The uptake of Cd by crops from soils is predominantly determined by the concentration and speciation of Cd in soil solution, which is controlled by soil physicochemical properties such as soil pH, soil minerals and organic matter, etc. Water management significantly affects soil pH, and especially soil pH is driven to be neutral under continuous flooding treatment. In the present study, the multi-surface models (MSMs) were modified to determine Cd partitioning in soils for prediction of Cd uptake by rice grain, with multiple parameter or setup changes including (1) soil pH was considered as variables to improve the accuracy of model prediction for paddy soils; (2) practical co-existing cation concentration and ionic strength were derived from electron conductivity to improve the universality of model. Our results suggested that the modified MSMs model provided a better prediction for the actual uptake by rice grain and a more consistent Cd distribution pattern in paddy soils. Artificial light at night has greatly changed the physical environment for many organisms on a global scale. As an energy efficient light resource, light emitting diodes (LEDs) have been widely used in recent years. As LEDs often have a broad spectrum, many biological processes may be potentially affected. In this study, we conducted manipulated experiments in rat-proof enclosures to explore the effects of LED night lighting on behavior of a nocturnal rodent, the Mongolian five-toed jerboa (Allactaga sibirica). We adopted the giving-up density (GUD) method and camera video trapping to study behavioral responses in terms of patch use, searching efficiency and vigilance. With the presence of white LED lighting, jerboas spent less time in patches, foraged less intensively (with higher GUDs) and became vigilant more frequently, while their searching efficiency was higher than under dark treatment. Although both positive and negative effects of LEDs on foraging were detected, the net effect of LEDs on jerboas is negative, which may further translate into changes in population dynamics, inter-specific interaction and community structure. To our knowledge, this is the first field study to explore how LED lighting affect foraging behavior and searching efficiency in rodents. Our results may have potential implications for practices such as pest control. Antimicrobial resistance (AMR) is considered an emerging public health problem. Greater AMR development rate is associated with "antibiotic-using" environments. Wildlife thriving in anthropized landscapes could be good indicators of the burden of AMR and antibiotic resistance genes (ARGs) in these areas. The aim of this study was to determine the presence and load of ARGs in fecal swabs of wild Andean foxes (Lycalopex culpaeus) from anthropized landscapes of central Chile. DNA was extracted from samples of 72 foxes; 22 ARGs encoding resistance against 8 antibiotic groups were evaluated using qPCR. Eighteen of the 22 ARGs were found and tet(Q) (65.3%; 15/72 of the samples) was the most common gene detected. Almost half of the foxes presented a 'multiresistant microbiome' (i.e. at least three ARG encoding resistance to different groups of antimicrobials). Prevalence of tet(Q) was higher in the cold-humid season than in the warm-dry season, but not for other genes. Up to 15 and 13 ARGs were detected in the fecal samples from two additional foxes that were kept 6 and 11 days, respectively, in a clinical environment (Wildlife Rescue Center) and received antibiotic treatment. read more Some of the ARGs detected (e.g. mecA and blaCTX-M) in the present study are of particular concern from the public health perspective. Wild foxes seem to be good sentinels for ARG environmental burden in highly anthropized environments of central Chile. Rain-fed corn system has varied optimal environmental requirements by growth phases and regions. Understanding spatiotemporal characteristics of such requirements are important to ensure food security. To capture the stage-variant growing requirements, we develop and compare statistical models with various spatial and temporal resolutions to quantify the relationships between corn yield and meteorological factors. Multilinear regression models are trained using cross-sectional datasets pooled at three magnitudes (state, district, county) with temperature and precipitation related predictors according to three temporal resolutions (growing season, fixed month, growing phase). The models are applied to the U.S. Corn Belt for the time period of 1981-2016. The results show that average corn yield variation explained by meteorological factors can be improved to 50.2% at the agricultural district scale with growth phase resolution from ~30% at the state-level with growing season resolution. The results reveal that corn yield is most sensitive to extreme heat stress during the grain filling phase. From a spatial perspective, the northern counties in the U.S. Corn Belt are less limited by precipitation resources but are more vulnerable to extreme heat. The spatiotemporal explicit statistic modeling approach quantifies the impact and adaptation potential of changing the planting date for production. Appropriate adaptions by changing plant dates can increase the potential of corn production by 0.87 million Mg year-1 in the Corn Belt. Depression is one of the leading causes of disability, but the etiology remains unclear. Recently, it has been suggested that air pollution is a potential risk factor for depression. However, the results remained inconsistent. So we conducted this study to assess the association between short-term exposure to ambient air pollution and hospital visits for depression in China. Daily hospital visits for depression from January 18, 2013 to June 10, 2018 were extracted from a regional health information system (HIS) covered 1.34 million population in Ningbo, China. We collected daily air pollutant concentrations and meteorological data from environmental air quality monitoring sites and meteorological stations in the study area. Quasi-Poisson regression models with generalized additive models (GAM) were applied to explore the associations between air pollution and hospital visits for depression. Stratified analyses were also conducted by gender, age, and season to examine the effects modification. The results disclosed that air pollutants including PM2.