Results 221 to 230 of about 9,518 (289)
A novel machine learning approach classifies macrophage phenotypes with up to 98% accuracy using only nuclear morphology from DAPI‐stained images. Bypassing traditional surface markers, the method proves robust even on complex textured biomaterial surfaces. It offers a simpler, faster alternative for studying macrophage behavior in various experimental
Oleh Mezhenskyi +5 more
wiley +1 more source
Factors Related to Oropharyngeal Dysphagia in Adults in a Healthcare Center in Colombia. [PDF]
Bernal Sandoval LM +2 more
europepmc +1 more source
CrossMatAgent is a multi‐agent framework that combines large language models and diffusion‐based generative AI to automate metamaterial design. By coordinating task‐specific agents—such as describer, architect, and builder—it transforms user‐provided image prompts into high‐fidelity, printable lattice patterns.
Jie Tian +12 more
wiley +1 more source
How the dynamic interplay of cortico-basal ganglia-thalamic pathways shapes the time course of deliberation and commitment. [PDF]
Yu Z, Verstynen T, Rubin JE.
europepmc +1 more source
Several simulation techniques are used to explore static and dynamic behavior in polyanion sodium cathode materials. The study reveals that universal machine learning interatomic potentials (MLIPs) struggle with system‐specific chemistry, emphasizing the need for tailored datasets.
Martin Hoffmann Petersen +5 more
wiley +1 more source
Workload of Queueing Systems with Autocorrelated Service Times. [PDF]
Chydzinski A.
europepmc +1 more source
Memristors based on trimethylsulfonium (phenanthroline)tetraiodobismuthate have been utilised as a nonlinear node in a delayed feedback reservoir. This system allowed an efficient classification of acoustic signals, namely differentiation of vocalisation of the brushtail possum (Trichosurus vulpecula).
Ewelina Cechosz +4 more
wiley +1 more source
Critical Assessment of Curvature-Driven Surface Hopping Algorithms. [PDF]
Jíra T, Janoš J, Slavíček P.
europepmc +1 more source
Large Language Model in Materials Science: Roles, Challenges, and Strategic Outlook
Large language models (LLMs) are reshaping materials science. Acting as Oracle, Surrogate, Quant, and Arbiter, they now extract knowledge, predict properties, gauge risk, and steer decisions within a traceable loop. Overcoming data heterogeneity, hallucinations, and poor interpretability demands domain‐adapted models, cross‐modal data standards, and ...
Jinglan Zhang +4 more
wiley +1 more source
RNA inverse folding can be solved in linear time for structures without isolated stacks or base pairs. [PDF]
Boury T, Gardelle S, Bulteau L, Ponty Y.
europepmc +1 more source

