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A causal relationship between type 1 diabetes and risk of systemic sclerosis: a bidirectional two-sample Mendelian randomization study

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Diabetology and Metabolic Syndrome 2025年第1期17卷 1-11页

作者： Huang, Haochen Yu, Bo Yu, Fengyun Wu, Zhen-Zhen Xu, Dong Zhang, Ling-Ling Department of Rheumatology and Clinical Immunology Peking Union Medical College Hospital Chinese Academy of Medical Sciences Peking Union Medical College Beijing 100730 China National Clinical Research Center for Dermatologic and Immunologic Diseases (NCRC-DID) Ministry of Science & Technology Beijing 100730 China State Key Laboratory of Complex Severe and Rare Diseases Peking Union Medical College Hospital Beijing 100730 China Key Laboratory of Rheumatology and Clinical Immunology Ministry of Education Beijing 100730 China School of Engineering Medicine Beihang University Beijing 100191 China Key Laboratory of Biomechanics and Mechanobiology of Ministry of Education Beijing Advanced Innovation Center for Biomedical Engineering Beihang University Beijing 100191 China Key Laboratory of Big Data-Based Precision Medicine Ministry of Industry and Information Technology of the People’s Republic of China Beihang University Beijing China Key Laboratory of Epigenetic Regulation and Intervention Institute of Biophysics Chinese Academy of Sciences Beijing 100101 China Department of Rheumatology and Clinical Immunology Beijing Shijitan Hospital Capital Medical University Beijing 100730 China

Background: Previous epidemiological observational studies have indicated an association between type 1 diabetes (T1DM) and systemic sclerosis (SSc). However, the potential causal relationship between T1DM and SSc remains unclear. Therefore, this Mendelian randomization (MR) study aims to investigate the bidirectional causal relationship between T1DM and SSc through a bidirectional two-sample mendelian randomization analysis. Methods: We utilized SNP data aggregated from previously published genome-wide association studies (GWAS) and selected appropriate SNPs as instrumental variables (IVs). To explore the bidirectional causal relationship between T1DM and SSc, we conducted bidirectional Mendelian randomization analyses using various methods, including inverse variance weighted (IVW), MR-Egger, weighted median, and weighted mode approaches. Additionally, sensitivity analyses were performed to further validate the accuracy of the MR results. Results: Our MR analysis using 66 SNPs as IVs for T1DM showed a genetically determined association with an increased risk of SSc (IVW OR = 1.294, 95% CI 1.140–1.469, p < 0.001), consistent across MR-Egger, weighted median, and weighted mode methods, with no evidence of horizontal pleiotropy or heterogeneity, and robustness confirmed by leave-one-out analysis. Conversely, reverse MR analysis using 7 SNPs for SSc revealed no significant association with T1DM (IVW OR = 0.998, 95% CI 0.979–1.016, P = 0.798), consistent across all methods, with no evidence of pleiotropy or heterogeneity, and results confirmed as robust. Conclusion: This pioneering mendelian randomization study reveals a causal link between genetic susceptibility to T1DM and an increased risk of SSc, though not vice versa, emphasizing the need for validation across diverse populations and further exploration of underlying mechanisms. © The Author(s) 2025.

关键词： Autoimmune disease Causal relationship Mendelian randomization Systemic sclerosis Type 1 diabetes

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Source Code Summarization in the Era of Large Language Models

Source Code Summarization in the Era of Large Language Model...

