Cross-sectional Dataset Research Articles

Revalidation of the Rosenberg Self-Esteem Scale in Algeria Using Network Analysis

The aim of the present study was to revalidate the Rosenberg Self-Esteem Scale using Network Analysis among Algerian university students. A cross-sectional dataset was collected from a sample of 1115 participants, including 738 females (66.2 %) and 377 males (33.8 %). Network Analysis was utilized to investigate the structure of the scale, identify the most influential items in measuring self-esteem, and compare the two networks between females and males. Results revealed a two-dimensional structure of the scale, with negative-worded items forming the first dimension and positive-worded items forming the second dimension. Item 8 was removed due to instability. Centrality indices were employed to identify the items that played a crucial role in enhancing an individual's self-esteem. The results indicated that items 3 and 10 held greater importance in the network for the entire participant group. The Network Comparison Test indicates no difference in Network Invariance between females' and males' networks. However, there is a difference in the global strength of the two networks. The comparison of their centrality indices reveals that items 3 and 10 continue to hold higher importance in the network for females. In contrast, for males, item 4 takes precedence over items 10 and 3. The findings provide evidence for the reliability and validity of the Rosenberg Self-Esteem Scale with nine items in measuring self-esteem among Algerian university students.

Happily ever after for coupled and single adults: A comparative study using latent profile analysis

Many people believe that “living happily ever after” involves finding a romantic partner. Regardless, it seems obvious that at least some single people are happier than some coupled people. But how likely is it, and what factors predict whether singles will be as happy as their coupled peers? The present paper addressed these questions via a secondary analysis of an existing cross-sectional dataset ( N = 2,000) of coupled ( n = 1,438) and single ( n = 562) adults. We identified 10 candidate predictors of life satisfaction, seven of which were significant in multiple regression models. Using the seven significant predictors as indicators in latent profile analysis (LPA), we identified four distinct profiles (or groups) of coupled adults and six distinct profiles of single adults. We then further conceptualized these profiles in terms of interpersonal indicators (friend satisfaction, closest friend intimacy, family satisfaction, and romantic satisfaction) and intrapersonal indicators (self-esteem, perceived stress, and physical health). Some profiles had very favorable levels of interpersonal and intrapersonal indicators and some had very unfavorable levels, while several profiles fell in between the two extremes with a range of nuances. Overall, people with favorable levels were happier than those with unfavorable levels—regardless of their relationship status, but disadvantages in one area (e.g., self-esteem) could be offset by advantages in another area (e.g., friendship satisfaction). Most importantly, in comparing single and coupled profiles directly, we found that the vast majority of single adults follow a range of life satisfaction patterns (from happy to unhappy) that is nearly identical to that of their coupled peers.

Considerations on brain age predictions from repeatedly sampled data across time.

Brain age, the estimation of a person's age from magnetic resonance imaging (MRI) parameters, has been used as a general indicator of health. The marker requires however further validation for application in clinical contexts. Here, we show how brain age predictions perform for the same individual at various time points and validate our findings with age-matched healthy controls. We used densely sampled T1-weighted MRI data from four individuals (from two densely sampled datasets) to observe how brain age corresponds to age and is influenced by acquisition and quality parameters. For validation, we used two cross-sectional datasets. Brain age was predicted by a pretrained deep learning model. We found small within-subject correlations between age and brain age. We also found evidence for the influence of field strength on brain age which replicated in the cross-sectional validation data and inconclusive effects of scan quality. The absence of maturation effects for the age range in the presented sample, brain age model bias (including training age distribution and field strength), and model error are potential reasons for small relationships between age and brain age in densely sampled longitudinal data. Clinical applications of brain age models should consider of the possibility of apparent biases caused by variation in the data acquisition process.

Establishing the associations between the Big Five personality traits and self-reported number of close friends: A cross-sectional and longitudinal study

Close friends are important across the life span, who spend time together, provide support, and share happiness. But what determines the number of close friends one would have? One of the most important factors would be personality traits, which capture the most basic differences among individuals in terms of how they feel, think, and behave. This report aimed to establish the associations between the Big Five personality traits and the number of close friends cross-sectionally and longitudinally. By analyzing a cross-sectional (N = 32, 990) and longitudinal dataset (N = 22, 383) from Understanding Society: the UK Household Longitudinal Study (UKHLS), the current report found that Neuroticism has a negative connection with the number of close friends, whereas Agreeableness, Openness, and Extraversion were positively connected to the number of close friends in the cross-sectional study. In the longitudinal study, Openness was positively associated with changes in the number of close friends.

