Site-specific labelling of proteins with a rigid lanthanide-binding tag

This paper describes a generic method for the site-specific attachment of lathanide complexes to proteins through a disulfide bond. The method is demonstrated by the attachment of a lanthanide-binding peptide tag to the single cysteine residue present in the N-terminal DNA-binding domain of the Echerichia coli arginine repressor. Complexes with Y3+, Tb3+, Dy3+, Ho3+, Er3+, Tm3+ and Yb3+ ions were formed and analysed by NMR spectroscopy. Large pseudocontact shifts and residual dipolar couplings were induced by the lanthanide-binding tag in the protein NMR spectrum, a result indicating that the tag was rigidly attached to the protein. The axial components of the magnetic susceptibility anisostropy tensors determined for the different lanthanide ions were similarly but not identically oriented. A single tag with a single protein attachment site can provide different pseudocontact shifts from different magnetic susceptibility tensors and thus provide valuable nondegenerate long-range structure information in the determination of 3D protein structures by NMR spectroscopy.

Automatic labelling of topic models learned from Twitter by summarisation

Latent topics derived by topic models such as Latent Dirichlet Allocation (LDA) are the result of hidden thematic structures which provide further insights into the data. The automatic labelling of such topics derived from social media poses however new challenges since topics may characterise novel events happening in the real world. Existing automatic topic labelling approaches which depend on external knowledge sources become less applicable here since relevant articles/concepts of the extracted topics may not exist in external sources. In this paper we propose to address the problem of automatic labelling of latent topics learned from Twitter as a summarisation problem. We introduce a framework which apply summarisation algorithms to generate topic labels. These algorithms are independent of external sources and only rely on the identification of dominant terms in documents related to the latent topic. We compare the efficiency of existing state of the art summarisation algorithms. Our results suggest that summarisation algorithms generate better topic labels which capture event-related context compared to the top-n terms returned by LDA. © 2014 Association for Computational Linguistics.

Phytoplankton-bacteria coupling under elevated CO2 levels: a stable isotope labelling study, 2010

The potential impact of rising carbon dioxide (CO2) on carbon transfer from phytoplankton to bacteria was investigated during the 2005 PeECE III mesocosm study in Bergen, Norway. Sets of mesocosms, in which a phytoplankton bloom was induced by nutrient addition, were incubated under 1x (~350 µatm), 2x (~700 µatm), and 3x present day CO2 (~1050 µatm) initial seawater and sustained atmospheric CO2 levels for 3 weeks. 13C labelled bicarbonate was added to all mesocosms to follow the transfer of carbon from dissolved inorganic carbon (DIC) into phytoplankton and subsequently heterotrophic bacteria, and settling particles. Isotope ratios of polar-lipid-derived fatty acids (PLFA) were used to infer the biomass and production of phytoplankton and bacteria. Phytoplankton PLFA were enriched within one day after label addition, whilst it took another 3 days before bacteria showed substantial enrichment. Group-specific primary production measurements revealed that coccolithophores showed higher primary production than green algae and diatoms. Elevated CO2 had a significant positive effect on post-bloom biomass of green algae, diatoms, and bacteria. A simple model based on measured isotope ratios of phytoplankton and bacteria revealed that CO2 had no significant effect on the carbon transfer efficiency from phytoplankton to bacteria during the bloom. There was no indication of CO2 effects on enhanced settling based on isotope mixing models during the phytoplankton bloom, but this could not be determined in the post-bloom phase. Our results suggest that CO2effects are most pronounced in the post-bloom phase, under nutrient limitation.