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Ongoing Research Projects supported by Research IT

Listing of project codes and abstracts, describing work undertaken which use the resources of the compute clusters hosted by the Research IT team.

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Showing 10 of 423 Results
Project Title Development of Whole Genome Sequencing in the Irish Mycobacteria Reference Laboratory
Project Code HPC_19_01059
Principal Investigator Professor Tom Rogers
Start Date 2019-04-30
End Date 2020-12-31
Abstract The advancement of whole genome sequencing (WGS) has not only allowed for rapid identification of species, but has enabled a wealth of information pertaining to the organism sequenced. This includes information governing genes related to the virulence traits of the organism and mutations in existing genes encoding antimicrobial drug resistance. In a clinical setting, this information is vital to guide appropriate patient management and for successful treatment outcomes. Furthermore, WGS data can be used to determine strain relatedness and track strains in outbreaks. The Irish Mycobacteria Reference Laboratory, situated at St James's Hospital is the National Reference Laboratory for Mycobacteria in Ireland. The primary focus of the IMRL is to recover and identify mycobacterial species [both TB and Non Tuberculous Mycobacteria (NTM)] from patients specimens; to perform drug susceptibility testing to guide patient management and to perform epidemiological typing of TB isolates to facilitate public health investigations. Currently, conventional phenotypic methods utilising culture media are used for organism recovery and for performing drug susceptibility testing. Current molecular methods involve PCR and target sequencing of specific genes however, these methods lack the greater insight that can be achieved with WGS. In addition to the IMRL, WGS will be performed in collaboration with the National MRSA Reference Laboratory. Resources will be pooled in order to enhance the services provided by both reference laboratories through translational research performed using both Illumina and Oxford NanoporeTechnolology. The translational research we are currently undertaking aims to provide rapid onsite WGS analysis of M. tuberculosis and nosocomial MRSA strains in order to improve the quality of patient care and treatment and ultimately provide a centre of excellence with regard to rapid identification of species and their genetic profile.
Project Title Athermal Operation of Semiconductor Lasers
Project Code HPC_19_01058
Principal Investigator Prof John Donegan
Start Date 2018-07-01
End Date 2020-06-30
Abstract We are working on the project, looking at how laser technology could deliver highly energy-efficient devices for future optical networks. Our main goal is to design the low power semiconductor lasers for optical network applications. The idea is that such technology could potentially lead to broadband speeds exceeding 1Gb/s.
Project Title EELS simulations for plasmonic metal particles
Project Code HPC_19_01057
Principal Investigator Dr Richard Hobbs
Start Date 2019-04-01
End Date 2020-04-01
Abstract Plasmonic materials can amplify optical fields via localized surface plasmon resonances (LSPRs), thus providing routes to enhance energy harvesting from light, photocatalysis, nonlinear optical processes, and optoelectronic performance. Scaling the dimensions of Indium nanostructures toward the atomic scale enables greater confinement of plasmonic hotspots. These confined hotspots can drive processes such as those listed above. Careful control of the size and position of Indium clustures is critical to controlling the location of plasmonic hotspots, which in turn determines how and where energy is transferred to neighboring materials for the applications described above. Here we carry out TDDFT simulations to calculate electron energy losss spectrum and optical spectrum of small indium clusters.
Project Title Penetration of Particulate Matter on the E3 Building
Project Code HPC_19_01056
Principal Investigator Prof John Gallagher
Start Date 2018-09-24
End Date 2019-04-08
Abstract Particulate Matter is one of the most detrimental forms of air pollution to the public. It is a complex mixture of extremely fine solid and liquid particles suspended in air mainly from fuel combustion and vehicle emissions. At present, outdoor particulate matter is filtered by mechanical ventilation to prevent it entering indoors. However, HVAC systems account for up to seventy percent of a buildings energy consumption which comes at an environmental and financial cost. Natural ventilation is preferred but This project aims to simulate and analyse particulate matter patterns for the planned E3 buildings on Trinity College Dublin Campus. Local wind and particulate matter will be used to simulate the particulate matter pattern around the E3 Building and how it interacts with the building. This Interim Report details the background of the project, examines the impact of air pollution and the planned work of the project.
