Thalidomide-induced teratogenesis in gray short-tailed opossums (Monodelphis domestica)

From 1957 to 1962, Thalidomide was prescribed to pregnant women as an antiemetic and was later discovered to cause serious birth defects. Thalidomide is currently being prescribed to treat erythema nodosum leprosum and multiple melanoma, and is being considered for other anti-angiogenic applications. This is of concern, as the mechanisms by which thalidomide disrupts development remain largely unknown. This lack of knowledge is largely due to the absence of an appropriate model mammalian system. Traditional mammal models, such as rats and mice, are resistant to the teratogenic properties of thalidomide. We propose to develop the gray short-tailed opossum (Monodelphis domestica) as a novel mammalian to study thalidomide teratogenesis. In M. domestica we have successfully recreated most morphological defects found in human thalidomide victims. Through RNA sequencing, we have also identified two gene that may play a significant role in the mechanism of thalidomide induced birth defects

On the need for a control line in selection experiments: A likelihood analysis

The question of whether selection experiments ought to include a control line, as opposed to investing all facilities in a single selected line, is addressed using a likelihood perspective. The consequences of using a control line are evaluated under two scenarios. In the first one, environmental trend is modeled and inferred from the data. In this case, a control line is shown to be highly beneficial in terms of the efficiency of inferences about eheritability and response to selection. In the second scenario, environmental trend is not modeled. One can imagine that a previous analysis of the experimental data had lent support to this decision. It is shown that in this situation where a control line may seem superfluous, inclusion of a control line can result in minor gains in efficiency if a high selection intensity is practiced in the selected line. Further, if there is a loss, it is moderately small. The results are verified to hold under more complicated data structures via Monte Carlo simulation. For completeness, divergent selection designs are also reviewed, and inferences based on a conditional and full likelihood approach are contrasted

The Austin Archives Bazaar: A collaborative outreach event

The Austin Archives Bazaar (AAB) is a biennial, multi-institutional, community outreach event organized by the Archivists of Central Texas (ACT), an all-volunteer group of archivists in Austin, Texas. It is designed to be free, fun, and appealing to the general public, including folks who may not even know exactly what an archives is. This paper looks at the planning and execution of the 2016 Bazaar and reflects back on how it built on lessons learned in 2014 with a focus on issues of governance, fundraising, publicity, logistics, and the participating repository perspective. This case study of a creative, multi-institutional outreach event can provide valuable lessons and ideas for outreach events at the regional, city, or repository level

Alternative implementations of Monte Carlo EM algorithms for likelihood inferences

Two methods of computing Monte Carlo estimators of variance components using restricted maximum likelihood via the expectation-maximisation algorithm are reviewed. A third approach is suggested and the performance of the methods is compared using simulated data

A comparison of strategies for Markov chain Monte Carlo computation in quantitative genetics

In quantitative genetics, Markov chain Monte Carlo (MCMC) methods are indispensable for statistical inference in non-standard models like generalized linear models with genetic random effects or models with genetically structured variance heterogeneity. A particular challenge for MCMC applications in quantitative genetics is to obtain efficient updates of the high-dimensional vectors of genetic random effects and the associated covariance parameters. We discuss various strategies to approach this problem including reparameterization, Langevin-Hastings updates, and updates based on normal approximations. The methods are compared in applications to Bayesian inference for three data sets using a model with genetically structured variance heterogeneity