5,363 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

    Analysis and Design of Non-Orthogonal Multiple Access (NOMA) Techniques for Next Generation Wireless Communication Systems

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    The current surge in wireless connectivity, anticipated to amplify significantly in future wireless technologies, brings a new wave of users. Given the impracticality of an endlessly expanding bandwidth, there’s a pressing need for communication techniques that efficiently serve this burgeoning user base with limited resources. Multiple Access (MA) techniques, notably Orthogonal Multiple Access (OMA), have long addressed bandwidth constraints. However, with escalating user numbers, OMA’s orthogonality becomes limiting for emerging wireless technologies. Non-Orthogonal Multiple Access (NOMA), employing superposition coding, serves more users within the same bandwidth as OMA by allocating different power levels to users whose signals can then be detected using the gap between them, thus offering superior spectral efficiency and massive connectivity. This thesis examines the integration of NOMA techniques with cooperative relaying, EXtrinsic Information Transfer (EXIT) chart analysis, and deep learning for enhancing 6G and beyond communication systems. The adopted methodology aims to optimize the systems’ performance, spanning from bit-error rate (BER) versus signal to noise ratio (SNR) to overall system efficiency and data rates. The primary focus of this thesis is the investigation of the integration of NOMA with cooperative relaying, EXIT chart analysis, and deep learning techniques. In the cooperative relaying context, NOMA notably improved diversity gains, thereby proving the superiority of combining NOMA with cooperative relaying over just NOMA. With EXIT chart analysis, NOMA achieved low BER at mid-range SNR as well as achieved optimal user fairness in the power allocation stage. Additionally, employing a trained neural network enhanced signal detection for NOMA in the deep learning scenario, thereby producing a simpler signal detection for NOMA which addresses NOMAs’ complex receiver problem

    Specificity Determining Features at the Interface of Biomolecular Complexes as Regulators of Biological Functions

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    Amino acid residues at the biomolecular interface play essential roles in many biological and cellular processes; relevant to this thesis, protein-protein interactions regulate signaling pathways and enzymatic activity, whereas protein-DNA interactions control gene expression, and protein-peptide interactions are central to the immune system. Biomolecular recognition and binding stability are largely determined by residues at the molecular interface. In this thesis, we focused on three biological datasets that are related to humans and human health: 1) dysregulated citrullination in the inflamed joints of rheumatoid arthritis patients, 2) a novel family of PRD-like transcription factors critical to the first few cell divisions in human life, and 3) epitopes that likely activate a cytotoxic T cell-mediated immune response against SARS-CoV-2 infection. For each dataset, in order to study the structural and functional consequences of molecular interactions, we applied a wide range of bioinformatics techniques to analyze sequences, structures and biological data retrieved from various databases, as well as taking into account experimental results from collaborators and from the literature. In rheumatoid arthritis, normally cytoplasmic peptidylarginine deiminase (PAD) enzymes citrullinate arginine residues in extracellular matrix (ECM) proteins. To examine specificity determining features that regulate the citrullination activity, we analyzed the sequence and structure data of the ECM proteins that were found citrullinated in chronically inflamed human joints. For citrullination, we found that an arginine side chain needs to be exposed to solvent but can arise from ÎČ-strands, α-helices, loops and ÎČ-turns. Moreover, there is no sequence motif linked to enzymatic activity. In addition, we studied the effect of citrullination on proteins important for a normal ECM, focusing on integrin binding to fibronectin and transforming growth factor-ÎČ (TGF-ÎČ). Citrullination of these proteins was found to inhibit cell attachment and spreading since PAD-treatment of the isoDGR motif in fibronectin and the RGD motif in TGF-ÎČ significantly reduced their binding with integrin αVÎČ3 and αVÎČ6, respectively. The expression of the human paired (PRD)-like transcription factors (TFs) are limited to the period of embryonic genome activation up to the 8-cell stage. We identified that one of these PRD-like TFs, LEUTX, binds to a TAATCC sequence motif. Sequence comparisons revealed that LEUTX protein is comprised of two domains: the DNA-binding homeodomain and a Leutx domain containing a transactivation domain. We identified specificity determining residues in the LEUTX homeodomain that are important for recognition of the TAATCC-containing 36 bp DNA motif enriched in genes involved in embryonic genome activation. We demonstrated using molecular models why a heterozygotic missense mutation A54V at the DNA-specificity determining position of LEUTX has significantly reduced overall transcriptional activity, as well as why the double mutant – I47T and A54V – form of LEUTX restores binding to the DNA motif similarly to that seen in the I47T mutation alone. At the onset of the COVID-19 pandemic we sought to understand the molecular factors that trigger the cytotoxic T cell-mediated immune response against the SARS-CoV-2 virus, taking advantage of binding data and 3D structures for related viruses and other pathogenic organisms. We first predicted the MHC class I (MHC-I)-specific immunogenic epitopes of length 8- to 11 amino acids from the SARS-CoV-2 proteins. Next, we predicted that the 9-mer epitopes would have the highest potential to elicit a strong immune response. For experimental validation, the predicted 9-mer epitopes were matched with the SARS-CoV-derived epitopes that are known to elicit an effective T cell response in vitro. Furthermore, our observations provide a structural explanation for the binding of SARS-CoV-2 epitopes to MHC-I molecules, identifying conserved immunogenic epitopes essential for understanding the pathogenesis of COVID-19. The three investigated datasets were made in concert with collaborative experimental studies and/or considering publicly available experimental data. The experimental studies generally provided the starting point for the in silico studies, which in turn had the objective of providing a detailed explanation of the experimental results. Furthermore, the in silico results could be used to devise novel and focused experiments, suggesting that bioinformatics predictions and wet-laboratory experimental investigations optimally take place with multiple advantages. Overall, this thesis demonstrates the synergy that is possible by applying this interdisciplinary approach to understanding the consequences of molecular interactions.Aminosyror i kontaktytan mellan olika biomolekyler spelar en viktig roll i mĂ„nga