New horizons in mouse immunoinformatics:reliable in silico prediction of mouse class I histocompatibility major complex peptide binding affinity

Quantitative structure–activity relationship (QSAR) analysis is a main cornerstone of modern informatic disciplines. Predictive computational models, based on QSAR technology, of peptide-major histocompatibility complex (MHC) binding affinity have now become a vital component of modern day computational immunovaccinology. Historically, such approaches have been built around semi-qualitative, classification methods, but these are now giving way to quantitative regression methods. The additive method, an established immunoinformatics technique for the quantitative prediction of peptide–protein affinity, was used here to identify the sequence dependence of peptide binding specificity for three mouse class I MHC alleles: H2–Db, H2–Kb and H2–Kk. As we show, in terms of reliability the resulting models represent a significant advance on existing methods. They can be used for the accurate prediction of T-cell epitopes and are freely available online (http://www.jenner.ac.uk/MHCPred).

Horizons régionaux et variations océaniennes des territoires français

« L’identité régionale […] repose sur une réalité concrète : la mer est aussi réelle que vous et moi […]. Je ne fais que constater qu’elle forge le caractère de cette planète, qu’elle est une source majeure de notre subsistance et que nous l’avons tous en partage, où que nous soyons en Océanie. Mais au-delà des épreuves quotidiennes, la mer nous lie les uns aux autres. Elle est source de sagesse infinie. La mer est notre métaphore la plus puissante. L’océan est en nous. » (Hau’Ofa, 2015 [1997] : 55-56). Souvent présentés de manière séparée, les territoires français dans le Pacifique Sud, objets de ce dossier, possèdent cependant des horizons régionaux intéressants qui mettent en exergue la variété des écosystèmes politiques de l’espace océanien1. Les facettes multidimensionnelles des évolutions humaines montrent une « mer d’îles » océaniennes en mouvement, selon l’intellectuel tongien Epeli Hau’Ofa (2008 [1993]). Sa pensée alimente la formation d’une identité régionale océanienne, à p... « Regional identity […] has been constructed on a foundation of concrete reality. That the sea is as real as you and I […], that it shapes the character of this planet, that it is a major source of our sustenance, that it is something that we all share in common wherever we are in Oceania – all of these are statements of fact. Yet beyond that level of everyday experience, the sea is our pathway to each other and to everyone else, the sea is our endless saga, the sea is our most powerful metaphor, the ocean is in us. » (Hau’Ofa, 2015 [1997]: 55-56). Often conceived of as separate entities, French overseas territories in the South Pacific, as the subject of this special issue, nevertheless possess interesting regional horizons, which highlight the variety of political ecosystems of Oceania. The multidimensional facets of human evolution show an Oceanian “Sea of Islands” in constant movement, according to the Tongan intellectual Epeli Hau’Ofa (2008 [1993]). His philosophy contributes to...

Model specification and forecasting foreign exchange rates with vector autoregressions

This study examines the forecasting accuracy of alternative vector autoregressive models each in a seven-variable system that comprises in turn of daily, weekly and monthly foreign exchange (FX) spot rates. The vector autoregressions (VARs) are in non-stationary, stationary and error-correction forms and are estimated using OLS. The imposition of Bayesian priors in the OLS estimations also allowed us to obtain another set of results. We find that there is some tendency for the Bayesian estimation method to generate superior forecast measures relatively to the OLS method. This result holds whether or not the data sets contain outliers. Also, the best forecasts under the non-stationary specification outperformed those of the stationary and error-correction specifications, particularly at long forecast horizons, while the best forecasts under the stationary and error-correction specifications are generally similar. The findings for the OLS forecasts are consistent with recent simulation results. The predictive ability of the VARs is very weak.