Network Analysis Research Articles

비대면 온라인 수업에서의 학습 참여도와 학생 만족도의 관계에 관한 실증적 연구

Objectives The purpose of this study is to empirically analyze the relationship between learning engagement and student satisfaction in a contactless online class environment. Methods The study employed regression analysis and text-based network analysis as research methods. Regression analysis was used to quantitatively measure the direct impact of learning engagement factors on stu-dent satisfaction. Text-based network analysis was used to explore the indirect effects on satisfaction by identify-ing information flow and centrality within the learning community. Results The analysis revealed a positive correlation between course completion rates and the frequency of the keyword ‘hard work’ in lecture comments, as well as the number of completed quiz submissions. However, the number of lectures and viewing time did not have a significant impact. The network analysis indicated that key-words such as ‘fun,’ ‘informative,’ ‘quiz,’ and ‘completion’ were central, suggesting that learner interaction is a crucial factor in satisfaction. Conclusions In conclusion, this study suggests that promoting interaction among learners and encouraging their active participation in course design can improve the efficiency and satisfaction of virtual classes. The empirical data provided here is expected to contribute to the improvement of educational technology.

Transglutaminase 2 serves as a pathogenic hub gene of KRAS mutant colon cancer based on integrated analysis

BACKGROUND Colon cancer is acknowledged as one of the most common malignancies worldwide, ranking third in United States regarding incidence and mortality. Notably, approximately 40% of colon cancer cases harbor oncogenic KRAS mutations, resulting in the continuous activation of epidermal growth factor receptor signaling. AIM To investigate the key pathogenic genes in KRAS mutant colon cancer holds considerable importance. METHODS Weighted gene co-expression network analysis, in combination with additional bioinformatics analysis, were conducted to screen the key factors driving the progression of KRAS mutant colon cancer. Meanwhile, various in vitro experiments were also conducted to explore the biological function of transglutaminase 2 (TGM2). RESULTS Integrated analysis demonstrated that TGM2 acted as an independent prognostic factor for progression-free survival. Immunohistochemical analysis on tissue microarrays revealed that TGM2 was associated with an elevated probability of perineural invasion in patients with KRAS mutant colon cancer. Additionally, biological roles of the key gene TGM2 was also assessed, suggesting that the downregulation of TGM2 attenuated the proliferation, invasion, and migration of the KRAS mutant colon cancer cell line. CONCLUSION This study underscores the potential significance of TGM2 in the progression of KRAS mutant colon cancer. This insight not only offers a theoretical foundation for therapeutic approaches but also highlights the need for additional clinical trials and fundamental research to support our preliminary findings.

대학 강의평가 서술형 문항의 감정분석 및 의미연결망 분석

Objectives The purpose of this study was to derive directions and implications for quality of education through sentiment analysis and semantic network analysis of university descriptive course evaluations. Methods The analysis data consisted of 243,900 descriptive lecture evaluations from the first semester of 2020 to the first semester of 2023 at University A. Emotion words were classified in the order of positive, negative, and neutral, and the frequency and ratio of descriptive lecture evaluation contents by year, subject, and college were calculated. N-gram and clustering were analyzed using Textome, and network centrality between keywords was analyzed and visualized using Ucinet 6. Results First, in the sentiment analysis trend for descriptive lecture evaluation questions by year, course com-pleted, and university, positive opinions were found to be dominant. Second, as a result of the keyword frequency and N-gram analysis of the lecture evaluation contents, the frequency of ‘class’ and ‘thank you’ was high in pos-itive evaluations, and the frequency of ‘class’, ‘assignment’, and ‘unsatisfactory’ was high in negative descriptive course evaluations. In addition, ‘class’ was expanded around the keywords ‘content’, ‘progress’, ‘time’, and ‘regrettable’. Third, the results of semantic network analysis between keywords in the lecture evaluation contents showed that in the positive type, ‘gratitude’, ‘class’, ‘hard work’, ‘explanation’, ‘student’, ‘kindness’, ‘help’, ‘understanding’, ‘assignment’, ‘time’ were the most important and connected keywords. In the negative course evaluation, keywords including ‘class’, ‘lecture’, ‘professor’, ‘assignment’, ‘disappointing’, ‘difficult’, ‘anxiety of test’ were analyzed as the most connected. Conclusions This study showed the possibility of suggesting directions for education improvement through de-scriptive lecture evaluation using sentiment analysis and semantic network analysis.

