Results 91 to 100 of about 341,780 (320)

An Open‐Source Pipeline for Calcium Imaging and All‐Optical Physiology in Human Stem Cell‐Derived Neurons

Advanced Science, EarlyView.
This work introduces an open‐source all‐optical platform for functional phenotyping of human stem cell‐derived neurons. The system integrates optogenetics, calcium imaging, automated acquisition, and analysis to resolve single‐cell and network activity, enabling longitudinal measurements, disease modeling, and pharmacological screening in preclinical ...
Wardiya Afshar‐Saber, Federico M. Gasparoli, Ziqin Yang, Nicole A. Teaney, Rachel Hobson, Lahin Lalani, Gayathri Srinivasan, Dosh Whye, Ranit Karmakar, Elizabeth D. Buttermore, Kellen D. Winden, Cidi Chen, Mustafa Sahin   +12 more
wiley   +1 more source

Semi-supervised learning

, 2017
Semi-supervised learning deals with the problem of how, if possible, to take advantage of a huge amount of not classified data, to perform classification, in situations when, typically, the labelled data are few. Even though this is not always possible (it depends on how useful is to know the distribution of the unlabelled data in the inference of the ...
Cholaquidis, Alejandro, Fraiman, Ricardo, Sued, Mariela   +2 more
openaire   +2 more sources

SKOOTS: Skeleton‐Oriented Object Segmentation for Mitochondria in High‐Resolution Cochlear EM Datasets

Advanced Science, EarlyView.
Skeleton‐oriented object segmentation (SKOOTS) introduces a new strategy for 3D mitochondrial instance segmentation by predicting explicit skeletons rather than relying on boundary cues. This approach enables robust analysis of densely packed organelles in large FIB‐SEM datasets.
Christopher J. Buswinka, Richard T. Osgood, Hidetomi Nitta, Artur A. Indzhykulian   +3 more
wiley   +1 more source

Long‐Tea‐CLIP: An Expert‐Level Multimodal AI Framework for Fine‐Grained Green Tea Grading Across Five Sensory Dimensions

Advanced Science, EarlyView.
Long‐Tea‐CLIP (Contrastive Language‐Image Pre‐training) presents a multimodal AI framework that integrates visual, metabolomic, and sensory knowledge to grade green tea across appearance, soup color, aroma, taste, and infused leaf. By combining expert‐guided modeling with CLIP‐supervised learning, the system delivers fine‐grained quality evaluation and
Yanqun Xu, Zhengfang Xue, Qing Luo, Xu Liu, Yongquan Xu, Jing Huang, Xingcai Zhang, Xinyuan Zhang, Zhonghua Liu, Zisheng Luo   +9 more
wiley   +1 more source

The Effectiveness of Semi-Supervised Learning Techniques in Identifying Calcifications in X-ray Mammography and the Impact of Different Classification Probabilities

Applied Sciences
Identifying calcifications in mammograms is crucial for early breast cancer detection, and semi-supervised learning, which utilizes a small dataset for supervised learning combined with deep learning, is anticipated to be an effective approach for ...
Miu Sakaida, Takaaki Yoshimura, Minghui Tang, Shota Ichikawa, Hiroyuki Sugimori, Kenji Hirata, Kohsuke Kudo   +6 more
doaj   +1 more source

Nanoscale Spatial Organization of ARC High‐ and Low‐Order Assemblies at Excitatory Synapses

Advanced Science, EarlyView.
ARC (Activity‐Regulated Cytoskeleton‐Associated protein) mediates synaptic plasticity by forming nanoscale assemblies in neurons. Using super‐resolution microscopy and time‐resolved anisotropy with targeted tagging, the study reveals low‐order ARC assemblies at synapses colocalizing with AMPARs, semi‐circular structures at endocytic zones, and 60–80 nm
Martina Damenti, Giovanna Coceano, Mariline Mendes Silva, Jonatan Alvelid, Chiara Sgattoni, Andrea Volpato, Lea Rems, Luciano A. Masullo, Eduard M. Unterauer, Rafal Kowalewski, Lucie Delemotte, Erdinc Sezgin, Ralf Jungmann, Ilaria Testa   +13 more
wiley   +1 more source

Semi-Supervised Learning in Medical Images Through Graph-Embedded Random Forest [PDF]

, 2020
Lin Gu, Xiaowei Zhang, Shaodi You, Shen Zhao, Zhenzhong Liu, Tatsuya Harada   +5 more
openalex   +1 more source

Integrating Spatial Proteogenomics in Cancer Research

Advanced Science, EarlyView.
Xx xx. ABSTRACT Background: Spatial proteogenomics marks a paradigm shift in oncology by integrating molecular analysis with spatial information from both spatial proteomics and other data modalities (e.g., spatial transcriptomics), thereby unveiling tumor heterogeneity and dynamic changes in the microenvironment.
Yida Wang, Yang Wu, Feng Zhang, Parthiban Periasamy, Haiyue You, Denise Goh, Rachel Elizabeth Ann Fincham, Xin Ning, Danping Wu, Lu Liu, Ying Jiang, Zhiwen Qian, Joe Yeong, Yan Zhang   +13 more
wiley   +1 more source

Imputations for High Missing Rate Data in Covariates Via Semi-supervised Learning Approach

, 2021
Wei Lan, Xuerong Chen, Tao Zou, Chih‐Ling Tsai   +3 more
openalex   +2 more sources

GraphFL: A Federated Learning Framework for Semi-Supervised Node Classification on Graphs [PDF]

, 2020
Binghui Wang, Ang Li, Hai Li, Yiran Chen
openalex   +1 more source