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arXiv 2023年

作者： Li, Zhongcan Huang, Ping Wen, Chao Rodrigues, Filipe National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu610031 China Kgs. Lyngby2800 Denmark Institute for Transport Planning and Systems ETH Zurich Zurich8093 Switzerland High-speed Railway Research Centre University of Waterloo WaterlooN2L3G1 Canada

Railway operations involve different types of entities (stations, trains, etc.), making the existing graph/network models with homogenous nodes (i.e., the same kind of nodes) incapable of capturing the interactions between the entities. This paper aims to develop a heterogeneous graph neural network (HetGNN) model, which can address different types of nodes (i.e., heterogeneous nodes), to investigate the train delay evolution on railway networks. To this end, a graph architecture combining the HetGNN model and the GraphSAGE homogeneous GNN (HomoGNN), called SAGE-Het, is proposed. The aim is to capture the interactions between trains, trains and stations, and stations and other stations on delay evolution based on different edges. In contrast to the traditional methods that require the inputs to have constant dimensions (e.g., in rectangular or grid-like arrays) or only allow homogeneous nodes in the graph, SAGE-Het allows for flexible inputs and heterogeneous nodes. The data from two sub-networks of the China railway network, namely the Guangzhou South network (GZS-Net) and the Changsha South network (CSS-Net), are applied to test the performance and robustness of the proposed SAGE-Het model. The experimental results show that SAGE-Het exhibits better performance than the existing delay prediction methods and some advanced HetGNNs used for other prediction tasks;the predictive performances of SAGE-Het under different prediction time horizons (10/20/30 min ahead) all outperform other baseline methods;the accuracies are over 90% under the permissible 3-minute errors for the three prediction time horizons. Specifically, the influences of train interactions on delay propagation are investigated based on the proposed model. The results show that train interactions become subtle when the train headways increase (e.g., when the train headways are over 20 min, canceling the edges does not decrease the prediction performance). This finding directly contributes to decision-ma

关键词： Graph neural networks

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Internal drivers of the global pandemic of the Omicron variants of SARS-CoV-2

Medicine Advances

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Medicine Advances 2024年第3期2卷 262-273页

作者： Yilin Niu Wuxiu Quan Yunyi Li Ziying Chen Yunfei Gao School of Public Health Southern Medical University Guangzhou Guangdong China Medical Big Data Center Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences) Southern Medical University Guangzhou Guangdong China Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application Guangdong Provincial People's Hospital Guangdong Academy of Medical Sciences Guangzhou Guangdong China School of Software Engineering South China University of Technology Guangzhou Guangdong China

Background The high transmissibility of the Omicron variants of severe acute respiratory syndrome coronavirus 2 continues to impose a significant burden on public health systems worldwide. Continuous mutations in the virus challenge the efficacy of vaccines and prior immunity from other variants, posing a severe threat to human health. Therefore, there is an urgent need to predict mutations of the Omicron variants and identify key factors influencing their spread. This study investigated critical amino acid mutations of the Omicron variants using epidemiological data and the mechanisms underlying their transmission. The aim was to understand the key factors driving the spread of the Omicron variants and provide insights for effective control and prevention strategies. Methods A total of 488,646 Omicron cases recorded between December 2021 and February 2023 were analyzed using a sliding time window and the Epi Score model, which has high accuracy for the identification of mutation sites. MutPred2, PolyPhen2 and VarSite tools were used to predict future mutations and identify factors driving the prevalence of the Omicron variants. Results Epi Scores showed fluctuating patterns at mutation sites, highlighting N969K, Y505H, N764K, T478K, and S371F mutations on the spike protein as significant for future prevention efforts. The spread of the Omicron variants was linked to changes in the viral entry pathway and improved angiotensin-converting enzyme 2 (ACE2) binding and immune evasion tactics. Conclusions The findings of this study reveal trends in the evolution of the Omicron variants, including altered cell entry, increased affinity for ACE2, and evasion of the immune system. These factors are critical for understanding the global spread of the Omicron variants and developing effective control strategies.

