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

Drug Predictive Cues Activate Aversion-Sensitive Striatal Neurons That Encode Drug Seeking

Drug-associated cues have profound effects on an addict’s emotional state and drug-seeking behavior. Although this influence must involve the motivational neural system that initiates and encodes the drug-seeking act, surprisingly little is known about the nature of such physiological events and their motivational consequences. Three experiments investigated the effect of a cocaine-predictive stimulus on dopamine signaling, neuronal activity, and reinstatement of cocaine seeking. In all experiments, rats were divided into two groups (paired and unpaired), and trained to self-administer cocaine in the presence of a tone that signaled the immediate availability of the drug. For rats in the paired group, self-administration sessions were preceded by a taste cue that signaled delayed drug availability. Assessments of hedonic responses indicated that this delay cue became aversive during training. Both the self-administration behavior and the immediate cue were subsequently extinguished in the absence of cocaine. After extinction of self-administration behavior, the presentation of the aversive delay cue reinstated drug seeking. In vivo electrophysiology and voltammetry recordings in the nucleus accumbens measured the neural responses to both the delay and immediate drug cues after extinction. Interestingly, the presentation of the delay cue simultaneously decreased dopamine signaling and increased excitatory encoding of the immediate cue. Most importantly, the delay cue selectively enhanced the baseline activity of neurons that would later encode drug seeking. Together these observations reveal how cocaine cues can modulate not only affective state, but also the neurochemical and downstream neurophysiological environment of striatal circuits in a manner that promotes drug seeking

Evaluation of a mechanical anchoring system to improve performance of carbon fiber reinforced polymer mitigated concrete slabs under close in blasts

"December 2013.""A Thesis presented to the Faculty of the Graduate School at the University of Missouri--Columbia In Partial Fulfillment of the Requirements for the Degree Master of Science."Thesis supervisor: Dr. Sarah Orton.Recent events, including the bombings of the Murrah Building in Oklahoma City in 1995 and the World Trade Center in New York in 2001, have drawn attention to the fact that explosive loads can cause extreme damage and loss of life through catastrophic damage to structural components. Reinforced concrete slabs are an extremely common structural component in transportation, military, commercial, and utility infrastructure. Often, reinforced concrete slabs are not designed for blast loads and a threat reassessment during the structure's service life suggests the need for improved resilience to blasts. This research identifies a promising retrofit system utilizing structural steel plating and carbon fiber reinforced polymer (CFRP) sheets. CFRP is used to lower dead loads and to permit the installation of the system in close quarters. A slab section is retrofitted with steel armor plating on the compression face and CFRP on the tension face, and a mechanical anchoring scheme is used to ensure the development of the full CFRP strength during flexure. A static three-point-bending test is performed in order to evaluate the efficacy of the anchoring system and to determine a static resistance function. Finally, nonlinear bending analysis is used to validate the test results, and the static test results are used in a single degree of freedom (SDOF) dynamic analysis to make predictions of dynamic behavior under blast loads.Includes bibliographical references (pages 88-90)

The Madden Julian Oscillation and its relationship with rainfall in Queensland

The Madden Julian Oscillation is a large-scale atmospheric phenomenon that is generated above the tropical Indian Ocean. It is associated with large convective systems that propagate eastward across the Pacific Ocean. Since it is an atmospheric event limited to the equatorial domain, it was believed that it has little effect on non-tropical regions. However, recent research found correlations between the positioning of the active Madden Julian Oscillation phase along the Equator and rainfall events northeast Australia. The correlations were significant throughout Queensland. The phenomenon is subject to a study by climate scientists at four Australian institutions. It aims to develop a simple predictive tool of rainfall events that are linked with the active phase of the Madden Julian Oscillation and that is applicable throughout Queensland and possible beyond. The outcome of this research is to be linked with agricultural production systems model in order to help Queensland farmers to better time planting and harvesting, as well as scheduling of contractors whose operations might be delayed by rain

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