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To gain further insight into the mechanisms underlying the change in the phenotype of BAT of the experimental models, we focused on the transcriptional control of the metabolic pathways, achieved by the coordinated actions of numerous transcription factors and associated coregulators or corepressors.

Nevertheless, since in our models we focused on higher risk drinkers, this distinction between abstainers and lower risk drinkers is inconsequential for our results.

When considering combined prediction models we focused on the multivariate Cox model with clinical predictors and omics score as covariates and with linear effects only.

In order to simplify the models we focused on variables for the most common conditions and medications and combined similar variables with comparable HRs where possible.

In our functional analysis of gene models, we focused on the common factors, rather than the species-specific factors, predicted in L. (L).

To minimize spurious findings and enhance interpretability of our models, we focused on covariates representing distance to road classes rather than individual roadways.

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For the virus spreading models, we focus on the most popular models, the SI model, SIS model, and SIS model.

As is common in such models, we focus on the equilibrium that results when there is costless migration between locations.

As a natural step for studying higher-dimensional models, we focus on systems with two fast and two slow variables.

Because of this, and because of its convenience for constructing and estimating hybrid models, we focus on Bayesian estimation for the remainder of the paper.

As the Industry Foundation Classes (IFC) is the most established and most comprehensive vendor-neutral schema for building information models, we focus on this data model in our discussions regarding data analysis and filtering.

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