Impact of different interaction behavior on epidemic spreading in time-dependent social networks

We investigate the impact of pairwise and group interactions on the spread of epidemics through an activity-driven model based on time-dependent networks.The effects of pairwise/group interaction proportion and pairwise/group interaction intensity are explored by extensive simulation and theoretical analysis.It is demonstrated that altering the group interaction proportion can either hinder or enhance the spread of epidemics,depending on the relative social intensity of group and pairwise interactions.As the group interaction proportion decreases,the impact of reducing group social intensity diminishes.The ratio of group and pairwise social intensity can affect the effect of group interaction proportion on the scale of infection.A weak heterogeneous activity distribution can raise the epidemic threshold,and reduce the scale of infection.These results benefit the design of epidemic control strategy.

Opinion consensus incorporating higher-order interactions in individual-collective networks

In the current information society, the dissemination mechanisms and evolution laws of individual or collective opinions and their behaviors are the research hot topics in the field of opinion dynamics. First, in this paper, a two-layer network consisting of an individual-opinion layer and a collective-opinion layer is constructed, and a dissemination model of opinions incorporating higher-order interactions(i.e. OIHOI dissemination model) is proposed. Furthermore, the dynamic equations of opinion dissemination for both individuals and groups are presented. Using Lyapunov's first method,two equilibrium points, including the negative consensus point and positive consensus point, and the dynamic equations obtained for opinion dissemination, are analyzed theoretically. In addition, for individual opinions and collective opinions,some conditions for reaching negative consensus and positive consensus as well as the theoretical expression for the dissemination threshold are put forward. Numerical simulations are carried to verify the feasibility and effectiveness of the proposed theoretical results, as well as the influence of the intra-structure, inter-connections, and higher-order interactions on the dissemination and evolution of individual opinions. The main results are as follows.(i) When the intra-structure of the collective-opinion layer meets certain characteristics, then a negative or positive consensus is easier to reach for individuals.(ii) Both negative consensus and positive consensus perform best in mixed type of inter-connections in the two-layer network.(iii) Higher-order interactions can quickly eliminate differences in individual opinions, thereby enabling individuals to reach consensus faster.

Lateral interaction by Laplacian‐based graph smoothing for deep neural networks

Lateral interaction in the biological brain is a key mechanism that underlies higher cognitive functions.Linear self‐organising map(SOM)introduces lateral interaction in a general form in which signals of any modality can be used.Some approaches directly incorporate SOM learning rules into neural networks,but incur complex operations and poor extendibility.The efficient way to implement lateral interaction in deep neural networks is not well established.The use of Laplacian Matrix‐based Smoothing(LS)regularisation is proposed for implementing lateral interaction in a concise form.The authors’derivation and experiments show that lateral interaction implemented by SOM model is a special case of LS‐regulated k‐means,and they both show the topology‐preserving capability.The authors also verify that LS‐regularisation can be used in conjunction with the end‐to‐end training paradigm in deep auto‐encoders.Additionally,the benefits of LS‐regularisation in relaxing the requirement of parameter initialisation in various models and improving the classification performance of prototype classifiers are evaluated.Furthermore,the topologically ordered structure introduced by LS‐regularisation in feature extractor can improve the generalisation performance on classification tasks.Overall,LS‐regularisation is an effective and efficient way to implement lateral interaction and can be easily extended to different models.

Construction of gene/protein interaction networks and enrichment pathway analysis for paroxysmal nocturnal hemoglobinuria and aplastic anemia

Background:To develop a protein-protein interaction network of Paroxysmal nocturnal hemoglobinuria(PNH)and Aplastic anemia(AA)based on genetic genes and to predict pathways underlying the molecular complexes in the network.Methods:In this research,the PNH and AA-related genes were screened through Online Mendelian Inheritance in Man(OMIM).The plugins and Cytoscape were used to search literature and build a protein-protein interaction network.Results:The protein-protein interaction network contains two molecular complexes that are five higher than the correlation integral values.The target genes of this study were obtained:CD59,STAT3,TERC,TNF,AKT1,C5AR1,EPO,IL6,IL10 and so on.We also found that many factors regulate biological behaviors:neutrophils,macrophages,vascular endothelial growth factor,immunoglobulin,interleukin,cytokine receptor,interleukin-6 receptor,tumor necrosis factor,and so on.This research provides a bioinformatics foundation for further explaining the mechanism of common development of both.Conclusion:This indicates that the PNH and AA is a complex process regulated by many cellular pathways and multiple genes.

