5,553 research outputs found

    UMSL Bulletin 2023-2024

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    The 2023-2024 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1088/thumbnail.jp

    Beam scanning by liquid-crystal biasing in a modified SIW structure

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    A fixed-frequency beam-scanning 1D antenna based on Liquid Crystals (LCs) is designed for application in 2D scanning with lateral alignment. The 2D array environment imposes full decoupling of adjacent 1D antennas, which often conflicts with the LC requirement of DC biasing: the proposed design accommodates both. The LC medium is placed inside a Substrate Integrated Waveguide (SIW) modified to work as a Groove Gap Waveguide, with radiating slots etched on the upper broad wall, that radiates as a Leaky-Wave Antenna (LWA). This allows effective application of the DC bias voltage needed for tuning the LCs. At the same time, the RF field remains laterally confined, enabling the possibility to lay several antennas in parallel and achieve 2D beam scanning. The design is validated by simulation employing the actual properties of a commercial LC medium

    Endogenous measures for contextualising large-scale social phenomena: a corpus-based method for mediated public discourse

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    This work presents an interdisciplinary methodology for developing endogenous measures of group membership through analysis of pervasive linguistic patterns in public discourse. Focusing on political discourse, this work critiques the conventional approach to the study of political participation, which is premised on decontextualised, exogenous measures to characterise groups. Considering the theoretical and empirical weaknesses of decontextualised approaches to large-scale social phenomena, this work suggests that contextualisation using endogenous measures might provide a complementary perspective to mitigate such weaknesses. This work develops a sociomaterial perspective on political participation in mediated discourse as affiliatory action performed through language. While the affiliatory function of language is often performed consciously (such as statements of identity), this work is concerned with unconscious features (such as patterns in lexis and grammar). This work argues that pervasive patterns in such features that emerge through socialisation are resistant to change and manipulation, and thus might serve as endogenous measures of sociopolitical contexts, and thus of groups. In terms of method, the work takes a corpus-based approach to the analysis of data from the Twitter messaging service whereby patterns in users’ speech are examined statistically in order to trace potential community membership. The method is applied in the US state of Michigan during the second half of 2018—6 November having been the date of midterm (i.e. non-Presidential) elections in the United States. The corpus is assembled from the original posts of 5,889 users, who are nominally geolocalised to 417 municipalities. These users are clustered according to pervasive language features. Comparing the linguistic clusters according to the municipalities they represent finds that there are regular sociodemographic differentials across clusters. This is understood as an indication of social structure, suggesting that endogenous measures derived from pervasive patterns in language may indeed offer a complementary, contextualised perspective on large-scale social phenomena

    k-Means

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    Efectos de un nuevo nutracéutico basado en aceite de oliva virgen extra, aceite de algas y extracto de hojas de olivo sobre las alteraciones metabólicas y cardiovasculares asociadas al envejecimiento

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    Tesis Doctoral inédita leída en la Universidad Autónoma de Madrid, Facultad de Medicina, Departamento de Fisiología. Fecha de Lectura: 23-07-2021Esta tesis tiene embargado el acceso al texto completo hasta el 23-01-2023Este trabajo de investigación ha sido financiado por la beca “Doctorados Industriales 2017” (IND2017/BIO7701) de la Comunidad de Madri

    ON EXPRESSIVENESS, INFERENCE, AND PARAMETER ESTIMATION OF DISCRETE SEQUENCE MODELS

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    Huge neural autoregressive sequence models have achieved impressive performance across different applications, such as NLP, reinforcement learning, and bioinformatics. However, some lingering problems (e.g., consistency and coherency of generated texts) continue to exist, regardless of the parameter count. In the first part of this thesis, we chart a taxonomy of the expressiveness of various sequence model families (Ch 3). In particular, we put forth complexity-theoretic proofs that string latent-variable sequence models are strictly more expressive than energy-based sequence models, which in turn are more expressive than autoregressive sequence models. Based on these findings, we introduce residual energy-based sequence models, a family of energy-based sequence models (Ch 4) whose sequence weights can be evaluated efficiently, and also perform competitively against autoregressive models. However, we show how unrestricted energy-based sequence models can suffer from uncomputability; and how such a problem is generally unfixable without knowledge of the true sequence distribution (Ch 5). In the second part of the thesis, we study practical sequence model families and algorithms based on theoretical findings in the first part of the thesis. We introduce neural particle smoothing (Ch 6), a family of approximate sampling methods that work with conditional latent variable models. We also introduce neural finite-state transducers (Ch 7), which extend weighted finite state transducers with the introduction of mark strings, allowing scoring transduction paths in a finite state transducer with a neural network. Finally, we propose neural regular expressions (Ch 8), a family of neural sequence models that are easy to engineer, allowing a user to design flexible weighted relations using Marked FSTs, and combine these weighted relations together with various operations

