Individual Attributes Research Articles

The Role of Optimism and Self-Efficacy in the Relationship between Academic Stress and Depressive Symptoms in Medical Students Including the Use and Knowledge of Structural Health Promotion Offers

Abstract Background In recent years, studies have highlighted that stress levels among medical students are alarmingly high. The study aim was to examine how academic stress and depressive symptoms in medical students are affected by individual moderators (self-efficacy/optimism) and structural influencing factors (health promotion offers). Methods Data collection occurred at three measurement points during the first 1.5 years at a medical school in Germany [baseline measurement (BM; Winter 2019, 226 participants), follow-up measurement 1 (FUM1; Summer 2020, 106 participants) and follow-up measurement 2 (FUM2; Winter 2020, 107 participants)]. Cross-sectional and longitudinal analyses were conducted with the data of FUM1 and FUM2, BM being only cross-sectionally analyzed as a baseline measurement. Interactions were calculated cross-sectionally with multiple linear regressions, and longitudinally with mixed effects models. Results Academic stress was positively correlated with depressive symptoms cross-sectionally. In the longitudinal sample, optimism was found to moderate the association between academic stress and depressive symptoms, while the moderating effect of self-efficacy was found cross-sectionally for FUM2. “University sports offers” was the most-used health promotion offer for both FUM1 and FUM2. The other services, especially “psychological counseling”, were rarely used by the students. The prevalence of depressive symptoms increased during the study course (BM: 4.9%; FUM1: 25.5%; FUM2: 23.4%). Conclusion The increase of depressive symptoms linked to rising academic stress within the study course is alarming. Individual attributes such as optimism and self-efficacy have a moderating role on the relationship between academic stress and depressive symptoms and prevention offers may affect those individual attributes. .

Equipping Teachers for Success in Rural Communities: Unveiling the Key Characteristics and Attributes of Thriving Rural Teachers

Australian education faces persistent challenges in preparing and retaining teachers within rural contexts. This qualitative study examines the attributes of thriving rural teachers through focus group discussions with 103 rural educational leaders. Utilising a theoretical framework combining attribution theory and ecological systems perspectives, the study synthesised leaders’ perceptions of key dispositions and behaviours contributing to rural teachers’ success. Four interconnected dimensions emerged: being community, classroom, professionally and personally equipped. This study revealed that while individual teacher attributes are crucial, school support and community context significantly influence teacher success. The application of attribute theory further revealed that findings attributed teacher’s success to internal, stable, and controllable factors, suggesting that key traits could be developed through targeted professional development and experience. This study provides nuanced insights into ’rural readiness’, informing education policy decisions and practices, initial teacher education institutions, schools, and communities. The study emphasises the need for a holistic approach to teacher preparation and support that considers both individual and systemic factors.

Radical networks and terrorist action: the importance of social relationships and network positions

ABSTRACT In most models on terrorism, interaction with other radicals is a key factor. However, systematic empirical evidence that radical ties affect behaviour is scarce. Our access to detailed police information allows us to apply Social Network Analysis (SNA) on a Dutch Salafi-Jihadi network and analyse the social relationships and network positions of 80 actors, out of whom 20 turned to terrorist acts. The results suggest that ties to leaders and terrorist offenders, co-attendance of radicalising settings and greater structural embeddedness relate to the likelihood of individual actors to turn to acts of terrorism. However, we also find some individual attributes that may offer competing explanations. In this paper we discuss our findings and address how future research may provide further insights into an important issue for agencies involved in countering terrorism: which radical actors, if any, are more likely to turn to acts of terrorism?

