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Self-adaptive bio-inspired optimisation of machine learning pipelines for rejection risk prediction in kidney transplant patients
No full textKidney transplantation is the treatment of choice for end-stage kidney disease. Kidney allograft rejection is one of the primary mechanisms of graft loss following organ transplantation. The histopathological diagnosis of kidney transplant rejection is complicated by a lack of disease-specific lesions, highlighting the urgent need for more quantitative approaches. Gene expression profiling of graft biopsies can provide evidence of rejection before a clinical phenotype becomes apparent. This thesis aims to improve the prediction of kidney transplant rejection using gene expression microarray data by leveraging bio-inspired optimisation techniques to improve classification performance. We used bio-inspired optimisation algorithms to perform numerical hyperparameter optimisation of classification pipelines trained on biopsy and blood samples of kidney transplant patients. Our results demonstrate that the optimised pipelines improved the predictive capabilities in many cases, particularly when adopting a particle swarm optimisation approach. The genes identified by the optimised pipelines as being important for predictive performance were relevant to the context of transplantation with a diverse immunological scope being independently selected by the different classification pipelines, underscoring the complexity of the immunological response underpinning kidney allograft rejection
A Big Data toolkit to support systematic health assessment of energy systems in buildings utilising AI techniques
No full textBuildings account for 40% of worldwide primary energy and 27% of the total CO2 emissions.
It is essential to make energy consumption in smart buildings more effcient by trying to keep the building operating in optimal conditions for as long as possible. New health assessment technologies, such as Prognostics and Health Management (PHM) can help to meet this goal. With the increasing availability of sensor and meter data in smart buildings, as well as advances in artificial intelligence (AI) techniques, the search for a PHM-based system that integrates these techniques may be a solution to the problem of energy effciency in buildings.
This thesis has achieved two objectives: to design a Health Management methodology for potential energy use optimisation in smart buildings, and to validate the methodology in a real case study. To fulfil the objectives, a number of tasks have been carried out which have led to several contributions.
A new methodology has been proposed to monitor the health of the system using mainly data-driven AI techniques. The proposal defines a set of high-level tasks (such as consistency checking, state assessment, KPI monitoring, FDD, prognosis and predictive maintenance) and the concept of health state vector. The solution can be adapted depending on the KPIs and tasks needed for each smart building.
It has been determined that one of the most relevant KPIs is the energy consumption of heating, ventilation, and air conditioning (HVAC) systems and that it would be important to be able to predict it in order to make decisions that lead to a reduction in consumption. For this purpose, an approach based on machine learning has been proposed: on the one hand, unsupervised techniques that allow estimating the operation mode of the system based on the meteorological conditions and, on the other hand, two prediction models have been developed using deep learning techniques that allow estimating the evolution of consumption in the short and medium term.
The usual machine learning (ML) methodology has been applied to build two possible solutions for the fault detection and diagnosis (FDD) task of an HVAC system, which would allow early detection of errors in the system and thus correct them, avoiding consumption associated with failure situations.The approach has been tested in the Alice Perry Engineering building, where the KPIs have been defined, weather-dependent operating modes have been detected and consumption prediction models have been successfully built (according to ASHRAE requirements); finally the FDD module has been successfully tested with promising results
A modelling and computational study of biofilm dynamics: Exploring the role of initial attachment and horizontal gene transfer
No full textBiofilms are ubiquitous in nature and one of the most successful lifestyles on earth. This success is largely due to the high level of interactions within biofilms, leading to the specialisation of subpopulations and subsequent heterogeneity. Biofilms have gathered research attention because of their potential use in engineering, but also because of the need to control their formation in man-made systems. However, due to their complexity and heterogeneity, biofilm systems can be cumbersome to analyse. In this context, model biofilm systems, whether they are experimental or mathematical, offer a reduction of the system to a more observable one through simplifying assumptions and hence allow easier analysis. This thesis aims to propose innovative biofilm models, focusing on diverse key aspects of biofilm ecosystems.
More specifically, this dissertation is divided between an original experimental model system, proposed to investigate cross-kingdom interactions in anaerobic communities during biofilm formation and four novel mathematical models, introduced to describe the main regulatory effects of trace metals on microbial biofilms. To this aim, novel mathematical functions are introduced to describe key phenomena linked to trace metals in biofilms dynamics: bacterial attachment (and subsequent biofilm initiation) and horizontal gene transfer, one of the drivers of genotypic diversity and antibiotic resistance.
