Results 171 to 180 of about 95,703 (260)

Complexity begets simplicity: self-supervised learning for palaeontological images with few or no labels. [PDF]

J R Soc Interface
Rodgers N.
europepmc   +1 more source

Non‐Covalent Reversibly Photoconvertible Fluorescent Tags for Wash‐Free Protein Labeling

Advanced Science, EarlyView.
FAST proteins combined with one or two fluorogens undergo reversible fluorescence switching or photoconversion through photoisomerization. At low fluorogen concentration and high light intensity, they act as negative or positive photoswitchers and photoconvertible tags, which are promising for discriminating FAST variants.
Mrinal Mandal, Yuriy Shpinov, Aliénor Lahlou, Fivos Pham, Lina El Hajji, Ian Coghill, Eve Laureau, Marie‐Aude Plamont, Franck Perez, Thomas Le Saux, Isabelle Aujard, Arnaud Gautier, Ludovic Jullien   +12 more
wiley   +1 more source

AARS2‐SIRT5‐Driven DLD K445 Lactylation Promotes Cuproptosis Resistance in Glioblastoma Stem Cells

Advanced Science, EarlyView.
Lactylation of DLD at K445 by AARS2 suppresses PDH complex activity and reduces DLAT lipoylation, thereby conferring cuproptosis resistance in glioblastoma stem cells. SIRT5‐mediated delactylation restores PDH function and enhances cuproptosis sensitivity.
Mingtian Ding, Haibiao Xu, Cen Liu, Yangyin Ding, Shuai Wang, Jun Yin, Tao Fu, Xiao Jiang, Yuning Chen, Hui Luo, Rui Li, Zhengxin Chen, Huibo Wang   +12 more
wiley   +1 more source

C-SOM SNN: A Continuous Self-Organizing Map Spiking Neural Network Based on STDP Unsupervised Learning Encoding with Designed Lateral Inhibition Distribution. [PDF]

Biomimetics (Basel)
Mo L, Liu X, Tang M.
europepmc   +1 more source

Metabolic Signatures for Liver Cancer Diagnosis and Mechanistic Insights: A Large‐Scale, Multicenter Study

Advanced Science, EarlyView.
A large‐scale multicenter serum metabolomics study (13 centers, n = 2,149) identified a 9‐metabolite + AFP diagnostic signature for liver cancer (AUC = 0.93). Among the signature, nicotinamide (NAM) was validated as an oncometabolite: NAM enhances NAD+ synthesis, activating SIRT1 to deacetylate and stabilize HIF1α, thereby driving glycolysis, MAPK ...
Yongjie Xu, Jiachen Wang, Chunmeng Ding, Changfa Xia, Yunyong Liu, Yuanjie Zheng, Sheng Chang, Shaokai Zhang, Yutong He, Ruifang Sun, Yunfeng Xi, Yan Liu, Kun Qian, Wanqing Chen   +13 more
wiley   +1 more source

Hardware Acceleration of Stochastic Neural Networks Enabled by Bit‐Cell Level Co‐Design of Magnetic Tunnel Junctions

Advanced Science, EarlyView.
An MTJ‐based probabilistic processing‐in‐memory architecture unites nonvolatile storage, local entropy generation, and stochastic computation within each memory cell. Stored multi‐bit weights are converted directly into programmable stochastic bitstreams, enabling neural‐network inference, adaptive Ising optimization, and uncertainty‐aware Bayesian ...
Qiuyuan Wang, Dooyong Koh, Brooke McGoldrick, Tudor Mocioi, Yabin Fan, Marc Baldo, Luqiao Liu   +6 more
wiley   +1 more source

Comparison of unsupervised classification methods for brain tumor segmentation using multi-parametric MRI. [PDF]

Neuroimage Clin, 2016
Sauwen N, Acou M, Van Cauter S, Sima DM, Veraart J, Maes F, Himmelreich U, Achten E, Van Huffel S.   +8 more
europepmc   +1 more source

Determination of Gene‐Level Fitness Contributions Across the Mucin‐Utilization Network of Bacteroides thetaiotaomicron

Advanced Science, EarlyView.
Genome‐wide CRISPRi and RNA‐seq resolve how B. thetaiotaomicron converts host mucosal glycans into fitness. The study reveals a layered mucin‐utilization architecture in which capsular polysaccharides constrain surface access, extracellular CAZymes and peptidases show functional redundancy, and intracellular catabolic pathways create discrete metabolic
Kangsan Kim, Jiwon Lee, Minjeong Kang, Sung Sun Yim, Donghui Choe, Byung‐Kwan Cho   +5 more
wiley   +1 more source

Classification of Acoustic Emission Signals from Failure Modes in Thermoplastic Composites Using Machine Learning. [PDF]

Materials (Basel)
Minhaj TB, Amoateng-Mensah D, Rijal M, Amevorku RD, Sundaresan MJ.   +4 more
europepmc   +1 more source

An Integrated Artificial Intelligence Framework for Nanomedicine Engineering

Advanced Science, EarlyView.
This review outlines a closed‐loop AI ecosystem for nanomedicine engineering. By synergizing multimodal data harmonization, objective‐driven AI architectures, and automated high‐throughput validation, this “Design‐Make‐Test‐Analyze” (DMTA) workflow transforms traditional heuristic formulation into a predictive, self‐optimizing paradigm, accelerating ...
Meichen Dong, Qin Luo, Xinyi Ding, Chaoqi Lu, Ru Zhang, Qingqing Tan, Zhiqing Pang, Defang Ouyang, Dongsheng Li, Jiaxuan Xia, Jianxin Wang   +10 more
wiley   +1 more source