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185 results on '"Xie, Guobo"'

16. Sex‐specific associations between haemoglobin glycation index and the risk of cardiovascular and all‐cause mortality in individuals with pre‐diabetes and diabetes: A large prospective cohort study.

Author

Yang, Jingqi, Shangguan, Qing, Xie, Guobo, Yang, Ming, and Sheng, Guotai

Subjects
MORTALITY, PREDIABETIC state, COHORT analysis, CARDIOVASCULAR diseases risk factors, HEALTH & Nutrition Examination Survey, PROPORTIONAL hazards models
Abstract

Aim: The aim of this study was to investigate the relationship between the haemoglobin glycation index (HGI), and cardiovascular disease (CVD) and all‐cause mortality in adults with pre‐diabetes and diabetes. Methods: This study included 10 267 adults with pre‐diabetes and diabetes from the National Health and Nutrition Examination Survey (NHANES) 1999‐2018. Sex‐differentiated relationships between HGI and mortality were elucidated using multivariate Cox proportional hazards models, restricted cubic splines and a two‐piecewise Cox proportional hazards model. Results: During the median follow‐up time of 103.5 months, a total of 535 CVD deaths and 1918 all‐cause deaths were recorded. After multivariate adjustment, in males with pre‐diabetes and diabetes, there was a U‐shaped relationship between HGI and CVD mortality and all‐cause mortality, with threshold points of −0.68 and −0.63, respectively. Before the threshold point, HGI was negatively associated with CVD mortality [hazard ratio (HR) 0.60; 95% confidence interval (CI) 0.41, 0.89] and all‐cause mortality (HR 0.56; 95% CI 0.43, 0.74), and after the threshold point, HGI was positively associated with CVD mortality (HR 1.46; 95% CI 1.23, 1.73) and all‐cause mortality (HR 1.40; 95% CI 1.23, 1.59). In contrast, HGI had an L‐shaped relationship with all‐cause mortality and no significant association with CVD mortality in females. To the left of the threshold points, the risk of all‐cause mortality decreased (HR 0.50; 95% CI 0.35, 0.71) progressively with increasing HGI. Conclusions: In the cohort study, HGI in pre‐diabetic and diabetic populations was found to have a U‐shaped association with CVD mortality and all‐cause mortality in males and an L‐shaped association with all‐cause mortality only in females. Further prospective and mechanistic studies are warranted. [ABSTRACT FROM AUTHOR]

Published
2024
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23. Insulator and Burst Fault Detection Using an Improved Yolov3 Algorithm

Author

Zhou Fangrong, Pan Hao, Qian Guochao, Ma Yutang, Wen Gang, Xu Chao, Kong Peng, Xie Guobo, and Zheng Xiaofeng

Subjects
Article Subject, Control and Systems Engineering, Electrical and Electronic Engineering, Instrumentation
Abstract

Insulators play an important role in the operation of outdoor high-voltage transmission lines. However, insulators are installed in outdoor environments for long periods and thus failures are inevitable. It is necessary to conduct timely insulator inspection and maintenance. In this paper, an improved Yolov3 target detection network (Yolov3-CK) is proposed in order to achieve higher detection accuracy and speed. First, Yolov3-CK uses the CIOU loss function instead of the mean square error loss function from Yolov3. Second, the Yolov3-CK model uses cluster analysis of the priori box via the k -means++ algorithm to obtain a priori box size that is more suitable for the detection of insulators and their burst faults. Finally, we use a dataset obtained by performing data enhancement on the China power line insulator dataset to train and test the data-enhanced Yolov3-CK model. The mean precision of Yolov3-CK reaches 91.67% with 47.9 frames processed per second. Yolov3-CK provides better detection accuracy and a higher processing rate than Faster RCNN, SSD, and Yolov3. Therefore, the Yolov3-CK model is more suitable for the detection of insulators and their burst faults.

Published
2022

37. Evaluating a Low-Power Dual-Core Architecture

Author

Liu, Yijun, Chen, Pinghua, Xie, Guobo, Liu, Guangcong, Li, Zhenkun, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Xu, Ming, editor, Zhan, Yinwei, editor, Cao, Jiannong, editor, and Liu, Yijun, editor

Published
2007
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49. LDA-LNSUBRW: lncRNA-Disease Association Prediction Based on Linear Neighborhood Similarity and Unbalanced bi-Random Walk.

Author

Xie, Guobo, Jiang, Jiawei, and Sun, Yuping

Abstract

Increasing number of experiments show that lncRNAs are involved in many biological processes, and their mutations and disorders are associated with many diseases. However, verifying the relationships between lncRNAs and diseases is time consuming and laborio. Searching for effective computational methods will contribute to our understanding of the underlying mechanisms of disease and identifying biomarkers of diseases. Therefore, we proposed a method called lncRNA-disease association prediction based on linear neighborhood similarity and unbalanced bi-random walk (LDA-LNSUBRW). Given that the known lncRNA-disease associations are rare, a pretreatment step should be performed to obtain the interaction possibility of unknown cases, so as to help us predict the potential associations. In the framework of leave-one-out cross-validation (LOOCV)and fivefold cross-validation (5-fold CV), LDA-LNSUBRW achieved effective performance with AUC of 0.8874 and 0.8632 $\pm$ ± 0.0051, respectively. The experimental results in this paper show that the proposed method is superior to five other state-of-the-art methods. In addition, case studies of three diseases (lung cancer, breast cancer, and osteosarcoma)were carried out to illustrate that LDA-LNSUBRW could predict the relevant lncRNAs. [ABSTRACT FROM AUTHOR]

Published
2022
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