Betweenness Centrality Values Research Articles

Graph-based ahead monitoring of vulnerabilities in large dynamic transportation networks.

Betweenness Centrality (BC) has proven to be a fundamental metric in many domains to identify the components (nodes) of a system modelled as a graph that are mostly traversed by information flows thus being critical to the proper functioning of the system itself. In the transportation domain, the metric has been mainly adopted to discover topological bottlenecks of the physical infrastructure composed of roads or railways. The adoption of this metric to study the evolution of transportation networks that take into account also the dynamic conditions of traffic is in its infancy mainly due to the high computation time needed to compute BC in large dynamic graphs. This paper explores the adoption of dynamic BC, i.e., BC computed on dynamic large-scale graphs, modeling road networks and the related vehicular traffic, and proposes the adoption of a fast algorithm for ahead monitoring of transportation networks by computing approximated BC values under time constraints. The experimental analysis proves that, with a bounded and tolerable approximation, the algorithm computes BC on very large dynamically weighted graphs in a significantly shorter time if compared with exact computation. Moreover, since the proposed algorithm can be tuned for an ideal trade-off between performance and accuracy, our solution paves the way to quasi real-time monitoring of highly dynamic networks providing anticipated information about possible congested or vulnerable areas. Such knowledge can be exploited by travel assistance services or intelligent traffic control systems to perform informed re-routing and therefore enhance network resilience in smart cities.

Keystone species of coastal ecosystems from the Bay of Biscay

Estuarine and coastal ecosystems play a fundamental role in human activities. Since millions of people depend on the numerous resources they offer (i.e., fisheries, transportation, and recreational activities), these ecosystems are also exposed to a high anthropogenic pressure. In order to better monitor and regulate such pressures, it is critical to improve our understanding of the functioning of these ecosystems. This is especially important for microorganisms, considering, for instance, their important roles in biogeochemical cycles. In this study we investigate estuarine and coastal microbial communities along the coast of the Basque Country using eDNA metabarcoding of bacterioplankton and microbenthos samples along a time series, from locations that present different degrees of disturbance. Metabarcoding of the 16S and 18S rRNA gene was carried out, and the OTU and taxa tables generated were used to reconstruct ecological networks, representing potential biological interactions. These ecological networks were used to identify on one hand the species that play a major role in the maintenance of the whole community structure (keystones), and on the other, complex network modules affected by environmental impacts. Here, we define keystone taxa as those that presented the highest ratio between the degree of connectivity (number of associations they established within the network) and their relative abundance. The underlying aim of this study is to develop novel “bioindicators” based on taxa that are both sensitive to impacts and important for community structure. Results of two seawater communities, one from offshore waters and the other from the coast, showed similar bacterial composition at family level, dominated by Flavobacteraceae and Rhodobacteraceae. Nevertheless, their ecological network properties differed strongly: for a similar number of taxa that established associations, the conectance was similar in both communities, but the number of associations in the coastal community was twice as high as for the offshore one, resulting in a higher modularity. Identified keystone taxa were taxonomically different between both communities: those from the coastal community belonged to Protobacteria, Bacteroidetes, Firmicutes, and Fusobacteria phyla, while the keystones from the offshore community belonged to Proteobacteria, Actinobacteria, Chloroflexi, Thaumarchaeota, PAUC34f and Verrucomicrobia. Moreover, we identified “connector” taxa that presented the highest values of betweenness centrality. These taxa, without having a high degree of connectivity, may be important for the interaction structure because they connect modules, i.e., highly connected subnetworks within the whole network. Further, hierarchical clustering was performed to identify seasonal trends and to better understand the associations retrieved. Taxa with different seasonal preference often grouped together in the same modules, which indicates that modularity was not caused primarily by seasonality.

