’Team GB’ and London 2012: The Paradox of National and Global Identities

This article explores the problems associated with ’national identity’ in the UK and examines the tensions arising between the international and local dimensions of the games through examples of domestic (UK) and international (Brazil, Chicago) media coverage of the key debates relating to London’s period of preparation. The chapter proposes a conception of London 2012 as exemplar of an event poised to generate insights and experiences connected to a new politics of ’cosmopolitan’ identity; insights central to grasping the cultural politics of contemporary urban development-and the paradoxes of national identity in current discourses of Olympism. Properly speaking, cosmopolitanism suits those people who have no country, while internationalism should be the state of mind of those who love their country above all, who seek to draw to it the friendship of foreigners by professing for the countries of those foreigners an intelligent and enlightened sympathy. © 2010 Taylor & Francis

Discovering study-specific gene regulatory networks

This article has been made available through the Brunel Open Access Publishing Fund.Microarrays are commonly used in biology because of their ability to simultaneously measure thousands of genes under different conditions. Due to their structure, typically containing a high amount of variables but far fewer samples, scalable network analysis techniques are often employed. In particular, consensus approaches have been recently used that combine multiple microarray studies in order to find networks that are more robust. The purpose of this paper, however, is to combine multiple microarray studies to automatically identify subnetworks that are distinctive to specific experimental conditions rather than common to them all. To better understand key regulatory mechanisms and how they change under different conditions, we derive unique networks from multiple independent networks built using glasso which goes beyond standard correlations. This involves calculating cluster prediction accuracies to detect the most predictive genes for a specific set of conditions. We differentiate between accuracies calculated using cross-validation within a selected cluster of studies (the intra prediction accuracy) and those calculated on a set of independent studies belonging to different study clusters (inter prediction accuracy). Finally, we compare our method's results to related state-of-the art techniques. We explore how the proposed pipeline performs on both synthetic data and real data (wheat and Fusarium). Our results show that subnetworks can be identified reliably that are specific to subsets of studies and that these networks reflect key mechanisms that are fundamental to the experimental conditions in each of those subsets