409 research outputs found
mgm: Estimating Time-Varying Mixed Graphical Models in High-Dimensional Data
We present the R-package mgm for the estimation of k-order Mixed Graphical
Models (MGMs) and mixed Vector Autoregressive (mVAR) models in high-dimensional
data. These are a useful extensions of graphical models for only one variable
type, since data sets consisting of mixed types of variables (continuous,
count, categorical) are ubiquitous. In addition, we allow to relax the
stationarity assumption of both models by introducing time-varying versions
MGMs and mVAR models based on a kernel weighting approach. Time-varying models
offer a rich description of temporally evolving systems and allow to identify
external influences on the model structure such as the impact of interventions.
We provide the background of all implemented methods and provide fully
reproducible examples that illustrate how to use the package
A Tutorial on Estimating Time-Varying Vector Autoregressive Models
Time series of individual subjects have become a common data type in
psychological research. These data allow one to estimate models of
within-subject dynamics, and thereby avoid the notorious problem of making
within-subjects inferences from between-subjects data, and naturally address
heterogeneity between subjects. A popular model for these data is the Vector
Autoregressive (VAR) model, in which each variable is predicted as a linear
function of all variables at previous time points. A key assumption of this
model is that its parameters are constant (or stationary) across time. However,
in many areas of psychological research time-varying parameters are plausible
or even the subject of study. In this tutorial paper, we introduce methods to
estimate time-varying VAR models based on splines and kernel-smoothing
with/without regularization. We use simulations to evaluate the relative
performance of all methods in scenarios typical in applied research, and
discuss their strengths and weaknesses. Finally, we provide a step-by-step
tutorial showing how to apply the discussed methods to an openly available time
series of mood-related measurements
Network intervention analysis of anxiety-related outcomes and processes of acceptance and commitment therapy (ACT) for anxious cancer survivors
Objective: Psychotherapies like Acceptance and Commitment Therapy (ACT) are thought to target multiple clinical outcomes by intervening on multiple mechanistic process variables. However, the standard mediation approach does not readily model the potentially complex associations among multiple processes and outcomes. The current study is one of the first to apply network intervention analysis to examine the putative change processes of a psychotherapy. Methods: Using data from a randomized trial of ACT versus minimally-enhanced usual care for anxious cancer survivors, we computed pre-to post-intervention (n = 113) residualized change scores on anxiety-related outcomes (general anxiety symptoms, cancer-related trauma symptoms, and fear of cancer recurrence) and putative processes of the intervention (experiential avoidance, self-compassion, and emotional approach coping). We estimated a network model with intervention condition and residualized change scores as nodes. Results: Contrary to the expectation that intervention effects would pass indirectly to outcomes via processes, network analysis indicated that two anxiety-related outcomes of the trial may have acted as primary mechanisms of the intervention on other outcome and process variables. Conclusions: Network intervention analysis facilitated flexible evaluation of ACT's change processes, and offers a new way to test whether change occurs as theorized in psychotherapies.</p
Musical and vocal interventions to improve neurodevelopmental outcomes for preterm infants
This is a protocol for a Cochrane Review (Intervention). The objectives are as follows:
We will assess the overall efficacy of auditory stimulation for physiological and neurodevelopmental outcomes in preterm infants (< 37 weeks' gestation), compared to standard care. In addition, we will determine specific effects of various musical and vocal interventions for physiological, anthropometrical, socialâemotional, neurodevelopmental shortâ and longâterm outcomes in preterm infants, parental wellâbeing, and bonding
Using network analysis to examine links between individual depressive symptoms, inflammatory markers, and covariates