Female Agency in the British Ministry of Education: Mary Medd's Contribution to School Design (1949-1972)

This paper describes and discusses architect Mary Medd’s input into school development within the Ministry of Education, responsible for England and Wales, during the post-war era, highlighting her agency and capacity to provide significant change in the discipline of architecture. Mary Medd’s contributions were outcast on two fronts: first, by an institutional framework that prioritised anonymous civil service expertise, thereby suppressing individual attribution, and second, as one half of a prosperous partnership, both professional and personal, with David Medd. Although the collective processes that inexorably characterise the work dynamics within public institutions normally imply that any attribution to a single person is ambiguous, this paper suggests that the institutional framework should not be interpreted as a hindrance to the recognition of Mary Medd’s authorship. Through archival work, and focusing on her design proposals, the Ministry of Education is interpreted as the very place where she decided to develop her agency as a woman, deeply engaged in education and architecture, to pursue the complete reconfiguration of school design in a national level. The ministry offered a place for the development of a different kind of architectural practice where her individual agency could be exercised both as a designer of spatial layouts and as the main catalyst of a holistic interdisciplinary collaboration. The paper embarks on a mission to reevaluate and celebrate Mary Medd’s crucial role in the evolution of education architecture through an analysis of her diaries, notebooks, and drawings. In addition to repositioning Mary Medd within the annals of architectural history, this research aims to contribute to the ongoing historiography of feminist research methods and ideologies within the field. By shedding light on the gendered disparities in architectural history and emphasising the importance of acknowledging women’s contributions, this study adds to the broader conversation on gender equity in design and education, ultimately enriching our understanding of the multifaceted history of architectural practice.

Resilience through adversity: a case of informal artisan entrepreneurs in Kenya

PurposeThe study aims to understand how informal artisan entrepreneurs demonstrate resilience while experiencing adversity. To achieve this, it explores how artisans handle adversities and how this differs from other informal entrepreneurs.Design/methodology/approachThe study engaged with entrepreneurial theories of resilience. It incorporated 46 qualitative telephone interviews with 32 artisans in the informal tourism industry of Kenya, conducted over two phases and analysed using thematic analysis. Notably, it draws key methodological considerations for conducting remote qualitative data collection and engaging with participants operating in an informal setting.FindingsThe findings suggest that informal artisans exhibit individual attributes and behaviours that are associated with resilience in entrepreneurship. Beyond these, their resilience is also influenced by cultural norms related to resourcing their business and culturally derived tacit knowledge.Originality/valueThe paper extends the understanding of resilience among informal artisan entrepreneurs, who display different characteristics due to the nature of their entrepreneurial activities. It shows that beyond the individualistic view of resilience, culture also influences resilience through social norms and values that govern behaviours. Furthermore, culture reinforces resilience as it is rooted in tacit knowledge held by artisan entrepreneurs. The paper thus contributes to resilience theory in entrepreneurship and to the unique context of artisanry.

Study on the travelers’ P&R usage behavior during snowfall in the corridor valley pattern city

According to the characteristics of corridor valley pattern cities, this paper presents four aspects of terrain space perception. Using a structural equation model (SEM), the four-dimensional terrain space perception is analyzed through second-order confirmatory factor analysis. A random parameter Logit model (RPLM) was developed to examine the impact of individual socioeconomic attributes, objective built environment, travel characteristics, and terrain space perception on travelers’ Park and Ride (P&R) use behavior during snowfall conditions. Additionally, a Logit model was utilized to assess how individual socioeconomic attributes of travelers in corridor valley pattern cities influence their perception of terrain space during snowfall weather. The findings indicate that the four latent variables related to terrain space perception proposed in this paper effectively explain travelers’ perception of terrain space in corridor valley pattern cities, and the observed variables also support these latent variables. The subjective and objective built environment of corridor valley pattern cities significantly affect travelers’ P&R usage behavior during snowfall weather, and there is heterogeneity in the terrain space perception during snowfall weather among different travelers in corridor valley pattern cities. Furthermore, the gender, age, income, and experience of using P&R of travelers in corridor valley pattern cities play a significant role in moderating terrain space perception.

Individual tree species classification using low-density airborne multispectral LiDAR data via attribute-aware cross-branch transformer

