721 research outputs found

    The development of bioinformatics workflows to explore single-cell multi-omics data from T and B lymphocytes

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    The adaptive immune response is responsible for recognising, containing and eliminating viral infection, and protecting from further reinfection. This antigen-specific response is driven by T and B cells, which recognise antigenic epitopes via highly specific heterodimeric surface receptors, termed T-cell receptors (TCRs) and B cell receptors (BCRs). The theoretical diversity of the receptor repertoire that can be generated via homologous recombination of V, D and J genes is large enough (>1015 unique sequences) that virtually any antigen can be recognised. However, only a subset of these are generated within the human body, and how they succeed in specifically recognising any pathogen(s) and distinguishing these from self-proteins remains largely unresolved. The recent advances in applying single-cell genomics technologies to simultaneously measure the clonality, surface phenotype and transcriptomic signature of pathogen- specific immune cells have significantly improved understanding of these questions. Single-cell multi-omics permits the accurate identification of clonally expanded populations, their differentiation trajectories, the level of immune receptor repertoire diversity involved in the response and the phenotypic and molecular heterogeneity. This thesis aims to develop a bioinformatic workflow utilising single-cell multi-omics data to explore, quantify and predict the clonal and transcriptomic signatures of the human T-cell response during and following viral infection. In the first aim, a web application, VDJView, was developed to facilitate the simultaneous analysis and visualisation of clonal, transcriptomic and clinical metadata of T and B cell multi-omics data. The application permits non-bioinformaticians to perform quality control and common analyses of single-cell genomics data integrated with other metadata, thus permitting the identification of biologically and clinically relevant parameters. The second aim pertains to analysing the functional, molecular and immune receptor profiles of CD8+ T cells in the acute phase of primary hepatitis C virus (HCV) infection. This analysis identified a novel population of progenitors of exhausted T cells, and lineage tracing revealed distinct trajectories with multiple fates and evolutionary plasticity. Furthermore, it was observed that high-magnitude IFN-Îł CD8+ T-cell response is associated with the increased probability of viral escape and chronic infection. Finally, in the third aim, a novel analysis is presented based on the topological characteristics of a network generated on pathogen-specific, paired-chain, CD8+ TCRs. This analysis revealed how some cross-reactivity between TCRs can be explained via the sequence similarity between TCRs and that this property is not uniformly distributed across all pathogen-specific TCR repertoires. Strong correlations between the topological properties of the network and the biological properties of the TCR sequences were identified and highlighted. The suite of workflows and methods presented in this thesis are designed to be adaptable to various T and B cell multi-omic datasets. The associated analyses contribute to understanding the role of T and B cells in the adaptive immune response to viral-infection and cancer

    When Deep Learning Meets Polyhedral Theory: A Survey

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    In the past decade, deep learning became the prevalent methodology for predictive modeling thanks to the remarkable accuracy of deep neural networks in tasks such as computer vision and natural language processing. Meanwhile, the structure of neural networks converged back to simpler representations based on piecewise constant and piecewise linear functions such as the Rectified Linear Unit (ReLU), which became the most commonly used type of activation function in neural networks. That made certain types of network structure \unicode{x2014}such as the typical fully-connected feedforward neural network\unicode{x2014} amenable to analysis through polyhedral theory and to the application of methodologies such as Linear Programming (LP) and Mixed-Integer Linear Programming (MILP) for a variety of purposes. In this paper, we survey the main topics emerging from this fast-paced area of work, which bring a fresh perspective to understanding neural networks in more detail as well as to applying linear optimization techniques to train, verify, and reduce the size of such networks

    Continuous Glucose Monitoring for the diagnosis of Gestational Diabetes Mellitus.

