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Quanxi Wang,Baocheng Wu,Mengxi Liu,Xiaoqin Yuan,Chunyan Li,Shiyi Chen,Yubin Zhuang,Yijian Wu,Yifan Huang 한국유전학회 2017 Genes & Genomics Vol.39 No.11
This study was to investigate the molecular mechanism underlying the apoptosis induced by Muscovy duck reovirus (MDRV) through a transcriptomic analysis. Muscovy ducklings were infected with MDRV strain YB and the apoptotic cells in their livers were examined with terminal-deoxynucleotidyl-transferase-mediated nick end labeling and flow cytometry. Genes differentially expressed in the livers of the MDRV-infected ducklings were screened by comparing them with those of uninfected ducklings and were analyzed with a transcriptomic method to illuminate the mechanism of MDRV infection. The results showed that MDRV infection strongly induced apoptotic cells in liver. Significant pathway enrichment determined by a Kyoto Encyclopedia of Genes and Genomes analysis showed that MDRV activated the death receptor family signaling pathway (Fas, TNFR1), the interleukin receptor signaling pathway (IL1, IL3), the phosphatidylinositol 3-kinase signaling pathway, NF-ҝB signaling pathway and calcium ions signaling pathway to induce apoptosis. This was verified by SYBR-Green-based fluorescence quantitative PCR. In conclusion, MDRV induce apoptosis by activation multi signaling pathways.
Fan Yu,Kailang Li,Hua Zhang,Rui Zhang,Zhang Gao,Yubin Huang 대한토목학회 2023 KSCE Journal of Civil Engineering Vol.27 No.11
This paper aims to establish an automatic and accurate pore identification method for pervious concrete. The residual module and mixed loss functions were introduced to the original UNet network to obtain the improved UNet. CT scanning was conducted on the six groups of pervious concrete samples with different aggregate sizes to obtain the initial dataset. The initial dataset was marked and enhanced, and then the pore recognition model was trained. The influence of image brightness and contrast on pore identification was analyzed. The fusion algorithm was used to improve the robustness of the model. The results show that during model training, R-UNet began to converge 20 epochs earlier than UNet and the loss value was smaller. Moreover, the maximum increase of mIoU and mDice was 10.3% and 11.7% respectively, and the maximum decrease of mHD was 14.1%. The fusion algorithm could improve the segmentation accuracy of pores in brightness anomaly images. Compared with threshold segmentation method, the method proposed in this paper could improve the accuracy of pore edge segmentation and the “fine pores” identification, and reduced the pore identification defects. The value of mHD was decreased by 48.7% − 72.4%, and the efficiency of pore identification was greatly improved.