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...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.展开更多
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 pairwi...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.展开更多
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...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.展开更多
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,give...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.展开更多
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 include...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.展开更多
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 ...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.展开更多
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 modalit...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.展开更多
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 ...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.展开更多
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 ne...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.展开更多
BACKGROUND Studies show that the antifibrotic mechanism of taurine may involve its inhibition of the activation and proliferation of hepatic stellate cells(HSCs). Since the molecular mechanism of taurine-mediated anti...BACKGROUND Studies show that the antifibrotic mechanism of taurine may involve its inhibition of the activation and proliferation of hepatic stellate cells(HSCs). Since the molecular mechanism of taurine-mediated antifibrotic activity has not been fully unveiled and is little studied, it is imperative to use "omics" methods to systematically investigate the molecular mechanism by which taurine inhibits liver fibrosis.AIM To establish a network including transcriptomic and protein-protein interaction data to elucidate the molecular mechanism of taurine-induced HSC apoptosis.METHODS We used microarrays, bioinformatics, protein-protein interaction(PPI) network,and sub-modules to investigate taurine-induced changes in gene expression in human HSCs(LX-2). Subsequently, all of the differentially expressed genes(DEGs) were subjected to gene ontology function and Kyoto encyclopedia of genes and genomes pathway enrichment analysis. Furthermore, the interactions of DEGs were explored in a human PPI network, and sub-modules of the DEGs interaction network were analyzed using Cytoscape software.RESULTS A total of 635 DEGs were identified in taurine-treated HSCs when compared with the controls. Of these, 304 genes were statistically significantly up-regulated, and 331 down-regulated. Most of these DEGs were mainly located on the membrane and extracellular region, and are involved in the biological processes of signal transduction, cell proliferation, positive regulation of extracellular regulated protein kinases 1(ERK1) and ERK2 cascade, extrinsic apoptotic signaling pathway and so on. Fifteen significantly enriched pathways with DEGs were identified, including mitogen-activated protein kinase(MAPK) signaling pathway, peroxisome proliferators-activated receptor signaling pathway,estrogen signaling pathway, Th1 and Th2 cell differentiation, cyclic adenosine monophosphate signaling pathway and so on. By integrating the transcriptomics and human PPI data, nine critical genes, including MMP2, MMP9, MMP21,TIMP3, KLF10, CX3CR1, TGFB1, VEGFB, and EGF, were identified in the PPI network analysis.CONCLUSION Taurine promotes the apoptosis of HSCs via up-regulating TGFB1 and then activating the p38 MAPK-JNK-Caspase9/8/3 pathway. These findings enhance the understanding of the molecular mechanism of taurine-induced HSC apoptosis and provide references for liver disorder therapy.展开更多
Based on the theories and methods of complex network,crude oil trade flows between countries along the Belt and Road(B&R,hereafter)are inserted into the Geo-space of B&R and form a spatial interaction network ...Based on the theories and methods of complex network,crude oil trade flows between countries along the Belt and Road(B&R,hereafter)are inserted into the Geo-space of B&R and form a spatial interaction network which takes the countries as nodes and takes the trade relations as edges.The networked mining and evolution analysis can provide important references for the research on trade relations among the B&R countries and the formulation of trade policy.This paper researches and discusses the construction,statistical analysis,top networks and stability of the crude oil trade network between the B&R countries from 2001 to 2020 from the perspectives of Geo-Computation for Social Sciences(GCSS)and spatial interaction.Firstly,evolutions of out-degree,in-degree,out-strength and in-strength of the top 10 countries in the crude oil trade network are computed and analyzed.Secondly,the top network method is used to explore the evolution characteristics of hierarchical structures.And finally,the sequential evolution characteristics of the crude oil trade network stability are analyzed utilizing the network stability measure method based on the trade relationship autocorrelation function.The analysis results show that Russia has the largest out-degree and out-strength,and China has the largest in-degree and in-strength.The crude oil trade volume of the top 10 import and export networks between 2001—2020 accounts for over 90%of the total trade volume of the crude oil trade network,and the proportion remains relatively stable.However,the stability of the network showed strong fluctuations in 2009,2012 and 2014,which may be closely related to major international events in these years,which could furtherly be used to build a correlation model between network volatility and major events.This paper explores how to construct and analyze the spatial interaction network of crude oil trade and can provide references for trade relations research and trade policy formulation of B&R countries.展开更多
