基于HubGLasso注意力机制的脑网络分类研究

脑网络分类有助于脑疾病的早期诊断,也有益于理解脑疾病发病机理,具有重要的研究与应用价值。其中,卷积神经网络应用广泛,可以提取脑网络的拓扑特征,是脑网络分类中的一个前沿热点。然而,现有方法未考虑脑网络中Hub节点对脑功能的重要贡献,这可能会导致特征提取不充分,限制了它们的分类性能。为此,该文提出了一种基于HubGLasso注意力机制的卷积神经网络模型,用于进行脑网络分类任务。该方法包含了一种新的卷积层结构,首先利用GLasso模型去除脑网络中的冗余信息,然后引入Hub约束与注意力机制,使其能够提取与异常Hub结构相关的重要特征,并用于脑疾病诊断。实验结果表明,该方法在包含1112个被试的真实自闭症数据集上取得了68.67%的准确率,显著优于目前已有方法,证明了其应用价值。更进一步,通过对训练后的模型进行特征分析,能够得到与脑疾病相关的脑区信息与Hub节点结构信息,为脑疾病病理机制的研究提供了全新的视角。

基于加权基因共表达网络和癌症基因组图谱临床数据分析并鉴定肝细胞癌的Hub基因研究

背景 肝细胞癌(HCC)是全球常见的癌症相关死亡的第三大原因,约占所有原发性肝癌病例的90%,其复发率和死亡率较高,目前发生的分子机制仍不清楚。目的 探索HCC潜在的分子机制,发掘新的生物标志物。方法 从TCGA数据库下载RNA-seq表达数据和临床相关信息,通过差异基因表达分析正常肝脏组织与HCC组织的差异基因;对差异表达基因进行富集分析;基于TCGA中HCC的基因表达数据概况,使用WGCNA R包建立共表达网络,进行加权基因共表达网络分析(WGCNA),选择具有临床意义的模块,并筛选候选Hub基因;进一步分析候选Hub基因在HCC组织和正常肝脏组织显著差异表达、与HCC患者总体生存期和无病生存期是否显著相关,最终确定Hub基因;通过人类蛋白质图谱数据库对Hub基因蛋白表达进行验证。结果 本研究的基因表达数据来自50个正常肝脏组织样本和373个HCC组织样本。通过差异基因表达分析发现7 230个在HCC和正常肝脏组织之间差异表达的基因(HCC中3 691个上调基因和3 539个下调基因)。富集分析表明,上调的差异表达基因主要参与细胞周期调控和有丝分裂过程;下调的差异表达基因主要参与小分子代谢和有机酸代谢等过程。WGCNA确定了19个与HCC患者临床特征相关基因模块,通过分析模块与临床特征之间的关系,筛选出青色模块和紫色模块。青色模块基因中同时与患者总生存期和无病生存期强烈相关的前两个基因为VPS45和FAM189B;紫色模块基因中同时与患者总生存期和无病生存期强烈相关的前两个基因分别为CLEC1B和FCN3,因此将VPS45、FAM189B、CLEC1B和FCN3确定为最终的Hub基因。人类蛋白质图谱数据库免疫组织化学染色显示:VPS45和FAM189B在HCC组织中的表达高于正常肝脏组织,FCN3在HCC组织中的表达低于正常肝脏组织,CLEC1B在HCC组织和正常肝脏组织中表达差异不明显。结论 初步确定VPS45、FAM189B、CLEC1B和FCN3可能是HCC的新型潜在生物标志物,这些Hub基因可能为HCC的靶向治疗提供理论基础。

Identification of hub genes associated with Helicobacter pylori infection and type 2 diabetes mellitus:A pilot bioinformatics study

