Comparison of machine learning approaches and statistical methods for estimation of airborne chemical exposures

Accurate quantitative estimation of exposure using retrospective data has been one of the most challenging tasks in the exposure assessment field. To improve these estimates, some models have been developed using published exposure databases with their corresponding exposure determinants. These models are designed to be applied to reported exposure determinants obtained from study subjects or exposure levels assigned by an industrial hygienist, so quantitative exposure estimates can be obtained. ^ In an effort to improve the prediction accuracy and generalizability of these models, and taking into account that the limitations encountered in previous studies might be due to limitations in the applicability of traditional statistical methods and concepts, the use of computer science- derived data analysis methods, predominantly machine learning approaches, were proposed and explored in this study. ^ The goal of this study was to develop a set of models using decision trees/ensemble and neural networks methods to predict occupational outcomes based on literature-derived databases, and compare, using cross-validation and data splitting techniques, the resulting prediction capacity to that of traditional regression models. Two cases were addressed: the categorical case, where the exposure level was measured as an exposure rating following the American Industrial Hygiene Association guidelines and the continuous case, where the result of the exposure is expressed as a concentration value. Previously developed literature-based exposure databases for 1,1,1 trichloroethane, methylene dichloride and, trichloroethylene were used. ^ When compared to regression estimations, results showed better accuracy of decision trees/ensemble techniques for the categorical case while neural networks were better for estimation of continuous exposure values. Overrepresentation of classes and overfitting were the main causes for poor neural network performance and accuracy. Estimations based on literature-based databases using machine learning techniques might provide an advantage when they are applied to other methodologies that combine `expert inputs' with current exposure measurements, like the Bayesian Decision Analysis tool. The use of machine learning techniques to more accurately estimate exposures from literature-based exposure databases might represent the starting point for the independence from the expert judgment.^

The role of specific regions of ribosomal RNAs in translation termination

The ribosome is a molecular machine that produces proteins in a cell. It consists of RNAs (rRNAs) and proteins. The rRNAs have been implicated in various aspects of protein biosynthesis supporting the idea that they function directly in translation. In this study the direct involvement of rRNA in translation termination was hypothesized and both genetic and biochemical strategies were designed to test this hypothesis. As a result, several regions of rRNAs from both ribosomal subunits were implicated in termination. More specifically, the mutation G1093A in an RNA of the large subunit (23S rRNA) and the mutation C1054A in the small subunit RNA (16S rRNA) of the Escherichia coli ribosome, were shown to affect the binding of the proteins that drive termination, RF1 and RF2. These mutations also caused defects in catalysis of peptidyl-tRNA hydrolysis, the last step of termination. Furthermore, the mutations affected the function of RF2 to a greater extent than that of RF1, a striking result considering the similarity of the RFs. The major defect in RF2 function was consistent with in vivo characteristics of the mutants and can be explained by the inability of the mutant rRNA sites to activate the hydrolytic center, that is the catalytic site for peptidyl-tRNA hydrolysis. Consistent with this explanation is the possibility of a direct interaction between the G1093-region (domain II of 23S rRNA) and the hydrolytic center (most likely domains IV–VI of 23S rRNA). To test that interaction hypothesis selections were performed for mutations in domains IV–VI that compensated for the growth defects caused by G1093A. Several compensatory mutations were isolated which not only restored growth in the presence of G1093A but also appeared to compensate for the termination defects caused by the G1093A. Therefore these results provided genetic evidence for an intramolecular interaction that might lead to peptidyl-tRNA hydrolysis. Finally, a new approach to the study of rRNA involvement in termination was designed. By screening a library of rRNA fragments, a fragment of the 23S rRNA (nt 74-136) was identified that caused readthrough of UGA. The antisense RNA fragment produced a similar effect. The data implicated the corresponding segment of intact 23S rRNA in termination. ^