Showing papers by "University College Dublin published in 2021"

Genetic mechanisms of critical illness in Covid-19.

Abstract: Host-mediated lung inflammation is present1, and drives mortality2, in the critical illness caused by coronavirus disease 2019 (COVID-19). Host genetic variants associated with critical illness may identify mechanistic targets for therapeutic development3. Here we report the results of the GenOMICC (Genetics Of Mortality In Critical Care) genome-wide association study in 2,244 critically ill patients with COVID-19 from 208 UK intensive care units. We have identified and replicated the following new genome-wide significant associations: on chromosome 12q24.13 (rs10735079, P = 1.65 × 10−8) in a gene cluster that encodes antiviral restriction enzyme activators (OAS1, OAS2 and OAS3); on chromosome 19p13.2 (rs74956615, P = 2.3 × 10−8) near the gene that encodes tyrosine kinase 2 (TYK2); on chromosome 19p13.3 (rs2109069, P = 3.98 × 10−12) within the gene that encodes dipeptidyl peptidase 9 (DPP9); and on chromosome 21q22.1 (rs2236757, P = 4.99 × 10−8) in the interferon receptor gene IFNAR2. We identified potential targets for repurposing of licensed medications: using Mendelian randomization, we found evidence that low expression of IFNAR2, or high expression of TYK2, are associated with life-threatening disease; and transcriptome-wide association in lung tissue revealed that high expression of the monocyte–macrophage chemotactic receptor CCR2 is associated with severe COVID-19. Our results identify robust genetic signals relating to key host antiviral defence mechanisms and mediators of inflammatory organ damage in COVID-19. Both mechanisms may be amenable to targeted treatment with existing drugs. However, large-scale randomized clinical trials will be essential before any change to clinical practice. A genome-wide association study of critically ill patients with COVID-19 identifies genetic signals that relate to important host antiviral defence mechanisms and mediators of inflammatory organ damage that may be targeted by repurposing drug treatments.

Towards complete and error-free genome assemblies of all vertebrate species

Abstract: High-quality and complete reference genome assemblies are fundamental for the application of genomics to biology, disease, and biodiversity conservation. However, such assemblies are available for only a few non-microbial species1-4. To address this issue, the international Genome 10K (G10K) consortium5,6 has worked over a five-year period to evaluate and develop cost-effective methods for assembling highly accurate and nearly complete reference genomes. Here we present lessons learned from generating assemblies for 16 species that represent six major vertebrate lineages. We confirm that long-read sequencing technologies are essential for maximizing genome quality, and that unresolved complex repeats and haplotype heterozygosity are major sources of assembly error when not handled correctly. Our assemblies correct substantial errors, add missing sequence in some of the best historical reference genomes, and reveal biological discoveries. These include the identification of many false gene duplications, increases in gene sizes, chromosome rearrangements that are specific to lineages, a repeated independent chromosome breakpoint in bat genomes, and a canonical GC-rich pattern in protein-coding genes and their regulatory regions. Adopting these lessons, we have embarked on the Vertebrate Genomes Project (VGP), an international effort to generate high-quality, complete reference genomes for all of the roughly 70,000 extant vertebrate species and to help to enable a new era of discovery across the life sciences.

Regulation of glycolysis by the hypoxia-inducible factor (HIF): implications for cellular physiology.

Abstract: Under conditions of hypoxia, most eukaryotic cells can shift their primary metabolic strategy from predominantly mitochondrial respiration towards increased glycolysis to maintain ATP levels. This hypoxia-induced reprogramming of metabolism is key to satisfying cellular energetic requirements during acute hypoxic stress. At a transcriptional level, this metabolic switch can be regulated by several pathways including the hypoxia inducible factor-1α (HIF-1α) which induces an increased expression of glycolytic enzymes. While this increase in glycolytic flux is beneficial for maintaining bioenergetic homeostasis during hypoxia, the pathways mediating this increase can also be exploited by cancer cells to promote tumour survival and growth, an area which has been extensively studied. It has recently become appreciated that increased glycolytic metabolism in hypoxia may also have profound effects on cellular physiology in hypoxic immune and endothelial cells. Therefore, understanding the mechanisms central to mediating this reprogramming are of importance from both physiological and pathophysiological standpoints. In this review, we highlight the role of HIF-1α in the regulation of hypoxic glycolysis and its implications for physiological processes such as angiogenesis and immune cell effector function.

