The temperature dependence of the F band in magnesium oxide

The position and width of the F band in magnesium oxide have been measured in the temperature range 4-400 °K. The data have been analysed in terms of the simplest adequate `configuration coordinate' model. The width results give an effective frequency of 7.8 × 10^12 s^-1, which is close to a peak in the phonon density of states and to the value extrapolated from data for the alkali halides. There is evidence that the effective frequency is reduced by about 5% in the excited state of the F centre. The Huang-Rhys factor is about 39, and luminescence is predicted at about 2.4 eV. The band shape indicates the existence of three small absorption bands on the high-energy side of the main F absorption band. These appear to be associated with the F centre, but their nature is not clear

Representation of probabilistic scientific knowledge

This article is available through the Brunel Open Access Publishing Fund. Copyright © 2013 Soldatova et al; licensee BioMed Central Ltd.The theory of probability is widely used in biomedical research for data analysis and modelling. In previous work the probabilities of the research hypotheses have been recorded as experimental metadata. The ontology HELO is designed to support probabilistic reasoning, and provides semantic descriptors for reporting on research that involves operations with probabilities. HELO explicitly links research statements such as hypotheses, models, laws, conclusions, etc. to the associated probabilities of these statements being true. HELO enables the explicit semantic representation and accurate recording of probabilities in hypotheses, as well as the inference methods used to generate and update those hypotheses. We demonstrate the utility of HELO on three worked examples: changes in the probability of the hypothesis that sirtuins regulate human life span; changes in the probability of hypotheses about gene functions in the S. cerevisiae aromatic amino acid pathway; and the use of active learning in drug design (quantitative structure activity relation learning), where a strategy for the selection of compounds with the highest probability of improving on the best known compound was used. HELO is open source and available at https://github.com/larisa-soldatova/HELO.This work was partially supported by grant BB/F008228/1 from the UK Biotechnology & Biological Sciences Research Council, from the European Commission under the FP7 Collaborative Programme, UNICELLSYS, KU Leuven GOA/08/008 and ERC Starting Grant 240186

A systematic search for massive young stars in the Galaxy - the RMS survey

We have selected red MSX sources (RMS) that have the colours of massive young stellar objects (MYSOs). Our aim is to generate a large, systematically selected sample to address questions such as their luminosity function, lifetimes, clustering and triggering. Other objects such as UCHIIs, PN, PPN and AGB stars have similar IR colours and a large programme of ground-based follow-up observations is underway to identify and eliminate these from the sample of the red MSX sources. These include radio continuum observations, kinematic distances, ground-based mid-IR imaging, near-IR imaging and spectroscopy to distinguish. We report the progress of these campaigns on the 3000 candidates, with initial indications showing that a substantial fraction are indeed massive YSOs.Comment: 3 pages, 4 figures Talk in conference: Milky Way surveys, the structure and evolution of our Galaxy, Boston 200

On the role of magnetic reconnection in jet/accretion disk systems

The most accepted model for jet production is based on the magneto-centrifugal acceleration out off an accretion disk that surrounds the central source (Blandford & Payne, 1982). This scenario, however, does not explain, e.g., the quasi-periodic ejection phenomena often observed in different astrophysical jet classes. de Gouveia Dal Pino & Lazarian (2005) (hereafter GDPL) have proposed that the large scale superluminal ejections observed in microquasars during radio flare events could be produced by violent magnetic reconnection (MR) episodes. Here, we extend this model to other accretion disk systems, namely: active galactic nuclei (AGNs) and young stellar objects (YSOs), and also discuss its role on jet heating and particle acceleration.Comment: To be published in the IAU Highlights of Astronomy, Volume 15, XXVII IAU General Assembly, August 2009, Ian F. Corbett et al., eds., 201

Developing a logical model of yeast metabolism

With the completion of the sequencing of genomes of increasing numbers of organisms, the focus of biology is moving to determining the role of these genes (functional genomics). To this end it is useful to view the cell as a biochemical machine: it consumes simple molecules to manufacture more complex ones by chaining together biochemical reactions into long sequences referred to as em metabolic pathways. Such metabolic pathways are not linear but often interesect to form complex networks. Genes play a fundamental role in these networks by providing the information to synthesise the enzymes that catalyse biochemical reactions. Although developing a complete model of metabolism is of fundamental importance to biology and medicine, the size and complexity of the network has proven beyond the capacity of human reasoning. This paper presents the first results of the Robot Scientist research programme that aims to automatically discover the function of genes in the metabolism of the yeast em Saccharomyces cerevisiae. Results include: (1) the first logical model of metabolism;(2) a method to predict phenotype by deductive inference; and (3) a method to infer reactions and gene function by aductive inference. We describe the em in vivo experimental set-up which will allow these em in silico predictions to be automatically tested by a laboratory robot

Combining inductive logic programming, active learning and robotics to discover the function of genes

The paper is addressed to AI workers with an interest in biomolecular genetics and also to biomolecular geneticists interested in what AI tools may do for them. The authors are engaged in a collaborative enterprise aimed at partially automating some aspects of scientific work. These aspects include the processes of forming hypotheses, devising trials to discriminate between these competing hypotheses, physically performing these trials and then using the results of these trials to converge upon an accurate hypothesis. As a potential component of the reasoning carried out by an "artificial scientist" this paper describes ASE-Progol, an Active Learning system which uses Inductive Logic Programming to construct hypothesised first-order theories and uses a CART-like algorithm to select trials for eliminating ILP derived hypotheses. In simulated yeast growth tests ASE-Progol was used to rediscover how genes participate in the aromatic amino acid pathway of Saccharomyces cerevisiae. The cost of the chemicals consumed in converging upon a hypothesis with an accuracy of around 88% was reduced by five orders of magnitude when trials were selected by ASE-Progol rather than being sampled at random. While the naive strategy of always choosing the cheapest trial from the set of candidate trials led to lower cumulative costs than ASE-Progol, both the naive strategy and the random strategy took significantly longer to converge upon a final hypothesis than ASE-Progol. For example to reach an accuracy of 80%, ASE-Progol required 4 days while random sampling required 6 days and the naive strategy required 10 days