5,557 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

    Graduate Catalog of Studies, 2023-2024

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    Using machine learning to predict pathogenicity of genomic variants throughout the human genome

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    Geschätzt mehr als 6.000 Erkrankungen werden durch Veränderungen im Genom verursacht. Ursachen gibt es viele: Eine genomische Variante kann die Translation eines Proteins stoppen, die Genregulation stören oder das Spleißen der mRNA in eine andere Isoform begünstigen. All diese Prozesse müssen überprüft werden, um die zum beschriebenen Phänotyp passende Variante zu ermitteln. Eine Automatisierung dieses Prozesses sind Varianteneffektmodelle. Mittels maschinellem Lernen und Annotationen aus verschiedenen Quellen bewerten diese Modelle genomische Varianten hinsichtlich ihrer Pathogenität. Die Entwicklung eines Varianteneffektmodells erfordert eine Reihe von Schritten: Annotation der Trainingsdaten, Auswahl von Features, Training verschiedener Modelle und Selektion eines Modells. Hier präsentiere ich ein allgemeines Workflow dieses Prozesses. Dieses ermöglicht es den Prozess zu konfigurieren, Modellmerkmale zu bearbeiten, und verschiedene Annotationen zu testen. Der Workflow umfasst außerdem die Optimierung von Hyperparametern, Validierung und letztlich die Anwendung des Modells durch genomweites Berechnen von Varianten-Scores. Der Workflow wird in der Entwicklung von Combined Annotation Dependent Depletion (CADD), einem Varianteneffektmodell zur genomweiten Bewertung von SNVs und InDels, verwendet. Durch Etablierung des ersten Varianteneffektmodells für das humane Referenzgenome GRCh38 demonstriere ich die gewonnenen Möglichkeiten Annotationen aufzugreifen und neue Modelle zu trainieren. Außerdem zeige ich, wie Deep-Learning-Scores als Feature in einem CADD-Modell die Vorhersage von RNA-Spleißing verbessern. Außerdem werden Varianteneffektmodelle aufgrund eines neuen, auf Allelhäufigkeit basierten, Trainingsdatensatz entwickelt. Diese Ergebnisse zeigen, dass der entwickelte Workflow eine skalierbare und flexible Möglichkeit ist, um Varianteneffektmodelle zu entwickeln. Alle entstandenen Scores sind unter cadd.gs.washington.edu und cadd.bihealth.org frei verfügbar.More than 6,000 diseases are estimated to be caused by genomic variants. This can happen in many possible ways: a variant may stop the translation of a protein, interfere with gene regulation, or alter splicing of the transcribed mRNA into an unwanted isoform. It is necessary to investigate all of these processes in order to evaluate which variant may be causal for the deleterious phenotype. A great help in this regard are variant effect scores. Implemented as machine learning classifiers, they integrate annotations from different resources to rank genomic variants in terms of pathogenicity. Developing a variant effect score requires multiple steps: annotation of the training data, feature selection, model training, benchmarking, and finally deployment for the model's application. Here, I present a generalized workflow of this process. It makes it simple to configure how information is converted into model features, enabling the rapid exploration of different annotations. The workflow further implements hyperparameter optimization, model validation and ultimately deployment of a selected model via genome-wide scoring of genomic variants. The workflow is applied to train Combined Annotation Dependent Depletion (CADD), a variant effect model that is scoring SNVs and InDels genome-wide. I show that the workflow can be quickly adapted to novel annotations by porting CADD to the genome reference GRCh38. Further, I demonstrate the integration of deep-neural network scores as features into a new CADD model, improving the annotation of RNA splicing events. Finally, I apply the workflow to train multiple variant effect models from training data that is based on variants selected by allele frequency. In conclusion, the developed workflow presents a flexible and scalable method to train variant effect scores. All software and developed scores are freely available from cadd.gs.washington.edu and cadd.bihealth.org

    A survey of Bayesian Network structure learning

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    Measuring the impact of COVID-19 on hospital care pathways

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    Care pathways in hospitals around the world reported significant disruption during the recent COVID-19 pandemic but measuring the actual impact is more problematic. Process mining can be useful for hospital management to measure the conformance of real-life care to what might be considered normal operations. In this study, we aim to demonstrate that process mining can be used to investigate process changes associated with complex disruptive events. We studied perturbations to accident and emergency (A &E) and maternity pathways in a UK public hospital during the COVID-19 pandemic. Co-incidentally the hospital had implemented a Command Centre approach for patient-flow management affording an opportunity to study both the planned improvement and the disruption due to the pandemic. Our study proposes and demonstrates a method for measuring and investigating the impact of such planned and unplanned disruptions affecting hospital care pathways. We found that during the pandemic, both A &E and maternity pathways had measurable reductions in the mean length of stay and a measurable drop in the percentage of pathways conforming to normative models. There were no distinctive patterns of monthly mean values of length of stay nor conformance throughout the phases of the installation of the hospital’s new Command Centre approach. Due to a deficit in the available A &E data, the findings for A &E pathways could not be interpreted

    Experimentally supported computational method for the optimal design selection of 3D printed fracture healing implant geometries

