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PANX2 and brain lower grade glioma genesis: A bioinformatic analysis

Science Progress 2021年第2期104卷 368504211011836页

作者： Xu, XiaoXue Hao, YueHan Xiong, Shuang He, ZhiYi Department of Neurology The First Hospital of China Medical University Shenyang China Key Laboratory of Neurological Disease Big Data of Liaoning Province Shenyang China Liaoning Academy of Analytic Science Construction Engineering Center of Important Technology Innovation and Research and Development Base in Liaoning Province Shenyang China

PANX2 forms large-pore channels mediating ATP release in response to physiological and pathological stimuli. Although PANX2 shows involvements in glioma genesis, the underlying mechanism remains unclear. PANX2 mRNA expression was analyzed via Oncomine and was confirmed via Gene Expression Profiling Interactive Analysis (GEPIA). The influence of PANX2 on overall survival (OS) of glioma was evaluated using LinkedOmics and further assessed through Cox regression analysis. The correlated genes with PANX2 acquired from LinkedOmics were validated through GEPIA and cBioPortal. Protein-protein interaction (PPI) of these genes was then obtained using Search Tool for the Retrieval of Interacting Genes (STRING) and Cytoscape with MCODE plug-in. All the PANX2-related genes underwent Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. The correlation between PANX2 and cancer immune infiltrates was evaluated via Tumor Immune Estimation Resource (TIMER). A higher expression of PANX2 only revealed a better OS in brain low grade glioma (LGG). PANX2-related genes in LGG functionally enriched in neuroactive ligand-receptor interaction, synaptic vesicle cycle, and calcium signaling. The hub genes from highest module of PPI were mainly linked to chemical synaptic transmission, plasma membrane, neuropeptide, and the pathway of neuroactive ligand-receptor interaction. Besides, PANX2 expression was negatively associated with infiltrating levels of macrophage, dendritic cells, and CD4+ T cells. This study demonstrated that PANX2 likely participated in LGG pathogenesis by affecting multiple molecular pathways and immune-related processes. PANX2 was associated with LGG prognosis and might become a promising therapeutic target of LGG. © The Author(s) 2021.

关键词： Brain Lower Grade Gliomas Immune Infiltrates Microenvironment PANX2 Tumorigenesis

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A branch-and-price algorithm for the trucking problem using trailer-swapping operation mode

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Transportation Research Part C: Emerging Technologies 2025年 178卷

作者： Peng, Wenxiang Chen, Xiangsheng Xue, Zhaojie You, Jintao College of Civil and Transportation Engineering Shenzhen University Shenzhen518060 China Ministry of Education Shenzhen518060 China State Key Laboratory of Subtropical Building and Urban Science Shenzhen518060 China School of Advanced Engineering Great Bay University Dongguan523000 China Great Bay Institute for Advanced Study Great Bay University Dongguan523000 China Dongguan Key Laboratory of Intelligent Equipment and Smart Industry School of Advanced Engineering Great Bay University Dongguan523000 China Shenzhen Research Institute of Big Data Shenzhen518172 China

This study investigates the trucking problem using the trailer-swapping operation mode (TP-TSOM), where trucks swap trailers at network nodes during transit and then continue to pull the swapped trailer to its destination. Traditionally, trucking involves transport tasks distributed across the network, completed by trucks pulling trailers directly from origins to destinations. The trailer-swapping operation mode (TSOM), which involves collaboration among trucks, can enhance trucking efficiency. Accordingly, a mixed-integer linear programming model for TP-TSOM is developed to determine the optimal truck trailer-swapping plan, and an integer linear programming model for the two-truck swapping case is also constructed. To solve the TP-TSOM efficiently, a branch-and-price algorithm utilizing a trailer-swapping group-based set partitioning model is designed. The pricing sub-problem is formulated as multiple elementary shortest cycle problems with resource constraints (ESCPRC) based on different sub-graphs. By iteratively fixing virtual terminals, a new representation of the ESCPRC is constructed, enabling the ESCPRC to be solved using the labeling algorithm. To accelerate the solution process, two tailored refinement strategies are proposed, and a heuristic pricing algorithm based on shortest path faster algorithm is applied. Extensive numerical experiments are conducted to validate performance. The computational results show that the proposed exact algorithm can solve instances with up to 200 tasks and that TSOM significantly reduces transportation costs, travel time, and empty load rates. © 2025 Elsevier Ltd

