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Examining Longitudinal Risk and Strengths-Based Factors Associated with Depression Symptoms Among Sexual Minority Men in Canada
No full textSexual minority men (SMM) experience anti-SMM stressors and have elevated rates of mental health issues compared to heterosexual men, such as depression. Importantly, strengths-based factors may directly increase wellbeing and provide a buffer against the detrimental effects of such stressors. In the present study, we integrated risk and strengths-based models to examine predictors of depression symptoms in a sample of 465 Canadian SMM across three time points using multilevel modeling. Higher scores on a measure of childhood physical abuse at baseline, and greater within-person (i.e., deviation from individual’s average) and between-person (i.e., deviation from group average) internalized homonegativity and heterosexist discrimination were associated with higher depression scores. Higher within- and between-person scores on measures of self-esteem, social support, and hope were associated with lower depression scores. Social support buffered the effects of between-person heterosexist discrimination on depression symptoms: at mean and high levels of social support, heterosexist discrimination was not associated with depression symptoms. This is the first study to disaggregate between-person and within-person effects of both risk factors and strengths-based factors among SMM, which has critical importance for the development of tailored individual-level interventions that target internalized homonegativity, hope, social support, and self-esteem to alleviate symptoms of depression among SMM
The effect of software and hardware version on Apple Watch activity measurement: A secondary analysis of the COVFIT retrospective cohort study
No full textThe objective of this study was to estimate the impact of software and hardware version on Apple Watch activity measurement using data from the COVFIT retrospective cohort study. We estimated the impact of software and hardware versions on activity measurement by comparing daily active calories and daily exercise minutes in the 7 days before and 7 days after upgrading from watchOS 5 to 6, 6 to 7, 7 to 8, 8 to 9 or between two hardware versions. For each transition, we fit mixed effect negative binomial regression models to estimate the effect of the upgrade on daily (a) exercise minutes and (b) active calories, overall and stratified by sex, with and without adjusting for weekday. We also calculated and plotted the mean person-level change in average activity levels between the two weeks. As a control, we repeated the entire analysis comparing activity data two weeks before vs. one week before each upgrade. 253 participants contributed data about at least one transition (software = 250, hardware = 74). Hardware upgrades were not associated with either outcome; however, some software upgrades were. Upgrading from watchOS 7 to 8 was associated with a large, statistically significant increase in daily exercise minutes (unadjusted rate ratio (RR) = 1.13, 95% CI: 1.06, 1.20). WatchOS 6 to 7 and 8 to 9 transitions were associated with statistically significant decreases in daily exercise minutes (6 to 7: unadjusted RR = 0.92, 95% CI: 0.86, 0.99; 8 to 9: unadjusted RR = 0.91, 95% CI: 0.86, 0.96) and active calories (6 to 7: RR = 0.96, 95% CI: 0.94, 0.99); 8 to 9: RR = 0.97, 95% CI: 0.94, 0.99). There was no significant change in either outcome during in the two-week control period for most transitions. Differences in software version over time or between people may confound physical activity analyses using Apple Watch data
Children of Iraqi Refugees: Risk Profile for Substance Abuse
No full textWhat is the risk profile of children of Iraqi refugees? Among adult refugees, higher prevalence of mental disorders has been associated with shorter time since resettlement and exposure to more potential traumatic events (PTE). We hypothesized that similar associations would hold in the children of Iraqi refugees
Optimal Direct Parameter Extraction of a Lithium-Ion Equivalent Circuit Cell Model for Electric Vehicle Application
No full textThe lithium-ion cell model is the heart of the battery management system—a more accurate model ensures operational safety, extends pack lifetime, and provides better tracking of battery charge and health. Catalyzed by the automotive industry’s shift towards electrification, optimal parameterization of the lithium-ion cell is of crucial importance. Two dominant methods of direct parameterization have emerged in the literature as the standard for parameter extraction of a lithium-ion equivalent circuit cell model. A direct comparison of their performance and suggestion as to the optimal method of cell parameterization has not yet been proposed; Thus, this paper aims to extract the optimal parameter set regarding the two dominant direct methods with an electrochemically based logic, compare the accuracy of cell parametrization over two transient load profiles, and ultimately suggest which method is preferable for electric vehicle applications. Additionally, this work will be conducted over multiple C-rates to quantify the robustness of each direct method of parameterization over a transient load profile.Canada Research Chair program (Award: CRC-2019-00319)Magna International and Mitacs (Award: IT40371
Predictors of Patient Stigma Perception Appraisal: Testing a Dynamic Stigma Model of Mental Illness
No full textThere is a lack of research on stigma as a socio-cultural, religious, and moral phenomenon. This study aimed to test a Dynamic Stigma Model of Mental Illness (DYSMO) among a cohort of outpatients. We used structural equation modeling techniques to examine hypothesized relationships within the DYSMO in a cohort of 330 participants. Confirmatory factor analysis produced a model with five latent variables and 17 indicators. All factor loadings were significant at p =.05). Mediation analysis on the full structural model produced standardized fit indices that include the following: (χ2/df = 335.403 (105)), p =.000; RMSEA = 0.08 (90% CI: [0.072, 0.092]; CFI = 0.921; SRMSR = 0.059; TLI = 0.90). Overall, the study revealed that religiocultural, and structural violence perspectives can promote and damage perceptions about mental illness. © The Author(s) 2025.
