Network Analysis Techniques Research Articles

Exploring the environmental contamination toxicity and potential carcinogenic pathways of perfluorinated and polyfluoroalkyl substances (PFAS): An integrated network toxicology and molecular docking strategy

The objective of this study was to investigate the potential carcinogenic toxicity and mechanisms of PFAS in thyroid, renal, and testicular cancers base on network toxicology and molecular docking techniques. Structural modeling was performed to predict relevant toxicity information, and compounds and cancer-related targets were screened in multiple databases. The interaction of PFAS with three cancers and their key protein targets were explored by combining protein network analysis, enrichment analysis and molecular docking techniques. PFOA, PFOS, and PFHXS exhibited significant carcinogenic and cytotoxic effects. These compounds may induce cancer by mediating active oxygen metabolism and the transduction of phosphatidylinositol 3-kinase/protein kinase B signaling pathway through genes such as ALB, mTOR, MDM2, and ERBB2. Furthermore, the underlying toxic mechanisms may be linked to the pathways in cancer, chemical carcinogenesis through reactive oxygen species/receptor activation, and the FoxO signaling pathway. The results contribute to a comprehensive understanding of the effects of these environmental pollutants on genes, proteins, and metabolic pathways in living organisms. It revealed their toxicity mechanisms in inducing thyroid, renal, and testicular cancers, and provided a solid theoretical foundation for designing new environmental control strategies and drug screening initiatives. Additionally, the integrated application of network toxicology and molecular docking technology can enhance our understanding of the toxicity and mechanisms of unknown environmental pollutants, which is beneficial for protecting the environment and human health.

Being insulted by parents is the most severe early adverse experience of anxiety in adulthood

IntroductionWhich adverse childhood experiences are causally associated with anxiety in adulthood is unclear. Sensitive Period Model states that early adverse experiences can influence anxiety levels in adulthood. However, it remains unknown which adverse experience is the most important early adverse factor for anxiety in adulthood, and whether early alleviation or aggravation of this adverse experience predicts a decrease or increase in anxiety levels in adulthood. MethodsA national cross-sectional survey was conducted from 20 June 2023 to 31 August 2023, with a total of 30,054 adults aged 18 years or older recruited on questionnaires that completed the Adverse Childhood Experiences Scale and Generalised Anxiety Disorder Scale. We used network analysis and computer simulation techniques to simulate aggravating and simulate alleviating interventions to observe changes in anxiety levels to identify early adverse experiences causally associated with anxiety levels in adulthood. ResultsThe results of both the network analysis and the nodeIdentifyR algorithm (NIRA) indicated that in the Adverse Childhood Experiences-Anxiety Network (ACE-GAD Network), being cursed and insulted (CUR) by parents in childhood was the most severe early adverse experience contributing to anxiety in adulthood. In particular, simulated aggravation interventions targeting CUR in young and middle-aged adults resulted in significantly higher levels of anxiety in adulthood. ConclusionsThis suggests that preventing verbal curses or insults in childhood can be an early preventive measure for future anxiety interventions. This study has important theoretical and practical implications, providing new insights for early prevention of anxiety and family education.

Blended learning dalam pembelajaran matematika: A systematic literature network analysis

As technology continued to advance, the educational landscape evolved, leading to the development of learning management systems, online platforms, and multimedia resources. Blended learning has since become a cornerstone in educational strategies. The purpose of this study is to describe the research trend data on Blended learning . The Systematic Literature Network Analysis (SLNA) method was employed for the investigation. This study's data is made up of non-time-limited research articles on Blended learning. The articles were retrieved from the Scopus database. The data was evaluated using a bibliometric and network analysis technique with programs R and VOSviewer. The findings indicate that the number of studies and research citations fluctuate. Keyword cluster analysis indicates that the predominant focus of research lies in Blended learning. Simultaneously, the terms 'personnel training' and 'academic performance' exhibit interconnections with inquiries into Blended learning . Subsequent scholarly investigations ought to broaden and elaborate upon facets of Blended learning conceptualization, assessment methodologies, pedagogical instruction, and organizational backing, to furnish a more exhaustive and all-encompassing research outlook.

