Cosmopolitans and 'cliques':everyday socialisation amongst Tamil student and young professional migrants to the UK

Among student and young professional migrants to the UK the opportunity for a global or cosmopolitan experience emerges as a motivating factor for migration. This article takes the example of student and young professional migrants to the UK from the South Indian state of Tamil Nadu, and explores how this cosmopolitan ambition plays out in the formation of UK social networks. Two 'types' of research participant are identified; 'self-conscious cosmopolitans' whose social networks are cross-ethnic, and others whose networks are largely co-ethnic and who are often derided by their self-consciously cosmopolitan counterparts as 'clannish' or 'cliquey'. The article asks how ethnicity emerges as salient (or not) in these migrants' talk and practice around UK social network formations. It then considers whether a co-ethnic social network necessarily limits the cosmopolitan experience, or whether this interpretation reflects a narrow understanding of cosmopolitanism which excludes the multiple inter-cultural encounters these migrants experience in their everyday lives. © The Author(s) 2013 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav.

Understanding motivations for entrepreneurship

The main aims of the study were to explore the different factors motivating entrepreneurs to start a business; explore whether motivations for entrepreneurship change and the impact of the recession; identify any correlates of motivations for entrepreneurship; and to examine the consequences of the different motivations for the entrepreneurial process and performance. The study is based on a re-survey of 1,000 entrepreneurs first identified in GEM and supplemented by in-depth interviews carried out with 40 entrepreneurs.

Assessing the motivation of MDs to use computer-based support at the point-of-care in the emergency department

A significant body of research investigates the acceptance of computer-based support (including devices and applications ranging from e-mail to specialized clinical systems, like PACS) among clinicians. Much of this research has focused on measuring the usability of systems using characteristics related to the clarity of interactions and ease of use. We propose that an important attribute of any clinical computer-based support tool is the intrinsic motivation of the end-user (i.e. a clinician) to use the system in practice. In this paper we present the results of a study that investigated factors motivating medical doctors (MDs) to use computer-based support. Our results demonstrate that MDs value computer-based support, find it useful and easy to use, however, uptake is hindered by perceived incompetence, and pressure and tension associated with using technology.

Accurate estimation of isoelectric point of protein and peptide based on amino acid sequences

Motivation: In any macromolecular polyprotic system - for example protein, DNA or RNA - the isoelectric point - commonly referred to as the pI - can be defined as the point of singularity in a titration curve, corresponding to the solution pH value at which the net overall surface charge - and thus the electrophoretic mobility - of the ampholyte sums to zero. Different modern analytical biochemistry and proteomics methods depend on the isoelectric point as a principal feature for protein and peptide characterization. Protein separation by isoelectric point is a critical part of 2-D gel electrophoresis, a key precursor of proteomics, where discrete spots can be digested in-gel, and proteins subsequently identified by analytical mass spectrometry. Peptide fractionation according to their pI is also widely used in current proteomics sample preparation procedures previous to the LC-MS/MS analysis. Therefore accurate theoretical prediction of pI would expedite such analysis. While such pI calculation is widely used, it remains largely untested, motivating our efforts to benchmark pI prediction methods. Results: Using data from the database PIP-DB and one publically available dataset as our reference gold standard, we have undertaken the benchmarking of pI calculation methods. We find that methods vary in their accuracy and are highly sensitive to the choice of basis set. The machine-learning algorithms, especially the SVM-based algorithm, showed a superior performance when studying peptide mixtures. In general, learning-based pI prediction methods (such as Cofactor, SVM and Branca) require a large training dataset and their resulting performance will strongly depend of the quality of that data. In contrast with Iterative methods, machine-learning algorithms have the advantage of being able to add new features to improve the accuracy of prediction. Contact: yperez@ebi.ac.uk Availability and Implementation: The software and data are freely available at https://github.com/ypriverol/pIR. Supplementary information: Supplementary data are available at Bioinformatics online.