21_One_Hot_Encoding.ipynb

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        "<a href=\"https://colab.research.google.com/github/shumshersubashgautam/Computer-Aided-Drug-Discovery-Learning/blob/main/21_One_Hot_Encoding.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "metadata": {
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        "id": "ZRwMpwBf2udq",
        "outputId": "3f72d28e-392a-4ee3-cef9-35d4c112e062"
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          "text": [
            "Cloning into 'teachopencadd'...\n",
            "remote: Enumerating objects: 17115, done.\u001b[K\n",
            "remote: Counting objects: 100% (2094/2094), done.\u001b[K\n",
            "remote: Compressing objects: 100% (971/971), done.\u001b[K\n",
            "remote: Total 17115 (delta 1311), reused 1784 (delta 1121), pack-reused 15021\u001b[K\n",
            "Receiving objects: 100% (17115/17115), 849.73 MiB | 32.24 MiB/s, done.\n",
            "Resolving deltas: 100% (11503/11503), done.\n",
            "Updating files: 100% (468/468), done.\n"
          ]
        }
      ],
      "source": [
        "!git clone https://github.com/volkamerlab/teachopencadd.git"
      ]
    },
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      "metadata": {
        "id": "eVR718vv3JOH"
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      "execution_count": null,
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      "source": [
        "The aim of the talktorial is to perform one-hot encoding of SMILES structures on a subset of the ChEMBL data set to gain a deeper understanding on the one-hot encoding concept and why it is useful as a pre-processing step in various machine learning algorithms.\n",
        "\n",
        "# **Contents in Theory**\n",
        "Molecular data and representation\n",
        "\n",
        "ChEMBL database\n",
        "\n",
        "SMILES structures and rules\n",
        "\n",
        "What is categorical data?\n",
        "\n",
        "What is the problem with categorical data?\n",
        "\n",
        "How to convert categorical data to numerical data?\n",
        "\n",
        "The One-Hot Encoding (OHE) concept\n",
        "\n",
        "Why using one-hot encoding?\n",
        "\n",
        "Example of one-hot encoding\n",
        "\n",
        "Advantages and disadvantages of one-hot encoding\n",
        "\n",
        "Similar: Integer or label encoding\n",
        "\n",
        "What is padding?\n",
        "\n",
        "Further readings\n",
        "\n",
        "Contents in Practical\n",
        "Import necessary packages\n",
        "\n",
        "Read the input data\n",
        "\n",
        "Process the data\n",
        "\n",
        "Double digit replacement\n",
        "\n",
        "Compute longest (& shortest) SMILES\n",
        "\n",
        "Python one-hot encoding implementation\n",
        "\n",
        "One-hot encode (padding=True)\n",
        "\n",
        "Visualization\n",
        "\n",
        "Shortest SMILES\n",
        "\n",
        "Longest SMILES\n",
        "\n",
        "Supplementary material\n",
        "\n",
        "Scikit learn implementation\n",
        "\n",
        "Keras implementation\n",
        "\n",
        "References\n",
        "Theoretical background:\n",
        "\n",
        "ChEMBL database: \"The ChEMBL bioactivity database: an update.\" (Nucleic acids research (2014), 42.D1, D1083-D1090)\n",
        "Allen Chieng Hoon Choong, Nung Kion Lee, \"Evaluation of Convolutionary Neural Networks Modeling of DNA Sequences using Ordinal versus one-hot Encoding Method\", bioRxiv, October 25, 2017.\n",
        "Patricio Cerda, Gael Varoquaux, \"Encoding high-cardinality string categorical variables\", arXiv:1907, 18 May 2020.\n",
        "Blogpost: Jason Brownlee, How to One Hot Encode Sequence Data in Python, Machine Learning Mastery, accessed November 9th, 2020.\n",
        "Blogpost: Krishna Kumar Mahto, One-Hot-Encoding, Multicollinearity and the Dummy Variable Trap, towardsdatascience, Available from one-hot-encoding-multicollinearity, accessed July 8th, 2019.\n",
        "Blogpost: Chris, What is padding in a neural network?, archieved from MachineCurve, Padding\n",
        "Packages and functions:\n",
        "\n",
        "RDKit: Greg Landrum, RDKit Documentation, PDF, Release on 2019.09.1.\n",
        "Scikit-learn:\n",
        "Scikit-learn: Machine Learning in Python, Pedregosa et al., JMLR 12, pp. 2825-2830, 2011.\n",
        "Jiangang Hao, et al. \"A Review of Scikit-learn Package in Python Programming Language.\" Journal of Education and Behavioral Statistics Volume: 44 issue: 3 (2019), page(s): 348-361\n",
        "Keras: Book chapter: \"An Introduction to Deep Learning and Keras\" in Learn Keras for Deep Neural Networks (2019), page(s):1-16.\n",
        "Matplotlib\n",
        "smiles encoder function: Blogpost by iwatobipen, encode and decode SMILES strings , Wordpress, accessed November 9th, 2020\n",
        "Theory\n",
        "Molecular data and representation\n",
        "ChEMBL database\n",
        "ChEMBL is an open large-scale bioactivity database, containing molecules with drug-like properties.\n",
        "The recent release (version 25) contains information extracted from more than\n",
        " documents. In total, there are now more than\n",
        " million compounds and\n",
        " million bioactivity data points available.\n",
        "It is maintained by European Bioinformatics Institute. Please refer to Talktorial T001 for more details.\n",
        "SMILES structures and rules\n",
        "SMILES (Simplified Molecular Input Line Entry System) notation is a chemical notation that allows a user to represent a chemical structure of a molecule in a linear way that can be read by the computer (see \"Modern Aspects of the Smiles Rearrangement\" (2017), Chemistry A European Journal, Volume23, Issue38, 8992-9008 for further information).\n",
        "It contains a sequence of letters, numbers and characters that specify a molecule's atoms, their connectivity, bond order and chirality.\n",
        "Some SMILES specification rules\n",
        "\n",
        "Atoms - are represented by their atomic symbols. Also metal atoms are represented with symbols in square bracket, e.g. Gold [Au].\n",
        "Bonds - single, double and triple bonds are represented by symbols -, = and #, respectively. Single bonds are the default and therefore do not need to be specified.\n",
        "Aromaticity - While atomic symbols are generally used in upper case, such as C, O, S and N; to specify aromatic atoms lower case symbols are used instead, such as c, o, s and n. Sometimes implicit bonds in rings (alternating = and -) are also used to describe aromatic atoms such as C1=CC=CC=C1.\n",
        "Rings - SMILES allows a user to identify ring structures by using numbers to identify the opening and closing ring atom. For example, in C1CCCCC1, the first carbon has a number \"1\" which connects by a single bond with the last carbon which also has a number \"1\". The resulting structure is cyclohexane.\n",
        "Branches - are specified by enclosing them in parentheses, and can be nested or arranged. For example, 2-Propanol is represented by CC(O)C.\n",
        "What is categorical data?\n",
        "Categorical data are variables that contain labels rather than numeric values. Some examples include:\n",
        "\n",
        "A \"pet” variable with the values: \"dog” and \"cat\".\n",
        "A \"color” variable with the values: \"red\", \"green” and \"blue\".\n",
        "A \"place” variable with the values: \"first”, \"second” and \"third\".\n",
        "What is the problem with categorical data?\n",
        "Machine learning consists of mathematical operations translated to a computer via low-level programming languages. Computers are brilliant when dealing with numbers. So we must somehow convert our input data to numbers. There are many machine learning algorithms which cannot operate on categorical data directly. Thus, categorical data must be converted to a numerical form, that all input and output variables are numeric (see Blogpost: Alakh Sethi, One-Hot Encoding vs. Label Encoding using Scikit-Learn , Analytics Vidya, accessed March 6th, 2020 for further information).\n",
        "\n",
        "Computer data\n",
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)\n",
        "Figure 1: Displays the categorical encoding required for computers to understand the input. The figure comes from this blogpost.\n",
        "\n",
        "How to convert categorical data to numerical data?\n",
        "There are many ways to convert categorical values into numerical values. Each approach has its own positive and negative impact on the feature set. Hereby, two main methods will be the focus: One-hot encoding and Label encoding. Both of these encoders are part of the scikit-learn library (one of the most widely used Python libraries) and are used to convert text or categorical data into numerical data which the model expects and and can work with.\n",
        "\n",
        "The One-Hot Encoding (OHE) concept\n",
        "The one-hot encoding is a vector representation where all the elements of the vector are set to 0 except one, which has 1 as its value. For example, [0 0 0 1 0 0] is a one-hot vector. Simply put, one-hot encoding, also known as binary encoding, is a binary representation of categorical variables as binary vectors.\n",
        "\n",
        "The figure shown below helps us gain an overall idea of the one-hot encoding concept.\n",
        "\n",
        "One Hot encoding\n",
        "![OneHotEncoding.png](data:image/png;base64,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)\n",
        "Figure 2 : Displays the one-hot encoding of the toluene molecule. Figure taken from the article BMC Bioinformatics. (2018), 19,526, more information can be found there.\n",
        "\n",
        "Let us take a deeper look into the concept with the help of a simple example that will describe the basic concept of one-hot encoding, why it is useful and how one can approach it.\n",
        "\n",
        "Why using one-hot encoding?\n",
        "One-hot encoding allows the representation of categorical data to be more expressive. It is difficult for many machine learning algorithms to work with categorical data directly that's why the label values which are categorical must be converted into numbers first as a preprocessing step. This is required for both input and output variables that are categorical.\n",
        "\n",
        "We could also use an integer encoding directly. This may work for problems where there is a natural ordinal relationship between the categories, and in turn the integer values, such as labels for temperature \"cold\", \"warm\", and \"hot\". There may be problems when there is no ordinal relationship and allowing the representation to lean on any such relationship by using integer encoding might be not useful to solve the problem. An example might be the labels \"dog\" and \"cat\".\n",
        "\n",
        "Example of one-hot encoding\n",
        "Let us take a look at a very simple example to understand this concept. Assume we have the \"color\" variable which has three labels red , blue and green. All these labels must be converted into numeric form in order to work with our machine learning algorithm. This can be done by creating three new columns having all three labels and use 1 for the color of the respective label and 0 for the other colors as shown in Figure 4.\n",
        "\n",
        "One Hot Encoding\n",
        "![OneHotEncoding_eg.png](data:image/png;base64,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        "Figure 3 : The visual demonstration of one-hot encoding done on the variable \"color\". Figure taken from the article: \"Building a One Hot Encoding Layer with TensorFlow\", George Novack, towardsdatascience, more details can be found there.\n",
        "\n",
        "Advantages and disadvantages of one-hot encoding\n",
        "Advantages\n",
        "\n",
        "If the cardinality (the number of categories) of the categorical features is low (relative to the amount of data), one-hot encoding will work best.\n",
        "One-hot encoding has the advantage that the result is binary rather than ordinal and that everything sits in an orthogonal vector space.\n",
        "Disadvantages\n",
        "\n",
        "Increase in dimensionality, after adding several columns based on categorical variables which may result in an increase in the computational cost.\n",
        "There is a high chance of multi-collinearity due to dummy variables (unique category added as a feature) which can affect the performance of the model.\n",
        "One-hot encoding can result in increasing the sparsity of a data set (a sparse matrix is a matrix in which most of the elements are zero).\n",
        "Similar: Integer or label encoding\n",
        "Label Encoding, or integer encoding, is a popular encoding technique for handling categorical variables and is easily reversible. In this technique, each label is assigned a unique integer based on alphabetical ordering, so that machines can work with it properly. Machine learning algorithms can then decide in a better way on how labels must be operated on. It is an important preprocessing step for structured data sets in supervised learning.\n",
        "\n",
        "Example of integer encoding\n",
        "\n",
        "Let us use a similar example as above: We have a color variable and we can assign red as 0, green as 1, and blue as 2 as shown in Figure 5.\n",
        "\n",
        "OneHotEncoding Example\n",
        "\n",
        "\n",
        "\n",
        "Figure 5 : The visual demonstration of label encoding done on the variable \"color\". Figure taken from the article: \"Know about Categorical Encoding, even New Ones!\", Ahmed Othmen, towardsdatascience, more details can be found there.\n",
        "\n",
        "Difference between label and one-hot encoding\n",
        "\n",
        "There is not much difference between these two encoding techniques, it mainly depends on the type of data and model used. For example, if we have categorical features which are not ordinal (dog or cat) then we can use one-hot encoding. Label encoding works best with ordinal data like good=0, better=1, best=2. Also when there are many categorical variables then it might be good to choose label encoding just to avoid high memory consumption and sparsity.\n",
        "\n",
        "What is padding?\n",
        "Padding is used to add zeros to the resulted one-hot encoded matrix. There are different types of padding, we chose to performed zero padding in here. For more details, please refer to this Article.\n",
        "\n",
        "Why is it performed?\n",
        "\n",
        "Padding is performed to make the dimensions of the matrix equal - or to preserve the height and the width - and to not have to worry too much about tensor dimensions when used as an input for the deep learning models.\n",
        "\n",
        "How is it performed?\n",
        "\n",
        "Padding can be performed by using the numpy.pad function which takes several parameters like the array which needs to be padded, pad_width which is number of values added to the edges of each axis and mode which by default is \"constant\".\n",
        "\n",
        "In this tutorial, padding is performed\n",
        "\n",
        "implicitly: when applying one-hot encoding using a python implementation on the preprocessed data, where we have given the maximum length of the string as the parameter so that all the resulting one-hot encoded matrices are of the same dimension\n",
        "explicitly: More info on this can be found in the supplementary section, where we apply one-hot encoding using keras and scikit-learn implementations.\n",
        "Further readings\n",
        "This section lists some resources for further reading:\n",
        "\n",
        "What is one-hot encoding and when is it used in data science?\n",
        "Categorical encoding using Label-Encoding and One-Hot-Encoder\n",
        "Hirohara, M., Saito, Y., Koda, Y. et al. Convolutional neural network based on SMILES representation of compounds for detecting chemical motif. BMC Bioinformatics 19, 526 (2018)\n",
        "How one can use matplotlib.pyplot.imshow() in Python"
      ],
      "metadata": {
        "id": "UASfPkWU3l_c"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install rdkit"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_GFN3noT5euO",
        "outputId": "b133892e-5c90-4120-cb57-c6b3a183e8f4"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting rdkit\n",
            "  Downloading rdkit-2023.3.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (29.7 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m29.7/29.7 MB\u001b[0m \u001b[31m50.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from rdkit) (1.22.4)\n",
            "Requirement already satisfied: Pillow in /usr/local/lib/python3.10/dist-packages (from rdkit) (8.4.0)\n",
            "Installing collected packages: rdkit\n",
            "Successfully installed rdkit-2023.3.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from pathlib import Path\n",
        "from warnings import filterwarnings\n",
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from rdkit import Chem\n",
        "from rdkit.Chem import Draw, PandasTools\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.preprocessing import OneHotEncoder\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras.utils import to_categorical\n",
        "from tensorflow.keras.preprocessing.text import Tokenizer\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Silence some expected warnings\n",
        "filterwarnings(\"ignore\")"
      ],
      "metadata": {
        "id": "CEV1YXMN47cx"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Set path to this notebook\n",
        "HERE = Path(_dh[-1])\n",
        "DATA = HERE / \"data\""
      ],
      "metadata": {
        "id": "pIMTcNuu5hRi"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Read the input data**\n",
        "Using the Pandas library, we first load the subset of the ChEMBL data set and draw the molecules using the rdkit.draw function. We then preprocess the data and apply our one-hot encoding python implementation.\n",
        "\n",
        "Let's load the data and quickly analyze its column values and check if there are any missing values:"
      ],
      "metadata": {
        "id": "Ivolf84f5xJ9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(DATA / \"/content/teachopencadd/teachopencadd/talktorials/T021_one_hot_encoding/data/CHEMBL25_activities_EGFR.csv\", index_col=0).reset_index(drop=True)\n",
