Hyperbolic topological data analysis mapper reveals dynamic trait-environment patterns in plant phenomics. [PDF]
Zdražil J +4 more
europepmc +1 more source
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
Inferring relationships among major psychiatric disorders in a resting-state functional connectivity-informed embedding space. [PDF]
Bai W +3 more
europepmc +1 more source
Magnetotransport Signatures of Spin–Orbit Coupling in High‐Temperature Cuprate Superconductors
Large angle‐dependent magnetoresistance and planar Hall effects emerge at the superconducting transition of YBa2Cu3O7−x$\mathrm{YBa_2Cu_3O_{7-x}}$ thin films. These anomalous transport signatures, absent in the normal state, point to spin‐polarized quasiparticle dynamics enabled by an unexpected spin–orbit coupling landscape in cuprate superconductors,
Aleix Barrera +6 more
wiley +1 more source
Machine Learning Approaches for Compound-Target Interaction Prediction: A Review. [PDF]
Zhang J +6 more
europepmc +1 more source
Causal‐Guided Ultra‐Long‐Term Time Series Forecasting Via Anticipated Covariates
Often treated as unknown, information from the future remains underutilized.We demonstrate that in a coupled dynamical system, providing the future state of the effect enables accurate forecasting of the cause for a long timesteps. A time series forecasting paradigm that introduces anticipated covariates to represent such known future states is ...
Jintong Zhao +4 more
wiley +1 more source
Decoding the unseen: unsupervised anomaly detection in metal-organic frameworks for discovery beyond the norm. [PDF]
Alimardani H, Abaei S, Asgari M.
europepmc +1 more source
Polarization Dynamics in Ferroelectrics: Insights Enabled by Machine Learning Molecular Dynamics
Machine learning molecular dynamics is presented as a route to capture polarization switching, domain wall kinetics, topological polar textures, and polar mechanical coupling beyond the limits of conventional atomistic methods. This Perspective surveys recent progress and identifies key methodological directions, including long‐range electrostatics ...
Dongyu Bai +3 more
wiley +1 more source
Integrating Biomimetic Reasoning Into Early-Stage Design Thinking for Sustainable Textile Development. [PDF]
Gerolimos N +7 more
europepmc +1 more source
Employing a digital single‐molecule activity tracker (dSMAT), this research demonstrates that high‐photon‐flux irradiation drives progressive oxidative scarring in polymerases. Unlike simple thermal denaturation, real‐time kinetic tracking dynamically visualizes enzymes degrading into multiple impaired subpopulations.
Anran Zheng +11 more
wiley +1 more source