The Every Mind Matters campaign: changes in mental health literacy and its associations with campaign awareness.

The aim of this study is to investigate the effects on population level mental health literacy (MHL) of Every Mind Matters over 30 months following campaign launch. To observe changes in MHL over time, we conducted regression analyses on a nationally representative, repeated cross-sectional dataset of nine survey waves from September 2019 to March 2022 and an individual participant data meta-analysis with data from October 2019 to March 2021 to examine the association between campaign awareness and the outcomes. There were small improvements in knowledge of management of stress, depression and anxiety, mental health vigilance, sleep literacy and psychological wellbeing self-efficacy from September 2019 to March 2020 and a deterioration in most MHL outcomes from March 2020 compared with September 2019. Campaign awareness was positively associated with symptom management of depression and anxiety, help seeking self-efficacy, stigma related to mental disorders and mental health vigilance. There is little evidence that the campaign improved MHL in the general population beyond March 2020. Those who were aware of the campaign may have benefitted from its resources.

Vector autoregression: Useful in rare diseases?-Predicting organ response patterns in a rare case of secondary AA amyloidosis.

Statistical analyses of clinical data are a cornerstone in understanding pathomechanisms of disorders. In rare disorders, cross-sectional datasets of sufficient size are usually not available. Taking AA amyloidosis as an example of a life-threatening rare disorder resulting from of uncontrolled chronic inflammation, we propose techniques from time series analysis to predict organ response to treatment. The advantage of time-series analysis is that it solely relies on temporal variation and therefore allows analyzing organ response to treatment even when the cross-sectional dimension is small. The joint temporal interdependence of inflammatory activity and organ response was modelled multivariately using vector autoregression (VAR) based on a unique 4.5 year spanning data set of routine laboratory, imaging data (e.g., 18F-Florbetaben-PET/CT) and functional investigations of a 68-year-old patient with multi-organ involvement of AA amyloidosis due to ongoing inflammatory activity of a malignant paraganglioma in stable disease for >20 years and excellent response to tocilizumab). VAR analysis showed that alterations in inflammatory activity forecasted alkaline phosphatase (AP). AP levels, but not inflammatory activity at the previous measurement time point predicted proteinuria. We demonstrate the feasibility and value of time series analysis for obtaining clinically reliable information when the rarity of a disease prevents conventional prognostic modelling approaches. We illustrate the comparative utility of blood, functional and imaging markers to monitor the development and regression of AA amyloidosis.

A TRANSCRIPTOMIC APPRECIATION OF CHILDHOOD MENINGOCOCCAL AND POLYMICROBIAL SEPSIS FROM A PRO-INFLAMMATORY AND TRAJECTORIAL PERSPECTIVE, A ROLE FOR VASCULAR ENDOTHELIAL GROWTH FACTOR A AND B MODULATION?

This study investigated the temporal dynamics of childhood sepsis by analyzing gene expression changes associated with proinflammatory processes. Five datasets, including four meningococcal sepsis shock (MSS) datasets (two temporal and two longitudinal) and one polymicrobial sepsis dataset, were selected to track temporal changes in gene expression. Hierarchical clustering revealed three temporal phases: early, intermediate, and late, providing a framework for understanding sepsis progression. Principal component analysis supported the identification of gene expression trajectories. Differential gene analysis highlighted consistent upregulation of vascular endothelial growth factor A (VEGF-A) and nuclear factor κB1 (NFKB1), genes involved in inflammation, across the sepsis datasets. NFKB1 gene expression also showed temporal changes in the MSS datasets. In the postmortem dataset comparing MSS cases to controls, VEGF-A was upregulated and VEGF-B downregulated. Renal tissue exhibited higher VEGF-A expression compared with other tissues. Similar VEGF-A upregulation and VEGF-B downregulation patterns were observed in the cross-sectional MSS datasets and the polymicrobial sepsis dataset. Hexagonal plots confirmed VEGF-R (VEGF receptor)-VEGF-R2 signaling pathway enrichment in the MSS cross-sectional studies. The polymicrobial sepsis dataset also showed enrichment of the VEGF pathway in septic shock day 3 and sepsis day 3 samples compared with controls. These findings provide unique insights into the dynamic nature of sepsis from a transcriptomic perspective and suggest potential implications for biomarker development. Future research should focus on larger-scale temporal transcriptomic studies with appropriate control groups and validate the identified gene combination as a potential biomarker panel for sepsis.

