Network Analysis Research Articles

A Critical Exploration of Tipping Practices in the American Hospitality Landscape from a Qualitative Approach

ABSTRACT This study provides a comprehensive analysis of tipping practices within the U.S. restaurant industry, integrating historical context, consumer experiences, and the impact of technological advancements. Utilizing a qualitative dataset via in-depth interviews with restaurant consumers, this study examines the complex nature of tipping, navigating its dual role as a voluntary gesture and a social norm. Thematic analysis via Latent Dirichlet Allocation reveals key themes such as the service-tipping relationship, customer interactions with touchscreen payment systems, cultural and economic factors, and the impact of the COVID-19 pandemic on both employees and customers. Network analysis and sentiment analysis further illuminate the intricacies of consumer behavior and attitudes toward tipping. Our findings underscore the necessity for a balanced approach to technological innovation in the service industry and advocate for a reevaluation of compensation models and labor practices. This research contributes to the academic discourse on tipping, offers actionable insights for the hospitality industry, and fosters dialogue toward a more equitable, transparent, and sustainable tipping culture.

Deep representation learning of protein-protein interaction networks for enhanced pattern discovery.

Protein-protein interaction (PPI) networks, where nodes represent proteins and edges depict myriad interactions among them, are fundamental to understanding the dynamics within biological systems. Despite their pivotal role in modern biology, reliably discerning patterns from these intertwined networks remains a substantial challenge. The essence of the challenge lies in holistically characterizing the relationships of each node with others in the network and effectively using this information for accurate pattern discovery. In this work, we introduce a self-supervised network embedding framework termed discriminative network embedding (DNE). Unlike conventional methods that primarily focus on direct or limited-order node proximity, DNE characterizes a node both locally and globally by harnessing the contrast between representations from neighboring and distant nodes. Our experimental results demonstrate DNE's superior performance over existing techniques across various critical network analyses, including PPI inference and the identification of protein functional modules. DNE emerges as a robust strategy for node representation in PPI networks, offering promising avenues for diverse biomedical applications.

Utilizing network analysis to identify core items of quality of life for children with autism spectrum disorder.

The purpose of this study was to develop and validate an ultra-short scale called the Quality of Life for Children with Autism Spectrum Disorder 3 (QOLASD-C3) from the full 16-item QOLASD-C scale. We first used network analysis to identify three core items to be retained on the QOLASD-C3 scale. Second, we used Cronbach's alpha and Pearson Product Moment correlations to determine the reliability and validity of the scale. Third, an optimal cut-off score of 6 was identified for the using receiver operating characteristic curve analysis. Finally, we used logistic regression to examine the similarities in the classification status based on demographic characteristics between the quality of life (QOL) status using the QOLASD-C and the QOLASD-C3 scales. Results were similar across the two versions and suggested variations in QOL status based on race/ethnicity, autism spectrum disorder (ASD) severity, and parents' socio-economic status. Implications for research and practice are discussed.

Network Structure of Transdiagnostic Dimensions of Emotional Disorders in Adolescents With Subthreshold Anxiety and Depression: Links With Psychopathology and Socio-Emotional Adjustment.

The transdiagnostic and network model approaches to psychopathology have emerged as an alternative to taxonomic systems. The main goal was to examine the network structure of the transdiagnostic dimensions of emotional disorders in adolescents with subclinical emotional symptoms. In addition, cross-sectional network analyses were conducted to investigate the relationships between transdiagnostic dimensions, psychopathology, and socio-emotional adjustment. The sample consisted of a total of 476 adolescents from 85 schools from different provinces in Spain with subthreshold anxiety and depression. The mean age was 13.77 years (SD = 1.43, range: 10-18 years), 73.9% were female. Several questionnaires assessing transdiagnostic dimensions of emotional disorders, mental health difficulties, self-esteem, perceived social support, sense of belonging at school, and prosocial behaviour were used. A strong interrelation was found between the transdiagnostic variables, except with the positive temperament dimension. The dimensions with the greatest weight were those of traumatic re-experiencing, intrusive cognitions, avoidance, neuroticism, and depressed mood. Negative relationships were found between the transdiagnostic dimensions of emotional disorders and the protective factors included in the study. Stability analyses indicated that the networks were accurately estimated. The present paper points to the value of conceptualising emotional disorders from a transdiagnostic and network model perspective. In addition, the work provides new insights into the nature of the relationships between transdiagnostic dimensions of emotional disorders, and the role played by risk and protective factors.

