Institution
Southwest University
Education•Chongqing, China•
About: Southwest University is a education organization based out in Chongqing, China. It is known for research contribution in the topics: Gene & Population. The organization has 29772 authors who have published 27755 publications receiving 409441 citations. The organization is also known as: Southwest University in Chongqing & SWU.
Topics: Gene, Population, Catalysis, Bombyx mori, Adsorption
Papers published on a yearly basis
Papers
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TL;DR: In this paper, the authors used the logarithmic mean divisia index method to investigate the impact of urbanization on renewable energy consumption growth (RECG) and found that urbanization contributed more to the total energy consumption than to RECG.
92 citations
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TL;DR: A novel sandwich-type electrochemical immunosensor based on functionalized nanomaterial labels and bienzyme (horseradish peroxidase and glucose oxidase) biocatalyzed precipitation was developed for the detection of α-fetoprotein and showed good selectivity, acceptable stability and reproducibility.
92 citations
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TL;DR: A highly sensitive "signal-off" sensing platform for the determination of dopamine has been developed upon effectively quenching of dopamine toward the Cu NCs/HZ-based ECL system, and this proposed method for dopamine detection possesses high selectivity, good stability, and excellent sensitivity with a detection limit down to 3.5 × 10-13 M.
Abstract: Cu nanoclusters (Cu NCs), which emerged as a new class of nontoxic, economic, and excellent phosphors and catalysts, have attracted increasing interest for a wide variety of promising applications in biolabeling and biocatalysis However, the electrochemiluminescence (ECL) behavior of Cu NCs has never been reported in previous works Here, anodic and blue ECL emission of Cu NCs was observed for the first time with the efficient coreactant of hydrazine (HZ), and the possible luminescence mechanism of Cu NCs/HZ ECL system was studied in detail Briefly, HZ was oxidized, and Cu NCs got the energy to generate excited state Cu NCs* for light radiation Furthermore, a highly sensitive “signal-off” sensing platform for the determination of dopamine has been developed upon effectively quenching of dopamine toward the Cu NCs/HZ-based ECL system As a result, this proposed method for dopamine detection possesses high selectivity, good stability, and excellent sensitivity with a detection limit down to 35 × 10–13 M
92 citations
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TL;DR: The proposed weighted averaging method has better performance in conflict management and fault diagnosis due to the fact that the information volume of each sensor report is taken into consideration.
Abstract: Sensor data fusion plays an important role in fault diagnosis. Dempster-Shafer (D-R) evidence theory is widely used in fault diagnosis, since it is efficient to combine evidence from different sensors. However, under the situation where the evidence highly conflicts, it may obtain a counterintuitive result. To address the issue, a new method is proposed in this paper. Not only the statistic sensor reliability, but also the dynamic sensor reliability are taken into consideration. The evidence distance function and the belief entropy are combined to obtain the dynamic reliability of each sensor report. A weighted averaging method is adopted to modify the conflict evidence by assigning different weights to evidence according to sensor reliability. The proposed method has better performance in conflict management and fault diagnosis due to the fact that the information volume of each sensor report is taken into consideration. An application in fault diagnosis based on sensor fusion is illustrated to show the efficiency of the proposed method. The results show that the proposed method improves the accuracy of fault diagnosis from 81.19% to 89.48% compared to the existing methods.
92 citations
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TL;DR: Results suggested that these SPI genes may be involved in resistance to pathogenic microorganisms, and may provide valuable information for further clarifying the roles of SPIs in the development, immune defence, and efficient synthesis of silk gland protein.
Abstract: In most insect species, a variety of serine protease inhibitors (SPIs) have been found in multiple tissues, including integument, gonad, salivary gland, and hemolymph, and are required for preventing unwanted proteolysis. These SPIs belong to different families and have distinct inhibitory mechanisms. Herein, we predicted and characterized potential SPI genes based on the genome sequences of silkworm, Bombyx mori. As a result, a total of eighty SPI genes were identified in B. mori. These SPI genes contain 10 kinds of SPI domains, including serpin, Kunitz_BPTI, Kazal, TIL, amfpi, Bowman-Birk, Antistasin, WAP, Pacifastin, and alpha-macroglobulin. Sixty-three SPIs contain single SPI domain while the others have at least two inhibitor units. Some SPIs also contain non-inhibitor domains for protein-protein interactions, including EGF, ADAM_spacer, spondin_N, reeler, TSP_1 and other modules. Microarray analysis showed that fourteen SPI genes from lineage-specific TIL family and Group F of serpin family had enriched expression in the silk gland. The roles of SPIs in resisting pathogens were investigated in silkworms when they were infected by four pathogens. Microarray and qRT-PCR experiments revealed obvious up-regulation of 8, 4, 3 and 3 SPI genes after infection with Escherichia coli, Bacillus bombysepticus, Beauveria bassiana or B. mori nuclear polyhedrosis virus (BmNPV), respectively. On the contrary, 4, 11, 7 and 9 SPI genes were down-regulated after infection with E. coli, B. bombysepticus, B. bassiana or BmNPV, respectively. These results suggested that these SPI genes may be involved in resistance to pathogenic microorganisms. These findings may provide valuable information for further clarifying the roles of SPIs in the development, immune defence, and efficient synthesis of silk gland protein.
92 citations
Authors
Showing all 29978 results
Name | H-index | Papers | Citations |
---|---|---|---|
Frank B. Hu | 250 | 1675 | 253464 |
Hongjie Dai | 197 | 570 | 182579 |
Jing Wang | 184 | 4046 | 202769 |
Chao Zhang | 127 | 3119 | 84711 |
Jianjun Liu | 112 | 1040 | 71032 |
Miao Liu | 111 | 993 | 59811 |
Jun Yang | 107 | 2090 | 55257 |
Eric Westhof | 98 | 472 | 34825 |
En-Tang Kang | 97 | 763 | 38498 |
Chang Ming Li | 97 | 896 | 42888 |
Wei Zhou | 93 | 1640 | 39772 |
Li Zhang | 92 | 918 | 35648 |
Heinz Rennenberg | 87 | 527 | 26359 |
Tao Chen | 86 | 820 | 27714 |
Xun Wang | 84 | 606 | 32187 |