Prediction of influential nodes in social networks based on local communities and users' reaction information.

Identifying influential nodes is one of the basic issues in managing large social networks. Identifying influence nodes in social networks and other networks, including transportation, can be effective in applications such as identifying the sources of spreading rumors, making advertisements more effective, predicting traffic, predicting diseases, etc. Therefore, it will be important to identify these people and nodes in social networks from different aspects. In this article, a new method is presented to identify influential nodes in the social network. The proposed method utilizes the combination of users' social characteristics and their reaction information to identify influential users. Since the identification of these users in the large social network is a complex process and requires high processing power and time, clustering and identifying communities have been used in the proposed method to reduce the complexity of the problem. In the proposed method, the structure of the social network is divided into its constituent communities and thus the problem of identifying influential nodes (in the entire network) turns into several problems of identifying an influential node (in each community). The suggested method for predicting the nodes first predicts the links that may be created in the future and then identifies the influential nodes based on an iterative strategy. The proposed algorithm uses the criteria of centrality and influence domain to identify this category of users and performs the identification process both at the community and network levels. The efficiency of the method has been evaluated using real databases and the results have been compared with previous works. The results demonstrate that the proposed method provides a more suitable performance in detecting the influential nodes and is superior in terms of accuracy, recall and processing time.

Phytochemical analysis, in-vitro and in-silico study of antiproliferative activity of ethyl acetate fraction of Launaea cornuta (Hochst. ex Oliv. & Hiern) C. Jeffrey against human cervical cancer cell line.

Introduction: Cervical cancer is one of the leading causes of death among women globally due to the limitation of current treatment methods and their associated adverse side effects. Launaea cornuta is used as traditional medicine for the treatment of a variety of diseases including cancer. However, there is no scientific validation on the antiproliferative activity of L. cornuta against cervical cancer. Objective: This study aimed to evaluate the selective antiproliferative, cytotoxic and antimigratory effects of L. cornuta and to explore its therapeutical mechanisms in human cervical cancer cell lines (HeLa-229) through a network analysis approach. Materials and methods: The cytotoxic effect of L. cornuta ethyl acetate fraction on the proliferation of cervical cancer cells was evaluated by 3-(4, 5-dimethylthiazol-2-yl)-2, 5-diphenyltetrazolium bromide (MTT) bioassay and the antimigratory effect was assessed by wound healing assays. Compounds were analysed using the qualitative colour method and gas chromatography-mass spectroscopy (GC-MS). Subsequently, bioinformatic analyses, including the protein-protein interaction (PPI) network analysis, Gene Ontology (GO), and Kyoto Encyclopaedia of Genes and Genomes (KEGG) analysis, were performed to screen for potential anticervical cancer therapeutic target genes of L. cornuta. Molecular docking (MD) was performed to predict and understand the molecular interactions of the ligands against cervical cancer. Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) was performed to validate the network analysis results. Results: L. cornuta ethyl acetate fraction exhibited a remarkable cytotoxic effect on HeLa-229 proliferation (IC50 of 20.56 ± 2.83 µg/mL) with a selectivity index (SI) of 2.36 with minimal cytotoxicity on non-cancerous cells (Vero-CCL 81 (IC50 of 48.83 ± 23.02). The preliminary screening revealed the presence of glycosides, phenols, saponins, terpenoids, quinones, and tannins. Thirteen compounds were also identified by GC-MS analysis. 124 potential target genes associated with the effect of L. cornuta ethyl acetate fraction on human cervical cancer were obtained, including AKT1, MDM2, CDK2, MCL1 and MTOR were identified among the top hub genes and PI3K/Akt1, Ras/MAPK, FoxO and EGFR signalling pathways were identified as the significantly enriched pathways. Molecular docking results showed that stigmasteryl methyl ether had a good binding affinity against CDK2, ATK1, BCL2, MDM2, and Casp9, with binding energy ranging from -7.0 to -12.6 kcal/mol. Tremulone showed a good binding affinity against TP53 and P21 with -7.0 and -8.0 kcal/mol, respectively. This suggests a stable molecular interaction of the ethyl acetate fraction of L. cornuta compounds with the selected target genes for cervical cancer. Furthermore, RT-qPCR analysis revealed that CDK2, MDM2 and BCL2 were downregulated, and Casp9 and P21 were upregulated in HeLa-229 cells treated with L. cornuta compared to the negative control (DMSO 0.2%). Conclusion: The findings indicate that L. cornuta ethyl acetate fraction phytochemicals modulates various molecular targets and pathways to exhibit selective antiproliferative and cytotoxic effects against HeLa-229 cells. This study lays a foundation for further research to develop innovative clinical anticervical cancer agents.