关键词： Travel time

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Utility maximization for multihop wireless networks employing BATS codes

arXiv

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arXiv 2021年

作者： Dong, Yanyan Jin, Sheng Chen, Yanzuo Yang, Shenghao Yin, Hoover H.F. The School of Science and Engineering The Chinese University of Hong Kong Shenzhen China Carnegie Mellon University The N-hop Technologies Limited Hong Kong The Institute of Network Coding The Chinese University of Hong Kong Hong Kong Shenzhen Key Laboratory of IoT Intelligent Systems and Wireless Network Technology Shenzhen Research Institute of Big Data Shenzhen China

BATS (BATched Sparse) codes are a class of efficient random linear network coding variation that has been studied for multihop wireless networks mostly in scenarios of a single communication flow. Towards sophisticated multi-flow network communications, we formulate a network utility maximization (NUM) problem that jointly optimizes the BATS code parameters of all the flows and network scheduling. The NUM problem adopts a batch-wise packet loss model that can be obtained from the network local statistics without any constraints on packet loss patterns. Moreover, the NUM problem allows a different number of recoded packets to be transmitted for different batches in a flow, which is called adaptive recoding. Due to both the probably nonconcave objective and the BATS code-related variables, the algorithms developed for the existing flow optimization problems cannot be applied directly to solve our NUM problem. We introduce a two-step algorithm to solve our NUM problem, where the first step solves the problem with nonadaptive recoding schemes, and the second step optimizes adaptive recoding hop-by-hop from upstream to downstream in each flow. We perform various numerical evaluations and simulations to verify the effectiveness and efficiency of the algorithm. © 2021, CC BY-NC-ND.

关键词： Packet loss

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Assessment of the Fuel Consumption Gap between Type-Approval and Real-World Driving Conditions

SSRN

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SSRN 2022年

作者： Fan, Pengfei Yin, Hang Lu, Hongyu Wu, Yizheng Zhai, Zhiqiang Yu, Lei Song, Guohua Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport Beijing Jiaotong University 3 Shangyuancun Haidian District Beijing100044 China Chinese Research Academy of Environmental Sciences Dayang Fang #8 Chaoyang District Beijing100012 China School of Civil and Environmental Engineering Georgia Institute of Technology 790 Atlantic Drive AtlantaGA30332 United States College of Science and Technology Texas Southern University 3100 Cleburne Avenue HoustonTX77004 United States

A widening gap has been found between the type-approval fuel consumption vs. real-world fuel consumption, which can bias the policy-making concerning energy saving and greenhouse gas reduction. An independent assessment of the gap’s contributions is significant to narrowing it. This study confirmed an average gap of 42% based on 0.95 billion second-by-second vehicle operating (speed, acceleration, etc.) and fuel consumption data collected from 395 light-duty vehicles through on-board diagnostics (OBD) devices. The impact of fuel consumption rate (FCR)-related (e.g., vehicle mass, tire pressure, temperature, etc.), engine load (or driving cycle)-related (e.g., congested, aggressive driving, etc.), and road grade discrepancies between the type-approval (or in-laboratory) test procedure vs. real-world driving conditions were independently assessed by setting the other impact factors as control variables. Results indicated that the FCR-related, engine load-related, and road grade discrepancies contributed 20.7%, 17.0%, and 3.2% to this gap, respectively (only1.0% of the gap has not been explained). © 2022, The Authors. All rights reserved.

关键词： Fuels

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Controlled-source electromagnetic signal detection using a hybrid deep learning model: Convolutional and long short-term memory neural networks

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Computers and Geosciences 2025年 204卷

作者： Liu, Yecheng Li, Diquan Li, Jin Hu, Yanfang Liu, Zijie Zhang, Xian School of Geosciences and Info-Physics Central South University Changsha 410083 China Key Laboratory of Metallogenic Prediction of Non-Ferrous Metals and Geological Environment Monitor Ministry of Education Central South University Changsha 410083 China Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing College of Information Science and Engineering Hunan Normal University Changsha 410081 China Hunan Provincial Key Laboratory of Finance & Economics Big Data Science and Technology School of Information Technology and Management Hunan University of Finance and Economics Changsha 410205 China

