Community structure in social and biological networks

Intervention effect of targeted workplace closures may be approximated by single-layered networks in an individual-based model of COVID-19 control

Individual-based models of infectious disease dynamics commonly use network structures to represent human interactions. Network structures can vary in complexity, from single-layered with homogeneous mixing to multi-layered with clustering and layer-specific contact weights. Here we assessed policy-relevant consequences of network choice by simulating different network structures within an established individual-based model of SARS-CoV-2 dynamics. We determined the clustering coefficient of each network structure and compared this to several epidemiological outcomes, such as cumulative and peak infections. High-clustered networks estimate fewer cumulative infections and peak infections than less-clustered networks when transmission probabilities are equal. However, by altering transmission probabilities, we find that high-clustered networks can essentially recover the dynamics of low-clustered networks. We further assessed the effect of workplace closures as a layer-targeted intervention on epidemiological outcomes and found in this scenario a single-layered network provides a sufficient approximation of intervention effect relative to a multi-layered network when layer-specific contact weightings are equal. Overall, network structure choice within models should consider the knowledge of contact weights in different environments and pathogen mode of transmission to avoid over- or under-estimating disease burden and impact of interventions.

Oncogenic R248W mutation induced conformational perturbation of the p53 core domain and the structural protection by proteomimetic amyloid inhibitor ADH-6

The involvement of p53 aggregation in cancer pathogenesis emphasizes the importance of unraveling the mechanisms underlying mutation-induced p53 destabilization. And understanding how small molecule inhibitors prevent the conversion of p53 into aggregation-primed conformations is pivotal for the development of therapeutics targeting p53-aggregation-associated cancers. A recent experimental study highlights the efficacy of the proteomimetic amyloid inhibitor ADH-6 in stabilizing R248W p53 and inhibiting its aggregation in cancer cells by interacting with the p53 core domain (p53C). However, it remains mostly unclear how R248W mutation induces destabilization of p53C and how ADH-6 stabilizes this p53C mutant and inhibits its aggregation. Herein, we conducted all-atom molecular dynamics simulations of R248W p53C in the absence and presence of ADH-6, as well as that of wild-type (WT) p53C. Our simulations reveal that the R248W mutation results in a shift of helix H2 and β-hairpin S2-S2' towards the mutation site, leading to the destruction of their neighboring β-sheet structure. This further facilitates the formation of a cavity in the hydrophobic core, and reduces the stability of the β-sandwich. Importantly, two crucial aggregation-prone regions (APRs) S9 and S10 are disturbed and more exposed to solvent in R248W p53C, which is conducive to p53C aggregation. Intriguingly, ADH-6 dynamically binds to the mutation site and multiple destabilized regions in R248W p53C, partially inhibiting the shift of helix H2 and β-hairpin S2-S2', thus preventing the disruption of the β-sheets and the formation of the cavity. ADH-6 also reduces the solvent exposure of APRs S9 and S10, which disfavors the aggregation of R248W p53C. Moreover, ADH-6 can preserve the WT-like dynamical network of R248W p53C. Our study elucidates the mechanisms underlying the oncogenic R248W mutation induced p53C destabilization and the structural protection of p53C by ADH-6.

Identifying highly connected sites for risk-based surveillance and control of cucurbit downy mildew in the eastern United States

Objective

Surveillance is critical for the rapid implementation of control measures for diseases caused by aerially dispersed plant pathogens, but such programs can be resource-intensive, especially for epidemics caused by long-distance dispersed pathogens. The current cucurbit downy mildew platform for monitoring, predicting and communicating the risk of disease spread in the United States is expensive to maintain. In this study, we focused on identifying sites critical for surveillance and treatment in an attempt to reduce disease monitoring costs and determine where control may be applied to mitigate the risk of disease spread.

Methods

Static networks were constructed based on the distance between fields, while dynamic networks were constructed based on the distance between fields and wind speed and direction, using disease data collected from 2008 to 2016. Three strategies were used to identify highly connected field sites. First, the probability of pathogen transmission between nodes and the probability of node infection were modeled over a discrete weekly time step within an epidemic year. Second, nodes identified as important were selectively removed from networks and the probability of node infection was recalculated in each epidemic year. Third, the recurring patterns of node infection were analyzed across epidemic years.

Results

Static networks exhibited scale-free properties where the node degree followed a power-law distribution. Betweenness centrality was the most useful metric for identifying important nodes within the networks that were associated with disease transmission and prediction. Based on betweenness centrality, field sites in Maryland, North Carolina, Ohio, South Carolina and Virginia were the most central in the disease network across epidemic years. Removing field sites identified as important limited the predicted risk of disease spread based on the dynamic network model.

Conclusions

Combining the dynamic network model and centrality metrics facilitated the identification of highly connected fields in the southeastern United States and the mid-Atlantic region. These highly connected sites may be used to inform surveillance and strategies for controlling cucurbit downy mildew in the eastern United States.

Memristors with Tunable Volatility for Reconfigurable Neuromorphic Computing

Neuromorphic computing promises an energy-efficient alternative to traditional digital processors in handling data-heavy tasks, primarily driven by the development of both volatile (neuronal) and nonvolatile (synaptic) resistive switches or memristors. However, despite their energy efficiency, memristor-based technologies presently lack functional tunability, thus limiting their competitiveness with arbitrarily programmable (general purpose) digital computers. This work introduces a two-terminal bilayer memristor, which can be tuned among neuronal, synaptic, and hybrid behaviors. The varying behaviors are accessed via facile control over the filament formed within the memristor, enabled by the interplay between the two active ionic species (oxygen vacancies and metal cations). This solution is unlike single-species ion migration employed in most other memristors, which makes their behavior difficult to control. By reconfiguring a single crossbar array of hybrid memristors, two different applications that usually require distinct types of devices are demonstrated - reprogrammable heterogeneous reservoir computing and arbitrary non-Euclidean graph networks. Thus, this work outlines a potential path toward functionally reconfigurable postdigital computers.

Comparative Study and Evaluation of Hybrid Visualizations of Graphs

Hybrid visualizations combine different metaphors into a single network layout, in order to help humans in finding the "right way" of displaying the different portions of the network, especially when it is globally sparse and locally dense. We investigate hybrid visualizations in two complementary directions: (i) On the one hand, we evaluate the effectiveness of different hybrid visualization models through a comparative user study; (ii) On the other hand, we estimate the usefulness of an interactive visualization that integrates all the considered hybrid models together. The results of our study provide some hints about the usefulness of the different hybrid visualizations for specific tasks of analysis and indicates that integrating different hybrid models into a single visualization may offer a valuable tool of analysis.

Microbial co-occurrence network demonstrates spatial and climatic trends for global soil diversity

Despite recent research efforts to explore the co-occurrence patterns of diverse microbes within soil microbial communities, a substantial knowledge-gap persists regarding global climate influences on soil microbiota behaviour. Comprehending co-occurrence patterns within distinct geoclimatic groups is pivotal for unravelling the ecological structure of microbial communities, that are crucial for preserving ecosystem functions and services. Our study addresses this gap by examining global climatic patterns of microbial diversity. Using data from the Earth Microbiome Project, we analyse a meta-community co-occurrence network for bacterial communities. This method unveils substantial shifts in topological features, highlighting regional and climatic trends. Arid, Polar, and Tropical zones show lower diversity but maintain denser networks, whereas Temperate and Cold zones display higher diversity alongside more modular networks. Furthermore, it identifies significant co-occurrence patterns across diverse climatic regions. Central taxa associated with different climates are pinpointed, highlighting climate's pivotal role in community structure. In conclusion, our study identifies significant correlations between microbial interactions in diverse climatic regions, contributing valuable insights into the intricate dynamics of soil microbiota.