A Longitudinal Examination of Four Processual Mechanisms in Career Decision Making

The dual-process theory of career decision making (DTC) proposes four processual mechanisms that explain the career decision-making process and outcomes. Although research has examined the validity of the four processual mechanisms using a cross-sectional dataset in China, the roles of the four mechanisms in career decision making cannot be fully established without longitudinal and cross-cultural analyses. Using a sample of U.S. college students ( n = 206), the current study examined the validity of the four processual mechanisms in explaining the joint operation of five major decision-making difficulties with subsequent career decidedness as the criterion. The results supported not only the standalone mechanisms of managing state and persistent decision uncertainty but also the joint mechanisms from managing state (persistent) uncertainty to managing persistent (state) uncertainty. Therefore, the current study adds evidence to the DTC, particularly regarding the scientific and practical necessity of differentiating between state and persistent decision uncertainty and between reducing uncertainty and reducing the threat of uncertainty. Additionally, the current study offers interesting implications for the interplay of major decision difficulties across different sociocultural contexts of China and the U.S.

Factors associated with Korean adolescent's e-cigarette use by the severity level of generalized anxiety disorder (GAD-7)

BackgroundThe use of e-cigarette among Korean adolescents is spreading with alarming rapidity, but it has not been studied along with the severity level of GAD-7. This study aims to identify adolescents' e-cigarette use and its associated factors among four different anxiety groups using the problem behavioral theory (PBT). MethodsA cross-sectional secondary dataset was retrieved from the 2020 Korea Youth Risk Behavior Survey. Of the total sample of 54,948, four anxiety groups were formed: minimal (score 0–4; n = 36,711), mild (score 5–9; n = 12,138), moderate (score 10–14; n = 4143), and severe (score above 15; n = 1965). A total of 11 variables were selected based on the PBT domains. Descriptive analysis, Chi-square, ANOVA, univariate analysis, and multiple linear regression analysis were performed. ResultsE-cigarette use was the highest in the order of severe, mild, moderate, and minimal. While cigarette use was associated with vaping in all anxiety groups, adolescents with minimal or mild anxiety levels were more likely to be influenced by tobacco accessibility and other behavior system factors such as sexual intercourse, alcohol use, and smartphone addiction. Meanwhile, secondhand smoke exposure in public areas was only associated with those with severe anxiety levels, and secondhand smoke exposure at school was a significant factor in all anxiety groups except for those with moderate anxiety levels. LimitationDue to the nature of cross-sectional dataset, the study could not draw causal links between vaping and other identified factors, include all the necessary PBT components. ConclusionThese findings underscore the need to consider different levels of anxiety when addressing the problems related to vaping among Korean adolescents.

The role of the espoused tight culture in value assessment of social media use

ABSTRACT To advance the stream of culture-themed Information Systems (IS) research, this study ushers in the cultural tightness-looseness (CTL) theory and conceptualises the focal construct, the cultural tightness (CT), into the social media usage context. By integrating the CTL theory and the customer value perspective, this study proposes a research model to examine how an espoused tight culture affects individuals’ value assessment and behaviour of social media use. The model is validated using a cross-sectional field survey dataset of Chinese social media users, sampling a typical tight culture. The findings demonstrate that the espoused tight culture plays a paramount role in influencing users’ value assessment and usage behaviour related to social media. This study contributes to the cultural IS literature by providing a context-specific understanding of overall cultural interactions of social media use in a tight culture.

