Browse
Submit Data
Databases
API
Help

Dataset Information

4 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Get Over It! A Multilevel Threshold Autoregressive Model for State-Dependent Affect Regulation.

ABSTRACT: Intensive longitudinal data provide rich information, which is best captured when specialized models are used in the analysis. One of these models is the multilevel autoregressive model, which psychologists have applied successfully to study affect regulation as well as alcohol use. A limitation of this model is that the autoregressive parameter is treated as a fixed, trait-like property of a person. We argue that the autoregressive parameter may be state-dependent, for example, if the strength of affect regulation depends on the intensity of affect experienced. To allow such intra-individual variation, we propose a multilevel threshold autoregressive model. Using simulations, we show that this model can be used to detect state-dependent regulation with adequate power and Type I error. The potential of the new modeling approach is illustrated with two empirical applications that extend the basic model to address additional substantive research questions.

SUBMITTER: De Haan-Rietdijk S

PROVIDER: S-EPMC4764683 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Multilevel regulation of non-photochemical quenching and state transitions by chloroplast NADPH-dependent thioredoxin reductase.

Project description:In natural growth habitats, plants face constant, unpredictable changes in light conditions. To avoid damage to the photosynthetic apparatus on thylakoid membranes in chloroplasts, and to avoid wasteful reactions, it is crucial to maintain a redox balance both within the components of photosynthetic electron transfer chain and between the light reactions and stromal carbon metabolism under fluctuating light conditions. This requires coordinated function of the photoprotective and regulatory mechanisms, such as non-photochemical quenching (NPQ) and reversible redistribution of excitation energy between photosystem II (PSII) and photosystem I (PSI). In this paper, we show that the NADPH-dependent chloroplast thioredoxin system (NTRC) is involved in the control of the activation of these mechanisms. In plants with altered NTRC content, the strict correlation between lumenal pH and NPQ is partially lost. We propose that NTRC contributes to downregulation of a slow-relaxing constituent of NPQ, whose induction is independent of lumenal acidification. Additionally, overexpression of NTRC enhances the ability to adjust the excitation balance between PSII and PSI, and improves the ability to oxidize the electron transfer chain during changes in light conditions. Thiol regulation allows coupling of the electron transfer chain to the stromal redox state during these changes.

| S-EPMC6850073 | biostudies-literature

Positive Affect Over Time and Emotion Regulation Strategies: Exploring Trajectories With Latent Growth Mixture Model Analysis.

Project description:The influence of Positive Affect (PA) on people's well-being and happiness and the related positive consequences on everyday life have been extensively described by positive psychology in the past decades. This study shows an application of Latent Growth Mixture Modeling (LGMM) to explore the existence of different trajectories of variation of PA over time, corresponding to different groups of people, and to observe the effect of emotion regulation strategies on these trajectories. We involved 108 undergraduates in a 1-week daily on-line survey, assessing their PA. We also measured their emotion regulation strategies before the survey. We identified three trajectories of PA over time: a constantly high PA profile, an increasing PA profile, and a decreasing PA profile. Considering emotion regulation strategies as covariates, reappraisal showed an effect on trajectories and class membership, whereas suppression regulation strategy did not.

| S-EPMC7396512 | biostudies-literature

Metabolic Control over mTOR-Dependent Diapause-like State.

Project description:Regulation of embryonic diapause, dormancy that interrupts the tight connection between developmental stage and time, is still poorly understood. Here, we characterize the transcriptional and metabolite profiles of mouse diapause embryos and identify unique gene expression and metabolic signatures with activated lipolysis, glycolysis, and metabolic pathways regulated by AMPK. Lipolysis is increased due to mTORC2 repression, increasing fatty acids to support cell survival. We further show that starvation in pre-implantation ICM-derived mouse ESCs induces a reversible dormant state, transcriptionally mimicking the in vivo diapause stage. During starvation, Lkb1, an upstream kinase of AMPK, represses mTOR, which induces a reversible glycolytic and epigenetically H4K16Ac-negative, diapause-like state. Diapause furthermore activates expression of glutamine transporters SLC38A1/2. We show by genetic and small molecule inhibitors that glutamine transporters are essential for the H4K16Ac-negative, diapause state. These data suggest that mTORC1/2 inhibition, regulated by amino acid levels, is causal for diapause metabolism and epigenetic state.

| S-EPMC7204393 | biostudies-literature

Analyzing Stability and Change in Dyadic Attachment: The Multi-Rater Latent State-Trait Model With Autoregressive Effects.

Project description:Previous research suggests that parental attachment is stable throughout emerging adulthood. However, the relationships between the mutual attachments in the dyads of emerging adults and their parents are still unclear. Our study examines the stability and change in dyadic attachment. We asked 574 emerging adults and 463 parents at four occasions over 1 year about their mutual attachments. We used a latent state-trait model with autoregressive effects to estimate the time consistency of the attachments. Attachment was very stable, and earlier measurement occasions could explain more than 60% of the reliable variance. Changes of attachment over time showed an accumulation of situational effects for emerging adults but not for their parents. We estimated the correlations of the mutual attachments over time using a novel multi-rater latent state-trait model with autoregressive effects. This model showed that the mutual attachments of parents and emerging adults were moderately to highly correlated. Our model allows to separate the stable attachment from the changing attachment. The correlations between the mutual attachments were higher for the stable elements of attachment than for the changing elements of attachment. Emerging adults and their parents share a stable mutual attachment, but they do not share the changes in their respective attachments.

| S-EPMC8280502 | biostudies-literature

How Does Environmental Regulation Affect the Location of New Polluting Firms? Exploring the Agglomeration Threshold of Effective Environmental Regulation.

