28 research outputs found

    Modelling the genomic structure, and antiviral susceptibility of Human Cytomegalovirus

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    Human Cytomegalovirus (HCMV) is found ubiquitously in humans worldwide, and once acquired, the infection persists within the host throughout their life. Although Immunocompetent people rarely are affected by HCMV infections, their related diseases pose a major health problem worldwide for those with compromised or suppressed immune systems such as transplant recipients. Additionally, congenital transmission of HCMV is the most common infectious cause of birth defects globally and is associated with a substantial economic burden. This thesis explores the application of statistical modelling and genomics to unpick three key areas of interest in HCMV research. First, a comparative genomics analysis of global HCMV strains was undertaken to delineate the molecular population structure of this highly variable virus. By including in-house sequenced viruses of African origin and by developing a statistical framework to deconvolute highly variable regions of the genome, novel and important insights into the co-evolution of HCMV with its host were uncovered. Second, a rich database relating mutations to drug sensitivity was curated for all the antiviral treated herpesviruses. This structured information along with the development of a mutation annotation pipeline, allowed the further development of statistical models that predict the phenotype of a virus from its sequence. The predictive power of these models was validated for HSV1 by using external unseen mutation data provided in collaboration with the UK Health Security Agency. Finally, a nonlinear mixed effects model, expanded to account for Ganciclovir pharmacokinetics and pharmacodynamics, was developed by making use of rich temporal HCMV viral load data. This model allowed the estimation of the impact of immune-clearance versus antiviral inhibition in controlling HCMV lytic replication in already established infections post-haematopoietic stem cell transplant

    Algebraic Topology for Data Scientists

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    This book gives a thorough introduction to topological data analysis (TDA), the application of algebraic topology to data science. Algebraic topology is traditionally a very specialized field of math, and most mathematicians have never been exposed to it, let alone data scientists, computer scientists, and analysts. I have three goals in writing this book. The first is to bring people up to speed who are missing a lot of the necessary background. I will describe the topics in point-set topology, abstract algebra, and homology theory needed for a good understanding of TDA. The second is to explain TDA and some current applications and techniques. Finally, I would like to answer some questions about more advanced topics such as cohomology, homotopy, obstruction theory, and Steenrod squares, and what they can tell us about data. It is hoped that readers will acquire the tools to start to think about these topics and where they might fit in.Comment: 322 pages, 69 figures, 5 table

    Learning Discriminative Representations for Gigapixel Images

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    Digital images of tumor tissue are important diagnostic and prognostic tools for pathologists. Recent advancement in digital pathology has led to an abundance of digitized histopathology slides, called whole-slide images. Computational analysis of whole-slide images is a challenging task as they are generally gigapixel files, often one or more gigabytes in size. However, these computational methods provide a unique opportunity to improve the objectivity and accuracy of diagnostic interpretations in histopathology. Recently, deep learning has been successful in characterizing images for vision-based applications in multiple domains. But its applications are relatively less explored in the histopathology domain mostly due to the following two challenges. Firstly, there is difficulty in scaling deep learning methods for processing large gigapixel histopathology images. Secondly, there is a lack of diversified and labeled datasets due to privacy constraints as well as workflow and technical challenges in the healthcare sector. The main goal of this dissertation is to explore and develop deep models to learn discriminative representations of whole slide images while overcoming the existing challenges. A three-staged approach was considered in this research. In the first stage, a framework called Yottixel is proposed. It represents a whole-slide image as a set of multiple representative patches, called mosaic. The mosaic enables convenient processing and compact representation of an entire high-resolution whole-slide image. Yottixel allows faster retrieval of similar whole-slide images within large archives of digital histopathology images. Such retrieval technology enables pathologists to tap into the past diagnostic data on demand. Yottixel is validated on the largest public archive of whole-slide images (The Cancer Genomic Atlas), achieving promising results. Yottixel is an unsupervised method that limits its performance on specific tasks especially when the labeled (or partially labeled) dataset can be available. In the second stage, multi-instance learning (MIL) is used to enhance the cancer subtype prediction through weakly-supervised training. Three MIL methods have been proposed, each improving upon the previous one. The first one is based on memory-based models, the second uses attention-based models, and the third one uses graph neural networks. All three methods are incorporated in Yottixel to classify entire whole-slide images with no pixel-level annotations. Access to large-scale and diversified datasets is a primary driver of the advancement and adoption of machine learning technologies. However, healthcare has many restrictive rules around data sharing, limiting research and model development. In the final stage, a federated learning scheme called ProxyFL is developed that enables collaborative training of Yottixel among the multiple healthcare organizations without centralization of the sensitive medical data. The combined research in all the three stages of the Ph.D. has resulted in the development of a holistic and practical framework for learning discriminative and compact representations of whole-slide images in digital pathology

