Bayesian Gaussian process classification from event-related brain potentials in Alzheimer’s disease

Event-related potentials (ERPs) have been shown to reflect neurodegenerative processes in Alzheimer’s disease (AD) and might qualify as non-invasive and cost-effective markers to facilitate the objectivization of AD assessment in daily clinical practice. Lately, the combination of multivariate pattern analysis (MVPA) and Gaussian process classification (GPC) has gained interest in the neuroscientific community. Here, we demonstrate how a MVPAGPC approach can be applied to electrophysiological data. Furthermore, in order to account for the temporal information of ERPs, we develop a novel method that integrates interregional synchrony of ERP time signatures. By using real-life ERP recordings of a prospective AD cohort study (PRODEM), we empirically investigate the usefulness of the proposed framework to build neurophysiological markers for single subject classification tasks. GPC outperforms the probabilistic reference method in both tasks, with the highest AUC overall (0.802) being achieved using the new spatiotemporal method in the prediction of rapid cognitive decline

New yeast expression platforms based on methylotrophic Hansenula polymorpha and Pichia pastoris and on dimorphic Arxula adeninivorans and Yarrowia lipolytica – A comparison

Yeasts combine the ease of genetic manipulation and fermentation of a microbial organism with the capability to secrete and to modify proteins according to a general eukaryotic scheme. Yeasts thus provide attractive platforms for the production of recombinant proteins. Here, four important species are presented and compared: the methylotrophic Hansenula polymorpha and Pichia pastoris, distinguished by an increasingly large track record as industrial platforms, and the dimorphic species Arxula adeninivorans and Yarrrowia lipolytica, not yet established as industrial platforms, but demonstrating promising technological potential, as discussed in this article.