It has been found that the self-assembling peptide RADA 16-I forms a beta-sheet structure and self-assembles into nanofibers and scaffolds in favor of cell growth, hemostasis and tissue-injury repair. But its biophysical and morphological properties, especially for its beta-sheet and self-assembling properties in heat- and pH-denatured conditions, remain largely unclear. In order to better understand and design nanobiomaterials, we studied the self-assembly behaviors of RADA16-I using CD and atomic force microscopy (AFM) measurements in various pH and heat-denatured conditions. Here, we report that the peptide, when exposed to pH 1.0 and 4.0, was still able to assume a typical beta-sheet structure and self-assemble into long nanofiber, although its beta-sheet content was dramatically decreased by 10% in a pH 1.0 solution. However, the peptide, when exposed to pH 13.0, drastically lost its beta-sheet structure and assembled into different small-sized globular aggregates. Similarly, the peptide, when heat-denatured from 25 to 70 degrees C, was still able to assume a typical beta-sheet structure with 46% content, but self-assembled into small-sized globular aggregates at much higher temperature. Titration experiments showed that the peptide RADA16-I exists in three types of ionic species: acidic (fully protonated peptide), zwitterionic (electrically neutral peptide carrying partial positive and negative charges) and basic (fully deprotonated peptide) species, called 'super ions'. The unordered structure and beta-turn of these 'super ions' via hydrogen or ionic bonds, and heat Brownian motion under the above denatured conditions would directly affect the stability of the beta-sheet and nanofibers. These results help us in the design of future nanobiomaterials, such as biosensors, based on beta-sheets and environmental changes. These results also help understand the pathogenesis of the beta-sheet-mediated neuronal diseases such as Alzheimer's disease and the mechanism of hemostasis.

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Quantifying source apportionments of nutrient load and their variations among seasons and hydrological years can provide useful information for watershed nutrient load reduction programs. There are large seasonal and inter-annual variations in nutrient loads and their sources in semi-arid watersheds that have a monsoon climate. The Generalized Watershed Loading Function model was used to simulate monthly nutrient loads from 2004 to 2011 in the Liu River watershed, Northern China. Model results were used to investigate nutrient load contributions from different sources, temporal variations of source apportionments and the differences in the behavior of total nitrogen (TN) and total phosphorus (TP). Examination of source apportionments for different seasons showed that point sources were the main source of TN and TP in the non-flood season, whereas contributions from diffuse sources, such as rural runoff, soil erosion, and urban areas, were much higher in the flood season. Furthermore, results for three typical hydrological years showed that the contribution ratios of nutrient loads from point sources increased as streamflow decreased, while contribution ratios from rural runoff and urban area increased as streamflow increased. Further, there were significant differences between TN and TP sources on different time scales. Our findings suggest that priority actions and management measures should be changed for different time periods and hydrological conditions, and that different strategies should be used to reduce loads of nitrogen and phosphorus effectively.

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Given the rising nitrous oxide (NO) concentration in the atmosphere, it has become increasingly important to identify hot spots and hot moments of NO emissions. With field measurements often failing to capture the spatiotemporal dynamics of NO emissions, estimating them with modeling tools has become an attractive alternative. Therefore, we incorporated several semi-empirical equations to estimate NO emissions with the Soil and Water Assessment Tool from nitrification and denitrification processes in soil. We then used the model to simulate soil moisture and the NO flux from grassland soils subjected to long-term grazing (> 60 years) at different intensities in Alberta, Canada. Sensitivity analysis showed that parameters controlling the NO flux from nitrification were most sensitive. On average, the accuracy of NO emission simulations were found to be satisfactory, as indicated by the selected goodness-of-fit statistics and predictive uncertainty band, while the model simulated the soil moisture with slightly higher accuracy. As expected, emissions were higher from the plots with greater grazing intensity. Scenario analysis showed that the NO emissions with the recommended fertilizer rate would dominate the emissions from the projected wetter and warmer future. The combined effects of fertilization and wetter and warmer climate scenarios would increase the current NO emission levels by more than sixfold, which would be comparable to current emission levels from agricultural soils in similar regions.