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  • 2005-2009  (116)
  • 2000-2004  (98)
  • 1975-1979  (26)
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  • 1
    Electronic Resource
    Electronic Resource
    Comparison of Sensitivity Analysis Methods Based on Applications to a Food Safety Risk Assessment Model (2004)
    350 Main Street , Malden , MA 02148 , USA , and 9600 Garsington Road , Oxford OX4 2DQ , UK . : Blackwell Publishing, Inc.
    Risk analysis 24 (2004), S. 0 
    Details
    ISSN: 1539-6924
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Energy, Environment Protection, Nuclear Power Engineering
    Notes: Sensitivity analysis (SA) methods are a valuable tool for identifying critical control points (CCPs), which is one of the important steps in the hazard analysis and CCP approach that is used to ensure safe food. There are many SA methods used across various disciplines. Furthermore, food safety process risk models pose challenges because they often are highly nonlinear, contain thresholds, and have discrete inputs. Therefore, it is useful to compare and evaluate SA methods based upon applications to an example food safety risk model. Ten SA methods were applied to a draft Vibrio parahaemolyticus (Vp) risk assessment model developed by the Food and Drug Administration. The model was modified so that all inputs were independent. Rankings of key inputs from different methods were compared. Inputs such as water temperature, number of oysters per meal, and the distributional assumption for the unrefrigerated time were the most important inputs, whereas time on water, fraction of pathogenic Vp, and the distributional assumption for the weight of oysters were the least important inputs. Most of the methods gave a similar ranking of key inputs even though the methods differed in terms of being graphical, mathematical, or statistical, accounting for individual effects or joint effect of inputs, and being model dependent or model independent. A key recommendation is that methods be further compared by application on different and more complex food safety models. Model independent methods, such as ANOVA, mutual information index, and scatter plots, are expected to be more robust than others evaluated.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1111/j.0272-4332.2004.00460.x
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  • 2
    Electronic Resource
    Electronic Resource
    Quantification of Variability and Uncertainty for Censored Data Sets and Application to Air Toxic Emission Factors (2004)
    350 Main Street , Malden , MA 02148 , USA , and 9600 Garsington Road , Oxford OX4 2DQ , UK . : Blackwell Publishing, Inc.
    Risk analysis 24 (2004), S. 0 
    Details
    ISSN: 1539-6924
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Energy, Environment Protection, Nuclear Power Engineering
    Notes: Many environmental data sets, such as for air toxic emission factors, contain several values reported only as below detection limit. Such data sets are referred to as “censored.” Typical approaches to dealing with the censored data sets include replacing censored values with arbitrary values of zero, one-half of the detection limit, or the detection limit. Here, an approach to quantification of the variability and uncertainty of censored data sets is demonstrated. Empirical bootstrap simulation is used to simulate censored bootstrap samples from the original data. Maximum likelihood estimation (MLE) is used to fit parametric probability distributions to each bootstrap sample, thereby specifying alternative estimates of the unknown population distribution of the censored data sets. Sampling distributions for uncertainty in statistics such as the mean, median, and percentile are calculated. The robustness of the method was tested by application to different degrees of censoring, sample sizes, coefficients of variation, and numbers of detection limits. Lognormal, gamma, and Weibull distributions were evaluated. The reliability of using this method to estimate the mean is evaluated by averaging the best estimated means of 20 cases for small sample size of 20. The confidence intervals for distribution percentiles estimated with bootstrap/MLE method compared favorably to results obtained with the nonparametric Kaplan–Meier method. The bootstrap/MLE method is illustrated via an application to an empirical air toxic emission factor data set.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1111/j.0272-4332.2004.00504.x
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  • 3
    Electronic Resource
    Electronic Resource
    [ohne Titel] (2002)
    Malden, USA : Blackwell Publishing, Inc.
    Risk analysis 22 (2002), S. 0 
    Details
    ISSN: 1539-6924
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Energy, Environment Protection, Nuclear Power Engineering
    Notes: This guest editorial is a summary of the NCSU/USDA Workshop on Sensitivity Analysis held June 11–12, 2001 at North Carolina State University and sponsored by the U.S. Department of Agriculture's Office of Risk Assessment and Cost Benefit Analysis. The objective of the workshop was to learn across disciplines in identifying, evaluating, and recommending sensitivity analysis methods and practices for application to food-safety process risk models. The workshop included presentations regarding the Hazard Assessment and Critical Control Points (HACCP) framework used in food-safety risk assessment, a survey of sensitivity analysis methods, invited white papers on sensitivity analysis, and invited case studies regarding risk assessment of microbial pathogens in food. Based on the sharing of interdisciplinary information represented by the presentations, the workshop participants, divided into breakout sessions, responded to three trigger questions: What are the key criteria for sensitivity analysis methods applied to food-safety risk assessment? What sensitivity analysis methods are most promising for application to food safety and risk assessment? and What are the key needs for implementation and demonstration of such methods? The workshop produced agreement regarding key criteria for sensitivity analysis methods and the need to use two or more methods to try to obtain robust insights. Recommendations were made regarding a guideline document to assist practitioners in selecting, applying, interpreting, and reporting the results of sensitivity analysis.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1111/0272-4332.00037
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  • 4
    Electronic Resource
    Electronic Resource
    Quantification of Variability and Uncertainty Using Mixture Distributions: Evaluation of Sample Size, Mixing Weights, and Separation Between Components (2004)
    350 Main Street , Malden , MA 02148 , USA , and 9600 Garsington Road , Oxford OX4 2DQ , UK . : Blackwell Publishing, Inc.
