Model Averaging Software for Dichotomous Dose Response Risk Estimation

Model averaging has been shown to be a useful method for incorporating model uncertainty in quantitative risk estimation. In certain circumstances this technique is computationally complex, requiring sophisticated software to carry out the computation. We introduce software that implements model averaging for risk assessment based upon dichotomous dose-response data. This software, which we call Model Averaging for Dichotomous Response Benchmark Dose (MADr-BMD), fits the quantal response models, which are also used in the US Environmental Protection Agency benchmark dose software suite, and generates a model-averaged dose response model to generate benchmark dose and benchmark dose lower bound estimates. The software fulfills a need for risk assessors, allowing them to go beyond one single model in their risk assessments based on quantal data by focusing on a set of models that describes the experimental data.

Model Averaging Software for Dichotomous Dose Response Risk Estimation

Model averaging has been shown to be a useful method for incorporating model uncertainty in quantitative risk estimation. In certain circumstances this technique is computationally complex, requiring sophisticated software to carry out the computation. We introduce software that implements model averaging for risk assessment based upon dichotomous dose-response data. This software, which we call Model Averaging for Dichotomous Response Benchmark Dose (MADr-BMD), fits the quantal response models, which are also used in the US Environmental Protection Agency benchmark dose software suite, and generates a model-averaged dose response model to generate benchmark dose and benchmark dose lower bound estimates. The software fulfills a need for risk assessors, allowing them to go beyond one single model in their risk assessments based on quantal data by focusing on a set of models that describes the experimental data

Fast increased fidelity approximate Gibbs samplers for Bayesian Gaussian process regression

The use of Gaussian processes (GPs) is supported by efficient sampling algorithms, a rich methodological literature, and strong theoretical grounding. However, due to their prohibitive computation and storage demands, the use of exact GPs in Bayesian models is limited to problems containing at most several thousand observations. Sampling requires matrix operations that scale at $\mathcal{O}(n^3),$ where $n$ is the number of unique inputs. Storage of individual matrices scales at $\mathcal{O}(n^2),$ and can quickly overwhelm the resources of most modern computers. To overcome these bottlenecks, we develop a sampling algorithm using $\mathcal{H}$ matrix approximation of the matrices comprising the GP posterior covariance. These matrices can approximate the true conditional covariance matrix within machine precision and allow for sampling algorithms that scale at \mathcal{O}(n \ \mbox{log}^2 n) time and storage demands scaling at \mathcal{O}(n \ \mbox{log} \ n). We also describe how these algorithms can be used as building blocks to model higher dimensional surfaces at \mathcal{O}(d \ n \ \mbox{log}^2 n), where $d$ is the dimension of the surface under consideration, using tensor products of one-dimensional GPs. Though various scalable processes have been proposed for approximating Bayesian GP inference when $n$ is large, to our knowledge, none of these methods show that the approximation's Kullback-Leibler divergence to the true posterior can be made arbitrarily small and may be no worse than the approximation provided by finite computer arithmetic. We describe $\mathcal{H}-$matrices, give an efficient Gibbs sampler using these matrices for one-dimensional GPs, offer a proposed extension to higher dimensional surfaces, and investigate the performance of this fast increased fidelity approximate GP, FIFA-GP, using both simulated and real data sets

Model Averaging Software for Dichotomous Dose Response Risk Estimation

Model averaging has been shown to be a useful method for incorporating model uncertainty in quantitative risk estimation. In certain circumstances this technique is computationally complex, requiring sophisticated software to carry out the computation. We introduce software that implements model averaging for risk assessment based upon dichotomous dose-response data. This software, which we call Model Averaging for Dichotomous Response Benchmark Dose (MADr-BMD), fits the quantal response models, which are also used in the US Environmental Protection Agency benchmark dose software suite, and generates a model-averaged dose response model to generate benchmark dose and benchmark dose lower bound estimates. The software fulfills a need for risk assessors, allowing them to go beyond one single model in their risk assessments based on quantal data by focusing on a set of models that describes the experimental data