The experimental model system is an undefined, engineered model system mimicking growth conditions in an Upflow Anaerobic Sludge Blanket reactor. It was studied with a sampling campaign during the first five days of the biofilm establishment, followed by biofilm characterisation with fluorescence microscopy and DNA sequencing at different time points. To complement DNA sequencing, a network-based analysis was carried out to investigate correlations between fungal and bacterial populations in different samples. This exploratory study into an unknown biofilm type brought to light the importance of the interactions between the bacterial microbiome and its fungal counterpart.
The mathematical models in this thesis are continuum models, formulated as systems of non-linear partial differential equations. Non-linear hyperbolic PDEs govern the advective transport and growth of the solid-phase species forming the biofilm, while parabolic quasi-linear PDEs model the diffusion-reaction of soluble substrates and bacteriophages. The first model presented focuses on the influence of ionic strength on bacterial attachment and subsequent biofilm formation, and is formulated as a 1D free boundary problem. This model focuses on drinking water distribution systems biofilms and their invasion by pathogenic bacteria Legionella pneumophila, with a focus on the necrotrophic metabolism of the latter, which gives it the ability to persist in biofilms. The second model is formulated as a free-boundary problem describing the impact of conjugation on plasmid spread in biofilm communities. More specifically, conjugation is modelled as a mass-action kinetics process subsequent to gene expression, modelled as a nonlocal term to account for recipient-sensing. The existence and uniqueness of the solutions are proved using the method of characteristics and the fixed point theorem. The third model is formulated as a multidimensional problem, and proposes a modelling framework to natural transformation in biofilms, modelled as a frequency-dependent process. It investigates the comparative influence of conjugation and transformation on the spread of antibiotic resistance and biofilm compartmentalisation due to differences in metabolisms and sensitivities to toxic stressors. Finally, the last model describes the interaction between bacteriophages and biofilm communities and includes generalised transduction. This model is the first biofilm model that includes the three main horizontal gene transfer mechanisms: conjugation, transformation and transduction.
All models are integrated numerically through the implementation of original code in MatLab and Comsol Multiphysics. Numerical simulations allow investigating the behaviour of the models, which are able to describe and predict key phenomena of biofilm dynamics. The results of the experimental section demonstrate the adequacy of model systems for investigating biofilm formation. The mathematical models can reproduce crucial elements of biofilm ecology, namely initial bacterial attachment and the main aspects of plasmid spread, such as horizontal gene transfer, the impact of selective pressure on vertical gene transfer or bacteriophage activity
Reclaiming residual knowledge: A novel paradigm to low-bit quantization
No full textThis paper explores a novel paradigm in low-bit (\ie~4-bits or lower) quantization, differing from existing state-of-the-art methods, by framing optimal quantization as an architecture search problem within convolutional neural networks (ConvNets). Our framework, dubbed \textbf{CoRa} (Optimal Quantization Residual \textbf{Co}nvolutional Operator Low-\textbf{Ra}nk Adaptation), is motivated by two key aspects. Firstly, quantization residual knowledge, \ie~the lost information between floating-point weights and quantized weights, has long been neglected by the research community. Reclaiming the critical residual knowledge, with an infinitesimal extra parameter cost, can reverse performance degradation without training. Secondly, state-of-the-art quantization frameworks search for optimal quantized weights to address the performance degradation. Yet, the vast search spaces in weight optimization pose a challenge for the efficient optimization in large models. For example, state-of-the-art BRECQ necessitates iterations to quantize models. Fundamentally differing from existing methods, \textbf{CoRa} searches for the optimal architectures of low-rank adapters, reclaiming critical quantization residual knowledge, within the search spaces smaller compared to the weight spaces, by many orders of magnitude. The low-rank adapters approximate the quantization residual weights, discarded in previous methods. We evaluate our approach over multiple pre-trained ConvNets on ImageNet. \textbf{CoRa} achieves comparable performance against both state-of-the-art quantization-aware training and post-training quantization baselines, in -bit and -bit quantization, by using less than iterations on a small calibration set with images. Thus, \textbf{CoRa} establishes a new state-of-the-art in terms of the optimization efficiency in low-bit quantization.Science Foundation Ireland Centre for Research Training in Artificial Intelligencepeer-reviewe
The diagonal application of the EU Charter of Fundamental Rights: From “Displacement” through “Agency” to “Scope” and beyond
No full textThe application of the EU Charter of Fundamental Rights to the Member States has given rise both to a controversial phraseology and a controversial case-law. This paper offers a reconstruction of the constitutional intent and proposes a conceptualization in conformity with the structural function and the constitutional contemplation of the pouvoir constituent. As to the phraseological debate, it demonstrates that the Charter’s application to the Member States may occur by reason of either “implementation” or “interpretation” of EU law and the two strands are embraced but not synthetized by “scope” as a collective term. As to the substantive debate, it demonstrates that the CJEU’s case-law on “implementation” is not only amorphous but also inconsistent with the Charter’s constitutional mandate. The paper proposes a novel approach based on the notion that the application to the Member States is accessory to the supremacy of EU law. The paper’s argument is presented in the following steps. First, the paper presents the pristine rationale and constitutional function of the application to the Member State through its emergence and historical context. Second, it provides a taxonomy and critical overview of the CJEU’s amorphous case-law and presents the Court’s futile attempt to create a coherent doctrine that faithfully reproduces the constitutional contemplation behind the diagonal application and that reflects the division of competences between the EU and its Member States. Third, it sets out the proposed doctrine of “displacement.”peer-reviewe
