Functional presynaptic HCN channels in the rat globus pallidus

Hyperpolarization-activated, cyclic nucleotide-gated cation (HCN) channels are expressed postsynaptically in the rodent globus pallidus (GP), where they play several important roles in controlling GP neuronal activity. To further elucidate the role of HCN channels in the GP, immunocytochemical and electrophysiological approaches were used to test the hypothesis that HCN channels are also expressed presynaptically on the local axon collaterals of GP neurons. At the electron microscopic level, immunoperoxidase labelling for HCN1 and HCN2 was localized in GP somata and dendritic processes, myelinated and unmyelinated axons, and axon terminals. One population of labelled terminals formed symmetric synapses with somata and proximal dendrites and were immunoreactive for parvalbumin, consistent with the axon collaterals of GABAergic GP projection neurons. In addition, labelling for HCN2 and, to a lesser degree, HCN1 was observed in axon terminals that formed asymmetric synapses and were immunoreactive for the vesicular glutamate transporter 2. Immunogold labelling demonstrated that HCN1 and HCN2 were located predominantly at extrasynaptic sites along the plasma membrane of both types of terminal. To determine the function of presynaptic HCN channels in the GP, we performed whole-cell recordings from GP neurons in vitro. Bath application of the HCN channel blocker ZD7288 resulted in an increase in the frequency of mIPSCs but had no effect on their amplitude, implying that HCN channels tonically regulate the release of GABA. Their presence, and predicted role in modulating transmitter release, represents a hitherto unidentified mechanism whereby HCN channels influence the activity of GP neurons. © The Authors (2007).

Investigation and development of techniques for the characterisation of the synthetic/biological interface

The purpose of this study is to increase our knowledge of the nature of the surface properties of polymeric materials and improve our understanding of how these factors influence the deposition of proteins to form a reactive biological/synthetic interface. A number of surface analytical techniques were identified as being of potential benefit to this investigation and included in a multidisciplinary research program. Cell adhesion in culture was the primary biological sensor of surface properties, and it showed that the cell response to different materials can be modified by adhesion promoting protein layers: cell adhesion is a protein-mediated event. A range of surface rugosity can be produced on polystyrene, and the results presented here show that surface rugosity does not play a major role in determining a material's cell adhesiveness. Contact angle measurements showed that surface energy (specifically the polar fraction) is important in promoting cell spreading on surfaces. The immunogold labelling technique indicated that there were small, but noticeable differences, between the distribution of proteins on a range of surfaces. This study has shown that surface analysis techniques have different sensitivities in terms of detection limits and depth probed, and these are important in determining the usefulness of the information obtained. The techniques provide information on differing aspects of the biological/synthetic interface, and the consequence of this is that a range of techniques is needed in any full study of such a complex field as the biomaterials area.

Histochemical and ultrastructural studies on the islets of langerhans of lean and obese hyperglycaemic mice with age

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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.