From 2be179f811e945cb63ee7570a6c288cd61c2a740 Mon Sep 17 00:00:00 2001 From: Taras Savchyn Date: Mon, 2 Oct 2023 01:41:39 +0200 Subject: [PATCH] Duplicate example notebook --- examples/tutorials/brca_subtypes_cnn.ipynb | 735 +++++++++++++++++++++ 1 file changed, 735 insertions(+) create mode 100644 examples/tutorials/brca_subtypes_cnn.ipynb diff --git a/examples/tutorials/brca_subtypes_cnn.ipynb b/examples/tutorials/brca_subtypes_cnn.ipynb new file mode 100644 index 0000000..c534b18 --- /dev/null +++ b/examples/tutorials/brca_subtypes_cnn.ipynb @@ -0,0 +1,735 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "source": [ + "# Modeling Breast Cancer Subtypes (CNN edition)" + ], + "metadata": { + "collapsed": false + }, + "id": "1a0084b42a4fe519" + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d8ce078a-a1aa-4f3e-be43-28655dea97e5", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import torch\n", + "torch.set_num_threads(4)\n", + "\n", + "import flexynesis " + ] + }, + { + "cell_type": "markdown", + "id": "db3f49dc-7a7a-48c0-b02a-e664e3aa4294", + "metadata": {}, + "source": [ + "Here, we demonstrate the capabilities of `flexynesis` on a multi-omic dataset of 2509 Breast Cancer samples from the [METABRIC consortium](https://www.cbioportal.org/study/summary?id=brca_metabric). The data was downloaded from [Cbioportal](https://www.cbioportal.org/study/summary?id=brca_metabric) and randomly split into `train` (70% of the samples) and `test` (30% of the samples) data folders. The data files were processed to follow the same nomenclature. \n", + "\n", + "- `gex.csv` contains \"gene expression\" data\n", + "- `cna.csv` contains \"copy number alteration\" data\n", + "- `mut.csv` contains \"mutation\" data, which is a binary matrix of genes versus samples. \n", + "- `clin.csv` contains \"clinical/sample metatada\", which is a table of clinical parameters such as age, gender, therapy, subtypes. \n", + "\n", + "## Data Download\n", + "\n", + "The data can be downloaded as follows:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "31189edf-56a1-4eaa-beeb-ce8c7f2c2f50", + "metadata": {}, + "outputs": [], + "source": [ + "# if not os.path.exists(\"brca_metabric_processed\"):\n", + "# !wget -O brca_metabric.tgz \"https://bimsbstatic.mdc-berlin.de/akalin/buyar/flexynesis-benchmark-datasets/brca_metabric_processed.tgz\" && tar -xzvf brca_metabric.tgz" + ] + }, + { + "attachments": { + "e46e5f0b-3a3f-4774-af82-ac3e1d52dbae.png": { + "image/png": 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RUAQUAUVAEcgFAko25AJFjUMRUAQUAUVAEVAEFAFFQBFQBBQBRUARUAQSCFRPHGV4sGHDBrN27VqzZPESs3zFCrN506YMY4gXPC8vz+TltzXXvTHNTF20OvKhjZs3m48u62c2rl8XGUZvKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKQMUjkBXZAMkwY/oMRzZUfBY1BUVAEVAEFAFFQBFQBBQBRUARUAQUAUVAEdiaEMiYbIBomDhhotlsLQmqVKliatWqZWrWqmmqV8s4qlg41a1XN1a4igi0abMxM5auNSuLNpkVazeYZWs2mHUbNpllazcaDDmq2UUoTetVN83q1XB/Te1vXp3qFpfk3MxcWmSGzSy0z280XZrXNv3bNzC1qpdvBcuGjZvN7BXrzOp1G81ym6+lqzeYIpu3pTYN+2pMw9pVTRObnxb1a5pGtauZ5vVrmNo1ypdmcqm2zDOwXmUxEWnRoKZpUrdi6qakob+KgCKgCCgCioAioAgoAoqAIqAIKALJCGSkhbF0YsaMmQmioaCgwEAG1KhRIznWHJ5Baiy3yv0vIWvXbzK3vj/TzFm+zrBMI5XUsYo8pMNunRqa0/q3NNVKCIfpVvn9/RtTHRnA89WqVjH92zUwfzywnakaZCVSJRC4N7dwnbnexltYtNGst8RDlJBew1rVTLvGtcxxfZqbndvXjwq6TVx/6Jt5ZsSslYmynDGgpTm6V7PEuR4oAoqAIqAIKAKKgCKgCCgCioAioAhUPAIZkQ1r16w1a9escRYNHSzR0KBhA5fDTRXkr6Hii586hc1ms1ltCYd0RAOxrLHhZi4rMs/9sNAqu6vMjQe2N43tjPr4+asTRAPhNlpzie9mrDDzrFVC60a1uJSVYNmwwpIwGzC/SCGktxTLB/v305xV5tSdW5gT+zZ3pEeKxyr8FgQJmDW0Vhe5FPBY55Ev22jVzCVkGpcioAgoAoqAIqAIKAKKgCKgCCgCOUcgI7v6xYsXuwywdKLeL7i8Ieco5DjCCQtWm1d/LsaquV3GEJQGtarb5RYVZw0STM8/f37EIvPuuKX+pUo9nrFkrXlm+AJzw5vTzBPD5ldq2pqYIqAIKAKKgCKgCCgCioAioAgoAopA5SCQkWXDisJCl6uaNWu6pRPbqkVDFPQsScAqoFvLunZpQlW3vGPqkiIzdOIyM90q0WJkgK3BN9NWmLOsCX/vNvXM2QPyzaeTlzt/Dy0a1DAX7t7K1K2ZEc8TlaWk67sVNDS7FDRwyzPw3zDOWlX8bK0ZFqxcnwiHz4n7vphrdrF+I/DjUJly75dzzeslJAzp1s+xVUNllkXTUgQUAUVAEVAEFAFFQBFQBBQBRUARiEYgI7JBtresXj2jx6JT38ruWK7B7JBf1/RtW+r3AGePB3TLM7e8N8OMmlu6Nef8wmIFn2eO26mZOaRHE+fAsb71n1BDHDrkuPx929W3eWmciPVQm+YM6zPixnem2WUbpYQDSw2+m17o8pQIXAkHq6x/CRVFQBFQBBQBRUARUAQUAUVAEVAEFIFtH4GtgjVAYW9ar6bbeSHqleCbIFPB58GS1esNjiBF2LGhUW2WOcSHhvC9W9dPIhskPn7ZMQL/kjUtyYBlwWZT1R37YTgudPnZYH0ZFCvl7PZRz1pA4Hgy250k2lvHkFcNaWuueW1qUnI4rvQFHxCL7Y4WhXbXDfEDUcNut9GoTjXTtG6NMjtsEKbIw61uzWouDDtBLLSWFLyOujjNtNYT6zZY7xcWAIlX0uXcJyCqW3yCu3QQBlzWWNzADh8M+NUEj8b2HWWLi+RBfxUBRUARUAQUAUVAEVAEFAFFQBFQBHKPQHyNOvdpx44RpfW3e7Uqo6z6EaDMb7K7ZcQRFNjXRy02H4xbFk42WCW2V6t65qxdW8aJzoVZj3btCcslRG77cJZZsqrUsuDQHZuag7qXWiAQ7rkfF5pPJy63+SklG9Cq65eQDUf2amoGb59nrJ6dseyQX88p/ji7FIHQIMsQOdPsEpDHv5tvpi4uclt8CnEjZEN3a81xht1hw192wfKMR7+d7xxeEufv92tnt5zcZO75fI6ZZ3fKIG526Lj10ALzyDfzzXx7zV/OwTNjrCXIta9P5dDJnp0buWUqnMyyzjY/mrDMjJ632iwDkxKyYaMtAnkWsgGLkX265P3iDi+LS6D/FQFFQBFQBBQBRUARUAQUAUVAEVAEQKDSyIZNywrNxilzzNqvfzLrxk83jU4+0Czq3cb87p273ax31Ovo3Xp7c9HOJ5hnRywx85Ynz8b7z2yybMPfj+xsNqxb518uc7zaKsT3W98BH0xY6qwNggHWbdzodnmolsG2lCjRP3jbLRJn79b1ElHPsH4dFqwszddSqzyLcPz3j2aakbNXySXvd7NZYpVsCIh/fjzLTF601pxid5NAic9Eiq0Kkp8Q0uIz60viXzZufwcHCQkWWCqw9SfEwA37tzedm9V2t1cWbTJTFq9NkA0QA+9Zx5N+2Xi2id2Rg106IA+Cwv0pi0uXVkBqiPzlvZl2CchaOS3zy5afWFBM+czWKUuiHN6zaZkwekERUAQUAUVAEVAEFAFFQBFQBBQBReCXQaBSyIaiT743i/7zjCmaONNsLrJKtyUG6gzZ2RRa/wKfTf7Rbi1ZOuMehGGTvbeh72Yzek6hmbqo1CdCMFwda8bvbOyDN7xzZtuftxYE749PvxtDz9aliq8XRdIh8X1kSYvnflhkZntECFteHhFD+bWPm4e/mRdBNCQl5SwFXhq5yG0VedxOzZNvpjkbNmOlJROSMWZHjEkLV5v/WmU9jGgIRjnXbtV54zvTzT+P6GjyG5bdYeM16/hxrSVGfGH5B8srspFdC+qnJBskTqxUHrVWGf3a1zdtyrGVqMSnv4qAIqAIKAKKgCKgCCgCioAioAgoAuVHoELJhirrNpgV979s5t/+pKlSNbPZ+EyL1rVlPbMhzTKK5dYfwcd25whf8KPQqVkd07lpbYPPgNl2Fn+eVax7WR8MQcGvAco5lgUsm1i8coNZXeJfgbAN7e4K2zevay7fq3XSkoNgPHI+ccEa84ldOuFLq0Y1zb52WUAzu2Xm1MVr7E4Xy82yNaWWEG+MXmIO7RF/Fh8HkXd9NttPwh23zqtp/v3JHIOFgAi7bfSzTiYHdGjoyIkvpyw3Y+etsWQQtIgxi+1SEAiPi/ZoLY8kfteULNGAaMGXAksxOjQptoLoZLHFMeZcS8gst/4XRBrYa23yasmpyW9QSmL0a9fAfDpphfWfUc20tmHa2fw2teQF/h++n1lovrcECtYsCJYNw6avNG16lcaViFQPFAFFQBFQBBQBRUARUAQUAUVAEVAEKh2BCiUbVjz+hll47/MVTjSA2i5WQU4nI+xSh0XeNpBV7VKJE/q2MEf3bmpqVy8mQ1CSl1rlvp6dkRdlVuJFtWWGP0ogCPbt0igW0UAc9381NykNyI4bD2ifUNKNyTPdWtR1yyxK9GqX/6GTlpvuLeuUyca4BatNyxKFHWLl57mrzI8zV7plGH7gPOv0EUJg7orkpQ0n9m1mju/T3NQsweJA61fiHx/NMl9NXZF4nDhxeBkm7NJx0R6tnHNNcLTcjJOr925rnWIac8fQWUnkyg6t6prr92ufiMoWPyFdWtRx/h5YhhF0Akm+/vT2dDNi9spE+OkpllwkAumBIqAIKAKKgCKgCCgCioAioAgoAopApSBQYWTDxuHjzfzbHqkUoqGRVZz7tS31kRCF3NdTC53SK/eZNcdRoxANXGd2H/N/BJ8CmciURWssMTDLvGadT7IDRFtv1j4YD04Px1jnh74cZZdeiDWAXN+tY0NHDOC3QeRDuwwkjGz4cPwyw18qwSrjmn3amtnL1pv1wgbYByjzkT2bJYgG4gCXfbs2dttkslwBwX/DSs8awl20/3gHlw9uY1rY3SeCIlt9Qu74wjmWJWFC2q2tlUeYEF9Hay3hkw1+WcKe+bVcW7RoUaKoVa01UYMGDUyNGmXfSSJQBR3gJ2TWLOsYdckSU6dOHdO6dWtTv35Za6EKSj5WtIWFhc4aqnHjUmetm+x2J5LnevVK+5SioiJD+GbNmkXGLc/m5eWZTLcHXmd9zaxYscK9r1q1ki10uM59Ed4reWa3GhHyRvqNGjWSSxn/Sh6iHqRMlK0iZeXKlc6HD/U2nSxbtsyAFfUrjlAnf/zxR9OyZUuHk18f5f02bNjQ1KxZ2u+Qn43Wd00qXAkD9jwbJlIveGfVqmW3rCws3oq+5vclflrgI2UF08WLF7v3EPXOKP/ChQvNggULXL3Nz8/PuH346QePJX3/fRJG6nOqNhuM65c6py7TnsPqGW2bOkj7A+8onHORd7+ukh/6wlzW2+XLl7s+t2nTstaZUW0wF+Xy4wj2p9Sb2rWLrTD9cFviMfhRF5o0aZKUvXR9UFLgLfxkzZo1ZtWqUh9m1EPGMNR7/5u3dOlS1/fHeXfBdx72Da1oWPy2FfYdoK1Rlrp10y/hzmVe/e8ouFO/pH/PJJ1Mvt2ZxKthtw0EKoZsWLvOLLzjqUohGrA+OHnnVtbMPr1CNcmSAb7g7BCz/7jCLgh7dGqUsB5geQGOD0fMWpXkE2Hc/DXmX5/MMrcf1Tky6vmFpbtTSKBB25VVFFCsIRzetMsnRCYsXJNyZw4JF/Z71oB806dNA/PDzHlJxEsbu0yhpt3qMihtrMLPdpQbSogXlHp2rwgKFgiZOq4MxhF1DrnBu1u8aoPdnrMYt7Hzk4maqGd/bdcffvjhMkVGGdtrr71M7969y9wLuzBlyhQzY8YMM3jw4LDbaa8xCHjqqafM6tWrDR91PrIIedh1113TPk+A9957z+y4446mTZs2scJnE+ibb74xP/zwg7nyyisThAzlfu6550zbtm3NKaeckoj2888/N8OHDzdXX311pMLIRxv8TzvtNEeuJB6OcfDZZ5+5+Lt162aOOOKIpCdeftkuRZs/P+kaigdh99lnHzdAeeuttwy4X3TRRUnhMjmZNm2aeeWVVyIfoR5ddtllkfcZCH/11Veu3sQlAIKRffTRRwYczzjjjOCtMudPPPGE6dOnjxk0aFCZe2EXJk2aZIifgRT4nXPOOYlg5Pu7774ze+65p9ltt90S15999lkX9swzz0xcCx68++67Zu3atebUU08N3nLnDMKoFyeffLJp165daJgt8WJYX0I+IWsED/qKF1980Q36w+oG96lTwSWOBx54YOz+KB02Dz30kHtH1H1/oD59+nRD27nwwgtDlfh08Vbm/Q8++MDQ/uiLgorIo48+6vIP0Re3bWSbd7+uQnzkut6OGjXKfPHFF+aAAw5wbdfPJ/0u/Rzvyyf8/DC5OI7qT3faaSfX/uOQ87no6zItC33Mfffd5x4L1ul0fVCmaYWFr4xvMunSD/NtDgpjiaOPPtp07lw8rn7yySdj9/9R79z/hgbTi3P+/fffO6K1Z8+eaYP7bSvsO0BbIx76xvJIpnXT/45++OGHsfuYYNkz+XaXp3z67NaJQHxNO4PybZy90BSNnZrBE9kFZWb8mJ1amsN7lFXSw2Jk9wJf2jZOnkH074UdY/VwgLWEYLmAL/g9YLeI76YXJi5DOHxgLRD2s5YBYRJ0pkiYDo3D2fWCEt8HEg9LFMKel/v+L3Of9a0FR4GN+4wBLUwPuw0m4vuB4JxlI7YvLyNsvRnkICBKmtRNJndK51jLRJHVBfww/GiXveCj4ie7zSZlTiXVYYJUHAK9evVyChOzYDD6kydPNgxGmF3cd99906I0e/ZsM2LECKc0pg0cEgDlgwHbBRdc4GbC+fgxePj0009NQUGBYWYznZA+Sk1Fkg2dOnVyZMOcOXNMhw4dXJZQSBGuQZIwwEFQBFq1alVGEXA3y/mPdEaOHOkGLePGjTMHHXRQmcE2M1nHH3+8S4lBJ1YjH3/8sZvtiatsp8tmx44d3UBfwjGw3WGHHRLKPAp6KmEG9qeffjIDBw6MbW0QjK9v375JVhzB++U5HzZsmKFtoMxBHDGDI4odbQThV8gGZsaZNZfzbNOGeEG5Cs5GZhtfZT4nfYmfpl8PwBHrEmYjaSO0b5Hx48ebV1991WEOycgMOXUEgo/+iGfiko8SZ9QvZAaE23HHHRcVZIu+DhEMMUP969KlSyKvKN/gRBvHGsC3cEoEqqCDiqi3tKUxY8YYyBX6X5lBxeJo7ty5TtGqSMsNgcrvT5nNpb+HBKEOQ6RJvy/hg7+56OuCcaY7//nnnxP5og0NGTIk3SM5vV8Z32Q/wxAqCOMYFHXqDKSBEHL77befaxP+M6mO/Xeeq28odZn6GodsSJW3XN6rrLoZLHtFfrtziY/G9csgkHr0mGWeNs6YazYstev8q1ScAojif85ubczB3eIRDRRFlgJkWazIx3B0eOHurZLIBgJjjRBFNoRFJssNgvfEh0LwevC8r3Xu2MruFIETRZTvPGtt0LEpzhVrOWsM3/LA9t9ZS0XhKBmCV7jTOuL8wjqojLs8gnegUowAg0Qxx2U2rH379s788NtvvzW77LKLG+CheH399dduIIvygBXB/vvv75QAPiIM3u+55x6n+KKERoX3zRoFf2bfYO7F5J68QHKg2KHgISjYQ4cONcx0rV+/3uXxkEMOcflhhgv55JNPzIQJE5ySPXbsWKdcM+ggvyhBDDbKIzK7wAyoTzaABfmCdCEM+UPpZNY7Kt/+jCp5fu2119wACQz23nvvxGxMWH4nTpzo8MaS4vHHH3dp8+H2hTLLO+UXIob3NHr06AQZ4IfP5hiCSNKQ5ymXXHvnnXfcYBfFWYTZJfDi3fI+Ea6R/913393MmzcvQXShlMpMEuFRMCB3sE7gXXfv3t3VDwgyZq+oKyilEDDUR54fbK1tCB+UdPWDejNz5kxXx4kfJRkFg/eL+TbvV967kBAQOgh5wUoHZRbLF2ZdyStWJVL/qSPM4KMwUjaeYYYKTIkfywnIKga51G/qBMoN1ijge+SRR5oWLVq49FK1NQgg6j2kDmldddVVDnewRBHlfYFRrga/fl/iMuf9A5OpU6ca2i2WP8x0CdlAO3nzzTedhRDkmQgDcyycUKAhH3v06JGTZQHgjaIOwdG1a1dJLuk36h1SR3l3Rx11lHtH5P3+++9375A+EaGvpJ2efvrpJl1dS0o05gn5R8FFofTJBvohhPpGHqRtQBy/8cYbrt5yn3qMRRTvK1U7ZfY+brvy6y3t56WXXiKpJCE+yEVIkbffftuw9Ib6PGDAgFDLFdoLs9NYo9BHYgWGcsSMKtZkkC6p+gzqPfXs7LPPTuSDvoP8HXbYYQ4Tls1QRhRy3qn084kH7EGwP2WZX/PmzQ2WTGDO94XvArP5kOUIYcg79SCsr4sKzzvJhVBu+k/yLpMB0v8Qf6o+iPupcKX/o1866aSTHClIeL5jENoQeGHfZMJkK7wv+ge+sQjv6OCDD05aainfHe7zHSUMS7EgHxD6cMhw+k3aLHmlbdLHQszRdqlTIsF3HvYNpX7RV1MnWV4DGUodR2hvft0iPogq6j5jJSzluBb3myX5CvslH/QF9GWM2+i7KAv1maUWUe0/qm6m+qaEpc+1VP3EAw884NqFX3b6X+mfeD4VltmWL1dtifypVC4CxdN2OU5z7ZgpOY7R8hY2RrZRzLfbG+7asZG598Ru5qgd85zzQD7S/Pkdb1gGGtVO5lYWhixlCHsuzjWU/CBZMG1JsgNGPx6b3TIy2y7JCJMldvlAUKqFEDm7FTQ0v9mztblySBtzmd0R4/T+LcyedtkHlhE+0UBcdazFgi9FdtvKMALCXk5absEzja2DyYoSPiNPf7/AOpJcliAa8OvQv30Dc4X1CfEPu/Xm3cd2NrvbpSW+NMpgOYz/3K/lWAawfKSYveHDzOAJRYEBDIMXrqN08/GmPTHgYuYpVfgw/CAnGHg99thjbsDHWkQUMAgHsVRgEMdgYeeddzaDBw92M/VPP/20UyhJF8ECYvvtt3cf2tdff93NDB9++OFOkWIgKQPxsDzEuYbSyOCB8iEoQAwsGVww0BArB1E6mYmLyreLoOQfgwPCohTSJ2HpwUc5ShhE8i4oLwMqztOJzMaRTmUJJJIMvCVNlAvywuBO8kJdQ7EmLOQJAyWwYGCIUsy7RBhworwz+42SDAY8gwKOsLSEdwxxgSLBzDj4B03yiT9d/SBd3jV/onzIe0dhpr6LhQikBMJ9rvNeKAeDdUgC6id1FwVJhEEwM7PMNqIwQQTJkhQGwgxeIQPIO8cMXMEJAotziAyENKPaJvfBh7CE43mUa+ob5AIYMXuH0segLxdC3iib/0d5EKxxwIf+gzbDYJOBPkJdoaxRS7ekjadqFy6imP/ot6hfKDCSB/9RFJSod4iSSTmlvfOewY93KEI9ROGJU9fkmUx+6R9RLCCQGOSLQCbSn0K0+W2DPoX+CiIH8oY6y8wvkqqdcj9uu/LrLYN83pn80V7BjPoGVvT1EGsQkShxKFwoPmFCX0GeIfvox2kLCH17uj4D/KV/kLg5p8wIxyhWX375petPMrGSEMKZug7R8vzz1rm57b/5btFO+Q5A+IT1danCSz7L8yt1kr6QP/oS2psvqfqgdLhKv+T3rfTrPEfdk/Yq32Q/3UyPaYssDYJo2GOPPdwECOOFIJklfQ7hsIykv4GUQ6FHGFfwDUHoE+mT6Yv41oAP4wlIlCgJfkNp4xB1tHPqMd+K999/342NiCNYtyAAwQbfTvRz5Ctu24rKk1ynnlPfIKSF8OBbKctLotp/WN3kW5HqmyJpBn9TlYXvTbDsfv+UDstsyxfMo55vPQgka99p8t2tezcXgg+jsIthjzQ+eh9Tb9dekco/z9bo1MZ0yKtrXjjt1jLKrB9nXu361sTfOjXcr8BsWL/O7XTAFohVSp6iw1i8aLHLz3bbb+c6Gf95/5htJdmlQWSc3XoSfwBsy1heWWodOLI1pi+pTPvFCaUf/hu7DOOYEKeSP88t3XWB8JQ/SB748cQ5bh5w5DjPEi/rN26yhEkyFkusj4SgdUHbvNomzOdEnHT9MMHdPri30m6N+dnk4pkECXtkr6bm1J1bJpE5LbxtMglXcTY0kout+1ecHTJARFDsYMlpyygtfBz4mDKQ4QPNHwMBhIFsVHgGwkFhho1ZSwYHMgBmsIoJLeQCg2SYbT7oMktN/KwdZNBIugwW+Zhzn0EHst1227lrXIcokEFHMP1MzlGQUe7pk0TpRFHmOoM5lEc+1qTFTEtUvhkYMABHGJjJ7D8zt3feeacbpDJ7HRTeB8+iKCLgz4wfZZaZbq6j1L/wwgscJmZTOCb+LUEgkcAQ5YhZTQZsMsN61llnJZZV8N5khpZ8M7j1fXlQD0VQGnn/Yi6MMoMZLcoNSo6IDDhT1Q/IBtJBiAcFkwEuSgRKJrNGKCb88d4LCgrce8cqiPukef755ydm/YiDOjrYEmUizHqLk0JmnxisUZ9RyINCurKEgPYgBBPh07U1lED8E5AG65sRBruUicE4A2SUn1wICrevdBPnJZdc4sqJokhbpG1Q52nztA+sp0T5EzP5YF7kuoQL3s/mnJlMZmNRdINtDcU31TuEgELR570wqKe/4r3wDDij2FB/4tS1bPLOM7xDZmbJByQrRAx5EEXPj5d8UEdp/yjE9D0M9uNIqnYV1afyvuR7ABa0YdomBAkEIHjhk4S6Tn7JH2EoR5hQ9ymrfB8gTWh7cfqMsPj8a2CGzxcU40wFBYo+mXqJ4nbooYcm+mHqNmUP6+t4V1HhM81DWHj6B/oWIet5H1yjz/Mlqg/iXfBuUvXFfjz+MekGv8n+/UyPIUhRNn0fNtRfSFOf7ICQCErYkjZ0APooxhZYmyH0S3x36aMhsZB031D6awiGE0880YWnHkNQcl3GKcG6xdiIeosVH5JN23IPRvzD0gTyDqFfgtBGotp/WN1kIindN8VFGviXqixYM9Gn+mX3H4+DJeEzLZ+fhh5vXQhkRDYwUBQRsoEP3QqrILJbAub1mOwvspPO86smK63ynPxu38B6ly3aZPrOTx2uep7dgtI6QC9oxIBNfBqUKvUwmChJkh+JP+x3R7vV4jjPseByu8Xl51NWuB0p/PB2zGzWl8ze+NejjrEKePmnRWVIkx3yo83nWtjy45wSkkLkpZGLzP7dGht/SQBbbeK7wJcBHRr4p1kdt7f+KiBDZEnE3OVFzjfCrtY6wpef5642lE8kz+46Qd5zQTbMXFrktv70d6lYY301+OmRbnvrb8K3GoEgmhxw9in5C/4uWpXspyN4/9dyzkcSYeDAAIWBKjMBfLxQulIJH9pMwqOIMRvEH0ozg2cUExwIMZhAmUdQAFFOfUHx9k0fuYfSzaw27DyzVihUzGLKDLX/fKbHkCUobAwW+Xgyu0WfBkYMLlHaICFQOoX0iMo3pA1C3kTAAuKCmakwYSCAMMtHHmRWk3fjm58TRhRIFAJmcDD7D2JFuC1FKDODHBQREbBh4CtYcr1///5yO+mX8jOrhYk+cUH6hEm6+kH9Azv/vfDehWTiPoMnhPcO2YBiiRUQA07eDYLFgAgDXJR+lA+EPAjRwDlpMeCinGFKj8yiEhZiRqwF4rQ18oQCjKDsgc8jjzzi8kDbQDGgveRCKAfkgS8oBrRTBrwoDZixI9RLMCW8kJsMWMME/BAfs7BwmVxDUaTOMFMenPVN9w5RTiBpaH8M6pm5gyzlmHgR6gztuaL6IvoYxlgQbijp/KIghins1BP61DvuuMPli74nTBELwy9uuwp7FlN9lhpA9jGDjIAt1//3v/8lHoGslv4qcTFwANnNchW+B9RpJG6fEYgq6RSFMazNJQWKOKEc1EkUPJa1oRjTF1PfGWf6JKcfRabh/WfTHZMniBnIbr+t8d2mHUlbS9UHpcMVpbGyhO8c9dr/fvfr18/w5wuEEd9ihHKyvIEleuedd17Se4BEQHzinTbLmIVy+yJ1MvgNFSseWTIhzxAn4xbeAZKubpWnbUma8gtGQjRwje+E9JuZtP843xRJ0//NtixxscxV+fw86/GWi0BGZEOwGPhbfHrYfLuzwQpnMVCjWlXz54M7mBfGvWfu+fKlYPCk80eP/4MZsqGFmXbC7yxRUKrMJgWyJ3kH7Gaa3nlN8HJW54M6NzIvj1zsFFwiQNF+9Jt5dhvIIrNv1zzT0DpSnLF0nflowlLTraVd+xrYHQL+gZ0n+K1hOZLNpoqZtnit+WLqcjPRWkn4Qh95Ut/iNbj+dTlmGcQJOzUz931Z2hlCPNz3xVxz8R6tTD1rbbHAAnyPPbfZTAgEwTF9mpkiq5SXR7o0r5O0ywRp3D50trlkz81ux40N1srh08krzAsjFtr3U5rSds1rO2eSpVfiH/mEAU9BpLw7dqnZtaCBw69m9apuu1Cw8+V/wxe66+weMt6SRU8MW+CIET9M1PHP1rnkDEtqrF630TSvX9NahZSrykcls8VfZ7CEMGBhFoABOYNmBtDMHPuKVLAwmYTnw8+ggDgZwDAA4g+FkvXmKPDMwCIMkoKKKB/zMGE9JAMuBn8MwlHSURDF9D3smTjXRFlHkYVUkPhEGURpQelkECKzLunyjbWIL5zLYMW/zjHEBR9df/aYc8rITK3EBS5Rux0E49xSzsFLBsKSJ5Q1RPBAuZIyShj5ZZaT+sI7YsYGBYK6FSap6gfKAoNGSZvnIYAgmahPEHEo6Qi/vBMxqeec5xGZjZdjlCRRRINl4B0iUk534v2LmkGO09YYdIrQnrFyoL5AlkHk8IczUdp2eYU27A94JT7BhMG8DOgpM7PCKJ/SjlGIhITjWSYHeOfSH0lfIPGW9xdyhPdH/wbZKSJtN+odkkfIBmYq+WOWlNlt+gTeMXVQ6k+quibpZfOLYkU9heSA9ICIhUwK1i3iRtGHGMOfCXWVegwRG2cXl0zaVbAcLFeiveBrQeo42JLHILZyPxiHnFOPCePXrzh9hjwvv0LUyTltIhvh20Vc5AsSkd0BeOeQPfT/kDtRkmn4qHjCrvPdI18Qd6zj94W6IiRTsJ4I/vRB2eAaZwLPz0vcYxT+qP7PjyNIGPENgFzDekmswggvbTtYftKQe4RL9Q2VOhTMl5xLPOnqVnnaFnn0Rd6ff02OM2n/cb4pEq//m21Z4mKZq/L5edbjLReBrDQvq4eakfPWmn9+ON3OzJfOHDeys94IndSmFARCAg4bbjOaO39hYu9Xb5e5KVxYVFxra60uurWsY8bMW50Iwk4SWBTw5wvbLAbJBra6RPnmL5WgK++9fZ7ZIb/YrDoq7CE9mpp3rLI93fPt8LH1VcBfQ+tfYoW35EPiGGAV83y7hGB6yPaTEibOL0sxTuzb3Dz2rZ1NteVCsFC57YOZlkywnvjtNawMfIHoOKh7kyQrA/9+uuN2gd02SPYu6wjyrs+Kn2SLz+v2bWdYYrLA86cx21pd/Pbl5PWJNivOkqQk64mlHr6VBLFOsWTQBc9NdAngx4L8/9qEQQpKGoNBBvesb+RXnGyhAKQiG1DK4obn48zgiIGBP1PBQADFhZlQUUQYwMmsHXnADDvsY44ywwdz8ODBbjafDz9LLiAchBzI9p2SXwa7DCQZBInSiUJEPocOHeqiRjkVSZdvlBUhMXiG2fmwGTEIDgbumG0KuUF4ZlOZJQRHrBe2FOEd+jPV5J33FiWUGSwYfMjAgrIhYMvgXmaugnHwDEQDJBV+EpDgbLU8k6p+oDRiuYD5sC/yfrCWQZmU98PsMiLXIeSkvjLAE4sCZufIn8zMQ0ihIMqAl3eOSLzuJMa/OG1NsCQ62gXvAWsC/rA2uPfee13eckE2hGWZd46Sy3IZ2qQISg3KAEQEy4JIX0hBcGJWDsIRk2MIEdp6pvhIWql+SZsZc9+nRrp3SD9AXrDMoH+hfrAsgX4TvMW6I1VdK29fRJmY4YcsIV3ea5jPC9oga7npX5l9RFiOQB/Gso9U7TSTduUi9v5BZkBsnHDCCa4vl1tgy5hPloJxnb4r7rIOiYdf3kGqPoP6zfvx+xTanrRD4pA2yHEmQptHsHKhDpPGxRdf7MgxrvNeogQSIJPwUfGEXadOQtgH+7DHHnvM5VPIhlR9UDpcJV2Z+efctz6T+7n4JS/0IRCTYlFBP4HVBnhHiViRgbMvxIdQb4S8pD5CaMq5Hz7smPbPNx9y1Le043tF+5dJkVR1i3zF/WaF5SHutXTtPxhPnG9K8JnylCUulsE05Txd+XxSU57R3y0fgarZZPGzqSvNX96ZkkQ0ZBNPumeq1Khuau3UNV2w2Pfxc3Byv+YmqJCGRQAhsXpdcqcWFi7s2m6dGppzB6YnSVDezxvYKjQ/YUQDfhbOGpBvw4elmvm1o6wvhMHbN7L2GcmyyloBBIkGMDvNOpwszxKOPSyZwC4iUTJi9kpXtjMHtHRLPKLCcf2wHZs5QkbCzLNWEkhb65cjSmZZq5VfgzDoYFDITCcfGjwHM3AVz+p8iDln5g6Fj3XwCANV7vGxQHHgQ0vHny68jymzQgw+GbgxYCNO/hg8s2QD83+UDGYtYM5JA5NbyA5mZ2XWlg88CpsMeBjoQkagsDCIZbAiAxU//WyOIRIY+DCg8MkOiAf/epx8kz6KAMoY+QQHyg9BERQGkQxwfKKBMGJOzf0tSVC8eYfUK94Zjh19kYEYs8GUHQWNwSuzzNQ1lH4UQOpH3EEgA0wUDAaOrMVHwDMoUfWDmT8GowzWfSGv1EHer08kMZNJWP86ygf1EQdmtBdm7vGrQd5oKwgKEA4hwYW8MktO/ZQlQ37aqY4zaWvEAxZginJHm5YBuV+PU6WXzT3aKSKm7xIH2IEldZ/2Kb42WOLBNTBiXTvtmPush48imyTObH4ZjOJ7wVec4rxD+iYILeq5tEvqHhj71hlRdS2bvAafoU6yTIX+UvIQDAM5huJLn4lpP3mExEWwJErXTgkXt10RFqEvpi/j/fKeOeeP9gChRz64Tzvn24OjTtpMppKuz5DvA2vxSYu6FNYfpEsXvOQbSR/xmFXc6bcg0CAuaIcIpCLxsyQKzHiOehzs69KFT5efqPukyfc8jHTiGvkRAjdVH5QOV1HgqHekSX8CLr4Ev8n+vUyOaYsI/SX1h+8J+GKpI5Zi3Oc674g/2pw43MXaxxfqO+0GsoKwfHuYUAEbIQn98FHHkHe0f8ZMvHPGL+CAH6UooR5Iny91INO2FRV31PV07T+sbqYa70Wlw/VUZQkru8SVDZbybLrySTj93boQyNiyYdzCImfREObcL9dFr97CzqLv0DGn0fZr18DcckgH8+DX85xFQVg5cBi5nV1mgGPE2ta0P45g1dHCkgGH9GhiDrB+F3ypUb2KYUcFEV/h7me3q7zjqE522clc54cg6IyRZ3CQ2bN1ffNbOzMv1iMsNSBOX3ev6p9IYil+yccle7Z2Sza+tL4rllj/Bt6KCfckYZpbS4PDezY1h+/YJEEWMI4g/Y0lxapuj0tLGJ4ovh5O2Km5syIJ+mUgnYa1qrulIz1b1TNX2R01WC6Bbwh5RxSvpbXqOMFaZLAkZtz8VWaNJUYQlpyA3T5d8sxX01YkWUZwn2chUX4NglLHH4ICjfKE4yRR8BiI8+Fm9g/h4809HDQxgGFGkkEHs+s4V0oXno+9L8zUMxBGCZLZRQYqKNzipI8txCA5ZJ0vcTAzxkAWkTXLzDQw28GSCWZDGQggDMhZ85sLobzELVYNEieDH9lZQq6lyrcoTgUFBU4ZlWcw+fT9BXAdRYiBjW/9IeH5xaKBmUoGTsQrAxk/TGUcS5lIi4E4SqOQU+CFQi1hUCQ5R9FGEWXWFUeZrHnlOYRBYZz3Rn0BN7xv33333e5ZfBFQL6m7V1xxhbsmcUbVDzzKi2O7xAMlB7x3Zqp5z75wDskl1xn8UKdJF9NqBF8U4gSUc8qNUsCWfgjKPlvGIYJP8NjdDPxL19YCwZ2ih7IhO3xwn7z5ZsbBZ8p7Tr2EwJEZRT8+3jsDfgbp9CVYT4Eb5IwI9YQBOsops+S+giFhMv31MeZZ6iqzjCjBSJx3CNlA/RLyj/Kh8KPA834R6m9UXXMBcvAPDKn3UVZNlBUHmCj0EMkIpBftDQUgVTtN167OPffcRAl8TFmmgfjfFs7Bg6UbLFnhfaIUkgZtBxIijvjpsI4/VZ9BvFid8J74o53RD0GKxxXSg6CR3Q94x7xrHBtTBxAUTKwVnnnmGXcOQUofjiUR11nu4vd1WOukCh9GNruI0/wTa4rg94PHUNrpa2mPSKo+KB2utA/KAInz4IMPuvoEBhI38Qe/yVzLRmhPxx57rOuz6E+pL/TFMjaQ+iDfGUkDQuSYY45xhKVck1+uQ37LOyVOxjxCJBNnum8o5YeggLQQ3xi8Z8mXpOX/8g6wKmLni8svvzzlN8tvW34cYcfkP0woB3+p2n/wO5zpN4V0ST/d9zdYdj+/6bAsT/n8dPR460GgimWugvplZO5X2pn+P7413Sl5YYFQhG87rKN5fuyL5u4vXwwLkrj2+Al/MkPWNzeTD740dBkFHUP+n843Dc44tIwCnIjEHsBuz5g+w3Uk/Xfp72ZM/PtRx/hHwMR+rLVgQFFF6lulvrMlGTo2rW3YyrKWJRpYrsDygsVWEcfaAGuHtRuKIbNt3voBqG7yG9ayv9bDuVXK69o4gjJ50VqrCJdaSTSzYYO7UbCcY5b1HTHaOmTExwCCMt/J+ino2qKOWwJCfkRQ1tla0+9A822eceCYqVCaOcvXmSnW6SI7dJAXpKl1YInvio5260zKR3lFCi0mc6w1gaRP3nA66RMpEtb/Jd+TLB7DZhQ6p6L4rmhu/Siw5KS13dZU0mFiYaF9L7PsEopZFg/Iglb2Pg5IJQwOJoVAqGWtVtrbe3TELLsYZnf2AEfKhp+G7i1sOex7BXuV4j2UsRLgw88AFbM5PrS+OaqPEybimYTnWWYvaZ98WBgUCpHgx0sYZmQgD9IJeWCQyMCafP+Ski7fWIaAJwMk6uS2JMz6MzhlkB5XeG88E1YHUsUBzigSDKTBkXP6HOIKSlj9wMoAhUgsEILPZHpOXqjPUWUHG8ooyy0yjZ/w2bY16hvkYhg22eQjl8/QHpglQ6kDH4gZlFPIu0zrRHnzle4dxok/rK7FeS6XYWgHtCt+pX348adqp5m0Kz/OdMfMCIflJd1zYfdT9Rl8i2iHYt4e9nx5r4Er6VA/pU2BKeULk0zDh8WRi2vp+qBUuFIvIMKjypiL/PlxkBf6ylz1z/Qz5D9qHOOnHXUs7YoxRpRSHPUs1yuqbQXTlHzym67NZfNNyUVZJI/ZYCnPxilfEBs937IQoE1kRDaMWVBkfv/6ZLPOKoxhkjOygVnuA3c3+Xdf5wZeYWnJtWzJBnlefxUBRUARUAQUAUVAEVAEFAFFQBFQBBQBRSB3CEA2lE6Vx4j3uxkrI4mGGI/HCuJYrCH9TbObLkhLNMSKUAMpAoqAIqAIKAKKgCKgCCgCioAioAgoAopApSKQkc39V1OWVVzmrDVDzbb5Ju/kA03eBcco0VBxSGvMioAioAgoAoqAIqAIKAKKgCKgCCgCikCFIpAR2dCkbo2UPgHqWQd/tWtUMx2atDK7dki9ZVuTOg1MtRp1TIPd+5iaHduYunv2MdW6FZhq+U2VaKjQV66RKwKKgCKgCCgCioAioAgoAoqAIqAIKAIVi0BGPhsiXDWU5tD6QbM+DU1lukOL67MBBymE9QWnJeLIC6dROKthixicf+HYTZwPsbQDx0c4skNwGMSzssWMHydhcSqEkx7C46iGuPyw3EeIQ4SweJ7HUZ6kyz2u+duY4fyGuMkneccxDL+siSH/pC/CPcpCHJJ3HPFwPcrxDU7GKCuOdvz4iRP8wFGEONhFgLh5hnKKox+ep5yyVRX5Is/knbwSN+HF8RLxEofkT9KQcoELz1B+nBkKRsRLufnjOphSPvAmTspOmUiHvFS2MzIph/4qAoqAIqAIKAKKgCKgCCgCioAi8GtBAD0uI8uGK14p3k4vCiC2jPzNoNamTcMtz+M/Cid7+bItjCj+bLfDNmEoxWw1haLLFkcovSipbP3DtoEosWwBhCKLR3S20+K5sK2p2M6ObaKIB6WXY37Zjgohrs8//9wpy/6WOijL7BndsmVLtwWRYPz999+bvffe26VLGdjuCYWZ+KdNm+YU6kGDBrmt2sgj5RNhuyTIBuIQgoS8Uw72HxYcJPzcuXPd3sp4DCcMW5ihpJN3wrI9IOdCEPDL9lbgNXToULd9kWzxRDojRoww+++/v4ue/arZxgmCAYKCPa0heNheh2tSNggFtuoRb+/gyTZqbAfGtlfkkbCyFZTgjcd5nhk9erRhmye2sGIrKrZYY697trLjfcu2ZlJm/VUEFAFFQBFQBBQBRUARUAQUAUVAEcg9AhmRDZMWrk6ZA3ajiNqpIuWDlXSTmXiUYZR/EWa/UeBR3kVRZrYcBZfr7PmMEgtJALnADDtKMtt2BckGrkMuEA9kBFJQUJBkRYDSC8tDnCjkKN4iMhOPIo5ijJAugvUAZETz5s1Nv379HPnAdRR9ykWeIUvY3zYoxMseye3bt3fWCewhzHN+2pAd3377rds/GqUeUoU9hiFihJQgLyjxUjZJR9KePn26I0vkPuki5B3igXjJh1yDBIGAIM/EQfxYRJA33gfnc+bMceG5j0g4jtlKDXIBwgKSBiEN8ABjCI6ePXu6/Mr+2S6Q/lMEFAFFQBFQBBQBRUARUAQUAUVAEahQBDLajaJCc5Jl5LI8IM7jKKoosiwH4I9n+UXRFuWeeFC0UYoJz8y4CNYOEATM+DMDH5SZM2c6BV6Ube6L+b+EJQyz8m3atDHDhw93acg9frt27eoUcNLxhXyi+AsRIPewChBBuZ83b577Q9EWBZ37WAMQx+zZs11wWYYgz1JOrB86deqUIDK4h4WDL+RL0iA+EZaLUK4xY8Y4RV+u80teICx8qwKIEUgZllWwNEKEMFgrIBANPAeGYTJlyhS3bzsEjAjlwuJCLDkgf8gv7xQSQkURUAQUAUVAEVAEFAFFQBFQBBQBRaDiEcjIsqHis5N5CmvXrHUPYSkgM+lRsUAuoAzLTD0WAsyAo4TyvC8oq/z5cY4aNSrh04HZ9KCgNMsSg+A9zkkLZV2UbDHzZxZfhOUAkBrjxo1zM/ZynRl/JEgSyH1+KR/PIbIkRMqKtcTEiRPddZZvBMkSnuWahEeRnzBhgiv/vvvumygXZMXChQtdGij54pOBCxAVkBYo+Cw/EcHiAyyDyj5YQQKI9QbhWQYC2QABxC+kDBYTYSIWIsF4Ccs1lopgofLTTz+5dDj3/V+ExanXFAFFQBFQBBQBRUARUAQUAUVAEVAEyo/AVk02oCAzM440ymuURAyEQYPCi8LJLDyCQopyDaGAcuvPoHOOMusr5RAMzNKjhPvXJS0Ub6weooTnsE7Ad4RYHaC8+2QDz/bu3duF8ZVssTDAmkCWDATTQXkfPHhw8LI77969u7NSwK+Dbw0hgSExxMqAeFhygYUGefWFpSO+5YZ/j2PSwSeFTyCwXIOy874gQUQgXiAbuCZkCktcIAQgCLDGwNLBx0Ge5ZdwvI9gvBIGAgksIYEgHSBb8DGhoggoAoqAIqAIKAKKgCKgCCgCioAiULEIbJV25SioEAHMvqOkQhr4M+ypIBOCAZKBeCAeUKxRRrEoYBYepXvkyJHORN93uMgzBdYHA8rxNOucMSgo4eQHiwVIBZRclk2IRQQKN44OcTyJVQWOEDHx9xVz4iQdlnGwzAJFGkEJJ59YZpBP4iaf+CwQ6wsIDK7JH2FEKLdYIpAfITvkPoQHWFBuiBbS5S8YTnaUkDSC93kPLPXAKkHKhcNJ8k/cPAfGWEAMGzbMOW/0rUHIZ5cuXdwSCqwaghYnkl9+SYcyQvBATBDvrFmz3LsBE+oH6ZFH4vWJDj8ePVYEFAFFQBFQBBQBRUARUAQUAUVAEcgtAqXTzDmMF8UPpbQiZMOGjWa1VeSJX5ThFi1bpFxeQD4gFsRsP5ivPn36ONP/L7/80imlKMkQB8zSo6CiuIpCzDnWERABnTt3dssDJD4UWnZugGzAIoBziAMcLTIDjzLPTL2v9GJlgILMLD3WEuQTIX2IDfHdwHUICpZJkE/ipfyE4R7nCLtqiOCHAoWcvMt9zr/77jvTtm3bJIsKlHocNUJmSN6JH4JCniUeFHuUeBGWZFBOwYfr+J3AAgPSQoS8E/cXX3yRKCNYkB+e9+NgCQbLSSgbQt4kD/xioYKQ5sCBAx3e5Ik4uIdTSAgG/HNAIvEMViuQPCqKgCKgCCgCioAioAgoAoqAIqAIKAIVj0AV69G/2M1/jLQOum9UylDsRnHbYR1NrTWLzaJFi1KGzcVNlMjmVjFt1So/MbtfnnhRUJkpR2EV5Tbb+FDU+UMhFgIh27iCz0k+UcJRsHMtkCtYaFRE/BUVN/kl7uDyFq5BHglBkWusND5FQBFQBBQBRUARUAQUAUVAEVAEFIFkBFiJUCGWDcnJ5P4MJbhxk8Zu5weUSBTKXAikQFBZzTZerBd8C4Zs4wl7Lpf5DIsfoqW8ZEtYvFyrqLipB2GEQkWlF1U+va4IKAKKgCKgCCgCioAioAgoAoqAImBXF2Ri2aCAKQKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKQCgEsG3Jvg58qRb2nCCgCioAioAgoAoqAIqAIKAKKgCKgCCgC2zwCSjZs869YC6gIKAKKgCKgCCgCioAioAgoAoqAIqAIVC4CSjZULt6amiKgCCgCioAioAgoAoqAIqAIKAKKgCKwzSOgZMM2/4q1gIqAIqAIKAKKgCKgCCgCioAioAgoAopA5SKgZEPl4q2pKQKKgCKgCCgCioAioAgoAoqAIqAIKALbPAJKNmzzr1gLqAgoAoqAIqAIKAKKgCKgCCgCioAioAhULgJKNlQu3pqaIqAIKAKKgCKgCCgCioAioAgoAoqAIrDNI6Bkwzb/irWAioAioAgoAoqAIqAIKAKKgCKgCCgCikDlIqBkQ+XirakpAoqAIqAIKAKKgCKgCCgCioAioAgoAts8AtWXLl26zRdSC6gIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKVg0C9evVMlVWrVm2unOQ0FUVAEVAEFAFFQBFQBBQBRUARUAQUAUVAEdjWESgsLDS6jGJbf8taPkVAEVAEFAFFQBFQBBQBRUARUAQUAUWgkhFQsqGSAdfkFAFFQBFQBBQBRUARUAQUAUVAEVAEFIFtHQElG7b1N6zlUwQUAUVAEVAEFAFFQBFQBBQBRUARUAQqGQElGyoZcE1OEVAEFAFFQBFQBBQBRUARUAQUAUVAEdjWEVCyYVt/w1o+RUARUAQUAUVAEVAEFAFFQBFQBBQBRaCSEVCyoZIB1+QUAUVAEVAEFAFFQBFQBBQBRUARUAQUgW0dASUbtvU3rOVTBBQBRUARUAQUAUVAEVAEFAFFQBFQBCoZASUbKhlwTU4RUAQUAUVAEVAEFAFFQBFQBBQBRUAR2NYRULJhW3/DWj5FQBFQBBQBRUARUAQUAUVAEVAEFAFFoJIRqF7J6WlyioAioAgoAoqAIqAIKAKKgCKgCGSFwPr1682qVatMUVGR2bx5c1Zx6EOKgCJQ8QjUq1fPVLGNVVtpxWOtKSgCioAioAgoAoqAIqAIKAKKQDkQgGhYsmSJadq0qalfv76pWlWNtMsBpz6qCFQoAvPnzzfaQgMQryzaaEbNWWlmLysyGzdtWzwM5flhZqGZsXRtoNR6GoXAtlwfosq8rV2fs7zItelC27a3JtG6t2W+rQWF61w/Wrh2w5aZQc2VIqAIKALbMAJYNEA0NGzYUImGbfg9a9G2HQQyXkZx5P0jzYqQQVaLBjXN7p3yzJG9m5t2jWtvEQh9P2OFy+uuBY1M3ZrVUubpm6nLzf99OM0sW106gKxWtYo5aed8c8aAVobjrV0+GLfE/N8H08x5u7cxw2tUNXcNnemK1KlZHfPQKTuUKd79X8wyzw2f767v0TnP/PnQzma+HWif9MjPZcL6Fy7as6259/NZ/qXQ47uO72p6tKpvhtv3dM0rE12YC+2zx/dtmRT+7dGLzD8/nJ50jRPeSZ+2Dczg7RubQ3Zs5u6nyh919IDuTc3hvZqbpvVquPBSn28+pLPZc7s8d41/uaoP4+evNhc9O9bFe8Xe7c1hPZsn0uDg6AdHujr372O6uLIk3Yw4WbxqvRk5u9C0qF/T7Ni6fkSoir+8bM0G8++PppsfZxWates3mQ5NaptzdmtjBnbKi8j5AABAAElEQVRslEgcRf+hL2ebb6ctNzWqVTUDbFukfuTVTe56sIL87UvjTZGN576Tuieel4OJC1abf388w0xauNpUq1LFdM+vZ67ap71pm6KvgVy7+uWJDiuJ56p9OiTqilwL/l7+4njz8+yVpmHt6uaFc3vZfJe2/ffHLjZ/e3+a2b55XXP/yWXzGYxLzrUvEiTK9xunHoD1M8PmmZ8taUwbGdK1sTlnYBtjq02SzF1RZH7/6iTTt31Dc9ngdkn3OPlu+grz7PfF8dS0dWDfbk0N/RPPXf3yBCN9YpkHM7xw01tTzGeTlpqqNoP3ndTNbGfrlooioAgoAopAOAIsncjPzw+/qVcVAUVgi0MgY8uGjSVro+rVqmaa24Ecfxwz2/PKyAXm4ufGmQ1biEXA361ScMs7U828FetSAv8/O6C8/vVJTulDCd3LKq8oEygrT30319z23rSUz28NN7HRePTrOS6rh1rF3H9FUxatMQtXlsXoQ0tOBGWTtzYOZT/sD+XMv+7H4V+vWb24+r3208JEkNdGlh7LRT+vUuf45f1AVPzLKryv2rqH+PnjXUp4FEfq6JP2fV5i66iEk/os58SRy/rgryW8+9NZJhezoT9a6xTq9RPfziW7v4is37jZnPnEaPPF5GUu/SZ1axjq0Q22HaHsIavXbTSXvzDeDJ241J1DSHwwbrG50ipqG+zzIhASf3hjklPwJ9s4gkK8F/xvrBk/f5UhHctZOALhrKfGmOWW8IiSN35e6MIVNK3jSBDqHnWFfKQSqeKQqtSXXIj2ReVHMU49gIy41hKXIywB1qB2NUcMQDzc9emMpAyMnrvSXP3SRGflNTWkzlGHr3u1OB76kTW2zlCfbnxzsundpoHrV6j75Km8IuQ9fdAq22ZUFAFFQBFQBKIRYFylSyei8dE7isCWhkDy9GIGubvazhCilIugUPz57SlmlTVVnrZ4zVYzO4OS/aCdeUVO7Jdvzt+jjRTJKrALzZ1DZ5iPJywxJ/fPN1gAbK3y1ZRljlBgNq6BVbyDArGAFYfIVPsOmUFPJa+c39vUt0RTmBzZu0Xi8l/fnWo+Gr/E7GdnBn9/QEHiOgcM4r+2ViVITatFMmvIAD4MaywTnj27pwvLP5TG860SOssuC2Hg76fJ/UdP65GUP2bXf//aJEc6TFu8NjSNiqwP6zZuMvd8Nsv8bv8CsrdVy+uWIEJJam8tCx46dQdT3Sryd38207z04wLz2DdzzM52tpj2Qx1C2X/YWs6s37TJnGitYugf6C/27dbE7HfXD2mXKz1gLWwQrEKwDoHMvMi+d4iJ160CeNourUKxxKoEOWvX1s5qBWIKJXT6krWma8t4s8eQjVjNtLR1r6KlIuteRee9MuKPUw9oX8gxO7Uwlwxq5+ra2ZaUoi6e0r+VgdiCAEsnkEPIHw/qZIZ0aexICcg1SIilq9eb4/q2cG35xR/nm2v3K3Bh9Z8ioAgoAoqAIqAIKAKKQDICZbXO5Puxz/q2a5AIW6dGsQKKqSmm1hATf7EzsSiSj5/ewy1p+Ic1ix9hZ2iRnm3qm98OaW/a5NVKxPHumMXmSTtzyzNIq4a1zHmWCMBkHkFxY6YYIgCCA0V1l4KG5sq9O5ila9abm61pqijLWC3sYM2u/3RwJ/es/09mh0nbJxoIw5IQZsxn2wHqT9ZsHQV4iR1o3vnJTGdmTx5Id7dOjcwVtowNrOK9aOV6Z2Lbzaa3k8UEawLywRKTE/u1NFwXef6H+W62DP8QdeyyBkzML9mrXcLEn3DMphGOMGCA4gPxgUhaPawpfU/7d5clRpC3Lt7J/fr/ZDlEUCFnthcLgXcs3j7ZIFYNct+PK5fHH1sSgvSZLWzbuJZ5a9QiV+bLbX1IJ7UtZpQbsqF2SZ1L9cwuHUrN+6mXYZJpfQiLI9W196wZ/tF9WpjtW0Qru6nq/u12KcHnk4stBVi+gALE0oDvpi83n09aZi4e1Na2g+Jy4pvjT29MNh1tvb3R1v0vLeH04BezzVE2fRT+Ny3Wu3RoaP56+HbO5PzuT2c6Bb6eXXLEUojL7DugXoYJyzgQ2ghEA7K/XaIC2TBraXGbxTQcYRkSJuy0lZMsoccSG+5BNuQ3rGmwkoA4khle95D3b/S8Ve7sWKtAIqS3d9cmNq+zzXRLGkVJq0bF/Ql9RI/W9cyYucXxxCUOpO7/wy49+ufRXaKSSdknLLJkZkX1RWSId0j/QBvAwqyXbQ9X2vogy4Qe/mq2qxenWELmueHzHJHHEhaWFdDvYrXF0pQ2FquDbd8iS5jK+1ycekjdoW+lviOQkSdbwrNZ/eIlTu6i9y9dPaAeSb0UAgqiC+ILkgDCtVvLegbSEmHJHH14UCCp6LOxioJoQCDV/nbEdtbyYJOpbuvxwT2aObLh4/FLzTWWbAis0AhGqeeKgCKgCCgCioAioAj8KhHImmxYu2GTEYdrmIY//k2xuTEDNCENxlolgZnrS+1MEgolgkJx3tNj3SCPQS/rr5lxPMfOPj1wSnc3qGPWE98CCGEa1qruZpawnMg/oZtT2P/xwXQ3W44Cw5KHKVZ5YnZ7llXKr7ez5yuKSpVJ8rfU88XgIi75N7pEATk8sJZewjxmyRFfLn52nJsZ51pnq8TNtIoV+SX+24/t4sqFkscf67vBgrKjXI2zZuAyM/+fT2YYWT7AQJbwxEN+njyzh1PMGPA/bU2AEcIwMH/IXmN27up9OyTS4vo71q8BEqUcYqmAdLKDb192tD4TJixY5ZQVlpug/CFCNuxjlTrKESbzLBEU9IXBEgrqQFxhhhw5YIemDivIhndGLzaX2jXUrGH2BfN2qXN2ryMzymL1UclSDxTXdDLMKuQiBU3D/YpkWh8kvnS/vJcDd2jmlhph6fGorVfJpSuOIV3dh1jDrwFCvaKer1m/0c3WU4cWWLJLZI1VjLhGG0SWWAWK8/9aUkHaI9dRxDA9R6ivhIMUGWVNzR89tYdVrsrmFCWOOHyfEaLMi7KI9QiyQ6tSgg0fHcjkhcX18ckzdnTnWBxc+dIEdxz8t5P1y0FaQh5wX96TpBV8hnN8c7AM4lPbrr6esty1l4Oskhj0FxH2LNdQfJ+xS6xwqooPj109XxT+M6n6hEv2althfdF9lrSBaEAgIlGQsRI65dFR5umzdnSEA1YcvPPb3pvqyAjqIUo2y0mETEH5JgzxtcurbQZa8rS8z8Wph3dbKwTeK3UOMhVid4NV/iFLwiRdPaBfRFgyxZ8IdZQ6PsnWOaxjpA/Goo0lU0EZO2+luwSRCVkMSVHXEjlDLNHtk7WQO7RHlmGEWWIF4/XPeVf4vgkuObzixeI2cIStu3EIVz9OPVYEFAFFQBFQBBQBRWBLQ6B0RJZhzsTMNPgYCndQIBRuO2o7g2J7l1V0mE1iZgiFGWGpAmauOJLDCeHbVuFEmPm6bHB7d4wlBAo1g0asA4ZZ513IrXa2CasKBsdXWAdzy6xVA8ruy+f1Nsc99JMbgN91fLfIweAcO8hFcG6XTlDYWffPIP35c3qaxnb9OGvGj3pgpFPMgs8zWGTQCNlxhHWsybPMKNeyA34hGu49sbsz6cbz/LEP/uSWOgyfXmi2a1HHEQ2khTVIazvziPPDUx8bZXCYiJVE1ZIZZQbsOMq75bDOprVVOoJSZIkhBsVIUNGyXI2BUGCGlPX0zAiCJSbdxNnWKgJRcv4zY8vcAvvnLDZxhPJMtLOqyCDrnLGOnVFHGZKlFViD+EKejrhvhH/JHTMjKhYv/s1brYIls+5zrSIi/gCoe6z9D5NM6kPY86munWsdc747ZpFT7CBV8J0RlHR1/w8HdXREEGVjxvb/jtreRcH7iyvUl727NDG/sYROfYv5KbZOIVj+gCP1Bb8WLGd5x+Y36NSSsGcPbM1PQlD07i0xYRdnnTJr3KhOaTcjxz4ZmIgk4oA+wRfIAxRABJIqSmbaeoxjvzW2vORl0HaNnZVVVPjgdYgMyolSivPY58/uFQxi0vUJOPuriL4IYlCIhr8dub2zUGFm/zJL7OLbAuLghgM7JvILGfHo6cXLXc6w1jAo91iwPHnmjs4iCwetkL44VYRsEMn2OXk+1S/1EMe0KOpY3fzRWuG8P3aJXSrToYwzR+JJVw9WlFgrNfTqG89JnUvl34NwIgsKiwk7LGJ8wWnoBHxC7FfgLudbkmZy0RrXh2VKNmB5Qn/AewoK/e6pth9WUQQUAUVAEVAEFAFFYGtHoFQLyLAkzOrLTOM6O8hlSQQD+vOeHmPusQq0r7yfu3trpxiRxJclDuVQJMeUmEezWwRkg6zdZ1C53saF93oG88yCfmfX2yOkhTAbtmLeBjtAneRmjFG8nj6zZ+TMvnso5J84CAy5VeZSQZM65rULejsz2rV2NpnZWAbJCANnXyAJmFlF8JEAXii706wCJIo/JImsHcf3wR2WqJlnFfAOdtb9+xIyhVlhTP7F7L+3NX1mppUZcEzJRW4+pJOzfpBz/xdrD0RMq/17HB9olW+U1XetRQFkw4eWdEAOsopcsFzuRsk/3gHvyJdU5IQfjmMUboQdJcRCgjK5pRTW4iFINoBpcKcDFGJIEpZUsJ7fF2ajwwSyIUoyqQ9RcURdh0hBkYIo+O/QmaEESZy6HxV/3Ovk47r9C5zFAgQYJA7XmOGWNrmbbZ9g+6Fd5hJGNvhp0W7/ZB3nUVeo0xBsfmvwLVRkO+xNgfbixxd1TJwsrcInBHKu3fmiY8BSx92w/3xrDQg2ZvNpryutJQhObJtYZe8/x3aV4JG/LCugbwIjrCSo875k0if4z4UdZ1L3KAtC/8BSGASrosstgUT5hIx1N+w/lkhgBYYwYw/ZwLItln4hLDODbAg6KMz2ORdpmn9YHIiSPqBkiRPfEN5RmF8ZiS6qHggmyT1S8bIbnuWbEkd8Z7EQNpCxUp9Y8oHFC4QA3z/6dCyBshFZsuITDsR7u92ZJqqvziYdfUYRUAQUAUXAjtM3bjSLFy829evXN3XrRi9lVawUAUUgtwhkTTYw8+I7iMTE/fQnRrlBLM4A/ZlP1uMjDOJkXfYNliQICsoKZqXMeGP2yyxSlFx/QEdz3WsTXXrsgsEfwgCO7cniCtt0Ym3AbL6sdfefxUHcSDujxWwts74vW8Xjebv2WUzT/bD+Mcqvb3xe3y4FEZm1rNi8vIMlL3zBYkN8OuCrAWE27TdWeQjKcmstIYISHqV0EYblDkjUbD6KBmbH+MdACWF2EWENvizPcBcC/7DKiHIQGQgaevrmqOIyoiQcVmKxIDsFsO0cSyZEGSICBuDB2U2WzqDogtel1t+FL/fYJTdYSyDUqXs+nemsCvDh8aoljcIk0/oQFkeqayz3eMGavmPREbY9aJy6nyr+OPe6tKiXWBohRBT1ObSeRSw/knRwyIeTPIQlCldawkfIBeolbRqrnbyS2WaOEb89uAtp/lEvrrI+YFiahWBqH2YZItGI5RVbcZ60c0tzgbXCQTE8z/5CsDBjH0coA05NWeIB2XBWwKKDlT5x+4R06WVS96QP6RLw/dGpeXGfQj/rEz4yu08eapXsAtPaI07ClsoQNtvneDadSJ0gHOlLfUn1XKp6IEsngoRJYcmSurwIa6ZgetKnsVQIogGBzOY7hk8IfEdACmDZhrDrSrbiEw5KNGSLoj6nCCgCikA0AuvWrTPPPPOMGTNmTCJQ27ZtzRlnnGEaNWpk7rzzTrPTTjuZPffc01x77bXmyiuvTLm1ZpwwiYS2kYMJEyaYJ554wtxyyy0ZlWjy5Mnm4YcfNrfeemus57JNx3+HsRLaAgOtWrXKfPrpp+bggw8uV+7+9re/mX333dfsvPPO5Yonlw+XasDljJVBN2tqUVaZIfPJhvp2CzIEJUQGlDilYzY1KNxn/TgzieycwCwpgz62LxNCgWeYYWS9N2uNf5hR6NZlMxDEtBjHjMzaxZHuVtGGbMCr/XGWqPAFRYk1vShikA1YEzxuZ1WZIWQWnfXnzAYfHmLa78cTPI4aFP9kiYWZtjy9rPWCDGT72UHuKSUOIf14WlrfChA8CMRGKpHBM84towRiAaXxfuv5H+zxg1GRs2uYazPbjIDn+g3FhWHJzcYSNQlfEcdYZ4apxHdMik+Mpp5zOQbvUnYsbfLtMhMcKqKIBYkMSSOT+iDPZPp7nVVe8VESRuTEqfup0vNnb2UZQzA8WwKKiDJJnbzJWscEhXXpUcKMLHWG9/fHgzuWsUTB/wf9AQ4IUdaQSSUEIsuC4grLA66wyj7LAyAJWDrlW04F46G9siYeQZmj32GpAb4MIBoQ6ntcwfIGixosOMSBqDybqz6B+DKpe9I2cRLry5JVpW3KJzv9MGHHmYT1n496Lk499OOJc5yuHojjR949dV8sOSaU7EoS1+qqWYnPGb+dkD85F3IB55+IWPi5kzT/fF89BIVMxfnozYd0du8fPz3iMJP7WHw9cHJ3t4yOcxVFQBFQBBSB+AiwTebdd99tx8ubHYnQsmVLM3/+fPPII4+Y++67z/zud79z9yTGE0880TRuXOwYWK7pr0nCqCLx8LeLzySdbJ/LJI2KDgvZMHTo0HKTDRWdz2zizxnZwKz4Z9YbPiID4bAMFViljxlGtg8bWGLOjuk2+5e3tc7Jbj2inlN2efZPdtsxmXGbtKjUygHrB1m7f+9J3Z1vB/w7sPsFSwywUvDJBkiDKMFTPkofStED1lO/vyPFw1/NcUQDBAgKxxPfznHRsDxCTMvHlcy0RsUfdl1mIz+3TiPx