AIM:To understand the complex reaction of gastric inflammation induced by Helicobacter pylori(H pylori) in a systematic manner using a protein interaction network. METHODS:The expression of genes significantly changed...AIM:To understand the complex reaction of gastric inflammation induced by Helicobacter pylori(H pylori) in a systematic manner using a protein interaction network. METHODS:The expression of genes significantly changed on microarray during H pylori infection was scanned from the web literary database and translated into proteins.A network of protein interactions was constructed by searching the primary interactions of selected proteins.The constructed network was mathematically analyzed and its biological function was examined.In addition,the nodes on the network were checked to determine if they had any further functional importance or relation to other proteins by extending them. RESULTS:The scale-free network showing the relationship between inflammation and carcinogenesis was constructed.Mathematical analysis showed hub and bottleneck proteins,and these proteins were mostly related to immune response.The network contained pathways and proteins related to H pylori infection,such as the JAK-STAT pathway triggered by interleukins.Activation of nuclear factor (NF)-κB,TLR4,and other proteins known to function as core proteins of immune response were also found. These immune-related proteins interacted on the network with pathways and proteins related to the cell cycle,cell maintenance and proliferation,andtranscription regulators such as BRCA1,FOS,REL,and zinc finger proteins.The extension of nodes showed interactions of the immune proteins with cancer- related proteins.One extended network,the core network,a summarized form of the extended network, and cell pathway model were constructed. CONCLUSION:Immune-related proteins activated by H pylori infection interact with proto-oncogene proteins.The hub and bottleneck proteins are potential drug targets for gastric inflammation and cancer.展开更多
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-pro...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.展开更多
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 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.展开更多
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 th...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.展开更多
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 wi...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,展开更多
The interactions of a colored dynamical network play a great role in its dynamical behaviour and are denoted by outer and inner coupling matrices. In this paper, the outer and inner coupting matrices are assumed to be...The interactions of a colored dynamical network play a great role in its dynamical behaviour and are denoted by outer and inner coupling matrices. In this paper, the outer and inner coupting matrices are assumed to be unknown and need to be identified. A corresponding network estimator is designed for identifying the unknown interactions by adopting proper adaptive laws. Based on the Lyapunov function method and Barbalat's lemma, the obtained result is analytically proved. A colored network coupled with chaotic Lorenz, Chen, and Lii systems is considered as a numerical example to illustrate the effectiveness of the proposed method.展开更多
neural network model based on backbone propagation was applied to Learn-ing and predicting the interaction between antiparallelly interactive peptide seg-ments in proteins.Hydrophobic properties pf residues were found...neural network model based on backbone propagation was applied to Learn-ing and predicting the interaction between antiparallelly interactive peptide seg-ments in proteins.Hydrophobic properties pf residues were found dominant in in-terpeptides.Weights of each kind of residues, obtained by this work,suggestedsome different scales for the hydrophobicity of the residue.These will be helpful in understanding polypeptide structure and protein folding.展开更多
Duplication and divergence have been widely recognized as the two domi- nant evolutionary forces in shaping biological networks, e.g., gene regulatory networks and protein-protein interaction (PPI) networks. It has ...Duplication and divergence have been widely recognized as the two domi- nant evolutionary forces in shaping biological networks, e.g., gene regulatory networks and protein-protein interaction (PPI) networks. It has been shown that the network growth models constructed on the principle of duplication and divergence can recapture the topo- logical properties of real PPI networks. However, such network models only consider the evolution processes. How to select the model parameters with the real biological experi- mental data has not been presented. Therefore, based on the real PPI network statistical data, a yeast PPI network model is constructed. The simulation results indicate that the topological characteristics of the constructed network model are well consistent with those of real PPI networks, especially on sparseness, scale-free, small-world, hierarchical modularity, and disassortativity.展开更多
Interactivity is the most significant feature of network data,especially in social networks.Existing network embedding methods have achieved remarkable results in learning network structure and node attributes,but do ...Interactivity is the most significant feature of network data,especially in social networks.Existing network embedding methods have achieved remarkable results in learning network structure and node attributes,but do not pay attention to the multi-interaction between nodes,which limits the extraction and mining of potential deep interactions between nodes.To tackle the problem,we propose a method called Multi-Interaction heterogeneous information Network Embedding(MINE).Firstly,we introduced the multi-interactions heterogeneous information network and extracted complex heterogeneous relation sequences by the multi-interaction extraction algorithm.Secondly,we use a well-designed multi-relationship network fusion model based on the attention mechanism to fuse multiple interactional relationships.Finally,applying a multitasking model makes the learned vector contain richer semantic relationships.A large number of practical experiments prove that our proposed method outperforms existing methods on multiple data sets.展开更多
基金This study was supported by the National Water Pollution Control and Treatment Science and Technology Major Project(2017ZX07101-002).
