4,909 research outputs found

    Undergraduate Catalog of Studies, 2023-2024

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    Bioactive Self-Assembled Protein Nanosheets for Stem Cell-Based Biotechnologies

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    Tissue and stem cell culture methods have been dominated by glass and plastic substrates such as Tissue culture plastic. These solid substrates, although widely used, are associated with poor scalability for adherent stem cell expansion in systems such as 3D bioreactors and the design of parallel culture systems. Therefore, investigating strategies to bypass these obstacles in stem cell expansion is essential to enable the wider translation of stem cell technologies. An alternative strategy recently proposed consists in using a liquid surface instead, such as an oil, and associated oil droplets. Indeed, emulsions can be formed using protein nanosheets to stabilise oil/water interfaces to promote the adhesion of stem cells and enable their proliferation. These nanosheets exhibit enhanced interfacial mechanics and allow the introduction of bioactive components via recombinant protein expression to promote bioactivity. Beyond the application of resulting bioemulsions for the expansion of Mesenchymal stem cells, the impact of these bioactive interfaces on the differentiation of iPSCs and the development of cerebral organoids will be presented. The Bovine serum albumin protein was recombinantly modified to attach an N-terminal Avi-Tag, this was expressed and purified from the yeast P. pastoris expression system. The Avi-tag was then biotinylated in vitro by recombinantly expressed BirA. Emulsions of a specific size were formed using the newly biotinylated Bt-BSA protein and functionalized with a cascade of components to mimic cell-cell ligands, this resulted in bioemulsions with a bioactive surface that can interact with surrounding cells. These functionalised droplets were integrated into developing cerebral organoids and their impact on phenotype was studied. The droplets were found not to deform sufficiently to allow mechanical forces to be measured, yet the many of these droplets were retained within the organoids which led to an interesting phenotype within the organoids. The developing rosettes were found to develop enlarged lumens shown by an increase in area, this phenotype did not impact the differentiation into the cerebral lineage depicted by immunohistochemistry of hallmark marker of neuronal differentiation within organoids retaining droplets. The interfacial mechanics of fibrinogen nanosheets treated with varying concentrations of thrombin was studied using interfacial shear rheology. The effect of thrombin significantly altered the interfacial mechanics with the lower concentration of thrombin significantly increasing the toughness multiple folds and decreasing the elasticity of the nanosheets. Additionally, the nanostructure of nanosheets was studied using SEM and TEM and traditional fibrin fibres were found to not form at these interfaces, but local rearrangements and retractions in the thrombin treated nanosheets were observed. Finally, these enhanced mechanical properties promoted the proliferation and expansion of Mesenchymal stem cells on quasi-2D and 3D interfaces

    Graduate Catalog of Studies, 2023-2024

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    Graduate Catalog of Studies, 2023-2024

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    Single-cell time-series analysis of metabolic rhythms in yeast

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    The yeast metabolic cycle (YMC) is a biological rhythm in budding yeast (Saccharomyces cerevisiae). It entails oscillations in the concentrations and redox states of intracellular metabolites, oscillations in transcript levels, temporal partitioning of biosynthesis, and, in chemostats, oscillations in oxygen consumption. Most studies on the YMC have been based on chemostat experiments, and it is unclear whether YMCs arise from interactions between cells or are generated independently by each cell. This thesis aims at characterising the YMC in single cells and its response to nutrient and genetic perturbations. Specifically, I use microfluidics to trap and separate yeast cells, then record the time-dependent intensity of flavin autofluorescence, which is a component of the YMC. Single-cell microfluidics produces a large amount of time series data. Noisy and short time series produced from biological experiments restrict the computational tools that are useful for analysis. I developed a method to filter time series, a machine learning model to classify whether time series are oscillatory, and an autocorrelation method to examine the periodicity of time series data. My experimental results show that yeast cells show oscillations in the fluorescence of flavins. Specifically, I show that in high glucose conditions, cells generate flavin oscillations asynchronously within a population, and these flavin oscillations couple with the cell division cycle. I show that cells can individually reset the phase of their flavin oscillations in response to abrupt nutrient changes, independently of the cell division cycle. I also show that deletion strains generate flavin oscillations that exhibit different behaviour from dissolved oxygen oscillations from chemostat conditions. Finally, I use flux balance analysis to address whether proteomic constraints in cellular metabolism mean that temporal partitioning of biosynthesis is advantageous for the yeast cell, and whether such partitioning explains the timing of the metabolic cycle. My results show that under proteomic constraints, it is advantageous for the cell to sequentially synthesise biomass components because doing so shortens the timescale of biomass synthesis. However, the degree of advantage of sequential over parallel biosynthesis is lower when both carbon and nitrogen sources are limiting. This thesis thus confirms autonomous generation of flavin oscillations, and suggests a model in which the YMC responds to nutrient conditions and subsequently entrains the cell division cycle. It also emphasises the possibility that subpopulations in the culture explain chemostat-based observations of the YMC. Furthermore, this thesis paves the way for using computational methods to analyse large datasets of oscillatory time series, which is useful for various fields of study beyond the YMC