TLE4 downregulation identified by WGCNA and machine learning algorithm promotes papillary thyroid carcinoma progression via activating JAK/STAT pathway.

Background: Papillary Thyroid Carcinoma (PTC), a common type of thyroid cancer, has a pathogenesis that is not fully understood. This study utilizes a range of public databases, sophisticated bioinformatics tools, and empirical approaches to explore the key genetic components and pathways implicated in PTC, particularly concentrating on the Transducin-Like Enhancer of Split 4 (TLE4) gene. Methods: Public databases such as TCGA and GEO were utilized to conduct differential gene expression analysis in PTC. Hub genes were identified using Weighted Gene Co-expression Network Analysis (WGCNA), and machine learning techniques, including Random Forest, LASSO regression, and SVM-RFE, were employed for biomarker identification. The clinical impact of the TLE4 gene was assessed in terms of diagnostic accuracy, prognostic value, and its functional enrichment analysis in PTC. Additionally, the study focused on understanding the role of TLE4 in the dynamics of immune cell infiltration, gene function enhancement, and behaviors of PTC cells like growth, migration, and invasion. To complement these analyses, in vivo studies were performed using a xenograft mouse model. Results: 244 genes with significant differential expression across various databases were identified. WGCNA indicated a strong link between specific gene modules and PTC. Machine learning analysis brought the TLE4 gene into focus as a key biomarker. Bioinformatics studies verified that TLE4 expression is lower in PTC, linking it to immune cell infiltration and the JAK-STAT signaling pathways. Experimental data revealed that decreased TLE4 expression in PTC cell lines leads to enhanced cell growth, migration, invasion, and activates the JAK/STAT pathway. In contrast, TLE4 overexpression in these cells inhibited tumor growth and metastasis. Conclusions: This study sheds light on TLE4's crucial role in PTC pathogenesis, positioning it as a potential biomarker and target for therapy. The integration of multi-omics data and advanced analytical methods provides a robust framework for understanding PTC at a molecular level, potentially guiding personalized treatment strategies.

Climate security dialogues on Twitter: An annotated dataset.

Climate security refers to the risks posed by climate change on nations, societies, and individuals, including the possibility of conflicts. As an emerging field of research and public debate, where conceptual definitions are not yet fully agreed upon, gaining insights into global discussions on climate security enables systematizing its various interpretations and framings, mapping thematic priorities, and understanding information gaps that need to be filled. Considering Twitter as an important digital forum for information exchanges and dialogue, the dataset was created through the development of a query strategy based on a snowball scraping technique, which collected tweets containing hashtags related to climate security between January 2014 to May 2023. The dataset comprises 636,379 tweets. Content analysis was performed using text mining and network analysis techniques to generate additional data on sentiment, countries mentioned in the body of tweets, and hashtag co-occurrences. With almost 10 years of data, the utility of this dataset lies in the ability to assess the discursive evolution of a particular topic since its inception.

Global insights from local actions: a case study on enhancing flood disaster management efficiency in China's Greater Bay Area.

This study examines the flood disaster management network within the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) from 2015 to 2021, identifying government department involvement and influence shifts. Key findings indicate a decrease in the centrality of the Public Security Office and Department of Transportation, suggesting a strategic shift toward more specialized, technology-driven disaster management. Conversely, the Science Bureau's increased engagement, from 8.43% to 12.84%, highlights a policy shift toward scientific research and technological innovation in managing flood risks. The analysis reveals underutilized communication between the Central Committee, the Poverty Alleviation Office, and the Publicity Department, highlighting opportunities for improved integration in disaster management and public communication strategies. To address these issues, the study suggests strengthening inter-departmental collaboration to leverage technological advancements in disaster management. It also recommends integrating flood disaster management with poverty alleviation initiatives to support affected populations comprehensively. Increasing the involvement of the Publicity Department is crucial for improving timely and transparent communication of flood-related data to the public. The conclusions advocate for an adaptive, strategically planned network approach to flood disaster management in the GBA, aiming to bolster responsiveness and preparedness for future flood events.

