Role of per- and polyfluoroalkyl substances in the cardiorenal system: Unraveling crosstalk from the network of pollutants and phenotypes.

Although per- and polyfluoroalkyl substances (PFAS) have been frequently linked to cardiovascular and renal disease separately, evidence remains scarce regarding their systematic effect. Therefore, we recruited 546 newly diagnosed acute coronary syndrome (ACS) patients and detected seven myocardial enzymes and six kidney function biomarkers. Twelve PFAS were also assessed with ultra-high-performance liquid chromatography-tandem mass spectrometry. Generalized linear model and restricted cubic spline model were applied to single pollutant analysis. Quantile g-computation was used for mixture analysis. Network model was utilized to identify central and bridge nodes of pollutants and phenotypes. In the present study, perfluorohexane sulfonic acid was positively associated with uric acid (UA) (ß= 0.04, 95% confidence interval (CI): 0.01, 0.07), and perfluorobutanoic acid was negatively associated with estimated glomerular filtration rate (ß= -0.04, 95% CI: -0.07, -0.01) but positively associated with UA (ß= 0.03, 95% CI: 0.01, 0.06). In mixture analysis, each quantile increase in the PFAS mixture was significantly associated with UA (ß= 0.08, 95% CI: 0.04, 0.11). Network analysis revealed that perfluorooctanoate, UA, and myoglobin were denoted as bridge nodes, and the first principal component of lactate dehydrogenase and creatine kinase- myocardial band was identified as the node with the highest strength and expected influence. This study investigates the systematic impact of PFAS exposure through cardiorenal interaction network, which highlights that PFAS may serve as an upstream approach in UA-modulated cardiorenal network to affect cardiorenal system comprehensively.

The bridge relationships of PTSD and depression symptoms among snakebite victims: a cross-sectional community-based survey.

BACKGROUND: The incidence of comorbid depression and post-traumatic stress disorder (PTSD) symptoms is higher in snakebite victims. However, the present state and contributing factors of depression and PTSD among Chinese snakebite victims remain unclear. METHODS: A representative sample of 6837 snakebite victims were assessed with the Post-traumatic Stress Disorder Checklist (Civilian Version) and The Center for Epidemiologic Studies Depression Scale. Multivariate analyses, including network analysis, evaluated the contributing factors of PTSD and depression symptoms caused by snake bites, as well as the bridge symptoms of comorbidity networks. RESULTS: Among 6,837 snakebite victims, 79.5% reported PTSD symptoms and 81.4% reported depression symptoms. Comorbidity of PTSD and depression symptoms was found in 75.1%. Key factors included the presence sequelae after snakebite (ORPTSD = 2.31, ORDepression = 1.89), time to medical facilities (6-8 h: ORPTSD = 3.17, ORDepression = 2.46), and marital status (divorced/widowed: ORPTSD = 1.78, ORDepression = 1.76). Symptoms I1 ("Repeated disturbing memories") and D1 ("Bothered by things that don't usually bother me") bridged PTSD and depression networks. CONCLUSION: The primary psychological challenges for snakebite victims in China are PTSD and depression symptoms, which is concerning. Standardized diagnosis and treatments, timely medical care, and stable marital relationships can reduce risks. Additional psychological support and management of negative memories, especially for those with severe bridge symptoms, can be beneficial. Further research should concentrate on understanding victims' psychological states and developing effective interventions.

Suicide warning signs of self-identification in patients with mood disorders: a qualitative analysis based on safety planning.