文摘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.
基金This work was supported by the National Natural Science Foundation of China(Grant No.12072340)the China Postdoctoral Science Foundation(Grant No.2022M720727)the Jiangsu Funding Program for Excellent Postdoctoral Talent(Grant No.2022ZB130).
文摘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.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.72031009 and 61473338)。
文摘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.
文摘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.
基金This review was supported by the National Key Research and Development Program of China(Grant No.2022YFD2100101)the National Natural Science Foundation of China to G.Z.(Grant No.32302623)+1 种基金the Joint NSFC-ISF Research Program(Grant No.32061143022)the National Natural Sciences Foundation of China(Grant No.32172639).
文摘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.
基金The research is granted by Japanese Ministry of Education as a part of Grants-in-Aid for Scientific Research,No.(C)22560533.The author records here warmest appreciation to the Resident Conference for Environment of Tokushima Prefecture for collecting the data in the field of actual travel behavior on the social experiment.
文摘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.
基金supported by the National Natural Science Foundation of China grants 61836014 to CL,and the STI2030‐Major Projects(2022ZD0205100)the Strategic Priority Research Program of Chinese Academy of Science,Grant No.XDB32010300+1 种基金Shanghai Municipal Science and Technology Major Project(Grant No.2018SHZDZX05)the Innovation Academy of Artificial Intelligence,Chinese Academy of Sciences to ZW.
文摘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.
文摘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.
文摘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.
基金the National Natural Science Foundation of China,No.81360595 and No.81860790Guangxi Natural Science Foundation Program,No.KJT13066+2 种基金the Bagui Scholars Foundation Program of Guangxithe Special-term Experts Foundation Program of Guangxithe Project of Guangxi Young Teacher Fundamental Ability Promotion,No.2017KY0298
文摘BACKGROUND Studies show that the antifibrotic mechanism of taurine may involve its inhibition of the activation and proliferation of hepatic stellate cells(HSCs). Since the molecular mechanism of taurine-mediated antifibrotic activity has not been fully unveiled and is little studied, it is imperative to use "omics" methods to systematically investigate the molecular mechanism by which taurine inhibits liver fibrosis.AIM To establish a network including transcriptomic and protein-protein interaction data to elucidate the molecular mechanism of taurine-induced HSC apoptosis.METHODS We used microarrays, bioinformatics, protein-protein interaction(PPI) network,and sub-modules to investigate taurine-induced changes in gene expression in human HSCs(LX-2). Subsequently, all of the differentially expressed genes(DEGs) were subjected to gene ontology function and Kyoto encyclopedia of genes and genomes pathway enrichment analysis. Furthermore, the interactions of DEGs were explored in a human PPI network, and sub-modules of the DEGs interaction network were analyzed using Cytoscape software.RESULTS A total of 635 DEGs were identified in taurine-treated HSCs when compared with the controls. Of these, 304 genes were statistically significantly up-regulated, and 331 down-regulated. Most of these DEGs were mainly located on the membrane and extracellular region, and are involved in the biological processes of signal transduction, cell proliferation, positive regulation of extracellular regulated protein kinases 1(ERK1) and ERK2 cascade, extrinsic apoptotic signaling pathway and so on. Fifteen significantly enriched pathways with DEGs were identified, including mitogen-activated protein kinase(MAPK) signaling pathway, peroxisome proliferators-activated receptor signaling pathway,estrogen signaling pathway, Th1 and Th2 cell differentiation, cyclic adenosine monophosphate signaling pathway and so on. By integrating the transcriptomics and human PPI data, nine critical genes, including MMP2, MMP9, MMP21,TIMP3, KLF10, CX3CR1, TGFB1, VEGFB, and EGF, were identified in the PPI network analysis.CONCLUSION Taurine promotes the apoptosis of HSCs via up-regulating TGFB1 and then activating the p38 MAPK-JNK-Caspase9/8/3 pathway. These findings enhance the understanding of the molecular mechanism of taurine-induced HSC apoptosis and provide references for liver disorder therapy.
