A new device for the study of electron-ion interactions: The Belfast EBIT

An electron beam ion trap ( EBIT) has been designed and is currently under construction for use in atomic physics experiments at the Queen's University, Belfast. In contrast to traditional EBITs where pairs of superconducting magnets are used, a pair of permanent magnets will be used to compress the electron beam. The permanent magnets have been designed in conjunction with bespoke vacuum ports to give unprecedented access for photon detection. Furthermore, the bespoke vacuum ports facillitate a versatile, reconfigurable trap structure able to accommodate various in-situ detectors and in-line charged particle analysers. Although the machine will have somewhat lower specifications than many existing EBITs in terms of beam current density, it is hoped that the unique features will facilitate a number of hitherto impossible studies involving interactions between electrons and highly charged ions. In this article the new machine's design is outlined along with some suggestions of the type of process to be studied once the construction is completed.

Identification of Peptide Inhibitors of Enveloped Viruses Using Support Vector Machine

The peptides derived from envelope proteins have been shown to inhibit the protein-protein interactions in the virus membrane fusion process and thus have a great potential to be developed into effective antiviral therapies. There are three types of envelope proteins each exhibiting distinct structure folds. Although the exact fusion mechanism remains elusive, it was suggested that the three classes of viral fusion proteins share a similar mechanism of membrane fusion. The common mechanism of action makes it possible to correlate the properties of self-derived peptide inhibitors with their activities. Here we developed a support vector machine model using sequence-based statistical scores of self-derived peptide inhibitors as input features to correlate with their activities. The model displayed 92% prediction accuracy with the Matthew’s correlation coefficient of 0.84, obviously superior to those using physicochemical properties and amino acid decomposition as input. The predictive support vector machine model for self- derived peptides of envelope proteins would be useful in development of antiviral peptide inhibitors targeting the virus fusion process.