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Statistical tools of uncertainty quantification can be used to assess the information content of measured observables with respect to present-day theoretical models, to estimate model errors and thereby improve predictive capability, to extrapolate beyond the regions reached by experiment, and to provide meaningful input to applications and planned measurements. To showcase new opportunities offered by such tools, we make a rigorous analysis of theoretical statistical uncertainties in nuclear density functional theory using Bayesian inference methods. By considering the recent mass measurements from the Canadian Penning Trap at Argonne National Laboratory, we demonstrate how the Bayesian analysis and a direct least-squares optimization, combined with high-performance computing, can be used to assess the information content of the new data with respect to a model based on the Skyrme energy density functional approach. Employing the posterior probability distribution computed with a Gaussian process emulator, we apply the Bayesian framework to propagate theoretical statistical uncertainties in predictions of nuclear masses, two-neutron dripline, and fission barriers. Overall, we find that the new mass measurements do not impose a constraint that is strong enough to lead to significant changes in the model parameters. The example discussed in this study sets the stage for quantifying and maximizing the impact of new measurements with respect to current modeling and guiding future experimental efforts, thus enhancing the experiment-theory cycle in the scientific method.

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The majority of patients undergoing orthotopic liver transplantation (OLT) have end-stage liver disease secondary to hepatitis C virus (HCV) infection. Although OLT does not cure the disease and recurrent virus is present in all patients, relatively few patients with recurrent viremia develop clinical disease. When the disease recurs, however, the results can be devastating. Factors associated with increased risk for recurrent HCV disease remain controversial. We hypothesized that preservation injury may predispose to the severity of HCV disease after OLT. We reviewed our series of OLTs performed for HCV cirrhosis between January 1994 and December 1998 (n = 56; 62 transplants). Patients were grouped according to the severity of recurrent hepatitis C. Group 1 had no or mild HCV disease (n = 36), and group 2 had moderate to severe HCV disease (n = 20). The duration of ischemic rewarming during graft implantation was significantly associated with the severity of recurrent hepatitis C (P <.04). The estimated chances of severe disease within the first year post-OLT after 30, 60, or 90 minutes of ischemic rewarming time were 19%, 40%, and 65%, respectively. Cold ischemia time, transaminase levels, and prothrombin time did not correlate with the severity of hepatitis C. In conclusion, our data suggest that the duration of ischemic rewarming predisposes to severe recurrent hepatitis C. This finding warrants the investigation of the pathogenesis of recurrent HCV disease after ischemic injury. Reduction of rewarming time should be stressed in OLT, particularly in patients with HCV cirrhosis.

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The explanation of patterns in species richness ranks among the most important tasks of ecology. Current theories emphasize the interaction between historical and geographical factors affecting the size of the regional species pool and of locally acting processes such as competitive exclusion, disturbance, productivity, and seasonality. Local species richness, or alpha diversity, of plants and primary consumers has been claimed to peak in habitats of low and intermediate productivity, which, if true, has major implications for conservation. Here, by contrast, we show that local richness of Neotropical primates (platyrrhines) is influenced by both historical biogeography and productivity but not by tree species richness or seasonality. This pattern indicates that habitats with the highest plant productivity are also the richest for many important primary consumers. We show further that fragmentation of Amazonian rain forests in the Pleistocene, if it occurred, appears to have had a negligible influence on primate alpha species richness.

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In recent years, many investigators have proposed Gibbs prior models to regularize images reconstructed from emission computed tomography data. Unfortunately, hyperparameters used to specify Gibbs priors can greatly influence the degree of regularity imposed by such priors and, as a result, numerous procedures have been proposed to estimate hyperparameter values from observed image data. Many of these procedures attempt to maximize the joint posterior distribution on the image scene. To implement these methods, approximations to the joint posterior densities are required, because the dependence of the Gibbs partition function on the hyperparameter values is unknown. In this paper, we use recent results in Markov chain Monte Carlo (MCMC) sampling to estimate the relative values of Gibbs partition functions and using these values, sample from joint posterior distributions on image scenes. This allows for a fully Bayesian procedure which does not fix the hyperparameters at some estimated or specified value, but enables uncertainty about these values to be propagated through to the estimated intensities. We utilize realizations from the posterior distribution for determining credible regions for the intensity of the emission source. We consider two different Markov random field (MRF) models-the power model and a line-site model. As applications we estimate the posterior distribution of source intensities from computer simulated data as well as data collected from a physical single photon emission computed tomography (SPECT) phantom.

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