Network Analysis Research Articles

Bacterial networks and enzyme genes in bacterial floccules from hydrolysis and aeration reactors in a dairy wastewater treatment system.

The dairy industry generates substantial wastewater, which is commonly treated using integrated anaerobic hydrolysis and aerated biofilm reactors. However, the bacterial composition and functional differences within the generated floccules remain unclear. In this study, we employed 16S rRNA and metagenomic sequencing to compare bacterial communities and enzyme gene profiles between suspended floccules from the hydrolysis ponds and the aeration ponds. Results revealed that the bacterial phyla Firmicutes, Proteobacteria, and Bacteroidetes, dominated the wastewater treatment system and the relative abundance of these bacterial phyla varied in each pond. Additionally, the aeration ponds exhibited higher bacterial OTUs and enzyme gene abundance. Network analysis demonstrated a more complex bacterial network structure in the hydrolysis ponds compared to the aeration ponds. Furthermore, enzyme gene abundance revealed higher metabolic enzyme genes in the hydrolysis ponds, while signal transduction enzyme genes were more abundant in the aeration ponds. Notably, the top ten bacterial genera, primarily Hydromonas in the hydrolysis ponds and Ferruginibacter in the aeration ponds exhibited distinct contributions to signal transduction enzyme genes. Hydromonas dominated the metabolic enzyme genes in both ponds. These findings provide crucial insights for optimizing dairy wastewater treatment technologies.

The use of big data analytics for human resource management in an organization

Aim. To examine the application of big data in the field of human resource management and in organizational network analysis as a methodology to study the patterns of employee interaction within a formal organizational structure to improve the effectiveness of the human resource management system.Objectives. Theoretical analysis of existing methodologies for evaluating data on human resources of an organization to provide new opportunities for enterprise transformation; identification of hidden information on human resources and its use for internal staff development, retention and training; study of the possibilities of using organizational network analysis (ONA) to improve the effectiveness of human resource management.Methods. The authors used system and logical approaches, general scientific methods (analysis, synthesis), methods of comparative and economic analysis, analytical processing of information, graphical presentation of information.Results. The sources of big data are analyzed, the main indicators characterizing the state of personnel and allowing to forecast its development, dynamics of preservation of human resources in the organization, its training are allocated. The directions of using organizational network analysis in the personnel management system are proposed.Conclusions. The identified potential advantages of using organizational network analysis in the analytics of data on human resources in the organization will contribute to the reduction of staff turnover, optimization of staff structure, improvement of experience and knowledge sharing within the organization. This, according to the author’s position, will lead the enterprise to a positive economic effect and open new opportunities for its transformation

Evolution and influential factors of inbound tourist flow network in China: a two-mode network perspective

ABSTRACT Different from existing studies focusing on tourist flow networks at the destination level, this study adopts a two-mode network analysis to identify the network structure, market groupings, and evolution of inbound tourist flows from 13 major origin countries to 31 destination provinces (municipalities, autonomous regions) in China. Furthermore, the quadratic assignment procedure (QAP) method is applied to analyse the influential factors of the formation of market groupings in the network. The results show that: (1) The connections within the destination-destination network and the origin-origin country network show a fluctuating increase trend. (2) Eastern provincial destinations occupy important positions in the network, and central and western destinations have played increasingly important roles in recent years. (3) Japan, South Korea, and the United States are the most important origin countries, and markets in Europe and Oceania have great potential. (4) Two market groupings led by Russia and Asian countries are relatively stable, while other origin countries have no specific regional preference. (5) Geographical distance exerts the most significant impact on the formation of market groupings, followed by tourism resources, economic development, and traffic accessibility.

Regulation of MicroRNAs After Spinal Cord Injury in Adult Zebrafish.