Background  Studies investigating the link between depressive symptoms and inflammation have yielded inconsistent results, which may be due to two factors. First, studies differed regarding the specific inflammatory markers studied and covariates accounted for. Second, specific depressive symptoms may be differentially related to inflammation. We address both challenges using network psychometrics.  Methods  We estimated seven regularized Mixed Graphical Models in the Netherlands Study of Depression and Anxiety (NESDA) data (N = 2321) to explore shared variances among (1) depression severity, modeled via depression sum-score, nine DSM-5 symptoms, or 28 individual depressive symptoms; (2) inflammatory markers C-reactive protein (CRP), interleukin 6 (IL-6), and tumor necrosis factor α (TNF-α); (3) before and after adjusting for sex, age, body mass index (BMI), exercise, smoking, alcohol, and chronic diseases.  Results  The depression sum-score was related to both IL-6 and CRP before, and only to IL-6 after covariate adjustment. When modeling the DSM-5 symptoms and CRP in a conceptual replication of Jokela et al., CRP was associated with âsleep problemsâ, âenergy levelâ, and âweight/appetite changesâ; only the first two links survived covariate adjustment. In a conservative model with all 38 variables, symptoms and markers were unrelated. Following recent psychometric work, we re-estimated the full model without regularization: the depressive symptoms âinsomniaâ, âhypersomniaâ, and âaches and painâ showed unique positive relations to all inflammatory markers.  Conclusions  We found evidence for differential relations between markers, depressive symptoms, and covariates. Associations between symptoms and markers were attenuated after covariate adjustment; BMI and sex consistently showed strong relations with inflammatory markers
Evaluation of Hydrodynamic Drag on Experimental Fouling-release Surfaces, using Rotating Disks
Fouling by biofilms significantly increases frictional drag on ships' hulls. A device, the friction disk machine, designed to measure torque on rotating disks, was used to examine differences among experimental fouling-release coatings in the drag penalty due to accumulated biofilms. Penalties were measured as the percentage change in the frictional resistance coefficient C f . Drag penalties due to microfouling ranged from 9% to 29%, comparable to previously reported values. An antifouling control coating showed a smaller drag penalty than the fouling-release coatings. There were also significant differences among the fouling-release coatings in drag due to biofilm formation. These results indicate that the friction disk machine may serve as a valuable tool for investigating the effects of experimental coatings, both antifouling and fouling-release, on microfouling and associated drag penalties
Moderated Network Models
Pairwise network models such as the Gaussian Graphical Model (GGM) are a
powerful and intuitive way to analyze dependencies in multivariate data. A key
assumption of the GGM is that each pairwise interaction is independent of the
values of all other variables. However, in psychological research this is often
implausible. In this paper, we extend the GGM by allowing each pairwise
interaction between two variables to be moderated by (a subset of) all other
variables in the model, and thereby introduce a Moderated Network Model (MNM).
We show how to construct the MNM and propose an L1-regularized nodewise
regression approach to estimate it. We provide performance results in a
simulation study and show that MNMs outperform the split-sample based methods
Network Comparison Test (NCT) and Fused Graphical Lasso (FGL) in detecting
moderation effects. Finally, we provide a fully reproducible tutorial on how to
estimate MNMs with the R-package mgm and discuss possible issues with model
misspecification
The permanently chaperone-active small heat shock protein Hsp17 from Caenorhabditis elegans exhibits topological separation of its N-terminal regions
Small Heat shock proteins (sHsps) are a family of molecular chaperones that bind nonnative proteins in an ATP-independent manner. Caenorhabditis elegans encodes 16 different sHsps, among them Hsp17, which is evolutionarily distinct from other sHsps in the nematode. The structure and mechanism of Hsp17 and how these may differ from other sHsps remain unclear. Here, we find that Hsp17 has a distinct expression pattern, structural organization, and chaperone function. Consistent with its presence under nonstress conditions, and in contrast to many other sHsps, we determined that Hsp17 is a mono-disperse, permanently active chaperone in vitro, which interacts with hundreds of different C. elegans proteins under physiological conditions. Additionally, our cryo-EM structure of Hsp17 reveals that in the 24-mer complex, 12 N-terminal regions are involved in its chaperone function. These flexible regions are located on the outside of the spherical oligomer, whereas the other 12 N-terminal regions are engaged in stabilizing interactions in its interior. This allows the same region in Hsp17 to perform different functions depending on the topological context. Taken together, our results reveal structural and functional features that further define the structural basis of permanently active sHsps
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