Camera for QUasars in EArly uNiverse (CQUEAN)

We describe the overall characteristics and the performance of an optical CCD camera system, Camera for QUasars in EArly uNiverse (CQUEAN), which is being used at the 2.1 m Otto Struve Telescope of the McDonald Observatory since 2010 August. CQUEAN was developed for follow-up imaging observations of red sources such as high redshift quasar candidates (z >= 5), Gamma Ray Bursts, brown dwarfs, and young stellar objects. For efficient observations of the red objects, CQUEAN has a science camera with a deep depletion CCD chip which boasts a higher quantum efficiency at 0.7 - 1.1 um than conventional CCD chips. The camera was developed in a short time scale (~ one year), and has been working reliably. By employing an auto-guiding system and a focal reducer to enhance the field of view on the classical Cassegrain focus, we achieve a stable guiding in 20 minute exposures, an imaging quality with FWHM >= 0.6" over the whole field (4.8' * 4.8'), and a limiting magnitude of z = 23.4 AB mag at 5-sigma with one hour total integration time.Comment: Accepted for publication in PASP. 26 pages including 5 tables and 24 figure

Participant concerns for the Learner in a Virtual Reality replication of the Milgram obedience study

In Milgram's seminal obedience studies, participants' behaviour has traditionally been explained as a demonstration of people's tendency to enter into an 'agentic state' when in the presence of an authority figure: they attend only to the demands of that authority and are insensitive to the plight of their victims. There have been many criticisms of this view, but most rely on either indirect or anecdotal evidence. In this study, participants (n = 40) are taken through a Virtual Reality simulation of the Milgram paradigm. Compared to control participants (n = 20) who are not taken through the simulation, those in the experimental conditions are found to attempt to help the Learner more by putting greater emphasis on the correct word over the incorrect words. We also manipulate the extent to which participants identify with the science of the study and show that high identifiers both give more help, are less stressed, and are more hesitant to press the shock button than low identifiers. We conclude that these findings constitute a refutation of the 'agentic state' approach to obedience. Instead, we discuss implications for the alternative approaches such as 'engaged followership' which suggests that obedience is a function of relative identification with the science and with the victim in the study. Finally, we discuss the value of Virtual Reality as a technique for investigating hard-to-study psychological phenomena

Error, reproducibility and sensitivity : a pipeline for data processing of Agilent oligonucleotide expression arrays

Background Expression microarrays are increasingly used to obtain large scale transcriptomic information on a wide range of biological samples. Nevertheless, there is still much debate on the best ways to process data, to design experiments and analyse the output. Furthermore, many of the more sophisticated mathematical approaches to data analysis in the literature remain inaccessible to much of the biological research community. In this study we examine ways of extracting and analysing a large data set obtained using the Agilent long oligonucleotide transcriptomics platform, applied to a set of human macrophage and dendritic cell samples. Results We describe and validate a series of data extraction, transformation and normalisation steps which are implemented via a new R function. Analysis of replicate normalised reference data demonstrate that intrarray variability is small (only around 2% of the mean log signal), while interarray variability from replicate array measurements has a standard deviation (SD) of around 0.5 log2 units ( 6% of mean). The common practise of working with ratios of Cy5/Cy3 signal offers little further improvement in terms of reducing error. Comparison to expression data obtained using Arabidopsis samples demonstrates that the large number of genes in each sample showing a low level of transcription reflect the real complexity of the cellular transcriptome. Multidimensional scaling is used to show that the processed data identifies an underlying structure which reflect some of the key biological variables which define the data set. This structure is robust, allowing reliable comparison of samples collected over a number of years and collected by a variety of operators. Conclusions This study outlines a robust and easily implemented pipeline for extracting, transforming normalising and visualising transcriptomic array data from Agilent expression platform. The analysis is used to obtain quantitative estimates of the SD arising from experimental (non biological) intra- and interarray variability, and for a lower threshold for determining whether an individual gene is expressed. The study provides a reliable basis for further more extensive studies of the systems biology of eukaryotic cells