Relationship between burnout and turnover intention among nurses: a network analysis

BackgroundNurse burnout and turnover intention significantly impact global healthcare systems, especially intensified by the COVID-19 pandemic. This study employs network analysis to explore these phenomena, providing insights into the interdependencies and potential intervention points within the constructs of burnout and turnover intention among nurses.MethodsA cross-sectional study was conducted with 560 nurses from three tertiary hospitals in Hangzhou, China. Data were collected via online questionnaires, including the Maslach Burnout Inventory-General Survey (MBI-GS) and the Turnover Intention Questionnaire (TIQ). Network analysis was performed using Gaussian graphical models to construct the network model and calculate related metrics.ResultsThe network analysis revealed that items related to personal accomplishment and emotional exhaustion were central, indicating significant roles in influencing nurses’ turnover intentions. Specifically, perceived meaningful work and self-efficacy emerged as pivotal nodes, suggesting that enhancing these can mitigate turnover intentions. The network’s stability and accuracy were confirmed through bootstrapping methods, emphasizing the robustness of the findings.ConclusionThe study underscores the importance of addressing nurse burnout by focusing on core elements like personal accomplishment and self-efficacy to reduce turnover intentions. These insights facilitate targeted interventions that could improve nurse retention and stability within healthcare systems. Future research should expand to multi-center studies to enhance the generalizability of these findings.

Developing the new diagnostic model by integrating bioinformatics and machine learning for osteoarthritis

BackgroundOsteoarthritis (OA) is a common cause of disability among the elderly, profoundly affecting quality of life. This study aims to leverage bioinformatics and machine learning to develop an artificial neural network (ANN) model for diagnosing OA, providing new avenues for early diagnosis and treatment.MethodsFrom the Gene Expression Omnibus (GEO) database, we first obtained OA synovial tissue microarray datasets. Differentially expressed genes (DEGs) associated with OA were identified through utilization of the Limma package and weighted gene co-expression network analysis (WGCNA). Subsequently, protein-protein interaction (PPI) network analysis and machine learning were employed to identify the most relevant potential feature genes of OA, and ANN diagnostic model and receiver operating characteristic (ROC) curve were constructed to evaluate the diagnostic performance of the model. In addition, the expression levels of the feature genes were verified using real-time quantitative polymerase chain reaction (qRT-PCR). Finally, immune cell infiltration analysis was performed using CIBERSORT algorithm to explore the correlation between feature genes and immune cells.ResultsThe Limma package and WGCNA identified a total of 72 DEGs related to OA, of which 12 were up-regulated and 60 were down-regulated. Then, the PPI network analysis identified 21 hub genes, and three machine learning algorithms finally screened four feature genes (BTG2, CALML4, DUSP5, and GADD45B). The ANN diagnostic model was constructed based on these four feature genes. The AUC of the training set was 0.942, and the AUC of the validation set was 0.850. In addition, the qRT-PCR validation results demonstrated a significant downregulation of BTG2, DUSP5, and GADD45 mRNA expression levels in OA samples compared to normal samples, while CALML4 mRNA expression level exhibited an upregulation. Immune cell infiltration analysis revealed B cells memory, T cells gamma delta, B cells naive, Plasma cells, T cells CD4 memory resting, and NK cells The abnormal infiltration of activated cells may be related to the progression of OA.ConclusionsBTG2, CALML4, DUSP5, and GADD45B were identified as potential feature genes for OA, and an ANN diagnostic model with good diagnostic performance was developed, providing a new perspective for the early diagnosis and personalized treatment of OA.

