{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "Welcome to the EPISOL Colab Playground!\n",
        "\n"
      ],
      "metadata": {
        "id": "2UZUSEe9RzKv"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        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)"
      ],
      "metadata": {
        "id": "JV402f06Qs2I"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The 3D reference interaction site model (3DRISM) provides an efficient grid-based solvation model to compute the structural and thermodynamic properties of solutes emersed in solvent. However, this solvent need not be aqueous, and can hypothetically be *any* molecule. In this notebook we will walk through a high-throughput calculation on the solvation free energy of a test set of small molecules, emersed in a common electrolyte solvent.  \n",
        "\n",
        "* **goals**:\n",
        "  * utilize an organic solvent\n",
        "  * Generate the coordinate and topology files for our test set using RDKit and openFF\n",
        "  * Perform high-throughput 3DRISM calculations to determine solvation free energy of 100 electrolyte molecules in the test set\n",
        "  * Be able to perform similar calculations on your own molecules\n",
        "    * use the colab-notebook to download and run calculations on your own molecules"
      ],
      "metadata": {
        "id": "2JyzyqCTrO2i"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#@title ##Download and Install Episol\n",
        "#@markdown ($\\approx 2$min) Stable as of 07/01/25 eprism v1.2.6\n",
        "%%capture\n",
        "import subprocess\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "#%cd ../home/\n",
        "%cd $HOME/\n",
        "%mkdir episol\n",
        "%cd episol\n",
        "!wget https://github.com/EPISOLrelease/EPISOL/raw/refs/heads/main/src/fftw/fftw-3.3.8.tar.gz\n",
        "!echo \"+++++++++++++++++++\"\n",
        "!echo \"downloaded fftw files\"\n",
        "!echo \"+++++++++++++++++++\"\n",
        "!tar -xzf fftw-3.3.8.tar.gz\n",
        "%cd fftw-3.3.8/\n",
        "#!./configure --prefix=/home/fftw-3.3.8\n",
        "!./configure --prefix=$HOME/episol/fftw-3.3.8\n",
        "!make\n",
        "!make install\n",
        "%cd ../\n",
        "!wget https://github.com/EPISOLrelease/EPISOL/raw/refs/heads/main/src/kernel/release.tar.gz\n",
        "!echo \"+++++++++++++++++++\"\n",
        "!echo \"downloaded Episol files\"\n",
        "!echo \"+++++++++++++++++++\"\n",
        "!tar -xzf release.tar.gz\n",
        "%cd release/\n",
        "#!./configure --with-fftw=/home/fftw-3.3.8\n",
        "!./configure --with-fftw=$HOME/episol/fftw-3.3.8\n",
        "!make\n",
        "!make install\n",
        "#%cd /content\n",
        "########################### WRAPEPR\n",
        "import subprocess\n",
        "import os\n",
        "import threading\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "!pip install episol\n"
      ],
      "metadata": {
        "id": "e9Zwmfx1MLyE",
        "cellView": "form"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Install some python packages for topology generation\n",
        "#@markdown ($\\approx$4min)\n",
        "\n",
        "#@markdown This will prompt a restart in our colab session, this is necessary, just keep moving\n",
        "\n",
        "#@markdown (if you are using the notebook offline this wont be necessary, as presumably you'll have your own forcefield to generate topologies)\n",
        "########################################\n",
        "#        FOR COLAB USERS ONLY          #\n",
        "#---------------------------------------#\n",
        "# if you are running locally you dont need\n",
        "# condacolab. Just use your local conda dist\n",
        "########################################\n",
        "!pip install -q condacolab\n",
        "import condacolab\n",
        "condacolab.install()\n",
        "########################################\n",
        "#!conda update conda\n",
        "#!conda install --yes -c conda-forge  python=3.11 numpy=1.26.4 openmm pdbfixer parmed mdanalysis py3dmol rdkit openff-toolkit\n",
        "#!conda install -y -c conda-forge numpy=1.26.4 openmm=8.3.1 python={PYTHON_VERSION} pdbfixer=1.11 parmed=4.3.0 mdanalysis=2.9.0 py3dmol=2.5.2 rdkit=2025.03.5 openff-toolkit=0.17.0 libgcc\n",
        "#!conda install -y -c conda-forge python=3.12 numpy=1.26.4 openmm=8.3.1 parmed=4.3.0 mdanalysis=2.9.0 py3dmol=2.5.2 rdkit=2025.03.5 openff-toolkit=0.17.0 torchvision\n",
        "#openmm pdbfixer parmed mdanalysis py3dmol rdkitconda install libgcc"
      ],
      "metadata": {
        "id": "Pz6E85eEOsUO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "35eef1c1-345c-4bf1-cf9e-b697313429ca",
        "cellView": "form"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "⏬ Downloading https://github.com/jaimergp/miniforge/releases/download/24.11.2-1_colab/Miniforge3-colab-24.11.2-1_colab-Linux-x86_64.sh...\n",
            "📦 Installing...\n",
            "📌 Adjusting configuration...\n",
            "🩹 Patching environment...\n",
            "⏲ Done in 0:00:11\n",
            "🔁 Restarting kernel...\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%capture\n",
        "!python -m ensurepip --upgrade\n",
        "!python3.12 -m pip install --upgrade setuptools\n",
        "#!pip3 install scikit-learn\n",
        "!conda install -y -c conda-forge python=3.12 numpy=1.26.4 openmm=8.3.1 pdbfixer=1.11 parmed=4.3.0 mdanalysis=2.9.0 py3dmol=2.5.2 rdkit=2025.03.5 openff-toolkit=0.17.0 torchvision pymol-open-source ambertools"
      ],
      "metadata": {
        "id": "KSSOftdLvj7_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title import our download packages\n",
        "%%capture\n",
        "ias!!ddmport py3Dmol\n",
        "\n",
        "def MolTo3DView(mol, size=(300, 300), style=\"stick\", surface=False, opacity=0.5):\n",
        "    \"\"\"\n",
        "    https://birdlet.github.io/2019/10/02/py3dmol_example/\n",
        "    Draw molecule in 3D\n",
        "\n",
        "    Args:\n",
        "    ----\n",
        "        mol: rdMol, molecule to show\n",
        "        size: tuple(int, int), canvas size\n",
