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- 저자 : Bingnan Yan (검색결과 2 건)
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Production of Transgenic Chimeric Chickens Using Blastodermal Cells
Yan, Haifeng,Lee, Chaeyoung,Xiao, Bingnan,Trefil, Pavel,Liu, Shixun,Kim, Younyoung,Wu, Xiaolin Asian Australasian Association of Animal Productio 2005 Animal Bioscience Vol.18 No.2
A practical approach was proposed to produce transgenic chimeric chickens using blastodermal cells (BCs). The chicken BCs were mechanically dissociated and transferred into the recipient eggs that had been exposed to 500 rads irradiation of$^{60}Co$ and windowed on the equatorial plane. Chimeric chickens were generated using two models: the crosses (MXL) from Black Minors (ii,EE,b/b) ♂${\times}$Barred Leghorns (ii,ee,B/-) ♀ as donors and White Leghorns (WL, II) as acceptors (Model 1), or the Black Heifengs (BH, ii,EE,bb) as donors and Hua-xing white (HW, II) as recipients (Model 2). The treated eggs were incubated in their original shells in normal conditions until hatching. Green fluorescent protein (GFP) gene was transferred into the BCs derived from MXL and BH via lipofectamine and the pEGFP-C1, and transfection efficiency into the BCs was examined under a fluorescent microscope. Potential transgenic chimeras were selected based on the proposed methods in this study. Using the fresh BCs, the best rate of phenotypic chimeras was 6.7% and 26.0% in model-1 groups, and model-2 groups, respectively. We also described the optimized conditions for transfection. Although 30% of the BCs transfected in vitro emitted green light under an inverted fluorescent microscope, no embryos injected with the transfected BCs expressed foreign GFP gene at 3-4 days.
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Wen Hou,Hong Guo,Bingnan Yan,Zhuang Xu,Chao Yuan,Yuan Mao 대한기계학회 2022 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.36 No.10
The tool is an important part of machining, and its condition determines the operational safety of the equipment and the quality of the workpiece. Therefore, tool condition monitoring (TCM) is of great significance. To address the imbalance of the tool monitoring signal and achieve a lightweight model, a TCM method based on WGAN-GP and ShuffleNet is proposed in this paper. The tool monitoring data are enhanced and balanced using WGAN-GP, and the 1D signal data are converted into 2D grayscale images. The existing ShuffleNet is improved by adding a channel attention mechanism to construct the entire model. The tool wear state is recognized through experimental validation of the milling dataset and compared with those through other models. Results show that the proposed model achieves an accuracy of 99.78 % in recognizing the wear state of tools under imbalanced data while ensuring a light weight, showing the superiority of the method.
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