关键词： amino acids coronavirus immune evasion mutation public health SARS-CoV-2 spike glycoprotein

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Transformer-based Annotation Bias-aware Medical Image Segmentation

arXiv

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arXiv 2023年

作者： Liao, Zehui Xie, Yutong Hu, Shishuai Xia, Yong National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University Xi’an710072 China Australian Institute for Machine Learning The University of Adelaide AdelaideSA Australia Ningbo Institute of Northwestern Polytechnical University Ningbo315048 China Research and Development Institute Northwestern Polytechnical University in Shenzhen Shenzhen518057 China

Manual medical image segmentation is subjective and suffers from annotator-related bias, which can be mimicked or amplified by deep learning methods. Recently, researchers have suggested that such bias is the combination of the annotator preference and stochastic error, which are modeled by convolution blocks located after decoder and pixel-wise independent Gaussian distribution, respectively. It is unlikely that convolution blocks can effectively model the varying degrees of preference at the full resolution level. Additionally, the independent pixel-wise Gaussian distribution disregards pixel correlations, leading to a discontinuous boundary. This paper proposes a Transformer-based Annotation Bias-aware (TAB) medical image segmentation model, which tackles the annotator-related bias via modeling annotator preference and stochastic errors. TAB employs the Transformer with learnable queries to extract the different preference-focused features. This enables TAB to produce segmentation with various preferences simultaneously using a single segmentation head. Moreover, TAB takes the multivariant normal distribution assumption that models pixel correlations, and learns the annotation distribution to disentangle the stochastic error. We evaluated our TAB on an OD/OC segmentation benchmark annotated by six annotators. Our results suggest that TAB outperforms existing medical image segmentation models which take into account the annotator-related bias. © 2023, CC BY-NC-ND.

关键词： Gaussian distribution

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Mutual Consistency Learning for Semi-supervised Medical Image Segmentation

arXiv

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arXiv 2021年

作者： Wu, Yicheng Ge, Zongyuan Zhang, Donghao Xu, Minfeng Zhang, Lei Xia, Yong Cai, Jianfei Department of Data Science & AI Faculty of Information Technology Monash University MelbourneVIC3800 Australia Monash-Airdoc Research Monash University MelbourneVIC3800 Australia Monash Medical AI Monash eResearch Centre MelbourneVIC3800 Australia DAMO Academy Alibaba Group Hangzhou311121 China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University Xi'an710072 China

In this paper, we propose a novel mutual consistency network (MC-Net+) to effectively exploit the unlabeled data for semi-supervised medical image segmentation. The MC-Net+ model is motivated by the observation that deep models trained with limited annotations are prone to output highly uncertain and easily mis-classified predictions in the ambiguous regions (e.g., adhesive edges or thin branches) for medical image segmentation. Leveraging these challenging samples can make the semi-supervised segmentation model training more effective. Therefore, our proposed MC-Net+ model consists of two new designs. First, the model contains one shared encoder and multiple slightly different decoders (i.e., using different up-sampling strategies). The statistical discrepancy of multiple decoders' outputs is computed to denote the model's uncertainty, which indicates the unlabeled hard regions. Second, we apply a novel mutual consistency constraint between one decoder's probability output and other decoders' soft pseudo labels. In this way, we minimize the discrepancy of multiple outputs (i.e., the model uncertainty) during training and force the model to generate invariant results in such challenging regions, aiming at regularizing the model training. We compared the segmentation results of our MC-Net+ model with five state-of-the-art semi-supervised approaches on three public medical datasets. Extension experiments with two standard semi-supervised settings demonstrate the superior performance of our model over other methods, which sets a new state of the art for semi-supervised medical image segmentation. Our code is released publicly at https://***/ycwu1997/MC-Net. © 2021, CC BY-NC-SA.

