Micromagnetic analysis of magnetic vortex dynamics for reservoir computing.

Reservoir computing (RC) has generated significant interest for its ability to reduce computational costs compared to traditional neural networks. The performance of the RC element is quantified by its memory capacity (MC) and prediction capability. In this study, we utilize micromagnetic simulations to investigate a magnetic vortex based on a permalloy ferromagnetic layer and its dynamics in RC.

Skeletal Muscle-Derived Irisin Enhances Gemcitabine Sensitivity and Suppresses Migration Ability in Pancreatic Ductal Adenocarcinoma.

Background: High skeletal muscle mass might be a prognostic factor for patients with pancreatic ductal adenocarcinoma (PDAC); however, the underlying reason is unclear. We hypothesized that myokines, which are cytokines secreted by the skeletal muscle, function as suppressors of PDAC. We specifically examined irisin, a myokine, which plays a critical role in the modulation of metabolism, to clarify the anticancer mechanisms.

Barley consumption under a high-fat diet suppresses lipogenic genes through altered intestinal bile acid composition.

We evaluated whether barley flour consumption in a high-fat environment affects lipid metabolism through signals mediated by bile acids. Four-week-old mice were fed a high-fat diet supplemented with cellulose (HC) or β-glucan-rich barley flour (HB) for 12 weeks. Bile acid composition in the intestinal tract and feces was measured by GC/MS.

Generation of Chemical Structures of Inhibitor and Activator Candidates for Therapeutic Target Proteins by a Transformer-Based Variational Autoencoder and Bayesian Optimization.

Deep generative models for molecular generation have been gaining much attention as structure generators to accelerate drug discovery. However, most previously developed methods are chemistry-centric approaches, and comprehensive biological responses in the cell have not been taken into account. In this study, we propose a novel computational method, TRIOMPHE-BOA (transcriptome-based inference and generation of molecules with desired phenotypes using the Bayesian optimization algorithm), to generate new chemical structures of inhibitor or activator candidates for therapeutic target proteins by integrating chemically and genetically perturbed transcriptome profiles.