Fluid Ontologies in the Search for MH370

This paper gives an account of the disappearance of Malaysian Airways Flight MH370 into the southern Indian Ocean in March 2014 and analyses the rare glimpses into remote ocean space this incident opened up. It follows the tenuous clues as to where the aeroplane might have come to rest after it disappeared from radar screens – seven satellite pings, hundreds of pieces of floating debris and six underwater sonic recordings – as ways of entering into and thinking about ocean space. The paper pays attention to and analyses this space on three registers – first, as a fluid, more-than-human materiality with particular properties and agencies; second, as a synthetic situation, a composite of informational bits and pieces scopically articulated and augmented; and third, as geopolitics, delineated by the protocols of international search and rescue. On all three registers – as matter, as data and as law – the ocean is shown to be ontologically fluid, a world defined by movement, flow and flux, posing intractable difficulties for human interactions with it.

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.