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185 results on '"Yuan, Zhiwen"'

9. Association analysis of PMEL gene expression and single nucleotide polymorphism with plumage color in quail.

Author

Yuan, Zhiwen, Zhang, Xiaohui, Pang, Youzhi, and Qi, Yanxia

Subjects
*COLOR of birds, *SINGLE nucleotide polymorphisms, *GENE expression, *QUAILS, *EMBRYOLOGY, *DISTRIBUTION (Probability theory)
Abstract

To explore the relationship between PMEL gene and quail plumage color, to provide a reference for subsequent quail plumage color breeding. In this experiment, RT-qPCR technology was used to analyze the relative mRNA expression levels of Korean quail (maroon) and Beijing white quail embryos at different developmental stages. Two SNPs in PMEL gene were screened based on the RNA-Seq data of skin tissues of Korean quail and Beijing white quail during embryonic stage. The KASP technology was used for genotyping in the resource population and correlation analysis was carried out with the plumage color traits of quail. Finally, the bioinformatics technology was used to predict the effects of these two SNPs on the structure and function of the encoded protein. The results showed that the expression levels of PMEL gene during the embryonic development of Beijing white quail were extremely significantly higher than that of Korean quail (p < 0.01). The frequency distribution of the three genotypes (AA, AB, and BB) of the Beijing white quail at the c. 1030C > T and c. 1374A > G mutation sites were extremely significantly different from that of the Korean quail (p < 0.01). And there was a significant correlation between the c. 1374A > G mutation site with white plumage phenotype. Bioinformatics analysis showed that SNP1 (c. c1030t) located in exon 6 was a harmful mutation site, and SNP2 (c. a1374g) located in exon 7 was a neutral mutation site. Protein conservation prediction showed that the coding protein P344S site caused by SNP1 (c. c1030t) site and the coding protein I458M site caused by SNP2 (c. g2129a) site were non-conservative sites. The results of this experiment showed that the PMEL gene was associated with the plumage color traits of quail and could be used as a candidate gene for studying the plumage color of quail. [ABSTRACT FROM AUTHOR]

Published
2023
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13. Machine learning guided 3D printing of carbon microlattices with customized performance for supercapacitive energy storage

Author

Yang, Hao, primary, Fang, Liang, additional, Yuan, Zhiwen, additional, Teng, Xiaoling, additional, Qin, Haiquan, additional, He, Zhengqiu, additional, Wan, Yi, additional, Wu, Xiaocui, additional, Zhang, Yunlong, additional, Guan, Lu, additional, Meng, Chao, additional, Zhou, Qiang, additional, Wang, Chongze, additional, Ding, Peibin, additional, Hu, Han, additional, and Wu, Mingbo, additional

Published
2023
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36. SF6 Gas Infrared Thermal Imaging Leakage Detection Based on Faster-RCNN

Author

Xing He, Zhang Jinli, Yuan Zhiwen, Haosen Yang, Xu Kan, and Ji Yiping

Subjects
Imagination, business.industry, Computer science, media_common.quotation_subject, Facial recognition system, Convolutional neural network, Image identification, Thermography, Infrared thermal imaging, Computer vision, Artificial intelligence, Temperature difference, business, media_common, Leakage (electronics)
Abstract

Infrared thermal image is an important medium for SF6 gas leakage detection. However, when the environment is complex or the temperature difference between the background and the measured gas is slight, a clear and obvious image can hardly be obtained. Moreover, the procedures for the leakage detection fully relies on human beings, which means lots of manpower are required and some subjective miss detection may occur. To solve this problem, aiming at the infrared thermal image and inspired by the breakthroughs in computer version (CV), this paper proposes a leakage detection method to realize automatic detection for the first time. Faster-RCNN, instead of human beings, is employed to handle SF6 infrared thermography image. Faster-RCNN is an advanced convolutional neural network, which has been widely used in numerous practical situations including image identification and face recognition. Under certain circumstances, our Faster-RCNN method can effectively improve the SF6 gas leakage detection. The method has great academic significance and can be promoted in practical engineering.

Published
2019

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