Spinal cord injury (SCI) is a central nerve injury that often leads to loss of motor and sensory functions at or below the level of the injury. Zebrafish have a strong ability to repair after SCI, but the role of microRNAs (miRNAs) after SCI remains unclear. Locomotor behavior analysis showed that adult zebrafish recovered about 30% of their motor ability at 2 weeks and 55% at 3 weeks after SCI, reflecting their strong ability to repair SCI. Through miRNA sequencing, mRNA sequencing, RT-qPCR experiment verification, and bioinformatics predictive analysis, the key miRNAs and related genes in the repair of SCI were screened. A total of 38 miRNAs were significantly different, the top ten miRNAs were verified by RT-qPCR. The prediction target genes were verified by the mRNAs sequencing results at the same time point. Finally, 182 target genes were identified as likely to be networked regulated by the 38 different miRNAs. GO and KEGG enrichment analysis found that miRNAs targeted gene regulation of many key pathways, such as membrane tissue transport, ribosome function, lipid binding, and peroxidase activity. The PPI network analysis showed that miRNAs were involved in SCI repair through complex network regulation, among which dre-miR-21 may enhance cell reversibility through nop56, and that dre-miR-125c regulates axon growth through kpnb1 to repair SCI.

Urban greenspace types and climate factors jointly drive the microbial community structure and co-occurrence network

The benefits of urban green space are socially widely recognized as a direct link between plant–microbe interactions and the maintenance of biodiversity, community stability, and ecosystem functioning. Nevertheless, there is a lack of knowledge about the factors influencing microbial communities in urban green spaces, especially those related to phyllosphere epiphytes and stem epiphytes. In this study, we analyzed the microbial community assembly in leaf and stem bark samples collected from Square, Road, Campus, and Park. Illumina sequecing of 16S amplicons was performed to characterize microbial diversity and composition. The α-diversity was significantly higher in the bark epiphytic community, compared to the phyllosphere. Moreover, urban greenspaces'type altered the way communities gathered. The main soil and air properties factors of the urban greenhouse (e.g. soil temperature, atmospheric moisture, air temperature) were shaping the characteristics of bacterial communities on the leaf surface and bark epiphytic. In addition, in the co-occurrence network analysis, keystone taxa were not mostly observed in abundant species, which may be necessary to maintain ecosystem functions. Finally, our findings provide a deeper understanding of the ecological dynamics and microbial interactions within plant phyllosphere and stem epiphytes microbiomes.

A regression-content analysis approach to assess public satisfaction with shared mobility measures against COVID-19 pandemic

IntroductionThe transportation sector was severely impacted by the COVID-19 pandemic, with shared mobility services being the most affected due to concerns from the public regarding the high likelihood of being a vector of the virus. Although studies have evaluated the impact of the COVID-19 pandemic on shared mobility, a deeper understanding of public satisfaction with the measures adopted during COVID-19 has not been explored. MethodsThis study utilized data collected in the Summer of 2020 across the United States to fill that literature gap. The study applied Ordered Probit (OP) models to explore the factors influencing an individual's confidence in not contracting COVID-19 while using shared mobility modes and Text Network Analysis (TNA) to understand the deeper reasons for their confidence levels. ResultsResults show a significant influence of sociodemographic factors, land-use/built environment, pre- and post-COVID travel behavior, and activity participation on respondents’ level of confidence for not contracting COVID-19. Only frequent public transit users showed that they have high confidence in not getting COVID-19 when they use any of the shared mobility options, while people who did not use public transit and those who frequently attend telehealth meetings had low confidence in the measures adopted by shared mobility providers. Furthermore, the text mining results indicated that cleanness was the key theme regardless of the confidence level of the respondents, except for rail and bus transit. However, we observed other patterns of themes across the types of shared mobility. ConclusionsThe study findings can be beneficial in the future to improve ridership during pandemics by considering perceptions and satisfactions of various users.

Characterisation of HBV and co-infection with HDV and HIV through spatial transcriptomics

Background and aimsThe intrahepatic processes associated with chronic hepatitis B (CHB), especially in the context of hepatitis delta virus (HDV) and HIV co-infection, require a better understanding. Spatial transcriptomics can...

Modulation of brain activity in brain-injured patients with a disorder of consciousness in intensive care with repeated 10-Hz transcranial alternating current stimulation (tACS): a randomised controlled trial protocol

IntroductionTherapeutic interventions for disorders of consciousness lack consistency; evidence supports non-invasive brain stimulation, but few studies assess neuromodulation in acute-to-subacute brain-injured patients. This study aims to validate the feasibility and...

