Identifying cancer subtypes in glioblastoma by combining genomic, transcriptomic and epigenomic data

We present a nonparametric Bayesian method for disease subtype discovery in multi-dimensional cancer data. Our method can simultaneously analyse a wide range of data types, allowing for both agreement and disagreement between their underlying clustering structure. It includes feature selection and infers the most likely number of disease subtypes, given the data. We apply the method to 277 glioblastoma samples from The Cancer Genome Atlas, for which there are gene expression, copy number variation, methylation and microRNA data. We identify 8 distinct consensus subtypes and study their prognostic value for death, new tumour events, progression and recurrence. The consensus subtypes are prognostic of tumour recurrence (log-rank p-value of $3.6 \times 10^{-4}$ after correction for multiple hypothesis tests). This is driven principally by the methylation data (log-rank p-value of $2.0 \times 10^{-3}$) but the effect is strengthened by the other 3 data types, demonstrating the value of integrating multiple data types. Of particular note is a subtype of 47 patients characterised by very low levels of methylation. This subtype has very low rates of tumour recurrence and no new events in 10 years of follow up. We also identify a small gene expression subtype of 6 patients that shows particularly poor survival outcomes. Additionally, we note a consensus subtype that showly a highly distinctive data signature and suggest that it is therefore a biologically distinct subtype of glioblastoma. The code is available from https://sites.google.com/site/multipledatafusion/

Testing in harsh conditions: Tracking resources on construction sites with machine vision

When tracking resources in large-scale, congested, outdoor construction sites, the cost and time for purchasing, installing and maintaining the position sensors needed to track thousands of materials, and hundreds of equipment and personnel can be significant. To alleviate this problem a novel vision based tracking method that allows each sensor (camera) to monitor the position of multiple entities simultaneously has been proposed. This paper presents the full-scale validation experiments for this method. The validation included testing the method under harsh conditions at a variety of mega-project construction sites. The procedure for collecting data from the sites, the testing procedure, metrics, and results are reported. Full-scale validation demonstrates that the novel vision tracking provides a good solution to track different entities on a large, congested construction site.

UK-NEES - Distributed hybrid testing between Bristol, Cambridge and Oxford Universities: Connecting structural dynamics labs to a geotechnical centrifuge

Distributed hybrid testing is a natural extension to and builds upon the local hybrid testing technique. Taking advantage of the hybrid nature of the test, it allows a sharing of resources and expertise between researchers from different disciplines by connecting multiple geographically distributed sites for joint testing. As part of the UK-NEES project, a successful series of three-site distributed hybrid tests have been carried out between Bristol, Cambridge and Oxford Universities. The first known multi-site distributed hybrid tests in the UK, they connected via a dedicated fibre network, using custom software, the geotechnical centrifuge at Cambridge to structural components at Bristol and Oxford. These experiments were to prove the connection and useful insights were gained into the issues involved with this distributed environment. A wider aim is towards providing a flexible testing framework to facilitate multi-disciplinary experiments such as the accurate investigation of the influence of foundations on structural systems under seismic and other loading. Time scaling incompatibilities mean true seismic soil structure interaction using a centrifuge at g is not possible, though it is clear that distributed centrifuge testing can be valuable in other problems. Development is continuing to overcome the issues encountered, in order to improve future distributed tests in the UK and beyond.

Centrifuge testing of monopile in clay under monotonic loads

The monopile is at present the most widely applied foundation concept for offshore wind turbines. Monopiles are designed utilising the well-established p-y method. Despite being well-established, there are multiple issues and limitations regarding its use. Investigation into the lateral behaviour of monopiles was carried out by performing monotonic and cyclic lateral load tests on an aluminium model monopile in the centrifuge. The monotonic responses and the behaviour of the monopile are described. Differences between the experimental and DNV design p-y curves and their implications are discussed. Efforts to characterise the shear force acting at the pile toe are also discussed. The results highlight the possible deficiencies of utilising the conventional DNV design p-y curves to design monopiles to resist cyclic lateral loads and the importance of research into the cyclic loading behaviour of monopiles to better improve their design to resist long-term cyclic loads. © 2014 Taylor & Francis Group.