Ecological network analysis reveals complex responses of tree species life stage interactions to stand variables

Tree interactions are essential for the structure,dynamics,and function of forest ecosystems,but variations in the architecture of life-stage interaction networks(LSINs)across forests is unclear.Here,we constructed 16 LSINs in the mountainous forests of northwest Hebei,China based on crown overlap from four mixed forests with two dominant tree species.Our results show that LSINs decrease the complexity of stand densities and basal areas due to the interaction cluster differentiation.In addition,we found that mature trees and saplings play different roles,the first acting as“hub”life stages with high connectivity and the second,as“bridges”controlling information flow with high centrality.Across the forests,life stages with higher importance showed better parameter stability within LSINs.These results reveal that the structure of tree interactions among life stages is highly related to stand variables.Our efforts contribute to the understanding of LSIN complexity and provide a basis for further research on tree interactions in complex forest communities.

Drivers for Inter-city Innovation Networks Across Chinese Cities:Modelling Physical Versus Intangible Effects

Cross-region innovation is widely recognized as an important source of the long-term regional innovation capacity.In the recent past,a growing number of studies has investigated the network structure and mechanisms of cross-region innovation collaboration in various contexts.However,existing research mainly focuses on physical effects,such as geographical distance and high-speed railway connections.These studies ignore the intangible drivers in a changing environment,the more digitalized economy and the increasingly solidified innovation network structure.Thus,the focus of this study is on estimating determinants of innovation networks,especially on intangible drivers,which have been largely neglected so far.Using city-level data of Chinese patents(excluding Hong Kong,Macao,and Taiwan Province of China),we trace innovation networks across Chinese cities over a long period of time.By integrating a measure on Information and Communications Technology(ICT)development gap and network structural effects into the general proximity framework,this paper explores the changing mechanisms of Chinese innovation networks from a new perspective.The results show that the structure of cross-region innovation networks has changed in China.As mechanisms behind this development,the results confirm the increasingly important role of intangible drivers in Chinese inter-city innovation collaboration when controlling for effects of physical proximity,such as geographical distance.Since digitalization and coordinated development are the mainstream trends in China and other developing countries,these countries'inter-city innovation collaboration patterns will witness dramatic changes under the influence of intangible drivers.

Stackelberg Game for Wireless Powered and Backscattering Enabled Sensor Networks

This paper investigates a wireless powered and backscattering enabled sensor network based on the non-linear energy harvesting model, where the power beacon(PB) delivers energy signals to wireless sensors to enable their passive backscattering and active transmission to the access point(AP). We propose an efficient time scheduling scheme for network performance enhancement, based on which each sensor can always harvest energy from the PB over the entire block except its time slots allocated for passive and active information delivery. Considering the PB and wireless sensors are from two selfish service providers, we use the Stackelberg game to model the energy interaction among them. To address the non-convexity of the leader-level problem, we propose to decompose the original problem into two subproblems and solve them iteratively in an alternating manner. Specifically, the successive convex approximation, semi-definite relaxation(SDR) and variable substitution techniques are applied to find a nearoptimal solution. To evaluate the performance loss caused by the interaction between two providers, we further investigate the social welfare maximization problem. Numerical results demonstrate that compared to the benchmark schemes, the proposed scheme can achieve up to 35.4% and 38.7% utility gain for the leader and the follower, respectively.

