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https://www.selleckchem.com/products/cl-amidine.html Breen, D, Powell, C, and Anderson, R. Pacing during 200-m competitive masters swimming. J Strength Cond Res XX(X) 000-000, 2020-Pacing strategies are key to overall performance outcome, particularly in swimming given the large resistive properties of water. However, no studies examining how swimming stroke, gender, age, or performance level affect pacing strategies during 200-m races. This study aimed to examine masters athletes pacing strategies categorized by stroke, gender, age, and performance level. Data were retrieved from World and European masters swimming championships and contained data for 4,272 performances. Performances were coded for stroke, gender, age, and performance classification (PC). Performance classification was based on comparison to the appropriate masters world record. Performances were then normalized, with split times being expressed as a percentage faster or slower than average 50-m split time to determine relative pace. Coefficient of variation (CV) of 50-m time was examined across splits. The main effect for stroke was examined at each split, whereas gender, age, and PC were examined for split-1 pace and CV. An alpha level of 0.05 was set to denote statistical significance. A main effect for stroke was identified at each split (all p 0.775). Masters athletes exhibit different pacing patterns across strokes, whereas lower ranked athletes also display less even pacing and a faster relative start compared with higher-ranked athletes. Individual analyses of pacing strategies may be necessary.This manuscript will review emerging applications of artificial intelligence, specifically deep learning, and its application to glioblastoma multiforme (GBM), the most common primary malignant brain tumor. Current deep learning approaches, commonly convolutional neural networks (CNNs), that take input data from MR images to grade gliomas (high grade from low grade) and predict overall survival will

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