Environmental consequences of geopolitical crises: The case of economic sanctions and emissions

Geopolitical crises, and economic sanctions, in particular, have created considerable disturbance in natural resource markets. This study focuses on a significant consequence of this disturbance, namely how sanctions may derail targeted states from their low-carbon pathways. Increasing capital costs and difficulties in accessing technology force sanctioned states to move away from capital- and technology-intensive greener alternatives to employ more carbon-intensive (predominantly coal) generation modes in their primary energy supply. We also show that the recovery process from sanctions further increases coal use in these targeted states. Our econometric results from a global cross-sectional time-series dataset support our expectations. Our findings call for a deeper understanding of the challenges in achieving sustainable recovery in the aftermath of geopolitical crises. These results call for assessing carbon-footprint implications of specific foreign policy actions before these actions are carried out.

The Dark Tetrad, online moral disengagement, and online aggression perpetration among adults

Online aggression is a form of antisocial online behavior that has been widely studied among adolescents, but adults of all ages are involved as well. The goal of the present study is to broaden the understanding of potential risk factors of online aggression perpetration. Associations between socially aversive personality traits and online aggression are examined. In contrast to previous research and to account for ICT affordances, the present study argues for and tests the mediating role of online moral disengagement, and not traditional moral disengagement, in the relationships between traits and perpetration. To this aim, a cross-sectional dataset, balanced in terms of age, was collected among 2,000 adults aged 19–79. A path model revealed indirect associations for two traits, psychopathy and sadism, with online aggression perpetration via online moral disengagement, while controlling for online aggression victimization. Theoretical and practical implications are discussed.

Electron Scattering from 1-Methyl-5-Nitroimidazole: Cross-Sections for Modeling Electron Transport through Potential Radiosensitizers.

In this study, we present a complete set of electron scattering cross-sections from 1-Methyl-5-Nitroimidazole (1M5NI) molecules for impact energies ranging from 0.1 to 1000 eV. This information is relevant to evaluate the potential role of 1M5NI as a molecular radiosensitizers. The total electron scattering cross-sections (TCS) that we previously measured with a magnetically confined electron transmission apparatus were considered as the reference values for the present analysis. Elastic scattering cross-sections were calculated by means of two different schemes: The Schwinger multichannel (SMC) method for the lower energies (below 15 eV) and the independent atom model-based screening-corrected additivity rule with interferences (IAM-SCARI) for higher energies (above 15 eV). The latter was also applied to calculate the total ionization cross-sections, which were complemented with experimental values of the induced cationic fragmentation by electron impact. Double differential ionization cross-sections were measured with a reaction microscope multi-particle coincidence spectrometer. Using a momentum imaging spectrometer, direct measurements of the anion fragment yields and kinetic energies by the dissociative electron attachment are also presented. Cross-sections for the other inelastic channels were derived with a self-consistent procedure by sampling their values at a given energy to ensure that the sum of the cross-sections of all the scattering processes available at that energy coincides with the corresponding TCS. This cross-section data set is ready to be used for modelling electron-induced radiation damage at the molecular level to biologically relevant media containing 1M5NI as a potential radiosensitizer. Nonetheless, a proper evaluation of its radiosensitizing effects would require further radiobiological experiments.

Maternal health care policy intervention and its impact on perinatal mortality outcomes in Ghana: evidence from a quasi-experimental design

ObjectiveThis study aimed to evaluate the impact of Ghana's free maternal health care policy on stillbirth and perinatal death since its implementation a decade ago. Study designThe study used the propensity score matching method, a quasi-experimental design technique and secondary data to construct two groups of mothers with a history of perinatal deaths who subscribed to the ‘free’ maternal health care policy versus mothers who did not. MethodThe study merged two rounds of repeated cross-sectional data sets obtained from the Ghana Demographic and Health Survey (GDHS), 2008 and 2014, and generated exposure variables; pregnant women policy holding status and outcome variables; stillbirth and perinatal death by constructing binary outcomes from the under-five mortality variables of the DHS data sets. Fetal and early neonatal deaths within the data set were categorized into two groups: those exposed to the free maternal health care policy and those who did not. The propensity scores of the two groups were then generated and analyzed after checking for bias and common support. The analysis applied sample weighting to account for clustering and stratification due to the complex design of the DHS. All analyses were done with STATA 15 and adjusted for confounding using independent covariates. ResultsStillbirth (43.3%) and perinatal death (60.2%) were high in the intervention group compared to the comparison group, and the differences were statistically significant (stillbirth, 0.0156, and perinatal death, 0.0012). Stillbirth and perinatal deaths were 12 and 13 percentage points higher in the intervention group, and these were statistically significant: adj. coef. = 0.12; 95% CI: [0.03–0.19]; P = 0.005 and adj. coef. = 0.13; 95% CI: [0.03–0.22]; P = 0.005. ConclusionThe results show that stillbirth and perinatal death were high in the maternal health care policy group, poorly reflecting as outcomes. However, the percentage point difference between stillbirth and perinatal death suggests a decline in early neonatal mortality and a positive impact of the ‘free’ maternal health care policy on perinatal death over stillbirth.