Traditional forest inventory supplies essential data for forest monitoring and management, including tree species, but obtaining individual tree-level information is increasingly crucial. Airborne Light Detection and Ranging (LiDAR) with multispectral observation offers rich information for improved forest inventory mapping with reliable individual tree attributes. Although deep learning techniques have shown promise in tree species classification, they are not sufficiently explored for individual tree-level classification using low-density (less than 30 point/m2) Airborne Multispectral LiDAR (AML) data. This study therefore explores the feasibility of using a deep learning (DL) framework for processing low-density AML point clouds to enhance tree species classification in challenging forest environments. A point-based deep learning network with a dual-branch mechanism combined Cross-Branch Attention modules named Attribute-Aware Cross-Branch (AACB) Transformer is designed for AML data to better differentiate tree species from delineated individual trees. In addition, a channel merging approach is introduced, which is suited to prepare the training samples of deep learning networks and reduces the computational costs. This study was tested with an average 9 points/m2 AML point cloud for 6 tree species including Populus tremuloides, Larix laricina, Acer saccharum, Picea abies, Pinus resinosa, and Pinus strobus from a Canadian mixed forest. The overall accuracies achieved 83.1 %, 85.8 %, and 95.3 % at species, genus, and leaf-type levels, respectively. The comparison between the proposed method and other widely used tree species classification methods demonstrates the effectiveness of the proposed approach in enhancing tree species classification accuracy. We discuss potentials and remaining challenges, and our findings allow to further improve tree species classification of low-density AML point clouds by DL technology.

Analyzing and predicting short-term substance use behaviors of persons who use drugs in the great plains of the U.S.

Substance use induces large economic and societal costs in the U.S. Understanding the change in substance use behaviors of persons who use drugs (PWUDs) over time, therefore, is important in order to inform healthcare providers, policymakers, and other stakeholders toward more efficient allocation of limited resources to at-risk PWUDs. This study examines the short-term (within a year) behavioral changes in substance use of PWUDs at the population and individual levels. 237 PWUDs in the Great Plains of the U.S. were recruited by our team. The sample provides us longitudinal survey data regarding their individual attributes, including drug use behaviors, at two separate time periods spanning 4-12 months. At the population level, we analyze our data quantitatively for 18 illicit drugs; then, at the individual level, we build interpretable machine learning logistic regression and decision tree models for identifying relevant attributes to predict, for a given PWUD, (i) which drug(s) they would likely use and (ii) which drug(s) they would likely increase usage within the next 12 months. All predictive models were evaluated by computing the (averaged) Area under the Receiver Operating Characteristic curve (AUROC) and Area under the Precision-Recall curve (AUPR) on multiple distinct sets of hold-out sample. At the population level, the extent of usage change and the number of drugs exhibiting usage changes follow power-law distributions. At the individual level, AUROC's of the models for the top-4 prevalent drugs (marijuana, methamphetamines, amphetamines, and cocaine) range 0.756-0.829 (+2.88-7.66% improvement with respect to baseline models using only current usage of the respective drugs as input) for (i) and 0.670-0.765 (+4.34-18.0%) for (ii). The corresponding AUPR's of the said models range 0.729-0.947 (+2.49-13.6%) for (i) and 0.348-0.618 (+26.9-87.6%) for (ii). The observed qualitative changes in short-term substance usage and the trained predictive models for (i) and (ii) can potentially inform human decision-making toward efficient allocation of appropriate resources to PWUDs at highest risk.

The combination of CSF neurofilament light chain and glial fibrillary acidic protein improves the prediction of long-term confirmed disability worsening in multiple sclerosis

Our objective was to evaluate the individual and combined prognostic attributes of baseline serum and CSF measurements of Neurofilament light chain (sNfL, cNfL) and glial fibrillary acidic protein (sGFAP, cGFAP) on long term clinical outcomes in MS. In this retrospective single center study, patients with serum and CSF stored at first MS presentation and > 15-years of follow-up were analyzed. NfL and GFAP were quantified from cryopreserved samples using a digital immunoassay and analyzed as predictors of confirmed disability worsening (CDW). Sixty patients (70% female) underwent baseline tandem CSF and serum sampling and were followed for a mean of 17.8 years (SD 2.5). 32 developed CDW. By logistic regression, while sNfL cNfL and cGFAP showed prognostic merit (AUC 0.72, 0.70, 0.62 respectively), sGFAP did not (AUC 0.5). The combination of cNfL and cGFAP improved CDW prediction compared to either measure considered in isolation (AUC 0.72). The optimal predictive cut-off for CDW (Youden’s index) for cNfL was 596 pg/mL and for cGFAP was 8160 pg/mL. Kaplan–Meier analysis of the cutoff-defined ‘high-high’ and ‘low-low’ combined cNfL and cGFAP groupings improved prediction of CDW compared to either marker individually (Hazard ratio 4.5 (95% CI 2.7–18.3), Logrank P < 0.0001). Cox Proportional Hazards regression demonstrated that high baseline cNfL and cGFAP were independently prognostic of subsequent CDW after adjusting for baseline age, sex, EDSS score and subsequent treatment exposure. Each unit increase in Ln(cNfL) and Ln(cGFAP) was respectively associated with an additional hazard of 2.36 (95% CI 1.12–5.52) and 2.26 (95% CI 1.03–5.21). CSF NfL and GFAP are independently prognostic of long-term clinical worsening in MS, and may represent a complementary pairing.