Salivary metabolites are promising noninvasive biomarkers of drug-induced liver injury

BACKGROUND Drug-induced liver injury (DILI) is one of the most common adverse events of medication use, and its incidence is increasing. However, early detection of DILI is a crucial challenge due to a lack of biomarkers and noninvasive tests. AIM To identify salivary metabolic biomarkers of DILI for the future development of noninvasive diagnostic tools. METHODS Saliva samples from 31 DILI patients and 35 healthy controls (HCs) were subjected to untargeted metabolomics using ultrahigh-pressure liquid chromatography coupled with tandem mass spectrometry. Subsequent analyses, including partial least squares-discriminant analysis modeling, t tests and weighted metabolite coexpression network analysis (WMCNA), were conducted to identify key differentially expressed metabolites (DEMs) and metabolite sets. Furthermore, we utilized least absolute shrinkage and selection operato and random fores analyses for biomarker prediction. The use of each metabolite and metabolite set to detect DILI was evaluated with area under the receiver operating characteristic curves. RESULTS We found 247 differentially expressed salivary metabolites between the DILI group and the HC group. Using WMCNA, we identified a set of 8 DEMs closely related to liver injury for further prediction testing. Interestingly, the distinct separation of DILI patients and HCs was achieved with five metabolites, namely, 12-hydroxydodecanoic acid, 3-hydroxydecanoic acid, tetradecanedioic acid, hypoxanthine, and inosine (area under the curve: 0.733-1). CONCLUSION Salivary metabolomics revealed previously unreported metabolic alterations and diagnostic biomarkers in the saliva of DILI patients. Our study may provide a potentially feasible and noninvasive diagnostic method for DILI, but further validation is needed.

Utility of intracranial EEG networks depends on re-referencing and connectivity choice

Abstract Studies of intracranial EEG (iEEG) networks have been used to reveal seizure generators in patients with drug-resistant epilepsy. iEEG is implanted to capture the epileptic network, the collection of brain tissue that forms a substrate for seizures to start and spread. Interictal iEEG measures brain activity at baseline and networks computed during this state can reveal aberrant brain tissue without requiring seizure recordings. iEEG network analyses require choosing a reference and applying statistical measures of functional connectivity. Approaches to these technical choices vary widely across studies, and the impact of these technical choices on downstream analyses is poorly understood. Our objective was to examine the effects of different re-referencing and connectivity approaches on connectivity results and on the ability to lateralize the seizure onset zone in patients with drug-resistant epilepsy. We applied 48 pre-processing pipelines to a cohort of 125 patients with drug-resistant epilepsy recorded with interictal iEEG across two epilepsy centers to generate iEEG functional connectivity networks. 24 functional connectivity measures across time and frequency domains were applied in combination with common average re-referencing (CAR) or bipolar re-referencing (BR). We applied an unsupervised clustering algorithm to identify groups of pre-processing pipelines. We subjected each pre-processing approach to three quality tests: 1) the introduction of spurious correlations, 2) robustness to incomplete spatial sampling, and 3) the ability to lateralize the clinician-defined seizure onset zone. Three groups of similar pre-processing pipelines emerged: CAR pipelines, BR pipelines, and relative entropy-based connectivity pipelines. Relative entropy and CAR networks were more robust to incomplete electrode sampling than BR and other connectivity methods (Friedman test, Dunn-Sˇidák test p < 0.0001). BR reduced spurious correlations at non-adjacent channels better than CAR (Δ mean from machine ref = -0.36 vs -0.22), and worse in adjacent channels (Δ mean from machine ref = -0.14 vs -0.40). Relative entropy-based network measures lateralized the seizure onset hemisphere better than other measures in patients with temporal lobe epilepsy (BH-corrected p < 0.05, Cohen’s d: 0.60 - 0.76). Finally, we present an interface where users can rapidly evaluate iEEG pre-processing choices to select the optimal preprocessing methods tailored to specific research questions. The choice of preprocessing methods affects downstream network analyses. Choosing a single method among highly correlated approaches can reduce redundancy in processing. Relative entropy outperforms other connectivity methods in multiple quality tests. We present a method and interface for researchers to optimize their preprocessing methods for deriving iEEG brain networks.

