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Apolipoprotein A-IV and its derived peptide, T55−121, improve glycemic control and increase energy expenditure

Life Metabolism 2024年第4期3卷 1-12页

作者： Zhen Cao Lei Lei Ziyun Zhou Shimeng Xu Linlin Wang Weikang Gong Qi Zhang Bin Pan Gaoxin Zhang Quan Yuan Liujuan Cui Min Zheng Tao Xu You Wang Shuyan Zhang Pingsheng Liu National Laboratory of Biomacromolecules CAS Center for Excellence in BiomacromoleculesInstitute of BiophysicsChinese Academy of SciencesBeijing 100101China University of Chinese Academy of Sciences Beijing 100049China The State Key Laboratory of Bioactive Substance and Function of Natural Medicines Institute of Materia MedicaChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijing 100050China Luxembourg Centre for Systems Biomedicine University of LuxembourgEsch-sur-Alzette 4362Luxembourg Guangzhou Regenerative Medicine and Health Guangdong Laboratory(Bioland Laboratory) GuangzhouGuangdong 510005China Department of Computer Science School of ComputingNational University of SingaporeSingapore 117417Singapore School of Basic Medical Sciences Southwest Medical UniversityLuzhouSichuan 646000China The State Key Laboratory for Diagnosis and Treatment of Infectious Diseases National Clinical Research Center for Infectious DiseasesCollaborative Innovation Center for Diagnosis and Treatment of Infectious DiseasesThe First Affiliated HospitalCollege of MedicineZhejiang UniversityHangzhouZhejiang 310003China Beijing Key Laboratory of Emerging Infectious Diseases Institute of Infectious DiseasesBeijing Ditan HospitalCapital Medical UniversityBeijing 100015China Beijing Institute of Infectious Diseases Beijing 100015China National Center for Infectious Diseases Beijing Ditan HospitalCapital Medical UniversityBeijing 100015China National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases Beijing 100015China

It is crucial to understand the glucose control within our ***/metabolic surgeries,including laparoscopic sleeve gastrec-tomy(LSG)and Roux-en-Y gastric bypass(RYGB),provide an avenue for exploring the potential key factors involved in maintaining glucose homeostasis since these surgeries have shown promising results in improving glycemic control among patients with severe type 2 diabetes(T2D).For the first time,a markedly altered population of serum proteins in patients after LSG was discovered and analyzed through *** A-IV(apoA-IV)was revealed to be increased dramatically in diabetic obese patients following LSG,and a similar effect was observed in patients after RYGB ***,recombinant apoA-IV protein treatment was proven to enhance insulin secretion in isolated human *** results showed that apoA-IV may play a crucial role in gly-cemic control in humans,potentially through enhancing insulin secretion in human ***-IV was further shown to enhance energy expenditure and improve glucose tolerance in diabetic rodents,through stimulating glucose-dependent insulin secretion in pancreaticβcells,partially via Gαs-coupled GPCR/cAMP(G protein-coupled receptor/cyclic adenosine monophosphate)***,T55-121,truncated peptide 55-121 of apoA-IV,was discovered to mediate the function of *** collective findings contribute to our understanding of the relationship between apoA-IV and glycemic control,highlighting its potential as a biomarker or therapeutic target in managing and improving glucose regulation.

关键词： bariatric/metabolic surgeries proteomics apolipoprotein A-IV glucose tolerance glucose-stimulated insulin secretion human islets

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Architecture and Key Technologies of Parallel Dispatching System for Railway Technical Operation Stations

Architecture and Key Technologies of Parallel Dispatching Sy...

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Complex systems and intelligent science (CSIS-IAC), International Annual Conference on

作者： Gang Xiong Donghu Yang Wei Xu Runmei Li Shichao Chen Bing Song Xisong Dong Fenghua Zhu State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Signal and Communication Research Institute China Academy of Railway Science Corporation Limited Beijing China The School of Electronic and Information Engineering Beijing Jiaotong University Beijing China Beijing Huairou Academy of Parallel Sensing Beijing China Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences Dongguan China

A railroad technical operation station is an extensive and complex operation system and a variety of random disturbing factors further increase the complexity of its command and dispatching. To enhance the railroad technical operation stations' intelligence, and integration level, and enhance its dispatching management ability of complex operation plans, based on the ACP approach, this paper designs the parallel dispatching system (PDS) architecture, and describes its key technologies including the artificial dispatching system (ADS), computing experiments, parallel execution, etc. On the basis of the artificial dispatching system, computational experiments can optimize various types of technical operations and the overall operational process, to achieve parallel execution between the actual dispatching system and ADS. PDS can provide more efficient and intelligent command and dispatching solutions for railroad technical work stations, and promote the development of the railroad transportation industry in the direction of higher quality and efficiency.

