Showing papers by "Dublin City University published in 2017"

Rare coding variants in PLCG2, ABI3, and TREM2 implicate microglial-mediated innate immunity in Alzheimer's disease

Abstract: We identified rare coding variants associated with Alzheimer's disease in a three-stage case–control study of 85,133 subjects. In stage 1, we genotyped 34,174 samples using a whole-exome microarray. In stage 2, we tested associated variants (P < 1 × 10−4) in 35,962 independent samples using de novo genotyping and imputed genotypes. In stage 3, we used an additional 14,997 samples to test the most significant stage 2 associations (P < 5 × 10−8) using imputed genotypes. We observed three new genome-wide significant nonsynonymous variants associated with Alzheimer's disease: a protective variant in PLCG2 (rs72824905: p.Pro522Arg, P = 5.38 × 10−10, odds ratio (OR) = 0.68, minor allele frequency (MAF)cases = 0.0059, MAFcontrols = 0.0093), a risk variant in ABI3 (rs616338: p.Ser209Phe, P = 4.56 × 10−10, OR = 1.43, MAFcases = 0.011, MAFcontrols = 0.008), and a new genome-wide significant variant in TREM2 (rs143332484: p.Arg62His, P = 1.55 × 10−14, OR = 1.67, MAFcases = 0.0143, MAFcontrols = 0.0089), a known susceptibility gene for Alzheimer's disease. These protein-altering changes are in genes highly expressed in microglia and highlight an immune-related protein–protein interaction network enriched for previously identified risk genes in Alzheimer's disease. These genetic findings provide additional evidence that the microglia-mediated innate immune response contributes directly to the development of Alzheimer's disease.

The 2017 Plasma Roadmap: Low temperature plasma science and technology

Abstract: Journal of Physics D: Applied Physics published the first Plasma Roadmap in 2012 consisting of the individual perspectives of 16 leading experts in the various sub-fields of low temperature plasma science and technology. The 2017 Plasma Roadmap is the first update of a planned series of periodic updates of the Plasma Roadmap. The continuously growing interdisciplinary nature of the low temperature plasma field and its equally broad range of applications are making it increasingly difficult to identify major challenges that encompass all of the many sub-fields and applications. This intellectual diversity is ultimately a strength of the field. The current state of the art for the 19 sub-fields addressed in this roadmap demonstrates the enviable track record of the low temperature plasma field in the development of plasmas as an enabling technology for a vast range of technologies that underpin our modern society. At the same time, the many important scientific and technological challenges shared in this roadmap show that the path forward is not only scientifically rich but has the potential to make wide and far reaching contributions to many societal challenges.

Glucose Sensing for Diabetes Monitoring: Recent Developments.

Abstract: This review highlights recent advances towards non-invasive and continuous glucose monitoring devices, with a particular focus placed on monitoring glucose concentrations in alternative physiological fluids to blood.

Alphas, Betas, and Incels: Theorizing the Masculinities of the Manosphere

Abstract: Since the emergence of Web 2.0 and social media, a particularly toxic brand of antifeminism has become evident across a range of online networks and platforms. Despite multiple internal conflicts and contradictions, these diverse assemblages are generally united in their adherence to Red Pill “philosophy,” which purports to liberate men from a life of feminist delusion. This loose confederacy of interest groups, broadly known as the manosphere, has become the dominant arena for the communication of men’s rights in Western culture. This article identifies the key categories and features of the manosphere and subsequently seeks to theorize the masculinities that characterize this discursive space. The analysis reveals that, while there are some continuities with older variants of antifeminism, many of these new toxic assemblages appear to complicate the orthodox alignment of power and dominance with hegemonic masculinity by operationalizing tropes of victimhood, “beta masculinity,” and involuntary celibacy ...

KVASIR: A Multi-Class Image Dataset for Computer Aided Gastrointestinal Disease Detection

Abstract: Automatic detection of diseases by use of computers is an important, but still unexplored field of research. Such innovations may improve medical practice and refine health care systems all over the world. However, datasets containing medical images are hardly available, making reproducibility and comparison of approaches almost impossible. In this paper, we present KVASIR, a dataset containing images from inside the gastrointestinal (GI) tract. The collection of images are classified into three important anatomical landmarks and three clinically significant findings. In addition, it contains two categories of images related to endoscopic polyp removal. Sorting and annotation of the dataset is performed by medical doctors (experienced endoscopists). In this respect, KVASIR is important for research on both single- and multi-disease computer aided detection. By providing it, we invite and enable multimedia researcher into the medical domain of detection and retrieval.

