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Background: Neighbourhood social fragmentation and socioeconomic deprivation seem to be associated with suicide mortality. However, results are inconclusive, which might be because dynamics in the social context are not well-represented by administratively bounded neighbourhoods at baseline. We used individualized neighbourhoods to examine associations between suicide mortality, social fragmentation, and deprivation for the total population as well as by sex and age group. Methods: Using a nested case-control design, all suicides aged 18-64 years between 2007 and 2016 were selected from longitudinal Dutch register data and matched with 10 random controls. Indices for social fragmentation and deprivation were calculated annually for 300, 600, and 1,000 metre circular buffers around each subject's residential address. Results: Suicide mortality was significantly higher in neighbourhoods with high deprivation and social fragmentation. Accounting for individual characteristics largely attenuated these associations. Suicide mortality remained significantly higher for women living in highly fragmented neighbourhoods in the fully adjusted model. Age-stratified analyses indicate associations with neighbourhood fragmentation among women in older age groups (40-64 years) only. Among men, suicide risk was lower in fragmented neighbourhoods for 18-39-year-olds and for short-term residents. In deprived neighbourhoods, the suicide risk was lower for 40-64-year-old men and long-term residents. Associations between neighbourhood characteristics and suicide mortality were comparable across buffer sizes. Conclusion: Our findings suggest that next to individual characteristics, the social and economic context within which people live may both enhance and buffer the risk of suicide.

Parks are increasingly understood to be key community resources for public health, particularly for ethnic minority and low socioeconomic groups. At the same time, research suggests parks are underutilised by these groups. In order to design effective interventions to promote health, the determinants of park use for these groups must be understood. This study examines the associations between park features, park satisfaction and park use in a deprived and ethnically diverse sample in Bradford, UK. 652 women from the Born in Bradford cohort completed a survey on park satisfaction and park use. Using a standardised direct observation tool, 44 parks in the area were audited for present park features. Features assessed were: access, recreational facilities, amenities, natural features, significant natural features, non-natural features, incivilities and usability. Size and proximity to the park were also calculated. Multilevel linear regressions were performed to understand associations between park features and (1) park satisfaction and (2) park use. Interactions between park features, ethnicity and socioeconomic status were explored, and park satisfaction was tested as a mediator in the relationship between park features and park use. More amenities and greater usability were associated with increased park satisfaction, while more incivilities were negatively related to park satisfaction. Incivilities, access and proximity were also negatively associated with park use. Ethnicity and socioeconomic status had no moderating role, and there was no evidence for park satisfaction as a mediator between park features and park use. Results suggest diverse park features are associated with park satisfaction and park use, but this did not vary by ethnicity or socioeconomic status. The reduction of incivilities should be prioritised where the aim is to encourage park satisfaction and park use.

Research suggests that exposure to the natural environment can improve mood, however, current reviews are limited in scope and there is little understanding of moderators. We aimed to conduct a comprehensive systematic review and meta-analysis of the evidence for the effect of short-term exposure to the natural environment on depressive mood. Five databases were systematically searched for relevant studies published up to March 2018. Risk of bias was evaluated using the Cochrane Risk of Bias (ROB) tool 1.0 and the Risk of Bias in Non-Randomised Studies of Interventions (ROBINS-I) tool where appropriate. The GRADE approach was used to assess the quality of evidence overall. A random-effects meta-analysis was performed. 20 potential moderators of the effect size were coded and the machine learning-based MetaForest algorithm was used to identify relevant moderators. These were then entered into a meta-regression. 33 studies met the inclusion criteria. Effect sizes ranged from -2.30 to 0.84, with a pooled effect size of $\gamma$ = -0.30 95% CI [-0.50 to -0.10]. However, there was significant residual heterogeneity between studies and risk of bias was high. Type of natural environment, type of built environment, gender mix of the sample, and region of study origin, among others, were identified as relevant moderators but were not significant when entered in a meta-regression. Quality of evidence was rated very low to low. An assessment of publication bias was inconclusive. A small effect was found for reduction in depressive mood following exposure to the natural environment. However, the high risk of bias and low quality of studies limits confidence in the results. The variation in effect size also remains largely unexplained. It is recommended that future studies make use of reporting guidelines and aim to reduce the potential for bias where possible.

Exposure to green space seems to be beneficial for self-reported mental health. In this study we used an objective health indicator, namely antidepressant prescription rates. Current studies rely exclusively upon mean regression models assuming linear associations. It is, however, plausible that the presence of green space is non-linearly related with different quantiles of the outcome antidepressant prescription rates. These restrictions may contribute to inconsistent findings. Our aim was to assess antidepressant prescription rates in relation to green space, and to analyze how the relationship varies non-linearly across different quantiles of antidepressant prescription rates. We used cross-sectional data for the year 2014 at a municipality level in the Netherlands. Ecological Bayesian geoadditive quantile regressions were fitted for the 15, 50, and 85 percent quantiles to estimate green space-prescription rate correlations, controlling for confounders. The results suggested that green space was overall inversely and non-linearly associated with antidepressant prescription rates. More important, the associations differed across the quantiles, although the variation was modest. Significant non-linearities were apparent: The associations were slightly positive in the lower quantile and strongly negative in the upper one. Our findings imply that an increased availability of green space within a municipality may contribute to a reduction in the number of antidepressant prescriptions dispensed. Green space is thus a central health and community asset, whilst a minimum level of 28 percent needs to be established for health gains. The highest effectiveness occurred at a municipality surface percentage higher than 79 percent. This inverse dose-dependent relation has important implications for setting future community-level health and planning policies.

Modelling genetic phenomena affecting biological traits is important for the development of agriculture as it allows breeders to predict the potential of breeding for certain traits. One such phenomenon is heterosis or hybrid vigor: crossing individuals from genetically distinct populations often results in improvements in quantitative traits, such as growth rate, biomass production and stress resistance. Heterosis has become a very useful tool in global agriculture, but its genetic basis remains controversial and its effects hard to predict. We have taken a computational approach to studying heterosis, developing a simulation of evolution, independent reassortment of alleles and hybridization of Gene Regulatory Networks (GRNs) in a Boolean framework. Fitness is measured as the ability of a network to respond to external inputs in a pre-defined way. Our model reproduced common experimental observations on heterosis using only biologically justified parameters. Hybrid vigor was observed and its extent was seen to increase as parental populations diverged, up until a point of sudden collapse of hybrid fitness. We also reproduce, for the first time in a model, the fact that hybrid vigor cannot easily be fixed by within a breeding line, currently an important limitation of the use of hybrid crops. The simulation allowed us to study the effects of three standard models for the genetic basis of heterosis and the level of detail in our model allows us to suggest possible warning signs of the impending collapse of hybrid vigor in breeding. In addition, the simulation provides a framework that can be extended to study other aspects of heterosis and alternative evolutionary scenarios., Comment: See online version for supplementary material