T Little

Los Alamos National Laboratory, Los Alamos, California, United States

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Publications (12)8.41 Total impact

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    ABSTRACT: The methods used to calculate radiological and toxicological doses to hypothetical persons inside either a U.S. Army Abrams tank or Bradley Fighting Vehicle that has been perforated by depleted uranium munitions are described. Data from time- and particle-size-resolved measurements of depleted uranium aerosol as well as particle-size-resolved measurements of aerosol solubility in lung fluids for aerosol produced in the breathing zones of the hypothetical occupants were used. The aerosol was approximated as a mixture of nine monodisperse (single particle size) components corresponding to particle size increments measured by the eight stages plus the backup filter of the cascade impactors used. A Markov Chain Monte Carlo Bayesian analysis technique was employed, which straightforwardly calculates the uncertainties in doses. Extensive quality control checking of the various computer codes used is described.
    Health physics 04/2009; 96(3):306-27. · 0.92 Impact Factor
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    ABSTRACT: Bayesian hypothesis testing may be used to qualitatively interpret a dataset as indicating something "detected" or not. Hypothesis testing is shown to be equivalent to testing the posterior distribution for positive true amounts by redefining the prior to be a mixture of the original prior and a delta-function component at 0 representing the null hypothesis that nothing is truly present. The hypothesis-testing interpretation of the data is based on the posterior probability of the usual modeling hypothesis relative to the null hypothesis. Real numerical examples are given and discussed, including the distribution of the non-null hypothesis probability over 4,000 internal dosimetry cases. Currently used comparable methods based on classical statistics are discussed.
    Health Physics 04/2008; 94(3):248-54. · 1.02 Impact Factor
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    ABSTRACT: Simulated-data internal dosimetry cases for use in intercomparison exercises or as a software verification and validation tool have been published on the internet (www.lanl.gov/bayesian/software Bayesian software package II). A user may validate their internal dosimetry code or method using this simulated bioassay data. Or, the user may choose to try out the Los Alamos National Laboratory codes ID and UF, which are also supplied. A Poisson-lognormal model of data uncertainty is assumed. A collection of different possible models for each nuclide (e.g. solubility types and particle sizes) are used. For example, for 238Pu, 14 different biokinetic models or types (8 inhalation, 4 wound and 2 ingestion) are assumed. Simulated data are generated for all the assumed biokinetic models, both for incidents, where the time of intake is known, and for non-incidents, where it is not. For the dose calculations, the route of intake, but not the biokinetic model, is considered to be known. The object is to correctly calculate the known true dose from simulated data covering a period of time. A 'correct' result has been defined in two ways: (1) that the credible limits of the calculated dose include the correct dose and (2) that the calculated dose is within a factor of 2 of the correct dose.
    Radiation Protection Dosimetry 02/2007; 127(1-4):361-9. · 0.91 Impact Factor
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    ABSTRACT: Biokinetic models are the scientific underpinning of internal dosimetry and depend, ultimately, for their scientific validation on comparisons with human bioassay data. Three significant plutonium/americium bioassay databases, known to the authors, are described: (1) Sellafield, (2) Los Alamos and (3) the United States Transuranium Registry. A case is made for a uniform standard for database format, and the XML standard is discussed.
    Radiation Protection Dosimetry 02/2007; 125(1-4):531-7. · 0.91 Impact Factor
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    ABSTRACT: One of the challenges to the dose assessment team in response to an inhalation incident in the workplace is to provide the occupational physicians, operational radiation protection personnel and line managers with early estimates of radionuclide intakes so that appropriate consequence management and mitigation can be done. For radionuclides such as Pu, where in vivo counting is not adequately sensitive, other techniques such as the measurement of removable radionuclide from the nasal airway passages can be used. At Los Alamos National Laboratory (LANL), nose swabs of the ET1 region have been used routinely as a first response to airborne Pu releases in the workplace, as well as for other radionuclides. This paper presents the results of analysing over 15 years of nose swab data, comparing these with dose assessments performed using the Bayesian methods developed at LANL. The results provide empirical support for using nose swab data for early dose assessments. For Pu, a rule of thumb is a dose factor of 0.8 mSv Bq(-1), assuming a linear relationship between nasal swab activity and committed effective dose equivalent. However, this value is specific to the methods and models used at LANL, and should not be applied directly without considering possible differences in measurement and calculation methods.
    Radiation Protection Dosimetry 02/2007; 127(1-4):356-60. · 0.91 Impact Factor
