Results 131 to 140 of about 3,284,626 (292)
Conceptual illustration of a Global Ecosystem Methane Observing System (GEM‐OS) integrating satellites, aircraft, atmospheric networks, and ecosystem measurements to quantify methane emissions from anthropogenic and natural sources. The multi‐scale observing framework improves source attribution, reduces uncertainty in regional methane budgets, and ...
P. Ciais +32 more
wiley +1 more source
Bayesian Networks and the Problem of Unreliable Instruments [PDF]
We appeal to the theory of Bayesian Networks to model different strategies for obtaining confirmation for a hypothesis from experimental test results provided by less than fully reliable instruments.
Bovens, Luc, Hartmann, Stephan
core
Machine Learning of Temperature‐Dependent Chemical Kinetics Using Parallel Droplet Microreactors
An integrated droplet microfluidics and machine learning framework enables high‐throughput characterization of temperature‐dependent reaction kinetics. Time‐resolved measurements from thousands of droplets train Neural ODE models that accurately predict nonlinear reaction dynamics across diverse thermal environments, bridging large‐scale ...
Mamoru Saita, Yutaka Hori
wiley +1 more source
Drought Stress Mediated Changes in Food Crops: Mechanisms and Remediation Strategies
This work presents a multi‐scale framework for flavonoid‐mediated drought tolerance in staple food crops, integrating ROS redox regulation, antioxidant defense, rhizosphere microbial interactions, and sustainable agronomic practices. This interdisciplinary model provides core insights to develop climate‐resilient cropping systems and enhance global ...
Xiaoyi Duan +5 more
wiley +1 more source
Fourier Bayesian networks: A novel approach for network structure inference with application to brain connectivity studies from magnetoencephalographic recordings [PDF]
This thesis proposes a novel approach for connectivity studies in Electrophysiology and Neuroimaging based on Bayesian Network (BN) analysis in the Fourier domain that is named Fourier Bayesian Networks (FBNs). FBNs use the complex information available
Peraza Rodriguez, Luis Ramon
core +2 more sources
The protein aggregates and gene expression in the middle temporal gyrus (MTG) and somatosensory cortex (SOM) of the postmortem brains of 13 Alzheimer's disease patients were studied in detail, revealing that small hyperphosphorylated tau aggregates increase with Braak stage driven by microglial inflammation.
Elizabeth A. English +9 more
wiley +1 more source
Bayesian MAP model selection of chain event graphs [PDF]
The class of chain event graph models is a generalisation of the class of discrete Bayesian networks, retaining most of the structural advantages of the Bayesian network for model interrogation, propagation and learning, while more naturally encoding ...
Freeman, Guy, Smith, J. Q.
core
Oligosaccharides with defined glycosidic bonds drive divergent gut microbial succession and shape unique metabolic profiles. Absolute time‐series profiling with Bayesian generalized Lotka–Volterra modeling identifies bond‐specific microbial responders, while monoculture validation and metaproteomics reveal linkage‐matched transport and cleavage modules.
Xiaoxuan Lu +10 more
wiley +1 more source
Non‐canonical amino acids (ncAAs) enhance peptide therapeutics but remain difficult to model computationally. SinCAA, a similarity‐enhanced pretraining framework, jointly optimizes contrastive learning guided by a novel conformational similarity metric with masked node reconstruction, capturing both functional relationships and chemical identity of ...
Chencheng Xu +8 more
wiley +1 more source
Bayesian networks have grown to become a dominant type of model within the domain of probabilistic graphical models. Not only do they empower users with a graphical means for describing the relationships among random variables, but they also allow for (potentially) fewer parameters to estimate, and enable more efficient inference.
openaire +2 more sources

