FINDING FOSSIL GROUPS: OPTICAL IDENTIFICATION AND X-RAY CONFIRMATION

We report the discovery of 12 new fossil groups (FGs) of galaxies, systems dominated by a single giant elliptical galaxy and cluster-scale gravitational potential, but lacking the population of bright galaxies typically seen in galaxy clusters. These FGs, selected from the maxBCG optical cluster catalog, were detected in snapshot observations with the Chandra X-ray Observatory. We detail the highly successful selection method, with an 80% success rate in identifying 12 FGs from our target sample of 15 candidates. For 11 of the systems, we determine the X-ray luminosity, temperature, and hydrostatic mass, which do not deviate significantly from expectations for normal systems, spanning a range typical of rich groups and poor clusters of galaxies. A small number of detected FGs are morphologically irregular, possibly due to past mergers, interaction of the intra-group medium with a central active galactic nucleus (AGN), or superposition of multiple massive halos. Two-thirds of the X-ray-detected FGs exhibit X-ray emission associated with the central brightest cluster galaxy (BCG), although we are unable to distinguish between AGN and extended thermal galaxy emission using the current data. This sample representing a large increase in the number of known FGs, will be invaluable for future planned observations to determine FG temperature, gas density, metal abundance, and mass distributions, and to compare to normal (non-fossil) systems. Finally, the presence of a population of galaxy-poor systems may bias mass function determinations that measure richness from galaxy counts. When used to constrain power spectrum normalization and Omega(m), these biased mass functions may in turn bias these results.

Comparative analysis of rating curve and ADP estimates of instantaneous water discharge through estuaries in two contrasting Brazilian rivers

This work quantifies, using ADP and rating curve techniques, the instantaneous outflows at estuarine interfaces: higher to middle estuary and middle to lower estuary, in two medium-sized watersheds (72 000 and 66 000 km(2) of area, respectively), the Jaguaribe and Contas Rivers located in the northeastern (semi-arid) and eastern (tropical humid) Brazilian coasts, respectively. Results from ADP showed that the net water balances show the Contas River as a net water exporter, whereas the Jaguaribe River Estuary is a net water importer. At the Jaguaribe Estuary, water retention during flood tide contributes to 58% of the total volume transferred during the ebb tide from the middle to lower estuary. However, 42% of the total water volume (452 m(3) s(-1)) that entered during flood tide is retained in the middle estuary. In the Contas River, 90% of the total water is retained during the flood tide contributing to the volume transported in the ebb tide from the middle to the lower estuary. Outflows obtained with the rating curve method for the Contas and Jaguaribe Rivers were uniform through time due to river flow normalization by dams in both basins. Estimated outflows with this method are about 65% (Contas) and 95% (Jaguaribe) lower compared to outflows obtained with ADP. This suggests that the outflows obtained with the rating curve method underestimate the net water balance in both systems, particularly in the Jaguaribe River under a semi-arid climate. This underestimation is somewhat decreased due to wetter conditions in the Contas River basin. Copyright. (C) 2011 John Wiley & Sons, Ltd.

Evaluating different methods of microarray data normalization

Abstract Background With the development of DNA hybridization microarray technologies, nowadays it is possible to simultaneously assess the expression levels of thousands to tens of thousands of genes. Quantitative comparison of microarrays uncovers distinct patterns of gene expression, which define different cellular phenotypes or cellular responses to drugs. Due to technical biases, normalization of the intensity levels is a pre-requisite to performing further statistical analyses. Therefore, choosing a suitable approach for normalization can be critical, deserving judicious consideration. Results Here, we considered three commonly used normalization approaches, namely: Loess, Splines and Wavelets, and two non-parametric regression methods, which have yet to be used for normalization, namely, the Kernel smoothing and Support Vector Regression. The results obtained were compared using artificial microarray data and benchmark studies. The results indicate that the Support Vector Regression is the most robust to outliers and that Kernel is the worst normalization technique, while no practical differences were observed between Loess, Splines and Wavelets. Conclusion In face of our results, the Support Vector Regression is favored for microarray normalization due to its superiority when compared to the other methods for its robustness in estimating the normalization curve.