BACKGROUND Helicobacter pylori(H.pylori)infection is related to various extragastric diseases including type 2 diabetes mellitus(T2DM).However,the possible mechanisms connecting H.pylori infection and T2DM remain unknown.AIM To explore potential molecular connections between H.pylori infection and T2DM.METHODS We extracted gene expression arrays from three online datasets(GSE60427,GSE27411 and GSE115601).Differentially expressed genes(DEGs)commonly present in patients with H.pylori infection and T2DM were identified.Hub genes were validated using human gastric biopsy samples.Correlations between hub genes and immune cell infiltration,miRNAs,and transcription factors(TFs)were further analyzed.RESULTS A total of 67 DEGs were commonly presented in patients with H.pylori infection and T2DM.Five significantly upregulated hub genes,including TLR4,ITGAM,C5AR1,FCER1G,and FCGR2A,were finally identified,all of which are closely related to immune cell infiltration.The gene-miRNA analysis detected 13 miRNAs with at least two gene cross-links.TF-gene interaction networks showed that TLR4 was coregulated by 26 TFs,the largest number of TFs among the 5 hub genes.CONCLUSION We identified five hub genes that may have molecular connections between H.pylori infection and T2DM.This study provides new insights into the pathogenesis of H.pylori-induced onset of T2DM.

Urban Traffic Control Meets Decision Recommendation System:A Survey and Perspective

Urban traffic control is a multifaceted and demanding task that necessitates extensive decision-making to ensure the safety and efficiency of urban transportation systems.Traditional approaches require traffic signal professionals to manually intervene on traffic control devices at the intersection level,utilizing their knowledge and expertise.However,this process is cumbersome,labor-intensive,and cannot be applied on a large network scale.Recent studies have begun to explore the applicability of recommendation system for urban traffic control,which offer increased control efficiency and scalability.Such a decision recommendation system is complex,with various interdependent components,but a systematic literature review has not yet been conducted.In this work,we present an up-to-date survey that elucidates all the detailed components of a recommendation system for urban traffic control,demonstrates the utility and efficacy of such a system in the real world using data and knowledgedriven approaches,and discusses the current challenges and potential future directions of this field.

无人车载网络高可靠HUB的设计

为满足无人车技术的不断发展及日趋复杂的车载网络带来的高带宽和高可靠性的使用要求,设计一种车载网络集线器(HUB)。采用时间触发协议(TTP),设计时间触发以太网(TTE)加星型TTP总线的架构。TTE提高数据带宽,在物理层与驱动设备采用点对点的连接方式代替总线型通信方式,提高可靠性,解决总线型通信方式一点断开,整体通信失效的问题。针对HUB的设计做详细阐述,通过实验验证了设计的可行性和可靠性,对车载网络的优化提供了可行的解决途径。

Spatiotemporal Prediction of Urban Traffics Based on Deep GNN

Traffic prediction already plays a significant role in applications like traffic planning and urban management,but it is still difficult to capture the highly non-linear and complicated spatiotemporal correlations of traffic data.As well as to fulfil both long-termand short-termprediction objectives,a better representation of the temporal dependency and global spatial correlation of traffic data is needed.In order to do this,the Spatiotemporal Graph Neural Network(S-GNN)is proposed in this research as amethod for traffic prediction.The S-GNN simultaneously accepts various traffic data as inputs and investigates the non-linear correlations between the variables.In terms of modelling,the road network is initially represented as a spatiotemporal directed graph,with the features of the samples at the time step being captured by a convolution module.In order to assign varying attention weights to various adjacent area nodes of the target node,the adjacent areas information of nodes in the road network is then aggregated using a graph network.The data is output using a fully connected layer at the end.The findings show that S-GNN can improve short-and long-term traffic prediction accuracy to a greater extent;in comparison to the control model,the RMSE of S-GNN is reduced by about 0.571 to 9.288 and the MAE(Mean Absolute Error)by about 0.314 to 7.678.The experimental results on two real datasets,Pe MSD7(M)and PEMS-BAY,also support this claim.

Segment routing for traffic engineering and effective recovery in low-earth orbit satellite constellations

Low-Earth Orbit Satellite Constellations(LEO-SCs)provide global,high-speed,and low latency Internet access services,which bridges the digital divide in the remote areas.As inter-satellite links are not supported in initial deployment(i.e.the Starlink),the communication between satellites is based on ground stations with radio frequency signals.Due to the rapid movement of satellites,this hybrid topology of LEO-SCs and ground stations is time-varying,which imposes a major challenge to uninterrupted service provisioning and network management.In this paper,we focus on solving two notable problems in such a ground station-assisted LEO-SC topology,i.e.,traffic engineering and fast reroute,to guarantee that the packets are forwarded in a balanced and uninterrupted manner.Specifically,we employ segment routing to support the arbitrary path routing in LEO-SCs.To solve the traffic engineering problem,we proposed two source routings with traffic splitting algorithms,Delay-Bounded Traffic Splitting(DBTS)and DBTS+,where DBTS equally splits a flow and DBTS+favors shorter paths.Simu-lation results show that DBTS+can achieve about 30%lower maximum satellite load at the cost of about 10%more delay.To guarantee the fast recovery of failures,two fast reroute mechanisms,Loop-Free Alternate(LFA)and LFA+,are studied,where LFA pre-computes an alternate next-hop as a backup while LFA+finds a 2-segment backup path.We show that LFA+can increase the percentage of protection coverage by about 15%.