Emerging trends of environmental accounting research: a science mapping approach

Purpose This paper aims to provide a comprehensive knowledge structure for environmental accounting (EA) research by identifying research hotspots and frontiers and suggesting future trends for scholarly investigation. Design/methodology/approach Using the bibliometric method, the paper analyzed 321 academic articles and reviews in international journals from the Scopus database. Science mapping, including strategic diagrams, network analysis and thematic maps, was used to analyze the evolution of topics and to recommend future research trends. Findings EA research is an emerging trend. This study presents the landscape of EA research by constructing the “synthesis house of knowledge” in EA. Significant EA research areas were identified and future research trends were suggested based on the results. Practical implications This paper provides insights into the current state of EA research and identifies potential future research trends that can help scholars and experts develop and stimulate further advancement of the research in this field. Social implications Results may motivate policymakers and government agencies to formulate regulations to enforce appropriate corporate environmental strategies to better manage environmental costs and reduce community environmental impact. Originality/value The study provides an intellectually structured literature review of the EA research field; identifies the main themes through the innovative use of network analysis, strategic diagrams and thematic maps; highlights research gaps; and offers potential research questions and suggestions for future research. The novelty of this paper lies in the “synthesis house of knowledge” in EA and the research gaps and potential and specific research questions constructed from the findings.

Mass Spectrometry-Based Proteomics Analysis Unveils PTPRS Inhibits Proliferation and Inflammatory Response of Keratinocytes in Psoriasis.

In this study, we used data-independent acquisition-mass spectrometry (DIA-MS) to analyze the serum proteome in psoriasis vulgaris (PsO). The serum proteomes of seven healthy controls and eight patients with PsO were analyzed using DIA-MS. Weighted gene co-expression network analysis was used to identify differentially expressed proteins (DEPs) that were closely related to PsO. Hub proteins of PsO were also identified. The Proteomics Drug Atlas 2023 was used to predict candidate hub protein drugs. To confirm the expression of the candidate factor, protein tyrosine phosphatase receptor S (PTPRS), in psoriatic lesions and the psoriatic keratinocyte model, immunohistochemical staining, quantitative real-time polymerase chain reaction, and western blotting were performed. A total of 129 DEPs were found to be closely related to PsO. The hub proteins for PsO were PVRL1, FGFR1, PTPRS, CDH2, CDH1, MCAM, and THY1. Five candidate hub protein drugs were identified: encorafenib, leupeptin, fedratinib, UNC 0631, and SCH 530348. PTPRS was identified as a common pharmacological target for these five drugs. PTPRS knockdown in keratinocytes promoted the proliferation and expression of IL-1α, IL-1β, IL-23A, TNF-α, MMP9, CXCL8, and S100A9. PTPRS expression was decreased in PsO, and PTPRS negatively regulated PsO. PTPRS may be involved in PsO pathogenesis through the inhibition of keratinocyte proliferation and inflammatory responses and is a potential treatment target for PsO.

Microbiome structure and function in parallel full-scale aerobic granular sludge and activated sludge processes.

Aerobic granular sludge (AGS) and conventional activated sludge (CAS) are two different biological wastewater treatment processes. AGS consists of self-immobilised microorganisms that are transformed into spherical biofilms, whereas CAS has floccular sludge of lower density. In this study, we investigated the treatment performance and microbiome dynamics of two full-scale AGS reactors and a parallel CAS system at a municipal WWTP in Sweden. Both systems produced low effluent concentrations, with some fluctuations in phosphate and nitrate mainly due to variations in organic substrate availability. The microbial diversity was slightly higher in the AGS, with different dynamics in the microbiome over time. Seasonal periodicity was observed in both sludge types, with a larger shift in the CAS microbiome compared to the AGS. Groups important for reactor function, such as ammonia-oxidising bacteria (AOB), nitrite-oxidising bacteria (NOB), polyphosphate-accumulating organisms (PAOs) and glycogen-accumulating organisms (GAOs), followed similar trends in both systems, with higher relative abundances of PAOs and GAOs in the AGS. However, microbial composition and dynamics differed between the two systems at the genus level. For instance, among PAOs, Tetrasphaera was more prevalent in the AGS, while Dechloromonas was more common in the CAS. Among NOB, Ca. Nitrotoga had a higher relative abundance in the AGS, while Nitrospira was the main nitrifier in the CAS. Furthermore, network analysis revealed the clustering of the various genera within the guilds to modules with different temporal patterns, suggesting functional redundancy in both AGS and CAS. KEY POINTS: • Microbial community succession in parallel full-scale aerobic granular sludge (AGS) and conventional activated sludge (CAS) processes. • Higher periodicity in microbial community structure in CAS compared to in AGS. • Similar functional groups between AGS and CAS but different composition and dynamics at genus level.

