Sarah Jane

Reduced mitochondrial quality and quantity in tumors is associated with dedifferentiation and increased malignancy. However, it remains unclear how...

Austen imitator is a poor relative from Sussex among Regency romances. Opened 07/01/22...

This study identifies the socio-ecological predictors at each level of the ecosystem that serve as risk or protective factors for the frequency of bullying, cyberbullying, and sexual harassment in U.S. schools, and examines how these predictors vary across the three behaviors, highlighting both common and distinct predictors. Using data from two waves (2018 and 2020) of the School Survey on Crime and Safety (SSOCS), a nationally representative cross-sectional survey of 5132 U.S. public elementary and secondary schools, we conduct a Generalized Ordinal Logistic Regression in hierarchical blocks of mesosystem-, exosystem-, and macrosystem-level factors to examine the relative contribution of each ecological level. We find that both mesosystem (teacher training, school size, school grade) and exosystem (neighborhood crime, parental involvement, and involvement of mental health & social services)-level factors play significant roles in predicting the frequency of bullying, sexual harassment, and cyberbullying. Urbanicity at the macrosystem-level is not a significant predictor of bullying and sexual harassment; however, schools in urban areas are less likely to have frequent and monthly cyberbullying and more likely to have occasional and no cyberbullying compared to schools in non-urban areas. We argue for the importance of multi-level interventions to address peer harassment in schools through evidence-based policies.

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Background: Quantitative gait analysis is essential in both clinical and research contexts; however, traditional marker-based motion capture systems are costly and burdensome. Advances in three-dimensional markerless motion capture (3D-MMC) offer more accessible alternatives; however, they lack standardized protocols. Objectives: The present study aimed to establish a standardized protocol and procedures for 3D MMC-based gait analysis using OpenCap and to quantify the reliability and within-session precision of key spatiotemporal gait parameters. Methods: Fifty healthy university students (mean age = 22.15 ± 2.12 years) completed walking trials along a 10 m walkway under single-task (ST) and five dual-task (DT) conditions of varying cognitive complexity. Gait data were collected using a two-camera OpenCap 3D-MMC system, with standardized calibration, lighting, clothing, and trial segmentation. Spatiotemporal parameters were extracted, and within-session relative reliability was quantified using two-way mixed-effects intraclass correlation coefficients, and absolute reliability was quantified using general linear model–derived within-subject error (standard error of measurement, SEM) and minimal detectable change (MDC). Repeated-measures ANOVA with Bonferroni corrections were used to examine condition-related differences. Results: Of 500 trials, 491 (98.2%) were successfully processed. Within-subject test–retest reliability ranged from moderate to excellent for all variables, with gait speed, stride length, and cadence showing the highest ICCs and smallest SEM and MDC values, and step width and double support exhibiting larger measurement error. Conclusions: This study establishes a standardized 3D-MMC protocol for gait analysis using OpenCap and demonstrates good to excellent within-session relative and absolute reliability for most spatiotemporal gait parameters in healthy young adults. Dual-task walking is used here to illustrate how trial-averaged OpenCap measurements and their SEM/MDC can be used to determine which condition-related changes in gait exceed measurement error.