Trends and hotspots in gastrointestinal neoplasms risk assessment: A bibliometric analysis from 1984 to 2022.

BACKGROUND: Gastrointestinal neoplasm (GN) significantly impact the global cancer burden and mortality, necessitating early detection and treatment. Understanding the evolution and current state of research in this field is vital. AIM: To conducts a comprehensive bibliometric analysis of publications from 1984 to 2022 to elucidate the trends and hotspots in the GN risk assessment research, focusing on key contributors, institutions, and thematic evolution. METHODS: This study conducted a bibliometric analysis of data from the Web of Science Core Collection database using the "bibliometrix" R package, VOSviewer, and CiteSpace. The analysis focused on the distribution of publications, contributions by institutions and countries, and trends in keywords. The methods included data synthesis, network analysis, and visualization of international collaboration networks. RESULTS: This analysis of 1371 articles on GN risk assessment revealed a notable evolution in terms of research focus and collaboration. It highlights the United States' critical role in advancing this field, with significant contributions from institutions such as Brigham and Women's Hospital and the National Cancer Institute. The last five years, substantial advancements have been made, representing nearly 45% of the examined literature. Publication rates have dramatically increased, from 20 articles in 2002 to 112 in 2022, reflecting intensified research efforts. This study underscores a growing trend toward interdisciplinary and international collaboration, with the Journal of Clinical Oncology standing out as a key publication outlet. This shift toward more comprehensive and collaborative research methods marks a significant step in addressing GN risks. CONCLUSION: This study underscores advancements in GN risk assessment through genetic analyses and machine learning and reveals significant geographical disparities in research emphasis. This calls for enhanced global collaboration and integration of artificial intelligence to improve cancer prevention and treatment accuracy, ultimately enhancing worldwide patient care.

Network analysis combined with experimental assessment to explore the therapeutic mechanisms of New Shenqi Pills formula targeting mitochondria on senile diabetes mellitus.

Background: The escalation of global population aging has accentuated the prominence of senile diabetes mellitus (SDM) as a consequential public health concern. Oxidative stress and chronic inflammatory cascades prevalent in individuals with senile diabetes significantly amplify disease progression and complication rates. Traditional Chinese Medicine (TCM) emerges as a pivotal player in enhancing blood sugar homeostasis and retarding complication onset in the clinical management of senile diabetes. Nonetheless, an evident research gap persists regarding the integration of TCM's renal tonification pharmacological mechanisms with experimental validation within the realm of senile diabetes therapeutics. Aims: The objective of this study was to investigate the mechanisms of action of New Shenqi Pills (SQP) in the treatment of SDM and make an experimental assessment. Methods: Network analysis is used to evaluate target pathways related to SQP and SDM. Mitochondrial-related genes were obtained from the MitoCarta3.0 database and intersected with the common target genes of the disease and drugs, then constructing a protein-protein interaction (PPI) network making use of the GeneMANIA database. Representative compounds in the SQP were quantitatively measured using high performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS) to ensure quality control and quantitative analysis of the compounds. A type 2 diabetes mice (C57BL/6) model was used to investigate the pharmacodynamics of SQP. The glucose lowering efficacy of SQP was assessed through various metrics including body weight and fasting blood glucose (FBG). To elucidate the modulatory effects of SQP on pancreatic beta cell function, we measured oral glucose tolerance test (OGTT), insulin histochemical staining and tunel apoptosis detection, then assessed the insulin-mediated phosphoinositide 3-kinase (PI3K)/protein kinase A (Akt)/glycogen synthase kinase-3ß (GSK-3ß) pathway in diabetic mice via Western blotting. Additionally, we observe the structural changes of the nucleus, cytoplasmic granules and mitochondria of pancreatic islet ß cells. Results: In this investigation, we identified a total of 1876 genes associated with senile diabetes, 278 targets of SQP, and 166 overlapping target genes, primarily enriched in pathways pertinent to oxidative stress response, peptide response, and oxygen level modulation. Moreover, an intersection analysis involving 1,136 human mitochondrial genes and comorbidity targets yielded 15 mitochondria-related therapeutic targets. Quality control assessments and quantitative analyses of SQP revealed the predominant presence of five compounds with elevated concentrations: Catalpol, Cinnamon Aldehyde, Rehmanthin D, Trigonelline, and Paeonol Phenol. Vivo experiments demonstrated notable findings. Relative to the control group, mice in the model group exhibited significant increases in body weight and fasting blood glucose levels, alongside decreased insulin secretion and heightened islet cell apoptosis. Moreover, ß-cells nuclear condensation and mitochondrial cristae disappearance were observed, accompanied by reduced expression levels of p-GSK-3ß protein in islet cells (p < 0.05 or p < 0.01). Conversely, treatment groups administered SQP and Rg displayed augmented expressions of the aforementioned protein markers (p < 0.05 or p < 0.01), alongside preserved mitochondrial cristae structure in islet ß cells. Conclusion: Our findings suggest that SQP can ameliorate diabetes by reducing islet cell apoptosis and resist oxidative stress. These insulin-mediated PI3K/AKT/GSK-3ß pathway plays an important regulatory role in this process.

