Interpretable Model Summaries Using the Wasserstein Distance

Statistical models often include thousands of parameters. However, large models decrease the investigator's ability to interpret and communicate the estimated parameters. Reducing the dimensionality of the parameter space in the estimation phase is a commonly used approach, but less work has focused on selecting subsets of the parameters for interpreting the estimated model -- especially in settings such as Bayesian inference and model averaging. Importantly, many models do not have straightforward interpretations and create another layer of obfuscation. To solve this gap, we introduce a new method that uses the Wasserstein distance to identify a low-dimensional interpretable model projection. After the estimation of complex models, users can budget how many parameters they wish to interpret and the proposed generates a simplified model of the desired dimension minimizing the distance to the full model. We provide simulation results to illustrate the method and apply it to cancer datasets

Gambling with Health: A Re-Evaluation of the Oregon Medicaid Lottery

In Baicker et al's “The Oregon Experiment – Effects of Medicaid on Clinical Outcomes,” the author’s utilize a “natural experiment” in which low-income adults were randomly selected to apply for Medicaid to estimate the causal effect of Medicaid on clinical outcomes, healthcare utilization, and health expenditures (Baicker et al, 2013). The 2008 Oregon Medicaid lottery provided something akin to a randomized clinical trial for Medicaid coverage across the state; however, the authors merely look at the effect of having any Medicaid coverage on these outcomes and not how long a person was covered by Medicaid. In our analysis, we look at the effect of 1-6 months, 7-12 months, 13-18 months, 19-24 months, and greater than 25 months of Medicaid coverage and we find significant health impacts from Medicaid coverage do not start prior to 18 months. This suggests that with an average follow up time of only 25 months, Baicker et al’s study ended too soon to adequately measure the impacts of the Medicaid expansion

Differential Requirements for NAIP5 in Activation of the NLRC4 Inflammasome▿

Inflammasomes are cytosolic multiprotein complexes that assemble in response to infectious or noxious stimuli and activate the CASPASE-1 protease. The inflammasome containing the nucleotide binding domain-leucine-rich repeat (NBD-LRR) protein NLRC4 (interleukin-converting enzyme protease-activating factor [IPAF]) responds to the cytosolic presence of bacterial proteins such as flagellin or the inner rod component of bacterial type III secretion systems (e.g., Salmonella PrgJ). In some instances, such as infection with Legionella pneumophila, the activation of the NLRC4 inflammasome requires the presence of a second NBD-LRR protein, NAIP5. NAIP5 also is required for NLRC4 activation by the minimal C-terminal flagellin peptide, which is sufficient to activate NLRC4. However, NLRC4 activation is not always dependent upon NAIP5. In this report, we define the molecular requirements for NAIP5 in the activation of the NLRC4 inflammasome. We demonstrate that the N terminus of flagellin can relieve the requirement for NAIP5 during the activation of the NLRC4 inflammasome. We also demonstrate that NLRC4 responds to the Salmonella protein PrgJ independently of NAIP5. Our results indicate that NAIP5 regulates the apparent specificity of the NLRC4 inflammasome for distinct bacterial ligands

Bone Response to Fluoride Exposure Is Influenced by Genetics

Genetic factors influence the effects of fluoride (F) on amelogenesis and bone homeostasis but the underlying molecular mechanisms remain undefined. A label-free proteomics approach was employed to identify and evaluate changes in bone protein expression in two mouse strains having different susceptibilities to develop dental fluorosis and to alter bone quality. In vivo bone formation and histomorphometry after F intake were also evaluated and related to the proteome. Resistant 129P3/J and susceptible A/J mice were assigned to three groups given low-F food and water containing 0, 10 or 50 ppmF for 8 weeks. Plasma was evaluated for alkaline phosphatase activity. Femurs, tibiae and lumbar vertebrae were evaluated using micro-CT analysis and mineral apposition rate (MAR) was measured in cortical bone. For quantitative proteomic analysis, bone proteins were extracted and analyzed using liquid chromatography-electrospray ionization-tandem mass spectrometry (LC-ESI-MS/MS), followed by label-free semi-quantitative differential expression analysis. Alterations in several bone proteins were found among the F treatment groups within each mouse strain and between the strains for each F treatment group (ratio ≥1.5 or ≤0.5; p<0.05). Although F treatment had no significant effects on BMD or bone histomorphometry in either strain, MAR was higher in the 50 ppmF 129P3/J mice than in the 50 ppmF A/J mice treated with 50 ppmF showing that F increased bone formation in a strain-specific manner. Also, F exposure was associated with dose-specific and strain-specific alterations in expression of proteins involved in osteogenesis and osteoclastogenesis. In conclusion, our findings confirm a genetic influence in bone response to F exposure and point to several proteins that may act as targets for the differential F responses in this tissue