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Graph Confidence Intercalibration Network for Intracranial Aneurysm Lesion Instance Segmentation in Dsa

SSRN

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SSRN 2024年

作者： Ye, Haili Mo, Yancheng Tang, Chen Liao, Mingqian Zhang, Xiaoqing Dai, Limeng Li, Baihua Liu, Jiang Research Institute of Trustworthy Autonomous Systems Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Department of Computer Science Loughborough University LoughboroughLE11 3TU United Kingdom School of Ophthalmology and Optometry Eye Hospital Wenzhou Medical University Wenzhou325027 China Center for High Performance Computing and Shenzhen Key Laboratory of Intelligent Bioinformatics Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Shenzhen People’s Hospital Neurosurgery Department Shenzhen518020 China China

Intracranial aneurysm (IA) lesion segmentation is significant for its treatment and prognosis. Although exiting deep network-based instance methods have good IA lesion segmentation results based on digital subtraction angiography (DSA) images, they still face great challenges with instance confidence bias and imprecise boundary segmentation, which may negatively affect IA diagnosis. To tackle these problems, this paper proposes a novel graph confidence intercalibration network (GCINet) to automatically segment IA lesions from DSA images. To be specific, we design a graph confidence intercalibration (GCI) module to mitigate instance confidence bias by dynamically adjusting their confidence distributions. At the same time, we propose an edge space perception (ESP) module to correct ambiguous segmentation boundaries. Extensive experiments on a clinical IA-DSA and a publicly available LiTS dataset demonstrate that our GCINet outperforms state-of-the-art methods. Additionally, visual analysis and ablation studies are provided to verify the effectiveness of each module in GCINet. © 2024, The Authors. All rights reserved.

关键词： Image segmentation

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Consistency and Diversity induced Human Motion Segmentation

arXiv

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arXiv 2022年

作者： Zhou, Tao Fu, Huazhu Gong, Chen Shao, Ling Porikli, Fatih Ling, Haibin Shen, Jianbing The PCA Lab The Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China Institute of High Performance Computing Agency for Science Technology and Research Singapore Riyadh Saudi Arabia The Research School of Engineering The Australian National University Australia The Department of Computer Science Stony Brook University Strony Brook NY United States The State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science University United States

Subspace clustering is a classical technique that has been widely used for human motion segmentation and other related tasks. However, existing segmentation methods often cluster data without guidance from prior knowledge, resulting in unsatisfactory segmentation results. To this end, we propose a novel Consistency and Diversity induced human Motion Segmentation (CDMS) algorithm. Specifically, our model factorizes the source and target data into distinct multi-layer feature spaces, in which transfer subspace learning is conducted on different layers to capture multi-level information. A multi-mutual consistency learning strategy is carried out to reduce the domain gap between the source and target data. In this way, the domain-specific knowledge and domain-invariant properties can be explored simultaneously. Besides, a novel constraint based on the Hilbert Schmidt Independence Criterion (HSIC) is introduced to ensure the diversity of multi-level subspace representations, which enables the complementarity of multi-level representations to be explored to boost the transfer learning performance. Moreover, to preserve the temporal correlations, an enhanced graph regularizer is imposed on the learned representation coefficients and the multi-level representations of the source data. The proposed model can be efficiently solved using the Alternating Direction Method of Multipliers (ADMM) algorithm. Extensive experimental results on public human motion datasets demonstrate the effectiveness of our method against several state-of-the-art approaches. Copyright © 2022, The Authors. All rights reserved.

关键词： Motion analysis

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Unity makes strength: Coalition Formation-based Group-buying for Timely UAV Data Collection

Unity makes strength: Coalition Formation-based Group-buying...

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GLOBECOM 2022 - 2022 IEEE Global Communications Conference

作者： Nan Qi Yeting Huang Wen Sun Shi Jin Theodoros A. Tsiftsis Qihui Wu Xiang Su National Mobile Communications Research Laboratory Southeast University Nanjing China College of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics Nanjing China School of Cyber Science and Engineering Southeast University Nanjing China School of Intelligent Systems Science & Engineering Jinan University Zhuhai China Department of Computer Science Norwegian University of Science and Technology Gjøvik Norway Center for Ubiquitous Computing University of Oulu Oulu Finland

ISBN: (纸本)9781665435413

With their high mobility, unmanned aerial vehicles (UAVs) become appealing data collectors in hard-to-reach wide-area distributed sensor networks. Different from existing works focusing on the perspective of UAVs for service order optimization and UAV utility maximization, we consider the utilities of both sensors and UAVs, and innovatively model the competition among sensors (buyers) for the service of UAVs (sellers) as an auction game. A “unity makes strength” strategy is exploited. That is, to strengthen the bidding competitiveness, a group-buying coalition auction method that encourages sensors to form coalitions to bid for UAV service is proposed. Besides, we propose a parallel variable neighborhood ascent search algorithm, we can quickly determine the approximately optimal group-buying coalition structure. Numerical results show that the proposed method outperforms the joint trajectory design-task scheduling (TDTS) UAV-to-community method and the single coalition formation game (CFG) method.