Applying Social Network Theory to Vaping in High School: Implications for Person-Centered Intervention.

BACKGROUND: Peer influence on risky behavior is particularly potent in adolescence and varies by gender. Smoking prevention programs focused on peer-group leaders have shown great promise, and a social influence model has proven effective in understanding adult smoking networks but has not been applied to adolescent vaping until 2023. This work aims to apply a social influence model to analyze vaping by gender in a high school network. METHODS: A high school's student body was emailed an online survey asking for gender, age, grade level, vape status, and the names of three friends. Custom Java and MATLAB scripts were written to create a directed graph, compute centrality measures, and perform Fisher's exact tests to compare centrality measures by demographic variables and vape status. RESULTS: Of 192 students in the school, 102 students responded. Students who vape were in closer-knit friend groups than students who do not vape (p < .05). Compared to males who vape, females who vape had more social ties to other students who vape, exhibiting greater homophily (p < .01). Compared to females who do not vape, females who vape were in closer-knit friend groups (p < .05) and had more ties to other students who vape (p < .01). CONCLUSION: Differences in vaping by social connectedness and gender necessitate school and state policies incorporating the social aspect of vaping in public health initiatives. Large-scale research should determine if trends can be generalized across student bodies, and more granular studies should investigate differences in motivations and social influence by demographic variables to individualize cessation strategies.

Severity of depressive and anxious symptoms and its association with birth outcomes among pregnant women during the COVID-19 pandemic: a prospective case-control study.

AIM: Comparing the anxiety and depression severity and their impact on subsequent birth outcomes in pregnant women before and during Omicron wave in Shanghai in 2022. METHODS: The depression-anxiety symptoms networks were compared between the pregnant women during the outbreak period (outbreak group; n = 783) and a matched control group of pregnant women before the outbreak (pre-outbreak group; n = 783). The impact of baseline mental state on follow-up pregnancy and neonatal outcomes was also explored by logistic regression. FINDINGS: Levels of depression and anxiety between the two groups were not significant different. Network analysis showed that central symptom "trouble relaxing" and bridge symptom "depressed mood" shared by both groups. Different symptom associations in different periods of the pandemic. Total scores and sub-symptom scores of prenatal depressive and anxious severities increased the odds ratios of maternal and neonatal syndromes. The influence of mental state on gestational and neonatal outcomes differed across different pandemic periods. CONCLUSION: The Omicron wave did not have a significant negative impact on the depressive and anxious mood in pregnant women. Targeting central and bridge symptoms intervention may be effective in reducing their adverse effects on co-occurring of anxious and depressive mood and birth outcomes.

Do Adolescents Adopt the Prosocial Behaviors of the Classmates They Like? A Social Network Analysis on Prosocial Contagion.

While the influence of high-status peers on maladaptive behaviors is well-documented, socialization processes of prosocial behavior through high-status peers remain understudied. This study examined whether adolescents' prosocial behavior was influenced by the prosocial behavior of the peers they liked and whether this effect was stronger when the peers they liked were also well-liked by their classmates. Three waves of data, six months apart, were collected among Chilean early adolescents who completed peer nominations and ratings at Time 1 (n = 294, Mage = 13.29, SD = 0.62; 55.1% male), Time 2 (n = 282), and Time 3 (n = 275). Longitudinal social network analyses showed that adolescents adopted the prosocial behavior of the classmates they liked - especially if these classmates were well-liked by peers in general. In addition, adolescents low in likeability were more susceptible to this influence than adolescents high in likeability. The influence resulted both in increases and - especially - decreases in prosocial behavior, depending on the level of prosociality of the liked peer. Findings suggest that likeability represents an important aspect of peer status that may be crucial for understanding the significance of peer influence with respect to prosocial behaviors during adolescence. Pre-Registration: https://osf.io/u4pxm .

