Results 81 to 90 of about 17,397,740 (295)

Design and analysis strategies for robust microbiome ageing research

open access: yesFEBS Letters, EarlyView.
The gut microbiome changes with age and associates with age‐related morbidity and mortality, establishing it as a potential biomarker and intervention target for ageing. Realising this potential requires methodological rigour, yet distinguishing biological signals from methodological artefacts remains challenging across cohorts. This review provides an
Mark Olenik   +5 more
wiley   +1 more source

ABL kinase‐dependent phosphorylation of SH proteins promotes their direct interaction with CRK family SH2 domains

open access: yesFEBS Letters, EarlyView.
CT10 regulator of kinase (CRK) and CRK‐Like (CRKL) are signaling adaptors driving cell adhesion, motility, differentiation, and proliferation. SH2‐domain containing (SH) proteins are enriched in YXXP motifs which when phosphorylated create preferred binding sites for CRK family SH2 domains.
Phoebe M. Cousens   +8 more
wiley   +1 more source

Consistency of Restricted Maximum Likelihood Estimators in High-Dimensional Kernel Linear Mixed-Effects Models with Applications in Estimating Genetic Heritability

open access: yesMathematics
Restricted maximum likelihood (REML) estimators are commonly used to obtain unbiased estimators for the variance components in linear mixed models. In modern applications, particularly in genomic studies, the dimension of the design matrix with respect ...
Xiaoxi Shen, Qing Lu
doaj   +1 more source

Investigating transcription factor dynamics in health and disease using FRAP

open access: yesFEBS Letters, EarlyView.
FRAP analysis of GFP‐tagged transcription factors reveals how molecular mobility and target engagement change in response to drug treatment. By combining live‐cell imaging, quantitative model fitting, and statistical analysis, this approach uncovers transcription factor dynamics linked to disease mechanisms, providing a powerful framework for ...
Kannan Govindaraj   +3 more
wiley   +1 more source

Robust Classification of Functional and Quantitative Image Data Using Functional Mixed Models [PDF]

open access: yes, 2012
This paper describes how to perform classification of complex, high-dimensional functional data using the functional mixed model (FMM) framework. The FMM relates a functional response to a set of predictors through functional fixed and random effects ...
Morris, Jeffrey S.   +5 more
core   +1 more source

Conserved binding mode but diverse interfaces of MreC‐PBP2 interactions

open access: yesFEBS Letters, EarlyView.
The crystal structure of abMreC reveals a conserved two β‐barrel architecture and provides structural insights into its role within the bacterial elongasome. The abMreC–abPBP2 complex model identifies the molecular basis of MreC‐mediated PBP2 recognition, contributing to the regulation of peptidoglycan synthesis.
Hyunseok Jang   +4 more
wiley   +1 more source

Linear mixed effects models.

open access: yes, 2022
Linear mixed effects models.
Theofilos Gkinopoulos (10284613)   +2 more
core   +1 more source

Variational Bayesian EM Algorithm for Quantile Regression in Linear Mixed Effects Models

open access: yesMathematics
This paper extends the normal-beta prime (NBP) prior to Bayesian quantile regression in linear mixed effects models and conducts Bayesian variable selection for the fixed effects of the model.
Weixian Wang, Maozai Tian
doaj   +1 more source

Microbiome‐blood–brain barrier interactions in aging — mechanisms and therapeutic potential

open access: yesFEBS Letters, EarlyView.
Aging reshapes the gut microbiome (↓SCFA‐producing commensals; ↑pro‐inflammatory outputs), shifting circulating metabolites (↓SCFAs; ↑LPS, ↑TMAO, ↑PAA) that act at the BBB to increase nonspecific transcytosis, alter transport, and promote astrocyte reactivity, heightening brain vulnerability.
Daniel Cuervo‐Zanatta   +3 more
wiley   +1 more source

Stochastic Differential Mixed-Effects Models

open access: yesScandinavian Journal of Statistics, 2010
Continuous processes \(X_t^i\) are observed for \(M\) different experimental units \(i=1,\dots,M\) at discrete time points. The stochastic differential mixed effect model (SDMEM) assumes that \[ dX_t^i=\mu(X_t^i,\vartheta,b^i)dt+\sigma(X_t^i,\vartheta,b^i)dw_t^i, \] where \(w_t^i\) are standard Brownian motions, \(\mu\) and \(\sigma\) are known drift ...
Picchini U, De Gaetano A, Ditlevsen S
openaire   +6 more sources

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