Institution
China Three Gorges University
Education•Yichang, China•
About: China Three Gorges University is a education organization based out in Yichang, China. It is known for research contribution in the topics: Catalysis & Landslide. The organization has 11161 authors who have published 8011 publications receiving 82224 citations. The organization is also known as: Sanxia Daxue.
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TL;DR: It is suggested that the partial planar backbone structure, due to the conjugation of π electrons in the presence of a 3,4-double bond and the carbonyl group at position C-2 in αβ-dehydrocurvularin, acts as a key factor for the inhibition of S. aureus, a Gram-positive low G + C bacteria that are often the hospital-acquired and/or community- Acquired pathogen.
Abstract: With the anti-microbial and anti-tumor composite screening model, bioassay-guided fractionation led to the isolation of two structurally related bioactive compounds, curvularin and alpha beta-dehydrocurvularin, from ethyl acetate extract of Eupenicillium sp. associated with marine sponge Axinella sp. Further study on the structure-activity relationship demonstrated that both compounds exhibited differences in bioactive profiles which are highly associated with their minor structural differences. Both curvularin and alpha beta-dehydrocurvularin have similar level of anti-fungal and anti-tumorous activity, while alpha beta-dehydrocurvularin is active against Staphylococcus aureus with a minimal inhibitory concentration of 375 mu g/ml but curvularin does not. No detectable activity against Gram-negative bacteria such as Escherichia coli and Pseudomonas aeruginosa exists for both compounds. It is suggested that the partial planar backbone structure, due to the conjugation of pi electrons in the presence of a 3,4-double bond and the carbonyl group at position C-2 in alpha beta-dehydrocurvularin, acts as a key factor for the inhibition of S. aureus, a Gram-positive low G + C bacteria that are often the hospital-acquired and/or community-acquired pathogen.
35 citations
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TL;DR: The results show that the mainstream of the T GR has a higher concentration of nitrogen and a lower concentration of phosphorus than that of the upper mainstream before the TGR, and it was found that nitrate-nitrogen is the main nitrogen component, while particulate phosphorus predominates the total phosphorus.
Abstract: A comprehensive monitoring program was conducted to investigate the nutrient spatial pattern in the mainstream of the Yangtze River from the Baihetan Dam down to the Three Gorges Dam located at the upper region of the Yangtze River in China. Samples were taken from 33 different sites from July 30 to August 19, 2011. The nutrient patterns of the three representative tributaries of the Three Gorges Reservoir (TGR)—the Modao, the Daning, and the Xiangxi Rivers—were also investigated. The results show that the mainstream of the TGR has a higher concentration of nitrogen and a lower concentration of phosphorus than that of the upper mainstream before the TGR. Moreover, it was found that nitrate-nitrogen (NO3-N) is the main nitrogen component, while particulate phosphorus predominates the total phosphorus (TP). It was found that the three representative tributaries of the TGR have lower total nitrogen (TN) concentrations compared to the corresponding sections of the mainstream TGR. Based on the nutrient spatial pattern, the nutrient flux was calculated. The total fluxes of TN, NO3-N, TP, and orthophosphate (PO4-P) from the upstream reach into the TGR are 2,155.06, 1,674.97, 212.98, and 83.42 t day−1, respectively. The amount of nutrients imported from the TGR into its tributaries is more than the amount exported. It was determined that the Xiangxi River has the largest net rate of imported nitrogen at 7.66 t day−1, whereas the Daning River has the largest net rate of imported phosphorus at 1.75 t day−1. In addition, compared with the nutrients imported from the TGR into its tributaries, the nutrient flux from the upstream reach into the TGR contributes approximately less than 3 %.
35 citations
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TL;DR: In this paper, two diel field campaigns under different weather patterns were carried out in the summer and autumn of 2013 to measure CO 2 and CH 4 fluxes and to probe the rates of gas exchange across the air-water interface in a subtropical eutrophic pond in China.