biologiska och cellulĂ€ra processer; relevanta interaktioner för den hĂ€r avhandlingen Ă€r protein-protein interaktioner som reglerar signaleringsrutter och enzymatisk aktivitet, protein-DNA interaktioner som kontrollerar genexpression, samt protein-peptid interaktioner som har en central roll i immunförsvaret. BiomolekylĂ€r igenkĂ€nning och bindningsstabilitet beror till stor del pĂ„ de aminosyror som finns i den molekylĂ€ra kontaktytan. I den hĂ€r avhandlingen fokuserade vi pĂ„ tre biologiska dataset som Ă€r relaterade till mĂ€nniskor och mĂ€nniskors hĂ€lsa: 1) felreglerad citrullinering i inflammerade leder hos patienter med reumatoid artrit, 2) en nyupptĂ€ckt familj av PRD (human paired)-lika transkriptionsfaktorer som Ă€r nödvĂ€ndiga för de första celldelningarna i mĂ€nniskolivet, och 3) epitoper som troligen aktiverar en cytotoxisk T-cell-förmedlad immunrespons mot SARS-CoV-2 infektioner. För att studera de strukturella och funktionella konsekvenserna av de molekylĂ€ra interaktionerna i varje dataset, anvĂ€ndes en mĂ€ngd olika bioinformatiska tekniker för att analysera sekvenser, strukturer och biologiska data frĂ„n olika databaser och dessutom beaktades experimentella resultat frĂ„n samarbetspartners och frĂ„n litteraturen. I reumatoid artrit citrullinerar vanligen PAD (cytoplasmatiska peptidyl arginin deiminas)-enzymer arginin-aminosyror i proteiner i det extracellulĂ€ra matrixet (ECM). För att undersöka egenskaper som avgör specificiteten hos citrullineringsaktiviteten analyserade vi sekvens- och strukturdata för ECM-proteiner som blir citrullinerade i kroniskt inflammerade leder hos mĂ€nniskor. Vi upptĂ€ckte att en argininsidokedja mĂ„ste vara i kontakt med det omgivande lösningsmedlet för att kunna citrullineras, att de kan finnas i beta-strĂ€ngar, alfa-helixar och beta-svĂ€ngar, samt att det inte finns nĂ„gra sekvensmotiv som Ă€r kopplade till enzymatisk aktivitet. Utöver detta studerade vi effekten av citrullinering pĂ„ proteiner som Ă€r viktiga för normal extracellulĂ€r matrix, med fokus pĂ„ integrinbinding till fibronektin och TGF-ÎČ (transforming growth factor-ÎČ). Citrullinering av dessa proteiner upptĂ€cktes inhibera cellvidhĂ€ftning och spridning eftersom PAD-behandling av isoDGR-motivet i fibronektin och RGD-motivet i TGF-ÎČ ordentligt reducerar deras bindning till integrin αVÎČ3 och αVÎČ6, respektive. ExpressionsnivĂ„erna av PRD-lika transkriptionsfaktorer (TF) Ă€r begrĂ€nsade till perioden av zygotens genomaktivering upp till 8-cells stadiet. Vi identifierade att en av dessa PRD-lika transkriptionsfaktorer, LEUTX, binder till ett TAATCC sekvensmotiv. SekvensjĂ€mförelser avslöjade att LEUTX proteinet bestĂ„r av tvĂ„ domĂ€ner, det DNA-bindande homeodomĂ€net och en leutx-domĂ€n som innehĂ„ller en transaktiveringsdomĂ€n. Vi identifierade specificitetsbestĂ€mmande aminosyror i LEUTX homeodomĂ€nen som Ă€r viktiga för igenkĂ€nning av TAATCC-innehĂ„llande 36 baspars DNA-motivet som Ă€r berikad med gener involverade i zygotens genomaktivering. Vi anvĂ€nde molekylĂ€ra modeller för att visa varför en heterozygotisk missense-mutation, A54V, i DNA-specificitetsbestĂ€mmande positionen i LEUTX har ordentligt minskad generell transkriptionsaktivitet, och varför dubbelmutanten I47T och A54V Ă„terstĂ€ller bindning till DNA-motivet pĂ„ samma sĂ€tt som observerats i enbart I47T mutationen. NĂ€r COVID-19 pandemin inleddes försökte vi förstĂ„ de molekylĂ€ra faktorer som startar den cytotoxiska T-cell-förmedlade immunresponsen mot SARS-CoV-2 viruset, genom att utnyttja bindningsdata och 3D strukturer för relaterade virus och andra patogena organismer. Vi förutspĂ„dde först MHC klass I (MHC-I)-specifika immunogena epitoper av lĂ€ngden 8 till 11 aminosyror frĂ„n SARS-CoV-2 proteiner. DĂ€refter förutspĂ„dde vi att epitoper bestĂ„ende av 9 aminosyror hade den högsta potentialen att orsaka en stark immunrespons. För experimentell validering matchades de 9 aminosyror lĂ„nga epitoperna med epitoper frĂ„n SARS-CoV som man vet att orsakar en effektiv T-cell respons in vitro. VĂ„ra observationer bidrar ocksĂ„ med en strukturell förklaring för bindningen av SARS-CoV-2 epitoper till MHC-I molekyler, vilket identifierar konserverade immunogena epitoper som Ă€r nödvĂ€ndiga för att förstĂ„r patogenesen hos COVID-19. De tre undersökta dataseten gjordes i samarbete med experimentella studier och/eller genom att ta allmĂ€nt tillgĂ€ngliga experimentella data i beaktande. De experimentella studierna gav en startpunkt för in silico-studierna, vilka i sin tur hade som mĂ„l att ge en detaljerad förklaring till de experimentella resultaten. In silico-resultaten kan ocksĂ„ anvĂ€ndas för att utveckla nya och fokuserade experiment, vilket indikerar att bioinformatiska förutspĂ„elser och experimentella studier optimalt sker med mĂ„nga fördelar. Över lag visar denna avhandling synergin som Ă€r möjlig genom att anvĂ€nda detta interdisciplinĂ€ra arbetssĂ€tt för att förstĂ„ konsekvenserna av molekylĂ€ra interaktioner

