Galaxy threshing and the origin of ultra-compact dwarf galaxies in the Fornax cluster

A recent all-object spectroscopic survey centred on the Fornax cluster of galaxies has discovered a population of subluminous and extremely compact members, called 'ultra-compact dwarf' (UCD) galaxies. In order to clarify the origin of these objects, we have used self-consistent numerical simulations to study the dynamical evolution a nucleated dwarf galaxy would undergo if orbiting the centre of the Fornax cluster and suffering from its strong tidal gravitational field. We find that the outer stellar components of a nucleated dwarf are removed by the strong tidal field of the cluster, whereas the nucleus manages to survive as a result of its initially compact nature. The developed naked nucleus is found to have physical properties (e. g. size and mass) similar to those observed for UCDs. We also find that although this formation process does not have a strong dependence on the initial total luminosity of the nucleated dwarf, it does depend on the radial density profile of the dark halo in the sense that UCDs are less likely to be formed from dwarfs embedded in dark matter haloes with central 'cuspy' density profiles. Our simulations also suggest that very massive and compact stellar systems can be rapidly and efficiently formed in the central regions of dwarfs through the merging of smaller star clusters. We provide some theoretical predictions on the total number and radial number density profile of UCDs in a cluster and their dependencies on cluster masses.

Galaxy threshing and ultra-compact dwarfs in the Fornax cluster

We have discovered a new type of galaxy in the Fornax Cluster: 'ultra-compact' dwarfs (UCDs). The UCDs are unresolved in ground-based imaging and have spectra typical of old stellar systems. Although the UCDs resemble overgrown globular clusters, based on VLT UVES echelle spectroscopy, they appear to be dynamically distinct systems with higher internal velocity dispersions and M/L ratios for a given luminosity than Milky Way or M31 globulars. Our preferred explanation for their origin is that they are the remnant nuclei of dwarf elliptical galaxies which have been tidally stripped, or 'threshed' by repeated encounters with the central cluster galaxy, NGC1399. If correct, then tidal stripping of nucleated dwarfs to form UCDs may, over a Hubble time, be an important source of the plentiful globular cluster population in the halo of NGC1399, and, by implication, other cD galaxies. In this picture, the dwarf elliptical halo contents, up to 99% of the original dwarf luminosity, contribute a significant fraction of the populations of intergalactic stars, globulars, and gas in galaxy clusters.

Bubble load measurement in the pulp zone of industrial flotation machines - a new device for determining the froth recovery of attached particles

A new device has been developed to directly measure the bubble loading of particle-bubble aggregates in industrial flotation machines, both mechanical flotation cells as well as flotation column cells. The bubble loading of aggregates allows for in-depth analysis of the operating performance of a flotation machine in terms of both pulp/collection zone and froth zone performance. This paper presents the methodology along with an example showing the excellent reproducibility of the device and an analysis of different operating conditions of the device itself. (C) 2004 Elsevier B.V All rights reserved.

SVMtm: Support vector machines to predict transmembrane segments

A new method has been developed for prediction of transmembrane helices using support vector machines. Different coding schemes of protein sequences were explored, and their performances were assessed by crossvalidation tests. The best performance method can predict the transmembrane helices with sensitivity of 93.4% and precision of 92.0%. For each predicted transmembrane segment, a score is given to show the strength of transmembrane signal and the prediction reliability. In particular, this method can distinguish transmembrane proteins from soluble proteins with an accuracy of similar to99%. This method can be used to complement current transmembrane helix prediction methods and can be Used for consensus analysis of entire proteomes . The predictor is located at http://genet.imb.uq.edu.au/predictors/ SVMtm. (C) 2004 Wiley Periodicals, Inc.

A comparison of surface brightness profiles for ultracompact dwarfs and dwarf elliptical nuclei: implications for the “threshing” scenario

Using imaging from the Hubble Space Telescope, we derive surface brightness profiles for ultracompact dwarfs in the Fornax Cluster and for the nuclei of dwarf elliptical galaxies in the Virgo Cluster. Ultracompact dwarfs are more extended and have higher surface brightnesses than typical dwarf nuclei, while the luminosities, colors, and sizes of the nuclei are closer to those of Galactic globular clusters. This calls into question the production of ultracompact dwarfs via threshing, whereby the lower surface brightness envelope of a dwarf elliptical galaxy is removed by tidal processes, leaving behind a bare nucleus. Threshing may still be a viable model if the relatively bright Fornax ultracompact dwarfs considered here are descended from dwarf elliptical galaxies whose nuclei are at the upper end of their luminosity and size distributions.

Detecting and sorting targeting peptides wtih neural networks and support vector machines

This paper presents a composite multi-layer classifier system for predicting the subcellular localization of proteins based on their amino acid sequence. The work is an extension of our previous predictor PProwler v1.1 which is itself built upon the series of predictors SignalP and TargetP. In this study we outline experiments conducted to improve the classifier design. The major improvement came from using Support Vector machines as a "smart gate" sorting the outputs of several different targeting peptide detection networks. Our final model (PProwler v1.2) gives MCC values of 0.873 for non-plant and 0.849 for plant proteins. The model improves upon the accuracy of our previous subcellular localization predictor (PProwler v1.1) by 2% for plant data (which represents 7.5% improvement upon TargetP).

Authorship Attribution with Support Vector Machines

In this paper we explore the use of text-mining methods for the identification of the author of a text. We apply the support vector machine (SVM) to this problem, as it is able to cope with half a million of inputs it requires no feature selection and can process the frequency vector of all words of a text. We performed a number of experiments with texts from a German newspaper. With nearly perfect reliability the SVM was able to reject other authors and detected the target author in 60–80% of the cases. In a second experiment, we ignored nouns, verbs and adjectives and replaced them by grammatical tags and bigrams. This resulted in slightly reduced performance. Author detection with SVMs on full word forms was remarkably robust even if the author wrote about different topics.