Prolactin blood concentration relies on the scalability of the TIDA neurons' network efficiency in vivo

Our understanding and management of reproductive health and related disorders such as infertility, menstrual irregularities, and pituitary disorders depend on understanding the intricate sex-specific mechanisms governing prolactin secretion. Using ex vivo experiments in acute slices, in parallel with in vivo calcium imaging (GRIN lens technology), we found that dopamine neurons inhibiting PRL secretion (TIDA), organize as functional networks both in and ex vivo. We defined an index of efficiency of networking (Ieff) using the duration of calcium events and the ability to form plastic economic networks. It determined TIDA neurons' ability to inhibit PRL secretion in vivo. Ieff variations in both sexes demonstrated TIDA neurons' adaptability to physiological changes. A variation in the number of active neurons contributing to the network explains the sexual dimorphism in basal [PRL]blood secretion patterns. These sex-specific differences in neuronal activity and network organization contribute to the understanding of hormone regulation.

Emergency communication networks on Twitter during Hurricane Irma: information flow, influential actors, and top messages

This study combined network analysis with message-level content analysis to investigate patterns of information flow and to examine messages widely distributed on social media during Hurricane Irma of 2017. The results show that while organisational users and media professionals dominated the top 100 information sources, individual citizens played a critical role in information dissemination. Public agencies should increase their retweeting activities and share the information posted by other trustworthy sources; doing so will contribute to the timely exchange of vital information during a disaster. This study also identified the active involvement of nonprofit organisations as information brokers during the post-event stage, indicating the potential for emergency management organisations to integrate their communication efforts into those of nonprofit entities. These findings will inform emergency management practices regarding implementation of communication plans and policies, facilitate the embracement of new partner organisations, and help with establishing and sustaining effective communication ties with a wide range of stakeholders.

Microbial community organization designates distinct pulmonary exacerbation types and predicts treatment outcome in cystic fibrosis

Polymicrobial infection of the airways is a hallmark of obstructive lung diseases such as cystic fibrosis (CF), non-CF bronchiectasis, and chronic obstructive pulmonary disease. Pulmonary exacerbations (PEx) in these conditions are associated with accelerated lung function decline and higher mortality rates. Understanding PEx ecology is challenged by high inter-patient variability in airway microbial community profiles. We analyze bacterial communities in 880 CF sputum samples collected during an observational prospective cohort study and develop microbiome descriptors to model community reorganization prior to and during 18 PEx. We identify two microbial dysbiosis regimes with opposing ecology and dynamics. Pathogen-governed PEx show hierarchical community reorganization and reduced diversity, whereas anaerobic bloom PEx display stochasticity and increased diversity. A simulation of antimicrobial treatment predicts better efficacy for hierarchically organized communities. This link between PEx, microbiome organization, and treatment success advances the development of personalized clinical management in CF and, potentially, other obstructive lung diseases.

Dynamical changes of interaction across functional brain communities during propofol-induced sedation

It is crucial to understand how anesthetics disrupt information transmission within the whole-brain network and its hub structure to gain insight into the network-level mechanisms underlying propofol-induced sedation. However, the influence of propofol on functional integration, segregation, and community structure of whole-brain networks were still unclear. We recruited 12 healthy subjects and acquired resting-state functional magnetic resonance imaging data during 5 different propofol-induced effect-site concentrations (CEs): 0, 0.5, 1.0, 1.5, and 2.0 μg/ml. We constructed whole-brain functional networks for each subject under different conditions and identify community structures. Subsequently, we calculated the global and local topological properties of whole-brain network to investigate the alterations in functional integration and segregation with deepening propofol sedation. Additionally, we assessed the alteration of key nodes within the whole-brain community structure at each effect-site concentrations level. We found that global participation was significantly increased at high effect-site concentrations, which was mediated by bilateral postcentral gyrus. Meanwhile, connector hubs appeared and were located in posterior cingulate cortex and precentral gyrus at high effect-site concentrations. Finally, nodal participation coefficients of connector hubs were closely associated to the level of sedation. These findings provide valuable insights into the relationship between increasing propofol dosage and enhanced functional interaction within the whole-brain networks.

Correlations reveal the hierarchical organization of biological networks with latent variables

Deciphering the functional organization of large biological networks is a major challenge for current mathematical methods. A common approach is to decompose networks into largely independent functional modules, but inferring these modules and their organization from network activity is difficult, given the uncertainties and incompleteness of measurements. Typically, some parts of the overall functional organization, such as intermediate processing steps, are latent. We show that the hidden structure can be determined from the statistical moments of observable network components alone, as long as the functional relevance of the network components lies in their mean values and the mean of each latent variable maps onto a scaled expectation of a binary variable. Whether the function of biological networks permits a hierarchical modularization can be falsified by a correlation-based statistical test that we derive. We apply the test to gene regulatory networks, dendrites of pyramidal neurons, and networks of spiking neurons.

A Social Network Analysis of a Multi-sector Service System for Intimate Partner Violence in a Large US City

About one in four women in the US report having experienced some form of intimate partner violence (IPV) during their lifetime and an estimated 15.5 million children live in families in which IPV occurred in the past year. Families of young children with IPV experiences often face complex needs and require well-coordinated efforts among service providers across social and health sectors. One promising partnership aims to support pregnant and parenting IPV survivors through coordination between IPV agencies and community-based maternal and early childhood home visiting programs. This study used social network analysis (SNA) to understand the interconnectedness of the system of IPV prevention and intervention for families with young children in a large US city. The SNA included 43 agencies serving this population across various service domains spanning IPV, legal, maternal and child health, and public benefit programs. An SNA survey collected data on four forms of collaboration between agencies, including formal administrative relationship, referral reciprocity, case consultation, and shared activities in community committees/organizing bodies. Density and centrality were the primary outcomes of interest. A community detection analysis was performed as a secondary analysis. The overall level of interconnectedness between the 43 responding agencies was low. Making referrals to each other was the most common form of collaboration, with a network density of 30%. IPV agencies had the highest average number of connections in the networks. There was a high level of variation in external collaborations among home visiting agencies, with several home visiting agencies having very few connections in the community but one home visiting program endorsing collaborative relationships with upwards of 38 partner agencies in the network. In serving families at risk for IPV, home visiting agencies were most likely to have referral relationships with mental health provider agencies and substance use disorder service agencies. A community detection analysis identified distinct communities within the network and demonstrated that certain agency types were more connected to one another while others were typically siloed within the network. Notably, the IPV and home visiting communities infrequently overlapped. Sensitivity analyses showed that survey participants' knowledge of their agencies' external collaborations varied by their work roles and agencies overall had low levels of consensus about their connectedness to one another. We identified a heterogeneous service system available to families of young children at-risk for or experiencing IPV. Overall inter-agency connectedness was low, with many siloed agencies and a lack of shared knowledge of community resources. Understanding current collaborations, silos, and centrality of agencies is an effective public health tool for allocating scarce resources across diverse service sectors to efficiently improve the system serving families experiencing IPV.

Temporal local clustering coefficient uncovers the hidden pattern in temporal networks

Identifying and extracting topological characteristics are essential for understanding associated structures and organizational principles of complex networks. For temporal networks where the network topology varies with time, beyond the classical patterns such as small-worldness and scale-freeness extracted from the perspective of traditional aggregated static networks, the temporality and simultaneity of time-varying interactions should also be included. Here we extend the traditional analysis on the local clustering coefficient C in static networks and study the dynamical local clustering coefficient of temporal networks. We demonstrate that the temporal local clustering coefficient TC conveys the hidden information of nodes' neighboring connectance when interactions occur at various rhythms. By systematically analyzing various empirical datasets, we find that TC uncovers different interaction patterns in different types of temporal networks. Specifically, we show that TC has a strong positive correlation with C in efficiency-related networks, whereas they are uncorrelated in social activity-related networks. Moreover, TC helps to exclude interference from accidental interactions and reflect the actual clustering properties of network nodes. Our results shed light on the importance of digging into dynamical characteristics to fundamentally understand the underlying temporal structures of real complex systems.

