Results 111 to 120 of about 89,082 (306)
Advanced Energy Materials, EarlyView.Machine learning interatomic potentials bridge quantum accuracy and computational efficiency for materials discovery. Architectures from Gaussian process regression to equivariant graph neural networks, training strategies including active learning and foundation models, and applications in solid‐state electrolytes, batteries, electrocatalysts ...
In Kee Park +19 more
wiley +1 more source
Hepatology, EarlyView., 2022 KAT7‐acetylated and cytoplasm‐translocated G‐protein GαS enhances IL‐6 effect and drives HCC progenitor cell progression. Abstract Background and Aims Hepatocarcinogenesis goes through HCC progenitor cells (HcPCs) to fully established HCC, and the mechanisms driving the development of HcPCs are still largely unknown.
Ye Zhou +15 more
wiley +1 more source
Neural Dynamics of Feedforward and Feedback Processing in Figure-Ground Segregation
Frontiers in Psychology, 2014 Determining whether a region belongs to the interior or exterior of a shape (figure-ground segregation) is a core competency of the primate brain, yet the underlying mechanisms are not well understood.
Oliver W. Layton +2 more
doaj +1 more source
Advanced Intelligent Discovery, EarlyView.This study introduces a tree‐based machine learning approach to accelerate USP8 inhibitor discovery. The best‐performing model identified 100 high‐confidence repurposable compounds, half already approved or in clinical trials, and uncovered novel scaffolds not previously studied. These findings offer a solid foundation for rapid experimental follow‐up,
Yik Kwong Ng +4 more
wiley +1 more source
Hepatology, EarlyView., 2022 Hepatic endothelial Alk1 signaling protects from development of vascular malformations while maintaining organ‐specific endothelial differentiation and angiocrine portmanteau of the names Wingless and Int‐1 signaling. Abstract Background and Aims In hereditary hemorrhagic telangiectasia (HHT), severe liver vascular malformations are associated with ...
Christian David Schmid +20 more
wiley +1 more source
Manipulating recurrent and feedforward plasticity.
, 2020 A. EI network with 80 excitatory neurons and 40 inhibitory neurons, trained with two uncorrelated time-varying inputs. Left panels: learnt feedforward weights of excitatory population.
Sophie Denève (355889) +4 more
core +1 more source
Nonlinear feedforward sequences of m-sequences I [PDF]
, 1993 The minimal polynomial of a nonlinear feedforward sequence of an m-sequence is studied, and algorithms for synthesising the nonlinear feedforward sequence are ...
Feng, Xuning +3 more
core +1 more source
Advanced Intelligent Discovery, EarlyView.Flexible tactile sensors have considerable potential for broad application in healthcare monitoring, human–machine interfaces, and bioinspired robotics. This review explores recent progress in device design, performance optimization, and intelligent applications. It highlights how AI algorithms enhance environmental adaptability and perception accuracy
Siyuan Wang +3 more
wiley +1 more source
Janus kinase 2 inhibition by pacritinib as potential therapeutic target for liver fibrosis
Hepatology, EarlyView., 2022 Diagram of the activation of the profibrotic and procontractile Janus kinase 2 (JAK2)/Ras homolog family member A/Rho‐kinase pathway and the inhibition of phosphorylated JAK2 by pacritinib to inhibit hepatic stellate cell activity. Abstract Background and Aims Janus kinase 2 (JAK2) signaling is increased in human and experimental liver fibrosis with ...
Sandra Torres +21 more
wiley +1 more source
Feedforward equilibrium trajectory optimization with GSPulse
Nuclear FusionOne of the common tasks required for designing new plasma scenarios or evaluating capabilities of a tokamak is to design the desired equilibria using a Grad-Shafranov (GS) equilibrium solver.
J.T. Wai +7 more
doaj +1 more source