Overview of "Systematic Reviews" of the Built Environment's Effects on Mental Health.

Good mental health is related to mental and psychological well-being, and there is growing interest in the potential role of the built environment on mental health, yet the evidence base underpinning the direct or indirect effects of the built environment is not fully clear. The aim of this overview is to assess the effect of the built environment on mental health-related outcomes. Methods. This study provides an overview of published systematic reviews (SRs) that assess the effect of the built environment on mental health. We reported the overview according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Databases searched until November 2019 included the Cochrane Database of Systematic Reviews, EMBASE, MEDLINE (OVID 1946 to present), LILACS, and PsycINFO. Two authors independently selected reviews, extracted data, and assessed the methodological quality of included reviews using the Assessing Methodological Quality of Systematic Reviews-2 (AMSTAR-2). Results. In total, 357 records were identified from a structured search of five databases combined with the references of the included studies, and eleven SRs were included in the narrative synthesis. Outcomes included mental health and well-being, depression and stress, and psychological distress. According to AMSTAR-2 scores, the quality assessment of the included SRs was categorized as "high" in two SRs and as "critically low" in nine SRs. According to the conclusions of the SRs reported by the authors, only one SR reported a "beneficial" effect on mental health and well-being outcomes. Conclusion. There was insufficient evidence to make firm conclusions on the effects of built environment interventions on mental health outcomes (well-being, depression and stress, and psychological distress). The evidence collected reported high heterogeneity (outcomes and measures) and a moderate- to low-quality assessment among the included SRs.

Identification of genetic network dynamics with unate structure.

MOTIVATION: Modern experimental techniques for time course measurement of gene expression enable the identification of dynamical models of genetic regulatory networks. In general, identification involves fitting appropriate network structures and parameters to the data. For a given set of genes, exploring all possible network structures is clearly prohibitive. Modelling and identification methods for the a priori selection of network structures compatible with biological knowledge and experimental data are necessary to make the identification problem tractable. RESULTS: We propose a differential equation modelling framework where the regulatory interactions among genes are expressed in terms of unate functions, a class of gene activation rules commonly encountered in Boolean network modelling. We establish analytical properties of the models in the class and exploit them to devise a two-step procedure for gene network reconstruction from product concentration and synthesis rate time series. The first step isolates a family of model structures compatible with the data from a set of most relevant biological hypotheses. The second step explores this family and returns a pool of best fitting models along with estimates of their parameters. The method is tested on a simulated network and compared with state-of-the-art network inference methods on the benchmark synthetic network IRMA.

Structural identification of piecewise-linear models of genetic regulatory networks.

We present a method for the structural identification of genetic regulatory networks (GRNs), based on the use of a class of Piecewise-Linear (PL) models. These models consist of a set of decoupled linear models describing the different modes of operation of the GRN and discrete switches between the modes accounting for the nonlinear character of gene regulation. They thus form a compromise between the mathematical simplicity of linear models and the biological expressiveness of nonlinear models. The input of the PL identification method consists of time-series measurements of concentrations of gene products. As output it produces estimates of the modes of operation of the GRN, as well as all possible minimal combinations of threshold concentrations of the gene products accounting for switches between the modes of operation. The applicability of the PL identification method has been evaluated using simulated data obtained from a model of the carbon starvation response in the bacterium Escherichia coli. This has allowed us to systematically test the performance of the method under different data characteristics, notably variations in the noise level and the sampling density.