Network Analysis Research Articles

Advancing the Measurement of Social Functioning in Schizophrenia: Applications of Egocentric Social Network Analysis.

People with schizophrenia face challenges with forming and maintaining social relationships, often resulting in poor social functioning. Commonly used measures of social functioning provide broad information relating to social relationships, but they do not adequately capture information regarding network structure and characteristics of network members. One method that can assess these more detailed aspects of social networks and provide a more comprehensive understanding of social functioning deficits is egocentric social network analysis (SNA). SNA is a scientific discipline that uses principles of network science and graph theory to analyze social relations quantitatively. Even though some types of SNA have been applied in prior schizophrenia studies, its application as a framework to measure social functioning has been extremely limited. Therefore, this article aims to formally introduce SNA and select quantitative SNA metrics, including measures of network composition, structure, homophily, and centrality, to schizophrenia researchers as novel ways of measuring components of social functioning. To demonstrate the application of SNA, we provide illustrative examples of the SNA metrics and graphical diagrams of social networks for two individuals with schizophrenia.

Protest and repression on social media: Pro-Navalny and pro-government mobilization dynamics and coordination patterns on Russian Twitter

In this study, we examine connective protest mobilization and suppression during the 2021 protests in Russia. We use time series analysis to study the dynamic interplay between the pro-Navalny movement and pro-government countermovement on Twitter, complemented by network analyses of co-retweeting networks to assess the movements’ coordination patterns. Findings show that pro-Navalny accounts were more active and coordinated within more centralized Twitter networks than pro-government accounts. Contrarily, the pro-government camp employed preventive communication tactics and coordinated in more clustered networks. Granger causality tests reveal that pro-Navalny tweeting activity triggered increased pro-regime reaction during the largest protests on 23 January and 21 April, whereas pro-government tweeting activity caused the escalation of pro-Navalny reaction during the 14 February protests. Both sides’ tweeting activity decreased after the February protests, presumably due to external repression. These findings contribute to a deeper understanding of online mobilization and coordination strategies via social media in authoritarian contexts.

Revisiting the relation between academic buoyancy and coping: A network analysis

Academic buoyancy, the capacity to respond to minor academic adversities, is expected to enable students to effectively deal with failure. Prior research, however, has shown negligible relations between buoyancy and coping, but only considered a limited set of coping strategies. In addition, academic buoyancy and effective coping are expected to positively relate to higher academic achievement. However, studies examining how coping could mediate relations from academic buoyancy to achievement are lacking. In the present study (N = 535 upper secondary students, mean age 16.4 years), we examined relations between students’ buoyancy, coping with an examination failure, and academic achievement. We considered an extensive set of nine coping strategies (five adaptive, four maladaptive) and used a novel network analysis, alongside traditional analytic approaches (correlation, structural equation modelling). Buoyancy and coping were assessed with self-report, and achievement from an end-of-year examination. Buoyancy was positively related with adaptive, and negatively with maladaptive, coping strategies both in structural equation modeling and in the network analysis. In addition, structural equation modeling showed positive and negative indirect relations between buoyancy and achievement that were mediated by adaptive coping strategies. Our findings suggest that buoyancy interventions to enhance adaptive, and reduce maladaptive, coping strategies could be suitable ways to help students overcome examination failure.

Research on rural tourism environment perception based on grounded theory A case study of Beishan Village, Zhuhai City, Guangdong Province, China

Drawing upon its regional culture, natural landscapes, and architectural features, Beishan Village in Zhuhai City, Guangdong Province, has forged a distinctive rural development model that combines an industrial park with tourism. While this approach has catalyzed rural progress, it also encounters numerous complex practical challenges. Utilizing the grounded theory method, this study employs participatory observation, in-depth interviews, and network data analysis to investigate the perspectives of three key stakeholder groups: villagers, tourists, and resident merchants. A model is constructed to capture their environmental perceptions of rural tourism. Based on an analysis of network and interview textual data, as well as the influencing factors identified through the theoretical model, this study proposes several optimization strategies. These include enhancing infrastructure development, cultivating a regional brand culture, strengthening the institutional management framework, establishing a rural sharing economy platform, and introducing digital tours. These strategies are designed to refine and enhance Beishan Village's development model, offering insights for similar villages and advancing the integration of beautiful China initiatives with urban and rural development efforts.

Genome-wide DNA methylation profiles and breast cancer among World Trade Center survivors.