A mixed-model approach for estimating drivers of microbiota community composition and differential taxonomic abundance.

Next-generation sequencing (NGS) and metabarcoding approaches are increasingly applied to wild animal populations, but there is a disconnect between the widely applied generalized linear mixed model (GLMM) approaches commonly used to study phenotypic variation and the statistical toolkit from community ecology typically applied to metabarcoding data. Here, we describe the suitability of a novel GLMM-based approach for analyzing the taxon-specific sequence read counts derived from standard metabarcoding data. This approach allows decomposition of the contribution of different drivers to variation in community composition (e.g., age, season, individual) via interaction terms in the model random-effects structure. We provide guidance to implementing this approach and show how these models can identify how responsible specific taxonomic groups are for the effects attributed to different drivers. We applied this approach to two cross-sectional data sets from the Soay sheep population of St. Kilda. GLMMs showed agreement with dissimilarity-based approaches highlighting the substantial contribution of age and minimal contribution of season to microbiota community compositions, and simultaneously estimated the contribution of other technical and biological factors. We further used model predictions to show that age effects were principally due to increases in taxa of the phylum Bacteroidetes and declines in taxa of the phylum Firmicutes. This approach offers a powerful means for understanding the influence of drivers of community structure derived from metabarcoding data. We discuss how our approach could be readily adapted to allow researchers to estimate contributions of additional factors such as host or microbe phylogeny to answer emerging questions surrounding the ecological and evolutionary roles of within-host communities. IMPORTANCE NGS and fecal metabarcoding methods have provided powerful opportunities to study the wild gut microbiome. A wealth of data is, therefore, amassing across wild systems, generating the need for analytical approaches that can appropriately investigate simultaneous factors at the host and environmental scale that determine the composition of these communities. Here, we describe a generalized linear mixed-effects model (GLMM) approach to analyze read count data from metabarcoding of the gut microbiota, allowing us to quantify the contributions of multiple host and environmental factors to within-host community structure. Our approach provides outputs that are familiar to a majority of field ecologists and can be run using any standard mixed-effects modeling packages. We illustrate this approach using two metabarcoding data sets from the Soay sheep population of St. Kilda investigating age and season effects as worked examples.

“Just Right”: The Goldilocks Theory of Sanctions Busting’s Causes

Abstract When sender states impose economic sanctions on target states, are the commercially lucrative opportunities for third-party sanctions busters the same across all target states? I develop a time-series cross-sectional data sets using UN Comtrade data between 1963 and 2011 to illustrate how variation in a target state’s economic size impacts the likelihood of sanctions busting. I develop a “Goldilocks” theory of target choice, arguing that target states with medium-sized economies are “just right” with a higher probability of receiving sanctions busting trade than smaller- and larger-sized economies. These “just right” economies likely generate higher rents and more commercially lucrative opportunities than smaller- and larger-sized sanctioned economies. This study has implications for policymakers in that the very policies imposing sanctions tend to target medium-sized economies more frequently. Thus, economic sanctions may stimulate sanctions busting trade and undermine the very behavior these foreign policies are meant to correct.

A Multi-Scale Rebar Detection Network with an Embedded Attention Mechanism

With the rapid development of computer vision and machine vision, methods based on deep learning have achieved good results in the field of object detection, identification, and tracking. However, for the detection and identification of rebars in smart construction sites, it is very difficult to perform accurate real-time detection of rebars by using object detection technology on the equipment in the field because of the problems of the dense cross-section between bundled bars, the mutual adhesion of cross-section boundaries, and mutual occlusion between cross-sections. To address the above problems, we propose a multi-scale rebar detection network RebarNet with an embedded attention mechanism based on YOLOv5, combining the K-means++ algorithm, attention mechanism, a newly designed SD_IoU Loss, and multi-scale feature fusion, aiming to solve the problems of missed and false detection in dense small object detection. Due to the problems of scarce rebar cross-section datasets, no publicly available large datasets, and weak rebar cross-sectional features, we constructed a new rebar cross-sectional dataset, used a semi-automatic annotation method to annotate part of the dataset, and then used the data enhancement algorithm to expand the rebar dataset. The experimental results show that the average accuracy (mAP) of our proposed RebarNet network is 97.9%, which is comparable to mainstream target detection algorithms such as Faster R-CNN, SSD, RetinaNet, CenterNet, CornerNet, YOLOv3, YOLOv4, and YOLOv5s. mAP0.5 is improved by 8.1%, 13%, 26.4%, 25.8%, 26.2%, 11.7%, 7.6%, and 9%, respectively. In addition, the frames per second (FPS) transmission reaches 89 frames per second, the model weight is only 17.0 MB. In summary, the proposed RebarNet can effectively reduce missed and false detections in the rebar counting detection task based on real-time detection.

Overweight/Obesity-related microstructural alterations of the fimbria-fornix in the ABCD study: The role of aerobic physical activity.

Childhood overweight/obesity has been associated with negative consequences related to brain function and may involve alterations in white matter pathways important for cognitive and emotional processing. Aerobic physical activity is a promising lifestyle factor that could restore white matter alterations. However, little is known about either regional white matter alterations in children with overweight/obesity or the effects of aerobic physical activity targeting the obesity-related brain alterations in children. Using a large-scale cross-sectional population-based dataset of US children aged 9 to 10 years (n = 8019), this study explored the associations between overweight/obesity and microstructure of limbic white matter tracts, and examined whether aerobic physical activity may reduce the overweight/obesity-related white matter alterations in children. The primary outcome measure was restriction spectrum imaging (RSI)-derived white matter microstructural integrity measures. The number of days in a week that children engaged in aerobic physical activity for at least 60 minutes per day was assessed. We found that females with overweight/obesity had lower measures of integrity of the fimbria-fornix, a major limbic-hippocampal white matter tract, than their lean peers, while this difference was not significant in males. We also found a positive relationship between the number of days of aerobic physical activity completed in a week and integrity measures of the fimbria-fornix in females with overweight/obesity. Our results provide cross-sectional evidence of sex-specific microstructural alteration in the fimbria-fornix in children with overweight/obesity and suggest that aerobic physical activity may play a role in reducing this alteration. Future work should examine the causal direction of the relationship between childhood overweight/obesity and brain alterations and evaluate potential interventions to validate the effects of aerobic physical activity on this relationship.

The role of development interventions in enhancing technical efficiency of sunflower producers

A key policy concern in African agriculture is low productivity even when new technologies are generated, disseminated and adopted among farmers. This study used a cross-sectional dataset from a sample of 202 sunflower farmers drawn from northern Uganda to determine technical efficiency and assess the influence of farmer management capabilities, development intervention and socio-economic factors on technical efficiency. Results reveal that technical inefficiency among sunflower farmers account for 81% of farm-level output. Further, farmer management capabilities (p<0.01), participation in development interventions (p<0.01) and reliance on certified seeds (p<0.01) significantly reduce farmer inefficiency and thus, improve technical efficiency. The study highlights that focusing on farmer management capabilities to improve farm efficiency is more cost-saving for realizing economic gains in sunflower production rather than introducing new technologies. We recommend a policy focus on using approaches such as farmer field schools that ensures farmer advisory services at all critical stages of crop growth.

The Narrowing Gender Wage Gap Among Faculty at Public Universities in the U.S

We study the conditional gender wage gap among faculty at public research universities in the U.S. We begin by using a cross-sectional dataset from 2016 to replicate the long-standing finding in research that, conditional on rich controls, female faculty earn less than their male colleagues. Next, we construct a data panel to track the evolution of the wage gap through 2021. We show that the gender wage gap is narrowing. It declined by more than 50% over the course of our data panel to the point where by 2021, it is no longer detectable at conventional levels of statistical significance.