Results 141 to 150 of about 299,242 (277)

Glymphatic dysfunction couples with cortical excitation–inhibition imbalance in epilepsy: Evidence from Rasmussen encephalitis

Epilepsia, EarlyView.
Cong Fu et al. demonstrate that glymphatic system dysfunction is linked to enhanced inhibitory cortical activity using diffusion MRI and EEG. These findings highlight a mechanistic link between perivascular fluid dynamics and neuronal activity, suggesting a role for glymphatic function in maintaining cortical stability in epilepsy.
Cong Fu, Yujiao Yang, Pan Gong, Kun Lv, Yilin Liu, Chongyang Tang, Xiongfei Wang, Lixin Cai, Qingzhu Liu, Yuwu Jiang, Taoyun Ji, Guoming Luan   +11 more
wiley   +1 more source

A unified EfficientSwinB-based framework for medicinal plant recognition and safflower germplasm classification. [PDF]

Sci Rep
Khuat PT, Thien Van H.
europepmc   +1 more source

Stereo‐EEG mapping of visual working memory with task‐related high‐gamma modulations

Epilepsia, EarlyView.
Abstract Objective We describe a safe, informative, and easy‐to‐implement approach for presurgical mapping of visual working memory (VWM) with stereo‐electroencephalography (SEEG). Methods Twenty‐four patients with drug‐resistant epilepsy, 11–23 years of age, performed a single‐probe change detection VWM task, during SEEG monitoring.
Brian Ervin, Jason Buroker, Taylor Cummins, Anna W. Byars, Craig Scholle, Leonid Rozhkov, Paul S. Horn, Jeffrey R. Tenney, Hansel M. Greiner, James L. Leach, Jesse Skoch, Francesco T. Mangano, Katherine D. Holland, Ravindra Arya   +13 more
wiley   +1 more source

Lightweight plant phenotypic feature extraction via transferable attention head pruning in Vision Transformers. [PDF]

Front Plant Sci
Xie Y, Zeng X, Wang R, Li W.
europepmc   +1 more source

AI‐based localization of the epileptogenic zone using intracranial EEG

Epilepsia Open, EarlyView.
Abstract Artificial intelligence (AI) is rapidly transforming our lives. Machine learning (ML) enables computers to learn from data and make decisions without explicit instructions. Deep learning (DL), a subset of ML, uses multiple layers of neural networks to recognize complex patterns in large datasets through end‐to‐end learning.
Atsuro Daida, Yipeng Zhang, Sotaro Kanai, Richard Staba, Vwani Roychowdhury, Hiroki Nariai   +5 more
wiley   +1 more source

Hallucination-aware learning and latency optimization transformer (HALL-OPT) for real-time edge intelligence. [PDF]

Sci Rep
Algawiaz D.
europepmc   +1 more source

Interpretable Tree‐Based Models for Predicting Short‐Term Rockburst Risk Considering Multiple Factors

Energy Science &Engineering, EarlyView.
Interpretable tree‐based models integrate microseismic, geological, and mining indicators to predict short‐term rockburst risk. SHAP analysis reveals the dominant role of energy‐related features and clarifies nonlinear factor interactions, enabling transparent and reliable early‐warning in deep coal mines.
Shuai Chen, Linming Dou, Atao Li, Wu Cai, Lei Zhang   +4 more
wiley   +1 more source

Efficient Super-Resolution Bayesian Eletromagnetic Brain Imaging. [PDF]

IEEE Trans Biomed Eng
Cai C, Qi X, Yan J, Shang Y, Sekihara K, Wu W, Nagarajan SS.   +6 more
europepmc   +1 more source

Exercise‐specific plasma proteomic signatures in racehorses: Candidates for training adaptation and peak load monitoring

Equine Veterinary Journal, EarlyView.
Abstract Background Racehorses undergo profound physiological changes with training and competition, but current biomarkers inadequately capture the complex molecular dynamics of exercise. This study aimed to identify novel plasma biomarkers of training adaptation and peak load using high‐throughput proteomics.
Jowita Grzędzicka, Bianka Świderska, Ewa Sitkiewicz, Izabela Dąbrowska, Olga Witkowska‐Piłaszewicz   +4 more
wiley   +1 more source

LMP-PM: a lightweight multi-path pruning method for plant leaf disease recognition. [PDF]

Front Plant Sci
Hua J, Zou F, Zhu Y, Deng J, He R.
europepmc   +1 more source