Targeting pro-inflammatory T cells as a novel therapeutic approach to potentially resolve atherosclerosis in humans

Atherosclerosis (AS), a leading cause of cardio-cerebrovascular disease worldwide, is driven by the accumulation of lipid contents and chronic inflammation. Traditional strategies primarily focus on lipid reduction to control AS progression, leaving residual inflammatory risks for major adverse cardiovascular events (MACEs). While anti-inflammatory therapies targeting innate immunity have reduced MACEs, many patients continue to face significant risks. Another key component in AS progression is adaptive immunity, but its potential role in preventing AS remains unclear. To investigate this, we conducted a retrospective cohort study on tumor patients with AS plaques. We found that anti-programmed cell death protein 1 (PD-1) monoclonal antibody (mAb) significantly reduces AS plaque size. With multi-omics single-cell analyses, we comprehensively characterized AS plaque-specific PD-1+ T cells, which are activated and pro-inflammatory. We demonstrated that anti-PD-1 mAb, when captured by myeloid-expressed Fc gamma receptors (FcγRs), interacts with PD-1 expressed on T cells. This interaction turns the anti-PD-1 mAb into a substitute PD-1 ligand, suppressing T-cell functions in the PD-1 ligands-deficient context of AS plaques. Further, we conducted a prospective cohort study on tumor patients treated with anti-PD-1 mAb with or without Fc-binding capability. Our analysis shows that anti-PD-1 mAb with Fc-binding capability effectively reduces AS plaque size, while anti-PD-1 mAb without Fc-binding capability does not. Our work suggests that T cell-targeting immunotherapy can be an effective strategy to resolve AS in humans.

Molecular dynamics investigation of DNA fragments bound to the anti-HIV protein SAMHD1 reveals alterations in allosteric communications

The sterile alpha motif and histidine-aspartate domain-containing protein 1 (or SAMHD1), a human dNTP-triphosphohydrolase, contributes to HIV-1 restriction in select terminally differentiated cells of the immune system. While the prevailing hypothesis is that the catalytically active form of the protein is an allosterically triggered tetramer, whose HIV-1 restriction properties are attributed to its dNTP - triphosphohydrolase activity, it is also known to bind to ssRNA and ssDNA oligomers. A complete picture of the structure-function relationship of the enzyme is still elusive and the function corresponding to its nucleic acid binding ability is debated. In this in silico study, we investigate the stability, preference and allosteric effects of DNA oligomers bound to SAMHD1. In particular, we compare the binding of DNA and RNA oligomers of the same sequence and also consider the binding of DNA fragments with phosphorothioate bonds in the backbone. The results are compared with the canonical form with the monomers connected by GTP/dATP crossbridges. The simulations indicate that SAMHD1 dimers preferably bind to DNA and RNA oligomers compared to GTP/dATP. However, allosteric communication channels are altered in the nucleic acid acid bound complexes compared to the canonical form. All results are consistent with the hypothesis that the DNA bound form of the protein correspond to an unproductive off-pathway state where the protein is sequestered and not available for dNTP hydrolysis.

Are different countries equally green with envy? A comparison of the everyday concept of envy in the United States, Spain, and Germany

Using a prototype approach to emotion concepts, we mapped the internal structure and content of the everyday concept of envy (as used in the United States) and its translation equivalents of envidia in Spanish and Neid in German. In Study 1 (total N = 415), the features of the concept of envy, envidia, and Neid were generated via an open-ended questionnaire. In Study 2 (total N = 404), participants rated the degree of typicality of the constitutive features on a forced-choice questionnaire. The prototype analysis of envy, supplemented with network analyses, revealed that the largest connected set of features of envy, envidia, and Neid shared a group of central features, including features related to success or to people with a better appearance. Still, envy, envidia, and Neid did differ with respect to their constituent peripheral features as well as the density of their networks, their structure, and the betweenness centrality of the nodes. These results suggest that a prototype approach combined with network analysis is a convenient approach for studying the internal structure of everyday emotion concepts and the degree of overlap with respect to the translation equivalents in different countries.

Unveiling Influence in Networks: A Novel Centrality Metric and Comparative Analysis through Graph-Based Models

Identifying influential actors within social networks is pivotal for optimizing information flow and mitigating the spread of both rumors and viruses. Several methods have emerged to pinpoint these influential entities in networks, represented as graphs. In these graphs, nodes correspond to individuals and edges indicate their connections. This study focuses on centrality measures, prized for their straightforwardness and effectiveness. We divide structural centrality into two categories: local, considering a node's immediate vicinity, and global, accounting for overarching path structures. Some techniques blend both centralities to highlight nodes influential at both micro and macro levels. Our paper presents a novel centrality measure, accentuating node degree and incorporating the network's broader features, especially paths of different lengths. Through Spearman and Pearson correlations tested on seven standard datasets, our method proves its merit against traditional centrality measures. Additionally, we employ the susceptible-infected-recovered (SIR) model, portraying virus spread, to further validate our approach. The ultimate influential node is gauged by its capacity to infect the most nodes during the SIR model's progression. Our results indicate a notable correlative efficacy across various real-world networks relative to other centrality metrics.

Comprehensive network modeling approaches unravel dynamic enhancer-promoter interactions across neural differentiation

Background

Increasing evidence suggests that a substantial proportion of disease-associated mutations occur in enhancers, regions of non-coding DNA essential to gene regulation. Understanding the structures and mechanisms of regulatory programs this variation affects can shed light on the apparatuses of human diseases.

Results

We collected epigenetic and gene expression datasets from seven early time points during neural differentiation. Focusing on this model system, we constructed networks of enhancer-promoter interactions, each at an individual stage of neural induction. These networks served as the base for a rich series of analyses, through which we demonstrated their temporal dynamics and enrichment for various disease-associated variants. We applied the Girvan-Newman clustering algorithm to these networks to reveal biologically relevant substructures of regulation. Additionally, we demonstrated methods to validate predicted enhancer-promoter interactions using transcription factor overexpression and massively parallel reporter assays.

Conclusions

Our findings suggest a generalizable framework for exploring gene regulatory programs and their dynamics across developmental processes. This includes a comprehensive approach to studying the effects of disease-associated variation on transcriptional networks. The techniques applied to our networks have been published alongside our findings as a computational tool, E-P-INAnalyzer. Our procedure can be utilized across different cellular contexts and disorders.

The influencing mechanism of scenic spot online attention and tourists' purchase behavior: an AISAS model based investigation

Introduction

In the era of the Internet, online digital traces have become a new way to study the online attention of scenic spots and tourists' purchase behavior. The public's information search on major search platforms is a series of manifestations of potential tourists' attention and interest in scenic spots, but there are few studies on how attention, interest and information search affect potential tourists to generate real purchase behavior.

Method

This paper selects four dimensions of short video platform, travel website, search engine and social media to comprehensively measure the online attention of high-quality scenic spots in Yunnan Province, and then establishes a gray association analytic hierarchy process based on the relevant variables of the AISAS model to empirically analyze the primary and secondary factors affecting tourists' purchase behavior.