Datestamping the Bitcoin and Ethereum Bubbles

Abstract: We examine the existence and dates of pricing bubbles in Bitcoin and Ethereum, two popular cryptocurrencies using the Phillips et al. (2011) methodology. In contrast to previous papers, we examine the fundamental drivers of the price. Having derived ratios that are economically and computationally sensible, we use these variables to detect and datestamp bubbles. Our conclusion is that there are periods of clear bubble behaviour, with Bitcoin now almost certainly in a bubble phase.

Metabolism of ketone bodies during exercise and training: physiological basis for exogenous supplementation

Abstract: Optimising training and performance through nutrition strategies is central to supporting elite sportspeople, much of which has focused on manipulating the relative intake of carbohydrate and fat and their contributions as fuels for energy provision. The ketone bodies, namely acetoacetate, acetone and β-hydroxybutyrate (βHB), are produced in the liver during conditions of reduced carbohydrate availability and serve as an alternative fuel source for peripheral tissues including brain, heart and skeletal muscle. Ketone bodies are oxidised as a fuel source during exercise, are markedly elevated during the post-exercise recovery period, and the ability to utilise ketone bodies is higher in exercise-trained skeletal muscle. The metabolic actions of ketone bodies can alter fuel selection through attenuating glucose utilisation in peripheral tissues, anti-lipolytic effects on adipose tissue, and attenuation of proteolysis in skeletal muscle. Moreover, ketone bodies can act as signalling metabolites, with βHB acting as an inhibitor of histone deacetylases, an important regulator of the adaptive response to exercise in skeletal muscle. Recent development of ketone esters facilitates acute ingestion of βHB that results in nutritional ketosis without necessitating restrictive dietary practices. Initial reports suggest this strategy alters the metabolic response to exercise and improves exercise performance, while other lines of evidence suggest roles in recovery from exercise. The present review focuses on the physiology of ketone bodies during and after exercise and in response to training, with specific interest in exploring the physiological basis for exogenous ketone supplementation and potential benefits for performance and recovery in athletes.

GRACKLE: a chemistry and cooling library for astrophysics

Abstract: We present the grackle chemistry and cooling library for astrophysical simulations and models grackle provides a treatment of non-equilibrium primordial chemistry and cooling for H, D and He species, including H2 formation on dust grains; tabulated primordial and metal cooling; multiple ultraviolet background models; and support for radiation transfer and arbitrary heat sources The library has an easily implementable interface for simulation codes written in c, c++ and fortran as well as a python interface with added convenience functions for semi-analytical models As an open-source project, grackle provides a community resource for accessing and disseminating astrochemical data and numerical methods We present the full details of the core functionality, the simulation and python interfaces, testing infrastructure, performance and range of applicability grackle is a fully open-source project and new contributions are welcome

Algorithmic governance: Developing a research agenda through the power of collective intelligence:

Abstract: The authors would like to acknowledge the funding of the Irish Research Council (New Horizons Grant) and the Whitaker Institute, NUI Galway, without whose support this paper would not have been possible.

Pretreatment techniques used in biogas production from grass

Abstract: Grass is being considered as a potential feedstock for biogas production, due to its low water consumption compared to other crops, and the fact that it can be cultivated in non-arable lands, avoiding the direct competition with food crops. However, biogas production is limited by the characteristics of the feedstock; in particular its complex lignocellulosic structure. Hence, different pretreatment methods are being investigated for grass structure disruption before undergoing the anaerobic digestion process. The aim of this paper is to review current knowledge on pretreatment techniques used for grassland biomass. Pretreatment techniques were categorized into mechanical, microwave, thermal, chemical and biological groups. The effect of the application of each studied methods on the biogas yield and on the energy balance is discussed. A further comparison between the covered techniques was revealed.

Lexically Constrained Decoding for Sequence Generation Using Grid Beam Search

Abstract: We present Grid Beam Search (GBS), an algorithm which extends beam search to allow the inclusion of pre-specified lexical constraints. The algorithm can be used with any model that generates a sequence $ \mathbf{\hat{y}} = \{y_{0}\ldots y_{T}\} $, by maximizing $ p(\mathbf{y} | \mathbf{x}) = \prod\limits_{t}p(y_{t} | \mathbf{x}; \{y_{0} \ldots y_{t-1}\}) $. Lexical constraints take the form of phrases or words that must be present in the output sequence. This is a very general way to incorporate additional knowledge into a model's output without requiring any modification of the model parameters or training data. We demonstrate the feasibility and flexibility of Lexically Constrained Decoding by conducting experiments on Neural Interactive-Predictive Translation, as well as Domain Adaptation for Neural Machine Translation. Experiments show that GBS can provide large improvements in translation quality in interactive scenarios, and that, even without any user input, GBS can be used to achieve significant gains in performance in domain adaptation scenarios.