Introduction: Warning signs serve as proximal indicators of suicide risk, making early recognition imperative for effective prevention strategies. This study aimed to explore self-identified suicide warning signs among Chinese patients with mood disorders based on safety planning framework. Methods: Researchers collaborated with patients to develop a safety plan and compiled warning signs based on it. Word frequency and network analysis were conducted to identify key warning signs. Directed content analysis categorized these signs into cognitive, emotional, behavioral, or physiological themes according to the suicide mode theory. Additionally, we examined potential variations in reported warning signs among participants with different demographic characteristics, including age, gender, and history of suicide attempts. Results: "Low mood" and "crying" emerged as prominent warning signs, with "social withdrawal" closely following. Patients commonly reported emotional themes during suicidal crises, often experiencing two to three themes simultaneously, primarily focusing on emotional, behavioral, and physiological themes. Males exhibited a higher proportion of concurrently reporting three sign themes compared to females (P < 0.05), while no difference was observed in warning signs among patients with other demographic traits. Discussion: This study offers a nuanced understanding of warning signs among mood disorder patients in China. The findings underscore the necessity for comprehensive suicide risk management strategies, emphasizing interventions targeting emotional regulation and social support. These insights provide valuable information for enhancing suicide prevention and intervention efforts.

Analysis of microRNAs and the microRNA-messengerRNA regulatory network in chronic alcohol exposure.

Introduction: Chronic alcoholism is one of the most common neurological diseases in modern society. However, the key mechanisms underlying learning and memory impairments caused by chronic alcohol exposure remain unclear. In this study, a microRNA-messenger RNA (miRNA-mRNA) network was constructed to explore the potential function of key genes in chronic alcohol exposure, their effects on the hippocampus, and their mechanisms which facilitate brain injury in mice. Methods: The Morris water maze test was used to assess the learning ability of mice in each group. Mitochondrial ATPase activity and H2S levels in the hippocampi of mice were determined. Differentially expressed miRNAs and mRNAs in the mouse hippocampus were identified using second-generation sequencing. Using the TargetScan, miRTarBase, and miRDB databases, we predicted miRNA target genes and constructed a miRNA-mRNA regulatory network. Furthermore, using the Gene Ontology and KEGG databases we performed functional enrichment and protein-protein interaction analyses. Real-time quantitative polymerase chain reaction (qPCR) and other methods were employed to verify the mRNA expression of related genes. Results: The Morris water maze test revealed that mice exposed to chronic alcohol exhibited a significantly reduced learning ability compared to the control group (p < 0.05). Compared with the control group, the activity of mitochondrial ATPase in the hippocampal tissue of alcohol-treated mice was significantly decreased (p < 0.01), suggesting brain injury. In the model group, H2S significantly increased in the mice hippocampi (p < 0.01), indicating that chronic alcohol exposure could activate cystathionineß-synthase (CBS) and catalyze the mass formation of H2S, suggesting brain injury. A total of 208 differentially expressed miRNAs and 377 differentially expressed mRNAs were screened through bioinformatic analysis. Enrichment analysis indicated that the main pathways were involved in neurodegeneration and regulation of the Wnt signaling pathway. The PCR detected a significant downregulation in the expressions of FOS and EGR1 genes. Discussion: Consequently, chronic alcohol exposure may regulate the expression of FOS and EGR1 in the hippocampus through miR-222-3p, miR-132-3p, miR-212-3p, and miR-191-5p, reduce the activity of hippocampal mitochondrial ATPase, activate CBS, catalyze the large amount of H2S formation, and destroy the mitochondrial structure, resulting in decreased learning ability. Our findings revealed valuable genes and miRNAs for the study of chronic alcohol exposure.

Integrating network analysis with differential expression to uncover therapeutic and prognostic biomarkers in esophageal squamous cell carcinoma.