关键词： Image segmentation

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Ginsenoside compound-K attenuates OVX-induced osteoporosis via the suppression of RANKL-induced osteoclastogenesis and oxidative stress

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Natural Products and Bioprospecting 2023年第1期13卷 159-170页

作者： Lingli Ding Zhao Gao Siluo Wu Chen Chen Yamei Liu Min Wang Yage Zhang Ling Li Hong Zou Guoping Zhao Shengnan Qin Liangliang Xu Key Laboratory of Orthopaedics and Traumatology Lingnan Medical Research CenterThe First Affiliated Hospital of Guangzhou University of Chinese MedicineGuangzhou University of Chinese MedicineGuangzhouChina Er Sha Sports Training Center of Guangdong Province GuangzhouChina School of Basic Medical Science Guangzhou University of Chinese MedicineGuangzhouChina Engineering Laboratory for Nutrition Shanghai Institute of Nutrition and HealthChinese Academy of SciencesShanghaiChina Master Lab for Innovative Application of Nature Products National Center of Technology Innovation for Synthetic BiologyTianjin Institute of Industrial BiotechnologyChinese Academy of SciencesTianjinChina CAS Key Laboratory of Quantitative Engineering Biology Shenzhen Institute of Synthetic BiologyShenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhenChina CAS-Key Laboratory of Synthetic Biology CAS Center for Excellence in Molecular Plant SciencesShanghai Institute of Plant Physiology and EcologyChinese Academy of SciencesShanghaiChina Bio-Med Big Data Center Shanghai Institute of Nutrition and HealthChinese Academy of SciencesShanghaiChina State Key Laboratory of Genetic Engineering Department of Microbiology and ImmunologySchool of Life SciencesFudan UniversityShanghaiChina Department of Microbiology The Chinese University of Hong KongHong KongChina Department of Orthopaedics Guangzhou Institute of Traumatic SurgeryGuangzhou Red Cross HospitalMedical CollegeJinan UniversityGuangzhouChina

Osteoporosis(OP),a systemic and chronic bone disease,is distinguished by low bone mass and destruction of bone *** Compound-K(CK),one of the metabolites of ginsenoside Rb1,has anti-aging,anti-inflammatory,anti-cancer,and hypolipidemic *** have demonstrated CK could promote osteogenesis and fracture healing in our previous ***,the contribution of CK to osteoporosis has not been *** the present study,we investigated the effect of CK on osteoclastogenesis and ovariectomy(OVX)-induced *** results showed that CK inhibited receptor activator for nuclear factor-κB ligand(RANKL)-mediated osteoclast differentiation and reactive oxygen species(ROS)activity by inhibiting the phosphorylation of NF-κB p65 and oxidative stress in RAW264.7 *** addition,we also demonstrated that CK could inhibit bone resorption using bone marrow-derived ***,we demonstrated that CK attenuated bone loss by suppressing the activity of osteoclast and alleviating oxidative stress in *** together,these results showed CK could inhibit osteoclastogenesis and prevent OVX-induced bone loss by inhibiting NF-κB signaling pathway.

关键词： Ginsenoside CK NF-κB signaling pathway Osteoporosis Oxidative stress

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Capacity analysis of ship-tugging operations in a large container port

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Asian Transport Studies 2020年 6卷

作者： Kang, Liujiang Gao, Song Meng, Qiang Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport Ministry of Transport Beijing Jiaotong University 100044 China Intelligent Transportation Systems Research Center Wuhan University of Technology Wuhan 430063 China Department of Civil and Environmental Engineering National University of Singapore Singapore 117576 Singapore

In this study, we deal with the simulation problem of ship-tugging operations in a large container port. First, we build a tugboat-service network using a directed graph, where the nodes consist of tugboat bases, anchorages, and berths while the arcs represent the directed links between nodes. Second, we develop a marine traffic simulation system, which can simulate maritime traffic conditions in restricted waters, such as large container ports and busy straits. The marine traffic simulation system is a customized system. Users are allowed to set their study areas and virtual traffic environment (such as traffic demand and network structure) at will. Finally, we conduct various simulations for a real tugboat-service network of Singapore Port, which includes one anchorage, one tugboat base, two berths, and two channels between the anchorage and berths. A detailed analysis of the tugging capacity is provided based on simulation results. © 2020 The Authors