Mapping leadership and communities in EU-funded research through network analysis

Background Horizon 2020 and Horizon Europe are flagship programs of the European Union aimed at supporting research and innovation, fostering collaboration among companies, academic institutions, and research organizations. Comprehensive data on projects, objectives, participants, funding details, and results of Horizon projects is available through the open access portal CORDIS (Community Research and Development Information Service). This paper introduces a novel methodology for utilizing CORDIS data to reveal collaborations, leadership roles, and their evolution over time. The case study focuses on the “hydrogen energy” sector, and specifically on the North Adriatic Hydrogen Valley project. Methods The methodology is based on network analysis. Data is downloaded from the CORDIS portal, enriched, segmented by year and transformed into weighted networks representing collaborations between organizations. Centrality measures are used to assess the influence of individual organizations, while community detection algorithms is used to identify stable collaborations. Temporal analysis tracks the evolution of these roles and communities over time. To ensure robust and reliable results, the methodology addresses challenges such as input-ordering bias and result variability, while the exploration of the solution space enhances the accuracy of identified collaboration patterns. Results A case study focusing on Horizon projects in the hydrogen energy sector demonstrates the application of this methodology, revealing the emergence of key leaders and stable communities, and highlighting significant collaboration within the sector. Conclusions The proposed methodology effectively identifies influential organizations and tracks the stability of research collaborations. The insights gained are valuable for policy-makers and organizations seeking to foster innovation through sustained partnerships. This approach can be extended to other sectors, offering a framework for understanding the impact of EU research funding on collaboration and leadership dynamics.

A network approach to understanding occupational psychological distress: linking depression, anxiety, and burnout among Chinese healthcare professionals

ObjectivesAs a population at high risk for psychological distress, healthcare workers typically experience varying degrees of anxiety, depression, and burnout. Studies have found that depression and anxiety have a negative impact on the mental health domain of burnout in healthcare workers. However, little is known about the symptom-to-symptom interactions between these psychological outcomes. This study aims to elucidate the characteristics of depression, anxiety, and burnout networks among healthcare workers.MethodsWe recruited 846 healthcare workers from March to April 2023 from three hospitals. A total of 826 healthcare workers completed the General Information Scale, the 16-item Quick Inventory of Depressive Symptomatology–Self-Report (QIDS-SR16), the Self-rating Anxiety Scale (SAS), and the Burnout Clinical Subtype Questionnaire (BCSQ)-36. The network models were constructed using network analysis. The expected influence and the bridge expected influence of nodes were calculated. The stability and accuracy of the network was assessed.ResultsThe results showed that the core symptoms in the symptom network mainly included QIDS8 (Energy/Fatigability), SAS3 (Easily upset or frightened), SAS11 (Dizzy), SAS8 (Tiredness), SAS10 (Tachycardia) and BCSQ3 (Worn-out), and the key nodes connecting these symptoms were QIDS2 (Sad mood), SAS20 (Have nightmares), BCSQ3 (Worn-out), SAS8 (Tiredness), QIDS8 (Energy/Fatigability), QIDS4 (Concentration/decision-making) and SAS4 (Madness).ConclusionUnique pathways of association between burnout, depression, and anxiety were found to exist. Interventions targeting core symptoms can maximize the improvement of depression, anxiety, and burnout, provide a deeper understanding of the relationship between the three conditions, and provide a target and basis for psychological interventions to improve the emotional wellbeing of healthcare workers and enhance their mental health.

Unveiling Key Genes Modulating Retinal Cell Survival and Autophagy in Glaucoma.

Glaucoma is a leading cause of irreversible blindness, with rising incidence globally. Effective treatment is challenging due to limited understanding of the disease mechanisms. Growth factor activity is crucial in glaucoma, with potential to reduce retinal ganglion cell (RGC) apoptosis and slow disease progression. This study aims to identify and analyze differentially expressed genes (DEGs) involved in growth factor activity to uncover new therapeutic targets. We analyzed the GSE9944 dataset from the Gene Expression Omnibus (GEO) to identify DEGs associated with glaucoma, resulting in 94 DEGs, including 29 down-regulated and 65 up-regulated genes. Functional enrichment and protein-protein interaction (PPI) network analyses were conducted using bioinformatics tools, highlighting the roles of Bone Morphogenetic Protein 1 (BMP1), Pleiotrophin (PTN), and f fibroblast Growth Factor 7 (FGF7). Aberrant expression vectors for these genes were transfected into RGCs derived from a glaucoma model to evaluate their impact on cell viability, apoptosis, and autophagy. Bioinformatics analysis of the GSE9944 dataset identified 94 DEGs, with 29 down-regulated and 65 up-regulated genes. Functional enrichment analysis revealed that these DEGs were involved in pathways related to growth factor activity, apoptosis, and autophagy, processes highly relevant to glaucoma pathogenesis. PPI network analysis identified BMP1, PTN, and FGF7 as central hub genes involved in extracellular matrix organization and growth factor signaling. In experimental validation using RGCs, we found that up-regulation of BMP1 significantly enhanced RGC viability and reduced apoptosis. Conversely, silencing PTN and FGF7 provided protective effects, enhancing RGC survival. Silencing BMP1 and upregulating PTN and FGF7 led to increased RGC apoptosis. Additionally, BMP1 was found to inhibit autophagy in RGCs, whereas PTN and FGF7 promoted autophagic activity, suggesting differential regulatory roles in glaucoma pathogenesis. Overall, BMP1, PTN, and FGF7 play critical roles in the regulation of RGC activity and autophagy in glaucoma, making them promising molecular targets for future therapeutic interventions.

