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Objective To investigate the evaluative values of peripheral blood leukocyte, neutrophil and lymphocyte counts on disorders of consciousness (DOC) in patients with acute cerebral infarction. Methods A total of 136 patients with acute cerebral infarction admitted to the Affiliated Central Hospital of Chongqing University from January to December 2018 were selected in this study. According to Glasgow coma scale (GCS) at admission, they were divided into DOC group (GCS≤14, n=59) and non-DOC group (GCS=15, n=77). The absolute values of peripheral blood leukocyte count and neutrophil count, percentage of neutrophils, absolute value of lymphocytes, percentage of lymphocytes, neutrophil/lymphocyte ratio, and common vascular risk factors (such as age, gender, hypertension, diabetes, atrial fibrillation and hyperlipidemia) were compared between the 2 groups. In addition, the relationships between the above peripheral blood indexes and GCS score were analyzed in the DOC group. Results The absolute values of leukocytes and neutrophils, percentage of neutrophils and proportion of patients with atrial fibrillation were significantly higher in the DOC group than the non-DOC group, but there were no statistical differences in the absolute value of lymphocytes, percentage of lymphocytes, neutrophil/lymphocyte ratio, age, gender, history of hypertension, diabetes and hyperlipidemia between the 2 groups. In addition, the absolute values of leukocytes and neutrophils and percentage of neutrophils in peripheral blood were negatively correlated with GCS scores. The higher the above 3 indexes were, the lower the GCS scores were. After adjustment for atrial fibrillation and other vascular risk factors by analysis of covariance, the above negative correlations still existed. Conclusion The absolute values of peripheral blood leukocytes and neutrophils, and percentage of neutrophils are closely associated with decreased GCS score in acute cerebral infarction patients.

Incorporating three-dimensional (3D) spatial measurement into the urban expansion measurement system plays an important role in understanding the degree of urban expansion and regulating and governing it. This article proposes an urban concrete forest and constructs an Urban Volume Sprawl Index to quantitatively study the spatiotemporal characteristics of the three-dimensional expansion of Chinese prefecture-level cities. Furthermore, geographic detectors are used to study its influencing factors. The following conclusions are drawn: (1) Between 1990 and 2018, the volume of Chinese cities has increased from 414.67 to 1,406.29 km3, with a growth rate of 239.13%. (2) The overall growth rate of urban volume shows a gradual slowing trend during the research period, from 55.99% to 40.83%. (3) Between 1990 and 2018, the Urban Volume Sprawl Index nationwide gradually slowed down (from 5.18% to 3.90%), with the largest rate reported in the western region (4.1%), the middle in the eastern region (3.46%), and the smallest in the central region (3.28%). (4) Government and economic factors have the greatest impact on the three-dimensional expansion of Chinese cities (over 69%), with social factors in the middle, and natural factors having the weakest explanatory power (less than 15%). (5) There are differences in the degree of urban expansion between two-dimensional and three-dimensional perspectives. A total of 38.1% of cities have a greater degree of urban expansion from a two-dimensional (2D) analysis perspective, which are mainly small cities located in the western and eastern regions. A total of 5.4% of cities (mainly located in the Yangtze River Delta and Pearl River Delta regions) have a greater degree of urban expansion from a 3D perspective. The remaining cities (56.5%) go through the same level of urban expansion in both analysis dimensions. A 3D urban expansion analysis can better explain and reveal the actual production and living space provided by urban expansion than 2D analysis. It can provide temporal data for urban land efficiency evaluation, ghost city identification, etc., and it has good application and promotion prospects. [ABSTRACT FROM AUTHOR]