Event camera-based eye motion analysis: a survey
No full textNeuromorphic vision sensors, commonly referred to as Event Cameras (ECs), have gained prominence as a field of research in Computer Vision. This popularity stems from the numerous unique characteristics including High Dynamic Range, High Temporal Resolution, and Low Latency. Of particular interest is their temporal resolution, which proves ideal for human monitoring applications. Capturing rapid facial movements and eye gaze can be effectively achieved with ECs. Recent studies involving the use of ECs for object detection and tracking have demonstrated success in tasks involving Eye Motion Analysis such as Eye tracking, Blink detection, Gaze estimation and Pupil tracking. The objective of this study is to provide a comprehensive review of the current research in the aforementioned tasks, focusing on the potential utilization of ECs for future tasks involving rapid eye motion detection, such as detection and classification of saccades. We highlight studies that may serve as a foundation for undertaking such a task, such as pupil tracking and gaze estimation. We also highlight in our review some common challenges encountered such as the availability of datasets and review some of the methods used in solving this problem. Finally, we discuss some limitations of this field of research and conclude with future directions including real-world applications and potential research directions.Science Foundation Ireland at the Insight Science Foundation Ireland Research Centre for Data Analytics, Dublin City University, in collaboration with FotoNation (Grant Number: 12/RC/2289_P
The influence of the convention on the rights of persons with disabilities on the European court of human rights in the area of mental health law: Divergence and unexplored potential
No full textThis article explores how the European Court of Human Rights has applied the norms of the UN Convention on the Rights of Persons with Disabilities (CRPD) in the area of mental health law. The European Court was initially receptive to the CRPD, including the UN Committee on the Rights of Persons with Disabilities' call for a repeal of legislation permitting involuntary psychiatric hospitalisation, but later distanced itself from it. The CRPD has nevertheless influenced how the European Court approached (a) involuntary hospitalisation, (b) separating detention from treatment, (c) restraints and other forms of ill-treatment in institutions, and (d) disability-neutral detention based on disability. Despite the two treaty bodies' different jurisprudential methodology and their different assumptions about the role of medical and legal professionals, the CRPD can continue to influence the European Court in areas such as less restrictive alternatives to coercive treatment, the relevance of capacity, and the importance of personal integrity for mental health treatment.This work was supported by the Hungarian Academy of Sciences' Janos Bolyai Research Scholarship no. BO/00785/20.peer-reviewe
Galway’s Local Authority Climate Action Plan: Research on the Galway City Council’s Local Authority Climate Action Plan (LACAP) and the city’s performance in terms of nature, water, and sustainable transport
No full textGalway City Council has made considerable efforts in climate protection over the past few years. In 2023, the minister required local authorities to create a local authority climate action plan as part of the path outlined in the Climate Action and Low Carbon Development (Amendment) Bill (2021) to achieve net-zero emissions by 2050. A closer look at the Local Authority Climate Action Plan (2024-2029) for Galway reveals working with stakeholders, designing programmes and projects to help achieve goals, and providing opportunities to involve the public to be integral to the creation of the plan. Some key goals are to be an environmentally sustainable, carbon neutral, and biodiverse city by 2050. The Galway City Council’s Climate Action Plan and Climate Action Strategy will work mutually to implement climate protection measures. The establishment of Sustainable Energy Communities (SECs), the Climate Adaptation Strategy (2019-2024), and general work to protect sand dunes, increase flood protection, and reduce air pollution are some examples of steps to be taken by the City Council. Other goals set by Galway City Council include improving energy efficiency by 50% and reducing greenhouse gases by 51% by 2030. In research conducted at various locations in Galway City centre, people were asked about their knowledge of the council’s Climate Action Plan, active travel initiatives, and the city’s performance in terms of protection of the environment and climate action in an eleven-question survey. One hundred and forty full and eight partially completed questionnaires were collected (n=148), with the vast majority of participants living in the city.The Social Sciences Research Centre (SSRC) at the University of Galway.non-peer-reviewe
SARS-CoV-2 outbreak management in nursing homes in Ireland: reflections of COVID-19 response teams from earlier to later waves of the pandemic
Get PDFBackground
A review of key learnings from the response to the COVID-19 pandemic in nursing homes in Ireland can inform planning for future pandemics. This study describes barriers and facilitators contributing to COVID-19 outbreak management from the perspective of frontline teams.