Identified VCAM1 as prognostic gene in gastric cancer by co-expression network analysis

The diffuse gastric cancer (DGC) is a malignant tumor distinct from intestinal gastric cancer (IGC). This study aims to identify genetic variances and potential diagnostic and therapeutic approaches for diverse types of gastric cancer utilizing an extensive dataset. Data from RNA sequencing and clinical pathological details were acquired from The Cancer Genome Atlas (TCGA) database and the Gene Expression Omnibus (GEO) dataset. Co‐expression gene modules were constructed via Weighted Gene Co‐Expression Network Analysis (WGCNA), followed by deciphering gene functions and protein–protein interaction networks within significantly associated modules. In total, analysis was conducted on 56,753 genes from 247 individuals with gastric cancer. Particularly, 621 genes from the green module exhibited strong associations with the Lauren type of gastric cancer. The prominent genes in the green module showed enrichment in processes such as signal transduction, immune response, and the positive regulation of GTPase activity. Noteworthy among these, VCAM1 was identified as the central gene linked to patients’ prognosis. Moreover, 72 gastric cancer specimens were collected from The First Affiliated Hospital of University of Science and Technology of China. Immunohistochemical analysis demonstrated a significantly higher expression of VCAM1 in DGC compared to IGC (p = 0.019). Furthermore, it was confirmed that VCAM1 expression serves as a prognostic indicator for patients with DGC (p = 0.002), a correlation not observed in IGC (p = 0.760). In conclusion, this study identifies VCAM1 as a promising diagnostic and prognostic factor, suggesting novel avenues for diagnostic and therapeutic approaches in gastric cancer.

Population and Co-Occurrence Characteristics of Diagnoses and Comorbidities in Coronary Artery Disease Patients: A Case Study from a Hospital in Guangxi, China

Coronary artery disease (CAD) remains a major global health concern, significantly contributing to morbidity and mortality. This study aimed to investigate the co-occurrence patterns of diagnoses and comorbidities in CAD patients using a network-based approach. A retrospective analysis was conducted on 195 hospitalized CAD patients from a single hospital in Guangxi, China, with data collected on age, sex, and comorbidities. Network analysis, supported by sensitivity analysis, revealed key diagnostic clusters and comorbidity hubs, with hypertension emerging as the central node in the co-occurrence network. Unstable angina and myocardial infarction were identified as central diagnoses, frequently co-occurring with metabolic conditions such as diabetes. The results also highlighted significant age- and sex-specific differences in CAD diagnoses and comorbidities. Sensitivity analysis confirmed the robustness of the network structure and identified clusters, despite the limitations of sample size and data source. Modularity analysis uncovered distinct clusters, illustrating the complex interplay between cardiovascular and metabolic disorders. These findings provide valuable insights into the relationships between CAD and its comorbidities, emphasizing the importance of integrated, personalized management strategies. Future studies with larger, multi-center datasets and longitudinal designs are needed to validate these results and explore the temporal dynamics of CAD progression.

