Environmental incidents in China:Lessons from 2006 to 2015

Environmental incidents are among the most significant environmental challenges in China. Hundreds of environmental incidents occur every year, endangering human health and ecosystems. In this paper, we conducted an analytical study of environmental incidents from 2006 to 2015 in China. We first examined the spatiotemporal characteristics of the total 5213 incidents based on the statistical data collected from the China Statistical Yearbook on Environment. We then examined the characteristics of the sources of risk, causes of harm and resulting damage of environmental incidents based on first-hand data from 1369 cases collected by the Ministry of Environmental Protection (MEP) of China, which obtains detailed incident information. The results show that (1) there was a significant downward trend in the overall number of environmental incidents between 2006 and 2015, and developed eastern regions were high incidence areas; (2) hazardous chemicals were the main risk stressors; (3) production safety accidents and traffic accidents were the two major causes, and (4) most of these incidents resulted in polluted water and air. This paper is the first to provide a longitudinal analysis of the full scope of environmental incidents across the different regions of China, which has useful implications for policy-making and environmental management

Manipulating cellular microRNAs and analyzing high-dimensional gene expression data using machine learning workflows

MicroRNAs (miRNAs) are elements of the gene regulatory network and manipulating their abundance is essential toward elucidating their role in patho-physiological conditions. We present a detailed workflow that identifies important miRNAs using a machine learning algorithm. We then provide optimized techniques to validate the identified miRNAs through over-expression/loss-of-function studies. Overall, these protocols apply to any field in biology where high-dimensional data are produced. For complete details on the use and execution of this protocol, please refer to Wong et al. (2021a)

Hyperandrogenism and Metabolic Syndrome Are Associated With Changes in Serum-Derived microRNAs in Women With Polycystic Ovary Syndrome

Polycystic ovary syndrome (PCOS) remains one of the most common endocrine disorder in premenopausal women with an unfavorable metabolic risk profile. Here, we investigate whether biochemical hyperandrogenism, represented by elevated serum free testosterone, resulted in an aberrant circulating microRNA (miRNAs) expression profile and whether miRNAs can identify those PCOS women with metabolic syndrome (MetS). Accordingly, we measured serum levels of miRNAs as well as biochemical markers related to MetS in a case-control study of 42 PCOS patients and 20 Controls. Patients were diagnosed based on the Rotterdam consensus criteria and stratified based on serum free testosterone levels (≥0.034 nmol/l) into either a normoandrogenic (n = 23) or hyperandrogenic (n = 19) PCOS group. Overall, hyperandrogenic PCOS women were more insulin resistant compared to normoandrogenic PCOS women and had a higher prevalence of MetS. A total of 750 different miRNAs were analyzed using TaqMan Low-Density Arrays. Altered levels of seven miRNAs (miR-485-3p, -1290, -21-3p, -139-3p, -361-5p, -572, and -143-3p) were observed in PCOS patients when compared with healthy Controls. Stratification of PCOS women revealed that 20 miRNAs were differentially expressed between the three groups. Elevated serum free testosterone levels, adjusted for age and BMI, were significantly associated with five miRNAs (miR-1290, -20a-5p, -139-3p, -433-3p, and -361-5p). Using binary logistic regression and receiver operating characteristic curves (ROC), a combination panel of three miRNAs (miR-361-5p, -1225-3p, and -34-3p) could correctly identify all of the MetS cases within the PCOS group. This study is the first to report comprehensive miRNA profiling in different subgroups of PCOS women with respect to MetS and suggests that circulating miRNAs might be useful as diagnostic biomarkers of MetS for a different subset of PCOS

Effect of carbon fiber crystallite size on the formation of hafnium carbide coating and the mechanism of the reaction of hafnium with carbon fibers

The effect of carbon source crystallite size on the formation of hafnium carbide (HfC) coating was investigated via direct reaction of hafnium powders with mesophase pitch-based carbon fibers (CFs) heat-treated at various temperatures. X-ray diffraction, scanning electron microscopy and energy dispersive X-ray spectroscopy analyses reveal that uniform and dense HfC coatings are preferentially formed on CFs containing larger and more ordered graphite crystallites. The carbide synthesis temperature and the sizes of crystallites in the CFs have a remarkable influence on the integrity and thickness of the coatings. The formation the HfC coatings can be attributed to the surface diffusion of hafnium and the bi-directional diffusion of hafnium and carbon sources inside the HfC coating. The reaction of HfC coated carbon fibers with zirconium powders leads to the growth of ZrC on the HfC coating and this has been shown to occur by the diffusion of carbon from the carbon fiber core through the carbide coating to its surface

