823 research outputs found

    2023-2024 Catalog

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    The 2023-2024 Governors State University Undergraduate and Graduate Catalog is a comprehensive listing of current information regarding:Degree RequirementsCourse OfferingsUndergraduate and Graduate Rules and Regulation

    Markov field models of molecular kinetics

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    Computer simulations such as molecular dynamics (MD) provide a possible means to understand protein dynamics and mechanisms on an atomistic scale. The resulting simulation data can be analyzed with Markov state models (MSMs), yielding a quantitative kinetic model that, e.g., encodes state populations and transition rates. However, the larger an investigated system, the more data is required to estimate a valid kinetic model. In this work, we show that this scaling problem can be escaped when decomposing a system into smaller ones, leveraging weak couplings between local domains. Our approach, termed independent Markov decomposition (IMD), is a first-order approximation neglecting couplings, i.e., it represents a decomposition of the underlying global dynamics into a set of independent local ones. We demonstrate that for truly independent systems, IMD can reduce the sampling by three orders of magnitude. IMD is applied to two biomolecular systems. First, synaptotagmin-1 is analyzed, a rapid calcium switch from the neurotransmitter release machinery. Within its C2A domain, local conformational switches are identified and modeled with independent MSMs, shedding light on the mechanism of its calcium-mediated activation. Second, the catalytic site of the serine protease TMPRSS2 is analyzed with a local drug-binding model. Equilibrium populations of different drug-binding modes are derived for three inhibitors, mirroring experimentally determined drug efficiencies. IMD is subsequently extended to an end-to-end deep learning framework called iVAMPnets, which learns a domain decomposition from simulation data and simultaneously models the kinetics in the local domains. We finally classify IMD and iVAMPnets as Markov field models (MFM), which we define as a class of models that describe dynamics by decomposing systems into local domains. Overall, this thesis introduces a local approach to Markov modeling that enables to quantitatively assess the kinetics of large macromolecular complexes, opening up possibilities to tackle current and future computational molecular biology questions