Disease networks. Uncovering disease-disease relationships through the incomplete interactome

According to the disease module hypothesis,the cellular components associated with a disease segregate in the same neighborhood of the human interactome,the map of biologically relevant molecular interactions.Yet,given the incompleteness of the interactome and the limited knowledge of disease-associated genes,it is not obvious if the available data have sufficient coverage to map out modules associated with each disease.

基于复杂网络理论的酵母菌PPI网络中关键蛋白质与核心蛋白质组的识别

识别关键蛋白质对疾病治疗、药物设计等领域有重要作用。本文首先采用5种节点重要性排序算法,对4种酵母菌PPI网络进行关键蛋白质识别,并通过分析不同网络之间共有的关键蛋白质,构建了关键蛋白质子网。再通过杰卡德相似度指标,筛选出子网中拓扑特征相似的关键蛋白质对,发现4种网络中存在Gavin-EPG 1、Gavin-EPG 2、Babu-EPG 1、Babu-EPG 2、LCMS-EPG、MALDI-EPG共6个核心蛋白质组。尽管它们大都是核糖体蛋白质,然而不同的酵母菌PPI网络中核心蛋白质组中蛋白质的组成差异很大。本文所发现的关键蛋白质以及核心蛋白质组对进一步研究核糖体上蛋白质如何相互作用影响肽链的合成以及折叠提供了重要的理论参考。

Analysis of Commuting Modal Shift in Consideration of Social Interaction of Consciousness for Environment

It is the matter for achievement of the low carbon transport system that the excessive use of private vehicles can be controlled appropriately.Not only improvement of service level of modes except private vehicle,but also consciousness for environmental problem of individual trip maker is important for eco-commuting promotion.On the other hand,consciousness for environment would be changed by influence of other person.Accordingly,it is aimed in the study that the structure of decision-making process for modal shift to the eco-commuting mode in the local city is described considering environmental consciousness and social interaction.For the purpose,the consciousness for the environment problem and the travel behavior of the commuter at the suburban area in the local city are investigated by the questionnaire survey.The covariance structure about the eco-consciousness is analyzed with the database of the questionnaire survey by structural equation modeling.As the result,it can be confirmed with the structural equation model that the individual environmental consciousness is strongly related with the intention of self-sacrifice and is influenced with the local interaction of the individual connections.On the other hand,the intention of modal shift for the commuting mode is analyzed with the database of the questionnaire survey.It can be found out that the environmental consciousness is not statistically significant for commuting mode choice with the present poor level of service of public transport.However,the intention of self-sacrifice for the prevention of the global warming is statistically confirmed as the factor of modal shift with the operation of eco-commuting bus service with the RP/SP integrated estimation method.As the result,the multi-agent simulation system with social interaction model for eco consciousness is developed to measure the effect of the eco-commuting promotion.For the purpose,the carbon dioxide emission is estimated based on traffic demand and road network condition in the traffic environment model.On the other hand,the relation between agents is defined based on the small world network.The proposed multi-agent simulation is applied to measure the effect of the eco-commuting promotion such as improvement of level of service on the public transport or education of eco-consciousness.The effect of the promotion plan can be observed with the proposed multi-agent system.Finally,it can be concluded that the proposed multi-agent simulation with social interaction for eco-consciousness is useful for planning of eco-commuting promotion.

Dynamic protein-protein interaction subnetworks of lung cancer in cases with smoking history

Smoking is the primary cause of lung cancer and is linked to 85% of lung cancer cases.However,how lung cancer develops in patients with smoking history remains unclear.Systems approaches that combine human protein-protein interaction (PPI) networks and gene expression data are superior to traditional methods.We performed these systems to determine the role that smoking plays in lung cancer development and used the support vector machine (SVM) model to predict PPIs.By defining expression variance (EV),we found 520 dynamic proteins (EV>0.4) using data from the Human Protein Reference Database and Gene Expression Omnibus Database,and built 7 dynamic PPI subnetworks of lung cancer in patients with smoking history.We also determined the primary functions of each subnetwork:signal transduction,apoptosis,and cell migration and adhesion for subnetwork A;cell-sustained angiogenesis for subnetwork B;apoptosis for subnetwork C;and,finally,signal transduction and cell replication and proliferation for subnetworks D-G.The probability distribution of the degree of dynamic protein and static protein differed,clearly showing that the dynamic proteins were not the core proteins which widely connected with their neighbor proteins.There were high correlations among the dynamic proteins,suggesting that the dynamic proteins tend to form specific dynamic modules.We also found that the dynamic proteins were only correlated with the expression of selected proteins but not all neighbor proteins when cancer occurred.

