Results 141 to 150 of about 28,479 (242)

A New Customizable Surfactant LLPS Strategy for Sustainable and Highly Efficient Radioactive Metal Ion Separation

Advanced Science, EarlyView.
A diluent‐free, customizable liquid–liquid phase separation (LLPS) platform enables highly efficient radioactive metal ion extraction and stripping. By encapsulating hydrophobic extractants into an ionic surfactant‐rich condensed phase, this LLPS system leverages synergistic electrostatic and coordination interactions.
Ruihan Yan, Yifu Hu, Wentao Wang, Zhi Cao, Meiwen Cao, Weifang Zheng, Guoan Ye, Taihong Yan   +7 more
wiley   +1 more source

Can satellites and AI reveal the Maxwell equations of the Earth? [PDF]

Natl Sci Rev
Feng L, Li D.
europepmc   +1 more source

Combining Spatial Multi‐Omics Data to Decipher Spatial Domains and Elucidate Cell Heterogeneity Based on Self‐Supervised Graph Learning

Advanced Science, EarlyView.
A self‐supervised multi‐view graph fusion framework integrates spatial multi‐omics, excelling in domain identification and denoising. It reconstructs spatial pseudo‐expression, jointly analyzes multi‐omics data, infers RNA velocity, predicts spatial omics features from single‐cell multi‐omics, and detects spatially dark genes and transcription factors,
Yuejing Lu, Rui Qiao, Ying Li, Junhong Li, Nuoya Yue, Jing Ge, Jiao Wang, Luonan Chen, Peiluan Li   +8 more
wiley   +1 more source

Beyond networks: Toward adaptive models of biological complexity. [PDF]

Phys Life Rev
Pessoa L.
europepmc   +1 more source

Decoding Spatial Heterogeneity and Multi‐Omics Regulation with Hierarchical Graph Learning

Advanced Science, EarlyView.
ABSTRACT Recent advances in spatial multi‐omics technologies have enabled the simultaneous profiling of multiple molecular layers within the same tissue slice, providing unprecedented opportunities to investigate tissue spatial organization. However, most existing computational methods identify spatial domains in a purely data‐driven manner, rarely ...
Jiazhou Chen, Jiahui Xie, Yi Liao, Junyu Li, Longqi Liu, Xin Gao, Hongmin Cai   +6 more
wiley   +1 more source

Impedance of Nonelectroneutral Solid Electrolyte Interphases With Nanopores: A Theoretical Model

Advanced Science, EarlyView.
Physical modeling reveals that often‐neglected non‐electroneutrality and nanopores in the solid‐electrolyte interphase (SEI) govern the impedance behavior. Under nonreactive conditions, the low‐frequency constant‐phase element (CPE) phenomenon can be attributed to the nonelectroneutral local conditions in the SEI.
Chenkun Li, Jun Huang
wiley   +1 more source

Topological metrics as evolutionary and dynamical descriptors of conformational landscapes within protein families. [PDF]

PLoS Comput Biol
Ramesh N, Ozkan SB, Panagiotou E.
europepmc   +1 more source

In Vivo Electrochemical Monitoring of Safinamide Pharmacokinetics in the Brain Explores Its Correlation With Vision‐Related Neuronal Activity

Advanced Science, EarlyView.
An electrochemical platform based on differential pulse amperometry enables real‐time, selective tracking of safinamide pharmacokinetics in the living brain with high spatiotemporal resolution. When integrated with electrophysiology, this approach reveals that drug‐induced suppression of vision‐related neuronal activity provides mechanistic insight ...
Xiaoke Nan, Chuqi Li, Qianhe Dai, Junlan Zhou, Lijiao Cao, Yuying Liu, Jinger Chen, Meiping Xiong, Yuhang Jiang, Xianchan Li   +9 more
wiley   +1 more source

Topological structure and influencing factors of the spatial CO₂ emission network in Chinese cities. [PDF]

Sci Rep
Yang F, Zhang X, Wang N, Zuo S, Yang M.
europepmc   +1 more source

Topology‐Aware Deep Learning on Higher‐Order Structures for Drug Response Prediction

Advanced Science, EarlyView.
We present TopDr, a topology‐aware deep learning framework that encodes both drugs and cell lines as multiscale simplicial complexes, capturing interactions at the 0‐, 1‐, and 2‐simplex levels. By jointly integrating local higher‐order neighborhoods and global topological structures, TopDr generates enriched representations for sensitivity prediction ...
Cong Shen, Guancen Lin, Chuan‐Shen Hu, Yong Wang   +3 more
wiley   +1 more source