基金National Natural Science Foundation of China(No.42171448)Key Laboratory of National Geographic Census and Monitoring,Ministry of Nature Resources(No.2020NGCMZD03)。
文摘Based on the theories and methods of complex network,crude oil trade flows between countries along the Belt and Road(B&R,hereafter)are inserted into the Geo-space of B&R and form a spatial interaction network which takes the countries as nodes and takes the trade relations as edges.The networked mining and evolution analysis can provide important references for the research on trade relations among the B&R countries and the formulation of trade policy.This paper researches and discusses the construction,statistical analysis,top networks and stability of the crude oil trade network between the B&R countries from 2001 to 2020 from the perspectives of Geo-Computation for Social Sciences(GCSS)and spatial interaction.Firstly,evolutions of out-degree,in-degree,out-strength and in-strength of the top 10 countries in the crude oil trade network are computed and analyzed.Secondly,the top network method is used to explore the evolution characteristics of hierarchical structures.And finally,the sequential evolution characteristics of the crude oil trade network stability are analyzed utilizing the network stability measure method based on the trade relationship autocorrelation function.The analysis results show that Russia has the largest out-degree and out-strength,and China has the largest in-degree and in-strength.The crude oil trade volume of the top 10 import and export networks between 2001—2020 accounts for over 90%of the total trade volume of the crude oil trade network,and the proportion remains relatively stable.However,the stability of the network showed strong fluctuations in 2009,2012 and 2014,which may be closely related to major international events in these years,which could furtherly be used to build a correlation model between network volatility and major events.This paper explores how to construct and analyze the spatial interaction network of crude oil trade and can provide references for trade relations research and trade policy formulation of B&R countries.
文摘AIM:To understand the complex reaction of gastric inflammation induced by Helicobacter pylori(H pylori) in a systematic manner using a protein interaction network. METHODS:The expression of genes significantly changed on microarray during H pylori infection was scanned from the web literary database and translated into proteins.A network of protein interactions was constructed by searching the primary interactions of selected proteins.The constructed network was mathematically analyzed and its biological function was examined.In addition,the nodes on the network were checked to determine if they had any further functional importance or relation to other proteins by extending them. RESULTS:The scale-free network showing the relationship between inflammation and carcinogenesis was constructed.Mathematical analysis showed hub and bottleneck proteins,and these proteins were mostly related to immune response.The network contained pathways and proteins related to H pylori infection,such as the JAK-STAT pathway triggered by interleukins.Activation of nuclear factor (NF)-κB,TLR4,and other proteins known to function as core proteins of immune response were also found. These immune-related proteins interacted on the network with pathways and proteins related to the cell cycle,cell maintenance and proliferation,andtranscription regulators such as BRCA1,FOS,REL,and zinc finger proteins.The extension of nodes showed interactions of the immune proteins with cancer- related proteins.One extended network,the core network,a summarized form of the extended network, and cell pathway model were constructed. CONCLUSION:Immune-related proteins activated by H pylori infection interact with proto-oncogene proteins.The hub and bottleneck proteins are potential drug targets for gastric inflammation and cancer.