Increased incidence of cancer has been reported among World Trade Center (WTC)-exposed persons. Aberrant DNA methylation is a hallmark of cancer development. To date, only a few small studies have investigated the relationship between WTC exposure and DNA methylation. The main objective of this study was to assess the DNA methylation profiles of WTC-exposed community members who remained cancer free and those who developed breast cancer. WTC-exposed women were selected from the WTC Environmental Health Center clinic, with peripheral blood collected during routine clinical monitoring visits. The reference group was selected from the NYU Women's Health Study, a prospective cohort study with blood samples collected before 9 November 2001. The Infinium MethylationEPIC array was used for global DNA methylation profiling, with adjustments for cell type composition and other confounders. Annotated probes were used for biological pathway and network analysis. A total of 64 WTC-exposed (32 cancer free and 32 with breast cancer) and 32 WTC-unexposed (16 cancer free and 16 with prediagnostic breast cancer) participants were included. Hypermethylated cytosine-phosphate-guanine probe sites (defined as β > 0.8) were more common among WTC-exposed versus unexposed participants (14.3% vs. 4.5%, respectively, among the top 5000 cytosine-phosphate-guanine sites). Cancer-related pathways (e.g., human papillomavirus infection, cGMP-PKG) were overrepresented in WTC-exposed groups (breast cancer patients and cancer-free subjects). Compared to the unexposed breast cancer patients, 47 epigenetically dysregulated genes were identified among WTC-exposed breast cancers. These genes formed a network, including Wnt/β-catenin signaling genes WNT4 and TCF7L2, and dysregulation of these genes contributes to cancer immune evasion. WTC exposure likely impacts DNA methylation and may predispose exposed individuals toward cancer development, possibly through an immune-mediated mechanism.

Development of novel MUC1-C targeting antibody-drug conjugate for pancreatic cancer treatment.

e15025 Background: Pancreatic ductal adenocarcinoma (PDAC) is one of the most aggressive forms of cancers, which is usually diagnosed in advanced stages and presents an abysmal 5-year survival rate of 3-10%. With late detection of metastasized/advanced disease, curative surgery is not an option for most patients. Accordingly, there is an urgent unmet need for development of therapeutics targeting the pancreatic cancer cells. MUC1 is one such cell surface antigen that is aberrantly overexpressed in most solid tumors including pancreatic cancer. In this study we report preclinical efficacy of pharmacological inhibition of MUC1 signaling by GO-203 and targeted delivery of toxin conjugated to novel MUC1-C antibodies (7B8 and 3D1) binding to the extra cellular domain (ECD) to the PDAC xenografts. Methods: Subjects from TCGA-PAAD were stratified into high (75th percentile) and low (25th percentile) MUC1-expressing cohorts. Differential expression was performed using DESeq2, followed by GSEA, and signaling network analysis using partial correlation networks, ARACNE, and BC3NET. Anti-MUC1 antibody-drug conjugates (ADCs) were generated by coupling monomethyl auristatin E (3D1-MMAE, 7B8-MMAE) via V-C-PAB linker and characterized for anti-tumor activity against pancreatic cancer cells including MIA PaCa-1, CAPAN2 and HAPF-II. Paired-end RNA-seq (50bp) was aligned and quantified to the Human Ensembl GRC38 reference using STAR aligner. Subcutaneous xenografts were obtained in nude mice by injecting 5X106 HPAF-II tumor cells. Results: Differential expression, pathway, and network analyses revealed consistent dysregulation of cancer survival-related processes, and P53-related pathways in the cohort with increased MUC1 expression. The ADCs (DAR of ~3.5) exhibited efficient internalization in cancer cells and associated apoptotic cell death (Kds = 2 - 5nM). Pharmacological inhibition of MUC1 signaling by GO-203 demonstrated reduction in canonical pathways including P53 and EMT. Treatment with 3D1-MMAE compared to both vehicle and GO-203 showed dysregulation of cellular metabolism pathways (cholesterol homeostasis, glycolysis) and exhibited several strong indicators of cellular stress including activation of unfolded protein response processes and mTorc1 signaling. Excellent tumor-suppressive effects of ADCs were observed in HPAF-II xenografts in nude mice. The 7.5 mg/kg (i.v., q7dx3), treatment showed complete tumor regression from 14th day (end of treatment) till the date of termination (Day 50). Doses up to 40 mg/kg were well tolerated by the mice and this anti-tumor efficacy was observed without any appreciable loss in body weight or adverse effects on tissues. Conclusions: Targeted delivery of MMAE toxin to the pancreatic cancer cells presents an exciting opportunity for a novel therapeutic intervention for the treatment of pancreatic cancer patients with MUC1 over expression in tumors.

