VictorJuiz/thesis huggingface dataset:
Acting the exegete [electronic resource] : homeric quotation and interpretation in imperial literary symposia
Keywords: Classics.

This doctoral dissertation analyzes the surprisingly common presence of Homer in imperial literary symposia, i.e. fictionalized dinner conversations in the early Roman Empire, where characters often cite the already ancient poetry. Unlike past studies of text reuse at these dinner parties, this dissertation takes the setting seriously and treats these quotations from the standpoint of performance. Through the societally approved use of Homer the elite justifies its privileged position and establishes a hierarchy within itself, and these texts either accept this ideology or reject it through parody. Such an approach not only explains the role of Homer at table, but also why learning how to present and interpret poetry is a vital skill cross-culturally.
Active nanophotonics [electronic resource] : inverse design and strained germanium light emitters
Keywords: Electrical Engineering.

Integration of optical devices into modern CMOS electronics processes offers a wide range of opportunities and challenges for optical design. Modern foundry processes provide an enormous amount of design freedom in feature shapes and sizes; such freedom is rarely fully exploited by traditional human-directed design methods. At the same time, fabrication considerations greatly limit the material selection available for on-chip optics, making integration of most existing light sources infeasible. The first part of this dissertation focuses on the use of strained germanium (Ge) as a Si-compatible light source. While Ge is naturally an indirect bandgap material and thus poorly suited to light emission, application of tensile strain can be used to modify the bandstructure of Ge, greatly increasing light emission efficiency. To this end, we present design and experimental analysis of an optical device which integrates 2.4% uniaxial tensile strain, a single-material pseudo-heterostructure, and a Q > 2,000 cavity in a Ge-on-insulator material system. The second part of this dissertation presents a method for computational inverse design of active optical devices. Rather than relying on a small number of human-chosen design parameters to determine the structure of the device, inverse design begins with an arbitrary structure and then adjusts all available degrees of freedom to create a working device. To illustrate the effectiveness of this method, we present inverse-design of an all-optical switch, resulting in a device that uses 22x less power, 2.5x less power density, and 8x less device area than similar devices reported in recent literature.
Adaptive particle filter in hidden Markov models [electronic resource] : a new approach and its applications
Keywords: Statistics.

Hidden Markov models have been widely used in many fields, such as bioinformatics, econometrics, targets tracking and population genetics. The particle filter, also known as sequential Monte Carlo method, is a powerful tool for latent state filtering of the hidden Markov models. In this thesis, we propose a new methodological advancement, the adaptive particle filter, for the problem of joint parameter estimation and latent state filtering of hidden Markov models. The adaptive particle filter is a hybrid algorithm that combines particle filter and a new MCMC scheme, called sequential substitution, to provide an efficient estimate for function of the parameter and latent state, and further give a consistent estimator of Monte Carlo standard error. Specifically we approximate the posterior distribution of the parameters and latent states by a representative population of N parameter atoms, in which each parameter atom contains P weighted 'particles' where each particle consists of a latent state generated by particle filter. Sequentially updated by the sequential substitution MCMC scheme, the representative population converges weakly to i.i.d samples from the target posterior distribution in the limit of infinite many iterations. By representing the adaptive particle filter as a standard sequential substitution on the extended space of joint parameter atoms and latent state particles, we establish the asymptotic normality of the estimate for the functional posterior mean and the consistency of the Monte Carlo standard error estimator based on the theoretical result of sequential substitution. In addition, we propose the Markov chain restart to deal with the case of long observed time series. Markov chain restart suggests using the representative population of parameter and latent state generated by adaptive particle filters up to time t as approximation of posterior distribution; then particle filters performed for newly proposed parameter atoms at time t + △t can start from the middle of time t. Markov chain restart greatly reduces the computation cost of adaptive particle filters and enables performing more sequential substitution iterations. It also increases the acceptance rate of the MCMC update in adaptive particle filters. Furthermore we demonstrate the effectiveness of adaptive particle filter and Markov chain restart algorithm with simulation results on an example of a highly nonlinear hidden Markov model. Finally as an application in econometrics, we apply our approach on parameter estimation and latent volatility filtering for the jump-diffusion models using both asset returns and option prices.
The adherens junctions control susceptibility to staphylococcus aureus α-toxin [electronic resource]
Keywords: Microbiology and Immunology.

