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• The causal impact of green space on air pollution remains understudied. • Here, the Normalized Difference Vegetation Index (NDVI) was used to assess green space level in each county of China. • Two-stage least squares estimation (2SLS) and multiplex IV was employed to verify the robustness. • The increased greenspace significantly decreases the PM 2.5 concentration and other air pollutants. • Green spaces show a more pronounced air pollution reduction effect in southern or higher administrative-level cities. Can green vegetation absorb air pollutants and control regional air pollution? The existing research has not reached a consistent conclusion on this issue. Using the multi-level data in China, this paper provides empirical evidence of the causal impact of greenspace on PM 2.5 through the abundant fixed effect and controls. Besides, this paper applied the soil humidity, Normalized Difference Vegetation Index (NDVI) last month, NDVI in the same month last year, and NDVI prefecture-level city average as the instrumental variables, respectively, to test the robustness of the estimation. The results show that increased greenspace significantly decreases the PM 2.5 concentration and other air pollutants. In further analysis, we found that the decreasing effect turns significant when NDVI exceeds 0.3. In addition, there are regional heterogeneities in the impact of greenspace on air pollution. Greenspaces show a more pronounced air pollution reduction effect in southern or higher administrative-level cities. This paper suggests that increasing greenspace is an economical and effective way to achieve co-management of multiple air pollutants. [ABSTRACT FROM AUTHOR]

Abstract: Until just a few years ago, the genetic determinants of obesity and metabolic syndrome were largely unknown, with the exception of a few forms of monogenic extreme obesity. Since genome-wide association studies (GWAS) became available, large advances have been made. The first single nucleotide polymorphism robustly associated with increased body mass index (BMI) was in 2007 mapped to a gene with for the time unknown function. This gene, now known as fat mass and obesity associated (FTO) has been repeatedly replicated in several ethnicities and is affecting obesity by regulating appetite. Since the first report from a GWAS of obesity, an increasing number of markers have been shown to be associated with BMI, other measures of obesity or fat distribution and metabolic syndrome. This systematic review of obesity GWAS will summarize genome-wide significant findings for obesity and metabolic syndrome and briefly give a few suggestions of what is to be expected in the next few years. [Copyright &y& Elsevier]

Abstract: Height is associated with mortality from many diseases, but it remains unclear whether the association is causal or due to confounding by social factors, genetic pleiotropy, 1 [1] Pleiotropy is the influence of one gene on multiple phenotypic traits. or existing ill-health. The authors investigated whether the association of height with mortality is causal by using a son's height as an instrumental variable (IV) for parents’ height among the parents of a cohort of 1,036,963 Swedish men born between 1951 and 1980 who had their height measured at military conscription, aged around 18, between 1969 and 2001. In a two-sample IV analysis adjusting for son's age at examination and secular trends in height, as well as parental age, and socioeconomic position, the hazard ratio (HR) for all-cause paternal mortality per standard deviation (SD, 6.49cm) of height was 0.96 (95% confidence interval (CI): 0.95, 0.96). The results of IV analyses of mortality from all causes, cardiovascular disease (CVD), respiratory disease, cancer, external causes and suicide were comparable to those obtained using son's height as a simple proxy for own height and to conventional analyses of own height in the present data and elsewhere, suggesting that such conventional analyses are not substantially confounded by existing ill-health. [Copyright &y& Elsevier]

Abstract: Determination of the order of a model is the key first step towards modeling any dynamic systems, particularly two-dimensional processes. In this paper, a new method for two-dimensional (2-D) Gaussian ARMA model order determination is proposed. In the proposed method, the AR and MA orders are first independently determined, then the procedure for model order determination of the 2-D ARMA model is outlined. The model is assumed to be causal, stable, linear, and spatial shift-invariant with quarter-plane (QP) support. Numerical simulations are presented to show the effectiveness of the proposed new approach. [Copyright &y& Elsevier]