关键词： Capacity estimation Container ports Simulation system development Tugging operations

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Microscopic Right-of-Way Trading Mechanism for Cooperative Decision-Making: Theories and Preliminary Results

SSRN

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SSRN 2023年

作者： Sun, Zhanbo Qin, Ziye Ma, Rui Huang, Tianyu Gao, Ziyan Ji, Ang School of Transportation and Logistics Southwest Jiaotong University Sichuan Chengdu China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Sichuan Chengdu China Department of Civil and Environmental Engineering The University of Alabama in Huntsville HuntsvilleAL United States School of Air Traffic Management Civil Aviation Flight University of China Sichuan Deyang China SenseTime IAG Shanghai China

In this paper, a microscopic right-of-way trading mechanism (Micro-ROWTM) is developed to encourage cooperative behavior in mixed traffic, which consists of cooperative vehicles (CoVs) and non-cooperative vehicles (NCoVs). Micro-ROWTM encompasses the following functions: (i) detection of traffic conflicts and system-improving opportunities;(ii) participants report their attributes;(iii) right-of-way trade proposed by the system;and (iv) trade settlement and cooperative control. In the experiments, the proposed mechanism is tested using an illustrative ramp-merging example, in which each right-of-way trade proposal is offered to a group of participants comprised of a mainline leader, a mainline follower and a ramp vehicle. Upon the acceptance of a proposal, the mainline leader and/or mainline follower agrees to behave cooperatively (yield) to the ramp vehicle, and receive monetary compensation from the ramp vehicle in return. To help design the trading mechanism, six important properties are introduced and discussed, including (i) strong individual rationality, (ii) system efficiency and group efficiency, (iii) dominant-strategy incentive compatibility, (iv) social welfare maximization, (v) utilitarian envy minimization and (vi) payoff difference minimization. A linear programming-based trading and taxation model which satisfies the above properties is then developed and solved. Using the proposed Micro-ROWTM, when the flow rates is 1000veh/h for both the mainline and ramp traffic, the ramp vehicles can save 6.8% travel time and 27.4% fuel consumption on average, and the mainline vehicles can eventually obtain a positive payoff by compensation. From a system perspective, the ramp-merging section can save 107.5s of travel time and 2450.3g of fuel consumption per hour. © 2023, The Authors. All rights reserved.

关键词： Game theory

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The Cauchy problem for two dimensional generalized Kadomtsev-Petviashvili-I equation in anisotropic Sobolev spaces

arXiv

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arXiv 2017年

作者： Yan, Wei Li, Yongsheng Huang, Jianhua Duan, Jinqiao School of Mathematics and Information Science Henan Engineering Laboratory for Big Data Statistical Analysis and Optimal Control Henan Normal University Xinxiang Henan453007 China School of Mathematics South China University of Technology Guangzhou Guangdong510640 China College of Science National University of Defense Technology Changsha Hunan410073 China Department of Applied Mathematics Illinois Institute of Technology ChicagoIL60616 United States

The goal of this paper is three-fold. Firstly, we prove that the Cauchy problem for generalized KP-I equation ut+ |Dx|αθxu + θ-1xθ2yu + 1/2 θx(u2) = 0, α ≥ 4 is locally well-posed in the anisotropic Sobolev spaces Hs1,s2(R2) with s1> -α-1/4 and s2≥ 0. Secondly, we prove that the problem is globally well-posed in Hs1,0(R2) with s1> -(α-1)(3α-4)/4(5α+3) if 4 ≤ α ≤ 5. Finally, we prove that the problem is globally well-posed in Hs1,0(R2) with s1> -α(3α-4)/4(5α+4) if α > 5. Our result improves the result of Saut and Tzvetkov (J. Math. Pures Appl. 79(2000), 307-338.) and Li and Xiao (J. Math. Pures Appl. 90(2008), 338-352.). Copyright © 2017, The Authors. All rights reserved.

