Results 61 to 70 of about 414,528 (301)

Temporal and Cell‐Specific Regulation of Synaptic Homeostasis by the Chromatin Remodeler Chd1

Advanced Science, EarlyView.
Chd1, the Drosophila homologue of mammalian CHD2 ‐ a gene linked to autism, epilepsy, and intellectual disability, is required for synaptic homeostatic plasticity. Chd1 in glia is necessary for the rapid induction of synaptic homeostasis, whereas Chd1 in motoneurons, muscle, and glia is critical for long‐term maintenance.
Danielle T. Morency, Tao Cui, Yimei Cai, Chloe Lok, Rachel E. Nokku, Ruoxian Huang, Grace L. Chu, Yumeng Xie, Saleem W. Abu‐Tayeh, Kaikai He, Chengjie Qiu, Junyi Wang, Paxton M. Paganelli, Ting Wang, Gabrielle Williams, Sreejith Nair, Huadong Pei, Dion K. Dickman, Stefano Vicini, Tingting Wang   +19 more
wiley   +1 more source

Integrated Single‐Cell and Spatial Transcriptomics Reveal Cell‐Type‐Specific Immune Regulatory Networks in Maize Responding to Southern Corn Rust

Advanced Science, EarlyView.
By integrating single‐nuclei and spatial transcriptomics, this study presents a stereoscopic landscape of maize leaf to Puccinia polysora infection. Epidermal and mesophyll cells initiate primary defenses via RLPs/RLKs and jasmonic acid signaling. Cell‐cell communication analyses further reveal the underlying the dynamics of the underlying immune ...
Qiongqiong Wang, Xinyan Sun, Yingchao Sun, Zeqiang Cheng, Zixiang Cheng, Shengbo Han, Ying Feng, Wenbo Yang, Huimin Li, Meichen Zhu, Xiaoling Wu, Jinghua Zhang, Jihua Tang, Honglian Li, Yanyong Cao, Canxing Duan, Yan Shi   +16 more
wiley   +1 more source

Adversarial Robustness on Image Classification With k-Means

IEEE Access
Attacks and defences in adversarial machine learning literature have primarily focused on supervised learning. However, it remains an open question whether existing methods and strategies can be adapted to unsupervised learning approaches.
Rollin Omari, Junae Kim, Paul Montague
doaj   +1 more source

Causal Prediction of TP53 Variant Pathogenicity Using a Perturbation‐Informed Protein Language Model

Advanced Science, EarlyView.
A TP53‐specific predictor, CaVepP53, is developed by fine‐tuning ESMC on experimentally validated variants, quantifying pathogenicity via Euclidean distances. It outperforms general‐purpose models and extends to five cancer genes, enabling interpretable variant classification for precision medicine.
Huiying Chen, Yang Zhao, Boqiang Hu, Wuke Wang, Minfang Song, Annabeth Xinyu Zhao, Xiangyang Li, Gefei Wang, Yanfen Wang, Weiyan Zheng, Xinpeng Zhang, Xia Lin, Yanbin Yin, Xingxu Huang, Jinfang Zheng, Tingbo Liang   +15 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

Multi-Objective Unsupervised Feature Selection and Cluster Based on Symbiotic Organism Search

Algorithms
Unsupervised learning is a type of machine learning that learns from data without human supervision. Unsupervised feature selection (UFS) is crucial in data analytics, which plays a vital role in enhancing the quality of results and reducing ...
Abbas Fadhil Jasim AL-Gburi, Mohd Zakree Ahmad Nazri, Mohd Ridzwan Bin Yaakub, Zaid Abdi Alkareem Alyasseri   +3 more
doaj   +1 more source

Reveal flocking of birds flying in fog by machine learning

, 2020
We study the first-order flocking transition of birds flying in low-visibility conditions by employing three different representative types of neural network (NN) based machine learning architectures that are trained via either an unsupervised learning ...
Ai, Bao-quan, Guo, Wei-chen, He, Liang
core  

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

Cell Consistency Evaluation Method Based on Multiple Unsupervised Learning Algorithms

Big Data Mining and Analytics
Unsupervised learning algorithms can effectively solve sample imbalance. To address battery consistency anomalies in new energy vehicles, we adopt a variety of unsupervised learning algorithms to evaluate and predict the battery consistency of three ...
Jiang Chang, Xianglong Gu, Jieyun Wu, Debu Zhang   +3 more
doaj   +1 more source

A General Approach for Achieving Supervised Subspace Learning in Sparse Representation

IEEE Access, 2019
Over the past few decades, a large family of subspace learning algorithms based on dictionary learning have been designed to provide different solutions to learn subspace feature.
Jianshun Sang, Dezhong Peng, Yongsheng Sang   +2 more
doaj   +1 more source