Dynamic Community Structure Research Articles

Metabolic cross-feeding interactions modulate the dynamic community structure in microbial fuel cell under variable organic loading wastewaters.

The efficiency of microbial fuel cells (MFCs) in industrial wastewater treatment is profoundly influenced by the microbial community, which can be disrupted by variable industrial operations. Although microbial guilds linked to MFC performance under specific conditions have been identified, comprehensive knowledge of the convergent community structure and pathways of adaptation is lacking. Here, we developed a microbe-microbe interaction genome-scale metabolic model (mmGEM) based on metabolic cross-feeding to study the adaptation of microbial communities in MFCs treating sulfide-containing wastewater from a canned-pineapple factory. The metabolic model encompassed three major microbial guilds: sulfate-reducing bacteria (SRB), methanogens (MET), and sulfide-oxidizing bacteria (SOB). Our findings revealed a shift from an SOB-dominant to MET-dominant community as organic loading rates (OLRs) increased, along with a decline in MFC performance. The mmGEM accurately predicted microbial relative abundance at low OLRs (L-OLRs) and adaptation to high OLRs (H-OLRs). The simulations revealed constraints on SOB growth under H-OLRs due to reduced sulfate-sulfide (S) cycling and acetate cross-feeding with SRB. More cross-fed metabolites from SRB were diverted to MET, facilitating their competitive dominance. Assessing cross-feeding dynamics under varying OLRs enabled the execution of practical scenario-based simulations to explore the potential impact of elevated acidity levels on SOB growth and MFC performance. This work highlights the role of metabolic cross-feeding in shaping microbial community structure in response to high OLRs. The insights gained will inform the development of effective strategies for implementing MFC technology in real-world industrial environments.

Withdrawn: The altered dynamic community structure for adaptive adjustment in stroke patients with multidomain cognitive impairments: A multilayer network analysis

BackgroundInternal capsule strokes can lead to multidomain cognitive impairments, and while the brain showcases adaptability post-stroke, the interplay between this adaptability and multidomain cognitive function remains elusive. In addition, although most studies indicate that adaptive changes after stroke manifest as brain functional network abnormalities, the changes in dynamic topological features, especially dynamic community structure, required for adaptive adjustment in stroke patients are not yet clear. MethodsResting-state fMRI data were acquired from patients with infarct in left-sided (n = 46, CI_L) and right-sided (n = 45, CI_R) internal capsule and 81 healthy controls (HC). Dynamic network matrices were generated using a sliding-window approach. These matrices were partitioned into communities utilizing a multilayer community detection algorithm. The network switching rate were employed as a key metric to characterize the differences of dynamic community structure between stroke and HC groups, and the correlations between these significant intergroup differences and cognitive performance scores was calculated. ResultsCompared to HC, CI_L patients exhibited an increase in network switching rates across all networks in the whole brain. However, these increases were largely unbeneficial for cognitive recovery; CI_R group show a more limited range of increased network switching rates and a more limited negative correlations with cognitive performance. ConclusionsThis study elucidates that following a stroke, the brain continuously adapts its dynamic community structure in an effort to address multidomain cognitive impairments, but this adaptation is often ineffective, unorganized, and disordered. Moreover, these findings elucidate the lateralized effects of stroke lesions on dynamic reorganization.

Brain connectivity patterns derived from aging-related alterations in dynamic brain functional networks and their potential as features for brain age classification.

Objective.Recent studies have demonstrated that the analysis of brain functional networks (BFNs) is a powerful tool for exploring brain aging and age-related neurodegenerative diseases. However, investigating the mechanism of brain aging associated with dynamic BFN is still limited. The purpose of this study is to develop a novel scheme to explore brain aging patterns by constructing dynamic BFN using resting-state functional magnetic resonance imaging data.Approach.A dynamic sliding-windowed non-negative block-diagonal representation (dNBDR) method is proposed for constructing dynamic BFN, based on which a collection of dynamic BFN measures are suggested for examining age-related differences at the group level and used as features for brain age classification at the individual level.Results.The experimental results reveal that the dNBDR method is superior to the sliding time window with Pearson correlation method in terms of dynamic network structure quality. Additionally, significant alterations in dynamic BFN structures exist across the human lifespan. Specifically, average node flexibility and integration coefficient increase with age, while the recruitment coefficient shows a decreased trend. The proposed feature extraction scheme based on dynamic BFN achieved the highest accuracy of 78.7% in classifying three brain age groups.Significance. These findings suggest that dynamic BFN measures, dynamic community structure metrics in particular, play an important role in quantitatively assessing brain aging.

