Dublin City University

About: Dublin City University is a education organization based out in Dublin, Ireland. It is known for research contribution in the topics: Machine translation & Laser. The organization has 5904 authors who have published 17178 publications receiving 389376 citations. The organization is also known as: National Institute for Higher Education, Dublin & DCU.

Activation of TRPV1 mediates calcitonin gene-related peptide release, which excites trigeminal sensory neurons and is attenuated by a retargeted botulinum toxin with anti-nociceptive potential.

Abstract: Excessive release of inflammatory/pain mediators from peripheral sensory afferents renders nerve endings hyper-responsive, causing central sensitization and chronic pain. Herein, the basal release of proinflammatory calcitonin gene-related peptide (CGRP) was shown to increase the excitability of trigeminal sensory neurons in brainstem slices via CGRP1 receptors because the effect was negated by an antagonist, CGRP8–37. This excitatory action could be prevented by cleaving synaptosomal-associated protein of Mr 25,000 (SNAP-25) with botulinum neurotoxin (BoNT) type A, a potent inhibitor of exocytosis. Strikingly, BoNT/A proved unable to abolish the CGRP1 receptor-mediated effect of capsaicin, a nociceptive TRPV1 stimulant, or its elevation of CGRP release from trigeminal ganglionic neurons (TGNs) in culture. Although the latter was also not susceptible to BoNT/E, apparently attributable to a paucity of its acceptors (glycosylated synaptic vesicle protein 2 A/B), this was overcome by using a recombinant chimera (EA) of BoNT/A and BoNT/E. It bound effectively to the C isoform of SV2 abundantly expressed in TGNs and cleaved SNAP-25, indicating that its /A binding domain (HC) mediated uptake of the active /E protease. The efficacy of /EA is attributable to removal of 26 C-terminal residues from SNAP-25, precluding formation of SDS-resistant SNARE complexes. In contrast, exocytosis could be evoked after deleting nine of the SNAP-25 residues with /A but only on prolonged elevation of [Ca2+]i with capsaicin. This successful targeting of /EA to nociceptive neurons and inhibition of CGRP release in vitro and in situ highlight its potential as a new therapy for sensory dysmodulation and chronic pain.

Overview of the ShARe/CLEF eHealth Evaluation Lab 2013

Abstract: Discharge summaries and other free-text reports in healthcare transfer information between working shifts and geographic locations. Patients are likely to have difficulties in understanding their content, because of their medical jargon, non-standard abbreviations,and ward-specific idioms. This paper reports on an evaluation lab with an aim to support the continuum of care by developing methods and resources that make clinical reports in English easier to understand for patients, and which helps them in finding information related to their condition.

High-level feature detection from video in TRECVid: a 5-year retrospective of achievements

Abstract: Successful and effective content-based access to digital video requires fast, accurate and scalable methods to determine the video content automatically. A variety of contemporary approaches to this rely on text taken from speech within the video, or on matching one video frame against others using low-level characteristics like colour, texture or shapes, or on determining and matching objects appearing within the video. Possibly the most important technique, however, is one that determines the presence or absence of a high-level or semantic feature, within a video clip or shot. By utilizing dozens, hundreds or even thousands of such semantic features we can support many kinds of content-based video navigation. Critically, however, this depends on being able to determine whether each feature is or is not present in a video clip. The last 5 years have seen much progress in the development of techniques to determine the presence of semantic features within video. This progress can be tracked in the annual TRECVid benchmarking activity where dozens of research groups measure the effectiveness of their techniques on common data and using an open, metrics-based approach. In this chapter we summarize the work done on the TRECVid high-level feature task, showing the progress made year-on-year. This provides a fairly comprehensive statement on where the state-of-the-art is regarding this important task, not just for one research group or for one approach, but across the spectrum. We then use this past and on-going work as a basis for highlighting the trends that are emerging in this area, and the questions which remain to be addressed before we can achieve large-scale, fast and reliable high-level feature detection on video.