Characteristics of SARS-CoV-2 variants of concern B.1.1.7, B.1.351 or P.1: data from seven EU/EEA countries, weeks 38/2020 to 10/2021.

Abstract: We compared 19,207 cases of SARS-CoV-2 variant B.1.1.7/S gene target failure (SGTF), 436 B.1.351 and 352 P.1 to non-variant cases reported by seven European countries. COVID-19 cases with these variants had significantly higher adjusted odds ratios for hospitalisation (B.1.1.7/SGTF: 1.7, 95% confidence interval (CI): 1.0-2.9; B.1.351: 3.6, 95% CI: 2.1-6.2; P.1: 2.6, 95% CI: 1.4-4.8) and B.1.1.7/SGTF and P.1 cases also for intensive care admission (B.1.1.7/SGTF: 2.3, 95% CI: 1.4-3.5; P.1: 2.2, 95% CI: 1.7-2.8).

Effectiveness of therapeutic heparin versus prophylactic heparin on death, mechanical ventilation, or intensive care unit admission in moderately ill patients with covid-19 admitted to hospital: RAPID randomised clinical trial.

Abstract: Objective To evaluate the effects of therapeutic heparin compared with prophylactic heparin among moderately ill patients with covid-19 admitted to hospital wards. Design Randomised controlled, adaptive, open label clinical trial. Setting 28 hospitals in Brazil, Canada, Ireland, Saudi Arabia, United Arab Emirates, and US. Participants 465 adults admitted to hospital wards with covid-19 and increased D-dimer levels were recruited between 29 May 2020 and 12 April 2021 and were randomly assigned to therapeutic dose heparin (n=228) or prophylactic dose heparin (n=237). Interventions Therapeutic dose or prophylactic dose heparin (low molecular weight or unfractionated heparin), to be continued until hospital discharge, day 28, or death. Main outcome measures The primary outcome was a composite of death, invasive mechanical ventilation, non-invasive mechanical ventilation, or admission to an intensive care unit, assessed up to 28 days. The secondary outcomes included all cause death, the composite of all cause death or any mechanical ventilation, and venous thromboembolism. Safety outcomes included major bleeding. Outcomes were blindly adjudicated. Results The mean age of participants was 60 years; 264 (56.8%) were men and the mean body mass index was 30.3 kg/m2. At 28 days, the primary composite outcome had occurred in 37/228 patients (16.2%) assigned to therapeutic heparin and 52/237 (21.9%) assigned to prophylactic heparin (odds ratio 0.69, 95% confidence interval 0.43 to 1.10; P=0.12). Deaths occurred in four patients (1.8%) assigned to therapeutic heparin and 18 patients (7.6%) assigned to prophylactic heparin (0.22, 0.07 to 0.65; P=0.006). The composite of all cause death or any mechanical ventilation occurred in 23 patients (10.1%) assigned to therapeutic heparin and 38 (16.0%) assigned to prophylactic heparin (0.59, 0.34 to 1.02; P=0.06). Venous thromboembolism occurred in two patients (0.9%) assigned to therapeutic heparin and six (2.5%) assigned to prophylactic heparin (0.34, 0.07 to 1.71; P=0.19). Major bleeding occurred in two patients (0.9%) assigned to therapeutic heparin and four (1.7%) assigned to prophylactic heparin (0.52, 0.09 to 2.85; P=0.69). Conclusions In moderately ill patients with covid-19 and increased D-dimer levels admitted to hospital wards, therapeutic heparin was not significantly associated with a reduction in the primary outcome but the odds of death at 28 days was decreased. The risk of major bleeding appeared low in this trial. Trial registration ClinicalTrials.gov NCT04362085.

Recent Advances in Enantioselective Pd-Catalyzed Allylic Substitution: From Design to Applications.

Abstract: This Review compiles the evolution, mechanistic understanding, and more recent advances in enantioselective Pd-catalyzed allylic substitution and decarboxylative and oxidative allylic substitutions. For each reaction, the catalytic data, as well as examples of their application to the synthesis of more complex molecules, are collected. Sections in which we discuss key mechanistic aspects for high selectivity and a comparison with other metals (with advantages and disadvantages) are also included. For Pd-catalyzed asymmetric allylic substitution, the catalytic data are grouped according to the type of nucleophile employed. Because of the prominent position of the use of stabilized carbon nucleophiles and heteronucleophiles, many chiral ligands have been developed. To better compare the results, they are presented grouped by ligand types. Pd-catalyzed asymmetric decarboxylative reactions are mainly promoted by PHOX or Trost ligands, which justifies organizing this section in chronological order. For asymmetric oxidative allylic substitution the results are grouped according to the type of nucleophile used.