Blood RNA-sequencing across the continuum of ANA-positive autoimmunity reveals insights into initiating immunopathology

ObjectiveMechanisms underpinning clinical evolution to systemic lupus erythematosus (SLE) from preceding antinuclear antibodies (ANA) positivity are poorly understood. This study aimed to understand blood immune cell transcriptional signatures associated with...

Identification and analysis of immunogenicity and immunotherapy efficacy by fatty acid genes: a novel prognostic features of lumbar disc herniation and Mendelian randomization analysis.

Background: Sciatica is a phrase used to describe radiating leg discomfort. The most common cause is lumbar disc herniation (LDH), which is considered to start in the nucleus pulposus. Advancements in lipidomics and metabolomics have unveiled the complex role of fatty acid metabolism (FAM) in both healthy and pathological states. However, the specific roles of fatty acid metabolism-related genes (FAMGs) in shaping therapeutic approaches, especially in LDH, remain largely unexplored and are a subject of ongoing research. Methods: The junction of the weighted correlation network analysis (WGCNA) test with 6 FAMGs enabled the finding of FAMGs. Gene set variation analysis (GSVA) was used to identify the possible biological activities and pathways of FAMGs. LASSO was used to determine diagnostic effectiveness of the four FAMGs in diagnosing LDH. GSE124272, GSE147383, GSE150408, and GSE153761 were utilized to confirm the levels of expression of four FAMGs. Results: Four FAMGs were discovered [Acyl-CoA Thioesterase 4 (ACOT4), Cytochrome P450 Family 4 Subfamily A Member 11 (CYP4A11), Acyl-CoA Dehydrogenase Long Chain (ACADL), Enoyl-CoA Hydratase and 3-Hydroxyacyl CoA Dehydrogenase (EHHADH)] For biological function analysis, mhc class ib receptor activity, response to thyroxine, response to l phenylalanine derivative were emphasized. Conclusions: FAMGs can help with prognosis and immunology, and provide evidence for fatty acid metabolism-related targeted therapeutics. In LDH, FAMGs and their interactions with immune cells might be therapeutic targets.

Starch and sucrose metabolism plays an important role in the stem development in Medicago sativa

The forage quality of alfalfa (Medicago sativa) stems is greater than the leaves. Sucrose hydrolysis provides energy for stem development, with starch being enzymatically converted into sucrose to maintain energy homeostasis. To understand the physiological and molecular networks controlling stem development, morphological characteristics and transcriptome profiles in the stems of two alfalfa cultivars (Zhungeer and WL168) were investigated. Based on transcriptome data, we analysed starch and sugar contents, and enzyme activity related to starch-sugar interconversion. Zhungeer stems were shorter and sturdier than WL168, resulting in significantly higher mechanical strength. Transcriptome analysis showed that starch and sucrose metabolism were significant enriched in the differentially expressed genes of stems development in both cultivars. Genes encoding INV, bglX, HK, TPS and glgC downregulated with the development of stems, while the gene encoding was AMY upregulated. Weighted gene co-expression network analysis revealed that the gene encoding glgC was pivotal in determining the variations in starch and sucrose contents between the two cultivars. Soluble carbohydrate, sucrose, and starch content of WL168 were higher than Zhungeer. Enzyme activities related to sucrose synthesis and hydrolysis (INV, bglX, HK, TPS) showed a downward trend. The change trend of enzyme activity was consistent with gene expression. WL168 stems had higher carbohydrate content than Zhungeer, which accounted for more rapid growth and taller plants. WL168 formed hollow stems were formed during rapid growth, which may be related to the redistribution of carbohydrates in the pith tissue. These results indicated that starch and sucrose metabolism play important roles in the stem development in alfalfa.

