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https://www.selleckchem.com/products/l-alpha-phosphatidylcholine.html In EXIT 360°, patients are engaged in a "game for health," where they must perform everyday subtasks in 360° daily life environments. In this way, the clinicians can obtain quickly more ecologically valid information about several aspects of EFs (e.g., planning, problem-solving). Moreover, the multimodal approach allows completing the assessment of EFs by integrating verbal responses, reaction times, and physiological data (eye movements and brain activation). Overall, EXIT 360° will allow obtaining simultaneously and in real time more information about executive dysfunction and its impact in real life, allowing clinicians to tailor the rehabilitation to the subject's needs.Deep convolutional neural networks (DCNNs) are widely utilized for the semantic segmentation of dense nerve tissues from light and electron microscopy (EM) image data; the goal of this technique is to achieve efficient and accurate three-dimensional reconstruction of the vasculature and neural networks in the brain. The success of these tasks heavily depends on the amount, and especially the quality, of the human-annotated labels fed into DCNNs. However, it is often difficult to acquire the gold standard of human-annotated labels for dense nerve tissues; human annotations inevitably contain discrepancies or even errors, which substantially impact the performance of DCNNs. Thus, a novel boosting framework consisting of a DCNN for multilabel semantic segmentation with a customized Dice-logarithmic loss function, a fusion module combining the annotated labels and the corresponding predictions from the DCNN, and a boosting algorithm to sequentially update the sample weights during network training iterations was proposed to systematically improve the quality of the annotated labels; this framework eventually resulted in improved segmentation task performance. The microoptical sectioning tomography (MOST) dataset was then employed t

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