Long-term warming and decreased precipitation regulated microbial community structure and potential function through enhanced predator-prey relationships in the Tibetan Plateau

Global climate change can shape the interactions among soil microbes and, in turn, mediate ecosystem functions. However, how these interactions were regulated remains to be investigated. This study utilized 16S rRNA, ITS, and 18S rRNA high-throughput sequencing to investigate the effects of simulated warming and precipitation changes on the major components of soil micro-food webs in the Qinghai-Tibetan Plateau through a field experiment. Adonis and non-metric multidimensional scaling (NMDS) analyses showed that compositions of bacteria, fungi and protists were all affected by changes in temperature and precipitation. Correlation and cross-trophic network analyses revealed that warming and decreased precipitation, both separately and together, enhanced the relationships between bacteria and protists, especially protistan predators. We found that the modified stochasticity ratio of the bacterial community assembly was best predicted by protists. The potential functional structures of bacteria were positively correlated with protistan predators under warming and decreased precipitation condition, suggesting enhanced predator-prey relationships between protists and bacteria might further influence the potential functional characteristics of bacterial communities. Our study indicated that climate change altered bacterial compositional and functional structure via enhanced predator-prey interactions. Therefore, in the context of global climate change, broader and more comprehensive studies on protist-regulated soil bacterial and fungal communities are imperative.

Exploration of effective biomarkers for venous thrombosis embolism in Behçet’s disease based on comprehensive bioinformatics analysis

Behçet’s disease (BD) is a multifaceted autoimmune disorder affecting multiple organ systems. Vascular complications, such as venous thromboembolism (VTE), are highly prevalent, affecting around 50% of individuals diagnosed with BD. This study aimed to identify potential biomarkers for VTE in BD patients. Three microarray datasets (GSE209567, GSE48000, GSE19151) were retrieved for analysis. Differentially expressed genes (DEGs) associated with VTE in BD were identified using the Limma package and weighted gene co-expression network analysis (WGCNA). Subsequently, potential diagnostic genes were explored through protein–protein interaction (PPI) network analysis and machine learning algorithms. A receiver operating characteristic (ROC) curve and a nomogram were constructed to evaluate the diagnostic performance for VTE in BD patients. Furthermore, immune cell infiltration analyses and single-sample gene set enrichment analysis (ssGSEA) were performed to investigate potential underlying mechanisms. Finally, the efficacy of listed drugs was assessed based on the identified signature genes. The limma package and WGCNA identified 117 DEGs related to VTE in BD. A PPI network analysis then selected 23 candidate hub genes. Four DEGs (E2F1, GATA3, HDAC5, and MSH2) were identified by intersecting gene sets from three machine learning algorithms. ROC analysis and nomogram construction demonstrated high diagnostic accuracy for these four genes (AUC: 0.816, 95% CI: 0.723–0.909). Immune cell infiltration analysis revealed a positive correlation between dysregulated immune cells and the four hub genes. ssGSEA provided insights into potential mechanisms underlying VTE development and progression in BD patients. Additionally, therapeutic agent screening identified potential drugs targeting the four hub genes. This study employed a systematic approach to identify four potential hub genes (E2F1, GATA3, HDAC5, and MSH2) and construct a nomogram for VTE diagnosis in BD. Immune cell infiltration analysis revealed dysregulation, suggesting potential macrophage involvement in VTE development. ssGSEA provided insights into potential mechanisms underlying BD-induced VTE, and potential therapeutic agents were identified.

Exploration of effective biomarkers for venous thrombosis embolism in Behçet’s disease based on comprehensive bioinformatics analysis