A Bibliometric Analysis Of Emotional Intelligence And Leadership

This research aims to conduct a bibliometric analysis of studies on the relationship between emotional intelligence and leadership. The investigation encompasses a compilation of 1136 articles sourced from Scopus, spanning from 1996 to 2023. The data analysis process involved utilizing the VOSviewer software, which facilitated the creation of maps depicting co-authorship and keywords. A combination of descriptive statistics and social network analysis techniques were employed to analyze the data. Within the 1136 articles, 105 author keywords were referenced at least five times. These keywords were subsequently categorized into 11 clusters. The prevalent terms included “training,” “competence,” “job satisfaction,” “self-awareness,” and “organizational citizenship behavior.” An exploration of the temporal aspect of the data revealed that recent scholarly interest has gravitated toward topics such as “higher education,” “job performance,” “leader development,” “soft skill,” and “employee engagement.” This study’s findings suggest potential avenues for further research in the realm of emotional intelligence and leadership. Subsequent researchers might consider conducting studies in these domains to contribute to the evolving knowledge system.

Personal Networks Supporting Workplace Learning

This paper is a case study researching personal social networks and their meaning to individual-level learning processes in the workplace. The study was based on the knowledge-creation metaphor of learning. Employees' personal networks are seen as an important component of their learning potential and competence. Methodologically, the paper presents a relatively new technique of Social Network Analysis (SNA), namely, the qualitative egocentric network interview, and a new way of presenting research findings in visual form. The context of the study is the Finnish Defence Forces (FDF) and it was conducted in three companies of one brigade-level unit of the Finnish Army. An egocentric network interview was conducted with ten Non-Commissioned Officers (NCO). The interview data was analysed with qualitative content analysis, and the networks were visualised with the Cytoscape software. The egocentric network analysis showed that the people in the same company created a major support structure for the NCOs’ workplace learning. However, nearly all NCOs had important network structures that were formed around their individual expertise and tasks. The networks varied considerably in size and composition, but had certain connecting features. The networks had three main components; one, the personnel of the NCO’s own unit provided important social support. Two, every NCO had networks related to their own specific task, and three, some NCOs had networks formed through various stages of their life that were still active and useful in their current job. The different ways in which the networks enable and support workplace learning are discussed. In addition, some methodological issues of social network analysis are addressed.

A Panoramic View on International Higher Education Studies from a Global Perspective

With globalization, international education has become much more accessible to more people around the world. International higher education (IHE) has been among the most studied topics in higher education in recent years. In this context, the present study aims to investigate the studies conducted on IHE through a systematic literature review by analyzing studies published between 1965 and 2021 and employs social network analysis (SNA) and text-mining techniques. All these findings show that global, national, and institutional strategies are effective in the increase of interest in international education and especially in the increase of research. In this connection, this study provides an overview of trends; academic production in IHE has been researched with the data mining and analytics approaches, bibliometric method and the analysis of social networks. Thus, a better understanding and interpretation of the leading themes in the literature of internationalization in higher education is possible to achieve a better understanding of the field of intellectual scientific knowledge in the literature on globalization and IHE.

Higher-Order Networks Representation and Learning: A Survey

Network data has become widespread, larger, and more complex over the years. Traditional network data is dyadic, capturing the relations among pairs of entities. With the need to model interactions among more than two entities, significant research has focused on higher-order networks and ways to represent, analyze, and learn from them. There are two main directions to studying higher-order networks. One direction has focused on capturing higher-order patterns in traditional (dyadic) graphs by changing the basic unit of study from nodes to small frequently observed subgraphs, called motifs. As most existing network data comes in the form of pairwise dyadic relationships, studying higher-order structures within such graphs may uncover new insights. The second direction aims to directly model higher-order interactions using new and more complex representations such as simplicial complexes or hypergraphs. Some of these models have long been proposed, but improvements in computational power and the advent of new computational techniques have increased their popularity. Our goal in this paper is to provide a succinct yet comprehensive summary of the advanced higher-order network analysis techniques. We provide a systematic review of the foundations and algorithms, along with use cases and applications of higher-order networks in various scientific domains.

Deciphering the immune-metabolic nexus in sepsis: a single-cell sequencing analysis of neutrophil heterogeneity and risk stratification.