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International Conference on Software engineering (ICSE)

作者： Weisong Sun Yun Miao Yuekang Li Hongyu Zhang Chunrong Fang Yi Liu Gelei Deng Yang Liu Zhenyu Chen College of Computing and Data Science Nanyang Technological University Singapore Singapore State Key Laboratory for Novel Software Technology Nanjing University Nanjing China School of Computer Science and Engineering University of New South Wales Sidney Australia School of Big Data and Software Engineering Chongqing University Chongqing China

ISBN: (数字)9798331505691

ISBN: (纸本)9798331505707

To support software developers in understanding and maintaining programs, various automatic (source) code summarization techniques have been proposed to generate a concise natural language summary (i.e., comment) for a given code snippet. Recently, the emergence of large language models (LLMs) has led to a great boost in the performance of coderelated tasks. In this paper, we undertake a systematic and comprehensive study on code summarization in the era of LLMs, which covers multiple aspects involved in the workflow of LLMbased code summarization. Specifically, we begin by examining prevalent automated evaluation methods for assessing the quality of summaries generated by LLMs and find that the results of the GPT-4 evaluation method are most closely aligned with human evaluation. Then, we explore the effectiveness of five prompting techniques (zero-shot, few-shot, chain-of-thought, critique, and expert) in adapting LLMs to code summarization tasks. Contrary to expectations, advanced prompting techniques may not outperform simple zero-shot prompting. Next, we investigate the impact of LLMs' model settings (including top_p and temperature parameters) on the quality of generated summaries. We find the impact of the two parameters on summary quality varies by the base LLM and programming language, but their impacts are similar. Moreover, we canvass LLMs' abilities to summarize code snippets in distinct types of programming languages. The results reveal that LLMs perform suboptimally when summarizing code written in logic programming languages compared to other language types (e.g., procedural and object-oriented programming languages). Finally, we unexpectedly find that CodeLlamaInstruct with 7B parameters can outperform advanced GPT-4 in generating summaries describing code design rationale and asserting code properties. We hope that our findings can provide a comprehensive understanding of code summarization in the era of LLMs.

关键词： Computer languages Codes Systematics Source coding Large language models Object oriented modeling Software Prompt engineering Object oriented programming Software engineering

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Digital twins in smart farming:An autoware-based simulator for autonomous agricultural vehicles

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International Journal of Agricultural and Biological engineering 2023年第4期16卷 184-189页

作者： Xin Zhao Wanli Wang Long Wen Zhibo Chen Sixian Wu Kun Zhou Mengyao Sun Lanjun Xu Bingbing Hu Caicong Wu College of Information and Electrical Engineering China Agricultural UniversityBeijing 100083China Key Laboratory of Agricultural Machinery Monitoring and Big Data Application Ministry of Agriculture and Rural AffairsBeijing 100083China The Bureau of Agriculture and Rural Affairs of Urumchi Xinjiang Uygur Autonomous RegionUrumchi 830000China Research&Advanced Engineering AGCO A/SDK-8930 RandersDenmark Beijing Agricultural Machinery Experiment Appraisal Extension Station Beijing 100079China Kunlun Beidou Intelligence Technologies Co.Ltd. Beijing 102200China

Digital twins can improve the level of control over physical entities and help manage complex systems by integrating a range of *** autonomous agricultural machine has shown revolutionary effects on labor reduction and utilization rate in field *** vehicles in precision agriculture have the potential to improve competitiveness compared to current crop production methods and have become a research ***,the development time and resources required in experiments have limited the research in this *** tools in unmanned farming that are required to enable more efficient,reliable,and safe autonomy are increasingly *** by the recent development of an open-source virtual simulation platform,this study proposed an autoware-based simulator to evaluate the performance of agricultural machine guidance based on digital *** photogrammetry using drones is used to construct threedimensional maps of fields at the same scale as reality.A communication format suitable for agricultural machines was developed for data input and output,along with an inter-node communication *** conversion,publishing,and maintenance of multiple coordinate systems were completed based on ROS(Robot Operating System).Coverage path planning was performed using hybrid curves based on Bézier curves,and it was tested in both a simulation environment and actual fields with the aid of Pure Pursuit algorithms and PID controllers.

关键词： autoware simulation platform autonomous agricultural vehicle digital twin autonomous robots

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Application Research of Vertical Federated Learning Technology in Banking Risk Control Model Strategy 21

Application Research of Vertical Federated Learning Technolo...