        "        style: str, type of drawing molecule\n",
        "               style can be 'line', 'stick', 'sphere', 'carton'\n",
        "        surface, bool, display SAS\n",
        "        opacity, float, opacity of surface, range 0.0-1.0\n",
        "    Return:\n",
        "    ----\n",
        "        viewer: py3Dmol.view, a class for constructing embedded 3Dmol.js views in ipython notebooks.\n",
        "    \"\"\"\n",
        "    assert style in ('line', 'stick', 'sphere', 'carton')\n",
        "    mblock = Chem.MolToMolBlock(mol)\n",
        "    viewer = py3Dmol.view(width=size[0], height=size[1])\n",
        "    viewer.addModel(mblock, 'mol')\n",
        "    viewer.setStyle({style:{}})\n",
        "    if surface:\n",
        "        viewer.addSurface(py3Dmol.SAS, {'opacity': opacity})\n",
        "    viewer.zoomTo()\n",
        "    return viewer\n",
        "def smi2conf(smiles):\n",
        "    '''Convert SMILES to rdkit.Mol with 3D coordinates'''\n",
        "    mol = Chem.MolFromSmiles(smiles)\n",
        "    if mol is not None:\n",
        "        mol = Chem.AddHs(mol)\n",
        "        AllChem.EmbedMolecule(mol)\n",
        "        AllChem.MMFFOptimizeMolecule(mol, maxIters=200)\n",
        "        return mol\n",
        "    else:\n",
        "        return None\n",
        "#free_energy(\"EXP\")\n",
        "!python -m ensurepip --upgrade # since we are using python 3.12 some pkg utils are now obsolete\n",
        "# after conda-initiate restart colab resets pip\n",
        "import matplotlib.pyplot as plt\n",
        "import openmm as mm\n",
        "from   openmm import app\n",
        "from   openmm.unit import *\n",
        "import py3Dmol as pymol\n",
        "import MDAnalysis as md\n",
        "import parmed as chem\n",
        "from openff.toolkit.topology import Molecule, Topology\n",
        "import numpy as np\n",
        "from MDAnalysis.transformations import center_in_box\n",
        "# from episol import epipy\n",
        "from rdkit import Chem\n",
        "from rdkit.Chem import AllChem\n",
        "from openff.toolkit.topology import Molecule\n",
        "from openff.toolkit.utils import get_data_file_path\n",
        "from openff.toolkit.typing.engines.smirnoff import ForceField\n",
        "from openff.interchange import Interchange\n",
        "\n",
        "# FIX IMPORTER ERROR\n",
        "!python -m ensurepip --upgrade\n",
        "!pip3 install episol\n",
        "!python3.12 -m pip install --upgrade setuptools\n",
        "%cd /content/"
      ],
      "metadata": {
        "id": "rjpO1aNMD_89",
        "cellView": "form"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Procedure"
      ],
      "metadata": {
        "id": "FPI8DegtS21r"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "* To use a solvent besides water, we need bulk solvent-solvent correlations\n",
        "* these can be acquired with MD or with 1DRISM\n",
        "* here we will use a mixture of water and cyanide"
      ],
      "metadata": {
        "id": "OxhknOidS6e_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "smi = \"N#C\"\n",
        "mol = Chem.MolFromSmiles(smi)\n",
        "mol"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 167
        },
        "id": "p22MJf7r0Bmn",
        "outputId": "0dfdaaee-f677-4ed1-815b-b70423b59c93"
      },
      "execution_count": 125,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<rdkit.Chem.rdchem.Mol at 0x7c1684cfff40>"
            ],
            "image/png": "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\n"
          },
          "metadata": {},
          "execution_count": 125
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mol = Chem.rdmolops.AddHs(mol,addCoords=True)\n",
        "AllChem.EmbedMolecule(mol)\n",
        "AllChem.UFFOptimizeMolecule(mol)\n",
        "MolTo3DView(mol).show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "id": "iofSqjI57UB_",
        "outputId": "93d4decd-db89-4467-fa3a-cc8085469b81"
      },
      "execution_count": 126,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/3dmoljs_load.v0": "<div id=\"3dmolviewer_17786927511833618\"  style=\"position: relative; width: 300px; height: 300px;\">\n        <p id=\"3dmolwarning_17786927511833618\" style=\"background-color:#ffcccc;color:black\">3Dmol.js failed to load for some reason.  Please check your browser console for error messages.<br></p>\n        </div>\n<script>\n\nvar loadScriptAsync = function(uri){\n  return new Promise((resolve, reject) => {\n    //this is to ignore the existence of requirejs amd\n    var savedexports, savedmodule;\n    if (typeof exports !== 'undefined') savedexports = exports;\n    else exports = {}\n    if (typeof module !== 'undefined') savedmodule = module;\n    else module = {}\n\n    var tag = document.createElement('script');\n    tag.src = uri;\n    tag.async = true;\n    tag.onload = () => {\n        exports = savedexports;\n        module = savedmodule;\n        resolve();\n    };\n  var firstScriptTag = document.getElementsByTagName('script')[0];\n  firstScriptTag.parentNode.insertBefore(tag, firstScriptTag);\n});\n};\n\nif(typeof $3Dmolpromise === 'undefined') {\n$3Dmolpromise = null;\n  $3Dmolpromise = loadScriptAsync('https://cdn.jsdelivr.net/npm/3dmol@2.5.2/build/3Dmol-min.js');\n}\n\nvar viewer_17786927511833618 = null;\nvar warn = document.getElementById(\"3dmolwarning_17786927511833618\");\nif(warn) {\n    warn.parentNode.removeChild(warn);\n}\n$3Dmolpromise.then(function() {\nviewer_17786927511833618 = $3Dmol.createViewer(document.getElementById(\"3dmolviewer_17786927511833618\"),{backgroundColor:\"white\"});\nviewer_17786927511833618.zoomTo();\n\tviewer_17786927511833618.addModel(\"\\n     RDKit          3D\\n\\n  3  2  0  0  0  0  0  0  0  0999 V2000\\n    1.1242    0.0000    0.0000 N   0  0  0  0  0  0  0  0  0  0  0  0\\n   -0.0329    0.0000    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -1.0913    0.0000    0.0000 H   0  0  0  0  0  0  0  0  0  0  0  0\\n  1  2  3  0\\n  2  3  1  0\\nM  END\\n\",\"mol\");\n\tviewer_17786927511833618.setStyle({\"stick\": {}});\n\tviewer_17786927511833618.zoomTo();\nviewer_17786927511833618.render();\n});\n</script>",