Project Title NICAP Ireland
Project Code HPC_19_01055
Principal Investigator Prof Jane McGrath
Start Date 2019-03-11
End Date 2020-12-31
Abstract Aberrant structure, function and connectivity of the basal ganglia and limbic system networks have been associated with core features of ADHD as well as with the emotional regulation difficulties commonly associated with the disorder. This study aims to use innovative neuroimaging analytic techniques to interrogate the morphology of these subcortical brain regions and to define the functional and structural connectivity networks between these regions and the rest of the brain. There is a dearth of longitudinal neuroimaging data in ADHD, and this dataset offers a unique opportunity to investigate developmental changes in the structure, function and connectivity of these subcortical regions as well as the correlation between these brain changes, neuropsychological measures and clinical symptomatology.
Project Title Structural Cortical Connectivity Mediating Motor Function
Project Code HPC_19_01054
Principal Investigator Prof Richard Carson
Start Date 2019-03-06
End Date 2019-09-30
Abstract The focus of the research is the relationship that exists between the structural connectivity of the human brain, and the facility with which movement skills are acquired. On the basis of diffusion weighted imaging (DWI) scans, estimates of the integrity of white matter projections between different regions of interest (RoIs) within the brain have been derived for 43 human volunteers. For each person, various behavioural and electrophysiological measures relating to skill acquisition were also obtained. The objective of the phase of the project for which access to the cluster is requested, is to determine the specific white matter projections that, through their variation across participants, are predictive of the individual differences in learning that are observed. This is achieved through application of a machine learning technique known as elastic net (EN) regularisation. As the particular (robust) variant of the EN technique being employed is based on quantile regression (rather than least squares), it is computationally intensive.
Project Title Evidence from Irish Migration
Project Code HPC_19_01053
Principal Investigator Prof Gaia Narciso
Start Date 2018-10-10
End Date 2019-04-01
Abstract What is the long run impact of large negative historical events on migration flows? The main aim of the proposed paper is to provide both a qualitative and a quantitative investigation on whether personal characteristics and radical historical events matter in the decision to migrate. In a previous contribution, Narciso and Severgnini (2016) exploited the information contained in a unique dataset based on Irish historical data during the first two decades of the 20th century. By combining different historical data sources, they identified the personal features and determinants of those who voiced their discontent and actively participated in the movement for the independence of Ireland from Great Britain. Furthermore, they tested the intergenerational transmission of rebellion generated by a large negative radical shock, the Irish Potato Famine (1845-1852) on the probability of joining the movement of independence in Ireland during the Irish revolution over the period 1913-1921. Starting from the abovementioned dataset, we plan to the test whether the Great Famine had also an effect on the individual decision after half century. Our paper will investigate and link two of the most relevant demographic changes of the recent European history in modern time. First, the Great Irish Famine is one of the biggest hungers in history, which led to the death of about 1 million people, about 20 percent of the total population, having a huge impact on the demographic of Ireland (� Gr�da (1989)). Second, between 1890 and 1924 the Irish migration flows to the USA contributed in a significant part on the economic and social conditions both in the sending countries and in America (see, for example, O�Rourke (1994), Hatton and Williamson (1998), Abramitzky, Boustan and Eriksson (2014)). In particular, we are interested in understanding how potential long-run effects of the Famine can impact the decision of migrating. The mechanism we have in mind and we would like to test is that migration networks formed soon after the famine in the US still have this driving force two generations afterwards. More in detail, during the 50-70 year period followed by the Famine, we 2 believe that the Irish counties hurt most had more short-term migration, which created even more migration due to the network effect and there has been an accumulated network effect. We suppose that county level inequality has resulted in bigger network effects for counties hurt most. Furthermore, there might have been a famine-induced increase in county level inequality. This has resulted in more migration from the counties that have been hurt most and "trapped" in a bad economic situation, resulting in more emigration. To test this, we exploit the methodology proposed by Abramitzky, Boustan and Eriksson (2014) for matching two different set of data. The first one is represented by the early waves of the 20th century Irish Census provide extraordinary pieces of information for creating a complete and realistic picture of the Irish societies before World War I. We consider the two Censuses held in 1901 and 1911, respectively. For the entire Irish population, we can have several pieces of information related to the personal characteristics of the individuals. More precisely, we can collect name and family names, gender, birth date and county of birth, the relation to the head of family, the religion beliefs, the level of education, the languages spoken, the types of occupation and eventually physical and mental handicaps in the family. Furthermore, the Census contains very precise information on the location