Results 61 to 70 of about 1,597,633 (258)
An efficient numerical method for uncertainty quantification in cardiology models
, 2019 Mathematical models of cardiology involve conductivity and massive parameters describing the dynamics of ionic channels. The conductivity is space dependent and cannot be measured directly.
Gao, X, Zhang, Z, Ying, W
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Uncertainty Quantification of Michaelis-Menten Kinetic Rates and Its Application to the Analysis of CRISPR-Based Diagnostics [PDF]
, 2022 Michaelis-Menten kinetics is an essential model to rationalize enzyme reactions. The quantification of Michaelis-Menten parameters can be very challenging as it is sensitive to even small experimental errors.
Juan G., Santiago, Alexandre S., Avaro
core +2 more sources
Molecular Oncology, EarlyView.Circulating microRNAs as biomarkers of cachexia and sex‐specific cancer in senior dogs. In 25 client‐owned dogs, four circulating miRNAs (miR‐15a, miR‐15b, miR‐16, miR‐140) were downregulated in cachexia, with miR‐16 the strongest individual biomarker (AUC = 0.899).
Soon‐Seok Park +6 more
wiley +1 more source
Verification and validation for trustworthy scientific machine learning
Machine Learning: Science and TechnologyScientific machine learning (SciML) models are transforming many scientific disciplines. However, the development of good modeling practices to increase the trustworthiness of SciML has lagged behind its application, limiting its potential impact.
John D Jakeman +3 more
doaj +1 more source
FEBS Open Bio, EarlyView.Time‐resolved X‐ray solution scattering captures how proteins change shape in real time under near‐native conditions. This article presents a practical workflow for light‐triggered TR‐XSS experiments, from data collection to structural refinement. Using a calcium‐transporting membrane protein as an example, the approach can be broadly applied to study ...
Fatemeh Sabzian‐Molaei +3 more
wiley +1 more source
PCE-GPR: A toolbox for high-dimensional uncertainty quantification
This is the first official release of the PCE-GPR Toolbox, a MATLAB and UQLab-based toolbox for uncertainty quantification hybridizing Polynomial Chaos Expansion (PCE) and Gaussian Process Regression (GPR ...
Paolo Manfredi
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Single‐molecule DNA flow‐stretch assays for high‐throughput DNA–protein interaction studies
FEBS Open Bio, EarlyView.We describe an optimised single‐molecule DNA flow‐stretch assay that visualises DNA–protein interactions in real time. Linear DNA fragments are tethered to a surface and stretched by buffer flow for fluorescence imaging. Using λ and φX174 DNA, this protocol enhances reproducibility and accessibility, providing a versatile approach for studying diverse ...
Ayush Kumar Ganguli +8 more
wiley +1 more source
, 2020 In shape optimization of complex industrial products (such as aerial vehicles or ship hulls), there exists an inherent similarity between global optimization (GO) and uncertainty quantification (UQ): they rely on an extensive exploration of the design ...
Andrea Serani, Matteo Diez
core
Aquaporin‐3 and aquaporin‐5 impact the development of pancreatic ductal adenocarcinoma spheroids
FEBS Open Bio, EarlyView.Schematic representation of the role of aquaporin‐3 (AQP3) and aquaporin‐5 (AQP5) in pancreatic ductal adenocarcinoma (PDAC). Both proteins are upregulated in PDAC and are associated with tumor progression and metastatic potential. Silencing AQP3 or AQP5 in PDAC spheroids results in decreased diameter, area, and overall growth, underscoring their key ...
Catarina Pimpão +3 more
wiley +1 more source
amz78/Shape-uncertainty-quantification-for-TESLA-cavities: v1.0
Software for the paper "Shape uncertainty quantification of Maxwell eigenvalues and -modes with application to TESLA ...
amz78
core +1 more source