Enamel Formation Genes Influence Enamel Microhardness Before and After Cariogenic Challenge

There is evidence for a genetic component in caries susceptibility, and studies in humans have suggested that variation in enamel formation genes may contribute to caries. For the present study, we used DNA samples collected from 1,831 individuals from various population data sets. Single nucleotide polymorphism markers were genotyped in selected genes (ameloblastin, amelogenin, enamelin, tuftelin, and tuftelin interacting protein 11) that influence enamel formation. Allele and genotype frequencies were compared between groups with distinct caries experience. Associations with caries experience can be detected but they are not necessarily replicated in all population groups and the most expressive results was for a marker in AMELX (p = 0.0007). To help interpret these results, we evaluated if enamel microhardness changes under simulated cariogenic challenges are associated with genetic variations in these same genes. After creating an artificial caries lesion, associations could be seen between genetic variation in TUFT1 (p = 0.006) and TUIP11 (p = 0.0006) with enamel microhardness. Our results suggest that the influence of genetic variation of enamel formation genes may influence the dynamic interactions between the enamel surface and the oral cavity. © 2012 Shimizu et al

Modelling and parametric study of the re-anchorage of ruptured tendons in bonded post-tensioned concrete

The contribution of ruptured tendons to the residual strength of bonded post-tensioned concrete structures is currently assessed based on pre-tensioned concrete bond models. However, this approach is inaccurate due to the inherent differences between pre-tensioned and post-tensioned concrete. In this paper, a non-linear 3D finite element model is developed for the re-anchoring of a ruptured tendon in post-tensioned concrete. The model is validated using full-field displacement measurement from 33 post-tensioned concrete prisms and previous experimental data on beams from the literature. The influence of different parameters was investigated, including tendon properties (i.e. diameter, roughness), duct properties (i.e. diameter, thickness, material), initial prestress, concrete strength, grout strength, grout voids, stirrups, and strands, on the tendon re-anchorage. The most influential parameters are found to be tendon and duct properties

Enamel Formation Genes Influence Enamel Microhardness Before and After Cariogenic Challenge

Abstract There is evidence for a genetic component in caries susceptibility, and studies in humans have suggested that variation in enamel formation genes may contribute to caries. For the present study, we used DNA samples collected from 1,831 individuals from various population data sets. Single nucleotide polymorphism markers were genotyped in selected genes (ameloblastin, amelogenin, enamelin, tuftelin, and tuftelin interacting protein 11) that influence enamel formation. Allele and genotype frequencies were compared between groups with distinct caries experience. Associations with caries experience can be detected but they are not necessarily replicated in all population groups and the most expressive results was for a marker in AMELX (p = 0.0007). To help interpret these results, we evaluated if enamel microhardness changes under simulated cariogenic challenges are associated with genetic variations in these same genes. After creating an artificial caries lesion, associations could be seen between genetic variation in TUFT1 (p = 0.006) and TUIP11 (p = 0.0006) with enamel microhardness. Our results suggest that the influence of genetic variation of enamel formation genes may influence the dynamic interactions between the enamel surface and the oral cavity

Evaluating Forecasting Methods

Ideally, forecasting methods should be evaluated in the situations for which they will be used. Underlying the evaluation procedure is the need to test methods against reasonable alternatives. Evaluation consists of four steps: testing assumptions, testing data and methods, replicating outputs, and assessing outputs. Most principles for testing forecasting methods are based on commonly accepted methodological procedures, such as to prespecify criteria or to obtain a large sample of forecast errors. However, forecasters often violate such principles, even in academic studies. Some principles might be surprising, such as do not use R-square, do not use Mean Square Error, and do not use the within-sample fit of the model to select the most accurate time-series model. A checklist of 32 principles is provided to help in systematically evaluating forecasting methods