Epiphytic and endophytic bacteria on Camellia oleifera phyllosphere: exploring region and cultivar effect

The epiphytic and endophytic bacteria play an important role in the healthy growth of plants. Both plant species and growth environmental influence the bacterial population diversity, yet it is inconclusive whether it is the former or the latter that has a greater impact. To explore the communities of the epiphytic and endophytic microbes in Camellia oleifera, this study assessed three representative C. oleifera cultivars from three areas in Hunan, China by Illumina high-throughput sequencing. The results showed that the diversity and species richness of endophytic microbial community in leaves were significantly higher than those of microbial community in the epiphytic. The diversity and species richness of epiphytic and endophytic microbes are complex when the same cultivar was grown in different areas. The C. oleifera cultivars grown in Youxian had the highest diversity of epiphytic microbial community, but the lowest abundance, while the cultivars grown in Changsha had the highest diversity and species richness of endophytic microbes in leaves. It was concluded that the dominant phylum mainly included Proteobacteria, Actinobacteriota and Firmicutes through the analysis of the epiphytic and endophytic microbial communities of C. oleifera. The species and relative abundances of epiphytic and endophytic microbial community were extremely different at the genus level. The analysis of NMDS map and PERMANOVA shows that the species richness and diversity of microbial communities in epiphytes are greatly influenced by region. However, the community structure of endophytic microorganisms in leaves is influenced by region and cultivated varieties, but the influence of cultivars is more significant. Molecular ecological network analysis showed that the symbiotic interaction of epiphytic microbial community was more complex.

Architectural learning networks: a study on eliciting the learning agendas of architectural schools

PurposeThe purpose of this paper is to demonstrate how network theory and methods can provide insights into the forces shaping architectural learning agendas and knowledge construction in architectural schools.Design/methodology/approachThe methodology involves conceptualising learning as a constructivist process and the agenda as an interconnected network of actors, concepts and relations. Network analysis techniques, including centrality and brokerage metrics, are used to identify roles and knowledge flows using the data locally collected from Turkish universities as well as from the OpenSyllabus open-source database.FindingsThe analysis reveals the enduring influence of early modernists, signalling imbalanced canon formation in the architectural learning system. However, marginal voices highlight struggles in integrating unconventional perspectives. Limited integration of local figures indicates a consolidation of Eurocentric epistemes. Identifying these hidden forces is vital for reimagining learning agendas and socio-culturally engaged forms of learning. Pioneering figures demonstrate potential for synthesis when situated as brokers, not bifurcated schools.Research limitations/implicationsThe outcomes are limited by the geographical and temporal boundaries of the data and the analysis method employed. Despite limitations, the diagnostic network framework reveals architectural learning as an open, contested ecosystem demanding pluralistic pedagogies concerning not only the global but the local, both canonical and marginal. Further research covering more data could enrich the understanding of qualitative complexities.Practical implicationsThe network perspective prompts critical reflexivity about power, ideology and exclusion in knowledge construction. Strategic inclusion and diversification of voices provide pathways to bridge divides and ground learning locally.Originality/valueThis research offers a methodology model to examine forces and influences shaping architectural education by elucidating hidden and remote roles and knowledge gaps in learning agendas. Extending the techniques more widely can enable strategic interventions toward inclusive, impactful learning across disciplines, time and geographies.

Layout analysis of the RCEP international airline network based on hub identification using improved contribution matrix

The signing of the Regional Comprehensive Economic Partnership agreement brings new opportunities for the development of international air transportation. Faced with fierce competition, it is worth studying how hub airports should enhance competitiveness, and how low-cost carriers and full-service carriers should optimize the RCEP international airline network layout for better development. Aiming at providing suggestions for the development of hub airports, low-cost and full-service carriers in the RCEP international airline network, this paper identifies the hub airports, analyzes the layout of the RCEP international airline network, and the multi-layered characteristics based on an improved contribution matrix using data from 2010 to 2019 collected from the Official Airline Guide (OAG). This method comprehensively considers attributes of hub airports and the multi-layered characteristics of the airports and routes. The layout analysis indicates that the RCEP international transportation market presents a more open environment for competition and cooperation where base carriers are often the biggest supporters of hub construction. The multi-layered characteristics analysis reveals that low-cost carriers contribute more towards opening up new RCEP routes than full-service carriers. It is advised that carriers newly entering the RCEP international aviation transportation market and low-cost carriers dedicate to establishing new routes around their hub airports to monopolize this market and enhance their market share, whilst full-service carriers consolidate existing routes and increase route density to achieve economic benefits.

