A new population of terrestrial gamma-ray flashes in the RHESSI data

Terrestrial gamma-ray flashes (TGFs) are the most energetic photon phenomenon occurring naturally on Earth. An outstanding question is as follows: Are these flashes just a rare exotic phenomenon or are they an intrinsic part of lightning discharges and therefore occurring more frequently than previously thought? All measurements of TGFs so far have been limited by the dynamic range and sensitivity of spaceborne instruments. In this paper we show that there is a new population of weak TGFs that has not been identified by search algorithms. We use the World Wide Lightning Location Network (WWLLN) to identify lightning that occurred in 2006 and 2012 within the 800 km field of view of Reuven Ramaty High Energy Solar Spectroscopic Imager (RHESSI). By superposing 740,210 100 ms RHESSI data intervals, centered at the time of the WWLLN detected lightning, we identify at least 141 and probably as many as 191 weak TGFs that were not part of the second RHESSI data catalogue. This supports the suggestion that the global TGF production rate is larger than previously reported

Adapting phrase-based machine translation to normalise medical terms in social media messages

Previous studies have shown that health reports in social media, such as DailyStrength and Twitter, have potential for monitoring health conditions (e.g. adverse drug reactions, infectious diseases) in particular communities. However, in order for a machine to understand and make inferences on these health conditions, the ability to recognise when laymen’s terms refer to a particular medical concept (i.e. text normalisation) is required. To achieve this, we propose to adapt an existing phrase-based machine translation (MT) technique and a vector representation of words to map between a social media phrase and a medical concept. We evaluate our proposed approach using a collection of phrases from tweets related to adverse drug reactions. Our experimental results show that the combination of a phrase-based MT technique and the similarity between word vector representations outperforms the baselines that apply only either of them by up to 55%.This work was supported by the EPSRC [grant number EP/M005089/1].This is the author accepted manuscript. The final version is available from the Association for Computational Linguistics via https://aclweb.org/anthology/D/D15/D15-1194.pd

Modelling the combination of generic and target domain embeddings in a convolutional neural network for sentence classification

Word embeddings have been successfully exploited in systems for NLP tasks, such as parsing and text classification. It is intuitive that word embeddings created from a larger corpus would provide a better coverage of vocabulary. Meanwhile, word embeddings trained on a corpus related to the given task or target domain would more effectively represent the semantics of terms. However, in some emerging domains (e.g. bio-surveillance using social media data), it may be difficult to find a domain corpus that is large enough for creating effective word embeddings. To deal with this problem, we propose novel approaches that use both word embeddings created from generic and target domain corpora. Our experimental results on sentence classifi- cation tasks show that our approaches significantly improve the performance of an existing convolutional neural network that achieved state-of-the-art performances on several text classification tasks.Engineering and Physical Sciences Research Council (Grant ID: EP/M005089/1)This is the final version of the article. It first appeared from the Association for Computational Linguistics via http://www.aclweb.org/anthology/W/W16

Bidirectional LSTM for Named Entity Recognition in Twitter Messages

In this paper, we present our approach for named entity recognition in Twitter messages that we used in our participation in the Named Entity Recognition in Twitter shared task at the COLING 2016 Workshop on Noisy User-generated text (WNUT). The main challenge that we aim to tackle in our participation is the short, noisy and colloquial nature of tweets, which makes named entity recognition in Twitter messages a challenging task. In particular, we investigate an approach for dealing with this problem by enabling bidirectional long short-term memory (LSTM) to automatically learn orthographic features without requiring feature engineering. In comparison with other systems participating in the shared task, our system achieved the most effective performance on both the ‘segmentation and categorisation’ and the ‘segmentation only’ sub-tasks

Pattern formation by lateral inhibition with feedback: a mathematical model of Delta-Notch intercellular signalling

In many developing tissues, adjacent cells diverge in character so as to create a fine-grained pattern of cells in contrasting states of differentiation. It has been proposed that such patterns can be generated through lateral inhibition—a type cells–cell interaction whereby a cell that adopts a particular fate inhibits its immediate neighbours from doing likewise. Lateral inhibition is well documented in flies, worms and vertebrates. In all of these organisms, the transmembrane proteins Notch and Delta (or their homologues) have been identified as mediators of the interaction—Notch as receptor, Delta as its ligand on adjacent cells. However, it is not clear under precisely what conditions the Delta-Notch mechanism of lateral inhibition can generate the observed types of pattern, or indeed whether this mechanism is capable of generating such patterns by itself. Here we construct and analyse a simple and general mathematical model of such contact-mediated lateral inhibition. In accordance with experimental data, the model postulates that receipt of inhibition (i.e. activation of Notch) diminishes the ability to deliver inhibition (i.e. to produce active Delta). This gives rise to a feedback loop that can amplify differences between adjacent cells. We investigate the pattern-forming potential and temporal behavior of this model both analytically and through numerical simulation. Inhomogeneities are self-amplifying and develop without need of any other machinery, provided the feedback is sufficiently strong. For a wide range of initial and boundary conditions, the model generates fine-grained patterns similar to those observed in living systems

Developing culturally competence in caring for people with mental health conditions

Adult nurses working in diverse environments will come into contact with people who happen to have a mental illness diagnosis. A lack of opportunity to develop specific skills, or a lack of confidence in approaching people who may behave in unfamiliar or challenging ways can result in adverse care experiences for patients. Person critical reflection, confidence building and specific knowledge can assist adult nurses in meeting this need

Conservation status of New Zealand freshwater invertebrates, 2013

The conservation status of 644 freshwater invertebrate taxa, across five Phyla, 28 Orders and 75 Families, was assessed using the New Zealand Threat Classification System (NZTCS) criteria. Forty-six species were ranked Nationally Critical, 11 Nationally Endangered and 16 Nationally Vulnerable. One hundred and seventy-two taxa were listed as Data Deficient. A full list is presented, along with summaries and brief notes on the most important changes. This list replaces all previous NZTCS lists for freshwater invertebrates