Results 121 to 130 of about 125,648 (258)

Multiscale Circuit Architecture Associated With Memory Dysfunction in Temporal Lobe Epilepsy

Advanced Science, EarlyView.
A multiscale precision‐mapping framework reveals that memory impairment in temporal lobe epilepsy arises from the convergence of focal medial temporal pathology, strategic white matter disconnection, and limbic‐centered metabolic network dysfunction.
Jiajie Mo, Qiwen Yuan, Pengfei Zhang, Lin Sang, Zhong Zheng, Baotian Zhao, Xiu Wang, Chao Zhang, Wenhan Hu, Xiaoqiu Shao, Jianguo Zhang, Ruijin Yang, Kai Zhang   +12 more
wiley   +1 more source

Automating Chemical Reasoning in High‐Throughput Phase Identification With a Probabilistic, LLM‐Guided Framework

Advanced Science, EarlyView.
Autonomous laboratories can now synthesize materials faster than experts can interpret the resulting diffraction data. A probabilistic framework combines refinement‐fit metrics with large language model‐derived chemical reasoning to rank competing phase interpretations and flag those unsuitable for autonomous use.
Olympia Dartsi, Lauren N. Walters, Amalie E. Trewartha, Steven B. Torrisi, Amanda Volk, Liam Joyce, Gerbrand Ceder, Anubhav Jain   +7 more
wiley   +1 more source

Bayesian Estimation of Vancomycin Exposure and Population Pharmacokinetics in Obese Patients. [PDF]

Antibiotics (Basel)
Chun HJ, Choi HG, Bang ES, Kyun JO, Kim YR, Kim EJ, Kim SH.   +6 more
europepmc   +1 more source

Multi‐Omics Profiling of High‐Grade Serous Ovarian Cancer Reveals an Inflammation‐Related Lipid Metabolism Subtype Associated With Platinum Resistance

Advanced Science, EarlyView.
Through integrated proteomic and metabolomic profiling, Zhao et al. identified three molecular subtypes of high‐grade serous ovarian cancer. The high‐risk subtype exhibits activated arachidonic acid metabolism and cyclooxygenase‐2 overexpression. This metabolic axis promotes M2‐like macrophage infiltration, which contributes to platinum resistance ...
Yuxi Zhao, Junyi Li, Bo Zheng, Wanshan Liu, Yaru Wang, Shufeng Wang, Ying Cui, Huiqin Guo, Hongxia Wang, Ting Xiao, Kun Qian, Jing Zuo   +11 more
wiley   +1 more source

StackingNet: Collective Inference Across Independent AI Foundation Models

Advanced Science, EarlyView.
ABSTRACT Artificial intelligence (AI) built on large foundation models has transformed language understanding, computer vision, and reasoning, yet these systems remain isolated and cannot readily share their capabilities. Coordinating the complementary strengths of independently developed, black‐box foundation models is essential for trustworthy ...
Siyang Li, Chenhao Liu, Dongrui Wu, Zhigang Zeng, Lieyun Ding   +4 more
wiley   +1 more source

Bayesian Estimation of Marginal Quantiles with Missing Data in a Multivariate Regression Framework. [PDF]

Entropy (Basel)
Morán-Vásquez RA, Mazo-Lopera MA, Escobar-Arias JA.   +2 more
europepmc   +1 more source

A Closed‐Loop Framework for Inverse Design: Dynamic Training and Intelligent Optimization for Heterostructured Materials

Advanced Science, EarlyView.
To accelerate the inverse design of heterostructured metal matrix composites, a closed‐loop scientific machine learning framework integrates continual learning prediction with NSGA‐II‐PMCP optimization. The framework maps microstructural descriptors to strength, toughness, and modulus, expands high‐quality Pareto solutions, and guides experimentally ...
Zhiyan Zhong, Xueru Zheng, Xiao Zhou, Zhengheng Tao, Wan Han, Jingyi Pan, Dian Wu, Ning Gao, Lei Liu, Zhongyang Wang, Fanchao Meng, Tongxiang Fan   +11 more
wiley   +1 more source

Bayesian Estimation

Journal of the Society of Naval Architects of Japan, 1985
Itagaki, Hiroshi, Asada, Hiroo, Itoh, Seiichi   +2 more
openaire   +3 more sources

Bayesian Estimation Improves Prediction of Outcomes After Epilepsy Surgery. [PDF]

Ann Clin Transl Neurol
Dickey AS, Reddy V, Rashied AA, Pedersen NP, Krafty RT.   +4 more
europepmc   +1 more source

Automation and Active Learning for the Multi‐Objective Optimization of Antibody Formulations

Advanced Science, EarlyView.
Successful antibody formulation necessitates balancing factors such as thermal stability, colloidal stability, and viscosity across a vast excipient design space. This work integrates robotic liquid handling, high‐throughput biophysical characterization, and multi‐objective Bayesian optimization in an iterative closed‐loop Design‐Build‐Test‐Learn cycle.
D. Christopher Radford, Matthew Tamasi, Elena Di Mare, Adam J. Gormley   +3 more
wiley   +1 more source