Results 81 to 90 of about 155,627 (235)
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 +16 more
wiley +1 more source
Directed acyclic graph kernels for structural RNA analysis
Background Recent discoveries of a large variety of important roles for non-coding RNAs (ncRNAs) have been reported by numerous researchers. In order to analyze ncRNAs by kernel methods including support vector machines, we propose stem kernels as an ...
Mituyama Toutai +3 more
doaj +1 more source
Mid‐infrared optoacoustic microscopy (MiROM) acquires lipid‐ and protein‐ associated vibrational contrast in intact fat tissue without dyes, preserving native tissue architecture. Through lateral and axial segmentation, MiROM tracks intrinsic intracellular changes during postnatal remodeling. A quantitative spatial analysis tool (Q‐SAT) maps white‐ and
Myeongseop Kim +7 more
wiley +1 more source
A real‐world model of structured animal product restriction practiced for religious reasons reveals the dynamic adaptability of the human gut microbiome to dietary change and uncovers reductions in diversity and rare taxa loss. Integrated microbiome, metabolomic, and proteomic analyses uncover coordinated taxonomic and molecular shifts and identify ...
Christina Emmanouil +7 more
wiley +1 more source
A Kernel Probabilistic Model for Semi-supervised Co-clustering Ensemble
Co-clustering is used to analyze the row and column clusters of a dataset, and it is widely used in recommendation systems. In general, different co-clustering models often obtain very different results for a dataset because each algorithm has its own ...
Zhang Yinghui
doaj +1 more source
Linearizing and Forecasting: A Reservoir Computing Route to Digital Twins of the Brain
A new approach uses simple neural networks to create digital twins of brain activity, capturing how different patterns unfold over time. The method generates and recovers key dynamics even from noisy data. When applied to fMRI, it predicts brain signals and reveals distinctive activity patterns across regions and individuals, opening possibilities for ...
Gabriele Di Antonio +3 more
wiley +1 more source
This study performs pan‐viromic profiling of 14,529 samples from 5,710 domestic herbivores across five Chinese provinces, establishing the DhCN‐Virome (1,085,360 viral metagenomes). It reveals species/sample‐specific viromic signatures and cross‐species transmission dynamics, aiding unified disease control.
Yue Sun +19 more
wiley +1 more source
Kernel discriminant analysis and clustering with parsimonious Gaussian process models
This work presents a family of parsimonious Gaussian process models which allow to build, from a finite sample, a model-based classifier in an infinite dimensional space.
Bouveyron, Charles +2 more
core +2 more sources
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 +3 more
wiley +1 more source
Tensorized Consensus Graph Learning for Incomplete Multi-View Clustering with Confidence Integration
Graph-based multi-view clustering has gained significant attention in recent years due to its superior ability to reveal clustering structures. However, existing methods often incur high computational costs when capturing local information and overlook ...
Guangqi Jiang +3 more
doaj +1 more source

