPV4q5iYQgH3PMDKBLTUfiMvPJ1JVjbqixDO5Ni2HGw+ssdKxHN8fnZ31Kzyvi6P1E+gS4oFPOJVNfKuMztwCUQk2xQsmEtRH0jUrFdJotDb+8S/UFeaTHSrll0YUfqpmyMjMGxbFHzNqczmJZNdTVbZCfuAunQuHqpxXq6ymXHWGIaSyrpIGYvWGEPEuX7m/MwNHHG0pypAghOJGRQAMTK/Z/Fo2RfiowFkOHP8gOb06NcGPq1EjHXqctAB4Ojng7uVB7Vyjbs93w+cAavxu7GWXvwfI3GhJ1LYkYIzUKUVYo7tDsL5gUF4zf7QNccbfDz+0CMJXk+WqufdiDZvre2P6eAwHmABR/OJ1dJ47SJCmBN1q0BsDPyIITbs35PPUcjs00PwCPfrtI9PbLOHWtRfzPCchkn5GfkIHxrRi6OTnT28oVp64S4vZeJ/5ACAPfb8pFq9N0DAggqFZtIh7dYV/BJaxMTBncsg2OllY08PTC2KDM5bf5rxPIiUzCzKfkqZu5OXQuSzAzAH9qY8/g7p8j7ylDKFScGfkdFv4upNQ0pWPwarpcdOAL+0/oWnCbYrWaqIZ9kUsqKzqhSw8RMmMLAXP74/9Jb3iidIHH6FZIJBImp+ZzaelR7k7vUqHkyBfZsCoPNlhCB31Iio2hvns29+7FIYRAIpEw2xQ+MIGwyZvYv+IwQTvMcO29ElhPcVY/ChIzMfN21LheSiRV1Gv0A9YCEH9kP7F7rmHra4n1nd9wVBtyJdSUwgRXjEtCKsO+P8r9b49gXM2GwjEd8dKpulTDn+Hhr+c5P+x7vCd0oMGKkS80pzgjl7zb0Zg19ERa4gp7bcovyAx1CZw74Knzrn3wC1mhcTh1rYN5zed/3v8t6JgY0PvRciRybRkMLS+HVCal/rLhmqyeEio+Gyr5DNdfOuyV1fM7dOgQDg4VH5h6e3szePBg4uLiKhVq/+677/Dz89O6fP7HkMlkjJn8ZpVZPR8zZtKbr72e32M+//xztm7d+kJjJRIJo0aNYubMmcyYMeOF5kyZMoWrV68yZ84cTE1N+frrr+nYseNfEVnLvxiJ+Bc8Ps7OzsbMzIysrCxMTf8d7lM/X3qAv6M59Vz+erHLqlCo1NjP2IaORELiov6AptafUi3oH1g5CUhaXhEus3fjYW3MzY+6AJpyDJ9H30AnWIfokb2xN31W9XYYv+0q358NZ80bDRnVuEbVg4SAxYfBygiGNyVm9yXc7MKRAE66hvTOkOCYoaCJvydB19I5HpzOxwola8wFt6pL6dCvK5Zm5qXLJZ0JpbriMhciquMokTBpTDtqt3bjI+UocLeHLvX5va0RA+6d4m17T370jIX8cJKLvTDXXcfDn/tRbfBAdM0f5/3PAg7wzvfRdNwYS1elLWv7ufCTi4K46mYkNB5AkVpFkVpF1r4b5Mdn4v2uxjoz6uiv7ChM44z5avxb3ATWI9ZUp/i3y+iZGsDyweCgUWJm/bqez7ds5Id3JjGuc/enntPizDwST97FuXtdpCU3tie6fkn8gRDan/oUuyBfssMSODtoBd4TO1Uok1CZ79DUxvigyt5zD1LwsjXBxrjqrK+PyYtNY5fbREx9nai2/x0avz+ezjiSn5xJ0rwBqItVXG4zgNQz98iJTMbzrdaolSoST9xB19KY2/N2UntWXyzruJeumfswmVtzd+LzbmsMlLlMmn2Mh7Vc2Tm/H0Z6mudHQkCNRI3T7M8W0FYfGNQfkZND3vzlGF07zX1SMWnRHifPekRtvcCNJXtouroWNrXXAt9zrN02kk7epcftLzHRlYKRPtg/PXGIqlhJ/KEbOAR5If9+KUonN7JqNceqXvXSMfnxGTzaeoGsoS0ZqjCkl6GEr8wrr5Ufn8GxtvNx69+IgM/7P/McPyYnIpEr763HZ3InHDu+WDmD2F9O49zYg7SoVKza1UZVWMxWs7eQ6ekwMFuTvU2o1aRcCMe6kUfpdZV0OpSsu3F4jWv3Qvto0fI6eB2/31XW8XOx0iSy+odKOWj536PKOn62VoyZ9Hrr+L1O3N3dmTx5MpMnT/6nRdHymtBa/KogODadNzdfoo6zBdendnole8ilEpp62qFX7il479ouTx1vpCunsbsVPuUKwH/mWpuIkDxiKMBE7znBtbfiGFyoIKOuG+3crGH/TU2hdaOyQKf8wgKi4uLwmdqhtKyAbo86SC6FI5dIiCnOJ9LZiWWd2gHtodZZTtn8TsiJPIKDrIh8cA+vqT9jOfUN8NYkC9mGjPxIKQG+Dxhp7EjXqYJGNQxhgxqKlTxQ5vFjYhwNja1pa+ZAXqgr0R9EUndKHjrJw2gutWCv6QKgLdAGmAd8xcIRC9lWrRPCwoux435liKcdu97rS3Im2JrL0JPKODJ2DYXJ2bj2bYiBnRmr6ndm2qyZOAybCsQCfZGMNkJvdHOepKVfbZrXrEVj72e7duiaG+Hau0GFNuchQdw3q4WBt+bvaerlQJdrC0r7By2eT8i9e1xasRJTw9PAZeATYAoateldoEy5O5OVxBcRt+is+hI7k0fYGAezISmBfLWScQ6V/f6NnK3ofHU+etYmGLlYkbvjAIf3HuPi6cs0WBRB+qHbJB3w5cp768mNTMK+dU1i917n2vsbqT27L612Tam0Zui+YNYamvLBmrX4RgfT/7o5OgbyUqUPNIa5MUagECVKH8Dn85Hk52OsyILtvyMVqey5sIuR8w5j2y2AzetXcXypkuVrjwNg37YWaqUaXRsTyMkn60EyRRFJ2DbXuHIFz9hCQWImTdaMQSKVItOV49KjnmavmZ8iR5O76DFhCjhvZsGwt1ojS8pgua6CIj3zKv+WxRl55EQkkRUah1qt5uapzbj6NsXSvsxN++bNDC5eTOOtt2oglUow8bAvLTIvhECpVD430F2/uh2x58IwLzkmmb4uHc/PRlqu3uD9b49wddIGAuYNwP/jXgDYBfliF/T6XY20aPmnce3TEOee9Uk+c4+ChEwMHMyxbeHzyix9Wv5/0qxlYxo3b8CdG6Gkp2ViaWWOX4DvP2bp06LlVaBV/KrA38GcTzr40cjt2T7ffwWJRMLet1u+8Hh9HRknxret0CaTSNnUs9lz50YV5tIl8ghvBmfy67tDYds1WHMGxgTxqK0t8xQpjHSpSdqBswRnxtO7bXtqe2kUCisdPYbZ1sAYOchgnludklVrEpsZRXKuDunz+zAvV8LR6k0Ys20lZJXF8vSo5cT+cGcO3orjulLBOs9N8P0d8LYEXxtOt7XgWEQo4+y9mBB5iVkSHX63MUYtBYVKoOYSMukC4CwaxW8gEIeZUX9Gd66uKWMxujmxVi6kh2cSa2uGjZmMC1GpNFwzFkVKFgZ2GiuejoUFvst/eKHz3SagDm0CNMdaWFxMVl4edhYvlrL+mLwxv8rB4CKM7BmLShlI8G1PZu1uzarx75N8L5rq9wvYu+0gQ4YvQJNgpgdwACimvNIHsC4pgoP5cbR1MMHVIh4oZGzEBYrUKkbY1kAX+G3RIMxsXJHVe4PolGTefqLWYMdOrelYsyaJ1x8RV9MVuyAf6i8dRvb9BExq2GHf2pMe90+jbxvD3tuNGfPbZda80YguNTWZTkOGtuVQoRz7hIaM27ySBR+NY1YVlunpTzzwL7Zx0rhu1rJH/e4EUh6cwmJWOnt3TKNH9DJs7Kyxc3hc6+9nas0Q1JrxqeattSkn235B3qNUesd+i6GDBQ82nKEoNYf6y4aja2rI85iXAyeLwMdYh/o6unQw00P+FMO4uZ8zfWJWoGtpTGTwUX5fPBTvBl0ZPmdf6Zj33rvG6dMpmEmTyTy3jr5zPsXaVXMeZn/wCTeC77N01Vzcq3pgUFgMBUVYN/GCphUV9vIWSgDrxh7YNPXCtvmLBfRr0fK/jlQmxb5VzX9aDC3/48hkMmrXrfVPi6FFyytDq/hVgVwmZW6XF8jKdS4CdGVQVeKWZ6AuLubmsIEY1vDAa8HzMy4B/BGexLQ9ISzvU48m1aw1teVU6tLEKM8iWVHAfUspt1q5QHoerD0Drpbkexuy6oO3WT19LEX3EpmzOQqFhw7ufaxL58olUkZKPWn/w0nmdPbH3OOxhXAZgYuDKFRkM6NJDmuj8tGt5kDhb++AVckNeXA0LolZ7BoexEd7b9C0mjVk5IN7IjidBR0pw+zXYa2rTxNDa27kZWCrq49/vxrkXUsgZ2wrCoUasAdK3CwiXDlfPJNDwaeZ2d8ZfV1deK8tVlceYNl4IvlvNGHlOx15d9tVprV0Z9v51bTPD2bV+PcrnRelSsU3u7fRslYADb1KkgMUK0CpAkON8hUcCUeCC/nh4Mccm/sxHo6aODtFsYIt67fh5lONBg3rsPPiWb7dv5sNkz9Cqu+EoyPUcAdQoFZlE5sexYFrl7kSfp9FY8bxzfxvcXV2BFahqTtYp5J8d6KjuB8Xw6f1alM/VskQ3a/RlZsCFmzzbUWBSomBTE5BTgb3Lu3FxMqRZRfiiU1NoWPd+tyJfkRSZgYj23bUZH3VkWPfqib2/TU1e5y7zYNuKUBHLAKcARWQStydfBKzC4nLyi+VpY+pnFQZvFG7Boe9FxObA/rP9jgF4HTfpST9cZdut7/ErGt3mtGd02eWIZRq9I0MWLHucUyFGngLEKRcaMydRQeos2gQXhM6kBuRhL6tRnnveHYWyvziF1L6AN4zhppycFDI+TbEGk9H6Nrg6eMN7M0BcPFtQsOu71CzSa8K/Z99VosTJ5LIu7GJ0z9vpFq9unSY8A7KvEKc1z7AQC4h+t75UsUvLCybwkIVtWtbQFoOFBSBro7GhfUZWDf0qLLsxb+J+CM3yY9Jw2N0639aFC1atGjR8ieIior6p0XQ8prRKn5/lkIFvPcr6Mjg0scvNVWZnU3q4QMYuLpVqfgdvZfI0F/OM6NdTSa30igklx6lcTUmneDIZJqY6qOeuZP1hhlM7GFF40R7Dg9sg1RadcaKhiY2RDXqh52OPkSlg5MFDGyArpUNE6N9sVp1mTdiHHBCl2rNWoFdRUunjkyKXCZFt5xbTXFKCldtpCRZW+Da41vmVrfiaL/6VEvLg9gMmNsLZu6AhCx0A1z4pnddCI6GNWdhVg8ImA7SPGR8R7el1+DkPc5vHQdO5jDzO+RRqVh0CwRfB2CsZtPfr7L76w3MqpHGjewkmvn60bGu5i7e0M4UM29HzGu50NDNiqbVrKnrZMp3mRnEpZVkn0zPg0mbudu+GiubGdMyR8r0dSto4d+QuaOn42xmiF1mNoYAbragIyenAHTlxng6VsPMyKj0+O/euseW9dtJ11Wh17ga3k4uXLh3l3txMfRo5kSgF9T3BKhG/INrfLLxCyAZcyNj6tcOZNOeNeXOcGVNJCs5maFLviDkYST9R7TFcMolnF0D6HFaU0Ovq2VZcg8DEwve+/4WukodfHdfJcqkGFcbO+p/MJ6UrEw6122osVa62j6xyy0gA417qQEQgVoJ/fWS6DG7J47mZcqVlQx6pt4kKi2eUQ060UAXFmTDVBOoV1KF5O6S/cQfCKHF75PQs9TUDbJv749Qiwrp1oN+q5gtTJlfhExfB4n0d0AQszOY2N3XcOhQm5ofVLRcGld78hieTQNdzSu3AOzMqw4XzE/IIHbXVaqPCEJuqHmwoW9oSs/x31ca27atPW3b2pOb7oJHfX8aD9TEAqqVaszkxlja69Cs24jS8c2aHSM7W0FaWh+MrUygQBcM9Sqt+6KoFUpCPv4N68Ye/3hs08VRP5Ifl45Dx9oYOb867wgtWrRo0aJFy9+DVvF7DgqVmvxiJWYGFWvsoa8Dk9pp/n1JdK2taXL1NhJDY74+eY+m1axp7F5mZdt0LYrk3CLWX3lYqvhNbeNDaw87GszYhyJsO7UauFHDxYhcuZIz8ckUKlUYPsP656JXorRUt4F9EwGQ//AH9jJXptRqCBN9wEAHNl2CvCIYXpYCuFl1GwoWV8w2GNZ7KD917Mbyetns99Xn7cQC3h7UCEWXZZwyLuTTswfYMbklDjG5pQlTuBULt+PgZizUWQXkAMYaaxSUpexuXB2iUuF0WIniV4KHLQNs7qHIVvPj+N60C4jisdKk52RJ97tfAHsBGecmtSfkQQRqtaCmS0ltvvQ8uBPPqlZ6rIhXE5+RhUPhOfzs2xK04jjOZgZ0djbn4yAv3EpiL1v4QQNPKe/3mlbh+GsF1sSnfX1+unSCusCC4WPo16QVTutWofK7RuNx40vHuvn6s3LaDI7fCKa+hyd+40fjYGnJsblVW3tPrl7L6rffpdvsD6lW243whGRCaufiFmBMj6f8fa2dPCEqk67CFRyNgdmsercTiRm2z3BRvQ4o0Sh9AEbcmreNW3N20OjHt+DtNhVGj5nTl6j4CC7+EsUFIzfOFkPjojLFL+HwTRJP3iU/Lr1U8as1vQe1pleW+uzgb8m8E0vzzRM41PBTHLsEliqE/p8VYtPM628o3p0IvAOMwNigF4Oe4ll9e94uwr4/ChLJCydOMba0JGjEsNL3umaG9In5FqmODKms7HM4ZkwN0tKKMTKSa4Ig9XWrWu6FybwTy93F+7Cs6/7KFL/bC3bxYMMZ2hyejrHb00vHNPj+TfIepWqVPi1atGjRouU/glbxew591p7heFgSoTO64mZpVLFzxDPqowgBs/aAkS4XBjTAx84UC8Oymz4jD0/ORCYzZXcwzapZc3ZS+9K+Hwc0oLqVEW83Kcu8KZNKaXjXCu7bEW+URrJMSnq6OZd9WmJdU/+ZSh9AUWIiV9o0xbpzN3yWLC+T39US2vhqlL64TDh8Gx6klCh+p4FjoJoBl+Khrivo6xCWnI28R2uqJyThp7LFva4XOGoUtOuzutP/7mky5en84efHoI7lXBiHNoFAV6jlBDTl+61wIQSWz+iJxaweZQrgMHPwSoHAJ2oz+TuzZsIHFKLmrQ5LgaPAH0zfa8bSU/e5NzMBd8v3gQnACs0cSbnC4B62HJlTBy+RxvmiQLJO7+eRgQ09GrUgxkAXF3MDwlNzkNtlg0QT2yaRgEGJgabljA8oVir4eMAQpBIJ7bq1YfPDYKb06s+ADZcIv3GHNb+swsjHF5dyih9A85r+NK/pT9eVx7mfmIpc9vS/l5mdHSbW1vSu24i53bty+9FDvpftYeyAIU+MPExWVjXOnDGiSxdHpO7m0M8XbM4Cc+jdpANw+Kn7gAmFxcV8uu47etSqS4smjTFys0bP2gTdcte6WqHk0e+XeKvzRB4mheNg7cwoKRTrJPBI9wYxNMEFM1r8NpH8uAzM/SqXGkg4fpvzw3/AumENWu78gJzIJPIepYIAHVODUkURQMdYH5ee9Z8h94tyFdiFRrHt9dRRnmPbggRcepXtue9OHMa6clp52r3wbvIqLHnz579Yps8XxTLQnWa/TqjyHP9dZN6NIyciiaK03GcqfqVJdbRo0aJFixYt/wm0it9zcLUwwsHUAAOdF8vqtOHyQz45cJPfBzWi0ZHbKHTkNE3IpE9tZ8Y18+BeUg7vBXkB0MTdmkXdA2heveLNlRpoUcsGU2MdUooLmfLwCkNsq9PR14nCwPa8Jwywd8rhm951aWBTZin89VoU7++8ztYRzSrdsKoLCyhKSiQvOgqA9DOnuPf+eLy/XIqVXm3YCHiZw8a3wPrxTfjnwHHY4QXzIzk5sT7XG7kz9aebjGzYinUfNsY3I46muqf4zsOGIUCjRtU55GpChCSHATbuFU+OdAfY34NvY8E7iKi4QcQmQV4BWJiWc1N1+gz6HAP6AHYQkQy2pjBzB0ODH8HOCWjcE+0Aa+RSFT4Zbbh8dTnuHeDAtbMER27i4wFDyN26tyQj12jgIObeRbylqIfesZqg9qdT4XRS/JrjnRjKW41r4G23pOS4twJlVk4hBNEpyRQLFd0v7kWanIFi4kLif9bU1jkZd52MAk88tu4n7GI4c9auYViXLlS3r1gDKrNIjbnXEEYFdmP/aQjIvs6dL/bQ6Me3MPdzJi07C0/fevQLfBdbiWZuLbdqfP/Ok8VUrwOdSEoKoHv3j9iypSkDB7qBkwnQHlgKtALuA18D0wFNLKqyUMHyYjkucgl1MlOoZ+lACzs3yCukMCGTotQcMm/G4Na3EQAPfznLhVE/YuFpj+6jTHLaJ2Be0xkbvWRCyCObQsAMXXOjcmU3KnJ53BoK4jPIikxi0fqLOGcW0HbXB5j7OdM3vrJL5fGMBG5GROHe/3ta7ozHss40oBNCrSb06wNYNazx1AyXS3PgdBGsseyGhfQw8GwFxaK2Kw2/fbP0fU6hgh6rT2OsKyd70YuVdfirnB/xAwUJmbTePw2pzrO/lqsNen5B3r9Ck7VjqfvlYAwdXyyZ0dNQq9SEfXsYm2beWNWv/vwJ5Ug6Fcr1D3+l/tJh2DTx+lP7F6FEBxnSp1b/1qJFixYtWv5/8dKKX0JCAsePH8fS0pJ27dqhq1tmxcrLy2PJkiX/U4VLv+v3fMuDQqWmww8nsTfRx9vWlJjMfOKLFLB1HJlFCjqduEePWk5M2HaNsJQcOnjb421nilwm5cO2lbOUDdp8jl3X46jb0YQ33N3ZmPqAYrWajr5O5K9QEvN9Pp09HOnk61hhXlxWAcm5RSTnFlVa08C9GhmrN3IhIR63jAzyb98i7+4dcm7dwMq5HSwHPIFfncrNWgKc5axZILpOaXTySEOVnkagiw2NzAwhLIksCwUuiTn8enITDfoMwsvXg0YONjSiKkvBeLBNgonAvV3M8+9GnrEJFpVKP31ESJwrnX5I4EDbe9T9YCs0rqGxSurrgM5qwBFNps8GzOuaSMhRPU5ea0/XoH0s3iEjr/A8Hw8YUi4NcyaQg5NVc/KkD9FrYg3NHcCuBVuDo/nq5D3isgpwNPNhetvaWBu7A/AgMZ7G0yYyvHV77n63hsjCHPxv7sc+MR/pZ3tgRhdQq/m6d12+3rWNPRfi2HTkCFcci1h4YDuRq3Zz+LoOdT0goBqcmtCWpHTBwCkyrMzhM71QUs6HkR4cxbqIi3ywZiWju79BmnMBxUduU93IhBHNPKlfyUPQG+iCgYEVffo406xZ+fMtBx4rih8DP4LaHaQzyItOZU+tj0hqUQt1YgaJFoXc6WJNhwaNsDTSw/u9jujbmeHar1Hpag4dalNtWHNQC1SFCuQlJUD6U5vaCWY8uBiCd/e2yOVP/zqp980wbs3bRb5USviVBziFxVOYmPnU8W9HnOdhYS6/BGVjWecEYAp0Iu3aQ65P+xXLetXocnV+6XilULM3LYbW5g5cKtblmgLS1WAh7VDl+qmXIzB0ssTQqXKdThN9HRZ1D8TcQIcU8ggjlSa4/ikF4q4CHqmg83MS4SSfC6MoORtVoeK5it+rRqYr/8tKH0DKuftcnbwR2xY+dDj9cr8JKRfCSbscSdrlyD+l+KWSxyyO4o894/jfrL+lRYsWLVq0vCwvdYdx5coVOnTogFqtRqFQ4OTkxK5du/Dz8wMgNzeXOXPm/E8pfi9CsVJNSFwGNsb6/Dq8KW83qYFTSWIMG+Cgl6amnZOZIfeSs/G2e3aR2yau1hyJSiA/Aj48fJNPRgbwbg3NzY+lkR7tvOwpUKgqzZvWxpcRDapha1L1XaapnR0meXno6uhgNW48lkGtOFBsQqRIpO2H9uCpUWITsvLR05FhZxIABLCnMJjFnrYMkbtQx9WCKS38oPtySMiiw2+jyAjJIDtXSUJcIl6+Hk8/MOV6SIuGBwcguga6fQzRPXYLDt8h7+3mSCyNMHSwANpx4I4dyXm3STfS1WRNbeYMEZsg1wFU34DQB9EPpImAIRnuf3DgYRrvpp+ktf9evtzxGyduBJeWZIDfgSKcrEri2cqFR42wNqZIqeJ6TAZLToKXzXb6VzPCxKqYIoWCnIJ80nOyMdDTo5aeHkert8Tl2EXo4IXqjVUUFSlZ+Elb5q5biYOZBfP8W6DiPs41nFEoZWTlQ0auZi+5TIqTDSycDOam4OsyALd+jbBu5IHdqeNYm5pxpKYRMT4eyI7KcXB3ZW8a3LIDkwolq4yAcFxcDrB9+6dAxSyX2fl5zP/tV95o0YM6D0LhlwuwJIWszHhCG/2KA02pbdkGg6ZeTL+wmbmjx4BUio6JQaUsjYZOltTZ8Dbfch4fWvMgT8ad5AcMsq3Ozu+2ce6Pi5hbmNGs1dNvsJ271eXmnB0U33hEz40Tqf5OK9z9HKscqypS8Na6R6R6WTDwh83APkCTidSqXjXqfT0UqwYVLUgbkiIZHX6eCQ4+/Fi9EWlqcC/5dtu4dzeZOdmMGzAIHbmc7PAEDjX+DB1TQ3rcX1Ja7qM809porInfcp4QEjBBl9o4VBpXHiFEmVtxCe9lQpgS/rCBGs/4tu18ZS7qIiU6Jk+pNfEv4sGG05j6OmHdoMYzx9k08SRgbn/sWr587cGa07ph37pmhb+zEIJ5P32Ig7UTb/WZ/Mz5cqToo4MRLx5TqVaribp9GhefxujovkDKWi1atGjRouU/xkspfjNnzqR3796sXr2avLw8PvroI1q2bMnRo0epU6dyOvr/LxjpyQn/uDu6cikSiaRU6XuSdt72tCspbP4sqvsZYqQPPSSOOEgNGOPmiYNu2Zo/XoikWKXmgyZ1yM6RVQiFe5rSt+/USW6FhzG2/0BMjY3h3ENEcDoDI25haahL2oK+ALz32zVWnY8AYO/bQXTzc6KapTGm+nLG2XnR3FmTVVHRqRbTHqRSNzEfv4Z1kZ17QONwJTyRG+PA1Uus2LeL78dNpNqqItiVAPorYK4z6EDW6Uect6pJ8YiNFEc/on/ySsjLYmbeOSb0dce0YXVoWB1YAKzmR+PdRG+8wazAZVxM8OaUrBkf9YWtI5vyIC0Xf0dzjuobkF9USE5BWTkCtVIQ9sNp7Fr6YlG7Yv05k0UHmVKkJHpaR+q7WtJclc46xzEY1Xag06VVpP6ynZ+2L2P8F0P5Zupa2jm5wxx3AJKvRLE3JJpUpQ4b35+Oo6UFbQLqMapk7ciEeLLyzzH4wFVEck2ON+5JTSNzmtfVxM4JNVg30ijLg1u2ZXDLtlzKTuHoo3DWZ66nMEpOLbfaGEt8EEKQr1ZiJHucUOgL4A5QdgMu1GokUimHrl/hyx1biUpOZKukERRcAuCkkRFFRlJ0mzrTst9oZGo1IbIW2JiZVzgnQkCiGqIjQpn003e8r/ZF51EU9zd2ZE5YJNdz0/E1NKfPGz2wtbehToPnlz9pd3wmypzCKq1skA8MA06Q83A17mtD8K9uh3yyIeVdbiVSKb7vd6k0u425A32t3Ohv44aJVKMkn+y+GCQSika4UaxQIIRApdYosgYO5hTEZ/Jg4xn8pnarUl5FbiEtFA5YWBjgiXWVYwpRcp5HGCy6TtTnB+h4dhaWddxL+ycYa6x+bs/xFNezMH72gH8JmXdiOT9iJWZ+znS//SW3F+wi804sTdaNQ/ZEnLFUR47/J73/1D5SmbT0c1G6d046P23/Gmtz2+cqfuYYsISuzxzzJNePrmPnsrdo0e9DOo1a9LIia9GiRcu/jtmzZ7Nr1y5CQkL+9BpRUVFUq1aN4OBgAgMD/zbZtPwzSJ8/pIxr164xffp0pFIpJiYmfP/990ydOpW2bdty5cqVVyXjfwJrYz1M/0SGz6pIVBSQoiiklrsZJ8a3xcWiXNzUw1S+b1KDXaOaM/s7GRMWQFxyWbdC9QUqtTfFysgKa4onN5m9C5PVJ8kIieWEXMayuLvon92Id2ocLgY62BrrYVPi0peeX0xOkZLMQkXp9Af96rNMpeL9fTe5I3Og8dEU2B1c6Vj2XL7AoetXuBx+D9yswMIcFAYo81X8fPwwU7vb8kt9I6IDalP8WEqJFKQyTLOUkJRdslJnSBzDyegOnLlRjdzc1kzcF8anZ89y8O5NnM0NCaphy+ViGNujH1mbd9Oz8ePi9rEkndzO1YnruT51UwX5HqTmknXgFooT93AtTOf9jD+4ev4w1tixr70P7le2cyAzgUUbPmfXiU38uH8r+UWFpfMt53QjrL8J/Xx0yfUXBNVuikYhKznNmzcwe/MqEvSkJOpCRGF2ad/Bhp+yq/pklAXFFWRqZGqDe2o+EXlpxMoLiMp8iBr44MEVLC5s4WpOKgOO7MDtfCxnM5rw+GP87ZbN6PftwvGrV+jZqCnL3h7PzAadKRo9Hjb/DtY2ZOkZs/vzW7hP+BmZkxU3zM1pq+fK1vzyEqhYnquiYTJsTMnmUtg9cnffxmZnPCMSfPjQ1IMPHf3wMzTHp5YXb00YgaHR8+vq6ZoaPkXpA9gP7AAyOTvgKGqFiuZb33vumj+v3MSIPmMxyMhnW81WBJlpHqykqfJICnlI2qUI3uzZm8lDRzD9Gx16T4ICoUeDkpi+xCO3nrr2kaDPCXWdS780Dwyo+rN9lih+JYSrzQRCqUKo1BX6exvAx6Yg/wthZsVZ+RxrO59b83b++UX+Jkx9HPGf1YfA+Rpl/MHGszzacoGilOwqx6uKlUTvvIIyr7DK/pfBwtSKLV8eZ+P8g395rapw8WlM9dqt8Qh8scyuWv63GTlyJBKJpNKrU6dOAMTHx2NhYcHy5csrzLt06RI6OjocOXLkmeufPHmSLl26YGVlhaGhITVr1mTKlCnExcVVGuvj44Oenh6JiYl/3wFq+VcwcuRIevXqVeG9RCJh4cKFFcbt2rWrkkfJk0gkEnbt2vUKpNTyv8RLKX4AhYUVf8CnT5/OzJkz6dChA+fPn//bBPv/zASLGiSbt2SYXZklR6VWc+JWLKqBq+g4dx8Ljt2lZxvoGgRS3SLafX+Cr06EsvPmYeKLUhi241CFNbu3bM1Ho97G8rFl581m0MQT85wCAoqUFKnVKFVq/E/dJ7phdZLm9aFRSYmJjzv4kTqvD+7uhqwNvooY+y0/3/oD255qBkYnYf/9CVALeDsIfjoNw9doCrUDi0eO4djcLxnQvBWMlMGJanDWiq2mf/DmTytYrQrlsM4FXI4dx/BxYhBDE2g9DOaEwJvrSo6gDpydzHKXUIYNiyC1VhtupNSHAU3YqafZ62C+mr5pMPnkA0wMyysifbBtMQj/YX74f1bRAhGalEBgPQc+HdkUJGpQKsmQFvJFxwLaj+xEdX0jdAtOIpfr06s1dG70NltOl30hn7t7hyU7NzP7wH6OYIBaCPILH2tRyUz2cmSeWxtu9h1LSO2u9LAqszbqWhiha2aIRFb5Yzi4ZRt61RuKUXZN0m1asaMAzOS6GMvk5KTpEBJlQbTKnLPxf/DzLs0chVKJSqhRqlXo6eiiE3+XnnMbsbLfe1AS65g5rTaD59ajo64S9HSINTQgb204UcHp5XZviKv8AxykguH1G3Bx8QoGnppH56vzkGUUo/SeT68F19GRPv/rI7gYCso9dci8G8udxXtRFRY/MbIbsBA4g9vATrj2aYCp5/Ot42kpaWRmZFFYUAQqNRQrSCCbmbLDJEW+Qfe7i5HJ5chkMiRSTZZWiQRcetajydox1F8+/KlrW9Zxx9zfBdkz6u7Vx4nWVKdv804MzFn73CQmIcRzlPDnHld5CpOzSPrjLglHn66kvi6kMikBs/uVZl1te3g6LXa8z4U3VxF3oPKDn/AfjnK6zzfcmr/rb9m/aUAr/DwC/5a1nsTOzY/RC0/gUbf98wdref0IAflFkJOv+VdUepz5t9OpUycSEhIqvDZv3gyAo6MjK1asYMaMGYSHaz7TBQUFjBgxgrfeeosOHaqOLwZYtWoV7dq1w97enu3bt3P37l1WrlxJVlYWS5YsqTD27NmzFBQU0K9fP9avX//qDlaLBpUarkTBwVuaf594mPc60NfXZ9GiRWRkZLz2vbX8P0C8BC1atBA//PBDlX2LFi0Senp6QiqVvsySQgghsrKyBCCysrJeeu7/JJ/uEiJgthBnw8WJsEThPme3eOe3y4KJm8TFkWvF4VHrxK9XH5YOvxqdJpj0q2i+7Ijwmb9dyI6tE/I/1ot8peLZ++QLIVblCBFWIMTSo6Jw+xUh5u0VIjWnyuEtQg4ITv8szu78TlzftlIYnFgnHrSZJ4o6fC1Ei8VCbLwgxLu/CFFnjhARyZW3U7qIfKWOECJRJGdmiDHffi3mb9gsTvf5QigXHhCK/KKywQqVKJqyRZz/dKNYeTFL7LkohIjPFhv2nRHS42vE4mtnRZfhJ0XA9j3iWHq8EEKIB3tuiIEHIsXqN39+YuflQpwMFKLRTCFiM8q1ZwkhrEVOYR2RW1hyrhRFIrcgX6RmZZaMmSYm/+QpdLo2EFuO1RJCIB4lf1u6glKpFCv27hQhDyLE8EPHRd/1a4RSqSzp9RHKQqn4zbq3KEjOElEKIfwShPg4U1TJ5ewUsS8tRggRLeZtDRLV3uov3jkdJoanqMQjhRCK3AIRPHOLOLAlTjQdphbvrg0WPeekiGZvCpGTp1mjqKjsHH634Qvh3E4i1k2bVdq2ddFgsWlufaFWbxdCCHH6dJKAX0WLFkc1h59XKNJDXER+gp0Qotzfo4TsiESxw32iuDlvZ9UHIYRIz0oTc374QKy8HSyc44WYWe54/+jztdjIIPFo26Wnzn8ZlEqlyMnO1byJSREiPE5kFuWIWeKo2CluVxqvVv8t21bgQMNPxL6A6UKtUj137DSxX4wW20SqyHupPTLvxYnC9Ko/l/80DzadFRsZJC6NX1upLzM0VvzR+2uRcin8H5BMy+vgtfx+5+QL8SBBiPC4steDBE37K2LEiBGiZ8+ezx3Xu3dv0bRpU6FSqcSkSZNE9erVRU7O0z+rMTExQldXV0yePLnK/oyMjArvR44cKaZPny4OHjwovLy8XuYQtLwsx+4K0eFrzf3X41eHrzXtr4gnr7MRI0aIbt26CR8fHzFt2rTS9p07d4pn3bK7ubkJNM5dAhBubm5CCCFmzZolAgICxIYNG4Sbm5swNTUVAwcOFNnZ2aVzDx48KJo1aybMzMyEpaWl6Nq1q4iIiCjtf/jwoQBEcHCwEELzuztq1Cjh7u4u9PX1hZeXl1i6dGmVxzV//nxha2srzMzMxJw5c4RCoRBTp04VFhYWwsnJSaxdW/a7cfLkSQFU+AwEBwcLQDx8+FAIIURUVJTo1q2bMDc3F4aGhqJmzZpi//79L3q6tQghXirGb/jw4fzxxx+MGzeuUt+HH36IEIKVK1f+VV1US6NqEJdBvLE1s9amEVWQh7GunHbe9hS096PDE6Ua6rlY8uOABoz57QpvNa5Bc1clWcpi9KTPCCy6lwgbYuBIfQqu3Uf/0jn0mnvCt4PLxsTHw+HZ4D4aWjfiY/tanIy9RX2VHjp1/AlPluI0PAOsrCEyE4K8oXcdTaF0l9XAT8AhoAZqIfC99jkCFQ8b2GBpoGboWTnm3nL8x/WAJjXAoFwiBrmUda0tGWcaT7OcWwwtboa6oQme/8feWUdHdXV9+BmLuyskgYQkEMHd3bW4u7UFirdYKVooBYq0FHco7u5uISGQBEhC3N0zM+f7YyCQBin1722etWZB7pF77p07M2efs/dv169AxZVHmXRrByenzqVlnUYAqIXA2cuOLVP3UdD+dbzZrqlfodDRpmvd7dArhhdNFnI9NJq6uz6lTMtyoG2GgcIepIlAe5B3R18+GX0djchGbkFHRrQOwlzaguYWzbhyV0INnwpF/ctkMsa264RareYbA0McLWPR7Fx9CaoWkB9Ky10P0bE0Qq2EAiDvHQvVXR5fIKogh5ia1/iy+2VSMuuyoJYTWgrNzlrk8UAezT+EQ8cofv7uC1IsfemVKWhUmIGBnkYw6E2V3dH9pjKkx3i0tTQ7VupCJe36LULP1hHoD3ShajULho7LwNdHzv6HkdwMjsVnSDNMK9jS5m5JYQzDctZ0Dlv+1vELIUi+85xzyVf4ad93tEpPpvrgDdRRwCvnAq+vOmPi5Yhd6z8nv51MJsPA8NVOsTZIJBjL9ZiR3xCplpxfC3F+wFPmd1GYnoMqX4kQJU5XgoFUJYVczPmwa+ybGFd4uxCOhhto4iMXAt0+qt8/A6deddC1NcH8LWIvxu72NNw//m8fUyn/Q2TlQtxbdj5Uas1xG8DgnxNFWrt2LZUqVaJPnz7s3buX8+fPY2Dw7pjdvXv3UlBQwOTJk99abmJiUvT/zMxM9u7dy61bt3B3dyc9PZ0rV65Qv379P/sySjn3BCbuKRkXk5ChOb6kOzT9eKGq34NMJmP+/Pn07t2bzz77DAeHD+dtvXPnDlZWVmzcuJFWrVq9oWgOz58/5+DBgxw9epTU1FS6d+/OwoULmTdPo4ydnZ3NhAkT8Pb2Jisri5kzZ9K5c2f8/PyQvsWzR61W4+DgwN69ezE3N+f69esMHz4cW1tbund/HZN//vx5HBwcuHz5MteuXWPIkCFcv36dBg0acOvWLXbv3s2IESNo3rz5b7pGgDFjxlBQUMDly5fR19fn8ePH7/28lVKSjzL8hg4dytChQ99ZPmXKFKZMmfKHB/W/THxmLlWXnKKluy3re9XUuKtk5oPRG6Isbb2hrTcvHoI62oQpda1Z2PH9D3ZjV2t87EyoXsaM4a7vUdZ8xbcn4d4LaG1J67Q87Ny+YoJVPsWSV+R8z7a2j5gU7MeedCda6lvTMiwOrJ25mJ1P04WT+aprL+Y4lYeMAjDSBv2XL4KAUCAJKIdUIsFB2xq1EEglUnKS04nYe4vU8tZ4zX77ZLXnrQzu2hljZe9FFTdNfvdaSXI+LXTmO54S1HENDR558nXOM5bHPOGGTxt8do1Ai5tAfQrz53L8u+WoFDKkAz+hs0dVlKtOo1KqNDFHOTKIuwCR6bBlAWy4rf/dOwABAABJREFUT/L9siTfPYfr8CZIJBJ6blFxNLA34eObIs8RxCSaon6L4TZrx2bm715P2p4gDHWTgXiQDEEm3YiRpxIAZ7lGnVP7HdbB7LK+hOVlYqPw4M7+GOwOS8luk0SUiQEnHgTgufkmlb7qhHPvuhi7QIQS3PNCaGT8I5o8fVYl+nxl9AHcGPwT4Tuv0+rW95hXtefuU7jkr+RU4GjOBskwij+If2w6d25+jU/Zt4uZvIvcuDSuD1iFtuVRPGp05+sxK2ju2wLH9DhQ6YKeJj2AWWWnYuInfypmhgBkRyVztOJkbFt40WDvOABik6IxNTJH5y9Qa2wXsAghNG6QH8KT354Q/rcTBjwHnvwFfWtQq9UIIYpNJl4hkUiwaVzxLzt3Kf9hhIDE9PfXSUoHfZ2/ZFXn6NGjJSaV06dPZ/r06UV/W1lZMXfuXEaOHMmoUaNo0KDBe/t8+vQpRkZG2Nq+XyUYYNeuXbi6uhYpp/fs2ZP169eXGn5/Nio1LD75FjEENMckaMobVYDf8D3/Z9C5c2d8fX2ZNWsW69ev/2B9S0tNSicTExNsbIqHSajVajZt2oShoeY3sl+/fpw7d67I8OvatWux+hs2bMDS0pLHjx9TqVKlEudSKBTMmTOn6G9nZ2du3LjBnj17ihl+ZmZmrFixAqlUSoUKFVi8eDE5OTlFn59p06axcOFCrl69Ss+ePX/LbSEiIoKuXbvi5eUFgIvLx+WILeUjDb+8vDxOnz5N48aNix6gV2RkZHDx4kVatmyJtva7Y2L+6xQo1aTnFZKS8zLGacU52HwdfuoP1Zw0xyJTwNaE2j5SNs8DB+sPr2aUtzTEb3LrD9a7/SKZ6PQcOo9sBHfCYJgjtquVTN4nxTdMDzoDXq86HUFksJQ4dMhLjgPdHBjkC1pS9MOfUtfCmRmpHpCWB71ffjncjwM9Obj/CHyDJt+ehqtR9rD9JsxPQs/Jgjb35qFlVvza1GpBdoESQx0FxtM68M31KBKv+GNbw5b8wh48f1KHYaZ9aeJmxuPnt5HpKFDliCL/hpdnIjbkGpc2fEP1bauZlRnOjtO7CWjZn0rBiyibW4BcTxvUaoK2X0crPhOXB+ZwcA93FvmRFf4DTr0moWU0gNpOzYnNyEHLVIG+gwldykPxNGtJgAUKVSq2edeY+F0TfvwyH1gJ0qtgEA0Gr4VB3mb05eTnkZGTwxAb16Jjt34x4/7hPXi6bmdmWS3u+hozQDufe4M7MtlKm35A1NNAdjv+jKn+U8CYpDvPiT8fiMeENkgVciKVmjT3Li/Ha+xhh46NCQHfqKi5tjlymUCuVNMysyW1J7Wkco1aBMVnUK3ib1t5KxrrExVPe62Ah0FY1tcncv9t2p//GksHF4hLKb6T+zcgkUmR6SiQ62uMvCdhAbQaWZkWLXsyYcJ3eLzFQP4j/Ja8ewlXg8iOSMa5d90P1n0bQggiX0RTxult701voCbw7h/Ai52WkhWaQKtbc5H/jvej9eiqZGSncWlDMFqKv/f9LOU/TG7Bh2OslGpNvffE4v5eGjduzJo1a4odMzMrLlClUqnYtGkTenp63Lx5E6VS+d6cpuItaV/exYYNG+jbt2/R33379qVhw4asXLmyxByslD/A/Yg3hOTegkBTfj8Cqjv9XaNi0aJFNGnShIkTJ/6hfpycnIo9L7a2tiQkvFYFfPr0KTNnzuTWrVskJSWhVms+cxEREW81/ABWrVrFhg0biIiIIDc3l4KCghKKnxUrViy2Y2htbV2sP5lMhrm5ebGxfIjPPvuMUaNGcfr0aZo1a0bXrl3x9v6wqngpr/mopYsff/yR5cuXv/ULx8jIiBUrVrBu3bo/bXD/izia6hM/tzP7BtXTHDDTB2NdMHj5o3XlKbRfyZVPtxOenEU5R9D+yHnWwaQIRj29waOENO5EJBcrO/PVfhoO3kSsSgWjG4NCxs7Py0MfCYFVQf2Gt9bzJCvMk4cSWa0bjR4/gKtHKchP5+b5yzhqGbBu/BwK1RICYtPJV6q4FBANVyLgUgSg4E2jD+BuQBDV28CpczcAMPUpi36+CsKTiuoM2XULy6/28zguHXQVWF8NoNLBc5gnLUNbcY8D2UdYa/2Y9TM+Yf2lb1Ea6rHYuRqZdXrjo2/KxKVDmftjDKdXfsKRRVcpF59BfWsnKkWkYmlkAhKJxugD0p/GcXfCNq4vOkT2nkbQOQafuV0oaKVAy+gB0ZE7qZwXy+0Ja7E2dEImjf2V0bcEsOTO7LkYTL2Jg4Uz1T06AF+gyeA4CjAEdLj8PIE2P17kSVzJ1evWs6fjPKwvUUmJRcfaDfiULh3HYSVsaLYphP6ZxjQa3IF0HS0S1HDp0UPqTvmcISuM0eS508Zv2i4eTN1F/EXNzk+7ZGiZ+FpcpdL0Zlg3yiHq4F0Srwbj6yKhr2ssC1f+wOAun1PZwYxeVZ2KjS0PJXeJopCSeSOV2XkEbr7OjRAZqSOGolfWEIsazqgLZQilCuQycLAEY40rZkYWbDgEW87Cg4w0Ct7S55+BlrEeLgMbUm5wQwBMDc0pX8YDvZ7NWcoVnieFQXTS3yIO8YqrfVdzrc8qssITP1z5LRzae4xRfcdxdN+7FC3L8T5H05zIZHJj0zTvy+9ALlcglyl+84S1lFL+FFS/8Xn9rfU+En19fcqXL1/s9WvDb8mSJYSGhnL37l2ioqKYP3/+e/t0c3MjPT2d2NjY99Z7/PgxN2/eZPLkycjlcuRyObVq1SInJ4ddu3b94Wsr5Q2SMv/cen8SDRo0oGXLlkybNu0P9aNQFFellkgkRcYdQPv27UlJSWHdunXcunWLW7c06Z8KCn4twqZh165dTJw4kSFDhnD69Gn8/PwYNGhQifpvO+/7xvLKSBRv/DYXFhYWqz906FBCQ0Pp168fAQEBVKtWjZUrV37wHpTymo/a8du+fTszZsx4Z/m4ceP4+uuvGTt27B8e2P8yem/mu+pXW/N6ha0xSVaGrE/Jxvb6Mxq7WmOqp0X1Mua/uf/lMU+4mB7HkROxxIblEThmOeXLHkOusKCjoxnGgbFIfuWytfAZROXAgesvsLzhD582ZdJhPw74R3HQsD7hySbkpyhxvfecxGu3SLsXQk75yniEh/KFUwVMDt5gxtUIjjbyok2VMr8aUQqwjwc1nbkrz+VGeC4tAfKV0GcdCbqmPFkymgZVwdJAG1M9LXQVsqL7k6qrhb5dReITddh8PZloiZL6jdWECik5QpO6XC6RkpuXw4Hz29HXNeDCsvu8KDBjasAttrefjVe7fiXuk5GrDTZNKqLKK0TXaRlwCNvm+3hywpvOP1lz0cUNn227aNlOH9AFfu3mZoNKmHIxToZlsja9R5xhaKNXlnMdYCOQRFyBPvseRxAWl05WXCpYGXKnUPCwUMogfQlzevshCMdQ9/VzEbnpJrmHIjDf2YEvx7bghq0VG7LhiCF4KyDBvgytq9agY806RW18F/Qk/nwgujXKUFBYSCcdBRkCXjs3XsJr0Q4cO7XGsbPGqde8ivM7n6McNRyTBHNKEkR3vGmBa7Hy4NVneDB5J24Tk/Ga2Ab74WuQyqT4zitEpl0y/cEPu4LZefwoNbp24bLRferhRIcH5tz9fAvec7r+JndBVYGSm0N/wqSSIxUnt39rnYQrQTxefITUhy+wbuCBjYUd59YFcIMXPCQO22wtUBWiVCo5dnUf9So3xdzE8oPnfheJKliRBb30wPMdGV1MvcuQ8yKJ9CfRGDi9PtcLUrlFJG1xf2+y8bLOjjiUscPxrTt+xYklgzTyiu1strw+B3WhCoXB+11dn647j9+Xe2h0aAKWtd2Kjh/74fYHz1tKKX86b3Et/kP1/mQCAwOZNWsWO3bswMPDgzVr1tCrVy86der0zl2Ibt26MXXqVBYvXsyyZctKlKelpWFiYsL69etp0KABq1atKla+ceNG1q9fz7Bhw/6Sa/pPYvEbd09/a70/kYULF+Lr60uFChU+WFehUKD6yEWQ5ORkgoODWbduXZEL8dWrV9/b5tq1a9SpU4fRo0cXHXv+/Pl7Wvw2XrmrxsbGYmqqCQ95W/5BR0dHRo4cyciRI5k2bRrr1q3j008/nP6pFA0fZfg9ffoUH593izJ4e3sXyRqX8jspb4X0wBg8bz6nXUV7Jk3ew9LQZFjZGyr/2qB6g4vBcCUEJrTgZ9fahD3MosFua/a5n8e5zD0eHNtM9U5fUGl+V5je7vUO40tmjoTYRLA8eBdOPoKaLkxo5I6TqT5NXK25IqvBfv9Ihtb2IS4xFdXP96ka/wxPSx16muVz7+xVRtk7YlfNARyMfzW4RcBiutdaisODJjTv9jJAWksGHXxYGluTayvgu0mwuIMDC9tNoOXaDFLi00mMvEOkbn2GnTbjpx5LqFfjJv6x6Ww2ViNRSLF44/deV0ePE6vvkZ6pzS83HUnw9SUg4BK9ou6wxcKYKgavjefUwnwCclJpdu5LOBQMB52hgyvotGDud12Y8PwOR2IeM/CzgcC73POCkElSCZznRcqUCfxY5s1J9Q/AN2QoVbjedcLWRpcNjeqhlyZh5t4dzLNRo2XogINdFVp5RQCxqFVq8lOy0DYzoPLCntg0qUiZrjWQKuRcSIerBfBCBT5aIA9LY8CyBCpq50FTzRktqpdD7WpGmeH9qFKuPBfnf1dstLtPxjHxuwJWf9mQsh9IxaAS0DARDBR2dDfNxFtSMrWCkZsmRkV96iqO3742wt5m9AEEh88iNXM3V46soWXzmbhZWZB0M4SEK0HEX3iMTeOKqAW0TNLsFx9/iy2Wn5hB+PZrGLrZvtPws2nmRY21Q7Bu4F7seG3KUpuy4KBZXTx4fhvjFg+gd5thLB7/03vvx/s4nQ+7o7Mw3XqKMiMaFDPsXuHQvgrpj6NL5DE8yzNuEIEjxpqxvYPK1X34cceKd5a/ySpuEkcmC2mFBZrdVpm24p3vy5vkxqSSn5RJfkr2bzrXX83jpccIXnGKJienYOxh/08Pp5S/G10tTUzV+9w95dK/zJ08Pz+/RO48uVyOhYUFSqWSAQMG0KVLF7p06QJoYqW6du3KwIEDuX379ltdPh0dHVm2bBljx44lIyOD/v374+TkRFRUFFu2bMHAwICFCxeydetWvv766xKudkOHDuW7774jMDCwKPavlD9IlTJgbaQRcnmbI4gEsDLS1Pub8fLyok+fPiXyRb4NJycnzp07R926ddHW1i4ynt6Hqakp5ubm/PTTT9ja2hIREcHUqVPf28bV1ZUtW7Zw6tQpnJ2d2bp1K3fu3MHZ+d0Lyb+F8uXL4+joyOzZs5k3bx4hISEl0puMGzeO1q1b4+bmRmpqKhcuXMDD4+8R3flf4aNcPZVKJYmJ73ZVSkxMRKlU/uFB/ZkExqbzNFGzPX/icQwTDtwnt+DfNcZfY6avzeSmnnhYG/G5rQnuWfkQqrnvWekZXN51mJSbIcUbbbwG++6T1GoZZY6HcO92GiglRCTb8UWPtrjW6vO67htGX8EVFetWh3BCO5CmtYDxzWFuJ2jiQT0XS77rXIXbuUno20n5uWdNzAx08PykOfZ96+OiL8NUnUaWUGJuY8WXXevh+/NlGLMdCt9cdeoPDKPFmvK03/uEyJyXW/cSCUildJCH0KaewN0F4AWF6qs8iFYTHPGUyKRYjEnD00qz0vZ57VpMq9USQ4kcq5dGnxCCy4/8ycnPw62sJ7cDyrH1CNR0GMqno0YSKPLYmpxARkhskQvBiGc3aOh/iq1PH4KZLuRXAfV8QBNzuNSlGml1etOjXCUm/biOlUcPvuWdCgXMmWRky24nHWyLLToPA6aQcs6XcoFpVCvQZc42S8ZstmDewf2oc3LQi4gmcNMuCjJPE3loE9cH7mKfzShS/SMwKGuJ67DGSBWavJlfGcIOIyUdXorXqXILUGbnU5heLPM62goFDhYWlLUsLiKSkBLH1QcXkEllyGXvMjB+AvRJvr+bp6tP4yATWAhTRklqYcOvVzqfYt/2GJ6TG+I1s0vR0exsJWFhWQBsy4bTb6T9nDRwCt1bDESZHkm57SH4ROjjOqIpLS7PpNJXmvyKAo1ras47vDD17M1oefNrmpx4LSIlhCA37/V9kEjAuXeddxsKMinIpNSr3IwuTfvStVnJ3eCPoYsufHX0ClYL9/Hku+NEk8FeAsjitduL67AmdHq2DFPv4hOHDnjSCx+q8f6dvIRrwRxyHU/EgTsfHE8zytMYF0zQPCzXB67l8iffF3OfeRfes7rSLWENDm0rf7Du30FWWAI5Ucnkp2T900Mp5Z9AIgHLXy8k/goL479Grhc4efIktra2xV716mnCNObPn090dDQ//PBDsTarVq0iNjb2vS6fo0eP5vTp00RHR9O5c2fc3d0ZOnQoRkZGTJw4kcOHD5OcnEznzp1LtPXw8MDDw+M3CX6U8huRSWFyK83/f/0ovfp7cqu/Tdjl13z99dfFXDPfxdKlSzlz5gyOjo5UrvzbvsOlUim7du3i3r17VKpUifHjx/Ptt9++t82IESPo0qULPXr0oGbNmiQnJxfb/fu9KBQKdu7cSVBQEN7e3ixatIhvvvmmWB2VSsWYMWPw8PCgVatWuLm5sXr16j987v8SEvFbZgMvqVWrFp07d36ncueCBQs4dOgQN2/e/KhBZGRkYGxsTHp6OkZGRh/V9n3kFigx+3I/egoZyfO70viHc1x8lsClT5vSoNyfK/DwW/CLSsXJTB8Tvd++OqlWqjly9CGV6pWnnIUhATfucOPEObx0HKk9ojPRSj0i4qA2kWQP2oi+WqA01kWriiPW2jpkJ0bRaN+37Aj1Q2VkQExBDnYSORGpDdCWu+DSfw/yZCl15xzHKE6X1Io5LKxQlUYmmh2efLUK/WvbkUsk5NUrPkFOy8wgKDSUqp4VX/ttt18BydlwZsJLdU8NWfmFmE3fhxQJ8d90xvjVCm2zpZCVT69BdbgSk0bAlDaY6gWRnG1LQaEOfs+e0nPvc9yt9bk1YRiHbx0gNK4GnZNuU3bvNcSS7myKfcHglTP5tF0nVgwfS2IKnLgKnZpAmraaapEJtN33iKaTN1BlaR88J7RlZ0Io3zy4QuuILGb2GoDRW+SA1So18zwnMLNCIHK5gsL9x98oTUITx2f+8v9vx3/OPvxn76POwtZcqtCPvHxwKn8bVYGSmNvBNG3dkMzNd3g0/xA2TSuS+TSG5hcLyI2rhZH7HbRN1wNXuPvUlHpTx9G/zmcMb9WaahU1MXavBEw+xJy1E1i3bxkzRyxleLcJ76i1DPiC0w0+JeFKIi0uz8Sqvvs76g4BNqBJ2aFR+r33+CbTJoVx7iTsvlCF8a4VsJDCNRNIzwbbl5tdKqWSfZYjEWrBJynrSqhhKl9+I8nf+AEOis/AVE+BtWFJ2fap349k7+lNHFt1F3fnStwY+hNhW6/S+u43mHr9vhXaB9N3kfU8gbrbRv8m8Zb8lCyCVpyk3IAG7HOO5Arh9KcKDfhjK6CvCN16hev91+A7rzuVpncCNK62L3bfoP7uz9C1MXln21+sR6LKK6RbwtrftOv3b0KtUlOQnImO1Qcm/6X8Y/xVv9/FyMrVqHu+ufMnl2qMvn8wlUMp/2Oce6JR73xT6MXaSGP0/U2pHEop5a/mo1w9Bw8ezIQJE6hYsSLt2rUrVnbkyBHmzZvHd999947Wfz86Chn9qzlhqKOZ7Kz5pDp3IpKp7/L743l+L/ciU6i29BRtPO04Nrzhb253+mkcnS4G0TYhg6PDG+JR1Rd5agHOFxOg/zrmVP+Mx6ESNnYTuKoFOeWt0BtQh/iTj8js6UNaghaZ9b/BwMyM1o/Ocio1mp9tKvKJ4xNScpORfyklPCIL02Q9jvnHgkEhl23iNYbfswS07U2Z51SZuiEqjUtk6/IaF00gU0vG5znhDAiVMjrRGyKBrUM1sXtFRl8QYIKuwormTZYQfAfuBjhi8jgSlxb1Md02FApVpB57SJ5ShUo9jYSU8qzbF07fdiNo6O2N8/l4KtqogTxaVlnA6aBCbufkYJFWk7hHWUTo+aI7qh/+Uj223Q3j5xuhbO/mhZG+JYpCNbPMLHHwtgNXm6JdoF5WLpS3zybFIB4DvSigpIEjlUmp4O2MlaENHRv8+kvfAo24i+d7379KX3aijFEoJo4ZuHRQg1QK1NAU1tbE56X2kJP1IolKM7uw1e4KBgY9kOluI3x3dSqMNAR0kUmlyKU6PH7YksmPwb3acnZducj9ZWtwsi7uhjk45BoSYL3baxfVfu1GIoSgY+NeRceCV50mcNERGh+ZiKlPWWA8MALvr8OJv/gYi1rvSwsyGSgDaKSbYxIi6TSuDvoKB2QGw9mxZyeLFx/GSgqn7sOLBOjZAGxMQSaX49CpGkKlfmsKBPmvVlxj0nPwWnQcD2sj/Ke0KVHfUN8YXR39IrVJHSsjtEz1f5d65Ste7LlFTkQSBek56Fh8eDKrbWaAz8u0JO3Qx0ZtgNGKAGK9srFt+nZVtI/BpV99LGu7YlDu9U5u3NlHJFwOIis88b2GX9uHC1ErVf/vjD7QfAZLjb5SMNDVpGzILdAIuchkGvfOUrGhUv5MmnpoUjbcj9AIuVgYatw7/6GdvlJK+Sv4qB0/0MgJ79ixA3d396Jg06CgIEJCQujevTs7d+786EH8LSuGfwMFShXdNl6jnIUByzpXKVaWlJVPzy3X6OTlwNj6bu/ooSSZuQVMP+ZPRy8HmlV4Y4Lfex2k53By8lgePpcxpE02kjVnMW/tBQFRsOoCZ5p5stjFnAND6mOgrWCe/00237vHC2cD6htJ2ODYgP3rjuFbzRv3WtW4Hp6EZRkFtYysUDyIhCGbULf0JGfKYwzCC+BqR+i/EEw6Af25Fv6CepEX6ZFtyq41HSAGOAgUedjFoTEOXIFAOu6qw+EdenTAjD1Rbmx0Cmfk/m2AJo2DUu2PltyXtXst+eanREZ8MpEZw78FbqOJsxvJih1JfNZ7FyFZcDpsEO2tN/Dd9ThWmJ7CPkefJsn2ZIYGc8Ajk8IK1XDcG4chqewaUI+q5X99378FZgN5QAiZ2ZZoKbSL5b57xYLIAPYkhHHcqxm2WnrAL8AnaPLnLXj/m5gQBQiwcixRlHTrGXHnA/Gc2JZ8uZy68dmctXTBuDCdiIMHcezYpMiAEUKw55QELQUcfvQte69d4uHyZZSztQA0vvxnctUMSMgkL/k2SZWbIX3XxCgjh6B157k7aSdNT0/FtpnXWyotxT9Eza0AOYM6fYpc9u51okJlIZO/G46OqQtBNi4MNTekbZ0OAASEQ1g8NPNWo6f78T+geYUqum+6hredCd+0/Xtkm7OjkinMyMXYw/6jlSzTg2M432oR2eGJmPqWpe2DDzwf70FVoCRs6xVsW3qj71Bc4KkgI4fs8KQS7qNKpZrbt5OpVcsCqbR0YlzKX8v/yu93KaWUUsp/gY+ehW3bto3du3fj5uZGSEgIwcHBVKhQgZ07d/4uo+9/icx8JaeDYzn2OKZEmYWBNmdHN/koow/AcNgWWuy6zfQjfjyMTn1dsH0oHPmMVg1lTBkMw47dxyI9hzt2xtC3FizowlwrAy4+SyAjLhPW+zE+zJBJ4+9gEpNHXIIJeUlKzhy/wInDZzBUgJexinrG1ijUoFZJwceRa0FX0DWajfBdBL0zKNA5gP/tHQDooM+nN8wp89wQpgPTeG30ZebDMwmItkAnAPZ0u8ia0Z+x0FONlncshaMbF12OVCpBS+4B1KK8hT49m1RieFdNLFpGdiGpGWqOXbmCX1AOQxYouBrcloJ709iyH75qasrQlArs8WzAdx0rky+REqeSg4Exbp57sPKcRKMZb1N8eoAQuWS9qMbxyzJq9C5Lty/evht7KCoSv+xUvrv+Kkm2C5pdwuJiR7lxaSUbWzmAlSO7c+DzNI1a5iv8pu/Gb/pu4i89QVcCN6y7YipNR6ZzhcI0Obv0B/HiF420skQioUcr6NwUNn4+ieTt+yln2xmNlH8mQYVwp1CKTMuYOeXqv9PoU2bmQkIa5duUp2vc/HcYfVHAROb+NIM5aydwK+DKW/vKiUnl5rB1ZDyMRB09lkWjXbkmbYmsisbouzt+C2LrfpzPHuSg8QASrgWjVCkZ/9CPgQn5xe7Fu9BRyDg8rMFfbvS9+OUW6cGaz66+gzm61sbssx3NxU5LP9BSQ+bzeNRKFbmxaWS/SMKyrhvVVw746HFEHXtAwDcHEGo1Eb/c4ubQdfhN212inpaRXgmjD2DBgsfUrXuW1atLhbZKKaWUUkoppZTXfJSrp0qlYsmSJRw+fJiCggLatWvH7Nmz0dUt9bEH8ItOpU9VJ75s8X73v49CKuGanhZ3olLxj0nDx/6lSpNEUiwIuXF5KxKz8nA00QNdLQK1k9jaw5tCPSPs1BJEdgEqGz2a3l3EhLUXcChviFurSLYfTkCpnMTJq5d59OwpPVu3xfZxPkZPMzje3RNJSBaPZn+Fa4vq6DVoRqeeHbGNvEO/sUdokGZHmrYVMzLMWVP4nFFdygHXAF84HwXh6dB5E5TRuGppy7WwsXcmcWQqj/QKaWbQGZXQiMDIJDKgM9mZgTRrnImXpxFWZjOBsSRekXN8UnceNk7nx+9+ISc9A+OdocTnZdHzsWBcP23Wda4FwLSdJzhxZwtNs10I7DmQymnPCMtNw8KipJubMvdHjvrkkx9hQIuKezCzK4+LgwNQBdBHk4PvR8CRGaY+dN91Fc/Wr1SyqgBP2LFpD7cCx1CjWX1qxNhwa/BPVF0+AI/PWpY43+4cuFMIw/Wh4ssll8qLehF/8THWjTyB02hLYoCygAtyvUBk2nJk73BZ1FZoARUBQ7LV2rRLAgMJXLIEF5k2JOeAuV6JdukhsWTe+gXd9g+5GdiaNmZlidx5BWMPe8yrvUoC7gBsZlDSQ1roFFCjUr23jiH66H2e/XwBqZYcK7vqmJpp0cNCQT0tyAiJJej7kwB4zepS5JZ14Nx29uq6IbFUEK2EsLvg7QY2Fm89xd9C0u1nXPlkOU9GVGbNAAe2V6hPY2GEUAvEhxJIA9HHH3Ch7be4j2tNtWX96BT2PXp2pr8pPvDX+E3dRdqjSBw7VcOupTduo5rh3O/t9/9t1K9vSZ06FtSqpdkh/JhY0FJKKaWUUkop5X+Xj5qVzJ8/n9mzZ9OsWTN0dXVZsWIFiYmJbNiw4a8a3/8rvj3/hFNBcfSsUgYX8z8p38uWIcxVqmj6PIHDj6LxtjN5bfy95FpoIu0q2vNZQ43r7ZNLl5nftDU+7dsRM3Aa5cwNmBsRjmmGLsFt2xP5dRf0tGTkF36OidkVErPuU8m1FWq1GnsrK+RXnwEQpyeo3LcHVnueofdUn5Ta+Zwe3RE7nQYstb8Ch7qjzNJHGlyOr3Iu0a/6Qwy0uwJtiW/xmItxtWlpPg8TXsfoJGYtopPXeWLTv8RG15Sxfp4UKqVYJuxgTD07Im6253GQDBOrXJx8kkjPvUX2t3swfSRh6icjUagkGN+KAbunGBg8ZkOvFxjqdwLALwiunmxFPccELi0YRGxKBOOd9jJp2/d83qx+iVsr0zHArllbZLfD0ZUJvh13gTpNlSiFMznCACNpFHAfcKStuz3Zs3u8bFkAnCMv9wU71p+hY89gHgVsRT90JLk2Rpz2j0H/wB3KdK5e7HwrTSFcCRXfsEHNq7m8YWwtAwLQuG3q4NJfikv/02jyNbxLoUuzE6QrgXbZyeQeP4lx79YQlgfXo6CpE1SyAjKBQUAzzKuOpCCtImsPV+HMrWaoE5LJ7r8Gk0qOtAtYVNSzWtmH9N3nMJBK3+nm6dK/PhK5DG0zA5y7L+LyPH0q1lxM5vNtRB58gkPnauhaG+MzuxveM7sgkUqpm6hPy23zaWKhQ1JiZb5eC/Uqw8LxGjGPhMtPsKxbAZnWxxtN6cExqAuUHy3qYupdhgpjWxDe2oZUZTwpygJ0rIzoGruau59v5mD58bS6Pvud8Wb6ZSww9rDH1EdzXoOyvy+OWAhBjbVDyAiKxqSSxjW4xurBH9VHWddMNmy3o4KTOQnXgjnT6Bs8J7al8oKeCLWam8N/RtfWBN+53X/XGEsppZRSSimllP+ffNTMasuWLaxevZoRI0YAcPbsWdq2bcvPP/+M9AN5wf4LrOhSlWthSTR1LZnz7GNJzs7n1otkWnvYopDLCE/J5ocrTylUCdZ2f21QvEjJpv7Ks3hYGxE4tS0ADpUqUr1LJ6zbdGTaped4WpnQ0tcR15fJR1+piv50fTAHA/RwNqvMqk+cqeD0UoGwtSuq6AwabjyFqyyEatVMuO1SGWO5gv42RthKL5GQ4Mm4mLI8i7BE2/Ep58rqoZdmQ2SSN5ZOx7DUk2BpqkOAsiX1mQJoJq+Ny08gNFngYj4YISAXSxLjzVl39jH3I/uwrZMneTl3qNooki6T+5NXoEuTT8bRg044S2Ww755GdWugI/rjnuD8UlESQFsL9HUl9G/TgcfJ9qw7EIpcepK9n31fVOd+ZAqGOnNwtUxDItlAjdWDuZMvmJsjmGiseYabJYYTrdLimtV9rGR1mfTdME5c3c+ptQ+wtyoDLAcmo6PblDlL/alaI5HcVC0uOIWjsLbEfP0ZLm46i92RYTRr3ajo3PYyzSts+1UiD92j6tK+6Du+Gbf1Axq32ESgEjAaTZxk1MvyPCANKPl8SSVQ9eIZdu05wClDLXo39gBzLU2qCkCTemLfy75HYtu0Ld2cwMgQGrUyJWJGZ8yqOGmevQzQ0wZdbRmtbn4NUgkSiYSgQjhych2bNk5hw5xD1PSqj0xHi/KDGxF/6Qknuzjy3CKdijwgYO5BQjffp/amkZQb0AAAycvvCDtLB9aP18gvZ+VAx8bQsBq8iA0le28w9z/fitesLkViKa94tuEi8RceU3Pt4HfuYJ2qPQtlTj7d/RcgL2fzmxM7y3S0qL5yINWB0YX5mCk0cZ5SmZTsiGRyY1JR5hS8s71JJUfaP36/BPZv4XS9OWRHJNEh+Le5l76Njp/XJjsnk0f7U5DpaiHX10ZhrNn5zQpLIHTzZXSsjP90w0+tVCGV/zOJtEsppZRSSimllA/zUYZfREQEbdq8VtVr1qwZEomEmJgYHBzen4fqv4CblRFuVn9OcPvn+++x/d4LDg6pT0cvB/pWdSKvUFVCkdTWSIeB1Z3xtjMpOmZobs64fbsA+GViAenhCka3k5CQUvwcn/hWIjq9H/2qORUvMNdDJhHY3ommk48NtZuVI9nkOVOfXaK/VWvCV0cwx1LG8RbnKX++CnPKJOKbKefGtWSCrzxn4DJIyzfi2JXZzGw0B7ADIDcfrl6rQoNq2wG4lB7HxqwhDHNzZaWxBY3KW2FuY0KjzhoDdmiXeO7e9aKcsR4eWYXszwlg7rO7HK3bDIcWrXhlTL7CwwVOrgWVMMbm+AxSicXR3YRJBbnYaGkMoC4brnLp0x1k5uijVGdiamDC5hwJh/Ik1NK5T3vdiww3GE+USoKlVKOKmV+QR0FhPkfO7qJCki31htZHodcKmEPVGmWAmyjSsshWn6YwMgUZEqKNlYRduPHS8FOTlH6e0Hg7Krt4ErxqM0k3silIvUezM1veuIJyaHb8vgTmoxHFiQM0So4qdTdU6hMM/L4vOyZuLvHMtOvSCoVCQfuuz8GgOzjVJDP7NDO/HUSL2h1oXe8avEwvcPVxAEFRkYzv34bMjBzWhl6jMKscTolgYAk2ZhoVTo3ap4bOyZAbm4QqK43M7HQSrgUTuf8O3rO7YlCvPNukFdCVypjBdDwnqtGzd8SxU7US43wTAz2YNAiOXdlHlwndGNZ0DHUaexZTwjyREkVFPROer79I4vUQPCe2RaKQIVTqEjt7rsObUBCTikyCRvr9Nxp+b2Km0ObeYzhzA0Z1z6DhvlEos0HLRP+j+/oYVAVKCtKz/7BSYfeWg0jPTEVHWxfdKs70SPsZgPQn0RzznYZN44rUWPNxu4gf4uHMvQQuOkKLKzOxqPE+NdhSSimllFJKKeWf4qMMP6VSiY5O8ZV2hUJBYWHhnzqofwtCCPpsvYGhtpwfe9T4W8/ds0pZ8pRqapTR7AjpaskJiE1n3IEHXPu8GbWcNAFRWnIZG3rXKtF+8AzNvNfItICI9Gy+3WJIQqIcL1eQJDzg0LxFdJs7izmtvYACYguqoC0xxExxSdOBmT56+8ayX1eLfUdPMeH0HnZ1b4C+dC8zh9TlXqyS9MwIZn6ShqtRWS5d9qenRSqWik5UybqEt0EU8mc6hDgHUv3lHP7HvfDLaQgKh28+h6u7D1G2rBQfAzPGuLmRmgEaN8rlQHMaVdfj+vVQcjPlnJHnkBmnx0P3FM6NcmaApx2rH93DLCSZnuZloa6rJq8T2cgk+jxrOZA+QZc5nRZLmvIX1LmNqbnsHu5WetxJ7Exk6FLkSgOGtYJpRlBPG1rp9AaC6a3XFjADCgE7VkzdytUH5+k5uSkuKVasyliL11cniFdBjySB9dkwUi9v5sf7uziw/TSBBXrc9HLlW99XRslyLIwnMP8XV4bNWIaJ8zUmDUjAUTnkHe/+XGA4mli/1wjhydO4q9xIkhCrAlOZEgmg/fJjbGpmQs8BXYFXefrKMG9cefaGJxIaHcpnW50Y37ELo9t0ZPiqZTyJjODatfXc8b+Ic1ZDMmXuPL8BowaC01s2rQfqQV7PaYzrPQpjAxMudf6OyIN3sapfAcdO1Tng2Rg9qRyww6QS+M7rUaKPH/cuZcXOeexYcBqfCq+NQgersjjZlcfbqxrN+7UDKxMAbmYk0ibwHE2MbTi8cyyZz+Ix8S7DbqMhCKWK7unri7mEVl7YC9RqzcP/O+LrXnHoPNx9nMzEAeWQyiuhZXL1d/f1PlIDIni27gLlhjTCf9YvpAdG4zWjM3K9ksqyv5Wvhi1+63GFkS76Zcwxq+qM4RtpIf4UpBIkUkmpvH4ppZRSSiml/Iv5qJmREIKBAweirf16UpKXl8fIkSPR13+9Gr5///4/b4T/IPlKNUcCo9FVyP52w69dRXvaVbQvdqy8hQFlzfQw13//pFAIze6aWg3eDV+wKdGfT+o2wU7Lmq0Bj0jYup60/QdxrWGHg6Uf2TpzcfMfiYU8j7Car/tRWxqSk5vHE/8gkp8XMF8ey2d2i5FrwwqrVNp3WoD0/HksHgZSpreEHxKrEOniy4KoBvQwiqBDg2ZEnR+BJKYm1VoMJunljmOVh/vZ5X4N94zd/JgRQ+OIRPbfyGPa6RjWtn1M42qTyStsRdvN4/i8zTLaW2txfOwNbClH6urDmOgY8MvdG4zJDcFEX0XPz2/A1NbQMxFSu8O9GRg3nsw+z8akKudhozWLrPw2JGcPoLZTNJdkuURII2hi7IiWzAj73AJ6PogHrx/BeBQphXWofn0eFw0v4VhlA6BLVc/a9G00BIcAbcp21xjacXt2MnVEfxZ0r0FohD+pynT6t27DiTqzMLM6z4rKWtQ/vgmoRkKaC2plXVA95nnKE54bTKNdn/lF9/r+E7jpD0M6g7aWlF9Ol2XVrkLaNtyJnWUB3VsORC5bzPfa8+FTCWlqNUtkp9BCxgJa/eoJWAwMBKQ46u6lvbmMhu2/oNfyFdy77oe6ZXuWDBpBwIswDvgfIl2lxKHpNLo3icPRxoXy7wiPm1K0mW1CzKmHlO1dG9tWPlg18iQnNpWOtm9vePo6rNoJs8dAWmYKWdkZ5ORlF6vjU6EaVzc/JccvDP/FR3Eb3wYdJysq6pnQz8qF1qb26FtZoF9Gs+DhOrIp6gLl2+MApdKX+RJ/O3EZyVgbmSF5qZj0aW949EwLmcweTUqS95PqHwHwVpXN9/F0zVlC1pwlLSCC+ItPMKnkgH37Kr+qdQBNjKfTR/X9a/Tszej4dNkf6uNdvBnDWUoppZRSSiml/Dv5KMNvwICS0uR9+/b90wbzb0NHIcN/cmsU/5LknZObejK56YcVQyUS2PoydZhM6smAGs44mOghhEB/8mOwqMHZ3Y2p5ngcLC+QfHo1WXGV0M2XQMJDqOtGzN4XLA20xO1QOxr4mjN45zrS9z7geZAj5tXtsKytTbkadUnNL0Tb9iBIptI1bhB4ewJfcPC8hO0nntBXZUDMiwNgX8C09q3p0qws2V9HEfE8nsNfzMLbMY2W5v7sk6sIaRLNiYIaNGYuYcm1uR+VwOYH3kS6WjMisAuGVkZo6ehRUFhIj29mIXN3ZrtFY6jpAtWdgWxY3goO5sOCQLRbe2GjZUpYNOQVKIn4vBlh3x1Gy8OdnW5h9HEoh1QKKdeeY/Ykk6eJSrRrF6JtLFBKpITc88KxSjDgS+KxANo89KLG6kFFCb3NVAXEKZXYRcrYsvkZjtZO5EQkY+Juh7kDNGz8GM3u3QwMcu/S7coOJo9qSIxtWXzciu/SbjsCtx9BNU+o4QX5hZCcvpzvt00CwMjAhHYNurHEXE6OGhLzU1AWyLHXL67aufVOGNHpuUxtpkl9MHr5KoTaA6msMcYnEni4Loov82KZPLMmbarV5H6jniRlFOC3WIvq0QtpXGNqiefpuh9ExkGPl/ZlQXoO51svRstEj+4p6zhVdzYp98Po+FyjZPlrElMhOR1SM2DK4HmM7jEFQ/3XLtF3iWInDxlKdfJ23iBwxWmkNqZUmtYRQ7mCLRU0wjzqQmWRSmaFL5tz/8xGstOT0Df+Y3KgB/bu59gngkbPTOlbvgkAlmbQuIYhEPjB9kKt5mStmQD0yNzw1qT0udk5qFUq9I2Kiz5V+qozBq42WNR2JWLPTSpO6YCu9ZsCMleALmgEfs7+vgv8myg1+koppZRS/t3Mnj2bgwcP4ufn908PpZR/iI8y/DZu3PhXjeNfQUp2PtOP+dOzchkauWpcoZzNDf7hUf0GMvLhbBhUsgQ3jWvoa40FCQ4mGuNAIpGwuU8FtKQF1PGuDr+UhaRLmNU8j+nIfIbEGIBtNJQbgl1eAdUNdMkxsUXxIg396CxsnFyIjVyDgUtVAMpNmQHTZiLwpzDqILKYcki9JwEtuffYi4JMR+p49KDQ3A8e56NvcoMqzeWotkRS9bsp9LHzIrGgHjCZLdV0sAzdwmcOZYCvsAgL4fHoiky6GMyKy5fxta9BG2ON/6GWQsHSQSPQUshp06YjAE9y0ohJdadpmyWQdx+qlCX1sD9a/pWY+6wuZS+fpZn7MAyqPcU+UYdEkwZ88uQSJys144cT+yibLeV++in2rX7G5EHzGBodiGO3w8AsIIbzW0+y49kd5pz3pGa3pkilUozqtmJ278HIzA0w0jajT4ehePl6Mj3wW5IKH+N17wxXnqSy2wPiLz4hfPs15HqCWj9NB/SAXUAk0I/P+sCRb3ZwcdBF/DZ2pn/b5ni6NGPG6hrYmNtT19eDDHUa9RNMMM1N5mbkETro2vC9iw+YiSIXu4mHHpCQlU/jsjqM/GERo1q3Z3grTb7EKh3qcio2E4WtH/cfW9O0lpxtZpBpoMBv0EZq+7zpsp0LnATa8P02bWISNMqb9tagZayH95yu6LwUCzKraI/IyUehVzJlBkCfttC6Hpi9tGfeNPoAUsghnTzSycNrbH2kOjLKD2lUrE7ao0hO1JiB+cDKVJjRnqfnNnNhx9coC/Np0ltjdKU/ieZkrZm4jmhKlcW93/+ZAbIoYA5nMaolRSciA0uxB6hE4s10ZNoKzCo7fbAP0Bg8bmOaA7zV6ANYMPAz8rJzmHdgEwrt1+k55PraXF+1nPDTZ+k8dg26ubmQqQWGr0R5KgPDgNa/aSyllFJKKaX8/2HgwIFs3ryZESNGsHbt2mJlY8aMYfXq1QwYMIBNmzb9KeebOHEin376trzGpfxXKF2ifYNLzxP48foz1lx79of6icvI5bN993gUm/a72idm5XEuJO69dQqVcPA8xCUBybkQmQHh6Zx6EovhV3uYeethiTY5BUq8bNvQtEIDIA06VAZjc/SNpGyYPo9vvjaDhXvAI4f0NuVQdi+g29CJlB08m+5JfkgjMrB3dtDsSOQUkDD4a9I//YF7WXZ8+mgwUyyS2PG4LRkF5Zg2SLC4dSJRkdE8f1YIVatA1cbAJmRaS9Cz201obiZTwiuQXGiETKLD9+Ua4KCtT+zp+5yqM5vQrnOZ27wCoTOm09qjBaAqupZxHbsy+qXRB9Ah8AzNHp0m3NsSFnYFayNyA2PRN9SntUUTaiZJKAy8ivu4VMyr3+dmZjx3s5LIVavo2K8TdUcc4fuvLtPCrSzGOWWYMWk5bs79gR4Ex0m4Xj6XW44p9NrYjs8W9SM/JYvjFacw7ZoJmz26cLfvWuR5KqQZBeTEpKIWzuQLC1Qx5Vk2YwnadZ0xWNgG24ltgRZoJvL9X74icbIHy4inZIxy47ZXDj88jEJX25ejK2/x8+z1mBrVQE4zzKQQEXwLxa0zfGdcAUlqFhQoi+7D3kH1+GVQPbJy0ihMaEXIierEJQoArBt4MHv9Qdy+OIOkxlYAFBIw05bQpMYgdLVnvPG0LEaz07ScKYM1Aiz2b4SFec/ogtsojbFT9cuO2NZzJenmuz83Zm/PggBAC9xYQhsqJwt0rL3xmbO2ZNoEqQSJTMr0F/NpMrQiFep1o37XSVRt8VqkRK1UoS5Qosp9u/pmXnY6G6Y35/JeTQycGjX5KDF0tOCHMvtp6XoKZW42p+t/zZlGc9894LdQ9ds+VP22D8rcAvzn7CPlQXixcofyLti5OCH7leqlVCFDbqyDkAjUhYWgVINS+UYNA+AnoPNHjaeUUv7XUKtUhPpf5OHFnYT6X0StUn240R9g4MCBSCSSEq9WrTSuDzExMZiamrJixYpi7W7duoVCoeD06dPv7FsikXDw4MG3ll28eJGOHTtia2uLvr4+vr6+bN++/a1158yZU+R1NWLECMqVK4euri6WlpZ07NiRoKCg33Hl/23UajVBQY+5efM6QUGPUas/nMf1j+Lo6MiuXbvIzc0tOpaXl8eOHTsoU+bjwgc+hIGBAebm5h+uWMr/LuJfQHp6ugBEenr6PzoOpUolttwOFREpWX+onx8uBws+3yHG/nLnd7Xv9PNlwec7xPmQuHfWOX1diLr9hDj86V0h6i8U4kKIEPlKsffBC8HuzYLLm8S19PhibdRqtbga2lE8TagphMh/eVQlhCh44/9JQggham06Ibi8SbQ9dUBc2XlJKK6sF42O7RHRqT4iIf+S6HhznzixZrA4tmKEuL53rxB7VohbO5aKLsZmYsXc8SJ6ywVx9atVIm3aCXHUd6ooVBUItVr9sv8pQog6QqneLSaH3hX5KgMhBCJ01wmRE5sqbjZpKnZLG4hDChcRf+iAEOKTl6930/h2f+F8pb+IygktOqbMKxCJJx8JtUolMh8HipywUCFEhBDCT6QV5ovY/Jyiun4z6orUAGNx8IfuIiuzrUhMviiOn50jfrx0T8g6dRAGbZqJEd16CJ9uVuKHNV8IZWKcuDFsglDmaosr3ceKmM0XRdqdp2K32TCx23SoUCnzRX5Soihs11qkDGggMjJNRIVPGohZNUaIh/dfiFlpQsQpjwohvi0aQ2F2nrhx7opYGecv6g0pEI0HC+EXJIQQeUKIJkKI4SI6LVvoDv5COI0eLURuvhDp2SXuRVDYI/Eg6La4suypEN/fEknXE4vKosVFMUT8Ir4RR997P4W4L4ToJIQI+EA9IRKuBomt9BJHK08TFzosEaE7roqEmyEiKyLpg21fkfE8TuzQ6S/Ot28thOgphBAi+UGYSPF/UVRHrVaLGT98JgbP6iQKlYVv7UeZV/DW40IIEf/isfiyjUQMajtM3LmjGVuBUAqVUAvNs58nhBDi/pQdwv+bA0Jk54mc8ChxKPu+SBe5xc9TqBR+9wJEYWHxcbzYf1tspZe42Pm733ztmv5ejrtQ+VHtSinln+bv+P1+dHWfWNjPQUxvTdFrYT8H8ejqvr/snAMGDBCtWrUSsbGxxV4pKSlFdbZu3Sr09PRESEiIEEKInJwcUaFCBTFy5Mj39g2IAwcOvLVs3rx54quvvhLXrl0Tz549E99//72QSqXiyJEjJepWrlxZ7N69WwghxI8//iguXbokwsLCxL1790T79u2Fo6OjUCpLv1N+K3fv3hYTJowVgwb1LnpNmDBW3L17+y8754ABA0THjh1FpUqVxLZt24qOb9++XXh7e4uOHTuKAQMGCCGEUKlUYv78+cLJyUno6OgIb29vsXfv3qI2Fy5cEIA4e/asqFq1qtDV1RW1a9cWQUFBRXVmzZolfHx8Spz/22+/FTY2NsLMzEyMHj1aFBS8/i2NiYkRbdq0ETo6OsLJyUls375dlC1bVixbtqyoTmpqqhgyZIiwsLAQhoaGonHjxsLPz+/Pv2Gl/GF+v+zd/yAyqZR+1Z3/cD8Da2gScnfxdvxd7XtULoNEAl62JkXH8pUqOq+/goe1EUs7VaGmF3RrATVic+BKHkgFaMnwtTdFdkmGmZY+a1YaIWsP1ubgZA+S3ELqOh0oprwnToeTl5mLbmfPl4IYmpWgzuXCCclS09g9mXpOmawPucxDHRes9YN5kJfK4fwMKlu7Uj4oA3HxKRubmBKZFItuZjbWWdf49Ng5biX7s2/sUWp+OoLB9+wpp1+VWZ4ngNrAIu7cc6VRwkpkdru4NeEHnq7eQrlBV7BupkRkx6JwbUVikASLtruQyqTkp2aR8SQGyzq5wDZgOlfvhxKXnESzzKqkKAKx1LYtujaZtgKLlhUBMPB4HRupVtohCUvAxlVTtzAzF+NKkaQFmtJhlA0S6QqOHc7i8s1L+NSUI9c2REdbm9OzNzFcLmfMwx/hykFq/TQYWEP1ZXZo57iCvg52Lb1x7vsLUpkJWnqXUbm4oG0fhK5ePn0l7jjdzuDyjxdYP2sADokNGFq+LZAOFCLXs6BWk3pUVUJoBbgdALFJ4FNBm9TC4+xOCucTMyndm3TDw9oIdLQ0r1/xycRGZGSl8ej7CFSPVZh7GJB4+xkbNn3LgBFjmeXmgZ7uh1yYK6MRFfkwFnXcqLF2CNnhiQQuPEzU4XsgkWDgbInk+XBCSWE0tYrUR49e2su6/ctYNmkTLg5uACgMdNArY4GhS1OgP6r8Qk7WmoVUIaNn5gZAs0r+9ZjlADxZdpxH8w7S5NRUzKtqPm83eMEZ7WcMpwY2GJYYp1UZD5p9dpvGrZ5yN+IW/v5tUPBqB06jjwovlUEB0rLQLZSQXZBDhF4ald7In3hwz1E2rN7KoFF96danU9Fx+za+VF7UC/s2vr/p3r1CJn/pJvvGjuCNh5c4cXU/kwbOLeEeW0op/xUCr+1nx7xugCh2PCMpmh3zutH7y1+oWLfLX3JubW1tbGzenZe3b9++7N+/n4EDB3LlyhWmTZtGYWEh3377+3N6Tp8+vdjfn3/+OadPn2b//v20a9eu6HhkZCSBgYFFO5DDhw8vKnNycuKbb77Bx8eH8PBwypUr97vH81/h3r07rFr1fYnjqakprFr1PWPGjKNq1eolG/5JDB48mI0bN9KnTx8ANmzYwKBBg7h48WJRnQULFrBt2zbWrl2Lq6srly9fpm/fvlhaWtKwYcOiel9++SVLly7F0tKSkSNHMnjwYK5du/bOc1+4cAFbW1suXLjAs2fP6NGjB76+vgwbNgyA/v37k5SUxMWLF1EoFEyYMIGEhIRifXzyySfo6upy4sQJjI2N+fHHH2natCkhISGYmZn9iXeqlD9KqeH3F6CvLWdMfbePbncmOJazwfHMae1FzyrFpfx3xoVxNj6WiNQclgJGBjCuL0B9GFGd3Nx8pIkp5O/cim+cGWVT6xIWqsP0MADB+gGhOI3fAd2qwlRNLkZlVBbqJ0moJSr2xj7nE/vX+bcm1/OhzZJh3LpRh7iOJ+l3ZS79kIAqAr1KRowXhgwwqkkW50lOu0XNOguxsLEju9dF9IxvoD7wBTaPXKnUwoQCmSGyaC0U0ldqpB15/Gwyj1smIS0cT8hzqDT1Eupcb8oN9sOq3hNcprXndENrErbtxqKmG9YNBYX35qJ47Emu3SN0nfaQkGJBwFNHXkSlsXiyJQrDZoT2kdOguppxA9/txXxj3EbCVp2n0ZGJOLSrgsJQizKd00AiBcksoAFN6lXCQH8XjeqMYlhHU76+AusMwS8Fttl4YK2dRHNaAhPRsp2qUWNRyKm3YyzwArhLYoEKk6UT0cs1grAUpn5rTFCt29xVJ/D57UB6RuWCjTcYVAOS0CRq10chh6UTITZpKk9C9+AXvIF92sZ8E+lPStl8NvXRCMNkZRWSkVGInV1xgZfBnT4jOT0R/Qo2SNw1xu3ZHzaTUi4bG31zyNIG3eJtSpCXBxfPc6ZKPbSMjXhSCLtyYLMZlH35reEXfIfBPw6m5dBPmTliENqFEkwqOhC85gyqnALs2/hyjDhekEYm+UWG342HF7n3+AZXr+7DpGEPzGxd0LEypuMbSctl2grcRjdDpqNAlV/I7dEbsahZDtfhTTXDS8ygIDWbwsy8ojbPnz1He9kFoibZYOOkMfgXX/wBf2fBCP1G1Lfwon6zKsycqU3VqiVFaEpgrE+2rgRvrXJUpPjkr5KvJ16+nlTyKS62JNNWUHFy+2LHlColUfEvcLL7uMnXz/uXcer6IepXaUbz2u0/3OA/wObNofj4mOLr+xvev1L+36NWqTj64+f82ujTIAAJx34ch0etjkh/R87OP4O1a9dSqVIl+vTpw969ezl//jwGBn+uNkB6ejoeHh7Fjh0+fJhGjRphZFRyUSg7O5uNGzfi7OyMo+PvW4D+L6FWq9mxY8t76+zcuYXKlasi/YtErPr27cu0adN48eIFANeuXWPXrl1Fhl9+fj7z58/n7Nmz1K5dGwAXFxeuXr3Kjz/+WMzwmzdvXtHfU6dOpW3btuTl5ZVIx/YKU1NTfvjhB2QyGe7u7rRt25Zz584xbNgwgoKCOHv2LHfu3KFaNU0Kpp9//hlXV9ei9levXuX27dskJCQUqf4vWbKEgwcP8ssvvxRblCjln6c0xu9fxPwzj1l8/gm3XiQXOx6Sk86gsGvYNFXQo3IZlKriPuc52bkcdP6ci3X7ETN1HIMu32bHdEs2fgMdGgiqGcTx+GIYQl8bjF9P+FUxWWgBq8yysDMwIi0ujmm+Ndg35xugNk8CvPHXbc2U9bvI1lKAWTY4x3MsVU7Lp4IyAenMcNXjVMo2Rs9oBkBB5CwO/1AfL4OGrGpfA33thnQPPsu+gsUMdtlZdG7P8pex8E0mxSecGc83I7FVcr6Wkt0RNYFlwAJ853Wl1Y0sLOteBE5hoK+Dsa0xCpkHK3ZY0XDwDNrUr8vwTq1w+ESGh09ZEjJlnD11lRvH3yZEdBuYytbAdSQaZiKzeZWCRIZU8YQ8iT9Od87T4J4e+/bKsC/7GVfzchBCMKMeHFDBggowt1wNDpVTIkQ8529toEafMmSp86FI2ONbgqIC+Wr7YO4+8wHDMeBQFrmZEXnuetzZvx/D87/w6amf+HnTTqAq4Atoo1TByu1w7HI+pobreb4/n7N1l9BDmDPzYgEdU3WLrqad3Vr62i/n2b0YAApR8ZxkxvWdwdwxK5C8sbNr2Nmdx5fvcSX6PpiV3A0DSM5I5ssfx/Eg6DacOAYrl/Ns1x5GpkKIEsJUkKyGR8/i8OrqxfJt63Hs0IpET3MCiUeqkOPctx6trs2h7YMF+M7rQZt1BYy9VhYLXqd7+WrEEnZ9c4SQrV+yZfbrFWyVGo5e0iiIAlT7rh+V5/ckKzyR5xsuErL2XFFd33k9+CRlHTaNXhtenpsTsV8divaOEJLQpIt4IVLQKWvHM2W85p2WSZkzx4sOHRzeeg+KIZGgr61PRUnJFf8Knq4s/OFrQiMM6NPnOtfv+lF/oBvbj68rUXf+uinUG1CeszePfvicbzBjxFKWfLGeJjXavLde1LPnHN+7HSHeNjn+3+HOnWQGDrzFiBF3/pHz52Wnk5YQ8Y+c+79KeOAVMpKi3lNDkJ4USXjglb/k/EePHsXAwKDYa/78+cXqWFlZMXfuXHbt2sXw4cNp0KDBnzqGPXv2cOfOHQYNGlTs+KFDh+jQoUOxY6tXry4a54kTJzhz5gxaWiW9QkopTkhIEKmpKe+tk5KSQkjIXxczaWlpSdu2bdm0aRMbN26kbdu2WFi8Vqx+9uwZOTk5NG/evNjzuGXLFp4/f16sL29v76L/29pqFn9/vUP3JhUrVkT2xsKJra1tUf3g4GDkcjlVqrxOM1S+fHlMTV8vvj18+JCsrCzMzc2LjS0sLKzE2Er55ynd8fuHeZKTxswQf6S76lDZqj79ekbSoJxlsTouuoZMcqjI0WuxzLwbQF1nC5q4vZ6MKgx0MK7ogGFFH24/dua553C0tcC1DHzeQ0VuWiTxUkO4OKmYm6e2kyGFIXqMquFGoW4uWVGpRD0KwNIpAJXoiO/cvqSfN2PTYzOOOJnRs2EhZNVgtPkhfkl8ynpMGJzhym0rL+o06EJOZhbB1xPYWOUqNjlhrL28D8wGYqdlgpUim3MX5VgbQbNaACfpcC6V3ZFbaKWsiCiYy7r95uhqP+XT3hqjzapeBHAEuA7sheoCSaW5yA1TaOpano6r87GxyGdiuda0Vycz7Zoz5/fs5MmVzhz9IQWfeh3QM3oziPlbCtUHaDi1Kr6uV7By2kiBeh4jnt3AKb6QzzwqoxaC+LMFjJx6h7LtdJn+0/dkFQRyau8cWnUcgIGWPouMAnlQYE+qOp4ff+mCShWMEILZGXAzH3aZg5GuDgObxFPbPQOwggIBm/2pYmlF1rjhmDpYc/LLJSQlJKNR99SwZeE+Hh0y4GrtmrRrmETv8XocXaOD4uRoXGcacWH7RfyWNiI6U3Azy4hCzBAvxV32E8gZnjKM6tT8Ve65x2lPeCANIlGZjt/MvQStOEXL67Mx9Xpd77u8c0T3r8b6jT9jkJ7Pc4Pn1MzIZ8z9RfQu34xxJs6U0TJj3cNUUjOCCI0qR91VMUTvC8bmMy9wLXZK0oNjCBy+BUNXGzwfehGx9yb27auga2pAmTgr3E/3xGKkE2d5ihZytB45s3A91PaBb794+fkgASrIaXbuS/TLvv4RlEgkaBkX37WsOL4t+jamBA+xZTknGUZ1ltabwqPUcKrafPwO/G9h8+Yw9uyJoGJVA8Kin/L0xeMSdTxcvCnv6I69dfFd/MiDdwhZe45aPw0tyk8IkJeQjpapPk525VCeiCD+7CPsWvq8cwxHGnyJbpzg7BEdmrft+udd3L8MX19Tpk3zpG7dP5a+4/ey8csWxIb68cWGUIwt7D/coJQ/TGZK7J9a72Np3Lgxa9asKXbs125rKpWKTZs2oaenx82bN1Eqlcjlf87U6sKFCwwaNIh169ZRsWLFouMZGRlcunSJ9evXF6vfp08fmjdvTmxsLEuWLKF79+5cu3btnTs9pWhIS0v7U+v9XgYPHszYsWMBWLVqVbGyrKwsAI4dO4a9ffHvnzdzawMoFK8Vtl8tAL9PpObN+q/afIyoTVZWFra2tsXcUl9hYmLym/sp5e+h1PD7hzmeEk2reyr2xIORXM7gWiXdweQSKYudq9GJRC5YJVC/nFWxcoWhLm3ufANADVUx2w605egOr4KTTPKrAiA2C0VyDoGhB5mTPIR5evv4fkc1DNveYtWT+ZxP383gljpsc3tI6Nd7SX5YBXNdM7R7BqOrn4lVpiXVU7WRVBmPqZk92779gfqdWuBzuSxWTwvIrpmFvv1PbJZLycqBViNBXxeqeiZjamRO7JkImhj0xLK2ZlK+77vL5ObpMPAraFoT+rUvQ2z+YgxlX2Eg/xLkdcEwldAdZXnQpyp1ttxAu18itm6uqNW6mMpy0F0zg7QKU+heKRgdXY0LjFL5hL17E6nXeBb1ItpgZBGPX9mveBYRiZ5tHpvjnmP7IovGK/0J3/0p6a6FzIl5hEMbbUJ3P8fndBI+iq0YGLnSqkNzOunVpJMepAZE0PtwFdwnzsBQ34igZHimhCw1OJjfwzAtn5iTjbFrtRy0VGChh9TGAKdyFdg1tTH1nPUZN3NnsbdEveM89R+nsj0ygtNt69GixVU6PgtEof+cozs6E/M0kkNfXuR5GUFB3fpUMZLiWrsPyUkpOOroU87AHEdMSjxDE/rPpkOjnrg7V+Le8W2oC1WIX+0cu5o7k1YQxtjuUxg7vTPhmTE0+/o8Zapkol7+LTHWlVjCeAZ2qs5y4wgaVjPjxk8/EHzlGha2diXOaVzBjuorB2Dkbsezn85xd9xWPCa2peq3fUAJqCQ4ejdimfBHoobu8hR6tKxK3ZcLi2oE33MVgNVNOiH7gIOCtpkBFca0IJMoLNDDDD10FNpUM63w3nbvIj9fRVhYNu7u746vW768Cr16laVjR3v69wnH5i0GwSctBvBJi5I5UKMO3yf2lD8p98M0hp9SRVZQNPvrzMRwfB1aDu/GzaHr0LUzpWv0qhLtX2HTpCIxt55QuWqd33Wd/19QKKTMn/9uA/ivpmzFesgV2ujov0eitpQ/FUMz2w9X+oh6H4u+vj7ly5d/b50lS5YQGhrK3bt3adiwIfPnz2fmzJl/+NyXLl2iffv2LFu2jP79+xcrO3HiBJ6eniXcOI2NjTE2NsbV1ZVatWphamrKgQMH6NWr1x8ez/8yv9U4+auNmFatWlFQUIBEIqFly5bFyjw9PdHW1iYiIqKYW+dfTYUKFVAqlTx48ICqVTWpvJ49e0ZqampRnSpVqhAXF4dcLsfJyelvG1spv49Sw+8fZoydOxEOarrVfIzOJxV4JTDxNuo4W1LH2fKd5fBaGyIvJgaFiQkyvWzQCQNqkBP6HLmxCVovpXwL3K3I1dIlwdgP3TufoEjTxjzUjNyOck7cNELbAwLS9XE8cJWYU4/ID7xA9dnzkT7pTfcRYbBBi4CsRGKS4tCJSkdXV4GRsRlfNNpPcsEJdLrXAfltwAUDPStmjYKHwYfx6tqRkb7VcPrODYmhDr1Tf0YikVDVcw8q1VZW7rhNcLgLT3Mz8LhrTQOj7zjvUxU4DeijazUbXbubCOtvCUgvZPq5kwD4fbmb/Jhj1PK+j3L1buJ8q2PX9zv27rWhd+9R1Gn6FL0FphjI0rnm14G5a7dxZp2EC471iVx7AMdOtZBJpJiZabN8eVWezfqS0CVRBFSSU7tHBxo2rA3idc68F4GhZIbEsXfbIb4a1pSNZhqjz0yqZlKoKc5dOmHkr6LtwwhMvctAz4qExzyn8WBPuujp4mHvWkze3y/4Dhv199K2dnOeRZqyceNAWrRog65Vf8jOpfu4eC51W4V5vil5llksmDuA1tXrkpebx9AeY5HJZGw7tA4d3ZKru3KZHHfnSgBUXdqXSl91QvYrYZiBsuoM1K0Ouhoj/MbEDWSZBlPlu1HIdIaTIzMjByW5sgK6NnMCoP3kL2g/+Yt3Po8Vxmp+vAzLWZNyPxznPnUBKNO1Bj2zNyLX1cL92haC6upzqsCfJX2qFrWVIqEbXgjEB42+N6kq7FEeO4xOeTNwf/fuUFpaAceOxdCtmyPa2sXjg4QQ9Gt2hL1Xczl7tjFNm75d4MFMV9ChrQ0SiQSHlzt6uZmpBN85RsV63VBovXulver3/XDuX1/jrppfCFGJGOjISTrQlMtN9ShDLtVXDkCvzPt3uDpvm/Hecg1hwLdkPuuNgUud0mTrv4M2w5Z+uFIpfypOFetjZOFARlI0b4/zk2Bs4YBTxfp/99AACAwMZNasWezYsQMPDw/WrFlDr1696NSpUzF3u4/l4sWLtGvXjkWLFr01PurQoUN07NjxLS1fI4RACEF+fv7vHsd/BTc3d0xNzd7r7mlmZoabm/tfOg6ZTMaTJ0+K/v8mhoaGTJw4kfHjx6NWq6lXrx7p6elcu3YNIyMjBgwoubj4Z+Du7k6zZs0YPnw4a9asQaFQ8MUXX6Crq1u0m9isWTNq165Np06dWLx4MW5ubsTExHDs2DE6d+5cFBtYyr+D0l//fxgdqQy3ll4Yj/RG21xj9BUqC+k+qQlj5r9OQn3icQwzjvtTqHr39ntGNizZBLcvxXLV0xm/bu2BXvgFTeSZ/1XOVWzJaa/eqFVq8gpVdJyRTLtFhoiw4UTs28XahFbQ6QtktMYu6VMaadVgYBlz3Cc1IK26I4ktyqOfrcIoIBUUDWFgfSqO7oRLGUcMQrLpcjcP+3JOSFwrE9F4OkIRCMoGkKdRhmpWS/AkNA+p1JLnh20J6GDJpoFlOZCsiZsJC3tBeipM/vQhes26sTKkIQ1172KU8ykXE58BM1Flx2DdqDddo1ezu8w2ZjxuwnfhO/j6xUN8vumOX+QGNg77jIs+1XhR1hFIpkmTRHxq+hOStoSJUQe44TuOx8/306uN5kurobMLfXd8gWHrutx4I/2htoMj2lY2bF1xhclDPkM3MYMV27YREB4KQK6LEYtb5vKsvR1CCHQlEB4axKnwUJZEh7CrnQdPu7hh9IZbrqmROd4VamDd/nOaVJrL9YFrUWZrBErMdM3o8bwu5rm6NHohoc/OusA0wB7STHD0cSaocXvu1BzH1PGrqF6+DdO+N+ZekAJtHW1yc3IJefL2XHp3PtvMkUqTyU/ORF2o5GClifxQtX+JeonxSSye/T2Rz2PQS5ZSmJmPno0Xpj6hNDHYz9xn1amVV3J3712ERj3F9xNrfjj/PXU2j8LM16moTK6rAM6g+yKQwq+PoS+TkU5esfbNcaUFH+emGf30LkdWj+HI2vcnqZ079xF9+95g0boAMik+OUq5F4bh1ZuU08vFyUn/re2zI5LYZzeGS52XFTt+cfc89i7px92TJeP93kRuoMvna1IZM+auZkFBAAoZFlaVccg1wQEjKoxtiWOHqkAO8DXg/4GrfxfbgTWEbR9FwNzfptZaiobYswEcrzqdxBsh//RQ/nNIZTLajVj+8q9feay8/LvtiO//MmGX/Px84uLiir2SkpIAUCqVDBgwgC5dutCli0ZVtGvXrnTt2pWBAweiLJaPsyRhYWH4+fkVe2VnZ3PhwgXatm3LZ599RteuXYvOm5KSUnTeEydOFIvvCw0NZcGCBdy7d4+IiAiuX79epLLYps37Y4RLAalUSu/eJX8P36RXr/5/mbDLmxgZGb1VsAdg7ty5zJgxgwULFuDh4UGrVq04duwYzs5/XI3+fWzZsgVra2saNGhA586dGTZsGIaGhkUuxBKJhOPHj9OgQQMGDRqEm5sbPXv25MWLF1hbW3+g91L+dv7BVBJF/Fvy+P1T/HA5WNjNPCDuRyYLIYTIyskUHh2NRd3+5YvqVF96UvD5DnH7xbtzo124oRKbp0SIrd8liBt1qorgaRNFbOIWUa+/SvSaVCDONm0sAg/4io1zF4tfNv4gWoy6Ldp+8lTsd54nFg4MEMevaPr5/mKQKD9sk1DdchGiQEv8tGSnqNtPiMHjQ8WFJ8uFiKkkhPhCqFRCBD4Touf21YLLm8SUX1YKkZgtlm7W5Bg8eilbiH27hPjhushPyhDblywXB88NF58vPCOOdftSHD5+Ugy+c05E5GUJZaFSrJ7xjVgwc7DYWAuxaKS26H3LVKy6uUF8ckNP3EjaL/JiY8V5ayNxpkELMXa+EPufbhPznnQSZW/8LLi8SUTnZYsNp7YKx9Y6onrNemJfm6+F/9jlIiMkQgSH7xNfrkTEJVV5fcNScoTY4i+EX5xQqYQYNlsz7nuPX5bnFQjxNFqImGSREZckYh88Ea0mjRfrT14UY+cL0annL6JN3a7i7oA+4rydqQh7+EBIOzYXxt0GikpfB4tDQaGiUK1663tVUCjEyXqzxVZ6iaR7mtyDhdl5Yq/VCLHP+wsxWhwQZ0RIUX1VXoHo3+W80NffI1LTNX0ev6wZ76qdQkRFxIgjo1eJvTajRNK9UJF486koyHidp/Bsi/lih05/kRmeINQqlRjSoLawb4bYe3pzsXGdOX5BtKnbVaxcvFaolKpifcRfeSK20ktc7rlC5OXniZT0189iXFLMW68z8PlD4dxaS0z6bthbSg8IIRDqvC/EpvTLYoj4RRwXQSK/IP8tdYuTn5slEiKDih1L8X8hLn3yvUh+GC7O7/xGnHt6QpwUwUJVWPjW/Hh37yaLzn0uiu6he8UicbFYmapQKfxm7RXRJ/1Efnq2SA8ueX25CeniQLlx4ubIn4sdj3p6T+xZ3Fckx4aWaPMmWVmFQk9vtzCTbxYXOiwRQqkST0PSBewQvr7Hf1V7j9As4Hd7b5/vJlVkhs0SJ2qOFS/2/3U5qf4XCZh/UGyllwhadfqfHsq/kn8qj9+ifo5/eR4/NMsxxV4VKlQQQggxZ84cYWNjI5KTk4u1S05OFjY2NmLOnDnv7Ptt/QLiypUr7zxvw4YNhRBCnD17Vjg4OBTrLzo6WrRu3VpYWVkJhUIhHBwcRO/evYvlbyvlw7wtj98XX/y1efz+PxIZGVmUL7CU/39IhPjnpeAyMjIwNjYmPT39nSsd/5PcioanKXyjp2TmlSBOj2xMswqa3aHktEQUCi2MXsaTPIhK4UFUaokYwIQjh9AtUxZDH1+UcdnIdwdSaKaPop8L8Cnq8x2JD7TmVvlylPfqiXFuBCe2DiTFMhSzJBc8b5kSfuoyZarWp8HdUQBEpmazeu9d5u65SkYjZ/aaeHDkSBQ9PnGg31BtYDQwld0nW7FyB9SsHcBhl8tsiqtA3Uwj/BtVZc8FGWN7gY1/OERnUtDehd0/bUBf+yRx9+4zem0ErPcDEzX0C0aIdhzfcZrYu1dwXLeaAg9rjnaaS2DwMBo4RNNddg33Txtzp341YmwbsrLMFuZ9Bg2rwY2MBOIKcmmlZ8Xes5uYvnI0a2Q9SD8lRaqjxPczIzwX7gdJb2Ac4AJ8AcFucLIquJhwUN+NJZs0+Q7XfKWJRZSqVRCTDAa6DBk5lZTkVCYum4qViTeDZ4CLdRKNHY9S//ZJXpw+TrVr9/j0yH6e3jXGPLMtM6vcp/psjTuOEIILpy7j6lGexGx75q+LYvPcWoikJhg4vZaRVubkI5FJkWkXD7YGePAwEplMgXclm5d9gn8ICHGNSuWdCZhxmcBFR/Cd3x2/abtx7lePultGA6DKL6QwMxcdC83n69ytY6zYMY+F437Ew9kLgMdLj6FUKkmvbolPVS+MjIurf2aGJnDE/QvkhjocHBbBrYDLXNoQxLlbx/hy5Ri+nfAzvVoPKTHurJxMFDo6REszceFNYYQoyF0C0ZNJlebz6ZMf8M6tzsOAcJzMI5n32aqisZ9p/A0GLlbU2zYGgB3zuvH4+n5GLruFg5smt5L/3P34z/wF33ndqTS9E3M4SyTpzIzxQTsuC7NKHii0irtS51DIz9zGXWmB/qCzKLMLaLBvXDFF1LPN5xN/PpC2AYvIiUjGrJpz0X38NUKIYm0/RHBAMhcbzMK+jAHtHi4kP1/FqFF3qF7dnFGj3lTLyQWWA+2Bim/vrJS/BKFWk+L3ArPKTh/13v5X+Lt+v9UqFeGBV8hMicXQzBanivX/sRQO/ySfffYZSqWS1atX/9ND+Z9ErVYTEhJEWloaJiYmuLm5/y07ff9mzp8/T1ZWFl5eXsTGxjJ58mSio6MJCQkpIQxTyr+f0hi/f5LEHEjJZXqLioxo4YGlwet4IHOTV7F8qYAplR3M8LYz4/5jqOQKWgrICQvlYY9O6Lm6YX0kmHDyifZRc1oWiUt4FHOd1iNNroetxJhO9Vcw/nk8+vIQnjabi1GwKc6nelHr8EQsJrvi9GUNAE6kRHEmLZZ5Q+oiH96QKePy6CcJof96f3Qt6kOGDtzeCLmFuLsK3J3z6dc4gk/1jDCSHwVREe+K1TEsn0n/pzcY4+NOxyYP0KI/cT2X8vVDOTV2WWJ5UUI1aQYmFY9gynIkEhva9pnE0+wu3M2JwKjiJwS/8EaluEzNx8GEnLmOPOkSeVGRNFjRn4oumvsAUNvIiujIWPr0GEKtajJOrN5NWa1VpN4OJ3N3LSqMrANsAJUVyMoDycAKhJMViaGrMfW1wMsFGsoDaGady+1H2sxd60bLsvGM7ZuIodlAqtVqQmKCFzU9zyOXy9g8ryI62nLil9ygXEoqzpt30V8ZR2QTHzY2q87M2Vn4XU3GMzIZfUdzAv2fsPSblXhVrkivsXPQ081BRzcJmVNhsUdCrqcxTLJzs9h54mfa1O+GnaUDmdkZ9J3jg66hBTndJrPcvjLNq1QlIjaIRRvqMqDDLobO74HHF21Q5RYSezoAu5avY0ziKMDU9LUKZtOabSnjr0vc9IsUrnNFrqeF37RdCAG9cjchlcuA74BTaNwL72DgbI959XJIteSUc9QlPjkafV0DbC0csDKzwdr87S6gUSmGHFQ85Jm1Jrl6DTSCBAlXsng0z4Z636kITXvOxe+/I6fKeOrVWkxG/FdF7VV5haQHRqF8I2df2Yr1yEiKwshcI6aSRDY3v7DG120Q7h00ge/t0+y5HfqUhPTHdF3YhY6Ne7FiyhZQq+HlhFEPBZ+qa3PIbTyJEcnI9bUJCw+hgELcnSuhUqkIq5SFZX5Z0vwjuNrrB8r2qEX9XZ+VuM5H8w/i//UBml/8CstariXK30YFL3NcIr5FqtCMR1tbxoYNtd5SUxdNnN4w4Czw9jyMd+4kM2WKHwsW+FCz5vtjAwsL1Tx9momn57vFStSFSrJfJGFY/t1JrP/XkUilmFd57UoVSTqHeUwHPHGkVOjl70Iqk+Hi3eifHsY/TqVKlYryuJXy5yOVSnF39/xwxf8QhYWFTJ8+ndDQUAwNDalTpw7bt28vNfr+n1Jq+P2TtCoHeUqkBlq8XbLlMNAJmA9M5fAFWLoZ+neA4d0EumWCcJn2OelGVRgyE6Rm2mT0O0V0vjlxGa7ASehkCgWnQH8Ik9z6klQQiaVTGYK2HsWslQP3H17nifsjbEx90QEWRwVyJT4C3VFzaDrcgbVLNiI7Pg9exJA58RsMoyygVxOQSPHxTufnsZ3A8hLbj44mKc2ZUb3N2ZUQSmKq4ExaLMRmE/A4gc+HhRFVmESOiT7S1vNxvPeE0/m30T0vp3+VtghxjLUB3xJVvQLDzyzhQaYHyv36tK68no5SHzJbp5AmJCQam/N47mFqbveBN+6alpYCfZkUowt6GDnvZ6Xsc74cpk1k/bbEpPrjEH8TiVX1l7XNgYuE743i2oq9uBWkUmONC977vid6Yy7Xpluif9cQ5ZbzXP42CjPPSIaf80Fm1ApoB7TFxeEoK3Zswe9RFLPN9Cjj7sG9pIe8OJfDvKeh2D59jm7AWZ5tMsNnRidc3cvRqUsrKlfxwqcCbJ7nhkQSD/w60W9P8gue8cvpvuzYuoLIyFDmfL4CLYU2Olq6xMU8IzEjjANxBbjZVGXzYVeszWOxMtfsNOlYLAMSaH5+Ja9CeJ/lZuB57xCNjW045dW86EzB68+RdiuM++bPcfczwWt2VyxruyKVy4g6dgKHtl+gVmohlcvZFv+Eg8nP2HT5S/TUEtRN5tHcZiimRua0qNOBFnU6cK7lAo4lXMbnyEhCokJpUqsxAE8ioUBqSRmzVBwUryfKiTefEnPKn2cnvagyvg17l1zEzqYmEqmMMhYLoFAJCjlaxnp0Cv0e6ctd0JzoFFz0m1Lnu8+L+gogjtt6cRj1KI/zjXAyn8YRl3+IyAPf4djzK8ra6VPe8RbEJkNuAZSxAsXrrz+FsR5yL7Bb1ooukxuQmZ3Ow18SOX5lH3MeLWDAJ2No2bgijl2q49Tz7ZMus2qH+CRhJ5lhbSiR2+JX5CVlIH15bQpD3RLlL365xaNvDlD1+/5v5Cq8CwQB2bzL8Lt4MYELFzSvDxl+Eyc+YMWKEA4dqv/OvIb3Jm4neMUpGh+bhH2byu/t77+CP7E8IIaymJYafqX87ZQmwy7l76Zly5YlVEZL+f9LqeH3TyKXgsH7kquaINQmhD+IwtI2GYfICKq4+1KtogTYi0TWA4lPDaZtmYlTBWjaMIj+lWeTo6qHSpwHFKAAFFWBNtjpGmGnuxcA23XjADh+9AQrXXXISnnBGAtz1pSvxaXn+Zy7FYVt+4o0khayKN6SLrV+5oVpDxqpTJH3OgGKO3BjCWR2RzTyQPjpYRHuz3qXSoxWXaVMoDv1k2tjkKjFyZTOGOxsyLIhL5hRph0WBgWor+SipXIn3hpgBHcO1OFA/kqiHQwZWceatjJtLpzrTn7yLYTLcgwrvCDDdiBJu1LJv/SClHthGJS1JOnOcwzKWmBpbcHWqV+hXryPfUllqddqKzCQFRuVLAxaCQ4pMG8LvEwkXlBYk/uSYCxbeJLa2YOr6fFUXzmAgtRsarZ2YPaCGoQpdKljYkTGY3vUo2sg29aYqJufoufQCP/YU8QkdCPE/DMudEugtZGafYpKDDr8nK3Xn/HDivNEnqnGE1kzfABthRbDOrR5qUdwGInEDiiudDVrx2Zy8zK5f/0SXSrGc75RHTLtNR9RbS1tBnX6lGVb5zD1oZoWqTLiqxQQl6RFfuFxPl80kqY122GgtxrNLvECQON2ZSrXwkvfBMeAZJ6cO4HHuNZcvneGqVrLMatmQPmA7zC/dhQDN3u8pncCoDDDmKu96+DSvwt2rWqwPzmcA8lqZuf5oJejJCkwGoOEjGLjz4lOJT85i6Xja6PIjodZ52hSsxHNfaFKph1lFcV3BD0mtMWqvjsWNcohkUio7aPZqUshh025D2mcZE5ZszKgr4O2+Wu30+sD1xJ39hHpHZ+itM3ls9UB1Jc4o48WlbDm7KBpJOSk4XC4Gw0UWtTt8Clt+28nNyeR4DvPcbF3QCF97bInkUpp+2ABw2Z34cS8NnRs3AtdbT30dPSp7dOI1vW60LZBN3StjWm4b/w7P616LcrzjVhEVR9DOryzFiiz8zjsOgFtC0M6Pl321joZN0IwdbEidNPlNwy/i2hEXizJJB/DtygAjx9fgSpVTGnQwKpE2a+pUcMcX18Typc3fGcdM9+yGHvao1/2/WrC/yWa44oNhnjx390FLaWUUkop5f8npYbfv5oGbJ80jBPfLadzFRl59xOZcmwS9h6VOXWtFga67YjcY4FzXhhd25nRvHZ5oB0SGqMqJn2tRLNb8FKdMDUPrkdCjWzqtZqLQ0g1DsXZMibIAPe25SkQBuT2WcOu27W5FjGHrBxtWnduSEQfMxr9ZMRBSQMsDK+TIMZjmlMXRVIPyh1pSkZgAGY7t9H02zkMru3HuR+/JlfIaF8/m2F9LiGVTMJCMRMqz0H6NIUqsfbkej6DE3uwS7SksmwAWddzuWSdiOPjACTSEdyIWE+QXgQeehkExiZxzKorPi2CsW/jS86yUzyavY+Cyo5UP/Y5I2zj8PmpPV+WqQB4A970aJKPOOQImY48ySmksf9uxlqUp01iGp9vGsbAjmNZbRhOhv9TYhq14l63FTiJukw50QtdoyiyD/bl0eKThOZJsX4QjsOtvjw8dY2+Vyfg7jQEZ5+fifayYvDAKuTK4lm18gQhQXY8SjTnskkLGr7a0JNKyJfJyIuPwrhcR8AZCC16h5ZumcG8/TdQyLRoaqOD1NgBDPIwdH2dgH5sz6mM6TGFcy0WEHb2Es361GfzXCf2nY0mL28U+go9iDwHDnn4PzXC0QZMjcBMrkP7Sy0p98Vg/LTleIxrjUKuRa6hivaDh5Me3JR6LmoyMnPo2vkyi76uSLmetTFqdJCp47+iatBWdn8Gd7NOk1iQznbLe6Qtb8/O9k3IT85Erq+NTEeLNvfmIVRqQr+JJub5fTyc3chPziTml9s496lLRGwsfae3okfLwYzpOQWpTPpWl0h/4riqG4OWsaCsvKRaWbmBDdC2NOSelh+8FLmVIy1yIa28sCdbPF5ws0IO49xHY6BjBdzhxpVrLJ37LY2a12PSrHEl+m3p3pS89HRmj1iKpbktCIGjgTXrvtpT5Br6PrL5lFjJWaIwfW89qZYcU18ndGyMuUwYuRTS8k3lUpUar1FNKczJRzi9qYhmCBhynmfs4CHtXujRqWxrAGbM8MfPL5U9e+q+M/XEr+nTx4k+fZzeW6fcoEaUG9ToN/X3X0ELGVUpTeBeSimllFLK/z9KDb9/OTW6diIp/AVuzZuTdv455tVcAIhPtmDevsNMbvaCtQOdILsQVIYI6WFc7/5CoThAdM1PkEukaHIDBgOayeuTS7chOBkPlziMLG+wqpw7svv2iLR0Nu1dj1JEYmBjQCO3atwP0mHFNDDJkRA8tgLdGpcnN98HIa4Sv2kGVrW+J+7WDe5vrUatbzwJ+GUv/R5foPfAR/Ty6EGOqgBpc0eEsg4rRhnh7B1M+1FAbQeIziTh2UPsc6wIVj+gscin1epjmEXmIu8/Erm2BJXQJ/dRMAVGZUgzyOGZTjUKk6Rk3gnFZPNNapez50fb5wwd7Eb02EU8yUnnyzIaow+gmjQGCgQ0a0C2SkmyMp+4zAyqWHowsvNMGlbog2HiC1KdDJDeiCT5bigyEyOafH4LuSSIuPyvSLjhjsX09uiXMedJ3h1ikGOhKkfr6k5s7wgX1dCy0IPZ3z/DukwdqlWJIDXWlX7OCVSoZwTokBuXxoXuK0m5G0rrO19gXs1Lo86Slg162lRxX8jTVUpeJHSnUWXZy/fKE2VeAXeHrcOiVnnKD2mMRCKhxupBJN8JxaqBOwO7jMSo8Aam0iAyHXpjFC4l2suZ0ZugljcsmQgqFSQduEparT5Mm6fJNVfbpyHPj+UCoIzNRLqrPEnZ/hj47+J2/UKeezvg11tNQnwa8bGuKKS1qG1khX9GArmRYJiuRUHACw63XIxlbVeanfsSmbaCq4TTY/4myr40fvb0mknBrmckRkej19ud8JhnPIt48t5nvi5lkSPFx8gWUCCE4HT9OSCR0PLKLJz71MO5Tz0qqYeQLslDJRHIAMlLeXfHTtVpn23Jg00XSDy4i/wNo4hI1uLBo3L0GtSThs3e7qrZrVFvutXtDmYvXSSz8iA+FQx1wfrdxtyLPTd5OHMvbsu7YhZ9labNh4HjO6sjVchpfkETwziWQ+SjpCHO6PAyXkImBRtTFFIp6JXc1ZOmpiORpOO39ydqdXXAxtmLI0eiCQxMJyWlAHv74l/r+fkqmjW7gLOzPlu2lMYGvQs1gmASKY85Cv57oiGllFJKKaX871Nq+P3LcatTG7c6LydrIzX/+IcEExZ9htmja9GkZjWU8RnIdgdBOVOyGrniGJlDdmwqWUYx5AffRNvegcwH94nduRWvrXu4+uAGAE4OEyjM9iFe7UDFjpY8vniNpKQ0DPUL6D+uIWsPKMjJ04hARFy8QdmHlZFrh2JusYoHR5uRfPgcytAv2bIohpjUCBJrRxBXbjQrpg8BmRWF1rcI+9EPebwNNhNHo3UrB+mjANTVo5F6WYPjz5QR14i7MAUvIx9UN56jO7AVV3Lv07aqD0sqFJDT7nt0jRQUqM2xk99kRo862OXroFXVjWO16pBTGEyLsYfZuzyflTYVqVbG4+WdO0Je/gAepvempsIWDLSpZmhBUq2eGBZkc/iMFJnxHO5lQfd4gbOTCVRV0O6rgRzq84SAQgcqa/2ATaMGZA1Uk/k0DmVZZ0YFNcXCoDJlxTVa1exKXSMYs0xNqrwHefkF5GbGki07hmlYXUzDkiBFGxyNuDDlZ1Luh+HQsSpGFUYDupCThzoulRPdV1JhrietvvFHLjvMo/2aeL3Ek37c/HwLz90kWB+PofwQTcyckastRq62ADi7OqGMC0WSF8P2xEf0sqzM/XvL8LQdSuMamkWCrJAoal3+GQMXa6w9X8Z+MqHoGZPbGsKIRKy0RzPr/mT8rqaBULFy31waVmvNl/MO8+qrwtvIiu1GXaFnOvmxqRi4WGJUQTOWcFLZxD3KYsIMmmqe1TLRpNk/xdy3PXXda3B7e+QbwkVvR4EMn+c66FiqNd6qQpAdkYxEAiI9C4meDijkrJHeIowUdFHgghnjqFfUh7deWZJ/SST6mB+pD1/w1do09uyJYM+eujiWfcdujc2vjDtdLTDQAUNNPF0hKvYSQAUsi+34pD2KJCM4lqsnj7Lt0TJkthY4O2bRA28M0SY/X1UiQfwrxlKbPJRFRl8g8VwilLoGTvzMHdpQgdZUKNamkWlVFAevEBiXw8ox3gz/9iqnTzcmJaWA8PBstmwJY9IkD+RyTYxnXp4Kf/80UlML3nvf/+tcJZwt3KcVbnTD658eTimllFJKKaX86ZQafv8S8pUqDj+KppW7LYY671dKMjUwxEyii21wFtQEv1t3KK+UcuqpNSt3g290DLWuBJJ7uBaP+nyCTlkn4iu6Y3z7Jt98E0HdDl2pWE7w5RoFJwutedLgAn0yZIgOQ2k5tQnVW1YkbNJP9OuYxIjVbQn4fD3PN1zCEBO0HVpR9mY31ibEoDu8FUuCJ5N12o5CJQRjQmpGVYJWDEBq1RxbD2P8Lt/CPEuC+yJjzCp1AqMKSK9FcyBXSu3a27GSPeBJ5c9p4lcG1HBdO5xIiRlDVyxhdt+BlDk+GrRkUJhNTXk15kcGcevBEwaODGYFnSlnYE9tw6Nc2XSJN4VShIhARzuVfQGrqHKtAIVcc0+N5Vogr86mC0fRMzCid0ewqeEItprJvWJ2C3yUK/DVOs1XWx2pUkZCzpCfkOlq0T6xJmPahVDfVRtT2594suIk4uk5vorMIjfgGYbx87mjzKdHuh8XLIKxL5RhkKSPVmYBTl1rou9oTt3to5HrvZSG1tVGGCnQsQvm0ReNaDPCC4VCgkQiIfLwT1zqeJGCKuY8OtKI+GwFvd54BlR5BQQvP8mk1t0waDCVbw/vZfSmdTxrnYHuxYU8/qoDz2xssf7qCBY+ZTg8xJJokUor7a3oUKPEM+UXUo+ffjrBzBm1aBegRKJQMO24Pg2rtUAuf8vXhIUx2hbGtH/07asbjkWUknopuvhUcIWXArXTZ68geFAgld0157Sx+HDy97THUeTefoZOFSfwdETyf+yddXgU1/7/XyvZ3bi7e0KEJCRYBIfgWqy4U7QttEApUqSUoqVAKaVFihR3d4ImgSAhStzdbe33R7i0XKhc6e393W9ez5On3TlnzpxZZs7OZz7yFovoE78GauoRFFaAVj1YGROINTpISKP0TQ+NQEDr7yZR+iQDiw5ezNUtxsFBm/Bwy989vgo1ZdRiJNYCi5/lJzIp5yovSKfsNcPPd8lAHEeGILTWQXzJmopOPtyvSaCFphXFV0SEh19nxQpfPv74Za5eUQXUN4ClER7Cxly8WPLRR0YkWTwkB2v0qUdBLa9Xff0bwf1mI9aQgQAMLRzRM5ZhZiYjNPQyERGFhISYEhpqRvyDM1SXFZCcPAyZrMmL9Vs4Y4wHpnjRJDjcRBNNNNHE/yZNht+fRGnETbQ9vZAYG/9+Z2BrRBLr9+3jUQt7Vk7+WQstLymZmvJynAJbvNpma27BDGkgiEVAAuae5twqeoyuQyBacWoeTumEwaYOjPJsRf3nH2I64AwfZ7twz+UaspxgzEoh3EWJW+ZdMvWs0JKZEtBgT5JPN2IfX6Pw+nl8H6tpKCzA/1otmpHpKPUkvHjPk32CAIrUWWxsVsJPPZ5zd/dDcmuMyCpxZOkCAbq1tVzxeIZKL4HSFTPwau9FoYUbPaZq0dehBYVKDY7UWrLKyhjL7K/JKblAZ/0iOgR/QHZDMDNPeGFhKuHMtVP4OXnTO7AnjtYglRrAzsd8UNnANxtu0ZBSyZTWCkLn+mPjcv6N71MgmMbpm3ICPPVeGn1KYDngBzTn+2WTUCiPYWIg45cVErWF4Cf5gTtxR1hx6DCDQ26xau80xNpSnr6/gEFb1yCob83VXuGk30riaPcVaFqLGJK2nCePyrlw7g6CTw25pFmD9fJz6FsaE37tE5qZB4JAAAwFzgHPQGiL0PIbOl04yYMZrVj74VyEokGAHRqyMwg1vBA/KYHv7qNwNKQ2uDM9pgXiZOPG4uYLqL+UjI7MAnRyMdKopbO3J2O690PiasfVSmMcTp4mftVxABK71BGnUUlh3hZs9ZsD9wFDeJlb9u23yWzdWoRJ80RyJ6fROk+XH898g62FA94u/iRlwNqdMLYftHqpElGTW4rMTB+hSAjlNejUyxmj7UfO6QwYZAeAlqY2/h4tKUx6zPGt0zHO8KbFkNHYv9MoWZB1Kpqcc48J+HI4Yu1Ga1Fmpke1Wo1KpXr5nb2UuZBpoKiqpfBhOhYWhnQVutH1l7lxf4emhQGaFgYABAYaExj49nvxaRK42YP0ZZ2lwzzlIknMJhjvVwU86nHCiCm0wuZlJcfoD38kZddNgs9PR6FdjZ1WG8b0nUZiyn0KZ48nvcsYjJovQ0ND+LrRVVsP9XJQqkEIN0llNw/RR0Z7nBhWYEnt8e2sfGcOJoa/nkvWqucUWvWc8tq2tWv9iYgopG3bxnDVI+vGUFNRxPx9PdHR/f2CL/+XsUaPOYT91dNoookmmmiiiT+NJsPvT6Dk1g2iu7XHtFdf/A4e/0P7hHuYU5C9EmGuENXEsa8EQ1d2CqcsN4/Neeno/s2IFAlhtC8I7qNSteXBhR/xVPjSrKMuPToCtEReXk7RhXPYz+iBQLiWwQVuxOS057PpEOwHafvvoPP1Vga1lfBYNJ6jaV54DTxBVtwmWvcuxNfCF0MDD3hcjU3zQo5PskCtUuNQrscqU326uX6PJi1pExTAndhEEtO8EKjB0t0IizlB1NQ/plZ0Ev/vpnLovjGV50QUlQeQLdKiQGLAehsJLuI2HBGLeVLzNQJJNPnqbDy2V/PuR18SPnYm50+HcfAILJsOHVoCYiFaCjWeEztgXqxAzy6HpC2RmAV/gqa5Pgp5PWKNn3OierWe3vhdkQvEAUtoFL9+hsHLQobZcfEcW7aSvgs+xtbbCygB9HBJs+Wnwe0JCllF/tPttOqVTu7lW5Q8MqE0WkH2tecgECE0NUGiK2Zg9mYiZ+7C79I9JvQeTeqiSIwndsR/VleoqgKJAIx0aXSFafC3fEuBMJSqdFd0XXojFJkDFpRXmnHimi/aCgUCIynqk8+w7tgFRWsFRWU52JdIiEjLxrK1Hw3GEgQ22ixZOA6xSIy3/VdUKQQs6LqamqoylFpSbPoFMi0+nvqMckz0HYFCoC1gR6M2HCxa5E3z5oa499HggDqD5IdSSss7kpqdBEBCKjxLhmPXq2nlq03+jTgudViO+/SuBH01GqRi8ssFTPrOlDbexsz7u+s7/d4V0uIjqCmoxHCP8yvDL37jefKuxGI/pA3m7RrDdCvissm/k8ix0+fRXN+V8XaNYaMIhcRsuED8xvP4LBqAQSd3IsMgAGuc1IZQ19BovQn/uMj25XuwZAsM6gqzRzRus0AXM2RY8wPQGcgA3gG2EcjEV/s2lNegqK7n1KbpZBTeYtY3sZjZNcNIYoimjgFaeiaEhZlRWzv49YNaGYNKRZpGBWeIR/ZyGdZEzAmeE3j4BJmn92Bi6YJJ72l/+FygsVJny5Y/G7j9Z26nsjQPHYO3G32K6rpXBvffo1Iq/08KZL8NhUL1KnS2iSaaaOKvoH379vj5+bFhw4a/dB47d+5k9uzZlJWVAbBkyRKOHz9OTEzMXzqvJv4YTb9kfwI6Hs0w6dEb8/6D/vA+HhaGdB/7Od3GrHxl9AF0mDSesLGj0DYwgIXHiOh+lLzEqsaHW4ENz59MYv2D4XwVb49aDRm5jfvN2L+J8LibLFxiTVL6PUrzV+Cs+ILqRx8glYCFozUOgR40uBoi0Y7GpuEJ41ttod3abnwTEkpWYR6ZO7ejFirI7niFZhkK6h5Gs862loEe/dARxQGXaIi9wCCLfLbMv4A4IoKsM4/osHgy+u5t0Fe5oBuRT2vrhwyx3UALj6P0cfuB+bNVhDTUY1HTwCDTmWxz3c3FuDAWTy9GpFaRufIsHRRxhJinoSlqQFT3HLhPoVE6m/aOpk+bOjaMtiDv6hHyrmRR9SKfq/s+Y+kAHVKf3mz8AhoUVGUUsjevmlJVMI16iN+jUn5HXeHPMgRRx05wd/9BIo8eB7IBWzIKwhmzcA7Vx9ZhbyPAwjSEtZPn8txESURDDheT1iPSlGLoa8fR7TJ2r2t8cPd4vzutd0ykwcmMwuvPObv5EicOnQe1AG5mQl7Vy3lUAddezqATOvaJeM76BAgBctl0IJtlUXvIXGtDdGYfyr3ceRyRiEKhy7LJn9DzhSuq3Yc4V2CDZIQfGlYGfD5zK5/P+obL907z6MZtes0ai5a+FUKxCKt3WvBYK5O8UHGjB0xtDIznVdIoYGGhyeTJLgwLeMhBYxXPz4dhYrCDYeGfANAzDMrE3/Dl5cHsu5jG86Rj4K5C1+VlWJxKjcrKlGq5mCrV6wVJlPVycnM0CNGfjZlZJyrW9mGe8gIvKKbVtgmEHJiBWZjHq/5PFh4kcf89DI5mInjvzGtjWff0x7KrD/Ebz3Ns427Ok8iP6kecrHoI2cVQ/LrERDHV7CaaLHXZG/ecSqWiImYp4RkzcL5+EAAFKnyxYCW6GLIA+JBGQ10MNLoEC6iiiGpab5/IO8Xf0nzQELxD3sHArLFwjomNG+NWXObO8Q2c2zH3jeMiEoKGmBhyeEQOlujyCR2YShu64UbfIZ/Rc/JG/DuPfnPff5Bmbfu98gqWlBeRU5j1qq3wbiI/GUzk4cf739ivqqyAz981Z8/iPlxasZi1410oevkS4B8lgjSekffPncB/AVlZNZiYHGX48Dt/9VSa+A8yZswYBALBG3/h4eEA5OTkYGhoyFdfffXafvfv30dDQ4OLFy/+6tgCgYDjx4//7hySk5PR1dXFwMDgXzmVJv6L+dt1NmXKlDfapk2bhkAgYMyYMQAcPXqUZcuW/Ydn+PvMmTOHK1eu/NXTaOIP0uTx+xOQmJrif/jkP7xf6KA3HxL7L5z/6v8fVxowz7QDQT8pWP8pgA0ePt8wwTkHU9UjvlhvzumYQOZPSGH1+M9Zkt6Xi2ttqF2txbKVS+nVZQd56S6gqEEroQ8hI8IpNk5HUJFPr9KN1Ghmc7xAk4tVQoalxFGvKCBP/IjmKb14x6IZ1cpi5n29i2/9biHSrkAUP4QWxR5E69pgkhPL00kRqOQaWMzrSolhf/x1z/C49iR6zo64j/mJy4+GEVDnikpDD3ZGg0CA+fgeqPMqaPdwJD/0ULPuHV8yz6Rg7xWGl0qHi8lySip2ABvRcruHllgb88Jy/O0DaLOzI97JYoytrdBNN0Qk1kAt+Lky4m6ZDusVGUTfMmNY2UC8gjrxeNF5XvywifZX52HTzodus6ajZ2ONdVgwoMWB+brs3xnLZb9mJEs9GFWyCQcTIU62h7h26SrTZk4jub0Ig69VmHhbNToUX3K5w3Lk5TW0jlzO5/rNyWp9GeH1HxjQqh3C9CrwqENlUsv9Kc3RdYnH++/dYi8Z2WsKAgSMH/g+2phzvVNrAqYLyX+UQ2+H4VyY7ETtPTHDZzm92mdEr8mcuAafXuhNr/B+BD6HNmtHca7vF8TOP8L0bxehZ21CdXwWDS/yMWy3EXReFw4vK2vAxERKkCKToJwTGI3qgZmxBJQqBDX1+LtLya+1wVIQz+Wjn2DfMxiPmeEoquoR55VgJBRxarM5Ug2oSMzl7thtuM8OJ+bj/VTXVLEr7C7zluzjqydKTFyrKKAaZ2c7dJ1fz6nS0JYhlAipME5Dy6jstTbLLj5YdvEhbsM5LJ6lURlwlNq2ptxf14WOmpbo/F0VzGiyefDiGcIWX+P2bhgtN4+ligYanuWhNpQTfXIJEnSp/qkavhvM90QSRTafEoYty4F2NBrkjYVRFKhYyhVECNgo6I1YS0rr3tPw9hlMxdM8TIKcf3F0NWqV6o1/X7VKhapBQTexC/oHU2nR0RS9l7mE7+ADxmDZd+bbL45fkBB5juykKNoPWfCHPHO9Z7SisDSf6AM56GrrIZJJ0NCRItF/U0C+8SFXSNnTLATHdJAH1lNTWQy4kkoJR3jGALxxwujNA/2CMmrZSTT6yFhLz9+d49u4N/k7Mo9F0ePhCrRt3gzXzTwRRWVyPs0+/OfG/z0EAhCJBIjFf9yT3MS/H6VSxa1bheTm1mJpqUloqCki0Z/77jo8PJwffvjhtW1SaeMaY2VlxaZNm5g8eTLdu3fH1dWV2tpaRo8ezYQJE+jateu/dGy5XM6wYcMIDQ3lzp2mlw7/KdRKJaW3b1Gfl4vUwhLD4FAEf3Lkg62tLQcOHGD9+vVoajaux3V1dezbtw87O7tX/YyMfnu9/S3UajVKpfLtOfv/Ijo6Oujo6Px+xyb+K2jy+P1/hMvn7ekRrKJPNzFQD4C4roExBlmEakF5wh5sLcDGvBRdcR2tqosJTV+C85FPkWoJEIoFWLskg3gbtLwBzc3oPXwgVVVDiQnrwo3nvRhZfIEi79n07H0C1z6dEJWpOJTbmYPCUuTR2Xyz8hwl2aeIPFPOyXuXeaAN/Z2z6F8rxqnlEUpa1LEvqycbH5mxJSaE4Q5GXCy9SheX+9jLtNl0qQdzrqaDQkVlLUSuDSR35hREm3LY0ftzjC0XkC5z54TvZtTPExh6fialY6uor/gA7SILJl28T+6gWcxx8EWivwTjZh/Dwef4FQTQr0sQq3cLScuGE0c/p9mezbTP302K5AHxDaWc8lhK1OUrlEqq+O7i1wDItLX5rjADz1mTeWfFKi4dM0a3qJoASxt2fLIUkb456Gii72jDu/Nnsiwvkjn6cTy/PBn/VUM53XweT5YdBcC0rRvGLZ2xcTQjriyc9R88Z8fMSoRtbGC0L+eqU3CYcIJjp21I3p7z6t/1/hOY96WCuuQCKKvG3tKJTyZ+gWapgK/2qPl8tyWJuebIn0RQkJ5GVUYSo/b0xc/r5wU8++wjMlfsxcR9KmmtDTgkS0Svkzt277SiIj6HioNP0HAy5mLfNZztt57avPI3rq89e9J49qyckko1ukcO0sX25RxLKiC/lD1Th5C9bT2CbbH4eYym88jGN49Hz+Sy41Au3x/OQ1MKQiHEn75N4Z1EEs4kUGLXDCM7Sy78FEuwnx9eOR60uNyVNti9dvyb0ZcIGGJJ1ft29I9aitfnPei8cOlb7wXP2d0xCXDE8ZEa2wMFTJYEE2+t4qx2OupfaFi2w4n+qmaIFKBqUJBOKV/s3sxZn3lkffuIofN+YuD7u+h2ewkAVTQgADZwj92Ewy8qhWYmviD1USz+9eY455hja3OCiRMfAHC1+xdcaL2IrNxsdhBJjaM1nx6uIGDiEnYSTT6VP5/noI0cNp9KxrYbZL+7j2fzDv3aLf+bXNw1nys/LiI//dkf6h/s34nWvu3QlDXmtBr5OzA44yu83+v8Rl9tfVPm7c2jz7AtmHdoxpjt57HzaAzPTaCQ+Jd/v4cBmryLHyPx/wfO7HUUlXUoaxtQy5VvbY+avYeHc/ZSlfb78/lnsLbWoqhoQJMUxl/I0aOZODicpEOHqwwffpcOHa7i4HCSo0cz/9TjSqVSLCwsXvszNPy5+u+IESPo1q0bY8aMQaVSMX/+fORyOV9++eW/fOyFCxfi4eHB4MGDf79zE/8W8o8f5ZaHA9HhHXg2ZjjR4R245eFA/vGjf+pxAwICsLW15ejRn49z9OhR7Ozs8Pf/ee1s3749s2fPfvW5vr6ejz/+GFtbW6RSKS4uLuzYsQOA69evIxAIOHfuHC1atEAqlRIREUF9fT0zZ87EzMwMmUxGSEgIkZGRr83n5MmTuLq6IpPJ6NChA7t27UIgELwK7fx7lixZgp+f36vPkZGRdOnSBRMTE/T19WnXrh0PHz7817+oJv4tNHn8/hJqgQS2HPBDLIZJfzAiVFtLwILJAuA7YBJwELQHQagdZbnlDAjoQfPQeoRCXzavmUSk+U8op6WgtSWZA2NsCBu4ErsB2sB68NkKijkIhULc1WdQ1OWSm3QPW/N7GBuWQXAZrbK/x8hsGRefm3On+jotzNOx7ZSLTo4GkfldURRCTW0pvZ8ocIy+T/FdffTrZVjJT6AMGkpSlR7Jmrocj/NjZG1vRpVa80NQFBGmeaQoAhh91BLn3Gjee3QKKxtzyp4k89hCg11VBgxQ5XNa+QBTRxnCWlPKI6dg9iwD6pWUSbOJKS7G2FUfL2d/hN6VlD4+h997N1kQt4uqhlh+sPmU8eqBbErvwC7NHxHVBKPvtQapjwu7tM4wvuuIV9+rj7U9ucZ17J+zDvnkvtRXnsPA0Q7YBlwFi9l0GjEQgLvPnrMvLZ3NhZfwqY9DEZtF6ctwx5B90wHIKcxCJrFmQtfdgAvyyjpuDFzHLbcGMkuKMFmQT6ceacAXgAlxKVBVoUSGAhoaqzjmXX/O5Q7LMRk7liDvLrhZNGDZI5T8tAzChvV+NfesU9EgFPJ05TEM7iSzbPpiEkYeRfHjUyoi2+K/aijZZx6Rtu8uJ76yQnOIMbbX7UmWm7xRsP7dd+0pLKxjSE8TzNSdMPJ3aGzQ0WwsRKIpoepZFllHojFv3wyndY3yEh06WvDJlQJGj2405M7cOsLhrz+jE97s1wgn2c6cLbOKEAgESCWwYoaA/JtZ3Bh4Hv/VQ9F2MEUkEpMX+4LSwkLyqgrRcLKhndPC37wfXN8NozAiAeve/thhwBbuUkQNLbHBBG0ApIjp5BqEqnQ7ArGIfFUlQk9jhN6mGAU4YBsWCNBopG65hOFUA5QCNZXU85yCl1ILjcvk1+8vRp1bRfNnuhj1DaG83IiiosYXMM5j21EQEU+SSQ13ySD+yn4Wt5jHA4MiIkjDGC1605jDqKGniVCmQbW9iIrWMgwGeP3mef6S2vxynq8+hcukjvSbvo28tKfoCq0ojkp5pfH5a6x+/9s3NxaWg0rdKFnxd94ToVCI/TutX+Vj/o3OuGKHAW68LsuhVqupSMhB36OxII2ipp6qtEI6NHPmbeTfjEPLyhBdl98WnA/eOw1VvRyRTPJqWzl1rOYGflgS+sNkqtMK0XH4bZmQfwWBoMnb91dx9GgmgwZFoFa/vj07u5ZBgyI4fDiEAQN+QzjzT+abb77B29ubd999l0OHDnH16tV/2ftx9epVDh06RExMzGvGQBN/HvnHj/Lk3UH8/YVWn5PNk3cH4bv3MOb9Bvxpxx83bhw//PAD7777LgDff/89Y8eO5fr167+6z6hRo7h79y5fffUVzZs3JzU1laKiotf6zJs3jzVr1uDk5IShoSEfffQRR44cYdeuXdjb27N69Wq6detGcnIyRkZGpKamMmjQIGbNmsWECRN49OgRc+bM+YfOpbKyktGjR7Np0ybUajVr166lR48eJCUloaur+w9/N038e2ky/P4SZiBX7ObwpVpEQhETBzaGE+UWgo4W6Gq/bZ90GvXXxtKYa9SYc6SoqEDgrktBmYzoa3dQifQIatcSo+0COieHY3tZRqxzGcLvQ7m57wkDMr9Gy+YBPM1ENWYriVal+E4zpSDXgSCbDFIcmrP5WGsMJVUM77kOP+tNbBM5knwgiysiXx5PUTF66gr0LCtIlqfT2SkI3d6j0DJy4ZLHu7jFNyCJ3gpx8/lp0UEin6iJoxapYDSapcbsaWfBumgl51SVuNtICH1xm8TiMvSmtketmUdNxC7ierbAsp0eo61cuGHij12VCLMWBqCtQOmsx33PZjz54hNmdV1KclZztkZd4Ispo6gt6IedZA5qpYR8dQ8ulVphYeLI5F5OgC+MrgCW0o9EGvP5GpnbbwBeqw6grBKhzBNSFplC6rajNF82hwMrrFGprjGwyybEj3KZ1NmegtRCbDamcKi3nDkHJlO27hL5N+MwD/OkqqaS9uM8MDEw486eFFCpUcRlcl/vLAbOOtxrNou6q6vJ1+mFrosJqNWM6lhHSHMJ2Ji9rNQK2jZGGHjb0DLoLhOnuoDKAYo0MXd46SVTq1EpVdzotw61QMAZnzr6TQnBr68bSq0gviu+xNrVwWya9yNmIe4o6+SEqR2I6mDC9rQW+B5SsWXR61eYkZGUzz7zffX58j0w0gexvIZ9+9KZN88Eg+adsP3WiSq5PcU5yRhbuWBqKuPbb3+WiLAytSXTvx5xsAsW3SvJy1DyncdjltOB8yQSggPZhx+QeTSSaOlWFBp1jJ1xCfnYW3zT7TO69f/tMMfsjBysbC2R6GvRft+sV9vHEUQJNRjUiH9ZqBVoFE3f9mEw5UWZfLItHsnTUa+1P5i+k9JHabQPXUykjwghAoqoIZ4CNL6M5unSo7Sf14FaVT0UxWHpDDdOyWgeGgyAxFCb7FOP8FnvhrvLTSqO70fuPYvOuGKAJkHYvDpWq2/GcdRmOl8t+ZjLhtGUZjXDKC8QC4s3Qy5/Scrumzxfc4ayp5moFEqCNo7G1qMVJz3nUJmYS7/0r94aCvk2lHUNjYaUhRGoVK+Mvh1EUkg1HxL6qyLmYoQ0e4vkQdy6szycs5eWW8fhNqUz9yZ9R9re23S9uQizUI/X+lalFnCp/XL0m1nT+9nq35yrQCB4zegDqENOMTUUUI1F+yZP3P8qSqWKWbOi3zD6oPH5XCCA2bMf0rev9Z8S9nn69Ok3jLgFCxawYMGCV5/NzMxYtmwZU6ZMYerUqYSF/WuVYYuLixkzZgw//vgjenp6/9JYTfwx1EolCXNnvWH0NTY2XmgJH83GrHffPy3sc8SIEcyfP5/09HQAbt++zYEDB37V8EtMTOTgwYNcunSJzp0bozacnN58+ffZZ5/RpUsXAKqrq9m6dSs7d+6ke/fuAGzfvp1Lly6xY8cO5s6dy7Zt23B3d3/ltXZ3d+fZs2esWLHiD59Lx44dX/v87bffYmBgwI0bN+jVq9cfHqeJP4cmw+8voR0a4li2fpqLUGiDQAAFJTDsI3BzgG8XN/aKrS7FRVMPqfAW0BOooVHRehQwCmVdHRE+doj19PG+dp/Vx305e1SXg8FC/Cb3RGq8CqcOEbTvGEKWVntqi0pQV8eS9F0AguJgbFTF3KnvQ3XZF0wcNYuc3O38sLIdHc/mo5g9kdq6Z2jK5uEZ5oZn2DzalVeSnpKBT3MvfIhFpXLjcr+FqLr6U/esnvG92hDVayfpe/VB3QxtmYSwBncyNASI8hrfyAqeXKGg2ViuPhxA98xMat+dj5OrFD13XayKHRCqRBxS5SAx9kMVpcX5h9YIUOP3rBAjJyMarsSgO2gyLcsOIJdLSMlsjoBMJHrXUcocKXx8jkTLeuoyp3DsUB+u1e0gpvdpRJJKwBFwRVFeRs1PdWi1SkPscgWR9lacx1RwyGQAmhOskdw9S3lsFga9DXkRm49CfourKY/o3sKclM2XsI2UolMmJ7hcC/3MapLvJpN74Qnxp87gNKQjrX3bYW78Uq8uJg/Nu3nEzUqlSprH+JiZZLhMxdDv5cNqVS3CgjJcdLVAw+DVFaLrYkHL6ADMJMNQqR8hFB4DM8OfNQcEAoRiEUGbx5KTnYcyJoprJsNwihVwI/cWp+XXoBQexd9nxoWfH1K0dq2i5GEizexdMDXNZMoUFxbPd2+US/gFxWWN1S6N9NUYlD5nz94MPD2d6T+8hENjlqB8Uc3diW5M3RCJtWuL1/b192jJ2HEJPIiFoZ1yqdV5gLPIhFukcZEk4qqKGORhhfPYdlQbpFFVUUiMQw06oc5Yh3n/5p1z4fQVvlq1lbFTRzDo3X6vtblhQtQHezj49UW63Vn6pgdMKKRg1CDOiJPoT/PXmryWD+Dwls3ElD7BFhMM0cQDM3yw4FlVPcp6BQFhbTEP84RF8ONn/YhbfQI9vYukBDjwqFku7l5WmPjY80GXNci7fYZMu1H2IQzH144l0BBj4GPHAD1rWg8fwceTrdm45BwFBW9/m/yATI4Ti19MAg1PM3Gb3hWvj/u8anceE0bZsyxkZvq/+d39jchZu0jadpXwe0sx8nN4rS2DMoqpQY7yVw2/X1J4J5Gny47hv3oYBl426LlboufReO2bhbhTlZyPtoMpiuo6bg7aiHErZ5ovGYSmtRHO49ph4GtH585X0dXV4Nix0FfjFlNNCbW4YvLW45qjy5f0QBMNFPIGlIoGpJpNOSb/a9y6VUhWVu2vtqvVkJlZw61bhbRv/+/XX+zQoQNbt259bdvf51kplUp27tyJlpYW9+7dQ6FQ/Et5VBMnTmT48OH/sgHZxB+n9PYt6rOzfr2DWk19Vialt29hFNb+T5mDqakpPXv2ZOfOnajVanr27ImJydvXP4CYmBhEIhHt2rX7zXEDAwNf/f+LFy+Qy+UEBwe/2qahoUHLli2Ji4sDICEhgaCgoNfGaNnyTe3f3yI/P5+FCxdy/fp1CgoKUCqV1NTUkJGR8Q+N08SfQ5Ph95cwEhiJu8PPW3S0wMcNPF8+q54vyaZ77GUmWbixzTWLRqMvBOhGfQPsPwttvMVoObuiYWCIvpEB0oIMGsRKhEKjl4UOHgJ3gWHYrDoPnOFR/zbkuFhQa2vJOK8vESvlDJ5kx+qd20i8eA9vl1LMM5rRPXAZmoWFUFyIQp2DKr8erVZW+Ph7AeVAAEKhCbaj1/Jw8knUJWKsu7fEQHKEh3qzeRTfmgqHr5BHGRBQLEfiDk/L4ll9awvWU9yZpdGOSA9PRiQWMqVHAB073yDvpB4SJznLOy5DR6JDcG03+rdIxn3xEPw7XMPRWsKi9MOkNDdCkBXIizuumEy/x7qPvgPW8WKHN/c/+Yn2uxREDlrFlPNdsNAwRSRpD2SiqNYn8+QTpKmrsNIOoebcE0RTLyIgGiM/S8T3Mhip6MKyURZYXipl+scN9OxQyYFvOvHQBcx147AfGUzzd8z5aY4NX4ZZIRGD7uY5PHj6nOi0j9BPsuWHIykMWhjJx3suUq0qpe/ZEqS5u+jomU5DqRjPj3shNdbl4YMY3Fyd0NHRBF1NVMrGoh9iTQmP5u0nIuY+wu0dsZF34+txQxCKe7NpSx/c3PSAOkCG25TOuAGqByNY9DWcvw1zRn+MnYUTc9aOY9epLcwYvgCUKp6vO0n+xcd0976C0P4DKirkiG884MCKLwg9OJP1j7/CPd+CiRtWYmwg5b0hULntILUXruH+8VjeeSeAm/0nI/q8ARMNDcxcWqBr9KYgesS1u1y5JCWpNICQ6ddpue804feXURioQ22+mjUt87HOu4SsoY7Rhee5YpLDT8TS+/pQvGn2apxiqimiBieMXhkh1raWWNtaYedggxo1V3mBNfp4vAw71NDTRKwtRSh9c2kb+uUl5qrPckOQQQUKOuOKNXoolUouVN9kQ/1OWh1P4UjYjdf2a750EF7z+iDW/NnrFNB5DBKZNlauLbhGMsnBMt55tgDrl8VOROJfN8KEIiFdrjWGsarVai4fuo1M9tJboVZDVR1XtbJ4JMpjEi3JpZICqrFe0h2Xfl0bjc9f8Esj8I8g1pYikmkglLz5Hc2nPXJUaCF5y55vkn0uhpzzj7Hq4YfHjG70iV/7qs1tSmfcpjS+ia5KLyT30lPqiytpvmQQIomYNt9Nor5eScxnx9HSen0um7lHBmUspysW6KJARSZlOP6imIwujS8rvl3QiYL0WD7YkYyW7j9f/OBfoR4FuVTigOHvd27iD5Ob++tG3z/T7x9FW1sbFxeX3+yzZs0aUlJSiIqKol27dqxcuZJFixb95j6/xdWrVzl58iRr1qwBGtcIlUqFWCzm22+/Zdy4cf/02E28nfq83H9rv3+WcePGMX16Y8rI5s2bf7Pv34rA/B7a2m8NIftTGT16NMXFxWzcuBF7e3ukUilt2rShoaHhPz6XJt6kyfD7L0FLBpteFvBskMOZo7q0UQtom18BriOA9sgVNpRXwvMU+O4oxKeKWXX1NgDKBgVdziyhQSEnY9EKUk/loEzVx25yNM18jIAuwDaMup0l6asLiF1KmG6wj5u5tuhUVpGXeA8dMws6TSsguG8hsudr4Kgz5bJ0NIqS0dKRsTtBhLJtMYKMGMJaDkVP/ZgbR65Q3kZOM08d7hzYSuHXUt7p84SFO8ajKcgh/YeFpMckYjF1L5+My8JSKmSJfS0Clx9oe2Q9Hz9JJ4g8xIM/QtKjI+MWXcBycks6WXbFxzAKbYd3MOuiQ79+Nrg7g/M9L4LcHYl5157S3JNUltWhyliK0LY7kQYKCjckYNt7BmBK316zSU1Of/kG1pq49cd4/OkhvN4PgZIbVOYlwFoPXpzpRk2WOZnzDKgLhKQ717j1sA8GRoZoCzux53M37Ds2A1N90NVCtx62h/flvjiTMJMKbAqqKWpbQqnUgsoab1YEDKdHvS9Ox6uwbNuMhxdvoar2QV7uxpVvviFApiDh1AOyHyRwfU4bPljaGK54revnFEe+oE/iOnLOP8HweTFRmQuIOLeFyNwayhNFbFnzjA2rzoPhChCcBboB0D4QVswAX3fQlGkxqMtIHic8wNLUBpRKqKmn/kURdbkCdNv3wQYJxZm9yD8fzYPoCNKKUqje8xzddDVJnlfwfL8H7YQxXDt1Al2pBr0nOaClJab9DwtonV+OgYctbBz/6vptqKth03s+6Jo6ceK2PgjVaDtn4ez8ERnuVqjNGj1ocwVhPDOMojC0H65OSeTLi/Cq1CYyLR1zoRZ4/Wz4rS29Q2ptOWYWElYJu6OJBt7Nm/Ht/sby6VmUs5/HWKHHZ3RBpVRy17aA7JhutLHXe+MR/BixqARq3Gn0PmojYRA+7D+/g2XffkjXNn1ZNKXRcFEqlJQWFGJi1Zh/9kujDxplEpq17UfJozQcv76M2vw6JUNkODX/xypLCgQCDh8OQYGKgzzBqU6HwHwZT2yziBOVUEwNvfGkLXaY6unw79AX9185FP+VQ9/aJkODX6r6KRQqbt4sIDTUDA2NN0PpfD7ph1mwO3lXY4n6YA+B60a+dVwde1PCrs/E1OP1kE+pVERiYi9Eotdz6MQIkSFG8+VP1DGecYEkJhJEq78rCqRnZEV1eSEikcbvnfqfxn4eE0EaM2mLL2++DGnin8PS8o893P7Rfv9uYmNjWbx4Mfv27cPT05OtW7cybNgw+vXrh6+v7+8P8Bbu3r2LUvlzIaMTJ07wxRdfcOfOHaytrf9dU2/iF0gt/tg9+0f7/bOEh4fT0NCAQCCgW7duv9nXx8cHlUrFjRs3XoV6/h7Ozs5IJBJu376NvX2j/JBcLicyMvJV0Rh3d3fOnj372n5/X/zl97h9+zZbtmyhR48eAGRmZr6Re9jEX0eT4fcfRlFVhfh3Er9LyiuJuaXD+KN3EVVXUHfAClmIHRtO1HD6vhZbF8KUwdD65e+KvLKWNbmJrNwXTN/t8WiGf4pJ/j4KunTifoSQqRbrMdbLI279HtQKXRriwCmkBVYrRvHNPCjpX4lT/UDW7c8j88c7JG0rApcSTBoMyDS7SMTdWUi0tbg1/AB3lFK2e15k9MJTtAs8Qn/fR5x9WI7UWoKPIIBEn3gK7j3H65uJ3FizlisuNjwIm8uW6lXMdyvlxb4cZny+CP+UWKxvb8NTARXXhOSM6ozUrABUYI8enq0X4N65gv0FXvgpKjl0KISG8ixiRhxAz0FMr8B+1NSNwjRmIByzge41GBnqcuzcOwztloGV+XjOHNtKVno2Q0YNwNzSDJNhh2kTlIaxzWqkOgri/DwwDOuA18cLuT9lF9bzCni0aT1P53lT3vIpk29vRywG0vJAoeLO6jPEfHGe6snD2LmpMyBBVbWTpE3BWIeuwXdEJTuXBCPIjUGzMJZcoMpchNOcboR/GIRmQyUvrAbhPKYdL/ZGoKvSwNzNkZ82fktQpzAkRtpoGGgj1BDRZtdkUKkZ4e/IDwVxlOT/RKthDoztaA5KKfC3v0aEQmgXqIaiGlBrIRQKWTFzc2PxjtR8sDJCe6of6VaP6d02DH0HU9AVU26ii7JWjkGxFFk3e6qf61IQkUDRvWSarxiMSSsX3KZ1QdepMYxKZqqHzPRteSdq5A21qBXVTP3gQ1bt+JiS5CTKajJRPx/LfO7wAaH4mZnxOKYHtx9dZchHc2gnv8R0477obf+O1NE+tPbq/mrEvW2ryMloYFREDXlORegr1RgZWb1qt0aPwfhgiwEZxyN5sOMEj3oqUdqbE5H4iGFuHV6boRsm5FJBHzzxwoIgHgLVtPFtR8eWPZg06AMcrBoLkRzb/D3XDp7kvbVLMJVrU28iwdrTESECVHIFQo3GpTN5xzXiL5wkzyuKnBIZtV+5Eypp9BDEUUAxNYTg8Kv3emVyHlp2JuRIqrhIEvYyAwJ1A5ioDKQYJXYYAGBK45ohR8mX3MQUbSby2+E3WYmR5Kc9I6DLGD76KAZzIzEz33NGoq+FWq2murzwV4Xd/8b69Ql89FEMn3/enHnzmr3RLpJJsOzmy81BG1ArVQR8MezVd/NLIs9/x/GvJtJz0gba9pv1WpuRkfSN/pJXYaYCamoUJN9owKaTATaSNz2pQ+f/9Jvn8J/AE1PyqcSSppysfyehoabY2GiSnV371vQrgQBsbLQIDf1zCvvU19eTl/e6BqVYLMbExASFQsHo0aMZMGAAAwY0hmkPHDiQgQMHMmbMGB48ePCbIZ+pqalvCF67urri6fm6Rz8qKgqhUIi392+HwTfxz2MYHIrU2ob6nOy35/kJBEitbTAMDn2z7d+ISCR6FXIp+p1cQgcHB0aPHs24ceNeFXdJT0+noKDgVyvBamtrM3XqVObOnYuRkRF2dnasXr2ampoaxo9vfJE7efJk1q1bx8cff8z48eOJiYlh586dwB8vcuXq6sqePXsIDAykoqKCuXPn/mEPZRN/Pk2G35/Ag6dQWw/tAl/fXnbvDlFd22E/8wNcl3/xq/tbmFzm28UrKXbxRfIiDGlODVyL5gOTeDJtR2JkoMuIl/mxKrmCTzufoEpag/44G6yKk6hTSVEKpQgQY6cpoPSkkDyBG/u3XON263PYb5ZyJegOwyOEGMj606y9Cm3VYjZ+dgznHRdpKBej+bkpmYa7cPPS4Ox5D2bcHcGRzT8RW22JMkcLqTiGguLHkCfGx6Y38uv5mIc6cs1STqpzEF6iNAQN+ehrKhGo1DyeY4bidixCiYjA2hJ8Mu5QqIC7gYMoMPFEuXQFzUdfxeI7U5zoTtIlMyoeLWB4wjlC9My41bw7BbcKSDxdQnZdHF6DulBi1A4dWxma5dVgronP1bscdtIk9lY/rAa58+nnH/Ns/kH2t5xK+jgLdn7VAXPzQOIfmcCxZNrvj0HQ+TACUQfK4j/EdcI1NC0rkdR8hE2vzohFakAAdmZQU8+puGQ85bVcvJlOo+LiEfLv7yFyQTbyZR2odzJEc+AuCn0yCBu4mdJiEcfOHKalVI+scwdIDtHhnU/bUZBUgOmSjrQUKaiwsuHa0efU190g9IA5DRUTiFhzhKzPz6PTzJJWR6czosdkxvabTs7FJ1xt/ymVi/rju6TmzQvncT7cyIAQW2jx8s2khqixcIdQgFvzQObYuHPW92OM/R0IO/0RGrqaaOhpommiz6ZPjgNwIXgpaWkVFD+up8+9pQgVqkZxdF1Nzh4+wfO70QSpnbBt50PE8gMoV4cxrO9A5u5M5/iJHF5k1nHsq1PcHrEZy/ZeyNFAhsarh/kLJNLgZUpYiy6sGLwco1IJ6dkPUHfsjRo1Ahp/XMICLbge/wSLKemsuKeF9OYd3hOH4BU8gNTsZErKi+jarLHi5E8rP0cemY+uqQLb62rkJ7NRFLVFrCWlrroaoUyCs8iItjS+5Sx+UoTYcwhomONsm83uFa8LxUusTDAb3Imiikqur9zFoythdKaSLlG6XAhZiufscDR6STAZ6Yh/83mk3r4D27JQuT2EDxoNvx+IpoQaPDHD+G/VZqpqIb8UzA3Je5DC8cHT0egoZuTuE0yWtcJKoAvm+mgDbwvQkaMilwoaeLu0AUBJbgq7FnWnvraKypIcdMy8Wb/+BcsEtzi+up5+qRu5fnwFNw+tYvSy87gG/LreWLt2ZrRv3/j3awgEAsLvfYZaqXqr0QdgaO6AgZk9RpZvr/D5N6rKCsh4foeZrXuhFKrRRINte5JZMCWJqVNdOLMlnlRK+ZSOfzgc9T9BK+ze8EQ28a8jEgnZuLEFgwZFIBC8/kz+t2fQDRsC/jQ9v/Pnz2Np+bqXx93dnfj4eFauXEl2dvYbQu2bN2/Gy8vrd0M+P/jggze23bp1i5CQkLf0buLPRCAS4f7lxsaqnr9yobmv3vCn6/kB/1BBn61bt7JgwQLee+89iouLsbOze63w0NtYtWoVKpWKkSNHUllZSWBgIBcuXHglU+Lo6Mjhw4f58MMP2bhxI23atOGTTz5h6tSprzQsf48dO3YwadKkVzIVK1eu/Icrgzbx5yFQq9/2euM/S0VFBfr6+pSXl/9PVLHqMhHqGuDCtsYQzr9REfOI6B4dcfjgYxzn/Ip6NwBy6uq/Y91u0NHSZmZAD1K/XUjK5bscDb/MrGmmuDk09lSrVHzvORS7zEMYfLIJ/9kGVJQ1Y+PUJzh4GTB0VnsuhC7lkPd7pOu6klPoh6KNAQXtB7Hi/F687Jrh2iqKiV/fR6BWsVTSmSyb9lz1OoFynwW9ovVwWJZF/MZO6IwqpTpPi4ovM1nb7jwCmQEr/ObTVrcNc+PtsL95kBcLSwjufIMpJneovrkTbYsgYj88y/PLt3EOb0EpyYh6K6lcqqa2tIovP52KaakRH1TvoipczYkBX2Ph7UO8ezmrvzDnlMZUmmmF0NvYFpVSxZ0N7zHwfAr6JoZs6eWKZa8f8ZI0UHtoDcVCPSIjMynsn0ara264+ATx5Nw9JiV+QKmsGmHOItp4B/PT2tZwJB4EwMxbqFXzudQ+DL8VCZiF6gCPoFLd+IBuqAPGjdfk3fIC0h5mMsTOEqGVMWgqUNbt4NESLWJ23KO8NheNFVH4m9gT2uo4Sk0JV4+extPbixU3f0S+OAyjNDty11nSM2UmFtHXcfh4AVo9hmDq2gmBIImk7d8yd81JvIuk5BuUIbHQJGxAT4Z8OIXCe0lc6/klzZe9wxOXHEoripk06APkVXXEfnESmURKyaU4WmwejdTn5cOKQgnpBWz79iTlddeZunAIVZdNKM8uxWNO7zcvvQY5ZBQSMuQhgik1eI3R4DNlO8yy6kBbxler1pEY/YTm0VoY9gjg4mhtVH0sGEpzOuOCtfVxcnJqufAeWNlo4T2/72vDK1HxHscpiU8iadcBDvbaiIupM+vuniBhtDkbq7uiVVLXGFYrk3DpYi4ycxVnnO6gPnWCdwMncdENrmzeSOzJ4zzYm4mFiRUvop/wzarFdHl/LMY3RVw7n0Xf7cMxlJbyUTN/jI9+TGlXZz4klMcHahk27A4ff1zCqlXawBjKqEWC6JUxERMfx4lrVwgPDqX87BPOvadFhxo7rFbHk7TtClYTm3Mx8QOMrVxJ+G4VelFlhH0Yh8FQS44ltScmppTlRx1JefiEfs7B6Ni/9EhU1EBBGZgZkJ5YxJ457anVSmX00vNU7M3H0N8BhyGtKbiVgHmHZgjFIoruJ6Ohr/lKJqGKejQQIUXME3K5Rgoj8MP4pamYEX+Pb+cE4+TbEYlJe77c5U+HDuZ0jLuGqKCYbneWEn11B9cOLGfkopNYu7Yg7sQdjpZFYzc6mJEE/OrKVFEhJzW1iubNGx8S4uLKMTGRYmoq+9V9/hEOfjmCx9f2Mvjj/bi1CEdTx4D8/FqWLYtl4kRnLjd/SjplrKDbqxy/Jv5a/hO/30ePZjJrVvRrhV5sbbXYsCHgL5VyaOJ/i/zjR0mYO+u1Qi9SG1vcV2/4U6Uc/ttZsWIF33zzDZmZf65uZhP/GZoMvz+BMzehpg7e+fUX6b+LUqnkpwtn0dPWpYtvIM9GDqD42mW2dn7ChA+96RkGBRHxPJyzD8+p/lTcPI7L4o6cCz2KsESX6ioVYh0FQyp1yIj7mP3nFOTL3RjZJ5mFDXeJLlPywfKPad2nJwFjdckqaE38dT2MG9KJy6kl3zSO9CumNMSeo42rL6HBC7ldexWlQM7j9V9SU9/Ai8GphPsrCCyahvRoBDVGYfT7sgtG4koK28wk/eQ2bFy7smL9T+im/8T7Fx5SnnATZYWI6Nu7MWqWz/qri1ktT8bKzp5l7ouxa2jF+ZN3uJV+idmjBczpdRLqgsHMAOrlrDugJmLzQnpmlhAUZozZ576Yq9sj0N4BxuuJfRTBwqMX2a7TEmM9DZgcwJ5z43mR/xN9Om+mhf3oxjd4t5ej1ksmXXcGSUVCVm71YbjgB8Z/PxmhSEjVk3R0tDQoLy8lrioPzSsluE7pjJamGHVxBVcz7nEr/hbaWnpU1VRQWJLHxYhjLBq5mFGD+0GeKehpoRAKqUwpIK+uhFuKF1w4FUpxniknpjxHW1tG1MofSTiUQNdbbbFsU0x11jSuLz/IgrrjNDOV0U+/C7bNm9G29+sx/M36GWChZ875lTcpfJjJtb7rEJvqoygsp93pudj29Ke+XsmFczl09BBj6nsFiWY9Jam3EJWsBH3tRuPq71ArlZQlFrJuewZx3vEYD5KyWNQZK5EuaIipraujoqiE6pupLPNu4JlxPt0yFHSPkBE8bzCXLuWSmVqBdNoyRJoShlZ8Dw1yFPkV7Liui429BKewAjaoI1CqVQyN1qZzi26kCcupQ4FHsRRKq3hSaURamYw+L6M161EQlSAgt6GOB1k70VQpqY5/wqrZ35B16AFSUz0sO3mTdeYRp3+MZ+oBmDnTjc8W2LAwsC0Wn4+jrH8LemX4YFCvydjR91j4STMGDHagino+4hzGaLGMn2/aqpoadLR+1oW4MWA9mcciCT0ym9gvTpJYdZLglbPI6dsGEUKevtuBqtI8zmSfJD6hhojl+sTP3Y3LxA54zO6OvLwG0zZu7FM9JCH1AXmTv+FeYgjr1vig2lVC3vmnaDmY4PJuKE+XHSPwq9E4Dm/LYfOpaFoYMCDr6zf+vXYRzS3SmEIrAl9KRihqG8i+8wibdgHcvVdGaOhlRo92ZOfO1/X4qtILybsSi9OoUA4aT0JRUUt16RQmG/x6ImH//rc4fjyLu3e7YGmpibPzKfz8DImK6kZMTCnTp0exeLE3Xbr8ei6MSqFEIBQgEL7ppUmMOs+jK7vITXlMVWkec3amIdP6+TdBiQoFKqRNwSr/Nfynfr+VShW3bhWSm1uLpaUmoaGmf5qnr4n/u6iVysYqn3m5SC0sMQwO/Y94+v6b2LJlC0FBQRgbG3P79m1mzJjB9OnTWb58+V89tSb+DTT9ev4J9PyDBRjkZWVUREdi1LHzG7HTIpGI4T16U19cyXH7mei5SQmOaoNemT4BAfWAlMK7SRTdT6Yi3AX98SPYtHAwVrldEWqJ8XQXoz01gqr8EuY32499WDsmfLmSZp4ZnFFMpSjTjqiBA8gSBJGyLQ/H5r6YmJ2ndbeFmMfO5Orzr4iy28R3PzynoCCNZuffR08vmOJ2rhQcPUxpdj7SvIsYnd5FYtVpvLLAY4iEC95f0pChw7oxQzDZdw2x4CZGEyKx79eZ24uO4xTogJm7CWm1ExHo7SUtx597VdfQMsvkwcPhODhq4eKjg2FJPoON+kBlS6ivBe1HkO9BuFMdlp0UaOxOIf/M9wi038Nwuh+ldQIaqgM4FeNOYZ43+61uMybUCF1NDUYN2A3sbtQrS81rTIhrX0PM/Vsszz6AVe5W5ASRfVNBycNUTHT0kd/K5GHMcx5UP0ZfZITyxxcgELCjkw/P9ipIz/oKn7wI4q21kSsakGhI2WOxgZQxtyk2Csc43AQEAq4EL6bgThKmwW7k2xdgknGM8X3fJftBHcatHXEf35/DN4fycEgUGzOSKH38CFlaJRFbfkSoVjFqxEw0Lt17w/D7dtERzAQ6SOqUyDQlmLZ2YYM8k+cul1lWOoVumRWMHh9FiLcmknoTwgwq0DTLoLbCAh1TfdB+00Nz4EA6I0fe5ccf27BsXUvujo+h9lg+go1VnDx6HPX2WOwGtMT/86Ew0obAukhEsjIku1NIX55EmzkD6dLFErXKnCOLdWkoqaahrBqJAMT19eioNLh0R4MNgfr0q7GjODGXVuYBIBTigCEKVOwzekyFQR23l5mSmgLerpA0dAmZxXlUrP0KjbxafMffRmGqgc3Jd1CU1BAx7Gukpnq8U/ANd8duQ6ewgtWfzGbYFE8SzIuZlfkAkzJdTPSPssvoPgWpPXh4xA9eSlgc5zkNaiUUquEXEY2/NPoAvD/pi4G3DdbhzUk//RiN050pFXamL/YoGxQ4jNtCTUMhc9t0o6JCjrmOGlFRKY6jQrnS5XPq8ssZlL+VHzZlo4q4T2vJY/w8mtOm13iOP9hEfawWmltDSLt0DeMwZ8zbeSIx1MZ1cie0bN5erXIwvgRhg+cvJh79/h6Stl2h/ckPCendguTkXtjYaL2x76OP9pN+8B4aepq0/X4y1aUVuBv8dg5L2yAhuVkaODpqo6urQbdulrRs2Ti3yMhibt8u4saNwl81/JT1ck66fYjURIce0SvfaHcLDMctMJz9Kwcj0dRBrPG6V0+EEBH/vof9kpJ6hg+/Q69e1kyf7vZvG7eJfz8ikfBPkWxooolfIhCJ/jTJhv9fSEpKYvny5ZSUlGBnZ8eHH37I/Pnz/+ppNfFvosnw+wtJ/PgDcvb8gO++I78aRiCSaaDjYo6+VxolyHhybi8xd9zo+e4APD/oiSzQlkP3T2P69UnyK/TI6K2HSUdfbuc7sWbKZ+xzGIsGd/EIDMDN3xfqD4O0AUluB2rONcCTB5i9pyY24hJjPmxHXY4RqSMb6BR8mr6f9kOmsRRnhxoEHY5jJ3mITd167LuMQmZgSa0cZLozEOl9RIFzGSL3ABrOf0rpegVmkQq07PSx6pBFgHASRx63oCJ9EObXjPDSfh8rtZLaPTXM7zyQ4h9liHQTWbPKlNLI2aTf2ULzhFQylwjJ/Hoxbby/QShdQOqL/ey4aUMnhyoutX0f8+eehMZEcKvDVr4PGUJFlQn9yubSo0cvjhVtQFTQhfcqpkCdAsy0Gz19Opo0CE5yv+AWWlWbMJN9SLOghwyO2EN62l1ubdTBz0AHmVqAfqg9JuWxdOw+jiSbhxyobknh2oc06PelmTyEwa3NqezQihC/TojFGtRuiEKipwkC4Stx4Ss2T1Gb52OpFjH608UUZuUivJzFwy9O0ePqPAwdTGg7fCC3r59nxfaPaXvDhNwLT4jc/h2tpkzEsFqIhkiTH0/Duz1fphuoVIT4d6S+pg5FvZwHwzZTHJWKwegqlNlFOOoXYVJbyar3LPH11EWhUBFwrBNF95LRtuveaPj+HTuOfUVqgjm7Vrujr2jMH2yzYxIAe1ZuJObwFfxT9KhI+LmcdS+ZKwU3nuK4JI78Oc0oENdggS5Xun6OvKwGy87eiHVkIBLy8JODFJzKpe2otlTkOnLaJh0pxdjO3k27zaORm+vxvfQxkYIsEEHP3tk0ZNtRYphKjqwMcZUCZctHDBME8ex+K7be/Jo2A0opF57Da0FfUnbd5M6orYTunkJFejFuk1tSRDXf8gATtPhcP5zZs90xN5eBpqQxhPel4Weh0sHxw0iEt+4R94Ee7oNbIhS/+YbXuIUTxi0a9VZK+3Zn+g86jNr2nOrDV7ghr6XFtRgG3FqGtoEEA4PGkFH/VcMA8JjZjZq0QiS6MmqvyLh/twtLTg7Eu4U3Bz/vjXXvgbxYMxGtgxe4HbuVdmPnY+jbmDPWcvPYV3OIiyunR48bzJzpxvvve6CJBhbospm7hOGIL5aYtfOk9EkGGZ4iVJTh7Gzw1rXFfUZXpKa6WHTyQmr4xzTwpFlT6WT6FLEqGS0tZ86c+VlDasIEZ5o10yco6DckFQQChBIRapWarDOPsOnp/9ZuwxYc/EPz+VdJT6/mwoU81Gr+5w2/pOgL6BpZYeHo81dPpYkmmvgvZv369axfv/6vnkYTfxJNht9/EJVCQe6PuzBq3xFNB0fM+vSnoagQ/cCgX91HrC2j58PPgRoqS88j1nnC1ThXai4qcLS+hrWZOXJdPQTNvKio+564DD3a5P6IQb2cLhOtGDf/e5bb52IT6EhuShKf7HrI55Zzyc1/gUrijDozB1dBP5yDbYlObIXo2TUKn61AS5pMRZ+HlMYMwmJoAZ133AfZVSovHkcoMkMkkaFvaIAIAeo6FdZBHTl78gy5KW74uBqQEXcP/cHluBl2x0g7kAlFuWg67mbfsy/oFtiCNHU5t+bcxTWsOwaL19Nm0XPO57oTqCmjvjKDAmsjvplehOLHTpS36oaHeTsuTdnA/dBLqIoHEqkdjoF7IINa2JL9gxpnhxpy7tcjeSZCr245iQ43+OlCNe/VtYfyeuqHenIjSo7EspCqysuk6zozzT6Q5uLb7G+3lOw2+Ui6teLDvbV8xiUkeproB5pSF3sNTRMH9Hr0JWNRAT2uHyTLMYpRO0qYeSqb+9vmsO79x+w87YWnUhP/kgus/DSVlpsekJl/iX2GxxD5ihg/oDWyEhVJ/X/AbVoXulxegKGLGbUl1QyY/B7rMk5Te7kjal8JgqC16FoboG1vRpuPV3Hsli3fHIT2QWBjqoL0AtQiIUunz0WqJeODXZ9CaRWB9xKoCRyOm5k3SrEYo7oyro7dj6adCcEHZmIW7A5AbX458vIa9NwavTJVNZUs3jKLQa3fYXjvDaSdf8LNaE/8PeCGdhw5H3jRUBnJIe94tq762UtjXaPJ8EhTHuhoUGEqpJw6EijkkvQxV4OusmXp+FcGlN2oUOwj91Kz/FsKbEdiPFKGloYeHhMDoEHBE1UOkWRhiCYBcTIUgQvImZHBzT7vI95uT7/L+YwxaUne9WuELh9L0eoqlGti0fbXw3V4G5K+uUKzd9tg4GyGRafG6nd3TpXi7mmFh4s+AoGA9zsJyDn3AEWtI2Ljn0PSOsgdyN2YjkpgQfS7mxAXjcR1ZmN1UXlDHae2TMe+WTAtuo6ltrSM8kfZ9Orpwb59bbCPfcTonUqSs42YPM+PwaWVVOZqMfXDJ3Rsqcu42Y1C8V4zukFOMRRVcPpUOwoL63F11SXp4UWSos+jqWPAlHYTqO3hjr5ED78O7751Pbh56R7paVU8i8kAGqURUighhly0keCLJY7D2iId5s0iLmFNIUvp8taxzEI8MAvxeGvb35OYWMHJk9m07jaFoozH6Ju8mVclEAgIDv7t6ooiiZi+Ses522IB13t9Sa+nX2Dg/fpYSRSxnUjewYegl+Grfxb+/kY8fNgNe/v/vNbVf5Kyggx2fhqOsZULH3yX9FdPp