Determining the Role of the Internet in Violent Extremism and Terrorism: Six Suggestions for Progressing Research

Abstract: Some scholars and others are skeptical of a significant role for the Internet in processes of violent radicalization. There is increasing concern on the part of other scholars, and increasingly also policymakers and publics, that easy availability of violent extremist content online may have violent radicalizing effects. This article identifies a number of core questions regarding the interaction of violent extremism and terrorism and the Internet, particularly social media, that have yet to be adequately addressed and supplies a series of six follow-up suggestions, flowing from these questions, for progressing research in this area. These suggestions relate to (1) widening the range of types of violent online extremism being studied beyond violent jihadis; (2) engaging in more comparative research, not just across ideologies, but also groups, countries, languages, and social media platforms; (3) deepening our analyses to include interviewing and virtual ethnographic approaches; (4) up-scaling or ...

Lexically Constrained Decoding for Sequence Generation Using Grid Beam Search

Abstract: We present Grid Beam Search (GBS), an algorithm which extends beam search to allow the inclusion of pre-specified lexical constraints. The algorithm can be used with any model which generates sequences token by token. Lexical constraints take the form of phrases or words that must be present in the output sequence. This is a very general way to incorporate auxillary knowledge into a model’s output without requiring any modification of the parameters or training data. We demonstrate the feasibility and flexibility of Lexically Constrained Decoding by conducting experiments on Neural Interactive-Predictive Translation, as well as Domain Adaptation for Neural Machine Translation. Experiments show that GBS can provide large improvements in translation quality in interactive scenarios, and that, even without any user input, GBS can be used to achieve significant gains in performance in domain adaptation scenarios.

Overview of the JET results in support to ITER

Abstract: The 2014-2016 JET results are reviewed in the light of their significance for optimising the ITER research plan for the active and non-active operation. More than 60 h of plasma operation with ITER first wall materials successfully took place since its installation in 2011. New multi-machine scaling of the type I-ELM divertor energy flux density to ITER is supported by first principle modelling. ITER relevant disruption experiments and first principle modelling are reported with a set of three disruption mitigation valves mimicking the ITER setup. Insights of the L-H power threshold in Deuterium and Hydrogen are given, stressing the importance of the magnetic configurations and the recent measurements of fine-scale structures in the edge radial electric. Dimensionless scans of the core and pedestal confinement provide new information to elucidate the importance of the first wall material on the fusion performance. H-mode plasmas at ITER triangularity (H = 1 at β N ∼ 1.8 and n/n GW ∼ 0.6) have been sustained at 2 MA during 5 s. The ITER neutronics codes have been validated on high performance experiments. Prospects for the coming D-T campaign and 14 MeV neutron calibration strategy are reviewed.

Talent management: a systematic review and future prospects

Abstract: This paper employs a systematic and comprehensive review to trace the evolution of talent management scholarship and propose a research agenda to move the field forward. Two primary streams of literature dominate: the management of high performers and high potentials, and the identification of strategic positions and talent management systems. The topic of global talent management has received significant attention; however, much of this literature tends to incorporate the two streams heretofore identified but on a global scale. The review also demonstrates that there has been limited attention placed on individual talents as the unit of analysis. Early criticisms of the disjointed nature of the field remain and we call for greater clarity around the conceptual boundaries of talent management. Our analysis highlights that most papers draw, to some extent, on primary research. There is however considerable need and scope for more comprehensive and nuanced methodological approaches.

Exploiting Cross-Sentence Context for Neural Machine Translation

Abstract: In translation, considering the document as a whole can help to resolve ambiguities and inconsistencies. In this paper, we propose a cross-sentence context-aware approach and investigate the influence of historical contextual information on the performance of neural machine translation (NMT). First, this history is summarized in a hierarchical way. We then integrate the historical representation into NMT in two strategies: 1) a warm-start of encoder and decoder states, and 2) an auxiliary context source for updating decoder states. Experimental results on a large Chinese-English translation task show that our approach significantly improves upon a strong attention-based NMT system by up to +2.1 BLEU points.