Explaining civilian control of militarisation in Indonesia: The case of military law amendment

This study explores the civilian control of militarisation practice in Indonesia, which refers to the discursive politics involving citizenry in the legitimisation mechanism of the use of military force for defence and non-defence objectives. To explore the empirical manifestation of the concept, the study applies the discourse network analysis method, which combines qualitative content analysis and social network analysis to investigate the public debates over the military law amendment. Accordingly, the study collected a dataset of 227 statements from 55 actors in 118 news articles on the military law amendment from May to July 2023. The analysis reveals that, first, the discourse was dominated by actors from non-governmental organisations, think tanks, and academia. Second, the discourse was driven by several main concerns, such as the return of military dual-function doctrine, the current state of military personnel occupying civilian posts, and the urgency of public participation in the deliberation process of the amendment. This article argues that the configuration of the dominant actors and the prominent issues reflect the prevalence of inward-looking defence policy orientation issue in Indonesia.

Networked corporate advocacy in a polarized public arena: analyzing discourse networks of U.S. Fortune 500 companies on controversial issues

Abstract This study investigates how U.S. Fortune 500 corporate social advocacy (CSA) discourse on multiple contentious issues is associated with public attention on Twitter/X. Our theoretical framework elucidates three discourse types and conceptualizes the diversity, structure, and stability of discourse coalition networks. Utilizing computational methods and dynamic network analysis of 43,791 corporate tweets over two years, findings reveal a prevalent use of aspirational communication in addressing climate change and healthcare disparities. Functional discourse is predominantly employed in tweets on racial equality and COVID-19 vaccination, while localization discourse is concentrated in tweets about gender/sexuality. Network analysis shows that discourse coalitions exist across industries, with a convergence in discourse types and low temporal stability in network compositions. Most discourse types and features of discourse coalition networks are positively associated with public attention to CSA tweets, though these associations vary across different issues.

Comprehensive analysis of lysine lactylation and its potential biological significance in clear cell renal cell carcinoma

BackgroundClear cell renal cell carcinoma (ccRCC) is a common histological subtype of malignant renal neoplasm. Protein lysine lactylation (Kla) plays a crucial role in tumor metabolic reprogramming. However, little is known regarding the distribution and potential biological functions of Kla in ccRCC. This study aimed to systematically investigate the role of Kla in ccRCC.MethodsA total of 12 ccRCC samples were collected from 6 patients. Western blotting was performed to determine the trend of Kla-modified proteins in ccRCC. Liquid chromatography–tandem mass spectrometry was used to quantitatively analyze Kla in ccRCC. Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and protein–protein interaction (PPI) network analyses were conducted to clarify the biological functions and interactional relationships of differentially lactylated proteins (DLPs).ResultsIn total, 239 DLPs, including 441 lactylated sites, were identified by comparing ccRCC tissues with adjacent normal tissues. Kla-related enzymes have a higher affinity for alanine than for other amino acid residues in ccRCC. Subcellular localization analysis revealed that most DLPs were localized in the cytoplasm and mitochondria. GO enrichment analysis showed that most of the DLPs were enriched in metabolism-associated biological processes, including the purine ribonucleotide, monocarboxylic acid, ribonucleoside triphosphate, purine nucleoside triphosphate, and ATP metabolic processes. KEGG analysis indicated that most DLPs were also enriched in metabolism-related pathways, including glycolysis, amino acid (valine, leucine, and isoleucine) degradation, pyruvate metabolism, fatty acid degradation, and the citrate cycle. The top 20 hub proteins were screened from the PPI network based on their degree ranks.ConclusionsThis study revealed the role of Kla in ccRCC, which will extend our understanding of the potential molecular mechanisms underlying ccRCC formation and progression. These key Kla-modified proteins may be promising therapeutic targets for the treatment of ccRCC. However, further molecular experiments are required to validate these findings.

Pasteurella multocida Serotype D Infection Induces Activation of the IL-17 Signaling Pathway in Goat Lymphocytes

