Results 101 to 110 of about 17,397,225 (294)
Linear Mixed-Effects Models in chemistry: A tutorial [PDF]
Analytica Chimica ActaA 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 +4 more
openaire +3 more sources
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
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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 +2 more
wiley +1 more source
Human Vaccines & ImmunotherapeuticsHeterologous boosting with aerosolized or intramuscular Ad5-nCoV following a two-dose CoronaVac prime has been shown to induce higher antibody levels than a homologous CoronaVac booster. However, no specific modeling has been reported to characterize the
Ruifan Shen +5 more
doaj +1 more source
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 +1 more
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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
Nonlinear Mixed-Effects Modeling of MNREAD Data
Investigative Opthalmology & Visual Science, 2008 It is often difficult to estimate parameters from individual clinical data because of noisy or incomplete measurements. Nonlinear mixed-effects (NLME) modeling provides a statistical framework for analyzing population parameters and the associated variations, even when individual data sets are incomplete. The authors demonstrate the application of NLME
Cheung, SH +3 more
openaire +4 more sources
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
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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 +2 more
wiley +1 more source
Trend-resistant and cost-efficient cross-over designs for mixed models. [PDF]
A mixed model approach is used to construct optimal cross-over designs. In a cross-over experiment the same subject is tested at different points in time.
Tack, L, Vandebroek, Martina
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