关键词： Sobolev spaces

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Exploratory study of highly susceptible to SARS-CoV-2 populations

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Life Research 2022年第3期5卷 10-14页

作者： Xiaodong Jia Chengliang Yin Shanshan Lu Yan Chen Qingyan Liu Junfan Bai Lei Zhi Yinying Lu Comprehensive Liver Cancer Center The Fifth Medical Center of PLA General HospitalBeijingChina.0000-0001-8262-5749 National Engineering Laboratory for Medical Big Data Application Technology Chinese PLA General HospitalBeijingChina Faculty of Medicine Macao University of Science and TechnologyMacaoChina Medical Big Data Research Center Medical Innovation Research Division of PLA General HospitalBeijingChina Center for Synthetic and Systems Biology(CSSB) Tsinghua UniversityBeijingChina Peking University BeijingChina Department of Chronic Disease Luzhou Center for Disease Control and PreventionLuzhouChina

The coronavirus disease 2019(COVID-19),caused by severe acute respiratory syndrome coronavirus 2(SARS-CoV-2),is threatening public *** human angiotensin-converting enzyme 2(ACE2)has a remarkably high affinity binding to *** study explored whether certain populations,including obese and cancer patients,are susceptible to *** expression pattern of ACE2 in normal and tumor tissues of cancer patients was compared by the search for network database and re-analysis of pubic *** level of ACE2 expression in normal adipose tissue was higher than that in normal lung tissue,which indicated the adipose tissue might be vulnerable to SARS-CoV-2;the levels of ACE2 expressed by adipocytes and adipose progenitor cells were similar between non-obese individuals and obese individuals(BMI>29),but obese individuals have more adiposes so as to increase the number of ACE2-expressing cells;the expression of ACE2 in tumor tissues posed by five different types of cancers increased significantly compared with that in adjacent ***,we proposed the following hypothesis:the obese individuals and five types of cancer patients might have a higher risk of SARS-CoV-2 infection,which might become the target population of SARS-CoV-2 infection in the future.

关键词： COVID-19 adipose tissue obesity cancer ACE2

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Counterfactual data Augmentation with Denoising Diffusion for Graph Anomaly Detection

arXiv

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arXiv 2024年

作者： Xiao, Chunjing Pang, Shikang Xu, Xovee Li, Xuan Trajcevski, Goce Zhou, Fan The School of Computer and Information Engineering Henan University China The Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng475004 China The University of Electronic Science and Technology of China Sichuan Chengdu610054 China The National Key Laboratory of Fundamental Science on Synthetic Vision Sichuan University Chengdu610065 China The Department of Electrical and Computer Engineering Iowa State University AmesIA50011 United States

A critical aspect of Graph Neural Networks (GNNs) is to enhance the node representations by aggregating node neighborhood information. However, when detecting anomalies, the representations of abnormal nodes are prone to be averaged by normal neighbors, making the learned anomaly representations less distinguishable. To tackle this issue, we propose CAGAD - an unsupervised Counterfactual data Augmentation method for Graph Anomaly Detection - which introduces a graph pointer neural network as the heterophilic node detector to identify potential anomalies whose neighborhoods are normal-node-dominant. For each identified potential anomaly, we design a graph-specific diffusion model to translate a part of its neighbors, which are probably normal, into anomalous ones. At last, we involve these translated neighbors in GNN neighborhood aggregation to produce counterfactual representations of anomalies. Through aggregating the translated anomalous neighbors, counterfactual representations become more distinguishable and further advocate detection performance. The experimental results on four datasets demonstrate that CAGAD significantly outperforms strong baselines, with an average improvement of 2.35% on F1, 2.53% on AUC-ROC, and 2.79% on AUC-PR. Copyright © 2024, The Authors. All rights reserved.

关键词： Anomaly detection

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