        "print(f\"Shape of dataframe: {df.shape}\\n\")\n",
        "# NBVAL_CHECK_OUTPUT"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cWcVmy3N5p38",
        "outputId": "f3c719eb-9d68-43f8-a550-30c16352dffb"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape of dataframe: (3905, 5)\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GLp2YxPs57Sr",
        "outputId": "d4f33352-78c8-462c-d5df-a696e844275a"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 3905 entries, 0 to 3904\n",
            "Data columns (total 5 columns):\n",
            " #   Column            Non-Null Count  Dtype  \n",
            "---  ------            --------------  -----  \n",
            " 0   chembl_id         3905 non-null   object \n",
            " 1   IC50              3905 non-null   float64\n",
            " 2   units             3905 non-null   object \n",
            " 3   canonical_smiles  3905 non-null   object \n",
            " 4   pIC50             3905 non-null   float64\n",
            "dtypes: float64(2), object(3)\n",
            "memory usage: 152.7+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.head(3)\n",
        "# NBVAL_CHECK_OUTPUT"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "5dRWw-q759Er",
        "outputId": "1942ca06-f59e-4b88-94a5-2d41419a5637"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
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            "text/plain": [
              "       chembl_id   IC50 units  \\\n",
              "0   CHEMBL207869   77.0    nM   \n",
              "1  CHEMBL3940060  330.0    nM   \n",
              "2  CHEMBL3678951    1.0    nM   \n",
              "\n",
              "                                    canonical_smiles     pIC50  \n",
              "0  Clc1c(OCc2cc(F)ccc2)ccc(Nc2c(C#Cc3ncccn3)cncn2)c1  7.113509  \n",
              "1  ClCC(=O)OCCN1C(=O)Oc2c1cc1c(Nc3cc(Cl)c(F)cc3)n...  6.481486  \n",
              "2  FC(F)(F)c1cc(Nc2n(C(C)C)c3nc(Nc4ccc(N5CC[NH+](...  9.000000  "
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          },
          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = df[[\"chembl_id\", \"canonical_smiles\"]]"
      ],
      "metadata": {
        "id": "uqEuW8-S5_Qi"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Using PandasTools and the respective Draw method\n",
        "PandasTools.AddMoleculeColumnToFrame(df, smilesCol=\"canonical_smiles\", molCol=\"Mol2D\")\n",
        "Draw.MolsToGridImage(list(df.Mol2D[0:10]), legends=list(df.chembl_id[0:20]), molsPerRow=5)"
      ],
      "metadata": {
        "colab": {
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          "height": 417
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        "id": "JBmWcI176BXJ",
        "outputId": "45bdf198-25d8-469b-e1f8-807450807f3c"
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      "outputs": [
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          "data": {
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Ny7djB4jFQCl1CnIyNe3HZpyT6sFSvOTP5vriiy/mzZsnFovHjBnz7ykxKwvS0gAANm8GPT0YNQoWLABai1CJuNwfNm708fEBAEQcMmTIjRs3goOP79gB/foBj1fyxE9PD6yt/33oR0//KkvlAvfP9PSscnIKCwsXLVoUFxeXkZFRN9N2tbWheXOQSIDLhfDwyIiIiDKbrV4NL15wXr3q+/r1H1u3RpmZmdVwP+uKkyfhxx9hwQK4fx88PGDMGBgwIGbcuBYtWlhYWBgbG8fFxYVpa+tMmVLeBgxYZA3AVtFShL6+Pigc6FPgXkc8efJk4sSJAwc+WLkS0tMhMhIMDGDMGDAwABcXGDoU3NzKTna/dg3WrgU+H5ycgBUgZVPE5BOJRCJF2tVS/YyMOBxOkVS6PT7+ZGqqsrtTOxUUAABUuArF+PHjeTxefHz8qVOnHjx4YGdnBzt3wsaNAACRkeDoCKam0Lw5JCRUe4+JXK6urjwej8/nHz16tGfPnlxKhalyyq5HWYbo6Ggej2dUapDDwMDAyMjI1NS0V69eiYmJyu5mtXv1CjU1sUGDJQAwevToMtvs3YtaWgiAXbqghGrgV58pUxARJRKcOhUtLFgR9ejGjWVfTi6X+/fff8vZgGxNpQkTJii4T1tbWwB49eqVIo2XL8foaIyOxps3Fdw8UTOvX7/u168fh8MBgK5dvx0zBu/f//fdGTMQALlcBEAnp7+fPHnCXi8uLj5+PL59+5J3J01CRHRzQ3d3bNgQDx8ud3c5OTn+/v5WVlYCgcDV1bWoqKhaj06VXc/IcAgO7hEaGldYqOy+1EJLl25q2LDX9u2H5LTJyclh8++//vrrf19NSMD4eERENzfMzUVEnD0b8/KqtbekQrdu3QKAzz//XNkdqbVU8U4oKChIIpGIRCJLS0sjIyM27ggAWVlZGRkZKSkpd+7c+fnnn5XbyRrQrBnMnv1TXNw6Ho938uRJliEtI5HAjRsweTIEBsLYsbBzJ83wqDZSKbAlablc4PPhfTUY4w/ShKVS6ZIlS+SktchG3BUcQZdIJOwjFbZn81dfvYJDhyAvD54/V2TzRP24uroGBwdramq6urru27fqjz+gY8d/3+3UCXR1ARFMTb0DAhw7deq0efNmHx+fpk2bLlw4/eFDMDeHVavAyQkAYNkyyM+H2Fj47juYMgVzc/9vDnRCQsLixYsbNmw4ffr0+Ph4sVi8a9eurl27vqqrU496GRoOqFcvXyrd9vy5lDIxqlp6elRs7A0eL0dOm1mzZoWHh7do0eLIkSP/vmphUbI6tYcHrFgBq1ZBly5Ai5ko27179wCAFTsm1UHlYr3s7OwFCxYAgFgsXr16dXp6elZWFrvJyMjICAsL69Gjh0gk8vHxSaoDJboWLpygp6cnkUhGjhx5+fLlXbt2BQSkHTsGZ85ASAi4uMCTJxARAYsWQbt2yu5rLcblgkQCz5/DzZtgZgbffAMjRsCkSRwnp+XLl2/evHnr1q1WVlaPHj1avjy4vG0oHrgXFhauWLGib9++PB4PAF6ypUrKIhbDhg1gYwNv3oCVFWhqQlnFc0ktkZWVlZWV1aVLl4EDB3722WcfvTtpEoSEgLPz/ZSUxQBYUFCwZMmSefPmvX37Vksr+uDBtNevYelSYA9+TE1h9244cAB0dCAqamubNq3v3LkDAGFhYdOnT7e1tfX29s7MzOzWrdvBgwfZoHtISIi9vb2vr2/NH7gq8GzY0D4qamuXLtsVWNSdVEphYSEAREdHl3dTtHXr1kOHDunp6Z08eVJPtgrdh6yt4aefYOJE8PWF/v1BIqnWDhP5Hjx4ANUQuEskkmPHjsmqTNZpyh3wL23WrFnwfu3333///aN379+/z+FwWEAze/ZspfSwhvXs2dOSDSoAAICNzTO21v2wYbhwIU6dir/+ig8fKruXtV5+Ph45gqdPl5eQ9PDhIweH+wC4eXPZGwgMDHR2dgaAJk2asJWVynT37t2WLVsCAIfD4XA4lpaWfD7fxcUlIiLio5aPHj0aOjSdfRnatcP587GwEAcOxF27/usxEsUEZWWtfP16d0JCUs1mjzRs2JCdBNatW1deG5FI5OXlxeVy+/fvDwAWFhYXLlyQSqXltX/2TNyxY2d2yrWzs2N5OHw+/9tvv71//z4iRkdHt2nTBgBkiaojRoxIS0urliNUbefOnQMAoVC4YcMGX19fLy8vNzc3Z2fnQYMGde7cuVmzZg0aNFi2bJmyu6lmxGJxnz59tLW1AcDKysrd3T00NPTDBnfu3BEIBBwO5/jx4xVsq6gIbWwQAPfvr8Yek4o0adIEAGTZelXlwIEDANChQ4eq3aw6Uq3A/cGDBzwej108+vfvX2abkSNHsquIhoZG6WimlgkPDxcIBFwu18XFZfr06a6urq6uKaNH49ChuHIlLl2K165hv34UuKuE48eRw0EeD1+8+L/XMzJwz54L9vb2AMCGi+rXr+/l5SV7lMTk5+d7enqym1J246qhodGhQwfZK9OmTYuJiUHE4uJib29vgUDQqNEIgUAKgDwebtiAiHjrFuW4V7tbmZkn3r2Lys8vNxyuBlKpVLZi1759++Q3DgkJ8fPzA4Dp06dXuOXi4mIvLy8+n1+vXj0dHR1XV9eXL19+2KCgoMDT05MF7nw+HwAaNmx4/fr1TzkcNTVw4EBjY+PyRsH4fL5AIKjyeKUWO3/+fLNmzdatW+fq6sqiPebzzz/fsmVLQkJCUlKSlZUVAHh6eiq0xYMHRXz+vuHDC2k2gpKkpKSwK51YLK7aLRcWFrLyG//880/VblntqFDgLhaL27dvz/5uhULhi4/Cn/devnypoaHBgnsnJ6ca7mRNkkqlffr0AYCZM2eWfrewEFevRkScOBHDwmq6b6RMa9fizp0lY963buH58+jiglpa2KPHMwAwMTEZN26cbPUlU1PTTZs25eXlIeI///zTrFmzD8c127ZtGxISgojR0dGurq4sYNLQ0HBxcblz546RkRGHwxEK9c3Nc5o2xRs3lHvcdcutzMwd8fFX09NrMnBnl0N2R3f58uUK2y9btgwAVqxYoeD22eq8kyZNYt+60s6fP29qairrg66u7qVLlypxALVCx44dAcDe3t7Nzc3Ly8vX1/fQoUMXLlwICgr6448/WDqcvb19cXGxsnuq6l6+fNm7d28jI6Mb709eUqn05s2b06dPl9Wl4PF4Ojo6ANCnTx+RSKTQdiWSbwYMAICtW7eWfjMtDaOj2d4xKwuTkhAR37xBCvLliIqKunTpkkTh2hd//PEH+5VVR2dWrFj1xRejXV2fVsfG1YgKBe5btmyRXRVWrlwpp+X06dPZXzWHwwkODq6xHtawvXv3AoCZmVl6erqy+0IUlZSEbdrgnTvo44O9epWU8hg2rDggIEBWlyMwMLBz587s4sRG39evX//hQLunp+dHRTxevnzp4uLCRt8FAgGbsc3n8zdsuEBXnRp2KzPz9v8/LakBjx49YiMaABCmwJ36nDnJbdvePHAgUpGNswV62U3jyZMny2uWnJw8ePBglu7F5/O/++67ShyA+vvrr7/Yn6eWltbVq1c/etfT05P9bQKAl5eXMjqo0iQSyblz5xAxJydnxowZ9evX79ChQ5nV4QoLC8+ePevi4qKtra2np2dpaZmQkKD4jk6ePAkA+vr6vr6+Bw8ePHbs1LlzGBiIt25hQAB26YKFhTh3LgYF4cGDiIje3hgbW0UHWescOHBAT09PX1+/adOmPj4+eXIr9kRHR3t6eurq6jZp0qRNmzaK3mtVRnIyamoil4uVTbY4evTo9u3ba01pLFUJ3B8+fMjurQGgWbNmBQUFchonJCTIGvfu3bvGOlmT0tLSTExMAOCwnIJtRPUkJeH69ThlCm7ejPv24fLl+Pp12S0vXrwoC9813xcx7ty5c3h4eHkbZ6PvPB6vQYMGtra29z8sB0hqSppIlC4SvS4oWPv6dXn1AZ/k5u5NTNybmFhYRVVaL168KLu1S01NrbD9oEEIgOfOKbRxtkwEuysICgqS01IqlQYGBrq5uZU3qFmL9erVi/2RGhsb5+TkfPRuYWEhmwzA4XD4fH5N/m3m5eXJmTajCmJiYnr37g0AX331VfPmzfX09CZOnFhhYJeWlubv7//u3btK7Ys9a+K9L/llaNiETQQCQFdX3LwZ160rCdzHjsWffsKvvqLAvQzp6emjRo1i/4aycKt+/fpLly4tfR918+bNb775ht35czgcdiaZOnVqdXRsyhQEQMVnOGZlZTk7O2tpaQkEAjMzMy8vL0XOnypOVQL33bt3a2pqGhoadu3a9cqVKxW2X7JkCZ/PFwqFWlpaQqHQyMiofv36NjY2NjY27du3d3Bw6Nat25dffvn27dtKdUMqlf7999/Pnz+PioqKi4v7sKZNDZs8eTIA9OrVS87EMqKCkpJwwwZ88ADbtMGoqIrbs9F3fX19PT09b29vRfIC2fnU39+/CrpL/qsVMTEOwcEbyznDHExKii8qklTdHy97/sbGdBU5J9jbIwAq+Dzyxo0bstHiN2/eVNiefQOPHj2q0NZrBVZ1h2WsrV27tsw2oaGhsjTOli1byh9+qqyCgoLFixcvXrx4+vTpo0aN6tu3b7t27Ro2bMiCKm1t7b1791bh7qqKVCrdsWOHLPLT0dGxtLT08/Orpt29fPlSKBSyX4Gpqel33303YcLcwYPR0RG7dsVly/D8eVy5EocNw6Ag/PVXLCzEtWspcP9YUFCQjY2N7Au/aNGis2fPduvWTXYKGj169L179yQSydmzZ7t27Sp73cXF5ejRo0ZGRuyDy5cvr/K+PXmCgweXTOWqML67e/cumzuhpaXFsgHZl3DWrFkKLpCimlQlcH/x4gX7Z71z544i7TMzM69evaqlpSU7I5RmYGAwefJkxfsglUo7d+5ceuEn2S/b3t6ezQ6sPhKJhKX6sdvW58+fV+vuSJVLSirJcV+1ChV/wPvPP/9EKRLmIyLinDlzAGDLli3/qYOkakTm53cIDu4WGpr5/wOHhRLJ+dTUA0lJW2Jj/0hJqardsXJb1tbWw4YNq7BxaCju349Ll+KjRwpt/OjRo/C+kJEiT5PZpbpOTRGTLaBmYGCQkZFRXjMvLy/Zg5Ey51MWFRUlJiaWN4NLfge0tLTKvDZxuVwOh9OgQQM5HVOKhISEIUOGyPpZv379xo0bDxo0qLr2J5XmjBzp3rIll8vl8/kPSxVtuHIFz5/HvDxs2RKvXy9ZfWzjRoyLq64eqR2JROLj4yNL2gQAExOT8+fPs3dv3749cuRI2dMMlhTAfvjxxx+TkpIQMSIigk2GYQPwvr6+Vd7JmTPR2xsRcc6ccttIpVLZgXTo0CEoKIg91mZ3FKx7jo6OZ8+erfLu1QBVCdzx/ROunj17Kth+6NChAODk5JSfn5+enp6SkhIVFRUVFRUcHBwcHLx//34NDQ0ej/f0qaLzGH799Vd216ipqSkUCgUCgYaGhuzXDAA8Hm/BggX/9fgUsm3bth49erD6j5QoqY4CA5HDwWqdNb1x47EuXTZu2XKtGvdBFLDo1q0BEyb89L4CaFpRkX98vOOjRw7BwV4xMYlVlE8puwJZWloKhUIfHx85jcPDsaAAhw/H/fsxMRHXr1doF2x+EbsAK9KeDWLV+qJeMiEhISwBpsJBRJFIxGafczgcLpc7YsSIMWPG9OvXz97evlGjRrIxP0NDw8WLFyvegefPn7NgVFYS9EPDhg3r0aMHAIwfP/6Tj7XKBAQEfDgK1rBhQ/YPaGdnV13lRH/9FQFQQyOmU6ctS5eW1yozE7/7Dps2xdqS8FxlIiMje/bsyX5f7KlFv3794tnatB9guexaWlomJiaWlpalc9/v3bsnG1HlcrnHjh0rb4+pqQfi43/Mzv54xoh88+fj2rX48iXOmYNz5+K+fRge/n+FmpOSkgYOHMiOwt3dvaioiOX4DRkyhB0X+/NkPXRwcGlVYvwAACAASURBVKjyAjjVTYUC9+zsbHajduHChQobyyaglP5WycycOZPH4+1wc1Nk77Kc8j179pQ5qrFs2TIul6upqVnZ9BvFJSYmGhoasu+6np4ezUlVRz/8gAC4aFE17mLzZgTAuXOrcRdEEWwpEDMzs/DwcHd3927Ozg7BwQ7BweOfP/85Pj6tKkqLJCfjxIlb2JWmdevW7Fz0zTfflA59rl0r/uor5HBwxw6cPx89PPDpU0UD94ULF7Itt23bVpH2bOhXxfOqq9DXX3/N/n10dHQqTLkODw9v1KhRmzZtGjRoUPo6IhAITExM+Hw+l8v9+++/FeyAi4sLAMycOdPT07NXr14jRoyYOnWqp6fnxo0b9+zZc+PGjaioKHZXUHGx8xrBhuFk6tevz65r7u7u1VWoMTsbjY1LktkbNsRSkxBkxGK0s0MA3L69WjqipiQSCRsxlA23b9y4UU5WHnsGaGxs3LVr19Lvnj9/XjboKRAIZPnP+fn5hYWvsrOvp6Udysr6MypqtFRa6Tms8+djfj7OmoUTJ6JsAsPAgcv69u27ePHi5cuXs++bubn5X3/99dFnHz16NGHCBJYWyOFwBAKBjo5O6bnmKk6FAnd8P/DTpk0b+bWH8vLyrK2tAUB+qlxSYuKz1q2Rw8G7dyvctSynXCwWszH7v//+OzAw8OzZswEBAYcPH46Kiho7diwATJkypdIHppgxY8bAB7Nqvv7662raEak+3bpJAfDPP6txF3v2IABOmlSNuyAKatq0qWx0isvlrnj4MKT8iKGyrl1DS0u0tBTb2nZkT6uPHz9er149drfAqjGyNNPOnTv37n0NAPl8NDNDDw9MSsKJExUN3L/99lsAaNKkySQFvlVpaWkAYGBg8GkHpzYyMjJ0dHR4PF6/fv0UrCaemZnJBqHc3d2PHj0aGBgYGhr6+vVr2ZTWFStWAECL5s0lCnxboqKi+Hy+hoaG/ERNHx8fADAxMUlOTlakk9Xq2LFjsgq27AcbG5sb1V229uLFktj9zBn5DU+fRgDs2jW49CTjOis9PZ2dxAYNGmRtbX379m357X/66ScWqJiZmZXZYM+ePSz7DgB0dXVbtmzJnsDcuqUbHAzBwRARMSgn5+br164ZGeVWsiotPx/nz0dEvHABnZ1x0yYcMwYbN0Y7uy6y/gCAo6OjnGJECQkJS5cunTBhwldffQUAZyr6wqga1Qrci4qK2JSIg6xQUzk8PDwAoEOHDhU/4Fi+HAGwRw/5rVhOuUAgkJ9THh0dLRQKK5V+o7jLly/LonZW6VLx8RiiIjIyMrS1jTp2XJCbW41Tio8dQ319nDat+vZAKiCWSP744w9W1VtXV5fD4bi4uDx79uzTtxwWhqweiZ8fcrkIgH36YFzcvye6N2/esMfZ7Cnwvn372OXKwKA9gBQAtbRwwgRExO3bFQ3cBw0apKmpaWJiIj9t/enTp5MnTz5+/DgAtGjR4r8fpFqRSCSNGzcGAE9PTwWrWbMRqM6dO5fXoLi4eMKwYWHNmqECK2RNmzYNACqcryWRSFjlluHDh5fZ4MWLkiGsK1fw9euSiuY3b6IiVfsKC1/Fxi5ITq54jFoqlfr7++vo6GhqarKxWy6XO2fOHPmVBD/VypW4ahXOno2RkXjggCKfcHFZxeFw5BeermtYYP3q1avs7OwKG//222/wPuekvIkxq1ev7tOnT7169dgqWgAgFAoDA7u9eNE9KurbpKTNYnG2RJIfEzNR8U5++y3264eRpercJiUlnThxgo2uNmzYUMGqHuyPa8eOHYp3QBWoVuCOiAcPHgSAxo0b79ixw8jIyMjIqEmTJjY2Nm3btnVwcOjRo0dYWBhLXi9voZD/k5WFVlY4e7acJRZEIlG7du1AsZzy2bNnA4Ai88MqpbCw8LPPPmPfbDY+UU2llEi1OnXqFFRmnobi2EzUyEg8exanTcM3bzA5GeXe3v7r2LFjP/74Y93JSK4O6SJRclFRukiUJ5GcePfum6dPOzk5AYCpqSlbdufPKnrIsm8fDhqE+fk4dy46O6OXF5YenRCLxStWrGAnCltbWzbpysCgQZMmEk9PLD95sFyhoaHsHMjn81euXFl6QOTKlSuDBg1ig2cNGzYUCoUWFhaVrdOnvu7evcvj8bhc7k0F1iUuLCxkYYpsSl/Znj1DTU3kcPDiRTmt3r59KxAIeDzeR8vZlikmJoatzfxRwZ+cHHzzBg8exD59MCcH587FS5dKSoV6eqIiGU8JCSvy8ysernr79m3//v3ZhezLL7/s1auXtbX1tWvVPBvn2TNk80ySknDJEgU/9M8//wCAtrb25cuXQ0NDo6KiYmJi0tPTMzMzq7Grqo0l45We1FsmBUtR7dq1CwAsLCyCgoJSSk3WT0n5JT7+x7y8u5GRw/PyKo7obt9GDge1tMousrx9+3b23TM0NFTkEPD9s6+l5c+IUE0qF7hLJBJ2CZFNWP6QQCBo1KgRAMyRM534I7m5uGABrlmD33+PZWWNr1u3DgCaNm2qSPWulJQUtvbNrVu3KnVc8rGMQDY+wfLG6s5FsXbIzc09f/48e16k+HKVimNlax8/xl27cOZMnD0bY2Nx48YKPpWfn88Wq9fW1ma1uhSvXUM+tCw6+mxq6qmUlN4PH7JE9tk3b/r7++fn52/YsAEA+vfvXyU72rcPT5zA1atx7lyUP2Z07949S0tLdp787LPPdu7cmZ//37OHRSKRl5cXe+LXuXNn9j0pLi4OCAjo1KkTOy+xJ4EAwHLcLS0tAwMD5W9WLBYfPXp07969ajf96yM//PADu02qMLni559/BgB7e/uKx/y8vVFHBw8dktOEDRU5Ozsr2E9/f38A0NHR6datm52dXadO4wWCkiRgd3f87TdcvrwkcHdxwTVrsGdPhQL34uL4t2/nxsf/mJLiFxU1JjPzrFT68RSOgIAAlsdlZGR06NAhRExKSqqJXJS7d3H/ftbLStT3Rhw0aBC7zymNy+VaWFioacmR/4xN6FRkkiEiRkZGwvvFH+6Wk42clYUDBmwDgCNHjsjZVFzc4uBgePr0M4lE3rdFIpE4Ob3icLC8C+yJEyfgfTUbBWfg7N69GwAmTqzEkL8qULnAHREPHTrEbuPKpKWlZWZmVony6leu4O+/IyI+fowrVmDXrujoiAMHbpg+ffTo0ePGjWPfvNKTGMrDCn516tSp9PoRN26giwsi4uLFGBqKAQGIiNu3VzAG9mHpWRa772enIaLyoqKifHx8HB0d2bcIAOrXr3/v3r0q31Hfvrh2Lbq54a5dOH8+HjuGO3bghg24dy/m55f9kWvXrrEbCQ0NDRMTE/YFEwqFs2bNqr4J1rXV8piYwPT0F/n5o54+nfziRWB6uqxGe1ZWFht0Dw0N/fQd7duHYWG4Zg1+803FjdmsmMmTJyu+ILl8165dY1Mq9fX1Dx8+vHjxYtmICftBKBS6uLhcu3aNpet06+a3aFHZ1TkKCwsPHDjQvHlzHo+nq6vbt2/fOHWuuidbX0n+mFFxcTGbf3XixImKNyoW4+vXuHIlrl6NCxaURNC5ufj27dNHj65evbp79252aVBkrVxEPHbsWP/+/Q0NDWXzYs3M7AFQVxcbNcK5c/H2bdy4Eb/6qrIj7hKJpAARX7+e/OxZe5agfP9+ozlz3FnElpSUNHz4cLbHr776Sk7FiGqRn4/jx2N4OK5fjwpfxxHx2bNnXbp0cXBwsLe3t7Gxady4sZGRERuYY9/5pk2bFlfFFHN1wWb67WL1jCuSn58vi5LLmxK9aBEC4LhxcfJvYiWSgvDwtsHBEBu7UE4zVjjE0dGrvKyroKAg2b2EgmXa2cJ2VTXsUmNUMXBHxKtXr+7cudPf3//nn3/29vb29vb29PT09PScNWvWmDFjHj9+XIltnTpVMlUwNhZnz5ZNQh7etCn7+7S2tra0tOzWrZsig+hv3rw5fPiwgYGB7E7d1LSpkRGamqKNDc6bhxMn4sWLOHcu3ruHvr4YG4tLllSwTMDq1avhfS1eAOjRowctuqTifH19nZ2d2fwz2WBkly5dli9fflHug+//7MMRdzY1x9kZx41DALS0RF9fLCj49zuTl5fHBtoBoE2bNg8ePGjVqtWHI6YCgcDV1TWW1h1R2NrXr6MLCtJEouyyRo7ZrBsXdtf+ac6exYgIzM/HESPQzw/lpGYUFxeznFRFkigUl5qaKgvCmjZtKgvZTU1NV6xYIXvYLRaLvb396tWTAqCDA37YheRk9PLCjh2zATgAYG5uzm5s6tevr3aTwD708OFDDQ0NVg3m5cuXp0+f/vXXX3/66afFixe7urqOHDlyypQpbJ2sli1bKnordesW/vorImJICC5ZgkIhuzx9ZWMjuzw1atSoU6dOipxYHBwc2KfMzc2vXr369OnThIREWZbolSv4+DEWFaGLC/r7I5spum0byn/SnJsbFB5u9+7droSElWlph4uL45KTfZ49a+/jU7IcT+PGjdnv18jISP7ktGqUmoonTuCTJ1W1vaKioubNm4P87Oc3b/DAAVQssUQt/Pjjj1CZOtSycp/btm0r/W5kJAqFyOWiIgNZBQXPYmImFRS8Skxcl57+f7cB+fn5L1++vHjxIqsVc+jQofICpNjYWNkog4JTBB8/fgwArVq1UqSx6lDRwL0qpabi5Mn49CkuXIhBQXjrFgYG4qVLgSdPBgQEbN26VXaH3blz5woj5mHDhgEAn8/X0tJigVH9+naygkSzZuH58+jqijNm4L176OqK+/fjmDEVBO4SicTCwkJTU5NNkK2SKW6k+gQEBBgbG8sukKNHjz5w4EB1VSZ+7/JlRMTkZHz4EFnpquho3LEDHRwQAI2NU62tbby9vfPz8y9fvszm0mloaHh6erJpQ8+ePXNxcWGJELJbRKFQ6OTkRHeJiihvhVQmNjZWQ0NDQ0Ojqh5lfPddycxUOSkSgYEZvXot6NevWmpPHThwQFtbm41d2drali7VzNy/j02bIoeDgwaVjOCOGiULPnHs2K0nTpyQSCQpKSmyhXhcXFzUt44ke9xqbW29aNEiKMXQ0JCdGeQnBvwf2bhSYiLOmYMAqKODDRt6DR3ap0+fwYMHswkMAFDh+kpnz56F989sf/75ZzktFyxAAFRk5WWRKCUsrGFwMMTGfryAyfPnjz08PCwsLDgcjomJSb9+/WrZQMDx48dNTHSOH/9aIinr65qSglOnYmwsrl6NtWUlsp07d4LC8+tEIhGbyzFixIgDBw48f/5SdiXJy0OJBN++xW++qVz1s7dvZxcXJ4jFmQkJK169GhgebnfpUjvZ3xd7JNK1a9eFC8semBeJRLLBqcNseS25UlJSnj17BmpYI6sOBO6ImJSEp0+XtwD9mjVrZOe706dPy9kMe6rC5oTJkqLy8grS0zEpCaOi8OxZPH8eX7xAKyu8d69k7qC3dwWB+44dOwDAxMTEzc2NFl1SfRMnTgQAR0dHVVjXVirFM2fQ2blkAR0DAwN22urYseOTUuNP4eHhTk5O7IaTneC0tLT27dunjI6rmcyKSm+wagaLqqiAPwutANDAQFpYWPbYrasrAmD1nTACAwO///77Xbt2yR88zsrCI0fQwwNnz8bcXJw+HXV0cMgQ/Oj5JSs2oq2tzQakFZwAp2qKi4vbt2/PRvVkC7h8SFtb29bWthIJ/YmJOH06vnuHa9bgzZuliyiwWIpdnuSvryRbed7c3Dy/vPw5REQ8ebLkBkH+fHWxWHzypGtICO/Fi+6lM9oR8cmTJ05OTux3mpiYKG9bakgqlYaHOwYHQ2Li2v97Izsbz5wpudgjYkoKLlumlB5WuXPnzgHAwIEDBw8eLL9M1ps3b9iaX2wUHADatetpYID9++PSpejiguxBxdy5CtUsksnJuRETMzEj41RExGCWkXX7to5QKGzSpMkXX3zBHuywv7vyFg63sLBg/dmwYYP8fV27ds3KyqpLly5cLpfL5aamplaio8pWNwJ3ufLz82UZgc2bNy+duc7k5eWx9QIBoF69emXWyk1OLlnlftMmnD69ZOLzixflZiEjYlJSEnvepCILZ5AKsfFsVYs8AgMD7e3t69evr62t7eXlJSc1k42+W1tbs1XcKXCvEsHBwSxu27Jly5kzZ+7fvx8XF1feyaRCQUEsat9oadmwzPQSiQQtLBAAK5U2WH08PDAyEleuxLlzyywBUOLRo0csa0tTU1NNBylu374te0irpaXVoEGDtm3b9u7dmy2KNGnSpEo/dYmIwJ078cGDMt+USqWDBg2SDRiVlzrPqgmz+L68mOZDY8Zgly4BQ4eOlnNXxmY4LF3qWFxcdj3sD0eyamUxlpycG8HB8PChgUiUiohSaWHu7T1oYoJ8Pvr74+7diIiPH2NZiSLq6Pr16wYGBnw+X7YwxdChQ0vnnJw4cYLNQm7YsOH27ds3btw4cuTIYcPWy1IPpk7FpUsxPv4/rhIYHT02J+dmZub5/PzHItG/gdbjx49Z7M7Kwx8oq+7nl19+yZ4DyHluUFxcvHjxYnYDwL69TZo0Ua8nzxS4IyL+8ssvsrPenj17ymzDFheU34bJyEBDQwRARapgjRs3DgC+/PLL/9Zz8kmKinDdOlyzpuQUrIC8yMixXbvaNW9eVdMBq1BiYiJLgNHW1q5wKbicnBw3NzcA8PHxqZnu1W65ubn16tX7aEV6Lpdrbm5ub2/v6Og4a9YsxeN4qRS//HIKS20qM3U+KwtnzMCylixUDg8PRERvb3R3r6BlQUGBu7s7h8PR19eXraeoXrKzs1++fCl/VLsKxcXFyZKJy1tfSbZSff369RUp5PLuXba5uTm8z05OS0uLiIgICgq6ePHi4cOHt23b5uzszOFw+Hy+nFWTPiziUV4lb3UXFTUqIWFlYeGr3Nx7YWGNQ4ME0vqGyOfjgAG4YgWuXo3z56PipTJUm1Qq7dWrF/sicTgc2VqQ9vb2/v7+BQUF+fn57u7u7MXhw4d/NEqdkICnT+PKlejhgRkZOHdupQP39PQ/4uO9kpI2l9fg2rVrsjoQGhoaHxXhzcrK8vHxMTU1NTc35/P5o0ePLl0o4vXr1926dYP3q+YBwIABA9Ru3jwF7oiIYrG4ZcuW7LdoaWlZOpvzyZMnGhoa7DfdvXv3Cm/O1q1DAOzSRSy/5d9//83SFahIn3Ls2VOSnvjTT4pObNq5EwFw9Ohq7dd/U1BQILuOnjxZ8Vp0rAgpLUFSJSZNmgQAjRo14nK5PB5PKBQKBALZtQEAdHV1vb29Fd8gm/AqFAq///77j96aNq2k/J3iRXGr27lzuG0bXrmi6FQ9Ozs7AKhr5fb+s/3798tGB78pVW8oJiZGR0eHvbtewTW3EE+fPg3vlw+DstSrV2/IkCFytnDkyBHZDep/PDB1kJq6PyFh1bt3/o8eGYeEaCRuaoUA2Lu3nMVh1NqjR49cXV1lkyvYYKW2tvatW7dYYSVNTU0fHx85sQ27jT94sFpWCfz9999lWWp6enpsPZ/IyMjZs2fr6uqy1xs0aCC76+jbt+/58+fZQNvRo0fZmD2bwCoUCuUfiMoqI0uvDuLxeKtWrQIADQ2NvLy8sLCwjxqsW7dOJBIBAJ/P9/PzK+9MJzNnDgwY8Fdqajs2JlGmoqKiGTNmIOLy5ctt3tcQIDUqKgpatQIAaNUKoqMV+si1awAAfftWY6/+K01NTaFQKJVKASArK6vC9uwUpkhLIt+JEyf27dunqan5+++/a2lpSSSSoqIilq3EGvTp0yc3N3ffvn3st6OIUaNGsR8uXbpka2s7YsQ5W1tg/wkE8PQppKVVy7H8N+/egbs7HDwI9vYKtWcXTllCKpFvwoQJI0aMEIvFAoHgiy++iIyMvH///p9//vnbb7/5+fkdPHiwU6dOYrHYyMho1qxZCm6zZ8+eWlpaPB4PEY2MjJo2bdqpU6cvv/zS2dnZzc1t2LBhGRkZgYGBT548KW8LhYWF7AdZkFfL5Obezs6+AgCIxYaGw83M5iOKMr7UxKuX4e+/4f3Qby3Trl07f3//6OjoJUuWGBsbs8gHEfv06fPkyZPWrVs/ePBgzpw5cqKgJUsAAMzN4eVLCAmp4u45OTlt27YNALhcrkAgePXq1fjx41u0aOHn55ebm9utW7ezZ8++ffs2MjLS09PTyMjo2rVrQ4YMad68ua+vb3h4eFZWFo/HKy4ubtGiRVBQkPwDUV3KvW9QHVKptEOHDgBgZ2c3evToCRMmuLq6zp4929PTc/HixStWrGA3c56engpukC2E0axZs/KyjVmBAjs7uzpVKVa1nDyJv/2GiLhgAb55g4GBWGppt4+NH4+6ulilBfiqkKxCpSIJMGztiSlTptRAx2qx2NhYlvG5c+dO9kpOTs7z58+vX79++PDhzZs3z5s3LywsjN2cKz7GvHnzZg6HI1uUrVevUx9Wr0pKwoULVWjEfe1aBECFz45oZ2fP4/HU7gm1EiUmJhoaGsoJkXk83qxZsxTfILsAaWtrr1+//qNBx+TkZB0dHTau+fnnn5d3hfLz82O7NjY2/qRjU0FhYWlnZwYHw+PHVhJJXkHB88jIERJJQUbGCUSVS5KsPmw1hlatWgmFQl1dXRcXlzKrS5WJzbBXeOmwypk/fz58sGalUCicPHly6XoMmZmZmzZtYtPS4IP1KKZNm6b4gaggCtz/NXbsWFkqYWk6OjpWVlaKrwMnEolsbGx4PJ65ubmNjU2bNm0cHBy6du3q6OjIZm2zKSD/1JZKUmpJKsUjR3D1arxxA729USDA1q0xKansxrm5uHgxrl6NFU1XV6JmzZqxr6siq7cGBAQAwMiRI2ugY7WVRCLp3bs3AAwfPlx+y61btwJAr169FNnskydPWH3Y3bt3R0VFRUVFRUenRUUh+489if7lF5wxA1VkqsWPP0bY2Dzftq3ixacRUSRCLhc1NCQikfo9pFYWqVRqa2srFAr19PRsbW07duw4cODAsWPHzp49e/ny5T4+Ptu3b1d8a9nZ2K+fLxtrLHP1mQULFsgCnfKy6TZt2sTONlZWVv/xqFSTjw9yuVKbBqH3NYKD4c2bGWlphyMjv5FK1Xv13/9MIpH89ttv1xSZtPeBN2+Qz0cNDYyNrfqTlFQqdXBwaNy4saGhoaenp/whAIlEcvbs2a5duxoYGNSrV+83NlqnzihwL8Gy2Llc7vr16wMCAvbt2+fv779t2zZvb++1a9d6enpOnTo1MjJS8Q0WFxfb2trKkq5Ka9SokbW1dfUdEamEmzdRIEANDQQo7tq1jNJmEglu316SwLttG1bFGpnVgT01AoB58+bJb1lYWMgqUTg6OtZM32qlFStWsMClwmpi2dnZhoaGABAUFCS/ZUEB9uiR0qnT16Wz2xlWL1ssxkOHsG3bkkpWyjVixAgACGCLRVckNhYB0MKiujtVq7CU9AYNGhRWRWq1tzcCoLn5bwKBbpnTTwsLC1u3bg0AbIrqg7KK3rBkaACwsbH59C4pn58fLl+Oc+fi778jj4cczrt1XR4+1E9K8i0sjMjO/vvFi565uXeV3Ut1MmdOSPv2jornKVQKm2NaqXkyISEh7969q47O1DAK3BERpVIp+xK4ublV1TY3btwI7+eWvXjx4vHjx8HBwbdu3QoMDLx06ZKXlxd76Hnp0qWq2iP577KzsUcPBCjW0Rlia/vZZ5+xO/jMmBjcuRNHjEATE5w/H9kqS+fOlVTwVT39+vVjgfvkyZPlNLt9+3bz5s3ZOnlNmzatklCgDrp+/Tq721ewOoqnpycAODk5yW82fToCYOvW0vx8eUl0Uin2748AOGCA8sfdv/jiCwC4KWeh1w/cv48A2L59dXeqVunYsSMA+Pr6fvqm8vLQ1LQk7Wro0HIfkoSGhspKMrRq1arg/QqrMTEx/v7+o0ePZmUxBQKBg4PDp/dKyfLySioiFRfjzJk4ejQKhZK1y0SikjgvLm5JcDC8eNFDmZ1UNw8ePAAAQ0NDxVMVFCQSibS1tTkcTrqc6rO1FwXuiIh7Evf0nt+7sXXjqipG+/o19u17SEtLG8rKYi8oKJDlIrdt21YFCwvWRTk5RYMHj2ralKXNGRoa2tradm/QAGWZxevWoZ8fFhTgzJnlptMoG1ufskePHuWFkrm5uW5ubix7VSgUsktvgwYNfHx8CuQvfU5K6dChA5/PHzdu3GW2sG1F4uPjBQIBj8eTU0UqPR0bN0ZNTQwLq3iDiYloaooNG0b5+h5SvNvVgdVOVuTfISMD/f3xyRPcvRuzslSlDr2Ku5x1+fMDn/ca2qtKalAmJOCwYez5Iv71l7yW7N6enRLHjRs3f/58Wfk1Nhjfvn17V1fXBFV46POJ3r3DpUtLfp4xA+Pi8M2bD98XizMfPTJ+8IAXEaGWNUyVpXv37gBQZs31TxEaGgoAzZs3r9rNqgsK3DFFlFLvcT0IgYAkhZ7zKmLoUPYgcouOjm6ZWey+vr6yDMJDh5R80SVMbm5unz59PkxnMjIyyps0CXftKllM68oV/OknfPpU2T0tl6urq46ODgB07dq19DPEmzdvsiR42Tz6Vq1aya7E02/eDEhJKVLD2ljK4ujoyP6KdXR00tjTGLlycnLGjx8PAH369Dlx4sTt27dfv06T3S7NmYPJyZiUhAcP4uzZJS+yBQjlCAxMMTAw1NDQuHPnDnslMzPz6dOnly5d2rNnj5eX1+TJkxXp2yfq0aMHl8vV0tL65Zdf5Ld8/RrbtMHQUNywAWNicPFiLFVqmXysx8seEAIbkzZ+4nbu3kVHR5RKce5cDA7+N1ItT1FRkb29vZaWFpfLZScWNt1ryJAh/v7+sSxnq9aYPBlDQvDgQdy1q8z3Hzz4pWVLW3t7exprUxzLMqjyzbK1d8pc46IuqLuBe64kN0+S9070bkTUCAiBAREDqmrLN24g/fN+4wAAIABJREFUAPL5rP5D2c9xWAY8Ow9aW1tTroKKyM/PZ7+Xli1b3rlzpxJLl6uG/Pz8hQsXypah7t69Oxt6z8nJmTFjBovX2fiZpqamt7e3WCyWSqUnTpwY7eHRITjYITj4q7Awn9hYv7i43QkJeXR9kissLIxlAAPAmjVr5DfOyMiwtrZ2dHRkVTiZ3r3vA6CxMbZrh1OnoocHxsTg1q0oS26XRfBysLXhjIyMWrVqVeakmuDg4E8+1gpkZmbKVmYZOHBgGbNEEBExIgLv3sXNm3HqVFy/HuPjsZbFftXhaPpRCIF6j+tli7M/cVN37+L33+P+/ZVYGefZs2dnzpxhC2YtWLDg+vXr/3k9YFWXn49nzuDdcrPYCwoKGjVqBABHjhypyX6R0ti6GZWakF2b1NHAfW/q3mXxy5YnLA9IDzAPM9d8qBlRGPHpm714EfPzsagIFy5EAwM0NkY5EyEOHz4si6K2bt366XsnVYKtQajIAkYqKzc318fHx8zMjAVSXbt2PXHiRPPmzWXrVnTt2vX58+cffkSKeCMz0/nZM4fg4FUxMW/oTlIxbNAdAExNTeXnGo0dO1b29969e/dhw4Z16dLlq68iBQIEQCsr9PDA/fvx119x61Z0csI1a3DNGixneur/KSgoMDAwkNVE19XVbdWq1YABAyZNmuTl5bVr166kmsrskq2FbmZmdvHixQ/fCg5GFxfk8XDcOPTxwZs3sU0b/OMP3LhR+Qn6KkiCknmx81YlrNqWsm1gxEC9R3qrElZ9+mbv3sWAAJwzBydOrMSnRo4cCQALFiz49A6ou71792pra9P1WunYCm6lF0atI+po4D7xdcl5617uvUNph67nXK+SzY4fj2vWYEYGenlhRgbevi2vsVQqbd++PbvW1q9fv6rS68mnyMvLEwqFPB6vBrILqlt2dvbatWuNjY3Zd0xbW5s95vb19S3vUa8U8VZm5u/JyZvevv2trJXVyUcuXboky3nbs2dPec1+/fVXeL/yZf/+/T/6909Kwlev0MMDpVIcNarSI+6srKe1tXVYWJjSTyNv375lq6ZzOBx3d/eCgoKzZ89269atQ4cUABQKcfJkZDfFnp5oYYEA6OiI8fHK7bXKuZZ9bV/qPvazR6xHqij104fb8X3gnpCAJiaKfuTZM+zTZ42FhVVtSGT/ZGKxOJ6+rMqWnZ3N4/EEAkGdTVWoo4H75NclNTfu5d47mn60qjbr4YHe3nj7Nnp5KdT+zz//hPfLzk2s1BgIqR6sQmKHDh2U3ZEqk5OT4+Pjo6+vb2VlZWdn91KBpaOOJifTiLvi2rVrx26NmjdvXuYdUWRkpJ6enuwWvbwLP6tUFB6ON2/imTMlL8p+kINVxNpRYTp8TRGLxatXr2Z3KVpaWuzAe/ZcumwZJiVhRAQePYqIeOQI7ttXUt7kyy/nnlHkUOuMUxmnzmeWlK7yiPWoqs1mZpbMq//zT3R0xIoKkyIiOjsjALq51dL0GKJWCgoKTp8+3bt3b5b26e7uXjfnG9TRwH1T0ia/FL8DaQcuZl6s2sC9sBCdnRUN3BHR19d3wIABWlpaU6dOrapukP+MFeyrprqzSvT9998DwKZNmxRp/DQ3N6u2prFWg3379slyYC5cuPDRuyKRqHPnzgDA4/E4HE6Vh6fBwcEswT03N7dqt/yJ7t+/b2VlZW1tbWNj4+PjI+vekye4fTsWFODWrfjiBSYloavrFTZCP2vWrCqpmlILJIuSXWJcnhc8D8oNqsLAXWbxYgTAFi1Q/r93ZGTJGjpscj4h1UpWSuvxY3z+vGSFiocPMS8Pjx1DJye0sxvOBgI4HA6L3VfPmoV1b+15bumZTHXBfLP5g/QHddbu3Fuv95f6X1bVZhs0AKEQJkwAXV3IylLoI926dbt69apIJGKhFVGuq1evAkDfvn2V3ZEqxqarZmdnK9LYTkdHn8+v5h7VHs7Ozg0aNBCJRG3btmUZ3h/y9va+d++eQCCQSCTff//90KFDq3bvW7ZsAQBZNSHV0bFjx+fPn586dSoiImLOnDkfdu/6dfj5Z/jnHwAAMzPYubOvv7+/pqbm8ePHsxQ8b9Z2pnzTtZZrHxU8EnKFE4wnVPn2ly8HOzuIjBRv3XpF9mJRUVFSkjQ8HG7ehDdvICkJTExg3jyYNw/erxlPSDXatevfH86cgaVLAQB+/hlMTGD0aDh6FExMZrZq1eq7777T0tJCxCba2osuXoRBg0CxS1utwUFEZfehtjl0CGbOBDc3WL++gpYSCUyYEH7+/OBp00b/9NNPNdI7Uq7MzMz69evzeLz09HRVC4M+0a5Luw4/Pzyg1YBlXy5Tdl9qoZUrV65Zs0YsFhsZGVlYWFhYWFhaWrL/1dXVXbZsWVJSUqtWrYKDg2WpI1UiISGhSZMmUqk0Ojq6YcOGVbjl6vP0Kdy6BTNmgJ8f9O8PzZuXvP7o0aPMzMzevXsrs3N1SWioZNKknk+fBl2/fn3atGnx8fG5ublNmxZERmoCwPLlcP06XL0KCxfC1q3K7iupG6ZPh0aNAABevgQ7OzAxAX19uH0bLl+GevVg5EgYMULauDEXAEJCQlxGjrzL5xu8fQsiEXTsCP/8A5qaSj6AmkLjalWvbVsoKAAfH5g1C+RfTH/5BY4csfv884gVK4prqnekbM+ePVu5cqVEIunevXsti9oBgN+Jf9Pspq2xrbI7UjtNnTr1zJkz4eHhGRkZGRkZz549+/BdgUAgEAiOHj1atVE7APj5+RUXFzs5OalL1A4AlpbQsycAQO/eYG7+7+v29vbK6lLd1L49b/hwx7CwOyNHjiwoKMjNzeVyeamp+UKhJiI8fw5ffw1+fsruJalLBIKSUfbZswEAhgyBVatAJIKQEHh/7ixJEnFwcAi7epU/cCCIRCAQQM+eoKEBGRlgZKScrtcsCtyrXrt2MHo03Lgh8fe/umbNgPKapaTAsmUAAF5eGjo6GjXXv7qqCItu5t5soNEgXZJuK7A10zBLE6ddzbn6YMeDozuPxsXFAYCpqWmtDCAMeYYAkCWhPIRqYWVlxVbye/fuXXJycnx8fFJSkux/4+LiJk2a1KZNm6rdaUFBwa5duwBgzpw5VbvlalWvHrB8otatld2VOu/HH3+8cOFCYmLiu3fvAMDAwDojoyTXy8AA2rWD69chIUGpXSR1ibb2vz9oagKXCwsWgIcHlDniwbe1hdu3YfBgsLWFCRPAzQ1atYKXL2HbNni/wmBtRaky1SIqKrt9+8/y89OCg4MXLFigqamppaWlr7+LxzPkcMDJCT7/HPT1wdsbIiNh715ld7dumPRmkks9l7fFbwukBUmipD9z/gzJD5GgpPdvva9vvm5mZjZgwIB+/fpNmFD1GaVKdyP3xojoEYP0Bx22PlyMxQKOQNk9Ip+qqKho//79t27dOnTokLL7QtRVQkLCnj17hEKhsbGxsbGJkdEwY2OoV69kjLNTJ+jSBZ4/V3YvSd3j5wcHDoCHB4wdK7ddbi5oasKCBbB2LejowLZt0LdvrR8VoBH3amFrq+/iMurnn38eP358WFgYe1FHxz8vDwAgNxesrGDDBkhOpqi9hmRLso14Rn31+gLA7tTdb4rf3M+7L+QIe+v1Hj5x+Nbv/sfefcdFcbx/AH/ujjuq9CIgVUHALipWFEFQxA4aWxR7iy32nnytsRtbbImKBQiKSmwYS+yKFQsWRAVEFJSqwHE3vz8m2d/lsBAbnn7ef/jydvfmBm6YfXb2mdnFNWrUEH25l+mVtSv3N+svFonP5J/Znb17ts3ssq4RvC9tbe2BAwcOHDiwrCsCGszGxmbatGklt9va/v0fRO3wLpRKWrGCvvuOLlygly/Jzo5++YW0tMjbm/55aN2bnThBcXFUUPC24/izohUKkkqJiKRSKi5+v6prAATuH8vUqVNjYmKSkpL4y8qVG9269fdazoxRxYp0/HjZVe7royPWyVPmCS+DTYK7mHZpatBUT6z3hnd9Mealz5tYfqKllmWOMmd39u6yrg4AAHy5GKPERCKi588pL4+2baMFC0hXl/r2JV/f0qSynD1LRFSvXuk+rls3mjKFvL3pwgUaMOA96q0ZELh/LFZWVuHh4WvXrpVIJFlZWdbW3s7OWnI5FRSQkRGFhtKwYSRDwsKnIhPJ3HXcpzyawog5azu7aLtU1qn89rd9KXIVuZZalkSkLdIu67oAAMCX7vx5mjWLEhOpbVtSKv9OVDcxoRcv6G3LPzx9+tTSso+razM3t+9L9VkNGlClSpSaSqtWkUTy3lX/3CFw/4i8vLz4s1fUzJhBYjENGkRLlnz6Sn29RlmOKusqlBlHbcez+Wdr6tXMUXxd690CAEAZqFuXJk+mQ4coL49MTOj2bbK1pYwMYoweP/7XklIlnD179ty5mObNX0gkpQvcicjCgiwsPkC1NQEmpwJ8+eRMvj5zfWZxZohxSLI82becb1nXCAAAvlCMUWws+ftTUhIVFZGjI23YQHl51KULDRxICQm0f///P8Th35RKZefOnaOioiZMmDDnrU/D+SohcAcAAACAjywriwID6fRpsrS888cfLnXqCHuUSuWpU6ciIyOjoqJSU1NtbW2XLFkSHBxchpX9bCFwBwAAAICPr6CAevTYlZHRPS4uPDzc39//8OHDUVFR0dHR/HkCROTs7NysWbP1WHTvNRC4AwAAAMCnoCwu7j9w4IYNGyQSib6+fk7O3zOv3NzcOnXq1LFjx9q1a5dtDT9zCNwBAAAA4BNhjIWGhu7du/fp06ceHh4hISFt2rTx9PQs63ppBqwqAwAAAACfiEgkcnZ2fvr0ab9+/dauXVvW1dEw4rKuAAAAAAB8Rc6dO0dELVq0KOuKaB4E7gAAAADw6Zw/f56I6pX24ajw/xC4AwAAAMAnkpSU9OTJE0tLS0dHx7Kui+ZB4A4AAAAAnwjPk3nlo+XhrRC4AwAAAMAnwgN35Mm8GwTuAAAAAPCJnD17lhC4vyssBwkAAAAAH92VK1ciIyPPnDkjEonq1KlT1tXRSAjcAQAAAOD93L5Nrq5ERHfukIsLPXpECQlUqxYZG1NcHEVFXbl9u+bOnUSkpaXl7e1tampaxhXWTHhyKgAAAAC8n2HDaPlyIqLvvqOBA2nDBgoJodWrycaG5s4lIqW5uZ1E4uvvHxIS0qJFCx0dnTKusGbCiDsAAAAAvJ/nz2nBAiIipZKio2nUKLKzo+xsunqV7OyoY0dxp04pDRuKJJKyrqhmQ+AOAAAAAO/H2Ji++46IaMwYMjCgnBwiouxsqlWLHjwgkYiIRGVawS8DAncAAAAAeD8SCWlrExGJxdSzJ02ZQo6O9OgRLVrEo3b4IJDjDgAAAAAfWnExaWGA+AND4A4AAAAAoAHwACYAAAAAAA2AwB0AAAAAQAMgcAcAAAAA0AAI3AEAAAAANAACdwAAAAAADYDAHQAAAABAAyBwBwAAAADQAAjcAQAAAAA0AAJ3AAAAAAANgMAdAAAAAEADIHAHAAAAANAACNwBAAAAADQAAncAAAAAAA2AwB0AAAAAQAMgcAcAAAAA0AAI3AEAAAAANAACdwAAAAAADYDAHQAAAABAAyBwBwAAAADQAAjcAQAAAAA0AAJ3AAAAAAANgMAdAAAAAEADIHAHAAAAANAACNwBAAAAADQAAncAAAAAAA2AwB0AAAAAQAMgcAcAAAAA0AAI3AEAAAAANAACdwAAAAAADYDAHQAAAABAAyBwBwAAAADQAAjcAQAAAAA0AAJ3AAAAAAANgMAdAAAAAEADIHAHAAAAANAACNwBAAAAADQAAncAAAAAAA2AwB0AAAAAQAMgcAcAAAAA0AAI3AEAAAAANAACdwAAAAAADYDAHQAAAABAAyBwBwAAAADQAAjcAQAAAAA0AAJ3AAAAAAANoFXWFSC5XL5nz54zZ85kZ2dXqFChQ4cOVatWJaKjR48eP358ypQpIpFIODg9PX3NmjWdOnXy8PA4efLk4cOH1Urr0aOHk5MTER05cuTcuXPe3t4NGjRQPSAuLu7PP/+sVauWv78/ET1//nzNmjV8l1QqtbW1DQgIMDY25luWLVvm4ODQrl27ktXOzc2NiIi4evWqnp5ew4YN27Rpo7o3PT09LCzs7t27ZmZmHTt2rF27Nt+ekJCwa9eukqWFhoZaWloSUXx8/M6dO9PT021sbEJCQlxdXVUPKyoq2rFjx7lz5+Ryuaura9euXc3NzfmuZ8+ebd68+fbt24aGhh06dKhXr96bfulfnIKCgujo6PPnz+fn59vb2wcHB/Nf3f79+69cuTJ+/HjVgx88eLBp06Zu3bpVrFjxyJEjJ06cUCutb9++NjY2RBQbG3vx4sXmzZvXrVtX9YDTp0//9ddf9erV8/HxIaKMjIz169fzXTKZzNbWtmXLloaGhnzLkiVLKlasqNZCuBcvXmzbtu3q1au6urr16tVr3769WPz/19IXL16Miop69uyZq6vrt99+a2ZmVrIEpVK5fPnyly9fVqtWLTAwUNielZW1efPmhIQEAwODNm3aNG7cWPVdhYWF27Ztu3jxokQiadasWdu2bVX/yogoOTk5MjIyMTHRwMCgYcOGQUFBEomE70pPT9+8eXNiYqJa2/6yHT58+MiRI0+ePDE1NW3ZsmXTpk2J6Pr16zt27BgwYICVlZVwpFKpnDVrVpMmTZo1a3br1q2IiAi1olq1alWnTh0iunz58oEDB9S+OCJKTEz8/fffnZycOnfuzAucP38+3yWRSMqXL+/n51e+fHm+5ddff5VKpT169ChZ56Kiot9///3cuXNSqbRmzZpdunTR0vpXh3/kyJFDhw49e/asQoUKnTp1cnNzI6IVK1bk5eWpFVW9evVWrVoRUX5+flhY2I0bN3R0dOrWrduxY0fVFpucnBwWFvbgwQMbG5tvvvlGtfu6cOHCoUOH1H4P1atXF14ePXr0xIkTPXv2dHBwKPmzfNkyMjKioqJu3LihUChcXV2/+eYbfjrYsGGDVCrt2bOn6sGnT58+dOjQmDFjdHV1t23bdvfuXbXSJk+ezL+UsLCw1NTUrl272tvbqx6wa9euhISENm3aeHh4ENGNGzf27NnDd+nq6jo7OwcEBEilUiJ6+fLlggUL/P39vby8SlZ7wYIFCoVCeGlqatq/f3/hZVxc3M6dO589e+bi4tKjRw/+E5W0bt26zMxMobVzL1++DAsLu3r1qkwm8/Pz421P8OTJk82bN9+7d8/c3Nzf379Ro0Yli2WMLV++/MWLFx4eHq/se78ejLHY2Nhjx45lZGRYWFgEBgY2bNiQiC5duhQTEzNs2DATExPh4IKCgvnz5/v6+jZs2DA+Pj46OlqttHbt2vE/23Pnzh05csTT09PPz0/1gJs3b+7evdvV1bVDhw5EVFhYuGTJEr5LS0vL2tq6RYsWFhYWfMuaNWuMjIy6dOnyypo/efJk06ZNFStW5EVxPIRTPSwwMLBatWr8/48fPw4LC7t//76JiYmfnx/vqIno1KlTx48fL/kR33//Pe8VY2NjDxw4UFBQULNmzR49eujo6PADli5dWlBQoPYuIYAsM6xMJSYmuru7i0SimjVr+vv7Ozs7i8Xi//3vf4yxSZMmEZFcLlc9/uLFi0S0bds2xtjs2bOJqHr16jVVnD17lh/Jz2R+fn5qn9i2bVsi6tixI395584dInJ0dPT09Kxataqurq6RkdGBAwf4Xjs7ux49epSs9o0bN+zs7IyMjFq3bs1D5I4dOyoUCr43Pj7ezMzM2Ni4bdu2Li4uYrF45cqVfNfBgwc9/61ChQpEdPPmTcbY6tWrxWJx3bp1Q0NDa9asqaWltX37duFDHzx44ObmJpVKPT0927RpY29vv2jRIqE+NjY2+vr6vr6+9vb2IpFo8eLF7/ylaJz4+HhHR0exWOzp6env729nZyeVSpcvX84YGzJkiL6+vtrx/Hpv3759jLGJEycSUc1/i4+P50d27NiRiNq2batWAu+qhLZx7do1InJycvL09KxSpYqOjo6Jicnhw4f53vLly/fu3btktRMTEytWrGhlZdWmTZtGjRqJRKKAgAChwS9cuFAsFlesWNHb29vAwKB8+fK3b98uWcjKlStlMpm+vr7qRzx48MDOzk5fXz8oKIhfBk+fPl3Ym5ubW6dOHS0tLT8/P96Dd+nSRWi9jLFNmzbp6OiYmpr6+fn5+fmZmZk9fPiQ77p69aqpqekr2/aXKjs7u0WLFkRUqVIlf39/ftLq1q2bXC7fsmULEV26dEn1+MLCQiKaPHkyY2zHjh1EVLlyZdXWFR4ezo+cMWMGbzZKpVK1hNGjRxNRjRo1+MuioiIisra29vT0rF69uqGhoY6Ozm+//cb31q9f38fHp2S109PTa9WqZWJi0rp1ax8fH7FYXKdOnfz8fL63oKCAnwsrV67cpk2bGjVqtGvXju8KCAhQ7aD4hdngwYMZY2lpafb29mZmZj169OjYsSMPqoQWGxsbW65cOXNz8+bNm1taWspkst27dwv1mT59Ou+7BEePHmWMFRcX7969u379+vx8tHPnzvf5sjRRdHS0oaGhtrZ248aN/fz8jI2NjY2Nee9Rr169pk2bqh3/008/EdHTp08ZYwEBAbq6umrdl/CNODs7q/3tM8YUCoW1tTURLVy4kG/hzdjDw8PT09PNzU0sFjs6Ot67d48x9vTpUyISTjSq+NUd7/S4Pn36CHuXLVsmFotdXFzatm1rZGRkbm5+7dq1koXs3LlTJBIZGxsHBAQIGzMzM6tUqSKTyVq2bMlHTPr37y/sPXz4sKGhoYuLS7t27apUqUJEEyZMKFny2rVrpVJpuXLlunfv/tpf/VcgIyOjSZMm/C/d39+f/8b69eunUCh++eUXIrp7967q8ZmZmUQ0d+5cxtimTZuIyN3dXbV17dmzhx85ZswYIqpWrZraJw4YMICIGjVqxF8+f/6ciCpUqODp6VmtWjUDAwN9ff2IiAi+t1q1akFBQSWrffv27eHDh+vp6RFRs2bNVHdNnTpVJpOp9iTHjh3ju86cOaOvr+/s7Ny7d29fX1+RSDR69Gi+a926dWqhl4WFhZaWVmFhIWOsT58+vMutX7++TCarXr368+fP+Rt9fHxU31WrVi0iGjVq1Dt+Hx9IWQbucrm8WrVqhoaGR44c4Vv42NKCBQtYqQP33NzcVxbeuHFjfX19iUTy+PFjYWNWVpa2tra+vn6tWrX4Fh64b9iwgb/MyMjw8PBwcHDgL18XuPfu3btmzZoZGRn85eTJk4lICPe9vLwsLCx4mCWXy9u2bautrS2EPmr8/f09PT0ZYwqFoly5cq1ateLbFQpFw4YNK1SoIPxmvLy8LC0tb9y4IRxQXFzM/9+kSRMzM7PExET+iSEhITKZLCkp6ZWf+IXJz893cnKytLQ8f/4831JcXDxt2rS1a9eyUgfuryu8du3a+vr62trawp8xYyw9PV1LS0tPT0/om3jgvmXLFuGASpUqubq68pevC9xHjx7dtGlTIZZavnw5EfFYJyEhQSwWh4SE8K/47t275ubmLVu2VCshNTXV2Nh48uTJDg4Oqh/Rvn17fX39uLg4xphSqezbt69IJBLiy0mTJolEot9//52/XLBggfA3xRi7cuWKVCrt1KnTy5cv+ZaioiKh5Hr16llYWNy5c4cxJpfL27Rp84a2/WXo2rWrWCxet26dsGX37t1DhgwpLCwsZeAutEw1vXv35memM2fOCBuVSqWdnZ2enp6hoSHfwgN3IfzKzc318fHR0dF58eIFe33gPn/+/OrVq2dmZvKXv//+OxGtWbOGvxwzZoxIJIqMjBSOV/2WVe3du5eITpw4wRgbP368SCQSzvSbN28WQu2ioiJra2sPD49nz54xxnJycnh/JbSi/v37e3h4lCz/4cOHtWvXnj59+m+//fYVBu4JCQm6urq1atVKSUnhW3Jycvr06fPXX3+x0gXutWvXfmXJxcXFUqlUT0+vcuXKqtv5aKWent6wYcP4Ft6MhcA6Pj7ewMAgJCSEvTFwv3XrFhHFxMSU3JWUlCSTyQICAnhU9PDhQ0tLS6G3FPB77H369Klfv75q4D5kyBAtLS3hfDp16lQi2rt3L3/p7e09ZMgQfq2rVCp79+4tEokePXqkWnJaWpqJicm4ceMqVar0lQfuQUFBUql069atwpbt27ePHj1aLpeXMnDno4olBQcH6+vrq7YcxphcLjc3N9fT07OxseFbeOD+008/8ZfZ2dn169c3MjLip7bXBe4DBgzo0aNHZGRkvXr11AL3vn37Vq1a9ZVVatGihY2NTV5eHn/JR0AePHhQ8kiFQlGpUqWuXbuyf3rpadOm8V3Hjh0Ti8Vjxox55UdERUURkTBAXFbKMnDnd2GWLVv2yr3vGbjb2tr269dPX1//559/Fjby28r9+/c3MTHhW9QCd8bYqFGjhGJfF7grlUrVSC4xMVFo6zdv3iSiSZMmCXvPnj2r2nBVxcfHi0Qi/kfFu8ixY8cKe4cMGSIWi/lv4MiRI0T0ynH0rKwsIhoxYoRqsa/rbb88PEclLCzslXvfM3A3MTEZNGiQTCZbv369sHH58uV6enp9+vSxtbXlW9QCd8bY4MGDRSIRP2+9LnBnjAldDPunba9YsYIxtnjxYiK6fPmysHf06NFisViIw7j27ds7Ojrm5eWpBu4ZGRkSiSQ0NFQ47P79+yKRSBgksLW1rV+/vrC3sLCQp3/wl6GhoTo6OqmpqSVre+PGDbW2febMmde17S/DvXv3xGKx6lCiqvcM3Js2bRoYGFixYkXV8Zu//vqLiIYNG0ZE/OtWC9wZY/zWM78v9LrAnf27daWlpRHRlClTGGO5ubn6+vrCEPub+fr61q1bl///m2++MTAwEG7O8Ga/dOlS9k9LWLVqlfDGyMhI1XjvIqZaAAAgAElEQVQrKCjI19f3DR/E2//XFrgPGjRIJBIlJCS8cu/7BO73798XGtLFixeF7f37969Ro0aTJk2EgEktcGeMNWrUiF9lvSFwP3r0qFrJAn5qVm32PFmRX/ALBgwYYGZm9uTJE9XAXS6XGxkZqQ5S5OTk6OnpdenShb8sKiri/SrHo0+1QKpjx4729va5ublfeeDOIwHV2EDVewbunp6ewcHB1tbWU6dOFTby6/yhQ4eKRCJ+0a4WuDPGZs2aRUR8YPF1gbugadOmaoF7YGBgixYtXnmwq6ur6vVhWFgYER0/frzkkarxd9++fWUyGR8H4fz9/YUxUzUNGzYs+Sf56ZXl5FQeP6lmL30oRUVFaWlpVapUCQgICA8PF7aHh4f7+fl5eno+f/48Ozu75BsLCwuPHj1asWJFAwODN5TP7+4JL3meH88S5oG7atZX3bp1DQ0N+XY1CxYssLW1DQ4OJiIzMzMXF5cNGzbExsYSUW5u7t69e4OCgngClvC7ksvl169fT0pKEgrhCVj82pdzd3eXSqX37t1782/py3D48GGRSPQxWlFOTs7z589r1arVvHlztVbUqlWrqlWrPnr0qGT2GxG9fPny+PHjbm5uMpnszR8hfGuMsd9++00qlfr6+vIS6N/faY0aNZRKJT8ZC9WIjo5esWKF6mFEdPv2bYVCodoCHRwcXFxceAvMzs5OTU3ln8LJZLImTZrwoJyIDh8+7OXlZWNj8/z580uXLvHLQq5k265Xr165cuVe2ba/DEePHlUqlR+jdRFRUlKSk5NTp06dtm/fLuQKh4eHu7i4tG/fnohUv26BUqk8dOiQkZGRo6Pjm8tXbRi//vorz8UionPnzuXn53fo0IExlpiYeP36ddVMZVVXr149fPjw2LFj+UsvL6+8vLzhw4fz9hkREaGjo8Pzj1/ZYolI6IXS0tKsra1TUlLOnj2bnJz81l/OV+LIkSNVqlSpXLnyBy+ZN57u3bvb2toK3ZdcLt+xY0dwcLCTk5PqSURVSkrK9evXVacfvBK/FDQ2Nr506dKlS5f45SV38+ZNY2NjPpGD452G0MkQ0bFjx9auXbto0SIh3Zl79OhRdna2agdVrly5evXqCe+VSqVCv8rn6tjY2AgpzkQUFRW1Y8eO5cuXv/kk/jX4eCEWESUlJfHs861btwobw8PDa9WqxQPrhw8flnxXcXHxoUOHLCwseL7WO3hDT1K/fv3Tp0+vWLGC35CJiIiws7Pz9PQsWcjChQubNm3K85xfvnyppaWlra0t7K1evXpqaiofglF1/vz5U6dOff/99+9W8w+oLCenPnz4UCaT8VmAr9OlSxfVaXOqYQTXqlUrYXZUnTp1Fi5cSEQPHjxQKpVOTk5WVlbdu3d/8OCBg4NDRkbGn3/+uXr1ap5Wfv/+fX5qIaJVq1bt27evsLAwLi7O2NiYj5OVXkxMjEgk4n1NSkoKEQlTx4hIJBJZWVnx7arS09PDw8NnzpzJpwGJRKLw8PCgoCB/f/8aNWqIRCIPDw9+M5qI7t27J5VK169fv2DBAn6ObNy4cXh4uI2NjaWlpbOzc0RExJgxY/gsk6ioKLFYnJqa+p9+Cg3F78PylINXKigo4JdGAj6MpEqYwkJE3t7e//vf/4iIn9icnJyCg4MHDhyYnp5uZWX16NGjkydPbtmyRUdHhzGWnJzs4uLC37hs2bLo6OiCgoLz589bWFjwEce3On78+OrVqy9evJiTk7N7925+CuczqtesWcNH13Jzc/nlXGpqKs85fvbs2YgRI7p166Y2r5EfQ/9ugUQktMBX7i1fvvyjR4/4SOrDhw9dXFwCAgIOHjxIRNra2t9//z0fIyl92/5i8HMPn+/+OmPHjjUyMhJeMsbUDhg4cKAQQ1haWvKGIZfLU1NTnZycvL29f/rppxMnTjRt2lSpVO7YsSM0NJR/YlJSkjD3NzIy8saNG3K5PD4+vqioaOfOnaWJS65cubJw4cKrV68+fPhw69atfI4yD6avXLlia2vLwy8HB4ewsDC1GcxENH/+fAcHB+HEP3To0HPnzq1YsWLLli3e3t4nT57cu3cvb/81a9bU1dXduHFj586dtbW1lUolv6Eq9EJpaWk3btzg53ilUhkYGLhp06ZXzrf+qjx48EBtbp+a69evq3Vft2/fVnup2n317dv322+/pX+6r4oVK3bs2HHbtm1z5swRiUSxsbGZmZmdOnUKDw/ng46C77//3tDQMC8v78SJEw0aNFi2bNmba/7o0SMiqlKlilwuLy4utrCw+OWXX3hTSUlJKdn/kEpjKCwsHDx4sLe3t9q8W3pVJ8NfXrlyRXXL5s2bo6Ojz5w5Y2VlFRsbq6ury7dnZ2ePGDEiJCTkK5+QypWm+xo2bJjq9bZcLlc74NtvvxV+vQ4ODnwYPjc399mzZ05OTq6uritXrrxw4YKnp2dhYeGuXbvGjh0rdF/C9PSwsLDz588XFRVduXJFJBJFR0erBsr/SVpaWkJCAh9NVyqVrVu33rRpk6mpKREtXLjw1q1bw4YNmzt3buXKlVNTUw8dOiRUXsDj7927d/OXDRo02Lp1a1hYGP/DSU1N5SPxjx49UvvVzZs3z9XVtXXr1u9W8w+oLEfci4uLZTKZ6qIEJRn/W7ly5dQOqFGjRu1/VKpUiW/kfZajo2ObNm10dHT4mTIqKoox1rZtW6FVCYXo6emZmJgYGxvb2dklJibyvItS/hQpKSmzZ8/+5ptv+IUdv0rjsbhAJpOVvHpbunSpVCrt16+fsOXYsWM5OTmTJk1ycnK6cePGiRMnhDndBQUFcrn8woUL+/btS0tLi4yMvHTpUu/evYlIJBKtWrUqNTXVxcWlefPmDg4O06ZNU1sk5AumUCiECeCvxG+PqCoZ8dRWwWd0kUor6tixo0Qi4ee58PBwmUwWGBjIxztVW5G+vr7Qiu7cubNt27bS1F9bW9va2rpq1arPnj3bsmULbyfNmjULDQ2dP39+1apVGzdubGtre/XqVdV3jR49mk//L1ngm1sg/1ftVoBMJlMoFAqFoqCggDF24sSJxo0bX7t27cGDB3369Jk9ezbvJfmgWmna9hejuLiYiN58jilXrpxq61IN4jk3NzehdfG5wkSUnJysUCgcHR3r1q3r7OzMx0SPHDmSlpYWHBxsZ2cnkUhUR9x1dXWF1vX48eNNmzaV5tcuk8nKly/v4eFRVFS0ZcuWnJwc+md0fN++fcuXL09JSTl16pSurm779u1zc3NV35uamhoRETFy5EhhLZr79+9fuHChRYsWAwYMuHjxYmZm5s8//8zfZWxsvGjRoiNHjlSsWLF58+bW1tY8AUOwb9++o0ePFhYWvnjxYtOmTbGxsQMHDnxr/b94b+2+tLS01LovtUBET09PtfsSBjLv37+vr69vYWERHBz88OFDnssUHh5etWpVd3d3R0fH/Px81SEMQ0NDExMTIyMjGxub8+fP85yHNxg4cOAff/yRnJwsl8sTEhL4Yjg88b2oqKhkL0H/dD5ENGPGjMTExFWrVpU8T/FOpmQHpdba9fX17e3tq1SpkpCQoDrQNmbMmNzcXJ5qCKXpvgwNDVVbl7AYmsDDw0NoXXwZIlI5OTZt2tTa2pp3X/v378/KyurYsSMPsV7ZfVWoUCElJWXTpk0lrxBKaf/+/ceOHeM9ycaNGw8ePDho0CC+6/Lly3fv3u3bt2/Lli0vXLhw9+7dtWvXKpVKtRJ++ukn1fi7X79+jRo16t27t5eXV7169dzc3Hi+kJqkpKTo6Gies/puNf+QyihFhzHGQkNDiYhPZirpfXLcefJWVlYWY6x9+/Z16tRhjPn4+PBEusLCQrFYzPPFS+a4r169moj41L3X5bgLnjx5Ur169cqVK6enp/Mt/HR1+vRp1cMsLCy6deumuiU/P9/MzEyY8swYO3nypEgkEjLy09LSWrRoIRaLr1y5whgbNGgQH8cSjh8+fLhIJBKSnhMTE2fPnj1+/PiwsLCMjAyRSMQXgvjitW3bViqVqi6Koup9ctwXL14sFot5LOvv7+/t7c0Y8/Ly4jkG/ObPL7/8wl6V47506VIi4hPw35DjrornjKqmk/7xxx/jxo2bNWvWqVOn+ADYuXPnGGMHDx4UiUTz5s179g87O7uuXbvyPyU++WzHjh2qhVerVo1nQj9+/JiIZs6cqbq3S5cuVlZW/P96enr9+vUTdsnlcjMzM56GyMN3tbZtbm6u1ra/JPx7FGbPq3mfHHe+NuKFCxcYY2PGjDE3N5fL5f3793dycuIH2Nvb8+mDJXPc9+3bR/+sCvKGHHdV165d09LS4lNotm/fTkSnTp0S9vKLUrU2M3bsWENDw+zsbGFLrVq1qlWrxjOMFQrF6tWrJRLJwIEDhQMuXrw4ffr0SZMm7dq169y5c/RPBnxJvXr1kkgkqnmlX2eOu7W1tZeX1+v2vk+Oe69evapUqcL+WUZmxIgRBQUFRkZGM2bMYP/0NnxkUS3HnU9n19LSSkxMfEOOuxreB/KOJTg4uHz58qp7+Up8fC2RS5cuaWlpjR49Wui+6tSp07x582fPnimVyoSEBCLia4IJWrZsKcz1V8MHL3geM0+bnDlzplCyk5NTSEjI62KML97MmTOJiK9SUNL75LjzIcVbt24xxgYNGmRnZ6dUKrt27SqshWVqaspX+ymZ484nyq9evZq9U467mm+//Zb3JFlZWcbGxny+KWPs5cuXI0aMEM7Rgnv37kkkEv7pAj6u8f333y9cuPDmzZsDBgwQi8Wqc4QYY8OGDTM1NVXbWFbK8tKBr/TE+/cP6/79+6ampnzoKzg4OC4u7vjx43/99VenTp2IiOfnvDJ/lIj4qu2XL19+66fcvHnT29s7Pz//0KFDwiK1fBFi1ft6jx49yszMVEtIXb9+fXZ29vDhw4UtPDjgua1EVL58+fnz5yuVymPHjhGRra1tYWEhvzvJWVpasn/W5CIiZ2fniRMnzp07t3v37jyS+EqWcq9bty6/F/HBS75//76NjQ0frggODj5x4sTx48fPnTvHW5GRkZGJicn7tyJB06ZNDQ0N+cAYFxgYOG/evEmTJjVo0ODMmTN6enp8vJafR8ePH2/6j+Tk5G3btvFGWLIF5ufnJyYm8hZoYWGhp6endt/56tWrQvu0tbVV/aG0tLTMzMx4MytZcmpq6rNnz96abK25eB/F55d/WPyXzIemOnXqlJGRsX///h07dgirWb8hC7lly5Y6Ojr/qXVVqVLF2dmZty5bW1v6980i3nJUR9xzc3PXrl07aNAgYQSOz3kIDAzko6FisXjgwIENGzbkISBXq1atGTNmzJo1q23btvyXpvYABIGNjY1CoSi5YPzXpm7dutevX8/Pz//gJfMZFEQkFos7dOgQHh4eExOTnZ3NE29KjokKRCJRu3btiouLr1+/XvqP4yP9fOaYo6PjkydPeBYWx28Y8o5i2bJlxcXFixYtErqvuLi4w4cPm5qaZmRkVKhQQSKRqHYySqXy2rVrr+tkeEoMb9h8MG7KlClCyUlJSZGRkaampqzUt9C/JB+1+xKJRPz5AMHBwcnJyYcOHdqzZw8/OdIbuy/+2JD/1H29Ae9J8vPzL1y4kJWVJURQOjo6ixYtKleuHF/YQ7B48WIjIyO1B19IpdJu3botWLBg9OjRbm5uZ86cqVKlimoG0fPnz3/77Te1tKIyVJaBe+fOncuVKzdp0iS1buv9/8bu378v/J3zFetCQ0NFIpHwpb6hVfEuQEhcfp1du3Y1aNDA0tLy1KlTPGmeq1evnpWV1YYNG4Q7Qb/++qtSqeTrx3MKhWLp0qXBwcGqjxrhZ1PV6Tv8bgCfu8OzGIW7z0qlcs+ePVZWVvxdqhQKxezZs42NjT/SlJTPTY8ePbS1tceNG6d2L/XDtqIOHTqIxeLQ0FCZTBYUFMQ3vrUVCblbr3ThwgV+VcYlJyfn5eW98jElCQkJkZGRXbt25XfJly1bFvdv1tbWQUFBvIOuWLGih4dHWFiY8Ge1ffv2Fy9e8BYoFotbt279xx9/CInpp0+fvnnzptA+mzZteuzYMWHSz/Xr1+/cucMzrfkCf29u21+Y+vXre3h4LFy4UG0W1AdpXTw5gYi8vLzs7OxGjhzJ84/5AW9oXZcuXSooKHhzH3Xnzh3hqTpElJWVlZaWxltX7dq1DQwMVFNZ+PiZ6iyudevW5efn8zVJOCMjI7WJyEVFRQ8ePFCbXMjl5eUtWrTIw8ODL9DO/rlLwBUXFx84cMDe3l54ftxXq2/fvnl5eXxBYVUftvsKDg5+/Pjx+PHj3dzc+Eretra2Uqn0dQ3s9OnT9Lbu69KlS6qhOU+t4U2oTZs2SqVy7dq1fJdSqfztt98qVKjAu5Fp06apdV/VqlVr0KBBXFyciYmJvr5+8+bNo6KihDSegwcPpqSkCJ1MdHS06roLvEHyRrho0SK1ku3s7Fq1ahUXF/f15I6qat68uaOj45w5c/iNVsEHaV3W1tY8y6tp06YWFhaDBw/Oy8sT5mO8ofvi93neGmK90it7EgcHB3Nzcx6GqUZQaWlpaqfU58+f//rrr2+Ov6Ojo69evcpXdhesWrVKLpcPHjz4Her8MZTl5FQzM7NffvmlZ8+etWvXHjBggIODQ1pa2h9//OHl5fXDDz+UspB58+ap5sPxZ7wJgw1EZGhoGBAQsHv3bj8/P+Ec4+joqHpNv3fv3idPnhDRw4cPN23a5OzsLES98fHxfKoiZ2VlNWDAgIkTJ86bN8/S0tLf3//XX3/lu/iz36RS6dy5c0NDQ9u1a9ezZ8/4+