Surveying the landscape of environmental social science: a bibliometric and network analysis

Social science research on the environment has grown exponentially in the past four decades alongside increasing awareness that environmental risks, such as climate change, cannot be understood by natural science interventions alone. While prior research examines how specific disciplines, like sociology, have attended to the environment or how specific problems have been engaged across disciplines, less attention has been offered to the entire landscape of research on the environment in the social sciences. In this article, we ask: What is the landscape of environmental social science? Focusing on 124,906 social scientific articles from the Web of Science, we analyze the relationship between journals publishing research on the environment. Specifically, we construct journal citation networks and topic models on bibliographic records from 1990 to 2022. Results suggest that journals form coherent communities associated with both disciplinary and interdisciplinary research topics, indicating that journal communities may be a central organizing feature of ESS. Moreover, research topics prevalent in this field have changed over time, potentially in relation to the ongoing neoliberalization of climate change politics. As scholars want to influence policy and industrial practice, their research implicitly reflects values consistent with the current political economic order. We conclude by highlighting the implications of these findings for the field of environmental social science.

Prediction of colorectal cancer chemotherapy efficacy using machine learning applied to gene expression data.

e15548 Background: FOLFOX and FOLFIRI chemotherapy are considered standard first-line treatment options for colorectal cancer (CRC). However, the criteria for selecting the appropriate treatments have not been thoroughly analyzed. Here, we aim to use machine learning and genetic profiles to identify precise multi-gene panels that can predict the response to 5-Fluorouracil-based chemotherapy in colorectal cancer patients. Methods: The genetic profiling data, including drug response profiles, were retrieved from the Gene Expression Omnibus (GEO) database. These datasets were used to train and validate machine learning models. Feature selection methods, such as least absolute shrinkage and selection operator (LASSO) and variable selection from random forests (varSelRF) and various algorithms, such as Random Forest and Support Vector Machines, were applied to develop predictive models. Functional enrichment and network analyses were performed using Ingenuity Pathway Analysis (IPA). Results: This study identified relevant gene signatures at two stages of colorectal cancer: primary and metastasis using two different chemotherapy regimens, FOLFOX and FOLFIRI. The predictive models achieved an average prediction accuracy of 93% in identifying drug response outcomes across multiple chemotherapy regimens. The application of the machine learning model suggested that 28.6% of patients who failed the treatment therapy they received would benefit from the alternative treatment. Conclusions: The developed machine learning models demonstrate potential for guiding clinicians in selecting the most effective treatment options based on individual genetic profiles. With additional clinical validation, this approach could lead to improvements in treatment outcomes for patients with CRC and other cancers.

Shared and unique features of epistemic emotions: Awe, surprise, curiosity, interest, confusion, and boredom.

Epistemic emotions are hardly ever studied together, making it difficult to predict what features are shared versus unique to each emotion. To address this, we conducted two autobiographical recall experiments. We compared awe, surprise, curiosity, interest, confusion, and boredom in terms of elicitors, subjective experience components, and action tendencies. Ratings were analyzed using network analyses, to describe the central features for the whole group of epistemic emotions. In addition, ratings were compared per emotion, to identify key features for each individual emotion. Results showed that valence, arousal, coping potential, and avoidance are central features of all epistemic emotions. Awe, surprise, and interest were relatively positive emotions, which together with curiosity, were associated with arousal, high coping potential, and approach. Confusion and boredom were relatively negative emotions, which were associated with low arousal, low coping potential, and avoidance. Further analyses revealed unique features of (groups of) emotions. For example, awe was associated with exceeded expectancies, while surprise was associated with both exceeded and disconfirmed expectancies. Moreover, curiosity and confusion were associated with having (too) little information, while awe and interest were associated with having sufficient information. All emotions except boredom were associated with exploration, but this was particularly high for curiosity and interest. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Coverage Analysis of RIS-Assisted mmWave Cellular Networks With 3D Beamforming

Coverage Analysis of RIS-Assisted mmWave Cellular Networks With 3D Beamforming

A comparative analysis of convolutional neural networks for breast cancer prediction

