Biomedical subjects
J R Lynch
Publications and source records attributed to J R Lynch.
A comparison of impinger and membrane filter techniques for evaluating air samples in asbestos plants.
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Evolutionary relationships of human populations from an analysis of nuclear DNA polymorphisms.
The genetic relationships of human populations have been studied by comparing gene frequency data for protein and blood-group loci of different populations. DNA analysis now promises to be more informative since not only do the DNA coding sequences have more variation than their corresponding proteins but, in addition, noncoding DNA sequences display more extensive polymorphism. We have now studied the frequency of a group of closely linked nuclear DNA polymorphisms (haplotypes) in the beta-globin gene cluster of normal (beta A) chromosomes of individuals from eight diverse populations. We have found that all non-African populations share a limited number of common haplotypes whereas Africans have predominantly a different haplotype not found in other populations. Genetic distance analysis based on these nuclear DNA polymorphisms indicates a major division of human populations into an African and a Eurasian group.
Measurement of dust exposures in the asbestos textile industry.
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Exposure to metals in the manufacture of asbestos textile products.
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Characterization and solubility of metals associated with asbestos fibers.
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Source and identification of respirable fibers.
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Exposure to fibers in the manufacture of fibrous glass.
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Evaluation of size-selective presamplers. I. Theoretical cyclone and elutriator relationships.
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Techniques for the detection, identification and analysis of fibers.
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The interrelationships of selected asbestos exposure indices.
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The evaluation of gas detector tube systems. I. Carbon monoxide.
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Quantitative level of protection offered to workers by ACGIH threshold limit values occupational exposure limits.
The details of the example or modeling methodologies used herein are not critical to the general point of this article, which advises the estimation of residual risk at the OEL by using some quantitative modeling structure. Specifically, the authors believe that an explicit attempt to gauge the level of residual risk at the OEL based on conceptual stochastic models with transparent and testable assumptions could be seen as an important enhancement to the process. This is especially true in sharing the OEL deliberations and explaining OEL decisions to the stakeholders. Indeed, if this approach is used, it is critically important to understand and continually communicate that this "cloud of uncertainty" represents model estimates in which the true risk would most likely be less than worst case estimates and could possibly be zero. It is also possible but highly unlikely that it could be higher than the worst case upper-bound estimate. The above quantitative estimation scheme represents a possible improvement that could provide a reasoned attempt on the part of the risk assessors to use rational science (i.e., conceptual models with transparent and testable assumptions) to inform all of the OEL users and stakeholders of their meaning.