RUFY4 deletion prevents pathological bone loss by blocking endo-lysosomal trafficking of osteoclasts

Mature osteoclasts degrade bone matrix by exocytosis of active proteases from secretory lysosomes through a ruffled border.However,the molecular mechanisms underlying lysosomal trafficking and secretion in osteoclasts remain largely unknown.Here,we show with GeneChip analysis that RUN and FYVE domain-containing protein 4(RUFY4)is strongly upregulated during osteoclastogenesis.Mice lacking Rufy4 exhibited a high trabecular bone mass phenotype with abnormalities in osteoclast function in vivo.Furthermore,deleting Rufy4 did not affect osteoclast differentiation,but inhibited bone-resorbing activity due to disruption in the acidic maturation of secondary lysosomes,their trafficking to the membrane,and their secretion of cathepsin K into the extracellular space.Mechanistically,RUFY4 promotes late endosome-lysosome fusion by acting as an adaptor protein between Rab7 on late endosomes and LAMP2 on primary lysosomes.Consequently,Rufy4-deficient mice were highly protected from lipopolysaccharide-and ovariectomy-induced bone loss.Thus,RUFY4 plays as a new regulator in osteoclast activity by mediating endo-lysosomal trafficking and have a potential to be specific target for therapies against bone-loss diseases such as osteoporosis.

Classified VPN Network Traffic Flow Using Time Related to Artificial Neural Network

VPNs are vital for safeguarding communication routes in the continually changing cybersecurity world.However,increasing network attack complexity and variety require increasingly advanced algorithms to recognize and categorizeVPNnetwork data.We present a novelVPNnetwork traffic flowclassificationmethod utilizing Artificial Neural Networks(ANN).This paper aims to provide a reliable system that can identify a virtual private network(VPN)traffic fromintrusion attempts,data exfiltration,and denial-of-service assaults.We compile a broad dataset of labeled VPN traffic flows from various apps and usage patterns.Next,we create an ANN architecture that can handle encrypted communication and distinguish benign from dangerous actions.To effectively process and categorize encrypted packets,the neural network model has input,hidden,and output layers.We use advanced feature extraction approaches to improve the ANN’s classification accuracy by leveraging network traffic’s statistical and behavioral properties.We also use cutting-edge optimizationmethods to optimize network characteristics and performance.The suggested ANN-based categorization method is extensively tested and analyzed.Results show the model effectively classifies VPN traffic types.We also show that our ANN-based technique outperforms other approaches in precision,recall,and F1-score with 98.79%accuracy.This study improves VPN security and protects against new cyberthreats.Classifying VPNtraffic flows effectively helps enterprises protect sensitive data,maintain network integrity,and respond quickly to security problems.This study advances network security and lays the groundwork for ANN-based cybersecurity solutions.

Scalable Temporal Dimension Preserved Tensor Completion for Missing Traffic Data Imputation With Orthogonal Initialization

Dear Editor,This letter puts forward a novel scalable temporal dimension preserved tensor completion model based on orthogonal initialization for missing traffic data(MTD)imputation.The MTD imputation acts directly on accessing the traffic state,and affects the traffic management.

An edge computing-based embedded traffic information processing approach:application of deep learning in existing traffic systems

To address traffic congestion,this study improves Mobile Netv2-you only look once version 4(YOLOv4)target detection algorithm(Mobile Netv2-YOLOv4-K++F)and introduces an embedded traffic information processing solution based on edge computing.We transition models initially designed for large-scale graphics processing units(GPUs)to edge computing devices,maximizing the strengths of both deep learning and edge computing technologies.This approach integrates embedded devices with the current traffic system,eliminating the need for extensive equipment updates.The solution enables real-time traffic flow monitoring and license plate recognition at the edge,synchronizing instantaneously with the cloud,allowing for intelligent adjustments of traffic signals and accident forewarnings,enhancing road utilization,and facilitating traffic flow optimization.Through on-site testing using the RK3399PRO development board and the Mobile Netv2-YOLOv4-K++F object detection algorithm,the upgrade costs of this approach are less than one-tenth of conventional methods.Under favorable weather conditions,the traffic flow detection accuracy reaches as high as 98%,with license plate recognition exceeding 80%.