Mapping science through editorial board interlocking: connections and distance between fields of knowledge and institutional affiliations

AbstractResearch is a global enterprise underpinned by the general belief that findings need to be true to be considered scientific. In the complex system of scientific validation, editorial boards (EBs) play a fundamental role in guiding journals’ review process, which has led many stakeholders of sciences to metaphorically picture them as the “gatekeepers of knowledge.” In an attempt to address the academic structure that governs sciences through editorial board interlocking (EBI, the cross-presence of EB members in different journals) and social network analysis, the aim of this study is threefold: first, to map the connection between fields of knowledge through EBI; second, to visualize and empirically test the distance between social and general sciences; and third, to uncover the institutional structure (i.e., universities) that governs these connections. Our findings, based on the dataset collected through the Open Editors initiative for the journals indexed in the JCR, revealed a substantial level of collaboration between all fields, as suggested by the connections between EBs. However, there is a statistically significant difference between the weight of the edges and the path lengths connecting the fields of natural sciences to the fields of social sciences (compared to the connections within), indicating the development of different research cultures and invisible colleges in these two research areas. The results also show that a central group of US institutions dominates most journal EBs, indirectly suggesting that US scientific norms and values still prevail in all fields of knowledge. Overall, our study suggests that scientific endeavor is highly networked through EBs.

Upregulation of HMOX1 associated with M2 macrophage infiltration and ferroptosis in proliferative diabetic retinopathy

The roles of immune cell infiltration and ferroptosis in the progression of proliferative diabetic retinopathy (PDR) remain unclear. To identify upregulated molecules associated with immune infiltration and ferroptosis in PDR, GSE60436 and GSE102485 datasets were downloaded from the Gene Expression Omnibus (GEO). Genes associated with immune cell infiltration were examined through Weighted Gene Co-expression Network Analysis (WGCNA) and CIBERSORT algorithm. Common differentially expressed genes (DEGs) were intersected with ferroptosis-associated and immune cell infiltration-related genes. Localization of cellular expression was confirmed by single-cell analysis of GSE165784 dataset. Findings were validated by qRT-PCR, ELISA, Western blotting, and immunofluorescence staining. As a result, the infiltration of M2 macrophages was significantly elevated in fibrovascular membrane samples from PDR patients than the retinas of control subjects. Analysis of DEGs, M2 macrophage-related genes and ferroptosis-related genes identified three hub intersecting genes, TP53, HMOX1 and PPARA. qRT-PCR showed that HMOX1 was significantly higher in the oxygen-induced retinopathy (OIR) mouse model retinas than in controls. Single-cell analysis confirmed that HMOX1 was located in M2 macrophages. ELISA and western blotting revealed elevated levels of HMOX1 in the vitreous humor of PDR patients and OIR retinas, and immunofluorescence staining showed that HMOX1 co-localized with M2 macrophages in the retinas of OIR mice. This study offers novel insights into the mechanisms associated with immune cell infiltration and ferroptosis in PDR. HMOX1 expression correlated with M2 macrophage infiltration and ferroptosis, which may play a crucial role in PDR pathogenesis.

Gender differences in connectivity patterns: Exploring autism traits, sleep disturbances, alexithymia, and aggression: A general population survey

BackgroundThe prevalence of elevated alexithymia trait is notably high among individuals with autism spectrum disorder (ASD), which will cause emotional and behavioral issues, especially aggressive behavior. However, few studies explored the inter links between autistic traits, alexithymia, sleep disturbance and aggression. Using network analysis, current study explores relationships between these symptoms within a general population sample. MethodSeven hundred and sixty-one (age 18–30, 351 male, 410 female) participants were recruited through an online platform. Network analysis was used to clarify relationship between these symptoms, and network comparison was conducted to determine whether network pattern differed between male and female. ResultsDiscernible patterns linking autistic traits to alexithymia, sleep disturbance and aggression. These patterns exhibited a satisfactory level of stability and consistency when tested and retested. Among the nodes examined, the node with the highest centrality and expected influence in the network is aggression. Network constructed using male and female data show different patterns of structure and strength. ConclusionUnderstanding the association between autism traits and other behavioral and emotional issues, as well as the possible role of gender, can help optimize targeted interventions and improve the quality of life of individuals with autism.