Controlled-source electromagnetic (CSEM) method using a periodic transmitted signal source suppresses random noise by superimposing and averaging the recorded signal over multiple periods. However, it still faces great challenges in strong interference environments. Here we present a CSEM signal detection method based on a hybrid architecture of convolutional neural network (CNN) and long short-term memory neural network (LSTM). Our method improves the quality of the recorded signal by filtering out all the noise periods and retaining the useful signal periods. The core lies in combining the spatial feature extraction capability of CNN with the time series modeling capability of LSTM, which can deeply excavate the feature differences between noise and CSEM useful signal. Meanwhile, a classification model of signal and noise is constructed using a large-scale training dataset. This hybrid model exhibits superior performance compared to other deep learning models such as CNN or LSTM. Also, we propose a novel signal detection mechanism that not only maintains the periodicity of CSEM signal, but also greatly enhances processing efficiency. The results of synthetic and measured data demonstrate that our method can obtain useful signals from CSEM data containing different noise types and significantly improve the quality of sounding curves. In particular, our method is useful for CSEM signal detection in strong interference. © 2025 Elsevier Ltd

关键词： Controlled-source electromagnetic method Deep learning Noise Signal detection

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Heterogeneous-length text topic modeling for reader-Aware multi-document summarization

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ACM Transactions on knowledge Discovery from data 2019年第4期13卷 1–21页

作者： Qiang, Jipeng Chen, Ping Ding, Wei Wang, Tong Xie, Fei Wu, Xindong Department of Computer Science Yangzhou University China Department of Computer Science University of Massachusetts Boston United States Department of Computer Science and Technology Hefei Normal University China Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Ministry of Education Hefei China Mininglamp Academy of Sciences Minininglamp Beijing 100084 China

More and more user comments like Tweets are available, which often contain user concerns. In order to meet the demands of users, a good summary generating from multiple documents should consider reader interests as reflected in reader comments. In this article, we focus on how to generate a summary from multi-document documents by considering reader comments, named as reader-Aware multi-document summarization (RA-MDS).We present an innovative topic-based method for RA-MDA, which exploits latent topics to obtain the most salient and lessen redundancy summary from multiple documents. Since finding latent topics for RA-MDS is a crucial step, we also present a Heterogeneous-length Text Topic Modeling (HTTM) to extract topics from the corpus that includes both news reports and user comments, denoted as heterogeneous-length texts. In this case, the latent topics extract by HTTM cover not only important aspects of the event, but also aspects that attract reader interests. Comparisons on summary benchmark datasets also confirm that the proposed RA-MDS method is effective in improving the quality of extracted summaries. In addition, experimental results demonstrate that the proposed topic modeling method outperforms existing topic modeling algorithms. © 2019 Association for Computing Machinery. All rights reserved.

关键词： heterogeneous-length text LDA multi-document summarization Topic modeling

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Finedge: A Dynamic Cost-Efficient Edge Resource Management Platform for NFV Network

Finedge: A Dynamic Cost-Efficient Edge Resource Management P...

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International Workshop on Quality of Service

作者： Miao Li Qixia Zhang Fangming Liu National Engineering Research Center for Big Data Technology and System Key Laboratory of Services Computing Technology and System Ministry of Education School of Computer Science and Technology Huazhong University of Science and Technology China

ISBN: (数字)9781728168876

ISBN: (纸本)9781728168883

With the evolution of network function virtualization (NFV) and edge computing, software-based network functions (NFs) can be deployed on closer-to-end-user edge servers to support a broad range of new services with high bandwidth and low latency. However, due to the resource limitation, strict QoS requirements and real-time flow fluctuations in edge network, existing cloud-based resource management strategy in NFV platforms is inefficient to be applied to the edge. Thus, we propose Finedge, a dynamic, fine-grained and cost-efficient edge resource management platform for NFV network. First, we conduct empirical experiments to find out the effect of NFs' resource allocation and their flow-level characteristics on performance. Then, by jointly considering these factors and QoS requirements (e.g., latency and packet loss rate), Finedge can automatically assign the most suitable CPU core and tune the most cost-efficient CPU quota to each NF. Finedge is also implemented with some key strategies including real-time flow monitoring, elastic resource scaling up and down, and also flexible NF migration among cores. Through extensive evaluations, we validate that Finedge can efficiently handle heterogeneous flows with the lowest CPU quota and the highest SLA satisfaction rate as compared with the default OS scheduler and other state-of-the-art resource management schemes.

关键词： Resource management Noise measurement Packet loss Servers Throughput Quality of service

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A Deep Concept-aware Model for predicting and explaining restaurant future status

A Deep Concept-aware Model for predicting and explaining res...