Introduction: Esophageal squamous cell carcinoma (ESCC) accounts for over 90% of all esophageal tumors. However, the molecular mechanism underlying ESCC development and prognosis remains unclear, and there are still no effective molecular biomarkers for diagnosing or predicting the clinical outcome of patients with ESCC. Here, we used bioinformatics analysis to identify potential biomarkers and therapeutic targets for ESCC. Methodology: Differentially expressed genes (DEGs) between ESCC and normal esophageal tissue samples were obtained by comprehensively analyzing publicly available RNA-seq datasets from the TCGA and GTEX. Gene Ontology (GO) annotation and Reactome pathway analysis identified the biological roles of the DEGs. Moreover, the Cytoscape 3.10.1 platform and subsidiary tools such as CytoHubba were used to visualize the DEGs' protein-protein interaction (PPI) network and identify hub genes, Furthermore our results are validated by using Single-cell RNA analysis. Results: Identification of 2524 genes exhibiting altered expression enriched in pathways including keratinization, epidermal cell differentiation, G alpha(s) signaling events, and biological process of cell proliferation and division, extracellular matrix (ECM) disassembly, and muscle function. Moreover, upregulation of hallmarks E2F targets, G2M checkpoints, and TNF signaling. CytoHubba revealed 20 hub genes that had a valuable influence on the progression of ESCC in these patients. Among these, the high expression levels of four genes, CDK1 MAD2L1, PLK1, and TOP2A, were associated with critical dependence for cell survival in ESCC cell lines, as indicated by CRISPR dependency scores, gene expression data, and cell line metadata. We also identify the molecules targeting these essential hub genes, among which GSK461364 is a promising inhibitor of PLK1, BMS265246, and Valrubicin inhibitors of CDK1 and TOP2A, respectively. Moreover, we identified that elevated expression of MMP9 is associated with worse overall survival in ESCC patients, which may serve as potential prognostic biomarker or therapeutic target for ESCC. The single-cell RNA analysis showed MMP9 is highly expressed in myeloid, fibroblast, and epithelial cells, but low in T cells, endothelial cells, and B cells. This suggests MMP9's role in tumor progression and matrix remodeling, highlighting its potential as a prognostic marker and therapeutic target. Discussion: Our study identified key hub genes in ESCC, assessing their potential as therapeutic targets and biomarkers through detailed expression and dependency analyses. Notably, MMP9 emerged as a significant prognostic marker with high expression correlating with poor survival, underscoring its potential for targeted therapy. These findings enhance our understanding of ESCC pathogenesis and highlight promising avenues for treatment.

Network analysis of anxiety and cognitive impairment among mental healthcare workers.

Introduction: With the rising demand for medical services and the associated burden, work-related stress and mental health issue have garnered increased attention among healthcare workers. Anxiety, cognitive impairment, and their comorbidities severely impact the physical and mental health as well as the work status of healthcare workers. The network analysis method was used to identify the anxiety and cognitive impairment among mental healthcare workers using the Generalized Anxiety Disorder Scale (GAD-7) and the Perceived Deficit Questionnaire for Depression (PDQ-D). We sought to identify the core symptoms associated with the comorbidity of anxiety and cognitive impairment in mental healthcare workers. Methods: The study was conducted by Shandong Daizhuang Hospital and Qingdao Mental Health Center in China from September 13, 2022, to October 25, 2022, involving a total of 680 healthcare workers as participants. GAD-7 and PDQ-D were utilized to assess anxiety and cognitive impairment, respectively. Regularized partial correlation network analysis was employed to examing the expected influence and predictability of each item within the network. Statistical analysis and visualization of the network were performed using R software. Results: The mean total score for anxiety was 3.25, while the mean total score for cognitive symptoms was 15.89. PDQ17 "Remembering numbers", PDQ12 "Trouble get started" and PDQ20 "Trouble make decisions" emerged as central symptoms in the anxiety-cognition network. GAD6 "Irritable", GAD5 "Restlessness" and GAD1 "Nervousness or anxiety" were identified as the most critical bridge symptoms connecting anxiety and cognition. Gender was found to be unrelated to the global strength of the network, edge weight distribution, or individual edge weights. Conclusion: Utilizing central and bridge symptoms (i.e., Remembering numbers, Trouble get started, Trouble make decisions, Irritable, Restlessness and Nervousness or anxiety) as primary intervention points may aid in mitigating the serious health consequences of anxiety, cognitive impairment, and comorbidities anxiety and cognitive impairment for mental healthcare workers.

Investigating the network ties between affect, attachment, and psychopathology.