            "text/html": [
              "<div id=\"3dmolviewer_17786927511833618\"  style=\"position: relative; width: 300px; height: 300px;\">\n",
              "        <p id=\"3dmolwarning_17786927511833618\" style=\"background-color:#ffcccc;color:black\">3Dmol.js failed to load for some reason.  Please check your browser console for error messages.<br></p>\n",
              "        </div>\n",
              "<script>\n",
              "\n",
              "var loadScriptAsync = function(uri){\n",
              "  return new Promise((resolve, reject) => {\n",
              "    //this is to ignore the existence of requirejs amd\n",
              "    var savedexports, savedmodule;\n",
              "    if (typeof exports !== 'undefined') savedexports = exports;\n",
              "    else exports = {}\n",
              "    if (typeof module !== 'undefined') savedmodule = module;\n",
              "    else module = {}\n",
              "\n",
              "    var tag = document.createElement('script');\n",
              "    tag.src = uri;\n",
              "    tag.async = true;\n",
              "    tag.onload = () => {\n",
              "        exports = savedexports;\n",
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              "        resolve();\n",
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              "  var firstScriptTag = document.getElementsByTagName('script')[0];\n",
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              "$3Dmolpromise = null;\n",
              "  $3Dmolpromise = loadScriptAsync('https://cdn.jsdelivr.net/npm/3dmol@2.5.2/build/3Dmol-min.js');\n",
              "}\n",
              "\n",
              "var viewer_17786927511833618 = null;\n",
              "var warn = document.getElementById(\"3dmolwarning_17786927511833618\");\n",
              "if(warn) {\n",
              "    warn.parentNode.removeChild(warn);\n",
              "}\n",
              "$3Dmolpromise.then(function() {\n",
              "viewer_17786927511833618 = $3Dmol.createViewer(document.getElementById(\"3dmolviewer_17786927511833618\"),{backgroundColor:\"white\"});\n",
              "viewer_17786927511833618.zoomTo();\n",
              "\tviewer_17786927511833618.addModel(\"\\n     RDKit          3D\\n\\n  3  2  0  0  0  0  0  0  0  0999 V2000\\n    1.1242    0.0000    0.0000 N   0  0  0  0  0  0  0  0  0  0  0  0\\n   -0.0329    0.0000    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -1.0913    0.0000    0.0000 H   0  0  0  0  0  0  0  0  0  0  0  0\\n  1  2  3  0\\n  2  3  1  0\\nM  END\\n\",\"mol\");\n",
              "\tviewer_17786927511833618.setStyle({\"stick\": {}});\n",
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              "</script>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "$$$$\n",
        "$$h(r)_i^{\\rm sol.-solv.} = \\rho\\sum_j\\int c(r)_j^{\\rm sol.-solv.}\\left[\\omega(r)_j^{\\rm solv.-solv.} + h(r)_j^{\\rm solv.-solv.}\\right]d\\mathbf{r}$$"
      ],
      "metadata": {
        "id": "ajoy1CuKNJp-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "$$$$\n",
        "$$\n",
        "h(r)_i^{\\rm sol.-solv.} \\equiv \\exp{\\left( - U(r)_i^{\\rm sol.-solv.} + h(r)_i^{\\rm sol.-solv.}+ c(r)_i^{\\rm sol.-solv.} + \\mathfrak{B}\\right)}\n",
        "$$\n",
        "\n",
        "$$$$"
      ],
      "metadata": {
        "id": "nZGk5jF5StW8"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "* we will download our correlations [here](https://github.com/p-swanson/solvents)"
      ],
      "metadata": {
        "id": "aiPITWmZEd-F"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%capture\n",
        "!wget https://raw.githubusercontent.com/p-swanson/solvents/refs/heads/main/organic/cyanide.gvv\n",
        "!wget https://raw.githubusercontent.com/p-swanson/solvents/refs/heads/main/organic/cyanide.sol\n"
      ],
      "metadata": {
        "id": "MnD1GgfFFwRT"
      },
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "* we can see that we have a mixture of cyanide and water.\n",
        "* this correspondings to 5 sites in total"
      ],
      "metadata": {
        "id": "d-e8Ona0H32E"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "* here we will test our system with the classic methane example"
      ],
      "metadata": {
        "id": "rxzfvPzpH3Zw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!wget https://raw.githubusercontent.com/p-swanson/solvents/refs/heads/main/tests/methane.gro\n",
        "!wget https://raw.githubusercontent.com/p-swanson/solvents/refs/heads/main/tests/methane.top"
      ],
      "metadata": {
        "id": "nsEf6MslIEQT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test = int()"
      ],
      "metadata": {
        "id": "SjmRtvY_HXM0"
      },
      "execution_count": 123,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mem = epipy(\"methane.gro\",\"methane.top\",gen_idc=True)\n",
        "mem.closure = \"KH\"\n",
        "mem.err_tol = 1e-04\n",
        "mem.ndiis = 15\n",
        "mem.solvent(\"cyanide.sol\")\n",
        "mem.solvent_path = \"./\"\n",
        "mem.report(f\"methane_cyanide_{mem.closure}\")\n",
        "mem.rism(step=2000,resolution=0.5)\n",
        "mem.kernel(nt=2)\n",
        "test +=1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4ClHr4wcF5DN",
        "outputId": "db15d35d-9967-46d7-aef1-d5c1d6b44e18"
      },
      "execution_count": 124,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "converted methane.top to methane.solute\n",
            "generated idc-enabled solute file to: idc_methane.solute\n",
            "Calculation finished in 42 steps \n",
            "err_tol: 0.0001 actual: 9.96146e-05 \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "guv = mem.select_grid()"
      ],
      "metadata": {
        "id": "e8WZP8TqIKg7"
      },
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "SLICE = 30\n",
        "names = [\"C\",\"N\",\"H\"]\n",
        "fig,axs = plt.subplots(1,3,figsize=(10,3))\n",
        "for ind,(ax,name) in enumerate(zip(axs.flatten(),names)):\n",
        "  # select values\n",
        "  t = mem.reader(f\"methane_cyanide_{mem.closure}.txt\",atom_to_select=ind+3)\n",