Behçet’s disease (BD) is a multifaceted autoimmune disorder affecting multiple organ systems. Vascular complications, such as venous thromboembolism (VTE), are highly prevalent, affecting around 50% of individuals diagnosed with BD. This study aimed to identify potential biomarkers for VTE in BD patients. Three microarray datasets (GSE209567, GSE48000, GSE19151) were retrieved for analysis. Differentially expressed genes (DEGs) associated with VTE in BD were identified using the Limma package and weighted gene co-expression network analysis (WGCNA). Subsequently, potential diagnostic genes were explored through protein–protein interaction (PPI) network analysis and machine learning algorithms. A receiver operating characteristic (ROC) curve and a nomogram were constructed to evaluate the diagnostic performance for VTE in BD patients. Furthermore, immune cell infiltration analyses and single-sample gene set enrichment analysis (ssGSEA) were performed to investigate potential underlying mechanisms. Finally, the efficacy of listed drugs was assessed based on the identified signature genes. The limma package and WGCNA identified 117 DEGs related to VTE in BD. A PPI network analysis then selected 23 candidate hub genes. Four DEGs (E2F1, GATA3, HDAC5, and MSH2) were identified by intersecting gene sets from three machine learning algorithms. ROC analysis and nomogram construction demonstrated high diagnostic accuracy for these four genes (AUC: 0.816, 95% CI: 0.723–0.909). Immune cell infiltration analysis revealed a positive correlation between dysregulated immune cells and the four hub genes. ssGSEA provided insights into potential mechanisms underlying VTE development and progression in BD patients. Additionally, therapeutic agent screening identified potential drugs targeting the four hub genes. This study employed a systematic approach to identify four potential hub genes (E2F1, GATA3, HDAC5, and MSH2) and construct a nomogram for VTE diagnosis in BD. Immune cell infiltration analysis revealed dysregulation, suggesting potential macrophage involvement in VTE development. ssGSEA provided insights into potential mechanisms underlying BD-induced VTE, and potential therapeutic agents were identified.

The domino effect of teacher cognitive presence triggering events and resolution on student’s grit

The aim of this current study is to assess the essential role of teacher cognitive presence within the context of the community of inquiry framework in relation to enhancing students’ academic perseverance, more specifically this type of presence influences the most students’ academic perseverance. In order to test this hypothesis, an online questionnaire was created, including the community of inquiry scale and the grit scale. A total of 529 valid and consented answers were collected. We have further analyzed the structure of the variables network with network analysis and then we have employed several sequential mediation analyses testing the impact of cognitive presence triggering events and resolution on the relationship between all the other presences and students’ academic perseverance. A circular structure has emerged in network analysis and eight significant full complementary sequential mediation analysis. Results confirm the domino effect of teacher cognitive presence triggering events and resolution on student’s grit under the community of inquiry framework.

Defined synthetic microbial communities colonize and benefit field-grown sorghum.

The rhizosphere constitutes a dynamic interface between plant hosts and their associated microbial communities. Despite the acknowledged potential for enhancing plant fitness by manipulating the rhizosphere, the engineering of the rhizosphere microbiome through inoculation has posed significant challenges. These challenges are thought to arise from the competitive microbial ecosystem where introduced microbes must survive, and the absence of adaptation to the specific metabolic and environmental demands of the rhizosphere. Here, we engineered a synthetic rhizosphere community (SRC1) with the anticipation that it would exhibit a selective advantage in colonizing the host Sorghum bicolor, thereby potentially fostering its growth. SRC1 was assembled from bacterial isolates identified either for their potential role in community cohesion through network analysis or for their ability to benefit from host-specific exudate compounds. The growth performance of SRC1 was assessed in vitro on solid media, in planta under gnotobiotic laboratory conditions, and in the field. Our findings reveal that SRC1 cohesion is most robust when cultivated in the presence of the plant host under laboratory conditions, with lineages being lost from the community when grown either in vitro or in a native field setting. We establish that SRC1 effectively promotes the growth of both above- and below-ground plant phenotypes in both laboratory and native field contexts. Furthermore, in laboratory conditions, these growth enhancements correlate with the transcriptional dampening of lignin biosynthesis in the host. Collectively, these results underscore the potential utility of synthetic microbial communities for modulating crop performance in controlled and native environments alike.

Coupling Hyperbolic GCN with Graph Generation for Spatial Community Detection and Dynamic Evolution Analysis