Mechanisms of residential spatial differentiation and heritage identity in residential historic district: a case study of the Barkhor Street, Tibet

ABSTRACT Under the dual background of urban renewal and heritage conservation, it is not clear what changes have taken place in the heritage identity and what is the connection with the residential spatial differentiation. This study takes a socio-spatial approach, uses a quantitative analysis approach to assess Barkhor Street’s residential space in terms of two factors: residential quality and heritage utilization. It also looks at how spatial differentiation affects residential historic districts’ heritage identities. It aims to re-understand the transformation process of heritage identity in different contexts. This study revealed that (1) variations in living spaces among people in residential historic districts can yield both positive and negative outcomes for their heritage identity. (2) Heritage identity is mediated in part by residential quality and the use of historical resources. (3) There is also a combined effect of individual attributes like the annual per capita household income, age, and ethnicity. Factors influencing people’ heritage identification include the per capita housing area, type of housing ownership, distance from religious heritage, and store form type. It is advised that mixed-use housing patterns be used to increase housing suitability and that procedures for community involvement and democratic, scientific decision-making be set up for preservation and renewal.

The role of leadership in promoting innovation within organizations in public sector

This study investigates the relationship between Knowledge Sharing, Person-Organization Fit (P-O Fit), and Innovative Work Behaviors (IWB) in public organizations in Iraq. It examines how the alignment between individual attributes and organizational culture affects employees' commitment to innovative work behaviors. Data were collected from 410 employees in public institutions, with surveys available in Kurdish and English. Findings indicate that information sharing enhances P-O Fit and promotes innovative work behaviors, both essential for organizational adaptability and growth. The study underscores the importance of fostering a collaborative environment to strengthen organizational cohesion. Organizations with strong P-O Fit are better positioned to retain talent and encourage creativity. Recommendations include refining recruitment and training processes to align with organizational culture, implementing programs to support development, and regularly evaluating knowledge-sharing practices. This analysis advances the theoretical understanding of how P-O Fit and knowledge exchange drive progress in the public sector and offers valuable insights for leaders.

Deep Reinforcement Learning Recommendation System Algorithm Based on Multi-Level Attention Mechanisms

Traditional recommendation systems, which rely on static user profiles and historical interaction data, frequently face difficulties in adapting to the rapid changes in user preferences that are typical of dynamic environments. In contrast, recommendation algorithms based on deep reinforcement learning are capable of dynamically adjusting their strategies to accommodate real-time fluctuations in user preferences. However, current deep reinforcement learning recommendation algorithms encounter several challenges, including the oversight of item features associated with high long-term rewards that reflect users’ enduring interests, as well as a lack of significant relevance between user attributes and item characteristics. This leads to an inadequate extraction of personalized information. To address these issues, this study presents a novel recommendation system known as the Multi-Level Hierarchical Attention Mechanism Deep Reinforcement Recommendation (MHDRR), which is fundamentally grounded in a multi-layer attention mechanism. This mechanism consists of a local attention layer, a global attention layer, and a Transformer layer, allowing for a detailed analysis of individual attributes and interactions within short-term preferred items, while also exploring users’ long-term interests. This methodology promotes a comprehensive understanding of users’ immediate and enduring preferences, thereby improving the overall effectiveness of the system over time. Experimental results obtained from three publicly available datasets validate the effectiveness of the proposed model.

