Spatiotemporal characteristics and drivers of Chinese urban total noise pollution from 2007 to 2019.

Noise pollution as a result of urbanization and socioeconomic development threatens human health and has become a major environmental problem worldwide, particularly for urban residents. Based on observed equivalent noise data of 113 major Chinese cities, a Bayesian spatiotemporal hierarchy model (BSTHM) was employed to investigate the spatiotemporal characteristics of urban noise pollution in China from 2007 to 2019. Meanwhile, the BART model was adopted to explore the drivers of urban noise pollution. The mean and medium of the equivalent noise of the 113 major cities decreased from 2007 to 2011 but increased from 2011 to 2019; the corresponding annual growth is 0.0793 dB and 0.0947 dB per year. The overall spatial pattern has a certain geographical feature. The cities located in the eastern and north-eastern coastal regions generally have a higher level of noise pollution, and the western and southwestern cities have a lower level. One hundred cities not only have greater noise pollution but also an increasing trend. Although the 52 cities located in Western China have less noise pollution, they have increasing local trends. The results indicate that economic and social factors are the main drivers of noise pollution of China; the explanatory power is 46.2%. Traffic factors are also relatively important drivers, of which bus ridership is the leading one. In terms of the natural environment, climatic factors, including temperature and relative humidity, and presence of green areas containing parkland and general green land are the main determinants.

Chain length-dependent inulin alleviates diet-induced obesity and metabolic disorders in mice.

Dietary fiber is regarded to improve host metabolic disorders through modulating gut microbiota. The study was to investigate the effects of inulin with different degree of polymerization (DP) on adiposity, related metabolic syndrome, and the possible mechanisms from the points of gut microbiota and metabolite changes. C57Bl/6J male mice were randomly allocated to normal diet (ND) group, high-fat diet (HFD) group, two HFD groups with short-chain inulin (HFD-S) and medium and long-chain inulin (HFD-ML) for 8 weeks. Compared with HFD treatment, ML-inulin supplementation significantly decreased weight gain, hepatic steatosis, chronic inflammation, and increased insulin sensitivity, energy expenditure and thermogenesis. This could be mimicked by S-inulin supplementation to some degree although it is not as effective as ML inulin. Also, mice treated with S and ML inulin had a remarkable alternation in the composition of gut microbiota and increased the production of short-chain fatty acids (SCFAs). However, reduced serum levels of essential fatty acids, vitamins B1 and B3 by HFD were further decreased by both inulin supplementations. ML inulin can prevent HFD-induced obesity and the associated metabolic disorders, and may be used as novel gut microbiota modulator to prevent HFD-induced gut dysbiosis and metabolic disorders.

[Effects of Shoutai pills on immune function and oxidative stress in pregnant rats with di(2-ethylhexyl) phthalate exposure].

