Search

COLL-P155 is an undergraduate public speaking course in which students give speeches on modern public controversies such as capital punishment, abortion, immigration, etc; in other words, issues for which many might hold a definite – at times inflexible – bias. In order to mitigate such biases, the concept of scopus, moving out of one perspective to inhabit another (Arthos, 2017a: Lecture 11), is situated in the goals of the speech assignments and combined with the theoretical and practical benefits of drama pedagogy as illustrated by Even (2008). Following a description of the speech assignments is a pedagogical reflection of activities that combine scopus and drama pedagogy to get students up and out of their seats in order to act out frames of mind that might embody perspectives drastically different from their own. From encouraging ad-hominem attacks in fictitious arguments about favorite foods to highlight the counterproductive and harmful nature of alienating language, to acting out a Grimm’s fairytale from the villain’s perspective to encourage empathy with an unpopular position, the lessons of open-mindedness and civility emphasized in these performative activities can be transferred to discourse surrounding real-world controversies.

After passing through the northeastern gate of University College Cork (UCC), my colleagues and I walk the bridge over the river Lee and onto a campus that, were it not for the beautiful wet, grey stone of its buildings and walkways, could just as well be some arm of an enchanting forest near the heart of the city. Although it is my first time here, the verdant space that opens up before me is both new and familiar; my eyes see a place they haven’t seen before, but in my mind echo years of conversations and expectations that mark this campus as a bastion of performativity. What a fitting venue for the vibrant palette of talks, workshops, panel discussions, and theater performances that graced the 2017 SCENARIO Forum International Conference, held May 25-28, 2017 in Cork, Ireland to mark the 10-year anniversary of the journal's inception! Brainchild of the online SCENARIO journal's co-founders, Susanne Even (Indiana University-Bloomington, USA) and Manfred Schewe (UCC), and aided by the talents of its advisory board members, Eucharia Donnery, Micha Fleiner, Dragan Miladinovic, Róisín O'Gorman, and Erika Piazzoli, as well as a number of volunteers who put the help in helpful, this conference brought a ...

Abstract 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.

Abstract A longitudinal approach is proposed to map QTL affecting function-valued traits and to estimate their effect over time. The method is based on fitting mixed random regression models. The QTL allelic effects are modelled with random coefficient parametric curves and using a gametic relationship matrix. A simulation study was conducted in order to assess the ability of the approach to fit different patterns of QTL over time. It was found that this longitudinal approach was able to adequately fit the simulated variance functions and considerably improved the power of detection of time-varying QTL effects compared to the traditional univariate model. This was confirmed by an analysis of protein yield data in dairy cattle, where the model was able to detect QTL with high effect either at the beginning or the end of the lactation, that were not detected with a simple 305 day model.

Abstract A Gaussian mixture model with a finite number of components and correlated random effects is described. The ultimate objective is to model somatic cell count information in dairy cattle and to develop criteria for genetic selection against mastitis, an important udder disease. Parameter estimation is by maximum likelihood or by an extension of restricted maximum likelihood. A Monte Carlo expectation-maximization algorithm is used for this purpose. The expectation step is carried out using Gibbs sampling, whereas the maximization step is deterministic. Ranking rules based on the conditional probability of membership in a putative group of uninfected animals, given the somatic cell information, are discussed. Several extensions of the model are suggested.

Abstract This study presents a multivariate, variance component-based QTL mapping model implemented via restricted maximum likelihood (REML). The method was applied to investigate bivariate and univariate QTL mapping analyses, using simulated data. Specifically, we report results on the statistical power to detect a QTL and on the precision of parameter estimates using univariate and bivariate approaches. The model and methodology were also applied to study the effectiveness of partitioning the overall genetic correlation between two traits into a component due to many genes of small effect, and one due to the QTL. It is shown that when the QTL has a pleiotropic effect on two traits, a bivariate analysis leads to a higher statistical power of detecting the QTL and to a more precise estimate of the QTL's map position, in particular in the case when the QTL has a small effect on the trait. The increase in power is most marked in cases where the contributions of the QTL and of the polygenic components to the genetic correlation have opposite signs. The bivariate REML analysis can successfully partition the two components contributing to the genetic correlation between traits.

Abstract A fully Bayesian analysis using Gibbs sampling and data augmentation in a multivariate model of Gaussian, right censored, and grouped Gaussian traits is described. The grouped Gaussian traits are either ordered categorical traits (with more than two categories) or binary traits, where the grouping is determined via thresholds on the underlying Gaussian scale, the liability scale. Allowances are made for unequal models, unknown covariance matrices and missing data. Having outlined the theory, strategies for implementation are reviewed. These include joint sampling of location parameters; efficient sampling from the fully conditional posterior distribution of augmented data, a multivariate truncated normal distribution; and sampling from the conditional inverse Wishart distribution, the fully conditional posterior distribution of the residual covariance matrix. Finally, a simulated dataset was analysed to illustrate the methodology. This paper concentrates on a model where residuals associated with liabilities of the binary traits are assumed to be independent. A Bayesian analysis using Gibbs sampling is outlined for the model where this assumption is relaxed.

Abstract 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.

Abstract 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.