PgFCxaEhISoPnlu586diYmJajmgrVu3Ll++fGho6IwZM9zd3W/evDl16tTKlSvzsdsmTZq0bNly8uTJ6enpVapUCQ8PP3v27Jo1a/jzLM+ePRsZGVm7du3Hjx/v3Lnz9OnTW7ZsKZlr+0VydHRctGjRsGHD6tWrx594mpycvGvXrg4dOvA7ZaWh+hUTUZs2bWrWrJmUlCRMXzY3N2/WrNmhQ4f480SEj1YdstqzZw8P7+7fv79582b+aBu+68qVK6ofYW1t3a9fv5UrV27evHnMmDFeXl5Pnz5dtGiRjo6OkPjLG7aVldX169fXrFljb2/PZ4jSq64qZTKZubm5MJFx/vz5bdq0CQgIGDRo0P379+fMmdOkSRPhzDdjxox9+/b5+vqOGzfuxYsXc+bMcXJyGjp0KN87fvz4iIgIHx8fvhrp/Pnzzc3N+RqpvG336dOHt+2rV68uXLhQrW1/YUQi0W+//daiRQtPT8/BgwdXq1bt2bNnx44dKyoqKuXkYyJav3696snG09MzMDBQtY8SiUTBwcGLFy92dHQU1uJwdHTkj2riL0+ePDlv3jwievr06caNG83MzPgUFyK6f/++auvS09P7/vvvN27cOGfOnKFDh/r4+OTk5PDH63733Xf8gMmTJ0+cODE4OLhly5aXL19euXJl9+7deUhHRAqFYvny5V26dLGzsxOK5Reuy5YtGzx4cHBwsFwuX7FiRUpKijCLMSoq6tq1a66urvzJxE+fPo2NjeUBU2xsbOvWrZs1axYSEqKlpRUWFnb58uVt27bxvdHR0bdu3eK3E3fs2HHr1i0vL69mzZqV+lvSYG3btg0NDV26dGlCQkJISIiBgcHdu3e3bt26Zs2aVz4QtKTHjx+rdV/9+/c3MzPjU5/5Fm9vb0tLy8TExGnTpvEtEonEzs5Otftav369lZWVUqmMj4+PiIho3bq1u7t7RkYGEcXGxqreG6lRo0ZQUFDfvn0fPnw4aNAgNze3+Pj4n3/+uXHjxvyJdd7e3m3btv3hhx/4E523bdt2/vz5LVu28LOVo6Oj2vC5vr6+oaGhcN3IHzzs6+vL02lmz57t4eHRt29fIiouLv7xxx+Tk5MnTZrk7Ox89+7defPm8WlI9KorDW1tbVNT01euK/I10NLS2rhxY2BgYO3atQcPHuzu7p6ZmXno0CF9fX3+2ITSWLVqleoFdoMGDfz8/FS7Ly0trQ4dOqxZs4ZPn+AbHR0d+cq2HF+ei4geP368cePG8uXLd+/ene+6c+eOagM2MjIaPnx4SkoKD5AePnzI80IlEsmYMWMOHjwYGBjYvHnzkJAQiUSyefPmK1eu8Ax7FxcXb29vfqSPj8/jx49nzZplYGAgZMAT0erVq0vG34wxPtCuo6MTFxe3bt06b29v4YRIREVFRcuXL+/Zs6fanOmy9MmTc9SdPHmyQ4cO5cuX19fXd3V17dKly8mTJxljS5YscXNzE54xxN24ccPNzY2vDbxmzRq3EjZu3MgYa9CggWoWb3h4uLOzs/CsQcbYokWLeDLW/fv3nf9RuXLlFi1ajB8//smTJ/yw5s2bq5XPFz/28vJyLqFXr15C+Vu2bKlbt66xsXHFihUnTZokPIWE69atG8+TVvPgwYP+/fs7Oztra2vb2NgMGDBASJ1njOXm5o4ePdrJycnU1LR+/frC8xcZY3v37m3QoIG5ubm1tXXbtm1fuXDpl41HBpaWlvr6+u7u7j179uTZwz/++CN/vpWqs2fPurm58d/S4sWLS7YiPsOhZs2awsMFGWMbN250dnZWfZbtzJkz+YLot2/fVm1F/v7+kyZNEp7P1aRJE7Xy+YN75XL5okWLqlatqqOjY2xszEeG+FuUSuXYsWNdXFyMjY0rV648atQoobRXatKkieoTABhjMTExjRo1MjU1tbe3Hz58OJ/vIYiLi2vZsiVfk6t79+5qT1DiGRF8b+fOndWSIN/ctr9Id+7c6dOnj729vb6+vqOjY6tWrfhM95iYGDc3N7Uc0KKiIjc3N57bHRsbW7J18STjLl26qD4P8vTp087Ozj/88IOw5ffff3d2ds7MzJTL5ULrcnFx8fHxGT58uPB4tW+++UatfN4mlUolf1ignp6egYGBj4+P8ChfbsWKFdWrV+cNbObMmaqziWJjY52dnflfkKri4uI1a9Z4enoaGRkZGBh4e3sLy7QzxtatW1ezZk1TU1M7O7vu3bur/VoOHjzIxyYsLCwCAgJUKzN06FC1vnTevHn/4evRcEqlcsOGDY0bN+az96pVqzZ69Gj+J9mlS5dvv/1W7fj169e7ubnxB4n079+/ZAO7ceNGcnKys7Oz6rczbtw4Z2fnq1evClu6d+/On/awa9cu4Tfv4eERFBS0cOFCPrfn+fPnJcufOHEiY+zp06djx451d3c3NjZ2d3efPn268Cw5xlh+fv748eOdnZ2NjY3r1aunerYqqWPHjqpnT8bYiRMnfH19zc3NbW1tQ0NDVZ+imJ2dPWbMGEdHR6lUWr58+W+//TY5Ofl1Jfv4+JT5Qy7L3PXr13v27FmhQgV9fX0nJ6egoCA++SoiIsLNzU2t88/KynJzc+PPm9u1a1fJb59nqwcFBakuD3/48GFnZ+f58+cLW/gA6MuXL7Ozs4XW5erq6uvrO2rUKOEra9eunVr5zZs3Z4ydP39erU9wc3Pjbzlw4EBgYKCVlZWlpWVAQIDqBKScnJwffvihSpUqurq6pqam7dq141MEOYVC0bhx45LtISsrq1evXg4ODsbGxrVq1Zo9e7baSW3Pnj3Ozs7Xr19/12/gwxOxrzL3CwAAAABAs3wG69oAAAAAAMDbIHAHAAAAANAACNwBAAAAADQAAncAAAAAAA2AwB0AAAAAQAMgcAcAAAAA0AAI3AEAAAAANAACdwAAAAAADYDAHQAAAABAAyBwBwAAAADQAAjcAQAAAAA0AAJ3AAAAAAANgMAdAAAAAEADIHAHAAAAANAACNwBAAAAADQAAncAAAAAAA2AwB0AAAAAQAMgcAcAAAAA0AAI3AEAAAAANAACdwAAAAAADYDAHQAAAABAAyBwBwAAAADQAAjcAQAAAAA0AAJ3AAAAAAANgMAdAAAAAEADIHAHAAAAANAACNwBAAAAADQAAncAAAAAAA2AwB0AAAAAQAMgcAcAAAAA0AAI3AEAAAAANAACdwAAAAAADYDAHQAAAABAAyBwBwAAAADQAAjcAQAAAAA0AAJ3AAAAAAANgMAdAAAAAEADIHAHAAAAANAACNwBAAAAADQAAncAAAAAAA2AwB0AAAAAQAMgcAcAAAAA0AAI3AEAAAAANAACdwAAAAAADYDAHQAAAABAAyBwBwAAAADQAAjcAQAAAAA0AAJ3AAAAAAANgMAdAAAAAEADIHAHAAAAANAACNwBAAAAADQAAncAAAAAAA2AwB0AAAAAQAMgcAcAAAAA0AAI3AEAAAAANAACdwAAAAAADYDAHQAAAABAAyBwBwAAAADQAAjcAQAAAAA0AAJ3AAAAAAANgMAdAAAAAEADIHAHAAAAANAACNwBAAAAADQAAncAAAAAAA2AwB0AAAAAQAMgcAcAAAAA0AAI3AEAAAAANAACdwAAAAAADYDA/aN7/pyKioiIMjPLuirwxVm3jg4fJiJataqsqwIAAAAfGQL3j27JEpo9m4ho5syyrgp8ce7epchIKiig27dJLi/r2gAAAMDHpFXWFfgqSCQUH09EtHw52dmRpydVqFDWdYIvxdChtGgREZGDA9nbk7c3+fpmeHlpGRsbl3XVAAAA4ENC4P4pDBtGEyaQREKjRlFxMRFR69ZXRKIpnp6eNWrU8Pf319fXL+s6guZ5+pSIqHJlUirp+nV6/JjS0ujsWeXZs0dOnuxavXp1Ly+vqVOn2tjYlHVNAQAA3uLFCzp2jFq1ovh4cnIiA4OyrtBnCYH7x3XiBBGRTEadO9PUqVRcTFpaJBIVX748NjU1NiYmxsDAwNfXNzo6uqxrChomOpp69KBRo0gsptGjKTGRGCMisrB4cfZsb8bYpUuXUlJSkpOTY2Jiyrqy8NnbsIGSkujFC2rViqyt6cEDCgws6zoBwNclO5tGjiR7ezp0iCGtkqoAACAASURBVDp2ROD+ashx/4iWL6cmTSgri/T0yNeXhg0jW1sqLiZ39/zU1Fgi0tHRkcvlu3btSkhIKOvKwmdEoVCcPn26mN+deZW9e1mXLpSfT1paJJGQnh7NmEErV9I331D9+pcLC18olcpatWplZ2fv3bv30aNHn7LyoJFOn6b//Y8WLqTwcKpSBVE7AJSJ3r1p6VIior176bvvKCqKMjLKuk6fmc9lxH1fzr6bBTeDjYP3ZO8ZajG0rKtTKowxkUj0ur27d+ePHKkvElHdusSP6taNunWjtDS6ejXn9OnpcXFxhoaGurq6GzZsiIiImDZt2qerOnzGHj9+3Lhx48zMTLlcXrNmzcaNG/v5+TVu3FhHR4cfcOLEicmTJ5Qrt7NHD4vp0/9+l4MDDR5MgwcTUePbt28dP37cwsJizZo1f/zxx4EDB0JDQ8vqxwENoFSSTPb3/yWSMq0KAHzVpFLq3JmmTCGlks6fp+XLycyMVahQu2lTbx8fn0aNGllYWJR1HcvYZxG478relVKUMsB8wK2CW4mFiWVdnVIJDQ2NjIw0NDT0/EejRo1MTU353nPnzvXtG1S79oYOHYJ69PjXG62tydraLiBgBn+5f//+HTt2Xr/OPm314TOVmZnZokWLxMREbW3twsLCkydPnjx5ct68ebq6uvXr1/f29rawsJgwYUJeXt64cWvmzp38ykJcXV1dXV2JKD0969kzvcuXnT7tDwGaRiwmmYwOH6anT8nFpaxrAwBfHcZo0CCqUIGcnMjPj3bvpk2biIi0tEgmW3LlyuUrVy4vW7bMzc0tNja2wte9voeIsbIPGcenjh9vNd5Uy5SIRqeMdpA5dDbpbC21Lut6vdbKlSuHDh0qkUgUCoWwUSwWu7q61qlTx8HBYdWqVc+ePRsyZOiKFcvfXJRcrrCzU6Sny65fJw+Pj1xv+Lzl5OT4+vrGxcVJpdJKlSpFRkbeu3fv5MmThw4dunTpklKpJCKpVCqXy3v16rV+/XrJ2wZHk5PJ3p4MDenp0/8fUYVP6WnybaZQmFWoJNGSlnVd3kihoGPHyMCAXrygn3+m7dtJ+nlXGAA0h1KpFItfm5vNo/Y1a8jAgBISyNaW8vJoxAg6epSSk0lb2yYvL42IDA0Nc3Jy1qxZ079//09Y98/OZ5Hjbiu1vVd0j///WsG1kSkjG95qmFDwmaZ9x8TEDB8+nIhMTU1jYmI2btw4fPjwRo0aaWtrJyQkhIWFzZo1Sy6XN2/efNSokW8tTSqVBAXJiCgy8qPXHD5nL168aNOmTVxcnEwmk8vlxsbGDg4Obdq0mTt3blxcXHp6+s6dO0eMGKGlpUVEly5d+uGHH95app0dVa1KOTl/T5KGT+9E5PJnaff3/DyGiJSKYqXitfMWyphEQs2bU61a1Lfvy+v3Ni18WtYVAoAvxLp162xtbS0tLTt37rxmzZqkpCTVvYyxCRNmxMbe0tOjPXvI1paIyMCA1q+nxERKTCxasWJur169PDw85syZQ0T79+8vk5/i8/FZjLjnKfN+SPtBV6zbUL/hhRcX9ufsP5F3wkRiEl0x2tvAu6xr9y/nz5/38fHJz88nIi0trT179rRs2ZLvksvlV69ejYuLW7x48a1bt8qVK2dgYJCSkvKGq0zuwAHq1Im++47mzPno9YfPU1FRUbt27fbv388H1GvWrHn48GETE5OSRwYHB0dFRRFRrVq1Ll68+NaSZ8ygM2doyhRq3PjDVxveKnLuAPsqXjkZaUbmNvk5mTWah5haO5Z1pd7k6ak7rn72lnbaV67QP7Mq/i0nh377jbS0qHdv0tP71PUDIiK6eiQqN/OxU43GNi41yrouAG+yZcuWXr16EZFqhoKTk1OzZs18fHyaNWu2dOnShQsX2tk5btiQ4Oen/YaikpOT7e3tq1b1vHgx7qu+I8g+Py8VL0PuhUgvSuuPqr9t27ayrs7/u3v3rqWlJRHxWHzFihWvPGzfvn1EJJPJiOjYsWNvLba4mPn6ssxMdvYs27fvQ1caPntFRUVBQUFEJJVKiahatWoZGRmvO3j9+vVEpKWlJRKJUlJS3lr49u2sd2/GGBs58gNWGUpr989jMlISoxYMPRO99tmjpLKuTqns2MHWrGEnTjDG2K+/MsbY8eMsPJwdGhjBfviBDRzIHj1iSUnsu+/KtJpftX2/TCkuKoycO/B5+sOHN84/f/ygrGsE8Ao7dz41MChHRHp6elu3bl2yZEn79u2F2YCcWCyWyWQxMTGlKbB58/tE7OjRj13xz9pnkSqjRkess81x28R7E88sPtO9e/elfGWg93Yy7+SxvGOP5Y+P5x1/h7dnZGS0atXqyZMnWlpaSqVy+vTpQ4YMeeWRfn5+FhYWRUVFRBRZigwYiYQqVKB586iggPLz36FqoNnat28fExPDx9orV64cGxtrZmb2uoMDAwNFIhH/6+WXiG9laEiHDn246sJ/UZCXfffCYSLia0vdOX8ofHZfplS85W1lqkMHun2btm6loiK6fJlu36Z9++jcObp0NIt27yaRiKytydGR5PKyrunXK/f5k6NbF2jrGdy/eiozNVHP0PTt7ykjt27dWrZsWZMmTdzd3bdu3VrW1YFPZ9cu6tzZ3MRkikgkKigoKC4uHjFixM6dOzMzMxMTE3/55ZeQkBA9PT1tbe06ders3bu3NGXWru1ARF95ssznGLgTkUQk+aHTD3PmzGGMjRw5cuzYse9Z4MzHM5PlyS+UL+Jfxt8ouPFf3/706dNWrVrduXNHKpUWFxd37959urAOXwlaWlrt2rXj///9999Vbw+VxHeam5OTE50581/rBV+C69evSySSli1b2tvbHzhwwMrK6g0Hly9fvnbt2rxRvbmnO3Hi78iqWzeKiKCiog9aaSidtsMX1mrxTcfvl9dp1dPA1Cpm5YSE0/uORywr63q93cCBtGwZEZGrK02YQE5O9JDZkVz+d6tijF7/nAH42MqZWPr2miTWkhYXFZZ3rpqXlRE5d0Dhi9wPVX5WFp07R0R05Qqlp//nt7948eLQoUMTJkzw8PBwc3MbMWLEiRMn7t69279//z///PNDVRI+Z1euUJcuJJdTWtpIL6/2y5cv79mzp7DX2dl5wIABERERK1asePny5blz51auXJmcnPzWYlu1IqK/G+dX6zMN3LkJEyZERERIpdKVK1fm5OS8QwnFrPhgzsHtz7ffL7z/jck3rQxb2cvs36GciIiIa9eu6erqyuXygICAX3/99Q0ruBNRSEgIEclkssePHx8//toB/hkzqGVLevKEiKh/fzpw4B2qBhqvYcOGCoXC19f34sWLDg4Obz0+8J+H48TGxhYWFr7ymN27qXlzatSI+P4RI+jIkQ9XYyg1bb1yMl0DIpJIZVJt3fajlohE4mPhS5JvXS7rqr1FtWqUl0e83y1Xjpo2JadW7tStG/XtSxMn0sSJNFQzHrjxRdLWK3c8YqmxRQWT8vbauvq7lo66cTJm87SuBfnvcqIsKTOTTp4kIoqLo7S0//DGSZMmNW3a1NjYuEWLFvPmzbt586ZUKhWLxbq6up07d37x4kXr1q337NnzllJycujaNQw2aKJLl2jCBCKi2Fhq25ZEIioulnXtGjV48OBXHt+6dWuxWMwYI6KDBw++tfzGjalPH4qNpYsX6e7dD1p1DVKWeTqlwGcfm5ub/6d3yeXygwcPTrgyweyKGV0gm6s2ve73UjIlYyzhZcLqp6v/azV+/fVXIqpYsWJkZGROTk5pKiA8I2DIkCGvKbNYJGIiEatcmf3xB2OMJSSwe/f+a9XgXSgUigMHDjx+/PijlJ6fzxYvZv/7H7t7tzSHh4WFEVGLFi1KWfyZM2fon0kUhw4dKnlAbOxJKysFESNizZqxZ88YYyw+vtT1h4/p4K//G9q9jbubW2l6kjKRnc3Gj2dFRSwnh4WFlXVtoBSy0pOX9qs/I8h61ZgOmZmZrzzmxYsXUVFRP/zww507d95a4N27LDCQzZzJOnRgly6Vthpbt27V1dUlIpFIxNe/4rS0tJo0aZKcnPzdd9/xvis6Ovq1pdy4wb77jkVHs1692MuXpf1s+Dz89Rdr146dOMFGjmQKBRsxgv3001veUrt2bd5OgoODS/MRjRqxDRtYZCT7668PUGFN9LkH7jxHvFWrVqU8Pj09PTQ01NzcnIiaLG5CF8jjusf0R9NjsmO+e/jdpNRJR3KPrM9Y/1+rIQTi169fL+VbhHVG69SpU3Lvn3/+6ezsa2paSMR0ddmpU/+1RvAu8vLydu/ePWDAABsbG21tbVNT0507d374jxk3jiUlsYIC1qNHaQ7PzMyUSCQymayUkZxCoeCtsX///g8fPlTbe+zYMT09PRubjiJRARH7+ef/XH34qAoKCmrUqEFEAwcO5FuKiopSUlIuXrz4xx9//Pbbb3PmzBk0aFBaWlpZ1XD+fEbEuncvq8+Hd5H9NHXpYL+a1arUrFnzyZMnwnYhmdjAwICIdHR0bGxs7r1tiOjuXbZoEWOMrVv3HwL3cePGEZGXlxc/95UvX75nz54RERHPnz/nByiVypEjR/LYPSoqSv39ubns4EE2dSpLT2eMsfBwFhtb2s+Gz8Nff7GoKNavHxs2rLRvmTz57ycJGhoaFhUVve4whYLxnd9/z0aNYqtXI3D/XE2YMIGIpk6dWsrjmzRpYmxsTETu7u6zls269vLah6pJv379iGj69OmlPJ4vNWpoaDh58uSdO3eqBljx8fHlypUjIgeH3lparGT3BR9WfHz8vHnzmjZtqjoIpKOjw88fkZGRH/jz/gnI2Pjx7J8z1ps1aNCAiN40CqXiwYMHJiYmQlINTxbcuHFjcnKyUqn09PQkIrFY4u19ec6cd/4Z4COKj4/nzc/Z2ZmPMqjR0dGpXr16cXHxp69bURGzs2NEbP/+T//h8F5SUlLc3NyIyMPDY/369QMGDLC3///UUJFIpKOjw9ubk5NTUlLSG4pKTWXh4YwxtmcPK8UA/d/q1q1LRAcPHly1atWNGzdedxgP1CQSSVhYmFKpvHTp0oEVK5iPD5PJGBEbPZolJzPGWFjY1756iAb66y+2cye7cIHZ2JT2LTydmN9D/us1wbhSyQYOZAEB7Plz9v33LD2d1ayJwP1z5efnV/qAhjHGRyJLua7Qf3LgwAEicnNzK+XxQ4YMkUgkqqnwxsbGjRo1Gj58+Pr16728vPiakuvWHf7gVQXBxYsXnZ2dha+ALzvF/+/v7z916lR+/tiwYcOH/NThw1lWFmOMhYYypbI075g5cyYfQX/rkcnJyU5OTkTk4uLSuHFjbe1/rXrr4eHRokUL/kTVadOmvefPAR9PkyZN+NqyPKKSyWTa2tr8wtLExISPPsyaNevTVywsbH/Dhkm1aytL13Lh85KWlubu7q46QiGVSlVPQzt27ODDBHZ2dm/ImcnMZJ07M6WSbd/+dxT9VllZWfzOYV5e3lsPnjhxIu97+QpatuXKMamUSSSsQQMWE8MGDGDr1rEBA1heHvsYN0Xhv9q1i/34Iztw4K0HJiez27dZejobOpTl5paq7OLiYmGByEmTJpU8QKlUjhxZSMQkEubpyTZvZoyxqKj/cEn5hfmsA3elUsm/ztKsV80Yu3fvHhFZWFh8jMoI2TLxpUgWnjt3Lh82mzlz5sSJE/39/dUWLtXT0yOiMWPGfIyqgoA/0VYmk6meulxcXEaOHHn48GH2zzclEomWLVv2wT710SM2ZQqbPp1FRpbyNjN/lJK1tXVWVtYb7mI/efLE3d2diLy8vHhejVwuj4uLmzt3blBQkJGREf8BjYyM+vbt+8F+HPjQ0tLSdHR0JBJJt27d1MbaZTJZtWrVjh49KhaLtbS0zpw58ykrplAoeAPbuvVD34aCT2Xbtm388k9oVBKJxNPTc/r06XFxcQqFIi8vz8fHh4jKly//uuTPlBTWvj1bt44tW1bKqTps165dRNSkSZPSHBwdHd2uXTt+7Wpra9unT5/sHTv+no7DGJPL2aNHTKlkgYFMJPo7awfKyrFjf+dc/vgju3q1NO9o1IgRsV27SvsJfD0Pd3f3VyRQMTZu3Dhn5za6usVEzNycJSSUttgv1WcduN+5c4d3LqU8PiIigogCAwM/Un142vpbBzK3b98uFovFYrFaDkZiYmJERMS4ceOaNm2qq6srlUqz+LgsfBxKpZIP58yaNUtLS6tRo0Zz584teaJavny5SCQSiUQLFix4XVFpafNSU6dkZ5c64XL9eubg8Pfk0NJV1dbWloh+/PFHHsGHhIQsWbIkLi5OGPl8/vx5rVq1iKhGjRqvnH9WVFR08uTJ6tWrE9Hq1f95BjZ8MjwVuHPnzmfPnl26dOn27duPHj1648YN1a919OjRRFSpUqXcUg5bvavCwsKUlJS4uLiYmBg+d9DR0VEul3/UD4WPZ8yYMUQ0efLkgICAQYMG7dy5s+Tkmfz8/ObNmxORlZWVMBSVk5OzY4dy4EDm6MhGjGBz57LRo9m0aaUN3HnyemmySYuLi/k9JXr94g1/W72aicXFEsnu1zzuED6FBQvY7duMMXbmDCvd3ekff2REbPDg0n5Ct27deBIXP/f98ssv9+/f57vmz5/PLz6rVt1iZMTOn3+Xn6A0soqzsouzP1bpH9T/sXffcVEcbwPAn2scvffeFDsodqKgItgQC/beiN1YiS22JGrsGhv2hoKo2GJBFEWiqCAoKIoFUarSpF+b94/Rfe93wHFGFE+f78c/vN25vTlYdp+deWbmmw7cjx07BgA+Pj4Klg8ICPiiGQJ0riInJ6e5c+c2atRoxIgRGzdujIqKKi0tZcpERkbS7IUNGzbIOVTHjh0B4MiRI1+oqogQkpCQQHuEi4uL5ffe7tixg2YuTZs27fbt2+fPnz948GBW1ub09MWvXk3Oylr3/PlAgeBTBguuW0cACJtNuFwF09ydnZ2NjY1pO6t0E6yJicnw4cMLCwtp/qiTk5P8yXACAwMBwNfX9xNqi76iwsJCGrXIb01nxrBOqubuJ5EQOn71/XtSUfHh/yUlpLCmW8+LFy+6d+/u7u7esGHDykt9WVpaKn7JRd8gOsqlyvmmpJWUlHTt2hUADA0NZ8yY4e7uzuPx6tcvpbNRdehAVq0iOTnE3p7cvatQtESbDCIVyEq/c+cO1JTT/P/27BndoQMA/P777zVXAn0Jp059SFjas0fBvPK7d4mGBunZU6EFfekimywWi46fZtSvX9/f33/r1q30gqmrq3/vnkI3008Smh867828OW/mhOSHhL9XjsHQ33TgPmzYMADw9/dXsHyXLl0A4LTi3TOfSCgU6ujosNlsmbsdj8dr3rz5ypUrk5KSaAflrFmz5B9qy5YtLBZr/Ph5in/669ev//jjj+vXr3/pFrjvxubNmwFgxIgRihTev38/m82WXvzo/n3je/fg3j148qRTRcXLN2/m5+T8rehnJycTAMLlEharRIEcTToIm8/n0+cHis1m04fAhg0btm3bFgAcHBzS09PlH+rNmzcNGjTv2vWP8nJFK0sIycjIwEbWr4NmZ3Xp0qXGknTtCAA4c+ZM5b2lpcTCgqSnk717yf37ZMoUQgi5cePDmEI5mjRpQofGUtIZ9paWlmw2m8fj3blz5798N1TX8vPzaaJ5SUlJjYWLioq6devGnAxcLnfs2Nu//07u3iVFRYQ+V4aFkWbNiJZWDQHbu3fv2Gy2qqpqmQITONI/AQBQV1cvV+A6dfDgQTpu51+cf61OiMXkr7/I8uVkzRpy86Yi75BIiKWlAwA8fvxYumWzsj179tAe7+3bt4tEosTERDoJknSul5aWlrq6+k3FPvpTjXw5kv7nn8J/MHCvBb/++quenp6Wltb169drLMwkxNcY2fxnNAdG+nxisVhqamo02PLy8rK0tASAAQMGiMVi+YfKzMy3tHylqlpz8xiVkZFhZmbGPI+amZn16tVr1apVUVFRiowE+jH5+fkBwK5duxQsT1uq6tWr17179+HDhz99Oj8jY1lOzt+FhRdLSx+Wl6ekpo5X/NPTfHw2ODg00NMbPXq0/JLr1+dqa1vxeLwzZ868f/8+PDw8ICDAzc2Nx+MxZxqXy7W2tmZ6D+VzdiYA5PJlheopkUj8/Py4XK6mpqanp+eSJUvouk4KvRl9ovLycnNzcwC4pMAwL0LI+vXrAcDAwGDfvn3btm1bunTpzJnlffoQNzcybx6ZPp1Mn/4hcPfyIocOkUWLagjcr1+/Tm+ENO1Khq+vL020cHBw+GanmUdynDp1CgDc3d0VKdyrVy96z7K2tj5x4kSVqZsiERk+nAAQDQ1y44bs5LOEkPfv34eFhXl7ewNAixYtFPlcWpjeNBUpTwgJCgr6q8b5wNGXExdHOncmPB7h8RSMWug6qatXr1ZTU3N1dZ0+fbr0xKAUbS9jsVh//y3bKCYUCmNiYlavXt24cWMA6N27d619l/81/tWH2zoG7rWjpKSkX79+AKCqqhoW9lh+4U9NiP9U76KjtT6OKN2zZ8+kSZNatWolM6cHm83u0KGDIk0OhBB3dwLwYXy0fO/fv6d3WS0tLVdXV2ZeFIrD4dSrV2/evE9ovP8RSCQS2nz+lCbn1UQoFGprawOApaVl5U6bwsKLOTm73r07VFBwXsEKTJ8+nf6CjI2N5TzIrV9PAEiTJsUhIbKDcgoLC8+fPx8QENCgQQMXF5fY2FgFP3rBAgJAZsxQqDDNpZbJz9HU1PTy8vr999+vX79eqODDJVIATWRydnZWcNKWkJCQFi1aSA9td3Qso8kM3buTRYvI4cNk6FBy/z4ZNYqkppLjx2sI3Hv27AkAS5cu3b179+LFizdv3hwcHHz9+vXHjx/Te2rlaeaREqGXnaVLl9ZYkrniAcDy5cvllBSLyZgxxN19o7q6+uWP7QHPnz/fuHGjp6cncxM0NDTkcrkhISHyP1cgEDAtUCtxwlpl8fgxASA8HgEgJ08q8o4VK1ZwOBymZZNpgWrXrt38+fMzMjJCQkLofWf16tVyjhMbGwsAVlZWtfRN/t+dkjt3Su5MSpsUXRz9b/G/YQVhGLjXDolEMnfu3A4d/FksImfQS0ZGBm1e/VLZmYmJRE8vo02b+TNnSm8WCASxsbE7d+6cMGGCvb19x44dFV+Mc+tWAkCmT6+hmEAgoO0TPB6Py+VevHhRKBQmJiYeOHBg+vTpdNYUANDR0VEkufDH8fjx4096kJNOu4yKiqpcIC/v+L17kJTUTMED0on86Ww2M2fOjI2NrTwtd3Q0YbEIm0327VPwqAq5eZM4OBBF7omLFi2iD5y6urpXrlwJCQmZPn26q6ur9CQ85ubmrxWcEA7JJRKJrKysAODAgQMZGRk1lr969Sp8nIHKxcXF39//t99+27PnXWgoiYoiyclk0SIikZDOnUlsLJk4kZCaUmUePXrEZrPV1dXfvn0r53OTkpJoik51aYdCIaEJDiUlRCT6sCpKebmCc5+iL6hJkyagSOL4/y7AXGMSgkRC/P39AYDP53t5eZmamkq3HLVv337p0qWOjo705cGDB+Uc6ubNm8znfuVJk9BnsbcnAARASNPy5IqPj9fT05O+j8D/5oLu2rWLRu01TnorkUhMTU319Q0eParNZIr7pff1E/S147Xvl94/mX/ydMHptIq0LMGXWUy9tn3rgTu1Zo2YxSIARGZ0SlYW2bZN2LlzF3o2mJubz549u/Y/vrSU2NoSANKvH6kpB0a+rCzyzz+EEBIRQR48IMOHE7GYxMSQ6paqkEgko0ePplE7ALRs2bJyYszx48dp5/vcuXM/p27fmR07dgDAoEGD5McoDDp0neY+VZl2KZFUBAZub9u2UMFEu/j4eA6HI93Y0KzZ4549yerV5ODBD6vKLV9OFi8m27Z9wveqTmLih+H+27eTJ09I48akqIgEB8ubjpKmYdBrq7a2tnRac2ZmZnBw8NSpU2n+ay3Pc/+jEgqFXC6Xw+E4Ojq2bt26xkEFTEaBvr5+5T98iYTQ3NEnT4ibG6FrAAgERE7O8MiRIwFg2rRpNVZ148aNAGBkZFTlAq7XrpEuXYhEQn75hdy+/aHbcOVKRSf8Rl/ImzdvWCyWurr6tWvXanzY/vPPP+nZpa6urkhqnEQiGT9+vJaWFr1imJiYDBgw4MCBA3l5eYSQCxcu0ExlGrvvq74pIiQkhI411NLSwnE1SiRnwYLzLVv2Nja2tbGRXzIxUdK4sRcA9O3bNzExcceOHYMGDZJ+2KMnCSi8ouUvvzzncETVz/om6/nz5+PGjRs6dOjZs2er7DFOepJkfdcaYqHv875CifKdhMoRuBNCQkOJhQUZMYLs3k0IISNHkk6dCIdDAEiLFp6qqqq+vr7yH/SrExQU1KRJk5YtW06cOHH37t2ChATCXE3S0silSyQvj5w8STw8iALDfeRLSSEbNxJCSGAgiY8nHh5k505y4gQJDydVXsFogyg9xe3t7atszmemgra2tsZFUxg+Pj48Hq9NmzZaWlqhoaGKlKc/xk6dOlVXhqagLFtW86c/e/aMPk116NBh586do0ePbtGiDYsloUkOw4eTIUNIZKSi2SyKSEggK1aQJ0/IokXk8WMybBhZvJgcOkQuXqx61tstW7YwUbu6unqV3TVjx46lDx5DhgyptYr+2MaOHcs8h8uf/yohIYHFYtG/ffmZD4mJRFWVsFjk7Fl5H/369WsVFRUOh/NMgen9JBJJjx49AMDDwyMqKurkyZM7dx5dsoRMmkT69CErV5K5c8mePR8C9wULyNWrZMIEDNzr2KVLl1RUVFRUVNTV1R0cHF69kjenB51SBgC6deum4PEPHz5MuzEPHz5ceW9gYCDTTsFisbZWmsBRIpHEx8evXr26Xr16bDZbwUR89I2g8/TTrpLExGrXpE9OJqamgnHHLwAAIABJREFUxMpKMGzYPJkHwufPn+/cuXPEiBHm5uYcDmfs2LEKfvTRowSAeHoqVLigoKBly5bMkGsOh9OoUSN/f38mwz45OdnU1NTZzdnvoV+FRClHcylN4E4IKSkhs2aRX38l2dlk4kTC4xE+n/TuTY4fT/nPabiPHj3S19dnRgHqaGtLWCyipkbatiXHjpHZs8mdO2TUKJKfXyvdwCkppGdP8vvvpE8fEh9PZs8mc+aQ7dvJ3LlETY20a0emTSNBQeeTkpJEItGuXbuY0MrQ0PDJkydVHpOORqJ/TjExMZ9fye/D6tWrmZVruVyu/Jk3xWIxk0Yspw0gM5OMG0eKisirVyQiotqjMYubdu7cWXpA/evX5PBh4u9PNm8m//xDxo8n8qcw/iQJCcTfn4SEkDFjyOPHJCCArF1L5s0jU6cSAGJqSgYOJDt2HHj48KFEIqFDguj3VVFR+Yd2A1Uyd+5cWkZPTw/bxmpFWVlZ06ZN6d81m82WM+x+yJAhtFiNmS2EkLVriYpKRa9ey3NycqorM3PmTAAYNmyYIvUsKSkJDw/X1tZmxuJraprRx04AMm4cOX2azJxJRo4kt2+T334j0dFk0iQM3OuYUCikk8bSO5q1tXV1D2kVFRUaGhr0Nys/w1ja+PHj6VuqW0F8z5490rH7li1bCCHv3r0LCQnx9/eneWKUgYEBzoasXIqLi5nxDD4+Pvfu3auc/FlYSCwtCQDp2pXIGesnFos/KWbLzSUcDtHVJTX2DBUXF3fo0AEA1NTURo8e/dNPP0mPCeRyucyQIS8vLwWHI36DlClwJ4TMmkXevSOzZpFffiGXL5PPXL8oIyPD1taWNjlERESsXbt2xbRppF49QvNyfvmF0Alqzp8nVU3H9h/ItLjPnk3evSMuLh+G7QMQdXXC5arSRlDa2Eb/f+vWreqOWVZWxowxwqVYpa1Zs4bpvWWz2XKml4mPj2cefuiKqtUZMoQsWUIePCDVHSw7O7tBgwYA0LZt2+rm5bh8mVy4QOLjSS2Ot0lIIDt3EkLIpk0fAneBgLRoQaZMISYmBICYmws/PJ3q6DA3Vx6PJ2f61GvXroHCWbBIQXFxccy9xM7Orsob2MuXL7lcLv01Ta9xHAwhYjEZNmwq/O8gn9zc3EePHkVGRh47dmzlypU0bT0uLk6RStJVyejAMhcXF19f34kTJy5dKvn7bxIaSg4dIqdPk6wsYmEhmypz7hxReBA1qn3l5eW085AmEFtZWaVUtS78jRs3mD9txaf+7NfvirFxC5C7atLRo0eZke4sFsvR0ZG5kcHHRVKDg4PlPGGib1NxcbGBgYF02rq399+9e5P168ndux/yfi9fJitWkA4dSK3MWR0URAghaWkkKopMmUKyssi7dyS8+uGjJSUlHh4ezIPryJEj6caoqKhVq1YxA6ltbGzq16+v1NPxKV/gTgjZvZuMGvW5h2KmamndurXslLf5+SQigqxd+yH3/MgRcu3a534eIYSQnBxCw8KbN8mbN4SurBoWRp4+Jfn5JDycbNyY379/fxsbGwAwMTFxcXExNTU9WdMgbjrhPb1MY7aMNGZlJXoXqXJVLKFQSKdi0NXV5fP58uc/nj2brF1LjhwhCxcSS0syfDgJDCRPnnxoEH379i0dHObs7JzHLN9dyePH5PFjQgipxSan9+9JWhohhLx8SYqLCb1Zv3z5Yeaux4/JwYOvhw0bRmd/MzExad26tYqKShC9NFZDKBQyCxzOnz+/1ur6w6PzWNO7S5X9xVOnTqVPmwpmthBCXr9+TVuSHBwcLCwsZOaeog8JhoaG8udUpkQikYODA31Xy5YtKxd4//7DqmIvX5KSkg8L1Wdnk4oKUlxMcNHeulVRUdGnTx8mdjc1Na2c2JCVlTVx4kT6GF+53bRKaWkEgKio5HfseC8sTF7XblBQEJfLdXR0pG0EzKrV0utAI+VSXl7evXt3ADAzM1u9evW4ceMcHBzq1XtIGxzbtSPdupFt28j+/SQu7jNHAv4/OhIsLu5DyLdgAUlNJevW1VBDel1t1qzZu3fvZMo8fPiQLhjsodiK5t8sJQvct28na9aQR49I9RlWCmGmanFwcMjOzq660Nu3ZPJk8scf5Jdfau1MVNjZs2cBwNraWpHllsLCwgDH6VfjyJEj0nMdrl+/nm6nHbgjRoygyQDm5uYmJibH6bNUVZ49IydPktmzSUUF8fYmgwYRJnPA1rariYmJj48P7Qtu0qSJ/NyGEyc+PL9NnVoHE3GsWLECAPr166fIrPADBgygPzdnZ+evULcfhFgs7tSpE3wcvlK5NyMoKIielgpmtlBDhw6VXhtOR0enQYMGHTp08PPz+/nnn+kz2FR6M5QrKCiIuZ7I+Yuo0pkzJCnpk96Bal9FRQWdSZle+mxtbWm2scwcjurq6nKG9MjYt48AEA6HsFikxgH/V65c2b59OwB07NhRqZs2ESGkoqKCTiNrYmLy+PH/T8ydlkYOHiRjxpA1a8iCBWTePLJ6NVGsS08hHh7k99/JlClk924yezbZvJmEhpIFC0jDhmTSJBIcTLKzy2VqSKN2JyenKkfVr127Fj4uOafU61QoWeDu50cAyP79NRTLzMzs2bOntra2m5vb9OnTDxw4kJiYyDzrSySSMWPGAICRkVHNk3x/9ZCdkkgk1tbWoFjaenl5uY6ODr1bf5F5dZRccHAw/XtWUVEJDAxcsWJFmzZtpOd7qVevHk2aAgA/P783b97IHCEqihgaEj6f0Kmr/v2X3LhBHj4kf/9NRo0SMOPldXR0bGxsapzp78SJD2P76NQcX1laWhpdXFqRNQv37dvH3P7lj3VDn4RpIHd1dV2zZs2sWbOGDx/u5eXVtGlTU1NT2k7JYrHi4+MVPGBZWRk9D7t37x4XFyeTvjllyhT6nMBisc6dOyf/UC1atKDns729vYLNsZRAQEJCiGJLS6EvSyQS0Z5YDofTu3fvESNGSC8LTVvBp06dqnj4smcPMTcnAKSZYjPiDh8+HAA2b978378D+gYIBILevXvTeCmpmofysjKyYAHJzSUNG9Zm4C7d4j57NhEKSZ8+ZODA/28ya9x4bOPGjSdPnty+fXvmPlWvXr3qbsFJSUlMk0RYWFitVfSrU7LA/cRvByb5JaYkyws4ioqKmBuPNCMjo27dui1atIj2p8hPHP8WzJgxAxROW1+2bFm/fv04HI6Tk9OXrpgyOn36NJ/Pl87PU1VV9fb23rx5M01FEAqFGzdu1NLSAgANDY1Vq1YxIcvRo6d1dUsASM+epLrbXHJycmBg4KJFix4+fFhjZU6cIKGhJCODjB1bN1Nf0/GREXIG2H6UlZVFEzYAYNOmTV+hbj+Ow4cP07zzKunp6c2iqYGK2bcvnMVi0wfUy5VWzX3+/Dk9twHA2NhYznITdP0BepwdmPWizIRC4ZAhQ5i5iaDSHI6K69yZvH1Lbt8mBw4QBWdA6NnzhJvbiMTEGlZORN8ykUg0ePBgANDV1ZWzAqBA8OFx/dKl2hyhfuAAIYSkppLIyA//v32bhIWRW7fIypWkVy+xjs6HDkZzc3PaieTg4FC53U0azUMGgIl05Qvl9I0F7iUlZMqUD70jZWWyrd2luWQpi/yhTiTVNwIJhWcnTgQAR0fHhISEM2fOLFmypFevXtKNDXw+n8PhnD+v6BKYdYUuVKFg2npqaqqZmRkA9O3b9yvUTRmdPHnS0NDQwMDg559/Pn36dJUduOnp6XShZgBwcXH5999/Fy1axGKx2rTpO2OG5FMaH+Wp21QZQkhAQAAAKBgXnj59ukGDBnw+HyeFrHWbN2/29PScNWvWmjVrDhw48M8//8THx2dkZHzqHD4iEXF0JCYmMSoq2s2bN6+yDJ2lijZK+fr6VncomgNK43tFEuLRt0wkEk2YMKFDhw7r1q2TM4VfjUaPJnPmkGvXyKlTCpV/8oQAEGNjXJNLWS1YsGDmzJm9evWqMWonhOTkEDpjbUgIqX6irNonEAhu3rxJ+3ZatGixYsWKGvM/6Tpi8GWWYv1qvrHA/dAhQueTvnKF7NtHtLSImxvZs4IkHCTZD0nqNXJlPrkub3FmMm4cAbjes2flEV2pqamhoaHz58/38PD4+++/v9h3qDVMtkyNaesFBQW0DdXDw0OR/Ack35kzZ+hzObOeSOU5iT+HUEjoM0BdzUZ1/fp1qH5ON2lisZiu2qOtrV3drJGozgUHf1iP3MhIfOpUVfP2E0I+jligsXtgYGDlAkKhcOrUqbTtqsYVDdGPY9YsEhhIVqxQNHDfsYMAkEGDvnC10JexZMkSAGCz2Xw+X1NTs8YIJCeHDB9OLlwgCxZ81cCdovNUcjic3NzcGgvT6bNpj+Kj6la+/Ob9/6C9b0JxMdBcbR0dOHIEioogOhpaFsDrJDBqCBZtwbwlCEurfXtYGOzZAxoaHX/7DT7OisCwsbGxsbHp37//l/wCtYnFYvXv33/Dhg3Hjx+n0xo2a9aMmUiVIRAI+vXr9/DhwyZNmpw6dapyAfSpfHx8unTp4uLikpKSAgAdO3acPHlyLR6fGSv7cY2Ir83NzU1fXz85OfnZs2caGhq0r6YyQsjUqVMPHjyorq5+7tw5Oj8u+gbFxgKLBUIhaGqye/Vyqq7Ytm3bbt68mZmZCQC//PILi8Xi8XjZ2dlZWVlv377NysrKysrKzs4WiUQaGhqTJk36it8AfevGjQMvL3B0hOPH4eOQ9aoVF4OlJfToAR9X/kXKZP369cuWLWOxWBKJRCQStWrVii4OIJ+GBhgZwceJqb8qDQ0NNze3q1evXrlyZeDAgfILd+nSRUVFRSgUAsDFixcbNmz4VepYyzhLly6t6zpIMTKCTZvAwgJ27QJTU7hxAwCgSylwxaBnBzo2oO8IZXkQFwh5z6BEDCq6oKoKFRUQFASxseDrC1wuzJgBnTrV9TepHdra2nv27Hnx4sWDBw8WLFiwatWq4ODgmJiYtLQ0kUhkbGzMZrOHDBly/vx5CwuLa9euSWcEoc/B4/Hu3buXkJAAAE5OTrQz7rvBZrPv37+flJRkZGTk4+Ozc+fOmJiY/Px8VVVVY2Njptivv/66adMmNTW18+fPu7u712GFUXVWrgQtLRgxAnJywMkJhg6F1q2rLayurt64ceOgoCBjY2ORSHTixImwsLArV67cvn37wYMHL168yMnJKS0tNTAwmDJlCp2lASEAMDUFMzNwc4OFC2H9etDSgnbtZMs8fAiHDoGeHnTtCtOmgbMzSCSgnHHRj2vTpk2zZs1isViEEA6HIxaLy8rKBg8ezMx+UaXSUkhOhiFDIDMTNDXhYxr515OTkxMeHq6lpeXr6yu/JJ/Pj4+PV1NTy8zMjI2NzcvLE4lEpqamlefP/ZaxCCF1XYf/lZkJcXHQsiXk5cGlS/D0ETSOgtyn0HkF5D4DV39Q1YOtDQAA8jvA5pvQpg20bQv+/sDlwsaNsHVrXX+B2kQIsbCwyMvLq6io4HK5YrFY+vc1ZswYHR2djRs3amtrR0VFNWvWrA6r+v2ZPXv2+vXrAaBVq1Z37typ6+rUsm3bts2cOVMkErFYLLFYzGy3sLBwd3dftWrVrl27VqxYoaKicvLkSYzhvlkzZ0J5OWzdCrNnw4YNCr3l6tWr27dvDw0NdXR0bNeunbGxsZmZmbGxMf2PkZGRsbGx9KI5CDECA2HSJJBIYOvWxMmTmxQUFFy5cuXixYuvXq28csUIAKZNA4kExGIYMgTevYN+/eq6xkhhu3fv9vf3pzEGm82WSCS6urpXrlxxdXWV/0axGPLzwdAQioqAy4Xqh9x/KYmJiXQ+royMDOkpKKqUlZXVoUOH1NRUkUhEtwzZu1e1ffuWmpquWlo3Cgv1uFxjHq+3oeGXr/h/9I2lygCAmRnQKMHE5P+f1iveQ0kWJB0Hy7ZQng+++yDjHpxKBz4ftLWhvPxDSan44/vAYrGCgoKmTJny7NkzgUAgvV1FRSUyMvLly5d8Pv/06dMYtde6aba2052dtbOzVb/H7KNBgwbt3LnzwYMHzBYOh8Nms9PT048dO1a/fv0VK1ZwOJxDhw5h1P4t43Bg2DDYvfsT3mJnZxcWFsbj8SIiIugoGoQU5O8PPB7s3x8yY8awtLTZqampwcHBAGBv34PH6ycSwdWr0K0beHhAaCh4eNR1dZHC9u/f//PPP0tH7To6OpcvX64xagcADgdolPtx5qqvrUmTJtbW1mlpaQkJCS4uLnJKvn371tPT89mzZ02bNv3tt99iY2MjIyNZDRs+KC5+UFy8Pyurt4HBADMzvtRs0d+gby9wrxJfGzLuQmokaJhAi/HgMhpcRkMPgO0iyM2FhQs/FPv4/PQ98fDwSEpKEolET548if3o3r17FRUVaWlpWlpaq1at8sAL5Bdgq6ICCQkAAKXVD6tQWgYGBgkJCTk5OTExMdHR0VeuXLl//z7N/JNIJGvWrGGz2QcOHKgxZRDVuZ9+gjNnQCJRtPzevdC6tZ+TkzpG7eg/GDMGeDxBdDRZvXq1hYUFj8cTCoX6+pwXL0BFBczNgRDo1QsOHKjriiKFFRYWBgQESCQSAKDZ7RoaGmfPnlUku/0b4e3tvWvXrosXL+7fv9/CwsLDw6NFixYyPYcFBQXdu3dPSkpq2rTp1atXDQ0N/fz8AKBULI4vLr5XVPROKBQAXMrPr6em1lxTs46+igLqcGDsp4lcSpYCuVzVpOa3bpGAADJ//ocZaX4AZWVlt2/fpk+WO3furOvqfKeOHycAhM0mAKSioq5r88Xl5uaePn161qxZjo6OPB6vTZs2dV0jVDO6mFJ2NgkMJMuW1Vz+7Vuirk5YLPLwIU7Uh/67kJAQQ0NDmvrs4OCweHHwmTOETrFLZ/LOyyP5+XVbR/QJ+vTpw+PxaJ6Jurr6tWvX6rpGn+bEiRMAoKenxyytqKmp6enpuWrVqqioKIFAUFBQQJ9D6tevL2eRxDVpaV+z2v+N0gTuE8aU+nbJTLz+pK4r8g2hq1p6enrWdUW+U9HRxMWF1K9PBg8mRUV1XZuv58WLFwCgra0tEAjqui5IIRUVxMqKAJB9+2oo+dtvBID4+HyNWqHv261bt7Zt25aSklLXFUGfa9OmTQCgoaGhpaWlqqoaHh5e1zX6ZFlZWdpSk9rQCR8ZR44cobOiOTo6pqenyzlOQlULvHxrvr3BqdWwsICMDEhJAUfHuq7KNyM/P9+vT89mVtp/bD2i/nEJMVRrCIFFi0BdHXJyYMkS0Nev6wp9PY0bN37y5MnNG5Ft2/9U13VBCjl0CEaOBAMD4d27+XZ2/z81UGkpZGdDbi4YG4OxMTx9Cr/9BnPmwE/4i0UIAezdu3f8+PEAsG3bNk9Pz5cvX3bt2rWuK/Vf3Lt3LywsLCYm5t9//y39mOBKJ73V0tLKzc21s7O7fv26lZVV3dbz831j00FWpbAQLlyAOXPAyAjc3ODhQ7C0hORkEAjqbCTEN0JNTY33+nrpi1v6Zrbmjs51XZ3vzu3bIBLBjBlgZwchIdC+fV1X6Osx5pc35L400ddxaN6xruuCFOLsDNnZqe/eeV+8eKJRo0aDBw9etWpVYODNX34ZsGkTHDwIAgEkJMCAARATAx9XD0QI/dAOHDhAo/YtW7ZMnjxZX1/fodIaOMrC3Ny8c+fOI0eODAgI8PX1tbe3B4DMzMyKigo2m62vr3/9+nVbW9u6rmYt+KZHzlJ5ebBoEUgkwONBZiZERAAA3L8Pqal1XLFvQWO3XgCQdPNsXVfke1RQ8GGovJER5OfXdW2+qlat27IqilLuRdR1RdAnWLlSt6TkbXR09IIFC6Kjo1NSUp49u8NmA50VSSAAPh8SE+u6lgihb0NoaOj48eMlEsnKlSunTJlS19WpNVwu19XVNSAgIDw8PD8//+rVq7NmzYqOjqah/HdACQJ3ABg79v+nKL5+Hf74A8LC6rRC3wyntt1smrSzb/aTWCiouTT6JG3bwunT8Pw5bNkCP9iUiNaNWvM1tN++flqUm1XXdUGK0tXV3bdvn5qa2u3bt+mW5s1nSSRQUfHh3/Tp8PffoCTZkQihL+jkyZNDhgwRiUR//PFHQEBAXVfnS+Hz+Z06dVq+fLny9iRUpgSpMgUF8OIFdOgAf/8Nvr4gEMDChSASga4uKH+q0ufiqvAznz8wsWt0I3hD/dZdOVxeze9BClJTg1at4N9/oXNnaNGirmvzVbHYHA6XZ2rXqLzkval94xqXtEDfCDs7OysrKy6X6+7u3qtXry5dWo0ebTdtGixcCA8fQo8ewOfDnTvQt29dVxQhVHeio6N9fHxEItGSJUsWL15c19VBn0YJ5nHn88HYGLy84MwZUFcHc3MAAAMD0NKCs2chOxvGj6/rKtY9loqaZk7q4zvn9zt3HmDv0qGu6/O9MDeHIUPquhJ1g0jErXuNTbh6PPNZQnnJexaLbeeM4xmVwMiRI0eOHFl5+7p1AABdu4JyDjxDCNWaVq1a+fj42NjYfPtNt6gypZlVpkoSCezd+6MH7hcCF7XpNS724mFLp+aCijLnzgPqukboe/DvyW2l7/PSUxJ8Z2yIPLLGe/wyNS3duq4UQgihWiAWi2XWJ0LKQgla3OWYPRtatqzrStQ1IiEvH0S/z83g8dsLKsrqujro+9G619i3aU+fx13TNbHGqB0hhL4bGLUrL+VucUcAUPo+T1BeoqVnAgBisVBFVaOua4S+B49unn37+qlQUN6q55jUB9HYk4MQQgjVOQzcEUIIIYQQUgLKMR0kQgghhBBCPzgM3BFCCCGEEFICGLgjhBBCCCGkBDBwRwghhBBCSAlg4I4QQgghhJASwMAdIYQQQgghJYCBO0IIIYQQQkoAA3eEEEIIIYSUAAbuCCGEEEIIKQEM3BFCCCGEEFICGLgjhBBCCCGkBDBwRwghhBBCSAlg4I4QQgghhJASwMAdIYQQQgghJYCBO0IIIYQQQkoAA3eEEEIIIYSUAAbuCCGEEEIIKQEM3BFCCCGEEFICGLgjhBBCCCGkBDBwRwghhBBCSAlg4I4QQgghhJASwMAdIYQQQgghJYCBO0IIIYQQQkoAA3eEEEIIIYSUAAbuCCGEEEIIKQEM3BFCCCGEEFICGLgjhBBCCCGkBDBwRwghhBBCSAlg4I4QQgghhJASwMAdIYQQQgghJYCBO0IIIYQQQkoAA3eEEEIIIYSUAAbuCCGEEEIIKQEM3BFCCCGEEFICGLgjhBBCCCGkBDBwRwghhBBCSAlg4I4QQgghhJASwMAdIYQQQgghJYCBO0IIIYQQQkoAA3eEEEIIIYSUAAbuCCGEEEIIKQEM3BFCCCGEEFICGLgjhBBCCCGkBDBwRwghhBBCSAlg4I4QQgghhJASwMAdIYQQQgghJYCBO0IIIYQQQkoAA3eEEEIIIYSUAAbuCCGEEEIIKQEM3BFCCCGEEFICGLgjhBBCCCGkBDBwRwghhBBCSAlg4I4QQgghhJASwMAdIYQQQgghJYCBO0IIIYQQQkoAA3eEEEIIIYSUAAbuCCGEEEIIKQEM3BFCCCGEEFICGLgjhBBCCCGkBDBwRwghhBBCSAlg4I4QQgghhJASwMAdIYQQQgghJYCBO0IIIYQQQkoAA3eEEEIIIYSUAAbuCCGEEEIIKQEM3BFCCCGEEFIC3K/8eaWlpSdPnoyNjS0vL7e1tR0wYIC9vT0AnD59+sWLFzNnzpQu/PTp0+Dg4NGjR1tZWV26dOnOnTsyR5s0aZKhoSEAnD9/PjExsVu3bs7OztIFbty4cevWrZ9++snNzQ0AMjIyDh06RHfx+XxLS8vu3btraGjQLX/99Zezs7O3t7ec+r97927Pnj0AIPNZL168OHbs2KtXrywtLQcNGlS/fn2ZN169ejUiIiIvL8/S0tLPz8/JyYluFwqFoaGht2/fZrFY7u7uffr0YbFYzLskEsnJkydv3rwpkUhat249ePBgLpcrc9gLFy6UlZU1atRoxIgRWlpazC6RSHTy5El6ZGdn58GDB6uoqMj5at+ZxMTEc+fOpaenq6mpubm5+fj4sNnsgoKC7du3d+/e3cXFRbrwvn371NTUBg8eXFZWtnHjRplDNWrUyNfXFwDy8/MDAwN1dHQmTpwoXUAoFG7YsIHFYs2dO5duCQ4OTk1NBQAWi2Vqaurq6tq4cWO669atW5GRkXPmzOHxeDIf9PDhw3/++Ud6S+fOnVu1akX/n5OTExQU9OzZMxMTEz8/v4YNG0qXTE5OPn78eHZ2tr29/fDhw42NjaX3Pn/+/NixY5mZmVZWVkOGDLG2tq7yhxYaGvr8+XMDA4Px48cDwNu3b/fu3Vu5mK+vb4MGDao8wo/m9evXJ06cePbsGZvNbtq06eDBg+nf4Nq1axs2bNizZ0/pwufPn3/8+PGcOXMAYMuWLQUFBdJ79fX1p0yZQv+/cePGioqKadOmqaurS5fZvXt3bm7umDFj6O83MjIyJiaG7tLW1m7YsKG7uzu9gLx48eLIkSOjRo2q8nf9+vXrw4cPv3r1ytzcfPDgwTLXq+jo6H/++ef9+/f0XDIyMqLbJRLJmjVrpEuamJiMHj1a5uBlZWVbtmwhhLi5uf3000904/379y9fvixdzNvbW+bPEEkrKio6ceJEfHy8QCCwt7cfNGiQlZUVABw9erSoqMjf31+6cHx8/NmzZ6dMmaKvr3/8+PHk5GSZowUEBNDr/+HDh9PT0ytfBE6fPp2cnOzj49OoUSMAePDgwYULF+guNTU1R0dHLy8vevfJy8vbunWrr69vs2bNKlf77du3hw4devbsmb6+vp+fn8yvOC4uLiwsLC8vz9raetiwYRYWFtJ7nz59GhIS0qVLl3bt2klvv3TpUlRUVGFhob29/ciRIw0MDKT3pqenHz582NnZuVu3bjX+VH8oAoEgLCzszp2u/lkDAAAgAElEQVQ7xcXFVlZW/fv3p9ftK1euxMTELFy4ULpwenr63r17Bw4c6OTkFBUVFRkZKXM05mJy7dq1O3fudOzYUebXdPfu3atXr7Zo0aJr164AUFhYuGPHDrqLx+OZm5t369ZNV1eXbtm6dauZmVm/fv0qV7ukpOT48ePx8fGqqqpt2rSRCYpyc3MPHTr09OlTHR0dX1/ftm3bSr/3xYsXR48eTU9PNzMz69+/Pz2ZGQUFBSEhIUlJSSwWq3nz5gMGDJC5wALAzZs3o6OjAeCXX37h8/kye3Nzc3fv3g11fvkiX9G9e/csLCw4HE7r1q29vLzMzMz4fP7evXsJISNGjDAxMZEpf/r0aQCIiooihEybNo3NZrv8rxcvXtCSXl5eADBs2DCZI9Bf6qRJk+jLW7duAYCjoyMNpPh8vqGh4a1bt+heTU3NadOmyf8KI0aM0NPTA4CdO3cyG8PDw9XU1MzMzHr37m1pacnn88+cOcPsLSsro2Gfk5OTj49Ps2bN+vbtS3eVlJS4ubmx2Ww3Nzf6GNCvXz+xWEz3CoVCHx8fFovVoUOHzp07czicDh06lJaWMkeePXs2ALi4uPTo0UNNTc3BwSEjI4PuysvLa9Omjba2dvfu3bt06cLlcps2bfr+/Xv53+77IBaLp0+fzmKxDA0NO3Xq1LJlSzab3b59+/z8/OfPnwPAjh07ZN7SvHlzT09PQsjbt28BwMzMrImUOXPm0GJRUVH0ryYlJUX67Uy0/e7dO7rF09NTQ0PD1dXVxcXF0NCQxWJNmDCB7vrzzz8BoKioqHLN161bx2azXaWcP3+e7oqJiTEwMNDS0nJ3dzcxMeHxeEePHmXeuG/fPnpl7NChg6ampoGBwYMHD5i9586dU1VVNTc379Gjh7GxsaamZmRkZOVPj42N5XK5urq6DRo0oFuePn3q+r/q1asHAKdOnVL0l/Fd27ZtG5/P19TU7NSpU6dOndTU1CwtLRMTEwkhenp648ePlyk/YcIEXV1d+n8bGxs9PT3pq1nv3r3prtLSUnqjCg4Oln57ZmYmh8MBgIsXL9ItAQEBLBaL/mrs7OwAoG3btvTUunTpEgBU+Yu+cuWKlpaWoaFhly5djI2NVVRUpK9XEydO5HK5HTt29PHx0dXV1dbWvn//Pt2VnZ0NAA4ODsz5MGXKlMrHnzdvHr1ILlmyhNm4fPlyHo8nfS5FREQo/qP+0URFRRkbG3O53Hbt2nl5eRkZGamrq9PzoVu3bk2bNpUpT4OJJ0+eEEL69Omjqqoqc68sLi6mJWlLmfSvhhAiFovNzMwAYN26ddIHbNKkiaurq5OTE5vNdnBweP36NSHk6dOnALBv377K1Y6LizMyMtLS0vL09LS0tGSz2bt27WL2Ll68mM1mt2vXrnfv3sbGxqqqqteuXWO+74ABA+jpzVxvqbFjx7LZ7G7duo0aNcrKykpXV/fhw4d0V3x8vL+/v6qqKgD079//v/ygv1+PHz92dHRksVgtWrTw8vKytrbmcrnr168nhMyaNYvD4ciU//fff5lr+/LlywHA2dlZ+hSKjY2lJYcPHw4AXbt2lTmCj4+P9C/ixYsXAGBjY+Pq6tqkSRN1dXVtbe0LFy7QvQ4ODgMHDqxc7ZSUFHt7ey0trR49etAHgx49egiFQmavqamplpaWj49PgwYNWCzW6tWrmfceOHCAx+M5Ozv7+vra2dmx2ezdu3dLf0EjIyMNDQ03N7cePXoYGBiEh4fLfHpeXp6pqSm9fOXn51eu3qhRo+je7du3y/nhf2lfL3DPz883Nze3sLBISEigWwQCwbx5844cOUIUC9w1NTWrO3j9+vXV1dU1NTVLSkqYjampqSwWS11dvVu3bnQLDdyZu9SbN2+srKxatGhBX9YYuNMWiO3bt0sH7uXl5VZWVvXr18/JySGEFBcXt2zZ0tjYmKnJrFmzWCzWiRMnmOMIBAL6n8WLF0uHQZs3bwaAoKAg+jIwMBAA1q5dS1+eOHECAP7880/68ubNmwAwbtw4iURCCLl//76qqurw4cPp3m3btjk5OWVnZ9OXFy9eBAD6R/vdW79+PQBMmzatoqKCbomLi/Pz88vJyVEwcK/uB0W7a9TV1VeuXCm9ffTo0fTB/e7du3SLp6dnmzZt6P/FYjH9RdN4S07gPmvWLEtLy8rbJRJJgwYNrK2t6YNZSUlJp06ddHR06JUlIyODz+d37dq1vLycEPLq1SsLC4t27drR9xYXFxsbGzdt2pQWzs/Pb9iwoa2tLXMSUkKh0NXV1d3dfeDAgUzgXtnMmTP19fWZIOBHFhERwWKxunfvnpeXR7dkZWUNHjw4KSmJKBa4Dx06tMojP3r0CAA0NDT69esnvX3Tpk30NGPuGQEBAVwulylAL5grVqwg1QfuAoHA3Ny8UaNGtNrv379v06aNsbFxWVkZISQ+Pl5HR4dpy8jMzNTX1+/Vqxd9ef/+fQBggq0qxcfH83g8eu2Sjg4nTpzo5OQk542IkZWVZWBgYG9vTwNxQkh5efm0adPCwsKIYoF7w4YNqzyySCTi8XgaGhoyv4uIiAh6ZZs6dar0AZ89e0ZfxsXFqampjRgxgsgN3F1cXMzMzGh8X1FR0atXL3V19aysLELIq1evdHR0mLitoKDAxsambdu2hBCJROLh4TF16tTw8HB1dXXpwP3hw4cAsGzZMvoyLy9PT0+PCQ0HDRo0duzY06dP169fHwN3aeXl5fXr19fX12f+lsVi8YoVK7Zu3UoUDtzpNaGyn376SV1dncPhMAEGISQ/P5/P52toaDABFQ3cmSe3d+/eNW7cmLnBVRe4T548uVGjRsyR//jjD+kYydPTU0dHhz65icXioUOHcjic5ORkQkhZWZmNjc3GjRtpSaFQ2KFDB11dXRog5efnm5qaOjs70ziNSIVh0saOHWtqarpkyZIqA3d6zafdCHUbuH+9HPeDBw9mZGRs3bqV6V/j8XirV68eOnToZx6ZEJKWljZ27NjS0tLz588z248dO6ajo9O3b1+atFCZhYWFh4dHUlKSIp9SWlo6ZcqUAQMG0MdKxtWrV1+/fj137lzaoayhoTFnzpycnBzaCltUVLRjx44+ffpIdwkxORLnzp1r1qxZnz596MtJkyaZmpoeO3aMvty/f7+Zmdkvv/xCX/br169p06YHDhygLw8cOMDlcv/44w/aOOfi4uLr6xsaGlpSUkIPFRsby+RLtG/fHgBev36tyDdVaiKRaPXq1S4uLhs3bmRSg5o3b378+HGmx/8/e/nypaqq6uDBg5nfEQBUVFScOnVqzJgxAFDlmcZms2kTRWJiovzjZ2ZmmpubV96ekpKSnJz8888/01YxdXX1BQsWFBYW0ifJy5cvV1RULFiwgPbrWVtb+/v737p1i1bm/PnzOTk5CxYsoH2Uurq6M2bMSE1NlekJXbt27cOHD7dv3y7dKSnj/fv3e/funTJlCpNd9iNbvXq1lpbWkSNHaAMMAJiYmBw9elSmc/Y/ePnyJQCMGTPmn3/+KSwsZLYfO3asb9++enp61V3QevfuraWlJf+Cdv/+/YyMjKlTp9Jqa2lp0evVtWvXAMDZ2Tk9PZ3pfTY1NaXtrPRlRkYGAFR5ilIikWjcuHEtWrQYO3aszK7MzEx69qIaBQYG5ubm7tmzh0lh4vP5mzdvpj23n+PNmzdCoXDMmDFPnjyhj2HUsWPHnJ2dXV1dqzu1mjdv3qhRI/mnVnp6enx8/NixYy0tLQFARUVl4cKFpaWlZ8+eBQBra+v09HQmm0VHR6dx48ZpaWkAwGKxrl27tmXLFk9PT5nrz6tXrwCAyTPU09OztbVlTshjx47t2bOnd+/eP1QWqCJOnDjx9OnTNWvWMH/LbDZ70aJFkydP/vyDv3z5cujQoaqqqsePH2c2njp1SiKRDB06tLpTyMDAoFu3bm/evJG+plW2devW6OhoJnShVxJ6rr558yYiImLEiBFNmjRhvpFYLD58+DAAqKqqPnr0aMaMGfSNXC63VatWBQUF79+/B4BDhw5lZWWtXbuWCQMqp6pGRkbu27dv8+bNOjo6lStWWlrq7+/fr1+/3r171/QT+uK+XuB+9epVPp8vk/RZKzIyMsrLy9u1a9e+ffvg4GBme3BwsK+vr5OTU2pqKiGk8huLiopu3bpVZaJeZfPnz3/37t2GDRtktj9+/BgAPD09mS30/7TZLCYmprS0lObGPH/+PCkpSSwWMyXLysqkYyAul9ukSRP6nAoAycnJnTp1or2HVJcuXVJSUoRCIf3cpk2bmpiYSO8tLy9n3i595H379gGA/PT978OjR4+ys7P79u3LZtf+uZ2amkoHZiQkJNDfLwBcuHChsLBw+vTpXC6XhlyVhYeHAwC93MiRlZVlZmb29u3b2NjYN2/eMNtLS0sBQFNTk9lCs+toB0JZWVmVe+mZQOtJkw4p6fOTSklJWb58+fz582Xy5mXs3LmzvLx80qRJ8r/Fj0AkEkVFRXl4eDBRey2iZ9Hs2bNFIhFtRAeA169f375928/Pz87OrrpbY3R0dFFRkfwLGj1bpC8ONEmvyutGbGzsgwcPmOtGVlYWAOjo6MTFxcXHx4tEIpmDr127NiEhYefOnZX/+mjgnp6eHhMTQ8M1VJ2rV68aGBi4u7vX+pHpmUOTy5l7pVAoPHnyJD21qruCpaamPnny5FNPrWbNmrFYrCpPrZSUlOjo6BpvSc2bN1dRUVm2bNmTJ08A4PHjxw8fPmSaulB1rl69CgBVZpB/poqKiszMzMaNG3t7e8uEW56enq6urnl5eVWG5hUVFZGRkba2ttra2vI/gsmDBwAaL9HHOdqyLh1rNWzY0MLCgsZgACCdsJ6fn3/69Om2bdvSKPzatWva2tqenp4lJSUJCQn0UiattLR0woQJ3t7eAwYMqLJWCxcuzMnJqTwErk58vcGpaWlplpaWMmMrpRUUFPj5+UlvyczMlH5ZVlYmfS3z9vZesGABfLzP2dnZ+fn5/frrr4WFhTo6Os+fP79///7y5csLCgrKy8tpSETfuHr16kOHDpWXl9++fdva2jooKKjGyt+5c2fr1q0bN260sLBIT0+X3kUDLOkAWl9fn8fj0WL0mhUfHx8QEEC/jo2NzZEjR+hg2Xbt2h05ciQuLq5Fixa02IsXL/Lz8+mXzcvLk2mjMjExkUgkmZmZ1tbWb968kQmzaB3S09ObNm1KtyQnJ69cuTIxMTElJWX//v0/QuBOW2hsbW3llNm9ezdtX2SkpqZKD3javn27dNfN4cOHTU1NAeDly5e2trZdunTR19cPCQlZunQpAAQHB7dq1ap+/foWFhbSEVVKSsrAgQMBID09PS4ubtWqVTX+/DMyMmJiYmjOGAB4e3sfPHjQ2Ni4QYMGenp6R48enTBhgpqaGiGENmLRc6xNmzYAsHfvXldXVxaLVV5eTkcB0r3p6ek8Hk9fX5/5FPpdmNOYEDJp0iRLS8tff/1VTt2EQuHff/89bNgwbDcFgLdv35aVlck/zSIiImQuaHFxcdIvr1y5In1BW7ZsmYeHBwCkpqYaGxvb2tp26NAhODh45MiRAHD06FENDQ1vb+/Dhw9LR1disZieZrm5uVFRUf7+/nToS3WcnZ3V1NQOHDgwaNAgPp8vkUjCwsJA6nwAAELI+PHj09LSrl+/PmXKlN9//51upy3udnZ2QqFQJBKZmZnt3r27R48edC99/Js3b56zs3PlhpLMzMyHDx/SO71EIunevfvBgwfp1AJIRlpamo2NjZy+r7S0NJlTS+ZZ7tWrV9Kn1vDhwydMmAAf75UODg79+vU7evToypUrWSxWeHh4bm5u//79g4ODaUImg2aoFhUV0cdUmoVYHVtbWzMzs2PHjk2dOpUO0Q4NDWWz2TJ3zJkzZyYnJ0dGRg4ePPjvv/+W/6MwNzc/dOjQyJEjGzVq1Llz56SkpJkzZwYEBMh/F0pLS9PV1ZWOgGVIJBKZUygvL0+mjJeXF3MStmnT5q+//gKAV69eSSQSOzs7ExOTYcOGvXr1ysbG5t27dxERETt37qSdLampqczUHTt37qR9wrGxsdra2qdOnZJzYld27tw5+NjYRGMtev9imJiYSDdyAcDatWtv3LgRFRXVunVrJkPh+fPnJiYm48eP379/v1gsZrPZAwYM2Lt3LxPrL1myJCMjg2YYVnb37t0tW7asX7/e0tJSJi6tE1+vxV0sFtNBJNVWhc3W/V8yPfJsNruFFBsbG7qdXrNsbW39/PwEAsGZM2cAICgoiD5g0Zur9HVNU1NTT09PR0fH0tLy8ePHISEh8msuEAjGjRvXvHnzKtsaKyoqWCyWdLcLfVlRUQEfGyEuXLiwdevW9PT06OhoVVXVPn36FBcXA8Dy5ctNTU3bt2/v7u7epEkTT09Ppj2evl2mN4fmQpSXl9MCMntpdyF9I8Xlck1NTRs2bMhisQ4fPlz5L/P7Q1sB1dTU5JTh8Xiq/0vmUmJiYtJQCtMPm5qaamdnx+PxfH19abZMaWnpuXPn6BWwuqZQHR0dDodz+vRp+lAhR2ho6IULF0pLS8vKyo4cORIZGUmn7FBVVd28efO9e/fq1avn7e1tY2MjfQdt3rz5tGnTtm/fTk8hc3Pz69evAwANnsrLy1VUVKS/IP069MwEgF27dkVERGzfvl3+n2dwcHBaWhrTEfmDo6eZ/J+YioqKzAVNpkNfX19f+oLGNN7T0wwA/Pz8aFAFAMHBwb169VJTU5M5zVgslp6enp6enq6urpGR0cWLF2/fvi2nVrq6uhs2bIiMjHRwcOjcubOZmdmRI0cqFzMyMrK3t7ewsDh79izNMwaAGTNmnD9/Pj09XSgUJiYm0gmy6FlNH/+MjY1pY0pl58+fj4yMrKioKC0tPXz4cEREhMy8KIjxH+6VMpNjqKqqSp9azOQtqampGhoaRkZGfn5+aWlp9FQJDg5u0qQJHfpSUlJCx/lQOjo69F5pbm5++/ZtOlaqOlwud+vWrU+ePHF0dOzcubOVldWaNWsqP8Lp6+vb2dnZ2tqGh4ffvXu3xh/FP//8o6uru2zZMtoAFxISUnl+OSSjxlMIAGROIek+W8rFxYU5hRwdHelGJtzy8fFRVVUNDQ0FgNDQUBaL5evrWzncUldXp1cnKyurFy9eHD58uMr0hyrl5OQsW7bM19eXTk4lEAigUlCkoqIiHfMAgJaWlq2tbf369e/cuUP7ugGgvLz82bNnHA7n33//zczMXLduXUhICB17BgDx8fGbNm1asmQJHbotQyQS/fzzzy4uLsysX3Xvq2XTd+7cWVtbu7q9nzM4dfny5aqqqnQIQvv27Xv27EkIady4MR1JQ5/G6BBYmcGp5OPQBzrcqrrBqUuXLmWz2XQyx7y8PJrnt2HDBjpJC22OouNvKBqUL1y4kBBCm/Nv377N7KVpYXSYESEkPz9/27Zts2bN2rZtW2ZmZqtWrZhxRerq6uPGjZOuCW0TpcNeXV1dmQGI1MGDB0FqfKS0lJQUVVXVyZMnV/kD/J7cuHEDpOZGkPE5g1OFQiEdlUEIoc0AcXFxNHynQ7jGjBnD/O6kB6cSQtLT001MTLy8vIjcwakypkyZwmKx3r59S18mJCQsXrx4zpw5R48epR3H0oP/Ll68OGfOnN9+++3KlSt0EO2lS5cIIfPmzWPOGaYyALBq1SpCSEZGhp6e3siRI5m9gwYNqnJwavPmzZlB3qikpITNZg8ZMqS6Ap8zONXV1XXQoEGEkKysLA6HExgY+OzZMwAIDQ0lhNBGSnr+yAxOLS8vd3NzMzExEQgEcmaVIYTExcUtWbJkwYIFdLY4ANi0aVPlYuXl5U2bNq1usDKdiXLDhg3k40j6o0eP0oskfdgICAgoKCio8r10qhBF/gp+QK6urlZWVtXt/ZzBqaNGjWrcuDEhRCwWm5ubz5gxo7y8XEdHZ+nSpYQQOu4lJiaGVBqcKhaLhw8frqKi8vr1azmDUwkhycnJv//+e0BAwNGjR2lO1Lx58yoXE4lEHTt2NDMzkxkjSAeJMS/XrVvHYrHu3LlDX8bHxzs6OhobG8uMj2/SpAkOTpU2cOBANptNpyuo7HMGp9KhmfTvuk+fPq1atSKEeHh4eHt7E0LKy8vZbDa9JsgMTiWE7Nq1Cz5OllXd4FRGXl5ey5Yt7e3t37x5Q7fQNll6X2PY29szo+dljB8/XkVFhb7dzc2NGTVLeXh4WFhYEEIEAoGzs3P9+vWzs7Pp5YuGhS9fvqQ/wOXLl7PZ7CtXrtC9NDNn3bp1dThN39drcW/VqtX79+8rzy/7+WjmMW1T7N+//+XLlyMjI5OSkvr37w8A5ubmqqqq1WWF0uE+8fHxco6/fv16iURCEyT09fXpQJmZM2fSfH3a8J+QkMCUf/DgAbOdNnVId23TURdFRUX0pa6u7qRJk9atWzdp0iQ1NbVHjx61bt2a7rK2tpY+LD2ysbExbVyxsbFJTk6mz6DMXhaLxXRESHN0dGzYsKH8prjvg7OzM4/Hq7Eh5z+g47poU6iXl5eenl5wcHBwcHCLFi0cHBwAwNbWtrrRFObm5u3atZMeDaYIc3NzQgiTL9isWbPly5evWbNm8ODB9+7dAwBmincA8Pb2XrNmzbJly7p06RITE8PhcGj+FT0fmEZT5v+0aSQwMDA/P//gwYOsj4KDg5OTk1ksFv0IKiIi4v79+/JzMH4o6urqjRo1og/JtX5wpsXdxMTkp59+Cg4ODgoKorNjAQDdVWXvDZ/P7969e3Z2do2duc2bN1+6dOkff/zh6+tL42/pc0n6gF5eXsnJyTJTzlN0lCo9P2kX0JAhQ+hFkiaerV69umXLllVWwNzcXCKR0DYOJKNVq1bp6ekyCQC14uXLl/T8YbPZffv2DQ4OPnfuXGFhIdNnCNWPsPfx8REIBEw+cXWcnJwWLly4atUq5jLF3NGkcTicnj17ZmZmVndrpq5du2ZhYcGcnM7OznQstfQQHVRZq1atJBLJl7gPpqam6uvr08Tx/v373717NyoqKioqip5CfD7fzMzsc8It6tmzZ+7u7jk5OVeuXGH6iyrHWvn5+W/evKkuZZGesTRB0cLC4tWrV9KXaxMTE3r9iYiISEhIePr0qYmJCb180Rnu7ezs6JMGjQA9PT3pXpqiPHv2bCZL8Ov7eoH7qFGjuFzunDlzZIY0ff6djwbu9P9+fn4ikWj8+PFaWlo0pZjFYllbW1c35oaZ2V3O8SMjI+9JodPFLFy4kP5Svby8+Hw+nR2Slt+zZw+Px6O/VFdXVw0NDenOaJpR6urqWvmD1q9fX1JSQucnAQAfH5979+7FxsbSl69fvw4PD2dGNPv4+OTn5zOjQ0pLS48dO9auXTs6aPrVq1fS2YrFxcVpaWkyi/J8l7S1tf38/EJDQ+kaCrWI6SIEAB6P5+Pjc+TIkYsXLzKZgnZ2dmVlZXSuaxkVFRUJCQk0vpdDeoUaQsjly5cNDQ0rL6BTUVHx119/WVhYSA85ZaSnp+/bt8/Hx4cmEHfv3p222jIF9u/fr6qqSrMGhw8fHv6/PDw8rKyswsPDpRflWbduXdOmTbt06SK//j+UcePGPXv2bMuWLdIbP/9qVlRUlJubK31Bi4yM3Lt3b48ePWjqII2uqrug/fvvv2pqajLr2shRXFy8YcOGRo0a0dknKioqduzYIT2A/tGjR3SmXQC4e/eudB4FHQdCnw+PHz8ufZGkEYO/v/+pU6cAgEitdQAAYrH44sWLFhYW0kODEGPs2LGEkDlz5kgkEunttX6vzMrKCggIaNCgAW2NsrCw4PF4n3OvlCYUCletWmViYkJvhWKxePv27dJZDY8ePeJwONLDbyqzsLDIycl59+4ds4X2Pn3+FGHft6FDh6qpqf366680sZZRu6eQj48Pn88fM2YMi8ViRgzLGeJMmw7pYiBynD9/vnXr1tra2jExMfRyRzVt2tTOzu7AgQPMlzp06JBAIGCCov3790vnA9OHTHofdHd3z83NZXK9CgsLr127Rq9d7dq1u/e/Zs2aBQDXrl2jw4euXr0qvZceZP78+bRXqk58vcGpDRs2/PPPP+fNm9euXbvRo0cbGxunpaWdPHlyzJgxdJnGGgkEghUrVkhvoStjvXz5kplkytraunXr1jExMXS6IrqRNoUy7zpx4gR9Xk9JSTly5IizszNdvwkA7t69K/0RNjY2I0eObN68ufSH0kwDa2truvqpsbFxQEDA8uXLR44c2bNnz+vXr+/du3fu3Ln03qmhobFgwYKFCxcOGDDA29s7Li5ux44dI0aMoA9t79+/nzNnjpubW0VFxfXr148ePTp58mRmUNGcOXMOHz7cq1evxYsX83i8tWvXampqMqudDRs2bMuWLT///HN6erq5ufnOnTuzs7OZgbZBQUELFy709/f38vIqLS3dvn17fn7+D5KgvG7dutu3b3t5ef3888/0ZxsTExMVFSXdhCxfRESE9PXO0NBwwoQJ9GLE5MD5+fnR3CRm5D7TXkVHz+Tk5NBwOScn59SpU6mpqatWrZKupHTG88iRI1+9etWtW7eOHTsOGTKEz+cHBwdfv359165dNKUvPDz85s2bjRs3fvPmzdGjRx//X3t3G9JU2wcAfJEv0ExtbpycG7UzbUtFzOZYRa+2TZc6zBRitpyFs8hQhmkDh8ZGnFCaSZqQlrNE0hlpvuBqvRGR5MsHmaEFli2WaNI3ZW7n/vDnPuzeurlfeJ67p+e+fh/Pm3PXtWvXzvn/r//09IMHD6grNDc3r6yssNnsmZmZlpaW8PBwKgiex+OdPXu2sbExNDT04MGDNputq6urtrYW7ony+Xy/nxM3b950uVy+mftv374dHh6+ffv2X0oq+r937ty5gYGBsrKyV69eZWRkBAUFORyOzs7O58+fQ4bWH5qamvIb0LjhpBQAAAY8SURBVC5evEil2sOWY8eOwfKdBEHAFni6SA1oXq8Xdrnd7hcvXoyMjOj1emopKovFAsFjQC6Xi8Viq9U6NTW1bdu2Dx8+WCyWhYUFm80GjTs6Onr+/Pmenp6TJ0/S6fT+/v6hoaGqqqqgoKC1tTW1Wr28vKzVauPi4sbHx5uamg4fPgwPHv1WTIL5ARQyo9Fodrs9MzMTqgQEBwffuXNnbGyss7MT9ajvSk1N1ev1JpPp06dPBQUFDAZjbm6uu7u7oqICZhJ/aHFx0a9raTQaDMOcTifVtSBS5f379waDAbasX7+ey+X6fle2tLRERUV5PJ7JyUmr1Zqbm8vj8WZnZ2k0Wl9fn+/6whKJRCqVPnv2bGhoKCkp6fPnz93d3RMTE1arFTKOpqenKyoq2tvbS0pKIiIi7Ha7xWLRaDQwEL158wbWkne73aOjowRBxMTEFBQUqNXqtrY2uVxeVVUVHR395MmTa9eu5efnw9xxdna2t7eXRqPB70mCIMLCwv6HApF/HDab3djYWFxcLBKJTp8+zeFwnE5nX1+fTCb786m9ly9f9l1NJCMjQyQSUQ9taDRaRESETCbr7++XSqVUovnWrVsh6AAMDw9D4Nz8/LzFYuHxeNQ3psPh8O2lTCbzzJkzly5dqqmpYTAY6enpVGoph8NRqVTr1q27cuVKfn6+QqE4derU7OwsQRByuRxuYC0tLcGnRqfTRUdHj42N1dXV7d69G25JFBYWXr169fjx4zqdDsOwpqam5eXl2tpa+C/8bqTCmJmcnAzZvX4zQHieSc0Af4x/ODRnYGBALpdD/ar4+PiioiIo8VhZWbl3716/g+12u1AohHpdJpNJGGBwcNDj8QgEAt/F8K9fv47jeH9/P7VFr9cfOnSIJMmJiQn8V0KhMD093WAwUFGYO3fu9Ls+RMn7cblcOI771q30eDwNDQ2JiYmRkZHx8fF1dXVra2vUXq/X29jYmJSUBDUpTSYTVQZsZmYmKysrOjqayWTu2bPn1q1bEKlPeffuXV5eHoZhLBYrKysLijJSFhcXtVoth8OBtcP8KhF2dHSIxeINGzbQ6fR9+/ZRlS/+DRYWFsrLy/l8fkhICIZhEomkoaFhdXX148ePsbGxvm0HlEplYWEhSZJfv36NDQCx3Q0NDRAeClZWVuLj4yFsHTidThzHHz58SJLkiRMnqJ4GIcsjIyNw2I0bNwL/BHTyx48fHzlyhMVihYeHHzhwwDcZ4+7du8nJyXQ6HcOwnJwcv0yG6upqPp9Pp9O3bNmi1WqpoEDgdrtNJlNsbCydTt++fbvZbPbrZr5KS0v9SuIZDIaUlBSqmhVCWV1dra+vF4lEGzduZLFYKSkpNTU1S0tLJElKJJLq6mq/4w0GA5X5kJaWFjigrays2O12HMep2GKSJHNycgQCgW84eGpqKpQAIwiC6mY7duzIzc3t6OiAxn358mXg9S0WC0mSra2tycnJDAaDy+WqVCqHw+H7Il+/fq1QKDZt2hQaGpqQkGA2m6lazi6Xq7y8XCAQREZGJiQkGI3G3yvR4vV6cRyniqGQJGmz2TIzMzdv3sxisWQy2aNHj/7mm/6v0dPTk5aWxmQyw8LCEhISSkpKYDm84uJipVIZeLBQKIRQvdLS0sCmHx8fn5+fx3F8cHCQOquyshLHcd8qyyqVSqPRkCR57949qmslJiZmZ2ebzWaIR5+bmwu8PnTI+/fvi8XiqKgoqGZPxaaDqampo0ePMpnMkJCQuLg4o9FIjSptbW34b+Xl5cGu0dFRpVIZExMTGhoaFxcHWaqwy2az+Z0FFZ0Q8PTp0+zsbAzD6HS6UChUqVTQIgRBJCYm+h08OTkpFArhg9nc3BzYxJAoKJFIjEYjdVZXVxeO4+3t7dSW+vp6WFcK+hsQCARSqfTChQtUZSW5XO53fYVCQZLk/v378QCQ8wN6e3t37doFK/rrdDrfgdHpdBYVFbHZ7ODgYC6XW1ZW5ptj43Q61Wo1m81msVhSqRSSJ7+rtbUVx/Fv3759d++XL19wHId340dZR/4XYjQRBEEQBEEQBPnP+udi3BEEQRAEQRAE+dvQxB1BEARBEARBfgJo4o4gCIIgCIIgPwE0cUcQBEEQBEGQn8Avy+Y9P6UT4JkAAAI6elRYdHJka2l0UEtMIHJka2l0IDIwMjMuMDMuMQAAeJx7v2/tPQYg4GWAACYglgdiJSBuYBRkUADSjGwOGkCKmQVGczCAaSY2hgSQFiaEfAaIZoZr4ITph4sTw4DoZWcACzBh2M4Gtp0JQcM0QGhmPGZjKCEswM3AyMDIlMHExKzAzMLAwsrAypbBxMaewM7BwM6ZwcTJlcDFDZTlSeDhzWDi5Uvg41fgF1AQEEwQFFIQEtZgFhZREBHNYBIVSxATz2ASl0iQkMxgEpTKYJKSTpCWyWCSkU3gk8tgkmNM4GZNkBXIYJIUSRBhAdrNxijHx8vDxMbGzsnFzcomKiYuISnCJiUtIysgKF7HCAxWaJQxyD+9qXdgV5LnARBn7qkf+2fkvt8PYl94qnng6y4eMLvkbcuBQi9GMHvL2tkHTl8NsAexHVacPuDnFgNmN2Y8OrB4JrcDiN1myHnwSCkfmL1sosjBy3+TwOw150QPhqsXg9X35HEdPPVx7z4Qu/vRkwOXLl0Bs4sfvdt/+IslWA3vw937HFr0wOw/Shvs/sxWBrvh3afX9g08+mD2j0plB31vBrD7O7Y3OgSoM4PZlz/Ocmh/6wpWI6ezxcHwEAPYnKy0sw5fdzwGszWXbXPoTHEDu630wXkHG44eMJvh9n8H7VldYPYCX25H9zUOYPa3hL8OmxnugfWeKJ/hEL8+HGzXr6o6h2Vrp4DZj1vlHKbZTwazD3M9spf9Hghm72x8vO+W6lewe8QAh1mns81fDHUAAALnelRYdE1PTCByZGtpdCAyMDIzLjAzLjEAAHicfVVbjhtHDPzXKfoCavDdzU/vrmMYgbVAsskdDOQz90eKI2zPGGhkpCZGgxqySBapW6vrj7fff/7b1iVvt1tr9D/fzGx/KxHdfrS6aS9fv31/tNePLy+fT17f/3p8/NmUmxrewedX7JeP9x+fT7i9/tPu0ocKWba7drYI50adjut8Wdpru3NP05had3OQq2yQWkjp4RYu7U7dLYfMDdLae7tbZ9eccSDJyXY+vXxan2HpyKpP9hE7YBQwuobJrJAzyGwXexRwdBouPpp0niy+8zgLOLsP82QARTx15zHbb+2eXaa7Z1OkHWObDFO5TJCURGxCUXNy7pD8DI6W5MyqkA7K6TuoPBNiF1S+oMYeY9dK1mcvZ4Sg7tSHhOYWaYVE1Ig8VBGT0fYdsjoEoBnxqFfgEWrdIaM9GoKbKlwWcuqYtkNWj6S7m6I6kBSR+dz6rCZZhyCP3IG0lLH1mQdympBQRR9DUaed4KtLjjxIjyI5SYbugNWkQLkntITMxkiau76LIHPvg8bACGlHfebchtanxwzGVBqaHrStulR/Rk8MBSoIoLJuhSTVnonQYeCI0EzOW4/VHXjkSaCGZNQ0txzHUcgRFsgLmwPKpLGNPeFSe2JgUWhMMV7hHDtkHg23EVk+gSR23u4YOkQ0wtO1osukQbupVH5K2My0VhxWGEaEd9Cvj7dftuNzX768P97OfVkfOXei4ei5+LBzm53bjXH8XGGME+eiMpxxriPDmefSYZw8V4vVo+sGsTLMl01hZXiR42IHSosext7K8GKI8bYyvEhysSyzeGJc+TCLKhdXGF5sueiWWYQhQi0j13HiMrIoS1GGkbOeR0EF+r9MgpWRxVmKM4wsztB2/YR0LyK24iOLsxRnGFmcoUorI4uzVJVh9KqyKg40dFGTlVmUtSij1Cu2FuOjSuvJwTgumZfQrrKq359/4bi//QfhZoCf4xWMZQAAAXJ6VFh0U01JTEVTIHJka2l0IDIwMjMuMDMuMQAAeJwlUjmOHEEM+4oBJztAbUH3gQkH2NB+RKXGvGAfb6q2g4aaoCiR6q/DB8/H6+8RvP89UP85+j7vc/T1+zXlOW8FLo/Dv74/PntLucfS7RZp6/lZ29O8l2wR70F6a0jHot3WxX1J4dy9PmlrUpcDy80u4YMZeyQDC7SaNForyKwAGUprH4g9Qy7Erj9qTk42mGCC/ai5dcq0MhbQqIWiklwvLVUINN1sEbMHxpup9dAaK0ENW5oR56iFNmHWExqmegdEaRacynY3lQVVoskApAzvKy9FY0m2ZTQ4toPYgehu2IiL2PgxNE2BJoRDNQi2HTMQtpYcpExIZnqmIq71dKxGOlk5IW64CwRZ6QtbZI+O76TMwrVgofoyOtiXYRIkcnc7UsanMow8Cw1hPg1MLpfBRT2SiOSmpRENHxMDI9Wbc0Xcq6UgLr7HpcR18Vdwsfh6fP8HFJqAu8R/L20AAAIxelRYdHJka2l0UEtMMSByZGtpdCAyMDIzLjAzLjEAAHice79v7T0GIOBlgAAmIJYDYkUgbmAUZFAA0oxsDAkgKSY2Bg0gzczCAaGZYDRCHkKzO4DFmdkc0NQ7QNVDxRF0Blg9I7oEOwNYgokRp0qYC6FcThgXrgzKQDgJQwDNZG4GRgZGJgYmZg0mJhYFFlYGVjYGNnYGdo4EDk4NJg6uBC7uBG6eDCYe3gRevgwmPv4EfgEFAUEFQaEMJiHhBGERBmHRDCZRMQZR8QRxiQwmfskMJkmpBCnpDCZpmQQZWaBO9gQZvgRZ7gQJwQQRFqCtrOwcXNw8bLx8MrJASkhYVFxCkE1SSlqGj1+8iBHoH2jkMMitvcJ68MPMvgMgTkrS3QORtxvB7DSPiwcmXlADs+NKPx+wP39sP4hdyLj4QFIwL1i8+Pm0AxxbHcHivxwCD8xu2L4PxP7HY3lA6k23PYht9bv0QJ2qgAOIzfpi1QHVUi4wO8vR6cBPlkQwe/K2U/v/XvAAsx+E7t8f9eMiWO8cb9l9bH/KwWz3RT323mufgtlR6jIOXzb1g9mbrys6rLu1H2zvy7XlDswfW8HucY6rdQg8qwR2p82slQ61p3zB7MyiNQ7Lzk8Ds7XXXnS4+UkCzHab8ceB5YMzmP3vyjkHlYoUsDmPXy52uMqbBDbf3DfHwSbpJ9jeu3PSHN61xoDdvPyYgEPvwWYwe4pjo33p6iAwO3n1yn0LV9SC2WIAd5adedi/KxwAAALFelRYdE1PTDEgcmRraXQgMjAyMy4wMy4xAAB4nH1VS25bMQzc+xS6gAX+RS4TOy2KIg7Qpr1DgS57f5SUkTwFFfps0ZIyTxTJGebU6vl2/frrT3t/6Ho6tQb/+UZE+8kAcHpuNWmPT5+/3Nrl9eHxbefy8uP2+r3ljDnfyc9H7MPry/PbDrbL73b2ziZC3M7SRY0CG3SYz/EytUs7Ww90MiwkqAPZBsl3pAqAeztTNzBx2CClvbTzyL87jNHO2FWZYIfUQmqH4EifeSaBE8oGaeVd+kAdmhFBH2pmsUGOQnInC82IE8nqArxBeruVTw8U9IZ1DZQdMO5HurBotLylEmUO/gUi3ANiyTg4kWg53UWOWEjuwEpCjbuiBu3iwVmiTKKzxUgk8sjgd0i+IyWyLhWQZdxBO6QUEjoqG5TTMDffetdEVmp8AOVkhLDuiIRVIerCEROIRCbbE0emnbqSj4BZnyFZ1h3S88hMu6U8oAIDsarUBhkTGeo+JjdVePguckoltSxQbhYyeYIqvmMcTRUl1D3M7uQjl517qhKlNJQ4k5ju88dp657bpzaScknK6R4MkwA7pMwzxVhhpsmDRLfedYaUCWeYhCeCPUFSY7dMkwVRcgV78oRxx04aM58GzHkP7pL9Y2xv6bOYZIR5DemII9m5A8adR0kkrBOzG+2LXq1usoKR3aqoKSHcHfl0u37oevc++Phyux59sD509LpcND4aWi6aHF0Lc6lHa8IcdvQfzDGOJoM5/OgkkiOOfkE5cG0LUhu4qF/KIC0qlzLIi5qlDMqiWimDuqizlim+RYY4zVj0htP4oispg7HoR8rQqhMsQ7joQeYOLbTHMsQLvWXuyEJjKUO60FXqhmQLLaUMjYV/UoZ8IZqUoVgYJWV4pc7M2PtLjHfIkTCuNM90v+/MK+uSnuLVyqJav/0nzvnpL73hdiI4/EybAAABbXpUWHRTTUlMRVMxIHJka2l0IDIwMjMuMDMuMQAAeJwtkUuO4zAQQ6/SSxuQBdW/CkFWBmY5OYS2g8wF+vBNKQ14oyeaVaRez/u473/n677fNI/n6/w/edLEd/ydMueBy3n8OeeU8z3f4Pz1fVzRfeTIdlE3E26Py7vpGNUuxpVrblSU7O3SPiyHAGUXV+WF1JwLyPooKVk/8kgmIO1BFtau0cPcfcmks5fVYmKp2417FsGNlgXpVqWKeoOX8a+9KGxByOUjGWKsTbqR1bJBhpQAIInwDbQ0Cb6OQctldDJxxBu9PD3B1tAMaKJUrD24q1ThTMy+z8YZn4VDEag9sJ0XNBgw1LE4SFlmrvCmEmgN+46xmkVgMv2QzIq2a+GED7o2lt21ZCHcI9CErHaQzsl4adRlV5jFatsHm8kizAO9rulezCsn4gptMATPKV3xPLFCsDNx004Ukjs1YhMUXBS0qxEhSQcqNMzt/P4BQYV7djFZ82oAAAKYelRYdHJka2l0UEtMMiByZGtpdCAyMDIzLjAzLjEAAHicdZFdSBRRHMXv3Lk7+2Hr7OzO7K7rujv7YRgUREkvpXOXInoJIYxwTbiQ1RAK0UNRvQj14BqFZRkEERWRFGEvEa3kzBIUCGIStQnSg/kilaaRRmA08/czpQuX85sz/5lz5s5U/5NPyFpetLCwtSutvcnabZwbqZZyAghZvFoyaZUlPBGobiu/bDgRGHjFWFBeQMxWbkE5flnXzv3vRaui1mWujsB4SZ3qNjuCI/9mrgysf+VKi7UZ62+shRLEIQ5bMYgjiDh07BCY4FSdLtXlZm4P8pQgzwbk9jJvqY5LRSb6VJ+kSn4d+wMsIOtYVlQliIIhFAqjcBkKR1CkHMlRFq3QsRjTcSzO4qqO1QQTkjpOplgqreM0YQmXZXpZhcTKFSQ7rDYCSaeSgsPhdiVUr1AqxuKW+ANytEISgqFwpFwJveasE1n876iyqdVVuIMUal8cDc2bJ6XTms17GoZNT3AA+NUJuXDzW2+tzb76CXOHMdRvc67zhSl+9Ro2Vw8+MC9P/gRuCLSYzeeIafOMv9qcI1nw727sMXxfDgHfHhjpf3eQh5mu6xeM81ojcO2Rdu3MlWmY6b3faLRMKtDh8H6stZNbwG8bqrT63Gbo/PwaR+fFeuDRS3W0W4wCG669NJI8BfM/UAflyNUam2XjGU3v3Ar+w75RWizT4FvCv4s0O/0Bch/P/qHy2RT0OZZVMu/H58B/+rEqMzyzHfwtfaFMMfsZnh3fRTJNPaNwPvfa8nTkRgl08F3solr3buA3HaKRzzUDT7ycNSS+BlgaIyb/Kw99EjUHTH+7H/j4YMFs+b4PsqY6p826Vg04+QgXysaGoE/wL3wdtR+VEFMDAAADcnpUWHRNT0wyIHJka2l0IDIwMjMuMDMuMQAAeJx9VsuO3DgMvPdX6AdaEB+iyEMO80iyiyQ9QDLJP+x9/x8pyjNyByusu0207ZJYJIt0X0oe35+//PNvWQc/Xy6ltP/5RkT5Ja21y7eSP8rjx89/38rT68Pj+52nl5+31x9FRlHCGnz+xD68vnx7v0PlU7l67WyjtcJV2MmktNrmcS7l8lSuo0YLaobnYcRNN0DJHa12ZdJRqPbhI3wD1ARG5WHNOp6LilHbAPvhmoM6cbm2qq2PbhukJbJX+POQRPau4rtwxoEkJ0ZCr1SDSZQ3SC+3cpVq7uZUrlybRYwdz8g9uTqJgR28e0cud6EjzFs6FUBHTKgi/N2mRLkrYuYuQ6f/zkS7mGjWiGrjEYOTNBYNjR1UAAXSeucZvvsg2/rXw7/3EZY5797Ntnt2BIXnzQPJp8pNeCslyjK1an0Yp+Ysuvsu9TSwIwJm7Q5hZbp07OpOjh2lskIjhBUounbaAWMCWyh7ijews2zljjYqCo9sND2O5m1XSs769IoyQpMZlbu2XTDMCMaqx4g2MqPkZrxFZnGsonI8ZnGwOeuuOJzFQVPaMJrahN55NtN/kFmcqCLaod3Uu5iN+xSVp78+f6Dy6+HrB12rslKUbcSBoKa4CeXaOciOiqouhG5PwSradBteFssrm/oUFHSfct0AY6bW2RnOOZu18a6qchTLWkDFWd5uTXckhbLvEI+1QYlEMqLtFCVvvYQSZJq4RheIYYeUo+0b1IzIqSrmWWwnox6jBNILzzj6YPFdK8kceZaBCGqMNSQo8nZTO6ZjV4eok0hgQmPybqBz6Hkls45lKSzIynfQj7fnP14Tx4vj8eX2fL448sPn6wEXRc6XQF4uHem87OdAV5x2Tm3FOc7RTDj9nL+EM84hqzjpfpTSNHQ3MWmaRY747c7ih/mnc9WiiDmnaWixpKQJQ4soJgFNs7hSkk2z6GIUaRpajCkpw/D9cNE0vDjzTCa+Z0JnRiH7xZlnUgVNf9f+lIYXZ7Q5peHFme3tzuLMyRmGF2dOzul9cebkDCP3PaYZuyzOaCZNI4szmkbTyKmDzDOMLM6SeR5Q+J3WNY0sypKCgJFFOf/KpDkXJWNstQhLHI7OgmoSngVZd2aS/S7JKfN7Uef1+z8p/L78Bjp/yJK0hgMrAAABtHpUWHRTTUlMRVMyIHJka2l0IDIwMjMuMDMuMQAAeJwdUjmO40AM/MqGFlZu8D7gUMBgI39gMJFyv2Aev8U2FBhUsS7quh7X8eH78b7lxu9xPb4OPF/HLccHs8+80nnztuv6fv/7+4ON67LjvnUQ/Of38eRFkp3nU5eJp5+vJ80/TTufssiF+XwBFe56Al6VHEDxUtaoE/AyNcVIVmEUe+QcWpjpiqqBgSsaQq+nLwZuuFpYDZOATqiMCdaOAeVyq94eupnGVy2OcJvFEKPeKGl2HkUjuI89a2qYptXBQrY3XcAqS6W2+QC7sJ+8PGts9pIMSiwhSvB2mV3VQ+1uWrrDSFkXUJ6i1TszZSiILJBv99KgGrF2Vd59OqjhSLEGa+eLVniGABPtow8WMS8gUKphACVDNCAgbT4DapORbgAVbgxYAQnBARUVgvqKbJ8UKN1Izles6uxdEFeE7FFag2lqzBasobFIhEYYRBUU1EvVQDRpcFEchKcG6XMfmXMgVnMyNG/oHsS1JKxG3ZQ6ZfyU1MTEwceNraAGQufec1OQUnVPf0I6yvvbycZXhHflgU6P3//3X5mvAkhAbwAAAwN6VFh0cmRraXRQS0wzIHJka2l0IDIwMjMuMDMuMQAAeJyNkl1IU2EYx9/zbjtzzs2zuQ/nNnfUqXMaRqUja9s5iV1EkkX5gRceceqBMjSENCqDpC6lUgxjddEHhYrZB2KingMxiYTwZpVkgRBeSFSYkHZR5zw6l131wsPzO//3ed73+b+cr1PDH5G0dGhjYSnypSiU4hJhQLSUCZLxSEmhjOUEBBmTiJNbcHyfl7Niq0ET69/S/wc2etUIBBwXNrJiqzIubB+PYjbHi+kwLo5neo88FSFdAfMTWz5gAxN/GdxuOD5TrGMzU2CUjNVtfyBCEX+of91qEYEIzGOsoBVKpFQhFcljUs2pE5Baw2NNIpeolXaTuCQdj3V6Tp9MJ1M0ZeCxwcgZU3icYuJMZh6bLZzFyllT6VSbR5FmUxJpdmR3IIcTOdOVON1Fu2gPdmXQGZkoMwtluZE7OwG7c6TIRU4PsubxWO/lsZfgtCrOTHGeNJQnnaySXRFevS4Jk6Rak6hVkQZjislMqSxmU55VZU/zOB1WvfTXEJs/EMpv8C8L6sheQf4IlkWFhmhkWuaxHd8Fa92dKZm9VymxfF8UuOmXU+z9dCwo87PlIrGjzQf8ra5UrBlbBw5U1IqH0Rfgcy95sf13GSNzxWyjeKAPgW6qrhbXmv1wV6SZERfWMoB7iz4Inxe6oebn8SFhpfc6cNepa8L9gcgk1FtLheHRnP0yRw/qhJLRMPQ6fT7hzO5s8FLQRAjik3bguRvkdMnbFuCdz5WBxvNW4LOW4umC9R7otZVVBfrCrX6Z+1vfBAfrl8DvgM/HzGfPBGRur73MVJ8MwzwO+yATvqIEX0efDjFJnSHgm3PzzKylC5h6scpc6C8EvvfeyBqbC4ALc9PZytMjcI6rw8Aurkbg3tdZxWyX7hHoVW0se+u2e0LmlZYaVghVgveLEyH2h/ExzNzdc4R9GFSBl46lTnZXeT7oGk0rWz9eALrN/45ZHJmBM4XkV8FDk2tQczf0QDjhGge2/AEFXM6R1CRRSAAAA+l6VFh0TU9MMyByZGtpdCAyMDIzLjAzLjEAAHicfVbLjhw3DLzvV+gHRhBfEnnIwbtrG0HgXSB2/A8Bcsz/I0WNrR4DRGa2iV5NjbrIKlLz1PL15+sff//bzotfn55aG//zFxHtu4wxnr60vGnPHz///tZevn14/rny8v7X27evTaXpwnfw/hX74dv7l58r1F7+abfVxYjxhRv3GLyI2+hjv64vc3tpt9lnWBiQ1M1EyQukJHJ1k+lC7Ta6EPGq9tT23m7Rh3DmBSQ2VKYCablndF/ia+JzZ1msBXAmkKjLGkMNgEXTlhXItZHcmUYwN+phvngUSN9I6WusEShadyMrE4r2CUjtpiumN+kjZNEqkDT2ptplsnimbIN5lVD68XxjsiFZpyXhs6o98Y+sBpuIJXYKFI0KK3dJ3ck00xri4FAhNZHWxQ1lB5LUaFU60RYqC6Aamo8fMaOsP8321m4okbpwajpo0Ko0pS0VlBpL1dN7LBT18z035e6TUaO8Myee5fPjvutg8knJBKZSqgwAV7xlLsY2QxIKV/GsCHCKNTox+mTl9jJnzEpW3lKlQYl9ZFboQS/rz9K+YhlOZd6pwKlaVYpTKOqTB1y1G8ooSv9xCnWv00xziHvUCWVHaaehARug9BwulfU4VYJHdA0LbI1mQZUqYGoEIDrEAz2CmrqXO6ZC8Cds6QZguK1Z7ZhTr60+F3IQPHopuqpKBvPovUWn5YTmYEwzWVECUxzMG1eDeHucmFUuEtk7jiC/mxi+1KrbRJF1DiaXdCZuYOdyhElKky0cI5ssewjdXA5lSXFyMniIbsGHwcQldMFDmDcw5kKX3TIns1UOcEdOIBALQ8R3jziPsjOQCaDWJ4Hs5gq7k1aO0xQJUCQDe+auqTtVRVXawi9bao5S2RyjrL7y9rvBarEPpTAaXpVf5T7CKGLBTICqooUr6Me3118Oyfux+fz+9nodm/nm62hUXHKdf4RLr0OOcNl1khGueZ1Ximtdh5Li8uvkIVxxHS+aS4+HiGYgejgrNAMdcpTsQOnQw9jXDHQYoq81Ax2SlCwzHJ7oZNrhUKXkikCHLYavZqBDmJIxAj+OUs3AhzInZQS+6rkLyhh7DwNQM/DhzFlWBD6cMdEkAx/OnJwxlnKEffr82/XE9WP9MOcsNAIf5pg9lEEehwy1+wzJ3S650wsIcvhjeHAGuTDbE1g5/CX5I8jhL1lzBDn8JfkjyKk5mpgzyGGO/ryvHOY5XHJFHztvM9dTAd01h+yHs2bNobgezvkDMsMF2Tbhy7v4fbV1ujyhyVizuGclXYLNL72zvR6bKf//+fsV90//ASxgDY9womPCAAACCnpUWHRTTUlMRVMzIHJka2l0IDIwMjMuMDMuMQAAeJwlUj2LW0EM/CuBNDasF32upDOBwAshla8IqUKq14ZrUlxzPz6jdWHvvkEzGo32+Hn58nrt33E8jj5ffx9ff/zh47HPy/H5OOU89bz8k+t5efTH5fiL6+txGu7fr+d5Gv7k+na+6fXgTx8X9kkkMm4yV2XyuLNNKc9x46nhuNxZZoV411AKdY3PxTVuNNcqtgCi07O0IXJRg2TzqEg2RuIMxp2hmhoDh1CgqGam+QaC3Bug4tQBUgVbRkMcyTraowZUYq7IWgDCdEFlzdSkGjorPUDxqbWyAcdctQELagrFbmyTyYrRWKTSxl1mLgGX4BDiPeZcQmsPYM7FPed00KvTKWd0vNNk5FXR8SjiQBHqg1l2hiyW0lXCyNl7LM8w9EM9IjDvKlHeRJ1kqXt4Jg4D82bTzWynTQV9WwB9clXsDmawDWghHa8t566bGlMdBtoaFhG8IdeVe1EKj53EDZHrRozThDeSgRTgI0VD2m1vrsMJXh7egPRMNTqJ3Dp4BAinI01nb0/c5hNbICxOngAWZtBxxBHxZLmwd//QypVPbbyY7WkpjNeeD+/TDeqEXcsOQdP3Y2LsJ3akvcM9sCfLEyLhXAPZYhbjvR4XPAttDOPI6gcU3skiu0Xk4zref73AGev7txemWR//AWCFuMa7M4MyAAACNXpUWHRyZGtpdFBLTDQgcmRraXQgMjAyMy4wMy4xAAB4nM3RPWgUQRgG4G9nb3cvd5fL7f1k9y6Xc70/T402NkIg82FllzSCsXFQhK0iSFRSqSCIhZWIxOIay3Ai4oGC3A5WGkRQkTSHaCMRC0WEdOK3s2MawdqB4X1n2Nmdh/02GnwAGpOQDEazS3M/zatGAYJ4T4eNPQoz5SRp2iAoDfPP/l8JSaaTZA6EKo3kIGNJmkY6edHu/u4a1QHz3+U/vFQWDDAYMFOYKTAtYdkhsx3hpIP0RI+lM0EmC9kc5CYhl4f8FNgFUXBD5hZFsRQyKEO5IirTUPGE54fMr4pqLajN9FitHtRnYbYBjT3QCCDYC35TNFsha7VFuxMyh4WsWg5ZyRIdT5RTdBuLiLZlF9xiybKtSrnqe3az1e54vvfWII/+79A1n38aLS7N8HhBfYF6FPcLZ3a4O/95FPcvg4P48fKyeuZiP4XDqTzG/f3mdTz+cELtH3pzE9ePrKqz/e/z+PjoC9Wf/NqHtdQxGffXJ8/jduOa6mvn1nn70WnVT5ln+Y9XfdXvzGWinfsD1S9tvovqN26pPvdzOdq6O1bdeenK8cpX1e9tDPHZgeRbD66McfX2YdVPrG3hytKiutt2fwO7T4dcuyJyLWiXJFekXZJcUrskuSLtkuTi2iXJxbVLkgu1S5ILtSsiF2pXRC7ULk4u1C5OLtQuTi7ULiQXapckF9cuSS6uXZJckXZJcqk+/RsL5cAuibUqawAAAwJ6VFh0TU9MNCByZGtpdCAyMDIzLjAzLjEAAHicfVZbbtswEPz3KXgBEdwHX5+NHRRFERto0t6h/70/OkspKwcgKIULiRkth7ND0pdg16/bz7//gl98u1xCSIu/3nv4Iymly1uwh/Dy+v3HPVw/vr189lwfv+8f70FaUMI3uL9iv3083j57KLyHLUXV1EVCiiWV0hUPaVznpwyg47YFUMI1UOzCpfOeugM5AWq4B46lsXb0xqZdW57gMhJyTJWpMh6Yek2zfAU4jaS9FsuXpTYtE1wdOEkF/zeCPUsnngDbGLhLyYXCRjEXZUkTYB/AgokwpiwxCeU0A1IKjyCxSq89h02BzIllhiSoQ5F6g+KWU3tmbTMkY3TI10qtyXL2wpmmSCvNZsr0qihijsqFy0xzUhDFlEvWQsWyKrN9NIHmPWvLJA3TL7F1yTLNWiwrSlhrs/nXKLlym2lPVqUcMSeUexdfWpr5jaxMNmjKrQxnNupSZ8g+kFXECgrGqTemmdfTGFyKECSnqOBAs7nj66uRO9y+MDuMcbepH3bfFn7nUSZ3/LawPKtB3fTbwvWcd+jh+4XtuezjH8Zf+J7rjjycvzA+N6u8O39hfO4mlDt/YXzb6YaQu/MXxhcaS+Tw/cL2wqDprl+YXmRfdLvnF5YXW0ju+IXhZdTHHb8wvIz6uOMXhpe6Iw/HbwvLS9uHPzy/LUz/er99OUL2Q+Xlcb+dh4rdfB4deAlyHhBq7TwGCC2fu73iLuemjpdQz71b0dq5QxNaP/dhRqPn7Zasg552VRrBuZGRs+D0MGcawRmSHj1OEnsejeA8zS5gRs6UjCoCOVlYUy2Q88WGpBb4eecZ0jljNnEQ+FRzyImT0BmzKWo9zphNQwR2xmy6IrAzxjInC+yUsZx5BKfMJrD1OGUsT7Igz8uQwlhkT8uNLIhzFj56ThOYygjinEV3huKcxTgjiHMWUxlBnLOYzAjinO1nD0xxjmQi2zydn5rpoLSnVRMZ350CmsefHW3vnz+x8Hz5D2FlzRmlO0LfAAABaXpUWHRTTUlMRVM0IHJka2l0IDIwMjMuMDMuMQAAeJxl0Ttu7DAMBdCtpLQBUZD4Jwavcv+myBLcZwVZfCjPSA4QV/YVJZHHx1c/t8/PE8/t2P499//HsT3347mfdG5nPrR/4bH/WcWzj1X8+N6wNsNuBSv2sF4eWNWRo7TqHOxSHr0GoUaBVplD8+0x3loQjUxbRlwesMJf2dx7bwXMSEVLr6KMdCWaN2Kh2qjnhUDVKLxwfkvD65gezj0rOATlusxdzbImFAVHS0JhTEUqo6KNvlVYMUsY0XiUuHRyLFo9SHgMi2YexSqJoee5XKmpeTYceV7HK+ocpmNWIXPWzKSSjl6zM365gWbTJFeZt3B8ZR5NxvjqPciundk2XtOTv4a/vOEGh/lT4NdfeaHBUptoMNXeaLDUJhostYkGSw0WGyw3mHCw5GDRwbKDiQdLb+LBrTfxbrtJt+Qm3O022eB2e7PB223//gFgMZy3NPULvwAAAlV6VFh0cmRraXRQS0w1IHJka2l0IDIwMjMuMDMuMQAAeJx7v2/tPQYg4GWAACYgVgRiZSBuYFRmUADSjGwOGkCKmYXNIQNEM8MF2BnAAkwIAQjNDFeJEIAZgcMoDgYwzcTGkAByBgbNAXYKEz+YYoWpZgdzmdEVC0LdjV2YHcxlZEJyA2EGNwMjUEsGExNzAjOLAgurAitbBhMbewI7RwYTB2cCJ1cGExd3AjdPBhMPrwIvHwMfPwO/AIOAIIOgEIOQMAeTkAiDiCiDqBiDmDiDiASDhCSDpBSDkDQDj0yCjGwGE7NcBpOcfIK8QgaTAmMCF2uCLGeCCDPQajZGBXk5YLCwsXNwcrGycfPIyHJyiU9jBHoAGmUMiowrOB0XcjUcAHF02JkcN/OrgtmLmK867PkpCGZ/P3jUYeOF6P0gttP2aQ7XKvjB7Oml7Q6Zc2PsQeyFdssdnuv+A7O//pnu0CwT5QBiNz4JdKgyygWzc4OEHB46qYLZR64aOCyqmgJWn9T+yP5trrAtiG1RNNOuWHa1HYht2G+4f7W+F9gus0quAwvX/94HYntyex2QPn0OLB4lMOWA7vwVYHbyzc0HDOxlwG6WePLogKUmJ5id9p3jYKvaG7Aa2cibB+z8dcDslwF/D8h/Wgy2693Rzwei1Z+C3XP3HvfBg6pGYHeGXlt9oIfdE2xvlPeiA484F4PVKE/MPNAv8ADMFs5/c2CxXTrYrp8BpvtqLkHE+b8E2F+PsACbw9j91CHjdjvYrtteXI6Zi9j2gtgbXws6GjvsArtHDAAyxp6Ud5OYdgAAAwt6VFh0TU9MNSByZGtpdCAyMDIzLjAzLjEAAHicfVZbjhw3DPyfU+gCI/AlUvz0PmAYhmeDZJM7GPBXkPvDRY23ewwI6R4RGm01u0SWavbS6vrz5ev3/9pxycvl0hr9zycz2z9KRJdvrSbt6fXzl1t7fv/09LHy/Pb37f2vptp04Bncv2M/vb99+1jh9vRvm91JB0e7WidiydkIk7rOZ6U9A8jiMbhdpY9ImroBKoDefbinFVAmTd9ltAXkyBS8mvoc08cu42i3Zj0oZ1ABh5PpLqMjo3VRZTL8eTpHxAYXSDi6iGuOxj3niPANbq58YemSTbsRuNIGl8inXSJHCCZBLrTLx2hOq4q4TMZkTBHaEWRewHAlCzDkgb7LDlhtYQAzxkqkQNoOWG2hLplRy13VhHIHtPZWRfZEKq5ZzIzttnkgJzrMrm6zoJY8Yvv6as1VsRHkzXbl7jxG7LoNHT6XEl080wuqHDp2/eZZXAegJDOKihnr2FY02x/tGp2S1JYu2ZSNd0qnyjqXzLAOAtMsZddQYXT+6j1SWFYFPJTmrgJSnQKBZGcphgrS2+aL3pFQb0II6G6S+DYnjtCPohrGCXFBMVPH2EJXr0ZXFBUtBlNOV96edL9DKdhhBQxha+hOK9hHvV+7Wxp2DaqDcO+gsyoVXeccI9czrLx3mqz3g2DgIMVK6iZbq6El6skuKIX0Oclkd0y0zlMgo8rassBobNdQvducm0qMIkEq5TobZHUpYYhEUBFEYmPsO/96e/nNc+8u/PR2ezlduG45rRabaXoaqtU4bZMxxmmOjOGnBRpGnE5nGPM0NMPI07cMgx/9ySowPxiRVeCDHBc7BD74wVp4hYMiF8cKB0sumhUOonAEXuHgioPPKxx00Ste4WCMYywrPJ5WXisHZ1nFxOcsqNSK4GQ9nDGuIAdnsV9PHZylOCPIwVmKM4IcnCV+vf3gLFVjVEMOzlJlrh+QR91aNfWgrLy+Qn4PQrQKespg6UBPmajde3PQw8+9tdXGR+k9Cq2+f/yrgPnlJ79GlCV0dGzJAAABh3pUWHRTTUlMRVM1IHJka2l0IDIwMjMuMDMuMQAAeJwlkrmO20AQRH/FIQVQg74PCJtYueR/YGps4Hg/3tWjhMA8VF9VfP05vt634/18Pt8XX5dcx3Xx7fv6vuQ14LqO3/9uQK8Dmr+3+fz6Oe65tMrjvOsKVjkfINSkdt5lsSkbUC0PAuFVZq0gvkJIekRmrAFkQDka5dxAF3v0Lgt2TyC0DA0/77Ss2XOa04pWbx6Y1dnng5Z050lL1YQAeGV0BghBCvn5ECAlPxlTunuAVEidsrxEeLoUh4iBVJHJnsWZFImq9BikS7Kxmy7wqbKV1qEARjl9fYmENkq6PGsUosrYpQLdcpdQV84B45NCExiEPQeVVzg8SyAVQZ3gbXCoVphi4/ucheVx8qMXC328NneqUbFE+ljt2R8SsEFOWE7EOtPCI3YaUlTjdKxs4b1ApFLZTrYZIY+vCI9yI9wA7+FHk8TOOo1B4G+p+85aoz/5MJzh2oySx0ZJ3Sni97G22saS83n7+Q94gYfEuaqnwgAAAkl6VFh0cmRraXRQS0w2IHJka2l0IDIwMjMuMDMuMQAAeJx7v2/tPQYg4GWAACYgVgBiFSBuYFQGsxnZHDSAFDMLm0MGiGaGC7AzgAWYEAIQmhmuEiEAM4KgUQwQAQ4IzcTGkAByFxO6SXB5NJMFHGDicBuow+BmYGRgZMpgYmJOYGZRYGFVYGXLYGJjT2DnyGDi4Ezg5Mpg4uJO4ObJYOLhVeDlU+Dj12DiE2AQEGQQFEoQEtZgEhJJEBHNYBIVSxAVTxCXyGCSkEyQlAIql06QlslgYpbNYJKVS5CTz2CSZ0zgYk0QE0yQ4UyQAupiBbqAjVFeThboLTZ2Dk4uVjZuHmkZIC0kKCYqwiYuISklIirexQiMLGhEMihM8mVxXH+55QCI46L520GpXhfMntZxxqF1ixiYvVVsv4OiYdZ+EJujbILDOjZ+MFudpdnBdVaiPYj9xWiJQ3LHbzBb+OA0h/w3kQ4g9hmHAAfu7QVgttFjXofsSG0w+8sxDYeYpHlg9VsunbG/YyFrB2J/rReyW1nBBBbPvJay/8ZfIbBdX8vEDkz1jd8HYm+LMjxwPG0OWE3/lYgDn5csg6jhmn2gRL4IzPZfvf/AQo2vYLaR5J4DFgFRED/WvTjwT3sCWPybxN0D96NWgu1lsl57YEPNIzD72PH/B14Lrgeb/+mt2MELU4rA7JkLZA7OKWAC6zX05D7Y+PkomC1wr3ffuZ6fYDVx7U72HCpOYD82uT5waBLVBZspUczu6DnFHux+Dh0+R2bnC2C9YgBNTKFRh0rgkQAAAv56VFh0TU9MNiByZGtpdCAyMDIzLjAzLjEAAHicfVZbjtswDPzPKXSBCOJLFD+7DxRFsQnQbnuH/ha9PzrUdmUvINSJCFsZU0NySORS8vr29PXXn7IufrpcSmn/+UZE+SmttctLyZvy8Pz5y608vn56eN95vP+4vX4vwkU63sHnI/bT6/3lfYfKw+8yKvcRMspVK1kbildqm9fxLpfH4nXo8OHlytWpjaEboADYqwR1lwSKqfexASqAVsPJmMu11SAdxBuglVvRaqTaRgLNRo++AXZ41NpC2Rw/D7jru1gcDq2SEDNSUKMbtR1uTH9O1HoUqdqs2e7cgD+pbBJmuPFO5LtACMUpXJl6BOOmu4vIDkgT2MVJBQxZXNx3wCwL1d4ajYyATEy2wCwLfldpPUOwhqh35SNFMEhyCiJ8ptuVxi5sMvicFaYGxoAyWCBVG2gv96kaJLNzRgTo2BL1dCpVhkcKiKo0irZ1OpKqQpQQ7aQaah7bzEd6hdpGdJwPr4FUtR0UkrgnFEJv3pOKEpHuEsBZpqujfUxpcoXgRHcV5SzUtVdEwpFxizQkY4eU8j3Ph0e1dKXKYrsGYn07PkIIrY5MpaB2ReVZqaiKU2d+Qt3Glmd/Q/oIRtKvUykmW6KzUqN6M0HREb1p19ien52U+nBvw7LlNDptx0xMnboF8o9eaiKqu+LnoMNAahhX8Ahl92i8E5RkkUbVbhYzIBogsXXJEzmGubWMp7M3tQ3y+fb0YYy+DdaH++3pGKz54WN6KpYcM1JzHZOQsOyYd4TVj6mmWH4ML8Uax4xSrDhGkWLReeRoGqLTbNE0tMhRsoOhxQ9ipmkWRUqOaRZLdD+noUUUTU5zZ3FFM9M0iy4lXxhajNGaPM25A3XuLM6cnGH4SOjMKIbo4szyb2dxRn9oGl6c0Qf5CJWf9K5peHHmTDBi58WZkzMML86caUYJz2rMR4jtJDtNI4uyJGWYY0PeKrHIyCQsp3TJJHwuqEzCfkpOivEsvXx+/z+A+8tfpZeOk3gPhpMAAAF/elRYdFNNSUxFUzYgcmRraXQgMjAyMy4wMy4xAAB4nCWSO24cQQxEr+JwF+gl+P9goMTKrUNMrsBOdXhX9074hqwqkv318fn4/JZ/t96PG58+b/n4ev655X4jeX7f37cecN+P339R8Ovn8VKqEsklpCo96wKxEJb1YgLh3MzIuqbWS8hYRoCc2oNnl41HjYIFuZjHYnJXiwZKGh4DMWM4gRRphsvWEk1zoKbisCMfnr4dh6pHTwq4hMlhDpVZ27Gi7YjNmDjiW1bZyTA9mVtrJN6kq7kXpnARCSCmHus9EOTLpXNdTOLG6DyOiLguoWSWBoHSDngppdXxUyvrDVRyZuEH/G3LVAwMlxHbXsZ20yruQNf4JEYx0rAJ1FSKQNcJV+ACcA5GmiAxUd0tGbILeFx3uG6sbYMQ9/eConNOz5SEnpOIo2xdRcztZ4IcVjg1eUbuGmmEmk26o2JvLBWH2F3oKdwWr0O4e9do9vjC2SWguK4kG8la57141Xr+/AeSF4W3DdUHDgAAAbl6VFh0cmRraXRQS0w3IHJka2l0IDIwMjMuMDMuMQAAeJx7v2/tPQYg4GWAACYgFgNiCSBuYFRmUADSjGwOGkCKmYXNIQNEM8MF2BnAAkwIAQjNDFeJEIAZgcsoBS2QVcwsHAxgASYOsMB/DMsYmZD04mRwMzAyMDJlMDExJzCzKLCwKrCyZTCxsSewc2QwcXAmcHJlMHFxJ3DzZDDx8Crw8mkw8fIr8AgkCAgqCAhlMDELZzAJiySIiGYwiTImcLEmCHEmiDADTWZjFBURZmZiY2Pn4ORiZePmERDi5BIXYgS6HBqADGKv1+0/wJOo5QDi1PAuOeB4aLs9iP2tNuNA/s8TYLa4teGBlGfr7UBsLZvF+yUPfwGzt968tE9+nv1+MDurdT8nM/sBEPuEuvm+L02hYDZP6Ar72Oc+YHYI50/7HdNfg9VPb+6yP6IvC2bfN79t72RnD7brk4Wvw9w1iWC22ORSh5Vt/GC38cxY5WCvJAFmWwTYOUxhiwOzX9g2Ocgu27APxM7k2eRwYVI4mL1GMcahK+EW2HzrqZEHsmybwGz5mXMOTC+ZDGb3We048HTzDFuwXQCcDW5CVjlzhgAAAjl6VFh0TU9MNyByZGtpdCAyMDIzLjAzLjEAAHicfVTLbhsxDLz7K/QDFvimdMghtoO0aGMDbZJ/6LXo/6OktvEqgNBdk1hrhwNKM9xDyevH5duvP+V+0eVwKAX+8+u9l3cGgMNLyYdyenr+ei3n18fTx8r59nZ9/VmICknUxP0Z+/h6e/lYwXL6XY5aeweEKKhmhM4FKoxrr6VyTiCSmVjBKqyGtgByArkak7UWQHVApAVQEkjVrRPnew5K1QVQy7UcsZIreeyoijdQXAAtGeM9YkPPJ5emsGrSN0oQYuzZBUJXgwWybZzorRnnxpgt+BfIHpxYGTWoEkiAcZgLIIZGgeyi2j3baAJqK0rEgYRGKJhtGEoItULSQHroIymfi4PICpj6ZHNmkM21hipLRon9cG3smzNISPvMWM5fnh+wvD9+f5B7kZZb0TgjBMMoYnfoS3YLINXunHbiKtbxk6aD/bjR473Ko3mp4AA6BGZRiMUFf4vutarFeWkiCUHXavRxIMI93RdqOBj4CkmwWZt7tM3DP+gSA7mApnBHqa1zbCyhsbveV1akf5Pl6ijDBC5qq4F5ul4+De82zqfb9bKPc960z6xE8D6ZkrHPH0boPmUYYfssSYTvAyMRbZ8Kiei79SUCZ4dLJsTJyZIJaXKsZEKerIkjyeRByoQ6GQzHik1OklHlk0twpDa5QcZKn1SXPJ1Z3Pwbyk0aSiaiSSoZ39X7Am+7ujdDSYrzUaRos0T5/+NrHc+Hv/mbHx7TIHAZAAABJnpUWHRTTUlMRVM3IHJka2l0IDIwMjMuMDMuMQAAeJwlkD1OBTEMhK9CuSuylse/iVY0HOBxAESVnoKaw2M/ynyxZ8bz2Nhb9rE3Ph+vX8fbx/n5cX2d+3j0z97H+8+5cX7vb3n5PZw8jGVcTAJ2wbiNOJmjkZozctxKpssLgZKDi4CW/YNp7E/AU2C9FTBGk4SEDaa0ZLOWEY4oMCf8+Z6aGEJi4mvcTqooZyHN5AJCKzV8lH8s+LhL3YBZma6WnV7Td4WQdMkStpzs5X0JZSzR0Teoe68q6Sq5zlxHNTGaS2M1gUsBp/QsH64K6jysJ6srKgOqA4//qbW4Y0dIt1PKoVI6IE8GnonYRKtWIXA318mRc0b5VxANSDekqLRX94KSPn//APD/XFVxG45YAAABlnpUWHRyZGtpdFBLTDggcmRraXQgMjAyMy4wMy4xAAB4nHu/b+09BiDgZYAAJiAWAWIxIG5gVGZQANKMbA4aQIqZhc0hA0QzwwXYGcACTAgBCM0MV4kQgBmBYRSamRwMYJqJjSEBZDczNpXoDG4GRgZGpgwmJuYEZhYFFlYFVrYMJjb2BHaODCYOzgROrgwmLu4Ebp4MJh7eBF4+BT5+Bl6BDCZmwQwmQaEEIeEMJmHGBC7WBAHOBBFmoJFsjMJCgkAnsLFzcHKxsnHz8Apwcon/ArkHGk4MIvuPHT0QzcbsAOI8K1184PXMensQ260y68D6ns1g9tTrqgd2Gm+1A7HlT03aHyjpBBbfmD1jb9KZKftA7M6FT/ZNNrq/Hxz6Zgr2WjuNDoDYbA0sDr43JcDsWwEyDvZp+WA1AXHr7cVOvbAFsb//v2cv03MCbGbjJ2+Ha95/wGxJrw4HbYkcMDumeZvD+dMrwWzTKzccLA36wO6JP1zhcPoZO9jMjBW2B3o29YDZ5by9B9yKDsDsOgC0C+xOMQCDr2g8v+d1YgAAAgB6VFh0TU9MOCByZGtpdCAyMDIzLjAzLjEAAHicfVRbjtswDPzPKXSBCHxT+twki6Io1gHatHfo76L3R0mlibWtUNkkZHlMUZyhDyXH18uXn7/Kc9DlcCgF/nP33ssPBoDDW8lJOb1++ryV8+3l9Fg5X79vt2+FoBDFN3F9xL7crm+PFSyn93K0ig0UsVAFcTIuUGGM/Vsq53LUitBQImztvcmY/A3kBHI1Y1MqWAXIGRdASSBV7Ugtd2TljrAAatnKESuKeU+ga8Mow79Ay4ix7JGZ5Iw6GK2S9AwJVcRUNIO78DrLFjGhGmnLkFS9k+oqZI+QUT8HxgHk1taVjDOeA5k5smYWnRVVVkgMJFZ2ALN4j8hmvgLSALoQNouJBsxtBUx2OOgOQAK7I9hy62RHKmmHKFDkSESyBGq5Fq1xbI3TRLJkyqv6YLJj1T1Dxntyd1wCfeTYmvU7i8rQTFfIdpdQN5aIGTRGf6ylgT2hEmr01gdUgaWtiklwl7o0SAJTJirrur9ulw/tdG+w03W77A2WF+1dJGG894qk7R2BYbrrHsNsV7eE+S5hCWu7TiWs72qUMJxFJ+kQJ3FJOqRJRZIOeZKLpEOZdIHD6SQAHM4mpmVgfKJUcq1NzOVj0DIRJOlo5kHGX+y58Cfj50Y0EvbpmEnIXP58fvwbY374DUfIA2fyd4E2AAABCHpUWHRTTUlMRVM4IHJka2l0IDIwMjMuMDMuMQAAeJwdkDtuAzEMRK+Scg3IBP8fqHP65BDqXaT24UNuyQfN05Dfv4fO4XOdQ49z/cx0zvX6e/T8Pm/++lwOEVYLgSOCeG2DQDFdBMJusrYCW6H1k2Jm1bUFMr14PRFMMN3WZlBllUElRp3fLQjE6ByRuMeQUKZqtfWcI6J0zwYVhL52xzHGdMu50LkrPQlIPaZlWFJVIwYr4mwkJkU3KRdd/RirJqWgGFlDDEUnZaCJ7CNX07tUM8Iknf0q++9G3r3QaDGgBk8vAXfpRamdHHI3VXVTG32oDENwtuTuwBDFdt8F+5w0RDJb9fj8A1ZLVAnyVLJnAAAB4npUWHRyZGtpdFBLTDkgcmRraXQgMjAyMy4wMy4xAAB4nHu/b+09BiDgZYAAJiCWBGJpIG5gFGRQANKMbA4aQIqZBUZzwoQzQFxmZAZEATsDWIAJIQChmeEqEQKoRuMxigEiwAGhmSB8JhYEnQByFDOGVXAGNwMjAyNTBhMTM1B5AgtrBhMrWwIbuwI7hwIHZwYTJ1cCF3cGEzdPAg9vBhMvXwIffwYTv4CCgKCCoJAGk6CwgrCIBrOIKAO/WIKYeAYTm0QGkwRjAi9HgjhPgggz0A42Rgk2VhYmNk4ubh5eDjY+fjFxHl5xM0ags6DByyB5ZNlFBz9fE3sQR990g0Osz759IPZr370Oal4v9oPYNjdeO+xezH0AxA6/M9vhE6MlmM2zJMrBq1kRzJ5c5ODQ6t8FVv949zH7rCVmYPbniEn2m09agc1fzM/jMEPuOZgduuex/eVX/g4gtpvPMrvqzHQwuynNYv+cHiUw28lx/j6tpB6I3mlr9z+QWmUDNvO+8YH3047Zgdiyh8sOsGoYgO26/3rlgZNRS8Dun82y/UDMgn6w3pLY0wcKL2wCq+E4/+aAuKsc2M3+CuwHvysmg9k5gn4HTsW8Aqt30uM+4MJtBXbDvVWNDtzHp+4GscUAMOF8CK+jt/YAAAJqelRYdE1PTDkgcmRraXQgMjAyMy4wMy4xAAB4nH1VW27bMBD89yl4AQv7JvkZ22lRFLGBNu0dAvSz90dnadhiAKKSuZDk0ZKzM0sdSh4/Lt8//pbnIZfDoRT6z6/3Xn4rER3eSl6U0+vXb9dyfn85PZ6cb7+u7z+LeJGKd3B+xr68394eT7ic/5TY3DkI4K2SKTkuaBz7q1LOxTensC7lSJtWjhoLoA5gZ68BIG+NrYYugFa+lIo8xBblKBs3btEWQEdG21rF3DWBrVM1WwADQN2MQzAhgC4auspYB5AoetdcIzVylwWwlWthVEeCerKurGYr1h0ZeWNT1Z5llB5GCxwTMibXZm54AxOH8ArII2OtnZtjsWIowIoLCzJCDwCIAQz2xqt6cyqTHISUDYtw7U1XrPHvQCozmWMV1KMuaXNqgwKqBwyUAKEaK/9wipPiMfkQT1sFoxWygtBRt+YePqoerTEvi9QyqWOhBN+OJQsul/P3ckto5a4oGbQSobZKKpRJA5wcpRzsevS2oi8p0rFuRiR1WM5S+bqCZgMd22bmYJ783Fq3laKi9wII91QSJlHqvkxq96Iy1OkNknZYeul4uTcROerTslAomNcV/dfr5dMOcd8zTrfrZd8z8pR9Z4CZiu79DxcW27vccOt7LxtG7B1rGHXvS8Zoe/cxRt97zDB47iXLwDw1jWVgmbrDMrBObWAZ2Ca7Wwb2ydZ5C9NO9uUR6mRTHqFNbpQM3CfT8Xg8m0szCE8e4gwik1Us5xKdHGEZxCblDaUSnwS2seU/H8Sd1XMx+BhYvreXIqWehc37x4cE14d/nZM6m026TYQAAAE/elRYdFNNSUxFUzkgcmRraXQgMjAyMy4wMy4xAAB4nB2RTYodMQyErxLI5jW4hUr/5pFVQ5Yzh/A2vBPM4SN7Zz6VyuXy8/x+ntef7+trYS1Zr/XB9VmfJQe8nn/Xev291sKvn9ddZOY6x63kVtPH+04yZslxC1nKzEZB6o4YN0hnzNnIScFs42ZS6ZMflpiKAYIIVxOlco/YqqgC0EyowG6jFytr27erR9szsXCG7c2eAmzeZjwj/aAUVtgQ6silxwtp2WByTDsXCmbtCKW9N947nhVjKAW8J29Q5kQ16EE2aJOqc1Oxh2wFTHUn7PeHbeAugf2MhJq1rxLzbqLDc691PiN2wRF1MZ5cDZ2cw+apKRHZm0G7S2x3NuUjmvA89RZsa7LVDN9/gMIuyaiS45CanLYTGEJiExftmNfPf+gZaEsOmCi7AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Process the data**\n",
        "SMILES preprocessing: Since in SMILES representation, atoms can be described by one or two characters (depending on the periodic element they represent) while the machine will read the input position-wise, we convert the SMILES in a chemical-aware representation.\n",
        "\n",
        "First, we search for all unique characters present in the current data set, which allows us to remove the characters which are not present in the current data.\n",
        "\n",
        "Second, we search for all double-character elements in our SMILES data set by comparing the atoms present in our strings with all the possible elements present in the periodic table and replacing all the two alphabetic elements by artificially selected characters, for example changing Cl to L.\n",
        "\n",
        "# **For padding:**\n",
        "\n",
        "SMILES strings have unequal dimension since their string length differ. For machine learning application, having equal dimension throughout the data set is required. In order to achieve this, we can first search for the SMILES string with the maximum length (e.g. len() method) and pass it as an argument in our function for all the strings.\n",
        "# **Double digit replacement**"
      ],
      "metadata": {
        "id": "UvjYmmLZ6HjN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def assess_two_letter_elements(df):\n",
        "    \"\"\"\n",
        "    Find the two letter elements in dataframe.\n",
        "\n",
        "    Parameters\n",
        "    ----------\n",
        "    df : pandas.DataFrame\n",
        "        Dataframe which requires preprocessing.\n",
        "\n",
        "    Returns\n",
        "    -------\n",
        "    two_letter_elements : list\n",
        "        List with found two letter elements\n",
        "    \"\"\"\n",
        "\n",
        "    # Search for unique characters in SMILES strings\n",
        "    unique_chars = set(df.canonical_smiles.apply(list).sum())\n",
        "    # Get upper and lower case letters only\n",
        "    upper_chars = []\n",
        "    lower_chars = []\n",
        "    for entry in unique_chars:\n",
        "        if entry.isalpha():\n",
        "            if entry.isupper():\n",
        "                upper_chars.append(entry)\n",
        "            elif entry.islower():\n",
        "                lower_chars.append(entry)\n",
        "    print(f\"Upper letter characters {sorted(upper_chars)}\")\n",
        "    print(f\"Lower letter characters {sorted(lower_chars)}\")\n",
        "\n",
        "    # List of all possible periodic elements\n",
        "    periodic_elements = [\n",
        "        \"Ac\",\n",
        "        \"Al\",\n",
        "        \"Am\",\n",
        "        \"Sb\",\n",
        "        \"Ar\",\n",
        "        \"As\",\n",
        "        \"At\",\n",
        "        \"Ba\",\n",
        "        \"Bk\",\n",
        "        \"Be\",\n",
        "        \"Bi\",\n",
        "        \"Bh\",\n",
        "        \"B\",\n",
        "        \"Br\",\n",
        "        \"Cd\",\n",
        "        \"Ca\",\n",
        "        \"Cf\",\n",
        "        \"C\",\n",
        "        \"Ce\",\n",
        "        \"Cs\",\n",
        "        \"Cl\",\n",
        "        \"Cr\",\n",
        "        \"Co\",\n",
        "        \"Cn\",\n",
        "        \"Cu\",\n",
        "        \"Cm\",\n",
        "        \"Ds\",\n",
        "        \"Db\",\n",
        "        \"Dy\",\n",
        "        \"Es\",\n",
        "        \"Er\",\n",
        "        \"Eu\",\n",
        "        \"Fm\",\n",
        "        \"Fl\",\n",
        "        \"F\",\n",
        "        \"Fr\",\n",
        "        \"Gd\",\n",
        "        \"Ga\",\n",
        "        \"Ge\",\n",
        "        \"Au\",\n",
        "        \"Hf\",\n",
        "        \"Hs\",\n",
        "        \"He\",\n",
        "        \"Ho\",\n",
        "        \"H\",\n",
        "        \"In\",\n",
        "        \"I\",\n",
        "        \"Ir\",\n",
        "        \"Fe\",\n",
        "        \"Kr\",\n",
        "        \"La\",\n",
        "        \"Lr\",\n",
        "        \"Pb\",\n",
        "        \"Li\",\n",
        "        \"Lv\",\n",
        "        \"Lu\",\n",
        "        \"Mg\",\n",
        "        \"Mn\",\n",
        "        \"Mt\",\n",
        "        \"Md\",\n",
        "        \"Hg\",\n",
        "        \"Mo\",\n",
        "        \"Mc\",\n",
        "        \"Nd\",\n",
        "        \"Ne\",\n",
        "        \"Np\",\n",
        "        \"Ni\",\n",
        "        \"Nh\",\n",
        "        \"Nb\",\n",
        "        \"N\",\n",
        "        \"No\",\n",
        "        \"Og\",\n",
        "        \"Os\",\n",
        "        \"O\",\n",
        "        \"Pd\",\n",
        "        \"P\",\n",
        "        \"Pt\",\n",
        "        \"Pu\",\n",
        "        \"Po\",\n",
        "        \"K\",\n",
        "        \"Pr\",\n",
        "        \"Pm\",\n",
        "        \"Pa\",\n",
        "        \"Ra\",\n",
        "        \"Rn\",\n",
        "        \"Re\",\n",
        "        \"Rh\",\n",
        "        \"Rg\",\n",
        "        \"Rb\",\n",
        "        \"Ru\",\n",
        "        \"Rf\",\n",
        "        \"Sm\",\n",
        "        \"Sc\",\n",
        "        \"Sg\",\n",
        "        \"Se\",\n",
        "        \"Si\",\n",
        "        \"Ag\",\n",
        "        \"Na\",\n",
        "        \"Sr\",\n",
        "        \"S\",\n",
        "        \"Ta\",\n",
        "        \"Tc\",\n",
        "        \"Te\",\n",
        "        \"Ts\",\n",
        "        \"Tb\",\n",
        "        \"Tl\",\n",
        "        \"Th\",\n",
        "        \"Tm\",\n",
        "        \"Sn\",\n",
        "        \"Ti\",\n",
        "        \"W\",\n",
        "        \"U\",\n",
        "        \"V\",\n",
        "        \"Xe\",\n",
        "        \"Yb\",\n",
        "        \"Y\",\n",
        "        \"Zn\",\n",
        "        \"Zr\",\n",
        "    ]\n",
        "\n",
        "    # The two_char_elements list contains all two letter elements\n",
        "    # which can be generated by all possible combination of upper x lower characters\n",
        "    # and are valid periodic elements.\n",
        "    two_char_elements = []\n",
        "    for upper in upper_chars:\n",
        "        for lower in lower_chars:\n",
        "            ch = upper + lower\n",
        "            if ch in periodic_elements:\n",
        "                two_char_elements.append(ch)\n",
        "\n",
        "    # This list is then reduced to the subset of two-letter elements\n",
        "    # that actually appear in the SMILES strings, specific to our data set.\n",
        "    two_char_elements_smiles = set()\n",
        "    for char in two_char_elements:\n",
        "        if df.canonical_smiles.str.contains(char).any():\n",
        "            two_char_elements_smiles.add(char)\n",
        "\n",
        "    return two_char_elements_smiles"
      ],
      "metadata": {
        "id": "RBR3rbKV6Dk4"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "elements_found = assess_two_letter_elements(df)\n",
        "print(f\"\\nTwo letter elements found in the data set: {sorted(elements_found)}\")\n",
        "# NBVAL_CHECK_OUTPUT"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DBHpssyA6RhA",
        "outputId": "8f82e419-a8fe-4f1c-9c17-c707b8b4a166"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Upper letter characters ['B', 'C', 'F', 'H', 'I', 'N', 'O', 'P', 'S']\n",
            "Lower letter characters ['c', 'e', 'l', 'n', 'o', 'r', 's']\n",
            "\n",
            "Two letter elements found in the data set: ['Br', 'Cl', 'Cn', 'Sc', 'Se']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Based on this finding, we defined our own dictionary for replacement. Note that their are several shortcomings with this simple implementation that we (partially) manually address here:\n",
        "\n",
        "We exclude Sc and Cn from the replacement, since it is more likely that sulfur S and an aromatic carbon c are contained in a molecule than scandium Sc, same for carbon C and an aromatic nitrogen n over Copernicium Cn. Thus, only the elements chlorine Cl, bromine Br and selenium Se are replaced.\n",
        "In isomeric SMILES @ and @@ are used to describe enantiomers, thus we also need to replace the latter by a one letter code.\n",
        "If you are working wit a different data set, you may want to adapt the below mapping dictionary.\n",
        "This resulted in the following dictionary to replace the two letter elements found in this data set"
      ],
      "metadata": {
        "id": "ygnHxZgz6XKn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "replace_dict = {\"Cl\": \"L\", \"Br\": \"R\", \"Se\": \"X\", \"@@\": \"Z\"}"
      ],
      "metadata": {
        "id": "5Ut_J3Pr6TmS"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def preprocessing_data(df, replacement):\n",
        "    \"\"\"\n",
        "    Preprocess the SMILES structures in a data set.\n",
        "\n",
        "    Parameters\n",
        "    ----------\n",
        "    df : pandas.DataFrame\n",
        "      Dataframe which requires preprocessing.\n",
        "    replacement : dict\n",
        "      Dictionary with mapping for replacement.\n",
        "\n",
        "    Returns\n",
        "    -------\n",
        "    df : pandas.DataFrame\n",
        "        Dataframe with new processed canonical SMILES column.\n",
        "    unique_char : list\n",
        "        List with unique characters present in SMILES.\n",
        "    \"\"\"\n",
        "    # Print warning if the data set has a 'Sc' element\n",
        "    if df.canonical_smiles.str.contains(\"Sc\").any():\n",
        "        print(\n",
        "            'Warning: \"Sc\" element is found in the data set, since the element is rarely found '\n",
        "            \"in the drugs so we are not converting  \"\n",
        "            'it to single letter element, instead considering \"S\" '\n",
        "            'and \"c\" as separate elements. '\n",
        "        )\n",
        "\n",
        "    # Create a new column having processed canonical SMILES\n",
        "    df[\"processed_canonical_smiles\"] = df[\"canonical_smiles\"].copy()\n",
        "\n",
        "    # Replace the two letter elements found with one character\n",
        "    for pattern, repl in replacement.items():\n",
        "        df[\"processed_canonical_smiles\"] = df[\"processed_canonical_smiles\"].str.replace(\n",
        "            pattern, repl\n",
        "        )\n",
        "\n",
        "    unique_char = set(df.processed_canonical_smiles.apply(list).sum())\n",
        "    return df, unique_char"
      ],
      "metadata": {
        "id": "II-Kwund6ZQM"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Calling function\n",
        "df, unique_char = preprocessing_data(df, replace_dict)\n",
        "df.head(3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 277
        },
        "id": "q_ZVN_0k6cmQ",
        "outputId": "0c5502e6-7551-4371-da6c-3d4e1b7db156"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Warning: \"Sc\" element is found in the data set, since the element is rarely found in the drugs so we are not converting  it to single letter element, instead considering \"S\" and \"c\" as separate elements. \n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       chembl_id                                   canonical_smiles  \\\n",
              "0   CHEMBL207869  Clc1c(OCc2cc(F)ccc2)ccc(Nc2c(C#Cc3ncccn3)cncn2)c1   \n",
              "1  CHEMBL3940060  ClCC(=O)OCCN1C(=O)Oc2c1cc1c(Nc3cc(Cl)c(F)cc3)n...   \n",
              "2  CHEMBL3678951  FC(F)(F)c1cc(Nc2n(C(C)C)c3nc(Nc4ccc(N5CC[NH+](...   \n",
              "\n",
              "                                              Mol2D  \\\n",
              "0  <rdkit.Chem.rdchem.Mol object at 0x7facf451a6c0>   \n",
              "1  <rdkit.Chem.rdchem.Mol object at 0x7facf451a730>   \n",
              "2  <rdkit.Chem.rdchem.Mol object at 0x7facf451a880>   \n",
              "\n",
              "                          processed_canonical_smiles  \n",
              "0   Lc1c(OCc2cc(F)ccc2)ccc(Nc2c(C#Cc3ncccn3)cncn2)c1  \n",
              "1  LCC(=O)OCCN1C(=O)Oc2c1cc1c(Nc3cc(L)c(F)cc3)ncn...  \n",
              "2  FC(F)(F)c1cc(Nc2n(C(C)C)c3nc(Nc4ccc(N5CC[NH+](...  "
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>chembl_id</th>\n",
              "      <th>canonical_smiles</th>\n",
              "      <th>Mol2D</th>\n",
              "      <th>processed_canonical_smiles</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>CHEMBL207869</td>\n",
              "      <td>Clc1c(OCc2cc(F)ccc2)ccc(Nc2c(C#Cc3ncccn3)cncn2)c1</td>\n",
              "      <td>&lt;rdkit.Chem.rdchem.Mol object at 0x7facf451a6c0&gt;</td>\n",
              "      <td>Lc1c(OCc2cc(F)ccc2)ccc(Nc2c(C#Cc3ncccn3)cncn2)c1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>CHEMBL3940060</td>\n",
              "      <td>ClCC(=O)OCCN1C(=O)Oc2c1cc1c(Nc3cc(Cl)c(F)cc3)n...</td>\n",
              "      <td>&lt;rdkit.Chem.rdchem.Mol object at 0x7facf451a730&gt;</td>\n",
              "      <td>LCC(=O)OCCN1C(=O)Oc2c1cc1c(Nc3cc(L)c(F)cc3)ncn...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>CHEMBL3678951</td>\n",
              "      <td>FC(F)(F)c1cc(Nc2n(C(C)C)c3nc(Nc4ccc(N5CC[NH+](...</td>\n",