A novel robust memetic algorithm for dynamic community structures detection in complex networks

A novel robust memetic algorithm for dynamic community structures detection in complex networks

Multilayer brain network modeling and dynamic analysis of juvenile myoclonic epilepsy.

Objective: It is indisputable that the functional connectivity of the brain network in juvenile myoclonic epilepsy (JME) patients is abnormal. As a mathematical extension of the traditional network model, the multilayer network can fully capture the fluctuations of brain imaging data with time, and capture subtle abnormal dynamic changes. This study assumed that the dynamic structure of JME patients is abnormal and used the multilayer network framework to analyze the change brain community structure in JME patients from the perspective of dynamic analysis. Methods: First, functional magnetic resonance imaging (fMRI) data were obtained from 35 JME patients and 34 healthy control subjects. In addition, the communities of the two groups were explored with the help of a multilayer network model and a multilayer community detection algorithm. Finally, differences were described by metrics that are specific to the multilayer network. Results: Compared with healthy controls, JME patients had a significantly lower modularity degree of the brain network. Furthermore, from the level of the functional network, the integration of the default mode network (DMN) and visual network (VN) in JME patients showed a significantly higher trend, and the flexibility of the attention network (AN) also increased significantly. At the node level, the integration of seven nodes of the DMN was significantly increased, the integration of five nodes of the VN was significantly increased, and the flexibility of three nodes of the AN was significantly increased. Moreover, through division of the core-peripheral system, we found that the left insula and left cuneus were core regions specific to the JME group, while most of the peripheral systems specific to the JME group were distributed in the prefrontal cortex and hippocampus. Finally, we found that the flexibility of the opercular part of the inferior frontal gyrus was significantly correlated with the severity of JME symptoms. Conclusion: Our findings indicate that the dynamic community structure of JME patients is indeed abnormal. These results provide a new perspective for the study of dynamic changes in communities in JME patients.

Dynamic community detection considering daily rhythms of human mobility

Detecting human mobility community structures is an important means to reveal urban spatial structures. Due to the circadian clock, human mobility has a daily rhythm, representing dynamic interactions among urban functional areas at different times of the day. However, few community detection methods have been developed by explicitly considering such daily rhythms of human mobility. To fill the gap, this study proposes a novel method for detecting dynamic community structures derived from the daily rhythms of human mobility. The proposed method employs a consensus clustering technique to detect consensus structures of dynamic communities throughout the day. The time series characteristics of the detected dynamic communities are further introduced. The hierarchical clustering technique is used to uncover evolving patterns of detected dynamic communities. A case study was carried out using taxi trajectories in New York City to verify the proposed method. Results indicated that the proposed method could well detect a robust structure of dynamic communities even when human mobility patterns changed dramatically at certain time periods. Results also suggested that the proposed method could identify underlying evolving patterns of detected dynamic communities.

Dynamic phytoplankton community structure in a subtropical reservoir during an extended drought, Central Texas, USA

Dynamic phytoplankton community structure in a subtropical reservoir during an extended drought, Central Texas, USA

A self-adaptive gradient-based particle swarm optimization algorithm with dynamic population topology