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    The development of AM technologies has brought about very promising opportunities in the field of tissue regeneration, especially due to the design freedom they enable. However, the tools and procedures needed to enable medical designers to make use of these revolutionary technologies still need to be developed. In particular, design tools to make implants with optimal geometries for tissue regeneration and procedures to manufacture and test such implants need to be developed to enable the adoption of these technologies by medical designers and biologists designing implants. This thesis aims to address this need. In order to best use the design freedom that AM brings; it is necessary to define the optimal geometries for specific applications. A novel tool that enables the design of optimal scaffold geometries and could be easily adopted by medical designers was developed here by proposing an intuitive design selection framework that graphically allows the user to gain an understanding of how design variables affect the chosen response variables. The novel framework is flexible, enabling the incorporation of any number of necessary computational models. Triply periodic minimal surface (TPMS) equations were used to simplify the design variables needed to generate an optimal porous scaffold geometry. The potential of this framework was demonstrated by using it to find the optimal TPMS type and volume fraction for a fracture fixation scaffold. Experiments were carried out to demonstrate that TCDMDA biocompatible scaffolds of appropriate pore size could be manufactured via projection micro stereolithography. The experiments successfully demonstrate for the first time that TCDMDA scaffolds can be manufactured via PµSLA by using a suitable combination of UV intensity and layer time. It was also demonstrated for the first time that hMSCs adhere to the surface of TCDMDA samples manufactured via PµSLA. To further enhance the cell adhesion, an oxygen plasma treatment was carried out. For the second part of this study it was found that the media could not penetrate the scaffold pores sufficiently, invalidating the results. The presented results highlighting a permeability challenge with TCDMDA scaffolds manufactured via PµSLA are nevertheless expected to contribute to future studies in this area. Experiments were also carried out to demonstrate the biocompatibility of scaffolds manufactured via stereolithography using Dental LT resin (Formlabs, UK). Successful adhesion of hMSCs to the surface of these scaffolds was shown in Chapter 4. Another novel finding of this thesis was that the Dental LT scaffolds manufactured via SLA were able to successfully enable cell growth, cell differentiation and mineralization in the presence of osteogenic media and BMP-2. The final part of the thesis focused on expanding the developed design selection framework to include not only a scaffold for fracture healing, but also a matching fracture fixation plate. Fracture fixation plates have been studied for centuries, but there is little research investigating the combination of a fracture fixation plate and a scaffold. The rise of AM has inspired the development of auxetic geometries, which have been applied to fracture fixation plates before and shown to reduce stress shielding. Moreover, stiffness grading has also proved very promising in improving fracture healing. In this thesis these two promising concepts are combined for the first time demonstrating reduced stress shielding compared to a conventional fixation plate geometry. Moreover, the thesis presents a novel computational design selection framework to find optimal scaffold and fracture plate geometries which lead to an improved healing outcome. The framework may be easily adopted by medical designers

    Ciguatoxins

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    Ciguatoxins (CTXs), which are responsible for Ciguatera fish poisoning (CFP), are liposoluble toxins produced by microalgae of the genera Gambierdiscus and Fukuyoa. This book presents 18 scientific papers that offer new information and scientific evidence on: (i) CTX occurrence in aquatic environments, with an emphasis on edible aquatic organisms; (ii) analysis methods for the determination of CTXs; (iii) advances in research on CTX-producing organisms; (iv) environmental factors involved in the presence of CTXs; and (v) the assessment of public health risks related to the presence of CTXs, as well as risk management and mitigation strategies

    Natural Toxins: Environmental Fate and Safe Water Supply

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    Plants, bacteria, cyanobacteria, algae and other organisms produce a vast diversity of bioactive and toxic natural compounds. We know that many of these toxins are mobile and can be produced in high amounts close to or within drinking water reservoirs. Natural toxins represent emerging classes of environmental contaminants for which we have very limited insight on occurrence, fate and effects. The konference “Natural toxins: Environmental Fate and Safe Water Supply” addresses knowledge gaps within the field of natural toxins, target, non-target, suspect and effect-directed analysis, distribution, fate, toxicity and management of natural toxins in aquatic environments and drinking water reservoirs. These proceedings are a collection of the abstracts to contributions presented at the conference

    Specificity of the innate immune responses to different classes of non-tuberculous mycobacteria

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    Mycobacterium avium is the most common nontuberculous mycobacterium (NTM) species causing infectious disease. Here, we characterized a M. avium infection model in zebrafish larvae, and compared it to M. marinum infection, a model of tuberculosis. M. avium bacteria are efficiently phagocytosed and frequently induce granuloma-like structures in zebrafish larvae. Although macrophages can respond to both mycobacterial infections, their migration speed is faster in infections caused by M. marinum. Tlr2 is conservatively involved in most aspects of the defense against both mycobacterial infections. However, Tlr2 has a function in the migration speed of macrophages and neutrophils to infection sites with M. marinum that is not observed with M. avium. Using RNAseq analysis, we found a distinct transcriptome response in cytokine-cytokine receptor interaction for M. avium and M. marinum infection. In addition, we found differences in gene expression in metabolic pathways, phagosome formation, matrix remodeling, and apoptosis in response to these mycobacterial infections. In conclusion, we characterized a new M. avium infection model in zebrafish that can be further used in studying pathological mechanisms for NTM-caused diseases

    Network polarization, filter bubbles, and echo chambers: An annotated review of measures and reduction methods

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    Polarization arises when the underlying network connecting the members of a community or society becomes characterized by highly connected groups with weak inter-group connectivity. The increasing polarization, the strengthening of echo chambers, and the isolation caused by information filters in social networks are increasingly attracting the attention of researchers from different areas of knowledge such as computer science, economics, social and political sciences. This work presents an annotated review of network polarization measures and models used to handle the polarization. Several approaches for measuring polarization in graphs and networks were identified, including those based on homophily, modularity, random walks, and balance theory. The strategies used for reducing polarization include methods that propose edge or node editions (including insertions or deletions, as well as edge weight modifications), changes in social network design, or changes in the recommendation systems embedded in these networks.Comment: Corrected a typo in Section 3.2; the rest remains unchange
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