Showing papers by "Dublin City University published in 2019"

Findings of the 2019 Conference on Machine Translation (WMT19)

Abstract: This paper presents the results of the premier shared task organized alongside the Conference on Machine Translation (WMT) 2019. Participants were asked to build machine translation systems for any of 18 language pairs, to be evaluated on a test set of news stories. The main metric for this task is human judgment of translation quality. The task was also opened up to additional test suites to probe specific aspects of translation.

Unsupervised Label Noise Modeling and Loss Correction

Abstract: Despite being robust to small amounts of label noise, convolutional neural networks trained with stochastic gradient methods have been shown to easily fit random labels. When there are a mixture of correct and mislabelled targets, networks tend to fit the former before the latter. This suggests using a suitable two-component mixture model as an unsupervised generative model of sample loss values during training to allow online estimation of the probability that a sample is mislabelled. Specifically, we propose a beta mixture to estimate this probability and correct the loss by relying on the network prediction (the so-called bootstrapping loss). We further adapt mixup augmentation to drive our approach a step further. Experiments on CIFAR-10/100 and TinyImageNet demonstrate a robustness to label noise that substantially outperforms recent state-of-the-art. Source code is available at https://git.io/fjsvE and Appendix at https://arxiv.org/abs/1904.11238.

Black holes, gravitational waves and fundamental physics: a roadmap

Abstract: The grand challenges of contemporary fundamental physics-dark matter, dark energy, vacuum energy, inflation and early universe cosmology, singularities and the hierarchy problem-all involve gravity as a key component. And of all gravitational phenomena, black holes stand out in their elegant simplicity, while harbouring some of the most remarkable predictions of General Relativity: event horizons, singularities and ergoregions. The hitherto invisible landscape of the gravitational Universe is being unveiled before our eyes: the historical direct detection of gravitational waves by the LIGO-Virgo collaboration marks the dawn of a new era of scientific exploration. Gravitational-wave astronomy will allow us to test models of black hole formation, growth and evolution, as well as models of gravitational-wave generation and propagation. It will provide evidence for event horizons and ergoregions, test the theory of General Relativity itself, and may reveal the existence of new fundamental fields. The synthesis of these results has the potential to radically reshape our understanding of the cosmos and of the laws of Nature. The purpose of this work is to present a concise, yet comprehensive overview of the state of the art in the relevant fields of research, summarize important open problems, and lay out a roadmap for future progress. This write-up is an initiative taken within the framework of the European Action on 'Black holes, Gravitational waves and Fundamental Physics'. © 2019 IOP Publishing Ltd.

Pseudo-Labeling and Confirmation Bias in Deep Semi-Supervised Learning

Abstract: Semi-supervised learning, i.e. jointly learning from labeled and unlabeled samples, is an active research topic due to its key role on relaxing human supervision. In the context of image classification, recent advances to learn from unlabeled samples are mainly focused on consistency regularization methods that encourage invariant predictions for different perturbations of unlabeled samples. We, conversely, propose to learn from unlabeled data by generating soft pseudo-labels using the network predictions. We show that a naive pseudo-labeling overfits to incorrect pseudo-labels due to the so-called confirmation bias and demonstrate that mixup augmentation and setting a minimum number of labeled samples per mini-batch are effective regularization techniques for reducing it. The proposed approach achieves state-of-the-art results in CIFAR-10/100, SVHN, and Mini-ImageNet despite being much simpler than other methods. These results demonstrate that pseudo-labeling alone can outperform consistency regularization methods, while the opposite was supposed in previous work. Source code is available at this https URL.