Research on saponin active compounds of Tuchao Baibiandouren for the treatment of type-2 diabetes based on UHPLC-Q-Exactive Orbitrap MS and network pharmacology

ObjectiveTo explore the pharmacological mechanism of active saponin compounds of Tuchao Baibiandouren (Lablab Semen Album fried with earth, TCBBDR) in treating type 2 diabetes (T2DM) using UHPLC-Q-Exactive Orbitrap MS and network pharmacology. MethodsUHPLC-Q-Exactive Orbitrap MS was used for a qualitative analysis of saponin compounds in TCBBDR. PharmMapper and CTD were used to screen drug active compounds and disease targets, and an active compound-target network was constructed via Cytoscape 3.8.0. Molecular docking was applied with the drug active compounds and key targets using AutoDock Vina 1.1.2, and a trajectory for the molecular dynamics simulation was completed by GROMACS 2019-3. ResultsSixteen saponin compounds were identified from TCBBDR, along with 292 saponin compoud targets and 792 T2DM targets. Through Venn analysis of target saponin constituents and T2DM related targets, a total of 91 intersection targets were screened out in the treatment of T2DM with saponin. The mean values of degree, betweenness centrality and closeness centrality were taken as the thresholds to screen out 22 key genes, among which 4 key proteins namely MAPK1, IGF1 EGFR, PIK3R1 were selected in the top 10 key genes. On this basis, the saponin active constituent-target-signaling pathway network was established. The Gene Ontology (GO) enrichment analysis showed that the related biological modules included activity of steroid hormone receptor, steroid binding, and insulin receptor binding, etc.; the related signaling pathways were EGFR, PI3K-Akt and MAPK, etc.; regulating signaling pathways like MAPK could induce the proliferation, inhibition and apoptosis of pancreatic β cells, increase the quantity of pancreatic β cells, improve the functions of pancreatic β cells and stimulate the insulin secretion. Docking experiment analysis showed that all selected saponin compounds could enter the active sites of targets and form 3 – 14 hydrogen bonds with residues of the active sites. Moreover, van der Waals forces were present between chemical compounds and active sites. By combining the docking binding energy, we determined that the chemical compounds showed strong binding energy to the targets. ConclusionTCBBDR exerts therapeutic effects on diabetes through multi-compound and multi-target collaboration. Specifically, saponin components mediate pathways including inflammatory reaction and signal transduction to treat T2DM by regulating several key proteins that interact with EGFR and a series of signaling pathways related to disease development.

Network Centrality Assessment (NCA): Assessing the Transport Network Resilience to Urban Flooding

This study presents a methodology to assess transport network resilience to urban flooding. The proposed methodology is developed based on the centrality measures and graph theory. The study utilises Open-Source GIS tools to compute betweenness and closeness centrality values. The case study was carried out in Greater Colombo - Sri Lanka, with reference to three significant urban flooding events in 2010, 2016, and 2017. The study assessed the resilience of road network in terms of topological impacts and accessibility changes.The results revealed three key findings. First, over 60% of road network revealed a significant change in its topological structural coherence during each flooding event. This was particularly pronounced in vehicular movements relative to pedestrian movements. Second, the study revealed a redundant depreciation of the transport accessibility as it shifted from city centre to peripheral areas creating temporary accessibility hotpots in the periphery. Third, a significant drawback of the resilience of road network was identified in terms of the deviation from the shortest path, increasing the travel time and trip length. In overall, the study concluded that the proposed methodology can be utilised as a planning and designing tool to assess road network`s resilience devising precautionary measures to mitigate disaster risk.

Network reconstruction from betweenness centrality by artificial bee colony

Reconstructing complex network structures from measurable data has become a central issue in contemporary network science and engineering. In this paper, we tackle the reconstruction of the network topology from a set of target betweenness centrality values. This metric evaluates the participation of the vertices in the communication along the shortest paths of the network and it has been widely used as centrality measure in analyzing social networks. In this case, the network reconstruction task is formulated as an optimization problem whose search space consists of all possible subsets of edges linking a given set of vertices. It should be noted that the development of optimization algorithms for tackling combinatorial problems involving betweenness centrality is very challenging, because this measure is notoriously expensive to compute. That is why we have made two innovative design decisions (not explored previously in the literature): firstly, to address the problem by proposing an artificial bee colony algorithm, which is a potent swarm intelligence metaheuristic inspired in the foraging behavior of honeybees and secondly, to incorporate in this proposal recent betweenness centrality update techniques that substantially reduce the number of shortest paths which should be re-computed when a network is changed. Extensive experiments verify that our optimizer can achieve better solution quality than the state-of-the-art metaheuristic for this complex optimization problem and other competing algorithms. We have completed the study of the proposal by evaluating its behavior as tool to reconstruct network topology.

