The examination and identification of bite marks in foods using 3D scanning and 3D comparison methods

Bite mark analysis offers the opportunity to identify the biter based on the individual characteristics of the dentitions. Normally, the main focus is on analysing bite mark injuries on human bodies, but also, bite marks in food may play an important role in the forensic investigation of a crime. This study presents a comparison of simulated bite marks in different kinds of food with the dentitions of the presumed biter. Bite marks were produced by six adults in slices of buttered bread, apples, different kinds of Swiss chocolate and Swiss cheese. The time-lapse influence of the bite mark in food, under room temperature conditions, was also examined. For the documentation of the bite marks and the dentitions of the biters, 3D optical surface scanning technology was used. The comparison was performed using two different software packages: the ATOS modelling and analysing software and the 3D studio max animation software. The ATOS software enables an automatic computation of the deviation between the two meshes. In the present study, the bite marks and the dentitions were compared, as well as the meshes of each bite mark which were recorded in the different stages of time lapse. In the 3D studio max software, the act of biting was animated to compare the dentitions with the bite mark. The examined food recorded the individual characteristics of the dentitions very well. In all cases, the biter could be identified, and the dentitions of the other presumed biters could be excluded. The influence of the time lapse on the food depends on the kind of food and is shown on the diagrams. However, the identification of the biter could still be performed after a period of time, based on the recorded individual characteristics of the dentitions.

The Importance of Scale for Spatial-confounding Bias and Precision of Spatial Regression Estimators

Increasingly, regression models are used when residuals are spatially correlated. Prominent examples include studies in environmental epidemiology to understand the chronic health effects of pollutants. I consider the effects of residual spatial structure on the bias and precision of regression coefficients, developing a simple framework in which to understand the key issues and derive informative analytic results. When the spatial residual is induced by an unmeasured confounder, regression models with spatial random effects and closely-related models such as kriging and penalized splines are biased, even when the residual variance components are known. Analytic and simulation results show how the bias depends on the spatial scales of the covariate and the residual; bias is reduced only when there is variation in the covariate at a scale smaller than the scale of the unmeasured confounding. I also discuss how the scales of the residual and the covariate affect efficiency and uncertainty estimation when the residuals can be considered independent of the covariate. In an application on the association between black carbon particulate matter air pollution and birth weight, controlling for large-scale spatial variation appears to reduce bias from unmeasured confounders, while increasing uncertainty in the estimated pollution effect.