and the quality of the houses. The 1901 and 1911 Census contains 4,429,866 and 4,384,519 observations, respectively and it has recently been digitized by the Irish national archive. The Ellis Island Administrative Records on Irish migrants represent the second type of data. This source contains characteristics of about 800,000 individuals derived from passengers� documents required to enter in the USA. Finally, information on the effect of the Irish Famine on different Irish county can be obtained by statistical work from Bourke (1959), which contains information on arable areas and the intensity of the potato cultivations for the 32 different counties in Ireland. After having matched the two datasets, we will analyse the main drivers of migration using the same methodology introduced by Krueger (2007 and 2009). We will start considering a simple linear 3 regressions analysis in order to understand the different level of correlations and the structure of our dataset: the dependent variable will be a binary variable, which takes the value of 1 if the individual in the dataset has migrated and 0 otherwise. We will add as explanatory variables our measure of the impact of famine and both individual and family characteristic. We can also construct more aggregate variables such as age structure, share of literate populations and share of different religious groups. We will use geographical information system (GIS) data and additional aggregate statistics, and include additional variables for controlling geographical and institutional variations at local level. Possible covariates can be urban densities, distance to water resources, agricultural productivity and different proxies for production. Preliminary results, also supported by different instrumental variable regressions based on the exogenous movement of the potato blight, suggest that the Famine was an important and significant driver of individuals� migration. We believe that our findings can shed some lights on the individual characteristics of migrants and the relationship between historical shocks and the long-run effects of mobility during the Age of Mass Migration.
Project Title The role of rare genetic variation in neurodevelopmental disorders
Project Code HPC_19_01052
Principal Investigator Prof Louise Gallagher
Start Date 2019-02-18
End Date 2020-02-17
Abstract Neurodevelopmental disorders affect the development of the brain which can result in cognitive, behavioural, language and motor deficits and lead to lifelong impairments. A number of rare genetic variants have been associated with neurodevelopmental disorders. This project aims to elucidate how previously identified rare genetics variants may contribute to behavioural and cognitive difficulties characterised in neurodevelopmental disorders such as autism spectrum disorder. Specifically, this study will use anatomical magnetic resonance imaging and diffusion tensor imaging to investigate brain structure and how it relates to behavioural and cognitive measures in individuals with rare genetic variants. We will assess surface area, volume and cortical thickness in individuals with rare genetic variants and compare to controls. We will also examine group differences in white matter structural architecture. This study will further our knowledge of the interactions between genes, brain structure and behaviour in neurodevelopmental disorders.
Project Title AV1 Video Encoding
Project Code HPC_19_01051
Principal Investigator Prof Aljosa Smolic
Start Date 2019-02-14
End Date 2019-03-03
Abstract I am working on a project about the development of full-reference quality metrics. For that, I need to test some metrics on videos encoded with the AV1 compression approach. In oder to encode the videos I need the computer cluster, since on a normal PC it would take too much time.
Project Title IoCT
Project Code HPC_19_01050
Principal Investigator Dr Nicola Marchetti
Start Date 2019-02-12
End Date 2020-07-01
Abstract The Internet of Things (IoT) is composed by a vast amount of devices, heterogeneous in function and connectivity, applications range from telemetry to smart city, traffic, health actuation and disruptive smart grid applications. Ensuring that the currently designed communication infrastructure will be capable to collect and transport the massive amount of data generated by the distributed devices and, at the same time, ensure a reliable and robust feedback to the actuators is no small task. Current networks are not meant to inter-operate and have been designed to be part of separate communications systems, rather than allowing seamless and heterogeneous data transfer; the effects of multiple communication technologies working at the same time, in the same space for dense and distributed networks are poorly understood. Network management Solutions, based on the interactions between all the layers of a communication network, need to consider the interactions between all the communication agents, in time and space, to determine causal relationships in a heterogeneous network and allow dynamic and distributed management. The first focus of this project is then to develop a functional model of hierarchical communication systems for the Internet of Things. The basis of this work is rooted in a new rediscovery of complexity theory as a powerful framework for the understanding of the interactions within large, heterogeneous systems in which a central controller is either impossible or undesirable. Secondly, the novel taxonomy developed will be used in cross-layer multi-agent simulators developed for the purpose to identify causality links within the communication networks. Techniques borrowed from climate science, econometrics and machine learning will be employed to determine how information spreads over a network, from node to node and across the layers, and which elements are relevant for a dynamic and proactive management of complex, large and dense IoT networks.