    Single-molecule detection and characterisation of alpha-synuclein aggregates

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    Aberrant protein aggregation is a predominant feature of many neurodegenerative disorders. It has long been recognised that aggregates of alpha-synuclein (α-syn) drive pathogenesis in Parkinson’s Disease (PD), and it is widely accepted that small α-syn oligomers are the key cytotoxic species in PD. Notably, however, these oligomeric species are difficult to characterise using traditional biochemical ensemble methods due to their high level of heterogeneity and low abundance. Single-molecule fluorescence microscopy techniques have emerged as a suitable approach to circumventing this problem, enabling the detection of individual aggregates amongst monomeric protein and thus facilitating the identification, quantification, and characterisation of rare oligomeric species. However, cellular mechanisms of α-syn aggregation are poorly understood. Furthermore, there remains some limitations to the singlemolecule techniques currently available. This thesis describes the work completed to address some of these issues. Chapter 1 provides the contextual background for the work presented in this thesis, detailing the biological aspects of α-syn, its aggregation, and its implications in PD, as well as outlining the single-molecule techniques used to investigate aggregate species. Chapter 2 describes the methodologies undertaken in this thesis, and chapters 3 to 5 describe the findings made using the single-molecule techniques which were utilised and developed in this work. One primary approach for studying species in single-molecule experiments involves directly labelling biomolecules of interest with a suitable fluorophore. Early steps in α-syn aggregation have previously been identified using fluorescently tagged α-syn and single-molecule Förster resonance energy transfer (smFRET) in vitro; however, the characterisation of early aggregate formation in cells has thus far been difficult to achieve. Chapter 3 describes the use of duallabelled α-syn to detect and characterise aggregates formed both intracellularly and in vitro via smFRET, using both single-molecule confocal microscopy coupled with microfluidics and iii total internal reflection fluorescence microscopy (TIRFM) to determine both the sizes and structures of the oligomers formed. This work reveals the presence of distinct oligomeric species in vitro and in neurons resulting from structural conversion during early aggregate formation. The approach taken in Chapter 3 is highly suitable for investigating aggregate formation resulting from the addition of exogenous α-syn to samples of interest. However, such an approach is not ideal for the detection and characterisation of endogenous aggregates due to issues with the covalent labelling of cellular protein. Extrinsic amyloid dyes are typically used as an alternative approach to labelled protein; however, such dyes are non-protein-specific and bind to the common amyloid beta-sheet motif. As an alternative, the work presented in Chapter 4 describes a novel single-molecule method to specifically detect and characterise α-syn aggregates with high sensitivity, making use of a high-affinity antibody labelled with orthogonal fluorophores which is combined with fast-flow microfluidics and single-molecule confocal microscopy. This enables the quantification and size approximation of α-syn aggregates at picomolar concentrations, both in vitro and in biological samples. Although the kinetics of α-syn aggregation have been studied extensively, much of our current knowledge stems from ensemble averaging techniques which are associated with high levels of variability and are not conducive to detecting the earliest steps in aggregate formation. In addition, there remains uncertainty surrounding the effect of familial variants and posttranslational modifications (PTM) on aggregation. Chapter 5 encompasses the study of the effects of the ubiquitous N-terminal acetylation PTM, in addition to the familial, rapid-onset G51D mutation, on α-syn aggregation, using the novel detection method developed in Chapter 4. This is used in conjunction with single-molecule detection with thioflavin-T (ThT) to reveal new insights into the aggregation of α-syn variants. Overall, the work presented here provides new insights into the aggregation of α-syn via the use and development of single-molecule techniques. The advancements made have added to the current understanding of the molecular mechanisms of α-syn aggregation, both in vitro and in neurons, and have also been used to develop a novel single-molecule detection method for α-syn aggregates. The work presented in this thesis has resulted in two published papers, ’Pathological structural conversion of alpha-synuclein at the mitochondria induces neuronal toxicity’ in Nature Neuroscience, and ’Single-molecule two-color coincidence detection of unlabeled alpha-synuclein aggregates’ in Angewandte Chemie International Edition. Furthermore, the novel detection method presented here holds promise for measuring α-syn oligomeric load in clinical samples due to its high sensitivity and specificity for α-syn aggregates. This may therefore be used in future studies for identifying, detecting, and studying potential biomarkers in PD, with potential use in disease diagnosis. It is therefore expected that the work from this thesis will be used to aid researchers towards better understanding the mechanisms of α-syn aggregation, both in vitro and in clinical samples

    University of Windsor Graduate Calendar 2023 Spring

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    https://scholar.uwindsor.ca/universitywindsorgraduatecalendars/1027/thumbnail.jp

    University of Windsor Graduate Calendar 2023 Winter

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    https://scholar.uwindsor.ca/universitywindsorgraduatecalendars/1026/thumbnail.jp

    Modeling and Simulation in Engineering

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    The Special Issue Modeling and Simulation in Engineering, belonging to the section Engineering Mathematics of the Journal Mathematics, publishes original research papers dealing with advanced simulation and modeling techniques. The present book, “Modeling and Simulation in Engineering I, 2022”, contains 14 papers accepted after peer review by recognized specialists in the field. The papers address different topics occurring in engineering, such as ferrofluid transport in magnetic fields, non-fractal signal analysis, fractional derivatives, applications of swarm algorithms and evolutionary algorithms (genetic algorithms), inverse methods for inverse problems, numerical analysis of heat and mass transfer, numerical solutions for fractional differential equations, Kriging modelling, theory of the modelling methodology, and artificial neural networks for fault diagnosis in electric circuits. It is hoped that the papers selected for this issue will attract a significant audience in the scientific community and will further stimulate research involving modelling and simulation in mathematical physics and in engineering

    2023-2024 Boise State University Undergraduate Catalog

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    This catalog is primarily for and directed at students. However, it serves many audiences, such as high school counselors, academic advisors, and the public. In this catalog you will find an overview of Boise State University and information on admission, registration, grades, tuition and fees, financial aid, housing, student services, and other important policies and procedures. However, most of this catalog is devoted to describing the various programs and courses offered at Boise State

    General Course Catalog [2022/23 academic year]

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    General Course Catalog, 2022/23 academic yearhttps://repository.stcloudstate.edu/undergencat/1134/thumbnail.jp
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