OBJECTIVES: Recent years have seen an increased interest regarding theoretical and empirical associations of adult attachment security and primary affective traits concerning psychiatric disorders. In this study, network analysis technique is applied to dissect the links between both psychodynamic personality constructs and an array of psychopathological symptoms. METHODS: A total sample of 921 (69.9 % female) participants from the general population was investigated. A regularized cross-sectional partial correlation network between attachment (Experiences in Close Relationships-Revised [ECR-RD8]), primary affective traits (Brief Affective Neuroscience Personality Scales [BANPS-GL]) and psychopathological symptoms (ICD-10-Symptom-Rating Questionnaire [ISR]) was estimated via the EBICglasso algorithm. Node centrality, predictability and bridge centrality were analyzed. To evaluate the stability of the network and evaluate the significance of differences, we employed bootstrap techniques. RESULTS: The network was found to be stable, allowing reliable interpretations. We observed SADNESS, as well as depressive, PTSD and anxiety symptoms as the most influential nodes within the investigated network. Attachment AV and SADNESS were observed as nodes with the highest bridge centrality. CONCLUSIONS: The results provide a data-driven in-depth look into the complex dynamics between psychopathological symptoms, attachment security and basic affective traits. Results underscore the critical interconnections between affect, attachment, and psychopathology, advocating for a psychodynamically informed systems approach in psychological research that considers the affective dimensions underlying human mental health.

Network structure of REM sleep behavior disorder symptoms in iRBD patients.

OBJECTIVE: Employing the REM Sleep Behavior Disorder Questionnaire-Hong Kong (RBDQ-HK) to investigate symptoms and their severity in rapid eye movement (REM) sleep behavior disorder (RBD) patients, this study delves into the construct of RBD through the RBDQ-HK and its links to depression and sleep quality. METHODS: Data from the RBDQ-HK, the Geriatric Depression Scale (GDS), and the Pittsburgh Sleep Quality Index (PSQI) were compiled from individuals with isolated RBD (iRBD) confirmed by polysomnography. We constructed a network analysis of the RBDQ-HK, measured the centrality of each symptom (node), conducted Exploratory Graph Analysis (EGA) to unveil the dimension structure of the questionnaire, and calculated bridge expected influence (BEI) to identifying critical bridge. Multivariate linear regression was also employed to discover relationships between RBDQ-HK dimensions and variables such as PSQI and GDS. RESULTS: In our cohort of 455 iRBD patients (299 males), the items in the RBDQ-HK were divided into three dimensions: dream, movement, and SRI/violence. The symptoms identified as most central to RBD were 'shouting or yelling in sleep', 'dream-enacting movements', and 'talking during sleep'. The highest (BEI) was 'violent and aggressive dreams', which has the potential to bridge three dimensions within the symptom network. Depression was significantly correlated with the movement and dream dimensions of RBD, and sleep quality was predominantly related to the dream dimension score. CONCLUSION: Our findings verify that the principal symptoms of the RBDQ-HK align with the established diagnostic criteria and reveal a three-dimensional structure within RBD symptoms. The relationships between the RBD symptoms, depression, and sleep quality need to be identified for the effective management of RBD patients.

Gradient Boosting Prediction of Overlapping Genes From Weighted Co-expression and Differential Gene Expression Analysis of Wnt Pathway: An Artificial Intelligence-Based Bioinformatics Study.

Introduction The Wnt (wingless-related integration site) signalling pathway is crucial for bone formation and remodelling, regulating the commitment of mesenchymal stem cells (MSCs) to the osteoblastic lineage. It triggers the transcriptional activation of Wnt target genes and promotes osteoblast proliferation and survival. Weighted co-expression network analysis (WGCNA) and differential gene expression analysis help researchers understand gene roles. Gradient boosting, a machine learning technique, enhances understanding of genetic and molecular mechanisms contributing to overlap genes, improving gene regulation and functional genomics. The aim is to predict overlapping genes in the Wnt signalling pathway. Methods Differential gene expression analysis was performed using the National Center for Biotechnology Information (NCBI) geo dataset-GSE251951, focusing on the effect of Wnt signaling on treatment. The WGCNA module was analyzed using the iDEP tool to identify interconnected gene clusters. Hub genes were identified by calculating module eigengenes, correlated with external traits, and ranked based on module membership values. The study utilized gradient boosting, an ensemble learning method, to predict models, evaluate their performance using metrics like accuracy, precision, recall, and F1 score, and adjust predictions based on gradient and learning rate. Results The dendrogram uses the "Dynamic TreeCut" algorithm to analyze gene clusters, aiding researchers in understanding gene modules and biological processes, identifying co-expressed genes, and discovering new pathways. The confusion matrix displays 88 actual and predicted cases. The gradient boosting model achieves 78.9% accuracy in predicting Wnt pathway overlapping genes, with a respectable area under the curve (AUC) and classification accuracy values. It accurately predicts 73.9% of samples, with a high precision ratio and low recall. Conclusion Future research should enhance differential expression analysis and WGCNA to identify key Wnt pathway genes, improve sensitivity, specificity, hyperparameter tuning, and validation experiments, and use larger datasets.

