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## page was renamed from brainsuiteBDP ## page was renamed from brainsuite |
'''[TUTORIAL UNDER CONSTRUCTION: NOT READY FOR PUBLIC USE]''' ---- |
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'''[TUTORIAL UNDER WRITING: NOT READY FOR PUBLIC USE]''' | In this tutorial, we describe the estimation of realistic conductivity tensors of living brain tissues using the [[http://brainsuite.org/|BrainSuite software]]. These results are used in FEM forward modeling, as described in the tutorials: [[https://neuroimage.usc.edu/brainstorm/Tutorials/Duneuro#DUNEuro_options:_Advanced|FEM with DUNEuro]] and [[https://neuroimage.usc.edu/brainstorm/Tutorials/FemMedianNerve#FEM_tensors|FEM median nerve example]]. |
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Describe brainsuite here : here we will describe the process of the brain tissues anisotrpy estimation and the different functions that brainstorm offers. | The realistic tensors are estimated from the Diffusion-Weighted Images (DWI): Brainstorm calls the BrainSuite software to compute the diffusion tensors on each brain MRI voxel (DTI), then Effective Medium Approach (EMA) is applied to estimate the conductivity tensors for each element of a tetrahedral FEM mesh. This is particularly interesting for the modeling the anisotropy of the white matter. |
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This tutorial explains how to use Brainsuite to estimate the anisotropy of the brain tissues. refer to this page [[https://neuroimage.usc.edu/brainstorm/Tutorials/SegBrainSuite?highlight=(anand)|https://neuroimage.usc.edu/brainstorm/Tutorials/SegBrainSuite?highlight=%28anand%29]] The realistic tensors are estimated from the Diffusion Weighted Images (DWI). For this purpose, Brainstorm calls internally the BrainSuite Diffusion Pipline to compute the diffusion tensors on each brain voxel. Afterwards, the Effective Medium Appeach is applied to convert the diffusion tensors to the conductivity tensors. The following section shows to the users how to do it from the graphical intefrace. Only the NIfTI are supported. All the diffusion data, inclusing the DWI file and direction and the value of the gradient files , respectively the the *.nii, the *.bval and the *.bvec are required. Ideally these files should have the same name and saved in the same folder. |
BrainSuite is also used for other purposes in Brainstorm, particularly the T1 MRI segmentation, as documented in this tutorial: [[Tutorials/SegBrainSuite|MRI segmentation: BrainSuite]]. |
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== Requirement == * You have already followed all the introduction tutorials * You have a working copy of Brainstorm installed on your computer |
== Download and installation == ==== Requirements ==== * You have already followed all the introduction tutorials. * You have a working copy of Brainstorm installed on your computer. * For the DWI data, only the NIfTI files (.nii) are supported. |
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== Brainsuite Installation == 1. Download the latest version of BrainSuite from http://www.brainsuite.org/download. |
==== Install Brainsuite ==== 1. Download the latest version of BrainSuite from http://forums.brainsuite.org/download/. |
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1. Note that you will be using BrainSuite Diffusion Pipeline(BDP), so you need to install a compatible [[http://www.mathworks.com/products/compiler/mcr|MATLAB Compiler Runtime]](last version). 1. Start BrainSuite to check if the installation (It's not required to open BrainSuite to run this tutorial). 1. The BrainSuite installation folder should be informed in the Brainstorm preferences |
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{{https://user-images.githubusercontent.com/6920058/81406567-1c785400-913a-11ea-9048-28c7459af7da.png|image}} | 1. You will be using BrainSuite Diffusion Pipeline (BDP), so you need to install a compatible [[https://www.mathworks.com/products/compiler/matlab-runtime.html|MATLAB Runtime]] (2019b for BrainSuite 21a). |
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== Dataset == In this tutorial we use the Brainsuite dataset example available on the Brainsuite tutorial webpag |
1. In Brainstorm, menu File > Edit preferences > Enter the BrainSuite installation folder:<<BR>><<BR>> {{attachment:brainsuiteInstall.gif}} |
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http://brainsuite.org/tutorials/. The T1w of the subject can be download from this link [[http://brainsuite.org/WebTutorialData/BrainSuiteTutorialSVReg_Sept16.zip|BrainSuiteTutorialSVReg_Sept16.zip]]. The DWI from this link [[http://brainsuite.org/WebTutorialData/DWI_Feb15.zip|DWI_Feb15.zip]] | ==== Download the dataset ==== * Download the files: [[http://brainsuite.org/WebTutorialData/BrainSuiteTutorialSVReg_Sept16.zip|MRI T1w]] and [[http://brainsuite.org/WebTutorialData/DWI_Feb15.zip|MRI DWI]] (from the [[http://brainsuite.org/tutorials/dtiexercise/|BrainSuite diffusion tutorial]]). |
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The first link contains the T1 MRI, with the name '2523412.nii.gz' | * Unzip it outside of any of the Brainstorm folders (program folder or database folder). * Start Brainstorm (Matlab scripts or stand-alone version) * Select the menu File > Create new protocol. Name it "'''TutorialTensors'''" and select: * No, use individual anatomy * No, use one channel file per condition |
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f%2BdiViU5n1OurHqe%2BJerqm43kFXroEXcTx2%2BRs1uuyVcestp11UMv7UMR5rNdiW5veDk20jth1ddbLNXEu5%2BpIBQO0fJEplcbKY/etczyX16aT%2Bup/KoPVUXWLsdJz9aCLrUfT1Le2jroc9lid9csadBFdJdhS169%2BLO9ESb2rR7nLNme/mi7Gbh62rexFl5xd2fbK7av8s5anbEpsIScH1yAAAQhAAAIQgAAEIAABCEBgNQlUgi4SeHn9618/syYpZ1rOkSpOX32Bt/3Fd93hpg7Sc35i7yjgccllr4y%2B6FudyFKm/PpcnIpSlmy1DJlExxyMej0WLFAHp5SrH6lL5ZS6dAVEymFsHZcig5STctJrubotdY7adKKP5tdtTkebN8ZHy7DpRFdxBss5uS7by1/1as8iJoOWo1ttsxQ3TaflS/uk2lDTWhljMtjrpbpKnbK6SR/NJHXJvtU3ZcPKvcl%2BNV3MBpVTn7aduzeVZZttFxuXevqWJyd7V1mtDc1yH2ifIjYjdlEvQ2Qv6VesHDlbjrEozau2p/eo2H1spZ/cC1JmrC49l/qe0Otttsdu23byvRX7vPZ1r8/Kkapn1qCLOH31Bd65X9qrM3fv3r2jgMcrX/nK6Iu%2B1YksZcqKAHGYy59stQzpk2IOZr0ec3hrYELK1Y/UpX9Sl66ASDmM7SPURAYpJ%2BWk13J1a4McMdlj6USf%2Bl9Ox1nqEF1l1Ybl/OpXv9qziMlQl0nbLMXNpp9FxpgMs5QjbSarm%2B68804vkuxbfVM2rNyb7FfTxWxQOfVp230HObrYuEDtWx7fUOxAAAIQgAAEIAABCEAAAhCAwEoQqARdur7bJeVMU0eqTPRTH3Hg5Zx16oyW/JI25TCTIIY4KmPXrYMx5tTWOmLX1EEq9ceuS33qmJQ0UlZMBpFPnOaWgxxL3lh6PWdlzzlWbbqYDDkdbd5cHcpCnMWpoJG2uTppVY/YVrmVpC2V0aaLcbDXS3SV9hKdYvLLOdUh1fbKXa7n7FfTxWxsHratbVnCPqV7/fysNi7lqN3Y%2ByO2H2vTuhwlx11kVXaz3geaX/TL8bfBoZhdlNpyjEdpXm0X0fUdR476oGqsbaQ/E5lj9cm51PdEKn3q/E6udInprefEma2O%2B9goQJ3Rkl7Spv4kiJF6tJV1tMec2lpH7JoGXaT%2B2HWRR53ikkbKiv2JfOI0V71lK8c2gBPLZ2Xf6aCLspAAQypo1MZ5rtyWMeiiuko7iU6pP9Uh1fZqW3I9Z7%2BaLmZj87Bt1a%2BEfUr3%2BvlZbVzKUbsRTrlP6v6qy8IxBCAAAQhAAAIQgAAEIAABCKwWAR906RpwEbVTzjR12OUmnnJNnHUpJ746o3POyZRzzp5XR6c4D%2BuOQa0j5tjUfCJnyvluHZixMqwc9p0eyiWnvy1bZLFl2X2bLuaYzulo86bqsA7gFAeRp6QslVsDFiVtW1quTRfjYK%2BX6JpKozrIVtJIW%2BZsq0nHXPvYumL7JfXX7TKXJ1ZHm3NtbVzKLu0rYm3aRrZ62ray9nEfKHuxGdmvy2SPNW3Mfkps2ZZl90vz1ttF5LAr3HTli/ZldTuzdaa%2BJ2yapv15BVykXpEv9VfqSM2t/FBndFfnsDqZYysStI6Yw1vzSVulnO82MBIrw/Kx7/TQ9s/pb8sWWVJ/Nl3MMZ3T0eZN1WFXMqQ4iGwlZakOGrAoadvScm26GAd7vUTXVBrVQbaSRtoyZ1tNOubax9YV2y%2Bpv26XuTyxOtqca2vjUnZpXxFr0zaykRYCEIAABCAAAQhAAAIQgAAElpPAKOjSR8BF1Es509RhJ446WYVy623b/iO/mv6dV1xe%2BeV0zJnfxRltHXjqvMw5xmMOw1y%2BWPmxMmw62ReHZ93Zq87MWFopUxwhOQetdaLGHNM5jjZvqg5tyxi/usxaV0wOm3ZZgy6qa8zRbeXXfdVD2qhuw8qiyS5K02mddpuz0VS5uTy27Fn329i41GGZ1/sK229IubPKlMrXRlaVs8t90IZ9zrZK7tucziX9iuortp27H1Sn2D2gMqS%2BJ/R603aeARepuyToIg5nWYWyvb3tP0ePHnWXX365fxyVMIg587s4o%2B0wIudkztWRyxcrv%2B7ctml0Xxz/dce0MJJz9b%2BSIIHkselijumcjjav6Bv7U6d4LLBQT691xeSwaZc16KK6NgVKVBfVI2bDyqLJLkrTaZ12m7PRVLm5PLbsWffb2LjUYZnX%2Bwrbb0i5/EEAAhCAAAQgAAEIQAACEIDA8Ais9RVwETQpZ5o67HLOOus4jDky1Wks2yannFyX8iSgc9UfHXC/%2BEsb7hnPOts97gln%2BMc85OqIOcbVmRi7ZuUpTWfziKz2vSDi6JBy6mlKnKOWY4yVcozpYfPW61dZVD%2BRsfQTk0PLk606lHP2oelLZJS0Nl2sfnu9SdeYrag8dptb/aDcY7LYMjRdrH00ncjel21re5aw1/pn2YrMTTYu5Zb0FbPU3yZPiazKrfQekHT1ttcySuzL2lbdXktsOaV/aV5tF9GjXr8t28pZ11fTpb4n9HpuO%2B%2BAi9RdGnQRx3Tszzr8Yw59dRrLtuRPypOAzoEDB9zGxoY7%2B%2Byz3RlnhO%2ByXB0xx7g6p2PXrDyl6WwekdW%2BF0TsRcqxf5ZP/VoqXYyVcozpUVKH6tfmHo7JYWXWYEVJcKNERinbpovVb6%2BneKquMVux8ut%2BbhWQco/Jovllq%2Bli7aPpRPa%2BbFt1LGGv9c%2ByFZmbbFzKtUGXVF8xS/3kgQAEIAABCEAAAhCAAAQgAIHVILB277339iZpypmmDrsmx64638UJInms802d0SlHnqYVJ%2BIvvOjCxoBAzNGpdcQc3uogjV3TumVbms7m0X15tJrIJfrXWZU6R226GKucjjZvyrGq%2BrVxVMXkUJ1lq%2B1e19mm0f0SGSWtTRer315v0rWpzVU263Cul6ncY7JoftlqulidInPftq33Zuyes3L1tZ%2BzcalD5Smxhb5kSpWTk7WP%2B0DLiPVFdZlytlViy/Xy9Lg0b5t2ydmw1Jv6nlCZUttFBFxUvtSXYqkjVZ3vcl9JHvunzugmp7U4dy%2B8sPm7LOZI1zpiDm91TseuWTlL09k8ui/vwhC5RP%2B6E7wkSCDl2HQxVjkdbV7RI/an%2BrX5LovJYcvWdq/rbNPofomMktami9Vvrzfp2tTmKpsNutTLVO4xWTS/bDVdrE6RuW/b1nszds9Zufraz9m41KHylNhCXzJRDgQgAAEIQAACEIAABCAAAQgsD4G1PkVJOdNKHXY5x6I68nJOa5tfJt7ioL7uLTeNnPryzgFxqOUcnVpHzOGt%2BWLXrJOwNJ3NY/eVlcgvZem1UueoTRdjldPR5rV1qwyy7aqfLUv3lz3oUuIUF12s/Uk7qn6yVe6xNomlq9uZLbtP27blNslm5eyyn7JxKVOvLUPQxcpTvx/7uA%2B0jBL7su1Ut62S%2BzbVXqV529yjaut1G1YZUt8Tej21fd/NH3KXXXbZ1OcPrrzKSUAmla/teZEv9VfqSO3qtLb5xfbEQX3TTTeNXlJ/5513jsTToMEyBl1EQGUl8lvHfUmQQPLbdDEHf5NTX5z99bptuyq/WFDApmuzv%2BxBl5itxPSz9iftaP%2BUe6xNYunqfG3Zfdq2LbdJNitnl/2UjUuZeo2gSxfC5IUABCAAAQhAAAIQgAAEILC6BAYVdLFOTPmVeszZZtOII9OmyTkLNV/KkajllKbT9PWtda5KWXrdOkdzzvGmdDkdbV5bt8ogW3WKlziKbb7cfhuHrpWxCwdbTpOupQGAnB7KPSezMNJ0dTtTuxLufdu21ikOsLpDP9dus15L2biUp/ZVynxWGUrzpWRVObvcB7ZN631RXT6tL8alxJbr5elxaV7LoclG1J5Stj5r0EVkPvy%2BP68EXfoOuEgdyxB00YCAOMnlF/WxP5tGHM72Tx3jdYe3pNF8sWu2jNJ0No/dt05wKUv/moIppelyOto6bN1atmzVKV4aiLB5U/uzBl1yQQKrSyydvd6ka2kAIKeHco/JYrlourqdqV3Nw7a1TvkuqweLrGx97adsXMpX%2Bypl3pdMlAMBCEAAAhCAAAQgAAEIQAACy0FgqYIu6liMOX%2BbHHniLGuTJuYs1fx1h7eUrQ7S2DW5rp/SdJq%2BvlXHvTCQsvS6dY7mZLD5Y07PnI62Dlu3yiBbW34qjU1fsq9lxhzK9fxWxi4cbDkpPayjOZXGyidppN1ythVrE1tGqn30fC6/psnVH2Om/FX2VFDHytll39ZX56p9QIktdJGhNG9K1tT50nIlndpLrL%2Brl6NtG2u/Eluul6fHbfKqDDkbLLlnugRdRG4NvMwj4CLl9xF0UYertG3d%2BauO4ZzTuk2aWNBA89cd3jLsUKd37JodlpSms3nsvjruhYENBtggQU4Gmz/GKqejrcPWXSKfTdN2X2UucbRbGbtwsOWkdM0FB2I6atvnbCvWJrasVPvo%2BVx%2BTZOrP8ZM%2BYvN5YI6Vs4u%2B7a%2BOnvtA0psoYsM5IUABCAAAQhAAAIQgAAEIACB5SSwNEEX66zLOY1zDj91CsYck%2BJMs47SXB2x/OogjV1TB6ZsU%2Bne/t4PjF4kLk5Om76%2BrzrEnM5aduyalGMdqOJ0iLHS8mN62PyxvCqrlpGSQ9OJ815enq7Hqa22S1N5mr8PDm11FZ65X/irDpJO5FNZdavMclwlraart0/qvJZv629r21KGMhX5pR3ecP2NUzpoXbqVNFf%2B4Wt9uj5sfFFBlz5k1TZpstvUfWCZ19tbGctWmaRsq9SWbZm6b/PG7FbT1eVI3QuqU8wGtayuQRcp5/3/5ZZeHymmssm2a9DFOrhzTuMSp3PMsSxDCevszdURy68O9dg1O0xJpfvABz4wepG4OPtzf%2Bo4jzmdtezYNSnTBhLE7mOstPyYHjZ/LK/KrWWk5NB0stpIXp7e9Kft0lSeltMHh7a6Cs96IFDlka3qIOlEvvqfMstxlTyart4%2BqfNaj62/rW1LGcpU5Jd2uPHGG7Xo5FbSvPa1r/XX%2B7Bxgi4eJzsQgAAEIAABCEAAAhCAAAR2JYGFB11ueNu73a23bfuPOIt/5xWXjxy9MkmWT8z5pw5O2VoHmd1Xh5%2BU8csvedkoCCHXxbEoDmJxjj75KeuNqxFiDlAtO3YtJkM9nXWcPuNZZ/t3zSiLq/7ogHvSDz15JJvIH9PTOtZFF2GpdYtjV/LL%2Bcc94YxRObEylGNdPi1Hr9vypV5xUmuauhwSWNF35kga2Rf%2BUkZMBi1Ht1qepJd9PZ/aanrhZOWU9KUcJG2JrhIMtO0ietV1Ff1FDpGniWsTD5WpXo7a3zxsWznbOqQesSPRTXirnb7jyNHK/Sp5NH8fNq5laLtqvbGt3Ndad9ut1iN69nU/tr0P6rzFzuw9LfpdctkrfZ9Qtwmrs9qNcpNr0m72vrXpdb9N0EXyaD3CTWTTNtB7Xs7Lx9qF1qXbPoIuWtY8tqVBl3e/%2B91ue3vbf8RZfPnll48cvcphVqe1dRy/7GUvGwUhZIQiDnZxEIszeX19/F3W1jGtZded4fURUCqdOpNFx7PPPtu/a0ZZHDhwwD35yeG7LOact4510UVY6p8EOCS/nD/jjPF3WayMJue9XrflS73iUNe/uhwSWNF35kga2Rf%2BUkZMBi1Ht1qepJf9pj9NLyytnJKvlIOkLdFVgoG2XUSvuq6iv8gh8qTsQ%2Btq4qHp6uWoXUkdfdu28rZ1SD1iR6Kb8FY7PXr0aOV%2BlTz614eNaxnarlpvbCv3NX8QgAAEIAABCEAAAhCAAAQgMCwCCw26yOQ39xGHof3lvHWoqbNPtva83bcOxFg94jBXR2fsl9haR8y5qfli16wMqXTWyRuTzZ6zASNbtuxr%2BTa97gu/IzffMnL8y7kYq5yOUr4NaGi5shX5rSypdDaP7Iu8Nl9sX8sS%2BWU/lqZ%2BrisHKU/rrctc17UeeKmn1%2BNcuyn3WJtY3TRd3c7madu2fnH62yCT6hbbSntZp34fNt6mjCaWVq/6fpt6cu2asqE6r9h9oDYsfZEEs4RnPZ8eS5uIHdb10OOUHHVb1vS6tXYVk1HT6damV9nq26ZyhhB0qetcPxZHq/3lvB02qDM657S2qxfqZcuxOKvVsbyTQUBUM3wAACAASURBVJeYbPacdapbBrKv8tv0ui/8brnllpHjX87FWCnHulNf67EBDS1XtuIMt3%2BpdDaP7FunvM1v97UskV/2S/66cpA6tN66zHVd64GXeno9zrWbco%2B1idVX09XbZ562beuXQJ4NMqlusa20lw3GacAklrZ%2BLsWqTRlNLK1e7EMAAhCAAAQgAAEIQAACEIDAahBYiqCL/KJeHJt2BYE6%2BXSrzugmR6s4Be3KA5kgS/n6uCTr6Kw7MbWOusNbZNB8sWsqY1M6WYUhq3pktU3dwSrn6r%2BUt%2BXafXGMy6/z7eT/F1504WiVh3WKxljldNQ6xIFrHe%2ByL%2Bf0um6Vta6sUXl0hUSuPbUM2arDuE3QRfJ14aD1l%2Bpq67NtJ/vCPsZH65Ctco%2B1SSxdzM6Ut62/D9u29eu%2BsBW9Ym37/J/52cqKDM0j26423iYY0sTSyhXb7yqrlqntEmOVu6e1T9EAsMgjzPU%2Bkq3c59IWWldu28aWtRzbXzQFSzSPbEUm20fofSA62HSx/SEHXeQX9eKEtSsI6kMBdUY3OVrFOW1XHog9SPn6uCR11i866CL6yCoMWdUjq23EYW1tVs7VV4zUGeixOMZltYzNf%2BGFF47Kt875GCvlWHfqa9mylWCEdbzLfiwYoqx1ZY3Koyskcu1Zr094tAm6SP4uHLT%2BUl1tfbbtZF/Yx/hoHbJV7rE2iaWLtY/ytvX3Ydu2ft0XtqJXrG1/9md/trLKSvPItquNE3SxNNmHAAQgAAEIQAACEIAABCCw%2BwgsJOgSc7xxzjU6J2EEI2xguDZQD7rslrZe5aDL7hsioDEEIAABCEAAAhCAAAQgAAEIQAACEIBAWwIEXb4yXKfubnHioic2vIo2QNBlOe02906Xtl%2BwpIcABCAAAQhAAAIQgAAEIAABCEAAAhDYfQQIuhB0YcUNNrCyNiCPCJRHcMnj%2BUo/qfdGLTpwQ9CFoMvuG3KgMQQgAAEIQAACEIAABCAAAQhAAAIQGD4Bgi443FfW4b5oJzn1LZ%2BTXIIu8k4UfQdDybbNu0vm2eYEXZbPnqS9Weky/IEPGkIAAhCAAAQgAAEIQAACEIAABCAAgXkSIOhC0IWgCzaADeyADRB0Iegyzy93yoYABCAAAQhAAAIQgAAEIAABCEAAAhDYGQIEXXbA2TrPX89T9nI6cmkX2qVuAwRdltMmWOmyM4MRaoUABCAAAQhAAAIQgAAEIAABCEAAAkMhQNCFoAurHLABbGAHbICgC0GXoQwk0AMCEIAABCAAAQhAAAIQgAAEIAABCEAgECDosgPO1vov3jleTucr7UK7YAP924CsJFlmrqx0CQME9iAAAQhAAAIQgAAEIAABCEAAAhCAAATaEyDoQtBlqR2gy%2BycRbbldp7TPsvZPgRd2n9RkwMCEIAABCAAAQhAAAIQgAAEIAABCEBgdQgQdCHoQtAFG8AGsIGF2QBBl9UZICApBCAAAQhAAAIQgAAEIAABCEAAAhCAQHsCBF1wti7M2crKg%2BVceUC70C6LtAGCLu2/qMkBAQhAAAIQgAAEIAABCEAAAhCAAAQgsDoECLoQdCHogg1gA9jAwmyAoMvqDBCQFAIQgAAEIAABCEAAAhCAAAQgAAEIQKA9AYIuOFsX5mxd5K/pqYvVG9jActoAQZf2X9TkgAAEIAABCEAAAhCAAAQgAAEIQAACEFgdAgRdCLoQdMEGsAFsYGE2QNBldQYISAoBCEAAAhCAAAQgAAEIQAACEIAABCDQngBBF5ytC3O2svJgOVce0C60yyJtgKBL%2By9qckAAAhCAAAQgAAEIQAACEIAABCAAAQisDgGCLgRdCLpgA9gANrAwGyDosjoDBCSFAAQgAAEIQAACEIAABCAAAQhAAAIQaE%2BAoAvO1oU5Wxf5a3rqYvUGNrCcNkDQpf0XNTkgAAEIQAACEIAABCAAAQhAAAIQgAAEVocAQReCLgRdsAFsABtYmA0QdFmdAQKSQgACEIAABCAAAQhAAAIQgAAEIAABCLQnQNAFZ%2BvCnK2sPFjOlQe0C%2B2ySBsg6NL%2Bi5ocEIAABCAAAQhAAAIQgAAEIAABCEAAAqtDgKALQReCLtgANoANLMwGCLqszgABSSEAAQhAAAIQgAAEIAABCEAAAhCAAATaEyDogrN1Yc7WRf6anrpYvYENLKcNEHRp/0VNDghAAAIQgAAEIAABCEAAAhCAAAQgAIHVIUDQhaALQRdsABvABhZmAwRdVmeAgKQQgAAEIAABCEAAAhCAAAQgAAEIQAAC7QkQdMHZujBnKysPlnPlAe1CuyzSBgi6tP%2BiJgcEIAABCEAAAhCAAAQgAAEIQAACEIDA6hAg6ELQhaALNoANYAMLswGCLqszQEBSCEAAAhCAAAQgAAEIQAACEIAABCAAgfYECLrgbF2Ys3WRv6anLlZvYAPLaQMEXdp/UZMDAhCAAAQgAAEIQAACEIAABCAAAQhAYHUIEHQh6ELQBRvABrCBhdkAQZfVGSAgKQQgAAEIQAACEIAABCAAAQhAAAIQgEB7Ar0HXcShxgcG2AA2gA1gA6tqA%2B2/SskBAQhAAAIQgAAEIAABCEAAAhCAAAQgAIExgbVPfvKTjg8MsAFsABvABrABbAAbwAawAWwAG8AGsAFsABvABrABbAAbwAawAWwAG%2BhmA72udCGSBQEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAR2KwGCLru15dEbAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIACBXgkQdOkVJ4VBAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCCwWwkQdNmtLY/eEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEI9EqAoEuvOCkMAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIACB3UqAoMtubXn0hgAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAoFcCBF16xUlhEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEI7FYCa1/4whdcyccCuu%2B%2B%2B5x%2B7Hn2IQABCEAAAhCAAAQgsEwEPve5zy2TOMgCAQhAAAITAvTPmAIEILAqBOivVqWlkHMnCHB/xKkTdIlz4SwEIAABCEAAAhCAwAAIMAkYQCOiAgQgMEgC9M%2BDbFaUgsAgCdBfDbJZUaonAtwfcZAEXeJcOAsBCEAAAhCAAAQgMAACTAIG0IioAAEIDJIA/fMgmxWlIDBIAvRXg2xWlOqJAPdHHCRBlzgXzkIAAhCAAAQgAAEIDIAAk4ABNCIqQAACgyRA/zzIZkUpCAySAP3VIJsVpXoiwP0RB0nQJc6FsxCAAAQgAAEIQAACAyDAJGAAjYgKEIDAIAnQPw%2ByWVEKAoMkQH81yGZFqZ4IcH/EQRJ0iXPhLAQgAIGVI3DnnXe6n/u5n3Oy5Q8CEIAABMYEmARgCRCAAASWkwD983K2C1JBAALTBOivpplwBgJKgPtDSVS3BF2qPDiCAAQgsJIEJNBy1llnjYIusiXwspLNiNAQgMAcCDAJmANUioQABCDQAwH65x4gUgQEILAQAvRXC8FMJStKgPsj3nAEXeJcOAsBCEBgZQhowOWaa64ZySxbAi8r03wICgEIzJkAk4A5A6Z4CEAAAjMSoH%2BeERzZIACBhROgv1o4cipcIQLcH/HGIugS58JZCEAAAitBoB5wUaEJvCgJthCAwG4nwCRgt1sA%2BkMAAstKgP55WVsGuSAAgToB%2Bqs6EY4hEAhwfwQWdo%2Bgi6XBPgQgAIEVIpAKuKgKBF6UBFsIQGA3E2ASsJtbH90hAIFlJkD/vMytg2wQgIAlQH9labAPgSoB7o8qDz0i6KIk2EIAAhBYIQJNARdVhcCLkmALAQjsVgJMAnZry6M3BCCw7ATon5e9hZAPAhBQAvRXSoItBKYJcH9MM5EzBF3iXDgLAQhAYGkJlAZcVAECL0qCLQQgsBsJMAnYja2OzhCAwCoQoH9ehVZCRghAQAjQX2EHEEgT4P6IsyHoEufCWQhAAAJLS%2BC8885zEkhp8yfpJR9/EIAABHYbASYBu63F0RcCEFgVAvTPq9JSyAkBCNBfYQMQSBPg/oizIegS58JZCEAAAhCAAAQgAIEBEGASMIBGRAUIQGCQBOifB9msKAWBQRKgvxpks6JUTwS4P%2BIgCbrEuXAWAhCAAAQgAAEIQGAABJgEDKARUQECEBgkAfrnQTYrSkFgkATorwbZrCjVEwHujzhIgi5xLpyFAAQgAAEIQAACEBgAASYBA2hEVIAABAZJgP55kM2KUhAYJAH6q0E2K0r1RID7Iw6SoEucC2chAAEIQAACEIAABAZAgEnAABoRFSAAgUESoH8eZLOiFAQGSYD%2BapDNilI9EeD%2BiINc%2BaDLnj173E5%2B4lg5CwEIQAACEIAABCCwDASYBCxDKyADBCAAgWkC9M/TTDgDAQgsJwH6q%2BVsF6RaDgLcH/F2IOjSMWgTx8pZCEAAAhCAAAQgAIFlIMAkYBlaARkgAAEITBOgf55mwhkIQGA5CdBfLWe7INVyEOD%2BiLfDYIIucfXmd1ZX18yvBkqGAAQgAAEIQAACEOhKgElAV4LkhwAEIDAfAvTP8%2BFKqRCAQP8E6K/6Z0qJwyHA/RFvS4IucS6NZwm6NCIiAQQgAAEIQAACENhxAkwCdrwJEAACEIBAlAD9cxQLJyEAgSUkQH%2B1hI2CSEtDgPsj3hQEXeJcGs8SdGlERAIIQAACEIAABCCw4wSYBOx4EyAABCAAgSgB%2BucoFk5CAAJLSID%2BagkbBZGWhgD3R7wpCLrEuTSeJejSiIgEEIAABCAAAQhAYMcJMAnY8SZAAAhAAAJRAvTPUSychAAElpAA/dUSNgoiLQ0B7o94UxB0iXNpPEvQpRERCSAAAQhAAAIQgMCOE2ASsONNsKIC3O1uvPQSt/eCS9xFh%2B6eWYc7jh11V07KkbL2Hjgxc1n9Z%2BxHx/7losTdQoD%2Bebe0NHpCYPUJ0F%2BtfhuiwfwIcH/E2RJ0iXNpPEvQpRERCSAAAQhAAAIQgMCOE2ASsONNsKIC5AMStxy4ehyQyQRR7jg0TjMKtkjAxQRdSvLPH1xex/nXTw27nQD98263APSHwOoQWJb%2BajnGD6vTbki6GALLcn8sRtvyWgi6lLOqpNyxoMuxG8YTttHE7Wp3410VsTIHJ9yVk8nelccyybgEAQhAAAIQgAAEBkSgt0lAZQw2caCrI32yvejSq92VB466G4/NvjJiQOhXXJVcQCKMq/decIO7JabpXUfdRWoXh064OyppCvJX0s/rIKfjvOqkXAgEAp3754J%2BuRL0NH02c%2BLQDuxBAALNBDr3V5Eqxqthr/bjBe2vRuPJY3fXxg5SwLKMHyLKcGpXE5jH/TEEoARdZmzF5Qi6XOL2Xno00hHHlAqdMwPMGB/OQQACEIAABCAwRAK9TQJaO/eudldOOduHSHioOuUDEk2/NPWrXBJj9ab8i6Ga13ExMlDLbibQuX9u3S%2BHgDlz4t1seegOgfYEOvdXtsq7ao8eNQFhDbzY1bE263KMH6xE7EPAuV7vjwEBJegyY2MuTdDlgktc2YCRoMuMTU02CEAAAhCAAARWmEBvkwDv3Lt6tJLljrvudvZzy7ET7sZDN7iL7Ps7ZBKdcLr3gdT/QrLDO0f6kGOYZXQJSIS8y/UOl3pLBTm7vLemXirHECgl0Ef/bPthv39MV5rF%2B2tJxx8EIACBNgT66K9G9ZmVsHsvuNpddOCou0VWtUzGlbccO%2BpunDzCdLnHEG3okXboBHq7PwYGiqDLjA2680GXG9yVB/SXOonHGlR0I%2BhSwcEBBCAAAQhAAAK7gkBvkwAbdGl6vOtdJ6ovT59L4AWH%2BXwNuAvfkHe5HSZBToIu87UmSo8T6K1/rhfvnZptHsddL4RjCEAAAoFAP/1V%2BN4dPZ40N56UseShE0EA9iCwxAT6uT%2BWWMEZRSPoMiO4ZQi63OIHk5e45gkdQZcZm5psEIAABCAAAQisMIHeJgFtgi4jXnZifYnr36kdyu%2B/7BVu8N5E78I35G0eo/cm8AwFBTmxoRnwkaUzgd7657okfp5M0KWOhmMIQGA2Ar30V75vmse4cDa9yAWBPgj0cn/0IciSlUHQZcYGWYqgi3POPy%2B68TFjBF1mbGqyQQACEIAABCCwwgR6mwS0DroItDD%2B6v8xYzjM52uWXfiGvARd5ttKlL7aBHrrn%2BsYvGOToEsdDccQgMBsBHrpr3zfVPqagNlkJRcEFk2gl/tj0UIvoD6CLjNCXpagi3NmUpd9dEWY9KffAXO302eDV17edWn%2BRbA%2B8KP133Vi9AzKi8zLwC46cMLdUWF9t7tFnntu0uyVeo6VPV83JudFl94wesZ6pRoOIAABCEAAAhDY1QR6mwTMFHSxP5BJOf/ajb/8uMuOoWr7qbHePMZPXp5FjgMjY00Zu7YdC6Z43DJ63EcYY8dWgdyij/lVveUu8zaijwCub4MNRPNH7tSUjDd2HDOX6BgRh1MQ6JVAb/1zXSrv2Az3nE3i778LSh7THfoCG0T1fZ8p445j9fd6Td7XkHuEkBGs6/1uimIXAhDomUAv/ZXvm0qeVhNXwPdfdvzhnPPna2PCim/PXFvkWDGuCWeHRKCX%2B2NIQCa6EHSZsVGXJ%2BjinDMdd2xSOFaxIegiz4s0HXC0Y6516orODzjlupFlugwd1Naec16rVwI06T8z6K3l8/Ul5EyXyRUIQAACEIAABIZKoLdJgHeox514SX5mbDQ1Tpth/OXHXalxUHT18/zGT16eRY0D9eWyGf33XnqDGwcVUq2SH4vKmFJ%2BCHTjpeOgyVS7WeeGHXd6G6kHW/Q42I53jtj8FXFD/X6MW9c5mVcK6q5jRRwOIDAHAr31z3XZfL8b7rlKEn%2B94NfmPm21LN/3jYIuzfdr/seFzfn7Xy1ZIcIBBCDQQKCv/sp//8uPRQ6V/ejYiubz18YA/nx9rBA7jvrc6IcsZ/bbEejr/mhX6/KnJugyYxstVdCl8pix6mAwqBeCKtGItg4mJ6tFwqqUuysR89iXgh9wXnr1eOXKpUfNRFfyX%2B38ZPHA0ckEVlbP3B1Wv1ReOJvSwX4JjFfFVOS0K2eiXyKBBnsQgAAEIAABCOwOAr1NArxDPTVOSfEMYzD7K%2BlR6g7jL7vaOTY%2BC9LMd/y0M%2BPAS9xoFbX99fhd9VXU%2BmOfQGK8V%2BNxqLoae/xLcw2StAy6%2BKpMHYkxqXeO1JwmURmPmTGzq%2BkZLd/Uf8H0ivVSHb067EBgTgR665/r8mnfekGqvzb3SPQeDAWm7lXf911wg7tytPKtPj91o6dIhCc7FMgi92vr%2Bz3Iyh4EIDA/Av31V2ZcKAGRxh%2BKVHVK9UnVVNNHPp9ZnRdSmT6RfihgYa%2BYQH/3R3GVK5GQoMuMzbRsQZfmZ4aHjj0edDmReTSX6YAjg9Iw4EwvkQwd/HjyGpfhaHjcWGQCGerJ/HpxZofIjIZANghAAAIQgAAElppAb5OAmccYZhxVH9/IY7KSj4ky%2BSLjr9Kgy7zHT6H8RY0DG36Z7p2tcXmCvCkHqJizYZ/4Jaof2za0zVSgbXK35PIHGWcb84b83XRc6hsb4QZBoLf%2BuU7D9wOZe6CoTw9z6HpwO9xnMr/N1NPwbq9Qzmz3e111jiEAgfkQ6LW/qvzoePJDj1HwpXnlS278kNI89DPxvipcpx9KMeR8nkCv90e%2BqpW6StBlxuZavqBL9TnS9UGhDcpEAx4NHCqdcC1tuBbvwEfJ/cBXIvlHwwqXWlnpL5Aw4M3LHybJ0wxqlXEIAQhAAAIQgMDgCfQ2CShy0MVwhrFJygEfyyXnwhgrtmojlJse88x//BRkXMw4MK1roBhkqnMLPBrLMWPXWNr0mFXkCG2TavN0/iDjbGPekD8md6BU%2BojiSg4OINArgd7657pU/v7N9EsmGJK8V3y/X%2B9LbP/c/Iig0CfV5Qn362z3e11xjiEAgXkRmEd/NXoPVP3xXw0rX9Ljh4Tmvj9M9VX0QwlynG5BYB73R4vqlzYpQZcZm2Ypgy72%2BdJTSwZLO9I4kDBQzA04p6%2BF0kL9yUGtlb8emMkMeEMd4z3/JVT/NWk9IccQgAAEIAABCAyeQG%2BTAD8WqTvNmhCGMVDKAZ8qITf%2Bso795NjKy5wbo41rn3X8lJdRNQsMkrIWjQML2RsHQ8WR6XmUlJOX2fOqj1lHKncIungZZ2wzn7%2B7jtp6bCEwLwK99c91AX0fkL8PutzHZX3fRDAvT22lnr9fZ7zf63pzDAEIzI3A3Pord7ebDr6MHzUYUybfb9VzhLFMcgxKP1SHxvEMBOZ3f8wgzBJlIegyY2Msa9DFrmipdqqhs61MPqf0lw7/hLvl0FF35YEb3JXynpbJS0TH72WZHhD6AWd00qkVlNWfKsufr/8KIHdM0EXhs4UABCAAAQjsOIEvfvGLnWSYNX9vkwA/Kc078aaUNM62dMCh/firJOiyiPGTr2Mh48DpcegU79GJ%2BLjTyzr146RYKSFwEmu3vNMj5K2Ox0M9qfxBxsnjRnJjXb1mxrwhfwmrIGdMxyAte0MnMGv/qlxmzd9b/6yC6Nb3uw39tU9XC4ZIOf5avIx291rok%2By9FsqY7X5XddlCYDcRmLW/UUaz5p9bf6WC1d/ZNtPjTX1hox0/1siMe%2BiHqsxW/WhW%2B1a9Z80///tDJVytLUGXGdtreYMu1ceMhQBLGOiFc1b5u92N9oX3Oomb2k5P4HwnvZDJNgNS22rsQwACEIAABCCQJ9DbJGDWoIvPF3HqyWOoZhx/7cqgS3asae0gjHujDs6icvIBCe/IiJYV8u5Y0CUql2Uk%2B0FOy6meimMIzItAb/1zXcCGgElIHu6B%2Br3aNMf11zPOzFBPQ580NefOzHlNkDWUzx4EIDBvAnPrr%2BqCy/tefJ8wHfTNjz9CYaGPmi4jpKo%2BKnH8Q%2BtM/6Ny0Q9ZhOw75xZ2f6wYbYIuMzbYUgddzARqrx8Eho57OugSrkkne9GBo%2B5GWe1y193ujrvGgEKHvcNBl6IJ5IyNSjYIQAACEIAABAZHoLdJgA%2Be5CevdYB%2BcuzHZJqi2/irxGHux29zHD%2BV1RF0nR6HKg8z8a/J6%2BuYYhjyVvfi9bUrJzhjYwEJ3641WcdyhLx1R67KmcrvZYyWq7nTW5%2B/iFWQM6ZjuhauQKAfAr31z3VxioMu9geLdp4b70NsNe3utXh5vowZ73crD/sQgMB8Ccytv4qJ7cec0%2B9hSY0fKsX4PnA6fyWdfX8g/VAdDcctCCz0/mgh104nJegyYwssd9DFLofWTjY%2B0BP1/WBPJmeTIEsdSyVN7aK/lu2k0/Xb4pJl%2BS8dOxi2OdmHAAQgAAEIQAAC0wR6mwT4sUiLoIvPo%2BOxIJ8f88w4/ioJujhf//zGT16PhYwDC9l7Z0MtvedROx%2BaxeyFsWssIJF3eoRgRtugS%2Bc261FHA4NdCMyFQG/9c126VB9QTzc6Dve6Dwr7%2Byjdd/q%2BryTA6cur9T3%2BfLqeqMichAAEFk5gbv1VVJPQL9XHIPnxhxQW8qbGIJUq6YcqODiYjcBi74/ZZNyJXARdZqS%2B9EGXSjBFBneh4/WDyYnuvtPOLBH0aSKDSj/gnOdk2w%2Bcp50WMzYh2SAAAQhAAAIQ2AUEepsE%2BElpzWmWYmjGLmHlcUjsx1Yzjr%2BKgi5GhvqkPUjSbW8h40DjQCjRw8tUH7e24eHbOz729O0XHf92CLq0kTHWdG3yN%2BgYK55zEOiTQG/9c10ofx%2BU9de%2Bz5j0x3p/5/obn%2BeC2KMjqwJpeXvr/YWXM97PVEvhCAIQ2EkCc%2BuvYkpl%2BoZkfzIpx1%2Bvj4Fi9ci5TF2pLJyHQJ3AQu%2BPeuVLfEzQZcbGWYWgi52M7730anfR5PmLyaBLfRCobEwnHHMa%2BAFnKv%2BonHTQR6uRba6s8OXRMHiWANOhE7ZY9iEAAQhAAAIQ2KUEepsEeAd1wzhk6kWo8fR%2BXJMaPzWMvyrjvKLATVwObxYzjp9yYzdftgma1MehIU3pOLDBwWm4TTtMTTDkggwP82Ol0aN3D91txRzt59vP1JNom1x%2Bfy0no0gRbTNTdy5/gY5TSnMCAj0T6K1/rsvl%2B4HMfW7zVNIfncyb83l93ydz7FQ/LnX47454YKXb/W6VYB8CEJgngV76q2NHR76qOxoEzfUL/lqk3wn9Ur7/qlfvy8yNGyRTdNxRL43j3Uigl/tjgOAIuszYqKsRdKlGrfWlWPXJbuiY5X0uJ1z4Arjb3XHshtGg86JLr3bj/NNLn33%2BSKcf8HYPutgI/N4Lrh5/WdnHod11t7vlwCS4lJjgBnnYgwAEIAABCEBgNxDobRLgHWdXuxuPyXvv7OeEu%2BXYUXfjAR0v6UtI05NeP34avU%2Bv/fhL2q4yST42CQzcJe/mMy3rnYkiU//jJ6/HvMeBEri5VLlOxqz1ceCh8bh1NGZNyePbccJD2tLjCmPfvZfe4OubDt4Y9tF6TOAjMSb1bRfL37XNetLRY2EHAnMi0Fv/XJfP30PpPriaJdyzft6buHc1n%2B/79MXSl94w%2Bm7Q6xIYv%2BWQ%2BU6I3euS2Ms6nz46yMMeBCDQhUAv/ZX5fr5o1Gfou5THY8pbDh31Y4/RWCbSDyXHD6YviY1bsrqbvPMYK2br5uIgCPRyfwyCRFUJgi5VHsVHKxN0qUzIxxPVetCl8ktJHTSa7SgQ4weMOxh0kdapfBmEibcGlHTb%2BkumuOVJCAEIQAACEIDAKhHobRJgJso63shtqz9kiRELTr5YOU3jr1GJiXHR1Fgvka5e7yzjJ%2B94TDkUR4L28OMbLccEXury63ETe/1Rkaaf2l4q493QPjEuSafHSM6QN/U89Xz%2B7mPePnQcqcI/CMyRQG/9c11G3%2BeVqo5AEAAAIABJREFUBl2qK1KkT5jqR2t1%2BL5PHuHj60vMT0d9Sq0Ae9iUfzI3j/VFthj2IQCB%2BRHopb9qMZZMjWXi44cwzpoa0xjfXrgW6Rvph%2BZnPLug5F7ujwFyIugyY6OuUtCl8iKt5ABSfoljfh0oHfOlV7srJ7%2BarAwqa8z8tYVMtqXyiaz1SbfIe%2BiEu8P%2B6rEmK4cQgAAEIAABCOwuAr1NApomyvIo18kvncPKiSbWs4%2B/fMl31X4VKTJEx0LzGT8tfhzo3B3yeA7z6NyxE%2BFqd9GBo%2B6Wu6YfBeZZ2Z27ToxWJunjd0dl6FhylC4ETmKOzrjTQysIeWcOukxkGI3PZx3zdtRRtWELgXkR6K1/rgvonYcRx2I9rT82TsvsvHacwfd9%2Bt6Eyf0WnJqX%2BO8EX0V2Zz59dLZKLkIAAsUEeuuvpK8Q39vUOEb6jGafVmz8EfqjROB3KvCS6hvph4oNgoQVAr3dH5VSV/%2BAoMuMbbhjQZcZ5SUbBCAAAQhAAAIQ2I0EmATsxlZHZwhAYBUILFf/HIIusUBrnWdwck4/CaKelmMIQGD1CSxXf7X6PNFgWAS4P%2BLtOZigiwZBFr2NY%2BUsBCAAAQhAAAIQgMAyEGASsAytgAwQgAAEpgksU/8cgiipX4BX5Q/pCbpUyXAEgWESWKb%2BapiE0WqVCXB/xFuPoMuePa5LoCaOlbMQgAAEIAABCEAAAstAgEnAMrQCMkAAAhCYJrA8/XPzIwHr0hN0qRPhGALDJrA8/dWwOaPdahLg/oi328oHXeJqcRYCEIAABCAAAQhAAALOMQnACiAAAQgsJ4Gl6Z/9O7vKVrkITYIuy2lTSAWBeRFYmv5qXgpSLgQ6EOD%2BiMMj6BLnwlkIQAACEIAABCAAgQEQYBIwgEZEBQhAYJAEdr5/vtvdcewGd5G%2BZPrAiWLOBF2KUZEQAoMgsPP91SAwosRACXB/xBuWoEucC2chAAEIQAACEIAABAZAgEnAABoRFSAAgUES2LH%2B2a9sucTt1YDLpUfdHS0oE3RpAYukEBgAgR3rrwbADhWGT4D7I97GBF3iXDgLAQhAAAIQgAAEIDAAAkwCBtCIqAABCAySwI71z5Wgy9XuykMnWgVcpDEIugzSJFEKAkkCO9ZfJSXiAgSWhwD3R7wtCLrEuXAWAhCAAAQgAAEIQGAABJgEDKARUQECEBgkAfrnQTYrSkFgkATorwbZrCjVEwHujzhIgi5xLpyFAAQgAAEIQAACEBgAASYBA2hEVIAABAZJgP55kM2KUhAYJAH6q0E2K0r1RID7Iw6SoEucC2chAAEIQAACEIAABAZAgEnAABoRFSAAgUESoH8eZLOiFAQGSYD%2BapDNilI9EeD%2BiIMk6BLnwlkIQAACEIAABCAAgQEQYBIwgEZEBQhAYJAE6J8H2awoBYFBEqC/GmSzolRPBLg/4iAJusS5cBYCEIAABCAAAQhAYAAEmAQMoBFRAQIQGCQB%2BudBNitKQWCQBOivBtmsKNUTAe6POEiCLnEunIUABCAAAQhAAAIQGAABJgEDaERUgAAEBkmA/nmQzYpSEBgkAfqrQTYrSvVEgPsjDpKgS5wLZyEAAQhAAAIQgAAEBkCAScAAGhEVIACBQRKgfx5ks6IUBAZJgP5qkM2KUj0R4P6IgyToEufCWQhAAAIQgAAEIACBARBgEjCARkQFCEBgkATonwfZrCgFgUESoL8aZLOiVE8EuD/iIAm6xLlwFgIQgAAEIAABCEBgAASYBAygEVEBAhAYJAH650E2K0pBYJAE6K8G2awo1RMB7o84SIIucS6chQAEIAABCEAAAhAYAAEmAQNoRFSAAAQGSYD%2BeZDNilIQGCQB%2BqtBNitK9USA%2ByMOkqBLnAtnIQABCEAAAhCAAAQGQIBJwAAaERUgAIFBEqB/HmSzohQEBkmA/mqQzYpSPRHg/oiDJOgS58JZCEAAAhCAAAQgAIEBEGASMIBGRAUIQGCQBOifB9msKAWBQRKgvxpks6JUTwS4P%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%2BJEbAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIDAYAgRdujUlQZdu/MgNAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEBgMAYIu3ZqSoEs3fjuS%2B7N33eVO/6lfdGvPPtc9TD4/dq572LnPc//juee775x8/vm557tHm8937/03Tj6P3nuee8ze57m/%2Bpu/2RHZqRQCEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAASWlwBBl25tQ9ClG7%2BF5/7wxz7mHn/h5e60F93sHnTZlttzYMvteceW27P1BfeLW3e7ja1vuo0t5353y7kDk891t97v/tOxL7q3fez/c2%2B89Q53/UfvdS961X90r3/72xcuPxVCAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIDA8hIg6NKtbQi6dOO30NyywkUCLmuXfMCt/V9bo0816PJxt7H1jUrQ5Y23fsW95aNfcDf91y%2B7//fT97sj2/e5m//%2Ba27rHufedOiQ23vxxY2fhSpJZRCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEILBjBAi6dENP0KUbv4Xmvu32293ak5/u1n70HLd21vhzylnnuFOefY475Zy97uHn/Jj79nPOG32%2B85zz3CPOOc898py9o893/di57lE/ca77j//5I6OAiwRdHvjqV93td97pP%2B%2B87U531eFPuovfe6d7yoG/dN//qveOPue89KUL1ZPKIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAYGcIEHTpxp2gSzd%2BC839zW9%2B033l/vs7fZ77a7/m3nnb50aBl098oSr%2BX93r3Js/%2BmX3iq2T7pk3HHOnHthyTzyw5Z5w/vnVhBxBAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIDAIAkQdOnWrARduvGba%2B5/%2Bsd/dJc%2B7xnu/Eevdf4895%2BvuT1ra%2B7md77TycqVN9/y39z9X6%2BKH4IuX3PPvOGT4/fFEHSpQuIIAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgMCACRB06da4BF268Ztbbgm4vO5Xf9x95hVrzh3o/nnNj625vQ8fB11E6H/7mte4y//07RX5Q9DlS%2B6ZN3x8HHT596x0qUDiAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIDAgAkQdOnWuARduvGbW25Z4dJXwOXlT1xzl/3LNffix4Sgiwj%2Bpne9y5178cX%2B8yMvudg98YKXuv/thRe7R/z0he47nvdC9x3PeqE7urU1Nz0pGAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABJaHAEGXbm1B0KUbv7nlPvMha%2B68R/Tz%2Bf0z1tyBPdNBlwe%2B%2BlV3%2B513umf%2Byq%2B4P9/ach/5rze7d7z3Z9wvXLLHXfu2t42uyXX%2BIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAYHcQIOjSrZ0JunTjN7fcz3/8mrv/vu6fc793zb3mifGgiwq/9%2BKL3ZOe8hT3qEc9yj384d/mvuVbHuy%2B9ZRT3Kmnnuruu%2B8%2BTcYWAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEBk6AoEu3Bibo0o3f3HKff8aac27NPXDvmrvvH9p9vnTHmrv35Dj/eaengy4nTpxwD33oQ93a2lrl8%2BAHP9g95CEPcY997GPd/fffPzcdKRgCEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAASWiwBBl27tQdClG7%2B55dagyz3Xr7m/fvGa29oo//zWv1pz1/xWc9Dl3nvvdT/90z/tHvnIR7pHPOIR7rnnPtNdcMH3u6c8%2B1%2B4n/ulX3Kf//zn56YfBUMAAhCAAAQgAAEIQAACEIAABCAAgTYEjh/c5/bt2%2Bf2bR5xJ9tkJC0EIAABCLQiQNClFa6pxARdppAsxwkNurj3rrkv/cGa%2B8Jfr7kvHC/77FkrC7qIpp/97Gfds37%2B592f3nSTO3HbzW5r6xfc297zbHf7Z7eWAwRS7AoChzfWR6ut1jcOT%2BmbuzaVmBMQgAAEIAABCEAAAhCAAATmSOD4wc2R03/z4PE51tKt6FWQMadhTn6CLjlyXIMABCDQHwGCLt1YEnTpxm9uuW3QxR1Yc%2B7r45Urr33tmjv99OnP%2B98/vi6PJGsTdBEF3vO%2B97mfecEL3L/%2B19/vvvd7/yf3mMd8m/ue7/lud/rpp/f%2BeLHtw/vdxvrYwe4fa7a%2B7jYOb7vtBprb%2B2v5ao9FG5e37vZnCupSv9vedof3b7j19erj2NbXN9z%2Bw5lKW%2Bi1nhM%2BVs72YbexvuZK83XSP1Z/L%2BcOuw3flhuuGnbJXeulcgqBAAQgAAEIFBM4eWTsaBv9wlZ%2BZRv9bLojLX9668stdOD59Pv2uc22lTnnfP5IfSePH3EHN2t6bm66g8dPFv6i%2BKQ7efyg29y0fDbd5sEj7nhLLqOGOXncHdycTU9tWK/vjLy0nOi2q3xd80eFCic76T5X2Y67g/7%2BOeiKXdcnj7hNn2%2Bfi5hwUD67Z%2Btvf89mi%2BbiwggE%2By5tw9DuRX2nsbeQPpSxb18L2y2kEnSyfWjZfrgf5itjoSryZVO5X%2BPfmRPdKqtW8vITdCluARJCYIEETrrjR2Ljv%2BPu5Czjv4rkHco%2BGcu7z21uHnRHigemPY9tVbfjBydziabvkkyf6MtIfE/IGP7I8cIxvAoWtgRdAotZ9gi6zEJtAXlSQZcrrqg6/DVwcehQ%2B6DLJz7xCXfqqae6U045xcl7XB70oAdV3u0iZX/lK1/pSdttt78WrFDZ/Xa97nCvVn14I667zz9y3KeCLh3rP7wxxaZa75qTVRrtQy82qFAePBEy24c33PokWNEcdOmof7Upej/KrWbJXetdEAqEAAQgAAEIZAh4R49x%2Bk47kUqdf1pRmEgFh5lei21Deqk7OAJjaWPnQv5qfSfdkUqgJDJ522yaFDaVIcGbmEzxc6PgzYR1ez21zKDvbLy0nOltV/m65p%2BWqH5mdt3nLlvNSVBsF3UnbnHGKpuqY7vtPVsti6MdJGDsoaiPsHZXcfLHdQh2UrWR3CqMeEnlZ0OdkT44991T02eeMhZrY9pn%2Bruypl8L%2Bf13cS1PsVwkhAAE%2BiXQeK%2B3G/9VhOtStu3zE/2nrFjMx4T6HdtWdPPy5cbXtv7qd9GoLF9GrU%2Bd0jeStyJM/ICgS5xL6VmCLqWkFpzOBl3%2B/j%2Bsuds/tuZuP1H2KV3pcscdd7gnPvGJ7iEPe5j71lNOcd/9mO9y3/d93%2Bke%2Ba/%2BV/e4H/5h97nPfa43rUPApLaqZbR6xKxgWd%2BfDFz4MiS4sb2d/MSE9nnXZqx/EnQZBVZMZGV7%2B3AlmNQc/KhKV1%2B9U5J/vFqlGoBqytdZ/6rYHEEAAhCAAAR2JQHv6JEJ2smTyU8rODpZKnQe1R1yRY5GK1CiPq/bvtqqltEvBM3Kl6Sc1UmhrIzRP1k9E1YnNE/6xqttqpPH1npOKu/MS5Uw267ydc1vRMnuzqL7omQL9jZp59LgyZTzpdmepiFVg1H79s1SxnSpnNkJAqbfSfZNQa6q3TW3u09fUHaopete%2Brul/r2jgZWltWFzv45WS2a%2BN9v8En5n2qVru5IfAkMlYL9Tx2NI1VTGFG3Gf5ovbDuWPRnzjgIrYVjqTp48XvmhUXqMab5jJuNjla27bs45HZMnV01W648ubvdlbI5W7tjvieP11eszfJcRdNEWn21L0GU2bnPPpUGX9/3emnvm/zB%2BZJgEU0o/1/zWeOXLeaevudc8cc0d2LPmXvyYNXfzO99Zkf3YsWPu%2B3/yJ90lr3iF%2B9BfvG30TpcDb/kR99f/7ZZKuq4HI6d/JqBigw%2BR13qMqtfAQVOAISZr5/oP7888tsyuIsmv1qnKVl3lIitncrpty6PN/GO4JOiy7o9z%2BaTOzvpXBecIAhCAAAQgsCsJqKMnPTlriyVMpsrKtJPPsbO6LJ/Kla5vpFtmMmYd%2BFH/uJ30mYmt1lx5zEy0AHkSzUEzORf9Nv1xOz211q68tJzxtqt8XfNXpWk6aqf7Tsl28GDpozUm%2Bnon7mZ4hF3CnpKE1FY31b6ane/Jsriw4wS0X25%2B1Nf0PZE3HZM%2Bn3BnGPh7YZYVjwsS2cjYJ0Lf5pnvrAVpSDUQ2PUEwvgw9V06e1/auezjRzKP/A1j4uT3h44XUj/OMH3cvlk6OV9%2BfKWL7%2Bv2ZR6n6stI8TePFc6Vk7Bkgi4JMIWnCboUglp0Mg263PaRNffBQ%2B0/n/lEWdBF9HrTn/2Z%2B7Xf%2BA33vOed6575zO91T3/6I9055/yo27t3r3vggQd6Uf3w/vQKlnEFIQARDyCEwEb8el7M7vXnyw9Bo9TjzabzhyDSfv9Ok7RuQf9RcGb0KLNwLp1vQrcz/2n5OQMBCEAAAhDYXQTC5Gy2AECElp%2BsxSdb9Rw6%2Bdo8csS/D6OVLJn6jh850vB4hTBpnq4zsMlNOsPkOaavKUMemzZ63EM4N11nnc70cWdelSKDLKPHlLWWr2v%2BijCNB%2B10X6xsFTvwNlnoOPbpN93BycvMk86SKKWg6%2BZBDfKlHRXRIji5XAS8wynjlBKJve0cdAcPFqyw8ukbyt0RGsGO9y1z4GFODLV/W2rdd8QuqBQCiydQcj%2BWpIlJXpKvJE2sbDkXxiOxcYDpZzMBlVBGbGybqnly3n9/TecN5TaMj3wZMR0m9Zi%2BuO14mqBLQxs2XCbo0gBopy5r0MW58K6WWfZzK10%2B9alPudNPP909%2BtGPdg972MOi73Xp750uTSRDAGEtutQlXG8KMDTVFL8eyo/XH8/lz/p3vhSudNH0o9U/TQEnqUXkWx%2B/N8Y/3izI3J1JKGsm/T0IdiAAAQhAAAJDJRAmX20nLCkifqKYmcz5vDqpGjnYcgEQn2Nqp1V9U7mD/lOBFTOZy6qSTSflb46DLX6lTKizNfMeeFURdJWva/6qNNmj1rovUDYX2nRsR%2Ba4xHnsbUicCzPcB8pGHuVRKStLlItLTSDYwVTfZOT2DqzNI%2B74EX1k4rSjS7P49MnHvmjKxW%2BDbBkn2%2BLFmq7R32P9Bq78d1lJnzEtFWcgAIHeCJjv8MwAMPRZ6T53WqR5lj2pzY4J6gKU9l%2Bl6erly3Gqfn%2B%2BIeBSKSP3fRC%2BJ9uOpwm6xBqu/BxBl3JWC02pQZdP/99r7q9e0u7z7p9dc6/5t80rXT796U%2BPAi72hfAPetCD3Lc85jHunz360e5v/uZvFqhzcPrHAwghMBGNyXSWtKn%2BfAV%2BpUvmEWqhhLou4Tiue8hZ3esmc19lTeu%2B7Q5vhEefjexrffwunWqd4UhX/axF%2BOWuhRJye9tu/B4c8%2B4geZTb%2BoY7PApgpTn6uiuPdau%2BT8feP/OxzZxuXIMABCAAgcURCBOWzLyyhThaXm6SpMVpWnVchePyyZPmKalP67XbMPmdqtNPDpsm0ypDwSRyVHWmTiva1H6oZ9xW4XhK9qm8bU7MKp/WUZ5f390gK4Dyf0HXbrqXy5aXp3Y15pzw9lNgmz7/OK13vhY5xms61cqqScrhChHwdpB0wtfavtHmQvrYaopcfd656GU56eT%2BDe812Of2bVbfe9AKtbfbfD9aJKO5b04ePxge2Td6AbMEwY8483quVmKOEhtZG7uuWuk5%2BXPXbDHjd1RpgG3ySM7Ng6P3Hth07EMAArMTKLkfS9LEJCjJV5ImVracm%2B6vTUr/PdH32LahDtNv5n5I4EvxcmbGUKbMtn0xQRdPeqYdgi4zYZt/Jg26uPeuuU/9xpo78e/W3InLyj7y3pfSd7rcdNNN7pFPfar7iZ/8SXfo7QdG73S59Oofdu/7yF/MX8lKDSHwEHdcN12vFDbDQZfyQ96SoIl35HtF2%2BUPyqWDBSFN6V6QwYtVmLUSdNkO5dhghO6n%2BHgmvQddDruN9XSQROTaOJzm6OUqCbq0BVfIl2QQgAAEILAsBOrO7G5yZSd6taL9hNLPlIIspUGENvXVqp8chjq9GJMr5WUbR2a9kGilIX2pnlJMH7yi4kydnE2%2BUExhfjNZbnphdn%2B6F8oWlCna8/IZZ69zwbYaHQyexcS54I81IJkRw6et5804KjLFcWl5CPg%2BqPG5%2B9rWwebifUv%2BurdjH1gJLLwscs2sxpLHEtY/8bpDWdN74b6MBYNs%2BiIZR/ehKTMio8h8cNbIi7/nCu5PK7ztxyOMc7qNi2nWqYlfTRwOIQCBFAHv9E/c56YfaN3nzbNsM/aIyVXpy1O6j86b/qZobGsK8/ppYCd89xT3Ub4M/X4z5U/ka%2B4z63nCMUGXwGKWPYIus1BbQB4bdHEH1pz7%2Bnjlyoc/vOauuGL6s70dHkPWJugiqvzegQPuNQcOuFe%2B8nfcxRf/kNu373Husste5q644gr3ta99bQHaOucd92upd6KknfmyYmH/eMnCzLI21x8penvbHTYvt18vcbrrY8XW7GPIgm6poESk9skjx8YBhXb5pkubSf9JMT7v%2BsY4wDFaQeKfgTZaZbLugxbx9vXBjV6DLoHr2tq629h/2AWp3GT1SzUg05ajl7vSntN8OQMBCEAAAkMgYCZCNcfUZutfzoayGudnfjKlEzJhGfLHJorTtEP6xvqmM4/O%2BMlnxKnpJ3MFhfu0EUfadNVhIlumZ%2BpRDUH/4nKmhYmcmUG%2BSinl%2BYtWuvRiKypguWyao3mbbgdvF5VgTKRE77wJzgWft8GmfDq100hZkRo5tQoEfFsmVn9E7g1vDzG78emDnVkMuby%2Br9w86A5uyqoWeZSdf2aik9UXYdVLvHxbl933ZTfdJw1BC1vO%2BP0245U3QUrXSU4vs2kXve38tYadHOPcNWcfYbivrtdJd/yIvstpn2sM8jbIyGUIQGBMIPQp1VV8lf6ubScwgdt72Ser/UBqBbHvZwrk9mlj3yc5I/HfNTLGD%2BOu0fdGLp%2B95suY/j4ZBf7le2g0b7HzCFtAfp%2BgS55P01WCLk2Eduh6KugiARddNWC3hw61D7p89rOfdXv37nVPfepT3SMe8Qj30Ic%2BdPReF1vuQt7psr3fqVM%2B6fQ2aax8lX3/uKiWjWbKTtYvRZp0tt7wmKqmekMQoBqfCeez9U8Vn16hMZU0d8Lo1a7%2BcaE%2B6CKBlUjQZJTK1BF7Z4wPXkTy567l1dLHicUDPRPp3X6zEqaN/kHvXPk5CbkGAQhAAAIrRcA4j%2Bq/VvbHI%2BdagVZ%2BgtQ0AQpO6uqcL5wvCiIU15eQ3egeq89PNqtCRgvzaYsmpmECGqt3uoLApSpKOF9WznTJ8TNt5auX0jW/LS/o2I/ufco2kdPb4bRjIDzXPPFLWVXV26IpI3ZO0%2Bs2liZ2TtOzXTECwV5jjnTf75ibIzjypvvh3DUB48uL9GMhrwRcjjgbyPBQve21cPybPEYNX2R9p1jGSCA9lBX6laQuIfH0npHZf0/WfrQwPm/u50kpOflz1wJ/CXZNizQ6k%2BuLElk4DQEI5AmctAHN2n0ugY3U7ZgvdXy1U9mJfkh%2BMJXsI2w/X9Dh5vqkrH6%2BLzroxgFwCZBMfyeVlaHBleltF/4EXbL0Gy8SdGlEtDMJUkGXf/qnNXf77dOf//7f2wddtre33WmnnVYJ4oze6XLaae7bTjvNffCDH3Tf/OY35wvAOuMjDndbucg7/ozPjvYP73cb6%2Bpcl4CUXUFicyf2W9SfCrqMAjC11R3TtYUAyXTQYQeDLm30n1ZqdCYEH%2BRRXYlEzugfaedcYCV3LVUhNZq4AAAgAElEQVSbcy2YGgbFQZdZ8qSF5QoEIAABCKwIgZMnT7rxZyzwaP/4EXdw0z4zvmmyFJyD%2BQBASDftRAyOsHwZImcopzltpCHsZDXqQAzlT8s5XV67iWkou1n2kHZajja8pmVOnwl1NssXK6Vrfi0zlNOf7qHM2XRT2cI23/ahjaZ1CGU4b4/WSRtkTeX1jlhrw9GyTF3srhQB38ZTDqtgWxXfmW//eqCv2Z5ythzkqJdrcZo6rE3aJJV9k76iRCVR5aBUxqb7O%2Bhj77lKVekDw3gxQZdEW09JGHg26T%2BVlRMQgMA0AXmUol9RMe30l5UbR3IRjukSw5muZef6odpKRFOpO6L6FPS5uf42lBnZ80EXw6zoO8GUFSujFvQa9b8NQSZTYmWXoEsFR%2BsDgi6tkS0mgw26PP9Ra2791DW3/rCyT%2BnjxSSg8prXvMb9sx/4AXf64x/v/uS6K9xf/uX/7l78iie5t77//XMPuGwf3vArXJIrJApxW8d/qeO8c/0SBBoFfcLqo1Td%2BcBBiwBBhUcIZKTqrSSvHXTWf1JeYJ8PePl0iwi6%2BMe4laxCacs/pJ8OoNUgcwgBCEAAAruGQHBMJR5voyT85C/vwMpP4IJjqdFhVFifime3oxcr68QtOQkMzquUw9uWmdfLppT9UHaTnvlyW/Cqi5A9LpcvXkzX/ONS56N7P7J5vb0dpu%2BPcA9l7g1fTi2NdzrUzo8ECO1f8Z2kyvJCs7NSBLwN1IId/nw9IJ6wcW8XtXIMjNw9F%2By4Xp8poOkFztWk4WXPUwGlWkJz2IeMo%2BIKeJhqq7uVvPqjhfi2mjG/miipW7Kt66Wb8iudwnQ6zkAAAg0EzH0uK%2BIqsZWTJ92Rg%2BGHSa1vt3mULT%2BgGv1gKgQ6pseY4fuh/7Gt4Wn7rJquxSuDfBmbo8BW%2BIGY6HncHamtQGrbBgRdTHvNsEvQZQZoi8iiQZcrfnXNyX7bz5uvGq98Oe/0NfeaJ665f/f4U9z1V13lRf/EZz7jzjj//Mrn9Oed70479/nu4T9%2BrnvYs891D3r62W7taXvdp%2B%2B4w%2BfrZ2fbHd4Iq1PkXSj2XRuz1RECEM0BnP7r9wGF0YvZqxqEa6mgRHDitwueBJ3b5uuTv9cvEkyxJHLpckGp3DVbvt33dRWtfGrH0ctTVLaVin0IQAACEBg2ATNBSwYoyhw9zU674ESenihWKXvnVKtZ1kmn7xCRX8c1PZagTR1t0pYGXfrkVaXXdBTavKkd4iV1ze8KnLHltlKVsbtstrzQRrGgyCSlcTgkefo09XLS8vq66/dlsiwrOfurQyBu68n2TwU%2BrAMr4fXy/VjdplJlRiDm5Kok9/Kkg0CV9JODIhmLgjhxrrE6p875e6yd7FJOTv7UNc9UfyxQsm313TilIScgsMsJhP4hF5wI92b9uzuHb55lj%2BsNck33Ub6fKegj2qStaOz790mQ3h%2B3ePSkz5NjG8ZIbR9fRtCl0mKtDwi6tEa2mAwadHEuPDZsln0JuvzKo9cqAZfXvvWt7revuWZKkffffr%2B7dOs%2Bd967ttyeK/6zO%2BVX/9h918Yh94Tzz59KO/uJw%2BOXrY9erL7u9h/uHm5RWcoc7fOqPzjuK6sf/IqL3KO3Fhl06V9/z30Zgy4NMo1tJ7RdU/DK67pWsoJGLZMtBCAAAQjsFgJh8pb6hbNOIDMTIz95mp4ABo5aTnrVwDitpsvUFwqd7NlHRIx/NTeVpHbCTzYjDshqUjPpK5jEFgVdeuVVlbb5KOiTDBJkC%2BmYf666d5StoncoK/%2BIofCr0%2BT7I7wTd9qm4/ef3gOReyVTVkV8DlaGwHRfFGwveo/6eyjY03QZ0%2Brn0ng7bOgPy9IF%2B805NKclzActfN0EXVxbrjHWnIPAbiUQ%2BpLQh8ZZhL4s2hdHMs2z7FBd%2BI6o9wW5fj7kl710GdV0kSP/HRTmDb5e%2BdHTkUTk3xbly2hoAz/myc0vbMHjfYIu00zanCHo0obWAtM%2B/eFr7uJzu3%2Be%2Bm1r7v1vfauX/N9fe627/j3vcV//xjf8Odn5uy87d8OtX3S/%2B6FPuqe%2BccvtefnN7l9uvME9aeNAf0EX8x6M5tUoFfHKDnyAI7GiZM71%2BxUQ3tEfnPmj976MAk3hUWT5cyVO/VB%2BU7BgBHBO%2BvtAhNc73ly5dNPsQhm5ayFVdc/XVbQapZCj4VfEuyoSRxCAAAQgsBsI%2BIlPmDxZtf0EMumMMxO3kl/oVtJMT7aa67PSjX6i7Ta1zKSMtTz2l92NDry2k%2B7AIz7xDNeLnfmqX/bl0dM6xs%2BE%2BuPyxXOFs13yh7zz0T2UP5tuQcvwHhYTVPHtkD4Xjct5p8G0vTsXsa/cPZkty8jP7uoQ8O09sY/GNg42M7a3Mrv3TrFIP1na75ak8/U09q3TTeTz5mQsKrfOaLqu5BnPv52TT8rLyZ%2B6VsI0KSsXIACB1gT8vdjYl4S%2BNfmjilrt8yzbVuXrqfWVvj9p1C30ka3HS/47y84bDKt9BX2nLyM2Lqpo6g5Oxl5t5CToYhm23yfo0p7ZQnLcefvtrq%2BPvLtF/mSFy%2B/9P%2B9xf/WPX3Mf/ydX%2BRy7x7k/vfWL7tW3brnnvGPL7fmjLbfnpQfdqb0FXcxqjl4eJzbdDHlH%2B/zrnw4OBGd%2BPsASC8T0HXSZn/6e%2BxIFXVxTAK5iPobN/tTKq5CmspKpUg4HEIAABCCw2wnkJ2hhEhV1Jo/ghTTdHemhrHR9tsXMpPHgcVfw27qQuXTC5x1wTRNDLTroEJ8ghuvdeWmdbbah/rh8TWV1yR/yzkf3UP5sugXdvUNDHBfyLPXs54h3CtR/dToqscGG/D04cp406NBQVtCAvZUh4Nt04qjyfZN1aFW18fYpHaXPn%2B%2BjfJ6ak05KrtpgtS571JjOy17gdLMFT/aLZGx0Jo6iH27cx%2BSZREQwPNvrkJM/ec0zS7d3VE5OQgACMxHw92JjXxK%2Bj1cl6OJ8f9LQ9xV%2Bb0QB%2BzrqfVYYk%2B/bt%2Bmy43hfRoOcsR%2BmRIWqniToUuXR9oigS1tiO5he3sNy6plnzvz5yd95lbv%2BI593rz/2jVGA5a3Hv%2Bze/RnnfvuYcy8%2B5tyvbP21%2B%2BWtrfHjxQ5suT37bnUP3ri2l5UuPiCxcXhOBE2AI%2BL8X2j9Vsftbbfd%2BAkO/fX9h336ZlBB56aVF/PUfymDLm1WpfgAzZpLcfT8ilbONLccKSAAAQhAYIgEGiaUflJUn1jVWGQd0uqwDpOxzSPHvRO7UlJpfZNMfuKcndlVajAHVp50uKbRyWhKHO8GpknHf1%2B8puouOVEgX7aYjvnnqntH2bzewTaiQRSfLux4W4w5cZqcG/76PnfwyJHJyq2EI8KnTVwPIrG3MgSC3Yq9eVvK9Gu%2BXxJ7K%2Bw3fblzC7q0v2/qTVQkY8GvqHPl1OucOvb32IKCLqa%2B5HfGlJCcgAAEZiUQ%2Bs%2Bm79H2fdo8yw76Vr8zwnnZCzLn%2BhMvZ%2BT7oFpe5Cj3nWP6Mwm8JJ805svIpBmpc3ASQG/XHxN0ibRbi1MEXVrA2umk8kL7J%2Bx7hfvBP/oLt0eCIlePt895x9%2B5ja2vu2u2nDuw5dy1W99w1916v3vLR7/gPnzXN9zWPc596B%2B%2B4V5/69%2B6X9/6mHvV1gPuskOfGQVT5H0tD3/a09zaM3/crT373PHnaWe5f/Gc5/jr3fXWoELJ6o3ZavOO/8iL7J3rWP/2/sZ3z4T6Z9FR5Us7/eNUSoMuWv4sssVrtme97pFgV2k6H9SIlJG7Zsuv7gc2axIoSS1g2VY249VGsaCL14/3uFQRcwQBCEAAAhUCftKVcGKp4yo3casUmD1ongi2q0/La5iwZWQK%2BifKMJPHjP%2BzVkOYDHfjpvoVPh%2B7JkX6sKt8XfOnJQtXZtW9J9m8M6DFJD%2BXx9tRws7sY4n0EWYpgysoK3Bkb1UI%2BL5oc9M/LjFlAiOdjB1sbk4ed5fNkH/0Vaj/SHbFYC6d9t9tX3hs28iXEXEE%2BrrlHolc9%2BWYe3GmPtizbXH/TyrPyV90LeeklDpOHncHjxz3qrIDAQjMQMDc47m%2BxN%2BziTFytOauZZ884o4cT/8QSOoMfWF8TNF0PayObN/HjXT2fWziB1n%2Bunw3NaWJ6zBmG8aCyXKijeAcQZcEmMLTBF0KQS1Lsj8/dsz98G9e4X7wdz/o9vzytW7P5e8dPQ5sY%2BsLPujy5o9%2BeSro8p5P3ueuuPVvnaR7%2BlU3uZdf84depb0XX%2Byedd2H3I%2B945j7xXe81u35zV9077z5Zn%2B9845fSSDO75KVH9uu7iPf3r/h1jf2j/MbgbbFab6%2B7vzju%2BwqE03XtX5dNbG%2BMQq%2BBNlEl2r9Mae9ipHeBsd/u/whsJDN11V/59zhjTHj9QhfH5SIBEyszrl0ucBK/lparvCIMQmorLuNw9autt324Q23Lu/ZWd9wG%2BuJoIu2/VrbgJjVnH0IQAACEBgCgZNHDrrNg0fGj0cyCp0Ux83mpv/1WPTX/H7imJsQmUIbd8PkKeoIa1ufn9SVPP5pstpmSsYg0%2BgXeWaie/K4rjhocPBNldmT47/w14pT1Tee6Cpfef7jB8c2ttngDJ4WObRL1FamM0zOlMuWLMIGQHKO3akCgsxTTpwS2/ZpxEmRued8ukyaKdk4sfQEfLvq%2B4Ka2jfYuj6qr%2Bk28w7EiF17J13kmmWXTOf7Y1mtpasby7a2/CIZNTC5ebDmnDzpjh8x32sNuth6K/umLZqYVvI19B053awTdPRYntFqUFP6yZNO%2BtPR%2B8vaCmWKYRcCEBgT8PfjKIg77kvCY0RljKx98T43PUYO/W9sjNKpbO1/Jv1bCL9If1odu8fqnmgXHnkq44lexrbGcnx/nwioVAJDiTG0L2MsX2A/1vOIzF%2B0r9/X/sdHBF1Me82wS9BlBmg7neUjt93mHvv8fe47nvk89x1nP9898gUvdN/zwpe673/hxe4HXnixe%2BIFL3U/%2BMKL3JMueKl72osvdk9/ycWj/ce/8JdH6a5793vc17/xDa%2BGBF0%2B9Im/cx/4wo1ua%2BsZbt/le%2BYUdIm9uyR%2Brh5E8A77zMvoJSAQAiJePYkYhKBMJr8P3NQd7MbxbtPU9%2BsyGwkadhcVdImzrushxxVdKvpPr5bxbbPooEuDXALdB1ZS7b6%2B4cRq9keDLqFdYoymz02zaWh4LkMAAhCAwAoR8A4yM3FRB51uxSEeJnVBOZ%2B3NwdPcErHJoqt6/MTNjMxzugp%2BsbqrTq7ImW1dtzlJ%2BOBcNNenldT7vT1rvIV5lfHwahNmhzIdWln1b1Qtnp19tjIHbUXm7a27224HjTxZeY4BNmngja2nqKybAb2V4NAsPlR31zQ71Qce3Wbiyjt00fK9rYbuWaLiqeryd7QD%2Bt3z3hbvSeKZJRfTvv7INJnS/2baWeg1Se6b8pu%2B/WXkz93bSSHqbfKqKpj234pqiMnIbDrCYwDmbl7Ta7JD5emxsj%2BXq32XwFpH2VX7/u6nI39gJcxUU5DXx90iez58Xe%2Bn/V9nvTJ9c7Ul5GQz3%2BPbI4C%2BREpsqcIumTxNF4k6NKIaDkT/P3Jk%2B72O%2B%2Bc6fPVr33NK/Xat77VfdtTn%2Br%2Bl%2Bc8xz3mvLPcY87Z477jR/a4084%2B20kdvfx1DXqIENuH3f6Ndbc%2BcY57h/f6%2BmQFTEbSOde/MXoPS6b%2BxkvBuV8JdjTmSwULahl70H8lV7ooBrUdG3hZX3ejdhuliXP0wSSbL7tP0EWRs4UABCAwSAInj7sj8utY%2B4u9kUNqc7ICJqV1cKLV50mpHM3nQ5nTk8Vwrbi%2B4glbmNBN16tSm18R60RvU14CenJ6sq1ZktvgPE/Xl8xsLgQm3coxRY52u8pXnn8VV7p4p3KBE7tO1gbwKm3mnR8pB82kpIlNZ%2B%2BB0rKmhOPEshOwDqqK/aQEt31ggQPNlx9J6%2B0%2Bcs1WH0vnz2nf2WpbvSeKZNTH1Uy%2B36wzcnNq9YuVvnDf32PtH72Tkz93LUgmq3UORr%2Bz5bFifbk6Qn3sQWCXE9BxcqXf0jFywreofW9DfymPAxyNwduULc2h%2BSJj93b9QJ9jW2Mnqr/2xeZSdTeMF8cBLPNoRF9GGKPbvnyfPGpTfhSWaIJqPdNHBF2mmbQ5Q9ClDa0VTPv45z/fnXrmmVHJP/GZz7jfvuYad9/997utk/e7N33obvcHH/6w%2B6UPf9g9480fdt9%2B9nPcfQ884PM%2B7%2BUvH5Vlz/mL7EBgZQnMGvRaWYURHAIQgAAEFklAJ0NNE8q%2BZFp0fX3JTTkQgAAEILAQAiG4k/919UKEoRIIQGDXEtC%2BqCg4vmsp7aziBF268Sfo0o3fUueWx4b94dE73a9sOfd9z32uu%2B322yufM84/38v/8X9y7k9v/aL7/a373Hnv2nJ7/vBDbs%2Bv3%2Boe/ENnjvL8H1de6Q598IPunce/5L71zKf4fOxAYOUJmMeURV5Zs/LqoQAEIAABCOwkgfDLtMVMKBdd306ypW4IQAACEJiFgDo6275QeZa6yAMBCEAgTkDHrNVVevG0nN0pAgRdupEn6NKN31LnlqCLPILsJX/l3MN/9Jnu3P/zVZVPLOiyMQm6nP7yG9wPXvOX7sFPPnOU5/EveBFBl6VubYSblcDhDX3XjbzfhT8IQAACEIBAjwT8o1UW9GviRdfXIyqKggAEIACBxRAg6LIYztQCAQjkCEwe/bqoleA5UbiWJEDQJYmm6AJBlyJMq5nIBl1OferT3JMueGnlEwu6vHISdHncy292T/z1j45Wuki%2Bxzznpwi6rKYZIHWGwLZ530279%2BlkCuUSBCAAAQhAAAIQgAAEIACBJSVA0GVJGwaxILCbCEweh7uYleC7CWy/uhJ06caToEs3fkudW4Iur3vfJ92Lt5x77E/91GjVi6x80c8Hbr3V/f4b3%2Bj%2B7svOHbtn/HixqydBl%2Be9fMv9z894gfvE3/7tKP1vXn21%2B6PDH3Q3/TWPF1vqRkc4T2B7/7pbX99w%2Bw8fdtvb2/68c9tue/uw27%2Bx7tbWJqtc1vc7m8IkZhcCEIAABCAAAQhAAAIQgMBgCBB0GUxToggEIACBuRIg6NINL0GXbvyWOvcDX/2qe%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%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%2BbzJvfGNb3QH5XPwoLvuuoPuDddd597whje4P5bPH/%2Bxe/3r/9hd%2B/rXu2uvvda97tpr3YHXvc4dOPA69%2Bbrr3f/eM89K0YCcSEAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACy0WAoMtytUejNBJw%2Bf/bu7Mdx41zgeP9XHog3fUrnBNvo/Gs8m7H9lh2zm1uG9BjKEAuAgVwgCDH8pIFCY6nt3EdFMX6qlisjYuGkvwfoEcSRX5V9SuKatbXRb569Sr6c//qlZKf%2B1fqXv/oZdXze%2BfxXv3yyy9KJ3G%2B/ctf1O9%2B9z/qxx9/zJbPCggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIBAWIOkSdjnapXqGi0663N3dVT%2B3d3eq%2Brm9U7e3d%2Brm9rb%2BuVM3N7f7n9tbdV09v6ker2/04426v7%2Bvki7//ve/1bfffqu%2B/uZ3JF6OtuepGAIIIIAAAggggAACCCCAAAIIIIAAAggggMCxC5B0OfYe8uq3T7r8MjDpopMwNunyn//8R/3zn/9Uf/7zn6vLkzHjxUPnJQIIIIAAAggggAACCCCAAAIIIIAAAggggAACBQIkXQqQjmkVfQ8XM9Pl97//vRry88c//rGa6fKvf/1L/eMf/6hmufzpT39SL75aqR%2B41NgxdTt1QQABBBBAAAEEEEAAAQQQQAABBBBAAAEEEDgBAZIuJ9BJbhW/%2BcYmXfxLi8UuL6YvLVb9XIcvL/b3v/9d/fTTT%2BqHH35Q3333ndLJmBcvXqgffuAeL649zxFAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQSAmQdEnpHOF733zzTTXTJZRwuZX7uej7uth7uuSSLjrZ8v3331cJl7/97W/qr3/9q/rDH/6gvnzx4ggFqBICCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAscpQNLlOPslWquvv/56n3S5vRt0aTF9WTJzebEnT56o0M/yvfei9eANBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQaAqQdGl6HP2rlZN00ZcTMz83kVkuerme6XJzYy4tpl/fqGt9qbHrG/W9vqTY7vv6Z6e%2B%2B26n/ve7nfrxx5/U8yVJl6PfIaggAggggAACCCCAAAIIIIAAAggggAACCCCAwNEIkHQ5mq4oq8hqtZKZLvuEi76UmPtjLytmEi7u/VxeVvd1sUkX/dr%2BXKuXL6/Vzy%2Bv1d3dvXq%2BXJZVirUQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEBAkXQ5sZ3gq69W6v7VqyrRoi8Rdogfk3R59nz6pMvVfKYuLi7UbH51Yj1FdRFAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQR%2BbQIkXU6sx1989ZUkXewMlzt1c%2BvMcNGXE5PLit3uLyV2U19WzLm0mJ3home72FkuJuny9Nnz8XSu5lXy5OJippbb0rBXan5xUW83V6RdSt1YDwEEEEAAAQQOJbDbrNVqsVCXl5f2Z7FQq81O7XoWulvX8VabdoTdTm1WC7VYeOWtNwcpb3j7dmq3WTXre7lQi9VabfoA7TZqtbhUi3V%2BY3F0%2B6b1fKEKQrX7IbakQ/10iOG%2BsYqMEXvkvjNV3a3VotUPzf15v38k%2BtiLEfqomOLSjxu1krqMvC%2BkC%2BbdQwhsVvY4LP3q7FuJZf33oUM0pEfMgrYv9HfTaq3WvQ6%2BPerEJggggMCvQaDj735REjmOr1TgDMDZzP3dxVtXYkS%2B%2B/T3wIBzBqcSPO0hQNKlB9qUm7x48aJOutSJlkiyZX8fFzfh0ky6NBMusaTLs/Ga2ivpohQzXcbrAiIhgAACCCCAwBCBnVq7iY/QYN7COxEqKs6eSPmDgFXyIlSOLFv1SGTEyhujfbkYOjlVhFKt5La/JOmyWUVOOMVLvz/eQHu3%2BuVsLtVlr/1HU40ROxejW981etlLmDQSlo2%2BuVSLVSSZ6MfosiM5lWkm5sbbF5wiePo6BXKDTd7%2B5e57PXeh19m6dFmd287AWxqUdxFAAIG8QLff/TLx5DieOn9wfz8L/N4iMXK/Awe2zVSPt4cLkHQZbvhaI3ypky73r9TNze1BLi2mL1dmZro8eTp90uW14v7KC9teLdV8NlOz8qlIncQOHb9TZVgZAQQQQACBjgJ2QN%2Bb1aJnopiZKnqAb7HuNgPFnCz525nlVcxmcsU94bu8TJ2oBRpp4nrlDW9f86RQz/wx//TsDjvTIX/St58N0jx57JR00QP3u130x9Sr72Of%2Bg33jdd2eOzx%2Bi5YS0mYLKq/uG/2zUZt1itn/7hUl6HRcIlh9ov8ftSui0047gff%2B8RoR2XJtALN/an%2B3MsxJ7TP7deZstZmxlvJcS1aT3Ms14lkPdPSO%2BZtNhu11p8t/48FvGN/ND5vnITAKPvSSbSUSiIwrUCf3/2yNZbjeOx3%2BebvZ8GZ2hKj/V2w8Wfnc/zPdsnYK5B0GVv0wPG%2B/PJLSbroxIu5xJie2WJ%2B9PLr6xt5rWe1XEcvK9ae5WKSLo%2BfPh2vNT1nuoxXASKlBbZqOdtfyu0wSZdDx0%2B3jncRQAABBBAYKlANbCdOVty/oA%2BNGYfLtydTzcE3Z3C44C//m9uGS9ovjZWn1OD2uSd9Nt9iK%2BMOmkeAdv7gu74sWf2X6iVtNMmHknVtxcqfDanfYN9ENQfHHqHvEtVTSvo%2BleSw%2B6ZOiLR2ESeGDCK3VkrWQu/k%2B0tRLcx%2BlapPJhZvH7eAs78EB6kmrb3d1zJYGDYAACAASURBVAcdq8z%2BXDJ7r74Ujsz0SXyXTUpD4R0FRtqXOpbK6gj8mgSG/O6XdZLjeDjpYn6vDf5eZIJLjPjvNP3OUUwBPA4RIOkyRG%2BCbb/44ku5p4tJspjHKgljkit1EqZPwkWSLk9IukzQxRMVeeikyKHjT8RGsQgggAACvxqBzTo3g8UmSooH0mRg0DvZKjiB0vByElU6gBYrTyk1rH124Cc4S6HeS6S%2Bwdk5ToxLc5kpuyxv2mXdPrutja9PfveXwbLLcvUb5puu77DYtg39%2By5dv7KkS7VDS5KtVRfZd/U9Ksw9lbzPTbIatp2LlZlZEx%2BgSIbizeMXcPYXki6mu%2BxnoDqGHR%2BMqSiPxQK2T3PfQcUhWREBBBwB%2Bxnr87ufEyj8VH7fb/8%2BY39nztzXUGIkfqeR78RMrHAtWTpAgKTLALwpNv38iy%2BqmS460aIvBXaIH5N0efT4yXhNZKbLeJYHiXTopMih4x8EhaAIIIAAAgh0EHBOzAr/Al/%2Bgs1bX060cskUOYlKnGg5LYiV56ySeJpon9QjMEPBjZhcT8df7JMZMlPGlpkfUOqyrlup0udD65crx9a/lXDIbZp9PxE72SdO4NL1nE3kqWyb20%2Bdevr7fiNGjwSnDEroS/WZy93l6iMt4MmpCRx1H9v9PH9cS8DLPt1lP7afnc6XwkxUhbemEhhpX5qq%2BpSLwNEL6M/YkN9NMw2U47iXdJHlBUkSWTf1XWCP/YO%2BdzLN4e22AEmXtslRL/n888/V/f39/vJh%2BrJhzmXEzIwXmd1iZr1c60uIhX6u1cuX19U9XHSixf25u7tT7z5%2BPJ5Fz6TL1Xx/yauL2VJtU7XZXqnlfKZm9SWyLi7qS2XN5mp5Fd/S3GfErK8fZ7ltljNVrS912qorXXZd5v69mZonyt03ZauulnM1m9XxpM4zNV9eJdvbp94hvq1pi1t37/n8Sim1Xdr2VQtC0ZRS0s8XSq9WHD8SjsUIIIAAAgicjkDXwQ9zAtQ%2BSZLkiD/w3MIwMTLJjmo7s267vFbY4IJE%2B%2BSEzztpbMUxdSg4iay2TZSZiO3lsFprjregS/1ypaZjberZHXqGTfd/idgH6zunlsUD4LaerQFhL4Z8RoKzppyyq6c2bjXY4MXy1%2Bb1GQh07GNzXwz9l8zmZ7FYVfdKaWlI7Mj9h8wG8tnaH58lme6UYcoyj50%2B3hK/2zHd1iO93W63UeuVuRRfgYtpt/9o4nj3lmn7ep9TP468tuuFEtTSPvP9acp33PczFSWgnmbXvrfUYn//Nnet2PNO%2B08dpFVPXQffO1IH2dZpk9mHzGOnfSnWMJYjgEBAwB6DBicw5Dju/P5c%2Bh1jaiYxEsd0JybHBgP3eh5Jurwe59FK%2BW2VdHkl92sJJlp0smVAwkUnX2510uXRKSRd9gkPN2nSeh5MEtiZF631TdJBkirN7pNEgn5/e6XmZv3AY/T%2BKG4SI7DdxcVc6VxH%2B1//erdjdUuKSLsv9gmVdjzHojZ3t4k5B7unHZwlCCCAAAIIHLmATSiUnNDIoIkZGHJaJwPKgfec1fRFwdSqHnTJnfilymvGjL2yZfntK49tT1RDg2Xtku36ufa5Fn792nHHWtKlfrky47728lx64DNxUh0tIh77cH3nVEZO9nN1t56t/cOPIa8LEo6ybl2%2B/9qpKk/PRKC4j519LjaAHTgOy%2BcmdP%2BhitB%2B5sy%2B7G5jBsb9x07HrpKBtlB3ik0s%2Bb2rki1%2B3VqvF3rWWKgAs2yfRGht5zo3Gmz7In28t%2BsZW1OifhRn3W9OW9v1MIOcG7XyEkLuuulEt1MXt13u88D%2B066ns7%2B429bPfQ9pY2BdU/cGrQvEcwQQGChgP/f%2BZ7NzYDmOO8cj87mOHDtaZUiM2O9Y%2BlhcJ85LY7YKYUFfAZIufeUm2u6z3/52P9Pl5uYglxbTlyv7%2BeeXVdLl4aNH47VSZkDM1DI%2B8aRVXm6mi7yvZ6jMl6oxuWS7VduruZot/fSFm7jYz0ixVapnn5hESCAjIImE2VzN9cya2VxdbZ0IV86skItQe53kRLWt22xd56Wa6%2BXu4ur5sHq3wjUW2NjRRJGy64RmHolLMGFkt43Hb1SIFwgggAACCJyUgB0EiZ30uM2xAyyhgZHiWHKiFRs8M2WmyzNrpR5TdZKTuVBjvKCybtGJX5cTW9tGM%2BhkHtt/Ue1VqvfLLvVLF5Ly1VsOmemSii39MXrfOe2Vgc/MZ0PWC%2BzP8p6NIXXP7EuynmljIJZTW56eg0BRH9vPr05mrjY7ZXMI3swHs%2B%2BIjbNtYP%2BznzkzkCYbVrMq1vUg/6ABOzn%2B28%2BEW0r8uXOsTLXL3LvKoii181wSM83kc1fFWTcTNLud2m1WarF2Z%2B5Z07SLXS%2BddKln6Szcsr1E0Gqt9n2xUKu10/87NxET83Xq0Xn/cZNDq33Sp0piWWw9e2ZhBl%2BjyXZbh7RZfG/gHQQQ6Cow4udOjuP6u8LGvdTHg9JqSYz2sUrPWLRJ5Q4xS8tmvawASZcs0XGt8NlnNulSzWbxZrVUlxsLXkrMXF4sfkmx6vJiP79U/1cnXRbvHnfSxQ7y65kXNumR6zG7nU6WRNZOJIns9jrhErnsmTuTxU/cJGJHalMttuX2q3cqtnISKsmkiNOuxnrOcr%2B5%2B3JJuqT9eRcBBBBA4KQFZIAvMFgcapicIEVOgJx4rcssSTzn5EwPaqVuipwrT2JGnjj1CZUjg2utAbx2PFk3MFDZXtu2MVRuY32njibZ0nrM/mV2I2LBiw71S0Vz6p5tZypO6L1MbOmP0fvOqYzUoT0gYNeylpehgdxQjNAyG3D/LLROaJm/Ha9PW6CgjxuJETvW3Wy3HDsD%2B66U4R1/neXhj5Xd1wd93lN1a7bCe2XL95MW1iQzg8xpox9DF9aME8P1quUMOKZd4vX3yw7VTa8jx706qRHsp%2BI2Jmb8JPrINYp%2Bz2fqoC%2BLNkoCz%2B8KXiOAQEJgxM%2BdHCNWamVmo4R%2BB0rURkkMexlI//ff9iUVUwF5b0wBki5jar6GWJ9%2B9ln7ni7m3i7JZItOumQSLi%2Bvq4SLJF0evjtei3omGmQmSyu54cwWCY/yR%2Bput0tvFk8S2ORH7DJbumi7fSsx08tieL0jIPViW99GMiWwkW2/mY1jt72Iotp1cvEDRbIIAQQQQACB4xVwB0VGTCQ0B2T29xbY6b8ONn8h7F0OJT5INfDkMNs%2BGz82wOV2ngx2jWhl4otPPca3t1qr1WIh92kIDuibAJ0fbdvj/pmgWd/M9qm3s7Ft/cfvO6diUo/AwLWq/%2BLd2Z%2BDlsEY%2BfrL58jd34KxnPry9PQFsn1sZ3sEB9xFwO5jof1S9i8ZJLPrxz9Tdp1QTCk690QG2kKfq9TGtvxmHa1JSb3abTdl2jjN%2BOb92KOtV7p8u14ovq1XwkX2j0sVTXi4yRn3%2BFFV37ax7/5j65lKcDltbdVBV8S%2BnzaLmbMcAQS6C4z4uZPjuJMwCX7WE7UMxZBZcm7cRII4EZ63hgmQdBnm99q3/uTTTyXpoi8Fdogfk3R58PDheO3rlWjQ92W/8G5aX1epZzx7o3eTMIg3Ucr2EgntpEM4hqznJ4ycWSE6IROdbeOGlfb2r7cbrv28S1LErquTLPoSbvv7taTqZrch6dLWZwkCCCCAwGkKVJdHMSc2pSdJMtiTGBCqOLzLoJhynEd9OZzsX7kWl9fug7L22TqEBsD8qIdMuvhlua/dAa7xBqds2/vELPN1W1H%2BvCy2rf/4fefUVfZB5%2BTf2Y/dv8iMOkoM73Mjgw3e8qr4yMBoLJZTZZ6euECuj2W/icw2dJovx6zg6HrzM6Q/d/v9ORXXbhPd353yo0%2BlrNC%2BH92qcS%2Bwxue%2Bazwx9pIGXeNIVUtd7HqN%2Btdx7LE%2B1Qf22JDqA%2Bl7//td2pgqY18hieHtP2X1dGYN%2BXWowluLVDuEmCcIIDCCwIifO/dY4hxTU8ngVgMkxkKt9WUyzR9oVX%2BktVHr9cq5VKF3vG4FY8HYAiRdxhY9cLxPPvlU3d3fq5fZWS31zJZ6dsvLl9equnxY7LG%2BrJhOuEjSZXG8SRdJaATvHxLvBLtdncwx925JPcaSLn4yxStWygqsJ%2B/V5c68%2B8J4oZS/fuym9I3lXr39mM3XHZMibuKobkO6uI7xm5XjFQIIIIAAAkcm0EyIdJm2HxuAiTVQX9e9OVPjUul7lOxvYGwHjrzxHAnXtbz9ht3a16WMLuuO%2B1e89iS508msSIae2JjdBry6%2BYZKji/rFrtLf3RZt1E/dyAhlmxZufddaGy9fyEx/AHmeB/IoKY/WBmNFSiXRacpkOlj2Tci%2B6ObCJTnsYOslGWTirFV6505nywvUXcH2kqv4KXjOvV1j1vWJJ9I2Fcv/P3TPY5pbPyzbNbYP9r1kkkX/3PfCBKue2MV9zJpXizbRtvnsp/E9ilvp5AYXuzSOvgWbl/6MXiNAAJjCthj0ODPnRzH6%2BOuvL5UoeNbsBWyjf/7kbu2rfO4M77dMngeEiDpElI54mUff/JJQdLlWi4llk226CSMl3AxSZd3HizGk5DZEKEby8eLkdkmXuJCkhDe8nik/TuyXSrJ4r/nZRMkRqbs3Hrbq6Waz/zkzyx4fxqJ5dct9dqrd9qme1JE%2BkbXIWPhXm6NmS7pnuBdBBBAAIFjF3BvSrn/q7LyGpuBntSJUXk0O3gWi9envO7tk8H4zOCRm0QpO5m0J4mDT2zdATS5FFAH6%2BCqferX3TdYdHBh99iH6zungjLIG/grTGe15FM3hjfALIOXjX41%2B753vw1dSCJWsg68eToCmT62%2B0z/QXMXQz5HesC9w3Fw0HGtaKDNrWX9XLZr/sWzmGTrb2KGP2Pd45h4pcdTu17oe6SsfFt3LxdiKlM9xmLJ8liCJbTcK0hiZLzT61mLQftSo9W8QACBtMCInzs5Httkt/t9UvS5lhixc4G6NfK92Dz2p9vKu0MFSLoMFXzN23/0sU26HOLSYjqmSbq8fQpJl74zXbJJgnjHSgIkE6N0PbW9Usv5TM3cBIoXuzhWvNqZdzomXVozXXLJtI7xM7XlbQQQQAABBCYRcE5Y8gNr7RqmB0/a62eXyImWPVlzt%2BlcXs/2STmNQW%2B3Jua5HegqOpEc%2B3r1GS9Ty/LHjifePX2L6tMz9uH6zqm11C0zIOBs0nqajBHYr1J9nYzVKpkFpyiQ6WPZ7zMD3kVNl7JMAie3n3c8bsQqIft4rrxmADug1/zeEJPscdzEs587N5/QPY6JV%2Bpi15s86TJg/xGnTIz0etai7DvVWPOIAAL9BUb83Mlx3D0e2/h6Bp17fA3WWWLkvgvsMZvjRVDyIAtJuhyE9XBBP/r443qmS3M2i57RUjSrxVxeLDC7xVxaTJIu7zwYryEjz3Sx92bJDfZ7TZB6pO4/4m3jvSxNgJSuZ8Nv1dV8Vt8f5UI1JqqMUG9bTuhZl6SIXVff00VmvHiJomYpdhtmujRleIUAAgggcCoCzsnKaqO8P7YvaIQ9icqeQBVE06vI4FkwYNfyBrSv9IRPBidzJ4YGwLZhjBNEGbwqHlQ09Yg9dqnfAN9Y8bJ8QOyD9Z1UbpyZJZl9R/q2GsDM9EsmllNznp6qQK6PZb93B7r6NNbuazoBIMfk5EC63WbQcU3aUHo8rb406nvOBGaAdY0XM%2B4aR9itSyiZIqs5yfjQes1jgd2q%2BcweM4Nfn/XK0VjSxv77TzR2s6IqvZ41G7QveWXyEgEEUgIjfu6ixxJ7jLq8XKQTLxIj911gY3K8SPXvuO%2BRdBnX8%2BDRPvzoI3V3d5%2B%2BP4tJrMQeMwkXk3R565iTLs5Mi06D%2BH23c3q2NJlSup4TWrmX4dIJDfk3Qr0lVvBJeVJE2mVmGRXVrTx%2BsHosRAABBBBAYGIBGUxLjdCk6ignRf0HaRrhYwNeZqWO5Q1rX9mJXHrwyFTcfRzxxNYdqEsOiLrl556X12%2BYb7oew2Ifqu%2BcOuf2VWfV6NNcDHn/Uq3W6/qmsZEBCFk38n60ErxxMgK5Ppb3A8mHDo2UY5pJ5BbFLT9uJKsix/jC/dipW/ia/mXHAlOnVtvtG3LT5m4De9YlOZPUbUfg%2B1jqlTzO27YGQpiWxBMeTh26tVFCx2PbVapn6fZYs7718IrjJQIIZAVG/NzJcTxwbuAcZ3TiZR37ay%2BJkVhHt0nWK5g9kzVghVIBki6lUkey3ocf2qTLoS8v9ubb74zXapmp0W1mSmoWhbx30TNmbrvtlZovncRHrSFJh%2BTMDqVK12si2%2BREI%2BmilJ1R0rPezXL8V/FyG2s6CZZmTsjM0In1RWH8RmG8QAABBBBA4FgEzABN5oQmUV0zMD7KwMjO1Cd%2Bo81u5Zl4/dsnA0OxE0Pn5DE1yNUkHO/E1tZvzJPN0voN9226uK%2BGx7Y2kf7v1XdOHWX7SHxn1ejTghhmn5cbWsd2tIJY0XrwxmkIFPSx3V8y%2B6U%2B3q437XZLGc1jSvbz5CaAY/tou7T2EhlAy9Rf7dRmvZJESGrwzpo029Qq3Gl76DvNxsnVrRm5k52%2Bb0rAT2IcMunizjSNfeeZpkX2n7J66omCi/3spGB77HdQyMJUgUcEEBhTwH7uQse/TiXJcTyQdNGB5H19%2BcrcOqnjrfldMRGnU8VZuVSApEup1JGs98GHH3af6VIws8W9tJiZ6fLmW2%2BP12o36XK1Vdtt4scpVRIrwQTHlZrLfVBmaja/Ututs/F2q66WczXzEydO4uDiYlYlVlrbmXusuJmFOnRpMiW2nl4%2Bmy/VlTZwqqsrby8vFkheDKy3ia2dQv/EWid1ruqabZfK8jmJk1Z/pN7bl5aPH6oVyxBAAAEEEDgCATnpWanNbqd2JT9utWWAKnVC5G6gB1pW1UCfLkv%2B7bzBs%2BAgTI8bhQ9tX1VB94Ruf8N0U%2B/dxsw8KLnJtNlKP5af2GqvxWq97x8nxE4PeC3qQavIIJ2zesenhfUbwXez2rdh4Q8yjhBbqUP0nUPZY/93tt4/LYkh6%2BhBhcRnTdZLrNOqAAtOSqCkj2Wd/f6iEyvu4Va/0J%2B7RfC4YT/77VkZqff2io2kxKY%2Bxu/WKpTbibrLZ39/vG1%2BL23UZrNW6/q4IYnI1OeiKkgfL7XH/kcfb1ombgIn9h3kHVOicfwGe32yNja6btWxfN9XC1NH/3hYff2lkhRG0x7zAiHMSumEh1fXbvtPLpkiVUjXwU/%2BGK%2Bu%2B5ItjmcIIJAVsMf4gydd3GOaPi6HjrmZ74K1e8y%2BHDa7M0vDCi0Bki4tkuNe8P4HpUmXl%2BrnHskWk3y5vbtTbxwk6XIh9yy5kIRJc5l7uTAZqG8N8tf9tL1S81lz%2B1bcUJKhkcCIb%2B/WxewZsWSKed88xtaT5ZH26/qHyq3i9q13Y7tAQkcHb6xjTQyfrXd%2B%2B2D9M/GNG48IIIAAAggcnYCc0NjBKDMoFXt0T8TkL1VTozteo2WbevDLL0cnGJx0TGNr2ba0vIHtk8Ibg1ABq9DJomwcelJ%2BYittjnhpv2rgL1RM72WF9Rvq23D1EgVDY5u2N8oYo%2B9M4GrEIH25L2fV6FOpn9f%2Bxga2P4IDE2bdolhmZR5PUqC0j2W9wD7vHEvc47n2sMebyP7oxPW3rTyd991je%2Bkhu4rR8bNffvxrJl7c%2BrnPs/EkSZKwDTTY2oa2097O5zy1ffL7ZoSky35HcGYQheq7XxbaB6SdyXo6%2B1psvTH2pWqH4j8EECgTsMeg0Ge7LEa9lhzHI7NYZDXn%2BOIf9ySGs47z/WWP2wu1il6jrFOtWbmDAEmXDljHsOr7H3wgM10OfXmx37z51nhNlpkudjC/lRypkxDugH026VLVcKu2V0s1n5lLXNVlzAKzWBotqmfC%2BEmbzHaSfIglguoyouttr9RSz8AJlNuardOor3nRr965mS5V9K12dPpoNldLPenF6T%2B3f0yNzKP0V%2BwSaLH4JgCPCCCAAAIIHKNA8QmNPeGxJ2JlgzutZu821V8pV39lLSdPC5nN0VpfFvQob1D7pOD6ifPX4abeC30T0F00SeRHsK87nNgaL/MX0E7Z%2BxkwNup4zwrrN4JvfqaL3ffsCXZ4md03fYkx%2B86JLYOCkQFqZ9Xo09IYtbU/JtGIWxqrsREvTkqgUx/XMwgDx47W7AWN4Hye458lvZr5/EX2%2B926MavkcrGKX7M/hO/UI/iZXyzUQsfsdezVE0t0/eqZPuZ4elnyHeRWdidxGnXU3wn%2BzCJnM1O2u409jtvjbuiSWmXJjLLvybJYHfefup1lsQuSLjre0H3JsecpAgjkBOwxKPUdkItSvS/H8XTSxZ35rY%2BLOukt/ySG%2Bc7xHvVx3J%2B1KBvz5NACJF0OLTxy/Pfe10mXu0GzWMxsltSjnukyatJlZAfCIYAAAggggAACJyFgToZif6U6diNed3lj1594CCCAAAIIIIAAAggggMCJC5B0ObEOXL7/vtIJkVTCZIz3dBn//cabJ6ZDdRFAAAEEEEAAgWMSGPGv4Yqa9brLK6oUKyGAAAIIIIAAAggggAACvyoBki4n1t3L996rki46KXLon//6zRsnpkN1EUAAAQQQQACBIxKQS9zkLhswUp1fd3kjVZswCCCAAAIIIIAAAggggMA5CZB0ObHefL58Tz1fLtWz50v19Nlz9fTZM/Xk6TP1%2BOlT9fjJU/Xo8RP17uPH6t1Hj9XDR4/U4t1HavHwXfXg4UP1YPFQvfNgod7WP%2B88UG%2B980C9%2BfY76s233lZvvPV2dTkxfUkxPcNFJ1xIupzYzkF1EUAAAQQQQAABBBBAAAEEEEAAAQQQQAABBCYVIOkyKT%2BFI4AAAggggAACCCCAAAIIIIAAAggggAACCCCAwLkIkHQ5l56kHQgggAACCCCAAAIIIIAAAggggAACCCCAAAIIIDCpAEmXSfkpHAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBM5FgKTLufQk7UAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIFJBUi6TMpP4QgggAACCCCAAAIIIIAAAggggAACCCCAAAIIIHAuAiRdzqUnaQcCCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAghMKkDSZVJ%2BCkcAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIFzESDpci49STsQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEBgUgGSLpPyUzgCCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgiciwBJl3PpSdqBAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACkwqQdJmUn8IRQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEDgXARIupxLT9IOBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQmFSApMuk/BSOAAIIIIAAAggggAACCCCA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| == Import the anatomy == === T1 MRI === * Switch to the "anatomical data" view, the left button in the toolbar above the database explorer. * Right-click on the TutorialFem folder > New subject > '''Subject01''' * Keep the default options you set for the protocol. |
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The second file is the DWI and should contain at least three files | * Right-click on the subject node > '''Import MRI''': * Set the file format: '''All MRI files (subject space)''' |
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{{/brainstorm/data%3Aimage/png%3Bbase64%2CiVBORw0KGgoAAAANSUhEUgAABjkAAAGcCAYAAACLC8IvAAAgAElEQVR4Aey9ebMcx3XoiU/g//0ZNBMzEY5xxCBmIp7jjf%2BYCIbt57HDi/Qs2pJgj2RSnjdvws%2ByKenZoq8WkwK106YkWJthkZJIgrgkSAoASREEF5C6JEACuMRCYrsAQRI7QIDAmcjqPplZ2ZlZVd1Vfavv/XVER225nPPLzNOZ53RVrRE%2BEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQGAGCayZQZkRGQIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQgIQQ46AQQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAjNJgCDHTDYbQkMAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIECQgz4AAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIDATBIgyDGTzYbQEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIEOSgD0AAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIDCTBAhyzGSzITQEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIEOegDEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIzCQBghwz2WwIDQEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAEEO%2BgAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIzSYAgx0w2G0JDAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCBAkIM%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%2Bqpw%2BfXpKtS5/NdPW2/xemN8N8/th6uYDAQhAAAIQgAAEIAABCEAAAhCYJoFokGPnzp3y9ttvT1OOmarrk5/8ZLGQN9uV%2BlkNOs5y23XVPgQ5ZrlXIHtIgN%2BykMjqO562s78vhKet9ywGObAPfemtyAEBCEAAAhCAAAQgAAEIQGByAiNBju3bt8tnP/tZufPOO6ce6HjooYeK4IH5J2Ds%2B4EPfEC%2B/OUvL%2Bs/zM0/Qc0/Qo180/hn6ObNm%2BU3f/M35Vd%2B5VdKTNauXSv33ntvJyymrWPYjav6Qaxv6DmTd6V/umyfpkEO49hK9VHTJqbfal9NtUsqv%2BaL3VHSpI%2B02SfGkTWl96yd379/v/z6r/96439pHz16VExQzthvHadma%2Bza1q1bO8PQ598yo7/p33/1V39VjJ8UBHVUm3G0sLCQSmbP%2B7ahzr/pTZn62%2BKn1/FVt14rQGLHr8fvA7H9tu8iU4Zd/V43GRcqS0xvPdcWc62rK73Dpp61IAf2ofpuG3/cdmkfwr7EMQQgAAEIQAACEIAABCAAgXEIlIIcuugzQY7lCHSoY0UX%2B6ltW06AcYCZPF39i96XxywujUMxxUDPG6d0F59p6JiSu24/UAb%2Btk2Hdkq%2BPpzvqn3qBjmMQ8sEHNVB6rdBbN93kCi/Sfp4kz7SRp%2BYRFbVd5a3JqCqbR1ry5Ru3/rWt2y%2Buv0iVVaT87PyW6ZMDNtYAD/lZEyx8MdFnWCBOsLD31QtJzyfqrfqvK%2BH6pza1pG7qj7/uurYhbO/6bhQ%2B5rS3Zxvi3mXevt8dX%2BWghzYh3qPpNQ%2BFPbJtu2D9iG2EIAABCAAAQhAAAIQgAAEJiFggxw7duwoAhsa4NDtNO/o8BdO5h/Ti4uL9nvfffcV//xV50DbjpBJILad13cImcXlxz72Mdm2bVuJhTlnWHQV5KjSSf/RbhbBXXz8ttd9U6fhYb5h/9A0XcjSta5dyDxumeqEy40v35mlTjnTH%2Bfn520fNe1hjo3j1vxjP%2Bwnk/bxnK3QvqDbcVlovkll1XL6sG3al036MNgatmVKL3WQmT5i%2BofhqB9zd4fpGyYI0van779lOjZuv/32kbtbwnHnj7XwWoybjl8dlz7zXPqwbH98VZURKzc8548hExjQsRnbmr7R5kf7YZtBjnHHhbaP2cZ013Nt6N%2BF3jm5/L5a10bkyuvqGvZhcKd0GLiI8db%2B2rV9iNXNOQhAAAIQgAAEIAABCEAAAk0J2CCHeQeHCWhocMPfTivQUcexogt348Qx6Vfax3cUGKeMeRRG6mOuGUfhtD%2B%2BjNN0ZqijrM7ivC0my6VrW/I3LSfl1NByzONwfKe3uaPEMGry8ZmO28fr2IomMqXStiFrquxpn/d1qRq34R0YGlw0drcqr9FL26du%2BjZZzMpvmeqsjzsyrMw3dCjqmKxy0vuPqtKycr%2BRfnpTh//R9mvL1qrtNnLlZPJlaGtf5wxV/OrUN%2Bm40LasM4bqyJNL06beuXr0WhP7onmWY4t9cI%2BDzY3FadqH5egH1AkBCEAAAhCAAAQgAAEIrDwCNshhVFvuxV8dx4rvLJmGo2DaTT4L%2Bi2XM0PZtOV4q9O2y6VrHdm6SKNOuNDRqnXpdeOsNP/IHuej7WjKGHcM17EV48gW5mlD1rDM5Tqu25f9dKaNTCDL/DaYPlGnzfz8ofN8WrrPwm%2BZz8JnFjLWvm7O55yS2leNM/%2Bmm26qvNNP08fK1TrbsrW5unwOXey35ewP22iScTGu3WvCpy2969bp85mGfnXliqXDPvTLPsTaiHMQgAAEIAABCEAAAhCAAASaEigFOUzm5Vz81XGs%2BP8u6/tCumljmPTL6QyqK%2B9yOTOUTVuOtzr6LpeudWTrIo0GMWJBDh2foRO2qRzajjHnat2yVJau%2B0IbstbVqet0dfuySffbv/3bRXBDHx1UN6/RQdumjX/OT8Kk779loW7%2Bb5s//vzzuaCROrVN3s997nNFkCPXBpo%2BlkbbsK3xtZzjKKdn2Aa540nHRZMxlJOj7rW29K5b37T1qytXKh32YY3Exr7yyvWftu2D1skWAhCAAAQgAAEIQAACEIDAJARGghymsOVa/NVZOFU5S9RJq84g/8WgvuPI6GkW5fqMeuNw1e8HPvCB6EtgFbTWEZZnruvCUK%2BZOsw/Po2zyC8/9S94Xz/VQeuts63raPDTxeqJ6ai6qR6xrWnD2CfG2byrwZyv%2B1E2dRxvvn5G7tTHT%2BdzmERXI6d5B4Hf5oZVHX2Vu8qS6r%2BaTvtZqJ/RK8Z83L7tc8o5RkI5YsfajoaJ6hlLlztXx1bk8te91oasfl2xNqnqF9oXfe6mX5i21DFo%2Bprpc7HH22l%2BTRvbpsatyu63f248mfTaN8dtW62zjW2ff8ti%2BmlbhTZOmebGu7lm2taUUTU%2B/PaMlVmVPyZ77pw/jqr6Wq6c5Rg/OXl8jlXjwg9WTcIgJ49/TfvSuHbDL0v3U/yN3ali0YU8Kte4W%2BzDr5TelaQc/bachn3QetlCAAIQgAAEIAABCEAAAhCYhEA0yGEKXI7FX5VjxSy8qpw9et1stTx16vmLNbMoN4t/vRbb%2Bul9yFpH7Lou5M0149jJ1ZFyimj56rDy667a9xenqfJNGX46U1/4URl8HVW3GCs9Fzpv/Ho0Tbj16wjl8I/VURY6AP00uu/XOw6HcXU1juZQv/DYvNMi5ow2sit3s831X00XY9dF31b2RpccT%2BVftVX5xy1P2dTpC1WyVF2fVFZTvt8fw/6gx7G2NHm1LxpbcuTIEfvYKM0XbsMAquYP0/nH4bgNmfjy59p/2o7cUM7YcR9/y2JymnOpcaZtmOrvmk%2Bv%2B%2B0Qa6%2Bq622PL5XP9LmqvhZj4/c/v9/6%2B22MH8MvHD8xefScL1eMs6YzW5/5OAz8sursa5%2BpYzeq9Day%2B%2B9i8rnrvuFm2sAcx1g0kceU0aQd6vBIpcE%2BjP4JxO%2BrsbZs2z6k2obzEIAABCAAAQhAAAIQgAAEmhBIBjlMIYuLi9EXkZuXkt9zzz1N6qmVNrdwMk4SXWSbRbtZhMU%2B6pA0zyQ36cxjO4wjIvyo08WUaf6dqGnMVstILdb1esypogt5U65%2BTV36MXUZh4IpWx1Sek23/gLTpDPlpJzimke3dZ0ufjqjT/jJ6ejnjS2AtSw/nTpRzDnzMVvz0nRlEZNBy9GttlmKm6bT8nMOF03ryxiTwb9eV1fTZubuHX3Uj6nL7Pv6pvqwcq/qv5ou1geVU5t9Ozc2lWWT7SR93NTTtjw52SeV1e9D44wDtSmmz5h%2BEZZhZK9jV3w5cn05xqJuXu17OkZNv4/dyWbGgilzWp8%2B/ZbldPb7mm%2BPlGvqN0nHg29XcjZC02s7hTJVXQ/TVx378puym3z8vhf2fXPNt6s%2BM62jrfGj5flbX7aqMeW3rWlH/1t1N5dfZ939tvT2dTT8w7FrbI/5rfH1ibFoS566%2BjdJh30o2%2BKq8V91vQl70kIAAhCAAAQgAAEIQAACEGiLQDLIsZz/bvMXy%2BG%2BcZiZRXfqo44dk8%2BkTX1M0MAszmMff1EfcyJrHbFrupA39ceum/p8h0/MKWPSGPlCx4E5NnlzH1/2mKNB8/rpYjLkdPTz5upQFsbxlgrSNFksK7eUY051M9u6MvrpYhz863V0Ne2ec%2BKpDiZdrD7lXtV/NV2sj3XRt7Ut67D32yG3P24fN2VqvzGcct8Y45xMqWuTyKrsxh0Hmt/omePvO1Fj/aJuX44xqJtX28Xoum3bNhvEjLWRsWdG5q4/y/lblmuvmN4%2BZ7/vps5rGWoP/Dzab/zAh6bPXTNptB2byq/lh1vf7sX6gp6L1efLOs7viOafdPyEOpljv11MPblPHQZmTKR0zJUdu9aW3lpOrG20Xp%2BD4RxjoeV00Q4qxzhb7MPon4a0rWK2wzBu2z6M027kgQAEIAABCEAAAhCAAAQgEBKIBjmWY9FnBNOFkzo8UtucI0CdPbkFeQghdpxb5GkdMUei5jOyG31iH98hECvDz2Me2WB08Vnk9PfLNrKkPn463zGm6XM6%2BnlTdfgO1xQHU1edslQmdRLVadu65frpYhz863V0TaVRHczWpDHtGXMgKPcqHTVdVf/x69X9OvWH5ebyaLnjbpv2cVNPXVsRa9Nx5TT5msraxjhQ9qbPmP3cR9PG%2Bk%2Bdvpwqu27esF2MHIaZyW8%2BemeH2rOwn6XqH/f8cv%2BWxdohp4vPOey72rah3Uj1MbWXhrVvg3N1qGzajk3l1/zh1pdF2z62DetL6RaW7%2BsUjhHlZuoLr4XlaNpQjjCdHufq1TT%2B1twxYL56l5/ZN3%2B2MHdyKI%2Bwff38TfZVl0n09vlXsfPbOJa2DXma6F8nLfZhMLdcbvtQp61IAwEIQAACEIAABCAAAQhAoIrASJBjuRZ9RlDfsWIW/uoQMFvzr%2BDbb7%2B95PD3F2aq6CTOXy3DbHVBHnM45OrI5YuVX8fJZxwpoXPVOGHMufBT1%2BnipwudaabMnI5%2B3pgzw%2BTXtozxC2XWumJy%2BGnViVLHAVVHRlO2ny5Wv3%2B9Stc6cpk6VQ/jfAr7sLKo6hd10/n8dD/XR1Pl5vJouZNsDee6fdzUo/3LMA9thW83TLltf5rIqnJOMg6asM/1rTp9OcWqbl7V1/Tt3HhQnWJjICVD0/N9%2BS0zbVL34zuVQ3vkszX7%2BtHzYR9LtVmuj4Rl5tpQ09bZ%2BnWace6P0XDfLy%2Blm59G99V2hdy0r4V8NJ%2B/9eX0Gftp/P0UYz9N3X2Vs05Qok6ZWt4keiv/Ov3A77um7vDThjxhmZMcYx8uRd%2BhUmcMNOkXk7QReSEAAQhAAAIQgAAEIAABCDQhUApyLOeizwhdZ%2BHkOxVii/eUoyMFxZRnAih33323rFu3rvhH5Qc%2B8IHsvyq1jpgjWhfysWu%2BDHXT%2BXmMrP7zx2POEJ9PzNGg5fnpQqeQSZPT0c%2BbqkP1MzLW/cbkUHnNVhffdRwudWQ0ZfrpYvX716t0jfVHX37d951BoSNNucdk0fxmq%2Bly/czI3lbf1vasw96Xs%2Bm%2Bkbmqj5sy69iKpnU3TV9HVuVWdwyYdGHbaxl1%2Bpfft8L%2BWqcvpxjUzavtYvQI6/fL9uUM9fXTjbs/C79lMd3UxsX4%2Bcx8tto/YrYgdk3bKDeW66SJyZ865%2Btlyq77Ufn7MH5iMtcdF7G84Tm/rFhbhumrjpXdJHajSRm%2B/CZf%2BGlSVqqvh2WOe4x9GJDTNvH7W52xXyfNuG1DPghAAAIQgAAEIAABCEAAAuMSsEGO5V70GQXqLpxyDhN1/lY5zsyC/GMf%2B1ilAz7mINA6/IWhNkBs0ajX/G3ddH4e3TfP7DZyGcdP6KiqcjRoGX66GKucjn5eo0fso/pN4pwKy9V2D3UO05njOjKG6WIc6pSjusb6Q0y2nANHucdk8cvSdLE6jcxt920dm6Y9zX7Xn1wfN3WrPHX6wnLKqn1jknGgZcRsUahbrm/V6ctheXpcN2%2BTdsn1Ya13nO0s/ZaF%2Bim/1DgLmVW1i5bnj5OwjFAGcxzLF0tX95za7pReqXK07/dh/MRkrOIfy5M7p/rWGeu5csy1JmWl7IaWEfudCeuvYqFl1dEtJU9Y5zjH2AdHLTbOl8M%2BOInYgwAEIAABCEAAAhCAAAQgMD4BG%2BTYsWOHfPaznx353nnnnWIWhdP4xBZcsXpzC2BdoJlt6uPnN84T4xC%2B7777ijsF9FnZuQW51hFb%2BGu%2B2DVfnrrp/Dz%2BvrIy8puy9FPlaIili7HK6Vinjkn1Uzn9rTrKfIedf93fryOjSe%2Bni3Hwr/uc/bpU1zrOG5PP73%2BmHf2Pco/JEksX9jO/7Db7tl9ulWy%2BnJPsp/q4KVOv1ekLk8hQN6/KE45H7RthO9Ut16TTMur0L7%2Bdwr5Vpy%2Bn5Kqbt8kY1b4%2BCZuYvLP0WxbKr0xSba39TPt9Fe%2BwP9Rtx7CeUM6mxyqnGR9hv8yVpX1/kj6iZaSY%2BvWHvPxrsf26PGN5Y%2BeUex1ZY/n9c23o3aSMKhZNymraDr7eVfvYB0co5FzVhppT%2B6naIT3PFgIQgAAEIAABCEAAAhCAwHISsEEOI8T27dtLQY5pBjhM/XUXTv7CzCyc/Y86iXKOWH%2Bxbf4xHvv4aUx9/kfriDleNF/sml9G3XR%2BHn8/xcBfpOYYVKXL6ejnNXrEPtqWbThrtHx1lNVZWPsyTsLBL6dK1zpyGV1yeij3nMymDE0X9jPtV4Z7231b62zqqNT2a7pN9XFTjvavusyb1t00fUpWlXOSceC3aWiLQjm1vhiXOn05LE%2BP6%2Bb1ORhZch/tT1V9PVdG6tqs/Jb58mvbhYEyP43aDh2DmifXv3zOmj/WP/x6tNyqdH6e3L7Wq3Ln0vrXVI6cfn762H5b4ydWdt1xEcsbO9dE1lh%2B/1yTspRz2N6p8349uu%2BPfVN3%2BGlDnrDMcY%2BxD45cH%2ByDk4Y9CEAAAhCAAAQgAAEIQAAC4xMoBTlMMbr4m3aAw9Rdd0Gt6WIOE3/BlsLSJE3MuaL5QwezqU8X8rFrvjx10/l5/H3faeQ7FHynS04GP7/RJ/zkdPTr8Ov2y/DLT6Xx09fZ1zJDR0wsry/jJBz8clJ6VDl3QvlMOabv5vpWrE38clLto%2Bdz%2BTVNrv4YM%2BWvsqeCKL6ck%2Bz79YXs1QbU6QuTyFA3b0rW1Pm65Zp02l9i9i4sR9s21n51%2BnJYnh43yasy5Ppg0zGjcjTZzsJvmerj95PYuNR0fjsYvnVYa/8x5X7ve99L2h6tw2zbHl%2B%2Bfqbsuh8/X2gD6pah%2Bk86fmL1%2Be0xrnxarl9WbPxqurrbNvRuwl/7jOEcY9GGPHV1r5MO%2BzCgpO2ynPahTnuRBgIQgAAEIAABCEAAAhCAQBWBkSCHybBz586pPaLKF1AXyTnHpe8cizmD6jh9NE3KkeAv7HN1xPLrgjF2zdc1lW7r1q3Fi5eNwyP3UR1irLTs2DVTpu9MMQ4JU1b40fJjevj5Y3m1LC0jJYemM85y87Lpqo%2B2S1V5Wk4bHJrqWuVIUx1SjiBlluNq9NN0YfukzisTv/6mfduUoUyN/KYd7r33Xi06uTVpvvWtb9nrbfTxOrbCVjjBThuyaptU9dvUOPCZh%2B3tq6ZMUn2rbl/2y9R9P6%2BRJ/fx5TD7sY/qFOuDsfTjnuvzb5nRyXA1ts/0DR1TZozmPsruAx/4gM2X4mzK0TFv6jB5TD1V9kXbsKrP5uT0r6kMpu6crH4e3e/L%2BFF5/G2TceHni%2B1ru47DqKq8ce2Gr1%2BuLxjbZcaykd18YzbC129ceWJ6TnIO%2B9AP%2BzBJG5IXAhCAAAQgAAEIQAACEICAEogGOfTitLe%2BY2Xz5s2yuLhov8ZJcvvtt1unTmohrQ6RnBPHX2x/8pOfLBxNRlezoDcOWbOYX7t2bbFYjznhtI7YQl3Ljl3zeabSKQOj32/%2B5m/ad4Uoi7vvvlt%2B/dd/3ToTYnr6DiWji2GpH%2BOMMPnN%2BZzDK6ejKUuv%2B%2BWbeo1TWD%2BhHMaZp%2B88MWnMvuFvyojpoeXoVssz6c1%2B1UfTG5a%2BnCZfXQ4mbR1dTfDNbxejV6ir78xM9Q%2Btq4qHpgvL0X5ldG67bytvvw5Tj%2BlHRjfDW/vptm3bSuPV5NFPG31cy9B21XpjWzOux/1oPW2Ox6bjIORt%2Bpk/po1%2Bn/vc56xNCPuEr7v2G%2BVmrpl288etn173fWen35Z6PdxqPYabkU3bQMe8OW%2B%2BdcoKy56FY%2B03ytnvl4a3eQeUeReUcjBbk9Zcq/qYNCat5q3K57ed5jHy5T6%2B/HVkypVlrvkyV9UdluXnNbou5/gJZfPZVvVlE%2Bg1bW700fGgbMxvvbZNle0PZUgdt2U3tC9oHzVzJJXfbHXOZOyS/gbGWLQlT0rfWTqvTE1/js11V5t9mKW2Q1YIQAACEIAABCAAAQhAoL8Eehnk0MV%2BamsWhv4/w3286lzLOQp8x0SsDuMc1gX5cgY5YrL553wnts/A7Kv8fnrdN/zMPxiNM9Sci7FSjimHqe940nLNNnRgpdL5ecx%2BzCkS6qRlGfnNfp3PpBxMHVpvKHOoaxjoCNPrca7dlHusTXx9NV3YPl32bb9%2B45hRh5bqldqa9vKd6OrgSaX3z6dYNSmjiqWvV7jfpJ6UrKbMVB/ydTX7sXGgfdjYIhM8MjzDfHps2sT0w9QnJUfYl8P8fr%2BKyZhLr7KF2zrlhOXOynGTfmO45PpOqLN/N6PJG9qAML05Vnth0texnyp/nbSx%2BsJzfr%2Br6mthXnPs5w/7kX8c61Ntjp9QtibjQuXw5Q33m/SDUJbwWOtrw25oICOUV4%2BN3Tly5IidU0y7HULd%2B36s40v5VW2b9ItZtA99by/kgwAEIAABCEAAAhCAAARmg8BMBTnMP8bNYs//h3yIWZ05VY5N45zw/1lvFpmmfLOYNx/fQRA6DbWOmHNJ88Wu%2BbLm0pm7DMxdK%2BZuktChac6F/2T1y/X3jSPa/4eo0dH8k9SU7ztnYqxyOmodxvHkO7rNvjkXfpS13jmiC3q9AyDXnn5Z6uhq6nibhIPWX1dXk17r89vO7Ou/eLXM2Fa5x9rET6/pYv1Mefv1t9G3/fp13%2Bhq9Iq17Qc/%2BMHSHQeax2wn7eNNnERVLH25YvuTyqplarvEWOXGtNoKDbgaeQxzHUdma8a5aYs6nyZ9Wcvz7YWRp%2B7HyOTbCB0HRoeV/Knqn6YPaJsZtk0/Ov5N29dpD1%2BemM0I69f0pr1iNj1MX3WsttvIa8oe59OX8ePL3mRc6LiNjX/9XfbLnnS/bbuh8oe/K8Z2GQ5VLNqWZ1I%2By5lfx5dvw/391WYflrMtqBsCEIAABCAAAQhAAAIQWDkEehXkWDlY0QQCEIBAOwRC52A7pVIKBCAAgekRwI5NjzU1QQACEIAABCAAAQhAAAIQWI0ECHKsxlZHZwhAYGYI4BycmaZCUAhAIEEAO5YAw2kIQAACEIAABCAAAQhAAAIQaIUAQY5WMFIIBCDQZwLmkXPm8UDmcW91v6n3/kxbT5yD0yZOfTECszyGYvrMwrmVxBw7Ngs9DhkhAAEIQAACEIAABCAAAQjMLgGCHLPbdkgOAQjUJGCcheadFv5zz6v2jVOuDx%2Bcg31oBWSY5TE0q623kphjx2a1FyI3BCAAAQhAAAIQgAAEIACB2SBAkGM22gkpIQCBVUoA5%2BAqbXjUhsAKIoAdW0GNiSoQgAAEIAABCEAAAhCAAAR6SIAgRw8bBZEgAAEIKAGcg0qCLQQgMKsEsGOz2nLIDQEIQAACEIAABCAAAQhAYDYIEOSYjXZCSghAYJUSwDm4ShsetSGwgghgx1ZQY6IKBCAAAQhAAAIQgAAEIACBHhIgyMT/mUoAACAASURBVNHDRkEkCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEqgkQ5KhmRAoIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAR6SIAgRw8bBZEgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCoJkCQo5oRKSAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEOghAYIcPWwURIIABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQKCaAEGOakakgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAoIcECHL0sFEQCQIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAgWoCBDmqGZECAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIACBHhIgyNHDRkEkCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEqgkQ5KhmRAoIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAR6SIAgRw8bBZEgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCoJkCQo5oRKSAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEOghAYIcPWwURIIABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQKCaAEGOakakgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAoIcECHL0sFEQCQIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAgWoCBDmqGZECAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIACBHhIgyNHDRkEkCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEqgkQ5KhmRAoIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAR6SIAgRw8bBZEgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCoJkCQo5oRKSAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEOghgTX79u0TvjCgD9AH6AP0AfoAfYA%2BQB%2BgD9AH6AP0AfoAfYA%2BQB%2BgD9AH6AP0AfoAfWDW%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%2BlkDX44MgRxY/FyEAAQhAAAIQgAAEVgOBrifdq4EhOkIAAhDoggD2uQuqlAkBCHRBAHvVBVXKXCkEuh4fBDlWSk9BDwhAAAIQgAAEIACBsQl0PekeWzAyQgACEFjlBLDPq7wDoD4EZogA9mqGGgtRp06g6/FBkGPqTUqFEIAABCAAAQhAAAJ9I9D1pLtv%2BiIPBCAAgVkhgH2elZZCTghAAHtFH4BAmkDX44MgR5o9VyAAAQisSAJHjx6VD33oQ2K2fCAAAQhAYECg60k3nCEAAQhAYDwC2OfxuJELAhCYPgHs1fSZU%2BPsEOh6fBDkmJ2%2BgKQQgAAEJiZgAhu/8Ru/UQQ5zJZAx8RIKQACEFghBLqedK8QTKgBAQhAYOoEsM9TR06FEIDAmASwV2OCI9uqIND1%2BCDIsSq6EUpCAAIQkCKgYQIbX/nKVwocZkugg54BAQhAYECg60k3nCEAAQhAYDwC2OfxuJELAhCYPgHs1fSZU%2BPsEOh6fBDkmJ2%2BgKQQgAAExiagd3BogEMLItChJNhCAAKrnUDXk%2B7Vzhf9IQABCIxLAPs8LjnyQQAC0yaAvZo2ceqbJQJdjw%2BCHLPUG5AVAhCAwBgEUgEOLYpAh5JgCwEIrGYCXU%2B6VzNbdIcABCAwCQHs8yT0yAsBCEyTAPZqmrSpa9YIdD0%2BCHLMWo9AXghAAAINCFQFOLQoAh1Kgi0EILBaCXQ96V6tXNEbAhCAwKQEsM%2BTEiQ/BCAwLQLYq2mRpp5ZJND1%2BCDIMYu9ApkhAAEI1CBQN8ChRRHoUBJsIQCB1Uig60n3amSKzhCAAATaIIB9boMiZUAAAtMggL2aBmXqmFUCXY8Pghyz2jOQGwIQgEAFgd/6rd%2ByLxmvSGovm0CHyccHAhCAwGoj0PWke7XxRF8IQAACbRHAPrdFknIgAIGuCWCvuiZM%2BbNMoOvxQZBjlnsHskMAAhCAAAQgAAEItEKg60l3K0JSCAQgAIFVSAD7vAobHZUhMKMEsFcz2nCIPRUCXY%2BPNTfffLOcOXOmta9P5fLly6Jf/zz7EIAABCAAAQhAAAIQ6BOBrifdfdIVWSAAAQjMEgHs8yy1FrJCYHUTwF6t7vZH%2BzyBrsdHEeRoM9Dhq6MBDrPlAwEIQAACEIAABCAAgb4S6HrS3Ve9kQsCEIBA3wlgn/veQsgHAQgoAeyVkmALgVECXY8PG%2BRoK9Dhq0CQw6fBPgQgAAEIQAACEIBAXwl0Penuq97IBQEIQKDvBLDPfW8h5IMABJQA9kpJsIXAKIGux0cpyNFGoMNXoU9Bjl/7tV%2BT5fz6XNiHAAQgAAEIQAACEOgXga4n3f3SFmkgAAEIzA4B7PPstBWSQmC1E8BerfYegP45Al2Pj5Egx6SBDl8ZghwusOJzYR8CEIAABCAAAQhAoF8Eup5090tbpIEABCAwOwSwz7PTVkgKgdVOAHu12nsA%2BucIdD0%2BokGOSQIdvjJ9DHL48k1jX%2B8emUZd1AEBCEAAAhCAAAQgMB6Brifd40lFLghAAAIQwD7TByAAgVkhgL2alZZCzuUg0PX4SAY5xg10%2BJAIcoh9RJbPhX0IQAACEIAABCAAgX4R6HrS3S9tkQYCEIDA7BDAPs9OWyEpBFY7AezVau8B6J8j0PX4IMiRo9/CNe7kaAEiRUAAAhCAAAQgAIGOCXQ96e5YfIqHAAQgsGIJYJ9XbNOiGARWHAHs1YprUhRqkUDX42PNmTNnxNy1Efuaa02/vu7cycGdHH5/YB8CEIAABCAAAQj0lUDXk%2B6%2B6o1cEIAABPpOAPvc9xZCPghAQAlgr5QEWwiMEuh6fBRBjlSgo2mAw6T3PwQ5CHL4/YF9CEAAAhCAAAQg0FcCXU%2B6%2B6o3ck1K4JTce9un5KabPyW3bjo1dmFHdm2TO4blmLJuunvP2GW1n7EdHduXixJXCwHs82ppafSEwOwTwF7NfhuiQXcEuh4fNsgRC3QQ5Ji8YXlc1eQMKQECEIAABCAAAQh0TaDrSXfX8lP%2BchHIBwB23n3XIACSCVoc2TRIUwQ3TIDDC3LUyd%2B95nkdu6%2BfGlY7Aezzau8B6A%2BB2SHQF3vVj/nD7LQbkk6HQNfjoxTkCAMdBDkmb%2BRlC3Ls2jhYIBULpbvk3hN1ddkjdwwXV3fsqpuHdBCAAAQgAAEIQGC2CbQ26S7NwYYOa3VcD7e33naX3HH3Nrl31/j//J9t2itJ%2BlwAwM2rb7p5o%2ByMqX1im9yq/WLTHjlSSlMjfyl9Vwc5Hbuqk3Ih4AhMbJ9r2OVSkNGz2ayJXTuwBwEIVBOY2F5Fqhjc7XmXnS%2BovSrmk7tOBXMHU0Bf5g8RZTi1qgl0MT58oCNBDj/QQZDDRzXefj%2BCHJ%2BSm27bFjF8MZ2cMWRCF%2BPDOQhAAAIQgAAEViKB1ibdjZ1pd8kdI87tlUh4peqUDwBU/ZPS3sWRmKtX5Z8O1byO05GBWlYzgYntc2O77ALUrIlXc89Ddwg0JzCxvfKrPBE8ytILwGqgw7/708/aj/mDLxH7EBBpdXxEgEaDHBroIMgRIdbwVG%2BCHDd/SupN0AhyNGxikkMAAhCAAAQgsAIItDbpts60u4o7NY6cOCX%2Bd%2BeuPXLvpo1yq//%2BBbNoTTi520Br/wE4wTsj2pBjZZYxSQDA5e3XOzjClnJyTvLekbBUjiFQl0Ab9tm3w3Z/l95JFbfXJh0fCEAAAk0ItGGvivq8Oz1vuvkuufXubbLT3LUxnFfu3LVN7h0%2BErPfc4gm9Ei70gm0Nj4SoJJBjnECHCaP/%2BHF48v44nG7wN4od9yt/0RJ3CbvN5p3W1u9oEgpMwcQgAAEIAABCEBgJgm0Num2c7Aajws9saf8sulOAh04qLvtkJPwdXn77aBwchLk6LY3UXqcQGv2OSzeOhFr2OswL8cQgAAEIgTasVfud7d43GXu8fNmLrlpT0QSTkGgfwTaGR9pvQhypNm0cmX57%2BTYKDvt5O1TUr2A4k6OVhqeQiAAAQhAAAIQmCkCrU26mwQ5CkL%2BQvZT0r4T2ZXfftkz1cQdCTsJX5e3eo7ekfi1inVy0odqASNRywRas8%2BhXHadTJAjRMMxBCAwHoFW7JW1TV3MC8fTi1wQaINAK%2BMjIwhBjgycNi71IsghIvZ5v5WPrSLI0Ua7UwYEIAABCEAAArNFoLVJd%2BMgh%2BHk5l/tP7YKB3W3PXESvi4vQY5uW4nSZ5tAa/Y5xGAdiQQ5QjQcQwAC4xFoxV5Z21T3sfPjyUouCEybQCvjIyM0QY4MnDYu9SXIIeItorKPQnCL7PTjqk6JPtu59LKj2/IvzrSBFq3/xJ7iGYK3ei9PuvXuPcEL0k/JTvPcai/NTaaeXfWejxqT89bbNhbPyG6jfSkDAhCAAAQgAIGVQaC1SfdYQQ7/DykpZ1uz%2BZedd/lzqGA/NdfrYv5k5ZnmPDAy1zRz16ZzwRSPncXjI9wcO3aXw059bKzqbYaL7SP6SNlw6/pANH9kyKVkvHfCOXMdHSPicAoCrRJozT6HUllHohtzfhI7/m6u89hnZwv8oKW1fV4ZR3aF72UaPm8/90gaT7BJx7tXFLsQgEDLBFqxV9Y21XkaS1wBa7/8%2BYeI2PPBnLDk2/OuTXOuGNeEsyuJQCvjIwOEIEcGThuX%2BhPkEBHPUMYWYQN9K4Ic5nl/nsGLGsLAiCpHO8Ez1z1ZRsvQSWTwnOqgXhMQSX%2B8SWaQz9aXkDNdJlcgAAEIQAACEFipBFqbdFsHdtxpluTnzY1G5mljzL/svCs1D4re3dvd/MnKM615oL6MM6P/TbeZx7omW2Rwh034gvigPPPHm3uHaUbazXcm%2BPNO20fC4IYeu75jnRF%2B/pLIrn47xw1kzN8dlJ9vmzKrdCyJwwEEOiDQmn0OZbN21425UhJ7vca/qW3aclnW9hVBjurxmv8zX3X%2B/HgvaccBBCDQAYG27JX9/Td/zthU70%2B%2Bvjo2fzB/sOfDuULsOOpzww75nNlvRqCt8ZGqlSBHikxL53sV5Cg9tqo8%2BXLquiBGNGKrk7fh3RBHbMZTpYhwzAjbCd5tdw3uzLhtm7ewNPnvErs4u3vbcMFo7g455e7uKL2gM6WDb3QHd32U5PTvDIkabasUOxCAAAQgAAEIrBICrU26rQM7NU9JAXVzMP9fwEXqCeZf/t28sfmZk6bb%2BdPyzAM/JcVdwn4g40R4l7D%2BucaRGOwFPDaV7zYe/JNagxKDbYyvdSYEToaROhJz0tr5bx5nztuOjiE5jiHQNoHW7HMomNrWm1P22hsj0THsCkyNVWv7bt4odxR3doVjVYqnJLgnF9SQZazx7mRlDwIQ6I5Ae/bKmxeaAETlHzPKOqVsUjnV6JHN59195lJ5NhE75LCwV5tAe%2BMjXiVBjjiX1s72LchR/cxnZ0jjQY49mUc9eQYvMgl0E7z0LXfOoA4Wi3EZtrnHV0UWhK6ezL/zxnZAtNY1KAgCEIAABCAAgR4RaG3SPfYcw5tHhfMb89il5GOHvHyR%2BVfdIEfX8ydX/rTmgRX/vLbOzbg8Tt6Uw9F0Xo994p%2BWdm5b0TYjga3h2MjldzKON%2Bd1%2BSfTsUfDGFFWKIHW7HPIx9qBzBioZdPdGjoMdrpxZta3mXoq3s3kyhlvvIeqcwwBCHRDoFV7VfqT7/CPFUWwo/rOjtz8IaW5szNxW%2BWuY4dSDDmfJ9Dq%2BIhURZAjAqXNU/0LcpSfAxxOwvwgSDTAUAGnZPSCtO5a3GAWye1E00Sqt7k7OIKy0gbbTTDz8rtF6SiDoDIOIQABCEAAAhBY8QRam3TXcojFcLq5ScrhHctlzrk5VuyuBFdues7T/fzJyTideWBaV0fRyRRyczwqy/HmrrG06TmrkcO1TarN0/mdjOPNeV3%2BmNyOUt1H3pZycACBVgm0Zp9Dqez4zdglL/iQHCvW7oe2xLfP1Y%2BccTYplMeN1/HGe6g4xxCAQFcEurBXxXt8wsdJVdzZkZ4/JDS39jBlq7BDCXKcbkCgi/HhV0%2BQw6fRwX4vgxz%2B84FHbkGra7jisNzELDfBG73mSnP1JyeRvvxhICQzwXR1DPas0Q//LRkm5BgCEIAABCAAgRVPoLVJt52LhE6qKoRuDpRyeKdKyM2/fEd6cm5lZc7N0Qa1jzt/ysuomjkGSVlrzQNrsvcW9CXHoeVRp5y8zJZXOGctVJ4gyGFlHLPNbP7JddTWYwuBrgi0Zp9DAa0NyI%2BDScZxPds3FMzKE9yJZsfrmOM91JtjCECgMwKd2Ss5JaPBjsHj72LK5O1WmMPNZZJzUOxQCI3jMQh0Nz4GwhDkGKNRmmTpa5DDv2OjbMSccSst9kaUNgZ2j%2BzctE3uuHuj3GHes1F6MePoBMxO8KKLPK2gXv2psuz5MMqdOybIofDZQgACEIAABJadwNmzZyeSYdz8rU267SIw7zQbUdJzbqUd/M3nX3WCHNOYP9k6pjIPHJ2HjvAuTsTnnVbWkT8DxUpxgYpYu%2BWdDC5veT7u6knldzIOH1%2BRm%2BvqNW/O6/LXYeXkjOnopGVvpRMY174ql3Hzt2afVRDdWrtbYa9tuiD4YMqx1%2BJlNBtrzib5Y82VMd54V3XZQmA1ERjX3iijcfN3Zq9UMAnfLRa/8yI1f7DFeDs2bWbegx3ygK2A3XH7t6o%2Bbv6uxwdBDm2hjrb9DXKUH1vlAhpuYuXO%2BXBOyb3%2BC8J10TSyHV0wWaM4lcUtE0C/1diHAAQgAAEIQCBPoLVJ97hBDpsv4kQzjzUac/61KoMc2bmm3w/cvDfqUKxVTj4AYB0H0bJc3mULckTl8hmZfSenzylMxTEEuiLQmn0OBawIULjkbgyEY7VqjWuvZ5yHrp4KmzSy5s6seb2gpiufPQhAoGsCndmrUHDzvg5rE0aDrPn5hyvM2ajRMlyq8qP3brL1ZmyQSYMd8hGyLyJdjw%2BCHB13s14HObwFy0120uUM5WiQw10zRu3Wu7fJveZujhOn5MiJAUhnIJc5yFFrwdZx41M8BCAAAQhAAAIzQ6C1SbcNVuQXiyEYuxi1czJNMdn8q46D2s7fOpw/1avD6To6D1Ue3kI7kNfWMcLQ5S3vxetrVo5zfsYCALZdA1kHcri8KUdAKr%2BVMVpuWcvYkc1fi5WTM6ZjrHzOQaBNAq3Z51Co2kEO/w%2BC/jo3bkP8apqNtXh5towxx7svD/sQgEC3BDqzVzGx7Zxz9G6O1PyhVIy1gaP5S%2Bn8979hh0I0HDcg0PX4IMjRoDHGSdrvIId/e60atfjEyuhuJ1dmMTQMaoRMSmmCi/Za1iim6/eLS5Zljbw/%2BfRzsg8BCEAAAhCAAARGCbQ26bZzkQZBDptH52NOPjvnGXP%2BVSfIIbb%2B7uZPVo%2BpzANrsreL%2ByC95RGcd83i7bm5aywAkHcyuOBB0yDHxG3Woo4eDHYh0AmB1uxzKF3KBoTpimM31m0Q1o6jtO20tq9OQNGWF9geez5dT1RkTkIAAlMn0Jm9imri7FI4B8nPP0xhLm9qDlKqEjtUwsHBeAS6Hh8EOcZrl9q5eh/kKAUvzGTKGTo7eRtqa41k5pYzmyYyibMTvC4Xt3aiOuokqN1oJIQABCAAAQhAYNURaG3SbReBgZMqRdSbu7g7a11iO7cac/5VK8jhyRAukp0kk%2B1NZR7oLdjr6GFlCuetTXjY9o7PPW37Ree/EwQ5msgYa7om%2BSt0jBXPOQi0SaA1%2BxwKZcdBPXttbcbQHuv4ztkbm%2Bfm2KMIywJpeTeF9sLKGbcz5VI4ggAElpNAZ/YqplTGNiTtybAcez2cA8XqMecydaWycB4CIYGuxwdBjpB4y8ezEOTwF783mReID5%2BvlwxyhJMuZeYZvdgi3U7wUvmLctJBFq3GbHNlOWNdMVk1AZ1Ne/xi2YcABCAAAQhAYJUSaG3SbR3CFfOQkRdHxtPbeU1q/lQx/yrN82oFSuJy2G4x5vwpN3ezZXtBinAe6tLUnQdWOBQ9bqMOSi/4cHOGh/fnoOJRrptO%2BWIW%2B/n28%2BpJtE0uv72Wk9FIEW0zr%2B5c/ho6jijNCQi0TKA1%2BxzKZe1AZpz7eUrptw3Xzfm81vaZNXbKjps67G9HPJAx2Xj3lWAfAhDokkAr9mrXtsJXdaRC0JxdsNcidsfZpbz9Cqu3ZebmDSZTdN4RlsbxaiTQyvjIgCPIkYHTxqXZCHKUo7L6EqFwcekMoXkfxx5xBveUHNm1sZjk3XrbXTLIP3orrc0fMbKO9eRBDj/CfNPNdw1%2BHPzHa504JTvvHgZzEgtKJw97EIAABCAAAQisBgKtTbqto%2BouuXeXeW%2BZ/90jO3dti7xEPL3ItPOn4n1ozedfpu1Ki9JdQ0f8CfNuNa9lrfPOvESy/fmT1aPreaAJlNzmXoRZzFnDeeCmwby1mLOm5LHtOORh2tLicnPfm27baOsbDZZ47KP1eIGGxJzUtl0s/6Rt1pKOFgs7EOiIQGv2OZTPjqG0DS5ncWPWrnsTY1fzWdunL%2Bq9bWPx26DXTSB65yZdQ2cCIVbWbmy0k4c9CEBgEgKt2Cvv9/nWwmbou3AHc8qdm7bZuUcxl4nYoeT8wbMlsXlLVncvbxdzxWzdXFwRBFoZHxkSBDkycNq4NDNBjtICeLAwDIMcpX8C6iTN2xaLSDtBW8Ygh2m4kvF1C10N4Oi2sVFvo1NQBgQgAAEIQAACvSPQ2qTbW5jqfCO3Lf9xJIbFOdVi5VTNv4oSE/OikbleIl1Y7zjzJ%2Bvoiznrrdot/NmlKKsc6Ajl1%2BMq9vonHk0/sr3NzHdd%2B8S4JJ0MhZwub%2Bp52Pn8k89529DRNh87EOiIQGv2OZTP2ry6QY7yHRfGJozY0aAOa/vMI2FsfYn1aWFTggL8w6r8w7V5zBb5xbAPAQh0R6AVe9VgLpmay8TnD26eNTKn8Xx77lrENmKHuus8q6DkVsZHhhNBjgycNi7NUpCj9OKh5ITN/NPE%2B/ebMYS33SV3DP8VWJrEBQDttaksbk3lQ1m9f/MVxtrIu2mPHPH/1RfIyiEEIAABCEAAAquLQGuT7qqFqXk06PCfvO7OgCrW48%2B/bMkngn/9GRmic6Fu5k/TnweKHDGPe/AexTpYtN8lt969TXaeGH20lGXl75zYU9x5o49zLc0li3QuUBFzLMadDFqByzt2kGMoQzE/H3fOO6GOqg1bCHRFoDX7HAponXURR16Y1h57TsLsunaQwdo%2Bfe79cLw5J%2BKn7G%2BCrSK7042NzlbJRQhAoDaB1uyVsRXG9zYyjzE2o9qnFZt/OHuUCLSOBDpSthE7VLtDkLBEoLXxUSrVHRDkcCw62Vu2IEcn2lAoBCAAAQhAAAIQWJkEup50r0xqaAUBCECgewL9ss8uyBELbIY0nFNx9EkHYVqOIQCB2SfQL3s1%2BzzRYGUR6Hp8rLoghwYdpr1dWd0SbSAAAQhAAAIQgMDKItD1pHtl0UIbCEAAAtMj0Cf77IIWqX84l7m49AQ5ymQ4gsDKJNAne7UyCaPVLBPoenwQ5Pi1X5NpBDxmuRMiOwQgAAEIQAACEFjpBLqedK90fugHAQhAoCsC/bHP1Y%2BYCxkQ5AiJcAyBlU2gP/ZqZXNGu9kk0PX4WDVBjtlsfqSGAAQgAAEIQAACEJgGga4n3dPQgTogAAEIrEQCvbHP9p1L9e7iMG1BkGMl9kh0gkCaQG/sVVpErkBg2Qh0PT4Icixb01IxBCAAAQhAAAIQgEBfCHQ96e6LnsgBAQhAYNYILL99PiVHdm2UW/WlvHfvqY2QIEdtVCSEwIogsPz2akVgRIkVSqDr8UGQY4V2HNSCAAQgAAEIQAACEKhPoOtJd31JSAkBCEAAAj6BZbPP9s6NT8lNGuC4bZsc8YWr2CfIUQGIyxBYYQSWzV6tMI6oszIJdD0%2BCHKszH6DVhCAAAQgAAEIQAACDQh0PeluIApJIQABCEDAI7Bs9rkU5LhL7ti0p1GAw6hAkMNrSHYhsAoILJu9WgVsUXH2CXQ9PghyzH4fQQMIQAACEIAABCAAgQkJdD3pnlA8skMAAhBYtQSwz6u26VEcAjNHAHs1c02GwFMk0PX4IMgxxcakKghAAAIQgAAEIACBfhLoetLdT62RCgIQgED/CWCf%2B99GSAgBCAwIYK/oCRBIE%2Bh6fBDkSLPnCgQgAAEIQAACEIDAKiHQ9aR7lWBETQhAAAKtE8A%2Bt46UAiEAgY4IYK86AkuxK4JA1%2BODIMeK6CYoAQEIQAACEIAABCAwCYGuJ92TyEZeCEAAAquZAPZ5Nbc%2BukNgtghgr2arvZB2ugS6Hh8EOabbntQGAQhAAAIQgAAEINBDAl1PunuoqHBlkAAAIABJREFUMiJBAAIQmAkC2OeZaCaEhAAERAR7RTeAQJpA1%2BODIEeaPVcgAAEIQAACEIAABFYJga4n3asEI2pCAAIQaJ0A9rl1pBQIAQh0RAB71RFYil0RBLoeHwQ5VkQ3QQkIQAACEIAABCAAgUkIdD3pnkQ28kIAAhBYzQSwz6u59dEdArNFAHs1W%2B2FtNMl0PX4IMgx3fakNghAAAIQgAAEIACBHhLoetLdQ5URCQIQgMBMEMA%2Bz0QzISQEIMDjqugDEMgS6Pr3nCBHFj8XIQABCEAAAhCAAARWA4GuJ92rgSE6QgACEOiCAPa5C6qUCQEIdEEAe9UFVcpcKQS6Hh8EOVZKT0EPCEAAAhCAAAQgAIGxCXQ96R5bMDJCAAIQWOUEsM%2BrvAOgPgRmiAD2aoYaC1GnTqDr8UGQY%2BpNSoUQgAAEIAABCEAAAn0j0PWku2/6Ig8EIACBWSGAfZ6VlkJOCEAAe0UfgECaQNfjgyBHmj1XIAABCEAAAhCAAARWCYGuJ92rBCNqQgACEGidAPa5daQUCAEIdEQAe9URWIpdEQS6Hh8EOVZEN0EJCEAAAhCAAAQgAIFJCHQ96Z5ENvJCAAIQWM0EsM%2BrufXRHQKzRQB7NVvthbTTJdD1%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%2BnKOCKTBwIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAwFgGCHGNh62em999/Xx557lX56JcfkL/8%2BsOJ7/zwvNm67y1fnxf9mvNm/y%2B/vnnk%2B4mvbZaPf%2B0h%2Bb%2B/%2BpD8xVc2yZ9/ZZN85M775UNfvE/u3f5LuX79ej/hIBUEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQisOAIEOVZQk7733nty/xMvyZ/d8aCsu/MR%2Bcgdj8hHi%2B/D8tE7H5aP3uG%2BH7ljfnDuTrOdl4/Z72b56Jc3y8fu3Cwf1e8dD8lH79gkH7ljk/zpPz0oH/7SA/KhL9wvfzz3U/nDf/yJ/P7n7pPf%2B9yP5UePPy9Xr15dQURRBQIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABPpMgCBHn1unoWwmyHHfthfk5i89KH/y%2Bc3yx/%2B4ST5ovnMPygfnzL7ZPigf%2BsdN8sdzD9jjD37%2BAfnQ3APyoeH2g5%2B/Xz70%2Bfvlg5//mXxw7mfyx%2Bb7jz%2BTP7r9p/KHt98nv/8P98nv/v298juf%2BXf5rU9vlN/57Eb5vc/dK997%2BBmCHA3bjOQQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAwPgGCHOOz613OQZDjefmzf3pQ/vSL80Wg40%2B%2B8JCY74e/sHm4NfsPyZ988SH58Bc3yZ98cZN8%2BEub5OYvbCqOP/zFB%2BVPvmju1hh%2Bi%2BMH5D9/YRj4mPup/NHcz%2BQPbv%2BJ/F9/f6/8p//%2BY/ndv/%2Bx/P7t98l3Nj1JkKN3vQKBIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQisXAIEOVZQ25ogx0O/%2BKWsW/8TueUbD8rHv/qAfPxrD8gnvua2n/jq4Nhuh9f/stjeL3/5tfuL9J8otmb/fvnEV8vfj3/1ftGvuf7Jr5s7RzbKDx/%2BBUGOFdSfUAUCEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAQN8JEOToews1kM%2B8eHzp1Gn57D2PyE937JXvbt0jG7btkX/dtke%2Bt22PfH/74PuD7Xvkh08Mvj96Yo/82xO75d%2Be3CP/9qTZDr4bvf3iuknzxG75UfD99yf3yJMvvia//9/ulv379/Pi8QbtRVIIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCYjABBjsn49Sr3jRs35OLl9%2BRLP35Onj58QR567axs3jv4PrzvrDyy76xs2XdWHtt/Vh4330XzPTP8%2Bvtn5Of2/Bl5fH/5%2B9j%2BM6Lfba%2BfkcWl8/Lhz/1Yjh8/3iseCAMBCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIrmwBBjhXWvpffuyZfvPcFefboFXlk8aJsef2iPGq%2BBy7KYwcuys8PXpTthy/J0sIWObawVZ44dFG2F98Lsv3QBXni0GBr9rcfHO6b7cELsi34bj14QZ48dFEOnb4if/qPP5GlpaUVRhN1IAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAoM8ECHL0uXXGkO3Se9dk7t%2BflWePXZFHXr9ggxwmwPH4MMix69Db8t4Dn5QLm/9Onj30TinIUQQ3/ECHBjuCAIcJeAyCHBfk4NuXCXKM0VZkgQAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAgckIEOSYjF/vcl%2B6ck1u/7dn5LkiyDG8k2N4F4cJcpi7Nt7adb9c%2B/GfytWf/LkceGWHPHHYnI/cyeEFOGJ3cphAx1OHL8qhd67IR%2BZ%2Byp0cvesNCAQBCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAYGUTIMixwtr30pWr8vff/0UR5DCPqioeV%2BU9quqXr78hlx78r3LtRx%2BUaz/6Izm97Rvy7MHTNshhgh1bD5yXTbvPys8W3pWfvvSu/PTFd%2BS%2BXW/LvS%2Bclh8/f1r%2B/bm35N%2BfOyWbd78rT79xUd48c1U%2B9sUHCHKssL60fOosyvq1a2TNmjWydv3i8olRs%2BbF9WsLWdesWSfzNfP0Jdn8ugHnNWvXS/9J94UackAAAhCAAAQgAAEIQAAC4xBY2HCL3HLLLXLL3BbhYdfjECQPBCAAAQikCBDkSJGZ0fMmyPGZDU/aIId5H4c%2BqmrboYtyfOeP5erPPiHXfvAHcu17vydXfvJxeeXV3aV3ccy/dk4%2Bf/9x%2Bet/PSz/77cPyF/98z75%2BNdflXV3vSx/dueL8idffE7%2B8%2BeflfWbDsozRy7KsXPX5C/ueIggx4z2mf6JvTxBjvl1g2DF2nXNQhUEOfrXg5AIAhCAAAQgAAEIQAACEFgeAgsb5opAxtyGhREBCHKMIOEEBCAAAQi0RIAgR0sg%2B1KMCXL83Xe2y/PHrpTu4jCPqtr1%2BlE5t/kz8u4z35MrD39arm34Hbn23d%2BW48/cK08fOmPv5jDv2nj4tXPy0O6zsumVM8X3wZfflQdeflfMdtMr78jmV96R7Yvn5Pmjl%2BXEuWvyifXzrQY5FufXy7q1%2Bg95/bf5Wlk3v1j5j3PndB7mWxPbrpXcTQKT1C%2BLizK/fp2sHd6NYO5IKO5KWLtO1s%2BP/395X6/Gdzgszsu6tfXvjJhI/4kHw3IEOeZlne0nze7IcO3SLN/EmFoogDs5WoBIERCAAAQaEFjaMnD8mH%2Bxzm1p/h9Wmz90HC0tycKWDTI3N/yHrPmXrKljboNsWWhej6qUrE9Elha2yIY5p8/gn7lzsmFhqea/c5dkaSGUeU7mNmyRsUReWpANczW5dsRLuUW3TeRrhW9UivjJBrLZPjHsY0W7j%2BzPyRjdW2Rpi8yNlOX16TntH5k%2BHZQRDpU4gNjZBdlgZRlTn1ixnFteAgsbBncR2Lb1%2Bpc5NzcnG7YsRG2Y6/tj9AevX6Ztv9/nNshoeCCBzuo0hlyJIovTnszxce6xK92VkdeDIEcOOtcgAIEVRaDB/Cqrt7XzVb8NGftry/Bst/9bmPn9y8rWs4sEOXrWIJOKY4Icn7pnaxHkMHdxPKqPqjpwURZfelKu/uwW2b1/UY7teUqufecmuXrP/ymXf/pJeX7xmA1yPLZ4Xn6082359vZT8i/bTso/bz0h3/r5cfnmY8fkm1uOyDe2vCnf3PKGPPjiaXnx%2BGVZOv%2B%2B/D9ff1SOHz8xqfgi4hzcGhwY2a7NO5Ot49Y6rZsEOSasf37d8NFFsTqHwY5185WBmlGQvhO%2BfrDClLM4v07WaqAlF9kpKp1Q/1HBxzjjZGgczBmjNs3CnRxKgi0EIAABCHRDwF941HTGlwRx%2BUuO26pFiwl2bIg77UrFjxwk6pMl2RIEU0YcYHNVi7CqMkywZESg5IkiWDJcqKUdiMPsnfFKijcI5tSVrxW%2BaVnCK43YiYh1UPoL45H9MZ2tDZyqyT4dltGkI3lwnEPbOAPG1Mcrj92eEKgx/gf2LNLmXt%2BqtDOBuq4/RcrVtIFstbuuzZcpW%2BtosvX0HbHx4ZgvBTmMneBOjiaoSQsBCKw8Ak3nV1kC1s7n5tf%2B3Drye2DLSAQ5rF2P5M0K16%2BLBDn61R4TS3PxylX5m3/5uQ1y6KOqnjrwrpza9nU5s%2B0rsvPAaXnm8Hm58uOPydW7/w%2B5%2Bq3/KIdffFyePHy%2BCHSYuzjmfnZc/r8Nh%2BW/fPuAfPLu/fKJr%2B%2BRP//Ky/KRO1%2BUm7/0vHz4C8/KNx8%2BLC8vvScnz78vf/vt7fLGkeMTy%2B8CFMFdG8XdEd6dHZl3CNgyTDBhcTH5jQlr864Zs/5hkMM88mjRu2ljcXHevmeiuKujMthQls7dLTAMlNTIP7gboxxsqQoaTKx/Wewxj5YnyDGmsOLaJh98G7f8LvPZ9s6Mpy7rp2wIQAACq4mAc3INFhdNnWSii5PAmaTnC6ev9wf3paWFUjCirfqckzu4a6O4O8K7syOU0zZ2eRFm7vzQj7k7xP2Tv3qRNbibpLxYq9RzyLF1XqqEtx1Hvsn5egJkdseRzRRn5TOBs6Wl5DdTdfqSdarOFXcglctfGNytZBfht8gtMS%2BwLUP7RXU/GhXIBfiSDu/RTJyZBQJqR03gytx15vXhhfDutBEb5tmukWs55evls2NL%2B3isf8eq8XVy5jSWstk5bywVd%2Bh5rHxug/36RVs9GzGsXz4pIQABCCwngXHnV1mZrZ1PBTm835nUHzNsGeP8/mWl69VFghy9ao7JhTl/6Yr8zT2jQY5f7l2U9%2B77uBzf%2BRN5de%2Be4ntm%2B9fk6jf%2Bg1z92v8ulzd%2BRJ47dLp4N4d58bh5VNUDC2fkgYV3i%2B%2BmhXfkoYV3ZPPCOzL/8tvyyMtvyzMHz8trb12Vkxevyz/%2B6BlZPHRkYgUKp2vG4eocymsk9eoEddxWOfRjwk5c//z6zGOwnPO%2B2Uuqy3dxVAVJFs2jskp3say1x1VMJtY/BrXxOcepSt7GRXeQwfVJghwd4KVICEAAAiuEQOg0bXonh1u8jDjxF7ZkHg3k8t1yS2phFEPs8oX1FQ6qjHPKD%2BZEfXT%2BIivmkPMca1EntnmUk3k0lzoCi%2B2cPQ7lHdGuE17lWiaRb2K%2BZVFGjiaRzRSmDspKziM11zhh2z4XmHB90wQgRvqYV4Z9fNtIogpZtI%2Bax2MN%2B9dYj9%2BqqIbLy0BA2zblBCrsiwvWhl3H2bdcHw30sn0y0l9tUvcbsWGDPlKrps2uoZOtpslOLbmbFDhIqzaEF483Z0cOCECgvwQmnV9lNbN2Pv67YO1qbF6kBdsy0r9f7jcu93ulBfZzS5Cjn%2B0ytlTvnLskn/7utsGdHMNHVW09dElO7fhB8f6Ny/92s1zc%2BFG5%2BG8fkcv/%2Bgdy9Wv/m1z9yv9afI8994A8efiiPPLaOflC8eLxN%2BS/fueA/Jd/2S%2B3fPM1%2BfhXX5F161%2BSj9zxgvzZl56Tbz/6hux/%2B6qcunhd1v90l7z6%2Bptjy60Z59evr3iUk3P4xx3gkznIJ69fNYlvnUM8/04QP7cL2qy3742I625yOf2LYEjxaCx3Lp1vUGPX%2Bvt6pffry5suY3pXXJsS5JgedWqCAAQgMFsEdPExt2WLfc5/IyexdTbFFzc5Gm7Bkl7UjOTP1LewZUv0efWuDOesG9XRc1CH3kNXgDiZY/p6ZdhHcblzo3V6BdfYdXU34FUq18linPCDxyq5c1XyTca3JEjkwMkxjmziPUqrSo9I5dWnbL%2BrYG/TRe7msNfmZMPwkTljB/g2aDCtQp5qzUjRFwI1nDz%2Bu2FG%2B7mzb6kgbKiqsykxezZIXUpj%2B3DNYHgdnUKh6hx7cmTMdZ2SSmn095AgRwkLBxCAwEwTmHR%2BVaG8tfOjvyPu96PiN8OWkZnTeHZ/9PevQsaeXCbI0ZOGaEuMt89elM9s2C4vHLsi5lFVPz9wUXYcPivv/fijcuypH8iuvYfkhb0H5MW9B2XhtX3y1qa/l6vr/xe5euevyZXv/q48%2B8YFefLQRfn5/vPy%2BN5zsnXfOdm%2B/7w8sf%2B8PPX6eXn69XOy48B52XngnCwcuySvv3OtCHJ8%2B5HdsrDvjbbUyJTjHOBrordyuOtVDv1MJZlLrvx4/Zms5pJ9Z0dNh7imL%2B5uqQrwmAqMfGul/LgsJ/PkTFxZY%2BlfgWdw2dUxuby1KpwoEUGOifCRGQIQgMDKJ6CLiuLuB%2Bcga7J4sE6hcTxNWn%2BDOzkmqs9zhI84Ab3FU1aVbDqzkDQvoDaPS9Lu4xaXTbhq7tJ2DF6l/IX%2BHcqX41sWJHI0KbsWOUekc87lzAK8yOfkGHGU2r5jyhhjvPntXyorJjDnZo6Abd9cH8v3G2sfa9lUV9aIPbTwvP5cGEbvOHPXnM1eSyebuv6O7f/t/qPX8qujW31pSQkBCEBgGQkYu93h3M/a%2BSDIYc9XBDgMGZt2/N%2B/ZQRcu2qCHLVRzUbCI6fOyu0//IW8cHwQ5Nh66KKcfOEBufSDD8ruvXtl%2B6GLsu3QhWL7xOGLcmDnJnnvG/9Rrv7T/yxX/%2Bl/kjcXtsuzRy7Kc0cvya5jl%2BWl41fk5aUrsufkFdn71tXizo3X374qB965JgfevSYH3x0EOTY%2BuV%2Bef/XQFCBVOcBdICAaA5lYwqr68xVYh3jmkVyuhFAXd9zM%2BT%2BZzE4es9dOWYP3hXjvWFmzRtaaF8oX7zGJ12HZrckFiOowiqfRO2bW1GobRyUmV/Gy97X%2B%2B1BM4Gn9UD%2BX1%2BzZerN6ldmPBpgWJcZ0zdq1sm59%2BkX3tu6GOpc14AgCEIAABNIEnINr4NR3x/Wd8ZontyhJS2D/4VXboTRZfdl/%2B9sFVrBIGxFfZaixaCvyOqdgfa4jlQ5K2jJ8VE1tXvFyymfbky/Lt1jDpl/4W5ZJj5rI5tolG6TSopturVO1qq87mfNBDvd4rXp3c7hyi35UW56mipJ%2B2QhYG5TpY7bdE859W0biuq9cnbSx%2Bmy%2BjJxaT5O0mqfONiZXnXzeY%2B1GxmfFNb/4wXPt3aPDirvP5jYU71Lx07EPAQhAoJ8EgjnFJEJaO%2B/Nnz0bnQ6ie5XaMjK/K16ZnczzPHG62iXI0RXZZSr34PF35IsbdxRBjm0HzsrLr%2B2X977/R3L%2B%2Bx%2BSFw6clO0HLwy%2Bhy7KE4cuyu69r8mFDX8oV7/0P8rVL/4Pcv6HfyavvPGWvHT8siycuCK7T16R1956T/adviqD4MbVIrBx6N1rcvjM%2B8XXPK7q4V1vylO/PDAFrZ2DOh7EqLo%2BqYiTlO/y1glSWAe0VbRZfqdpPGjgrjfZczJYsZpkl3lZV3L%2B%2B4GAwf66%2BYS8i%2Bvtu0WSdeudL%2BadJCnHvS2n/MgwyzuVL6FnOcjhZDePCxv9Dl5oXyrKkzmpl8lg5Q7eR7Po2mS0vqEMCZ3G1bkkPwcQgAAEIJAkYP%2BxalcKzklc1xnfPEjhizPt%2Bkzdrk6r9lCk%2Brq4hWGthZt3d0Ndrj4lt%2B9kn6wcV%2BJgz%2BkzeblOxpCvuxPCvHQ7s4gtiddEtkzdpTLHPLCL6yrZncwj/SMswx7XcEjbtMP6w%2BMx1SJbjwhUOnmW7HtnYg76gSZuHIz0v0BV%2BxuQCZraNKU7Q%2BrXUe/fuYFgdQ5t/68xdoLyrE4RvXPXBsV447v07iVj14bfSLmBCBxCAAIQWGYCzpZNPPezv10a5PB%2BI%2BraQ1tGao5V5/dvmZHWqJ4gRw1Is5Rk96GTsv4nz8qLJ67Iay8/L%2Bcf%2BZxc3vhRuXTvJ2TpmY2ya%2B/B4uXi5o6OHQfekmPP3CcXN/6FXPnOf5Ir3/4dufKvfyinn/6%2B7Dvylrx66orsO/2evP724K6NQWDjmrxx9n15036vF4%2BrembfSXn0%2BeJv%2BJ3icg7lsoPaVZp2%2BJo7BdYPbhVwyRvuVdcfKXBxUea9l4GbR0lVfqzj279rwelWJ0ji6nCO92b5XAm6N5b%2Bmtm7C2TNmtE7DAZ3IpQDA2V5nR6jdzIMKrFO%2ByLAEO8jVofA8W/zBuet%2BIkdW96adbJunZF/EMjwR4PRzb0MPpSrWi9Tta0nlE%2BDH8P%2B7epd9O4SWSNllgGvsMyErpyGAAQgAIEGBOxiQhckJq9blNRb8Lj0Iw7tnChLS7LgvZzbPNqp3mfM%2BrzCbSAj4mS3zq0a8ti0tRZvEy4kx%2BblKZ7dnVA%2Br%2BwcX5NsYfgeivpt3kQ21z%2Bss3HodJxr4x/W1qmaWoAPQdh0kTt97DVXRt2%2BZNNp/4yU5TUFu7NIwNpl1z9UjSXzeLM5daT7dltTuK3tK6XAhLs%2B2HPjJW3v02nq1VEM%2BqHzf1SnUKJGx7b/TzPI4eyRCdRuWFjy3gFV/l2rCjA10pXEEIAABFon4OxZ%2BjegZqX2t8v8Nrlyb5nL/1aVSrdljP5WNPn9K5XZwwOCHD1slElEevbVI/KNTbvkxeNXZOfeN%2BWXe16VheK7RxZ275ZnXl%2BS7YcuFC8Yf%2BbQu/Lanldk32uvyOt7X5YDe1%2BRw/tekcMH9sni0jlZHD6WSoMbJrBx5Ox1OXrufTl6zmyvy5Fz1%2BXEheuy79g5%2Bekv9k0ienVedeaaRxutd67cUkYvTfpf7fpYpFLO6gOv7GT9phQvnS%2BDexxTVVUumFGOh7jz2fpHindO9Gb5goI8vcYpxzrp14ROfr8eJ6thF9Zjy4g65ZXPWlk7vFukzG9QjwYzwrL1fPIOEF9Mb9/KlAmsDGu2L44P63Bl%2BEEtrxIvQBTKLYvzmeCdxzPCbFydfcnYhwAEIACBGAHnuFJ/6SCVO19rwWMXJBWLGM8Z5TufjeN5wb63IiZncK5ufUE2e%2BjJEdPPOu3KUGx2f8em7SLI4ck5ES9f4Oy%2BW5DGuGSz%2Bhc9uScqxy/TWyxXlunV73Mr7Tftc74stvzRBbhL5lhGH0EVKyN2zhU42IuliZ0L83E8WwSsjdNgxui2eN9PlVa2b2QCALauTH/OpbHXMnUYOW26TD1V%2BsSuezqWxrjeTWG3o/Xm7HfumgviZn67utI3xoBzEIAABMYm4OYrlfOrqjqs3dsgGzbo71bF2iAs05ah%2BUe3tX7/wnJ7dkyQo2cNMqk42186JN955OXiTo7HzYvHD16UrQcvyraDF2X7wcEjqp48fFGefvOS7Hzzkjxv3r0xfDTVKyevyKun3pP9p9%2BTA28P3rdx%2BMw1efPsNTlydhDYOHb%2Buhwffs3%2B0fPXpTh35or8YOveScVP5/cc7KGDOMy0uLgog%2B/gSrE/v17WrfXfAZFyJoelDY8b1J8KchQBj%2BK9E4kATVGV55Qe8dCrE3/U%2BZ%2BQWoWX9UOn/4iDPJ/RXW2iv8vl7TWQ3atrRF57LRIo0Wvr5t1dDxmG4aVxHf4uQFHdLi5tIL/KviZ4FJUStNeDfHo9s3V1jvb5cXXOVMclCEAAAhDwnMaj/zRtEuRosDjKOaMKp3OdSEeD%2BmKt7MsQDUy48ke5jBaYc4SNpnZl11pI%2BrJaR91wsVeb16gU6TMN5YsV5Msc5RvLVOdcM9mWlpZk8B2UXewvbJENc/6z8xsuvFVMq%2BOo09S8i2RpYYPM2X/aR%2B7iMOVEy3A6pvqeda76bKNlqbBsZ5JADSdP4dCvDNZ5fcrvMxZK1fVBwrydc78XqX5blGJ1io0bK1DzHdv/Rx1h5aDHaL05vdLXnL75OLhjW8veN9ecHBCAAARaINCirbJ23rPH0d%2BejNixMsI5sDn46VOSAAAgAElEQVSu/P3L1NGDSwQ5etAIbYpw/1Ovycbte0eCHCbQYR5R9eQbF%2BXpNy7Js0cGAY4XTYBjafDujb1vmQDH4KXi5u6NN2xw4/1BIOP84K4Nc%2BfGiQs3ijs4TIDjyLkbcvbKdbnnkT1y40ab2gzKKl7irO83iPwbvUmNzuFb7ZDWcieu3wRdiiCLexTTiPN%2BWFne6dwgUKDCF1sXOEnVW0oeHEysvynPPn6rjpM%2Bp2f6mrZtEbywQYHAsW/lCM77LwBv2Me03jWVLw4v3%2BVTDrK4Noo9isvWUc4UtFT80OaNyJfvb/HyOAsBCEAAAnkCaQeOyeecOJXOGetgGnUg5SUwjl7jEDaOZ7cY6rK%2BwvmsC6Xkosst9rIOu6FyeY4hAVd2pZ5hVnM8Dq9YOclzk8lXj2%2By8ooLk8nmF24DBbckAhB%2B4ti%2B7fOu35adqe58sp1tGcG4sYv74HwhhxuXJedqqqyY7JybDQJ%2BPzCPQrJBO2MzF2SL95g/0/dK/SHQ0PX3SJ%2ByfSdThpcm1Z%2Bzdag8vk514tmar2rryVc8NspnFeyHReXsd/Ka1aM6SGrLyDVQKBTHEIAABKZKoL35lbtjz9zltkXmKufcEUWtjZ2TLRP%2B/kVK780pghy9aYp2BPnhYwty/84DxeOqHj9wQeaf2Sebn3hZHn7yFXnkqVdky1O75dFf7JHHduyRn%2B/YI1t3virbn31Ntj%2B7V558bq88%2Bfw%2BeeqFffKLF/bJ07v2F98du/aLfp82157fJ88uHJCj71wpgh9Hzl%2BXM1duyA%2B27pcLl6%2B2o0hRinmngLv7wrzLIncPRL2KPWdypTO7/fqdw3n0H/vu2qgDfqBb2sGf193p3CzI0Z7%2B1br5GuTltY75ksNf82gQRVnp8aB8K0cp75Bu8T6NzAvLfRG9fVtmJIjgJRvuqlyRIFsyAOPyRMT2qjDBtHmZX79e1q1bV9y5pI/tGjw2bbRfWZaVY8Grhl0IQAACEEgScA6plJPGOVNTTi0tvC0njpMp42wrnngydCA3chqZlxS6f/BX3ebeRKcmac2//LcMAzpVXJVvaluXVyp//Py48jXjG6%2B76uy4ssXKdWWlX9ocyzc85y/cdQEfbOc2bMk/gs2WETqenWxhH7FtHgbokmVldOBSvwn4Tp5kQMD1legj0VRD2z9Gg3q2T2Xe2eHShH1VK/DvTBqtw6aqpZNNXX/H06/Rz4L/exKOqcw1x8MFM1NBTnu%2BqWD1tSclBCAAgQkJuN%2BScN7RuGBr54frC3t8i9T541BRn82T%2Bc3x5tPZ37/GCkwvA0GO6bGeSk3fuP85eezlY/L8sSvy89fPy6O/PCqPvviGPP7Sm7L1l0dk%2B8tH5MlXjsovdh%2BVHXuOyrOvHZPn9x6XF/adkBf3n5CXFpdk4fUlefnAkrxy4KTsPnhS9pjvoZPy6qGTcuL8NTl58UbxNXdzHDt/o3gvx1sXb8hDzx6W429faEnPeVk3fMSSeZHzpC8M94Wq55Tuqn51xq%2BR0j/2rYN7NPjhZHfO7qbBiuaPq2pXf8u8ljPdMYrqaVl5Tnu9c8MrXx34rgxXbixYoOmrHofm2mOwZ3WbNMghrn1L8sX0LQmxKOu9YKD/HpjyvsdrmH9cnUvVcwABCEAAAgMCdvGQCybUDXJoutxCpC54t8hKL4TGqc9/Se/gX2FVEtnARcTxVc5bR2Y/h0s/8ULSX%2BC15kAbR77mfH0i9ffHkS1dunNUpgJ96bylR02F/zLMZCtdso7Z0bETl037fsSJnCmrVCcHs0PA2unR/lFSwrZ9zp6b12EMHfIlm%2BbGVNrmemmCQJ514IfnS3V40tbVyctSa7cmg1hZcS6DlKlrbnwS5Igx5RwEIDBrBJydn3huau28m1tZW1r37llbRju/f31tDYIcfW2ZMeWa%2B/4TsmP/W7Lz6GXZdvCCbHvtLdn%2B6kl5au8peXrfW7Jz8S157vXTsuvgafnlobfl5Tfekd1vvCOvHnlHXjv6ruw7ekYWj52R14%2BflQMnBt%2BDJ87KwaXB99SF9%2BXUpWGQ4%2BINOXr%2BhrxxbvCejh2vnpA9h98eU3IvmzqszSOqPKe1l2Ky3Sqnccf1jzqWnfO97JR2j7dKny/fqRAH48p3Dv94yuJsB/o3CwRUyTsaDNDyS/ppO2sfsnqNOvuN3qPtkmHkXdK6az2uKhXIGJZnZfCiHHqupJut37EwfWTtuvWy3tzNYR6RNrztKSeflt3JOLMysgMBCEBgdRDQOwmSDqrQYWWPRxcb1tmTcmo1RGoXQonyGtfnOb%2Ba/GPf1pP5d/NAtYzjOap7iwvJzD%2BNo1XXOtlQvjH51hJlJFFD2UbyByfsItotxIMU6UOr9%2BiYSGcKrmTLiPSrnLzZsoJ6OZwNAra9q/pYpK/ENIyVZ89lAiS2bzVw6Kcen2Xrq9IppkDmnCdj03hv7jcndc3%2BPiR%2BpzKScgkCEIBADwm0OL%2Bydt6fW7nyzdqj0k7bMqp%2BK2r%2B/vWQuBGJIEdPG2Zcsf76W4/KwtFzsuPNy7L11ZMy/%2BQrsnn7gjz8xMvyyJMvy5anXpFHf7FbHnt6t/z86d2y9Zk9st08smrnq/LEs6/Jk8/ulaeeG3zNY6neunhdTl%2B%2BUfpqkOP4hcFdHAfPvC8H3r0mrx59R57afWxc0Yf5nNO2ncdTjYqTc/qK54Tuqv5Rx7Jz6qeDGamAR9tBjo74a8DB3JVT%2BcwxT4ZEYmU4cPwrv7BsLWcY1FAZvACC3zu0zKYO/3x/8muo8W4SldHeFaI6hLoNyi3VneBaSjMizrBfaSAouM4hBCAAAQjUJ9BekMMtWioXLDXFSzmVBtmb1uctfjYsSPKJLzHZ6i6wrHOtaiGmlTgdJv633LIHOSbgqzgabdtlZx2VlYGsiJCN2715GVa%2BwpFaoXsb8kRE5NQyEqhrg2q/P8kbr1sG1jBvbwe62zRmnATvtyi9J6S4tkU2aFA89qNQW6eG3G3/r%2BE8C4q2%2BkUCFslrVg/fiRcUzCEEIACBmSFQMcdookfSPrrfoFtumcsHOmwZVXNrV2Ybc%2BomaraRliBHGxR7VMaff%2BkB2X/6ijz95mXZ8ebgBeMvHL0kLx2/LK%2BcuCyvnroi%2B956T14/fVUOvnNVDp%2B5KkfOXJOjZ6/J8XPvy9L563LywnU5ZYIbF6/LO5dvDL8ib5tgx6UbxZ0cS8VdHNfl8Nn3ZfGd92XPW9fkjbcuypYXDsnV96%2BPTcQ6mhOO6LELthnVIR6/S2Sq9fs6Fv%2B8N/%2B%2Bz33V4W3e5zBv01rVkjtO5/jdAC5jZ/rbuygi76Jw1Q/2rKM/k1bTGOe8lj3iqHd6G9SqW4qBXh8/yJF71JiqVhVUcG1cdA9fz5CTf/eJ35eCdFYvGzhxCey1EXYuDXsQgAAEIFCTQKWjyrzk1l84LNiX3pZqsIuQthw9bpEVfXRKw/qsgyrmbCspEjvw9U%2BHR8qO6Fg54Tmn4%2BQLMldWlFdYda1jV2aVfJPxrSVMkKi%2BbEHGyKErq8kdPrYg61StWoDbHKM7VWXY67fIhi368s5EfTZt4vpo7ZzpOwFr7yra1KardvDbMVs49OvYOJemro2xdcSCh1bWCp2ato3t/9UMwqKtvE2CHF59VXYyrI9jCEAAAv0j4OZEE9s0a%2BcjawPPdppAxzDePorDlpFJY3LZdM1t/2il0z9DkGP6zDur8e1zl%2BSvvjpf3FVh7uTY9tIbxcvGzQvH9e6Nx58evnD8Ge/uDXPnxvN7ixeKD144vk927DLf/bLjxf3yzIvuxePm%2Bonz14sXjpsAx%2BvvXJNX37omv1y6KodOX5aHnz8kp89eGlNHdfDG/7U%2BZqGlbO5f7TGH9IT1L5pHBSX%2BTj%2BUwtU/jo4qX8b5X9JWD5yzP%2BXgH6TU8seRTetKbZ0MxTtWUpgWVYZBMCAprwY2zJ0h69dJ8aimyF0flrd5jFPxjpe0buM6/G0dVY9X04CFeaxURFYlZ8tbN28DM6kYRqXMlpPhOfqYrsr8KhRbCEAAAhBoiYBzbqUWPOocSl0vCbK0RbYspIMFJq0NGCQWPo3qs/9urlgglYQsH1TJ497L0GRxVXMh2QKvsjZ1j2rK1wLfuhK5dHVlczlSe65tm7SdV5pdqI/fv1z/SZehfd4%2BWi4VsGtDHk89dntAwDpv0v1D7Dg0j5KKOJRCNfwyt2yQQb/KlG/TNxgnuTz2WqbOUOY6x7b/N5BzWK4dY02CHP5ddInfKyu2%2BcPAlgV7yA4EIACB/hFob37lAg%2BJ3yT7O5D53bJpcr8Vbp1S6/evf9B5XFUP22RskfYcPiVf2rhDDr57TZ47elmefu1k8ZLxp3cflWf2HPNeMn68eMn4L/efkJdfNy8YX5LdB07KqwcHLxd/7fBJ2Vt8T8new4PvG29dkLcu3RBzB8ex89fljVKA4z157tgVeWXpimx%2B/oC8fuxduXHjRnM9rBN43fCdArm7GobXgloW168r3ktQvJPAu7ZonOdr1xbO8OKRUDGv8aT1q0N57boi2OH8%2BEbWcv05J7cndrDrAgDN8rsAQzbfpPoXdxYMGJtHfY18bPnG4b5W1s0vSonR/DpZWwQK1tmXzqfldToNHvGVCF7YNlk7LHu9V2dZwpzDf374Yu%2BYXjYoYWQfyl8Odi3K/Hqv71XdNaEyr1kra4vAzGhwQiX36y4/Xm1RFoc819p%2BP1pOTmetgy0EIAABCLRJwC0eokEM61TKLUA8eTT93IYi2OHCHcO7Rubmhg63yEuVTTGav8qhpFXaBVKdR6wYGZYij7NyDIp/nHlBmqUF/Wf9LdLsToCaC0nVd1xeyqHxtqZ8LfBd2DBo87mU435E9pqyFd1lg8xt2DJ4vI5XTnGHktfX6v473StisKvtU7c/jhRQs0/beowzIDPWbLpMmpgMnOsvATvG5gY2s3QH3oJs2bJB5vTRUHVf5ireGNJxkBl/uQBAGpxnN8PAQVanoR1WPdMVjF6x/X96QQ73mzQYmyaQseR%2B2MQcGBtXtFGG8agynIEABCAwbQLeb0Py9oqaMlk7nwhylP7UlJhD2zLa/P2rKf8Uk3EnxxRhd13V47sOyA%2B3vSqHz1yTl45fkV2HzsiuA6flpYOnZeHQ2/LKG2/LnjffkdfefFf2HXlX9h81Lxg/IwdOnJFDS2fk8NLZ4vvGyXOi38Mnz4n5Hn33spy8YF40PnhE1eAOjquysDQIcPzizcuy88hleWL3MXnqlSNy9doYj6wqOcGHDmN1HCe2oRPcd/oOnN%2Bj5ZSdwV6rTFq/dU6P1unLEsrsSVCxO60gR17%2BpC4l/eNBB3W8%2B2WU9tcaR7wLYORYldo6GThwZZl6cuUlHf4Vejk5THBu/SCYkuivawr9KprZ079gEwsY2SLK%2BpVYFi8in5eSfDbfYCepc5COQwhAAAIQaIuAc1TFghz2n/B1nTeeE8r%2BK91z0Om5WF1Go8b12QWScUDV%2B0brrpI7dOJV4q%2B5kKyqd6hTVOZKGXIJaso3Kd%2BSfnUd8zVl8/tLpu1NcMX3SeaojFyz8teVfaSEmoE7p3M2mNaGPBERObWMBGqPsTnZ0MApZW3pcGwkTbjtU4nAcwaNqyMYH7V1alinJ2tSn4S8uUBO7lpRnFdv7nemfTudUIbTEIAABMYi4OYaE9sra%2BfTQQ4jorWv5rcoNNy2jKr5e7Pfv7HQdJiJIEeHcKdd9L889II8ve%2BkHDt3XY6de19ODN%2Bt8dal68X7NN69fEPOvndDzr13Q85fvSEXrt6Qi9dELl0Tuex/3xe5/P7gvLl%2B8aoUed69ckPevjR4X4d5ZNWRs%2B/LwXffl31vX5Pdb12V3Sevyt7j5%2BVHW1%2BVsxeuNFd/0iCDqXFxXtav03/Ae876tWuHd3hkxOq4/nXFezQy9Vde6nmQo%2BpODtVP28gPBKxdKwWfIo1z3OeCEuIFFHLprCO/4qXnNl0kYFLvTo7hnRJD/fyAw9rh3T2KoGrrAhPxYFE5v7lTZHgXjDI1PIePTnNlcSdHmRtHEIAABJaDQC7I4a6F65KspEsLssX8s3UuWLTMmUVK8C/YUkFj1Fd7geRkSS/svH/kqtPcyLwQu/ujJHjkoMFCcmxekWprn6opXwt8u7yTQzLsBnd41AYST2idm4ETN546frZuGUPW2bFWt6y4JJztI4GqMTY3J0WgrmmkzvYVY/vSTqhkoKIOK6%2BOkl2t0knta%2B07U4bCePVlx0lEdutoiwSsc9dcUUuyYO6qafy75kpgDwIQgMDyEqg596sjpLXz6d%2BXQTGuThMkLt3Va8twc/RSIHnc37868k8xDUGOKcLuuqpfvn5C/uprm%2BXmuZ9O5funn/%2BZfOQL98vHvvSArPunTfIXX94sf3vPVtm8c1Euv3eta3UpHwIQgAAEIAABCKwcArr4iDiFOlFy2vV1ogSFQgACEIAABCAAAQhAAAIQEN7JsdI6wTcfeE7u/Pcn5Mv3bpd/2rhVvvCjx2Xuh4/J7d9/VP7hXx%2BR/77hYfnMd%2Bfl09%2BZl9u%2BbYISD418/%2B6eh4prJs1nvvuw/P2GR%2BRz33tU5n7wWFHelzb%2BXO788Xa5674n5Gs/e0q%2B%2BcAO%2BdamZ%2BSe%2BefkyYXD8v71MR5VtdIaAn0gAAEIQAACEIBAbQLun1elf%2BjWzt804bTrayof6SEAAQhAAAIQgAAEIAABCNQnwJ0c9VnNRMqDx9%2BRv/6mCUo8Lv%2Bw4TH57He2yGe/87B8%2Btvz8ul7Nsvf/ctm%2Bbt/fkj%2B9lub5G%2B%2B9aD8zTcflP9mvt8Yfr85OGeumTQm7d/982b59L9slk/fMy%2Bf%2Bc58Ud4/fHeL3P69x2TuBz%2BXz/9wm/zN3Y/KY8%2B%2BKu%2BevzwTnBASAhCAAAQgAAEI9IaAfSxI1W3oLUn8/7P3LlByHeW9bwdCDLmHk5t7OYTg3JMTZ3FuSMjD0coJNJCTOGQ5OdgGHJxAIBhjaMD23OiQkISEmJdN%2B81gjI2wLbVsyQjjlyyrpbatp23Jkka2W2%2BpNRrJerXez9G8pPnuqr27vqq9d1XtR/fM9LT%2BvdZevR9VX331q9q1d33frqrxTq9FakMMCIAACIAACIAACIAACIAACJgIwMlhojLJz81%2B9jW69ZGlVJy1hG5%2BeJG/zVxEN3nb83TTTN8BIpwg3xLbjNBWes5zkIjr3yn54UWcm2cu8jbPsTFrCd06exndMecF6v7pcrr/8SV06NChSU4O6oMACIAACIAACIAACIAACIAACIAACIAACIAACIAACEwmAnByTKbSSqjr6TNDtGvfQdqwZTut27RtDLcardtco/Wba9S3azcNDGAUR8IiQjAQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAIEWEICTowUQ21XE6OgojefWrhygFwiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAQGcSgJOjM8sVuQIBEAABEAABEAABEAABEAABEAABEAABEAABEAABEACBjicAJ0fHFzEyCAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAKdSQBOjs4sV%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%2B/725dMMPF1i2cuO8%2BFdb1w/LJDdxXuzf8MP5ke36e%2BbTdfc8Q1/%2BwTP0pbvn0RfvnkfX3vUUffq2x%2BhnS1%2Bjc%2BfOpdQawUEABEAABEAABEAABEAABEAABEAABEAABEAABEAABEAgGwE4ObJxa8tYQ0ND9NSyV%2Blzdz5NhbsW0rV3LqTPe9sC%2BvxdC%2Bjzd6rt2jvL/rm7xH%2BZvsDbfPr89%2BbTF%2B6aT5%2BX253P0OfvnEfX3jmPrrnjabr69rn06Vufor8rPkGf%2BO7jdNVNj9HHb3qUHnl%2BDQ0PD7clGygFAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiDQeQTg5OigMhVOjseW9NBnb3%2BaPnPLfPq7786jT4mt%2BDR9qij2xf/T9OnvzqO/K87l40/dMpc%2BXZxLn278f%2BqWp%2BjTtzxFn7rlSfpU8Un6O7F990n65M1P0Cdufoyu%2Bs5jdOW3f0Yf/cZP6Yob59BHvzmHPn7Tz%2BihBSvh5Oig%2BoSsgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgEC7E4CTo91LKIV%2BvpNjDX3ujqfpmtvKnqPjM7c%2BQ2K7%2Btb5jX%2Bx/wx95rZn6Orb5tFnbptHV98%2Bjz576zzv%2BOrbnqbP3CZGazQ273gu/f2tDcdH8Qn6ZPFJ%2BtubH6e//vbP6GPfepSu/PajdNXNj9H0ecvh5EhRXggKAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiDQHAE4OZrj11axhZPjmRdfo0L349R179N03Q/m0nX3zKXr71H/1//AP%2Bb/xvUbvP%2Bn6IZ7nvLCX%2B/9i/2n6PofBLfrfvAUyU1c/4cfipEjc2j2ghfh5GirGgFlQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQKCzCcDJ0UHlKxYerx88TN98YCE9sWILzVi8kUpLNtLMJRvpoSUb6eGl/jZr6UaavczfHlm2kX6ybAP9ZPlG%2Bsly8e9vc7R977oIs2wDPRLafrp8Iy1/ZTNd9a/TaNu2bVh4vIPqE7ICAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAu1OAE6Odi%2BhFPqNjo5S/8AQ3f7oanpp52l6ZvMJmr/F3xZsPUELt56gytYT9Ny2E/S82GpiO97Y9P3jtIjPH6fntwW357YdJ7kt2X6cavVTdPVNj9K%2BfftSaIugIAACIAACIAACIAACIAACIAACIAACIAACIAACIAACINAcATg5muPXdrEHhkbotp/10Ko9g7Sw1k%2BV7f30rNh6%2B%2Bm53n5atKOflu48Q/VqhfZWF9Oyvn5a6m2naWnfaVrW5/%2BL/aU7Gvvif8dpWhLaFu84Tcv7%2Bqnv8CBd893HqV6vtx0PKAQCIAACIAACIAACIAACIAACIAACIAACIAACIAACINC5BODk6LCyPTM0QsWfrqJVewdp4fbT7OQQDo7nG06OtX1HaGjuP9Dp%2BV%2BnVX1HA04Oz7mhOzqksyPk4BAOD9/JcZp2HBmAk6PD6hGyAwIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAKTgQCcHJOhlFLoeGZwhG7%2ByUpa7Tk5GiM5GqM4hJNDjNo4tPYpGnn0Ghp%2B/IvUu34FLdspzhtGcmgODtNIDuHoeGFnP/UdHaRri09gJEeKckJQEAABEAABEAABEAABEAABEAABEAABEAABEAABEACB5gnAydE8w7aScGZwmL798Iuek0NMVeVNV6VNVfXa9l105ul/opFHPkUjj3ySDi%2B5l1btOMxODuHsWNx7iuZtOEFPVo/RE68eoydeOUqPrT1CP%2Bs5TI%2BuOUw/XX2Ifrr6IM3fcIxe2tVPrx8fpi/cNhdOjraqCZNZmRp153OUy%2BUo311r%2B4zUuvOerrlcgcptry0UBAEQAAEQAAEQAAEQAAEQAIGJJ1AtdVFXVxd1FSuEia8nvjygAQiAAAhMdgJwckz2EgzpL5wc3ygtZyeHWI9DTlW1pK%2Bf9r38KA0/eT2NzPpbGnno4zT4%2BHW0ftOGwFoc5c0n6Zan9tG/zNxJ//hgL0398Va67oebqPD9dfS5u16hz9y2mv7%2BllXUPW8HrdzdT3tPjtCX7nwGTo5QWeAwK4GJcXKUC76zIl9I56qAkyNrOSMeCIAACIAACIAACIAACIBAJxOoloqeI6NYqkayCSdHBAlOgAAIgAAINEEATo4m4LVjVOHk%2BPr0pbRm72BgFIeYqmrt9j10cv436NjKh2hwwY00Uvoojcz4CO1b%2BTN6qe84j%2BYQa20s2HySntlwguatP%2B5tT687RnPXHSPxP2/9UZq//igtrZ2kNXsGaP/JEbq%2Bu9xSJ0et3E2FvPxC3v%2BqP5fPU6Fco7hv%2B5XRuREvZ/rPk2uQQDPpU61G5e4C5RujEcSIBG9UQr5A3eU47e21Ss9X6hEOtTIV8slHRjSVf3sWEl6ZCCdHmQpcT9KNyFDlki5eQhgIBgIgAAIg0EEE6hXf2CO%2BXC1W0n%2B3yvHDxqJ6naqVEhWLja9ixZexIo1iiSrV9OlI5Nb0iKherVCpqPLjf41bpFK1nvCL3DrVq2Gdi1QsVSiTyvUqlYoJuY4RL8nN%2BJ9Gv5bwNWphPplCN64TjTrmlXtkv0gZqjdRvULFiCytThdl/XDU6ZCM8K1iBmA6W6US65IxPyaxODexBKolfyZiEjoAACAASURBVOQAl61Wv8S5YpFKlaqxDVN1P0N90OploO2P00fTM1ldFu2q3zab76UiFUUe0zaymv7me17jGBiVod9HJQq7OeDkmNjbAamDAAiMPQH17Ej4jmpSiZ8V0XY0GNzR5rIMrb3WnjGu518wjfY%2BgpOjvcsntXbCyfFvDyz2nBxiFMezcqqq3n6qvbqchp/sog3barR34ws0Mv0yGn7gr2jgiX%2BgNbW97OR4rnaKHnn5CD249CDdv%2BQA/XjxfvrRon1033N76b7Kbrq38jrdV9lFT79ymF7ZN0D1U2fpf//wWdq3b39qfaMRlIFbOgci/3m3MblcMDk1wudsTo4m0y8XGlMXhdNTx2KkQHpXh26ET%2B6sEHxr5QLlpaPF5dnxCqPJ/EcLNMMZpUNqZ06G1GQUjOSQJPAPAiAAAiAwNgT0jkeWjo6KHzB2xXVahLOjZDbaufNpSY/qVAk5UyJGr2JcJyxOhjDCubXTr3rOkkZHLWBA1APJ/THjJROI/qfSryV8ozrYzqTTjYiNknrHOLKfwQgsFExhSLXW6bCMNBVJg6QbJbq6MuZHk4fdNiGQ4P732zNDmWt1K7adCWVX1aeQ3MT6dMW0iQ1Hd%2BReNBuz0uqf5t4MTz2FkRyhyoBDEACB84iAepcWz5bUba8kxc8K1/u1/m4detYIOSzD/FxQ7/KGuFKPSfAPJ8ckKKQ0KvYPDtPX7l/ETg45VdULvcfo4JIf0vEld9PLvYdp5c5TNPjoF2h42odo%2BEd/TjtfeZ6W7zzlOTrEKI7ik/voq6Wd9JUHe%2Bkfpm2j63%2B4kb549zq69q5X6LO3r6Grb11F9y3YSevqQ3Tg1Fn69weX0q7d%2B9KoagyrHBShURve6AhtZEe%2B2%2BooYBnCmVCrWTeTAhw3lzH9hpPDc2RonoxarczrTHijOmKdDUHt1GgB31mSxPjvj8ZQzpUk6Tad/6DaGY8mxsmRUVlSZeN2vmWVj3ggAAIgAAKdQUAZufzOReqOjuycBL6SVZ0Wz%2BirfeBer1cDzohWpaeM3KFRG97oCG1kR1hPLsZgJ0z/olh8gay%2BPo7vZPlfLAc7a7H5bHBsOS/On9rJol/zfFX6rr0sugl5rJ9wnNXr1s2VtvUaG5GL3gikoPyqP1pJN%2BKaHBgsQ9aL%2BHoU1SdolICTI0po0p6R7ahwXIlRZ1odroZHp0XaMK3tilxzEXHEc%2Bij6yb27T9Nvnd/NEY8VYP3aLXq30NiBF5sOxlOTLuvvNF6Grewnk5VQ3K5PUnFMyQEhyAAAiDQpgSafveX%2BeJnhc3JoT8HLO89LCPL808q0v7/cHK0fxml0vDUmUH62gNRJ8drW2o09Nh1tO/lx2nTlo3ednzpPTR875/S8D0fpIE519LqvsPe2hxi4XExVdXc6nGaWz3mbfOqR%2BmZ6lGaXz1K5XVHaOG6I7RyxynafGiYDvSfo%2B8%2BspJqfbtT6WoK7BnZHQ4MZVDOkW3pBGmoT%2BIICOvQdPrlbsc0WMp4n26R6uAojjhnRU1MlcVTLwknR56P45g0nf8w0EzHilOcvpnEtziSqpNwcrQYLcSBAAiAQAcRCBtN037NpTovEeNUteKYGkjF6%2BqydYxMmFW8cHqeUcphkNI7dCYbtPqSzNIJ04xpXUYB4oP/kuYMEcbsIh%2BH9Y3kbkx4BVNpRr%2Bm%2BQZViRw1o5sQJo2SsZwjKSc4wWVvqRueCFU3xVeHkSqiyeDp2yKBYnSRhgBhDG4YjTNNvxWTDC5PAAFZto7ROa42TF1z1dFQvrhOGuprAn1C0kKH/jR98gtc6winUKzUh648pBamIsj2JDz6Q4XAHgiAAAhMVgLNvvtr%2BeZnhfldnttS03uRFMMy7M8v9YwzPK%2BknDb/h5OjzQsorXpHT56hG2cs8UdyNKaqWtx3hg6umOWtvzHwk89S/5zPU/9PrqWBmX9Lw/d8gIbvfp%2B37V09l5bv7KeFm0/Srd7C47von6b30lfu30Zd922m636wngrdr9K1d/bQ525fTQ8%2Bu4u2HRmmg/3nqPuJtbRp%2B%2Btp1Y2EL3fbR2j4gZXB32wAb85A3nz6kSwFTiiDuG26rEBw70A5bbp53Qhz3kVwlX/PGeJNjaXO2eP56Y51/qO5M51Jrq8p9nifU2UKJ8d4s0d6IAACIDBZCMjOR7FS4Xn%2BUxmJ2cBk7ty4OKgOi71TE4nvSK9aqRjnq1cyVKcumkfNQO0wPCudTfnVZPBUXOpcNE2lWZI9lXYKXgHBShdvagJvqjB1Lk6/5vgGFDEcKD2y6EbaVFpx%2BTAkHn%2BK610Mew7XRRFHGF8rUqmx4HFmB19JOtNi9InPGUK0C4EERh59aqZoPVftW6TuWfKo2hRDe5ZEH4tccVo%2BW3yHn2u0h0NIkkt8X7XW8MX6OxznSdRDGBAAARBoNwKyfcv87q9niJ8V0eeIesbEfEDFMhzvNFpbH33%2B6Qq17z6cHO1bNpk0O3Kin75RWko9ewdJTFW1qLefVuw8QUOPfp72vjCL1m7po54tvfTKlh1U3byVDs37Ng13/xEN3zWFBmdcSat2nablff20aNspen7LSVq89SQt3XaKlm07RS9sP0UvbT9JK3pP0cu9J6m69wxtPzriOTkeXLiBqlt3ZdI5XSRlAM8Zh3Ko63EG/XTpytBKvjl9Gc7yz2t2JDSIy/De6JY4B49IU%2BiXp%2BB0WUrn5pkoWZnyb8ESPK3SaF7foOSxOIKTYyyoQiYIgAAIdBAB2anwjDjKQJam8yA7SkmNagF6Mv0UIzmaSk8zhEf01TpPDh9HYG2GaDhhqBfTsYipWGROlfE%2BDVcZO/CfgVcgvpf/MdTPxTeoiOGoWXYt5GzQThmXHR1wL57SI/IFONcxISPD/aaXf0CWSWGcm3QEuHxddcxdb7h9TNSmKlmR9lDAS6SPhTLHjTFsWaKnOs33ApwcqbghMAiAwPlJQLbPTbz7B8BJeeHnDp9P8BzgsNmffwGd2vQATo42LZisau0%2BeIJunv0i9ezznRyL%2B/rpQM9cOjPrU7RhyxZa2tdPS/pOe//LdvZT78vzaOjeP6fhO/6Ahu/4fXq9upRW7e6n1XvO0Nq9A/TqvkFaVx%2BkjQcGacuhYW/kxvYjw9R7dIR6j43QjmO%2Bk2PO8m20ZlNfVrVTxIszgCtHgNEHkiIlc9C49M2x5Fk2iDum5JJhicJ5UcfpjP/N6az0EXutkeWvF6KtsZLLUV4sKO%2BtY2JOg9nlXA6iJIzMYeSImVyislFUTHp5i73n9fVQhOOpu5E/FVfscbrOfAXZ2xxMNq7dPthgwqYjsXZMIU/5gO5%2B2SSWYZKLcyAAAiBw3hJQBi7fWK%2BOkxvjZRxXp8QOmL/wSvylbHPpOb/25w5W9Eu0YA6kDgk6bV5EZfROzjWYojxKz0vGdP23Tj8nX89m6q%2BLIpxAyX5pdFPlklh8MiX8UGxIjavrSme3k0P/0j2uzgkVlFyvHiXWJ00mEXZCCXAb5KhjXO4Wgz7LsFzXMxgXlq879NHl8b6qq%2BlGKrGAdDtxTBzS2ClkeAa5ruki/TWEtDWfxCi%2BYslbV0UPh30QAAEQmHgC4XcldZz5HZWfFdq7jNYuG53oYRAsw/G80WSOyXteWKcxOIaTYwygTqTIHfuO0m1zVnhOjiW9J2jd5m009PAn6dTDn6ae3gO0dMdpf%2Bvrp2V9/bRhy2Y6XfoEDd/%2BuzR823vo1OzP0fpdh%2BjVfQNU3T9IGw4M0uZDQ7T18DD5zo1hz7HRd2yEdh4/621iuqoFa1%2BnF17rHYesKwO12YkRd71ZFZuRr%2BImcVKw8Zszmi6%2ByqnZaaCup9lTOrBaaaILx03IgC6m1dK3Qtmib62b1xaxpi1HvgiZNmcFywlOGca8bfEs%2BQw6OZTuep7Uvr%2BgfUCUprM1XyIC621ajyYuXQcPT5kalQtBp5PSuVE%2BTuUCOcIBCIAACIBAgwAbcLinkL6j05zRfbzTExlXaXK2GzyS50Uz4IWFGGuXCp%2B5A%2BnJVbo3JyesZKv0E3KVjhE0Wuc0%2BWLZaXRzpB3OcpZj1t/RAffkKp0jHfuwDD5OYJDmsI30w8dZ8oQ47UUg1shTV1NAGYzyfmbUfRCpf6Hc8jPAJitWn5BAech1M6kjWEbM%2BK%2BlF2l3YkS6GLiu%2BWK1e91bH0eswRTabGxj9MJlEAABEBgLAtyucWOpnhmZ3y35WSGdHEpm5GMPW6ZYhu0dK8nzzya8fc7DydE%2BZdESTTb0HaDux1fRK/sHafO6NXRq4U00MOfzdOZn11N95Rxau2WHt7i4GNGxovcQ7V35GPXP%2BRINTv8YDT74URqc%2BQk6/NLDtHX3Idp0cJC2Hh6i7Uf8URu%2BY2OEdp04S6/zds6brmrl1gP07BrvM/yW5MMmRBmUgwZqFV4Z4cNGWjFSoNmv0ePTV5rwXq1GZW0xcDGVVOyPDd/6qAWVtyROEpWGMoCni6ckyL1M%2BZeRtVEgYjH0QneZ9Brjj0IIOjyC%2Bqp82EYysKPCc5yY6wjnIeTM4Lih86y%2BZYfl5QpUKAj9fUdGOG9qMfiwXvH5EklzOhH9tPiRtIN1Lwm3yIiTWo28kSndCeqthRFOgwAIgMB5SYA7E7JDIiioTkmyjo4Kz32lJDDrdapqi3Mn/6o/Y3qaTuzIMCzsG%2B34aRFDuxw2kQFLGcKScQ0llplXSI71sEn9NLkuviJYtbEORfIyT6Obqh9hQ2NLvqpmQ6qtA94AweEMBl6%2BpmQkrUscTt5sBllaUWB3MhLgdlnVD5mNupjerCgN6Hq7LUOof64r4alDVJBk7b1Dn4Co8EHWeGE5SY/5XkjgLAzJZFaGttx1TR9ZJZy2pWpdWw8q%2BIyLczaFVMIhCIAACIwNAW6b9WeIenfK9I4qNA3IVe9tXUU9nZgssYzmnn8xqUz4ZTg5JrwIWqvAqk276d55a%2BmVfYP08pbX6bWNm6jqbRupumEDrdxep6V9p70Fxlf2HaPNG9fT1s3rafuWddS7ZT3t3LqedvZupVr9JNUa01JJ54ZwbOw%2BcY72nDxLe06K/3O0%2B%2BQ52n/6HG3de5KeeHFrazMTlqZ9yR40fmsBtTBhJwcf87RIWrwku5psa/pCjhaO0wxMxxSXmHJmBP0h6rwz/Yh4ZQRPFy8kSMtXFjlspM%2BFjfx6OkpXwS6cDsuIGPqFDMlHTbcU5OenI50ZYdnyvHUEiK6mts86ORwrjZR54fhwGkqG7tTSEtEcRGG9A3F1z4oenZ1mUfYqvhghYhOgC8M%2BCIAACIBAPAHVoZH2Uj%2BOOp%2Boo8MdkphOjGaA0o3PwvBc5XUr4rUOdqIShA8H0fQw5Y8NWkEoYSneMYc1GMaiEVSHz5RuJLymZ1O8IoJtJ1LqZxVToWLjK%2BZE%2BbTJCZxPoZuFm87Q63CnqXO6Liw/2gFXwZS%2Bxml6TDJM55RAf88UxnQuHA/Hk4sAt6nSmRH999b7icsV1w2H0Z/TctRnDhPVI3BfhdrMOGdnnPqpr2v5DegVHlXhcm4b2nJXO6/y6HiOMT8H49SZRQQQAAEQyEJAveMHm2x1PvO7G7d1JSqV5PMipm8QzgLLkPGj/4mef2G5bXYMJ0ebFUiz6ix9tY%2BmL1znjeR4Xiw8vqOfFu/opyU7%2BmnpDn%2BKquU7%2B%2Bml18/Qy6%2BfoTVi7Y3G1FTrDwzSpoNDtO3wEPUe8dfb2Hl8hF4/MUK7T/iOjb2nztG%2Bxib295w6R96544M0a/GWZtW3x9cM7GEDcThSTXx57m3%2BFW%2B/3E2FvD4dj82YHJbWOE6Rvs3J4Tk8PAeLy5CsGfkjFnppxI8a/y1aS%2BWpuzFFVNhA7o6nXU2Tfy2a2k2hu5ZWRF%2B%2BFjXWM/dCWY16cDAMX2qFkyOirwLg7SmHQkh/zpdpKirdcRaKx44dSzxOX9WroI6qXGyjPFgEdkAABEAABBIS0IywwV5Osi97ORUlJ7ZT5DJAeY6OJFbnFOmxjtqOroPBmBX4KjfCRZPT2HUZv6KhU%2Bqu6xo20iXmFdXCfialfiZBus5GvqZISc6l061er5O/%2BbK9/WqFSkV9vvyUHW%2BpJufRZLCsU71aoiJ/aW8YxSHkGGWoPNq%2B%2BGaDqs7WKEsqi/9JSSCBkccz4sc6iLU6pdcZhhJ3vREwqT6hNpPrq3MkCSvT/A7fC1GjWNDpEb13XW25/ZoyCoayHsqL4hz7nAzFxCEIgAAItI6Aaoui7xmqPcvcTpmeFcZnjyNHJhnhd2BxHPv8c6TRBpfg5GiDQmilCk%2B9sJnmLN0ScXIIR4eYomr5rn56adcZWrXbd3C8IhwcdX/tjS2HhIPDX1RcjN7Yxc6Ns74j45Q/akOM3Nh/etQbwSEcHLtPjtKJwXP0wMKNNDraytz4srypcuS6DcYv%2BJOnqYzMyR0FTafvTfcjnCxqKqagoVnp7za0K4O0Lb6SpO/ZDNx6GPt%2B0/kXoh0jCaIpu/JpvybL1nNesNMg5MxiPULn9QXAU9YxmW4uduFw3VkRdkqoMjI5GziNqGemsZ5JND9hrly3dBnMI%2Bw8CcfGMQiAAAiAQFICdqONkJCio8NGpajRKFYXYYj2DM/KIBXbsWoiPc/4LDtK1k6XqwMYzZGbYzi8kh2bz3BUcZyFl0mO9Vxz%2BiXja0085kJzuunCleHV4oDQA5v2uQ6qehs0oKrz1nJmGaH7hjv3ofOeHuq%2BDBhUbbJMuuPc5CCg1wMx/RE77USbWaWKNs2fqHuB%2BhDKoarvhjrFdcctQ42eK3qLaAf00XWzpp3RoRiSF3sYyE%2BIm65nPepQd7Xl1mtcTvH5YxmuworNIAKAAAiAQHYC3A4Z34HVO4b13SUuab1N1NrjxOtxCPksw/C8Sfn8i1N3Iq/DyTGR9Mcg7dnPVempl3u96aqe7z1N5ZVbaf6ydbRg%2BXpa%2BMJ6qrywgZ59cSM9t2IjLVqxkRa/vImWrtpMS1dtoeWrt9DyNVvphZ6t9GLPVnpp7TZvW7F2G8ntJXFtzVZaVe2lPUcHPefH7lPn6PjgKM1avI1ODwy3MFfBxZDFWhauMRDJEtaMybHG7Nanz8bqXNjIra25YDWW2w387ryrPKd1juiLUTfDX%2BU73hhPjqmZRD6NxnqOI431kpU89gmxHrqhvwGP5cbWiyBtlmktNz281MvgZGOHQ5iRihNWW6WtHGj6FGnGfU2Iih9OU9cZ%2ByAAAiAAAkkJKKOXzTCTvKPDHaYmDTdKJ7exLVt6YpFC9QV/3DD3NGmkCauPEsncgWwUclJeSeuEHy6rIyEd33Q6ydBZdZPx9X8lK1XHW4rQO%2B7SaRb6L5Yq7inYWEbY8Kx0C9cRLvOwccIqSyqM/0lHQDfyRO3xjeyoumKcEk1mmutH1KnHdSpupEUifWSC6l/JD9dzFaale1pe0z6SuC0P31%2Beza3huAxdU/lTjk2bw5PPp1WspYAgDARA4HwloNqr5t/9rQz5WdFIg4%2B7KDpyxCKF47ieGwmff5Yk2uE0nBztUAot1OHep1bTc%2Bv20pq9g7Ro%2Byl69rU99Owru%2Bj5V1%2Bnxa/tpqXrdtPy9XvoxQ17aMXGPbRq815as2Uf9WzdT69s20%2Bv1upU3V6ndb11Wt97gDbsOEAbxdZ3gDb1HaD9p0boQP%2Bot4nRHHtPjXrrchzqH6VnVu2kfUdOtyg3ZW3kQ77pBcN1pZIZdscqfeVwCHyxzwbuqPND6a6M3WmdFemnq2pt/pl5IgeCYmTMJ7PSDPNy5IYmXzotlAwlV7PzM14ZPm46NI7Q2OG8NevksE09ZcpvJO0mnRwat3D%2BcAwCIAACIJCQAHceXM6EpE4OGc7VEUmoF2kdFqsRKEt6%2BiK9/ldhcRq5jF3BuEl01mOo8GEDth4q2b6SlbjjGCtYyUyuX3q%2BsWoYA2TRzSjIOxnf2bfHDUw1Ff7K3hEtcImNsdF7x6ybrPtRQ3VAH6tBPJA6DtqdALfT0foRUJ3rkas9Fx/Gmoz06p6KbUOS6hNQTv8i161fOFrm44Q8TPLNjPyQtmvqXoWTw8QU50AABNqEALfhrrbY8Z6RNBucjnKkcPvZZXh/McllGa15/pmSaIdzcHK0Qym0UIfiw8toxbZD9PKeAVqy4zQt2XyIlm46QC9sOUgvbT1EL9cO0erth2ntjsP0Wt8RWrfrKG3YdZQ27T5Km/cco617jlNt73Havu8E9e73tx37T9COur8dPH2WDp5pODn6R2nPqVHaddJfp2PFpv20ceeR5nMjDdZiiqqxML46jMae8mOcftSYrozvxi/v5VRdxv/gSAUzfCVfGfzNIccq/%2BkcAXH6KmePdFZI%2BYH8yXKWdYjLVXOOaBii5aJddOzKtBNNV2VzZDTksw4yY9rIlUDeGuE5bZlHh56mSxw/kYPGJAHnQAAEQAAEJIGKtlYAf1ka%2BgrdfD7a2WADT%2BjrVplW2n/uCFnkpU5PM3il%2BWKf04n7ujnNtF4eDGVUTO5EsFOM42WPabuSUr%2BMfG2pu8%2Bn1M0tTJsOQXXE46Lwdc539J7gMHE7ThkGQwN3%2Bg36OmXFKYLrbUmAyzuujhnqiilDJnl8zmX0agjjsHH6hBNX%2BsU6UsJRsxzzvZAgTyH5rvbUdo2fFZZnVigJHIIACIDABBBQ70/m93uXkzZlm8/PCv1dJZi%2B9TsmSYZlxKWtni%2BteKeWyY/XP5wc40V6nNL5lx89S9U9J2nF6wO0eNMBKi9fT/OXVmnBsnW0cPk6qrywnp59cQM999IGWvTSBlq8ciMtFVNWvbyJlq3aTMtXbaEXVvubmJbqUP85OjwwGtikk2PfaX8Ux47jZ6n32Aht2nOUXtiwt8mcKgN2M9MjuZRwG3bHPn02ZLNhWhn1J97JMUb5lw6HXBKnjKZDt3mCMsnQN/xLfmHZUk7DqSF10BwIej2RMtM61tz1SU8hwdokUkd2Osg8hPPWkBsJH0ov7pDjW%2BTHxcd1EAABEAABJtA6J4fqtMR2WDh1947NkOTHSpue1vkpVSnVB%2B5JO1hsUIvriMl8qzy0okPm5iXTTPOfRr8m%2BKZRicOm0Y0jWXfYOBnryDKISF3u6WWwfp7xNCbvrdDHoCJOTSCBpG1QYkerdr9W/NYwVfuRWJ8oM04nZu2QaMwMZ/heGB8nh5o7XjfoZdAbUUAABEBgzAiod4iJcXKIjKlnUFdX0bmOlGpX496tlcxWvFOPGX6LYDg5LGAm6%2Bkv3j6Xth0epJdeH6AVr/sLjPfsOUOv7hug9fsHaNPBQdp6aIi2Hx6mHUeHaefxYdp9fIT2nBihfSfPUv3UOTpw%2BhwdFM6N/nN0dGC0sREdEc6OM6PeSI66N4rjHO08cZZqR8/SxkMjtOtQP1V6%2Bmj47LnM%2BNjQbDFEZxbMEaVB3DxKZFzT1/MoFieP3aTBW6znUObwnDXrjsqzaTSAHm3M8s%2BjKAxrUegKiH02vDvCyjDCUSRls9NIClT5Fqhl3mwM5PXsTg7XVGO%2BTvFpqDL2qoeeT5kt/V/mPedgpYcP7zcbPywPxyAAAiBwPhMILb5qXkBW7zhUedHbADY2erXKuKN1wkxek5TpsWHNJCuQEdOBnn%2B7eyRoiDbJCZ9TeWy%2BQ6Zkte4LaSUzTr/m%2BIa5JDlOrlu8NCUrzQgflsuG1LgOOMeI7sTJ4OtdVKpUqOiNtrKkx2Et16Op40y7E%2BD2LqZMOVy8UZ/vWc9xlqyNY0ycTow%2BHEHb4fopvhYuudeq0aJl2tXSStv0B/kEU7de09KLazODEnEEAiAAAuNIoFXv/nEq87PC0DfQ2kvh6Gj426MSWYYjjIjF4eKff9FEJv4MnBwTXwYt0%2BDIyTM09Qdlb1SFGMmx5NVd3mLjYsFxOXrj%2BZcaC46v1EZviJEba7Z4C4r7C45vpRVrxbaNVryyjVa%2BohYeF9f3nzrnLTguHBzbj47QpkMj9Fp9mPoOD9CCNX10%2BMSZjHmSBt6x%2B6pcfXVvMkg3mX6tO3btEJV%2BljxK/dIatJWx32bg9wtMys%2BiW1yRKx1yrtEcNamDv8aEVV82zuepu7tAYgSMKSzzLnSTvy6JPW/xDghzHjmNuOnVpMPCoquUzvIKZXbM6P4wGU7%2Bs94uriKwYNtdltH4P3F8joEdEAABEACB7ATiDWDS6JPIsFOvUKVqdxYIPdlhYOn4pEqPvxiL6SA5AMXpo9ZBSNO5UsZ1J7cW8HJkzXEpoX4t4OtQwnIpqW6W6NppVbZpyi4gwO100IJad7mzb6%2Bjss7zl5c2q20CWVY9cKE9CbDxxl4/gl/GGgxK4ZzpMisl8uuVS74mQI/rbsq1SGpXv%2Bf8r3jjhdSrJSpZrWBKdmCP74X09zbfb4appxJdszy7WL96lUqVKh9iBwRAAATai0D8u3%2BsvvyssDyT%2BHrD6W0SyGFczyelq%2Bc8N8lp83NwcrR5AaVRb%2BPOg3T7nBW049gIrd4zQC9tPuAtMv7Shj20cuNebZHxfd4i469t20/rtosFxuu0ofcAbdrhLy6%2BeecB2uJtB2nLTn/bdeg0HTozSmIEx95T52hXwMExRKv3DtL6%2BiDNX9NL2/ceo9HR0TSq%2B2HZCFygcuyohsbIh1Aqte4C5QvdfnztWk0YePN5zxjuTQllsho3m740vOcLnrNDTbQkdA2mbzLIa%2BpadpUDIF185WBwxms2/94gDJ%2BxmGos8mP5woGRp0K5RgFG5QLlPUdBgRedt%2Bur8uRP8WVxXnCZ5Buyu7U0gxqysT8yIkSMArHni50Scs2URvkr6TUqd2t1zyBfhRXOiG5f11ye8nnByryGCMfh8A2u3igfvkpUq3n6e2xN5aKtEyLKxZsmThWMH1/cVwYHiZYKdkEABEAABBIRUJ0HozGeDUmuDoiWkAxfLHnODmXeqlNdGH6KxYbBzbIooYwfZ0SSSXIHSXw1LNJIsMm4/K8YeF%2BcaU6aelV%2BWd9F6UYCJDTU5pikKwAAIABJREFUy/xm5cV5SLuTUL8W8K2W/DIv2gz3EdUT6uY5zEpULFX8stfkhOta5hEwsnyS1kdNB95NIoPDCGOA417jcI4wnDB2JgUBvseKfpsZaMOqVKmUGo42fy51Yzsdyah2D8k2N%2Bn959Qn1L5G0vVPBB0dou0sekb/QBtdDeYtWb60BPleGD8nh%2B7w9hw4FTH6UdepTqK980ZjJeWtRccuCIAACIwPAfXem7rtlQrys8Li5Ah81GR5h2YZrXz%2BSQXb5x9OjvYpi6Y1eX5tL81esol2Hh%2BhV/cN0tq%2B47S29zC9uuMwVfuO0PpdR2jj60dp8%2BvHaOvuY7Rtj1hg/Dj17j9OffXjtLN%2Bwtt2HThJctt54CSJbc%2BxATpwWiw07k9R5Y/gGKZq3XdwvPj6AL28e4CWbdhLL6zfTcMjGaasChjB/S/549aoCBvBIwZnaXjW/q1rfTSbfsDYbNc/rHPygh8vJ4dd93B5BPISyL/Z6VCTjgytPAIy88KgrxwYAfkhUIGytjoOlCyRjkue1ckRky%2Blh3DOSQeFhaGXv1BGIodBnXNGx0QoUly6Dd7W/HtOOIvOsqyS6BFSC4cgAAIgAAJhAu6ODhurkhpsNMMTf5VuWOzc1qlKnR53kHwDoCtNec2Ydpzehi9%2BwySDx5qR0fWFcly6DXZGnYMJpjxKqF%2BzfAP5S2qYT6hbuANtqGeizIVzRbdDpgLF%2BifV3SA9kQyVZ6czLZEsgw441b4EEt9jwlGQvCZzW9q4L5I24fq0ILLNtP47hAoHcamYtF0WBq6URcT3wjg6OYSKWrpWLqLdSVFWKXOO4CAAAiDQJAH3u38i4fzssjs5hBweHSeeReFnBsuIe1ake/4l0n8cA8HJMY6wxzqp%2B5/poZe2HqC9J8/R3pNnaX9jbY1DZ85562kcGxilE0OjdHJolE4Nj9Lp4VHqHyE6M0I0oG9niQbO%2BufF9f5h8uIcGxylI2f89TrElFW7T5ylHcfO0tYjI7Th0DBtODBMW/adokcWb6ITpwfTZ7dZJ4NIsVam7oL8Al4z2ubFF%2BpihIdDrTFOX0wVVHOl71DNv9TmTo64kRwyf7KMpPFc/Ofz3lRKPh5l5Lca5YUszbDvCsfOi5jpnDicwWGSbCRHY8RFI3%2B68yYfGd0hYZj/lePE7CyyxKKyGHHhjf4I1v1kda9GtXJ3cMSTXjZN1V2zxjgLAiAAAucfAVdHR10L90ucnOpVqoivWcMGrsbXvIEvXwOCMqSXuIOkOlB245P2Fa40mAudq/UMRnJltLan18h8Zl4BeCkPEurXAr5jOZKDHOz8ER4psYSDs0FzrJ0cas5p573WCn3CecTxxBKIu8eKRd9Rl9y/4eeH64po%2B9xGqACAOH1k22gyWAUENdSoVqzPg2LJH/FniBZ/Ssuf854xSGKjm8F57bqmRNWpKkbYpH7GKQnYAwEQAIGJI6Det2PfUW1K8rMi7vmi3jflhycskmWod/SA8zjr848TaI8dODnaoxxaosVr2/fT1Hvm02eLT4zLds0tT9K1tz5FX7h9LhXumEdf%2Bt58%2BvcHFtP8l2s0MDTSkjxBCAiAAAiAAAiAAAicFwRk58NgCBqT/I93emOSCQgFARAAARAAARAAARAAARAAASI4OTqsFtw3dzXd9dNl9L2fLaU75iymWx95noqzn6ObH36WvjNzIX2rtIC%2BMaNMN04v0388KJwSz0S2rz/wjHdNhPnGjAX07dJCuumhZ6k46zlP3u1zFtFdjy6l7z%2B2jO558gW6b%2B4K%2BtG8lfRAeTUtr%2B6ks%2BcyTFXVYeWA7IAACIAACIAACIBAcgLqy6vMX3klT0zMAUKVxlex45NeKuUQGARAAARAAARAAARAAARAAARSEYCTIxWu9g%2B8Y99R%2Bpf7hFPiefpO6Tn65vQKfXP6ArrxwTLd%2BMB8%2Bvr98%2BnrP36G/v1H8%2BhrP3qavnbf0/SvYru3sd3nnxPXRBgR9us/nk833j%2BfbnygTN%2BYXvbkfWdGhW5%2B6DkqzlpEt8xeQl%2Bb9iw9t2oTHTs10P6QoCEIgAAIgAAIgAAItBMBngokbhh6i5Qe7/RapDbEgAAIgAAIgAAIgAAIgAAIgICJAJwcJiqT/NzsZ1%2BjWx9ZSsVZS%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%2BQKBEAABEAABEAABEAABEAABEAABEAABEAABEAABEAABDqeAJwcRDStig0MUAdQB1AHUAdQB1AHUAdQB1AHUAdQB1AHUAdQB1AHUAdQB1AHUAdQB1AH0tSBdvCgwMnRcHK0Q2FABxAAARAAARAAARAAARAAARAAARAAARAAARAAARAAARCYDASEM6QdfnBywMnRDvUQOoAACIAACIAACIAACIAACIAACIAACIAACIAACIAACEwiAnBytFFhtUthtBESqAICIAACIAACIAACIAACIAACIAACIAACIAACIAACIAACVgLtYlfHSA6M5LBWUlwAARAAARAAARAAARAAARAAARAAARAAARAAARAAARAAARMBODlMVCboXLsUxgRlH8mCAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAQCoC7WJXx0iODh7JsWv/frroY1dT7kOX01vE9peX01suv5L%2Bz8uvol9ubP/l8qvondr2a5f9NYntnZddQRdediW9smVLqoqNwCAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAp1PAE6ONirjdimMViJZuW4dvfsLN9M7PreMfu6bPTRlWg9NmdtDU3qO09U9B6nQM0qFHqIbe4imNbYZawbpp2tP0JPrTtPMNbtp9uoj9Lnbf0gPPPVUK1WDLBAAARAAARAAARAAARAAARAAARAAARAAARAAARAAgUlOoF3s6hjJ0YEjOcQIDuHgyP3bYsrd1uNtQSfHeir0nAs4OWauOUOPrD5Oj712ip7dPkiV2gAte32Eeg4TPTRvHl02dWrsNsnvSagPAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiCQkACcHAlBjUewdimMVuV1Q28v5f7HByn3Py%2Bl3J/52wV/dild8KFL6YJLL6O3XvqX9J8vvcLbfvnSK%2Bhtl15Bb7/0Mm/7lb%2B8nH71f11OP3xulefgEE6OoeFh6t2zh7enN%2Byhu8pbaerCPfS%2BaS/Re25f6G2X3nBDq7IAOSAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAm1MoF3s6hjJ0YEjOUZHR%2BnM4GBT20e%2B8hV6esNez9Gx6XjwTnrlCNGs1afoWz11umTOWnrztB66eFoP/fZVVwUD4ggEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQKAjCcDJ0UbF2i6FkRXJsUOH6D%2Bu/FO66p25preP/JccTcnlaNnTT5MYmTHr5c00eDaomXJyjNAlc7b6633AyRGEhCMQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQ6GAC7WJXx0iOST6SQzg4fnzdX9GOb%2BWIpjW/3fuXObrsrb6TQ9x/X7/3Xrr5J8GFx5WT4yRdMme97%2BT4NkZydHB7hayBAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAQIAAnBwBHBN70C6FkYWCGMHRKgfHVy/O0Tf/e46%2BfKFycgidHnrmGbp86lTe/uT6qXTxZ2%2Bg//eaqfS2v/kC/dKV19Av/fk1tKSnJ0sWEAcEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQGCSEWgXuzpGckzykRzvfWOOrnhba7ZbfydH06ZEnRxy4fFLvvQlWtrTQ6teW0ZzF36CPvNvU%2Bj%2BJ5/kBckn2T0IdUEABEAABEAABEAABEAABEAABEAABEAABEAABEAABDISgJMjI7ixiNYuhZElbx9/d44GB5rfLv%2BNHN17sdnJIfW6bOpU%2BsP3vY9%2B9Vd/ld761l%2Bkn//5N9AvXHABvfnNb6aBgQEZDP8gAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIdTqBd7OoYyTHJR3Jc9Ts5IsrR0JEcDexLt53cnaMjdT/%2BFRfZnRwbN26kN73pTZTL5QLbG97wBnrjG99I73rXu2hwcLDDb1lkDwRAAARAAARAAARAAARAAARAAARAAARAAARAAARAQBKAk0OSaIP/dimMLCikk%2BPw7By9%2BuUc9RSSb//yuzm6%2B1/inRxHjhyhv/mbv6G3v/3t9La3vY0%2Bcvkl9NnPvofe96H/Sp%2B%2B9lo6evRoFtURBwQsBGrUnfcdavnumiUMToMACIAACIAACIAACIAACIAACICAmUC11EVdXV3UVaxQ3RwEZ0EABEAABFpAoF3s6hjJ0SEjOWhhjk7ekaPjr%2BboeDXZNiWXzMkh6vuuXbvoz//%2B7%2Bknjz1GGzcso56ez9CTCz5Evbuw2HgL2gOICBCYGCdHuZD3RirlC%2BWANuNx4ErbdW08dEMaIAACIAACIAACIAACIAAC6QlUS0XPyF4sVdNHHqcYk0FHFwqX/nByuMjhGgiAAAi0jgCcHK1j2bSkdimMLBmRIzmEk4Om5YjO%2BiMzfvSjHF10UXRbtMi/Lqa4SuPkELoteP55%2BsQnP0m///vvod/4jf%2BLLrzwF%2BnXf/3X6KKLLmr5dFW1cjcV8r7RmafJyuepUK5R3Lf9te5QvNA0W768PLkGCTSTPtVqVO4uUL4xGkHqn88XqLscp729Fuj5Sj3CoVamQj5HSeM1lX97FhJemQgnR5kKXE8KNL5uDlfarmsJcSIYCIAACIBA2xCoV3yDl/iytFhJ/10pxw8bzOp1qlZKVCw2vloVX66KNIolqlTTpyOBWdMjonq1QqWiyo//tWyRStV6wi9m61SvhnUuUrFUoUwq16tUKibkOka8JDfjfxr9WsLXqIX5ZArduE406phX7pH9ImWo3kT1ChUjsrQ6XZT1w1GnQzLCt4oZgOlslUqsS8b8mMTi3IQQUPU2aVmq8k/UVmv1ToVXMrq6StRqN4fKk3aPcJ11n1P3xdjqmLiwNX7mNkXLT2BUhlt/ODkSlwACgkAiAsnaHUc7O%2BbvX82%2BW5rep8W7R5XqjlcPBa/Z%2BEpSYK9a8kelxT5LHG0iy9DaU/2ZUSxSqVJN%2BA4f0M47aBe7OkZydNBIDt3JccstwfUzpKF93rz0To5NmzZ5i4tfcMEFJNbh%2BLmf%2B7nA2hxC9pkzZ6K1PNMZZeCWOkf%2B824jdLlgzntQjs3J0WT65UKETTDdHImRAuldHbqxO7mzQhRBrVygfMOAH%2B/kaDL/mco8HEnpEK9vOG7244kcMeFK23Ute24REwRAAARAYPwJ6B2PhMb4gJIqvjJQEVFcp0U4O0TnLCAryYElPapTJeRMiRilinEGvTgZwlmSREc/jOcsaXTUlIHREn/MeFnS8xwWJTbex%2BrXEr52XcJXUrHzqpulc6x3lLscBo6wAvpxCkOntU6HZaSpSJouQUNOxvxo8rA7wQS0ehF/D4ba1YBR3ZwPVV%2BCdcU1ysAsKflZlWaSe1ILE8rPWOqYODda%2BUSeJ4G2JTr1lEt/ODkSlwACgkAiAnxPhe/LwHGwHWTBY/7%2B1eS7ZWw7FPNu2mx8BmXYYXau92s9/4YyYBna8yBQbvK8Ia5BpfApODnCRCbwuF0KIwsCfSTH69/JUe%2B6HPVuTLYlHcmxe/duuvjii%2BmNb3kL/cIFF9CvXfgr9Ju/%2Bcv09t/9b/Rbf/zHtHfv3iyqG%2BMoB0Vo1IY3OkIboZHvtjoKWIZwJtRq1s2kAMfNZUy/4eTwHBmaJ6NWK/M6E8LpkdZ4r4/iSBrfH40RdPjEpdt0/k1QU5%2BbGCdHajURAQRAAARAAARSEAgbpBIZ2nT5snMSMlBJJ0f4K7N6vRpwRrQqPdXBDY3a8L7O00Z2hPXkvAQ7YWLkh/yJ0SHqS/74TpY/mkR2yvz/2Hw2OLacl8yE9p9Fv%2Bb5ago4drPoJsSxft5XjXWq182bI2n7JTYQFL0RSEHZVX%2B0kt4hNzkwWIasF/H1KKqQcvD5BtcsMqJScWYiCWjtjrVtUvpxPffqW3z5c/gEslUqze6Z773gfeOHkY6ArqwOyGZVjYuv3bfeaEBLu%2BLnLU6Yuj4x5aLSxx4IdBoBvqeyvAOM6fuX1sZ3%2Be%2Bnkn2yd0v9uT8R8aW2ln/ZB7CO5Ajm3zialmVE37Gq4dHZGZ5l7WJXx0iODhnJ8fx3c3TJ/%2BFPQSWcF0m3JAuPi9ts7dq19J6PfpT%2B7VvfohUvPumtyTHtkT%2BhVze/bLkLs532jOwOB4Zu7LctnSAN9XEGfZOGTadf7nZMg6WM97mcezRKULfgKI44J0dNTJXFUy8JJ0eej%2BOYNJ3/oOIZjxSnOH0zJoBoIAACIAACIDDOBPTOU0JjfEBD1XmJGPGrFcfUQCpeuulSVLxwel4H19H50Z05Jhu0dMpYjW2asavLKEDMaqRGRkgjtHSOhPUNYBQHY8IrmEoz%2BjXNN6hK5KgZ3Tx8jYV8YzlHUk5wgsveZVRWdVOUfaSKaDJ4%2BrZIoBhdpCFATI%2BV0MgdIxGX24AAG%2BesRiKpZLS9dlchLbw7oExgfP/5nsgygnCcVNV0bCVCLnPHM2uccohkQKAjCMh7KtM7wFi%2Bf8nnts2Rq7UxpndL9e5qe/9wt/PNxo%2BtHJw/80gOWS7G9yIpnGXY8ijer9XHSmnbYjg5JOg2%2BG%2BXwsiCQo7k2LAqR8vnpd92bPKnr7riohzde3GOpk3J0ZcvzNGyp5%2BOqPPQE0/QV/7pn%2BjKKy%2BnSy75DfrgB99Ol176P%2Bmyyy6joaGhSPgsJ8rd9hEavjxl8DcbwJszkDefvjvXykljmy4rGl85bbp53Qhz3kVclX/PGeJNjaXO2eM16DbNP6p/%2BjPJ9U0vGzFAAARAAARAYPwJyM5HsVLhef5TdRC5c2bu3LhypDos9k5NJL4jvWqlEjP1leoIRvOoGagdvSelsym/mgyeikudi6YZyZ3zhEo7Ba%2BARKWL6Gz60yqpc3H6Ncc3oIjhQOmRRTfSptKKy4ch8fhTXO9i2HO4LooYK/hakUqNRZ8zO/hK0pkWo098zhCiHQiwgcfgHNP14zpUolLDqRepZ8bwMXL1OOO2r93z7WzoZ%2BatZSifvV3tnPdxqwtICASaJaDak7F4B8j%2B/qX0crXVSn703TJJW%2BEK47omqScJI8NG/vn5FdVd5SvGkc0yHO80Wluctozbxa6OkRwdMpJDLCTezOZycmzbts1bXPyd73wnveUtbzGuy9G6NTkit3PohDKA54xDOdT1OIN%2BSHDCQyXfnH6MGF6zI%2BFIDhneG90S5%2BARaQv98v66HzxdltK5eSZKVqb8x%2BDxL6s0mtc3UYIIBAIgAAIgAAJjR0B2Kjwji8sBYFeBO0YOx4A1tkw/9utlJaGp9DRDeKSzqXWenFlxhhOd2fAikKqDm7ZTpnLd2MvAKyhjjPVz8Q0qYjhqVrcWcjZopxYed3TAvXhKj4jxkuuOkJHhftPLPyDLpDDOTS4Cqj5E2iYtI2wwKlaoyl%2B1Rg1LMgqHT9HGyrhj/a90i7unxlqTGPl8r8HJEUMKl0FgAgmoZ2/T71qmXOjPX9N127mk7Yc1nMpXomdDpK1vNr4tY9p5Gxs%2BH%2BPgEKI4rOt5oJ6TacsYTg6tvCZ6t10KIwsHOZJj%2B305euX6dNv8T%2BXo3q/Hj%2BTYvn07CQeHGBkgN7H4%2BM9feCH9p3e%2Bk7Zs2ZJF9Yxx4gzgyhFg9IFkTFVFi0tfhTTt8UgOx5RcKl44L%2Bo4nfG/OZ2VPmKvNbL89UK0NVbEOiViQXnPMWNOg9k5p/pKwsgcRo6YySUqG0WF9eJ4NRKLhQemDMv7a7yoWME9V9qua0EptqMaZeEtpHHa2r0v2wDT/9jcc7Z84TwIgAAITAYCqrPgG/XVcfLOg4zj6pTYWbCRK/GXrM2l5/zanztYdoOhnxOpQ4JOmxdBdTCTczUzS8/LLCd4tnX6Ofl6fVh/qgExgiTZL41uqlwSi0%2BmhB%2BKDRBxdV3p7HZyaGuIRIwSJsWUXK8eJdbHJAvn2pEAO3Ct7WGoDnCbZauTKnykLupr2BjSi7Y1dRJrZ8ip98Roq65icG74VEy5/rrbURcT1lG7f%2BrVEvFUcI3p3IqlCmnLK6VS0wus6Zq2bXHp77qmK%2BmvUaSmafFGuhVL3tpAejjsg8D5TWBs3wG4vTG0l07u3E5nf7dM0la4wriuSd2ThJFhI/%2BmPGrtpss5w7JYhu15RmK%2BKn4GpW2L28WujpEcnTKSY2GOtv1TjjZ%2BI0cbv5lsS7rwuLgpHnvsMXr7%2B99P/%2BujH6V5T03z1uT4j%2B//MT2/6kW%2BZ8ZnRxmozQbVuOvNatmMfBU3iZOCDcyc0XTxVU7NTgN1Pc2e0oHVShOdylTIK2eZ2VBu0bfWzY4Da9py5IswyrPTIaQgywlOGca8bfFCYuRhwMlRU3xMebOVuytt1zWpg/2/Cd5pnRzWQrFrhysgAAIg0OkEuEPDPQXVQUxqjM/c6fPgjnd6IlGVJme7UdDJ86IZDsNCjJVGhU/K1ShG0705OWHprdJPyLXz1Tun1jVPwqppI0Pi8%2BxIOyI3wwnuXDs64J5YxTPSsQ/L4OMEX4hz2Eb64eMMWUKU9iLAbVDsvO2yDqo6b74/3Nf5GWAw2rEu4po26shzbnjOA3/9Js/gblxF1sVWu0cMaesxE%2BnoOTk0mSH9pM5i0fBMP77XEtynoQRc%2Bruu%2BWLi82RyXoVUwCEInCcEVHuX6NUsFRUl29zW2oUF2lJ7MGHBp0qx0a6GM8AOAEsbpLVRRv2aje/U2/OYk9/OSkeO4pW4jWId5fMtnKhwtDf4xDw3wjHFMZwcJioTdK5dCiNL9uVIDlqYI5qWIzrrj8xYuTJHt9wS3Wo1Na1VGieH0O2706bRvdOm0U03/TtNnfpH1NX1W/TNb/4D3XLLLTQyMpJF/dRx2KCcCxqolSC7kVmMFOj2hwqo4Cn34tM3CKzVqKwtBp5PYgxmY70%2BrZXKm81Ybki9ZaMvhOxM%2BWellPNCLIZe6C4Tz6glZJe7Iw6QYD61%2BBaG7BDwRh6Y6wjnIeTM4Lih86y%2BZUfJK/j6eyNSVM5EvtSoDrNOrrRd1ywqNU6r%2BpKNt1u6uMq6OUfXxMtBCBAAARDoSALcmZAdEpFL1SkxdpIiIFT4cH8sElQ/Ua9TVVucO/lX/RnT09LmzqbBiMidpwSZ4bCJOlqq45qMq6aw2M3MKyTHetikfppcF18RTHwJ7hlFEzD2xabRTdUPadSU/8VWfPXMRgRbB7wBgsMZvlDna0pG0rrE4SQ7gyytKLA7GQlwmRrqjsiPod3memFqizi8qm86FldcvpeLJSoJw1uxRNW6chKI0QVqVIdZvp6Wvs%2BytREY%2BnV9P5GOXXJ9En9kidKSqBk9WQ%2BtXOTtx9didlz6u64FDJ5d4XwFn6ERZ2qMTrgMAp1JYAzeAVrw/sX3eYLGg8Ma2nPVbgZH0AXaOEcazcZ31hl%2B1og%2BhXpv854bzojaRZYRfZ54jnbpAErw3NCk8m672NUxkqONPE5cO1Ls2JwcwsFh%2BpJ83rz0To5du3Z5i4u///3vp7e97W30pje9yVuXQ5c/Lmty8Bf4OQoavzVgWhhdv8A%2BT4ukxUuyq8m2pi/kaOH0dNV0THGJKeN00JavzjvTj4hXzoF08UKCtHxlkcPOAKuDyoNH3dpIj3A6LMPoiJB88pRvyAjy8/MjDfNh2fK8dQRICIc8ZJ0SjR7JkWktE1farmtSB9O/0svsWPHjqLoh6mqYiUmuPJdMvgyNfxAAARA43wiojmCwP6TOJzLGc4dEd5QYWGoGIml09gzdntHMEN52Kml6tviaHqb8cecyCMUojcMaOqLRCKrDZ0o3El7TsyleEcG2Eyn1s4pRRs9E%2BbTJCZxPoZuFm87QN9QGEkh%2BwPKjHXAlROlrXFDcJMN0Tgn090xhTOfC8XA8yQho9cfQDnG7o11ThqtoO%2By6JsCwPEM7puIKB0eFdMcBQ%2BU62EWJDe1aHC0bLDK8k1hHg%2BNayVLPNmteVODonqZzoD2JjBiJtg0u/V3XFH/hXIqq5J3hZ2I0XUsMnAaBziWQ5D6Ne%2B%2B0yBAfSljvwxiifJ8naPA4rKXNrWsfCIXbIvHBkK2pkCo2G1/KifxzWyQdzmLERfSZFImnn2AZapRgljzqIvV9ODl0GhO83y6FkQWDzclx7FiOenujW39/eidHrVajd7zjHQGnibcmxzveQb/4jnfQ8uXLaXR0NIv6yeNoBvY4I7TQ19988d6wG%2BWcAAAgAElEQVS%2BN0pAXwNCHyGRQI0U6ducHJ7DI/SVfzRlzegcsdBLI346Y3RL1tFIk/9opsQ3/1RorOsQa0jX0oqE5WsGw728ViirEScOhuFLzTsTchSWqVBo5Wpw0LjSdl1T8sN7LeIdFiuPJeuUjhEZHf8gAAIg0NkEXEY0ZQiKN1IrObFhLZ1Gr/MS%2BjrYzj5FeiYhug7GzqOSn8RYF9cRDaqgZMeyEhF1XcNGtMS8ghq4j1LqZxKm62zka4qU5Fw63er1OvmbL9vbr1aoVNTns0/Z8ZZqch5NBsU6hdcCMJa1UYbKo63uscFTZ2uUJZXF/2QlwGUdMRCp9jlgK%2BN6EJ7GJL5eudoxpUdYrk5WS0Ovm3qQwL4WPpCJQKDAQVIdjfebJknlx3T/agFNuxrjsNEteByV7dLffs1S1hHdFM%2B4/Eei4gQIdCCBpt8BXPd6pvcvdY/anu96MdjbBPFuWPVH1YXfC%2BVx3IjVZuPriob3TQ6KRM8ETZBJhsyb/p/R4dQudnWM5OigkRwf/9Uc5d%2Bco/xbkm1Jp6sSDox7772X/tPv/R5d9O5308MzbqGXXvo7%2BvK3/pAeX7RozB0ctXJBTfdjMBBrt23srvr6PLmjoOn0hdMlNBVTxHjf0Nxt0E5huA6QUAZ2W7qB4KGDpvMv5PH0WwbnRCg9t0PEzkCWredoYCN8yJnFeoTO61MvpaxjMt1czJRNHM4g31XurmsRdPIE57NZ3lKg/q/KwDQqRQ%2BJfRAAARA4Hwk4O1BppqvijmDUqBPLVRiiPcOz%2Blor1kDTRHqe8Vl2kKydrhZ2RCMAlOzYfEbi%2BtNVpeZlkmM915x%2ByfhaE4%2B50JxuunBl5LRMBaQHNu1zHVT1NmjgVOet5cwyQvcNd%2B5D5z09LAZPmyyT7jg3eQhwXQg5F/h82ElnuUe4foTkaCRczwN1v4TT0wR4PtmGA9HatqrwSWWqGElHm7h19OQl4KGnG9gPxJWOVPN/IF7MaBkrf2tZh6VrfBI6jaIScAYEzg8Cqv1J8Q6Q5X01gFO1z005ObQ2SIxGC4wqqdep0pgKVLyTGJuCZuMH8mQ40NusUFpxo0tYGssoUqUaal%2BrVaqERrEY88nCojtwckSZTNiZdimMLADkSI5brsuR2E%2B7zbrLH9lxxUU5uvfiHH3j3RfQ7LvuYlU27dhBv3PVVYHtoiuvondc/nF6619dTm/50OX0cx/8C8p94DLavns3x2vNTo3KBTX6QqxloVY6yJqCMvjHjQgRIyBanT4bunPRr/7VtagB3s%2BtMi6nc1aoPKeN16r8x%2BdNL0%2B3vmz0DwybkHGkUV%2Bykse%2BfNYjELdBt9CY4s3ghNC1C%2B%2BzzJh4rnCcJ4MM17WwLvKY04pxvPjhJbtkjj/WJ5FsqRH%2BQQAEQOD8IKA6eDaDkDKmWo20DVRsnEnbywihVjpZOmZNpScWKVRf8McN5U%2BTpzRh9bnV47iG8EQOk/KKRHSeUJ3wdPql4%2BtUwXoxq24mgUrWWE1ZUyyFjA9hNbjzH3ZmKN3CZcBlHjYiW2WFE8Xx5CJgboet9cDmaNANRhYrE7dj4bplk2kA6dIrEJz1cbf1gTgxTgJOOzLqJSxFHJu5mkJGzvG9lk53L1XHQrk2/ipfynFqc6jy%2BSafxZE84wQIdBwB9ZzN8g6g35dpbje%2BzxNEModVbZfLUaL0C79fNBs/QUXg9r3Rv%2BDjFFMZcpyw/nr6WhkmavdV3Haxq2MkR6eM5CA1DRVl2BdOji%2B9MxdwcPzo8cfpX%2B%2B%2BW9Xaxt6i3kH6j54BuuKZHppyy3N0wXUP0q8U5tFvX3VVJGz2E2VtEep80wuG63okMwCPVfrKoBz4Cp6/vI86P5Tu0nCfzBit4qk0kzs5Wpt/Zm4w4is95V6MvsxKcwbJkRuafGmMV3lWcg0%2BDrWItiZDauT6T5o3Vzipq8nx5rpm08uVVjSO4qJYRUOJMyzXua6KOS7OggAIgEDHE%2BDOg8tIozpCYUNrkI8M5%2BqIBGPYj7QOi7UDmCU9fVi//1WYXQf/CncuDQa/YNwkOusxVHg3Vz2ObV/JcnV2bbHN55XM5Pql52tOO%2B5sFt3sMpURwObos8dV04gZvjJ0RAtcYmNp9N4x6ybrvuHLU4esQJo4mHQEom1RzH3A7buqV1EZUQyuMFwfY9rDZOFUPU7bbiXSMZGxS%2BmQvJ1rMON7zfX8jPIVZ1z6264xUzkCMcm/9flp1gtnQeB8JKDurQzvAPqC2inuN9t9HuWv2nm9nVQ6q/Y9GlecMbdxzcY3pxU6y88gxZXz3WV4fwlF9w5ZRkw%2BM7bHcHKYoE/QuXYpjCzZ/%2BBbczT18ua39/9ijhY9/jir8O3776fZCxbQ2XPn%2BJzY2XmKaM6aE3Tjiq30/pk9NOWry%2Bi/F6bTHxamtc7JIQ3WroWcA1qlPDAZyXURY5x%2B1GitjMz6QuXJ9oMjFfRsqH0lP86I7cUZg/wr47jmmFAKhvbi9FXOHumskPID%2BZPlLJ0WnC%2BzDtFyCallOZRpmxwUehRXOFfarmu6fH2f00o02iKOd0My80vrZNM1wz4IgAAIdC6BSjHFF6EBg0q0s8EdphjjV1Ka3BGyyEudntYBSvO1HqcTazAzdyTt%2BVUd19TGNYPQOF6GKDGnUuqXkW%2BMEpbLKXWzSOHT3IlWHXG%2BFrfD%2BY7eE3FR%2BbpThqFeufR1yuIUsTMZCXC5N%2BpabFmruuPb3pLdN662hNtDS7sssSYJx%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%2B9xszs5W3UEydBAAScBLi9StJmGCRZ71lDWD7F93NM28PtTDAcpxmrs3pnaA8nhyCg3hm7uorm9UIkqKScNJlp3qnh5JCg2%2BC/XQqjVSjEOhpvfu97M28f/ffbafaqo/TA2nOeQ%2BPx6imav4PoX9cSfXkt0Zd6XqUv9vT401VN66EpXWvoDYX7WzKSgw26FkN084ykQTxHJmP2uKav51EsTh67KUNzvrvM4eOZqDwHRjoYIo5Z/tOMApDOiJzD6C7DCIeBlB1xHqh8C9QybzYG8rqpXhhQ8SmX84ID6VM9RfRUupnSzqSXZOJiKJWTLB1hWYdEI0OkYPyDAAiAwHlGQCyeGLupzkixUuXwAVLcCWmV8UXrlJm8JinT486gSVYgI6YDPf929wh3kg2GQZPUdE4EswR1NoaXCphiT8mM6zA2xzeFShw0uW4cxbqjZOkGAGvw8AWLASIczHkcJ4Ovd1GpUqGiN6oqaPBg%2BRzWcp0DYmfyEdDqaqmqDP2Odo3bJWEIS9hu8v1saMtYnuGaztMdTrWp%2BvQrevy4/UQ6JvhK2CUnTgflNBonJwff2wmneYnNAAKAAAjo72KZ3gG0jy7StWeqHXS9Y9naUj7fFfesV%2Bno%2BjUbP1HNcT1ztPZMODoqttdrluEII5ThcOna43axq2MkxyQfyWG6IcQC4L/d9S36g3tepCnCCfF9///Dc3dSoecs3d1DNK2H6P6eczRjzSA9svo4rdx/jnoOE63Yd44eWNNH/9izjm7vGaJvztvhOS/Eehtv/cAHKHfJX1HuQ5f72wf%2BjP7rhz/M1026pDsnjfjhL/LTSXGFZoO0YeFvoibTr3XHrh2i0s%2BSR6mfw/hvzLwy9tsM/H40KT%2BLbsaEtZNKh5xrNEdN6uCPVrHqy0b8PHV3F0iMgDGFZd6FburOC5n2vLEh3%2BCE0DIS2eU0YuK5wrnSdl2LKMMndN4FKpsHxBAl4M16O9hxstgBARAAARCIIaA6SLaOmDQU2a4HEqhXqFK19Wb8kHGdr1Tp8dddMR2kgJLBgzh9shm7lMHSya0FvIK5SXqUUL8W8E2qkQqXVDcVw7anyjZdx5jlcUc9e/1S9ccuQ9b52AWFW6EPZw477UaA62ux2HB2xdRbrT4U5fSEDqeIyC/XNYMjQ6VfcY6Ic4Vj%2BbFfINvpswyXjsIZaLjOUjWjmLMN5gihHWYbUwahaOLQpX%2Biay6joEigXqVSpWpIGadAAAR0AtxWmZyiY/z%2BpdK2PPtdbYx2zdXOcXsSzl%2Bz8XWItn1uYy0fQPF1MXVuXBgLIy9t1U%2BxyrHoCCeHBcxEnG6Xwmhl3peuXUt//M%2B30B/cuJymfPF%2BmnLzQm96qULPcXZyzFp9KuLkWLB1gG5Z00ci3Afveoy%2BevcPWK3Lpk6lP5%2Bxgv5y7lq6eu6PaMo/X01PL1vG15ve4S/KhVE2yciGGoVtt7XuAuUL3X58TaGaMObm854x3JsSSh9FIcM1m740vOcLnrND6SbyEkzfZJCXatj/lQMgXXxl8HbGazb/JEYk%2BIzFVGORH8v3nQ2Fsl5%2BNaqVC5T31mEp8KLzdn1VnvwpvizOCy6TfEN2d6TOSD1dzgRXvtgJMAFODpde1Arekp/FiSTZ4R8EQAAEQCApAdV5MBqCuKPk6oBoacnwxZLn7FDuDjGqRCxcXWxMH2L5WlXGjzPyyCS5E1WiauyolcbIFhmX/xUD74szzUlTr8ov62MMaixL7iQ01Mv8ZuUlk0v9n1C/FvCtlvwyL8YYX1UWEuombH2VEhVLFb/slYBIXdO/cNSCxe/K8klaH00Sk8jgMMIY4LjXOJwjjEkHnJscBLh85XpKceWs7hXpIIu7zdgoZnAQsFHOcE0HaA3H7YUYlZRkJKEKo8tPpKNcR6rRdqr4dapW1HPGZSBUcQx7WlnEMQ3HdunvuqYcon47IBwZdfUQJXEg2lNvtFdapcJK4hgEOoBAU%2B8A8h7P/P6l2l/j%2BzN/JNJ4rqd8t%2BS2wnPoNt6p%2BT1XvE/L50QXmd5xmo0fWz24vbc4MLx3tJi2mGUU/T4D58/vM1TEO55s65MuZq4p3i52dYzk6MCRHLKerdqwgd718S76pUuupF/6i4/T2z95Df36NTfQe66ZSr93zVS6%2BLM30B9c8//RH372BvrAl6fSB6%2Bf6u2/%2B5oveuFmzA8uPC6cHCs27aTFx39GPT1/Sl03TxkjJ4e%2B5oR7P2wEZ4Ozt16FOa51rY%2BAUdgcN7xmRiB9zSAcDqcfB%2BLIwkr0P15OjmR5F3kK5CWQf7PTgR0ZtvLJizUklAMjID/EKFDWVgeDkhXRNyTP6uSIyRfrYdXBT8gVzpq25zhqlEdYfoxeItXmeKv6ptdf%2B765zEOYcQgCIAAC5zkBZeA3ddLYmJXUoCI7jVqnRBrf9H9TWqIgUqfHHSSts5cl7Ti9Y4x%2B0UoU1/ltxIhLt5EXG69ouknPJNSvWb6B/MUZbKXuCXXT64ujzIVzRbcTylQS/bP%2BSXU3SE0kQ%2BXZaZRNJMugA05NEgKqPfbaywTtTsCQ5XKQNQhweINsbn8N13SA5nAh3R33pP4s8PeD91ciHcWXwXw/WNr/ot34pufHuK/JTvr4k3Jc%2BruuefG1dKOcVD5b/0yQ2uMfBCYPAW6LHO2N9R2g2XuN4wfbrwA9DqPu3cB97WxrfadmILwhn%2BJDD/M7TrPxAzmJHvD7obud5TZP6B5uTFmGhQ/nt%2Bg5zqNKuM/AyeHmM65X26UwxiLTr9fr1LtnT6ZteGSEVRKLkf/i%2B99P/8%2BHP0wXXvFndOGlU%2BiX/mQKveMv/oJEGi35NetkEErUytRdyFPem5pIM9bn840RHg5Nxzj9greOhiP92EvK6Owy/kfFKEO/M14L8u8cWSAVk2WkOzryefL4eGES6qsZ%2BV35YgdCzHRLHC7sTIgZoeJyXsgsi39XOHfaFidHjF6cdkberK9eTs59ODmYOXZAAARAwEpAGaaiRhN1LdwvsYoTF%2BpVqoivTfWvzERHpSg6KaEvUwOCMqSXuIOkOlDRfEoltK9kZcdK6FytWzqQMp7pXxmt7ek14mXmZUo36bmE%2BrWA71iO5HDVNX%2BER1IelnBsoHAYMSxR%2BXRSGQ3WznstqSxOHDuTjYBuEIptO0Tm9HvUaTDzSbB8Q1g2GBqu6RxN4ficbDtT/Qfvr0Q6yulPGu2nbggsRkZ36Non3Od7bfymq1KaidEopQzPUCUBeyBwXhBwvD8legdwxHe/r2ptb0x7KdYF4RFYsl1M824pdZRxvf8ij2KNLedm49sS4GeP28kRWBdFjMbQX3JYhnpH19ty0W/wnFQZzbvtYlfHSI4OHslhuz9M59/98Y97C5WbromFzP/17rtpYHCQeuqD9NCKg3THypV07cqV9KezVtJ//osP08DQEEe98qtf9WTp5/gidkAABNqMQFbnWZtlA%2BqAAAiAwGQnIDsfsR24FmV0vNNrkdoQAwIgAAIgMD4ElDMlzrA2PvogFRAAgfOTgGyLEjmjz09EE55rODkmvAiUAu1SGEqj8d27bOpU%2BsGSPfSlHqLf/MhHaENvb2D7nauuYoXWHyP6yZoTdGvPAF3xTA9N%2BcEKmvKPa%2BgNf/ReL87/vvNOmrd8OT1dPUm/8N73cTzsgAAItCkBbUSMaSmVNtUaaoEACIBAhxFI%2BKV/y3I93um1THEIAgEQAAEQGCcC0rCYdgHacVIPyYAACJwXBOQ7a3AU2nmR9UmUyXaxq2MkB0ZykHByiCmtrn%2BF6K3/8xK6/Gu3BzaTk6PQcHJc9NU59Ad3v0Rv%2BB/v9eK8%2B5Ofg5NjEjVEUBUEeKqsnFgPBT8QAAEQAIEJIcBTdYzT17Ljnd6EQEWiIAACIAACzRCAk6MZeogLAiDQGgKN6VXHa6Rza5Q%2B76TAydFGRd4uhTFRSHQnx5vf/wFv8XGxGLncTE6OmxpOjt/66jK6%2BB9XeyM5RPgLP/wxODkmqiCRLgikJCAWJpcLibvWNUkpFsFBAARAAARAAARAAARAAAQmOQE4OSZ5AUJ9EOgEAo3pVTFVVXsXZrvY1TGSAyM5vJEcP35%2BK325h%2BhdH/tYZJHyxWvW0K0zZ9LOU0RrD/vTVX2/4eS48qs99H//6SdpU1%2BfF%2B%2Bfv/99uqe8nB57FdNVtXcTBO06nYBYODyfL1B3uUy1Wk3Lbo1qjcXIpYMjZ1hsXYuAXRAAARAAARAAARAAARAAgfOMAJwc51mBI7sgAAIgkJEAnBwZwY1FtHYpjLHIWxKZQ8PD9K4r/5re%2BN48DWoLiMu4o6OjVJwxgy54X97bxFobb3pvnt74x%2B%2BlN/7Rez0Hhww7cvYsfeQrX/HW4zh68qQ8jX8QAIFxJiCcHOzEyOXs%2B3BwjHPJIDkQAAEQAAEQAAEQAAEQaH8CcHK0fxlBQxAAARBoBwLtYlfHSA6M5GiH%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%2BkHnnkEZo9ezbNmjXL2x5%2B%2BGF66CGxPUQzZ86kkthKJZoxo0TTZ8yg6dOn04Nie/BBeuCBB%2Bn%2BBx6g%2B%2B%2B/n358//007cc/pmnTfkyzZs%2BmQ4cPj2c2kBYIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIWAnAyWFFM7kvCAfHuXPnrNvZc%2BeIt7Pn6KzYxDlv/6z2f5ZGR0dJOE36du6khx%2BeRYcOHZrccKA9CIAACIAACIAACIAACIAACIAACIAACIAACIAACIBARxCAk6MjijGaCTGCQzg5RkZGvG14ZIS8bXiEhodHaGh4uLGN0NDQsL8ND9Ogtz/k/Q8Oif8hOnv2rOfkOHnyJPX19dHMhx6GoyOKHGdAAARAAARAAARAAARAAARAAARAAARAAARAAARAAATGmQCcHOMMfLyS850co006OYTTQzk5Tp06RceOHaPt27d7011hRMd4lSbSAQEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQMBGAk8NEpQPOiTU45EiOxx9/nJrZNm7c6I3kOH78OB09etQbxbF161aaPqNEBzF1VQfUFmQBBEAABEAABEAABEAABEAABEAABEAABEAABEAABCYnATg5Jme5xWotFhmXTo7wVFW26arEVFXeNmierurIkSN0%2BPBhOnjwIO3fv5%2BE80MsWH7wINboiC0QBAABEAABEAABEAABEAABEAABEAABEAABEAABEAABEGg5ATg5Wo60PQQ%2B9NBDnpPD5OAY5vU4xLocak2OOCeHcG4cOHDAc3Ds3buXdu/eTevWraMHp09vj0xDCxAAARAAARAAARAAARAAARAAARAAARAAARAAARAAgfOKAJwcHVrcM2fO9J0cwyNNTVUlprmS01XdcccdZNq6v//9DqWIbIEACIAACIAACIAACIAACIAACIAACIAACIAACIAACLQzATg52rl0mtCtpDk5xPRUchuyjOIQ58VIjqEhOVWVv%2Bj4oJi6anCIDogpquoHGlud9u%2Bv0779dTp06DB9rxtOjiaKClFBAARAAARAAARAAARAAARAAARAAARAAARAAARAAAQyEoCTIyO4do9WKpV4JIfv4BBTU%2BmbmqZKOjj09TgGvHU5fAeHcHKIY7UN0sDAIJ0ZGKSRkbP0ve7udscB/UAABEAABEAABEAABEAABEAABEAABEAABEAABEAABDqQAJwcHVioIkszZpTo7LlznmNDTDk1Fpt0ctz1vYl3cpQLecrlcpQvlDu0RJEtEAABEAABEAABEAABEAABEAABEAABEAABEAABEACBMAE4OcJEOuR4%2BowZ7ORQIzhGAguND4npqXiaqmFvWip/8XExZVX8KA7p5Ljzru%2B1jlq54Dkrcrk8ddeSii1TIZdrxCsQ3BxJuSEcCIAACIAACIDAWBGoVytUKhapq6tLbcUilap1qmdMtF5pyCtVoxLqdaqWilQshtKrVMckvebzV6d6tRTUt6tIxVKFqlkA1atUKnZRsRIfmTnqZRPZL1ICUdFysJ1JoZ8Q0TxfmyKtkN3ispOq1itUjJRDsD779cNRxiEZpltFJuf%2Br1KJdWlxXXAnjKtjQaBaUu0wl6tWtxznstehschIBpkJ8l4Uz6ZShSqZGt8MOiEKCIAACJwPBFK%2B%2B1mRcDteIkMPQIumv7uEwrIMy7NPPAea6DNoSkzoLpwcE4p/7BKfPn16w8nRcGwMa9NTac4Nfx0O3cHRWIuj4eRQU1TJ6arUVFXKyXFX6zKSyclBhJEcrSsCSAIBEAABEAABEGiGQJ0quqPBZDwrhjoeiZJTHZew0c1zFpjS4XOlDI4DW3qtyF%2BcDOEMSgTFC6TnP4mTo1qydPCYl7jeOsN2Ov3i2HRRV6b6I1C1QnacjHRlFyjlkIMi4CAMlE0XFUsW511YRpqKpCkTdIS1ri5oSWB3PAnEGXdC9Uuvexmr0Hjmzp1W6rx3hqHLDQVXQQAEQGBsCaR794vRhdtxV/9Bfz8zvLewjLh3YEPcGPXa6TKcHO1UGi3U5UHh5Dh7jsRojbGYqkrIlE6OO%2B6ceCdHC9FBVAyBWrmbCvk85ZMPtYmRGLw81vKDqeEIBEAABEAABFpLQBnQQ6M2xEgLORJDGNSKlXQjLGTnJBxPnvdkBp0Zegerq8vVMTIwkHJD6TWfv2AnTIxskT8xekF9yR/fyfJHOwQ7a6mcHMJQXq9bN6lX1v8s%2BjXP121QuE4AACAASURBVK5t87JbV3ZGLdlBUfS%2BKA%2BWTZWqlZJWP7qoy2R9ZhmyXsTXo6guysHnG7uzyIhKxZmJJRCsT437ntscU53zw0yk1nJEV5J2zaqnbMuF41aMJAy1edVqlSri3go750Ntv1U%2BLkwKAi2pS5Mip1ASBCaWQJZ3v1iNuR23vcsH38%2BMI5FZRvRZUA2PPp/E7T%2BcHLG1aXIGePDBB9nJIaelklNT%2BVNSDXsOELGouDx2LzYuRnIER3FIJ8ftd97ZOkgZR3K0TgFIchOoUXfenxpsbJwcYy3fnTtcBQEQAAEQAIFmCXiGZEfnQP9C3GSjNaevOi9BY5dmjE3wZXswrjkl/6wtPaKm86d3spR/QymjG6ktgOphY7eY5qrxJXaSPEpjf5KwSrHke83o1zRfh5pNy25B2TnUI%2BKydzkVVN0UDohIFdFksNE2Esiphajk/tRGRVmvXPrEyMLl9iag1RejUWhCtVd1vam2StbnJKPTGlOr8EgWx7NsQtEg8ZQEWlSXUqaK4CBwPhFo5t0vlhO342Ynh3yvNb4XSeEsw/5Ok62PIhNoj384OdqjHFquxQMPPMhrckgnhvz3nB5yzY0hMT3VMGVxcLCT4w44OVpegG0rcKydEGMtv23BQjEQAAEQAIEOIVCtxI3QUI6JxIYrNsSFOjcJOiz/f3vnluQ2jqzhWpcWpDdv4Zxpt2350u5W32/zoA30a0VoGfWsDdQGZtwz9gmcAElkJkHcKFEu0fE5wiFegETiAwgV8hdAj1UmLa0Bq1x5zrnL6qeBluSv8Ic%2BIP4mV58YG8/CtkV6rc50TtpzOqXa95PNflslvVbz7zK%2BZX8vs611OL/tyv61iRxdhxZRa%2BKL9F3/joHwTpzouSm6ofXcHcLKkXxAoGiKm7dPwPQXRI7QXPoMdGPY7YEJjvLZTEDbtPYd1GyShBCAgCGgz9g5f/sZQ%2BlD%2BXt/%2BveM/s1ceS%2Bd2Cj8TSPfiRVbaS9v4ioix000w/JO/PnPf3YrObyAce3tqt6%2B%2B2a5CrCSYzmWV7F0bRHi2vavAgWjEIAABCAAgRkEzESo8Rfm8gutKL1MbGrihUxaChMbU4NceSZJ4bBQP/Ej8Qt8a7GYztv3Lyj3W02FTFpmPYAzJ22wP%2BfzUv9qZan/kwB/LWv1fsF2sU2M4dZ0JoscSt5aPzV%2Bxn1/ZOMMQVGCAH7rt7B9Ws0fqQEHayNw022s/bw%2BrhXAS5%2Be04/12Zm9tWLBFW49FYGF%2BtJTuU%2B5ELh5Av4Zu%2BRv00oFZRyPRA653iBKSNrSd4GO/Rd971Sqc83biBzXpPuEtv/880/36dMn121H5bekMttShRUdsnojrOr4O7xcPP6cblPlV3H0Kzk%2Bujfv3i1X0zNFjvttv4XS3WbvTiVvTvduv924zbDl0t3dsPXSZuv29/mc4T0RIb3/3NTy7DeuSy8%2BnboXpG%2BGMvt7G7ctlNtX5eTu91u32Qz2xOeN2%2B7vi/U9x%2B8UvlOoi/U9Ot7eO%2BdOeyf16y6krDn/pviezd2d88ma7WfMcRkCEIAABCCwHgJzgw1hwjGdlIgYEQd6JzCCjYq40OULaaflTcwmLxTqJxOsaJI2sRN8aJi0dXkLZRZsR5rRJOVyF%2Bb4Vyu1bOthWL3gRaD5/wq2r9Z2xsvmgLP6OQnARjbkGUmuCjJld4dqt5vcR7bi1Jx/AQRmtnF4r4H/pW74v9sdunddTGiI7cz7Y0IGebb68VnEa1NGKCt8znq8xf68MV39KOd7fHxwx0PY2q2BS6h3/BnsRO8GmfKNntPYjpxrupQgLPUL35%2BhfMO9X4knBv0ysum7gXb9%2B7dsqtzxrP4zGJn46X2IeWd8kLymTqEPhc9ZfSlXMa5DAAIJAjoGXSwYyDhu/n5u/Y4JnomNwphubK51bEDkCA3%2BhX3%2B0Ykc/yfv20gKG17cuEDg8CLHfz9%2BdG/erkHk6AUGK1JMjpNBeV1ZMEkfgvwiYow7kQTu/f3TvduG9InP7PstrGiQyHd3t3VeW5j%2BO9/vqa15IoTUexAwpvYMi4G5zZPjnGyeqXGuQAACEIAABG6cgAbwWyYQEqQIgRhTOwngJu6ZZH6TKXcYghy1iVapvLHN3JmWFdev3bZODFPBqWnJmr5WP8si9m9qd6krc/yrlZnnq9s9%2BUBjYRKbLSJv%2B3ptZ5yRyXXNd%2BU56R%2BxDTlvEPgk7VB%2BfG5c5fALIdDcxqbP5QLGiXFYnpvU%2B2M6hPrMhb5s84RAdPw5a%2BxqCWylmlPY5MTmx07ciH2bnO/8qqhUAeFaH7Sf5LOcRxXWtiiP95ousA0l%2Bk/h7NvN1HXqRwgqPrhDJMDYtGVh2fhi62WPE/1n6qfpLzbvcBzzkDom0gbfR2gtII4hAIELCehzHz%2Bbsw3LOG7Go/BcZ8aOSRliI/c3lh%2BLB6G61eakkKe/gMjx9G1wFQ9%2B/%2BOPfiXHf/5zve2q/v2hEzlev327XB3kF/4bt88vrJiUV1vJIff9Cozt3o0WT5xO7nS/dZt9LBdYoaBfcaEuDasrgvCQiMBL4H6zdVu/cmSzdfcnY%2BHerHq4S9XXiAFdXltt7/Pebf11e7k7vszvibnRBbWdFWacpkmtrBEuSYFG8%2BbtjxziBAIQgAAEILAqAhp0yE0ybHU0oJEKRDTbkolNLlgVyiyXF1KVPks%2ByeQpVZnIqKRtmmjNmUhqHUOQJ3xOfzEcOXX26Rz/yoWU%2BPqcl6zkKNmW9li87Ux9JdBYeTYkXaI/yz21Ib5X%2BpKkC3VM2DLecvglEGhqY31%2BvXh4eHh0GrOPftkf%2Bo6wMXkT/U%2BfuRC4kozdqoHjEFS/KEAm478%2BE7aU/LEZK0v1Cu8eUijO7yX4cAzvtPFBs1T9%2BpLluevsHMeCyOOje3w4uN3RrkxTpmUumq4scgyrUHa27Eh4ORxd3xY7dzia9h%2B9qD3H1/gxu/9YMebQiyydaKSw/eqQXQh2ZsVt9aHMLN8buAMBCMwlsOBzJ%2BO4H0vV7jM/HrS6JTamY5Vfkaci7gybrWV/xnSIHJ8R9ucs6vffVeToVmtEqzbCNlZ%2By6r0//wWVd1WVf/%2B4P41iBy7N7ctcmhQ3W%2BNpCJDrT00nxcnMqkLoozm9wJHZhstu1IjFkoKtjPedJe13PP8Ltl2RsAoihCmXqN05npc3b5cRI4yf%2B5CAAIQgMCqCUhALRGcTVVMJiSZCYexN9m2R%2ByZyZAPIpVeIlsrT2xmDow/qXIkmDUJmE3tSdpEYHCaWuuYKneU3vgYxI3JZ/WXxyOLDScz/CtZM75X61myk7pXsS3tsXjbGWfEh%2BkEXFMpy2TgNGUjdU0N9kepNKlrcT7O102goY1HQoTGlsf1lrEz0XeljGj8NdfTj5X29Yue95Jv41pEZ1p%2BLBIok8oKKVPH2IYvbGwnBzdyywT4ylzy/sdlp3zzaWTcG0SEZDs117GwoqXQRpZR9nu%2B4oPfZmsRwSxuCs4hAIECgQWfOxkjDu4QVlsUxOOkU2JDtxWM//6dbtGXtHTTFxE5brp5znfut99/n76TI7ybIytsBMGjInB8%2BLsTOETkeP3mfEfjnGcG9mWlxkRMMKsh0lH12IPhXPOVs%2BWD8io29O%2BdSBek%2BSdCyFksLvc77We4qv6OxItw23xq/cNqE817l4WqaWr2TVEcQgACEIAABG6fgA1CLBi4HwdA%2Br3hH/2vX8MvYKPtNfJBoQsnY9X6qf1cQMk2ogSXFmQV7AufIabWszq6w24n%2B%2BwnA%2BjBwOxPrXuef8VolW8lf%2Bl21bb6v3zbGcfEj0Sg2A2/6Db9OckyaaPuvzxHtr8lbRl/OVw/gWob62qGZIBbCGgfS/VL6V8SlNL0%2BWdK06RsStG1AwlspZ6rUmYtf%2ByjMmnxa1r3UKbaGdsP93Of6le5fE2Xsq9%2BFbhI/3jmsgKDFUPs%2BNG5r3U8t/%2BonyVBydR14oN3RO%2BXmeWYcx0CEJhPYMHnTsZxI1Akn/WClykbsgrM2i0IsgXzt3ILkeNWWmJhP3797TcROf7666%2BrbFkVRI5Xr18v5/1ZgX3/HuvMi8fPtKcvxg4B%2BnwVpewocD8N8qdtSLpYoDGrHrwAkl1NYs1Kfc/325qbHs8RITStFzX8lmD9%2BzZKvmkeRI4pfa5AAAIQgMA6CXTbbYSJROukRIIrhQBMhyPaViOUYz799irVX3E2lzdtg7b6qQ%2BpgFNs9ZoiR1yWPbcBpeWCQVr3c2y28bW1aD9us63%2BL992xlfpg2aybfqx/cVhlqPYiJ4bmdxH17viM4HInC3jMocrJ1BrY%2Bk3mdV0pvoyZiWj2eNnyD93fX8u2dU82f5uys8eSlmpvp/NNXqX0%2Bi5n2tPGEdB%2Brl2xNVWLppu5P9gR8f6Uhvo2FBqA2n7%2BPtd6lgqo3dIbET9p81Psyom9qEzryxK9RDEHEAAAgsQWPC5s2OJGVNL4uukAmJj545%2B28Xwg6juR1EP7jjaYjAaryfGbvcCIsftts1Fnv3662/u46dPma2owoqN8Pm3%2B/B3v3rjw4e/XbcdVe5z2KbKCxwicuxuV%2BQQASH5/oc8Ys03iCfh3Rulz5zIEYsXUbFSViKd3BvK3UTv9YhMuTh97iXeo%2BuR37HN8flMEcIKNUMdysXNtD92jjMIQAACEIDAjREYCxBzloHnAh65Cvp9uccrEZ45/46J/oWvGqiJ4idibm55fcZ59ZtTxpy0y/5KVSelsyaPQjJ1oDbnBZjm8U2VnL82z/ac9piTduSfnbjnxI2D3Td/lLs/ERtxQDffBhJEjIODWVuJcrm0TgKVNpa%2BkemPVniT49wgK2WpiJdLOnTmujjdQt0Gtlp3hPJ2jb923FIm9cB97176%2B2e%2BnVDZ/LMcUvSfmq4ocsTP/chI2vdRErvtVmRL66htLv0k16eiTiE2ItutPsQsbFvGNjiHAASWJKBj0MXPnYzjw7gr589canxL1kLyxH8f2dTq87Irmm0Z1z1G5Lgu3yez/suvvzaIHDPEDS96RAJHEDlevtotV0/5tX/qRdz5YmQ1RSQUSNA/up631N%2BRfCVRI74XRe/FRqXsWrr%2BBeOx2NK/CD2uh9iKfSudR37HNsfn80UIaRvvQ4VF8zs/xk5xBgEIQAACELhBAvYlfv2vptqdDIGV0kSk3ZoGq3L2zilvfv0k%2BF0J1ljRom3yppOyiyeSNmAlW8vMYJ1Meo5/8/kmi05enG/7em1nHJSgauJXhiZZ8dDaiAK6EiwctWvo%2B9H7EnwhBVtFH7i5HgKVNtY%2Bc36Q2sKQ58gHuGeMgxeNa02BLevlcCz5xr/oFSZV/4PN9DM2306w1zqearrU90hb%2Bep7pD0EZ7rPnC25nhM0UtejgsRGhXc5nbK4qC%2BNas0JBCBQJrDgcyfjsYrL9vuk6bkWG7m5wFAb%2BV4cj/3lut7OXUSO22mLRT35%2BRcVOa69XdWLNYgc567kqAbl880mgkPFRms6d7p3%2B%2B3GbaxgEdlutpV3u3JnpsgxWclRE69m2q94y20IQAACEIDAkxAwE4R6IGvqYTlYMU1fvSITG50c2TyzyzuzflLOKMhsPQnHGlhqmrgtvd94hVfwsv1z5kT3TL5N/pxp%2B3ptZ7wW3yoTcJNlcli0kehXpbYu2pqUzIU1Eqi0sfT7SoC5qepSVhBMav185riRc0L6eK28sQENoI2/N4RJdRwP9vS5s/H7%2BXaCvVYumu7JRY4L%2Bo9wqtgop1MWbd%2BpgTWfEIDA%2BQQWfO5kHLfjsdr3K8Ts%2BJr0WWzUvgt0zF7jeIHIkWz99V/8%2BZdfhpUc49Uafjuq6pZUdquqxOqNsFVVWMnx4uWr5YAtvJJD361RC65HVRA/Su%2BPiPJEp62CQ2s6NX9y99vN8H6L6KXmC/it5aSO5ogQmta/k0NWdETCzLgUzcM7OcZkOIMABCAAgbUQMJODw4OLfkzeUAmdtFQnLA3WfBIJViUNzi3vgvq1TrAkGFibiAUAWoclJmQSLGoO4gU/cp9z/LuAb654uX6B7au1nTi3zMqJSt%2BRtu0ChpV2qdgynnO4VgK1NpZ%2BbwNL51RW%2B5oPuMuYXAxca56LxjWpQ%2Bt42n1pDO8MSaxwmmsvx3iuHcGuXFLihSQz4ncq3Xgs0FzjIx0zk1%2BfQ%2BKsLanj%2Bf0na3vsqCunU2YX9aWoTE4hAIESgQWfu%2BxYomPUs2e7stAhNmrfBWpzjeMFIkepT6743k8//%2Bw%2BfvxUfr%2BGFTNSxxWBI4gcX9%2ByyGFWEswKmp%2Bbz/SZVvGiNZ0x7ey2Tl5AkH8L%2BC22kgftIoTUK6yiafKt3X7SPS5CAAIQgAAEnpiABK9KEZGSjzIJOT8oMjKfCzCFRDPLu6x%2BbROncrAmOG4/F5xI2sBYMQBpy68dt/t3Gd%2ByH5fZvlbbGZ9rfdUkzR7WbMj9Z%2B5wPLpdt11MZsIvaTP3s05wYzUEam0s9xPB/hmVlDEtCKdNdtvHjaIrMsY39mPjW3pP9raxIPg0qbveGJ6/uWyVS3GlpK1H4vtY/CqO81rXhIlQk7zAYHw4N1jY5icvHpfG4AACN0NAx6pzn3%2BpiozjibmBGWe80HHM/bpKbBTS%2BAIlXcPqEHHwdg4QOW6nLRb15KefVOS49nZVz1%2B8XM53WYkwb%2BVFaZWA3Ls702Yt3%2BnebfdGaBhoSJC/uHLB6cvCK%2BnGkFUMGIkczumKiTP9HpcTn%2BXLHaU0gsZYgwkrUHJt0Wh/VBgnEIAABCAAgVshEAIilQlEwd0QiL54QuTLeAz%2B5F9MOK%2B8YO/8%2BknAJjcRM5O1UlBpjHC5iaT6t%2BTkrtW/y/mOudizy20rm0z7n9V2xkfJn7FvkmYPG2yEPi8vAM51tAZbWT%2B4sQ4CDW2s/aXSL/14e3yY1lvKGI8p1efJCq65PjotbXpFAlYV/92jezgeRHgoBcuUybhOk8JN3VPfaWqn5tvY8ix2XshM8BMb1xQ57ErK3HdeqFqm/7T52S5ypFgEF/iEAASWJND6t19DmTKOJ0QOn13u%2B%2B0Qa2lK4234W7Fgp8Hdp0yCyPGU9K9Y9o8//TR/JUfDyg27VVVYyfH86xfL1cSKHPcndzoV/ptSRchICgX3bivvsdi4zfbenU4m8%2Bnk7vdbt4mFChOov7vbdELGJF94R4aN5A%2BmLxU5fP7Ndu/uPQPjrndet6tKiAUX%2Bh1se06pf8Laiyj3g2envVN8RqiYtEfpXl9a3X7KK65BAAIQgAAEboCATDIO7uHx0T22/LduS0CoNAGxGXxg49AF1nxZ8u8xClblgjhzy7u0fp2DdgI1fiH740P4ZX3LS3mltl7Nccddv899KpA2SumDeIdj3z7mxqMPMO12skXLsoGgRv8W4Ptw6Ouwi4N6C9h27hptZxshtH97/ze5%2B8OWPi1pfJ8plCXpCmkmDnBhVQRa2ljS9P3FCxl2uPUn/rnrVgXFz50Zm6arDnRcmN7rKY5EgIdhjH88upSWkuUuz34/3o6/lx7cw8PRHYdxQ4S/0nPRFeTHy37M9Xn8eDNhYgWT3HdQNKZk7cQVjtrkGNh437qxvG%2BrXfBx0i41USDQ1DEvYSIkyq/k6PwJ49o5/afVz3q6RfqS1JgDCECgTkDH%2BNrfplVbMo5nBAw/9B3N37CpMVdspL8LjnbM9uN6dklI1dsnTYDI8aT4r1f4Dz%2B2ihwf3L/PEDeC2PHfjx/dV1cROe7knRN3IlCMr9ntpyQwPgmqD4z9aovNOP/EbiqoPxIM8vmtL6FVlxA5Jj5GLFLlduWf6/coX0JA8cZHaZRJwCf1zq0kMfmT/pv7tv7BfuDLJwQgAAEIQODmCMgEQoM/GjRKX7OTCJmglKIpUaUlT7ftzrQMH9A38scot%2BRtLe/C%2BknhowDV1OdcwE/yTw7aJ5JS5wwvCdhNyrjkQqN/l/IdcY0C85faDtUflbFE2wXD3Qy9vH2USZo9FP%2Bi%2Bo8yaHsU%2B1qTrZFhTtZGoLWNJV2iz5uxxI7nHoWON5n%2BaOzGeTuU5r79LmkdsjsbM5/9Tmhoasex0GH9s8dVeyJKFNgmKqxsU/k8b/Ocl/KngoFS/wVEDm8r046Wkz9O9QGpZ9FP09dy6TI%2BJNBI7TmAAAQuIaBjUOrZnmVZxvG8yOHtqZiZWMEmNlJjpr22c4eVChyeASLHrJ61nsQ//PijrOS49nZV/3j%2B9XJgZCWHBs9toNse2wB5VeToPDy50/3ebTdhy6ShjE1ilcaoRsNKj1gkqeSTYH9OeBnKyKY73bu9X2GSKHeyGmXkbzg5z%2B/aSo7O%2BslzNG202bq9X9Rh2s%2B2T/AofEp7FYSQpP1ggE8IQAACEIDALRJonkDoZEInPm3BlEm1Hx%2B6X%2BH27xYIdneyWmGSXi6cUd5F9ZOChwPz6%2BcQJNz5lyY%2BZkWZ2IKez5hIBl7hF76m7H6Fh1pd7qjRvwX41ldyhD5S/9S%2BGZNYsu2MbQnCZQLCJmn2sNXGwLoY4Gu1lXWGGzdPYFYbDyvkEmPHZHWHr7h5nvPPkg1KZfr943G0auLZ7pDfcz0F3PgRB9W7893O7bzNs8Zev3DC%2BzesZAnj6bOW7yDr7KPYGfnovxPilTMmWyjb5tFxXMfd1Mq8NvGg7XuyzdbM/jPUs812g8jh7V3alwx7DiEAgRoBHYNK3wE1K919GcfLIodd2ezHRS8yyz%2Bxkfn7z4/j8ao8ybyeA0SO9bTVLE%2B//8GLHB8vWqURVmuUPv1KjkVFjlm1JDEEIAABCEAAAhD4QgiEyUfuV5hLV/Nzl7e0/9iDAAQgAAEIQAACEIAABCAwEEDk%2BEK7wv6HH5wXIEoCxRL3fBn/%2B9XzL5Qi1YIABCAAAQhAAAKfg8CCv/Zqcvdzl9fkFIkgAAEIQAACEIAABCAAAQicRQCR4yxst59p//33ncjhRYhr//%2Bff3x1%2B0DwEAIQgAAEIAABCNwqAdkypbYMfaEKfO7yFnIbMxCAAAQgAAEIQAACEIAABFIEEDlSVL6Aa9/tv3ff7ffu2%2B/27v2337n3337rvnn/rXv3/r1798179/bdN%2B7Nu3fuzdt37vXbt2735q3bvX7jXr1%2B7V7tXruXr3buhf//8pX7%2BuUr9/zFS/f86xfdS8b99lT%2Bv1/B4QUORI4voMNQBQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACKySAyLHCRsNlCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEnEPkoBdAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCCwSgKIHKtsNpyGAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAkYM%2BAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAwCoJIHKsstlwGgIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAUQO%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%2BAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAwCoJIHKsstlwGgIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAUQO%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%2BnwZbLeiqJ9gAAAABJRU5ErkJggg%3D%3D?action=content|data:image/png;base64,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|width="100%"}} | * Select the T1 file: BrainSuiteTutorialSVReg/'''2523412.nii.gz''' |
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Where the *.bval is a text file that contains the value of the gradient, and the *.bvec is also a text file that contaons the orientation of the gradient. The nii.gz file is the NifTi file of the DWI where the images are stored. | * Click on the link "'''Click here to compute MNI normalization'''": option "'''maff8'''". This estimates an affine transformation to the [[https://neuroimage.usc.edu/brainstorm/CoordinateSystems#MNI_coordinates|MNI space]] and sets default positions for the anatomical fiducials. The NAS/LPA/RPA fiducials are needed for defining the Brainstorm [[CoordinateSystems|subject coordinate system]], in which the surfaces and FEM meshes are stored. <<BR>><<BR>> {{attachment:importT1.gif}} |
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== Realistic condctivity tensors == First you need to create a new subject in your protocole, let call it the 'BrainSuiteSubject'. Then import the T1 MRI of the subject and set the fidicials points as explained in the previous tutorial. |
=== Diffusion imaging === This computes the This requires BrainSuite to be installed on your computer, with the bdp program available in the system path. |
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BynZK5gDARCwCXhFWl78UdZ08%2BbNxMZvE5P7yWvWrCE2jqSvv/56ZRdffDGxnXHGGXTCCScoO%2Buss4iNv5Yl97Elyuf7zPJWMRksxt%2BuNgeMmYPG%2BH70zp07lQVF0qkR19LAO6ZdjaZ/AFSyU5IIyPmYjPooxIR%2BnWWqQaxKpNn11OtIHfXVXNKNd1IvlQm19HZ2Hi2azmrIs%2BrFmRvrhFeKif0hE2f57HPKU/2n1nobQtuaSPa51FNPnS/PSY6jNA8FLX0xGcTE6HlwpXMd94X2k0aOGRAAAYuAV6RlMFZZU3mG%2BeDBg/oZZ3lVKHdvy1vFuEub7eSTT6bjjz9embw%2BlAeScVc2m1w88GAx/nQl26FDhwrZjh07iG358uXFu7tdDZNe1qRmoS8ruSPNZL%2B0I/LUW65S78w2GkpTXQzBMhebQpZanhSYG5G2N5WIuakvEkQomin/zIyt%2Bc6XnJK6GV9y6kWkHRcQ3aLEFxkTXV/%2B4veQq69IGdG15W3nb031NkWaLyIcX8Fq6fe/2577RLq9bQwTdfFivsyG978R5dve8H/7gse1n1zpsAwExp1Apkjzt5z561Jl2nCK9LgfIqj/8BHIF%2BnhqxM8BoHxJJAp0vwxCn539urVq0sxfg5606ZNyvjjFhL58mNRbPwGMX40i%2B3AgQPK9u/frz89KR/EMKdm17Y892xO5TEriZp5Kl/ikgh%2B2bJlOZH0eB4YqPUwE0hEOrP3ZJirB99BYIwIZIp0FQwg0lVQRZ4gkCbAn%2BFs34pwD6hLb41/IAACg0wgWqQ5Ei3627hxI7Hdd999%2BhOUd9xxB7GtXbtWPXbF96ZXrVql7LrrrqOVK1dmmryhjN%2B/zS8lyTK%2B72wb%2B83GX%2BDyP4JVtHbYDgTqJJBEzzJWoOsd6nW6h7JBAAR6IhAt0iGlynPLLJjy1rCFCxcS24IFC2j%2B/PnK5s2bR2xz587VAipCWuU0pC7YFgQGisDMRGr0fLNlfeRkoJyFMyAAAkUJRIt0SCQNkS66W7AdCIAACIAACHg%2BsAE4IAACIAACIAAC9RLoayRdb1VROgiAAAiAAAgMF4FokR6u6sJbEAABEAABEBgeAtEiHXJPeniwwFMQAAEQAAEQqJ9AtEjXXwV4AAIgAAIgAAKjSSBapBFJj%2BaBgVqBAAiAAAjUTyBapOuvAjwAARAAARAAgdEkEC3SiKRH88BArUAABEAABOonEC3S9VcBHoAACIAACIDAaBKIFmlE0qN5YKBWIAACIAAC9ROIFum6qnDGVU8TDAzKPAbqOpZRLgiAAAhkEYgW6TIj6ZC8uHF%2B4tVPYWBQyjHAxxN%2BIAACIDBoBKJFuq4KQaRxgVLmRRpEuq4zGeWCAAj4CESLdEj063OE14XkBZGGSEOk884orAcBEBh2AtEiXRcAiDREGiJd19mHckEABPpFIFqkQ6LfvEqF5AWRhkhDpPPOKKwHARAYdgLRIl0XAIg0RBoiXdfZh3JBAAT6RSBapEOi37xKheQFkYZIQ6TzziisBwEQGHYC0SJdFwCINEQaIl3X2YdyQQAE%2BkUgWqRDot%2B8SoXkBZGGSEOk884orAcBEBh2AtEiXRcAiDREGiJd19mHckEABPpFIFqkQ6LfvEqF5AWRhkhDpPPOKKwHARAYdgLRIl0XgBiR/t%2Bl/0e2SYO/6s6Hu9bxMlmP6WheHPDxhB8IgAAIDBqBaJEOiX7zKh%2BSV4xINxoNsk3El8XbXsfLZD2mEOm84xjrQQAEQKAsAtEiXZYjofkMs0hv37eXVmxbRLM2NKixLrFZGxbRin2feC8I1m%2BbnUpjptfzU5POPGLK5YuT9bssn9fOpllTk7Q%2Bx2fXhc32fcto1toGNdbOpqUB6bcf3EtLmdvaRbTioJ9VSH0RSYeegdgeBECgHwSiRTok%2Bs2rUEheoSJtRshcjm0SPf/nP//pWsfLZL1LcEKWbWehY3EyxNmen7Vtr1NkuZwVU/60Ki%2BHSMeUq4Uxy%2BdehZbzC0yrL1JyRDq0vhDpvLMT60EABOogEC3SdTjNZQ6jSOvokcVJRc2JGG/fN9mOEDtCOHdXd5S4/eAkze0l%2BpSotcdy9YXB2tk0d1fa57nSG5AjmuaFjBbaQJFOp8uOpHvhDJGu60xGuSAAAj4C0SIdEv36HOF1IXkVFWkZIPZf//0/xBGxK1LmciVSdq1fsmSJTiv59TKYTItMhqClItYNy2i91Z2rxcexzhRBez6mXF3mugZ5LxzWNcjXAyA%2BmfmpqL9AJM1cVkxZ3fwZDLmcXuoLkc47O7EeBECgDgLRIl2H01xmUZH2ia/Z5R26XehgspQAO7qjtYjtWtTuCneIF3fhZnVnS3p7GltunuBxeTrS9tSLtzN9mTuVXU%2BzDut3LSMdrfOFwIaOWGeItFlGw%2BOPZtnhDJGu60xGuSAAAj4C0SIdEv36HOF1IXkNm0ibwuOb15GmQ6RFMF0RrS/PIut85ealLyrS4j%2BLpy2SrjJ0936nW5y72pN02d3drrzsZXZ9IdJ5ZyfWgwAI1EEgWqTrcJrLjBFp8XnPnj1dg8TM6Nq1nUTcoZG0LRJZ/xMR6h71rMRw7SJayvevp2anBqC1R4Yn94uz8s9a7is3Kw0v12KX0R0uafV2nQi4SHki0moEeafrP0kXKdLSK4FIWg5zTEEABAaQQLRIh0S/efUPyWtURVpHpVZ3rgiWPRLc/l/kvrAIpznNKtfcxp7nrmg9Ut1zn9zsgpZegERsuy9GzHLsx7uSdHEibdcXkXTe2Yn1IAACdRCIFuk6nOYyR1GkdbTJ3bvW/dTUOh5lvW1SDyxTzwMbA6tChTqVt1WuKZg8bwquXCDMMnyxt%2Bf/Zje3rE/E1i/Ssr1Mk3S9i7SrvhDpus5klAsCIOAjEC3SIdGvzxFeF5LXqIl0SvysKJoFSouTp1vZjg5F2HzTvHLttO2IfrYawKWjaB7QNdU9Gl35LY9/WXXS9XHce7fLNP8n6XoT6az6QqTzzk6sBwEQqINAtEjX4TSXOUoinRIOjwibYuWaN7vEi0TTZZTr6/I285dubvE7Edv%2BRdKmP9wLYPoEka7rTEa5IAACPgLRIh0S/foc4XWSV5HpqIi0LRxFxFWEzp6m8grsto4qVwZhWcLn6uYWn/st0ik2jme6IdJ5ZyfWgwAI1EEgWqTrcJrLHAWRzhMOEbSQqe7y9oh02eWm8uuUq%2B/7Wt3cUpd%2BinTKP4dAs08Q6brOZJQLAiDgIxAt0hL1%2Bgopum7Lli1FNx16kVZd0/JKTRWB9v74lAgfT/NEul/l6iiaB8EVNc%2BFhdQxEfdi96SL1hciXfjUw4YgAAJ9JBAt0n30NVXUMEfS/J5u9Q5u/ZKO7vd0iyiFTM2I0dV93Y9yZVT6IIh0SH0h0qnTC39AAAQGhEC0SJcZSYcwGVaRNgd3hXwBSkfIvueRZSS1dW%2BYhb7ScuXDHxldya4LjSQirmbgWGh9IdIhZx%2B2BQEQ6BeBaJHul6N2OTEizRcWtsmbxFwf2DC3le16eeOYGemGCLQSWRmclfHIUipvS8hT6zLSu4Q0Va5D%2BCWNvoAIyLtKke6lvhBp%2BwzDfxAAgUEgEC3SwxhJm6Ir8yK%2BVYq02QVsPv4jYueb2sKT%2BclIh5iWXa58nYtfosKvJ5V7zq4u9qw6VSnSvdQXIj0IzRF8AAEQsAlEi7SdYb/%2BF42k5dOSdX%2BqMtX9WnAglS166h6rMdhMxFFPre89s0CWUi53Z/vKDejmFtGuSqR7rS9Eul9nLsoBARAIIRAt0oMeSYsosFhLtCzRszmVda5ImpfJeskvdKofSSoo0Cy8tkhzmRxRr9i2iGaZorl2tnrj14p93QPQyix3/a7i5ebxqUykjfvy%2BuIlhzlzhkiHNBvYFgRAoF8EokW6X47a5RSNpEUs6hZp8QPTT2kQGUCk7TMM/0EABAaBQLRID0skbQqDRMXmVNabYi7reZmsx3QwRTZ2v0CkB6E5gg8gAAI2gWiRtjPs1//QSNpsxEV8zamsh0iPpgjL/s2aQqT7deaiHBAAgRAC0SI9jJE0C7Ft0nivuvPhrnW8TNZjOpoiDpEOaTawLQiAQL8IRIt0vxy1y4mJpCG0oym0MfsVIm2fYfgPAiAwCASiRXoYI%2BmYxhxpR1PgIdKD0BzBBxAAAZtAtEjbGfbrPyLp0RTLui6CINL9OnNRDgiAQAiBaJFGJA2xrEtYyywXIh3SbGBbEACBfhGIFul%2BOWqXg0gaFwcQafuswH8QAIFRIxAt0oikIZZlimVdeSGSHrWmDfUBgdEgEC3SdWFAJI2LgzIFHSJd15mMckEABHwEokUakTTEskyxrCsviLSvmcA6EACBughEi3RdjiOSxsVBmYIOka7rTEa5IAACPgLRIo1IGmJZpljWlRdE2tdMYB0IgEBdBKJFui7HEUnj4qBMQYdI13Umo1wQAAEfgWiRrjOS5oYVBgZlHQO%2BEwXrQAAEQKAOAtEiXYfTKBMEQAAEQAAExoFAtEjXFUmPw85BHUEABEAABMabQLRIjzc%2B1B4EQAAEQAAEqiMQLdKIpKvbOcgZBEAABEBgvAlEi/R440PtQQAEQAAEQKA6AtEijUi6up2DnEEABEAABMabQLRIjzc%2B1B4EQAAEQAAEqiMQLdKIpKvbOcgZBEAABEBgvAlEi/R440PtQQAEQAAEQKA6Av8PFZ1QCPgOoaAAAAAASUVORK5CYII%3D?action=content|data:image/png;base64,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|width="100%"}} | * Right-click on Subject01''' '''> '''Convert DWI to DTI''' |
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=== Diffusion tensor generation from DWI === Right click on the subject and then select the item "Convert DWI to DTI". |
* Select the DWI file: DWI/'''2523412.dwi.nii.gz''' |
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Then follow the popup windows by selecting the DWI, bval and bval. If these files are in the same folder, Brainstorm will detect them automatiquely, otherwise user will be asked to browse the files one by one (as it's the case in this tutorial). | * The associated text files '''*.bvec''' (orientation of the gradient) and '''*.bval''' (value of the gradient) must be in the same folder, with the same file name. Theses files are created from for the DWI acquisition. If you don't have them, ask the person who programmed your DWI sequence and get the files that are specific to your use case. |
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In this step, Brainstorm calls the Brainsuite internally, and the the diffusion tensors are computed. At the end of this process, a new node will appeare in the Brainstom database with the name 'DTI-EIT'. This name refers to, DTI: diffusion tensors images, and EIG for eigen value, since the eigenvalues and eigenvectors are computed at voxel and stored in Brainstorm database. | * The process can take up to 30min. At the end, a new file '''DTI-EIG''' appears in the database (DTI=diffusion tensors images, EIG=eigenvalue). This file contains 12 volumes, ie. 12 values for each voxel. From 1 to 9: components of the three eigenvectors; from 10 to 12: the values of their norm to the eigenvalue. <<BR>><<BR>> {{attachment:importDTI.gif}} |
