Reflections on conducting research on the ‘war on terror’: religious identity, subjectivity and emotions

In recent years there has been an increase in literature which has explored the insider/outsider position through ethnic identities. However, there remains a neglect of religious identities, even though it could be argued that religious identities have become increasingly important through being prominent in international issues such as the ‘war on terror’ and the Middle East conflict. Through drawing on the concept of subjectivity, I reflect on research I conducted on the impact of the ‘war on terror’ on British Muslims. I explore the space between the insider/outsider position demonstrating how my various subjectivities – the ‘non-Islamic appearance I’, the ‘Muslim I’, the ‘personal I’, the ‘exploring I’, the ‘Kashmiri I’ or the ‘Pakistani I’, the ‘status I’ and the ‘outsider I’ – assisted in establishing trust, openness and commonality. I conclude by demonstrating how the ‘emotional I’ allowed me to manage my own emotions and participants emotions.

Ultra-deep sequencing provides insights into the virology of hepatitis C super-infections in a case of three sequential infections with different genotypes

The current epidemic of Hepatitis C infection in HIV-positive men who have sex with men is associated with increasing use of recreational drugs. Multiple HCV infections have been reported in haemophiliacs and intravenous drug users. Using ultra-deep sequencing analysis, we present the case of an HIV-positive MSM with evidence of three sequential HCV infections, each occurring during the acute phase of the preceding infection, following risk exposures. We observed rapid replacement of the original strain by the incoming genotype at subsequent time points. The impact of HCV super-infection remains unclear and UDS may provide new insights.

A Single-Frame Super-Resolution Innovative Approach

Super-resolution refers to the process of obtaining a high resolution image from one or more low resolution images. In this work, we present a novel method for the super-resolution problem for the limited case, where only one image of low resolution is given as an input. The proposed method is based on statistical learning for inferring the high frequencies regions which helps to distinguish a high resolution image from a low resolution one. These inferences are obtained from the correlation between regions of low and high resolution that come exclusively from the image to be super-resolved, in term of small neighborhoods. The Markov random fields are used as a model to capture the local statistics of high and low resolution data when they are analyzed at different scales and resolutions. Experimental results show the viability of the method.