OBJECTIVE: To investigate the effects of Shoutai pills (a traditional Chinese medicinal preparation) on immune functions and oxidative stress in pregnant rats exposed to di(2-ethylhexyl) phthalate (DEHP). METHODS: Thirty-six mature female SD rats were randomly divided into 3 groups (n=12). After pregnancy was confirmed, the rats were given 10 mL/kg corn oil +10 mL/kg saline (control group), 500 mg/kg DEHP+10 mL/kg saline (model group), and 500 mg/kg DEHP+10 mL/kg Shoutai pills (treatment group). At 19 days of gestation, the rats were sacrificed and the fetal rats were weighed and the numbers of live and stillborn fetal rats were recorded. Serum levels of interleukin-6 (IL-6), interleukin-2 (IL-2), tumor necrosis factor-ɑ (TNF-ɑ), estradiol (E2) and progesterone (P) levels were detected. The appearance, color and quality of the placenta in each group were recorded, and the placental tissues were examined pathologically. The total antioxidant capacity (T-AOC), superoxide dismutase (SOD), glutathione peroxidase (GSH- Px), catalase (CAT), reactive oxygen species (ROS) and malondialdehyde (MDA) in the placental tissues were measured. RESULTS: Compared with the control group, the rats with DEHP exposure showed slow weight gain in the middle and late gestation period and significantly lower fetal weight (P < 0.05) with lowered serum levels of IL-2, IL-6 and TNF-ɑ, increased estradiol level (P < 0.05), decreased placental T-AOC, GSH-Px, SOD and CAT levels, and increased ROS and MDA levels (P < 0.01). Compared with the model group, the rats treated with Shoutai pills had significantly increased weight gain in mid and late pregnancy and greater fetal weight (P < 0.05) with significantly increased serum IL-2 and IL-6 levels, decreased estradiol level (P < 0.05), slightly increased TNF-ɑ expression (P> 0.05), increased placenta T-AOC, GSH- Px and CAT levels, decreased MDA level (P < 0.05), and slightly increased SOD and decreased ROS levels (P>0.05). No significant difference was found in progesterone levels among the groups (P>0.05). HE staining showed that the trophoblast in the placental tissue sponge in the model group was loose and irregular with numerous vacuoles. In the treatment group, the structure of the placenta remained intact with clearly visible labyrinth zone, sponge trophoblast and giant cell trophoblast, and the cell distribution in each layer was better than that in the model group. CONCLUSIONS: Shoutai pills can regulate the immune function of DEHP-exposed pregnant rats possibly by antagonizing the estrogenlike effect of DEHP and regulating serum immune factors; Shoutai pills can also reduce placental tissue damage and improve pregnancy outcome by correcting DEHP-induced imbalance of oxidative stress in the placental tissues.

Spatiotemporal variation and determinants of population's PM2.5 exposure risk in China, 1998-2017: a case study of the Beijing-Tianjin-Hebei region.

PM2.5 pollution has emerged as a global human health risk. The best measure of its impact is a population's PM2.5 exposure (PPM2.5E), an index that simultaneously considers PM2.5 concentrations and population spatial density. The spatiotemporal variation of PPM2.5E over the Beijing-Tianjin-Hebei (BTH) region, which is the national capital region of China, was investigated using a Bayesian space-time model, and the influence patterns of the anthropic and geographical factors were identified using the GeoDetector model and Pearson correlation analysis. The spatial pattern of PPM2.5E maintained a stable structure over the BTH region's distinct terrain, which has been described as "high in the northwest, low in the southeast". The spatial difference of PPM2.5E intensified annually. An overall increase of 6.192 (95% CI 6.186, 6.203) ×103 µg/m3 ∙ persons/km2 per year occurred over the BTH region from 1998 to 2017. The evolution of PPM2.5E in the region can be described as "high value, high increase" and "low value, low increase", since human activities related to gross domestic product (GDP) and energy consumption (EC) were the main factors in its occurrence. GDP had the strongest explanatory power of 76% (P < 0.01), followed by EC and elevation (EL), which accounted for 61% (P < 0.01) and 40% (P < 0.01), respectively. There were four factors, proportion of secondary industry (PSI), normalized differential vegetation index (NDVI), relief amplitude (RA), and EL, associated negatively with PPM2.5E and four factors, GDP, EC, annual precipitation (AP), and annual average temperature (AAT), associated positively with PPM2.5E. Remarkably, the interaction of GDP and NDVI, which was 90%, had the greatest explanatory power for PPM2.5E ' s diffusion and impact on the BTH region.

Exploring Spatial Influence of Remotely Sensed PM2.5 Concentration Using a Developed Deep Convolutional Neural Network Model.

