Results 221 to 230 of about 70,734 (340)

Cancer heterogeneity explored through immune cell deconvolution of the tumor microenvironment

Quantitative Biology, Volume 14, Issue 3, September 2026.
Abstract Cancer is a highly heterogeneous disease, characterized by significant variability across multiple dimensions. This diversity has been extensively studied from various perspectives. In this study, we aim to examine cancer heterogeneity through the lens of immune cell composition within the tumor microenvironment.
Limin Jiang, Chung‐I Li, Rui Xin, Jijun Tang, Yan Guo   +4 more
wiley   +1 more source

SBCS-Net: Sparse Bayesian and Deep Learning Framework for Compressed Sensing in Sensor Networks. [PDF]

Sensors (Basel)
Gao X, Yao X, Chen B, Zhang H.
europepmc   +1 more source

Potential Trophic Competition Between the European Perch Perca fluviatilis and Pikeperch Sander lucioperca

International Review of Hydrobiology, Volume 111, Issue 1, June 2026.
ABSTRACT The European perch Perca fluviatilis and pikeperch Sander lucioperca can co‐occur, potentially competing for food resources. However, few studies have addressed the potential feeding competition between these species. We used carbon and nitrogen stable isotope analysis to assess the potential trophic competition between these species in two ...
Mathieu Toutain, Phillip J. Haubrock, Nadège Belouard, Irmak Kurtul, Sadi Aksu, Özgür Emiroglu, Ali Serhan Tarkan, Paride Balzani   +7 more
wiley   +1 more source

Enhanced Lateral Resolution in Acoustic Imaging: From High- to Super-Resolution. [PDF]

Sensors (Basel)
Xia Z, He H, Zhou Z, Pan S, Zhang S.
europepmc   +1 more source

An Interpretable TCN– Transformer Framework for Lithium‐Ion Battery State of Health Estimation Using SHAP Analysis

Quality and Reliability Engineering International, Volume 42, Issue 4, Page 1426-1442, June 2026.
ABSTRACT Accurate state of health (SOH) estimation of Li‐ion batteries is essential for ensuring safety, reliability, and prolonging battery lifespan in energy storage systems and electric vehicles. This study proposes a hybrid temporal convolutional network (TCN)–transformer framework that effectively captures both short‐term temporal dynamics and ...
Fusen Guo, Zhibo Zhang, Xuzhe Ma, Lin Li, Hailing Zhou, Chaojie Li, Huadong Mo   +6 more
wiley   +1 more source

Data compression of Bridge Resilience Control: Algorithm and case analysis. [PDF]

PLoS One
Chen M.
europepmc   +1 more source

ConvCGP: A convolutional neural network to predict genetic values of agronomic traits from compressed genome‐wide polymorphisms

The Plant Genome, Volume 19, Issue 2, June 2026.
Abstract The growing size of genome‐wide polymorphism data in animal and plant breeding has raised concerns regarding computational load and time, particularly when predicting genetic values for target traits using genomic prediction. Several deep learning and conventional methods, including dimensionality reduction techniques such as principal ...
Tanzila Raihan, Chyon Hae Kim, Hiroyuki Shimono, Akio Kimura, Hiroyoshi Iwata   +4 more
wiley   +1 more source

Machine learning in predicting failures of buried water supply networks affected by mining impacts. [PDF]

Sci Rep
Chomacki L, Rusek J, Słowik L.
europepmc   +1 more source

Genetic control of wheat flour end‐use quality and rheology by genome‐wide association studies

The Plant Genome, Volume 19, Issue 2, June 2026.
Abstract Improving end‐use quality in bread wheat (Triticum aestivum) requires dissecting the genetic basis of complex processing traits and deploying robust prediction pipelines in breeding. We performed genome‐wide association studies (GWASs) using 1767 high‐quality single‐nucleotide polymorphisms generated by genotyping‐by‐sequencing in a diverse ...
Juan Menor de Gaspar, Richard D. Cuthbert, Ruan Yuefeng, Ron Knox, BinXiao Fu, Kun Wang, Jatinder S. Sangah, Samia Berraies, Brad Meyer, Firdissa E. Bokore, Julio Isidro y Sánchez   +10 more
wiley   +1 more source

Digital twin-based machine learning framework for predicting nonlinear seismic response of reinforced concrete shear walls using analytical data. [PDF]

Sci Rep
Kuria KK, Kegyes-Brassai O.
europepmc   +1 more source