English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Gene-environment and nutrition-environment studies often involve testing ofhigh-dimensional interactions between two sets of variables, each havingpotentially complex nonlinear main effects on an outcome. Construction of avalid and powerful hypothesis test for such an interaction is challenging, dueto the difficulty in constructing an efficient and unbiased estimator for thecomplex, nonlinear main effects. In this work we address this problem byproposing a Cross-validated Ensemble of Kernels (CVEK) that learns the space ofappropriate functions for the main effects using a cross-validated ensembleapproach. With a carefully chosen library of base kernels, CVEK flexiblyestimates the form of the main-effect functions from the data, and encouragestest power by guarding against over-fitting under the alternative. The methodis motivated by a study on the interaction between metal exposures in utero andmaternal nutrition on children's neurodevelopment in rural Bangladesh. Theproposed tests identified evidence of an interaction between minerals andvitamins intake and arsenic and manganese exposures. Results suggest that thedetrimental effects of these metals are most pronounced at low intake levels ofthe nutrients, suggesting nutritional interventions in pregnant women couldmitigate the adverse impacts of in utero metal exposures on children'sneurodevelopment.

A Cross-validated Ensemble Approach to Robust Hypothesis Testing of Continuous Nonlinear Interactions: Application to Nutrition-Environment Studies