Network Analysis Research Articles

Trade network dynamics in a globalized environment and on the edge of crises

At the edges of crises, the roles of trade networks and supply chains are evident. Global trade networks aggregate all legal trade activities. Thus, researchers believe that network structure changes reflect phenomena such as crises, globalization, deglobalization, or even technological development, including those related to green technologies. Although trade network analysis has been widely researched, researchers have focused mainly on one or a few products or product groups. While the impact of crises and technological changes in the case of individual products has been demonstrated in some studies, less attention has been given to structural pattern changes in trade on these products. To understand how a shock, crisis, or technological change in one product may affect the trade network of another product, the patterns and relationships of node- and network-level indicators across products need to be identified. In this study, the dynamics and interdependencies of trade networks are analyzed using a combination of network indicators, cluster analysis and causality analysis. Variations and implications of the temporal patterns and causal relationships of trade network indicators are revealed for different products and product groups. Researchers have argued that the deterioration of structural indicators exacerbates crises and amplifies their consequences. A causality analysis shows that disruptions in one product’s supply chain quickly affect the trade of other products, altering the structural characteristics of trade networks. The proposed temporal pattern analysis shows that while the 2008 financial crisis did not cause structural changes in the product trade network, indicators improved before, during, and after the crisis. Nevertheless, before the pandemic, there was a noticeable deterioration in almost all structural indicators, signaling more deglobalization. This study contributes to a comprehensive understanding of trade networks, equipping decision-makers with the knowledge to promote the resilience and sustainability of these networks in an evolving global landscape.

Genetic basis of phenotypic convergence in pig terminal sires using pathway-based selection signature detection methods.

The primary purpose of genetic improvement in lean pig breeds is to enhance production performance. Owing to their similar breeding directions, Duroc and Pietrain pigs are ideal models for investigating the phenotypic convergence underlying artificial selection. However, most important economic traits are controlled by a polygenic basis, so traditional strategies for detecting selection signatures may not fully reveal the genetic basis of complex traits. The pathway-based gene network analysis method utilizes each pathway as a unit, overcoming the limitations of traditional strategies for detecting selection signatures by revealing the selection of complex biological processes. Here, we utilized 13 122 398 high-quality SNPs from whole-genome sequencing data of 48 Pietrain pigs, 156 Duroc pigs and 36 European wild boars to detect selective signatures. After calculating FST and iHS scores, we integrated the pathway information and utilized the r/bioconductor graphite and signet packages to construct gene networks, identify subnets and uncover candidate genes underlying selection. Using the traditional strategy, a total of 47 genomic regions exhibiting parallel selection were identified. The enriched genes, including INO80, FZR1, LEPR and FAF1, may be associated with reproduction, fat deposition and skeletal development. Using the pathway-based selection signatures detection method, we identified two significant biological pathways and eight potential candidate genes underlying parallel selection, such as VTN, FN1 and ITGAV. This study presents a novel strategy for investigating the genetic basis of complex traits and elucidating the phenotypic convergence underlying artificial selection, by integrating traditional selection signature methods with pathway-based gene network analysis.

Metabolome profiling and transcriptome analysis unveiling the crucial role of magnesium transport system for magnesium homeostasis in tea plants

Abstract Magnesium (Mg2+) is a crucial nutrient for the growth and development of Camellia sinensis and is closely related to the quality of tea. However, the underlying mechanisms responding to low-Mg2+ stress in tea plants remain largely unknown. In this study, the photosynthetic parameters, metabolomics, and transcriptomics were utilized to explore the potential effects of low-Mg2+ on the growth and metabolism of C. sinensis. Low-Mg2+ treatment increased the ratio of shoot dry weight to root dry weight but decreased the photosynthesis of C. sinensis. Forty and thirty metabolites were impacted by Mg2+ shortage in C. sinensis shoots and roots, respectively. Integrated transcriptome and metabolome analyses revealed the possible reasons for the decreased contents of chlorophyll (Chl) and catechins and the increased theanine content in C. sinensis roots. Weighted gene co-expression network analysis indicated that Mg2+ transport system was essential in regulation of Mg2+ homeostasis in C. sinensis, in which CsMGT5 was identified to be the key regulator according to CsMGT5-overexpressing and complementary assays in Arabidopsis thaliana. Moreover, silencing of CsMGT5 in vivo reduced the content of Chl in C. sinensis shoots. In addition, CsMGT5 might collaborate with ammonium transporters to keep the steady of amino acids contents, suggesting its potential application for tea quality improvement. All these findings demonstrate the key roles of CsMGTs for Mg2+ homeostasis in C. sinensis, providing theoretical basis for Mg2+ efficient utilization in plants.

Network Analysis of Depressive Symptoms in Chinese Sexual Minority Women During the COVID-19 Pandemic: An Intra-Group Perspective

ABSTRACT The prevalence of depression among sexual minority women is a significant concern, yet no prior research has conducted a network analysis of depressive symptoms in this population. This is the first study to address this gap by examining the network structure of depressive symptoms in Chinese sexual minority women during the COVID-19 pandemic, considering both sexual orientation and gender expression as part of an intra-group perspective. 1420 Chinese sexual minority women completed the Center for Epidemiologic Studies Depressive Symptoms (CES-D). Network analysis was employed to calculate edge and centrality measures, and the network structures of lesbians and bisexual women were compared based on sexual orientation and of femme, androgyny, and butch based on gender expression. Network analysis revealed that the core depressive symptoms of Chinese sexual minority women are “Felt depressed,” “Fatigue,” “Sad,” and “Failure.” Although no significant differences were found in the network structure and global strength of depressive symptoms between different sexual orientations and gender expressions, there were significant differences in the core symptoms. This study suggests the unique associations between depressive symptoms and social and historical contexts among sexual minority women and emphasizes the importance of considering these differences when providing targeted mental health interventions.

