Results 161 to 170 of about 150,269 (267)

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

Effects of ferulic acid on meat quality and fat metabolism in broilers. [PDF]

open access: yesPoult Sci
Zhang X   +6 more
europepmc   +1 more source

Ultrafast Multilevel Switching and Synaptic Behavior in a Planar Quantum Topological Memristor

open access: yesAdvanced Science, EarlyView.
Dry‐transferred Bi2Te3 layers enable a planar quantum topological memristor framework. In‐plane topological surface states facilitate ultrafast & low‐power operations. Coexisting analog and digital modes support current‐controlled multilevel states. PQTM exhibits 105 s retention, 103 cycles endurance, and reproducibility across 24 devices.
Mamoon Ur Rashid   +12 more
wiley   +1 more source

Reconstructing Coherent Functional Landscape From Multi‐Modal Multi‐Slice Spatial Transcriptomics by a Variational Spatial Gaussian Process

open access: yesAdvanced Science, EarlyView.
This study introduces stVGP, a variational spatial Gaussian process framework for multi‐modal, multi‐slice spatial transcriptomics. By integrating histological and genomic data through hybrid alignment and attention‐based fusion, stVGP reconstructs coherent 3D functional landscapes.
Zedong Wang   +3 more
wiley   +1 more source

SpatialESD: Spatial Ensemble Domain Detection in Spatial Transcriptomics

open access: yesAdvanced Science, EarlyView.
ABSTRACT Spatial transcriptomics (ST) measures gene expression while preserving spatial context within tissues. One of the key tasks in ST analysis is spatial domain detection, which remains challenging due to the complex structure of ST data and the varying performance of individual clustering methods. To address this, we propose SpatialESD, a Spatial
Hongyan Cao   +11 more
wiley   +1 more source

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