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21st IEEE International Symposium on Parallel and Distributed Processing with Applications, 13th IEEE International Conference on big data and Cloud Computing, 16th IEEE International Conference on Social Computing and Networking and 13th International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Luo, Yong Lu, Zhi Yin, Xiaofei Lu, Songfeng Weng, Yiting Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Wuhan China China Minsheng Banking Corp. Ltd. Beijing China Shenzhen Huazhong University of Science and Technology Research Institute Wuhan China Shu Lun Pan Beijing China

ISBN: (纸本)9798350329223

This study centers on the application of vertical federated learning technology in the context of Internet banking loans, with a particular focus on innovations in data privacy protection, risk control model algorithms, and secure multi-party computation. Currently, banking risk control strategies mainly rely on traditional data processing technologies, which often fall short in protecting user privacy and ensuring data usage efficiency. We adopt vertical federated learning technology, offering an innovative solution for the Internet banking loan scenario. Firstly, regarding data privacy protection, we propose a differential privacy mechanism to safeguard user-sensitive data. Secondly, we innovatively apply risk control model algorithms, facilitating collaborative modeling across multiple Internet loan platforms through federated learning. Furthermore, we introduce secure multi-party computation technology to ensure the secure transmission of data and confidentiality of computation processes during federated learning. Through empirical experiments on real Internet loan datasets, we validate the effectiveness and feasibility of our proposed methods. After implementing our risk control model, the credit approval rate increased from 3.44% to 18.2%, with a single-day high reaching 25.53%. The average loan amount increased by 7,700 yuan, and the average interest rate slightly declined by 0.48%, marking a significant improvement and breakthrough compared to traditional risk control models. This study offers innovative solutions for data privacy protection and risk control in the Internet loan scenario, providing safer and more reliable services for financial institutions and users. Moreover, our methods possess high practicality and promotional value. The potential widespread impact on the industry is profound. © 2023 IEEE.

关键词： Risk management

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Generation of functional oligopeptides that promote osteogenesis based on unsupervised deep learning of protein IDRs

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Bone Research 2022年第2期10卷 354-366页

作者： Mingxiang Cai Baichuan Xiao Fujun Jin Xiaopeng Xu Yuwei Hua Junhui Li Pingping Niu Meijing Liu Jiaqi Wu Rui Yue Yong Zhang Zuolin Wang Yongbiao Zhang Xiaogang Wang Yao Sun Department of Oral Implantology School of StomatologyTongji UniversityShanghai Engineering Research Center of Tooth Restoration and RegenerationShanghai 200072China The First Affiliated Hospital of Jinan University School of StomatologyClinical Research Platform for Interdiscipline of StomatologyJinan UniversityGuangzhou 510630China Key Laboratory of Big Data-Based Precision Medicine School of Engineering MedicineBeihang UniversityBeijing 100191China Guangzhou Laboratory Bioland LaboratoryGuangzhou Regenerative Medicine and Health Guangdong LaboratoryGuangzhou 510320China Institute for Regenerative Medicine Shanghai East HospitalShanghai Key Laboratory of Signaling and Disease ResearchFrontier Science Center for Stem Cell ResearchSchool of Life Sciences and TechnologyTongji UniversityShanghai 200092China

Deep learning(DL)is currently revolutionizing peptide drug development due to both computational advances and the substantial recent expansion of digitized biological ***,progress in oligopeptide drug development has been limited,likely due to the lack of suitable datasets and difficulty in identifying informative features to use as inputs for DL ***,we utilized an unsupervised deep learning model to learn a semantic pattern based on the intrinsically disordered regions of~171 known osteogenic ***,oligopeptides were generated from this semantic pattern based on Monte Carlo simulation,followed by in vivo functional characterization.A five amino acid oligopeptide(AIB5P)had strong bone-formation-promoting effects,as determined in multiple mouse models(e.g.,osteoporosis,fracture,and osseointegration of implants).Mechanistically,we showed that AIB5P promotes osteogenesis by binding to the integrinα5 subunit and thereby activating FAK *** summary,we successfully established an oligopeptide discovery strategy based on a DL model and demonstrated its utility from cytological screening to animal experimental verification.