Results

(1) From the perspective of the online attention of scenic spots on different platforms, the intensity of the public's scenic spots online attention on the four types of media platforms is in the order of travel websites, search engines, short videos and social media (2) From the perspective of spatial distribution characteristics, the online attention of high-quality scenic spots in Yunnan Province is unevenly distributed, that is, there is a big difference between the attention of higher star scenic spots and their star rating and popularity, while the attention of low-star scenic spots is not much different from their star rating and popularity (3) From the perspective of spatial agglomeration characteristics, the comprehensive online attention of high-quality scenic spots in Yunnan Province presents the spatial agglomeration characteristics of "the multi-core linkage of high-density in the east and north, and sub-high-density in the south" (4) The factors influencing the purchase behavior of potential tourists are sharing experience, attracting attention, generating interest and searching information.

Discussion

By exploring the formation mechanism of high-quality scenic spots online attention in Yunnan Province and the mechanism of its spatial differentiation, this study not only enriches the logical chain of "tourism information source → potential tourists → demand driven → tourism information search → travel preference → destination selection → purchase decision → travel experience → real tourists → feelings after traveling → focus on feedback → tourism information source," but also broadens the application scenarios and application boundaries of travel preference theory and AISAS behavior model to a certain extent.

Prediction of Conformational Ensembles and Structural Effects of State-Switching Allosteric Mutants in the Protein Kinases Using Comparative Analysis of AlphaFold2 Adaptations with Sequence Masking and Shallow Subsampling

Despite the success of AlphaFold2 approaches in predicting single protein structures, these methods showed intrinsic limitations in predicting multiple functional conformations of allosteric proteins and have been challenged to accurately capture of the effects of single point mutations that induced significant structural changes. We systematically examined several implementations of AlphaFold2 methods to predict conformational ensembles for state-switching mutants of the ABL kinase. The results revealed that a combination of randomized alanine sequence masking with shallow multiple sequence alignment subsampling can significantly expand the conformational diversity of the predicted structural ensembles and capture shifts in populations of the active and inactive ABL states. Consistent with the NMR experiments, the predicted conformational ensembles for M309L/L320I and M309L/H415P ABL mutants that perturb the regulatory spine networks featured the increased population of the fully closed inactive state. On the other hand, the predicted conformational ensembles for the G269E/M309L/T334I and M309L/L320I/T334I triple ABL mutants that share activating T334I gate-keeper substitution are dominated by the active ABL form. The proposed adaptation of AlphaFold can reproduce the experimentally observed mutation-induced redistributions in the relative populations of the active and inactive ABL states and capture the effects of regulatory mutations on allosteric structural rearrangements of the kinase domain. The ensemble-based network analysis complemented AlphaFold predictions by revealing allosteric mediating centers that often directly correspond to state-switching mutational sites or reside in their immediate local structural proximity, which may explain the global effect of regulatory mutations on structural changes between the ABL states. This study suggested that attention-based learning of long-range dependencies between sequence positions in homologous folds and deciphering patterns of allosteric interactions may further augment the predictive abilities of AlphaFold methods for modeling of alternative protein sates, conformational ensembles and mutation-induced structural transformations.

Elucidating transport dynamics and regional division of PM2.5 and O3 in China using an advanced network model

Air pollution exhibits significant spatial spillover effects, complicating and challenging regional governance models. This study innovatively applied and optimized a statistics-based complex network method in atmospheric environmental field. The methodology was enhanced through improvements in edge weighting and threshold calculations, leading to the development of an advanced pollutant transport network model. This model integrates pollution, meteorological, and geographical data, thereby comprehensively revealing the dynamic characteristics of PM2.5 and O3 transport among various cities in China. Research findings indicated that, throughout the year, the O3 transport network surpassed the PM2.5 network in edge count, average degree, and average weighted degree, showcasing a higher network density, broader city connections, and greater transmission strength. Particularly during the warm period, these characteristics of the O3 network were more pronounced, showcasing significant transport potential. Furthermore, the model successfully identified key influential cities in different periods; it also provided detailed descriptions of the interprovincial spillover flux and pathways of PM2.5 and O3 across various time scales. It pinpointed major pollution spillover and receiving provinces, with primary spillover pathways concentrated in crucial areas such as the Beijing-Tianjin-Hebei (BTH) region and its surrounding areas, the Yangtze River Delta, and the Fen-Wei Plain. Building on this, the model divided the O3, PM2.5, and synergistic pollution transmission regions in China into 6, 7, and 8 zones, respectively, based on network weights and the Girvan Newman (GN) algorithm. Such division offers novel perspectives and strategies for regional joint prevention and control. The validity of the model was further corroborated by source analysis results from the WRF-CAMx model in the BTH area. Overall, this research provides valuable insights for local and regional atmospheric pollution control strategies. Additionally, it offers a robust analytical tool for research in the field of atmospheric pollution.

The volume and structural covariance network of thalamic nuclei in patients with Wilson's disease: an investigation of the association with neurological impairment

OBJECTIVE

This study aimed to examine the volumes of thalamic nuclei and the intrinsic thalamic network in patients with Wilson's disease (WDs), and to explore the correlation between these volumes and the severity of neurological symptoms.

METHODS

A total of 61 WDs and 33 healthy controls (HCs) were included in the study. The volumes of 25 bilateral thalamic nuclei were measured using structural imaging analysis with Freesurfer, and the intrinsic thalamic network was evaluated through structural covariance network (SCN) analysis.

RESULTS

The results indicated that multiple thalamic nuclei were smaller in WDs compared to HCs, including mediodorsal medial magnocellular (MDm), anterior ventral (AV), central median (CeM), centromedian (CM), lateral geniculate (LGN), limitans-suprageniculate (L-Sg), reuniens-medial ventral (MV), paracentral (Pc), parafascicular (Pf), paratenial (Pt), pulvinar anterior (PuA), pulvinar inferior (PuI), pulvinar medial (PuM), ventral anterior (VA), ventral anterior magnocellular (VAmc), ventral lateral anterior (VLa), ventral lateral posterior (VLp), ventromedial (VM), ventral posterolateral (VPL), and right middle dorsal intralaminar (MDI). The study also found a negative correlation between the UWDRS scores and the volume of the right MDm. The intrinsic thalamic network analysis showed abnormal topological properties in WDs, including increased mean local efficiency, modularity, normalized clustering coefficient, small-world index, and characteristic path length, and a corresponding decrease in mean node betweenness centrality. WDs with cerebral involvement had a lower modularity compared to HCs.

CONCLUSIONS

The findings suggest that the majority of thalamic nuclei in WDs exhibit significant volume reduction, and the atrophy of the right MDm is closely related to the severity of neurological symptoms. The intrinsic thalamic network in WDs demonstrated abnormal topological properties, indicating a close relationship with neurological impairment.

Disparity-driven heterogeneous nucleation in finite-size adaptive networks

Phase transitions are crucial in shaping the collective dynamics of a broad spectrum of natural systems across disciplines. Here, we report two distinct heterogeneous nucleation facilitating single step and multistep phase transitions to global synchronization in a finite-size adaptive network due to the trade off between time scale adaptation and coupling strength disparities. Specifically, small intracluster nucleations coalesce either at the population interface or within the populations resulting in the two distinct phase transitions depending on the degree of the disparities. We find that the coupling strength disparity largely controls the nature of phase transition in the phase diagram irrespective of the adaptation disparity. We provide a mesoscopic description for the cluster dynamics using the collective coordinates approach that brilliantly captures the multicluster dynamics among the populations leading to distinct phase transitions. Further, we also deduce the upper bound for the coupling strength for the existence of two intraclusters explicitly in terms of adaptation and coupling strength disparities. These insights may have implications across domains ranging from neurological disorders to segregation dynamics in social networks.

