The Northern HIPASS catalogue - data presentation, completeness and reliability measures

The Northern HIPASS catalogue (NHICAT) is the northern extension of the HIPASS catalogue, HICAT. This extension adds the sky area between the declination (Dec.) range of +2 degrees < delta < +25 degrees 30' to HICAT's Dec. range of -90 degrees < delta < +2 degrees. HIPASS is a blind H I survey using the Parkes Radio Telescope covering 71 per cent of the sky (including this northern extension) and a heliocentric velocity range of - 1280 to 12 700 km s(-1). The entire Virgo Cluster region has been observed in the Northern HIPASS. The galaxy catalogue, NHICAT, contains 1002 sources with nu(hel) > 300 km s(-1). Sources with -300 < nu(hel) < 300 km s(-1) were excluded to avoid contamination by Galactic emission. In total, the entire HIPASS survey has found 5317 galaxies identified purely by their HI content. The full galaxy catalogue is publicly available at http://hipass.aus-vo.org.

Mathematical Morphology: Star/Galaxy Differentiation & Galaxy Morphology Classication

We present an application of Mathematical Morphology (MM) for the classification of astronomical objects, both for star/galaxy differentiation and galaxy morphology classification. We demonstrate that, for CCD images, 99.3 +/- 3.8% of galaxies can be separated from stars using MM, with 19.4 +/- 7.9% of the stars being misclassified. We demonstrate that, for photographic plate images, the number of galaxies correctly separated from the stars can be increased using our MM diffraction spike tool, which allows 51.0 +/- 6.0% of the high-brightness galaxies that are inseparable in current techniques to be correctly classified, with only 1.4 +/- 0.5% of the high-brightness stars contaminating the population. We demonstrate that elliptical (E) and late-type spiral (Sc-Sd) galaxies can be classified using MM with an accuracy of 91.4 +/- 7.8%. It is a method involving fewer 'free parameters' than current techniques, especially automated machine learning algorithms. The limitation of MM galaxy morphology classification based on seeing and distance is also presented. We examine various star/galaxy differentiation and galaxy morphology classification techniques commonly used today, and show that our MM techniques compare very favourably.

The dendritic architecture of the cholinergic plexus in the rabbit retina: Selective labeling by glycine accumulation in the presence of sarcosine

The cholinergic amacrine cells in the rabbit retina slowly accumulate glycine to very high levels when the tissue is incubated with excess sarcosine (methylglycine), even though these cells do not normally contain elevated levels of glycine and do not express high-affinity glycine transporters. Because the sarcosine also depletes the endogenous glycine in the glycine-containing amacrine cells and bipolar cells, the cholinergic amacrine cells can be selectively labeled by glycine immunocytochemistry under these conditions. Incubation experiments indicated that the effect of sarcosine on the cholinergic amacrine cells is indirect: sarcosine raises the extracellular concentration of glycine by blocking its re-uptake by the glycinergic amacrine cells, and the excess glycine is probably taken-up by an unidentified low-affinity transporter on the cholinergic amacrine cells. Neurobiotin injection of the On-Off direction-selective (DS) ganglion cells in sarcosine-incubated rabbit retina was combined with glycine immunocytochemistry to examine the dendritic relationships between the DS ganglion cells and the cholinergic amacrine cells. These double-labeled preparations showed that the dendrites of the DS ganglion cells closely follow the fasciculated dendrites of the cholinergic amacrine cells. Each ganglion cell dendrite located within the cholinergic strata is associated with a cholinergic fascicle and, conversely, there are few cholinergic fascicles that do not contain at least one dendrite from an On-Off DS cell. It is not known how the dendritic co-fasciculation develops, but the cholinergic dendritic plexus may provide the initial scaffold, because the dendrites of the On-Off DS cells commonly run along the outside of the cholinergic fascicles. J. Comp. Neurol. 421:1-13, 2000. (C) 2000 Wiley-Liss, Inc.

Machine learning for matching astronomy catalogues

An emerging issue in the field of astronomy is the integration, management and utilization of databases from around the world to facilitate scientific discovery. In this paper, we investigate application of the machine learning techniques of support vector machines and neural networks to the problem of amalgamating catalogues of galaxies as objects from two disparate data sources: radio and optical. Formulating this as a classification problem presents several challenges, including dealing with a highly unbalanced data set. Unlike the conventional approach to the problem (which is based on a likelihood ratio) machine learning does not require density estimation and is shown here to provide a significant improvement in performance. We also report some experiments that explore the importance of the radio and optical data features for the matching problem.