64,100 research outputs found

    FEASIBILITY STUDY OF TOWER DESIGN AND VIBRATION SUPPRESSION FOR AN ANTARCTIC INFRARED TELESCOPE

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    There is increasing interest to develop a dedicated near-infrared (NIR) observatory in Antarctica due to the advantages of a colder, darker sky in the NIR band and because the turbulent ground layer, responsible for seeing, is limited to the first 30 m above the ice shelf. A telescope mounted atop a 25 to 30 m tower will have enhanced performance by operating at the top of the boundary layer and unprecedented sensitivity due to the Antarctic climate. Cryoscope is one such telescope and will be mounted atop a 30 m tower at Dome C, Antarctica. This presents a challenge for image stability since vibration-induced image motion must be less than 0.1 arcsec while the tower is subjected to 10 m/s wind buffeting. Seven tower designs were assessed using structural analysis programs to determine each tower’s stability and natural frequency from wind loads determined using ASCE Standards 7-16 and wind data from Dome C. Power law analysis was conducted to evaluate the relationships between stability and mass if a tower was linearly scaled. Furthermore, in case the tower designs provide insufficient stability, vibration isolation mounts are explored to provide additional compensation, mainly damping mounts and friction mounts. In addition, we hold in reserve the possibility of active cancellation of forces on the telescope by applying torques with direct drive motors in response to forces measured by rotary flexures and encoders incorporated into the bearing mounts. The primary purpose of this work is to assess the feasibility and degree of difficulty of designing a 30 m tower for Antarctica with sufficient stability through passive and active mitigation methods while considering construction and transportation constraints

    RESPIRATORY OUTCOMES AND IMMUNOLOGICAL BIOMARKERS ASSOCIATED WITH STAPHYLOCOCCAL EXPOSURES IN ASTHMA

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    This dissertation project aimed to characterize staphylococcal exposures and explore the effect of Type-2 immune responses in the associations between staphylococcal exposures and asthma morbidity. Staphylococcus aureus (SA) is a common colonizer of the human skin and mucosa but is also an opportunistic pathogen with well-known roles in acute and chronic diseases. The staphylococcal enterotoxins (SE) secreted by some strains of SA have known immunomodulatory effects behaving as both superantigens and allergens. Individuals with certain allergic diseases are commonly colonized by SA, which frequently express SE. The household environment acts as a reservoir for SA and SE thus exposures are multimodal, occurring through contact with settled dust independent of colonization. It is not known if these different routes of exposure have different implications in allergic disease. Further, allergic sensitization to SE (SE-IgE) is associated with severity of allergic diseases and is also associated with polysensitization to environmental allergens. How these staphylococcal exposures are associated with asthma, independent of other allergic diseases, is not well understood. Last, it is not known if asthma subtypes are differentially associated with SA or SE. To carry out the work in this dissertation, preexisting data and biospecimens from larger studies were leveraged. Staphylococcal exposures were characterized according to respiratory and household dust presence of SA and SE, and via serum SE-IgE. These exposures were then compared with asthma morbidity outcomes and both peripheral immune markers and local immune mediators. This body of work identifies that exposure to SA or SE in dust and respiratory carriage of SA is associated with asthma, potentially via immune mediators related to eosinophilic infiltration. Further, the magnitude and direction of association between staphylococcal exposures and asthma depend on the route of exposure, virulence of the strain, and vulnerability factors of the individual, including age, race, and socioeconomic status. These data suggest that the Type-2 biased immune response is important to the influence of staphylococcal exposures on asthma outcomes. This work adds to an emerging literature that investigates the biological mechanisms through which staphylococcal exposures contribute to asthma morbidity