Metabolic dysregulation following sepsis can significantly compromise patient prognosis by altering immune-inflammatory responses. Despite its clinical relevance, the exact mechanisms of this perturbation are not yet fully understood. Single-cell RNA sequencing (scRNA-seq) was utilized to map the immune cell landscape and its association with metabolic pathways during sepsis. This study employed cell-cell interaction and phenotype profiling from scRNA-seq data, along with pseudotime trajectory analysis, to investigate neutrophil differentiation and heterogeneity. By integrating scRNA-seq with Weighted Gene Co-expression Network Analysis (WGCNA) and machine learning techniques, key genes were identified. These genes were used to develop and validate a risk score model and nomogram, with their efficacy confirmed through Receiver Operating Characteristic (ROC) curve analysis. The model's practicality was further reinforced through enrichment and immune characteristic studies based on the risk score and in vivo validation of a critical gene associated with sepsis. The complex immune landscape and neutrophil roles in metabolic disturbances during sepsis were elucidated by our in-depth scRNA-seq analysis. Pronounced neutrophil interactions with diverse cell types were revealed in the analysis of intercellular communication, highlighting pathways that differentiate between proximal and core regions within atherosclerotic plaques. Insight into the evolution of neutrophil subpopulations and their differentiation within the plaque milieu was provided by pseudotime trajectory mappings. Diagnostic markers were identified with the assistance of machine learning, resulting in the discovery of PIM1, HIST1H1C, and IGSF6. The identification of these markers culminated in the development of the risk score model, which demonstrated remarkable precision in sepsis prognosis. The model's capability to categorize patient profiles based on immune characteristics was confirmed, particularly in identifying individuals at high risk with suppressed immune cell activity and inflammatory responses. The role of PIM1 in modulating the immune-inflammatory response during sepsis was further confirmed through experimental validation, suggesting its potential as a therapeutic target. The understanding of sepsis immunopathology is improved by this research, and new avenues are opened for novel prognostic and therapeutic approaches.

Uncovering Names and Connections: The “Polish Jew” Periodical as a Second-Tier Record for Holocaust Remembrance and Network Analysis in Jewish Genealogy

This paper explores the Polish Jew journal as a pivotal second-tier record for advancing Holocaust studies and Jewish genealogy. Traditionally underutilized in academic research, this periodical provides a unique repository of names and narratives of Holocaust victims, filling crucial gaps in primary record collections. The investigation centers on the journal’s potential not only to contribute names to existing databases of Holocaust victims—many of whom are still unrecorded—but also to enhance genealogical methods through the integration of network analysis. By examining Polish Jew, this study illustrates how second-tier records can extend beyond mere supplements to primary data, acting instead as vital tools for reconstructing complex social and familial networks disrupted by the Holocaust. The paper proposes a methodological framework combining traditional genealogical research with modern network analysis techniques to deepen our understanding of Jewish community dynamics during and after World War II. This approach not only aids in identifying individual victims and survivors but also in visualizing the broader interactions within Jewish diaspora communities. This research underscores the significance of Polish Jew in the broader context of Holocaust remembrance. It offers a novel pathway for the future of Jewish genealogical research, advocating for the strategic use of second-tier records in scholarly investigations.

Experience of implementing discourse analysis and conceptual mapping of healthy eating communities

The article presents the experience of implementing discourse analysis methods as interpreted by E. Laclau and C. Mouffe and the concept mapping method as interpreted by W. Trochim through the network analysis technique using the example of healthy eating. The result of the analysis is a graph that makes it possible to highlight the struggle of discourse agents for the meaning of key discursive signs, as well as the conceptual map of participants in an online discussion on controversial issues within the topic discussed in the article as an example. The empirical basis for the discourse analysis was 3 000 collected comments in four communities about healthy eating on the social networks VKontakte and Odnoklassniki. The modified versions of the methods of concept mapping and discourse analysis were adapted for the analysis of online discussions. Subsequent validation of the methods seems to be a promising direction for further research. The proposed designs have a number of limitations, which are discussed in the article.