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    ABSTRACT: Workers are routinely monitored by urinalysis for exposure to uranium at Los Alamos National Laboratory. Urine samples are analysed by alpha spectroscopy for 234U, 235U and 238U. Interpretation of the data is complicated by the presence of natural uranium in the workers' drinking water and diet. Earlier methods used drinking water samples to estimate the dietary component in urine excretion. However, there proved to be insufficient correlation between drinking water concentration and excretion rate. Instead, an iterative calculation is used to identify a typical excretion rate for each individual in the absence of occupational intakes. Bayesian dose-assessment methods, first developed for plutonium bioassay at Los Alamos, have been adapted for uranium. These methods, coupled with an algorithm for identifying each individual's baseline level of uranium from environmental sources, yield improved reliability in the identification of occupational intakes.
    Radiation Protection Dosimetry 02/2007; 127(1-4):333-8. · 0.91 Impact Factor
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    ABSTRACT: This paper describes the design and implementation of the Los Alamos National Laboratory (LANL) dose assessment (DA) data system. Dose calculations for the most important radionuclides at LANL, namely plutonium, americium, uranium and tritium, are performed through the Microsoft Access DA database. DA includes specially developed forms and macros that perform a variety of tasks, such as retrieving bioassay data, launching the FORTRAN internal dosimetry applications and displaying dose results in the form of text summaries and plots. The DA software involves the following major processes: (1) downloading of bioassay data from a remote data source, (2) editing local and remote databases, (3) setting up and carrying out internal dose calculations using the UF code or the ID code, (3) importing results of the dose calculations into local results databases, (4) producing a secondary database of 'official results' and (5) automatically creating and e-mailing reports. The software also provides summary status and reports of the pending DAs, which are useful for managing the cases in process.
    Radiation Protection Dosimetry 02/2007; 127(1-4):347-9. · 0.91 Impact Factor
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    ABSTRACT: The methods used at Los Alamos for calculating internal dose for plutonium and americium are described. The main method, the ID code, is a straightforward use of Bayes' theorem, evaluated using Markov Chain Monte Carlo. A supercomputer cluster is used to do mass calculations on many cases at once. As an alternative, a single workstation continually does calculations as new bioassay data comes in, spreading out the calculation load over the year. Both methods are being used successfully.
    Applied Modeling and Computations in Nuclear Science, Edited by Thomas M' semkowo, 01/2005: chapter 7: pages 93-103; American Chemical Society.
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    G Miller, T Little, R Guilmette
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    ABSTRACT: The inverse problem of internal dosimetry is naturally posed as a problem of Bayesian inference. The Bayesian approach is of practical importance in three areas: (1) avoiding false positives in the detection of rare events, (2) the calculation of uncertainties, and (3) the calculation of multiple intakes, all of which are important for internal dosimetry. In this paper, the Bayesian approach to the interpretation of measurements is first reviewed using a simple conceptual example. Then, a simple 239Pu case using IMBA expert is discussed, and finally a current cutting-edge example is discussed involving real 238Pu data calculated with a Markov Chain Monte Carlo algorithm and with exact calculation of poisson likelihood functions.
    Radiation Protection Dosimetry 02/2003; 105(1-4):333-8. · 0.91 Impact Factor
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    ABSTRACT: A technique for computing the exact marginalized (integrated) Poisson likelihood function for counting measurement processes involving a background subtraction is described. An empirical Bayesian method for determining the prior probability distribution of background count rates from population data is recommended and would seem to have important practical advantages. The exact marginalized Poisson likelihood function may be used instead of the commonly used Gaussian approximation. Differences occur in some cases of small numbers of measured counts, which are discussed. Optional use of exact likelihood functions in our Bayesian internal dosimetry codes has been implemented using an interpolation-table approach, which means that there is no computation time penalty except for the initial setup of the interpolation tables.
    Health Physics 11/2002; 83(4):512-8. · 1.02 Impact Factor
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    Guthrie Miller, Harry F. Martz, Tom T. Little
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    ABSTRACT: SUMMARY A new numerical method for solving the inverse problem of in- ternal dosimetry is described. The new method uses Markov Chain Monte Carlo and the Metropolis algorithm. Multiple intake amounts, biokinetic types, and times of intake are determined from bioas- say data by integrating over the Bayesian posterior distribution. The method appears definitive, but its application requires a large amount of computing time.