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Solar-driven catalysis is a promising strategy for transforming CO2 into fuels and valuable chemical feedstocks, with current research focusing primarily on increasing CO2 conversion efficiency and product selectivity. Herein, a series of FeO–CeO2 nanocomposite catalysts were successfully prepared by H2 reduction of Fe(OH)3-Ce(OH)3 precursors at temperatures (x) ranging from 200 to 600 °C (the obtained catalysts are denoted as FeCe-x). An FeCe-300 catalyst with an Fe:Ce molar ratio of 2:1 demonstrated outstanding performance for photothermal CO2 conversion to CO in the presence of H2 under Xe lamp irradiation (CO2 conversion, 43.63%; CO selectivity, 99.87%; CO production rate, 19.61 mmol h−1 gcat−1; stable operation over 50 h). Characterization studies using powder X-ray diffraction and high-resolution transmission electron microscopy determined that the active catalyst comprises FeO and CeO2 nanoparticles. The selectivity to CO of the FeCe-x catalysts decreased as the reduction temperature (x) increased in the range of 300–500 °C due to the appearance of metallic Fe0, which introduced an additional reaction pathway for the production of CH4. In situ diffuse reflectance infrared Fourier transform spectroscopy identified formate, bicarbonate and methanol as important reaction intermediates during light-driven CO2 hydrogenation over the FeCe-x catalysts, providing key mechanistic information needed to explain the product distributions of CO2 hydrogenation on the different catalysts. Catalysis: Reclaiming carbon dioxide A nanomaterial that helps convert carbon dioxide to more useful chemicals has been developed by researchers in China. One potential method is to convert the carbon dioxide into carbon monoxide using a reaction known as reverse water-gas shift, and then use further reactions to convert this into fuel, or produce useful chemicals such as methanol or methane. This reaction normally requires high temperatures, and a catalyst is required to make the conversion efficient at lower, more practical temperatures. Tierui Zhang from the Technical Institute of Physics and Chemistry in Beijing and co-workers developed a nanocomposite based on iron and cerium with excellent performance in converting carbon dioxide into carbon monoxide with hydrogen only under light irradiation. This result indicates the potential of solar-driven catalysis for transforming carbon dioxide into fuels. A series of FeO-CeO2 nanocomposite catalysts (FeCe-x) were successfully fabricated by hydrogen reduction of hydroxide precursors at temperatures (x) between 200–600 °C. A FeCe-300 catalyst with a Fe:Ce ratio of 2-1 exhibited excellent performance for photothermal CO2 hydrogenation to CO (CO selectivity = 99.87%, CO production rate 19.61 mmol h−1 gcat−1, excellent stability). The FeO phase was effective in promoting the reverse water-gas shift (RWGS, CO2 + H2 → CO + H2O). Catalysts prepared at higher reduction temperatures contained both Fe0 and FeO, with the Fe0 catalyzing the Sabatier reaction (CO2 + 4H2 → CH4 + 2H2O) and thus lowering FeCe-x catalyst selectivity to CO. [ABSTRACT FROM AUTHOR]

Social area analysis has been applied to analyze how urban space is spatially arranged. Related studies have divided urban residents into different groups whose socioeconomic status are similar. However, few studies have attempted to explore the mobility of these social groups, especially for developing countries where census data with travel information are not readily available. This study explores whether people in the same social area have similar work mobility and whether location matters from an aggregated neighborhood perspective. A case study is conducted in Shenzhen, China with fine-grained census and mobile phone data. The neighborhood committee based census and cell tower based mobile phone data are integrated into 500 m grid for analysis. Results show that different social areas have significantly different work mobility, and the within-group heterogeneity is also prominent. Three location factors are examined and it is found that there exist variations in the influence of spatial factors on work mobility for different social areas. Findings of this study will supplement current knowledge about social areas, location and mobility in urban China at the mesoscale. In addition, the methodology provides a feasible framework of integrating city-scale socioeconomic information and mobility data from different sources for future urban studies. • Similar social groups have different mobility at the aggregated level. • The difference is related to interactions of socio-economic status and location. • Variations in the influence of location on mobility for different social group • A framework of integrating census and mobile phone data [ABSTRACT FROM AUTHOR]