Methods
A qualitative study involving ten online focus group meetings was conducted. Data was collected between April and June 2023. The focus group discussions explored the views, perceptions and experiences of COVID-19 Response Team (CRT) members, clinical/public health experts who worked with them, and care professionals who worked in frontline managerial roles during the pandemic. All nine Community Healthcare Organisations and six Public Health Areas in Ireland were represented. Inductive reflexive thematic analysis was carried out using NVivo Pro 20.
Results
In total, 54 staff members participated in focus group meetings. Five themes were developed from a thematic analysis that covered topics related to (1) infection prevention and control challenges and response to the pandemic, (2) social model of care and the built environment of nursing homes, (3) nursing home staffing, (4) leadership and staff practices, and (5) support and guidance received during the pandemic.
Conclusions
The response to the COVID-19 pandemic has resulted in a steep learning curve, internationally and in Ireland. Preparing better for future pandemics not only requires changes to infection control and outbreak response but also to the organisation and operation of nursing homes. There is a great need to strengthen the long-term care sector’s regulations and support around staffing levels, nursing home facilities, governance, use of technology, infection prevention and control, contingency planning, and maintaining collaborative relationships and strategic leadership. Key findings and recommendations from the Irish example can be used to improve the quality of care and service delivery at local, national, and policy levels and improve preparedness for future pandemics, in Ireland and internationally.This work was funded by the Health Service Executive, Ireland. The funder had a role in the conceptualisation of this study since this work was one of the recommendations in the Nursing Homes Expert Panel Report (Recommendation 6.6). The funder did not have any influence in the publication content
“We want to get out” – A qualitative study on reasons for nurses’ early retirement intentions
No full textBackground
Nurses, the largest workforce in healthcare, play a central role in health systems. There is a severe global shortage of nurses, which is expected to worsen as a significant portion approaches retirement age. Early retirements can harm care quality and healthcare access, thus significantly impacting public health. Identifying reasons is crucial to prevent this trend. This study examined factors behind nurses’ early retirement intentions.
Methods
A descriptive qualitative study was conducted in an Irish hospital (July to September 2023) including individual interviews with 10 nurses aged 55 and over. A thematic analysis was conducted collaboratively by three researchers to identify key factors influencing nurses’ intentions to retire early.
Results
Five main themes emerged: 1) Health concerns, encompassing physical strain and psychological stress, significantly influencing early retirement intentions; 2) Struggling to balance job demands with personal responsibilities such as caring for ageing parents due to inflexible work schedules; 3) Need for more support from senior leaders to address staffing shortages and investment in aging nurses’ career and retirement planning to make staying more desirable and easier; 4) Feelings about nurses’ experience being undervalued in the evolving healthcare; and 5) Facing constant heavy responsibility in guiding new nurses, while simultaneously fearing patient safety risks and encountering generational differences.
Conclusions
Developing support systems for ageing nurses, encompassing strategies to ease workload strain, enhancing career opportunities, and promoting intergenerational collaboration are critical for reducing early retirement trends and sustaining a resilient healthcare workforce. By implementing tailored approaches, such as flexible scheduling and mentorship programs for ageing nurses, healthcare organisations can promote workforce longevity and enhance public health outcomes through improved care delivery.
Key messages
• Health challenges and struggles in work-life balance influence early retirement intentions among nurses, urging flexible work arrangements and improved recognition and leadership in healthcare.
• To extend work careers, organisations must invest in ageing nurses’ career and retirement planning, value experience, and acknowledge potential challenges and benefits in intergenerational nursing