关键词： functional thereby utilized

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DiffECN: Differential ECN Marking for datacenter Networks

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IEEE/ACM Transactions on Networking 2024年

作者： Huang, Hanlin Xu, Ke Li, Tong Liu, Zhuotao Du, Xinle Gao, Xiangyu Tsinghua University Department of Computer Science and Technology Beijing100084 China Zhongguancun Laboratory Beijing100094 China Renmin University of China Key Laboratory of Data Engineering and Knowledge Engineering Beijing100872 China Tsinghua University Institute for Network Sciences and Cyberspace Beijing100084 China Huawei Shenzhen518129 China

ECN marking has been integrated into datacenter switches to enable high-throughput and low-latency transport. We observe that current marking schemes are coarse-grained: they blindly mark all flows when congestion occurs, causing large flows to occupy undeserved bandwidth and preventing newly arriving small flows from finishing quickly. In this paper, we propose, a differential marking strategy that marks only the flows that are the culprits of congestion and protects the remaining flows from being limited. We have implemented it in the Barefoot Tofino switch and performed extensive evaluations via both physical testbed and large-scale simulations. The results show that can restrain flows responsible for congestion successfully while providing desirable network performance. For instance, compared to the legacy way of ECN marking, achieves up to 32.5% (40.1%) lower average (99th percentile) flow completion time (FCT) for small flows while delivering similar FCT for large flows under production workloads. © 1993-2012 IEEE.

关键词： Congestion control (communication)

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Integrated Building Information Modelling and 3D Printing for Prefabricated Building

Integrated Building Information Modelling and 3D Printing fo...

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Chinese Control and Decision Conference, CCDC

作者： Yukang Wang Yu Zhou Xiaoping Zhou Junjun Han Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing University of Civil Engineering and Architecture Beijing China Beijing Pi-Innovation Technology Ltd Beijing

The prefabricated components can be connected to form buildings in prefabricated building based on the design drawings. The production of components in the factory is not standardized and accurate, which leads to difficulty in connecting prefabricated components and low efficiency in construction. Excessive construction materials are piled up on the construction site and these wastes have caused a lot of pollution to the environment. Study proposes to integrating Building Information Modelling (BIM) and 3D printing for prefabricated buildings. Triangulation of BIM models is discussed. Prefabricated components information is extracted by Industry Foundation Class (IFC) files so that BIM can provide prefabricated component models. Additionally, the conversion relationship between BIM and 3D printing is established. Thirdly, the conversion rules and methods for converting BIM data into STL files are developed so that the 3D printer can print the prefabricated components according to the corresponding path. Finally, testing the data format conversion on BIM models shows that our presented approach enables us to convert a BIM model into a data format recognizable for 3D printing. Applying BIM and 3D print in prefabricated buildings will boost the fineness of components and the efficiency of construction and have good performance in decreasing engineering waste.

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No-Reference Stereoscopic Image Quality Assessment Based on Binocular Collaboration

SSRN

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SSRN 2024年

作者： Guo, Wenzhong Wang, Hanling Ke, Xiao Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing College of Computer and Data Science Fuzhou University Fujian Fuzhou350116 China Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou University Fuzhou350116 China

Stereoscopic images usually consist of a left view, a right view, and depth information. Stereoscopic/3D image quality assessment (SIQA) is often more challenging compared to 2D-IQA due to the usual scene differences between left and right views and the complexity of the binocular vision fusion process. To address the problem of quality prediction bias of multi-distortion images, we investigated the visual physiology and the processing of visual information by the primary visual cortex of the human brain and proposed a no-reference stereoscopic image quality evaluation method. The method mainly includes an innovative end-to-end NR-SIQA neural network with a picture patch generation algorithm. The algorithm generates a saliency map by fusing the left and right views and then guides the image cropping in the database based on the saliency map. The proposed models are validated and compared based on publicly available databases. The results show that the model and algorithm together outperform the state-of-the-art NR-SIQA metric in the LIVE 3D database and the WIVC 3D database, and have excellent results in the specific noise metric. The model generalization experiments demonstrate a certain degree of generality of our proposed model. © 2024, The Authors. All rights reserved.

