Predicting failure in mammalian enamel

Dentition is a vital element of human and animal function, yet there is little fundamental knowledge about how tooth enamel endures under stringent oral conditions. This paper describes a novel approach to the issue. Model glass dome specimens fabricated from glass and back­filled with polymer resin are used as representative of the basic enamel/dentine shell structure. Contact loading is used to deform the dome structures to failure, in simulation of occlusal loading with opposing dentition or food bolus. To investigate the role of enamel microstructure, additional contact tests are conducted on two­phase materials that capture the essence of the mineralized­rod/organic­sheath structure of dental enamel. These materials include dental glass­ceramics and biomimicked composites fabricated from glass fibers infiltrated with epoxy. The tests indicate how enamel is likely to deform and fracture along easy sliding and fracture paths within the binding phase between the rods. Analytical relations describing the critical loads for each damage mode are presented in terms of material properties (hardness, modulus, toughness) and tooth geometry variables (enamel thickness, cusp radius). Implications in dentistry and evolutionary biology are discussed.

Survival Analysis with Large Dimensional Covariates: An Application in Microarray Studies

Use of microarray technology often leads to high-dimensional and low- sample size data settings. Over the past several years, a variety of novel approaches have been proposed for variable selection in this context. However, only a small number of these have been adapted for time-to-event data where censoring is present. Among standard variable selection methods shown both to have good predictive accuracy and to be computationally efficient is the elastic net penalization approach. In this paper, adaptation of the elastic net approach is presented for variable selection both under the Cox proportional hazards model and under an accelerated failure time (AFT) model. Assessment of the two methods is conducted through simulation studies and through analysis of microarray data obtained from a set of patients with diffuse large B-cell lymphoma where time to survival is of interest. The approaches are shown to match or exceed the predictive performance of a Cox-based and an AFT-based variable selection method. The methods are moreover shown to be much more computationally efficient than their respective Cox- and AFT- based counterparts.