    RNA FOLDING AND THE FITNESS LANDSCAPE OF THE glmS RIBOZYME

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    The discovery of catalytic RNAs suggests that life emerged from an RNA molecule. However, the principle of how an RNA sequence evolved to acquire stabilizing tertiary motifs for optimizing the function is beginning to be understood. By mapping an activity landscape of numerous variants, one can decipher the connection between sequence and function and deduce the evolutionary rules that favor a particular structural motif. The glmS ribozyme is part of a negative feedback loop that maintains cellular levels of glucosamine-6-phosphate (GlcN6P). The self-cleavage of the ribozyme in complex with GlcN6P leads to the turnover of the glmS mRNA. The cleavage of RNA depends on proper folding of the ribozyme sequences, which provides a convenient readout of the sequence-function relationship. In addition to developing a medium-scale screening approach to generate a hierarchy of mutation tolerance for structural domains, I combined the self-cleavage assay with high-throughput sequencing to estimate ribozyme activity and project the activity profile onto the secondary and tertiary structures to rationalize the mutational effects. By surveying 456 single mutants, I observed that many deleterious mutations are clustered in the phylogenetically conserved catalytic core. Other detrimental base substitutions lie in the internal loop IL4, which forms tertiary interaction with the core helices. The thermally metastable conformer caused by the IL4 mutation suggests that sequence conservation in the core-periphery contact enhances the ribozyme function by preventing RNA misfolding. The investigation of 14205 pairwise epistatic interactions in a condition that stabilizes the folded RNA showed that mutations in different structural domains often create negative epistasis. In contrast, mutation pairs in the same motif show positive epistatic interactions. Moreover, the analysis of epistasis change among different folding conditions demonstrated that folding cooperativity is stronger in the crowding condition. On some rare occasions, one harmless mutation compensates for the other deleterious mutation in the same peripheral motif. This compensatory interaction indicates an alternative folding pathway that may exist during RNA evolution. This study has provided indications of molecular evolution by scrutinizing the link between sequence and cleavage activity, allowing a prospective understanding of molecular decisions for conserving tertiary motifs that promote RNA functionality

    Multimodal MRI analysis using deep learning methods

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    Magnetic resonance imaging (MRI) has been widely used in scientific and clinical research. It is a non-invasive medical imaging technique that reveals anatomical structures and provides useful information for investigators to explore aging and pathological processes. Different MR modalities offer different useful properties. Automatic MRI analysis algorithms have been developed to address problems in many applications such as classification, segmentation, and disease diagnosis. Segmentation and labeling algorithms applied to brain MRIs enable evaluations of the volumetric changes of specific structures in neurodegenerative diseases. Reconstruction of fiber orientations using diffusion MRI is beneficial to obtain better understanding of the underlying structures. In this thesis, we focused on development of deep learning methods for MRI analysis using different image modalities. Specifically, we applied deep learning techniques on different applications, including segmentation of brain structures and reconstruction of tongue muscle fiber orientations. For segmentation of brain structures, we developed an end-to-end deep learning algorithm for ventricle parcellation of brains with ventriculomegaly using T1-w MR images. The deep network provides robust and accurate segmentation results in subjects with high variability in ventricle shapes and sizes. We developed another deep learning method to automatically parcellate the thalamus into a set of thalamic nuclei using T1-w MRI and features from diffusion MRI. The algorithm incorporates a harmonization step to make the network adapt to input images with different contrasts. We also studied the strains associated with tongue muscles during speech production using multiple MRI modalities. To enable this study, we first developed a deep network to reconstruct crossing tongue muscle fiber orientations using diffusion MRI. The network was specifically designed for the human tongue and accounted for the orthogonality property of the tongue muscles. Next, we proposed a comprehensive pipeline to analyze the strains associated with tongue muscle fiber orientations during speech using diffusion MRI, and tagged and cine MRI. The proposed pipeline provides a solution to analyze the cooperation between muscle groups during speech production

    Data-assisted modeling of complex chemical and biological systems

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    Complex systems are abundant in chemistry and biology; they can be multiscale, possibly high-dimensional or stochastic, with nonlinear dynamics and interacting components. It is often nontrivial (and sometimes impossible), to determine and study the macroscopic quantities of interest and the equations they obey. One can only (judiciously or randomly) probe the system, gather observations and study trends. In this thesis, Machine Learning is used as a complement to traditional modeling and numerical methods to enable data-assisted (or data-driven) dynamical systems. As case studies, three complex systems are sourced from diverse fields: The first one is a high-dimensional computational neuroscience model of the Suprachiasmatic Nucleus of the human brain, where bifurcation analysis is performed by simply probing the system. Then, manifold learning is employed to discover a latent space of neuronal heterogeneity. Second, Machine Learning surrogate models are used to optimize dynamically operated catalytic reactors. An algorithmic pipeline is presented through which it is possible to program catalysts with active learning. Third, Machine Learning is employed to extract laws of Partial Differential Equations describing bacterial Chemotaxis. It is demonstrated how Machine Learning manages to capture the rules of bacterial motility in the macroscopic level, starting from diverse data sources (including real-world experimental data). More importantly, a framework is constructed though which already existing, partial knowledge of the system can be exploited. These applications showcase how Machine Learning can be used synergistically with traditional simulations in different scenarios: (i) Equations are available but the overall system is so high-dimensional that efficiency and explainability suffer, (ii) Equations are available but lead to highly nonlinear black-box responses, (iii) Only data are available (of varying source and quality) and equations need to be discovered. For such data-assisted dynamical systems, we can perform fundamental tasks, such as integration, steady-state location, continuation and optimization. This work aims to unify traditional scientific computing and Machine Learning, in an efficient, data-economical, generalizable way, where both the physical system and the algorithm matter