Currently, more and more remotely sensed data are being accumulated, and the spatial analysis methods for remotely sensed data, especially big data, are desiderating innovation. A deep convolutional network (CNN) model is proposed in this paper for exploiting the spatial influence feature in remotely sensed data. The method was applied in investigating the magnitude of the spatial influence of four factors-population, gross domestic product (GDP), terrain, land-use and land-cover (LULC)-on remotely sensed PM2.5 concentration over China. Satisfactory results were produced by the method. It demonstrates that the deep CNN model can be well applied in the field of spatial analysing remotely sensed big data. And the accuracy of the deep CNN is much higher than of geographically weighted regression (GWR) based on comparation. The results showed that population spatial density, GDP spatial density, terrain, and LULC could together determine the spatial distribution of PM2.5 annual concentrations with an overall spatial influencing magnitude of 97.85%. Population, GDP, terrain, and LULC have individual spatial influencing magnitudes of 47.12% and 36.13%, 50.07% and 40.91% on PM2.5 annual concentrations respectively. Terrain and LULC are the dominating spatial influencing factors, and only these two factors together may approximately determine the spatial pattern of PM2.5 annual concentration over China with a high spatial influencing magnitude of 96.65%.

Globally analysing spatiotemporal trends of anthropogenic PM2.5 concentration and population's PM2.5 exposure from 1998 to 2016.

Air pollution in the form of particulate matter (PM) is becoming one of the greatest current threats to human health on a global scale. This paper firstly presents a Bayesian space-time hierarch piecewise regression model (BSTHPRM) which can self-adaptively detect the transitions of local trends, accounting for spatial correlations. The spatiotemporal trends of the approximately anthropogenic PM2.5 removed natural dust (PM2.5_No Dust) concentrations and the corresponding population's PM2.5_No Dust exposure (PPM2.5E) in the global continent from 1998 to 2016 were investigated by the presented BSTHPRM. The total areas of the high and higher PM2.5_No Dust-polluted regions, whose spatial relative magnitude of PM2.5_NoDust pollution to the global continental overall level was between 1.89 and 14.68, accounted for about 13.4% of the global land area, and the corresponding exposed populations accounted for 56.0% of the global total population. The spatial heterogeneity of the global PM2.5_NoDust pollution increased generally from 1998 to 2016. The areas of hot, warm, and cold spots with increasing trends of PM2.5_NoDust concentration initially contracted and then later expanded. The local trends of the global continental PM2.5_NoDust concentrations and PPM2.5E can be parted into three changing stages, early, medium, and later stages, using the BSTHPRM. The area proportions of the regions experiencing a decreasing trend of PM2.5_NoDust concentrations and PPM2.5E were greater in the medium stage than in the early and later stages. The local trends of PM2.5_NoDust concentration and PPM2.5E in the two higher PM2.5_NoDust polluted areas, northern India and eastern and southern China, increased in the early stage and then decreased in the medium stage. In the later stage (recent years), northern India displayed a stronger increasing trend; nevertheless, the follow-up decreasing trend still occurred in eastern and southern China. In the first two stages, more than half of the areas in Europe experienced a decreasing trend of PM2.5_NoDust concentration and PPM2.5E; later, more than half of areas in Europe exhibited increasing trends in the later stage. North America and South America experienced a similar local trend of PPM2.5E to Europe. The PPM2.5E trend in Africa generally increased during the study period.

Identification of gene products that control lipid droplet size in yeast using a high-throughput quantitative image analysis.

Lipid droplets (LDs) are important organelles involved in energy storage and expenditure. LD dynamics has been investigated using genome-wide image screening methods in yeast and other model organisms. For most studies, genes were identified using two-dimensional images with LD enlargement as readout. Due to imaging limitation, reduction of LD size is seldom explored. Here, we aim to set up a screen that specifically utilizes reduced LD size as the readout. To achieve this, a novel yeast screen is set up to quantitatively and systematically identify genes that regulate LD size through a three-dimensional imaging-based approach. Cidea which promotes LD fusion and growth in mammalian cells was overexpressed in a yeast knockout library to induce large LD formation. Next, an automated, high-throughput image analysis method that monitors LD size was utilized. With this screen, we identified twelve genes that reduced LD size when deleted. The effects of eight of these genes on LD size were further validated in fld1 null strain background. In addition, six genes were previously identified as LD-regulating genes. To conclude, this methodology represents a promising strategy to screen for players in LD size control in both yeast and mammalian cells to aid in the investigation of LD-associated metabolic diseases.

c-Src phosphorylation and activation of hexokinase promotes tumorigenesis and metastasis.