Government Digital Transformation and the utilization of Basic Public Health Services by China’s migrant population

BackgroundThis research delves into the ramifications of Government Digital Transformation (GDT) on Basic Public Health Services (BPHS) utilization among China’s migrant populace. Within the framework of escalating digital incorporation into public services, it becomes imperative to assess how GDT initiatives could ameliorate healthcare access for migrants, a group habitually confronted with systemic obstacles. Such scrutiny is crucial for the formulation of evidence-based policies that harness digital innovations to mitigate healthcare disparities.MethodsThe analysis employs data from the 2018 China Migrants Dynamic Survey, matched with details on the National Pilot Cities for Information Benefit to the Public policy at the prefecture city level, yielding a dataset comprising 113,905 observations. To gauge BPHS utilization among migrants, two indicators were developed: the establishment of health records and registration with family doctors. The influence of GDT on BPHS access was estimated utilizing Ordinary Least Squares regression for the initial analysis, incorporating a comprehensive array of individual and household attributes, alongside province fixed effects, to ensure a thorough evaluation.ResultsBaseline regression analyses revealed that GDT is significantly correlated with an enhanced probability of migrants establishing health records and registering with family doctors, with increases of 7.53% and 2.75%, respectively. Subsequent robustness checks, including sensitivity analyses with alternate dependent variables, the Propensity Score Matching method, Double/Debiased Machine Learning method, and placebo tests, further substantiated these findings. Additionally, the analysis explored mechanisms through which GDT facilitates BPHS access, identifying health information accessibility as a significant mediator in promoting healthcare engagement among migrants. Notably, both individual and regional heterogeneity were observed in GDT’s impact: female migrants, those with lower educational levels, older individuals, and rural hukou holders experienced more pronounced benefits, while the effects of GDT were also stronger in cities with lower GDP and smaller populations. These findings suggest that GDT is particularly effective in bridging healthcare access gaps in economically or demographically constrained areas.ConclusionsThe outcomes underscore that GDT significantly enhances BPHS utilization among migrants, positioning digital governance as an instrumental mechanism in bridging healthcare access gaps. The observed differential impacts across various subgroups and regions emphasize the importance of customized digital health strategies that cater to the diverse needs of migrant communities. For instance, targeted interventions in lower-GDP or smaller cities could maximize the benefits of GDT in under-resourced areas. Contributing to the body of empirical evidence on digital governance and public health, this research advocates for the expanded deployment of GDT initiatives to foster more equitable health service provision for migrant demographics, promoting inclusivity within a rapidly urbanizing society.

Selection and daily occupancy of artificial retreat-sites by a declining Mediterranean island specialist, the European leaf-toed gecko Euleptes europaea.

The European Leaf-toed Gecko, Euleptes europaea, a rock-dwelling nocturnal gecko characteristic of Mediterranean Islands, is facing local extinctions and population decline at the margins of its range. Population monitoring through artificial retreat-sites (ARS) was implemented on French’s Grand Rouveau and If islands to study the effects of management measures. We used Generalized Linear Models to identify: (i) the environmental variables (such as substrate, vegetation, and exposure to wind and sun) influencing the maximum number of individuals observed in ARS (studied in both islands); and (ii) the factors influencing ARS daily use (occupancy), including individual attributes such as age and weight, external temperature, and disturbance (Grand Rouveau only). The maximum number of geckos appeared to be determined by the thermal properties of ARS, as mediated by exposure to the dominant wind and sunlight, rather than by the structure of the habitat and nearby vegetation. An individual gecko’s presence in an ARS was positively related to its presence in the same ARS on the previous day and negatively related to its age, to the temperature of the previous night, and the number of days of disturbance. These results provide insights into the factors governing the selection and use of ARS by the European leaf-toed gecko and open perspectives on the use of ARS for the monitoring and conservation of this and other elusive terrestrial reptiles.

Employing biological sex as a primary variable can restrict our understanding of behavioral diversity (and sex differences) in psychostimulant activity.