基金supported by grants from the National Natural Science Foundation of China (No. 91130009)Science and Technology Planning Project of Guangdong Province of China (No. 2003A3080503)
文摘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.
基金Under the auspices of China Scholarship Council。
文摘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.
基金supported by National Natural Science Foundation of China(No.61901229 and No.62071242)the Project of Jiangsu Engineering Research Center of Novel Optical Fiber Technology and Communication Network(No.SDGC2234)+1 种基金the Open Research Project of Jiangsu Provincial Key Laboratory of Photonic and Electronic Materials Sciences and Technology(No.NJUZDS2022-008)the Post-Doctoral Research Supporting Program of Jiangsu Province(No.SBH20).
文摘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.
基金Project supported by the National Natural Science Foundation of China (Grant Nos 60374037 and 60574036), the Program for New Century Excellent Talents of High Education of China(Grant No NCET 2005-290), The Special Research Fund for the Doctoral Program of High Education of China (Grant No 20050055013).Acknowledgments The authors would like to thank Réka Albert for useful discussion and are grateful to the anonymous referees for their valuable suggestions and comments, which have made this paper improved.
文摘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,
基金supported by the National Natural Science Foundation of China(Grant No.61463022)the Natural Science Foundation of Jiangxi Educational Committee,China(Grant No.GJJ14273)the Graduate Innovation Fund of Jiangxi Normal University,China(Grant No.YJS2014061)
文摘The interactions of a colored dynamical network play a great role in its dynamical behaviour and are denoted by outer and inner coupling matrices. In this paper, the outer and inner coupting matrices are assumed to be unknown and need to be identified. A corresponding network estimator is designed for identifying the unknown interactions by adopting proper adaptive laws. Based on the Lyapunov function method and Barbalat's lemma, the obtained result is analytically proved. A colored network coupled with chaotic Lorenz, Chen, and Lii systems is considered as a numerical example to illustrate the effectiveness of the proposed method.
文摘neural network model based on backbone propagation was applied to Learn-ing and predicting the interaction between antiparallelly interactive peptide seg-ments in proteins.Hydrophobic properties pf residues were found dominant in in-terpeptides.Weights of each kind of residues, obtained by this work,suggestedsome different scales for the hydrophobicity of the residue.These will be helpful in understanding polypeptide structure and protein folding.
基金Project supported by the National Natural Science Foundation of China(No.11172158)
文摘Duplication and divergence have been widely recognized as the two domi- nant evolutionary forces in shaping biological networks, e.g., gene regulatory networks and protein-protein interaction (PPI) networks. It has been shown that the network growth models constructed on the principle of duplication and divergence can recapture the topo- logical properties of real PPI networks. However, such network models only consider the evolution processes. How to select the model parameters with the real biological experi- mental data has not been presented. Therefore, based on the real PPI network statistical data, a yeast PPI network model is constructed. The simulation results indicate that the topological characteristics of the constructed network model are well consistent with those of real PPI networks, especially on sparseness, scale-free, small-world, hierarchical modularity, and disassortativity.
基金This work is supported by the Fundamental Research Funds for the Central Universities(Grant No.HIT.NSRIF.201714)Weihai Science and Technology Development Program(2016DXGJMS15)Key Research and Development Program in Shandong Provincial(2017GGX90103).
文摘Interactivity is the most significant feature of network data,especially in social networks.Existing network embedding methods have achieved remarkable results in learning network structure and node attributes,but do not pay attention to the multi-interaction between nodes,which limits the extraction and mining of potential deep interactions between nodes.To tackle the problem,we propose a method called Multi-Interaction heterogeneous information Network Embedding(MINE).Firstly,we introduced the multi-interactions heterogeneous information network and extracted complex heterogeneous relation sequences by the multi-interaction extraction algorithm.Secondly,we use a well-designed multi-relationship network fusion model based on the attention mechanism to fuse multiple interactional relationships.Finally,applying a multitasking model makes the learned vector contain richer semantic relationships.A large number of practical experiments prove that our proposed method outperforms existing methods on multiple data sets.