Results 71 to 80 of about 401,511 (278)

Strain tracking with uncertainty quantification

, 2023
AbstractThe ability to detect and quantify microbiota over time has a plethora of clinical, basic science, and public health applications. One of the primary means of tracking microbiota is through sequencing technologies. When the microorganism of interest is well characterized or knowna priori, targeted sequencing is often used. In many applications,
Younhun Kim, Colin J. Worby, Sawal Acharya, Lucas R. van Dijk, Daniel Alfonsetti, Zackary Gromko, Philippe Azimzadeh, Karen Dodson, Georg Gerber, Scott Hultgren, Ashlee M. Earl, Bonnie Berger, Travis E. Gibson   +12 more
openaire   +2 more sources

CLOSURE LAW MODEL UNCERTAINTY QUANTIFICATION

International Journal for Uncertainty Quantification, 2022
The prediction uncertainty in simulators for industrial processes is due to uncertainties in the input variables and uncertainties in specification of the models, in particular the closure laws. In this work, the uncertainty in each closure law was modeled as a random variable and the parameters of its distribution were optimized to correctly quantify ...
Strand, Andreas, Kjølaas, Jørn, Bergstrøm, Trond Harald, Steinsland, Ingelin, Hellevik, Leif Rune   +4 more
openaire   +2 more sources

A light‐triggered Time‐Resolved X‐ray Solution Scattering (TR‐XSS) workflow with application to protein conformational dynamics

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, Fredrik Orädd, Konstantinos Magkakis, Magnus Andersson   +3 more
wiley   +1 more source

Uncertainty Quantification For Learned ISTA

2023 IEEE 33rd International Workshop on Machine Learning for Signal Processing (MLSP), 2023
to appear at the 33rd IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2023)
Hoppe, Frederik, Verdun, Claudio Mayrink, Krahmer, Felix, Laus, Hannah, Rauhut, Holger   +4 more
openaire   +2 more sources

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, Mohammad Nour Alsamsam, Ugnė Bagdonaitė, Van Truc Vu, Chun‐Jen Huang, Polina Kuzhir, Mindaugas Zaremba, Aurimas Kopūstas, Marijonas Tutkus   +8 more
wiley   +1 more source

Verification and validation for trustworthy scientific machine learning

Machine Learning: Science and Technology
Scientific 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, Lorena A Barba, Joaquim R R A Martins, Thomas O’Leary-Roseberry   +3 more
doaj   +1 more source

Self-optimized construction of transition rate matrices from accelerated atomistic simulations with Bayesian uncertainty quantification

, 2018
A massively parallel method to build large transition rate matrices from temperature accelerated molecular dynamics trajectories is presented. Bayesian Markov model analysis is used to estimate the expected residence time in the known state space ...
Perez, Danny, Swinburne, Thomas D
core   +3 more sources

Screening and epitope characterization of Nidogen‐2‐specific nanobodies

FEBS Open Bio, EarlyView.
Camel immunization and phage display were employed to generate high‐affinity VHH nanobodies against Nidogen‐2. After library construction, biopanning, ELISA screening, sequencing, and recombinant expression, selected nanobodies were purified and characterized, leading to the preliminary exploration of a nanobody‐based sandwich ELISA for specific ...
Jianchuan Wen, Qianqian Cui, Zhongyun Lan, Yingjun Wang, Shuaiying Zhao, Wenxuan Feng, Yunfeng Liu, Qiting Huang, Dongna Zhang, Jianfeng Xu   +9 more
wiley   +1 more source

Probabilistic analysis of concrete beams during fire [PDF]

, 2011
In this paper a simple computational tool is presented, which provides insight in the time and temperature dependent reliability of concrete beams during fire.
Annerel, Emmanuel, Caspeele, Robby, Taerwe, Luc, Van Coile, Ruben   +3 more
core  

Combined 5‐aminolevulinic acid and ferric ammonium citrate treatment promotes hair follicle growth by activating dermal papilla cells

FEBS Open Bio, EarlyView.
5‐Aminolevulinic acid combined with ferric ammonium citrate (5‐ALA/FAC) stimulates dermal papilla cell activity and promotes hair follicle growth. The treatment enhances ERK and AKT signaling, increases hair‐inductive gene expression, and restores dermal papilla function suppressed by dihydrotestosterone and oxidative stress, resulting in enhanced hair
Han‐Wook Ryu, Eok‐Soo Oh, Sewoon Kim
wiley   +1 more source