In addition to representing distinct biological sex groups, males and females are also individuals expressing behavioral diversity. Our MISSING (Mapping Intrinsic Sex Similarities as an Integral quality of Normalized Groups) model suggests that, with respect to behavior, grouping according to the individual reveals groups with differences that exceed biological sex-related differences, but this needs further clarification. We hypothesized that, compared to the current model (grouping by biological sex), the MISSING model (grouping by individual attributes) was the more effective grouping strategy to identify behavioral diversity. We conducted experiments in rats to determine the locomotor activity (in 90 min) following intraperitoneal injections of saline (n = 12 males, n = 11 females) and cocaine (n = 8 males, n = 11 females). For the current model, we compared males versus females using unpaired t-tests. For the MISSING model, we identified clusters of individuals (males and females) using normal mixtures clustering analysis of several behavioral variables and employed unpaired t-tests to compare clusters and Two-way ANOVA to determine if there were any SEX by cluster interactions. For both models, we employed linear regression analysis to compare relationships between variables and Two-way repeated measures ANOVA to analyze locomotor activity time course. For both the saline and cocaine groups, the MISSING model identified two behavioral clusters with differences that exceeded any differences due to biological sex. The MISSING model suggests that employing biological sex as a primary variable can obscure our understanding of sex and individual differences in psychostimulant activity.

Time Framing and SDGs: Can Imaginary Future Generations Alter People’s Perceptions and Attitudes?

This study verifies the effectiveness of Imaginary Future Generation (IFG) in promoting the Sustainable Development Goals (SDGs). Using data from an online survey, we calculated sustainable development component measurements to assess the understanding of SDGs and demonstrated whether IFG can influence futurability components. Statistical analysis shows that knowledge significantly impacts people’s attitudes towards SDGs, with informed individuals more likely to cooperate in addressing these issues. Comparing the treatment and control groups revealed that participants asked to envision themselves as residents of 2050 (IFG) better grasped the essence of Agenda 2030 and exhibited more cooperative attitudes toward sustainability. Regression analysis confirmed these effects after controlling for individual attributes and SDG knowledge. The study finds that while raising awareness is crucial, IFG makes people perceive sustainability problems as personal concerns. IFG enhances respondents’ futurability, encouraging positive attitudes and long-term thinking. Additionally, IFG promotes participation, inclusive ideas, and increased SDG knowledge.

Reimagining maternal resilience: Incorporating the socioecological framework, lifecourse theory, and weathering hypothesis.

Women of color are at least twice as likely as non-Hispanic White women to die during the perinatal period or deliver infants who are low birthweight, preterm, or die within the first year of life. Maternal stress before and during pregnancy is associated with adverse obstetric outcomes. A growing body of literature has explored maternal resilience as protective factors contributing to healthy maternal and child health (MCH) outcomes. However, several gaps exist in how this construct has been conceptualized and operationalized. First, extant research has primarily conceptualized maternal resilience as individual attributes that enable women to "bounce back" after facing adversity during pregnancy, thereby failing to incorporate the broader systemic and environmental factors that contribute to chronic stress, particularly among vulnerable groups. Second, the literature has largely neglected to examine resilience in relation to maternal stress, therefore not acknowledging that women who experience greater stress will likely require more resources. Third, though resilience has been investigated at discrete life stages, longitudinal research has not been conducted to explore how it develops over the lifecourse. This article critically evaluates the resilience literature, expands upon the gaps described, and proposes a conceptual framework that reimagines material resilience using three population health theories, including Bronfenbrenner's socioecological framework, Elder's lifecourse theory, and Geronimus' weathering hypothesis. The proposed framework will inform future research that examines the development of multilevel resilience resources over the lifecourse as well as interventions to increase resilience and ultimately yield healthier MCH outcomes among vulnerable communities. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Computed tomography study of cranial vault thickness in Malaysian subadult population.

Cranial vault thickness (CVT) and its variations provide valuable insights into an individual's biological attributes such as age and sex. This paper aimed to assess the correlations of CVT with age and sex and develop a regression model for age estimation in Malaysian subadults using computed tomography (CT) images. A total of 521 CT images (male/female: 279/242; age range: 0-20 years; Malay/Chinese/Indian: 221/145/155) were included in the study. Correlations of CVT measurements with age and sex, including frontal bone thickness (FBT), occipital bone thickness (OBT), left parietal bone thickness (LPBT), and right parietal bone thickness (RPBT) were assessed and regression formulae were developed for age estimation in subadults. A significant correlation between CVT measurements and age was demonstrated (p < 0.001). Age estimation was most accurate in the younger age group (< 2 years) at frontal and occipital, and accuracy decreases in the older age groups. Additionally, sexual dimorphism was evident in the frontal and parietal bone thickness within the age range of 3-6 years and 16-20 years, respectively. In conclusion, the findings suggested CVT measurements could be used to corroborate other age estimation methods for subadults.