Contemporaneous Symptom Networks of Breast Cancer-Related Upper Limb Lymphedema: A Network Analysis.

BACKGROUND: Upper limb lymphedema (ULL) is a common and deliberating complication for breast cancer survivors (BCSs). Breast cancer survivors with ULL reported a wide range of symptoms. However, little is known about symptom patterns and interrelationships among them. This study was designed to explore symptom clusters and construct symptom networks of ULL-related symptoms among BCSs and to identify the core symptoms. METHODS: This study is a secondary data analysis using datasets from three cross-sectional studies of BCSs in China. A total of 341 participants with maximum interlimb circumference ≥2 cm and complete responses in Part I of the Breast Cancer and Lymphedema Symptom Experience Index were included. Symptom clusters were identified through principal component analysis, and multiple linear regression analysis was employed to explore factors associated with severity of overall ULL-related symptoms. A contemporaneous network with 20 frequently reported symptoms were constructed after controlling for covariates. RESULTS: Three symptom clusters, including lymph stasis symptom cluster, nerve symptom cluster, and movement limitation symptom cluster, were identified. Postsurgery time, axillary lymph node dissection, and radiotherapy were associated with the severity of ULL-related symptoms. Tightness (rs = 1.379; rscov = 1.097), tingling (rs = 1.264; rscov = 0.925), and firmness (rs = 1.170; rscov = 0.923) were the most central symptoms in both networks with and without covariates. CONCLUSIONS: Breast cancer survivors with ULL experienced severe symptom burden. Tightness, tingling, and firmness were core symptoms of ULL among BCSs. Our findings demonstrated that the assessment and targeted intervention of specific core symptoms might help to relive effectively the burden of ULL-related symptom among BCSs.

Exploring stroke discourse on Twitter through content and network analysis among Indian users.

The study aimed to understand stroke-related Twitter conversations in India, focusing on topics, message sources, reach, and influential users to provide insights to stakeholders regarding community needs for knowledge, support, and interventions. Geo-tagged Twitter posts focusing on stroke originating from India and, spanning from November 7, 2022, to February 28, 2023, were systematically obtained via the Twitter application programming interface, using keywords and hashtags sourced through Symplur Signals. Preprocessing involved the removal of hashtags, stop words, and URLs. The Latent Dirichlet Allocation (LDA) topic model was used to identify recurring stroke-related topics, while influential users were identified through social network analysis. About half of the tweets about stroke in India were about seeking support and post-stroke bereavement sharing and had the highest reachability. Four out of 10 tweets were from the individual twitter users. Tweets on the topic risk factors, awareness and prevention (14.6%) constituted the least proportion, whereas the topic management, research, and promotion had the least retweet ratio. Twitter demonstrates significant potential as a platform for both disseminating and acquiring stroke-related information within the Indian context. The identified topics and understanding of the content of discussion offer valuable resources to public health professionals and organizations to develop targeted educational and engagement strategies for the relevant audience.

Identification of the Core Characteristics of Vulnerable/Hypersensitive Narcissism and its Association with the Dark Triad in a Large International Sample: A Network Analysis Study.

The current paper aimed to investigate the network structure and centrality indexes of hypersensitive narcissism using the hypersensitive Narcissism Scale (HSN). Additionally, we aimed to explore its relationships with dark triad personality aspects. A globally diverse sample of "53,981" participants (47.9% non-United States responders) completed the HSN and Dark Triad Dirty Dozen scale (DTD). We estimated the network structure across genders to determine the core characteristics of hypersensitive narcissism. Additionally, bridge and central nodes (characteristics) were identified. All analyses were performed using R-Studio programming software. The network comparison test indicated significant differences in the network structures between males and females (Network-Invariance: 0.0489, P < 0.01; Global Strength Invariance: 0.101, P < 0.01). In the network of HSN for male participants, characteristics with the highest strength centrality were "Highly affected by criticism" (HSN.2, strength = 1.08) and "Self-absorbed in personal pursuits" (HSN.8, strength = 1.28). For female participants, "Self-absorbed in personal pursuits" (HSN.8, strength = 1.32) and "privately annoyed by others' needs" (HSN.10, strength = 1.21) were the highest central characteristics. The assessment of bridge strength indicated that nodes HSN.2 (Highly sensitive to criticism), scoring 0.42, and DTD.1 (Tendency to manipulate for gain, a component of Machiavellianism), scoring 0.428, showed the highest bridge strength values. The current study identified core characteristics of hypersensitive narcissism and its correlation with dark triad personality, revealing gender-specific patterns and bridging symptoms between the two constructs. These findings showed that focusing on these core characteristics may be advantageous in treating individuals exhibiting elevated levels of narcissism.