Structural characteristics of carbon information disclosure networks and the impact on carbon emission performance: Evidence from China.

Improving corporate carbon emission performance is an important driving force for realising the low-carbon development of China's economy. Carbon information disclosure is an environmental regulatory tool proposed in the context of carbon neutrality, this study examines whether carbon information disclosure effectively influences carbon emission performance. Based on the data from 30 provinces in China from 2012 to 2021, this paper examines the structural characteristics of carbon information disclosure using social network analysis, accounting for the carbon emission performance of industrial enterprises in each province in China. Using the spatial econometric model, we apply the undesirable slacks-based measure model and examine the impact of the spatial network characteristics of carbon information disclosure on carbon emission performance. Results show that the overall density of carbon information disclosure networks of industrial enterprises in Chinese provinces is low, so there is much room for improvement. Additionally, there is spatial dependence on carbon emission performance among neighbouring provinces. Moreover, the out-degree centrality and betweenness centrality of carbon information disclosure networks significantly negatively affect carbon emission performance. However, the inhibitory effect of the in-degree centrality of carbon information disclosure networks on carbon emission performance is not significant. The unique findings of this paper are relevant to environmental policy formulation and assessment.

Deciphering the differences of bacterial communities between high- and low-productive wheat fields using high-throughput sequencing.

Microbial communities have been demonstrated to be essential for healthy and productive soil ecosystems. However, an understanding of the relationship between soil microbial community and soil productivity levels is remarkably limited. In this study, bulk soil (BS), rhizosphere soil (RS), and root (R) samples from the historical high-productive (H) and low-productive (L) soil types of wheat in Hebei province of China were collected and analyzed by high-throughput sequencing. The study highlighted the richness, diversity, and structure of bacterial communities, along with the correlation networks among different bacterial genera. Significant differences in the bacterial community structure between samples of different soil types were observed. Compared with the low-productive soil type, the bacterial communities of samples from the high-productive soil type possessed high species richness, low species diversity, complex and stable networks, and a higher relative abundance of beneficial microbes, such as Pseudoxanthomonas, unclassified Vicinamibacteraceae, Lysobacter, Massilia, Pseudomonas, and Bacillus. Further analysis indicated that the differences were mainly driven by soil organic matter (SOM), available nitrogen (AN), and electrical conductivity (EC). Overall, the soil bacterial community is an important factor affecting soil health and crop production, which provides a theoretical basis for the targeted regulation of microbes in low-productivity soil types.

Antibiotic resistance genes (ARGs) and their eco-environmental response in the Bohai Sea sediments.

Antibiotic resistance genes (ARGs) are an important class of pollutants in the environment. This study investigated the characteristics and ecological effects of ARGs in the Bohai Sea sediments. The results showed that ARGs are widely distributed, and exhibit significant spatial and subtype variations, with absolute abundance following the decreasing order of Liaodong Bay, Laizhou Bay, Bohai Bay, and Bohai Strait. Tetracycline ARGs dominated, comprising 50 % to 62 % of all ARGs, with tetM having the highest abundance at 1.43 × 107 copies/g. Symbiotic network analysis revealed that the phyla Deinococcota, Dadabacteria were serve as the primary likely host of ARGs. The ARGs have a wide range of potential hosts, and bacteria often carry multiple ARGs, enhancing the mobility and ecological niche adaptation of ARGs. This study will provide an important reference for assessing ARGs pollution in semi-enclosed seas.

