Results 221 to 230 of about 72,594 (306)

Giant Conductivity Modulation and Chemical Neuromodulation via Proton‐Electron Coupling in a Hydrogen‐Bonded Coordination Polymer

Advanced Science, EarlyView.
A “de‐doping” strategy positions mixed protonic–electronic conductors (MPECs) as adaptive neuromorphic platforms with dynamically tunable transport. Co‐BAND achieves giant conductivity modulation (>106) and chemically tunable synaptic plasticity. Analogous to biological neuromodulation, solvent vapors dynamically reprogram the device's learning rules ...
Kwangmin Park, Jumin Park, Huiyeong Ju, Nasim Arafat, Byoung Gwan Lee, Joohee Oh, Eejin Jang, Hyunseob Lim, Seok Min Yoon, Dae‐Woon Lim, Intek Song   +10 more
wiley   +1 more source

Switching metastable dynamics in many-body open quantum systems. [PDF]

Natl Sci Rev
Xiang YX, Li W, Bai Z, Ma YQ.
europepmc   +1 more source

Triple‐Mode Ferroelectric Thin‐Film Transistor for Hybrid Electrical–Optical Reservoir Computing

Advanced Science, EarlyView.
A triple‐mode ferroelectric thin‐film transistor is developed by integrating Si3N4/HZO/IGZO layers to realize three independent memory modes: electric long‐term, electric short‐term, and optical short‐term. This single‐device architecture functions as both a reservoir and readout layer, achieving 92.43% MNIST accuracy. It offers a fully hardware‐based,
Hyeonho Lee, Seungjun Lee, Tae‐Hyeon Kim, Yonghee Jeong, Gwangmin An, Minsu Ko, Junsu Yu, Heung Soo Kim, Yang Chai, Sungjun Kim   +9 more
wiley   +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

Dynamic Analysis of Bi-Stable Galloping Energy Harvesters Under Random Excitation. [PDF]

Micromachines (Basel)
Zhang Y, Qin R, Zheng K, Zhang J.
europepmc   +1 more source

Transposase‐Assisted Donor Tethering Boosts Large‐Fragment HDR in Plants

Advanced Science, EarlyView.
A transposase‐assisted donor tethering strategy is developed to enhance homology‐directed repair in plants. By recruiting donor DNA to double‐strand breaks and synergizing with repair pathway reprogramming and transcription‐coupled donor design, this system markedly improves large‐fragment targeted insertion efficiency, providing a robust platform for ...
Sha Wei, Kai Zhang, Shuke Deng, Jinjie Chen, Xiaomei Huang, Jingfei Guo, Yuqing Wu, Yingjie Guo, Zhen Liang   +8 more
wiley   +1 more source

Inferring accumulation times of mitochondrial DNA deletion mutants from cross-sectional single-cell data: methodological framework and validation. [PDF]

NPJ Aging
Kowald A, Kirkwood TBL.
europepmc   +1 more source

Shear‐Induced Emergence of Aromatic Superlow‐Friction Interfaces in Amorphous Carbon: Triggering Chemical Impurities and Atomic‐Scale Mechanisms

Advanced Science, EarlyView.
High‐throughput quantum‐mechanical simulations reveal that amorphous carbon undergoes shear‐driven structural transformation into aromatic, graphene‐like interfaces. This mechanochemical process is governed by dopant chemistry: dopants with valency less than four promote the emergence of superlow‐friction amorphous graphene, whereas tetra‐valent ...
Takuya Kuwahara, Koki Horiguchi, Leonhard Mayrhofer, Gianpietro Moras, Michael Moseler   +4 more
wiley   +1 more source

Cellular Heterogeneity in Drug Uptake Amplifies Pharmacodynamic Variability: A Stochastic PK-PD Analysis. [PDF]

CPT Pharmacometrics Syst Pharmacol
Duong NH, Do TN, Nguyen TT, Phan KQ, Nguyen LT.   +4 more
europepmc   +1 more source

STAID: A Self‐Refining Deep Learning Framework for Spatial Cell‐Type Deconvolution with Biologically Informed Modeling

Advanced Science, EarlyView.
STAID is a unified deep learning framework that couples iterative pseudo‐spot refinement with neural network training through a feedback loop and exploits gene co‐expression information to model higher‐order interactions, achieving accurate and robust cell‐type deconvolution in spatial transcriptomics.
Jixin Liu, Shuli Sun, Zhengliang Lv, Xinyu Liu, Yihua Wang, Bingqiang Liu   +5 more
wiley   +1 more source