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226. Introduction

Author

Duckham, Matt, primary, Sun, Qian (Chayn), additional, and Worboys, Michael F., additional

Published
2023
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230. Architectures

Author

Duckham, Matt, primary, Sun, Qian (Chayn), additional, and Worboys, Michael F., additional

Published
2023
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233. Investigating the causal impact of polycystic ovary syndrome on gestational diabetes mellitus: a two-sample Mendelian randomization study

Author

Guan Guixue, Pu Yifu, Tang Xiaofeng, Sun Qian, Gao Yuan, Yang Wen, Han Conghui, and Zhu Zuobin

Subjects
polycystic ovary syndrome, gestational diabetes mellitus, causal effects, Mendelian randomization study, genome-wide association studies, Diseases of the endocrine glands. Clinical endocrinology, RC648-665
Abstract

IntroductionDetermining the causal relationship between polycystic ovary syndrome (PCOS) and gestational diabetes mellitus (GDM) holds significant implications for GDM prevention and treatment. Despite numerous observational studies suggesting an association between PCOS and GDM, it remains unclear whether a definitive causal relationship exists between these two conditions and which specific features of PCOS contribute to increased incidence of GDM.MethodsThe causal relationship between polycystic ovary syndrome (PCOS), its characteristic indices, and gestational diabetes mellitus (GDM) was investigated using a two-sample Mendelian randomization study based on publicly available statistics from genome-wide association studies (GWAS). The inverse-variance weighted method was employed as the primary analytical approach to examine the association between PCOS, its characteristic indices, and GDM. MR Egger intercept was used to assess pleiotropy, while Q values and their corresponding P values were utilized to evaluate heterogeneity. It is important to note that this study adopts a two-sample MR design where PCOS and its characteristic indices are considered as exposures, while GDM is treated as an outcome.ResultsThe study results indicate that there is no causal relationship between PCOS and GDM (all methods P > 0.05, 95% CI of OR values passed 1). The IVW OR value was 1.007 with a 95% CI of 0.906 to 1.119 and a P value of 0.904. Moreover, the MR Egger Q value was 8.141 with a P value of 0.701, while the IVW Q value was also 8.141 with a P value of 0.774, indicating no significant heterogeneity. Additionally, the MR Egger intercept was 0.0004, which was close to zero with a P value of 0.988, suggesting no pleiotropy. However, the study did find a causal relationship between several other factors such as testosterone, high-density lipoprotein, sex hormone-binding globulin, body mass index, waist-hip ratio, apolipoprotein A-I, number of children, diabetes illnesses of mother, father and siblings, hemoglobin A1c, fasting insulin, fasting blood glucose, years of schooling, and GDM based on the IVW method.ConclusionWe observed no association between genetically predicted PCOS and the risk of GDM, implying that PCOS itself does not confer an increased susceptibility to GDM. The presence of other PCOS-related factors such as testosterone, high-density lipoprotein, and sex hormone-binding globulin may elucidate the link between PCOS and GDM. Based on these findings, efforts aimed at preventing GDM in individuals with PCOS should prioritize those exhibiting high-risk features rather than encompassing all women with PCOS.

Published
2024
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234. Modeling the clearing of intra-provincial power market considering the equivalent value of power supply from outside Guangdong Province under the environment of Southern Regional Market (SRM)

Author

Wang Haohao, Huang Yuanming, Luo Jinqing, Shu Kangan, Sun Qian, and Chen Qing

Subjects
power supply equivalence, homotopy methods, electricity markets, clearing models, 00-02, Mathematics, QA1-939
Abstract

Focusing on the complex market environment of Guangdong Province, this research pioneers a model for assimilating external power sources into the intra-provincial power market, a critical move to support the region’s high demand for electricity driven by robust economic activities. Our study innovatively tackles the province’s fluctuating power supply and demand by employing a novel power supply equivalence and co-modulation technique, enhancing system modeling efficiency and computational effectiveness. Comparative evaluations demonstrate that our approach significantly reduces the power market’s purchase costs by 15% while safeguarding power supply stability and security. The proposed model presents a breakthrough in optimizing Guangdong’s power market’s economic and reliability aspects, offering a promising pathway for future market clearing practices.

Published
2024
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235. Superpixel Segmentation with Fully Convolutional Networks

Author

Yang, Fengting, Sun, Qian, Jin, Hailin, and Zhou, Zihan

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

In computer vision, superpixels have been widely used as an effective way to reduce the number of image primitives for subsequent processing. But only a few attempts have been made to incorporate them into deep neural networks. One main reason is that the standard convolution operation is defined on regular grids and becomes inefficient when applied to superpixels. Inspired by an initialization strategy commonly adopted by traditional superpixel algorithms, we present a novel method that employs a simple fully convolutional network to predict superpixels on a regular image grid. Experimental results on benchmark datasets show that our method achieves state-of-the-art superpixel segmentation performance while running at about 50fps. Based on the predicted superpixels, we further develop a downsampling/upsampling scheme for deep networks with the goal of generating high-resolution outputs for dense prediction tasks. Specifically, we modify a popular network architecture for stereo matching to simultaneously predict superpixels and disparities. We show that improved disparity estimation accuracy can be obtained on public datasets., Comment: 16 pages, 15 figures, to be published in CVPR'20

Published
2020

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