Developing and applying heterogeneous phylogenetic models with XRate

Modeling sequence evolution on phylogenetic trees is a useful technique in computational biology. Especially powerful are models which take account of the heterogeneous nature of sequence evolution according to the "grammar" of the encoded gene features. However, beyond a modest level of model complexity, manual coding of models becomes prohibitively labor-intensive. We demonstrate, via a set of case studies, the new built-in model-prototyping capabilities of XRate (macros and Scheme extensions). These features allow rapid implementation of phylogenetic models which would have previously been far more labor-intensive. XRate's new capabilities for lineage-specific models, ancestral sequence reconstruction, and improved annotation output are also discussed. XRate's flexible model-specification capabilities and computational efficiency make it well-suited to developing and prototyping phylogenetic grammar models. XRate is available as part of the DART software package: http://biowiki.org/DART .Comment: 34 pages, 3 figures, glossary of XRate model terminolog

Correlated fragile site expression allows the identification of candidate fragile genes involved in immunity and associated with carcinogenesis

Common fragile sites (cfs) are specific regions in the human genome that are particularly prone to genomic instability under conditions of replicative stress. Several investigations support the view that common fragile sites play a role in carcinogenesis. We discuss a genome-wide approach based on graph theory and Gene Ontology vocabulary for the functional characterization of common fragile sites and for the identification of genes that contribute to tumour cell biology. CFS were assembled in a network based on a simple measure of correlation among common fragile site patterns of expression. By applying robust measurements to capture in quantitative terms the non triviality of the network, we identified several topological features clearly indicating departure from the Erdos-Renyi random graph model. The most important outcome was the presence of an unexpected large connected component far below the percolation threshold. Most of the best characterized common fragile sites belonged to this connected component. By filtering this connected component with Gene Ontology, statistically significant shared functional features were detected. Common fragile sites were found to be enriched for genes associated to the immune response and to mechanisms involved in tumour progression such as extracellular space remodeling and angiogenesis. Our results support the hypothesis that fragile sites serve a function; we propose that fragility is linked to a coordinated regulation of fragile genes expression.Comment: 18 pages, accepted for publication in BMC Bioinformatic

Ground-Based Submillimagnitude CCD Photometry of Bright Stars Using Snapshot Observations

We demonstrate ground-based submillimagnitude (<10^-3) photometry of widely separated bright stars using snapshot CCD imaging. We routinely achieved this photometric precision by (1) choosing nearby comparison stars of a similar magnitude and spectral type, (2) defocusing the telescope to allow high signal (>10^7 electrons) to be acquired in a single integration, (3) pointing the telescope so that all stellar images fall on the same detector pixels, and (4) using a region of the CCD detector that is free of nonlinear or aberrant pixels. We describe semiautomated observations with the Supernova Integrated Field Spectrograph (SNIFS) on the University of Hawaii 2.2m telescope on Mauna Kea, with which we achieved photometric precision as good as 5.2x10^-4 (0.56mmag) with a 5 minute cadence over a 2hr interval. In one experiment, we monitored eight stars, each separated by several degrees, and achieved submillimagnitude precision with a cadence (per star) of ~17 minutes. Our snapshot technique is suitable for automated searches for planetary transits among multiple bright stars.Comment: Accepted to PAS

A Cryogenic Silicon Interferometer for Gravitational-wave Detection

The detection of gravitational waves from compact binary mergers by LIGO has opened the era of gravitational wave astronomy, revealing a previously hidden side of the cosmos. To maximize the reach of the existing LIGO observatory facilities, we have designed a new instrument that will have 5 times the range of Advanced LIGO, or greater than 100 times the event rate. Observations with this new instrument will make possible dramatic steps toward understanding the physics of the nearby universe, as well as observing the universe out to cosmological distances by the detection of binary black hole coalescences. This article presents the instrument design and a quantitative analysis of the anticipated noise floor

Advanced Genomic Data Mining

As data banks increase their size, one of the current challenges in bioinformatics is to be able to query them in a sensible way. Information is contained in differen

Reactome knowledgebase of human biological pathways and processes

Reactome (http://www.reactome.org) is an expert-authored, peer-reviewed knowledgebase of human reactions and pathways that functions as a data mining resource and electronic textbook. Its current release includes 2975 human proteins, 2907 reactions and 4455 literature citations. A new entity-level pathway viewer and improved search and data mining tools facilitate searching and visualizing pathway data and the analysis of user-supplied high-throughput data sets. Reactome has increased its utility to the model organism communities with improved orthology prediction methods allowing pathway inference for 22 species and through collaborations to create manually curated Reactome pathway datasets for species including Arabidopsis, Oryza sativa (rice), Drosophila and Gallus gallus (chicken). Reactome's data content and software can all be freely used and redistributed under open source terms