A realistic model for complex networks with local interaction, self-organization and order

In this paper, a new mechanism for the emergence of scale-free distribution is proposed. It is more realistic than the existing mechanism. Based on our mechanism, a model responsible for the scale-free distribution with an exponent in a range of 3-to-5 is given. Moreover, this model could also reproduce the exponential distribution that is discovered in some real networks. Finally, the analytical result of the model is given and the simulation shows the validity of our result,

Transcriptome Profile Based on Protein-Protein Interaction Networks Provides a Set of Core Genes for Understanding the Metabolic Mechanisms of the Egg-Protecting Behavior in Amphioctopus fangsiao

Marine organisms cannot grow and reproduce without proper metabolic regulation.Within a metabolic network,problems with a given link will affect the normal life activities of the organism.Many metabolic mechanisms associated with behaviors of Am-phioctopus fangsiao are still unclear.Moreover,as a factor affecting the normal growth of A.fangsiao,egg protection has rarely been considered in previous behavioral studies.In this research,we analyzed the transcriptome profile of gene expression in A.fangsiao egg-unprotected larvae and egg-protected larvae,and identified 818 differentially expressed genes(DEGs).We used GO and KEGG enrichment analyses to search for metabolism-related DEGs.Protein-protein interaction networks were constructed to examine the interactions between metabolism-related genes.Twenty hub genes with multiple protein-protein interaction relationships or that were involved in multiple KEGG signaling pathways were obtained and verified by quantitative RT-PCR.We first studied the effects of egg protection on the metabolism of A.fangsiao larvae by means of protein-protein interaction networks,and the results provide va-luable gene resources for understanding the metabolism of invertebrate larvae.The data serve as a foundation for further research on the egg-protecting behavior of invertebrates.

基于边权重信息深度网络嵌入的PPIN功能模块检测

现有基于网络嵌入的蛋白质相互作用网络(PPIN)功能模块检测方法通常仅对蛋白质节点信息进行网络嵌入,并未对蛋白质间的边权重信息进行网络嵌入,导致蛋白质功能模块检测质量不理想。针对该问题,提出一种基于边权重信息深度网络嵌入的PPIN功能模块检测方法。结合PPIN的拓扑结构以及基因本体的属性信息,通过图注意力网络的注意力系数来衡量蛋白质间的一阶边权重信息,基于邻域聚合对蛋白质的一阶边权重信息进行嵌入。利用长短期记忆网络的遗忘门和输入门来衡量蛋白质间的高阶边权重信息,并对蛋白质的高阶边权重信息进行嵌入。根据网络嵌入得到的低维向量,通过核心附属聚类算法挖掘出核心团并添加附属蛋白质,从而获得最终的蛋白质功能模块。在Collins、Gavin和Krogan蛋白质数据集上的实验结果表明,该方法相较于基于核心附属聚类的蛋白质功能模块检测等方法在准确率和F1值上最高提升了18.1和12.9个百分点。

基于谷物蛋白质序列与PPI网络的功能预测研究

谷物中现存大量未经注释、功能未知的蛋白质,且难以通过实验验证,因此计算方法成为预测谷物蛋白质功能的主流方法之一。作者以玉米、小麦、籼稻、粳稻4种谷物蛋白质为研究对象,利用数据库获取结构域相互作用信息。从蛋白质中较为稳定的结构域信息出发,结合AdaBoost算法获得蛋白质相互作用信息并构建蛋白质相互作用网络,将其与利用blast所获得的蛋白质序列相似性网络相结合,利用协同分类和多层感知机两种算法实现对谷物蛋白质的功能预测。研究结果显示,两种算法均能较为准确地预测蛋白质功能,其中协同分类在召回率方面表现更优,而多层感知机在准确率方面表现更优。本研究为谷物蛋白质的功能注释提供了新思路、新方法,对谷物的加工与营养研究提供了依据。

