Results 51 to 60 of about 2,621 (151)
Advanced Science, EarlyView.Tau populations with different aggregation states associate with distinct sets of proteins in the Alzheimer's disease brain. Proteomic discovery combined with single‐molecule analysis in neurons reveals dynamic associations involving proteostasis, metabolism and RNA biology.
Dorothea Böken +8 more
wiley +1 more source
Real‐Time Monitoring of Mass and Mechanical Properties in Single Cells and Multicellular Spheroids
Advanced Science, EarlyView.This study introduces photothermally actuated microcantilevers combined with optical microscopy to simultaneously measure morphology, mass, and mechanical properties of cellular systems in real‐time. The findings link cytoplasm‐dominated mechanical energy dissipation to the Q‐factor as a novel biophysical parameter.
Ilaria Incaviglia +8 more
wiley +1 more source
Thermal Shifts Dominate Single‐Gene Responses to Transcription‐Targeting Antibiotics
Advanced Science, EarlyView.Thermal shifts reshape bacterial responses to transcription‐targeting antibiotics. In E. coli, temperature can dominate antibiotic‐induced genome‐wide expression, particularly outside 30°C–39°C, through changes in adenosine triphosphate (ATP), nucleoid organization, and drug‐target engagement.
Rahul Jagadeesan +5 more
wiley +1 more source
PrOMet: Accurate and Robust Metal‐Binding Site and Type Prediction
Advanced Science, EarlyView.PrOMet is a two‐stage deep learning framework for jointly predicting metal‐binding sites and ion identities from protein structures. Surface candidate sites are first pruned, followed by refinement of the most probable sites and metal types. This hierarchical strategy enables robust and accurate metal prediction for cryo‐EM and AlphaFold‐derived ...
Gangeun Park, Chaok Seok, Hahnbeom Park
wiley +1 more source
A Biomimetic Solvatochromic Chip With Optimized Chemical Diversity for On‐Site Liquid Identification
Advanced Science, EarlyView.A compact biomimetic photonic‐gustatory chip uses Hansen‐solubility‐parameter‐guided solvatochromic disks to create distinct color patterns in organic liquids. An optimized four‐disk array distinguishes solvent types, ratios, homologues, isomers, and alcoholic beverages. Coupled with smartphone imaging and deep learning, it identifies 80 liquid classes
Jintao Zhang +9 more
wiley +1 more source
Advanced Electronic Materials, EarlyView.ABSTRACT Magnetic 2‐dimensional (2D) van der Waals (vdW) materials have garnered tremendous attention. The vdW ferromagnet Fe5Ge1Te2 has a Curie temperature Tc of ≈ 270 K, which is tailorable by tuning the stoichiometry and the Fe deficiency to reach room temperature.
Chih‐Yu Lee +7 more
wiley +1 more source
Advanced Electronic Materials, EarlyView.A compact passive engineered surface converts changes in microwave frequency into many distinct illumination patterns, allowing nearby objects to be imaged without mechanical scanning, active tuning, or a large antenna array. Operating from 18 to 28 GHz, the single‐port system uses measured field patterns to reconstruct metallic targets and distinguish
Mohammed H. Arif +6 more
wiley +1 more source
Advanced Energy Materials, EarlyView.A combination of discrete and finite element method models for the current collector deformation and electrochemical performance analysis, respectively. The models are calibrated and validated with electrochemical and imaging data of hard carbon electrodes. These electrodes were manufactured with different parameters (slurry solid contents of 35 and 40
Soorya Saravanan +12 more
wiley +1 more source
Composition‐Aware Cross‐Sectional Integration for Spatial Transcriptomics
Advanced Intelligent Discovery, EarlyView.Multi‐section spatial transcriptomics demands coherent cell‐type deconvolution, domain detection, and batch correction, yet existing pipelines treat these tasks separately. FUSION unifies them within a composition‐aware latent framework, modeling reads as cell‐type–specific topics and clustering in embedding space.
Qishi Dong +5 more
wiley +1 more source
Advanced Intelligent Discovery, EarlyView.Phonons‐informed machine‐learning predictive models are propitious for reproducing thermal effects in computational materials science studies. Machine learning (ML) methods have become powerful tools for predicting material properties with near first‐principles accuracy and vastly reduced computational cost.
Pol Benítez +4 more
wiley +1 more source