Results 181 to 190 of about 1,980,897 (317)

Gate‐Tunable Polarity‐Reconfigurable Photovoltaic Response With Dual Linearity in NbS3/WSe2 Heterostructures for In‐Sensor Image Processing

Advanced Science, EarlyView.
An NbS3/WSe2 van der Waals heterostructure realizes a continuously gate‐tunable self‐powered photovoltaic response through interfacial band‐alignment modulation and reversible built‐in electric‐field switching. The short‐circuit photocurrent scales linearly with incident power density, while short‐circuit photoresponsivity varies linearly with gate ...
Jidong Liu, Jianxiong Xie, Qiaoyan Hao, Yutao Hu, Zhen He, Haolin Liu, Danting Lin, Haibo Gan, Yudi Tu, Wenjing Zhang   +9 more
wiley   +1 more source

Advances in artificial intelligence-driven MRI research in Alzheimer's disease. [PDF]

Front Neurol
Deng L, Zeng Y, Ou P, Zhou S, Gou Y, Huang C.   +5 more
europepmc   +1 more source

NICE: A Two‐Step Non‐Invasive Framework for Embryo cfDNA Read Enrichment and Quality Assessment

Advanced Science, EarlyView.
The non‐invasive NICE framework, built on an ensemble stacking machine learning model, prioritizes embryos by analyzing cell‐free DNA from spent culture medium. By integrating multimodal signals, including genomic and epigenetic profiles, this automated approach standardizes morphological assessment without human bias, paving the way for more precise ...
Xueya Zhou, Shu Ding, Zhenyi Zhang, Qiaoling Shangguan, Jie Qiao, Peijie Zhou, Yidong Chen   +6 more
wiley   +1 more source

Artificial Intelligence‐Empowered Single‐Cell Phenotyping for Rapid, Automated Pathogen Diagnostics

Advanced Science, EarlyView.
This work presents an integrated diagnostic platform that combines microfluidic single‐cell bacterial detection with artificial intelligence‐driven analysis for rapid antimicrobial susceptibility testing. Single‐cell phenotyping enables assessment of antibiotic response in only a few cell replication cycles, while AI analysis supports precise bacterial
Sabita Khadka, Bushra Raahat, Spyros Tragoudas, Hui Li   +3 more
wiley   +1 more source

A Two-Stage Localization and Refinement Neural Network Structure for Data-Efficient Microbleed Detection. [PDF]

Brain Sci
Rau L, Granert O, Margraf NG, Schneider S, Jensen-Kondering U.   +4 more
europepmc   +1 more source

Convolution Neural Networks by Image Net

International Journal of Emerging Trends in Science and Technology, 2017
openaire   +1 more source

A Unified Hierarchical Multiscale Fusion Framework for Drug–Target Affinity Prediction: From Benchmark Performance to Nanomolar Inhibitor Discovery

Advanced Science, EarlyView.
A multimodal fusion framework integrating sequence, atomic, and fragment representations captures drug–target interactions across multiple scales. The model delivers strong predictive performance and enables efficient virtual screening. Applied to hematopoietic progenitor kinase 1 (HPK1), it identifies structurally diverse inhibitors with nanomolar ...
Shuo Liu, Xiang Zhang, Haixia Feng, Yuquan Li, Xiaoqing Gong, Yong Liang, Xiaojun Yao, Huanxiang Liu   +7 more
wiley   +1 more source

Artifact segmentation using the U-Net architecture for powder X-ray diffraction images. [PDF]

J Synchrotron Radiat
Vong A, Yanxon H, Roberts E, Parraga H, Weng J, Xu W, Ruett U, Hexemer A, Zwart PH, Schwarz N.   +9 more
europepmc   +1 more source

AI‐Assisted Tumor Boundary Delineation via Targeted Ultrasmall Iron Oxide Nanoprobe for High‐Contrast HER2‐Positive Tumor Imaging

Advanced Science, EarlyView.
A novel targeted and pH‐responsive MRI contrast agent was integrated with a 3D nnU‐Net deep learning framework to enable accurate delineation of tumor boundary morphology in HER2‐positive breast cancer imaging. ABSTRACT Breast cancer continues to be a leading cause of cancer‐related mortality in women globally, where precise diagnosis and clear tumor ...
Jiaying Zheng, Yi Zhu, Xinrui Li, Sunxian Dai, Xiangyuan Ma, Hongwei Lu, Zhongling Wang, Benqing Zhou   +7 more
wiley   +1 more source

A multi-layer active intellectual property protection framework for hyperspectral image classification models. [PDF]

iScience
Xiao S, Chen H, Zhao J, Liu Y, Guo C, Dong S.   +5 more
europepmc   +1 more source