    Natural and Technological Hazards in Urban Areas

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    Natural hazard events and technological accidents are separate causes of environmental impacts. Natural hazards are physical phenomena active in geological times, whereas technological hazards result from actions or facilities created by humans. In our time, combined natural and man-made hazards have been induced. Overpopulation and urban development in areas prone to natural hazards increase the impact of natural disasters worldwide. Additionally, urban areas are frequently characterized by intense industrial activity and rapid, poorly planned growth that threatens the environment and degrades the quality of life. Therefore, proper urban planning is crucial to minimize fatalities and reduce the environmental and economic impacts that accompany both natural and technological hazardous events

    Learning and Control of Dynamical Systems

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    Despite the remarkable success of machine learning in various domains in recent years, our understanding of its fundamental limitations remains incomplete. This knowledge gap poses a grand challenge when deploying machine learning methods in critical decision-making tasks, where incorrect decisions can have catastrophic consequences. To effectively utilize these learning-based methods in such contexts, it is crucial to explicitly characterize their performance. Over the years, significant research efforts have been dedicated to learning and control of dynamical systems where the underlying dynamics are unknown or only partially known a priori, and must be inferred from collected data. However, much of these classical results have focused on asymptotic guarantees, providing limited insights into the amount of data required to achieve desired control performance while satisfying operational constraints such as safety and stability, especially in the presence of statistical noise. In this thesis, we study the statistical complexity of learning and control of unknown dynamical systems. By utilizing recent advances in statistical learning theory, high-dimensional statistics, and control theoretic tools, we aim to establish a fundamental understanding of the number of samples required to achieve desired (i) accuracy in learning the unknown dynamics, (ii) performance in the control of the underlying system, and (iii) satisfaction of the operational constraints such as safety and stability. We provide finite-sample guarantees for these objectives and propose efficient learning and control algorithms that achieve the desired performance at these statistical limits in various dynamical systems. Our investigation covers a broad range of dynamical systems, starting from fully observable linear dynamical systems to partially observable linear dynamical systems, and ultimately, nonlinear systems. We deploy our learning and control algorithms in various adaptive control tasks in real-world control systems and demonstrate their strong empirical performance along with their learning, robustness, and stability guarantees. In particular, we implement one of our proposed methods, Fourier Adaptive Learning and Control (FALCON), on an experimental aerodynamic testbed under extreme turbulent flow dynamics in a wind tunnel. The results show that FALCON achieves state-of-the-art stabilization performance and consistently outperforms conventional and other learning-based methods by at least 37%, despite using 8 times less data. The superior performance of FALCON arises from its physically and theoretically accurate modeling of the underlying nonlinear turbulent dynamics, which yields rigorous finite-sample learning and performance guarantees. These findings underscore the importance of characterizing the statistical complexity of learning and control of unknown dynamical systems.</p

    Simultaneous Multiparametric and Multidimensional Cardiovascular Magnetic Resonance Imaging

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    Undergraduate Catalog of Studies, 2022-2023

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