Adaptation to hard-object feeding in sea otters and hominins

The large, bunodont postcanine teeth in living sea otters (Enhydra lutris) have been likened to those of certain fossil hominins, particularly the ’robust’ australopiths (genus Paranthropus). We examine this evolutionary convergence by conducting fracture experiments on extracted molar teeth of sea otters and modern humans (Homo sapiens) to determine how load-bearing capacity relates to tooth morphology and enamel material properties. In situ optical microscopy and x-ray imaging during simulated occlusal loading reveal the nature of the fracture patterns. Explicit fracture relations are used to analyze the data and to extrapolate the results from humans to earlier hominins. It is shown that the molar teeth of sea otters have considerably thinner enamel than those of humans, making sea otter molars more susceptible to certain kinds of fractures. At the same time, the base diameter of sea otter first molars is larger, diminishing the fracture susceptibility in a compensatory manner. We also conduct nanoindentation tests to map out elastic modulus and hardness of sea otter and human molars through a section thickness, and microindentation tests to measure toughness. We find that while sea otter enamel is just as stiff elastically as human enamel, it is a little softer and tougher. The role of these material factors in the capacity of dentition to resist fracture and deformation is considered. From such comparisons, we argue that early hominin species like Paranthropus most likely consumed hard food objects with substantially higher biting forces than those exerted by modern humans.

Modeling multilevel sleep transitional data via Poisson log-linear multilevel models

This paper proposes Poisson log-linear multilevel models to investigate population variability in sleep state transition rates. We specifically propose a Bayesian Poisson regression model that is more flexible, scalable to larger studies, and easily fit than other attempts in the literature. We further use hierarchical random effects to account for pairings of individuals and repeated measures within those individuals, as comparing diseased to non-diseased subjects while minimizing bias is of epidemiologic importance. We estimate essentially non-parametric piecewise constant hazards and smooth them, and allow for time varying covariates and segment of the night comparisons. The Bayesian Poisson regression is justified through a re-derivation of a classical algebraic likelihood equivalence of Poisson regression with a log(time) offset and survival regression assuming piecewise constant hazards. This relationship allows us to synthesize two methods currently used to analyze sleep transition phenomena: stratified multi-state proportional hazards models and log-linear models with GEE for transition counts. An example data set from the Sleep Heart Health Study is analyzed.