LncRNAs exert indispensable roles in orchestrating the interaction among diverse noncoding RNAs and enrich the regulatory network of plant growth and its adaptive environmental stress response

With the advent of advanced sequencing technologies,non-coding RNAs(ncRNAs)are increasingly pivotal and play highly regulated roles in the modulation of diverse aspects of plant growth and stress response.This includes a spectrum of ncRNA classes,ranging from small RNAs to long non-coding RNAs(lncRNAs).Notably,among these,lncRNAs emerge as significant and intricate components within the broader ncRNA regulatory networks.Here,we categorize ncRNAs based on their length and structure into small RNAs,medium-sized ncRNAs,lncRNAs,and circle RNAs.Furthermore,the review delves into the detailed biosynthesis and origin of these ncRNAs.Subsequently,we emphasize the diverse regulatory mechanisms employed by lncRNAs that are located at various gene regions of coding genes,embodying promoters,5’UTRs,introns,exons,and 3’UTR regions.Furthermore,we elucidate these regulatory modes through one or two concrete examples.Besides,lncRNAs have emerged as novel central components that participate in phase separation processes.Moreover,we illustrate the coordinated regulatory mechanisms among lncRNAs,miRNAs,and siRNAs with a particular emphasis on the central role of lncRNAs in serving as sponges,precursors,spliceosome,stabilization,scaffolds,or interaction factors to bridge interactions with other ncRNAs.The review also sheds light on the intriguing possibility that some ncRNAs may encode functional micropeptides.Therefore,the review underscores the emergent roles of ncRNAs as potent regulatory factors that significantly enrich the regulatory network governing plant growth,development,and responses to environmental stimuli.There are yet-to-be-discovered roles of ncRNAs waiting for us to explore.

Maximizing Influence in Temporal Social Networks:A Node Feature-Aware Voting Algorithm

Influence Maximization(IM)aims to select a seed set of size k in a social network so that information can be spread most widely under a specific information propagation model through this set of nodes.However,most existing studies on the IM problem focus on static social network features,while neglecting the features of temporal social networks.To bridge this gap,we focus on node features reflected by their historical interaction behavior in temporal social networks,i.e.,interaction attributes and self-similarity,and incorporate them into the influence maximization algorithm and information propagation model.Firstly,we propose a node feature-aware voting algorithm,called ISVoteRank,for seed nodes selection.Specifically,before voting,the algorithm sets the initial voting ability of nodes in a personalized manner by combining their features.During the voting process,voting weights are set based on the interaction strength between nodes,allowing nodes to vote at different extents and subsequently weakening their voting ability accordingly.The process concludes by selecting the top k nodes with the highest voting scores as seeds,avoiding the inefficiency of iterative seed selection in traditional voting-based algorithms.Secondly,we extend the Independent Cascade(IC)model and propose the Dynamic Independent Cascade(DIC)model,which aims to capture the dynamic features in the information propagation process by combining node features.Finally,experiments demonstrate that the ISVoteRank algorithm has been improved in both effectiveness and efficiency compared to baseline methods,and the influence spread through the DIC model is improved compared to the IC model.

