1,842 research outputs found

    Computational techniques to interpret the neural code underlying complex cognitive processes

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    Advances in large-scale neural recording technology have significantly improved the capacity to further elucidate the neural code underlying complex cognitive processes. This thesis aimed to investigate two research questions in rodent models. First, what is the role of the hippocampus in memory and specifically what is the underlying neural code that contributes to spatial memory and navigational decision-making. Second, how is social cognition represented in the medial prefrontal cortex at the level of individual neurons. To start, the thesis begins by investigating memory and social cognition in the context of healthy and diseased states that use non-invasive methods (i.e. fMRI and animal behavioural studies). The main body of the thesis then shifts to developing our fundamental understanding of the neural mechanisms underpinning these cognitive processes by applying computational techniques to ana lyse stable large-scale neural recordings. To achieve this, tailored calcium imaging and behaviour preprocessing computational pipelines were developed and optimised for use in social interaction and spatial navigation experimental analysis. In parallel, a review was conducted on methods for multivariate/neural population analysis. A comparison of multiple neural manifold learning (NML) algorithms identified that non linear algorithms such as UMAP are more adaptable across datasets of varying noise and behavioural complexity. Furthermore, the review visualises how NML can be applied to disease states in the brain and introduces the secondary analyses that can be used to enhance or characterise a neural manifold. Lastly, the preprocessing and analytical pipelines were combined to investigate the neural mechanisms in volved in social cognition and spatial memory. The social cognition study explored how neural firing in the medial Prefrontal cortex changed as a function of the social dominance paradigm, the "Tube Test". The univariate analysis identified an ensemble of behavioural-tuned neurons that fire preferentially during specific behaviours such as "pushing" or "retreating" for the animal’s own behaviour and/or the competitor’s behaviour. Furthermore, in dominant animals, the neural population exhibited greater average firing than that of subordinate animals. Next, to investigate spatial memory, a spatial recency task was used, where rats learnt to navigate towards one of three reward locations and then recall the rewarded location of the session. During the task, over 1000 neurons were recorded from the hippocampal CA1 region for five rats over multiple sessions. Multivariate analysis revealed that the sequence of neurons encoding an animal’s spatial position leading up to a rewarded location was also active in the decision period before the animal navigates to the rewarded location. The result posits that prospective replay of neural sequences in the hippocampal CA1 region could provide a mechanism by which decision-making is supported

    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

    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

    La traduzione specializzata all’opera per una piccola impresa in espansione: la mia esperienza di internazionalizzazione in cinese di Bioretics© S.r.l.

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    Global markets are currently immersed in two all-encompassing and unstoppable processes: internationalization and globalization. While the former pushes companies to look beyond the borders of their country of origin to forge relationships with foreign trading partners, the latter fosters the standardization in all countries, by reducing spatiotemporal distances and breaking down geographical, political, economic and socio-cultural barriers. In recent decades, another domain has appeared to propel these unifying drives: Artificial Intelligence, together with its high technologies aiming to implement human cognitive abilities in machinery. The “Language Toolkit – Le lingue straniere al servizio dell’internazionalizzazione dell’impresa” project, promoted by the Department of Interpreting and Translation (ForlĂŹ Campus) in collaboration with the Romagna Chamber of Commerce (ForlĂŹ-Cesena and Rimini), seeks to help Italian SMEs make their way into the global market. It is precisely within this project that this dissertation has been conceived. Indeed, its purpose is to present the translation and localization project from English into Chinese of a series of texts produced by Bioretics© S.r.l.: an investor deck, the company website and part of the installation and use manual of the Aliquis© framework software, its flagship product. This dissertation is structured as follows: Chapter 1 presents the project and the company in detail; Chapter 2 outlines the internationalization and globalization processes and the Artificial Intelligence market both in Italy and in China; Chapter 3 provides the theoretical foundations for every aspect related to Specialized Translation, including website localization; Chapter 4 describes the resources and tools used to perform the translations; Chapter 5 proposes an analysis of the source texts; Chapter 6 is a commentary on translation strategies and choices

    Microcredentials to support PBL

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    Mining Butterflies in Streaming Graphs

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    This thesis introduces two main-memory systems sGrapp and sGradd for performing the fundamental analytic tasks of biclique counting and concept drift detection over a streaming graph. A data-driven heuristic is used to architect the systems. To this end, initially, the growth patterns of bipartite streaming graphs are mined and the emergence principles of streaming motifs are discovered. Next, the discovered principles are (a) explained by a graph generator called sGrow; and (b) utilized to establish the requirements for efficient, effective, explainable, and interpretable management and processing of streams. sGrow is used to benchmark stream analytics, particularly in the case of concept drift detection. sGrow displays robust realization of streaming growth patterns independent of initial conditions, scale and temporal characteristics, and model configurations. Extensive evaluations confirm the simultaneous effectiveness and efficiency of sGrapp and sGradd. sGrapp achieves mean absolute percentage error up to 0.05/0.14 for the cumulative butterfly count in streaming graphs with uniform/non-uniform temporal distribution and a processing throughput of 1.5 million data records per second. The throughput and estimation error of sGrapp are 160x higher and 0.02x lower than baselines. sGradd demonstrates an improving performance over time, achieves zero false detection rates when there is not any drift and when drift is already detected, and detects sequential drifts in zero to a few seconds after their occurrence regardless of drift intervals

    Computational Approaches to Drug Profiling and Drug-Protein Interactions

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    Despite substantial increases in R&D spending within the pharmaceutical industry, denovo drug design has become a time-consuming endeavour. High attrition rates led to a long period of stagnation in drug approvals. Due to the extreme costs associated with introducing a drug to the market, locating and understanding the reasons for clinical failure is key to future productivity. As part of this PhD, three main contributions were made in this respect. First, the web platform, LigNFam enables users to interactively explore similarity relationships between ‘drug like’ molecules and the proteins they bind. Secondly, two deep-learning-based binding site comparison tools were developed, competing with the state-of-the-art over benchmark datasets. The models have the ability to predict offtarget interactions and potential candidates for target-based drug repurposing. Finally, the open-source ScaffoldGraph software was presented for the analysis of hierarchical scaffold relationships and has already been used in multiple projects, including integration into a virtual screening pipeline to increase the tractability of ultra-large screening experiments. Together, and with existing tools, the contributions made will aid in the understanding of drug-protein relationships, particularly in the fields of off-target prediction and drug repurposing, helping to design better drugs faster
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