Phylogeny, Genetic Diversity and Population Structure of Fritillaria cirrhosa and Its Relatives Based on Chloroplast Genome Data.

Fritillaria cirrhosa and its relatives have been utilized in traditional Chinese medicine for many years and are under priority protection in China. Despite their medicinal and protective value, research on their phylogeny, genetic diversity, and divergence remains limited. Here, we investigate the chloroplast genome variation architecture of 46 samples of F. cirrhosa and its relatives collected from various regions, encompassing the majority of wild populations across diverse geographical areas. The results indicate abundant variations in 46 accessions including 1659 single-nucleotide polymorphisms and 440 indels. Six variable markers (psbJ, ndhD, ycf1, ndhG, trnT-trnL, and rpl32-trnL) were identified. Phylogenetic and network analysis, population structure analysis, and principal component analysis showed that the 46 accessions formed five clades with significant divergence, which were related to their geographical distribution. The regions spanning from the southern Hengduan Mountains to the Qinghai-Tibet Plateau exhibited the highest levels of genetic diversity. F. cirrhosa and its relatives may have suffered a genetic bottleneck and have a relatively low genetic diversity level. Moreover, geographical barriers and discrete patches may have accelerated population divergence. The study offers novel perspectives on the phylogeny, genetic diversity, and population structure of F. cirrhosa and its relatives, information that can inform conservation and utilization strategies in the future.

Deployment of Lean Manufacturing in Palm Oil Mill for Maximum Yield: A Case Study of Leading Palm Oil Producer in Nigeria

The deployment of lean manufacturing in the palm oil mill companyaimed at maximizing yield, was thoroughly investigated. The company has been experiencing a decline in palm oil production, adversely affecting productivity and customer satisfaction. The methodology employed for optimization encompassed a comprehensive system description, detailing the case study company's history, operational modalities, production machinery, and system challenges. To gather a holistic understanding of the company's current situation, data was meticulously collected through questionnaires, examination of company records, and other relevant sources. Microsoft excel software was then utilized to analyze the acquired data. The implementation of lean manufacturing tools, such as the fishbone diagram, identification of eight deadly wastesand takt-timeplayed a pivotal role in streamlining operations. In addition, a quantitative model of the supply chain was applied, utilizing critical path method (network analysis) to determine the optimal route. The results derived from the application of lean manufacturing tools and the supply chain quantitative model exhibited a remarkable improvement between (2012-2016) and (2018-2022). The average oil palm extraction increased substantially from 8.67% (2012-2016) to an impressive 30.52% (2018-2022). Furthermore, the management tools led to a significant reduction in machine downtimes, decreasing from an annual average of 5,498.6 hours to 2,472.4 hours. This improvement resulted in a considerable reduction in the annual total average cost of machine maintenance, plummeting from #1,899,578.6 to #261,853.8. The optimization efforts also extended to the supply chain, where selecting Path-2, exhibiting the least total supply time of 11 hours, significantly reduced the cost of supply from an average of #2,168,402 to #1,893,186. Overall, the net revenue of the company witnessed a remarkable surge of 71.99% post-implementation of these management tools. This comprehensive approach not only addressed production challenges but also had a substantial positive impact on the company’s financial performance. The strategic deployment of lean manufacturing emerged as a catalyst for sustainable growth and enhanced operational efficiency.

Experience-dependent regulation of dopaminergic signaling in the somatosensory cortex

Dopamine critically influences reward processing, sensory perception, and motor control. Yet, the modulation of dopaminergic signaling by sensory experiences is not fully delineated. Here, by manipulating sensory experience using bilateral single-row whisker deprivation, we demonstrated that gene transcription in the dopaminergic signaling pathway (DSP) undergoes experience-dependent plasticity in both granular and supragranular layers of the primary somatosensory (barrel) cortex (S1). Sensory experience and deprivation compete for the regulation of DSP transcription across neighboring cortical columns, and sensory deprivation-induced changes in DSP are topographically constrained. These changes in DSP extend beyond cortical map plasticity and influence neuronal information processing. Pharmacological regulation of D2 receptors, a key component of DSP, revealed that D2 receptor activation suppresses excitatory neuronal excitability, hyperpolarizes the action potential threshold, and reduces the instantaneous firing rate. These findings suggest that the dopaminergic drive originating from midbrain dopaminergic neurons, targeting the sensory cortex, is subject to experience-dependent regulation and might create a regulatory feedback loop for modulating sensory processing. Finally, using topological gene network analysis and mutual information, we identify the molecular hubs of experience-dependent plasticity of DSP. These findings provide new insights into the mechanisms by which sensory experience shapes dopaminergic signaling in the brain and might help unravel the sensory deficits observed after dopamine depletion.

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

Performance Analysis of SWIPT Relay Networks Over Arbitrary Dependent Fading Channels

Performance Analysis of SWIPT Relay Networks Over Arbitrary Dependent Fading Channels

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