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Dose response modelling of Escherichia coli O157 incorporating data from foodborne and environmental outbreaks

INTERNATIONAL JOURNAL OF FOOD MICROBIOLOGY 2005年第1期103卷 35-47页

作者： Strachan, NJC Doyle, MP Kasuga, F Rotariu, O Ogden, ID Univ Aberdeen Sch Biol Sci Aberdeen AB24 3UU Scotland Univ Georgia Ctr Food Safety Griffin GA USA Natl Inst Hlth Sci Div Biomed Food Res Setagaya Ku Tokyo 1588501 Japan Univ Aberdeen Sch Med Dept Med Microbiol Appl Food Microbiol Grp Aberdeen AB25 2ZD Scotland

A human dose response model for Escherichia coli O157 would enable prediction of risk of infection to humans following exposure from either foodborne or environmental pathways. However, due to the severe nature of the disease, volunteer human dose response studies cannot be carried out. Surrogate models from Shigella fed to humans and E. coli O157 to rabbits have been utilised but are significantly different to one another. In addition data obtained by animal exposure may not be representative for human beings. An alternative approach to generating and validating a dose response model is to use quantitative data obtained from actual human outbreaks. This work collates outbreak data obtained from global sources and these are fitted using exponential and beta-Poisson models. The best fitting model was found to be the beta-Poisson model using a beta-binomial likelihood and the authors favour the exact version of this model. The confidence levels in this model encompass a previously published Shigella dose response model. The potential incorporation of this model into QMRAs is discussed together with applications of the model to help explain foodborne outbreaks. (c) 2005 Elsevier B.V. All rights reserved.

关键词： dose response E. coli O157 food outbreaks environmental outbreaks risk assessment Markov Chain Monte Carlo metropolis algorithm epidemiology

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Ultra-fast calculation scheme for small-angle multi-scattering problems

Ultra-fast calculation scheme for small-angle multi-scatteri...

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12th Advanced Laser Technoloies 2004 Conference

作者： Shuvalov, VV Shutov, IV Tret'akov, EV Voronov, AV Moscow MV Lomonosov State Univ Moscow 119899 Russia

A new ultra-fast (increasing the calculation rate about 9 orders and more) calculation scheme, enabling one to solve small-angle multi-scattering problems for large (size about 1000 scattering lengths and more) object... 详细信息

ISBN: (纸本)0819458473

关键词： diffusion optical tomography multiple small-angle scattering path-integration technique metropolis algorithm ultra-fast calculations

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Conductance bounds on the L² convergence rate of metropolis algorithms on unbounded state spaces

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ADVANCES IN APPLIED PROBABILITY 2004年第1期36卷 243-266页

作者： Jarner, SF Yuen, WK Danish Labour Market Supplementary Pens Quantitat Res Fixed Income DK-3400 Hillerod Denmark Brock Univ Dept Math St Catharines ON LS2 3A1 Canada

In this paper we derive bounds on the conductance and hence on the spectral gap of a metropolis algorithm with a monotone, log-concave target density on an interval of R. We show that the minimal conductance set has measure I and we use this characterization to bound the conductance in terms of the conductance of the algorithm restricted to a smaller domain. Whereas previous work on conductance has resulted in good bounds for Markov chains on bounded domains, this is the first conductance bound applicable to unbounded domains. We then show how this result can be combined with the state-decomposition theorem of Madras and Randall (2002) to bound the spectral gap of metropolis algorithms with target distributions with monotone, log-concave tails on R.

关键词： conductance Markov chain Monte Carlo metropolis algorithm rates of convergence spectral gap

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Bayesian estimation of the spectral density of a time series

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JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION 2004年第468期99卷 1050-1059页

作者： Choudhuri, N Ghosal, S Roy, A Case Western Reserve Univ Dept Stat Cleveland OH 44106 USA N Carolina State Univ Dept Stat Raleigh NC 27695 USA Univ Maryland Baltimore Cty Dept Math & Stat Baltimore MD 21250 USA

This article describes a Bayesian approach to estimating the spectral density of a stationary time series. A nonparametric prior on the spectral density is described through Bernstein polynomials. Because the actual likelihood is very complicated, a pseudoposterior distribution is obtained by updating the prior using the Whittle likelihood. A Markov chain Monte Carlo algorithm for sampling front this posterior distribution is described that is used for computing the posterior mean, variance, and other statistics. A consistency result is established for this pseudoposterior distribution that holds for a short-memory Gaussian time series and under some conditions on the prior. To prove this asymptotic result, a general consistency theorem of Schwartz is extended for a triangular array of independent, nonidentically distributed observations. This extension is also of independent interest. A simulation study is conducted to compare the proposed method with some existing methods. The method is illustrated with the well-studied sunspot dataset.