Land use has been demonstrated to have an important influence on PM2.5 concentrations; however, how the scale effects and regional disparities in land use influence PM2.5 concentrations remains unclear. This study investigated the scale differences in spatial variations in PM2.5 concentrations, in spatial associations between PM2.5 concentrations and land use, and explored the effects of the spatial heterogeneity and action scale of land use on PM2.5 concentrations. The main findings indicated greater intra-unit variation at small scales and greater inter-unit variation at large scales. PM2.5 concentrations had a positive association with the surrounding cultivated land and artificial surface, and had a negative association with surrounding forest and grass; the positive spatial association between PM2.5 concentrations and the surrounding artificial surface was stronger at small scales. Cultivated land and forest negatively influenced PM2.5 concentrations generally. Artificial surfaces showed a strong positive influence on PM2.5 concentrations in most urban areas. The action scale of cultivated land in influencing PM2.5 concentrations was the largest (4698.05 m). The findings provide a new interpretation of the relationship between PM2.5 concentrations and land use, and may contribute to effective policy making from the perspective of land use planning to PM2.5 pollution control and abatement. [ABSTRACT FROM AUTHOR]

Air pollution is one of the top environmental concerns and causes of deaths and various diseases worldwide. In this paper, PM2.5 observation data at 1,497 automatic air quality-monitoring stations in 367 cities of China in 2015 were utilized, and the study on spatiotemporal distribution of PM2.5 concentration found that the average annual concentration of PM2.5 in urban China in 2015 was 49.74 µg/m3 and exceeded the annual average limit in 287 cities. PM2.5 concentrations were highest in winter and lowest in summer in most cities, but it reached the highest in spring in the cities around Taklimakan Desert. There were 320 fi ne days in 2015 and the maximum PM2.5 was prone to appear at night, the minimum was usually in the afternoon, but in the early morning in Lhasa, and the minimum in winter was even in the early morning, midday and afternoon in some cities. Higher concentrations of PM2.5 were found in the East China Plain and the cities around Taklimakan Desert, preceded by the Yangtze River Delta economic zone, Chengdu-Chongqing economic zone and Harbin-Changchun megalopolis, while the lower values covered the northwestern region of Xinjiang, Heilongjiang Xing'an Mountains region, northeast high latitudes of Inner Mongolia, southwest high altitudes in the border area of Sichuan, Yunnan, Qinghai-Tibet Plateau and the southeast coastal areas. [ABSTRACT FROM AUTHOR]

The prediction of PM2.5 concentrations with high spatiotemporal resolution has been suggested as a potential method for data collection to assess the health effects of exposure. This work predicted the weekly average PM2.5 concentrations in the Yangtze River Delta, China, by using a spatio-temporal model. Integrating land use data, including the areas of cultivated land, construction land, and forest land, and meteorological data, including precipitation, air pressure, relative humidity, temperature, and wind speed, we used the model to estimate the weekly average PM2.5 concentrations. We validated the estimated effects by using the cross-validated R2 and Root mean square error (RMSE); the results showed that the model performed well in capturing the spatiotemporal variability of PM2.5 concentration, with a reasonably large R2 of 0.86 and a small RMSE of 8.15 (μg/m3). In addition, the predicted values covered 94% of the observed data at the 95% confidence interval. This work provided a dataset of PM2.5 concentration predictions with a spatiotemporal resolution of 3 km × week, which would contribute to accurately assessing the potential health effects of air pollution. [ABSTRACT FROM AUTHOR]

Land use change has an important influence on the spatial and temporal distribution of PM2.5 concentration. Therefore, based on the particulate matter (PM2.5) data from remote sensing instruments and land use change data in long time series, the Getis-Ord Gi* statistic and SP-SDM are employed to analyze the spatial distribution pattern of PM2.5 and its response to land use change in China. It is found that the average PM2.5 increased from 25.49 μg/m3 to 31.23 μg/m3 during 2000-2016, showing an annual average growth rate of 0.97%. It is still greater than 35 μg/m3 in nearly half of all cities. The spatial distribution pattern of PM2.5 presents the characteristics of concentrated regional convergence. PM2.5 is positively correlated with urban land and farmland, negatively correlated with forest land, grassland, and unused land. Furthermore, the average PM2.5 concentrations show the highest values for urban land and decrease in the order of farmland > unused land > water body > forest > grassland. The impact of land use change on PM2.5 is a non-linear process, and there are obvious differences and spillover effects for different land types. Thus, reasonably controlling the scale of urban land and farmland, optimizing the spatial distribution pattern and development intensity, and expanding forest land and grassland are conducive to curbing PM2.5 pollution. The research conclusions provide a theoretical basis for the management of PM2.5 pollution from the perspective of optimizing land use. [ABSTRACT FROM AUTHOR]

