Results 101 to 110 of about 17,397,740 (295)

Peripheral lysosomes recruit PLEKHG3 to focal adhesions and restrain protrusion dynamics

FEBS Letters, EarlyView.
Proximity‐dependent labeling at the LAMTOR complex revealed the Rho GEF PLEKHG3 as a lysosome‐proximal protein directing the study toward the influence of lysosome positioning on actin dynamics and cell motility. We show that PLEKHG3 colocalizes with lysosomes at focal adhesion sites and observe that forced peripheral dispersion of lysosomes hinders ...
Rainer Ettelt, Ana‐Maria Sandru, Georg Vucak, Sebastian Didusch, Biljana Riemelmoser, Karin Ehrenreiter, Markus Hartl, Lukas A. Huber, Manuela Baccarini   +8 more
wiley   +1 more source

Smooth-car mixed models for spatial count data [PDF]

Penalized splines (P-splines) and individual random effects are used for the analysis of spatial count data. P-splines are represented as mixed models to give a unified approach to the model estimation procedure.
Dae-Jin Lee, Maria Durban
core  

Engineering peptides into antibodies—opportunities and strategies for therapeutic innovation

FEBS Letters, EarlyView.
Peptides and antibodies occupy complementary therapeutic niches. Peptides recognize difficult targets in a compact format, while antibodies add specificity, long half‐life, and effector functions. This review examines strategies that merge both modalities—peptide grafting into loops, terminal and Fc fusions, and bioconjugation—highlighting how ...
Jinling Wang, Ruslana Romaniuk, Hristo L. Svilenov   +2 more
wiley   +1 more source

Cross-validation for nonlinear mixed effects models

Journal of Pharmacokinetics and Pharmacodynamics, 2013
Cross-validation is frequently used for model selection in a variety of applications. However, it is difficult to apply cross-validation to mixed effects models (including nonlinear mixed effects models or NLME models) due to the fact that cross-validation requires “out-of-sample” predictions of the outcome variable, which cannot be easily calculated ...
Colby, Emily, Bair, Eric
openaire   +4 more sources

S-estimation and a robust conditional Akaike information criterion for linear mixed models. [PDF]

We study estimation and model selection on both the fixed and the random effects in the setting of linear mixed models using outlier robust S-estimators.
Tharmaratnam, Kukatharmini, Claeskens, Gerda   +1 more
core  

Unit of analysis issues in laboratory-based research

eLife, 2018
Many studies in the biomedical research literature report analyses that fail to recognise important data dependencies from multilevel or complex experimental designs.
Nick R Parsons, M Dawn Teare, Alice J Sitch   +2 more
doaj   +1 more source

Liver organoids: modelling complexity in homeostasis and disease

FEBS Letters, EarlyView.
Studying liver in vitro has been challenging because simple 2D cell cultures fail to capture liver's cellular and architectural complexity. To bridge this gap, scientists increasingly use organoids, 3D liver models which better mimic liver composition and function. This review examines recent advances in liver organoid complexity and realism, discusses
Anna M. Dowbaj, Meritxell Huch
wiley   +1 more source

Diagnostics for generalised linear mixed models [PDF]

Generalized linear mixed models are generalized linear models that include random effects varying between clusters or 'higher-level' units of hierarchically structured data. Such models can be estimated using gllamm.
Sophia Rabe-Hesketh, Anders Skrondal
core  

Autophagy and mitophagy in pancreatic β‐cell homeostasis and their involvement in diabetes pathophysiology

FEBS Letters, EarlyView.
This review focuses on the role of autophagy and mitophagy in maintaining pancreatic β‐cell function and homeostasis. We discuss how genetic defects affecting these pathways contribute to the development of type 1, type 2, monogenic, and gestational diabetes. We further explore their potential as therapeutic targets. Created in BioRender.
Yunkyeong Lee, Mingming Tong, Anna L. Gloyn   +2 more
wiley   +1 more source

Linear Mixed-Effects Models in chemistry: A tutorial [PDF]

Analytica Chimica Acta
A common goal in chemistry is to study the relationship between a measured signal and the variability of certain factors. To this end, researchers often use Design of Experiment to decide which experiments to conduct and (Multiple) Linear Regression, and/or Analysis of Variance to analyze the collected data. Among the assumptions to the very foundation
Andrea Junior Carnoli, Petra oude Lohuis, Lutgarde M.C. Buydens, Gerjen H. Tinnevelt, Jeroen J. Jansen   +4 more
openaire   +3 more sources