Results 141 to 150 of about 35,760 (243)

Multimodal Cross‐Attentive Graph‐Based Framework for Predicting In Vivo Endocrine Disruptors

open access: yesAdvanced Science, EarlyView.
A multimodal cross‐attentive graph neural network integrates molecular graphs with androgen and estrogen adverse outcome pathway (AOP)–anchored in vitro assay signals to predict in vivo endocrine disruption. By fusing information on Tier‐1 AOP logits with chemical structures, the framework achieves high accuracy and provides assay‐traceable ...
Eder Soares de Almeida Santos   +6 more
wiley   +1 more source

Learnable Diffusion Framework for Mouse V1 Neural Decoding

open access: yesAdvanced Science, EarlyView.
We introduce Sensorium‐Viz, a diffusion‐based framework for reconstructing high‐fidelity visual stimuli from mouse primary visual cortex activity. By integrating a novel spatial embedding module with a Diffusion Transformer (DiT) and a synthetic‐response augmentation strategy, our model outperforms state‐of‐the‐art fMRI‐based baselines, enabling robust
Kaiwen Deng   +2 more
wiley   +1 more source

Evaporation‐Driven Solutal Marangoni Control of Rayleigh–Taylor Instability in Inverted Films

open access: yesAdvanced Science, EarlyView.
Inverted liquid films, like paint on a ceiling, are inherently unstable under gravity. This work shows that selective evaporation in volatile binary mixtures generates solutal Marangoni stresses that either suppress or amplify the Rayleigh Taylor instability.
Minwoo Choi, Hyejoon Jun, Hyoungsoo Kim
wiley   +1 more source

A Guide for Spatial Omics Technologies: Innovation, Evaluation, and Application

open access: yesAdvanced Science, EarlyView.
This review presents a strategy‐centric framework for spatial omics technologies, organizing methods by how spatial information is experimentally encoded. It compares key performance trade‐offs across sequencing‐ and imaging‐based approaches, examines computational and practical limitations, and highlights biomedical applications. The analysis provides
Xiaofeng Wu   +5 more
wiley   +1 more source

Machine Learning‐Guided Engineering of Protein Phase Separation Properties in Immune Regulation

open access: yesAdvanced Science, EarlyView.
PScalpel, a machine learning model integrating protein structure extraction, graph contrastive learning, and a genetic algorithm, guides the engineering of protein phase separation ability. It adopts transfer learning methods to provide predictive recommendations for protein phase separation ability changes through single amino acid mutations in a ...
Chenqiu Zhang   +9 more
wiley   +1 more source

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