Characterisation of a C1qtnf5 Ser163Arg Knock-In Mouse Model of Late-Onset Retinal Macular Degeneration

A single founder mutation resulting in a Ser163Arg substitution in the C1QTNF5 gene product causes autosomal dominant late-onset retinal macular degeneration (L-ORMD) in humans, which has clinical and pathological features resembling age-related macular degeneration. We generated and characterised a mouse

A constraints-based resource discovery model for multi-provider cloud environments

Increasingly infrastructure providers are supplying the cloud marketplace with storage and on-demand compute resources to host cloud applications. From an application user's point of view, it is desirable to identify the most appropriate set of available resources on which to execute an application. Resource choice can be complex and may involve comparing available hardware specifications, operating systems, value-added services, such as network configuration or data replication, and operating costs, such as hosting cost and data throughput. Providers' cost models often change and new commodity cost models, such as spot pricing, have been introduced to offer significant savings. In this paper, a software abstraction layer is used to discover infrastructure resources for a particular application, across multiple providers, by using a two-phase constraints-based approach. In the first phase, a set of possible infrastructure resources are identified for a given application. In the second phase, a heuristic is used to select the most appropriate resources from the initial set. For some applications a cost-based heuristic is most appropriate; for others a performance-based heuristic may be used. A financial services application and a high performance computing application are used to illustrate the execution of the proposed resource discovery mechanism. The experimental result shows the proposed model could dynamically select an appropriate set of resouces that match the application's requirements.

A feasibility study testing four hypotheses with phase II outcomes in advanced colorectal cancer (MRC FOCUS3): a model for randomised controlled trials in the era of personalised medicine?

Background: Molecular characteristics of cancer vary between individuals. In future, most trials will require assessment of biomarkers to allocate patients into enriched populations in which targeted therapies are more likely to be effective. The MRC FOCUS3 trial is a feasibility study to assess key elements in the planning of such studies.

Patients and methods: Patients with advanced colorectal cancer were registered from 24 centres between February 2010 and April 2011. With their consent, patients' tumour samples were analysed for KRAS/BRAF oncogene mutation status and topoisomerase 1 (topo-1) immunohistochemistry. Patients were then classified into one of four molecular strata; within each strata patients were randomised to one of two hypothesis-driven experimental therapies or a common control arm (FOLFIRI chemotherapy). A 4-stage suite of patient information sheets (PISs) was developed to avoid patient overload.

Results: A total of 332 patients were registered, 244 randomised. Among randomised patients, biomarker results were provided within 10 working days (w.d.) in 71%, 15 w.d. in 91% and 20 w.d. in 99%. DNA mutation analysis was 100% concordant between two laboratories. Over 90% of participants reported excellent understanding of all aspects of the trial. In this randomised phase II setting, omission of irinotecan in the low topo-1 group was associated with increased response rate and addition of cetuximab in the KRAS, BRAF wild-type cohort was associated with longer progression-free survival.

Conclusions: Patient samples can be collected and analysed within workable time frames and with reproducible mutation results. Complex multi-arm designs are acceptable to patients with good PIS. Randomisation within each cohort provides outcome data that can inform clinical practice.