(1) Background: Pasteurellosis is a global zoonotic bacterial disease, which has caused significant economic impacts in animal husbandry. Nevertheless, there is limited understanding of the immune response between goat peripheral blood lymphocytes (PBLs) and goat-derived Pasteurella multocida (P. multocida). (2) Methods: To investigate the immune response of host PBLs during infection with P. multocida type D, we established an in vitro cell model utilizing isolated primary goat PBLs. Utilizing this in vitro infection model, we employed an enzyme-linked immunosorbent assay (ELISA) to assess the cytokine profile variation in goat PBLs following infection. Meanwhile, RNA sequencing and quantitative PCR (qPCR) methods were employed to analyze the gene expression profile. (3) Results: The ELISA test results indicated that the expression levels of pro-inflammatory cytokines, such as IL-6, IFN-γ, CXCL10, and IL-17A, were significantly elevated within 12 h after infection with P. multocida. In contrast, the levels of the anti-inflammatory cytokine IL-10 were found to be reduced. RNA sequencing and functional enrichment analysis identified 2114 differentially expressed genes (DEGs) that were primarily associated with cytokine-cytokine receptor interactions, viral protein-cytokine interactions, and the IL-17 signaling pathway. Furthermore, protein-protein interaction (PPI) network analysis and qPCR highlighted CD86, CCL5, CD8A, CXCL8, CTLA4, TNF, CD274, IL-10, IL-6, CXCL10, IFNG, and IL-17A that were crucial for the response of PBLs to P. multocida infection. (4) Conclusions: This study systematically revealed the characteristics of PBLs in goats following infection with goat-derived P. multocida type D through the analysis of cytokines and gene expression, providing important theoretical insights for a deeper understanding of the defense mechanisms in goats against P. multocida.

Impact of Petty Tyranny on Employee Turnover Intentions: The Mediating Roles of Toxic Workplace Environment and Emotional Exhaustion in Academia

Based on social exchange theory, social psychology theories, and despotic leadership theory, this study explored the impact of petty tyranny on employee turnover intentions. Specifically, the authors examined the mediating effect of toxic workplace environments through emotional exhaustion on this relationship among academicians. The authors surveyed 421 employees using a five-point Likert scale across six universities in Lahore, Pakistan and employed a time-lag research design. Partial least squares structural equation modeling (PLS-SEM) and artificial neural network (ANN) analyses, including performance comparisons of various algorithms, were used to test the relationships among the variables. The analysis results of the study suggested that petty tyranny does not significantly and directly contribute to employee turnover intentions; however, this relationship is positively and significantly mediated by toxic workplace environments and emotional exhaustion. The results indicated that toxic workplace environments and emotional exhaustion also have a direct effect on employee turnover intentions. A serial full mediation was found between petty tyranny and turnover intentions, mediated through a toxic workplace environment and emotional exhaustion. Similarly, results from the performance comparison of various algorithms reveal trade-offs between precision, recall, and processing time, with ZeroR and Stacking REP Tree emerging as the most effective in terms of overall model accuracy. This study contributes to the literature by examining petty tyranny, workplace environment, and emotional exhaustion, highlighting the need to address tyrannical behavior to improve employee retention in academic organizations. Our study offers valuable practical implications, emphasizing addressing these issues to reduce turnover in academic organizations. Our study also provides recommendations for future research directions.

The conformational space of RNase P RNA in solution.

RNA conformational diversity has fundamental biological roles1-5, but direct visualization of its full conformational space in solution has not been possible using traditional biophysical techniques. Using solution atomic force microscopy, a deep neural network and statistical analyses, we show that the ribonuclease P (RNase P) RNA adopts heterogeneous conformations consisting of a conformationally invariant core and highly flexible peripheral structural elements that sample a broad conformational space, with amplitudes as large as 20-60 Å in a multitude of directions, with very low net energy cost. Increasing Mg2+ drives compaction and enhances enzymatic activity, probably by narrowing the conformational space. Moreover, analyses of the correlations and anticorrelations between spatial flexibility and sequence conservation suggest that the functional roles of both the structure and dynamics of key regions are embedded in the primary sequence. These findings reveal the structure-dynamics basis for the embodiment of both enzymatic precision and substrate promiscuity in the RNA component of the RNase P. Mapping the conformational space of the RNase P RNA demonstrates a new general approach to studying RNA structure and dynamics.

Profiling immune cell-related gene features and immunoregulatory ceRNA in ischemic stroke