Breast cancer continues to be a substantial worldwide health concern, affecting millions of individuals each year; this emphasizes the critical nature of early detection in order to enhance patient prognoses. The present study aims to assess the classification performance of three convolutional neural network (CNN) architectures-visual geometry group 19 (VGG19), AlexNet, and residual network 50 (ResNet50)-with respect to breast cancer detection in medical images. Thorough assessments, encompassing metrics such as accuracy, precision, recall, and F-score, were undertaken to evaluate the diagnostic performance of the models. ResNet50 consistently outperforms other models, as evidenced by its highest accuracy and F-score. The research highlights the significant importance of carefully choosing suitable architectures for medical image analysis, with a specific focus on the detection of breast cancer. In addition, it demonstrates the capacity of deep learning models, such as ResNet50, to improve the diagnosis of breast cancer with exceptional precision and sensitivity, which is critical for reducing the occurrence of false positives and negatives in clinical environments. In addition, computational efficiency is taken into account; AlexNet is recognized as the most efficient model, which is advantageous in environments with limited resources. This study advances medical image processing by demonstrating the potential of CNNs in the detection of breast cancer. The results of this study establish a fundamental basis for sub- sequent inquiries and suggest approaches to improve timely detection and treatment, which will ultimately be advantageous for both patients and healthcare professionals.

Performance Analysis of Deep Learning and Machine Learning Methods for Music Genre Classification System

Performance Analysis of Deep Learning and Machine Learning Methods for Music Genre Classification System

Embedding Principle in Depth for the Loss Landscape Analysis of Deep Neural Networks

Embedding Principle in Depth for the Loss Landscape Analysis of Deep Neural Networks

BER Analysis of Multi-User NOMA Networks in the Presence of Interference

BER Analysis of Multi-User NOMA Networks in the Presence of Interference

Innovations in science education: A bibliometric exploration of trends and future directions

This article carries out a bibliometric research review to present the evolving landscape of innovations in science education. The study acknowledges the dynamism of the contemporary educational practices and uses the bibliometric analyses for identification of trends, assessment of impacts of scholarly outputs, and mapping the future directions in the field of science education innovation. Starting with a review of the standard bibliometric indicators, e.g., citation analysis and co-authorship, the review lays down the theoretical basis for the understanding of the scientific impact of the innovations in science education. The analysis, moreover, consists of identifying thematic foci and emerging topics through keyword mapping, which together present a holistic picture of the intellectual map of the field. The review points to seminal works and trends, demonstrating the nature of interdisciplinarity in research in science education innovation. By the analysis of collaborative networks and research hotspots the research not only demonstrates the present status of the field, but also predicts its future directions, which could help researchers and educators at the same time. Also, as a retrospective tool, the article explores the general relevance of bibliometric insights for educators, administrators, and other researchers. It highlights the application of bibliometrics in evidence-based decision-making, curricula design and international collaboration, because it is common knowledge that it can trigger the great revolution in innovative science education. This bibliometric review generates useful outcomes, which are beneficial to academic discussions and provide guidelines for future research efforts as a key reference for researchers who would like to have a deep, general overview of the innovations in science education.

Classification of Circular Mass of Breast Cancer Using Artificial Neural Network vs. Discriminant Analysis in Medical Image Processing

Classification of Circular Mass of Breast Cancer Using Artificial Neural Network vs. Discriminant Analysis in Medical Image Processing

Classification of Circular Mass of Breast Cancer Using Artificial Neural Network vs. Discriminant Analysis in Medical Image Processing

Classification of Circular Mass of Breast Cancer Using Artificial Neural Network vs. Discriminant Analysis in Medical Image Processing

Prediction of Treatment Mechanisms of Scutellariae Radix on Viral Pneumonia Through Network Pharmacology: Focus on Hypoxic State Regulation Through HIF-1<i>α</i> and HSP90