Semantic Consistency and Correctness Verification of Digital Traffic Rules

The consensus of the automotive industry and traffic management authorities is that autonomous vehicles must follow the same traffic laws as human drivers.Using formal or digital methods,natural language traffic rules can be translated into machine language and used by autonomous vehicles.In this paper,a translation flow is designed.Beyond the translation,a deeper examination is required,because the semantics of natural languages are rich and complex,and frequently contain hidden assumptions.The issue of how to ensure that digital rules are accurate and consistent with the original intent of the traffic rules they represent is both significant and unresolved.In response,we propose a method of formal verification that combines equivalence verification with model checking.Reasonable and reassuring digital traffic rules can be obtained by utilizing the proposed traffic rule digitization flow and verification method.In addition,we offer a number of simulation applications that employ digital traffic rules to assess vehicle violations.The experimental findings indicate that our digital rules utilizing metric temporal logic(MTL)can be easily incorporated into simulation platforms and autonomous driving systems(ADS).

Effect of speed humps on instantaneous traffic emissions in a microscopic model with limited deceleration capacity

As a common transportation facility, speed humps can control the speed of vehicles on special road sections to reduce traffic risks. At the same time, they also cause instantaneous traffic emissions. Based on the classic instantaneous traffic emission model and the limited deceleration capacity microscopic traffic flow model with slow-to-start rules, this paper has investigated the impact of speed humps on traffic flow and the instantaneous emissions of vehicle pollutants in a single lane situation. The numerical simulation results have shown that speed humps have significant effects on traffic flow and traffic emissions. In a free-flow region, the increase of speed humps leads to the continuous rise of CO_(2), NO_(X) and PM emissions. Within some density ranges, one finds that these pollutant emissions can evolve into some higher values under some random seeds. Under other random seeds, they can evolve into some lower values. In a wide moving jam region, the emission values of these pollutants sometimes appear as continuous or intermittent phenomenon. Compared to the refined Na Sch model, the present model has lower instantaneous emissions such as CO_(2), NO_(X) and PM and higher volatile organic components(VOC) emissions. Compared to the limited deceleration capacity model without slow-to-start rules, the present model also has lower instantaneous emissions such as CO_(2), NO_(X) and PM and higher VOC emissions in a wide moving jam region. These results can also be confirmed or explained by the statistical values of vehicle velocity and acceleration.

Optimization Model Proposal for Traffic Differentiation in Wireless Sensor Networks

Wireless sensor networks(WSNs)are characterized by heterogeneous traffic types(audio,video,data)and diverse application traffic requirements.This paper introduces three traffic classes following the defined model of heterogeneous traffic differentiation in WSNs.The requirements for each class regarding sensitivity to QoS(Quality of Service)parameters,such as loss,delay,and jitter,are described.These classes encompass real-time and delay-tolerant traffic.Given that QoS evaluation is a multi-criteria decision-making problem,we employed the AHP(Analytical Hierarchy Process)method for multi-criteria optimization.As a result of this approach,we derived weight values for different traffic classes based on key QoS factors and requirements.These weights are assigned to individual traffic classes to determine transmission priority.This study provides a thorough comparative analysis of the proposed model against existing methods,demonstrating its superior performance across various traffic scenarios and its implications for future WSN applications.The results highlight the model’s adaptability and robustness in optimizing network resources under varying conditions,offering insights into practical deployments in real-world scenarios.Additionally,the paper includes an analysis of energy consumption,underscoring the trade-offs between QoS performance and energy efficiency.This study presents the development of a differentiated services model for heterogeneous traffic in wireless sensor networks,considering the appropriate QoS framework supported by experimental analyses.