        "  p = ax.pcolormesh(t[SLICE],cmap='bwr')\n",
        "  ax.set_title(name)\n",
        "  fig.colorbar(p,ax=ax,label=\"$g(r)$\")\n",
        "  ax.set_axis_off()\n",
        "fig.tight_layout()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 307
        },
        "id": "VQ04OVZuIFXR",
        "outputId": "1eeeffd5-c9cd-4d96-9c0d-587be78721f6"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x300 with 6 Axes>"
            ],
            "image/png": 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mlI+JAK9Ye8yPp86tMQZM13VrXAqv2qBNFROd5Fi7mXOtchLLAhEHVWwruiqlOtZEZIDBkohIx441EVEM24quYseaKKgYLImIdOxYExHFsK3oKp4loqA6Eyzj+Wdg3rx5GD58ONq3b4/MzEyMHz8eu3fvtn3d888/j759+yI9PR0XX3wxXnnllUSPjIgoMR7ERCKilOVRW7G54hXrr5nkt7j12TL50TxZOY1W+S4meTN2rI7Pq3zHwPPoV8g33ngDU6dOxfDhw3Hq1CnMmjULV155JXbu3Imzzz67wdds3rwZN998M+bNm4fvfve7WLVqFcaPH4+33367wSkVqHF+GGvCSUx0MyfZqk5227KSaEyUZdN8QZPjMcFxKBS8Yh14XuUZm35sEm1Xehn33Ppom8Q2N2OmH+e8TrS96hu8Yu0qdqyJgqpVq6/+2bEYtbEh69at08olJSXIzMzE9u3b8c1vfrPB1zz++OMYO3Ys7rvvPgDAgw8+iPXr1+OJJ55AcXGx0f6JiBIWT1w0jIlERCnLo7Zic8WfH4iCyvD2nqqqKu3fyZMn49rN8ePHAQCdOnVqdJ0tW7agsLBQe27MmDHYsmVLggdHRJQA3vJIRBTDW8FdxbNEFFRn5ia0+/f13IS5ubnIyMiI/ps3b57tLk6fPo177rkHl156qeUt3RUVFejatav2XNeuXVFRUeHsGImITMQTFzmPNRE1F4ZtRbLm+1vBvfqBxKv8Qav92G1X3omhluUyJzk3kkl+y6lTDT9uqOzW3IROzr+b+S4pl0djmDdz4MABhMPh6NNpaWm2L506dSp27NiBTZs2JVxNsuaHnGpZtquTyffDKrZZxURZNomRdqzilV3cs4qRVvG0oeUqt/4ueRm7UiJGMsc6cEzfrkTzqk1ipJPXmsQy07iXaFvRSe6zXZyziq9u5WM3VI53mclYEoEdd+LMemTL9x1rIkqQYbAMh8Nax9rOtGnT8NJLL2Hjxo3o1q2b5bpZWVmorKzUnqusrERWVlbc+yMicowdayKiGHasXcWzRBRUHuXNRCIRTJs2DS+++CI2bNiAXr162b6moKAApaWl2nPr169HQUGB0b6JiBxhLiERUQxzrF3FK9ZEQeXRr5BTp07FqlWrsHbtWrRv3z6aJ52RkYE2bdoAACZMmIBzzz03mqd9991341vf+hYee+wxXHXVVVi9ejW2bduGpUuXmh0TEZETvGJNRBTDK9au8l3HOhk51bKcaP5NQ2WrHMDWra3LVq+1y6uxWmaSZ2KSP1hb23jZJB/bro5+yLlOiTwaj4LlkiVLAACjRo3Snn/qqadw2223AQD279+PFsp2R44ciVWrVuH+++/HrFmzcMEFF2DNmjWcwzrJTHL1VE5yqE3ioEmMNM1/tOIkf1CNe1Yx0a7sJCZaxSeTvO5AYseaLLiVJw2YxSeT9p4aB+3iqVVcN4kbJuPvyLhm11ZUl8s6mIzdI9d1olnFRXasXeW7jjURucSjYBmJYy7DsrKyes9df/31uP766432RUTkKnasiYhi2LF2FTvWREHFYElEpGPHmogohm1FV7FjTRRUDJZERDp2rImIYthWdFVgO9amuTCJ5lWb5Aump8e/rty2k5xqO4nmXJvkWJ84Ef+6ctt2uS6JftcDn0MTCsV3ckIh7+tCRppqrAmrHEDJJMdX3a6MazIOWpVNxpZoqr/5JnOlyrgm46BV2S5P0SrX0OrvmzyHXuVc+3YcinjiImOi7zkZC8dqmUmOtV27zCq+mowtYRUj7dY1GZfCisnYEnbtPZO4ZzIOheRWDrZdfE35ea3ZVnQVf34gCipOoUBEpPMgJs6bNw/Dhw9H+/btkZmZifHjx2P37t22r3v++efRt29fpKen4+KLL8Yrr7yS6FERESWGbUVX8SwRBRWDJRGRzoOY+MYbb2Dq1Kl48803sX79enz55Ze48sorUVNT0+hrNm/ejJtvvhl33HEH3nnnHYwfPx7jx4/Hjh07nB4hEVH82FZ0Fc8SUVC1ahX/PyKi5sCDmLhu3Trcdttt6N+/PwYNGoSSkhLs378f27dvb/Q1jz/+OMaOHYv77rsPF110ER588EEMGTIETzzxhNMjJCKKn0dtxY0bN+Lqq69GTk4OQqEQ1qxZY7l+WVkZQqFQvX8VFRXaeosXL0bPnj2Rnp6O/Px8bN261fSIPZX0FrWbP4BY5cK4lVMNWOfCtG2rl9V8F5n7Ypc/mOgcrU5yrE3yB2W+ijwX6nK5TObYfPGFXraaC9aPc1z7Mo+GA1I0C+pnzc2xJUy+Z1Z5fjImyrKTHEC38iwlk++v1XbtxuCQfxPUOChjolXeol3cNvnbKJnkcqeEeOKiwwM7fvw4AKBTp06NrrNlyxYUFRVpz40ZM8a28dlcufldT7StaJJTLcsmedPt2unLrGKoXXy1yrk26SfZjamjxicZu0zKduuq51zWQZJ1Vo/XyZzXVnHQdEygILcVa2pqMGjQINx+++247rrr4n7d7t27EQ6Ho+XMzMzo42effRZFRUUoLi5Gfn4+Fi1ahDFjxmD37t3aesmU9I41EXmEHWsiIp1Bx7qqqkp7Oi0tDWlpaZYvPX36NO655x5ceumlGDBgQKPrVVRUoGvXrtpzXbt2rXd1hojIUx61FceNG4dx48YZVyczMxMdOnRocNmCBQswZcoUTJo0CQBQXFyMl19+GStWrMCMGTOM9+UFtqiJgop5M0REOoOYmJubi4yMjOi/efPm2W5+6tSp2LFjB1avXu31kRAROWfYVqyqqtL+nTx50tXq5OXlITs7G1dccQX+9Kc/RZ+vra3F9u3bUVhYqFS9BQoLC7FlyxZX6+AEr1gTBRWvWBMR6QyuWB84cEC7JdHuavW0adPw0ksvYePGjejWrZvlullZWaisrNSeq6ysRFZWlnXdiIjcZNhWzM3N1Z6eM2cO5s6d67ga2dnZKC4uxrBhw3Dy5EksX74co0aNwltvvYUhQ4bgyJEjqKura/BOn127djnev1tSumNtkmfsVk41YJ3f4mROVqt8PJO5Cu3ygKzm9rObb9Aq91nmrKhl02OXeTWN1QHwZ861L3BuwpThJP/XrbElTHLD7MaWUHME7XKqrepkV3+TGGkVF+WxW82dajfPqtV5tMonl2W7OG5SB5M8fBMmOde+yS00mMc6HA5rHevGRCIR/PCHP8SLL76IsrIy9OrVy/Y1BQUFKC0txT333BN9bv369SgoKLB9LbnH7vtgNeaD3RgKatkuF1r9mMmPnFVZLgtVf64/IRtTx5SyXeBQDjBNHOzZ9YJ+rBzJaq8tEhkVlmW5zCpGWo3N0xCrNqlVznXKtwXtGLYVTX9wjFefPn3Qp0+faHnkyJHYu3cvFi5ciN/+9reu7KMppHTHmogs8Io1EZHOg8HLpk6dilWrVmHt2rVo3759NE86IyMDbdq0AQBMmDAB5557bvR28rvvvhvf+ta38Nhjj+Gqq67C6tWrsW3bNixdutT8mIiIEmXYVoz3B0c3jBgxAps2bQIAdO7cGS1btvT9nT5sURMFFXOsiYh0HsTEJUuW4Pjx4xg1ahSys7Oj/5599tnoOvv378fhw4ej5ZEjR2LVqlVYunQpBg0ahP/6r//CmjVrLAc8IyJynY/biuXl5cjOzgYAtG7dGkOHDkVpaWl0+enTp1FaWuqrO314xZooqHjFmohI58EV60gkYrtOWVlZveeuv/56XH/99Ub7IiJylUdtxerqauzZsyda3rdvH8rLy9GpUyd0794dM2fOxKFDh7By5UoAwKJFi9CrVy/0798fJ06cwPLly7Fhwwa89tpr0W0UFRVh4sSJGDZsGEaMGIFFixahpqYmOkq4HySlY+1WO94kx9pkW27mD1rlPsscQDl3oTravJwOU5bPbv1lrCATo60m8xMVqak9S1v0j3+g0fKxY/qy6mq9rFZDVsEuV8nk/bOaz1Vykstqwip/s8mwYx1ITnKSTfJcrfKZZayymnfV7rttFQtkfJVxr3Pnhh8DevxsaFshxDpDEejjDMjcPTXuHTmiL5NlNS7K7djNnaqeY5P3UsZeq9RJJ3NRcx5raipOxsWxW57ouBQmOdWAHhet2neyLONcx3Cd/oQadA4aBCRAT2KWbUWrL7hssMpbgZUDCIlgnCHLOXr5aLuWje5GnlP1cOR7JeOgFbs2abzLAOsxLEzapEFrK27btg2jR4+OlouKigAAEydORElJCQ4fPoz9+/dHl9fW1uLee+/FoUOH0LZtWwwcOBCvv/66to0bb7wRn376KWbPno2Kigrk5eVh3bp19QY0SyZesSYKKnasiYh07FgTEcV41FYcNWqU5d08JSUlWnn69OmYPn267XanTZuGadOmGdWlKbFjTRRU7FgTEenYsSYiimFb0VXsWBMFFYMlEZGOHWsiohi2FV2VUh1rN/8WWuUPWuVUy+V2+SBWeYmZmXq5Wze93PH0Z7HChx/qC7cd1MtfT+8BoP5EgHY51kquzNmiUmd3766Vc887L/r4aPdztGVKqgQAPdVH5r7Y5TUlyskc13b5gyk3lyGDpW+5mddqki9oUiersSasYmJDr1XJ8CNfq6bj9eypLzu/p8g1/NvfYo93iACkxkSgfq6hEixCosJniwTIs5WpPHJFTMTlF2rFvR/F8gU/+khf9ZNP9LLMwVZZnUPJLja5NQ6FXUy0yjU03ZZn2LEOPJO8abvxXqzmlrfKqQb0NGS7cXLUplda9Wf6wh2ivXfwYMOPGyqbDAJh9cfGauJtQD8A2ZiVZRFDO+bkxHbTTW9XynNsNa+4JNudJjFGbTY31dg8ScO2oqtSqmNNRAZatqzfEmhsPSKi5iCeuMiYSETNBduKrmLHmiio+CskEZGOV6yJiGLYVnQVO9ZEQcVgSUSkY8eaiCiGbUVXNUnHuinmrTZ9rVWujEwlkWU1r1rmf8icazWPRqbm5YaP60+8/bZeLi+PPd65U19mktBsl6SpJgbJyWBlpfv2jT7sOGSItkiWD3TKaLS6cn5sE/Jw1FwYq2UNLVeZzFVowte5hGfWI19xkqsa73Yli+ntAehhwklOtZy/VeZR5+XFHoe2/Vlf+No2vbxjR+yxXUKzVVyUJ1EeoJo/qIwzAQAYMEArnj9sWGzVy4dry9SQDtSvspr+KGOX1Tm2ynWWZbtxKJzkWFvNyeqbMMOOtW+5OXd1vK81yce2GncCqJ9jrbb/ZNNKSSsGAIQOHogV5Jg66lgSsizXlTnWsrGlxkGTYGCXUK4erMypljHzwgsbLaeJdbt1y220SpJJHHQydolJG87ktWwrBgOvWBMFFYMlEZGOHWsiohi2FV3FjjVRUDFYEhHp2LEmIophW9FV7FgTBRWDJRGRjh1rIqIYthVd5fuOtUm+l8l2rHJlTOamluvKPBo1leScf3ygL1xXppc3b9bL774beyyTlOVc1WqujJNkYbuESDV3Rs1vBIA9e7Ri7qhR0cdt+16gLZMpQ3KqRZWsrjznVjnWdmWVk/xBX2Kw9JWmGGvCdB8m+YNq2W5mDnW5nK+1Xz+9fEG4Un9i1euxxxs36svkOBRqkrKcp1omKTtJRlcPSM7fKpPElTqGRKAbXFioldu166qV1aE0ZGqkPBy