The Perceived Weight Stigma Scale and Weight Self-Stigma Questionnaire: Rasch analysis, confirmatory factor analysis, and network analysis among Chinese adolescents.

OBJECTIVES: The objectives of the present study were to (i) re-evaluate and expand the psychometric properties of two weight stigma instruments-the Perceived Weight Stigma Scale (PWSS) and the Weight Self-Stigma Questionnaire (WSSQ) among a large sample of adolescents using advanced psychometric methods and (ii) examine how the different types of weight stigma (i.e., PWSS and WSSQ) are associated with psychological distress. STUDY DESIGN: Cross-sectional study. METHODS: In September 2023, a cross-sectional survey utilising convenience sampling was used to recruit 9995 adolescents (mean age = 16.36 years [standard deviation = 0.78]; 57.8% males). They completed the PWSS, WSSQ, and a measure on psychological distress. The data were analysed using Rasch analysis, confirmatory factor analysis (CFA), structural equation modelling (SEM), and network analysis. RESULTS: The CFA and Rasch model results showed acceptable psychometric properties regarding factor structure, factor loading, difficulty, and infit and outfit mean squares (except Items 4 and 7 of the PWSS). There was no substantial differential item functioning for any tested items across the sex and weight categories. The CFA and SEM results showed promising validity indices with significant associations between both weight stigma scales and psychological distress (i.e., depression, anxiety, and stress). Network analysis showed inter-variable connectivity between nodes PWSS3 ("People act as if they are afraid of you") and WSSQF7 ("I feel insecure about others' opinions of me"). CONCLUSIONS: Both weight stigma scales had acceptable psychometric properties and were significantly associated with psychological distress, although each assessed different types of weight stigma. This suggests that researchers and clinicians can use these scales to reliably and validly assess weight stigmas among adolescents.

Adolescent anxiety and depression: perspectives of network analysis and longitudinal network analysis.

BACKGROUND: Anxiety and depression often co-occur, exhibiting high comorbidity, with their trends evolving over time. However, the specific pathways through which comorbid symptoms of anxiety and depression evolve and interact remain unclear. To investigate these questions, this study employed Network Analysis (NA) and Longitudinal Network Analysis (LNA) to explore the central symptoms of anxiety and depression, as well as the temporal evolution of these central symptoms. METHODS: The study focused on 606 high school students who were not in their final year in Shandong of China, with assessments conducted from March to September 2022. The bootnet package in R was used for establishing NA and LNA models, as well as for conducting accuracy analysis and node stability analysis. RESULTS: The results of the NA indicated that adolescent highly susceptible to anxiety and depression. And uncontrollable worry was a common central symptom, while irritability emerged as a central bridging symptom across all three NAs. The LNA results revealed that suicidal ideation and worthlessness were key central symptoms in the LNA. Furthermore, worthlessness played a pivotal role in the developmental pathway of "suicidal ideation → worthlessness → anxiety and uncontrollable worry." A reduction in suicidal ideation was associated with decreased severity in other symptoms. CONCLUSIONS: The findings suggest that adolescent anxiety and depression are in a state of vulnerability, and that irritability, worthlessness, and suicidal ideation are potential targets for interventions to address adolescent anxiety and depression.

Extending Copper Interconnects and Epoxy Dielectrics to Multi-GHz Frequencies.

Future multichip packages require Die-to-Die (D2D) interconnects operating at frequencies above 10 GHz; however, the extension of copper interconnects and epoxy dielectrics presents a trade-off between performance and reliability. This paper explores insertion losses and adhesion as a function of interface roughness at frequencies up to 18 GHz. We probe epoxy surface chemistry as a function of curing time and use wet etching to modulate surface roughness. The morphology is quantified by atomic force microscopy (AFM) and two-dimensional fast Fourier transform (2D FFT). Peel test and vector network analysis are used to examine the impacts of both type and level of roughness. The trade-offs between power efficiency and reliability are presented and discussed.

How do different goals affect students' internal collaboration script configurations? Results of an epistemic network analysis study.