WT-FCTGN:A wavelet-enhanced fully connected time-gated neural network for complex noisy traffic flow modeling

Accurate forecasting of traffic flow provides a powerful traffic decision-making basis for an intelligent transportation system. However, the traffic data's complexity and fluctuation, as well as the noise produced during collecting information and summarizing original data of traffic flow, cause large errors in the traffic flow forecasting results. This article suggests a solution to the above mentioned issues and proposes a fully connected time-gated neural network based on wavelet reconstruction(WT-FCTGN). To eliminate the potential noise and strengthen the potential traffic trend in the data, we adopt the methods of wavelet reconstruction and periodic data introduction to preprocess the data. The model introduces fully connected time-series blocks to model all the information including time sequence information and fluctuation information in the flow of traffic, and establishes the time gate block to comprehend the periodic characteristics of the flow of traffic and predict its flow. The performance of the WT-FCTGN model is validated on the public Pe MS data set. The experimental results show that the WT-FCTGN model has higher accuracy, and its mean absolute error(MAE), mean absolute percentage error(MAPE) and root mean square error(RMSE) are obviously lower than those of the other algorithms. The robust experimental results prove that the WT-FCTGN model has good anti-noise ability.

应用生物信息学筛选结直肠癌Hub基因及验证

目的通过运用生物信息学分析,筛选出与结直肠癌相关的差异表达基因(Differentially Expressed genes,DEGs),并验证其生物学功能。方法云浮市人民医院检验科从基因表达综合数据库(Gene Expression Omnibus,GEO)中下载结直肠癌芯片数据GSE 21815、GSE 31905、GSE 35279资料,应用GEO2R语言进行处理得出结直肠癌和正常结直肠组织之间的差异表达基因,并通过生物信息学工具DAVID、STRING、Cytoscape构建差异表达基因的蛋白互作网络,筛选Hub基因,应用基因本体论(Gene Ontology,GO)、基因百科全书(Kyoto Encyclopedia of Genes and Genomes,KEGG)分析筛选出的Hub基因的生物功能,并利用MiRDB工具找出可能调控Hub基因的miRNA,并于2017—2022年8月收集30例结直肠癌组织和30例正常结直肠组织样本,通过实时荧光定量PCR(Quantitative Real-time PCR,qPCR)验证。结果经生物学信息分析和蛋白质相互作用网络图分析催产素受体基因、基质金属蛋白酶11基因、间质上皮转化因子基因、基质金属蛋白酶7基因、激肽释放酶8基因、激肽释放酶10基因为结直肠癌组织发生发展的关键Hub基因。结直肠癌组织中基质金属蛋白酶11基因(4.38±1.58)、间质上皮转化因子基因(2.69±0.29)、基质金属蛋白酶7基因(0.88±0.14)、激肽释放酶8基因(11.09±3.90)、激肽释放酶10基因mRNA(7.88±2.20)的表达,显著高于正常结直肠组织组织,差异有统计学意义(t=9.605、25.339、26.376、9.541、3.726,P均<0.001)。结论结直肠癌组织中基质金属蛋白酶11基因、间质上皮转化因子基因、基质金属蛋白酶7基因、激肽释放酶8基因、激肽释放酶10基因异常表达,可能参与结直肠癌发生过程,有望为后续结直肠癌基础研究及临床诊疗提供依据。

Real-Time Prediction of Urban Traffic Problems Based on Artificial Intelligence-Enhanced Mobile Ad Hoc Networks(MANETS)

Traffic in today’s cities is a serious problem that increases travel times,negatively affects the environment,and drains financial resources.This study presents an Artificial Intelligence(AI)augmentedMobile Ad Hoc Networks(MANETs)based real-time prediction paradigm for urban traffic challenges.MANETs are wireless networks that are based on mobile devices and may self-organize.The distributed nature of MANETs and the power of AI approaches are leveraged in this framework to provide reliable and timely traffic congestion forecasts.This study suggests a unique Chaotic Spatial Fuzzy Polynomial Neural Network(CSFPNN)technique to assess real-time data acquired from various sources within theMANETs.The framework uses the proposed approach to learn from the data and create predictionmodels to detect possible traffic problems and their severity in real time.Real-time traffic prediction allows for proactive actions like resource allocation,dynamic route advice,and traffic signal optimization to reduce congestion.The framework supports effective decision-making,decreases travel time,lowers fuel use,and enhances overall urban mobility by giving timely information to pedestrians,drivers,and urban planners.Extensive simulations and real-world datasets are used to test the proposed framework’s prediction accuracy,responsiveness,and scalability.Experimental results show that the suggested framework successfully anticipates urban traffic issues in real-time,enables proactive traffic management,and aids in creating smarter,more sustainable cities.

