Further Wolf-Rayet stars in the starburst cluster Westerlund 1

We present new low and intermediate-resolution spectroscopic observations of the Wolf Rayet (WR) star population in the massive starburst cluster Westerlund 1. Finding charts are presented for five new WRs - four WNL and one WCL - raising the current total of known WRs in the cluster to 19. We also present new spectra and correct identifications for the majority of the 14 WR stars previously known, notably confirming the presence of two WNVL stars. Finally we briefly discuss the massive star population of Westerlund 1 in comparison to other massive young galactic clusters.Comment: Accepted for publication in Astronomy & Astrophysics. Eight pages, six figures. Replaced with final version, some minor change

The effect of genomic information on optimal contribution selection in livestock breeding programs

BACKGROUND: Long-term benefits in animal breeding programs require that increases in genetic merit be balanced with the need to maintain diversity (lost due to inbreeding). This can be achieved by using optimal contribution selection. The availability of high-density DNA marker information enables the incorporation of genomic data into optimal contribution selection but this raises the question about how this information affects the balance between genetic merit and diversity. METHODS: The effect of using genomic information in optimal contribution selection was examined based on simulated and real data on dairy bulls. We compared the genetic merit of selected animals at various levels of co-ancestry restrictions when using estimated breeding values based on parent average, genomic or progeny test information. Furthermore, we estimated the proportion of variation in estimated breeding values that is due to within-family differences. RESULTS: Optimal selection on genomic estimated breeding values increased genetic gain. Genetic merit was further increased using genomic rather than pedigree-based measures of co-ancestry under an inbreeding restriction policy. Using genomic instead of pedigree relationships to restrict inbreeding had a significant effect only when the population consisted of many large full-sib families; with a half-sib family structure, no difference was observed. In real data from dairy bulls, optimal contribution selection based on genomic estimated breeding values allowed for additional improvements in genetic merit at low to moderate inbreeding levels. Genomic estimated breeding values were more accurate and showed more within-family variation than parent average breeding values; for genomic estimated breeding values, 30 to 40% of the variation was due to within-family differences. Finally, there was no difference between constraining inbreeding via pedigree or genomic relationships in the real data. CONCLUSIONS: The use of genomic estimated breeding values increased genetic gain in optimal contribution selection. Genomic estimated breeding values were more accurate and showed more within-family variation, which led to higher genetic gains for the same restriction on inbreeding. Using genomic relationships to restrict inbreeding provided no additional gain, except in the case of very large full-sib families

Characterising a solid state qubit via environmental noise

We propose a method for characterising the energy level structure of a solid-state qubit by monitoring the noise level in its environment. We consider a model persistent-current qubit in a lossy resevoir and demonstrate that the noise in a classical bias field is a sensitive function of the applied field.Comment: 3 Figure

Heterogeneity in reported well-being:Evidence from twelve European countries

Dans cet article nous modélisons la relation entre le revenu et le bien-être déclaré à l'aide de techniques à effet aléatoire appliquées sur des données de panel issues de douze pays européens. Il n'est pas possible de distinguer empiriquement une hétérogénéité des fonctions d'utilité (transformation du revenu en utilité) et une hétérogénéité des fonctions d'expression (transformation de l'utilité en bien-êttre déclaré); neanmoins, nous montrons que l'on peut fermement rejeter l'hypothèse selon laquelle ses deux opérations sont menées de la même façon dans les douze pays étudiés. L'"effet marginal du revenu sur le bien-être" diffère en effet très significativement entre les quatre classes mises en évidence; ce qui laisse supposer des préférences pour la redistribution et des comportements très différents entre ces classes. Nos résultats nous amènent à penser qu'agréger sans précaution des données issues de populations et de pays différents peut s'avérer une pratique dangeureuse.Revenu ; Utilité ; Bien-être ; Hétérogénéité ; Classes latentes

Multi-mass solvers for lattice QCD on GPUs

Graphical Processing Units (GPUs) are more and more frequently used for lattice QCD calculations. Lattice studies often require computing the quark propagators for several masses. These systems can be solved using multi-shift inverters but these algorithms are memory intensive which limits the size of the problem that can be solved using GPUs. In this paper, we show how to efficiently use a memory-lean single-mass inverter to solve multi-mass problems. We focus on the BiCGstab algorithm for Wilson fermions and show that the single-mass inverter not only requires less memory but also outperforms the multi-shift variant by a factor of two.Comment: 27 pages, 6 figures, 3 Table

The Optical Counterpart of the X-ray Transient RX J0117.6-7330: Spectroscopy and Photometry

We conducted spectroscopic and photometric observations of the optical counterpart of the X-ray transient RX J0117.6-7330 in the Small Magellanic Cloud, during a quiescent state. The primary star is identified as a B0.5 IIIe, with mass M = (18 +/- 2) M(sun) and bolometric magnitude M(bol) = -7.4 +/- 0.2. The main spectral features are strong H-alpha emission, H-beta and H-gamma emission cores with absorption wings, and narrow HeI and OII absorption lines. Equivalent width and FWHM of the main lines are listed. The average systemic velocity over our observing run is v(r) = (184 +/- 4) km/s; measurements over a longer period of time are needed to determine the binary period and the K velocity of the primary. We determine a projected rotational velocity v sin i = (145 +/- 10) km/s for the Be star, and we deduce that the inclination angle of the system is i = (21 +/- 3)deg.Comment: submitted to PASA; 6 figure