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IEEE International Conference on Web Services (ICWS)

作者： Hao Liao Xiaojie Zhang Xin Li Mingyang Zhou Alexandre Vidmer Rui Mao Guangdong Province Key Laboratory of Popular High Performance Computers College of Computer Science and Software Engineering Shenzhen University Guangdong Province Engineering Center of China-made High Performance Data Computing System Shenzhen City Key Laboratory of Service Computing and Application Shenzhen China Institute of Big Data Intelligent Management and Decision Shenzhen University Shenzhen China

ISBN: (数字)9781728187860

ISBN: (纸本)9781728187877

Nowadays, with the development of web services, the survival of online service has captured public attention significantly. Future prediction of business is crucial to the shopkeepers. Predicting how the business will develop and explaining what key factors are leading to it are two most important tasks. Existing literatures usually tackle only one of these two tasks, ignoring that they are two closely related tasks and complement each other. In this paper, we propose a review-based neural model, named Deep Concept-aware Model (DCA), to predict restaurants' future status and provide explainable sentences simultaneously in an end2end framework. Specifically, we use co-attention to select concepts and implement prediction and explanation tasks through Factorization Machine and Gated Recurrent Unit, respectively. We conduct extensive experiments on three Chinese cities' datasets. The proposed joint model outperforms the state-of-the-art baseline methods for both prediction (average 40.96% improvement in AUC) and explanation (average 9.72% improvement in BLEU and 86.05% in Precision metric of ROUGE).

关键词： Web services Urban areas Predictive models Prediction algorithms data models Task analysis Business

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A data driven time-dependent transmission rate for tracking an epidemic:a case study of 2019-nCoV

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Science Bulletin 2020年第6期65卷 425-427,M0003页

作者： Norden EHuang Fangli Qiao First Institute of Oceanography Ministry of Natural Resources of ChinaQingdao 266061China Key Laboratory of Marine Science and Numerical Modeling Ministry of Natural Resources of ChinaQingdao 266061China Laboratory for Regional Oceanography and Numerical Modeling Pilot National Laboratory for Marine Science and TechnologyQingdao 266237China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Qingdao 266061China

Ever since the first report of the discovery of an unknown novel coronavirus that had caused pneumonia in Wuhan,China,had filed to WHO by China,on December 31,2019,the government had instituted drastic measures to contain the outbreak of the *** keep transparency,the China's National Health Commission reports confirmed,suspected,recovered and death case numbers daily,yet the epidemiological community is still calling for more ***,they want to have the critical"Basic Reproduction,Number"*** to Jones[1]and van den Driessche and Watmough[2],Ro,is defined as the expected number of secondary cases produced by a single(typical)infection in a completely susceptible population.

关键词：数据驱动 filed transparency

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Subequivariant reinforcement learning in 3D multi-entity physical environments 24

Subequivariant reinforcement learning in 3D multi-entity phy...

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Proceedings of the 41st International Conference on Machine Learning

作者： Runfa Chen Ling Wang Yu Du Tianrui Xue Fuchun Sun Jianwei Zhang Wenbing Huang Dept. of Comp. Sci. & Tech. Institute for AI BNRist Center Tsinghua University Dept. of Info. Eng. Xi'an Research Institute of High-Tech School of Elec. Eng. Naval University of Engineering and Dept. of Comp. Sci. & Tech. Institute for AI BNRist Center Tsinghua University College of Arts & Sci. New York Unviersity Dept. of Comp. Sci. & Tech. Institute for AI BNRist Center Tsinghua University and THU-Bosch JCML Center TAMS Dept. of Informatics University of Hamburg Gaoling School of Artificial Intelligence Renmin University of China and Beijing Key Laboratory of Big Data Management and Analysis Methods

Learning policies for multi-entity systems in 3D environments is far more complicated against single-entity scenarios, due to the exponential expansion of the global state space as the number of entities increases. One potential solution of alleviating the exponential complexity is dividing the global space into independent local views that are invariant to transformations including translations and rotations. To this end, this paper proposes Subequivariant Hierarchical Neural Networks (SHNN) to facilitate multientity policy learning. In particular, SHNN first dynamically decouples the global space into local entity-level graphs via task assignment. Second, it leverages subequivariant message passing over the local entity-level graphs to devise local reference frames, remarkably compressing the representation redundancy, particularly in gravity-affected environments. Furthermore, to overcome the limitations of existing benchmarks in capturing the subtleties of multi-entity systems under the Euclidean symmetry, we propose the Multientity Benchmark (MEBEN), a new suite of environments tailored for exploring a wide range of multi-entity reinforcement learning. Extensive experiments demonstrate significant advancements of SHNN on the proposed benchmarks compared to existing methods. Comprehensive ablations are conducted to verify the indispensability of task assignment and subequivariance.

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