Factors that predict stigma perception and appraisal: Testing a stigma model of mental illness Not only does stigma affect people with mental illness (PWMI), but also their family relatives, and all who care for them. There have been many studies on stigma over the years. However, researchers have a gap in studying the stigma of mental illness as a sociocultural, religious, and moral phenomenon. The purpose of this study was to test a model named the “Dynamic Stigma Model (DYSMO)” among a convenience sample of 330 persons receiving care in Ghana. The cross-sectional study examined relationships within the newly developed stigma model using statistical analysis techniques. The analysis found that religious and cultural beliefs positively influenced extreme social injustice, otherwise known as structural violence towards PWMI. The study also revealed that existing religious and cultural belief systems influenced how much a person appraised stigma as positive or negative. The study further found that stigma perceptions of PWMI influenced their anticipation of discrimination and subsequent social withdrawal, especially when in public places. Overall, the study revealed that factors such as religious, cultural, and structural violence can promote and damage perceptions about individuals with mental health problems. All stakeholders, including mental health practitioners, policymakers, and community members, must gain increased awareness and knowledge of the role religious and cultural beliefs play in the perpetuation and outcomes of mental illness stigma
Using in vitro data to derive acceptable exposure levels for environmental chemicals: A case study on p,p’-DDE obesogenicity
No full textBackground: Current acceptable exposure levels are largely based on animal models, which are costly, time- consuming, and may poorly predict adverse outcomes in humans. Alternative testing methods are needed to adequately tackle the large number of environmental chemicals. Objective: To evaluate a method integrating human in vitro data and computational modeling to calculate acceptable exposure levels through a case study on early-life p,p’-dichlorodiphenyldichloroethylene (p,p’-DDE) and developmental obesogenicity. Methods: We reviewed in vitro studies of p,p’-DDE and obesogenicity-related endpoints to select points of departure (PODs). Nominal PODs were converted into lipid-based cellular concentrations using a dynamic mass- balance model. Cellular concentrations were converted into tolerable daily intakes and biomonitoring equivalents in pregnant individuals using a toxicokinetic model and uncertainty factors. We compared estimated biomonitoring equivalents to maternal and cord plasma levels measured in epidemiological studies reporting associations between early-life p,p’-DDE exposure and child adiposity. Results: We estimated PODs for phenotypic (181,897 ng/g lipids) and transcriptomic (14,405 ng/g lipids) endpoints. Application of the toxicokinetic model and uncertainty factors led to tolerable daily intakes of 2.50–8.65 ng/kg/day (phenotypic) and 0.198–0.685 ng/kg/day (transcriptomic). Corresponding biomonitoring equivalents were 54.4–188 ng/g lipids (phenotypic) and 4.31–14.9 ng/g lipids (transcriptomic). Mean/median concentrations measured in epidemiological studies of p,p’-DDE exposure and child adiposity were mostly within or above the range of concentrations produced using the phenotypic in vitro POD. Conclusion: This study adds to a growing body of literature on the potential of in vitro data combined with computational modeling for chemical risk assessment, while also identifying challenges to regulatory adoption
Hypergraph Neural Networks Reveal Spatial Domains from Single-cell Transcriptomics Data
No full textI. Abstract Spatial transcriptomics enables the measurement of gene expression while preserving spatial context within tissue samples. A key challenge is detecting spatial domains of biologically meaningful cell clusters, typically addressed using graph-based models like SpaGCN and STAGATE. However, these methods only capture pairwise relationships and fail to model complex higher-order interactions. We propose a hypergraph-based framework for spatial transcriptomics using Hypergraph Neural Networks (HGNNs). Our approach constructs hyperedges from top- K densest overlapping subgraphs and integrates histological image features and gene expression profiles. Combined with autoencoders, our model effectively learns expressive node embeddings in an unsupervised setting. Evaluated on a mouse brain dataset, our model achieves the highest iLISI score of 1.843 and outperforms state-of-the-art baselines with an ARI of 0.51 and a Leiden score of 0.60
The distraction potential of driving a partially automated vehicle through a construction zone
No full textPartial driving automation is designed to control the vehicle’s speed and acceleration without input from the human driver on the condition that the driver maintains alertness. These systems are promised to make driving more convenient and safer, especially in increasingly demanding road conditions such as construction zones. Despite this, little knowledge is available on how these systems are used in these accident-prone areas and the effect they may have on drivers’ workload and glance allocation. This study aims to fill this gap by having participants drive a vehicle in partially automated and manual mode through three road sections: pre-construction, construction, and post-construction. Results show no differences in cognitive workload by driving mode or construction zone. An increase in glances directed away from the forward roadway toward the vehicle’s touchscreen was observed during partially-automated driving in the pre-construction zone, a pattern that, notably, continued on when driving throughout the construction zone. These findings adds to the literature on the human factors of partial automation. More importantly, because drivers failed to increase the amount of time looking at the forward roadway when entering the construction zone, they show the potential perniciousness of partially automated driving and the detrimental effect certain systems may have on safety risk