Mechanistic interactions as the origin of modularity in biological networks

Biological networks are often modular. Explanations for this peculiarity either assume an adaptive advantage of a modular design such as higher robustness, or attribute it to neutral factors such as constraints underlying network assembly. Interestingly, most insights on the origin of modularity stem from models in which interactions are either determined by highly simplistic mechanisms, or have no mechanistic basis at all. Yet, empirical knowledge suggests that biological interactions are often mediated by complex structural or behavioural traits. Here, we investigate the origins of modularity using a model in which interactions are determined by potentially complex traits. Specifically, we model system elements-such as the species in an ecosystem-as finite-state machines (FSMs), and determine their interactions by means of communication between the corresponding FSMs. Using this model, we show that modularity probably emerges for free. We further find that the more modular an interaction network is, the less complex are the traits that mediate the interactions. Altogether, our results suggest that the conditions for modularity to evolve may be much broader than previously thought.

A simple and effective convolutional operator for node classification without features by graph convolutional networks

Graph neural networks (GNNs), with their ability to incorporate node features into graph learning, have achieved impressive performance in many graph analysis tasks. However, current GNNs including the popular graph convolutional network (GCN) cannot obtain competitive results on the graphs without node features. In this work, we first introduce path-driven neighborhoods, and then define an extensional adjacency matrix as a convolutional operator. Second, we propose an approach named exopGCN which integrates the simple and effective convolutional operator into GCN to classify the nodes in the graphs without features. Experiments on six real-world graphs without node features indicate that exopGCN achieves better performance than other GNNs on node classification. Furthermore, by adding the simple convolutional operator into 13 GNNs, the accuracy of these methods are improved remarkably, which means that our research can offer a general skill to improve accuracy of GNNs. More importantly, we study the relationship between node classification by GCN without node features and community detection. Extensive experiments including six real-world graphs and nine synthetic graphs demonstrate that the positive relationship between them can provide a new direction on exploring the theories of GCNs.

An Entropic Analysis of Social Demonstrations

Social media has dramatically influenced how individuals and groups express their demands, concerns, and aspirations during social demonstrations. The study of X or Twitter hashtags during those events has revealed the presence of some temporal points characterised by high correlation among their participants. It has also been reported that the connectivity presents a modular-to-nested transition at the point of maximum correlation. The present study aims to determine whether it is possible to characterise this transition using entropic-based tools. Our results show that entropic analysis can effectively find the transition point to the nested structure, allowing researchers to know that the transition occurs without the need for a network representation. The entropic analysis also shows that the modular-to-nested transition is characterised not by the diversity in the number of hashtags users post but by how many hashtags they share.

Revisiting the Modularity-Disease transmission Link: Uncovering the importance of intra-modular structure

Studies have shown that the internal structure of modules is hardly important for the spread of epidemics. However, most of these studies have assumed that intra-module connectivity and inter-module connectivity do not affect each other. In reality, changes in the internal structure of modules may affect inter-module links and thus change the modularity of the entire network. Therefore, we have developed a theoretical network model with adjustable modularity to investigate the impact of this situation on disease transmission. Our findings indicate that the intra-module structure plays a crucial role in disease outbreaks. Changes in intra-module structure lead to significant numerical changes in peak prevalence and duration of disease. This implies that the potential impact of changes in exposure patterns within modules should also be considered when investigating the exact impact of modular social networks on disease burden.

Explainable prediction of node labels in multilayer networks: a case study of turnover prediction in organizations

In real-world classification problems, it is important to build accurate prediction models and provide information that can improve decision-making. Decision-support tools are often based on network models, and this article uses information encoded by social networks to solve the problem of employer turnover. However, understanding the factors behind black-box prediction models can be challenging. Our question was about the predictability of employee turnover, given information from the multilayer network that describes collaborations and perceptions that assess the performance of organizations that indicate the success of cooperation. Our goal was to develop an accurate prediction procedure, preserve the interpretability of the classification, and capture the wide variety of specific reasons that explain positive cases. After a feature engineering, we identified variables with the best predictive power using decision trees and ranked them based on their added value considering their frequent co-occurrence. We applied the Random Forest using the SMOTE balancing technique for prediction. We calculated the SHAP values to identify the variables that contribute the most to individual predictions. As a last step, we clustered the sample based on SHAP values to fine-tune the explanations for quitting due to different background factors.

Proof of biased behavior of Normalized Mutual Information

The Normalized Mutual Information (NMI) metric is widely utilized in the evaluation of clustering and community detection algorithms. This study explores the performance of NMI, specifically examining its performance in relation to the quantity of communities, and uncovers a significant drawback associated with the metric's behavior as the number of communities increases. Our findings reveal a pronounced bias in the NMI as the number of communities escalates. While previous studies have noted this biased behavior, they have not provided a formal proof and have not addressed the causation of this problem, leaving a gap in the existing literature. In this study, we fill this gap by employing a mathematical approach to formally demonstrate why NMI exhibits biased behavior, thereby establishing its unsuitability as a metric for evaluating clustering and community detection algorithms. Crucially, our study exposes the vulnerability of entropy-based metrics that employ logarithmic functions to similar bias.

Community detection in directed weighted networks using Voronoi partitioning

Community detection is a ubiquitous problem in applied network analysis, however efficient techniques do not yet exist for all types of network data. Directed and weighted networks are an example, where the different information encoded by link weights and the possibly high graph density can cause difficulties for some approaches. Here we present an algorithm based on Voronoi partitioning generalized to deal with directed weighted networks. As an added benefit, this method can directly employ edge weights that represent lengths, in contrast to algorithms that operate with connection strengths, requiring ad-hoc transformations of length data. We demonstrate the method on inter-areal brain connectivity, air transportation networks, and several social networks. We compare the performance with several other well-known algorithms, applying them on a set of randomly generated benchmark networks. The algorithm can handle dense graphs where weights are the main factor determining communities. The hierarchical structure of networks can also be detected, as shown for the brain. Its time efficiency is comparable or even outperforms some of the state-of-the-art algorithms, the part with the highest time-complexity being Dijkstra's shortest paths algorithm ( O ( | E | + | V | log | V | ) ).

Research on a Critical Link Discovery Method for Network Security Situational Awareness

Network security situational awareness (NSSA) aims to capture, understand, and display security elements in large-scale network environments in order to predict security trends in the relevant network environment. With the internet's increasingly large scale, increasingly complex structure, and gradual diversification of components, the traditional single-layer network topology model can no longer meet the needs of network security analysis. Therefore, we conduct research based on a multi-layer network model for network security situational awareness, which is characterized by the three-layer network structure of a physical device network, a business application network, and a user role network. Its network characteristics require new assessment methods, so we propose a multi-layer network link importance assessment metric: the multi-layer-dependent link entropy (MDLE). On the one hand, the MDLE comprehensively evaluates the connectivity importance of links by fitting the link-local betweenness centrality and mapping entropy. On the other hand, it relies on the link-dependent mechanism to better aggregate the link importance contributions in each network layer. The experimental results show that the MDLE has better ordering monotonicity during critical link discovery and a higher destruction efficacy in destruction simulations compared to classical link importance metrics, thus better adapting to the critical link discovery requirements of a multi-layer network topology.

LDA2Net Digging under the surface of COVID-19 scientific literature topics via a network-based approach

During the COVID-19 pandemic, the scientific literature related to SARS-COV-2 has been growing dramatically. These literary items encompass a varied set of topics, ranging from vaccination to protective equipment efficacy as well as lockdown policy evaluations. As a result, the development of automatic methods that allow an in-depth exploration of this growing literature has become a relevant issue, both to identify the topical trends of COVID-related research and to zoom-in on its sub-themes. This work proposes a novel methodology, called LDA2Net, which combines topic modelling and network analysis, to investigate topics under their surface. More specifically, LDA2Net exploits the frequencies of consecutive words pairs (i.e. bigram) to build those network structures underlying the hidden topics extracted from large volumes of text by Latent Dirichlet Allocation (LDA). Results are promising and suggest that the topic model efficacy is magnified by the network-based representation. In particular, such enrichment is noticeable when it comes to displaying and exploring the topics at different levels of granularity.