Residue Interaction Network Analysis Predicts a Val24-Ile31 Interaction May be Involved in Preventing Amyloid-Beta (1-42) Primary Nucleation.

Alzheimer's disease (AD) patients could benefit from a more effective treatment than the current FDA-approved options. Because amyloid-beta (Aβ) is thought to play a central role in AD pathogenesis, many experimental drugs attempt to reduce Aβ-induced pathology. Preventing amyloid accumulation may be a more effective strategy than clearing Aβ plaques after they form. If preventing Aβ accumulation can treat or prevent AD, then understanding Aβ primary nucleation may aid rational drug design. This study examines Aβ residue interaction networks and reports network and structural observations that may provide insight into primary nucleation. While many studies identify structural features of Aβ that promote aggregation, this study reports features that may resist primary nucleation by examining Aβ42 studies in more and less polar solvents. In Aβ42 in a less polar solvent (PDB ID: 1IYT), Val24 and Ile31 have higher betweenness and residue centrality values. This may be due to a predicted interaction between Val24 and Ile31. Residues in the central hydrophobic cluster (CHC) of Aβ40 and Aβ42 had significantly higher betweenness values compared to the average betweenness of the structures, highlighting the CHC's reported role in oligomerization. The predicted interaction between Val24 and Ile31 may reduce the likelihood of primary nucleation of Aβ.

Association of built environment attributes with the spread of COVID-19 at its initial stage in China

Evidence of the association of built environment (BE) attributes with the spread of COVID-19 remains limited. As an additional effort, this study regresses a ratio of accumulative confirmed infection cases at the city level in China on both inter-city and intra-city BE attributes. A mixed geographically weighted regression model was estimated to accommodate both local and global effects of BE attributes. It is found that spatial clusters are mostly related to low infections in 28.63 % of the cities. The density of point of interests around railway stations, travel time by public transport to activity centers, and the number of flights from Hubei Province are associated with the spread. On average, the most influential BE attribute is the number of trains from Hubei Province. Higher infection ratios are associated with higher values of between-ness centrality in 70.98 % of the cities. In 79.22 % of the cities, the percentage of the aging population shows a negative association. A positive association of the population density in built-up areas is found in 68.75 % of county-level cities. It is concluded that the countermeasures in China could have well reflected spatial heterogeneities, and the BE could be further improved to mitigate the impacts of future pandemics.

The Social Engagement to Agricultural Issues using Social Network Analysis

Twitter is one of the micro-blogging social media which emphasizes the speed of communication. In the 4.0 era, the government also promotes the distribution of information through social media to reach the community from various lines. In previous research, Social Network Analysis was used to see the relationship between actors in a work environment, or as a basis for identifying the application of technology adoption in decision making, whereas no one has used SNA to see trends in people's response to agricultural information. This study aims to see the extent to which information about agriculture reaches the community, as well as to see the community's response to take part in agricultural development. This article also shows the actors who took part in disseminating information. Data was taken on November 13 to 20, 2020 from the Drone Emprit Academic, and was taken limited to 3000 nodes. Then, the measurements of the SNA are represented on the values of Degree Centrality, Betweenness Centrality, Closeness Centrality, and Eigenvector Centrality. @AdrianiLaksmi has the highest value in Eigenvector Centrality and Degree Centrality, he has the greatest role in disseminating information and has many followers among other accounts that spread the same information. While the @RamliRizal account ranks the highest in Betweenness Centrality, who has the most frequently referred information, and the highest Closeness Centrality is owned by the @baigmac account because of the fastest to re-tweet the first information.

Comparison of path-based centrality measures in protein-protein interaction networks revealed proteins with phenotypic relevance during adaptation to changing nitrogen environments

