The Influence of Reproductive Experience on Milk Energy Output and Lactation Performance in the Grey Seal (Halichoerus grypus)

Although evidence from domestic and laboratory species suggests that reproductive experience plays a critical role in the development of aspects of lactation performance, whether reproductive experience may have a significant influence on milk energy transfer to neonates in wild populations has not been directly investigated. We compared maternal energy expenditures and pup growth and energy deposition over the course of lactation between primiparous and fully-grown, multiparous grey seal (Halichoerus grypus) females to test whether reproductive experience has a significant influence on lactation performance. Although there was no difference between primiparous females in milk composition and, thus, milk energy content at either early or peak lactation primiparous females had a significantly lower daily milk energy output than multiparous females indicating a reduced physiological capacity for milk secretion

Evolution of sex-specific pace-of-life syndromes: genetic architecture and physiological mechanisms

Sex differences in life history, physiology, and behavior are nearly ubiquitous across taxa, owing to sex-specific selection that arises from different reproductive strategies of the sexes. The pace-of-life syndrome (POLS) hypothesis predicts that most variation in such traits among individuals, populations, and species falls along a slow-fast pace-of-life continuum. As a result of their different reproductive roles and environment, the sexes also commonly differ in pace-of-life, with important consequences for the evolution of POLS. Here, we outline mechanisms for how males and females can evolve differences in POLS traits and in how such traits can covary differently despite constraints resulting from a shared genome. We review the current knowledge of the genetic basis of POLS traits and suggest candidate genes and pathways for future studies. Pleiotropic effects may govern many of the genetic correlations, but little is still known about the mechanisms involved in trade-offs between current and future reproduction and their integration with behavioral variation. We highlight the importance of metabolic and hormonal pathways in mediating sex differences in POLS traits; however, there is still a shortage of studies that test for sex specificity in molecular effects and their evolutionary causes. Considering whether and how sexual dimorphism evolves in POLS traits provides a more holistic framework to understand how behavioral variation is integrated with life histories and physiology, and we call for studies that focus on examining the sex-specific genetic architecture of this integration

An interactive framework for visualizing foreign currency exchange options

Analyzing options is a complex, multi-variate process. Option behavior depends on a variety of market conditions which vary over the time course of the option. The goal of this project is to provide an interactive visual environment which allows the analyst to explore these complex interactions, and to select and construct specific views for communicating information to non-analysts (e.g., marketing managers and customers). In this paper we describe an environment for exploring 2- and 3-dimensional representations of options data, dynamically varying parameters, examining how multi-variate relationships develop over time, and exploring the likelihood of the development of different outcomes over the life of the option. We also demonstrate how this tool has been used by analysts to communicate to nonanalysts how particular options no longer deliver the behavior they were originally intended to provide

Mimicking human texture classification

In an attempt to mimic human (colorful) texture classification by a clustering algorithm three lines of research have been encountered, in which as test set 180 texture images (both their color and gray-scale equivalent) were drawn from the OuTex and VisTex databases. First, a k-means algorithm was applied with three feature vectors, based on color/gray values, four texture features, and their combination. Second, 18 participants clustered the images using a newly developed card sorting program. The mutual agreement between the participants was 57% and 56% and between the algorithm and the participants it was 47% and 45%, for respectively color and gray-scale texture images. Third, in a benchmark, 30 participants judged the algorithms’ clusters with gray-scale textures as more homogeneous then those with colored textures. However, a high interpersonal variability was present for both the color and the gray-scale clusters. So, despite the promising results, it is questionable whether average human texture classification can be mimicked (if it exists at all)