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

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

Dynein structure and power stroke

Dynein ATPases are microtubule motors that are critical to diverse processes such as vesicle transport and the beating of sperm tails; however, their mechanism of force generation is unknown. Each dynein comprises a head, from which a stalk and a stem emerge. Here we use electron microscopy and image processing to reveal new structural details of dynein c, an isoform from Chlamydomonas reinhardtii flagella, at the start and end of its power stroke. Both stem and stalk are flexible, and the stem connects to the head by means of a linker approximately 10 nm long that we propose lies across the head. With both ADP and vanadate bound, the stem and stalk emerge from the head 10 nm apart. However, without nucleotide they emerge much closer together owing to a change in linker orientation, and the coiled-coil stalk becomes stiffer. The net result is a shortening of the molecule coupled to an approximately 15-nm displacement of the tip of the stalk. These changes indicate a mechanism for the dynein power stroke

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

Investigation of Non-Stable Processes in Close Binary Ry Scuti

We present results of reanalysis of old electrophotometric data of early type close binary system RY Scuti obtained at the Abastumani Astrophysical Observatory, Georgia, during 1972-1990 years and at the Maidanak Observatory, Uzbekistan, during 1979-1991 years. It is revealed non-stable processes in RY Sct from period to period, from month to month and from year to year. This variation consists from the hundredths up to the tenths of a magnitude. Furthermore, periodical changes in the system's light are displayed near the first maximum on timescales of a few years. That is of great interest with regard to some similar variations seen in luminous blue variable (LBV) stars. This also could be closely related to the question of why RY Sct ejected its nebula.Comment: 11 pages, 6 figures, 2 table

Ontology of core data mining entities

In this article, we present OntoDM-core, an ontology of core data mining entities. OntoDM-core defines themost essential datamining entities in a three-layered ontological structure comprising of a specification, an implementation and an application layer. It provides a representational framework for the description of mining structured data, and in addition provides taxonomies of datasets, data mining tasks, generalizations, data mining algorithms and constraints, based on the type of data. OntoDM-core is designed to support a wide range of applications/use cases, such as semantic annotation of data mining algorithms, datasets and results; annotation of QSAR studies in the context of drug discovery investigations; and disambiguation of terms in text mining. The ontology has been thoroughly assessed following the practices in ontology engineering, is fully interoperable with many domain resources and is easy to extend

Gene Function Classification Using Bayesian Models with Hierarchy-Based Priors

We investigate the application of hierarchical classification schemes to the annotation of gene function based on several characteristics of protein sequences including phylogenic descriptors, sequence based attributes, and predicted secondary structure. We discuss three Bayesian models and compare their performance in terms of predictive accuracy. These models are the ordinary multinomial logit (MNL) model, a hierarchical model based on a set of nested MNL models, and a MNL model with a prior that introduces correlations between the parameters for classes that are nearby in the hierarchy. We also provide a new scheme for combining different sources of information. We use these models to predict the functional class of Open Reading Frames (ORFs) from the E. coli genome. The results from all three models show substantial improvement over previous methods, which were based on the C5 algorithm. The MNL model using a prior based on the hierarchy outperforms both the non-hierarchical MNL model and the nested MNL model. In contrast to previous attempts at combining these sources of information, our approach results in a higher accuracy rate when compared to models that use each data source alone. Together, these results show that gene function can be predicted with higher accuracy than previously achieved, using Bayesian models that incorporate suitable prior information