    Risk analysis 24 (2004), S. 0 
    Details
    ISSN: 1539-6924
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Energy, Environment Protection, Nuclear Power Engineering
    Notes: Variability is the heterogeneity of values within a population. Uncertainty refers to lack of knowledge regarding the true value of a quantity. Mixture distributions have the potential to improve the goodness of fit to data sets not adequately described by a single parametric distribution. Uncertainty due to random sampling error in statistics of interests can be estimated based upon bootstrap simulation. In order to evaluate the robustness of using mixture distribution as a basis for estimating both variability and uncertainty, 108 synthetic data sets generated from selected population mixture log-normal distributions were investigated, and properties of variability and uncertainty estimates were evaluated with respect to variation in sample size, mixing weight, and separation between components of mixtures. Furthermore, mixture distributions were compared with single-component distributions. Findings include: (1) mixing weight influences the stability of variability and uncertainty estimates; (2) bootstrap simulation results tend to be more stable for larger sample sizes; (3) when two components are well separated, the stability of bootstrap simulation is improved; however, a larger degree of uncertainty arises regarding the percentiles coinciding with the separated region; (4) when two components are not well separated, a single distribution may often be a better choice because it has fewer parameters and better numerical stability; and (5) dependencies exist in sampling distributions of parameters of mixtures and are influenced by the amount of separation between the components. An emission factor case study based upon NOx emissions from coal-fired tangential boilers is used to illustrate the application of the approach.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1111/j.0272-4332.2004.00459.x
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  • 5
    Electronic Resource
    Electronic Resource
    Quantitative Analysis of Variability and Uncertainty with Known Measurement Error: Methodology and Case Study (2005)
    350 Main Street , Malden , MA 02148 , USA , and 9600 Garsington Road , Oxford OX4 2DQ , UK . : Blackwell Publishing, Inc.
    Risk analysis 25 (2005), S. 0 
    Details
    ISSN: 1539-6924
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Energy, Environment Protection, Nuclear Power Engineering
    Notes: The appearance of measurement error in exposure and risk factor data potentially affects any inferences regarding variability and uncertainty because the distribution representing the observed data set deviates from the distribution that represents an error-free data set. A methodology for improving the characterization of variability and uncertainty with known measurement errors in data is demonstrated in this article based on an observed data set, known measurement error, and a measurement-error model. A practical method for constructing an error-free data set is presented and a numerical method based upon bootstrap pairs, incorporating two-dimensional Monte Carlo simulation, is introduced to address uncertainty arising from measurement error in selected statistics. When measurement error is a large source of uncertainty, substantial differences between the distribution representing variability of the observed data set and the distribution representing variability of the error-free data set will occur. Furthermore, the shape and range of the probability bands for uncertainty differ between the observed and error-free data set. Failure to separately characterize contributions from random sampling error and measurement error will lead to bias in the variability and uncertainty estimates. However, a key finding is that total uncertainty in mean can be properly quantified even if measurement and random sampling errors cannot be separated. An empirical case study is used to illustrate the application of the methodology.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1111/j.1539-6924.2005.00620.x
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  • 6
    Electronic Resource
    Electronic Resource
    Identification and Review of Sensitivity Analysis Methods (2002)
    Malden, USA : Blackwell Publishing, Inc.
    Risk analysis 22 (2002), S. 0 
    Details
    ISSN: 1539-6924
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Energy, Environment Protection, Nuclear Power Engineering
    Notes: Identification and qualitative comparison of sensitivity analysis methods that have been used across various disciplines, and that merit consideration for application to food-safety risk assessment models, are presented in this article. Sensitivity analysis can help in identifying critical control points, prioritizing additional data collection or research, and verifying and validating a model. Ten sensitivity analysis methods, including four mathematical methods, five statistical methods, and one graphical method, are identified. The selected methods are compared on the basis of their applicability to different types of models, computational issues such as initial data requirement and complexity of their application, representation of the sensitivity, and the specific uses of these methods. Applications of these methods are illustrated with examples from various fields. No one method is clearly best for food-safety risk models. In general, use of two or more methods, preferably with dissimilar theoretical foundations, may be needed to increase confidence in the ranking of key inputs.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1111/0272-4332.00039
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  • 7
    Electronic Resource
    Electronic Resource
    Sensitivity Analysis of a Two-Dimensional Probabilistic Risk Assessment Model Using Analysis of Variance (2005)
    350 Main Street , Malden , MA 02148 , USA , and 9600 Garsington Road , Oxford OX4 2DQ , UK . : Blackwell Publishing, Inc.