4kmmmiiib+IJsPvP0jh6ZM8f28CZv0H0XzvIUx79sa051sKbLwVLXQNu2N54gzhzzbiNv5jzsUkUFdfz9wJk5FoaGBzT8it68VMDF1DgvI9InbCD1HWeIVZYFWVS0OWBrYHE4iqj0BSXE3m3I9w1lTjqmFHyfaHaCZHsKj5OByHjafKPBWb/VfxCO2JpbMjyNrCk2MUxbtTFL0Bl0/fx7h1AKkLj3P365O4ZXbFp78XyoOx+LRtiUZbHWoqE9j5/UPCfFUYuAvQVXrgXtiD2zkJXHYoRzBdjW9fLRxajKa+fiFaX3Ti0pbtmAW+S0FgGZWmpyhwyuLArSJGenXDrOoFE493ho4WDBpqRNsAa2qVs3FeuA7XrBnc2zcaPV9LxEdcmC5qg6lDMnnKTERmpuhc3Y9zdhYOjhMQaH3OhLv92al/kS4eHWjIL6fwdjwGbazx7NmO4oZ6Khpe8I7fAByqRGiKxZhlPSZKNgGPAf0ZFJLC9S5K3p1kj0e4N5FRTlRUQa6RNcEhzbEvSkEeFU2NIB07RxHDJ89h2uTPOROwAEVVHaqYXMzn9ObxuuM8/fwsLVYMIMxoMDeru1GIkk+/P4uBmxeHL+1hx7neCIUipgyuIunMDfbufM7IZb0xkAro0LUjcUlJ6Bprg0SISXsn4g5cQXOiIyKpBnZO5iiVSgxdLUhMjUUtAHcHLy53XE5lUh79Y1ai6WiGjpYu3y46gpZUi7QrsdyusWf1wiJGDTWhYXQ+KbJyGjSriCu15UF8Lf1eKipkH7mPe78WWLR2QhHigDHanKiJ4oH+A3Ibitlx6C5BgWF8990Lpk2LZuusYOSppUQX6PBZQwAEaSNXKyiqKMVHZEQf6vDDCp26MsrWDCNb/yHZp8+jdbWaVKETpQkTsWi/g6efT6L/R18SU3GQwPe7IRaLGJCyHqFcAXIliIQkJlbQt88tRldISCYXK/Q5rfkI259uUtDVmspepgzCBzFCRAol4QdnUpZXQe6ZR1h424BCSUVqAenX7xJ9cQd5aU8QS2RcH7ca41RP2nw/iWFj2yOvNGdU2SVOl6p5b3YIUrkmF+6UsXd/Jo/2FzKgizEGXjYg1WgMr9WRYaD7s0fQNaArE1ffxNzBh4byGjQkWgT3m/3qrs+/EUd9SRXm3TyY+e4Cvj3eljDXg4wfGE4GZTwih264Mpvg18TNzdEhHDfsMKAOBTLE1CHnCsk4YfxaaOgfYenSZ+zbl87evT0YPuO9n9c0qtFD+g/n3HnM7k7R3SR0XS2Io4BzJDCU5lihRym1lFBDIVX/0Jj/LP7+f43o+38SPWNrgvu9j6md5+93bqKJJppo4n+WJsPvP4hR+47YTp2B+cDXNVaUigaKMp9hYuuDSPwLEeJHeZBYDD1dQUcCaKJl7k1DQQb+3jaYanXDurCQ4ry9/HRdQEhgEL4bNnBrkSteq1cxoHl3rsUZoi+oZ932Qq6U+zKmsh/V0vvo9jehrbE/t+MPknBLhOyRBXY6Wox2uIyWwJd0pTMWne+S2MYSPaUeuXdUJET4cUM3Bu3uPXmx5y6Ke49pH3oQvXNSFNmPKT+koOpcEpdvnqVzQhQn9q3lilkcOXXPMas1wnL1fQQKBYIrTzHYFkxRVTwVkS041fkx2WZdsc4rBaCiR3PEJjKmZnVjzZUvkRu9Q2ZWGyRFuxChja26Cg/9xwiENlSXDWHXRnNSKx0Zq6tP9C43nCb64NLlGC3faaDz5IXczDXhSGA46+9sp1dqBq30O1AR6cEK8wv0X94D4ffDMRn1I6KzsfQ/doDUvDbEbKuiS7UAcUErju97iskcCXXKcgxzlKTsNgdBHuKLRrQUaOCzswJPGwWDhxpQdDIUm4gE5IEh+LQfhk/uM7pPm0n+jTjKYtKRS2sodjoK8hHcblaHzFxKXY0Sz+Z2jO0B5nVl6KjNuLLkR+5O1CVgdBQuBXps3TuJH8KWYX3rEcfmZzNm2ww6delEl+mjQa4AmYQDm/qSmxSFe343zOw84V42jjZWYCTj0KW9rD61mvjjFdj0aUH50wwkajXfbXrA08QLDB7ZiyB3XzQsq5k/PZYn52OocDPkScznTBw3H7+p+xmcrMkPx9T0bttAUbmSI+m69FYkY9nSEUGVNndfQA+RhNLSdHQrXGl9Wc6dMd9QH9QRhUJNRoguXp+PZVCqCYrSCkR6WpySxzKg0JQ6jRL62Dcjeu5e0s00OPyhFMdMN/THf01FnifP9CqpHe6PVrE+slYBSEx18Vs+mMK7CZi3dUOsp/Wq7Hb68fukbDrH0PF+6CRp0T7Akodkk9leD+XsbmyOLiJvYxFuuy0JsDQDhRJTP3tMTfVx7d8CVGoQi7gzcitF95MZvHc/th1akpv6mFqLYnSMTDFp4wqAhq4mE/o70Xr5MVJDrpNva8x27cY8OR9LkJm99CyKhGDZaGBUV1WRdf0ZruFB1IgV1Ht7sH1tNgmLD9DdWY7l4xnEkMMw/Lg5aAP1RZW0f/wBldk30NNqzjsTxtO6Zw+2EUk02Vij96qwy98QIWQQPlwnhemcoCPOxJBDCbUY1Wlx0EPO+++7M2uW+++uW5Up+cx53xVHRx169/5ZQDqTMj7jCn5YMY02f3whBJxGhuI0svF7ekYezykgmWKs0KMltrhgjCH/f+k+DRt2m7IyOWfOtEMo/GN6V/8phCIRPSat+6un0UQTTTTRxF9Mk+H3H0TDwACPtV+9sT318QWSI4/j1mogTv49Xm5Ng5wayKuGqoaXhh+0O/r+q/0cSzQgVsyN04+ptHIgPSYJ8zaO5J85x4lzBlj1SGS37yPE6s5Men8rrT70JqOoAI1mFoTuOoM6q4jvbl8grFVn8suhPNyC9mO/p8P7aUgaSlk5JIik3HKKC6rJ+0yAaXUOikANcpWW6OsWIqivw6B9Mv7TbIg7JURbp55uHw3g2uZTxG1ScCSpHcb1qQg8Jag8dKjaKcJ4uTESuZqhIyaiHjmeyvjvKdpajXWaHliAeJgORVGn6Gbaj66bptGi83scu1JIlxBDyjW+pCS+lvoiN84MuEKzeQakmKRhKGugd1tNalPGYBx0B9cJa3imHMih49XIi5PQRMXR1La01tlOXtEuzmfupwUyBhzqw81HX6LxPAerD/Xwe/cnMqT1RAnkfPXDFaLy7pF3JxrjezmkPnDhxaY00o9HIzXSpjStkNivLqGoqiN12S6kV55T57OEqFm7qSuooP+tBVTXVuI9ci4iiRgTNwtse/vzoPJTam7X01JxCucRYVwYYsbz2FJypx1DW63EdfxwatLreR7zjHLHEAwFhgwN9OPLU7Z8EnWB7pb61CeX83BPBCHe/mBkBKZaRGbe55v4h4S4BaFvaEv64bnY2MkRJY8CextauLdmXP1UBHIV/oPbwwe63IoqZuEX92nnEM9dA30ywrfi+V4X+vUNQispEdevTpEg9eaHsngO/RDOxIFqjGqKqI8q5P3Zj9kfDbc26jPDq4E72+D8bVgxoxPN/MJpITZEvj6ZjJQ0puycRPMnERzomMkloRAjHQXJ2RmY9Iogr4cpD+b0wkNqiUZDHgmbzlHvqIdybje07Zx5cldJQ20koUb90dGZSrTxeC7G7MP2m0XYFkkpuPYcuyGtCTswk9O+H1MWm0W9XRnSNH1c1lZToecL5kfpePQ9goLbcM+omoSFD6kuUlEerwZLQF+bytxybnVaSfiPU0jPl1OcJ8R9VjiGN+OQOTUj58Bzms3si9ed/q/uvxhysEaf3ItPSXt0C6nMgMrSQoysG40g1/HtXxO7z6MSQzTZuXQT+muessazNebXvMkzL+XcWTnFtR4McS3kFqk8I59gHGix9l1q88qx8Q1kzf5dfGts/VJ0vYq+uOOIIc359QqaZdS+XE1KsUIPZXUDThdVZGRU8+LF73vU8m/GcandMpzHt2f5d5Nea9NDhh0Gr3kaf49CqsilEt9fzLkPzfDCHI9feCGNeLMYzX87t28XUVkpp6FBhUz2f6sKYBNNNNFEE/9/0GT4/RdgZu9LRWEapnZ/S7rPAtygaytoexEMG9983xy0gfqSKjpdWoBQJISWVtToJlLzRRX2pQ8JvtSDJx+bkRlUgSA8BadaOSe7PkVD9pABGT8RekBFyTwvxNqOCEQNnE/wJE7qxrj8ByxbksaLn0IRpJnSsdlSAk/upHR4BiZf7aRtshKZfQ3R313G+LYapb0eDX2t0P36CS8qnEiJbsMJbRO+kJlgbGmDREeHogw3WmQnYh9VTKUrOM44TLhdBjG5LXj2mTeZmVk4OOdg6DOVDofa82CWF2XditnfeTvd3u1OUVok+daX0Z2eRVjeGoo3d8C6Zwx2I9UcKPTlAj9y8MgYVHkj6BL3NUcO7sciKh55pRaqsgH4m3/K9xnGjPZ5xm4feK6IYJttEnrxIlxPKxFrK0BTTnVgLnLNFpw4fIDUOAuGfLqDYRGROHawY8a24djvn47WzebEmekyXQA23XyR6kgxQ4BegBNp+++g62JB6eNMNHRlBO+bTl1WMQnLZlN8+yaGn6wm8N3hiBsUtFv3LsJJu6lLeobikphuY105r5FIjbcuBhK4ceA4NQeL+abOk8kNRvTrFc8Z9w58LBNQK9AkojgDt3oJniUS5E/zEVjXQVkdmGqREnGIviIV+ibWFF5PwKTlFkR2NeA2DZQyAqx9EB3J4cnEnfgFNqdIW0bzwR70UdUR9lCCjaOYUjN9FCo1Q8a54968mshBMaSmOWFd0ajrNjSkipIzzyl/oSYwqJycltbodCnnCbk4d5IRIlLRzNmW0PHfIUTAjwnfcmedM1VVd9HecxO/WAMEC6eQ8eMptPc+okJfiOG3Jeyu7EhVyTOmeR/Ed1k0+6cvwghNWsjNkX2xgLS7FWitG8Ba82d0xBnzfZlIHpah0auxMEhuVTQvYq5QlVKAWqUiw/4aUmNDeg85RmpYoyFeeyMFv2Bf/CbDsKH2xMdX0KqVyat7sDKjiJLoVB7vuMGQ4/okp1SxI6sZAcN68CV3sL2RgOltZ8zDGkPlkhQFfC2+i1lMDXNmBHP02QhUYgsE8hycBcn89NNBeve2ejV+GqUs5yq6SDEf6kjZ1UJOP9Skp7gCgRoGV6ahbyMn7PCneFNDJuV4yA2he/PGQjSArMKQ037z8FvZCo8ZH2JFVwwUB3gv9RxGNdp4NtdDEw2G4PvquH1phq3SgJjDhfQMcWHv3GVE7WvJ3nIvBms15kIWUU0elXhj8cbapGVthIGPLUb+Dm+06SPjUzq9dU0rKKhDT0/jNQOoulrBlAM30epfwzKjTjjQqAUoRUwz/rVqjSqV+i/3ssXEhKNQqJuMviaaaKKJJv5raTL8/s1k5sH5CBgSDjejob4BBr69tsIr9EzsCQif8YstBkAwaLR8ZfQBlDxMpaG8FlWDAqGmBGRiNBMt0bM3BTMFpkFO1N/NptbMjilOEhpu1tAgOkVlQwCX1o3AudtzXINyeDh3GBJ9YyKNN1GnW8mzPhdYKNPlQislNz4qoF7yGVUtJaSpPdGQP8KzZX+kAk0Un/Tl2ZensfKMx9O9mjQXPTLPhfJZh9lo1Vjw7b0laBU+wL6/CuUJXTq4J3Db6wF6SUFYilcTU/YRCV4zSV1cQtGi45gevI/nPC9qslrT6dvJXF/+HZVtOnPgQ33GrFWTmZpO1WdJZH8dCFQSt6YjpuEi8kOeoA4S0+XCBjRsQ9GOyEQpyCVb34rOcySYO01ArVYT2iKHsk8vcWvJcxp0GvBY5MmgCc9xbG5A7DofLoceRVQp5aMbu7GYr4tAVs7Jb/dz5vvtFBpWMrC5OwNPOpHrUY/ms3q+kGSwMKg1VNcirqxDFOTHraJWDO0Og1YNIzP5EY99DAmyN+LJzBw0kGAW6AYNCkp1qtlW/BHd50xFd/0DajIaH+YX0wmVWM16ozOU5BUQEZnJA7kZP34ViEP35lz8Tk1eIRy0GcDDmxGkBDbgH94X5+HBIJKASaNnxK9FV1JuHkCdHImify0FdxZhqpagY+9K1Iyd6FsbkHH+CTX5lZR7t+Dj9ckMrZKw+EBv7o3PpfB2AvHBKrIzH9FCOZzYFgLup3ZiSowHg5wbqx0eLr5Pcv4D9DIqSR2nx/ARD9HeVU/bF8s5uH4rXttSEfgspVRcgrG+DZZdLNHU1MRG24Q20SuIT6mldfdIBpvKGelhidFIH6pya9i/O43qinrCDpajZe9D/VwtdO/lcXfOKe4Ee7NvnzUeegkEbxWjj4zWJ2YhzqlFcCmNpIiLpKsuU743BSvXYKpSCtHNtwW1kIs/aNFtXCAxP+xDHlD56j7SqKpCfvQa9a7dkRhpI0CAVVdfWid+hoOdHZ2HXcJLKOahcRqdsMeoVgNriSEaej/fi3e+L6AyOp9ArXL0v7TFv9MokrMMeHr/AX179eGdwXav3eMGyNBHSjn19GkRhEZUK9yPZpHz9Da1wS1xrS5EatxYZVRZWomdSAINNVBeDWIhGOqirJejqpcjL1cBhoApGQXVyG3qSEutp4IypIgZgi8qpYroD/ag62LB3bLn5N79gBWn5hM8oz05lbW46hq9kkXYQRRJFPEpHbF/aYz9DV1nc3o9+eK3F7C/IzW1Ck/PM7Rvb8b58x1ebf/mmySOzimnR5Ihlqt0/6Exf4s1yx4xf2kCZ8+1+039wD8bI6MmQfkmmmiiiSb+u2ky/P7N/HQejl8F9e0THKjog1whoFc7kEr+kVF04MgW5LkVREVvo8XGkUj0tPC/tIov92pg+lRMh5aAGtSlNYR38Odx0XkubjyEm64J4RMm8eLhBR4fuY+RqhSxdRX6RmYUXTSl4mFL3LX0Ob82gbatVtBh+lS+kLhgm1XPsykyBPq1SDwluPVuINLEFZ0H2ZwyP4BMbyDrE3tgGuLJgVNBCIQ3MCzbz9mtN9GrskQtFJPiJiYoIIaOQVe4caIbydXxlNun0H/iCmxqOvDou28oXXWa/C6dsYnYikAp48UWT+pL0qnWXkPRtSzCBgXz/H4D4rQU0i3SaPXVRLTV96gvqaY6u5CblZ8jvyJFfeUZBhlfYOKv5k5nX+qyLfB9fpFmC3Kh2ovKF2qCmnUhdXQoYmNdzmt1wEctwbZhFpmXDdCx609w8Bfoaosp3nOd4q+uYd07AP85g0nPTMG+rS8bGlYjfKRG0kmFukGJ8dx+1LvVED1jJ049fDmVbMn5F64YG6j5cUQIsQUpHN6+ijm5uvjXtsBh+EzsOncnPbmc2wVnuFd9Ag8rf5QJWlRdPUxGniHvfRpI+ZUYwocPIvZ0BJp3UhB1q+dhA6ieJxLitx6PqDFUHbzN7NMfcv3+Qy59uJeyh+kE73mPo1f2snb3YrYsOEDLnlO5uncJshI5Zv7vknA7H3crBVWJucR/fRHTQEfafj2GNH0r9K6U450dy7X3j+DwbjDKskrSY59iEF9F/Joz+JloYutuRnCI5ysDwUqly41BFgiX/Ije4jiEueEoVSZYaBpi6elA7kcyKtV1GAol3Ln1Pb7d3qGTsTvyqjqe/3Qa+16BdOsFgqpY3Of04UZOHGM+WsRKnVuUr9qLcc8ArD9qw2Ba8+zqcZ7cLqYzGaR3DSMrqxzrCAv8QqzAGrAGRZAv94doovrOCO+2A6iNeYGqoo6u721m8ZoXTPA3RFNfl7Tbl7DPC3x1d+0Z/iPaN+/xLL0CyTfONDx7hEuLzhxwTeQdVDQ4FaMvFRH0RIVdoAHTzlpw64MD3G+VQ/d7y5DX1DNu8mPAgN1V4xGJNRj4wQ+v3cG1VWWoVUq09IwBMECTz+nOC4pxxxShQMD2+2PIf3aTjgYryY3Lo+3VT6itr6XdWHd0tPR4sCe10ejTbTTuTVu7Mrh8B2JNCTAVAD2rcmQKET7WZgx7WawGoC6/nIRNF9G2NyFgSwin7mlhPVCHy21qCMaep+RRRh2dcSEMR0zRxoJ/jzGmp6eBm5suXl76AAweHEFhYT3z5nlgbi6jh63Ta8Vg7pHBU/IYgT+aaPzasL9K8rdXUSktqMoqhd8Ie22iiSaaaKKJ/+s0GX7/Zob3BPX1g1jtmMDc5cfQCe70Dxp9UFML38aYEYScjL0ROI9vh76XDSm5GsQkizG/XUuIRika7kY09JpE/OQsnvRdSgElFGy5h1EvOy4OWYxMaE+l20p0RRW0lvcAXQ2ixHXUVKUgrE0k77ICzcilmAxbQkVWJmqVEM2aDnSxBIdmT2n1o4IfkjSIjctEw01Kg0Sbcj0L7l/oQlFlDCrjjRh49WFm9mbMpjhwYecyeoY3Q99RBYVZWJQ9wrRfTxzdWqOur+XF4j3oAKbPv6K+xXXEWTLMZR2wXjILs5E+LEpLYNKVKj7oYc1j4X0ayqoxc7HFdrY5aIhQ64h4PH83WbIsnKwvMrDzZEptFLxYNg2jZ26YhrsjEvRBXXiW3Z/noKs8i7WWDyvvy2no6suLrFJ2D45m1M2uwAn29tuIq42MtTZPcO8RhO37XbEI88LjQghffvGc/mnbcLWU8FRxFJGmBN01x7n3LIXMc09IjHyC0tuYBdOWYhVzle0xGWgqqvC7nY/O3SR8z0+jWbuW1NQq8Q64hK6uJqmZRpQXB7PC4WsEljJOb4vD6sBBKjsZYhbsTnjffhQaJdCykwtmJrrous3ByeA8i5+2oU1vH4TiOgJc88iuEFIvLuXqvmWkPiygLCWX9LRE8p4IeVE2gx/PazJzaAPJi3+k1MiY7/XsGdpBiXOfAIydTDE2lHLxYgfSfrpLqakeTqPDEOjo8N0CD0TyRyhVamIm/ICeuyWi+J8FvX3KjCjsfofSMf40mBpRoeFKVYgNMfrFqKb7kUg6X2zdx5xmvWnbcSKogKxCss4/5enSo1Qk5JKpsZgkcSkffX+e7v3a8OWX51mwqhwRYbSrteJKWGN+nIGnDSKpGG6n4h8djEeACPsoAwDSKUUcVcijuKfcHaGGaeHU3ZbR6dQc1BkFZFx8xpdHerFwTRpr5rVl8uSLWHRo++o8TIa141xMOT2HB9C/yJxsfR3SSioxs9Ym7dQuPO7cpMgpAFO7mWRkVCPSfHkDqxv/I5JqMMezEJG2FKn2m16eJIq45jsWUbmKkZkHkWg1SgVIEOGJGWXUcoZ4PPpNRtFQx4v6XAROWiSZ19BWLKFFs7bo6xqCSAQGr+vriV/NRQ0qNbGiAhRiFfn65Vjxcz6hlpUhna8sQGaqh4G3LW27v0M8BeziIbdJB8CCYoqpJp5CPiQMKWIqqecYsbTBDldM+GcwNpby5EmPV5+jo4ooLVOQnV1Hfn7dG7mFt0kjjkI64owzxv/w8Wa+78GQuy8IHeryT823iSbexpgxY9i1a9cb27t168b58+fJycnBy8uLpUuXMnPmzFft9+/fJyQkhDNnztC1a9e3ji0QCDh27Bj9+vV7oy0tLQ1HR8c3tt+9e5fWrVu/tm3p0qUkJSXx448/vurzySefcP/+fUQiEX5+fly4cAFNzf+/CiX9XyQzM5PFixdz/vx5ioqKsLS0pF+/fixatAhj48Z1sX379ty4cQMAqVSKk5MT06dP5733fq603NDQwIYNG9i7dy9JSUloaWnh7u7OhAkTGDFiBBoa//jLtSb+t2gy/P7NWJnCewv9KfRbhO3Etoj+ifX2SRIcTjcmw1WHD46+j1ELJ45YTkVZp2DBihkULd3Eze8s6LRhErIuFQSeN0U7pTuxdx8TvLUTRpbOGJd5oq5Wg+Ip9aZSMnLysDI1Z1GUBmMOn8BA9BiV2B6FUsEWu0RSzSppmHSCczZSRokWECU1Q9nDmfaP7pGkYYqt3RPCSmrRi9jB43uxPLGuptU0OxraZfDC4hiC56H0dVtK9mcNFJGN3CifhkoLdJ+4crLbl1i1dcSsuSm277TlvsYPPAoVoJumwn9JGi72CVRnHsHtoRVe7buSEHONC7kLcHjaifIWqXQc3gc9K2ME3WwY/P63HF6yim4pCrQ6KSh4movzqF4kbz5J+kU9ste0J1CnG/cujkZTpIWvsozr6oH0T9iIrGQ/nfq0Q3hfia63iD55X1Jk0w0NBwuCwoeBRw7kHSOv2B9ZXDJ1u0/xFOizaCxZMck8PHkTI3dj0itkXJ19BrvM1uh+vZiS4+m8028Gzb8aSMz0r7kbn8m2xU9Zd24/MpmU8HBLDAyiqC4sQVxVgq2HM2L0mR6fjLS8guo6fYqUNdzsv56WW8dhF+aDRnYJ8vxlPIruRYvjaaSrcrCZNw+3Nquxt+/D5o4vuLh9Ja7X+/Kh3kiiRiagtHhKtcCBuV+kopeYSNi7bbn39XG6nn1A5LhgzlfsITjqBZL+ofhjR+oQM8KGrEF4L5cvp57nxxgDhOMGoJ13gxoXAc7h3sTHV/A0spD+g+2R55fTUFFLoa47WgsmMCHJljX6j9lBFL0TpZgvn0Zl947sbFNKmKCELlmGVOYWs807lQ6fdMD33R6sr3EhwiAPz9RyBvvkcydhLm3arMVQX4CH9SWykw0wc2nO+T4Kmi8MR69ehKe3FzfkmQwIdCKZYn765Gtsvs+k1kwDhnfCVqzHrZ9GgnIOhjEuFN5LIk/Dnp9+ymBRVwUPJ+zEps8T2h//EICBU5rTbIol69W3cE0RoptVTfmYC0x4vwcnbx2jqiCdoYP24R8YQVCQEffvdaHZmj5kVNwh5+5jrPw8+OhsX+KMalGieuURBcinii+4QSuROZK6KlC/nu+VRTlniCeSLDq39aJscwbFzaWUJ83Cq94QkUjEnpVn37ouRBxdS8LhCwRP38S3u4qZ8o4ZYWG2VEjqaYH1G/0tOni99tkDM6bThlPE4aM2x6VYQpqglGvGldQhRwcJzyngJqnIUf7Tht8vKc1Px9tgEy06mjBu3CA8PPQICHi9GMxYAsmlEmeMiXj3a4ruJtE9esUfFpVv9kFPmv3LM23ivx0VahIpopw69JHhhglC/ty8zvDwcH744XVvvlTa+LLHysqKTZs2MXnyZLp3746rqyu1tbWMHj2aCRMm/KrR90e5fPkyXl4/38N/e/j/JSdOnGDevHlAo9EXHh7O/Pnz2bRpE2KxmMePHyMUCt/Yr4lfR6VSkpx2i4rKXPR0LXFxCEUo/HPzdlNSUmjTpg1ubm7s378fR0dHYmNjmTt3LufOnePevXsYGTWumxMnTuSzzz6jpqaG3bt3M23aNAwNDRk2bBgNDQ1069aNx48fs2zZMoKDg9HT0+PevXusWbMGf39//Pz8/tRzaeK/nybD709A28UV7dlzfrVdrVYjELz+g/XZN42evpWzoKU3fDIRdBVpCB1uI5TkIDPTpzIpD+uKO3TJ+onaZDcw+QRIBAS42QlwsbZF46V7MWTHTB5M+57C1rVoODjxyKgImbkN7Q98gFJlT7GgP2JdTXrFTEZ+oRLZmTPMkki4YhBIXUVnVpbZoDYWsDs7kwoLS1Z20WHSnZ2MDTqI32dw86wuL0zqyMi8QY1WCXkxidSdFCMQCFCr1dj0DOPFIzPyQnTQv1VB81HNkNVIePEki7b6Ui5kO5D3wII01wSej3kfyZwuRHUax66oB9gGVqMUN6CjZUBVSinZ9WXcfpHJtJXrmVPghOVpc4qb5VJVVEZxbi6t2vSi2KEeveAnlGzdyZOSeoa88z76omTqTxxiubaUOq0CXjyIIdGsnJDwOeTr6zFxyzBUyh2gVwI/xUKFFCxbURufwoLqb+nTx5MhiVBiBNo+BrRu04ucqAJsPm1Dhd5O8ivuIG6Q0CG8OebNHRDXf0HQV3upGBeM9XVjzgd8Qs9L8zl0KITYq/msHafGvGQlcy5EEPHpSdIqTpHgF06zqX3p51iKtE8f8itBmleOwFAXkb4F/mIr0guX0lBZx8ZgD+wEtfg4meHi0pXy3sUIVYZsuqnmtsCGTYWJTL81ATEijE21iS2uxSAhl3pNKaKKLC4/OMe1+xcZlDSbC3M8UGgK8W4wx3pFDEEFcexe1IsdGb7kldrgZXKVwLoGvl96h9XLfCmIz8eylz/9nq3gonkePrUW6GlKaBclJU2qiXN6GZElReim5FGoUcchnuPyzJZzI9YgaamN4NQctoriMRUYkSOuQdfCGkFOT0LbCvhqcTZnDiwmZ/FH/JR6kSFa5kRa5ZC90JSl6s4gEHD5i2rO2ubTc7gFtl+lIKiRo87TQNfyHu0n2HBFUUdDbRX+SwZCTjFKfW3yX+zjwIF52DXvzoO2StqgQKoWgUCAJhoYZCmI8V6LgZsVZU8yKLqbxKglZ1ArVWgptRgxzI62oWYohbChk5IzIf50+eww688M5GS7ciK1i5D9nZSCEZq0xo6C7WNoXWOLRFv22n2+jxgSKaITLnTHnYYvrlNfU0lc1wtkPjhLbeI6NM3137puJJ+4hnS/CTciv2d9sh8oVaxr78hAvF/rp0JNJfXo8/qxGxqUnN1VSM9wH2ytNKEsDzOxCR7GLq/6OmPEMHwJeHlOdTUVXPlxMZnx9xi7/CJSrdfDQfOpYjcP6YQzAW8xPm/cruPkownkK0tYBLRt+6bAuxFar6p41hdWUl9SjVqufKNfTY2C48ez6NvXBm3tpp+u/0tEk80BHlP6skotgCGaDKX5W196/LuQSqVYWLxZ9OhvjBgxgqNHjzJmzBhu3brF/PnzkcvlfPnll//ysY2NjX/z2JmZmcTGxhIeHg7A+++/z8yZM18ZggDu7r8v19LEz8Q8O8rh07Moq8h6tc1Az4ZBvTbi5z3gTzvutGnTkEgkXLx48ZV31s7ODn9/f5ydnfnkk0/YunUrAFpaWq+uiyVLlrBv3z5OnjzJsGHD2LBhAzdv3iQqKgp/f/9X4zs5OfHOO+/Q0NDwp51DE///0PTr+R8maftVIqf9QPuTc7AKbw7VcriZDqkmxJQboFCCRAO6tVXx/YpDBLdfBGjQJ6GO4gcvMArQQaixFg0/f3gZ2vXwegTP7kWjUqkY3GUI4nu5VNZWUJ9fgZlUn4i21/k0bD+LlxbSU/oAuSqDioZ2aOioKYkZwO0PgtHXS2GKcxtC7Joz3fAUFfciyT/kDGVaiFsaIheoqAur4MEdXXQua/MwYSGeG4qpji/BwvQIzje2UGVUjdhYilyuou3+Wdyf+j6GZtroTLQj7XktNXUQv+ohLhNK6NrSnncjJnHP9D7yZjcwjl9KiqAVmu4NZCiv4Hp5ICJNbbp+0pXC7AzunLxLenA5ubUmGMo0yH6eRnZiBm03vouxiyVuM2Q073qNpPUdKLzWmoCxvaiqSuVxxG0csqtR3DBHNbob5YJVLHE3QGR6ih5JgVh4+3Nux1wK70XSL3QNUpWS3Opn1MnlPFJY051ydILsMLH2Jf6762QciaTb0FnssXmIZER7ipvlcK2rlMwLT6ju0pcHZ43RupuFSKWk/EUBCjVIACN7F8yy/LDS94PCclqObUVSXAQ3zIaRGlHJMA8Jibki1p4zYOOoYhqU9ehJDTh0+AiZ/iqsLslxz7DHQ3gXrt3lcoiACB03gtYPpfcwFSPVtXTxCMTIyZyD7nNRKpSk+Tlhdy+B0uFOtOjszahWt8k//oibsU9RaAoxjSjjWVs1opIEhgzqjJmRMWmSQ9SlF9K3y2ASl50mdGAoCZnuWPros1Z0G2cPYwbUB4BMArpKnB4YovlhDOsFx7hRr2TOA1e6ndOmrKyG2OfF5LfRRPVATbz5V6QlhZFeXg+u+lhmqjhyaAFKcRn56SdpyM9G8+xF6o5LuXb+Cf2ODaNNv2AUigY2H17GyiVmSLQt2fOtgL37Z1Aam0WXebmkFhowrFMwM9/phYGbDQgFqAUCbh7bR9mz5ZjYu5H5Uzjf9Wzg0uEzXNjdAoWeFg5WRizT6cll90eYhXli+2lXakzUfH76fXrIAunQZhx7NnqDqT4q1IhqdCmtqaLU2Bwdd0vaqUzQfuca0k7aMMUGFWpqaEAHKZ1SPXHqcApHxxJSUuxfu/9740kcBfTGEw1EYOOOGjXJDpHI0oqol6nJfvGABwdW03nkZ5jZNUONmgjSaL18Lel5+3Fo24LRW2JxacimXtwaRUYR13p9idPo/8feWcdJXe3//zm9M7Pd3d1BLUsv3Y2ASkgYiIBggIqIYmEgioJSIpLS3Z27sMB2d3fM7vTvj70X5apXvVe/3++9v33+w2PO59SwM2fO65x39CL4xWHs4h7nyGEJvfDnR6G1e3chc+bc5sknPdm6NRbc7CgVNnGYewwy+vOdtpQcYTF2Yj3x+HH37Lfs/Wga1k4+NFYX06Zq/JnwK6KeDKpwxPQXhV+ffl7MeKoO53GeJFFKJM4/q/NT+h57CYNGh1jxqAltU3Y5z/XYzrYKZ956K4zOna35+usc1qyJwdX1Py/1Qwe/n0RK+JIbPyuvo5UvucEzdPtLxd9v8dVXXxEaGsrUqVPZs2cP586dw9T0991W/zNGjhxJW1sb/v7+vPTSS4wcOfKR54cOHaJPnz6Ym5tTWVnJzZs3mTp1Kt27dycnJ4fAwEDeeecdevTo8W/P5f8HkpL38c3343lo0/836htL+Ob78cyasvcvEX+1tbWcPHmSd95552cmuY6OjkydOpVdu3axbt26X2wvl8sfCrrt27fTv3//R0Tf35FIJB1mnh0AHcLvfxyDVo9Rb8Cg+9uJdnkz6qRS5vnW8lgPV7Z9t4sbhVdY+dxaeg0aTtH1UkxSW2nJO4jXO6P/1ku7j05VLdTknuWHDwdh6jwccbMV7+45wYvjXuTBoWQQCLBKsabQYTNvXTUQtf1TtApHrEOi6PXKYuTOppTcz0DfKsK97ySMLRm4Jd/AeshQPhW1oZ3SF/0sEScLHLjh8QPjZ5zA/9YgLp41x0/0MhUh00gN38v9XHsm3AeH+hIs3/IBpQyxXs/c559GJJUjFgv44b27VFRKcNAZMdaOJ0wbx3CrPHIr0/BsDaCr7TJU8rE0hH2A7mYE2glOeEgasLV1ReSgYrhtJiW6KkxLdYh8Asi2OIIbMXg/sYHU2zY0Vgs5EjUOh+hI+qyfhL5Fw/HYA1iPX4xVbh1V17O5sjOa2HFNaHrnkxUkYn5OIabFdUTeEFJTkcuALm7EKgx82vsZioY8zfdP+SJI34FIpQatHp+JXcnJBmtHa4QiE2oSncnZJqZ6pDuiehWHLuQidHWh2xPOmAnF+E6OQyoADAbsXYOxSQ6mzajmxtLd2MT6EuhSxEfPjcFBsQqwxd5TSWaVgckr9+PUcoknZs2mKb2cKn0jDhNFCHcBBpDZKDEL82DDRR++nnMbjWo8QrEQympBpcb0CX/0CilmXXuS+p2Mbj2cGfLYGCipwWlMbzJvt9LzXQMX9dVUdTNHZ64jLTuLCj8ppF+kPreQpO0K/EZ1psTjNLk5OVj3eZMMYzVqgZ7RJn8zPxKL8JoSR8nRRKyTDTxbNhKnQEtCHf05NmwZlSiwWjkV7e3jmDZWUT/rHn6ienQTg7DLt+LWg0OYWmgYNGMpudd7kH7lOI3CZtpcR5PgWMcwFFw5so7Kbe8w6ck4qq4HYtdcz7E7CxkY4cIgwXWqIyKxlDWyOucsgVcUTH9+Nnn5+RzfvgmpuReDZ62m/GA+6xMF9Dek8ODzGhye6MPosQm4WqQQ5v05T794k1XTX0c9OwLd7Ce4cymNvnYW8LfbOiECNncfybulrdjayhCLhTjcL4S9mZSWQtjTg/meJC6Rxyv0wdvLmg8/jMTP7+fBUoKwJwh70BugvBoUJuy0yubcegdsPnfkxumvEaRcQHDzGG4BXbF3DyaHWrZyB183Gwp9hrF7RTIfyVJpKLcmmxocypuoTy6m9k4eALYosUKBkkfF05Ahzjz3nB9PPvk3/yGZhNuUkUAxlVoolBbTqrYjUNy++ZApLJDJzYif+iZe4X2xsP355roTrryCHDd++ZbS0lLKm98E8hZnOYzqZ8KvkTa2cocuuNEVN4wGIy2F1VgE/jjWhQsVvPvkUfpVZNDf246xY91YvTqNffuKGfiYHRETFHTD/R+H7uC/AANGdnLvn9bZyT2icP5LzD6PHDnyMxG3dOlSli5d+vC1vb09K1eu5Omnn+aZZ56hV69e/9aYpqamfPTRR8TFxSEUCvnhhx8YPXo0Bw4ceET8HTx4kFGjRgHtpoLQfgO0evVqIiMj+fbbb4mPjyc5ORk/P79/a07/7RgMevYeeYF/FH3tGAEBe48uIDx41J9u9pmVlYXRaCQoKOgXnwcFBVFXV0dVVdUj5Xq9nh07dnD//n3mzJnzsK8+ffr8qfPr4L+PDuH3L6Ktr+dG1wjMO3UhYvue393OIsgZuas1Rr2hvcDbkvN7TlLzoBDFp/344co27lffZUy/qcg/zKXsSBmRfa1w7S0F7gFaoBM5RTDzdYhxECAzCqgWx2Gdf5p6r3TW3V5D59AJNDtLUfmXokyuING2M65yV3SOA9ENi0Cr1rNinxtOZZ8y7vG7eEWHcupEMo3Tb5LVV8GU3uMRmNTT5O+GUgpBimH0bAnAptUUK1cRknotXtJ6MmQiut3fzZAZ65FZibggikZpLkeg1mJpY09yQhmqozewqK6Gi1lYuztgF+2JTZ2Ow5YepPhO4e6IZfSZXMK12/fI1dUQcSmAqfu2cjy1jQvvgU+xI+abZlArfJGEB3U4ZTjSZlFLQ/V50pbpcFvQj8zX+1LYoEZ1Ko+waRUoXawwKISEDA3DJSYQn8gTlGlMWD/LmpiNV7iwOw/bkb1xrAlhxON9OZZyiK3ZGQQG+nJw2QT6PTaZRQPlqEcvQhrrj66+gYTkg1xyH4TjeS3uvUx48P40nOICmTE5mNWrY1HmetCn0xdI3WyIClSC0YhRq+NM/DtYR3th292P6quZ5J+4T9TXc7iW4oCXUYqbzBakYiztpNzZvY9Jpw1oI8dRvrEMxZY8hof5k6W6jAnOmAfb85HlYEykMr77yh4TSzm1J++xbu5B6kJDGTUgjHm565FL61jj8gKKoBYGzVwMEiGYK9h3vIpPsh1J+DAGuSIYx4REDM6mzDjnyxdT/Jjdezzpgfk01ieT3naH3OQTJJ0axcLUk3Q7c44+H06HgUbQ6UEiBvYR990inPY/xayJXhRfN+VUkBUWr0zi5OlmFoyP5ts5bVRcFvNGvYRTq7ZgezABm/mDePGbLxCKN2EifIpgexMivEZR3ZJH7eauDBY7gFZH7vkK5OW+9Izrh9ZURuWuO5jvPoRi7isENgymuOQOBxJuUv14T0oLxKhKatnz4hQkzvmExU0jqOsILizswVMxmbjc7E/Fg2KsnBy4e/c2gnHm+HtGcLHzp/QODUJv8EV9UsDQQYvgHxKIG41GHB1/PI21CnfHzM+RhrRitE2tWJnJMTfKWDb0IKEpBSxOWozM+pdP/etTihGJBJiJ2zeqCisp6gYj69drWJ6WhqXUlOCdG4kaOgUAT6wYSgAB2HHMqxZTdxtcv3sdx1A9Qdgj7OLA6NxPkdiZM2LyOeythfiskbBXfJ8X+PG0XyQSUFenoaCghS5dbGigDQlCphPNsc8auB9uT3eNPdOH+IAIgrqO4MU1eShdH/UtMmh1JC7ejnWkBz4z+uD7GwFZ3LHkKTrhbDTn3Q9SsOlpZFr3QGSIKaGRe5QhRkhX3Eh4YStZX52l3/GX2y0igPT0Rk4VKRj66nSmXL3J5Tkb+ejwc4wb50bS0Ay+oQ57TPH+A4nk/1WSKccEyW++5w7+HDKpfsS885eoo5VMqgnk52bE/y59+/Z9aGL3d/7ua/V39Ho9W7ZsQaFQcOPGDXQ6HWLxv761srW1ZdGiRQ9fd+7cmdLSUj788MOHwq+xsZGLFy+yceNGAAyG9v3E3LlzmTFjBgBRUVGcPXuWTZs28e677/7L8/n/gez8y4+Yd/4cI/UNRWTnX8bfu89fMgej8ZdE589Zt24d33zzDRqNBpFIxMKFC3nmmWf+UB8d/P9Nh/D7FzHqdOiam9A11P+hdi0F1agKa2jKqWgvEAhwHBKOyFJOcP/e2Pg6ozbT0D92OOkJx9He30vJ9QOYDgnBnMcANdCAhakJ7k7gF9mPbJs2OpvcIXbwJ5x/LRxxsgVB16IpKi6joSaH/ke+o6E6EavmbOwa7Bgs645eYkVahRzlvTxuJFzkwchCRg15htqwSm63JaIaPofT42IQqoLw69PE3TNpjKwX4DjNle7r3iXlXE++OjaBtCZLVsQa8ZxrQGUiZMYtf1wajTQ/9zWuFSrKr2Zh7ORI62A7Gp28OBStI7ppHUvcvqI1p5SyXamYSseSUahmVi83FPPOYplsQ8mOpay/G07nrBs0FRewYW0iok5OWBnncGzAJFYOmU7+5LEYoqO5MiGM5jupSCxcMLY0c37mNxiNBu7IMrl+9D2G5fgweog/x2sGsuCDPJ6w1zBIZU7JMRsUunpSQrPZenUPtgXh1NbX8SDjDB7pgwhz7oJIKUGoNCE54SBHNiygQtWLu7ldyVhVgUFvoOvwCJR36uhTOZOSsiZiN+ehkBfQdDUWgVKEUWeg6loWRr0BE08buApp4d3ZM+kq+S/Xs9zcH4PRyIffljNgiJhje7sTotTQXGrEYqCU24NKiOgSRbiwCze0F/Ca05+0VxqRiARoT2/AY+V4ikvq8S7LZqfWldccgwi2PcT4wacom5aITmbFy4IM3GWnSM/I4/FZzxMaakFefhXpzpWMzA7CIjyQby6UkJbWxJAAaxwnBZAd6U3i6y8gdWxFo27jerWSWXNG4OHj8vBmEVtzkJohUBjxGRtLdYANxfkqXm85w9BVPmx7xoW0+hzu79tEv/HTyOh8Ett8I0ITCSb25ijMRwOzwGCgtjqdpG4WyMVd6Kp2wKxRCJpGvHFDeLczgf28iH5zEncE+8j49ATnB71HWlI3lAf303DxKuPCuhMXPhSZRoyLQyfUrfV4hnYBoO/k1yi6kECLVw0uQyNxd1eSkzOCVU4nqRIsRP3VMRxt7BAcrqFg9w1OPpXGY18vRSAQUFdZypYVa8lNTmPAlLGMnPvkw++yRZALzflShBIRwwikj8qXxfM2U+HpQdntTDwHRWPUaGnIrkAbbMU7rRdImKQl5NhN+ikqmVj5JQKJmFHYUjRrJ7LsNiLH1dKQ7EP0wKnUSDVYIkaGmLF/8+MLWeLAkiVB9O17loSEWrKyPHB0lGPqaUdGYzXmn9ailYBvpRXXj7xPVlc1fhHtSdbv3avn++8LqKvTMGGCO6fI4iSZjCeUnl7VJH3USt9JD1g+MohJL+9Ed0xPyvuH6XNkCa7Doh5ZwzI+O4lFsAurbymprlaza1fcownUtXpoUoGFEkRCYvEgO6eJrdnJ9HhZjCUGJhJOEPYsoRcufzNbl3VxxXjDnjZv5cOunn7ajz59HPD3N8VSnI3KKGKyEIYPd8EVAVlU/+qNI0BubjMNDRqiov49YahCyxquIkfCZ4z87QYd/Ns00Pan1vujKJVKfH3/ebTY1atXk5ubS0JCAr1792bVqlW88cYbf+o8unbtyunTpx++Pn78OMHBwbi5uQHg5NSexiQ4+NEwR0FBQRQWFv6pc/lvpLGp7E+t90fw9fVFIBCQlpbGmDFjfvY8LS0NKysr7OzaDzamTp3KsmXLkMvlODk5PRK8x9/fn/T09D99jh38d9Eh/P5FpLa29MopRfgrNtN6o573M8ZjKbHnWZ/1D8t9pvempDmfI8c24PRYFFbOzkS+M+nhc5cAb/RGAxF3DqPsK+bM8G8p2bAex8cfB5qAFsAEWyvY9o4RhAJAhLqkBJlLGaGtnqTe74+p0kDrs+/QFj+MT0KusGp+H+riDbTUN9GgbUFgEPCs9EUuDXqN7LpKIpcXUftkPnS2RlxezZGIOHQtOkSSXIq3bAMh5H38OV2GfY7R8DjhzT35wj+H+mAn8OvE/Ld98bI2JULVk0HSMxiuZlDSoEEsEmBvY0JhXTlnY605EezLqEJPMBFT6ORIi5M7zRevkrzJlJobJ5CVCBBojKjTHdHr47jcvRfrdh/ErvhV1FWVjJSvRy66xHmHkcwsXIRYOwzJzAe0lBo53m0xitZ6VvjeorGskvmDRvEg6Tx531whu9c72Jra0piXwpS1T1LX2MYPvVchbFZRFwYb3viB3FVJHFl7HvcFL2Pa6I6otw23lu6mZNoawmb3JfRyf/K1ftR4WWFaW4XYRI9swGsY015EMDKaGlUp33bWY9mvG60KAw1VOnZcvMPchJVI9Hq2vbcNmaclFwol9Cw5zLIlU6nUw6FEDa++nsKlA9m86l+CaFZv7G3MwKBD26ji6rdHMbe2pri1jvAoF27u0iM0s+DgotNcTT1PcJ4NenNTztWb4V2Qw2tvBxPhH8J699W0KuyouHuaWmMio2ucaVm8kdf2Tydb2UzYHgMnVnyNPNKFbw7PxF8iRJVTi9xEhoObJ2GifpS5daEmM44TqUbi7cqxrvbEzN6i3SCmqgGBIAQ8WkEi5co1Dbmqeg7Y3sPDYAl6AwfP7SDr6EHGW3ZjwHdP05BVjs3wGAoLCyhMz8Y90Jeqkkw+PfQ4ouVvIylsodhBTYjGmiClOVGrJqOWlhM5rS9txUVELh7GiVOVnGh05LPCOJjuQWvYDbr6DkOcWw9mch4/uQe1QUuusA4DRgKfH4xNjDcne6ygIb0UDAbEdmoUVyoxu1aNTCBCZmuGY/9Q7l+8QU5KAmNa2zi99SVuHP4cVWsYao0vL74r4uidW6xf3y4o+xxsjxJaV5EPDWDl4Il9P3dqBSocgkOhvhlBdSMpS3cimBpOZUwbYYdvEEcJmgXdyDBpeHhL0c3dARvxVRLeiKPWM5BgYy0fcI0onIk+fAc7tyB8IvsBUHwzHTdjAenmVohqG0HbDC42NJm3IRGDrUCOPLsExYEjXCsxPBR+vXvbc+xYb8LDLdvnjxdgxCWjnJ0bB/B4z7F4eI4hQSBEIBRh5m+P0tMOuZPlI2ubma8jfY8uQelhx9MDE6iv19Daqn804Ep9c3vyeYEA/had09fXjBeGhlFRW0GE9Y/59gJ+clNTPMOdizN6Yo4G75+MGRjYLgznLwmhtlaDuUV7EKtInH/Tb7Bfv3OUlKgoKxuDre2/nmRdgYSRBGPKH8zP08G/zD8GKPp36/3ZpKSksHz5cr7//nuCgoL48ssvmTx5MqNHjyY8PPxPGycpKemhuINHzTwBPD09cXZ2JiMj45F2mZmZDBky5E+bx38r5ma/L//n7633R7CxsWHAgAGsW7eOhQsXPuLnV15ezvbt23nyyScfBgS0sLD41cOIKVOmsHTpUu7evfszPz+tVotGo0GpVP5i2w7+/6FD+P0biEx+/cdGY2jlfsMZTMU/P2W+dDWDU8r1BO6qZuQ0K7TmEiQ/MQ0xGKFBr0FrNCAWWuHnOglBais4vvljJzdLKN+whYLU/YRu3Y7c81OaM0QkVPWiws2EhpoK9Bo191OqqXbMYOXn21nga0JrWguJ126SauvNzT7dkW5IIDLrGk3A4aMqCqYE4K41QdJUi4OPKxWOaRg1syhNryLg/AU85qSx3/ctlBJ/bNzssMr0o7Q5nYbcADKeiGFrsIqT6zVIq9qQWSkJe7Mvpy8uQ3cjD0eVF3PFU4jVB9HyXSonvzhLjlsXpr8ykDbXelp36em8tZYDa49QXn+RV9u0FFka0Sxtgq2DaKupRdc1C6O3CnvB54iFKopKdBQPiaXv2CiaZ7yIxFFBS28nwpfMRaEx4NkvkrfLv2HKwGaWvZvK5BEaMg4k4tPZH8vPXLhQsoWyBktuvKajX7KYEG01Y8Y8y9bNW0gsuI/kXhHaBhU53+zGVn+GMdIcAhasI3NjQ/um27kFgZeeGq05DZVSGq6cpsbtJKH+i5i2MIdLt7REuLbQt5sFhmArhi+eyDiFgnPPlRBsZkWU0hTNECs+/SgSl0sXcQh2IffQXTyndOf2sj1UaWqpVFUgaMwibVcfEqOzWSkYiF2bhOe3LOfKsfMU7DiGRApv91bh4pmM/JNDWH71FN5DY7B2UWIWUIODZ18ejPyKJpWWPfaF5CiaeDbeH/VCFRWGOnq1tiCQmnPm2W8Qd5EzdOd7DN25CLkeMnauJze3iOTEHIp2XWXqhbcQGAy01bTQVFZPWfFFSk7excdtAFEzehFVHwtSMXnNeZjXWrN46gqmDpmDaY0aiULGXqfnuOlhpMucZ+leb0JKYD1xk1YytCiSS2/vJfPYETaUDCNUWEWuvAHhWwGcLT+Bra07w0RK0r1DuXqwhrXPbGP1lrnYDI0GjY5j+97kbsIPjHp+PTs/mEzTlPGMemwl2kn7aSuuZ2TGRwjMxJBbzin3Yup72yI3MUG0toSs9WcRSsSMvfYuOp2OB199y40znyMQCBkxZyZuERPZ3vkK9+7VP/wK3j37LdgrObBqDiKtjid25eIst8QbW+RIQaxHr9MjMBHjaCrjG+8+XHtNSuOJ65ydbUUaiayiPSKf2wu9OVTxHeXrqzB85Mq+OxfwjHXAqbCew1/Ow9Y1gMc3JLCGa/iP2MWAqlb2MYgjx8uZMdoeDEbctWbMO25N8KCumMSY4PSCFhePTlTdyMKuW7uPz5AhP4qkhK7vY9KixuP8Anwi4/HrNITIfo8T0Xdq+yajB3hP78XJzS9jWexO5yFzEEvahZPL0PZNxe3b1mg0BpRKMafJ4hgZLCAOD0uz9kMp80eDFTwzJhj+IfnCT6Mc98MXJVK64PaL6+rb78f86pr7a8yY4UV+fgtWVv9+YIMR/LIfTgd/Df7YYoX8n5p7WiHH/09IPfJLqNVqysvLHykTi8XY2tqi0+mYNm0aY8eOZezY9qAf48aNY9y4cUyfPp1bt279U5PPvLw8kpKSHinz8/Nj7969SKXShxv3ffv2sWnTJr755hsAdDodx48fZ/HiHyOHCwQClixZwvLly4mIiCAyMpKtW7eSnp7O3r17/4z/iv9qfD17YmnuSn1jCb/s5yfA0sIVX8+ev/Ds3+fzzz+ne/fuDBo0iLfffvuRdA4uLi688847v6ufBQsWcPToUeLj41m5ciU9evTAzMyMhIQE3n//fTZu3NiRzqGDDuH3VyEXmbI2MgWJ4OcnzAMWvYz/6mRGCiW0HEphdesNxsQPINw/AFVzCyZyE7I6jUHTqubomm8ZqAzB1OofRKZAQH3BPRrvJbJ/8zHGL89FYmkk/HYu5TfVpPqUIFj1OE1Fcwi/W09k6kkK/D04F/w4fZK280pvT2ZXqlF90p+MAxFkf+pNly8uUHPbCUsLT3RPZBLj24NXC3vS0GROhVdXppe/yt4uo2jNkdNCIZXpRVTdqcdFXU7m4c+pDHBlVEEOr1laM6qLNzN0aSgahIyxWkWmzxdENhqpfK2E096V3LSLIq6ljpiUg7i98Ry2Ee50nfkctevfZlDEZZyGvIeN51B2Dn8TrYsTdkPdqLyax/BP1qORabAsVsB2MZdl+WS1FSLXChFrDJi5QJ1VMTunLKDN2oiTZwSZbcOZY1WL3vFxKjIrsQk8gdjFjK6GmVgGubBh6XmEbjJse/ri9+wABNZmeFm5cOnQWYIqzRHoDegDQqm+d4WTMe5sjw/HI84PPBwwGkToDRI87uwmstsadEsa+OJefwTpGmJuXUUgdMLX1B+hwJIRfXugUasxtTBj2IEXwExOfUoxZTfvMKyrHvf+A5Eq2v/OxZUVCGcGEWgbR82hLWRu96R8k5KQLiJMkkopsq7CTC3DYMhGKBMR+/4YrCN0JH2UxZ2zF5g9dRlPdJtKvNMU8vbno/Ztw2VwJOW5hQSuKSLs1e6E+/hie/5Vilz03F98FKmFCZq+1pTkHkejVoOZgpwbyYQ0FrKFeJKlSt7uWUnCyv10eX0MGzZl0z/AyOkjy7DeGUJtazH2vrZYeTsAcGPPcapTC3nOaxrCr++iHxxMRVopSmdL7Py86DvUgeMuuWTXlODsa0/NzUoqM1QUDwzCKBWhMuhRIKFZYaDSy46IeksCKoX0fA9UASWEbr7BLsnJl50AAQAASURBVM9MBlx6jiv7j2NiqETT1oJep0EA2BnkeGHNh1+6ImpwwM5ER++sUyxQuhNf4YjRFcr8LFh8bhnnhrxPxtqTBMwbiLmnC3VlUqzy/YmZNo2+kxcAkHy1B6o7ORj0Bhqqi0i9Po3JSyF9wyRKj1jzjugCAkCOhGnEgKkcUaAbdf0KOPb5ImY4nabXyqHoVg5CST5O/Bj8JfHMNvT1N7Crh+akPnjYxPEYfTC6GRGEP4u9WzCNqKmgCc+3uqFKaGaE3ptBk3zB0YQr4iLOrN2J76L7iJdVE/n2RDoNeorLj31G9q5bFD83i0nzo/H3/zHJu665DZ1Kg6mlPTNXnQFAr9ZyvMvryJ0sqRuQRsbtY6gaq5HI5JzaupT56x5g5eD5sA9n5x99IRtRY9y8iaP5a5i97DBi6/b3989CbqxZk8FLLyVx4kQf+vZ1QIGEvvgA0IzmT7ldW7487N/uo4P/HYQIeIyIX4zq+XceI+Ivy+d34sSJR27aoD1FQnp6OqtWraKkpIRTp0498vyLL74gJCTkN00+f+rH93cuX74MwMqVKykoKEAsFhMYGMiuXbsYP348ABcvXsTU1JTo6OhH2i5YsIC2tjYWLlxIbW0tERERnD59Gh8fn3/pvf//hFAoYvzwNX+L6ingUfHX/tkaP+zTvyyfn5+fHwkJCSxfvpyJEydSW1uLo6Mjo0ePZvny5T/zK/01ZDIZp0+f5pNPPmH9+vUsXrwYhUJBUFAQ8+fPJzQ09Lc76eC/HoHx/4A3aGNjIxYWFjQ0NGBubv7bDf7TKamHEWsx9gmneIwfW9MuMjZ+AI6m5vzw5Ra8QwKInzAKnVbH0S07MLO2pN+4EQDoVGrytryD26B9SFu/5ESChtutaRgMZjz7WB9Ut6u5PPRjqt3smXXnRWpu57P3/QJO2I7AveIaWZ6D6V98iDffDKa5sh61ayxGiYHDs/ozbvtdZIIIzl/wwGA0Yu0ew5lP1uPhao6N+cs0F15BFmyDmZcjd868SWw/azTFdpS8Y8nNGQOoCHNheONhZlZ3Y2ZtHcPu3qYlx4hBo0PZ35mS9OuUB99EXjaG45F78Cv+kvjzlzH3sceqiwsRn07i4vC+mCTn0mbfF4MgDImlCdVZhVSGluFeoOTUnvvUiyr5NOErXA0h5Fg1c/7EFSZ/8BwiqZicgxdI+XovRRlKEvVnqPMK5oSXipW+8UzqXUN++TlCeltx7spe0ipkvNI1iePjPkcgNUVd3UyLnwUWRjG63FrsO3thGeBC6s7bHNJ6kO7ow5p1NgwIjWBraQYLVfn0/bSI+9fceaw5gVdLNiIy0YO0ksNd36QtpRK7GE86vzMR6wgPDNVNIJMgFBhBKkZtpuAH+2cQScSUDDZisLREIVcw85UX+Oadj0l7kMpzH68guHMUCsUuWtVGGj/2Qquv4OPzY3DwCMOltQv12fnM2LiZlrI6xHoZN7cfYlrRK4zrMpGnfZ/l5ox1yJ0saS2rp9BdjY3RlMnJnyGvakakNyL0y+DW9ibSn9iFPNAOy2VD+PKTU/gKCwjIVlAq17CmbgCt5qm4Rmxg6dmRrPEYQ1peG1+5JNHvzCQydl7m1ve3sB8WTKeunXBycOHakxvxe7I7FTmpxMeOJE9biufozuiMcPzYu6R4+pIzyg37bQW4fZKNaZqKzaPWYRQI6Pn4t4RWmyLcnYHfqrF8+fHH9B89lDERvVhjl0LZjSwi3i1g8PrZXDt7lfIXj2Hl58yo08tILhKScLuSmU96cTz2DS5vD6XNz5x4TSgz7t9isWsIQ9XOfGR7AVGZiD7nDtLWfwTdcxwJHxZHZkYDm2b+wPCRrvR4+cckzJcmrKFw703segYw6NJyLh17nK4Dd7NdPBcpM6ihFU8s6Y4ndkYFVNaDUMCNm99zZd9qHn/9IOu8ymhEzfsMQfaTc7fWpjq2vvEq2j13EPYfTOuW/jzXHE76ucOkjDqOzMaU0MsfYuElxN5EifgnSeMB0qjk+/RzdH4tj7hXJ2ET024ombr6CNs3pLEqy5Xx493Ys+fHYC8GnR6MRoSSH+ehU6k54L0AEwcLGidmc/l8GtazXqZmtAT7N95n0ZLDWNr98m2cESNrng2ntjiD0E2nUPXag7VIycgHH/zqUrh6dRqvvHKPEyf60L//jznLkilnDVcZQRAjaU9p0UAblsh/ta8O/nf4n/j9/t/K4/d/kfnz56PT6X41vH8H/zq/mMfPwo3xwz79S/P4ddDB/yQdN37/C2xPsuR2vwWsHFOHW5w7r/UMJjEVnvmskcHOljRorflyF8waJ2bU7CceaZv9zXkSXsimYYGUTuNzEN1roKnOljhrc+5+dJbs7beRysyotA1m59uTadvfglNBIEGdLbAOMqHL3nkEd2pAVTMbibgJZfAwrjzpgbKwHHnRQZIqrmDdYkTX1EDXZ5ZT9F0/egWMxUFpz+nzAnLv3eHSM1VYC3LpPjcXfZuIne9MpOvm0+QNGsegLccpSk/A6f5HZMwIIWvhFzTnifF6wpWsm84Y8/SoTeV4FdzETr0XhGY05FRy7Lk9fJuyghdm6ylab8LRyEB8sk0IUYpQVbaQMy2b8K/6Y/utBXJPMVc/fwdniYiwb7by1NSJCPbncDOghTp5EIY8U+5FPIGZeiydkzdi1zmcBsXj+A3pha+kgMuLw5AUb2dKfDRmnp6AHHVNM12/NvD1VyqGe4ajdbIl83ICNeH5BEXaMvZWBtfLm9G8JebyEjXXZE3U2QkxhrswoNmONTUSsud2RaQtwmOAgIHDe9N1ayhavR4za3NQymjdk4LEUo54RCBCRfu238rNnpq8MqyPmcAoS/Q2Uo50exNreyEmMeb4RYWBAL75yIOD21Yg6PohhgoHgu73w94iGP9JL9CUUoRAKKS1ogm7Pt6EBkfzseAt7tbGkWuwpHhGT6YvGMT1rw4gKMtEcqqRT9/+giGLhuNSmIuzYAwRQ5dQ5ueIQ7cAvrtuS0m1jBgHU2T1OszFMN/9JEnCJGTGEKp6mrLAsZwX6xx5VRtHSUAXzJ7z5UziPTwdbfGMj0XSBm21LVTdzEPsbs7GnR9T/8VwYs2qKRq8BXVyJQ29j9KY8yVhNUG4qstoaKvHuigB025GSofZotmSg/u5LBq2XyTWNwp3hQMiRxumpDhy+9UzCJ6OJGXeHlquZSIWmlCpa2Pb7I/ZVyHFUbSZ7MR36VRSj8/I29zO6c3tS/d44cs5BMSNJ3LRZhZ8piD1lR3c+0REfSA4BVoRDuzdmEbotTNk31diOaETLy5KYuHTgfhP7kTRgVs051QCoBJ+gH3wMCZutWRFrC/O/HTTa0RTW0ttg57LaX2Z/v5M7OxMsKcJKWJEPxFuRqMRE1NLevaYStJZGVc+CERLFXt2reCWuwZ5wRB67nAkOPgYM2d6s3FjVwDKL6Ry/429xHz6BPJoSx4L7EvI3sce9nvz6FdkvXkRb40JSxb1ZcJjXo+sJULxz0+vxQoZo/PWIBAJEUpEPPPBXpov6niiTsr0D85h+U8iZwoQMGvVWWoqqug9MpWF5VosnfSPmHL+I4sXBzF/vj9S6aNzkSNBiRTzv/luHSGdg6T+r+ds+6sppZHbFDMQP+R05Nz6OzG4EIUzmVTTQBsWmOCP7V920/d/mdDQUGJjY/+3p/FfSWToWMKDR5Gdf5nGpjLMzZzw9ez5l930ddDB/wYdwu9/gRv3IanKjMQbDwjSt+DQK4iaeiipM0c+eA6Hj9UgvXWSy0la+r07nIJSuPkAEpLhxRGd8ZtbiM+o6RDdF9V3gQzI8SJMOZcqWyuqCxswLBnNnGfi2bxBTrm7kF4FR+mRdZT+LwXT4nWbzA9ukfhGJoEfmKK+lE7BQQVuY6O437iEQ9/exlrti1NBEBn6aoKHKjE0WHPz6DlUQW3o4tXMin2BO8quGLTvU5JvStUEFVZXrHn1wxhSP3qR+6svI5+/i6GTepMlaEHXYsX1Oz7ovaSYewdj8X4FXvVrUShsaRabItO04nDBG1fXFmr3e1J3X8bhF0KY92wkAxQeXMzdzaDSx/Ca6Yv3jWS0D7Qkll2kppspRwatpe/CsTh7u7F+X2csbxwnurqBma7v4BSvxW/6CW5e17Jkg5RrB8bgbnaDdYPvYK74CLfkxeze+R1dj+Rg653H4UlhhCcKqTNWoS2rp8Sqli15R+huF8VLCxfimFyEmbcDmgBXop7awnZ7GyYMHYxkgojvz2fi7t+HqMsN+NoV02lkV4xGI1KlDEwVYCrHpLszIgsTNFodUqmS/K2XaCyvRWxuCo3NsDuTUVsXcKLqc/SlWloGaWhMLsYm2otQ5ybUmSacffwbuq4YS2en2TSYCQkblsj6ybaEBrcgc7Hlftpham7m0XtKPAMcrTCzNUWny+Prdzpj885aJhZ1JznpeyQfJrN7pB29T6TgENEbTW13ZMqbxCwYROldLV+tj+SlZU9C0VrcdEZ6fjIF8ZUL9Jk2ghjPcGQyEVvjzyEUtvuXWNrZMP+jFZiayDE3N6e8uJJtkeMYP8ID7771FCXL8LhXzJmDudhoWjFamGI5Yj17FtQgmOjOsMXDuPf2Ptw1H2EhCUJLX4JiIrnZL5e8K5dZ9sHLdOnRGYByURH5a/0ZGT2A/LPbMeiNvNzaC7FQznzPdGTWRh6c683tqwK++Hg+daYqbozNZszZU9AHRJJ280G51ohQbaBzgTXXt2zH1L4vDA3m+ddj+CFvEJ2HePG51WlC3N9k0/v9OHm7K/38S1n8Wntwl8GDnbm6bwi+vmaY0L4xSKcKMOKrs+Tjt3tyMX0YJ+70o6lJy1tvhTOP7pz7/i0SLTLpOuxpAI5FL0XX3MaZW91RD4qlf5oVbSbmqF9NR3BpMCpXU7wnxRCz5zaxsT/6MlVcSKXycjrV17PYEK2lwdBK5Iovee7NM+TeO8+Nw2sxOkgI6/QYMz+MRiB89Jbw1xDLfzSvfP55f1atSuWIv5Etpb9tbmRqaU99syllicl8Fz2GW9cG/6ro+zv/KPoAfLDhU0Y8fG2LEiujHAujFH7f2/hdtLbqmDr1Op07W/PqqyF/qO0OkmhGw1N0/tMEyAkyuUYBNijogeef0ud/C0IEf0nKhv80/p6zrYO/BqFQ9JelbOigg/8LdAi/vwid0YBY8Ms7lPcX7Kcqv4TLwddoCnFlRPIHDOwO0cFgawn7vi4nK2AQyZueRzEgksVbXLGxgMgjn5N4sZahhxYDG9Abh5I0T88k52KcLU/jLBAQOLc/Mpt23xqd4+NkuOkINDmGWNqK1O8gFgNuUXs/gozL9hRMNkWgckAg0FFw8CrC2zL8S0ZgFAips6qgOEFI7dWjeORV01ZgikApomunTgQpY6jVN7MvaDhZ5q2UKPREOEngfDkGowylTxP+kencfdmf+gdWSCwVbK8dgbimlbG1U8l2tqIbqWi8JZSNn0VjujlBXVWMqfMlX5eAxCKFuSo5r5p70Vhfjg+eZK29Rob2DgNmj0coFFKdV0ZqShKWZjZUJRchDNIRIfsYy67DcJB7E/7+JtRiI6215VRee41lymga35Ag2vwN3vLZWBituXblQ1R5DYREKGhIMUV31ZwWkQzuFuLg58bwHhMx5Jox4eOXCbL2Zu/GVDLlZgx+0EpRRhPCnFbODC2iT99O3I56i/ytRyl4kEH+4rXYvjMRzz6BCGRSMFdQnlOASGFE3tjGwaCXCV4yDJf4UEZcfp2UizlkvLgZsYUcq2YhIVN74jOrB34fn+Z4zDJiNszgzBvfYF+nQKWqw1RiyqWuDezdY0lTi57LuiZCGkoxC7Rl18LJzJ1zBJtQN9pa22g2amktzaOtqZbG93diPXo5TfnVyCzkOGQ2EzhpJqJGd7IbMmjt7oFILqW2wYBQr+PYM98xNsKKyL6dcckyIWLq03ywr4per+/h9Om+nD3b7+Fnurm+gdoHWThHhEF9C45mAqIjrfhmVxnbJ/oysKcpm11yUN6pp3BFCFfnaZkfWMe7L9zmg28lNCe2MDm3mtDOIbiFdWIAgyAMrIR3qElswcfUFgor0Svl7Ht7Kvq2eux2jUH6VD9ydt4mPM4cxUApOW9EULtNT7xtPCF5qVRc7Iyp/xiaK0YR3SmYvu+lYRnkCkDwi8MIeG4AeakXyXpvMuvrQ7iakMwbb4Qyfc80CvbdxC6/EX1BLqaySBpbJIgjByAf2I9tu3L58PNU+m2R8GJLBJod6bhPjuUTk3YfnbXC4cjNbejqfZzQ2AE0NGh57bX7LHvZjbPfLUduZvVQ+P0dezNLCsSNCDo7M7nZj4thPYjY4UDwiqGYespISBj0sG59chEOfYJxig/FLs6f4cY8Thz7iObcDIwGAzvfm4iqsYZpu0/gHzPokXGaUVNEQ3si+b9h0OpIfHE71lHtufn+zttvR9C/v+OjETt/A1dXBTk5I7CwkPym6Pu9xOJObdSXJDefxjvtQ4xCMBoMiCX/ng9geXkbBw+WUFDQ8oeFXwIltKJFgx6TP+mndCgB2GNKJ1z/lP466KCDDjro4Kd0CL+/gElpFzlVX0py9EhcZD8PnauQT8UjqBXVp3uwCPJ/WG5r2f7vSNVXpBU4I3lmAkV6KwI8oV9nA8JtCTRfaWLn4BHETASf6QaECBGI5rWHTYe/ib4iSnNfZOFjE4izd2bH9z/Qk/4cirSnz9GXCd75MpdmfYrFoXzEWi0NChP2d41iYH0mU14dR9b35ym7JUJ5ajxCo5A2BMjszLGysiSoNYrsT9agMfFEnavA3KUOpcYCDzsDYqMQexMV/qdvIj+6B3W0gSrZbewcTZl5cCWa7BQutxzD1t2PkScOsG/KB8SWPqDr+z3RJh6gfGc0Zs7u3K/siaegFxqNhps3juBsakd1WglGuZEcpwqUyUY6TepH2QfFyEUKdAoFq7/YgKnVdawvQIVRyFdPWWGwFdInYjv3Du/H0SaVuBHzuXv6CrNtX6cmIR/bUbO5OmkX9x8XM658BcVvJaO+W4zd/WwGPDuOssxCgvdYY5XUyqWdG+F6NqZApp8rI5c8wfG0YsKGdUWsMiC+U871kk8Qh8sxuW7JpZU78HB5CYG4DeJNsWzV09KsokEnQmIuRySXIhUYUViYENnfl1xrBVI3K2ojrLAz90fpYIGZpSmWgU4Un0hH0GaDuLsLPRb3xzTMCed0S4r0a3jppS/oa5JFmb2Oz/3SCYhZgaRcjNpPzZEDh/D3CqLSpidFq0JJXLKameKVxKwcj30nT24l3sJXbkuNpokLq/Zgt6+UFSZ+BGhhk9MtJC3NKJwDcXmxP4akMloFOmQyEVKpEO2tVK59u5+Yjx5HZm2KzlRCqrMG06py3B0CuDxvDzeOaRm5Ow6XRhOstCaY2BVh/0BFQ6017s22KK6tIeXedTStMSgjPBm45Q3sewSC3gClNaA0wdbdgRazXK5YlXHNqYYRpQHck2wi3L8GhZk1rXEiyj4IY8q3Z7gzLQztNT3O2QHMX9iZoqQMDq5txVqdz1BPe4JeH4xloDl6DNygiEDssDFR4BHSiwlT9jDfzY0nlhcBsGB+Ase+SWZx62WGXLmCXVQAczfc4oh9JV9ZZ3F1TwWpVyzxWHeTBO8qWuYdQ9+mZewzoegxUCtSE9XvCU5ufpnqQ/vYUjQKiUTA+Kj2HEsOnu2h3rMST6KdWcegGe8RKTayl2TCcURiakL/Yy9BcTXUqMHm0SBRZwe+S1tlI+Mrv0IgFNIXH+L6r4b+q7n/oJG4iR8gpg6/6IG8914qvr6mjB/vDsA27pJICYvoQTDtgXia86vIWHsSsxAnUtt+wMknik4DZwLQp4/DH14DXV0Vv13pjyIUIBAKQCBg3fxoVI01LNqYjUT6r4fy9/Iy5c6dQdjb//E+ltEX7Z8o+gAcMWM4gX9afx100EEHHXTwUzqE31+AWCBAhADhr552bwWaCXph/C8+DbMpx+HyFxQ9cYPmB0VsfCsQEKJKnUdOystcnuBJfr4tfqoVLHGS0pieD+Pa2zaklVCceICrmdFYm99n1NzhyPosoeJCNgCrVvZm/MED2GzLQmJqgpZWkkM9uRrljWWjkZcGRCNUl1BxrwChpv00XSARIbeSk9gvhLENDbxX6kZXXzOKMWJaZkujdyY1pQ3cv3sRScMiTDFF4JZKVbEGl+5BaPacxsOqO+VqMQMfm4lXn9FoW9QIynWUfdJERlku0sYB3Dt2jTqncwSUuZCxWsWeCjEtWinWElekT4g4XmXKxoO9iL/2Dm7SakS1ega//TjmUgVFW4ooSVchFImIHNOdKbHPs/X6YvKXJuEpGope0MIN2QP0gRao16bSkl5JfL+n8RwRRVLmHu69e5ShT46hKsCaAlsx1XW15CW0b9Ib5Ho8RnemrtmItk1La4A5E99N5JLOkdJX9Nz6+BgllzKxb+qOTaw10R9NZMv+HZDX0P5HCbWlUaWiqa4BUauECbmfoC2ooupWDuau1jRpBUS9NIJzrx3k8RmXebL0Ii7xwdQkFqCub6H/EyOxVxmJGNyNqgeV5FZWI5eYYF6zkFbJXa5UnaWXYhDGLnK09VpaGmtw6i9j6IQxXJjwBebJxTz33nDc++7i0pQvaKttpvvUgdTvv87xvfmYz49DPdUfocicmDtnSBNsx03eA6W5E74ze7EncjF1Rhkm1dUM3L2ERdWjuDj6I3JPJ+M2sStuQ6K4LC7gRBc1uvJm1o6dQZTGlVuaSKQjzyK/3gW3gu4sdx2J7fhWMpsKsX3FhDbDW8gL1xB+7xSFa0fw+Z199NiixD1yEYGmGmpzq7GN9SPm48d5WXKKeqGGJMtiegwew4C/ubhckxSRt8QXUxcr5tw2snvT88x6fj1OTnIc7J+i5nQmzk6hxE3wxNfDBE2jigeiMjYrkwirNKf/5lZU9U0I1Wqkk1z48k1v0m9dY8eWDGrVCkLXzsItrjM6lZqshZsIkIvJUFsiu3mbad0v4dKoplfsC6TPUWE1Mgx/HNnGHV7jFPNGTaLL2UI2n2w3kZwe3kbSlIO4Tu9GVHx7Ivjri9dT7GWOYeBFrgaIUSL5Ma+d0YjRaETVrEVp8+P6sGpVCu/X92TdJC1SSwWathakJkqkJgry8pqJiTlJRIQzd+7M/NtN1j08PJSEjjfBGgUxuKDHgOtPkp6b+znR99hLaBRNfPN+Lxw8QikbGEsiJSwgjpasVC7v/YD+T76NrYvfw3bFmbc5teVVBk5/F1f/zr+9OP4TLpGHGTKifiUv37A7qx76C5ooLdHrtAh+xariHzEajahU+l+8uYyIsPqX5mvNXyBuO+iggw466OAvpEP4/QVsD+z1TwMawIRHXhWVQ0oODOre3ib0662oFryK1Td3qEzMwzB/BVnL30SVX0xVrS1H+r5FuEMpnePtyJr3BPXXryD38MKiU2fODngXVVkdokFOtDYa2LN0Dp3XTqet8j4N6XLEHm7oK64itzcnYGIAZbt38OG7r/G+0oS0HSoqzt6j4OAFyv1yUOvq0QqFOPRwZPxTr3Pj8gOas9VkJOfi0n4xQotjORV+9zBGCakoiMdMXYtf20l0gVpOn3wGt8IeXB0jwaqihZjHJ5Bw8S3kr6o4JzPDqNW39yFPpsyxGrJkmOfZoVWIsAnTcb7Ig0hLDZe+eItq1wRE4k1oxBaozERUcBVnYzf04aZobCzwEPXH4hsRrUMyqTAmY1Vuhbd2AAyQUXTyASZyA8JbybRVBRDz8kguz9pE5rar1PdToXCT4N8lCINej8xLyV35Ziqsu5JacwOJqylT9z/O4JjhvLftXfJvZLL40su4hPbgE4mAopMPMLZq6flYPGdXVxMcNQSHGC+6SV5gfn4o8yyzCDiaQ0usgsPD1mBbLcHmxpuY2Zii0umJHXaJhGwD01zXcrNHCt4qLzT5GsoTcujz6ZN8q6kgyr6UY5E9uF1+m733zmC/3AL3/gFceuBK0S01/SIaqV1zhZ7LmrnQ/RlcX7MBvRGFjwsRS0dR/aCYUP9wRIVN1IvMMBE0c7HyO8RSa2qSCmmYX8nYjx4nPTuXmG1P0F03kU3zxxIfsRxEYtRVTShoQi3WojdVkLnpPFGvDKficXe2xZbx3B1Lkq2qOTVHh303EQgEmI2O4TVhC84XUnH4TM6AN0PBVM7d+ny+cE7F/kQhXVcX0Wf9K1xTnuK2I0gczSk4e5Fr+ZOxO3AE+8Ic1HUqCvbcIH6BHQfcKtDdLmZhzyDEDpaAnN5iX0pa64jq5YNWKWO81beE9ZrIsSPFfP9FMk+7h2ATFIZTJ2f22D+NUW9gaNo7dNPbkl1WwJmLSXjlCWlMLyVvx22Cvx/C9g9H0c0rjqLKOXSf1wtoD3rSY8c8RAoZ86dlo2rpzdPr/YjpNBgbey/urg9lOzd4iwF4YkUOtThKrYn4ci0b8s/DyQruFIKre1cWLpmOmW97BEuLKk8un+hGnlSHo0qMheInN08mUoY9n0FllyqeW+DHDKv2vF719RpUWgHe84Zy99w2fvh4OuNf3EpWTR8qK9sYM8aVzp3bxaaHh5Lt22NRugtYzWXcsGA5/X+WJ0+LHsMQT5wxZZryOJb2HuyhnlIaaUFD8pW9PLi8G/eg7ti6vPCwXe69c+QknSX33rl/S/g1oeZb7mCK9FeFH/BwTZ39wcWfPdNp1RRn3MIjpMfP1t558xL55pscbt4cSGTkvyb0Ouiggw466OA/nQ7h9xfxz3xbVG0gFoFUAjV38ti2MhvluGQGxr6CQHASkaIPZp2jiYoIRyAS0nQ3kfvvXUQpSUUirCV+8Yvc+vgJPr5rz2gHJSZu7uz7ZivX+g/nyTkraTqXT8OVcvRNbRgE4GAtxzfhAlq1mnHqkRz/1A7byU9RvO8Luj4Tj9JMyIFRn6JtVtP5vbkklUqw6mZJZVMlbc11lJdWcy2tFvfSYrb7mOEe7ETeoUQECBFKo6m2HozYvRvdXtBx6aWNiERPgkBOhHc/EiNM8DrqSkOZild8R7B8oD1OjjkkfXgMqZkMnVhLjZ8eE4kjhUGHiOw8ld7Pz+XygY00X1yBiVcnqBchN7GnX+YluhlvIWmsp801GKc4P8zd7BFKRATF2lEu6oF9yHhOP7aOM5UHyI07zvhu6zAIwMqmnuY7PxA0fynWYZYoh1pxrTiF4i9ETHl9IHIXNwRCIaeOvE1RbgIaQxuCuFrKCvPRGEwoa6hiyZv1uKUnMz4jkJEnZmDmacvC0WvonFlO11WdGfjiY7R5KZDbmaP7eAOlgVYUTHIiIFqPuY2A4nGVJMWlE840ahfep/RiOk9Z2TIzSE6SvxmNqhYWL/Xl3rwcLCzNsI30ILTAQJkgCztfW3Jz8vnk2xEIhUbe/NqSqS5aOr0GD64YMX0igpavkxivP0+peiymWeXY+znhMiQCl/HdSLlfx+xFadSb9+TsbnucXD05veFjpOmOFB25Q+nWK7iNieKtwHTc2hQMmvMZEaMHIFaaEP3x41x5+1NqAzO507uGAr9GuixI4MDCSM57VOL6pZKcKdWELhJhm2LNohM7+HjFXV74wJzPp9cgv1LAlVd302PPAtzMXfCctpPyq1pybWBUz0BG97xIn6KXOTbLn0Gffo6Fqw1brphRX1KK1q4RDkOzwhv9jigKhfbUiFSY5am58vy3BM/th/3b+0luayb0wTI6xT+BXq3lg/kXuJgnou90MzwMBo5X30PX3IZBoUVbW0Gk0p4bEdU4vN+T4ERrSo/epeuGWVy4ns/58qfpryokrHUz8OTD763zuBiKJS0c+qKem1O3Y/u2CTHX2sWYNQrMkSFAQE+8CCzWkHtvO3aDZ/H4NG8KE4tJrJJh7xWI3MMKvUZH6bG79Dv0CmYVJVQkZqMfu53YTXNgxo9rRXCIJXH1F2h+7DptBwMxMZHzwQdRvPFGKKamEpKvKJFI5dQ1iJg16yZNTTrq68dhYfGj79uUKZ5o0VPdko9DRTO0Z3qgOa8SUy97Nm/O5U6vTFp9WvDAktc7tSeVn4OeJtRYo8B20lKcfaIIih31yFoWN2YRzj7ReIX3+bfWSzNkTCfmYd4+g8FIQUELXl6mv7uPs9+9yaU97zF6/td0HjzrkWdWVlLMzMSYmDwaSMZgMJKV1URAwP9cGiG9RkfV1QzsewUhFP2JkWo66KCDDjro4DfoEH5/EgatDm1zGzKrX96o1KcUk7RsF75LxjB3uzeOtrDxLUiYvxX3q5l4zXVFINAD+odtRNL2P4+5UwAO4aEUJ9txptssWlo6ETe0E36x3Whe/ykqtYby8hbUbWpcRrlzbfVFjEYjuaFdGdDXhMI7Fwmq+Q5D/PNUp72G6r2uNMhTkJtJEAydibHVlCJpHXbmSmoyy7AYHMog73nkCFN4OWgW86+vpWu5iOPlfVk6SYpKexzV6d1o7IfT4/Wneaq7Nxt+qEUa2IfO74s48uR7CKaKOVbnyZdLHBhNK+OkSdg4ZBIbZIckxI7qlEKcephzIfMe4uxiHOz8adHWIo0Ro7S1wizLGc87fckwO0CEbjaWCd7ojGrsg6ypb2zARueMzNaME099Tp1pDmkuZ1n+chLaqhbUzW0IEKKsiaQltAWVbR6dYp6nyCuSzN23qf/gGs03hEQpD9LJoOLsDkhefwaxtxB1QzNPT9mHg7cPutZWLo/rSneljKN7ZlL0hQUOlS5ITO+Tvu0qJv5OfJ4ZzSDzSmrXbaHaV85M7zE05FfR6/NpuJy8TuacM+RunIPrqBjEQyTU21eRU32HliMPMEgkTN4+A7mtKatfiuGFoeuRFlRiXayhqbiE/X3eQTrUhwWtySR3Os/dS5ewsnwT1E3ICiWYysC5UzXjnvkMY4mGUx9cofZaGneeWktbdSPYQsgrPXALjiV4UCTx/S2J626Ho6sNep2WXk/OQVOppj69FL8RXbExt8CyII+G7FK2rDuFXagvovwWVFdyiY4eQGaSI9ViLc+LOmH1cXeyvitmR2MTVQ8kTK/wwaOnKyJRKfnGOlRj25iwqRyn0544BmfSUloPgK3UHLedBThrdNh/9+LfPumXsHTLpenaLPJbRETVGbDOSUdVZ4XC0grrLj4oCrUIdyTTPDmUbRkPUG5KxexSOq5xfpiIJdQXCXg26CqJhR60ltZhm5eFs5cXufJP2fRVLjV7PqN4gifS5MuYHf0ElWIRPr4ePDkimEJS8I7uiczKlOdn3SarrBfdhDeJlFZyjhxOkEHQ4Ztkr3+butdfonvn7lgqK9FZ/BhhMPxOGdfDVKyRXGEMIdzes5yC07uwsHVl8uThDImSsOn1K4hL1vPmqLH0inidyveycFoYjPOkCAJkvtyL8cIywuORdWP16ij2RXxPa3oNxgYNRfn3uXV8PQOefBtMnQntMZ4Nu72IH5fJihWB+PiYPSL6AAwGA3l3TiM6uIbriSeQTVpG2fYUhKfldPtmNotfqsV3qZ6oRRLcBZYP20kQPTRnlCnMCOs18WfrmkgswTd6wO9YIX+bn0axXLkymTffTGbXrjgmTnT/Xe19IvpRmHoVt4CuP3v29tvhvP12+M/K3303lddeu8/mzV2ZPt37X577HyHl/UPcf2MvnT59gsAXhvyPjNlBBx100EEH0CH8/jQujV9D2an7DE/5ADPvnwdDKD1xj+KDiZiHemBr5Y3D33x2wt8cT+62S3SOnYZA8A78Qu4mQXETFg6BFCY10N9Dg+cTQrpHbiflw8PcqehC2/MR3Kl+krLxG0jLngGmYgwqA1cCp2HedBQby8NUijU0DommOUeGz9y+lF9Jo7rBhEVf3CPGrJCG2T6kf38X/ZpdaG0c0D2hJ9k2m0XOWwjtG4M6oZ6QxlSOb6vHTZMLIiFucyNoqS9H1WKH3Zb17Ht3AE6DG2i4WUtZWBG61kR6C4Yzxk9MgEMXJh16iVM3XUgdFssmw5vEF8fQ+ZYbKokj8sBmJvm/jbTVl0uvbMUy4nuiuurQLxuFXt6Gpb8j9RnlmPk64RYfQt7hJHJu3Ob4d4eRFGtx265Gr9MiMpGg1+hx7OzPY+vfo6qliGtnVqPfJKcxvQqbzt7U3G4/Za/u8RidOslps7qHZWIMuQdKcI/qxf01Vxi8O4riOwXUaE2R1doTNySBSLMKIibGkuoTR/rZRHwDHOkRasKxe/YYXewRtGUyGS0e/k40ZFeQvvYkUksFeTZN5Ly+lcCvbRm/fSsxZUEkLfHh/ppjpO1JwGZIDAcLLVnlaY65IY9KVzVeUldKc+tpaPDhtbce4+iw95Hn9qFaLMXD0ZmnzpmzZd5ibjklo32jAJcenZEoTRAAfiNiuLXnPOU+p0g7swPvN4ZR9v04Nt+XUlLQwsA4awRiCaXFpTg7OBH22miKUgq4e+wSuq0pxE4dRuW0QfygfoDFvJOMe3wcVo62VBdXMfCUAo1NMXeyb/P45LHYJCbiWlhMlGEqWe+eJXnVQWpXd8HqaB7DSlrJfKwTysL59JeFApDxxSkcYjy4/3kIN2rPkvfSASYseouTjuO5rTBgqqnDRixmjcCKrnoRXbr4MOTYfO68tYe4bxPRD7AjxM6R7XPq8FS34TgogsBXR3MkZB0Li46Qt90dr6k9MBkRh41vLRppCwJpM31eq2bc7jCU8mA+mxyBX1Q6jY06THeloN2XgqC8mM6fvEyLrA7PMc4EtVjiGxzJFu5hwMgdcQVmQiGIRPj4hRNWtBGFeXtqBaPRyPZvnqH1s/dpNTbxheAG9nMnITE30hLiT9HBBBz7hTDrnV58O/ATsBBi18UPwRAJFzLewrBCy9M78vFImI8NDtTWqhGLhZibt68Fw869gbaxFbmDBYm7N3Pn9GbsrYKpfrMQ2QQTrKsO0yVsHuPHuxMcbPGz9ePBxZ3s/nAqHsE98I7oR1HmLSpqUvBzHYGptz329i3cWtzEyxMj0Lqp0WFAjJBVq1JobtaxalXEI/0d37gEVUM1YxZsRPg7U0T8UQIDzfHxMcXD4/f70flGD/jDIjQ01IKAADP8/c3+6BT/ZRz7BlPRJxi7HgH/Y2N20EEHHXTQAXQIvz8NpactchdrxMpfjg4XMG8gZj72OA0MZ+tP9jKqklryvr2CzMaMTh8/we3kB5ir23CztELh49teqZMTni8NpsVBSuiyrpj7g16lRlKrwcrNjaYLtXSx3oNZn3pihx/FOEpE4oOByE6k4/79LZx6DaP7a7NJuruV4rOpHK/MISdlHV5xU+hxIw+pXoB/8AAOhI5AkPc5kpoK8j9dS8CKYbhdc+TE4o8QyiWIyhvRCSB9ogfmvpNQ3aqk8eABbnW9gqi4EWGbiOIfrBGPNCM6OhLrbWX0Etxn3BOzkZsp2N9/N85b9PTvN4wzW5zw2mtBp1H9iX5pOJcXbqVgzQNk1vn4Tu2FutaL5uwGRDopvtN7YBW2h6ytRurTk6m9Z0afDTPBSciZ0uO4aP0IHrkOvVGJqTV0m2qD7RO9ECvEOCt8mDXjALWdiri35hQiITiM68Zn++sxO3mQnm8K6WmaxZHFbuilauShttzafoq97zyJ7elGGjUyYr164e7pSvntYhxziknZdxV1SxtKlZGBPnpu39MS7+BK3NzeeDj6Unw+lZILaQB4fTeZzv5B3ChsoK17CQ7p1ggUBkoP3UWk0ZC95TL4BnN9hoxsQwGzPp9LdmAOT7vP5LDdEG7taYWwZp4/8CI3mvJ4zkTNcykO3Hx7B9bZofgO7EPDlQYqL2UwdNnjmClMoZsdWXOcaDwtRpGagt7XmZLuMhz6CSn9IptWWTe2ffEVaTcP07nEH3IMuAR7MXzOKExuW6O70Yh2nBt3uojwHe3MzV3nsHSypqm6nrSiHA6tPUBvxzDSp72GVWd3zqk/4cqc9wi4NQGLbp5Ynmik/nwVAJF9LjOqbfZD0+eiAwlUX8/CX9SXuk2nSS8oZ+dIP5Id5UwijF51CvLTsgl0FXNaHMioZwfTfD2D1BUHEBkh+MoeLhzcgL5xIFax/bHuHQzAyx/34M6LBSjc2k9UGowyUr8y8vSVTfSoVHH1pZ08cz6OztIYmrwd2LnThoYGLeIfttO2u5iSMXbcaWhj4HYhou4SbhFGQXIBpkduYjJwGE+Ev4Bg68sYrZTtaRB+4iYmEAgIlUzjvFGIBTKkiPFV2HDtqYlkr79B2tMn8HqyB7WJ+cQveAO/2f0QK03IcD2OYbkWJ58otpvlkU0NS9p6093vIpaWUnJy2vPYpdzdx+3j63Hp8SWvfDGE56fH4esdSWHRe9QVVaNtTGfTl06/KPoA3IO7E9BlOLEj5uEXMwhVUy01T2ThFth+M7ZokYyrV6u551JICY30xBNnzHnvvVRaW/W89loICsWPPxVJ57bR1lzP8GfWIpO3WzjU0MJmEumNN53/hFQEkyZ5MGmSx29X/CdUVrbRo8cZhg1z5pNPon+xzqhRrowa9T+bOsG+RyADzr/2PzpmBx108J+Pp6cnCxYsYMGCBUD7b8/+/fsZPXr0/+q8OvjPokP4/Ul0XjONzmum/epzkUyC2+ifBz+w7eaLY/9QnAdHUFlbw7HLF1FUVRKz9mNyr85AKlXypNNKLPr6073fjyfEact+IOnTI5g6WaF/UEdkRD7Su7VooyQ49K5iYM983G1suf5xI2VXawlr1OP3XD/8nh/O4eXHkZpbYXW/EKnenqwIP3TrLlD9ZD+OTl/E+Oz9NFZWkNVylJqns9DhjUAjQ2AK6lgnBHojxhYJTQeasHSypiGnFLHJLRRCBbZ9JmHfPxzv+K488BUQ7KTlzKkr1BrukjrgPuQbuLfoE5YPeY6S00lY2lijV2k4I03E39EcdXkL7lJLCkt6U3I0kcbJISwdacG+LiIch13kUFh/RPYCrHwcMBqNzPlyKTdUXnzp1I1LN6/y5XBzCm6D2iWVwJdW0la2iumrWnATttGrVY1NSAAxr47EzGIf2ZsCEORPw6KLDULLNUjrlRRsuYW9yJrjuTkoHRwJjAqj01ODKLiURX52GnUyAWXU4WZui73QnLGCfMastCHosf5cuZnDvXcOUpRdgsooRgSUv36GjJG1ZF7YQZHiCoc/exdPux7YB7vSZUgsl5yc6TLEEU1qAwk/7GZ3/7WcE97hsfEziLh7hWdes6f3ABfMHLQEpcmonPw1B+PlOGa1IBKK6DplAYWyW6S8f5jU6/eInDMAVUsDlzrZ83ry85gIWpi/8yTPvq/Dte4s8VN6ovSwo/+UsVTlf0VNoQ4nuyhcx3emwaihaKg9kqnH0V5KwSXdCe9DdRRN9aLWzZaJw2Yj1miprK7AxmCHxrmMoAXDyDl8DIFGRFtlAyaBNqgflGIT6U6fL2dQnlSIoLmVrB3XMXGwQFPfAoAmfjt+jU3oBwWS0sUUV72CziI3UnISOD/zc0xODqGTt4yQRgnbPingS2NXXu3cQM6bd9HILBk4SknLh8nk2FzAZ0YfPMZ3xSrKg5pbuRiNRj79NJqRl11wsazmm0VrMH+5B5o+CrIp4ippTBoSznh80PZ7lnWT95M1ygzvJUfpc6aIhDfvIR46AllNPSbf7mJ03ELKJc30abGl1cqCncXH4MA+Bk14HSuHdnGiuNqG91tpTFs4F1ujApmNGX3wwSq+jfujq2m0qOfKO24kX7pG14nJ9N6/CDNrJ3yjB+IvGcG1p04Q8XY/XBzMCQ+3xOYn6Rvy7l+gMO06FbICMjJUqESdcOoVxNjSdYgtTagtz8bB49EcdCkpDRw8WMyCBQFYOXjy5JuHHz5TmFmjCPzRHHL2bF9mz/almAYqaMaZdn+38+fjUav1j4g+gLkfXUenaX0o+gCKaSSdKqxR/CnC78+guVlHYWELOTnN/9tT6eA/kOnTp7N169aflQ8aNIgTJ05QWlpKSEgIK1asYP78+Q+f37x5kx49enD06FEGDhz4i33/1obdaDTy0UcfsWHDBgoKCrC1teXZZ59l2bJlf8p76+D/HkVFRSxfvpwTJ05QXV2Nk5MTo0eP5o033sDGxuYX25SVlWFl1RGsqoM/Rofw+xcxqNXcju+BzMWVyF37f1cbnUpN4ovf4Rgfisf49o2X3MmS+FOvIhAIMBqNDIiNQ73re2zHT+TsR5cZZP8kTKui6GYOl/MtqXAL4/nJeqSmBThH2eA7bzwPFjyNNqeOinv9aEjoz4jkwwgEr7DidgX7Z/TDuy2OsIwSzHXlRDh78OnqUbR8Gs+BlU9iInRGGeJI2wUxtSJXJEYXhizTcGVYDyqEOiykILSoxnTw8+Tvu475tXqkZkaSW3JxENnjFColxeIS0UeqEQiMGDP2ctenO81FVzmcIKPV8gdu9+qEsc2W6LfjcL1qj6RBQtntFIRiASatjRSuPYLHASFajxJ8Y7pjEW5JouNwwqcM51zIe5wqceWrxvcJ7zaG7OCjCGTm3Pu8NxHzBuL3WCzJzx9EbW3G+aZPOPluNDXJFTQ268hUjCF/x0GOp0Ux3bUWVWk9aZsu4TM1Dqf4UJoLK3GLCUVgEOAyLIy83TcRWOlx9Y1mVmgu6s2mdB0zmIJiHXvO1xFicKS1KgHlM+e5nPQyY8MdsXI3YNnJD3VTG10rJYgG9+CzzzI4kqenh6k9WclSnn1wGHOpAQeHGNwcY+m+YRZmdUYMEj1WlSWU1NgjlKqpNTfBXmlDX1kcKq0a54QHLA3VEtRghaHOhvo7eRgBXXkLotBWvLtUcfeVlZSdbkVoKsPoZEZ5Yz1Fm25T8sEssuTFmLWp2WIMp0+JLaH2vmR9e5VuX83CNzKEaSv2UlWYSkjIaBJf3UVNvA2mZ+vI8lRTa9mCzVd1aIrllJlrqRgiIFLWgFejBOFXGTT5NDA25xPQ65nqt5/yi6lcsL6OV5UVBdUF2IS7Ibc1w3NoOEe+PMrKN3IYKatm+IJuqIpraStvwEQuo66qlRaRjmihDZbIcfX1xr5aSuPBNJSJa1kp68q6I8/RZrQjv7wWt0ITejyxAO+QAVys/JjW8vaUGZ8vWk7ulSQiEhVIrBT4DI7kgX0qpza8hVimxONiI2NGRZNy6haGoUp2mdxHhwEPe0vSRptjhxLnbXmoJTraho7CHgmv9VqBIe513hFdolBQj7EAssgl9dQGTI8dRKI10FRbxtAZn1A7x4/cOVbsuXsN2+g99N6/EPtIT3Z1f4+IqZ1pHtlMTV97tNZiygdcR9fUSvLl3WTfOYXaXk5+lp6RO4MxfVHK9ldsuTF3IxfnJuO7ZiIjnv2C7qMX4OQdwfjHGwgKahdm1Y3FfLwigydm9cHhHy7HVqx4wJ49Rfj4mDJpkgfNaNjJPTrhgktxExX5Dwjt8Wg6GVcsHknxEBNj/YvrmLWj18/KInDiVfrgwi/fOv6UWlS8x0U64cJEfu5393v5kEs0oeYN4hHzc5NTb29TSkpGY2bW8TP334DRYKC2LBO1qgGZwgJrJ38Ef5Gp8d8ZPHgwmzdvfqRMJms/lHF2dmbt2rXMnTuXIUOG4OfnR2trK9OmTWPWrFm/Kvp+Dy+88AKnTp1i9erVhIWFUVtbS21t7b/1Xjr4feiNBi43VFKmUeEkVdDTwh7R70wb86+Sm5tLbGws/v7+7NixAy8vL1JSUliyZAnHjx/nxo0bWFv/fD12dHT8S+fVwX8nHb+IgFoDd9KgcwiIf+f/iEGrpbUgD4O67XePU/+giKyvzlJ3r5DkVQdRultTcvQeAfMG0umTJ7iUmMDlOwlMX/Airg6O5Pd9BYfgE2iw4+ITJxCYGHB71oQru63RnvsY20FDcZ/UHef6b8guyiWzMAWn0mNUHbCmKKCE4Z4uiDMUzPUrJbixDWu/YIxGI5RWcXjoYorDbqPS62kN8kBmaOWDp+o49P5+Mi/J0RtNEVYpKY32w9MYgN7CiNfT/Slcd52m8jYilFXU611pblDxyrokdl95FkXtDR5UBrBpbQZDRXmEGypp9CrG46oQVZdSSvTXMQqG8EB4H9cKX6wN1mReTaDsQSNKqQJv13Bk0Q6Iu/sQce4oxz9cQnBBTw723smwNUFUvrwQw+dnqc6W8eDsKUyjLSiovonbc828ZnqS6vcTqamxwndyLIYoK/bs30tEqyOpmz1peGCgOj+Esl0pnJu2nqGJRTgPaMQom0xTngLpQBnVLXfwzelDp+n+mE06gvG596i+V8DaNR9iojCS7RvOvDcTCZ4KiR+lcP/gVsS6Qhz6reF+poH1d50IP78ZefAoJtUc4ZTEA31zK0ozIYHjRpC39SItpjVcnPgZgdsfJ8DeFauCFG6fPELVqoukxKh4vjqf+QueY935vfh9kEMmDlQGp6Nvcid57Sm8nhlIwaE8xp5QI3I5xtUnokHQyg33FpYd0jJ8SDOrR/Rk9/F0vnssghB/B15okjNwlisfTTrJbb0fY+rbUNTUYSd0x31QLMVH7pD33RV0t+/hHRtBc60ccbOazbpAwi0r8FA2oduWRIGvGddfPg5KEU4BTuRcOIiluTP3Vlwm9+oFbOPtMHH3o9+lZRyf9BE1Pd7CY2I3Pjl+l1vqbtg72bLszXGEzO1HS2UjW5Z/gupuMc9edCKiXzRwBXvPadj3GIz2vSzyewto1DWgEGh5K7gYx9RMthDKtO4j6D0qlC6fPYnS2hR0eloaGtFJodFLjHXn9iAd1nfTUJ49jpXKH/nXEmRe9xEuPUjkUn+CR3bDIcACU0treuJJJM6UrLjF1YMfIE3vRFhoPAKBgOviEgRXSvA6lkf+zhPcPeyOvLkZ1cB+XB8Vg2jlSgIvXUCw9Aa+yq50U/pTZGuG1NqUbTuLeLYqlgVXVXzyyTN41mdh5W6CWcpYZDZm9J74Kg6eYWi0DqS/+wX20T4ANKSWoCqoRrXhEqf7a1g24RmcvNv97H5qzrnyhdf5+vgEkjOuc/rssEfWmtdfDyU83JIRI1wAyKeWGxSiRofk0yUUpF7l6Y9vPDT3/DWa6ytRqxqxcfZ9WPbUjAtUVOg4cKgfYvGPGyIffvlU+h/RoKeRNupp/V31f3VuqFGhwYjxV+vY/EPi+w7+MynPTSTt6g7ULXUPy2RKK4LiJuPoHfOXjSuTyf7p5vrxxx9n3759TJ8+ncuXL/Pqq6+i1Wr58MMP/+Ux09LS+PLLL0lOTiYgoN3Kx8vr54ctHfz57Ksu4IWcWxRrVA/LXKUK1vh0Yaztv2d6/s947rnnkEqlnDp1CrlcDoC7uztRUVH4+PiwbNkyvvzyy5+1+8eb42vXrvHss8+Snp5OaGgor732GmPGjOHu3btERkb+ZfPv4D+LDuEHbD8Km/bDvMnw2O8MsiY2NaVHSi4Cyc+Dsfwatl196bVvIXInS872XwUGI1ILOTIbUyjJwZh172HSZoCBB8chM38fo/E8Pm/1J6P0CmGRSu5tjiPqydkI7UMxGPXcPnSJnIsPkA8yYrj3gJw3A7nyvBiyzzLo/SRUVudJaCtHRQRtUywQ3/FDUmuKf85YNE1VWNy5jV6u4bbfBlwFDaS8lYu1+0ySJKGkuXvR88FMfFTNeA6sIMpEzP2PQGvmBC0g07Vh0LQhjxlM/VEFVhoBL3ODul5CnJvzUDibo7IqwCO6Gy3ri5DVmxFKDD6z47DQWCFUStGZpCKylZPePRbj3tPYRmRQeyAHo8KAqkJB/ItnEVp2x3rIZRqWJWA0tGLuZYe+Xk9DTjlVgjRiddPR1nkjsDOl21vjaa1uYvWHeThE+XPujUNIcwuo9MrF8dnpBMRFscv5cxyjQol5pYqzey7iMd2L8BF19JvyLfsXnSQ+Zh7WZmKklkoCMkyQRbsz++iztDYNpfrWY5RuNNCalYSNZQKmR/ZzUD+deyixkjrSM2UP4sZ8Jj7nRdWBuyit7ZCay9GIjai1begK1Zx4ZwdVUlcKkrPJ7muKk5sF/UOjcBoeQ9GKo/icud/+OYu1IKWxBO9CGc4Dw4iZEcfdHB3f7A7H6csaBGI99p/1xvbKfjT3TGgb3cLdcwpiK62Ib27jYkYjH30YScqdar5LDaACUy4fymVQFzNuv/MDxW9F0Sc+hPI4OQWaYg4rU9h4egtXX/uBwstOVEocefHexzQtmkmDpwcFtf44bing3v0CCkvm4OwWgUt2H0yarai/VMxdww7uvOdH6pHuxA69jdLbgZcWxOK6Mom5s3uDWotY1YaFjRKfIZ24VlKEIaGErB5l2EnTsCYXh16gqQlg0jfD2d/3OmEjM5j+5csc2JrOg3cTST3ZnW/L+iF8SwFCAY8NCGPx+tUY9HqEIiFrF7yOmZUl05cvwuQZNS07mzB/0Qn/eQMx6g1kiw4RZjMHVXkTCktHphGDASMHhscRHnWGb2cd5IUvK3n7AxvMhi6lVSQg+IMspF7WhFgGcG9+MNKqWjR21ox+fTNdPQfj6dmLyoYUju6eyqhD63GIDcKzpRQry1w+v6ukdPJ65sU74jJ+CBu35DJligX29mZ4xgzg9dLN2H3cnajIOAACFwxB6WHLlaRbuA+JRPYry/TM50ailhbw/LKZGPR6vpgfjUQq5+lPbhAWZklYmCUAedTShJrniMULK0rGLMLJJwpHr1++bbt64FMMOi09xy/hmbHPc/RmLAePDqdnH1/Urc3s2/2AFo05KpUec/M/fhLuiBkfMwyTXwhk9Ud4nXiMGJEg+u3KHfzHUp6bSNKpdT8rV7fUkXRqHZEDn/1Lxd9v8dVXXxEaGsrUqVPZs2cP586dw9T096ch+UcOHz6Mt7c3R44cYfDgwRiNRvr3788HH3zwi7c+Hfw57KsuYHzahZ8dI5VoVIxPu8DeoD5/ifirra3l5MmTvPPOOw9F399xdHRk6tSp7Nq1i3Xrfv4d+CmNjY2MGDGCoUOH8v3331NQUPDQF7CDDn5Kh/ADuoVDWi50Cvntuj9FbP7Hcj/l7biGtr4F9zGdGZ2/BrHSBLG8PfR65pEDaErqmNItGrmsPUDMweudULUt44nhH9DoUkjWtWZyaw3oexxk7NNJGMqqKDp8A+noQJrv3SJdHcLQVR9hHhKJmZcbqrAyygpKkGX7oEkpR6irpvV+EdWORUg9LbHJD0SEJfkx13EojoM2HcI2DQFP9sc/KICyd9ZgfTsWp+xuqJINmMY3o7WppaUlBpGqCttuYjRVljx46wA9psQheKYTRSfu0HC7DJv6o+jTb2Pa71W8Il0QCIR4VvZCK8xBYDCn4KNbGDR6LPwcaMiqIGT5WPqO8eWAuZxjK77GpF7D7DUHyeAa587a0tWugtKSFuJ3Poe+qJbUdR9QrJ1Atu55nn/Ohr0DX8GhyyDM3G248c5hNOV1uCptuZRVx8dz/Sm4Aldqp7CvaTizc+9RYjTFzNyS/MvZnP/KDzNTJUvujEYk/RajexuJSc3opbeonX8YR5EFkQO7U5tSTPntIu6+25PYWUPw3tMHg17Pa2uX0L38EuZ2T+NsyCRu9Tj2v3GKfTdN2Hp0KYlLviPjsxPQzZbqglJcymR4pOmRfd0FU0kIRb1aUN9TobKxoZN1OEprdzz7h2Ad7MIgsQE7TydaxAIkE3ogVWsY+0ZfbJ3NuHjSAZFUTLceA6l79QbLxWfI2tgTh6f6knrtEm+8lM4Pe3WMjpJw7VQZ5miQ0kDP7rYIDFpKfcTcrs6k5Xwm/SbEsq5+C1MfW4alSMGwXc+RpxVz5ZXl5F+SYzOiFEpLMbtbj3lCHdtM3Jj77lM4u3Uib9t1RKYSmpxKqPPIJOTuSCzi/OlXMgk7lAQBg557rP1LYDSCjTl6oYCqMzuJGqYgZmwPLlxJIXVFHgrZ69QnV6ItK6Y2cQI31prQNlhBHM30HiVlTvP36K81kZNzHcnrz6EOsuCLxss4dT1Br83PoozyIDUxE5GkEbOP7xPjN4fM79dT1NeN+09lIDtcQOfDT/Ag+yzBfSbw6qtJ5Oe3sGFbJzpLzRhq40qnRSNJvbGdtvoGPGYkYGciJXzVJJJe3kHfU9Y4zXAg1M4REQL8vNojezr2CSbn7A3qbE1pqi0FYMgQZ86e60ds3Gl6H7tFwc5WbmbbseT9AsrL23jvvUiaNc3IvtlMs1AEke3pLQQCAe6jOzEl0h1qhWD6yzlBuw2bTre/XfQ11pbT2lyPTqb+Wb3NJFBKE28xAEvkWMaNJSRu7C/2aTQaObnpZQwGPbGjXkAr70adyoHK6nZxJZaa8NYz55EonDA3n0bpyXvUPygi6MVhCAQCNi8bSH1VIfM+T0Ii/eVAVwAKpL/67PfyS+adHfx3YTQYSLu645/WSb+6EwfPqL/E7PPIkSM/E3FLly5l6dKlD1/b29uzcuVKnn76aZ555hl69er1b42Zm5tLQUEBe/bs4dtvv0Wv17Nw4ULGjx/PuXPn/q2+O/hl9EYDL+Tc+kXbASMgABbk3mKUjdufbvaZlZWF0WgkKCjoF58HBQVRV1dHVVXVP+3n+++/RyAQ8PXXX2NiYkJwcDAlJSXMnj37T51vB//5dAg/INgHPnzxt+v9u9yauxFtUyueU+MwsX1UNGbXqilu1JOQmIx9RS2zxk0gxMfIyldmw2YXJn03EVuXQ9y48i1VJQ9oSk/jwcyJSLoP49zoIwS8G8KwxcGUi2JxW7AI/d189n31Fvm15wkqn4RELUElkyEsVRJEE3UTLmF5fBDODnZkBn+EzldC86VORGumEfPqCGqSCxDXm+DWdB+tiQIzQzrlS0sQR7ab0aksZYz/eiUGnYaNg2cijHBHXdhAwKjOmPlquX/USIm9Kw8+LaL75zEUfPQGbgH+NCaeQ613oN6+H0qRJeY+9ph72CKXCrmbWklshDXnJUauD0nG9odrVJ9ModLeF+z6If/0PDHb5iLyd+TsAid6mbdhNKmiOF1AbWAaQ1YsxcbFlmX9NtFH3sRnC/pxd/eXFK3YAk79cZF50lCShG3LDbrvnIUht4Tbr+wkssQHzQQPUrYE0trvSXwDuxE9ci6FOy9xqb4NtcGIWCajprCGlK/PYe5rj9LBCsRCqu7kEvKNhMgvn2R4vD+CN14GnR6H+WOJzFFhphQjjrZDnGWJKLkZl2YZTr0C6PrWOKruFxE+pw/NdXdo8taSdS+DNCcfnBvLqTNrxtvXgZNT1+E9OoayjBIabhcQ8/FUau8Wcvf7q3iNisEsxplzX+yjNdwaSYsrq5ujSfv2Ftt3jsHJO5ous99AqpAwwKIGU64iDfNCWNOAXi4m0icU/didGIsbORt8CwePHJLjbzHMJwaZQoa9TELTD3XYN4YT+70/VZoU8tYEoh4VxmuW8fTsYQdGIyGJIxDVNZFwdS9J6buJM0QR9UE1lo+pwLX9Vu4hAgFaiZg7FZk8mNsTuy92ktw7A1uZGWZ+HqRtvIg+3AZRGRSeTOFolQlBBiNuQy05PvhlvIosyI83oznAE/GrUWAU8ECixkSmpa28HiuTAK6WjiHWdSv3LnyPf/QQzjzei8PXzJjnmE6QvRKX4BgUzu0O8bt2FeLwnJrFoqPMbfQjff8tAkZ3xkPwMpHBwyjrXIyFkwdKD1tMHC2xtLdhPGG/+P1Oig+iNv59TOmBASNCBERFWdPSPIH7R50wyW3BbFwkJU0iZj/ZfnLsYO6MRXEVEpmCEhq4SykD8EOk02NEi0Si/M11JeHkJvavmYWLXyfmfnT1Yfk6rlNKEyMIpJxmHPntlAUCgQCTt9+nVF9HnUTL94cX8O5PEqmLRGKeX73vYf3Ehd/RkFaC89BILINdaW2uo62lAaNB/2tD/CpZWU0A+Pn9z6VW6OD/NrVlmY+Yd/4SbS211JZlYuMS+KeP37dv35+Z2P3jrZter2fLli0oFApu3LiBTqdD/Ht9Rn4Bg8GAWq3m22+/xd/fH4CNGzcSExNDRkbGQ/PPDv48LjdUPmLe+Y8YgSK1issNlfSx/Gv86v5u6fWvkpGRQXh4OCYmPx64denS5d+dVgf/hXQIv/9Beuych7axFan5z3NT9Rk7nJqKKh6UFuDh5AxAkKeRiTc+RCgTIxGZUbGlkNZUMW4RT2PavSuW3XuQat+Hc7eg0s6G8cFxWPfoRWtFA8c6LUNmZ4O9LBpjMaglTrS4VUBTOeP3JqF0V3NcEkuQnwt6M3D31FNmvoik/ae4/8VpdBixDHGlIasEsUaFlV01mrIimgRNFHbJwEE1kMwjidTbPKA8MoFre8+ivNNA9ncn6byoEpdnXqZ6hyn6A0lkbkyi/6FTaJtUXBicillwMNcVO4hynYF/rziSvzhD1tYriGU3KRPqaaptY0ObNfsFtzjz3nQqfOQE5Ek4nlrB9Tf30mRWiEpaRMIX5+g0Lx59VSU9+7xKysYLyN1d6eOmxb1fJ2qa6qhu1SGwsqaypRyx5Vs8afI4DVpb1s/aR//XB+PcKwDf+O5ozldg2y2aCy05XD62hXXbfBgjySX0mX6oKhq59tIOCidMpDoino++6knVg2IEBiOlV7MQmUi49MI2tLPKmb5wG1W1dXjo8/DU1nLsscPETxhCQeEFEApQetlRkZTPtZ3n0Vwppi69DOmO60SNiMT026vIRjdzrSKX+8IiLMSmGLV60jdfQuFqS3NuJRfGfIqhVQOAuZs1gU9054uje3A2E+ISbonKYOCCdxPWfR9H6efOC6+f49omAda2Smwi3NEaTXiw8hCKEZ2Q9Y3maNl5htKIwnYw9iPMGCmahMzJHnQG2J7MFrkpQoECr5U2eH2u4KBHG88dsid1w+d4Ng/DLcAdM297miuN3H4hjgjpJMqfvoRLlDsSjRZVXgXlVzPxnNwdoUSMQW/gUMCLVDVpODUznhfzgvG1csXETMnVTlU4Bw3j5JMm9Lkqo0u/frz4bTE2DVIsMMHcyhVqBDyzKY3v7LLp1mxHQLUJ1RZGTF53oPTkfXLdHZhXcQmzGjdcN3xJeN/JmJy5TW1uHqr3BzPh+1hEP7kpOrIljO2JR6jVOmPlZI/XiyFQXodYp8fNLQK3Zf1A3u4nNijlDQqtQE8dJfc0fPJeJm+9EU5goDkZGY3IhUr8/G1Zx3UCsON5Y3dKjtzBtqsvUSP+dgug1vLJQjcQa9Bo1UglMhZ/k41AIGQzqSRQjD2m3HhhKKqmGl7clPubBpH1lfmAEZnCHJFYQmt5PXq1lgYPNc2oCcORrr9yw6bTGR7x0wMICO+PgCqUSBEKBQ9F3y/R5auZNKQUYxncHslz7sfXMRr0iCV/zLfOYDASHX2i/f3Uj0Mk+u1T9XpaqUH1u30LO/jPQ61q+FPr/VGUSiW+vr7/tM7q1avJzc0lISGB3r17s2rVKt54441/eUwnJyfEYvFD0Qc8vA0qLCzsEH5/AWX/RPT9K/X+CL6+vggEAtLS0hgzZszPnqelpWFlZYWdnd2fPnYH/3/SIfz+h7iTBm+fiuKFqeCm0dFW0YDybznHtM1tVJ1IRiAU4FOuIaCnHwBCsYiRmR8hEAooO5tC7Y4MLJ3dkH6Xi+wVEzov2kyAkwLN7Ua2XrHBetwpVo2vobG1BWxNoLqFqgm+2A2IwSatCcE10Dg0cndVFIIYJeHecuQ9nNi+zZrqS0K+GzoUh3l9OTvofZqDq5GN8UPpqSVdWEif2ldpvpRF4pE8OsmcqM2t5u6reymJbcQpqwtmwY04DSuh5KgLeBdy8+sPiHq9DLk4joZCPRiN1BuV3H01AQ/rZtxvFiM6a8L9+6doq2lCVV2H4yxnqjaU0WflVHonbcLCaInP8Bgsmhs49cCU0pff5vKnkXj01ZHYrYE2dQ57Qh9HZBCQOPkoLYU1IGg/Obsv+Y4B3V5Dp3dGHDuZ8tRTNDuUUtuvjtJl2YSrZTj6WBO5cTaqumY0GinmrjaMDl/B5XM3OLEin7ryy9QZDLgMCGHyg/dw7H6DqloDPeduQ5WVQ9S0eCz8HXDoF0LepUvk5V5CpVex5O1PqW9s4uk2X2oT8zlTvpcuS0aiCHLgzUOtpPZrIuwxPT1sr4MQNA0q8psk2EV7IjNXYHFJwyvfLOXBqkPUJBWC0UjPiYMx+X4e50d+SFNOBWZ+jkgtFWSk5nDxoIynq0poM73E4VP2JMYKGSmYQmydPUeSP2VYagNiw/9j773Dqyraxe171+z03ntCeg8kJLQQeu+EKk0EFBUFAVEBEURFFAtIEaQJgvTeewsthZKE9N573X1/f+QV5WB9X/2d852T+7pywZ6ZNWtNsmb2PPO0KqwMfVBKjLjaxxRJ72A0YhH24maEUglqDzMUvbpgIrGC2kYw0KesqIDo9l+hFUuo9O6KOFKLY6w5qh/nU58o51GNDIv3RnJ/wV6MF3Ql1bkaeVEDsQo1SZ+fovx+Dmk7r6OVq9FptGjtynF0CMCyvRsJxwvIWKumZMBlhHoSFHIFW7ftYEH8N1RxFZ8oXyQVdbw60wMkrctU/1sr0Gl1CEVC1KSzUy+VSTetCY8J5O6BoxQcustuYxFeCiMcxE3cXn+KoNjhrFoRRuWUQuS+lU+FvoxvL1J8OhmvGT3wm3sL0y1O3Eg5QP3iEYQsG4VGqea7DelYu2gZNsyJI7e2EP/VXCp2b8EEGQ1+Spqma7j4YSmrlRZs3V+OTgcJ6X34wes21TTzUeVJHMfuwWNQBF33vs4TKnCWmmBgYsBZQSb7xNd5lU6EGNoDMBg/nDAlkWLqzGSYCqwQCv/Yf63nxGWExE7A2ql1Q3gq4j2UNU28WbYOtaGIrdzHoUWf4fqhz1x37FgRw4ZdY9269sya1brmrFz5mJISOWu+jEEsFLKPh1wjh3eI/VWNoW03P6y7+ZJKOe2wRCISg+ivf60IhQImTnRDp+NPCX0AG7hNJlV0xY0JhLWZfv4vRM/gj6PE/pV2fzePHz9m6dKl7N69Gz8/P9avX8+4ceMYNmwYwcH/XsTazp07o1arycrKwtOzNeBTeno6AK6u/1yAkf/L2EufP4z/T9r9FSwtLenduzfffPMNb7755jN+fqWlpezatYtJkyY9zYX7W/j4+PD999+jUCieRp69e/fu3/68bfz/nzbB7x9AUdWASCZ5Jpl7eXXrT3El3J6xmewd1+h3awRWHUfy+JOjPFpxGKGhPtqmFpyHtsfAsdWcRGzwr9DRfYKoGuVKk48psQp7xAWNkFFNZUspk8eHExQF3pZaaIGqghJ0tQowayJkjAGPiiSYJUixcrBFWgw/3J3J47reLDPfh31HH2b4ZbI/tYxqW0+qDiYS9l4/zhTvQSqx4YnlTRJsk3BIOY1bmis6rY7ylGIEEiGgJTCrmNpad8wKytEfJsbEJxVDnzcxfeFt7KObqS0xIfGtDA5FPMJh1RQWDo7icqoeHlFjeXIwn12ek+iZ9hWho0PQnf4WC0Ee1YebiW2v4PbAI3y5vIIGPzdGdhvNRFcP4qvfwve1oQxc1oeC8nx2vfE1zka2NBfXomdrgl1sAMJmBSMWv0v8gn1UPSigS2h/9HuYcSpjJYHB/djXbxm+xPD9yiwOveWOtEGOwQAvMg/cRSetQqVWsWGxG9deuYxfj/YEvj0AgUDAge0duLnyDI0XkwFI23Wc/ucNsXabRuxLQ2gIXMeJzh/h5S2l/ytTEd4vp/ZxETXFFdgZWJGXq+K7gzVY3RMzurYQU08bRDIxhSPa89VhDcYO7Uno5YmvvzdffbMBj0QVFhHuVN/JJrO4iOMb8pn53WzMbQ0pu5xC0ckkHuWfp1fIFxQ1zOGNL6aj1WrpmifFwcUEIswZmbmaXaNuYCSBErmIktx69PoFYO9hTn1NCxGG9Yy++gGNVQ2csahDVdeCqFKJwl7KLVcfLB17YeDnSLVOQPYQKeH5Z1HHZ9BkJab6bCr37I5zM2cNQWsLmTK4NxkzdsPQaCy9nPF19qLSNYtmpRxbb2MMbbzQaJREbZ2GwRdXKNrxhPtJQ/liwdtoTL3p7taRsq07WPbCVGy1WjTKFpRldZReSeXxJ8fosvtVzAKdKUjPpjz/CaWGloyemcFa4z30v7sC655+PHDKoNbQC193M0wyMzA2N0Uq1mO2bxglugaStEWEihzJ/uEGFZdSaffuQNp/8QL339iJUCJCz6LVtDLlSSMz5iQT4G1AZ7sWxGbmaEUi3HPraO8ezHVxLo42Okx3bafOogMymRN9+tji52zGKgawgovkWikImxKFe/9ozi7fwcMnjzHbOpwXrTuiVy9DghDJL4QVR0xwwJg3OI525Xtc6GXIg1kJbN78fOTN2xQgQUg4jggEgqdCH4DjwDCqiht4nNqCZwcDyjfdwGRWAnk/znmaRgZAKGz9+aXG76uv0qltVqD3ZRnWGOGAMSo0aNA+bVNTo+TNNxMYOsSSnjGGJFrK2Uki/fFhJIH/9rq5fv3zeU6bUJJDNQHYIuDZjU8kzhRTzzVyicUTF8z+7Xu38T8TC3tv9AzNf9fcU2ZogYW992/W/ycoFApKS0ufKROLxVhZWaFWq5k8eTIjRoxgxIhWn9mRI0cycuRIpkyZwp07d37X5DMnJ4ekpKRnyry8vOjVqxfh4eFMmzaNL774Aq1Wy+zZs+ndu/czWsA2/j66mtrgJDWgSNn8q35+AsBJrzW1wz/B2rVr6dSpE3379mXFihXPpHNwdHTkww8//MM+xo8fz7vvvsuMGTN4++23yc/PZ/Xq1a3P/wdCYxv/t2gT/P5mFNWNHHZ/A2NvOwbc+3my9onWEuYrxNYSUh47YeqrRWYzGTDBeXgE9WnF7CkNQqURMuJfPgSX967EyMyWDn1fRCgRM37TfPKO3yd9+k4kzVpqKqsoPZKEz2ejiZo9FNAne/tdNGhRmzeQGXYCm4sOaCvMUCemkWVgh5dMD9/yO5gO74bHkD4I9NS4WSmw3LaEkx+0niYaOpnjYxVI+NDe6HsOY8e55fSOmoRiZhPVn1QgNpPQktWIqZs+0rIkbA0fEbb8e8zCI1mzMoYHq5YglKmpa3Hnux/sCAtxxOvMBoSHdDwwaqIu7yLhoxbiflDG+aXVGNdVU3JCi629FVpdKVmXVLRYlqOVaFAFFqO8eZRyPTGl2YMo+DGNB/tP87rtADxf6oStuxtGpjKOd16NoqyS9p8MQ/GoHHlBE8gFOPUNQhTjTJfRcfg1TsXU0hmXZdtpVufyam08hqV2yEVayvJK0IqE2Hh4otcspTEhh+C5/cndew8/PdBotHQd6EppkwhVoA+GRm50euErZB4J1KwPxcQpnJzDN1CU19O7TyfCI4JIaL5DcbiYAd0GIhQIeH1rJVEUMVpfTj+VL/eyEilv0ZAvfkCcswmhZr7c+fIBxjZmvPXpeyjLG3m47Sphbw3EPsyVL+c85PxHqzFyNif8yGh8vHpzZf56bDJjMc8tIvXGA0KHdsZIBaBi69idFBU0cu7CZATVja1+CiZa8m20rFdcJj/VjRuNHXC5U4VXOxlxNe60CDWo1TpWLriO9Tc7EVh68jiwCKeeckrvP0EQX47X4E/R//wRCnUD+Vvvo/9iER5xB7C1GoFuWGdevWPH0AhzAhUtlD8pQM/cECNbezSaBrR2NlCrwkyipVf+Q8rd0/FynoCThQOPVx4h6WYGARE9qX7UyNlx32DkYYNR73aUZOZSfPYBjz89To2PiMiWJu4dUjBdXIp1d3++2llKnz6RDF+XBwIBQTN7kfpIibKkHqm7jE64MltwhCuKdL5WD0K9ewCJmTZ8/MpDKGhh1mw/fK1dcBwYBpwiKGg3P/74BtkzfuBs1/2MrtzIxsZDZA1q5M27HvQz8EYXoOXB4nqq6y14ckHBBx8EI5OJyDscj8fi7+iytCvd180G4M7cndhkl+O12pRmbS1lQV8xYWAo/jtsn1k/BAh4n17U1ikJvnOW/IwSUm6V4h89FIAqmiijic3cQYSQDTxvGtRxw4uMHHmNgxFnuXGjF0P0g3kifYBI9qzR6MCBjiiVY57ZFFy82IPaZgWHhfcAHRMJYwwhSH8ROfP27Uq2b88h8do5koJWMnHrA3zNrQng798U7eUBN8njZaJoj+MzdT3wpB2WFFHfJvT9L0UgFOLXedyvRvX8Cd/OY/+xfH6nT5/G3t7+mTIfHx/S0tJYuXIlRUVFnD179pn6devWERAQ8Icmn3Pnzn2u7Nq1a3Tp0oVjx47x2muv0a1bNwwNDenfvz+fffbZ3zOoNp5DJBDypWcko1IvI4BnhL+fVscvPCL/sXx+Xl5e3Lt3j6VLlxIXF0d1dTV2dnYMGzaMpUuX/qloriYmJhw7doyXX36Z0NBQgoKCWLJkCePHj3/G76+NNtoEv78ZkUyCia89pn4/b1Ka6ir4tm9PrIx8mHh+H/5vDcL/rQb2nRxK8v4uvDpVn2773qDxGjQ2g4E+NDdUc37He+gbW9Ch74sAWMsMMfJ1o6GTF0k/xlMrFGKr1GIr0OP2lPWILIxIW3MKhAKswsTkNRtiVuqHY6YMOY6AEK1czd2OQ6nI0XEx9kN8RrhDDxXtfLtSWViAbScvKu7n0P6jMdj7uaJuVhJ0OAjzYEceXT+PsryFIrO7DLz6CU8e1XPjiJyIHiaYBoWhaW5meMy7XEn8EbVUjjhxBNbqx3hauiH27YCBXwAHNs3GtMSdHkNlGArkzA1Po9zCG+vQAFS5YeQkXEJsIkO73wGPk/p4OD+mQM8STW4ehRnp3PfR4nfDCmqbCYyMpCIxl2/nT8FN6IBYWEvRhv3cX/sQt5HBVKcXYxXuRuGZZJK/OU9puQJpdCBuAjuaGhuw6+FBDg3MSzxC/t06Jt0txajIgb4hk8k/+5C+e2bjOSSMsy9soNu+ORioFMzdMgMLtTFbQtZy/ktPLD1AeTufrj260nipBgSQkpiKTX4hAo0Ok1Id91YcJMPHnlv54ehjSXFuKjqZBIFEhIu1LbWVuShl7gzaMI4zXT/A0NUK33oFjTkV3L+5jYKb9wgc05fhZioEDmaowirYNL8LIYEv4HxeTfRX8yg6mYBvZACPMvJJWHwR7I1467gR1VjR+e1DKG+k4DWwA9+skFCnUfFjsBqBbQNbz/bHVarG2loKVibkZzQS1vUSbvrZvC4WoGypxz1VgnRDCkMXT0LX8IT4zWeQRfugS7tHfbgpPaeE4t8pifz9a+mwahNFgWe5mNHC0o8iibDVIRQLOfjtJs5+t5m3z57ALSQIk2BXtG5aWqJMyc7Lp27jXaz6tUPrbkrFjVLuztqB2EiG07D27Eo6AlHgn5xPzo5rRH06mnZxg7B3v0Xhi7cpuqVk0Q/JXLlSztRbdxEJRTSLZJRsOoPUzIBU/0gcHfXpkFhGU14eqkU96WjnS5WdgIOPH1DXqKXMUYTZO2e4e7uU2JP3EbAP+9GR+GV2RV5ej8REn9JiOWVlctTq1m2BQCgk5IPRfD8/kUeP0sjMbCQ42JyM78+jfaQl+/vDdB81E4A+595BWd2EhZ0bTYVVraavSvXTNeIJFVTSRGfcKKMBmZmEuzdC2fJWILtXqPGNGkLOw8sovt/JwwctdKyxpVfv387t1bevPTU1Sjw8jLDr1If2L/x6Mun/ehL8U57AKPoj/NeWR/pf0iX07WvPwYNdqE29i6q6Pa76DryF1x+ujQ8pZQcJvEAYwdj/YXuADjjSggoPfn3j44JZm9D3vxw7j/aE9nnluTx+MkMLfDuP/cdSOWzbto1t27b9Zv2SJUt+VbCzsLCgpKTkd/v+o0AeDg4OHDhw4E89Zxt/DyOsXNnv1/35PH56Bnzh8c/m8YNWM97fe98AcnNzn/n8X9+jTp06kZyc/PTzrl27kEgkuLi4/F2P2cb/AtoEv78ZsYEe/e+seK7cLNkL5K2BF/TtzIBxdLarYKRXDbkNErASM6Drz+3jx24mPPcVQneNBkDeVEdtzhPszJ3wW9CXgpHfIJKIMNjwIq49/Ci9lk5jYSViIz28X+6NiUUx6tWVNA0dgWnXUgQtSuz7hvBw54/YhJZRX2GPnlU5eccUSE6IiZrRm/1OR9DqahBcNyP53f1kDbTFrV0NVY8b2LdlLzo9HTKg0aKIe6MXoS31Itdfg9nlS4ju2NNz0SScw3syte8wknac4fF7p+hhpI+ttYKWGaFUtuhwutYZvQxzkj86QdTbgwh5qTupD5U8fHyQbrGj0NPvw7nMsyw/MZjJCjPk2bbYqKQUtVRR59DAddfvKBJcove1jRyb8jWKx5U4SMKos9bRY/oA7Pr3wzVuKFIjGQKnvWSsuItQKkarVCMDKCzCAiGrjQcxJtiaG69uIcxNzOIXw/lsxFwabaDdJn+ay+pBIMDQwZyhV99DIBSAQsfL/q/TsdSR+sQcBi9/H5GZHlUjtegUUiLfH0Hx7SwEpvq4uTihX6rgcXozapGOrIp8foiV8DDbGMeSapKWHUKn0hD67jjOxB3gstCRFT72dNz8ElWXU6jPreTyrK00dShFc8+HO7vPYNKkh+/krtgMc6BidypXisNICPViQIQHtfJaVHYyQgRmuH3Yn4+HfM+8IZEkXsih6nAu8op6jI2MMLjRQhNKDJrbY6/SMdBGg0QipQwF299aglfXwQhFQgzMxYR8P567Y7dgUS6hpV7EpZc2oOdkxv3bsdjsyMV/Kxg8aMLZZSd3v5yDp/8r6NfVk3WrKzKNGpqaSVPtp6WpgZvHs2isrkarUYNQiN2wCHpGfMcrG67iXX+fw1dceBxvx+CqR0hESRi6WBGyZDgeoyLJ3tKMUCQkZNgw1A0tXMtcR/mVx4gLTUEowMLVjI9mhtCnlw1G+0bSzs6CsdsKeX/hIBLPZDBxuRpnZ32ungzi2wWzOfFdIXELdjGJcLrtgqIzD7B6cQL553cir2ygtmoZey3deVJogNn5SQR0G04HwQtcv96LjM2XOeU8i5jDc7GLbc3/8tFHIbz4oie+vq2Rerttmcvd9puxCf05P4yRqzW4tjrnGzpZMrpyI0LxzwLVVu5RSTMumPEZ1zBASt9X4zFXWdNkXkRLQxUqeTNkZnK6uyXHWwrY7FpLcHAOhw8/Hz5+xox2zJjxc1AKLTp+IIl2WNER5z9cx4T8tmmQQCBg+HBnGP75H/bzS6pppuZfAVn+LMHY/2khsY3/vdh5tMfWLaw1ymdzHXoGpljYe/9jmr42/m8ywsqVoZbOXKsrp0TZjL201bzzn9L0/d3s2LEDDw8PHB0dSU5OZuHChcTFxT2XH7CN/9u0CX7/DzA0tabf6eUoqxrRv14MLfkwLgBbV31UTWI8nJ4P4NCYVYa2Ro2LTzQAW9/tQ3FWAr2Hz8WuXRgea71x9R1E16ggLk55DUuNATXhJaQqfsCvpxJ1ygQaqxNQbEqiZbASZZmGe9XfUNbxIYMq1UgcmslJ9wIaUAENuWVU1+Sh726BT0Qncu+lYZKTjfvay9Tl9uOuzBmtRgV6KjrYjKEhOQ+lVsyMbl1o128eZ4es42btHiKXjYDaZpJzt9Ho2IStRwCBS8aRcOhLqu+X0GvaG5RFF+PR3R9Vk5ym+jrKirIxXJ/Bw5pROIx/EQPleeLapxLzyAuBQgoiEBeZo7N8glfFdnzzLiLUCrF1tyX/cQWoxOiXgVlELAY2JqDToWqS05xZi+8b/bD2tafsWhrpu28hNtBAE3QyrsXU2RuBSEiw0JzM5UeJGACVtjYINBKaTXUkfX4am0gPHDp7gVDIoS3ZHDzVjXdORNFYVINQKEAmk5Gx5BC6kmoyQ6OJmdOXvEdZ3Fl6kOJiOSWeHagWKDCT3KHpUi7uWiGVVo6YO0iwD3NG7GVJZ1kFEYpSrr/QSOGxRADKbmcgtxajqw1E8YI5/RfOw9TAmLSTN7Gx9yW2x0dc+16Htv4gm9e/zrAxazEs1KFqqePqqQz6TwtHlp6PS9MD5E2AQIDFmDBetLXg/tkt2MfMo3zWZD49eJ+xvUdQ39RIXmoGHoGZVBaOR1lfh4mrA2Xzc8lPz8bNzpb6XRm0uNVjetsB8wvlVFuqCegZQcatPHy7rkVPpkdzZQOPvzyD29AwLINcSTq3B61YxerzeahEEm5N3EBL72J8xkQz88UkTpyv4bPPRnCHhzTXajD36kTY5N74fh2COq2Ao+HvYO3nSNfjbyFGSMzBuTSuz+dk6g0cztlTErufQbuy6GvrRNOTIjRRruQk1JBWpsGksw8mJx8w3iiHgR+OwcxWirNPR1wDWk9YWkprEWk1dFo7mfz8ZpIfN2JXlk/CxCMMOvY27tt3cU3cRG15PgBSqYja7Ceo6luoSc97KviJxcKnQh+A1NQA+dVmbr+zBbNbzlhHPe+X85PQV5n5GFmjlkEO1jTaGOOEKf3xwQg9Hmg+pcm8CInDLKZ/8g0qRQuJzencidyIhUVHLlxRU1Wl/MP1J/X2Mb599B31L07kLoV/SvD7qzShRIzwN5PNA8TgQRB2WPD3B0ho438/AqHwH0nZ0EYbv0QkEP5jKRv+aUpLS1myZAmlpaXY29szevToP+Uf2Mb/LdoEv/9H2MX4t/7nu0RQakCuQmRphOhXIpHff2sXpoHORH07/WmCd7fArjTnVBPqNx5jK0vy068RZK+m4loaxd9XUyKtR+5ZiUatAckP3JurQyAU0Chuh9DUGrlPIq6m3bB39+aG9jO0OeDO2wBYvdeN6iA7nHdFIbydwqde0xF4iNk9uwxlWj6SHhk4Jj0he9tDUDhRc7IePRN7aGnC1FCGia8rjv1CsAp1prGghqQvThMyajT3y36g5WQ9dd8l0ct1EhkumTh09aUkMY+bb++hPrMMobUe/Y69yfWAElruF5FRkoDdVSMGKXwR+oPUUg/VfRV23bzp8fUMehUVs2Pyl7iPjsPGoZJtLj6YN8iJtVVz6tNNdJs8mszdt/Fs50n1qRKUwUIc27uS7+iOvGsO1s51ePhE0NUvDAMLQwJmxmLV3o0T87LQf9SNYa5TCXmlGzZJGdydup2sg3eQVzbweM4jjIqXcz9dwY3bVRgnJKFvYYiqUUG+ugLFk2rKFFlYLu6Iz6hILr2yjaZmsM96hB2gZ2aJUtuMLsiLr2pDuPx1KC4OMoqKihl/ezFF9/KQ6jQ0ZpRSm1bCJUUx9/1beFMUgbdbCI6+7lSUlLF19xYefXCI0LAwvh/TjaNOMhKzChDYipAo9eBhFYMX9qPBSY+KY0k4jA3jyY1krKI88eoRhsbeCpH0JIrqEgSVZbjGDMbN3JKcW8XE4MeAiXEccnkFVbMc+42+PFwQjX9zAB30vaixcaI+zIxR9t60TKnk/M4PSc59SNYXF4l7YSxSSQ4nNrWwYtdsqlOL2NP7U1yqe2IWYAdKNXXpZZSee0RTVhkZX58loNYY41E9mTrVk56drNi4Ixed3J52wQGoVQrOX/ySSl02FaU6DtQfYNZOQ+xDPQl5eRmHOI8iv4XBt30ws3VC1dDC0fbvMeTMAuary7FaC1kdzDD1sWNON1MiX2iNjjdrTfzTeXZvzg7yfoyn+9F5XK21YFmZH2sMqii8cp+Ikhb6vDObkH6xmLq2mm2rFC2kPNiPPnZkH72C38wB6HS6X3ecN9NS4n+XG/v30CNqQWsk0Jo6hCIhhiY/R8esyHpE+uKrKITNxMVvBWDEvwKk6N5ayv61S+k1eCR3BYVEyJzoKAvl7MXWvGItLWokktaT6IbqUuKPryNywExMrZyeeRS1ogXxk3T0FBr66P1nwSEaUXKdHKJxxbRVh04zSt7mNFYYsJRev3v9T0KfSqVl48ZMeve2w8fH5Ll2arQkU4IfNhgg4cGyA9SlFNFp5yuIpG1fW2200UYb/5UFCxawYMGC/+7HaON/OG3foL9B/IxvqXtcRM/z7zwVvv6IK/fukpqdyYSBQzA2fDbxsrpZ0Rqhc7QvyJWU3snCLNAJmc3zYaiz9t5GXlxDcUh3xi0OBaD/9NU0vddE/oGH+L3Zh/YD3qDmYhk3X1jNnYjxNEpM0JfJ6NHxMpX7OyI11UfTuzuHhN3oYXkM8wkBDJn9EoLSGhoulWLcRURiuoCqpkqyBbXIC5QIFO3Rq3BCZNmCUF+AUtSCoTQEf/8g8jd9iX5fHVFuo9E0N3B3xQlAxINvvkMeKify8yE0Vyi5MW0b6iY5ZXMzsAoNoNQ8jaKbKbi4ueD5Si8eH7hB5ubLCERCEICeSMbdHdd5MCIM7bAgPCou4Oc8htSNSdjYBGBgokd2cwKa9i0o6hswqdTjpXc2UFlQzpM1wznQvAGtUEoXs3uEzxzBkzNJlF1Oo/ZJKS11jVjoibm58iDb9CUMGWiDyi6Igi0ZVC+5ilmQEzUPCxAIhVRHJ9JoUky/IQsQmxjg0y8SH00fSk4ncy3uK7oa9uezx00M9dMS0d2O7HQ9dA9rQE9AyzBXQsZ3YYa/Hze+vsjDEynkduvGxNfC2NhxNYK6RsKG+CAONqXJ3IkRF9W4OLRums2MzFA3yKGuCZG1MeGLhmBga8JET1sST90jde4+Ei7lcCU/m5VnHAm0CaGbUzMu4d6I6lSID2mIdH2TnLm3sfrKF2mMBde3L+fIok7Yd5cxvcKL6CBnqiSGNCmEGJVU0i3iBdLuldK3qT8BQifSjlxGrVRjXiVEsu8J7cIDSL2VxE2BkrGlLviozdi57QpORxORpjqwd5mCGV160P/cYkqzc6lKPk1dqDGVGxU8yLAg814hppYyKKlCaCal0CUepbEIizA3DF2tcB0TRfmVx4SLCwjx34R5WQjmt3Owyq5g+Zl6Ioq/oeOnIVw7/hluI2MoGtEB/XtVJL16iLsBZqgfTaEX7fBwsSDSpVV7JTbUw2V4BFf2P+bQFypMPKBg8V1ExxORWhpRn1qMOEhKvukVhry6HjNrZzwmd0UgFmIV5cV4CyNMTXsSYBXGzlXRfP9ZHjNX3eRa3y8RyaSMKFzL4xsHKFTH4+bbk7C3JqKRKznqNx99W1P6xX/wzBwWdtFRW5dJvPAsOfTgVWVHlo9/GYmelA8Pb3vazqVDL+4k7kcnUqNWKRFLWteaDcRTP1zM+uFX+YZbnOMO2huFhPkHUWmu5TipDNL3w5FWoenemc1c3rMCnU5Ln8nPnvAGdYvjw46Dkej95+Y+V8nmII9pRvVUQBUjwhYjbPjtfH//lRMninnttfsMG+bEoUNdn6u/SR47SKAX7RhLCLk/3KQhs4wOX076l6l8G2200UYbbbTxV2kT/H6Dqns5NKSXoG6S/2nBr6yygvLqaprlLc8IfhXxGZzr9gE+r/ej/eoJlCXkcr7nhzgODif26FvP9eP41TxyRixCtXoDLN7wtHxg0kfotJmIHMAcLwjWwzLSk44T/blU6UHcwCPEBEagvbUWxWgR+s6mfD7iPg46Hf7OIir3HqL8UREZ35wn6su5mMbE0HhuAz0jO3Lzzd0IMpvQCgSMynqXDl/e58ERNS52n2Md7EHV6SKMTX2w2WlI3tbt2ISrUdWY4j44GnfnTlwK70K9titqpQEIBeiLMxE+rsTWuje5t5/Q7rVYDIrKSV6yH6mpPsq6FgBaSuuo+yGBwN5DqLuWgWy3A0aLwxEbptFh/gBuzv8BAQLCmjsjSVEgqGxBqa3CwdwK4cQNbD6VREWlkqacBrTxlTTfKEKkL6VCnIrYwBBNOxWeXaIZt+IYZtHGWHZ0xnC8Gdbt7HlyOREdoO9rRezIN7iWXsS46TVcPqVG8i+tgn13P0bteQthRgO5bsXUXFhOxSUhPhNi0OU1okmuxPRKIY4Bphx/mE/tutOI9KVMSVqBQKXBvlMAwe1EqO0teGXWFboOyWT12lm0SPWYNfk6BsfP092kmgeOSh5byPnwvXncyhTS27iBJ4sOodVBS1QspxK3U1g6GzOhPlIrBeoQc2r0xUg9rai8mQFAyeG7lBnf5axzE6LsepQ2EhZ7JPOisTfueWa8v1vK4iE1iCRiZi1YRu6aM9y7fpiKlBw6rowjZsMMJMmVRIzoTpLFj8iypBgEajly6zh3btxm4IgJWJgbkbroBC2vu6K8mE6QjQPF7TqSXdOI+b5C4pqyMLXqQ1VjA1oDCdrhA3jtu4mo1VrOnCmhb8rnXHvrA7KyLqNpL8f8Qx8yBTdoZ++Mv0ker5OJXpEQ14BXGfrqBlz8ojHRc6bi+hPK5so41rkRqGU5fWioLuXkxwuovabBcfNYYna+giazhPlNWWQ/PMetbxOYc2wpDVmlpM7ajbBIyBOPEzxJv05H63E4DgjDcUDY0/k1ZIgTSrk5Nm4B2LkFIxQLyYoK5IfbpjjeriSk42A6TZyDSaorqP4V4OVXlH1VNHHhpRDcPJbSEhlOOE401pYiFNZgavWsk72hpSUhPw5HJ9Q+FfoA8qmlESUatMTiidmlcjJ7fE1ZFxvSPrIio0ur0PeT4Bc5YCY6nY4O/ab/6tr0dwh9ANG40oSSrvwcWEaKiPfo8Zf66dXLloUL/Rg2zOlX6/2woQOOdKC1vuf5d1DWNLUJfW200UYbbbTxH9Am+P0Gfa4sRt2sRGb1vBnSbzGiVx+a5C2YGrWacmmam0mY/z0Z391AqCdBYtyq5THxd8BhVARuwyN/tZ/Ow13RWzoGM1fzZ8r17TRAF8CV5pYEVpxbRvul/dl51AMPJwiXulG3dg6mkmb0HAzIEcjx8TGmR78Q3Lt4krj/KpWfzcEAeLgxhObsPDy9XchZeQlBZiMgIGROL5Ti+zj0K6Z2Rz/M2jlSmZxHh9cHopWquPvwMOq8JsRmAfTaNZfKG5fRt3dE590P9QMdOj0NobP6UHconpaKHBzHBeMyKAYjJwvWfpeLtZEePXfMIu9EEjmn7mPg4UzEG70x8rIlPVnFg9L71CbkYB/kgapBTvh7Qym5k4tWp+XEkq2UOyThONwPF4UdNlILgjuHUlRcRMXdHJK+OY1ALMTc3xE8lWSVnkW8Vw/LaR70/m4W5n4OrL53FksbQ1rS8zDXN0enr0GeUoGpsgcN53KYtVlGgV4DFpbGrbECpWKE7R3QGdfSUVXAhtMOJP94FeuAKGpLyrHoakftzQfIyoyZ96MaS8J4e5Q1Oo2O4tQWtPtPc7uhGSMXS15UVlFW3R2VTEpqlpwdx6oIcwpjtFcmupocEIsor1QSVFWApsadIWcW8OPrB7kuTeOjN2fygWMUJ+eXMO6mC6NeOI5t90ycAt2wvgkIBPgMCkdWaIHnxsOYTD6DcH1vtJOssNEZYGcr5PP5ElS1+kicrFE/KcQ62h1xiZzylBwK72bjNXcAaFRUFechC3Zg6szFfOiQgircCpfpYyjarqL4ziPcqw1QKTXc+OIIQYM7YS5VMtqlPTUfjSFrx3XW9V7PMfcI4pr1aNel1Zdtw4ZMXnvtPosW+RP4XS4mChdcPDpQQAK3d17j4sJZuA53w7XwPo6xPRAKhUQOaI2IqZQ3E1+wk9TsrUx59Sr2hFBXVcP+uW+gPFmKcaUD109eICYgApGFEas+DCI124DKkiiuXj5P4qUbTN8zB88uAajLY/k2VIKEIsJx5IcfcqmsVPLaa60mkFKZIXHzDnNi6x6uZdznQFc7HpzQcO1aBR07+hERPpOTL75D9fk8BiZ+xNCsLxAIBCiaG9j6bm8cvDoQ/MpyyqQK3PoPZNCOFh4s+4L8VVGIdKdx8Yx7br6HDh//XNlieqJBix5igrCjnX8PbvVPJ4cL1H12hVFe54m1/TmSpqGpNT3GL/6zS9W/jTn6jObfS0z9S4yMJHz8cehv1ltjyCyinn42dLLE0OlX7OLbaKONNtpoo40/TZvg9xtIjPWRGP+1U3KxWPxU6AOIjwqlpagMgWAI/eI/xCyg9fT64O0rFPQzJHJY6NO2qtpaam9ex6rfAARCIR3eH/pM38q6ZhpzqrAI7QNaDzLyU9h7+jvO3sjH2ag/kvQcqncexvGFychTKkg/vJ52r8/hlcb7YDSCx0VSbn5zA3N5HKbSG/T+fCKaqmYM05UU7tmDnmEz/tO74P9KH0oyArg73RrvuAAs/BwwcjRHfKUClTH4dOnBE/l1Ej44TuLGo6StjSfoVR3tJsdSeSWP5nodeWdS0Io6E/7pAoysQ6nLqeajz5+Q9KiStz+Zy7IzFgxueYz9SyE4GLtQeP4xFsU1pH15msgN07jz8lb0DGSUfv0IvZLH9D46jzN9P6E44BYRI+Lo2HMSANkbt+G1biqOa+Mpc3Im+cId2r8zFPtJXZGaGlJ2PY2srVcw9bDDOtAFpAJei+jJkfVr8TAwozQpE5GHA8X5jVgEmRPQXYmb1AoPtQlNCi3K0gq0lfXITPURtLdj6yYrNqvHIzWyo1t9C821DeR+nkjJjTQ83+3Dt70MWL/JkrAJJhzs+xmXbPvg2HcqIyNquLjlNoaiWsZO9OL6qC/ouP1lTh3uhI+JGlf3HnTVk6Ipr0FeVkdVWT1CiQh9GxNiPwpEcnEjAWHzKC1r4ZzOFwW1GHbwxtSqGgsnW7pu6IdOoo/AUErirB8xKa3HplM7oprcGP+tIQyyAE9ztJnFxM/ZiamvA76TuqBRamjR1yI3McWumx9IJWjLlZjVmNHF9z1ORq5Duj8CcaA+mUYN3M7LRrYziei09zHz9iJ692z07c1wkCsxdTDDql8YjYom7iUlkZAjI+iDkUR1VHFPmYv52Yt08TBhwAB7qovMoUBCp9Wvcnj/G+yttGH/zLssX+qP+x0F2SnnifxwAgBajYZlYzxp0bRgINIhv1nM8fANaDpYIbqkw1jjQIZvPQ0XklDO1/DIop4g7PCzDgeguSyX+mBrPHq1x8TSHC/HXjwmBTNa5/bs2fepqVEyYYIrFhZ6NDaq2DXgXdJNKhGYyug/z4uh9nXEdZQDYB7iSuT6aZiHtGrufvLvU8gbKclJBoGAIZjzCf0xQcbDtB9pzC7HQtyO2V8nYmnviQ4dAgRo5Eruv7UL225+uMZFPTPf9Xk2556+rSk9Ti6kpmwsVcUZtLN93jzy30XeVIfM8FmT87unv0WrUdNx4Mt/233aaKONNtpoo43/XtoEv38QfY92SCws6X5mC2K9n8249GUyZHp6iIQiapLzyNl9g6J9p9Er2Y3JgFfQ8+lE2MqxQBNgAAi4NWUDBYfv0ffSx1g7mREiELJ5+Hfc2lpBUEIMSq0T9y0NMe9TR5OiieLTp7EbPoSGJ8W430njyL0G3CUimoUijCLjkNmYgJczCIuxmdaRSM9hGMiMaFa1kH3yAiXXnmBkp8Guayj3Pz6Gpb45Ogl4RNji0NUH5QuVPBGlolVryNgfT0hsFJ16DabK9QGXX7yNUgb3ogrwP26OnrkxY/SP0L10J8dPXiDBLJCWFCFLB3rh1N6fO4sP8Oib8+SLbLh0rJFoV0fw8Oex9g5dLC2gupGum6ezc+15Mm/fxEUWRV1pKT5vvoTA3BBBZ2sqje/QdfJ0BBohpyMW01JSi9vEzmR/d4Xcndcx2/smjzeeo+DCIwz7GaDSaPF+tQ9Cbwvi+rQnP+c2D4q3YHrdkMboGYglEm5O34SemQHhS0awe9MTFsx0RCd5wvhoJ3acqeVmsogXaquprajj+r7bSDPLmVJaizSjC/LCctyM87CfNIsKxxJeWdiAzDgAh3lFrDIq41zMcoamfw4ZlXCzFGGEPUmfneLYzk0IQ4x5e8HXCGQqzH2D0dyScPWb76g8UM69/HDecCrmoyOvIrOeDDod1allfLczj5fCtLgMCOXh0ZuUl5ZSWVODSbQD2LYG1Mg3UJN/7hGGKUWU3suh57fTyLYD+bk0PF7oAo1ycovkOAl0nL5Qwd5mB6ZHH0NlKSMydyluQ/xI7hXOR94PmIcpR4c2oaWRt7XdoFkB1Q1I/Y0xVH/JK71eZWQ7U/Y21aGUKghTpPOmUombjReH0r/G2tmXIXqr6XH/Y0r2FtJ87BpGd76m2r+KlD1daEzdznS/ySAQIDWxofKVYdR3DOTSqKOY1TZjWitEoxFh0ycQTacmSjL38WPyTi6HGBOLBxMIo/jsA8pnXUUx2ZAfTB7TFS+icSWan/Mx7dnTiYKCZq5dq2DQIAc6dTrHmDQZgUGOjJgyEalazIr9Zmw7qs+S/XVoFSo8p3V/LsCIiYU9b32XQ42BgNc5SjfcGUUQoSvi8JrZszWdA5BLDZ9wmH46byx2nSZ920Wq77d/TvD7CbVcwbd6iYgFQl4iEnNbV8xt/558UjtIIOv2KdQfLKbP5JXExLUGe9LpdBxd9wo6nZYOfacjEkv+oKc22mijjTbaaOP/D7QJfv8g4YdP/mr5iF59qLiVTunhBG6MX4dAJESn1eI/agIZR4tQHz2BQFhByMAUBIIQiJpFS7QFGtcgdDb6IBFTdfIJkq+eENcjmLR0a9QtGiq1pezY9RXGDwSY6XfDKtSakC253H3tEjaPCwn6eCyew8O5mJVKsb4AB6kAYl0R3U5hyroGekcb89pAA3xiu5G18R7uH45DKDbCOswdmxhfSu7m8NULR8nuNAyv6H1cDr1B7/39EOQJiN95BvRExL9+Edse/mx7/1MUNS0Me38UYkM9/F9wQaU1JabsHCrtDZwqT3D79VByurUjb38Cdl28SYwYh1FKPsLcQuS5hfjFNCEQWVFw4TFN1c1YnwzDZXEf7pal0ad7DwTNKlCquXjvS/Kz7hLeeyw5BxMYfHwuWYfucX//RcRGUvTtLZE4mqJQq9GqNZQnZmEwKo8GoQO3bj+g7kASNanF+LzwMobaLhg5mlObUoheOwfi7zzAoUpBQW4jLoOsWPflKRpbMujvGUn7UBN+MPdiv86d3p+kM9qqAE2TEtNvpjN8yYuo9QQ0lF2k8nQun/hVsKrUFbnMADNbS/yigiCrAgrqoKIFLheQdzyJKmEF9Y+yaIw2J/9mHjdqClFe8aToYSF1hnoMjkqlk48RytpGZFI9OJ2Jua2U6lolWw8UMWZKB7alncDU0JTxr/eA+mZQKgE9PjNPQpLYk46XvAlQ17Lq6CHeuXqETiFiRqdmYWdng4GgiXt7r2HRxxqjo67ELzTlxSm+BIjtwakGQ3MpLoAlBlTTjA4dOqEANFoEciUBkR354sl9LJycePDlj3iVZCHyNWTkV3MwkhkgczDDSCBl8vsZXBw5lx+T3dmYZcXoLtdQVd5GYx2Cyteb0koNAEKhkJCduympSMZ6fzEGCWosO3pi7GWPY2wgoR/GceX016TfzsAoNROTkCgukU0ULpiaGiAwlVA/IJAsSRnl2hYC5N3AQO/pXDTto2bHjBJ2fVvA999HExBgyhnfoXTcq+WqoIQB+NBp2Jvo9GWs0F5AtuoePnsrGfz40+fmtbGFHXXUoUSDnNbk7AKh8KnQB6359HRAdUU+dw68hf2YcKIXzPrVdaIxt4KjYW/zoLAfeoayp5rCtDsnyHl4md6TVlAikXORTAbhh+VfTJNQRD01BmBtaIaBidXTcoFAwMQlR9Bq1G1CXxtttNFGG238L6JN8PuLaJRqGjJKn5pt/lWqEnIw9XXg0oBVKOuaQQdiYz3ch0cQvmgwvkvUHO20jEcfxRNo9TpisQ466qiRyFGYCLh+8RLhVr40VlVS8iSfAqNyhiRs4tiMkRTqJ9HOrT3OIaFoT+cS/+5hopatwf+lFKozSvF4uSdZKU/QiAXY1qmgoRoEAlR1EmokscRnNzBLqCb9h3jE+obED9jIoGNv0W50qy+iSe8grLoEYWYMLapNyBo/pviDJzjPN8OnRzQWPTxhMxg4WxK1oiuWfo5ErR6ExMQAy4FhNMqdKGjRw+30j1R1eEDZQxGNR/YjMLIhbNVSiC/DqKMY46FDUSvlBMV0QuhsSPbxRPSdrej25WQsIz24cH0dqz6ej9piC+t+ULFr01j6+PRCLJFg5WpHY2ENzgPCyZVngLmW0nPJvDXiCGtT5/Lok6PczPqcWMc5OErbUZuQS/HVNCRGMsbMX8P10V9w6Xwa8rIGvotWst/xERHKIkaN8eFxTRbGFTpKN+qx3esBvVdNpFLpRWX2LVyajaiINcfe3o6ilGzc3S0ouHUDhdKGlM1XcFCp+f4VIyIn9qN4ZQkO7ZyguBFcDcDHBm4UEPpqP0wyPYl8rT+5n1/i0dpzSMVCJBotX+nCuV/twPF37egT6wv2Fq0CY2kTakMJowdnYm/qjq2NCyOnjifA1ANhuRKcTcFYn5xd1/Eoz8J6SgQdOjjj6ehFTJohseeuEVWoQ15Ri8bcgqsrd6GsbkLSXEXXFcOZcWMQ5uhTeOgu5ScTCVkwiBDTnlSXlvOeaTek+noIEZD0+SmqrzzG+80BOA2NoOhkIk/eOUPooDBi5/bjXzFIKD6dTJhgBMLKcxQfLMUWBc7OfZg6cAwtW8zp8sGraAT2mNv8LMRE4UKDtYKAWjNMl4USP20T1Qm56FQa5AvD2TXQid5eexjsNRILCkigCBuMMO5oycTaHZwjg6u6HMYXO6NtLOfmiqN0+X422VTzLXfQG2PIyGpnunWzZsIEN7Kp5mMuU0UTAP2mfUIeNRzUXcSusyV6F387d54TpnzFECT8esJfq3pjLFd7EDbcBtthb+IZ1htTH4dfbSuUiJBKJYxaVkf7VYMQ/Cup+tUfPyIv5QYBnUZwy8+Aa+TCiROor19hzMIfMDKz+VNr0Vy6oAyKxvjHd5+r84kY8Kf6aKONNv5ZcnNzcXd3JzExkdDQ0P/ux2mjDQQCAYcOHWLYsGH/3Y/yH+Hm5sYbb7zBG2+88d/2DL/8Xf6/muu/vjtp4zdJXLCb44ELKDhy7y9fW3w6mVPt3+Vi/09wn9SV4PdH0fXH14n+biZV97JpqWzAwNkKgUCATqsjV14DUXbQrKD/pDicvTwQJ1ZzfdxaTAKdKLQr5vz9H8hNecDkC3dYsDqHnkEj6T5nLEEfjCA/Poc7iw7giQURcT0QyqS0s7Gnj3cQ1Q8K0Gq1oCcmrMSGnWOLGZ+6k+svbyXnwH08B7Sn86px1KQXU5NV3joApQaRspjUB6doaqpmluVHjO+wiD7zJ+ET1ZG9xl9z7fPLVCSm4ZZiiJ86kvxTD7AJc8GgRY5weG/G7pVxUdeLrh0/xtGgAgdxFTZmlTSV1SO7fBvTgjKKjz1C38CIre+/RF1FOY++Pkv6zuvY9A2hLL8QayNrPMw7cHdXAjUtcG7XWrwCYkALCZ+d4P6cvehdLKVX+EAk0Y48NnJF2sEPibE+jq8PZeXVRfxY0g68TAgZ3h4DRzMMbE2Q6kvQKFQoaprRnzWWHG0MHZzaIc5ppGDONxxfuoMLXWWkb88mZtlQxCIBbo4y3l/Zgb1yTyZNmUzctPHot6hJPb+a9B2voWdTR4fPJ2Ib64+jtT3mFjLqxEoeJCRw/+A58hLyebTzMtqu9tj19EFSqgKdBBNXK6QWhtgPCCLss3FEx9hzc397wrsEgABoaAY3Mw7omWA6IoHb4gh+sKzhxqEr+Ht2wLZYADcLwdSQ/CP3uTHxG/x3V2I36Ta2/jZgKKNTWCiLGvzwz5SwY/E6vnptBZ22zcJv/iAGHl3EQkVnzP/lC/fok6OkbL5MTX4V5YXFjO72ES8O2PA0YfeFxxoilsdh620PWi023fzwfbM/fm/0B11rBEylvImM7y7QeBOUVTvofHQB2peH8/0WB0yFcpoyq2jKrcBeYILsX/3enLyezPHbiavyImh8DzwnxzDkyWdEfDWZsI/H8tiyHoRCFGV6pK46QfAjMQPw5XOukUrre6tAQ4mggWxZE9fv3GtNnwE4Y0pv2vFqz2D27++Cs3NrJF4PLPiMgYyu8yFl9XGai2twxZwVgj6803MyJdu7cEj36DfnuRTRUyFNKW9CpWh5WnfoUAErl6ew5otcBsz4HHO5OzUP8n+1HwNHC0aVbaDnqulPfRIBhr62kVHztuPiF01/tRfD1rSgunCV7OSLVJdm/+n1SA8xxuj9ccM22vg/yJQpUxAIBM/99OvXD4Di4mLMzc356quvnrnu9u3bSCQSzp49+5t9/9RXfHz8M+UKhQJLS0sEAgGXL1/+28fUxv88KioqePnll3FxcUFPTw87Ozv69u3LjRs3/rsfjffff7/tsOF/IW0av7+IZaQn5qGuGHvZ/eVrTXzssejgTvnVNBqzyxlRsPZpnbG7DQJ7MzAxwH/+QJKX7Kf4ahJ2nVwxymvAzMeS/i/EUWCZQHalDvnhFHz0/RGiwTu81T/o5rw9hE3txPVNH1Gcfg+zRRZos3UoWuop3XEYp+C3Eel0NBXVcCZuLcaeNgx98jkN1iWoL96m/MYVaoOf4BHYCzMfe5rK6rnx5vcIRELGJK5AoCdC2dBMdsZNbMNiyKpu5tEdZ2RedVhpYJz3QnKs0glw6YWdkQk35h+g8mElddnl2IS742olZLC/llEp90n8qpoQTSZKIyMqp73OkXUbCX6ixfpGBvKqRsrvZKOndaC+tJgeu2dTnlLM4YU7uBf2Gp8F2tEppC+xpins2/EDFYITnN3Vjs6T36DxQ1+6W3aC9CYyauq5kJLE8K9HYJx2nqRhawn4eCNJB0PZseUJLU42fFPzFr2E3TFSmqGTSfBbNogrA77CYOW3vLNyLB5XQ6jado+Ku9k42RugE4mJWDeOsjtZSAz1EFkb490nnNQEB0wb6mgur8ehqy8mLjORuofg2nsg2SeTubTCgUYysZ/3GrEe7/Lgq0uACL3v76OoakReWElVfBbVegoM23tiEuaJSirBPswVfS87ppaeIijImG0HChkzyBZ9A0CjwaWdAUamEowleqiW3CZ3Xz5vUsIbIxyZ8NVAAKSmBvj2CMexfyBdP61l0tIElsx2RS2TErZoMFmnEwgzM6K5qgmnGD+c/OxpTink2KrXCH3hZZw7xxL12QSqb2UgttXn3NZ9XMnqgDZDQNrWa/hO7YpbFw+Of3SBnssGYSMQsO2HQhotg3nDxZyvl91nwAs+nFzTlfqWYsb+sA+nrqEIapvwvDGfk2dr8Q18FevM13CyDePJurO4ju6InrUJhccT0ShVFB5PwNDFisGPVmHczg5jExnIlSi1KnI05WgH7iWpSUPmd5cxzphNAXXkU4tXsykuW7IZMd6TH62ycJpowpKJbwIg1gl58B6UWNcT9sazWjdj9EjZcp6E+btpKaml/WcTsVBKyPjhKqIpW8j4JBQWBP7ufFerFHw2zROZgSlvbn4CwKhRLpSVyRk+3BlFdSPnui9Hz9qEUaXrn15Tnp+Cg2fYb/Zr6xqArWsAAM1Xc6idexLH2CgGfLsaB8/QP7ka/YxWo0WrULXmGW2jjf+haDQarqU8pKS6GnsLC7r6ByESif7Re/br14+tW7c+U6an1zpPHBwc+Prrr5k5cyb9+/fHy8uLlpYWJk+ezPTp0+nTp8/v9u3s7MzWrVuJivrZv/fQoUMYGRlRXV399w+mjT+HTgsVxSBvBpkBWDuA4J/TkYwcORKlUsn27dvx8PCgrKyMCxcuUFVV9Y/ds43/27Rp/P4i7uM7MzDxI8z8/7qpp5G7DQPufkjYqnG0X/PCM3XmIa7IbEwpPvuA0guP6XXxXexGdaY4TQznciClEoBbjz6jouI+d7afwdjYHE/7IKTG+mhbVBiN70qp7gFF+x+iWqNPw2Y1zU+aufD+aHKv7kFY0QAiIfpe9nRYGYf3jB6QXYJZFzecZnbDtrcHiuZmjNRGGJfouP/eAczCXRBZCHmw5D0q71zHxt6bHiNWcDZwBedmfsHBxHR+fCSixroOM6E1fYwn0Oyo5azmNjHfvkrXr14g63AC6cmZ1FHF/p1RmAc50zluAE12YxCNfheBgS35LbUUaZooMzSmJbQCnUCLoZcMp8AQDKxNqInPQPGklsbmcqoNdGAqwXFqD8Ys7kdMVRid+0zEUC1iWNQwzIJdUEuFrDuh4IsDQdz66Dz55+XYDZ6GRCzE2lJKT2khpecfYWgjpSAwB+fhYQgr6rh1/w617cTYDvfBUPk9j7+/TsntDOwW9sTE2ASZrxU+PcOwCXcj72QyzoPbk3jiFF+/MY9KdQsG3vYA6Du54zBkGMqGFoovp1DnIkXj5YzQQIQ6uRxDS3P0zES0X/MC0Vtn4jWuE333vkqPFZOoKa+i6OwD7s7ZQeLyQ+jqm0n+9AQXXv8e4ZKv2TZkPdfn7UZX1UCEm5jyjL5MiPVizGtT0EYGUSQ0Jd2whXUWSSTO34lecwsdJvbEWmSKSVUFN66VknnwHpk/3MJ8UBgH19jjNzkGz/FdOV2Zxyset9mjvcm17IvEX/4WAI2eFMfxnTGTGdB/QD++tolnAfHk7j/LuR2Lsb59E82pmzQ+KQSBgPXrM9mzr4D6Jg2pGU1UVyuxdQnAwtMdlxEdkde3oJOIcfOKIeCYL+IpZ/DYlE/m5kvcfXUbjz46Ssrq40RteQnzQGfUjXKMPFtNGIupZ6npTW7oFRMmt2R0rTO5H/ghDzEiYOEQeuDJEnrSGy/yfoznwevfY/ZWPHGHZES/l8ch59e40O9j6upUfPxxKitWPP71uT6hC35vDcT75V60NNayapIzt059hJ6rBf3a/Xowll8iEIowNbXHzMQelCoADA3FLFjgj5eXMVJzQ3zf6I///EFPrzmz9W3WvRbOo+sH/tSaYt3Fh5Dlo+mwYtK/JfQBXBq4igMOs2kprf23rm+jjX+agzev4TZ9ArHvvsX4z1YS++5buE2fwMGb1/7R+/6kgfnlj7n5z2mWJk6cSN++fZkyZQparZZFixahUqn49NPnfYD/K5MnT2bPnj20tPxsEfDdd98xefLkX22flpZGp06dkMlkBAYGcuXKlf98gG08S2EmnNgOVw7B7TOt/57Y3lr+D1BbW8u1a9f45JNPiI2NxdXVlcjISBYtWsSQIUOethMIBGzcuJFBgwZhYGCAn58ft27dIjMzk+7du2NoaEinTp3Iysp6pv/169fj6emJVCrFx8eHnTt3PlOfn5/P0KFDMTIywsTEhLi4OMrKygDYtm0by5YtIzk5+amGetu2bU+vraysZPjw4RgYGODl5cXRo0d/d6xubm6sWLGCSZMmYWRkhKurK0ePHqWiouLpMwQHB3Pv3s/WdL+mcfziiy9wc3N7+vny5ctERkZiaGiImZkZnTt3Ji8v72n9sWPHiIiIQCaTYWVlxfDhw3/37zF9+nSsra0xMTGhR48eJCcn/+64CgsLGTduHBYWFhgaGtKhQwdu3779tP7IkSOEh4cjk8nw8PBg2bJlqNXq3+3zJ2pqapgwYQLW1tbo6+vj5eX13EHUv0Ob4PffQMD8wbiO6virdSVnH1J2KYWm9FL0FS0U7TpIhlYGrqakJz2kRR1IiXsBTkNDab9zJv1SPkbcXISwqISc5au5/uouTIKdEEiFKO6rqLyQhdjJlRsiKw7nVlObX83FoZ9h5mSO36gImivqORy5mBuzvqP6SCm+omE4B3niGOCO75sdUVY1oChowdDVm4xjuRzu/TFCsQipiT6GNmas/rA74S7naaoqobqghF56Y9h6bQtvfLGQnHuPqcosJOuHW1xavY/Tnx/k/u6rdFgVR4dXB2LrGUp7iyDG9+/NolFx9J8+nlP2ZuTan6dmYBpNGXJyjiRQfuMJqZsuYSMF95zObHivDykP74JKS3NGFoKHiTQ8SACpmOaCSi4NWsX+aZ+z48cs3H3MmX1gMr13vYLAyg5EQuqyykj59golN9L5wPwEmtMhXFl/F3lhNSPjRjD7xEdEvD4cL/feMN6Hb4PrWZh0krr0UurqG7gw6zuaGxVEb56O2ECPy+cvUlZUQtaDVGhRgr6U2zfiMTQzQm0kxSLMjWFzlXy6z4P508/TZelEwmb3w+nkGmbv1tAUFoxpsCs6AVg7WVKbnI5jgCO6yCAqU4qoL21g6ONP0DOSIlQqMSstovDofTQyKRjpQ7MS6ptR1zYhuveIcx9a8/b7vYhNsEJibYG5rwMV2gYycnNY7JzJ7k0hVOVU4NwpANGtErrizpydWQSNSmbzmVs82qFi+w4tTU3RBHeaRda2K5yKeI9zfT9BZyQj/2QyxqVVBFJJpukpLu9ZQXNDJQgE6Nu1pgXYt68zKz/rgGGgE0tXRxDhLCBu5iZe/uI2Z0qT2TjnU67N/wFP6/6Qo0PPxYzzjy5gM8AP79m9MQ10JHHBDzxacRjbWH+sItsRvn06OnRkn7yH6eTL5LXUkH3gLoIv0njjjTeYmrQBr+mxCBDQhJIyGnAa2h7/+YPwe3MALW9fpOLjS2hVGnRqDWZmUs6fj+X06e6/Ohf1bU1p/+kEjNvZIRSKkBmaoudvxKicrzFsgPr0kt+d5yKRmLFvX+Zs7jL2H3m+rUAgoP1nE/GfN/Bpmat/Zxy9I7Bx8f9Ta4lIKiboveFYd/L+3XY6nY6qe9loNdrn6mTWJkjNDRFK2wxA2vifx8Gb1xj18TIKqyqfKS+qqmTUx8v+ceHvj9iwYQMZGRlMmDCBtWvXsnXrVoyMjP7wuvbt2+Pm5saBA62HPPn5+Vy9epUXXnjhV9vPnz+fefPmkZiYSHR0NIMHD27TCv2dFGbCrVPQ0vhseUtja/k/IPwZGRlhZGTE4cOHUSgUv9t2+fLlTJo0iaSkJHx9fRk/fjwzZ85k0aJF3Lt3D51Ox6uvvvq0/aFDh5gzZw7z5s3j0aNHzJw5k6lTp3Lp0iUAtFotQ4cOpbq6mitXrnDu3Dmys7MZM2YMAGPGjGHevHkEBARQUlJCSUnJ0zqAZcuWERcXx4MHDxgwYAATJkz4Q031mjVr6Ny5M4mJiQwcOJAXXniBSZMmMXHiRBISEvD09GTSpEno/uUe8keo1WqGDRtGTEwMDx484NatW8yYMeNpmqUTJ04wfPhwBgwYQGJiIhcuXCAy8tfzZwOMHj2a8vJyTp06xf379wkPD6dnz56/Oa7GxkZiYmIoKiri6NGjJCcns2DBglY3KuDatWtMmjSJOXPmkJKSwsaNG9m2bRsffvjhnxrf4sWLSUlJ4dSpU6SmprJ+/XqsrKz++MI/oO2b/r+J4u+3U3fvDt4ff8a623mEOpnTzdOGkOWjcRocjk0XH+7O3krR1WQsX+jKg8TDXDpwEH2zMHqOmo8kLRlsZJz55gs03+YTuWAQggIpBgpbmo/WoVO1vng6tIib8wnXgcTFivLLKZTeysQy2BmpqQEiPQnNRTWIZBK8X+6FdTcfauMzOHdnMwWNF/DpNACvKcNoN6oLJvdzyDqwjuobTxh9YynoS6FBiUmvmUiqmxHpJLRUNWBRqkcXg1Byv0+g3bwYGhK+w15uS+OBXG6+JsXMJpJb128R6uCAtZU18ifn8fV5gKn7At6vrSPpyCDSw95iyGkHHOxbX9GwxcOw7R1MSE0vig+mo+/ggM5EgtWAMTzam0bWvgrufbwSAzdLxPoSBBIBV7aEEhhhi6JFSV1SEbInLRy6touOEZGEzh+Ix9goTg9cTbbWhJ7KPJJ3xtPxrb6g1iAWiXF360SeoAzPqTGEVujBmXjM1gxg70Atk4uMcTY0hIo6Jr80lXOXLtMxoj0oVOh0OioupXMkrYCeC6fhP6c/mR/eJD5NQUllJUNjHXGb1o3EK5WcPl1CTy8R1h20NFc3ofM2RirVo6lZw5UcEf0VSrL23CBybn+i3x9BbXoprnFRtBTVIDTQa/X3K6uFFgWihmaEMgliQxl6iEm+q6B06XGqLj7A6I3R+I7thN+XrcnCLdd7wI8pUFLJILE1WYIW+sbYEHahlHfOamlssGHqysH8OPsW+p1CsDQ0pPZRAU3NKrym9UQo00ejA9NBdhRm3cE4zYXm5Br07VtPwz09jfH0bM1paWspgfxalBX11Dg1c9ilENnn3igWfUh9cTZlBRPJOnwBq+Nr2bP2Pq98fYTyB6MxcLbG2MOGduOiqbeB+dITdCcAl+9TsNlbiH+PKh58cp3G7HLavdQDkVtrQJhKmviMa9ihYYV5HOGrWsfc/chcWkrrMHSzpqWodSGPjbXlAI84wF3m0w2L34iMqWdgzLwtrV/8pRcfc2vKBux7B9Hz7KLn2m7fns3Onbns2BFFWlYLew4V06iCUaNd/nBdCOwyisAuo/7MEvKXSF9/nruztxK6cgyBi57NEdp55yt/+/3aaOPvQKPRMOfbdfzaNlBH6/L3xuZvGNqx0z9i9nn8+PHnhLh33nmHd9555+lnGxsbli9fzqxZs3j55Zfp1q3bn+5/2rRpfPfdd0ycOJFt27YxYMAArK2tf7Xtq6++ysiRI4FWTc7p06fZsmULCxYs+DdG1sYz6LSQ9AcHCEnXwNHjbzX7FIvFbNu2jZdeeokNGzYQHh5OTEwMY8eOJTg4+Jm2U6dOJS4uDoCFCxcSHR3N4sWL6du3LwBz5sxh6tSpT9uvXr2aKVOm8Morrev73LlziY+PZ/Xq1cTGxnLhwgUePnxITk4Ozs7OAOzYsYOAgADu3r1LREQERkZGiMVi7Oyed22aMmUK48aNA2DlypV89dVX3Llz56kP7K8xYMAAZs6cCcCSJUtYv349ERERjB49+plxlZWV/eo9/yv19fXU1dUxaNAgPD09AfDz83ta/+GHHzJ27FiWLVv2tCwkJORX+7p+/Tp37tyhvLz8qTn36tWrOXz4MPv372fGjBnPXbN7924qKiq4e/cuFhYWALRr1+5p/bJly3j77befavE9PDxYvnw5CxYsYOnSpX84vvz8fMLCwujQoQPAM5rO/4Q2we9vpKW0lsSFP+A5rTu2MT+/fPmH7mIR7oaRixX86yTiyZrVHEt6grOeHXMV7ehkKOXKe4MR60ufXtv+q8l4zuiBZbg7Nw9/SWXOfnr1D+PCR3MoN89nrIcjxdvPYZbrzPU39qFT6jCxt6E5pxYjKzNa6hrQNKlw+OA73Hv7omeuT4FD68Zcq9ZSk5qPmbcTbkPCyN5/F61cRfqG85R3vMa0NfuJNV1E+vmjFO56iO+MfijNzWh4eTZZ5SKqNl0i5J2hoFCRdm4PAaEDKSotQlmlxuNzCVO3f0nNkxLujP6Obh+8SXNWJXnNELRsBH4mfvjEN6MMbKSkLhs7mRN6Nc4IvIQImhS4RcTw2D8CV89a8q88IOfOHTove5WsPTfxGhCKfpw5aZsvYxnizN2Vp8gRBuA/rANRPbwpvplF58XVrF3aDeHNh2jsDUnceIqHZ0/QrmM3Hmgz8Xgk5uGZ28gb5SAREjA4kIpHUlh3mqqUIjrMjMHQxZwmuRx9mT4zU/SJeHUweVY+FEfbohNnIZWJoK4ZADdPd6aG+pO5/Spmblbkn31Ew/ePMfW2w2BmMw3iBqKGmrNq3w4+WxLFyg9NWTRGyrBORrxrkECsRkHu8Vqy9t0h/LMJ1D7MJ1mTzoK53Wmn142jH26nLq4jZoHODLq6hOqbaew9dBKjvW74DIpEJxAgr21Go9Ex4N6HmPo5gk5Hmbqa2O0vExpuxZg5KWi3lLJva3uQSaFaDnI1eJqAuZQRXs28NsELAV7oTJ9wtlDHoJdGcFVwjxGj3Eno44j4SQbpWaeoKc+jx7zFCBpawFCGk38EDG7VZLe+XFooqQaZlBYDA5YOPUDI1VNIZGLMQlzx9pYjKamgVlxBZV4qbpKx1IzvhFH8MaydA8hKmk+7sHg6/TgLO6vR3Li0CUt7Z6ziZlAbN4O+KxZwKjKbu6FJLPnxdZoLqjBy+3mzZI4+sVzHkRRKsvWx92h9LlNfR0x9HTnVcTFVd7IY9OgTzAKcqdA1Uk0zcsGfM7+wimqH7xv9cej/618gJ0+WcOFCGWlp9fToYcepUzGEhJj/atvf4sk35xDJJLSb1v032yRSRAtqOv0iL+FvYRHmikV7dywjPf/Sc7TRxn8n11IePqfp+yU6oKCygmspD+keFPq33z82Npb169c/U/bTBu8nNBoN27Ztw8DAgPj4eNRqNWLxn9taTZw4kbfffpvs7Gy2bdv2XKCYXxIdHf30/2KxmA4dOpCamvoXRtPGb1JR/Lym77/S0tjazubfi+j+W4wcOZKBAwdy7do14uPjOXXqFKtWrWLz5s1MmTLlabtfCoK2trYABAUFPVMml8upr6/HxMSE1NTU54SVzp078+WXXwKQmpqKs7PzU6EPwN/fHzMzM1JTU4mIiPjd5/7l8xgaGmJiYkJ5efmfvua3xgBQXl7+pwQ/CwsLpkyZQt++fenduze9evUiLi4Oe/tWl5ukpCReeumlP+wHIDk5mcbGRiwtLZ8pb2lpec6E9ieSkpIICwt7bk34ZZ83btx4RsOn0WiQy+U0NzdjYPD7KZhefvllRo4cSUJCAn369GHYsGF06tTpT43n92gT/P5GSi8+JnvHNRAKngpvpZdTuDpiDfbd/ei5eTo4W5Ox/Sql6e1RaqtRPU5gzejuWE3fyLXkbGKPz3/an0gqxjLcHXWTHLOsdry1JpaMr39E2aCPwEJC4vxztOvbjXppGQEvjiBv1006fDQWM5kMlaaW4sv3KX3UjOOQaPQEWmiSY+BojqGrFYaOemQvG4XE3gHbuV9gnJCLxFAPqY2MHv0mYGblTENpPi6BPRANy0JQ3UidUEzkcB/auytJ236TJ0f2U1RSS5r0JMHiYWgFILHUI3rbTJw7tUOo1mAW5EzKF+cQCIXIqxrpfL2CeqckkLdQnVaILtIanE1RVylpyigl9M2+iK6Ukf/gM+KzOnH9wJvM+ewEovoWEt7+kaKEJ8S8E4djjwAy993BIs4d7ycyvPr5o1aqKRYbo5NVo9DW42Rsi/hBHfcfX6Qk6jwuIzrQ03wMBrlm5Jm0R/H9XmrN3dB0H4rzlTkoBQLcO3tw5a09CIe0o+V2FtyvwMTGnIwqLVaeDlQvimf1vP6Y2piBgRoUajDQI3PXTe4t3Pv0b+c2tD0eQ8IQ6HToK+DuuyfoJrWnbogFo3tbga0pAo2WcXMi0chVyCI8MbyTjW2MPxaDQgmUq3lyuZr6+ioCenVAcSkPHM1BJsWinR1GPd0oTkxBl1pG4spj2HX3o/RSCh03voipnyM15fm89JINhhigqmhAz0hKeHtzLjWnEi+t4uU8F8xq5CTmHUff0JtzfkL8r5TSHifMtm4lSGfB8R8dmfxae6qKG9FcvUfKunPkjzhHU0sl5acf4GvYn7BlI8H2vwg1Gm2ryatWR1aWguvnCghGhaGvPY4Dw6hecQjvl3vh++7HSJUCxFVyxCcec+6JF25mwVzpf4bMZQ6EdR2NXN5IQW4ipmEO6En08cIaYzdnamZFItYJsNT3oLm9BcnlqQQ0miJ2sSX30WVcsxNpaErg64OXGTHrGzr0fRGAlrI69CyNMAt24UTIImKOzGN6kBtynDByNgYBaLU6FixMwtRLwMIZQUh5VpOgkatQNysQSn4uLz6TzONPjhG5birvvOPPyZPF7NmTT48edvTr9+vpGn4LVZOcu69uQygV/67gt5l7KFAThgP6/H6+Petobwbc+3PmJf8u6hYlpRceYd8n+LkE92208e9Q8ieDnPzZdn8VQ0PDZ07wf43Vq1eTnZ3NvXv3iImJYeXKlSxZsuRP9W9pacmgQYN48cUXkcvl9O/fn4aGhr/j0dv4K8ib/952fxGZTEbv3r3p3bs3ixcvZvr06SxduvQZwU8i+XmN/8mU8dfKfjIz/Kf55b1/uv8f3fuvjkEoFD5n9qlSqZ75vHXrVl5//XVOnz7N3r17ee+99zh37hxRUVHo6+vzZ2lsbMTe3v5Xo+mamZn96jV/1H9jYyPLli1jxIgRz9XJZLI/fKb+/fuTl5fHyZMnOXfuHD179mT27NmsXr36D6/9Pdq+nf8OWpQgEeEaF4VAKMA2NuBplWV7dzxf7I5TN190Oh23Z36LslmFpknFS+u+wWtiLEK5jgsBzpiH/frJfc4Pt0h4axf1aTb4v/WQmkvhuA5Zg908ByQm+sg2tOPolu8RzvBGJ1cgdbNAKjXBy9EBLwADMfKqRm7M3UXYqvEMz/0KnVaLui6ea1e+5ebxVwmxmUCPL1+i+nEhlmF26HQ69G0dONH/cxpzKzEONSHv4V4U4lAav5VjKbiFe+wb+HR2p7asgknlIayQHaT40yTyjiVy10CK8/AOxBx+k/uzvkNR14xllAdPZo7GKqY3nl9sRHRYgXPPAKozy9B6W1Nz/gGK2mbM2tnQrfOLuHpbsWZGOFs//QjvMlcsIzwJGN8DoUCAxFqPC+XLkYnMiTWcz8nZu5A/zqXHpheZnWLMBb1GwneYUplcjOWEsTjXWmOl8KDDsH7MH3iFWzaxBLnno3IOIS3JiNFh0VgIG8i7V4CisJLEI+aEFVUgEAmpL6/h7oHL2Ho5E7x5CgbtnLh0OJMgCyXNhVU4RXvi3NWbdD8HDO1McR4cTtWDAgoupGDfxZumnEqMXawo3nGdee+64+Chx9IXXsXdyJ4pveJgcjDxL31LU14lqvpmrmy6Su65FCxNhFT7WNAptCNiqRhVkxKRlQl3vzhBl25dcA9ox6PvbyCUiGgurCb4hR54DutAXVIKXyxtj4G+NW9uTEPazo63Am5ScvQiKTO7kSWtp8IeZMFGXH/jIHYNvriueoc73gX4ZFlhGuSCRZEKn7vXEAy2wtLLmnS5CoQCeri9zYn0RTyqPIhmnxTDGYOx01dhaAAl2ck4eXdAJVdxb/lhPEN8CRQZsXbvCIx8JuMd0hqcJWDB4KfvdlNBFWJPeyIH96appQX7cn3yy6wxS1iG7cwetDTW0mvyclBpUCqbuZV5HgvhDD6RDUP4LxflTKrQ1yrRKdTkHbrNvewtPLhyBbWDHVibYOX2c/TN9A3nKT6VjHVXn6dlYqEII+3P5juF2bXor95CnrEJB2cIGMuzmr2yK6lkbrqIsqYJu3/N9Z98cyvvZCHqGohGoKVB/dv5/n6LRysP8+D9A4SuGI15aOt6oGhpRCAQIpU9ezo4lfa0oPpDoQ+gOCsRrUaNk/fvn+L+Jzz+5CgPlx0k/LMJ+M8d+McXtNHGH2D/Gyfp/267v5vHjx+zdOlSdu/ejZ+fH+vXr2fcuHEMGzbsOVO932LatGkMGDCAhQsX/q65anx8/FMzUrVazf3795/x6WrjP0D2+5qXv9zuP8Tf35/Dhw//R334+flx48aNZ4IF3bhxA39//6f1BQUFFBQUPNX6paSkUFtb+7SNVCpFo9H8R8/xn2BtbU1paSk6ne6pUJiUlPRcu7CwMMLCwli0aBHR0dHs3r2bqKgogoODuXDhwjMmsL9FeHg4paWliMXiP21SGRwczObNm6murv5VrV94eDhPnjz5w8Oj38Pa2prJkyczefJkunbtyvz589sEv/92FCooqkTRoKB47U1axBr0x/6sipUY6xO9uVXdXnLhEZnbruEyMpK46k08vn+A796JYczbe4g5PJeCQ3epKpWzZIuMaB8FHYou4jwiEpcRETRklGLsbUfiogiij01EIBIiv5sJQgF6CgVVGRmgUnHj9U24jBmDgaMVj9aep8vaSVj6OyISCim9+JjSc4+wCHND0KzAd9ZC7gszsKyzZOBX81A2t2Dm74iehTkoVYgR4zEykoaiGkTWBjSp6jG9X0bB/nQc3u6MoYs7Go2GgWbT6do4GMs6M+ymWhE2bwDodBwbsJrcfbcZlfsVqZ8cJfjlnlTedsUmqis6lRaLaC/qssuRWhpj5GnLndcS0DTIif7+dXSVzeRX5vPxB7M4eyyXhrIndNr4ImaBTjTnlGM7rBOh8kk4eobjaRFB8xenybhQh7yhBVeFIWqBlrqyWmTOMro5u+IYthx5TRPklPL6Ox6YH09HaFZH1yHGxM2O540MI5oap/Ddlw/48LQp3QKdmPRiECXnH1J9PwfX8Z3wfKErtr72TB3xCdtOunM6ppLqW3eJ3fgihgEOePQPQWtigPcbAzjs9jryigaKr6ch1tcjZuNURGUK9Mq1NBXV0FhThyJXSIlLPvYEE7F2Cu0XDkSir0dyciaS/CL8Px+P54BQUtZdoCarkvCJ09CotJTsv0/7abEodQICl4wEOzFJL++jWCVAMX83lY9zkWGBqEVGwuytRH0/m/KrqZTfSGfAsW4M7RaCo9KAA+M+wMU0is5dJmKFP3qDD3Dlxk56X1tCU/cVVN3WgWVr5nULXweytDrswiKZNOYINekFiKZ2xC/iEt2727Bg4jmu7vuYUfO24+nSm+zvb2CoEmPTI4LwzjbgaPzc1EnfeIE7s7YQ+c1UMoQnqMw7T1TMF+QLBBg4mHOVHM6rEzD9ZBEuvh3RdIqmaMo41nOb1+lEMK3mHBU0cUWeTHboOVTSZkqH3WbM23uo79wBoUiMG64o5c1c+H4JxgatfnaV8Zl4r+9JSuV+bK3fQ6L388mdtbEQH2k9Tk4C/Hg+GbrTkPZ02vnKM+bcISvicBrSHptuvlwWZDO2WkSs+N/YJAgEIBBg080Xmy6+aDRq1rzkjVgq463vns3R14E/NjnS/ss7atNbXdBq1Cw50IBYIv3rz/UncBwQSvW9HOx7Bf1x4zba+BN09Q/CydKKoqrKX/XzEwBOVtZ09f9n3jmFQkFpaekzZWKxGCsrK9RqNZMnT2bEiBFPT/RHjhzJyJEjmTJlCnfu3PlTJp/9+vWjoqICExOT3223bt06vLy88PPzY82aNdTU1DBt2rR/f3Bt/Iy1A+gb/b65p75Ra7u/kaqqKkaPHs20adMIDg7G2NiYe/fusWrVKoYOHfrHHfwO8+fPJy4ujrCwMHr16sWxY8c4ePAg58+fB6BXr14EBQUxYcIEvvjiC9RqNa+88goxMTHP+JTl5OSQlJSEk5MTxsbGT/3f/l/QvXt3KioqWLVqFaNGjeL06dOcOnXq6VzJyclh06ZNDBkyBAcHB548eUJGRgaTJk0CYOnSpfTs2RNPT0/Gjh2LWq3m5MmTLFy48Ll79erVi+joaIYNG8aqVavw9vamuLj4aYCYn34nv2TcuHGsXLmSYcOG8dFHH2Fvb09iYiIODg5ER0ezZMkSBg0ahIuLC6NGjUIoFJKcnMyjR49YsWLFH45/yZIltG/fnoCAABQKBcePH3/Gh/HfpU3w+0+RiMBQBrUq3IO9qKmq+c2m9j0D6X15MWaBTkjNDClIu01JdhLVJVlUb7xC1pbLeGr0yXgYjENCEhzcSXViLp22vUz4J+O4PWsLBYfu4jAgFLG+hIa7WQS/2pv8H24RkpZAc2kxJSpTnuy+g6GDGcr6FsrTy7g57wcCR3Qm9rUR2L3ci7Nl36Jpbqa/eDSTX/2e2zv3UJT/gKyjdxCWmBK1dDg6jZb6omoCp8cgEAlBq2N03GdcvrQNdHDnk7vkHLxGn9PfIDLSRz+lBWFxPUaAxtUUtViLQAA6pYbCsw+x7uIDOiiIb6LxwgXcw3wwjHUAj39trGsaUFY10lJcg4lAhZ6lFHVDC+adPRltZ8ypwWd5+P4+UGspOvuQfreX03vyzxPHZ2oMeQfuUJqQg+0br2Fv5kTKk0A8uypQevvhEPIKYoPWDa+jrZA5QzVETLZn/mwRa98woNkkipyvT2L2zh4m9RyEh3ENqZ+fJ2hOH5pzKvCZ2BWrICdoVuDtUICFoT6V9VpQa8i/loaDRkPgyz0pTS0GjZa++16nqUFJ2o5r5O64Snl6Be5R/ohUkPHFRRbNWsjpcWu4XX+eYR8OYtvOPMZ11EOokRO7fipH+36CzFCP6tRiso4n4Do2Gn2dliaJjOzBQ/nucAUz38tg544omtM/IztMR3ieIZU7rhEwbwC++0Zg7GyBXafWkybn1S+RHTYPYwNDTBUy9m7cTINdNsGRo8Cy1VRz/yMRLkJzZO52DE75FImJPhjoIUeFT1wkbrG+6AW6gp6ELMsGzM2ldO5cwYC+tvToNANXK38MHP1IephA1ytvY+Jixult66kPq8S2ix8xB998Zj4Yulhi4GiBgYsVhfGPqCrOxMLfktF3l6Hn58ROUijV1SKoLqKuooCXVh3lIaVU0YwHP5+ueWBBqpsD1jviuHl4GTIrK4K6xj09IQQoSIvn+sHP8AjqgWvvXpSee8i9WxspLL2Nq39nfCIGPG2rb2uKT/Fi6gw0hPxLuPwlQpEQj4ldnin7pW9uILbYio3+reTogYuGErBwMAKhEGVtE7VpxZjbuj8jmP5ZtOhYzFlECIgZOR+1Sv6PCX0AVpHtnjFVb6ON/xSRSMSXL81m1MfLEMAzwt9Ps/uL6a/8Y/n8Tp8+/dRf6Cd8fHxIS0tj5cqVFBUVPZeofd26dQQEBPxpk0+BQPCnIvV9/PHHfPzxxyQlJdGuXTuOHj36t0T4a4PWgC2hXVujd/4WoV3/9nx+RkZGdOzYkTVr1pCVlYVKpcLZ2ZmXXnrpmQBC/w7Dhg3jyy+/ZPXq1cyZMwd3d3e2bt1K9+7dgdb37siRI7z22mt069YNoVBIv379+Prrr5/2MXLkSA4ePEhsbCy1tbVs3br1GfPTfxo/Pz+++eYbVq5cyfLlyxk5ciRvvfUWmzZtAsDAwIC0tDS2b99OVVUV9vb2zJ49+2kAme7du7Nv3z6WL1/Oxx9/jImJyW8GXxIIBJw8eZJ3332XqVOnUlFRgZ2dHd26dXvqe/hfkUqlnD17lnnz5jFgwADUajX+/v6sW7cOgL59+3L8+HE++OADPvnkEyQSCb6+vkyfPv1PjV8qlbJo0SJyc3PR19ena9eu7Nmz56/+Gp8fq+7Pxk39B6mvr8fU1JS6uro/PPX6n4wmvRKhjSECM30assowdLVCKP7tLyS1Skl1SSY2Lv7UPiqg9FQSrj3cuTemPyKfzmSfFOI8IoKYA60bZmVtE2c6v09zUQ3dDr5J1pcnCXqlN8nrztFt7+ugJ6U5L5+zn79P/cYmdDoNw0q/Rl9riLSuCURCdG62jLqth06n5cegaoQqAae7ryDwlZ5cf30nlsEu9P1hNqW3s1A3ynGM9Ucg/HkDrdZqaalroSE5C/MwV/Tb+5Fy8zC7ln/NEP9oTPTNScg/yZPym3STL0BjpCPR5Fu8bQYQ7j6SmrRitE9q8e7qjzrcCGNvZw4ffIcRL29EUVqHWiLG2MmSwouPSF9/gU5fvoDA1IDEud+TufM6NpEeKOpaiD3zNka2pqDWtKY0qKyD2ia0YgEnu82gttAYdGAofoy1dzNXXCMYHTsW7+EdyDmRyI03dpE2ZAS+vXwwc0ng7e1z2Rv9LY8/OknMwbnU77hHk0RB6PyBHDlZjNhAj4HdW51+66ubUOgJSNh0h5bz92lML0HP3ICOn03AZnwUK186z80HKnJrBHwywgKzRzfo8PlEDB9XIy5TUm4nxGZEKGmrjiFxtKD04mPWlLfjzoNG9n4bipuqksQvThAwtxclF9KR59XSbctLCGsauXnwMR8vvEVlZCRJSTVkXevE3iMlvLkym4/tDiNT6Xit5Ae0ai3lV1Ox6xGAUCJGV93Atz2+wqipjvIuUsoF+7C9ZIhlri/t17xAXUohibvus9d3AOdvD6JeLGdD4Xn0H9byuL8hMxRhRKrswUifrVuzmTbtNp98EsKCBf5U3nhCxelEvKbEEP8omR9WraPLsP64++p4MvwSIo0UUz9HBqd8iqpRTtXdLGy7+z8jmL2pOkRLcy1r9MeiL9eBsT5qgY4qmjBpUCORGSCW/IogpdNBoxz0pSSKS/mGeAbiyzACnmmm1WpJPL8dF79orJ19aSmtpV5RxK1pmxBVyOi657VncnO+xQlqkbOK/r8Z6fO3yKOG5VwkEFsm3LQhe8c1QpaPRmb919a2KyPWUHDoLn2uLsGmq+9fuhZaBb8lnEOEgGX0/svXt9HGn+H/xff3wZvXmPPtumcCvThbWfPF9FcY0anrP3LPNv4PUpjZGr3zl5o/faNWoc/p3zfXa6ON/0m0afz+RkTeradvhcfuc3nIZ/jNG0j71RN+s71YIn2ar8ss0BkzS0NokmPbdwjY2NGikeAxufV0QqNQcTp6KcqaRvTtzbCK8EA1KYaUfXdo/8000Nej7t5d0jd+zt2iPUi7GaNFTUt5CU2ZYGlvgsRYhhBY5LIX63pTxKWNlCXnIzWWoaxuQqvUUFSayPkf1xDsMYyGokLUaiMkduXQGA1KNSVN+jibCzGKCW4NUKrWkH3vLAgucqo5lDExUVTt/gHf3gOJWL6A3MTLNH71IdIOEtwGhuLUw5+iK6ncXDgCnzffwrjdFOqri9HVN6IvFYGlEUjFOA0MxzLUnuO+M5A5WBF7YAkyYxlWke44ju2CoKEZiipBB5Vl+ZiHuCBSNxI/bw8aqSdS00aEEhG24dFIVKkYDk1jQ+PbTPh2ISXHH4JQwKyheqhv7cDSZCgdbEOxD3SleVA4Jl622C4YAJVNKBAydIADCpWW5K/O0C6uIw8+PYGiroWKK2mtf0ihAP+ZPbEfHknM9ACSj0+nudadIcEGdLeRYnn4LTCUgpc9adfvs3vDRsbZzWNrrhU9M7Oo2Hmdie1LmfflCPyD9bnc4wcaciv4vDmBYfNfInbZGBCJwNaMqh3niKMQSXUDKmURukxnXA20uEpryPdoQaIVce+1bSgqG+ny4+tolWoU6fnkFCrxe7UveTPXYJWpw8wiDPPRNthJQnEdE0X8i5swFyg4tjeSgh9vUT3IngJZI/YJBUh7+6HVE8O/tE6enkZ4ehphJqtny7AF6B0pbH3/Ha0IjutIcVYeUsdoUsqEGIckoc3WEnN0HtQ3U3f5MZfHrSN4RRzKqkb85g1Az9yIHhJfGkwVyJChluoQAWKE2GIM/7ISlaPmLOlEKh2wK1OBqSEIBVBWQ6GxinW29xiAzzNCX/b31zHxscfQxYrCVx+iGyjGeo0v+nZm6GOGOkVJY2kFd17dRp+L7wFwbtpyzGwfEDP3DSysfxb6Si48wtDZEhPv57WAv8QVc5bSC0v0SdywmZyd17Hp5ov7+M5/ah35CcdBYagb5Rj/wf1+CyECltMbAYI/btxGG/+DGdGpK0M7duJaykNKqquxt7Cgq3/QP6bpa+P/KE7tWlM2VBS3BnKRGbSad/7Nmr422vjvpE3w+wcwdLXGxMcesyDnP2z76PZ9SnfcxtzAlPB3hiBokuP9/ocU38/DxbEa5yHtAdBptKjqmjFwtGDA/ZUAuIyMxGVkJCjVkFdGy4PH1O7aQ/93X8Wp11DMJOaUnSnj4erjyItqsfBMxG/GCzS7CLD2G4JOpMMmxAWHXoHo2ZniPbkTJ0veofDedboNno15rAxJgz1NZY5cm7YWrzEdOWXSi/b2WsSJB3APDMPKyoQArSeyRGNCxoRhHdYRj9sXEAjg3cDPudpgwXdfn6Zk9x3KXAtQlNVy7bWdWFvYkrl2NbLoDkT7zWfD+GFY9HJg3KRvQK0Fa1NaisqRNxoiT5dTeu4x/i/GkLD3EBuH92XuiruYmVhTm1fJ6V4f4/1yLyKHxVB+IwOlWoXnuChSN1xEbOhJ7amNdDCL4eHb5bjb9eWdm7ZEzjDEb+8aci4U452pYrwuhqymeJ7svEH++Uf0PP8OZl1c0atpgKoG9CRCqtLLeNT7E8pMLQkbEYqrgxluMX6YuFlh6mkLQgFWZjZ06HyEBf2/YuCEEKhrpD4hk8qsCtwnd6O4qoyqknK+3ZXJms1NtMQ50CfQicBXYtBrqObK2ydwDPSgws2GWR+NxNfFBirrQSQEdzuivp1O7aNCsrZeobpAjNjZCsG5r9niZUKTdQc8Fw7kYa+1qFtU+KSW8uWI3fgUpvKRUU9yK3XssDNHUVyNuFqIZaMPHTeNR5lTjqZBToevp5Cz+yYPlu7HbUJnOhfmwZVC+hr40vHNn3PQdetmQ2bmYA58vYWU5EzckSGzM8NlZCQyCxPi5s6kXbtjZGU1kpn5+dN8fhRWYuXviPvYaKruZJG35xYGzpZ4z+zJYFrNJBtR8h5ncMWMN3n2JD+RYo6SilShop/CGmoawdGSOhMhK60TECLAnJ9NIutSi7j5wjeY+jvS/dhbNOVX0Zj9bLjp/ndW8OD9A+hZGlKfXoKJtz2ZmadpKb2JwfEAmNoqrDVklXGh18rWlBoPP3l6/XVyyaCSCYQ9E/3TmdZE9mEfjcUu1h/X0R0B0KBFxJ/bRLSb1v13o3r+GdqEvjb+tyASif6RlA1ttPEMAuHfnrKhjTb+J9Em+P3d1DRgbmnAkMeftm7Wf4FOp0Oj1SL+xSnl7TMXkW57SJlCS9C7w5Aa60NdM5l7rpK//RYWYW5YtndHbKDHsJwvaSmro+5JMaY+v3Ay1mhBpcG6Z18CNm7FLm4cQj09Et7+gZRPjoGNISKvAryGD6Xl4WPuPrxO8oXveWXhGUQIsG2n5fbsl6ipD6Hr1DeovJvNDy+9jveGI1Sm11FVWIU4PhMTb3vi1uhRmvAZD97Np9TrAV33TGfbhXcwcVESXC5HdzgHvWALrn92iJCyVFwcnGlONESgVGNgboDMVIZZgCPei77HfUKrn9Rlo6mYNrVD0K11k3pr9lbq8qvodfE9vN4aQFZ6A/Hzf+C+kQxlXTOmAW7knr1A2KixlF5pzWOUvukCynYlhG7uhWtAFLV1cuT1cnzf7I+ivxemjoF0CQ4m91E5F27VUNusY1T3vsjPXiErXYHMWYXvkhE0/n/t3Wd0VNXXgPFn+iQzyaT3XgkQSIDQS4DQlSpdETsqKhYUK4iKvIIdRf2rgA1FQVR6L9J7bwFCSO+9TH0/3JAQEiAgiOL5rZWluXNumUJm9uxz9s4uJnXFQcrTC9hyVkZAgI7oSGkdYvzS53l/xmGee/Ewcz9bglIup8PHY5GXG6HCCGYLH8S9w9ZZn1Gw4mN2hU4lNljDpqfnU7DnNPYuOpr6NeLsPj3BTXKJeqc19wTI0M54GYubhp99HsNcWEHblx+gy9vpBI45xdH17pTnFFNSVIF7sBfu7SJwbxdB6NjOWCrNqPRaBnb6BGNRGTK5HJVeS3pcCBl/nuDDD08x7aQvfRVGxjwdyYkD2ZQvycN/UCscgz0IH9cd8ktQa5Q4+DhRmVskretDmlbs1CqA04eTaBpes44ke9tJjn+0ktjpI+h7/0hCmjbCrcwOj/YRVDor+YIttDL7ML6tidQEb06GpOJKIE7YkX02hz9Hf0LM28Nxbx+Bc7MAgkbW9KaCqnU7ZWYy1x7gvEWL/8CaKpS6XQfQH/mBCqeWED0WgAqllVcdd7H+QRPt2rnR9cGaPnUOEd5EvzYY55hAHEI8GZL2CSqH2iWUdf6uhIzpxOr4N8jbk0TC2pcZveAXDm/+hZZ33EdZWj52Xgbs/V0Jf6Q7zrFBWE1mTs5eg3fPaDY1OssZ8uhBOH5Vwd7F7H1dCL0vHoBdpPAFOxhLKzo0oPeeIAiCIAjCjSQCvxutrFJq72Cx1An8fly+lOSMdHrFtiPpVBID2rdh7KhRnG+fjr1Gj9pJB8UyCtOzyPXJxd1jI+ta30uH5bPw6dkMhUbFmu5vUZacy+CUWShUVs69PwP/UfehUdijcHfG556x1efL3nwC7ZAmnPG2Eds4gbDuHbGaKsn8/CcKkxMpsa/E4OBN6pIFqExniLxnOHlHrJgOW0ltsZs7fDI4neSK3dzhtJ7cDf9n7iVl2VZUaUqaTUjAuZEXagdHnDwDcQ+MIPMIuHdSgo8dUfd34dzyg9w9bxQOXlKhGXsPR3JOZVJwJJWiE+nSY5WVT6s37+Lpycf5ukUEpz//gLwjVoqT8yg9l8M3ZVEsWLee5wZ5ErnPgiHSm359x6AP9sbq4YTKSS/dWYuNk4mrULsqCZC1pTyzCEMTPxxahpC86hDrJ35KwpN3EdQviiPL4pBVlOEZHo2+aSQHJi8kd8dpTnyymnbfPc7hNxeTY9Vyxx2baNU2FH1EC16+u4KEHnoefiAUrdmC67pkTGfTMSFDUy4DmQoMOrwSopGrFViNFvYvP0XMuCao2zUlsok3bkGulKrUuLi74xXpQbc+HrD6LJwpQBEQSOCwdmSuPkzikWME+xoIjrMDDyf+HPEJmRuO0m7uOJya+OHaKgS5SolcpZQCzvQ81E56sJMKd8Qvn0RlbjHRJ0v46tdsUn1ieP71WNal7+NMpQ9WRxXH3luGUqOk+f2dKS2zsMHNRtG6uxjz6i90+O4xPLs1wd7bGfPzo1GarVJTdrmcpPnbOPfjNry6NSH8oW7Edq2ZvniWbA6SgSmvBM/vf8G7WzALZS0oopLhNKc8q4jS87kUn84ieHRHmr5YU7UsL/0MJ3cvp1XvBxk/34ntD/2C9cfaVbTcXPzw33yAgBF3QoCUYQUrpWdlHP/agu6ACS5aMy1XyGn++l3Vv2tc9DU3msxSJtWgwzUuhMgne+HdUyq/7ujlTfuhT5C++hBre00n6pm+tJw5mjafSb0Az/+2m91PfYPfgJY8tHgcWZTUG/RdSo4MGTLkIgsnCIIgCMItIAK/G83LRQr61HV7a6lUKpQKBXNmfEVi8nl6Nm+GvUJJULAPhFZl8BzsOVdWiMFYjrUsE3tnb5z9nLHmFJK84RjFvnJkWkfK8pL5dFIHnBNLuSswAs/OPaRCF3nFoNNilctwbhFEYJgbAVEajO+uYf+BIux8XEj9+BDIoHRaLgafUPRPPUhOnC+tnnmLLaNmwc4zhAYnsGfpx3Rz6YHhs5noPFzBZObkp2tI33acO/4cg2uwB+i09HcaQO7BnUTM6ERBWTnOMhmGME8GrHqBkpxinIPcUWpVoFaCsx6dnwtqtYKFoRPwbh9Oi9cHcfy1DHZNfRPVmZ20nLuQzCNW/mg8kX7P3IWhzSyKSpNpvWgLPvZBFKbko/JzJnXRTva8uRj3jpEE92lG+NDW5Bfkk1IiZ9+EbyhOzCRwWFuMBWWYSiqxlBohy4ibj5LdD/1IyDePYBjUisr8MjJXHiD2se5k/Xmc4x8sJ6SonBdHB2C3Zw8rba05vXgfCU0a46CQ8/gIT9aurKAwOYfKMzlotmaDQQNh7tj7OJOw5mXWf/ADsm0rWKms5Lt9Knrd2YYAlR7PKHcGnfsIgP/1nk2ErYL29zyMCsjecoqSpGyMvl4MSY4klXzSKCZwRDtUBju2P/gFMpWSyMd6EP5INxzDvaWAzGKF/GLSFu+iIKOQqBf6o/NzpbmvCxkZ/pz+7k9Wd3qdjO+7sHtVO9ocCMOWWsySFAcGdt/JHT08mPWVJz0aJ3BvQQnB7eOoVClIWboPr3APbIAxuwiVuwG3h/vi0TES/0F1Sxs3wp0X6IKnsz3n/8+IvmsYCsy0I5CFHCZySACDzn+MvW/dfjerv3mZgxt/xN7RlWYPjCB4dAcU2toVKH1CY3n2q8Ra27TImeHVg9/dluLmdg1VNMuNUFoBSgVKdwNxH95bZ4jW04AuwBWHsNoVvbwTmmJ41JMk+zXEFY6hiaH+il+XaokvnzNITL8UBEEQBOGWEIHfjWSxQFYh6LX1Bn539egFwP8+fAr7bAUlJSbsAw1wSeXPZrHNwcMP4wMPoQwJQH4+B3NGAZuHfoR6Slv0Ps5Y7ZRUVpRRqvRH3bwFhHhDZhGUlnLilYmc/2MDGSktcI0Loc/GSZhdPFE6uZG+/RQyhZzAobFkvzUD5fDRLFs5hfSzB2g08H6avTmMgH4xlClT+P77ezl+bBV97n8Jt+hKnLs1IfiN9mye8Qabd7oysM3nYLORtW45JSePUZR6mspAV5yDvbAcOIvNZkPprAOFTMp+Gs38vC8Dw//1xc8vgH1TfyXpj334PD+SY9t6sP250/gN7IVbs1Zsfe4dlHotUQFaIhq9z7olH+HhG0XZuVx2vPAjjR+KJ+9UJnK5jOw/T+AVG4hNq2bm14V8Ou8Q51c/QmlBGQ5hXrR4ZxTRrw1GpVGBUk7FeSME+XFu2QFCH+hK42f60vjhrpCRj0erYJq/PICAUR1Iin0Jq9HMYwVH6b96opSh1KpAIafTx/dSUW7EsbTqQ3yoc/XzZ9bJ+DN1H05errzxaCuc48v5v/+lMmx8zZiNG7N4eKUTrWXpzH5rEWWnM8m5awDTp+7H/5iMV1ZBVscAPOz1+DzSnYhHurP/lQXk7D7NsXeXYrNaafXePWCvhSBPyC9h91u/UXQ6C98e0SjcHVFoVNh5OXHk7EnOuBQz5JQrjb7LouTEn3RbOpG5I/4kNT2TDgk+pGRWMvW1r4jUmMEGe579njPfbMZ/YCsKD54j7LGe/JDvz1tvHeH33zsTqFKCxcKGjdnoHFT4FaWz7b7PiftoDI4D46qbtAciNVhfzglOksOLfvEApC7fjy7QrbqSZodBz1BkCmHvyaZE+6ej0GlB27DWA1YLWK02qLfT12U42EmvSbva59g69jNMxeV0/mUCzs0CGJT0UZ1dlTotxT7JnNq6grTT+whv0ROAg6Tjig5fLl/Z8OKg7+DrCyk8lkr7bx5DoRZ/igVBEARBuLnEp40bqdIsZREAHC5fAr7/lHvI23Ua96YBoKkbIKJQgFKB2s0LNGrwcSF/60lco/1p0649i9dMZEPWWpoeHItCrkYbGiBNezuQDXILlenpyErO0WrmW+gjg0nfchbv1mGUHjlPyo5EIhbF42tz4sjI51A7u9Kr9/OkpB3GKygauUKBg0oGxlBCP+tBQFwXsvaeY8fLv9B29v043xGAvcENvZ0LlFdy6ts/cRnxCuHhdpQ7qvBvIhWx0LQIRWOx4pBfDAWlYLWw66yZvWeP4BdspqhjM5wb6bBZtCzdo2NcQglNXn8Q52BXSlLzKE/NJ2hgKwL7NKPoTDah+fEo88upKDWicdWx6bG56EM8GJIxm4xvNuLROhSZh4Gz50ro4lGCg6cjDqFSJiZvXxJp6w+gjdcT1qI7H/6iIWzZQbIX/YnvvV3QgtQSIkCForyS6LGdMCsUIAOFnZqB+9/AzsMgBfXlFWCxog73RmWvwXooC7m7HeVOMuyAknPZbGj3Fm3cPHFu3xQ8negQ4sXvd4bWeoojg+xoH66gyalMzs5PpDy9gM4P9OJrjzI6ZZ1F9yMMDx0IIbLqhlXuY+P4ZuOPxPVvTOOJdwBgKi5ndbe3cIsLofXHYynan4R9qCcL/Z9A7aJncPLHbH3Nlxy5M2NK/Mkc9zXFSdmYSyr4etowPnwtHNcYP7yUhznx6ZdEx/SksrEz53/fg9bLgN/kPmx0PIqLRzhhvygJDLTH0VIGFgvFh9Po3uNP9I5Ktr/vQun5XErOZkN5pfTvwMUB5HJCcWEsLQlECnyLTqWzvu87GJr4cefhdyg9n4u7WxS/7yhj/+6TjO7cFtVFXWbyD5zj8PTfaTZlSO21rVXc3DSkpQ1EpZJz6n/rODJzKfbr7qaJb+jlgzCZTOq/eYm0FQewlBs5d2gL817vQ/fRU+g4+Nm6/4bHzyauz8PVQV8mxXzEVnxwZGoDWyckzd9KcWImLd+/B3tv56vvIAiCIAiC8BeIwO8a2Gw2zn67GZeWITg1qafqk70GfFxBc+WH1bNtGJ6NfaCwFNwN0ofQi6mVUhYH6UPv2l7TUbvoUCrkuIb40LXbeNYs+BBdZgAVPnm8Ny6MZ748hV20K3un/kzY+KlEvf0hqkZBLG3xEvn7krjz2AzWDvuYspQ8zuxZQYfHn6L5gj8oSlXgH9SY8PZSIEF2ATg7YCytYMCnn+PU2I/UP/ZScDQVJ08H3IwOvPjFGUjPx3I+lx3jvkaulDPqxAzpvgCUVZCy8hD2QW6UZCVSXJJMk+Z9iOnii8ef0QS5yPAN8EM3bRBOjaKZO+sAH+YG8cxEP9Cq0ZdVMnD9S2jdHJHbaTiz9giebaUeOulbTnJ++UHsvZ3QehnI2nwcn+HtSVm+n+KfdzD/o45UFIVRYQWtkw6A3ePnkLX1FH6TQlDkGZmmcmNdr3YU7tjBooAnaPXRvUTc21lqD+FoDwYdSgc7Bp75ELnZjNbVQXqOTCap4qiPC9hriWm+nJycSlaMV5A39xCefZoTPLYLhsa+KHUamvVLqPkCwGaTMoZ2apDL8bKzsPGXONJ2u+Ma7klpbgkedzTlrTePoD3vTvQjd4KbY/VrIyenkqRz5cjsVNj1CqsOFMyllRQeSUEug9bvjMSrTwxWixXvHtFoPaXno/0+DQc3HqLE3Y3ucx5i5YhPSFtxEL56GFdn6TEyVZiJHXEnO/0teChsKJQKnGMCWZu8i+T+Oo5SwMNjW9Noy1oSB/1E5i9P4tkqmMcm+LPTN43Px5ahvHsUQ5U9IK0IyirZY0rBcWcx4QPa0pGg6pe3PsidyCd64RIbSEVOEb9HPINDmBfPzXqW339PodLHjYqMXE69/wONn+1H8sKdHF73M8pGKuIm3sfBKQvx7Rdb3TAdQKORsuYFh8+T4m5kv+8ZIijiebrU+feXn5lEeUk+PqGxtbbv2pXLC3a9eeWVUKzkYK4sp/Lifk5Vpkw5hNVqY+rUXtXbXNHRnVACcKoz/nK6r34JY36pCPoEQRAEQfhbiMDvGuRsO8XWez/DvWMkvTZPrn+QfQPWGbkbpIxIURk4O4Dq8r2IjIVlVGYX4d4xAp2vCxaNCt8WnRjVrBPmZyv54/vxaDIckMvkpCzbydEvNlCRU0Kbj6Q1SxEPdyV3w1H0OjUylZTF8vFtia9daw5+sJeMtUfIf6IXcW/cBUlZUoBitaH2ckbtJX0g9U1oiu/vz0gXZLVJGUlXR3ZvmINsmBmXxFD2frCSFh/eK7WWSMtDp5azptd0KrOLSW61AcNHgXj5uPLGq23ZPOwjdm05RehzAziyKxmXP37BVamAkc9VBV726Br5gqsjyGW0mHG39HjlFhHyaE+UOg0KlZJNj85h44B3Gb73TVz8nNk2bg4RI9qhVMlRypECOZmMVm8OJfmXHTh0MGDZVYnC3kpUmAN2Ib0pSMsha+tJIsZ0wma1YTIaUXq6kvrHXnaM+wpDkBsmOzvifnoady8XaT2dRiUVVTGZyc2pYMuco+hPJeLYKgSVgx0qBzvUzjq8uzeteSKLyiC7UMoylVUCNpQOdgSM7ADnstF5ukGZiS6xbaGrGpTyWtngO+/cyM6deZw69RUhITVFSuy8nOizfSop326iaFcijl2jkSvkdPn1meoxjffLKHp2P6avW6NvF0nvXW9iLq1E7VrVZsFmIys7AO+mGr5ofASnQi0ztrxKVlkJix6fSMS5Ttz5RE9+4RD+nfwxbPHB3skedFrefjeOz9lBLmVUKi3YgK1uBZwpT2eTYybBGw/ygItTrQbkcpWSuKrXp6XCiHvHSAyNfIjr4kGXLlIF1Z0vruDkJ6tJ/mUnsYuGkrJ7M7ICE4Gb2rH9m9loD+m5r/OyWk3gAVq+ezfhKb3xJIso3Ov9N/X1i90pSc7i6fkncXSv6ZGXnFzGmaQyzmZZua9ZPK8tLEalsaO8OJ/lX00kutNQQmN7Mn36UWw2eO21piiVUgEnW4mRLlvleCcE0MBuDej8XdH5uzZssCAIgiAIwl8kAr9r4NIymKjn+uHVtfFfO5BMBl7OUvao0giFRinIqfoQayoupzKvBH2gO56do7gr+3PUzjpkMhkV2UXk7Ehkw50zUdipuevsN9h5Gjj4+kJyt57E3s8Zj7gQ5EYTWG2EJzQhPKEJYCNhzUuc/Hwtx95Zwukzm8nfm4RTI2+CEhpTvHsPDoFhoFZi0SrJWbwQt979sNnkpK88gE+XRiTbjrMkZzYjZFNQnofNn31EkSWZ8D2DKUrMwqNbUyqyC9FZoOBoKvbOFZhyy7CrrGDVrJlof/mGbqsmcXj5cYwuLsR9OIaTB+0pN9oI6hopPTbF5eDvDk41L80Dr/2MMbeYVu+MQqHTEjy+N1lbTqAy2OHdMxrsNczZnMszRV15LWEqEWUmBmyYjFqvAasNl0Y+uHw4FhRyslUHsPo4sbPnXBwNBjo/NRi1l4ykxbv5PONbflz5Nc/t7o8hRwq6zMXlWKw21q1KY/jdoeRm57Dgvc8YfPdwJo3zZNzkU+zv1oiH+kdQkVWEqbicwmOpqAxSps9otDDz8bUEHtnLwA+HoHPSScG1QsYZXRnuZfY4rEylwlhJWbQzLmcrIEAHUbUDmn79fDAYVHh41P1i4ez8rRyZuQxTcQWxzYOkKZYXUdhrUOo1aNwdQa1C5ycFGwcm/0LBwWQ6zh9PQk8/nn/rKGc6WnArliN72IBHiYbXPngbm4eBfaSxgpP0HhPBXffMkAJxew165DxV3ha5RoFNLkOBHI1ay251PmHpWlqqAnCODeLcgu1krD9Cy/fuQXnRujqFVk3C6pfq3KdGT/Xm7Hdb0Hoa8IlqSZt+jxEc3QWvDtEUdkwktSyD/MwkXLyCa+0nVypwCvJkKJcvuNIkcBg5c89xeNLvNH6mH1ajGZfYIIYM8efMmTsJDJSyoKqqhvXJx7exZ9VXlBXnEt6yF5s3J2CzUR30Aex/+SdOfLSSdnPHEXpv58ueWxAEoT5jx46loKCAxYsXX/cxNmzYQNeuXcnPz8fJyemGXZvwzxcUFMSECROYMGHCrb4U4R9OBH7XQKFR0XLGaEBqDq1xc0DrfvlCDpeyGM2Unc+V1p7ZVX2AP58NlSZpjVlVMYsN/d8l688TdPr5Sby7N60uQ3/845XsfnIeTV8ZiFytRK5RUnQyjYy1h0n8agPl6QXEvDWUsMGtsJnMUoBxgU6Lg4sjTSf1x6owEdi/PTlbThLarRFqJ3u2D0og8oXXcRl4J6fffosD09bh238H9k1akPfTx5QP6MmvsbvZFrwU7wwfwn9vhNeOVjQdNBzHeD3N4+NYMmoWprJKALxiQgiJdybl/DcoIprh4t6W5Z06s/DnCDq88Dhvv7eW8JnjuGPQZCI3P8/87ZtwLckjTO8COYXSlNkqJz5eibm0kpiHu6KKDgK5jLPfb8E/oSmefWPBxxWLJgez2Qo5u9G56JFrlDW1PiqMkCJN4zSWGdk8cT7lGYWUZxWhGdWULV6PYqkw4Tm9DwsjW5HpWEDZhlM0eSge/7vasn1zGtHh0qHOHDrO4a27sdPrSJd3pahAjlynI2fbXrK3niRyWAcGnHoPWVXBnkWLUnj5y1z6NQmkb6UZnVW6qPMeMu6bv5snKiJpEaon6ZXfUYW60unzR8BUCcaq3oBVr4lXXrkoe3iJsIe6Up6SS/jItmzZmoNftJxAf3upZUF5JabCMswllZiKK7CYTaz7YSpBTTux66cDKBOTicstwbOJB/N+8ODUqWJcXdVgUCHLK8HTwx0CPTFwjgTCSLCGsnfdN3j7xrDjzrk4hHmSs+0U3ec9gltMIAS409LoTktzAnjbQVWv8+MfriB760nCHuiKa6sQbDYbNrNFaklRD8dwb4blfYFMLgVX/R//pPq2kVMW1Bv0NVSH0U+x7vt3cGrsx8oOU7CUGxlW8CVKOzXBwfo648Nb9mb4Cz8S0Lg9AHFxdbN0fne2oPhkBu7tw6/rmgRB+GcZO3Ys8+bNq7O9V69erFixgrS0NJo0acLrr7/Ok08+WX37jh076NixI0uXLqVnz5519k9KSiI4OJh9+/YRExNzM++C8C8QHx9PTEwMH3zwQa3tc+fOZcKECRQUFNyS6xJuXyLwuw4l57JZEv0CzrFB9N31ZoP2SU6HKZPSiVj1Dff9OLhmfZKHExhNWCwmTox7FN8howka1IryzAI2DXqfoNEd6Pjd4wDYeRnQuDvi0akRMW8MA2B561fI3XWGzoufRh/gjlyjpKKgFI3BnsITaZyY8QeNH+uBPlSa0paUuInfDtxHz9i36ThhIqc/X4tvhzBkai17HhhKrN9e5D6xVFoSyT1hJvAed7Lf3Uruyjzalj+M6cccIps3InpcN1ya+uIzsiOKYhOsOkObt0aS6HSCfXariXfqjV9oCNmndfQYNxyXIDe2/OpM3lEF0R0c6dXNgeMHVxDTZggENiI7P4/DSWcIa+srFao5kw5KJQS4EztjFBm/7SZ5xUFCG/uTn51KRfMkWoQPBUd7zKZKHnnIlxFB5zj1vSexb36EMsxPWkNZVikFUBYr5BUjLyqjssxI01cGovVwhKIS2k4bxrojakpWFHN0w3IKW7UgIyiW/kPboNKpCSrP4bf7vkG2ewyRblImsKy4FP9GevauiCM2PoDipDiKvtyOy7FSaKMEvRTY9+rlzeMPB9HW/zBKB7MU8BvNGNRaApVO9O7kgs5ejt+c+zEqFFIW+FwxuCmlLwTqqWy56IMHUajUDHj8U7DZcLBT0f7d0ZzKk9ExfhktmjmyZ2GsNFW00kTE6PaE3RGD3ElHcuIeNvz4Js6+rXj+5DPc1a8LYy9qrxAeflG2MNADZDLSKOJrdtO/MBCHzDwOrfiG/ZbvcCgIxlRQilKvlRq/W21SsJ2RD2YLBKqgKrBrN/cRMjccxSHME5vNxtp+b5O3I4n+x2ag9ai/B96FoO9SQU07EdS0U/Xvhzb/jIt3KL5hLa7yr1DiEObFgJPvAWDML8VUXF4rC3kpuVxOsy7Dr3hM74RovBOiG3R+QRCundUGqbnSZAOdFnxdq1p53kS9e/dmzpw5tbZpNNLfdh8fHz7++GMeeeQR+vTpQ3h4OOXl5dx77708+OCD9QZ9wr+A1QZpxVBqAp0KfBxu/gtNEP5GDVyNIlxM6+aAd69m+N3ZsA+aAIdOwUmbPyWBERycspCCIykA2FQK1gx6n039J2Pn7IkhuBHhfZvRfcUkPOMb49Oz6sOk0Uxg6xCGnpyJT1WjaYCmLw0g6rl++PaJxSU2CCc/F7ShXshCvDnz3Z+cnLuZc+uOVk8jVWt1qLV6jCllHH77N/K2HMdep8G13zPIgruyps9H7H1xKUEj21N4LAOFszeBr3xEyBsf02LCSOIdR3Pmu52kbjqBV0I0iuxC0CthZBOCnujOwQ472BKwhDMtviL16DlS/jzPiXmbQQavDsjn+6FH8dm/hVlTEnj84z34NG2Ln78fT/UcwJ1NW0oBU1GZ9MfXaAKLlbAHuuI/KA7Pbk1AqaAgK4mda+fxR98ZrBn6EV9M7MSMsYH8eeJ7lhWv5HxBIshklGYVUXgwWXqglAosWjV5ybk0fu4OYt4YRqMneoNSSfCdsUTf2YhEsyfJGnc+3KfHvPMwaUfTyUsr4PQvO8h/OILt9unkNNHRoX8vfGPiOXc0j9hQe8gsxEFhw/futtDIVXqzqOLsrGbcPakc2/owy5ZNk9oI+LnhqNLzzdie2Ed6ItNpUfu4oI8OgHVJsCMDNPbSekcga8sJzv28HQCL2cShTT9ycMMP2Gy2mqIxZRX4B+m5554g7h8dID3fjjqoKnAjrzCSvnAHspNKBj75P7re+yHNmjnRvns9RYqq7FdmkqkoxRM9vQgnvMQFOeBV0JaOo55h0L5p9Jz7MENOvYuha1OpIJFCTqmqApuTrlabEqVey64nvmFNj7dZ8+1rnNy3DCvmuoWNrlF2ygl+fHsYP8+4+7r2j3lzWL09/K5FHmVsJxnrtbSTEAShwRLT4OtVsHALrNgj/ffrVdL2m0mj0eDl5VXrx9m5phjT3XffTa9evRg7dixWq5UXX3wRk8nEjBkzLnvM4GBppkJsbCwymYz4+Phat8+cORNvb29cXV15/PHHMZlM1bd9++23tGrVCgcHB7y8vBg1ahRZWVmXPVdubi4jR47E19cXe3t7oqOjmT9/fq0x8fHxPPHEE0yYMAFnZ2c8PT353//+R2lpKffddx8ODg6EhYWxfPny6n3mzp1bZyrp4sWLa625PnDgAF27dsXBwQFHR0datmzJ7t27L3ut/wiJeTDnACw8DitOS/+dc0DafguNHTuWgQMHXvG1cakvv/wSJycn1q5dC0jP85NPPsnzzz+Pi4sLXl5eTJkypdY+ycnJDBgwAL1ej6OjI8OGDSMzMxOAwsJCFApF9XNotVpxcXGhbdu21ft/9913+Pv7A1JmWyaTsWjRIrp27Yq9vT3Nmzdn27ZtN/KhEa6DCPwAq8lM0k/bqMwtbtB4pU5Lt6XP0+y1wbVvMJkhq0AKWC7RpyO8/zwM6GIlc8NRMtYdkc5ttpJ/IJm8ExW4DOiPVWYDvRadvys91r9CyJiq9UIWKzaTGVuFsdZx/QfG0XLGaNJXH6LwWKp0/qr70fjZfrT+9D7CH02oHh8c2YE7/GeR8cwpDr76M+eWH2DPjGUsWVyI3zufEDisLUEj2+PWNgx7HxeslSZ2vryNPa+v48Az35L0/VaKz+Ww+alvUNlrpGxapUnK8pzNZLTXZMYFt6Cd61xUbmuxVppJW38UAj1R6DXsfvor9r2zlPVDPsDOrEKeVwKpOThptCiQgUwuBSsXMjDpechsENS/BfoAaYpdcJNO3DN9OT53xOA/KA47mTM6vQf+TTvhGxGHu1swnE5ndY+3Wdr/PSrzSjF7OLF70s84lMp5d4WFgIBfSU0tgzKp/UbL5ipGO26g66BQ9m/owkOz7sB/aBsM0QH0/OFxHu4ziDG0oI+mMaNeGE9asQ/zV+ax9ZyVLUdKuXfSCdKcHKBXKAfeWMSOR7+i+GwWNpuNoKadKVH3YdoXEWTsT6s1BVeuVUvTWj2dKC8tIztIBW19wbem2uufIz5m87CPKD2fg0Kh5PGP9/H4h3ulN1m5HALcwc8drVbBN/Pa8vh9AeS4yHnSsIaN2lTILCBt7zm2TfoJY34Zcb0fpGXH9uzf34cJE2qKrlzsTFkms9jGV+xGgZyhNCPcyYcfmkwi+6uzuCmjUDraIdeopGuQyUAu48Su5bz9cDBrl79bK6hTG+xxbxeOR6dInDwCKUo4S/stj1zTVOlLVeaVYDpRSZdhL9Jt1GWKLQFFJ9P5vdGznJi1stb2U3tWsnreK5hNxsvs2TA/cZAv2cUBbvKnUEH4D0pMgyW7oKSi9vaSCmn7zQ7+ruazzz7j1KlTjB49mlmzZjFnzhz0+rpTxi/YuXMnAGvWrCE9PZ1FixZV37Z+/XpOnz7N+vXrmTdvHnPnzmXu3LnVt5tMJt544w0OHDjA4sWLSUpKYuzYsZc9V0VFBS1btmTp0qUcPnyYhx9+mHvuuaf6Gi6YN28ebm5u7Ny5kyeeeIJHH32UoUOH0r59e/bu3UvPnj255557KCsra/DjMnr0aPz8/Ni1axd79uxh0qRJqFT1tK/6p0jMg6WJUHLJ+0GJUdp+i4O/q702LvbOO+8wadIkVq1aRffu3au3z5s3D51Ox44dO3jnnXeYOnUqq1evBqRAbsCAAeTl5bFx40ZWr17NmTNnGD5cmu1iMBiIiYlhw4YNABw6dAiZTMa+ffsoKZGqX2/cuJEuXWpX0n755Zd57rnn2L9/PxEREYwcORKz2XyDHx3hWoipnsDZ7/5k2/1fEPFoAq0/vf/6D1RSValTLge32n/g5HKIawrmKYPw7hxRnclTqJU0fv5O9r0wn8J0BYa2vlJgUFQmTdWrmuqXsSOR7aPewbNTEO1+mlLr2IXHUtlwxwycmwfQ79cJUt83hRy5RkVZSh7FiZm4tqhaD5VdgHeLQDJaBFF0Ih3vHs1I7zGAheXuFCdb6aXeilfnRgQkNKHRvZ2w6bU0mdQfx0Y+2GMFmYzzqw8T8WgCFxbRrc/7AR9ZEKGWKFQn5Dh/HUveMHccIu9BF/weMpmMvP1JuLQKxTHEg5KkHMIGtZSuR6vGVFxEqaUEJ1epnQM6rRREp+ZCTqkUCBaXg9UKJjkUF+OlcsFr4bOUZxZy3Gs5zTq1Jy6iL3ER/cAiNfMO7NuckrQCVMHu5B5M5tT3m5H1b83xdCtpqWUc3XgA305RoLax4cOFZC47QlGMF+2C3AmJDZYyVno7KpMycfJ2oTM13/T+/nsqR44WkfL7HhYekLFgYykJA7IZ0dvCsXeXYi6v5NRna4mZNpymE+8gR/UCyXlZWMuM0vN7UVBktVhY8+2rHNl+hsRDpTz3xUyCL5paEvt/I7EWlGJfUQl7knEL9JBeH0YTqGumU6aklGHQK3AoLMVJAeXOJkqUZlDI8enfkn49otE4X/4DycX2tZyO/wuBdB3dAqpeykqdlhYzRlOelodLbJC00aDDZrNRciYThxBP7B1d0Tl54ujqW+t4SnsNPda/Uv17XO8HG3QdV7Jj3Fck/7yDbisn4dOl2WXHlacXUHQyg4LDKbW2L/nsSXJSTxIR15fAqvV71yOeEHSoCL9MFVFBEK6P1QYbDl15zIbDEOJ9c2bjLVmypE4Q99JLL/HSSzUFqTw8PHjjjTcYN24cjz76KJ07X7m4k7u79HfC1dUVLy+vWrc5Ozsza9YsFAoFjRo1ol+/fqxdu5aHHnoIgPvvr/l8EhISwkcffURcXBwlJSX1Bpu+vr4899xz1b8/8cQTrFy5kgULFtC6devq7c2bN+eVV6S/zy+++CLTp0/Hzc2t+ryvvfYas2fP5uDBg7UyPFeSnJzMxIkTadRI+nIxPPwfvP7ZaoONyVceszEZQpxv2bTPq702LnjhhRf49ttv2bhxI02aNKl1W7NmzZg8WfqSNDw8nFmzZrF27Vp69OjB2rVrOXToEGfPnq3O2n3zzTc0adKEXbt2ERcXR3x8PBs2bOC5555jw4YN9OjRg+PHj/Pnn3/Su3dvNmzYwPPPP1/rnM899xz9+vUD4PXXX6dJkyYkJiZWvy6Ev58I/ACvhGiCRrUnaHSHv3Ygg730R0Fvd9khSjs1fv1q9w/TuumRq5UotFWfsCtNUul/jUqqcAnYeTthV7qUkiV5VKQ8gNbPv3p/fagnEY8m4BbuKU0PrKrqmLZ8P4en/UbRqQw6L3hKGuykx7tPc7zv7sTqhGnk7T9Hu4+c6H0ojYjPPufUwVMEdAiTmq4D6T98S+zb46R9LVbUAe6YzFaC7+4EhWXYgE258yktK2LMHxNIXLgLTfpPFH4HCbk+aFz05O05i/VcNsQGI1fIsZkt+HRuJDWnd3Hklzce5siu33j8o714K6rWm6mUkGOG1FJQFkCkuxQwnS6AMjPYSX98Vc465Bol7i0CWdz1beI/vw+nRt5gsRH7ykDwdQPA3dVA128fxadNCL+bzHz26Td0C2wnfZM3Mpqg/m0w7C7Ep3UE+En7YLSwqef/kbzxMI0f7Y5H7xj8+ksB67wvWrB5yPuUf3SCHk7ODPxhIsO6O5P01XrMpZW4tQ+nPDUfhzBPyC7ku2lh2GZGIfd1BbmcnJ2JpB3OQNexGQ6Fh9i44G30zgGEx47GzaeqIqXFAgoFwaM6SM3ji0pBVf+bTmpqGWFhfxAb68y2dfEoZPAh/bHXqqAq5tfUs17wclzCfGj7VSFt7gmstT1qQh9WcZLJrOYJ2lO4fwdLpz2Nbpk/3b98iaAR7Xnx+/Tq8ZkFZXg6SVNWFyxI5pFHdvLDD+3p06duI/ZrFXhXGywVJpybBVxxnGeXKAYlfYidt1Ot7ZUV0reUemevevZquCg8iMLjLx1DEIS6UnPrZvouVVIujfN3u/Hn79q1K7Nnz661zcXFpdbvFouFuXPnYm9vz/bt2zGbzSiV1/fRqkmTJigUNVPkvb29OXSoJvLds2cPU6ZM4cCBA+Tn52O1WgEpyGrcuG61cYvFwrRp01iwYAGpqakYjUYqKyuxt7evNa5Zs5ovzhQKBa6urkRH16xZ9vSU3pOuNK30Us888wwPPvgg3377LQkJCQwdOpTQ0NAG7/+3Siuum+m7VIlRGud3/bNU/oqrvTYA3n33XUpLS9m9ezchISF1jnHx83zhGBee02PHjuHv718d9AE0btwYJycnjh07RlxcHF26dOGrr77CYrGwceNGevbsiZeXFxs2bKBZs2YkJibWmbp88Tm9vaVaE1lZWSLwu4XEVE+kflodvx+PR4fIv3Qcqw2OfLaWjI3HLjNAKjCCsXaaO/S+eEZVzMO3T4y0QaOSgreLyvIbIn2IeOUZPIcMReXqxsE3FnF+8S5Ayhq2fv8eQu6MlY5dlU06V7kRz4cb0fyNodXHsSjk7HlnKWmrD2EqKsdcUoGLg5XRkWcxHjyFa9swSvKkoK8s5TypP31Tc6EKOa5dm9D103vRKiycPZeCDLg7eBovhf5A00e6EffWMJxbNcGhSTgyhYLOCyfQ8vXB2AW4sqzta1iVCgYcn4lr/zhwsCPp5+3IUrV4BjRFZ7goY1JWCS4qaGwArQ2rzUZFQRlEuUOIFzTyhrJKlEoFA069T1lBOSUpeWzbkMru91dTeD5XevwsVvasOMOYoRuwdIhBJpOhVqsY/+KjKDztIcoFdCqCYxqhddaj1GsBpPVzacVUnJemd5ycu4kNA97l7PytAOgwUbEvEX2IB3cseoyRI4NQ6DQEDGhJs9cG0+7rRxiU9BGBQ9uCoz0yuYy8gnxmv/gmuRlZbB7+MQcf+JRO0QtR4Mfwh//HmNd/5amP38LB2Ul6nZzNpKy0GBMWKjQyzp5NgkAXqQ9kiJeU7avi6KiiZUsX2rZ1I/doCj+6PEzi679TeCyVs9//2YBXb21d/5hIr82TkSvq/ok4TyFpFFFIBUc2LSKz5CAVbnnoAt0oTc5hXd//4/ziXUz+cg9ezr8xcZ40pz8np5KCAhP5+VefWrl/fz7x8WvZsCHzsmMCh7Wl6+/PYefldOWDmSzoZDbkl3yCHDlpAcMmfo+rd903SEEQbr3SqwR91zruWul0OsLCwmr9XBr4zZw5kzNnzrB7925SUlKYNm3adZ/v0qmQMpmsOrgrLS2lV69eODo68v3337Nr1y5+/fVXAIzG+v+mzpgxgw8//JAXXniB9evXs3//fnr16lVnfH3nvXjbhbV7F65FLpdL75EXuXS92ZQpUzhy5Aj9+vVj3bp1NG7cuPp6/3FKL79W7rrGXQNHR0cKCwvrbC8oKMBgqCl8dqXXxgWdOnXCYrGwYMGCes/VkGNcSefOnSkuLmbv3r1s2rSJ+Pj46izgxo0b8fHxqZPZvdLrSLg1RMbvBsrfl8S+ST/i0jKYvrvfqjuguFz6QG+2SNU8gZydiciUClxbBPPuPKk3+oS7ZXV6sQEET3wRgMITaRx87Rc8OkTg3yZUyvLptFIVxqoP6qd/2cjZ+3Zh1Zhpfudu0iq8OP/lXlyjwzk2cymnv9pAh+8fx6t7UxRqJUEj26MLcOXPUZ+w6/n5+A+MQx7qQ/OFf0gnt9mk1hMKOaiUpGXmsGbzdu6/bxihzq2xmS2c2bkK3zs74juyZvGu3kVH1Kj2GO21lJzOxGa2UPL7UVTNS1F2DGD3hG8wZ0DCB//H8tDX6Dh/PL5dG0NmPlit2FwcsCoVbBv7Oed+3kHfPW9JGZ7CUikr6qRH5+9K668eIbN5C954ZhOPsY+KtFw6do2GknJahmkZ2NWZ7DwTAc08WHvnTPL2nqX/rjfRdPYGmYycLSfYv2Aj7qnpbC33YubMY/y5uTvdFz5LhcrG/v9bTPLCnagNUjZX5+PMneteRO3jjLaqYio6Laoof5q9XvON2YXthPqw6v9mcXjrblr3OkHzN4byzdubsM/KI7k4ndj+97P+lyW88+AbPP7eVCLCQihSmXnJfh0BNie8Z+1j+7K1PDpjMk3bt6pTFMXBQcWWLT2qX1M2ixWr0cz2h78k+88T6IM9cG8f0fAX8+8nIacM7o4Gdc23jMaCUu4xxDJA1hhXmz0552IJTx5AZEIf3ONCOb94N2nLD6B00BK07zx9Nc64OEvnfeyxcEaMCMDFpW4vwktt3ZrDxo1ZrF+fRXz85XvyNYjFImXRL2kdEdikA4FN/mKWXxCEm0anvbHjbrQjR44wefJkfvjhB6Kiopg9ezYjR45k4MCBdbIrF6jV0swLi8VyTec6fvw4ubm5TJ8+vTorc7ViKVu2bGHAgAHcfbdU/MpqtXLy5Ml6s4PXwt3dneLiYkpLS9HppAJi+/fvrzMuIiKCiIgInn76aUaOHMmcOXMYNGjQXzr3TaFTXX3MtYy7BpGRkaxatarO9r179xIRcQ3v2UDr1q0ZP348vXv3RqlU1prmezVRUVGcP3+e8+fPV7++jh49SkFBQfXrxcnJiWbNmjFr1ixUKhWNGjXCw8OD4cOHs2TJkjrr+4R/JhH43UAuLYOJ+/heXC6sp7uU3k5qK1A1FdRiNLOq01RkSgV3FX7N8s0yOkWWYyuVIbvCO5kh0of23zyKWxNf6atOuVz6YFtUVlXjWs7x6atQmNVoDXbsGT6Ag1H++O7pSFlCHmEPdiXxy/WkLT9QnWWUyWR4tAtn4OoXqCgqp6Kkkpwdpwm976J/yJaqb2mcdFTsPo9s+jZSIloSPNKTIzsXMX/aUBpHD8F+gS9xs8ZKU1ovfClogzuPziBn8wnWDPsQv+aB6JqaqcjIwa+7FzKFDFNhGabCMmnuTtXjtOmhL8lYe4TQ+7qgD3ZH7Sy9yWCnln7spSBCrpATFmyPwsURRVx7mjxXVUpbp8Xs4kiH/g5s2VnAV1+dps2Zc1gKi8jIyyVwTy6YLLi52dFyYGfc72nNhl0WulWe4lj8KnJfGYV1yWa8EpoysnRu9UOx8/E5nPt5O32XP0+hKRVzSSWura6cORo0bgyx8e1p1Fqq5tZdV0z2zE8xGmNBLsdiNmO1WrFaLOCoQ+WoxsV0BLcKOVFxMeRlZONt1UNGCXjpSfxyHUUHkgl/qjcOYTXTFd1ahzG8+GsUaiUpS/aSEReCS4ugK15bHSaL1Friom91s7efYlXH14l4vAdxH96LsaiMxA/Xo3PyoN2Xj0BSJv6xASSseQmbDJK7T+OFIX50rpoeCzQo6AMYNy6MMC85nXr6X33w1WjVVV+KKK4+tspCDqNCTn/+2gckQRCun68r6LVXnu6pt5PG3QyVlZVkZGTU2qZUKnFzc8NsNnPvvfcyePBgBg+WCr0NGTKEIUOGMHbsWHbu3FnvlE8PDw/s7OxYsWIFfn5+aLXaWpmdywkICECtVvPxxx8zbtw4Dh8+zBtvvHHFfcLDw/nll1/YunUrzs7OvPfee2RmZv7lwK9NmzbY29vz0ksv8eSTT7Jjx45ahUbKy8uZOHEid911F8HBwaSkpLBr1y6GDBnyl8570/g4gF595emeerU07gZ79NFHmTVrFk8++SQPPvggGo2GpUuXMn/+fP74449rPl779u1ZtmwZffr0QalUNrihe0JCAtHR0YwePZoPPvgAs9nMY489RpcuXWjVqlX1uPj4eD7++GPuuusuQJr6HBUVxU8//cQnn3xyucML/yBiqucNJJPJiBzf6/KZFYVcyuSppR5tCqWcqGf70nhiP1RKGV+/buW1QfnIMvLr3b3waApnvt2M1WQm5J5OFKbkk52cB1oVlBulTGJVcNZm9v349m+BKdeCutFgfPUxKPzkNJs8iDafP0D3VS8SM+2S3mQyGXI7NfYBbux8fA7bH/iCrM3Hq28j0FNac2inwV6vR2eUVQcGvuGtCIvtiZd9c0qTsilJzKDgaArH527C6ObIbzEvsrjLdEa+m8/64A7I7E7jqCvCO/gkpq2zcHTJYljeFwR1iqjJiHo4odRrUdiriRrSigGbXkHnX/UOr1ZJ6/fsawIJv0gXxpt2EtHECee2EdXXrXTR4+nnwIcfnmD27EQyB7YjsZcKnYcTlBnBUYHcW0/UkA64dW7E22/HMGViKOaScpa98DlZm09wcPJCzOezpf6CRjPGkgrMFSasTnpWx7/JinaTMRZeoeJZhRG77BKigkOqpzt0GtSH/1v2PW16dwMgYdRg3l/7C43iYgBI/2YbvUYc457zQbTq0YXuw5/k5RdOc/7HEyQv2om60kiLp3pSciy17ktNLX3g8LujBa3euwfFNazvA2BwI3ggBjQ1H1xUGiUaZx12DtKXEmqDPQlrX6bbKikTjb0Gk9XG4em/k/3nSe48PpP288Zd23mrFB45T/bwKex+8LPr2r8OlbLBi/JNWFjFSVZy8sacWxCE6yKXQfxV2mPGN7159TZWrFiBt7d3rZ+OHTsCMG3aNFJTU5k1a1atfT755BPS09MvO+VTqVTy0Ucf8fnnn+Pj48OAAQMadC3u7u7MnTuXn3/+mcaNGzN9+nRmzpx5xX1eeeUVWrRoQa9evYiPj8fLy4uBAwc26HxX4uLiwnfffceyZcuqW0Rc3BpAoVCQm5vLmDFjiIiIYNiwYfTp04fXX3/9L5/7ppDLoMuV14rTJeCmvNBCQkLYtGkTx48fJyEhgTZt2rBgwQJ+/vlnevfufV3H7NixI0uXLuWVV17h448/btA+MpmM3377DWdnZzp37kxCQgIhISH89NNPtcZ16dIFi8VSay1ffHx8nW3CP5fMdulE7VugqKgIg8FAYWEhjo63ZuHs36q8UlqNrteCV+31AhSUSn9cHGsvvj7383Y2D/sIgPbfPkbwqPZ8EtwOR0cfxvz6iVTJ08tFmvNisUJaLuk7T7Nv+hJafzKWHQ9/Rf6Bc+gC3PDuFU3bL6RKUOaSEtK++RqPgXeh9akpuJG++hDpaw7T/PUhlw0arBZr9RqwE5+sYveT8+j86zO4NfXFztPAgSkLOf7ZOtp/9xhung4cOJBHh8fO0cyxlAVPyQm/uxep29ay7t0Z9PryazxiW0BSJijlUpBZXgnApjGzyVh1iP7bpqAN8ay3qfkFNputVi+hL75IxMlJzTAvOLn2PMuOpvH4nDsw5pWiC3CT2l8UlYGrA5QBlWapchcw/+sTLHz+MWLzSukxeTytH70DisvAzx2bWomlwoTSTs3BqYuoyCggbtbYyzYdx2iCtDywV0vrMN0M0v04lAV5JRDiAJ5OtdbtreoylaxNx+mz+01cW4bw7DN7ee/9E7z/ciPSc8twzMnksUfCcIpvjOw6iwkkfrWewmNpxP7fyHrX89VSaYKUHDDowK2ef6dFpdgyCzjy2TpSt5+m15Yp13VNAKUpuaxNmEbgiHY0n3LXdR/nep0hDyVyAnD6288tCP8mf8f7d2KaVN3z4syf3k4K+sL+ep0oQZAk5knVOy/O/OnVUtAX5nL5/QThX0QEfreC2SL1vXOwkz5EN0D62sNsGvw+zrFBdPrxCdJ3HeSr2V1QWux5/etkKK8kaeUhKnKKafRsP8jIx2w1c+LdKQQ//xJWhYH8/UlsuftTCLYS+/Vw9McyKdy8hfyVq3EdcieN3p919Qu5yLkF20n8aj1tZ41FYzSxa+qvBN7XBZ+wmgqHZ5NKGPXyWZZ+1hgXdztOlWtImbEQWWoWnb54gNULfuDbZybS76GHGfXONOndvKonHGfSwQZ/TvmVwoPn6LdogpQ19XKWgqYLhWzUFwU9xWVSIOXlTGGFDSenhTg6qij8pQWWQ5ksfec7HFoFkrrsAD03vopHmzBpvaCTHr4+ABVmeDgWLGaw12A2mynJy8PpQtltq+2av/WzGM0kfrken97NcDDYQX4JZSYrK3u/Q6OusUSNal8T+DlUBfzllZgyCsg9k41X96YA5JwvYuEf6dx9byhhYX+Qm2skO3sQBsM1ZvOy0e7UAAAlHElEQVQusjjsaUpOZzIw6UP0gQ1oR3BJK4pazmaAxUqF2YrMwwmNS8NaRwiC8O/1d71/W23S96WlFdL3m76ut6yyvnA7s0qF3Sg1SWv6fBzEC024rYg1freCUlHTMgDI3/onGu8A0lafwq9XM7T1fADPXHeE4NEdiPvkPmQyGT5tY2i1+AF8WzcHHxeMJ1IJ6hXNvplLpT9cgR6cm/E2ad/OwT48guDnJqEPcqfFqpHMm9qb0kUpBHy8gdaPfkXUMw9S1vnaP6SnLNlL+qpD5B84h19MAO1m3w8uDuSuOYjW3QGdXsOuE5Xs2pPHybPFtI3xI1wmI/yLsaQu28eC8GeJeKMPHUeOoH2/O6V3dIOuJrBwcQAbdPzucWxpuVKlT6Vcevd3d4TsIikQDL6oHH+lWeoBaDJjMNjx/XftcDSoIMGHIncFjewHYak0UXw6C62XQQoa3avWV3Twk/bNyJWyps56lK6ONUEfXPUNwFRczsbB7+PRMZJmk6X1DOd/3cWux+cQOLwtneY/AfZainedpvR8LiV2NmjsDa6a2pnMglJUFgtebaTy16e/XMe+ST8y6PP70emUbNzYndJS818K+gC6LHqastS8qwZ9VquV0sIiqeLo5bg5QqUJravj5YPDa1R8OpOCIyn4X7ROUBCE/x657Oa0bBCEWuSyW9ayQRD+DiLjd4sV7dvLjg4tIbgvmUcNNLq3E61mPyAVLrnIz24PYyquYNiR6SgNOilYScqUsoeBHlBuxJyeh8VOgybAnYrsIlL/2I5GlozX0BEoq5q72mw2Dmz4Ab/wOCy7D6MvcETn5A/9wkDV8OIXAMaiMgoOJuMY6UPGqoMEDGtLWUoei0MmoA92Z0Di++ydsYItby3DraKQoXn/Q1XVLiF54U5WP/U/8ie1oHP7trT29JfWKXq71F+iraBEqopqp4aicvBykub9KORS8GY011RCNVmkbRUmSM2W1ncFeFCSno3ODDIPJ6llRnEl2KmkYPKCnEJpuq1GJT3GWjW//55CXp6RsWOvXva/+Ewmv0c8i2vrUHpvldYzGAvLOPTGrwQOa4Nb67DqseWZhWjcHOpOsSwug8wCqS+kmwFkMg69+SsHXv2ZLt8/jv+omkqU2ZTggBZtQ77DKTbC3nRo7glO11YGb/Gnc1nzwyLGvz+1eg3i32FlxylkbzlJ751v4BbX8B5QVpMZY2EZ2vqmpAqCcMP8l9+/BUEQ/m1Exu8Wsw8Nw2PgEHQtuqA/pCTkng61Cmpc0HPLFKyllShlSJkvkAITk1kKbFRKlI721U/ovkk/cvrrDbT/9rHqoA+zBVl6HjHN+0tBkt8VSgWbzJBbBAZ9nSD0ArWjPR4dG7HtgS84/fUGYlPz8egUScxbw3CM9CF72ymOTfoOX19nZAYDxz9eSfSL0kL2gCGt6Ryi44ft68kvLIBGjUFTUf+5jGYpS+rvLgVEVitYbFIBmOxCaZ2eDWldpEpZM/WzoFjabraQfCKRw0vW0XfAHVKAWWKGZSegqQs08anZx9VRmm6pqVlrd++92ykoMDFggB/OzjXXZy6tIGXJPvz6t0RZdd0OIZ7cceQdtO41H4DUBntazhwt/WKzseOPRNKyTQx6oJ7KaiXl0n0C0GqqM2fRrwwi/JHutY6bShFTWE1TvHiKmmCwPLMQm8WKvY9z7WMfz4H90hpKY7QrO8d9jU/fGELu7ljf01uLs6c7jq7O6Ax/vaqZpdKEQtOwstiRT/TCqak/Tk1rqnuePVvC/v35DBp0+Yqff46cRcofe+l3cDqGSLEISBAEQRAE4T8f+J3/32fIFAr87n/o2neuNEF6HjjrG7xW71JKR0ea//ALAGFw2TVU1R9eTeaa6YY6rZTdysgDR111hcvKjAwChzTHZrbg1b1JzUEsVumalYqaMv2Xm5JXVlmVUVNcNvC7IOTeTlhNZo69t4z9L/3EXZmz0bg6YDVbMDTxQ2GnJn/vWTJWHaoO/ADCYxrzTLAveoNBylyqlPVfT1YBVBjBTyFNJ9Rra6p5llZIwZ2HoU6fNhzspfvg7ICuQs6evftwDfGnzcDe2EqNmN3VqByUUhGZC4GfTAYaFcVnMjk2cylRz/bjq6/akJdnxNlZTeLXGzj42i90XjSB1GX7OfT6ImKnj6DJC/3rPlcgBanF5Xw8J5nsPBNTXmlMywh7nO3LsVisKC7N9pkt0lRdd0dpDehFLg76ABzREIwLvumlfPp/rWnT91Fa9ryPpTEvYimr5K6sz2oHWM08pOxmpCuFB5NImr+ViqxC/Pq3YOuY2XgnNCVyfK96n+MuQ/rRZUi/em/LoJijX64hQO5M2P3x9Y4BMFZU8MfrX1Dx3i4iH+9Jq/fuAeD0vE3YzBbCHuhaZ5+g4e0IGt6u1rYHH9zJunWZbN6cQMeO9U9RdQjzRBfohuqSx7DweCoO4d5XL2QjCIIgCIJwm/lPB35Ws5njTz+OTC7H974Ha1WDbBCLRfqgbjLfmAsymaUm6ToteDrXP+bS4KaiKkCTy0Ehx1xUzNaYSFSubnQ8crr2WI1KmhYqk1VVz6zKotXH0V6aRml39b5rnm3D8Qx0Zb+LmvSV6yg7dRiNaztkchkVGQUA9DswHTtfZ2xWK7uemIfWy0CzF+7EwUFaD1ZxIg2tVknqqkP4Dm1b+wQGnRSYqVVS0KvTkrXlBMdmLqXljJHoAzxqF3i5QKeVSr5VmnAtLuPVz97Faq9l5/i5qJzsOfHhCoL6t6Dt9+OlAi9FZdJUU6WCs99t4eTsNWg9DQyeXNN7qORsFkWp+bw4LZEurX0JHN4WvyutPysqg5wiZAUlvP32aSZNakyhRo9zsBZFfesFnfTSdV/6PNfDAQ0v0ZXE9NXMObmLc0HNaNnzPrx7NMVcXIH80sdEo4RYab2ie9twuq96EUNjX0qTckj5bQ/GgjIix/eisNCIVqtAo2nY1N//s26gZIyZbo1+vmLgl3jgKNuXr6WZTY9CKwWkNpuN7Q/+D5vFSsiYTsgbcL8ffzyc0FA9MTFOlx0TO30ksdNH1tp2dv5WtoyaRfSrg2g+dWiD7psgCIIgCMLt4j8d+MmVSmJ+Wgxy+bUHfQD2WgjylAKkW+VCFUw7DZzLRG404diqNRov7zpDD0z5hTPzNtNj3cvoL1TOBCkrlV0oZdEuVJWUyaRjlxul9XWujnWDK4tVuu8WKfh1Cy4l+9x8Mhe44ty2HTK5nDuOzACbDTt7NWhUGIvKOfXFOlya+dNsZFupoImfG2VF5VAmQy6rZ8mpg12d7Ffywp2cX7wbnz7NCX+4+5UfI7NFmi5aYcJYbuLUZ2uw83Wh409PStMhZTIp61dpktbXOTvQ6MleaFz1BI/uUOtQzacOxdajHfd1+ZPtp60cOvTklc+ttwOjmS4d3Vj3jQ57uRV7nQ2KqgL2S+4X0KCg72JhLXrw5OzDmJwMLCrbQ89596OXXX0Nn3cPqUGWva8LvXe+gT7QjdzcSkJD/6BJEwNbtvRo0Pm7yENIT0uh6zfjrziuUavm9J8yjvCYpngFSdM0ZTIZnX95CpvZ2qCgD2DwYH8GD772xu6O4V4YGvvi3DzwmvcVBEEQBEH4txPFXf4mpYU5bP5pJpHB/Qju0enyA69ULv9q8oqlaYKXKWixZcynJM3fRt/db9b+8FtuhNQcaUqn7yVl0y4UO3E31J7OWlohTXN1cZB+TGasVguZvy3CLaEXKhep501pSi6Jn64mon8sdn4u4OdO7u4zKPUaDA4aKWB1cYAKI7aUHGT19Tash6m4nIx1R/DtF4tc2YDMlNFUPZU0e+tJ1M46DFG+F91uhuSsulVCL+O331IIDdXTtKlT7dMUlLK05UvklGXQaGo/Oj00Rrohv0Rav+ftIl1LYZlUuOVKBXVsNsgtpiglj50TfyD27RG4trp8gZnvS7axXp/GaGNTuqojr3of6lNSYqJdu9U0b+7Ed9+1v+LY0/M2kTR/K+3nPYqdp6H2pVutFB5NrbU2TxCE289/4f1bEAThdvGfzvj9nQ5t+olTb64j53wy6mUO+PaJqX/gXymD73Llwhvtvn6Elu/dXbfSoZ0avF2wqFTIL407XRykrNyllTblVRnDC9lOlRK5TYH3gLtqFUY5+elqjrz9O3Ig+uWBALi2DJayiGpVzXG1amSBHtKawgZQOdjhP6BVg8YCtRqju7evp6jNhbYODQkigQED/Ordbi4zUpyUiZ1VwZoXZtUEfs566Qekc9g3oKqmyQIFJagtFjLWHCYj4fAVA7/eqsa4lOtpqw1u0H2oj16v4tChvg0am/L7HtJXHqToeFqdwO/IO0vY/+KPtP7sASIeuUpGVhAEQRAEQbjpRIWDv0lM93sI7d8NQ4wfjuFSRslaWcmOTq05MHJI7cEmszTt8EYqq0SeVYDW0b7+mxVahkxUMH7aJTfI5dJ0RWDbolP8+r+j0hRPjQpCvGtnAXOKpDWKJeXVmyLH96LZ63cRPqFPTZBnskBusZRNtNmkqaYgZeQuWvdms1rJ25+EraBEmop5FZkbj7GmxzQKDp+v3lZ6Mo19T8wh6adtdXew2qTKpReObdDV30riKsxllVTmlwBg7+NMzJRh6PsEMeKXmZffyWqTHscrUSvByxl1Ix+6rZxE1DNXDshcNQb62EVjJ/trvf2uJnH/Eea/8wnR742g19YpeHaJojKvhOMfLqcyT3ocXGICq6px1h8gC4Ig/Nds2LABmUxGQUHBXzrO3LlzcXJy+kvHSEpKQiaTsX///ht6bYIg/LOJwO9vorV3pO+s6dy57x0cwqoCP5OJ8rOnKUs8WXtwaq4UQFks0u8XqnFeh4rsIkpTcjFm5kuVOi8TQMmqkneXXa5og2HjDzH44QOk7UzGcvIce/olcGLSsxfdSWkd38VrAe19nGn22mC0HhdlhNRKqRWDh5M0xTQps94g6Mg7S1gW+xKnPlwBWYXV2y0WKz17rmfEiC01gwtL0Vot5O06Tdaqg5CSA2YLGrOZ2Kd6UbA3qZ4Hxwj5JVizCtn52NdkbDh6mTt/ZSvaTea3kAlSX8MjKRx87ReUKRaiunW+/E7V99ty5YPr7ZDr7PDp2azBa+BulvTVhzg8bTGbFy3jz99WcDYxEfd2Uvb0+Ecr2D3hW058vBIAn97NuePQ/+HR4fqmnAqCINxKY8eORSaT1fnp3bv333odQUFBfPDBBzf9PO3btyc9PR2DwXD1wYIg/GuJqZ63kFKvp8ORM8hVl/Q0c7CTCpLIq6KwjDxpHZ6/e61plFdTnlnIr8FPYTOZ0QW4MfDg9Jo2CFVOzFoJcjmRj/Xgl/euMNNULmPa9BjOni3F20+HMT+H/C2bMeXl1b7uC8VKyitBJuP8igPk7DxNsyl3obi4OMyFzKNSIWW/6jmva6tgnGODcG4TXmvdotFoZdeuXJycLspslVZg8Heh6+/P4hriIQV1ZgtKfzfKzuUQPWVw3RPYqcHdQNbWk5ycvQa5DLy6RDV8uq3FCmWVGKJ8UGhVKNRKHBv50PTlgbi0vMp0S7VSynSaLA2e3lpLaYXU0N7dcMOLC23fnkPz5k7Y2dX+87Bv0nzy9ibRdc1Eotq2IKZLzRrA0LFdMOaXEnLvFYJdQRCE62S1Wsk4d56y4lLsHXR4Bfojl9/c76579+7NnDlzam3TaK5e6frfSK1W4+V19fXtgiD8u4mM3y2mMhhQ2F8y/dLVUWrncCEAsddKRVAauP7sAqVOgyHKB427I84xgdI0xouCGqvZwu4J37L7qW+wWa31xztWa3XPv3vGhPDa5Ghk/u5omkXR4cAJWi5bW/8+qbnYUnPZOOQDjrz9O2krDtR/kV4uEOBRE+RexDu+Mf2WT8S9S1StKZh2dkpO70wgcXIgHMmWNno6g58b7p0bI/dxkYJkmw3MFuyb+KOory2FTAYGHWabjLARbWj1dG+pCEtD5RdDZj6dPnuAPjveQKFVI1fIiXlzGAGD4q68r6eztO4wJafh2dyySukLAZDaRJSUQ6Wx4dfbAAsWJNOu3WpeeKHu8xX3yX20/vQ+ArvF0LZvdxQXvR71Qe7EfXgv+qDLtAcBik6mk3/g3A29XkEQbn9nj55g/nuzWTJnPut++Z0lc+Yz/73ZnD164qaeV6PR4OXlVevH2VlqtSSTyfjyyy8ZNGgQ9vb2hIeH8/vvv9faf9myZURERGBnZ0fXrl1JSkqqc46FCxfSpEkTNBoNQUFBvPvuu9W3xcfHc+7cOZ5++unqjOPFVq5cSVRUFHq9nt69e5Oenl7r9i+//JKoqCi0Wi2NGjXi008/vex9vXSq55QpU4iJiak15oMPPiAoKKj697FjxzJw4ECmTZuGp6cnTk5OTJ06FbPZzMSJE3FxccHPz69O8CwIwq0jAr9/A2c9+Lpec2ZHpdfSb8807kr7lC4Ln65zu1ypoOuSiXRd8hwyuVzKKhaW1gyoNMHZDKnVQz3sAoNQOdfTb1Aury5m4ndHC9zahVe3DrgmZZVSVqu4rM5NLipQFFZCTtVtCrk01bTq/Ol5ZhJ3pElrCSuuHFhpnO0pTS/CWFrPNFiTWbqOC4ymmumZejvpR3ed3wBr1aBVNex5rTBCWi5k5ku/uxukCqEN6LN4LZo3d6JjR3e6dfOsc5t723AiHu1xfa1PgJUdX2d561cxFZdffbAgCAJS0Lf6x18pLSqutb20qJjVP/5604O/K3n99dcZNmwYBw8epG/fvowePZq8qlkw58+fZ/Dgwdx5553s37+fBx98kEmTJtXaf8+ePQwbNowRI0Zw6NAhpkyZwquvvsrcuXMBWLRoEX5+fkydOpX09PRagV1ZWRkzZ87k22+/ZdOmTSQnJ/Pcc89V3/7999/z2muv8dZbb3Hs2DGmTZvGq6++yrx5827oY7Bu3TrS0tLYtGkT7733HpMnT+aOO+7A2dmZHTt2MG7cOB555BFSUlJu6HkFQbg+IvD7j/Pp3RyfXs2lX7ILpCDvQgZKJpN+rmc6jasjMhcH4n97lt5bX0dpdx0FRxzswNMJXOupVhrkBPc2g04B9e66dm0GY545yo9r8qXgCqSgtp41ju7tIui+4VXUBnupJYb5onV3mflSwFVpkoLA5GxIq5reqlWDl/MV++6dO1fKl1+exmSqp5CLsx783Gtlcm1WKydnryb/YHLtsWol6LU1fRaVijoZ3BshMtKRzZsTGDjwxhdlCX+4G6H3dUGpv/YCOoIg/PdYrVa2LltzxTFbl63Bar1KoazrtGTJEvR6fa2fadNqKqCNHTuWkSNHEhYWxrRp0ygpKWHnzp0AzJ49m9DQUN59910iIyMZPXo0Y8eOrXX89957j+7du/Pqq68SERHB2LFjGT9+PDNmzADAxcUFhUKBg4NDdcbxApPJxGeffUarVq1o0aIF48ePZ+3amhk4kydP5t1332Xw4MEEBwczePBgnn76aT7//PMb+hi5uLjw0UcfERkZyf33309kZCRlZWW89NJLhIeH8+KLL6JWq/nzzz9v6HkFQbg+Yo3f7aTSJE3/c9Jf37ovV0fpGBfW4qmVUuXOv8n+l38ie+sp4n9/FpVDVWN6h/qrkALgdPkAYsSIQJRKuZS5ksmk+5VdKN2nAA9pkNUmVRa110iZOye9FNxd/Ng56kBVWdNvz15Tk1lsgBdfPMD8+edwcFAyfPjVG4dnbjzGzsfm4Nm1MT3WvVJzg1zeoP6G/2Qxbw671ZcgCMK/SMa583UyfZcqLSom49x5fIKv/vf1WnXt2pXZs2fX2ubiUvN3uFmzZtX/r9PpcHR0JCsrC4Bjx47Rpk2bWvu2a9eu1u/Hjh1jwIABtbZ16NCBDz74AIvFguIK67/t7e0JDQ2t/t3b27v63KWlpZw+fZoHHniAhx56qHqM2Wy+4cVbmjRpUmutpaenJ02bNq3+XaFQ4OrqWn1tgiDcWiLwuwb5B86RteUkEeO6S1Mjb4LiM5mYCstxiQ269p1zCqXpmmrllQOmSyQv2knaigO0fO8eVFfpBXitrBYrYEPegAImGeuPkrvrDJV5JVLg9xcoZTBiqH9NEKdWSpnDi/r5YTRJa+WM5qrAT1f3QI72NYVoAHxcr+k6nngiAldXNT17NiyAdm8fQdOXB+KV0PTqgy9y6n/r2PXEPOJ/e6YmgysIgvAvVlZcevVB1zDuWul0OsLCwi57u+qSwmwymeymZR8bcm5b1Xr8khJprfr//ve/OsHnlYLJi8nl8urjXWAy1V02Ud913MrHRRCEKxOB3zXYPeFbMjccxdDYF6/4xpcfWFohrQtzdazVl64h1nSfRllKHkPSP6nbaP1qjGbpv+qGV/4EOPnJajLWHSFkTCc8Ojaqd8zZ+VuxlFUS9kDXazr2z598idViYfhTj1y1Alu3Zc9TmVeCPvDyBUKwWqWplxabNA20vuybzQbJ0reLK+/+jIqsQu44/A4K50uC2gtTNeUyaUqnk/6yVVN79VqP0Whl3bpudde4lRulTKurQ51pse3audGundsV7/fFFBrVdWXGrJUmbCYz1guvAUEQhH85e4d6voz7C+P+TlFRUXWKvWzfvr3OmC1bttTatmXLFiIiIqoDNLVajeVqbX8u4enpiY+PD2fOnGH06NHXcfXg7u5ORkYGNput+j3vQs8/QRD+vUTgdw2iJw/Go1Mk7u3Crzwwv0QqxqG3k1oGXIPQ+7pQcjYLdX3Zp5JyKUPl4VR/hU93gzRVUX1tT2vbLx8ib/+5ywZ9ANvv/xxLpZmgUR2uab2eSq3GarEgq69fwyXUTrr67/fFLLaaYi0lFZefdqlWgQwsFUasRkt1ZdI69HbS81VcLrVVqCfws9lsHDtWhMlkxWKxoVRecl8KSqRg305d3ez+7xY5vhdhD3ZFcQ3TUC9WnlmInafo3yQIwj+HV6A/OkeHK0731Dk64BXof1POX1lZSUZGRq1tSqUSN7erf5k3btw43n33XSZOnMiDDz7Inj17qou2XPDss88SFxfHG2+8wfDhw9m2bRuzZs2qVX0zKCiITZs2MWLECDQaTYPODVLhmSeffBKDwUDv3r2prKxk9+7d5Ofn88wzz1x1//j4eLKzs3nnnXe46667WLFiBcuXL8fR8Rq/kBYE4R9FFHe5Bl7xjWk+dSiKq/XS8zBI2ajrKGjS7LXBtJ8zDnl9gd2FTOLlyv/r7cDZ4ZoLfuiDPa7afqDD/PFEPdcPc3E55BZJAWYDDH54DHcNGois4BraJFyJSgEB7uDuKBVHqY9MJlVB9XGlz643GXD6/SsHRAZ7KZi+zPFkMhlHjvTlxIk7UCrr+Sfj5igF3bpbW7TkeoO+w2//xkKvR0n+ddcNviJBEITrJ5fLad834Ypj2vdNuGn9/FasWIG3t3etn44dOzZo34CAABYuXMjixYtp3rw5n332Wa3CMAAtWrRgwYIF/PjjjzRt2pTXXnuNqVOn1ioCM3XqVJKSkggNDcXd/QqzYS7x4IMP8uWXXzJnzhyio6Pp0qULc+fOJTj4Kj1mq0RFRfHpp5/yySef0Lx5c3bu3FmraqggCP9OMtulk7hvgaKiIgwGA4WFheLbpCuxWKV1aTe4hH+t4xeVSusDLwk8k3/dxabB79Nh1r0E94oGg04Kdq7GZIZzWVImzb/hb1q3DYsVKsygu76g7O9w5tvN7H1+Pp1/fvKKWV9BEIRL/R3v32ePnmDrsjW1Mn86Rwfa900guHHkTTmnIAjC7UgEfkKN/GKp752TXspiXaQsPZ/dT84j7IF4fFqHSq0WrtDGoJZKEyjl0lTK/5rliXAqD0Y2BfeGF9z5xzBbQMZ/87kTBOGq/q73b6vVSsa585QVl2LvoMMr0P+mZfoEQRBuV2KNn1DDwV5aQ2eoG6DYezvT+ecJ13fcq02NvZ252EltJ7T/wsDJapWK5MjlEFS3obsgCMLfRS6X35SWDYIgCP8l4uuyW6z01EnSf/y+TtnkW0KpkDJ9crlUnOZGSs2Bc5lS77zbTO6eMxQcTan/xja+MKYZONyk6bk3k0wmFc+5jrWqgiAIgiAIwj+LCPxusWNPjuPw/XdTsGXzrb6UGpn5kJJTJ/gzl1Zw9octmEsrrv2YVltV0Hd7BX6m4nJWtpvM6i5v3OpLufFkMqlvoafzrb4SQRAEQRAE4S8SUz2vRXGZFLwYblzPoMAnn0XfJBrHFq1u2DH/Mp1Wis9UtacnHvtgBQdeWUDzN4YS/cqgazumX1UJ6musOPpPp9RrCX+kOxq3G9v4/t/MajKz55nvcGoWQPhD3W715QiCIAiCIAiIwO/aZBVK/eAc7Oo06r5e7n364d6n3w051g1j0NUb3PoPakXB4fP4X6X1Q72uNeArKZeKwrhce3uKv5NMJiPu47F1tptLKyg9n4uhke/ff1G3WOn5XE7MWoUhylcEfoIgCIIgCP8QoqrntSitkDJ+DremSfd/SnIWGM1Szz71v684zKa7PiB54U5675iKW+uwW305f7u0VQex93PBqbHfrb4UQRBuon/N+7cgCIIgMn7X5BY36P5P8XCSegD+m4I+mw3SckEmw6NTJGWpedj7ud7qq7olfHo2u679zny7Gadof1xigm7sBQmCIAiCIPzHiYyfIFwsuwCsgKfTte9rs0FSpjQ1VbQ/uGa5e8+yvOXLuLQKoe+uN2/15QiC0ADi/VsQBOHfQ2T8/mmsNkjLkZqji2qKf7/icqmwjYfh2tcWymQQ4CE1PBeumXO0P01e7I97u/BbfSmCIAiCIAi3HRH4/dPYbFBpvi373TWY1QrZhWCnAce6zeRvKn93KfC73oIyCtEh5XrJVUpip4241ZchCIIgCIJwWxKB3z+NQg5BHv/oSpY3nckiZd5Mlr8/8FOJfxKCIAiCIAjC7Ud8yv0nUiiuPuZ2plFJjcNV//HHQRAEQRAEQRBuEBH4Cf9M9ppbfQWCIAiCIAiCcNsQC5L+jcwWqdWBIAiCIAiCIAhCA4jA798oJQeSs6UiKIIgCIIgCIIgCFchpnr+G+m0Utbvv1wARhAEQRAEQRCEBhOB37+Ru+FWX4EgCIIgCIIgCP8iYqqnIAiCIAiCIAjCbU4EfoIgCIIgCIIgCLc5EfgJgiAIgiAIgiDc5kTgJwiCIAiCIAiCcJsTgZ8gCIIgCIIgCMJtTgR+giAIgiAIgiAItzkR+AmCIAiCIAiCINzmROAnCIIgCIIgCIJwmxOBnyAIgiAIgiAIwm1OBH6CIAiCIAiCIAi3ORH4CYIgCIIgCIIg3OZE4CcIgiAIgiAIgnCbE4GfIAiCIAiCIAjCbU4EfoIgCIIgCIIgCLc55a2+AACbzQZAUVHRLb4SQRAEQRAa6sL79oX3cUEQBOGf6x8R+BUXFwPg7+9/i69EEARBEIRrVVxcjMFguNWXIQiCIFyBzPYP+JrOarWSlpaGg4MDMpnsVl+OIAiCIAgNYLPZKC4uxsfHB7lcrB4RBEH4J/tHBH6CIAiCIAiCIAjCzSO+nhMEQRAEQRAEQbjNicBPEARBEARBEAThNicCP0EQBEEQBEEQhNucCPwEQRAEQRAEQRBucyLwEwRBEARBEARBuM2JwE8QBEEQBEEQBOE2JwI/QRAEQRAEQRCE25wI/ARBEARBEARBEG5zIvATBOGajR07FplMhkwmQ61WExYWxtSpUzGbzQDYbDa++OIL2rRpg16vx8nJiVatWvHBBx9QVlYGwJEjRxgyZAhBQUHIZDI++OCDW3iPBEEQBEEQbm8i8BME4br07t2b9PR0Tp06xbPPPsuUKVOYMWMGAPfccw8TJkxgwIABrF+/nv379/Pqq6/y22+/sWrVKgDKysoICQlh+vTpeHl53cq7IgiCIAiCcNuT2Ww2262+CEEQ/l3Gjh1LQUEBixcvrt7Ws2dPiouLefrppxk+fDiLFy9mwIABtfaz2WwUFRVhMBhqbQ8KCmLChAlMmDDhb7h6QRAEQRCE/x6R8RME4Yaws7PDaDTy/fffExkZWSfoA5DJZHWCPkEQBEEQBOHmE4GfIAh/ic1mY82aNaxcuZJu3bpx6tQpIiMjb/VlCYIgCIIgCBcRgZ8gCNdlyZIl6PV6tFotffr0Yfjw4UyZMgUxe1wQBEEQBOGfR3mrL0AQhH+nrl27Mnv2bNRqNT4+PiiV0p+TiIgIjh8/fouvThAEQRAEQbiYyPgJgnBddDodYWFhBAQEVAd9AKNGjeLkyZP89ttvdfax2WwUFhb+nZcpCIIgCIIgIAI/QRBusGHDhjF8+HBGjhzJtGnT2L17N+fOnWPJkiUkJCSwfv16AIxGI/v372f//v0YjUZSU1PZv38/iYmJt/geCIIgCIIg3H5EOwdBEK5Zfe0cLma1Wvniiy/4+uuvOXLkCEqlkvDwcMaMGcNDDz2EnZ0dSUlJBAcH19m3S5cubNiw4ebeAUEQBEEQhP8YEfgJgiAIgiAIgiDc5sRUT0EQBEEQBEEQhNucCPwEQRAEQRAEQRBucyLwEwRBEARBEARBuM2JwE8QBEEQBEEQBOE2JwI/QRAEQRAEQRCE25wI/ARBEARBEARBEG5zIvATBEEQBEEQBEG4zYnATxAEQRAEQRAE4TYnAj9BEARBEARBEITbnAj8BEEQBEEQBEEQbnMi8BMEQRAEQRAEQbjNicBPEARBEARBEAThNvf/QBrV4Z6v8nMAAAAASUVORK5CYII=", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sc.tl.pca(data)\n", "sc.pl.pca(data, color='annotation')" ] }, { "cell_type": "markdown", "id": "35a4bbcb", "metadata": {}, "source": [ "#### Load other necessary data\n", "**Auxilliary datasets:**
