DISTINCT BRAIN MORPHOMETRY PATTERNS REVEALED BY DEEP LEARNING IMPROVE PREDICTION OF POST-STROKE APHASIA SEVERITY

Distinct brain morphometry patterns revealed by deep learning improve prediction of post-stroke aphasia severity

Abstract Background Emerging evidence suggests that post-stroke aphasia severity depends on the integrity of the brain beyond the lesion.While measures of lesion anatomy and brain integrity combine synergistically to explain aphasic symptoms, substantial interindividual variability remains unaccounted.One explanatory factor may be read more the spa

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Initially purported hepatotoxicity by Pelargonium sidoides: the dilemma of pharmacovigilance and proposals for improvement

Background.Spontaneous reports of herb induced liver injury (HILI) represent a major regulatory issue, and it is in the interest of pharmacovigilance to identify and quantify previously unrecognized adverse reactions and to confirm or refute false positive signals of safety concerns.In a total of 13 spontaneous cases, liver disease has initially be

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Estimation of 2D pressure and cavitation fields from sparse pseudo-pressure sensor point data using super-resolution machine learning

The peperomia double duty two-dimensional pressure field around a hydrofoil is estimated from the pressure values of sparse sensors flush-mounted on the hydrofoil.The sparse data is expanded to pressure field data using a combination of multi-layer perceptron (MLP) and super-resolution convolutional neural network (SRCNN) techniques, where MLP is e

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