Spatial community detection is a method that divides geographic spaces into several sub-regions based on spatial interactions, reflecting the regional spatial structure against the background of human mobility. In recent years, spatial community detection has attracted extensive research in the field of geographic information science. However, mining the community structures and their evolutionary patterns from spatial interaction data remains challenging. Most existing methods for spatial community detection rely on representing spatial interaction networks in Euclidean space, which results in significant distortion when modeling spatial interaction networks; since spatial community detection has no ground truth, this results in the detection and evaluation of communities being difficult. Furthermore, most methods usually ignore the dynamics of these spatial interaction networks, resulting in the dynamic evolution of spatial communities not being discussed in depth. Therefore, this study proposes a framework for community detection and evolutionary analysis for spatial interaction networks. Specifically, we construct a spatial interaction network based on network science theory, where geographic units serve as nodes and interaction relationships serve as edges. In order to fully learn the structural features of the spatial interaction network, we introduce a hyperbolic graph convolution module in the community detection phase to learn the spatial and non-spatial attributes of the spatial interaction network, obtain vector representations of the nodes, and optimize them based on a graph generation model to achieve the final community detection results. Considering the dynamics of spatial interactions, we analyze the evolution of the spatial community over time. Finally, using taxi trajectory data as an example, we conduct relevant experiments within the fifth ring road of Beijing. The empirical results validate the community detection capabilities of the proposed method, which can effectively describe the dynamic spatial structure of cities based on human mobility and provide an effective analytical method for urban spatial planning.

The puzzle of loneliness: A sociostructural and transnational analysis of International Chinese Students' networks in Germany

AbstractThe experience of loneliness is common among international students who study in a foreign country, away from their home. Loneliness refers to the difference between the actual and desired social relationships highlighting not only the quantity but also the quality of relationships. We argue for a perspective on loneliness that emphasizes its social context rather than relying mainly on individualistic or psychological interpretations prevalent thus far. In this article, we explore network predictors of loneliness among Chinese international students in Germany. Through our analysis of a unique data set, we show that when compared to the local students, international students tend to feel lonelier. Surprisingly, when we discover that when international students have the same network size, those with more emotionally supportive ties tend to experience more loneliness compared to those with fewer emotionally supportive ties. Not surprisingly, having a romantic partner is associated with lower levels of loneliness, especially when they also live in the country of education. Additionally, we find that international Chinese students who struggle to form cross‐national friendships report higher levels of loneliness. By delving into these findings, our research offers a sociological account of loneliness among international Chinese and local students in Germany.

Identifying Diagnostic Markers and Constructing Predictive Models for Oxidative Stress in Multiple Sclerosis

Multiple sclerosis (MS) is a chronic disease characterized by inflammation and neurodegeneration of the central nervous system. Despite the significant role of oxidative stress in the pathogenesis of MS, its precise molecular mechanisms remain unclear. This study utilized microarray datasets from the GEO database to analyze differentially expressed oxidative-stress-related genes (DE-OSRGs), identifying 101 DE-OSRGs. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses indicate that these genes are primarily involved in oxidative stress and immune responses. Through protein–protein interaction (PPI) network, LASSO regression, and logistic regression analyses, four genes (MMP9, NFKBIA, NFKB1, and SRC) were identified as being closely related to MS. A diagnostic prediction model based on logistic regression demonstrated good predictive power, as shown by the nomogram curve index and DAC results. An immune-cell infiltration analysis using CIBERSORT revealed significant correlations between these genes and immune cell subpopulations. Abnormal oxidative stress and upregulated expression of key genes were observed in the blood and brain tissues of EAE mice. A molecular docking analysis suggested strong binding potentials between the proteins of these genes and several drug molecules, including isoquercitrin, decitabine, benztropine, and curcumin. In conclusion, this study identifies and validates potential diagnostic biomarkers for MS, establishes an effective prediction model, and provides new insights for the early diagnosis and personalized treatment of MS.

A Post-Implanto-Prosthetic Rehabilitation Study Regarding the Degree of Improvement in Patients’ Quality of Life: A Before–After Study

Background: Implant–prosthetic rehabilitation has the ability to improve the quality of life of patients, because, in addition to the role of restoring masticatory function, they also have many other benefits, such as restoring aesthetics or improving speech. This study aimed to analyze whether patients’ quality of life was improved by implanto-prosthetic rehabilitation and which were the most important aspects. Materials and Methods: In this before–after study, we applied the Oral Health Impact Profile (OHIP-14) questionnaire to analyze the degree to which complex implanto-prosthetic rehabilitation led or not to an increase in patients’ quality of life. The present study was carried out at the level of a private medical center in a city in the central region of Romania and included patients who visited this medical center between January and June 2022 and who benefited from a complex implanto-prosthetic rehabilitation, with the total number of patients eligible for inclusion in the study being 116. Results: Overall, an improvement in quality of life after implant-prosthetic rehabilitation was found. Patients’ gender, age, or educational level did not significantly influence their responses. The network analysis offered an overview (intuitive visual representation) of the similarities but also the differences in the OHIP-14 item relationships in both situations: before and after oral rehabilitation. Conclusions: A better understanding of how patients perceive implanto-prosthetic rehabilitation and the aspects that influence this perception can lead to an improvement in their quality of life, increasing the addressability of this type of medical procedure.

