Results 191 to 200 of about 12,744 (261)

Weakly-supervised learning for lung carcinoma classification using deep learning. [PDF]

Sci Rep, 2020
Kanavati F, Toyokawa G, Momosaki S, Rambeau M, Kozuma Y, Shoji F, Yamazaki K, Takeo S, Iizuka O, Tsuneki M.   +9 more
europepmc   +1 more source

Machine Learning‐Enhanced Ultrasensitive Immuno‐CRISPR Array Facilitates Early Diagnosis of Alzheimer's Disease by Detecting Multiple Plasma Biomarkers

Advanced Science, EarlyView.
This work presents a CRISPR‐based, ultrasensitive multiplex protein detection array capable of simultaneously analyzing six plasma biomarkers associated with Alzheimer's disease (AD). By integrating antibody‐mediated signal transduction, recombinase polymerase amplification, and spatially encoded CRISPR‐Cas12a, the system achieves detection sensitivity
Liding Zhang, Changwen Yang, Qian Yao, Xuewei Du, Shuai Ding, Yaoqiang Shi, Can Sheng, Ming Wang, Ying Han, Haiming Luo   +9 more
wiley   +1 more source

3D‐Printed Magnetoelectronics for Interactive Appliances and Self‐Aware 4D‐Printed Mechatronics

Advanced Science, EarlyView.
3D‐printed magnetoelectronics integrate high‐performance magnetic field sensing directly into complex structural components. Flexible spring and cross‐shaped sensors exhibit giant magnetoimpedance and 3D Hall sensing for vector field reconstruction. Applications include smart‐home switches, robotic joysticks, volumetric magnetometers, and self‐aware 4D‐
Eduardo Sergio Oliveros‐Mata, Anna Martin Vilardell, Fabian Ganss, Christoph Leyens, Lukas Stepien, Denys Makarov   +5 more
wiley   +1 more source

Pressure‐Induced Drift Artifacts in Stretchable Liquid Metal ThinFilm Electrocardiogram Electrodes

Advanced Science, EarlyView.
A stretchable LM electrode integrated with a strain sensor enables in situ quantitative investigation of drift artifact and skin deformation. This reveals the significance of pressure‐induced drift artifact and its intimate relationship with the skin potential model.
Ding Li, Zi‐Gan Xu, Zhi‐Kang Chen, Shuo‐Yan Xu, Jia‐Yi Cui, Si‐Yuan Wo, Zi‐Xu Wang, Yi‐Kun Liu, Jia‐Ju Yin, Hou‐Fang Liu, Xiao‐Ming Wu, Lu‐Qi Tao, Yi Yang, Tian‐Ling Ren   +13 more
wiley   +1 more source

Weakly supervised learning for subcutaneous edema segmentation of abdominal CT using pseudo-labels and multi-stage nnU-Nets. [PDF]

Proc SPIE Int Soc Opt Eng
Bhadra S, Liu J, Summers RM.
europepmc   +1 more source

Robust Semi-Supervised Traffic Sign Recognition via Self-Training and Weakly-Supervised Learning. [PDF]

Sensors (Basel), 2020
Nartey OT, Yang G, Asare SK, Wu J, Frempong LN.   +4 more
europepmc   +1 more source

Mechanistic Understanding of Protein–MOF Integration Through Surfactant‐Driven Interfacial Design

Advanced Science, EarlyView.
This study reveals how surfactant‐driven interfacial design governs the assembly and stability of protein@MOF composites. Using lipid‐based nonionic surfactants, we modulate protein–MOF interactions to improve encapsulation efficiency, MOF crystallization, and catalytic performance.
Ehsan Rashidniyaghi, Mohammad Khavani, Carlie Coerver, Ruibin Liang, Raheleh Ravanfar   +4 more
wiley   +1 more source

Multiferroic‐Centric Materials and Systems Engineering for Battery Applications: An Insight Into Mechanisms, Strategies, and Characterizations

Advanced Science, EarlyView.
Multiferroic order parameters – polarization, magnetization, and ferroelastic strain – are positioned as dynamic design variables for batteries. Their mechanistic roles, practical tuning through fabrication and external fields, and ferroic‐resolved characterization routes are unified into a closed‐loop framework, revealing how coupled ferroic responses
Jiaqi Su, Yanda Zhu, Hao Peng, Manman Li, Xuzihan Zhang, Meixiao Wu, Siyuan Zhang, Yuhan Zeng, Jiwen Liao, Ming Luo, Hetaishan Huang, Yutong Wang, Sean Li, Wenxian Li   +13 more
wiley   +1 more source

AI‐Physics‐Experiment Trinity for Integrated Protein Dynamics Modeling

Advanced Science, EarlyView.
This review unites experiments, physics‐based simulations, and AI as a synergistic triad for protein dynamics modeling. It highlights integrative strategies, resolves sampling and forcefield bottlenecks, and outlines challenges and future directions for accurate, interpretable conformational ensemble prediction.
Chen Shi, Minying Low, Peng Xiu, Kresten Lindorff‐Larsen, Yong Wang   +4 more
wiley   +1 more source

Label-driven magnetic resonance imaging (MRI)-transrectal ultrasound (TRUS) registration using weakly supervised learning for MRI-guided prostate radiotherapy. [PDF]

Phys Med Biol, 2020
Zeng Q, Fu Y, Tian Z, Lei Y, Zhang Y, Wang T, Mao H, Liu T, Curran WJ, Jani AB, Patel P, Yang X.   +11 more
europepmc   +1 more source