    Risk analysis 25 (2005), S. 0 
    Details
    ISSN: 1539-6924
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Energy, Environment Protection, Nuclear Power Engineering
    Notes: This article demonstrates application of sensitivity analysis to risk assessment models with two-dimensional probabilistic frameworks that distinguish between variability and uncertainty. A microbial food safety process risk (MFSPR) model is used as a test bed. The process of identifying key controllable inputs and key sources of uncertainty using sensitivity analysis is challenged by typical characteristics of MFSPR models such as nonlinearity, thresholds, interactions, and categorical inputs. Among many available sensitivity analysis methods, analysis of variance (ANOVA) is evaluated in comparison to commonly used methods based on correlation coefficients. In a two-dimensional risk model, the identification of key controllable inputs that can be prioritized with respect to risk management is confounded by uncertainty. However, as shown here, ANOVA provided robust insights regarding controllable inputs most likely to lead to effective risk reduction despite uncertainty. ANOVA appropriately selected the top six important inputs, while correlation-based methods provided misleading insights. Bootstrap simulation is used to quantify uncertainty in ranks of inputs due to sampling error. For the selected sample size, differences in F values of 60% or more were associated with clear differences in rank order between inputs. Sensitivity analysis results identified inputs related to the storage of ground beef servings at home as the most important. Risk management recommendations are suggested in the form of a consumer advisory for better handling and storage practices.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1111/j.1539-6924.2005.00679.x
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  • 8
    Electronic Resource
    Electronic Resource
    Continuous magnetic reconnection at Earth's magnetopause (2003)
    [s.l.] : Macmillian Magazines Ltd.
    Nature 426 (2003), S. 533-537 
    Details
    ISSN: 1476-4687
    Source: Nature Archives 1869 - 2009
    Topics: Biology , Chemistry and Pharmacology , Medicine , Natural Sciences in General , Physics
    Notes: [Auszug] The most important process that allows solar-wind plasma to cross the magnetopause and enter Earth's magnetosphere is the merging between solar-wind and terrestrial magnetic fields of opposite sense—magnetic reconnection. It is at present not known whether reconnection can happen in ...
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1038/nature02084
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  • 9
    Electronic Resource
    Electronic Resource
    The involvement of the 3′:5′-cyclic-AMP phosphodiesterase in transformation and growth of crown-gall tumors in Bryophyllum daigremontianum (1977)
    Springer
    Planta 137 (1977), S. 165-168 
    Details
    ISSN: 1432-2048
    Keywords: Bryophyllum ; Crown-gall ; 3′:5′-cyclic-AMP phosphodiesterase ; Theophylline
    Source: Springer Online Journal Archives 1860-2000
    Topics: Biology
    Notes: Abstract In crown-gall tumor tissue obtained from leaves of Bryophyllum daigremontianum an adenosine 3′:5′-cyclic phosphate (3′:5′-cyclic-AMP) degrading activity increases up to 2.5 fold until the fifth day after inoculation with Agrobacterium tumefaciens, declining to the value of the control in the solid tumor. Theophylline up to 1 mmol l−1 given to wounded leaves of Bryophyllum daigremontianum has no effect on the number of tumors. The effect of higher concentrations given over extended periods can be explained otherwise. Therefore it seems likely that the 3′:5′-cyclic-AMP phosphodiesterase (EC 3.1.4.17) has no effect on transformation and growth of crown-gall tumors in Bryophyllum daigremontianum.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF00387554
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  • 10
    Electronic Resource
    Electronic Resource
    Bioavailability of oral testosterone in males (1979)
    Springer
    European journal of clinical pharmacology 16 (1979), S. 345-349 
    Details
    ISSN: 1432-1041
    Keywords: testosterone ; testosterone undecanoate ; arachis oil ; serum level ; oral administration
    Source: Springer Online Journal Archives 1860-2000
    Topics: Chemistry and Pharmacology , Medicine
    Notes: Summary Twenty-six male volunteers received a single oral dose of testosterone as free crystals or as the undecanoate ester. The latter was given either in crystalline form or in arachis oil. All preparations were tested three times in the same individual, whilst fasting on 2 days and on one day together with a breakfast rich in fat. Serum testosterone concentration was measured at intervals for up to 6–24h after the dose. A significant and reproducible rise in serum testosterone level was found after ingestion of free testosterone. Testosterone esterified with undecylenic acid was only effective when administered in arachis oil. The meal increased the bioavailability of the ester, but had hardly any effect on that of the free hormone. It is concluded that bioavailability of oral testosterone can be improved by pharmaceutical means to an extent sufficient to produce adequate blood levels in substitution therapy.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF00605634
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