    Blending generative models with deep learning for multidimensional phenotypic prediction from brain connectivity data

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    Network science as a discipline has provided us with foundational machinery to study complex relational entities such as social networks, genomics, econometrics etc. The human brain is a complex network that has recently garnered immense interest within the data science community. Connectomics or the study of the underlying connectivity patterns in the brain has become an important field of study for the characterization of various neurological disorders such as Autism, Schizophrenia etc. Such connectomic studies have provided several fundamental insights into its intrinsic organisation and implications on our behavior and health. This thesis proposes a collection of mathematical models that are capable of fusing information from functional and structural connectivity with phenotypic information. Here, functional connectivity is measured by resting state functional MRI (rs-fMRI), while anatomical connectivity is captured using Diffusion Tensor Imaging (DTI). The phenotypic information of interest could refer to continuous measures of behavior or cognition, or may capture levels of impairment in the case of neuropsychiatric disorders. We first develop a joint network optimization framework to predict clinical severity from rs-fMRI connectivity matrices. This model couples two key terms into a unified optimization framework: a generative matrix factorization and a discriminative linear regression model. We demonstrate that the proposed joint inference strategy is successful in generalizing to prediction of impairments in Autism Spectrum Disorder (ASD) when compared with several machine learning, graph theoretic and statistical baselines. At the same time, the model is capable of extracting functional brain biomarkers that are informative of individual measures of clinical severity. We then present two modeling extensions to non-parametric and neural network regression models that are coupled with the same generative framework. Building on these general principles, we extend our framework to incorporate multimodal information from Diffusion Tensor Imaging (DTI) and dynamic functional connectivity. At a high level, our generative matrix factorization now estimates a time-varying functional decomposition. At the same time, it is guided by anatomical connectivity priors in a graph-based regularization setup. This connectivity model is coupled with a deep network that predicts multidimensional clinical characterizations and models the temporal dynamics of the functional scan. This framework allows us to simultaneously explain multiple impairments, isolate stable multi-modal connectivity signatures, and study the evolution of various brain states at rest. Lastly, we shift our focus to end-to-end geometric frameworks. These are designed to characterize the complementarity between functional and structural connectivity data spaces, while using clinical information as a secondary guide. As an alternative to the previous generative framework for functional connectivity, our representation learning scheme of choice is a matrix autoencoder that is crafted to reflect the underlying data geometry. This is coupled with a manifold alignment model that maps from function to structure and a deep network that maps to phenotypic information. We demonstrate that the model reliably recovers structural connectivity patterns across individuals, while robustly extracting predictive yet interpretable brain biomarkers. Finally, we also present a preliminary analytical and experimental exposition on the theoretical aspects of the matrix autoencoder representation

