37 research outputs found
Distinct disease mutations in DNMT3A result in a spectrum of behavioral, epigenetic, and transcriptional deficits
Phenotypic heterogeneity in monogenic neurodevelopmental disorders can arise from differential severity of variants underlying disease, but how distinct alleles drive variable disease presentation is not well understood. Here, we investigate missense mutations in DNA methyltransferase 3A (DNMT3A), a DNA methyltransferase associated with overgrowth, intellectual disability, and autism, to uncover molecular correlates of phenotypic heterogeneity. We generate a Dnmt3
An exploration of the dynamic longitudinal relationship between mental health and alcohol consumption: a prospective cohort study
Nonlinear growth curves in developmental research
Developmentalists are often interested in understanding change processes and growth models are the most common analytic tool for examining such processes. Nonlinear growth curves are especially valuable to developmentalists because the defining characteristics of the growth process such as initial levels, rates of change during growth spurts, and asymptotic levels can be estimated. A variety of growth models are described beginning with the linear growth model and moving to nonlinear models of varying complexity. A detailed discussion of nonlinear models is provided, highlighting the added insights into complex developmental processes associated with their use. A collection of growth models are fit to repeated measures of height from participants of the Berkeley Growth and Guidance Studies from early childhood through adulthood
Mixing by wind-induced flow and thermal convection in a small, shallow and stratified lake
Highly Crystalline Chlorapatite Films Prepared by the Evaporation of a Sodium Chloride Flux
Teaching through Interactions: Testing a Developmental Framework of Teacher Effectiveness in over 4,000 Classrooms
Validating frameworks for understanding classroom processes that contribute to student learning and development is important to advance the scientific study of teaching. This article presents one such framework, Teaching through Interactions, which posits that teacher-student interactions are a central driver for student learning and organizes teacher-student interactions into three major domains. Results provide evidence that across 4,341 preschool to elementary classrooms (1) teacher-student classroom interactions comprise distinct emotional, organizational, and instructional domains; (2) the three-domain latent structure is a better fit to observational data than alternative one- and two-domain models of teacher-student classroom interactions; and (3) the three-domain structure is the best-fitting model across multiple data sets