关键词： Wireless sensor networks Games Data collection Autonomous aerial vehicles Search problems Information age Trajectory

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Sequential Recommendation via Cross-Domain Novelty Seeking Trait Mining

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Journal of Computer science & Technology 2020年第2期35卷 305-319页

作者： Fu-Zhen Zhuang Ying-Min Zhou Hao-Chao Ying Fu-Zheng Zhang Xiang Ao Xing Xie Qing He Hui Xiong Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences Institute of Computing Technology Chinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 100049China School of Public Health Zhejiang University School of MedicineHangzhou 310027China Meituan-Dianping Group Beijing 100102China Microsoft Research Asia Beijing 100080China Department of Management Science and Information Systems Rutgers UniversityNew Jersey 07102U.S.A.

Transfer learning has attracted a large amount of interest and research in last decades, and some effort has been made to build more precise recommendation systems. Most previous transfer recommendation systems assume that the target domain shares the same/similar rating patterns with the auxiliary source domain, which is used to improve the recommendation performance. However, almost all existing transfer learning work does not consider the characteristics of sequential data. In this paper, we study the new cross-domain recommendation scenario by mining novelty-seeking trait. Recent studies in psychology suggest that novelty-seeking trait is highly related to consumer behavior, which has a profound business impact on online recommendation. Previous work performed on only one single target domain may not fully characterize users' novelty-seeking trait well due to the data scarcity and sparsity, leading to the poor recommendation performance. Along this line, we propose a new cross-domain novelty-seeking trait mining model (CDNST for short) to improve the sequential recommendation performance by transferring the knowledge from auxiliary source domain. We conduct systematic experiments on three domain datasets crawled from Douban to demonstrate the effectiveness of our proposed model. Moreover, we analyze the directed influence of the temporal property at the source and target domains in detail.

关键词： sequential recommendation novelty-seeking trait transfer learning

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SMedBERT: A knowledge-enhanced pre-trained language model with structured semantics for medical text mining

arXiv

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arXiv 2021年

作者： Zhang, Taolin Cai, Zerui Wang, Chengyu Qiu, Minghui Yang, Bite He, Xiaofeng School of Software Engineering East China Normal University Alibaba Group Shanghai Key Laboratory of Trsustworthy Computing School of Computer Science and Technology East China Normal University Dxy Shanghai Research Institute for Intelligent Autonomous Systems

Recently, the performance of Pre-trained Language Models (PLMs) has been significantly improved by injecting knowledge facts to enhance their abilities of language understanding. For medical domains, the background knowledge sources are especially useful, due to the massive medical terms and their complicated relations are difficult to understand in text. In this work, we introduce SMedBERT, a medical PLM trained on large-scale medical corpora, incorporating deep structured semantics knowledge from neighbours of linked-entity. In SMedBERT, the mention-neighbour hybrid attention is proposed to learn heterogeneousentity information, which infuses the semantic representations of entity types into the homogeneous neighbouring entity structure. Apart from knowledge integration as external features, we propose to employ the neighbors of linked-entities in the knowledge graph as additional global contexts of text mentions, allowing themto communicate via shared neighbors, thus enrich their semantic representations. Experiments demonstrate that SMedBERT significantly outperforms strong baselines in various knowledge-intensive Chinese medical tasks. It also improves the performance of other tasks such as question answering, questionmatching and natural language inference. Copyright © 2021, The Authors. All rights reserved.

关键词： Knowledge graph

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Fuzzy Granule Density-Based Outlier Detection with Multi-Scale Granular Balls

arXiv

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arXiv 2025年

作者： Gao, Can Tan, Xiaofeng Zhou, Jie Ding, Weiping Pedrycz, Witold The College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen518060 China The National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China The School of Artificial Intelligence and Computer Science Nantong University Nantong226019 China The Faculty of Data Science City University of Macau Macau999078 China The Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Systems Research Institute Polish Academy of Sciences Warsaw Poland The Department of Electrical and Computer Engineering King Abdulaziz University Jeddah Saudi Arabia The Department of Computer Engineering Istinye University Istanbul Turkey