Identification of tumor stemness and immunity related prognostic factors and sensitive drugs in head and neck squamous cell carcinoma

The presence of cancer stem cells (CSCs) contributes significantly to treatment resistance in various cancers, including head and neck squamous cell carcinoma (HNSCC). Despite this, the relationship between cancer stemness and immunity remains poorly understood. In this study, we aimed to identify potential immunotherapeutic targets and sensitive drugs for CSCs in HNSCC. Using data from public databases, we analyzed expression patterns and prognostic values in HNSCC. The stemness index was calculated using the single-sample gene set enrichment analysis (ssgsea) algorithm, and weighted gene co-expression network analysis (WGCNA) was employed to screen for key stemness-related modules. Consensus clustering was then used to group samples for further analysis, and prognosis-related key genes were identified through regression analysis. Our results showed that tumor samples from HNSCC exhibited higher stemness indices compared to normal samples. WGCNA identified a module highly correlated with stemness, comprising 187 genes, which were significantly enriched in protein digestion and absorption pathways. Furthermore, we identified sensitive drugs targeting prognostic genes associated with tumor stemness. Notably, two genes, HLF and CCL11, were found to be highly associated with both stemness and immunity. In conclusion, our study identifies a stemness-related gene signature and promising drug candidates for CSCs of HNSCC. Additionally, HLF and CCL11, which are associated with both stemness and immunity, represent potential targets for immunotherapy in HNSCC.

Сетевая платформа автоматизации прогнозирования отказов карьерных самосвалов

The article considers the possibilities of developing an automated system for monitoring and predicting the technical condition of in-pit vehicles at the operation stage based on failure statistics and network analysis of data received from health sensors of the mining machines. This study seeks to reduce emergency downtime in the mining industry by introducing modern information and communication technologies. The applicability of existing methods to analyze digital signals received from the sensors installed on the mining equipment was assessed. A promising approach is considered, using the progress achieved in network engineering and conversion of the time series signals into the integrated networks. A sequence of operations is proposed as an innovation, including collection and analysis of data, development of network prediction models and practical implementation of the results. It is expected that using such a sequence of steps will be able to promptly notify of the need to repair equipment, thereby reducing downtime, which in turn will increase productivity and reduce the operating costs. The main stages of the study are formulated and presented, the implementation of which is aimed at predicting the health of the equipment, identifying the need for unscheduled repairs, which will lead to a decrease in the number of emergency failures or their prevention in real operating conditions of mining enterprises.

Identification of tumor stemness and immunity related prognostic factors and sensitive drugs in head and neck squamous cell carcinoma

The presence of cancer stem cells (CSCs) contributes significantly to treatment resistance in various cancers, including head and neck squamous cell carcinoma (HNSCC). Despite this, the relationship between cancer stemness and immunity remains poorly understood. In this study, we aimed to identify potential immunotherapeutic targets and sensitive drugs for CSCs in HNSCC. Using data from public databases, we analyzed expression patterns and prognostic values in HNSCC. The stemness index was calculated using the single-sample gene set enrichment analysis (ssgsea) algorithm, and weighted gene co-expression network analysis (WGCNA) was employed to screen for key stemness-related modules. Consensus clustering was then used to group samples for further analysis, and prognosis-related key genes were identified through regression analysis. Our results showed that tumor samples from HNSCC exhibited higher stemness indices compared to normal samples. WGCNA identified a module highly correlated with stemness, comprising 187 genes, which were significantly enriched in protein digestion and absorption pathways. Furthermore, we identified sensitive drugs targeting prognostic genes associated with tumor stemness. Notably, two genes, HLF and CCL11, were found to be highly associated with both stemness and immunity. In conclusion, our study identifies a stemness-related gene signature and promising drug candidates for CSCs of HNSCC. Additionally, HLF and CCL11, which are associated with both stemness and immunity, represent potential targets for immunotherapy in HNSCC.