Vitamin D supplementation to prevent acute respiratory infections: individual participant data meta-analysis

Abstract: Background Randomised controlled trials (RCTs) exploring the potential of vitamin D to prevent acute respiratory infections have yielded mixed results. Individual participant data (IPD) meta-analysis has the potential to identify factors that may explain this heterogeneity. Objectives To assess the overall effect of vitamin D supplementation on the risk of acute respiratory infections (ARIs) and to identify factors modifying this effect. Data sources MEDLINE, EMBASE, the Cochrane Central Register of Controlled Trials (CENTRAL), Web of Science, ClinicalTrials.gov and the International Standard Randomised Controlled Trials Number (ISRCTN) registry. Study selection Randomised, double-blind, placebo-controlled trials of supplementation with vitamin D3 or vitamin D2 of any duration having incidence of acute respiratory infection as a prespecified efficacy outcome were selected. Study appraisal Study quality was assessed using the Cochrane Collaboration Risk of Bias tool to assess sequence generation, allocation concealment, blinding of participants, personnel and outcome assessors, completeness of outcome data, evidence of selective outcome reporting and other potential threats to validity. Results We identified 25 eligible RCTs (a total of 11,321 participants, aged from 0 to 95 years). IPD were obtained for 10,933 out of 11,321 (96.6%) participants. Vitamin D supplementation reduced the risk of ARI among all participants [adjusted odds ratio (aOR) 0.88, 95% confidence interval (CI) 0.81 to 0.96; heterogeneity p < 0.001]. Subgroup analysis revealed that protective effects were seen in individuals receiving daily or weekly vitamin D without additional bolus doses (aOR 0.81, 95% CI 0.72 to 0.91), but not in those receiving one or more bolus doses (aOR 0.97, 95% CI 0.86 to 1.10; p = 0.05). Among those receiving daily or weekly vitamin D, protective effects of vitamin D were stronger in individuals with a baseline 25-hydroxyvitamin D [25(OH)D] concentration of < 25 nmol/l (aOR 0.30, 95% CI 0.17 to 0.53) than in those with a baseline 25(OH)D concentration of ≥ 25 nmol/l (aOR 0.75, 95% CI 0.60 to 0.95; p = 0.006). Vitamin D did not influence the proportion of participants experiencing at least one serious adverse event (aOR 0.98, 95% CI 0.80 to 1.20; p = 0.83). The body of evidence contributing to these analyses was assessed as being of high quality. Limitations Our study had limited power to detect the effects of vitamin D supplementation on the risk of upper versus lower respiratory infection, analysed separately. Conclusions Vitamin D supplementation was safe, and it protected against ARIs overall. Very deficient individuals and those not receiving bolus doses experienced the benefit. Incorporation of additional IPD from ongoing trials in the field has the potential to increase statistical power for analyses of secondary outcomes. Study registration This study is registered as PROSPERO CRD42014013953. Funding The National Institute for Health Research Health Technology Assessment programme.

The crisis of democracy and the science of deliberation.

Abstract: Citizens can avoid polarization and make sound decisions That there are more opportunities than ever for citizens to express their views may be, counterintuitively, a problem facing democracy—the sheer quantitative overabundance overloads policymakers and citizens, making it difficult to detect the signal amid the noise. This overload has been accompanied by marked decline in civility and argumentative complexity. Uncivil behavior by elites and pathological mass communication reinforce each other. How do we break this vicious cycle? Asking elites to behave better is futile so long as there is a public ripe to be polarized and exploited by demagogues and media manipulators. Thus, any response has to involve ordinary citizens; but are they up to the task? Social science on “deliberative democracy” offers reasons for optimism about citizens' capacity to avoid polarization and manipulation and to make sound decisions. The real world of democratic politics is currently far from the deliberative ideal, but empirical evidence shows that the gap can be closed.