1SvK9kuuajEMhOcm5NtFk8ZUd68AxyZOWZbt1rdp78nsnm0tq7KuXU/3RPv2JXbtij9W2X0NlNa7Y5VTLPGr1AGUgt/pCy0BRUaGX1RMl26uy/PHHelmts5hbOyT2m9OzV4PVa6iKJnHQqr3nZHwnthWbH993rIkoQaFQfIEwFPK+LkREfhBPXGRMJKLmgm1FV7FjTRRU/BWSiEjHK9ZERDFsK7qKHWuioGKwJCLSsWNNRBTDtqKrPOlYu3nuE80fNJm3GtBzZ0xyrGVOjZye75yP34sVXnlFX7hhg16Wk5yqicfygKySeWQOoDx4mXii5ibK/ByrssyTscj1Oec739EWXXjhxVpZ5qGoh26XC6MejkwnsprH2i73xWS+RBNNNlchg2XKcDIvppPtmuQPquva5Zyp4aleTnXrv+tPrF6jl9etiz2WOdVyMAa1kn376stkuXv3xl8r8vrq5QSq+Y9y8mlZR/W1MiaK+l8wfry+vF+P6EM5hIVVCrldfrwaF+3yEN2c1zrR7XqKHWvfSMa81bJs0laUMVI2tWSzLDMz9libpxrQYwoAbNvW8GNAH3wB0OPKiRP6Mvnll8ndaltRzkVt1VZU578G6rcN1dgm869l8JJlq7m1hZBSx8xMfY5r+VKrskkctPt7l/LzWrOt6CpesSYKKgZLIiIdO9ZERDFsK7qKHWuioGKwJCLSsWNNRBTDtqKr2LEmCqpWreznRTqzHhFRcxBPXGRMJKLmgm1FV/nuLHk1b7X8PMhcGbUs15VlNS1FTmFab65qNa/6pZf0ZTKnWub5qZNiDxyoLxs5Ui8rc0bXW1cm/sj8FnWORJn3bTW39ief6MvefFMvy9wfxTnX6W9A9+76PNfqS63mbwX0984ux8ZqHkPJKr+lyXJfnOCvkEnlVk6gmxfXTPIHZd6uVZ1kWp8aF+vNUy1zqteI8tatscfyCzxkiF7+3vdij2+/XVu0vryLVpah7eBHscdZWfqywsl6ecywz2KF5cv1hc89p5fVGCnn4baIiQBwwU03RR9X99TnuBZTYtdLeVRZjSFiNw6F1bzWdnHPrXEpPI2vvGKdNE11Wp3MY20VI2VKssyxVtOXASCtWokbH36oL5RzU6t51XKZHPNBrWTv3voyObaEHORCXS7zr2UgV9ukckwdmSOu5oHLZbL+8lxYBR2LASTSRH07dTpHK8twq5bt2opW4/FYfZ68bAt6th+2FV3Fs0QUVGeCZTz/iIiaA8ZEIqIYj9qKGzduxNVXX42cnByEQiGskT+iCy+88AKuuOIKdOnSBeFwGAUFBXj11Ve1debOnYtQKKT96yt/TEoy/vUgCqpQKL5AGQolu6ZERE0jnrjImEhEzYVHbcWamhoMGjQIixcvjmv9jRs34oorrsArr7yC7du3Y/To0bj66qvxzjvvaOv1798fhw8fjv7btGmTUb285rtbwYnIJby9h4hIx1vBiYhiPGorjhs3DuPGjYt7/UWLFmnlX/7yl1i7di3++Mc/YvDgwdHnW7VqhSyZv+UjrnSsvfwblGhetXyd1VzUgJ6yYpePraY+54aP6wvXlenl11+PPbbLqZa5MtdcE3s8Y4a26KeL9FySlbfFHsupXmX6SosW+ms7dRodfXzLLaO1ZQ8995lWxi9/GXssc8b37NHL6vHK3B2RjJR7Q6ZWPpaZEX0s82Sscq7t3mc1j8Y0j8+r/BbP8gk9CpYbN27EI488gu3bt+Pw4cN48cUXMV7Ox6soKyvD6NGj6z1/+PBhXwdHv3KSx2oVF2WcsxqnRC7L1L++yMtTCqte1xeq81QDek41oB+EGgMBYP58rfiTktjYDMXibjA5lITJ92rBAr3crl0sZt55539oyx5ZfZ2+shqr1TE2gPrHavHHJe9f/01bJKeNtcoXtPobZpV32BCv5nNNGnasm1Sip9LkLTLJqZZluzF11O+ObMbIHOuO4Tr9iZ1KXrIcJEHmUas5yjInWcaJAQNijy+5RF/23e9qxeMjrtDKL7wQe/zmC9qiejFTPT65m+v0Jik6blsfK8i4J8fq2bFDL6vHazWoEaC3HcX4QR0H6OXq6paiHHssm99W7UwnOfuSVb62k3EoHDFsK1ZVVWlPp6WlIS0tzcUKfeX06dP4/PPP0Un0Fz744APk5OQgPT0dBQUFmDdvHrp37+76/hPFvx5EQeVR3ozp7T1n7N69W7t9J1P2xoiIvMYcayKiGMO2Ym5uLjIyMqL/5s2b50m1Hn30UVRXV+OGG26IPpefn4+SkhKsW7cOS5Yswb59+3DZZZfh888/96QOieCt4ERB5ZPbe87IzMxEBzlCPRFRU+IVayKiGMO24oEDBxBWbmfw4mr1qlWr8LOf/Qxr167VLsKobc+BAwciPz8fPXr0wHPPPYc77rjD9Xokgh1roqDy2e09eXl5OHnyJAYMGIC5c+fi0ksvdW3bRERxYceaiCjGsK0YDoe1jrXbVq9ejcmTJ+P5559HYWGh5bodOnTAhRdeiD0yFTWJfNexduvvmdVchA2VrXKsZYqHdiv/22/rC63mfZYTj154oV6+7Tat+H/fmBl9PF6sKvPtnFCnJ3z4YX3Z0qV6PvaaNY9FH39LTTYHgJISvazmFMn8cjl/osgv75YXy8m1yi0EzHKs1XWtpk60kxK5hYbBMjc3V3t6zpw5mDt3ruNqZGdno7i4GMOGDcPJkyexfPlyjBo1Cm+99RaGyHmJqR4nMVG+1iouWs1bLctW81YDQGjbn2OFsjJ9oTpfK1A/QVjNqxZzRPcfqOfMqWmJbpLfZ/U3p0cf1Ze99NIFWvmvO5Q6K7ewAQD++7/1svz70a1b9GFI/H3o2XO4Vlbjol0ss4qR8jMht5Vo3p8vYyLAjrXHUuHUmYzHY5JjXW+AGzV3WOZYy/LBg7HH8kup5lQDwJVXxh5Pm6YtemRlV628XF+s7cZq7mbpmWf0sjrcDgBMnhzL5Z45K09f+KtfNb5hQG8fqhUE6uVRa21H2Y4UY7aEw/q5UHPIZdq6yXg8geOjgW5///vf4/bbb8fq1atx1VVX2a5fXV2NvXv34tZbb/W8bvHyXceaiFzik9t7+vTpgz59+kTLI0eOxN69e7Fw4UL89re/dWUfRERxYceaiCjGo451dXW1diV53759KC8vR6dOndC9e3fMnDkThw4dwsqVKwF8dfv3xIkT8fjjjyM/Px8VFRUAgDZt2iAj46sBjX/84x/j6quvRo8ePfDxxx9jzpw5aNmyJW6++WajunmJfz2IgspwQIozt/ec+edF3swZI0aM8NWtO0TUTHDwMiKiGI8Gut22bRsGDx4cnSqrqKgIgwcPxuzZswF8NTPMfuWujqVLl+LUqVOYOnUqsrOzo//uvvvu6DoHDx7EzTffjD59+uCGG27AOeecgzfffBNdunRx4US4g1esiYIqFIovEIZC3tdFKC8vR3Z2dpPvl4iauXjiYhJiIhFRUnjUVhw1ahQikUijy0tE+miZTBtrwOrVq43qkAwJd6zd+kHXzR+G1W3ZzU1oVZa5FvXmKsTRWEHmyMlc4k8+iT2WOclivsE3Rs7UylcqOfsyF6apyHkN1dSe117T6/stmQz99e0dAOrnHsk5HMV56zhwYPRxhw56nrcYY0vLCTR5n93Mk/ZyW4425IPbexYtWoRevXqhf//+OHHiBJYvX44NGzbgtddeM9qv37mZC+3VulY51iZ3x8q0t/N7ivlbX1PyqGWMlLFA5tkrc1VfNEDPqd61y7qOySDrpOaBv79Gn3e73vy0Mt9cjYNi2fl36ufpo49i+5HDd8j4Y5JjbRLLnKxrwtULyLwV3LcSPe1W40HYle3aDOr3xTbH+qCIbWq+8IcfNr4M0AePEWPO1JtE+v/9v+jDKbP0K3Wy3yFjQ6Lk2DbycH7+c3WZXqdlv/x/+sqyEae2HeWdbPI8qTs+7zx9mRjsI9xPz7FW3z+7OGg1Ho/V58lJWzBpY/f4KMc6CHjFmiioWrWq/9eisfUMbNu2DaNHxwaXKyoqAgBMnDgRJSUl9W7vqa2txb333otDhw6hbdu2GDhwIF5//XVtG0RETSKeuGgYE4mIUpZHbcXmimeJKKg8+hXS9Pae6dOnY/r06Ub7ICLyBK9YExHF8Iq1q3iWiILKowEpiIhSlkcxcePGjbj66quRk5ODUCiENWvWWK5fVlaGUChU79+ZkXCJiJoE24quivuKdbLOZ6L5hKY51uprZe6FTI3WckBkgp3MoVM3LOcinDVLK14j0kWSlVdtRa3Tddfpyz772wz9CTVfUA5KIM+TnJBWyaPplKXnWMt2h5r74yTH2o66vm/naFXxV0hPeXnarHK2JJO8ViffBzUu1ouJck7WHTtijz/6SF8mExXFXM/3FsfmhfZjTrUdNZTNXKHPcT3ve9/TV5YHqJ4r9RwC9c5x584XRR/LmGiVE2jyt1CSnzWvcg095dEV65qaGgwaNAi33347rpN/HC3s3r1bm+YwMzPTeN/NgV3etMlrrZaZzGMdqv5cf0KOH6HmB3/8sb5MjknTrl3scd+++jIxHs+85bEc5lWr9FW/+AJJoe5X5nlfeKGec32fOB4tb9ruPFmdU3H+5fvTtm376GO7eaxNPjNWnPz9bjJsK7qKt4ITBRWDJRGRzqOO9bhx4zBu3Djj12VmZqKDHA2QiKipsK3oKp4loqDi7T1ERDqDmFhVVaX9O3nypOvVycvLQ3Z2Nq644gr86U9/cn37RESW2FZ0Fc8SUVCdmZvQ7h/nbCWi5iKeuPh1TMzNzUVGRkb037x581yrRnZ2NoqLi/GHP/wBf/jDH5Cbm4tRo0bhbTk9HRGRl9hWdJXvbgV3krtgld9ll0emlmXuRadOYsdblfxgmSss5+dTJzocOVJbdO8vredn9juZ+vKTBfrx/EI9Xjm/tzxYeR6VPJpO/YZri+T7o87TaPK+2+Vs+SL3xQne3tPsufl9sMyx3im+v2rO3LFj+rJ+/fTy5MlacWlPBMbSpXp53q7b9SeeeUYvqznXMjddxMjOA2I51jI/UM45qzL92xg4BreCHzhwQMt/TktLc60affr0QZ8+faLlkSNHYu/evVi4cCF++9vfuraf5sA0/9qkrah+t2Tbo15Cs4x1as6vzL+Wg+jk5MQeixh5dNgVWnnpnY1XwQ/k3NnLl+vlO7fqx5PR7/VY4d139ZVl21BteH7yib5Mnn9xctLTYznWdvNYm4xzkhJjS1hhW9FVvutYE5FLGCyJiHQGHetwOKx1rL02YsQIbNq0qcn2R0TEtqK72LEmCioGSyIinUeDl7mhvLwc2dnZSdk3ETVTbCu6ih1roqBisCQi0nnUsa6ursYeZSrOffv2oby8HJ06dUL37t0xc+ZMHDp0CCtXrgQALFq0CL169UL//v1x4sQJLF++HBs2bMBrr71mvG8iooSxreiqwHasneTcyNyLs9Pr9CfUvA6ZNyMnEFWTES+/XFv0zL9a1ynVyHTBX5SMihXkxIbyvMmyMjHr2a2/1Ba1bn2WVrZ6b00/B/FKiTwaBksSnHwf1LhYb3YgOZGyGiPll0PM0fo/W/WxGWR+XiqTKX/ry/X5XK+Q89Wqc1fL/EFxjjt8I/ZY5n5ajdfhVUxMGR51rLdt24bRo0dHy0VFRQCAiRMnoqSkBIcPH8Z+JV+0trYW9957Lw4dOoS2bdti4MCBeP3117VtBF2yPnsmY0uoubeybYhjIsFZfvHUACADm9yROpiPiAsvvKCvKqdv9jt16mkAWLNGL09Uj1cO4CFzrNXzKM+3LIsc69bKVOEm4y754XMKeNjOZFvRVYHtWBM1ewEMll9++SUqKirwxRdfoEuXLuhUb2RBIiILHnWsR40ahUgk0ujykpISrTx9+nRMnz7deD8NYVwkooQFsK0YD6/iZrDOEhHFxDN9QrwBNYk+//xzLFmyBN/61rcQDofRs2dPXHTRRejSpQt69OiBKVOm4M9//nOyq0lEqSAAMRFgXCQilwSkrRiPpoibqX+WiKhhAQiWCxYsQM+ePfHUU0+hsLAQa9asQXl5Of72t79hy5YtmDNnDk6dOoUrr7wSY8eOxQcffJDsKhORn6V4TAQYF4nIRQFoK8ajqeJmoG4FN8mJsMqnkLkX9SYKVPM47BIC1ak6Bg7UFsn8u1Qn06SRlxd7LKcskW+API/qORaTsrZq1XiOtcn7nuIxwl7Llg18mBtZz6f+/Oc/Y+PGjejbty/++Mc/YuTIkWjfPjYX5YgRI3D77bejuLgYTz31FP7v//4PF1xwQRJrnFx2+YJu5Vi3aycW2swfquneXSu+/noj6wWAzInbsEEvXyHOhfaGyJgoznHbtrHHdvmCaj2cfEZ8OZaEqXjioo9jIsC46CaTz3+9j40cU0dOIK/GQTlvtdyYGlTVOa0BbH7Oerd+Jw/9zTf18sRrsmIF+cdFnid1Y/LvjDz/Ysfqppz8bQxc2zEAbcV4NFXcDFTHmogUAcib+f3vfx99fPPNN+P999/XAuEZaWlpuOuuu5qyakSUijzKsW5KjItE5JoAtBXj0VRxM7XPEhE1LmC39wwfPhz79u1LdjWIKJUFKCYCjItE5FDA2orx8DJuBucsEZEuYMHyhz/8IWbNmoUDBw4kuypElKoCFBMBxkUicsijtuLGjRtx9dVXIycnB6FQCGvkPGsNKCsrw5AhQ5CWlobevXvXm00BABYvXoyePXsiPT0d+fn52Lp1q1G9AG/jZrO5Fdzk81BvXZnQopZl0pl8scXkr4HIV1PUy/tRj7feBJCCPBnqxsSGHb2XzUnAbu+58cYbAQD9+/fHNddcg1GjRmHw4MG4+OKL0dru80XGrD4WIYhphWQSnfqdlRtSk4MBVHyYQOVSlJzuG930c2GZ0CzOceh0nfKy1M59a1IBuBVcxbhoz7O3U35Hrcp2bUU1x1VMTC+nZ061tqOsb73xhdS/CXbJzlbn1K5sIYW+8u7zqK1YU1ODQYMG4fbbb8d1111nu/6+fftw1VVX4a677sLvfvc7lJaWYvLkycjOzsaYMWMAAM8++yyKiopQXFyM/Px8LFq0CGPGjMHu3buRmZkZd928jJvNpmNN1OwErGO9b98+/OUvf0F5eTn+8pe/YN68efjoo4/QqlUr9OnTB++++26yq0hEfhewjjXjIhE54lFbcdy4cRg3blzc6xcXF6NXr1547LHHAAAXXXQRNm3ahIULF0Y71gsWLMCUKVMwadKk6GtefvllrFixAjNmzIh7X17GTXasiYIqYB3rHj16oEePHrjmmmuiz33++ecoLy9n45GI4hOwjjXjIhE5YthWrBK3UKSlpSEtLc1xNbZs2YLCwkLtuTFjxuCee+4BANTW1mL79u2YOXOmUqUWKCwsxJYtW4z25WXcZMeaKKgC1rFuSPv27XHZZZfhsssuS3ZViCgVBKxj3RDGRSKKm2FbMTc3V3t6zpw5mDt3ruNqVFRUoGvXrtpzXbt2RVVVFf75z3/i6NGjqKura3CdXbt2Od6/W3Gz2XSsTXJS6q0r53ezmgzPKi9OJJa0atVFK6fa3IRSvWnw1OOVOZiSVb6R2LCj97IZiSCECEJxredX+/fvR3c516+FQ4cO4dxzz/WwRs2H1Xen7rT+mWkpc5Ks5sQUc49262Zas9SVlSWekPOwWk04Lc5xRMmrbs5xzlQ8cdHPMRFgXDTl2ffDLh9YLdu1FdUGoIgLnTrpq6rhNRXajfLQxXBD+vHKA7LKTTc5/zaacww1bSseOHAA4XA4+rwbV6ubQlPFzdT+WZaIGnX6dPz//Gr48OH493//d/z5z39udJ3jx49j2bJlGDBgAP7whz80Ye2IKNWkekwEGBeJyD2mbcVwOKz9c6tjnZWVhcrKSu25yspKhMNhtGnTBp07d0bLli0bXCer3q/W9TVV3Gw2V6yJmpt4G4h+bkTu3LkTv/jFL3DFFVcgPT0dQ4cORU5ODtLT03H06FHs3LkT77//PoYMGYKHH34Y3/nOd5JdZSLysXjiop9jIsC4SETu8UtbsaCgAK+88or23Pr161FQUAAAaN26NYYOHYrS0lKMHz/+6zqdRmlpKaZNm2a7/aaKm7xiTRRQQbhifc4552DBggU4fPgwnnjiCVxwwQU4cuQIPvjgAwDAv/3bv2H79u3YsmULG49EZCvVYyLAuEhE7vGqrVhdXY3y8nKUl5cD+Gok7vLycuzfvx8AMHPmTEyYMCG6/l133YUPP/wQ06dPx65du/DrX/8azz33HH70ox9F1ykqKsKyZcvw9NNP469//Su+//3vo6amJjpKuJWmipuBumJtNb2d1bqyXC9nRcy7qiWItGtnvaPq6tjjHTu0RZmZo7Xy15+1lCXzgKCOrGc3EaM8j0r+hpzT0SoFx+R993vjySm//ArphjZt2qBfv3647rrr0CLFBxbykum0nibvvTpMgkwNbi+T5tTvs9yJCHRX3qUvfvTRxOrnR/KjevnlYoWlIuirByxjojjHJmmJVsvc/IykgiBcsT6DcdE5k8+//J6lybEkRFtFazvKcSjkxtS2opjwfuRIfdVnnml8M34kT8sll4gVPv449lg9D0D9A1TPo2ybyx2Jc65uykncS5X4EC+v2orbtm3D6NGxfk5RUREAYOLEiSgpKcHhw4ejnWwA6NWrF15++WX86Ec/wuOPP45u3bph+fLl0am2gK/mn/70008xe/ZsVFRUIC8vD+vWras3oJkVr+NmoDrWRBQTpI41AAwYMADp6eno168fBg0apP3rUG80FCKi+oLUsQYYF4nIGa/aiqNGjUIkEml0eUlJSYOveeeddyy3O23atLhu/bbiZdzkT5xEAXXqVPz/UsEbb7yBcDiMc889F59//jmWLVuG0aNH45xzzkGfPn3w05/+FMfEyPtERKogxUSAcZGInAlaWzEeXsZNdqyJAioIOdaqu+++G0uWLMHatWvx3HPP4b333sP69evRq1cv3HLLLdi4cSMGDx6MTz/9NNlVJSKfClJMBBgXiciZoLUV4+Fl3AzsreCmeWLqcjnlcs2Jllr57MzMWKFzZ31lmUfzj3/EHpeVaYsmTNBzrH/+c+s6+p0yBsFX1ONVzwNQ/zzJ86ic45ras7RF8v1xkk8YZJFIfMdrcaeOr+zatQv9+/fXnvv2t7+NhQsX4umnn0ZZWRluuOEGzJo1C8uWLUtSLf3NrRxr+UNueznVhRojZR7irl1a8dsD9KkzOnSI5UrJsJFq5B1lV+SJP9LiXGjnSj2HQL1JsNX34MSJ+OvUnGMiEF9cTJWYCKR2XJTvQ1OliVuNtSLLatyTbY+zZY6vOjYMYD0ej8ijxpEjscc7d2qLrputrzp/fuyxDCF+1K2bXr7uOrHCA8pB2AV99TzK8y3L4v1R3z+7cSn8MB5PU+03aG3FeHgZN3nFmiiggvYr5NChQ/G73/2u3vMDBgzAa6+9hlAohPvuuw+vv/56EmpHRKkgSDERYFwkImeC1laMh5dxkx1rooAKWrB89NFHsWDBAtx6663Y9fXP9LW1tVi4cCE6fT0kfZcuXVBZWWm1GSJqxoIUEwHGRSJyJmhtxXh4GTcDeys4UXMXtFHB8/PzsWXLFtx9993o168f0tLScOrUKbRq1QpPPfUUAOCdd95BTk5OkmtKRH4VtFHBGReJyImgtRXj4WXc9F3H2iTnxionQuZPmORTyHw1mfJxtpow0r27vlDmeKiJcJs3a4sefO6oVi4u7qiV1ZQbP5Jp0b/4sX48uGFT7LFMypTJh/I8KmV5/uX7Y5ULYzLntUnQSIUAE8RgOWDAAJSWlmL//v0oLy9Hy5YtMXToUGR9nX/apUsXzFcT0Jo5J98HSc1Pk7Ept2dP/Qm1LL/rH32kl1es0Io/+MHM6ONf/lJfNRU+q+rfrLvEHN3yWOudC/VcyXMqYqT6HtiNO5FojAyioHWsAcZFJ2Pq2H0f1HK9sQzaiRxrGevURpJsMO0Xc9hb5Fi33/yqVr7zzti8vnPn6pupqkLSyWbw5Ml6WR6Pdrzyj4scj+frq4kA6o9DIc+/yLE+oUyRLWOmk7+NfsjHdiKIbcV4eBU3fdexJiJ3BDlYdu/eHd3ljzEALrvssiTUhohSRRA71mcwLhKRqSC3FePhdtxkx5oooJp7sCQikoLcsSYiMsW2orvYsSYKKAZLIiIdO9ZERDFsK7or7o61Se6zm9T92u0z0bwZub7MvZA5vrm9e8cK/frpC99+u/EXv/uuvkzcu//CCw9p5bFjY4+/+AK+oKas/Nd/iYUyF2HHjthj+YbI2y7keTzvvOjDfxzUF1nlE9q9z2rZNEikWlBhsPSWlzHRKpbJ/ahlu1xDk++D3I/6vas31ejA3np5wIDY461b9WUyRr7wglZ88JnYJKf//d99tGUyhPqRGsp+MWG3vvAWETRlYO/bN/ZYPYcAcOGFWvFIWeyxjImSk/FHVE7+BvsFO9apya3Ppd0y+V1S86rl1zWS1V4rh2QetToejxwESY6voM5rLSenfuUVrfijGQOjj//2t2xt2apV+kubKudazav+13/Vl937r4f1J+brx6MdrzzJX+e7RqnnVE6QLc5/pJ3+/nzxSeyxzJe3G6ci3mV26/oxtrCt6C5Ot0UUUJFIfNMnRCJm2924cSOuvvpq5OTkIBQKYc2aNbavKSsrw5AhQ5CWlobevXujpKQkoWMiInIinrhoGhOJiFKVV23F5ooda6KAiidQxvtLpaqmpgaDBg3C4sWL41p/3759uOqqqzB69GiUl5fjnnvuweTJk/Hqq6/av5iIyEVexEQiolTlVVuxuWKONVFAeXV7z7hx4zBu3Li41y8uLkavXr3w2GOPAQAuuugibNq0CQsXLsSYMWNsXk1E5B7eCk5EFMNbwd2VcMfaJPc53u24uS2TXFtZlrkWcgrmo91j8013zMvTF8okwINKgrCcn++//1srXqbOzwdg3br/iD6+7jptUZPNcS1ThtR0yMs26znh8njwiZLQIjc0cKBeFufxaItzoo/l+beaf9DLHGsrftzWqVP1j7+x9QCgSiRjpaWlIS0tzXE9tmzZgsLCQu25MWPG4J577nG8bT9xEstMXutkXfl5UL9Lclkr8ddB3ZbMsd77UUutfP6wYbFCebm+spy/VS6fNSv68C/bVmuLLh5yllZWh3FoKvJ8y1Tov2z7Mla4aZa+UB6rjItDhsQeq+cQwL79+jlW3wO7mGH1983JPNZO1nXy/XAinrgYT9wk9yXarrTLY7Uq27UZ1O+LTP+V+csZVjnWyrgxAOrHQbWhI5dt3qyXn3gi+nDJ7Gnaot699ZzrpUv1l6pNUru8YvU9SE/Xl8n0ZnWu6no51Up9AdQ/HvV47XaknkebHGv5/qjvn10ctGorWpWdxKpkdVxN24pkjbeCEwWU6e09ubm5yMjIiP6bN2+eK/WoqKhA165dtee6du2Kqqoq/POf/3RlH0RE8eAtj0REMbwV3F28FZwooExv7zlw4ADCytCeblytJiLyE94KTkQUw1vB3cWONVFAmQbLcDisdazdkpWVhcrKSu25yspKhMNhtGnTxvX9ERE1hh1rIqIYdqzd5buOtVs511Z5Mg2VW7du/LXV1XpZzVHpqObEAcCHH+rljz+OPZZ5JXv26GUxBdFlSiL1p7tmaMt+suAcrbxyZeyxzL+2O6eZmbHHEyboyx4sOqo/oc5VLXOq5fGok17LXPSRI/WyOI8HlZQbef5N8uPt8mgSlQoBxi/BsqCgAK+IeTjXr1+PgoICb3ccEG7mpprkWFt9LmSuoZyS9bzLh0cfh/72N33hQTEx/caNeln9rNx0k7bovf/6pVb+ycrYPNe//rW+GZlfZ/I5l+e4Q4fY4x/8QF/24C1irmo1r1p87uvlD4o8anzzm9GHkWHDtUUfbtBXVd8DuxxAr3KspaDExUSOY+PGjXjkkUewfft2HD58GC+++CLGjx9v+ZqysjIUFRXh/fffR25uLu6//37cdttt5jv3ETfHzfGKVU6syTzW9XKsc0SOdffuscdiHnqtbQjoOdayHWk1mIRoIN37ne9o5Tu36QOEquPkyCapPB71d3bZZJPj/rTfrMzyIeepljuSx6O+CTJvWp43tWyXYy1Osfr+mcxjnQpxzQm/tBWDwochj4jc4NXchNXV1SgvL0f51wMx7du3D+Xl5dj/9QAkM2fOxATlF5q77roLH374IaZPn45du3bh17/+NZ577jn86Ec/cutQiYji4tU81pyGkIhSkdfzWC9evBg9e/ZEeno68vPzsXXr1kbXHTVqFEKhUL1/V111VXSd2267rd7ysWPHJlY5D/juijURucOrXyG3bduG0aNHR8tFRUUAgIkTJ6KkpASHDx+OdrIBoFevXnj55Zfxox/9CI8//ji6deuG5cuXc6otImpyXl2x5jSERJSKvLxi/eyzz6KoqAjFxcXIz8/HokWLMGbMGOzevRuZ6u2yX3vhhRdQq9w+8Nlnn2HQoEG4/vrrtfXGjh2Lp556Klr205hA7FgTBZRXwXLUqFGIWPx0WSJSGs685p133jHbERGRy/ySY91cpiEkIn/zsmO9YMECTJkyBZMmTQLw1Q+KL7/8MlasWIEZM2bUW7+TmHZ49erVaNu2bb2OdVpaGrKysswr1ARc6Vh7mVOjbttuuyZ5MyZzIctcDHV65gMdMrRluUqOHAA94Vlu6O239bLMRVQTp8W6v/jGN/TyylGxgpwjWk0QBOpPDK3OvV1Wpi+7QeTGqOvKZG41pxrQ86Yvv1xfNmqUVjxQpZ9H9RzL0+Ykx9pJ3oxXjS0vt8u8meCxircyRtrl3qrfD/k9U8edkGX5HZShQJ2uebDoPNSbBFsmLm7bFnssx3EQydy/+N73Yo933a4tW/+uPsXbBpGjrKY45uToy2SVvz1AGXxvxQp94S0v6GX14O1yqq+8stEdq6EWqH+OrcaLsPobZjd3rWQyT3oqMOlYV4mk07S0NNeujNhNQxiUgR1N2nCNva6h11q9h3Zxz6odYPXdscuxPtpOn2u+oxpYZK6wjINqrrSshJzXWo0xcjtirJv2r7+ulSf26xd7PF50TGQbTj3gigp92QM79fJOpbxrl75M1l+edDVXWqkfgPrtWYsc66NV+vm3msfarl1p9Rkx+Tx59SOem7HXtK0Yb1ysra3F9u3bMXPmzOhzLVq0QGFhIbZs2RJX3Z588kncdNNNOPvss7Xny8rKkJmZiY4dO+Lyyy/Hz3/+c5xzzjmNbKVpMceaKKDiyZmJN6ASEQWBSUzMzc1FRkZG9N+8efOSW3kiIpeZthXjjYtHjhxBXV1dgz8gVsgfahqwdetW7NixA5MnT9aeHzt2LFauXInS0lI89NBDeOONNzBu3DjU1dUldgJcxlvBiQKKV6yJiHQmV6wPHDigTUHoZh4fpyEkIj8wbSt6GRdVTz75JC6++GKMGDFCe/4mZbaQiy++GAMHDsT555+PsrIyfPvb3/akLiZ4xZoooHjFmohIZxITw+Gw9s/NBmRBQQFKS0u15zgNIRE1NdO2YrxxsXPnzmjZsmWDPyDa5UfX1NRg9erVuOOOO2zrf95556Fz587YI6f8TRLfXbG2yqsxyeU2yZsB9PzBVuKsWM1rLVNH2va9QCufI+YU1Mj8OzVvBtCT6uRcr3Ld1atjj9XJBwH7A1LzJWS+jszHVk+smDOw3lzVal61OA+fddLP036RkmOVbmT1XprkzdgxyeHyI16xTi4nY09YvSdWuYZ2+7Cax1p+d2R4kmFEZTWvdbt2+m1gF1xzjXUl1Tw/Nd8asB6XYtUqbdEVffvqZXVOWQDoruxHHkCxCOxqzqCctFu+Vo2LdjnVYp7jD6pi50pOZSt3YzKmiFWOtVWutmSSL+gkrngZk0yuWJuorq7WGnZnpiHs1KkTunfvjpkzZ+LQoUNY+fXYKXfddReeeOIJTJ8+Hbfffjs2bNiA5557Di+//LL5zlOEk5joZD9WZfn5l3VSvy9iyuh6MVKW23aL5XymnXeevlB+oeUXUyWD78GDsccyUMhbbOWc0Wp8atdOX2b1x0UevBz0QS3LY5MnRs4/reZVy5gpc6yV83iynZ5T+4+D+qoyx1o9BLs4qJZNPk9e8mo/XrUVW7dujaFDh6K0tBTjv/5bd/r0aZSWlmLatGmWr33++edx8uRJ3HLLLbb7OXjwID777DNkZ2ebVdAjvGJNFFC8Yk1EpPMqJm7btg2DBw/G4MGDAXw1DeHgwYMxe/ZsAGh0GsL169dj0KBBeOyxxzgNIRE1OS/bikVFRVi2bBmefvpp/PWvf8X3v/991NTUREcJnzBhgja42RlPPvkkxo8fX29Asurqatx3331488038dFHH6G0tBTXXnstevfu7ZvY6bsr1kTkjrq6+K5G+WS8ByIiz8UTFxOJiZyGkIhSkZdtxRtvvBGffvopZs+ejYqKCuTl5WHdunXRAc3279+PFuJOid27d2PTpk147bXX6m2vZcuWePfdd/H000/j2LFjyMnJwZVXXokHH3zQN3NZs2NNFFC8FZyISOfVreBERKnI67bitGnTGr31u0xO8QugT58+jf5I2aZNG7z66quJVaSJeNKxdjOPxiTX0Op1VrmFgJ6DJtNZ5H7UskxJllNRX3jhxdHH51wn8kxkjrKcTFWdyFQmc8vcZ1kRlckE4HLyWjkHtpqnKHNfxNzaUOb0ljnV8jzJ6qvvj9V7Jct2eTNOcqzdamw1ZT4OO9apwckcrVZfb7vtqt8P+b2SaXJqXJRhQn7P1PAkpzRF3x5a8YIbbtCXd+oUeyxz8WSOtZpfKHckcwudJLmrBy9josjlxpAhscdKDARQb4JsNaca0A9BhnirWCbjnnzv1PfW7sqEW2MBONmul9ix9g8n4+Y4+ZxajQVgMg6FzOGVcVEtd+uWqy0LWX0R5YZkLrQagw6KxGLZmJJtR7Vs19i1SkaX1DrLgalkHJdzeqttSdmuFPE1opzHT8Shy5gp359E24putgXdGufHTWwruotXrIkCisGSiEjHjjURUQzbiu5ix5oooBgsiYh07FgTEcWwregudqyJAorBkohIx441EVEM24ru8n3H2mqOVif5OVY5aSY51pKc2k/db/fuep5x7vcy9ZXlPIfqXNU7d+rLZN6MumM536DdiVLzd2Tet5z7VZ1vUM5breYWAjhQlRGrrkh/lGlAMnfJJMdafe/kum7ms6RaUGGw9BerWJbodhoqq0zmtbbL01XjotyuVc61jIky9bm6p55nnPev/xZ9HJK5eHJea3Vjcn7pTz4RO7KIi1YxEQAylVjds6e+bMAAvazMwxoZNlxbpA6bAdSfglbNEbRLaVSXy/fKaopck3mrAe9yDa324yV2rIPH9P0y+Uyr3x0ZJmRbUeb4quvL1+b07KWVQ+rG2rbVV1bHnQD08XhkEPn4Y70sA7DVZM5WbUW7vG+17SjHC5JtWxnX1bJYNyJy09XDk4cmz7/VWBN2h27yGbFaNxWwregu33esiSgxDJZERDp2rImIYthWdBc71kQBxWBJRKRjx5qIKIZtRXexY00UUJFIfIGwkekCiYgCJ564yJhIRM0F24ruapKOtVvzWjuZM9NkrkKZl2GSYy2pOSAyN/gfnTO0cve80Vq5o5rDvGeP/mI5d6GaT2g3AarMlQmHY48zRd63nH+wd+/ow6PoqFdJpH2rVZLpjTK/xSqP2kn+oMl8wKk+b3VD++WvkKlJjTF28dMkd9tkmlIZr6zyBSU1xFjNcQ1Yz2nfs6eeo3z+nfo4DlpclDnWFRXWO1YrJpMl5VzV6rysMsdaiYkAsPejlrEqbdBXlTmBMpZZ5T9b5cDL+CrfO6vtujlvtVvjCHiJV6z9y+Tz4+Szlmj+rN04FCZtRbmfzMxYLnGaSY61iD/12oZWich2gUKNi+np+jIZI9Uca9lulGU5do9yPCfbnaMtknNVq4cjQ7qMg1ZtxabKm/bjvNUN7ZdtRffwijVRQDFYEhHp2LEmIophW9Fd7FgTBRSDJRGRjh1rIqIYthXdxY41UUAxWBIR6dixJiKKYVvRXSnVsXYrV7uhbcncGbe2a5LDKPNFOnSI5TB3ytJzDTv108tnp9fFCnaJezKfUMnnqTnRUlsk55v+h5JHbZffoh6fVU57Q2Wr/EGTvBknUj2InDoV35y1pvPaknMmscwk19DuM2uS82cVE01irww38vNmFTdk/PnwQz0+de7cJ/a4Xx9tWYeR+mvltKshxEZiiSBkWSc11smUxX+UibJSZ7tQbPL3Qm5LraNVTGxoP1ac5Bq6ma/tlXjiImNianPyeTd57+X3TMYNq/1YxdtOnfQ84479Ougrq2PhyDEf7CZ3rqqKPZaNUKs/TDLHWh2bB9BzrtV86zjKR6ticf0fIqdaVl8ty/NtN5aP+t7K99kqNjfVeDzJwraiu1KqY01E8eOvkEREOl6xJiKKYVvRXexYEwUUgyURkY4dayKiGLYV3cWONVFAcW5CIiId57EmIophW9FdSelYuzXXpZN5ra22ZZJvLesg01DU5Xa5hjLdRU2FkVOyyv20bh3LUWnVqr22zGROb6v5pOVyq/wVWbY7VllWcwa9yh/0Mk/GD7/s8VfIYLJKgzN9L61ea5UDKHPZrF4rp2RV57huiNW89HK/n3zS+Hbtyi1axPKq7fLLreKeVTwyvTKqbssqp1ouN4mJdn8PTOKim+OeNBVesU4NJp8tu3VNPsNWy01zTNXvrF0ut/odlu2h6mp9bIlwuGvscb+u2rJQ9ef6i2UgUct2gz6oJ9JibB5ZjrTT26BqWxYAqj5Go8vrrSvKJm1Dqzaqk3msnXyenK7vBbYV3cUr1kQBxWBJRKRjx5qIKIZtRXexY00UUAyWREQ6dqyJiGLYVnRXCty4RUSJOBMs4/lHRNQcMCYSEcV43VZcvHgxevbsifT0dOTn52Pr1q2NrltSUoJQKKT9Sxe5r5FIBLNnz0Z2djbatGmDwsJCfPDBB4lVzgMpfcXayR8/t+ZztZsLTy3LvGiZsmKVg10/50Yvq8djmueW6Hx9JsduklMNWOc0JppTLcte5lj7AX+FTB0m+YQm+YN2rOKG1ffDbhwKqzhhl3Mt46BVnaxijB2TvwFWTPLtrPIqAev8QavxLuzqa3KsJjEzFeatlnjFmlR277X6nbUbJ8dq285yrPWyOpdz/VRnPb85PV0vt24Xe2wVayW72HVCqeMXn+jLrNK8ZVkeq1Xb0WScH8D671LQ24NWvGwrPvvssygqKkJxcTHy8/OxaNEijBkzBrt370amOh+7IhwOY/fu3dFyKBTSlj/88MP41a9+haeffhq9evXCT3/6U4wZMwY7d+6s1wlPBl6xJgooXrEmItIxJhIRxXjZVlywYAGmTJmCSZMmoV+/figuLkbbtm2xYsWKRl8TCoWQlZUV/de1a2yAvkgkgkWLFuH+++/Htddei4EDB2LlypX4+OOPsWbNmgSO3n3sWBMFFDvWREQ6xkQiohjTtmJVVZX27+TJkw1ut7a2Ftu3b0dhYWH0uRYtWqCwsBBbtmxptD7V1dXo0aMHcnNzce211+L999+PLtu3bx8qKiq0bWZkZCA/P99ym02JHWuigGLHmohIx5hIRBRj2lbMzc1FRkZG9N+8efMa3O6RI0dQV1enXXEGgK5du6JCziH8tT59+mDFihVYu3YtnnnmGZw+fRojR47EwYMHASD6OpNtNrWk51g7ydmy25aJRHOu7fIyrHKF7eZZVfNfZC6MSQ6mnURzBO1ybqxyhpzMN2jFSb6gE35siEUi8dUrEvG+LuQdJ7nC6nK5Hauca6s4J5fbxQmZEqWW7cahSMbfC7sYYxUj7caaMMkftKq/WznVJttNFfHERcZE/zFpK9qtm+h3X34HTXKuTeKG1dgLgB4jZbvRbiwfdX0nYyRYxSe79p5J3LMqW51Du+VuthWdtFH9wLSteODAAYTD4ejzaWlprtWloKAABQUF0fLIkSNx0UUX4Te/+Q0efPBB1/bjpaR3rInIG/FeefFjoCci8kI8cZExkYiaC9O2Yjgc1jrWjencuTNatmyJyspK7fnKykpkZWXFVbezzjoLgwcPxp49ewAg+rrKykpkZ2dr28zLy4trm14LwO/PRNQQ3gpORKRjTCQiivGqrdi6dWsMHToUpaWlyr5Oo7S0VLsqbaWurg7vvfdetBPdq1cvZGVladusqqrCW2+9Ffc2vcYr1kQBdeoU0LJlfOsRETUH8cRFxkQiai68bCsWFRVh4sSJGDZsGEaMGIFFixahpqYGkyZNAgBMmDAB5557bjRP+4EHHsAll1yC3r1749ixY3jkkUfw97//HZMnTwbw1Yjh99xzD37+85/jggsuiE63lZOTg/Hjx5tX0AOB7Vh7lW8tt22S0+FmjrWTnOtEc6pl2STH2mR+Qbs6Sla5ME62m+p4K3jqssp9dmu7gPUcrVY51yZjS9jFFKv8O5MYaZcj7iSf0CrGWB2PSb6g3JZJI8bNv1lu8Wtc4a3gwWASI61yrp281/K75KQdZtVWlDnJahy0yqFuaLlaD7diImBdf7uYbxX3rOKVaY61W23FVM+plrxsK95444349NNPMXv2bFRUVCAvLw/r1q2LDj62f/9+tFA+iEePHsWUKVNQUVGBjh07YujQodi8eTP69esXXWf69OmoqanBnXfeiWPHjuEb3/gG1q1b54s5rIEAd6yJmjt2rImIdOxYExHFeN1WnDZtGqZNm9bgsrKyMq28cOFCLFy40HJ7oVAIDzzwAB544IHEKuQx5lgTBZTXOdaLFy9Gz549kZ6ejvz8fGzdurXRdUtKShAKhbR/fvl1kYiaD+ZYExHFcDwed/GKNVFAefkr5LPPPouioiIUFxcjPz8fixYtwpgxY7B7925kZmY2+JpwOIzdu3dHy6FQyHzHREQO8Io1EVEM7250l+861la5MG5u14Rbc4La5ZlY5VHLOriVNyPraJKz4iQXxsn74SRvxi2pEGC8nMd6wYIFmDJlSnQAiuLiYrz88stYsWIFZsyY0eBrQqFQ3FMsUNOw+j5b5Sg7yem1K6v5hHZjS1iNQ+FVjrXJ8ZiMwWHHrb9DqRC7vMR5rMmKm98P9fttF4+sYoyMbWrb0TTueZVj7Vbus5d50sloK6YCL9uKzRFvBScKKNPbe6qqqrR/J0+ebHC7tbW12L59OwoLC6PPtWjRAoWFhdiyZUuj9amurkaPHj2Qm5uLa6+9Fu+//76rx0tEZIe3PBIRxfBWcHexY00UUKbBMjc3FxkZGdF/Z6Y/kI4cOYK6urroqI5ndO3aFRUVFQ2+pk+fPlixYgXWrl2LZ555BqdPn8bIkSNx8OBBV4+ZiMgKx50gIophx9pdvrsVnIjcYZo3c+DAAYTD4ejzaWlprtWloKAABQUF0fLIkSNx0UUX4Te/+Q0efPBB1/ZDRGTFqxxrjjtBRKmIOdbu8n3HWn0jvZzP1USieSl2+eNWOY4m+YJOcqztlpnk6rmZR+1WnZxItaBiGizD4bDWsW5M586d0bJlS1RWVmrPV1ZWxp1DfdZZZ2Hw4MHYs2dPXOs3Z3Zxw6v9mDCJTyZ5xm7mfTvJJ7Ra5mY8cutvS7LyB1MhRnrVsea4E8ljGiNN2pUmscAkPpmsa7Ufu3EanIw1YcUkxriZ++xW3DP5jnu1rl+wY+0u3gpOFFBe3d7TunVrDB06FKWlpcq+TqO0tFS7Km2lrq4O7733HrKzs812TkTkgElM5LgTRBR0vBXcXexYEwWUl8GyqKgIy5Ytw9NPP42//vWv+P73v4+ampro1ZoJEyZg5syZ0fUfeOABvPbaa/jwww/x9ttv45ZbbsHf//53TJ482a3DJSKyZRITOe4EEQUdO9bu8v2t4ESUGC9v77nxxhvx6aefYvbs2aioqEBeXh7WrVsXbVju378fLZR7zo4ePYopU6agoqICHTt2xNChQ7F582b069fPfOdERAkyuRWc404QUdDxVnB3sWP9tURzKNycK9VkuVd5liZ18HI/TvJ16CteB8tp06Zh2rRpDS4rKyvTygsXLsTChQsT2xFpUiHnWmUaI01y6JzUI946uMmrvw/MqY6fScea406kJpMYafdZUF/r5PPuZj52vNsxZZfbHS/TeORV3nRT5UanYhxUsWPtLt4KThRQdXVfDWxi96+uLtk1JSJqGvHERdOYyHEniChVsa3oLl6xJgoo/gpJRKQzuWJtoqioCBMnTsSwYcMwYsQILFq0qN64E+eee240T/uBBx7AJZdcgt69e+PYsWN45JFHOO4EETU5thXdxY41UUAxWBIR6bzqWHPcCSJKRWwruiulOtbJyi10kq9jMs+qiVT7gLuZc9NUUu0cSwyWzYPVmA9ubddumZM8Py/HqUh0XRNOzrmTfEEn70+ighArvOpYAxx3Ami6dpoJJ3Uyia9u5ek6ybE2Xe4Wk3hktdzJd5M51YlhW9FdKdWxJqL4MVgSEem87FgTEaUathXdxY41UUAxWBIR6dixJiKKYVvRXexYEwUUgyURkY4dayKiGLYV3RV3xzoV8mZUXtbPKh8k0fxrO34434B7OSzJmnc12ftsSgyWzY+bcdpqTlO7+U5N8gVN6uzmnK1eaapcQ6vtNufxPOywY920vBoDwolE62T3ufBqHujmlGPtZD9O1m2K7bjJzTqxregun4Q5InJbJBILmFb/IpFk15SIqGnEExcZE4moufC6rbh48WL07NkT6enpyM/Px9atWxtdd9myZbjsssvQsWNHdOzYEYWFhfXWv+222xAKhbR/Y8eOTaxyHmDHmiig4gmU8f5SSUQUBIyJREQxXrYVn332WRQVFWHOnDl4++23MWjQIIwZMwaffPJJg+uXlZXh5ptvxv/+7/9iy5YtyM3NxZVXXolDhw5p640dOxaHDx+O/vv973+fyKF7gh1rooBix5qISMeYSEQU42VbccGCBZgyZQomTZqEfv36obi4GG3btsWKFSsaXP93v/sdfvCDHyAvLw99+/bF8uXLcfr0aZSWlmrrpaWlISsrK/qvY8eOiRy6JxIevMyPeTMqL3POTPZrkpfoVX62CScNCic5Nk3FL/Ww4mZeEPNmmjc340ai8ckqJja0XbdiZlNJVm6heuzMqY5fPHEx6OcgWVLt+wu4l4PtVv613T692o8J030mWkc3jy0VvvNe1dG0rVhVVaU9n5aWhrS0tHrr19bWYvv27Zg5c2b0uRYtWqCwsBBbtmyJq25ffPEFvvzyS3Tq1El7vqysDJmZmejYsSMuv/xy/PznP8c555wT1za95oOwRkRe4BVrIiIdYyIRUYxpWzE3NxcZGRnRf/PmzWtwu0eOHEFdXR26du2qPd+1a1dUVFTEVbf/+I//QE5ODgoLC6PPjR07FitXrkRpaSkeeughvPHGGxg3bhzq6uoSOwEu43RbRAHFK9ZERDpesSYiijFtKx44cADhcDj6fENXq90wf/58rF69GmVlZUhPT48+f9NNN0UfX3zxxRg4cCDOP/98lJWV4dvf/rYndTHBjjVRQJ065e56RESpLp54x5hIRM2FaVsxHA5rHevGdO7cGS1btkRlZaX2fGVlJbKysixf++ijj2L+/Pl4/fXXMXDgQMt1zzvvPHTu3Bl79uwJTsfaj3kzkpt5NF7VwUnOdTL4oQ6SH+tkxy95MxR8JjmAJpoqf9Buv8ng1bgUklvH2ty/77xi7R+p1nZ0Uj8nsSzVPo+pkPucCue0qeroVVuxdevWGDp0KEpLSzF+/Pivt/HVQGTTpk1r9HUPP/wwfvGLX+DVV1/FsGHDbPdz8OBBfPbZZ8jOzjaroEd4xZoooM7MTRjPekREzUE8cZExkYiaCy/bikVFRZg4cSKGDRuGESNGYNGiRaipqcGkSZMAABMmTMC5554bzdN+6KGHMHv2bKxatQo9e/aM5mK3a9cO7dq1Q3V1NX72s5/hX/7lX5CVlYW9e/di+vTp6N27N8aMGWNeQQ+wY00UUKdPA6FQfOsRETUH8cRFxkQiai68bCveeOON+PTTTzF79mxUVFQgLy8P69atiw5otn//frRQbtdYsmQJamtr8b3vfU/bzpw5czB37ly0bNkS7777Lp5++mkcO3YMOTk5uPLKK/Hggw96luttih1rooBix5qISMeONRFRjNdtxWnTpjV663dZWZlW/uijjyy31aZNG7z66quJVaSJeNKxToW8Gcmr3EMn+Me9Yal+Xpoyb4Yda4qXm/nMJvmCTvKM/fjZdVIn5lF7jx1r/3Irn9krXo754FYbNBU/u805j1qVrPqyreguXrEmCigGSyIiHTvWREQxbCu6ix1rooBisCQi0rFjTUQUw7aiu9ixJgooBksiIh071kREMWwruqtJOtapmHOt8mP+ddAF6QvMvJlg8kMubVNyKw6m4rGbaKrjC9r31k9jTwTt3KaiVGw3etVWTPU2KHOoG+eHY2Bb0V28Yk0UUAyWREQ6dqyJiGLYVnQXO9ZEARWJxBcIIxHv60JE5AfxxEXGRCJqLthWdBc71kQBFe+vi/wVkoiai3jiHWMiETUXbCu6Kykda7/PVWjCy3kNgyzoX1A/HB+DpX+lYv6glVTPAfSjoH8vkzn2hBvrUNNK9ZjpVVvR9LPq1nlryjERgsSPx8O2ort4xZoooE6diu+PKIMlETUX8cRFxkQiai7YVnQXO9ZEAcVfIYmIdLxiTUQUw7aiu9ixJgooBksiIh071kREMWwruivpHetUz5ux05xzD5vTl9CPx8pgSX7AcSgax+9e02PHOhiCNFYP0HTH4/fPtt/rZyoVjodtRXclvWNNRN5gsCQi0rFjTUQUw7aiu9ixJgoozk1IRKTjPNZERDFsK7qLHWuigDp9GgiF7NdjsCSi5iKeuMiYSETNBduK7vJdxzroOdeqVM89bM63haTCsTNYUiow+S75PSYCqREbmjN2rIMnaO3GoB2PnSDFzFQ8FrYV3eW7jjURuYPBkohIx441EVEM24ruYseaKKAYLImIdOxYExHFsK3oroDfYELUfJ0+Hf+/RCxevBg9e/ZEeno68vPzsXXrVsv1n3/+efTt2xfp6em4+OKL8corryS2YyKiBDEmEhHFpFpbMRKJYPbs2cjOzkabNm1QWFiIDz74ILHKecD3HWunb2gq8+Ox+7FOTSXVjt3LYPnss8+iqKgIc+bMwdtvv41BgwZhzJgx+OSTTxpcf/Pmzbj55ptxxx134J133sH48eMxfvx47Nixw+FRUnPixh95t/mxTtQ4xsTgC9p3ksfjb6l+LKnWVnz44Yfxq1/9CsXFxXjrrbdw9tlnY8yYMThx4kSip8BVoUgkvov73/++11WxF/QBHKz45dhTNXC4IRnHvmSJ+WuqqqqQkZGBli2PIxQK264fiVShri4Dx48fRzhsvz4A5OfnY/jw4XjiiScAAKdPn0Zubi5++MMfYsaMGfXWv/HGG1FTU4OXXnop+twll1yCvLw8FBcXx3lk/jJpknvb8sv3O9X44bw155johJPz9tRT5q8xiYuMiYn72c+SXYP6/BAn3JTqxxO0mOmH45kzx/w1qdhWjEQiyMnJwb333osf//jHAIDjx4+ja9euKCkpwU033RRXvbyU4l9PImqMV79C1tbWYvv27SgsLIw+16JFCxQWFmLLli0NvmbLli3a+gAwZsyYRtcnIvICYyIRUUwqtRX37duHiooKbZ2MjAzk5+f7JnZy8DKigIpEquIcbKLqq/9WVWnPpqWlIS0trd7aR44cQV1dHbp27ao937VrV+zatavBPVRUVDS4fkVFRTwVJCJyRXxxkTGRiJqHVGornvm/n2Nn3B3rRG5JJaKm17p1a2RlZaGiIjfu17Rr1w65ufr6c+bMwdy5c12uXXAkcjsqESWHaVxkTExMIrekElHTY1vRG7xiTRQw6enp2LdvH2pra+N+TSQSQUjMt9DQL5AA0LlzZ7Rs2RKVlZXa85WVlcjKymrwNVlZWUbrExG5yTQuMiYSUZClYlvxzP8rKyuRnZ2trZOXlxf3cXiJOdZEAZSeno5wOBz3v4yMjHrPNRYsW7dujaFDh6K0tDT63OnTp1FaWoqCgoIGX1NQUKCtDwDr169vdH0iIreZxEXGRCIKulRrK/bq1QtZWVnaOlVVVXjrrbf8EzsjRESGVq9eHUlLS4uUlJREdu7cGbnzzjsjHTp0iFRUVEQikUjk1ltvjcyYMSO6/p/+9KdIq1atIo8++mjkr3/9a2TOnDmRs846K/Lee+8l6xCIiFzDmEhEpPMiLs6fPz/SoUOHyNq1ayPvvvtu5Nprr4306tUr8s9//rPJj68hvBWciIzdeOON+PTTTzF79mxUVFQgLy8P69atiw4osX//frRQ5gQZOXIkVq1ahfvvvx+zZs3CBRdcgDVr1mDAgAHJOgQiItcwJhIR6byIi9OnT0dNTQ3uvPNOHDt2DN/4xjewbt06pKenN/nxNSTueayJiIiIiIiIqD7mWBMRERERERE5wI41ERERERERkQPsWBMRERERERE5wI41ERERERERkQPsWBMRERERERE5wI41ERERERERkQPsWBMRERERERE5wI41ERERERERkQPsWBMRERERERE5wI41ERERERERkQPsWBMRERERERE5wI41ERERERERkQP/H3SgbWT+4tpmAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}