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    UMSL Bulletin 2022-2023

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    The 2022-2023 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1087/thumbnail.jp

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

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    GeschĂ€tzt mehr als 6.000 Erkrankungen werden durch VerĂ€nderungen 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 begĂŒnstigen. All diese Prozesse mĂŒssen ĂŒberprĂŒft werden, um die zum beschriebenen PhĂ€notyp 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 PathogenitĂ€t. Die Entwicklung eines Varianteneffektmodells erfordert eine Reihe von Schritten: Annotation der Trainingsdaten, Auswahl von Features, Training verschiedener Modelle und Selektion eines Modells. Hier prĂ€sentiere 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 fĂŒr 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 AllelhĂ€ufigkeit 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 verfĂŒgbar.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

    Synthetic Aperture Radar (SAR) Meets Deep Learning

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    This reprint focuses on the application of the combination of synthetic aperture radars and depth learning technology. It aims to further promote the development of SAR image intelligent interpretation technology. A synthetic aperture radar (SAR) is an important active microwave imaging sensor, whose all-day and all-weather working capacity give it an important place in the remote sensing community. Since the United States launched the first SAR satellite, SAR has received much attention in the remote sensing community, e.g., in geological exploration, topographic mapping, disaster forecast, and traffic monitoring. It is valuable and meaningful, therefore, to study SAR-based remote sensing applications. In recent years, deep learning represented by convolution neural networks has promoted significant progress in the computer vision community, e.g., in face recognition, the driverless field and Internet of things (IoT). Deep learning can enable computational models with multiple processing layers to learn data representations with multiple-level abstractions. This can greatly improve the performance of various applications. This reprint provides a platform for researchers to handle the above significant challenges and present their innovative and cutting-edge research results when applying deep learning to SAR in various manuscript types, e.g., articles, letters, reviews and technical reports

    The Trypanosoma brucei MISP family of invariant proteins is co-expressed with BARP as triple helical bundle structures on the surface of salivary gland forms, but is dispensable for parasite development within the tsetse vector

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    Trypanosoma brucei spp. develop into mammalian-infectious metacyclic trypomastigotes inside tsetse salivary glands. Besides acquiring a variant surface glycoprotein (VSG) coat, little is known about the metacyclic expression of invariant surface antigens. Proteomic analyses of saliva from T. brucei-infected flies identified, in addition to VSG and Brucei Alanine-Rich Protein (BARP) peptides, a family of GPI-anchored surface proteins herein named as Metacyclic Invariant Surface Proteins (MISP) because of its predominant expression on the surface of metacyclic trypomastigotes. The MISP family is encoded by five paralog genes with >80% protein identity, which are exclusively expressed by salivary gland stages of the parasite and peak in metacyclic stage, as shown by confocal microscopy and immuno-high resolution scanning electron microscopy. Crystallographic analysis of a MISP isoform (MISP360) and a high confidence model of BARP revealed a triple helical bundle architecture commonly found in other trypanosome surface proteins. Molecular modelling combined with live fluorescent microscopy suggests that MISP N-termini are potentially extended above the metacyclic VSG coat, and thus could be tested as a transmission-blocking vaccine target. However, vaccination with recombinant MISP360 isoform did not protect mice against a T. brucei infectious tsetse bite. Lastly, both CRISPR-Cas9-driven knock out and RNAi knock down of all MISP paralogues suggest they are not essential for parasite development in the tsetse vector. We suggest MISP may be relevant during trypanosome transmission or establishment in the vertebrate’s skin

    A Comprehensive Review of Data-Driven Co-Speech Gesture Generation

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    Gestures that accompany speech are an essential part of natural and efficient embodied human communication. The automatic generation of such co-speech gestures is a long-standing problem in computer animation and is considered an enabling technology in film, games, virtual social spaces, and for interaction with social robots. The problem is made challenging by the idiosyncratic and non-periodic nature of human co-speech gesture motion, and by the great diversity of communicative functions that gestures encompass. Gesture generation has seen surging interest recently, owing to the emergence of more and larger datasets of human gesture motion, combined with strides in deep-learning-based generative models, that benefit from the growing availability of data. This review article summarizes co-speech gesture generation research, with a particular focus on deep generative models. First, we articulate the theory describing human gesticulation and how it complements speech. Next, we briefly discuss rule-based and classical statistical gesture synthesis, before delving into deep learning approaches. We employ the choice of input modalities as an organizing principle, examining systems that generate gestures from audio, text, and non-linguistic input. We also chronicle the evolution of the related training data sets in terms of size, diversity, motion quality, and collection method. Finally, we identify key research challenges in gesture generation, including data availability and quality; producing human-like motion; grounding the gesture in the co-occurring speech in interaction with other speakers, and in the environment; performing gesture evaluation; and integration of gesture synthesis into applications. We highlight recent approaches to tackling the various key challenges, as well as the limitations of these approaches, and point toward areas of future development.Comment: Accepted for EUROGRAPHICS 202
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