The origins of postmating reproductive isolation: testing hypotheses in the grasshopper Chorthippus parallelus

Although there are several well-established hypotheses for the origins of postmating isolation during allopatric divergence, there have been very few attempts, to determine their relative importance in nature. We have developed an approach based on knowledge of the differing evolutionary histories of populations within species that allows systematic comparison of the predictions of these hypotheses. In previous work, we have applied this methodology to mating signal variation and premating reproductive isolation between populations of the meadow grasshopper Chorthippus parallelus. Here we review the principles behind our approach and report a study measuring postmating isolation in the same set of populations. The populations have known and differing evolutionary histories and relationships resulting from the colonization of northern Europe following the last glaciation. We use a maximum-likelihood analysis to compare the observed pattern of postmating isolation with the predictions of the hypotheses that isolation primarily evolves either as a result of gradual accumulation of mutations in allopatry, or through processes associated with colonization, such as founder events., We also quantify the extent to which degree of postmating isolation can be predicted by genetic distance. Our results suggest that although there is only a weak correlation between genetic distance and postmating isolation, long periods of allopatry do lead to postmating isolation. In contrast to the pattern of premating isolation described in our previous study, colonization does not seem to be associated with increased postmating isolation

B cell receptor accessory molecule CD79α : characterisation and expression analysis in a cartilaginous fish, the spiny dogfish (Squalus acanthias)

Copyright © 2013. Published by Elsevier Ltd.Peer reviewedPublisher PD

NUMERAIRE CHOICE IN AGRICULTURAL SUPPLY ANALYSIS

Economic theory provides little guidance for selecting a numeraire in estimating dual function profit functions. In this study, we examine the choice of numeraire price (equation) for profit function models of Iowa agriculture. The choice of numeraire is evaluated by forecasting accuracy and with a non-nested specification test.Research Methods/ Statistical Methods,

XML document design via GN-DTD

Designing a well-structured XML document is important for the sake of readability and maintainability. More importantly, this will avoid data redundancies and update anomalies when maintaining a large quantity of XML based documents. In this paper, we propose a method to improve XML structural design by adopting graphical notations for Document Type Definitions (GN-DTD), which is used to describe the structure of an XML document at the schema level. Multiples levels of normal forms for GN-DTD are proposed on the basis of conceptual model approaches and theories of normalization. The normalization rules are applied to transform a poorly designed XML document into a well-designed based on normalized GN-DTD, which is illustrated through examples

A Seeded Genetic Algorithm for RNA Secondary Structural Prediction with Pseudoknots

This work explores a new approach in using genetic algorithm to predict RNA secondary structures with pseudoknots. Since only a small portion of most RNA structures is comprised of pseudoknots, the majority of structural elements from an optimal pseudoknot-free structure are likely to be part of the true structure. Thus seeding the genetic algorithm with optimal pseudoknot-free structures will more likely lead it to the true structure than a randomly generated population. The genetic algorithm uses the known energy models with an additional augmentation to allow complex pseudoknots. The nearest-neighbor energy model is used in conjunction with Turner’s thermodynamic parameters for pseudoknot-free structures, and the H-type pseudoknot energy estimation for simple pseudoknots. Testing with known pseudoknot sequences from PseudoBase shows that it out performs some of the current popular algorithms

New Burnout Evaluation Model Based on the Brief Burnout Questionnaire: Psychometric Properties for Nursing

Health care personnel are considered one of the worker sectors most exposed to heavier workloads and work stress. One of the consequences associated with the exposure to chronic stress is the development of burnout syndrome. Given that evaluating this syndrome requires addressing the context in which they are to be used, the purpose of this work was to analyze the psychometric properties and structure of the Burnout Brief Questionnaire (CBB), and to propose a more suitable version for its application to health professionals, and more specifically nurses. The final study sample was made up of 1236 working nursing professionals. An exploratory factorial analysis was carried out and a new model was proposed through a confirmatory factorial analysis. Thus, validation of the CBB questionnaire for nursing health care personnel showed an adequate discrimination of the items and a high internal consistency of the scale. With respect to the factorial analysis, four factors were extracted from the revised model. Specifically, these new factors, called job dissatisfaction, social climate, personal impact, and motivational abandonment, showed an adequate index of adjustment. Thus, the Brief Burnout Questionnaire Revised for nursing staff has favorable psychometric properties, and this model can be applied to all health care professionals

Penalized EM algorithm and copula skeptic graphical models for inferring networks for mixed variables

In this article, we consider the problem of reconstructing networks for continuous, binary, count and discrete ordinal variables by estimating sparse precision matrix in Gaussian copula graphical models. We propose two approaches: $\ell_1$ penalized extended rank likelihood with Monte Carlo Expectation-Maximization algorithm (copula EM glasso) and copula skeptic with pair-wise copula estimation for copula Gaussian graphical models. The proposed approaches help to infer networks arising from nonnormal and mixed variables. We demonstrate the performance of our methods through simulation studies and analysis of breast cancer genomic and clinical data and maize genetics data

Managing in conflict: How actors distribute conflict in an industrial network

IMP researchers have examined conflict as a threat to established business relationships and commercial exchanges, drawing on theories and concepts developed in organization studies. We examine cases of conflict in relationships from the oil and gas industry's service sector, focusing on conflicts of interest and resources, and conflict as experienced by actors. Through a comparative case study design, we propose an explanation of how actors manage conflict and manage in conflict given that they tend to value and maintain relationships beyond episodes of exchange. We consider conflicts in relationships from a network perspective, showing that actors experienced these while adapting to changes in their business setting, modifying their roles in that network. By identifying conflict with the organizing forms of relationship and network, we show how actors formulate conflict through pursuing and combining a number of strategies, distributing the conflict across an enlarged network

The Role of Promotion Programs for U.S. Poultry Exports

This study examines the effectiveness of price versus nonprice promotion programs for U.S. poultry exports. A comparative static simulation framework is specified for this purpose. The elasticities needed for the simulation model are estimated using seemingly unrelated regression and time-varying parameter regression techniques. Results from this study indicate that a price subsidy is more effective than nonprice market promotion programs in raising export demand for U.S. poultry.International Relations/Trade,