Introduction: Research has suggested that how learners act in CSCL environments is considerably influenced by their internal collaboration scripts. These scripts are knowledge structures that reside in an individual's memory and consist of play, scene, scriptlet, and role components. In its "internal script configuration principle," the Script Theory of Guidance suggests that as learners work in a CSCL environment, these components are dynamically (re-)configured, and that this (re-)configuration is influenced by the goals of the individual learner. However, this principle has not yet been tested empirically. Methods: In this study, upon entering a CSCL environment, we therefore experimentally manipulated the goals that students pursued while learning. In one condition, we induced learning goals while in the other condition, no goals were induced. A total of 233 pre-service teachers collaborated in dyads on the task to analyze an authentic, problematic classroom situation by aid of educational evidence. We measured their internal scripts both at pre-test (i.e., before collaboration and before goal induction) and post-test (i.e., after collaboration and goal induction), focusing on the scriptlet level. Results: Results show that goal induction had no effects on the kinds of scriptlets participants selected during collaboration. However, results from Epistemic Network Analysis show that learning goal induction led to significantly different combinations of scriptlets (especially to more relations between scriptlets that are indicative of pursuing learning goals) than no goal induction. Furthermore, participants from the learning goal induction acquired significantly more knowledge about educational theories and evidence than students from the control condition. Conclusion: This study is among the first to provide direct evidence for the internal script configuration principle and demonstrates the effectiveness of inducing learning goals as a scaffold to support students' knowledge acquisition processes in CSCL.

Dissecting the infodemic: An in-depth analysis of COVID-19 misinformation detection on X (formerly Twitter) utilizing machine learning and deep learning techniques.

The alarming growth of misinformation on social media has become a global concern as it influences public opinions and compromises social, political, and public health development. The proliferation of deceptive information has resulted in widespread confusion, societal disturbances, and significant consequences for matters pertaining to health. Throughout the COVID-19 pandemic, there was a substantial surge in the dissemination of inaccurate or deceptive information via social media platforms, particularly X (formerly known as Twitter), resulting in the phenomenon commonly referred to as an "Infodemic". This review paper examines a grand selection of 600 articles published in the past five years and focuses on conducting a thorough analysis of 87 studies that investigate the detection of fake news connected to COVID-19 on Twitter. In addition, this research explores the algorithmic techniques and methodologies used to investigate the individuals responsible for disseminating this type of fake news. A summary of common datasets, along with their fundamental qualities, for detecting fake news has been included as well. For the purpose of identifying fake news, the behavioral pattern of the misinformation spreaders, and their community analysis, we have performed an in-depth examination of the most recent literature that the researchers have worked with and recommended. Our key findings can be summarized in a few points: (a) around 80% of fake news detection-related papers have utilized Deep Neural Networks-based techniques for better performance achievement, although the proposed models suffer from overfitting, vanishing gradients, and higher prediction time problems, (b) around 60% of the disseminator related analysis papers focus on identifying dominant spreaders and their communities utilizing graph modeling although there is not much work done in this domain, and finally, (c) we conclude by pointing out a wide range of research gaps, for example, the need of a large and robust training dataset and deeper investigation of the communities, etc., and suggesting potential solution strategies. Moreover, to facilitate the utilization of a large training dataset for detecting fake news, we have created a large database by compiling the training datasets from 17 different research works. The objective of this study is to shed light on exactly how COVID-19-related tweets are beginning to diverge, along with the dissemination of misinformation. Our work uncovers notable discoveries, including the ongoing rapid growth of the disseminator population, the presence of professional spreaders within the disseminator community, and a substantial level of collaboration among the fake news spreaders.

Depressive symptoms, cognitive functions and daily activities: An extended network analysis in monozygotic and dizygotic twins.

