On the accumulation of deleterious mutations during range expansions

We investigate the effect of spatial range expansions on the evolution of fitness when beneficial and deleterious mutations co-segregate. We perform individual-based simulations of a uniform linear habitat and complement them with analytical approximations for the evolution of mean fitness at the edge of the expansion. We find that deleterious mutations accumulate steadily on the wave front during range expansions, thus creating an expansion load. Reduced fitness due to the expansion load is not restricted to the wave front but occurs over a large proportion of newly colonized habitats. The expansion load can persist and represent a major fraction of the total mutation load thousands of generations after the expansion. Our results extend qualitatively and quantitatively to two-dimensional expansions. The phenomenon of expansion load may explain growing evidence that populations that have recently expanded, including humans, show an excess of deleterious mutations. To test the predictions of our model, we analyze patterns of neutral and non-neutral genetic diversity in humans and find an excellent fit between theory and data

Detection of Johne\u27s disease in an Iowa (United States) dairy herd : comparisons of the milk ELISA, serum ELISA, Gamma-Interferon and fecal culture tests and the effect of a skin-test using a cell-free sonicate of Mycobacterium avium subsp. paratuberculosis (19698) on the production of Gamma-Interferon

Analysis using kappa returned a value of -0.116 indicated a very low percentage of agreement between the two testing means. With the SELISA being the regulatory US standard it is not recommended to use the MELISA for purposes of culling decisions. Results of the skin testing portion of the study indicates that inoculation of animals with 19698 MpS significantly increased production of [Gamma]-IFN in cattle declared to have positive (MpS-P) baseline [Gamma]-IFN responses for Johne\u27s disease. Peak production of [Gamma]-IFN occurred at 144 hr following skin testing and was statistically significant P = 0.0005 as compared to day 0. Levels of [Gamma]-IFN dropped on day 9, but remained at levels that were significantly higher than day 0 (P = 0.0325). Levels of [Gamma]-IFN again significantly increased on day 27 when compared to day 15 (P = 0.0362), and was significantly higher than day 0 (P = 0.0004). Further research is warranted to determine if the addition of skin testing to the [Gamma]-IFN assay could increase test sensitivity or durability.Cattle from the Iowa State University, Ames dairy herd were characterized for Johne\u27s disease using results from the milk ELISA (MELISA) (Dairy Lab Services; Dubuque, IA), serum ELISA (SELISA) (IDEXX Laboratories, Inc., Westbrook, ME), [Gamma]-IFN assay (CSL Limited Parkville Victoria Australia) and fecal culture. Using the results of the initial [Gamma]-IFN assay and the SELISA determination, all herd members were divided into four groups consisting of: a negative SELISA and [Gamma]-IFN, suspect [Gamma]-IFN, positive [Gamma]-IFN and SELISA positive individuals. The first three groups were enrolled in the skin testing trial and were randomly assigned within group to one of two treatments. This consisted of an injection of either 100[Mu]g (0.1m1) of a M. paratuberculosis strain 19698 cell free sonicate (19698 MpS) or 0.1 ml of 0.9% saline solution as an intradermal injection. Blood samples were obtained on days 0, 3, 6, 9, 15, and 27 days post-injection for [Gamma]-IFN assay, while skin lesion measurements were made on days 0, 3, 6 and 9. The MELISA test returned the greatest number of test positive individuals at 12.96%, The SELISA was 7.41% test positive, [Gamma]-IFN assay was 4.40%, and fecal culture yielded 0% positive. Further evaluation indicated the SELISA had a significantly lower age (37.2 vs. 50.5 and 48.3 months respectively) at positive test (P\u3e [T] = 0.0192) than did MELISA or [Gamma]-IFN. Due to the low number of fecal culture and [Gamma]-IFN positive individuals, and the significantly lower age at positive test it could be suspect that the SELISA had an inordinate number of false positive individuals. MELISA and SELISA results were compared using an XY plot. No common individuals were identified as test positive by both tests

Restricted maximum likelihood estimation of genetic principal components and smoothed covariance matrices

Principal component analysis is a widely used 'dimension reduction' technique, albeit generally at a phenotypic level. It is shown that we can estimate genetic principal components directly through a simple reparameterisation of the usual linear, mixed model. This is applicable to any analysis fitting multiple, correlated genetic effects, whether effects for individual traits or sets of random regression coefficients to model trajectories. Depending on the magnitude of genetic correlation, a subset of the principal component generally suffices to capture the bulk of genetic variation. Corresponding estimates of genetic covariance matrices are more parsimonious, have reduced rank and are smoothed, with the number of parameters required to model the dispersion structure reduced from k(k + 1)/2 to m(2k - m + 1)/2 for k effects and m principal components. Estimation of these parameters, the largest eigenvalues and pertaining eigenvectors of the genetic covariance matrix, via restricted maximum likelihood using derivatives of the likelihood, is described. It is shown that reduced rank estimation can reduce computational requirements of multivariate analyses substantially. An application to the analysis of eight traits recorded via live ultrasound scanning of beef cattle is given