    Multi-Scale Mathematical Modelling of Brain Networks in Alzheimer's Disease

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    Perturbations to brain network dynamics on a range of spatial and temporal scales are believed to underpin neurological disorders such as Alzheimer’s disease (AD). This thesis combines quantitative data analysis with tools such as dynamical systems and graph theory to understand how the network dynamics of the brain are altered in AD and experimental models of related pathologies. Firstly, we use a biophysical neuron model to elucidate ionic mechanisms underpinning alterations to the dynamics of principal neurons in the brain’s spatial navigation systems in an animal model of tauopathy. To uncover how synaptic deficits result in alterations to brain dynamics, we subsequently study an animal model featuring local and long-range synaptic degeneration. Synchronous activity (functional connectivity; FC) between neurons within a region of the cortex is analysed using two-photon calcium imaging data. Long-range FC between regions of the brain is analysed using EEG data. Furthermore, a computational model is used to study relationships between networks on these different spatial scales. The latter half of this thesis studies EEG to characterize alterations to macro-scale brain dynamics in clinical AD. Spectral and FC measures are correlated with cognitive test scores to study the hypothesis that impaired integration of the brain’s processing systems underpin cognitive impairment in AD. Whole brain computational modelling is used to gain insight into the role of spectral slowing on FC, and elucidate potential synaptic mechanisms of FC differences in AD. On a finer temporal scale, microstate analyses are used to identify changes to the rapid transitioning behaviour of the brain’s resting state in AD. Finally, the electrophysiological signatures of AD identified throughout the thesis are combined into a predictive model which can accurately separate people with AD and healthy controls based on their EEG, results which are validated on an independent patient cohort. Furthermore, we demonstrate in a small preliminary cohort that this model is a promising tool for predicting future conversion to AD in patients with mild cognitive impairment

    Uncertainty in Artificial Intelligence: Proceedings of the Thirty-Fourth Conference

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    Review of Particle Physics: Particle Data Group

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    The Review summarizes much of particle physics and cosmology. Using data from previous editions, plus 2,873 new measurements from 758 papers, we list, evaluate, and average measured properties of gauge bosons and the recently discovered Higgs boson, leptons, quarks, mesons, and baryons. We summarize searches for hypothetical particles such as supersymmetric particles, heavy bosons, axions, dark photons, etc. Particle properties and search limits are listed in Summary Tables. We give numerous tables, figures, formulae, and reviews of topics such as Higgs Boson Physics, Supersymmetry, Grand Unified Theories, Neutrino Mixing, Dark Energy, Dark Matter, Cosmology, Particle Detectors, Colliders, Probability and Statistics. Among the 118 reviews are many that are new or heavily revised, including a new review on Neutrinos in Cosmology. Starting with this edition, the Review is divided into two volumes. Volume 1 includes the Summary Tables and all review articles. Volume 2 consists of the Particle Listings. Review articles that were previously part of the Listings are now included in volume 1. The complete Review (both volumes) is published online on the website of the Particle Data Group (http://pdg.lbl.gov) and in a journal. Volume 1 is available in print as the PDG Book. A Particle Physics Booklet with the Summary Tables and essential tables, figures, and equations from selected review articles is also available

    Review of Particle Physics

    Get PDF
    The Review summarizes much of particle physics and cosmology. Using data from previous editions, plus 2,873 new measurements from 758 papers, we list, evaluate, and average measured properties of gauge bosons and the recently discovered Higgs boson, leptons, quarks, mesons, and baryons. We summarize searches for hypothetical particles such as supersymmetric particles, heavy bosons, axions, dark photons, etc. Particle properties and search limits are listed in Summary Tables. We give numerous tables, figures, formulae, and reviews of topics such as Higgs Boson Physics, Supersymmetry, Grand Unified Theories, Neutrino Mixing, Dark Energy, Dark Matter, Cosmology, Particle Detectors, Colliders, Probability and Statistics. Among the 118 reviews are many that are new or heavily revised, including a new review on Neutrinos in Cosmology. Starting with this edition, the Review is divided into two volumes. Volume 1 includes the Summary Tables and all review articles. Volume 2 consists of the Particle Listings. Review articles that were previously part of the Listings are now included in volume 1. The complete Review (both volumes) is published online on the website of the Particle Data Group (http://pdg.lbl.gov) and in a journal. Volume 1 is available in print as the PDG Book. A Particle Physics Booklet with the Summary Tables and essential tables, figures, and equations from selected review articles is also available. The 2018 edition of the Review of Particle Physics should be cited as: M. Tanabashi et al. (Particle Data Group), Phys. Rev. D 98, 030001 (2018)
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