Project description:Some scholars have already proved the important role of agglomeration in studying how environmental regulation (ER) affects the location of polluting firms. However, further research is needed on both the mechanism and the empirical evidence. This paper reports the construction of a location database of new chemical plants in China's Yangtze River Economic Belt (YREB), where a fixed-effects panel threshold regression model was used to explore the agglomeration threshold of effective ER. We found a single agglomeration threshold for the whole YREB region that represented the turning point of ER from excluding to attracting new chemical enterprises. Additionally, there were two agglomeration thresholds in the lower reaches. If agglomeration reached the lower threshold, the effect of ER changed from repulsion to nonsignificant attraction. Once above the upper threshold, the attraction effect became large and significant. The results for this region were consistent with the Porter hypothesis. Furthermore, there was a single agglomeration threshold in the middle reaches. When agglomeration level exceeded the threshold, the repellant effect of ER was no longer significant. In the upper reaches, we found no valid threshold and ER always exhibited a small and nonsignificant exclusion effect. The pollution haven hypothesis was more explanatory in the middle and upper reaches. In the end, some suggestions are provided to support the government to formulate differentiated environmental policies.

| S-EPMC7068494 | biostudies-literature

Metabolic control over mTOR dependent diapause-like state

Project description:Regulation of embryonic diapause, dormancy that interrupts the tight connection between develop- mental stage and time, is still poorly understood. Here, we characterize the transcriptional and metabolite profiles of mouse diapause embryos and identify unique gene expression and metabolic signatures with activated lipolysis, glycolysis, and metabolic pathways regulated by AMPK. Lipolysis is increased due to mTORC2 repression, increasing fatty acids to support cell survival. We further show that starvation in pre-implantation ICM-derived mouse ESCs induces a reversible dormant state, transcriptionally mimicking the in vivo diapause stage. During starvation, Lkb1, an upstream kinase of AMPK, represses mTOR, which induces a revers- ible glycolytic and epigenetically H4K16Ac-negative, diapause-like state. Diapause furthermore activates expression of glutamine transporters SLC38A1/2. We show by genetic and small molecule inhibitors that glutamine transporters are essential for the H4K16Ac-negative, diapause state. These data sug- gest that mTORC1/2 inhibition, regulated by amino acid levels, is causal for diapause metabolism and epigenetic state.

2020-01-14 | GSE143494 | GEO

Multilevel Models for Intensive Longitudinal Data with Heterogeneous Autoregressive Errors: The Effect of Misspecification and Correction with Cholesky Transformation.

Project description:Intensive longitudinal studies, such as ecological momentary assessment studies using electronic diaries, are gaining popularity across many areas of psychology. Multilevel models (MLMs) are most widely used analytical tools for intensive longitudinal data (ILD). Although ILD often have individually distinct patterns of serial correlation of measures over time, inferences of the fixed effects, and random components in MLMs are made under the assumption that all variance and autocovariance components are homogenous across individuals. In the present study, we introduced a multilevel model with Cholesky transformation to model ILD with individually heterogeneous covariance structure. In addition, the performance of the transformation method and the effects of misspecification of heterogeneous covariance structure were investigated through a Monte Carlo simulation. We found that, if individually heterogeneous covariances are incorrectly assumed as homogenous independent or homogenous autoregressive, MLMs produce highly biased estimates of the variance of random intercepts and the standard errors of the fixed intercept and the fixed effect of a level 2 covariate when the average autocorrelation is high. For intensive longitudinal data with individual specific residual covariance, the suggested transformation method showed lower bias in those estimates than the misspecified models when the number of repeated observations within individuals is 50 or more.

| S-EPMC5323419 | biostudies-literature

Metabolic control over mTOR dependent diapause-like state

Project description:Metabolic control over mTOR dependent diapause-like state

| PRJNA600822 | ENA

Multilevel cross-dependent binary longitudinal data.

Project description:We provide insights into new methodology for the analysis of multilevel binary data observed longitudinally, when the repeated longitudinal measurements are correlated. The proposed model is logistic functional regression conditioned on three latent processes describing the within- and between-variability, and describing the cross-dependence of the repeated longitudinal measurements. We estimate the model components without employing mixed-effects modeling but assuming an approximation to the logistic link function. The primary objectives of this article are to highlight the challenges in the estimation of the model components, to compare two approximations to the logistic regression function, linear and exponential, and to discuss their advantages and limitations. The linear approximation is computationally efficient whereas the exponential approximation applies for rare events functional data. Our methods are inspired by and applied to a scientific experiment on spectral backscatter from long range infrared light detection and ranging (LIDAR) data. The models are general and relevant to many new binary functional data sets, with or without dependence between repeated functional measurements.

| S-EPMC3865135 | biostudies-literature

Estimation of a Semiparametric Varying-Coefficient Mixed Regressive Spatial Autoregressive Model.

Project description:A semiparametric varying-coefficient mixed regressive spatial autoregressive model is used to study covariate effects on spatially dependent responses, where the effects of some covariates are allowed to vary with other variables. A semiparametric series-based least squares estimating procedure is proposed with the introduction of instrumental variables and series approximations of the conditional expectations. The estimators for both the nonparametric and parametric components of the model are shown to be consistent and their asymptotic distributions are derived. The proposed estimators perform well in simulations. The proposed method is applied to analyze a data set on teen pregnancy to investigate effects of neighborhood as well as other social and economic factors on the teen pregnancy rate.

| S-EPMC6364846 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data