Dentin tubule numerical density variations below the CEJ

Aim: To evaluate dentin tubule numerical density variations below the CEJ. Methodology: Three human non-carious permanent canines were sectioned parallel to the CEJ to obtain dentin disks 1 mm thick whose surfaces were 1 mm and 2 mm below the CEJ. Each disk was sectioned into quarters resulting in four segment locations: facial, lingual, mesial, and distal. The outer (PDL side) and inner (pulp side) surfaces of the specimens were shaped to expose dentin with SiC papers and polished. Numerical tubule density was determined from SEM images. All data were statistically analyzed using a three-way ANOVA. Results: The dentin tubule density (number/mm2) ranged from 13,700 to 32,300. Dentin tubule density was relatively uniform at 1 mm and 2 mm below the CEJ and increased by a factor of about two from the outer to the inner surface, which was significantly different (P < 0.0001). Conclusions: The tubule density variations at the cervical root did not present marked

Towards Machine Wald

The past century has seen a steady increase in the need of estimating and predicting complex systems and making (possibly critical) decisions with limited information. Although computers have made possible the numerical evaluation of sophisticated statistical models, these models are still designed \emph{by humans} because there is currently no known recipe or algorithm for dividing the design of a statistical model into a sequence of arithmetic operations. Indeed enabling computers to \emph{think} as \emph{humans} have the ability to do when faced with uncertainty is challenging in several major ways: (1) Finding optimal statistical models remains to be formulated as a well posed problem when information on the system of interest is incomplete and comes in the form of a complex combination of sample data, partial knowledge of constitutive relations and a limited description of the distribution of input random variables. (2) The space of admissible scenarios along with the space of relevant information, assumptions, and/or beliefs, tend to be infinite dimensional, whereas calculus on a computer is necessarily discrete and finite. With this purpose, this paper explores the foundations of a rigorous framework for the scientific computation of optimal statistical estimators/models and reviews their connections with Decision Theory, Machine Learning, Bayesian Inference, Stochastic Optimization, Robust Optimization, Optimal Uncertainty Quantification and Information Based Complexity.Comment: 37 page

Preferences and utilities for the symptoms of moderate to severe allergic asthma

Asthma, Valuation, Willingness to pay (WTP), Utility, Preference,