The aggregation of individuals facilitates local information exchange, and the migration of individuals from one population to another leads to a dynamic community structure. In addition, the negative feedback regulation mechanism of organisms helps them in good living conditions. Based on the above knowledge, a novel particle swarm optimization algorithm with a self-organizing topology structure and self-adaptive adjustable parameters is proposed (KGPSO). During the optimization process, the K-Means clustering method periodically divides the particle swarm into multiple distance-based sub-swarms, and the optimal number of sub-swarms is determined by maximizing the Calinski-Harabasz index. This strategy helps maintain the population diversity and gives particles the ability to perceive the surrounding environment. The parameters used to update the particle velocity are adjusted based on the gradient descent of its fitness error, ensuring a dynamic balance between exploration and exploitation. The hyperparameters of KGPSO are tuned by Bayesian optimization method to improve the algorithm performance further. Two benchmark suites are used to evaluate the performance of KGPSO. Both ranking results and Wilcoxon signed-rank tests show that KGPSO performs best among the PSO algorithms tested. Moreover, the excellent optimization capability of KGPSO are proven in the process of X-ray CT image enhancement, making it possible to analyze the structure and motion of heterogeneous granular materials efficiently and robustly. In conclusion, the proposed KGPSO can provide a stable and powerful support for the frontier experimental research of granular materials and expand the research scope.

Detecting Dynamic Communities in Vehicle Movements Using Ant Colony Optimization

Detecting dynamic community structure in vehicle movements is helpful for revealing urban structures and human mobility patterns. Despite the fruitful research outcomes of community detection, the discovery of irregular-shaped and statistically significant dynamic communities in vehicle movements is still challenging. To overcome this challenge, we developed an evolutionary ant colony optimization (EACO) method for detecting dynamic communities in vehicle movements. Firstly, a weighted, spatially embedded graph was constructed at each time snapshot. Then, an ant-colony-optimization-based spatial scan statistic was upgraded to identify statistically significant communities at each snapshot by considering the effects of the communities discovered at the previous snapshot. Finally, different rules defined based on the Jaccard coefficient were used to identify the evolution of the communities. Experimental results on both simulated and real-world vehicle movement datasets showed that EACO performs better than three representative dynamic community detection methods: FacetNet (a framework for analyzing communities and evolutions in dynamic networks), DYNMOGA (dynamic multi-objective genetic algorithm), and RWLA (random-walk-based Leiden algorithm). The dynamic communities identified by EACO may be useful for understanding the dynamic organization of urban structures.

A novel influence maximization algorithm for a competitive environment based on social media data analytics

Online social networks are increasingly connecting people around the world. Influence maximization is a key area of research in online social networks, which identifies influential users during information dissemination. Most of the existing influence maximization methods only consider the transmission of a single channel, but real-world networks mostly include multiple channels of information transmission with competitive relationships. The problem of influence maximization in an environment involves selecting the seed node set for certain competitive information, so that it can avoid the influence of other information, and ultimately affect the largest set of nodes in the network. In this paper, the influence calculation of nodes is achieved according to the local community discovery algorithm, which is based on community dispersion and the characteristics of dynamic community structure. Furthermore, considering two various competitive information dissemination cases as an example, a solution is designed for self-interested information based on the assumption that the seed node set of competitive information is known, and a novel influence maximization algorithm of node avoidance based on user interest is proposed. Experiments conducted based on real-world Twitter dataset demonstrates the efficiency of our proposed algorithm in terms of accuracy and time against notable influence maximization algorithms.

Assessment of dynamic microbial community structure and rhizosphere interactions during bioaugmented phytoremediation of petroleum contaminated soil by a newly designed rhizobox system

To understand the plant (Vigna unguiculata) and plant-growth promoting bacteria (PGPB) (Microcococcus luteus WN01) interactions in crude oil contaminated soil, experiments were conducted based on the newly designed rhizobox system. The rhizobox was divided into three main compartments namely the rhizosphere zone, the mid-zone, and the bulk soil zone, in accordance with the distance from the plant. Plants were grown in these three-chambered pots for 30 days under natural conditions. The plant root exudates were determined by analyzing for carbohydrates, amino acids, and phenolic compounds. The degradation of alkane, polycyclic aromatic hydrocarbons (PAHs), and total petroleum hydrocarbons (TPHs) were quantified by GC-FID. Soil catalase, dehydrogenase, and invertase activities were determined. The microbial community structure was assessed using denaturing gradient gel electrophoresis (DGGE). Results showed that the inoculation of M. luteus WN01 significantly enhanced cowpea root biomass and exudates, especially the phenolic compounds. Bioaugmented phytoremediation by cowpea and M. luteus promoted rhizodegradation of TPH. Cowpea stimulated microbial growth, soil dehydrogenase, and invertase activities and enhanced bacterial community diversity in oil contaminated soil. The rhizosphere zone of cowpea inoculated with M. luteus showed the highest removal efficiency, microbial activities, microbial population, and bacterial community diversity indicating the strong synergic interactions between M. luteus and cowpea.