Generative Adversarial Networks in Computer Vision: A Survey and Taxonomy

Abstract: Generative adversarial networks (GANs) have been extensively studied in the past few years. Arguably their most significant impact has been in the area of computer vision where great advances have been made in challenges such as plausible image generation, image-to-image translation, facial attribute manipulation and similar domains. Despite the significant successes achieved to date, applying GANs to real-world problems still poses significant challenges, three of which we focus on here. These are: (1) the generation of high quality images, (2) diversity of image generation, and (3) stable training. Focusing on the degree to which popular GAN technologies have made progress against these challenges, we provide a detailed review of the state of the art in GAN-related research in the published scientific literature. We further structure this review through a convenient taxonomy we have adopted based on variations in GAN architectures and loss functions. While several reviews for GANs have been presented to date, none have considered the status of this field based on their progress towards addressing practical challenges relevant to computer vision. Accordingly, we review and critically discuss the most popular architecture-variant, and loss-variant GANs, for tackling these challenges. Our objective is to provide an overview as well as a critical analysis of the status of GAN research in terms of relevant progress towards important computer vision application requirements. As we do this we also discuss the most compelling applications in computer vision in which GANs have demonstrated considerable success along with some suggestions for future research directions. Code related to GAN-variants studied in this work is summarized on this https URL.

Molecular methods for assessment of non-covalent metallodrug–DNA interactions

Abstract: The binding of small molecule metallodrugs to discrete regions of nucleic acids is an important branch of medicinal chemistry and the nature of these interactions, allied with sequence selectivity, forms part of the backbone of modern medicinal inorganic chemistry research. In this tutorial review we describe a range of molecular methods currently employed within our laboratories to explore novel metallodrug-DNA interactions. At the outset, an introduction to DNA from a structural perspective is provided along with descriptions of non-covalent DNA recognition focusing on intercalation, insertion, and phosphate binding. Molecular methods, described from a non-expert perspective, to identify non-covalent and pre-associative nucleic acid recognition are then demonstrated using a variety of techniques including direct (non-optical) and indirect (optical) methods. Direct methods include: X-ray crystallography; NMR spectroscopy; mass spectrometry; and viscosity while indirect approaches detail: competitive inhibition experiments; fluorescence and absorbance spectroscopy; circular dichroism; and electrophoresis-based techniques. For each method described we provide an overview of the technique, a detailed examination of results obtained and relevant follow-on of advanced biophysical/analytical techniques. To achieve this, a selection of relevant copper(ii) and platinum(ii) complexes developed within our laboratories are discussed and are compared, where possible, to classical DNA binding agents. Applying these molecular methods enables us to determine structure-activity factors important to rational metallodrug design. In many cases, combinations of molecular methods are required to comprehensively elucidate new metallodrug-DNA interactions and, from a drug discovery perspective, coupling this data with cellular responses helps to inform understanding of how metallodrug-DNA binding interactions manifest cytotoxic action.

Accelerometer-Based Human Fall Detection Using Convolutional Neural Networks

Abstract: Human falls are a global public health issue resulting in over 373 million severe injuries and 646,000 deaths yearly Falls result in direct financial cost to health systems and indirectly to society productivity Unsurprisingly, human fall detection and prevention are a major focus of health research In this article, we consider deep learning for fall detection in an IoT and fog computing environment We propose a Convolutional Neural Network composed of three convolutional layers, two maxpool, and three fully-connected layers as our deep learning model We evaluate its performance using three open data sets and against extant research Our approach for resolving dimensionality and modelling simplicity issues is outlined Accuracy, precision, sensitivity, specificity, and the Matthews Correlation Coefficient are used to evaluate performance The best results are achieved when using data augmentation during the training process The paper concludes with a discussion of challenges and future directions for research in this domain

Secretome of adipose-derived mesenchymal stem cells promotes skeletal muscle regeneration through synergistic action of extracellular vesicle cargo and soluble proteins