A Genome-Wide Association Study of Diabetic Kidney Disease in Subjects With Type 2 Diabetes

dentification of sequence variants robustly associated with predisposition to diabetic kidney disease (DKD) has the potential to provide insights into the pathophysiological mechanisms responsible. We conducted a genome-wide association study (GWAS) of DKD in type 2 diabetes (T2D) using eight complementary dichotomous and quantitative DKD phenotypes: the principal dichotomous analysis involved 5,717 T2D subjects, 3,345 with DKD. Promising association signals were evaluated in up to 26,827 subjects with T2D (12,710 with DKD). A combined T1D+T2D GWAS was performed using complementary data available for subjects with T1D, which, with replication samples, involved up to 40,340 subjects with diabetes (18,582 with DKD). Analysis of specific DKD phenotypes identified a novel signal near GABRR1 (rs9942471, P = 4.5 x 10(-8)) associated with microalbuminuria in European T2D case subjects. However, no replication of this signal was observed in Asian subjects with T2D or in the equivalent T1D analysis. There was only limited support, in this substantially enlarged analysis, for association at previously reported DKD signals, except for those at UMOD and PRKAG2, both associated with estimated glomerular filtration rate. We conclude that, despite challenges in addressing phenotypic heterogeneity, access to increased sample sizes will continue to provide more robust inference regarding risk variant discovery for DKD.Peer reviewe

Genetic Drivers of Heterogeneity in Type 2 Diabetes Pathophysiology

Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes1,2 and molecular mechanisms that are often specific to cell type3,4. Here, to characterize the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study data from 2,535,601 individuals (39.7% not of European ancestry), including 428,452 cases of T2D. We identify 1,289 independent association signals at genome-wide significance (P \u3c 5 × 10-8) that map to 611 loci, of which 145 loci are, to our knowledge, previously unreported. We define eight non-overlapping clusters of T2D signals that are characterized by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type-specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial cells and enteroendocrine cells. We build cluster-specific partitioned polygenic scores5 in a further 279,552 individuals of diverse ancestry, including 30,288 cases of T2D, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned polygenic scores are associated with coronary artery disease, peripheral artery disease and end-stage diabetic nephropathy across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings show the value of integrating multi-ancestry genome-wide association study data with single-cell epigenomics to disentangle the aetiological heterogeneity that drives the development and progression of T2D. This might offer a route to optimize global access to genetically informed diabetes care

Genetic drivers of heterogeneity in type 2 diabetes pathophysiology

Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes1,2 and molecular mechanisms that are often specific to cell type3,4. Here, to characterize the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study data from 2,535,601 individuals (39.7% not of European ancestry), including 428,452 cases of T2D. We identify 1,289 independent association signals at genome-wide significance (P &lt; 5 × 10-8) that map to 611 loci, of which 145 loci are, to our knowledge, previously unreported. We define eight non-overlapping clusters of T2D signals that are characterized by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type-specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial cells and enteroendocrine cells. We build cluster-specific partitioned polygenic scores5 in a further 279,552 individuals of diverse ancestry, including 30,288 cases of T2D, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned polygenic scores are associated with coronary artery disease, peripheral artery disease and end-stage diabetic nephropathy across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings show the value of integrating multi-ancestry genome-wide association study data with single-cell epigenomics to disentangle the aetiological heterogeneity that drives the development and progression of T2D. This might offer a route to optimize global access to genetically informed diabetes care.</p

Seasonal Characteristics of the Chemical Composition of Fine Particles in Residences of Nanjing, China

Indoor fine particulate matter (PM2.5) and its chemical composition is important for human exposure as people spend most of their time indoors. However, few studies have investigated the multiseasonal characteristics of indoor PM2.5 and its chemical composition in China. In this study, the chemical composition of PM2.5 samples in residences was analyzed over four seasons in Nanjing, China. Indoor water-soluble ions exhibited similar seasonal variations (winter &gt; autumn &gt; summer &gt; spring) to those from outdoors (winter &gt; autumn &gt; spring &gt; summer) except in summer. Whereas, indoor metallic elements exhibited a different seasonal pattern from that of outdoors. The highest concentrations of indoor metallic elements were observed in summer when the outdoor concentrations were low. The different seasonal variations of the chemical composition between indoor and outdoor PM2.5 indicated that people should consider both indoor and outdoor sources to reduce their exposure to air pollutants in different seasons. The carcinogenic risks for metallic elements were within the acceptable levels, while manganese (Mn) was found to have potential noncarcinogenic risk to humans. More attention should be paid to the pollution of Mn in the study area in the future. Moreover, the cumulative effect of noncarcinogenic PM2.5-bound elements should not be ignored