Network analysis of trait aggression among community youths and juvenile offenders.

OBJECTIVE: The mainstream view in trait aggression research has regarded the structure as representing the latent cause of the cognitions, emotions, and behaviors that supposedly reflect its nature. Under network perspective, trait aggression is not a latent cause of its features but a dynamic system of interacting elements. The current study uses network theory to explain the structure of relationships between trait aggression features in juvenile offenders and their peers. METHODS: Network analysis was applied to investigate the dynamic system of trait aggression operationalized by the Buss-Perry Aggression Questionnaire in a sample of community youths (Mage = 17.46, N = 715) and juvenile offenders (Mage = 18.36, N = 834). RESULTS: The facet level networks showed that anger is a particularly effective mechanism for activating all other traits. In addition, anger was more strongly associated with physical aggression and the overall network strength was greater in juvenile delinquency networks than in their peers. The item level networks revealed that A4 and A6 exhibited the highest predictability and strength centrality in both samples. Also, the Bayesian network indicated that these two items were positioned at the highest level in the model. There are similarities and differences between juvenile delinquents and community adolescents in trait aggression. CONCLUSION: Trait aggression was primarily activated by difficulty controlling one's temper and feeling like a powder keg.

Dataset on global trade networks of COVID-19 medical products.

This paper presents a comprehensive dataset on the global trade dynamics of COVID-19-related medical products for the years 2019 and 2020. The dataset, derived from the BACI database, focuses on eight distinct product categories identified by six-digit codes. The trade flow data for 224 countries is structured as a multilevel network, with countries as nodes and product categories as layers. Directed edges represent trading activities, and edge weights are determined by the difference in exported values between 2019 and 2020. The dataset is provided in an edges-and-nodes format. Additionally, the associated R script transforms the data into the MuxViz R package format, facilitating further analysis and visualization of the dataset. The dataset is valuable for researchers in the field of foreign trade or medical products, and for decision-makers in these fields, whether at company or national level.

A Network Analysis of Perinatal Depression, Anxiety, and Temperaments in Women in the First, Second, and Third Trimesters of Pregnancy.

Background/Objectives: Although depression and anxiety are found to be affected by temperaments, little research has studied these relationships in pregnancy. The present study explored the associations among perinatal depression (PD), anxiety dimensions (state, trait, and generalized anxiety disorder (GAD)), and temperaments between women in the three trimesters of pregnancy through a network analysis approach. Moreover, differences in the severity of PD and anxiety between women in the three trimesters were evaluated. Methods: Women in first (N = 31), second (N = 184), and third (N = 54) trimesters of pregnancy were recruited in the present cross-sectional study. The network analysis included PD, anxiety dimensions, and temperaments. Three network models were estimated, and ANOVAs evaluated the differences in the severity of PD and anxiety, including trimesters as a between-subject factor. Results: PD and GAD were the nodes most strongly connected across the three groups. Cyclothymic, depressive, and anxious temperaments were most frequently associated with PD and GAD. Hyperthymic temperament was in the periphery of the three networks. Lastly, women in the first trimester had the highest severity of PD and GAD. Conclusions: PD and GAD showed the strongest associations. Anxiety dimensions had positive associations with PD and GAD, suggesting their role as possible risk factors. Temperaments were differently associated within the network between the three groups. Clinical interventions during pregnancy should target the central variables, considering their direct and indirect relationships.

Identification of PgRg1-3 Gene for Ginsenoside Rg1 Biosynthesis as Revealed by Combining Genome-Wide Association Study and Gene Co-Expression Network Analysis of Jilin Ginseng Core Collection.

Ginseng, an important medicinal plant, is characterized by its main active component, ginsenosides. Among more than 40 ginsenosides, Rg1 is one of the ginsenosides used for measuring the quality of ginseng. Therefore, the identification and characterization of genes for Rg1 biosynthesis are important to elucidate the molecular basis of Rg1 biosynthesis. In this study, we utilized 39,327 SNPs and the corresponding Rg1 content from 344 core ginseng cultivars from Jilin Province. We conducted a genome-wide association study (GWAS) combining weighted gene co-expression network analysis (WGCNA), SNP-Rg1 content association analysis, and gene co-expression network analysis; three candidate Rg1 genes (PgRg1-1, PgRg1-2, and PgRg1-3) and one crucial candidate gene (PgRg1-3) were identified. Functional validation of PgRg1-3 was performed using methyl jasmonate (MeJA) regulation and RNAi, confirming that this gene regulates Rg1 biosynthesis. The spatial-temporal expression patterns of the PgRg1-3 gene and known key enzyme genes involved in ginsenoside biosynthesis differ. Furthermore, variations in their networks have a significant impact on Rg1 biosynthesis. This study established an accurate and efficient method for identifying candidate genes, cloned a novel gene controlling Rg1 biosynthesis, and identified 73 SNPs significantly associated with Rg1 content. This provides genetic resources and effective tools for further exploring the molecular mechanisms of Rg1 biosynthesis and molecular breeding.

Identifying Core Genes Related to Low-Temperature Stress Resistance in Quinoa Seedlings Based on WGCNA.

Quinoa is a nutritious crop that is tolerant to extreme environmental conditions; however, low-temperature stress can affect quinoa growth, development, and quality. Considering the lack of molecular research on quinoa seedlings under low-temperature stress, we utilized a Weighted Gene Co-Expression Network Analysis to construct weighted gene co-expression networks associated with physiological indices and metabolites related to low-temperature stress resistance based on transcriptomic data. We screened 11 co-expression modules closely related to low-temperature stress resistance and selected 12 core genes from the two modules that showed the highest associations with the target traits. Following the functional annotation of these genes to determine the key biological processes and metabolic pathways involved in low-temperature stress, we identified four important transcription factors involved in resistance to low-temperature stress: gene-LOC110731664, gene-LOC110736639, gene-LOC110684437, and gene-LOC110720903. These results provide insights into the molecular genetic mechanism of quinoa under low-temperature stress and can be used to breed lines with tolerance to low-temperature stress.

Coping with Examination Stress: An Emotion Analysis.

Stress is an important factor affecting human behavior, with recent works in the literature distinguishing it as either productive or destructive. The present study investigated how the primary emotion of stress is correlated with engagement, focus, interest, excitement, and relaxation during university students' examination processes. Given that examinations are highly stressful processes, twenty-six postgraduate students participated in a four-phase experiment (rest, written examination, oral examination, and rest) conducted at the International Hellenic University (IHU) using a modified Trier protocol. Network analysis with a focus on centralities was employed for data processing. The results highlight the important role of stress in the examination process; correlate stress with other emotions, such as interest, engagement, enthusiasm, relaxation, and concentration; and, finally, suggest ways to control and creatively utilize stress.

Multi-layer Bundling as a New Approach for Determining Multi-scale Correlations Within a High-Dimensional Dataset.

The growing complexity of biological data has spurred the development of innovative computational techniques to extract meaningful information and uncover hidden patterns within vast datasets. Biological networks, such as gene regulatory networks and protein-protein interaction networks, hold critical insights into biological features' connections and functions. Integrating and analyzing high-dimensional data, particularly in gene expression studies, stands prominent among the challenges in deciphering these networks. Clustering methods play a crucial role in addressing these challenges, with spectral clustering emerging as a potent unsupervised technique considering intrinsic geometric structures. However, spectral clustering's user-defined cluster number can lead to inconsistent and sometimes orthogonal clustering regimes. We propose the Multi-layer Bundling (MLB) method to address this limitation, combining multiple prominent clustering regimes to offer a comprehensive data view. We call the outcome clusters "bundles". This approach refines clustering outcomes, unravels hierarchical organization, and identifies bridge elements mediating communication between network components. By layering clustering results, MLB provides a global-to-local view of biological feature clusters enabling insights into intricate biological systems. Furthermore, the method enhances bundle network predictions by integrating the bundle co-cluster matrix with the affinity matrix. The versatility of MLB extends beyond biological networks, making it applicable to various domains where understanding complex relationships and patterns is needed.