Bayesian Nonparametric Differential Equation Models for Functions

Bayesian nonparametric methods develop priors over a large class of functions that essentially allow any continuous function to be modeled. Though these methods are flexible, they are black box approaches that do not explicitly incorporate additional information on the shape of the curve. In many contexts, though the exact parametric form of the curve is unknown, additional scientific information is available in the form of differential operators. This dissertation develops nonparametric priors over function spaces that are specified by differential operators. Here two novel approaches to nonparametric function estimation are considered. In the first approach the prior is specified by a linear differential equation. The Mechanistic Hierarchical Gaussian process defines a prior over functions consistent with a differential operator. The method is applied to muscle force tracings in a functional ANOVA context, and is shown to adequately describe the between subject variability often seen in such tracings. In the second case a novel spline based approach is considered. Here prior information is specifies the maximum number of extrema (changepoints) for an arbitrary function located on an open set in R. The Local Extrema (LX) spline models the first derivative of the curve and puts a prior over the maximum number of changepoints. This method is applied to animal toxicology studies, human health surveys, and seasonal data; and it is shown to remove artifactual bumps common to other nonparametric methods. It is further shown to superior in terms of estimated squared error loss in simulation studies.Doctor of Philosoph

Korg: fitting, model atmosphere interpolation, and Brackett lines

We describe several updates to Korg, a package for 1D LTE spectral synthesis of FGKM stars. Built-in functions to fit observed spectra via synthesis or equivalent widths make it easy to take advantage of Korg's automatic differentiation. Comparison to a past analysis of 18 Sco shows that we obtain significantly reduced line-to-line abundance scatter with Korg. Fitting and synthesis are facilitated by a rigorously-tested model atmosphere interpolation method, which introduces negligible error to synthesized spectra for stars with $T_\mathrm{eff} \gtrsim 4000\,\mathrm{K}$. For cooler stars, atmosphere interpolation is complicated by the presence of molecules, though we demonstrate an adequate method for cool dwarfs. The chemical equilibrium solver has been extended to include polyatomic and charged molecules, extending Korg's regime of applicability to M stars. We also discuss a common oversight regarding the synthesis of hydrogen lines in the infrared, and show that Korg's Brackett line profiles are a much closer match to observations than others available. Documentation, installation instructions, and tutorials are available at https://github.com/ajwheeler/Korg.jl.Comment: Submitted to AJ, comments welcom

NCERA-101 Station Report from Kennedy Space Center, FL, USA (April 2019)

This is our annual "station report" of activities related to controlled environment research to the North Central Education Research Activity (NCERA-101) committee. The committee is sponsored the USDA National Institute for Food and Agriculture (NIFA). Kennedy Space Center has participated in this committee for over 30 years

Regenerated sciatic nerve axons stimulated through a chronically implanted macro-sieve electrode

Sieve electrodes provide a chronic interface for stimulating peripheral nerve axons. Yet, successful utilization requires robust axonal regeneration through the implanted electrode. The present study determined the effect of large transit zones in enhancing axonal regeneration and revealed an intimate neural interface with an implanted sieve electrode. Fabrication of the polyimide sieve electrodes employed sacrificial photolithography. The manufactured macro-sieve electrode (MSE) contained nine large transit zones with areas of ~0.285 mm2 surrounded by eight Pt-Ir metallized electrode sites. Prior to implantation, saline or glial derived neurotropic factor (GDNF) was injected into nerve guidance silicone-conduits with or without a MSE. The MSE assembly or a nerve guidance conduit was implanted between transected ends of the sciatic nerve in adult male Lewis rats. At 3 months’ post-operation, fiber counts were similar through both implant types. Likewise, stimulation of nerves regenerated through a MSE or an open silicone conduit evoked comparable muscle forces. These results showed that nerve regeneration was comparable through MSE transit zones and an open conduit. GDNF had a minimal positive effect on the quality and morphology of fibers regenerating through the MSE; thus, the MSE may reduce reliance on GDNF to augment axonal regeneration. Selective stimulation of several individual muscles was achieved through monopolar stimulation of individual electrodes sites suggesting that the MSE might be an optimal platform for functional neuromuscular stimulation

New Frontiers in Food Production Beyond LEO

New technologies will be needed as mankind moves towards exploration of cislunar space, the Moon and Mars. Although many advances in our understanding of the effects of spaceflight on plant growth have been achieved in the last 40 years, spaceflight plant growth systems have been primarily designed to support space biology studies. Recently, the need for a sustainable and robust food system for future missions beyond Low Earth Orbit (LEO) has identified gaps in current technologies for food production. The goal is to develop safe and sustainable food production systems with reduced resupply mass and crew time compared to current systems