Effects of Water Content of the Mixed Solvent on the Single-Molecule Mechanics of Amylose

It is generally recognized that water is deeply involved in the structures and functions of DNA and proteins. For polysaccharides, however, the role of water remains unclear. Due to the force-induced conformational transition of the sugar rings, a fingerprint plateau can be observed in the single-chain force–extension (F–E) curves of amylose and some other polysaccharides in aqueous solutions. In this study, the effects of water content of the mixed solvents on the fingerprint plateau of amylose are explored by single-molecule AFM. The experimental results obtained in a series of water/alcohol mixed solvents clearly show that both the appearance and the fingerprint plateau height in the F–E curves of amylose are dependent on the water content. Since water is a good solvent for amylose but alcohols are not, the higher water content of a mixed solvent corresponds to a better solvent quality. Thus, the observed results can be associated with the solvent quality to amylose. The present study implies that water is not only a solvent but also an active constituent in the amylose solution

Identification of long-term trends and seasonality in high-frequency water quality data from the Yangtze River basin, China

<div><p>Comprehensive understanding of the long-term trends and seasonality of water quality is important for controlling water pollution. This study focuses on spatio-temporal distributions, long-term trends, and seasonality of water quality in the Yangtze River basin using a combination of the seasonal Mann-Kendall test and time-series decomposition. The used weekly water quality data were from 17 environmental stations for the period January 2004 to December 2015. Results show gradual improvement in water quality during this period in the Yangtze River basin and greater improvement in the Uppermost Yangtze River basin. The larger cities, with high GDP and population density, experienced relatively higher pollution levels due to discharge of industrial and household wastewater. There are higher pollution levels in Xiang and Gan River basins, as indicated by higher NH₄-N and COD_Mn concentrations measured at the stations within these basins. Significant trends in water quality were identified for the 2004–2015 period. Operations of the three Gorges Reservoir (TGR) enhanced pH fluctuations and possibly attenuated COD_Mn, and NH₄-N transportation. Finally, seasonal cycles of varying strength were detected for time-series of pollutants in river discharge. Seasonal patterns in pH indicate that maxima appear in winter, and minima in summer, with the opposite true for COD_Mn. Accurate understanding of long-term trends and seasonality are necessary goals of water quality monitoring system efforts and the analysis methods described here provide essential information for effectively controlling water pollution.</p></div

Main pollutant emission in industrial waste water in main cities from 2005 to 2013.

<p>Main pollutant emission in industrial waste water in main cities from 2005 to 2013.</p

Weekly probability distribution functions for regional mean concentrations in the Yangtze River basin, between 2004 and 2015 for the three time periods: 2004–2007 (black curve), 2008–2011 (green curve), and 2012–2015 (red curve).

<p>(a) pH, (<b>b</b>) COD_Mn (mg/L), (<b>c</b>) NH₃-N (mg/L), and (<b>d</b>) DO (mg/L).</p

Changes of number of different grades of river water based on 17 stations in the Yangtze River basin from 2004 to 2015.

<p>The dashed lines are the trend lines for each Grade.</p

Seasonal trends (the value of Z) of stations with positive or negative trends for water quality parameters in the Yangtze River basin during 2004–2015.

<p>Seasonal trends (the value of Z) of stations with positive or negative trends for water quality parameters in the Yangtze River basin during 2004–2015.</p

Location and description of 17 environmental monitoring stations.

<p>Location and description of 17 environmental monitoring stations.</p

Seasonality analysis: The results of the DHR seasonal cycle extracted from the weekly time series (see the main text for more detail on how the seasonality analysis was carried out).

<p>Seasonality analysis: The results of the DHR seasonal cycle extracted from the weekly time series (see the main text for more detail on how the seasonality analysis was carried out).</p

Spatial distribution of seasonal Mann-Kendall trends (the value of Z) at 17 stations in the Yangtze River basin, between 2004 and 2015.

<p>(a) pH, (<b>b</b>) COD_Mn (mg/L), (<b>c</b>) NH₃-N (mg/L), and (<b>d</b>) DO (mg/L).</p

Geological map of the Yangtze River basin and studying stations.

<p>Geological map of the Yangtze River basin and studying stations.</p