Assessing the infringement risk of patent portfolios using network analysis and IF-TOPSIS: A case of standard-essential patent portfolios in the ICT industry

With the intensification of technological innovation competition and the surge in the number of patents in modern society, companies' technology activities such as technology R&D, technology licensing and technology transfer now regularly involve large patent portfolios, as well as a rising risk of patent infringement. However, the existing patent infringement risk assessment methods are only designed for individual patents, which is not enough to meet the companies' practical need for assessing and managing the infringement risk of large patent portfolios. Therefore, this study proposes a combined approach using network analysis and intuitionistic fuzzy technique for order preference by similarity to ideal solution (IF-TOPSIS), which can assess and rank the infringement risk of a company's patent portfolio relative to each of its competitors' patent portfolios, thereby providing a scientific base for the company's management decisions on patent portfolios. In this approach, one-mode weighted patent proximity networks are constructed to extract topology information of patent portfolios, and the IF-TOPSIS method is combined to obtain and rank the risk results under a proper fuzzy framework. The case study uses patent portfolios containing a total of 6736 standard-essential patents (SEPs) in the information and communications technology (ICT) industry as test data, and verifies the practicality and validity of this intelligent approach. Managerial implications for technology managers and patent attorneys are elaborated.

Perceived usefulness of English (L2) learning apps and language mindset mediated by flow and motivation intensity: a serial mediation model and a network analysis

ABSTRACT Existing research has explored the connection between perceived usefulness and growth mindset, but there are still gaps in our understanding of how the perceived usefulness of second language learning apps (PU-L2LA) relates to L2 learner’s language mindset (LM). Our study seeks to investigate the serial mediating role of flow and motivation intensity in the association between PU-L2LA and LM, as well as the network-level interaction between flow and motivation intensity (MI). This is a cross-sectional study conducted with a convenience sample of 524 Chinese university students, aiming to assess PU-L2LA, flow, MI, and LM. The results of the mediation analyses reveal that flow and MI play sequential mediating roles in linking PU-L2LA to LM. Furthermore, through network analysis techniques, two key bridge indicators (“The experience is extremely rewarding.” and “After I graduate from college, I will continue to study English and try to improve.”) were identified as important links between flow and MI. These findings contribute to a deeper understanding of how PU-L2LA influences LM and the underlying mechanisms involved. They highlight the importance of considering flow experiences and MI in promoting a positive language mindset among learners. Educators, app developers, and researchers can utilize these insights to design effective interventions, enhance engagement, and optimize language learning outcomes.

Network analysis of students’ conceptual understanding of mathematical expressions for probability in upper-division quantum mechanics

One expected outcome of physics instruction is for students to be capable of relating physical concepts to multiple mathematical representations. In quantum mechanics (QM), students are asked to work across multiple symbolic notations, including some they have not previously encountered. To investigate student understanding of the relationships between expressions used in these various notations, a survey was developed and distributed to students at six different institutions. All of the courses studied were structured as “spins-first,” in which the course begins with spin-1/2 systems and Dirac notation before transitioning to include continuous systems and wave function notation. Network analysis techniques such as community detection methods were used to investigate conceptual connections between commonly used expressions in upper-division QM courses. Our findings suggest that, for spins-first students, Dirac bras and kets share a stronger identity with vectorlike concepts than are associated with quantum state or wave function concepts. This work represents a novel way of using well-developed network analysis techniques and suggests such techniques could be used for other purposes as well. Published by the American Physical Society 2024

Emerald Ash Borer Infestation-Induced Elevated Negative Correlations and Core Genera Shift in the Endophyte Community of Fraxinus bungeana.

Endophytes, prevalent in plants, mediate plant-insect interactions. Nevertheless, our understanding of the key members of endophyte communities involved in inhibiting or assisting EAB infestation remains limited. Employing ITS and 16S rRNA high-throughput sequencing, along with network analysis techniques, we conducted a comprehensive investigation into the reaction of endophytic fungi and bacteria within F. bungeana phloem by comparing EAB-infested and uninfected samples. Our findings reveal that EAB infestation significantly impacts the endophytic communities, altering both their diversity and overall structure. Interestingly, both endophytic fungi and bacteria exhibited distinct patterns in response to the infestation. For instance, in the EAB-infested phloem, the fungi abundance remained unchanged, but diversity decreased significantly. Conversely, bacterial abundance increased, without significant diversity changes. The fungi community structure altered significantly, which was not observed in bacteria. The bacterial composition in the infested phloem underwent significant changes, characterized by a substantial decrease in beneficial species abundance, whereas the fungal composition remained largely unaffected. In network analysis, the endophytes in infested phloem exhibited a modular topology, demonstrating greater complexity due to an augmented number of network nodes, elevated negative correlations, and a core genera shift compared to those observed in healthy phloem. Our findings increase understanding of plant-insect-microorganism relationships, crucial for pest control, considering endophytic roles in plant defense.

