Predicting Tau Accumulation in Cerebral Cortex with Multivariate MRI Morphometry Measurements, Sparse Coding, and Correntropy. [PDF]
Wu J +9 more
europepmc +1 more source
Learnable Diffusion Framework for Mouse V1 Neural Decoding
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
Sparse Coding in Temporal Association Cortex Improves Complex Sound Discriminability. [PDF]
Feigin L, Tasaka G, Maor I, Mizrahi A.
europepmc +1 more source
In this study, the orange‐muscle giant abalone (Haliotis gigantea) is used as a model to identify a non‐coding SNP that disrupts the interaction between ITGA8 pre‐mRNA and the splicing factor ILF2, leading to altered ITGA8 splicing. These splicing changes promote carotenoid accumulation in abalone muscle through the regulation of tissue remodeling ...
Xiaohui Wei +17 more
wiley +1 more source
Selectivity and robustness of sparse coding networks. [PDF]
Paiton DM +5 more
europepmc +1 more source
A Guide for Spatial Omics Technologies: Innovation, Evaluation, and Application
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
Learning an Efficient Hippocampal Place Map from Entorhinal Inputs Using Non-Negative Sparse Coding. [PDF]
Lian Y, Burkitt AN.
europepmc +1 more source
Machine Learning‐Guided Engineering of Protein Phase Separation Properties in Immune Regulation
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
Pansharpening of WorldView-2 Data via Graph Regularized Sparse Coding and Adaptive Coupled Dictionary. [PDF]
Wang W, Liu H, Xie G.
europepmc +1 more source
SpatialESD: Spatial Ensemble Domain Detection in Spatial Transcriptomics
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

