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The nicotine yield of cigarettes measured by machine-smoking does not reliably predict the exposure of smokers.
We made use of this approach to predict the exposure of β-tubulin epitopes and tyrosine residues on the surface of A. thaliana microtubules.
The consensus opinion was that it is difficult to predict the exposure of OC in the wild fowl gut and its implications for hastening the generation of OC-resistance in avian influenza.
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Like other microarray studies, the primary objective of this study was to search for an optimal or near optimal subset of genes that could be used to predict the exposure history of unknown samples.
When algorithm was applied on the data set (training set) to predict the exposure type of a data set obtained from an exposure to Y. pestis (test set), we were able to correctly predict the type of exposure with a p < 0.02.
To determine whether the microarray data obtained in this study can be used to predict the exposure type of an uncharacterized sample or condition, we applied a supervised learning method for class prediction (GeneSpring) that uses the k-nearest neighbor algorithm.
The indirect approach predicted the exposures of three different population subgroups (i.e. pedestrians, shop vendors and residents) by multiplying the pollutant concentrations with the duration of exposure within a specific micro-environment.
Appropriate models can provide a framework for predicting the exposure, response, and time course of a drug for different dosage regimens in a target population.
We report an inter-comparison of eight models designed to predict the radiological exposure of radionuclides in marine biota.
This study provides a general framework and specific procedures to predict the contaminant exposure of mid-sized mammals using a geographical information system (GIS -based Monte Carlo simulation model.
The predicted increase in the exposure of DOM with KETO (area under the curve ratio) was 2.8 ± 0.4-fold, which correlated well with the observed 3.5-fold increase.
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