Evaluation of Drug Concentrations Delivered by Microiontophoresis

Microiontophoresis uses an electric current to eject a drug solution from a glass capillary and is often utilized for targeted delivery in neurochemical investigations. The amount of drug ejected, and its effective concentration at the tip, has historically been difficult to determine, which has precluded its use in quantitative studies. To address this, a method called controlled iontophoresis was developed which employs a carbon-fiber microelectrode incorporated into a multibarreled iontophoretic probe to detect the ejection of electroactive species. Here, we evaluate the accuracy of this method. To do this, we eject different concentrations of quinpirole, a D2 receptor agonist, into a brain slice containing the dorsal striatum, a brain region with a high density of dopamine terminals. Local electrical stimulation was used to evoke dopamine release, and inhibitory actions of quinpirole on this release were examined. The amount of drug ejected was estimated by detection of a coejected electrochemical mar..

Why HR is set to fail the big data challenge

Few organisations have made much progress in developing HR analytics capabilities, write Andy Charlwood, Mark Stuart, Ian Kirkpatrick and Mark T Lawrenc

COMPROF and COMPLACE : shared-memory communication profiling and automated thread placement via dynamic binary instrumentation

Funding: This work was generously supported by UK EPSRC Energise, grant number EP/V006290/1.This paper presents COMPROF and COMPLACE, a novel profiling tool and thread placement technique for shared-memory architectures that requires no recompilation or user intervention. We use dynamic binary instrumentation to intercept memory operations and estimate inter-thread communication overhead, deriving (and possibly visualising) a communication graph of data-sharing between threads. We then use this graph to map threads to cores in order to optimise memory traffic through the memory system. Different paths through a system's memory hierarchy have different latency, throughput and energy properties, COMPLACE exploits this heterogeneity to provide automatic performance and energy improvements for multi-threaded programs. We demonstrate COMPLACE on the NAS Parallel Benchmark (NPB) suite where, using our technique, we are able to achieve improvements of up to 12% in the execution time and up to 10% in the energy consumption (compared to default Linux scheduling) while not requiring any modification or recompilation of the application code.Postprin

Sex Differences in Recombination in Sticklebacks.

Recombination often differs markedly between males and females. Here we present the first analysis of sex-specific recombination in Gasterosteus sticklebacks. Using whole-genome sequencing of 15 crosses between G. aculeatus and G. nipponicus, we localized 698 crossovers with a median resolution of 2.3 kb. We also used a bioinformatic approach to infer historical sex-averaged recombination patterns for both species. Recombination is greater in females than males on all chromosomes, and overall map length is 1.64 times longer in females. The locations of crossovers differ strikingly between sexes. Crossovers cluster toward chromosome ends in males, but are distributed more evenly across chromosomes in females. Suppression of recombination near the centromeres in males causes crossovers to cluster at the ends of long arms in acrocentric chromosomes, and greatly reduces crossing over on short arms. The effect of centromeres on recombination is much weaker in females. Genomic differentiation between G. aculeatus and G. nipponicus is strongly correlated with recombination rate, and patterns of differentiation along chromosomes are strongly influenced by male-specific telomere and centromere effects. We found no evidence for fine-scale correlations between recombination and local gene content in either sex. We discuss hypotheses for the origin of sexual dimorphism in recombination and its consequences for sexually antagonistic selection and sex chromosome evolution

Visualizing genetic constraints

Principal Components Analysis (PCA) is a common way to study the sources of variation in a high-dimensional data set. Typically, the leading principal components are used to understand the variation in the data or to reduce the dimension of the data for subsequent analysis. The remaining principal components are ignored since they explain little of the variation in the data. However, evolutionary biologists gain important insights from these low variation directions. Specifically, they are interested in directions of low genetic variability that are biologically interpretable. These directions are called genetic constraints and indicate directions in which a trait cannot evolve through selection. Here, we propose studying the subspace spanned by low variance principal components by determining vectors in this subspace that are simplest. Our method and accompanying graphical displays enhance the biologist's ability to visualize the subspace and identify interpretable directions of low genetic variability that align with simple directions.Comment: Published in at http://dx.doi.org/10.1214/12-AOAS603 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org