A paradigm shift in our thinking about the intricacies of the host-parasite interaction is required that considers bacterial structures and their relationship to bacterial pathogenesis. It has been proposed that interactions between extended macromolecular assemblies, termed hyperstructures (which include multiprotein complexes), determine bacterial phenotypes. In particular, it has been proposed that hyperstructures can alter virulence. Two such hyperstructures have been characterized in both pathogenic and nonpathogenic bacteria. Present within a number of both human and plant Gram-negative pathogens is the type 3 secretion system (T3SS) injectisome which in some bacteria serves to inject toxic effector proteins directly into targeted host cells resulting in their paralysis and eventual death (but which in other bacteria prevents the death of the host). The injectisome itself comprises multiple protein subunits, which are all essential for its function. The degradosome is another multiprotein complex thought to be involved in cooperative RNA decay and processing of mRNA transcripts and has been very well characterized in nonpathogenic Escherichia coli. Recently, experimental evidence has suggested that a degradosome exists in the yersiniae as well and that its interactions within the pathogens modulate their virulence. Here, we explore the possibility that certain interactions between hyperstructures, like the T3SS and the degradosome, can ultimately influence the virulence potential of the pathogen based upon the physical locations of hyperstructures within the cell.
Numerous variable selection methods rely on a two-stage procedure, where a sparsity-inducing penalty is used in the first stage to predict the support, which is then conveyed to the second stage for estimation or inference purposes. In this framework, the first stage screens variables to find a set of possibly relevant variables and the second stage operates on this set of candidate variables, to improve estimation accuracy or to assess the uncertainty associated to the selection of variables. We advocate that more information can be conveyed from the first stage to the second one: we use the magnitude of the coefficients estimated in the first stage to define an adaptive penalty that is applied at the second stage. We give two examples of procedures that can benefit from the proposed transfer of information, in estimation and inference problems respectively. Extensive simulations demonstrate that this transfer is particularly efficient when each stage operates on distinct subsamples. This separation plays a crucial role for the computation of calibrated p-values, allowing to control the False Discovery Rate. In this setup, the proposed transfer results in sensitivity gains ranging from 50% to 100% compared to state-of-the-art.
Assessing the uncertainty pertaining to the conclusions derived from experimental data is challenging when there is a high number of possible explanations compared to the number of experiments. We propose a new two-stage "screen and clean" procedure for assessing the uncertainties pertaining to the selection of relevant variables in high-dimensional regression problems. In this two-stage method, screening consists in selecting a subset of candidate variables by a sparsity-inducing penalized regression, while cleaning consists in discarding all variables that do not pass a significance test. This test was originally based on ordinary least squares regression. We propose to improve the procedure by conveying more information from the screening stage to the cleaning stage. Our cleaning stage is based on an adaptively penalized regression whose weights are adjusted in the screening stage. Our procedure is amenable to the computation of p-values, allowing to control the False Discovery Rate. Our experiments show the benefits of our procedure, as we observe a systematic improvement of sensitivity compared to the original procedure.
No abstract
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.