How Do Nonprofits’ Organizational Characteristics Shape Environmental Philanthropy in Texas? A Network Science Approach

Abstract In efforts to address the far-reaching effects of climate change and associated impacts in communities, research on environmental philanthropy suggests that more resources are being allocated to environmental societal challenges. However, understandings about which environmental nonprofits benefit from these funding flows is limited. This study integrates resource dependency theory with elitism and pluralism perspectives to analyze a network of environmental nonprofits and their funders in Texas. Resource dependency and a network-analytic approaches share underlying relationality principles, and we connect those dots by conceptualizing the funding dynamics in Texas as a network of funder-grantee relations. Drawing on statewide survey data (n = 114), we use a network analysis technique – exponential random graph modeling (ERGM) – to analyze funding allocations in Texas through the organizational attributes of environmental nonprofits, their funder-grantee relations, and their community context. We specifically observe elitism in funding allocations in Texas, which is evident in network effects (preferential attachment) and the focal areas of environmental work. However, we find limited evidence that age or resources of the nonprofit are predictive of funding and the most influential factor determining a funder-grantee relationship is the natural hazard risk of the community served by the nonprofit. Our findings suggest interconnected funding dynamics of pluralism and elitism in the Texas environmental philanthropy landscape, prompting further discussion about the potential synergies of these patterns and the implications for environmental funding practices.

Association of M2 macrophages with EMT in glioma identified through combination of multi-omics and machine learning

BackgroundThe incidence of glioma, a prevalent brain malignancy, is increasing, particularly among the elderly population. This study aimed to elucidate the clinical importance of epithelial–mesenchymal transition (EMT) in gliomas and its association with malignancy and prognosis. BackgroundThe incidence of glioma, particularly among elderly individuals, is on the rise. The malignancy of glioma is determined not only by the oncogenic properties of tumor cells but also by the composition of the tumor microenvironment, which includes immune system macrophages. The prevalence of M2-type macrophages typically fosters tumor progression, yet the underlying mechanism remains elusive. Our study explored the clinical importance of epithelial–mesenchymal transition (EMT) in gliomas and its association with malignancy and prognosis. MethodsOur study used the gene set variation analysis (GSVA) algorithm to classify different levels of EMT activation based on the transcriptomic and multi-omics data. Machine learning (ML) and single-cell analysis were integrated into our model for comprehensive analysis. A predictive model was constructed and in vitro experiments were performed to validate our findings. ResultsOur study classified 1,641 samples into two clusters based on EMT activation: the EMT-hot group and the EMT-cold group. The EMT-hot group had elevated copy number loss, tumor mutational burden (TMB), and a poorer survival rate. Conversely, the EMT-cold group showed a better survival rate, likely attributed to lower stromal and immune cell scores, as well as decreased expression of human leukocyte antigen-related genes. Driving genes were identified through weighted gene coexpression network analysis (WGCNA) and dimensionality reduction techniques. These genes were then utilized in the construction of a prognostic model using ML and protein-protein interaction (PPI) network analysis. Furthermore, the impact of the core genes identified through single-cell analysis on glioma prognosis was examined. ConclusionOur research underscores the efficacy of our model in predicting glioma prognosis and elucidates the connection between the M2 macrophages and EMT. Additionally, core genes such as LY96, C1QB, LGALS1, CSPG5, S100A8, and CHGB were identified as pivotal for mediating the occurrence of EMT induced by M2 macrophages.

Research Progress of Central Serous Chorioretinopathy in Recent 20 Years Based on Visual Bibliometric Analysis

