Results 151 to 160 of about 57,147 (259)
SKALE 2.0 maps disease‐associated protein aggregation as a phase‐resolved structural process, linking mutation‐induced geometric perturbations to nucleation, elongation, and suppressor design. Across neurodegenerative proteins, the framework reveals cryptic aggregation vulnerabilities, separates phase‐concordant and phase‐switching mutations, and ...
Jia Shen Sio +6 more
wiley +1 more source
Sparse coding-based multiframe superresolution for efficient synchrotron radiation microspectroscopy. [PDF]
Igarashi Y +5 more
europepmc +1 more source
A gap‐free genome assembly and multi‐omics comparison of the terrestrial slug Laevichaulis alte with an aquatic relative reveal that expansion of the VEGF family orchestrates mucus production, lipid metabolism, and immune defense—highlighting key molecular innovations for conquering life on land.
Gang Wang +19 more
wiley +1 more source
Emergence of sparse coding, balance and decorrelation from a biologically-grounded spiking neural network model of learning in the primary visual cortex. [PDF]
Ruslim MA +5 more
europepmc +1 more source
Chemically Programmable Underwater Sound‐Absorbing Metamaterial via MXene Self‐Assembly
MXene self‐assembly creates a chemically programmable underwater metamaterial with quasi‐periodic layers and a chemical‐physical multi‐level constraint system. A crosslinker as a ‘chemical scissor’ precisely edits microscopic interactions, exciting deep‐subwavelength strong local resonances that induce near‐zero/negative effective bulk modulus (Keff ...
Ziwen Gan +5 more
wiley +1 more source
Denoising pediatric cardiac photon-counting CT data with sparse coding and data-adaptive, self-supervised deep learning. [PDF]
Clark DP, Cao JY, Badea CT.
europepmc +1 more source
Correntropy-Induced Discriminative Nonnegative Sparse Coding for Robust Palmprint Recognition. [PDF]
Jing K, Zhang X, Song G.
europepmc +1 more source
CauFinder: Steering Cell‐State and Phenotype Transitions by Causal Disentanglement Learning
CauFinder combines causal disentanglement modeling and network control to prioritize causal drivers of cell‐state transitions from observational transcriptomic data. The framework separates transition‐relevant signals from spurious associations, nominates intervention targets across biological and disease contexts, and identifies DAAM1 as an actionable
Chengming Zhang +11 more
wiley +1 more source
Decoupling biological signals from unwanted variation in multi‑condition single‑cell RNA sequencing data remains challenging. CAPER disentangles condition‑associated biological effects from sample heterogeneity through matrix factorization, producing interpretable latent factors and a batch‑corrected expression matrix.
Ye Li +6 more
wiley +1 more source
Upon mitotic entry, RTN4 relocalizes to the pericentrosomal region, forming a more tubular ER network around centrosomes. CDK1‐mediated phosphorylation of RTN4 increases its interaction with Rab11 GTPase, facilitating dynein‐dependent transport of RTN4 to the pericentrosomal region.
Xiangyu Xu +9 more
wiley +1 more source