Plants must rapidly adapt to changes in nutrient conditions. Especially adaptations to changing nitrogen environments are very complex involving also major adjustments on the protein level. Here, we used a size-exclusion chromatography-coupled to mass spectrometry approach to study the dynamics of protein-protein interactions induced by transition from full nutrition to nitrogen starvation. Comparison of interaction networks established for each nutrient condition revealed a large overlap of proteins which were part of the protein-protein interaction network, but that same set of proteins underwent different interactions at each treatment. Network topology parameter betweenness centrality (BC) was found to best reflect the relevance of individual proteins in the information flow within each network. Changes in BC for individual proteins may therefore indicate their involvement in the cellular adjustments to the new condition. Based on this analysis, a set of proteins was identified showing high nitrogen-dependent changes in their BC values: The receptor kinase AT5G49770, co-receptor QSK1, and proton-ATPase AHA2. Mutants of those proteins showed a nitrate-dependent root growth phenotype. Individual interactions within the reconstructed network were tested using FRET-FLIM technology. Taken together, we present a systematic strategy comparing dynamic changes in protein-protein interaction networks based on their network parameters to identify regulatory nodes. SignificanceProtein-protein interactions are known to be important in cellular signaling events, but the dynamic changes in interaction networks induced by external stimuli are still rarely studied. We systematically analyzed how changes in the nutrient environment induced a rewiring of protein-protein interactions in roots. We observed small changes in overall protein abundances, but instead a rewiring of pairwise protein-protein interactions. Betweenness centrality was found to be the optimal network topology parameter to identify protein candidates with high relevance to the information flow in the (dynamic) network. Predicted interactions of those relevant nodes were confirmed in FLIM/FRET experiments and in phenotypic analysis. The network approach described here may be a useful application in dynamic network analysis more generally.

Central Proteins of Plasma in Response to Low-Level Laser Therapy Involve in Body Hemostasis and Wound Repair.

Introduction: Low-level laser therapy (LLLT) is accompanied by protein expression change in the body. There are many efforts to find a clear relationship between the differentially expressed proteins. This study aims to find the central differentiated expressed proteins of plasma after LLLT. Methods: Six proteins are extracted from a proteomics study and the network including these query proteins plus 100 first neighbors was constructed. The central proteins were determined based on degree value, betweenness centrality, closeness centrality (CC), and stress (The centrality parameters). Results: Among 106 nodes of the network, 10 proteins were characterized with the most values of degree, betweenness centrality, CC, and stress. These proteins were determined as central proteins in response to LLLT in plasma. Conclusion: Three query proteins, AHSG, FGG, and SERPINA1, plus 7 first neighbors, namely FGA, ALB, KNG1, FN1, APP, TIMP1, and F5, were identified as central proteins which were dysregulated.

Alpha-Carbonic Anhydrases from Hydrothermal Vent Sources as Potential Carbon Dioxide Sequestration Agents: In Silico Sequence, Structure and Dynamics Analyses.

With the increase in CO2 emissions worldwide and its dire effects, there is a need to reduce CO2 concentrations in the atmosphere. Alpha-carbonic anhydrases (α-CAs) have been identified as suitable sequestration agents. This study reports the sequence and structural analysis of 15 α-CAs from bacteria, originating from hydrothermal vent systems. Structural analysis of the multimers enabled the identification of hotspot and interface residues. Molecular dynamics simulations of the homo-multimers were performed at 300 K, 363 K, 393 K and 423 K to unearth potentially thermostable α-CAs. Average betweenness centrality (BC) calculations confirmed the relevance of some hotspot and interface residues. The key residues responsible for dimer thermostability were identified by comparing fluctuating interfaces with stable ones, and were part of conserved motifs. Crucial long-lived hydrogen bond networks were observed around residues with high BC values. Dynamic cross correlation fortified the relevance of oligomerization of these proteins, thus the importance of simulating them in their multimeric forms. A consensus of the simulation analyses used in this study suggested high thermostability for the α-CA from Nitratiruptor tergarcus. Overall, our novel findings enhance the potential of biotechnology applications through the discovery of alternative thermostable CO2 sequestration agents and their potential protein design.

Conformational Changes Induced by S34Y and R98C Variants in the Death Domain of Myd88.

Myeloid differentiating factor 88 (Myd88) is a universal adaptor protein that plays a critical role in innate immunity by mediating TLR downstream signaling. Myd88 death domain (DD) forms an oligomeric complex by association with other DD-containing proteins such as IRAK4. Despite its universal role, polymorphisms in Myd88 can result in several diseases. Previous studies have suggested that, out of several non-synonymous single-nucleotide polymorphisms (nsSNPs), the variants S34Y and R98C in the DD of Myd88 disrupt the formation of the Myddosome complex. Therefore, we performed molecular dynamics (MD) simulations on wild-type (Myd88WT) and mutant (Myd88S34Y, Myd88R98C) DDs to evaluate the subtle conformational changes induced by these mutations. Our results suggest that the S34Y variant induces large structural transitions compared to the R98C variant as evidenced by residual flexibility at the variable loop regions, particularly in the H1–H2 loop, and variations in the collective modes of motion observed for wild-type and mutant Myd88 DDs. The residue interaction network strongly suggests a distortion in the interaction pattern at the location of the mutated residue between the wild type and mutants. Moreover, betweenness centrality values indicate that variations in the distribution of functionally important residues may be reflected by distinct residue signal transductions in both wild-type and mutant Myd88 DDs, which may influence the interaction with other DDs in TLR downstream signaling.