ABSTRACT Objective To dynamically track the publications on central serous chorioretinopathy (CSC) and depict the research status and hot spots to guide future research. Methods Gather all papers published in this area between 2004 and 2024 in the WOSCC databases comprehensively, assess their trends, and characterize the contributions of various nations, authors, institutions, and journals. In addition, VOSviewer, CiteSpace, and R software are used to obtain the most popular keywords for the topic. Results A total of 2,203 papers were published across 1,863 institutions in 59 countries. Among these, 6,907 authors contributed to publications in 300 journals and generated a total of 35,638 citations. The number of publications continues to grow steadily. Notably, Jay Chhablani’s team/Lab stands out as the leading contributor with ownership of 84 publications. Through keyword network analysis and clustering techniques, risk factor-related clustering, imaging-related clustering, pathogenesis-related clustering, and treatment-related clustering were identified. Furthermore, keyword analysis has unveiled emerging frontier areas including pachychoroid disease, choroidal vasculature abnormalities, PDT therapy, and optical coherence tomography that have garnered increasing interest. Conclusion This study presents a comprehensive review of central serous retinopathy research conducted in the past two decades, highlighting key trends and exploring emerging research frontiers within this field. As such, it provides valuable references and suggestions for researchers engaged in studying this topic.

Analyzing supply chain technology trends through network analysis and clustering techniques: a patent-based study

Analyzing supply chain technology trends through network analysis and clustering techniques: a patent-based study

Major Problems in Clinical Psychological Science and How to Address them. Introducing a Multimodal Dynamical Network Approach

Abstract Background Despite impressive dissemination programs of best-practice therapies, clinical psychology faces obstacles in developing more efficacious treatments for mental disorders. In contrast to other medical disciplines, psychotherapy has made only slow progress in improving treatment outcomes. Improvements in the classification of mental disorders could enhance the tailoring of treatments to improve effectiveness. We introduce a multimodal dynamical network approach, to address some of the challenges faced by clinical research. These challenges include the absence of a comprehensive meta-theory, comorbidity, substantial diagnostic heterogeneity, violations of ergodicity assumptions, and a limited understanding of causal processes. Methods Through the application of multimodal dynamical network analysis, we describe how to advance clinical research by addressing central problems in the field. By utilizing dynamic network analysis techniques (e.g., Group Iterative Multiple Model Estimation, multivariate Granger causality), multimodal measurements (i.e., psychological, psychopathological, and neurobiological data), intensive longitudinal data collection (e.g., Ecological Momentary Assessment), and causal inference methods (e.g., GIMME), our approach could improve the comprehension and treatment of mental disorders. Under the umbrella of the systems approach and utilizing e.g., graph theory and control theory, we aim to integrate data from longitudinal, multimodal measurements. Results The multimodal dynamical network approach enables a comprehensive understanding of mental disorders as dynamic networks of interconnected symptoms. It dismantles artificial diagnostic boundaries, facilitating a transdiagnostic view of psychopathology. The integration of longitudinal data and causal inference techniques enhances our ability to identify influential nodes, prioritize interventions, and predict the impact of therapeutic strategies. Conclusion The proposed approach could improve psychological treatment by providing individualized models of psychopathology and by suggesting individual treatment angles.

Technical-scientific production and knowledge networks about medicinal plants and herbal medicines in the Amazon.

This paper explores the role of Brazilian research institutions in the global and national context of study of medicinal plants. Most of these plants have ethnopharmacological use and herbal medicines related to the Amazon. It highlights Brazil's position in scientific production and the importance of Amazonian resources in developing phytomedicines. The study aims to provide an overview of the technical-scientific production of medicinal plants and herbal medicines related to the Amazon, focusing on scientific impact, collaboration, Technology Readiness Level (TRL) of scientific production, and innovation system maturity. The study employs a comprehensive methodological approach, including data collection from Scopus covering the period from 2002 to 2022. The data was cleaned and analyzed using bibliometric and network analysis techniques. Advanced natural language processing techniques, such as Latent Dirichlet Allocation and Jaccard distance measure, were used for TRL classification. The findings reveal a predominant contribution from Brazilian institutions and authors, with 1,850 publications analyzed. Key areas identified include Pharmacology, Toxicology, Pharmaceuticals, Medicine, and Biochemistry. The study also uncovers various collaborative networks and technological maturity levels, with a significant focus on early-stage development phases. The research concludes that Brazilian institutions, particularly those in the Amazon region, play a significant role in the scientific exploration and development of medicinal plants and herbal medicines. Despite this, countries like the USA were proportionally more productive in clinical trial research. The study underscores the potential of Brazil's rich biodiversity and traditional knowledge in the pharmaceutical industry, particularly for neglected diseases. It suggests the need for stronger research systems and international collaboration to leverage these resources for global health benefits.