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https://www.selleckchem.com/JAK.html BACKGROUND Contralateral strength training (CST) is increasingly investigated and employed as a non-conventional way to induce an indirect gain in strength in the weakened untrained limb. However, its effects on gait performance are more controversial. RESEARCH QUESTION To assess and compare the effects of contralateral (CST) and direct (DST) strength training on spatio-temporal parameters, kinematic and kinetic descriptors of gait in persons with relapsing-remitting multiple sclerosis (PwMS). METHODS Twenty-eight PwMS (EDSS 2.0-5.5) with inter-side difference in ankle dorsiflexors' strength ≥ 20 % and moderate gait impairment (walking speed 0.70-0.94 m/s), were randomly assigned to a CST (undergoing training of the less-affected dorsiflexors) or DST group (where the most-affected dorsiflexors were trained). Before and after a 6-week high-intensity resistance training (three 25-minute sessions/week), PwMS underwent bilateral measurements of dorsiflexors' maximal strength and assessment of gait spatio-temporalking speed. The estimation of patient-specific tissue properties in the form of model parameters is important for personalized physiological models. Because tissue properties are spatially varying across the underlying geometrical model, it presents a significant challenge of high-dimensional (HD) optimization at the presence of limited measurement data. A common solution to reduce the dimension of the parameter space is to explicitly partition the geometrical mesh. In this paper, we present a novel concept that uses a generative variational auto-encoder (VAE) to embed HD Bayesian optimization into a low-dimensional (LD) latent space that represents the generative code of HD parameters. We further utilize VAE-encoded knowledge about the generative code to guide the exploration of the search space. The presented method is applied to estimating tissue excitability in a cardiac electrophysiological model in a range of synthetic

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