\n", "Transcription factor gene list: a list of transcription factors of interest. This list is requried for the network inference step. Data can be downloaded through pySCENIC_TF_list and TF_lists.\n", "\n", "Ranked whole genome databases: Databases ranking the whole genome of species of interest based on transcription factors in feather v2 format. Data can typically be downloaded from public databases, through cisTarget databases.\n", "\n", "Motif to TF annotation databases: Motif databases (in tbl format) serve as a reference for identifying potential TF binding sites in the genome and inferring the regulatory relationships between TFs and target genes. Data can be downloaded through Motif2TF annotations." ] }, { "cell_type": "code", "execution_count": 13, "id": "313fb32b", "metadata": {}, "outputs": [], "source": [ "tfs_fn = 'mouse_tfs.txt'\n", "database_fn = 'mm10_10kbp_up_10kbp_down_full_tx_v10_clust.genes_vs_motifs.rankings.feather'\n", "motif_anno_fn = 'motifs-v10nr_clust-nr.mgi-m0.001-o0.0.tbl'" ] }, { "cell_type": "markdown", "id": "3af8f5d7", "metadata": {}, "source": [ "### 2. Create a spagrn object\n", "pos_label is the obsm key where spots coordinates were stored." ] }, { "cell_type": "code", "execution_count": null, "id": "0ff9b0dd", "metadata": {}, "outputs": [], "source": [ "grn = irn(data, project_name='mouse_brain')" ] }, { "cell_type": "markdown", "id": "8d3205a6", "metadata": {}, "source": [ "[optional] set parameters of the model:\n", "1. rank_threshold: number of ranked genes;\n", "2. prune_auc_threshold: fraction of the ranked genome when computing AUC in prune modules step;\n", "3. nes_threshold: normalized enrichment score (nes) threshold to select enriched motifs;\n", "4. auc_threshold: fraction of the ranked genome when computing AUC;" ] }, { "cell_type": "code", "execution_count": 15, "id": "13f83012", "metadata": {}, "outputs": [], "source": [ "grn.add_params({'prune_auc_threshold': 0.05, 'rank_threshold': 9000, 'auc_threshold': 0.05})" ] }, { "cell_type": "markdown", "id": "abcde939", "metadata": {}, "source": [ "### 3. Set Ligand-Receptor data\n", "Pre-computed ligand-recpetor datasets obtained from CellPhoneDB." ] }, { "cell_type": "code", "execution_count": null, "id": "1d23696d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " from to database source\n", "0 2300002M23Rik Ddr1 omnipath omnipath\n", "1 2610528A11Rik Gpr15 omnipath omnipath\n", "2 9530003J23Rik Itgal omnipath omnipath\n", "3 a Atrn omnipath omnipath\n", "4 a F11r omnipath omnipath\n", "... ... ... ... ...\n", "4981 ZNRF3 FZD4 omnipath omnipath\n", "4982 ZP3 EGFR omnipath omnipath\n", "4983 ZP3 CHRNA7 omnipath omnipath\n", "4984 ZP3 MERTK omnipath omnipath\n", "4985 ZPBP2 CD80 omnipath omnipath\n", "\n", "[10654 rows x 4 columns]\n" ] } ], "source": [ "niche_human = pd.read_csv('resource/lr_network_human.csv')\n", "niche_mouse = pd.read_csv('resource/lr_network_mouse.csv')\n", "# concat the databases together if you are using multiple ligand-receptors databases\n", "niches = pd.concat([niche_mouse, niche_human])\n", "print(niches)" ] }, { "cell_type": "markdown", "id": "c452c883", "metadata": {}, "source": [ "### 4. Construct the network\n", "In order to construct a GRN:\n", "- database_fn\n", "- motif_anno_fn\n", "- niche_df\n", "- gene_list: A list of interested genes to calculate co-expression values with TF genes. e.g. a list of HVGs.\n", "\n", "- num_workers: number of thread.\n", "- cache: if use saved intermediate result files.\n", "- output_dir: output directory.\n", "- save_tmp: if save intermediate results onto disk.\n", "\n", "- layer_key: Key in adata.layers with count data, uses adata.X if None.\n", "- latent_obsm_key: Key in adata.obsm where cell/spot coordinates are.\n", "- cluster_label: Key in adata.obs with cluster/cell type labels.\n", "\n", "- model: Specifies the null model to use for gene expression.\n", "- n_neighbors: n cell/spots to consider as \"neighbors\" when computing spatial autocorrelation and gene co-expression.\n", "- methods: a list of spatial autocorrelation statistics, options are 'FDR_C', 'FDR_I', 'FDR_G', 'FDR'.\n", "- operation: options are 'intersection' and 'union'.\n", "- mode: bi-variate Geary's C or bi-variate Moran's R when computing gene co-expression. options are 'moran' and 'geary'." ] }, { "cell_type": "code", "execution_count": null, "id": "32df5bdc", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/Oreo/anaconda3/envs/spagrn/lib/python3.8/site-packages/hotspot/hotspot.py:98: UserWarning: Hotspot will work faster when counts are a csc sparse matrix.\n", " warnings.warn(\n", "100%|█████████████████████████████████████████████████████████████████████████████████| 9788/9788 [03:48<00:00, 42.82it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Computing spatial weights matrix...\n" ] }, { "ename": "ModuleNotFoundError", "evalue": "No module named 'pysal'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", "Cell \u001b[0;32mIn[18], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mgrn\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minfer\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdatabase_fn\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2\u001b[0m \u001b[43m \u001b[49m\u001b[43mmotif_anno_fn\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3\u001b[0m \u001b[43m \u001b[49m\u001b[43mtfs_fn\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 4\u001b[0m \u001b[43m \u001b[49m\u001b[43mniche_df\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mniches\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 5\u001b[0m \u001b[43m \u001b[49m\u001b[43mnum_workers\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m12\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 6\u001b[0m \u001b[43m \u001b[49m\u001b[43mlayers\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mcounts\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 7\u001b[0m \u001b[43m \u001b[49m\u001b[43mlatent_obsm_key\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mspatial\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 8\u001b[0m \u001b[43m \u001b[49m\u001b[43mn_neighbors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m10\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[1;32m 9\u001b[0m \u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mmoran\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 10\u001b[0m \u001b[43m \u001b[49m\u001b[43mcluster_label\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mannotation\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 11\u001b[0m \u001b[43m \u001b[49m\u001b[43mproject_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mmouse_brain\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n", "File \u001b[0;32m~/Library/CloudStorage/OneDrive-BGIHongKongTechCo.,Limited/PycharmProjects/SpaGRN/spagrn/regulatory_network.py:170\u001b[0m, in \u001b[0;36mInferNetwork.infer\u001b[0;34m(self, databases, motif_anno_fn, tfs_fn, gene_list, cluster_label, niche_df, receptor_key, num_workers, save_tmp, cache, project_name, layers, model, latent_obsm_key, umi_counts_obs_key, n_neighbors, weighted_graph, rho_mask_dropouts, local, methods, operation, combine, mode, somde_k, noweights, normalize)\u001b[0m\n\u001b[1;32m 167\u001b[0m dbs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mload_database(databases)\n\u001b[1;32m 169\u001b[0m \u001b[38;5;66;03m# 3. GRN Inference\u001b[39;00m\n\u001b[0;32m--> 170\u001b[0m adjacencies \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mspg\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 171\u001b[0m \u001b[43m \u001b[49m\u001b[43mgene_list\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgene_list\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 172\u001b[0m \u001b[43m \u001b[49m\u001b[43mtf_list\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtfs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 173\u001b[0m \u001b[43m \u001b[49m\u001b[43mjobs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnum_workers\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 174\u001b[0m \u001b[43m \u001b[49m\u001b[43mlayer_key\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlayers\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 175\u001b[0m \u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 176\u001b[0m \u001b[43m \u001b[49m\u001b[43mlatent_obsm_key\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlatent_obsm_key\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 177\u001b[0m \u001b[43m \u001b[49m\u001b[43mumi_counts_obs_key\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mumi_counts_obs_key\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 178\u001b[0m \u001b[43m \u001b[49m\u001b[43mn_neighbors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mn_neighbors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 179\u001b[0m \u001b[43m \u001b[49m\u001b[43mweighted_graph\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mweighted_graph\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 180\u001b[0m \u001b[43m \u001b[49m\u001b[43mcache\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcache\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 181\u001b[0m \u001b[43m \u001b[49m\u001b[43msave_tmp\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msave_tmp\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 182\u001b[0m \u001b[43m \u001b[49m\u001b[43mfn\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43mf\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[43mproject_name\u001b[49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[38;5;124;43m_\u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[43mmode\u001b[49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[38;5;124;43m_adj.csv\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 183\u001b[0m \u001b[43m \u001b[49m\u001b[43mlocal\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlocal\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 184\u001b[0m \u001b[43m \u001b[49m\u001b[43mmethods\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmethods\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 185\u001b[0m \u001b[43m \u001b[49m\u001b[43moperation\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moperation\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 186\u001b[0m \u001b[43m \u001b[49m\u001b[43mcombine\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcombine\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 187\u001b[0m \u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 188\u001b[0m \u001b[43m \u001b[49m\u001b[43msomde_k\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msomde_k\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 190\u001b[0m \u001b[38;5;66;03m# 4. Compute Modules\u001b[39;00m\n\u001b[1;32m 191\u001b[0m \u001b[38;5;66;03m# ctxcore.genesig.Regulon\u001b[39;00m\n\u001b[1;32m 192\u001b[0m modules \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_modules(adjacencies, df, rho_mask_dropouts\u001b[38;5;241m=\u001b[39mrho_mask_dropouts, prefix\u001b[38;5;241m=\u001b[39mproject_name)\n", "File \u001b[0;32m~/Library/CloudStorage/OneDrive-BGIHongKongTechCo.,Limited/PycharmProjects/SpaGRN/spagrn/regulatory_network.py:581\u001b[0m, in \u001b[0;36mInferNetwork.spg\u001b[0;34m(self, data, gene_list, layer_key, model, latent_obsm_key, umi_counts_obs_key, weighted_graph, n_neighbors, fdr_threshold, tf_list, save_tmp, jobs, cache, local, methods, operation, combine, somde_k, fn, mode, **kwargs)\u001b[0m\n\u001b[1;32m 578\u001b[0m hs_results \u001b[38;5;241m=\u001b[39m hs\u001b[38;5;241m.\u001b[39mcompute_autocorrelations()\n\u001b[1;32m 580\u001b[0m \u001b[38;5;66;03m# 1: Select genes\u001b[39;00m\n\u001b[0;32m--> 581\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mspatial_autocorrelation\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 582\u001b[0m \u001b[43m \u001b[49m\u001b[43mlayer_key\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlayer_key\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 583\u001b[0m \u001b[43m \u001b[49m\u001b[43mlatent_obsm_key\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlatent_obsm_key\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 584\u001b[0m \u001b[43m \u001b[49m\u001b[43mn_neighbors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mn_neighbors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 585\u001b[0m \u001b[43m \u001b[49m\u001b[43msomde_k\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msomde_k\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 586\u001b[0m \u001b[43m 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local\u001b[38;5;241m=\u001b[39mlocal,\n\u001b[1;32m 594\u001b[0m combine\u001b[38;5;241m=\u001b[39mcombine,\n\u001b[1;32m 595\u001b[0m operation\u001b[38;5;241m=\u001b[39moperation)\n", "File \u001b[0;32m~/Library/CloudStorage/OneDrive-BGIHongKongTechCo.,Limited/PycharmProjects/SpaGRN/spagrn/regulatory_network.py:371\u001b[0m, in \u001b[0;36mInferNetwork.spatial_autocorrelation\u001b[0;34m(self, adata, layer_key, latent_obsm_key, n_neighbors, somde_k, n_processes, local, cache)\u001b[0m\n\u001b[1;32m 368\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mComputing spatial weights matrix...\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 369\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mind, neighbors, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mweights_n \u001b[38;5;241m=\u001b[39m neighbors_and_weights(adata, latent_obsm_key\u001b[38;5;241m=\u001b[39mlatent_obsm_key,\n\u001b[1;32m 370\u001b[0m n_neighbors\u001b[38;5;241m=\u001b[39mn_neighbors)\n\u001b[0;32m--> 371\u001b[0m Weights \u001b[38;5;241m=\u001b[39m \u001b[43mget_w\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mind\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mweights_n\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 372\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mweights \u001b[38;5;241m=\u001b[39m Weights\n\u001b[1;32m 374\u001b[0m more_stats \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mDataFrame(index\u001b[38;5;241m=\u001b[39madata\u001b[38;5;241m.\u001b[39mvar_names)\n", "File \u001b[0;32m~/Library/CloudStorage/OneDrive-BGIHongKongTechCo.,Limited/PycharmProjects/SpaGRN/spagrn/autocor.py:129\u001b[0m, in \u001b[0;36mget_w\u001b[0;34m(ind, weights_n)\u001b[0m\n\u001b[1;32m 127\u001b[0m nei \u001b[38;5;241m=\u001b[39m nind\u001b[38;5;241m.\u001b[39mtranspose()\u001b[38;5;241m.\u001b[39mto_dict(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlist\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 128\u001b[0m w_dict \u001b[38;5;241m=\u001b[39m weights_n\u001b[38;5;241m.\u001b[39mreset_index(drop\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\u001b[38;5;241m.\u001b[39mtranspose()\u001b[38;5;241m.\u001b[39mto_dict(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlist\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m--> 129\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpysal\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlib\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m weights\n\u001b[1;32m 130\u001b[0m w \u001b[38;5;241m=\u001b[39m weights\u001b[38;5;241m.\u001b[39mW(nei, weights\u001b[38;5;241m=\u001b[39mw_dict)\n\u001b[1;32m 131\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m w\n", "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'pysal'" ] } ], "source": [ "grn.infer(database_fn,\n", " motif_anno_fn,\n", " tfs_fn,\n", " niche_df=niches,\n", " gene_list=None,\n", " num_workers=20,\n", " cache=True,\n", " output_dir='exp/ouput',\n", " save_tmp=True,\n", " layers='raw_counts',\n", " latent_obsm_key='spatial',\n", " model='bernoulli',\n", " n_neighbors=10,\n", " methods=['FDR_I','FDR_C','FDR_G'],\n", " operation='intersection',\n", " mode='geary',\n", " cluster_label='annotation')" ] }, { "cell_type": "markdown", "id": "db9108c4-fb19-4f90-b00c-bf7557efa9a2", "metadata": {}, "source": [ "## Part II. Interpreting the results\n", "The results of Gene Regulatory Network were stored in an AnnData object." ] }, { "cell_type": "code", "execution_count": null, "id": "f2afbf39-b86b-436d-8fe0-1210beb3f135", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "AnnData object with n_obs × n_vars = 50140 × 23472\n", " obs: 'annotation', 'n_genes', 'n_counts'\n", " var: 'Gene', 'n_cells', 'n_counts'\n", " uns: 'adj', 'annotation_colors', 'method', 'pca', 'receptor_dict_all', 'receptors', 'receptors_all', 'regulon_dict', 'rss'\n", " obsm: 'X_pca', 'auc_mtx', 'isr', 'rep_auc_mtx', 'spatial'\n", " varm: 'PCs'\n", " layers: 'counts'" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "adata = sc.read_h5ad('spg_spagrn_gearyc.h5ad')\n", "adata" ] }, { "cell_type": "markdown", "id": "376704db", "metadata": {}, "source": [ "Regulons were stored in adata.uns under key `regulon_dict` in dictionary format." ] }, { "cell_type": "code", "execution_count": null, "id": "ef57c05e-f510-4f49-994b-0a0de6bc4f11", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'Atf2(+)': array(['Atp2b1', 'Atf2', 'Galnt13', 'Ube3a', 'Lrrc7', 'Adgrl3', 'Syt1',\n", " 'Sgcz', 'Akt3', 'Pafah1b1', 'Foxp1', 'Basp1', 'Hivep2', 'Elavl2',\n", " 'Stmn2', 'Rims2', 'Gphn', 'Stxbp5l', 'Kansl1', 'Cnksr2', 'Pja2',\n", " 'Tanc2', 'Mef2a', 'Grid2', 'Meg3', 'Fam19a2', 'Lingo2', 'Atxn1',\n", " 'Cdkl5', 'Sept7', 'Zfp385b', 'Zfp804a', 'Ntng1', 'Sntg1', 'R3hdm1',\n", " 'Ppp3cb', 'Epha6', 'Cdh8', 'Nlgn1', 'Rora', 'Zfr', 'Ttc3', 'Fgf12',\n", " 'Oxr1', 'Lrrc4c', 'Prr16', 'Dlgap1', 'Ptpn4', 'Arhgap21', 'Nbea',\n", " 'Camk2d', 'Lrfn5', 'Myh10', 'Gabrb2', 'Ralyl', 'Magi1', 'Kcnt2',\n", " 'Apba2', 'Frmpd4', 'Grm5', 'Cntn1', 'Ccser1', 'Kif1a', 'Nrn1',\n", " 'Kcnc2', 'Tenm4', 'Inpp4b', 'Bcl11a', 'Kcnj3', 'Vsnl1', 'Adgrb3',\n", " 'Pak3', 'Cadm3', 'Osbpl6', 'Ncor1', 'Dok6', 'Wdfy3', 'Efna5',\n", " 'Gria4', 'Peak1', 'Chsy3', 'Pam', 'Syndig1', 'Hecw1', 'Dpp10',\n", " 'Ppp3ca', 'Tmeff2', 'Arpp21', 'Gria2', 'Herc1', 'Snap25',\n", " 'Cacna2d1', 'Kif1b', 'Anks1b', 'Lmo4', 'Unc5d', 'Matr3', 'Csde1',\n", " 'Brinp1', 'Caln1', 'Nap1l1', 'Car10', 'Ppp1r9a', 'Cntnap2',\n", " 'Rgs17', 'Erc2', 'Arap2', 'Cltc', 'Rab6a', 'Nckap1', 'Gabra1',\n", " 'Cadps', 'Cadm2', 'Ywhaz', 'App', 'Calm2', '5730522E02Rik',\n", " 'Scn8a', 'Ppp2r2b', 'Mdga2', 'Ppm1l', 'Rims1', 'Gnas', 'Ppfia2',\n", " 'Ywhae', 'Aplp2', 'Ctnna2', 'Chd4', 'Trank1', 'Mapt', 'Rock2',\n", " 'Pcdh9', 'Sptan1', 'Dnm3', 'Mef2c', 'Fgf14', 'Camk1d', 'Sestd1',\n", " 'Psip1', 'Nkain2', 'Tcaf1', 'Atp6v1a', 'Zfp804b', 'Nucks1',\n", " 'Akap11', 'Grm1', 'Nptn', 'Ptprd', 'Ehbp1', 'Nrxn1', 'Map1a',\n", " 'Dlgap2', 'Map7d2', 'Abi2', 'Cdh6', 'Ndrg3', 'Eif4g2', 'Dock3',\n", " 'Elavl4', 'Unc80', 'Magi2', 'Epha5', 'Lrba', 'Gls', 'Xkr4',\n", " 'Ctnnd2', 'Unc79', 'Rad23b', 'Ssh2', 'Pak7', 'Pcsk2', 'Map1b',\n", " 'Ahi1', 'Plcb1', 'Sub1', 'Robo2', 'Csmd1', 'Hcn1', 'Grin2b',\n", " 'Grm7', 'Atrnl1', 'Thsd7b', 'Myrip', 'Dclk1', 'Jarid2', 'AI593442',\n", " 'Pcdh7', 'Camta1', 'Celf1', 'Rap1gds1', 'Kcnq3', 'Rtf1', 'Prkar1b',\n", " 'Hsp90ab1', 'Nefm', 'Ttll7', 'Syt7', 'Fam19a1', 'Frmd5', 'Kcnh7',\n", " 'Homer1', 'Hspa4l', 'Pkm', 'Cacnb2', 'Negr1', 'Gsk3b', 'Cacna1e',\n", " 'Tmem56', 'Dync1i1', 'Samd5', 'Nrg3', 'Ywhag', 'Serbp1', 'Map9',\n", " 'Robo1', 'Ppp3r1', 'Arf1', 'Osbpl1a', 'Fam155a', 'Tcf4', 'Mmp16',\n", " 'Dnajc6', 'Gabrg3', 'Ncam2', 'Herc3', 'Adcy8', 'Srrm2', 'Kcnq5',\n", " 'Gnao1', 'Tnik', 'Frmd4a', 'Cacna2d3', 'Pdp1', 'Pde10a', 'Stmn1',\n", " 'Fam135b', 'Eloc', 'Cyfip2', 'Rbfox1', 'Nrg1', 'Rbms3', 'Kcnd2',\n", " 'Bex2', 'Rtn3', 'Tmtc2', 'Dmxl2', 'Cntn4', 'Fam126b', 'Prkar1a',\n", " 'Snap91', 'March1', 'Pten', 'Kif21a', 'Ank2', 'Arhgef9', 'Ppp2ca',\n", " 'Kcnma1', 'Macrod2', 'Map2', 'Gabrg2', 'Lrp1b', 'Gap43', 'Man1a2',\n", " 'Ccdc136', 'Pgm2l1', 'Pacsin1', 'Lars2', 'Sv2a', 'Nrxn3', 'Grin2a',\n", " 'Cux2', 'Clstn1', 'Napb', 'Morf4l1', 'Astn2', 'Otud7a', 'Nmnat2',\n", " 'Cit', 'Atp2b2', 'Pfn2', 'Phf24', 'Adam22', 'Atl1', 'Cntn5',\n", " 'Phactr1', 'Hs6st3', 'Thy1', 'Rtn4', 'Clasp2', 'Dlgap4', 'Dcc',\n", " 'Slc24a2', 'Fgfr2', 'Pclo', 'Kcna2', 'Lsamp', 'Syne1', 'Park2',\n", " 'Prkcd', 'Cdk14', 'Snrpn', 'Grik2', 'Hdac9', 'Efr3a', 'Elavl3',\n", " 'Ywhab', 'Asap1', 'Rtn1', 'Tmod2', 'Zc3h7a', 'Celf2', 'Cd44',\n", " 'Eef1a2', 'Pak1', 'Pnisr', 'Cacnb4', 'Dnaja1', 'Nrip3', 'Cdc42bpa',\n", " 'Set', 'Rasgef1b', 'Dst', 'Slc25a4', 'Dmd', 'Cacna1a', 'Syn2',\n", " 'Pde1a', 'Rgs7bp', 'Plcxd2', 'Ghitm', 'Gabrb3', 'Rfx7', 'Atp6v1b2',\n", " 'Evi5', 'Oxct1', 'Rasgrp1', 'Dgki'], dtype=object),\n", " 'Atf4(+)': array(['Slc25a3', 'Sult4a1', 'Tspyl1', 'Ndufb2', 'Chgb', 'Cycs', 'Cds1',\n", " 'Scn1b', 'Ptprs', 'Map1lc3a', 'Slc6a17', 'Gabra1', 'Ap2s1',\n", " 'Rab6a', 'Atp6v0b', 'Hint1', 'Nsg1', 'Stxbp1', 'Gnas', 'Stmn1',\n", " 'Tspan7', 'Gabarapl1', 'Mat2b', 'Tmem59l', 'Faim2', 'Scamp5',\n", " 'Sst', 'Ociad1', 'Gde1', 'Dnm1', 'Gnb1', 'Vgf', 'Ttc9b', 'Cox4i1',\n", " 'Nat8l', 'Dctn3', 'Kif1a', 'Ndrg4', 'Cox5a', 'Rab3c', 'Cpe',\n", " 'Hsp90b1', 'Med26', 'Slc3a1', 'Cspg5', 'Nap1l5', 'Maged1',\n", " 'Spock2', 'Tbc1d9', 'Tbca', 'Slc25a4', 'Pitpna', 'Clstn3', 'Kif5a',\n", " 'Sod1', 'Bsg', 'Cox5b', 'Atp5j', 'Scg5', 'Atp1a3', 'Myl12b',\n", " 'Prkacb', 'Pcp4', 'Vamp2', 'Slc32a1', 'Usmg5', 'Syp', 'Rap1gds1',\n", " 'Osbpl1a', 'Mgst3', 'Nceh1', 'Arpc4', 'Lgi2', 'Cadm3', 'Mdh1',\n", " 'Atf4', 'Sept8', 'Atp5f1', 'Atp6ap2', 'Atp6v0e2', 'Stmn2', 'Ywhag',\n", " 'Atp1b1', 'Ndufc2', 'Ldhb', 'Hras', 'Stmn3', 'Caly'], dtype=object),\n", " 'Bcl11a(+)': array(['Kmt2e', 'Mpped2', 'Tanc2', 'Meg3', 'Atf2', 'Satb1', 'Cdk17',\n", " 'Slitrk5', 'Atp2b1', 'Stmn2', 'Cnksr2', 'Zfr', 'Syt1', 'Stxbp5l',\n", " 'Akt3', 'Sgcz', 'Ube3a', 'Rims2', 'Kcnt2', 'Ppp1r12a', 'Ttc3',\n", " 'Gnas', 'Fbxw7', 'Lrrc7', 'Wasf1', 'Foxp1', 'Sept7', 'Atxn1',\n", " 'Tceal3', 'Cdkl5', 'Pafah1b1', 'Grm8', 'Mef2a', 'Pja2', 'Nrn1',\n", " 'Galnt13', 'Frmpd4', 'Epha6', 'Dab1', 'Vamp2', 'Trpc4', 'Gabrb2',\n", " 'Snca', 'Kcnf1', 'Acsl4', 'Ppp3cb', 'Adgrl3', 'R3hdm1', 'Auts2',\n", " 'Pak3', 'Slc39a10', 'March6', 'Gnb2', 'Fut9', 'Lmo4', 'Exd2',\n", " 'Dapk1', 'Erc2', 'Nbea', 'Rab6a', 'Calm2', 'Hspa8', 'Cadm1',\n", " 'Ccser1', 'Anks1b', 'Kansl1', 'Ssbp2', 'Basp1', 'Prmt8', 'Sntg1',\n", " 'Abi2', 'Cntn1', 'Gria2', 'Nlk', 'Tox', 'Apba2', 'Ppp3ca',\n", " 'Sipa1l1', 'Mctp1', 'Ppp3r1', 'Kcnj3', 'Bcl11a', 'Sub1', 'Magi2',\n", " 'Mapt', 'Cntnap2', 'Adgrb3', 'Hecw1', 'Scn8a', 'Ctnnd2', 'Ncoa7',\n", " 'Camta1', 'Rock2', 'Ptprs', 'Morf4l2', 'Ndrg3', 'Brinp1', 'Cadps',\n", " 'Oxr1', 'Nlgn1', 'Ehbp1', 'Dpp10', 'Csmd2', 'Hprt', 'Unc80',\n", " 'Chsy3', 'Pls3', 'Ywhae', 'Serpini1', 'Hivep2', 'Gabra2', 'Ube2w',\n", " 'Kcnma1', 'Nap1l1', 'Mef2c', 'Cdc42', 'Psd3', 'Tspan7', 'Camkv',\n", " 'Chrm1', 'Rab26', 'Osbpl6', 'Zfp365', 'Nrxn1', 'Lrrc4c', 'Unc79',\n", " 'Atp6v1a', 'Pak7', 'Gabra4', 'Ywhag', 'Prkacb', 'Dlgap1', 'Trank1',\n", " 'App', 'Thrb', 'Cadm2', 'Sh3rf1', 'Nrxn3', 'Vgf', 'Capza2',\n", " 'Map1b', 'Mapk8', 'Cltc', 'Etv1', 'Unc5d', 'Celf5', 'Elavl4',\n", " 'Bcl11b', 'Rtn3', 'Cnih2', 'Kcnq3', 'Car10', 'Actr3', 'Dock3',\n", " 'Prkar1b', 'Herc1', 'Maged1', 'Slc22a17', 'Nckap1', 'Hnrnpk',\n", " 'Ppp1r9a', 'Tceal6', 'Cacna2d3', 'Glrb', 'Cttnbp2', 'Setbp1',\n", " 'Rims1', 'Fgf12', 'Dnajc6', 'Arf3', 'Agap2', 'Grm7', 'Robo2',\n", " 'Fgf14', 'Epha5', 'Adgrl1', 'Nrcam', 'Fam19a1', 'Efna5', 'Cplx2',\n", " 'Sptbn4', 'Gnao1', 'Grm5', 'Cacna2d1', 'Nck2', 'Adgrb2',\n", " '5730522E02Rik', 'Snap25', 'Cacna1e', 'Stmn4', 'Dlgap2', 'Gls',\n", " 'Btbd10', 'Grin2a', 'Matr3', 'Ctnna2', 'Nptn', 'Dlg3', 'Hsp90ab1',\n", " 'Grin2b', 'Psip1', 'Tcf4', 'Ppm1l', 'Cdk5r2', 'Syp', 'Nrg3',\n", " 'Epha7', 'Adgrb1', 'Homer1', 'Atxn10', 'Ptpra', 'Pacsin1',\n", " 'Sptan1', 'Ldb2', 'Runx1t1', 'Mdga2', 'Ppp2r2b', 'Myh10', 'Syne1',\n", " 'Trim32', 'Sqstm1', 'Otud7a', 'Atrnl1', 'Gpm6b', 'Ppp2ca',\n", " 'Mir124a-1hg', 'Fam171b', 'Stmn1', 'Nucks1', 'Nrg1', 'Slc35f1',\n", " 'Rap1gds1', 'Mlf2', 'Kcnab1', 'Flrt2', 'Itpr1', 'Chl1', 'Phactr1',\n", " 'Esyt2', 'Ssh2', 'Mtcl1', 'Srcin1', 'Trim9', 'Ptprd', 'Pcsk2',\n", " 'Kcnh5', 'Camk2b', 'Dmxl2', 'Caly', 'Ptms', 'Ank2', 'Dlc1',\n", " 'Sphkap', 'Mllt11', 'Crmp1', 'Hs3st4', 'Syngap1', 'Dlgap4',\n", " 'Gpm6a', 'Meis2', 'Slit3', 'Slc25a4', 'Hpca', 'Pdlim7', 'Necab1',\n", " 'Lingo1', 'Csmd1', 'Bex2', 'Osbpl1a', 'Arhgef9', 'Ywhaz',\n", " 'Neurod2', 'Dock4', 'Etv5', 'Ywhah', 'Slc17a7', 'Large1', 'Cacnb4',\n", " 'Kcnq5', 'Hcn1', 'Tceal5', 'Serbp1', 'Ube2e2', 'Atp6v0a1',\n", " 'Cacna1d', 'Ogfrl1', 'Pfn2', 'Dlg2', 'Gabra1', 'Atp6v1c1',\n", " 'Atp1a3', 'Cabp1', 'Tlcd2', 'Plxna4', 'Tcaf1', 'Tsc22d1', 'Adcy2',\n", " 'Fam155a', 'Gabrg3', 'Gabrg2', 'Syn2', 'Sptbn2', 'Thra', 'Ncdn',\n", " 'Syt13', 'Napb', 'Dnm1', 'Cpne8', 'Gabra5', 'Arpp21', 'Tpm3',\n", " 'Srrm2', 'Dmd', 'Tspan13', 'Nwd2', 'Snrpn', 'Prkcg', 'Gria3',\n", " 'Enc1', 'Ralyl', 'Herc3', 'Nkain2', 'Stxbp1', 'Mapre3', 'Npr3',\n", " 'Plcb1', 'Pgm2l1', 'Akap11', 'Kcnd2', 'Fam49b', 'Fhl2', 'Efhd2',\n", " 'Ppfia2', 'Tmsb4x', 'Usp46', 'Crtac1', 'Nol4', 'Syt7', 'Etl4',\n", " 'Lingo2', 'Lmtk2', 'Sprn', 'Cntn4', 'Map2k1', 'Pclo', 'Gdi1',\n", " 'Hpcal4', 'Serp2', 'St3gal5', 'Chn1', 'Khdrbs2', 'Efr3a', 'Thsd7b',\n", " 'Snn', 'Fry', 'Plekha5', 'Tagln3', 'Spink8', 'Kcnmb4', 'Celf1',\n", " 'Ttc9b', 'Eif1', 'Tomm70a', 'Dmtn', 'Cacna1a', 'B4galt6', 'Cnrip1',\n", " 'Macrod2', 'Madd', 'Eef1a2', 'Ssb', 'Pcdh7', 'Negr1', 'Dync1i1',\n", " 'Pak1', 'Nsf', 'Mff', 'Gng13', 'Ociad1', 'Pde10a', 'Lmo7',\n", " 'Rgs7bp', 'Ube2ql1', 'Rnf150', 'Itm2c', 'Cacnb3', 'Fnbp1l',\n", " 'Lrfn5', 'Camk2a', 'Kcnb1', 'Chst1', 'Camk2n1', 'Grik5', 'Svop',\n", " 'Rtn1', 'Akap8l', 'Arpc2', 'Lrba', 'Ptprn2', 'Itm2b', 'Atp6v1b2',\n", " 'Mycbp2', 'Abhd12', 'Psma1', 'Hs6st3', 'Mtpn', 'Rgs6', 'Camta2',\n", " 'Fscn1', 'Kctd16', 'Tenm2', 'Nptxr', 'Syn1', 'Phf24', 'Slc12a5',\n", " 'Cox6c', 'Slc1a1', 'D430041D05Rik', 'Camkk2', 'Chn1os3', 'Pkm',\n", " 'Tusc3', 'Epha4', 'Oxct1', 'Dkk3', 'Map9', 'Cpne6', 'Eif3e',\n", " 'Atp5a1', 'Dgkh', 'Calm1', 'Ddn', 'Celsr2', 'Shank2', 'Nptx1',\n", " 'Rapgef4', 'Grik3', 'Fxyd7', 'Olfm1', 'Ngef', 'Sorbs2', 'Arhgap26',\n", " 'Prnp', 'Sidt1', 'Fam126b', 'Pde4d', 'Adam22', 'Tenm4', 'Kcnip4',\n", " 'Hivep3', 'Ube2b', 'Ncald', 'Prickle1', 'Agbl4', 'Dnaja1', 'Stmn3',\n", " 'Tcf25', 'Pitpna', 'Unc13b', 'Tshz2', 'Thy1', 'Rap2b', 'Shank1',\n", " 'Dbn1', 'Sorl1', 'Syt11', 'C1qtnf4', 'Dynll1', 'Pfkp', 'Rasl11b',\n", " 'Hlf', 'Dgkg', 'Mapk10', 'Eif4g3', 'Ppp1r1a', 'Ckmt1',\n", " 'C230004F18Rik', 'Klhl2', 'Caln1', 'Synpo', 'Med26', 'Slit1',\n", " 'Msra', 'Actn1', 'Slc24a2', 'Syt16', 'Ap2b1', 'Elmod1', 'Nrgn',\n", " 'Myo5a', 'Rasgrp1', 'Tspan5', 'Cpne5', 'Tubb2a', 'Tubb5',\n", " 'Tmem158', 'Camk4', 'Nedd4l', 'Atpif1', 'Asic2', 'Cpne7', 'Kif5c',\n", " 'Coro1a', 'Snx10', 'Dgkz', 'Gpr158', 'Vsnl1', 'Ypel3', 'Prkce',\n", " 'Tmem181a', 'Atp2b2', 'Atp6ap2', 'Ap2s1', '2010300C02Rik', 'Uchl1',\n", " 'Aak1', 'Hk1', 'Sult4a1', 'R3hdm2', 'Snap91', 'Cyfip2', 'Lrrtm4',\n", " 'Ksr1', 'Mapk8ip2', 'Myt1l', 'Prkcb', 'Kcnh7', 'Ids', 'Foxg1',\n", " 'Dclk1', 'Plppr2', 'Cox6b1', 'Evi5', 'Tmsb10', 'Mapk1', 'Rbfox1',\n", " 'Rtf1', 'Tpi1', 'Idh3a', 'Mmp17', 'Lancl1', 'Miat', 'Ptprn',\n", " 'Actr2', 'Tspyl1', 'Selenow', 'Exoc5', 'Map2', 'Atp5g3', 'Gas7',\n", " 'Rasgrf1', 'Sgip1', 'Arfgef3', 'Ghitm', 'Ubb', 'Egfem1', 'Ksr2',\n", " 'Cers6', 'Micu3', 'Serinc1', 'Rapgefl1', 'Tnrc6c', 'Rasgef1b',\n", " 'Mef2d', 'Scai', 'Phyhip', 'Rheb', 'Prickle2', 'Arhgef25', 'Pde1a',\n", " 'Stum', 'Ext1', 'Actr3b', 'Chd5', 'Tmem132d', 'Dip2c', 'Sox5',\n", " 'Eif4a2', 'Fbxl16', 'Sptbn1', 'Gabbr2', 'Megf9', 'Gabarapl1',\n", " 'Rasgef1a', 'Nsg2', '4930481A15Rik', 'Junb', 'Sort1', 'Trim2',\n", " 'Fam135b', 'Ywhab', 'Mgst3', 'Frrs1l', 'Ephx4', 'Slc6a17',\n", " 'Fam49a', 'Ptk2', 'Arf1', 'Mkl2', 'Celf2', 'Trio', 'Gnaq', 'Lamp5',\n", " 'Ppia', 'Dtnb', 'Rnf14', 'Fars2', 'Sh3rf3', 'Klc1', 'Ryr2',\n", " 'Cdkl2', 'Rab2a', 'March1', 'Arpp19', 'Lpgat1', 'Clstn1', 'Kcnip3',\n", " 'Ptprg', 'Tpm1', 'Eno1', 'Ppp1r2', 'Gabrb3', '1700020I14Rik',\n", " 'Tub', 'Mal2', 'Pi4ka', 'Skp1a', 'Sun1', 'Rbfox3', 'Atp5j',\n", " 'Ndufa4', 'Rgs7', 'Sh3gl2', 'Actb', 'Ubqln2', 'Nell2', 'Arhgap44',\n", " 'Rtn4', 'Kcnh1', 'Ak5', 'Psma3', 'Stim2', 'Plk2', 'Add2', 'Vdac1',\n", " 'Hint1', 'Apbb1', 'Ptk2b', 'Mpped1', 'Synj1', 'Hsp90aa1', 'Gnb1',\n", " 'Nos1ap', 'Egr1', 'Syngr1', 'Agtpbp1', 'Ryr3', 'Calm3', 'Atp1a1',\n", " 'Ldha', 'Plppr4', 'Tmx4', 'Ppp2r1a', 'Sgcd', 'Brk1', 'Satb2',\n", " 'Galnt9', 'Dctn6', 'A830018L16Rik', 'Adcy1', 'Cobl', 'Gria1',\n", " 'Chrm3', 'Npy', 'Arl15', 'Kcnv1', 'Ntrk3', 'Plxna2', 'Amph',\n", " 'Atl1', 'Cap2', 'Cnih3', 'Diras2', 'Cadps2', 'Dusp14', 'Nuak1',\n", " 'Trim37', 'Rab15', 'Ppp2r2c', 'Kcnj4', 'Ankrd33b', 'Slc15a3',\n", " 'Nov', 'Ndst3', 'Pex5l', 'Nyap2', 'Mat2b', 'Arpc5', 'Sec14l1',\n", " 'Mmd', 'Snph', 'Fam81a', 'Unc13a', 'Vopp1', 'Celf4', 'Kcnq2',\n", " 'Ier5', 'Rmnd5b'], dtype=object),\n", " 'Bcl6(+)': array(['Nwd1', 'Nfia', 'Wdr17', 'Zbtb20', 'Nrxn1', 'Lsamp', 'Ptprz1',\n", " 'Atp1a2', 'Gpc5', 'Lama2', 'Appl2', 'Bcl6', 'Slc1a2'], dtype=object),\n", " 'Bnc2(+)': array(['Bmp7', 'Fmod', 'Atp1a2', 'Atp13a5', 'Alcam', 'Adam12', 'Igf2',\n", " 'Stra6', 'Trpm3', 'Sdk1', 'Rcsd1', 'Tbx15', 'Notch2', 'Slc26a2',\n", " 'Bmp6', '1500015O10Rik', 'Car13', 'Gjb2', 'Pear1', 'Slc6a20a',\n", " 'Mpzl2', 'Gm5860', 'Cdh1', 'Fn1', 'Slc13a4', 'Dapl1', 'Slc13a3',\n", " 'Ptgds', 'Prdm6', 'Lbp', 'Col6a2', 'Foxc2'], dtype=object),\n", " 'Cebpb(+)': array(['Neurod2', 'C1ql2', 'Olfm1', 'Ppp3ca', 'Hpca', 'Rfx3', 'Snca',\n", " 'Wasf1', 'Sema5a', 'Prox1', 'Calm2', 'Ppfia2', 'Bex2', 'Camk2b',\n", " 'Pter', 'C1ql3', '2010300C02Rik', 'Dsp', 'Ppp1r1a', 'Nrgn',\n", " 'Cpne6', 'Chn1', 'Dgkh', 'Dynll1', 'Rps7', 'Cebpb', 'Tmsb4x',\n", " 'Ncdn'], dtype=object),\n", " 'Cebpd(+)': array(['Fam163b', 'Dsp', 'Rfx3', 'Dock10', 'Ahcyl2', 'Nr3c2'],\n", " dtype=object),\n", " 'Dbp(+)': array(['Gpr162', 'Atp1a2', 'Kcnv1', 'Ptn', 'Slc1a2', 'Plec', 'Rorb',\n", " 'Dbp', 'Nr2f1', 'Clu', 'Fzd3', 'Itm2c', 'Olfm1', 'Pld3', 'Cdk5r2',\n", " 'Camkv', 'Cst3', 'Ttyh1', 'Apoe', 'Gpm6b', 'Fxyd7', 'Ddn',\n", " 'Shank1', 'Prnp', 'Itpka', 'Bsn', 'Bcan', 'Gja1', 'Synpo',\n", " 'Hpcal4', 'Slc1a3', 'Atp2b4', 'Napa', '1110008P14Rik', 'Tsc22d1',\n", " 'Klc1', 'Thra', 'Cck', 'Satb2', 'Lamp5', 'Nptn', 'Tbc1d9', 'Id2',\n", " 'Gapdh', 'Fam107a', 'Camk2n1', 'Cabp1', 'Cx3cl1', 'Slc2a1',\n", " 'Htra1', 'Stmn4', 'Psap', 'Sparcl1', 'Sirpa', 'Pcdhga1', 'Scg5',\n", " 'Gpr37l1', 'Pcsk1n'], dtype=object),\n", " 'Dlx1(+)': array(['Dlx1as', 'Dlx6os1', 'Cnr1', 'Gad1', 'Grip1', 'Tcf4', 'Maf',\n", " 'Dner', 'Nxph1', 'Dlx1', 'Rpp25', 'Adarb2', 'Erbb4', 'Gad2',\n", " 'Vstm2a', 'Synpr', 'Serpini1', 'Vip', 'Npy', 'Cck', 'Pcdha1',\n", " 'Slc6a1', 'Sst', 'Fxyd6', 'Slc32a1'], dtype=object),\n", " 'Ebf1(+)': array(['Acta2', 'Tcf7l2', 'Igfbp7', 'Cntnap2', 'Rbms3', 'Slc17a6',\n", " 'Atp13a5'], dtype=object),\n", " 'Egr1(+)': array(['Meg3', 'Satb1', 'Cdk17', 'Atp2b1', 'Stmn2', 'Cnksr2', 'Zfr',\n", " 'Syt1', 'Stxbp5l', 'Ttc3', 'Fbxw7', 'Lrrc7', 'Wasf1', 'Foxp1',\n", " 'Sept7', 'Cdkl5', 'Pja2', 'Nrn1', 'Vamp2', 'Snca', 'Ppp3cb',\n", " 'R3hdm1', 'Slc39a10', 'March6', 'Gnb2', 'Lmo4', 'Nbea', 'Rab6a',\n", " 'Calm2', 'Anks1b', 'Basp1', 'Prmt8', 'Abi2', 'Cntn1', 'Gria2',\n", " 'Nlk', 'Gabbr1', 'Cadm3', 'Apba2', 'Ppp3ca', 'Acvr1b', 'Ppp3r1',\n", " 'Kcnj3', 'Bcl11a', 'Sub1', 'Hecw1', 'Scn8a', 'Rock2', 'Ptprs',\n", " 'Ndrg3', 'Brinp1', 'Cadps', 'Oxr1', 'Nlgn1', 'Dpp10', 'Hprt',\n", " 'Ywhae', 'Serpini1', 'Hivep2', 'Kcnma1', 'Nap1l1', 'Mef2c', 'Psd3',\n", " 'Tspan7', 'Camkv', 'Chrm1', 'Sept11', 'Nrxn1', 'Unc79', 'Mark1',\n", " 'Atp6v1a', 'Gabra4', 'Ywhag', 'Prkacb', 'Dlgap1', 'App', 'Cadm2',\n", " 'Vgf', 'Egr3', 'Celf5', 'Nckap5', 'Rad23b', 'Rtn3', 'Kcnq3',\n", " 'Car10', 'Slc30a4', 'Prkar1b', 'Herc1', 'Nckap1', 'Hnrnpk',\n", " 'Ppp1r9a', 'Tceal6', 'Glrb', 'Rims1', 'Fgf12', 'Higd1a', 'Spred1',\n", " 'Arf3', 'Robo2', 'Adgrl1', 'Nrcam', 'Nrxn2', 'Gnao1', 'Cacna2d1',\n", " '5730522E02Rik', 'Snap25', 'Stmn4', 'Slitrk1', 'Gls', 'Trhde',\n", " 'Ovol2', 'Btbd10', 'Grin2a', 'Matr3', 'Nptn', 'Dlg3', 'Hsp90ab1',\n", " 'Grin2b', 'Tcf4', 'Cdk5r2', 'Syp', 'Adgrb1', 'Homer1', 'Sptan1',\n", " 'Trim32', 'Gpm6b', 'Ppp2ca', 'Mir124a-1hg', 'Fam171b', 'Stmn1',\n", " 'Rap1gds1', 'Mlf2', 'Itpr1', 'Phactr1', 'Ssh2', 'Trim9', 'Ptprd',\n", " 'Pcsk2', 'Fzd3', 'Camk2b', 'Caly', 'Ptms', 'Kif1a', 'Ank2',\n", " 'Mllt11', 'Crmp1', 'Syngap1', 'Dlgap4', 'Gpm6a', 'Slc25a4', 'Hpca',\n", " 'Ptn', 'Necab1', 'Lingo1', 'Csmd1', 'Bex2', 'Osbpl1a', 'Clip3',\n", " 'Ywhaz', 'Etv5', 'Ywhah', 'Slc17a7', 'Cacnb4', 'Tceal5', 'Pfn2',\n", " 'Ncan', 'Gabra1', 'Ildr2', 'Atp6v1c1', 'Atp1a3', 'Cabp1',\n", " 'Tsc22d1', 'Lrrc40', 'Gabrg2', 'Syn2', 'Sptbn2', 'Syt13', 'Napb',\n", " 'Dnm1', 'Tmem50a', 'Tmem151a', 'Arpp21', 'Tspan13', 'Nwd2',\n", " 'Snrpn', 'Prkcg', 'Gria3', 'Enc1', 'Iqsec2', 'Prkar1a', 'Stxbp1',\n", " 'Kcnv1', 'Plcb1', 'Pgm2l1', 'Fam49b', 'Efhd2', 'Usp46', 'Syt7',\n", " 'Clstn3', 'Pclo', 'Hpcal4', 'Serp2', 'St3gal5', 'Chn1', 'Efr3a',\n", " 'AI593442', 'Stx1b', 'Tagln3', 'Celf1', 'Ttc9b', 'Eif1', 'Tomm70a',\n", " 'Dmtn', 'Cacna1a', 'Ppp1r9b', 'Madd', 'Eef1a2', 'Negr1', 'Pak1',\n", " 'Nsf', 'Gng13', 'Pde10a', 'Rgs7bp', 'Ube2ql1', 'Itm2c', 'Cacnb3',\n", " 'Lrfn5', 'Camk2a', 'Kcnb1', 'Chst1', 'Pantr1', 'Camk2n1', 'Svop',\n", " 'Rtn1', 'Akap8l', 'Arpc2', 'B3galt2', '1190002N15Rik', 'Itm2b',\n", " 'Pitpnm3', 'Atp6v1b2', 'Abhd12', 'Hs6st3', 'Mtpn', 'Camta2',\n", " 'Fscn1', 'Tenm2', 'Nptxr', 'Syn1', 'Phf24', 'Cox6c', 'Camkk2',\n", " 'Chn1os3', 'Pkm', 'Nr2f1', 'Tusc3', 'Epha4', 'Oxct1', 'Dkk3',\n", " 'Atp5a1', 'Calm1', 'Celsr2', 'Shank2', 'Nptx1', 'Fxyd7', 'Olfm1',\n", " 'Ngef', 'Sorbs2', 'Arhgap26', 'Prnp', 'Sidt1', 'Adam22', 'Kcnip4',\n", " 'Pdp1', 'Epop', 'Ube2b', 'Ncald', 'Prickle1', 'Dnaja1', 'Stmn3',\n", " 'Tcf25', 'Pitpna', 'Igfbp4', 'Thy1', 'Shank1', 'Slc1a2', 'Nrsn1',\n", " 'Sorl1', 'Eif5a', 'Syt11', 'C1qtnf4', 'Dynll1', 'Pfkp', 'Rasl11b',\n", " 'Hlf', 'Tbc1d30', 'Mapk10', 'Dlk2', 'Ppp1r1a', 'Ckmt1', 'Synpo',\n", " 'Sgtb', 'Slc24a2', 'Nrgn', 'Myo5a', 'Rasgrp1', 'Tspan5', 'Tubb2a',\n", " 'Tubb5', 'Dlg1', 'Smim13', 'Camk4', 'Mir9-3hg', 'Atpif1', 'Asic2',\n", " 'Kif5c', 'Dgkz', 'Vsnl1', 'Ypel3', 'Hs3st2', 'Prkce', 'Tmem181a',\n", " 'Atp2b2', 'Atp6ap2', 'Ap2s1', '2010300C02Rik', 'Uchl1', 'Aak1',\n", " 'Sult4a1', 'Atp8a1', 'R3hdm2', 'Snap91', 'Cyfip2', 'Myt1l',\n", " 'Prkcb', 'Ids', 'Foxg1', 'Dclk1', 'Gfra2', 'Tmsb10', 'Mapk1',\n", " 'Tpi1', 'Idh3a', 'Miat', 'Ptprn', 'Actr2', 'Selenow', 'Kif5a',\n", " 'Gas7', 'Arfgef3', 'Ubb', 'Igfbp6', 'Micu3', 'Serinc1', 'Rasgef1b',\n", " 'Mef2d', 'Jph3', 'Arhgef25', 'Pde1a', 'Actr3b', 'Golga7b',\n", " 'Eif4a2', 'Fbxl16', 'Sptbn1', 'Gabbr2', 'Gabarapl1', 'Rasgef1a',\n", " 'Cds1', 'Tacc1', 'Nsg2', 'Junb', 'Sort1', 'Ywhab', 'Mgst3',\n", " 'Frrs1l', 'Ephx4', 'Rtn4', 'Unc13a', 'Tpm1', 'Nr4a1', 'Calm3',\n", " 'Tsnax', 'Ppme1', 'Eno2', 'Fam212b', 'Slc8a2', 'Cnih3', 'Baiap2',\n", " 'Ndrg4', 'Scamp5', 'Ppp2r2c', 'Syt4', 'Plxna2', 'Fabp3', 'Atp6ap1',\n", " 'Adcy1', 'Kcnh1', 'Fam49a', 'Rab15', 'Dctn2', 'Psma3', 'Celf2',\n", " 'Camk2g', 'Kctd17', 'Ctsb', 'Atp1a1', 'Fam131a', 'Copg2', 'Mmd',\n", " 'Nme1', '2010107E04Rik', 'Neurod6', 'Apbb1', 'Clstn1', 'Tyro3',\n", " 'Strn4', 'Actr1b', 'Cyb5a', 'Rbfox3', 'Opcml', 'Ldhb', 'Khdrbs3',\n", " 'Nat8l', 'Kcnj4', 'Mdh1', 'Mical2', 'Tmem38a', 'Tmem178',\n", " 'Sec14l1', 'Grin1', 'Atp1b1', 'Zbtb18', 'Arf1', 'Rprm', 'Lamp5',\n", " 'Ptk2', 'Cap2', 'Mkl2', 'Slc6a17', 'Diras2', 'Gabrb3', 'Fars2',\n", " 'Tbc1d9', 'Slc30a3', 'Arpp19', 'Pi4ka', 'Atp5j', 'Agtpbp1', 'Eno1',\n", " 'Ptk2b', 'Rangap1', 'Ppia', 'Gnaq', 'Eif4h', 'Ryr2', 'Rab6b',\n", " 'Tppp', '1700020I14Rik', 'Lancl2', 'Mat2b', 'Ppp2r1a', 'Fam81a',\n", " 'Vopp1', 'Pnrc1', 'Nell2', 'Sh3gl2', 'Celf4', 'Kcnq2', 'Nos1ap',\n", " 'Gnb1', 'Rgs7', 'Ppp5c', 'Napa', 'Cacng3', 'Ppm1k', 'Itpka',\n", " 'Klc1', 'Adap1', 'Tuba1a', 'Egr1', 'Slc25a3', 'Ndufa4', 'Scg5',\n", " 'Cx3cl1', 'Plk2', 'Ttc7b', 'Stx1a', 'Rap1gap2', 'Syngr1', 'Myl12b',\n", " 'Tmem59l', 'Brk1', 'Sept8', 'Spock1', 'Gapdh', 'Kifap3', 'Faim2',\n", " 'Sirpa', 'Ccl27a', 'Gda', 'Kalrn', 'Cfl1', 'Tbc1d24', 'Rnf187',\n", " 'Sncb', 'Tbr1', 'Arpc4', 'Rnf112', 'Clcn4', 'Tuba4a', 'Crip2',\n", " 