Survey on Network Slicing for Internet of Things Realization in 5G Networks

Abstract: Internet of Things (IoT) is an emerging technology that makes people’s lives smart by conquering a plethora of diverse application and service areas. In near future, the fifth-generation (5G) wireless networks provide the connectivity for this IoT ecosystem. It has been carefully designed to facilitate the exponential growth in the IoT field. Network slicing is one of the key technologies in the 5G architecture that has the ability to divide the physical network into multiple logical networks (i.e., slices) with different network characteristics. Therefore, network slicing is also a key enabler of realisation of IoT in 5G. Network slicing can satisfy the various networking demands by heterogeneous IoT applications via dedicated slices. In this survey, we present a comprehensive analysis of the exploitation of network slicing in IoT realisation. We discuss network slicing utilisation in different IoT application scenarios, along with the technical challenges that can be solved via network slicing. Furthermore, integration challenges and open research problems related to the network slicing in the IoT realisation are also discussed in this paper. Finally, we discuss the role of other emerging technologies and concepts, such as blockchain and Artificial Intelligence/Machine Learning (AI/ML) in network slicing and IoT integration.

k-Nearest Neighbour Classifiers - A Tutorial

Abstract: Perhaps the most straightforward classifier in the arsenal or Machine Learning techniques is the Nearest Neighbour Classifier—classification is achieved by identifying the nearest neighbours to a query example and using those neighbours to determine the class of the query. This approach to classification is of particular importance, because issues of poor runtime performance is not such a problem these days with the computational power that is available. This article presents an overview of techniques for Nearest Neighbour classification focusing on: mechanisms for assessing similarity (distance), computational issues in identifying nearest neighbours, and mechanisms for reducing the dimension of the data. This article is the second edition of a paper previously published as a technical report [16]. Sections on similarity measures for time-series, retrieval speedup, and intrinsic dimensionality have been added. An Appendix is included, providing access to Python code for the key methods.

Pseudomonas aeruginosa: An Audacious Pathogen with an Adaptable Arsenal of Virulence Factors.

Abstract: Pseudomonas aeruginosa is a dominant pathogen in people with cystic fibrosis (CF) contributing to morbidity and mortality. Its tremendous ability to adapt greatly facilitates its capacity to cause chronic infections. The adaptability and flexibility of the pathogen are afforded by the extensive number of virulence factors it has at its disposal, providing P. aeruginosa with the facility to tailor its response against the different stressors in the environment. A deep understanding of these virulence mechanisms is crucial for the design of therapeutic strategies and vaccines against this multi-resistant pathogen. Therefore, this review describes the main virulence factors of P. aeruginosa and the adaptations it undergoes to persist in hostile environments such as the CF respiratory tract. The very large P. aeruginosa genome (5 to 7 MB) contributes considerably to its adaptive capacity; consequently, genomic studies have provided significant insights into elucidating P. aeruginosa evolution and its interactions with the host throughout the course of infection.

Tracking the international spread of SARS-CoV-2 lineages B.1.1.7 and B.1.351/501Y-V2

Abstract: Late in 2020, two genetically-distinct clusters of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) with mutations of biological concern were reported, one in the United Kingdom and one in South Africa. Using a combination of data from routine surveillance, genomic sequencing and international travel we track the international dispersal of lineages B.1.1.7 and B.1.351 (variant 501Y-V2). We account for potential biases in genomic surveillance efforts by including passenger volumes from location of where the lineage was first reported, London and South Africa respectively. Using the software tool grinch (global report investigating novel coronavirus haplotypes), we track the international spread of lineages of concern with automated daily reports, Further, we have built a custom tracking website (cov-lineages.org/global_report.html) which hosts this daily report and will continue to include novel SARS-CoV-2 lineages of concern as they are detected.

Survey on Multi-Access Edge Computing Security and Privacy

Abstract: The European Telecommunications Standards Institute (ETSI) has introduced the paradigm of Multi-Access Edge Computing (MEC) to enable efficient and fast data processing in mobile networks. Among other technological requirements, security and privacy are significant factors in the realization of MEC deployments. In this paper, we analyse the security and privacy of the MEC system. We introduce a thorough investigation of the identification and the analysis of threat vectors in the ETSI standardized MEC architecture. Furthermore, we analyse the vulnerabilities leading to the identified threat vectors and propose potential security solutions to overcome these vulnerabilities. The privacy issues of MEC are also highlighted, and clear objectives for preserving privacy are defined. Finally, we present future directives to enhance the security and privacy of MEC services.