Abstract: The mechanisms underpinning the regenerative capabilities of mesenchymal stem cells (MSC) were originally thought to reside in their ability to recognise damaged tissue and to differentiate into specific cell types that would replace defective cells. However, recent work has shown that molecules produced by MSCs (secretome), particularly those packaged in extracellular vesicles (EVs), rather than the cells themselves are responsible for tissue repair. Here we have produced a secretome from adipose-derived mesenchymal stem cells (ADSC) that is free of exogenous molecules by incubation within a saline solution. Various in vitro models were used to evaluate the effects of the secretome on cellular processes that promote tissue regeneration. A cardiotoxin-induced skeletal muscle injury model was used to test the regenerative effects of the whole secretome or isolated extracellular vesicle fraction in vivo. This was followed by bioinformatic analysis of the components of the protein and miRNA content of the secretome and finally compared to a secretome generated from a secondary stem cell source. Here we have demonstrated that the secretome from adipose-derived mesenchymal stem cells shows robust effects on cellular processes that promote tissue regeneration. Furthermore, we show that the whole ADSC secretome is capable of enhancing the rate of skeletal muscle regeneration following acute damage. We assessed the efficacy of the total secretome compared with the extracellular vesicle fraction on a number of assays that inform on tissue regeneration and demonstrate that both fractions affect different aspects of the process in vitro and in vivo. Our in vitro, in vivo, and bioinformatic results show that factors that promote regeneration are distributed both within extracellular vesicles and the soluble fraction of the secretome. Taken together, our study implies that extracellular vesicles and soluble molecules within ADSC secretome act in a synergistic manner to promote muscle generation.

Formation of massive black holes in rapidly growing pre-galactic gas clouds

Abstract: The origin of the supermassive black holes that inhabit the centres of massive galaxies remains unclear1,2. Direct-collapse black holes-remnants of supermassive stars, with masses around 10,000 times that of the Sun-are ideal seed candidates3-6. However, their very existence and their formation environment in the early Universe are still under debate, and their supposed rarity makes modelling their formation difficult7,8. Models have shown that rapid collapse of pre-galactic gas (with a mass infall rate above some critical value) in metal-free haloes is a requirement for the formation of a protostellar core that will then form a supermassive star9,10. Here we report a radiation hydrodynamics simulation of early galaxy formation11,12 that produces metal-free haloes massive enough and with sufficiently high mass infall rates to form supermassive stars. We find that pre-galactic haloes and their associated gas clouds that are exposed to a Lyman-Werner intensity roughly three times the intensity of the background radiation and that undergo at least one period of rapid mass growth early in their evolution are ideal environments for the formation of supermassive stars. The rapid growth induces substantial dynamical heating13,14, amplifying the Lyman-Werner suppression that originates from a group of young galaxies 20 kiloparsecs away. Our results strongly indicate that the dynamics of structure formation, rather than a critical Lyman-Werner flux, is the main driver of the formation of massive black holes in the early Universe. We find that the seeds of massive black holes may be much more common than previously considered in overdense regions of the early Universe, with a co-moving number density up to 10-3 per cubic megaparsec.

Applications of Carbon Nanotubes in Bone Tissue Regeneration and Engineering: Superiority, Concerns, Current Advancements, and Prospects.

Abstract: With advances in bone tissue regeneration and engineering technology, various biomaterials as artificial bone substitutes have been widely developed and innovated for the treatment of bone defects or diseases. However, there are no available natural and synthetic biomaterials replicating the natural bone structure and properties under physiological conditions. The characteristic properties of carbon nanotubes (CNTs) make them an ideal candidate for developing innovative biomimetic materials in the bone biomedical field. Indeed, CNT-based materials and their composites possess the promising potential to revolutionize the design and integration of bone scaffolds or implants, as well as drug therapeutic systems. This review summarizes the unique physicochemical and biomedical properties of CNTs as structural biomaterials and reinforcing agents for bone repair as well as provides coverage of recent concerns and advancements in CNT-based materials and composites for bone tissue regeneration and engineering. Moreover, this review discusses the research progress in the design and development of novel CNT-based delivery systems in the field of bone tissue engineering.

Advances in Density-Functional Calculations for Materials Modeling

Abstract: During the past two decades, density-functional (DF) theory has evolved from niche applications for simple solid-state materials to become a workhorse method for studying a wide range of phenomena ...