'Atp6v1d', 'Car11', 'Eid1', 'Rph3a', 'Cyp46a1', 'Pcdha1', 'Pgbd5',\n", " 'Tuba1b', 'Grina', 'Hras', 'Atp2a2', 'Ddx5', 'Satb2', 'Flrt2',\n", " 'Efna5', 'Dlgap3', 'Cux2', 'Kctd1', 'Galnt9', 'Tshz3', 'Pcdh10',\n", " 'Rorb', 'Ddit4l', 'Kcnmb4', 'Pak7', 'Kcnt2', 'Dact2', 'Ankrd33b'],\n", " dtype=object),\n", " 'Egr3(+)': array(['Tanc2', 'Meg3', 'Slitrk4', 'Cdk17', 'Atp2b1', 'Cnksr2', 'Syt1',\n", " 'Ttc3', 'Fbxw7', 'Wasf1', 'Tceal3', 'Cdkl5', 'Pja2', 'Nrn1',\n", " 'Vamp2', 'Snca', 'Ppp3cb', 'Slc39a10', 'Lmo4', 'Dapk1', 'Erc2',\n", " 'Nbea', 'Rab6a', 'Calm2', 'Anks1b', 'Basp1', 'Prmt8', 'Abi2',\n", " 'Gria2', 'Gabbr1', 'Cadm3', 'Apba2', 'Ppp3ca', 'Sipa1l1', 'Acvr1b',\n", " 'Ppp3r1', 'Kcnj3', 'Sub1', 'Ptprs', 'Ndrg3', 'Brinp1', 'Nlgn1',\n", " 'Ywhae', 'Kcnma1', 'Mef2c', 'Cdc42', 'Tspan7', 'Camkv', 'Chrm1',\n", " 'Pdcd4', 'Nrxn1', 'Atp6v1a', 'Ywhag', 'Prkacb', 'Map1a', 'Dlgap1',\n", " 'App', 'Cadm2', 'Nrxn3', 'Vgf', 'Zfand5', 'Capza2', 'Egr3',\n", " 'Celf5', 'Nckap5', 'Cnih2', 'Anp32a', 'Car10', 'Prkar1b',\n", " 'Slc22a17', 'Nckap1', 'Hnrnpk', 'Ppp1r9a', 'Arf3', 'Shisa7',\n", " 'Nrcam', 'Cplx2', 'Grm5', 'Cacna2d1', 'Adgrb2', 'Snap25',\n", " 'Cacna1e', 'Stmn4', 'Dlgap2', 'Gls', 'Btbd10', 'Grin2a', 'Matr3',\n", " 'Sorcs3', 'Nptn', 'Dlg3', 'Hsp90ab1', 'Tcf4', 'Cdk5r2', 'Syp',\n", " 'Adgrb1', 'Homer1', 'Sptan1', 'Syne1', 'Trim32', 'Sqstm1',\n", " 'Ppp2ca', 'Fam171b', 'Stmn1', 'Mlf2', 'Itpr1', 'Dpf1', 'Phactr1',\n", " 'Pcsk2', 'Fzd3', 'Camk2b', 'Dmxl2', 'Caly', 'Ptms', 'Mllt11',\n", " 'Crmp1', 'H2afz', 'Syngap1', 'Dlgap4', 'Gpm6a', 'Slit3', 'Hpca',\n", " 'Ptn', 'Lingo1', 'Csmd1', 'Bex2', 'Osbpl1a', 'Arhgef9', 'Ywhaz',\n", " 'Ywhah', 'Slc17a7', 'Tceal5', 'Serbp1', 'Ogfrl1', 'Dlg2', 'Ppp1cb',\n", " 'Prpf39', 'Gabra1', 'Atp6v1c1', 'Cabp1', 'Pter', 'Sertm1',\n", " 'Tsc22d1', 'Fkbp3', 'Ccsap', 'Cux2', 'Lrrc40', 'Syn2', 'Sptbn2',\n", " 'Thra', 'Ncdn', 'Napb', 'Dnm1', 'Arpp21', 'Tpm3', 'Grasp', 'Snrpn',\n", " 'Prkcg', 'Gria3', 'Enc1', 'Ralyl', 'Herc3', 'Stxbp1', 'Kcnv1',\n", " 'Plcb1', 'Pgm2l1', 'Fhl2', 'Efhd2', 'Tmsb4x', 'Klhl23', 'Syt7',\n", " 'Pclo', 'Kcnip2', 'Hpcal4', 'Serp2', 'St3gal5', 'Chn1', 'Snn',\n", " 'AI593442', 'Celf1', 'Ttc9b', 'Eif1', 'Dmtn', 'Dcaf7', 'Eef1a2',\n", " 'Negr1', 'Nsf', 'Gng13', 'Ociad1', 'Pea15a', 'Ube2ql1', 'Itm2c',\n", " 'Cacnb3', 'Camk2a', 'Kcnb1', 'Chst1', 'Camk2n1', 'Grik5', 'Rtn1',\n", " 'Arpc2', 'Atp6v1b2', 'Ddit4l', 'Mtpn', 'Camta2', 'Fscn1', 'Tenm2',\n", " 'Nptxr', 'Syn1', 'Phf24', 'Slc12a5', 'Cox6c', 'D430041D05Rik',\n", " 'Camkk2', 'Chn1os3', 'Pkm', 'Nr2f1', 'Tusc3', 'Epha4', 'Dkk3',\n", " 'Cpne6', 'Calm1', 'Rilpl1', 'Ddn', 'Shank2', 'Nptx1', 'Fxyd7',\n", " 'Olfm1', 'Ngef', 'Shank1', 'Rtn4', 'Unc13a', 'Tpm1', 'Ncald',\n", " 'Prnp', 'Thy1', 'Eif5a', 'Calm3', 'Ubb', 'Vsnl1', 'Scamp5',\n", " 'Ap2m1', 'Plppr4', 'Nsg2', 'Baiap2', '4930481A15Rik', 'Ndrg4',\n", " 'Ppp2r2c'], dtype=object),\n", " 'Egr4(+)': array(['Atp2b1', 'Syt1', 'Snca', 'Arpp21', 'Dlgap1', 'Camkv', 'Cnksr2',\n", " 'Ywhaz', 'Snap25', 'Dclk1', 'Homer1', 'Lmo4', 'Grin2b', 'Ppp3ca',\n", " 'Mef2c', 'Syp', 'Dnm1', 'Slc17a7', 'Calm2', 'Sub1', 'Gpr22',\n", " 'Ensa', 'Camk2n1', 'Nrgn', 'Cdk5r2', 'Itm2c', 'Gria2'],\n", " dtype=object),\n", " 'Elk1(+)': array(['Snca', 'Snap25', 'Ppp3ca', 'Camk2b', 'Camk2n1', 'Nckap1', 'Gpm6a',\n", " 'Ptms', 'Itm2c', 'Slc17a7', 'Hsp90ab1', 'Tmsb4x', 'Ndrg4', 'Ddn',\n", " 'Elk1', 'Bex2', 'Arpc2', 'Calm3', 'Nrgn', 'Rtn1', 'Atp5g1',\n", " 'Olfm1', 'Syp', 'Atp1a1', 'Enc1', 'Snrpn', 'Ncdn', 'Calm2', 'Chgb'],\n", " dtype=object),\n", " 'En1(+)': array(['Uncx', 'Pou3f2', 'Foxa2', 'Gucy2c', 'Ankrd34b', 'Chrnb3', 'Kcns3',\n", " 'Oprk1', 'Ctxn2', 'Foxa1', 'Col25a1', 'Sv2c', 'Ddc', 'Serpine2',\n", " 'Fam204a', 'Grip2', 'Klhl13', 'Klhl1', 'Aldh1a1', 'Asb4',\n", " 'C130021I20Rik', 'Chrna4', 'Grb10', 'B630019K06Rik', 'Lmx1b',\n", " 'Tmem255a', 'Ret', 'Ndnf', '1700001C19Rik', 'Slc6a3', 'En1',\n", " 'Ntsr1', 'Th', 'Ntn1', 'Lix1', 'Vat1l', 'Trpc6', 'Iqcj', 'Pbx3',\n", " 'Slc18a2', 'Zdbf2', 'Drd2', 'Nr4a2', 'Fgf13', 'Kcnd3', 'Zfhx3',\n", " 'Dlk1', 'Apba1', 'Nwd2', 'Plcxd2', 'Zcchc12', 'Chrna6', 'Fam184a',\n", " 'Lbh', 'Slc10a4', 'AW551984'], dtype=object),\n", " 'Ets2(+)': array(['Atp2b1', 'Syt1', 'Ttc3', 'Vamp2', 'Snca', 'Rab6a', 'Calm2',\n", " 'Basp1', 'Gria2', 'Ppp3ca', 'Ppp3r1', 'Sub1', 'Snap25', 'Prkar1b',\n", " 'Camk2b', 'Chst1', 'Ptms', 'Ncald', 'Cacnb3', 'Slc17a7', 'Enc1',\n", " 'Bex2', 'Dnm1', 'Rgs7bp', 'Rtn1', 'Camk2n1', 'Vsnl1', 'Gpm6a',\n", " 'Lamp5', 'Nrgn', 'Ets2', 'Atp1a1', 'Tmsb4x', 'Cdk5r1', 'Chn1',\n", " 'Tubb2a'], dtype=object),\n", " 'Etv1(+)': array(['Tollip', 'Prss12', 'Chst8', 'Ndst4', 'Tmem200a', 'Igfbp4',\n", " 'Man1a'], dtype=object),\n", " 'Etv5(+)': array(['Meg3', 'Vamp2', 'Syt1', 'Dlgap1', 'Slc17a7', 'Adgrb3', 'Gpm6a'],\n", " dtype=object),\n", " 'Foxa1(+)': array(['AW551984', '1700001C19Rik', 'Ndnf', 'Chrna4', 'Rab3c', 'Aldh1a1',\n", " 'Chrna6', 'Fam167a', 'Drd2', 'Lmx1b', 'Sv2c', 'Ddc', 'Foxa1',\n", " 'Slc18a2', 'Chrnb3', 'Ret', 'Pbx3', 'Th', 'Dlk1', 'Aldh1a7',\n", " 'Slc6a3'], dtype=object),\n", " 'Foxa2(+)': array(['Chrnb3', 'Foxa2', 'Ebf3', 'Gm2694', 'Ebf2', 'Ntn1'], dtype=object),\n", " 'Foxc2(+)': array(['Lmx1b', 'Zic2', 'Zic4', 'Ahnak', 'Bgn', 'Gjb6', 'Msx1', 'Igfbp5',\n", " 'Fn1', 'Prelp', 'Tgfbi', 'S100a11', 'Tgfbr3', 'Trpm3', 'Gm5860',\n", " 'Nfatc4', 'Rspo3', 'Slc26a7', 'Bmp7', 'Aph1a', 'Slc13a4',\n", " 'Slc26a2', 'Anxa3', 'Car13', 'Fzd2', 'Wfikkn2', 'Mgp', 'Col5a2',\n", " 'Tns2', 'Bmp4', 'Igfbp6', 'Bnc2', 'Tspan8', 'Six2', 'Cmss1',\n", " 'Pkd2', 'Tbx15', 'Serpind1', 'Col1a2', 'Islr', 'Cped1', 'Aldh1a2',\n", " 'Hmgcs2', 'Rbp1', 'Ptgds', 'Slc47a1', 'Foxc2', 'Slc6a13',\n", " '1500015O10Rik', 'Tnfrsf11b', 'Slc6a12', 'Fxyd5', 'Lypd2',\n", " 'Slc38a2', 'Slc16a9', 'Foxc1', 'Smoc1', 'Prg4', 'Alpl', 'Slco4a1',\n", " 'Adamtsl3', 'Ifitm2', 'Fgl2'], dtype=object),\n", " 'Foxd2(+)': array(['Bdh2', 'Vsx1', 'Aldh1a2', 'Slc22a6', 'Slc6a12', 'Foxd1',\n", " 'Tnfrsf11b', 'Osr1'], dtype=object),\n", " 'Gata3(+)': array(['Gata3', 'Gad1', 'Tcf7l2', 'Plp1', 'Lhx1os', 'Gad2', 'Scn9a',\n", " 'Six3', 'Pld5', 'Slc32a1', 'Chrm2', 'Pbx3'], dtype=object),\n", " 'Glis3(+)': array(['Fut8', 'Dtna', 'Nr3c2', 'Smarca2', 'Rfx3', 'Maml2', 'Slc4a4',\n", " 'Dab1', 'Nlgn1', 'Acsl3', 'Msi2', 'Grm3', 'Rora', 'Cadm1', 'Ncam1',\n", " 'Garem1', 'Gria2', 'Gli3', 'Tead1', 'Myo6', 'Nfia', 'Tmem164',\n", " 'Mdga2', 'Zfp217', 'Chd7', 'Zeb1', 'Diaph2', 'Nrcam', 'Adgrb3',\n", " 'Gabra2', 'Pip5k1b', 'Abi1', 'Zfpm2', 'Prkg1', 'Rgs20', 'Negr1',\n", " 'Pcdh9', 'Cadm2', 'Nfib', 'Plekhg1', 'Farp1', 'Ptchd4', 'Glis3',\n", " 'Mapk4', 'Gpc6', 'Hectd2', 'Ptprz1', 'Auts2', 'Macrod2', 'Ppfia2',\n", " 'Grik2', 'Nrxn1', 'Ctnnd2', 'Csgalnact1', 'Trps1', 'Setbp1',\n", " 'Plcb1', 'Paqr8', 'Magi2', 'Npas3', 'Tnfrsf19', 'Wdr17', 'Sox6',\n", " 'Syne1', 'Dennd1a', 'Cttnbp2', 'Sorbs1', 'Kirrel3', 'Prkd1', 'Qk',\n", " 'Pde8b', 'Frmd4a', 'Gpc5', 'Ston2', 'Nebl', 'Celf2', 'Dgkh',\n", " 'Rorb', 'Prox1', 'Tjp1', 'Arhgef26', 'Gabrb1', 'Gja1', 'Slc1a2',\n", " 'Ptprt', 'Tcf4', 'Phka1', 'Pitpnc1', 'Pnisr', 'Dock4', 'Zbtb16',\n", " 'Ccdc85a', 'Tnik', 'Fmn2', 'Tmem108', 'Macf1', 'Iqgap2', 'Adcy7',\n", " '4930402H24Rik', 'Kcnn2', 'Trpm3', 'Nedd4l', 'Gli2', 'Pde7b',\n", " 'Ryr2', 'Appl2', 'Mcc', 'Lrp1b', 'A330033J07Rik', 'Carmil1',\n", " 'Dlg2'], dtype=object),\n", " 'Hivep2(+)': array(['Kmt2e', 'Atp2b1', 'Slc39a10', 'Cdk17', 'Satb1', 'Lrrc7',\n", " 'Pafah1b1', 'Atf2', 'Akt3', 'Cnksr2', 'Galnt13', 'Hivep2',\n", " 'Smarca2', 'Fbxw7', 'Rims2', 'Ube3a', 'Man1a', 'Slitrk5', 'Lingo2',\n", " 'Sgcz', 'Tanc2', 'Kat6b', 'Stag1', 'Sept7', 'Adgrl3', 'Kansl1',\n", " 'Mef2a', 'Zfp385b', 'Slitrk4', 'Zfp804a', 'Ttc3', 'Enox1', 'Syt1',\n", " 'Stmn2', 'Atxn1', 'Stxbp5l', 'Runx1t1', 'Grid2', 'Pja2', 'Zfr',\n", " 'Ppp3cb', 'Ccser1', 'Mapk8', 'Ntng1', 'Mctp1', 'Hook1', 'Cdh2',\n", " 'Dlgap1', 'Basp1', 'Ncoa7', 'Btbd10', 'Fut9', 'Dpp10', 'Nap1l1',\n", " 'Slc16a10', 'Cacna2d1', 'Dab1', 'Nlgn1', 'Numb', 'Car10', 'Cdh8',\n", " 'Fgf12', 'Fbxo11', 'Trpc4', 'Grm3', 'Pparg', 'Usp32', 'Cdkl5',\n", " 'Pam', 'Arap2', 'Sh3rf1', 'Rora', 'Osbpl6', 'Kif1b', 'Thrb',\n", " 'Arhgap21', 'Camk1d', 'R3hdm1', 'Nbea', 'Ralyl', 'Anks1b', 'Efna5',\n", " 'Il1rapl2', 'Sptan1', 'Foxp1', 'Cadm1', 'Wasf1', 'Ppp1r12a',\n", " 'Grm8', 'Xkr6', 'Ncor1', 'Lyst', 'Frmpd4', 'Sipa1l1', 'Prr16',\n", " 'Kcnt2', 'Fam19a2', 'March6', 'Gria4', 'Mllt3', 'Gria2', 'Unc5d',\n", " 'Ssbp2', 'App', 'Matr3', 'Chsy3', 'Hecw2', 'Ube2w', 'Dok6', 'Tox',\n", " 'Sntg1', 'Lrfn5', 'Ppp1r9a', 'Runx2', 'Mdga2', 'Baz2b', 'Arl15',\n", " 'Snca', 'Tmem200a', 'Calm2', 'Tceal3', 'Ldlrad4', 'Alcam',\n", " 'Diaph2', 'Kctd1', 'Cdh11', 'Nckap1', 'Chl1', 'Nrcam', 'Gabrb2',\n", " 'Ppm1l', 'Arpp21', 'Oxr1', 'St6gal2', 'Epha6', 'Ppp3ca', 'Unc80',\n", " 'Cadps', 'Apba2', 'Adgrb3', 'Gabra2', 'Gabbr1', 'Npas2',\n", " '5730522E02Rik', 'Morf4l2', 'Cadm3', 'Capza2', 'Rock2', 'Fzd3',\n", " 'Trhde', 'Ube2k', 'Hspa8', 'Flrt2', 'Abi1', 'Trank1', 'Hprt',\n", " 'Gabra4', 'Erc2', 'Rab6a', 'Cux1', 'Abi2', 'Enc1', 'Hecw1',\n", " 'Cacna2d3', 'Cyfip1', 'Cdc42', 'Kcnc2', 'Prkacb', 'Homer1',\n", " 'Ube2e2', 'Nkain2', 'Pdp1', 'Herc1', 'Gabra5', 'Prkg1', 'Lrrc8b',\n", " 'Cnot2', 'Cdc42se2', 'Prickle1', 'Ahi1', 'Grm5', 'B3gat1', 'Kcnq3',\n", " 'Myh10', 'Man1a2', 'Large1', 'Psip1', 'Gnas', 'Negr1', 'Rgs17',\n", " 'Phf20l1', 'Cltc', 'Cdh12', 'Mir124-2hg', 'Cntnap2', 'Cntn1',\n", " 'Efnb2', 'Snap25', 'Brinp3', 'Syt14', 'Nol4', 'Pcdh9', 'Cadm2',\n", " 'Map1b', 'Fnbp1l', 'Syndig1', 'Nkain3', 'Lmo4', 'Gls', 'Mef2c',\n", " 'Robo2', 'Cd47', 'Mlf2', 'Nfib', 'Zfp365', 'Pdcd4', 'Bcl11a',\n", " 'Hcn1', 'Atrnl1', 'Tenm4', 'Pls3', 'Farp1', 'Rap1gds1', 'Myrip',\n", " 'Ptchd4', 'Magi1', 'Grip1', 'Kif21a', 'Kcnj3', 'Rasal2',\n", " '9530059O14Rik', 'Dock3', 'Ssh2', 'Gpc6', 'Xkr4', 'Arid5b',\n", " 'Sestd1', 'Stmn1', 'Elavl4', 'Rad23b', 'Sub1', 'Tspan7', 'March1',\n", " 'Atl1', 'Ralgapa2', 'Nrg3', 'Mapt', 'Nin', 'Kcnmb4', 'Auts2',\n", " 'Macrod2', 'Tiam2', 'Ctnna2', 'Kif5c', 'Pak7', 'Frmd6', 'Ppfia2',\n", " 'Plxna4', 'Nucks1', 'Gnao1', 'Map9', 'Rgl1', 'Akap11', 'Slc23a2',\n", " 'Atp6v1a', 'Dnaja1', 'Man2a1', 'Samd5', 'Ehbp1', 'Camta1',\n", " 'Dennd5b', 'Amph', 'Cpne8', 'Dmxl2', 'Grik2', 'Adcy8', 'Asap2',\n", " 'Map3k5', 'Ndrg3', 'Spred1', 'Pde10a', 'Snap91', 'Nrxn1',\n", " 'Osbpl1a', 'Rims1', 'Zfp706', 'Sobp', 'Ctnnd2', 'Eml5', 'Nrn1',\n", " 'Prmt8', 'Nckap5', 'Rtn3', 'Prkar1a', 'Ywhae', 'Zfp804b', 'Acvr1b',\n", " 'Ptpra', 'Spcs3', 'Dclk1', 'Kcnq5', 'Ppp3r1', 'Agtpbp1', 'Fstl4',\n", " 'Cacnb2', 'Setbp1', 'Grm7', 'Tcaf1', 'Ext1', 'Elmod1', 'Arf3',\n", " 'Etv1', 'C130071C03Rik', 'Cds1', 'Atp8a1', 'Lrrc4c', 'Zbtb18',\n", " 'Trim9', 'Gabrg3', 'Plcb1', 'Gnb2', 'Fgf14', 'Ywhaz', 'Srrm2',\n", " 'Dync1i1', 'Mtcl1', 'Hs6st3', 'Khdrbs2', 'Magi2', 'Rgs6', 'Celf1',\n", " 'Ptprk', 'C1ql3', 'Bex2', 'Sept11', 'Pak3', 'Meg3', 'Brinp1',\n", " 'Mapre3', 'Tceal6', 'Tsc22d1', 'Epha7', 'Pcsk2', 'Olfm2', 'Caln1',\n", " 'Sptbn2', 'Kcnd2', 'Ppm1h', 'Tnrc6c', 'Napb', 'Wdr17', 'Csmd2',\n", " 'Dlgap2', 'Mef2d', 'Fam19a1', 'Grin2b', 'Rasgef1b', 'Actr3',\n", " 'Kcnv1', 'Mtus2', 'Ptpn5', 'Gnaq', 'Sbf2', 'Scn8a', 'Fam155a',\n", " 'Mkl1', 'Syne1', 'Unc79', 'Necab1', 'Arntl', 'Arf1', 'Fam126b',\n", " 'Mkl2', 'Ptprd', 'Itpr1', 'Phactr1', 'Ncoa1', 'Dennd1a', 'Immp2l',\n", " 'Cttnbp2', 'Dpy19l1', 'Rap2b', 'Herc3', 'Park2', 'Zcchc7',\n", " 'Sorbs1', 'Epha5', 'Tshz3', 'Kirrel3', 'B4galt6', 'Gm20754',\n", " 'Esyt2', 'Atp5g3', 'Grm1', 'Nlk', 'Esrrg', 'Sms', 'Glrb',\n", " 'Prkar1b', 'Cdh6', 'Pfn2', 'Ppp2ca', 'Cplx2', 'Adcy2', 'Asap1',\n", " 'Pi4ka', 'Cacna1e', 'Mtpn', 'Ncam2', 'Pfkp', 'Rfx7', 'Robo1',\n", " 'Dlgap3', 'Gnl3l', 'Ptk2', 'Htr1f', 'Ociad1', 'Nwd2', 'Grin2a',\n", " 'Fhl2', 'Clasp2', 'Ola1', 'Gpm6b', 'Pan3', 'Arhgap26', 'Gucy1a2',\n", " 'Ank2', 'Slc35f1', 'C2cd5', 'Gsk3b', 'Lmo7', 'Nos1ap', 'Kcnh7',\n", " 'Vamp2', 'Serbp1', 'Pde8b', 'Pgm2l1', 'Rtn1', 'Frmd4a', 'Cntn4',\n", " 'Pvt1', 'Slc35f4', 'Osbpl3', 'Evi5', 'Ust', 'Calm1', 'Rtf1',\n", " 'Serpini1', 'Efr3a', 'Synj1', 'Nrg1', 'Actr2', 'Nebl', 'Pclo',\n", " 'Fbxo34', 'Dapk1', 'Pfkm', 'Pkm', 'Celf2', 'Sorcs3', 'Slitrk1',\n", " 'Gabra1', 'Adgrl1', 'Dgkh', 'Rorb', 'Lmtk2', 'Mbnl2', 'Hsp90ab1',\n", " '2010300C02Rik', 'Ube2b', 'Rtn4', 'Myo16', 'Chrm3', 'Slc24a2',\n", " 'Hs3st4', 'Scai', 'Serp2', 'Arhgap44', 'Fars2', 'Snph', 'Map2',\n", " 'Ncald', 'Ywhag', 'Ttc14', 'Abcc5', 'Atp6v1b2', 'Dst', 'Vps13b',\n", " 'Kcnab1', 'Tbc1d30', 'Appl1', 'Cacnb4', 'Lin7a', 'Chrm1', 'Ywhab',\n", " 'Trim37', 'Foxg1', 'Nrxn3', 'Klhl29', 'Cntn5', 'Gabrb1', 'Epha4',\n", " 'Clstn2', 'Arnt2', 'Dmd', 'Gnaz', 'Ppp1r2', 'Caly', 'Rasl11b',\n", " 'Slc2a13', 'Mapk1', 'Camkk2', 'Ppp2r2b', 'Gabrg2', 'Ywhah',\n", " 'Ptprt', 'Tcf4', 'Rnf14', 'Cers6', 'Map2k1', 'Prnp', 'Abr',\n", " 'Myo5a', 'Plekha5', 'Atp6v1c1', 'Kcnb1', 'Nptn', 'Dscaml1',\n", " 'Lpgat1', 'Set', 'Megf9', 'Lrrtm1', 'Ankhd1', 'Hdac4', 'Micu3',\n", " 'Pnisr', 'Kcnh1', 'Acap2', 'Abhd12', 'Plk2', 'Otud7a', 'Dock4',\n", " 'Usp46', 'Sh3gl2', 'Cap2', 'Exoc5', 'Zfp697', 'Tmx4', 'Mat2b',\n", " 'Tnik', 'Fmn2', 'Kcnip3', 'Nae1', 'Rnf150', 'Camk4', 'Mllt11',\n", " 'Rbfox1', 'AI593442', 'Tmem108', 'Adam22', 'Agbl4', 'Hace1',\n", " 'Macf1', 'Kitl', 'Cacna1c', 'Ildr2', 'Chn1', 'Nsf', 'Oxct1',\n", " 'Nuak1', 'Sorcs1', 'Dnajc6', 'Arfgef3', 'Kcnn2', 'Sun1', 'Slc4a3',\n", " 'Tusc3', 'Snrpn', 'Ptms', 'Lancl1', 'Gpm6a', 'Trio', 'Rab15',\n", " 'Spata7', 'Nedd4l', 'Bcl11b', 'Cyfip2', 'Cdk14', 'Galnt9',\n", " '1700020I14Rik', 'Meis2', 'Acvr2a', 'Pde7b', 'Csmd1', 'Nmnat2',\n", " 'Trim32', 'Pten', 'Etv5', 'Apba1', 'Mmp16', 'Mast2', 'Cpne4',\n", " 'Phf24', 'Dip2c', 'Etnk1', 'Gng13', 'Nme1', 'Ryr2', 'Cnih3',\n", " 'Tomm70a', 'Camkv', 'Zdhhc14', 'Med13l', 'Lrp1b', 'Tspoap1',\n", " 'Dynll1', '2900055J20Rik', 'Syn2', 'Rab2a', 'Slc6a17', 'Rab26',\n", " 'Syt13', 'Vti1b', 'Arhgap33', 'Sphkap', 'Rgs7', 'Ngef', 'Fry',\n", " 'Dlg2', 'Pbx1', 'Fras1', 'Pcdh7', 'Efhd2', 'Ogfrl1', 'Sybu',\n", " 'Actn1', 'Nell2', 'Ctnna3', 'Kcnh5', 'Miat', 'Dyrk2', 'Rgs7bp',\n", " 'Fam49a', 'Plxna2', 'Ddit4l', 'Snx10', 'Ak5', 'Frrs1l',\n", " 'Gabarapl1', 'Ssb', 'C230004F18Rik', 'Snn', 'Tspan13', 'Pde4dip',\n", " 'Lamp5', 'A830018L16Rik', 'Apbb1', 'Hdac9', 'Akap8l', 'Ndufa4',\n", " 'Dlg3', 'Mycbp2', 'Arhgef9', 'Ptpn3', 'Camk2g', 'Crmp1', 'Rapgef4',\n", " 'Ppia', 'Syn1', 'Adgrb1', 'Prickle2', 'Kcnh3', 'Dtnb', 'Hivep3',\n", " 'Tmem191c', 'Arid1b', 'Calb1', 'Smyd3', 'Kctd16', 'Dock9', 'Nrsn1',\n", " 'Syt7', 'Myl12b', 'Msra', 'Vopp1', 'Slc17a7', '2010111I01Rik',\n", " 'Dpp6', 'Fam193a', 'Serinc1', 'Ntrk2', 'Cacna1d', 'Fhod3',\n", " 'Atp5a1', 'Dgkb', 'Kcnma1', 'Pkd1', 'Fam49b', 'Arpp19', 'Cdh10',\n", " 'Vgf', 'Agap2', 'Hpca', 'Ckmt1', 'Ano4', 'Mical2', 'Atp5j',\n", " 'Adam23', 'R3hdm2', 'Fam135b', 'Dgkg', 'St3gal5', 'Tia1', 'Slit3',\n", " 'Gnb1', 'Lrrtm3', 'Ap2a2', 'Brk1', 'Sidt1', 'Sh3rf3', 'Gm1976',\n", " 'Shisa9', 'Prkce', 'Ptprs', 'Egfem1', 'Mir9-3hg', 'Pcdha1',\n", " '3110039M20Rik', 'Tspan5', 'Slit2', 'Ubb', 'Cadps2', 'Ncan',\n", " 'Unc13b', 'Rab12', 'Lrrc40', 'Dennd6b', 'Slc22a17', 'St13', 'Ank',\n", " 'Tceal5', 'Prkca', 'Pde4d', 'Pacsin1', 'Neurod2', 'Srcin1',\n", " 'Ube3c', 'N4bp2l2', 'Madd', 'Nr2f1', 'Cnrip1', 'Pde1a', 'Arpc2',\n", " 'Ptprj', 'Sec14l1', 'Psma1', 'Add2', 'Tspyl1', 'Syt16', 'Fxyd7',\n", " 'Ly6h', 'Mapk10', 'Cd34', 'Cdh18', 'Xist', 'Junb', 'Asph', 'Astn2',\n", " 'A330008L17Rik', 'Cit', 'Hsp90aa1', 'Psma3', 'Sgip1', 'Fam171b',\n", " 'Mink1', 'Pde4a', 'Ndst3', 'Celf5', 'Ogt', 'Mir124a-1hg', 'Coro1a',\n", " 'Nrgn', 'Galnt18', 'Ppp2r1a', 'Synpo', 'Kcnj6', 'Vsnl1',\n", " 'Tmem132d', 'Shank1', 'Rasgrp1', 'Actr3b', 'Gabrb3', 'C1qtnf4',\n", " 'Sv2b', 'Dlg4', 'Ndufs4', 'Iqsec2', 'Nov', 'Zmat4', 'Dcc', 'Tpm1',\n", " 'D430041D05Rik', 'Gda', 'Rasgef1a', 'Slc25a3', 'Sept8', 'Nav2',\n", " 'Sptbn4', 'Gabbr2', 'Pex5l', 'Nsg2', 'Ldha', 'Rasgrf1', 'Kcna2',\n", " 'Mkx', 'Sesn1', 'Samd12', 'Scn2b', 'Tsnax', 'Sel1l3', 'Vps29',\n", " 'Ksr2', 'Ano3', 'Eif4g3', 'Ksr1', '9330182L06Rik', 'Fcho1',\n", " 'Tubb2a', 'Chd5', 'Sort1', 'Svop', 'Hk1', 'Thy1', 'Gramd1b',\n", " 'Ube2ql1', 'D630045J12Rik', 'Me3', 'Gria3', 'Hsph1', 'Cntnap5a',\n", " 'Prkag2', 'Hspa12a', 'Prkcg', 'Egr3'], dtype=object),\n", " 'Jun(+)': array(['Crlf1', 'C1ql2', 'Tspan18', 'Slc39a6', 'Car12', 'Tanc1', 'Ehd3',\n", " 'Tdo2', 'Neurod1', 'Prdm8', 'Jun', 'Ptma', 'Sema5a', 'Bcl11b',\n", " 'Ak5', 'Pgbd5', 'Fam19a2', 'Tiam1', 'Dgkh', 'Epha7', 'Plxna4',\n", " 'C1ql3', 'Sybu', 'Cplx2', 'Prox1', 'Shisa6', 'Pter'], dtype=object),\n", " 'Junb(+)': array(['Cox5b', 'Enc1', 'Lingo1', 'Olfm1', 'Lmo4', 'Wasf1', 'Nckap1',\n", " 'Cfl1', 'Tubb2a', 'Synpo', 'Ppp3ca', 'Dclk1', 'Snap25', 'Atp1a3',\n", " 'Hsp90ab1', 'Atp1b1', 'Ptms', 'Nrgn', 'Ndrg4', 'Atp1a1', 'Cox8a',\n", " 'Calm2', 'Rtn1', 'Serinc1', 'Junb', 'Ube2ql1', 'Sorcs3', 'Lmo7',\n", " 'Sptbn2', 'Egr3'], dtype=object),\n", " 'Kdm5a(+)': array(['Grin2b', 'Meg3', 'Cntnap2', 'Malat1', 'Sox5', 'Cacna1c', 'Ptprd',\n", " 'Nrxn1', 'Grid2', 'Lsamp', 'Fgf14', 'Celf2', 'Kcnd2', 'Ctnna2',\n", " 'Dlg2', 'Grm7', 'Pcdh9', 'Prickle2'], dtype=object),\n", " 'Klf12(+)': array(['Dtna', 'Lrrc7', 'Akt3', 'Hivep2', 'Rims2', 'Ube3a', 'Sgcz',\n", " 'Kat6b', 'Gphn', 'Stag1', 'Adgrl3', 'Kansl1', 'Zfp385b', 'Zfp804a',\n", " 'Sema6d', 'Enox1', 'Atxn1', 'Stxbp5l', 'Maml2', 'Slc4a4',\n", " 'Runx1t1', 'Grid2', 'Ccser1', 'Ntng1', 'Cdh2', 'Fut9', 'Dab1',\n", " 'Nlgn1', 'Acsl3', 'Msi2', 'Fgf12', 'Grm3', 'Arap2', 'Rora', 'Thrb',\n", " 'Camk1d', 'Cadm1', 'Xkr6', 'Gria4', 'Garem1', 'Mllt3', 'Ssbp2',\n", " 'Gli3', 'Chsy3', 'Peak1', 'Nfia', 'Dok6', 'Tmem164', 'Sntg1',\n", " 'Dach1', 'Mdga2', 'Baz2b', 'Arl15', 'Zfp217', 'Zeb1', 'Nrcam',\n", " 'Gabrb2', 'Cadps', 'Adgrb3', 'Nxph1', 'Erbb4', 'Trhde', 'Erc2',\n", " 'Cacna2d3', 'Kcnc2', 'Ube2e2', 'Jarid2', 'Zfpm2', 'Rgs20',\n", " 'Ppp2r3a', 'Negr1', 'Phf20l1', 'Cdh12', 'Cntnap2', 'Thsd7a',\n", " 'Brinp3', 'Nol4', 'Pcdh9', 'Cadm2', 'Robo2', 'Nfib', 'Cdh20',\n", " 'Plekhg1', 'Tenm4', 'Farp1', 'Ptchd4', 'Magi1', 'Grip1', 'Glis3',\n", " 'Dock3', 'Mapk4', 'Gpc6', 'Xkr4', 'Nrg3', 'Ptprz1', 'Auts2',\n", " 'Macrod2', 'Ctnna2', 'Ehbp1', 'Camta1', 'Grik2', 'Unc5c', 'Pde10a',\n", " 'Nrxn1', 'Rims1', 'Ctnnd2', 'Csgalnact1', 'Utrn', 'Fstl4', 'Trps1',\n", " 'Grm7', 'Pcdh17', 'Ext1', 'Zc3h7a', 'C130071C03Rik', 'Sash1',\n", " 'Lrrc4c', 'Gabrg3', 'Plcb1', 'Fgf14', 'Srrm2', 'Hs6st3', 'Magi2',\n", " 'Rgs6', 'Meg3', 'Npas3', 'Kcnd2', 'Wdr17', 'Dlgap2', 'Anln',\n", " 'Sox6', 'Sbf2', 'Fam155a', 'Syne1', 'Ptprd', 'Sdk1', 'Dennd1a',\n", " 'Nrg2', 'Magi3', 'Park2', 'Zcchc7', 'Sorbs1', 'Kirrel3', 'Esrrg',\n", " 'Olfm3', 'Ncam2', 'Rfx7', 'Robo1', 'Srgap1', 'Lhfp', 'Cdk8',\n", " 'Clasp2', 'Tmtc2', 'Arhgap26', 'Ank2', 'Prkd1', 'Kcnh7', 'Frmd4a',\n", " 'Slc35f4', 'Gpc5', 'Nrg1', 'Nebl', 'Celf2', 'Rorb', 'Mast4',\n", " 'Ttc14', 'Tjp1', 'Vps13b', 'Arhgef26', 'Nrxn3', 'Cntn5', 'Gabrb1',\n", " 'Dmd', 'Ppp2r2b', 'Zfhx3', 'Ptprt', 'Phka1', 'Pitpnc1', 'Rbms3',\n", " 'Pnisr', 'Otud7a', 'Dock4', 'Sik3', 'Ccdc85a', 'Tnik', 'Fmn2',\n", " 'Rbfox1', 'Agbl4', 'Macf1', 'Cacna1c', 'Ptprm', '4930402H24Rik',\n", " 'Sorcs1', 'Kcnn2', 'Trpm3', 'Gli2', 'Pde7b', 'Csmd1', 'Mmp16',\n", " 'Appl2', 'Mcc', 'Lrp1b', 'Carmil1', 'Rgs7', 'Fry', 'Peg3', 'Dlg2',\n", " 'Pbx1', 'Pcdh7', 'Prex2', 'C230004F18Rik', 'Mycbp2', 'Tnrc6a',\n", " 'Camk2g', 'Bcl2', 'Hivep3', 'Arid1b', 'Smyd3', 'Lrch1', 'Kctd16',\n", " 'Msra', 'Dpp6', 'Fam193a', 'Ntrk2', 'Cacna1d', 'Neat1', 'Kcnma1',\n", " 'Lars2', 'Astn2', 'Cit', 'Dcc', 'Fgfr2', 'Lsamp', 'Cd44', 'Hdac8',\n", " 'Ptprg', 'Lama2', 'Pde4d', 'Ank1', 'Xylt1', 'Tia1', 'Cdh18',\n", " 'Samd12', 'Shisa9', 'Ptprn2', 'Ntm', 'Mertk', 'Shc3', 'Kcnip4',\n", " 'Pde4b', 'F3', 'Mapk10', 'Luzp2', 'Cpeb3', 'Sfxn5', 'Adarb2',\n", " 'Nav2', 'Rmst', 'Csmd3', 'Grik1', 'Cacna1a', 'N4bp2l2', 'Dock1',\n", " 'Asic2', 'Sox5', 'Dscam', 'Lrrtm4', 'Cntnap5a', 'Malat1', 'Sgip1',\n", " 'Egfem1', 'Pcdh15'], dtype=object),\n", " 'Klf9(+)': array(['Atp2b1', 'Fbxw7', 'Gphn', 'Sept7', 'Grin2b', 'Cnksr2', 'Rora',\n", " 'Nlgn1', 'Stxbp5l', 'Magi2', 'Camk1d', 'Syt1', 'Mef2c', 'Pcdh9',\n", " 'Dlgap1', 'Lsamp', 'Cadm2', 'Ppp3ca', 'Gnao1', 'Nrxn1', 'Prkce',\n", " 'Plcb1', 'Dclk1', 'Tcf4', 'Jarid2', 'Phactr1', 'Pitpnc1', 'Mdga2',\n", " 'Adgrb3', 'Enc1', 'Peak1', 'Syne1', 'Kcnq3', 'Tmtc2', 'Cyfip2',\n", " 'Cd44', 'Trpm3', 'Gria2', 'Zc3h7a', 'Cdk8', 'Qk', 'Calm1', 'Celf2',\n", " 'Slc24a2', 'Ywhah', 'Macf1', 'Chn1', 'Klf9', 'Cmss1', 'Malat1'],\n", " dtype=object),\n", " 'Lef1(+)': array(['Vangl1', 'Shox2', 'Edn3', 'Lef1', 'Adgrl4', 'Sh3d19', 'Prkcd',\n", " 'Mctp2', 'Vav3', 'Lhcgr', 'Synpo2', 'Olfm3', 'Ptpn3', 'Samd5',\n", " 'Rbms3', 'Atp10a', 'Lrrc7'], dtype=object),\n", " 'Lmx1b(+)': array(['Lmx1b', 'Foxa1', 'Sv2c', 'Crabp2', 'Drd2', 'Klhl1', 'Ddc', 'En1',\n", " 'Fmod', 'Ret', 'Th', 'Chrna6', 'Slc6a3', 'Chrna4'], dtype=object),\n", " 'Maf(+)': array(['Gad1', 'Igf2', 'Alx4', 'Erbb4', 'Rbms3', 'Gad2', 'Dlx1as',\n", " 'Atp1a2', 'Maf', 'Camk1d', 'Slc6a1', 'Fmod', 'Mrc1', 'Dlx6os1',\n", " 'Sst', 'Nxph1', 'Sema3b', 'D130009I18Rik', 'Slc13a4', 'Csf1r'],\n", " dtype=object),\n", " 'Mbnl2(+)': array(['Atp2b1', 'Cdk17', 'Lrrc7', 'Pafah1b1', 'Atf2', 'Akt3', 'Cnksr2',\n", " 'Galnt13', 'Hivep2', 'Rfx3', 'Fbxw7', 'Rims2', 'Ube3a', 'Lingo2',\n", " 'Sgcz', 'Tanc2', 'Gphn', 'Sept7', 'Adgrl3', 'Ttc3', 'Enox1',\n", " 'Syt1', 'Atxn1', 'Stxbp5l', 'Pja2', 'Ccser1', 'Dlgap1', 'Basp1',\n", " 'Dpp10', 'Cacna2d1', 'Dab1', 'Nlgn1', 'Numb', 'Car10', 'Fgf12',\n", " 'Cdkl5', 'Kif1b', 'Thrb', 'R3hdm1', 'Nbea', 'Ralyl', 'Anks1b',\n", " 'Sptan1', 'Foxp1', 'Wasf1', 'Ppp1r12a', 'Xkr6', 'Ncor1', 'Lyst',\n", " 'Frmpd4', 'Sipa1l1', 'Gria2', 'App', 'Chsy3', 'Hecw2', 'Dok6',\n", " 'Sntg1', 'Lrfn5', 'Ppp1r9a', 'Zeb2', 'Mdga2', 'Calm2', 'Nckap1',\n", " 'Nrcam', 'Gabrb2', 'Arpp21', 'Oxr1', 'Epha6', 'Ppp3ca', 'Tmeff2',\n", " 'Unc80', 'Cadps', 'Apba2', 'Adgrb3', '5730522E02Rik', 'Capza2',\n", " 'Rock2', 'Flrt2', 'Trank1', 'Erc2', 'Rab6a', 'Enc1', 'Hecw1',\n", " 'Cacna2d3', 'Frmd5', 'Homer1', 'Ube2e2', 'Nkain2', 'Jarid2',\n", " 'Herc1', 'Prickle1', 'Grm5', 'Kcnq3', 'Man1a2', 'Plcl1', 'Negr1',\n", " 'Phf20l1', 'Cdh12', 'Cntnap2', 'Cntn1', 'Snap25', 'Ttll7', 'Strn',\n", " 'Pcdh9', 'Cadm2', 'Map1b', 'Lmo4', 'Gls', 'Mef2c', 'Map7d2',\n", " 'Mlf2', 'Tenm4', 'Farp1', 'Kcnj3', 'Rasal2', 'Dock3', 'Ssh2',\n", " 'Gpc6', 'Xkr4', 'Sub1', 'March1', 'Nrg3', 'Mapt', 'Auts2',\n", " 'Macrod2', 'Ctnna2', 'Pak7', 'Ppfia2', 'Map9', 'Akap11', 'Atp6v1a',\n", " 'Camta1', 'Amph', 'Grik2', 'Nrxn1', 'Osbpl1a', 'Rims1', 'Ctnnd2',\n", " 'Eml5', 'Ywhae', 'Dclk1', 'Kcnq5', 'Ppp3r1', 'Agtpbp1', 'Grm7',\n", " 'Ext1', 'Zc3h7a', 'Atp8a1', 'Lrrc4c', 'Zbtb18', 'Trim9', 'Plcb1',\n", " 'Fgf14', 'Ywhaz', 'Hs6st3', 'Magi2', 'Meg3', 'Brinp1', 'Pcsk2',\n", " 'Edil3', 'Caln1', 'Kcnd2', 'Tnrc6c', 'Csmd2', 'Dlgap2', 'Fam19a1',\n", " 'Grin2b', 'Gnaq', 'Scn8a', 'Fam155a', 'Syne1', 'Unc79', 'Mkl2',\n", " 'Ptprd', 'Itpr1', 'Phactr1', 'Cttnbp2', 'Park2', 'Zcchc7',\n", " 'Kirrel3', 'Kcna1', 'Cacna1e', 'Ncam2', 'Ptk2', 'Cdk8', 'Grin2a',\n", " 'Clasp2', 'Tmtc2', 'Arhgap26', 'Ank2', 'C2cd5', 'Kcnh7', 'Serbp1',\n", " 'Pgm2l1', 'Rtn1', 'Frmd4a', 'Cntn4', 'Calm1', 'Rtf1', 'Nrg1',\n", " 'Actr2', 'Pclo', 'Celf2', 'Dgkh', 'Mbnl2', 'Hsp90ab1',\n", " '2010300C02Rik', 'Rtn4', 'Chrm3', 'Slc24a2', 'Mast4', 'Hs3st4',\n", " 'Scai', 'Ncald', 'Ttc14', 'Dst', 'Cacnb4', 'Nrxn3', 'Cntn5', 'Dmd',\n", " 'Mapk1', 'Ppp2r2b', 'Ywhah', 'Tcf4', 'Map7', 'Plekha5', 'Nptn',\n", " 'Dscaml1', 'Palm2', 'Micu3', 'Pnisr', 'Acap2', 'Sytl2', 'Otud7a',\n", " 'Dock4', 'Sh3gl2'], dtype=object),\n", " 'Mef2d(+)': array(['Snap25', 'Atp1a3', 'Hsp90ab1', 'Ppp3ca', 'Atp1b1', 'Ptms', 'Ttc3',\n", " 'Syt11', 'Dclk1', 'Map1b'], dtype=object),\n", " 'Msx1(+)': array(['Osr1', 'Wnt5a', 'Ahnak', 'Msx1', 'Atp1a2', 'Col1a1', 'Wnt6',\n", " 'Ptn', 'Cnn2', 'Serpinf1', 'Dab2', 'Bmp4', 'Id1', 'Fbln1', 'Mpzl2',\n", " 'Tnfrsf1a', 'Col1a2', 'Stab1', 'Pear1', 'Maf'], dtype=object),\n", " 'Mtf1(+)': array(['Stmn2', 'Gnas', 'Wasf1', 'Tceal3', 'Pafah1b1', 'Bex3', 'Vamp2',\n", " 'Snca', 'Lmo4', 'Rab6a', 'Calm2', 'Hspa8', 'Anks1b', 'Basp1',\n", " 'Gria2', 'Ppp3ca', 'Ppp3r1', 'Sub1', 'Morf4l2', 'Ndrg3', 'Ywhae',\n", " 'Nap1l1', 'Cdc42', 'Camkv', 'Atp6v1a', 'Ywhag', 'App', 'Capza2',\n", " 'Map1b', 'Rtn3', 'Cnih2', 'Prkar1b', 'Maged1', 'Nckap1', 'Arf3',\n", " 'Snap25', 'Stmn4', 'Hsp90ab1', 'Syp', 'Atxn10', 'Ppp2ca', 'Stmn1',\n", " 'Gap43', 'Ssh2', 'Caly', 'Ptms', 'Rimklb', 'Mllt11', 'Crmp1',\n", " 'Gpm6a', 'Slc25a4', 'Hpca', 'Lingo1', 'Bex2', 'Ywhaz', 'Reps2',\n", " 'Ywhah', 'Slc17a7', 'Tceal5', 'Pfn2', 'Syn2', 'Thra', 'Ncdn',\n", " 'Dnm1', 'Tspan13', 'Snrpn', 'Prkcg', 'Gria3', 'Enc1', 'Pgm2l1',\n", " 'Abr', 'Tmsb4x', 'Gdi1', 'Hpcal4', 'Serp2', 'Chn1', 'Eif1',\n", " 'Rpl17', 'Rpl29', 'Nsf', 'Itm2c', 'Chst1', 'Camk2n1', 'Mtch1',\n", " 'Rtn1', 'Arpc2', 'Atp6v1b2', 'Nptxr', 'Cox6c', 'Pkm', 'Cpne6',\n", " 'Atp5a1', 'Calm1', 'Ddn', 'Aplp1', 'Olfm1', 'Prnp', 'Ube2b',\n", " 'Dnaja1', 'Rpl37', 'Stmn3', 'Pitpna', 'Thy1', 'Eif5a', 'Syt11',\n", " 'Dynll1', 'Ckmt1', 'Nrgn', 'Tubb2a', 'Tubb5', 'Rps14', 'Atpif1',\n", " 'Cpne7', 'Atp6ap2', 'Ap2s1', 'Uchl1', 'Sult4a1', 'Ndufb6', 'Mtf1',\n", " 'Tspan3', 'Atp9a', 'Cox6b1', 'Tmsb10', 'Mapk1', 'Tpi1', 'Psmb1',\n", " 'Actr2', 'Selenow', 'Atp5g3', 'Ubb', 'Serinc1', 'Pde1a', 'Rpl13a',\n", " 'Eif4a2', 'Fbxl16', 'Gabarapl1', 'Nsg2', '4930481A15Rik', 'Ywhab',\n", " 'Frrs1l', 'Rtn4', 'Calm3', 'Chgb', 'Ldha', 'Atp5d', 'Sncb', 'Arf1',\n", " 'Rpl14', 'Cox17', 'Sdhb', 'Atp5g1', 'Gapdh', 'Gnb1', 'Rplp1',\n", " 'Gtf2h5', 'Tuba1b', 'Ap1s1', 'Rpl22l1', 'Wdr89', 'Tpt1', 'Cck',\n", " 'Mdh2', 'Atp1b1', 'Atp6v1g2', 'Celf4', 'Nme7', 'Gm21949', 'Apbb1',\n", " 'Rab3a', 'Rps17', 'Dctn3', 'Tomm7', 'Usmg5', 'Ndufa4', 'Napa',\n", " 'Atp5j', 'Brk1', 'Fam162a', 'Mt3', 'Nov', 'Klc1', 'Cox5b', 'Dctn2',\n", " 'Rps10', 'Rpl5', 'Rpl21', 'Pcsk1n', 'Actb', 'Crym', 'Mal2',\n", " 'Cox4i1', 'Atp5j2', 'Clta', 'Rrp1', 'Zwint', 'Ly6h', 'Snhg11',\n", " 'Gpi1', 'Cox5a', 'Myl6', 'Ndufv3', 'Hint1', 'Rpl11', 'Rprml',\n", " 'Vdac1', 'Hdac11', 'Mif', 'Dpysl2', 'Dynlrb1', 'Zbtb18',\n", " '2410015M20Rik', 'Rpl36', 'Ptk2b', 'Vps29', 'Ppia', 'Ndrg4',\n", " 'Tuba4a', 'Ndufb7', 'Atp6v1d', 'Sec11c', 'Rogdi', 'Hsp90aa1'],\n", " dtype=object),\n", " 'Nfatc4(+)': array(['Zic2', 'Ahnak', 'Bgn', 'Atp1a2', 'Col1a1', 'Crabp2', 'Bmp5',\n", " 'Fmod', 'Nfatc4', 'Bmp7', 'Slc13a4', 'Lama1', 'Perp', 'Vsx1',\n", " 'Hmgcs2', 'Rbp1', 'Ptgds', 'Slc47a1', 'Gjb2', 'Foxc2', 'Txnip',\n", " 'Vwf', 'Slc6a12', 'Fxyd5', 'Olfml2a', 'Slc16a9', 'Foxc1',\n", " 'Adamtsl3', 'Mpzl2', 'Gfap', 'Wfdc1', 'Wnk4', 'Asgr1', 'Wnt5a',\n", " 'Zic1', 'Pbxip1', 'Fgfbp1', 'Mgp', 'Lum', 'Apod', 'Krt80',\n", " 'C230024C17Rik', 'Tnfrsf1a', '1500015O10Rik', 'Anxa1', 'Islr',\n", " 'Fbln1', 'Myoc', 'Igf2', 'Fn1', 'Slc26a2', 'Slc6a20a', 'Col1a2',\n", " 'Colec12', 'Slc6a13', 'Lbp', 'Slc22a8'], dtype=object),\n", " 'Nfe2l2(+)': array(['Igf2', 'Plp1', 'Qk', 'Nfe2l2', 'Cldn11', 'Glul', 'Gfap', 'Ptgds',\n", " 'Mt1'], dtype=object),\n", " 'Nfib(+)': array(['Atp2b1', 'Zcchc11', 'Ube3a', 'Nr3c2', 'Rfx3', 'Cdk17', 'Dtna',\n", " 'Lrrc7', 'Tanc2', 'Akt3', 'Stag1', 'Cnksr2', 'Ccnd2', 'Adgrl3',\n", " 'Atxn1', 'Kansl1', 'Stxbp5l', 'Hivep2', 'Smarca2', 'Syt1', 'Maml2',\n", " 'Nbea', 'Mctp1', 'Epha6', 'Nlgn1', 'Wasf1', 'Dlgap1', 'Lingo2',\n", " 'Apba2', 'Basp1', 'Dab1', 'R3hdm1', 'Cacna2d1', 'Frmpd4', 'Gli3',\n", " 'Zfpm2', 'Ncor1', 'Sema5a', 'Cadm1', 'Mllt3', 'Ppp1r9a', 'Meg3',\n", " 'Large1', 'Msi2', 'Snca', 'Anks1b', 'Calm2', 'Slc4a4', 'Sipa1l1',\n", " 'Ywhaz', 'Prkg1', 'Gucy1a2', 'Arhgap21', 'Sub1', 'Ptprz1', 'Rora',\n", " 'Adgrb3', 'Pip5k1b', 'Csgalnact1', 'Ncoa1', 'Gria2', 'Diaph2',\n", " 'Npas2', 'Nfia', 'Garem1', '5730522E02Rik', 'Gabra5', 'Psip1',\n", " 'Unc80', 'Lmo4', 'Cdc42se2', 'Gabra2', 'Mdga2', 'Atrnl1', 'Glis3',\n", " 'Lyst', 'Pdcd4', 'Ncam1', 'Caln1', 'Dock3', 'Slc7a14', 'Ywhae',\n", " 'Erc2', 'Mapk4', 'Nfib', 'Matr3', 'Nckap1', 'Capza2', 'Eef1a1',\n", " 'Ssbp2', 'Pcdh9', 'Camkv', 'Dlgap2', 'Ctnna2', 'Rbfox1', 'Ppp3r1',\n", " 'Arpp21', 'Nrcam', 'Phf20l1', 'Ppp3ca', 'Nkain2', 'Zeb1', 'Srrm2',\n", " 'C130071C03Rik', 'Npas3', 'Nucks1', 'Dlg3', 'Plcb1', 'Atp6v1a',\n", " 'Mef2d', 'Magi2', 'Robo2', 'Dclk1', 'Auts2', 'Zbtb18', 'Ptprd',\n", " 'Xkr4', 'Arhgef28', 'Farp1', 'Bcl11b', 'Dapk1', 'Fam19a1',\n", " 'Zcchc7', 'Ppfia2', 'Zfp706', 'Nrxn1', 'Fgf14', 'Cplx2', 'Hs3st4',\n", " 'Rasal2', 'Brinp1', 'H3f3a', 'Anp32a', 'Gls', 'Pcsk5', 'Nrg3',\n", " 'Gnao1', 'Ube2e2', 'Fam155a', 'Fam49a', 'Cttnbp2', 'Rock2', 'Ptk2',\n", " 'Prox1', 'Trps1', 'Pgm2l1', 'Dennd1a', 'Rgl1', 'Setbp1', 'Frmd4a',\n", " 'Trim9', 'Negr1', 'Serbp1', 'Arf1', 'Ptpra', 'Sptbn2', 'Kirrel3',\n", " 'Ncam2', 'Abi1', 'Ctnnd2', 'Prkag2', 'Rgs20', 'Gm20754', 'Csmd1',\n", " 'Csnk1a1', 'Grm5', 'Arf3', 'Cadm2', 'Mkl2', 'Nkain3', 'Bex2',\n", " 'Tnrc6c', 'Mmp16', 'Celf2', 'Enc1', 'Kcnma1', 'Epha7', 'C1ql3',\n", " 'Wdr17', 'Plxna4', 'Grm7', 'Camk2b', 'Gnaq', 'Pde1a', 'Cnrip1',\n", " 'Cap2', 'Otud7a', 'Gabrb1', 'Iqgap2', 'Tsc22d1', 'Syt17', 'Sorbs1',\n", " 'Cacna1d', 'Eif5a', 'Foxo1', 'Rprm', 'Dscaml1', 'Tspan13', 'Lmo7',\n", " 'Nrp1', 'Hmgn3', 'Pitpnc1', 'Macrod2', 'Pkig', 'Cacng8', 'Acap2',\n", " 'Hpca', 'Cnih3', 'Grik2', 'Fam135b', 'Fars2', 'Dgkg', 'Thra',\n", " 'Csmd2', 'Slc44a5', 'Macf1', 'Actr3', 'Celf5', 'Grin2b', 'Foxg1',\n", " 'Nebl', 'Ywhab', 'Pnisr', 'Kcnn2', 'Ryr2', 'Caly', 'Gpm6a',\n", " 'Lsamp', 'Rtn1', 'Pter', 'Neat1', 'Cacna1c', 'Cnih2', 'Map2k1',\n", " 'Hsp90ab1', 'Park2', 'Neurod2', 'Prkd1', 'Celf3', 'Chn1', 'Fmn2',\n", " 'Dock4', 'Hmgb1', 'Tcf4', 'Syt7', 'Myo5b', 'Adgrb2', 'Kcnd2',\n", " 'Kcnh7', 'Tnik', 'Cers6', 'Arhgap26', 'Ptprg', 'Syne1', 'Dynll1',\n", " 'Ank2', 'Ncald', 'Plxna2', 'Asic2', 'Lrp1b', 'Tpm1', 'Nfix',\n", " 'Gabrb3', 'Calm1', 'Cacna1e', 'Sgip1', 'Trpm3', 'Egfem1', 'Ptms',\n", " 'Chst1', 'Gpc5', 'Phactr1', '2010300C02Rik', 'Syn2', 'Dpyd',\n", " 'Mertk', 'Mapk1', 'Serp2', 'Grin2a', 'Ptprt', 'Plekha5', 'Chd3',\n", " 'Shisa6', 'Dip2c', 'Arpc2', 'Klhl2', 'Plekhg5', 'Rbfox3', 'Astn2',\n", " 'Nsf', 'Clstn2', 'Kctd16', 'Ntrk3', 'Tle4', 'Nptx1', 'Appl2',\n", " 'Med26', 'Actb', 'Miat', 'Slc17a7', 'Hivep3', 'Agbl4', 'Ttll6',\n", " 'Dgkb', 'Nell2', 'Acvr2a', 'Tjp1', 'Mmp17', 'Ak5', 'Psma3',\n", " 'Ogfrl1', 'Ccdc85a', 'Stim2', 'Tceal5', 'Shisa9', 'Dmd', 'Nedd4l',\n", " 'Dlg2', 'Tpm3', 'Ahcyl2', 'Fbxl16', 'Fgd4', 'Zbtb16', 'Npy2r',\n", " 'Dsp', 'Meis2', 'Pbx1', 'A830018L16Rik', 'Rapgef4', 'Ptprj',\n", " 'Cyp7b1', 'Hrk', 'Mink1', 'Neurod1', 'Rasgef1a', 'Syngap1',\n", " 'Ppp1r1a', 'Slit3', 'Gria3', 'Rgs7bp', 'Slc1a2', 'Synpr', 'Sybu',\n", " 'Hpcal4', 'Itm2c', 'Fam171b', 'Prickle2', 'Add2', 'Pde4d', 'Cpne6',\n", " 'Ptk2b', 'Sox5', 'Myt1l', 'Prkce', 'Lama2', 'Mycbp2', 'Tubb2b',\n", " 'Id4', 'Prex2', 'Msra', 'Plppr4', 'Dgkh', 'Kcnip2', 'Tusc3', 'Fry',\n", " 'Plppr2', 'Neto1', 'Camk2a', 'Mmd', 'Nt5dc3', 'Me3', 'Galnt18',\n", " 'Adcy1', 'Ryr3', 'Tmem132b', 'Xist', 'Kcnip4', 'Csmd3', 'Celf4',\n", " 'Btbd9', 'Hnrnpa3', 'Nos1ap', 'Xylt1', 'Dbn1', 'Tspan18', 'Tmsb4x'],\n", " dtype=object),\n", " 'Nfyb(+)': array(['Slc8a2', 'Lypd1', 'Rbfox3', 'Egr3', 'Adgrb2', 'Lin7c', 'Mmp17',\n", " 'Crmp1', 'Pop5', 'Gpr39', 'Plppr2', 'Abr', 'C1qtnf4', 'Syt7',\n", " 'Atp2b4', 'Mef2d', 'Prkar1a'], dtype=object),\n", " 'Nkx6-1(+)': array(['Igf2', 'Foxd2', 'C1ql4', 'Apod', 'S100a9', 'Tagln'], dtype=object),\n", " 'Nkx6-2(+)': array(['Ddr1', 'Mobp', 'Nmral1', 'Gltp', 'Vmp1', 'Mbp', 'Hcn2',\n", " 'Arhgap23', 'Sept4', 'Cdc42ep2', 'Myrf', 'Jam3', 'Csrp1', 'Tubb4a',\n", " 'Ugt8a', 'Gpm6b', 'Rnf13', 'Gpr37', 'Inpp5a', 'Myo6', 'Pls1',\n", " 'Dock10', 'Gjc3', 'Qdpr', 'Efnb3', 'Grb14', 'Rap1a', 'Rpl37a',\n", " 'Pde4b', 'Car2', 'Pdlim2', 'Dbi', 'Pip4k2a', 'Phldb1', 'Cdk8',\n", " 'Otud7b', 'Serpinb1a', 'Atp1b3', 'Slc38a2', 'Mapk8ip1', 'Ppp1r14a',\n", " 'Cntn2', 'Glul', 'Rtkn', 'Gatm', 'Fa2h', 'Evi2a', 'Tmbim1', 'Ermn',\n", " 'S100a16', 'Stmn4', 'Cd81', 'Nkx6-2', 'Tnni1', 'Gsn', 'Unc5b',\n", " 'Tmem63a', 'Mag', 'Ppp1r16b', 'Traf5', 'Plekhb1', 'Tmem88b',\n", " 'Rhog', 'Bin1', 'Cers2', 'Ndrg1', 'S100b', 'Apod', 'Smad7', 'Mcam',\n", " 'Ninj2', 'Lgi3', 'Lpar1', 'Efhd1', 'Ptma', 'Mal', 'Scd2', 'Cmtm5',\n", " 'Foxn3', 'Josd2', 'Qk', 'Plekha1', 'Tsc22d4', 'Sept7', 'Slc44a1',\n", " 'Lamp1', 'Cldn11', 'Clic4', 'Plp1', 'Pllp', 'Kcnj10', 'Gjb1',\n", " 'Sgk1', 'Slc12a2', 'Il33', 'Gjc2', 'Tfeb', 'Slc6a9', 'Cd82',\n", " 'Adssl1', 'Sema4d', 'Plekhg3', 'Gng11', 'Micall1', 'Gstm7',\n", " 'Ypel2', 'Scd1', 'Cdc42ep1', 'Gm15417', 'Erbb3', 'Tmem229a',\n", " 'Plxdc2', 'Klk6', 'Kazn', 'Map7d1', 'Wnk1', 'S100a1', 'Phgdh',\n", " 'Agpat4', 'Tmcc3', 'Tmem123', 'Phlda3', 'Mog', 'Degs1', 'S100a13',\n", " 'Dock5', 'Arsg', 'Padi2', 'Dixdc1', 'Galnt6', 'Litaf', 'Cdr2l',\n", " 'Opalin', 'Aph1a', 'S1pr5', 'Lap3', 'Trf', 'Ddit4', 'Olfml1',\n", " 'Trim59', 'Prr18', 'Cnp', 'Sec14l5', 'Npc2', 'Gprc5b', 'Cryab',\n", " 'Gal3st1', '9630013A20Rik', 'Tspan2', 'Rdx', 'Cdh19', 'Cd9',\n", " 'Klf13', '5031439G07Rik', 'Daam1', 'Pde8a', 'Arrdc3', 'Hapln2',\n", " 'Rhou', 'Mtmr2', 'Zdhhc9', 'Cd63', 'Serinc5', 'Pla2g16', 'Slain1',\n", " 'Bcas1', 'Rnf7', 'Ppp2r3a', 'Sh3gl3', 'Itgad', 'Dbndd2', 'Olig2',\n", " 'Nudt4', '2310022B05Rik', 'Kif13a', 'Cyp2j12', 'Epb41l3',\n", " 'Slc48a1', 'Hhip', 'Pacs2', 'Fth1', 'Olig1', 'Endod1', 'Ddc',\n", " 'Elovl1', 'Sox10', 'Nfasc', 'Tmeff2', 'Kcna1', 'St18', 'Edil3',\n", " 'Phactr3', 'Elmo1', 'Gphn', 'Itpk1', 'Ano4', 'D7Ertd443e',\n", " 'Plxnb3', 'Ptgds', 'Fez1', 'Secisbp2l', 'Kndc1', 'Hipk2', 'Creb5',\n", " 'Kif5a', 'Arhgef10', 'Map7', 'Slc6a3', 'Reep3', 'Sparc', 'Sec11c',\n", " 'Hepacam', 'Chd7', 'BC049715', 'Rcan2', 'Zfp536', 'Enpp2', 'Daam2',\n", " 'Selenop', 'Plekhh1', 'Wscd1', 'Cd44', 'Plcl1', 'Frmd8', 'Gfap',\n", " 'Carhsp1', 'Rab31', 'Arpc1b', 'Tmem125', 'Aldh1a1', 'Psat1',\n", " 'Sez6l2', 'Ccp110', 'Clasp2'], dtype=object),\n", " 'Nr2f1(+)': array(['Meg3', 'Cdk17', 'Atp2b1', 'Stmn2', 'Cnksr2', 'Syt1', 'Ttc3',\n", " 'Fbxw7', 'Tceal3', 'Pja2', 'Bex3', 'Nrn1', 'Vamp2', 'Snca',\n", " 'R3hdm1', 'Tceal9', 'Auts2', 'Slc39a10', 'Lmo4', 'Erc2', 'Rab6a',\n", " 'Calm2', 'Hspa8', 'Cadm1', 'Basp1', 'Prmt8', 'Abi2', 'Gria2',\n", " 'Gabbr1', 'Cadm3', 'Ppp3ca', 'Sipa1l1', 'Ppp3r1', 'Sub1', 'Scn8a',\n", " 'Ptprs', 'Ndrg3', 'Brinp1', 'Ywhae', 'Serpini1', 'Hivep2',\n", " 'Nap1l1', 'Mef2c', 'Cdc42', 'Tspan7', 'Camkv', 'Chrm1', 'Nrxn1',\n", " 'Atp6v1a', 'Gabra4', 'Ywhag', 'Cd47', 'Prkacb', 'Dlgap1', 'App',\n", " 'Cadm2', 'Nrxn3', 'Vgf', 'Egr3', 'Unc5d', 'Celf5', 'Anp32a',\n", " 'Car10', 'Prkar1b', 'Slc22a17', 'Nckap1', 'Ppp1r9a', 'Tceal6',\n", " 'Nlgn3', 'Spred1', 'Arf3', 'Adgrl1', 'Nrcam', 'Cplx2', 'Nrxn2',\n", " 'Gnao1', 'Grm5', 'Tmem191c', 'Myh7', 'Cacna2d1', 'Snap25', 'Stmn4',\n", " 'Slitrk1', 'Grin2a', 'Nptn', 'Dlg3', 'Hsp90ab1', 'Grin2b', 'Tcf4',\n", " 'Cdk5r2', 'Syp', 'Adgrb1', 'Homer1', 'Sptan1', 'Sqstm1', 'Gpm6b',\n", " 'Mir124a-1hg', 'Stmn1', 'Phactr1', 'Ptprd', 'Pcsk2', 'Fzd3',\n", " 'Camk2b', 'Caly', 'Ptms', 'Crmp1', 'Syngap1', 'Gpm6a', 'Hpca',\n", " 'Rab33a', 'Ptn', 'Necab1', 'Lingo1', 'Bex2', 'Osbpl1a', 'Ttyh1',\n", " 'Ywhaz', 'Etv5', 'Ywhah', 'Slc17a7', 'Tceal5', 'Serbp1', 'Cabp1',\n", " 'Pter', 'Tsc22d1', 'Cux2', 'Syn2', 'Sptbn2', 'Thra', 'Napb',\n", " 'Dnm1', 'Tmem50a', 'Arpp21', 'Tpm3', 'Gria3', 'Enc1', 'Stxbp1',\n", " 'Kcnv1', 'Pgm2l1', 'Abr', 'Fam49b', 'Syt7', 'Lingo2', 'Map2k1',\n", " 'Farp1', 'Hpcal4', 'Serp2', 'Chn1', 'Snn', 'AI593442', 'Celf1',\n", " 'Ttc9b', 'Eif1', 'Dmtn', 'Madd', 'Zfp706', 'Eef1a2', 'Negr1',\n", " 'Gng13', 'Itm2c', 'Cacnb3', 'Camk2a', 'Kcnb1', 'Chst1', 'Camk2n1',\n", " 'Grik5', 'Svop', 'Rtn1', 'Akap8l', 'Arpc2', 'Atp6v1b2', 'Mtpn',\n", " 'Camta2', 'Fscn1', 'Tenm2', 'Nptxr', 'Syn1', 'Phf24', 'Slc12a5',\n", " 'Camkk2', 'Pkm', 'Nr2f1', 'Tusc3', 'Epha4', 'Calm1', 'Ddn',\n", " 'Aplp1', 'Shank2', 'Fxyd7', 'Olfm1', 'Ngef', 'Sorbs2',\n", " 'A830082K12Rik', 'Prnp', 'Tenm4', 'Kcnip4', 'Ncald', 'Marc2',\n", " 'Thy1', 'Rap2b', 'Shank1', 'Slc1a2', 'Eif5a', 'Syt11', 'C1qtnf4',\n", " 'Dynll1', 'Ntm', 'Dlk2', 'Ppp1r1a', 'Synpo', 'Syt16', 'Marcks',\n", " 'Nrgn', 'Rasgrp1', 'Tspan5', 'Tubb2a', 'Tubb5', 'Camk4', 'Atpif1',\n", " 'Coro1a', 'Vsnl1', 'Ypel3', 'Atp2b2', 'Atp6ap2', 'Dbp',\n", " '2010300C02Rik', 'Uchl1', 'Sult4a1', 'Cyfip2', 'Nek6', 'Prkcb',\n", " 'Ids', 'Dclk1', 'Plxnd1', 'Atp9a', 'Mapk1', 'Tpi1', 'Miat',\n", " 'Ptprn', 'Nsg1', 'Gas7', 'Ubb', 'Micu3', 'Serinc1', 'Mef2d',\n", " 'Phyhip', 'Calb1', 'Ext1', 'Golga7b', 'Fbxl16', 'Sptbn1', 'Gabbr2',\n", " 'Gabarapl1', 'Nsg2', 'Ywhab', 'Frrs1l', 'Clstn1', 'Prex1', 'Cfl1',\n", " '6330403K07Rik', 'Faim2', 'Rtn4', 'Icam5', 'Stx1a', 'Fam212b',\n", " 'Fxyd6', 'Calr', 'Cspg5', 'Arpp19', 'Grin1', 'Dusp14', 'Scn2a',\n", " 'Vdac1', 'Bsn', 'Slc1a3', 'Strn4', 'Klc1', 'Pcsk1n', 'Mapk8ip3',\n", " 'Rbfox3', 'Tpm1', 'Tuba1a', 'Cbx6', 'Ppia', 'Tbc1d9', 'Calm3',\n", " 'Pi4ka', 'Kctd17', 'Kifap3', 'Apbb1', 'Nme1', 'Clu', 'Car4',\n", " 'Ap3m2'], dtype=object),\n", " 'Nr2f2(+)': array(['Cnr1', 'Bex3', 'Stmn2', 'Nr2f2', 'Basp1', 'Camk2d', 'Snca',\n", " 'Gnas', 'Bex1', 'Ywhae', 'Camkv', 'Tceal9', 'Zcchc12', 'Pgrmc1',\n", " 'Bex2', 'Eif5a', 'Rtn1', 'Cnih2', 'Dynll1', 'Penk', 'Syn2',\n", " 'Hpcal1', 'Tceal5', 'Tubb2a', 'Pnck', 'Nlgn3', 'Calb1', 'Gjb2',\n", " 'Mtch1', 'Cpne7', 'Rpl21', 'Calb2', 'Itm2c', 'Col1a1', 'Ndn',\n", " 'Nov', 'Ly6h', 'Tuba1b', 'Fxyd6', 'Cpne6', 'Stum', 'Pcolce',\n", " 'Timp2', 'Gap43', 'Pea15a', 'Nptxr', 'Uchl1', 'Pnmal2', 'Nsg2',\n", " 'Fmod', '6330403K07Rik', 'Icam5', 'Lypd1', 'Rpl22l1', 'Igf2',\n", " 'Pcdh19'], dtype=object),\n", " 'Nr3c1(+)': array(['Nr3c1', 'Atp2b1', 'R3hdm1', 'Sept7', 'Kcnd2', 'Ldlrad4', 'Homer1',\n", " 'Mef2c', 'Kctd1', 'Pcdh9', 'Nrxn1', 'Plcb1', 'Tcf4', 'Ppp2r2b',\n", " 'Phactr1', 'Ptprd', 'Celf2', 'Osbpl1a', 'Kcnip4', 'Pde1a', 'Ntm',\n", " 'Pcsk2', 'Dlg2', 'Camk4', 'Arhgap26', 'Clstn1', 'Nrgn', 'Sv2b',\n", " 'Rgs6', 'Kctd16', 'Sub1'], dtype=object),\n", " 'Ovol2(+)': array(['R3hdm1', 'Snap25', 'Syt1', 'Mef2c', 'Car10', 'Pcsk2', 'Camk2n1',\n", " 'Nrg1', 'Phactr1', 'Vsnl1', 'Lmo4', 'Col8a1', 'Dclk1', 'Slc17a7',\n", " 'Arpp21', 'Nrgn', 'Ovol2', 'Kcnh7', 'Egr1', 'Kalrn'], dtype=object),\n", " 'Pbx1(+)': array(['Atp2b1', 'Dtna', 'Lrrc7', 'Akt3', 'Cnksr2', 'Hivep2', 'Rims2',\n", " 'Ube3a', 'Sgcz', 'Tanc2', 'Gphn', 'Stag1', 'Adgrl3', 'Kansl1',\n", " 'Ptpn4', 'Ttc3', 'Sema6d', 'Syt1', 'Stxbp5l', 'Slc4a4', 'Grid2',\n", " 'Ccser1', 'Mctp1', 'Cdh2', 'Dlgap1', 'Basp1', 'Foxp2', 'Fut9',\n", " 'Dpp10', 'Dab1', 'Nlgn1', 'Acsl3', 'Msi2', 'Fgf12', 'Grm3', 'Pam',\n", " 'Rora', 'Thrb', 'Camk1d', 'R3hdm1', 'Nbea', 'Ralyl', 'Anks1b',\n", " 'Foxp1', 'Cadm1', 'Xkr6', 'Frmpd4', 'Prr16', 'Kcnt2', 'Garem1',\n", " 'Gria2', 'Ssbp2', 'Gli3', 'Chsy3', 'Peak1', 'Nfia', 'Hecw2',\n", " 'Dok6', 'Sntg1', 'Mdga2', 'Zfp217', 'Zeb1', 'Cdh11', 'Nrcam',\n", " 'Gabrb2', 'Arpp21', 'Oxr1', 'Unc80', 'Cadps', 'Apba2', 'Adgrb3',\n", " 'Npas2', '5730522E02Rik', 'Dnm3', 'Erbb4', 'Lrba', 'Erc2',\n", " 'Cacna2d3', 'Homer1', 'Nkain2', 'Jarid2', 'Zfpm2', 'Herc1',\n", " 'Rgs20', 'Prickle1', 'Grm5', 'Kcnq3', 'Large1', 'Negr1', 'Cntnap2',\n", " 'Cntn1', 'Brinp3', 'Syt14', 'Nol4', 'Pcdh9', 'Cadm2', 'Map1b',\n", " 'Lmo4', 'Gls', 'Robo2', 'Nfib', 'Cdh20', 'Atrnl1', 'Tenm4',\n", " 'Farp1', 'Rasal2', 'Dock3', 'Mapk4', 'Gpc6', 'Xkr4', 'Tspan7',\n", " 'March1', 'Nrg3', 'Thsd7b', 'Ptprz1', 'Auts2', 'Macrod2', 'Ctnna2',\n", " 'Gnao1', 'Ehbp1', 'Camta1', 'Asap2', 'Pde10a', 'Nrxn1', 'Rims1',\n", " 'Ctnnd2', 'Csgalnact1', 'Nckap5', 'Rtn3', 'Dclk1', 'Kcnq5',\n", " 'Tmsb10', 'Setbp1', 'Grm7', 'Fam184a', 'Ext1', 'Zc3h7a',\n", " 'C130071C03Rik', 'Lrrc4c', 'Trim9', 'Gabrg3', 'Plcb1', 'Fgf14',\n", " 'Paqr8', 'Srrm2', 'Khdrbs2', 'Magi2', 'Rgs6', 'Meg3', 'Npas3',\n", " 'Caln1', 'Kcnd2', 'Wdr17', 'Csmd2', 'Dlgap2', 'Grin2b', 'Sox6',\n", " 'Gnaq', 'Sbf2', 'Fam155a', 'Syne1', 'Unc79', 'Pard3b', 'Ptprd',\n", " 'Phactr1', 'Dennd1a', 'Magi3', 'Cttnbp2', 'Park2', 'Zcchc7',\n", " 'Sorbs1', 'Epha5', 'Esrrg', 'Adcy2', 'Asap1', 'Cacna1e', 'Ncam2',\n", " 'Pfkp', 'Robo1', 'Ptk2', 'Cdk8', 'Grin2a', 'Tmtc2', 'Arhgap26',\n", " 'Gucy1a2', 'Ank2', 'Slc35f1', 'Dnajc1', 'Nos1ap', 'Kcnh7',\n", " 'Pgm2l1', 'Frmd4a', 'Cntn4', 'Evi5', 'Gpc5', 'Efr3a', 'Nrg1',\n", " 'Pclo', 'Celf2', 'Rorb', 'Lmtk2', 'Kcnd3', 'Hs3st4', 'Scai',\n", " 'Map2', 'Tjp1', 'Dst', 'Vps13b', 'Cdc73', 'Cacnb4', 'Nrxn3',\n", " 'Cntn5', 'Gabrb1', 'Dmd', 'Slc1a2', 'Ppp2r2b', 'Ptprt', 'Tcf4',\n", " 'Phka1', 'Pitpnc1', 'Myo5a', 'Plekha5', 'Hdac4', 'Pnisr', 'Kcnh1',\n", " 'Otud7a', 'Dock4', 'Sh3gl2', 'Sik3', 'Tnik', 'Fmn2', 'Camk4',\n", " 'Rbfox1', 'Agbl4', 'Pid1', 'Macf1', '4930402H24Rik', 'Kcnn2',\n", " 'Gpm6a', 'Trpm3', 'Gli2', 'Galnt9', 'Meis2', 'Csmd1', 'Nmnat2',\n", " 