Diurnal variation of small and large ion concentrations in an urban location

A Neutral cluster and Air Ion Spectrometer (NAIS) was used to monitor the concentration of airborne ions on 258 full days between Nov 2011 and Dec 2012 in Brisbane, Australia. The air was sampled from outside a window on the sixth floor of a building close to the city centre, approximately 100 m away from a busy freeway. The NAIS detects all ions and charged particles smaller than 42 nm. It was operated in a 4 min measurement cycle, with ion data recorded at 10 s intervals over 2 min during each cycle. The data were analysed to derive the diurnal variation of small, large and total ion concentrations in the environment. We adapt the definition of Horrak et al (2000) and classify small ions as molecular clusters smaller than 1.6 nm and large ions as charged particles larger than this size...

Association of oestrogen-receptor gene (ESR1) polymorphisms with migraine in the large Norfolk Island pedigree

BACKGROUND: Oestrogen receptor 1 ( ESR1) is located in region 6q25.1 and encodes a ligand-activated transcription factor composed of several domains important for hormone binding and transcription activation. Progesterone receptor ( PGR) is located in 11q22-23 and mediates the role of progesterone interacting with different transcriptional co-regulators. ESR1 and PGR have previously been implicated in migraine susceptibility. Here, we report the results of an association study of these genes in a migraine pedigree from the genetic isolate of Norfolk Island, a population descended from a small number of Isle of Man "Bounty Mutineer" and Tahitian founders.

Standard preanalytical coding for biospecimens: Review and implementation of the Sample PREanalytical Code (SPREC)

The first version of the Standard PREanalytical Code (SPREC) was developed in 2009 by the International Society for Biological and Environmental Repositories (ISBER) Biospecimen Science Working Group to facilitate documentation and communication of the most important preanalytical quality parameters of different types of biospecimens used for research. This same Working Group has now updated the SPREC to version 2.0, presented here, so that it contains more options to allow for recent technological developments. Existing elements have been fine tuned. An interface to the Biospecimen Reporting for Improved Study Quality (BRISQ) has been defined, and informatics solutions for SPREC implementation have been developed. A glossary with SPRECrelated definitions has also been added.

Epstein-Barr virus-positive diffuse large B-cell lymphoma of the elderly expresses EBNA3A with conserved CD8+ T-cell epitopes

Post-transplantation lymphoproliferative disorders (PTLD) arise in the immunosuppressed and are frequently Epstein-Barr virus (EBV) associated. The most common PTLD histological sub-type is diffuse large B-cell lymphoma (EBV+DLBCL-PTLD). Restoration of EBV-specific T-cell immunity can induce EBV+DLBCL-PTLD regression. The most frequent B-cell lymphoma in the immunocompetent is also DLBCL. ‘EBV-positive DLBCL of the elderly’ (EBV+DLBCL) is a rare but well-recognized DLBCL entity that occurs in the overtly immunocompetent, that has an adverse outcome relative to EBV-negative DLBCL. Unlike PTLD (which is classified as viral latency III), literature suggests EBV+DLBCL is typically latency II, i.e. expression is limited to the immuno-subdominant EBNA1, LMP1 and LMP2 EBV-proteins. If correct, this would be a major impediment for T-cell immunotherapeutic strategies. Unexpectedly we observed EBV+DLBCL-PTLD and EBV+DLBCL both shared features consistent with type III EBV-latency, including expression of the immuno-dominant EBNA3A protein. Extensive analysis showed frequent polymorphisms in EBNA1 and LMP1 functionally defined CD8+ T-cell epitope encoding regions, whereas EBNA3A polymorphisms were very rare making this an attractive immunotherapy target. As with EBV+DLBCL-PTLD, the antigen presenting machinery within lymphomatous nodes was intact. EBV+DLBCL express EBNA3A suggesting it is amenable to immunotherapeutic strategies.

The efficiency of corpus-based distributional models for literature-based discovery on large data sets

This paper evaluates the efficiency of a number of popular corpus-based distributional models in performing discovery on very large document sets, including online collections. Literature-based discovery is the process of identifying previously unknown connections from text, often published literature, that could lead to the development of new techniques or technologies. Literature-based discovery has attracted growing research interest ever since Swanson's serendipitous discovery of the therapeutic effects of fish oil on Raynaud's disease in 1986. The successful application of distributional models in automating the identification of indirect associations underpinning literature-based discovery has been heavily demonstrated in the medical domain. However, we wish to investigate the computational complexity of distributional models for literature-based discovery on much larger document collections, as they may provide computationally tractable solutions to tasks including, predicting future disruptive innovations. In this paper we perform a computational complexity analysis on four successful corpus-based distributional models to evaluate their fit for such tasks. Our results indicate that corpus-based distributional models that store their representations in fixed dimensions provide superior efficiency on literature-based discovery tasks.