Results 101 to 110 of about 298,037 (275)

SemanticST: A Scalable Multi‐Contextual Graph Learning Framework for Uncovering Spatial Niches and Robust Multi‐Sample Integration in Spatial Transcriptomics

open access: yesAdvanced Science, EarlyView.
Technical limitations often let dominant signals overshadow rare cell types and fine‐grained heterogeneity in spatial transcriptomics. SemanticST, a graph neural network using multi‐semantic graph fusion and a novel min‐cut loss, recovers these subtle patterns.
Roxana Zahedi   +7 more
wiley   +1 more source

Switching Spike Plasticity Shapes ACE2 Engagement Across SARS‐CoV‐2 Variants

open access: yesAdvanced Science, EarlyView.
Conformational plasticity governs SARS‐CoV‐2 spike function and ACE2 recognition. Using high‐speed AFM and single‐molecule force spectroscopy, we analyzed the ancestral spike and nine variants, revealing progressive rigidification from Delta, variable plasticity in Omicron sublineages, and JN.1 compaction.
Sarah Stainer   +17 more
wiley   +1 more source

Deep‐Learning‐Based Denoising for Improved Phase Precision in Electron Holography of Electromagnetic Fields in Nanoscale Materials

open access: yesAdvanced Science, EarlyView.
Low‐dose electron holography is limited by shot noise, which buries weak phase signals. HoloDenoiser, a physics‐informed network that works simultaneously in the spatial and frequency domains, locates and protects the holographic sideband while suppressing noise in the hologram.
Ye Luo   +10 more
wiley   +1 more source

An ATP‐Driven N Protein–DDX21 Molecular Switch Dynamically Controls SARS‐CoV‐2 RNA G‐Quadruplex Heterogeneity

open access: yesAdvanced Science, EarlyView.
An ATP‐driven molecular switch, comprising viral nucleocapsid (N) protein and host helicase DDX21, dynamically modulates SARS‐CoV‐2 RNA G‐quadruplex (G4) heterogeneity. These viral G4s feature non‐canonical ion‐dependence and act as energy‐sensitive structural checkpoints.
Ya‐Ting Zheng   +8 more
wiley   +1 more source

MAPA: A Semantic Network Framework for Functional Module Discovery and Interpretation in Multi‐Omics Data

open access: yesAdvanced Science, EarlyView.
MAPA transforms complex multi‐omics data into biologically coherent functional modules by integrating pathway information with molecular interaction networks. Retrieval‐augmented large language models then generate structured, literature‐informed interpretations.
Yifei Ge   +13 more
wiley   +1 more source

Beyond representations: from active inference to active interference

open access: yesFrontiers in Human Neuroscience
Active inference describes the system–environment boundary through statistical constructs such as the Markov blanket, a substrate-independent formalism that specifies a boundary's functional role without committing to the physical structure realizing it.
Tommaso Firaux   +3 more
doaj   +1 more source

Exceptional Antimodes in Multi‐Drive Cavity Magnonics

open access: yesAdvanced Electronic Materials, EarlyView.
Driven‐dissipative cavity‐magnonics provides a flexible platform for engineering non‐Hermitian physics such as exceptional points. Here, using a four‐port, three‐mode system with controllable microwave interference, antimodes and coherent perfect extinction (CPE) are realized, enabling active tuning to antimode exceptional points.
Mawgan A. Smith   +4 more
wiley   +1 more source

blanket with beaded strip

open access: yes
See notes2.49 mBlue wool blanket with beaded blanket strip down center, two sides bound in red braid. Blanket is made by two pieces sewn together. Blanket strip is 54.0 long, with chalk white background with 5 designs spaced at intervals in green, blue ...
Omaha
core   +1 more source

Vacation request for Sophia Good Blanket (Drunkard)

open access: yes, 1919
Vacation request for Sophia Good Blanket (Drunkard) from her ...
Good Blanket, Irving (Drunkard)
core  

From Top to Bottom: Manufacturing Process‐Context Aware Resolution of Energy Device Electrodes Through a 3D Diffusion Generative Model

open access: yesAdvanced Energy Materials, EarlyView.
The application of a generative diffusion model, enhanced with a training data augmentation pipeline retaining the manufacturing process context of electrode microstructures, leads to improved fidelity of the through‐plane tortuosity factor in the AI generated samples.
Victor Ramirez‐Camacho   +5 more
wiley   +1 more source

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