Clustering on heterogeneous IoT information network based on meta path

As the Internet and Internet of Things (IoT) continue to develop, Heterogeneous Information Networks (HIN) have formed complex interaction relationships among data objects. These relationships are represented by various types of edges (meta-paths) that contain rich semantic information. In the context of IoT data applications, the widespread adoption of Trigger-Action Patterns makes the management and analysis of heterogeneous data particularly important. This study proposes a meta-path-based clustering method for heterogeneous IoT data called I-RankClus, which aims to improve the modeling and analysis efficiency of IoT data. By combining ranking with clustering algorithms, the PageRank algorithm was used to calculate the intraclass influence of objects in the network. The HITS algorithm then transfers the influence to the core objects, thereby optimizing the classification of objects during the clustering process. The I-RankClus algorithm does not process each meta-path individually, but instead integrates multiple meta-paths to enhance the interpretability and clustering performance of the model. The experimental results show that the I-RankClus algorithm can process complex IoT datasets more effectively than traditional clustering methods and provide more accurate clustering outcomes. Furthermore, through a detailed analysis of meta-paths, this study explored the influence and importance of different meta-paths, thereby validating the effectiveness of the algorithm. Overall, the research presented in this paper not only improves the application effects of HINs in IoT data analysis but also provides valuable methods and insights for future network data processing.

An allosteric mechanism for potent inhibition of SARS-CoV-2 main proteinase

The main proteinase (Mpro) of SARS-CoV-2 plays a critical role in cleaving viral polyproteins into functional proteins required for viral replication and assembly, making it a prime drug target for COVID-19. It is well known that noncompetitive inhibition offers potential therapeutic options for treating COVID-19, which can effectively reduce the likelihood of cross-reactivity with other proteins and increase the selectivity of the drug. Therefore, the discovery of allosteric sites of Mpro has both scientific and practical significance. In this study, we explored the binding characteristics and inhibiting process of Mpro activity by two recently reported allosteric inhibitors, pelitinib and AT7519 which were obtained by the X-ray screening experiments, to probe the allosteric mechanism via molecular dynamic (MD) simulations. We found that pelitinib and AT7519 can stably bind to Mpro far from the active site. The binding affinity is estimated to be -24.37 ± 4.14 and - 26.96 ± 4.05 kcal/mol for pelitinib and AT7519, respectively, which is considerably stable compared with orthosteric drugs. Furthermore, the strong binding caused clear changes in the catalytic site of Mpro, thus decreasing the substrate accessibility. The community network analysis also validated that pelitinib and AT7519 strengthened intra- and inter-domain communication of Mpro dimer, resulting in a rigid Mpro, which could negatively impact substrate binding. In summary, our findings provide the detailed working mechanism for the two experimentally observed allosteric sites of Mpro. These allosteric sites greatly enhance the 'druggability' of Mpro and represent attractive targets for the development of new Mpro inhibitors.

Nonlinear social evolution and the emergence of collective action

Organisms from microbes to humans engage in a variety of social behaviors, which affect fitness in complex, often nonlinear ways. The question of how these behaviors evolve has consequences ranging from antibiotic resistance to human origins. However, evolution with nonlinear social interactions is challenging to model mathematically, especially in combination with spatial, group, and/or kin assortment. We derive a mathematical condition for natural selection with synergistic interactions among any number of individuals. This result applies to populations with arbitrary (but fixed) spatial or network structure, group subdivision, and/or mating patterns. In this condition, nonlinear fitness effects are ascribed to collectives, and weighted by a new measure of collective relatedness. For weak selection, this condition can be systematically evaluated by computing branch lengths of ancestral trees. We apply this condition to pairwise games between diploid relatives, and to dilemmas of collective help or harm among siblings and on spatial networks. Our work provides a rigorous basis for extending the notion of "actor", in the study of social evolution, from individuals to collectives.

A generalized simplicial model and its application

Higher-order structures, consisting of more than two individuals, provide a new perspective to reveal the missed non-trivial characteristics under pairwise networks. Prior works have researched various higher-order networks, but research for evaluating the effects of higher-order structures on network functions is still scarce. In this paper, we propose a framework to quantify the effects of higher-order structures (e.g., 2-simplex) and vital functions of complex networks by comparing the original network with its simplicial model. We provide a simplicial model that can regulate the quantity of 2-simplices and simultaneously fix the degree sequence. Although the algorithm is proposed to control the quantity of 2-simplices, results indicate it can also indirectly control simplexes more than 2-order. Experiments on spreading dynamics, pinning control, network robustness, and community detection have shown that regulating the quantity of 2-simplices changes network performance significantly. In conclusion, the proposed framework is a general and effective tool for linking higher-order structures with network functions. It can be regarded as a reference object in other applications and can deepen our understanding of the correlation between micro-level network structures and global network functions.

Coarse graining and criticality in the human connectome

In the face of the stupefying complexity of the human brain, network analysis is a most useful tool that allows one to greatly simplify the problem, typically by approximating the billions of neurons making up the brain by means of a coarse-grained picture with a practicable number of nodes. But even such relatively small and coarse networks, such as the human connectome with its 100-1000 nodes, may present challenges for some computationally demanding analyses that are incapable of handling networks with more than a handful of nodes. With such applications in mind, we set out to study the extent to which dynamical behavior and critical phenomena in the brain may be preserved following a severe coarse-graining procedure. Thus we proceeded to further coarse grain the human connectome by taking a modularity-based approach, the goal being to produce a network of a relatively small number of modules. After finding that the qualitative dynamical behavior of the coarse-grained networks reflected that of the original networks, albeit to a less pronounced extent, we then formulated a hypothesis based on the coarse-grained networks in the context of criticality in the Wilson-Cowan and Ising models, and we verified the hypothesis, which connected a transition value of the former with the critical temperature of the latter, using the original networks. This preservation of dynamical and critical behavior following a severe coarse-graining procedure, in principle, allows for the drawing of similar qualitative conclusions by analyzing much smaller networks, which opens the door for studying the human connectome in contexts typically regarded as computationally intractable, such as Integrated Information Theory and quantum models of the human brain.

Network analysis reveals context-dependent structural complexity of social calls in serrate-legged small treefrogs

Vocal communication plays an important role in survival, reproduction, and animal social association. Birds and mammals produce complex vocal sequence to convey context-dependent information. Vocalizations are conspicuous features of the behavior of most anuran species (frogs and toads), and males usually alter their calling strategies according to ecological context to improve the attractiveness/competitiveness. However, very few studies have focused on the variation of vocal sequence in anurans. In the present study, we used both conventional method and network analysis to investigate the context-dependent vocal repertoire, vocal sequence, and call network structure in serrate-legged small treefrogs Kurixalus odontotarsus. We found that male K. odontotarsus modified their vocal sequence by switching to different call types and increasing repertoire size in the presence of a competitive rival. Specifically, compared with before and after the playback of advertisement calls, males emitted fewer advertisement calls, but more aggressive calls, encounter calls, and compound calls during the playback period. Network analysis revealed that the mean degree, mean closeness, and mean betweenness of the call networks significantly decreased during the playback period, which resulted in lower connectivity. In addition, the increased proportion of one-way motifs and average path length also indicated that the connectivity of the call network decreased in competitive context. However, the vocal sequence of K. odontotarsus did not display a clear small-world network structure, regardless of context. Our study presents a paradigm to apply network analysis to vocal sequence in anurans and has important implications for understanding the evolution and function of sequence patterns.

