Terrestrial feeding in aquatic turtles: environment-dependent feeding behavior modulation and the evolution of terrestrial feeding in Emydidae

Evolutionary transitions between aquatic and terrestrial environments are common in vertebrate evolution. These transitions require major changes in most physiological functions, including feeding. Emydid turtles are ancestrally aquatic, with most species naturally feeding only in water, but some terrestrial species can modulate their feeding behavior appropriately for both media. In addition, many aquatic species can be induced to feed terrestrially. A comparison of feeding in both aquatic and terrestrial environments presents an excellent opportunity to investigate the evolution of terrestrial feeding from aquatic feeding, as well as a system within which to develop methods for studying major evolutionary transitions between environments. Individuals from eight species of emydid turtles (six aquatic, two terrestrial) were filmed while feeding underwater and on land. Bite kinematics were analyzed to determine whether aquatic turtles modulated their feeding behavior in a consistent and appropriate manner between environments. Aquatic turtles showed consistent changes between environments, taking longer bites and using more extensive motions of the jaw and hyoid when feeding on land. However, these motions differ from those shown by species that naturally feed in both environments and mostly do not seem to be appropriate for terrestrial feeding. For example, more extensive motions of the hyoid are only effective during underwater suction feeding. Emydids evolving to feed on land probably would have needed to evolve or learn to overcome many, but not all, aspects of the intrinsic emydid response to terrestrial feeding. Studies that investigate major evolutionary transitions must determine what responses to the new environment are shown by naïve individuals in order to fully understand the evolutionary patterns and processes associated with these transitions.

Temperature-Dependent Defect Dynamics in the Network Glass SiO2

We investigate the long time dynamics of a strong glass former, SiO2, below the glass transition temperature by averaging single-particle trajectories over time windows which comprise roughly 100 particle oscillations. The structure on this coarse-grained time scale is very well defined in terms of coordination numbers, allowing us to identify ill-coordinated atoms, which are called defects in the following. The most numerous defects are O-O neighbors, whose lifetimes are comparable to the equilibration time at low temperature. On the other hand, SiO and OSi defects are very rare and short lived. The lifetime of defects is found to be strongly temperature dependent, consistent with activated processes. Single-particle jumps give rise to local structural rearrangements. We show that in SiO2 these structural rearrangements are coupled to the creation or annihilation of defects, giving rise to very strong correlations of jumping atoms and defects.

Yolk Androstenedione in Domestic Chicks (Gallus Gallus Domesticus): Uptake and Sex-Dependent Alteration of Growth and Behavior

In birds, causes and consequences of variation in maternally-derived steroids in egg yolk have been the subject of intense experimentation. Many studies have quantified or manipulated testosterone ("T") and one of its immediate precursors, androstenedione ("A4") - often lumping the two steroids as "androgens" and treating them as functionally equivalent. However, yolk A4 is deposited in substantially higher concentrations than T, binds only weakly to the androgen receptor, and is readily converted into either T or estrone by steroidogenic enzymes present during embryonic development. Thus it may not be appropriate to assume that A4 has the same effect as T. In addition, A4's metabolic fate is likely to differ between females and males. The goals of this study were to examine the sex-specific uptake and metabolism of yolk A4 and consequences of elevated levels of yolk A4 on development and behavior of domestic chicks. Eggs were injected with 2mu Ci of tritiated androstenedione; radioactivity was detected in all tissues of day 7 and day 16 embryos and found in both aqueous and organics phases of day 7 yolk, with no difference between sexes. A second set of eggs was injected with 125ng of A4. A4 increased growth of morphological traits (tarsus, beak) in females, but not males. A4 males had smaller combs than controls; there was no treatment effect in females. A4 reduced tonic immobility behavior in both sexes. The results of this study illustrate the importance of distinguishing both between androgens and between sexes when investigating avian endocrine maternal effects. Copyright 2013 Elsevier Inc. All rights reserved.