Determinants of diet and physical activity (DEDIPAC): a summary of findings.

Abstract: The establishment of the Determinants of Diet and Physical Activity (DEDIPAC) Knowledge Hub, 2013–2016, was the first action taken by the ‘Healthy Diet for a Healthy Life’ European Joint Programming Initiative. DEDIPAC aimed to provide better insight into the determinants of diet, physical activity and sedentary behaviour across the life course, i.e. insight into the causes of the causes of important, non-communicable diseases across Europe and beyond. DEDIPAC was launched in late 2013, and delivered its final report in late 2016. In this paper we give an overview of what was achieved in terms of furthering measurement and monitoring, providing overviews of the state-of-the-art in the field, and building toolboxes for further research and practice. Additionally, we propose some of the next steps that are now required to move forward in this field, arguing in favour of 1) sustaining the Knowledge Hub and developing it into a European virtual research institute and knowledge centre for determinants of behavioural nutrition and physical activity with close links to other parts of the world; 2) establishing a cohort study of families across all regions of Europe focusing specifically on the individual and contextual determinants of major, non-communicable disease; and 3) furthering DEDIPAC’s work on nutrition, physical activity, and sedentary behaviour policy evaluation and benchmarking across Europe by aligning with other international initiatives and by supporting harmonisation of pan-European surveillance.

Incorporating Global Visual Features into Attention-based Neural Machine Translation.

Abstract: We introduce multi-modal, attention-based neural machine translation (NMT) models which incorporate visual features into different parts of both the encoder and the decoder. Global image features are extracted using a pre-trained convolutional neural network and are incorporated (i) as words in the source sentence, (ii) to initialise the encoder hidden state, and (iii) as additional data to initialise the decoder hidden state. In our experiments, we evaluate translations into English and German, how different strategies to incorporate global image features compare and which ones perform best. We also study the impact that adding synthetic multi-modal, multilingual data brings and find that the additional data have a positive impact on multi-modal NMT models. We report new state-of-the-art results and our best models also significantly improve on a comparable phrase-based Statistical MT (PBSMT) model trained on the Multi30k data set according to all metrics evaluated. To the best of our knowledge, it is the first time a purely neural model significantly improves over a PBSMT model on all metrics evaluated on this data set.

A life course examination of the physical environmental determinants of physical activity behaviour: A “Determinants of Diet and Physical Activity” (DEDIPAC) umbrella systematic literature review

Abstract: Background: Participation in regular physical activity is associated with a multitude of health benefits across the life course. However, many people fail to meet PA recommendations. Despite a plethora of studies, the evidence regarding the environmental (physical) determinants of physical activity remains inconclusive. Objective: To identify the physical environmental determinants that influence PA across the life course. Methods: An online systematic literature search was conducted using MEDLINE, ISI Web of Science, Scopus and SPORTDiscus. The search was limited to studies published in English (January 2004 to April 2016). Only systematic literature reviews (SLRs) and meta-analyses (MAs) of observational studies, that investigated the association between physical determinants and physical activity outcomes, were eligible for inclusion. The extracted data were assessed on the importance of determinants, strength of evidence and methodological quality. Results: The literature search identified 28 SLRs and 3 MAs on 67 physical environmental characteristics potentially related to physical activity that were eligible for inclusion. Among preschool children, a positive association was reported between availability of backyard space and outdoor toys/equipment in the home and overall physical activity. The availability of physical activity programs and equipment within schools, and neighbourhood features such as pedestrian and cyclist safety structure were positively associated with physical activity in children and adolescents. Negative street characteristics, for example, lack of sidewalks and streetlights, were negatively associated with physical activity in adults. Inconsistent associations were reported for the majority of reviewed determinants in adults. Conclusion: This umbrella SLR provided a comprehensive overview of the physical environment determinants of physical activity across the life course and has highlighted, particularly amongst youth, a number of key determinants that may be associated with overall physical activity. Given the limited evidence drawn mostly from cross-sectional studies, longitudinal studies are needed to further explore these associations. Registration: PROSPERO CRD42015010616.

