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36. Siderophores provoke extracellular superoxide production by Arthrobacter strains during carbon sources‐level fluctuation

Author

Ning, Xue, Liang, Jinsong, Men, Yujie, Wang, Yuhan, Chang, Yangyang, Bai, Yaohui, Liu, Huijuan, Wang, Aijie, Zhang, Tong, and Qu, Jiuhui

Subjects
Microbiology, Biological Sciences, Arthrobacter, Carbon, Iron, Siderophores, Superoxides, Evolutionary Biology, Ecology
Abstract

Superoxide and other reactive oxygen species (ROS) shape microbial communities and drive the transformation of metals and inorganic/organic matter. Taxonomically diverse bacteria and phytoplankton produce extracellular superoxide during laboratory cultivation. Understanding the physiological reasons for extracellular superoxide production by aerobes in the environment is a crucial question yet not fully solved. Here, we showed that iron-starving Arthrobacter sp. QXT-31 (A. QXT-31) secreted a type of siderophore [deferoxamine (DFO)], which provoked extracellular superoxide production by A. QXT-31 during carbon sources-level fluctuation. Several other siderophores also demonstrated similar effects to A. QXT-31. RNA-Seq data hinted that DFO stripped iron from iron-bearing proteins in electron transfer chain (ETC) of metabolically active A. QXT-31, resulting in electron leakage from the electron-rich (resulting from carbon sources metabolism by A. QXT-31) ETC and superoxide production. Considering that most aerobes secrete siderophore(s) and undergo carbon sources-level fluctuation, the superoxide-generation pathway is likely a common pathway by which aerobes produce extracellular superoxide in the environment, thus influencing the microbial community and cycling of elements. Our results pointed that the ubiquitous siderophore might be the potential driving force for the microbial generation of superoxide and other ROS and revealed the important role of iron physiology in microbial ROS generation.

Published
2022

43. Research on Teaching Quality Monitoring Mechanism of Colleges and Universities Based on Internet+Education Mode

Author

Ma Wei and Liu Huijuan

Subjects
data mining, cluster analysis, factor analysis, association rules, teaching quality monitoring, 97c50, Mathematics, QA1-939
Abstract

This paper focuses on the “Internet + education” model and describes the feasible strategy of using data mining technology to establish a teaching quality monitoring mechanism in colleges and universities. Through the study of teaching quality monitoring pathways, such as teachers’ teaching level, students’ academic status, and course learning effectiveness, this paper’s main content provides a directional guide. Based on the teaching quality monitoring pathway, we choose the related algorithms of data mining technology, such as cluster analysis, association rule, and factor analysis, to be applied to the various aspects of the teaching quality monitoring pathway. The clustering algorithm is used to classify 1500 teachers in a university into 4 categories, such as excellent teaching effect, good teaching effect, etc., as well as to cluster students into 6 categories, such as excellent in all the courses and low to medium level, according to the course grades. In addition, this paper analyzes students’ course learning effectiveness, academic level, and career guidance using association rule algorithms, factor algorithms, and decision tree algorithms, respectively. From teachers’ teaching to students’ learning and employment, this paper constructs an all-round teaching quality monitoring mechanism using data mining technology, which contributes to the improvement of teaching quality in colleges and universities.

Published
2024
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