Results 111 to 120 of about 25,940 (257)

scTIDE: Deciphering Critical Transitions Through Cell‐Perturbed Manifold Graphs and Optimal Transport Conditional Flow Matching

Advanced Science, EarlyView.
scTIDE identifies single‐cell tipping points by combining manifold‐based graph representations with optimal‐transport conditional flow matching, which preserves intrinsic topology and models distributional dynamics. It supports critical‐transition detection at individual‐cell resolution, prediction of unseen cells, and dimensionality reduction and ...
Jiayuan Zhong, Bowen Niu, Yongbo Yu, Shiyang Nie, Xuerong Gu, Pei Chen, Rui Liu   +6 more
wiley   +1 more source

A Sparse PCA Approach to Clustering

, 2016
We discuss a clustering method for Gaussian mixture model based on the sparse principal component analysis (SPCA) method and compare it with the IF-PCA method. We also discuss the dependent case where the covariance matrix $Σ$ is not necessarily diagonal.
Cai, T. Tony, Zhang, Linjun
openaire   +2 more sources

Experience‐Dependent Reorganization of Hippocampal CA3 Neuronal Ensembles Associates With Memory Generalization

Advanced Science, EarlyView.
Mice can transfer the learned rule of spatial working memory to guide similar but novel tasks. Hippocampal CA3 populational activity dynamically reorganize during memory generalization, shifting from task‐specific to generalized coding over testing days. Sparse yet redundant neural representations of CA3 enable rule transfer and cognitive map formation,
Da Song, Zilu Zhu, Yujun Deng, Yujie Song, Hui Li, Junjun Ni, Zhenzhen Quan, Yan Yan, Hong Qing   +8 more
wiley   +1 more source

Engineering‐Modulated Molybdenum Enzymes Strategy for Tumor‐Specific Metabolic‐Immunotherapy

Advanced Science, EarlyView.
Biodegradable molybdenum sulfide nanoparticles were synthesized via a one‐pot strategy to increase molybdenum enzyme activity and thus effectively potentiate anti‐tumor immunity by integrating molybdenum‐based metalloimmunotherapy with hydrogen sulfide gas therapy.
Xiaoxiao Pan, Zifan Pei, Jie Wu, Nan Jiang, Qian Li, Yuqi Yang, Jie Cao, Yechen Huang, Shumin Sun, Qialu Du, Zhicheng Liu, Lin Zhang, Fei Gong, Jinhua Zhou, Liang Cheng   +14 more
wiley   +1 more source

Kernel-Based Dimension Reduction Method for Time Series Nowcasting

IEEE Access
Nowcasts are closely related to big data. Currently, the most popular nowcast model-building approach is to use the factor bridge equation (BE) model or factor mixed data sampling (MIDAS) model, where the factors are extracted from large datasets using ...
Thanh Do Van, Hai Nguyen Minh
doaj   +1 more source

Physics‐encoded transfer learning for scale‐up modeling of CHO cell bioreactors

AIChE Journal, EarlyView.
Abstract Developing reliable predictive models for mammalian cell bioreactors, particularly Chinese hamster ovary (CHO) cultures widely used in biopharmaceutical manufacturing, remains challenging due to severe data scarcity in industrial‐scale reactors.
Muyang Li, Ming Xiao, Zhe Wu
wiley   +1 more source

Decoding Tattoo and Permanent Makeup Pigments: Linking Physicochemical Properties to Absorption, Distribution, Metabolism, and Elimination Profiles Using Quantitative Structure–Activity Relationship (QSAR)‐Based New Approach Methodologies (NAMs)

Advanced Intelligent Discovery, EarlyView.
This study applies QSAR‐based new approach methodologies to 90 synthetic tattoo and permanent makeup pigments, revealing systemic links between their physicochemical properties and absorption, distribution, metabolism, and elimination profiles. The correlation‐driven analysis using SwissADME, ChemBCPP, and principal component analysis uncovers insights
Girija Bansod, Ajay Vikram Singh, Preeti Bhardwaj, Tulika Rai, Sweta Vijay Nakhale, Amruta Shelar, Rajendra Patil, Peter Laux, Andreas Luch, Christopher J. Osgood, Michael W. Stacey   +10 more
wiley   +1 more source

Deep Learning‐Assisted Coherent Raman Scattering Microscopy

Advanced Intelligent Discovery, EarlyView.
The analytical capabilities of coherent Raman scattering microscopy are augmented through deep learning integration. This synergistic paradigm improves fundamental performance via denoising, deconvolution, and hyperspectral unmixing. Concurrently, it enhances downstream image analysis including subcellular localization, virtual staining, and clinical ...
Jianlin Liu, Rujia Liu, Xinyan Zhang, Xun Chen, Shuhua Yue   +4 more
wiley   +1 more source

Interpretable Machine Learning for Solvent‐Dependent Carrier Mobility in Solution‐Processed Organic Thin Films

Advanced Intelligent Discovery, EarlyView.
This work establishes a correlation between solvent properties and the charge transport performance of solution‐processed organic thin films through interpretable machine learning. Strong dispersion interactions (δD), moderate hydrogen bonding (δH), closely matching and compatible with the solute (quadruple thiophene), and a small molar volume (MolVol)
Tianhao Tan, Lian Duan, Dong Wang
wiley   +1 more source

A Generalized Framework for Data‐Efficient and Extrapolative Materials Discovery for Gas Separation

Advanced Intelligent Discovery, EarlyView.
This study introduces an iterative supervised machine learning framework for metal‐organic framework (MOF) discovery. The approach identifies over 97% of the best performing candidates while using less than 10% of available data. It generalizes across diverse MOF databases and gas separation scenarios.
Varad Daoo, Jayant K. Singh
wiley   +1 more source