On the accretion flow geometry in A0535+26

The geometry of accretion flow in the Be/X-ray transient A0535+26 is explored. It is shown that neither moderate nor giant X-ray flaring events observed in the system can be interpreted within the spherically symmetrical accretion model and hence the formation of an accretion disk around the neutron star magnetosphere during the both types of flares is required. The accretion disk can be formed at the periastron if (i) the expansion velocity of the Be star envelope in the equatorial plane is V_wr < 150 km/s and (ii) the parameter accounting for the accretion flow inhomogeneities, xi, satisfies the following condition: xi > 0.16 (Mdot_17)^-1/7, where (Mdot_17)^-1/7 is the rate of mass capture by the neutron star expressed in units of 1017 g/s. We suggest that the `missing' outburst phenomenon can be associated with the spherically symmetrical accretion onto the interchange-stable magnetosphere of the neutron star. The average spin up rate of the neutron star during moderate flares < 3.5 x 10^-12 Hz/s is predicted.Comment: 6 pages, published in A&A 372, 227 (2001

Different models of genetic variation and their effect on genomic evaluation

<p>Abstract</p> <p>Background</p> <p>The theory of genomic selection is based on the prediction of the effects of quantitative trait loci (QTL) in linkage disequilibrium (LD) with markers. However, there is increasing evidence that genomic selection also relies on "relationships" between individuals to accurately predict genetic values. Therefore, a better understanding of what genomic selection actually predicts is relevant so that appropriate methods of analysis are used in genomic evaluations.</p> <p>Methods</p> <p>Simulation was used to compare the performance of estimates of breeding values based on pedigree relationships (Best Linear Unbiased Prediction, BLUP), genomic relationships (gBLUP), and based on a Bayesian variable selection model (Bayes B) to estimate breeding values under a range of different underlying models of genetic variation. The effects of different marker densities and varying animal relationships were also examined.</p> <p>Results</p> <p>This study shows that genomic selection methods can predict a proportion of the additive genetic value when genetic variation is controlled by common quantitative trait loci (QTL model), rare loci (rare variant model), all loci (infinitesimal model) and a random association (a polygenic model). The Bayes B method was able to estimate breeding values more accurately than gBLUP under the QTL and rare variant models, for the alternative marker densities and reference populations. The Bayes B and gBLUP methods had similar accuracies under the infinitesimal model.</p> <p>Conclusions</p> <p>Our results suggest that Bayes B is superior to gBLUP to estimate breeding values from genomic data. The underlying model of genetic variation greatly affects the predictive ability of genomic selection methods, and the superiority of Bayes B over gBLUP is highly dependent on the presence of large QTL effects. The use of SNP sequence data will outperform the less dense marker panels. However, the size and distribution of QTL effects and the size of reference populations still greatly influence the effectiveness of using sequence data for genomic prediction.</p

The importance of information on relatives for the prediction of genomic breeding values and the implications for the makeup of reference data sets in livestock breeding schemes.

<p>Abstract</p> <p>Background</p> <p>The theory of genomic selection is based on the prediction of the effects of genetic markers in linkage disequilibrium with quantitative trait loci. However, genomic selection also relies on relationships between individuals to accurately predict genetic value. This study aimed to examine the importance of information on relatives versus that of unrelated or more distantly related individuals on the estimation of genomic breeding values.</p> <p>Methods</p> <p>Simulated and real data were used to examine the effects of various degrees of relationship on the accuracy of genomic selection. Genomic Best Linear Unbiased Prediction (gBLUP) was compared to two pedigree based BLUP methods, one with a shallow one generation pedigree and the other with a deep ten generation pedigree. The accuracy of estimated breeding values for different groups of selection candidates that had varying degrees of relationships to a reference data set of 1750 animals was investigated.</p> <p>Results</p> <p>The gBLUP method predicted breeding values more accurately than BLUP. The most accurate breeding values were estimated using gBLUP for closely related animals. Similarly, the pedigree based BLUP methods were also accurate for closely related animals, however when the pedigree based BLUP methods were used to predict unrelated animals, the accuracy was close to zero. In contrast, gBLUP breeding values, for animals that had no pedigree relationship with animals in the reference data set, allowed substantial accuracy.</p> <p>Conclusions</p> <p>An animal's relationship to the reference data set is an important factor for the accuracy of genomic predictions. Animals that share a close relationship to the reference data set had the highest accuracy from genomic predictions. However a baseline accuracy that is driven by the reference data set size and the overall population effective population size enables gBLUP to estimate a breeding value for unrelated animals within a population (breed), using information previously ignored by pedigree based BLUP methods.</p

Regeneration in gap models: priority issues for studying forest responses to climate change

Recruitment algorithms in forest gap models are examined with particular regard to their suitability for simulating forest ecosystem responses to a changing climate. The traditional formulation of recruitment is found limiting in three areas. First, the aggregation of different regeneration stages (seed production, dispersal, storage, germination and seedling establishment) is likely to result in less accurate predictions of responses as compared to treating each stage separately. Second, the relatedassumptions that seeds of all species are uniformly available and that environmental conditions are homogeneous, are likely to cause overestimates of future species diversity and forest migration rates. Third, interactions between herbivores (ungulates and insect pests) and forest vegetation are a big unknown with potentially serious impacts in many regions. Possible strategies for developing better gap model representations for the climate-sensitive aspects of each of these key areas are discussed. A working example of a relatively new model that addresses some of these limitations is also presented for each case. We conclude that better models of regeneration processes are desirable for predicting effects of climate change, but that it is presently impossible to determine what improvements can be expected without carrying out rigorous tests for each new formulation