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R4AxzltcEjIcHRtHyqUP79mDxp5M9aCS9Zftn2XJ0lsMylMZF08ck/%2Btsw19vaZH5a5zfOcdu0ZoOXSpVdNIb5b0PVBhTV65rXTpouobrFrwXbX%2B1zXe0W0KXjydpx39MXmpK/pJMXfpcesZpozcWDU7p/iujfpaN9nADs0a48pcCmd1HTW/BS2jQC29X5sK34p6rQpDmaVAF23ccLQyKJgT2OP%2BHlf5ehFy9xRExT7cjoyUWa7kfG/wZD/%2BHIL2lyLeXwBw97yCUPgXA0TUt%2BYyBH9vSv776bjnV3bNTfHBBOy7GQwJa/tprkP4CdGNijFefPBWADPk2Cxqz6Qq%2BOpaETGjaRry9oUnsBbD8ry/aWDomUZbC3U4Ra9XPLLbcosvXr15vXpl5zzTWKbM2aNXoqH41l0385kl1wwQXmPx35vx3lkp%2BMpPeB0AMwZUbj1LaR32T0pkBE2LiQNAlmo/gio2ozzj3khVaj1JMqD4BdlZr5dNHAzhfn/8YvYyJ48tOKBx54oCLbd999zX8r0r/PkO2%2B%2B%2B4GpgzVcS5TdUaUgwvApPsA3ZydF7Nw6NUGrlfdTtqvsvoAbD8ry/ZGAzskwgawAbKyExjbF1bfALDLkOzfHg1sf/H5vXSQcGIurBMTxxvH29UHAOw8G6t%2BiwZ2SIQNYOPkdZ282Lbw%2BgWAXRXR%2BXTRwM4X5/8GYC%2B8ExMwxjF39QEA28/Ksr3RwEaEjRPSdUJiG/qFrw8A2GVI9m%2BPBra/%2BPxeRNg4iX0nMfYtnP4BYOfZWPVbNLARYS%2BckwxAxbFO1QcA7KqIzqeLBna%2BOP83RNg44VOd8ChnuvsSgO1nZdneaGAjwp7uEwfgw/Grow8A2GVI9m%2BPBra/%2BPxeRNiAQx1wQJ3N63cAdp6NVb9FAzs0wqYDBYMG6APoA1UhhXSZAtHAzorCGhSAAlAACoxTgWhgh0TYwxqSsqxhdWE/FIACUGDaFIgG9rQ1GP5CASgABaZVgWhgp4yKU5Y1rQcEfkMBKAAFyhSIBnZZwdgOBaAAFIACaRWIBnbKqDhlWWllQmlQAApAgfoViAZ2/U2AB1AACkCBhaFANLBTRsUpy1oYhw%2BthAJQYCEpEA3shSQW2goFoAAUqFOBaGCnjIpTllWnqKgbCkABKDAOBaKBPQ6nUCYUgAJQAAoUFYgGdsqoOGVZxaZiCxSAAlBguhWIBvY0Nn/lypXKNm7H5s2bC/toGz5QAApAgboViAZ2yqg4ZVk%2BYefm5pRtnJ5Abu%2BjbfhAASgABepWIBrYdTdglPptINN3/kwE2Dt6qru1rVob5tTc2sxaG9qqu4M9cS97W1u5PDK/Wd/UdWeOqJcK7G23fF7TUq1NXdUb4rPTmR0d1Vozp%2BbWtFQnKH9PdUi3NW1V0sqsusj2ZgVhDQo0Q4FoYKeMilOWZcsrQUz12MYQX7x4cWEfbeP9drnB3wl6BCoBanu9tbVXWmx3kz%2BvLssF7Kh6B5As83lU6FJ5gXnNBWsYsKPaWyo/dkCBWhWIBnat3gdU3ghgc1RJoNLRtADzjm4/chxAsb3d1biuao8SlUbWay4Sa1qqvT3vc5t/JQwDqGiOgW4gsPP5PBF2ZHuFq1iFAo1SIBrYKaPilGWxygRqsmXLlimKlF0RNNXLEbRrv8zL5Y1yI9IApxRuIpLd0FECjf3mMIhc%2B7jBjmVUvVzn2jnlvYisnVO%2BXwbGLVGe/jVQKcLuqe4mayioVEOlotprHMUKFGieAtHAbl6T8h75QCyHRULTEbjDPgLGriELLmx7uz9c4gIZ7/Pl53LMMq7e4fBTykTgQ/3KfGlv8rTT%2BE7j5h1loni6KGwYgLsU2Fkdcz5/WEuXzqJ%2BrEKBJikQDeyUUXHKsljkUBC7ImwX2MOBzR4NWXIE6gAJw9Md6Q4pd9huT73DslYFNvuvQVoJmIMhoMHQiR6OMfk8QyLDHKb9Ee2tUjzSQIFxKBAN7HE4lbJMF7C5/C1bthRuMEo4u9JxeWMDtgFScfaEBuOaturQePemVu7mZX%2BGSWEQhZswfOmp15uZwVc6ZDLIzek4Mq5UXx/YeiYKO2HyRQLblFPUmavCEgo0TYFoYKeMilOWxUIzYGXkzPuaCGwTrTLY2Fklos3BjUl7dgl9rzSObMrMVsrrzdLYazRcYWa8eMfVs2EK8%2BugIjALUwZNvjhgj9Jeu/34DgUmrUA0sCftcGh9UwVsjkIJyPb4q9xHszW2drObkjTfWNyUC4a2LNuutyB4Bl%2B%2BYLSkL4X04iagLNuANzDCNfkigB3UXkeDsAkK1KRANLBTRsUpy2I9pwfYAoSF6FopxaDyDD2MFjUOqZeFNEuK9Fv65p%2BJrimy3%2BSY1UJ5GI52m7g9jrF6U5VrxeQbFdih7XU5gW1QoB4FooFdj9vVa50OYAuIeIA8vNXZsEm1KDu%2BXv%2BwSFa%2BGQrhRhjwTjLCzvyhXwcFn9g3LKFAQxWIBnbKqJjLqrqsomnzgZ2HSDXQlrVclCWHH5zJRdqIsW9dNMPXgmBuVojtA%2BeZWISdsL12W/AdCkxIgWhgT8jPkatpNrDTQ8QMi3iBnbpeUR7XWzYUwkdyosAW/sVenNh/LKFADQpEA5uj4RS%2BpyyL/WkusLu5B0Jyj3yz8yMshwN7MvWa6Nozo4VvWpolw97XbgP6qmPY42mvz0XsgwLjUiAa2ONyLFW5jQT2jmysmV5%2BlG4sNYsknUMrE6iXZ7c0Athja2%2Bq3olyoECYAtHAThkVpyyLZWgesPOwrvpqURM5%2B%2BY78zCENZbc12KM9Yo54s4LBR8MuTSR8rhuOo7WXuki1qFA0xSIBnbTGmT74wI2XRhsC0032pOOWQQc%2BlpRM62v9CadKLsAdbGvNL%2Bt3OA7g9V5EeinMReTkLK53JA8VJ3J5xsSiWhviQzYDAWaoEA0sFNGxSnLYnFDQSyfiLShTt%2B5vFGALYcJwodB8hDKjXnTT39%2BzakDrKnrNQ/A7%2BjpR%2BR5DLpydE0Hx4A3fYQd117uOVhCgeYpEA3s5jUp7xGBlUy%2BItUHYhewZV4uL/z1quInesUbcQUAWmBmUJql/b5qLUWCemnIQ1wQTH2iHQVf84eh%2BG1swE7R3qK72AIFmqBANLBTRsUpy7LFJdBydBwKbII457XLrfxdjC%2B7gOfa5oag4%2B/F9F91ddx/L5awXvdfhJXUO0yYcQE7WXuHNQD7ocDkFYgG9uRdHq3G2oE9mtvIBQWgABQwCkQDO2VUnLIs00LHCkfLcsnJJNh5P23DBwpAAShQtwLRwK67AaPUzyCWSy4HwGYlsIQCUKBpCkQDO2VUnLIsn9AEZds4Pd1MtPeF32Dk0rCEAlAACqRTIBrY6VxBSVAACkABKOBTIBrYKaPilGX5Go19UAAKQIFpVCAa2NPYaPgMBaAAFJhGBaKBnTIqTlnWNB4M%2BAwFoAAU8CkQDWxf4dgHBaAAFIAC6RSIBnbKqDhlWekkQklQAApAgWYoEA3sZjQDXkABKAAFZl%2BBaGCnjIpTljX7hw4thAJQYKEpEA3shSYY2gsFoAAUqEuBaGCnjIpTllWXoKgXCkABKDAuBaKBPS7HUC4UgAJQAArkFYgGdsqoOGVZ%2BWbiGxSAAlBg%2BhWIBvb0S4AWQAEoAAWmQ4FoYKeMilOWNR3yw0soAAWgQHUFooFdvSqkhAJQAApAgRgFooGdMipOWVaMKMgLBaAAFGiiAtHAbmKj4BMUgAJQYBYViAZ2yqg4ZVmzeLDQJigABRa2AtHAXtjyofVQAApAgckpEA3slFFxyrImJyFqggJQAApMRoFoYE/GTdQCBaAAFIAC0cBOGRWnLAuHFgpAASgwawpEA3vWBEF7oAAUgAJNVSAa2Cmj4pRlNVVw%2BAUFoAAUGFWBaGCPWnGd%2BVauXKlsY382b95c2Efb8IECUAAK1K1ANLBTRsUpy/IJOzc3p2zj9ARyex9twwcKQAEoULcC0cCuuwGj1G8Dmb7zZ9zA7raLF4uiPy3VarVVp9tTPXbMWlYrx11XqyNK7bYHF6iWkput6jxfe6rTctdTbJdM56gv2Jee6nY6qt1qqZZ1ESb92p1uqX6eBmEXFGisAtHAThkVpyzLVlyCmOqxjeGyePHiwj7axvvtckO/B4OWwC34yvUFlyOANgvA7nXaBUjzMcovW6rtEpCFxBIKTJEC0cCelrY2Dtjtbrl0vZ7qdSWQHNFoWe7QKDU0faFeEWH72lTI59hQyRdRH12EnL9EBvqJyD93kXJUjU1QYBoUiAZ2yqg4ZVksPoGabNmyZYoiZVcETfVyVObaL/NyeaPeiDSRcSW4dVXbRMZt5UE8N1epStDLkgenF1n7qwKgldpUKCDbUMF3o9/cnGp3HT89stKUUj2VpQ%2B46OXKwBco0BwFooHdnKa4PfGBWA6LhKYjcI/yMQCpCrdex/z0rxQlVoBezu/Q9LnM9GWCwA7VQvsqLnpVNS%2B0ERugQDMUiAZ2yqg4ZVksbyiIXRG2C%2BwTA7aioHlws67VGX4TLRTAoelZWLOcHLCDdDD%2BKdXrtPq/oKroJ/JhFQo0TYFoYDetQbY/LmBzmi1bthRuMEo4u9JxeZMEdtCwRSiAQ9OzKGY5KWBnkXKlXxrGP1opn22TS4YvUKDhCkQDO2VUnLIs1p0BKyNn3jc1wBZDAUN/1YcCODQ9i2eWEwJ2tJ/GYaxAgalVIBrYTW/5TABbBUSXoWALTV844JMBthnWmKt487XgJzZAgelXIBrYKaPilGXxoQGwWYmSZUpgmxkt8gEZ97rzl4LHFwNsjEOXHEhsXggKRAO76SIB2EOOkAeSQ3IOdosIu3ZgD/fFeaGo1lCkggK1KxAN7JRRMZdVdVlFPQB7iEopgR1LQ48vJsL2DokA2EOONnZPuQLRwG56%2B2cC2HXcdBR1sobZUj6EIiA5RmAHzZSxO6VoS6yLdtH4DgUmqUA0sDkanqTTIXUxZKZ6logn8ixoEZKWMpelF5BjDbNlDcAWN16DoSvaEpy3IDA2QIH6FIgGdn2uV6uZITPNwA56YKQMwGVyhaYvlDOhCDv0ASLpJ4At1cD6FCsQDWxE2GFH38C3aqgXCptQAIemLzR3csBWQough2dEvqqyF5qJDVCgAQpEA7sBbfC64Iqw6SJjW2i6yTzpmM2/nqs6nS0UwKHpC2pPENgyyp6bU5Wg3euqtnhrH4BdOIDYMEUKRAN7GiNsG9b0vUnA7vXoxfx4var7PKI38A3eDaJfr9rSb%2B3r5V7cR69X7f%2BxAR9XWrba%2BEMDt6bYOi0KRAO76Q2lSJhMviI1FNgyL5cX/XrVqnOWW2019C2i8iCERsyh6WVdel1E2FXbNEhXiJADfMm/L9z9cI6B9eCd2QXXsQEKTJkC0cBueoTNx4NAyydwKLDphiXn5fJGXZoxbB/c6C%2Bv2h39F2HB9QRAT5cdmr7gUD3AZjd0JN12/UUY/dNMJ%2Bxix4ViCQUaqkA0sBvaroJbTQF2wTFsgAJQAApUVCAa2NMSYUs9OFqWS94vwc77aRs%2BUAAKQIG6FYgGdt0NGKV%2BBrFccjkANiuBJRSAAk1TIBrY0xhhE5Rt4wNDNxPtfaPeYOQysYQCUAAKpFAgGtgpnEAZUAAKQAEoMFyBaGBPY4Q9XBakgAJQAAo0T4FoYDevSfAICkABKDCbCkQDGxH2bHYMtAoKQIHmKRAN7OY1CR5BASgABWZTgWhgI8KezY6BVkEBKNA8BaKB3bwmwSMoAAWgwGwqEA1sRNiz2THQKigABZqnQDSwm9ckeAQFoAAUmE0FooGNCHs2OwZaBQWgQPMUiAZ285oEj6AAFIACs6lANLARYc9mx0CroAAUaJ4C/w9XVJXtbq6wHAAAAABJRU5ErkJggg%3D%3D?action=content|data:image/png;base64,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|width="100%"}} | == FEM mesh == The FEM approach requires a segmentation of the head volume in different tissues, represented as hexahedral or tetrahedral 3D meshes. The methods available within Brainstorm are listed in the tutorial [[https://neuroimage.usc.edu/brainstorm/Tutorials/FemMesh|FEM mesh generation]]. Here we illustrate only the use of '''Brain2mesh''': this is not the most accurate solution for MRI segmentation but it is probably the fastest solution to obtain a tetrahedral mesh of the head with 5 tissues (gray matter, white matter, CSF, skull, skin). For more accurate results, we recommend using SimNIBS, as illustrated in the tutorial [[https://neuroimage.usc.edu/brainstorm/Tutorials/FemMedianNerve#FEM_mesh_with_SimNIBS|FEM median nerve example]]. |
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You have already generated the FEM mesh as explained here (link to the FEM mesh tutorial) | * Right-click on the T1 MRI > MRI segmentation > '''Generate FEM mesh''' > '''Brain2mesh'''. * After less than 15 minutes, you will obtain a new FEM mesh in the database. <<BR>><<BR>>{{attachment:femMesh.gif}} |
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The FEM head model to use for tensors should be selected and highlighted with the green color (double click on the FEM mesh node to select it) | === Conductivity tensor generation from DTI === The Effective Medium Approach is applied to convert the diffusion tensors to the conductivity tensors. |
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When this is done, then right-click on the subject > Convert DWI to DTI, | www.pnas.org/content/98/20/11697 |
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Brainstorm will load the avialbale tissue in the FEM head model and the following windows appears. | ==== FEM mesh head model ==== {{attachment:Mri&femMeshView.JPG|Mri&femMeshView.JPG|width="260",height="300"}} {{attachment:femMeshView.JPG||width="280",height="300"}} |
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Select the WM anisotropy and kee all the oher tissues as isotropic. | Note that this mesh is obtained only from the T1, the use of the T2 is highly recommended if it's available, as recommended in the [[https://neuroimage.usc.edu/brainstorm/Tutorials/FemMesh|FEM mesh tutorial]]. |
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The process of conversion from DWI to Conductivity tensors use the EMA, furthermore, brainstorm propose the option to use the adaptative EMA with the volume constraint option [ref]. In this example we select the EMA with the VC. | ==== Computation of FEM mesh tensors ==== Once the FEM mesh and the DTI tensors are available in the Brainstorm database, the next step for the FEM tensors can be performed by the following: |
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The process will take around 10 min, and then the FEM tensors are computed and stored in the FEM strucutre. [explain how it is organised and how to use it outside brainstorm ] | - Right-click on the FEM mesh - Compute FEM tensors |
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=== Display the tensors === == Artificial/simulated conductivity tensors == In the case where the DWI is not available, or the users desire to evaluates the effect of the conductivity change on the model, the artificial conductivity can be use. |
{{attachment:menuGenerateFemTensors.png||width="250",height="280"}} |
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Two approaches are integrated within Brainstorm. Either the | Brainstorm checks the available tissues in the FEM head model and displays the following panel {{attachment:FEMConductivitiesIsoPanel.JPG||width="250",height="220"}} This panel lists the tissues available in the FEM head model and assigns a default value of the conductivity for each compartment. Users can change these values to their own if needed. DTI values can be used to generate conductivity tensors for the white matter (and in some cases for the grey matter). Please, note that the DWI can be used only for the brain tissues and not for the outers compartments (skull and skin) In this tutorial (and in most cases) we select the white matter. Select the WM anisotropy and keep all the other tissues as isotropic, then these additional options appear asking for the method to use. {{attachment:FEMConductivitiesAnisoPanel.JPG||width="250",height="300"}} The available methods are: - Effective Medium approach (EMA) - Effective Medium approach with volume constraints (EMA + VC) - Simulated or the artificial anisotropy Only the two first methods require the DTI. More information about these methods can be found on these references [ref1][ref2] and in our main paper [link] In this tutorial, we use the method "EMA + VC", where the final tensors are constrained to fits the volume of the equivalent isotropic tensor volume. ==== Visulation of FEM mesh tensors ==== Once the FEM tensors are successfully computed, they are stored in the FEM head node. By right-clicking on the FEM head, new menu items are added that gives the possibilities to display the FEM tensors either as ellipsoids or as vectors in the direction of the main eigenvector. {{attachment:menuDisplayTensors.jpg||width="250",height="300"}} The tensors can be displayed either on the FEM mesh or overlaid on the MRI. The following figures show an example of the obtained tensors displayed on the white matter. {{attachment:meshViewTensorsLines.JPG||width="350",height="300"}} {{attachment:meshViewTensorsTensorsTops.JPG||width="350",height="300"}} On the left, the tensors as a line on the direction of the main eigenvector. On the right, the tensors displayed as ellipsoids. The orientation of the tensor is color-coded as follows: red for right-left, green for anterior-posterior, and blue for superior-inferior. Note that the quality of the tensors depends on the DWI data and the number of acquisition direction. Users can also display the tensors on specific tissues, for example on the white matter (left figure) or overlay on the MRI (right figure). {{attachment:meshViewTensorsLinesWM2.JPG||width="350",height="300"}} {{attachment:tensorsOnMri.JPG||width="300",height="300"}} ==== Recommendation ==== In the case where the user wants to use generate isotropic tensors, then the DTI is not required. For that case, keep all the options to 'isotropic', the recommended display is the 'Ellipsoids', and the final shape will be a sphere (isotropic direction). In the case where more than one FEM head model is in the database, the highlighted one in the green color will be used. <<TAG(Advanced)>> == Simulated conductivity tensor == In the case where the DWI is not available, or in the case where the users desire to evaluate the effect of the conductivity change on the head model, the artificial conductivity can be used. Users can reach this option by following this tutorial and select the third method in this panel. {{attachment:artificialTensors.JPG||width="280",height="450"}} Two approaches are integrated within Brainstorm. Either Wang's constraint or the volume's constraint (Wolters). The common feature between these methods is the ratio between the transversal and longitudinal conductivity ratio. A common example is the skull anisotropy simulation, where the longitudinal conductivity can be higher than the transversal conductivity, the ratio can vary from 2 to 10 [ref]. In this tutorial, we keep all the tissue as isotropic, except the skull, we use a ratio of 0.1 and select the volume constraint. The following figures show the results of this example. "eigenvalues parallel (longitudinal) and perpendicular (transverse) to the fiber directions" for 1:10 anisotropy (transverse:longitudinal) {{attachment:skullAniso.JPG||width="300",height="250"}} {{attachment:skullAniso2.JPG||width="300",height="250"}} == Troubleshooting == To be completed soon and linked to BrainSuite website |
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===TODO=== Check the error in the simnibs mesh in X direction and overlay on mri check the error with the brain2mesh Correct the ratio from integer to float check the meaning of transversal/longitidunal in the code add an interactive way yo change the size of the tensor.. important correct the name of the simulated method, correct the EMC and remove the VC and change the coefficcient |
[TUTORIAL UNDER CONSTRUCTION: NOT READY FOR PUBLIC USE]
FEM tensors estimation with BrainSuite
Authors: Takfarinas Medani, Francois Tadel, Anand Joshi and Richard Leahy
In this tutorial, we describe the estimation of realistic conductivity tensors of living brain tissues using the BrainSuite software. These results are used in FEM forward modeling, as described in the tutorials: FEM with DUNEuro and FEM median nerve example.
The realistic tensors are estimated from the Diffusion-Weighted Images (DWI): Brainstorm calls the BrainSuite software to compute the diffusion tensors on each brain MRI voxel (DTI), then Effective Medium Approach (EMA) is applied to estimate the conductivity tensors for each element of a tetrahedral FEM mesh. This is particularly interesting for the modeling the anisotropy of the white matter.
BrainSuite is also used for other purposes in Brainstorm, particularly the T1 MRI segmentation, as documented in this tutorial: MRI segmentation: BrainSuite.
Contents
Download and installation
Requirements
- You have already followed all the introduction tutorials.
- You have a working copy of Brainstorm installed on your computer.
- For the DWI data, only the NIfTI files (.nii) are supported.
Install Brainsuite
Download the latest version of BrainSuite from http://forums.brainsuite.org/download/.
Install it on your computer by following the instructions in BrainSuite's quick start installation guide.
You will be using BrainSuite Diffusion Pipeline (BDP), so you need to install a compatible MATLAB Runtime (2019b for BrainSuite 21a).
In Brainstorm, menu File > Edit preferences > Enter the BrainSuite installation folder:
Download the dataset
Download the files: MRI T1w and MRI DWI (from the BrainSuite diffusion tutorial).
- Unzip it outside of any of the Brainstorm folders (program folder or database folder).
- Start Brainstorm (Matlab scripts or stand-alone version)
Select the menu File > Create new protocol. Name it "TutorialTensors" and select:
- No, use individual anatomy
- No, use one channel file per condition
Import the anatomy
T1 MRI
- Switch to the "anatomical data" view, the left button in the toolbar above the database explorer.
Right-click on the TutorialFem folder > New subject > Subject01
- Keep the default options you set for the protocol.
Right-click on the subject node > Import MRI:
Set the file format: All MRI files (subject space)
Select the T1 file: BrainSuiteTutorialSVReg/2523412.nii.gz
Click on the link "Click here to compute MNI normalization": option "maff8". This estimates an affine transformation to the MNI space and sets default positions for the anatomical fiducials. The NAS/LPA/RPA fiducials are needed for defining the Brainstorm subject coordinate system, in which the surfaces and FEM meshes are stored.
Diffusion imaging
This computes the This requires BrainSuite to be installed on your computer, with the bdp program available in the system path.
Right-click on Subject01 > Convert DWI to DTI
Select the DWI file: DWI/2523412.dwi.nii.gz
The associated text files *.bvec (orientation of the gradient) and *.bval (value of the gradient) must be in the same folder, with the same file name. Theses files are created from for the DWI acquisition. If you don't have them, ask the person who programmed your DWI sequence and get the files that are specific to your use case.
The process can take up to 30min. At the end, a new file DTI-EIG appears in the database (DTI=diffusion tensors images, EIG=eigenvalue). This file contains 12 volumes, ie. 12 values for each voxel. From 1 to 9: components of the three eigenvectors; from 10 to 12: the values of their norm to the eigenvalue.
FEM mesh
The FEM approach requires a segmentation of the head volume in different tissues, represented as hexahedral or tetrahedral 3D meshes. The methods available within Brainstorm are listed in the tutorial FEM mesh generation. Here we illustrate only the use of Brain2mesh: this is not the most accurate solution for MRI segmentation but it is probably the fastest solution to obtain a tetrahedral mesh of the head with 5 tissues (gray matter, white matter, CSF, skull, skin). For more accurate results, we recommend using SimNIBS, as illustrated in the tutorial FEM median nerve example.
Right-click on the T1 MRI > MRI segmentation > Generate FEM mesh > Brain2mesh.
After less than 15 minutes, you will obtain a new FEM mesh in the database.
Conductivity tensor generation from DTI
The Effective Medium Approach is applied to convert the diffusion tensors to the conductivity tensors.
www.pnas.org/content/98/20/11697
FEM mesh head model
Note that this mesh is obtained only from the T1, the use of the T2 is highly recommended if it's available, as recommended in the FEM mesh tutorial.
Computation of FEM mesh tensors
Once the FEM mesh and the DTI tensors are available in the Brainstorm database, the next step for the FEM tensors can be performed by the following:
- Right-click on the FEM mesh - Compute FEM tensors
Brainstorm checks the available tissues in the FEM head model and displays the following panel
This panel lists the tissues available in the FEM head model and assigns a default value of the conductivity for each compartment. Users can change these values to their own if needed.
DTI values can be used to generate conductivity tensors for the white matter (and in some cases for the grey matter). Please, note that the DWI can be used only for the brain tissues and not for the outers compartments (skull and skin)
In this tutorial (and in most cases) we select the white matter. Select the WM anisotropy and keep all the other tissues as isotropic, then these additional options appear asking for the method to use.
The available methods are:
- Effective Medium approach (EMA)
- Effective Medium approach with volume constraints (EMA + VC)
- Simulated or the artificial anisotropy
Only the two first methods require the DTI. More information about these methods can be found on these references [ref1][ref2] and in our main paper [link]
In this tutorial, we use the method "EMA + VC", where the final tensors are constrained to fits the volume of the equivalent isotropic tensor volume.
Visulation of FEM mesh tensors
Once the FEM tensors are successfully computed, they are stored in the FEM head node. By right-clicking on the FEM head, new menu items are added that gives the possibilities to display the FEM tensors either as ellipsoids or as vectors in the direction of the main eigenvector.
The tensors can be displayed either on the FEM mesh or overlaid on the MRI. The following figures show an example of the obtained tensors displayed on the white matter.
On the left, the tensors as a line on the direction of the main eigenvector. On the right, the tensors displayed as ellipsoids. The orientation of the tensor is color-coded as follows: red for right-left, green for anterior-posterior, and blue for superior-inferior.
Note that the quality of the tensors depends on the DWI data and the number of acquisition direction.
Users can also display the tensors on specific tissues, for example on the white matter (left figure) or overlay on the MRI (right figure).
Recommendation
In the case where the user wants to use generate isotropic tensors, then the DTI is not required. For that case, keep all the options to 'isotropic', the recommended display is the 'Ellipsoids', and the final shape will be a sphere (isotropic direction). In the case where more than one FEM head model is in the database, the highlighted one in the green color will be used.
Simulated conductivity tensor
In the case where the DWI is not available, or in the case where the users desire to evaluate the effect of the conductivity change on the head model, the artificial conductivity can be used.
Users can reach this option by following this tutorial and select the third method in this panel.
Two approaches are integrated within Brainstorm. Either Wang's constraint or the volume's constraint (Wolters). The common feature between these methods is the ratio between the transversal and longitudinal conductivity ratio.
A common example is the skull anisotropy simulation, where the longitudinal conductivity can be higher than the transversal conductivity, the ratio can vary from 2 to 10 [ref]. In this tutorial, we keep all the tissue as isotropic, except the skull, we use a ratio of 0.1 and select the volume constraint. The following figures show the results of this example.
"eigenvalues parallel (longitudinal) and perpendicular (transverse) to the fiber directions" for 1:10 anisotropy (transverse:longitudinal)
Troubleshooting
To be completed soon and linked to BrainSuite website
References
===TODO===
Check the error in the simnibs mesh in X direction and overlay on mri check the error with the brain2mesh Correct the ratio from integer to float check the meaning of transversal/longitidunal in the code add an interactive way yo change the size of the tensor.. important correct the name of the simulated method, correct the EMC and remove the VC and change the coefficcient