Community analysis of large-scale molecular dynamics simulations elucidated dynamics-driven allostery in tyrosine kinase 2

TYK2 is a nonreceptor tyrosine kinase, member of the Janus kinases (JAK), with a central role in several diseases, including cancer. The JAKs' catalytic domains (KD) are highly conserved, yet the isolated TYK2-KD exhibits unique specificities. In a previous work, using molecular dynamics (MD) simulations of a catalytically impaired TYK2-KD variant (P1104A) we found that this amino acid change of its JAK-characteristic insert (αFG), acts at the dynamics level. Given that structural dynamics is key to the allosteric activation of protein kinases, in this study we applied a long-scale MD simulation and investigated an active TYK2-KD form in the presence of adenosine 5'-triphosphate and one magnesium ion that represents a dynamic and crucial step of the catalytic cycle, in other protein kinases. Community analysis of the MD trajectory shed light, for the first time, on the dynamic profile and dynamics-driven allosteric communications within the TYK2-KD during activation and revealed that αFG and amino acids P1104, P1105, and I1112 in particular, hold a pivotal role and act synergistically with a dynamically coupled communication network of amino acids serving intra-KD signaling for allosteric regulation of TYK2 activity. Corroborating our findings, most of the identified amino acids are associated with cancer-related missense/splice-site mutations of the Tyk2 gene. We propose that the conformational dynamics at this step of the catalytic cycle, coordinated by αFG, underlie TYK2-unique substrate recognition and account for its distinct specificity. In total, this work adds to knowledge towards an in-depth understanding of TYK2 activation and may be valuable towards a rational design of allosteric TYK2-specific inhibitors.

CoVar: A generalizable machine learning approach to identify the coordinated regulators driving variational gene expression

Network inference is used to model transcriptional, signaling, and metabolic interactions among genes, proteins, and metabolites that identify biological pathways influencing disease pathogenesis. Advances in machine learning (ML)-based inference models exhibit the predictive capabilities of capturing latent patterns in genomic data. Such models are emerging as an alternative to the statistical models identifying causative factors driving complex diseases. We present CoVar, an ML-based framework that builds upon the properties of existing inference models, to find the central genes driving perturbed gene expression across biological states. Unlike differentially expressed genes (DEGs) that capture changes in individual gene expression across conditions, CoVar focuses on identifying variational genes that undergo changes in their expression network interaction profiles, providing insights into changes in the regulatory dynamics, such as in disease pathogenesis. Subsequently, it finds core genes from among the nearest neighbors of these variational genes, which are central to the variational activity and influence the coordinated regulatory processes underlying the observed changes in gene expression. Through the analysis of simulated as well as yeast expression data perturbed by the deletion of the mitochondrial genome, we show that CoVar captures the intrinsic variationality and modularity in the expression data, identifying key driver genes not found through existing differential analysis methodologies.

A Comprehensive Survey on Community Detection With Deep Learning

Detecting a community in a network is a matter of discerning the distinct features and connections of a group of members that are different from those in other communities. The ability to do this is of great significance in network analysis. However, beyond the classic spectral clustering and statistical inference methods, there have been significant developments with deep learning techniques for community detection in recent years-particularly when it comes to handling high-dimensional network data. Hence, a comprehensive review of the latest progress in community detection through deep learning is timely. To frame the survey, we have devised a new taxonomy covering different state-of-the-art methods, including deep learning models based on deep neural networks (DNNs), deep nonnegative matrix factorization, and deep sparse filtering. The main category, i.e., DNNs, is further divided into convolutional networks, graph attention networks, generative adversarial networks, and autoencoders. The popular benchmark datasets, evaluation metrics, and open-source implementations to address experimentation settings are also summarized. This is followed by a discussion on the practical applications of community detection in various domains. The survey concludes with suggestions of challenging topics that would make for fruitful future research directions in this fast-growing deep learning field.

Knowledge Development Trajectories of Intelligent Video Surveillance Domain: An Academic Study Based on Citation and Main Path Analysis

Smart city is an area where the Internet of things is used effectively with sensors. The data used by smart city can be collected through the cameras, sensors etc. Intelligent video surveillance (IVS) systems integrate multiple networked cameras for automatic surveillance purposes. Such systems can analyze and monitor video data and perform automatic functions required by users. This study performed main path analysis (MPA) to explore the development trends of IVS research. First, relevant articles were retrieved from the Web of Science database. Next, MPA was performed to analyze development trends in relevant research, and g-index and h-index values were analyzed to identify influential journals. Cluster analysis was then performed to group similar articles, and Wordle was used to display the key words of each group in word clouds. These key words served as the basis for naming their corresponding groups. Data mining and statistical analysis yielded six major IVS research topics, namely video cameras, background modeling, closed-circuit television, multiple cameras, person reidentification, and privacy, security, and protection. These topics can boost the future innovation and development of IVS technology and contribute to smart transportation, smart city, and other applications. According to the study results, predictions were made regarding developments in IVS research to provide recommendations for future research.

Community cohesion looseness in gene networks reveals individualized drug targets and resistance

Community cohesion plays a critical role in the determination of an individual's health in social science. Intriguingly, a community structure of gene networks indicates that the concept of community cohesion could be applied between the genes as well to overcome the limitations of single gene-based biomarkers for precision oncology. Here, we develop community cohesion scores which precisely quantify the community ability to retain the interactions between the genes and their cellular functions in each individualized gene network. Using breast cancer as a proof-of-concept study, we measure the community cohesion score profiles of 950 case samples and predict the individualized therapeutic targets in 2-fold. First, we prioritize them by finding druggable genes present in the community with the most and relatively decreased scores in each individual. Then, we pinpoint more individualized therapeutic targets by discovering the genes which greatly contribute to the community cohesion looseness in each individualized gene network. Compared with the previous approaches, the community cohesion scores show at least four times higher performance in predicting effective individualized chemotherapy targets based on drug sensitivity data. Furthermore, the community cohesion scores successfully discover the known breast cancer subtypes and we suggest new targeted therapy targets for triple negative breast cancer (e.g. KIT and GABRP). Lastly, we demonstrate that the community cohesion scores can predict tamoxifen responses in ER+ breast cancer and suggest potential combination therapies (e.g. NAMPT and RXRA inhibitors) to reduce endocrine therapy resistance based on individualized characteristics. Our method opens new perspectives for the biomarker development in precision oncology.

Research on regional ozone prevention and control strategies in eastern China based on pollutant transport network and FNR

O3 pollution in China has worsened sharply in recent years, and O3 formation sensitivity (OFS) in many regions have gradually changed, with eastern China as the most typical region. This study constructed the transport networks of O3 and NO2 in different seasons from 2017 to 2020. The transport trends and the clustering formation patterns were summarized by analyzing the topological characteristics of the transport networks, and the patterns of OFS changes were diagnosed by analyzing the satellite remote sensing data. Based on that, the main clusters that each province or city belongs to in different pollutant transport networks were summarized and proposals for the inter-regional joint prevention and control were put forward. As the results showed, O3 transport activity was most active in spring and summer and least active in winter, while NO2 transport activity was most active in autumn and winter and least active in summer. OFS in summer mainly consisted of transitional regimes and NOx-limited regimes, while that in other seasons was mainly VOC-limited regimes. Notably, there was a significant upward trend in the proportion of transitional regimes and NOx-limited regimes in spring, autumn, and winter. For regions showing NOx-limited regime, areas with higher out-weighted degrees in the NO2 transport network should focus on controlling local NOx emissions, such as central regions in summer. For regions showing VOC-limited regime, areas with higher out-weighted degrees in the O3 transport network should focus on controlling local VOCs emissions, such as central and south-central regions in summer. For regions that belong to the same cluster and present the same OFS in each specific season, regional cooperative emission reduction strategies should be established to block important transmission paths and weaken regional pollution consistency.