    DISSECTING TRANSCRIPTIONAL DEVELOPMENTAL HISTORIES WITH SINGLE-CELL RESOLUTION IN C. ELEGANS

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    One of the most intriguing questions in developmental biology is how cells acquire their specific cell identities during embryonic development. With the significant development of methods in molecular biology during the last several years and specifically single-cell RNA sequencing, there are more pieces of evidence that development is not necessarily a canalized, linear process. There are more and more pieces of evidence that cells can follow different developmental trajectories to achieve the same terminal identity, through a process that is called convergent differentiation. Nowadays, with the development of single-cell RNA sequencing methods, we have an opportunity to explore changes in cell transcriptomes during development and reveal unconventional ways of lineage specification. In this project, we made an attempt to study the mechanisms that might be responsible for convergent differentiation of a small group of body wall muscle cells in the mesodermal (MS) cell lineage in C. elegans embryos. We obtained single-cell RNA-seq data for ~6500 individual cells, derived from MS cell lineage. We tried to identify potential mechanisms that could be responsible for the activation of the body wall muscle identity in cells that are originally committed to different developmental trajectories. This included experiments with NHR-67 transcription factor, as well as components of the Notch signaling pathway. In addition, we tried to explore the role of pha-4 and hnd-1 transcription factors in the process of establishing commitment of MSxa and MSxp cell lineages during early stages of embryogenesis. Another aspect of the body wall muscle biology that we highlighted in this work is the surprising diversity of terminally differentiated cells in the embryo, diversity that is partially kept at later larval stages. This study serves as a framework for further research projects, providing a good resource for addressing various questions regarding cell identity establishment

    Computational Analysis of 3d Cleared and Labeled Pancreatic Cancer Samples

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    Pancreatic ductal adenocarcinoma (PDAC) is the most common form of pan- creatic cancer and with a 5-year survival rate of only 11%, it is amongst the deadliest cancers. Understanding pancreatic cancer growth pattern on a microscopic scale in 3D will help to understand mechanisms of metastasis and invasion. Modern Tissue Clearing can be used to obtain microscopic fluorescent 3D images of real patient tissue. In this thesis, we build the pipeline for analyzing PDAC structures in 3D using fluorescent microscopic images by generating surface models and quan-tifying them by centerline, persistent homology, endpoints, and sphericity analysis

    INSIGHTS INTO THE INFLUENCE OF TRANSCRIPTIONAL VARIABILITY AND CHROMATIN REPRESSION ON STOCHASTIC CELL FATE SPECIFICATION

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    Cell types are predominantly specified through lineage- or signaling-specific cues, resulting in robust and stereotyped outputs. Alternatively, cells can choose randomly between two or more cell fates in a stochastic process. Stochastic mechanisms in gene expression and cell specification diversify cell fates in otherwise uniform tissues. Despite the importance of these developmental and regulatory processes, little is understood regarding the inputs controlling stochastic processes. In the visual system of D. melanogaster, R7 photoreceptors undergo stochastic differentiation, resulting in two subtypes. We observe a consistent 1:2 ratio between these two subtypes in wild type eyes. While the ratio between the subtypes is consistent, the pattern of these subtypes across the eye is random, even between eyes of the same organism. These fates are characterized by the expression of their rhodopsin proteins. The pale R7 fate expresses Rh3, while the yellow R7 fate expresses Rh4. This decision is controlled by the stochastic expression of a transcription factor called Spineless (Ss), which inhibits the pale R7 fate and promotes the yellow R7 fate. We investigated how spineless (ss) activation and silencing are regulated temporally during development in a dynamic, antagonistic process, which patterns the locus and sets the subtype in each cell. We find that a pulse of expression early in development is necessary to enable expression during terminal differentiation into the yellow subtype. Moreover, we investigated how transcriptional variation at the early developmental timepoint affects the capacity for repression and ultimately terminal fate. Finally, I outline preliminary findings into the dynamics of these processes and future avenues in identifying additional genes undergoing these regulatory mechanisms. These findings are significant to the investigation of gene regulation and developmental processes that are still understudied