Why do users continue to contribute to darknet Child Sexual Abuse Material forums? Examining social exchange, social capital, and social learning explanations using digital forensic artifacts

BackgroundThe darknet hosts an increasing number of hidden services dedicated to the distribution of child sexual abuse material (CSAM). Given that by contributing CSAM to the forum members subject themselves to criminal prosecution, questions regarding the motivation for members contributing to darknet CSAM forums arise. ObjectiveBuilding on insights gained from research into clearnet communities, here we examine the extent to which social incentives generated by the online CSAM community may explain members' posting behavior on darknet CSAM forums. Participants and settingWe analyze digital forensic artifacts on the online behavior of members of a darknet CSAM forum that was shut down by law enforcement agencies in July 2015. MethodsWe apply group-based trajectory modelling (GBTM), social network analysis, and mixed-effect survival models. ResultsApplying GBTM three posting trajectories can be distinguished. Social network analyses finds the reply network to be more centralized than predicted by chance. Mixed-effect survival models show positive associations between the length of members' first post and the time since members' first registration on the forum and subsequent posting. Contrarily, the number of replies received appears to mitigate subsequent posting. ConclusionsFindings show posting activity on the forum to be concentrated in a minority of forum members who show posting trajectories that are both frequent and persistent. Results further suggest persistence in posting is motivated by social identity and, to a lesser extent, differential association processes.

Alteration of multilayer network perspective on gray and white matter connectivity in obstructive sleep apnea.

The objective of this research was to examine changes in the neural networks of both gray and white matter in individuals with obstructive sleep apnea (OSA) in comparison to those without the condition, employing a comprehensive multilayer network analysis. Patients meeting the criteria for OSA were recruited through polysomnography, while a control group of healthy individuals matched for age and sex was also assembled. Utilizing T1-weighted imaging, a morphometric similarity network was crafted to represent gray matter, while diffusion tensor imaging provided structural connectivity for constructing a white matter network. A multilayer network analysis was then performed, employing graph theory methodologies. We included 40 individuals diagnosed with OSA and 40 healthy participants in our study. Analysis revealed significant differences in various global network metrics between the two groups. Specifically, patients with OSA exhibited higher average degree overlap and average multilayer clustering coefficient (28.081 vs. 23.407, p < 0.001; 0.459 vs. 0.412, p = 0.004), but lower multilayer modularity (0.150 vs. 0.175, p = 0.001) compared to healthy controls. However, no significant differences were observed in average multiplex participation, average overlapping strength, or average weighted multiplex participation between the patients with OSA and healthy controls. Moreover, several brain regions displayed notable differences in degree overlap at the nodal level between patients with OSA and healthy controls. Remarkable alterations in the multilayer network, indicating shifts in both gray and white matter, were detected in patients with OSA in contrast to their healthy counterparts. Further examination at the nodal level unveiled notable changes in regions associated with cognition, underscoring the effectiveness of multilayer network analysis in exploring interactions across brain layers.

A Psychometric Network Analysis Approach for Detecting Item Wording Effects in Self-report Measures across Subgroups

In this study, we explored psychometric network analysis (PNA) as an alternative method for identifying item wording effects in self-report instruments. We examined the functioning of negatively worded items in the network structures of two math-related scales from the 2019 Trends in International Mathematics and Science Study (TIMSS); Students Like Learning in Mathematics (SLLM); and Students Confident in Mathematics (SCM). We also explored how the negatively worded items functioned in network structures across demographic subgroups. Data were drawn from eight countries that represented diverse levels of math performance and cultural attitudes toward school ( n = 75,972). We found that negatively worded items were distinct from the positively worded items in the SLLM and SCM item networks, and that this effect was consistent across all age- and country-level subgroups. Based on these findings, we recommend PNA as a data-driven approach for detecting wording effects effectively.