THE RESONATE-AND-FIRE NEURON: TIME DEPENDENT AND FREQUENCY SELECTIVE NEURONS IN NEURAL NETWORKS

The means through which the nervous system perceives its environment is one of the most fascinating questions in contemporary science. Our endeavors to comprehend the principles of neural science provide an instance of how biological processes may inspire novel methods in mathematical modeling and engineering. The application ofmathematical models towards understanding neural signals and systems represents a vibrant field of research that has spanned over half a century. During this period, multiple approaches to neuronal modeling have been adopted, and each approach is adept at elucidating a specific aspect of nervous system function. Thus while bio-physical models have strived to comprehend the dynamics of actual physical processes occurring within a nerve cell, the phenomenological approach has conceived models that relate the ionic properties of nerve cells to transitions in neural activity. Further-more, the field of neural networks has endeavored to explore how distributed parallel processing systems may become capable of storing memory. Through this project, we strive to explore how some of the insights gained from biophysical neuronal modeling may be incorporated within the field of neural net-works. We specifically study the capabilities of a simple neural model, the Resonate-and-Fire (RAF) neuron, whose derivation is inspired by biophysical neural modeling. While reflecting further biological plausibility, the RAF neuron is also analytically tractable, and thus may be implemented within neural networks. In the following thesis, we provide a brief overview of the different approaches that have been adopted towards comprehending the properties of nerve cells, along with the framework under which our specific neuron model relates to the field of neuronal modeling. Subsequently, we explore some of the time-dependent neurocomputational capabilities of the RAF neuron, and we utilize the model to classify logic gates, and solve the classic XOR problem. Finally we explore how the resonate-and-fire neuron may be implemented within neural networks, and how such a network could be adapted through the temporal backpropagation algorithm.

Large Sample Theory for Semiparametric Regression Models with Two-Phase, Outcome Dependent Sampling

Outcome-dependent, two-phase sampling designs can dramatically reduce the costs of observational studies by judicious selection of the most informative subjects for purposes of detailed covariate measurement. Here we derive asymptotic information bounds and the form of the efficient score and influence functions for the semiparametric regression models studied by Lawless, Kalbfleisch, and Wild (1999) under two-phase sampling designs. We show that the maximum likelihood estimators for both the parametric and nonparametric parts of the model are asymptotically normal and efficient. The efficient influence function for the parametric part aggress with the more general information bound calculations of Robins, Hsieh, and Newey (1995). By verifying the conditions of Murphy and Van der Vaart (2000) for a least favorable parametric submodel, we provide asymptotic justification for statistical inference based on profile likelihood.

A Nonstationary Negative Binomial Time Series with Time-Dependent Covariates: Enterococcus Counts in Boston Harbor

Boston Harbor has had a history of poor water quality, including contamination by enteric pathogens. We conduct a statistical analysis of data collected by the Massachusetts Water Resources Authority (MWRA) between 1996 and 2002 to evaluate the effects of court-mandated improvements in sewage treatment. Motivated by the ineffectiveness of standard Poisson mixture models and their zero-inflated counterparts, we propose a new negative binomial model for time series of Enterococcus counts in Boston Harbor, where nonstationarity and autocorrelation are modeled using a nonparametric smooth function of time in the predictor. Without further restrictions, this function is not identifiable in the presence of time-dependent covariates; consequently we use a basis orthogonal to the space spanned by the covariates and use penalized quasi-likelihood (PQL) for estimation. We conclude that Enterococcus counts were greatly reduced near the Nut Island Treatment Plant (NITP) outfalls following the transfer of wastewaters from NITP to the Deer Island Treatment Plant (DITP) and that the transfer of wastewaters from Boston Harbor to the offshore diffusers in Massachusetts Bay reduced the Enterococcus counts near the DITP outfalls.

Mixture Cure Survival Models with Dependent Censoring

A number of authors have studies the mixture survival model to analyze survival data with nonnegligible cure fractions. A key assumption made by these authors is the independence between the survival time and the censoring time. To our knowledge, no one has studies the mixture cure model in the presence of dependent censoring. To account for such dependence, we propose a more general cure model which allows for dependent censoring. In particular, we derive the cure models from the perspective of competing risks and model the dependence between the censoring time and the survival time using a class of Archimedean copula models. Within this framework, we consider the parameter estimation, the cure detection, and the two-sample comparison of latency distribution in the presence of dependent censoring when a proportion of patients is deemed cured. Large sample results using the martingale theory are obtained. We applied the proposed methodologies to the SEER prostate cancer data.

Causal Inference in Observational Studies with Outcome-Dependent Sampling

In this paper, we consider estimation of the causal effect of a treatment on an outcome from observational data collected in two phases. In the first phase, a simple random sample of individuals are drawn from a population. On these individuals, information is obtained on treatment, outcome, and a few low-dimensional confounders. These individuals are then stratified according to these factors. In the second phase, a random sub-sample of individuals are drawn from each stratum, with known, stratum-specific selection probabilities. On these individuals, a rich set of confounding factors are collected. In this setting, we introduce four estimators: (1) simple inverse weighted, (2) locally efficient, (3) doubly robust and (4)enriched inverse weighted. We evaluate the finite-sample performance of these estimators in a simulation study. We also use our methodology to estimate the causal effect of trauma care on in-hospital mortality using data from the National Study of Cost and Outcomes of Trauma.

Guest Speaker

David Salmela is the special guest speaker for the opening reception.