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    Gestational Diabetes Mellitus (GDM) incidence and negative outcomes are increasing worldwide. The validity of the oral glucose tolerance test (OGTT) for GDM diagnosis remains contested. Continuous Glucose Monitoring (CGM) could represent a more acceptable and replicable test. Aim of this project was to assess CGM for GDM diagnosis. This PhD thesis is based on five projects: a systematic review of the diagnostic indicators of GDM, an online questionnaire to recruit women at high and low risk of GDM, a retrospective cohort study on the use of the Medtronic iPro2 CGM device for GDM diagnosis, a prospective cohort study on the use of the Abbott Freestyle Libre PRO 2 CGM and a survey study on women and healthcare providers perception of both methods. CGM data were analysed as distribution parameters (mean, CV, SD, maximum value), variability parameters (MAGE and MODD) and time spent in the recommended range, then combined in a CGM score of Variability (CGMSV). In the systematic review were included 174 full-text articles on blood, ultrasound, post-natal and amniotic fluid biomarkers. The ultrasound gestational diabetic score (UGDS) was the most promising biomarker for triangulation. In the GDM risk questionnaire (n=45), triangulation of a composite risk factors score (RFS) with CGMSV and OGTT results highlighted six possible OGTT misdiagnoses (discordant with RFS and CGMSV). In the Medtronic pilot Study (n=73), GDM women (n=33) had significantly higher RFS and CGMSV. The triangulation analysis (n=60) suggested 12 probable misdiagnoses. In the Abbott pilot study (n=87), no significant demographic nor CGM data difference was found between NGT and GDM, possibly due to the small GDM sample size (n=13). With triangulation, 11 OGTT results were potentially false. UGDS (n=22) was positive in only one woman, considered a true negative otherwise. In the survey study, women reported significantly higher acceptability of CGM versus OGTT (n=70 and n=60, respectively), and 94% would recommend CGM for GDM diagnosis. HCP (n=30) scored CGM acceptability significantly lower than women and expressed doubts about the correlation between CGM data and perinatal outcomes. CGM represents a more acceptable alternative to OGTT for GDM diagnosis. HCP expressed doubt about CGM accuracy, and issues of establishing superiority to OGTT remain. Further research on larger cohorts of patients with additional triangulation elements is needed to confirm CGM acceptability and accuracy and refine its use

    Dual Bounds for Redistricting Problems with Non-Convex Objectives

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    We study optimization models for computational redistricting. We focus nonconvex objectives that estimate expected black voter representation, political representation, and Polsby Popper Compactness. All objectives contain a sum of convolutions with a ratio of variables. The representation objectives are a convolution of a ratio of variables with a cumulative distribution function of a normal distribution, also known as the probit curve, while the compactness objective has a quadratic complication in the ratio. We extend the work of Validi et al. [30], which develops strong optimization models for contiguity constraints and develop mixed integer linear programming models that tightly approximate the nonlinear model, and show that our approach creates tight bounds on these optimization problems. We develop novel mixed integer linear relaxations to these nonconvex objectives and demonstrate the effectiveness of our approaches on county level data

    Health technology assessment for digital health technologies

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    Health technology assessment (HTA) frameworks used for making public funding decisions on digital health technologies (DHTs) have not been informed by large stakeholder preference studies and rarely cover all nine domains of the widely used EUnetHTA “Core Model”. Our aim was to develop a literature-informed and stakeholder-prioritised checklist of DHT-specific considerations for DHTs that manage chronic disease that extends an internationally established HTA framework. We conducted two systematic reviews to identify: (i) DHT evaluation frameworks and (ii) primary research on DHTs published until 20 March 2020. Stakeholder prioritisation of issues was performed using a best-worst preference study among a broad cross-section of patients, carers, health professionals, and the general population in Australia, Canada, New Zealand, and the UK. Systematic review issues were prioritised and adapted for use as a practical checklist. DHT evaluation content was recommended by 44 identified frameworks for 28 of the 145 issues in the Core Model and for 22 new DHT-specific issues. A coverage assessment of 112 clinical studies of remote treatment and self-management DHTs for patients with cardiovascular disease or diabetes revealed that less than half covered DHT-specific content in all but one domain, or traditional HTA content in clinical effectiveness and ethical analysis. The preference survey of 1,251 stakeholders identified broad agreement on the 12 most important DHT attributes, six of which were related to safety. The most important attribute was “helps health professionals respond quickly when changes in patient care are needed”, which is not a focus of existing DHT HTA frameworks. Using the thesis-developed checklist in conjunction with the Core Model can enable users to perform a DHT-specific and comprehensive HTA on DHTs that manage chronic disease and can assist primary researchers to collect appropriate data to inform this HTA

    Hemodynamic Quantifications By Contrast-Enhanced Ultrasound:From In-Vitro Modelling To Clinical Validation

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    Aerobic Exercise for the Promotion of Healthy Aging: Changes in Brain Structure Assessed with New Methods