Drug–Target Interaction Prediction Model Using Optimal Recurrent Neural Network

Drug-target interactions prediction(DTIP)remains an important requirement in thefield of drug discovery and human medicine.The identification of interaction among the drug compound and target protein plays an essential pro-cess in the drug discovery process.It is a lengthier and complex process for pre-dicting the drug target interaction(DTI)utilizing experimental approaches.To resolve these issues,computational intelligence based DTIP techniques were developed to offer an efficient predictive model with low cost.The recently devel-oped deep learning(DL)models can be employed for the design of effective pre-dictive approaches for DTIP.With this motivation,this paper presents a new drug target interaction prediction using optimal recurrent neural network(DTIP-ORNN)technique.The goal of the DTIP-ORNN technique is to predict the DTIs in a semi-supervised way,i.e.,inclusion of both labelled and unlabelled instances.Initially,the DTIP-ORNN technique performs data preparation process and also includes class labelling process,where the target interactions from the database are used to determine thefinal label of the unlabelled instances.Besides,drug-to-drug(D-D)and target-to-target(T-T)interactions are used for the weight initia-tion of the RNN based bidirectional long short term memory(BiLSTM)model which is then utilized to the prediction of DTIs.Since hyperparameters signifi-cantly affect the prediction performance of the BiLSTM technique,the Adam optimizer is used which mainly helps to improve the DTI prediction outcomes.In order to ensure the enhanced predictive outcomes of the DTIP-ORNN techni-que,a series of simulations are implemented on four benchmark datasets.The comparative result analysis shows the promising performance of the DTIP-ORNN method on the recent approaches.

Mobile Communication Voice Enhancement Under Convolutional Neural Networks and the Internet of Things

This study aims to reduce the interference of ambient noise in mobile communication,improve the accuracy and authenticity of information transmitted by sound,and guarantee the accuracy of voice information deliv-ered by mobile communication.First,the principles and techniques of speech enhancement are analyzed,and a fast lateral recursive least square method(FLRLS method)is adopted to process sound data.Then,the convolutional neural networks(CNNs)-based noise recognition CNN(NR-CNN)algorithm and speech enhancement model are proposed.Finally,related experiments are designed to verify the performance of the proposed algorithm and model.The experimental results show that the noise classification accuracy of the NR-CNN noise recognition algorithm is higher than 99.82%,and the recall rate and F1 value are also higher than 99.92.The proposed sound enhance-ment model can effectively enhance the original sound in the case of noise interference.After the CNN is incorporated,the average value of all noisy sound perception quality evaluation system values is improved by over 21%compared with that of the traditional noise reduction method.The proposed algorithm can adapt to a variety of voice environments and can simultaneously enhance and reduce noise processing on a variety of different types of voice signals,and the processing effect is better than that of traditional sound enhancement models.In addition,the sound distortion index of the proposed speech enhancement model is inferior to that of the control group,indicating that the addition of the CNN neural network is less likely to cause sound signal distortion in various sound environments and shows superior robustness.In summary,the proposed CNN-based speech enhancement model shows significant sound enhancement effects,stable performance,and strong adapt-ability.This study provides a reference and basis for research applying neural networks in speech enhancement.

酒精性肝炎自噬关键基因的筛选及生物信息学分析

目的筛选酒精性肝炎(AH)的自噬关键基因,探讨AH潜在的生物标志物和治疗靶点。方法采用基因表达综合数据库(GEO)中的2个AH基因芯片和从MSigDB、GeneCards数据库中获得的自噬相关数据集,通过加权基因共表达网络分析(WGCNA)获取关键基因。对筛选的关键基因进行基因本体(GO)、京都基因和基因组百科全书(KEGG)功能富集分析,蛋白质相互作用(PPI)分析,免疫浸润分析,构建信使RNA(mRNA)-微小RNA(miRNA)网络,进行酒精性肝病不同分期的自噬相关关键基因的表达差异分析,并进一步通过实时荧光定量逆转录聚合酶链反应(RT-qPCR)在AH患者和小鼠肝脏组织中验证。结果本研究筛选得到了11个与AH自噬相关的基因(EEF1A2、CFTR、SOX4、TREM2、CTHRC1、HSPB8、TUBB3、PRKAA2、RNASE1、MTCL1、HGF),均为上调基因。在AH患者和小鼠肝脏组织中,SOX4、TREM2、HSPB8、PRKAA2在AH组中的相对表达量均高于对照组。结论SOX4、TREM2、HSPB8、PRKAA2可能是AH潜在的生物标志物和治疗靶点。