'Pten', 'Etv5', 'Mmp16', 'Phf24', 'Dip2c', 'Appl2', 'Mcc', 'Lrp1b',\n", " 'Cdc42bpa', 'A330033J07Rik', 'Rgs7', 'Fry', 'Peg3', 'Dlg2', 'Pbx1',\n", " 'Pcdh7', 'Miat', 'Prex2', 'Plxna2', 'C230004F18Rik',\n", " 'A830018L16Rik', 'Camk2g', 'Prickle2', 'Bcl2', 'Tle4', 'Hivep3',\n", " 'Smyd3', 'Kctd16', 'Plpp3', 'Slc17a7', '2010111I01Rik', 'Ntrk2',\n", " 'Cacna1d', 'Nr4a2', 'Astn2', 'Kcnj6', 'Dlk1', 'Xylt1', 'Cdh18',\n", " 'Gabrb3', '9330182L06Rik', 'Satb2', 'Dcc', 'Kcnn3', 'Sgcd', 'Sdk2',\n", " 'Adam23', 'Slc6a3', 'Tnr', 'Th', 'Wnk2', 'Mical2', 'En1',\n", " 'Ppp1r12b', 'Nav2', 'Ogt', 'Fgfr2', 'Nyap2', 'Grik3', 'Galnt18',\n", " 'Cntnap5a', 'Fam189a1', 'Slc10a4', 'Malat1'], dtype=object),\n", " 'Pbx3(+)': array(['AW551984', 'C130021I20Rik', 'Slc39a4', 'Serpine2', 'Gucy2c',\n", " 'Pcbd1'], dtype=object),\n", " 'Pknox2(+)': array(['Lrrc7', 'Akt3', 'Cnksr2', 'Hivep2', 'Rfx3', 'Fbxw7', 'Rims2',\n", " 'Ube3a', 'Lingo2', 'Sgcz', 'Tanc2', 'Stag1', 'Adgrl3', 'Zfp385b',\n", " 'Ttc3', 'Syt1', 'Atxn1', 'Stxbp5l', 'Ccser1', 'Dlgap1', 'Fut9',\n", " 'Dpp10', 'Cacna2d1', 'Dab1', 'Nlgn1', 'Car10', 'Fgf12', 'Pparg',\n", " 'Rora', 'Thrb', 'R3hdm1', 'Nbea', 'Ralyl', 'Anks1b', 'Efna5',\n", " 'Sptan1', 'Foxp1', 'Cadm1', 'Ppp1r12a', 'Frmpd4', 'Sipa1l1',\n", " 'Prr16', 'Kcnt2', 'Fam19a2', 'Gria4', 'Gria2', 'Unc5d', 'Chsy3',\n", " 'Dok6', 'Sntg1', 'Lrfn5', 'Mdga2', 'Calm2', 'Nrcam', 'Arpp21',\n", " 'Oxr1', 'Epha6', 'Ppp3ca', 'Unc80', 'Cadps', 'Apba2', 'Adgrb3',\n", " 'Trhde', 'Trank1', 'Erc2', 'Cux1', 'Hecw1', 'Cacna2d3', 'Ube2e2',\n", " 'Nkain2', 'Herc1', 'Prkg1', 'Grm5', 'Kcnq3', 'Negr1', 'Cdh12',\n", " 'Cntnap2', 'Snap25', 'Brinp3', 'Syt14', 'Nol4', 'Pcdh9', 'Cadm2',\n", " 'Fnbp1l', 'Lmo4', 'Gls', 'Mef2c', 'Robo2', 'Atrnl1', 'Tenm4',\n", " 'Myrip', 'Grip1', 'Kcnj3', 'Rasal2', '9530059O14Rik', 'Dock3',\n", " 'Xkr4', 'Arid5b', 'Nrg3', 'Auts2', 'Macrod2', 'Ctnna2', 'Ppfia2',\n", " 'Ehbp1', 'Camta1', 'Grik2', 'Pde10a', 'Snap91', 'Nrxn1', 'Rims1',\n", " 'Ctnnd2', 'Dclk1', 'Kcnq5', 'Fstl4', 'Grm7', 'Ext1', 'Lrrc4c',\n", " 'Gabrg3', 'Plcb1', 'Fgf14', 'Srrm2', 'Hs6st3', 'Magi2', 'Meg3',\n", " 'Brinp1', 'Pcsk2', 'Kcnd2', 'Ppm1h', 'Csmd2', 'Dlgap2', 'Fam19a1',\n", " 'Grin2b', 'Scn8a', 'Fam155a', 'Syne1', 'Ptprd', 'Phactr1',\n", " 'Cttnbp2', 'Park2', 'Zcchc7', 'Epha5', 'Kirrel3', 'Prkar1b',\n", " 'Asap1', 'Cacna1e', 'Ncam2', 'Grin2a', 'Arhgap20', 'Arhgap26',\n", " 'Gucy1a2', 'Ank2', 'Kcnh7', 'Vamp2', 'Rtn1', 'Frmd4a', 'Cntn4',\n", " 'Osbpl3', 'Calm1', 'Nrg1', 'Celf2', 'Chrm3', 'Tbc1d30', 'Cacnb4',\n", " 'Nrxn3', 'Klhl29', 'Cntn5', 'Gabrb1', 'Clstn2', 'Slc2a13', 'Mapk1',\n", " 'Ppp2r2b', 'Ptprt', 'Tcf4', 'Plekha5', 'Kcnb1', 'Nptn', 'Otud7a',\n", " 'Zbtb16', 'Tnik', 'Fmn2', 'Camk4', 'Rbfox1', 'AI593442', 'Tmem108',\n", " 'Adam22', 'Agbl4', 'Cacna1c', 'Chn1', 'Camkk2'], dtype=object),\n", " 'Pou3f2(+)': array(['Ntm', 'Pou3f2', 'Slc6a3', 'Dlk1', 'Slc1a2', 'Ret', 'Gpc5', 'Glul',\n", " 'Ptn', 'En1', 'Msi2', 'Fam107a', 'Plpp3', 'Ddc', 'Aldh1a1'],\n", " dtype=object),\n", " 'Prox1(+)': array(['Kif26b', 'Slc39a6', 'Prox1', 'Lmo1', 'Stxbp6', 'Nt5dc2'],\n", " dtype=object),\n", " 'Prrx2(+)': array(['Tbx15', 'Slc26a7', 'H2-Aa', 'Car13', 'Igf2', 'Lbp', 'Zic2',\n", " 'Slc38a2', 'Anxa3', 'Col1a2', 'Ddr2'], dtype=object),\n", " 'Satb2(+)': array(['Kmt2e', 'Atp2b1', 'Slc39a10', 'Cdk17', 'Satb1', 'Lrrc7', 'Akt3',\n", " 'Cnksr2', 'Galnt13', 'Hivep2', 'Smarca2', 'Fbxw7', 'Rims2',\n", " 'Ube3a', 'Lingo2', 'Sgcz', 'Tanc2', 'Stag1', 'Sept7', 'Adgrl3',\n", " 'Kansl1', 'Zfp385b', 'Ttc3', 'Syt1', 'Atxn1', 'Stxbp5l', 'Pja2',\n", " 'Zfr', 'Ppp3cb', 'Ccser1', 'Dlgap1', 'Basp1', 'Btbd10', 'Fut9',\n", " 'Dpp10', 'Cacna2d1', 'Dab1', 'Nlgn1', 'Numb', 'Car10', 'Cdh8',\n", " 'Fgf12', 'Grm3', 'Pparg', 'Cdkl5', 'Pam', 'Thrb', 'Camk1d',\n", " 'R3hdm1', 'Nbea', 'Ralyl', 'Anks1b', 'Efna5', 'Il1rapl2', 'Sptan1',\n", " 'Foxp1', 'Cadm1', 'Wasf1', 'Ppp1r12a', 'Frmpd4', 'Sipa1l1',\n", " 'Prr16', 'Kcnt2', 'Fam19a2', 'Gria4', 'Gria2', 'Unc5d', 'Myo1b',\n", " 'App', 'Chsy3', 'Hecw2', 'Sntg1', 'Lrfn5', 'Ppp1r9a', 'Mdga2',\n", " 'Nectin3', 'Calm2', 'Ldlrad4', 'Kctd1', 'Nckap1', 'Nrcam',\n", " 'Gabrb2', 'Ppm1l', 'Arpp21', 'Oxr1', 'St6gal2', 'Ppp3ca', 'Unc80',\n", " 'Cadps', 'Apba2', 'Adgrb3', 'Npas2', '5730522E02Rik', 'Cadm3',\n", " 'Rock2', 'Fzd3', 'Trhde', 'Flrt2', 'Trank1', 'Gabra4', 'Erc2',\n", " 'Rab6a', 'Abi2', 'Enc1', 'Hecw1', 'Cacna2d3', 'Prkacb', 'Homer1',\n", " 'Ube2e2', 'Herc1', 'Prickle1', 'Grm5', 'Kcnq3', 'Large1', 'Negr1',\n", " 'Cdh12', 'Spats2l', 'Snap25', 'Brinp3', 'Syt14', 'Pcdh9', 'Cadm2',\n", " 'Fnbp1l', 'Lmo4', 'Gls', 'Mef2c', 'Robo2', 'Bcl11a', 'Atrnl1',\n", " 'Tenm4', 'Myrip', 'Kcnj3', 'Rasal2', 'Dock3', 'Ssh2', 'Gpc6',\n", " 'Xkr4', 'Stmn1', 'Sub1', 'Tspan7', 'March1', 'Atl1', 'Nrg3',\n", " 'Kcnmb4', 'Auts2', 'Macrod2', 'Tiam2', 'Ctnna2', 'Kif5c', 'Pak7',\n", " 'Map9', 'Dnaja1', 'Cpne8', 'Dmxl2', 'Adcy8', 'Ndrg3', 'Spred1',\n", " 'Pde10a', 'Snap91', 'Nrxn1', 'Osbpl1a', 'Rims1', 'Ctnnd2', 'Eml5',\n", " 'Nrn1', 'Rtn3', 'Dclk1', 'Nrep', 'Kcnq5', 'Ppp3r1', 'Agtpbp1',\n", " 'Fstl4', 'Setbp1', 'Grm7', 'Ext1', 'Atp8a1', 'Lrrc4c', 'Zbtb18',\n", " 'Trim9', 'Gabrg3', 'Plcb1', 'Fgf14', 'Ywhaz', 'Hs6st3', 'Khdrbs2',\n", " 'Rgs6', 'Celf1', 'Ptprk', 'Sept11', 'Meg3', 'Brinp1', 'Tsc22d1',\n", " 'Pcsk2', 'Caln1', 'Sptbn2', 'Kcnd2', 'Dlgap2', 'Fam19a1', 'Grin2b',\n", " 'Rasgef1b', 'Mark1', 'Ptpn5', 'Scn8a', 'Fam155a', 'Syne1', 'Unc79',\n", " 'Necab1', 'Arntl', 'Fam126b', 'Mkl2', 'Ptprd', 'Itpr1', 'Phactr1',\n", " 'Herc3', 'Epha5', 'Tshz3', 'Nlk', 'Prkar1b', 'Cdh6', 'Asap1',\n", " 'Pi4ka', 'Cacna1e', 'Mtpn', 'Pfkp', 'Ptk2', 'Nwd2', 'Grin2a',\n", " 'Fhl2', 'Arhgap26', 'Gucy1a2', 'Ank2', 'Nos1ap', 'Kcnh7', 'Vamp2',\n", " 'Serbp1', 'Pde8b', 'Pgm2l1', 'Rtn1', 'Cntn4', 'Extl3', 'Calm1',\n", " 'Serpini1', 'Nrg1', 'Pclo', 'Pkm', 'Celf2', 'Sorcs3', 'Gabra1',\n", " 'Adgrl1', 'Lmtk2', '2010300C02Rik', 'Rtn4', 'Chrm3', 'Smim13',\n", " 'Slc24a2', 'Hs3st4', 'Map2', 'Ncald', 'Ywhag', 'Ptn', 'Cacnb4',\n", " 'Trim37', 'Nrxn3', 'Cntn5', 'Epha4', 'Clstn2', 'Dmd', 'Slc2a13',\n", " 'Mapk1', 'Camkk2', 'Ppp2r2b', 'Ywhah', 'Ptprt', 'Tcf4', 'Myo5a',\n", " 'Kcnb1', 'Nptn', 'Hdac4', 'Micu3', 'Kcnh1', 'Otud7a', 'Sh3gl2',\n", " 'Zfp697', 'Fmn2', 'Rnf150', 'Camk4', 'Rbfox1', 'AI593442',\n", " 'Adam22', 'Agbl4', 'Cacna1c', 'Chn1', 'Nuak1', 'Wdr82', 'Sorcs1',\n", " 'Arfgef3', 'Sun1', 'Tusc3', 'Snrpn', 'Ptms', 'Gpm6a', 'Trio',\n", " 'Rab15', 'Nedd4l', 'Cyfip2', '1700020I14Rik', 'Csmd1', 'Pten',\n", " 'Phf24', 'Ryr2', 'Med13l', 'Tspoap1', 'Cdc42bpa', 'Slc6a17',\n", " 'Sphkap', 'Rgs7', 'Ngef', 'Dlg2', 'Pbx1', 'Pcdh7', 'Efhd2',\n", " 'Nell2', 'Gfra2', 'Ctnna3', 'Kcnh5', 'Miat', 'Rgs7bp', 'Fam49a',\n", " 'Plxna2', 'Frrs1l', 'C230004F18Rik', 'Pde4dip', 'Lamp5', 'Hdac9',\n", " 'Akap8l', 'Arhgef9', 'Camk2g', 'Prickle2', 'Kctd16', 'Nrsn1',\n", " 'Msra', 'Lancl2', 'Vopp1', 'Slc17a7', '2010111I01Rik', 'Serinc1',\n", " 'Cacna1d', 'Fhod3', 'Kcnma1', 'Pkd1', 'Fam49b', 'Arpp19', 'Cdh10',\n", " 'Vgf', 'Hpca', 'Mical2', 'Adam23', 'R3hdm2', 'Fam135b', 'St3gal5',\n", " 'Slit3', 'Sidt1', 'Sh3rf3', 'Gm1976', 'Prkce', 'Ptprs', 'Egfem1',\n", " 'Pcdha1', 'Malat1', 'Satb2', 'Ntm', 'Opcml', 'Ephb6', 'Coch',\n", " 'Sema7a', 'Trpc3', 'Cabyr', 'Lamc2', 'Lrrk2', 'Ankrd33b', 'Stard8',\n", " 'Ier5', 'Hipk4', 'Plekha6', 'Usp2', 'Igsf9b', 'Pantr1', 'Acsl5',\n", " 'Tnnt2', 'Frmd6', 'Mkx', 'Cd34', 'Cpne5', 'Kcnj9', 'Pamr1', 'Cux1',\n", " 'Ddit4l'], dtype=object),\n", " 'Sox10(+)': array(['Chn2', 'Dock10', 'Pacs2', 'Pdlim2', 'Sema6a', 'Desi1', 'Clic4',\n", " 'Tmem88b', 'Gpr37', 'Frmd8', 'Nfasc', 'Edil3', 'Fnbp1', 'Hhip',\n", " 'Mapk8ip1', 'Rnf13', 'Daam2', 'Plekhh1', 'Mt2', 'Ptgds', 'S100a16',\n", " 'Itpk1', 'Tprn', 'Adssl1', 'Dennd5a', 'Gsn', 'Tmbim1', 'Ddr1',\n", " 'Sema4d', 'Clmn', 'Cdc42ep2', 'Plxnb3', 'Plekhg3', 'Ppp1r16b',\n", " 'Mcam', 'Kndc1', 'Gng11', 'Tulp4', 'Abca2', 'Phactr3', 'Tmeff2',\n", " 'Elovl1', 'Gstm7', 'Ptma', 'Hcn2', 'Gjc2', 'Ptn', 'Smad7', 'Cd81',\n", " 'St6galnac3', 'Myrf', 'Pllp', 'Plat', 'Sox10', 'Rhou', 'Trf',\n", " 'Tppp3', 'Grb14', 'Ttll7', 'Gpr17', 'Fgf1', 'Nkx6-2', 'Cntn2',\n", " 'Dbndd2', 'Gab1', 'Ugt8a', 'Creb5', 'Cpox', 'Klk6', 'Gatm',\n", " 'Tspan2', 'Fth1', 'Scd2', 'Cnksr3', 'Ftl1', 'Mbp', 'Tmem229a',\n", " 'Csrp1', 'Sept7', 'Foxn3', 'Ppp1r14a', 'Otud7b', 'Rassf2', 'Efnb3',\n", " 'Il1rap', 'Rcbtb1', 'Tsc22d4', 'Carhsp1', 'Erbb3', 'Bcas1',\n", " 'Tmem63a', 'Lpar1', 'St18', 'Syt11', 'Nmral1', 'S100b', 'Bin1',\n", " 'Rdx', 'Kcnj10', 'Rhog', 'Olig2', 'Olig1', 'Arhgef10', 'Rhob',\n", " 'Cd9', 'Ankrd13a', 'Myo6', 'Anln', 'Slain1', 'Gltp', 'Serpinb1a',\n", " 'Car2', '5031439G07Rik', 'Gabrr2', 'Atp1b3', 'Pde4b', 'Plin3',\n", " 'Galnt6', 'Opalin', 'Rtkn', 'Traf5', '2700046A07Rik', 'Plp1',\n", " 'Gstp1', 'Zdhhc9', 'Gna12', 'Plekhb1', 'Ndrg1', 'Tubb4a', 'Mal',\n", " 'Sgk1', 'Mobp', 'Qdpr', 'Gprc5b', 'Nkain1', 'D16Ertd472e',\n", " 'Secisbp2l', 'Fa2h', 'Enpp6', 'Tns3', 'Scd1', 'Cd82', 'Sept4',\n", " 'Scrg1', 'Cryab', 'Pde8a', 'Arhgap23', 'Sparc', 'Rcan2', 'Cers2',\n", " 'Map4k4', 'Tmem123', 'Gjb1', 'Padi2', 'Mast4', 'Phgdh', 'Sec14l5',\n", " 'Ano4', 'Plekha1', 'Unc5b', 'Zfp536', 'Fez1', 'Qk', 'Tmod1',\n", " 'Sez6l2'], dtype=object),\n", " 'Sox9(+)': array(['Id3', 'Tmem47', 'Atp1a2', 'Rassf3', 'Slc6a1', 'Id4', 'Cldn10',\n", " 'Fgfr3', 'Oaf', 'Syn3', 'Clu', 'Slc1a2', 'Mlc1', 'Fgfr1', 'Mfge8',\n", " 'Sox9'], dtype=object),\n", " 'Srebf1(+)': array(['Atp1a2', 'Qk', 'Aldoc', 'Clu', 'Cst3', 'Paqr8'], dtype=object),\n", " 'Tbx18(+)': array(['Igf2', 'Fbln1', 'Serpinf1', 'Tbx18', 'Col1a2', 'Elf1', 'Zic2',\n", " 'Osr1', 'Lrp1', 'Cald1', 'Bgn'], dtype=object),\n", " 'Tcf4(+)': array(['Gphn', 'Snap25', 'Cadm2', 'Syt1', 'Lsamp', 'Dlg2', 'Nrg3',\n", " 'Magi2', 'Setbp1', 'Cd44', 'Dok6', 'Camk1d', 'Nrxn1', 'Gria2',\n", " 'Cdh2', 'Ctnnd2', 'Hsp90ab1', 'Tmsb4x'], dtype=object),\n", " 'Tcf7l2(+)': array(['Otx2', 'Gata3', 'Zfhx3', 'Tcf7l2', 'Zfhx4', 'Pax3', 'Lhx1os',\n", " 'Cbln2', 'Six3', 'Calb2', 'Htr2c', 'AW551984', 'Slc17a6',\n", " 'Mab21l2', 'Tal1', 'Syndig1l', 'Glra1', 'Steap2', 'Chrm2',\n", " 'Klhl13', 'Scn4b', 'Fign', 'Zcchc12', 'Cacna2d2', 'Patj', 'Pnoc',\n", " 'Prkcd', 'Esrrb', 'Plxnb3', 'Abat', 'Nrsn2', 'Smim17', 'Arhgap36',\n", " 'Ctxn3', 'Phlda3', 'Pld5', 'Enpp6', 'Kat2b', 'Cygb', 'Rasgrp2',\n", " 'Camk2n2', 'Nacc2', 'Nova1', 'Rgs10', 'Scd1', 'Cnksr3', 'Tmem163',\n", " 'Irx1', 'Svs5', 'Cd59a', 'Rgs16', 'Foxp2', 'Gad2', 'Zic1',\n", " 'Sema6a', 'Gphn', 'Plp1', 'Ntng1', 'Rbms3', 'Kcnj10', 'Hdac8',\n", " 'Cdh20', 'Kcnc1', 'Rmst', 'Atp1a2', 'Camta1', 'Grid2', 'Ntsr2',\n", " 'Mal', 'Camk1d', 'Rora', 'Aqp4', 'Aldoc', 'Nefm'], dtype=object),\n", " 'Thra(+)': array(['Dapk1', 'Syt1', 'Cdk17', 'Ccnd2', 'Slitrk5', 'Ttc3', 'Lmo4',\n", " 'Chd3', 'Meg3', 'Fbxw7', 'Zfr', 'Tceal9', 'Arf3', 'Basp1', 'Jph4',\n", " 'Tceal3', 'Stmn2', 'Pja2', 'Gnb2', 'Pafah1b1', 'Cadm3', 'Prmt8',\n", " 'Cadm1', 'Bex3', 'Smarca2', 'Atp2b1', 'Snca', 'Thra', 'Ppp3ca',\n", " 'Slc39a10', 'Cacna1e', 'Dpf1', 'Sipa1l1', 'Shank1', 'Grin2b',\n", " 'Chrm1', 'Vamp2', 'Tceal6', 'Ywhae', 'Anp32a', 'Nrn1', 'Morf4l2',\n", " 'Rab6a', 'Nlgn2', 'Abr', 'Gnas', 'Bcl11a', 'Matr3', 'Abi2',\n", " 'Gpr162', 'Tlk1', 'Hspa8', 'Snap25', 'Tcf4', 'Stmn4', 'H2afz',\n", " 'Nrxn2', 'Anks1b', 'Hnrnpk', 'Adgrl1', 'Ywhag', 'Cacng8', 'Gria3',\n", " 'Tsc22d1', 'Erc2', 'Adgrb2', 'Cnksr2', 'Ildr2', 'Tceal5', 'Set',\n", " 'Vgf', 'Gap43', 'Scn3b', 'Mef2c', 'Cntn1', 'Capza2', 'Gabbr1',\n", " 'Arhgef9', 'Nap1l1', 'App', 'March6', 'Cd47', 'Gria2', 'Celf5',\n", " 'Nlgn1', 'Grin2a', 'Usp46', 'Gnao1', 'Calm2', 'Dlg4', 'Gabra2',\n", " 'Kif1a', 'Bex1', 'Wasf1', 'Ppp3cb', 'Cnih2', 'Miat', 'C1ql3',\n", " 'Ndrg3', 'Acsl4', 'Nfib', 'Egr3', 'Hsp90ab1', 'Eloc', 'Prkar1b',\n", " 'Pnck', 'Tmem50a', 'Hprt', 'Fscn1', 'Prkar1a', 'Camk2b', 'Ywhaz',\n", " 'Cplx2', 'Cltc', 'Nlgn3', 'Sptan1', 'Bcl11b', 'Rtn4', 'Camk2d',\n", " 'Bhlhe22', 'Nptn', 'Chst1', 'Maged1', 'Cdk5r2', 'Myh7', 'Mapre3',\n", " 'Ptms', 'Pcdh8', 'Unc13a', 'Pls3', 'Grik5', 'Ppp1r9a', 'Pkm',\n", " 'Ptprs', 'Nckap5', 'Ssh2', 'Chn1os3', 'Syngap1', 'Syp', 'Syt11',\n", " 'Tpm1', 'Ube2e3', 'Enpp5', 'Atxn10', 'Tenm4', 'B3gat1', 'Dyrk2',\n", " 'Tmem35a', 'Pdcd4', 'Arhgef28', 'Slc6a15', 'Dmtn', 'Neurod2',\n", " 'Ncald', 'Ube2w', 'Cacnb3', 'Ctnnb1', 'Rtn3', 'Slc17a7', 'Ppp3r1',\n", " 'Mlf2', 'Pea15a', 'Kcnh3', 'Fam19a1', 'Gls', 'Grasp', 'Fam171b',\n", " 'Rin1', 'Negr1', 'Grm5', 'Pcdh19', 'Nckap1', 'Hpcal4', 'Cdc42se2',\n", " 'Sorbs2', 'Atp1a3', 'Actr3', 'Ddn', 'Sfr1', 'Gpr39', 'Mmp17',\n", " 'Enc1', 'Gdi1', 'Homer1', 'Adam11', 'Crmp1', 'Sprn', 'Psip1',\n", " 'Bex2', 'Prkacb', 'Ankhd1', 'Sub1', 'Prnp', 'Thy1', 'Dlgap4',\n", " 'Palm', 'Syt17', 'Prkaca', 'Snrpn', 'Gpd2', 'Eif5a', 'Rps23',\n", " 'Pter', 'Prpf39', 'Celf3', 'Syn1', 'Calm3', 'Arpp21', 'Syt7',\n", " 'Gnl3l', 'Ppp1r9b', 'Eef1a1', 'Pcbp1', 'Fxyd7', 'Serpini1',\n", " 'Itm2c', 'Dlgap1', 'Akirin2', 'Dcaf7', 'Nptxr', 'Dnm1', 'Lrfn5',\n", " 'Smad3', 'Stxbp1', 'Tsnax', 'Slc22a17', 'Rab26', 'St3gal5',\n", " 'Pgrmc1', 'Atpif1', 'Ube2b', 'Jph3', 'Akap8l', 'Sult4a1', 'Tomm20',\n", " 'Tusc3', 'Mras', 'Rab3b', 'Pfn2', 'Actn1', 'B3galt2', 'Rapgefl1',\n", " 'Psma1', 'Klhl2', 'Prkcg', 'Arl6ip1', 'Rheb', 'Synpr', 'Akap11',\n", " 'Atp5a1', 'Chl1', 'Sertm1', 'Ppme1', 'Eno2', 'Efhd2', 'Galnt9',\n", " 'Celsr2', 'Pcsk2', 'Plppr4', 'Camk2n1', 'Map2', 'Gpm6a', 'Slc8a2',\n", " 'Nos1', 'Nsg2', 'Ptprn', 'Mef2d', 'Cnih3', 'Mpc2', 'Pgm2l1',\n", " 'Baiap2', '4930481A15Rik', 'Ryr3', 'Tmsb4x', 'Ndrg4', 'Grp',\n", " 'Ramp2', 'Rpl29', 'Scamp5', 'Dcn', 'Ap2b1', 'Bsg', 'Ppp2r2c',\n", " 'Atp6v1a', 'Syt4', 'Hpca', 'Slc15a3', 'Tpm3', 'Ckmt1', 'Fez1',\n", " 'Prex1', 'Cnr1', 'Rpl6', 'Marcks', 'Neurl1a', 'Ppp1r1a', 'Lingo1',\n", " 'Ipo5', 'C1qtnf4', 'Crtac1', 'Pam', 'Gria1', 'Camta2', 'Arpc2',\n", " 'Rwdd1', 'Fabp3', 'Fzd3', 'Atp6ap1', 'Kcnma1', 'Ndufv2', 'Syn2',\n", " 'Caln1', 'Snx10', 'Slc12a5', 'Tcaf1', 'Amph', 'Adcy1', 'Atp6v1c1',\n", " 'Clip3', 'Gpm6b', 'Synj1', 'Dlg3', 'Cers1', 'Nov', 'Scn2a',\n", " 'Map1b', 'Dynlrb1', 'Hspa2', 'Cacna2d1', 'Apbb1', 'Dnaja1',\n", " 'Pcsk5', 'Prkce', 'Dclk1', 'Ndufb5', 'Ppp1cb', 'Slc7a14', 'Hpcal1',\n", " 'Shank2', 'Nucks1', 'Mapk8ip2', 'Ran', 'Pnrc1', 'Slit1', 'Cdc42',\n", " 'Cyfip2', 'Cpne6', 'Mea1', 'Rps10', 'Rps21', 'Dbn1', 'Ywhab',\n", " 'Kcnip2', 'Rab2a', 'Calb1', 'Hrk', 'Ssbp4', 'Prkca', 'Celf1',\n", " 'Zbtb18', 'Ociad1', 'Emc2', 'Ubb', 'Hap1', 'Arhgef25', 'Rtn4rl1',\n", " 'Cyth2', 'Sirpa', 'Dynlt3', 'Ubxn11', 'Eif1', 'Gprin1', 'Ptpra',\n", " 'Hk1', 'Adprh', 'Atp1b1', 'Ubqln2', 'Snn', 'Rps16', 'Hnrnpa3',\n", " 'Sncb', 'Ankrd63', 'Arl6ip5', 'Plppr2', 'Sptbn2', 'Fbxl16',\n", " 'Sez6l', 'Atp2b4', 'Rilpl1', 'Rpl36', 'Ephx4', 'Ppm1e', 'Mal2',\n", " 'Serinc1', 'Ccl27a', 'Mkl2', 'Ppp1r2', 'Polr1d', 'Atl1', 'Elmod1',\n", " 'Pfn1', 'Ptk2b', 'Atp1a1', 'Mtpn', 'Psd', 'Rasal1', 'Arpc4',\n", " 'Foxg1', 'H3f3b', 'Reps2', 'Tspan13', 'Psmb3', 'Fam131a',\n", " '3300002P13Rik', 'Cryl1', 'Eef1a2', 'Cmas', 'Rasgrp1', 'Nptx1',\n", " 'Fars2', 'Gnb1', 'Coro1a', 'Camk2a', 'Park7', 'Actb', 'Actr2',\n", " 'Rab15', 'Nsg1', 'Tex264', 'Dynll1', 'Vti1b', 'Synpo', 'Rap2b',\n", " 'Clstn1', 'Atp5j', 'Itpka', 'Rnf112', 'Celf2', 'Strn4', 'Grin1',\n", " 'Tesc', 'Dctn2', 'Pi4ka', 'Csnk1a1', 'Rbfox3', 'Tbca', 'Opcml',\n", " 'Gabrb3', 'Stk11', 'Sez6', 'Stum', 'Chmp4b', 'Aes', 'Cbarp',\n", " 'Caly', 'Serp2', 'Creg2', 'Rhob', 'Wdr89', 'Atp5g1', 'Npy',\n", " 'Trim32', 'Rpl38', 'Serbp1', 'Clcn4', 'Wfs1', 'Meis2', 'Atp5d',\n", " 'Astn1', 'Eif4a2', 'Tuba4a', 'Dbp', 'Nr2f1', 'Lamp5', 'Nrgn',\n", " 'Pfkl', 'Timp2', 'Ndufb7', 'Celf4', 'Tpi1', 'Pfkp', 'Atp6v1d',\n", " 'Rasgrf1', 'Slc6a17', 'Mllt11', 'Ppp5c', 'Mapk3', 'Kcnj4', 'Gpr22',\n", " 'Brk1', 'Tomm70a', 'Ensa', 'Ube2ql1', 'Rasgef1a', 'Phykpl', 'Nsf',\n", " 'Rbbp7', 'Dennd6b', 'Atp9a', 'Dgkz', 'Sptbn1', 'Tyro3', 'Adgrd1',\n", " 'Oxct1', 'Atp6v1b2', 'Phyhip', 'Rasl10a', 'Arpc5', 'Khdrbs3',\n", " 'Mapk1', 'Calr', 'Vstm2l', 'Trim2', 'Rps26', 'Slc25a22', 'Dgkg',\n", " 'Ap1s1', 'R3hdm4', 'Atp8a1', 'Nfyb', 'Tbc1d9', 'Eif4h', 'Sdhb',\n", " 'Nme2', 'Ncoa1', 'Cyp46a1', 'C2cd2l', 'B4galt6', 'Ldha', 'Klc1',\n", " 'Calm1', 'Lrrtm1', 'Svop', 'Fam163b', 'Plec', 'Ppia', 'Myo5b',\n", " 'Chn1', 'Rtn4r', 'Zfp706', 'Fxyd6', 'Nhp2', 'Txn1', 'Gabarapl1',\n", " 'Madd', 'Dpysl2', 'Eif1b', 'Cpe', 'Rpl19', 'Hnrnpab', 'Fam49a',\n", " 'Sort1', 'Rpl17', 'Hdac11', 'Rps7', 'Cttn', 'Olfm1', 'Kcnc4',\n", " 'Pgbd5', 'Klf13', 'Gnaq', 'Sdc3', 'Rpl11', 'Camkv', 'Gng13',\n", " 'Rprm', 'Tspan7', 'Arl4a', 'Gabra5', 'Sv2b', 'Cbx6', 'Cdh13',\n", " 'Cnrip1', 'Stmn1', 'Junb', 'Sh3gl2', 'Spock2', 'Rab6b', 'Fam81a',\n", " 'Ctsb', 'Myt1l', 'Kcnf1', 'Napa', 'Galnt16', 'Dnajb6', 'Mtch1',\n", " 'Nell2', 'Tagln3', '1700037H04Rik', 'Rtn1', 'Eno1', 'Nedd4l',\n", " 'Lancl2', 'Fam84a', '2010300C02Rik', 'Slc4a10', 'Slc25a4', 'Gnaz',\n", " 'Kalrn', 'Frrs1l', 'Ndufa10', 'Sqstm1', 'Mff', 'Slc30a3', 'Arf1',\n", " 'Ak5', 'Med26', 'Tubb2a', 'Ncan', 'Kif5a', 'Nme1', 'Apaf1',\n", " 'Map2k1', 'Hsp90aa1', 'Nos1ap', 'Mgst3', 'Cycs', 'Pdss2', 'Scoc',\n", " 'Ogfrl1', 'Ppp2ca', 'Vdac1', 'Slc25a3', 'Rpl37', 'Aak1', 'Rpl21',\n", " 'Hspa5', 'Spcs1', 'Rangap1', 'Gas7', 'Cox6c', 'Rpl31', 'Pja1',\n", " 'Atp5g3', 'Ypel3', 'Cadps2', 'Tspan3', 'Sem1', 'Tmx4', 'Arl8b',\n", " 'Ssb', 'Ly6h', 'Minos1', 'Ap2s1', 'Ier3ip1', 'Tuba1a', 'Rpl30',\n", " 'Ywhah', 'Angel1', 'Phf24', 'Cpne7', 'Gm5441', 'Lgi1', 'Ppp2r1a',\n", " 'Ociad2', 'Itm2b', 'Vps29', 'Ndufb6', 'Ap2m1', 'Ndufb3', 'Cetn3',\n", " 'Arl3', 'Tuba1b', 'Mat2b', 'Mtf1', 'Icam5', 'Uchl1', 'Fabp5',\n", " 'Gapdh', 'Rps20', 'Cspg5', 'Kcnq2', 'Gadl1', 'Lrp11', 'Ccsap',\n", " 'Cacybp', 'Akap5', 'Capzb', 'Fcho1', 'Napb', 'Lancl1', 'Trh',\n", " 'Ap2a2', 'Mpped1', 'Kifap3', 'Rab3c', 'Skp1a', 'Fibcd1', 'Otub1',\n", " 'Tmem191c', 'Pkig', 'Ndufa6', 'Chgb', 'Gal3st3', 'Selenow',\n", " 'Atp5k', 'Tmem59l', 'Rpl39', 'Marcksl1', 'Fam189a1', 'Nme7',\n", " 'Arhgap32', 'Rpl5', 'Nek6', 'Pcsk1n', 'Ssr2', 'Rpl18a', 'Tmem30a',\n", " 'Snap47', 'Ncdn', 'Gpx4', 'Limd2', 'Ttc9b', 'Dkk3', 'Aplp1',\n", " 'Stmn3', 'Pitpna', 'Ssu72', 'Syt16', 'Tubb5', 'Rps14', 'Pcp4',\n", " 'Med28', 'Atp6ap2', 'Prkcb', 'Ids', 'Cox6b1', 'Kctd4', 'Pdcd5',\n", " 'Rps15a', 'Psmb6', 'Psmb1', 'Tspyl1', 'Swi5', 'Pnmal2', 'Ccdc106',\n", " 'Pop5', 'Pde1a', 'Actr3b', 'Pnmal1', 'Ctbp1', 'Dctn6', 'Cfl1',\n", " '6330403K07Rik', 'Ndufv3', 'Stx1a', 'Matk', 'Cbr1', 'Arpp19',\n", " 'Sowaha', 'Bsn', 'Rpl3', 'Mapk8ip3', 'Rps13', 'Dctn3', 'Strap',\n", " 'Rpl27', 'Tmem38a', 'Rps19', 'Car11', 'Hras', '6530403H02Rik',\n", " 'Arpc3', 'Lrpap1'], dtype=object),\n", " 'Thrb(+)': array(['Kmt2e', 'Atp2b1', 'Satb1', 'Dtna', 'Lrrc7', 'Akt3', 'Cnksr2',\n", " 'Galnt13', 'Hivep2', 'Rims2', 'Ube3a', 'Lingo2', 'Sgcz', 'Tanc2',\n", " 'Kat6b', 'Stag1', 'Adgrl3', 'Kansl1', 'Zfp385b', 'Ttc3', 'Enox1',\n", " 'Syt1', 'Atxn1', 'Stxbp5l', 'Runx1t1', 'Grid2', 'Pja2', 'Ccser1',\n", " 'Mapk8', 'Mctp1', 'Hook1', 'Dlgap1', 'Fut9', 'Dpp10', 'Cacna2d1',\n", " 'Dab1', 'Nlgn1', 'Msi2', 'Car10', 'Cdh8', 'Fgf12', 'Trpc4', 'Grm3',\n", " 'Pam', 'Rora', 'Osbpl6', 'Thrb', 'Camk1d', 'R3hdm1', 'Nbea',\n", " 'Ralyl', 'Anks1b', 'Efna5', 'Il1rapl2', 'Sptan1', 'Foxp1', 'Cadm1',\n", " 'Ppp1r12a', 'Grm8', 'Xkr6', 'Ncor1', 'Frmpd4', 'Sipa1l1', 'Prr16',\n", " 'Kcnt2', 'Gria4', 'Mllt3', 'Gria2', 'Unc5d', 'Ssbp2', 'App',\n", " 'Chsy3', 'Peak1', 'Hecw2', 'Dok6', 'Tox', 'Sntg1', 'Lrfn5',\n", " 'Ppp1r9a', 'Mdga2', 'Arl15', 'Calm2', 'Zeb1', 'Ldlrad4', 'Diaph2',\n", " 'Nrcam', 'Gabrb2', 'Ppm1l', 'Arpp21', 'Oxr1', 'Epha6', 'Ppp3ca',\n", " 'Unc80', 'Cadps', 'Apba2', 'Adgrb3', '5730522E02Rik', 'Nxph1',\n", " 'Rock2', 'Trhde', 'Flrt2', 'Trank1', 'Erc2', 'Hecw1', 'Cacna2d3',\n", " 'Prkacb', 'Homer1', 'Ube2e2', 'Nkain2', 'Jarid2', 'Herc1', 'Prkg1',\n", " 'Prickle1', 'Ahi1', 'Grm5', 'Kcnq3', 'Large1', 'Negr1', 'Phf20l1',\n", " 'Cdh12', 'Cntnap2', 'Snap25', 'Brinp3', 'Syt14', 'Nol4', 'Pcdh9',\n", " 'Cadm2', 'Fnbp1l', 'Lmo4', 'Gls', 'Mef2c', 'Robo2', 'Bcl11a',\n", " 'Hcn1', 'Atrnl1', 'Tenm4', 'Farp1', 'Myrip', 'Ptchd4', 'Magi1',\n", " 'Grip1', 'Kif21a', 'Kcnj3', 'Rasal2', 'Dock3', 'Mapk4', 'Gpc6',\n", " 'Xkr4', 'Tspan7', 'March1', 'Nrg3', 'Mapt', 'Auts2', 'Macrod2',\n", " 'Ctnna2', 'Pak7', 'Ppfia2', 'Ehbp1', 'Camta1', 'Dmxl2', 'Grik2',\n", " 'Adcy8', 'Asap2', 'Ndrg3', 'Unc5c', 'Pde10a', 'Nrxn1', 'Osbpl1a',\n", " 'Rims1', 'Sobp', 'Ctnnd2', 'Zfp804b', 'Dclk1', 'Kcnq5', 'Fstl4',\n", " 'Setbp1', 'Grm7', 'Ext1', 'Zc3h7a', 'C130071C03Rik', 'Lrrc4c',\n", " 'Trim9', 'Gabrg3', 'Plcb1', 'Fgf14', 'Ywhaz', 'Srrm2', 'Dync1i1',\n", " 'Mtcl1', 'Hs6st3', 'Magi2', 'Rgs6', 'Celf1', 'Sept11', 'Meg3',\n", " 'Npas3', 'Brinp1', 'Pcsk2', 'Caln1', 'Kcnd2', 'Tnrc6c', 'Wdr17',\n", " 'Csmd2', 'Dlgap2', 'Fam19a1', 'Grin2b', 'Rasgef1b', 'Sox6',\n", " 'Mtus2', 'Sbf2', 'Scn8a', 'Fam155a', 'Syne1', 'Unc79', 'Fam126b',\n", " 'Mkl2', 'Ptprd', 'Itpr1', 'Phactr1', 'Ncoa1', 'Immp2l', 'Magi3',\n", " 'Cttnbp2', 'Herc3', 'Park2', 'Zcchc7', 'Sorbs1', 'Epha5', 'Tshz3',\n", " 'Kirrel3', 'Gm20754', 'Esyt2', 'Nlk', 'Prkar1b', 'Adcy2', 'Asap1',\n", " 'Cacna1e', 'Mtpn', 'Ncam2', 'Robo1', 'Ptk2', 'Htr1f', 'Grin2a',\n", " 'Clasp2', 'Tmtc2', 'Arhgap26', 'Gucy1a2', 'Ank2', 'C2cd5',\n", " 'Dnajc1', 'Nos1ap', 'Kcnh7', 'Slc24a4', 'Rtn1', 'Frmd4a', 'Cntn4',\n", " 'Slc35f4', 'Gpc5', 'Serpini1', 'Efr3a', 'Nrg1', 'Nebl', 'Pclo',\n", " 'Celf2', 'Gabra1', 'Dgkh', 'Lmtk2', 'Mbnl2', 'Myo16', 'Chrm3',\n", " 'Slc24a2', 'Hs3st4', 'Scai', 'Ttc14', 'Tjp1', 'Vps13b', 'Appl1',\n", " 'Cacnb4', 'Nrxn3', 'Klhl29', 'Cntn5', 'Gabrb1', 'Dmd', 'Slc2a13',\n", " 'Ppp2r2b', 'Ptprt', 'Tcf4', 'Myo5a', 'Plekha5', 'Nptn', 'Pnisr',\n", " 'Kcnh1', 'Otud7a', 'Dock4', 'Sh3gl2', 'Dlc1', 'Exoc5', 'Sik3',\n", " 'Ccdc85a', 'Tnik', 'Fmn2', 'Rnf150', 'Camk4', 'Rbfox1', 'Adam22',\n", " 'Agbl4', 'Macf1', 'Cacna1c', 'Adcy7', 'Chn1', 'Ptprm',\n", " '4930402H24Rik', 'Sorcs1', 'Kcnn2', 'Gpm6a', 'Trio', 'Trpm3',\n", " 'Nedd4l', 'Cyfip2', 'Cdk14', 'Csmd1', 'Pten', 'Mmp16', 'Phf24',\n", " 'Dip2c', 'Ryr2', 'Med13l', 'Lrp1b', 'Cdc42bpa', 'Slc6a17',\n", " 'Carmil1', 'Rgs7', 'Fry', 'Dlg2', 'Pbx1', 'Pcdh7', 'Sybu', 'Nell2',\n", " 'Ctnna3', 'Kcnh5', 'Miat', 'Fam49a', 'C230004F18Rik', 'Hdac9',\n", " 'Akap8l', 'Mycbp2', 'Camk2g', 'Ttll6', 'Syn1', 'Adgrb1',\n", " 'Prickle2', 'Hivep3', 'Arid1b', 'Smyd3', 'Lrch1', 'Kctd16', 'Msra',\n", " '2010111I01Rik', 'Dpp6', 'Fam193a', 'Cacna1d', 'Fhod3', 'Kcnma1',\n", " 'Fam49b', 'Cdh10', 'Mical2', 'Adam23', 'R3hdm2', 'Fam135b', 'Tia1',\n", " 'Slit3', 'Sidt1', 'Sh3rf3', 'Prkce', 'Egfem1', 'Tspan5', 'Slit2',\n", " 'Tshz2', 'Unc13b', 'Pde4d', 'Pacsin1', 'N4bp2l2', 'Pde1a', 'Syt16',\n", " 'Mapk10', 'Cdh18', 'Xist', 'Astn2', 'Cit', 'Sgip1', 'Ndst3', 'Ogt',\n", " 'Mir124a-1hg', 'Grik1', 'Nrgn', 'Galnt18', 'Vsnl1', 'Ntrk3',\n", " 'Ldb2', 'Cux2', 'Atp2b2', 'Lingo1', 'Dcc', 'Fgfr2', 'Lsamp',\n", " 'Pknox2', 'Cacna1a', 'Gria3', 'Gabrb3', 'Dgki', 'Cobl', 'Ksr2',\n", " 'Cpeb3', 'Sox5', 'Camk2n1', 'Dlg1', 'Pcdh15', 'Myt1l', 'Psd3',\n", " 'Rnf169', 'Adgrl2', 'Hdac8', 'Ptprg', 'Rbfox3', 'Xylt1', 'Plppr4',\n", " 'Rasgrf1', 'Samd12', 'Ptprn2', 'Satb2', 'Aak1', 'Cacng3', 'Dnm1',\n", " 'Shank2', 'Asic2', 'Nav2', 'Sv2b', 'Ntm', 'Csmd3', 'Slc4a10',\n", " 'Eif4g3', 'Gramd1b', 'Camk2a', 'Kcnip4', 'Grin1', 'Rimbp2', 'Rarb',\n", " 'Il1rapl1', 'D430041D05Rik', 'Sgcd', 'Chd5', 'Wnk2', 'Gria1',\n", " 'Kalrn', 'Ddx17', 'Arhgap32', 'Gpr158', 'Sorbs2', 'Slc8a1',\n", " 'Pex5l', 'D130009I18Rik', 'Mgat4c', 'Id2', 'B3galt1', 'Ftx',\n", " 'Cntnap5a', 'Tmem132b', 'Csrnp3', 'Copg2', 'Mdh1', 'Etl4',\n", " 'Lrrtm4', 'Astn1', 'Pdzrn3', 'Klf12', 'Cmss1', 'Galntl6', 'Srpk2',\n", " 'Malat1', 'Scn2a', 'Tenm3', 'Dscam', 'Scn1a', 'Nyap2', 'Tmem132d',\n", " 'Srsf11', 'Prkcb', 'Fam189a1', '1600010M07Rik', 'Adgra1'],\n", " dtype=object),\n", " 'Vsx1(+)': array(['Osr1', 'Zic4', 'Foxd2', 'Igfbp5', 'Col1a1', 'Tfap2a', 'Fn1',\n", " 'Bmp5', 'Pdzk1ip1', 'Gm5860', 'Nfatc4', 'Thsd4', 'Bmp7', 'Slc9a2',\n", " 'Krt80', 'Dapl1', 'Foxp2', 'Bgn', 'Slc26a7', 'Slc26a2', 'Col6a2',\n", " 'Tbx15', 'Lypd2', 'Foxc1'], dtype=object)}" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "adata.uns['regulon_dict']" ] }, { "cell_type": "markdown", "id": "ff55ef4e", "metadata": {}, "source": [ "Receptors of each regulon were stored in adata.uns under key `receptor_dict` in dictionary format." ] }, { "cell_type": "code", "execution_count": null, "id": "f572ee04-8bce-4811-859f-0df1335dcc91", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'Atf2': array(['Scn2a', 'Scn1a', 'Dscam', 'Cacna1b', 'Lrrtm4', 'Cacna1c', 'Ntm',\n", " 'Atp1a3', 'Il31ra', 'Nrsn1', 'Il1rapl1', 'Scn2b', 'Opcml', 'Sv2b',\n", " 'Stx1a', 'Adam23'], dtype=object),\n", " 'Atf4': array(['Gp1bb', 'Thy1', 'Nptn', 'Rtn4', 'Nptxr', 'Sv2b', 'Stx1a', 'App',\n", " 'Lingo1'], dtype=object),\n", " 'Bcl11a': array(['Gp1bb', 'Sirpa', 'Scn2a', 'Tyro3', 'Dscam', 'Scn1b', 'Il1rapl1',\n", " 'Lrp11', 'Opcml', 'Sv2b', 'Stx1a', 'Tmem59l'], dtype=object),\n", " 'Bcl6': array(['Ptprt', 'F3', 'Mertk'], dtype=object),\n", " 'Bnc2': array(['Mrc2', 'Thbd', 'Tgfbr3', 'Asgr1', 'Anxa2', 'Cd36', 'Slc6a12'],\n", " dtype=object),\n", " 'Cebpb': array(['Neurod2', 'C1ql2', 'Olfm1', 'Ppp3ca', 'Hpca', 'Rfx3', 'Snca',\n", " 'Wasf1', 'Sema5a', 'Prox1', 'Calm2', 'Ppfia2', 'Bex2', 'Camk2b',\n", " 'Pter', 'C1ql3', '2010300C02Rik', 'Dsp', 'Ppp1r1a', 'Nrgn',\n", " 'Cpne6', 'Chn1', 'Dgkh', 'Dynll1', 'Rps7', 'Cebpb', 'Tmsb4x',\n", " 'Ncdn'], dtype=object),\n", " 'Cebpd': array(['Grin2a', 'Adcy1', 'Crlf1'], dtype=object),\n", " 'Dbp': array(['Gp1bb', 'Camk2a', 'Scn1b', 'Nptxr', 'Sv2b', 'Stx1a', 'Lingo1',\n", " 'Nrxn1'], dtype=object),\n", " 'Dlx1': array([], dtype=float64),\n", " 'Ebf1': array(['Flt1', 'Cldn5'], dtype=object),\n", " 'Egr1': array(['Gp1bb', 'Rtn4r', 'Ephb6', 'Scn1b', 'Pcdhga1', 'Lrp11', 'Chrm3',\n", " 'Cckbr', 'Igsf9b'], dtype=object),\n", " 'Egr3': array(['Gp1bb', 'Sirpa', 'Rtn4r', 'Tspan5', 'Kcnj4', 'Sort1', 'Pde1a',\n", " 'Scn1b', 'Atp6ap2', 'Sv2b', 'Stx1a', 'Tmem59l', 'Cckbr', 'Nectin3',\n", " 'Adcy1'], dtype=object),\n", " 'Egr4': array(['Gp1bb', 'Lingo1', 'Sv2b', 'Nptxr'], dtype=object),\n", " 'Elk1': array(['Gp1bb', 'Plxna4', 'Camk2a', 'Lrfn5', 'Grin2a', 'Nptxr', 'Crlf1',\n", " 'Epha7', 'Lingo1'], dtype=object),\n", " 'En1': array(['Il13ra1', 'F2r'], dtype=object),\n", " 'Ets2': array(['Gp1bb', 'Camk2a'], dtype=object),\n", " 'Etv1': array(['Epha5', 'Tspan13', 'Pde1a', 'Grin2b', 'Atp1a3', 'Ntm', 'Sv2b',\n", " 'Lingo1', 'Ptprs', 'Ptprd', 'Cntn1', 'Sirpa', 'Scn2a', 'Adora1',\n", " 'Scn1b', 'Chl1', 'Rtn4', 'Ntng1', 'Nrcam', 'Rxfp1', 'Hrh3', 'Nptn',\n", " 'Gnas', 'Sorl1', 'Sv2a', 'Gpr161', 'Scn8a', 'Stx1a', 'Atp6ap2',\n", " 'Grm5', 'Sort1', 'Grin2a', 'Cnr1', 'Thy1', 'Nlgn1', 'Cadm1',\n", " 'Npr3', 'Scn2b', 'Opcml', 'App', 'Aplp1'], dtype=object),\n", " 'Etv5': array(['Epha5', 'Camk2a', 'Tspan5', 'Pde1a', 'Grin2b', 'Atp1a3', 'Ntm',\n", " 'Sv2b', 'Lingo1', 'Nrxn1', 'Ptprs', 'Ptprd', 'Gp1bb', 'Sirpa',\n", " 'Scn2a', 'Cntn1', 'Kcnj4', 'Scn1b', 'Rtn4', 'Nrcam', 'Nptn',\n", " 'Sorl1', 'Htr1f', 'Scn8a', 'Chrm3', 'Stx1a', 'Atp6ap2', 'Nrxn3',\n", " 'Grm5', 'Grin2a', 'Thy1', 'Cd47', 'Nlgn1', 'Nrxn2', 'Opcml', 'App',\n", " 'Cadm2', 'Tmem59l'], dtype=object),\n", " 'Foxa1': array(['Gucy2c'], dtype=object),\n", " 'Foxa2': array(['Ret', 'Ntsr1', 'Gucy2c'], dtype=object),\n", " 'Foxc2': array(['Mrc2', 'Gpr182', 'Thbd', 'Stab1', 'Notch2', 'Adam12', 'Cd248',\n", " 'Lsr', 'Cdh1', 'Anxa2', 'Asgr1', 'Mpzl2', 'Gjb2'], dtype=object),\n", " 'Foxd2': array(['Mrc2', 'Cdh1', 'Thbd', 'Anxa2'], dtype=object),\n", " 'Gata3': array([], dtype=float64),\n", " 'Glis3': array(['Fgfr2', 'Lrrtm4', 'F3', 'Ntsr2', 'Mertk', 'Ntm', 'Il1rapl1',\n", " 'Fgfr3', 'Trpc6'], dtype=object),\n", " 'Hivep2': array(['Camk2a', 'Fgfr2', 'Ntm', 'Il31ra', 'Atp1a3', 'Epha10', 'Oprm1',\n", " 'Lingo1', 'Sdk2', 'Adcy1', 'Gp1bb', 'Sirpa', 'Scn2a', 'Scn1a',\n", " 'Scn1b', 'Ntrk3', 'Ptprg', 'Il1rapl1', 'Cd44', 'Rxfp1', 'Tyro3',\n", " 'Sorl1', 'Cacna1b', 'Lrp11', 'Stx1a', 'Atp6ap2', 'Igsf9b', 'Rtn4r',\n", " 'Lrrtm4', 'Nptxr', 'Nrxn2', 'Opcml', 'Dscam', 'Tmem59l'],\n", " dtype=object),\n", " 'Jun': array(['Camk2a', 'Igsf5', 'Grin2a', 'Nptxr', 'Orai2', 'Cacna1e', 'Gpc4',\n", " 'Adcy1'], dtype=object),\n", " 'Junb': array(['Camk2a', 'Gp1bb', 'Sirpa', 'Epha4', 'Tspan13', 'Grm5', 'Grin2a',\n", " 'Pde1a', 'Grin2b', 'Atp6ap2', 'Nptxr', 'Nrxn2', 'Cacna1e', 'Sv2b',\n", " 'Stx1a', 'Nptn', 'Ptprs', 'Adcy1'], dtype=object),\n", " 'Kdm5a': array(['Cntn5', 'Lrrtm4'], dtype=object),\n", " 'Klf12': array(['Scn1a', 'Opcml', 'Il31ra', 'Il1rapl1'], dtype=object),\n", " 'Klf9': array(['Fgfr2', 'Ntm', 'Il31ra', 'Ptprd'], dtype=object),\n", " 'Lef1': array(['Ntng1', 'Vipr2', 'Cldn5', 'Il31ra'], dtype=object),\n", " 'Lmx1b': array(['Chrnb3', 'Ntsr1'], dtype=object),\n", " 'Maf': array(['Mrc2', 'Thbd', 'Stab1', 'Tgfbr3', 'Anxa2', 'Mpzl2', 'Gjb2',\n", " 'Gjb6', 'Slc6a12'], dtype=object),\n", " 'Mbnl2': array(['Camk2a', 'Sorcs1', 'Fgfr2', 'Pde1a', 'Ntm', 'Sv2b', 'Ptprs',\n", " 'Adcy1', 'Scn2a', 'Scn1a', 'Cacna1c', 'Ntrk3', 'Ptprg', 'Il1rapl1',\n", " 'Adam23', 'Cd44', 'Cacna1b', 'Cacna1d', 'Lrrtm4', 'Nptxr', 'Opcml',\n", " 'Dscam', 'Aplp1'], dtype=object),\n", " 'Mef2d': array(['Camk2a', 'Tspan5', 'Tspan13', 'Pde1a', 'Grin2b', 'Ildr2', 'Sv2b',\n", " 'Lingo1', 'Nrxn1', 'Ptprs', 'Adcy1', 'Ptprd', 'Acvr1b', 'Sirpa',\n", " 'Scn2a', 'Epha4', 'Lrfn5', 'Gp1bb', 'Cntn1', 'Scn1b', 'Chl1',\n", " 'Rtn4', 'Nrcam', 'Epha7', 'Nptn', 'Gnas', 'Tyro3', 'Igsf5',\n", " 'Sorl1', 'Pcdhga1', 'Negr1', 'Scn8a', 'Stx1a', 'Nlgn2', 'Atp6ap2',\n", " 'Nrxn3', 'Grm5', 'Rtn4r', 'Grin2a', 'Sort1', 'Grm7', 'Thy1',\n", " 'Lrrtm4', 'Nlgn1', 'Nptxr', 'Ramp1', 'Nrxn2', 'Opcml', 'Cacna1e',\n", " 'App', 'Cadm2', 'Tmem59l'], dtype=object),\n", " 'Msx1': array(['Thbd', 'Ntsr2', 'P2ry6', 'Tgfbr3', 'Cdh1', 'Mrc1', 'Anxa2',\n", " 'Asgr1', 'Fcgrt', 'Gjb2', 'Gjb6'], dtype=object),\n", " 'Mtf1': array([], dtype=float64),\n", " 'Nfatc4': array(['Thbd', 'Lsr'], dtype=object),\n", " 'Nfe2l2': array(['Mpzl2'], dtype=object),\n", " 'Nfib': array(['Gp1bb', 'Igsf5', 'Lrrtm4', 'Adcy9', 'Ntm', 'Nptxr', 'Il1rapl1',\n", " 'Lrp11', 'Orai2', 'Sv2b', 'Opcml'], dtype=object),\n", " 'Nfyb': array(['Camk2a', 'Gp1bb', 'Grm5', 'Epha4', 'Tspan13', 'Thy1', 'Pde1a',\n", " 'Grin2b', 'Nptn', 'Rtn4', 'Nptxr', 'Cacna1e', 'Sv2b', 'Stx1a',\n", " 'App', 'Lingo1', 'Ptprs'], dtype=object),\n", " 'Nkx6-1': array(['Sell', 'Cd248', 'Cd79b', 'Gjb2'], dtype=object),\n", " 'Nkx6-2': array(['Il1rap', 'Rpsa', 'Aplp1', 'Adipor2'], dtype=object),\n", " 'Nr2f1': array(['Gp1bb', 'Sirpa', 'Rtn4r', 'Lrpap1', 'Scn1b', 'Pcdhga1', 'Sv2b',\n", " 'Tmem59l', 'Adcy1'], dtype=object),\n", " 'Nr2f2': array(['Gpr39'], dtype=object),\n", " 'Nr3c1': array([], dtype=float64),\n", " 'Ovol2': array(['Camk2a'], dtype=object),\n", " 'Pbx1': array(['Dscam', 'Lrrtm4', 'Pde1a', 'Mertk', 'Ntm', 'Il31ra', 'Ptprg',\n", " 'Il1rapl1', 'Ret', 'Opcml', 'Sv2b', 'Igsf8', 'Cd44'], dtype=object),\n", " 'Pbx3': array(['Sv2c', 'Chrnb3', 'Ret', 'Chrna4'], dtype=object),\n", " 'Pknox2': array(['Camk2a', 'Gp1bb', 'Scn2a', 'Tspan5', 'Dscam', 'Cacna1b', 'Lrrtm4',\n", " 'Ntm', 'Nptxr', 'Il1rapl1', 'Opcml', 'Sv2b', 'Dcc', 'Stx1a',\n", " 'Lingo1', 'Igsf9b'], dtype=object),\n", " 'Pou3f2': array([], dtype=float64),\n", " 'Prox1': array(['Plxna4', 'Gpc4', 'Cldn11', 'Adcy1', 'Cacna1c', 'Mag', 'Lin7c',\n", " 'Il1r1', 'Epha7', 'Pde1b', 'Ncam2', 'Igsf5', 'Il1rap', 'Orai2',\n", " 'Thsd7a', 'Trpc6', 'Itga8', 'Crlf1', 'Npy2r'], dtype=object),\n", " 'Prrx2': array(['Anxa2', 'Tnfrsf11b', 'Gjb2'], dtype=object),\n", " 'Satb2': array(['Camk2a', 'Tspan5', 'Pde1a', 'Il31ra', 'Sv2b', 'Lingo1', 'Cckbr',\n", " 'Acvr1b', 'Gp1bb', 'Sirpa', 'Scn2a', 'Scn1a', 'Kcnj4', 'Scn1b',\n", " 'Il1rapl1', 'Tyro3', 'Cacna1b', 'Crhr1', 'Stx1a', 'Rtn4r', 'Sort1',\n", " 'Lrrtm4', 'Nptxr', 'Dscam', 'Plxnd1'], dtype=object),\n", " 'Sox10': array(['Lamp1', 'Itgad', 'Jam3', 'Mag', 'Mog', 'Cldn11', 'Aplp1',\n", " 'Adipor2'], dtype=object),\n", " 'Sox9': array(['Cd81', 'Ntrk2', 'Ezr', 'S1pr1', 'Plxnb1', 'F3', 'Mertk', 'Ntsr2',\n", " 'Ntm', 'Gpr37l1', 'Ptprz1', 'Cadm1', 'Sdc4', 'Fxyd1', 'Gjb6',\n", " 'Cd63'], dtype=object),\n", " 'Srebf1': array(['Cd81', 'Lamp1', 'S1pr1', 'Adgrg1', 'F3', 'Mertk', 'Mag',\n", " 'Gpr37l1', 'Ntsr2', 'Cd38', 'Mog', 'Sdc4', 'Ptprz1', 'Gjb6',\n", " 'Fgfr3', 'Cldn11'], dtype=object),\n", " 'Tbx18': array(['Thbd', 'Stab1', 'Tnfrsf11b', 'Cd74', 'Cd248', 'Tgfbr3', 'Cdh1',\n", " 'Anxa2', 'Acvrl1', 'Gjb2', 'Gjb6', 'Slc6a12', 'Vcam1'],\n", " dtype=object),\n", " 'Tcf4': array(['Camk2a', 'Plxna4', 'Epha5', 'Tspan5', 'Ptprj', 'Il31ra', 'Plxna2',\n", " 'Dcc', 'Lingo1', 'Ptprd', 'Sirpa', 'Ntrk2', 'Scn2a', 'Epha4',\n", " 'Lrfn5', 'Robo2', 'Alcam', 'Cdh10', 'Cacna1c', 'Scn1b', 'Cntnap2',\n", " 'Unc5d', 'Rtn4', 'Nrcam', 'Cntn5', 'Epha7', 'Ncam2', 'Kcnd2',\n", " 'Cacna1b', 'Trhde', 'Ptprz1', 'Pcdhga1', 'Ptprt', 'Negr1', 'Stx1a',\n", " 'Grm5', 'Sort1', 'Grin2a', 'Thy1', 'Grm7', 'Cd47', 'Cadm1',\n", " 'Nrxn2', 'Lrrc4c', 'Cacna1e', 'App', 'Epha6', 'Kcnh1', 'Fgfr2',\n", " 'Tspan13', 'Pde1a', 'Grin2b', 'Ntm', 'Adcy2', 'Sv2b', 'Ptprs',\n", " 'Adcy1', 'Cntn1', 'Gp1bb', 'Kcnj4', 'Chl1', 'Cdh11', 'Ntrk3',\n", " 'Ptprg', 'Il1rapl1', 'Nptn', 'Sorl1', 'Ezr', 'Cacna1d', 'Ncam1',\n", " 'Scn8a', 'Chrm3', 'Nrxn3', 'Cnr1', 'Lrrtm4', 'Erbb4', 'Nlgn1',\n", " 'Htr4', 'Mertk', 'Nptxr', 'Opcml', 'Dscam', 'Scn3a'], dtype=object),\n", " 'Tcf7l2': array(['Fgfr2', 'Ptprz1', 'Mag', 'Gpr37l1', 'Il31ra', 'Ptprm', 'Mog',\n", " 'Cd44', 'Cd9', 'Cldn11', 'Gpr37'], dtype=object),\n", " 'Thra': array(['Gp1bb', 'Pcdhga1', 'Ramp1'], dtype=object),\n", " 'Thrb': array(['Gp1bb', 'Opcml'], dtype=object),\n", " 'Vsx1': array(['Mrc2', 'Thbd', 'Adam12', 'Cd55', 'Cdh1', 'Anxa2', 'Mpzl2', 'Gjb2',\n", " 'Tgfbr3'], dtype=object)}" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "adata.uns['receptor_dict']" ] }, { "cell_type": "markdown", "id": "fcaf2ab0", "metadata": {}, "source": [ "ISR matrix (n cells x m regulons) was stored in adata.obsm under key `isr` in pd.Dataframe format." ] }, { "cell_type": "code", "execution_count": null, "id": "3196f952-e366-4129-afa6-e33b682b7ed2", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Atf2(+)Atf4(+)Bcl11a(+)Bcl6(+)Bnc2(+)Cebpb(+)Cebpd(+)Dbp(+)Dlx1(+)Ebf1(+)...Satb2(+)Sox10(+)Sox9(+)Srebf1(+)Tbx18(+)Tcf4(+)Tcf7l2(+)Thra(+)Thrb(+)Vsx1(+)
Cell
Cell_10.1564310.1116440.0984570.0000000.0334530.0855400.0000000.1706510.1224740.000000...0.0991240.0305810.0439820.1084480.0000000.2825130.1218340.1239720.1039470.0
Cell_10000.2122920.2180800.1643890.0532360.0569760.2153770.0600050.1808530.0533700.000000...0.1242000.0235780.0239900.0591540.0000000.3685840.0470020.2188430.1124900.0
Cell_100000.1110010.0604510.0757370.0000000.0322330.1075420.0000000.1346290.0408310.000000...0.0791510.0218870.0697030.3497200.0000000.3209390.0769050.0855740.0741820.0
Cell_100010.0813700.1482190.1013230.0868410.0286920.1771240.0000000.1878420.0347860.000000...0.0624230.0400460.1354910.3369810.0000000.1721920.0357830.1446330.0529060.0
Cell_100020.1393630.3718020.1867180.0747400.0000000.2860480.0000000.3246440.0756730.075543...0.1044460.0408200.1379170.3597950.0000000.3118380.0665020.2794410.0900390.0
..................................................................
Cell_99940.0815290.1929220.1215660.0000000.0114340.1073560.0000000.0797880.0913600.000000...0.0727960.0482700.0594780.1163940.0000000.1248700.0110980.1169620.0609930.0
Cell_99960.2532490.2731790.1740620.0583100.0785240.2596390.0000000.1788990.2046840.163190...0.1400810.0419060.1233770.3154870.0000000.3471890.1142360.2202110.1467680.0
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50140 rows × 63 columns

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" ], "text/plain": [ " Atf2(+) Atf4(+) Bcl11a(+) Bcl6(+) Bnc2(+) Cebpb(+) \\\n", "Cell \n", "Cell_1 0.156431 0.111644 0.098457 0.000000 0.033453 0.085540 \n", "Cell_1000 0.212292 0.218080 0.164389 0.053236 0.056976 0.215377 \n", "Cell_10000 0.111001 0.060451 0.075737 0.000000 0.032233 0.107542 \n", "Cell_10001 0.081370 0.148219 0.101323 0.086841 0.028692 0.177124 \n", "Cell_10002 0.139363 0.371802 0.186718 0.074740 0.000000 0.286048 \n", "... ... ... ... ... ... ... \n", "Cell_9994 0.081529 0.192922 0.121566 0.000000 0.011434 0.107356 \n", "Cell_9996 0.253249 0.273179 0.174062 0.058310 0.078524 0.259639 \n", "Cell_9997 0.190197 0.204892 0.185552 0.089932 0.016441 0.182882 \n", "Cell_9998 0.240043 0.216039 0.157211 0.226659 0.027269 0.211629 \n", "Cell_9999 0.143029 0.303464 0.129246 0.171726 0.023894 0.087445 \n", "\n", " Cebpd(+) Dbp(+) Dlx1(+) Ebf1(+) ... Satb2(+) Sox10(+) \\\n", "Cell ... \n", "Cell_1 0.000000 0.170651 0.122474 0.000000 ... 0.099124 0.030581 \n", "Cell_1000 0.060005 0.180853 0.053370 0.000000 ... 0.124200 0.023578 \n", "Cell_10000 0.000000 0.134629 0.040831 0.000000 ... 0.079151 0.021887 \n", "Cell_10001 0.000000 0.187842 0.034786 0.000000 ... 0.062423 0.040046 \n", "Cell_10002 0.000000 0.324644 0.075673 0.075543 ... 0.104446 0.040820 \n", "... ... ... ... ... ... ... ... \n", "Cell_9994 0.000000 0.079788 0.091360 0.000000 ... 0.072796 0.048270 \n", "Cell_9996 0.000000 0.178899 0.204684 0.163190 ... 0.140081 0.041906 \n", "Cell_9997 0.052197 0.216455 0.096070 0.000000 ... 0.139947 0.031018 \n", "Cell_9998 0.000000 0.173408 0.153566 0.083944 ... 0.146612 0.053716 \n", "Cell_9999 0.000000 0.420941 0.067038 0.000000 ... 0.073910 0.077494 \n", "\n", " Sox9(+) Srebf1(+) Tbx18(+) Tcf4(+) Tcf7l2(+) Thra(+) \\\n", "Cell \n", "Cell_1 0.043982 0.108448 0.000000 0.282513 0.121834 0.123972 \n", "Cell_1000 0.023990 0.059154 0.000000 0.368584 0.047002 0.218843 \n", "Cell_10000 0.069703 0.349720 0.000000 0.320939 0.076905 0.085574 \n", "Cell_10001 0.135491 0.336981 0.000000 0.172192 0.035783 0.144633 \n", "Cell_10002 0.137917 0.359795 0.000000 0.311838 0.066502 0.279441 \n", "... ... ... ... ... ... ... \n", "Cell_9994 0.059478 0.116394 0.000000 0.124870 0.011098 0.116962 \n", "Cell_9996 0.123377 0.315487 0.000000 0.347189 0.114236 0.220211 \n", "Cell_9997 0.139405 0.583368 0.000000 0.314701 0.095673 0.242292 \n", "Cell_9998 0.107629 0.365332 0.000000 0.579174 0.114361 0.212512 \n", "Cell_9999 0.173375 0.530861 0.018987 0.336947 0.084933 0.217689 \n", "\n", " Thrb(+) Vsx1(+) \n", "Cell \n", "Cell_1 0.103947 0.0 \n", "Cell_1000 0.112490 0.0 \n", "Cell_10000 0.074182 0.0 \n", "Cell_10001 0.052906 0.0 \n", "Cell_10002 0.090039 0.0 \n", "... ... ... \n", "Cell_9994 0.060993 0.0 \n", "Cell_9996 0.146768 0.0 \n", "Cell_9997 0.116345 0.0 \n", "Cell_9998 0.162407 0.0 \n", "Cell_9999 0.068903 0.0 \n", "\n", "[50140 rows x 63 columns]" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "adata.obsm['isr']" ] }, { "cell_type": "markdown", "id": "e52be4bd", "metadata": {}, "source": [ "## Part III. Visualization of the network" ] }, { "cell_type": "code", "execution_count": 25, "id": "13c85f0e-9ea0-44a0-a605-7fa0ca83831f", "metadata": {}, "outputs": [], "source": [ "import spagrn.plot as prn" ] }, { "cell_type": "markdown", "id": "c3587377", "metadata": {}, "source": [ "### Regulon 2D spatially distribution plot\n", "`plot_2d` plots the spatial distribution of the regulon activity level, with each point representing a spatial location and the color indicating the ISR value of the corresponding regulon." ] }, { "cell_type": "code", "execution_count": 29, "id": "d265482b", "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "prn.plot_2d(adata, adata.obsm['auc_mtx'], pos_label='spatial', reg_name='Pbx3', fn='Pbx3.png')" ] }, { "cell_type": "markdown", "id": "133b72fc-9ac3-4d03-810c-d9737c9bc43a", "metadata": {}, "source": [ "" ] }, { "cell_type": "markdown", "id": "29a499b8", "metadata": {}, "source": [ "### Heatmaps\n", "`auc_heatmap` visualize the activity levels of regulons across cells, allowing for easy comparison and identification of trends or patterns. By clustering the cells based on regulon activities, one can see whether there are groups of cells that tend to have the same regulons active, and reveal the network states that are recurrent across multiple cells.\n", "#### 1. regular heatmap" ] }, { "cell_type": "code", "execution_count": null, "id": "b1c504d4", "metadata": {}, "outputs": [], "source": [ "prn.auc_heatmap(data,\n", " auc_mtx,\n", " cluster_label='annotation',\n", " topn=10,\n", " subset=False,\n", " save=True,\n", " fn=f'{out_dir}/{method}_clusters_heatmap_top10.png',\n", " legend_fn=f\"{out_dir}/{method}_rss_celltype_legend_top10.png\")" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.12" } }, "nbformat": 4, "nbformat_minor": 5 }