Roles of external circuit and rhizosphere location in CH4 emission control in sequencing batch flow constructed wetland-microbial fuel cell

Connecting external circuits can effectively inhibit the CH4 emissions from constructed wetlands (CW). In this study, the effect of external circuits, plant roots position and external resistance value on the electrical, CH4 emission and pollutant removing performance of sequencing batch constructed wetland-microbial fuel cell (CW-MFC) was investigated for the first time. After connecting external circuit, the CH4 emission from rhizosphere-anode-closed (RAC) constructed wetland-microbial fuel cell (CW-MFC) and rhizosphere-cathode-closed (RCC) CW-MFC was significantly reduced by 49.39% and 74.19%, respectively. When the external resistance was 1000, the highest power density was observed in RAC CW-MFC with a number of 73.32 mW m−3. In comparison, CW-MFC with rhizosphere at cathode emitted less CH4, while CW-MFC with rhizosphere at anode had a better electrical performance. Based on the high-throughput sequencing analysis, dynamic community structure of bacteria and archaea and the related microbial processes in the anode and cathode of CW-MFC were studied. Proteobacteria, Bacteroidetes and Methanosaeta were observed to be dominant in CW-MFCs, and the circuit conditions, external resistance and rhizosphere location all played important roles in the microbial community structure of CW-MFCs.

IMPLEMENTATION OF RELIGIOUS MODERATION EDUCATION FOR SANTRI AT THE MIFTAHUL ULUM SUREN ISLAMIC BOARDING SCHOOL KALISAT JEMBER EAST JAVA

<div class="page" title="Page 1"><div class="layoutArea"><div class="column"><p><span>Today's religious moderation is an interesting topic in the interaction and relations of modern society. The emergence of a group or individual actions that are intolerant and disrespectful of other people or groups in physical or verbal treatment is a serious problem in religious moderation. This study aims to explain and describe the implementation of religious moderation education in Islamic boarding school Miftahul Ulum Suren Jember which has a positive influence in forming a safe, dynamic, and conducive community structure. The type of this research was qualitative with a phenomenological approach. The participants in this study were 7 people including clerics and students who meet the predetermined research criteria. Data were collected through interviews and observations using interview and observation instruments. The data analysis technique used in this research was the reduction, presentation, and interpretation of the data. The study’s findings showed that in the implementation of religious moderation education at the Miftahul Ulum Islamic boarding school, there are two important themes which are the concept of religious moderation education and the obstacles and supports in its application. The value of mutual respect and tolerance is the main concept that is taught. Group fanaticism and culturing of figures are negative values that need to be eliminated in religious moderation education. Constraints and obstacles in applying religious moderation education in Islamic boarding schools include the regeneration of Kiai who have equal knowledge to continue the existence of the pesantren. The implication of this religious moderation education has a positive impact in constructing the society’s knowledge and culture. This research still has many limitations both in terms of themes and studies and research implementation techniques. In the future, further research can enrich and strengthen the results of this previous research.</span></p></div></div></div>

Dynamic Community Structure in Online Social Groups

One of the main ideas about the Internet is to rethink its services in a user-centric fashion. This fact translates to having human-scale services with devices that will become smarter and make decisions in place of their respective owners. Online Social Networks and, in particular, Online Social Groups, such as Facebook Groups, will be at the epicentre of this revolution because of their great relevance in the current society. Despite the vast number of studies on human behaviour in Online Social Media, the characteristics of Online Social Groups are still unknown. In this paper, we propose a dynamic community detection driven study of the structure of users inside Facebook Groups. The communities are extracted considering the interactions among the members of a group and it aims at searching dense communication groups of users, and the evolution of the communication groups over time, in order to discover social properties of Online Social Groups. The analysis is carried out considering the activity of 17 Facebook Groups, using 8 community detection algorithms and considering 2 possible interaction lifespans. Results show that interaction communities in OSGs are very fragmented but community detection tools are capable of uncovering relevant structures. The study of the community quality gives important insights about the community structure and increasing the interaction lifespan does not necessarily result in more clusterized or bigger communities.