5G-Slicing-Enabled Scalable SDN Core Network: Toward an Ultra-Low Latency of Autonomous Driving Service

Abstract: 5G networks are anticipated to support a plethora of innovative and promising network services. These services have heterogeneous performance requirements (e.g., high-rate traffic, low latency, and high reliability). To meet them, 5G networks are entailed to endorse flexibility that can be fulfilled through the deployment of new emerging technologies, mainly software-defined networking (SDN), network functions virtualization (NFV), and network slicing. In this paper, we focus on an interesting automotive vertical use case: autonomous vehicles. Our aim is to enhance the quality of service of autonomous driving application. To this end, we design a framework that uses the aforementioned technologies to enhance the quality of service of the autonomous driving application. The framework is made of 1) a distributed and scalable SDN core network architecture that deploys fog, edge and cloud computing technologies; 2) a network slicing function that maps autonomous driving functionalities into service slices; and 3) a network and service slicing system model that promotes a four-layer logical architecture to improve the transmission efficiency and satisfy the low latency constraint. In addition, we present a theoretical analysis of the propagation delay and the handling latency based on GI/M/1 queuing system. Simulation results show that our framework meets the low-latency requirement of the autonomous driving application as it incurs low propagation delay and handling latency for autonomous driving traffic compared to best-effort traffic.

Stories from the field: Women's networking as gender capital in entrepreneurial ecosystems

Abstract: Women are under-represented in successful entrepreneurial ecosystems and the creation of women-only entrepreneurial networks has been a widespread policy response. We examine the entrepreneurial ecosystem construct and suggest that it, and the role networks play in entrepreneurial ecosystems, can be analysed in terms of Bourdieu’s socio-analysis as field, habitus and capital. Specifically, we develop the notion of gender capital as the skill set associated with femininity or from simply being recognized as feminine. We apply this to the development of women’s entrepreneurial networks as a gender capital enhancing initiative. Using data from qualitative interviews with network coordinators and women entrepreneurs, we reflect on the extent to which formally established women-only networks generate gender capital for their members and improve their ability to participate in the entrepreneurial ecosystem. The paper concludes by drawing out the implications of our analysis for theory, entrepreneurial practice and economic development policy.

Building routines for non-routine events: supply chain resilience learning mechanisms and their antecedents

Abstract: Purpose Organisations must build resilience to be able to deal with disruptions or non-routine events in their supply chains. While learning is implicit in definitions of supply chain resilience (SCRes), there is little understanding of how exactly organisations can adapt their routines to build resilience. The purpose of this study is to address this gap. Design/methodology/approach This paper is an in-depth qualitative case study based on 28 interviews across five companies, exploring learning to build SCRes. Findings This study uncovers six learning mechanisms and their antecedents that foster SCRes. The learning mechanisms identified suggest that through knowledge creation within an organisation and knowledge transfer across the supply chain and broader network of stakeholders, operating routines are built and/or adapted both intentionally and unintentionally during three stages of a supply chain disruption: preparation, response and recovery. Practical implications This study shows how the impact of a supply chain disruption may be reduced by intentional and unintentional learning in all three disruption phases. By being aware of the antecedents of unintentional learning, organisations can more consciously adapt routines. Furthermore, findings highlight the potential value of additional attention to knowledge transfer, particularly in relation to collaborative and vicarious learning across the supply chain and broader network of stakeholders not only in preparation for, but also in response to and recovery from disruptions. Originality/value This study contributes novel insights about how learning leads both directly and indirectly to the evolution of operating routines that help an organisation and its supply chains to deal with disruptions. Results detail six specific learning mechanisms for knowledge creation and knowledge transfer and their antecedents for building SCRes. In doing so, this study provides new fine-grained theoretical insights about how SCRes can be improved through all three phases of a disruption. Propositions are developed for theory development.

