Study design and patients
This retrospective, observational, registry-based cohort study was conducted at the emergency intensive care unit (ICU) of a tertiary care university-affiliated teaching hospital in Korea. Data were extracted from the OHCA registry containing prospectively collected data of consecutive patients with OHCA since January 2010 [20]. The Institutional Review Board of the University of Ulsan College of Medicine, Seoul, Korea, reviewed and approved the study protocol (no. 2019-1883). Informed consent was waived due to the retrospective nature of the study.
This study included patients with successfully resuscitated nontraumatic OHCA >18 years old and treated with TTM due to neurologic impairments after the return of spontaneous circulation (ROSC) between January 2014 and December 2018. Routine EEG was recorded in the first 48 h after ROSC. All patients were comatose during EEG recording and TTM. We excluded patients with poor EEG quality which is impossible to extract the representative 5-min EEG or periodic/rhythmic EEG pattern. All patients were followed up to 1 month after cardiac arrest with neurologic assessment using the Cerebral Performance Category (CPC) score. The primary outcome of this study was a good neurologic outcome at 1 month, defined as CPC scores 1 and 2.
Management and data collection
All patients were treated according to the current advanced cardiac life support guidelines [21, 22]. TTM was performed for all unconscious patients using Arctic Sun Energy Transfer Pad (Medivance Corp., Louisville, CO, USA), and the target temperature (33°C or 36°C) was maintained for 24 h. Patients were rewarmed at a rate of 0.25°C/h after 24 h, following maintained normothermia until 72 h from ROSC. The temperature was monitored using an esophageal temperature probe. A combination of propofol, midazolam, fentanyl, remifentanil, and morphine was used for sedation and analgesia. A neuromuscular blocking agent was administered to control shivering if necessary. Patients with seizure activity on EEG were treated with valproate or levetiracetam. All patients received standard intensive care according to institutional protocol. WLST was legally prohibited in South Korea until August 2017 [23], and none of the patients in the current study underwent WLST.
Demographic and clinical data, including age, sex, previous medical history, resuscitation profiles such as the presence of a witness on collapse, initial documented rhythm, and resuscitation duration) were obtained.
EEG recording and preprocessing
Standard EEG examination was started during TTM after ICU admission. The 30-min scalp EEG was recorded by the Stellate EEG system from 21 electrodes that were placed according to the international 10–20 electrode system (Fp1-2, F7-8, T7-8, P7-8, F3-4, C3-4, P3-4, O1-2, Fz, Cz, and Pz) with a sampling rate of 200 and 0.1 Hz high-pass filter. EEGs were initially reviewed to select clean data without apparent artifacts or muscle activities, and 5 min of each artifact-free resting-state EEG were collected. The extracted EEGs were extended temporally, and 0.5, 15, and 30 Hz high-pass filters were used to differentiate visible background amplitude and frequency, including beta (14–30 Hz) and gamma (30–100 Hz) with sensitivities of 10 and 5 μV/mm, respectively (Figure 1). Before quantitative analysis, the data were preprocessed using the EEGLAB toolbox of MATLAB 2020a. The 0.5-Hz high-pass and 60-Hz notch filters were used to remove the alternating current at 60 Hz, and independent component analysis was applied using runica (), plugged in EEGLAB, to remove artifacts. Finally, the extracted 5 min of EEG data of each patient were divided into 10 epochs (30-s duration) per patient. The EEG from the C3 and C4 electrodes were selected for data extraction to reduce the muscle and eyeball movement artifacts.
Spectral power analysis (power spectral density)
The PSD estimated the power of each frequency band directly from the signal itself. The PSD (log) was computed by Welch power spectral estimation for each patient with a sampling rate (200 Hz) and 15-s window size length segments in each 30-s epoch. The average in each PSD frequency including delta (0.5–4 Hz), theta (5–7 Hz), alpha (8–14 Hz), beta (15–29 Hz), and gamma (30–100 Hz) extracted from the C3 and C4 electrodes of 10 epochs were calculated.
Variability analysis (event-related spectral perturbation)
ERSP (log) was used to show dynamic brain changes with the zero point in each epoch set as the baseline. ERSPs were analyzed with fast Fourier transform and Hanning window tapering in each 30-s epoch. The mean baseline log power spectrum was subtracted to produce the baseline-normalized ERSP from each spectral estimate, and then deviations from baseline power were calculated. The ERSP formula for averaged estimates across data trials (n trials) is defined below:
![](data:image/png;base64,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)
where
is the spectral estimate of trial k at frequency f and time t. The averaged each frequency ERSP including delta (0.5–4 Hz), theta (5–7 Hz), alpha (8–14 Hz), beta (15–29 Hz), and gamma (30–100 Hz) extracted from the C3 and C4 electrodes of 10 epochs were calculated.
Randomness analysis (spectral entropy)
SE based on Shannon entropy in physics, quantifying the regularity/randomness of power spectrum during a given time, was used to establish one of the biomarkers for neurological prognosis after cardiac arrest in the recent study [24]. In effect, SE reflected the randomness of the power spectrum distribution. The greater SE represents the more uniform power spectral distribution [25]. Using previously calculated PSD (Psd(f)) via Welch’s method, the normalized PSD, which was defined as the Psd(f) divided by the total power of each frequency band, was analyzed to obtain probability density function.
![](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAokAAAFuCAYAAADtfoKGAAAgAElEQVR4Ae2927HySJe1S9vyecE24POEHXXR121D3dEXvw0V7QDbgPLiJ8qV1o4hGKzBJHXguAA9iuBNKXMen0yJ+UrAWnVsEIAABCAAAQhAAAIQKARW5ZhDCEAAAhCAAAQgAAEIdBSJLAIIQAACEIAABCAAgQsCFIkXSOiAAAQgAAEIQAACEKBIZA1AAAIQgAAEIAABCFwQoEi8QEIHBCAAAQhAAAIQgABFImsAAhCAAAQgAAEIQOCCAEXiBRI6IAABCEAAAhCAAAQoElkDEIAABCAAAQhAAAIXBCgSL5DQAQEIQAACEIAABCBAkcgagAAEIAABCEAAAhC4IECReIGEDghAAAIQgAAEIAABikTWAAQgAAEIQAACEIDABQGKxAskdEAAAhCAAAQgAAEIUCSyBiAAAQhAAAIQgAAELghQJF4goQMCEIAABCAAAQhAgCKRNQABCEAAAhCAAAQgcEGAIvECCR0QgAAEIAABCEAAAhSJrAEIQAACEIAABCAAgQsCFIkXSOiAAAQgAAEIQAACEKBIZA1AAAIQgAAEIAABCFwQoEi8QEIHBCAAgdcT2G1W3Wq16labXdv5fttt1utuux8a3nRr6R9t7Lfrbr3ZdrsB+bYVeiEAAQj8EKBI/GHBHgQgAIFZBFSA9cWYi7K+XXebuyqyfbddr7pWjdgXfOttN1Tv9fEcx3tZV5L7bbceKSxnJYsQBCCwWAIUiYudehKHAARuJ3Ao6H7u+u27XV84Dt/pm/a16zarhr4KvdWmG7i/2HWd9NrFZe9zt+lWo/rTkSEBAQgskwBF4jLnnawhAIG7CDQKs76YaxR5c/30d/3q3cJDMbr2ncGGrX1fBI75nbbRMEsXBCAAgY4ikUUAAQhA4FoCjcLs8Aj6/I6f7i6ePie4WnXrze7nkfF+123Wx88Q6g7idnP5ecSJwvP0OcbTY+9z/04rH0e7jxYCEIDAFAGKxClCjEMAAhAoBGrRpbt5fTGYHyj0Y97jBwn7O34e74u/LBoPdyYv7hjaRvGfh32haLs5kPsz7KQ4+xCAAAREgCKRdQABCEDgKgLHzyOe7t7pbuC629RHwse7gK0vs/SF3dkXUdqfR6zF6GWYMx8lT9yRvLRLDwQgAAGKRNYABD6awOU3bP348rPaz5qExucRBxI43WFcb+KnaBr6fRF3+ah4skicW/zNlRvIg24IQGCZBLiTuMx5J2sIQOBWAo3PI46b2ndndw4bBdvQI+PJInHuY+SGz/GYGYUABCDAnUTWAAQgAIGrCBwKvsu7fmdG9BnF7c+P1pwVe33B5p+s0U/nbLuNfkhbnyvUD2bnY+uJInCouDyLRQcTdi7k6YAABCDAZxJZAxCAAATmE+iLPX8WceTLIqfHzJY9e9zcHX9TcdWtjv39l1rqt58V1ugdwIEvuzTSOStSG+N0QQACEGgR4HFziwp9EIDANIGJPxNXDahQ4c/EVSpTxyNfTBktINPuiI0UYx8CEIBAIUCRWIBwCAEIHAj0jzJ1J6xxx6wv+M6+nXtJbb89/1vCvYQKG/5M3CWssZ7mo+LyOcer9ccUGIMABCBwIECRyEqAwBcQOHsM6kec+lmWZ/wt4f4O1vhn8vLxZl9QXvE5uy+YjoenkDz9Z/j8qHrU2ey7jaNWGIQABBZKgCJxoRNP2t9G4Pjbfae7fs/6W8JzHl02fuLlDPccG2cKHOjjibttt7niLmxfnG+28dM7YIQABCBwHQGKxOt4IQ2BNyXQKMzuvYvUPxouf0t4hs3DlzDG/pawvo+x7lYTj6vfFDRhQQACEFgMAYrExUw1iX41gf5za+eFWV+Irc4fC9/9t4Sbn4/7IXv6HOPpkfe5/5PkhJ2uO94ZPdkZ+HFwCs0TUnYgAAEIPJoAReKjiWIPAr9AoN6ZO/0Ey+nxc/xW3h1/S7j6aaU667f7ZtyRbNmmDwIQgAAEXkeAIvF1rPEEgScRaN11428JJ+xv+fOF75ZHMmYfAhD4PgIUid83p2S0OAKNzyMOMDjdYTz7ceeGfn+n7/JR8eSdxLl3CCflWoVv45Ezj5sHZppuCEAAAvcToEi8nyEWIPC7BBqfRxwPqPzGXqNgG3xkPPVZwqlxBzZXzvK0EIAABCDwcgIUiS9HjkMIPJZAX9DpzuCY2Uf9LeFGQZluB4vLFOr/2hzfbi5IOIQABCDwdgQoEt9uSggIAvMJHL7BfHwMm19SKSZOj5n9beGzx81X/C3h47eO1/nj2Cdfh8fW7bGTkErEbrteddNyqfNL+yquj8xG8F4E1xfLq/Nvm18I0QEBCEDgzQlQJL75BBEeBN6OwNCj4om7jKc8hvRPAu+yo6J3c/5j1H2Ojc9GXhSER913SYU4IAABCNxAgCLxBmioQGDpBC6/wFI+5zgEaG4hOaT/qv5SDF5zF7EPUfr6ayebdadvJK+vNvCqRPEDAQhAYJgAReIwG0YgAIERAj9/Ju7wmHnqbwmrsOwLp+PvNI6YfvjQ2WN5P3K/aM8fD/c6NxZ3hyL6eBdSBSPfwn74nGIQAhB4PgGKxOczxgMEIPDrBPyTOueFoMM6FJHnY/pc4UWNWO4w/vxuYerKVxzr8fqLi8TDZyJX3eoigWPG+/G/A73f/nwWUzbE57cKfM8RLQQg8HoCFImvZ45HCEDgNwi4wGsWbLobGoVdV4+vCTg+j7jfdZt1o9gMc4cCtX7Ocd1tdvfccj0Uxa0asS/4mgwOQfXxHMd7WX9Jqb8jmowiCXYhAIGvJECR+JXTSlIQgECLwKkga1VPqXDP3T99I3p9+CziatX4yzfpp98/3uU8xbTv9De2pev67EJlsmOgyO0L5bGfS5LeSFH7MV86mgSEAAQgMIMAReIMSIhAAALfQ8CPYk81WSO1XmZMoKFze1ejMOuLuTuKxP6u37Y7vxd5KEbHfnpIP5U0XpxO27idA5oQgMC7EaBIfLcZIR4IQODJBA5FWbsYOt7Vm/px8kdG2CjMDnc8z+/46e6if7PR35g+FYHHx9qHz0iuu+12c/l5xInC08Xzz+csz/075Xwc7T5aCEDgOwlQJH7nvJIVBCAwRqAvzN7jB71r0XX64fO8k+nHvMeqsL/j5/G++Dv8zM5h+FAEX9wxtI0RLrPuoM6wM+KCIQhA4IMIUCR+0GQRKgQg8CACb1PoHO9cnv0cT+NzjMe7gK0vs/SF3dkXUVQkXj6qrsXoJcmZj5In7khe2qUHAhD4VAIUiZ86c8QNAQjcRuCtipzG5xEHsjrdYTz7k4oN/T6/y0fFk0XiXC5z5QbyoBsCEPgcAhSJnzNXRAoBCNxNoFFU3W3zDgP9Hc3Lu37DFstftmkUbEOPjCeLxLl3Vxs+h+NlBAIQ+GQCFImfPHvEDgEIXEFg/HGqiit/zO8Ko3eJHh4VX971OzOqn9TZ7k5dZ8VeX7A5bv10zrbbbI4/oq0fzM7f0JkoAoeKy5Nj70zYsRgtBCDw+QQoEj9/DskAAhCYQUDF1eDfUD4rtsKYCrTj5wUfXUD2xZ4/izhi/PSY2bJnj5u7428qrjr/WcT+Sy3Hvxd9+vazUhq9A3i4w3rxZZdA4d2zItWdtBCAwFcSoEj8ymklKQhA4IxAf/er/lWTelwf+6pwOv79ZRs7FpM/PxNjG1XXCu/UjtxJHS0gM4cRGynGPgQg8BUEKBK/YhpJAgIQGCZwKGwuCzsXeG6j0CvF4MiNvgG3+267+fmrK6/+280DQXVd81Fx+ZzjoHI3oD+mwBgEIPDJBCgSP3n2iB0CEHgagf6x6vXVoZ7rdtu1foNx1//Fk9vtPCe188fFh8fMflQ96nH23cZRKwxCAAIfRIAi8YMmi1AhAIHXEWh+kaXcYfy5O1nuQsbvFjbtvC6Npqf9bttt1hFzU+qnU4XlerPtdmcfcvwZZw8CEPhOAhSJ3zmvZAUBCNxFQHfY5hdRZ670SPdYJB6+dKLPNe67HRXWGSYOIACB9ydAkfj+c0SEEIDAqwlEoXe96123WetzjutOfyFFj577x7nXG0IDAhCAwK8SoEj8Vfw4hwAE3pHA7N8MfMfgiQkCEIDAgwhQJD4IJGYgAIFvIMCdv2+YRXKAAAQeQ4Ai8TEcsQIBCEAAAhCAAAS+igBF4ldNJ8lAAAIQgAAEIACBxxCgSHwMR6xAAAIQgAAEIACBryJAkfhV00kyEIAABCAAAQhA4DEEKBIfwxErEIAABCAAAQhA4KsIUCR+1XSSDAQgAAEIQAACEHgMAYrEx3DECgQgAAEIQAACEPgqAhSJXzWdJAMBCEAAAhCAAAQeQ4Ai8TEcsQIBCEAAAhCAAAS+igBF4ldNJ8lAAAIQgAAEIACBxxCgSHwMR6xAAAIQgAAEIACBryJAkfhV00kyEIAABCAAAQhA4DEEKBIfwxErEIAABCAAAQhA4KsIUCR+1XSSDAQgAAEIQAACEHgMAYrEx3DECgQgAAEIQAACEPgqAhSJXzWdJAMBCEAAAhCAAAQeQ4Ai8TEcsQIBCEAAAhCAAAS+igBF4ldNJ8lAAAIQgAAEIACBxxCgSHwMR6xAAAIQgAAEIACBryJAkfhV00kyEIAABCAAAQhA4DEEKBIfwxErEIAABCAAAQhA4KsIUCR+1XSSDAQgAAEIQAACEHgMAYrEx3DECgQgAAEIQAACEPgqAhSJXzWdJAMBCEAAAhCAAAQeQ4Ai8TEcsQIBCEAAAhCAAAS+igBF4ldNJ8lAAAIQgAAEIACBxxCgSHwMR6xAAAIQgAAEIACBryJAkfhV00kyEIAABCAAAQhA4DEEKBIfwxErEIAABCAAAQhA4KsIUCR+1XSSDAQgAAEIQAACEHgMAYrEx3DECgQgAAEIQAACEPgqAhSJXzWdJAMBCEAAAhCAAAQeQ4Ai8TEcsQIBCEAAAhCAAAS+igBF4ldNJ8lAAAIQgAAEIACBxxCgSHwMR6xAAAIQgAAEIACBryJAkfhV00kyEIAABCAAAQhA4DEEKBKPHFerVfcNr8csC6xAAAIQgAAEILB0AhSJS18B5A8BCEAAAhCAAAQaBCgSG1DoggAEIAABCEAAAksnQJG49BVA/hCAAAQgAAEIQKBBgCKxAeXlXfttt1mvu+1+nuf9dt2tN9tuN1N+nlWkIAABCEAAAhCAwA+BXy0S//777/7LIv/8889PRF+4t9scvxSz2V1k1xd86203Vu/tt5tu7S/W2MZ+262vKCwvHL9Bh74o9Ndff71BJK8Jwetdrbc//vij+9e//uXDh7XiKr6tc8txtL6olbEpGOmnnI4Vszb7yPHc//PPPx+WT8Y8xCtlMo4h+YcFN2FI/h2PmOmlWCtby2Rb52PC1ccMe67eMT/Pi+ZpaNOcPnJ9D/kZ6n8Hfub0iDn897//fTpHcv2rv24pqzmQf89V6tb9Z10HZNfXxBqrjhXjlG/F+pvrqRW3+36tSPQiFxwttnfZVLTVxbVarbvNXbft9t12vepc351yVaG32nSXpeNJouvjORaRfUGZtxt3m241of9j6b32zNgn93tF9/hocr0/4qI6FqGYmu/YuaWLbV6EffHNOZGdvHjpYqe+3FoXSV00HUPK3rLvNyPpej9jTJutccWXeab8s/crY3PJNaC+1puIchzK89lxL9W+14/W7hB7nwN5XryS1yuvJUN5Jadcy0Pyc/qdl+35OM+Neq5oDupcuS992laVTZln7DuWzOEZfp5p8/xq/0xPDdueOC2499kOBd3qVNHtu11fOM5/HHyZy67brKr+wc86i74LRek1isuT3BwbJ+Ff36lv1K8+YX8bgNe72mdveoMT37FzqxYwvvB7nmyjxupx9+sCqIt33axf5avc1LEutHNtmHHa9IU6+16xb551vhVP9tU3vlfE9mofynFsLT46Hvlqrck5fjxvQ0WibGjNax5/a/M6z3X0iljyPDSnR8XQyklzqOuYfbTeMzRPOVdj57v0p66L93JUrLk2tE+ReCNVL4pXXjymQ20UZv0dv1rkTVs6SfSPhssj5Rk29/2dwnG/eafx5O8Nd1wwZGitEz7Hv23f690XvGfmZ95j55Yu+HnRVzy6mPmC5njzgteKWfJDb8iyf+9FWbZrnK041NeSla5zGtJ7Vr9yn4pdMdf4NH9jc/eseJ9h1+volfmI+dCanMpRcU5dmzRfU+fFlJ97xs30FdcSx1nPLXN6VAytnHwdU6tN3Ou54vjcal40f63N9p45d3VtyNdUzK1Y36WvTfLG6ARCk6OXFpAWlY89Ke5T60UhWetWmJ5U23FoXqBeEBr3Zlvqs1+PTbaNwuzwCPr8sbDuLp4+J7hadevN7udzhftdt1n7x7nX3Xa76X7uTB4jmHhUfPocoz+LOPRYecJOzVcXT7MU/+SnOdHmPr+5JU/pir0325OO9T3m1vbs13I61vzq2GNp23PssbGLkXwoztSxn6E40pf1HYta25Jf5+n1aTnFlnHV9ZoxSK7Ky645K07nWlvn4Dg0nrY1npy9nznahtvqW/2ym/GkvyFbYlJjsQ/HIS5DW2WWPCuHIT+2rVjSl+RlI21atrYtWcXvvGVbtq7ZnL/0Mq60Ib9eV+5Xn/26L1vHIruSc+w6lk9t7lOrTWP56juP/9ieYrR8jue+7cpWrhXFYfvirTjq3Fre/c7Rx/JjG47Dsak/t4xDY7KhLeXV77nPuZBMbpKxX8vZXsp5X/qSGzo/hmKTvu1LV/tmoLHUMyv7NCPFaRvOzTK1dU5qzdMy6pPN9JmxWE5t5mk9z7diyPHUS67SG7IvHctmTjXPyiB9ed86Ps7WMVe2llGMfqkv8/J6cJ/8aJN87ltfrfxpTOvFvtWfc+F+27fNuXPTB/HEf87Pugc4UvIJwJNq04amYy8KwzQsLxJDsq7sVth1sj0uHdtL+LY11NY7c7qb1xeDp8fPXde5MDt+26S/4+fx/g5hFo2HO5P1sXL104qnLxRttyWgvhl3JKuqFm1y8zyIlzfPoRe4+8XXY+Lqk6PKWd5tXQfq98nk+ZFt21MsOvZm/YzRY9KxLetUeclkzsrB6y71HYvXju2KUfZZTjbTruQ9VmMwZ69v6Uk+9c1WuTku5yw562pcupKxrHPXsfY1bt1eqPwje9V3xm9xx6ExveomXxl3jpuBmeSY9tWfcdtX5inbGWe14WPzdZxuPT6nzfzk13zNXn0Z2xybNa6qL5uONduxuZNf6enlzax9LH3HL7lkKD8eU+v5qXK25VbjllVfzr3sO2b1277z95jjVAzqy2P1acs+62XM1abiynWkWNTnTbE4HvVp3DxatuQr87Qdt/IlGc+lbDn2lj3HplzG/KZP6TgH87B/+7N/92eb8cmvjm1P+37ZZ/pLO96XrmNXn22mH+3bnmJLefHX+NBmbs7J9s3Oeu6v8Xt8yo/0Mi7rudVYrhXtZwyKzzk6hpSXbB5rX3K2kXOZuVSb0nGfdD13jvNV7fCM3RhBApAJHztZtQKjzYvCx+pLMJosT0K2kjdcL6i0l7Lanw/3+HnEOIH6L63Uzw0eC7PWl1n6wu7s28qtzyOqtlt3qzO5Hkn8M/PzhjcUiZW7j72wdZxcHZS5mqfnNufPsrW1bPbLnteF+jXftu0Tyz7dpnzakrxPQvU7J8cm/ZpTnnhVXzZaa0w65iQZxdu64GT8jsExZRzSbembl/N1LObg1ro1P+vbd7LyvnRtx23mZjm3KZ92k6Nl3ZqD83C/W+nWMflxXpIbYmwbbuUr9dw/t9W8WF+2HFf2y5bX6Fy7ljMLsbZt2xOH3OQjGeeY92VDtrz52LbVVhs6rnPtuGxnrLVuto69zpvteN1nLI7VfT62juPMc0V+FGvd5FfxOA6Nqy/nSWMZs/ctm3bt2xyrPx3XWIZ0amxDcrLpmLJ1TmozHjNNPhmn8pHv3MxYMWiTn7RZmaWu9ut56FwyhuQi+czF+ymfPpyT5dymTO47H8nl/Glffa3NMVc2KSt9c1e/7Tnu9KXx6j8ZWD/tOU/Pg2PKuZDNPJ6am4z/0fttknd6yQQFNBdXnrgVltymrsB6YmpIBpvjgjo2+dXG5XHj84iXQn3P6Q7jehO/V9jQ74u480fVMjBZJM4t/ubKlTxyIWuO9PJCzjmSmuZEL211sUrH4/XkSZc+obMv57raVgxj9tKO9jN+Hefaaq0V+/N6qfoab+klN8nk2tax7CgvtRmDxnyca1b+HYNktNmvbHiTjufHfW4tn3bNW2NDW8t3yspeXqg0pj7nZ1nFVdeMx+RD8kNx1DUgPdnKXCtj265tnZs6rmPJyGcyd5/8iLli9bjzlY72c6xlv/a1uFT/NV/ZEPchZukj+Sn2ZJW+ZUuy5lpZacyvOuf2Zxs+rm2yki3H734fS6+uTx/bpn1J11uN2WtLMsrduUleY5l/xmN7bqtd+x7iIL2qoz75sM5YbIrLrL3O7NMx1TZta8xMk0/q5Dpwf9WpNisz67mtNh1zxpBcZM88bGOsrfG1ZHNOPS6fennTWlBurc3rTDJDW81LssmmxiBfaS8ZyIfGMj7nKT/a7C9ZXTs3Q7k8or9N8k7LXkyCocQTSsLMfrtMOJqYlLeMWoOVDW+25+Or28bnEcdt7LuzO4eNgm3wkbEfWQ85mBq33lw5yx9bL1xx1H7yTOZ5cki1Htus+nPu3O/WJ6eP1Vb5tK0YdDx3cz6W91pQXs4t85Jc9Zcnssatl2usXgC81iWfPseO055iqHmKS+1zLGrr5rG8yJh3S976Ld8eU6s46wVR/dLLfjHJY9twDK0xyyjXOq7jzL8eWzdbM0i2Oe79tKs+xW5u2pe+1kmuFfm3jMarDdtutbJZt7pWZb8lJz2Njc2h2SguxahWTKWTOcj+2LFjlJz0Zae1jY1Z3nMhWW0Zk2W8Npybjz1uGxlH5lAZ1uPWGvUc2odb2RVHb/Y9JC+5jEXH1lFbY6nH9qN+MbLvMbYak7w3M00+HlMrm+bvfusoRm0azxwrM+u59VrzsXPOGJKL5J2bdcZax5f2qrzs162uHXOtcjpWzpVLS84sZFvx2IdyTma2mXOTDDSusYzbeXoezDHtXjs3rRwe1feUItEQKjiBygVgOcMycMPyxKSObEreYHNM+vJRL+JzF+qh4Lu863cGW59R3P78suHZHcG+SPRP1uinc7bdRj+krc8V6q+q5GPrRkGZfgaLyxSac0eyyPvQ/JKNuImf+UtWfclTi9c6kss5llzq2pdaz6X2rTN2Ijg+y0pPcz1kX3JjJ6Jilj+vF9tX65hSX32WsY76JJMxya75SC59SE7H9lHHZa/y1XGNw7ynxuTLm2R1nP495rb6dr9bx5v5uk+tN8XrGN1n3jUXj7s1o5xXxZz2k7H1aiuZKV/SkV37Uuu4PdeSERfPmY5l18eKN3nUOOqxcpG93NSXNoZiV3xVN+1o3/OR9hSvXslQxzVX66g1E9msuulTNhR/brab3JKnY1Sffcqf7Jirj23X+jUH66vNOORbcXvTseKSHdm2vP1JznF7zL58LPvJxbbVylfOjf1pzPqWz9jkP/Uk6zjUZk7Sr2OO37KSd9z2p1ZyGktf0rE9ydT8Moe05X3p2p7ito/0Ly4a06b+6kM8U962U35oXDKyn/Psvsyr8k/bQ7wkk5vizFw0Jl31eQ4sr37nrD7rKQ+9NJYxq086tmOOudY0nsdTc+NYntGen+0P9FDBVVACIBm/6rGhq7WMbRqy+xOmUnC/2zlp9cWe4xn5ssjpMbNlzx43d8ffVFx1q2N//6WW+u3nPqCxzxy2v+xymceYjUvp2qOFmyeX5yDlKmvJi6sWreR9rL60lTa07xNBcvbj+fHa8LEvRNW3+6vtjKFlX3a0VTmfpLVfstV3jVk6iscx+yKQfWn3//yf/3OSlY7sp6z2U9521dq24tJ+jjmHOmZbOS4ZbTU3x3McPjWS06ueg+rTVplkXNrX+JxtyL50p2zmurLskE8x1iY20lPrGKXrcbeSlVwei7+ZOn/baPlVbpW3+mzbMQ+1Y7btT7q2qT7t55pRn2OVrMaUk/bFQPJeL9WWfWRrXcnq5fVgv7Vfuu5THBmL+v/rv/7rNK7jOl6PFWvalE7GrzHFov7kkDL20xsq1wbLye/YJtuyo5f85eZ+tbanfa87j2d80h9iW8ds0+zTt/fly34ch8Zqf523XO+2pTbnoa7pHEtfQ/1pV/s176EYFGuNXyy8Zb51f8imdbO1j1wD8lNtpA+vAc+NZL1vucrjf/7nf87m6L//+7/PjufOTcb+6P2nFYmPDvQr7Q09Kp64y3hiMaR/EmAHAhAQAV/0dbH2G3NesHUx1pje/PJNRzK++GtfLzYIQAACSyFAkfjLM332uLqPpXzOcSi+uYXkkD79EIBAk4AKQReSWTDmflORTghAAAJfRoAi8Q0mdL/bdpu1/rLK4TGzH1UPhabCcr3ZxreqhyTphwAEbiGQj4f8aO0WO+hAAAIQ+GQCFImfPHvEDgEIQAACEIAABJ5EgCLxSWAxCwEIQAACEIAABD6ZAEXiJ88esUMAAhCAAAQgAIEnEaBIfBJYzEIAAhCAAAQgAIFPJkCR+MmzR+wQgAAEIAABCEDgSQQoEp8EFrMQgAAEIAABCEDgkwlQJH7y7BE7BCAAAQhAAAIQeBIBisQngcUsBCAAAQhAAAIQ+GQCFImfPHvEDgEIQAACEIAABJ5EgCLxSWAxCwEIQAACEIAABD6ZAEXiJ88esUMAAhCAAAQgAIEnEaBIfBJYzEIAAhCAAAQgAIFPJkCR+MmzR+wQgAAEIAABCEDgSQQoEp8EFrMQgAAEIAABCEDgkwlQJH7y7BE7BCAAAQhAAAIQeBIBisQngcUsBCAAAQhAAAIQ+GQCFImfPHvEDgEIQAACEIAABJ5EgCLxSWAxCwEIQAACEIAABD6ZAEXiJ88esUMAAqL+VFMAACAASURBVBCAAAQgAIEnEaBIfBJYzEIAAhCAAAQgAIFPJkCR+MmzR+wQgAAEIAABCEDgSQQoEp8EFrMQgAAEIAABCEDgkwlQJH7y7BE7BCAAAQhAAAIQeBIBisQngcUsBCAAAQhAAAIQ+GQCFImfPHvEDgEIQAACEIAABJ5EgCLxSWAxCwEIfC+B1WrVfcPre2eIzL6NwDecb8rh07bPi/jTCBMvBCAAAQhAAAIQ+EACFIkfOGmEDAEIQAACEIAABJ5NgCLx2YSxDwEIQAACEIAABD6QAEXiB04aIUMAAgsmsN92m/W62+7nMdhv1916s+12M+XnWUUKAgsisOBz7iOKxD///LP717/+dfWK/Oeff84+XH61gS9S0Adm//rrr5dkpLn6448/XuLrWidaS/4A9N9//z2pLhnLq31VXpor+dMaHtoylyGZR/bL37///e9ZJjO2ZzGT3bnXhTk8ZyX2AqHd5vilmM3uwltf8K233Vi9t99uurW/WGMb+223vqKwvHD8oo56zZ673u4Nz+f52DXBMjovx+TuiUVrOq833h+7Dsif17fkn8XM+U/Fck/+v6W75HNuivnbF4l+s5n7ZpAJ64Txgta+bC1x84XmFUWi5kn+nlUY3DN/tchRnFMX+9/IIy/4Xr9DeUv2FTEqDvGa8wakmCznXKbyGMpvqN9vpnOuC45B8T86DhVtsnv+Wnebu27b7bvtetW5vjsxUKG32nSXpeNJouvjORaRfUGZtxt3m241of9j6Xf2XrGWa2YufjSHU9cDrZ85a676mHtc36Pm+FPMjsm5TOUxNx7L2e4zziH7mNtyzs0l9Ri5ty8SlaZOHJ8Ec9PWG8O1OnNtf6KcTm4xecUm7r9xsZ/Krb4J1KKx6uvC+Cpm1bcLm6miRsXYo98Qaiw61nzq5eKvJeM+ydQ3O489slU8c8/xFk/pT/GdjvdQ0K1OFd2+2/WF4/zHwZc+dt1mVfUPftZZ9F0oSq9RXJ7k5tg4Cb98R3PxinXTSsxF0NS5pPieFWPLt9btlD+fm6287u3L892Mbj1nlEcrx+tj5Jy7ntntGl9bJGpBzn0DuR3f52guvUj0BS5nrNWX47pAiptet14Y0941+62ipqWv2J69KRaxmlsk6rybemN7RMz3FIme+/vntVGY9Xf8apF3Rcb9o+HySHmGzX1/p3Dcb95pvCKil4hqPn2+aX5euXk9TPnVNWFK5pFxz/EnGbF79FbPdzO65ZyRjub2Mew45x4912P2nvIOozcJn+z5ZuE3Po/VhZ0XiSzwXPB5oUm/6maS6V+yOom0+c3L/r1gNe6+ale+PaZ9jSsOx+o4M7c8iSyXcSgWy2vf9h1nH+zxDqrH1GrLeOw7bTgn23ArfflMNh5Tmwwkm3asm7lkjtKXjF6SkQ+13uzTx0Ntzq/ikZ5izi1l7LO2VUf6XkNpSzlKt+ZiGfebeeZkmWw9p44n5e0/489x2bEf6XvfMaQf78ufbOhlny359Gm52raYyY90HafaukYdi1rzTNu2W9nk+vKYcx7z4XXqNaY2N681xSB73uxD+Xjfcdqf/bs/9W3nom0UZofHYeePhXV38fQ5wdWqW292P58r3O+6zdqPrNfddrvpfu5MHj1OPCo+fabq9Oj73P8p7gk7J7mBHbMRZ3FrMbLMUNvSkTuvXc+P52UglIv1lvK2IV3HkePqt4zGPfe5Lqtfxae8U0/7rc0+h9ohBmnL/rIv9zVe7dtuPRczTo8553oOyYdYpW3pW89xabzqJhuNa7Oe7Vmnyvo6kzk29xd0zomVuYnPbEZNcLd1PrxIVFJaRNq8iL1Alaz3vUAsW9+AJOsFXxezdcdSlo4Xo+QSdurpZNAi9lb9pg3FqHHHXH34ZPC45J2vY3Cf7PilMes6lmpbcUg3Ze1HfZK3bi9U/rEv68iec6s8xcQXVOupdS4Zi9zkmG051hLG4KH1nIPykd1HbZWn7Jq5mYz5sqzja8m2OMi2c9F4ZW7fNT7JSd7jLX9inD5z3lryt/TlPGrf62LMlmJXPt40t85bfeYhlp535ZE61s1Wvh2PuCRPycmHeXlc9rXZj8c9nz62vOfXMab/1n69M6e7eX0xeHr83HWdC7Pjt036O34e7+8QZtF4uEtSHytXP61Y+kLRdlsC6ptxR7Klal5DPFs69/R5PuyvZSvXmeNT67nW+tBLW47r2DK2q7UlWckNbdbxGpy7RobsTfXL39Q5IRt5XujYudq+41brMeXqPCxX23q+p67mx3NkZrKfNrXv877KypdikI42xyi5qa2eC994zpmX59/szXqK0SPHH/cuHItTk5+vXDgK3ieXZATDQIYWiOS92KRvYEPy9pE66suLio7tN2PVvt8ItZ+TUhfyVFzVro4dk20pDm2Oxf6q76PYqakXhsr4JHjckT2fkOoyQ/uzvOKTrBmov+qmbzFIWcnLxlQ89qfWseR8KtZqN3Wu3a9zNeR3zK5ykp2pTTJippdzqv5rzpLNufD6sH7Lp3Ryk49HMlMMGZPyn2O/nmc6zrWnmGXHtpxr5lL3zSv7FY9sa/O4ubv1OrQP87S8j6td62f/5f7xs1Fn17t1t6mfGzwWZq0vs/SF3dm3lVUkXj4yrm+MQ7HU4vJC7oYiUYzEI9eC9s3+wseDOrSePX9jJj23GaP7rFdzqGvS6yFztK5brdcct47HH91Wf0P2JZecdCx2ubXOldbarzqy5c35pp6Yi7U2yfq8yVbylb9tqlWslk/bKfOzv4xzLrkqd/P74fC6vfN3mTv9arHkoqrmvBjU5oLL/aqjY8nnBWlKvqWjPtmQLW+yk3bdr9aTIhlvvvB4IY/FZX3r1ta23G95+ct9j9c2GWjfJ2qV8/HQonN+4iAZ+dYFJeex6mrMF6UqK3+y5XH7H2tlL+dFsq0+9ZuNYhp6tViYV8ZR5yDHWvuSr3GmnMYUk1r7U7za1JdrLcedk+dC8o7N+ulH+5KtjB/NTPaGGGesNTblmZxko86JYjcP51rt5HFLptpQvEOb9c0z+VtH9hyr5TXmfbP48dP4bJSNlfZ0t2O9id8rbOj3Rdzlo+LJInFu8TdXLuIXF71ya/V53JyG2lwb1mm1rTWecp4XxVLPIY9Zvo57nj3u9TC1ri2vVnn8rIUc+XnEfSsDxevz49zy5ZFiyPmp559jVSzanKvPhUuLhx7ZzPxaeslRfof4Vf7yIH7SV5u2LZvsfmJsnDM/g2d7n3rOtdZVq+8s2ScePLRI9ES34vWYF2Yee1Gor7UJUJ4wqduSV1/VUV89eezXMaUtj2VMvvBYvvqocWmRp37aty33VX958lmmtr445AWiyvi42nOsGq8Xg3pcde3Xujk36tPxnJgk67zNVH1mk33qv3er86H5mxunfEt2aD7N0zHX47G1YgbK29sUA8WS8vZn/We08plvGkM+6nkm7pVz2nKuQ/bUbxnzVZ9seO1N5V/1LW97HncM9dj9Z23js1Fn4xcH++7szmGjYBt8ZOxH1hc2jx1T49abK2f545ME8fJmdtnnsUe2Omc0D63N54xjqMd1/uq41qTse5vKSfbqGq7XE9t6RNvyN2Q3r8eS0TlRz9O8/jhXr/0hu3mOSqalJwaeI/lMpmm38q+26nHqnu0v4JzT/Jmpcje77Dtj8uSDhxaJirW1QLXYvAjUatNi0gLzQtUC03FuPilzgWvctqybOt6vOupXbHURy6/6c7Nf9edY9escrOscnJfsDOWkCc8xLwTzsW7m6Ljsz/HUfo9nK18plyzUn3lqX7l4k24uUI3ZlmMwVx9Xf7ZVW+ftPM1UMZhF1bn1WLadV/U7ZVP5O+eWrPN2zM4j80rGlve4145t+1gcLeMxtS1b9p1yj9xX/uY3ZlexeT1IzixyDSkvx1vPhSHb0kn/OtbLLNTmuOx4zuzDLJO/4qvj0pPtse1Q8F3e9TvT0WcUtz+/bHh2R7AvEv2TNfrpnG230Q9p63OF+gsP+di6UVCmn8HiMoX6jySuu9XZ4+0i0DgUhzpX2ddQubtL/upcplHNo2LwmvL81TitY3mPe369Hnw8lJfGrSubkss1bj+PapV7+huzK1nF580sMj6dG2Ylu4rfuVuvtrLpOZCtlt7YHMie9OQn+act56i+OTEt4ZxrzVVyrvP07OPxq+CN3pVQvmxGC879rZMyxyWnhZVy6vMJYDteZPahVpA9rtYL0H15QrXk8+SxTrZD4/aT4zUnxWs526w5+WSuubdylf30lxzqvv2ptQ/JSD/Hql+PKe5kK9/aMn71SSYZW6fG4+NkJB/mY/uWe0SbuVWeytM+Nea81WY+Q3FkHunH+dte8lKf40g56w/NbbUxFNMj+xWT+bTsVmbKzfF7Ts3AOdd+y7fst9apmOVm+241VlnZh2U0rq3FXzKWTz99sefr3MiXRU6PvCx79ri5O/6m4qpbHfslL59n337uHY/9xuHhEdzk5xG7tg2vW3PIPLXvtai4tG+e0muxqfpzjuvcjq0z26txeT5zHtXneD3uPJ2381LrdWkfbmt8Q3KWv6eVr7quW/ZqTBn/UM61fyyPlM19+anHOo+11fPZ/RrzvLivxT9zqDl/yznndWsONc9kKEaaI3HxfpV/9vFTisRnB/1b9n1i6OR8l00Ljg0CEFgAgaFHxRN3GU9khvSPAkNvWid9diCwNAJD58wDzrlPOd8oEq9Y9O9WJCoevdggAIFlEDh7XN2nXD7nOIRh5E3Nd3SGVOmHwJIJPPqc853Ed7rZNDa/FIljdGLMBaJu++r1WxOcjxi4ixgTxC4EFkJgv9t2m7V+JufwmNmPqofS15vcerONb1UPSdIPAQi0CCz5nKNIbK0I+iAAAQhAAAIQgMDCCVAkLnwBkD4EIAABCEAAAhBoEaBIbFGhDwIQgAAEIAABCCycAEXiwhcA6UMAAhCAAAQgAIEWAYrEFhX6IAABCEAAAhCAwMIJUCQufAGQPgQgAAEIQAACEGgRoEhsUaEPAhCAAAQgAAEILJwAReLCFwDpQwACEIAABCAAgRYBisQWFfogAAEIQAACEIDAwglQJC58AZA+BCAAAQhAAAIQaBGgSGxRoQ8CEIAABCAAAQgsnABF4sIXAOlDAAIQgAAEIACBFgGKxBYV+iAAAQhAAAIQgMDCCVAkLnwBkD4EIAABCEAAAhBoEaBIbFGhDwIQgAAEIAABCCycAEXiwhcA6UMAAhCAAAQgAIEWAYrEFhX6IAABCEAAAhCAwMIJUCQufAGQPgQgAAEIQAACEGgRoEhsUaEPAhCAAAQgAAEILJwAReLCFwDpQwACEIAABCAAgRYBisQWFfogAAEIQAACEIDAwglQJC58AZA+BCAAAQhAAAIQaBGgSGxRoQ8CEIAABCAAAQgsnABF4sIXAOlDAAIQgAAEIACBFgGKxBYV+j6awGq16r7h9dGTQPAQGCDwDeemcmCDwBIIsNKXMMvkCAEIQAACEIAABK4kQJF4JTDEIQABCEAAAhCAwBIIUCQuYZbJEQIQgAAEIAABCFxJgCLxSmCIQ2CUwH7bbdbrbrsflToN7rfrbr3ZdruZ8idFdiAAgesJcH5ezwyNRRN4myLxzz//PPuywStnRR9Clv+xzR+2npIbszFn7K+//uo5/P3336PiimdKxgYcu9p//vnH3U9r//3vf3d6zd3++OOPq+Tn2n2G3G5z/FLMZndhvi/41tturN7bbzfd2l+ssY39tltfUVheOH5Rh8/RoTX0r3/961fOYa3rqfPS58CU3D0otY7tp7X+fW5LRqzmbrapdoj9XFuS03XjUbau8fsK2W8+P6+9rj6Lt68DY+tYsXrdPiuOll2dg1Pnls/TKbmW/Wv7xED+WpvjGDoXp8ZbNp/R9xZFoi+evgDq+BUTKKBeyHPePBSTY3zGZOSJNVYAevGMyTg+xWw57Q8tWMvf2zoHtXM25zJXfo5Ny6ho8/z+tOtuc9dtu323Xa8613f21anQW226y9LxJNH18RyLyL6gzNuNu023mtD/sfT6Pa0dM2ydAzpnvc5eGZ1j+u3zV/7FQJv4KK5c077GmY3WvZhObY8+fzVHZtaax6l4HjnO+Tmf5rXX1fmWr5PUOs5zrfWeolgt4/V2nZfbpP1eMue8UnyO8TZv41o+33Wutd5z1ed+Xy/yfJwaH/f+2NG3KBIFJC+ohvbYVIetaSKnFowWe8Y4bO2+EZ9Ualub+hWrYh6SsZ5t+fhVrThdw6rO/+PiPBR0q1NFt+92feE4/3HwZSy7brOq+gc/6yz6LhSl1yguT3JzbJyEf2XHF768mDkQX/B8/Mr2Hc5fscnN56j7dD5UGcVd+yyv9lnnr+225jH9D+0rt6lrz5DueT/n5zmP8aNrr6vj1m4brWtGayGv9V5baV3jknvFpuvQnCJRMjWXZ8TXKqLlV+d++lfcvoZOjT8jzjGbb1Ek1guqIM2Z6LHErhmb8yajGF+x0H2SDV2EtZC8iIZknLvf1H38qvbai5lyygvN4+JsFGb9Hb9a5F3hsX80XB4pz7C57+8UjvvNO41XRPQyUa+nvLjJufvnnEfPCHaO31edv87PTHysGNWXm9a83xiy3/vVhvvvbX2NqfM4x+7ca88cW13H+TmP00Hq2uvqNbZvldX6zfchnWf1Wt7qu9XflJ7imaodtIanZKb8zB1vFYk6r6v/7Mt9+2n1eezZ7UOLRC0OXQyHXnXxZHLSMTi3OV73qw+P50Us4/G4WsvIhhaw25Sp+4rJF1fJD13c1V9jy+Op3OwjTzzHItuK3fG3ZCybuct/+s2x7JeuxnyiSU+Ls7VlTtrPTTb08iZ7OpYt64m7N487d8nkeOppTPKztkZhdnjEdf5YWHcXT58TXK269Wb387nC/a7brL2m1912u+l+7kweo5h4VHz6nNTp3Dj3f8plws5JrrGjeTRb8Wkx0hxYptXmnDVcnOZP6y83H3tdyvbUVv1b3ja0FjJej6u1jGy82/nrOLVmk6dirXOi8dpn/cxdunme5lj2S9c2vR5a56/PM7eyL73c6jmnsZTPmKrsUE5pv99fwPmZcyVmYuhNc6T16zVcxyXneZQdv6w/1KY9zYXs1q3OmWTqy+d11fWx/NT1pRjr/EuurlPbUOsY7T9t2l7Gm+PSl4x05UO2xnxJXvHoZT3ptra63h1ftjmfLRuOKccUo3zn5vzUNzWeeq/Yb9N5hefiIy/8U4uzQtSkadLThvo8gR6XS8t4THo5XsLqD63jxefFYxstnVv7hmyr3yeH45nynwvP8WhxKmdvysl55UkjH0PbEH/Ly45PAsmKr17a12bmjt8y1tG4Y5K8dJ27cxqLz3HUO3O6m9cXg6fHz13XuTA7ftukv+Pn8f4OYRaNhzsf9bFy9WP/2faFou3mQO7PuCOZ4tr3WvCcDq2fqnfL8Vz2mjvPdcuPxjzXGvf551y8Xrw+PC5Zy3jMa8n5t/xZx2vqmYzsX/nJrzev8eyb4mTetqFWdjNX2XBeGjO79JP62nf+1jMfz5n8el/y2q+y5q9x+ZSONsc85r8X7Lqzz+iq79vOT7NwvpofvbSJp+fK85njlvGY58z6tllbjXuubCPnssrfepzxp335z2PZVw4ZU/p0Xl4vudZky4xsM8dlJ/15HQ/5sl/b9RqWvO1b5lFty7Z8KYbccq1MjafeK/bfpkjUQhKoOoFjELwotJB8MrnPC0D6miiPawLqgkj9lj/pWt/j0kkf7r+39UlTbWfMrRxbfnPhady2U9a2JKstT7qUa+1bt/KTjTwJWos+T546XuO2b82BfOkl3+Pb8fNOZ/9DXneb+rnBY2HW+jJLX9idfVtZReLlI+PpInHm5w1vKBLFwnMnHp6TcTa3jXpepthrnWl+pzbHmuvHfbn+P+n8Vc7iVK8X6ve1zWu4zl3lZd7uf9T5azs5jz63huJUrJJvzY/jsw3Lur/dLuP8dO5aw+KS18Vc15LL66Dmvp5D9bpq225T333Ska1nbcop51v+FEduWhc1lxz3vnRlK2WrvbTldWx9tfKd+jmmfa3fOi6dGnPVu/VYvqptHSuv3PJcnxpPvVfsP7RI9CR74dS2gnGCApRjvtjkRcyyan2h8mSrlU6ODb3JyE+dNMVp/fTjfelkLK3FaVnZrnnnsWO2fG1tO+N3X9rxfnKrtnLhaaweW162nH+Lj+XcjvGXjGxkXK1Fn37qeI3T60GtWSgG75vFOdvG552cQGlPdzDWm/i9woZ+X8RdPiqeLBLnFn9z5Y7xi0dyVnerz+lK1qxabbVlPbeelzwXPFbb87k4Hx1bPx7L9Z/nd64bW831675spZMxe92kjPfvPX/lRzamNs3TGCPpm7dt1WP3Z/4tPpZz6/yTSdpWXMnfempb86NcHEPa9r7X2nm+jfMrHcX+p56fSkE5K3+vizzHNCZ23vI62Fof0k1966n1vOSctvqs4/n23LTatGW92lYfirvGmHlVfR1LXv61XmreGsvzKcedQ9qU7Pk6y9H2f+AkL1t1q+u3xUgyY5tsZ/ySla8aY81rbHzM3zPGHlok3hpgXQiyMzRxHhNUb5L1sRdtTl6Oy5deuWnyrZ/92pe9OmHSH5Kv+tcee2Fm/NVGK8cqo+N6Evm42lb+Pklac1FtJ0+N1ePKuHWRSJ067jhl2zyUc+u472z90/i8U0vsp2/fnd05bBRsg4+M/cj6x9j53tS4pefKHeXF0POmLq+L7LPpR7SeF8/FkE3NWb0wpmzOvfrz2DnkGs3xurak/07nr2Kd2lo5tnTM22M+TjYau/b8reeUbOh6Jrba1A5d32rs1VY97g22/lnA+Vmva/U417UQ5bj4a15za619j+f8uW9M3jKPaDNOz3/aVRxD1yTFnedMPZZuXkty3OdDXo8km/YyDu3LnmL0Jhtj8pa7tZXtjF92fA7VuJXbnPFbY7lV73wV3mrlTj3B0UIztBbEdJHgLVsB50KQvMe9sLxofSz/lklfGs/+Z594Psky/oxH+855TEZyzi31xSJPasmoz1s9Kd2f7Rh/yVVGOklyfmtcGpeOtxyvPDQXacs6tT0UfJd3/c7k9BnF7c8vG57dEeyLRP9kjX46Z9tt9EPa+lyh/mpDPrZuFJTpZ7C4TKHGZ7TK8MWh5kGstJmT2LjvQuHODs+Lz9Mhc7m+WjJj66e1tiXvc9AxOEcfy6dl0qfGs7+uzZS9d7/mrVzqG4TXr+Mf8+ncUkYs0o9k1OdtzvnrteIYKnP7zeuL4pZcyqrPtizr/CQ3ti3h/NTc59xoP69zOhYvb3kdNGevHx9r7tOGdWUn+2Xb54TnxrKPbBVf5iDbisN9dX1W35LL9SzdZKZjM5Cu5HO88tCxXimTPrPf63xqrab+tfvyl/FbX33u9znkMbVT4yn77P23KBINxROsdmziPLleDFpI3k8bKad+T4oXpvvUelG3gGuibXdMrqV7TZ/isx/H1tL3BWPs5DcT23Puspf55EmT/dIf2pKrdOxLPrwvv7bh/rTv+U15jefcyIbkUiYZDeXfF3v+LOLIl0VOj7Ese/a4uTv+puKqWx37Ja+Yzr793EMa+8zh4bFa/bLLJdtLG851aM0lKzHyxcb7lz5u78k5EIOMKedVY1Pb0PqpdlJOdsVDW+Y9xUjyaTfjnopz7rjPR8VYX3WdK5Y5W+Xt3KWb+aS97Jf+2Oa14ngdp3WSsWSSm/24L2P1fEhn6ednXRdmY35mr/5k6DnNOVKfZIbmtfrSsf3oPHrUlnEq/iHb9i2Zqc0c1JqR9tOG/OaY7da8JSO9oS3X9ZjckP7c/pw751d1k6XyqNvUeJV/1vH0DD7LM3YXQ8AXwa9OeOhR8cRdxhOTIf1jUXSSYwcCELiewND5xfl5PUs0FkWAInFR0/07yS6iSGw+Li6fcxzCP/BG5f85t/6XOWSKfghAoE3g7OMkvQjnZ5sUvRD4IUCR+MOCvScQyFvmz7y9/4TQbzK53227zVo/k3N4zOxH1UPG9Ma13mzjW9VDkvRDAAL3EuD8vJcg+ksjQJG4tBknXwhAAAIQgAAEIDCDAEXiDEiIQAACEIAABCAAgaURoEhc2oyTLwQgAAEIQAACEJhBgCJxBiREIAABCEAAAhCAwNIIUCQubcbJFwIQgAAEIAABCMwgQJE4AxIiEIAABCAAAQhAYGkEKBKXNuPkCwEIQAACEIAABGYQoEicAQkRCEAAAhCAAAQgsDQCFIlLm3HyhQAEIAABCEAAAjMIUCTOgIQIBCAAAQhAAAIQWBoBisSlzTj5QgACEIAABCAAgRkEKBJnQEIEAhCAAAQgAAEILI0AReLSZpx8IQABCEAAAhCAwAwCFIkzICECAQhAAAIQgAAElkaAInFpM06+EIAABCAAAQhAYAYBisQZkBCBAAQgAAEIQAACSyNAkbi0GSdfCEAAAhCAAAQgMIMAReIMSIhAAAIQgAAEIACBpRGgSFzajJMvBCAAAQhAAAIQmEGAInEGJEQgAAEIQAACEIDA0ghQJC5txskXAhCAAAQgAAEIzCBAkTgDEiIQgAAEIAABCEBgaQQoEpc24+QLAQhAAAIQgAAEZhCgSJwBCREIQAACEIAABCCwNAIUiUubcfKFAAQgAAEIQAACMwhQJI5AWq1W3Te8RlJkCAIQgAAEIAABCDQJUCQ2sdAJAQhAAAIQgAAElk2AInHZ80/2EIAABCAAAQhAoEmAIrGJhU4IQAACEIAABCCwbAIUie86//ttt1mvu+1+XoD77bpbb7bdbqb8PKtIQQACEIAABCCwVAIUib8087vN8Usxm91FBH3Bt952Y/Xefrvp1v5ijW3st936isLSjv/+++/JL+j89ddfvfi//vWv7s8//7Tq3e2///3vk2/ZVSz2NfWlIcuNBSGZP/74I0T23W6z/mG3WnXrg6gc/gAAHWdJREFU9easuBb/Qd9mHRZbu8pLubD9HgGv66F50NrQPL/DpjWqc+tVm9n8888/D3WZ582UbfNPOek/8vpSkxPj8+tBlfieY12D9GKDwD0E3uMKeU8GL9BtFw3rbnPXbbt9t12vuouaQ4XeatNdlo4/ifbxHIvIvqDM2427Tbea0P+xdNirhVS9uOiirZcusI+8iNc3RvmQfcXjzX0+dqs3OY2NbbJfL5J9cb6OO65N3oe5WZ1NzsB8jQTw6IJ6xBVDhYCLIK2nVpHoAkXjS9uSTRZo93LQejfrqWIs+TsGzYVeU+f1vXGiDwEIzCewvCvkfDYhWYuGfbfr7zbNfxwcxo67u26zqvoHP+ss+i4UpdcoLk9yc2ychPsdXbB9oVZHLRLV5wv3IwsfvSFkQSg/Os6+oSKxD3zkH9moBWLXTbGzwbac7kCOTo3Vo803zuhmdyYBFR1eezNVTmIuhly4nAaOO1ojSyoS83wwmzzvK59rjm3vGh3zzxgoEq8h+CMrhku5Q/qTNXuvIECROItyo2jo70BdXzSc3PWPhssj5Rk29/2dwnG/eafx5O+KnVaRaPVHFomypdfY1ioS9YY09MZvW3qzuZQ5Fvt5J9EK2Za7i+J5dlOx23fbzeGRdN+/33Wb9eEuSC3wFcNUjuma/XMC96w3sW+vg4MPFynnHr/zqN5VN5ss0O7J/BaW1skYKBJvmwVdsykSb2OH1jiBpxSJOtHrywvYb/oez7sEvpD5AiYZXUh0EbH80P+G9WYiGY+rtU5ehMb8D6JqFGZ9IVYe6+ru4ulzgvqs22b387nCKCRWuoO43XTnjzO7rpt4VHz6HOOJ78Bj6Qk7g3keB8TOHKus37STo+YrN3NXq/kb2vwmIblcBylvP7Wv+sxx2R0szPovBGl9Dn9c4DC3uYbPi/Ld9vAZRs3HervtttvDPA8V58qvFa9zc/657h1/9ilHnSOS9yv52p7mTvu57pOP9vP8sC37rH5yLXjOJGO9HFe/49B42rSux61XYzEr23frfJKBbcivNvuQjv3Y3lHk1Fg2Gfs61fKR1yHHYmP2Jb/a0qb9p776pONNeWQu8q/jtJPy0hNbs3GbNtK2x9Uqb9tVTLaTcyVd87Gu7dVWPi2jNu3kWPan/WQp/bE8M7/0KXvpS7Fnn21W+4pJY3rZnudL+jlnHq82ekfxT8o5Dg9njPLjfBSH9Or6S2YZY8ZgXft1/Oq3PftPWfvOsTEWjj3ny7q030vg4UViLkyfYFp42nzsRexFr7HWRdl9sqlNejoR1HrfJ4ZsZ5/2tbXi8Vj674UH/qlv/rqb1xeDeXvJhdnx2yb9HT+P93emsmg83Jmsd52qn1Y4faFouy0B9c24Izmkqn5dDOoFxPK+yHhOJZcXIs2HN8+Hebs/W68Jz2O9qHqOPO52zKbiGYr/4Pvw5RXZqnOg8XPGuvtY7vj2Ro53JWMuzvV+slQs5vXTe9gzo+xXXMnBfC0rZtrU73NDfc65yqXtqqdj2bCux9O/zyH1mb9abfalVpvyzFxlV6/UzXH398qNtSffKa+cW7FJv2VLcTo2+3BreefuXOzP45ZXK//mn/3ar/Je2/YvP9bNvNSvOB2HfJiz+xSTmcuX+j2mY41Jb2jTWMo7V+kpphqrckl72lfMQ1vNXXLyZ5Y6ln7asI6ZSEbxVB3JedN45lF9SDd9KE/rSzftS672yV7aT3kzsz3HlK3kvVlerTbZTaaVRx3PXOr8aCx9VV3nVv1l7NJ3rpbPfDXmcedEuzwCPyv6QblrkeVCrIvXbrR4JauXN/XlopSdHK8nik/CepG51b/jOG+PxUDcvenvQtUPpx0Ls9aXWfri4azQUJF4fndKPqeLxEMsrcLmLOYnF4l5Ec8583x5Xt2m/FmccZC6KV8vhlJRny+8YeK0O7TmTgLHnct50UDjowVVUccXjA9zEzXjSWsqHnHKNavjPA+Sh41qXHK6uGvzuZF2LFvbGo/s245kPW/ZetzzZJv26/nIN5vUl3zVtQ231s3c1Zf5p03vOza1mb+vD47Nfty24tF6tj3JVf8aH9qqvcpGeWVuaaeO5XkluWq7FdeQbelXe2YzdO2ULfPNNuUz/hqf7aeMeUhWm3XSpnx5vjWecyEd2/Wc1rWrY9nI8Ywh7at/jKPjta2WfNp2PsnL/hx3yte1Jua5vmpu1pWMfbiv6qpfudneHJZjLOyHdnkEfiq0B+WuRZkXKy1mLVBvXuDq80mVY6lbx+tJ6xOvXmTSXz15hvzblk++n4vTzKJBdYPvMJ79pEpDvy8wLh8VTxaJF4WJyZV2rlxR86GY5Ty4X+3YhUQXNV+UUmdovyUr+z/sfx5dpg3NlV5DW53zIbnmnb8mO33msMyX7xzb+MCcangqHnEw7zwvtK51nLlKTmtUffVNxGtb47bn8LKt51XG53Ms5XO/6lreMcp3no9juh7TfFsvWWg815t9Wa+2sqH4vCkm9Tk297utuai/Mk0Z7Q/Zkm7K6tjxWsfxKCbn61g0BzlnlUO1XeNMTraZbbXnWHKukp/sOe60M7Rf46vH1pMPxa7NMjUGj9ccpWOm0s1jxyqdXM/KO7f0r/7KrXJKW/ZtX2lX+/Jd/VnGufpYrWTl31v6Ul/NX/Keo2qv6kpfth1PtaVx52OWUywcJ+2yCDy8SBQ+LWS/vEjVXxd2PZasFru3Ou5F7ZNUrfyo35tPIh/nyVPt1WPrnLV9MXB51+9M5uxAjzJX3cp3DhtFR7M4kY1aeJzZnTFu+Sk7lhtoxSznIcXGLiTimRe91Gvtt2TrnOjipjltbRrzWsjxuo40pgI878Dud/qpoctviV8W6sf5LLcIL+ZQzPs515dathlOz1KxDm25jn2+mHPqaT+Z1WPbV7+Y2Zb7s9X8SkavtCkZ9bW4aqzOTz0nZUsyra3qSqbOVT02B9sbi01jyctc5+bieCo3x1D7HZPbml9lYzn3K15v9ZyrHKpt6Sku2dBr6Hy1/WrPbIaunbKXLG1nqK3x+biyV6wa02aZGoP9eh3nuNmlXcWq/GRP/WnXvhy3/Nu++jy3Hq+c7M+cU9c6buVL9lqbY8ox+Up552EZ+fJ41a/HVVc2pCsf2uawlHzmV1k4LtplEfi5Sj0oby3WPKnTrBe2x7UIdfJ5q4vS8h73CesLhFrp257kdCw9b3ny2J7lq3/rZHso+MpdpBTQvu4gbn9+2fCs0OiLRBcj+umcbbdREamiQ1+iyMfWjYIyXV0UJjkY+2f+o3/urpjp1dqmLiTiL67exDovPO5XK1t65abj1PfFLWW0r7mvupbRPNex+gPaq3Xjiyuaj/gPjnLxKwvMTt9uXpfPM+ZdZAdybGXDa7YMnQ6dt+WUt/ssVFlojpynOOecSTY52oZajeU5kmPab50XtuVzyDrym/k5Rp9jtqe26nrMOehY+5mHjmVTXPQai81j9u3jjE8+vDkeM1crWetXOcUxttme9at/5eUxtfLlTWOZt3Tz2LYtr3HH7b6xNu2ZZ81Vx/Kjzf7Sh/Qcf/Vl+ezX3GWOksm5tk7alHxy1nHqaCy5OFavE/u3XtrWWLVf9ZKT5NO3bY+1si8b3uTf3OqY+tN+XQOS10sylZV8aMybdNUnf5LVJr2MxbasU1lKXn3epFtZe4x2OQR+VtmDctZC8+LO1hebHPdC9+K1vBerj9X6JHHff/7nf575qeM+AS3vxT7kv15MhKMvtlwolDtJiev0mNmyejQZfy7l8JuKurt46O+/1FK//dwbbBQfJ0eHx9bnxcppMHbaNsRDLDwPoXDa1Zh5uU1596n1BcR94qpNHN2n1v0nJ7GjmKp8vailrbqfF7Qw2+9KNmOv4686VgxjDByH8k45c6k5JIM8fySfx2nLPtymXNpLnjpfckxxeA25v55zfnOq9tVfdRWvNudpm9Z1/D72+SudVmzOLcesWxlaVm3NIcdy3/FkX2tfcs7FOdu/jnPc/Rmz9vNY8rZju2KWMu5X6zmosWWeuW+dtCF/2qpf91fbNRZx95b5at9bjaGug/SVsclX3aybuSuGKpt2am6Wt4xj9XFt5bO1ORbL245kNd/udzs2rhhzXPvW89rWsXw6H8tXWcdqfbXJJ/uHWHieh3K3D9rvIvDwIjFP7kQ11J8y7I88Up64y3hid+ej5pOdD9/RG0ZeBH8rHcWQb16/FUf6HToXh/pTd6n7euN8p21orob63yn2T4pliOdQ/zW5aU25qLtGD1kIvJLAQ4tEnTitN2b1+3/Nr0zuU31dPi4un3McSmxuITmk/2X9Wne/eRGW70e8mTxyWnw3o9pU/7vFWmP8reN3u37pPx2tda3+d/sPyW/N2SP8qohr/edAfY+4m0aR+IhZwsazCTy0SFSwviWdt69587l+GvXFis1aX5g5PGb2o+ohSyos15v4m8RDggvrV/HTutA/G4POg3f9j5HOxzw/66OnZ7P5BPsupsXmHa9fWtN1Dn9jnX/CXN4To4rxyvkRhXjOX6vgvydmdCHwSAIPLxIfGRy2IAABCEAAAhCAAAR+hwBF4u9wxysEIAABCEAAAhB4awIUiW89PQQHAQhAAAIQgAAEfocAReLvcMcrBCAAAQhAAAIQeGsCFIlvPT0EBwEIQAACEIAABH6HAEXi73DHKwQgAAEIQAACEHhrAhSJbz09BAcBCEAAAhCAAAR+hwBF4u9wxysEIAABCEAAAhB4awIUiW89PQQHAQhAAAIQgAAEfocAReLvcMcrBCAAAQhAAAIQeGsCFIlvPT0EBwEIQAACEIAABH6HAEXi73DHKwQgAAEIQAACEHhrAhSJbz09BAcBCEAAAhCAAAR+hwBF4u9wxysEIAABCEAAAhB4awIUiW89PQQHAQhAAAIQgAAEfocAReLvcMcrBCAAAQhAAAIQeGsCFIlvPT0EBwEIQAACEIAABH6HAEXi73DHKwQgAAEIQAACEHhrAhSJbz09BAcBCEAAAhCAAAR+hwBF4u9wxysEIAABCEAAAhB4awIUiW89PQQHAQhAAAIQgAAEfocAReLvcMcrBCAAAQhAAAIQeGsCFIlvPT0EBwEIQAACEIAABH6HAEXi73DHKwQgAAEIQAACEHhrAhSJbz09BAcBCEAAAhCAAAR+hwBF4u9wxysEIAABCEAAAhB4awIUiW89PQQHAQhAAAIQgAAEfocAReLvcMcrBCAAAQhAAAIQeGsCFIlvPT0EBwEIQAACEIAABH6HAEXi73DHKwQgAAEIQAACEHhrAhSJbz09BAcBCEAAAhCAAAR+hwBF4u9wxysEIAABCEAAAhB4awIUiW89PQQHAQhAAAIQgAAEfocAReLvcMcrBCAAAQhAAAIQeGsCFIlvPT0EBwEIQAACEIAABH6HAEXi73DHKwQgAAEIQAACEHhrAhSJbz09BAcBCEAAAhCAAAR+hwBF4u9wxysEIAABCEAAAhB4awIUiW89PQQHgS8gsNt069WqW61W3WY3L5/dRvLrbrufJ48UBCAAAQg8ngBF4uOZYhECEDgR2HWb1abbZbG3356KRhWOP68sCo96JzvsQAACEIDAqwlQJL6aOP4gsBQCpRicexexxyPdzbbbbdZ9Ebm+SnkpgMkTAhCAwHMJUCQ+ly/WIbBoAvvtulvdUOD1euvjHUgVjOttlzcjFw2V5CEAAQi8iABF4otA4wYCSySgzxZe1IjlDuPl4+Z9t13Ho+fdpltRJC5x+ZAzBCDwywQoEn95AnAPge8loM8VRrE3O9H4POJ+123WjUJzti0EIQABCEDgVgIUibeSQw8CEBgncOsdQH0ben34LKK+4bzhK87jnBmFAAQg8CQCFIlPAotZCCydQP8zNhfPmpdOhfwhAAEIfA4BisTPmSsihcCHENBnClfdSl88+ZCICRMCEIAABC4JUCReMqEHAhCAAAQgAAEILJ4AReLilwAAIAABCEAAAhCAwCUBisRLJvRAAAIQgAAEIACBxROgSFz8EgAABCAAAQhAAAIQuCRAkXjJhB4IQOBOAv1fTDn7u8z5N5qv2+cL0ndOBuoQgAAEbiRAkXgjONQgAIExAsdvOPeF4i3fcv7Rp0gc48wYBCAAgecRoEh8HlssQ2DZBPLP791S6R31b1FdNniyhwAEIPAYAhSJj+GIFQhAoEEgHzuvr/7LKYe7iRSJDbB0QQACEHgBAYrEF0DGBQSWTKD/yyv9Y+fr/46zdIeKxNWDPvP4bnZeuVbeLfdb4vnf//3fVyLDFwQWRYAicVHTTbIQ+A0Cu27jgm697fa/EQI+IQABCEDgagIUiVcjQwECELiawL2fT7zaIQoQgAAEIHAvAYrEewmiDwEIzCKQn08ceoQ8yxBCEIAABCDwEgIUiS/BjBMIQEAEfj6feMvP4rwRw/2226znf8ZSBfJ6s+12PGs/TCL83mgxEwoEhglQJA6zYQQCEDgR+Plc4fXfUj4ZUZn4EZ9PPBWzjVuefcE38dnK/XbTrf05TNvQI/cZheXpjuuEj6R61f6VBdpVtkN4iOGj+e3/+/85FeB8hSUmgF0IPIAAReIDIGJiKQT23W6z/nnzX6269Xpzdnfo9AbvAiFbFwufiqv/XOH8u2eDae42nb/Fel/BefDQZr7uNnfdthv4+Z2ewfhd0D6eY4HXF0T50z997uP6yiptDHK8YaCPp1F89v5yrfb7T2D4LH77/z4V4BSKNywMVCAwQIAicQAM3RCoBPo7I+t4ZNh8wzv+pZCzgnCg4KgO3v24me9tQWdRcobqJnOV+b7bbdfdanVPQas7nlX/4Ge8sD3cKR3OaY6NJxWJo/P3CoZzcr+D3//3/3b/sdp0u//tOgrFm04klCBwQYAi8QIJHRBoEZh687JOW053IPOGkqU/qX3s3a1jUdLfsZq+szbOqcG8L4juYC79esdths19f6dw3O8cjnNkxpnU0akC7QUMn85POf5Ht97+346fTqzzzzEEbiNAkXgbN7QWR+BY1OSdxBaD/o3wp+jRm/35XaV9t93oLtfxR6L3u26zXvXHw3eosqA6yPpx7Vlbi5pWfHf06U7qKUbFfXwkeXvxaxurbnUO6booG4VZX2TprlJY0t3F0+cE9VGBze7nNxtjHvo7kNvNZUwTj4pPn8E7PbY9938KZcKO5C6LxMNHHX7mu/Eo+CyHWCdaF1MF2isYTuR9C796x1CfT/yP9bb7vyfY7EAAAvcQoEi8hx66yyLQf+Bfb76NN+gjiUNxEm/Q5ZHlbnv4DOOh4Np22+2hULksCt4NbbnTpKKjfB7zpoj7wkG8BgqqGUYrO93N64vBLDxdoBy/Xdzf8fN4X0Bl0XjI9VQQ59xOFOJ9oWO7Q7FPFWyNIrG3G74Pdyzzr9Ec7xSeCt/jfywci/MfiOkVDKuPVijX8qtFYhePnFv26YMABK4jQJF4HS+kF0/g545OLSKE5vxNTm/Urb8wUt7AL/SeDfno/3THK4va2M/YT4WNPu+3Ob8Ld0+4LtBuvh3ZymXdbaq9Y/ytL7PUAuzwDezLR8bTRc4hlta6OEN0YnnWe3Zw7qsU6L1kXUOXMmkj988cpa2z9fB4huMxKJDr+V0UifoCy3+suy23Ei+nmR4I3ECAIvEGaKhA4LKwEJPLN+omqYsi4fDm6Js+lzrHguDsTTyKOfdnUXdp5K6e/g3efvyo/C6LUj7mdVfcM5nLm+8wnt0Bbej383N5Z3OyyLmY1wFAM+TOfA3IX/6H5PDY/nCz9Mj2uKjO7F2E1WBwIXPouIfheAz97dNuXe68N8MIHhSJTUJ0QuBhBCgSH4YSQ0sicP4Gfcw83rx+WOgzh6XgqI/+er0i82PgLfZ+iuLz4uOe4Pqi4Y7HzL3vnuXlXb/huHQneNWtXJg25qw5tzJY5606mRq3/Ay5s4KqjzEfLcvQYR7O7loe5fy5Rf14t3+7+8ye43D7KoZTeU+Nn8V7OF8oEg2FFgLPIUCR+ByuWP0iAnqDzTfj/W7bf+at3vm7fCM+FiRF8KII0ZtjX7ToSy0/b+zvg/Bwp8kM+jz74u6OeJuFz/UZHwq+iQJbdxC3P19hOZunszj0KH3bbVREas70GdR8bN3LDhekF/M6kM6Z/1kyP3dc/dOPh88kZiySyeNieKQAexnDV/DjM4ll4jmEwH0EKBLv44f2AgjUH9BerRtfXFFBEY9jfTdHrYurA6rGHaB8DPqOPOub+7GwWp09tr0m8EPRedc3mvunk4dvifesSyGe0ZwekXp+StyH31TU3cXDl4oOBVh+kcXWGnPnoeNHDc7n+jQYO2M2jmJ9QXf8OMEpr59vxff5rjflJ5UOdnPdWa4vj+scHl0dCv7qK8K1nNfoXQzHcj//j8hlBO4Zs9F1fLvZnGgh8BgCFImP4YgVCEBgJoH+ztW9j5ln+nq42NAduYEi7ML/kP6F4JUd8l8/1nD2SHq8uLrS2+3iQ/k/hJ9y5HcSb58cNCFwSYAi8ZIJPRCAwLMI9EXCyGPRZ/l9oN3Lx8Xlc45DvuYWQkP6I/2HO4LnfyKyO/6W5elm5FCBNmL3GUNP4xePmi8+q/iMRLAJgQUQoEhcwCSTIgTegkBfJNXH728R2dVB6HOpm/4zgMdH5+URdjWowkhfJPFnCuv4/ceHn2bKHwtvfSziskC73/MtFh7OL376hgLxlhlBBwJtAhSJbS70QgACDyVweNx5+lbxQ21j7BoCPwXaNVrvK6vPIboAp0B833kiss8kQJH4mfNG1BD4KAKHx6Gf/Zj5o4ATLAQgAIEHEKBIfABETEAAAiMEjo+ZT5+NGxFlCAIQgAAE3ocAReL7zAWRQOALCdz3czfv8hm6L5wYUoIABCAwSYAicRIRAhCAwK0E7v25m7k/UH1rfOhBAAIQgMAwAYrEYTaMQAAC9xDof3Kl/jm5+Qb9l23u/dHt+R6RhAAEIACBJECRmDTYhwAEHkPg+DnEi78A4r/YcUU7/VdMHhMyViAAAQhA4JwAReI5D44gAIG7CRx/7uaKQnCsmKRIvHtCMAABCEDgJgIUiTdhQwkCEIAABCAAAQh8NwGKxO+eX7KDAAQgAAEIQAACNxGgSLwJG0oQgAAEIAABCEDguwlQJH73/JIdBH6XwG7T+e8J82PavzsVeIcABCBwLQGKxGuJIQ8BCMwkoB/S3nS7fYgPfuuZP9kXlNiFAAQg8BYEKBLfYhoIAgJfRqAUg9ffRdx32826O3zred2t1tsua80vo0U6EIAABN6SAEXiW04LQUHg8wn0f1Lv+uqw67rDT+ist7u+MLzdzuczJAMIQAACv0mAIvE36eMbAl9MQH9S76JGLHcYf34fMR43SybuHDbtfDE3UoMABCDwLgQoEt9lJogDAl9FQJ9HjMLvmtz05/yOReK+/+KLPte473ZnH268xiCyEIAABCBwCwGKxFuooQMBCIwTiEJvXLA1uus261W3Wq27ze7411vWm27XEqUPAhCAAASeRoAi8WloMQyB5RLQI+LVxbPm5fIgcwhAAAKfSIAi8RNnjZgh8LYEuPP3tlNDYBCAAASuJECReCUwxCEAAQhAAAIQgMASCFAkLmGWyRECEIAABCAAAQhcSYAi8UpgiEMAAhCAAAQgAIElEKBIXMIskyMEIAABCEAAAhC4kgBF4pXAEIcABCAAAQhAAAJLIECRuIRZJkcIQAACEIAABCBwJQGKxCuBIQ4BCEAAAhCAAASWQIAicQmzTI4QgAAEIAABCEDgSgIUiVcCQxwCEIAABCAAAQgsgQBF4hJmmRwhAAEIQAACEIDAlQQoEq8EhjgEIAABCEAAAhBYAgGKxCXMMjlCAAIQgAAEIACBKwlQJF4JDHEIQAACEIAABCCwBAIUiUuYZXKEAAQgAAEIQAACVxKgSLwSGOIQgAAEIAABCEBgCQQoEpcwy+QIAQhAAAIQgAAEriRAkXglMMQhAAEIQAACEIDAEghQJC5hlskRAhCAAAQgAAEIXEmAIvFKYIhDAAIQgAAEIACBJRCgSFzCLJMjBCAAAQhAAAIQuJLA/w/2MvjaKE06UwAAAABJRU5ErkJggg==)
where k = 1. The average of each frequency SE including delta (0.5–4 Hz), theta (5–7 Hz), alpha (8–14 Hz), beta (15–29 Hz), and gamma (30–100 Hz) were extracted from C3 the C4 electrodes of 10 epochs were calculated.
Statistical analysis
Continuous variables are presented as median with interquartile range (IQR) due to the non-normal distribution using the Kolmogorov–Smirnov test. In addition, categorical variables are expressed as absolute numbers and percentages. The patients were categorized into two groups based on their CPC scores at 1 month: good neurologic (CPC 1 and 2) and poor neurologic (CPC 3–5) outcome groups. Comparisons of demographic and clinical characteristics between the good and poor neurologic outcome groups were performed using the Mann–Whitney U-test and chi-square test for continuous and categorical variables, respectively.
The linear mixed model analysis for PSD, ESRP, and SE values for all and each frequency band was performed to adjust the random effect of clustering from the epochs of the same patients at 10 time-points and fixed effect of ages of each patient. Each variable was compared between the good and poor neurologic outcome groups, and receiver operating characteristic curves with 95% confidence intervals for good neurologic outcomes were analyzed to determine the cutoff values to predict neurologic prognosis. Sensitivity and specificity for good neurological outcomes at 6 months were calculated. All statistical analyses were performed using IBM SPSS for Windows, version 21.0 (IBM Corp., Armonk, NY, USA).