Objectives: In this study, we used network-based systems pharmacology analysis and molecular docking methods to predict the therapeutic mechanism of Scutellariae Radix on viral pneumonia.Methods: We screened active components of Scutellariae Radix and its’ genes by TCMSP. Also, we extracted viral pneumonia related target genes through Gene Cards, CTD and DisGeNet. To construct Protein-protein Interaction, STRING database was used. For functional enrichment, using SRplot platform, genes were classified by 3 categories: cellular component (CC), molecular function (MF) and biological process (BP). Molecular docking was conducted by AutoDockTools (version 4.2.6).Results: 32 Network-based systematic pharmacology analysis identified 37 target genes associated with baicalein. Based on the network and gene ontology analysis of the active ingredient's target genes and disease target genes, we identified nine core genes (<i>AKT1, BAX, BCL2, CASP3, HIF1A, PTGS2, RELA, TP53, VEGFA</i>) and HSP90 as involved. Notably, HIF1A showed the highest relevance, overlapping with two or more utilized programs. Hypoxia-inducible factor 1-alpha (HIF-1<i>α</i>) has been implicated in the expression of inflammatory cytokines, the induction of hypoxia, and the triggering of cytokine storms. Baicalein, a major component of SR, binds to both HIF-1 <i>α</i> and HSP90, suggesting that it may be a possible targeted treatment for viral pneumonia.Conclusions: Baicalein may bind to HIF-1<i>α</i> to control inflammation caused by viral infectious diseases and may also regulate hypoxic conditions to prevent impairment of lung function caused by an overactive immune system. These findings suggest further research into the molecular mechanisms involved in hypoxia and provide a scientific basis for improving the treatment of viral infectious diseases.

Network-based integration of omics, physiological and environmental data in real-world Elbe estuarine Zander

Coastal and estuarine environments are under endogenic and exogenic pressures jeopardizing survival and diversity of inhabiting biota. Information of possible synergistic effects of multiple (a)biotic stressors and holobiont interaction are largely missing in estuaries like the Elbe but are of importance to estimate unforeseen effects on animals' physiology. Here, we seek to leverage host-transcriptional RNA-seq and gill mucus microbial 16S rRNA metabarcoding data coupled with physiological and abiotic measurements in a network analysis approach to decipher the impact of multiple stressors on the health of juvenile Sander lucioperca along one of the largest European estuaries. We find mesohaline areas characterized by gill tissue specific transcriptional responses matching osmosensing and tissue remodeling. Liver transcriptomes instead emphasized that zander from highly turbid areas were undergoing starvation which was supported by compromised body condition. Potential pathogenic bacteria, including Shewanella, Acinetobacter, Aeromonas and Chryseobacterium, dominated the gill microbiome along the freshwater transition and oxygen minimum zone. Their occurrence coincided with a strong adaptive and innate transcriptional immune response in host gill and enhanced energy demand in liver tissue supporting their potential pathogenicity. Taken together, we show physiological responses of a fish species and its microbiome to abiotic factors whose impact is expected to increase with consequences of climate change. We further present a method for the close-meshed detection of the main stressors and bacterial species with disease potential in a highly productive ecosystem.

Constructing fine-grained spatiotemporal neonatal functional atlases with spectral functional network learning.

The early stages of human development are increasingly acknowledged as pivotal in laying the groundwork for subsequent behavioral and cognitive development. Spatiotemporal (4D) brain functional atlases are important in elucidating the development of human brain functions. However, the scarcity of such atlases for early life stages stems from two primary challenges: (1) the significant noise in functional magnetic resonance imaging (fMRI) that complicates the generation of high-quality atlases for each age group, and (2) the rapid and complex changes in the early human brain that hinder the maintenance of temporal consistency in 4D atlases. This study tackles these challenges by integrating low-rank tensor learning with spectral embedding, thereby proposing a novel, data-driven 4D functional atlas generation framework based on spectral functional network learning (SFNL). This method utilizes low-rank tensor learning to capture common functional connectivity (FC) patterns across different ages, thus optimizing FCs for each age group to improve the temporal consistency of functional networks. Incorporating spectral embedding aids in mitigating potential noise in FC networks derived from fMRI data by reconstructing networks in the spectral space. Utilizing SFNL-generated functional networks enables the creation of consistent and highly qualified spatiotemporal functional atlases. The framework was applied to the developing Human Connectome Project (dHCP) dataset, generating the first neonatal 4D functional atlases with fine-grained temporal and spatial resolutions. Experimental evaluations focusing on functional homogeneity, reliability, and temporal consistency demonstrated the superiority of our framework compared to existing methods for constructing 4D atlases. Additionally, network analysis experiments, including individual identification, functional systems development, and local efficiency assessments, further corroborate the efficacy and robustness of the generated atlases. The 4D atlases and related codes will be made publicly accessible (https://github.com/zhaoyunxi/neonate-atlases).