6wDpwkAaRZNBMuvwUfaRybbcmBuL74Gb7C4b7BF/jfV3gTIdR/LqFM/AiQIW9syBdskEiiOWAshBPeIrY2HxpbsmlhuYgKMI+sKOC9zHr0UqsoF6IOvWed4nGvz45BgnmyKYPvtWE3I9k/rAMyw/oZPDNB1SLI5ghUL9EQzkGZRkiB4kqu5LWH6pMyJCGvxo6/9RJduOjiwxtZcwYb+igEHAoKwJCUBdYAcTrBWkLvrPs/MF2FN3H7ZOJMWxqB+GZTCEefPnRQmy4XV7jOxhfXTEFRxaQjR0tTsJ/Oe4LgklMup5yDchNWkrtF36pIN3bOqWREQ9l+o6lhRs2xnsS9ilBonbJwSf99PMpO6JbxEIEEhEsVrCagbpXGLh4MdfGcfZ1sM4eUtXDyAWWZbhllZZ6ywIYvzlfGWdgyIsDYojA+yuRggOTyFEWYZD24RER8SKjG8GwlKauMK3yRdIT/xsYDWH5QzEnC8QG/QJ0i79e3qsCCgCioAikBqBcePGmblz55rrr7/e5OUVfwPy8/PNueeea7766iuzZk2xs2qJ5e233zbt2rUzzZs3N6NGjTKvvfaaWb58uWnatKk55ZRTDBYRvnz44Ydm+PDh5vzzzy9DUowcOdK8+eabprCw0FlOzJo1y5x66qkGwuOjjz5ys9ib7JbgWFUcffTRZv369YZZ6b322svdxyJj8ODB5sADD3RJUpaXXnrJxVdQUGBOO+00U69eqQNuAlFWLBBYLlKzZk2nuPbs2dPcfvvt5rzzzjOtWhX7A6JcVexETJ06dVx8s2fPNvwRL+mTzsqVK80OO+zg0iFu8vrkk0+a0aNHm9q1a5v99tvP7L777twyY8eONa+88opZsWKFw+r44483HTp0cPfkX1jedtttN7md+E2VThwMKPvTTz9t5syZ45bL7L///mbXXXc1jz76qOnYsaPDlDCQUCeffLLZbrvtXJl4l5dffnkiHxzwDl544QV3v6pdB9y3b19zxBFHmGrVqpl77rnHDBw40HzyyScu3qOOOirp2UzecZ8+fVx8RPDnP//ZnHXWWa6ekPbPP//slv6A94ABAxzW33zzjaunkBM33HCDe49+4ljxvP7662bt2rVm++23d/FVr17dROEXLMugQYMSGDZrZncqs++zffvkpep+eumOsyYb/mm9mTP4Q1hrLLPUnJ9gnRdGyXl7tDXXvTrR/NM6fERRZxux96wTOgZzePpGeWFGCqX7qlcmGPw5OMdldtYf+dr6SDjAzkq2tDOcWCRc8/JEO0vZxJqybnLLHQiDhQWCAzQUbHa2GGQHc7IdmrtZ8g8FC1NrZpSetcsjWP/vZhjtoJDt5BCWZWDym19ieo2FBeujbbHNyyOKl28QDuW4f8n6ac6jBGsF2YHirCdHu4EmyhtlRilCOYJcQHmljOwTz2CUJRVYb4BPT2vSC2ETR9rZLSVF+cL3g2++LM8zSMe5oyi7vj8ICRP8xTFmmLCNqTgJDLtPOT+ZsNTdQpkNCk7/UMTB3x+cMyuN008RyiLKG9v4MestW6VKmOCv1C3KGTY7nkl9IO5/23aAXGTrSFyygfAX2G1c37Prv8FCJG7dlxlWdotgucCxdvaepTD4uoAsuu29adZUvoojQiTuqF9M6o/dqaUjmvC3gtK80DrIE+d4ongFn/+kxHkePlQufT55mQ8Y3m2XsBDXS7Z9sBMLXvbZwQbSgHLu6/kbCcYdPMchJDJjyRpnneDfx8w9uGyK2XYsEcDiBrtkhl1AaCtYIYj83fYJ1HnxGSDXo35pl5ABj9jdCXyJ0ydgnZXrvog2DAk5yloInf3kGOcoE+VXHGdeMih5SZGf54o8zrYexslTnHpwSv9WdpvlKeYOu9vPt9bPD8tuaGP4tghr72HpYlWGJQ5+Gs60/fPu1sIOHzp8o7Cok2WBOOlE2sdwLhyWjlzDISu7v4hcZv1uQNJhleOTqXJffxUBRUARUATiIYCCy1IJIRrkqRYtWpgjjzxSThO/KMvi2wGlHcW7e/fu5r333jMPPfSQuemmmxJhMXlHQb3gggvKEA0LFixwyhrm7F26dDFvvPGGs6jYsGGD+eGHHxyZgKKLAvjUU0+5/O2xxx5OwUeRPPPMMw1LCj7++GODQk6esMbAxB6F+dVXXzWPP/64ufjiixP54eDll192cZ1++ulu2Qjh+vXr55RR0j3kkEPcBNm3337ryJPx48cb0jvssMPMQQcd5Mo4bZodVx57rMsby09mzizW9cg7+Fx44YVmypQpjoiBuAFbFHnySVoowPfee6+58cYbY+WtVq1kS9eodJo0aZIWA4gKSASsUy666CKXTzBBYYZAggCCwGF5B2QKyjdkA8o5YYKCsg9G4Anx8OyzzzqihfewZMkS89xzz5nevXs7EsJ/NtN3DIkBmUA9OeaYYxxhA+EDQQXe1GPOwRsCgTxNnz7dQKQE8SMflAdyjMlQ6heEBURSVB3yywL5xfujnkGgfPfdd84K6OabbzY1ahRPXvtljXOc2v4+RQw4OUSR5w+iAXNrFD52o5Dt7WS3BD8aZrJ/t3+BU5iZFWa7MwZxEBSyXIA10ijHKNd4gh81Z1VitharCGYTb7XLMHC6iNLI9pAQAHiev8rOQG5fso4ZvxLEwzPMrkYJ6f71sO2c3wLKg5KCootiSl5ldh2lgUErCi7LKyAndmxdz+WDuNkmrQqaTogEr4MTg1YUaAaUKMlYKmBSK1YM/zp6exeGvBT7jih0g917TrS+CKzCFowzJFl3CTNyCBRE/GBYWJzAbIqIZ3/eoyz1kHvyW8WUhgeHsD/fwZo85/9+OXm5e453EzbbKD4keAark9IUTaLO8Z4QFNuTrMO222x9QPz8SRndjZJ/sk5dtsyUe+RFJG59kPDZ/GIefcU+ZVnCOHW/b7uGTkkGe5QUlt7w7iBuKAeEGW1L1oPLK/bftZ9nrHmOtstVaIdYM0A0UL/+aC2LpC374TkeVjLLSx6kH5BfmfFlNvY2u+SBmWFIMogG3tcd1jGjzIAH4w2e48NC/LyQP0lDfmVZRPC5a/YrcI4TeYa+AaKB9sWWkBAR5Bs/DNwPiuAVvH6irWdBq604fQLx5LovIs6/WWzZYQN8sGiAaKAfpi+jn8pGvGaQ0ePyXHnqYaoE49aDIV0aO/KY5Vj049QTsPi/gPVUqrS4d8vhnR3hALb0QXxnsPChf6Z+EC8kBv2kLNVKFyf34xAedw6daf3srLO7NM2073ZjRlsJx8mDhlEEFAFF4NeCAFYFzMJnKsOGDXMKH8p5p06dnOLGLDbWBwhWEW+99ZazkEApC8r3339vIDRQBgsKCtwMuoT58ssvTf/+/d3MNJYGKL8o/CIo+qTJs8jSpUudwodFBIogyj3kAKQApve+MOO+bNkyU1RUZIYMGWLOPvtsZ8HAzP6PP/7ogk6dOtX9QqIg5B9/FSjdrVu3dooz6/2Zbcc6AqVcBD8XlGfvvfc2nTt3dvlGGSVPkDdYhUCiYAXw00+lk4M8H5U3idv/jUonHQaUjfxiGYBlBRhQLt4XFh5Yb0BmQJZgpQLpgEDs9OrVy8+Cs+SAnED579atm7t/wAEHJL0rLD9OOumkMlYcmb5jLGy6du3q0u/Ro4cjh3hfWLVAnJAOeIM1Aqly6aWXGiwQwDookGSUlzLxTrDk4NlU+ElZICgglSCfGjZs6Pw/EL/v8ySYXrrzjC0b3riwT7o4E/ejwmKZwB8KNhYJmHKL0zIexvEke9rjj4EZQWasq9gB3smWFGCNMiQDyysetNumMWM53w7MGPChSIpzOuLBGR+DTxyGyW4HXA8TtnvDaSCDS3bAQCli1tImmxDy+F+7jSYKzlJLsGAxQLyY87PXOkoV1gEfX94v8Ywc/Nt6GPeF2TMIB5SdGdaKgm32gopMMExjGz/5EgGXsLTkvv/LMhNmQV+0SgmED6b2Ym4v4fCz4fva4DpKEn8imMvHTVOewaO7vw0f72RIl7IYSXh+gw4cU1lK+M+ly98jp+7gBzdRdTROfSCiF87t5awturSsmxSvnODwMwovaQcSlt+4dZ8tA1F2NljLAqnbLK1hVpS62NkqRljf+LP+OFQMc6oIufUb61wT6wwsSXgOosAnYPw8chyFWzAcM8Rs/YjyRLzBOi7hseYJw6mlre9h1+W5qF+WyLA8B4eUkB+tG9VM7HCAv5hUErVLBTjxvn2J0ycQPtd9EXHy3vGzwXIaCCcUX/oRX4IOVbnH0i3+fAm282yfo//Nth5+cGlfP0tJx5nUA8hCvhVYhdGHQ5yFyXl260n+wgSimeVstK+p1qKmhe3XfYIMkhwhnUwkDPtMntewioAioAgoAvERYDYcZT0ozFIz4y0KffA+yhmKtwhKF4qmCOQASh6zyyizQSFNWbLAPfIhQtxYC3z99ddyKUlhZMkGQvz8ofwtWrTIWUb89a9/TTzDAcSCv5SCmWisETCL51lIAywrsDgQ6woUaIgE7iOUTQRCwJ/hlzDch3hgJw+RNm3aGCEuOBYhDpRayunHFZU3eU5+U6WDv40wDORZcA8+j7KNBQPLALAkgaSBbIBEwCIDxZqlMqLsS1wQVYi/dIZjLAt4JwjYhkk279iPh/wgLAfxhXSpbxBoUk/8+3Ls4w4ekBOp6hDPSVkIh7CkxxesH7KVjMmGbBMKew5lOUoYxAfXiqOoBJUVFHL+ogSFKWqWPuwZwoqZbNh9rqHw+0o/SohYU0Q9E3WdgbBsf1meMFHPynWWYdz5yUxnGswsHaSISnoE0tWHC/831mHZq2THlfQxpg8Rt+6jEFl9M0l4VnY0SboR44S2EndZQYzokoKE+XRIClBBJ5CQ6dpXLpKO0ydURF9E3iFxKuq9ZYtNeephtmn6z0ESi28F/3qmx5Dawe8Qllssn6P9YdmioggoAoqAIrBlIoCCCbGAgsmMvMjEiRPdcgZ8JYQJW2Niui7CbDlLJmSnAMzqUSixbsAEPuhUEgJAlh8Qh6+oNWjQwAy21gzi72D16tVO4ZW0wqxQIQRQMpmxR1AeMbHHrN4Xrl9yySVu9n7EiBFuuQWWC5ANrN1nthyyQeLxn013DI5YBaCwIyjplBMCAtLFF5RlyulLqrz54aLSiYMB+eF5lp1AeiAQMuQFXLHmwOKCvIMHecfnAvUEpdwXIXHEZ4fERfnlHeHzIkyyecd+PELqYL0g75g6BPnDew9bOuE/L/nzr6XDT8pCONLBd4TEQ1tIRW746YQdh0/5hIXUa1s1Asw4ymzmSz8Wz8pt1QXaQjLPjPDD1loC5UZFEVAEtn0E8K+DRdqJdktVSBUVRUARUAQUgS0TAUzpUdYee+wxp6QxM4w/Bda/o3gGFUwpBWbzEBTM3LN04p133nEKtYRn5pilB1gssI4/KDvuuKMjG3CmCJmA7wQR7n3xxRfOOSX5Yd2/b+Ug4fxfTOsnTZrknB6i7H7++ecuXd/ygPCsyX///fedGT4kCGFRvBGWgXz22WfuOOi80V2M8Q8HmpAGKJ8o7ZjeY66PAkxZEZYsQM5wz5dUefPDcRyWThwMIFbABBIIbMkX5Ap5RFhWgFUKlhiEY2kCSx6CSygIC3ZYMuDkEwKDMuG7Q5afECZKsnnHothDhECUkPYHH3zgFH6sKfBFwTKQbCUOfsQtpBx1EgwgzbCUkSVE2aT/i1o2ZJNhfSZ7BFi+MKCgT2KHj+xj0icFAZ3ZFCT0VxH4dSDALitvXtQn7ZbDvw40tJSKgCKgCGzZCODAkV0U7rzzzkRGsRI44YQTEuf+AUofCiWWBzjKQ1gWgC8FEcKgrOKE76677nIO+EShJQzx4xySdFHO5R5kBX4EUIJvueUWFx3LLfBzIMqmpOH/4sNhn332cQon13F6yTadwWfwm4DpPWQG6bKEQBRpysQ1/EX4EozDv8cx9/mjvOw6QdwIjhF32WUXd53lKDisRAgnTg5lOQDXU+WN+0i6dNJhwPKCM61zTZwiSj4pr5RZiAKsGhCx9kARDxPeLyTJH/7wB3cbPIO7ToQ9l807xu8FFg3UC5xC4gvi4YcfdjupgCl1iOUv4nsjLF25FvZO49YhrCbY6YR6BGlDncWih/xlK1Wsc5HihSfZxqDPKQKKgCKgCCgCioAioAgoAoqAIlDBCMybN885UMw0GZYAsPSBJQ9ioZAuDpRzZpVZVpGJYD2BXwAcM2JZwDnEBabpIswUcy8TB5bkB2eCYuIvcfm/hMHfAGUU03juY2Fx0003mWuuucbtzOA/k8kxM+/gway3L1gSkK4sWfDvyXFU3uS+/xuVThwMiAdLBMovyyn8uDM9BnPiiVtvJP5s3rE8K7+8NwiAXJSDOOPiR1gwpK6FkRfcjyP42UiuKXGe0jCKgCKgCCgCioAioAgoAoqAIqAIbCUIoBxjoZCJMKOcKdFA/GwRyNIJLAHYcpGdACAefCFMplsJkp9URAPxEwbLB19Y2sDSC2a3yU95hDX9YYJCGnVPwoflTe4Ff6PiioMBcYnfg2C82Zz7pE0mz2fzjoPxZ1P/gnH453Hx45lcYaiWDf4b0GNFQBFQBBQBRUARUAQUAUVAEdgiEcjWsqGyC4NjQtn+kSUHQQKgMvPDbgzgNmDAgCRrh8rMg6b160QAywYlG36d715LrQgoAoqAIqAIKAKKgCKgCGxVCGwtZMNWBapmVhGoIAQgG9SVdgWBq9EqAoqAIqAIKAKKgCKgCCgCioAioAgoAr9WBGL7bMCJCVuRRAkePdkqZksWtnwpKChw+6luyfnMNm94KGV9EFuuVJbgOIdtZBYuXGiOO+64Msni2GT8+PFub96uXbsmrefCGy9OV0RwfsJ6MpEZM2a4LVfY27VLly5uHZrcE+c7rCfCu2wuHKfg2Gbo0KHmwAMPzHgdneQrl7/Dhg1za/OC2wflMg2NqywC1Du2u9prr73K3GQrIKmPZW4GLuAsia2qfKG+4nk6KMRL/+nv1c2+1eSFLbZoO6yz2xKEvZ7ZKurQQw8tl9MgysIMFWameLLeGuSHH35wDqLK28eOmFVoxs1flShyDftudyloaNo1rp24luqA7YsHdmpkWjeqVSaYrXbmuR/mmXo1q5nDepZdmytpD+yYZzo0iZeeJLJ+42bz0oj55uAezUzD2rGHD/K4/ioCioAioAgoAorArwyB2KMFvFd+8sknCXi+//57t6WKOFvBU2ZFkw0o0+wz+tBDDyXykckBz7GVSPv27TN5bKsJ+8Ybb5gWLVpUKtnAtiwff/yx2XvvvcvgNHHiRHP11VcnPNbanU/Mrbfeatj7F7n88ssN16g7COvZnnjiCXf86KOPGv7YA3fy5MnOsc5tt93mSIUXX3zRbWHEPfb65X3efvvtaZ3muIhT/Fu6dKn5xz/+YQYPHrxFkA2vv/66YS/kX4psgAg69dRTHdbsSRyU8rbHYHxbyjnKL46dIBuCZXzllVfc9kOQX+kE07FzzjkniUAgziuvvDLpUfrVG2+80W2fJWQD/Rx7b0v932mnncy//vWvRFtKiiAHJ+netZ8EbY52csghh5SbbICMoaxbC9nAftu56GM/Gr/EvDVqkenUrI6Ddn7hOnP3ZzNNZ3v+4CnJe5P72MsxYVs0qBlKNqzbuMk88EXxXty92jQoQyj89d2pZvGq9SavTo0y9yT+qN/Cog0u7l0LGinZEAWSXlcEFAFFQBFQBBSBBAKxyQZmzP/9738nHhw0aJDb4/Wwww5LXKvog6KiIrc3bUWns7XGf+2115Z78J9p2ceMGeMsGtjvNygoEexd+5e//MXlC6KB/YghEdhOBWsIlLomTZokPTpnzhwXBgWMPXWZ3UXp/fTTT53izV7Jf/zjH81+++1nIAjYU5d42A+3PNK2bVu3pyzb9qgUb4/DO1q3bl0oHNtqezzggANcvaPQ5Skj1hHsycxexVECvnfccUfS7UWLFjmigfayxx57OLLtrLPOMli6DBw4MClsrk4gk1O961ylo/GUItC8fk3zkEcsDJ241Pz57SlmzLxVZof8eqUBy3H0+k8LzaWD2yViIG6IBhVFQBFQBLZWBNj1gG/WlmLtt7XiqPlWBCoLgdhkQ7oMvf/+++bxxx93Zu/MzqF8YkWAB9T77rvPnd9///3ul5lorjFr2LNnT9OnTx83qP/Nb37j9pxlcP7aa6+5We8hQ4a4GXC2bOEZzPJPP/10c/311xuWboSFZc9aFKQHH3zQkC8U0iOPPDJSaWIGkllDlgOwxQhECrOPxMM+r//9738NSzAwnz744IMTSi2D8//85z8GKw9m54866ig3y09cPHfzzTebxx57LNEhojz079/fKcmp4kWxOPbYYw2zqBMmTDA777yzKy/m1OzZSpzvvvuuUw522WUXc9lll7nZffDPy8szJ5xwgkkVB3sNY5EANhwff/zx5sMPP3T5DVp9cP+pp55y6eFZF/Nh3hPLUbBM+Pnnn50yNHPmTLd3r19PMDn++9//nljigBdc0kSmTZvmsGY7GZZTYCEDoYUQJ++Bd4+Qp913393VF9njFhICoa4xK8q2QkGygXipJ8TDjCRWFLvttpvLN1YU1DHeIXl4+eWXzR/+8AdnIcFeyJh3Q65hsfHMM884nJilZjkReBAXSikY8MGLW/95X+QV0kW2lHnrrbecWTr1IyiY0v/2t781YMksN6QLZee9nH322eaKK65w7Q6i5Pzzz4+sq7RD0hRsqeNXXXWVq7fUM8oEFrzXiy66yGDJ8Pvf/95lh1/a8hFHHJHI3gcffFCmPVIOZvyFgISIQpF+4IEH3BIsltOAH3WNtsRezx999JEhLtoP+We2HMGaBbKKttW6dWtnsk8eWJoQLPdpp50W2X4lw7QbysU7RGFnycwll1ziynXQQQe5gcull17q2g3vFmsdFP1gn0N8kAg8C5aQCeATZlbPHtss8aH/YbkRddwfHDFYwlqHPNEegyJWY2CFhO1zzNK2uLiSHn0iz5An8k7fwbKOsHdNvNQZLD3YKotlUj6piBXSs88+6/rkww8/3NVFljOl6ttGjRrl3uvo0aNdXRMrp2DZOacfpV5DstBmglhjEUL/Tx9JvaPPo58Iaxtgl5+fb4YPH+7qVK9evVzb5ZtE3NT76667zpGZ9HNYSn377beubIS94YYbyix/EasX0qcfoh8N9kFh5Yq6tnunPHdrpF1iwYKZ69+YZF46t7d978VPXPfqRLt0Is8c0at4acQnE5aYO4fOcORBe7v84tr9C5JIip3bNzTvjllkfrNXu0Qcr41caHZsXd+MmrMykY2lq9ebv78/zQyfWeiWXhzUo6m5YI+27v68FevMLe9OMePn/z97ZwGvR3G18RP3hIQkRIAIIcGCBXcrUCDFvbhD8RZo+QoUhyKluBR3p1AguAQnkGAJCSFCQtzdv/1Pci57N6/e+159n3N/712bHXlmdmfOM2fOzrV6UUa4JhECQkAIVCUCjBeYsEr3acSqzJvSFgJCYGUEGNOUW8aOHWtXXnllUMRRWhj8o7DNmDEjrMlHUcLslkE9ZuGnnHJKIApQmnfbbTd75JFHDIUf4X4Gw8xcM+BD6WBADiEBYYBABKAMpQtLGBQcFEAG0SyfYJ0xM+aphAE4CgaD5xtuuCEMMlFEUWzIB+VA8UHBIK+YtzNwx5KAQTv5vOyyy4JCjhIN0YFywX5cSJ+4MsVLeMpM/lGOGOyjcLJ0AAEzFGcURTBmYI2SjIAh+UEyxYFiA8YMriED+vfvH8KjkCUFAgOF4vTTTw8Y8H3fU089NShrKIcM0vFx4HUTvx9CBOUaAS+UA1cuwAbiCIX9iCOOCAq0ExHM7KJkxBUz6hs/Df59YS8ncXMeYiEpzEqTDkQR9UMbRFG85JJLQlDiAFeUXxRYlH/Ck1fqD2US5QSMUCRoG4QFb+oeJR2FOp/2D1mFGTrkmQttLd1SCaw5wJB6x5IDAmvQoEGhDZFX2jfLl3i20rVV0rnwwguD+TdWJZAa1A1KHFYjtF/q8oknnghkDGFRniCVEMg9r8dwIvqX6nmk/aGouYA/JAdCuwcvFGfabY8ePYKiS/uFsCP/4MwxcfCsEZb88r7gWSd/PDvxcoNnuufX88EWIgvzd9oCgrJLPF4PEAPUN0orRAR1mqqM3EsbgFDhvYKinrRMIAzCM8izBUkDAUg7RzF1AQ/auJMzfh5SEcWe+rz//vvDUiQIR392PBzbfHDl+UI5p+2CJ22O9xpEQrKueTZOOukk69atW8gDz8cdd9xRgh9pQxzwfsICiXciSnumdxvPNe8RMKZe6Scg+dIJ7Yn80UcksYaEIl2u8Y5ieQrPAnWYqo0QF8QCZBbvP/ICyUO7Ji9YV4E1QvtkeQfPO9cgT5577rlS2SQN3ikQEbRLSBvi9/ZeKnAOB9PnLbaHPlveP2231io2f/FSmz53sS2L/lx+jRR/iAGXD4dPtxO26Wy3HbqOtW3ewM5+5kebEcXjsvu6q9q8RUvts5Ezwin8LbwTERT7rSArOEns5z471Ej/1oN72VmRFcSzkT+IJ74cH+FodubTQ0KcNxywtl22d3d7K1r+IRECQkAIVCUCjAOnTJkS3s2M1yRCQAhUbwTqFyJ7sIwM/JhBZu2vz9gyEHZhhrlPnz5B4WBwy6CTgTYOARmkujBwO/HEE8MsFOeYyUW5g2DAkoEZb5QAJF1YZnuffvrpMKjeaqutQlgGoq5ohBOxf1gwMJMNGcC3cBlUIygjzF4xu8nLjbxivYCSwIwlygSDdrcGQEljpjubZIqXGUIEZQu8EJRMV6YZ9HKNGXoEwgDncihpSUkVB4NkFA7wwMoCYeYcxSKVPPPMM0HZYyYc4T6IBZQ0FD2WHDDjSN0khbpCaAdYCUA6oTwgdBBYJECowE6jTENYMXhn9jtONBAeZZHz4E77gTCgbTD7ConhaRE2KShX3IdQRyjWKJMIhAfLMigHindSmH1H8UbxRZlgyQbr55Hrr78+lI0Z01zbP/fhWK9fv35BwSYfKCiUPZUwC4+SitD+IBqwIHBywq1YIFHStVUULJRJFFaeTRQp8KJeeHYR/GvghJDybb/99sGqh7pAwC7p1JA6SD6PIXCGfxBTzD4jWGdQb7RR8oTFChYk1AfnyRfYcx6LDp5PSC/Ki3i52Yc4S/X8ci0u+OKg/fEMgCMKKmQO7QpSCXziMyXpygieEGzIgQceGNpUPB3fB2PyiUUGBOTll18eyCKeP+oLkgTyMmmxANGFpRCkC+GcWGNwlawH0soVV3DkOaSckK+80yDqsOxJ1jXvONo97wasFSCHKE98yRPvZKxOaAe8i6mDTO82yEziRDEnTvJD+yetdJIOa/zTUH9uaQEpAtEA6eG+g+JthPixFnOLKN6fgwcPDiQi17Bg8nywz73gCjHIuyFOohGeOgM33hnUD+9H6tOtUQiTTSbNXmi73DKgJFi9unWCQ0ecROay1OGwPqvZnusttzS4sm8P63vXQHtryFTbp3fbECcOIiEuXoyWUmzVrZWxTAPZoUfr6P+IsD80slgYPW2+3XRgT2sROXzsFf32iEiK5wdOtA07Nw/5wIcEviSQi/foZn/7709hX/+EgBAQAlWBAONB+iKsG+gX6dMlQkAIVE8EGGsWhGzAJB1lBSUdCwEGkUnxwSwKPQPDuDKJtQODTmYUXflDAYwLL5S4ZArLQB0hXhcKy+AxlTDDxcsKZYD0ySsDYk+Tmfy4MOjG5BxhBt6FcuUiDMqRVPH6/SiwLpAhKOde5ng6hIMASSXp4iAsBIELCkMq4UUOHvEvRJAX0memMBeBlLjqqquCcgyh4HmCVHFihXiYOUXxQgGgI0HhigskFoon6dM2iAvrDxQLyCkU1HQSx8vbgNcBdZ3JRwNtG/GvXaRSJvJp/8SFAnXMMccERY+vX0CIMeueSpIkDseQCi5OvHl5UrUp8o6yi3LoDjX9fsrDDC2KPAQdbRulzMkZD1eIbVxRJU+k5cSk40s6KMDUky+Z4RzKLkod1hKIl5v9dM8v1+ICzpA6KPCQC1hMsEQAawNM5iEjcpG4wk97TCc4R3UhHO0UootZdZ4JZuV57/GjXLxDqQ/yw5IurG5oF1yjXrECgJxJSq64QqRACkDe8j6MEyvJOGlPtLV4vfiyJqxCkPizQPkgbTK1Q+qVviEeZ6q+Ip6XdFiDmROgHh6LiVTPhl+H6HAhD3Gnp/E8QSLwjoEEBH/6tHg+PA580WBxAXGOQGTkQjb7/c0a1bNr9+0RDlmi0KN9U6sfEQ65Ss/2v/l1aNygrrWPfECMnVH6vcmSi7+8MMxmLVhiLw6aaLv1amP16/2Whoc/77nfLG5IH+JjQmRJgXRfdTnRwP7aUR4lQkAICIGqRoBxIsuGJUJACFRvBNBdC0I2MMv02muvhQEaCj5KelIB5sWAMEPMemiUe5/Rw4SZmUxXPDCndkdoKJ0ooCjHDFZdMoVlhhZh4ItPCIRZtfj94eSKfyjOKAIsTcBaAbNolm5gjYEwc+rpQTKgLHjeidMV6LgC7tfjs/Tch2AmjaSKN1xI8w/lDHGig30G3czIocTkIl4OSB9fZ55qRp+4PD1wxOIDoTzcG1dwwoUU/1iPj9k/yk3S2zxLVpidZ5Y1KSgxzJCiZMVn3iEWIB1QxlCcUQoQlkfECYVkfCjYft3bgHdSmYiGZDzpjvNp/8RBXmiXECRYOND20okTZ36duvD2wzmvIz+Xqk2hxPJMsXyCGV2IPl9bjgIP2QRxwwuBZ5O18pANcYXe08+2pX24MHteFkEpxJIHgs1JSeqQsnp78HITf7rnFxP4uKBcYzaPNQOKJM5LwQPLIMzy/XmP35Nq35/tVNf8HO8trCjA2d8PcWwoF0QHPwRSDyIAggWzfawq+CGUmecv3XMaAuXwjyUPEIiQSmAB0XXttdemvJPnA8zjAtnBs+nPTiocMrVDysq7I16vTk7E04nvp0qD62CTzB/vQk+fMPE2wnEuQh1hCUX/heUS9UF/AG5xoT/hXQR55ZYyLHsh/VSEUPxe32/aoJ6t37G5H6bcRh+WsLr1ll+KL6HgzJjpvxELS6P+FGuINk2X97Me2aaR3wZIjcc+H2eDI+eQZ0b+G+KyWvRFC+TFUzYq+boEfhomzFpgdaI/5NcZC6zzKsstoNiXCAEhIASEgBAQAkIgVwTq5howUziUChQ/iAQUFmadEZ+FDAcr/mGijbLMLDaKH2avzOIhDCxZ38yaWgaOxMX6WeKrX79+mBFjUM41BoXpwkJsMHtMPMxiMjPFDBT3phLSYEDJ4JEZVZRBZrqY0YfkQGkgLwyMcRTGLCizfihpzIBhYguR4MsvSMOVcdbGo3hACDDQRjLFGwKk+UeemF1EiQID8CPv4BmfBU5zeziNkgM2rE9GCUWpZ818KiEs1gcoopAwlAMcUVB9GUeq+zhH/UACYDbPsgPyyI97EQgpfAWgQHm8nEep8mUKmCVj6g+GmEhDQDGDClFBPqgTZqhR9llqkk6oP+qHuFgSQ51Sz4WSfNq/p0nbpR6pQyfW/Fp8CyGBcgxGPCesu0/O6BI+U5tyE3BmclGSWIYBiUGc4A9RhSIIEeWkEnXvs72E5dlICtf9eWQZDwogeQVr2jpr6csiOPmjrXA/eaSd8iyz/CSVpHt+U4XF9B7csWihLdHWeL9A/sQVVb83WUY/n21L3CwbIm+QB7R7ygBhRjo8U7Rj/0GwsDSI5w0ShPJj2YCSC9nKM+B1ky3tdNd55qgjlHDeH3xaE+FZTdY1zlypdwgG6vjjjz8O78h0VlCeZqZ2iMNN2gVLz6hXlkCxXxbBisAdq2K1A3ECuYm1SHmEuCgvlgxgwnvH3+FxU10IE5YmgQ/PCgQ399BPIbxz0i3byyV/fKkCeW7ghOB34dGILJgTWSfEhXPfj4uszxYusbujT13yycud1v7NeoOw0AV7r9/Wnv5qghHnOomvXPRaLbJwiZZOXPvGyOC3Yez0BXbOsz/aB8Om23odmwWi4up+yz+Vyec5b3xrVDwL2hcCQkAICAEhIASEQEYEykU2MMhCmLVGiWENP4osA20GtZhnJwWFB6sBZmAx14ZswDmcWz6g+BCGdc7EhTLMumGICExuGexyjUFgprCYtmJ6jbk68WP6GzejjecLL/SsYyccg1hmOn1dMTNXzIKSF5wEMqBknTADUcrBDCwKwgEHHFDKLJnZTJy8oXCDD8sJfCkJ96aL1/Pl2Pqxb8GCuMEA/JC4v4X4TGC6OJh5w6oA83PMs504cIXD02LLdZQcSAPKgbLLzDf1m0nAhUE7ih3r2v13bOQTAGE2H18X+F0gXpRqSA9IGuoJE2UcYeI8jxl50scKgvLhk4P6p07YJ65MCjuzuNQPcZEvsPeyxvEKGYv+cS6JnYeLn/dZ9nzav6cBYQA+5Mnbvl/zLWnRZlhnTxrMuOKs0c3ZCef5ydSmUKRRqnkWIGUgBEgfvFkGxPp0FCcUceIHV/x58BxjCYBliiumnje2yeeR9gjxAtYsi4nXCfn0vHIv+46fH7NFeP6xxOBzppSbdsoSCLYu8bjSPb8eNr71tu4WNa7A75RmCUWyjKnaRjz++D7LsiA7IUkgl1DqOZdOvI1RVzyjWCLwTmW5B0o075Ok5IMr9YwlDT4JwJL6oQ2STrKuIQ0gViFF8E8BqUobgYiNY5/MT6Z2SL3y7OGbg3rlveX+FZLxcJwJa8rA/fiQARuIGt75kNku8Xwm4yKfjrenxRbSnHcrziF5zrD84BmFOHDygPtou1gy8H4jL9QxJA7vOYTnxa1WwonEP0iATIIlwa7Rkod7IhJh7zu+Dv4WVo98ObjUi5Y5dI2WN5z59I+2z50Dg4+FC3fvavEwUTaD/GGFQ8i+K3w5eBxsWbbxz/3XtmET59oB9wyyox76zjq2bBQ5nuwUrt10QE8bN3OBHXzfN3b4/d9ayybLyZR4HNoXAkJACAgBISAEhEA6BOpEs/4F8azCTA+KBia2zO5g1cA5N3X3DDCTy7paBpvMhqJosbaZ9bbxpQDcD4HhZv9+f6ptprDMoJFOtniYtWLmozAkxQAAQABJREFUmy1liA9ESZMZRqwHUlkQMHPJYJ3ZXwammEMzsPb7iDOduX6meEMEaf5RLuJN4psmeKnTH330USBNWPJCOZk5xVEjS2FYy51KqAssRNyEOlWYspwDM+JOtX6c9sOMMD4RfMYwnga4k18nDuLX2McaA5ICggRFj1+qdJL3leU41/bvcVMuFFBm1VHsMonHDQmTrqzx+9O1KdKkndJmiJNwjgfPEHjSTglTViFeZtCxcoore2WNjzzz7KYjZDzebM+vh6uKrTvizBdXr3faf7by51ouZu6pZ0g96oc2QL5SPV/ECa5YxqQjajOlm64dEif1yrukvG0EjMhfIeKKl4V3Hf2G+2yhDnlWkv0C7xTKAvmQ7h0fjzff/Znzoy9SRD10qzRK/oLoqxV8gaJdtBwiG4GRLW3SalQ/IgGjX1L4MkaThqmvJcPqWAgIASEgBISAEBACIIAVesHIhlwhdX8OKOXMLmJqzKf/WFYRd+iYa3zVKRwD0iTZUJ3y53nhqwoo4Mw+M1jGXJ3P6jGTWZskTjbkq+hVJA5Y9fD5RASrBYkQEAJCQAgIASEgBISAEBACQqA2IQDZUC9SMC+rzEJhaorZKebFLG1g5ohP3GGeWxuEWTsU9+qk3CZxxUyb2WI+/8f6esyQsQAo7yxjMp2qPmbWkxlh6iMXi4DKym///v1D+8BUvixO7Corn0pHCAgBISAEhIAQEAJCQAgIASFQFgSwqK10y4ayZFT3CAEhIASEgBAQAkJACAgBISAEhIAQEAI1AwEsG1ZenFkz8q5cCgEhIASEgBAQAkJACAgBISAEhIAQEALVFAGRDdW0YpQtISAEhIAQEAJCQAgIASEgBISAEBACNRUBkQ01teaUbyEgBISAEBACQkAICAEhIASEgBAQAtUUAZEN1bRilC0hIASEgBAQAkJACAgBISAEhIAQEAI1FQGRDTW15pRvISAEhIAQEAJCQAgIASEgBISAEBAC1RQBkQ3VtGKULSEgBISAEBACQkAICAEhIASEgBAQAjUVAZENNbXmlG8hIASEgBAQAkJACAgBISAEhIAQEALVFAGRDdW0YpQtISAEhIAQEAJCQAgIASEgBISAEBACNRUBkQ01teaUbyEgBISAEBACQkAICAEhIASEgBAQAtUUAZEN1bRilC0hIASEgBAQAkJACAgBISAEhIAQEAI1FQGRDTW15pRvISAEhIAQEAJCQAgIASEgBISAEBAC1RSB+vnma+jEufbwZ+NswOiZtmDx0nxvV/gMCDSqX9f6rNnSjt6yo/Vs3zRDSF0SAkJACAgBISAEhIAQEAJCQAgIASFQfRGoM2fOnGW5Zm/O/MV20H++FcmQK2BlDAfp8OwJG1izxg3KGINuEwJCQAgIASEgBISAEBACQkAICAEhUDUIzJo1y/JaRrFwwVwRDZVQV1iMLFwwrxJSUhJCQAgIASEgBISAEBACQkAICAEhIAQKj0BeyygWzp9nl/XtZZ1bN7benVsWPjeK0b4dO9PGTptvC+fPMWsljNUkhIAQEAJCQAgIASEgBISAEBACQqDmIZAX2bBs2TLbc4P2Na+UNSjHkDj8fv755xqUa2VVCAgBISAEhIAQEAJCoCIQYPxdp06diohacSYQENYJQHQoBMqJQF5kQznT0u1CQAgIASEgBISAEBACQkAI5IjApEmTbP78+TZ79uwc76gdwRo1amQLFiyo9MI0b97cGjdubO3atcsr7Wte/t7e+O5XWyTn+XnhVpMDN4h87O2+QSf7a9/18ypGMbWV187ZxkQ25NU8FFgICAEhIASEgBAQAkJACFQ8AhMnTrTFixdb9+7drUGDBuaz7pW5/eqrr2zTTTcNlhVVka6jjGUH6btU1DF4jxo1yiB5ciUcUB7fGzrV6jRZxRrWycsdnhdH25qIwLKlod4tqv9cCYdibCt6Impi41aehYAQEAJCQAgIASEgBGotAijWzOx37drV6tfX3GCcaKDSK+oYrLt06RKsSZYuXZpT+woWDfUam4loyAmvWhMoqu9FUb1T/7lKMbYVvb1ybR0KJwSEgBAQAkJACAgBISAEKgEBZu5ZOlG3bt2gWLtyXdlbikqaVZVuJUC9UhL16tUrwX6liylOsHRCFg0pgCmGUxAOeSydKca2IsuGYngQVEYhIASEgBAQAkJACAgBISAEhIAQEAKViIAsGyoRbCUlBISAEBACQkAICAEhIARyRcAtCnINXxHhqioPVZVuRWCoOIVAsSIgy4ZirXmVWwgIASEgBISAEBACQkAICAEhIASEQAUhIMuGCgJW0QoBISAEhIAQEAJCQAgIgfIgUB1m96sqD1WVbnnqS/cKASFQGgFZNpTGQ0dCQAgIASEgBISAEKiRCMyaNcumT59eI/OuTGdHIKl8ZzueNm2aDRo0qKRNZAtf3a5nRyR1CD5d+cknn9jQoUNTB9BZISAEKg0BWTZUGtRKSAgIASEgBGozAlOmTLFhw4aFT6a1b9/e1llnneBJPpcyz5w501AUmzdvbq1atcrllkoJM3ny5PD5vVVWWcWaNWuWc5ooOY0aNbKmTZvmfE+6gHPnzg15aN26dcog4A5mVfF5wLFjx4Y8dezYMee6TlmIAp3873//a5MmTbJzzjknbYx8zg/MqNMGDRqkDZfrBeq6YcOGKdvHkiVLjOurrrqq8XWFmiTVpW7LQgC8/vrr9vDDD4c64TOOKN9z5syxo48+2vbcc89S1ZAtfgLHw8T3k9eo67fffrtU+Hhibdu2tZ133jl+aqWw8fhpq7QbzuWz/de//hXIhjXWWMOefvrpvO+P56FUZgt8UDd6JNbp2MIWLFpqIybPsaXLCpxABUTXpllD6962qf06Y779On1+BaSgKFMhsHrrJta6aQMbMn62LVqS2ydZU8VTFedENlQF6lWY5rzohfbjhDnRS2KBdWrVyHp3am71eNtFsiR6y42Zvvx8g3p1bGy037JJfWvRqF7GHPt9naP46kf3lUfIV/MovZaNV26aE2YttPlR/l3aNGuQNW8eVlshIASEQEUicN9994XBfTyNTp062ZVXXmk9evSIn065/+KLLxpx7Lvvvnb++eenDJPPSRSlV155JSjhhx12WD63lgp79dVX25dffmn/93//Z7vvvnupa6kOPvjgA7vxxhuDgsn1TTfd1P7yl79Y586dVwo+fvx4O+SQQ1Y6j0J04oknGkTHtddea59//nkIA57kY4MNNgjHybR23HFHu+iii1IqvSslUoYT4DBgwABbb731bPvttzcU6cMPPzzE9OyzzxoEU3klmUZ544vfT34feeQRu//++0tO097OOOMMa9y4cck530Fp/Mc//uGHJdvrr7/ettpqK/v+++/tiiuusF9/Xf6Neer64osvtnbt2gVs4mlBOh144IF2wgknVAtSpqQwaXYqom7TJJXT6VyVbZTzv//978anG++8805r2bJlID+pXwhNns0PP/ww1Jt/UjObEk8Gc02f99g999yTsUxvvfVWICFzSdfTzhRhJmKAa5mu5xJ/prTLeo1h91X7r2s79WprPmpGgby+30/28qAJZYqWuDZZo6Xd/NbPZbo/200N6tW1h4/fxLpFRAOcCPmeMmehHfWfr21qtE0ne2+4ml28V0/b5toP0wXJeD5Zrk//ur2d+cS39sXI4rDgOmzzzvanXbpZ/RW6Gti/PGi8Xf3qMFslIh9eP3srO/K+r2z4pDkZccx08cTt1rRJsxfaSwPHZwpW5msra3Rljir3G+fPnx86KUz92rRpEzpuZkAkFYvAE1+Ot3s/Wj4L4yk1ixT7y/deK3pBtYheGovsuEe+t5sP6mkbdW5hZzw1xPr2bmsnbLPyINHvZ5u8L34t3/3Tn0yf5iUvD7dhk+aWirJh9PLbd6N2dtxWnaxxg+yrgsbNXGCPfj7eTtmuc0pCo1TkOhACQkAI5IAAShmziChURxxxhDVp0sQ++ugj++qrrwJx8Pzzz4fBfw5RFSzIuHHj7LHHHjNm9spDNuSToU8//TSQAdzD7OnPP/9cgsGjjz66kuXBiBEjQvRrrbVWKUKma9eutnjx4kAcYAa99dZbh7HC//73Pzv99NPt8ccfN8gUiAdk//33N8r7/vvvB4Li3//+d0Fm7EPksX/ffPNNwBSlGbIBZem4444LIQphwUFEyTRiyZd7FzKLNgEZsNlmmxn19dJLL9nChQvtr3/960rxY6WDbLHFFha3KmHcBlF02mmnhet77713MNOnzUOUkc5DDz0UiA0Iou222y4QRpAPpAW5IckdAQgExJXmTFtm8bFYoR6GDx8eiAcIsj59+tiZZ55pl112WSAcCOdEX6b4PJeEyRaO65A02WTRokUhrmzxEQ9hnJTw4+Q203XCIpnSyuX+5bEU7v9Nh2xgm3ddxW56Y7i9GCl4q7dubNccsK79LVLKvxkzy0ZNKT3WzSXlrbq3tp17rVphZMPf9lrb1mzTxE54aKB9/+ss22atNnbDwevb9QeuZyc+PDCXLJYpTLJcD378iw2bWHbFukyZqKKbDtiko52zW3f7b0Qu3P7uyOhZMDtz5272h4062Oip8+yVb8pGTCWLs+cG7W3MtPm1h2z47LPPDPMmXkgwr2wx46Oj89mKJAg6Lj8Cd3wwxl4YNNFOjpTsPddtG1ks1LMhE+ba3f3H2PnPD7XHjtvA6tGKY3LWzmvYmtELMJtghXDerl1yCpstrmzXeSFfv9/aEau6zKbNXWyDx8+xBz/91QaMnml3H75uiZVGungmz15kr30/2Y7cvIPIhnQg6bwQEAJ5IfDtt9+G8GuuuWYYwDOLiLL9n//8J5yfOnVqUPBGjx5td911VyDbMfvfdtttw0xvOvN/lEGU9J9++slQyPfZZx/7/e9/X5I3FFNmqYcMGRJm8zFPRvnlvnvvvTeE++WXX4Jyh5K+/vrrh2uZ4nzqqacMpX7GjBm22267BeXQE0TB9FnxP/3pT2HW1K+xfe2118Ihig4z5phtk19mviEeevbsGQ8eysWJP/7xj7bJJpuEGW8UJYQZfogGyBKsG1AIWMaBkoTFxsSJE0M4FNdDDz00KBPHHHNMwPaLL76wbbbZJlz3fy+88IIxo7rDDjsE6wSwA1PIIeoBwZKCWVnGKZiDo5STfzDlPKbpyDvvvBPy9s9//jPkk3P77bcfG8tUx999912YbWasw7KDV199NWDUt29fO/XUU1OmwUz0jz/+GK6hPCJYVlCfuVjMhBtW/AMD5IYbbrBu3bqFvJ933nmh3lKRDbQr5M9//nPYUjduAeHtgHITB4JFysiRIwO+zz33XDiHcstyIvCkTdC+jjrqqJXaDvUJSUS9IyxLoT532mmncPzDDz8EEoMty2q87n73u9+FdnTzzTfb2muvHRTtl19+OYwrSadDhw7hPogtcKcsnMulPYSEY/9ozzxXPF/z5s2zzTffPNQD5AvCEhbKxzMHOYP1B89JixYtYrFU3C64UK4HHnggJALpw7OAsOX4mmuuCe8D2jTvk0KRZCGRCvqXiSggyfh1yjdw4MDwLHPNraeo81tuuYVTpcJnOg6BK+AfyxBQoFGanxmw3CpoxOS5dtLDg+zqyNqh66pN7NQdu0RWvvXDDD5ZODOa2eYeZrBP2aGLHbX1GmGmG2vfq18dar07twwKaN1oGP/hBdva9td/FM5df9B6YfZ7cWQ18eLX4+3GN4dbqyYN7JUzt7D3fpxiO6+z3LLivv6jbY/121mXVZva4iXL7NrXhtn/vi2tyHZapXG0zGNZRITMC6h8PHyqXff6sIgoaRKOW0S6wF1/3NC6t2tmixZH6UUkyk1Rekm5cr91Siw6fohIi1Me/SbEe3RUpuO3XTNMHM5duMT+8fKPtlmXVVYq1xFbdDbSnj53kR0azfqDVeMG9WzugiV2TZTvtwZPsv027mDHRnFNjWbr1+3UwpZGltu3vTvCnvi89IRrMm/V7fiMiFgYOmF2sGLwvF3xv+V+SJo2/M3qHCJovaicyEfDptqfn/0+7F8XEUHbr72q0S5oKxc9/4P9HE3aPnfaZnbn+6Ps1KgtzZq/2No0b2ido3p88uQ+dvpj39r9x25sHVo2ChYsP0bLNo5/cGCooxBpGf5VqmXD7NmzA6O65ZZbho6Vlxwd09133x1egMwMQUBICovALxFb9ezXE+yPW3S0w/p0KIl8vQ7N7Mq+Pezmt0fZL1PnhxdcycVo5/mBE223Xm2sR7umduzD39uBm7S3N4dMDQo+VgTn7Lym7RpdnxO9FB75bJz1at80rCfiRfXv90bbBz9ND9fWiAiC83dd09bv2DxET16wLpgZNXAsE3Zft42du0uXwNjF00+13yhaptGhZcNwqWP0IFCG7tGLGcKk3+Apttf6be2rX2bZze+MsvEzl5t1rRuFuWKftezjn6dH5MryF82fnh5i+27Y3o7ZsmPa8K2iJSQSISAEhEA2BFD+sF5AOUP5wpyf2WAG9O5/AWUKpRpBgabv48fa+b/97W8rJdG/f/+S84SH0HBSAwUYxRVFBmH2GIUeRZwZf0iHVH1ptjgxgb799ttDnKT5zDPPhH3/hzWiK9yYw2OiHReUPZTRjTbaKJymz3eJz4z7OUgUBAXOTfGZcb/00kuD3wGugS1EA+JkBVYMKH4ISjtCGFeEPa5wYcW/CRMmlGDI2APiAjxRsu+4446giFIP1GHXrl0DWQHpwrhk9dVXTzlji5LjdcJsbbY6xidHvB49fyiovXv3TpkGlqDMSCOQP7QXFHLqnvylqmePN75lYod2iUCKIRBKCO0nKZTNyQbIIxRohNlwLBrAE+nVq1fY8g8yizbNNRRfxIm0uPUqCmCy7bBcB0ugDTfcMJA8WAtdcsklQXFGmYeMQWgfKO/vvvtuWApA+yCtOK7UL+euu+66cI8fsxyH9s3Sj2ztYd111w33+j/wO/vsswPJRFtesGCBvfHGG+E5pI2AFSQOAnmEwgv5Rv2lWori8eaydWU6W1jywLMH1uTXlx/5fRxznuuEIzwkXy6Sax5yCUeYXMKRr1zDeRkOPvjgsEwE3BEspHguzz333Lzj8jgLve0dWQ4jSZN1lD6WByBHbrm6xZXJVSOCgvX6mNKjRH/001R7NiIqzv/dWnbR79cO9224estAFtz9wcgQ7o4jewdF8u8vDokUzjZ28GadgkXAB8OmGEsiIC8ujxT6P261RiAwfolmylHwz4qIjdN26roS2fD0F2PD0o9+52xlg8fNtveHTrYXIgJj9oLFIc+3H9HbOrZqbJf990cjL4dE6X0zZma45v9O3bGr7bJOO7vzvRG2ICIkzt61u12xby978otf7fQozU9/nmb9vp8Y7XezK/db1059dFCICxKEciEN69e1RtEP5frcaNb/69Ez7Okvf7WTI8X5iojIGDRmhkF8oCzPifJG+c/YuaudFqWdJBt2jciWrtGykFTyzpDJkR+N/C1MUsVV1nNNI+vzD4ZOWel2JxxYRoHgQ4O63DXCdruorsFr7fbNbMeeqwZC67MR0+ySvXsF0urC5waH+j9jBd6fR8tRWEYxLSJv7vtwtP1597WM9vbX5wcbBBNLOA7dvFMp7FaJCKsD+3RcKV+cmBZZyT//9bhS1ypVm/LOCTbeHU3BrtOR8tJm8EBnzBpXBhswkQizDJjf3XbbbaGDee+990KHA4tMh7LHHnsYBAYDBl4qdAR0jjDfdEj5hCc9OnAGjuQFqws66JNOOikMZujgGWTREX799dfWvXv3MHA8+eSTuTXIE088EUwhYVirg/jSA4iBpOCP4ZK9uofTEyOfCHEZM21BWCLBubGRL4Wb3xkdyAXieXrABLv2jZG2TfdVgq8H1vrMXbQk3H51vxH23rBpdtAmq1n7Fg3sqSjsOc8OtRdO3ij4i8DKYqturWzHtVvbwIgYeOW7ybZDtL/ZmqUHrvG8ZNpnCQgyfNI8gw298MVh0QuvkZ210xrRi3aJ3ffxWHs8WkKyY4/WtmXXVhFhMsW2X6u1QUJkCn/a9qtnSlbXhIAQEAIBAZRAZlzpN+iTUDJ8lp9BLmb+WBMgEA70F/RVBx10UFDemcFNCv0dwuw5/RtLBFiLzewkfd6DDz4YrqP80S8xc0s8mLJfcMEFYdkBM86QBk4g+HKKdHHSzyIok/gigLhwnwScR5lnlhpxJT8crPjnZAqHOClk/T4C8YKVQFKYsUdQmLEEgchAkaYvd4XYCQTC+bgBC4SNN944KFMo6iiGWE64cpzpawzkA+xwxMmsO2MESBXGIijA5BW/DziyQzEjT2ALxiit3LvLLrsExZPrccmnjrHwRFE+66yzQr4HDx6cMg1fykA6Rx55ZCBCWIuPsks5cbyYi4Bx3HoByw7aAYJynBTqj7aMgAX44CMDQouxFdeReP2AKYIlD+2E+gAvxkws13BxksOP2UI0ILRtlmVAOlCnjAuxIqBesKygTUEsocxjLYHjQ0+X+7G4wCEibZ08YsnCmLJfv3521VVXhbEZ4VzStQeIjrjw/GFpQ76wokB4TjjHGJPxItI1IqrwN0L9sGQljk8IUIH/aCtOyOGPAdLR30MkyzHnEcIRPleyIdxUA/7RNrGQ4bminfGeYizOO7S6SIdIIUfwd1BWwcp3XjTehZyANGBZw+BoBrpDNPZFocZPAoQC1hJjIyeOzPZvtHorO2LLzgbZgFwZzZC/HymydSMC47K+vezcp7+PTOnnBYuI30dm9Ul5O1K+Jz0yKJAC63RoYRtEpMnpO3eLiIOR9uinY6znas0jQmBUmIlnNn7bHm3s8MgKIa54/mGj1SICYob1j8gShPS37bFqRB5ElglRec556rtwHieIkA9YUcTLFS6u+IcVxLxI7zjtsW/CmU9+nmrvnL+tHRWRJ5NmLQjnTo7yOyeyeMCfHGVMytRIwb5iuy5h5j9+bXKk0zwSlakqpUlkuVAnysD0ecvJnEx5ueHN4fbadxOjep4cLFsgGijDY5+NsVvfGRFIA3S0lo0blETz3FfjSixPDo6IA5x90k4gpupFbaJLNJH77IBxNj5yBMqkdVymz1sUltGsv8KaIn4NvyNJqVSygZcwnTUvYF7QmDli1objKGaAEFh7fswSuODUhoEZwj6dMjMXDNTYx6SSFyrx7bXXXmEmApNHWO18w8OOs9YQR0esE2X2CNNLXlQwweSDQSD5w0SOTg6ihPy7V2c6NTfL9DJU5XbCihn+ds1/a2RlyQ9+HC7es1u4dfVVGtlfXhgWOZScb60iBtEFZgyigWUK7uuhd9QY/+/ln2x4xBCOi/KCkv+PyE8ETih3iAgALBJYK1RWsoG08T0B4QEp0qVNY/vrHt1srbZNQrZe+2FyiH+9js1sScSoQzYc2me14CATIiZdeC+TtkJACAiBTAgwgMf7P7NqWA98/PHHgYzmHjfv9hlwPscGMRGXuELJeRQXn53HPwEEtveJKFAoWfRNCH0egjJIX8hMYCqz7WxxQiy4Aune6umb6QtdEaQPz+b8jbxQ/ssvvzwoqyidTjpwLS6QI+R3p8hUnrEBSjxECf05s8iIr1dnn1lKBJ8Y+E2gvOAUJ/u5TlzphDQcnz/84Q+BbID0gMggz9QfBAZLFtykP56HdPFyPlsd++w++UPJQ2kGX5TyuBVIPA3qgAkU6pxxBlYIjD2woMmVaIjHR1kgRSCtEBR7J6Hi4VDSLrzwwoAl9UNesUJl/IZy7RjHsfH6QcFGEacOISj4xcXvjZ9jzARRBgECOcaYi3aIco/gZJR2xfMEzuCBxGe+wcmXljghAkmBcIz4feEg+peuPfh13zoxhlUNeUH8eSE/vrwJyw6Wi6DM46uC/fJKvIyZ4qI98BwTHkUbcoq6wCEkeWHMDEGFtRXvA9pSrnHnGi5T/vwaceUaX67haGNeH4zFb7rppkDOMvGIHoADV4S253Xl+ans7c8rHPmhEEISxOXGyAcCs9DpZHG0HOCOaDkAs/h3H7WRcZy0kODebtHMNs7bIRpcforSdasKzjHuRrBGXhaOly+PYMlFKum5WrPgJ+DUaNkDsla0XOLGQ9YPVhCfRRYJCEs8+LnU8Z0V2xaRsrvxGg3tiZP6lFwh7Y6RTjEuUmpdsCj4y7Ol+0m/5lssFybOXE4qcI5lAjPnL7I1ouUAkA2LonJBNCBYjaQSrCIueO57++dB6wfFnjAzIkX6sHsG2MLI8qIqBTIpqsKg9CfzgeVJ28j64NaoLSAsdUBwMgqekAU4jMTfw0cXbhcIJZaSTI2sDlxejyxIUsktb4+wXh2aR8tTuobfhAhjJ4Hi4U+K/HQ8csKmoR34+Tsi4un5iMRIym9aYvJKBRzDrOPFmPVkrB/ztWTMvBx77LHBjDDXZDFL40VJR0EHBGnhpoYMPmCh45JreKwvMAXlBcULy2eU6GDcNJTZDGYWGJyMGTMmDIwYlEBOYBbKbADrCKuL8HUHZEb0sMXNsjx/OHjEJCmb9FlzuQUB4bAcQPhcj8XcOoyKlmMgWC249FqtqT1z4vIBA4TFppElAv4jhkT+FljyUAjhJdM2WnO0drTk418H9YqWcEyzF6JlIN/8Ojt8VSOd74l8wxcir4pDCAiB2oWAE9CsgcZ/AD/W7qME0x9gBcfsLEL/4ablrgi5EuqoeFiOUfIIz8/DM2vqs84o3i7sMzjnnqTkEqffE7daiO/79UxbFHW3pAAHrAJTxYGZMxYJ9LOufMZN192KwS0iSROLBmS11VYLlhuYr2N5QP+MUsrEAGRBpq9CxGeaHTuwYQadGXjGD8x2QwawLIAvLuQqjnG2OmZZhteRlz1dGlyHGIBYQaGCBMGChh+kgVuApLs/fh7CCkLFx0dYOmRSvCg/EyqeV44RJl18RtwngjjvdeX4MXGD5QjtFTIBIol2i6VEUrBmhWQib5Bb3McPoqpr166hHXEPpAHjPawnksSB1yfhPM/+rPmMPtfikq49xMOw788bhIrHyVgRoTxYqUDEUQbIGCbU+NE+IWhyXe4SIizjP4g9LHOpUwgkcOeZ4r0BWYLlEe+aJ598MliGMH6uaMFCmTYSJ6UqIk2svuJp0N5o30wG4sMjLozlUy0dioepyP0fxs0KCiE+Ci587jeFGuUZa4AvR00P1+Ov8dbR19cQZruZ7ccnw7rR0uQ/797DDty040oKHpNvKJwseWZ8jLRv0bBE+Q4n8vx355Eb2Vejp5eQACizt70zIlrusI41qL+8z7koMr3/csVXIlBYUX7XiJxKusyPZtffHjzVbnhjeDjVLspT14gYwUzffT9woXNkvn/x3j2DKb/fm9xCCuAfIi7NInyY0c9H+kc+Dlj68Y8/9ArWFYdGRIMvDcknnooIO23uwmhpRFu7cQVensbBfTqFT2D6cdTtrySX7tMrEAFnPfmdDYja1D8j/x1YpLjgzyKVrBYtVWepxeTZC2zfjTsGC5PL910n+urIcuszvwci5Jj7v7anT9ksWE5gCfLwJ7/45VLb7BpmqeDlP2AQAFvOy5fOBVad9XusoePFnIvw0vYXhVsTeMfH/cnOO5/wmMNifkUHAYOeymsyL2gfCDFowJGRM/cw87DLdI7VRXhoke9/Xb7GNZ4vWNGD7/vGHvrs1/jplPu5fO0B/w1I4zTkxds/TrU/PvidvfztpOC05tQCLFUYGb14YXA7tWoYmNr97xlkd344Jpzbt3e76KW1nBhJVSiY3XzCp4pD54SAEChuBDBJxzEbJuY+E0e/4M4OUYKwPEDoP1jbzY/+j1lqV1ocxbgyxuw/YTEFZgCNBR8DeEh6xGfTmb3bddddw7p+9l187XK2OFkS6H0n3usR7qVsLsyGsoSQn8fr19hiteFEA0oi/WecaBg0aFC4F8sOfADgUJBlGowBELckIB/uC4B15q5U4pgRAUuUOsYQKJVYlbCsAqIBwfN+OkGR9Tpy7OjH2YdoYGwBYcKXLtIp8qnKTnr51HG6/Pl5TwOrF/JDnTNRg6WL11PSQsbvTbfFYSnKPPcTV5JoQLmnbiFv2FI/WCe4EseSVgSF38c43AOetDnHn2UMmLLjkI96wcoBMgeFnXshi+LCNQgVyCSWl1C3PqajvlDeEfJLvpkMKpSkaw/J+L0t8Nz584ulCc8vyjx5hOziGcT3iS+jpU15+07Gmesx+Oby453D80DaEA0I1r84ImWLcJ7rhKOt5xIv9+USzsMQHuG9BgHDkpQ42eLhsm2Jg7aXKZxf9zbKPQhjcSYgIRqwDmIJCT/2/R7ije8n01keU+H/M9uOJQDr6U/cvkvwQYDSjaUCs/F8+pJPSa7ZpmlEEDQKCmOfyFEigt8GnD4eFCmb+E1g+QICsYDzxobR2Jv19G/+MCkQFnwtIroU0lo7Wubw4YolFOGmPP99/+vMyB/AqsaXCxAcWB699eph9v27sbPCmv8/7dw1WBDzafpbDtvAtoi+uBGXgb/MiHw2tDXKGzVBuzn6KsdRW60enFUyYUjcxIty2z2aPIRQiJcrHter30bL5iM8joj8WyAX7NkjLB3hyw35Cn4i8FdxxL0DguPJfO+vqPD/+XC04VAULPHPwNIYSAMmjtMp9p6X9hFpAGmC9QafK92yW+vIwsGvlt5CHODsHyvxv0QEFo4+kSejJTn4YKANpRJ0yMMizPjc6u3vjkgVJJyrVMsGPPgy2DjllFPCbAYsOT/MQBmU0FHxMkhKcl1kKoY61TmPJ9W1VOcITx7oJOnY6SRhgrFiiEty1gRTP2ZYmDXg/mQHHr+3KvZ7d2puTSJ28+HPx9lOPVuXfKuVvKD8I5tG3+YthHReYfHwfWS1wJoyBGuHUx4fbFfv2yP4eugeLW+498jI6Vd0bV7EuF7/5kiClUkg8/ABgezcs01wagnx8NTxvUusOF6KiI108toPUwIpkWv4dPHovBAQAsWLAGveMYVnCR0KICbUvtwPVLB6QwFjAMyacpQ5+guUE0y/MeePC/0Tih79ymWXXRYIBOJHIcaH0AEHHBCsJ1B6MBdm1hKlCcGaADNpn+XFtBiCn3Xk2eLEtwQTATjrI/8QDT6jS9yYaLuzO/wcxGeFuU5+XfC7wM8FhQNTZjCgvBAEKDsoY1g2spzAlVW+IoDijhIH2cB1nAT61xhY1++f1aTcEBeMLRDiBtN0AoY4giRtd4DJGMRJEfIDqYG5OUov4mMQLy+zxyx7SDr2pF5yreN0+UumAdkEiYXw2ci4FQxtKldBoYeoQKhT/4KE3881yBuEuuJLCwjY8sUOxkTexlgaAGlA/TJJdPzxxwdLA8JjYUL7Z7xHePxdMK5jAgfBtD8ptFUsN8gXWGMl4IQZJJi3QdJiYod4nYDyuknGmetxuvaQvJ+JKMgangksNGg/WJcgPNMQcdQTbZgxLpZNLklyxc9XxJa88ZxnEqx14s9qprBlvYZFBe8nlixBsPJ80b58OVi+8UIEQJAktx4PGLtljZ/zLe9blmcjkB7kyePxMH4c3/q1itj++ZnvgwKJYz5+CP4H+FoACuIDH40OXxH475+2CKTBuGg5BBN+rKtnJh4nfudHP8bRgyInjPhIgMDYP/pU4uuRA8etrvnQbn5zeHDA+PFF24f4WbLB+n2UeQQlPp2kunRBZIXxn2M2Dr4PLo38H5A2Sw0ueWlIiAYrjVsO3cDePX+bkGeWi/CVi717/0YuXvm/YeFLB76MAjLhkid+jCYKI2uNyFeA+1Vg2cN1r/8U4k2Wi5Pkj69lbBNZguDQkh+leSpyYvnt2Jm2URl0GnweVDfB30X7yOIFK5jXz94qZC9ScUL7wJkmxBKSqi6fipxu/t8+Pe2ji7YLYYZNmBP51WgWLGGW3xNOh39fRYQEX/B49tTN7byno68mRVYsb5+3vB5ZZoN/j3RCGwD3TFKpZIPPimBqCrvo4p8NotNw9jO+fhEHQJUlsKAw2Kwb5MXG7EKSbHATOs8TDohg2+l4WCeXijDxsFWxxTfC8dt0ttvf/8XOfubH4EuB2f4vR820f707OnyFYqvIcSJmV+UVPtlD3LdFafG1CJyykG6LxvUMR448EDMiZyfjIt8KMLi3vre8Q544i+8uZ08dRymf/DzDWJU0Pfr05X+/mWT4XcAHw6oRk+px/BQ5i+SrFVhQYL2AadXCyJyrwQp67ouo7DiLzBaer2VIhIAQEAKZEIBswLz6wQcfDMqZe+5HIcNSjv6OH0r/rbfeWqLEomD77D/9TVxQuJmFZFYbpZjZaBQelHSEfpQ+lXX0fh0lEMUQYe26K+tYHEAkZIsTxZYBOyQIpAOzuHwlAYUsmb8kYc8gPb7kwJXBkJnoH4pyXOhHfYYY5dGJBmaL3YcAeOHcj2UoxAeJANmBsoApNLPuzJ6780EnMeLpJPf5mgFKK6QAAjnjPiogcpj5J03wZtYapRllBeE6jqkhJAjna/fDxegfCnqmOvZwqbaOZzINnFWiQLJUh89guqDQxped+Pl0W5Z8xiVZP9RfXHwchIUn5eUH/rQ/2gTCOAkyzEkgzmMRgriDUQgbxkbcS55pw0mh7LQFZtydECEMYfFNAfFAmyQd4u/atWsgOzjGasAtiOLxOp7xc6n2M7WHeHjy73mkTSA830xI8axBjpEfyuqkDfdgyp8cM8bjzWWfuokrwamUbr+OZS2f13XCMBk/RA55Ilyu8RIHFgCZ0vX0WXpM22IJGRbAfh8WWSyjhixiaY6f9/tSbUmX87lseU/mkr94OrnEGxKvgH/MCJ/x+LfRjHGd4EcB0/+4f4WhkWK4w/X9oyUITaOvxc0rpUzyWUOWU2C1PD4aS7vJP84Wt45IBhe+0MCP5QkTIx8G7oMA536QES44BeTnwiw1v6SwHINPb/Klh16RlQTx/DTxN4tpvjyx840fB2sMfCf48g1IAf+MJuTCgXd+EQgPrDCw4HABD2buGcuPj/liSJYrnveLXxhsf48wZKnGL1PnRjgtjw2LD7f64Axf74jf52nWhO1d7480fj0iHx98jQRiycuZrEvKE28Dbw+ZFL4QMmrKcmz4ugQOH295u3T98qlTfi473fBRaF9YzIyO2l95pU7EdpfuYTLEyEsdlrmswgvIPTrzIsLEisESLwlmL1hzRfwMNhhIsIUV92/jwhozqGDLQMgFb9ww6z5gID46OAZ++YZn8EDHxrpGGFhIBAZaPohjYELekx0m3wFnwESZ8HhcXmHWhpmEQsrr0Sw+yj3WBC59oi9AXLR71/BwQzYc+p9v7eaDehq+Ffa7e5D17d02kBO/uzX6tu92ncMXJrj31+gFx3KI2w5ZJzKHalDqPpT7/3t5uI1e4XymV8Sknbxt50A2QBRc8r/hwZqAePDtgJfTnyfPs0eO2cDOeGpISZpcj8upTwy2oRPnlpzCWoPPah6+WYcSHxEToi9qnPbk4EBEEBDyo0e7JvZW9MnOs6NPde6+7qrhM56U9ffRZzKPjj59mSn8vhu2K0lPO0JACAiBTAgwgGYGjX6NdevuiDB+D4NdzMW55rPY8evJfUh4rBOIz8n4eBjSZE10uuv0Y8TBjLgTBtnixPcAhD9xFlKIF+UMcgZrBRdm3bEkYHYylWJGnwzxwlIQL4PfS/nAB4vDVPd6OBQwFFnIGJReMENJjVsKEJZxCmmlw5MwvsQhXf3lW8fEmZRkGtQZ7Yb6BgdfQpq8j7EReDgplbye6ZjxFoQTYy5fxkBZqBswoX6S+BMf6YFj8nOWXMMqgfpLdy9hXEgL/GknKOq+XITrlJt0KLdPUPl9Zdnm2h5SxU15yE8qkoN64x3ATD4Kfa6kR6p0OIeFkS8pShcm1XnyBymCRRTkJ8Qh5BRkQL55wnKqLHlIla98znm6+ZIIhQpP+rkSettd0c8atlzZF0k+5VXYmovAwpmTrf/f98ipAMXWVl46vqdVqmUDnQeML2s6/ZNL1AwdNp2/s+V4zMXcks6SjgWTScxTkXxfkvmGZ0YF9tqdTTKzAcMPacFavHSCQ0jIht133z1dkCo/v+d60Vqr6IdDSD73snrEcKGwu7SL1ku9c/Zva11fPGUjv2RvnrlpyT47naLlEvGw8X2WTzx49PrB0Qph404pt+7eyl47fZOQPmutsLpg2QMWC7CZ8TS5Ny53Hb5u/DDl/mrROrDnTtwoxI/TS3eOeVJEdrAejLnDp05YPivjEWQK72G0FQJCQAhkQ4D+BiU1k5LOQDjT9WQaEAyZiGfSzHSdPjSpmGaLE7N2X4aRzE95jiHl6e+THvqZAXU/SKniT6XEejjKhkl7PkIdoPymEsYp2SQdyeD35VvHfl98m0yDOkuX5/h95dnHtwKWFe4Mm7goSzZMMrVnCIM4aZApf6SVjkignVdU+TO1h1T5zdQeqbe45W6q+/M9BwmDkE/fz3ZMWKwJWMLl4vd7HH6cvJ7qmHvyCU8c5Q1PHKTr+a3sLelLhIAQKD8ClWrZEM8uyw1g6TGnoqNKkgKww8x2wAxXtmAO658HYiDDjAJMdqYO98033wzr9h6MrCmSMyVlyX9FWDaUJR+6RwgIASEgBIRAIRDA5wMWCEnyoxBxZ4uDr1+xzAM/Ue6PINs9NfE6n0llbOUTJvmUgc824gcCJbG2S01pD1g24AOjKoVlR1WRh6pK17EmfVk2OBraZkJAlg3p0al0y4Z4VnBgxS+dwA4nmf10YQt9HlPMuOduZhTSEQ106njupoPHd0MhiIZCl0fxCQEhIASEgBCoagQKPeObT3kgGGozyeBY4CuhrNI18oVQLFKT2oPP6Fdl3VRVHqoq3arEWmkLgdqGwG829LWtZJVUHtbmQTQwG5DNA3AlZUnJCAEhIASEgBAQAkJACAgBISAEhIAQqFIEKtVnQ5WWtIISx2kOSyckQkAICAEhIASEgBAQAkKgkAhUh9n9qspDVaVbyPpTXEKg2BGQZUOxtwCVXwgIASEgBISAEBACQkAICAEhIASEQIERkGVDgQFVdEJACAgBISAEhIAQEAJCoDwIMKuPE3WclOO007/uUNlbysCnNKsi3aqybKC8YM826cA+VZ02iL5+Zsuiz8rX0RxuKnxq9bmo3kP951jIYmwreipybBwKJgSEgBAQAkJACAgBISAEKgMBlHscpU+cODEovZWRZnVLw8mG5NbzmTzvx+W5DsEwadKk4PA9F6KBtHbfoJM1WDJ/OeHgiWtb+xGAaIjqnfrPVYqxrciyIdfWoXBCQAgIASEgBISAEBACQqCSEODT8Ci+fPls9uzZlZTqysnwOfbKFr7uVhXpYtEAyQP2ucpf+65v9vL39sZ3v9qixZGFg6QoEMBKYaeIaAj1n2OJi6+t9LQ6c+bMWZYjPjZ+/Hjr3r17rsEVrhwI8ILt0KFDOWLQrUJACAgBISAEhIAQEAI1HYFczflrejmrQ/6FdXWoBeWhtiAwa9Ys0zKK2lKbKocQEAJCQAgIASEgBIRArUMgV3P+WlfwKiiQsK4C0JVkrUZAZEOtrl4VTggIASEgBISAEBACQkAICAEhIASEQOUjILKh8jFXikJACAgBISAEhIAQEAJCQAgIASEgBGo1AiIbanX1qnBCQAgIASEgBISAEBACQkAICAEhIAQqHwGRDZWPuVIUAkJACAgBISAEhIAQEAJCQAgIASFQqxEQ2VCrq1eFEwJCQAgIASEgBISAEBACQkAICAEhUPkI1OdzlrlK69atcw2qcOVEAKzzqZtyJqfbhYAQEAJCQAgIASEgBISAEBACQkAIFASBZs2aWZ1lkRQkNkUiBISAEBACQkAICAEhIASEgBAQAkJACBQ9AhMmTDAtoyj6ZiAAhIAQEAJCQAgIASEgBISAEBACQkAIFBYBkQ2FxVOxCQEhIASEgBAQAkJACAgBISAEhIAQKHoERDYUfRMQAEJACAgBISAEhIAQEAJCQAgIASEgBAqLgMiGwuKp2ISAEBACQkAICAEhIASEgBAQAkJACBQ9AiIbir4JCAAhIASEgBAQAkJACAgBISAEhIAQEAKFRUBkQ2HxVGxCQAgIASEgBISAEBACQkAICAEhIASKHgGRDUXfBASAEBACQkAICAEhIASEgBAQAkJACAiBwiIgsqGweCo2ISAEhIAQEAJCQAgIASEgBISAEBACRY+AyIaibwICQAgIASEgBISAEBACQkAICAEhIASEQGERENlQWDwVmxAQAkJACAgBISAEhIAQEAJCQAgIgaJHQGRD0TcBASAEhIAQEAJCQAgIASEgBISAEBACQqCwCIhsKCyeik0ICAEhIASEgBAQAkJACAgBISAEhEDRIyCyoeibgAAQAkJACAgBISAEhIAQEAJCQAgIASFQWARENhQWT8UmBISAEBACQkAICAEhIASEgBAQAkKg6BEQ2VD0TUAACAEhIASEgBAQAkJACAgBISAEhIAQKCwCIhsKi6diEwJCQAgIASEgBISAEBACQkAICAEhUPQIiGwo+iYgAISAEBACQkAICAEhIASEgBAQAkJACBQWAZENhcVTsQkBISAEhIAQEAJCQAgIASEgBISAECh6BEQ2FH0TEABCQAgIASEgBISAEBACQkAICAEhIAQKi4DIhsLiqdiEgBAQAkJACAgBISAEhIAQEAJCQAgUPQIiG4q+CQgAISAEhIAQEAJCQAgIASEgBISAEBAChUVAZENh8VRsQkAICAEhIASEgBAQAkJACAgBISAEih4BkQ1F3wQEgBAQAkJACAgBISAEhIAQEAJCQAgIgcIiILKhsHgqNiEgBISAEBACQkAICAEhsBICS5cuXemcTqRGQFilxkVnhUBNQ6B+ITI8b948mzRpks2ePdv0cigEooqjJiBQt25da968ubVr186aNGmSNst6PtJCowu1GAE9H7W4clU0ISAEyozAggULynxvdbqxUaNGVpFlYVzVrFkz69KlS9pia3yVFhpdqMUI5Dq+qi4Q1FkWSXkyw4M+YsQI69SpkzVu3FhkQ3nA1L01CgEe9vnz59uvv/5q3bp1S0k46PmoUVWqzBYQAT0fBQRTUQkBIVCjEWBCbtGiRUFxrl+/IPN8KfH46quvrE+fPimvFfrkgAEDbNNNNy10tCXxLV682EaNGmUNGjRISThofFUClXaKDIFcxlfVBZIJEyZYuZdR8AKFaGjYsKGIhupSs8pHpSCAFQ/tnvbPc5BK9HykQkXnigEBPR/FUMsqoxAQAtkQYE6PiQlm6CuSaPB8kB7v34reenoVtQUrMJszZ05K/ULjq4pCXvFWdwRyGV9VpzKUm2xg6QQWDRIhUKwI0P55DlKJno9UqOhcMSGg56OYaltlFQJCIIlAnTp1whihXr16gQCABKioH2kTd2VsPY2KKgvxghkWDMzkJkXjqyQiOi42BDKNr6oTFuW25YJd4ScRAsWKQKZnINO1YsVL5S4uBDI9A5muFRdKKq0QEAK1HQEnASq6nJWVDuWozLSSuKn/SCKi42JDoKY8A+UmG4qtYlVeISAEhIAQEAJCQAgIASGQDwK5KuaEGzZsWPCHtsEGG1jnzp3zSSYtAYCFRaY8lOV6pvjyyrQCCwEhUGsRENlQ4KplXd7UqVOtffv2lbI2r8DZV3RCoEYjMH369LC+ky+E4E9DIgSEQO1GgGcex3tt27Y1lCWJEKiuCKCYu0Kfbvv888/bvffea61atbJ11lnHbrrppvDVq7/97W/BGWO6+/w8ZU9HAKQ773iV5XqmMr366qs2ZMgQjz7tduedd7ZNNtkkLTZpb9SFgiCwZMkSGz9+vK266qpaFl8QRBVJEgGRDUlEyniMt83rrrvOvvzyyxBD06ZN7ZRTTrH999+/jDEW7rbRo0fbH//4x5QR/t///Z/tvvvuKa/ppBCoaAROO+00+/7771dK5qGHHrJ33nnH2MZljTXWsHPPPdc222yz+GmbOXOmXXbZZSXPHxeJ+/DDDw+KyKOPPmo9e/a0bbfdttR98QMUllzCxe/RvhAQAlWDwDfffGNXX311+BoQOcBR74UXXhiUlqrJ0W+pVnaf+8UXX9j5559fkgHGH8yIn3rqqdajR4+S86l2GLN8++23dsghh4TPDKYKw7k999wzKL//+te/Su2nC588v8MOOxhK5T/+8Y/kpaI5RjF3hT7V9plnnrGbb7454LHPPvvY6aefbmeccYZ9/fXXoX5vuOGGkq8/pLo/HyD5ygNEBm1k3XXXDbfyJYuHH37Y/vrXv9pqq62WU3SZyvT6668bbTOb8HnLjTfeOC022e7X9bIhwOToXXfdZRBcLn/4wx/s7LPPDl8A8XPJLVY3J5xwQgh34IEHJi/nfDx27Fh74403jHfDWmutlfN9CljzEBDZUIA6Q0nh4eQTiPvtt5917drVnn766dBp8MLeZpttCpBK2aNo2bKlHXnkkSGCF154webOnRsGFuk+J1T2lHSnEMgPARh1hIEu7dGFNstaNITOj0+Ljhs3LjxXEGTMmMQdRt1///2BaIDg4/l74IEH7M477wyKhx/37ds3I9lAXrgvWzjPo7ZCQAhUDQIo83/6058MpZpBL+8CZoPphxk4Y+VQlVJVfS4z4RtttJGBzyeffGKff/65Pfvss8HSMh0eAwcODArm73//+4xkw1/+8pcww54uHp0vHwLUmRMNxEQ7fu+994yJLATF8OKLL7b//e9/pfq+cDHxz/vOxOlSh5D29Kk//PCD3XLLLWFcSB2TDqQdfWp55corr7QZM2Zkjaaqn9esGaylAWhvr732mm211Va29dZb20cffWT//e9/w3uAyZp0kiS60oXLdh6diTHX6quvLrIhG1g1/LrIhgJU4LvvvhuIhkMPPTSw0ETJzCszAL/88kuYWb3jjjvCQ42izwzrn//8Z2vSpIn9/e9/ty222ML69+8f4oAUQGFC3nzzTXvwwQdDHJiYnXPOOUHpYkbntttuC+ZpnGdGY80117STTz45MNSkifdelC1klVVWKYkTlnno0KEh7K233mrXXnut3X777WHQdtVVV9mYMWNs3333tccffzwMWsgDplW8eLbbbrvQEd13330hb3yWiLBYTcQVv5Co/gmBPBA48cQT05rv7bTTTiWWDAygad8LFy4sFd4HZB07dgzPU4cOHYznBGWE5wJ5++23je92kxYzRMSF0Mkyk3PWWWeFYw/HM/zII4/Y5ZdfHggMnpPPPvssDMxffvnl8Gzy6S3IDO5NWluEyPRPCAiBgiPwxBNPhDjpP91aiT7wpZdespEjR4YZ0lTPOIpPuj6XSQMGvihzCxYsCJME5513XlCwGYDTJ7JkY/vttw998eTJk0NcvJ94Z+y4444l75p8+9xevXrZTz/9FPpaxhO9e/cOCh9lmjhxYhhLMOvM8kzGB17mJLBbbrllIF84/9hjj9ndd98dlAfeeanK8MEHHwQygvCMLy699FL78ccfw3uPdxvL0bgXIoKJCizLSCMu6cYjP//8s/3zn/8MfgewaJBYINB9uUNyCzHkQhtjbIZSh3UDGCO0X5YlQCol7/djjyPbdu+99zbGcnzRAZKOvhGiAeFargKx4Wknt+Q/03UPny1crnlRuNwRYNkERAP6CG2NMTwTLViITpkyJUTEknCIKMY9WJ9gbXP00UeXJAJRhc7CmMx1l+R7FL0BYph3YlxHmTZtWlhyTmToM3zelMlaSe1EYOVvydTOclZoqUaMGBHij1swMEjARA0C4q233rLnnnsu7ENAoOxjrs2AhoERCj0PMWagDBBQnHiIr7jiiuAYCJaZAQAzugxweHCZOUFBIuwFF1xgzMrSCTEgoPNINxiJA4Hp3PDhw8PsB51Yv379rHv37uF+8sVggcEWnx5ivSBpQ35gtXHssceGwZUTDwNewQoAAEAASURBVPF4tS8E8kWA9nTYYYeFH+05LhBxtGuUfTo12nbyc7s+OKKj3HXXXe2ee+4JA3Oew+OOOy5Et95664XnjDYL0cCsC4Ms9hngJ8PxTPAcQGwgDPo5ZkDGIBo2HhNEfEP8+9//DmH0TwgIgYpHADNeBKLeBWWfPhfSL90znqnPpY+mX2ZZIQQ6/bbHA3HBe4fBMv0kg2OPiz6xS5cuQQH0vKTbputzebc4OcpEBMsaUPzp1+l76f+ZiYZsoN/H/DibQIIgvLN4x6UqA6SGkwe+1BIMIT+YrEDBQNlAGYQMYSY8LunGIygckDqMjc4888z4LUW/D5ZIcstSCRdw8+UHyYkcD5e83489bo4z/ehzjz/++JAkY0YnGugTDzjggIz3eryeX087uWXMSr/OODjTlqUbnu9UW09H28Ih4HoLZIC3MSYQGRdRbwjL1BgbsdQGwhAyNr7kYsCAAYGIjOsu6Ae8RyEoIUZZJsEScySuo5Cukwu/+93vSpYHhYD6V+sQkGVDAarUX7CwtC6cY0DQqFGjMHhhppWHG2IB4eXu4oMbjlGSYBM//vjjcJnZBh5kBimc9w4IZYvOv3nz5oG88MEXMxEMEiAIsgnrpGA0eZkwcEL22GOPEB/7vGAYjJAWLx8GQDChrVu3DhYQbqr3/vvvh/u4RyIEyoLA5ptvXuJQlfYVF9Z98hzBhCO026TQcdHB4eeBZ4fBtRMKWC4gWD3QnlnDjOKACd+nn34arvE80jnGwzHATyUs9yCPDPp4XsmP/J6kQkrnhEDlIYCyhPILEU+/meoZ99yk6nPpx+g/mUmmL+ddAZH44osvhtvo7+jTsZbC+sH9MfHegADIRdL1uU8++WS4HXKf9wvr6Vm/P3jw4NAfM5PNJIUvNeO9lW2tdHw8gjk+kiwDExlYKyCQNJAZ+Mmhr//www8DwYo1po9xQsDYP/dRlRyPMBMKMXzEEUeEWXKUCTArdkmHI7gwRnTBwhRLhz59+vipki39WKZ4COjX3XIgufXI8JOAgolVA0I94usj1/u5x8OynxSeM4i4bEI5M8WT7X5dLzsC8fcEsUB8QhYyxqENYu0AKUD9oL/wnmSpFpLpPYr+gDBxCkHrkzZxHcX1GchNJoYktRcBkQ0FqFusARBmYHl5IygiDHgYiDBQYG0UzCAv1WSn6454IA4QBgTOMvPyR3gh0CH4A8ugg0EV5nb8WrRoEcJhEpUL0UBg0uNlAfPILC4vAToaBjWID2xY7oGQtpMSpI2wXpYBikQIlAcBnGB5W0/GA9POQBgrnoMPPjhYEzgxQFieF9ay8hzxzDFjw9pDFAAIh/jsJ+GdYMNPBM8r5smpxDthb/NuWsjzxUwMnS6dMbOfmFijlKQrQ6r4dU4ICIGyIbD22muHfgqlFqIRYWkFM2889yjjkOjpnvFUfS5mvDy//twTJ5YF3ufSD0J64nQW4b2D5OPYLF2fGyKK/nnf7e8RFH0X+lx+jCd8zOHXUm2dSCV/+ANAkmWgfHHB0pE+nTSwEMNXTioHvn6PY5Mcj0DIICiyiJcrHBTxPxQ22leqLYS7Lw/ChwFjOcLR91EHKIDMQG+66aYp7/d4gZf7Mm25xhgVssmJBs4x7sSCBusW2kC6eAjrkqosnhf6aRyL+nFZt56WtoVDoGu0/BOBVMSqiWeUSZeDDjooEA1M3iCuB1B3PM/+zHMt1XuUdurvL8LwzkT8XZOPjhJu1L9agYDIhgJUI+aKONPBfAjFBB8HmH0jMPqweghMH+sgcxGUIDdHgkxA4WLQ4E5beKAhLpj1hRzATA3hhZCPYMkA2YDChUmdm1MRBx0fA6unnnoqREnnw1IRzEthIZn1we/DUUcdlU+SCisEVkIAC4H4gBRfIEmhY2PgxWwaHZ5/2pI2y6Ac8z5It/XXXz8Qf9zvHSr7WCowaMMhGuePOeYYe+WVV7hUMqhi38M5gYd/BtY3+rpZ9lFi+KHYMECDdIgP2ohHIgSEQMUgQL8Eac8SA/otyHKWQaDk4uyM5U2ZnvFUuYKUpM/Feo93C19NoL9FucMnA4Nu9iEXsTb0vtK3qeJMdS5TnwtpueGGG4b3EjOLzCCyhQjhPH09eXSCJRk/5AvvI2YnIVsQZiYhXVOVgXeu5x/y1BULSF0sO0gLcaUzHMT+EQZJjkdcYeHdyTsbSwnJchwdy+QWLBljYWrOkj8mpwjD+AoiG6sdlHeWtnA+eb8fO84cZ1Luad8+qYXPIb7o9GC0JAiFE/N5LFwy3e/psY3vk74fQ8rnUvcsg2QZlN+X3HqZtC0cAljI8C5CP4F04jllbI8w/mL8g86BjkHdYKmE/hKf6EmVG+JBZ+A9ygQmcTIm80nLuI7i+4zpaPOdO3dOFaXO1QIERDYUoBIZmOCL4frrrw8OqoiSQQ9rz3nwUEJ4oCEEGKQweEhFOviDx/0MJnDEwssab9I4gmT9EzMNxEtaOHfiYb7oootKMYncn6sweCKvKGsQI3FhJted65E25nCYmHKe9aQIHSLMtUQIlAcB2nhc4v5P4uchueiYMGfmWXBB6bjmmmtKfSqTAQxrTxlA8zxhecQSI8yfIQe5TieIsEQDNj4ejgEXJsZ0tjxrhIWswNwVooLBmA/GcaLmlkmeJ22FgBCoGAR4D0B00w+613z6Viyc6F/TPePJ3MT7XCyiWFrFewShb8N8mP4dJR+rCX7MzEFw+LKuZJzZjjP1uZACEK/0yUwwMCuI42b6W9bScx7lM501BYoqP8KxfIwJDmbJ99prr5RlIK9MWvAewycOCihjCvxFIRA2jFUgc8iLExO+n248gkJ82WWXBTIWRZb8kidJegRoZzfeeGNw+I1VCktQEBRvxlwohvjzyEXc6iaptMePIdJYS8+WftIFv2GM+wgbD8/15LHfk27LMiAIsGzipFW2cLpeWARwLs+zykQNEyYIy7N8AvGSSy4JDrJ59yC8E90hZDix4l/yPYpvF3+P8r5Mt8yMJa28tyEmID94B0tqJwJ1opfHcnurMpbvu+++y8mkr4zR17jbYIp9SYJ3zBSC8yj0DITiD2a2AjJjwL0+y+rh6UxIh/jKKihsmJvj7AoyA0dQCIofDu98BodOkF9cYL8xqYqbS8WvF9s+zjRZgpIUPR9JRCr2eNasWWFmBuuiZJvlWeLZo90SjlefLweK5yoejucMXylt2rQpGWh7WKyYmA0iDjcV9GvalkZAz0dpPHRUOAR4BiH0eUbjkukZj4dL7tNP03cn+zasqXjmk31x8v5Mx+n6XMhSyFBITb54wTslPn4gTsiNVq1arXQ+U3rJa+nKwHuOa7zH2CctvMf7MohkPMnjdOORdOeT9xfDMYo3ihd9EH1Pui3jPcZlON/DzxfjCgghHHlyT7r7/DzEGGvgc5G4haCHT3XOryW3WAHmUibPW1m3lAkMkqLxVRKRsh2zxAFSkXdoctxEjLxjOZ98J2ZKjXZMW0o1xkreR1jiT77zkuF0nBqBdOOr1KEr/yxLoGXZUGDceRhTPZDpzmdLnllZN22Mh+WhLA/RQFyYRUE0YLoHw+lCh4CwTTdbm+68x6GtEKgKBFAE0ikD8ecoXRjyHA/Hc5buG+AMzJkFlAgBIVB1CKQbzGZ6xjPlNt0MPIPhVAPxTHElr6Xrc+ODbJT8VFLe/p4405WB9H3cwtKKdO+8VPniXLrxSLrz6eIphvM+v5duSz3gL4NfKkl3n5/nHvb5ZVPu6esghOLhIJhyvd/z52lX1NbT0bZiEOCZjzsoTaaS7h2bDBc/zkfn8XdP/H7t1y4ERDbUrvrMqzSsbWUmJSmYUWXzdJ28R8dCQAgIASEgBIRAegTS9bksmZDUfgRcsfeSOiFQyGPiqiilPxkvaVVWmUhLIgSEQM1EoCBkA6yYexqtmTAo10Kg7AjQ/jOJno9M6OhabUdAz0dtr2GVTwgIgVwRcIXdwxf6mHiTcXpaFbVNplfo40z51vgqEzq6VtsRyDa+qi7lLwjZgCmWyIbqUqXKR2UjEDe7T5W2no9UqOhcsSCg56NYalrlFAJCIBUCzP6z9JRxcnzJTKqwhT6H4u9LY4m7Io4Lned4fGDnvkRSYafxVRwt7RcbAtnGV9UFj4KQDbwAWHODYzWRDtWlapWPikYARpEHPVUHGE9bz0ccDe0XCwJ6PoqlplVOISAEMiHgYwCc8OHnJ9uYIVNcuVxDQY9LoS0NkvHF0yrkPuUAM8iSdJg5ttI/Com84qruCOQ6vqou5SgI2UBheODlkb26VKvyUd0Q0PNR3WpE+alOCOj5qE61obwIASFQaAT4dPiYMWNs8uTJ4WtIhY4/Hh/e6StDGPMPHz68wpLCySufZuQLLZlE/UcmdHRNCFQ9AgUjG6q+KMqBEBACQkAICAEhIASEgBCofgisvvrqwaEiyrEkOwJYNoBVNrIhe0wKIQSEQFUioDdeVaKvtIWAEBACQkAICAEhIASKAgERDblXs7DKHSuFFALVGQGRDdW5dpQ3ISAEhIAQEAJCQAgIASEgBISAEBACNRABkQ01sNKUZSEgBISAEBACQkAICAEhIASEgBAQAtUZAZEN1bl2lDchIASEgBAQAkJACAgBISAEhIAQEAI1EAGRDTWw0pRlISAEhIAQEAJCQAgIASEgBISAEBAC1RkBkQ3VuXaUNyEgBMqNQPKb4+WOUBEIASEgBISAEBACQqDIEdD4qsgbQI7F16cvcwSqqoJ9PnpBSPqLUfPtixX7nhe/5sdst1izUcnh5vH9Lo1LXSsJpB0hUEMRWDxjvC0c+70tHDfUFk0aboumjLb5e15ji5cssUWLFtmyZctCyYYvnW1nDHgx7G/doXtJaUvtd+xu8eOSQNoRAkJACAiBCkdgypQpNm7cONtggw3shx9+sHbt2oVfhSdcgxOYOXOmvf322ylLsP7661vPnj1TXtNJIZANgSXTZtvCkRNs0ZhJ9nr7x23Kkl9s0uzhNnfhNFu4ZJ51+rSh/f6Pb9qIeS3tgjd/i61Lm/rWpkk9a9eirnVsWd/WXKW+dVu1vvVo18DaNqv3W0DtFRUCIhuqUXU7eXDHhzNCrvw4nyzG74nvW/+ZJdGcsV3LsH/G9q1KzmlHCFR3BOaP+MLm/fihzRv2sc0f8aUtnvrLSlleuOMlK52Ln/hk/M8lh/F9G7j8NISDkw5bi4AowUo7QkAICIGKROCll16yJ5980l577TU7+uij7eKLL7b999+/IpOs8XFPnTrVrrrqqpTlOOuss0Q2pERGJ1MhsGDYWJv/zQib/8Mom/PBt6WCvH3yraWOOVhztb4lEzrxi6OmLrZRtjh+qmS/WcM6tm33JrZJ54a2WTQZun6HhiXXtFO7ERDZUMX1CyEAuVCKGKjgPN2+gnjwrciHCgZc0ZcNgaVLbPZXL9rsga/YnG/62ZKZE8oWTx53QUCUkBArCIjzNt7NRDzkAaKCCgEhIATyRGDMmDHWpUsXmzRpUrizc+fOecZQ3MEvuOCCUgBgIVIZcskll9j06dNTJnXiiSfahhtumPKaTlY9AnM++t7mfjrYZr3+ZcbM9Gm4lw1Y+GqpMIvqpSYUSgVKHMxZuMzeGDI3/LjUpmld26lHE9tp7Sa2W88midA6rE0IiGyogtp0goGkK5NkSFdUJx3YQjxsriUX6aDS+UpAYO4P79isTx63WZ8/bUsXzMkrxaZLZtvces3zuidb4JsGvmVu+QDxgJy3yfJttnt1XQgIASEgBNIjwHK3a6+91vr372/169e3G2+8MQR+6qmnbL311rPmzQv7Pk+fk5p95aCDDrK6dUu7YXvggQfCMouDDz7YnnvuubC88IknnrAPP/zQ7r333rBcBYyPPfZY22WXXQIAAwYMsFtuuSVc23zzzW3ttde2r776ys477zzbdNNNVwJpnXXWsZtuumml8+uuu65169ZtpfM6UbUIvPfrTHv0p0l20Q0PW915y5dpZ8vRKvNWNUusgFhcP3+yIZnO1LlL7flv5oQf1w7aqJnts34z26LLb8vBk/fouGYiILKhEuvNSYbqQDCkK3YgHiLSAd8P+HzQUot0SOl8IRFYFlkxzHj3Lpvx/n9swehBZY667oJoCVLTihucBuIhljuRDjEwtCsEhIAQyBOBOnXq2Lx584wlASi2o0aNCjE0adLE5HwudzDvu+8+A0ukZcuWduihh9qECRNsyJAhdsUVV4TzW2+9tX366ad27rnnGpYjkBD4fMAq4o477rA111zTTjnllBC2b9++Nn78eHv88cfD8Zw5qYn/I444IlyPEw4QDbfffru1aNEiXNO/qkVgSUTo3T14gt03ZKJ9PXl5PfbZZQvb438f5pSxVaZFS6/blg66qN7C0icKcPTsoDnGb93VGtpBGzezQzdpbnWXN+kCxK4oqhIBkQ2VgH5NIBmSMJBnfm7tINIhiZCOC4HA0vmzbVq/f9n0t261JbMmlzvKOnOiOJpWvPmtkw5ssXYQ6VDuqlMEQkAIFCkCKLuvvvqqnXDCCTZs2DBbsGCBXXnllUWKRtmKfc8995Tc2KZNm0A2+ImNNtrIbrvtNoPAOe2008LpM844I1gedOrUKVgy4C/DlzwceeSRgZCA7DnssMPs559/83Xkcca3ccLBiQYID0nVIjB70RK7+dtx9u/vxtvk+YtKZea6ZqvYHqXOpD9oOb7xSmTDwgogGzwHgycstCv68ZtmZ+7Qyo7ZooU1bSDWwfGpiVuRDRVYa7dHvhh8iUIFJlPhUVMGkQ4VDnPRJTD1lets6stX5b1UIhNQy2aON2u3UaYgBb8G4SDSoeCwKkIhIASKAIH33nvPrr/++lBStvPnz7e5c+cGx4c4iZTkhgDLI+rVW27r7lu/c5999glEA8fDhw8Pp//2t7/55bAdOXJkydc/+JIFwrIMiIpsZANhIRzWWGONEF5EA4hUrVw7cKxdN/BXm74w9XKHUcvq2vRNe9oqXw3NmtEWoyJVcYPSwRbWLbxlQ+kUlh/d+sEMu++TmXbati3txK1FYKXCqCacE9lQAbVUEy0ZcoFBpEMuKClMNgRmfvyoTXnhsuhzlSOyBc3/+rTR+d9ToDtEOhQISEUjBIRA0SDAuv4tt9zSXn75ZTvuuOPszjvvDMc77rhj0WBQiIKutdZaK/ls8HibNWvmu9ahQ4ewZAXfGL1797bFixfb0KFDg2+ML7/8MoQbO3ZsSfjRo3PvU7fffvuS+7RTNQg8OmySXTZgjA2fOT9rBl5fo7MdlgPZ0CRqDg3rNY0+eTm3JM6F9XLz91ByQzl25i1aZje9N8OeGTjH/hR9Ra/vBk3LEZturQoESnuTqYoc1KI0IRmOfWxi+LFfWyWQDis+z1lby6hyFR6BhWN/sLH/2tfG33NMxRANUZaXTVo+a1P43OceYyAdvo6cSkqEgBAQAkIgIwJ8gaJ79+7WtGnTYLKPVQNEw3bbbZfxPl0sGwL4YkDuuusu69evn1133XV2zjnnBP8OW2yxRbiGvwV8NeCHAYeRkuqPwOBp8+wP/YbYUe/+lBPRQIkubdjC6jTJ7fOTbRt2KQXCgrrZyYxSNxTg4Jfpi+3Cl6fYGc9OtuGTSy8LKUD0iqICERDZUCBwWTIB0VCbSYY4VBAO613zi1FuiRDIhsC012+ykRf3tjnRZywrUpZMGFKR0eccN4TD6g9cZDeJdMgZMwUUAkKgOBFgJp2Z+WnTpgUA8CMgyY6AO4RMF9Kvx79SccABB9hJJ50UfGNg3fDFF1/YMcccY3vssUfw2XDWWWeF6CAa3n//feNrE5LqjcAVrw6z9Z4ZaC+PWv785JrbxVbHxm3cM6fgbZZ1LBVuYd151ijy6VEV8u6wedb33vH20OezqiJ5pVkGBLSMogygJW8pJpIhWXZIB0QOJJPI6BgEFk8baxMfOsNmD3y5UgBZOnlE8MjN59Sqg0A6IHIgWR1qQ3kQAkKgOiJw0UUXlWTLTflLTmgnLQJYhWTC68ILLzR+ccGfA1+cOPHEE23y5MnBT0OcjDj66KPtkEMOsZkzZ4Zrl156afiiRTwO7VcPBOaOm2v9T+tvDesuMdtzlTJl6rmOHewM+y7rvW0WtCsVZkFENkSfiyl1rrIPrnt7un0RWZFfsmdra9888W3Oys6M0suIgCwbMsKT+aIvmygWa4Z0aLiVQ7HjkA6fYj2PFcOoSzevNKLBca5fzd5qbuXwyfjMHr09/9oKASEgBISAEKhIBCAdVltttZR+Hho3bmzt27cv+ZRmReZDcZcNgdGvjrbn+zxvI18aaa1f+MVOnVE2Zfumes2szqrZHS+2mtGqVEbn1Yn8N1Qx2UCG3omsHA55YIJ9MLzyl3WUAkQHGRGoZsPyjHmtVheLbdlELuBj4aFlFbkgVfvDTHvtxuCfYcnMCZVe2HrLUntfrvSMJBI8+LV7tKwigYkOhYAQEAJCoPoicOihh4avhfBJS0n1QOCbm76x1/d+3bBscNnkgoG2bZPoE5VlkJ82WCvrXa0m/eZklMAL8dmwNLKoqAYycfYSO/XpSVpWUQ3qIl0WRDakQybDeRRqXz6QIVhRXgIXEQ5FWfUlhZ746Nk26akLSo4re6fuot864MpOO1t6ch6ZDSFdFwJCQAgIgeqCAJ/B3GWXXaxt27bVJUtFnY+Pz/7YPj3/05QYHHD/6JTns518tF37bEGsxZgGK4VZGn3JpDoJyyqufWt6dcqS8rICAZENeTYFEQ3ZARPhkB2j2hpi3J1H2PS3bqvS4tWdX72dlopwqNLmocSFgBAQAkJACNQ4BN4+/G377t/p/Ss0f2+CnTM+f39VD9dpbEvXKO2TIQlOi+F1kqciw4aFK52r6hMPfzHL/vzSlKrOhtJPICCyIQFIpkMRDZnQKX1NhENpPIrhiM9azvrsqSovap05k6s8D9kyIMIhG0K6LgSEgBAQAkJACFjEH7ze93Ub/mT2T3uve+l3tnuj/JdTfNurW0ag60RGDKs2Lv35y2WLqufnJ1/9Ya6d+Vz1HwdmBLyWXRTZkGOFimjIEahYMAgH/DhIaj8CY2/uW+GftcwZxZnjcw5alQFFOFQl+kpbCAgBISAEhED1R+C1fV6z0a/kvkRi7xuHWcsG+TmMvLt19mUyq9btXAqspYurn2WDZ/DtofPspCcn+aG2VYyAyIYcKkBEQw4gpQnCFyrAT1J7Efj1toNtzqBXq00Bl00bVW3yki0jIhyyIaTrQkAICAEhIASKE4E39n/Dfnn1l7wK3/jb6Xba4AV53dPPGtiitVfPeE/rhaV9OyxdlF8aGSOvgIsfjZhv57ygJRUVAG3eUdbP+44iu0FEQ/krHAsH5IztS386p/wxK4aqRmDC/SfZ7C+fr+pslEp/6aTspoalbqjiAwgH5LxNdqvinFRN8t98803VJKxUhYAQEAJCoNojULducc6Lvn/8+zbyxZFlqp/u/xxs+923mb24JHdC4NMea9r2w8akTa/17NZmsRUaSxdVX8sGL8QbQ+baJa/Wscv3auOntK0CBEQ2ZABdREMGcPK8JMIhT8BqQPApL1xqMz64v9rldOn4IdUuT9kyVMyEw4YbbpgNHl0XAkJACAiBIkXgu+/SO0WsrZB8cfEX9uMDP5areL/7+3f2xbXr2dj5uflWuLlFG9s+Q4qtprQwi62kqO6WDV6UZwfNsXbN69mZO2jC0zGp7G1x0oU5oCyiIQeQ8gwC4cCyCknNR2DmRw/blJeurJYFWTp1lNWps7Ln5GqZ2VimIBw+Gf9z7Ix2hYAQEAJCQAgIgWJCAJLh66u/LneR64+bbyd+MivneAYtq2dzendPG77luEalri1dWHPG83d+NNNe/HZOqfzroPIQENmQBmufiU9zWafLiAAOI0U4lBG8anLb/JFf2f+zdx7gURVdGD6pEJKQhN6rIKAgKKD4A6KgiNgFLIhdURErKDZQFHvvomBBsGChCaKAqCi9KE0EpPdOIITU/74TJ9ndbEuym93NzuHZ7C1Tv5m73PPNOWd2jro5SFrjvBnRIfrL1nvaSEM4OB9Sc9UgYBAwCBgEDAJlGoE9C/cI7hO+klrvrZVrsmO9Lm5Wg7ou0yZutH+xyslId5k2GG88MmW/rNwZ/K4fwYhdSdtkP3NKWloZyW92UPDvQL5jAkb6F2A/l74LoiEnx8+1lKz4qBzvzAZLVot/cr+yNC+Gg39KN6UaBAwCBgGDgEHAIBCMCPzW/zefN+usgUukWQV7qwRXlYyIs1wNXMTIiNkvkhBbsGtFdghZNuj+PjHtgD4036WIgCEbHMDGfcKsvDuA4uNTs0OFjwEtxeJ2f3aPHN8S/AH9IrPSShEV31aFK4UhHHyLqSnNIGAQMAgYBAwCwYzA7wN/l71L9/q+idm50m+ad+XuyY2Qfac1ddmGKtEFlg85WaG3qINlw4ifDOHgcoD9dMOQDTbAmjgNNmD4+dDEb/AzwH4o/siSiXJwxlt+KNn3RUamh/Z2qyZ+g+/nhCnRIGAQMAgYBAwCwYjAxokbZeVbK/3WtCrjNsrNad7tCTCldi2X7aiUXSP/Xm5Edv5xKB2MXXREZq09FkpNDvm2ejfzQr6b3nXAxGnwDidfpcKdon1f+317fVW2Kce3CORmZ8meLx70baF+LC3iqMXixxcw8H6sym9FY90wvsdtfis/XAvOysqSyZMn23W/XLly0qBBA2nRooXd9UCezJkzRypUqCCnnnpqqTZj06ZNsn79ejnnnHN8Uu/OnTtl7ty5+WUlJiZKnTp1pGnTppa1bumud0ydOlVat24ttWo5f5nevXu3auuaNWuEdrZr107atm2b33ZfHBw+fFhmzpwpPXv2lNhY732pfVF3MJbB/Pjuu+9k48aNHpvHM3rZZZdJjRoFCo/HTFaCP/74Q0455RSJj493mfzYsWOyfPly4ffhxBNPlMqVK9ul3b9/v/z9999q3lapUmBKvmfPHuGerdBOflNKW3bt2iUHDx4sVG21atUkJcXattBGVq5cKeXLl5fGjRvbXC04zMjIkNWrVwvz9YQTTpCaNWsW3LSO1q1bJ4zdySefLMnJyXb3vv76a2nUqFGp/3bZNSKETnJzcmXuvQW/kf5qevv7lsqfY9rJojT3sRaGxyTKDYlxkptaWCFPTrOei6i8FmZHZPmrqX4v94WZB6XLCXESGeH3qkwFFgKGbPhvGmDVYKR0EdDuFAM6WT5iRoIagb1fPyqZu9cFdRvtGndoh0i1NnaXQu1Eu1Pc36ZbqDXdL+1t8eyWIpW76mHnZBMv0SNGjJAmTZpIpUqVrPAjOcJL+ubNm+X++++Xa665pkj1FDfx4sWL5dVXX5XPPvvMaRGff/65UspLm2xYunSpoCz4imz4999/Fd6nnXaaREdHC8oZ1yB2hg8frkgepwB4efGBBx6Q7t27y3nnnecxx3PPPSfPPPOMU7IBhXTIkCFKkQXzBQsWyKhRo+SSSy6Rxx57zGc73KCYMv+6du1qyAZrxCAaIIDuuOMOj+MHaUV6b9LqwlCq7777buF54pl3JhBst956a/4txui1116Tjh07qmuffvqpvPHGG1K7dm3Ztm2bXHfddapMbr733nsyceJE9VuiC/jwww+lXr16+rTUvt99912ZMmVKofoeffRRRdLoG1u2bJHrr79e4eiMbIBg6N+/v+prQkKCQMI9/PDDcsUVV6giIO3eeusthQ/P8Pjx4yUpKe89btq0aTJ69Gj54osvdHXm2wMCXw74UlI3pnpI5ZvbfT7fJosusSfSnJW8pVUTqfN7YZfZ5IMVRf7LniOh50ah+7r5QJa8OvugPHC2PVGm75tv3yJQussKvm27T0szVg0+hdPrwow7hddQBSzh8c3L5MC0lwJWf3Eqzj2wuTjZgi6Pcafw35AMGDBA3n77beEF/dtvv5Urr7xSPv74Y/9V6FDy8ePHFcHhcDngpxdccIGMHDnS5+148cUXFd5fffWVoJAgKDDZ2SUzxYW8SE0t2Ys6yhVEA9YGKEkcf/TRRzJ06FClSM6fP99neKCEzpo1SypWtF7ajSiLhg4dOniFBOm8sYCgsIULFypCAKXakzDmWEugRE+fPl26deumSATyrVq1ShENEAiQCu+//75APuzbt08VixXMCy+8ID/++GP+JxBEA4154oknZNGiRfkfiDUIEogtLTxvzGusplzJN998I/w+/fDDDwoTCNjXX39dcnNzVRbuQ8w+8sgjUr9+fdHPByTGs88+qwg9M79doWt/feW+VXJHq/vk34c3SVyDOPubfjhLmrpdBuzzvJz/pQvroYq7CtqYHRm6lg1AO2peqqzZHbqEiR+mh9+KNGSDBa2xavDb/PKqYLM7hVcwBSzRvolPB6zu4lacs2d9cbMGXT4TLLJ0hgQT67i4vBcpFAxWT1ntvvrqq9ULN2bDXMOs/sYbbxTcHLSgpFx++eXqHivsKCMIVhTPP/+8dO7cWd1jtRALCpTtp59+WtLS0qRPnz5KodFlOfvmBX7s2LEycOBAVQ51/fXXX5Keni7XXnutXVu4zjXqph8333yzykMbUEZQIhCUfogF+kMbdH/4ZrXSU9mHDh1S5VEu7fnkk0+cNd3ptapVqyqFfu3atbJixQrhmzZgZaKFtmpSAnNuVp5pK20eM2aMSsYKM31EAXzyySfVNXd91mU7fuvVYPC1de248MILlcKqr7kaZ2fzhdVxlDHw4fPUU0+p8d6xY4fccsstcvToUdVvxgdLEiwo6B8KoiZgMIkHB+YUH8qAGEEmTZqk5hbp9RgsWbJEKXuck540CLiyEs016mC8mNtlWXCDQMl+6KGHvOomcxLLm4iICKlevXr+PMDl5eyzz1bWF7hYYKEzYcIEpawzTrhW1K1bV1C0jxw54lVdpZFow4YNav5B8tkq/jynuER06dLFZTPoJwSDJiRwleC3Ss/LAwcO5Fty4FJCvzMzMwULin79+imsXBZubtgh8MKiV9T5+3VHy/2DH5HMG0tGvtoV7uKk1SN/yVmWC407eSfS+r+wur3rDekrborOz5YVkZF/HKoH7/6e93saqu0PlXaHPdlggkIGfqriTmF2AAn8ODhrQdqKn+TI4u+c3Qrqa9k7Vwd1+4rSONwp+BjxLQKYZKOMYdWAMs/qJsomwos1K6MQAhdddJHya77pppukVatWSknv0aOH3HvvvUpZRMlgpfDiiy8WVvwgIjC5RlFEWf7+++/zV/RRZl555RWlsPTq1UvVxSo6MQzcCQoqLhfnn3++crtgtZLVVHyuyauVZcpgZZaYBMQDoI0oTpiQv/zyyyrdvHnzBLNx8t91113KLB1zcdKiZKDMbt261W3ZMTEx8uCDD6o+4mpw3333KZcDsPRWmjVrppJCvkBs4FphK5irgyFKOwoMPuDjxo1T5t2ssv78889qdRUswOWqq65S2V312bZsx2PIDpRwrVzp+5AMEBrt27dXyqSrcXacL8QHwL2DccN6Bqx/++03tVqOUkZfUdzo959//im//PKLSgNxAIbMTVaRKQOS66WXXlL3iSmANQgCNpivo+xhkUMMjNtuu00pfZAvECWQRowp85A0uIMwR1EecRMoy8J8gcABB0/Su3dvFa8BMoffAlw1br/9dpWN+CXEJIAUOuOMMxRhA6kDMcmzj2AlRSwJFHieb20BoG4G6A9zhn4xL7RAivE754mAgdjj94l5yjNHWTxX/H4huF7gngJxyfwlRgXWUPwu8PuHWxrPhBH3CPy89ReZ9O/3+YmyrBgIj7UbLlNe+FHiurq2PMnPUIKDS9/dILGR7i0c/m7RqFANcdaUj4nKI+XLAtnw499pMm+j+xgWhUAwF4qMQNiTDUVGzGTwCwLGusEvsJa40P0/vFziMgJRQO6BLdbKlPv/SAPRruLWaawbiouc63yQCShhP/30k3px5uUYxddWWFVmhY/Vd3yXUV5QMjp16iRt2rRRRAIKP0oiSimrqXoVkZV/7lEuyiz5UWawjiBgG8q2DgCp89jW7XgMmYGZP/lw+WBFFUGZmjFjhqqHFWz6wzWUWUiAQYMGKVNn6uKDkqqD11EGyi9KKm4DtpYF7spGCSHmBCv09IsAclgmoPx6KyjytIf63Qkry8TWgNxAcWIMiL0A1vj64yuOKTdB/dz12V0drNTSD3fibpx1Pj1fGHOIAciJli1bKiUVhQ2yyplAONF+LA8Y3+3btyuSAUVu2LBhKh99ffzxx5XJup6nKNSMHd/kRRjz5s2b5yvZtIXxeeedd5QFBP3kQ3A/I3kIQBpA/PCcQhCBGZYBCGQX5APzDuIQ4gsSB2sb3Hdw7YD8IsYHRAPKOXM2kAKhiGsDBKkWAmBCNjEndXwFfc/VN/MQSykw4BnRlg0QMQTZhYjF6gNC68svv1RlY701ePBgRQCWdesZV7h5e/3Npe86Tfpbwu9y9xWDZcPDmyWuXoHbgtPExbxYYd5euXuTeyuKjytXdVp6ldi8eCRZEXlWck4ThdDF0fNL5oYXQl0NWFML7GEC1oTAVmxiNQQWf127tm5oX6/0IzjrNphvewTSVs0ULBtCVaIsrqHAKDtUe5HXbm3d0KFG4ZWG0O5Z4FpP0DgdAI5WQD5ABNgGRkTBQ1hlJ0gaCr+tsOLJiztKOyvFmBPbBqGjrDvvvFO5GEBIsArPKiiKZVHF1voBxVcLq60o7bg/QGKgNHEtKipKuVJQH+1n9VEL/um0h+CUKEfkx13kpJNO0knUt6uyUayQG264QX3rP5TjrWBBgVLnaWcBlD/woj9a8Kl3Ju767Cy9voYlCMqjMwEjxpgglK7GWefT80Ur8pAgWjTR4CzmgC0GkFkodawOIxAJWiANEF2+7Y4JrDqDP7toILZ40X5cTyCeIBq8VTZVQWHwB+sTLBMgahDiL+ACA7nHeEAo6GcfsgHygWeAeBBvvvlmPkLaZQbLB1dzND+xHw9w8zjrrLME1xAtxJxgfvChfcQ6YY78888/dtYPOj3f/D7yYc5i6UCZzGMsG3DDQiAhID8hMSDBIGCwnoCYYc6deeaZKp35Y4/Az1t+ESwb3Ml7dUdJ7EOxMnThEIn5uOD3z12eotxr8vRKuWB0O5ma6Xxlf3xEORnRoIZEbbQnJitLLdkhayTDIhvKghI55990mb/puJxe3+gfRZk/RUkbWZTEZS2tidUQXCNqrBuCazwOznTOugdXK123JirH/Yqp65zBecdYN/h3XPC7RvSKJsd6a0JcEVhxtg2+xss2bhe82OPGgMLy66+/Kj968iIoyigdBJYjDco7SgzWBb4SlEwUASwaCDyIUkS7CVyHJQWrkLNnz1ZxJPSWfaxUQl4QW4I899xzj3KD0HEbdNtcla2VGFwZNCbEMyDYpreCwoaAK37yiF455RhyB4HQgSyxFbAHa0dx12fHtLbnkARYIuCyYCuQR5iHE//C3TjrPHq+6K0AdRBB7rPKS9wNZ6L7b3sPaw5EEwscM58QW5JBXfDwBxca2sJqNOOERYqRPASYc1jq2Lob6B1gNEmHe4Ct8FxgBYRFkaM1D6v8gXSjYM7SLsgxW8HlgXmMVRAfyFUsMHCxchRieui4KNyDqISk0G4jOj395DcGSy9+5/bu3auICKyW+H3RhJlOb74LEPhgxeiCEzdHGVZchMfaD5epL/wkced4T+a6KdLuVo9n/pYq5VxTBkubNrBLz0lKeh6JlRlxrNC9UL3w+RJj3eDPsQtvsmGOCQziz8lV1LK1dUNR85n0vkcgY/vqkIzVYItEVFbZ8hnV1g22fTTHxUcAE2GUDD68eBNIDcGf3VFY3WNVG396LAfwfcZtgNVBlDhWP1HkWa3/4IMPVHbSQQDg64ySiNWBLpuXcT6s7PMCj4JSEiGGBIo/CjHHCCuOCC/9uCpAeOj4CKxU4vbBNpeshOMSgtAmR3FWNivsRNwnjgR9pn+smrszm6ZOsEaph5hhRRirC5RqrTxDyIAbCpwmGFgZpd0QDOBF7ANiIGAlgrA6y1jituKuz479sj0nkCAWKVi7MBdoA/Vjdo4Vybnnnut2nG3L4hjFlX7RR/CBBCFQqK1FimMex3PKAGPmJeQQcw3SgHY2bNjQMbnbc5RACCKCoBKPQ2+3akvuuC2gjN1kvuLygNsE84f5j9LNODHHtBUAODM3ILYg4lDWIWyYx3rFnnHFyoFnjGeQZwDlO1BC2xC2lrUVnk+eIf0h0CqWGbjpIPy26SCv4EHQUiwgeBa4Di7690uXy3PKc03ZCL+BxLiAiIG0DdSuHLp9wfq9Yt9KmbbxxyI175eEOXJ3r8Gy8aEtElfXd64VsetSpf8y1+9KbydVLtTOpMN520UeL0Nkw49/H5MN+0r2/3AhoMyFfARc01n5ScrmgbFqCM5xXbgpXYwrReDH5tBvHwe+ESVsQUT6IZH4iiUsxWQvqwigsNoKL+cE1kMpI4CiraDY4nOP6wEfFEl85YlMzws2Cgi+ywg7QUAsEMiPl/Rly5Yp32busTpIcDZiNKA0Ui7m29RLWY6ilX9NTjje1+eUxYfVS61ksFpPmZg4IyhHtBHllS3taCeB4LTgk47lhW2wSe45KxsFDTyIPq/dTlDIKdNRdB+0/zgYEMcA/HVeouMTG2PEiBHqc/rpp+cTIBAb+MizGqtJBrbdw5QbQbEjHgG+5Sj3rvrszqyd/jAGBAfElUYLWEKMMF4oZ67GGWsVWyEmBm41kBX0kT6Tn1ga2lIBawaNjW1efczqOQox5uk6HgOBJ8FAzwfb/Bzbkhm21hKMC/MOqwbacoPl/gLpA3lBLIxwE8gX3IewYGB+o3Dz6du3r4KC55LxAyuCg+ptHlG4ucaY4NoDIQSWuEoh3ANPTUSoi6X8R/924RrkTpgvtnMEogRLBAgILLaeeOIJ9RtHGfze8btn6xYEUcNcxFoHshXh9wMiCxwhYZlfRgoj8Nnqzwtf9PLKu/U/lLghcfLYggcl+pPC5LCXxdglq/faGuk1sq18nVs4BsOvlqNEerP6Uv7vgv8Tk/bEi1jeWhkRrkkKuwpC5OTbv47IA2fnESkh0uSQaWaEZQaVt3FuMZtM4CxHtrOYRZVqthbPbinV+kxl3iEA0fBx32reJQ6iVK6eA1fXg6jpTpvy7z21JeuQvZ+e04RBfDG230dypPqpsj7niAxYPCGIW+p904jZML7Hbd5nCJKUrp4DV9edNbuov9mrHs5zi3BWVkmuscqMmbytosdKHi/XKSkpKmI7Sj+rxigfCKbNBGjT90tSf1Hz0l6UUNpCO/Gp1j77tBOLAJRpW0W1KHVQHsq1diEoSl7HtJTFK4mzgJlcx3IBxceTuOuzp7ysUGMpwVg5tsPTODsrG3yJkwChUVxh/jDf9HwqTjmsytMWrEgoC6xREiE1AimQIFgHeCtFTe+qXLZKxbrH1kqE7UjBibF3FD3/uGerpJOOOaOxdbznWE4onWPVAMHirM9YyPCbBlnjKDynPDslmfO2Zbr6f8LVddu8wXbMPGo4upkcyii5ZfXZqda2w9MukrTZJVf6c5Jj5YU3WsqGYxmFIBt5dJ90nTon//rhZrnybucXpHl6Ozmh+Tfy4E/5t0L+wF/vDf4EJtifA0hM39Bi/kTRD2UbqwY/gOqjIo0rhY+ALEExR5ZMDHmige5HHN5eAhSCM6txpQj8uKDs2hINtIhzLCK04obybasYomza3i/NXtBe3RbaqYkG2kA7scIoLtFAGbhh+IJo0GU5KvhcR1DivCEaSOuuz9x3J/jns4LrrB2extlZueBdUqWL+aPH0Fkd3lxjjJiDeu4ybnq+epPfX2mIB8BWn94I6UhfUsHVAAXasSzcTJwRDdSn558zMoE5g4WTs3slbWsg8/O7wLPkrF+4ZzkjGmgvGJZ0zgey3/6s+5t1E3xCNNDGnxN/lYF9Bsvmh7ZJ+dolc62IPJghN8zOc71z7P+L8fbkW8K/ESpJuhxxTBry57PWlp04FME0GIGltIMJCdOWoEGAQJHtQ9C6IWgALGFDjiz6toQlBEf23P2bg6MhPm4FgSJD0brBxzCY4gwCBoEygABuRAQLJd6IJ4EcIH1JhR0+iM9hxCBQ2ghMXD/Z51W+XX+kxD8cL48uGCxRn+YRAcWppMbof+U6y7Xo05hMu+xrrXXpw62bSMVla9X1SMv4oVL5upKemSYRORjHF79Ou4qC4OSH1WlyTpOSETdB0I2ga0JYkg1mu8ugm4d2DcK6wUjgEDiydGLgKvdhzTl77CPL+7DogBaFdUM4SiiaN4bjOJk+GwSKggDbftrGyShKXpPWIBBKCKRZyvn3G37wS5OPRh6VR854Qrqe1EUundpT0n4pnmvF/+5aIkvHtJXlafbv4T/Wqy29/iMb6EDlyDqyL3K9RTYQVNF+txa/dLCUCp29zvk2oKVUfZmtJuzcKIwLRWjMZUM4BGacji6fLjnHfLQFUESkpJx3j9S8Y5wktL3cZYeq9X1N4ltf6PR+8jm3S6WeDzq95+lizs6/PSUJ2fvhSjiE7ICZhhsEDAIGAYNAWCPw4+aZkmv986fMTJwtA68cLFse3CHla5UvVlXXTNhVKN/wchUlIraAVKiUWU3S2Y3CigNUluTI8RyZ868hHHw9pmFHNvgaQFOefxDAlcJI6SOQttJ3kX4avrhWql79ssQ1O0tq3TVe6j/9Z6EOVbl8uCSfO1ASTytsGlu+8elSrd9bktSleAERcw5us/yTy455ny14c3eEp3WDLQbm2CBgEDAIGAQMAqGCwM9bZpdaU99q8J48/sjTktOv6FVW+maL9D9sH9D2qOUqsatNQTDQ5CMpkm5ZU0hOdtErcJKjQmyE3P6/ijL4nGRJibNXTaOt97jerePlhYsrS4/meUGXdRFR1ite/zMrysuX5t3zxSvfHxsM2aDx9dW3/Yj6qtQgLmehMdEP4tExTQs0AmmrfvZJE5K73ikxVRrIjveuEXa22PXJnVKuzslSvmHb/PI5r3SR/ZZx+mZEVIzUeWAqUbn0pWJ9R5cse7HqLI1MxrKhNFA2dRgEDAIGAYOAQcA3CPy67XffFORlKYcjU+XhDsNkxnO/SIXO1naVRZBTBy+TDhXsLSMm2GynmrQvUXKsf5JjH9+hCFXkJ21cJUYWPlBHkQ192iTI7/fWlp4tCkiFWXfVlCd7VJIzGpRTpMKInnk7EkEs/DywltxzVpL8r2F5de/bm2vkl1vcg2lW3AYjvkUg7MgGY57v2wnkr9LMOPkLWdflZh/ZL8c3L3OdoAh3ks6+Te1okTr/K4mt1UIOz/lENgxqLOmbluaVYrlY1LH2Szq+baXkZhf+z6rWwG8kIqa8ZO4qWdyFKB/8R1iEbpdaUkM2lBrUpiKDgEHAIGAQMAiUCIFNhzfLxsObSlRGcTP/VHGWDLxqkGwdbLlW1LAnENyVecXHW+xuPx8dLxEpCepaxR3/lZNTeKtMu0xenDxybrJkW4EmT3txq7R7eavsPZotAzsnqZwDOiVJlfgoufKTXdL5je0yacVRubRVvFQsHym3dKio7r32yyE549Vtcu2Y3dK0aozcckZFL2p1nWRXarYcOla23ENc97Z07oQV2WAU2NKZVL6qxYyXr5D0rpz09fO8S+hFquiK1SyjhEhp8uExafDMcmnywVFJ6TEo3+SuZv9PJdLaTmnr890KlZbYrpcVw6Gn7Hj3aouIKNl/ZJGZlplfGRVDOJTRgTXdMggYBAwCBoEyhcDi3UsC3p83G74nTzz2jORe611TEmfulHscwjdsOPkElTlx43/qow/IhnL/maDqaBbZlp6faJEJyJmWNcM+i3xYvj3vXfDV2YfU3he4U7SpHasiYIyelxdnbMnW43IsM1fa1y+n8pbkz5//1VeSMkzeAgTCajeKhZuMH07B0Af/EePVvl7JfzSCv6fB0cL0jb77zzCyfKIVTKiCpM7/Ug7OfMeKvfCGJHe9Q1IXjpfImHKSeMbVsvPDGyU7dY9d56MSKkuN/mPkyJJJ6lOl1wi7+0U9iUg/KFKhclGzmfSliEBGRskIpVJsqqnKIGAQMAgYBAwCRUZg2Z7lRc7jjwwHIg/JkDOHSfeTusmF358vaXPcL8i0GLpcun3cTmYcz9OfxlarLo9aDYvdJxIfY7kzlHBBiD4+P/OgjLm2miwdXEcyrBAQxG94avoB1f0aFaNlX1qBlQFWB0jd5GhhQfKsE+KkXkq0/LsvUxLKRUr5mAh1rhKV4M/KnRnSubH3ViAlqCossoaVZUNYjGgZ6qSJr1G6g3l8S+EAjsVtQc7xNMnNSLOsE66RY//MkW2vXaqKqnj6VVJzwFfqXlyTjlL9xpESERltBZHsrAJF1rQCSUZEx0rO0f3qXnRKHYmyrCTYsaI4EnF0b3GyhUSeV5bOCIl2mkYaBAwCBgGDgEEgnBFYtW9VUHV/etIMGXjNINk+aJeUq+Z+Ue+i19ZJQnRewMjRkeUlt1YV1ZcqMfV8QjZcfFK8xFqRHg+n51juC3lkwql18tpUwSIP0i1rBVuxPC6kohVEcuyiI8LxlzdUl+cuqiQ/3F5DWT1QVkllze7C7r0lLTOc84cV2WCU13Ce6qbvnhDI2Oa7/wyzU3dLblbhFevsI3slJz3Vco/IksT2vdWHIJDRlepKwqmXWP9xZVpbbx5WW2VyP7J8gkRaFhIJ1nFxJPfQ9uJkM3kMAgYBg4BBwCBgEDAI+ASBNQfW+qQcXxfyeqN3ZPjjL0huX9cKevllB+TOf47nV72yeUN1XCnbCsbog7hYvdvEC8p9JysmQ7d3dsiUFWly4UkV1K4URzNyLIsF+7YRGHLj/kzLCiJXBk/cJ2lWmm5NK8jaPZmy9WCWbD+cld/W4h6s32vIhuJi5yxfWLlRmBgAzqZA8F4z41W6Y5Oxy3f/Ge6f8rzUuO0TqXbtG7J/6gtS684vVGcO/zFW9n7zuF3HmoxKl1Tr+s5RN9td54R4DxExcbJhcJ6fYKEEni7s3+wpRcjeNzEbQnboTMMNAgYBg4BBIEwQSM9Kl61HtgVtb/dH7Zch/xsqPU4+Vy6Y0l3Sfi/sWtH4+dVyyai2MjHruHxQqYq8bvUm5ZjlohpfMqUcKwQ+xFvQMndjulx0cgVpWi1GcJtoWi1W3xJt8bBmV6awi8VJNWLl7Le2KwsHSIhlD9aRBZsKysrPWMQDQzYUETAPycPKssEDFua2QSBsEcjcuzE/eKMvQDj8x2dyeO44Se42QBq9sknKN2one74YLBk7/nZafK4K81P4Vm5Oga9e4buer+Ts8R2B4rk2k8IgYBAwCBgEDAIGAYNAAQJbjmwtOAnio2lJP8nAvoNkxwO7pVzVwq4V5w5bJTXLx8gUiZWsxrUl6aC160NOyawIsE44YO38cFnLeEUcQB7c2yVJuVTMt0iDkXNTJd6K4fDypZWltRUQ8tXLKluWDLnCvUwr701nJMrQ7imSaMVreLtXFYm0LGXf/+OwT1DecTjPpcMnhYV5IWFj2WBWyUNzpjNuJkik/8cua7/vWfed7/eTXR/eZG192VyOb/nLZSfW3uw6CM+mx05xmc+bGzk7nZMb3uQNhTRYN3So0SgUmmraaBAwCBgEDAIGgbBDYMfRnSHV59cavy1Vh1aRQXPvFhlXEC8hZmua3LIgVZ5qVV4WNKkvjXdtkgPVS2bZADC3fbFHRl9dVcbfWF3hBInw4KT96viXdcdk5j/HhN0n+EA03PvdXsmygjVsPpAl3/55VK44JUH6tElQsR2G/3BAtliuFL6QXalZUrNiXqwKX5QXzmWEDdkQzoNs+m4Q8IRA9mGH/Y08ZfDyfq4Vg8Ed0eBlMcVOlnNoh0Rm+eY/nmI3wmTeFaxvAABAAElEQVQMKgSyrPkwdepUuzbFxsZK/fr1pXnz5nbXA3kyZ84c2b9/v3Tr1k0qVKhg15Rdu3bJ/PnzVZtPOeUUWbJkicTExEjLli3t0pXkZOfOnfLnn39K9+7dCxWzcuVKWb9+ff71ypUrq7bUqVMn/1ppHGzcuFH4dOnSxa/VzZ49Wxo2bKj6aFuRnkvt2rWTmjVr2t7yeLxgwQKJj7dW8046yWNaxwSLFy8W5qwvx9uxjtI63717t0yePFmNo6c6GzRoIBdddJFUq1bNU9Ii309LS5O///5bGNNmzZpJxYrWqq2NHDhwQN1v2rSpMN9thWeBZ7J69erSuHFj21sBO6YviYmJUrt2bbs2bN68WfhUqVJF6EtkZGEDZ/py6NAhu3ycgHtycrK6rvvcokWL/Gs6w7fffiuNGjWS1q1b60th/b33WOgFqt4TtVce6jhULji5u+Vaca4c/SPPtaLO22vl6ndPkzcqVpLRq6PlwEklf79i54fTX90mtZOihW0w2VnCVgZ+s1ftUFEtIcqK1WBf32NT98uwafultrU7BeSDL2Xf0ZJZ1vqyLaFeVtiQDWbby1Cfqqb9/kQg+4i1j1EZlajMtDLas/Dq1pYuRVPm6s7e4RQgttp87rnn5IQTTpBKlSpJdna27NmzR72A33333XLVVVc5zefrixAEb775pnz00UdOi/7ss8/kr7/+UiRCjx497NJ89dVX8vnnn0vv3r0FsmHSpElKEfCl8rl27Vp54403nJINv/32m3z66afSvn17hd+WLVsEpfG8886TwYMHKyXarsFFODl27JhcffXV8tZbb4kn8gJ8vvvuO7+TDR988IFcc801hcgGPZdeeumlIpMNEyZMEJTn4pANKOcofr4c7yIMkU+T0heU0jvuuMNjuXPnzlXExM03F47v4zGzmwT//POP3HfffepZI9mRI0fk+eefl9NOO03l4ll85513pFatWrJ9+3bp27evDBgwQHIsN79hw4bJzJkz1e/JunXr5PLLL5dBgwa5qc3/tw4ePChDhgyRfv36yRVXXJFf4ahRo4QPv320tUOHDvLCCy9IVJT96i3z3ZGQpRDKvPjii2X69Ony7rvvqvwjRoyQcePGSVJSkqqHe/w2jBkzJr/ecD84eLwwcRMqmExNni5Tr50u93W4S+p9bG1Nuc/abvKeZbJ4VBuRxIYSYS0o+Uq2HXJNFmDR4Eg06HotQwifEw2UfdBy7zDiGwTChmzwDVymlNJGAJLIuFH4H3V2gCirEp15rKx2TebuMG4UxR3c22+/Xc4888z87K+++qp6QS4tsuH48eOCku5JIBJsyYbMzEyZOHGiXbaHHnrI6QqlXSIfn5x44ony2msFW9Ji7XDPPfdISkqK+i5udShwEBco8kbKPgJYpnhDNIAEyjEkja8F0u/kk0+WZ555xtocKUKeeuopef3115XSjIUARAPKtbYiuuuuuxQpuXTpUkU0QP5BjC1fvlz69++vCLpAEEGQphCEkDJYatgKJAlEw/Dhw5W1FNYN/Nb9/PPP6tw27WOPPSZ8tMyYMUPee+89Ofvss9UlCD6I2XPOOUdZpCxcuFCVsXXrVnnxxRfl5ZdfVlYVOn+4fx/JPBLyELza+C2p/kQ1eeCPgSKfZ8t10/fJ7LZ1pEnm1pDvm6sOHLF2uTDiGwQK20/5plxTikHAIBBCCOSUYYU8OsP+pSuEhsU0tRQRwFUhLi5O1bh69WoZOHCgemm/7rrr5IcfflBuA1yDoLj11lvljz/+yG8dq3m8uHOvZ8+eMnbsWHUPhZlVb1whuIeSAsFAelZOUQhYJaU+Z4ILA64MvMRrmTdvnjpE8dKCdQSmy6xosurLSiOC1QZEBMoSgumzqz6gKNEv2kmbUCCKIqzQU/eXX36pTNFROiBKtEBG2K5IgwHYUh/fKEibNm3KT/Pggw8qqwXIBxQ9cCUtOGsMdNnOvsFUE0rg//TTTwsED0J9rKjfeOONqsx7771X9u7NM3VmzFA+dX2Mn87nrB5311g9pmza3atXL6W8MiZaULT1eNAmlDotnK9Zs0afqvFFAXYUxhzFUM8xFHcUS4RVZ8qk76VFojm2LxTOcUu55JJL1Ao/bgVnnHGGWvmn7bNmzZKzzjpLEQ24WJx66qkyfvx45dq0atUqOf300/MtcCAYsBqgvEBIuXLllLURpJ+jqwlWQPzGQRAg9erVk44dOwqEiTthjg4dOlRZg+GWgTDnsApDcMc4evSoQIJi5YEFEKSMkQIE0q0dHMqC7IraLQ92elx+GzFPGqzbLauyUiQiq+ySwsczC+JVlIXxC2QfDNkQSPRN3QaBYEEg27X5WrA0sbjtiM4ou5YNxcXE5BMV82DKlCnKSgDFGAXizjvvVNBgyo/CACFwwQUXKP9jVixRJiASIAEwlUaZhAh48sknlXL6xRdfyPXXXy9vv/22eiFHoYaoQEkkX3R0tDpu06aNXHbZZaouynHlLsB1XBV+/PHH/CHDvJm8+OxrwRIAn3L8qfv06aNcEFiR/eSTT2TFihXKLQEfbFd9QNHmHtYKmECj6H799de6eK+/W7VqpdIS74EPbdKCwq5JFeIVgBkKHvWBxwMPPKCUPVZMEYgAFDkwxCT74YcfFvDFRxwywJOAKwoX5TOO4Ea9COPGSi2m8BAxKFSsTiNcZ8xQ4CFx9u3bl6+8qwRe/kEhg+xAKaMNzC2+NRFFMcSCoI/0DxcUlLply5apGmhjenp6fm2Up0mE/IvWAXONtFjmUP7hw4fzx465icsQ5XhrPWBbdrgcQ8gwDgjkFgSDdqH4999/lYsAY9m5c2f1nKOgQ0wmJCTIjh07VB7yQkYwX5j7gRDiTBDTgk/VqlXtmoDVQ926de0soPh9IT6DO8F6CdcQSBQtxC+BaOGZhqQk3g1WE8SN4beDMvkNNZKHQHZuAcFYFjCZkjJN7rpukNTaNltyjhf8RpWFvtn2IcsYNtjCUaJj40ZRIvhMZoOAQSAoELBMX7Mr15cc68P3+ph4WZQbKfsTqsmCfQWrwkHRVtOIoEBg0aJFgiKB4KONlYGjWwMKZ9u2bdUKO4oFL/GYWbMiiJ/2tGnTlCKPaTIrhpShV/9Q+sqXL6+uoQyyus3KHy/9KMEEZ2OlEUXbnVx44YWKPLjhhhsUgfHLL78opZKXe2cCEULfUN5R9rEKwLUB02dXfahRo4ZKg986/tsoFtu2bZNvvvnGWRUur7GyirDK6U6weEC51v7kWHygsEDG6FVRgnXiI8/YQNYQhBFFjj54UpCwHoC0gKghPfEnwBqFXQv3GVuEtjD2ubm5itDAN11bjjAHwLyogsIKHpQFMQSmKKZYnaCQIcyJa6+9Vh0TWBCiAQKsKIH1unbtqqwWUAAhI5h/tiQP9WLZYMQ1AswNhPmBJQtEI6QTApZYMT3yyCOKIOO5x90CXLF4+PDDD5XrAEQEvwektyWJVCFB8IdnwjEYJM+braWNYzMh5/jwDNgKFlCPP/64Is6wqOF5h5wcPXq0st7iHCsl5h1WIuEuERIRdBBER0ZLYkyCJEbH5X2iykliZIz1iZYEiZJE6/+5ROv3MMHa9SExN0sSLCItMfu4JFpWGgmWJWzC8aOS0GiMbFtRT15PPirPx/eR7Tl5li9B19liNsjYNRQTOCfZDNngBBRzySAQbghERMWEQJctQqFKPYtQaFCIUJhyNFVyHf9D5//3o6HvKxkCAxOSTWRVG2VPCwoGJu1dunTRl/IVX5RErAe0cqwTYElAYDQCzBHQEMUYJUQLZd12221q5ZrVdZRnVkghGrwViA294o0bBOXb1uGsHNwgvv/+e7WDgvYdd9cHlJAmTZrYBYrzVIezevWKrqMJt2Na/MVR8LWwIsrqKYJJtq2ALwo6lg0QB467BNim1ccQJrhDYE6OAg6R4SgQLFoghVjRZvyQBg0aqG/+sGMEinxRBSzIZ2uBAsYoo9otw3EecE7g0KIIK+ysPmM1wm4I4Ge7MwaEixHPCPz6668qngGxG7A00RiCL1YPkH4IbgKQZRB6EEXESMDiZuTIkYqEhPxx3K3Cc+3+T8Ez5kiCcG47Px1bQT87deqkXCVs77HThLbQgaDh9wYyBsIuNTVV4QfxQhpDNojEWEq8ryQuurxFEsT/RxCUlwRNEkRAEFgfS0NOsN6GEq3fs4RsCIIMiyjgky6JVrDsxIyjEn88VeIyIV5LsEtGUmPZ+3uEJGxcZNW3SEbGjpSNHW+SlyteKRuyk33V3YCWE21s/32GvyEbfAalKcggELoIRMTmrewEQw+UhUIVbaGQIIstC4V9CVVlikUcuCYUgm/lIBiwNG3wHgHtyoBJvbZO4AUdwSQZFwPbnSM2bNigVuIJ1ohijzk7Siqr71hAICicKB83WFYJrMZj5g5xoFfUVSIPf1CEcZvg5R3TZa2Uu8qG0kyANszAUUBR1FGK3PWBbTYxuSevXv2EnCiKkBfrCSwSUNAR21VTtvHUQlsgb7SwEspKsjPzbxRpSACsLCAeCGhHLAJ3Avnz7LPPKveJ//3vf6pP3sQs0O2m79olhLZ5sqRw1hbcJ5xhyiq6tgBxtKShXnzgtdjix7xyFO5jwQIJxpijOL7yyitK4dNpdZ/0ufkujABuFDyXWB45bvUKUYU1kq1AZjHfIbIgjrCGQLCM4Vm96aabbJMHxTFEFL9ZtFfPP54TV9v98sxhnYPVliuhv1h5QIhi2QU5ARHBbwi/p464uSqnrF+Pi4rNsyKIqZBPEiRyLcKyIlAkQWQeSZCb8x9JkGmRBNbHsiKIt6wI8kiCIxKfnioxOQXWWQHDLbmJ7Pkly9rWvMAFJzfjuNSf9a68ETVStna+SV5PulL+zi74LQtYW0tQcXlrG04jvkEgbHibdvXL+wYxU4pBoAwiEBlXsVR7BaGQ2bSzpJ/ZT1aedYd82nmAvHrBE9Kt8yA59+Q+0r3G6XJBTA0ZaHHmH0dUkMnWal0hoqFUW2wqK2sIoNjx8s0HqwaUNMTZVoQorATrIxI+q4Eo8azm8TKNEs3qJ0oiq3qYEiOkw+SarSAhHVCwiTeAoKzwwe0ChRRfb3dy/vnnK0KDtkJeuBO2xKR9TzzxhKqbmASQFO76gAk4ihNkCL7WxHvg2J2QjvZgbQF+jz76qAryiEsEQn+JSUA8CMq2JWpYLcXsmlV8vRJKnAQsPzTZgfUDdZCfslCace2wDX7pqn3aXaJ27drKxJtgkJTnuLLrmJ+6ITzY+o++YSXAijXj5E5s5xL5+GBRAKFCv6kXxY6y2DpQC/MDjLhPgEziU3Tp0kXdxjqEc9pAkFCIHEfBeoO2sQrPfCIIILEpKA9F0JMwp1mZ18ExmYd6NwOdF+w8zQWdNhS/IWyIa0HwUlyaGDM+mhxjBwbIOFwpwBtikfFlxR6lneebcWIcsHAgL89TsImOQfH+++8rdxHIS+aLtu6CNGWLTy30EXFFRnCPoLQ8VwRBRbDowDUNIob8msBVN8P4T/+IOFm7e6ss2faP/LLpT5ny73z5fO1vMvKfn+WVNTPkyb9/lEFrfpTb/5khfdfNkks2/CbnbJ4n7bYvlRZ7/pa6BzdLctp+i2hw755WKhAnN5PdszLsiAa7eq3nqc7PH8iLE7rJyMOvS6uoAkLCLl0InCSUCxsV2e+jYSwb/A6xqaAkCBiSqCToeZ83KiEvsrT3OTynzI+hYFkpEENhcU6U7EusKt9bKyY5EQ6MMafWS7XlEO+5YJMiH4EONRvlH5sD7xAg5gLCSrCt8FKNKwSkAS/QtoISjEKCKwT70hMDAeWEyPSkR8HTbgFXX321isnASin+3CiK2hqBVW0CF2I5gY8+5eqAjpTlKLqtmHaTtlmzZioIpGM6fU5sCCws8JXmxZ9VWpRZ2oIi4aoP5GeFknwoxAhKCKSFM6FdYAThgtA2XARQSrX7Bf3CLQXlmn6z4qsVGBR6VvU1MYEVAT7g2qQbs3XMsm+55RZltg6RARlBOVhpsLMFcRx0XY5tpDzw1PEQiL+Afz1BFHU0fk1qOOalTWCh+0YMCcbbmejxcZxLpCWmBiveWGHo+BrscIG/O0L9lE1wUh1j4QbLAka3j3Ywbij7EA9gpkkUVYD1B5KLdjIn+bDDAEE3IZx0nAlX/aQMxgAFGTKLeYwVB+e4q+iYFb/99ptSIHWcCV13WfkGA4gCxkiPE31jzCEWsEIivgdjQTrmIHNTu8BANkBOEfCUPMwF3KuCTWibfsYZY4TfMGKhIPxOQJToZ0b/Bmp3EpXI5g+/NfT1448/zt/FB4KNucc8xhKHOWnEeq0p7/z3I+SwSWkhu6Yflszd+71qes1fPpIR8pHs7tRP3qtylSzMqu1VvmBJlBRnyAZfjUWExX57pr/d1Eaka16Egl0WbD4uN4wtMNsM9vaa9uUh8HHfatK+Xl7QsWDGxNVz4Op6sPXl2Lq5suXpjkVuVnZlYij85/IQa7k85FguD4nVnBMKRS7dZPCEwPget0mHGsFPOLh6DriuX9o99XVLl5qektjdrzu7aG4AdpndnKAYYs5vq8SxksdKKEoGQdcwVeYayiCCSTKrzfq+m+JL5ZazPlAxrwP0A8XEtn/FbRQYoHRAfDgrj1VlsAFPd8KKMgE3cU2gHPLgXgLW7oR+ko5xoC1YUXiqS5fHeLHSj+JdUqEdlKPdcmzL03MHzLFOsBXq1/22ve54jPUDRIFWciEl6KcmQxzTB9M5JBMknrfCVq5PPfWUt8l9lo5ng3FknJzhCuYaf59V6oeCmG/6+ff0/LirHisjLI+cEX5gAaHqOJ/dlefuHlZBzvQMV/+vuCsrEPdyts6VzE/PCUTVvqsz5WTZNW2/ZO4tvhvH3v9dLR9Wu1p+z6rnu3b5saSx11WTNrVDV//wIzRFKho3RPf/UxepuOBOHAoKa3AjGJjWmXErHdxjUlwzznaEghW9mBgK+y1CYYozCwWIYGOhUDqDZtUSCkRDqYFRShWhbDgKCjArw1q0T7Q+R9H0hdKqyyvpt7M+UCZKlC+D24ELLhCuBGXEG+UfiwdbfL3F0raftMWbunRbISl8JbbtcCzTce7Y3kcZhGDxJI5xGUJB6dV9atCggXIh0ZYU+rqzb1xNSB8I4dlwNxahgjnzzRfPOC5KriRUsHDVfl9fj0goGlHu6/pLXF6llrJz0m7JOphaoqKq/P65DJHP5UCH3vJRjavl56zgXiipkRg2KnKJxtWbzAZJb1AyaQwCZRyBaMtCIbNpp/92eUiQJf8FZfzeCsqYHQGDYCNYoYchoXB/m27y94GdMnXjChsw7A9rxyfLgFZdZP2hPfLJ3/MkK6dgf+3E2PJyY/MzpWpcgryz/BfZcfRQfubeJ5wmdRPtldj3V/wqRzMz8tOYA4OAQcAgUNYQwD0EVxHioXgSiAbSGzEIhBICEUmhsZLvDNPcSqfIzm+3SfYR93FrnOV1dS1l7ni5X8bLzadfJmNqXSPTs5q4ShrQ6zUr2luaBbQxIV55WJENrJLjTmEkNBAwVg2lO05PtLlG5u78N69SCAXLP1UciYbSbVLQ1Daw1dlyf+tu8uXaRS7JBkiGh087Xw5lHJP46Fh5vF1PafPF03LgeJp0r9dCPuzazyIfciTd2o4K0uGj1X/I4/MmqT4+d+ZlEmut8to6tX27fqlFNhSOQK9BCTerBn+5RWg8zbdBwCBQ+ggQj4LYAUYMAmUWAes9KiKlkeQe+O/9KkQ6mlupjewYv1FyjvlHb0qa/53cJd/J9e0uknF1rpEpWc2DBpnqiYZo8OVghBXZ4EvgTFn+R6BdCMRq8D8KpoZAIhAfEytfdL9V2lSt67EZd7Y8S9ZZFg1dvn1ZsGJY1XeYDLHIh4f++FYeadtD9h47qsgHCvrq/Fulb9P2imwgbbmoaHls3kT5ePVcj/XoBOFGNuh+m2+DgEHAIGAQMAiEEgIRlZuGFNmQW/k02TFuneRYsWD8LYkLJ0t/63PtaT3kq3rXyLdZLf1dpcfyTwmBWA0eOxFECRzso4OoZX5oilFe/QCqKbLMIICbgBF7BE6pUldaVq4lLy750c4lwj5V3lm0tV+2lhxrv2ysFKpXSFSXFuzaKE8t/F7flrWHdluWDNFSuXy8nF27qbr+67a1cnnjNnJSpVr56cyBQcAgYBAwCBgEDAKhjUBE1ZNCpgO5ldvKtk//LhWiwRaU+MXT5Mbv+slXWwfLVVHLbG+V+vGJ1WJKvc6yXGFYWTaobRTnHC7L41mm+ma2vSxTwxmSnVm0e5M0/WyYZFiuD/e17uq2D88t/kGGn3GRsmioYLlRZFrxGl5c8pPKM/j3b/LzYi1BjIaDlnvFvvSjcmbNxureL1c8IBHWPwR3lt7T8rYgVBec/DHbXjoBxVwyCBgEDAIGAYNAkCEQWf0UKYjgFGSNs2lObuX2su0T13GpbJL67TBu6U/S1/r0stxXJzbqK2Oy2/qtLlcFn1Qj1tUtc70YCISVZYOJAVCMGRLALGa8Shd8Y5ZfGG9IBj7eyNVN2ymyYK9FIKRlZUhMZJS0rlrHLmtSbJz8evkgKR8VIzfPHKPuEQRyuxUssuekt6TluOHyi2XhwFj0a3a6XV7HEzNejoiYc4OAQcAgYBAwCAQfAhE1S19hLioKuZVPDzjRYNvmcn/9LH0m3CLfbLhLboqcZ3vL78etahmywZcghxXZAHBGgfXl9PFfWWac/Ietu5KNAusOHdf3miZXkxaVasq7y3+Vzt+8JC3GPik70g7Jo1asBi31EivJ/D5DlOtEnx8+kPm7NqhbuFe0/+pZ+WvfNhVM8s7Z49T1c+o001kLfZtxKgSJuWAQMAgYBAwCBoGgRCAipaFEVPQc+ylQjc+pfIZFNCwPVPVu641dMUcum3i7fLfudukfOcdtWl/crJMcLclxYace+wI6l2WElRuFSxTMjaBDwMTXCMyQoMTm70gRmCaEZK1tqzVQ7Z66qeA/61X7d0jX/wgDyIhpF98tOVYghx6WBcPqAzvy+/l65z5S0bJ4uHHGJ+paUrk49b1sz5b8NI4HZYlsiI01KwiO42vODQIGAYOAQaBsIRBZ39pefHneYkIw9SynUgfZ/smfwdQkp22JXj1PLrQ+PZq2lRnN+slbuWc5TVfSi12b5r2DlbQck78AgbCjbu7slFTQe3NkEDAI2CFg4gDYweH2pG5CirzT5RppVbm2zNi6WnKtf6926iNV4xLk4oatpIsV+HHOjnWqDHa0YGvL1/6cqVwrcLngQ/yGLakH5Ny6zeWWkzrKCUlVZXTX61RZo1f97rJ+M04uoTE3DAIGAYOAQcAgEHQIRDQ4J+jalFPpTNn+afATDbbARf2zSLpPukcmrr5B7pWZtrd8cvy/huV9Uo4ppACBsLNsMOb5BYMfzEcDDCkUkOEpSyvm/gDQ2mAiXxpUrKxIhSV7NsuHK+fII3MnyFOnXyJLr3pMpdlvxW4Y8sd30qpKHan2364UD1tbYdrKcst14tVlM6VTrSYytF1PeaL9hcr64Yn5UyQ10/Xe1macbFE0xwYBg4BBwCBgEAhuBCIbnRtUDcyp9D+LaFgaVG0qSmMi1y2TrtbnnIYnyW8nXScvRnQvSnaXaTs2MmSDS3CKeSPsyAZwgnBYsNn1i3wxsTTZfITAgI4VfVSSKaY4CBhXCueoNfjkUbsbv21fJzvTDsvag7vV9TF/zxc+uEwczkhX97ixOXW/1PloiF1ex5NLvn9HoiIipXFSFfnnv/Ic0+jz+1t304fm2yBgEDAIGAQMAgaBEEAgIr6aRDboIjkbZwe8tdmVOsqOT5cEvB2+aEDEhpXSecND0rneaPmj5XXybGTPYhd78cnxxc5rMrpGIOzcKIDCuFK4nhDmjkHg/jZGmfVmFjRPqWkFdDwqcyzSwVYgCyAhiirZuTkeiYailmnSGwQMAgYBg4BBwCAQHAhEnnhpwBuSndKpzBANdmBuXiNnfv+oTF5yhTyeM8nulrcn551o4jV4i1VR0oWtZUMwWjfUtSKg3nB6ouw4lC2fLU6V9Exbo+2CYSV4Yvv69mY+M9akyZrdmSpR4yox0q9dghxJz5Uxi1JlV2rB7r5s53JlmwQ5eCxHxi0+ItsOZRUUHCRHxoUisAOBZYOxbvA8BgR5PHfC654T+jiFIYN8DKgpziBgEDAIGAQMAqWAQGTzXiLT7y2FmpxXkZ3cWXaMWeT8pouriRdcJTF1GhW6e2zhL3JsqRVbKjJSknrfJuVPOk2OzJokR2dPtksb36mHJHS9VNKXL5BD330kkpNjd9/nJ9vWS/ttQ2VKjQ9lcZvrZVj0FV5VUTUhSs4xwSG9wqqoicKSbAAkFPZgcqUYfkEl6XVKvKSm50j5mAi57+wk6f/lHpnzb3qhMX2oa7K0qBFr+XYX3MrKzlVkAyZAz11USREVUZbdyrUW6XDhyJ2y9WCWXHVqggztniLZ1nOeaaWH2Ljl8z0yd2PhOgpKLt0j40JRuni7qg2Ftve0ka5um+sBQsC4UJQc+KysLJk82f5lqFy5ctKgQQNp0aJFySvwUQlz5syRChUqyKmnnuqjEr0rZtOmTbJ+/Xo555xzvMvgIdXOnTtl7ty50rNnTynqziPz5s2ThIQEOfnkkz3UUvj21KlTpXXr1lKrVq3CN4t4JdfaRWb+/PmyYsUK2b9/v5or559/vlSs6FuXv0WLFimMWrVqVcQWhm5y5sd3330nGzdu9NgJntHLLrtMatSo4TFtcRP88ccfcsopp0h8fIE5dWZmpqxevVoOHz4sTZs2lWrVqtkVT9s3b94s1atXlxNPPNHuXrCc0PYdO3YUak5iYmKhZ2TXrl1y8ODBQmnpd0pKirq+bp3lRmiNHc9mcnKyXdqvv/5aGjVqVOq/XXaNCNKTiAqVJbJFH8lZ9VWpt1ARDZ8VjWigkSnX3W+RDQ0L2hsRIWJ9omvWVWRD/fGLJbpGXcnev0cSul0uGf+uli3XdlTp646bK7H1m+TdO6+XRUr0l81XtS8oy49HuTs3y6nTnpIpVUfJn6fdIMNiekmWuDbov/jkCn5sTXgXHb5kA5YBc4pu6uyP6RIdGSGXtqwgP689JgO+3ivWqcy7r7bc1yXJKdmABQQEwc0WUeAoT/ZIUaTDFaN3SlL5SPn17toypFuy3GWVO/icZDlyPEe6vLVd0jJyZcptNeTNK6pI25e3OhYTsHNj1RAw6E3FIYBAOFs1HH+maOaN5R455nREMzIyZMSIEdKkSROpVKmStciSI7xcoyzcf//9cs011zjN5+uLixcvlldffVU+++wzp0V//vnnUqdOnVJ/YV+6dKmgLPiKbPj3338V3l27di0y2fDtt99Kw4YNi0U2PPfcc/LMM88UUqScgu3m4rFjx2TYsGEya9YshQkE0FtvvaU+o0aNUvPITfYi3ZowYYJSZMOJbIBogBS64447PGIFaUV6b9J6LMxJgpUrV8rdd98tPHv8PiB79+6VO++8UynWVapUUb8Tw4cPlwsuuEDdf/bZZ+Wbb76RZs2ayd9//y1nnXWWvPDCCxJl7T4UTDJ79myh3Y4Cafb000/bXX733XdlypQpdtc4efTRRxXZA5HHM9CxY0dV5vjx4yUpKW+nt2nTpsno0aPliy++KJTfXMhDIOqUfqVONmQlnSU7P1tYrCFwJAfqfbFAEQ37Xn9Mkq642TquJ7uG3iJHZnwnFS+7UaoOfknKNWstUSlVJbZBU9n99ABJnfqFJF97j1S+c6iUa3GqHF9VevEicvdsl1Y/PCMTUj6Ule1vkOHlesvR3JhCWFzeqoBgLHTTXCgRAmFLNuBGESyuFNUTo2TWP8fkzd/yyA8sFvYcyZH6KYUfhiiLiEi0SITFW47L2U3iJCMrVxEP5KkcHyXloiNk1LzDyurhgOUqsXJnhpxWp5wVfE4kzrKYmLzimCIamDWTV6TJPWclqTzHrXICLcaqIdAjUFC/caUowCJYjoxVg29HYsCAAeplWZf64osvyscff1xqZMPx48eV4qLrD5ZvlKjzzjsvWJoT8HaMHTtWsLBAeTrhhBNUe4YMGSJXXnmlIjM++ugjn7URZS7SMkkOJ8EqwFvyoEOHDgIh42tZuHChjBkzRrBqcJQPPvhAEQfTp0+XuLg4+eSTTwQiq0uXLrJmzRpFNHDtpJNOkrVr18rVV18tv//+u3Tu3NmxqICeX3zxxcJHCxZM/fr1c/p798QTTwgfLT/++KO8/fbbAmGIQK5AzHbr1k02bNigrH74zdiyZYtAvrzxxhs+t/rRbSkL35ENu0lEzbaSu6PoVgbF6X9WRYtoGFs8osGxvuR+90pMvcaKQMg+uE8qXnqDZO/brYiG2IYnSur34yRt3kzJ2rXNIhuqyJ7n7lNEA+VEJeZZgkVEF9ZtHOvxx3nugd3SYvoL8mXFD2TNGTfKiLjesj8nbxHjvGZx0rByYNrlj74GW5nh9b+aA/rBEiiSuAn3frdP1u/Ni7nQ0oqr0LBytCzdVnjHjNYWcYBgAfB2ryrywVVV5bd7aktyXKScXDNW3Vu7J68cTnYczpIKsRFieU3IUcuagXK1UA9CHIdgEGPVEAyjUNCGcF5FL0AheI7MePh3LDCbRplAVq1apRQglA+UB1byMBtGKWrbtq3ceOONgpuDFu5ffvnl6h4v3Z9++qm6hRXF888/rxQP8vXvb5mQWhYUrP6xmpiWliZ9+vRR9emynH3zAo/SO3DgQFUHdf3111+Snp4u1157rV1buM416qYfN998s8qD8oMCAcmBfPXVV2p1lnbRBt0fvlkB9VT2oUOHVHmUS3tQuIojKGgQP7TjkksuEZT37OyCOEMoMxp3xgLFRwvnrCZrYYX1lVde0af5365wwKWGvuO+QB1YQtgK7XjvvffklltuyScauF++fHmVFuUXE/sDBw4IBARY0I9bb71Vtm/frop68sknVZvBHpwQ5gttJy3fWsn98MMPhT4grsZc3bT+zJgxQ40zZTDev/76q76lykQhpGzaRL9QqHWd4E2bEVfY5BcWBgeVK1dWivRDDz1UqLdY+rD6r38bcOPguWXeMXexaIBoQLCGwNUA94JgFn4D7rvvPmXF4cl1jOfvkUceEchY7TbE3MEqDMHa48iRI+o5gCyDwMBSxYh7BKLa9nefwEd3Myt2kZ3jfEM0QBJUunWIZKxbWUAgWNYLxGxo9MsOqTv2D2k0e7sk973LisuQbZEQu+TwpE8lxnKjwBoCy4bja6z/t/6a76PeFa+Y3MMHpOmPr8inP50nrx0bLTUjj8jVpyYWrzCTyysEwpps0NYNXiFVSola146Vz66tpoiB+ywCwlFwjdh3NFuG/3BAWj63RV75+ZCkWETDi5dUlhqWhQRC8EctR4/nShR+GZb8ZAWRhJAYd111RVJ0OSHvxTqhXOCngbFq0CMWPN/auiF4WhS+LTFWDb4fe0yyJ02aJJjqo9ixco0yj6BMoJxBCFx00UXSuHFjuemmmwTzdpT0Hj16yL333quUDVbzhg4dqlYNUfAgIljZw+cZUuH7779Xq4Lki46OVsrwaaedJr16WYHCLEFJxV3CneBrjcsFSg9uF7Vr11am2ii95LU1eWYFljgFxEegjfiRYxb+8ssvq3Ss0rOqian3XXfdpczSMYcmLco3vt1bt25VCrWrsmNiYuTBBx9UfcSVAMWFb7AsiqC0QIag7NFG8Ids0GQNZeG+gFLP+IA7is+SJUtUNSh7kCJawHzbtm36NP/bFQ640ODmwSpt8+bN7VZ+yax93Nu1a5dflj5AwbztttsELBjvf/75R5mW0w+IGG1GDpbMI9pJ/8Cf+YLSOm7cOGEuYLq/Z88e5c5DTAjE1Zhzb8GCBWredO/eXc1HXF7ow59//sltNS/BkDFinBmXBx54QGGN9Q64MfcRV9iom2Hyh/gCEF0XXnhhoR7jHoCblRbcKhCuQR5h+aAF4mb37t2KdNDXgvGbeYcwBz3JSy+9JL1791axKnRafg9xOYG0YM4RS2PkyJHqWeD3D2z4DTXiGoGoltdKRJXmrhP44E5mYhfZNW6BD0rKKyLpytsFwmHfO0/mlxlZIUFZMBz9ZYpsu6OnRUSskKTLb5a4Nv/LTxOVXFmyUw9Iblamit9Q/uTCv6f5iUvxIDctVRrPeEM+3TFUTq+ft5BbitWHVVUFy9xh1e2CzmLdsGDs7oILATxiy5VXLqtikQXZcpEV1JH4Co4yy4rrwEfLh5bLxE1nJErz6jHyyYJUdZmIqnoHiuQKkfm7WgyffkCqWffaWNYRhyxC4nNrN4q+bRPkTycWFLp88x3eCJhAkcEx/saqwffjAJmAoomkpqaql2OsDmyF1ej27dsrs2ECFaKQRFiBsTp16iQ//fSTIhL69u2rVo4xKeYFW6/+oXBCBnAN5Q6F/qmnnlJKJaufrIgWJQAkJtAEWUQw4UdJRFCQOKYe6qNdjz32mLIOgAQ4/fTTVZBFLB2oD4WcgJgIq7O0A6X57LPPVvEr1I3//rgqG9yIOYHiCi64F2AhwKq8Xr23LcfVMavztJn2Qo6wMoxFAIQKSgsCbtddd506ph6IBhRlbwNnYp3gCgdVqPUHywAwcBTGEAE3d3LuuecK8wClFbKDoHu2AfboF6b3COQSpBF4Iffcc4/Ur19fET3qgs0fV2MOeQDBwCoyQr3Lli1T85TghgjltmnTRh3Xq1dPaCNzFIHggOjxBhuVIYz/QHBhGQLBV7duXSGeAYKiDXnIB/n5559VbA/mv8Zd3QiyP8xLXCKwVPAUVwJijMCo/A7ayu233y4PP/ywcj1hXkFSfvnll+oc6y2sfXCPYc6feeaZtlnNsQ0CUWfcJ1lTCv/u2CQp9mFmvEU0fO47ooGGJPXpLzmWVQBuElpy0tNUyMVdQ29Vl3YM7iv1v/tTEs69PG+nCutq+p/zZNut3SUyPlEa/rhBKvV/VLYPvFQXEfDvStdalhhG/IpA3q+kX6sI7sK1dUOgd6a43NqJ4ilrR4otB7Lk0lE78wkCR/T6npagdpXo9dEu0XEWyltxGv7ZkyXafYKdNlbsyFBZG1k+SPvT8kxSe7dOsLbCPCK3fLFH3Xv03BQV22F/WmFSw7Fef55j1WBcKPyJcPHL1tYNc3fmKWXFL8nkLC4CxqqhuMi5z8dqMoqsFsgHTOltAyNqc2AUSFYsbX2eyUcUdlY+Udpfe+01ZU6sA8txn7IILoeLgVZYMGEvTsR6W+sHFHQtZ5xxhlKGcX+AxGAFnWsoEhAM1Ef7WX3UQjR/2oNSzyonyjQm9tocXKdzVTYr68gNN9ygvvUfT0q5Tqe/MTcnAKTtLhVE+2d1X7t7OGLFOW4P3oo7HHQZrogLrEIQrAwgBGwF6xBcWxhf+o3lCKu9YItZOcqpFnDUQj7IBi1YRrBy7ExcjTnWEpBDtoJlhi0u2sydNJBLtrtyUCfiDTYqYRj/ufTSSxUJh8UQAvGFlZHGF0sgLKOI0wDp5421QCDhhOBjvtr+9rlqD/ExCHhZtaplKm8jWDZgqYVAWkF+Yq0DWQtxi1UPFl3EwTBkgw1wDodRraxAkUtHSc62+Q53SnaaUaGL7P7St0QDREF01Zpy6Kv37RqXfWCvRFQtvDtM9sH9ipyAoNhydQfJzTwuOUdTJSf9mAooaVdIAE/iz75Y4tqeFcAWhEfVYU82MMyBtm4gsOPwHnnbVY6elyo9W1RQs48dI6atTpOzLHcHtmR5aNJ+WWQFhnz0vBR59bLK8qTlSnF35yS1VebYxanKmmG7Ff9hQMckRTx0aFBeGlSKltd+yVud6W4FQGlePVbYqQLrhytPjZdf1xdYSQRqyhuiIVDIe1evsW7wDid/pTJWDf5C1r5cVi0RfJRZmUa0EozSiQWA7c4RrO6zqskLOW4M77//vlKcMbPWyiRKKqvJuGCgWOMigBsA1hK+EtqASwYWDTVr1lSECO0meB2WFCjBWGIQeFBbHaDMo8hiao+yRF4UJkeywVXZWvlgNVdjBRmDK0BRBPcJ3FBwZ9CBEbFsQBnS1heO1iZgquunLtv4DtrE3bYN7nDQ6Wy3OdTX+KZ9kAYTJ05UBI7tvZkzZ8oPP/wggwcPVm4QWCq8/vrras7gugDhoMW2fNpua5bPKjArzVjNeCuQSo7uIuDI9aKIN9gUpbyymBbXCJ597fbE/OT54fcCMo+VfIgHXKhs52WwYgEJQBBYTTi5aifzF2ICUtKVsCUsvzH8vvA7x04hEBE8y/y+2M5zV2WE+/WoTo9KzhcX+wyGjDiLaPjKt0QDjdNuEWlzZ9i19eCY16Ta0Helyv3Py8HPXpfqT49W91OnfyUR1jyocu8z1rVRsueFB6x4Dw9LZFwFSZ38g10ZgTypdOOgQFYfNnUH3lk/CKDGuiGQMQNuOj1RbXfJbhFPWFtXYuHA5/mL8wLwnNs0Tno0r6BIhTW7M+WLJUcEIuHnu2rJZdZWLT+uOaZ2lgDK+ybsU2WNvLKq3GCVi8XGyD8OK5Qf+36/HMvMkan9a8rHfavJv/uy5O5vCseFKM0hCSTupdnPUK4L6wazuh6YETS4+w93lAYIAz5YNWBWjLBvvKOwCoj1AubrWA6ggKBcolzv27dPBY8jUBqKu/bhJh1KPKudKMgogrpsXsb54PqAkogZckkEU28Uf4gPjhFWHBFe+lFoUZhR3GkXJs6s0BL8DrJAm31rhV9l/O+Ps7JxZ8A0nxVe+kz/Bg0alB/o0Da/PqZOjbf+JnghJAWBEWkXyi8BGVlN1gKGxG3gPoEUCa6orU/AlPOjR4/muxHofPrbHQ46jatvXGZwR6ANWK7QT9pBkMp33nlHBY4EW8YRywEsBfBhh3xiy0yUMUfp0qWLWhXGDQXscEWhD7aWEI55HM+J1YArCUEhUXghPmgT14siJcGmKPWEclqIBaxXeP4ZrzfffFORD1jkTJ48WZE+uMYw1sxlPtoqJ9j6DTG3fPlyadmyZaGm8dtmG+SVZxRxF0AStymea559hN/A9evXK/IQ0pbfCCPuEYhsdK5g4eALyShvEQ3jfU800LZy/8VZOLb0d7umpv7wlRyZbm192usWqT9huZRv3kb2vTlUMjetlYwNa+TQ+JFS4fRzpMHkVVLx4n5ybMFs2fvaI3ZlBOok+dq7JfaEkwJVfVjVaywb/htuVtcXWop5INwpXpx1UPi4kudmHpSeJ1XIj+FA7AU+javEyKb9WZKVU/BCs3x7hpz60lbBfWK7tRNFembBvY1W2o6vbxe22jyUnmN3z1Xd/r5urBr8jbBvymd1HVcK407hGzy9KUWRPBbuRvyDAKvPtsJLNdYJrE5i6m4rKIIESmOVjw8rmfj44/vOCzZKhzZrx/8fJRifZl7e8aUnyCTCij0R74nrgLsF5WJ2Tb2U5Sha+dfkhON9fU5ZfFBytHKACwhlYuKMYM5MGyFVWJGnneyaoIWVdcz9bYNNcs9Z2SjV4EH0ea3447tNmY6i+4B1h6Ng9o8Sz+4XBJhDwAqfcIS8ECH4fqP0IQSU1LEHIHKwFIFIAXP6YBsrgfTucMBEHNFtVCcOf+gfc4U22Fq24HJDf8kLcaPnBgoWFiSkhQBCwEsLbWR+sSKOEGMBn3htRQPBgVCuq3aBNZYNBIXUAinSxSIytNjmpX5drr7Ptzts9LyxTR+OxxCNEG4875BKPA/MfQSyjmuOrhPDhg3Lf+aDCTMIVsTRJYhrzFUsEa6//npO838Dbd1v1I3//hCHhucCVyK9Uwe/H8x74pdAwmJVZcQzAlFnPyXZ/0yxghvkEcSecxROcbxcF9nztX+IBmrb/95T6lO4ZitY6pO3y+4RAyWmQVO1U4Vtmr2vPix8yjVpKRlb1kmu5UYRDBJTt5FUvv2xYGhKWLQhwmJjC7TRYnR5xYoV+as1xcgeVFkgGm4IkmCRtsDc/r+KUjspWh6fmveyZXsvlI/LUqwGV8+Bq+uhOG4QDb2n5SkEodj+UGszVg1lxYXC1XPg6rqzsTr+TJyzyy6vlXvEPy81KL3EarBV5nAD4OU6JSVFuVag9LOKCLmAYJLMSre+77LRfrhBe4nxQFtoJz7VxJlAaCdBECE/bONAFKUZlIfLg1aWi5LXNi3txMrCmXm3xheSx1ZxJ7/eQUP70NuWaXvsDgfbdO6OKQPlEncVx3ZgXYGVA3MDgfQAZ2dKPveZH8wLPRZcK6qAC9YJzCvb+VjUcnyBTVHr1Okh39ge1lspanpvy/WUjvFijHUAWE/py/p94obwmwb54ijMSXByfEYc03l77ur/CVfXvS03mNJl/zWm2MEij8daRMO3/iMaggknX7WlxvOfSXynPCtAX5UZqHKC/TmAxDSWDTazQ7tTvD0nz+3A5lZAD9/7Pbja4wswyhLR4As8QqEM7U7xyjJ7n71QaHuotbEsEQ2hhr2n9jpTalH0bP21dbwBXRY7NvAJhNi2l3baKre0E4uAkoiO2VCSMshr207Hshzxtb1PXAl3eXVa2zSOOOg0nr4pw7Yc2/S2cRm4rkkH2zS2xyhitmNhe8/bY/pBXImSim2fiotNcdtA4FK2oWV7U09COtIHQhgvQzQUIG8bvLTgat4R5JeRoiGAK0Xupl8le/lnRcqYHn2W7DVEQ5EwS+p9a5khGorU8QAmNmSDA/iBdKdwaEqZPVWkjuW2YiT0EDDuFP4fM+M+4X+MTQ0GAYNAcCCACwKBBYk34kkgGhxdFjzlMfcNAqGCQPT5r0vO9oWSu2+NV01Oj7SIhgkLvUprEuUhEF2tllS57zkDRykjYMgGJ4AHencKJ00qU5cITmkkdBGAcDDuFP4bv/E9/LPvtv9aXDol+8stonRab2oxCBgEnCHAVqHEvzBiEAh7BGIqSPQF70jmmK4eoTgWcZbsm2SIBo9AOSTAfcJI6SNgdqNwgjkr70YhdgKMDy6Z3Sd8AGKAi2Dl3SjE/hkEs/uEf3A1pRoEDAIGAYOAQSDYEYise6ZFOLzttpnHcjvLvsmGaHALkpOb1R5/W8qdeIqTO+aSvxEwZIMLhHX8Bhe3zeViIGDiNBQDtCDNouM3BGnzQrJZJk5DSA6babRBwCBgEDAIGAR8hkBU65sk6swHnZaXlt1J9n2/yOk9c9E1Aik3DZbEHle5TmDu+BUBQza4gZf4DWYl3g1ARbhliIYigBUiSXGnMCvxvhksQzT4BkdTikHAIGAQMAgYBEIdgeguT0rUKXnbkOq+pGV2lP3TFutT8+0lAhUv7ieVbhniZWqTzB8IGLLBA6qGcPAAkBe3DdHgBUghmsQQDiUfOEM0lBxDU4JBwCBgEDAIGATKEgLRPd+TyGaXqS4dzbCIhulLylL3SqUvCWdfLFWHvFYqdZlKXCNgyAbX2OTfgXDArcJI0REwREPRMQu1HIZwKP6IGaKh+NiZnAYBg4BBwCBgECjLCMRcPk6OHD1DDvxoiIaijnN8x+5SfcRHRc1m0vsBAbMbhZegEjDy7d8OydtzDnuZwyQzREP4zAEIB+SVZTPCp9Ml7KkhGvIAzMjIKCGSJrtBwCBgEDAIGATKJgKVR/wsGQ9cKWlzzfuVtyMc3+kCqfH8GG+Tm3R+RsBYNhQBYONS4T1YhmjwHquyktJYOHg/koZo8B4rk9IgYBAwCBgEDALhjEDNl7+UhO69whkCr/uecF4vQzR4jVbpJDRkQxFxNoSDZ8AM0eAZo7KawhAOnkfWEA2eMTIpDAIGAYOAQcAgYBAoQKD6sPcl6crbCy6Yo0IIgE/1J94vdN1cCCwChmwoBv6GcHANmiEaXGMTLncM4eB6pA3R4Bobc8cgYBAwCBgEDAIGAdcIVLlnhFS++ynXCcL4TuW7nxbwMRJ8CJiYDcUcEwiHdvXLyw1jdxezhLKVjQCad5pAmmVrUEvQGxPDoTB443vcJh1qNCp8w1wxCBgEDAIGAYOAQcAg4AUCyVfdKbH1msiOQVd5kbrsJ4mqXF2qPfK6VOhwbtnvbIj20Fg2lGDgULBXPVxXWM0PZwEHAmiaHTvCeRYU7juEw9YbnxNW88NZIBjAwRAN4TwLTN8NAgYBg4BBwCDgGwQqnHmu1J+0UgiEGM4S3/F8qfvRLEM0BPkkMJYNPhggrByQcNypwrhN+GAClfEiwtnKwVgzBOfknjRpkpx66qlSp06dUm3gkiVLJCYmRlq2bOmTeleuXCnr16/PLysiIkIqVqyo+paYmJh/PRgOdu/eLfPnz5e1a9dKQkKCtG3bVrXT122bPn26tGrVSmrWrOnrok15fkCAeTF58mTZuHGjx9IbNGggF110kVSrVs1j2uImmDt3rpo/8fHx+UVkZmbK6tWrJTU1VRo3biw1atTIv8fBunXrZMeOHWrOnXDCCXb3gulk7969sn//frsm1a9fX8qVK7y1u6c+87uza9cuadGihSQnJ9uV+e2330qjRo2kdevWdtfNiW8RiK5SQwVCPPjFu7Lvjcd8W3gIlIbbRPJVd4RAS00TDdngozmg4jhYpEO4bI9p3CZ8NHHCpBgVx8GydHhl6Yyw2B4TKwb6bKwZfDfBq4+qX6TCdt28yWn6nJwcee6552T48OGlTjZAcqAo+Yps+O233+TTTz+V9u3bq74eP35c/vzzT1XHxx9/XEgJcAqIDy4+9NBDcu6550q3bs6tmFDgHn/8calevbq0adNGFi5cKLQPxXHIkCECSeIrefHFF9XYGrLBV4j6txyIBpTSO+7wrDQwj0h/8803+6VRq1atkgceeEA9U5o0gGAYMGCAbN++XZFkkCODBw+Wyy67TLXh6aeflqlTp0q9evVk8+bNct5558mwYcN8Oqd91dkPPvhA4ZeSkpJf5HvvvSd169bNP+fAU58h9N59913p0KGDjBgxQsaNGydJSXmLbtzjN2nMGLPtoB2ofjxB4a5wRlfZ/86TcnTOD36sKTiKju/YXSrdOUxiG5wYHA0yrfCIgCEbPEJUtARl3crBkAxFmw8mtT0CZd3KwZAM9uNtzuwRQCmPjPSt9+KJJ54or732Wn5Fe/bskUsuuUR+//136dmzZ/51fx6wYoqC4ky4DtFw/vnny/3335/f/ylTpsgzzzwjXbt2zSdLnOUv6rUJEyZIXFxcUbOZ9AFCAIsGb4gGmodyy/j6WhYvXqwUZsgMR/nuu+8kIyNDIAorVKggb7zxhrz99tty6aWXyvLlyxXRMHbsWGnYsKH8+uuvijzr16+fsoBwLCvQ5//884965rp06eK2Ke76DDHI/bvvvlvOOeccZZECeQjRuHXrVoHse/nllyXYLKvcdrgM3Ixt0FRqvDBWUqePl31vDZPsfbvKQK/suxBTu4Gk3PyQJJ7fx/6GOQt6BHz71hP03S2dBkI4lMVYDrhMmNgMpTOHynItEA5lMZYDLhPGbSL0Zu51112nVvtuvPFGOfPMM+Xee+8VlGdcFPr27auOda/Gjx+vVvI4ZwXvqquuUnlQ6lE4EBSTl156Sb18U95dd90lW7ZsUfc++ugjwcTYU9mYKA8cOFCVfeutt8off/yh8nvzp3z58iqZVrhZeZwxY4awAkt7s7OzlVUByhIKwrPPPivHjh1TeQ4ePCiPPfZYfttRAlnRRXAB0RjRX71yySozJuYffvhhPjYqw39/vv/+e3XE6rAt0XLBBReoFWNt1eAKT8oGCxRBxuqHH35QpuBDhw5V7XTsA6vOf/31lzJtv/322xXevXr1Ulg+//zzqv80iL6CCX3hAw6aMKHNjCHpKR/cli5dqhQpzkkPWYJgKcMqL9cYb9LOmzdP3TN/QgOBSpUqCQr4oEGDCjU4KytL+vTpo4gGbuI2kJaWpubRoUOHlHUORAPSJVD7tQAAQABJREFUvHlz9Y0LQrAJz/2aNWuUNRekwJEjR1w20V2fycSzA2ZIlSpV5OjRo0Kfsei45ppr5JRTTlH3zJ/SRyCxe29pMHmVVL5jaOlX7scaK9/xuNQbv9gQDX7E2J9FG7LBj+iWBdJBB39U5Ml/sSn8CJkpOowQKAukg7JksAJgmgCQoTtx8bfGlBhl+J133lErdV999ZVgMYBPMquVWr755htp1qyZWsF78sknlYL5xRdfyPXXX69WO3kJR2lGIX799dcVAREdHa2OKQMT7AMHDrgtGwWmf//+ytUCAqN79+5KCaKdzoTyUHz5YL6M9QBKj3atQLHAbSQ9PV2tIJOGtCgGrNJSLhYGCCu2nL/66quqrMOHD8vXX38tubm5yjqBWAhgc9999ykFGyKAFc5atWqpdvbu3btQEyFOiI+hyQ+dAOIBMqddu3Zu8YQIYeUZxR+CgjZgIYKPPBjT9jlz5sjIkSNV0cSEIA8uJZAOuJqQBreZiRMnqrgR9Af3DdoGNtxfsWKF6iOFMI6QQihSmJ5jUs/8QKF666231LiTB6WM8YZ4efjhh4W5gDL65ptv6m6a7xBAALIAl54ePXoUau1NN90kl19+uWzYsEGNL88G5BfPdadOndS4M1+wfGBOdezYUZo2bVqonEBf4HcAueeeexR5grsHzw/PgqO46zNpwQuXE54xrDuI+zBq1CgVjwZCkN9NTWA6lm3OSweB5H73SMOZm6XSrQ+XToV+qCUyLl61n34k97vXDzWYIksLAeNGUQpI28ZzWLj5uCywPsEuxl0i2Eeo7LQP0oEP8RyQV5blfQdzD427RDCPTtHbhsJMwEKEl3AsEVAmUDJ++umnfGUDn2xMh1klRHnlmFVObTKMco5lAddQ2lnpRqnHtcFW3JU9a9Ys5RuO8sOqP8rLzJkzZdq0aUrJsS3n/+ydBbwU1fvGX+rS3R0WICq2WIiCHdh/G7sL9Sd2K3Z3dyuKiQFY2IqBSSkpJUjnf74Hz3Xu3t27u/fu3q3n/Xxmd2fmzIln5sye9znv+x5+Q2Cg8CIoDlgiEMAO6wwCMSIoy8ziI1dccYUdd9xxxQEUUQ5QlPFVx6WBmXmUCfKhXZAZ1AOygPaz37t3b3vooYdcOQSHw18bv+9oShbpCVpZloBZLDz9dVhccI/AFcsQ/MQ7BQEDEQiDSIzdieADSwfIEOoGeQNJAckAEQHx4n3zzz//fMOKxFuhgIGPDYA1A/eF5wRMCaj3xBNPuPtMwECUNkiTWbNmufMoW5L8QoDnBkKK55mNd0C1atVcI5nZx/KH+088hPDMf7aggCXD5ptv7ohM+gJuVpB2xI/hPRZNYrWZfoJrFP2JvgEJBynJOwFLLvYnTpzo3jlbbLFFtKx1rBIQQFlvfOTZ1viIgTb35Yftn6GP25Jfv6+EkitWRM2117f6ex5mDfsPsMAcrmKZ6eqsQEBkQyXeBh/PAbLhi4mLs271CggG5KTAgsH/rkR4VFSBIwDhgPRq3cVGTR2XdaSDD/ZIPf3vAr9ledP8cHR5FF9M4xGIB5RKFHosHJjJRJlgMI3/M7PcDOK9wso1mGOjzDPbzWw8ii7m/JGKeKy8GeBT3r777kt2xRIZ8d2fwAIjPJPObDuWBwRgvPTSS10yb+WAmwBECDEewnEeSISiBKHAcSwWCOaIEuUDLTKTj/UAVhcISgZWFPGE9pNfNHn66acdEQEWsfD013nTbK/IE5DPS1kBN2mHF+4tSiL4IhAKXoiej/j8vZk4xyCH8Nf35I1XMjkH0QLxAWHD+XjECtdIcg8BiEM2lOiDDjrIvQv8c9e2bVvX17AeOvTQQw03HOI2ZJOsu+66zmLJ14l3GUTmt99+G5NsiNVm+op3G4NYwUIJsg6ijncM7z7IUdKIbPCIZ/A7UNgb7nu02xZ9/ZHNH/aCzX/nRVu5aGEGK1W66AYBwVBvx/2s9kZblz6pIzmNgMiGDNw+FHk2yAdWr0AyuWwmdRHBkIEHQUVGRQBF3lsOjJo2LqPEgycVRDBEvVV5fxASgQ2iYdiwYW7mm0Zjjo9CgdsBs+so6lgiINOmTXMWAgMGDHCKK7PnxBfwlhMuUfARK+/mzZs7NwviO3jBhBuFNxEhHcoP5s1e/DJ+XlkmHgGKBIK5M2QA1+y+++6O5CDAW1FRkd10001OeYBcIR1xDbCeQEFBuaCumJSXJbg9MOuJNQFWAF4gaTC9xj2hLDx9epYMRTzpwhJ+uDkgxEgAI5TASPExIcLHPZEAsQAZgnDfEH/O7STwATlDW3CxgXgYPnx41NgVCWSlJFmIAJY+e+65p4tFQPVwGYB4wi0Bq6fJkye7gIicg8zChQkLoGwTLHMgBrDW8gLx5olVf4zvstrsCRbS8S6AhIS0wDoCVxKICFykWFY4lrUR10oygwCKPFvzQbfYgpGv24KP37YFw4faygXzMlKh+rsfYnW32snq9q6cYMYZaaQKNdmnZPgh8C4WxETwQSUJxJgu8UQHgR7ZKFNBH9OFtvKtKAKedCAmAsEXBwbxEdg8CVDR/COvJ182H+zRf6ervMjytZ99CLCyA4o/iinR8BEUXawAUHaZycN8GGFmE5cHTPdRXlHG8eFHwrPh7kDwES3vrbbaygVyI+o++UEEMHMYa+AOCYCizYaLAZHiGfR7MsGXxTeKN0v2EYcAZYm6Q5hQf5QOrB6wZKCuuBmwpB91QCkhWCSkAYoEcStQ0n2bOIZFBvEmIqVPnz6OWMFNg9gL5IeLxqWB1QXuCLhulIVnZH6QNFiXUG/qj0UE5AmKXqJCHlhGQKZQNi4nuEJwHPIoGeFa7jOEDoonVg4ImElyHwEsaojfQYBQnl3vPoOlABuBSyEd6Ieff/656/8s5ZmNQj9h5QjIw5EjR7q6QxQgEH60DSmrzS7Bvx/gArFCUF2kadOmNm7cOPcu4X0E4SDJXgRQ8Fucf5t1fie4Vw+9Z01Pu8Lq9tnDqrdMz32r2qBRkP+eQTlXuvLW+GSWK19EQ/Y+I6mqWWJTJakqTfnERcC7Wvhv73LhLyTmg5dosR8gE8KyabC/acfVg7DIc+F0+i0Esh0BTwSE6+ktH/wx9r2Ef/tjkaSByzNw20Aiz/lr9J2fCKAgewn/9sf8NzN2zPQzI4hyjLCMI4o45v8IM+otWrRwFgysyjB69OjiGURmQYly7+M6uAv+/YiWN0o8MQhwwbjuuuucYk3sAIIsRgrkAYN9yAgvKOKsvuDjDXA83D5cOsibCPsI+eIOQT3JhzLZUMYhQ3B1wHKA2BMsa+ddNrBY8HmgsNx7773OmoKVGcICIQExQJ5hKwhmgFHwcTsoC8///e9/4ezcPeB+QFYQPJN6cz3BI8MSzaLBn8f6AwyIE4E1B0J7mKUFKzZPpHCOfX/v2Q/nTfT9Cy64wAUFpS6Y0RNTgrYl4mZCfpLsRQCSjXgnWOAg9C+eG/oHyjQEH33DCzFQogWa9Ocz9Q3ph2sHASIRnlWeT+/mMGLECOdexPNbVpt9/Wk3/RB3LR/8FXct3hdYdWHtRZ+X5AYCNbv2NDb7v5NchVfM/suWjvvJlk783ZZPnmDLpk+yFTOm2oq5s2zF37Nt5T9/R21YjfZdrFqjZlatWSurEZAW1YPlKos6rmVFXbpatSYtol6jg/mPQJXADKp0KNok2k3AnB49eiRxhZIKgfxDIFY/iHU8/xBQi4RAbARi9QOOR8YyiJVLywc7xjoV9fj0oydGPZ6qg1gCMCuOWT/KK5HZOeYH3pjWMxPqz5enXALRYZofJgvKk0+0a6gvM5zetcKn8cvYeXcFTK+pAwo2s/XUCcU7Gnni84j1TXlYQKCwRV4fD89oeWJJQf3DxEC0dGUd4z6BL8pXeYXlTgkOigsGeZEnlhaJur6Ut9x8uI5AgxA/iQpBDQlyWtlCX8byh2c3TDZRD+Kk0E84V5FnsTLaRB/0z2pkOyLLL6vNWPFgzYE1UKSABf07VVhgvRRNz4j1vxJZH+0LgXxGINv7AVahsmzI5ydQbRMCQkAICIG0IIBS6WMGUEB45pt9lOBIRZ7jyQjKS7qE+kbWmbJ8fAdfricd2Ed5CLfZp0n0m7gL4cCO4evi4RlO639DglRUKnqPKJ/4FmFcUpFnRduVK9fjtoIrgndRKqvepCN9JgTyKJarDqRS+P5non6JlkkfxN0hESmrzcR4iSXhd0asNDouBIRA4SAgsqFw7rVaKgSEgBAQAkJACAiBrEGAwKpDhw41YpTEE4gGH4g1XlqdFwJCQAgIgexAQGRDdtwH1UIICAEhIATKQCDdbhFlFK1TQkAIpAkBYp2E44ukqRhlKwSEgBAQAhlC4L8IWRmqgIoVAkJACAgBISAEhIAQEAJCQAgIASEgBPILAZEN+XU/1RohIASEgBAQAkJACAgBISAEhIAQEAIZR0BkQ8ZvgSogBISAEBACQkAICAEhIASEgBAQAkIgvxAQ2ZBf91OtEQJCQAgIASEgBISAEBACQkAICAEhkHEERDZk/BaoAkJACAgBISAEhIAQEAJCQAgIASEgBPILAa1GkV/3U60RAkJACOQUAkVFRTlVX1VWCAgBISAEhIAQEAJCIDEEZNmQGE5KJQSEgBAQAkJACAgBISAEhIAQEAJCQAgkiIDIhgSBUjIhIASEgBAQAkJACAgBISAEhIAQEAJCIDEERDYkhpNSCQEhIASEgBAQAkJACAgBISAEhIAQEAIJIiCyIUGglEwICAEhIASEgBAQAkJACAgBISAEhIAQSAwBkQ2J4aRUQkAICAEhIASEgBAQAkJACAgBISAEhECCCGg1igSBUjIhIASEgBDIfQSWL19uQ4cOLdGQmjVrWqdOnax79+4ljmdy56OPPrI6derYRhttVKnVmDhxoo0dO9a23377lJQ7Y8YMoy0dO3aM2pb333/f5s6da7vssostXbrU3nvvPdttt90slauUfP/997Z48WLbdNNNS7Rp5cqV9sorrzicd9pppxLn2OG633//3V3Xrl27UucTPfDll1+69qy//vqJXlIw6aZNm2Yvv/yyTZgwIW6b6aN77723tWrVKm7aVCZYsWKF/fLLLzZr1ixbY401rE2bNsXZ//HHH7Zo0aLi/WrVqtmaa65ZvJ+NP8CcfrneeuvFrB594+eff7bZs2dbhw4d3BZOTL8gnx49elijRo3Cp+yFF16wLl26RO3vJRJqRwgIgYJAQGRDQdxmNVIICAEhkNsI3FflvqQacNyq46KmR6G96qqrbK211rImTZoYg+rp06cbSsPAgQPt4IMPjnpdqg9+9dVXdvPNN9sTTzwRNeunn37aUHArm2z45ptvnLKQKrIBJRK8W7RoYa+99ppVrfqfQSWKzP/+9z/X/m233db++ecfl3aHHXZIKdnwzjvv2Lx580qRDRBP1A2BaGrfvr377T+uvvpq++233+y6665z98IfT/Z7yJAhrv0iG0ojB9HQs2dPO/HEE0ufjDgyatQoR0wkkjbi0nLv8kyedtppjoBr27atex7YP/zww12eJ5xwgs2fP99q1arl9lG8n3vuuXKXl+4LIU5uvPFGq1Klinuuo5VHvzj11FPtiy++cO9J+sBJJ51kRx11lEv+xhtv2B133GFbb721XX755fb8889bw4YN3bk333zTHnroIXvmmWeiZa1jQkAIFCACIhsK8KaryUJACAiBQkfg5JNPdoNlj8P1119vjzzySKWRDUuWLHEEhy8/W7533XVX23HHHVNenb/++ssgMjbeeOPivN99993i3/xgBhVLhwYNGpQ4Xhk7b731lh177LHFRf36669OsSw+UIEfF1xwQQmSpQJZ5d2lkFGJkge9evUyiJvKFBRpyEisoVCoX3rpJYOE2muvvdw95bl+++23rWnTppVZrXKVdc0119inn35qkydPLtNyadiwYY5ogAiCgHv99dftkksucRZHLVu2tBdffNERs3379rXx48fbZ5995t4Zf/75p1HGbbfdlpE+XC5QdJEQEAJpR+C/KYa0F6UChIAQEAJCQAhkJwJ169a12rVru8qNGTPGKUCffPKJHXTQQcZMHmbDKEWbbLKJHXnkkc41wLeE8/vss487h6L+2GOPuVNYUVx77bXGrD3XHX/88Y5gYPbvyiuvtIULF9oBBxxglFeWMIB/8skn3Wwj+VDWd99951wDDj300BJ14TjHKJt8jz76aFc2dbj00ksNkgNh9hVigfyoA64OCN/MVuJ2UFbeuD6QH/lSn0cffdRdH+uDsrBsCAvKDMe9TJ061Y455hhbsGCBPfXUU272GOsHxN8LFBoEUoJ7Q/2ZacYyxQv5+vvBDO2kSZP8qajf1IFrsHLxgoIVad1BeZiWe0ERvemmm9zusmXL7K677irGlHJxSUEeeOABN/vL71j3knPMKN9///1OkQVX8hg3bhynHPHBfQrXEfx5lpBY99Od1Ee5EeB+4+LjZ+4hPBBcCFC0cXXivcHzR5/LZtlggw2cdcKWW25ZZjVx++HZ95Y+O++8s0s/evRo9z1nzhxnFcZOs2bNnGUHzz+k2mGHHeYsVVxCfQgBISAEAgRENugxEAJCQAgIgYJDAJPsV1991c1UogBi9otyh0ACYEIMIbDHHns4P21MiDGDR6lD+TjjjDOcAojye/HFF9uee+7pZvwgIpjZ+/vvv50iiNJ65513uuuqV6/ulFNm9/fbbz9X1qBBg+Ka6KOE43LBoB+3C8y5Me3HdBtXi7ASzywrPuXEPKCOzETikoHpNOmY2UQJ5vpTTjnFKdmYQ5MWZRd3A5TzsvKuUaOGc3+gjQ8++KCdeeaZ7ptZ31iy++67u9lhiAQEH3jMs8NkAwoLyjWm3pAFEB7cm5kzZxo4QeSgAH399deu/AEDBtizzz7rFB5ICpS9kSNHOtcI3GG4p9yzDz74IFa13HFmaL3lBQeoB+QD9z4s1Jc6eaH9zBIjkATMup933nn28MMPOyzPOeccd46ZcU+axLqXJISUePzxx+300093eEKA0UbuCeV64sFlGnxQNnUo6376tPouHwIQb5CEXoYPH+5+8hxyP3hXeMINJR7iMVuFvoZFRrdu3cqsIs+rJxpISBwKYjAQ5wEhbsWPP/7oiEsICGJp3HfffcZ7gfcf14OLRAgIASEAAiIb9BwIASEgBIRAwSEAmcCsMP78DJwZHIdnxwGEmWOU1h9++MHq1avnBurMYm6zzTa24YYbOvNilHLMqvHhxpTauwAw88858kVJ5forrrjCWUcQv6Br167FASD9NWXdBMgMAidy3YEHHlg8w44SjzsC5TDrTXs4hsIOCXD22We74IzMwLKhnBIQE2HWFsX6uOOOcwpyeNac87HyRski5gQKPu0iIB6z7sz0xxJmVSFJRowY4ZJ4y4HGjRtHvQTsBg8ebCh33AP8+o844giXlnIgHiASaBMKOco8RAoEEqQMZA71wjWCe1WWcE/33XffYtIGKwrEz2KXda0/B7GB5QvPBoH3IKD2339/Rzr4NP471r3EmgMCiFllYopguo54zPz1kd+J3s/I67QfHwGeQ4g7iDjIICxZICB47ugvKPC44EBeQkhy3yGUclloFwRDWDwGHCNOBW4lkHF9+vRx2ED60XaIGUg2+qDvR+F89FsICIHCQ6B64TVZLRYCQkAICIFCRwDTe2b0vUA+oCyi6HlBwUWYQWbmGyUxLASDw7wapf2WW25x5sQoiV7Ii8BquBhASKBsEytinXXW8UkS/g6vhoAC5GWLLbZwig/uD5AYzIBzDGWBmX7Ko/7MPnohmj/1wUoCBRfFCReBdddd1ydx37Hy/vzzz915lPywkE8sISAdKwkw+9+vXz/37YMzxrqG2VSUFpS5/v37F8c9wJoEzPEtDwtK3pQpU0rFnGAmF+uAsgQiB2URcgbCArICS5REhLwhe5jx9dK6dWtHNvj98He0e0kgQvIIr2QACUKemOx37tw5nEWJ34nezxIXaSdhBFid5cILL3T3hxl8H7QV6xs2LyjaBEeEvOT+56pgoRC24KEdPJt+hRieSR8EE5cKyE+IBshanmOIN8hErHTiuWzkKkaqtxAQAokjkNg/aeL5KaUQKAgEYOxZBivSzLYgGq9GCoE4CORi//Bmw/hh169f37XQD65xRcCiILxyBLP7KKMoz7gn3HvvvU4hxOQfBRlB+cVEHyUWhRHz+vPPP98222wzdz4VH9QBxRiLBhQcCBHqjZsClhS4TzDbzioQXjHCCgCFl9gSKMpci7tCJNkQK+/mzZu7qmN14LGCjPFm1rHaBS5EsUcZQbAcQJGLJfiOQzT07t3bWTmg5EHwcD8233zzYrcXrsecm3vIDDOEQ1hwC4lnPYI1AmQQShSuGLieRBMsRrxwrxHcHRAw8AIhggUGlgqJiM+D58QLZUEUYTEDWYNwzK/o4csr635i4i8pPwK4qOAaQN+BOPTvBHLEhYjnJpX9ufw1Td2VuGHx/vDC0p4QCZFLjq5atcq9Y3i/8J7D9QgigueT9wvuFBIhIARSi0Aujq/kRpHaZ0C5FQgCrM2Ob7RECAiB0gjkQv9AIYUwYMOqgdUoENaNjxQsIJhJJyYBM34EXsRtAOUa0pEZaAKlobjju4+QDiWeWAiQDlgd+LwZjLMxW4hSiol2RYQYEij+EB/8RphxRBj04yrBPUFhoF6sAIDbB6tDQBZ4NwOvxLoL//2Iljez76wcQRwJ2kz7sAiIZzaNsgJJQEyLeJYDuKFAzOAGARECyQB5goKz0047OUWe2A20B5Nu4m1gzYF7BUoPlh6cw/ohXswGmuotL4ivAbEUtlDxeHAP8ckn7sS3337rYnRwjnKx1mDWG2IDJYs28nyElVOfT7RvsEehhYzB7QYFj4CTEAkoc361A4gt2gWRARGBJHM/mXnGFYB77wViBTN4L+BKGh9fwx8vxO977rnHEQq48uCCBMHDhqsBZBOuPhAS3JO7777bQeT7Uy7hxbvNB3mFoMJNijEObeaZRMIrybDPM8izR99HeAdCHoINpC3vCIkQEAKpRSAXxleRLZZlQyQi2hcCQkAICIG8R4AAiWHp3r27s05g1h7lISzMeN9www3O9QD3gyZNmrg4Bwy+GWCj7OK7jLCCA0opgQIZvKOUegso3AzOPfdcN8uOMku+uBZgFRE5kCcvr/zz7X9zPFLIi42VJmgHggsIeWLijGDOTB0hVbAWoJ7hpR4JHIfbRDjYJNdFyxvlGjyIPu/dTlC2yTOeUA5L5XlSJJzez97zDcGAgs2sMgr7ZZdd5iLdM9DCegMlh1gTCO4Wl156qXNpwXqC+wfJg3AvmH0uSzy23vKCOkYT8oQAoQ7kSzqUMQQ/deqAuwcC1uElHX3byrqXECYQULi0IJSBosc3gsKL6wkbpI1XavmOdT/dhaEP8sd1BhLKXw8hgxWLf1Yg1kjDygLe4iKURUH9/Pjjj909CQcyBQAILe4vxBLxPhD6M+SEJ4bcwSz88M9iuGqQlbSFuCi8K+h39G+E9xYWUmHrIAgx3qGskgPZivD+wPrrkEMOcSQs10iEgBAQAlWCWYJVFYGBwFl+tqYi+ehaIZBLCKAwMOvjGf9Y/SDW8Vxqq+oqBJJFIB39474q9yVVjeNWrVZEk7oogcTMNDPL7hVULmEmDwsHgh2itKH0Y+7uYxjMnz/fzVT78wkUk7Ik1JcYD9SFejKz7Zfxo55YEKBEhONAJFM4+RGgMNEZ/GTyjpeW9lD/aEEmwZ9zEEOpFL9iR6x8sUhgWOXvfXnKJtYG/y/R2gXe5B9W/HwZqbifPq/K+uZdwfKwiUqy6RPNtyLpIHCwHvL9qiJ5ZdO1tMn3ofD7jjpiwcOzDhkZKVhV8XxCSqZCYo2jYh1PRZnKQwhkKwKJjq+ypf6QmLJsyJa7oXpkNQKYBzOb5UXmpR4JfQsBc+bzhdI/oimZDMR9HAOeB786gH82WLGBLRMSri/1DCtE1NPPmpe3bj5mQ3mvr8h1tCeaQk6eKDrhtleknPC1kEll5etnecPXJPsb4iYWeVMW3qm4n8nWtaLpCVxKnI1EVv4gHemzTSpCLGVbW8L1IVAkllvRBMuYWBKrT8ZKr+NCQAjERiAf9I8Kkw382TO7wLdECOQrAsxaxiIYynr+1T/y9YlQu8IIlLd/hPPQbyEgBAoPAdyIcEkg3kg8gWggvUQIgIDGV3oOCgGBfBhfVdiNgoBTzNiUxfQXwsOgNhYuApgpYyYdLRiS+kfhPhdq+WoEyuofMoPVUyIEhIAQEAJlIRDrf0Ljq7JQ07lCQKCs8VW2tB83igqbI2A6ylJNNJgZXokQKBQEeN557nn+wybU4farf4TR0O9CQiCR/lFIeKitQkAICAEhkDoENL5KHZbKKbcQyLXxVYXdKPBP7Ny5s1viCaVLhENuPbCqbfkRwIQPqx6e/1h+uuof5cdXV+Y2Aon0j9xuoWovBISAEBACmUJA46tMIa9yM41Aro2vKkw2ADgdPpoJeaZvhsoXAtmAgPpHNtwF1UEICAEhIASEgBDIJwQ0vsqnu6m25CsCFXajyFdg1C4hIASEgBAQAkJACAgBISAEhIAQEAJCoHwIiGwoH266SggIASEgBISAEBACQkAICAEhIASEgBCIgUBK3Chi5K3DQkAICAEhIATKRGDp0qVlntdJISAEhIAQEAJCQAgIgdxEQJYNuXnfVGshIASEgBAQAkJACAgBISAEhIAQEAJZi0BKLBsWLVrkVqOYP3++VqPI2lutiqUaAR8NluWXCFIUS9Q/YiGj4/mMQKL9I58xUNuEgBAQAkIgfQhofJU+bJVz9iKQa+OrCpMNdPTx48dbmzZtrEWLFiIbsvfZVM1SjACdffHixe75j7X8pfpHikFXdjmDQCL9I2cao4oKASEgBIRAViGg8VVW3Q5VphIRyLXxVYXdKGbMmOGIhqKiIhENlfigqajMI7By5UrjuYdoox9EE/WPaKjoWCEgkEj/KAQc1EYhIASEgBBIPQIaX6UeU+WYGwjk2viqwmQDrhO1atXKjbujWgqBNCDA808/iCbqH9FQ0bFCQqCs/lFIOKitQkAICAEhkDoENL5KHZbKKTcRyJXxVYXdKGBX2CRCoFARKKsPlHWuUPFSuwsLgWzrA8uXL7c33nijxE3AQqljx47WrVu3Ese1IwSEQHoR+Ouvv2zo0KE2YcKEuAV16tTJ9thjD+eyGzdxChOsWLHCfv31V5s9e7bhMok1Y6T8/PPPVr9+fWvbtm3kqaza/+OPP4ytWbNmtvbaaxvm2NGE9/ZPP/3k2sy7sUOHDiWSjR071qZPn27du3e3Ro0alTj30ksvWZcuXaxnz54ljqd6J9v+W1LdPuUnBOIhkCt9oMJkQzwgdF4ICAEhIASEQEURqPnIV0llsWTAxlHTs9Tm4MGDbc0117QmTZoYigTmuAzATzvtNPu///u/qNel+uDXX39tt99