Titans of the early Universe: The Prato statement on the origin of the first supermassive black holes

Abstract: In recent years, the discovery of massive quasars at has provided a striking challenge to our understanding of the origin and growth of supermassive black holes in the early Universe. Mounting observational and theoretical evidence indicates the viability of massive seeds, formed by the collapse of supermassive stars, as a progenitor model for such early, massive accreting black holes. Although considerable progress has been made in our theoretical understanding, many questions remain regarding how (and how often) such objects may form, how they live and die, and how next generation observatories may yield new insight into the origin of these primordial titans. This review focusses on our present understanding of this remarkable formation scenario, based on the discussions held at the Monash Prato Centre from November 20 to 24, 2017, during the workshop ‘Titans of the Early Universe: The Origin of the First Supermassive Black Holes’.

Calcium Phosphate Nanoparticles for Therapeutic Applications in Bone Regeneration.

Abstract: Bone injuries and diseases constitute a burden both socially and economically, as the consequences of a lack of effective treatments affect both the patients’ quality of life and the costs on the health systems. This impended need has led the research community’s efforts to establish efficacious bone tissue engineering solutions. There has been a recent focus on the use of biomaterial-based nanoparticles for the delivery of therapeutic factors. Among the biomaterials being considered to date, calcium phosphates have emerged as one of the most promising materials for bone repair applications due to their osteoconductivity, osteoinductivity and their ability to be resorbed in the body. Calcium phosphate nanoparticles have received particular attention as non-viral vectors for gene therapy, as factors such as plasmid DNAs, microRNAs (miRNA) and silencing RNA (siRNAs) can be easily incorporated on their surface. Calcium phosphate nanoparticles loaded with therapeutic factors have also been delivered to the site of bone injury using scaffolds and hydrogels. This review provides an extensive overview of the current state-of-the-art relating to the design and synthesis of calcium phosphate nanoparticles as carriers for therapeutic factors, the mechanisms of therapeutic factors’ loading and release, and their application in bone tissue engineering.

‘Systematizing’ constitutional deliberation: the 2016–18 citizens’ assembly in Ireland

Abstract: Ireland has become something of a trail-blazer in the use of deliberative methods in the process of constitutional review. It is the first case in which the process has been employed a second time:...

Acquired Resistance to Antibody-Drug Conjugates

Abstract: Antibody-drug conjugates (ADCs) combine the tumor selectivity of antibodies with the potency of cytotoxic small molecules thereby constituting antibody-mediated chemotherapy. As this inherently limits the adverse effects of the chemotherapeutic, such approaches are heavily pursued by pharma and biotech companies and have resulted in four FDA (Food and Drug Administration)-approved ADCs. However, as with other cancer therapies, durable responses are limited by the fact that under cell stress exerted by these drugs, tumors can acquire mechanisms of escape. Resistance can develop against the antibody component of ADCs by down-regulation/mutation of the targeted cell surface antigen or against payload toxicity by up-regulation of drug efflux transporters. Unique resistance mechanisms specific for the mode of action of ADCs have also emerged, like altered internalization or cell surface recycling of the targeted tumor antigen, changes in the intracellular routing or processing of ADCs, and impaired release of the toxic payload into the cytosol. These evasive changes are tailored to the specific nature and interplay of the three ADC constituents: the antibody, the linker, and the payload. Hence, they do not necessarily endow broad resistance to ADC therapy. This review summarizes preclinical and clinical findings that shed light on the mechanisms of acquired resistance to ADC therapies.

Overview of the JET preparation for deuterium–tritium operation with the ITER like-wall