              "      <td>&lt;rdkit.Chem.rdchem.Mol object at 0x7facf451a880&gt;</td>\n",
              "      <td>FC(F)(F)c1cc(Nc2n(C(C)C)c3nc(Nc4ccc(N5CC[NH+](...</td>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Print unique characters\n",
        "print(f\"All unique characters found in the preprocessed data set:\\n{sorted(unique_char)}\")\n",
        "# NBVAL_CHECK_OUTPUT"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jLIKcIDI6eKZ",
        "outputId": "552b45c5-065c-4379-f3e6-1676ba695f2d"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "All unique characters found in the preprocessed data set:\n",
            "['#', '(', ')', '+', '-', '/', '0', '1', '2', '3', '4', '5', '6', '7', '=', '@', 'B', 'C', 'F', 'H', 'I', 'L', 'N', 'O', 'P', 'R', 'S', 'X', 'Z', '[', '\\\\', ']', 'c', 'n', 'o', 's']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Compute longest (& shortest) SMILES**\n",
        "Here, we compute the length and the indices in the data frame of the longest and shortest SMILES, which we will use later in the sections for visualization purpose."
      ],
      "metadata": {
        "id": "5Qu4GemK6i4F"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Index of the longest SMILES string\n",
        "longest_smiles = max(df[\"canonical_smiles\"], key=len)\n",
        "longest_smiles_index = df.canonical_smiles[df.canonical_smiles == longest_smiles].index.tolist()\n",
        "print(f\"Longest SMILES: {longest_smiles}\")\n",
        "print(f\"Contains {len(longest_smiles)} characters, index in dataframe: {longest_smiles_index[0]}.\")\n",
        "smiles_maxlen = len(longest_smiles)\n",
        "# NBVAL_CHECK_OUTPUT"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QiGWTwR-6gMT",
        "outputId": "2af2538a-e259-432b-9317-32c1e0394886"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Longest SMILES: O=C(N[C@@H]1C(=O)N[C@H](CCC[NH3+])C(=O)N[C@@H]([C@H](CC)C)C(=O)N[C@H](Cc2ccccc2)C(=O)N[C@@H](Cc2[nH]cnc2)C(=O)N[C@H](CC(=O)[O-])C(=O)N[C@@H](CC(=O)N)C(=O)NCCCC1)[C@@H](NC(=O)[C@H](NC(=O)[C@@H](NC(=O)[C@H]1N=C([C@@H]([NH3+])[C@H](CC)C)SC1)CC(C)C)CCC(=O)[O-])[C@H](CC)C\n",
            "Contains 267 characters, index in dataframe: 2704.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Index of the shortest SMILES string\n",
        "shortest_smiles = min(df[\"canonical_smiles\"], key=len)\n",
        "shortest_smiles_index = df.canonical_smiles[df.canonical_smiles == shortest_smiles].index.tolist()\n",
        "print(f\"Shortest SMILES: {shortest_smiles}\")\n",
        "print(\n",
        "    f\"Contains {len(shortest_smiles)} characters, index in dataframe: {shortest_smiles_index[0]}.\"\n",
        ")\n",
        "# NBVAL_CHECK_OUTPUT"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e7FTMC9m6mqo",
        "outputId": "61a76b0f-c285-4ea9-dbd2-6d5e181fe532"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shortest SMILES: Oc1c(O)cccc1\n",
            "Contains 12 characters, index in dataframe: 3428.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Python one-hot encoding implementation**\n",
        "One-hot encode (padding=True)\n",
        "We define a function smiles_encoder that takes SMILES, the maximum length of the SMILES string (max_len) for padding and the list of unique characters (unique_char) present in the processed_canonical_smiles column; and returns the one-hot encoded matrix of fixed shape."
      ],
      "metadata": {
        "id": "3xSwCoq06sc8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Function defined to create one-hot encoded matrix\n",
        "def smiles_encoder(smiles, max_len, unique_char):\n",
        "    \"\"\"\n",
        "    Function defined using all unique characters in our\n",
        "    processed canonical SMILES structures created\n",
        "    with the preprocessing_data function.\n",
        "\n",
        "    Parameters\n",
        "    ----------\n",
        "    smiles : str\n",
        "         SMILES of a molecule in string format.\n",
        "    unique_char : list\n",
        "         List of unique characters in the string data set.\n",
        "    max_len : int\n",
        "         Maximum length of the SMILES string.\n",
        "\n",
        "    Returns\n",
        "    -------\n",
        "    smiles_matrix : numpy.ndarray\n",
        "         One-hot encoded matrix of fixed shape\n",
        "         (unique char in smiles, max SMILES length).\n",
        "    \"\"\"\n",
        "    # create dictionary of the unique char data set\n",
        "    smi2index = {char: index for index, char in enumerate(unique_char)}\n",
        "    # one-hot encoding\n",
        "    # zero padding to max_len\n",
        "    smiles_matrix = np.zeros((len(unique_char), max_len))\n",
        "    for index, char in enumerate(smiles):\n",
        "        smiles_matrix[smi2index[char], index] = 1\n",
        "    return smiles_matrix"
      ],
      "metadata": {
        "id": "SQu6jUMO6pSX"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Apply the function to the processed canonical SMILES strings\n",
        "df[\"unique_char_ohe_matrix\"] = df[\"processed_canonical_smiles\"].apply(\n",
        "    smiles_encoder, max_len=smiles_maxlen, unique_char=unique_char\n",
        ")\n",
        "df.head(3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 311
        },
        "id": "b22lGBBV6wGB",
        "outputId": "3d3e3052-3b4a-4bc1-8fa8-8e30c0ff35a6"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       chembl_id                                   canonical_smiles  \\\n",
              "0   CHEMBL207869  Clc1c(OCc2cc(F)ccc2)ccc(Nc2c(C#Cc3ncccn3)cncn2)c1   \n",
              "1  CHEMBL3940060  ClCC(=O)OCCN1C(=O)Oc2c1cc1c(Nc3cc(Cl)c(F)cc3)n...   \n",
              "2  CHEMBL3678951  FC(F)(F)c1cc(Nc2n(C(C)C)c3nc(Nc4ccc(N5CC[NH+](...   \n",
              "\n",
              "                                              Mol2D  \\\n",
              "0  <rdkit.Chem.rdchem.Mol object at 0x7facf451a6c0>   \n",
              "1  <rdkit.Chem.rdchem.Mol object at 0x7facf451a730>   \n",
              "2  <rdkit.Chem.rdchem.Mol object at 0x7facf451a880>   \n",
              "\n",
              "                          processed_canonical_smiles  \\\n",
              "0   Lc1c(OCc2cc(F)ccc2)ccc(Nc2c(C#Cc3ncccn3)cncn2)c1   \n",
              "1  LCC(=O)OCCN1C(=O)Oc2c1cc1c(Nc3cc(L)c(F)cc3)ncn...   \n",
              "2  FC(F)(F)c1cc(Nc2n(C(C)C)c3nc(Nc4ccc(N5CC[NH+](...   \n",
              "\n",
              "                              unique_char_ohe_matrix  \n",
              "0  [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...  \n",
              "1  [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...  \n",
              "2  [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...  "
            ],
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>chembl_id</th>\n",
              "      <th>canonical_smiles</th>\n",
              "      <th>Mol2D</th>\n",
              "      <th>processed_canonical_smiles</th>\n",
              "      <th>unique_char_ohe_matrix</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>CHEMBL207869</td>\n",
              "      <td>Clc1c(OCc2cc(F)ccc2)ccc(Nc2c(C#Cc3ncccn3)cncn2)c1</td>\n",
              "      <td>&lt;rdkit.Chem.rdchem.Mol object at 0x7facf451a6c0&gt;</td>\n",
              "      <td>Lc1c(OCc2cc(F)ccc2)ccc(Nc2c(C#Cc3ncccn3)cncn2)c1</td>\n",
              "      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>CHEMBL3940060</td>\n",
              "      <td>ClCC(=O)OCCN1C(=O)Oc2c1cc1c(Nc3cc(Cl)c(F)cc3)n...</td>\n",
              "      <td>&lt;rdkit.Chem.rdchem.Mol object at 0x7facf451a730&gt;</td>\n",
              "      <td>LCC(=O)OCCN1C(=O)Oc2c1cc1c(Nc3cc(L)c(F)cc3)ncn...</td>\n",
              "      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>CHEMBL3678951</td>\n",
              "      <td>FC(F)(F)c1cc(Nc2n(C(C)C)c3nc(Nc4ccc(N5CC[NH+](...</td>\n",
              "      <td>&lt;rdkit.Chem.rdchem.Mol object at 0x7facf451a880&gt;</td>\n",
              "      <td>FC(F)(F)c1cc(Nc2n(C(C)C)c3nc(Nc4ccc(N5CC[NH+](...</td>\n",
              "      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-175d7705-c21a-4c1f-a093-ad6c7c1bfc66')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-175d7705-c21a-4c1f-a093-ad6c7c1bfc66 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-175d7705-c21a-4c1f-a093-ad6c7c1bfc66');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Visualization**\n",
        "Matplotlib is a plotting library for the python programming language and Pyplot is a state-based interface to a matplotlib module which provides a MATLAB-like interface. The imshow function in the pyplot module of the matplotlib library is used to display data as an image i.e. on a 2D space.\n",
        "\n",
        "We now visualize our one-hot encoded matrix using imshow by defining the one_hot_matrix_plot function as shown below."
      ],
      "metadata": {
        "id": "1NozqrQZ60S3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def one_hot_matrix_plot(ohe_matrix, smiles_char, smiles):\n",
        "    \"\"\"\n",
        "    Visualize one-hot encoded matrix\n",
        "    using matplotlib imshow() function.\n",
        "\n",
        "    Parameters\n",
        "    ----------\n",
        "    ohe_matrix : numpy.ndarray\n",
        "       One-hot encoded (ohe) matrix of shape\n",
        "       (`smiles_char`, `len(smiles)`).\n",
        "    smiles_char : list\n",
        "        List of all possible SMILES characters.\n",
        "    smiles : string\n",
        "        Original SMILES string of respective molecule.\n",
        "\n",
        "    Returns\n",
        "    ------\n",
        "    None\n",
        "\n",
        "    \"\"\"\n",
        "    im = plt.imshow(ohe_matrix, cmap=\"hot\", interpolation=\"None\")\n",
        "    plt.xlabel(\"Length of SMILES string\")\n",
        "    plt.ylabel(f\"Char in SMILES ({len(smiles_char)})\")\n",
        "    plt.title(\"Visualization of one-hot encoded matrix\")\n",
        "    plt.show()\n",
        "    print(\"Shape of one-hot matrix : \", ohe_matrix.shape)\n",
        "    print(\"Associated canonical SMILES: \", smiles)\n",
        "    return None"
      ],
      "metadata": {
        "id": "YB6RShIh6x3M"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Pass the index of the longest SMILES string to visualize the matrix\n",
        "one_hot_matrix_plot(\n",
        "    df.iloc[longest_smiles_index[0]][\"unique_char_ohe_matrix\"], unique_char, longest_smiles\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        },
        "id": "Q6LBts-g63rP",
        "outputId": "2292b6c4-68a5-4d53-b3f5-57d50a6f3699"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape of one-hot matrix :  (36, 267)\n",
            "Associated canonical SMILES:  O=C(N[C@@H]1C(=O)N[C@H](CCC[NH3+])C(=O)N[C@@H]([C@H](CC)C)C(=O)N[C@H](Cc2ccccc2)C(=O)N[C@@H](Cc2[nH]cnc2)C(=O)N[C@H](CC(=O)[O-])C(=O)N[C@@H](CC(=O)N)C(=O)NCCCC1)[C@@H](NC(=O)[C@H](NC(=O)[C@@H](NC(=O)[C@H]1N=C([C@@H]([NH3+])[C@H](CC)C)SC1)CC(C)C)CCC(=O)[O-])[C@H](CC)C\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Pass the index of the shortest SMILES string to visualize the matrix\n",
        "one_hot_matrix_plot(\n",
        "    df.iloc[shortest_smiles_index[0]][\"unique_char_ohe_matrix\"], unique_char, shortest_smiles\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 205
        },
        "id": "QV-cz3Cp65z-",
        "outputId": "31ed9665-4f5a-4288-e24d-1712130a3108"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape of one-hot matrix :  (36, 267)\n",
            "Associated canonical SMILES:  Oc1c(O)cccc1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "From above, the matrix visualization was performed using matplotlib imshow function, we can also visualize the entire matrix using the numpy.matrix function, e.g. the one-hot encoded matrix of the longest SMILES string as shown below."
      ],
      "metadata": {
        "id": "Obz91Pbz6-k1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Draw the molecules with the shortest SMILES\n",
        "shortest_smiles_mol = Chem.MolFromSmiles(shortest_smiles)\n",
        "shortest_smiles_mol"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 167
        },
        "id": "HOn6HMiN68XS",
        "outputId": "f5c6d06d-eb23-4fa9-d0d7-3ff1f8c9f6e1"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<rdkit.Chem.rdchem.Mol at 0x7facf518df50>"
            ],
            "image/png": 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\n"
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Print the some rows of the matrix for the molecule with the longest smiles\n",
        "np.set_printoptions(threshold=np.inf)\n",
        "subset = 3\n",
        "unique_char = list(unique_char)\n",
        "print(\n",
        "    f\"First {subset} rows of the ohe matrix, representing the characters {unique_char[0:subset]}\\n\"\n",
        ")\n",
        "print(np.matrix(df.iloc[longest_smiles_index[0]][\"unique_char_ohe_matrix\"])[0:subset, :])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nzPDwNor7Axm",
        "outputId": "1e80750c-8731-4c63-8fae-8c2a8287bad5"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "First 3 rows of the ohe matrix, representing the characters ['o', ')', '0']\n",
            "\n",
            "[[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            "  0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            "  0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            "  0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            "  0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            "  0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            "  0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            "  0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            "  0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            "  0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            "  0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            "  0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            "  0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            "  0. 0. 0. 0. 0. 1. 0. 1. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            "  0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            "  0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1.\n",
            "  0. 0. 0. 0. 1. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0.\n",
            "  1. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            "  1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            "  1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1.\n",
            "  0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 1. 0. 0. 0. 1. 0. 0. 0. 0. 1. 0. 1. 0. 0.\n",
            "  0. 0. 0. 0. 1. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0.\n",
            "  0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            "  0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            "  0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            "  0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            "  0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            "  0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            "  0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            "  0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            "  0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            "  0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            "  0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            "  0. 0. 0.]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Discussion\n",
        "In the fields of computer aided drug discovery and development, machine learning techniques have been used for the development of novel drug candidates. The methods for designing drug targets and novel drug discovery now oftenly combine machine learning and deep learning algorithms to enhance the efficiency, efficacy, and quality of developed outputs.\n",
        "\n",
        "But to work with any machine learning or deep learning algorithms, the input data should be in machine readable format. In computed-aided drug design, we mainly deal with the categorical or textual data where we work with drug molecules which are represented in SMILES string format, therefore we have to convert these categorical data to a numerical format.\n",
        "\n",
        "One-hot encoding is one of the popular and efficient encoding technique which converts the data to a numerical format. It can be used an important preprocessing steps before applying any machine learning or deep learning algorithms.\n",
        "\n",
        "In this talktorial, we have applied one hot encoding after preprocessing the data to overcome some of the shortcomings such as:\n",
        "\n",
        "Make equal dimension of the one-hot encoded matrices because SMILES strings could have unequal dimension since their string length might differ and for most machine learning application having equal dimension throughout the data set is required.\n",
        "\n",
        "Replacing two character element such as Cl to a single character because while one-hot encoding, it would split Cl into two characters, namely C and l, and that could lead to discrepancies.\n",
        "\n",
        "Looking for unique characters in the data set to produce less sparse one-hot encoded matrices.\n",
        "\n",
        "One-hot encoding has several applications in various fields such as:\n",
        "\n",
        "Machine learning (neural networks): In machine learning , one-hot encoding is a frequently used method to deal with categorical data because many machine learning models need their input variables to be numeric.\n",
        "\n",
        "Natural language processing (NLP): For NLP, most of the times data consists of corpus of words which is categorical in nature. Consider we have vocabulary of size\n",
        ". In the one-hot encoding technique, we map the words to the vectors of length N\n",
        ", where the\n",
        "th digit is an indicator of the presence of the particular word. The\n",
        "the n bit of each vector indicates the presence of the\n",
        "th n word in the vocabulary. For example if we are converting words to the one-hot encoding format, then we will see vectors such as\n",
        ",n and so on. Using this technique normal sentences can be represented as vectors and numerical operations can be then performed on this vector form."
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