Cortical Alterations Associated with Executive Function Deficits in Youth with a Congenital Heart Defect

Abstract Adolescents and young adults born with a complex congenital heart defect (CHD) are at risk for executive function (ExF) impairments, which contribute to the psychological and everyday burden of CHD. Cortical dysmaturation has been well described in fetuses and neonates with CHD and early evidence suggests that cortical alterations in thickness, surface area, and gyrification index are non-transient and can be observed in adolescents with CHD. However, cortical alterations have yet to be correlated with ExF deficits in youth with CHD. This study aims to use a data-driven approach to identify the most important cortical features associated with ExF deficits in adolescents and young adults with CHD. To do so, we combined two comparable datasets acquired at the Research Institute of the McGill University Health Centre and the University Children’s Hospital Zurich each including both youth with CHD and healthy controls. For each participant, a high-resolution T1-weighted magnetic resonance image, a self-reported ExF assessment (the Behaviour Rating Inventory of Executive Function— Adult Scale), and their clinical and demographic characteristics were available. Corticometric Iterative Vertex-Based Estimation of Thickness (CIVET) was used to extract cortical thickness, cortical surface area, and local gyrification index measures. Using orthogonal projective non-negative matrix factorization (OPNMF), we identified non-overlapping spatial components that integrate cortical thickness, cortical surface area, and local gyrification index and capture structural covariance across these features. Behavioural partial least squares correlation (bPLS) analysis was then used to compute correlations between the individual variability in the OPNMF covariance patterns and ExF outcomes for each subject. A total of 56 youth with CHD who underwent cardiopulmonary bypass surgery before three years of age and 56 age- and sex-matched healthy controls were included in our analyses. Cortical grey matter volume, cortical thickness, and cortical surface area were found to be significantly reduced in CHD patients compared to controls. OPNMF identified 12 stable cortex-wide components summarizing the inter-subject variability in cortical thickness, cortical surface area, and local gyrification index. bPLS revealed two significant latent variables (LV) accounting for a total of 82.8% of the variance in the sample, each describing distinct patterns between the brain and cognitive data. LV1 summarized a pattern of belonging to the CHD group, worse scores on most BRIEF-A scales, younger age at MRI, and female sex. This pattern was associated with increased cortical thickness, local gyrification index, and decreased cortical surface area in several OPNMF components. Finally, we identified a positive relationship between the LV1 brain-behaviour pattern and total aortic cross-clamp time in the CHD group, indicating that longer aortic cross-clamp time was associated with worse neurodevelopmental outcomes. In this study, we uncover novel multivariate relationships between ExF and alterations in cortical thickness, surface area, and local gyrification index in adolescents and young adults with CHD using a data-driven approach. Although our findings highlight the important role played by the cortex in higher order cognitive processes, future studies are needed to elucidate the individual contribution of individual and clinical attributes into the deficits observed in this population.

Children's friendship stability in the United States, China, and Indonesia: Associations with individual attributes and dyadic similarity.

Predictors of friendship stability from individual attributes and dyadic similarities were assessed using cross-classified multilevel analyses in this 6- to 8-month longitudinal study of 10-year-old US (White, Black, Asian, other; n = 477, 50% girls), Chinese (n = 467, 59% girls), and Indonesian (Sudanese, Javanese, other; n = 419, 45% girls) children with complete participation and reciprocated baseline friendships. Across countries, individual attributes of social preference, popularity, and academic achievement and dyadic social preference similarity positively predicted friendship stability. Dyadic similarity of popularity, academic achievement, and aggression respectively predicted friendship stabilities of US, Chinese, and Indonesian children. Both individual attributes and dyadic similarity predicted friendship stability, with results that varied across countries consistent with attributes' reputational salience.