+uz388MOpzlr5CYGcQQCiAaX0xBNPjFvnUaNGOWLi6KOPjps2VQmYPT/zzDOLY5P9/vvvdvLJJ9shhxxSXMTff/9tgwYNssMOO8z23Xff4uPZ9uPBBx80Nt59tKNXr1523XXXWbVq1UpUlVhUZ5xxhksDKcG78cgjj7Rjjz3WpXv77bft7rvvdtdfddVV9tRTT1nDhg2Lzz322GP2+OOPl8hTO0JACBQuAiIbynnv+XNZtmyZY4erVKlSzlx0mRAQApWFwMKFC23OnDluFqZu3bqVVazKyVIETjjhBNtyyy2La3fzzTe7AXJlkQ1LliyxP//8s7h8/RAChYgAFg2JEA1gg3I8ZMiQSoXphRdecGTkiy++6BRqykdB33333Q3i8rbbbjNIEP5fslmmTJniiIbLL7/c+vbt6wgE3nXDhw93++G6f/LJJ/bdd9/Zq6++6sa4tP3GG290RCzWGy+//LIjZrfffntnkfLFF1+4PCZNmmTXX3+9S0s6iRAQAkIABEQ2JPkc8AK++uqrjRc3gjndueeeaxtuuKHxQr711lvdC32ttdZKMufyJ//yyy/t+++/twMOOMDVC9b/9NNPz2qGvfyt1ZW5hgADyR9//NGGDRtWHN9lr732cor/Bx984PrLo48+WqpZZ511ls2cOdMiz7Vv397NNG2yySZuNZB77rnH3nrrLatZs6bttttudswxx5QwDcXM7M4777Tnn3++uIwdd9zR/ve//xlE4RNPPOHMSbfaaqvi89F+MAOHgrjffvtFO61jOY5AnTp1ipewxXz4rrvusoMPPtjN4PHNO/2WW26xr776ytZdd1030+fJCmb6sFBgBrBx48buOmY+vTLC84kystFGG7n/izFjxrh8OUa6Cy+8UC4cOf78qPr5iQDuE/xf+Jn7LbbYwjUUF4JWrVrZZptt5jYsBrJZGLvyjoMgQHCL2Hrrre2bb74pRTZwnrTePYKV5sLCZBtWYQiWDwsWLHCTb5dccol7922wwQbh5PodBwEsSXjG+F/BYgRBx4AM6t27t11xxRVxcsiu0358F1krxmdz5861jz76yBj7SQoHAZENSdxrBpKnnHKKewmj0OPrdv/99zvFHh+1VatWudz8dxJZVyjpt99+a5it7bLLLhmrQ4UaoIvzGgHM1MsSyACkT58+JXxh11hjDcOfF9lzzz2dr+zUqVPtueeec8oZfvdPPvmk0ff4E+PPGeIAn1n2vfCnBtHAbA5/6JAebPirki9KIn7A8cgGyqM+Ihs8srn9/dlnnzl/ZO+PDWFw/vnnu0axpBqkApYHDPjwPz7++OMdoTtw4EB37uyzz3bvXQI0XXbZZW52loHhp59+6kgJnsEPP/zQEWGQ0Aze+WaD6Np7773t3nvvNfJp165dboOp2guBPEXg4osvLuFmMHLkSNdS+ix9mv8OBCuAbBZcxSDqwzEaaAPj2kiBUMHVAvcR4ti8/vrrxpjXWysQtwLClHNMdOE6AtlSo0YNO/zww10shwYNGhSTt5H5a78kApWtM5QsPfV7++yzj5sI4j+UWCZ+bMeY65133kl9gcox6xEQ2ZDELXr66add6osuuqhYMYEdfuWVV5wpmc+KF/M555zjdpmd3XbbbZ2SwqwYfrqwxAxcUW4YbEIWoFi9++677gWPed4jjzxisMdHHXWUG+DisoFSRN7MrsJIQ3xgvoeZH4KPHX51CH8EzJgxWOab8mBPH3jgAdfZq1evbrCPhx56qEtDm7bbbjt77733HJO6+eabO39iWH3+UBk446d4xx13uEBJTZs2deZ36623npup4w/n2Wefde3mGGZ2bAzC+WOSCIF4CDBow1ohLDzfCM+mP8cxnmtmjWHI6TtYF02bNs31FZ7ZMNngCQv6HTMHBLTiGaXvHnfccS5/nnsCB6L4MaP95ptvuplo/hw5Rr/DOgNhMMVMN36qmKQStAzLCZRXSD8sIOi/DO44RxwAX3eXgT6yAgEswsaNG+fqgl82VgaRbg1YHHDveJfVq1fPKRZYw/D+5ZnhOTnooIPcc8CMIXn4Afm8efOcJQ/H8I/GCoKZP54LnkWeLd6tWMVJhIAQyE4EIBMR/h+IQ8AEE/2YvptLAqkaJhqoO+PAaJMBWBQy/qTNvPdwP5w8ebIbQ4IHsRsYM0L2Q+IzPmUc+tBDD7lxKfsTJ060K6+80rwlSC5hlY11JTgpRDXjDNxA0RMYi+C6Ejl+x4qF/xiuQedgrI8FCjoM115wwQXOyi5enljtMMZiIieeHhF+to444ggHIfoNZAPlY9WHeLKBeEVMAjFGwnKDIKTUGx2Da/hfRH/y4zSILf6fmQjAdSnaOK127doOi/XXX9/pRjy/EB/8R6MvMRFF30UXwuIWCxz0Jkn6Eaia/iLyp4TffvvNNYYO6IWZrJtuuqmEMoFSwuwnL2heDszcMmP2yy+/OKaYl8B5553nXt6Y4pEeZnOnnXZynQ1Sgs6BCRsdj87FbC6ztlgvQBwwM3vttdc6pQliAIE44IWCwCiSFjcP/hAoBwWIfAYMGOAIBU88QF5MCPwmOU+H79q1q8HmU3+UqG222ca9FBiYo9B5czyUMEgGOm+PHj1cHgzAEVh+lEFeJBIhAAJYFUC8sfFsRQp/OMwi+w1zOy/84aHw8TzyhwNRx6CHP00ILQSyDkGJCwvPLwNDyAFICIgJBlj80XhyDgKCP2/ywB3qwAMPdLPTEBf0O/omfYl86H8okvQZnnEEQoN9/sTwWWXGCPcOVjngj1GSfQhAnjLg8UEa+WZwxCDZizcHxqKGe8wMHoMXttGjR7tBGObVPCcc69+/vyOf/fWQZBBaKCkMuBikR5tJ9On1LQSEQPYhwNiHiR9IZ/6D+D/INcHqgP+nsLDPf1Sk8F+LUopyes0117jxH65gxGZAsPRiXAke/IdeeumlbozLf/M///zjCBmsIkgjSRwBdAE/RmIcFBbct4mvQawhLAWYfMSqM9r4HXKbsQy6Cko+E6JY3PH/A9kNYYbEyxNigHFRInpEuK6J/IbQ4v+UcROkA/sQAljEoB+hs2AByFgN8oFnkkkBxn6xxmkeC87jTsukEhY3n3/+udNt0E8oB/2I1VSYfJJUDgKybKggzryseXjpIF54+dIh8IVj9oyBKoNRlHi+eekjdH4vdDI6Eh1q5513dgNTFHte1ihdmO41b97cvWi4BssFzH5RtDCNQ5iBQwlCUOwgH5D77rvPMZzMwuFPDBPqTdfJ11seQE7QyRGULzokG2UhzNB5wZSYdjAw50XBHxMdmz8kiBY6Mp2dWUCJEAABFDE/sIH0ihT6B39qXph18eJjMniSgj9kBFNPBjf0A/5IeO7pP2Fp3bq1I9k4T5/DMoI/ddh6b9lAGp5fnnv8cMePH1/MwNMvIdPoO8zY0Le9NVG4HH7TJ0hH36ffUU/qJMl+BLwrA4Mfb53g39W8e9dZZx03wPMt4RnhGWUg55UQyNVZs2YVm1bzDt9hhx0cwcvgCcsXiFxZungU9S0EshsByEH+JyALIZr9f1h217p07Vq2bOn+11DIiG+EMB6Nttwv7zYIBf/+Y0aYGWbGjmFhkgyFFUsvLLuYZOI6Zrl5n6LYShJHgMkMlHuEcQ1jdoSxNwoz1p+Mo8AdIoDxuyfEw+N37gnjKSwwue9YNxDTDZICvQHiPJE8k9EjkiXgsFrAEoHJHZ4TdCWESSTIEixqeD79JC//wcSzYOUUCIho4zSXQfCB7gMeWNXgRgtJRv1oO8FPicmE8NxKKgcBWTYkgbMP+ogZkxeYXzr0xx9/7A85UoAdP2BFQfECKcFLAP83XspeePl7MyRvthdWtsjDH+ca/2cRzQSO87xgEDosArnAnwwbdUAhog7M+nrBHB2BQOEcZkpINPbPL5Xk68QM76677upeYMwQIpjXSYSAR+Ckk05yzDXEGs9fpOy///4u/gnBTdkgsLxgjolSB7GFYDWAoMzB1kMkYPLO5p9NlyD4gMXnev5ssAZiNgDxvrdu598PBkuUzax1tEGYT+tJNPoTgoKJUDYKJVY/9D2shwYElkSRM0ousT4yigAkMINqNkhVLNSQaO87CCYs04hEz71ksIJZKYMkTFEZNGFVxgARU2KEdDyXuNTxnDJYgsxCeE7YGPAxgMfcUyIEhED2IcD/B2Qz1m64FqCosfkJm+yrcfQabbzx6qWAsd6iHSiyWKn6ILf8v2LFh2CGDoGKgst7jN+QLhwPC/+lHPem6LjXYgUCNrxXPYEbvka/YyPAxB/jIzbvikBqP8735A/jD/QDb1lJGj9+5zfi3Xz8eMiP1f1+InmWR49YXXr8T/QQ2kM9IU98WzjGOXQT9BCvR2Gx6useb5zmCUHfZvLGLYNxJ4QDFiJYufK/LakcBP6bOqyc8nK6FFwbeOniEoFJHRYHsHJ0Fhg0BpbRBFaYh5yIvbysYeuY2U2GVWMmjJgI/EEwaMVMiEExncWTFChPkSbk4frwp8J1MNR0RkgB1oX24vPhj5QZZJh8zJxY5ilSMEmnLa+99pprGy8lZnFR5pg55lx4ljryeu0LgUgEUOTCRF602V+eM47DgvMHcsMNNzgLBZ49rHqwOOC58wMoyuBZx6IBc1AIMW/GjlWEFxRJrB28FQ+mipGzMvQPjuHS4Uk84jOgSDJoQ/jNDAIbVkIM1BiwSZn0SGf+2xNFLOUWFsgl3NYgDfwz4s/zTA0ePNi933gferIWE1XSEzzUW7DwP4GrHBYMkE0QV7hYIPxXQEQxgGJwSL48K5TrfVp9mfoWAkIg8wgwnoEUZEY5LIzhckmZ5p3FjDfE+zPPPOOagjK36aabut8jRoxwJAqzwhCpWP55EoEEvLfCRCyzz7xDHwncb73Shtk+E3AQ7BDw0caOrjB9JIWA/7/AwhPXbdxVGIswe+/Fj9/9frzvRPKMlkc8PSLaNYkcw7IUYWIVYoy2oitB8iH+f5vfZY3TOA9xBrHvJ4F5biEqsAxhMgjx/9duRx9pR0BkQxIQo6SjoPMC9bNXDBbxG+dFHktg6q666ioX24BZUwacKPkMNj1Tx7XhzhSZFy9vZuJQmBBIBe/yQMfkjw9fQh+Y0l8fzpOZZf4AiLGAYFKFyTkuD2GBHEGho8Oy+T8Yb8JOWv6AMROnLfx50Q5eXuTpWcNwnvotBCIR4NkJS+RSSGHLhnA6+iFkAy48KPII1/rreQbDZAOWRxBoEAPemgEyDJ9SmG9IPwgETPQYaMF88wdH36Zfe9IBQoMYJZCNECO4L/GHSLn0EcgKTPuYkWC5Tu8qgjuRJyfC7dDvzCDAwBhTyrIExT8yDfefjfcgcRr84I7nkRk+LByIs8OME2bXzO5R1s033+xc5CCe/HnKZhYwljtOWXXTOSEgBCoPASZoEhHvB59I2kylIYZM+B0Wtp7lPeUFy1msBPGZh8RnmUv/vvNpeLehuIVJe8a6EKdYTjAeDI9v/XX6jo1AJMak9McgrwlIjQ6BMM6BFIJ4iJSwpXRYB4hMl2ie4Txi6RGReSe7j1UHzxv6FWMqJlUHDRpUwqLb54lrYqxxGmmwjKCeCDh5YgFXH55Z8vbuJy6RPtKOQJXAfGX1eo3lLOqHH34o4Q5Qzmxy7jJewMxW+rWGE21A5EA10et8OgaszOjSmcLCwJbjvGTCL4ZwGv8bH3T+ZLyJkT8e/uaxoK4ohOF0KFooZrwM+EOhHrwMKRurDwbPvPywwIilLIbLyZffmA5iahkphdo/InHIhn36DgohimLkswmbTr+hX5COmSyIhsi+hOkh/Z5+Rp8jv2gDMdwreEfQP0hb6FJW/yjLGiuMW81Hvgrvxv29ZMBqs+G4CZVACAiBjCFAALeePXtar1694taBSQ5W72JGXlI4CDDDrfHV6vvNuAJrzfC4vKJPQnnyTESPKE+9GFdhNV7WBC75Rhun8ZwwucMKYCxrjl7iJ3qYKEJvwUKc90fYTaU89cyma2KNr7KljsSrkmVDOe9GpLKfaDbxOlC8fHjBRHvJoPBHOx4tP9/5op3zx1CyohEpnmUlHbN0XlCuYMaZDWZVgUhlzqfTtxDIFAL0D57PaMKMjJdYfYzzzNT42Rr6gl/9xV/rvyEYYM8lQkAICAEhEBsBTJuxOsNaLJ4QgJX0EiFQqAiUV/coC6/y5JmIHlFWmbHOMa5KRE+KNk7zk0N8Q8iwecGVFqIBS1bvmuHP6Tv9CMiyIf0Yq4QCQCAWsyjLhgK4+WpiXATK6h+JWjbELUQJhIAQEAJCIO8QkGVD3t1SNSiFCMQaX6WwiAplhWVDSlaj8DN9FaqNLhYCOYpAvOc/3vkcbbaqLQQSQkDPf0IwKZEQEAJCQAgkiYD+X5IETMnzCoFcef5TQjaETZDz6i6qMUIgAQTiPf/xzidQhJIIgZxFQM9/zt46VVwICAEhkNUI6P8lq2+PKpdmBHLl+U8J2eDjBeQKw5Lme6/sCwQBnnf8xsJxLKI1Xf0jGio6lu8IJNo/8h0HtU8ICAEhIATSg4DGV+nBVblmNwK5Nr5KWYBIOryirmf3w6naZQ4B9Y/MYa+ShYAQEAJCQAgIgfxEQOOr/LyvalX+IJASy4b8gUMtEQJCQAgIASEgBISAEBACQkAICAEhIAQqikDKLBsqWhFdLwSEgBAQAoWHQHh5qsJrvVosBISAEBACQkAICIH8RUCWDfl7b9UyISAEhIAQEAJCQAgIASEgBISAEBACGUFAZENGYFehQkAICAEhIASEgBAQAkJACAgBISAE8hcBkQ35e2/VMiEgBISAEBACQkAICAEhIASEgBAQAhlBQGRDRmBXoUJACAgBISAEhIAQEAJCQAgIASEgBPIXAZENeXBvV65cmQetUBOEQHoQUP9ID67KVQgIASEgBISAEChcBDS+Ktx7n0zLtRpFMmhVctrly5fb4sWLbcmSJbZ06VJbtmyZcYyNDs43Um/2GFv60EHud52uvd23/6gd7Nfuul3Ucz6NvoVALiJQnv5BO30foW94oY/44/6YvvMTAZ6boUOHlmhczZo1rVOnTta9e/cSx7Nh588//7Qvv/zSevXqZa1atUpJld544w3r2bOntWnTJiX5KRMhUF4Epk2bZi+//LJNmDAhbhb00b333jtl/SBugQkmoO5//PGHtWzZ0tZZZ50Er8pMsokTJxpbs2bNrGvXrla1atlzjj/99JPVr1/f2rVrV6LCv//+u3HvevToYY0aNSpx7oUXXrAuXbrYRhttVOK4dnIHgUTHV2NXzreTvxriGtarVZcSDWS/V+vVxyLPlUionbxHQGRDFt3iRYsW2YIFC2zhwoXGb8iFZGXhzyNLXLJ6//LiY16hatL/EnfM7xcn0A8hkKUIpKJ/0DTfR/z36uau7iP0B09CiIDIrgdhXL9BSVWoyzuDo6aHuL3qqqtsrbXWsiZNmjjidvr06U5ZGDhwoB188MFRr0v1wa+++spuvvlme+KJJ8rM+qGHHnLkCPWifqmQwYMH29VXXy2yIRVgKo8KIQDRAPF14oknxs1n1KhRjphIJG3czFKU4JprrrEXX3zRKe4///yz9e7d26677jqrVq1aikpIXTb33XefsfHu++2332zLLbd076BYdf3777/trLPOsgEDBtgBBxxQXBHIyjvuuMO23npru/zyy+3555+3hg0buvNvvvmm8c565plnitPrR/YjkIrx1ahp40o01O1/+98hTzgM3LCvO+j3/0uhX/mKgMiGDN/ZefPm2T///OM2b6mQzip5BWvh4O1dMZ5sgHzwv9NZvvIWAskgkIn+4fuI2WoComn/i511kPpHMncu+9OefPLJbrDsa3r99dfbI488UmlkAxZrzIaWJfw3YIWx11572ZAhQ4w6Y4VRUUFZqF27dkWz0fVCoMIIYBWQKHmAdQ/9IFvkm2++cUTDo48+auuuu65T4A866CD7+OOPbdttt82Warp6TJ482RENkIw77rijs27Yd9997b333nP74cr+9ddfjoSgHUx+RQrkCsRn3759bfz48fbZZ5+5PLDCgny57bbbrEGDBpGXaT/LEKjs8ZUnI/Z/8z6HhCcbIB/87yyDSNVJEQIiG1IEZDLZYL0AYzx37lw3q5bMtalO6xWrMPkg4iHVKCu/ZBDIpv5BvWcNgXQQ8ZDMPczFtHXr1i1WwMeMGWO33367HXbYYcXfa6+9tt144432xRdf2HrrrWdHH310MVmB8v7AAw848gBriUMPPdQOP/xw5/6G9cLrr7/uBu0bb7yxXXDBBfbjjz+6fBnIM2N46aWXRnXhePfdd61FixZ25pln2iuvvGIjR44sVgwY1Hfo0ME+/fRTY8aX3+Sz/vrrO/ghKZ566ilDydhuu+2cuTTK2k477eTyQ8HDzJn/oltuucU++eQTd90222xjp59+ulMWMJNGOeFcnTp1bPvtt7dBgwZZrVq1DIzAY/To0cXnzjvvPBs7dmwxZh7DLbbYwm644Qb76KOPXBn9+vVzM6bkWVb5r776qmHGvWLFCnvrrbec6feFF15ob7/9tjGDSj1OOeUU23PPPd1/6V133WVcM3v2bIfH2Wef7WZvc/F5VJ2zHwGsA3BFgGhAsBigv9JvskGw4qKveMsF+hsEAdKxY0dHiGBhBfkQFvoVfZbt3nvvDZ9yv+fMmeOswtjBHWP+/PnOEpd3G+9MLFUk2YlANo2vwuSDJxtEPGTnc1PRWlWtaAa6PjEEVq1a5QZA+LnBBPOyzsbAKpAPkwKrBzZPRCTWQqUSAuVHIFf6B8QDfWPWkMvcVv4W68pMI4CCjmL60ksvudk4zH5PPfVUVy1IAEiFK6+80vbYYw9bY4017KijjnKK/HPPPWe77LKLnXHGGW4mk9m8iy++2Cm8zPgdeeSRbmYPJRqFGKLhzjvvNK6rXr263XTTTQbpsN9++7myUN4j/aE9NuSHVUO9evVs1113dSbk/tzUqVPd7OPOO+/sXDHatm3rzLc5j1J/2WWX2YEHHmgPPvigU8pfe+01R3Bz/pdffnHkB/0OM2n+lyADMP/+/vvvDdIAQXlncErdMZuGdKBOCO3HR/3pp592pAP5Q3xEYrfBBhu4MqgvOFDGhx9+aPfcc4/FKx8MMdFGocHqBMLnuOOOc4oNStDuu+/uzLixCgRr0kBGUEd8ySFRJEIgXQjss88+dv/99xdnDwGHVQCkQyaFfvPwww+7/kGf7datm6sXhGQ4RgPvHVzIIgWrBN47bPTxSOF9CGGKdRZkI7E0cM+oUaOGe/+RZzSLiMh8tF85COTC+ArigQ2rBzZPRFQOQiol3QhUT3cBhZ4/hMLMmTNt1qxZbnYmV/CAaMDaAdNxWTrkyl3LvXrmav9Ybe2w2uoBN4um/8ZAyb07ULg1hkwYN261jynuCgyOI90asBTYbLPNnPKKws/gu0qVKsbs/zvvvOOIhEMOOcTN/jNjSB7efBjLNWYIOcYMKP7NV1xxhc2YMcPNfjIjykxjrCBqv/76q+EDjk80AtnALP6kSZOKyQlm9HfbbTd3HmIBAgDBCgKSpH///m6fGX4sAyIFkgFlAcLAK0gXXXSRUxjAgvpDEmAdseGGGzrigvZjaYC1xeabb+6IEGZQvZUCFiKIx462Q2BAGnTu3Nmdg9hAKYtXPokJNAfBgDADi7UHZRO0DpeSxx57zGG85pprOnKE+8X/LfcrW2aYXeX1kXcIQB6yIcOHD7dLLrnEICDoK5kQiE9IU6ya6PsQDpCQCPULEw0cgxygLycrJ5xwgiMkH3/8ccNKCbLv2WefNfaPP/54RwbiHkNsGOJCSDKDQK6OrzzpgLWDLB0y8+ykulSRDalGNJQfg0qIhvK8zEPZZPSnSIeMwp/XhedD/+AGQTywiXTIrcf1tNNOK3aDoOaQD7gW4CrgxZsDo2yjHKPch4Uo7ARGgxRgFh1zYq+0k468TjrpJMOnG3cEBv7EXUgkYj2WAkg4MBv7WEowoEfCFhEQA14gJLB48FJUVOTcCvy+//azmij0XlDaERR1lApmxTCPhjRBicISArNsCAbaAjbMbEaKx84r/Jhte/GuHt6tIlb5pG/atKm/zCl2kBoQDYg3D+c39wFlB2IFosEHrOOcRAikCwH83nFpIr4BZB+rZWRCIPWIFwHpybvDk56+LpAirG4WFoIC8m5IVrBswNoJwUoXohPrLghKiFsID95T9EeRDcmim5r0+TC+8qQDiDy/y3GK65CaRyMjuciNIg2wY8LGrBQDuVwmGsLQyL0ijIZ+VwSBfOwf4AHh8OuAanKvqMjDkcFr27dv70rHzc2LH4hjSowlAktQ+o3BNm4XBKxjcI+LwAcffGDXXnutv9xZBTD4x9KANPhAn3/++S5OQXGiKPJ6eKAAAEAASURBVD9QCsgXUuHJJ58s3pg1xUUg3v9K8+bNS1hpkF+k1QbFEl8C8YQAv7FkQFDyOQ4BM2LECFcHZspwz8ANAysNyAjOYV2Aq0NYPHZ+WTysDbxg2k274pXv0yfyTfwIymBWlxgaxxxzTCKXKY0QKDcCEG70UQgH+iX9E8ufTAguEueee66LcQI5+Pnnnzui0NeFZXOx5ML1wQv92Fs++GPJfENE8h7A0ov3HJNrEBFYUMRy0Ugmf6VNHoF8HV/JvSL5ZyGbrhDZkMK7wUuc9YuZVeJPKB9FpEM+3tXKaVMh9A+QXG3pcFnlgKpSyo3AlClT3OCbAThWDaxGgeDrHym4QGC9QHwHFHd8s7E4YPYIBZeVHVC2UTq8DzfpcLVgthMFnsBxPm8G42xYC2D6HLkSEaQF5ygDKwi/EeeB4IfEmyhLsGrwASUpmyCV5BcpxEBASaHt5Et7UNqxzsDl4dZbb3XnuJZ9FAmsCZjNRFAoWKKZsiAzaHOkUAakAsEiwQcFB0IGS4x45UfmVdY+ig4kC24c/Af7JUXjETNl5alzQqAsBCC2sOwh7gqKN9ZPbGGFvqzrU3kOl6L999/fvaMgPe6++25ncQDxxnh0k002ccVBiqKQYnmACxXvNoR3GxZYyQiuUbhLYE2E8A4kQCykJKQt7xZJ5SBQCOMrb+mgmA6V80ylshSRDSlCk4EOZmyYkBWCeNKBQHkSIRAPgULrH7JyiPdEZP48gQpR5tmYvUfRJuggCmukMPtHnAGCGjI4h0AgjgCBHomlgMLdp08f5zYB8QCxQJBF8uZa4icQSwClntlHTJxR6DmH2TWD/rCgvOMLHekKgHKOywGBLT1hEb7O/4ZsoE3EX6Bs/pe6d+9eymTaB6yEaCAeAoEvIQzAhvxxmYAc2G677Zw5NG4PKBa4SNB2zKe32mort3we7ad9EAphQQnCxeSHH35w+Bx77LHGqhgEd4xXfmQb2Q+7i4RnkVkBBMUK65GDDz7YzbZCkkCeSIRAOhBg6UueMfow7wG/DRs2LB3FJZQnZCB9kXgN9H+IS4g3CD/6NRZTWCEQX4J3GO8lhJgOEBCJCuNd8sOFxC+jS99r3LixEceGdyUr9kjSj0Chja886cC3JDcQqBKwsasqUlUGEH62piL55Oq1zEgxQxY5wKrM9tSbPcaWPnRQZRZZoiz5qpsbSEfrB+of6h/qH6npH+P6DSrx3om30+WdwfGSlOs8ijmuASi+XpjJw8KBgTYKNLNMzKgTXwAhlgP+0f68vy5d30SKh2Bg8M9/FGQIS16WFbCNOtImX2dfN4YIzITyTf3DCj5YoPxzDRhQZiRB4vPhm6CZxFMIx1rw52OV788n8s0MLmXgAkJbqA+KEPdEkp0IQL6FXY/i1TLZ9PHyK4Tz9E3/vuK3f4dVpF9gPcQ7LRyjxmOJ5ROEarR+7tMk8x1rHBXreDJ553LabNA/xq6cbyd/NSRjMBJEkngOhSzZ3g8IKfDfaKmQ71Q5285AhmjamSQayln1lF7GLK6WykwppHmRmfrH6tvorRywBpLkPgLMEPqBu28N+1hE+ME7s/lhpR0FO3zeX5eubwb7rFzB7CazrAMHDnREgTeljlYudQzX2aeBXIBkoN1hooHzHPPXgEFZRAPpOR9LAYlVPtclKsSJAGd/fwgk6e9JonkoXeUiQHDReG5BvkakI70kOQR8f+AqfuPuUNF+gQtVNKKBMnhfxOrnnJdUHAGNr1ZjKCuHij9LlZGD6P5yoozZUjioVjmzyZvLUKRYKrPdoPfdcpl50zA1pFwIqH+Uhg1CTlYOpXHRkdQjgKvHHXfcYe+//77zoWZ5TcydfdDG1JeoHIVA+RDABeHll1925v3xcoBoyNRqD/HqpvNCoLIQ0PiqJNKecBjYs69bKrPkWe1lAwIiG8pxFwi4FY6sXY4s8vYSKVR5e2sTbpj6R2yosHJAmva/JHYinYmKQLrcIqIWlgcHcaFgkwiBbEaAVRKILyIRAkIgPgIaX8XG6KZv33UnB27YN3YinckIAiIbkoQdPzX8VyWxEZBCFRubfD+j/hH/Dqt/xMdIKYSAEBACQkAICIH/END46j8sYv0S4RALmcweF9mQBP4s7VXo8RkShUsKVaJI5U869Y/E76X6R+JYKaUQEAJCQAgIgUJGQOOrxO++CIfEsaqslCIbEkR64sSJLrJ1gsmVLEBAClXhPAbqH8nfa/WP5DHTFUJACAgBISAECgkBja+Sv9siHJLHLJ1XaDWKBNCFUSTyqyR5BFCoZg25LPkLdUXOIKD+Uf5bpf5Rfux0pRAQAkJACAiBfEZA46vy310Ih5u+WR3Hofy56MpUICCyIQ6KkydPlutEHIzinZZCFQ+h3D2v/lHxe6f+UXEMlYMQEAJCQAgIgXxCQOOrit9NEQ4VxzAVOciNogwU//rrL2O9cknFEUChQhSFv+JYZksO6h+puxOF3D+WLl2aOiCVkxAQAkJACAiBHEdA46vU3UC5VKQOy/LmJMuGGMix4gSdXZI6BFCoFv48MnUZKqeMIaD+kXro1T9Sj6lyFAJCQAgIASGQSwhofJX6uyULh9RjmkyOIhuioLVo0SJjiRlJ6hGYNHj71GeqHCsVAfWP9MFN/xAhlz58lbMQEAJCQAgIgWxFQOOr9N0Zb+GQvhKUcywERDZEQWbKlClRjupQqhBQwMhUIZmZfNQ/0ov7bAVUTS/Ayl0ICAEhIASEQBYioPFVem/K/m/el94ClHtUBEQ2RMAydepUg1mUpA8BzMVFOKQP33TmrP6RTnRX541lg/pH+nFWCUJACAgBISAEsgUBja/SfydGTRunFSrSD3OpEkQ2hCCZN2+ezZo1K3REP9OFgAiHdCGbvnzVP9KHbWTOit8QiYj2hYAQEAJCQAjkJwIaX1XefVX8hsrD2pek1Sg8EsH3tGnTQnv6mW4EFilYZLohTmn+6h8phTNuZrhT1Bn0ftx0SpAcAitXrrTXXnvNateubf369St18Y8//mhjx461jTfe2Nq2bVvqfDoO/PPPPzZ8+HDbZZddrEaNGikp4uuvv3Z5rbfeegnnt3z5cnvjjTdKpC8qKrKOHTtat27dShzP9M6qVavsiy++MO4Xq0ZRR+5ngwYNMl01lZ8EAgTiHjp0qE2YMCHuVZ06dbI99tjDWrRoETdtZSaYOHGi/fHHH9ayZUtbe+21K7PopMuinmzNmjVzda1atew5x59//tnq169f6l3IO3L69OnWvXt3a9SoUYl6vPTSS9alSxfr2bNniePaiY2AxlexsUnHGSwcJJWHgMiGf7Gmo2sJtsp78CjJm4trOczKxb08pal/lAe1il2j/lESv1NfqVnyQJy92/daEjUFCvXgwYPdORTodu3alUh37bXX2u+//25XX311qQF2iYTBzhNPPGEzZsywM888M/JUUvuzZ892derTp0+5yQbIhdtvv90efvhhV/arr77qlLJkyAb+A8FmzTXXtCZNmtiKFStc+1BOTjvtNPu///u/pNpV3sSRbYnMB1fHK664wkaMGGHbbbed1alTx+6++2633XPPPa7+kddoPzsRgGhAKT3xxBPjVnDUqFGOmDj66KPjpq2sBNdff729/PLLts4669gvv/xi22yzjXt3VKtWrbKqkHA5Dz74oLHRv3nH9erVy6677jqLVVdWRRg0aJAddthhtu+++xaX8/bbb7u+xvVXXXWVPfXUU9awYUN3nnOPPfaYPf7448Xp9aNsBDS+KhufdJz17hQDN+ybjuyVZwQCZVOaEYnzdXfx4sU2c+bMfG1eVrcLc3FJdiOg/pG5+yN3ivRiP2zYsBIFMABnS1SwSIAoyAZZsmSJ/fnnn8VVOffcc628StkJJ5xgt9xyiyMvnnnmGdt///0rVXmIbEtxo/79QZ0+//xzVydIoQsvvNBZqzDDClkkyR0EsGhAaU1ESJeIBUQieaUizejRox3R8MADDziSDyX7ww8/NEiRbBMCD0I0XH755Y4MoA9RTyyqIgUC9aKLLrL99tsv6hLwkCuQj7xjOnTo4CyMyINV3CBfLrvsMmcNEZmv9ksjoPFVaUwq64hWp6gspM1ENgRYY8YnyRwCCoaXOewTKVn9IxGU0pdGq1OkB9udd97ZmP3HrcLLm2++6WbK/T7fc+fOtSuvvNL69u3rZvaxZkBuvfVWe/HFF+29996zQw45xFkBMBOI4kvaLbfc0s3W+ujit912m91www126qmnunOHH364vfvuuy6vyA9mB7EiII/ddtvNnnzySZcEywPy8PmfcsopjmAgPUr2woULXV1++uknp/xgzowsW7bM7r33Xuvfv7/LE0sMrBUSFSwHcDtByJs2oKjQhrfeesu5nfh2HXvssfbJJ58UZ52KthRnFvzA2uL++++3AQMG2BprrFF8qlatWk6R2mKLLVx7y7oXKERPP/20s0gBY7D+/vvvi/OizrSNc3yHlUesKfy5s846q5jgQQG77777nLULOEvyHwGISSwacCVAsBjAxSNbTOJ5X7z++uvG8/zdd985C6Dtt9/e1RWSYOutt7ZvvvnG7Yc/atasaZtttpmdfvrpUV1W6FtYPiG4YyxYsMD1uUsuucQOPvhg22CDDcLZ6XcZCGh8VQY4lXDqpm+i/wdXQtEFVUTBkw3z5883ArNIMoeArBsyh328ktU/4iGU/vO4U7BJUosALgsM9L799luXMQr5K6+84pR7XxJxAS644AJjcI15Pgr1I488YkOGDLE999zTUGw32mgjO/vss61KlSp25513OsuIm2++2c0e8t/ywgsvuOxQQFD+N998czcjv+OOO9rFF19cXL4vk9lBZgYhGZh9POKII1y+1AGlAeUeogMConr16u73hhtuaHvvvbfLgrrgGkLbiGWAPPTQQ45YOeecc5yijivJeeed585F+/jss8+cpQB4oJg///zzdtJJJ7mkuDB89dVXjtzYddddnW/28ccfb7hrUKeddtrJ4YEilqq2hOvoFblNNtkkfNj9RunDmoO4F/HuBS4n3APcTlq3bm033XSTywOLCfDfa6+93D0EW0gFrB9RzM4//3xnVg7p1LRpU8MKhGcHrJ977jnDWuaYY44pVTcdyD8EeEbuuuuu4oZBxNHv1lprreJjmfjBuwIrC94Jn376qSNEsFZo3769hWM08J4g7kKkEPeE2BhszZs3jzxtnTt3tjFjxhgWSJB0xEvBaoJ+BxFHnlrVrRRspQ5ofFUKkko/IOuGyoG84GM2yH2ich60eKVg3aDYDfFQqvzz6h+Vj3m0EhUsMhoqFTvGbD2DcawZIAwYlCOQAV7Gjx9vxA9gJr1evXpuw3cZ0oDZawJIQjKgkCI77LCDmyVnMI5FA4HVvMLPeWbKDz30UH66WXmIDoJV4hPtxc/QMwOJpQJ5IBAXnOMYijx5MZOIEsFsKoHpaJOvi8+Pb8gCzJ632mordxgCBesDSAcIi0j58ssvbdy41QG0GBBTZthFg/RYcKDwM6MPNigmYMFsKdYe4HrQQQc5a4NUtgVLE4S2liXx7sXuu+/uAnKSB24ikDQI1i6QEN5HHesRlCmw4r5jVeLjYHDfuH+QMwg4QXL4e+YO6iNvEaDv+P4zcuRIF0cEAiJTM/uQe/R1rBkgQ3lvtWnTxuGPRU6YaOAgdcdSKFnBegk3C8hF+gNkG6QqpCb9hX2CZmIRBiEriY6AxlfRcanso1g3KHZDelEvPcpIb3lZlTumXwykJJlHAOuG2l2DQF9de2e+MqqBQ0D9I3seBG/doP6R2nuCKwWz8meccYZTGiEfvPJASX4WncF1WGIpurgaEOuAGU4i09OHmDX3Ehmpnn3IjLAQaO3XX3+1O+64w/0/YZrthWCIxx13nLOMwG0CRYKZ9ch8fXq+iSuBEgwB4qVVq1a2zz77+N1S3yeffLIjM/wJLBmw6qB8L16hYm14ZnO9cu7PEz8h1W0hb3BFuDeYgocF1xDcI7in8e6FV8K4HhLHC3lANnhhttZjhTJHEMBI9xcwQMBERINHrjC+6V8EWcTVBkUbsiETAgGJVQHEHgRg5HPIc0x8gLCwz2ozyQorTXjXLqwocCPD4gdCEjwIDgnZSBqRDdHR1fgqOi6ZOIp1g8iG9CJf0G4Us2bNSi+6yj0pBBb9PCKp9EqcXgTUP9KLb7K5K3ZDsojFT9+jRw+nsBN7gcBuuACEBX9kBNN4LAHYcC0g/kKkMEOIuT2m/CwfyUB72223LZEs0joAJdWX4ROSPzOTkBYotUR794KCzYw9s4goFFhh4IrBgD+WeGIECwgvKM20IdEVmPyKHeHgfCgvCGbW+K17fPim7bhdpLotlIevOEQBqxhECsHu3nnnHatbt27ce4EVRjShPZAnXpilxeWCtmNBgmVKuK241zC7i0QqeD4PfecnAvQfiDkUbFyesHaK9VylGwFcJLDOYalK3gksC4sbmBdIOiy1cH3wAqkZJt388US/yZ8ArVgzQXIwZoCIwIKCd0b4nZNonoWSTuOr7LrTit2Q3vtRsGQDL1zFakjvw5Vs7ordkCxi6Uuv/pE+bMubs7duKO/1uq40AigGzESiMKIwh60ISE0AQmbPUcxRKFD2WQrOu1ywZBymsCinKB5YEGDJwHECskE6MHvoB/24F2DOzDFmQonBELYWoExWt2BWHhKCMjFNRriG64m7QD1Qin1gOspjo3yIBEz+vXAcggKf6smTJzt/akz9UQRizWpCgqCYsGHV4OMZrLvuuj7b4m9cM5jtJ44FdcSqg5lO8k91WyiUe8YMMlhAAoAF5ULMEATzyCOPdO2Pdy+KGxDxg6ULIXOwOPGztMTJQCnr16+fc6XA/YUyIYUGDhxYyjw9Ikvt5ikC3H/cpVDyCTTLe4AtrNBXVtMJ6ogFzrPPPutID4KVQozxjoEw23jjjV1V6COQk1ge8I7CHQuBGPTBb92BBD5wK8ISCMswhBgmuF+BBe8OT1ImkFVBJdH4Kvtut2I3pPeeFKwbRdiPNr0QK/dkEFDshmTQSl9a9Y/0YVuRnLH+kStFRRD871o/A4nJ/N133+1iDvx3dvUvFPVrrrnGxUbwVg8o7sQiQIiPQFBAZjTx2UbJxqSaDZICIgOzfs4h66+/vgu46PvXgGBFBWYEIQgQ6oRrBySFN+WnLGbUma1kiT2W2/Nm/VgtoOgwow4xgkJ8wAEHOBcMl+G/H6w+gYUEsQmQ3r17O3eMf08Xf3lMbrzxxuJj/OjWrZvLEwIkchULyhw8eLALGEm7Gzdu7II0EgeD9KloC3mFBYKGGVW/qoQ/h4sJeDGzWta9oJ2R/us+D2JPYIECoYFwz/BPh5jhXuGL7oNl4ppC7ArcRRCPn9vRR94jQF+E1KLPhYWYKAR4zYTwzqJ/s/3444/u/QMhR6BZ+gxxFLDCQAimuummm7rfkKAQJT6mjDtYxgduG7wnHnnkkeKValjBgvcd7zVm7nkfSEoj4N//pc/oSCYRUOyG9KFfJZhx+c/Oqhzl/PDDD4Ypaq4Jpmbh2Z9cq3+4vvVmj7GlD60e/IaP5+JvFKl2g97PuarH6gexjmd7A9U/svMO5WP/KCveQPgunPpKzfBu3N+37/WfuXDcxAkkIL4PCmcsawCfBb64zCQSswBhFhFlFAWkU6dObqUCZvxRylEMogkzg6QhD2JIMBPGMb/8JHVhZt2fj5ZHtGM+QrzPJ1qaihxjEE1bw4p8utri6wlOtIs4FJF4xroXiZACuMWAsycSfHl80yYsI/09Dp/T7+QQgMgh/kiicu6557pAjImmV7rVz6vvkzy7vp+G49MkixNWUvS7SGsw8uGdBwEa2R+TLcOnx90jmp6h8ZVHKHPfY1fOt5O/GpK5CqS45ElHDk5xjunPLtv7AavTFKQbBYOEfCEa0v8YV24JmIpj3SDJHALqH5nDPl7JcqWIh1D6zrPiQjyigdKJFxBWQvkdVm4Z9DPjX9ZA3KfxygAm0mGCgLqQhz+faKvJI5xPotclmg4CxSs1/pp0tcXnTwwHVgWJhme8e+HziPZNftGIBtLSpvA9jna9jiWGAAQcLkWJCOlIL0kOgXCf5DfuDsm+OyJLpM9FIxpIR9+I1h8j8yjEfY2vsvuuK3ZDeu5PQbpR0NklQkAIREdA/SM6LtlyVMtgZsudSK4effr0UQDB5CBT6gJAALcVgn0S8yOeQDSQXiIEchUBja9y9c6p3hVBQGRDRdDTtWlBgECRTftfkpa8lWl8BPRnGB+jTKbAuqEQJdVuEZWNIcEFJUJACJREgHgkxA6QCIFCQEDjq+y+y1oGMz33p+DcKPDBxGdNkt0IFKpClem7ov6R6TuQWPnqH4nhpFRCQAgIASEgBLIBAY2vsuEuxK/DqGnj4idSiqQQKEiyISmElDgjCBB1X1L5CPBnKMl+BNQ/sv8eqYZCQAgIASEgBDwCGl95JLL7e9RUkQ2pvkMFRzb8+fdMq1qt4Jqd6udG+eUpAuofuXFjFxWoK0Vu3B3VUggIASEgBIRASQQW/jXRqgUBOiXZjYAsG1J/fwoqZsOcJQtt71FPOBR3bdvN+jTrYmvVaWJ1VlWxlSvkWpH6x6v8OSpuQ/mxK++V6h/lRa7yr5MbReVjrhKFgBAQAkJACJQHgRXzZ9vym7Y0FjuutdG+VrXbLraiZXdbUr2+rZBrd3kgTds1IhtSD21BkQ3fzPijGME3Jv9kbF52g3xo3sXWrB2QDysD8kGd30OTsW8Uqjpde2es/EIrWP0jt+64+kdu3S/VVggIASEgBAoTgcXjPitu+LKvXzRjC2Q1+bC/Ve2+s61oAflQT+RDMVKZ+wHh0KtVl8xVIM9KLiiy4buZk2PevtcD4oHNyx7tulvvwPJhzTqNrc4KkQ8el8r8xi9dZEPlIT5a/aPywE5BSfnSP4qKilKAhrIQAkJACAgBIZCdCCye8FXMii37+vmAfAi2QBz5sPGBAfmwU0A+dLPF1epp8jMmcuk7QdwGkQ2pw7egyIYxs6cmjNzQSWOMzcue7da13s0725q1GlttWT54WPSdRwiMmT0l4daofyQMVdoSKm5D2qBVxkJACAgBISAEUobAkj++SzivZV89a8YWCEpajU3+z6p028lWOvKhrsgHh4w+cgmBgiIbfv17ernvzauTfjQ2L/3b9bBtm3eyNWoH5IMsHzwsKf2WMpVSOONm9ov6R1yMlEAICAEhIASEgBAQAskgsHTKf5OXyVxH2mVfPmPGFogjHzY92Kp23TGwfOgaWD6IfHDApPhDcRtSC2jBkA2rbJWNnTczZegNmfSDsXnZu/16tm0zyIdGVkvkg4elQt8Kglch+JK6eMWqlTZurvpHUqBlOLH6R4ZvgIoXAkJACAgBIZAAAkun/OemnUDyMpMs++IpM7ZAUOKKNjvEqkA+NId8qB1YPqxy5/RRfgRENpQfu2hXFgzZ8Mc/c2zVqvR1wJf//N7YvOzzL/nQJSAfagbkwyoFnPTQ6DsLEfjjn9lprZX6R1rhVeZCQAgIASEgBIRAFiKwbNZ/wenTUb2lnz9pxhaIIx82PywgH/rZ8mbr2BKRD+mAXHkmiUDBkA1TF8xNEpqKJX8pIB7YvOzbYX3btmkn6wz5sDyws0gj8eHL1LcQSBSBKeofiUKVVem0IkVW3Q5VRggIASEgBIRACQSWz5lUYj/dO0s/e9yMLZDV5MPhq8mH5mvbkiqB5UMO6B8DN+xrP8+ZZm9M+M+CPBK3DvWb2PE9trHJ8/+2h3/6xBYtXxaZxKLlU7VKFTuq+1a2YfP29vqE72OWoRUpSsFZ7gMFQzbMWPRPuUFKxYUvBsFh2LzsH5AP2wRuF51rNbIikQ8ellLfUqZKQZKWA3+pf6QFV2UqBISAEBACQkAIFC4CK+aWP15cKlBb+tljAfkQbIGg9NXodYRVWaefrWi2ti2uUivrJj9PXb+PDezZ15797cuYRMD1W+1rB629qc1bushqVy+y8zbZ2Q4b9rCNmPyraycfsfL5eL//Wbt6jWzmovm2V+cN7Jc5022HITcXX6cfqUegYMiGOUsWph69CuT4fEA8sHk5oMMGtrUjHxqKfPCgBN/5srxfqElZ+XP24gVZVS/1j6y6HaqMEBACQkAICAEhUA4EVixIr5tqslVaNupRM7ZAarBteaRVWbtvQD6slVHyoW6NIntmp2OdxYGrXIyP6lWr2f5rbmTD/hhjR733mGGp8OMhl9qgjXd2ZENZ+Qzo1sva12tsJ414yl4d/50d1nVzu6bX3rZ+s3b23cxJJUrU8pcl4KjQTsGQDf8sXVwhoNJ98XN/jDY2pEqwHdCxp23dtKN1qiXywYGij7QioP6RVnjTlrnIuLRBq4yFgBAQAkJACFQYgZWL5lU4j3RmsOyTh83YAqkRaCCQD1XX6WvLm0I+1Kw0y4cNmrW39Zq2seu/HmZn9twhZpNb12lgbwdEw43fvOPS4Bby18J51rlBM7dfVj6HrrO5YekO0bB2oxb23G9f2YhJv1pluxLHbFyenigYsmHRitK+PNl6Twlj+ezEb91GHatWqWoHBm4XW/1LPtSQ20W23rqcrdfCKL5u2doY9Y9svTOqlxAQAkJACAgBIRBGYFVg6p87ssqWffJQQD4EWyA1AiuCol4DAsuHHYKAkwH5YOkjH778a6Kt/cQltnTF8jLJhj/nz7Hjh68OiEkdewaxF9Zo2NxGTv6NXSsrn6a16llgCGHjj7jKtY30j/48yi4Y9Qo/JWlCoGDIhuUrV6QJwvRnuzJYlvDpgHxgQ6pXrWq4XWzVpKN1rNUgWNRTIgQqhsAy9Y+KAairhYAQEAJCQAgIASEQgcCqQHnOWQnGhks/ftCMLZAa1Wr8Sz5s7ywfqqRwpT1IhmRl4xYd7IVdjrf5y5bYCcOfcJeXlU+9GjWDGA817JXxo+3Rn0bZlVvsZUd07WVDx39vn04bl2zxSp8gAgVDNiSIR04kWx507qcmfOM2zIauGz/cmuZEzVXJ7EUgfygr3z9+nT3B+k762np/92rAxUuEgBAQAkJACAgBISAEyo1AYCW+9KP7zdgCKdp4f7N6ndzvyv7YtVMPu2e7Q2z2kgW2/cs32z8B4RBPFi1f6pKcPOJp933ku4/aZwcMsv5dNhDZEA+8CpwvGLKBgCL5Jo5oGPe+Nf3x7XxrmtpTyQhUC1x18kGa1KprezZubn1+eM3aj3wzH5qkNggBISAEhIAQEAI5ikCVavmratWcPysjZMP/rbWJXb/1vjZx3mzr98otUZe9jPa4zAyCobeqU3q8m21B0qPVPZeP5W8PiLgrtQLTn3ySdBMNTftfYksmfW/zv3wpKmzVm7SzhtseXeLckj++sflfv+qOVavfzBrveLpVb9zO/n73Dls84asSaRtseYjV6ba9zXn75qCc2OvolrhIO2lDoFZgVpbLsk2j5tZ34qe2zcjoz2sut011FwJCQAgIASEgBHITgSpFtXOz4gnUusXyyolHwQoS522yi93z/UibunCuXRcsfbkoiDV29w8jA6uEnq6mCwLLBgI/liV3fjfCbt32ALtiiz3triCve/oc4pK/NPabsi7TuQoiUDBkQ/2i/DGkhmi4No0WDU32OM+a9r/Y5n34SEyyodF2x1mTPS8wC+JJeFn0y4eObKjRvIt1uvr7ILJlNVu5+B9rsPXhNmfYbTbjqTNd0jXvnmNVa9W3Ff/MsAbbDLB5o56yafce5rMp8V2763Yl9rWTHgTqB35suSZtata23aoss36Bq0SzkbLuybX7p/oKASEgBISAEMh3BKrWbpC3TawajOPTJWHn3k4Nmtqende3r2f8Ya3rNHTLXdapXmTXbrlPcfHE5otGNoTzeXHs17Zd27XsyG5buo2VLK744nX7fW7pdvRq3aU4b/2oGAIFQzY0rlmnYkhlydWeaGiWBteJqkGU1nbnDLNaa2wet7W11trSViyYY2NPXr3UTPiClgPuCYiG6jb+7C62fM5ka3f2W9Zo+xMc2dBoh5OMF+8fV2xpi8d+Zm1OfdHqb3aATX/gKFuVQyuGhNubD78b5VD/2KlJK9th3Ie2yegh+QB9hdogMq5C8OliISAEhIAQEAJpRaBa3fyNqla0IHCjSIN0ejSYzAzJh1N+t2nB8pa//f2XPfDjR44gCJ2O+TMyHxKe+sGzduZHL9haDVvYT3OmxrxWJ1KHQMGQDc1r108dahnKKZ1EA02q1XkTq9lpI5v50sXWdK+LymxlUeuutmzar1ar08ZWo+UatmD0G4EVw3x3Te11trEF37xqy+dOs5rtetiUOw+wqjXrunMQGmFZtXyJ4c+GmdmqRcvCp/S7EhHI9v6xZu16tuOMn2znRbOs3sh3KhGZkkWV5V6EaxEWP5ECiTbrlSvc4Wr1m1uTXc+xKjVq2dyRD9iSP/8z+atSvWZAyh0f9MGN7Z/PnnV9KjIv7QsBISAEhIAQEAK5g0C1QKnNV2HSsWjlclsaTDCmU7o1bm1zgkCQHwWkQyoEKwgRDalAMrE80vt0JFaHSknVpm7DSiknXYWkm2ig3ot+G2W/H9/QIADikQ3VG7a06g1bWYdLP3dNXhVEeJ188+62cMxwqxKYNtVsv76t/WAQGTZY0BZla9p9hwcK1HP29/v3GNYNHS782LlRVGvQwh1fuWheVOjqdO0d9bgOphaB1lnYP4KlkG2v5m1t+1/fsx4jh6a2weXILZ57Uc0OPY00JYTAm4GZHmRDnXV3cJZDK4M/zCpBv6AfQMQRF6VKUR1b865ghiBIv3LRXGuw5aE2d/i9Nv3Rk0pkF7mj/hGJiPaFgBAQAkJACGQPAsQuy2dpunSBTa2VXh0LYqDfkFsrFcZereRGkSrAC4ZsaF+vSaowq/R8Oge+SsRoSIfrRLgxkAyJCG4Qy/+eagt/Gm7THzzG6qy3U+AO8YK1PukZm3jRhi6LGi3WCCwkLrKlk3+0lkc/ZK2OeyxQql622mtvHQSNbGurli2y5fOmGzO9NTtu4GZ6Vy1bnEjxSpMGBDoEwXeyRdar19D6Tf3OtguWrKy3PPPPRKLuRQu+fc1+PfK/QJt1evRzLkRzP3rEQdvyqPttxby/bOxprd3+mvfMtWb7XO7IhpaH3xlY+NQIzrUKSLiZ1v6CD6zBtkfFJRuy5Z6pHkJACAgBISAEhEBpBGo07VD6YAWOEAetqMWapXJYMOY9W/TzSHe83kZ7Wb1N9rUlf3xrf793VzDmLj2Wwr2Z8fzs168rlVcyB7YNxmzPLk/mCqUtNAQKhmyoGswkrtGwuY2NEgQkm2/6aqJheNqJhmQwwAph3Jn/vTxRshaOed/qBqQDihKyeNznNnvoNe53UevrrNn+1wSxILZwytWqFcvtt8CCguCSdTfY1dqeOdQa9j46WLXiTpfef2jW1iOR/u+6QYBIrBumLpib/sKilFC3RpHtFcRi6PPTW7bGyNejpMjcoWTci3wtIQ7anPK8cyWCkEP+euREW7Hw79VJguCpVVgB5N8leecMuzUIrjqkuP+sdjvCtiO2qH/ExkZnhIAQEAJCQAhkCwJFbbrZ0ik/paQ6TXY/z4pahsmGYKwQ6DjVm3VyZEOr4x511pErggm9Br0Osmb7XWW/n9DIWS37ChCbrcVhd9iymRMqTDbUXxyMG6un17LB17uyvmXVkFqkC4ZsALa1G7XIKbKB6KvXjssuogEca3bc0Fqf+JRNf+SEYha1Wp3gRbZ0oWNPcZtYGfz+T1YrTbz4qgcM77KZ4x3RwPlFv3zgktVsv8F/yfUrIwis06hlpZMNmzdoYn0nfWW9v33Zqq9akZF2xys0Gfcin1fzQ25xK65MGryDP2QLvn/b/W5x6G3BCi1HOEuGGU8NdMeYfWDDfaLZgdc6F6V4sw215WJUjK1+CAEhIASEgBDIVgSK2nRPGdkwYVC3Es3sNPgnqxEQDTOeHmjEjmrQ6xAX92nq3Qdb9UatrcvNf1qzfa+wGc/+z13HZEi7s95wBEWJjMq5s36NYOIkvORDOfPRZfmLQOBQXDjSvUmbnGksRMN1WUQ08CJrfeLTLiDkkj9GW43ghdY6YE9rBOxq453OsFpdNrUFP7zr8F3w3VtWp+t2Vn/T/Vz6xrucZfipLw0CSrICRVHLta3hNke6l2Cb014OXlKBT/vLl5S6N036lz5WKpEOpAyBdSupfzQLgj0e3aazPTHrB7tm6Hm2wzcvZC3RALi4FyXqYuRvBqTBslkTbfGEr/yh4m+sFlYQwTmIz9Cwz/HFx/lRrX6zwNViuusTDbb4PxfLoUQC7QgBISAEhIAQEAI5hUDNDuunpb5NdjvXilqtHUz+He8sIxvveLojEabec6gjIJj8G3dWJxf43VeAVeAIUr1semqCLdZcOMdnnTffAzfsmzdtyYaGFJRlw/pN22YD5nHrkC1Ew6oQVcmKE/U3PyAgCz51CtT0x0625gdeb52v/cW1Z+n032z6Q8e439PuO8I6XPSRtT75WbePxcOf1/Rxv6fceaB1DIJKtjz6AbcP0fD3u3e4GBCrD+gzUwis3yy9/WO7xs1thwmf2lYjX8pUEyulXHwlifMw45mzo5Y37cGj3fHWJz8XEHL7urglK/5dq3rO27cYG30Ncs+5F71ze9R8tOxlVFh0UAgIASEgBIRAViHAym2pFiwUmu5zmVvVat5Hj7nsawSxHCAY1rh1klVr0NIdWzLxG5t42eol7ZkErNtzN5ty297OvaJK1doVrlbNBbPNaqd3/FjhSiqDjCJQUGTDRi3+izOQUdTLKDxbiIbfjq5VopYLf3zPls+ZbEunrvY548XGhsUD0fNZ/sYL+xPOX8+q1W1sVes1LcGeQjxMOL+HO4d/2dJJP7gXo782/C2f9DAa6f+9cYuOKS+kfa06tpstda4STUYOS3n+2Zhho76nuGd67oj7S1Sv09U/2D+fP2+zhlzmjuM2AdnAjEfDbY9xfWLSDTu7c4vHrl7lpWaHDUrkEd5R/wijod9CQAgIASEgBLITgVpdViv7qaxdo8CKAcJh5nODirNlpTiOrZg/26bccWDgsnl4ML44yloccquzIG51/ONBfKhX3UYsh1RIqxWlg0+mIt9M5qGYDalFv6DIhsY161i3xq2CtVWnpRbFFOWWLURDtOawlOWK+bNsQUA6hIXgMrEEAiJMQoTTlXWOdFKkwmhVzu9WdRoEcU1a2q9/B2b8FZRdmra2HcaOtI1Gv1LBnLL7csg2gp/OefOGYpeJolZrOWIusuaQcE12H+SCqVYJAmI22fWcwD1jabCqy4jADWlz51PZqO/JtvCHd4qtguZ9+GhkNm5f/SMqLDooBISAEBACQiDrEKgWrIhHbLIlf45OWd0a73haMMaeXRwPiowZpyOTb97Tls0YZ4t+/dDqb3aA1enR14radndL068Mrml55H3BynDtXJDqFkGMqb+ePMNdV56PqlhmrjaiKM/lWXeNiIbU35KCIhuAb5s2a2Ul2eCIhvHZFwzSP3JL/vyueFlLfyyd3wp+l050Y+e9Ves1yk02rF2nvu341xjb7vuh1iRYdzlfpSz3ItpcrUELRyBEtn/aA0db2zNesfbnj3CniGMy5da9zFausNmvDQ5Wc9kxmH24xcVyYKUWLCAYKEQT9Y9oqOiYEBACQkAICIHsRKDOutunjGxgyUqWkZ8z7LYSjV0axGGoGxxZGY6jEKxUsWrx/ICJCIK3B6vJ1dtkH3cN7p7BgMPqbbZ/hciGWrhR5JGIbEj9zSw4smHbtmvZfT9GH8CnHt7EcoRouBai4YfV0eoTuyq/U8kfPTP3d7u2a9vDP32ScOHVq1a1vZq1se1/ede6jRya8HW5mjCeexHt+u2YOlGbt3Tqzzb+3HVcEMhqdZu4gKnFCQNy4c+rt3Pmj0XtegQrUwSzH8GxWKL+EQsZHRcCQkAICAEhkH0I1Fm3n8156+aUVKz2Otu6fBZ+/2aJ/GYPvdoa9zvV2v3vHZt0427WJAjQTlBqXDhnv35tibSdrv4+CBRZ28afs2aJ48nuLJ+bndbiybbDp+/Vuov/qe8UIVBwZAPKVL0aNW3+siUpgrBi2XiiobmIhmIgMRGXmXgxHJX6Y/v2Xa1O9SJbGJj3lyU96zeyflO+td6Bm0SdFWWnLSufXD8Xy72orHat+GemixodLQ2BnQjmFE/UP+IhpPNCQAgIASEgBLIHgbrr7ZSyytRes5fLa+HPq5eP9xkTbHr6oydai8PusDVum+JWtlow+o1SRAPpV61cGdg1pECCQO9Ys84uwqYi90WWDam/hwVHNgDhjh2620tj4w/oUw93yRw71V9t0SCioSQu2sscAvzx7Nxx3aj9o35RLdurSUvrM+YN6zwyWKNZ4qJAT7xow0pFomn/iyu1PBUmBISAEBACQkAIVBwBlsWe98kTFc5o5gsXGFs0ITg1W1Gb7i5uw6pl0QM4Trxwg2iXl+tYn2AC6sUly8p1bTZdJKIhPXejIMmGXTv1iKpMpQfi6Lk6omHCcBPRUBqfJv0vKX1QRyoNgd06rVeif/Rq2NT6/vmF9f72JasaMNgSISAEhIAQEAJCQAgIgeQQqBesQJUKsiGRUpdOGZNIspSkqb94bhD+IboLaUoKqKRMBm7Yt5JKKqxiCpJs2LnDutasdj2buSgImJIB6fj/7J0HfBzF2cZf9S7LknvDBVewsY2x6cWY3gMh9BYgBEggBAgBEiDkS0ILAQIJgdAh9F5NMaaDMcZg4467LRfJsmT1k/TNM/LIp9Pt1d27vbtn9Fvd3e7szOx/5t2ZeeedGVg0UNHglzynUPjFEtOThynLn95qZ4rjuveQA797RfrMfDum8TOywATKqIwLDIhXSYAESIAESMCFBAonHOvCVEWfpN2yc0QS3LABVg20bIi+LPgLId3fyVQ4d+Kw2Jo+G6ZQNNxKRYPB0eWTq+x3QRKXE9NzmuWUp34pfeZR0RCXDLCIlFMoLMDwNAmQAAmQAAkkAIHSI69OgFSGl8Rk2JGCVg3h5Xk4vlNW2XDyzpPC4WSLXyoagmPkKvvBGcXCB/MhFpTDj4NWDeEz4x0kQAIkQAIk4BYCxfud45ak2JaOnLrk2v7SNjAMSBNIWWXDyO699UKRsSoHVDQEJ80pFMEZxcoH8yJWpEOPh1YNobOiTxIgARIgARJwI4HsviMFazckk+vX4o4d/iJlyikUkZIL7b6UVTYAz9mj9gyNUpS+qGgIDSAXhgyNU6x8MT9iRTq0eGjVEBon+iIBEiABEiABNxMomXqxm5MXdtrS1Zabiew4hcLZ3EtpZcMB/UfIPn2HOUp4p6JSrtEQAmGOpIcAKcZemCcxBh4gOlo1BIDDSyRAAiRAAiSQQATyRx8oBWMPS6AUB05qXl1VYA8uvkqrBuczJ6WVDcD7i133d4xyu6LhQ25vGQJhjqKHACkOXpgvcYDuJ0paNfiBwlMkQAIkQAIkkKAEuh9xZYKmvGuyPVvXdz2ZIGdo1eB8RqW8smHqgJEybeBo20lD0XDLiplUNIRAliPoIUCKkxfmTZzAe0VLqwYvGPxKAiRAAiRAAklAIH/M1KRZu6GtqV4KPQ0Jlyu0aohNlqW8sgGYLx8/1VbaRtHQi9sGhsSVo+chYYqbJ+ZP3NDriGnVEF/+jJ0ESIAESIAEnCBQduz1TgQblzCnFpfGJd5oIqVVQzT0Qr+XygbFanyPgfLzMfuETi2ATyoaAsDxcwmjthg9p3MvAeQPR9fjkz/kHh/ujJUESIAESIAEnCaQM3CclB55tdPRxCT8ooatMYnHrkiuGD9NYNlA5zwBKhu2M75m98Olf0FJVMQHbZ86QYuG0DC2d2JvCM0zfcWVAEbXqRSKbRZQPmLLm7GRAAmQAAmQQKwJ9Djpz5Lde3iso7U9vok5ubaH6VSAUDLQqsEpul3DpbJhO5O8zCz5w+QjuxIK8QwUDbeqNRqoaAgRmPJG8/zQWbnBJ/MrtrmQR4uf2AJnbCRAAiRAAiQQawLpGdLjlFtjHavt8eXUVtoeplMBUtHgFFn/4VLZ4MXl6MHj5IyRU7zOhPaViobQOHn74vQJbxqJ8Z3TKWKXT5APrtUQO96MiQRIgARIgATiRaBwwrFScsil8YrelngTRdnA6RO2ZHdYgVDZ4IPr//Y6TkaX9vU5a/2TigZrNlZXaB5uRcb95zmdwvk8onw4z5gxkAAJkAAJkICbCPQ6/S7JHby7m5IUVlpy69xv2cDpE2FlqW2eqWzwQZmRli637v0Tn7P+f1LR4J9LsLM0xw9GyN3XmX/O5s+Aaz5wNgKGTgIkQAIkQAIk4DoCvc75t+vSFGqCennqQ/UaN3+cPhEf9FQ2+OE+oedAuX3fk/xc2XGKioYdLML5xukT4dByp1+MvLND7EzecPcJZ7gyVBIgARIgARJwO4HcwROlzwUPuz2ZftOXXrPJ73m3nHzuiAu5+0ScMoPKBgvwpwyfJL/ebarfq1Q0+MUS9CTnoQdFlDAeuH6D/VlF+bCfKUMkARIgARIggUQiULzPWVJ23B8SKck6rdnb3DuNgus0xLc4UdkQgP/VEw+VU0fs0ckHFQ2dcIT8gx2pkFEljEes38CReHuyi/JhD0eGQgIkQAIkQAKJTqDshBul2/7nJdRjtKjdKLJbPa5LMxQNnD4R32yhsiEI/9v2OVGOGjxW+6KiIQgsi8vsSFmASYLTVDhEn4mUj+gZMgQSIAESIAESSCYCvc97QIr2ODGhHqm0qdZV6aWiwR3ZQWVDCPlw/0GnyyEDR8stK2ZKr3lvh3AHvRgCXFnfkEjeTyocIs9bKhoiZ8c7SYAESIAESCCZCfS95FkpGH90wjziAUXdXJNWKhpckxVCZUOIefHwtLNlSAKstBri48TEGzpSXEgwJqjjHgkVDuFnARUN4TPjHSRAAiRAAiSQSgT6X/6KFE48LiEeuai+2hXppKLBFdnQkQgqGzpQBP+CjjM6CHTBCbAjFZxRsvmgwiH0HKV8hM6KPkmABEiABEgglQn0+/WLUrzXaa5HMC47K+5ppKIh7lnQJQFUNnRBEvgEO1SB+eAqO1LBGSWrD8pH8JylfARnRB8kQAIkQAIkQAI7CPT5xePS/dDLdpxw4bcctUhkPB0VDfGkbx03lQ3WbCyvsENliYaKBms0KXOF8mGd1VQ0WLPhFRIgARIgARIgAWsCPU/7u/Q89Q5rD3G+Ek9lAxUNcc78ANFnBrjGSwEIoEOVN+pAWfO3qQF8pdYlTDPBgpB0JED56FwGIBel6p1B+ejMhb9IgARIgARIgARCJ9D9sMslu+9IKX/gHGmp2Rz6jTHw2aelMQaxdI3iuSMulL36DO16gWdcQYCWDVFkAzoOIx5pSfkOBEZrySGKgpSktxr5QPlIZQcOVMSlcgngs5MACZAACZCAfQQKxh0hO938resWjkyv2WTfQ4YQEqwZ1pz7NyoaQmAVTy9UNthAP5UXjqRZuA0FKMmDSOVpFZAPvB/oSIAESIAESIAESMAuApklfQULR7ppWkVuDNds4LQJu0qS8+FQ2WATY3SoUmn00lgz4LnpSCAYAZQTWL+kipWDsWagfAQrGbxOAiRAAiRAAiQQKQFMqxj8l3lSMP7oSIOw7T7P1nLbwrIKyFgzXDFhmpUXnncZASobbMwQ08FIdqUDrRlsLDQpFhQ638mscEiVd0CKFVs+LgmQAAmQAAm4lkB2v9HS//JXpM+Fj0pWr6HxS2dbq5Q21ToWP60ZHEPraMBUNjiA13Q4kq1ThedpH52mNYMDxSZlgkxGKwcj88muaEyZQsoHJQESIAESIIEEI1C89xky5NYl0uOkv8Qt5QcWldgeN60ZbEca0wCpbHAQd7J0qqhkcLCQpHDQySAfVDKkcAHmo5MACZAACZCACwmUHv072fn+rVL2k5tE0tJimsKihmrb4qOSwTaUcQ2IW1/GAH+76fgNUrdwplS+fJP+jEG0UUWBTlSeOpB2OhJwkoCRjwolG/VKRiAnbneQD25l6fZcYvpIgARIgARIIDUJpOcUStmx10vZ0ddK1Yz7ZevMB6Rx1VzHYYzPzpZHmyOPBgoGOK7JEDlDt91JZUMMcwQdlHy1Mr2blQ6wYsgbdWDKb+cZw2LBqLYTMIotyEf9wg+l4uU/uY4NlQyuyxImiARIgARIgARIwIpAerqUHPxLfdQt+FBqvnhKKR7+a+U76vM5dZUiWT3DDgdKhr36DuU2lmGTc/8NVDbEIY+M0gFRG8WD+R6H5HQs2Gc6e/FIA+MkAUNAy8d2qxpYO8DFS/GAtMDRikFj4D8SIAESIAESIIEEJZA/Wg0