Detecting Dynamic Community Structure in Functional Brain Networks Across Individuals: A Multilayer Approach.

We present a unified statistical framework for characterizing community structure of brain functional networks that captures variation across individuals and evolution over time. Existing methods for community detection focus only on single-subject analysis of dynamic networks; while recent extensions to multiple-subjects analysis are limited to static networks. To overcome these limitations, we propose a multi-subject, Markov-switching stochastic block model (MSS-SBM) to identify state-related changes in brain community organization over a group of individuals. We first formulate a multilayer extension of SBM to describe the time-dependent, multi-subject brain networks. We develop a novel procedure for fitting the multilayer SBM that builds on multislice modularity maximization which can uncover a common community partition of all layers (subjects) simultaneously. By augmenting with a dynamic Markov switching process, our proposed method is able to capture a set of distinct, recurring temporal states with respect to inter-community interactions over subjects and the change points between them. Simulation shows accurate community recovery and tracking of dynamic community regimes over multilayer networks by the MSS-SBM. Application to task fMRI reveals meaningful non-assortative brain community motifs, e.g., core-periphery structure at the group level, that are associated with language comprehension and motor functions suggesting their putative role in complex information integration. Our approach detected dynamic reconfiguration of modular connectivity elicited by varying task demands and identified unique profiles of intra and inter-community connectivity across different task conditions. The proposed multilayer network representation provides a principled way of detecting synchronous, dynamic modularity in brain networks across subjects.

Abnormal Dynamic Community Structure of Patients with Attention-Deficit/Hyperactivity Disorder in the Resting State

Objective: Some static network studies have suggested that the community structure in the brains of ADHD patients is altered. However, ADHD is now increasingly regarded as a disorder of neural dynamics, but the dynamic reconstruction of brain communities in ADHD patients is far from being understood. Method: Forty-two ADHD patients and fifty healthy controls participated in this study. We constructed a multilayer network model and calculated several metrics for quantifying community reconstruction at different levels. Results: Regardless of the level of research, the flexibility and cohesion of the ADHD patients were significantly higher than those of controls. In addition, the frontal lobe of ADHD patients presented a phenomenon of increasing peripheral areas and decreasing core areas. Conclusion: Our results indicate that ADHD patients do have abnormalities in dynamic community structure. These evidences provide a new perspective that advances the present understanding of the dynamic organizational principles of communities in ADHD.

An Online Dynamic Social Network Evolution Community Discovery Method

Online social network in the study, dynamic implicit community or group structure of discovery and detection is a very key question at the heart of evolution, it is in the medium (Mesoscopic) view to observe the online social network hidden structure characteristic, predict the evolution trend, control of the situation, found that network abnormal mass incidents, etc. It is of great significance. The purpose of this paper is to provide technical advice for community discovery research by studying online dynamic social network evolution community discovery methods. This paper analyzes the characteristics of local clustering coefficient and node similarity calculation in unsigned network, and proposes the extended local clustering coefficient as the structural attribute of the edge in unsigned network. This property can better reflect the characteristics of local network density and network structure. Combining the new edge structure measure with the label propagation algorithm with linear time complexity, a label propagation algorithm combining the extended local clustering coefficient is proposed. The results showed that the accuracy of community discovery was improved by 32.6 percent in dynamic social networks.