Abstract: Two diel field campaigns under different weather patterns were carried out in the summer and autumn of 2013 to measure CO 2 and CH 4 fluxes and to probe the rates of gas exchange across the air–water interface in a subtropical eutrophic pond in China. Bubble emissions of CH 4 accounted for 99.7 and 91.67% of the total CH 4 emission measured at two sites in the summer; however, no bubble was observed in the autumn. The pond was supersaturated with CO 2 and CH 4 during the monitoring period, and the saturation ratios (i.e. observed concentration/equilibrium concentration) of CH 4 were much higher than that of CO 2 . Although the concentration of dissolved CO 2 in the surface water collected in the autumn was 1.24 times of that in the summer, the mean diffusive CO 2 flux across the water–air interface measured in the summer is almost twice compared with that in the autumn. The mean concentration of dissolved CH 4 in the surface water in the autumn was around half of that in the summer, but the mean diffusive CH 4 flux in the summer is 4–5 times of that in the autumn. Our data showed that the variation in gas exchange rate was dominated by differences in weather patterns and primary production. Averaged k 600 -CO 2 and k 600 -CH 4 (the gas transfer velocity normalised to a Schmidt number of 600) were 0.65 and 0.55 cm/h in the autumn, and 2.83 and 1.64 cm/h in the summer, respectively. No statistically significant correlation was found between k 600 and U 10 (wind speed at 10 m height) in the summer at low wind speeds in clear weather. Diffusive gas fluxes increased during the nights, which resulted from the nighttime cooling effect of water surface and stronger turbulent mixing in the water column. The chemical enhancements for CO 2 were estimated up to 1.94-fold in the hot and clear summer with low wind speeds, which might have been resulted from the increasing hydration reactions in water due to the high water temperature and active metabolism in planktonic algae. However, both the air and surface water temperatures decreased continually, and relatively lower temperature and overcast weather with occasionally light rain dominated the second campaign in the autumn. The concentration of dissolved oxygen in the surface water and U 10 controlled gas transfer velocities of CO 2 and CH 4 , respectively, in the cool autumn. When the surface water temperature was higher than the air temperature, higher CO 2 flux was observed because the water body was unstable and overturned quickly, inducing quick CO 2 emitted from plankton algae in surface water to the atmosphere. Keywords: gas transfer velocity, the chemical enhancement, convective cooling, wind speed, pond, subtropical, primary productivity (Published: 9 December 2014) Citation: Tellus B 2014, 66 , 23795, http://dx.doi.org/10.3402/tellusb.v66.23795
34 citations
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TL;DR: To study the system stability and facilitate the design of fuzzy controller, Takagi–Sugeno (T–S) fuzzy models are employed to represent the system dynamics of the nonlinear discrete-time NCSs with effects of the approximation errors taken into account.
34 citations
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TL;DR: In this article, the mechanism of the photocatalytic activity related to different defects remains disputable, although defects play an important role in the photochemical activity of TiO2.
Abstract: Although defects play an important role in the photocatalytic activity of TiO2, the mechanism of the photocatalytic activity related to different defects remains disputable. Moreover, the reported ...
34 citations
Authors
Showing all 11222 results
Name | H-index | Papers | Citations |
---|---|---|---|
Shu Li | 136 | 1001 | 78390 |
Yu Huang | 136 | 1492 | 89209 |
Jian Zhang | 107 | 3064 | 69715 |
Tao Li | 102 | 2483 | 60947 |
Jian Chen | 96 | 1718 | 52917 |
Jing Zhang | 95 | 1271 | 42163 |
Qichun Zhang | 94 | 540 | 28367 |
Bin Li | 92 | 1755 | 42835 |
Xianhui Bu | 87 | 290 | 20927 |
Dawei Wang | 85 | 934 | 41226 |
Guangshan Zhu | 77 | 369 | 21281 |
Fei Xu | 71 | 743 | 24009 |
Jian Zhang | 70 | 317 | 14802 |
Ying Wu | 70 | 489 | 22952 |
Chao Zhang | 69 | 331 | 23555 |