Approximation of Interactive Betweenness Centrality in Large Complex Networks

The analysis of real-world systems through the lens of complex networks often requires a node importance function. While many such views on importance exist, a frequently used global node importance measure is betweenness centrality, quantifying the number of times a node occurs on all shortest paths in a network. This centrality of nodes often significantly depends on the presence of nodes in the network; once a node is missing, e.g., due to a failure, other nodes’ centrality values can change dramatically. This observation is, for instance, important when dismantling a network: instead of removing the nodes in decreasing order of their static betweenness, recomputing the betweenness after a removal creates tremendously stronger attacks, as has been shown in recent research. This process is referred to as interactive betweenness centrality. Nevertheless, very few studies compute the interactive betweenness centrality, given its high computational costs, a worst-case runtime complexity of O(N∗∗4) in the number of nodes in the network. In this study, we address the research questions, whether approximations of interactive betweenness centrality can be obtained with reduction of computational costs and how much quality/accuracy needs to be traded in order to obtain a significant reduction. At the heart of our interactive betweenness approximation framework, we use a set of established betweenness approximation techniques, which come with a wide range of parameter settings. Given that we are interested in the top-ranked node(s) for interactive dismantling, we tune these methods accordingly. Moreover, we explore the idea of batch removal, where groups of top-k ranked nodes are removed before recomputation of betweenness centrality values. Our experiments on real-world and random networks show that specific variants of the approximate interactive betweenness framework allow for a speedup of two orders of magnitude, compared to the exact computation, while obtaining near-optimal results. This work contributes to the analysis of complex network phenomena, with a particular focus on obtaining scalable techniques.

Identification and role of opinion leaders in information diffusion for online discussion network

Social networks are playing a vibrant role in spreading information among its users. With the ongoing boom in technology, social networks are becoming very popular nowadays and are made up of a multitude of users. Some of these users may have a strong influence on the other network users depending on their uncommon elevated values of betweenness centrality (BC). In the online discussion network such as Twitter, the extremely important users are called Opinion Leaders, who play a vital role in the spread of information in an efficient and fast way and keep the isolates interested in the online discussion network. One of the most significant problems in the associated sector is the identification of opinion leaders. In this paper, opinion leaders are chosen based on various centrality measures. The central users are identified based on their in-degree and out-degree links and are ranked within the network by their BC values. Furthermore, we analyze community evolution by using the standard Louvain algorithm. The experiment is performed on publicly available Higgs Boson data from Twitter. The conversation starter and influencer have been observed as an opinion leader for each network. These users have been observed to play a crucial part in the dissemination of information in an online discussion network.

Diffusion Delay Centrality: Decelerating Diffusion Processes Across Networks

This paper presents a methodology to identify agents who should be put into isolation to decelerate a diffusion process spreading through-out a network. We refer to this measure as to Diffusion Delay Centrality (DDC). We show that DDC assigns a high rank to agents acting as the gatekeepers of the fringe of the network. When these are isolated, the spreading of diffusion from the periphery to the core of the network (or vice versa) is prevented or, at least, decelerated. We also show that the ranking of nodes obtained from the DDC is predicted by standard measures of network centrality. Specifically, the ranking can be recovered using the difference in the values of betweenness and eigenvector centrality of network agents. We suggest that the findings presented in this paper might represent a useful tool for policies intending to reduce diffusion processes.

