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25. Examination of the Expression Profile of Resistance Genes in Yuanjiang Common Wild Rice (Oryza rufipogon)

Author

Wang Kan, Ling Chen, Bo Wang, Li Liu, Fuyou Yin, Qiaofang Zhong, Jinlu Li, Dunyu Zhang, Suqin Xiao, Yun Zhang, Cong Jiang, Tengqiong Yu, Yunyue Wang, and Zaiquan Cheng

Subjects
expression patterns, interaction network diagrams, induced expression, Oryza, rice blight, tissue-specific expression, Genetics, QH426-470
Abstract

The rice blight poses a significant threat to the rice industry, and the discovery of disease-resistant genes is a crucial strategy for its control. By exploring the rich genetic resources of Yuanjiang common wild rice (Oryza rufipogon) and analyzing their expression patterns, genetic resources can be provided for molecular rice breeding. The target genes’ expression patterns, subcellular localization, and interaction networks were analyzed based on the annotated disease-resistant genes on the 9th and 10th chromosomes in the rice genome database using fluorescent quantitative PCR technology and bioinformatics tools. Thirty-three disease-resistant genes were identified from the database, including 20 on the 9th and 13 on the 10th. These genes were categorized into seven subfamilies of the NLR family, such as CNL and the G subfamily of the ABC family. Four genes were not expressed under the induction of the pathogen Y8, two genes were significantly down-regulated, and the majority were up-regulated. Notably, the expression levels of nine genes belonging to the ABCG, CN, and CNL classes were significantly up-regulated, yet the expression levels varied among roots, stems, and leaves; one was significantly expressed in the roots, one in the stems, and the remaining seven were primarily highly expressed in the leaves. Two interaction network diagrams were predicted based on the seven highly expressed genes in the leaves: complex networks regulated by CNL proteins and specific networks controlled by ABCG proteins. The disease-resistant genes on the 9th chromosome are actively expressed in response to the induction of rice blight, forming a critical gene pool for the resistance of Yuanjiang common wild rice (O. rufipogon) to rice blight. Meanwhile, the disease-resistant genes on the 10th chromosome not only participate in resisting the rice blight pathogen but may also be involved in the defense against other stem diseases.

Published
2024
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48. Design and Implementation of Smart Teaching for Computer Courses in Colleges and Universities Enabled by Artificial Intelligence Technology

Author

Wang Kan

Subjects
similarity matrix, coded net clustering, decision support system, data mining, brilliant teaching, 01a25, Mathematics, QA1-939
Abstract

In this study, an intelligent decision support system for education was successfully constructed by integrating data mining techniques, evaluation similarity matrix and novel braided net clustering method. Based on the evaluation similarity matrix, this system brings together decision-making members with similar views through clustering analysis. It calculates these categorized groups’ preferences, consistency indexes, and decision values to derive the optimal solution. The research in this paper empirically validates the effectiveness of the intelligent decision support system against the background of actual teaching scenarios and with all students in their classes as the research subjects. The validation results show that in the Powerpoint Practical and PS Practical tests, the average scores of the students in the experimental class reached 87.1 and 86.2, respectively, and this score is significantly higher than that of the control class. In addition, in the Analysis of the four dimensions of pre-course learning behavior (including interest in learning, attitude toward learning, etc.), the mean values of the experimental class (4.46, 3.99, 3.89, 4.18) were also significantly higher than those of the control class (3.2, 2.73, 2.94, 2.89). The application of the Intelligent Decision Support System increased students’ learning interest and effectively improved their performance in both machine and written tests. The main effect of its teaching method is significant with an F-value of 18.029 and a P-value of less than 0.001. The results of this study confirm the practical application of intelligent decision support systems in teaching and learning, and provide a new methodology and perspective for computer-intelligent teaching.

Published
2024
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