Not every credible interval is credible: Evaluating robustness in the presence of contamination in Bayesian data analysis.

As Bayesian methods become more popular among behavioral scientists, they will inevitably be applied in situations that violate the assumptions underpinning typical models used to guide statistical inference. With this in mind, it is important to know something about how robust Bayesian methods are to the violation of those assumptions. In this paper, we focus on the problem of contaminated data (such as data with outliers or conflicts present), with specific application to the problem of estimating a credible interval for the population mean. We evaluate five Bayesian methods for constructing a credible interval, using toy examples to illustrate the qualitative behavior of different approaches in the presence of contaminants, and an extensive simulation study to quantify the robustness of each method. We find that the "default" normal model used in most Bayesian data analyses is not robust, and that approaches based on the Bayesian bootstrap are only robust in limited circumstances. A simple parametric model based on Tukey's "contaminated normal model" and a model based on the t-distribution were markedly more robust. However, the contaminated normal model had the added benefit of estimating which data points were discounted as outliers and which were not.

Deletion of the Stress Response Gene DDR48 from Histoplasma capsulatum Increases Sensitivity to Oxidative Stress, Increases Susceptibility to Antifungals, and Decreases Fitness in Macrophages.

The stress response gene DDR48 has been characterized in Saccharomyces cerevisiae and Candida albicans to be involved in combating various cellular stressors, from oxidative agents to antifungal compounds. Surprisingly, the biological function of DDR48 has yet to be identified, though it is likely an important part of the stress response. To gain insight into its function, we characterized DDR48 in the dimorphic fungal pathogen Histoplasma capsulatum. Transcriptional analyses showed preferential expression of DDR48 in the mycelial phase. Induction of DDR48 in Histoplasma yeasts developed after treatment with various cellular stress compounds. We generated a ddr48∆ deletion mutant to further characterize DDR48 function. Loss of DDR48 alters the transcriptional profile of the oxidative stress response and membrane synthesis pathways. Treatment with ROS or antifungal compounds reduced survival of ddr48∆ yeasts compared to controls, consistent with an aberrant cellular stress response. In addition, we infected RAW 264.7 macrophages with DDR48-expressing and ddr48∆ yeasts and observed a 50% decrease in recovery of ddr48∆ yeasts compared to wild-type yeasts. Loss of DDR48 function results in numerous negative effects in Histoplasma yeasts, highlighting its role as a key player in the global sensing and response to cellular stress by fungi.