It is well known that c-Src has important roles in tumorigenesis. However, it remains unclear whether c-Src contributes to metabolic reprogramming. Here we find that c-Src can interact with and phosphorylate hexokinases HK1 and HK2, the rate-limiting enzymes in glycolysis. Tyrosine phosphorylation dramatically increases their catalytic activity and thus enhances glycolysis. Mechanistically, c-Src phosphorylation of HK1 at Tyr732 robustly decreases its Km and increases its Vmax by disrupting its dimer formation. Mutation in c-Src phosphorylation site of either HK1 or HK2 remarkably abrogates the stimulating effects of c-Src on glycolysis, cell proliferation, migration, invasion, tumorigenesis and metastasis. Due to its lower Km for glucose, HK1 rather than HK2 is required for tumour cell survival when glucose is scarce. Importantly, HK1-Y732 phosphorylation level remarkably correlates with the incidence and metastasis of various clinical cancers and may serve as a marker to predict metastasis risk of primary cancers.

Spatiotemporal Variability of Remotely Sensed PM2.5 Concentrations in China from 1998 to 2014 Based on a Bayesian Hierarchy Model.

With the rapid industrial development and urbanization in China over the past three decades, PM2.5 pollution has become a severe environmental problem that threatens public health. Due to its unbalanced development and intrinsic topography features, the distribution of PM2.5 concentrations over China is spatially heterogeneous. In this study, we explore the spatiotemporal variations of PM2.5 pollution in China and four great urban areas from 1998 to 2014. A space-time Bayesian hierarchy model is employed to analyse PM2.5 pollution. The results show that a stable "3-Clusters" spatial PM2.5 pollution pattern has formed. The mean and 90% quantile of the PM2.5 concentrations in China have increased significantly, with annual increases of 0.279 µg/m³ (95% CI: 0.083-0.475) and 0.735 µg/m³ (95% CI: 0.261-1.210), respectively. The area with a PM2.5 pollution level of more than 70 µg/m³ has increased significantly, with an annual increase of 0.26 percentage points. Two regions in particular, the North China Plain and Sichuan Basin, are experiencing the largest amounts of PM2.5 pollution. The polluted areas, with a high local magnitude of more than 1.0 relative to the overall PM2.5 concentration, affect an area with a human population of 949 million, which corresponded to 69.3% of the total population in 2010. North and south differentiation occurs in the urban areas of the Jingjinji and Yangtze Delta, and circular and radial gradient differentiation occur in the urban areas of the Cheng-Yu and Pearl Deltas. The spatial heterogeneity of the urban Jingjinji group is the strongest. Eighteen cities located in the Yangtze Delta urban group, including Shanghai and Nanjing, have experienced high PM2.5 concentrations and faster local trends of increasing PM2.5. The percentage of exposure to PM2.5 concentrations greater than 70 µg/m³ and 100 µg/m³ is increasing significantly.

C-terminus of MUC16 activates Wnt signaling pathway through its interaction with ß-catenin to promote tumorigenesis and metastasis.

MUC16/CA125 has been identified as a prominent cancer biomarker, especially for epithelial ovarian cancers, in clinical test for over three decades. Due to its huge mass, limited knowledge of MUC16 was acquired previously. By utilizing a well characterized self-made MUC16 monoclonal antibody, we identified the endogenous interaction between a C-terminal fragment of MUC16 (MUC16C) and ß-catenin for the first time, and further elucidated that trans-activation domain of ß-catenin is required for this interaction. Such interaction could activate the Wnt/ß-catenin signaling pathway by facilitating cytosol-nucleus transportation of ß-catenin, consequently induce cell proliferation and the migration, eventually lead to tumorigenesis and metastasis in nude mice. Consistently, knockdown of MUC16 significantly weakened the capabilities of cells for proliferation and migration. Based on our discovery, we suggest that MUC16 appears as an attractive target for the development of effective anticancer drugs.