T143. RESTING STATE FMRI BASED MULTILAYER NETWORK CONFIGURATION IN PATIENTS WITH SCHIZOPHRENIA

BackgroundNovel methods for measuring large-scale dynamic brain organisation are needed to provide new biomarkers of schizophrenia. Using a method for modelling dynamic modular organisation, previous evidence suggests higher ‘flexibility’ (switching between multilayer network communities) to be a feature of schizophrenia.MethodsThe current study compared flexibility between 55 patients with schizophrenia and 72 controls (the COBRE Dataset). In addition, novel methods of ‘between resting state network synchronisation’ (BRSNS) and the probability of transition from one community to another were used to further describe group differences in dynamic community structure.ResultsThere was significantly higher schizophrenia group flexibility in cerebellar (F (1,124) = 9.33, p (FDR) = 0.017), subcortical (F (1,124) = 13.14, p (FDR) = 0.005), and fronto-parietal task control (F (1,124) = 7.19, p (FDR) = 0.033) resting state networks (RSNs), as well as in the left thalamus (MNI XYZ: -2, -13, 12; F(1, 124) = 17.1, p (FDR) < 0.001) and the right crus I (MNI XYZ: 35, -67, -34; F (1, 124) = 19.65, p (FDR) < 0.001). Flexibility in the left thalamus reflected transitions between communities covering default mode and sensory-somatomotor RSNs. BRSNS scores suggested altered dynamic inter-RSN modular configuration in schizophrenia.DiscussionThis study suggests higher flexibility in a schizophrenia group at an RSN and node level and provides novel methods of exploring dynamic community structure. Mediation of group differences by mean time window correlation did however suggest flexibility to be no better as a schizophrenia biomarker than simpler measures and a range of methodological choices affected results.

How do spatial and environmental factors affect the fish community structure in seasonally flooded karst systems?

Spatial and temporal variation of the physical and chemical conditions of coastal karst wetland ecosystems, which annually range from flooded to completely dry, generate a very dynamic fish community structure. We assessed the relative influence of spatial dependence and environmental factors on fish community structure in a seasonally flooded tropical karst system on the north-western coast of the Yucatan Peninsula, Mexico. Sampling was carried out between November 2009 and April 2010, while the area was inundated by seasonal floodwater. Fish biomass, abundance and species richness, as well as environmental variables and geographic location, were recorded. A variation partitioning analysis was performed to assess the influence of the spatial and environmental variables on the fish community using multiple regression, and a principal coordinates analysis of neighbor matrices (PCNM). Spatial factors had a greater influence on the changes in the community structure than the environmental factors. After spatial variation, environmental effects from conductivity, oxygen saturation, temperature and depth further shaped the community. These results imply that, despite the diversity of niches and wide variation in environmental conditions, the structuring force of the community is the spatial dynamic probably linked to species dispersal mechanisms and reproductive strategies.

Resting state fMRI based multilayer network configuration in patients with schizophrenia.

Novel methods for measuring large-scale dynamic brain organisation are needed to provide new biomarkers of schizophrenia. Using a method for modelling dynamic modular organisation (Mucha etal., 2010), evidence suggests higher 'flexibility' (switching between multilayer network communities) to be a feature of schizophrenia (Braun etal., 2016). The current study compared flexibility between 55 patients with schizophrenia and 72 controls (the COBRE Dataset). In addition, novel methods of 'between resting state network synchronisation' (BRSNS) and the probability of transition from one community to another were used to further describe group differences in dynamic community structure. There was significantly higher schizophrenia group flexibility scores in cerebellar (F (1124)=9.33, p (FDR)=0.017), subcortical (F (1124)=13.14, p (FDR)=0.005), and fronto-parietal task control (F (1124)=7.19, p (FDR)=0.033) resting state networks (RSNs), as well as in the left thalamus (MNI XYZ: -2, -13, 12; F(1, 124)=17.1, p (FDR) < 0.001) and the right crus I (MNI XYZ: 35, -67, -34; F (1, 124)=19.65, p (FDR) < 0.001). Flexibility in the left thalamus reflected transitions between communities covering default mode and sensory-somatomotor RSNs. BRSNS scores suggested altered dynamic inter-RSN modular configuration in schizophrenia. This study suggests less stable community structure in a schizophrenia group at an RSN and node level and provides novel methods of exploring dynamic community structure. Mediation of group differences by mean time window correlation did however suggest flexibility to be no better as a schizophrenia biomarker than simpler measures and a range of methodological choices affected results.