Traffic Flow Prediction with Heterogeneous Spatiotemporal Data Based on a Hybrid Deep Learning Model Using Attention-Mechanism

A significant obstacle in intelligent transportation systems(ITS)is the capacity to predict traffic flow.Recent advancements in deep neural networks have enabled the development of models to represent traffic flow accurately.However,accurately predicting traffic flow at the individual road level is extremely difficult due to the complex interplay of spatial and temporal factors.This paper proposes a technique for predicting short-term traffic flow data using an architecture that utilizes convolutional bidirectional long short-term memory(Conv-BiLSTM)with attention mechanisms.Prior studies neglected to include data pertaining to factors such as holidays,weather conditions,and vehicle types,which are interconnected and significantly impact the accuracy of forecast outcomes.In addition,this research incorporates recurring monthly periodic pattern data that significantly enhances the accuracy of forecast outcomes.The experimental findings demonstrate a performance improvement of 21.68%when incorporating the vehicle type feature.

Effects of connected automated vehicle on stability and energy consumption of heterogeneous traffic flow system

With the development of intelligent and interconnected traffic system,a convergence of traffic stream is anticipated in the foreseeable future,where both connected automated vehicle(CAV)and human driven vehicle(HDV)will coexist.In order to examine the effect of CAV on the overall stability and energy consumption of such a heterogeneous traffic system,we first take into account the interrelated perception of distance and speed by CAV to establish a macroscopic dynamic model through utilizing the full velocity difference(FVD)model.Subsequently,adopting the linear stability theory,we propose the linear stability condition for the model through using the small perturbation method,and the validity of the heterogeneous model is verified by comparing with the FVD model.Through nonlinear theoretical analysis,we further derive the KdV-Burgers equation,which captures the propagation characteristics of traffic density waves.Finally,by numerical simulation experiments through utilizing a macroscopic model of heterogeneous traffic flow,the effect of CAV permeability on the stability of density wave in heterogeneous traffic flow and the energy consumption of the traffic system is investigated.Subsequent analysis reveals emergent traffic phenomena.The experimental findings demonstrate that as CAV permeability increases,the ability to dampen the propagation of fluctuations in heterogeneous traffic flow gradually intensifies when giving system perturbation,leading to enhanced stability of the traffic system.Furthermore,higher initial traffic density renders the traffic system more susceptible to congestion,resulting in local clustering effect and stop-and-go traffic phenomenon.Remarkably,the total energy consumption of the heterogeneous traffic system exhibits a gradual decline with CAV permeability increasing.Further evidence has demonstrated the positive influence of CAV on heterogeneous traffic flow.This research contributes to providing theoretical guidance for future CAV applications,aiming to enhance urban road traffic efficiency and alleviate congestion.

Traffic Sign Detection Model Based on Improved RT-DETR

The correct identification of traffic signs plays an important role in automatic driving technology and road safety driving.Therefore,to address the problems of misdetection and omission in traffic sign detection due to the variety of sign types,significant size differences and complex background information,an improved traffic sign detection model for RT-DETR was proposed in this study.Firstly,the HiLo attention mechanism was added to the Attention-based Intra-scale Feature Interaction,which further enhanced the feature extraction capability of the network and improved the detection efficiency on high-resolution images.Secondly,the CAFMFusion feature fusion mechanism was designed,which enabled the network to pay attention to the features in different regions in each channel.Based on this,the model could better capture the remote dependencies and neighborhood feature correlation,improving the feature fusion capability of the model.Finally,the MPDIoU was used as the loss function of the improved model to achieve faster convergence and more accurate regression results.The experimental results on the TT100k-2021 traffic sign dataset showed that the improved model achieves the performance with a precision value of 90.2%,recall value of 88.1%and mAP@0.5 value of 91.6%,which are 4.6%,5.8%,and 4.4%better than the original RT-DETR model respectively.The model effectively improves the problem of poor traffic sign detection and has greater practical value.