ABSTRACT
A global priority for the behavioural sciences is to develop cost-effective, scalable interventions that could improve the academic outcomes of adolescents at a population level, but no such interventions have so far been evaluated in a population-generalizable sample. Here we show that a short (less than one hour), online growth mindset intervention-which teaches that intellectual abilities can be developed-improved grades among lower-achieving students and increased overall enrolment to advanced mathematics courses in a nationally representative sample of students in secondary education in the United States. Notably, the study identified school contexts that sustained the effects of the growth mindset intervention: the intervention changed grades when peer norms aligned with the messages of the intervention. Confidence in the conclusions of this study comes from independent data collection and processing, pre-registration of analyses, and corroboration of results by a blinded Bayesian analysis.
Subject(s)
Academic Success , Students/psychology , Adolescent , Humans , Psychosocial Support Systems , United KingdomABSTRACT
Human disease studies using DNA microarrays in both clinical/observational and experimental/controlled studies are having increasing impact on our understanding of the complexity of human diseases. A fundamental concept is the use of gene expression as a "common currency" that links the results of in vitro controlled experiments to in vivo observational human studies. Many studies--in cancer and other diseases--have shown promise in using in vitro cell manipulations to improve understanding of in vivo biology, but experiments often simply fail to reflect the enormous phenotypic variation seen in human diseases. We address this with a framework and methods to dissect, enhance and extend the in vivo utility of in vitro derived gene expression signatures. From an experimentally defined gene expression signature we use statistical factor analysis to generate multiple quantitative factors in human cancer gene expression data. These factors retain their relationship to the original, one-dimensional in vitro signature but better describe the diversity of in vivo biology. In a breast cancer analysis, we show that factors can reflect fundamentally different biological processes linked to molecular and clinical features of human cancers, and that in combination they can improve prediction of clinical outcomes.
Subject(s)
Breast Neoplasms/genetics , Computational Biology/methods , Gene Expression Profiling/methods , Genomics/methods , Biomarkers, Tumor , Breast Neoplasms/diagnosis , Female , Gene Expression Regulation, Neoplastic , Humans , Prognosis , Treatment OutcomeABSTRACT
We describe studies in molecular profiling and biological pathway analysis that use sparse latent factor and regression models for microarray gene expression data. We discuss breast cancer applications and key aspects of the modeling and computational methodology. Our case studies aim to investigate and characterize heterogeneity of structure related to specific oncogenic pathways, as well as links between aggregate patterns in gene expression profiles and clinical biomarkers. Based on the metaphor of statistically derived "factors" as representing biological "subpathway" structure, we explore the decomposition of fitted sparse factor models into pathway subcomponents and investigate how these components overlay multiple aspects of known biological activity. Our methodology is based on sparsity modeling of multivariate regression, ANOVA, and latent factor models, as well as a class of models that combines all components. Hierarchical sparsity priors address questions of dimension reduction and multiple comparisons, as well as scalability of the methodology. The models include practically relevant non-Gaussian/nonparametric components for latent structure, underlying often quite complex non-Gaussianity in multivariate expression patterns. Model search and fitting are addressed through stochastic simulation and evolutionary stochastic search methods that are exemplified in the oncogenic pathway studies. Supplementary supporting material provides more details of the applications, as well as examples of the use of freely available software tools for implementing the methodology.
ABSTRACT
O Objetivo desse estudo foi investigar o comportamento de algumas medidas antropométricas relacionadas com a composiçäo corporal em escolares de 7 a 11 anos da cidade do Rio de Janeiro. Participaram deste estudo 246 crianças de uma escola localizada no bairro de Tijuca, área sócio-economicamente privilegiada. As medidas antropométricas consistiram de peso, estatura, perímetros (braços, perna), diâmetro (bi-estiloidal, bi-epicondiliano, bi-condiliano, bi-maleolar), dobras cutâneas (axilar média, subescapular, supra-ilíaca, abdominal, bíceps, tríceps, panturrilha). Os índices somatórios das dobras cutâneas, área do braço, área muscular do braço e área de gordura do braço foram calculados. Constata-se neste estudo: quanto ao sexo, as meninas apresentavam valores relativos a gordura subcutânea maiores do que os meninos nas faixas etárias dos 7 aos 8 anos; enquanto os meninos apresentaram maiores valores nos diâmetros ósseos e valores semelhantes nos diâmetros ósseos e valores semelhantes na gordura subcutânea aos 10 e 11 anos. Quanto a faixa etária, näo existiram diferencas significativas nas medidas antropométrica entre os 7 e 8 anos tanto nos meninos quanto nas meninas. O mesmo ocorreu entre os 9 e 10 anos e 10 e 11 anos. As meninas näo apresentaram diferenças significativas entre as idades nos índices relativos a gordura subcutânea. Ao contrário, os meninos foram diferentes entre os 7 e 9 anos. As crianças investigadas parecem aprsentar excessiva adiposidade, característica que pode em parte estar relacionada a obesidade na vida adulta e que em nada colabora para uma vida mais saúdavel