Mechanism differences between typical yin and typical yang personality individuals assessed by Five-Pattern Personality Inventory (FPPI): Evidence from resting-state brain functional networks

BackgroundMost studies assessing brain–personality mechanisms have used Western personality questionnaires. However, Western personality questionnaires may not objectively reflect the personality characteristics of individuals in Eastern cultures such as China. Hence, we adopted the functional magnetic resonance imaging (fMRI) and the Chinese localized scale, FPPI, to explore the brain mechanisms differences of typical yin and typical yang personalities of individuals in China. Methods30 typical yin personality participants (TYI) and 34 typical yang personality participants (TYA) were enrolled according to the FPPI. The group differences of the functional brain networks among 90 specific brain regions were mapped using fMRI data and then analyzed by the conventional network metrics (CNM) and frequency subgraph mining (FSM). ResultsThe CNM and FSM differences between two typical personality groups were traced to the frontal, temporal, and parietal cortices. The yin group, reflecting the rich emotions and feelings of individuals, showed higher betweenness centrality (BCi) and nodal efficiency (Ei) values in putamen and middle frontal gyrus. The yang group, reflecting active behaviors and tendency to adapting to the changing surroundings, showed higher BCi and Ei values in precuneus, posterior cingulate gyrus, and inferior parietal lobule, brain areas in the default mode network (DMN). ConclusionThese results supplied evidence for the neurobiological differences between typical yin and typical yang personality participants based on Chinese culture. These results also provide a new perspective to help researchers understand brain mechanism differences between yin and yang personality groups in the Chinese culture.

Optimizing node infiltrations in complex networks by a local search based heuristic

Over the past few years, the task of conceiving effective interventions on complex networks has arisen as different optimization problems. An interesting intervention scheme that has many important real-world applications is to introduce (infiltrate) in a network a certain number of new nodes and connect them to certain existing nodes with the aim of making them as central as possible. The idea is that they should occupy strategic positions in the network to gather a lot of information or to decisively influence others. In this work, we present an optimization problem that concerns the selection of nodes in a network with which to link each of a particular number of infiltrated nodes in order to maximize the lower betweenness centrality value obtained by them. This metric evaluates the participation of the nodes in the communications through the shortest paths of the network and it has been widely used as centrality measure in analyzing social networks. To address the problem, we propose a local search based heuristic whose performance is driven by two search strategies; a constructive greedy procedure that is employed to create an initial solution and a local improvement method that makes use of two neighborhood operators designed for exploring the search space of this problem. It should be noted that the development of metaheuristics for tackling combinatorial problems involving betweenness centrality is very challenging, because this measure is notoriously expensive to compute. That is why we have made two design decisions: firstly, to conceive a very simple metaheuristic approach and secondly, to incorporate in this proposal recent betweenness centrality update techniques that substantially reduce the number of shortest paths which should be re-computed when a network is changed. The performance of our optimizer, with respect to other metaheuristic models from the literature that can be adapted to face this problem, such as a randomized greedy multi-start algorithm and a steady-state genetic algorithm, is empirically shown.

The effects of high-frequency repetitive transcranial magnetic stimulation on resting-state functional connectivity in obese adults.

We conducted a 4-week randomized, sham-controlled, single-blind, parallel-group trial to examine the effect of repetitive transcranial magnetic stimulation (rTMS) delivered to the left dorsolateral prefrontal cortex (DLPFC) on functional brain connectivity and body weight in adults with obesity. Of the 45 volunteers with obesity, aged between 18 and 70 years (body mass index [BMI] ≥25 kg/m2 according to the obesity criterion for an Asian population), 36 participants (54.1 ± 11.0 years, BMI 30.2 ± 3.5 kg/m2 , 77.8% female) completed the 4 weeks of follow-up, undergoing two resting state fMRI scans (20 in the real stimulation group and 16 in the sham stimulation group). A total of eight sessions of high-frequency rTMS targeting the left DLPFC were provided over a period of 4 weeks (5-second trains with 25-second inter-train intervals, 10 Hz, 110% motor threshold; 2000 pulses over 20 minutes). Participants in the real stimulation group showed significantly greater weight loss from baseline following the eight session of rTMS (-2.53 ± 2.41 kg vs 0.38 ± 1.13 kg, P < 0.01). For intrinsic brain connectivity comparisons, the between-ness centrality values within the right frontoparietal network tended to increase with rTMS, and a significant interaction effect was identified for time (pre vs post) × rTMS (real vs sham) in the right frontoparietal network (P = 0.031, FDR corrected). We observed that rTMS selectively increased resting state functional connectivity within the right frontoparietal network. Our findings suggest that high-frequency rTMS to the left DLPFC might strengthen the frontoparietal network that orchestrates top-down inhibitory control to reduce food intake.

