Bio-indicators in cadmium toxicity: Role of HSP27 and HSP70

Environmental Science and Pollution Research - Heat shock proteins (HSPs) are a family of proteins that are expressed by cells in reply to stressors. The changes in concentration of HSPs could be...     

AI-based runoff simulation based on remote sensing observations: A case study of two river basins in the United States and Canada

Data-driven techniques are used extensively for hydrologic time-series prediction. We created various data-driven models (DDMs) based on machine learning: long short-term memory (LSTM), support vector regression (SVR), extreme learning machines, and an artificial neural network with backpropagation, to define the optimal approach to predicting streamflow time series in the Carson River (California, USA) and Montmorency (Canada) catchments. The moderate resolution imaging spectroradiometer (MODIS) snow-coverage dataset was applied to improve the streamflow estimate. In addition to the DDMs, the conceptual snowmelt runoff model was applied to simulate and forecast daily streamflow. The four main predictor variables, namely snow-coverage (S-C), precipitation (P), maximum temperature (T_max), and minimum temperature (T_min), and their corresponding values for each river basin, were obtained from National Climatic Data Center and National Snow and Ice Data Center to develop the model. The most relevant predictor variable was chosen using the support vector machine-recursive feature elimination feature selection approach. The results show that incorporating the MODIS snow-coverage dataset improves the models' prediction accuracies in the snowmelt-dominated basin. SVR and LSTM exhibited the best performances (root mean square error = 8.63 and 9.80) using monthly and daily snowmelt time series, respectively. In summary, machine learning is a reliable method to forecast runoff as it can be employed in global climate forecasts that require high-volume data processing.     

New bacterial strain of the genus Ochrobactrum with glyphosate-degrading activity

Thirty bacterial strains with various abilities to utilize glyphosate as the sole phosphorus source were isolated from farm soils using the glyphosate enrichment cultivation technique. Among them, a strain showing a remarkable glyphosate-degrading activity was identified by biochemical features and 16S rRNA sequence analysis as Ochrobactrum sp. (GDOS). Herbicide (3 mM) degradation was induced by phosphate starvation, and was completed within 60 h. Aminomethylphosphonic acid was detected in the exhausted medium, suggesting glyphosate oxidoreductase as the enzyme responsible for herbicide breakdown. As it grew even in the presence of glyphosate concentrations as high as 200 mM, Ochrobactrum sp. could be used for bioremediation purposes and treatment of heavily contaminated soils.     

Nanoparticles Addition in Coir‐Basalt‐Innegra Fibers Reinforced Bio-synthetic Epoxy Composites

Journal of Polymers and the Environment - In the present investigation, the influence of coir micro-particles and titanium carbide (TiC) nanofillers on mechanical characteristics and thermal...     

Scale,context, and decision making in agricultural adaptation to climate variability and change

This work presents a framework for viewing agricultural adaptation, emphasizing the multiple spatial and temporal scales on which individuals and institutions process information on changes in their environment. The framework is offered as a means to gain perspective on the role of climate variability and change in agricultural adaptation, and developed for a case study of Australian agriculture. To study adaptation issues at the scale of individual farms we developed a simple modelling framework. The model highlights the decision making element of adaptation in light of uncertainty, and underscores the importance of decision information related to climate variability. Model results show that the assumption of perfect information for farmers systematically overpredicts adaptive performance. The results also suggest that farmers who make tactical planting decisions on the basis of historical climate information are outperformed by those who use even moderately successful seasonal forecast information. Analysis at continental scales highlights the prominent role of the decline in economic operating conditions on Australian agriculture. Examples from segments of the agricultural industry in Australia are given to illustrate the importance of appropriate scale attribution in adapting to environmental changes. In particular, adaptations oriented toward short time scale changes in the farming environment (droughts, market fluctuations) can be limited in their efficacy by constraints imposed by broad changes in the soil/water base and economic environment occuring over longer time scales. The case study also makes the point that adaptation must be defined in reference to some goal, which is ultimately a social and political exercise. Overall, this study highlights the importance of allowing more complexity (limited information, risk aversion, cross-scale interactions, mis-attribution of cause and effect, background context, identification of goals) in representing adaptation processes in climate change studies.     

Spatial and temporal variability of fluoride concentrations in groundwater resources of Larestan and Gerash regions in Iran from 2003 to 2010

Environmental Geochemistry and Health - There is discrepancy about intervals of fluoride monitoring in groundwater resources by Iranian authorities. Spatial and temporal variability of fluoride in...     

Estimation of Aerobic Capacity and Determination of Its Associated Factors Among Male Workers of Industrial Sector of Iran

Introduction. The aim of this study was to estimate maximal aerobic capacity (V_O2max), to determine its associated factors among workers of industrial sector of Iran and to develop a regression equation for subjects’ V_O2max. Methods. In this study, 500 healthy male workers employed in Shiraz industries participated voluntarily. The subjects’ V_O2max was assessed with the ergocycle test according to the Åstrand protocol. Required data was collected with a questionnaire covering demographic details (i.e., age, job tenure, marital status, education, nature of work, shift work, smoking and weekly exercises). Results. The subject’s mean V_O2max was 2.69 ± 0.263 L/min. The results showed that there was an association between V_O2max and age, BMI, hours of exercise and smoking, but there was no association between V_O2max and height, weight, nature of work and working schedule. On the basis of the results, regression equations were developed to estimate V_O2max. Conclusion. Final regression equation developed in this study may be used to estimate V_O2max reliably without the need to use other laboratory instruments for aerobic measurement.