关键词： Bernstein polynomial Dirichlet process metropolis algorithm periodogram posterior consistency posterior distribution spectral density time series

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Robust analysis of generalized linear mixed models

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JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION 2004年第466期99卷 451-460页

作者： Sinha, SK Univ Winnipeg Dept Math & Stat Winnipeg MB R3B 2E9 Canada

The method of maximum likelihood (ML) is widely used for analyzing generalized linear mixed models (GLMM's). A full maximum likelihood analysis requires numerical integration techniques for calculation of the log-likelihood, and to avoid the computational problems involving irreducibly high-dimensional integrals, several maximum likelihood algorithms have been proposed in the literature to estimate the model parameters by approximating the log-likelihood function. Although these likelihood algorithms are useful for fitting the GLMM's efficiently under strict model assumptions, they can be highly influenced by the presence of unusual data points. In this article, the author develops a technique for finding robust maximum likelihood (RML) estimates of the model parameters in GLMM's, which appears to be useful in downweighting the influential data points when estimating the parameters. The asymptotic properties of the robust estimators are investigated under some regularity conditions. Small simulations are carried out to study the behavior of the robust estimates in the presence of outliers. and these estimates are also compared to the ordinary classical estimates. To avoid the computational problems involving high-dimensional integrals, the author proposes a robust Monte Carlo Newton-Raphson (RMCNR) algorithm for fitting GLMM's. The proposed robust method is illustrated in an analysis of data from a clinical experiment described in a biometrical journal.

关键词： generalized linear models metropolis algorithm mixed models Newton-Raphson algorithm robust estimation

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Static and dynamic properties of the nonframework cations in Na-mordenites zeolite

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MOLECULAR SIMULATION 2004年第9期30卷 587-592页

作者： Maurin, G Bell, RG Senet, P Devautour-Vinot, S Univ Aix Marseille 1 Ctr St Jerome UMR 6121 CNRS Lab Madirel F-13397 Marseille 20 France UCL Royal Inst Great Britain Davy Faraday Res Lab London W1S 4BS England Univ Bourgogne Fac Sci Mirande UMR 5027 CNRS Phys Lab F-21078 Dijon France Univ Montpellier 2 Lab Physicochim Mat Condensee UMR 5617 CNRS F-34095 Montpellier France

This work consists of exploring the effect of Si/Al ratio and water content on both the static and dynamic properties of the extra-framework cations in a typical zeolite system Na(+)-Mordenites. Atomistic simulations based on interatomic potentials and minimisation techniques have been used to determine the location of the cations as a function of the Si/Al ratio and the hydration level. Our calculations showed two different cation behaviours depending on the type of channels that they occupy, the positions of the cations in the main channels being substantially perturbed upon the sorption of water molecules whereas those of the cations located in the small side channels being only slightly shifted. By contrast, the populations of the cation sites have almost the same trend when the Si/Al ratio varies. We have then developed a Monte Carlo technique specialised for the simulation of rare events to study the activated cations motions in this zeolite. Mean activation barriers are obtained from minimum energy paths (MEP) calculated for lattices with various Si/Al ratio, by using a metropolis algorithm. Finally, Molecular Dynamics (MD) simulations allowed us to compare the mobility of the cations in hydrated and dehydrated states and confirmed the results expected from the previous static calculations. This modelling has been successfully compared with experimental data obtained by dielectric relaxation spectroscopy (DRS) and provided a microscopic description of the static and dynamic properties of the extra-framework cations.

关键词： Na-mordenites Si/Al ratio Monte Carlo technique metropolis algorithm

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Slice sampling

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ANNALS OF STATISTICS 2003年第3期31卷 705-741页

作者： Neal, RM Univ Toronto Dept Stat Toronto ON M5S 3G3 Canada

Markov chain sampling methods that adapt to characteristics of the distribution being sampled can be constructed using the principle that one can sample from a distribution by sampling uniformly from the region under the plot of its density function. A Markov chain that converges to this uniform distribution can be constructed by alternating uniform sampling in the vertical direction with uniform sampling from the horizontal "slice" defined by the current vertical position, or more generally, with some update that leaves the uniform distribution over this slice invariant. Such "slice sampling" methods are easily implemented for univariate distributions, and can be used to sample from a multivariate distribution by updating each variable in turn. This approach is often easier to implement than Gibbs sampling and more efficient than simple metropolis updates, due to the ability of slice sampling to adaptively choose the magnitude of changes made. It is therefore attractive for routine and automated use. Slice sampling methods that update all variables simultaneously are also possible. These methods can adaptively choose the magnitudes of changes made to each variable, based on the local properties of the density function. More ambitiously, such methods could potentially adapt to the dependencies between variables by constructing local quadratic approximations. Another approach is to improve sampling efficiency by suppressing random walks. This can be done for univariate slice sampling by "overrelaxation," and for multivariate slice sampling by "reflection" from the edges of the slice.