mqqP3uf+RbbNfltpvX5etHz9s69Pk1m4RKQlN2UAFg63oXRsYlQ1xzhpfxQOSg6kWcE6Zk5uFK2nBoDHzn4sJGAWY+XRa+eCtXAAW89vFiJg0EiABEiABEiABEgiLQOHuxwuO3j9/UBpWzJb6RR9L/ZLPpHbOq9LWEvk8iH6tjZbpMFMkaMFgiSgpL1DZ4KJsNR0bTLUwzigcYFaO+ezGmfPmt/enCQdrLhhHxYIhwc9EJmCUDuYTcgDZMC6YjBjZMP4hI5ANON9r+iT/kQAJkAAJkAAJkEASE8gdvLvg6H7Y5fopW7ZukMa186Vp/SJp3rRMmjevkqa186R5w1Jpa23xSyItO08yu/URT/VGuWLqtA4/VCx0oEjZL1Q2uDzrTQfIfLo8uUweCcSUAOSCshFT5IyMBEiABEiABEggiQlkdOst+TjGTI3oKcdEdBdvSlYC3PoyWXOWz0UCJEACJEACJEACJEACJEACJEACcSJAZUOcwDNaEiABEiABEiABEiABEiABEiABEkhWAlQ2JGvO8rlIgARIgARIgARIgARIgARIgARIIE4EuGZDnMAzWhIgARIgAWsCdXV1sn79eqmurpbW1lZrj7xCAhYE0tX+8sXFxdK3b1/Jz8+38MXTJEACJEACJEACThGgssEpsgyXBEiABEggIgJQNCxatEh69eol3bt3l7S0tIjC4U2pTaCtrU3q6+t1WRo5cmTECodVq1ZJZWWltLT4X4U9GShnZGRIaWmpDBo0KBkeh89AAiRAAiTgEgJUNrgkI5gMEiABEiCBdgKwaDCKhpycHCobWDAiIgBlQ25urr4XZWrYsGFhhwNFAyxrdtllF8nKyhKEaRyUYPjt+5mI15ubm2Xt2rWC56XCweQgP0mABEiABKIlQGVDtAR5PwmQAAmQgK0EMHWiT58+uqMIU3g6EoiUAJQNJSUlsnjx4oiCgEUDFA2ZmZmdFA0IzCgefD9NRL7nzW83Xsfz9e/fX+bPn09lg8kgfpIACZAACURNgMqGqBEyABIgARIgATsJYCQZnUQqGuykmpphoQyhLEW67gemTmCKga+iIBlp4jmTeapIMuYZn4kESIAE3E6Ayga35xDTRwIkQAIpSIDrNKRgpjv0yNGWpVRQNDiEnsGSAAmQAAmkOAEqG1K8APDxSYAESCCZCDQ1NcnKlSultrZWioqKtEk45trH2nk8HtmyZYuUlZXRQiPW8G2OL5CyAWsdbNy4UbC2CPI6WsWGzUlncCRAAiRAAiQQVwKcDBtX/IycBEiABEjALgILFy6URx99VGbMmCGzZs2S999/X/9esWKFXVGEHM7mzZvlpZdeEnzSJT4Bo3Dw/nz55ZflsMMOkzPPPFOOPvpoOe200+THH3/UD+vtDye8f++55556IUbf86H8xnomjz/+eKfwQrkPca5evVrmzZsnRxxxhOX9CIuOBEiABEiABOwiQGWDXSQZDgmQAAmQQNwIYIvDjz76SHr37i1nnXWWXHDBBXLsscdKQUGBvPdz3CyoAABAAElEQVTeexHP2Y/bAzFi1xDAeg9QFnh/Qqnwt7/9TW6//XZdvl577TW9qOnNN9/cyZ/vfd5KB+/w/Pnzd33r1q1y7733dklPsPsB04SH71b+cY2OBEiABEiABOwiwGkUdpFkOCRAAiRAAnEjUFVVpePGlplmu0PsaHHIIYfI3LlzpaGhQfLz86Wurk7efvttwS4DWBBv4MCBcvDBB2vzdyyON336dL0FIDpmuD5x4kSZMGGClJeX6/sOP/xw+fDDD/XUiJNPPllmz56tR4sxfQMr+sPv+PHjOzjA2uL1118XTKtAujASDoUIXWITMBYrKG9wmEJx1VVXyaeffqo79ViY8sUXX5SnnnpK5z2sCaAA81309Ntvv5W77rpLWx3stddecvXVV+vpP9iGEoqM77//Xnr06CEXXXSRDB06VK644god3+mnny433XST7Lzzzvq3+ffMM8/IY489pqfwjBw5Um688UbZaaedzGV+kgAJkAAJkEBMCdCyIaa4GRkJkAAJkIATBNCBh3Lgu+++k1deeUVv4Yd1G9BRgzIBigaM5j733HOC0WEoEYYMGaLN3jHdAg6WETA1HzZsmOy99976HkzHgKIAygQcb775pv4cNGiQVmJA2VBcXCy777679v/VV1+J97QNKBvQIRw7dqy+z8TlBAOG6QwBf1YAo0eP1ooqWNHccsst8u6772pl0gknnKAVVzNnzpT//ve/cuWVV8of//hHeeutt7TyAUosOISJtR4uvvhiOf744+Wf//ynXmfkr3/9q94RAkoFrDly9913awXVtddeq+8755xz9Ofll1+ulVbGWgGfixYt0oqLyy67TE+1QLl89tlndVy4CXHigPO+z/v59EX+IwESIAESIAGbCNCywSaQDIYESIAESCB+BDBifOKJJ2qrgw0bNggOjDJDybDPPvtoxQI6Y42NjXLSSSdJaWmpTmx2drZWTGChP1zDKPBBBx3UcQ1WDFBOGIcOHOLBQoCPPPKI7hCigwkHhcLTTz8t69ev1/GZcxixhoN1xdKlS/V3/kscAqaD7v2JcvXggw/KO++8Ix9//LFWcOGJfvnLXwqsDmDNAiWCsTww537yk590PDgUT1BSoXzC/fznP9fWD1BgQen1wAMPSGFhocBCAYoHuN12201/4j7jTLowZQjTOFB+a2pqpFu3bgKLH3Pd+Def5rzvp7nOTxIgARIgARKIlgCVDdES5P0kQAIkQAKuIFBSUqI7eLBEgBn6smXLtOUCRp2PPPJIPRUCCcUaDsZBAYDOFsziMUVizZo1uvO4adMmqaioMN46PmERAUUDRoZh6TBq1KiOa9j1AosFwmHaBZy3CTs6f6Zjpy/yX0IQ8Jdn6MyjDEDRhAPlCAuC3nfffbLHHnvoBSCh7Hr44Yc7ntFY1+AEwly3bp2ehoO1Rbwdym2/fv30eiMmbqPQQrmGM+e974MCbf78+doaAlY9UD7suuuuHX5xj7nPfHrfz+8kQAIkQAIkYDcBKhvsJsrwSIAESIAEYk4A0xWw5SXWRMDaCejk44ByAKbkuG4cOmHGme9YTwFrOaxatUqgtMBcfEzB8L4P90BhAAeFBlywbTWRFrrEJ4DOOZRM5vP555+XBQsW6CkUOI+tL0855RStbIBCoGfPnvo3yiOub9u2Ta+jYDr5+IRyYNq0afL73/9eh4tzsHzBuiKwSMAaIrDYwf2wmIGFDKYKwZl0eH9C2QEFB6Ze9O/fX5d7KB+8HfzDed/n/VzefvmdBEiABEiABKIlwDUboiXI+0mABEiABOJOALtRQNlgFu4zCcK0BziMQmMRP7ipU6fKUUcdpQ+Yo2MqBZQOUDTA7B0LPx544IHSt29f7d/7n+ns4R50BDFlwjjEgakVWPuBLnkIIF/ROff+xEKgn3/+uZ5GAcUTrpn1OLDmBxQDWCASSgYoDe655x7t17uzP2nSJG1ls3z5cq1EeOONN+TOO++U4cOHa4UD1h7BvVj/ARYSUGBAMQCHBU4Rr3e6YI0zYMAAvSsGrmOdCEwPQtrgTPrx3fs+c974w3U6EiABEiABErCDAJUNdlBkGCRAAiRAAnElACUBOv/YgnDx4sW6k4fOF37DoQM3ZswY3VnDOVzDCDR2n8CaDMYCobq6Wt8LxcMnn3yi78U5fw6LRMIUHrtdIDxMz8DUCu/dKPzdh3OIEzsVYAFKf79hUYHrmLtP5z4C48aN04s/QjmANRIwTQcWBddff722KsDUCJQ5rNtw6KGHCsrQaaed1vEgUBpgCs4ll1wi559/vg4Di4/+5je/0dYzWCjy/vvv1+exNgQWmoT1DRRmgwcP1uFiDRJvByUayiIUaYgLVj0ow1i0FA5xGmWF9338TgIkQAIkQAJOEaB9p1NkGS4JkAAJkEDMCGABPay5MGPGDMGijsZBATF58uSOBRth1g6lAEzO4WD5gN0q4A8L8aEDh04+HJQTMJXHiDU6jL7uQGX9gNHjL7/8Ul+CKT06eAgTlhaBHBajxKi32bLT97dRegQLJ1AcvGYPAVgB4EBH3fvz6KOP1mUOa3vgPCwPUI7wHZYv11xzjVYewGIAigLcj+8on/CDA4uVYsFRlAWUG3N+zz33FFg6oByYtT5wDUoxWM/guwnPpAvKC1hDICzcg7RgDRHIBuQC/mCtg7KPdJj7vD/tIcZQSIAESIAESKCdQJqqsNon8EVIZN68eXoBoghv520kkBQErOTA6nxSPDQfggRCJGAlB1bnsRo/zNTRWYrEYXE8WA7k5eV1dLp8w0EnHlMi0Cn0drBMwGJ/6KChE4bfprPo7c/7O0zdcY9Z/8H7Gr/HnwDyb86cOXrnh3BTg7I4YsSIcG9LWP+wCvLe6SJhH4QJTxoCVvWE1fmkeXA+CAmEQMDtcoCdwWjZEEJG0gsJ+BL47LPP9Er1xxxzjO8l/iaBlCcQb/lApz9Yxx+KCH8OygdvBYT3d3/+cQ5Ki2DxWd3L8+4n4D0mYywKTKqT7bd5Ln6SQLwIxLv+iNdzM14SCIVAIspHZMNGodCgHxJIYgIwVcX+6nQkQAJdCVA+ujLhmcQlAIWCOfAU5js+k/G3fij+I4E4EWD9ESfwjDYhCCSifNCyISGKFhNJAiRAAiRAAiQQawKwWsGuD2YXkljHH8v4MB0oFZ4zlkwZFwmQAAmkOgFaNqR6CeDzkwAJkIDLCGCtBsyzpyMBOwigLEW6/kdpaaneZhId8WR2eD5sl4nnpSMBEiABEiABuwjQssEukgwnqQlgjhRMl4zDAnR0JEAC7QTslg+syo+OT69evYiYBKImgLKEMhWJw/am2AYVW5Ams8IBFg1QNOB56UgglgTsrj9imXbGRQJOE0gG+Yha2WBGoCIdNXA6kxg+CdhBAFubWSkYAo2aUT7soM8w3E7AbvnA9nzYghKurKyMpt1uLwAuTR+UAyiba9eu1duaRppMdMDZCY+UHu8jgcAE7K4/AsfGqySQWAQilQ83PWXUW19C419YWEjTOzflKtMSUwIYNcO+5v4ao5SPmGYFI3MhgUjlo66uTtavXy/V1dWcUuHCfE2EJEHZC4sGKK/y8/MTIclMIwmQgBeBSOsPryD4lQSSlkAg+XDLQ9uy9WXPnj1l+fLl+plKSkoinhfpFihMBwmESgAWDVVVVVJeXi5Dhgzxexvlwy8WnkwBAtHKBzqHw4YNSwFSfEQSIAESIAFvAtHWH95h8TsJJBuBUOTDTc8ctWUDHqa+vl42bdqkR3cBgI4EUoEARs1g1QOFQl5enuUjUz4s0fBCEhOgfCRx5vLRSIAESMBBAqw/HITLoBOeQKjy4YYHhWWDLcoGNzwM00ACJEACJEACJEACJEACJEACJEACJBB/AlA2cOvL+OcDU0ACJEACJEACJEACJEACJEACJEACSUWAyoakyk4+DAmQAAmQAAmQAAmQAAmQAAmQAAnEnwCVDfHPA6aABEiABEiABEiABEiABEiABEiABJKKAJUNSZWdfBgSIAESIAESiB8BLhIdP/aMmQRIgAQSmQDrj9BzL5FYZYb+WNY+udq+NRteSV4Coa4GS/lI3jLAJ7MmQPmwZsMrJED5YBkgAWsClA9rNrxCAqHKh1tIRb0bBTpSy5cvl379+klubq4kkqbFLZnAdCQmAQh7Q0ODrFu3ToYMGeJ3+0vKR2LmLVMdPQHKR/QMGULyEqB8JG/e8smiJ0D5iJ4hQ0heAqHIh1ue3patL1etWiXFxcWSnZ3tludiOkggpgSampqkurpaBg0a1CVeykcXJDyRYgQoHymW4XzcsAhQPsLCRc8pRsDIx4ABAwQdLG/H9pU3DX5PRQKB5MMtPGzZ+nLbtm3aosEtD8V0kECsCcCiB3Lgz1E+/FHhuVQiQPlIpdzms4ZLgPIRLjH6TyUCRj58FQ1gwPZVKpUEPqs/AoHkw5//eJ3rrCaMIBWYNsGpExGA4y1JQyCQDAS6ljQA+CAkEIBAIBkIdC1AkLxEAklDIJAMBLqWNAD4ICQQgEAgGQh0LUCQvEQCSUMgUWQgamVD0uQYH4QESIAESIAESIAESIAESIAESIAESMAWAlQ22IKRgYRDAJq4jRs36qOlpSWcW+mXBEiABEjA5QSam5tl7dq1smXLFpenlMlLBgJoR6C8YcFmOhJwKwGUUywoznLq1hxiupwikHDKhlmzZsn+++8v//3vfwMyQYf2lltukcMPP1xefPHFgH7tuoh03XDDDZ2Cw0sF53H8+c9/7riGHTzM+UceeaTjfLJ8AffLL7+8y+MsXLhQTjvtNDnppJP08ZOf/ESQp3Bff/21PPzww1JbW9vlPu8TaFTA37Jly7xP87sLCPzyl7/sKNemfF922WWyfv36sFL3u9/9TssubrIqS1YBHnfccV3SgLTgfXDttdfqa1b38jwJgMD//d//+S1DJ554YswBffPNN3L++ed3KbemLjRyhs9TTjlFZs6c6XgaX3vtNXn++ef9xvPWW2/JMcccI6eeeqpAFi+88EIpLy/365cnSSAaAmhf/eMf/5CDDjpIl7dDDz1Ubr/9doGyK5D705/+pOUJ7cRQ6pfXX39d+//uu+8CBdvpGhYvhEw+/vjj+nwgmel0I38kBIGamhpBOUL5Qb0QqJ/hXU7xjkY5veuuuyReg22+ZdMO4L5hggfe/9dff7089NBDWhbCbQdGk65Abb1Q+xrRxM97OxNIOGVD5+Rb/5o7d6688cYbcsQRR8iee+5p7dHhK21tbR0xTJ8+vUOj+emnn3acT6UvN998s1RVVWmlDF5CjY2N8tvf/la/dL/99lutRMBLPJCDZhjKhh9//DGQN16LAwFTeV500UXy61//Wvbdd1+ZM2eO3HfffRGn5qqrrtINyVADgALr9NNPl1GjRulb0BDF78mTJ4caBP2lOAHUGSgz6CzA7bLLLvr38ccfH3MyaKitWbNG/va3v/mNe+LEiQKF3tlnn61Hzf7617/69WfnyTfffFP+97//dQly8+bNgvj79u2rlXsXXHCBQMGMhjUdCdhN4M4779SdPMjrb37zG/2Of/XVV+XBBx+0O6qwwysrK5Pf//73stdee+l7rWQm7IB5gysI/Oc//5H33ntPK1YHDhyolV7od/hz3uX0V7/6lYwbN05eeOGFDkWUv3sS7Zx3eceW71ACgssZZ5wh++yzj5aFkpISVzxWqH0NVyQ2SRKRmcjPsXLlSvnDH/6gK5hPPvlEN7TQQNx7773lxhtv1I/20UcfyR577CGZmZm68GOUqFevXvKLX/xCC8Ddd98tK1askCFDhshzzz0n6Njg85BDDpEnn3xSbrrpJsnLy5N//vOfutE0YcIE3TnGNofo7N52220CKwV0aEJxX331lW7Avv/++128Q2vuLx6MDCF9GNGHH1ReeD6kCwqMRx99VFavXi1I26WXXirDhw8XPNfixYsFL4AZM2bI2LFjBaPFSDdeiPfcc4++np+fL0cddZRccsklcu+99+prpqLGKPXOO++sLRSQLoxY1dXVCRq311xzjfTp00defvll/cLctGmT9OzZU4/AQcHjz2EUAens3r279O/fX0aOHKm3TYW2EyMHZqQMFhGwEMGWLr7phBbZWIggTbCCwDmk+d1339X5DG0qXnD+Vi/2ly6es58AOvxYJRdyAdmsrKzUkViVFyiYMCL15Zdf6nKBrUSNe+mll3SlNWXKFC2DKBPff/+9DB48WM4999wusodOF9z999+v/aM8oMzCoYzAIQzIOcKAAmynnXbSsmUlf6NHj9ZlF5UoRjOQ1s8//1yHBXlEo3Lr1q36fYSGBK55PB4BB7yPEAdGe3/605/K/PnzdUcMFj4ou3TuI3DwwQcLji+++EJQh0BRtd9+++m8R7n2zkeMUuE97JvvZ555pn4HofPz1FNPaSUrwsD7raCgoNNDQz7QIUf5x7Wjjz5azjrrLP2uM6OpGClCWfJ1eLdjZA3vV8RlnG/dBtlDOnzfk1AQQhlo3u8jRoyQK6+8Utcj/t77qBdRhuGQRihDUL/CQdkA161bN11nQTbQwMzKyhKMfEHBjHczmKEORN2Fdz0GBrzrYdQFqI9Qd4ErwkB9cOutt+rpd2jIBqvL8Rxo7CJ8jCyiPtttt938WtzpRPNfQhGAtQzKLPIZijjU93jHom1UUVGhnwXTNf2VFasHRVsLikUo76Dgw+goRqK9Hdo6/t7/aNs98cQTHW1HKNuffvppbcH5yiuvdMgM3gt4h2ArR5ZNb7KJ9R1tASif0XbGSDneR6ad4/0k/sop2sjoO+C9BIdPf21YKDNQptBZRzsoJydH9z+gBA+1bON9D+tp00dA/ZORkdGRRFgB+Xv/Y7AG1ji4F2UebSWUafQz/NULqH+ggEZ7C+UabunSpbothPc30j9p0iRJS0sL+KzefS+0+Yzzlxb05azqFHMfOKOuQd8H8rxgwYJOfY1p06bpOh59P9SlSCcO9BHRhoymXjdp4KdIQls2YFQcDRQIExpn/fr10woCCCQaM3A4P2zYMF3IFi1apLXfKKDoHKDzjhcBXhQffvihFhJspYMwH3jgAd2JQSMKHfji4mJ9D/YLvfrqq7WZHhQdUDRAUxnMmc4OCj1G5jEFAC8Q49BI8xcPGoIYGUKlioYSGr3oyMAyYt68eboxCiGCwCFtaCRCIYDnQiMVygScQ+cMLwBYWvzxj3/Uc2nRmEPjF50uKE7Q6YeCwjg8G8JEONDC4iUCwUV6cA/4/f3vf9edw3/961+6kYwGs7c1hwkLn2gMwLQW83hRqeOFaxqTu+++u5gXCxqjGBnzl048mxldxEsJXPEyfPbZZ+Wcc86RAw44oONF5h03v8eWADo9eGFDow932GGHBSwvkDeUBTTsUA68p8igwkLZhEICFSUakijvkGOUaZTHcB3kDR00yDrKcjD5w7Pg3QCZRaMAMgilFxql+I60m/cRGggwe0fFheleSDsUEejsweFdg3jHjx8fbrLpP44Ehg4d6jcfUb8gP33z/bPPPtNlAx0TlBu889555x3dUPN9jL/85S+6DMEiCAo6WG5BuYB3HZSzqNvw7vTnUH5R3k444QT9bkXjF863boOiwd97EunG+/1nP/uZ7pihDkAD1+q9D1lGelC3oEHmrdQFC3TW0PA++eSTtTUI6jvINBrU4GQUiZBn/MZ5f2mFTJ133nnaMhEKHygr0KBFgzGUuhz1IupbyPbs2bP1OwVpo0sOAmifwMF6zpRBtNfwXkb9EKisWBFAXYI2DxyUWyifeHd7O6v3P8o1/Ju2I9qM5n5vmYGMQ7nNsulNNfG+o4xdccUV+n2O7+iM+7Oe9C2neB+ibkCdgDoF5dSqDWvKFKbMYSAVbWe0sUMt2z/88IMe6MDgHgYb8d5EWr2d1fsf72W096EU+/e//y3Z2dl6ENOqXsDgCso72kkYSIGDcgGKEVgz4xr8BHtWIz89evToSKZVWgLVKeZmxAmZg2yjnkW7zLuvAYU40gZ5hEPbFbKP+HE+mnrdpIGfIu3DEQlOAooFdFDhYNqEDroxoR4zZowu4GhA4Rw+McoCh1Er4zAKBIE0o+tQRqAz/Pbbb2sv0ESj01NYWKjDwAgUNIUQKlgGoOOL0Rkrh5EeCB0aULBSgMPImZlOAYUHnG88S5Ys0efRiINJKpQG0KCjw2VeYujwwMoAGkrMS8cLxTi8DPG8sAJBoxT3oQOExhcOvIzg0Im3csb0CVpLNAoxQoTnRVoxMoU0ffzxx1rTinCslA0IH/dCuOEfzw4lCg40gjEKBYcXFOL0l05oRXfddVftD5YRaFDifjTIMRKBlzAc5i2jgqeLDwGMdsKhPKBTAjmEAsmqvKAsYIQKCiM4yJd3OcY5VBYID9NuUP4gz5B7yLGRd/gLxSEMWAahkwWtfTD5g9UOFGoYEcCIBhoKaDSYdwgqWOOQJrw7YNqLThLkDIo6VLJQ6n3wwQdaSQaZpUscAujQ+MtHWI/B+eY7GohoDMHhvQTlLGQB9QQafsahTOOdjlFZKBfw/oQCHe8wWFCgnKLjAoWsP4eyj3cqGqYoz1AAezd6Td2GtPt7T8LqBmUR9QnihUN5tnrv4xkQDuobyIG3Q10DqyGUeyjh8J6HJQTk+7rrrvP26ve7SSssJlAfmPcBFHRIk1GIh1KXQ6bRUETDHqNZcOiY0iUXAbQJvB1GfM2aDcHafd73hfodCm9/73/IKZxpO0I5ZhzaLN4ygwExWEOxbBpCifuJtjneR1Cwoq7HOx/tWTi0kaEkgDPlFJ11dHqNmzp1qmUb1gxSwoITZQ5TUvFeC/Qe9F5XCOULDmUWCmJYaML6wrSTcQ39J3/vf7zLUWYRJ/pV6L/AL9o/cL79ASh14VBPwpocDu1z8EF9YJxVe908q5Ef4x+fVmkximtvv77f0daDogR1AOIuLS3t1NfAs0MBgb4e1pLDQBeU9ya/Iq3XfdOR6r8T2rLBZF7v3r31VygC4LwFCb9NxYPvaLThxfDzn/9caxVxDg6KBm9nFALQcMGhsONeWALgXjS44Iz5qLdZkr7g5x/MdeDQEUPHCtpt46ziKSoq0l7QUYMz5rd4Rmjs4JA2OFRgcOY8vpt04aUHh9EkpN/Mofc30gNlDUZp0QiGAwuMdGFkDhX5HXfcoaeXQChhso4pEHgpGWb6Jj//oCWEVQiUArAGwcsKo2lweAF7O7ywg6XT+EdacSB/kA7ch3yiix8BjOLj5Y1RJpSjZ555Rr/ErcoLyqwpx0i193fzFEaOzTVT3s154y+UT5QVhAM5RucumPxBXo0swWQXowOoXP1ZJ6BigzMyh7CN7MMsD8oNKCPoEo9AoHz0l++mXBmFAawT8H7CO9Y4892UazRyUK+Ye40/q080lPAehTIaDU3IG6zejDN1m9V7EiM5UFhjeh0ao8ZZvffNdX+fUKbD8gB1MpQLMB9H+vDuNybGRgGD0S5fZ9IKWYNywSiuDQtvWQ9Wl8PcF/UBrEmQLgwKmA6hb7z8nXgEMJIMB4WWkSGUGXQYLr744pDafToAn3+mfPpaNBhvwd7/wdpBCIdl09BMzE90uGFBBotH1OVGkQqlKN5L6GDjgOWCbzlFhx2KJrybjLN6N5vrGOyAM/2BUN6D8G/KsmmLoG5BW8u8V+HH6v2Pd/Bjjz2mLaPRt4JFD5S/6Iv46w8grFBcsGf1Jz9WaTH1gnlOf3WK6aOZ9qJ3/8ik98gjj9T1JhTlcKaex/dI63XcS7eDQFIoG3Y8jv9vaMBAsDG/H/NvIHgQMLwUrJwxy0NDCQ7CjZEldBQwSoNRdYSJeURYbAQmqsGc90KVGJn1dlbxmFEzb7/mO54F7kk1coRRX6NNxZoNxiFd6MgbhQBeNjDFgpYSDVOjpYR/WF/AYXQMYRqH0S5o98ARGll00GAhAUsPOIy+YfoCFBFw3i8yfWL7P7wwMRKM+V7gBq2rsayABtQwx4geRoCt0mk0jtDwYrQQ85jRwEYYWGMC2ktfhZN3Ovg9dgTMyCesAAKVF4zEYh44KmHIF0Z6fR06/HCw0EF5R4UH56/Dry+E8S+Y/JkyhyAh72hAQHFiKjDvMo8OFjp7qKjhoNBD2cQnRnvhqAzTGBLuX6B89JfvZrQGigR8x+gXrMOM4goA0IBEwxTKOYwioW5CPeNtnRAIFCzLYJEHecB7Hs4op73vs3pPmncwTE1Rro2zeu/jOt7VSCPWY/Eu+6gT8Y6HhR06+OgIQrmMOgPmuHBokOO8saLQJ33+Ia14/9+o5t9jjRQoK+DCqcvBGNYi4IP6AY18uuQhgDIO60VYL8BSCOsjYAQXDlY84ZQVQwVtFMgD2iBoB/lzwd7/ph3je6+3zOA7y6YvocT5DaUl2sAYPMB7zlhyoqOMdj5G53HAsti3nKL9AoUs3m/GWb2bzXXfz1DLtmkboW5A3YIp1Zie513/WL3/UU+hjMJCB8+C+gjv0UD1gm86/f0O9qz+5McqLUZ5HKhOQV8Gz462Jaw70O8zcUDOYR2B/hAc2mfoV8GfcZHW6+Z+frYTSNhpFN6Nf5OZ/s7hGhp62MoM87sxgoOGDxbpQQMvmMPLA6PxWJgKpkCojLA4IiwM0BCCEGLRFISFcAM5KDlQOWKkBZ1zWBkYZxWP0Ugaf97PiI4cNI2PPPKIbmyioYdGnjF/xT0QHphAIW0YZYZpKqYpQIBwGMsGvPiwvgVenFAGwPoD4cGhY4S4zKIvCAtmRtDaggfMcOHQUEUDFCMC6IQZgdYX1T8ww2JNYIm5YHAIC/wQHzSeUJhgLQnM9bdKJ9auwMsASgW8yDGKgRe/SR/mPHPhPY037v9QqaHMwQoGFZ9VecGoLJQRKKMoEyhLRnllyhLm0OE6FF6wsoFDOTQme9E8bKjyhzgwNx6NC4yUesuPiR+VGcokHMqi6eRA9qFQwUgInpEuMQlY5aO/fEf5R2cIazDggMIM6xD4OqxPg0416ic4lBtjfuvr1/c3FBg44PBexDoK/uo2q/ckFNqok2Adgfvx3sd73Oq9j3jQOEMnHo1mzHU1VhmQcXT4UOeY9zFkHt/xHsBzoY7BSCAa5ZiK6M9hGgUUxlCUgxnqBzQKw63LYaIMpR/SgHqDLrkIwEQa7Qp0oIyCGoMoaN8FKivenS1Tv4AMBlXQ9sB6XEZRjvOm3YVPq/e/1Tvd3OsrMyybIJu4Du1/HOY9h9FwrFPjz/mWU5QVDNJB8QBn9W6G5YQ/F6hse/vHtDG8S/EOhEIaFjWoH4xD2cR0bn/vf0wvwIAKpr6agUy0tzBQioFC89x4Fpw3DmGaMm/OmU+cD/dZca9VWlDPBapTkDZMW8U6EPiO/hocBo5NXwP1C7iYcNCu83bR1Ove4aT69zQ1KtEWDQSM4KHTmSgOnWqM4Pt2hIOlHw0fdKJNB9z4tzpvrof7GUl4MAtCQwxpM0KORiBGnaAggWmRtzYPWQ4OED5fZQYsOBCWP4sKWIbAEgLxmMoa5osICwoOY64U7JlN/Eg34jFh4T48P0yjzIixVTrhF6ZTMHEyeQkTSqTB95ng12kHSwyzloR3XIkmH95pd+J7sPKCsoqRXu8y4ZsOU34gx4H8+d4Xyu9Q5Q+KQqQDcmUcOpWodKE8O/bYY3U5NlO70MEyVhuYl25GHMy9yf6ZLPLhLx8D5bvJV7zToEw1ZrDmvO8n3r14pzn5DvP3nsS7FKNW3nWISZu/9z6uQZbxDjfvauPfXMM0IzwL6gZTL+Ea3ukYkQr0jBilRiMRpsoIH8pFdNagbDQO4QSqy6EMQf2HKVyYuoKGs1tdsshHvPiiLEJBhvnYxuzZOy3Byoq3X5R31AOBZNXf+987DKvvRmbwzkiUsmn1LLE872b5COV9ZliZcgrlp7+2i793s7nX6jOUso12Pd7xVmU60Psf9RbqJbR1vN/1VvWCVTp9z0fyrFZpCZYHuI70ezM3fQ3UTdiWFsoYKL8xiAkFpp31uu+z2/3bSj7sjifS8LDobmakNyfqfb7KglCfAx1af/danQ81XF9/kYSHDjYqWW9nOuA4523pgN8QLl//OA8Hjak/RQOuQQBxeDsIr/eqsd7XrL4Hih/p9m6EWqUTYXv7w2/TscN3OncSCFZefMuqv6cIVH78+Q/nXKjy56/SRrrg8IkGr3ejF1p0LMCHEbdUUzSEw9/tfv3lY6B8N8/jWx7Med9Pb+WV7zW7fvt7T+Jd6vs+NfH5e+/jGmTZu/Fm/JtrZi0l7/P47q8e9fUDqwo0AM1OT7BswOLC3i5YOJjOAUUDRvfM2kDe9/N78hBAOcTop5ULVla87/Nt43hfM9/9vf/NtUCfRmZYNgNRSqxr4ZStYOXU37s5GI1Q4ke7HoeVC/T+h4IByhFfZ1Uv+Pqz+h3Js1qlJRgDf9dNXwOKQ1gzwaoPVtpG/u2s160YpNL5lLNsSKXM5bPGjoCVZpGWDbHLA8bkXgKUD/fmjVtTBsshWDoFs4Jwa/rDSRflIxxa9JtqBCgfqZbjfN5wCFjJRzhhOOkXlg22LBBpNbrhZOIZNgm4hUCw8h/sulueg+kgAScIBCv/wa47kSaG6X4CGFnCiJSVxYX7nyC0FAYr/8GuhxYLfZFAYhIIVv6DXU/Mp2aqSSA0AolS/m1RNgQyzwkNF32RQOISCFb+g11P3CdnykkgOIFg5T/Y9eAx0AcJJC6BYOU/2PXEfXKmnASCEzDlH3Ps/Tlz3d81niOBZCdgyr+VfLjl+W1RNpi5L4miYXELfKYjsQmgvGPUDeU/kKN8BKLDa8lKgPKRrDnL57KDAOXDDooMI1kJ+MqHVTuL7atkLQF8rkAEQpWPQGHE8pptC0RC4L1XKo3lQzAuEnA7AcqH23OI6YsnAcpHPOkzbrcToHy4PYeYvngSoHzEkz7jJoHgBAIPyQa/nz5IgARIgARIgARIgARIgARIgARIgARIoBMBKhs64eAPEiABEiABEiABEiABEiABEiABEiCBaAlQ2RAtQd5PAiRAAiRAAiRAAiRAAiRAAiRAAiTQiQCVDZ1w8AcJkAAJkAAJkAAJkAAJkAAJkAAJkEC0BKhsiJYg7ycBEiABEiABEiABEiABEiABEiABEuhEgMqGTjj4gwRIgARIgARIgARIgARIgARIgARIIFoCVDZES9AF97e2trogFUwCCbiTAOXDnfnCVLmDAOXDHfnAVLiTAOXDnfnCVLmDAOXDHfng9lRkuj2BqZw+j8cjDQ0N0tjYKE1NTdLc3Cw4hwMCjk+4FQ3FcvW7afr75EE5+tP820P93mOnXL/XjB9+kkAiEghVPpa1bpNLZr+sH7E0t0C6ZecJPnvnFUn/whLZqahMhnXrISO795Fe6hwdCSQDgVDlg/VHMuQ2nyFcAqHLRzfVvtoR+k6lmVKalyE9i9Klb3GmDCzJlCFlmTK8Z5b0KMjY4ZHfSCCBCRj5QN/DHDhXV1fX6am864/u+elSlJMupeqzZ2FGJ/nYWclHL3WOLjUJUNngonyvr6+X2tpaLcz4DuVCuO6rVY2dbtG/P6nuOGeUERfv102fM787PPALCbiUgB3yUdlQKziWV2/2+5T9CrrJ+B4DZVKvnWTPPkNkXI8Bfv3xJAm4jYAd8sH6w225yvTYRcAO+UBaVlZ6ZKW0D/T4pq1vcYaM7ZcjE/pnCwZ6xvTJ9vXC3yTgSgLe8gGFAhQL4botda2CY9UW/3f2LlLy0TdbJgzIkUlKPvCdLjUIUNkQ53yurq6WmpoafUQi3OEm3zQmv3pyo77VKBugfDDfww2T/knAKQKxlo91tVsFx5sr5+lHgqXDQQNGyiEDR8vhO+3i1GMyXBKIiECs5YP1R0TZxJviRCDW8rG+ukXWV9fJ9IXto79lytLhgJ1z5aCd8+TgEXlxosBoScA/ASMfW7du1dbS/n3Zd3ZDTYtsqKmX9xbX60BhCbTvUCUfw/NkmpKPtHYDbfsiZEiuIUBlQxyyAtYLVVVVEisBD/SI/hqPVDwEIsZrThNwk3xsrK+RZ5Z8rY+cjEw5cdhEOWHYeNmrz1CnMTB8EvBLwE3ywfrDbxbxZBwJGPlAG6utrS2OKRGpqG2RF+fW6iMrI02O2zVfjt6lQCbv1Hm6a1wTychTioCRjy1bLMwPYkhjs5KPl7+v1UdBdpocPrpdPqZQPmKYC7GJisqG2HDWlR6Eu7KyUq/DEKNow44GjUdYPcDKgUqHsPHxhggJoFHodvlobPHIU4u/0scotb7DmaOmyJkj95R0quMjzHXeFg4B1B2sP8IhRr+pQiAR6o/mljZ5XikecIzunSUn7lYoP5tQKBlcpj1Vimlcn9Pt9UdtU5u8oGQDx+je2XLS+AItH+m0dohrubErciob7CJpEQ4Wcty8ebNUVFRIS0uLhS/3nfZWOmDu4SXb13hwX0qZokQmkKjysXBLuVz3+Styx5z35LzRe8sFu+wrBVkcrUrksujGtCeqfLD+cGNpSr40Jap8LNjQLH+evkXu/WSrnLF7kZw9uUjy1cguHQnYScDIB/og+J4obsGGJrn5nSb5+4wqOW9KsZyl5AOWD3SJS4DKBgfzbtOmTVrRkEhKBl8cutGorB1mqYOWDr50+DsaAskgH1hs8vY578p/5n8sF489QC4dd1A0SHgvCXQQSAb5YP3RkZ38YjOBZJAPLKZ3z8db5ZGvauT8vYrkgr2KbabE4FKVQDLIB6wdIB8PQz72LJIL96Z8JGp5prLBgZzDXMGNGzfq7WIcCD4uQXqPVFHpEJcsSJpIk1E+qpsa5G+z31FTLGbJFeOnyUk7T0ya/OKDxJZAMsoH64/YlqFkji0Z5aOmsVXu/HCrPP9trbIiLZZjdy1I5izkszlIAPKxfv36hLKkDoZjm5KPf8zcKs9tl4/jx1I+gjFz23XOFrMxRxobG2XlypWyZs2apFI0eCNCo/EctaYDDnynI4FQCaSCfKyqqZTLP35Wzn7vEVlUtSFUNPRHApIK8uFdfzDLSSAcAg0NDUnfvlpd5ZFrXquUi5/bLMs2h7/1eTg86Te5CHjXH4lsTR0oV9Zu9ci1r1fKRc9ukiWbKB+BWLntGpUNNuUI5kQtWbJEb2FpU5CuDsY0Gu9VJk50JBCMQKrJx/urF8rBL90p/5n3cTA0vE4CerpdqtUfY/66mgprlv2QCKD+WLp0acq0rz5cWi/HPFAujyrzcToSCEYg1dpXHy1rkOMeLJeHvqR8BCsbbrlOZUOUOeHxeGTVqlVSXl4eZUiJefu9n1QLFQ6JmXexSHWqy8efZr0h573/mJTXVccCN+NIMAKpLh/GSi7Bso3JjRGB5uZmbc2Qqu2rW96v0lYOm7YlzuLiMSoajEYRSPX64/YP2uWjvIby4XaBoLIhihyqqanR2vbq6tTuSEDhwGkVURSkJL2V8tGesdNX/SBHvnqPvL9mYZLmNB8rEgKQj8WLF0uq1x+wkqOVQyQlKLnvgXwsW7YsZawZrHITVg5H3L9eMJpLRwKGAORj0aJFKV9/QD5++vAGwSedewlQ2RBh3sBsCeszQLNIJ9ocFgoHWjmwNIAA5aNzOdhYXyNnv/uIPDD/k84X+CslCWArZNQfibQdmdMZxfrDacKJEz7rj855VadW5cc8dU6r6MwlVX+Z+qOtrS1VEXR67oraFm0B9DCnVXTi4qYfVDZEkBtY6TVVzfqC4eK0imCEkv865cM6j2/66nW5UR10qUsA8oGDrisB1h9dmaTaGdYf1jmOaRV/fa/K2gOvJD0B1h/WWXybmlbxl3cpH9aE4neFyoYw2WOnCWgV6awJsMFozSbZr1A+gufwg8q64VcfPR3cI30kHQHKR/AsZf0RnFGy+qB8BM/Zx2fVyNWvsg0anFTy+aB8BM/TJ76ukasoH8FBxdgHlQ1hAMdCkNjDli44ATYYgzNKNh+Uj9Bz9KVl38rP1cKRdKlDgPIRel6z/gidVbL4pHyEnpOvz6+TS5/fHPoN9JnwBCgfoWfhG5SP0GHFyCeVDSGCxvzaVF/IK0RUHd7YYOxAkfRfKB/hZ/E7auHIc957NPwbeUfCEaB8hJ9lrD/CZ5aod1A+ws+5D5bU63nq4d/JOxKNAOUj/ByjfITPzMk7qGwIgS40ilj5lS58Amwwhs8s0e6gfESeY++tXiAXzngi8gB4p+sJUD4izyLWH5GzS5Q7KR+R5xRW4L/8JVo4RE7Q/XdSPiLPo3b54JSjyAnadyeVDUFYrl27lhYNQRgFu8wGYzBCiXud8hF93r25Yp5c+ekL0QfEEFxHgPIRfZaw/oieoVtDoHxEnzPTF9bLH9+sjD4ghuA6ApSP6LNk+sI6ykf0GKMOgcqGAAg3btwoW7ZsCeCDl0IlwAZjqKQSxx/lw768enrxLLntm+n2BciQ4k5gw4YNrD9sygXWHzaBdFEwlA/7MuP5ubVyz8db7QuQIcWdANtX9mWBlo+PKB/2EQ0/JCobLJhhIUgIO519BNBg/GpVo30BMqS4EaB82I/+rrkfyLNLZtsfMEOMOQEoqTdt2hTzeJM5Qiockid3KR/25+W/VPvqle9r7Q+YIcacANtX9iP/16fV8tJ3lA/7yYYWIpUNfjjV19cLtpihs5/AOU9SgWM/1diGSPlwjvcVnzwnczfz3eMcYedDhnzA/JXOfgJUWNvPNNYhUj6cI37dG5Uyv7zJuQgYsuME2L5yDjHkY956yodzhK1DprLBD5t169b5OctTdhG4l+Z+dqGMSziUD2ex/+6zF52NgKE7SoDy4SheuY/1h7OAHQ6dijjnALe2iaBDRZe4BFh/OJt3N7zFqfHOEvYfOpUNPlzWr18v0CzSOUeA5rDOsXU6ZMqH04RF5lWsk+u/eMX5iBiD7QQoH7Yj7RIgpuJRYd0FS0KcgHw0NDQkRFoTNZGLNzbLX95lhyoR84/1h/O5tmBDk/x5OuXDedKdY6CywYtHdXW1VFRwmxQvJI59pcLBMbSOBUz5cAxtl4AfWfC5vLNqfpfzPOFeApSP2OUNp1PEjrVdMVE+7CIZPJwnvt4mHyzhoFlwUu7xQfmIXV48NXubvLeY8hE74iJUNnjRLi8v9/rFr04TmMXFIp1GbGv4lA9bcQYN7OZZb0pLW2tQf/TgDgIYlaKLHQGu/xM71tHG1NbWJqw/oqUY3v23vV8lmFZBlxgEVq9enRgJTZJUQj48FJCY5SaVDdtRoyJsauLCITEreSoimsPGknZ0cVE+ouMXyd0rqivkltnvRHIr74kxAWzj19zcHONYGR2nUyRGGYB8sH0V27xaucUjd35YFdtIGVtEBNC+gkKOLnYEVldBPrgdZqyIU9mgSGMO4ebNm2PFnPF4EYA5LJ27CVA+4pc/930/UxZUcsQ8fjkQPGbIB7e5DM7JCR+sP5ygam+YrD/s5RlOaP/9okYWqTUc6NxLgPIRv7x5+MsawRoOdM4ToLJBMd64kdsxOl/UrGPg6JQ1GzdcoXzENxf+MfeD+CaAsQckQEVDQDyOX2T94TjiqCJg/REVvqhv/tcnHL2NGqKDAVA+HIQbQtD//pQDniFgitpLyisbtm3bJliYhS5+BLjYV/zYB4uZ8hGMkPPX31jxvXyyfqnzETGGsAlAPrZuZWM+bHA23sD6w0aYNgfF+sNmoBEEN31RvXy+gjuARIDO8VsoH44jDhrBu5SPoIzs8JDyygZOn7CjGEUfxqyVrAyjp2h/CJQP+5lGEuL9338cyW28x2EClA+HAYcYPOuPEEHF2BvlI8bALaKDuTid+whQPtyRJw9RPhzPiJRWNtTW1go0i3TxJ8C5t/HPA98UUD58icTv94y1i+Sz9T/GLwGMuQsBykcXJHE7QeuGuKG3jJjyYYkm5hc++bFBvlrZGPN4GaE1AcqHNZtYX/lUyceXlA9Hsae0sqGiosJRuAw8PAKcexseL6d9Uz6cJhxe+I8u/Dy8G+jbUQKUD0fxhh04rRvCRuboDZQPR/GGHfhT33BgLWxoDt5A+XAQbgRB/+8bWv9EgC3kW1JW2dDY2Mi1GkIuJrHxSOuG2HAOJRbKRyiUYusHazcs3boptpEyNr8EKB9+scT1JOuPuOLvFDnloxMOV/yYvrBOlld4XJGWVE8E5cN9JWD6wnrKh4PZkrLKhi1btjiIlUFHSoDWDZGSs/c+yoe9PO0K7dklX9sVFMOJggDlIwp4Dt7K+sNBuGEETfkIA1YMvb70XW0MY2NUVgSqqqqsLvF8HAm8+B2tf5zCn7LKBgq7U0UqunA5OhUdP7vupnzYRdLecJ5f+o29ATK0iAhQPiLC5vhNrD8cRxxSBJSPkDDF3NOr86hsiDl0PxFWVlb6OctT8Sbw6ry6eCchaeNPSWUDtrr0eGhO5tZSzdGp+OYM5SO+/APFvrG+Rt5Z9UMgL7zmMAHKh8OAowye9UeUAKO8nfIRJUAHb9+4rUU+WFLvYAwMOhgByEdLS0swb7weBwKbKB+OUU9ZZYNjRBkwCSQ4AVSGdO4lgLUb6OJHgPIRP/aM2f0EKB/uziPMTaeLHwHKxw723fPS5bIDuslfji6VXfpk77jg9a04N13uOL5M+nfL9Dorss+QXLn12DI5b88iyUxP63Qtmh+Uj2joWd9LZYM1G16JEwGawsYJ/PZoWRnGl3+w2GnZEIyQs9cpH87yjTZ01h/REozufspHdPycvptTKZwmHDh8TjFq5zOwJFM+uLSf/FwpCw7aOU+eO7e3/H5aSRd4D53aU44YnS+DS3coG+4+sYc8cEpP2Wdorvz2oBJ58xd9utwX6Yn3F1MZFym7QPelnLJh27Zt0traGogJr7mAwFeruCd0PLKB8hEP6uHFWdvcKDPWLArvJvq2hQDlwxaMjgfCqRSOI/YbQU1NDdtXfsm46+QnPza4K0EpkhrUH9G6kpIS6d27d5ejoKCgS9B9+/aVoqKijvNZWVnSq1evTkdxcXHH9Vh+ufGI7toi4ZD71ste/1grny5vkFMmFnZKwrlTimSMj8XDyF5ZMm1Enjz8ZY3so+67+LnNMkApLo7ZNb/TvZH+qG1qlY8pH5His7wvJZUNljR4wTUEuGd6fLLCjsowPilPrVg/WrcktR7YJU9L+XBJRjAZriRA+XBltnRJFDp2dLEnYId89OzZU/wd3bt37/RAUEiUlZWJtzKhtLS0k6IBigf4iYebNDBH3lfrh2xW6yQM75klv3mpQqYpxYNxsHyA1cKHSztbGuw/LE97ufujrfpzprre0Nwmx+7aVdliwgr38zPKR7jIgvrfYZcS1GtyeKhbt1oycgukhdYNyZGhfApbCdRtXCkZmUWUD1up2h/YZ+uX2R8oQwxKwI7GYtBI6CFqArNoGRc1w0gCqK3lbgeRcIv1PV+tpOVorJkjPjvqjyVLOg80DB8+XLKzs2X9+h0d9dzcXK2Q8H3G/Px8vTjlggULfC/F9DeWWMjKSJNRykrhu2sGClZc8LS2ye9erZS3FrTvCPHo6b1kfbVH7v24Wg5U0yyM27lHljS3tEmjp82ckprGVulRkNHxO9ovX66gMi5ahr73p5RlQ0v1Fmm79HDJPn8/KXrydile8q3kN9dLRnpKYfAtA678zXm3sc+Wlm2V4vn73pJx61gpeP9PUrR+luS31lI+Yp8VQWOcX7letjRym6agoGz0gBXEGxrYCLERqWNBcRqeY2gtA6Z8WKJx3YUFG5qkqp7TiWOZMU7IBywccnJyZO3atZ12uBg8eLCuq9radnTI8azw29TUJHl5edKtWzdJj1Pfp2dhu2JgYPdMuWvmVrn0+c1S19QmtxxbqqdW3HB4d+lVlCHnPLmpSxaVFqSrwbDOp6F4yMuyb5HIhRubZUudTySdo+SvMAmklGVD47yvO/B4Zr4mgkM5rIGaeeBxkjb5YPHsNEIac2j5oMHE+R8ajJMH5cQ5FakTfcOPX3Y8bPOcF0RwKIdqIXfiTyV9zOHS0muMNGYW0vJBk4nvv282rZKDB4yKbyJSKPa6Oip3Eim7WX/ENrcoH8F5j+uXLT+bUKg7+k/N3iZrt+7Ygh2jvWdMKhL4mb6wTqYv2mE+vodqB03eKbdTBO8tqpNFqlMUqZu7rkkOGNY5zEjD4n3BCdgtH2lpaXpKBBTg3otODhgwQDIyMmTp0qUycuTITgnLzMwUHMOGDdPnoYxYuXKlLRYXnSIK8qOitr0j/70qg//5rH33swc/r5ErDuomh4zM0zIC2fjFPsVSlt8+GIz1G9QjC7anzPQxYoCiYeWWHbIUJPqQLs9d19jJoiKkm+jJkkBqKRsWfmsJwvPhKyI4lNPKh6nHS9oeSvkwaIQ05ORz0SNLcs5dwLoNVDY4x9c35IYVs31Pdfxu/uY5ERzKaeXD7idvVz6MloaMQspHB6nYffl+81oqG2KHW+qqq2IYG6OKlgDrj2gJhnd/ff2OznF4d6aGbyx+98fDuutRWZiBn6M6T+f/b5N8vt1k+51f9pV+anu/itoWOXJMvizZ1CzHPViu4fzu4BK9UJ6yNO9wHhVGNMqGH8qpbOiAGYMvdlvFYa0FKBzKy9vLCB6hsLBQsIDkmjVrxOPp3PmGFUNzc7NgqhMsIeB30KBBMnDgQIn1tApMmcBR37zDegCKBLhmdX6bmhbRTW15efioPGVZ235h4oAcmbO2SZZXeLT1Q29l+bChpkVwuSQvQz5aZq/V4fzyZiob2rPElv+ppWxYOi9kaJ4PXhbBoRzG1jMP/olSPkxVyofh0pBN5YMGw39JRaBx1XchP0/z7GdFcCiHl0jWpFMkbfRh0toLyocCKh80GWf/LdiyY46mszExdBBou+8PUqg6VOlj95S2nUaKp6yPNGflqkbTjgYTSZFAqhKwuzOVbByvmlqiO1EH/nOdNhl//cI+co/awm/SHWvktN0Lpb9SNPz25Qo9Zx3WDzAl37Vvtsxb3yRYLA9KiZ8r5YRdbtHGJruCYjghELBbPqBswNQM73UgoDzAbnvYmQIHlBH4hN+KigpZtGjHLlbYOQaKBygd4M93ykUIjxSVl4+VcuDA4Xly2Kh8WVPlEVgu1KuFHt9TFj3vLVrbEfYuajcKbIv5qxc26x0r8rPT5LIDusnDp/WSq16pkCuVXGE2yONf13TcY8cXyocdFHeEkVLKhqblOwRtB4LQvnnef1EEh3Ja+TDtRJE9DpKWgcryITuPnavQMIbli4t8hYUras9N636IOIzmr58WwaGcVj7scZqkjzpUTbsYReVDxFQD37i4amNgD7xqK4GmJfOlZc2P0vLFex3hZqlvuUNHS/rEA6RtxFhp7T1Imgu6SZP3EGSHb36JJQHWH7GkLdLYyEUHrYirtfD0nPLX5tVrRQP8vTavTneacjLT5GSlXNisLBqwON4wtQDey9/XCranLFcL5OHeIjXKO3t1oxykOmdNan46FA/RvmKWbe488m2Vdp63h4CdygZYKWAbSygQvB0UDbiG9RiMgz/sSAHFAqwY1q1bp7/jOqZb4J5YKxoQ9zWvVcpTZ/WSO08ow0+9o8RZT3Zt05hybj712g7vV8k100q0EgIWEg98Xi0LN0Q+pUgnwOffss32hucTfMr9TB1lg5qb1LxqqW0Z7HlPzWfHoZxWPhxyUrvyYQAsH3KVAHvZu9kWa2oFxEW+YpvfTevsW6G4edZTIjiUw0sme/LpkgblQ08oH6Cco3xoOFH8W0JlQxT0wr+1WSka/LmWHxcIDuMwzSi/oFgypkyTtDG7S+ugnaWlW09pzMiiUtpAisEn648YQPaKgsoGLxg+X9WMB6lVC+ANKdvR5B6r1maAwxoNmJcOM/K5Vw/Qq/Tj/P++2SY3v7NFdldbBMJdsl83vWo/vm9Rizsedf/6qBZ5ZGcKJGPn7JQPHhiWFAAAQABJREFUWCvAwTrB2y1cuND7p+yyyy56PQdMm4CD4gFrOixfvlyKior0QpHV1e1rJnS6MQY/sIPEMQ+US7FSpJXkpcsqizUXsJjpmL+u7pSix2fVyFPKkmGoUsxhupETbmUllXF2ct3x5rMzVBeG1bxupbKDda6D43n3eREcymnlw6EnS9qkA8UzcGdpyFTKBwfjdiFuJinBCDRXrHI0xU1fPSmCQzmtfJhyplI+HCKeHiNVJywxlQ9XTJgmC7eUy5srAk/PykhLl7sP+JncMvsdWVVTqRlctttUyc7o+vp9YtGXsr52q7525sgpMq5Hf3l1+Xfy/urOjQgdiPq3trZK+heUmJ/8dIiAZ8OasEJuq60WzwfKEg7Hdod6IWPcXpK2294iQ8coq5/+0qwWI25O0mkY6BwtUaba3gvdGRbenxi5veXYMvmHWpUc5rRwF6mFwbJxwcc9M2ebnqebqSbqnjAuX/YYlCsz1F7tZrs0H+/8GSMCmAtOF5jAu2pBx+PHFqjR3N5K8dAqew3O1TcU5qRLQXa65KpF7t78oU6wcOQf1NoOp6o1Ht5Wlg5F6jrWcfin2gLw+W+3KXPzYr2Q3m3HlckFT0c3rWJ9dYv0LfZZbS/wY/BqBATslg9sYQkX7lazsGro27evjBgxQt+PnSmMIkKfiMO/6oZWwRGugwLPKUUD0oLxMCxSielNdNETSBmKno3roqcVRgie6Wo+Ow7ltPLhsJ+1Kx8GQPmQQ+VDiCy5oniIoKL05qkMrzMVZXTS9OXjIjiUw0soe8pZ7cqHnmo3mDSlfHC5cu5X4w6SK8ZPk2eWfB1Q2ZCuhqueO+JCmdx7sDyr/Bplw2/GH6wWNtqx5S78wWGHiSq1peUPp9+oRrHSpKa5QU4cNlEeX/il/P7zl7Qf739QTFDZ4E3Eme921R8t330ugmO702W/zyBJV4rptFHjpbXfEGku6i5Nkh4X01aTrmg/L9y7WC7Zt1he+q42oLIBi3s9ekYvweJf8GuUDRere41MIC3b1wgTrKC/oaZePri0r95XHabnR++SL/sOzZXr3mhX5HmnnfWHNw3nvtvdmXIupfEL+U/KSqGX2vJvgirrW5Vlwv+UUuH0SYUyd22j1KupEXBXqjnocBc/t0nev6SfHKUWirzx7S3ygVKoGffgF9Vy3p5FMro3JnFF5zbUeKhsiA5hSHfbLR8bNmwQHMHc/PnzO3nBrhU4srOz9XoPWPOBzpoAFqCkssGaTzhXUkbZ0FIZXDDDAReuX887z4jgUE4rHw4/RWR3ZfkwYJhWPsRjzlS4z0D/yUugpTq+8tH05WNK+aAO5fBSytrzbK18aOmh1kRJy3VNx6sgK1uePuwCmdBzoE5roH8ju/eWZw6/QHrkFnbxNvjR6zrOFalpV9+d+gdt0QALhjv3+6labTlddvvfn6WyoVZeOuqXcuqISX6VDZvrt3WEwy/OEWipjG4EMVDKWstXSevrquzjUC5dHRjzzNj7UEnfdUr7YpSlWIwyx/WLUWLxrodO7aVNw/Esgdzwnllqka+eUprfdWR13C1rOm7FyO4nl/XTFg0zl9Zrc/IeBRnys0c3CLZO+9sxpXL8uAK5Rc3jjWSErCMifomYQObqGTJo4fPSUjhAWgr6SktOmXhy1M4LmUXiSc+TlrQs8bSlqQ5O+COYESfKZTf+dHyhWsRum5y/3RrhukO669HTyrpWqVRKMygifB2mS5yuFo/EThYnPbxBGrcrJXLVOg+LN0Vv5m22IPSNl7/tJeC7M4S9oYcfGiwa6IIToHwEZxSqj9RRNmztOuoRKiQn/HneVovp4VAODcvMI08TUYuMefpD+ZDtms6VTiD/JT2Blm3uko/mLx4VwaEcxm+y9j5X0kZMk5Yeak2UOCofdusxUMaW9ZPbvpkusE4I5M4fs69kpWfI/339plw36UhLr48fcq5aBCxdfvrWf7SfB+d/Ku+s/EErGnAiPxME2i0ftAevf1uUFQSd8wRaqmMvHy2fTRccxqEU5A4fK2kT9pM09dnSe6A05xe7ajHKsWr1/DF9suTuj7YKrBMCuTP3KNJbmN0+o0quPMh6KtC/T+6htz87e/viYXsPztFm5VA0wN354VY5dtcCOWJ0vmCaBV3sCWQ0bJKcxe3tmUCxt2Uqq7WS4WoNk8FKMTFIWvKVYiKvl1JMlEpLVpG0ZOSLJ00p1drSxZNkionD1DZ+o3tny4kPlUtPpVj42cQCtV1fu8XCA5/XaKXZdYd2lwfVYnd3ntBDY8QikllKB4HzWEjvJmXl8Ov9u+kpF0/O7jxfPxB3q2tVSplB5zwBWhA4z9iJGCgf9lFNGWVD67boX8z2Ye8akudNtZgeDuVyVcci66jTpE0rH4aqBfWofOhKjGfsJNDaEJ9FgkJ9hubPHhbBoVwW5EMpH9JHTlPbD0L5kBMz5dzXG1fKiCdukKYWT1Blw5++el2u+vQFGVfW3/Ixp/QeIpN67ST/+v4jvf4CPM6vXKcPTJ+4fo8jpGdekdz3/Uy/YWxr5grwfsHYfLK11h2d2JYl36uJqurY7jAWml9cIhmTty9GqabpeUrKpCk9PotRzlnTJJNuX6vkoy2osuFWZYnwxzcrBVubWTksjgez84e+qBHML4frU5wpFWo02DiYusJhe0BfN2tlg0weBFtCOkcJNIZWf6R56iVj83f6CCU9rSXDlGJiqFJM7KQsJvop5UTvdquJrG7KaqJAWU0oxYSyhWtR5c3tU++uV9N8njizl7z5i76CSROYb/7rF9qnTbw6r1ZPBYIVAw7MF7/tgyr5saJ9LYyn1WKRJyjrnRmX9tPYsA4KFBHRum1qkT465wlQ2eA8YydiwNoqdPYQ6Fo72xOu60Jpa9wx5811ieuSILVzxhtPiuBQLleNjrZbPuwvHjWnN5WUD2wsdikcjpxoa0ow+fjsIaV8UIdysB7I3guWD1PVgpNK+aAmKjk1LQlKhlBdTQiKgN/tfphuJN8+Z8cItgm/NDdfNqlpEj3yCuW4obvJnd++p+b2dl6Ird7TPrpr7uGnMwTcXH+0VVeJ5z21ODEO5WADg+51xoR9JV0tSNmGxSh79FOLUeY7vhgllAyhulA6Or85sJvueN3z8daOYPPVQnqbtnWOB52zYrWiOV2cCDRH3/H1l/L0qmWCA1Y9wVxrQR9p7aaUE8XKaqJATefI7yOe3DJpyS5pn86RkSstKiRPq5rOEYdFWVeo1e33vWud9C7KkK1qQbyG5s5l+OpXK+Ta1yvV1peZsmhj5/c81nvAgW0xsUo+tvuzwzVsn5ZhR1gMw5qAU+0R6xh5xQ4CZtqSHWGlehipo2zwaaQnVMa3tojn9cfVnF51KJerzKozDz9VTbvYTykfhkqarnj8m1kn1HMysXEj0BZGJzpuibSKWMlH06cPiuBQLkttMZi91zntygdl+ZAWh4alVVK9z+cpOYZVw8frlkijH/4PzP9EcBw7ZJzcd+Bpat2GyfLQD596B+H6OfydEpvIPzyhK5nc8pgtcz4RHMahss/uP0QtRnmAko3ti1EWlkiaPf0WE41tn1idf3z/HPlseUPHXHUEjtGmwpzO9R0WkFxR2bmDZltCGFBQAm0t8WefXlsuODLXdX5H+kt8m5qy0VqirIC6DZFWrDOR3088eT2V1cT2dSbUdI6WtGw9nUNtY6aC6Fze/IUZ6jljiePPP5QIvooGb392b1fp4cCtN17Hvie1ssHToLjlOcYungFTPuyjnzLKBvuQuSAkpTjxbF9QLLN3f/lw2n9Uovq7IGFMAgm4gIBq+DZ98oAIDuXKJhyvhj2HuyBhnZNwyvA99Ir72BLT20Gx0C07T06f/l99es6m1fpzTGlfb2/6u0v7iV3SyRPuINC6drngMA62AIV7nibS82pzyjWfJyqzcSgR7lJbYno7dNZG9Nox9QI7WcAt2hD/Dq93OlPre2K9idKaayRj0xx9WOZTXqk0pY2TbLU2wk5j/yUrW6zXFbEMgxdIIAkJpGemy8aWTTKz/D05/ocv5PV3flCabPUexpGlFvRWn21qQePWzFxp1Z9qupPaha9FHZ4MHNnSrD5xNKVnS5P6bFSfjWpaVKNS8jWqqbH16lMfyk4Pn3XSftS2Zck29b22NVtq1HePYDIhndsJpIyyIc3PnvZuz5xg6cso7i7/PuAueaOaioZgrHg9MIFklA/zxD02LY2JsmFgYXf5/aQj5N9qfYXvKtaa6C0/J6odLdrUn6/fhVvK5eqJh8q5o/eWmWsXy/1TT9dhPLdkdpewsGsFXQwIZCZvVdln/VylbHCeIbYQu+Kgbnr9hfnlwaf/7KasGtCF9fX7H7WY3r9+2kPuOL5MHp9VoxfOq2tqky9Xcv0S53PRfwxp6UkkH2o6RmPLKKl4eY60Nn+lH/jOnGvlJ8Pu8//wCXxW9RnpYkAgbfvW1jGIyrkoskR+bFghb6x7W55d8YIMLuwnT9Y1ysDWgbKxSVk34PBysAWCGsCoAtpVwl4ebPqahgW0taJDLbWvlRxK2aEUHO3KDqXgUN+h5GiBkqND2QFFR7uSo0kpOKDwMIqOBvW7Ybuio+eP+SL7HmZTSlM7mCSqIQJnZFpucpn5pCmheujgu+WV+p0DP3iEVy/Zr5ss2dhkuUc65h2epLZy8nYLVAPS7Ad90T7FegVxcx37Sj/xdfsinRitOmNSkd4ebfrCOss4zL38dJ5AWrZ6qSapS1szTwp2PlJq2+wfffMOcXBxmZ7y8M2mVV0UCEDru4DZ8JLeUuVnLZl/fjdDDug/XG6acoy2fMB9f5/znny5YXmXHMpTU0bonCeQlpNc9Yc3scyV8yV3TJ3aBcn+d4C3yA3qnql3jJi7tqmLAgHp8Z2GjrnrqDd8Hba/fH9xvQ4LO1BA0XD5S5ttm8fuGx9/h0BAjWAmvCsaJI0NQ2XTM1Aw4Njhsn74TJ4q+IOc1ufmHSeT4FuO2kKTznkC6Qk6KNCU2STf1cyXZ1e9IJ9v/LID1CC1ve3j9U0yrGKZNOUH3wa840YHvrRhijyOuh2LOKNU44AuLZpObtmv/+xAilMzyGjyIaGIpRcUJVR6gyX2qUPvkec9Y4N5i+j6hXsXyyVq27KXvqu1VAT8bEKhQKHg3UCcvbpRKxvQqMT2TOiImcZmrVr12Cgb3vllX+mnRrkq1N7SR47J16syH/dgud+07rFTEjRi/D6Zu06mqx0PktkdkeaR59uMjt2eJx386HWdAvp43VIpr6uWJVUbO52H5cKAh6/pdA4/Dn/17i7ncALKhRPfvF9tC5gho7r3lh8q13dRVJgbC6HRp3OcQLLVH77Ajs1eLM+2jvc9HdXvcbes6XT/5ysaBFMgzAr75iIsF8b8dbX52fF54kMbOr77fvnVC5slPztNeqktBLHwnpVj/WFFxubz2Z0HHmwO3dng1KKSDco6dPP/vlbxbLaMq2jWa/LA/mVyQbfLLf0k2oXCHJo2xCLPEkXZkJ6RLlvaquSzyi/koaWPSXl913fwAGX583hDiwzfrCxGk9ylFyTwe81leZMyyoaM4lKXoY88Oc8f/g95Km1y5AFY3InG20On9tIWBxZeOk5PGJAt1WpF5T3v7Gouvv+w9lHAKX9fK74rjp+mtnWCOe1vX66QtxbUCZQWNxzeXXZV+7PPWx/ctLYjAfxiK4GMgjJbw3NbYJObq+X5jO6OJmt0976ypbFWPlFKBzucRy18Oa9iXcCgStQOA3TOE8CUtWR2U5p+kGcz7VU2+PIa2StLsG/552rBRzscLBoCKRrsiINhhEYgLTcB5aNklNRV9JTKJzE9bX1ID9rno0fkjkO6y29zzw7Jv9s9dculsiEWeZSRYe9Ah51pTlPWLaub18q7G96Xh5Y8FjDofmrr2ccb22Tk5sU7/Hmbd+44mxTfkr3ej2UmpY6yoaxXLLk6Ftf3J98mj9Yf6Ej4Y1WHf0yfLLn7o61B90gfWpalG3rYIx2WDDOX1WtzViRs0sBsva1TgVJeTBtRIGZEC9dOVsqFzcqiAYoGbOP08veqc/Zjg5RXW49O4T46ZwlkdEsO+bCitHO16rR3d7ZBvGDLejnk5buskuDI+Z651Lw7AtYn0IzSGCxq4BNnLH92W/2dyBC1UKSDDqvsn/Bf/xZsDkbLoGNBoCCB6o/SXaV2fTfZ8sQcRWZV2HRGvHunXH9Uqfw5/Ziw73XbDT0KqGyIRZ5kumzNn5asFllQu0heWv2qTF/3fkgI+qrdWp5oTpPRmxaG5D8ZPGV0T6D3msuBp4yyIbNXP5dnRfDk/XD8n+Ta+kOCe4zQx5w1TTLp9rWCvdIvVtMoArmyggzpoUxYnzu3t/bWrO656NnNWrEwUq0Ujm3LPri0n543BcXnfZ9Uy71qr/Sy/HTBWjlzrx6gtihsny/4v2+2yc1qD2l/bvIgmon742L3uczuA+wO0lXhlWxYopQNu7gqTXYkpm9BNzuCYRhBCGT2TPz6I9Aj9t/4vVI2BPKRmNdYf8Qm39KK3C8fbaXjpXZ1nlS9PjdqKFPe+IP84rjucn/rvlGHFc8AehenTBcgnpglKyu+aythgcpt6bXyddU38tjyp2TxVtUeCsP1zushj7dkyZiNP3S9K4ktGzJ7dd0BrCsAngmFQMqoNbP67hQKD9f6WXTkNfK7lmMdTR+UDDiCOczz27StRV6bVyuYl3vJ85u1AuHvanVwOFgu/KDm4e6lplgc80C5bFTzdKG86KmUEwXZ6VKanyHvLqqXMx7fqPeUPnVioexBpUIw7I5ezyob5Gj4cQ9cKRv6JtnbLiMtXQaoHTDonCeQ2Se5lXGejWulZ31opuTO02YMiUYgrZt721dtZZOkpmqKrH1ssVTNiF7RYPLmmNcvl+Mz5pufCfeppudLv2L3mvcnHNAACY6HskFvTymb5JlNL8iBHx8uh394nFz/7U1hKxp65pYqRUOO7LrBj6JBPXPwHkMAMG6+pNbMyuwT38Uv3Ywn3LSljlpTzZnKGjhMmlcvC5dR3P0vnXaZXJlxStzTYRKAdRgO+ueOueQzltTLFysaZd+huQJjhTOf2LFAHtZ1uO+TrXLTEaWyz5Bcqfe0v5qufKVCB3fxc5vk/Uv6yVFqochZqzpvXcZRKUM8Np/Z/UZL07oFtkdWvPfpkj96qmx5505pVDtDeLv8XQ+R4n3OksZVc6Vq+l3S1tIsmaUDpOTAC7296e+4VvHKzV3Oh3riELUj82NRrU0cakyx8TekuEdsImIsmkD24JHStGJR0tL4dZ8f5Q9bk2ckh/VHDIuq2kYurWSwtFWtiGGkgaNqK5siNQs9Uv26/05S4LuDX21r8cj5H/9W1u3/oHzlSTxl5ODS+I62ByecXD5ycnKksbFzG9fuJ2zLapOl9T/K62vfkhdXvRJ18GVqLZbHW/Nl3IbO7bZOASeptiFr4NBOj8kf0RFIHWWD4pQ9ZFTCKRuW73+B/Cbv3Ohy2ea7R/fO1nuc3/BWZYeCoFgtNNTQ3CYwjHjhvN56azJMnYDDlAs47FZRqawesIK4r9viZ4szXz/87SyB7H5jbFc27PyvLZKeWyQtNZukeL9zpPrzp6T8/jP1g/T71QtSuPvx6tpmKd7zVCmZepEsv2pnyRk0XkqP+X3nh1Wj+NjaJBplw6TGLfJYVvLMvR/B+YSdy4jDv7KGJLeyodtqNepbvI/DFBl8shJI6zHKFcqG1tK9Zev3dVL7upoa5LBrqyhXo8XXyIW73S/lLQUOx2Zv8Nhali52BJxSNjRmNsq31d/L0yufk683f2PbA3XPKZEn2gpl/IYgcpSkygYMLtDZRyDJDIsDg8kZvmtgDy67umqvM+TX3S5xRaqwg8QdapoEFoRctLFJ+hRlyC3HlOnFIc+eXKR3k/hMbW0Gh+0wf6G2z5yitq2EtcN5U4r0NperqzzywOc1UqQUE9cd2l16qzDuPKF9dPa1eXVdnvPi/TgfvQsUB0/kDBpna+glB18s6XnFsurP+8iyX/eVbbNflqLJJ0taRpbkDBynFQ1b3rpDlv2qt6z9x7GS1XOIwAqi9tvXZfG5WR3HmjuO1Ona+skjUaVvcFXXnVOiCjDON49Ru1/QxY5Azs7Jt+aHN70BmwKMXnl7TJDvrD9im1FpvcbGNkKf2FpL95XK5bvIuse+ldo5i32uOvczbfk8+eeya52LwKGQsbYWXewI5Obas4273p4yvUpe2/KWHP/Vz2TajKPlytm/t1XRUJJdLE+mFcuE8iCKhtjhi3lMyV7fxxpoSqk2c0Y5u7WXnZm3dvefyCU9rrQzyLDDUgPJHQ47ThwxOl/mrm0S7Iv+J7Wg41UHl8jbF7V3eFaqvc6vf6NS+7/mtUq596Qe8vBp7aPItWqLskue36SvvarWeYAC4nS1BSYOKCZu+6Cqy97rHRHzS8wI5A7e3da40n12SmjzNCpFQ6akZedJwbgjdFybX/yj/qyd+6a0NdUpZcOZUv3Zkx3pgGKi36XPiWdruWz47/kd5yP5UrRhkUjPxHkHBHvGcT36B/PC6zYSyBm1m42huS+o7B+/FRnmvnQxRYlBIL3PRGmJQ1JbSvaTqi83Sv0S+0Z1w32MnO9nyhMFN8kZPW8I99a4+cfAEV3sCOTltW8JH0mM2J5yZdNqeaf8XXls2VORBBHyPcXZRfJEeqlMXK/qg1Ccd0chFP8J4ifZ6/tYZ0NqKRt2nRRrvhHF1zTlKLmoV3snLKIAbLgJCz96O7N95Y8Vzfo0tqzEAYuHGrWGA9ZmMG7Z5mY5/N/rpSQvXfLUrhTrqzs3Qa5+tUKufb1SbX2ZqReINPf5fnLOrS8RZ3/nDp1sawRVH/xbYN0w6PpP9TSKjOJeUvPls9JaXy3Z/XeRNk+TtDW3W8Mg4pa6rZLRrX13E5OQnqf/Q0/DWPO3g82piD/bKlbJCGXLtXhHUY04LDfcOKHnIDckI2XSkDPGXmWc28C1NdTJsXk/yqv1yTFXlfVHbEtYWn9764+AqVfT6jzF+8mWj1er9X5mB/Qaq4vdvnhJ/n1gmVxUdGmsoowqnnH9qGyICmCYN+fn54d1hyfTI/NrF8gLau2FGeUzw7o3Us+FWQXyREYPmbRuTshBtOk950L2njAec3ZJjP5iogBNKWVDRrdSyRkxVhoXu9c0qHn8QXJir/9zXfkZ2StLqtS6Cp8v39E5RCLXbvVYphX+q+r9X/Yokwbsu27l2FC0IuPc+YzCMr1eQuOqEDXaQZKSN2JfyezeXykU6sVTvUEyinpKzk67SVpWrmQqxYO0di47UDykZ3eukIv3PkOaK1ZKwwp7GpRTpUkWS+I3ssaU9pXuOZ1ZBckOXo6SAOqPjB69pWXzhihDcu/te3sWyKuS+MoG1h+xL2PY/lKv27B5oXORZ+ZJc94UqfxgmTRvmOVcPBGG3P/DB+XWw0rl6uzTIgwhNrdh3S0MBtHFjsD/s3ce8FEV2x//bUnb9J4AAULvHQQBQQUREGkqVh4+e336t5dn7/qe2LtYUZ+9V6yIiooF6b0mgfTe87/n4g2bZJPsZu/u3t39zeez2btzZ87MfOdOdufMmTMWxUl9aGgoqqurHRZqNptRZCrGyoJf8PyWl7CtdIfDdJ6KtClj66WQVIzZ46KFkJ0FtKfq5m25ob0GwRKX6O1iA7q8oFI2SE9GjJhoWGVDTf+xWND5bkM+cKIYmPtMttfqxqMwvYa6SUG2/pOVlSJ9lA1J825RTpeoxaZzFN8bDfWIHDoDnS99H7GTzkBtoXLMnrJFwj6YlclzdfamxqioEbMVq4Yo7H9Vv+1EwyqUU1DC/N/XwYT0Xo2ceOE9AtFT56PwlUe9V+DfJUXPOBEhXVoqASp+/gYVv32vpoqedgIiRikm5Ur9qrcePFUmYvh45XtvQpM6l37zAao3tzy2T3USmTazSVp+IAFnCZi7TVKUcR5QNijO6qqtw5D/6Trlu2Ols9XxSbr+n96Dq46Jx92mA1sFfVKJdgo9pFtYOyl42xMEYmJikJub2yhajqfcW5uFZfu+weMbnmqM9/ZFuDUcL4d2wtg9HVjUCcBtFBEjm35fers/ArG84FM2jJmMwle9/2OxvYenNnMI/tH1btSY/X/Vtb22OnN/tOJcksH7BGyDjlKOqFysS8HWxK6oyd2mKhpEYMWGb1W5YRlDUZ21XnUUKZYPtQV7AMUs1hyZqKxWfdxYdtyUC9WjMIu+1u9LOKNgF5Dm/8qGwzr3buTEC+8RiBhzuE+UDfEL/09RNmQebKjJpIwZE6zpGaqyIfPz7TDbolBXmIfomSej9NPXkXPzuWr6xItvQ1hfxflr/cH9Q3KMrCNlQ7p4Hk87WIy/XtE5pG96ztxjCup+fUK/wm0pqG4YgNz3/0B9pbGVDPaNnvDBNdg/Ox7P1o+1jzbM9aHKMeQM3icQFRWFfUX7sbF8E97b8yHe3/WR9yvRrMQw5djal8O6YNzuX5rdcfJjAFo22JTveQZ9CQSdssE29kiYwiPQUNmKfb++fJ2SVtepF87ueQ9KQnn6ggATE1iawTr16OieKHLwNN1kVm75SXUEGTvxdJSt/gRpZ79w4PjKt29UfjiWIOm429Dlqi+Q/fipSFpwj6JvsKDg84cayw9N631AEdEY4/5FZJay4ps2xn1BPpRgUiaZkzv38WENgrdo2yFHKEqxaNSXlXgVws4Tmz6zXV9dqSoa8h64HrHzz1DrtOeso1C55lek3fkCIqfMhen2ixS/KDUI6dwNYgGx91/z2q2zZec62AaWodwa2W5aoybg94fvesbcU1nNV1ZJUVvpXiWiuqCquhf2/08UDP6jZLBv9LxPLkXOjCX4sLaffbQhrsVJN4P3CYiyYeZ381BaU+r9wh2UGGIOwUsR3TB+l/G2JDmorleiRGlvGzfFK2UFUyFBuWkravIsw/SxNaUznpz8APZHBMBykmGosiLuEBA/CXqEvY8sUC0YUs94Gj0W74atv7Iq/MXD6haK+spS7H/lMoSm9kbXm1Yios945H94d5MtHOJQsjpLOUFCx1BfvA8jLToK9IGoeT2G+aBUFqkRiJxwtHbpk/e40y5BSNee2H/3paolgykiqkk9Gmr+PvUlTJlQKAo8c1QsKv74AVJvdcVG2RvcVpgVqu+Ya6ss3gswAvK89Tm2442KyUSlZRJ2v5qP/W/5p5JBa3xDVQXO+/4KDLUay8fLsYP8V5GosfXn95mZvv3+0NhZTBa8ZMvERDcVDYG2iyJyonG3P2l954/vQWfZIJ0UqSgbSj75n8/7yxwTh8cnLcZHJZ19XhcjVYAmsL7tjajR85XjJ19yuxJylOX2axVHO5HxsCZ1R/Xuv9RtEZrggs8eRMEXjyCsU39UKfeah01n2ppH6fJ5Ul0FfkWELrJ8IWRG98G+KJZl/k0g8ojZKFG2KfgimKwhSDjranULRMlHr6pVKH77WdW6ofOTn6rKB0t8Ekq/eFu1vggfOk5Nk3DGVeq2C/kgWy12nTQWdUX56r3mf8ZUrcNrISOaR/vNZ35/+LarLP3noX6ti7+v4vqgoiANeUvFlNtYk3N3aDbs24VbVl+Ffwx4HIUNivLPAOGofv773WcAfG5X4diex+C1jW+4LccdAWId+XJUL0za+ZM7Yg7kDTBtQ+QRbihL3acZsBKCU9lw2AxYElJQl7/PZx1rCgnFc0c+iHcruffavhNoAmtPwzfXUcOPhTUu/YATRx2qUFdWAHk5DPV1DhUNDtPqFDmoPBewZegkzbtiUiKiMa3rAO8WytKaEFBXPpQVXCjPrrdD7IJzIQqHvEdvbixaFArW5HQ0VFWq32niRVtOXTKFhsGiKLTr8vcj/5m7UfzuC4g7+UIknn8DUm5+ElmXHNcow/5CdRLZ4xT7KL+55veH77vK3Hc2TJEpaChz4vdV/ECU74tH/kurlIrv9mjl23OyagoJQ8zcRQjrN0xV1pWv+Eytj4wv2yEt93DX7N4KTeHXVsXNm3/HU1HX4fiM/7SVzCv3kqMsOKI3lQ1egd1KITO6T0Oq4oskp9yJ8dGKDHejX47qi8k7f3RXzIH8AeSzwZKYisiJM/ThQilNCASlskEIRB99AgqXPtwEhjc/LJ32EF6vHeLNIv2iLJ5CYYxukq0U+R/da4zK6FyLTvk7/FbZML/XcJ1pUFxHCMSdfAEKX3qwI1ndyhN7wjmoLy5A+Y/LGuUknH2NeurL1ildVSeQtkOPQvp9ryDm2NNQ9MbTKPvuoNPVwpceQNwpFyKsd+vWMZ33KU4iezSK96sLWjUYo7vMg09B3Y/3t16ZhKEo3ROJwg/k5CPFibAXQltOVivXrULmp1uVbUdm1JcWq78Pi99egv33Xo6YmSchesZJTWuopKvN2e2UskEyhv++DC/Y7sDCxGubyvHyp2MH2bxcIotzRGBBn+Pw4O++cVT/UnR/HLHzB0fVCvq46OkLgp6BpwC0vXnTU6UaQG608gXiq/DG0Yux1HSIr4o3dLk8hcIY3ROjOHUM1BC2t+WRf/7S1gW9RvlLVQO6njHKiQ/eDuKYUiwYmm8BtKZ2QW3WzsbTJir/PgpTzgqPPe5MZLy8QrVy0OprDotAzV5F4dZKqN2fhdTyva3cNXY0HQsbo38sQ//hsCINiSNQUjwWu1/YhMJl+hyx7LAgB5HiZHXLhOTGV82urWioqYY4WU2+/D7VYmjHrAHYPr03Kv/8CdGzDvgu2nfHxY15JH/xO8+p0nP/o2xNciHEr/gfHil73IUc+iedN4T+GvSn6rrEk/v5ZlL7QswATNFZ0aCcah4wwRff6wEDr52GBK2yITSzn2IuM70dPPrfXn3CvXg+ZLL+ggNAIk1gjdOJoel9ETVqvnEqpGNNGiqKcZjF/2z/pncbhF5xKTqSoKiOEgjp1huRh3t3b2fE8PFqdct/+KJJtavW/IKQjJ6IPuYUiBlo2t2KvxVlH23+U3ei4vcfEJrZF2m3L4E1pRNSrntYPY2pWLF4aCtcmLalrduGvEdFg3G6xZTYF+Z+cxsr1JA4BsV5o7Hn+fUo+vbPxnhfXTR3slr02uPIvmah6s9E6mSKUCwAlH3tzYNYBMXMVU5X+vYjlC3/pPntdj93/fJx3F77WrvpPJFAfDVkJoZ4QjRlukigb3wfzMz07vzj+ZiBOGrHChdr6kxy//st5ahV8n0u3+sMniEQtMoGwRkz7wzPUG1F6to5N+Paiqmt3GU0TWCN9QzEHXm+sSqkY20m1pXrKM07ohb1P+DszzulsZT2CMiRk94MYYNGq8VV/G25oJWdff0/Ub1jE1KufRDd31+LiJETle0TT6EuL0d1JFn81rOIGD0Z3d5ZDbHoK/v6fcXB5f+07A7fY3f5fkLosGJtRPL7ow04PrhlGXku6hPHoSh7hKJk+AvFPxjDosyRk9WqTatVBYJsr+3+/jp1m1HRK4+0oJZ66zPKcbLVyFHGXEfDkI/vxGX4vKPZO5zv5BHRHc7LjPoTOGPQIv2FtiJxSexATNvxfSt33YwODF2D6mTZTRLM3gaBoFY2iNMf+WHmjbBhxtW4qm62N4ryyzJo1WC8brP1n4zIwdOMVzEdatS/NEcHKd4TMalzH4xP7+m9AllSuwQiRkyAbeyR7abTK0H+47diy6GJaKiuaiKyobICu04eh23TemL3osOxdVI6cu+/pjHN/vuuwNbJnbDrlEOx9bA0ZF+7qPFeaxdd9it+G/wo8PvDeJ1l7nYYCtcnouSX9YaqnCMnq1oFxblqXcE+1TIoaup81QpIu2dN76oeOVv6+duKwqFGi+7Q++T3r8BpFjl5wzthYo9wjOkW5p3CWIpTBA7vchgmdfH8/OOZ2EGYvt1DigZpaQCcRiHf4/J9zuA5AkGtbBCscSdd4Dm6f0vePOViXG450ePl+HMBXJUyZu/FT7/MmBVzs1apudvdlODd7OcO8vyPEu+2KDBKk9MdjBLqSwpRtfHPVidC1ds2tHqveRtCt3p3P33z8l39zO8PV4l5J33cKRd5pyAXSnHkZFXLXvjqY9i1cBJybjwL4gslZtZp2i0knvdvdWKV9+hNjXHuXCz4/FJMtW52R4TTeU8/hFYNTsPyYsILh57r0dKeihuEmduXe7SMANA1qKc0eRQShSPolQ22Q6fCNv4ojz0K2w47C5dGdNzkzmMVM5BgrkoZqDOaVcU24MiA9N1g3eM/K7fiq2FiJ+4lbPZoGuJjxKjDEOVl3w3eaLgcozk7wjsTIXfbw+8Pdwl6Lr/RxkdrTlZTb3ka6YvfaARRueZX9Tqs96DGOFn9rN6+UT1KtjHSjYuGshL866cr0M+S64aU9rNO62fD2O7h7SdkCq8TmNL1CEzv7pn5xxNxgzFrm2cVDSowP9c2RB4+C/J/isGzBIJe2SB440+/3COUd449BRfHet5ywiOV96JQrkp5EXYHikqcfX0Hchk7i+y7nWHxDzfKlw7znqm+sXvNmLXz1PeHr1s7rmadr6vgVPn8/nAKk88SGWl8tOZktXrrWtjGHK6e3hLStSfSxcmqEoo/fKWRmzkyBlVrVzV+1uOiYe823LXuakSYavUQ51DGueNjHMYz0hgErhh5qe4VeTx+CGZv+053uYEoMGGRZ+Z/gcjKnTZR2aDQCx8wErHHn+0OxxZ5946YhwuSr2gRz4imBC6YEANZmWIwLoGwjCFImHGlcSvYwZqNqyntYE7vZTtv8CQMSEj3XoEsyWUCob0GIu7Ui13OZ/QMcX7gJJLfH0Z/igAjjY/WnKwWvLAYlX/8gKRL7kTXV1citEd/5D9ztxonhFUv9crpFJWrV+oO3LLhFzy3+zrd5YrAM8fFoG9KiEdkU6g+BIanDMN5Q/SbfzwWPxRztn6rT+WckNLgx5YNss0r1M56yYnmMkkHCVDZ8De4hHP/rRwN1rmDGJtmqz5kJs7pfEPTSH5qQUCUDBdMjG0RzwjjEUg67jaYwwNr32efkmzjgbarUWZMIq4eGZgOOu2aGRCXicr3R0iXzIBoi9aItBxjn0jB7w+tp4z/ro6PjB4+r2hrTlZRX4895x2jOlIVJ6tbDktFwTP3NNa3RjntRZyzFr/3QmOcnhe2VZ9iScHB8vSQ3SXOiksm8feVHiw9LeOGQ65BRnQXt4t5JGEY5m79xm05LgnwDwPRFk2S7+uEcwPPardFQw0SQWXD3x1hVs5VTrzoZre7pWbo4ZifcrvbcoJBAM1f/aiXzRakneOZH1q+opC0f4uvinaq3OtHz4TFxH/RTsHydSKzWfn+uNXXtdC1fMuuDYg0sPUPvz907W7PCpPxceEtni1DB+lyyoQ4WRXlg7dD0vKleLD8ad2KvXpKHMwm3cRRkAcJhFvDces49xYoH1IUDfO3fO3BWrYm2j/Pvky84GaYLNbWGsV4nQnwl6wd0Kgj5yJm9kK7GNcua/odghM73+VapiBNTfNX/+v4qOHHIm6Kcbzvu0vQtOcvRBv0x9g/+x+KaV0HuNtE5vcigciJ03XfjufF6jssalboBofxvo7k94eve8D18g+Mj7NczxhEOTKXPYyba990u8WnjorCEb0j3JZDAd4jMLvnLJw+sGPzjwcSh+F4nygaFD5+qGuInX8mIifN9F7nsiSeRtH8GUi67B51v17z+PY+12YOxqJud6PaQv8D7bGi+Wt7hIx7P+XUBxDefaRxK+hizY4217mYw/PJByd2xi1jj/V8QSxBdwJJl96JsH7DdJfrK4Fjqtb6quhWy+X3R6toDH8j6dK7EJLR0/D19GUFR3x8Ky42fdXhKgxMC8W1U+M7nJ8ZfUfg3ol3YFCia4sMixOGY8Hmr31WaX/z2RDWZwiSLrvbZ7yCtWBaNjTreZM1BMlX398stu2PdZ164uye96A4NK7thLyrEqD5q38/CCmLHvPvBtjVfkxVkd0nY1zePX6eMSrCWnSIQPKV/+1QPiNmit31h+Gqxe8Pw3WJSxVKvfkpl9IHY+Kp712KEy2/u9x0xYclbppORYPL4AySwWKy4P5J9zpdm/8mDseJWzqumHK6oLYS+pllQ/JVgfP93Fa3GO0elQ0OeiR80GhF4bDYwZ2WUdaUTnhy8gPYH0GP8S3ptIx57pQUnj7REotfxYhlQ9qZS/yqzq1VtmdxVmu3fBJ//8TjMUSxbGDwXwJh/YYi5fpH/LcBdjVPz1lt98n3l/z+8H0fuFuDQBof7rJoK/+pX/0fDrNubytJi3u3z0yAWDYw+C+BUakj8NDh7U+I70scgZM3+1jRIJj96DSKlOseQlj/4f77cPhxzalsaKXzYo49DfELL23l7oFoc3Qcnpj0AD4qcd+LbJsFBchN7rMNkI5UmhEzYSESZ//b7xsUt2+TYdpwybAjcXyvwNmiYhiwPqhI9IwTEX+6/x99XJeXg7Ty3T4g2LJIfn+0ZOKvMYEyPjzJv6EoH1f8ciUyLYVOFXPe+BjMGRzpVFomMjaBU/udhMtHXtJqJe9JHIlTN3/Z6n2v3vATy4b4RZcjeubJXkXDwg4SoLLhIIsWV3IsSvQxp7SIlwhTSCieO/JBvFPZ2+F9RjYlID8UecxlUyb+/ilx7k2InXSGfzcjZxM6ie2pj8NJfUbj8uFTfVwLFq8ngYSzroYorf09XJi61edN4PeHz7tA9wqo42OW/48P3cHYC9y1Ef/deI3iXK3tGd38oZG46DAec2mPzt+vrxtzFU7r33JyfFfSSCzcvMw4zfMDy4aYWaci4exrjMMsCGtCZUM7nZ5y7YOInHxMi1RLpz2E1+uGtIhnREsC/KHYkkmgxKSe/iSiRvm3j4GjTNU+7Y4Z3Qbh3vHzfVoHFu4ZArIdL+rwYz0j3EtSfe23gd8fXupoHxSTfM1iRPr5+PA0NuvaH/BydutWhEf1jcCtMxI8XQ3K9wGBByf/B8f2ODj/uFNRNCzaZCBFg8LE6LoG+f+SfM0DPug9FmlPgMoGexqtXKfd8Txs46c13n1z2v1Yajqk8TMvWidAz+GtswmUO50ufB2RQ/33GKERVc6ZqXqiv47M6IcnjzjVE6Ip0yAEUm9fAtuh/mu10mW/7/w2UNFgkIfYg9VIk/Fh9/vKg0X5reionz/A00Ut9/FP7hWBxfOS/LZdrHj7BJ6f9hSmdZuK2xVFw+kGUzSota9v2+qm/RZ6LkXkhKMh/18YfE+AygYn+yD93qWQc6JXH38Pngs93MlcwZ1MfiiKQy+GwCfQ+dL3EDXCP1dwuxf6Zk/61Iz+eH7KosB/ONhCpN/3KiIPm+GXJEK2/O6TelPR4BPsPilU+33lk8L9pNDUb1/Afyufa6ztEb0j8OjxVDQ0Agngi6XTn8NZJptBW2hMZYN836bd87JBmQVftahscKHP0+5+CXtGHedCjuBNyh+Kwdf3nS5+G9HjWu4xNDqJ6OwNXq/inB5DsWTKP7xeLgv0HYG0u15E1LTjfVeBDpbcUFONOeHedaTK748OdpYfZ5PfV1FH8fdVW13Y+/PF+Hf9e5g5wIaHj6OioS1WgXTPbDIj5Lj/wTxwgfGaZUBdg3zPyvctg3EIUNngYl+Ik0P5IcTQOgH+UGydTaDfST/nRcQfdbFfNbMhfxf6WrxX5X8OGI+HJ53kvQJZkmEIpN74OGIXnGuY+jhbkXE165xN6nY6fn+4jdBvBaTe9IRfjg9vAp8atRf3zk70ZpEsyyAEQmY/B8uo8wxSmwPVaDCY04bYE86BfM8yGIsAlQ0d6A8qHFqHxh+KrbMJljvJJ9+P5JPu86vmHlFf5ZX6/nv0TNxyyCyvlMVCjEkg6V+3I/Hi24xZuVZq5S0nkfz+aKUDgijaH8eHt7pH/m8IH4bgJWA96r+wHn6rcQDUG6cqiRfdiqRL7jBOhViTRgJUNjSicO1CFA70R9CUmfDg8ZZNmQTrp/hpl6Lzpe/DGpfuFwiGVeZ7tJ4pEdGqf4ZzBk30aDkU7h8E4k48T/XjYElK9YsKp2V73kkkvz/84lHwSiXV8fGfV2FJ9I/x4Q0o4vdFuDCQgGXc5QiZtxQIjfY9jAbfaxssCSlIv/cVxJ10vu95sAYOCVDZ4BCLc5Fy0sLaazIg78EcZDWKHIL5CXDc9sihM9D1ppV+4TiyS8FOx43QIfaorgPw8bEXQU6eYCABjYCcUNHl2S9Vx8NanFHfLXs2Iqqm2CPV4/eHR7D6vVDbuKnIWCLjwz8dq+rVAeKYvNt7a/z6RBu9WFDOQQLmfnMRevpymLv6dgHD17so5MSJLkuWKSfaHHUQDq8MR4DKBh26RF2RCVI/DjR71eEBCmAR1rhOEMeRySf9x9CtjMxa75H63TjmGDx75EKk2ujnxSOA/VyoNSkN4hjPH7ZVzArR35Eqvz/8/AH2cPUt6vh40S/GhydQyP8F+f8g/ycYSKA5AVNiH4Sc+hksh17R/Jb3PvtQ25B44S3qiRPW5E7eay9L6hABKhs6hK1lJm1bRbBYOWirUdw20fJZYExLAvHTLkH3O/5C5LBjWt40QEx98T6M1NFJ5BTlWMuv5v4fzho4wQCtYxWMTkDMozOWroBtjHGPVR5dpZ+TSH5/GP2JNFb91PHx8gpEjp9mrIp5qDaRE6ap/w+4bcJDgANMrHXyLQg5+WOY0oZ5vWUN9d4/jkKsAjNeWo64ky/wentZYMcIUNnQMW4Oc4miQawc5BXISgeuRjnsfka2QyC0U390vuRdpJ39guLLwXia6Mn1Fe20oP3b3aIT8MBhC/Cccqxl77iU9jMwBQn8TSC0e1+kL34DKTc8hpDO3Q3HRS8nkfz+MFzX+kWFQjP7Iu3eper48IsKd6CS1rQMtX1p9yyF/D9gIAFnCZi7T0boP3+AddJNgHJUpteCFy0bQtK7IuXfj6j+jkJ79PdaE1mQ+wS8+ES6X1l/kaApHeRHVSAFrkYFUm/6ri0xh56CHot3Iel4Y3kNHlSW22Eo0aHhuHrkNHx/3JWY33N4h+UwIwlEH30Cur7+KxLO+7ehYKTnuOckkt8fhupOv62MjI+eK/IMNz7cAWqOjEbieTeg21u/Q9rHQAIdJWAZfxVCz18Ly9BFHRXhWj4vGDaYo2KQcO716Prmb4iefqJr9WNqQxCgssGD3SBbDMRxor8rHfgj0YMPSRCLTph5FXo9UYTEeTfDEp3kcxKd8re7XIf4MBsuGz4VP59wDS4cYlwTeJcbxgw+JxB/2iXIXLYTCWdd4/O6SAXq8vchvWyXy3Xh94fLyJjBCQL248MSl+hEDmMmkfEtDiDjTvuXMSvIWvkdAVNsN1hnPqZaOpgHeXhy7kHLBhnXCWddjW7vrEb8wkv9rh9Y4YMErAcveeUpAqJ0kNfKnVV49Lsi9d1TZeklV6wzRisv+mTQiyjlOCJgDotC4rHXI/GYa1D41RMo+vYZVO343VFSj8eF7l0LdHHOs3P/hHSc3Gc0Tus7FlYzdbYe75wgLcAcEYn40y9H/D/+D0VvL0HJ+y+iaqN7FgbuoLwgdQuuL81oVwS/P9pFxAQ6EGg+PorffBrV2zfqINmzIsL6DEb0rNMQO/d0gN8fnoUdxNLFh0PIsUvQMO4K1P32FOp+XwLUVulKpMEDyoaw3sr4mK2MjzkcH7p2lg+FUdngRfjyA2yM4s/ByEoHWYUa3S08oH1OeLHLWZSzBMwWxB15vvoqX/c1Sn5ciuLvX0RDbbWzEtxO11BRjEmKk8hv6hyLigwJw6zugzFX2SYxPr2n40SMJQFPEFAmJLHzz1BfFauWo/SzN1D83oueKKlNmarfhvjJrabh90eraHjDkwQMMj7aa2LMsach6qjjEDGCjoPbY8X7+hEwJQ+A9aj7YZ18G+pWv4z6ta+hftcKfQrQy0GkyYSYWadyfOjTK4aTQmWDD7pEUzpI0ZriQbv2QXUat3nQisEX9FlmcwK2/pMhr9TTn0TpqndQ9vsHKP39Q9QpJ0Z4OkyoLcU3pqjGYlIiojG5S19MzeiHo7sOhEn5QmQgAV8SkImKvJKvXoyybz5E2fJPUPHTl6jNzfZ4tbrs+wuIb1qMKBgk8PujKRd+8g2BFuPj+09R8sHLPqmMJTEFtnFT1VM0IifN9EkdWCgJNBIIjYRl5NnqqyFvI+o3vo/6LZ+gfufyxiQuX7hh2WBJTIVt7JGQ01ciJxnzpDKXeTCDQwJUNjjE4r3I5ooHKVm2WkgQRYQngvbjkBYMnqBLmXoSiBoxB/JKVYRWbl+Fig3fonLzClRu/Rk1eTv1LEqVNaBsH6YPHIsxqd1xSFomhiR21r0MCiQBvQjIBEabxFRt+AMVv61A1eqVKP3qPb2KaCInvChLUU5HK3EmKheakOEHIxLQxkfKtQ9CxkelMj4q/vhBva7N3q17lU1WK2wTZyB88BhEDD8UYX2H6l4GBZKAHgRMiX1gGXeZ+kJlAep3fKtaOzTsXYn6rN8UJz1Ozj9cUDZYUzohrP8IhA9RxscwZXz0H65HUyjDDwhQ2WCgThLFgwTZaqEFTeHw845K/GynfNDitXT275oc8bmgBSoWNBJ891cC4d2VLynlhWmXqE2oK8pB1Z41qM7agJr9W1CrKB+qszehvrJEeZWqr4aapsdZWqKTYbHFwRKjvMelIySxK0KSeyI0vS/COg9En9hUTPJXQKx3UBOQiY06uTnxPFU5Jw4dq7euQ/WOzajdsx01ObtRp1g/1GbvQn1VJRqqKpRXZQtmIRk9lLGRBEtSGkJSu8CqHMMZ2q035KgxS0IyeLJ5C2SM8AMC2viIVcaHhLr8/QfGx04ZH9uUcbFbtQ6qK8xDza4tTVukWLSZlBOHTGHhMIeGwaocwWdVxoc1TRkfnbTx0U8ZHwd/uzUVwE8kYGAC4fEw952tvrRaNuSuQ0PuejQUbEFD4Q40lOxBg7IYg4o8oKoYDTXlBxQSdsoGcehojopVvj8Sle+PVFiV7w85xjmka2+E9VDGhzJmGIKTAJUNBu93TXGgvRu8uqweCXiNgEVRDNjkNeAIr5XJgkjAXwjIxCdCXqOoPvOXPmM9vUdAFGcR8hp1mPcKZUkk4CcETEn9Ia/2Qnp7CXifBBQCdKPOx4AESIAESIAESIAESIAESIAESIAESEBXAlQ26IqTwkiABEiABEiABEiABEiABEiABEiABKhs4DNAAiRAAiRAAiRAAiRAAiRAAiRAAiSgKwEqG3TFSWEkQAIkQAIkQAIkQAIkQAIkQAIkQAJUNvAZIAESIAESIAESIAESIAESIAESIAES0JUAlQ264qQwEiABEiABEiABEiABEiABEiABEiABKhv4DJAACZAACZAACZAACZAACZAACZAACehKgMoGXXFSGAmQAAmQAAmQAAmQAAmQAAmQAAmQAJUNfAZIgARIgARIgARIgARIgARIgARIgAR0JUBlg644KYwESIAESIAESIAESIAESIAESIAESIDKBj4DJEACJEACJEACJEACJEACJEACJEACuhKgskFXnBRGAiRAAiRAAiRAAiRAAiRAAiRAAiRAZQOfARIgARIgARIgARIgARIgARIgARIgAV0JUNmgK04KIwESIAESIAESIAESIAESIAESIAESoLKBzwAJkAAJkAAJkAAJkAAJkAAJkAAJkICuBKhs0BUnhZEACZAACZAACZAACZAACZAACZAACVDZwGeABEiABEiABEiABEiABEiABEiABEhAVwJUNuiKk8JIgARIgARIgARIgARIgARIgARIgASobOAzQAIkQAIkQAIkQAIkQAIkQAIkQAIkoCsBKht0xUlhJEACJEACJEACJEACJEACJEACJEACVDbwGSABEiABEiABEiABEiABEiABEiABEtCVAJUNuuKksGAhsGLFCrz//vvB0ly2k2g9bP8AAEAASURBVARcIsDx4RIuJg4yAhwfQdbhbK5LBDg+XMLFxEFGwB/HB5UNQfaQsrn6EHj33Xfx6aef6iOMUkggwAhwfARYh7I5uhLg+NAVJ4UFGAGOjwDrUDZHVwL+OD6obND1EaAwEiABEiABEiABEiABEiABEiABEiABKhv4DJAACZAACZAACZAACZAACZAACZAACehKwKqrNAojgQAlIHukxHRJC2VlZdol30kg6AlwfAT9I0AAbRDg+GgDDm8FPQGOj6B/BAigDQKBMD7cVjaYzWbU19dD3hlIIFAJ5OXloTUFQ1vPP8dHoD4RbJc9AY4Pexq8JoGmBDg+mvLgJxKwJ8DxYU+D1yTQlEBHx0dTKb79ZGpQgjtV2LlzJ6KiopCQkOCOGOYlAb8lkJ+fj9LSUnTt2rVFGzg+WiBhRJAR4PgIsg5nc10iwPHhEi4mDjICHB9B1uFsrksE2hofLgnyYOKcnBy4bY6QnJyM7OxsSINlhZeBBIKFgDzv8tzL8y/jwFHg+HBEhXHBQIDjIxh6mW3sKAGOj46SY75gIMDxEQy9zDZ2lIAz46Ojsj2Rz23LBqlURUUF9u/fr67uCgAGEggGArJFQqx6RKEQERHRapM5PlpFwxsBTIDjI4A7l01zmwDHh9sIKSCACXB8BHDnsmluE3B2fLhdkA4CxLJBF2WDDnWhCBIgARIgARIgARIgARIgARIgARIggQAgoMs2igDgwCaQAAmQAAmQAAmQAAmQAAmQAAmQAAnoSMBtnw061oWiSIAESIAESIAESIAESIAESIAESIAEAoAAlQ0B0IlsAgmQAAmQAAmQAAmQAAmQAAmQAAkYiQCVDUbqDdaFBEiABEiABEiABEiABEiABEiABAKAgFWPNtDbvh4UKcPfCDjrDZbjw996lvXVg4Cz40OPsiiDBEiABEiABEiABEjAeATcPo1CJlLbtm1Dp06dEB4eDh59abxOZo08Q0AmU5WVldi7dy8yMzMdHn/J8eEZ9pRqfALOjA/jt4I1JAESIAESIAESIAES6AgBXU6j2L9/v6poCA0NpaKhI73APH5LQBRr8tyLok3GgaPA8eGICuOCgYAz4yMYOLCNJEACJEACJEACJBCsBNz22VBaWqpaNAQrQLabBMSiR8aBo8Dx4YgK44KJQFvjI5g4sK0kQAIkQAIkQAIkEGwE3FY2yOoVt04E22PD9toTaGsMtHXPXgavSSBQCXAMBGrPsl0kQAIkQAIkQAIk0DYBt5UNbYvnXRIgARIgARIgARIgARIgARIgARIggWAjQGVDgPa4rCbu27dPfdXV1QVoK9ksfyagOdesra3152aw7iRAAiRAAiRAAiRAAiRAAg4I6HL0pQO5QRN1++2349NPP23R3uTkZLz55pst4r0RsX79etx0003qKQlSXnx8PK6//nqMHj0av/zyC1avXo0TTjgBkZGR3qgOy9CJgEzOjzrqqCbSunfvjnPOOQfjx49vEt/RDxdccAFKSkoasx9zzDE4/vjjsXTpUvzvf/9DVVUV5syZgzPPPBNWa8f+fYhn2rvvvlt9FqUgm82mtmHu3Ln44IMPcM899+Dhhx/GkCFDGuvR0YurrroKf/zxBz755BMcffTR6NevHxYvXuy0uJ07d+LUU0/FWWedhdNOO83pfHon3LRpE8444wz861//wvz585uIr6mpwUsvvYQ+ffro9hw0KYAfSIAESIAESIAESIAESKADBDo2W+hAQYGaZezYsUhKSsKuXbvw7bffYuDAgRg2bJg6gfJVm2+99VYUFhbixhtvhFg1/Pe//8Vll12Gr776Cr///jteeOEFTJ8+ncoGX3VQB8ttaGhQc4ryaOHChapTyldeeQXXXHMN3nrrLfU57KBoNVtBQYGqiDrssMOQkZGhxnXt2hU//fQTnnjiCRx++OGIjo5WFQ+dO3fGrFmzXC5OJsYyYZbjQkVpIcoSUWLcf//9SE1NdVmeKxmuuOIKREVFuZLFMGm1vtfe7SsmY3zJkiVqf+ildLKXz2sSIAESIAESIAESIAES6AgBKhs6Qs0uz5FHHgl5/fjjj6qyYcyYMTj99NPx+eefqxPCW265RZ1QPfLII+qkTSb6F154IVJSUpCfn49Vq1Zh9uzZ6kRRJo5ibXDddddhxIgR6v0HHnhAzSfxssosk8xly5apK5lTp07Fyy+/jJtvvhmHHHKIWivZPiGKD5mQyoSwb9++iImJQVZWlrpq/MYbb6jpLrnkElUZUV1djYceeggbN25UFSQzZ86ErG6Xl5fj3nvvVRUUssIcFxcHSSurzrI9Q1aHpe7SDj1X1u3Q8rIVAnLUpra6Lf0tE81t27ZBJqL33XcffvjhBzXnuHHjVEWEKJieffZZyLMpz2VERIS6Ui/Pwpo1azBlyhT1mdu+fbuaT55ns9mMzMxMiLLhzjvvVONFqSFHfX733Xf44osvHCobysrK1PTyDKWnp6v57P+IwksUDQsWLFCfM7k3atQo9XmS59be2kYUE48++ig+/vhj9XmUlfvLL79cfU7FUkcsDsTSQ8bD888/rz7P8jwKA1GQyLNfXFzcWPzbb7+tKlHS0tLw73//W+WxfPlytT6nnHKK+hw3Jm7lQqyGZLyIdZAoSmSsDx48GP/3f/+HyZMn45///Ccee+wxtQ9krFgsFlXRJ2PV3jLir7/+wn/+8x9s2bJFHavSFrEgEdliifH000+rNTjvvPPQq1evRtYy5qQdeXl5anqxdjj77LPVtMJBmEk/ytgXZaNYw4iMoUOHYu3atejSpQvEWmP37t2YMGECLr30UpX5n3/+qVqTSPuGDx+u1ln6noEESIAESIAESIAESIAEOkqAPhs6Sq6dfEVFReqPfpmgS5AJukwCJGzevFmdrMnkT5QK7777rqqsEFPt/fv346mnnlLT3XHHHepk/9xzz1VXlWVSKSvYMoESWZJOVpvFskILMkk86aSTIKvUMgkRCwaZ4InCYOTIkY1KCZncyGTwhhtuUNOKMmTixIl4/fXXsXXrVjz33HNqPpk8ifWGWG3IJEUmt9deey02bNigTlRkcieT0D179mhV4LuHCUj///zzz2r/yKRfgiggZIIqiobbbrtNtR6Qa+l7OX5Tnpd169ZBJtUy2RcF1aGHHgpRSIiM3377TVVYiCyZpMpkXJ4RmcBmZ2er8uUIQ3m+evbsqU5WJW3zIJNdeVZE6eAoiFJEgpStBZnUivWNKCDsg9RLtiJJvCi3RCEm2wVkAi3t0RQJsu1DPku8jAlp84knnqg+6zKZ14KMO1G6yVYQSS+KF1HgCTtR2sn2jraClCNKOpnoi7JDnn1hlZubq1oQffjhh2p22bIh8mUCL1zlunfv3k1EP/7442pd5F3GlygZZOxL/aSdWhBe9vUSZYP0iygPRMEiSk5ReEgYMGCAqpTo1q2b2m9Sr19//VVVaIjFlSgShKkok4477jh89tlnqqJK0okCVJSSMpalvCuvvFJtk1YPvpMACZAACZAACZAACZCAqwRo2eAqMZ3Sy97xk08+WTUdlwmE+FAQxYH4fxDFhFgWrFy5Up08iLm5rFrL5Oibb75RV1ClGjIxEGVC8yArmTKJlBXo77//Xl0ZltVhMVfXzONlNVmsFZ555hl1QiKTEln5lKCVLWkXLVqkxn399dfqpFWbDEn9ZVIUEhKi3pdJj7barkbwj8cIiAWAbIvRwvnnn6+uZMtEWMzoRZkg/SFBFA1i5SJB+lL8dojCSlbcZaVd0olSQqxsRPkkE1fZHiH3FynpxSJCy68K+ftPc6eOopCQiWxFRYWaQnwviBJMnkNRTmhB2wZgMpm0KPXZFmVVWFhYY5xciNWCWCHIhFuefQnSnraCPO9iASF1lyAWDqIYcxREvkzcJTz55JMqA1G2iSWIBFHOiaWPFmSyLmND2IulgowByS8MjzjiiMbJvyj6hJkoGjROYi1gH0RRIcqIu+66S2Uk1hDi56W9IP8zZMyL8lD6TWSItZME6T+xshBlivwfWbFihapgkntixSBBxr0oIyWIUkgsO8RyQoIok0QhI1tNZGyLnwhpIwMJkAAJkAAJkAAJkAAJdIQALRs6Qs2JPNpkSn74S5DVUPsgTvEkyKROgvzQl6B91k6Q0CbzIk8c8mmWEpJWzNybB1lFlVVJMZO+6KKLIFsztBVjmZjYB1kJFjNsMVWXIKufWpB6yMROmxxq5crKtRZkJVQmIyKjR48eWjTfPUxAJu+ywi99K5YHsoovQSausuIuK9XiN6R50Cbz0reylUKCvZNH6WMxvxclgUyWZZuAPLcyiRUFhtb3O3bsUM3x7eWLjxCZ6MsEV4Io0OSzrNbbB+05kUmuFmRSLpNobeuAFv/ee++pFhqyraB///5aNLSxJc+vBClbCzK518aMxNlfa2m0d81HhObHQax2ZMIvfOUlig77oLVfk6nxlHjNV4L4tpCxLRN6YSEKP1Eiamk1eaIoFOsSGXNi2SQKQrE80YKMf/nfIcoN+6DJke0sErT/E/ZpRLEh/ScKB7HyEIWJ1t9afkkv/3NEOaSNbWmXjGmxcJIxLf45GEiABEiABEiABEiABEigowRo2dBRcu3k036ov//++6oZevOJfjvZ1R/6MuERk+xJkyapfhhk4mZvySAm7c2DTJZkpVXKE0VBQkJCo8WCmKuLEkKCWEjIKqisworPCLFKkD3mWhAz9xdffFE91UImp6LEEHNzmYzKREbM5GV1VVZAxWJCWznV8vPdcwRkkuhoP72syIuC4B//+Ifqn0NqoCmLnKmNKABkgipWCvL8ihJA/DmImb/Ey1ab2NhYVYHQ3IpFlE6yOi8T/2OPPVbdnqOtmNuXPXnyZNVaQjvZIjExUfVBIGnEWkDK1oJmaSPbiOyVFtrYEmsb2TKgWT1IPtmaJHWVLQLi/0GsgzTFnia3rXdNMaelka1DWhCLCQli/SCKCG3bhCh25J6MC9m2Icxke5SmxBOHm82DWJVI28UfhihzRNEiW6+ErwTZPuLIikP8NQwaNEhtn6QTvxRaEAsO8cEhCgyxThH/MBLsTzARBZC8RJEhlhpSV/k/IEGUJmLNIf9zZFw3Z6Em4h8SIAESIAESIAESIAEScJJAy9mqkxmZrG0CMkGTbQjyw10cwdlbDcjqoqYo0FZpHUkTfwoyERHv/eL0TlZIZc99W0EmWLK/XbZIiINHWUGVCdDFF1+sWiHIZEImXzJxlFVNMauWlVVZ/ZQVcQmigBDTbClL4mQCpJlTy+qnHPcpq8pSL/EjMW/evCam8m3Vj/fcJ6A9O80lydGRohSSvhQTeQnSl9ozpr03zyef5Z5M6kWBJBNfOR1CJsxiHSMKApEp2y+kv0WxJGU5CloZ2nvzNLIiL8+n9tzJNg2Z+Io1jsRp+eRdfAtIkEmv+C+RybwoHWSiL2NBJsTiTFUUYFoQvyeipBO/FXIqiyhftGA/7rQ4edfKtI9rfi1pxOJD5Mp4En8oMm7kCFDZmiJ9otVXuImiRVNyiAKkeRAHmiJHfEYIb1FISD75LPnk6E9RGkqb7YMoKMQKQpQOwkD6RpRP0ifCQxQ+EmRbhwRRPop/By3I/yRRKt50000qG2mHWEgJf3lmZEuObM8Qjpq1lZaX7yRAAiRAAiRAAiRAAiTgCgGTsvJ54Dw9V3LZpRWv6ppptF00LxUCYpYte+HFuqC1CaIzoMQRnkzSXPnxL90qE00xK5cJirY9Q8qTeonptGZSLelkgmMv/9VXX8WXX36pnhgg6WRSIhMimWxpQfKJIsKdtmmy/P1dnGrKinPz4O3xIU4Mpe/FHL6jQWRI0CwINDminBKzfW3bgRbf0XdRWMlqvkyIW3uGJI1sJZBJd3OlgDx/sj3A/rnV6iIWFlJ/++deu+fuuza25Nl3R76w1NrQ/BQOGfMybh0FGbvCpXkfi2WCMJLtGKI4eO2119TtEGLpIkHGryglZAwL9+aKDPm/4CjeUR1cjWttfLgqh+lJgARIgARIgARIgAT8g4A4Hec2Cg/2lUyg7E+K6GhRzScVzsiRSYcoORwFqZf9BM1ROtm3/dFHH6kr2yJDLBtkRdU+NJ+s2N/jtW8INFcQdKQWrcnQ9v13RKajPPIM2j+HrqZp6/kTyx5PhbbGlitliqLC0f8HsR5qTdEg8kXxqPlssC9P8yUhFhGiaBDFQvOtEDL25eWIXWvx9mXwmgRIgARIgARIgARIgAScJUDLBmdJBWE6WcGVFeLWVo+DEEmrTW5t5dbblg2tVpA3SMCHBFobHz6sEosmARIgARIgARIgARLwIAHdLBtkhc6RV3QP1p2ivUBAVnAdrYB6oWi/KqI9U3qOD7/qTlZWZwLtjQ+di6M4EiABEiABEiABEiABgxAw61EPzXxXD1mUQQL+RqC957+9+/7WXtaXBFwhwOffFVpMSwIkQAIkQAIkQAKBQ0AXZYPmA4ArWIHzYLAl7ROQ5118Dsjz31bg+GiLDu8FKgFnx0egtp/tIgESIAESIAESIIFgJ6Cbg0iZUGmnGwQ7VLafBJoT4PhoToSfSYAESIAESIAESIAESIAEAplA20uygdxyto0ESIAESIAESIAESIAESIAESIAESMAjBKhs8AhWCiUBEiABEiABEiABEiABEiABEiCB4CVAZUPw9j1bTgIkQAIkQAIkQAIkQAIkQAIkQAIeIUBlg0ewUigJkAAJkAAJkAAJkAAJkAAJkAAJBC8BKhuCt+/ZchIgARIgARIgARIgARIgARIgARLwCAEqGzyClUJJgARIgARIgARIgARIgARIgARIIHgJUNkQAH1fX18fAK1gE0jAMwQ4PjzDlVJJgARIgARIgARIgARIoC0C1rZu8p5vCdTW1qKyshLV1dWNL4krLy9vUrHtlbG48vODUd3irYi3WZASZUZ6rBUZcVZkJlrROzkESZGWgwl5RQJ+TEAbH1VVVer4qKmpgcTJSxQM8i6h+fiQuOFdwpAcZUGnGAsylPGSmXBgfCRyfAgeBhIgARIgARIgARIgARJwmwCVDW4j1E9ARUUFysrKVGWCXMvkqSNhR0Et5OUopCuTq8GdQjG8cxhGdQ3DwLRQR8kYRwKGI6DX+JCG/ba7ymH70pTxMbRTGIZ1DsVoZXwM4PhwyImRJEACJEACJEACJEACJNAeASob2iPk4fvFxcUoKSlBYWEhGhoaPFwakFVcp7wq8Nn6CrWseJsZk3tF4PDeEZjSJ8Lj5bMAEnCFgDY+ZIxolgqu5Hc1bbYyPrKLy/Hp+gPWQyYTMHdwJCZzfLiKkulJgARIgARIgARIgASCnACVDT54AMR6QZQLBQUFPii9aZEF5fV4+88y9RVmNWHWQBuOGRiJMd3CmibkJxLwEgFtfBQVFanbIbxUrMNiRP/3ljI+5CXhuGGROGYAx4dDWIwkARIgARIgARIgARIgATsCVDbYwfD0ZX5+Pvbt2+eVFdqOtKWqtgFv/FGmvvqnhmD+0CgsGBEFi7K6y0ACniQgVj2ifJMxIn5KjBre+F0ZH8qrq+Ln4R9jorFgeBTMHB9G7S7WiwRIgARIgARIgARIwIcEqGzwMHxxVJebm4u8vDzU1dV5uDT9xK/LqcFtnxXgkeVFOHVktDqxsoVyVqUfYUoSAv46PnYqPlFu/VQZH98p42NUNBYqigdbCMcHn2oSIAESIAESIAESIAES0Ajw6EuNhAfe9+/fj/Xr16vWDP6kaLBHIdssHlImVIc/vBdP/VBsf4vXJOAWARkfGzZs8Ovxka+Mjwe/LcKh9+/h+HDraWBmEiABEiABEiABEiCBQCNAZYMHelT8MaxZswY5OTk+33OuV/NKqupx/9dFmPZYFt7768D+db1kU05wEZDxsXHjRnV8+KsSrnmPVdc1qONjwJ27OD6aw+FnEiABEiABEiABEiCBoCRAZYOO3V5VVYUdO3Zg9+7dXjlZQseqOy1qV2Etrn4/H+e/nostuR07mtPpwpgwoAjYj4/q6uqAapt9Yzg+7GnwmgRIgARIgARIgARIIFgJUNmgU8+LX4ZNmzapx1jqJNLQYr7eXIFZT2Xj+ZUlhq4nK2cMAhwfxugH1oIESIAESIAESIAESIAEvEWAygY3SdfW1mLnzp3Izs52U5J/Zr97WSEufCMX+0v9x/mlf5L2z1pzfBTi7Nf2c3z45+PLWpMACZAACZAACZAACbhBgMoGN+CVlJRg8+bNKC4ObseJX246YOXw7RbjHlnoRjczawcJcHwcALd8ayWOW5IDjo8OPkjMRgIkQAIkQAIkQAIk4JcEqGzoYLfJUZbin0FWbhmA4sp6nPu//dxWwYdBJSDbJjg+Dj4MYvnD8XGQB69IgARIgARIgARIgAQCnwCVDR3o46ysLMiLoSUB2VZx5xeFLW8wJmgIyNgI1m1F7XWyjI+7OD7aw8T7JEACJEACJEACJEACAUCAygYXO1FOmhCrBobWCbz4cwmufI+MWicUuHc4Ptrv2xc4PtqHxBQkQAIkQAIkQAIkQAJ+T4DKBhe6UBxBFhZy1d4ZZB+sKVcdRzqTlmkCgwDHh/P9KOPjojdznc/AlCRAAiRAAiRAAiRAAiTgZwSobHCyw2T/ebA7gnQSVWMycRx5/uucUDUCCeALjg/XO3fZxgpcoJzkwkACJEACJEACJEACJEACgUiAygYnelVWbMWzPoPrBL7eXIFL3uKEynVy/pOD46PjffWVopC79G1uOeo4QeYkARIgARIgARIgARIwKgEqG9rpmT179tCioR1G7d3+bEMFbvgov71kvO+HBDg+3O+0T9eX44aPOT7cJ0kJJEACJEACJEACJEACRiJAZUMbvbFv3z4UFBS0kYK3nCXwxh9leOi7ImeTM50fEOD40K+T3vi9DA9zfOgHlJJIgARIgARIgARIgAR8ToDKhla6QBxBymSKQT8Cjy0vxrury/QTSEk+I8DxoT/6Rzk+9IdKiSRAAiRAAiRAAiRAAj4jQGWDA/QVFRWQI/wY9Cdw7Yf5WJNdrb9gSvQaAY4Pz6G+5oN8rOX48BxgSiYBEiABEiABEiABEvAaASobHKCWfegMniHQ0ADc+BG3pniGrnek7t271zsFBWkpF/KEiiDteTabBEiABEiABEiABAKLAJUNzfozKysLlZWVzWL5UU8Ca3OqccfnVDjoydRbsmR8iGUDg+cIZJfUcXx4Di8lkwAJkAAJkAAJkAAJeIkAlQ12oIuLi5GXx2Po7JB47PKlX0rxpXLsH4P/EOD48F5fcXx4jzVLIgESIAESIAESIAES8AwBKhvsuO7cudPuEy89TeDeZYWoV7ZVMPgHgezsbP+oaIDUUrZTcHwESGeyGSRAAiRAAiRAAiQQhASobPi70zmR8v7Tv6OgFvd/Xej9glmiywRkfFRX07Gny+DczLCY48NNgsxOAiRAAiRAAiRAAiTgKwJUNijkxUdDbm6ur/ogqMt95scSbNhXE9QMjN54jg/f9dDTHB++g8+SSYAESIAESIAESIAE3CJAZYOCb9++fW5BZGb3CDy2vMg9AcztUQIcHx7F267wB7/l+GgXEhOQAAmQAAmQAAmQAAkYjkDQKxtKS0shju8YfEfgsw0V+GE7TwDxXQ+0XjLHR+tsvHXnK8WR6o8cH97CzXJIgARIgARIgARIgAR0IhD0ygZun9DpSXJTzJKfStyUwOyeIMDx4Qmqrst8luPDdWjMQQIkQAIkQAIkQAIk4FMCQa1sKCsrg6zcMviewPKtlfhpR5XvK8IaNBLg+GhE4fMLGR8rOT583g+sAAmQAAmQAAmQAAmQgPMEglrZkJeX5zwppvQ4gVdW0brB45BdKIDjwwVYXki6dBUVo17AzCJIgARIgARIgARIgAR0IhC0yoaqqir6atDpIdJLzGfrK7Atr1YvcZTjBgGODzfgeSjrZ+vLOT48xJZiSYAESIAESIAESIAE9CcQtMqGgoIC/WlSotsE3vqTq7duQ9RBAMeHDhA9IILjwwNQKZIESIAESIAESIAESMAjBIJW2UDHdx55ntwW+s7qcrdlUID7BAoLC90XQgm6E3jmR2410h0qBZIACZAACZAACZAACXiEQFAqG3jUpUeeJV2E5pXV4UvlqD8G3xGQ8VFby+0svuuBtkvm+GibD++SAAmQAAmQAAmQAAkYgwCVDcboB9bCjoD4bmDwHQEq43zH3pmSOT6cocQ0JEACJEACJEACJEACviZg9XUFfFE+TcR9Qd35Mj9cW4a7ZiU4n4EpdSVAZcNBnOeOj0GoxXQw4u+r134rRU5JHaxmE+YOsWF013B8pVjkfLyu6Tag8ZnhmD04Euv3VeOFlaWorW9oIcvViPf+4vhwlRnTkwAJkAAJkAAJkAAJeJ9A0CkbSkv1cUAYFxeHsLCwFj0m8svKytT4yMhIxMfHo6SkBEVFRS3SmkwmdOnSBTk5Oaiurm5xP1gj6uqB5VsrMaFHeLAi8Fm75fmtr1c6gEElcP6EGJiVcaoFRbeghj/2VivKhgp8eWE6kiItyFW2/xwz0KY+s9d9mK+meXB+Eqb0iUBBRT1mKvdOHB6Fox7L0kS59c7x4RY+ZiYBEiABEiABEiABEvACASobOgg5OTnZobIhJCREVTb07t1bvS9730UxIUqFtWvXoqHh4MpmZmYmbDYbxPM/lQ1NO+L7bVQ2NCXinU96KeO8U1vPlzLk7t2NhUSHmbH8X51Ui4ZvNlfggomxqqJhwfM5WK0oH8QaZ86QSNy9rBDpMRZV0bDkpxLc+2UhJvWKwGPHJ2HWIBve/6up9UNjAS5crOD4cIEWk5IACZAACZAACZAACfiCQNApG8TKQI+wadOmJmJEuRAaGoqsrCxVgSBWD7t374Zs2RAFRN++fZGSkqJaMYSHh6N79+6wWoMOfxNmbX1YuaOqrdu85yEC5Vm5sCiT6jpaN7Qg/PgJSbAopg3/eHmfeu/Q7mEQh6aiaJBw/9dFOHZQJKb3tyEm/IA7nAe/PWDRJMqJypoG9b4eyoafOD5U5vxDAiRAAiRAAiRAAiRgXAJBNdutq6tDVZX+k1jNykGUC1KGxWJRt01oviFqampUiwbZViEhMTERsoUiOzsbaWlpxn06fFizdTnVKFTMz+MigtKHqU/I55dWAle+BNkcVJ+RiPqeKbDOHIWqUFPQKx9GZoRheJcwPKscPZlVXKf2T1qMFXnlB7eciA8HCRlxViRHWVBT14Cq2oOWTCVV9aolhJrIzT8cH24CZHYSIAESIAESIAESIAGPEwgqZUN5ufvmy817RJQGYrFQWVmpWjHIfbGesLegSE1NVZULmvJBrB/27NmDiIiI5uL42Y7An8qK8WE96bfBDolHL3/Mr8DM42erZYQrvgkW1lXgiM83YfRnP6K+ayJC545FbZeEoFQ+XDo5FuLb8aHvDvpesYWYsL/0oDJBwEmaGEVBlhAp1iFNu0sUDxFKHr0Cx4deJCmHBEiABEiABEiABEjAEwSCStlQUaH/kYr2VgqOOkjui+WDlJ2ff8BxnJEd8A3pFIoFiiM7sSpY+msp9hTVqs2ao3jU7xrf8nH5YXslft5ZBXHYf+a4GPRJCcEXGyrw6fpydeLliImzcWuyqGxwlpUe6X7NPeDYVGRVKpPmJ80ReDJWUYj9rYCYu6ca0zdvw9B9eUjI2acoH8Ypyod4VCqWD0Z+pt1lE64oCIZ1DoP4SbC3VCirrkdUWFPlgTiQ3J5fo5xSAVgtTUsWRcOOggPjqemdjn1ak83x0TFyzEUCJEACJEACJEACJOANAi1nj94o1UdliPWB3kGUCbJ1wpFjvU6dOiEhIUF1GLlt2za9i9Zd3okjonDDtHh1RVZMwBcdEo0zX9kPUSicfWhME2WDYtABmWZ1jrXi111V+OqiTqqJeHFlvbpn/Zz9MZjzdLZbddygHBfI4D0Cf+S1bfnztikUb9sSge7y6oND99ZizradGJNfgM679h6wfOgceMqH+YrTR1EiPPDNQasG6RXZNtEnJbSxg0Yo2ywkbMipUZRvJvVYzNRoi5pO8sdFWPDtFv3+B23YV9NYNi9IgARIgARIgARIgARIwGgEgmpDvHYkpV6dYDabVeeP2vYIe7kZGRmqokHu+YOiQep+xRFxKFX2lR9y/26MuG83tuXV4CHl+D4JM57IwqC7djW+duTXqnvS7/qiQLVokOP/FiuTsbH378GpL+5Dn+QQnDk2Rs3b0T9b8vRbBe5oHYIp39qCtpUNzVmsgBVXhsVhcnomeo8Zj8P3mPDUir3Y/s4aWN7+DdE7CmGrM0HGiT+HoYpVg2yWEEsC+/DkDyWIVKw6/jMnUbF8CMX9cxNRXt0Acd748q8lqmXPkpNTMDAtFM+clKJwAF78RR8HtVKPzxTrIQYSIAESIAESIAESIAESMCoB/54FuEhVLBD0DJrDR3v/DCI/NjZWfYkzSlFwxMfHqy8tvZ510EuWbIMQM++vNlWqEyaRK17zbcpkKswqNgwHw1nKdonuCVbc+HEBCpTtFsOViZZMxsR5noRVu6tQoXjeH9PtwEqvGtmBP2KOzuA9AhuL3Ft13woz7rJGYWZSFwxI7YW+ey246wflWMgPN6L+o79g81PlQ88kK4qU57x5kBMmlm2sUC15li5MVRQPZlzydi5qFccNonSQIzC7KePk9dNTMTIjFE/9UIz1itUDAwmQAAmQAAmQAAmQAAkEA4Gg2UYhJ0LoHWw2myqyucWE+GiQIMdfdu7cWb2WP7KNY/PmzY2fjXSh7JpAmTJBykw8+EgMVvw3SBA/DuKXQYJVsQe/6LAYiAn3O6sP7PFfqdyb1CtC3WaxVbGGiFKOTpR97o58PKhCnPwjDvb2Kp7/O8U02/zuZH4mc57AjpIq1DWIyki/UKWIe0rx+/CU+H2QsAeYsycXM8pKMBxViOmdCmuGsu1CUWbVN7SczB/I5Pu/85/NabUSF72ZqyrkUpTTJ7Yr1j724cWfS7BUsWTokRSCTfv1//8jZcnJGOkcH/bYeU0CJEACJEACJEACJGAQAgdnlgapkKeq4QllQ05ODuTVPDirUBCnkX/99Vfz7D77/PmGcogjSFmlFed347ofOAlClAdaWDgmSlU4/OerQi0KL/9SissOj8Nri1KVld5yTOgRrvpzCBVzCTdDTnEtlQ1uMnQm++6yplsEnMnTkTTvIBTvRCo+HyTsBcbtzcfcqjIcaqpCfLcEhHdLRKWlQVE+6Kv4OFCgZ/6KFUNzRYNWkijxPKVokDJySmqpbNBg850ESIAESIAESIAESMBQBIJG2VBb23TV0VC9YJDK3PJpAWSFdrji6E7Mxl9RTqM4ZVQU/thzwKpBqnnaqGj13vKtB03uq5UZ1RXv5uHqKXGY0seG1VlViuPIBuwrdX/bSl65cVe8DdJtulQju8IzK+/tVe4Hxe/DD2GxB5JlAZlZBVhQW44plmokp8cgUnFGKcqHBj9SPrTXZj3v55ZxfOjJk7JIgARIgARIgARIgAT0IxA0yga9/TXo1wXGkXT8sCjFgV0pznx1v1qp66bGq07u8v+e8IuFg3jXF/Nw+9BTMRMXJ3iHP7xXTS+e93+/sgtWKo7y3A2O9sq7K5P5WxLIr/SNsqF5Tbb97ffhLrmhHGYSll2EhfUVmBVSgy5JEaryoUb5r0XlwwFyHB/NnyB+JgESIAESIAESIAESMAqBoFE21NdzBbC9h25avwj0Tw3F/GezkaxYOCwYEakc1VfRmG101wMOH7+zs2qQm3JM5j/HRiu+GhTP/F8V4Z5jE5SjAk14YkVxY96OXsjpGAyeJ1Bc474ViidqKeoq1e9DneL3QXYs5RRjNqpxUmgNesSHIkrZdlEfErzKh1JluxMDCZAACZAACZAACZAACRiRAJUNRuwVH9Xp+g/z8dJpKfjonHT1dAnZa37xm3mNtRmqnDohQXMWqd3YWVCLt/4ow/yhUThheBQqlZMobvmkALsK3d+6UlnrP3v3NR7++F5e6z+T1ncVvw/vVivP4t/Kh0NRi9MjajEw2oxoRflgUk5QCRbLhyplrDGQAAmQAAmQAAmQAAmQgBEJBI2yIZAnHw3lRcqz9fe+dzeeMnFyN+GBvepWiaLKelVpYC9u8ddFkJejcP1H+cpRmPnoHGeFKB/0Cn40B9aryT6RI8c1+mtYofh9WFGh/CsTI5x9yukPpnpcGFmLUYoxRIzidDKkTCxsYvy1eW3Wm+OjTTy8SQIkQAIkQAIkQAIk4EMCQaNs8CFj3Ys2m82oLKzEnlWbUF/2M/qtW4GE/s8hv16ZXekQcko6ZlIvnvf1VDTo0BSKCBICIYqjkMzwUGTUm5CuOCbdtdGE0u3lsOWtQ/cxz+K99RUoSuuHnbF9sCa8F1agB7bXxQUJHTaTBEiABEiABEiABEiABLxPIGiUDSbFh4C/BovZgqq8auz5bTf+KvkR3RN/x4yi79AvZgyy3v1VbdZj0TdjQSfVrZ6/NtNhva0HT910eJ+R+hCQybrRQ4TFjMywEHSpUxQKikIsKUc5LnNbGaLXFCH894IW1a9b2IAH5j+MPxSrh93bdiJu218Q9cIQ5XWS8jIld0JF5/7ISeyLjZG98aulJ76v7dpCjpEjFCQMJEACJEACJEACJEACJGBIAkGjbBBrAH8JVpMVNftqsHvlHmyq3IT1cd9jckoBjq78CVNMisu8fKAqfDKyXlzZ2CTbr5/gsZgMnBd1QWNcIFyEW40/CQ4EzhEG0epEh1jQPcSKLrWKQqG4FknKMapxW8sQ9VchwtY63sLTnL9JUZzsvHwPHu7+BM6PHgNkfQ5Yo4DaphY7Dfv3Ilx5dcMy5QVMVV6msHDUdR2A/NS+2BHTB6tDe2F5Qw/k1Ec2L8YQnzk+DNENrAQJkAAJkAAJkAAJkIADAlQ2OIDi7agQhKB2by12rdiN7WU78F3CCoT03o/p3WqxaOdKhJVVAtv+rlVoDEoLB6Lwg4OKBq2+Xb56CjfM6IJbLLO1KL9/j1SO22TwPIHYUO/9K4hXyuputaKz4twwTXEiqioUtpQi6s9ChGxueqyqqy2PHBiJt0/7AMtivlazzi3cLUdVIHJwD5T9tqldcQ1VlTBvWoUkeSmpRyqvRcrLlN4dpZ36IyuhD9bbeuNnc0+sqk1X7vg2yHG0DCRAAiRAAiRAAiRAAiRgRALem2H4uPUWi8XHNThYfEhdCKp3Ktsivt2LvYV78VvnP/FB0seYNLgnptfU4lFFwRC+UVEwNA/x/ZD/swnl6/9ofqfx8+iPbsRpczrjxbpRjXH+fBEXzsmUN/ovMUzffwXJirxuFsVCoaoBqYrD0IS9FYjbXIpIUSjsKPNIk0JnheGqaTeizHxA/uj4vhi89Se1rIiucYqyoePFNmRtR6Ty6oWPlRdwjPIyRcWgJmMA8pL7Ymt0b/yhWEF8W9cDJQ0HTm1Rkng8xEZwfHgcMgsgARIgARIgARIgARLoEAF9ZxgdqoJ3MlmVlVRfBPEVYa2yonJLJbK+zEZObg429diCZenfYFPPzZiZNBTTa2tx2859CN+wo9Uq1ieMQ/ara1FfXdNqGu3Ggq+vxrrJz+OXus5alN++J0ZyMuWNzku1hbhcTHp4CLop/kQ6VSoKhfwaJO6uQOymUtj+yIc1y4GyzOUSnM9Qcl4Zbht8Y5MM8y3K1om/Q0hM0y0UWrw77w2lxbCu+xGp8lIEjVNe54rAjD4oSe+H3YqyY53ijPJHU0+sqxM7Cf1DEseH/lApkQRIgARIgARIgARIQBcCvpmB61J114SEhLg+mXKthAOpxZmjqUSsD8qR80UOcvfmYe/AbKzI+BHfDvkeFpNFUTAMwb9qw3D0rnJE5H/ZbjE1UZOR80LLbROtZWwozMX1a2/AcX2fQi38e7KeFhM0j2hr3emV+C6RjlfjMyJC0BWKQqGiHil5BxQKMRtKEPlbAcz5iv8QH4ew5DD8cv7veCn11RY1mbN7VWOcpWF/47XHL3ZtRLTy6q8UJK95yssUl4SqLv2xP7kfNkf1xiprT3xTm4k6N8dnajTHh8f7kwWQAAmQAAmQAAmQAAl0iEDQ/FL1lLLBaraiIb8BpatLkfNxDoqyilAwpBCrMv/E24e+p3aK2WRWLRgerR2C6bt+URQMXznXWYpVRHntBOS/6ryiQRNs2fgrnou/Baem3KRF