Temporal betweenness centrality in dynamic graphs

Measures of centrality of vertices in a network are usually defined solely on the basis of the network structure. In highly dynamic networks, where vertices appear and disappear and their connectivity constantly changes, we need to redefine our measures of centrality to properly capture the temporal dimension of the network structure evolution, as well as the dynamic processes over the network. Betweenness centrality (BC), one of the most studied measures, defines the importance of a vertex as a mediator between available communication paths. BC value of a node is expressed as the fraction of the shortest paths passing through this node. In other words, given the context that information flow follows the shortest paths, a node with higher BC potentially has greater influence on the information flow in the network. In temporal dynamic graphs, a communication path should be seen as a path both in space (i.e., the network structure) and in time (i.e., the network evolution). Toward this goal, in this paper we propose the bi-objective notion of shortest–fastest path (SFP) in temporal graphs, which considers both space and time as a linear combination governed by a parameter. Based on this notion, we then define a novel temporal betweenness centrality (TBC) metric, which is highly sensitive to the observation interval and the importance of space and time distances of the vertices, that can provide better understanding of the communication mediators in temporal networks. We devise efficient algorithms for exactly computing all-pairs SFPs and the corresponding BC values both in a single graph window and sliding graph windows. We also present a distributed implementation of our approach on Apache Spark which shows great solution effectiveness and efficiency. We provide a thorough experimentation on a large variety of datasets. An application to the analysis of information propagation proves that our notion of TBC outperforms static BC in the task of identifying the best vertices for propagating information.

Signature microRNAs and long noncoding RNAs in laryngeal cancer recurrence identified using a competing endogenous RNA network.

The aim of the present study was to identify novel microRNA (miRNA) or long noncoding RNA (lncRNA) signatures of laryngeal cancer recurrence and to investigate the regulatory mechanisms associated with this malignancy. Datasets of recurrent and nonrecurrent laryngeal cancer samples were downloaded from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus database (GSE27020 and GSE25727) to examine differentially expressed miRNAs (DE-miRs), lncRNAs (DE-lncRs) and mRNAs (DEGs). miRNA-mRNA and lncRNA-miRNA networks were constructed by investigating the associations among these RNAs in various databases. Subsequently, the interactions identified were combined into a competing endogenous RNA (ceRNA) regulatory network. Feature genes in the miRNA-mRNA network were identified via topological analysis and a recursive feature elimination algorithm. A support vector machine (SVM) classifier was established using the betweenness centrality values in the miRNA-mRNA network, consisting of 32 optimal feature-coding genes. The classification effect was tested using two validation datasets. Furthermore, coding genes in the ceRNA network were examined via pathway enrichment analyses. In total, 21 DE-lncRs, 507 DEGs and 55 DE-miRs were selected. The SVM classifier exhibited an accuracy of 94.05% (79/84) for sample classification prediction in the TCGA dataset, and 92.66 and 91.07% in the two validation datasets. The ceRNA regulatory network comprised 203 nodes, corresponding to mRNAs, miRNAs and lncRNAs, and 346 lines, corresponding to the interactions among RNAs. In particular, the interactions with the highest scores were HLA complex group 4 (HCG4)-miR-33b, HOX transcript antisense RNA (HOTAIR)-miR-1-MAGE family member A2 (MAGEA2), EMX2 opposite strand/antisense RNA (EMX2OS)-miR-124-calcitonin related polypeptide α (CALCA) and EMX2OS-miR-124-γ-aminobutyric acid type A receptor γ2 subunit (GABRG2). Gene enrichment analysis of the genes in the ceRNA network identified that 11 pathway terms and 16 molecular function terms were significantly enriched. The SVM classifier based on 32 feature coding genes exhibited high accuracy in the classification of laryngeal cancer samples. miR-1, miR-33b, miR-124, HOTAIR, HCG4 and EMX2OS may be novel biomarkers of recurrent laryngeal cancer, and HCG4-miR-33b, HOTAIR-miR-1-MAGEA2 and EMX2OS-miR-124-CALCA/GABRG2 may be associated with the molecular mechanisms regulating recurrent laryngeal cancer.