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548 results on '"Teng, Da"'

7. Screening chronic kidney disease through deep learning utilizing ultra-wide-field fundus images

Author

Zhao, Xinyu, Gu, Xingwang, Meng, Lihui, Chen, Yongwei, Zhao, Qing, Cheng, Shiyu, Zhang, Wenfei, Cheng, Tiantian, Wang, Chuting, Shi, Zhengming, Jiao, Shengyin, Jiang, Changlong, Jiao, Guofang, Teng, Da, Sun, Xiaolei, Zhang, Bilei, Li, Yakun, Lu, Huiqin, Chen, Changzheng, Zhang, Hao, Yuan, Ling, Su, Chang, Zhang, Han, Xia, Song, Liang, Anyi, Li, Mengda, Zhu, Dan, Xue, Meirong, Sun, Dawei, Li, Qiuming, Zhang, Ziwu, Zhang, Donglei, Lv, Hongbin, Ahmat, Rishet, Wang, Zilong, Sabanayagam, Charumathi, Ding, Xiaowei, Wong, Tien Yin, and Chen, Youxin

Published
2024
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49. Reveal the microbial communities and functional prediction during the fermentation of Fen-flavor Baijiu via metagenome combining amplicon sequencing

Author

Teng-da Xue, Jin-hua Zhang, Tian-rui Wang, Bao-qing Bai, Zhi-xing Hou, Jian-feng Cheng, Tao Bo, and San-hong Fan

Subjects
Metagenomics, Amplicon sequencing, Baijiu, Microbial community, Spearman correlation, Microbiology, QR1-502
Abstract

Abstract Purpose Microbial resources are abundant in fermented grains of the Chinese Fen-flavor Baijiu, which is closely related to the quality of Baijiu. The purpose of this study was to investigate the microbial community structure and function in Daqu and fermented grains. Methods We systematically compared two technical approaches, amplicon sequencing, and metagenomic sequencing, to analyze the microbial communities during Baijiu fermentation. Result The results showed that lactic acid bacteria (LAB) and yeasts were the main microorganisms in the fermentation process. Firmicutes (Lactobacillus, Pediococcus, and Weissella) were the dominant bacteria, and Ascomycota (Issatchenkia or Pichia) was the dominant fungus in fermented grains. Moreover, Pichia kudriavzevii, Lichtheimia ramosa, and Companilactobacillus paralimentarius were the dominant species at the initial stage of fermentation by metagenomic sequencing. Latilactobacillus curvatus, Loigolactobacillus coryniformis subsp. coryniformis, and Lentilactobacillus parabuchneri became dominant during the middle stage of fermentation. Lentilactobacillus parabuchneri and Lactobacillus acetotolerans were the dominant species in the final stage of fermentation. Spearman correlation analysis showed that LAB inhibited the growth of yeasts. Conclusion Combining the two sequencing methods provided valuable insights into the dynamic succession of microorganisms during the fermentation of Baijiu. It had had a particular significance for mining microbial species resources in fermented grains.

Published
2023
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50. Reliability analysis of flap deflection angle based on collaborative intelligent moving Kriging model

Author

LIU Lei, TENG Da, and FENG Yunwen

Subjects
民机, 襟翼偏角, 可靠性分析, 基于协同智能移动kriging方法, qar, Motor vehicles. Aeronautics. Astronautics, TL1-4050
Abstract

To effectively monitor the reliability of civil aircraft flap deflection angle, combined with the quick access recorder(QAR), the collaborative intelligent moving Kriging(CIMK) method is proposed by absorbing the Kriging model, decomposition and co-ordination strategy, equilibrium optimizer(EO), and moving least square(MLS). Among them, the decomposition coordination strategy is used to deal with the relationship between the flaps left and right deflection angles. MLS is employed to select effective modeling samples and solve the undetermined coefficients of Kriging model. EO method is applied to determine optimizing the local compact support region radius of MLS. Firstly, the fault reason for flap left-right asymmetry is analyzed to clarify the main characteristic parameters in QAR data. Secondly, combined with the QAR data of relevant influencing parameters, the civil aircraft flap deflection model(limit state function) is constructed by using CIMK. Then, the reliability and influence of civil aircraft flap deflection angle are analyzed by Monte Carlo(MC) sampling method. The results show that when the flap deflection angle is 3°, the reliability is 0.450 2, and the important factors affecting the flap deflection angle are Mach number, left angle of attack, right angle of attack, etc. Compared with the response surface method(RSM), Kriging, support vector machine(SVM), and back-propagation-artificial neural network(BP-ANN), the average absolute error accuracy of the proposed method is relative improved by 53.02%, 51.43%, 49.03%, and 44.04%, the average relative error accuracy is relative improved by 68.36%, 66.76%, 64.41%, and 62.64%, and the modeling efficiency is relative improved by 50.62%, 26.35%, and 43.01% respectively compared with Kriging, SVM and BP-ANN. When the number of simulations is 103, the analysis accuracy is relative improved by 8.82%, 7.25%, 6.22%, and 3.98% respectively.

Published
2023
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