Focused molecular analysis of small cell lung cancer: feasibility in routine clinical practice.

Background: There is an urgent need to identify molecular signatures in small cell lung cancer (SCLC) that may select patients who are likely to respond to molecularly targeted therapies. In this study, we investigate the feasibility of undertaking focused molecular analyses on routine diagnostic biopsies in patients with SCLC.

Methods: A series of histopathologically confirmed formalin-fixed, paraffin-embedded SCLC specimens were analysed for epidermal growth factor receptors (EGFR), KRAS, NRAS and BRAF mutations, ALK gene rearrangements and MET amplification. EGFR and KRAS mutation testing was evaluated using real time polymerase chain reaction (RT-PCR cobas®), BRAF and NRAS mutations using multiplex PCR and capillary electrophoresis-single strand conformation analysis, and ALK and MET aberrations with fluorescent in situ hybridization. All genetic aberrations detected were validated independently.

Results: A total of 105 patients diagnosed with SCLC between July 1990 and September 2006 were included. 60 (57 %) patients had suitable tumour tissue for molecular testing. 25 patients were successfully evaluated for all six pre-defined molecular aberrations. Eleven patients failed all molecular analysis. No mutations in EGFR, KRAS and NRAS were detected, and no ALK gene rearrangements or MET gene amplifications were identified. A V600E substitution in BRAF was detected in a Caucasian male smoker diagnosed with SCLC with squamoid and glandular features.

Conclusion: The paucity of patients with sufficient tumour tissue, quality of DNA extracted and low frequency of aberrations detected indicate that alternative molecular characterisation approaches are necessary, such as the use of circulating plasma DNA in patients with SCLC.

Hate crime legislation and violence against sex workers in Ireland: Lessons in Policy and Practice

Drawing on data from UglyMugs.ie (a reporting mechanism for sex workers) this paper considers whether crimes against sex workers should be considered as hate crimes. In many ways, the debates around hate crime in the UK are more developed than in Ireland. As yet the Irish State has yet to criminalise the ‘hate’ element of crime and has been severely criticised for its relatively lacklustre approach to recording incidents of bias or hate crimes against certain social groups. The paper adopts the structural understanding of hate crime espoused by Barbara Perry (2001) who frames the dynamics of hate crime within a complex interplay of political, social and cultural factors. In our analysis we consider what is termed ‘whorephobia’ through the ambit of criminalisation and stigmatisation, gender and heteronormativity in Irish society, and the gendered nature of policing in both parts of Ireland.

Video anomaly detection in real-time on a Power-Aware Heterogeneous Platform

FPGAs and GPUs are often used when real-time performance in video processing is required. An accelerated processor is chosen based on task-specific priorities (power consumption, processing time and detection accuracy), and this decision is normally made once at design time. All three characteristics are important, particularly in battery-powered systems. Here we propose a method for moving selection of processing platform from a single design-time choice to a continuous run time one.We implement Histogram of Oriented Gradients (HOG) detectors for cars and people and Mixture of Gaussians (MoG) motion detectors running across FPGA, GPU and CPU in a heterogeneous system. We use this to detect illegally parked vehicles in urban scenes. Power, time and accuracy information for each detector is characterised. An anomaly measure is assigned to each detected object based on its trajectory and location, when compared to learned contextual movement patterns. This drives processor and implementation selection, so that scenes with high behavioural anomalies are processed with faster but more power hungry implementations, but routine or static time periods are processed with power-optimised, less accurate, slower versions. Real-time performance is evaluated on video datasets including i-LIDS. Compared to power-optimised static selection, automatic dynamic implementation mapping is 10% more accurate but draws 12W extra power in our testbed desktop system.