Molecules in immune cells plays a vital role in the pathogenesis of ischemic stroke (IS). The aim of this study is to profile the landscape of molecules on the basis of immune cells in IS peripheral blood and construct an immunoregulatory competing endogenous RNA (ceRNA) network. We collected and combined multiple public transcriptome datasets from the peripheral blood of IS patients and healthy controls. CIBERSORT deconvolution revealed that the proportions of CD8 and CD4 naive T cells, monocytes, and neutrophils changed significantly in the IS group. Intersecting the immune cell-related genes identified by weighted gene co-expression network analysis (WGCNA) and differential expression analysis, 38 overlapping candidate biomarkers were selected. Three machine learning algorithms, including least absolute shrinkage and selection operator (LASSO), support vector machine-recursive feature elimination (SVM-RFE), and random forest were applied, and 11 distinct immune cell-related genes were identified. We obtained the mRNA-miRNA and miRNA-lncRNA interactions from StarBase v3.0, and constructed a ceRNA network based on the differentially expressed mRNAs, miRNAs, and lncRNAs. The aberrant expression of HECW2-centered ceRNAs in the peripheral blood of in-house patients was validated using quantitative PCR. We also revealed that the expression of HECW2 was positively correlated with lncRNAs LINC02593 through miRNAs miR-130a-3p, miR-130b-3p and miR-148b-3p in cells. These results show that there are distinct immune features between IS patients and healthy controls. The ceRNA network may help elucidate the mechanism of immune cell-related genes in IS and may serve as a therapeutic target.

Effects of pepper–maize intercropping on the physicochemical properties, microbial communities, and metabolites of rhizosphere and bulk soils

BackgroundIntercropping increases land use efficiency and farmland ecological diversity. However, little is understood about whether and how soil biota, metabolites, and nutrients change under interspecific competition among plants. Thus, this study aimed to explore the changes in the physicochemical properties, microbial communities, and metabolites of rhizosphere and bulk soils of pepper monocropping and pepper–maize intercropping systems.ResultsIntercropping significantly increased the contents of available phosphorus (AP) and available potassium (AK), and decreased the pH value, whereas it had little effect on the total nitrogen (TN) and organic matter (OM) in the rhizosphere and bulk soils, compared with those in monocropping pepper. Moreover, the OM content was higher in rhizosphere soil than in bulk soil. The microbial community structures and metabolite profiles also differed between the two systems. The diversity of bacteria and fungi increased in intercropped pepper. The relative abundances of Actinobacteria, Chloroflexi, Cyanobacteria, and Ascomycota were higher while those of Proteobacteria, Planctomycetes, Mucoromycota, and Basidiomycota were significantly lower in the rhizosphere and bulk soils from the intercropping system than in those from the monocropping system. Linear discriminant analysis revealed that the predominant bacteria and fungi in the rhizosphere soil from the intercropping system belonged to the order Sphingomonadales and genera Nitrospira, Phycicoccus and Auricularia, whereas those in the bulk soil from the intercropping system belonged to the phylum Acidobacteria and genera Calocera, Pseudogymnoascus, and Trichosporon. Intercropping promoted the secretion of flavonoids, alkaloids, and nucleotides and their derivatives in the rhizosphere soil and significantly increased the contents of organoheterocyclic compounds in the bulk soil. Furthermore, the AP and AK contents, and pH value had strong positive correlations with bacteria. In addition, co-occurrence network analysis also showed that asebogenin, trachelanthamidine, 5-methyldeoxycytidine, and soil pH were the key factors mediating root-soil-microbe interactions.ConclusionIntercropping can alter microbial community structures and soil metabolite composition in rhizosphere and bulk soils, enhancing soil nutrient contents, enriching soil beneficial microbes and secondary metabolites (flavonoids and alkaloids) of intercropped pepper, and provided a scientific basis for sustainable development in the pepper-maize intercropping system.

My Interests Are (Y)ours? A Multilevel Social Network Analysis of the Co-Evolution of Adolescents’ Interests and Friendships

Both interests and peer relations are crucial for adolescents in finding out who they are and want to be as a person. Yet, our knowledge of the co-evolution of friendships and interests is limited to studies focusing on single interest domains and/or practices predetermined by researchers. A total of 1599 Dutch adolescents participated in this study, which employs a unique design to examine the co-evolution of all participant-defined interests and in- and out-of-school friendships. In total, 368 specific interest areas were identified. Multilevel social network analyses (i.e., using multivariate stochastic actor-oriented models) of a subsample of 935 adolescents in seven schools indicate that adolescents are more likely, over time, to have similar interests to their friends. Interests that were reported rarely or very often contributed less to these dynamics. Additionally, being similarly interested increased the likelihood of being friends. Our findings show that social structures are related to interest development and how being interested is mediated by the places and spaces of adolescents’ lives.