SARS-CoV-2 variants with mutations at the S1/S2 cleavage site are generated in vitro during propagation in TMPRSS2-deficient cells.

Abstract: The spike (S) protein of Severe Acute Respiratory Syndrome-Coronavirus-2 (SARS-CoV-2) binds to a host cell receptor which facilitates viral entry. A polybasic motif detected at the cleavage site of the S protein has been shown to broaden the cell tropism and transmissibility of the virus. Here we examine the properties of SARS-CoV-2 variants with mutations at the S protein cleavage site that undergo inefficient proteolytic cleavage. Virus variants with S gene mutations generated smaller plaques and exhibited a more limited range of cell tropism compared to the wild-type strain. These alterations were shown to result from their inability to utilize the entry pathway involving direct fusion mediated by the host type II transmembrane serine protease, TMPRSS2. Notably, viruses with S gene mutations emerged rapidly and became the dominant SARS-CoV-2 variants in TMPRSS2-deficient cells including Vero cells. Our study demonstrated that the S protein polybasic cleavage motif is a critical factor underlying SARS-CoV-2 entry and cell tropism. As such, researchers should be alert to the possibility of de novo S gene mutations emerging in tissue-culture propagated virus strains.

Data-driven predictive control for unlocking building energy flexibility: A review

Abstract: Managing supply and demand in the electricity grid is becoming more challenging due to the increasing penetration of variable renewable energy sources. As significant end-use consumers, and through better grid integration, buildings are expected to play an expanding role in the future smart grid. Predictive control allows buildings to better harness available energy flexibility from the building passive thermal mass. However, due to the heterogeneous nature of the building stock, developing computationally tractable control-oriented models, which adequately represent the complex and nonlinear thermal-dynamics of individual buildings, is proving to be a major hurdle. Data-driven predictive control, coupled with the “Internet of Things”, holds the promise for a scalable and transferrable approach, with data-driven models replacing traditional physics-based models. This review examines recent work utilising data-driven predictive control for demand side management application with a special focus on the nexus of model development and control integration, which to date, previous reviews have not addressed. Further topics examined include the practical requirements for harnessing passive thermal mass and the issue of feature selection. Current research gaps are outlined and future research pathways are suggested to identify the most promising data-driven predictive control techniques for grid integration of buildings.

Achievable Rate Optimization for MIMO Systems With Reconfigurable Intelligent Surfaces

Abstract: Reconfigurable intelligent surfaces (RISs) represent a new technology that can shape the radio wave propagation in wireless networks and offers a great variety of possible performance and implementation gains Motivated by this, we study the achievable rate optimization for multi-stream multiple-input multiple-output (MIMO) systems equipped with an RIS, and formulate a joint optimization problem of the covariance matrix of the transmitted signal and the RIS elements To solve this problem, we propose an iterative optimization algorithm that is based on the projected gradient method (PGM) We derive the step size that guarantees the convergence of the proposed algorithm and we define a backtracking line search to improve its convergence rate Furthermore, we introduce the total free space path loss (FSPL) ratio of the indirect and direct links as a first-order measure of the applicability of RISs in the considered communication system Simulation results show that the proposed PGM achieves the same achievable rate as a state-of-the-art benchmark scheme, but with a significantly lower computational complexity In addition, we demonstrate that the RIS application is particularly suitable to increase the achievable rate in indoor environments, as even a small number of RIS elements can provide a substantial achievable rate gain

Electron and photon reconstruction and identification with the CMS experiment at the CERN LHC

Abstract: The performance is presented of the reconstruction and identification algorithms for electrons and photons with the CMS experiment at the LHC. The reported results are based on proton-proton collision data collected at a center-of-mass energy of 13 TeV and recorded in 2016-2018, corresponding to an integrated luminosity of 136 fb$^{-1}$. Results obtained from lead-lead collision data collected at $\sqrt{s_\mathrm{NN}}=$ 5.02 TeV are also presented. Innovative techniques are used to reconstruct the electron and photon signals in the detector and to optimize the energy resolution. Events with electrons and photons in the final state are used to measure the energy resolution and energy scale uncertainty in the recorded events. The measured energy resolution for electrons produced in Z boson decays in proton-proton collision data ranges from 2 to 5%, depending on electron pseudorapidity and energy loss through bremsstrahlung in the detector material. The energy scale in the same range of energies is measured with an uncertainty smaller than 0.1 (0.3)% in the barrel (endcap) region in proton-proton collisions and better than 1 (3)% in the barrel (endcap) region in heavy ion collisions. The timing resolution for electrons from Z boson decays with the full 2016-2018 proton-proton collision data set is measured to be 200 ps.