    Metabolic impacts of weight loss intervention on morbid obesity

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    Morbid obesity can result in life-altering health issues, such as type 2 diabetes. Roux-en-Y gastric bypass (RYGB) surgery has been demonstrated to be one of the most effective treatments for morbid obesity and its co-morbidities in long-term. This aim of this thesis is to investigate the metabolic impact of weight loss intervention (RYGB, caloric restriction, and gut hormone treatment) on urine, plasma, and faecal profiles from morbidly obese patients, and to answer two hypotheses: 1) RYGB-induced metabolic changes are partially attributed to caloric restriction and increased gut hormones; 2) RYGB alters metabolic profile of faecal bacterial pellets separated using a newly developed method. Samples at pre-intervention time point were compared with post-intervention time point, and multivariate and univariate analysis were applied based on different types of datasets using different software to avoid missing potential biomarkers. Samples at post-intervention time point were compared across the intervention groups using the same strategy as above. At 1-month-post-intervention, RYGB-induced metabolic changes could be attributed by caloric restriction via increased metabolisms of ketone bodies, lactic acid, and tricarboxylic acid, and decreased concentrations of total apolipoprotein A1, high-density lipoprotein (HDL) subfraction 3&4, and very-low-density-lipoprotein (VLDL) subfraction 5. RYGB-induced distinct metabolic changes included metabolisms of amino acids, short chain fatty acids, creatine, increased concentration of low-density lipoprotein fraction of triglycerides, and decreased concentration of HDL subfraction 2 of phospholipids. Gut hormone treatment exerted limited metabolic effects on urine and plasma samples. A separation method was developed for faecal bacterial pellets profiling and applied on RYGB and caloric restriction cohorts. Propionate and butyrate productions via dicarboxylic acid pathway were increased significantly 2-5 years after RYGB and 3 months after caloric restriction, respectively. My study showed RYGB-induced metabolic changes could not be fully explained by caloric restriction nor increased gut hormone levels; Gut hormone treatment induced limited metabolic changes and could be an alternate therapy for morbid obesity followed by clinical trial with increased sample size and follow-up study in long term.Open Acces

    Dissecting the Role of ZAK-beta in Skeletal Muscle Using Zebrafish as a Model Organism

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    Congenital myopathies are a group of inherited, heterogenous, rare muscle diseases, associated with progressive muscle wasting, chronic disability and a reduced quality of life. Inheritance of mutations in ZAK, a gene encoding a MAP triple kinase, has been identified as a novel cause of congenital myopathy in humans. This thesis utilises zebrafish to model the impacts of absence of ZAK on skeletal muscle, with investigations ranging from development studies to the analysis of the aging process of muscle in adult zebrafish. In contrast to other vertebrates, where the two isoforms are achieved through differential splicing of a single gene (to produce ZAK-alpha and ZAK-beta), in zebrafish the two isoforms exist as different genes on separate chromosomes, simplifying efforts to target each isoform. ZAK-beta is shown to be the isoform expressed in zebrafish skeletal muscle, and CRISPR-Cas9 gene editing was used to create mutations in each ZAK isoform and raise lines of zebrafish lacking either individual or both isoforms. Sequencing of the mRNA transcript and qRT-PCR confirmed mutations to each isoform result in a premature stop codon, and that there was a significant reduction in transcript levels. Breeding the ZAK-beta-/- line into transgenic reporter lines allowed the assessment of the structure of developing skeletal muscle, and the immune response of neutrophils following wounding in larval zebrafish, using confocal imaging. Individual muscle fibre area was significantly reduced in ZAK-beta-/- embryos at two and five days of development, compared to wild type, potentially indicating growth defects in the skeletal muscle. Aged ZAK-beta-/- zebrafish (35-months-old) show significantly decreased swimming capabilities, as well as abnormalities in the ultrastructure absent in age-matched wild type controls, suggesting a potential accelerated aging process in skeletal muscle with loss of ZAK. Thus, ZAK-beta signalling may represent a promising target for developing novel therapies for the treatment of sarcopenia

    On noise, uncertainty and inference for computational diffusion MRI

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    Diffusion Magnetic Resonance Imaging (dMRI) has revolutionised the way brain microstructure and connectivity can be studied. Despite its unique potential in mapping the whole brain, biophysical properties are inferred from measurements rather than being directly observed. This indirect mapping from noisy data creates challenges and introduces uncertainty in the estimated properties. Hence, dMRI frameworks capable to deal with noise and uncertainty quantification are of great importance and are the topic of this thesis. First, we look into approaches for reducing uncertainty, by de-noising the dMRI signal. Thermal noise can have detrimental effects for modalities where the information resides in the signal attenuation, such as dMRI, that has inherently low-SNR data. We highlight the dual effect of noise, both in increasing variance, but also introducing bias. We then design a framework for evaluating denoising approaches in a principled manner. By setting objective criteria based on what a well-behaved denoising algorithm should offer, we provide a bespoke dataset and a set of evaluations. We demonstrate that common magnitude-based denoising approaches usually reduce noise-related variance from the signal, but do not address the bias effects introduced by the noise floor. Our framework also allows to better characterise scenarios where denoising can be beneficial (e.g. when done in complex domain) and can open new opportunities, such as pushing spatio-temporal resolution boundaries. Subsequently, we look into approaches for mapping uncertainty and design two inference frameworks for dMRI models, one using classical Bayesian methods and another using more recent data-driven algorithms. In the first approach, we build upon the univariate random-walk Metropolis-Hastings MCMC, an extensively used sampling method to sample from the posterior distribution of model parameters given the data. We devise an efficient adaptive multivariate MCMC scheme, relying upon the assumption that groups of model parameters can be jointly estimated if a proper covariance matrix is defined. In doing so, our algorithm increases the sampling efficiency, while preserving accuracy and precision of estimates. We show results using both synthetic and in-vivo dMRI data. In the second approach, we resort to Simulation-Based Inference (SBI), a data-driven approach that avoids the need for iterative model inversions. This is achieved by using neural density estimators to learn the inverse mapping from the forward generative process (simulations) to the parameters of interest that have generated those simulations. By addressing the problem via learning approaches offers the opportunity to achieve inference amortisation, boosting efficiency by avoiding the necessity of repeating the inference process for each new unseen dataset. It also allows inversion of forward processes (i.e. a series of processing steps) rather than only models. We explore different neural network architectures to perform conditional density estimation of the posterior distribution of parameters. Results and comparisons obtained against MCMC suggest speed-ups of 2-3 orders of magnitude in the inference process while keeping the accuracy in the estimates
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