Background: Dengue fever (DF) is a common mosquito-borne viral infectious disease in the world, and increasingly severe DF epidemics in China have seriously affected people's health in recent years. Thus, investigating spatiotemporal patterns and potential influencing factors of DF epidemics in typical regions is critical to consolidate effective prevention and control measures for these regional epidemics. Methods: A generalized additive model (GAM) was used to identify potential contributing factors that influence spatiotemporal epidemic patterns in typical DF epidemic regions of China (e.g., the Pearl River Delta [PRD] and the Border of Yunnan and Myanmar [BYM]). In terms of influencing factors, environmental factors including the normalized difference vegetation index (NDVI), temperature, precipitation, and humidity, in conjunction with socioeconomic factors, such as population density (Pop), road density, land-use, and gross domestic product, were employed. Results: DF epidemics in the PRD and BYM exhibit prominent spatial variations at 4 km and 3 km grid scales, characterized by significant spatial clustering over the Guangzhou-Foshan, Dehong, and Xishuangbanna areas. The GAM that integrated the Pop-urban land ratio (ULR)-NDVI-humidity-temperature factors for the PRD and the ULR-Road density-NDVI-temperature-water land ratio-precipitation factors for the BYM performed well in terms of overall accuracy, with Akaike Information Criterion values of 61 859.89 and 826.65, explaining a total variance of 83.4 and 97.3%, respectively. As indicated, socioeconomic factors have a stronger influence on DF epidemics than environmental factors in the study area. Among these factors, Pop (PRD) and ULR (BYM) were the socioeconomic factors explaining the largest variance in regional epidemics, whereas NDVI was the environmental factor explaining the largest variance in both regions. In addition, the common factors (ULR, NDVI, and temperature) in these two regions exhibited different effects on regional epidemics. Conclusions: The spatiotemporal patterns of DF in the PRD and BYM are influenced by environmental and socioeconomic factors, the socioeconomic factors may play a significant role in DF epidemics in cases where environmental factors are suitable and differ only slightly throughout an area. Thus, prevention and control resources should be fully allocated by referring to the spatial patterns of primary influencing factors to better consolidate the prevention and control measures for DF epidemics. [ABSTRACT FROM AUTHOR]

The aim of the study was to explore the association between mucin 5ac expression and cancer prognosis. A systematically comprehensive search was performed through PubMed, the Web of Science, and the China National Knowledge Infrastructure (CNKI). The prognostic value of mucin 5ac expression in cancer patients was evaluated. The overexpression of mucin 5ac was found to be significantly associated with a poor prognosis in cancer patients (pooled HR: 1.53, 95%CI: 1.158-2.028, P = 0.003). This association was also detected in a biliary subgroup (pooled HR: 1.83, 95%CI: 1.269-2.639, P = 0.001) and a gastrointestinal subgroup (pooled HR: 1.44, 95%CI: 1.069-1.949 P = 0.017). In the geography subgroup analysis, a statistical association was found in the Asian subgroup (pooled HR: 1.69, 95%CI: 1.200-2.384, P = 0.003). In the clinical characteristics analysis, a statistical association was found between the hyper expression of mucin 5ac and lymphatic metastasis. We indicated that mucin 5ac is a promising prognostic predictor for cancer, especially for biliary and gastrointestinal cancer, and is more suitable for predicting cancer prognoses in Asians.

Objective: To better understand the characteristics of spatial distribution of malaria epidemics in Hainan province and to explore the relationship between malaria epidemics and environmental factors, as well to develop prediction model on malaria epidemics., Methods: Data on Malaria and meteorological factors were collected in all 19 counties in Hainan province from May to Oct., 2000, and the proportion of land use types of these counties in this period were extracted from digital map of land use in Hainan province. Land surface temperatures (LST) were extracted from MODIS images and elevations of these counties were extracted from DEM of Hainan province. The coefficients of correlation of malaria incidences and these environmental factors were then calculated with SPSS 13.0, and negative binomial regression analysis were done using SAS 9.0., Results: The incidence of malaria showed (1) positive correlations to elevation, proportion of forest land area and grassland area; (2) negative correlations to the proportion of cultivated area, urban and rural residents and to industrial enterprise area, LST; (3) no correlations to meteorological factors, proportion of water area, and unemployed land area. The prediction model of malaria which came from negative binomial regression analysis was: I (monthly, unit: 1/1,000,000) = exp (-1.672-0.399xLST)., Conclusion: Spatial distribution of malaria epidemics was associated with some environmental factors, and prediction model of malaria epidemic could be developed with indexes which extracted from satellite remote sensing images.