The Clustal Omega Multiple Alignment Package.

Abstract: Clustal Omega is a version, completely rewritten and revised in 2011, of the widely used Clustal series of programs for multiple sequence alignment. It can deal with very large numbers (many tens of thousands) of DNA/RNA or protein sequences due to its use of the mBed algorithm for calculating guide-trees. This algorithm allows very large alignment problems to be tackled very quickly, even on personal computers. The accuracy of the program has been considerably improved over earlier Clustal programs, through the use of the HHalign method for aligning profile hidden Markov models. The program currently is used from the command-line or can be run online.

Hybrid LiFi and WiFi Networks: A Survey

Abstract: In order to tackle the rapidly growing number of mobile devices and their expanding demands for Internet services, network convergence is envisaged to integrate different technology domains. For indoor wireless communications, one promising approach is to coordinate light fidelity (LiFi) and wireless fidelity (WiFi), namely hybrid LiFi and WiFi networks (HLWNets). This hybrid network combines the high-speed data transmission of LiFi and the ubiquitous coverage of WiFi. In this article, we present a survey-style introduction to HLWNets, starting with a framework of system design in the aspects of network architectures, cell deployments, multiple access and modulation schemes, illumination requirements and backhaul. Key performance metrics and recent achievements are then reviewed to demonstrate the superiority of HLWNets against stand-alone networks. Further, the unique challenges facing HLWNets are elaborated on key research topics including user behavior modeling, interference management, handover and load balancing. Moreover, the potential of HLWNets in the application areas is presented, exemplified by indoor positioning and physical layer security. Finally, the challenges and future research directions are discussed.

Algorithmic injustice: a relational ethics approach.

Abstract: It has become trivial to point out that algorithmic systems increasingly pervade the social sphere. Improved efficiency-the hallmark of these systems-drives their mass integration into day-to-day life. However, as a robust body of research in the area of algorithmic injustice shows, algorithmic systems, especially when used to sort and predict social outcomes, are not only inadequate but also perpetuate harm. In particular, a persistent and recurrent trend within the literature indicates that society's most vulnerable are disproportionally impacted. When algorithmic injustice and harm are brought to the fore, most of the solutions on offer (1) revolve around technical solutions and (2) do not center disproportionally impacted communities. This paper proposes a fundamental shift-from rational to relational-in thinking about personhood, data, justice, and everything in between, and places ethics as something that goes above and beyond technical solutions. Outlining the idea of ethics built on the foundations of relationality, this paper calls for a rethinking of justice and ethics as a set of broad, contingent, and fluid concepts and down-to-earth practices that are best viewed as a habit and not a mere methodology for data science. As such, this paper mainly offers critical examinations and reflection and not "solutions."

State of the Art in Defect Detection Based on Machine Vision

Abstract: Machine vision significantly improves the efficiency, quality, and reliability of defect detection. In visual inspection, excellent optical illumination platforms and suitable image acquisition hardware are the prerequisites for obtaining high-quality images. Image processing and analysis are key technologies in obtaining defect information, while deep learning is significantly impacting the field of image analysis. In this study, a brief history and the state of the art in optical illumination, image acquisition, image processing, and image analysis in the field of visual inspection are systematically discussed. The latest developments in industrial defect detection based on machine vision are introduced. In the further development of the field of visual inspection, the application of deep learning will play an increasingly important role. Thus, a detailed description of the application of deep learning in defect classification, localization and segmentation follows the discussion of traditional defect detection algorithms. Finally, future prospects for the development of visual inspection technology are explored.

A Phase 3 Open-Label Study of Elexacaftor/Tezacaftor/Ivacaftor in Children 6 through 11 Years of Age with Cystic Fibrosis and at Least One F508del Allele.

Abstract: Rationale: Elexacaftor/tezacaftor/ivacaftor (ELX/TEZ/IVA) was shown to be efficacious and safe in patients ≥12 years of age with cystic fibrosis and at least one F508del-CFTR (cystic fibrosis trans...