Doubly-Attentive Decoder for Multi-modal Neural Machine Translation

Abstract: We introduce a Multi-modal Neural Machine Translation model in which a doubly-attentive decoder naturally incorporates spatial visual features obtained using pre-trained convolutional neural networks, bridging the gap between image description and translation. Our decoder learns to attend to source-language words and parts of an image independently by means of two separate attention mechanisms as it generates words in the target language. We find that our model can efficiently exploit not just back-translated in-domain multi-modal data but also large general-domain text-only MT corpora. We also report state-of-the-art results on the Multi30k data set.

Altered miR-29 Expression in Type 2 Diabetes Influences Glucose and Lipid Metabolism in Skeletal Muscle

Abstract: MicroRNAs have emerged as important regulators of glucose and lipid metabolism in several tissues; however, their role in skeletal muscle remains poorly characterized. We determined the effects of the miR-29 family on glucose metabolism, lipid metabolism, and insulin responsiveness in skeletal muscle. We provide evidence that miR-29a and miR-29c are increased in skeletal muscle from patients with type 2 diabetes and are decreased following endurance training in healthy young men and in rats. In primary human skeletal muscle cells, inhibition and overexpression strategies demonstrate that miR-29a and miR-29c regulate glucose uptake and insulin-stimulated glucose metabolism. We identified that miR-29 overexpression attenuates insulin signaling and expression of insulin receptor substrate 1 and phosphoinositide 3-kinase. Moreover, miR-29 overexpression reduces hexokinase 2 expression and activity. Conversely, overexpression of miR-29 by electroporation of mouse tibialis anterior muscle decreased glucose uptake and glycogen content in vivo, concomitant with decreased abundance of GLUT4. We also provide evidence that fatty acid oxidation is negatively regulated by miR-29 overexpression, potentially through the regulation of peroxisome proliferator–activated receptor γ coactivator-1α expression. Collectively, we reveal that miR-29 acts as an important regulator of insulin-stimulated glucose metabolism and lipid oxidation, with relevance to human physiology and type 2 diabetes.

Paediatric early warning systems for detecting and responding to clinical deterioration in children: a systematic review

Abstract: Objective To systematically review the available evidence on paediatric early warning systems (PEWS) for use in acute paediatric healthcare settings for the detection of, and timely response to, clinical deterioration in children. Method The electronic databases PubMed, MEDLINE, CINAHL, EMBASE and Cochrane were searched systematically from inception up to August 2016. Eligible studies had to refer to PEWS, inclusive of rapid response systems and teams. Outcomes had to be specific to the identification of and/or response to clinical deterioration in children (including neonates) in paediatric hospital settings (including emergency departments). 2 review authors independently completed the screening and selection process, the quality appraisal of the retrieved evidence and data extraction; with a third reviewer resolving any discrepancies, as required. Results were narratively synthesised. Results From a total screening of 2742 papers, 90 papers, of varied designs, were identified as eligible for inclusion in the review. Findings revealed that PEWS are extensively used internationally in paediatric inpatient hospital settings. However, robust empirical evidence on which PEWS is most effective was limited. The studies examined did however highlight some evidence of positive directional trends in improving clinical and process-based outcomes for clinically deteriorating children. Favourable outcomes were also identified for enhanced multidisciplinary team work, communication and confidence in recognising, reporting and making decisions about child clinical deterioration. Conclusions Despite many studies reporting on the complexity and multifaceted nature of PEWS, no evidence was sourced which examined PEWS as a complex healthcare intervention. Future research needs to investigate PEWS as a complex multifaceted sociotechnical system that is embedded in a wider safety culture influenced by many organisational and human factors. PEWS should be embraced as a part of a larger multifaceted safety framework that will develop and grow over time with strong governance and leadership, targeted training, ongoing support and continuous improvement.

A Preliminary Review of Enterprise Serverless Cloud Computing (Function-as-a-Service) Platforms

Abstract: In line with cloud computing emergence as the dominant enterprise computing paradigm, our conceptualization of the cloud computing reference architecture and service construction has also evolved. For example, to address the need for cost reduction and rapid provisioning, virtualization has moved beyond hardware to containers. More recently, serverless computing or Function-as-a-Service has been presented as a means to introduce further cost-efficiencies, reduce configuration and management overheads, and rapidly increase an application's ability to speed up, scale up and scale down in the cloud. The potential of this new computation model is reflected in the introduction of serverless computing platforms by the main hyperscale cloud service providers. This paper provides an overview and multi-level feature analysis of seven enterprise serverless computing platforms. It reviews extant research on these platforms and identifies the emergence of AWS Lambda as a de facto base platform for research on enterprise serverless cloud computing. The paper concludes with a summary of avenues for further research.