Vulnerability of exporting nations to the development of a carbon label in the United Kingdom

Carbon labels inform consumers about the amount of greenhouse gases (GHGs) released during the production and consumption of goods, including food. In the future consumer and legislative responses to carbon labels may favour goods with lower emissions, and thereby change established supply chains. This may have unintended consequences. We present the carbon footprint of three horticultural goods of different origins supplied to the United Kingdom market: lettuce, broccoli and green beans. Analysis of these footprints enables the characterisation of three different classes of vulnerability which are related to: transport, national economy and supply chain specifics. There is no simple relationship between the characteristics of an exporting country and its vulnerability to the introduction of a carbon label. Geographically distant developing countries with a high level of substitutable exports to the UK are most vulnerable. However, many developing countries have low vulnerability as their main exports are tropical crops which would be hard to substitute with local produce. In the short term it is unlikely that consumers will respond to carbon labels in such a way that will have major impacts in the horticultural sector. Labels which require contractual reductions in GHG emissions may have greater impacts in the short term.

Vulnerability and disobedience:new repertoires in French environmental protests

The emergence of the counter-globalisation movement in France has been accompanied by an apparent diversification of social protest repertoires. Protest events carried out by groups associated with a wide array of issues have been remarkable for their use of spectacular and novel actions, while civil disobedience campaigns have been prominent features of environmental and civil rights protests in particular. Drawing on a number of examples of contemporary environmental and global justice campaigns, opposing advertising, four-wheeled drive vehicles, nuclear energy and, especially, open field trials of genetically modified crops, this article discusses the rise of such new forms of protest, placing them in the wider context of transformations in protest repertoires in France. It identifies key examples of innovation, before discussing the twin processes of diffusion and domestication that shape them. It is argued that, although transnational agents and processes are key determinants of repertoire innovation, it is vital to identify the national, movement and sectoral contexts and discourses which enable the naturalisation and legitimisation of new action forms.

Towards person-centered neuroimaging markers for resilience and vulnerability in Bipolar Disorder

Improved clinical care for Bipolar Disorder (BD) relies on the identification of diagnostic markers that can reliably detect disease-related signals in clinically heterogeneous populations. At the very least, diagnostic markers should be able to differentiate patients with BD from healthy individuals and from individuals at familial risk for BD who either remain well or develop other psychopathology, most commonly Major Depressive Disorder (MDD). These issues are particularly pertinent to the development of translational applications of neuroimaging as they represent challenges for which clinical observation alone is insufficient. We therefore applied pattern classification to task-based functional magnetic resonance imaging (fMRI) data of the n-back working memory task, to test their predictive value in differentiating patients with BD (n=30) from healthy individuals (n=30) and from patients' relatives who were either diagnosed with MDD (n=30) or were free of any personal lifetime history of psychopathology (n=30). Diagnostic stability in these groups was confirmed with 4-year prospective follow-up. Task-based activation patterns from the fMRI data were analyzed with Gaussian Process Classifiers (GPC), a machine learning approach to detecting multivariate patterns in neuroimaging datasets. Consistent significant classification results were only obtained using data from the 3-back versus 0-back contrast. Using contrast, patients with BD were correctly classified compared to unrelated healthy individuals with an accuracy of 83.5%, sensitivity of 84.6% and specificity of 92.3%. Classification accuracy, sensitivity and specificity when comparing patients with BD to their relatives with MDD, were respectively 73.1%, 53.9% and 94.5%. Classification accuracy, sensitivity and specificity when comparing patients with BD to their healthy relatives were respectively 81.8%, 72.7% and 90.9%. We show that significant individual classification can be achieved using whole brain pattern analysis of task-based working memory fMRI data. The high accuracy and specificity achieved by all three classifiers suggest that multivariate pattern recognition analyses can aid clinicians in the clinical care of BD in situations of true clinical uncertainty regarding the diagnosis and prognosis.