关键词： Markov chain Monte Carlo auxiliary variables adaptive methods Gibbs sampling metropolis algorithm overrelaxation dynamical methods

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A method for classifying metabolites in topological pathway analyses based on minimization of pathway number

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BIOSYSTEMS 2003年第3期70卷 255-270页

作者： Dandekar, T Moldenhauer, F Bulik, S Bertram, H Schuster, S Max Delbruck Ctr Mol Med D-13092 Berlin Germany Univ Wurzburg Dept Bioinformat Bioctr D-97074 Wurzburg Germany European Mol Biol Lab Biocomp & Struct Program D-69012 Heidelberg Germany Humboldt Univ Inst Biol D-10115 Berlin Germany

Metabolic pathway analysis based on the concept of elementary flux mode is a valuable tool for reconstruction of bacterial metabolisms and in predicting optimal conversion yields in biotechnology. However, pathway analysis of large and highly entangled metabolic networks meets the problem of combinatorial explosion of possible routes across the networks. Here we propose a method for coping with this problem by suitably classifying metabolites as external or internal. External metabolites are considered to have buffered concentrations while internal metabolites have to fulfil a balance condition at steady state. For many substances such as nutrients and excreted products, there are biochemical reasons to classify them as external. In addition. other substances (especially at central branching points) can operationally be considered external in order to avoid combinatorial explosion. We suggest to find such a classification of metabolites that minimizes the number of elementary flux modes (pathways). This is motivated by the objectives of finding such a description of the system that reduces as much as possible the amount of necessary data and of removing the ambiguity and arbitrariness in the classification of metabolites in an automated, systematic way. For networks of moderate size, the solution to this combinatorial minimization problem can be found by exhaustive search. To tackle also larger systems, a stochastic optimization program based on the metropolis algorithm was developed. Both methods are applied, for illustration, to several reaction schemes including a larger network representing glutathione metabolism. (C) 2003 Elsevier Science Ireland Ltd. All rights reserved.

关键词： elementary flux modes biochemical networks pathway analysis external metabolites combinatorial explosion metropolis algorithm

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Threshold accepting as limit case for a modified Tsallis statistics

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APPLIED MATHEMATICS LETTERS 2003年第1期16卷 27-31页

作者： Franz, A Hoffman, KH Tech Univ Inst Phys D-09107 Chemnitz Germany

Simulated annealing with different types of acceptance probabilities is widely used in stochastic optimization. Based on the-metropolis algorithm describing thermal relaxation, threshold accepting was developed to speed up the computation. Tsallis statistics generalizes the metropolis acceptance probability by introducing a new parameter q is an element of R, where for q --> 1, the metropolis statistics is recovered. In this paper, we will show that not only the metropolis acceptance probability is a limit case of Tsallis statistics, but threshold accepting can also be considered as limit case of a modified Tsallis acceptance probability for q --> -infinity. (C) 2002 Elsevier Science Ltd. All rights reserved.

关键词： simulated annealing metropolis algorithm threshold accepting Tsallis statistics

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Subset threshold autoregression

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JOURNAL OF FORECASTING 2003年第1期22卷 49-66页

作者： So, MKP Chen, CWS Hong Kong Univ Sci & Technol Dept Informat & Syst Management Hong Kong Hong Kong Peoples R China Feng Chia Univ Dept Stat Taichung 40724 Taiwan

We develop in this paper an efficient way to select the best subset threshold autoregressive model. The proposed method uses a stochastic search idea. Differing from most conventional approaches, our method does not require us to fix the delay or the threshold parameters in advance. By adopting the Markov chain Monte Carlo techniques, we can identify the best subset model from a very large of number of possible models, and at the same time estimate the unknown parameters. A simulation experiment shows that the method is very effective. In its application to the US unemployment rate, the stochastic search method successfully selects lag one as the time delay and five best models from more than 4000 choices. Copyright (C) 2003 John Wiley Sons, Ltd.

关键词： arranged autoregression hierarchical Bayes Markov chain Monte Carlo stochastic search variable selection metropolis algorithm unemployment rate

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