关键词： database systems

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An Improved Weighted Differential Evolution Algorithm Based on the Chaotic Mapping and Dynamic Reverse Learning Strategy 4

An Improved Weighted Differential Evolution Algorithm Based ...

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2022 4th International Conference on Artificial Intelligence Technologies and Applications, ICAITA 2022

作者： Wu, Fengbin Li, Shaobo Zhang, Junxing Lv, Dongchao Wu, Xuefang Li, Menghan State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang550025 China College of Mechanical Engineering Guizhou University Guiyang550025 China College of Mathematics and Statistics Guizhou University Guiyang550025 China

The weighted differential evolution algorithm has the disadvantage of slipping into local optima and low convergence. To address these problems, an extended weighted differential evolution algorithm based on the chaotic mapping and optimal-worst dynamic opposite learning strategy is proposed. Firstly, the chaotic sequence generated by improved one-dimensional Logistic-Chebyshev mapping is employed to modify the starting population generated uniformly within the original algorithm, which aids in broadening the richness of the population. Secondly, to augment the probability of obtaining global optimum, an optimal-worst dynamic reverse learning strategy is used. Finally, a new evolutionary step size generation method is incorporated into the algorithm, which helps to acquire better solutions. The experimental outcomes obtained by testing 10 benchmark functions in CEC 2020 indicate that the proposed method has been greatly improved in accuracy. © Published under licence by IOP Publishing Ltd.

关键词： Optimization

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Rtr-Cnn: Rotated Tray Region Selection and Seedling Health Information Detection by Cnn

SSRN

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SSRN 2024年

作者： Li, Yatao Lin, Hao Tong, Junhua Wang, Xiaoqin Jiang, Jiahang School of Mechanical Engineering Zhejiang Sci-Tech University Hangzhou310018 China Key Laboratory of Transplanting Equipment and Technology of Zhejiang Province Hangzhou310018 China Fujian Key Laboratory of Big Data Application and Intellectualization for Tea Industry Wuyi University Wuyishan354300 China

Transplanting seedlings is the primary technique employed in fruit and vegetable production facilities. Utilizing mobile inspection transplanting machines in seedbed technology could effectively reduce manual labor. The detection of plug seedlings and the acquisition of health information are crucial for achieving automated seedling transplantation. Herein, a two-stage detection algorithm called RTR-CNN was developed to locate and identify multiple rotating trays with varying angles on seedbeds within greenhouses while differentiating the growth status of individual seedlings. This study focused on a seedling tray containing 200 cells. Corresponding matching templates were designed on the basis of the binary morphological characteristics of the seedling trays to identify trays at various angles. The recognition results were then segmented and distorted for each cell before being inputted into the backbone network for classification. Masked generative distillation was utilized to optimize the performance of the deployed network for edge computing hardware. The accuracy rate of the deployed network using the WideResNet101_2 teacher network reached 88.80%, and the two-classification accuracy rate reached 98.95%. The MobileNetV2 lightweight network was used for deployment, reaching an accuracy rate of 87.45% through distillation learning, and the two-classification accuracy rate reached 99.15% at a test speed of 159.30 fps with a batch size of 200. Compared with the training accuracy rate without distillation method, that with using distillation learning had a 0.95% increase, and the two-classification accuracy rate exhibited a 0.5% increase. © 2024, The Authors. All rights reserved.

关键词： Distillation

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