    NOVEL STATISTICAL METHODS FOR LATENT STRUCTURE IDENTIFICATION AND DECISION MAKING IN MEDICAL APPLICATIONS

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    This dissertation focuses on developing different statistical methods that are targeted for challenges in a variety of medical applications. The first main contribution of this dissertation is the development of a Bayesian tensor-on-tensor regression approach to predict a multidimensional array (tensor) of arbitrary dimensions from another tensor of arbitrary dimensions. Our contribution is that we develop an efficient Markov Chain Monte Carlo (MCMC) algorithm to estimate both the model dimension and parameters for posterior inference simultaneously. The proposed Bayesian framework provides a natural way for uncertainty quantifications. This proposed regression framework has multiple possible applications such as predicting patterns in brain images using biomarker trajectories. Our second contribution is the development of a flexible Bayesian semiparametric framework to model the longitudinal trajectory of the AD biomarker with a changepoint relative to the occurrence of symptoms onset, which is subject to left truncation and right censoring, in a heterogeneous population. Our model can cluster subjects based on longitudinal and disease manifestation patterns and predict the latent disease progressor status. Moreover, the proposed model can predict the not-yet-observed disease manifestation event time for progressors. Lastly, our model can determine the timing of biomarker changes before disease manifestation while accounting for subjects' heterogeneity. The proposed model is evaluated through simulation studies and then applied to analyze the BIOCARD Alzheimer's disease dataset. Lastly, we propose a novel decision-making framework that can help answer the new clinical question of when to apply a particular oxygen treatment to COVID-19 patients. The proposed decision-making framework involves a classification model and a reinforcement learning model where the classification model is used to predict universally recognized clinicians' actions and the reinforcement learning model is used to recommend optimal treatments that maximize the long-term target of our interest when the universal consensus treatment does not exist. The proposed model is applied to analyze the JH-CROWN COVID-19 data and shows efficacy in detecting patterns of clinical variables and suggesting earlier interventions for patients with bad outcomes. Through policy evaluations, we show that the learned policy performs better than the observational policy

    SOLVENT RESPONSIVE SELF-FOLDING ARCHITECTURES FOR PHOTO ELECTRONICS AND BIOELECTRONICS

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    Self-assembly is omnipresent in nature. The concept has been transferred from natural context into many applications in engineering. Several engineering approaches for self-assembly with varied scales in nano- and micro-manufacturing. Among those approaches, self-folding is one of the most established methods. Self-folding broadly refers to assembling 3D structures by bending, curving, and folding from a 2D planar form without manual or mechanized intervention. In this thesis, we introduced a novel self-folding method using thin films of SU8, a negative photoresist widely used in microfluidics and micromechanical system. We rationalized the folding mechanism with theoretical methods, including coarse-grained and Finite element method (FEM) modeling. With the help of this methodology, we further demonstrated a wafer-scale patterning process and integration of functional electronic devices. We focused on the development of self-folding structures with 2D layered materials, which are important for ultrathin and flexible electronics. However, the realization of wafer-scale integration of on-chip or free-standing and reversibly reconfigurable integrated polymer-functional 2DLM hybrid devices has proven challenging. This has been the focus of the first part of my study. This research demonstrated a comprehensive framework for the rational design and scalable fabrication of 3D self-folding optoelectronic devices with the integration of 2D monolayer graphene. This approach also enabled the fabrication of microscale bioelectronics. In the second part of my study, we further developed shell-shaped multielectrode arrays (MEAs) for brain organoids. Brain organoids are important models for mimicking some three-dimensional (3D) cytoarchitectural and functional aspects of the brain. Inspired by the shape of electroencephalography caps, we developed miniaturized wafer-integrated MEA caps for organoids. The optically transparent shells are composed of self-folding polymer leaflets with conductive polymer–coated metal electrodes. Our studies suggest that 3D shell MEAs offer great potential for high signal-to-noise ratio and 3D spatiotemporal brain organoid recording
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