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Evaluating the effect of γ‐oryzanol on MASLD pathology using a medaka fish model
FEBS Open Bio, EarlyView.This study explores a liver disease called MASLD, which is increasing worldwide and can lead to serious damage. Researchers used medaka fish instead of rodents to test a food compound, γ‐oryzanol. Fish fed this compound had less liver fat and healthier gut bacteria.
Yukako Ito +7 more
wiley +1 more source
FEBS Open Bio, EarlyView.KDAC6 has been associated with cell motility and actin structures, but specific domain contributions are not established. The two catalytic domains have differential effects on motility and F‐actin regulation, affecting cortical F‐actin density, motility, stress fibers, and cell spreading.
Taylor V. Joseph +7 more
wiley +1 more source
Minimal detectable change of gait and balance measures in older neurological patients: estimating the standard error of the measurement from before-after rehabilitation data thanks to the linear mixed-effects models. [PDF]
J Neuroeng RehabilCaronni A +6 more
europepmc +1 more source
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Functional Mixed Effects Models
Biometrics, 2002Summary.In this article, a new class of functional models in which smoothing splines are used to model fixed effects as well as random effects is introduced. The linear mixed effects models are extended to non‐parametric mixed effects models by introducing functional random effects, which are modeled as realizations of zero‐mean stochastic processes ...
Wensheng Guo
exaly +3 more sources
2010
Mixed effects models, or simply mixed models, are widely used in practice. These models are characterized by the involvement of the so-called random effects. To understand the basic elements of a mixed model, let us first recall a linear regression model, which can be expressed as y = Xβ + e, where y is a vector of observations, X is a matrix of known ...
Donghui Zhang, Chunpeng Fan, A Gould
+4 more sources
Mixed effects models, or simply mixed models, are widely used in practice. These models are characterized by the involvement of the so-called random effects. To understand the basic elements of a mixed model, let us first recall a linear regression model, which can be expressed as y = Xβ + e, where y is a vector of observations, X is a matrix of known ...
Donghui Zhang, Chunpeng Fan, A Gould
+4 more sources
Mixed-effects models in psychophysiology
Psychophysiology, 2000The current methodological policy in Psychophysiology stipulates that repeated-measures designs be analyzed using either multivariate analysis of variance (ANOVA) or repeated-measures ANOVA with the Greenhouse-Geisser or Huynh-Feldt correction. Both techniques lead to appropriate type I error probabilities under general assumptions about the variance ...
E, Bagiella, R P, Sloan, D F, Heitjan
openaire +2 more sources
2021
Abstract Mixed effects models are powerful techniques for controlling for non-independence of data or repeated measures, and can be harnessed for both normal and non-normal data structures. Chapter 8 teaches readers how to code, assess, interpret, and troubleshoot both linear and generalized linear mixed models using the same RxP dataset
openaire +2 more sources
Abstract Mixed effects models are powerful techniques for controlling for non-independence of data or repeated measures, and can be harnessed for both normal and non-normal data structures. Chapter 8 teaches readers how to code, assess, interpret, and troubleshoot both linear and generalized linear mixed models using the same RxP dataset
openaire +2 more sources
2007
Statistical models provide a framework in which to describe the biological process giving rise to the data of interest. The construction of this model requires balancing adequate representation of the process with simplicity. Experiments involving multiple (correlated) observations per subject do not satisfy the assumption of independence required for ...
Ann L, Oberg, Douglas W, Mahoney
openaire +2 more sources
Statistical models provide a framework in which to describe the biological process giving rise to the data of interest. The construction of this model requires balancing adequate representation of the process with simplicity. Experiments involving multiple (correlated) observations per subject do not satisfy the assumption of independence required for ...
Ann L, Oberg, Douglas W, Mahoney
openaire +2 more sources
2018
We have covered a variety of statistical models in this book, but all have shared a common feature: The criterion and error term were treated as random variables, but all of the predictors were assumed to be fixed. In this chapter, we will consider models that include a broader mixture of fixed and random variables.
openaire +2 more sources
We have covered a variety of statistical models in this book, but all have shared a common feature: The criterion and error term were treated as random variables, but all of the predictors were assumed to be fixed. In this chapter, we will consider models that include a broader mixture of fixed and random variables.
openaire +2 more sources
1989
In most design set-ups such as block designs or row-column designs classification effects such as block effects or row (column) effects are regarded fixed. When these are also considered random variables, we have one or more additional sources of information for estimating treatment effect parameters. Such models are known as mixed effects models.
Kirti R. Shah, Bikas K. Sinha
openaire +1 more source
In most design set-ups such as block designs or row-column designs classification effects such as block effects or row (column) effects are regarded fixed. When these are also considered random variables, we have one or more additional sources of information for estimating treatment effect parameters. Such models are known as mixed effects models.
Kirti R. Shah, Bikas K. Sinha
openaire +1 more source