BACKGROUND: The network theory posits that depression emerges as the result of individual symptoms triggering each other. Risk factors for depression can impact these between-symptoms interactions through extended networks. The study aimed to model the extended network of depressive symptoms and known depression risk factors - objective cognitive function, intellectual, physical, and social daily activities, and then, compare the observed networks between monozygotic (MZ) and dizygotic (DZ) co-twins. METHODS: Twin pairs, 722 MZ and 2200 DZ, aged 40-79, were selected from the Dansh Twin Registry for having complete measures of depressive symptoms (e.g., sadness), cognitive functions (e.g., verbal memory), physical (e.g., brisk walk), intellectual (e.g., reading newspapers) and social activities (e.g., phone calls). Gaussian graphical models were used to estimate and compare the networks first between co-twins and then, between MZ to DZ twin pairs separately. RESULTS: Specific intellectual, physical and social activities were central in the extended networks of depressive symptoms and, with the exception of processing speed, more central than cognition. The extended networks' structure was more homogeneous between MZ co-twins relative to DZ co-twins. Cognitive nodes were more central in MZ than DZ co-twins. LIMITATIONS: Cross-sectional design, participants were middle-aged or older, mostly affective (non-somatic) depressive symptoms. CONCLUSIONS: In depression networks, core connecting elements were intellectual, physical and social activities. The interaction between cognition and daily activities seems critical for triggering depressive symptoms. Thus, clinical interventions aimed at preventing depression and associated cognitive deficits should focus on maintenance and/or engagement in stimulating daily activities.

Inter and intra-island genetic structure and differentiation of the endemic Bolle's Laurel Pigeon (Columba bollii) in the Canary archipelago.

Islands provide excellent settings for studying the evolutionary history of species, since their geographic isolation and relatively small size limit gene flow between populations, and promote divergence and speciation. The endemic Bolle's Laurel Pigeon Columba bollii is an arboreal frugivorous bird species distributed on laurel forests in four islands of the Canary archipelago. To elucidate the population genetics, we genotyped ten microsatellite loci using DNA obtained from non-invasive samples collected across practically all laurel forest remnants, and subsequently grouped into eight sampling sites. Analyses including F-statistics, Bayesian clustering approaches, isolation by distance tests and population graph topologies, were used to infer the genetic diversity and the population differentiation within and among insular populations. Additionally, we evaluated the effect of null alleles on data analysis. Low genetic diversity was found in all populations of Bolle's Laurel Pigeon, with no significant differences in diversity among them. However, significant genetic differentiation was detected among all populations, with pigeons from La Palma and El Hierro exhibiting the closest affinity. Bayesian clustering supported population separation between islands, and also detected fine-scale structure within the Tenerife and La Gomera populations. Our results suggest that, despite columbids have a high movement ability, they can show signature of genetic divergence among populations, particularly on oceanic islands. Geological history of the islands and distribution range of habitats could have close influence on the evolutionary trajectories of these birds. This approach can provide practical tools to implement appropriate conservation measures for range-restricted species and their habitat.

Sleep disturbance and other co-occurring conditions in autistic children: A network approach to understanding their inter-relationships.

Autistic children frequently have one or more co-occurring psychological, behavioral, or medical conditions. We examined relationships between child behaviors, sleep, adaptive behavior, autistic traits, mental health conditions, and health in autistic children using network analysis. Network analysis is hypothesis generating and can inform our understanding of relationships between multiple conditions and behaviors, directing the development of transdiagnostic treatments for co-occurring conditions. Participants were two child cohorts from the Autism Treatment Network registry: ages 2-5 years (n = 2372) and 6-17 years (n = 1553). Least absolute-shrinkage and selection operator (LASSO) regularized partial correlation network analysis was performed in the 2-5 years cohort (35 items) and the 6-17 years cohort (36 items). The Spinglass algorithm determined communities within each network. Two-step expected influence (EI2) determined the importance of network variables. The most influential network items were sleep difficulties (2 items) and aggressive behaviors for young children and aggressive behaviors, social problems, and anxious/depressed behavior for older children. Five communities were found for younger children and seven for older children. Of the top three most important bridge variables, night-waking/parasomnias and anxious/depressed behavior were in both age-groups, and somatic complaints and sleep initiation/duration were in younger and older cohorts respectively. Despite cohort differences, sleep disturbances were prominent in all networks, indicating they are a transdiagnostic feature across many clinical conditions, and thus a target for intervention and monitoring. Aggressive behavior was influential in the partial correlation networks, indicating a potential red flag for clinical monitoring. Other items of strong network importance may also be intervention targets or screening flags.