Results 121 to 130 of about 8,346,191 (267)

Nanozymes Integrated Biochips Toward Smart Detection System

Advanced Science, EarlyView.
This review systematically outlines the integration of nanozymes, biochips, and artificial intelligence (AI) for intelligent biosensing. It details how their convergence enhances signal amplification, enables portable detection, and improves data interpretation.
Dongyu Chen, Wang Zheng, Zhihui Zhang, Shenping Yu, Xinxin Hang, Han Wu, Xiao‐Wei Xiang, Wei Mu, Yanli Jiao, Zaizai Dong, Lingqian Chang   +10 more
wiley   +1 more source

cuteHap: Haplotype‐Aware Structural Variant Detection in Phased Long‐Read Sequencing Data

Advanced Science, EarlyView.
cuteHap is a haplotype‐aware structural variant detection method designed for phased long‐read sequencing. By employing self‐adaptive clustering and credibility‐prioritized beam search algorithms, cuteHap generates accurate haplotype‐resolved calls and outperforms state‐of‐the‐art tools.
Shuqi Cao, Yadong Liu, Miao Cui, Runtian Gao, Weimin Guo, Guohua Wang, Yadong Wang, Tao Jiang   +7 more
wiley   +1 more source

Reconstructing Coherent Functional Landscape From Multi‐Modal Multi‐Slice Spatial Transcriptomics by a Variational Spatial Gaussian Process

Advanced Science, EarlyView.
This study introduces stVGP, a variational spatial Gaussian process framework for multi‐modal, multi‐slice spatial transcriptomics. By integrating histological and genomic data through hybrid alignment and attention‐based fusion, stVGP reconstructs coherent 3D functional landscapes.
Zedong Wang, Bowen Fu, Chuanchao Zhang, Xiaoping Liu   +3 more
wiley   +1 more source

A Formal Framework for Knowledge Acquisition: Going beyond Machine Learning. [PDF]

Entropy (Basel), 2022
Hössjer O, Díaz-Pachón DA, Rao JS.
europepmc   +1 more source

SpatialESD: Spatial Ensemble Domain Detection in Spatial Transcriptomics

Advanced Science, EarlyView.
ABSTRACT Spatial transcriptomics (ST) measures gene expression while preserving spatial context within tissues. One of the key tasks in ST analysis is spatial domain detection, which remains challenging due to the complex structure of ST data and the varying performance of individual clustering methods. To address this, we propose SpatialESD, a Spatial
Hongyan Cao, Gaiqin Liu, Jingyi Xia, Runle Chen, Tong Wang, Xiaoling Yang, Ruiling Fang, Yanhong Luo, Ping Zeng, Hongmei Yu, Yanbo Zhang, Yuehua Cui   +11 more
wiley   +1 more source

Precise or Imprecise Probabilities? Evidence from Survey Response Related to Late-Onset Dementia. [PDF]

J Eur Econ Assoc, 2022
Giustinelli P, Manski CF, Molinari F.
europepmc   +1 more source

A Subset of Pro‐inflammatory CXCL10+ LILRB2+ Macrophages Derives From Recipient Monocytes and Drives Renal Allograft Rejection

Advanced Science, EarlyView.
This study uncovers a recipient‐derived monocyte‐to‐macrophage trajectory that drives inflammation during kidney transplant rejection. Using over 150 000 single‐cell profiles and more than 850 biopsies, the authors identify CXCL10+ macrophages as key predictors of graft loss.
Alexis Varin, Jovanne Palvair, Lennie Messager, Jamal Bamoulid, Jasper Callemeyn, Mélanie Chaintreuil, Ludivine Dal Zuffo, Didier Ducloux, Imane Farhat, Mathieu Legendre, Laurent Martin, Florian Renosi, Xavier Roussel, Thibaut Vaulet, Maarten Naesens, Claire Tinel, Baptiste Lamarthée   +16 more
wiley   +1 more source

Alternative Types of Ambiguity and their Effects on Climate Change Regulation. [PDF]

Open Res Eur, 2022
Koundouri P, Pittis N, Samartzis P, Englezos N, Papandreou A.   +4 more
europepmc   +1 more source

A Statistical Mechanics Model to Decode Tissue Crosstalk During Graft Formation

Advanced Science, EarlyView.
We introduce a statistical mechanics framework to decode the genomic crosstalk governing plant grafting. By integrating evolutionary game theory with transcriptomics, we reconstruct idopNetworks (informative, dynamic, omnidirectional, and personalized networks) that map scion–rootstock interactions.
Ang Dong, Yihan Meng, Stephen Shing‐Toung Yau, Shing‐Tung Yau, Rongling Wu   +4 more
wiley   +1 more source

ML Workflows for Screening Degradation‐Relevant Properties of Forever Chemicals

Advanced Science, EarlyView.
The environmental persistence of per‐ and polyfluoroalkyl substances (PFAS) necessitates efficient remediation strategies. This study presents physics‐informed machine learning workflows that accurately predict critical degradation properties, including bond dissociation energies and polarizability.
Pranoy Ray, Andrew R. Castillo, Manoj Kolel‐Veetil, Surya R. Kalidindi   +3 more
wiley   +1 more source