Abstract: For the past several years, the JET scientific programme (Pamela et al 2007 Fusion Eng. Des. 82 590) has been engaged in a multi-campaign effort, including experiments in D, H and T, leading up to 2020 and the first experiments with 50%/50% D–T mixtures since 1997 and the first ever D–T plasmas with the ITER mix of plasma-facing component materials. For this purpose, a concerted physics and technology programme was launched with a view to prepare the D–T campaign (DTE2). This paper addresses the key elements developed by the JET programme directly contributing to the D–T preparation. This intense preparation includes the review of the physics basis for the D–T operational scenarios, including the fusion power predictions through first principle and integrated modelling, and the impact of isotopes in the operation and physics of D–T plasmas (thermal and particle transport, high confinement mode (H-mode) access, Be and W erosion, fuel recovery, etc). This effort also requires improving several aspects of plasma operation for DTE2, such as real time control schemes, heat load control, disruption avoidance and a mitigation system (including the installation of a new shattered pellet injector), novel ion cyclotron resonance heating schemes (such as the threeions scheme), new diagnostics (neutron camera and spectrometer, active Alfven eigenmode antennas, neutral gauges, radiation hard imaging systems…) and the calibration of the JET neutron diagnostics at 14 MeV for accurate fusion power measurement. The active preparation of JET for the 2020 D–T campaign provides an incomparable source of information and a basis for the future D–T operation of ITER, and it is also foreseen that a large number of key physics issues will be addressed in support of burning plasmas.

Surface molecules of extracellular vesicles secreted by the helminth pathogen Fasciola hepatica direct their internalisation by host cells.

Abstract: Helminth parasites secrete extracellular vesicles (EVs) that can be internalised by host immune cells resulting in modulation of host immunity. While the molecular cargo of EVs have been characterised in many parasites, little is known about the surface-exposed molecules that participate in ligand-receptor interactions with the host cell surface to initiate vesicle docking and subsequent internalisation. Using a membrane-impermeable biotin reagent to capture proteins displayed on the outer membrane surface of two EV sub-populations (termed 15k and 120k EVs) released by adult F. hepatica, we describe 380 surface proteins including an array of virulence factors, membrane transport proteins and molecules involved in EV biogenesis/trafficking. Proteomics and immunohistochemical analysis show that the 120k EVs have an endosomal origin and may be released from the parasite via the protonephridial (excretory) system whilst the larger 15k EVs are released from the gastrodermal epithelial cells that line the fluke gut. A parallel lectin microarray strategy was used to profile the topology of major surface oligosaccharides of intact fluorogenically-labelled EVs as they would be displayed to the host. Lectin profiles corresponding to glycoconjugates exposed on the surface of the 15 K and 120K EV sub-populations are practically identical but are distinct from those of the parasite surface tegument, although all are predominated by high mannose sugars. We found that while the F. hepatica EVs were resistant to exo- and endo-glycosidases, the glyco-amidase PNGase F drastically remodelled the surface oligosaccharides and blocked the uptake of EVs by host macrophages. In contrast, pre-treatment with antibodies obtained from infected hosts, or purified antibodies raised against the extracellular domains of specific EV surface proteins (DM9-containing protein, CD63 receptor and myoferlin), significantly enhanced their cellular internalisation. This work highlights the diversity of EV biogenesis and trafficking pathways used by F. hepatica and sheds light on the molecular interaction between parasite EVs and host cells.

Perceptions and experiences of diabetic foot ulceration and foot care in people with diabetes: A qualitative meta-synthesis.

Abstract: Diabetic foot ulceration (DFU) is a common and debilitating complication of diabetes that is preventable through active engagement in appropriate foot‐related behaviours, yet many individuals with diabetes do not adhere to foot care recommendations. The aim of this paper was to synthesise the findings of qualitative papers exploring diabetic people's perceptions and experiences of DFU in order to identify how they could be better supported to prevent ulceration or manage its impact. Five databases (MEDLINE, PsycINFO, CINAHL, EMBASE, Web of Science) were searched in May 2016 to identify eligible articles. Findings were synthesised using a meta‐ethnographic approach. Forty‐two articles were eligible for inclusion. Synthesis resulted in the development of five overarching themes: personal understandings of diabetic foot ulceration; preventing diabetic foot ulceration: knowledge, attitudes, and behaviours; views on health care experiences; development of diabetic foot ulceration and actions taken; and wide‐ranging impacts of diabetic foot ulceration. The findings highlight various barriers and facilitators of foot care experienced by people with diabetes and demonstrate the significant consequences of ulcers for their physical, social, and psychological well‐being. The insights provided could inform the development of interventions to promote foot care effectively and provide appropriate support to those living with ulceration.