Whiteness and diasporic Irishness: nation, gender and class

Whiteness is often detached from the notion of diaspora in the recent flurry of interest in the phenomenon, yet it is a key feature of some of the largest and oldest displacements. This paper explores the specific contexts of white racial belonging and status over two centuries in two main destinations of the Irish diaspora, the USA and Britain. Its major contribution is a tracing of the untold story of ‘How the Irish became white in Britain’ to parallel and contrast with the much more fully developed narrative in the USA. It argues that, contrary to popular belief, the racialisation of the Irish in England did not fade away at the end of the nineteenth century but became transmuted in new forms which have continued to place the ‘white’ Irish outside the boundaries of the English nation. These have been strangely ignored by social scientists, who conflate Irishness and working-class identities in England without acknowledging the distinctive contribution of Irish backgrounds to constructions of class difference. Gender locates Irish women and men differently in relation to these class positions, for example allowing mothers to be blamed for the perpetuation of the underclass. Class and gender are also largely unrecognised dimensions of Irish ethnicity in the USA, where the presence of ‘poor white’ neighbourhoods continues to challenge the iconic story of Irish upward mobility. Irishness thus remains central to the construction of mainstream ‘white’ identities in both the USA and Britain into the twenty-first century

Estimating the motion of plant root cells from in vivo confocal laser scanning microscopy images

This is an author-created version of a paper to be published in the Springer journal Machine Vision and Applications. The published version will be available at www.springerlink.com) Images of cellular structures in growing plant roots acquired using confocal laser scanning microscopy (CLSM) have some unusual properties that make motion estimation challenging. These include multiple motions, non-Gaussian noise and large regions with little spatial structure. In this paper, a method for motion estimation is described that uses a robust multi-frame likelihood model and a technique for estimating uncertainty. An efficient region-based matching approach was used followed by a forward projection method. Over small timescales the dynamics are simple (approximately locally constant) and the change in appearance small. Therefore a constant local velocity model is used and the MAP estimate of the joint probability over a set of frames is recovered. Occurrences of multiple modes in the posterior are detected, and in the case of a single dominant mode, motion is inferred using Laplace’e method. The method was applied to several Arabidopsis thaliana root growth sequences with varying levels of success. In addition, comparative results are given for three alternative motion estimation approaches, the Kanade-Lucas-Tomasi tracker, Black and Anandan’s robust smoothing method, and Markov random field based methods.