Developing a novel algorithm for comparing cluster patterns in networks on journal articles during and after COVID-19: Bibliometric analysis

BACKGROUND

Cluster analysis is vital in bibliometrics for deciphering large sets of academic data. However, no prior research has employed a cluster-pattern algorithm to assess the similarities and differences between 2 clusters in networks. The study goals are 2-fold: to create a cluster-pattern comparison algorithm tailored for bibliometric analysis and to apply this algorithm in presenting clusters of countries, institutes, departments, authors (CIDA), and keywords on journal articles during and after COVID-19.

METHODS

We analyzed 9499 and 5943 articles from the Journal of Medicine (Baltimore) during and after COVID-19 in 2020 to 2021 and 2022 to 2023, sourced from the Web of Science (WoS) Core Collection. Follower-leading clustering algorithm (FLCA) was compared to other 8 counterparts in cluster validation and effectiveness and a cluster-pattern-comparison algorithm (CPCA) was developed using the similarity coefficient, collaborative maps, and thematic maps to evaluate CIDA cluster patterns. The similarity coefficients were categorized as identical, similar, dissimilar, or different for values above 0.7, between 0.5 and 0.7, between 0.3 and 0.5, and below 0.3, respectively.

RESULTS

Both stages displayed similar trends in annual publications and average citations, although these trends are decreasing. The peak publication year was 2020. Similarity coefficients of cluster patterns in these 2 stages for CIDA entities and keywords were 0.73, 0.35, 0.80, 0.02, and 0.83, respectively, suggesting the existence of identical patterns (>0.70) in countries, departments, and keywords plus, but dissimilar (<0.5) and different patterns (<0.3) found in institutes and 1st and corresponding authors, during and after COVID-19.

CONCLUSIONS

This research effectively created and utilized CPCA to analyze cluster patterns in bibliometrics. It underscores notable identical patterns in country-/department-/keyword based clusters, but dissimilar and different in institute-/author- based clusters, between these 2 stages during and after COVID-19, offering a framework for future bibliographic studies to compare cluster patterns beyond just the CIDA entities, as demonstrated in this study.

Microbial community organization designates distinct pulmonary exacerbation types and predicts treatment outcome in cystic fibrosis

Polymicrobial infection of the airways is a hallmark of obstructive lung diseases such as cystic fibrosis (CF), non-CF bronchiectasis, and chronic obstructive pulmonary disease. Pulmonary exacerbations (PEx) in these conditions are associated with accelerated lung function decline and higher mortality rates. An understanding of the microbial underpinnings of PEx is challenged by high inter-patient variability in airway microbial community profiles. We analyzed bacterial communities in 880 CF sputum samples and developed microbiome descriptors to model community reorganization prior to and during 18 PEx. We identified two microbial dysbiosis regimes with opposing ecology and dynamics. Pathogen-governed PEx showed hierarchical community reorganization and reduced diversity, whereas anaerobic bloom PEx displayed stochasticity and increased diversity. A simulation of antimicrobial treatment predicted better efficacy for hierarchically organized communities. This link between PEx type, microbiome organization, and treatment success advances the development of personalized clinical management in CF and, potentially, other obstructive lung diseases.

In Silico Investigation of Parkin-Activating Mutations Using Simulations and Network Modeling

Complete loss-of-function mutations in the PRKN gene are a major cause of early-onset Parkinson's disease (PD). PRKN encodes the Parkin protein, an E3 ubiquitin ligase that works in conjunction with the ubiquitin kinase PINK1 in a distinct quality control pathway to tag damaged mitochondria for autophagic clearance, i.e., mitophagy. According to previous structural investigations, Parkin protein is typically kept in an inactive conformation via several intramolecular, auto-inhibitory interactions. Here, we performed molecular dynamics simulations (MDS) to provide insights into conformational changes occurring during the de-repression of Parkin and the gain of catalytic activity. We analyzed four different Parkin-activating mutations that are predicted to disrupt certain aspects of its auto-inhibition. All four variants showed greater conformational motions compared to wild-type protein, as well as differences in distances between domain interfaces and solvent-accessible surface area, which are thought to play critical roles as Parkin gains catalytic activity. Our findings reveal that the studied variants exert a notable influence on Parkin activation as they alter the opening of its closed inactive structure, a finding that is supported by recent structure- and cell-based studies. These findings not only helped further characterize the hyperactive variants but overall improved our understanding of Parkin's catalytic activity and nominated targets within Parkin's structure for potential therapeutic designs.

Ground air pollutants explanation based on multiple visibility graph of complex network by temporal community division

In air pollution studies, the correlation analysis of environmental variables has usually been challenged by parametric diversity. Such variable variations are not only from the extrinsic meteorological conditions and industrial activities but also from the interactive influences between the multiple parameters. A promising solution has been motivated by the recent development of visibility graph (VG) on multi-variable data analysis, especially for the characterization of pollutants' correlation in the temporal domain, the multiple visibility graph (MVG) for nonlinear multivariate time series analysis has been verified effectively in different realistic scenarios. To comprehensively study the correlation between pollutant data and season, in this work, we propose a multi-layer complex network with a community division strategy based on the joint analysis of the atmospheric pollutants. Compared to the single-layer-based complex networks, our proposed method can integrate multiple different atmospheric pollutants for analysis, and combine them with multivariate time series data to obtain higher temporary community division for ground air pollutants interpretation. Substantial experiments have shown that this method effectively utilizes air pollution data from multiple representative indicators. By mining community information in the data, it successfully achieves reasonable and strong interpretive analysis of air pollution data.

Comparing community detection algorithms in psychometric networks: A Monte Carlo simulation

Identifying the correct number of factors in multivariate data is fundamental to psychological measurement. Factor analysis has a long tradition in the field, but it has been challenged recently by exploratory graph analysis (EGA), an approach based on network psychometrics. EGA first estimates a network and then applies the Walktrap community detection algorithm. Simulation studies have demonstrated that EGA has comparable or better accuracy for recovering the same number of communities as there are factors in the simulated data than factor analytic methods. Despite EGA's effectiveness, there has yet to be an investigation into whether other sparsity induction methods or community detection algorithms could achieve equivalent or better performance. Furthermore, unidimensional structures are fundamental to psychological measurement yet they have been sparsely studied in simulations using community detection algorithms. In the present study, we performed a Monte Carlo simulation using the zero-order correlation matrix, GLASSO, and two variants of a non-regularized partial correlation sparsity induction methods with several community detection algorithms. We examined the performance of these method-algorithm combinations in both continuous and polytomous data across a variety of conditions. The results indicate that the Fast-greedy, Louvain, and Walktrap algorithms paired with the GLASSO method were consistently among the most accurate and least-biased overall.

Improving the Walktrap Algorithm Using K-Means Clustering

The walktrap algorithm is one of the most popular community-detection methods in psychological research. Several simulation studies have shown that it is often effective at determining the correct number of communities and assigning items to their proper community. Nevertheless, it is important to recognize that the walktrap algorithm relies on hierarchical clustering because it was originally developed for networks much larger than those encountered in psychological research. In this paper, we present and demonstrate a computational alternative to the hierarchical algorithm that is conceptually easier to understand. More importantly, we show that better solutions to the sum-of-squares optimization problem that is heuristically tackled by hierarchical clustering in the walktrap algorithm can often be obtained using exact or approximate methods for K-means clustering. Three simulation studies and analyses of empirical networks were completed to assess the impact of better sum-of-squares solutions.