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1,722 results on '"Zhou, Guanghong"'

14. Effect of High-density Culture on the Fate of Porcine Muscle Stem Cells

Author

LIN Ling, ZHU Haozhe, JIANG Yichen, ZHENG Yanyan, LIU Zheng, WU Zhongyuan, DING Shijie, ZHOU Guanghong

Subjects
muscle stem cell, high-density culture, senescence, proliferation, stemness, rna sequencing, Food processing and manufacture, TP368-456
Abstract

In this study, four different initial densities (2.7 × 103 as a control, 5.4 × 103, 1.1 × 104, and 2.2 × 104 cells/cm2) of porcine muscle stem cells were cultured for three days to explore the effect of high-density culture conditions on the fate of porcine muscle stem cells. The counting results showed that the suitable culture density of porcine muscle stem cells was 2 × 104–3 × 104 cells/cm2, and the proliferation-related protein phosphorylated extracellular signal-regulated kinase 1/2 (p-ERK1/2) was down-regulated when the initial cell density was outside this range, causing a proliferation inhibitory effect. The transcriptome sequencing results showed that 2 125 differentially expressed genes were identified between the highest density and control groups, mainly enriched in some signaling pathways such as those related to cell senescence and cell cycle and the PI3K-Akt signaling pathway. Furthermore, the expression of the stemness genes PAX7 and PAX3 and the quiescence-related gene SPRY1 was upregulated under high-density conditions. The expression of the differentiation genes MYOD, MYOG and MYH3 and the senescence genes P21, P53 and P15 was upregulated. The results of real-time PCR and Western blot assay showed that the expression of PAX7, SPRY1, MYOG, and P21 were upregulated as the cell density increased. The above results showed that porcine muscle stem cells had multiple fats such as returning to a quiescent state, differentiation and senescence under high-density conditions. The results of this study can help in understanding the mechanism of cell fate determination and provide a theoretical basis for studying the regulation of cell proliferation and differentiation under high-density condition.

Published
2023
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25. Optimization study of anomaly detection algorithm in machine vision inspection technology

Author

Zhou Guanghong, Wang Hairong, and Zhuang Er-xing

Subjects
machine vision, yolov5 network, deformable convolution, attention mechanism, anomaly detection, 97m50, Mathematics, QA1-939
Abstract

In recent years, target detection algorithms based on machine vision have been a hotspot in computer vision research. The You Only Look Once (YOLO) algorithm, as an excellent target detection algorithm, has played an important role in improving detection speed and accuracy with the improvement of the network architecture in its development process. This paper introduces the concept of integrated learning to the YOLOv5 network architecture, incorporating deformable convolution and attention mechanisms. It also chooses the focal EIOU loss function to replace the GIOU loss function, thereby addressing the issue of localization loss, prioritizing abnormally detected targets, and enhancing the detection efficiency of these abnormal targets. Finally, we examine the practical value of the improved YOLOv5 algorithm by testing its performance and applying it to real-world anomaly detection. The results show that the improved YOLOv5 model outperforms the original YOLOv5 model in terms of performance and practical application advantages. In terms of performance, the classification accuracy of sea_person and earth_person in the improved YOLOv5 model is 37% and 26%, respectively, which is a significant performance gain overall. In actual application tests, the proposed method is more accurate than the traditional method. The accuracy is significantly higher.

Published
2024
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26. Research on image recognition and processing of motion targets of warehouse logistics robots

Author

Zhao Aodong, Zhou Guanghong, and Zhang Nan

Subjects
image recognition, camshift algorithm, gamma correction, surf feature points, motion targets, warehouse logistics, 97p39, Mathematics, QA1-939
Abstract

In developing robots for warehouse logistics, image recognition and processing for moving targets are the cornerstone of subsequent work. In this paper, the Meanshift algorithm is extended to continuous image sequences, and the Camshift algorithm for motion target tracking in a warehouse environment is proposed to obtain effective tracking of targets through the probability distribution when the color of continuous images changes dynamically. Based on target tracking, a feature-matching-based image recognition method is constructed. The scene image is first treated with improved Gamma correction for light equalization, and then image features are extracted using SURF feature points. Regarding running time, the feature matching method is, on average, 2.03 seconds faster than FLDA and 0.96 seconds faster than PCAFLDA under the same external conditions. By optimizing the computational structure, the feature-matching method can address the need for efficiency in warehouse logistics.

Published
2024
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