Outlier detection refers to the identification of anomalous samples that deviate significantly from the distribution of normal data and has been extensively studied and used in a variety of practical tasks. However, most unsupervised outlier detection methods are carefully designed to detect specified outliers, while real-world data may be entangled with different types of outliers. In this study, we propose a fuzzy rough sets-based multi-scale outlier detection method to identify various types of outliers. Specifically, a novel fuzzy rough sets-based method that integrates relative fuzzy granule density is first introduced to improve the capability of detecting local outliers. Then, a multi-scale view generation method based on granular-ball computing is proposed to collaboratively identify group outliers at different levels of granularity. Moreover, reliable outliers and inliers determined by the three-way decision are used to train a weighted support vector machine to further improve the performance of outlier detection. The proposed method innovatively transforms unsupervised outlier detection into a semi-supervised classification problem and for the first time explores the fuzzy rough sets-based outlier detection from the perspective of multi-scale granular balls, allowing for high adaptability to different types of outliers. Extensive experiments carried out on both artificial and UCI datasets demonstrate that the proposed outlier detection method significantly outperforms the state-of-the-art methods, improving the results by at least 8.48% in terms of the Area Under the ROC Curve (AUROC) index. The source codes are released at https://***/Xiaofeng-Tan/MGBOD. © 2025, CC BY.

关键词： Granulation

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A Kind of Lean Approach for Removing Wastes From Non-Manufacturing Process With Various Facilities

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IEEE/CAA Journal of Automatica Sinica 2019年第1期6卷 307-315页

作者： Guangyu Xiong Xiuqin Shang Gang Xiong Timo R.Nyberg IEEE the Silk Road Business School Sanya University the Department of Industrial Engineering and Management School of ScienceAalto University the State Key Laboratory for Management and Controlof Complex Systems Institute of AutomationChinese Academy of Sciences(CAS) Qingdao Academy of Intelligent Industries the Cloud Computing Center CAS the Beijing Engineering Research Center of Intelligent Systems and Technology Institute of AutomationCAS the School of Science at Aalto University

It is important to identify and remove the wastes not only from manufacturing process, but also from nonmanufacturing process. In the last several decades, significant research achievements and practice benefits have been achieved about removing wastes from manufacturing process. Since the1990 s, some researchers and lean practitioners have paid more attention to removing waste from non-manufacturing *** on the authors' research work and industrial practice, the paper introduces a kind of lean approach for removing waste from non-manufacturing process. In its case study, the order handling process in a value chain is described with respect to a factory and its downstream distribution centers(DCs). The paper proposes a lean approach solution for creating the improved order handling process, and analyze how great improvements in performance can be achieved. As a result, the significant achievement has created a win-win scenario for both the nonmanufacturing process in a factory and non-manufacturing facilities(like DCs) across the value chain. It demonstrates that improvements have been made by removing waste from the non-manufacturing process that takes place within a factory as well as with external participants through the whole value chain. Likewise, the proposed lean approach has helped the case companies to achieve greater levels of efficiency and more benefits. Finally, some conclusions are drawn.

关键词： Lean approach non-manufacturing process optimized order handling process removing wastes value chain win-win scenario

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RCGA-Net: An Improved Multi-hybrid Attention Mechanism Network in Biomedical Image Segmentation

RCGA-Net: An Improved Multi-hybrid Attention Mechanism Netwo...

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2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Xiao, Feng Shen, Cong Chen, Yu Yang, Tian Chen, Shengyong Liao, Zhijun Tang, Jijun Tianjin University of Technology School of Computer Science and Engineering Tianjin China Engineering Research Center of Learning-Based Intelligent System Ministry of Education Tianjin China School of Basic Medical Sciences Fujian Medical University Fujian Fuzhou China College of Intelligence and Computing Tianjin University Tianjin China Key Laboratory of Systems Bioengineering Ministry of Education Tianjin China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Shenzhen China Department of Computer Science and Engineering University of South Carolina ColumbiaSC United States

ISBN: (纸本)9781665401265

Drawing support from an effective Medical Image Segmentation (MIS) is conducive to a substantial diagnostic basis for the physicians to identify the focus lesion in the patient body and give the subsequent clinical assessment of the patient status. Although various works have tried the challenging quantitative analysis problem, it is still difficult to conduct precise automatic segmentation, especially the soft tissue organs. In this decade, with the increased amount of available datasets, deep learning-based networks have achieved remarkable performance in image processing. Inspired by the state-of-the-art deep learning works, in this paper, we propose an end-to-end multi-layer network named RCGA-Net. It consists of an encoder-decoder backbone that integrates a coordinate attention mechanism based on space and channel and a global context extraction module to highlight more valuable information. To evaluate the performance of RCGA-Net, we apply it to different kinds of clinical and experimental MIS tasks to testify its generalization ability. Extensive experiments represent that our schema has taken the outperform or compatible results among the comparison methods group. Specifically, the numeric result of RCGA-Net on the pulmonary dataset has achieved a 99.12% optimum F1-score. © 2021 IEEE.

关键词： Diagnosis

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