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    As the proportion of older individuals in the population increases, so does the scientific concern surrounding age-related deterioration of brain tissue and related cognitive decline. One modifiable lifestyle factor of interest in the pursuit to slow or even reverse age-related brain atrophy is aerobic exercise. A number of studies have already demonstrated that aerobic exercise in older age can induce maintenance (i.e., reduction of loss) of both gray and white matter volume, particularly in the frontal regions of the brain, which are vulnerable to shrinkage in older age. Other magnetic resonance imaging (MRI)-based techniques, such as quantitative MRI and diffusion-weighted MRI, have been used to measure age-related deterioration of gray and white matter integrity in both voxel-wise analyses as well as on the latent level, but whether these negative changes can be ameliorated through exercise has yet to be shown. The current dissertation includes three papers which used a number of both established and novel MRI-based metrics to quantify changes in brain tissue integrity resulting from aging, as well as to investigate whether these changes can be ameliorated through aerobic exercise. In Paper I (Wenger et al., 2022), we tested the reliability of quantitative MRI measures, namely longitudinal relaxation rate, effective transverse relaxation rate, proton density, and magnetization transfer saturation, by measuring them in a two-day, four-session design with repositioning in the scanner. Using the intra-class effect decomposition model, we found that magnetization transfer saturation could reliably detect individual differences, validating its use to investigate changes in brain structure longitudinally, as well as correlations with other variables of interest, such as change in cardiovascular fitness. In Paper II (Polk et al., 2022), we tested the effects of aerobic exercise on a latent factor of gray-matter structural integrity, comprising observed measures of gray-matter volume, magnetization transfer saturation, and mean diffusivity, in regions of interest that have previously shown volumetric effects of aerobic exercise. We found that gray-matter structural integrity was maintained in frontal and midline regions, and that change in gray-matter structural integrity in the right anterior cingulate cortex was positively correlated with change in cardiovascular fitness within exercising participants. These results suggest a causal relationship between aerobic exercise, cardiovascular fitness, and gray-matter structural integrity in this region. In Paper III (Polk et al., 2022), we tested the effects of aerobic exercise on white matter integrity, measured with both established and recently developed metrics. We were able to replicate findings from a previous study on the effects of aerobic exercise on white matter volume, and we also found change-change correlations between white matter volume and cardiovascular fitness as well as between white matter volume and performance on a test of perceptual speed. We also found unexpected exercise-induced changes in the diffusion weighted imaging-derived metrics of fractional anisotropy, mean diffusivity, fiber density, and fiber density and cross-section. Specifically, we found increases (or decreases in the case of mean diffusivity) within control participants and decreases (or increases in mean diffusivity) in exercisers. Furthermore, we found that percent change in fiber density and fiber density and cross-section correlated negatively with percent change in both cardiovascular fitness and cognitive performance. This casts doubt on the functional interpretation of these measures and suggests that the “more is better” principle may not be universally applicable when investigating age-related and exercise-induced changes in white matter integrity. In sum, this dissertation showed that regular at-home aerobic exercise, which may be more accessible for older individuals than supervised exercise, can be an effective tool to ameliorate age-related decreases in a latent measure of gray-matter structural integrity as well as white matter volume. It also illuminated potential limitations of other measures of white matter integrity in the context of aging and aerobic exercise, and calls for further research into these novel measures, especially when considering functional outcomes such as cognitive performance

    Risk identification and assessment of human-machine conflict

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    The process industries are fully embracing digitalization and artificial intelligence (AI). Industry 4.0 has also transformed the production structures in the process industries to increase productivity and profitability; however, this has also led to emerging risks. The rapid growth and transformation have created gaps and challenges in various aspects, for example, information technology (IT) vs. operation technology (OT), human vs. AI, and traditional statistical analysis vs. machine learning. A notable issue is the apparent differences in decision-making between humans and machines, primarily when they work together. Contradictory observations, states, goals, and actions may lead to conflict between these two decision-makers. Such conflicts have triggered numerous catastrophes in recent years. Moreover, conflicts may become even more elusive and confusing under external forces, e.g., cyberattacks. Therefore, this thesis focuses on human-machine conflict. Five research tasks are conducted to explore the risk of human-machine conflict. More specifically, the thesis presents a systematic literature review on the impact of digitalization on process safety, highlights the myths and misconceptions of data modeling on process safety analysis, and attempts to clarify associated concepts in the area of human-machine conflict. In addition, the thesis summarizes the causes of conflicts and generalizes the mathematical expressions of the causes. It illustrates the evolutional process of conflicts, proposes the measurement of conflicts, develops the risk assessment model of conflicts, and explores the condition of conflict convergence, divergence, and resolution. The thesis also iii demonstrates the proposed methodology and risk models in process systems, for example, the two-phase separator and the Continuous Stirred Tank Reactor (CSTR). It verifies the conflict between manual and automated control (e.g., proportional-integral-derivative control (PID) and model predictive control (MPC)). This thesis proves that conflict is another more profound and implicit phenomenon that raises risks more rapidly and severely. Conflicts are highly associated with faults and failures. Various factors can trigger human-machine conflict, including sensor faults, cyberattacks, human errors, and sabotage. This thesis attempts to provide the readers with a clear picture of the human-machine conflict, alerts the industry and academia about the risk of human-machine conflict, and emphasizes human-centered design
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