Deep pelagic biology

The deep pelagic habitat is a vast volume of cold, dark water where food is scarce and bioluminescence is the principal source of light and communication. Understanding the adaptations that allow animals to successfully inhabit this daunting realm has been a difficult challenge because investigators have had to conduct their work remotely. Research in the deep water column is going through an essential transformation from indirect to direct methods as undersea vehicles provide unprecedented access, new capabilities, and new perspectives. Traditional methods have accurately documented the meso- and macro-scale zoogeographic patterns of micronekton and zooplankton, as well as their distribution and migration patterns in the vertical plane. The new in situ technologies have enabled advances in studies of behavior, physiology, and in particular, the role of gelatinous animals in deep pelagic ecology. These discoveries reveal a deep-water fauna that is complex and diverse and still very poorly known.     

Nonparametric screening and feature selection for ultrahigh-dimensional Case II interval-censored failure time data

For the analysis of ultrahigh-dimensional data, the first step is often to perform screening and feature selection to effectively reduce the dimensionality while retaining all the active or relevant variables with high probability. For this, many methods have been developed under various frameworks but most of them only apply to complete data. In this paper, we consider an incomplete data situation, case II interval-censored failure time data, for which there seems to be no screening procedure. Basing on the idea of cumulative residual, a model-free or nonparametric method is developed and shown to have the sure independent screening property. In particular, the approach is shown to tend to rank the active variables above the inactive ones in terms of their association with the failure time of interest. A simulation study is conducted to demonstrate the usefulness of the proposed method and, in particular, indicates that it works well with general survival models and is capable of capturing the nonlinear covariates with interactions. Also the approach is applied to a childhood cancer survivor study that motivated this investigation.