Aspergillus awamori attenuates ochratoxin A-induced renal and cardiac injuries in rabbits by activating the Nrf2/HO-1 signaling pathway and downregulating IL1β, TNFα, and iNOS gene expressions

Environmental Science and Pollution Research - Ochratoxin A (OTA) is one of the most dangerous and that pollute agricultural products, inducing a variety of toxic effects in humans and animals. The...     

Integrated application of plant bioregulator and micronutrients improves crop physiology,productivity and grain biofortification of delayed sown wheat

Environmental Science and Pollution Research - Delay sowing of wheat is a common problem in Punjab that exacerbates serious yield loss. To reduce yield loss and improve yield, different...     

Source apportionment of surfactants in marine aerosols at different locations along the Malacca Straits

This study aims to determine the source apportionment of surfactants in marine aerosols at two selected stations along the Malacca Straits. The aerosol samples were collected using a high volume sampler equipped with an impactor to separate coarse- and fine-mode aerosols. The concentrations of surfactants, as methylene blue active substance and disulphine blue active substance, were analysed using colorimetric method. Ion chromatography was employed to determine the ionic compositions. Principal component analysis combined with multiple linear regression was used to identify and quantify the sources of atmospheric surfactants. The results showed that the surfactants in tropical coastal environments are actively generated from natural and anthropogenic origins. Sea spray (generated from sea-surface microlayers) was found to be a major contributor to surfactants in both aerosol sizes. Meanwhile, the anthropogenic sources (motor vehicles/biomass burning) were predominant contributors to atmospheric surfactants in fine-mode aerosols.     

Empirical Estimation of Stream Discharge Using Channel Geometry in Low‐Gradient,Sand‐Bed Streams of the Southeastern Plains

Manning's equation is used widely to predict stream discharge (Q) from hydraulic variables when logistics constrain empirical measurements of in‐bank flow events. Uncertainty in Manning's roughness (n_M) is the major source of error in natural channels, and sand‐bed streams pose difficulties because flow resistance is affected by flow‐dependent bed configuration. Our study was designed to develop and validate models for estimating Q from channel geometry easily derived from cross‐sectional surveys and available GIS data. A database was compiled consisting of 484 Q measurements from 75 sand‐bed streams in Alabama, Georgia, South Carolina, North Carolina (Southeastern Plains), and Florida (Southern Coastal Plain), with six New Zealand streams included to develop statistical models to predict Q from hydraulic variables. Model error characteristics were estimated with leave‐one‐site‐out jackknifing. Independent data of 317 Q measurements from 55 Southeastern Plains streams indicated the model (Q = A_cR_H^0.6906S^0.1216; where A_c is the channel area, R_H is the hydraulic radius, and S is the bed slope) best predicted Q, based on Akaike's information criterion and root mean square error. Models also were developed from smaller Q range subsets to explore if subsets increased predictive ability, but error fit statistics suggested that these were not reasonable alternatives to the above equation. Thus, we recommend the above equation for predicting in‐bank Q of unbraided, sandy streams of the Southeastern Plains.