+d27eNpPjzHO9he/A+hChbtGheHE+lCk5NYgflcZYtaXwLYqH+bSWhekeDep7dBIPHncEqwOX9Oi4PkpIxG3YdnB+MINMIfFor6qfcugg5n0uxIFYGjhd+gMeQGTlNelitPahm4DGo/k/EuxgvjBxSM5OT706yNKIgESIAESIAESIAES0JdA0Cgb9MImzhzr9tWh6NciZL+fjbKsMlSOrMK63hvw8dTPkWXNVosSBcOspOGYUVuPabt/hs1ZBYNW0ZjuKFwfh9JVB4621KJdeY/96R08MCUD/4o4w5VshknbPcE71iiGabCPKzL3gS3KM1fo41o4WfzJJlw04fJWE8+raOkXwjYoE6W/bmw1j9dv1NfDJMdx2h3JebJSCe1IzmzlSM5NypGcvyhHcq5wcCRntwT++/Z6n7FAEiABEiABEiABEiABpwkE1a/VmJgYFBcXOw1HEobUh6BmTw0KfyrEnjf2oDJH2Ys+xoTt/Xfi67nf4ffwPxvliX+GY/5WMBy9+xfXFQx/S2pIHIV9H+5FTa77E6MeXzyEK2d1wT2Y1lhPf7nomRhUj6fPuyV+YLxfKBuyLtuHxT0faZVXRmQ6jtz5Y4v74RmxirKhRbThIrQjObsrR3J2V2rn6EjOP8N6oTpWNmkwkAAJkAAJkAAJkAAJkIAxCQTVbC48PLxNZYPqzLHaiqrtVchfno8tT25BXWkdbBMjkTN4H3467Rd8HtPUx4IJJsxMHnbAgmHPr4h01YKh2XNRGzsJ2c//3CzWvY8T378KW+Z0wpt1g90T5OXcfVMd+xHwcjWCprjEIYnY9qZ2xqrxmm3rY8NHp3+Gj2M/b7Ny8yMzlPvrWqQJ9YCTyBaFeCjC0ZGcCWddo5TWunWHh6pCsSRAAiRAAiRAAiRAAiTgFIGgUjZEREQ0gWJW9kyby82o2FiBDfdvQMlvJWioa1DT1C1swJ9X/IV3kz5EhamiST75MDNZ2SJRV4+jdysKho1O+mBoIaVpRKVlMnJfdt0/Q1Mpjj+dvuIarDn0Oaz3kFd8x6W6FzswjcoG9wi6ljtphGdOTHCtFo5Th08Px80z70SuOd9xArvYeXnb7T4dvDTX7zv4IQCuwvoODYBWsAkkQAIkQAIkQAIkQAKBSiColA02mw2WEgs2P7pZVSyUrilt7Ffr/BBsmboDn6ctw9aQ7Y3x9heqBYOijJimeLmP0knBoMq3paBkbyaKlntG0SBlNOzbjTs23Yh5PVo3P7dvqxGuh3SissGb/ZB8SLI3i3O6rIqzq3DVsBudSj8+cQD6bF7hOG3hRpjD41Ff6ftTNBxX0LXYsIEjXcvA1CRAAiRAAiRAAiRAAiTgRQJBpWywWCxYd+E65K7KVRHnXlKAF6KXIie99RXPGbJFQlEwHK0qGL7Wv2sShmD/t2XK1o3V+stuJjFkzfd4Lu4uLEq4utkd433smxKCuAj/PrbTeFTbrlFEcgQSBicgf3X71gNtS9LnbkhsCP68aC2e6/SS0wLnI6zNtJGDuqPklw1tpvGHm6G9BsISm+APVWUdSYAESIAESIAESIAEgpRAUCkbpI/XnbUR9zU80GZ3H1AwANP2rEL0xq/bTOvOzfqECdj7wip3RLicN/H7V3HPtAxcGXqKy3m9mWFs93BvFsey/ibQaXInQygbIsdEYsmCl/FrxG9O902IOQRzdv7UZnpxElnyS5tJ/OJmxIgJflFPVpIESIAESIAESIAESCB4CQTd0vFhsyY67O3pigXDQwnDsKmiHs8qCobjtnyN6ErXTq5wKLiVyOqIyV5XNGhV6f/pvbjI9LX20ZDv4zOpbPBFx3Se2tkXxTYp03yCBRcuutwlRYMIOC5pKGzV5U1kNf8QEl3TPMovP9tGT/LLerPSJEACJEACJEACJEACwUMg6CwbJnYej4RwxVS88oCpuCgYpu35DTEetGBo8jiF2FBWMhwFH3jOP0OT8lr5MO3jK7HpmJfwSW2fVlL4LtpsAib0oLLBFz2QMT0DljAL6qrqfFE89l+Sh/v6PNihsueVF7Wbz1yX024aoycwhYbBNnaK0avJ+pEACZAACZAACZAACQQ5gaCzbJD+vikiAxsLi5BdUIjjFQuGmMr2Jym6PCdxfZC/MRMFXzhvGq5LuQ6ENNRU44JfrkMn80EnmQ6S+STqmIGRPimXhQJmqxndZnfzOoqITBu+v21lhxUNvaIzMHGXE0fGFm2GObLpqTReb6ybBUZOnA4o/mcYSIAESIAESIAESIAESMDIBIJS2XDKyIsQ09Dg1X6pTxyLvW/lonzNNq+W22ZhuzbhgR03tpnEFzeP6uffk0FfMNOzzB7ze+gprl1ZEVNtuPvS/+K9hA/bTdtagnkR6a3dahEfOdD7ypQWlXAjInLyMW7kZlYSIAESIAESIAESIAES8A6BoFQ2mHvPBEKjvUNYKaUmWvHP8PyfqC+vdL5MsxmxC85F6i1PIXLyrCb5TCFhiD3hHKTc8Bhshx7V5J58sI05HKk3Po64Uy6CyRrS4r59RPjvy/BUcdsOM+3Te/o6OcqCI3pT2eBpzm3J73FCD4TFt32qQ1v5XblX/c9aXDz3CuyxZrmSrUXaefs2tohrLSK8S0xrtwwfb46KRdSRcw1fT1aQBEiABEiABEiABEiABIJS2SDdbhl5tld6v6LhMOS84rp/hm6v/4qki29DxPAJSLvjOWS8tFytryk8AplfbEfiRbfANm4K0u97BclX3NfYlrQ7X0D64jcQccgRSDz/RmS8+lPjvdYu0r5Zgltr32zttlfjjx1k82p5LMwxgd6n9XZ8Q6dYq82KLddux79H3eq2xMOTBqN7wXan5YRE+a+TyOijj3e6nUxIAiRAAiRAAiRAAiRAAr4kELzKhiELPctd2UNelDMCeR+6fs5e7PwzYE3vipwbz8L2Wf2x/97LEdqjP8L6DUPy5fep1go7Zg3A9um9UfnnT4iedaraltBeAxE5aSYKlz6M7TP6IOuKkxHSqRuip53QbluHfXwrzjD/2G46TyeYO4T+GjzN2Bn5fU/v60yyDqWJHB6JN29+D092WdKh/M0zza937d+YuS67uQi/+Rw942S/qSsrSgIkQAIkQAIkQAIkENwEXPuVHkCsTIl9YO43xyMtakgcgZwvG1Dy8/oOyY+Zswh1eftQ+sXbCM3si5IPl2LH/OGo2rgaRa89juxrFqKuME+VbYpQLAFMyvENSrCNm6q+5z95h/pevuIzNFRWIGp6+8oGyTB32VWYYN2p5vXFH/HV0COx7W0fvqhXMJaZOCwRXWd21b3p1vkhuPCsy7Eisn2LG2cKjwqJxJxt3zuT9GCaoi2wRPufUsumWCuF9Rt6sB28IgESIAESIAESIAESIAEDEwhaZYP0iWXkObp3TV3cYdjz/HrU5Bw4WrMjBVjik5VjAczo8U0WMl5egR5f71X8L1wI1NehatNqlH37EaKPPgHd31+HsN6DUfTKI2oxoT36Qk6ZaKiuaiy2vqwY1oSUxs9tXTSUFuHKP65DlKm6rWQeu3fSCO/50fBYIwJI8MALBuramoKLi3DZkdfqKnN+wgBY62tdlumPTiJj5p3hcjuZgQRIgARIgARIgARIgAR8RSColQ3mbpNh7nHAGkCPDqgKnYysl1zfNtG8bLMtCpb4JJR98wH2nDcT1Zv/Qqwy0YgYPr4xqSUuEXUF+wDlVI2oqfMhvhxUJUVdXWMauaivqlTuOe8HwbR1NZ7ae3MTGd74MKFHOA7p5h2nhN5oTyCUkTE9A50O7+R2UyK6RODnW37DXf3+67as5gLmleQ2j3Lqc1jnKKfSGSVR+OAxiJx4tFGqw3qQAAmQAAmQAAmQAAmQQLsEglrZIHQsh1zSLqR2E0QkoKR4LPa/tbLdpM4kqK8sV7c/5NxwFir/+FHxvXCKmi1q6rzG7IWvPoZdCyepfh2sqV0QM+s01OXmAFZrYxq5MCuKhro8Jd6FEPXLh3ik7HEXcrif9J+H0KrBfYr6Sxhy+RC3hNom27D48sfwRtI7bslxlHlAbCYO2fObo1vtxoVE+sZ6p92KtZIg7mTFsomBBEiABEiABEiABEiABPyIQNArG8yZR8Dc1w3fDfGDkLsqCUXf/qlbt9cV5KKhtuVkqK4wXzkK82n1tAmtsMo1v6qXYb0HoXrHJtV5pDU5/cBtZSuGOTYeNbu3asmdfu/65eO4tv4Dp9O7k/CofjaM7R7ujoj/b+9eoKMs7zyO/+aWy0zIZXIhBEhAioAg94uCImELIggWQj1VKZVddReVU+SiQNFDa11FrcVqu1vdRS1eWFaryLpno8furhzRuh6sy81uBeQqyE0CMRAhs/OOTWFCJpd55528b+Y758zJvO/7PP/3/3yeeTnkn/dCX4sESieWqmxKWVzRz/ygTnOuX6gdaTvj6t9cp4q0guaaxNzuPmPuUZsxA1uwwT+iPHLjVwtCExIBBBBAAAEEEEAAAcsEUr7YYMh6r1gcF3BdcJT2rtqhU9v3xtU/VqcvV62QOytHBfOWy1tUoo4/XRlpeqJyjWp3bJV/eLlypt8iX2kPdVr+fGRb1Rsvqerlp8PXTdSp5Im1kRvJlfziVbncHh1f81SsXTW5/vI3lup7nvj+ctxk4AYbZ4/KbrCGRTsJDL53cKvScXvd2nXPXv1ohLWX40w9sLVVeUU1rtopb44zzqbJm7UwKnUWEEAAAQQQQAABBBBwggDFhvAsuTr2l+eyea2ar1r/GO3/jTW/iJ/4jzU6WfmvkYJC2WublNFnkI48cZ++Dp+5cOw3K8KXVryngrkPqnT1B5FHYh795+WRdXU11Tr8i6Xydb1IXVb+Thn9R+jYqscjN5Vs1eDOazxj/SIN8LbuMozzujf78ZbLstWriCdQNAvVhg0Khxaq/7yWXU4R6BfQvz/wpn5VFi58WfiaUDhAJVX7TO3Bf0nin7ZhKqFGOhuPwTWOY14IIIAAAggggAACCDhNwBUKv8wkvXnzZvXr189MCHv0Dd/RvvbXAxU6tr3pfDzpqj41TMcqNzbdLgFbXV6ffN0uDt8gcssF0YxtaRf10enwzSONsxmiXuGzGdKMfju2Ra2Od+FM7+Ga2jO+syOa2me3oFf/dlsnub95cmdTTW2/LdZxEGu97QfUIMG62jqt6bNGVTuqGmw5t+i7Ll1Lxi3TKfepcyst+vRU7qWasnO9qeinM8bo0MuJuc+KqURidPYWFKvrS+/LHXDGGRgxhhFZ3V6Og6bGyDYEEEAAAQQQQACBcwIHDx4UZzbUe7i98v7Vg/VLjf/M6aFjO3slpdBgJBA683WjhYb6baf/L3yfiIaFBmNj+BGZiSo0GOG8n3ygVYfuNz4m9LVwbG67KDQkFMWmwdxpbl326GUxs6u6vVrzrl6UlEJDMD3XdKHBGIjPf+4RsTEH1oYb8uf8pF0UGtqQkF0jgAACCCCAAAIItKEAxYbz8N0XT5Zn6Ozz1pz7GMofrs9fr1L1/356bmUKfcp9/xU9durZhI14xtAslffMTFg8Alkv0G1qN/W9s2/UjtKL0vXxsi16oN/DUeutXKjIvTgh4e18k8jsKTMjj7RNyEAJggACCCCAAAIIIIBAGwhQbGiA7h3/mFzFg6LWnskeo33PbdbZE9VR61NtoedbKzRPb5kedt/itPDp9nmbB2qHAAAP/0lEQVSm4xAg+QKjnhilgiHfPAXCf0VATy1+Vi8WrUlqIhVfJuhJElWfyZtnv5uTGpdHFSxIXvEmqZPHzhBAAAEEEEAAAQRSRoBiQyNT7Zv4y7+sPeUarQMv2ve67r8kmqQP5evu1hRP/E8BMO7PsOwaCg1Jmi5LdjP616MVukmac+MCbU3/xJJ9xAo6MLenBh7YFGtzq9f7+9jvJpGFi34eeYRtqwdDBwQQQAABBBBAAAEEbCRAsaGRyTDObPCM+ZmqDg/V4XUfNtIilVeFdNvvl6iH58u4EH46KSjjzAZezhUwzmzIvje3TQZQ4ctJ6H4zOgcSGs9ssMLFjyuj3zCzYeiPAAIIIIAAAggggECbC1BsiDEF3pG3y3PJpBhbU3t16PPP9PD2+1qNMHtUtr5zqb1+uWv1IOgQEbih1/VaOOSupGtM3f9xQvfp9dckNJ6ZYHk3z1f25BlmQtAXAQQQQAABBBBAAAHbCFBsaGIqgrcsCv/n//tNtEjdTWmb3tHKLx9tMUDFgIDmjE7sX6VbvHMaWiKwZPjdmtH7BktiNxZ0SuEgFZw81NimuNe5a/fH3TeRHY0iQ/C2JYkMSSwEEEAAAQQQQAABBNpUgGJDM/yFi1coMGZyM61Sc3Ph+uf1YO3qZgc/vlem7p8YbLYdDZwn8ET5Y7q2+zVJSXxarQWPqjyxW76CtrkkpB4tUD5FxuUTvBBAAAEEEEAAAQQQaE8CFBtaMJvFf/+s/KPGt6Bl6jXpV/mQZrvWxxz4mG9lasW0b55eELMRGxwtsGrCSn27tNzSMRT7CzXhsw2W7MPfu6slcVsSNDDqahU/8ExLmtIGAQQQQAABBBBAAAFHCVBsaOF0dXrkJQWumNDC1qnVbNKbd2usd/sFgx7bM1O/+i6Fhgtg2uGK1RNXaVzpWMtGNj2ru2Wx0zv5LYvdVODAldeo+JEXm2rCNgQQQAABBBBAAAEEHCtAsaEVU1f88AvKGjetFT1So2noVI3u2rhUhe5zN9u7tq9fT06n0JAa3wDJ4/JozaQXNOWiay0Z8tRjeyyJawRti5tEZo2vUPHy5y0bE4ERQAABBBBAAAEEEGhrAYoNrZyBjj9+WjnfvbWVvVKg+a5tenLPsshAZw7roIen5KfAoBliQ4Hnrn5as/rObLja1PLwYG/1/WKbqRhNdXbX7mtqc8K3Gf9+dFz2VMLjEhABBBBAAAEEEEAAATsJUGyIYzYK7npI+Xf+OI6e7buLf2OlnvS+okXfbtsb7rVvZfuP7rHRy7V0xKKEJVrhtvhxqSf2yFeUl7B8mwqUf8cyGf9+8EIAAQQQQAABBBBAoL0LUGyIc4Zzb7xTncLXW3uChXFGaF/dPAUd1enR1Rq7cG77GhijiUtg/uAfyjjLISctO67+9Z1ccmnq3g/rFy376e/dxbLYRmBPdp6KH1ql3JvmWLofgiOAAAIIIIAAAgggYBcBig0mZsIfvpN8l5W/S/kbRxo3ujMc/CPHmdCka3sTMO7f8J/TK1Xe5aq4h1ZRNFjZNcfj7t/SjunF1t0k0j+8XJ1Xvq3A6IktTYd2CCCAAAIIIIAAAgg4XoBig8kp9BaVyLhxZP6cn5iM5Mzu+XPuj9zozltQ7MwBkLWlAt1zuum3k1dr6fD4LquYVlNtaX71wX2ZX9V/TOjP4G1L1GnFy/KVlCU0LsEQQAABBBBAAAEEELC7AMWGBM1Q7g13qOsL78o42yEVXv6R49X1+XeVe8PtqTBcxmhSYP6QH+q/wmc5jOkyusWRygIlGrv7/Ra3N9PQdXqvme4X9M0cfIW6/NNbyrt5/gXbWIEAAggggAACCCCAQCoIUGxI4Cynde8duY9D0X3/IG9x1wRGtk8ob6dSFS39Zfj+DC8p7aLe9kmMTGwvMKCwv16d/C96fMzPVJJV0my+FQFr76MQlcDJffJ1NP8EFU9ugQoWPKKSJ9cq/ZLBUbtgAQEEEEAAAQQQQACBVBKg2GDBbHeYcL3KfvsH5c++V+5ABwv2kPyQxjiCf/sjlb3ykTpM/F7yE2CP7UZgZp8b9fFNv9fiYQsV8MV+0sTUIzuSOmZ/787x78/tVt7MuSp9ZaNypv11/HHoiQACCCCAAAIIIIBAOxGg2GDhROZ+f67KXt+i4K2L5ck1/1dTC1ONGdqTE1Twb+5R2WublPeDeTHbsQGB1gh43V7dPXSetszcqHuGzldeRvSjJ6/M76uehz9tTUjTbdOLM1odw5WWEXnCRLfw8RH8u3BxMTN28aTVwemAAAIIIIAAAggggICDBbwOzt0RqRu/fOTNWhD+q+ddOv7aM6p6fZVq/7TZ9rmn9+ynDpNnKPs7s+Ty8jWx/YQ5NEHj0ZiLhi3QgiFz9czWVVq17UVtOrxZ05SW9BH5Mlp+k0hfaQ9lX2scHzfLnWXu8Z5JHyg7RAABBBBAAAEEEEAgCQL8FpkE5MguPB7lVNwSedd8+I5OvPmyqt9+VXU1Lf8Fx/JUw6eCZ0+6UVnjpytzyJWW744dIFAvYJzpcGu/WZH3+n0bNOgPz0m+TOnrmvomlv90ndrT5D5cvjRljb0ufHxUyH85j3ltEouNCCCAAAIIIIAAAikvQLGhDb4CmUNHy3hr0c9V/d9vqPrdSn31/ts6e/RQ0rPxBIvkHzlOgfBTNAJXTUr6/tkhAg0Fruw8UjLek/5RdX98XXV/ekN12ysVqv6iYdPELld/rrTwIypr9587Dj3BQvlHjA0fI+MVGD1RRsGBFwIIIIAAAggggAACCDQvQLGheSPrWrg9CpRPibyNnZzetlE1H72nU5s/0OmtH+nMF/sSvm9vUefIXfIz+g9X5sCRSu89MOH7ICACiRFwyd3rusjbiFe3/38U2vWO6va+p5Dx2YLiQ+bFJfL1ulwZlw5T5oDLld5nUGKGQhQEEEAAAQQQQAABBFJMgGKDjSY8vc/g8C83xuPy7ohkdfbIQdXu/ES1uz7Vmf2f6esDe3T28EEZ6+u+Ohm+BKNaodrTUigUaW/81dW4R4S7Q074hpQF8hR0lLdjF/m6dFdaaU+l9egjT35HG42YVBBouYC7ZJgUfnv+3CV0fJdCh7YodPiPCh3boVDVHoVOfi5VH1Lo9PHwJRgNLlFyhe+Hm5Yll3EzykChXB1K5MoplSuvh1z5veQqvEQFAY6Pls8ILRFAAAEEEEAAAQQQiC1AsSG2TZtvMQoDmcZ76FVN5xKqk4xfpHghkEICrpyycLGgTPrWxMZHbRThzv65GBc+i0geLoFoHIq1CCCAAAIIIIAAAggkXoBiQ+JNkx+RQkPyzdmj/QVcLsnb+sdZ2n9gZIgAAggggAACCCCAgP0F+HO4/eeIDBFAAAEEEEAAAQQQQAABBBBwlADFBkdNF8kigAACCCCAAAIIIIAAAgggYH8Big32nyMyRAABBBBAAAEEEEAAAQQQQMBRAhQbHDVdJIsAAggggAACCCCAAAIIIICA/QUoNth/jsgQAQQQQAABBBBAAAEEEEAAAUcJUGxw1HSRLAIIIIAAAggggAACCCCAAAL2F6DYYP85IkMEEEAAAQQQQAABBBBAAAEEHCVAscFR00WyCCCAAAIIIIAAAggggAACCNhfgGKD/eeIDBFAAAEEEEAAAQQQQAABBBBwlADFBkdNF8kigAACCCCAAAIIIIAAAgggYH8Big32nyMyRAABBBBAAAEEEEAAAQQQQMBRAhQbHDVdJIsAAggggAACCCCAAAIIIICA/QUoNth/jsgQAQQQQAABBBBAAAEEEEAAAUcJUGxw1HSRLAIIIIAAAggggAACCCCAAAL2F6DYYP85IkMEEEAAAQQQQAABBBBAAAEEHCVAscFR00WyCCCAAAIIIIAAAggggAACCNhfgGKD/eeIDBFAAAEEEEAAAQQQQAABBBBwlADFBkdNF8kigAACCCCAAAIIIIAAAgggYH8Big32nyMyRAABBBBAAAEEEEAAAQQQQMBRAhQbHDVdJIsAAggggAACCCCAAAIIIICA/QUoNth/jsjQhgIbNmzQunXrbJgZKSHQ9gIcH20/B2SAAAIIIIAAAgi0tQDFhraeAfbvSIG1a9eqsrLSkbmTNAJWC3B8WC1MfAQQQAABBBBAwP4CFBvsP0dkiAACCCCAAAIIIIAAAggggICjBCg2OGq6SBYBBBBAAAEEEEAAAQQQQAAB+wt47Z8iGSLQ9gLGNejGqeH1r+rq6vqP/EQg5QU4PlL+KwAAAggggAACCCBwgYDpYoPb7VZdXZ2Mn7wQaK8CR44cUawCQ1Pff46P9vqNYFznC8R7fJwfg88IIIAAAggggAAC7UvAFQq/zAxp9+7dysrKUjAYNBOGvgg4VuDo0aM6efKkSktLLxgDx8cFJKxIMYGmjo8Uo2C4CCCAAAIIIIBAyggcPHhQpk9HKCws1IEDB2T8h9L4Cy8vBFJFwPi+G9974/tvHAeNvTg+GlNhXSoItOT4SAUHxogAAggggAACCKSqgOkzGwy4mpoaHTp0KPLXXQoOqfpVSr1xG5dIGGf1GAWFzMzMmAAcHzFp2NCOBVp6fLRjAoaGAAIIIIAAAgikrIBxZkNCig0pK8jAEUAAAQQQQAABBBBAAAEEEEAgSiAhl1FERWQBAQQQQAABBBBAAAEEEEAAAQRSXsD0PRtSXhAABBBAAAEEEEAAAQQQQAABBBCIEqDYEMXBAgIIIIAAAggggAACCCCAAAIImBWg2GBWkP4IIIAAAggggAACCCCAAAIIIBAlQLEhioMFBBBAAAEEEEAAAQQQQAABBBAwK0Cxwawg/RFAAAEEEEAAAQQQQAABBBBAIEqAYkMUBwsIIIAAAggggAACCCCAAAIIIGBWgGKDWUH6I4AAAggggAACCCCAAAIIIIBAlADFhigOFhBAAAEEEEAAAQQQQAABBBBAwKwAxQazgvRHAAEEEEAAAQQQQAABBBBAAIEoAYoNURwsIIAAAggggAACCCCAAAIIIICAWQGKDWYF6Y8AAggggAACCCCAAAIIIIAAAlECFBuiOFhAAAEEEEAAAQQQQAABBBBAAAGzAhQbzArSHwEEEEAAAQQQQAABBBBAAAEEogQoNkRxsIAAAggggAACCCCAAAIIIIAAAmYFKDaYFaQ/AggggAACCCCAAAIIIIAAAghECVBsiOJgAQEEEEAAAQQQQAABBBBAAAEEzApQbDArSH8EEEAAAQQQQAABBBBAAAEEEIgSoNgQxcECAggggAACCCCAAAIIIIAAAgiYFaDYYFaQ/ggggAACCCCAAAIIIIAAAgggECVAsSGKgwUEEEAAAQQQQAABBBBAAAEEEDArQLHBrCD9EUAAAQQQQAABBBBAAAEEEEAgSoBiQxQHCwgggAACCCCAAAIIIIAAAgggYFaAYoNZQfojgAACCCCAAAIIIIAAAggggECUAMWGKA4WEEAAAQQQQAABBBBAAAEEEEDArMD/A0oecj4FzMWwAAAAAElFTkSuQmCC" + } + }, + "cell_type": "markdown", + "id": "f2cc8751-c22c-457a-afc5-0e6aebd50596", + "metadata": {}, + "source": [ + "![Screenshot 2023-09-22 at 14.14.17.png](attachment:e46e5f0b-3a3f-4774-af82-ac3e1d52dbae.png)" + ] + }, + { + "cell_type": "markdown", + "id": "6fb30e76-afa6-4fdb-a619-2b50710990bb", + "metadata": {}, + "source": [ + "Let's check the number of samples and number of features in the corresponding files under train and test folders:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4fdf7567-8028-458e-899e-df1898645aa5", + "metadata": {}, + "outputs": [], + "source": [ + "# train data\n", + "!wc -l ./brca_metabric_processed/train/*" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c0cef6f9-b22b-4b51-8001-0ba49ff67717", + "metadata": {}, + "outputs": [], + "source": [ + "# test data\n", + "!wc -l ./brca_metabric_processed/test/* " + ] + }, + { + "cell_type": "markdown", + "id": "3c829c1d-2d0a-425a-92e6-639bc1d68aff", + "metadata": {}, + "source": [ + "## Importing Multiomics Data Into Flexynesis \n", + "\n", + "### Procedure\n", + "We use the `flexynesis.DataImporter` class to import multiomics data from the data folders. \n", + "Data importing includes:\n", + "1. Validation of the data folders\n", + "2. Reading data matrices\n", + "3. Data processing, which includes: \n", + " - Cleaning up the data matrices to: \n", + " - remove uninformative features (e.g. features with near-zero-variation)\n", + " - remove samples with too many NA values \n", + " - remove features with too many NA values and impute NA values for the rest with few NA values\n", + "4. Feature selection **only on training data** for each omics layer separately:\n", + " - Features are sorted by Laplacian score\n", + " - Features that make it in the `top_percentile` \n", + " - Highly redundant features are further removed (for a pair of highly correlated features, keep the one with the higher laplacian score). \n", + "5. Harmonize the training data with the test data.\n", + " - Subset the test data features to those that are kept for training data \n", + "6. Normalize the datasets \n", + " - Normalize training data (standard scaling) and apply the same scaling factors to the test data. \n", + "7. (Optional): Log transform the final matrices. \n", + "8. Distinguish numerical and categorical variables in the \"clin.csv\" file. For categorical variables, create a numerical encoding of the labels for training data. Use the same encoders to map the test samples to the same numerical encodings. \n" + ] + }, + { + "cell_type": "markdown", + "id": "879de45d-a7df-449b-8113-5a086fb25809", + "metadata": {}, + "source": [ + "### Usage\n", + "\n", + "- Here, we import both train/test datasets from the data folder we downloaded and unpacked before. \n", + "- We choose which omic layers to import \n", + "- We choose whether we want to concatenate the data matrices (early integration) or not (intermediate integration) before running them through the neural networks. \n", + "- We want to apply feature selection and keep only top 25% of the features. In the end, we want to keep at least 1000 features per omics layer. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6ea8d80c-3c74-403f-a78d-97450459f6f1", + "metadata": {}, + "outputs": [], + "source": [ + "data_importer = flexynesis.DataImporter(\n", + " path=\"./brca_metabric_processed/\", \n", + " data_types=[\"gex\", \"cna\"], \n", + " concatenate=False,\n", + " top_percentile=20,\n", + " min_features=1000,\n", + ")\n", + "train_dataset, test_dataset = data_importer.import_data()" + ] + }, + { + "cell_type": "markdown", + "id": "6fd80fb2-0b4a-4392-a7d6-cf64cbb26d86", + "metadata": {}, + "source": [ + "- **dataset.dat** contains the data matrices" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9fc6cd1c-6f40-4346-82ef-63fe534d3b21", + "metadata": {}, + "outputs": [], + "source": [ + "train_dataset.dat" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1c791757-d11d-41f3-b57d-dc379f39dcc1", + "metadata": {}, + "outputs": [], + "source": [ + "train_dataset.dat['gex'].shape, train_dataset.dat['cna'].shape" + ] + }, + { + "cell_type": "markdown", + "id": "9513251f-a3f7-445e-a312-0a65da4491d4", + "metadata": {}, + "source": [ + "- dataset.ann contains the sample annotation data (from clin.csv), where the keys are variable names and values are tensors. \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8b96afc5-a2e3-4f22-a82b-d8918d3f0eb2", + "metadata": {}, + "outputs": [], + "source": [ + "train_dataset.ann" + ] + }, + { + "cell_type": "markdown", + "id": "0c91c942-d262-44f8-b851-00d7e789fd35", + "metadata": {}, + "source": [ + "- A mapping of the sample labels for categorical variables can be found in dataset.label_mappings" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6f6a1e0a-3aa4-4aee-acc2-42530f09d7cb", + "metadata": {}, + "outputs": [], + "source": [ + "train_dataset.label_mappings" + ] + }, + { + "cell_type": "markdown", + "id": "f502edc6-8024-42f6-a173-3c40103fc647", + "metadata": {}, + "source": [ + "- As the data matrices are stored as tensors, the row and column names cannot be stored as tensors. These are stored in the same dataset object as:`dataset.samples` and `dataset.features`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6551aa8c-65d4-49ca-9708-ce35bad055d7", + "metadata": {}, + "outputs": [], + "source": [ + "train_dataset.samples[1:10], train_dataset.features" + ] + }, + { + "cell_type": "markdown", + "id": "da1e6100-0390-49ff-ba5e-9ac8be66126a", + "metadata": {}, + "source": [ + "- We can get a summary of sample metadata using `print_summary_stats`. For categorical variables, we can the sample counts per label and for numerical variables, we get mean/median statistics. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f0e0d3c6-80dc-44a7-ac80-7ac70d3e37f0", + "metadata": {}, + "outputs": [], + "source": [ + "flexynesis.print_summary_stats(train_dataset)" + ] + }, + { + "cell_type": "markdown", + "id": "611d767b-df4d-44f1-a738-e1b1f2032faa", + "metadata": {}, + "source": [ + "## Training flexynesis models\n", + "\n", + "We create a `tuner` object by specifying: \n", + "1. `dataset`: the training dataset (as we constructed above)\n", + "2. `model_class`: which model architecture to use:\n", + " a) DirectPred: a fully connected network (standard multilayer perceptron) with supervisor heads (one MLP for each target variable)\n", + " b) Supervised Variational Autoencoder: A variational autoencoder (MMD-loss) with supervisor heads (one MLP for each target variable)\n", + " c) MultiTripletNetwork: A network structured in triplets to enable contrastive learning (using triplet loss) and additiona supervisor heads (one MLP for each target variable)\n", + "3. `target_variables`: A comma separated list of target variables (specify the column headers from the clin.csv). \n", + " - One MLP per each target variable will be created. \n", + " - The target variables may contain NA values \n", + "4. `config_name`: which hyperparameter search space configuration to use. \n", + "5. `n_iter`: How many hyperparameter search steps to implement. \n" + ] + }, + { + "cell_type": "markdown", + "id": "6fc34517-f45e-4365-a6ee-5f161bee9c10", + "metadata": {}, + "source": [ + "- This example runs 1 hyperparameter search step using DirectPred architecture and a hyperparameter configuration space defined for \"DirectPred\" with a supervisor head for \"CLAUDIN_SUBTYPE\" variable:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "810c00cd-21d1-45d9-a9c6-78d4d023acc1", + "metadata": {}, + "outputs": [], + "source": [ + "tuner = flexynesis.HyperparameterTuning(dataset = train_dataset, \n", + " model_class = flexynesis.DirectPred, \n", + " target_variables = \"CLAUDIN_SUBTYPE\",\n", + " config_name = \"DirectPred\", \n", + " n_iter=1) " + ] + }, + { + "cell_type": "markdown", + "id": "a0a18601-d42c-4f78-8950-336c20f30828", + "metadata": {}, + "source": [ + "- We use `perform_tuning` function to run the hyperparameter optimisation procedure. At the end of the parameter optimisation, best model will be selected and returned. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a531d24e-99f6-46d7-8e5d-6cd3268677f6", + "metadata": {}, + "outputs": [], + "source": [ + "model, best_params = tuner.perform_tuning()" + ] + }, + { + "cell_type": "markdown", + "id": "0682d719-cb11-46f2-84d0-eb00b8b0824b", + "metadata": {}, + "source": [ + "One can also visualize the training setting `plot_losses` to `True`. This will print the loss values training/validation splits and also the individual loss values for each target variable. \n", + "In this case, the total loss value for the training equals the loss value of the single variable we chose. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d07595c9-463b-47d0-9746-44c22842f2ed", + "metadata": {}, + "outputs": [], + "source": [ + "tuner = flexynesis.HyperparameterTuning(dataset = train_dataset, \n", + " model_class = flexynesis.DirectPred, \n", + " target_variables = \"CLAUDIN_SUBTYPE\",\n", + " config_name = \"DirectPred\", \n", + " n_iter=1, plot_losses=True) \n", + "model, best_params = tuner.perform_tuning()" + ] + }, + { + "cell_type": "markdown", + "id": "4855a65f-a100-4a78-9483-932672b7fe31", + "metadata": {}, + "source": [ + "- One can also provide own parameter optimisation spaces via a `yaml` file as input:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "24db5e23-a71b-4c01-b1d7-340eda94dd05", + "metadata": {}, + "outputs": [], + "source": [ + "tuner = flexynesis.HyperparameterTuning(dataset = train_dataset, \n", + " model_class = flexynesis.DirectPred, \n", + " target_variables = \"CLAUDIN_SUBTYPE\",\n", + " config_name = \"DirectPred\", \n", + " n_iter=1, plot_losses=True, \n", + " config_path='./conf.yaml') \n", + "model, best_params = tuner.perform_tuning()" + ] + }, + { + "cell_type": "markdown", + "id": "eeb6b628-526f-4a84-956d-f11e7e82e7ba", + "metadata": {}, + "source": [ + "- We can also provide multiple target variables as input. This will create multiple MLP heads (one per variable) and the network will be trained to learn to predict both variables. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9f7f4c8b-29dc-41a9-a255-ba31d6fa890d", + "metadata": {}, + "outputs": [], + "source": [ + "tuner = flexynesis.HyperparameterTuning(dataset = train_dataset, \n", + " model_class = flexynesis.DirectPred, \n", + " target_variables = \"CLAUDIN_SUBTYPE,CHEMOTHERAPY\",\n", + " config_name = \"DirectPred\", \n", + " n_iter=1, plot_losses=True,\n", + " config_path='./conf.yaml')\n", + "model, best_params = tuner.perform_tuning()" + ] + }, + { + "cell_type": "markdown", + "id": "d45daee9-e576-47f1-945c-27467465d4cc", + "metadata": {}, + "source": [ + "- We can mix numerical and categorical variables. The relevant network structure and evaluation procedures will be applied depending on the type of variable" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dab8522c-f2a0-49eb-9bd0-1cdc4a5c8557", + "metadata": {}, + "outputs": [], + "source": [ + "tuner = flexynesis.HyperparameterTuning(dataset = train_dataset, \n", + " model_class = flexynesis.DirectPred, \n", + " target_variables = \"CLAUDIN_SUBTYPE,CHEMOTHERAPY,LYMPH_NODES_EXAMINED_POSITIVE\",\n", + " config_name = \"DirectPred\", \n", + " n_iter=1, plot_losses=True,\n", + " config_path='./conf.yaml')\n", + "model, best_params = tuner.perform_tuning()" + ] + }, + { + "cell_type": "markdown", + "id": "170c9294-08d1-4be7-9c96-801504226d5b", + "metadata": {}, + "source": [ + "### Early Stopping\n", + "\n", + "Training a model longer than needed causes the model to overfit, yield worse validation performance, and also it takes a longer time to train the models, considering if we have to run a long hyperparameter optimisation routine, not just for 1 step, but say more than 100 steps. \n", + "\n", + "It is possible to set `early stopping` criteria in flexynesis, which is basically a simple callback that is handled by `Pytorch Lightning`. \n", + "This is regulated using the `early_stop_patience`. When set to e.g. 10, the training will stop if the validation loss has not been improved in the last 10 epochs. " + ] + }, + { + "cell_type": "markdown", + "id": "fc9fe5ed-f85a-413d-8ea7-4fba7f49a332", + "metadata": {}, + "source": [ + "Let's run the model for more HPO steps with early stopping patience set to 10:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0d1ac83a-7ed6-41db-a74c-090a6feadfd5", + "metadata": {}, + "outputs": [], + "source": [ + "tuner = flexynesis.HyperparameterTuning(dataset = train_dataset, \n", + " model_class = flexynesis.DirectPred, \n", + " target_variables = \"CLAUDIN_SUBTYPE\",\n", + " config_name = \"DirectPred\", \n", + " n_iter=20, plot_losses=True,\n", + " config_path='./conf.yaml', early_stop_patience=20)\n", + "model, best_params = tuner.perform_tuning()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b0e71cbe-3f54-4243-92ea-1777d37cfd80", + "metadata": {}, + "outputs": [], + "source": [ + "model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cbb5d492-8295-4360-884f-c74dfc76717e", + "metadata": {}, + "outputs": [], + "source": [ + "best_params" + ] + }, + { + "cell_type": "markdown", + "id": "f1d462ad-304c-43e9-9508-8b14bb5fabf0", + "metadata": {}, + "source": [ + "### Prediction and Model Evaluation\n", + "\n", + "We can use the best model (chosen based on the hyperparameter optimisation procedure) to make predictions on the test dataset" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "97f37b27-ab0f-4dcb-8b91-609f091774b0", + "metadata": {}, + "outputs": [], + "source": [ + "y_pred_dict = model.predict(test_dataset)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a3387255-26dc-47c5-ae22-79971cef3f96", + "metadata": {}, + "outputs": [], + "source": [ + "y_pred_dict" + ] + }, + { + "cell_type": "markdown", + "id": "0eb0dafc-4934-4d9c-97c0-687ab5d8d58a", + "metadata": {}, + "source": [ + "- The predictions are class labels for both variables. Now, we can run `evaluate_wrapper` to evaluate all predictions. \n", + "The wrapper goes through each variable and figures out which type of evaluation to apply to the corresponding variable (whether to report metrics relevant to regression tasks or classification tasks) " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "626579b3-0b52-43b3-9954-11c101ae2d62", + "metadata": {}, + "outputs": [], + "source": [ + "metrics_df = flexynesis.evaluate_wrapper(y_pred_dict, test_dataset)\n", + "metrics_df" + ] + }, + { + "cell_type": "markdown", + "id": "c96a16ad-23da-4f28-aef3-2cc7efddd1e9", + "metadata": {}, + "source": [ + "### Extracting the sample embeddings\n", + "\n", + "All models trained within `flexynesis` comes with a `transform` method, which extracts the sample embeddings that are generated by the encoding networks (whether it is an MLP or a variational autoencoder). The embeddings reflect a merged representation of multiple omic layers. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a79437eb-c9fb-4e3f-9637-b5bee317cc2a", + "metadata": {}, + "outputs": [], + "source": [ + "ds = test_dataset\n", + "E = model.transform(ds)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "37f7e926-f908-4d61-827c-e53bc6624474", + "metadata": {}, + "outputs": [], + "source": [ + "E.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6c004af8-41f8-4c86-84f9-e1303bd3f17f", + "metadata": {}, + "outputs": [], + "source": [ + "best_params" + ] + }, + { + "cell_type": "markdown", + "id": "be57264e-ee33-400a-9beb-4d351b598da4", + "metadata": {}, + "source": [ + "### Visualizing the sample embeddings\n", + "\n", + "Let's visualize the embeddings in a reduced space and color by the target variables. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c00e8272-2baa-487f-b555-0b064f606832", + "metadata": {}, + "outputs": [], + "source": [ + "f = 'CLAUDIN_SUBTYPE'\n", + "labels = [ds.label_mappings[f][x] for x in ds.ann[f].numpy()] #map the sample labels from numeric vector to initial labels. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "aac22aeb-45b9-4fdf-a536-4173743d7992", + "metadata": {}, + "outputs": [], + "source": [ + "flexynesis.plot_dim_reduced(E, labels, color_type = 'categorical', method='pca')" + ] + }, + { + "cell_type": "markdown", + "id": "9c8ed438-db56-4b19-96a0-f0a699c11a48", + "metadata": {}, + "source": [ + "We can also use UMAP visualisation" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "57ed36e5-3854-4be2-9d93-bad6c079b467", + "metadata": {}, + "outputs": [], + "source": [ + "flexynesis.plot_dim_reduced(E, labels, color_type = 'categorical', method='umap')" + ] + }, + { + "cell_type": "markdown", + "id": "403e4942-3f79-4de3-a059-eca305895c0d", + "metadata": {}, + "source": [ + "We can pass further arguments to matplotlib.pyplot.scatter to change the figure parameters " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "57383617-d7c9-4cba-ab4b-8194e8690809", + "metadata": {}, + "outputs": [], + "source": [ + "flexynesis.plot_dim_reduced(E, labels, color_type = 'categorical', method='umap', scatter_kwargs={'s':100},legend_kwargs={'fontsize': 15}, figsize=(15, 12))" + ] + }, + { + "cell_type": "markdown", + "id": "4b5b1596-801e-41f6-acde-40955f9c8198", + "metadata": { + "tags": [] + }, + "source": [ + "### Feature Importance Calculation (Marker Discovery)\n", + "\n", + "Flexynesis makes use of the `IntegratedGradients` method implemented in the `captum` library to compute the feature importance rankings for a given trained model.\n", + "Below, we compute the feature importance for each target variable and class label within the target variable. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "def79dc2-766e-4b78-b14b-7db149a98896", + "metadata": {}, + "outputs": [], + "source": [ + "for var in model.target_variables:\n", + " model.compute_feature_importance(var, steps = 30)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e1217755-6669-42ed-be46-caab46334828", + "metadata": {}, + "outputs": [], + "source": [ + "top_features = flexynesis.get_important_features(model, f, top=10)\n", + "top_features" + ] + }, + { + "cell_type": "markdown", + "id": "7bedc314-1358-4801-91ce-e60ba5c5d131", + "metadata": {}, + "source": [ + "Let's get a subset of the input data for the top features and see if the clusters cluster similarly when we use these features instead of the sample embeddings. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "10839ef3-5b40-47f5-b40e-f322b08ab17f", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np \n", + "tf = top_features.groupby('layer')['name'].apply(list).to_dict()\n", + "tf = {x: np.unique(tf[x]) for x in tf.keys()} # get unique sets per layer" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c7fc63f9-8f93-4769-b3d8-19d340d1a4fb", + "metadata": {}, + "outputs": [], + "source": [ + "df = flexynesis.subset_assays_by_features(ds, tf)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "08589d64-b992-429f-a8d9-9fd7ffeed570", + "metadata": {}, + "outputs": [], + "source": [ + "df" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "57850b40-cbb2-4c0a-be7a-55a9048f092a", + "metadata": {}, + "outputs": [], + "source": [ + "flexynesis.plot_dim_reduced(df, labels, color_type = 'categorical', method='umap', scatter_kwargs={'s':100}, legend_kwargs={'fontsize': 15}, figsize=(20, 12))" + ] + } + ], + "metadata": { + "kernelspec": { + "name": "python3", + "language": "python", + "display_name": "Python 3 (ipykernel)" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.16" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}