The lockdown of cities in the Yangtze River Delta (YRD) during COVID-19 has provided many natural and typical test sites for estimating the potential of air pollution control and reduction. To evaluate the reduction of PM2.5 concentration in the YRD region by the epidemic lockdown policy, this study employs big data, including PM2.5 observations and 29 independent variables regarding Aerosol Optical Depth (AOD), climate, terrain, population, road density, and Gaode map Point of interesting (POI) data, to build regression models and retrieve spatially continuous distributions of PM2.5 during COVID-19. Simulation accuracy of multiple machine learning regression models, i.e., random forest (RF), support vector regression (SVR), and artificial neural network (ANN) were compared. The results showed that the RF model outperformed the SVR and ANN models in the inversion of PM2.5 in the YRD region, with the model-fitting and cross-validation coefficients of determination R2 reached 0.917 and 0.691, mean absolute error (MAE) values were 1.026 μg m−3 and 2.353 μg m−3, and root mean square error (RMSE) values were 1.413 μg m−3, and 3.144 μg m−3, respectively. PM2.5 concentrations during COVID-19 in 2020 have decreased by 3.61 μg m−3 compared to that during the same period of 2019 in the YRD region. The results of this study provide a cost-effective method of air pollution exposure assessment and help provide insight into the atmospheric changes under strong government controlling strategies. [ABSTRACT FROM AUTHOR]

PM 2.5 pollution is an environmental issue caused by multiple natural and socioeconomic factors, presenting with significant spatial disparities across mainland China. However, the determinant power of natural and socioeconomic factors and their interactive impact on PM 2.5 pollution is still unclear. In the study, the GeogDetector method was used to quantify nonlinear associations between PM 2.5 and potential factors. This study found that natural factors, including ecological environments and climate, were more influential than socioeconomic factors, and climate was the predominant factor (q = 0.56) in influencing PM 2.5 pollution. Among all interactions of the six influencing factors, the interaction of industry and climate had the largest influence (q = 0.66). Two recognized major contaminated areas were the Tarim Basin in the northwest region and the eastern plain region; the former was mainly influenced by the warm temperate arid climate and desert, and the latter was mainly influenced by the warm temperate semi-humid climate and multiple socioeconomic factors. The findings provided an interpretation of the influencing mechanisms of PM 2.5 pollution, which can contribute to more specific policies aimed at successful PM 2.5 pollution control and abatement., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

Numerous evidences demonstrated that type 1 diabetes (T1D) is due to a loss of immune tolerance to islet antigens, and CD8(+) T cells play an important role in the development of T1D. Zinc Transporter 8 (ZnT8) has emerged in recent years as a target of disease-associated autoreactive T cells in human T1D. However, ZnT8-associated CTL specific-peptides have not been identified. In this study, we predicted and identified HLA-A*0201-restricted cytotoxic T lymphocyte (CTL) epitopes derived from ZnT8, and utilized it to immunize HLA-A2.1/Kb transgenic (Tg) mice. The results demonstrated that peptides of ZnT8 containing residues 107-115, 115-123 and 145-153 could elicit specific CTLs in vitro, and induce diabetes in mice. The results suggest that these specific peptides are novel HLA-A*0201-restricted CTL epitopes, and could have therapeutic potential in preventing of T1D disease., (Copyright © 2012 Elsevier Ltd. All rights reserved.)