Staphylococcus aureus is both a transient skin colonizer and a formidable human pathogen, ranking among the leading causes of skin and soft tissue infections as well as severe pneumonia. The secreted bacterial alpha-toxin is essential for S. aureus virulence in these epithelial diseases. To discover host cellular factors required for alpha-toxin cytotoxicity, we conducted a genetic screen using mutagenized haploid human cells. Our screen identified a cytoplasmic member of the adherens junctions, plekstrin-homology domain containing protein 7 (PLEKHA7), as the second most significantly enriched gene after the known alpha-toxin receptor, a disintegrin and metalloprotease 10 (ADAM10). Here we report a new, unexpected role for PLEKHA7 and several components of cellular adherens junctions in controlling susceptibility to S. aureus alpha-toxin. We find that despite being injured by alpha-toxin pore formation, PLEKHA7 knockout cells recover after intoxication. By infecting PLEKHA7−/− mice with methicillin-resistant S. aureus USA300 LAC strain, we demonstrate that this junctional protein controls disease severity in both skin infection and lethal S. aureus pneumonia. Our results suggest that adherens junctions actively control cellular responses to a potent pore-forming bacterial toxin and identify PLEKHA7 as a potential non-essential host target to reduce S. aureus virulence during epithelial infections.
Adipose-Derived Stem Cell-Seeded Hydrogels Increase Endogenous Progenitor Cell Recruitment and Functionality
Keywords:  Department of Biology
Biology - Wound Healing
Thesis

Chronic wounds afflict over 6.5 million individuals and cost over $25 billion annually in the United States alone. These nonhealing wounds are caused in large part by impairments in neovascularization, the formation of new blood vessels. Stem cell therapies have emerged as a potentially viable approach to treat chronic wounds. In particular, adipose-derived mesenchymal stem cells (ASCs) are promising due to their ease of harvest and production of pro-regenerative molecules, including those that promote neovascularization. However, a challenge with stem cell therapies is maintaining cell survival in the harsh wound environment. Therefore, stem cell delivery to the wound must be optimized to make cell-based therapies for wound healing translatable. Previously, the Gurtner laboratory found that delivering ASCs by seeding these cells in a hydrogel composed of collagen and the polysaccharide pullulan and topically applying the ASC-seeded hydrogel to a cutaneous wound accelerated wound closure. This project focused on further elucidating how ASC-seeded hydrogels have this therapeutic effect. Specifically, we tested the hypothesis that ASC-seeded hydrogels increase the recruitment and heighten the functionality of endogenous progenitor cells that promote neovascularization. We used techniques including a model in vivo to study cell recruitment to cutaneous wounds, fluorescence activated cell sorting (FACS) of recruited cells, and in vitro measurement of recruited cell proliferation, migration, and expression of provascular genes. We found that application of ASC-seeded hydrogels to wounds in vivo, compared to injected ASCs or a saline control, increased the recruitment of circulating bone marrow-derived mesenchymal progenitor cells (BM-MPCs, p < 0.05), a population we previously found plays a role in neovascularization. In vitro, we found an increase in expression of genes related to angiogenesis, as well as BM-MPC proliferation, migration, and tubule formation, all of which play a role in neovascularization (p < 0.05 for all assays). These data support our hypothesis, demonstrating that treating cutaneous wounds with ASCs seeded in hydrogel increases recruitment of progenitor cells that contribute to neovascularization and improves recruited cell provascular functionality. Overall, this study demonstrates that our pullulan-collagen hydrogel can optimize delivery of ASCs to cutaneous wounds, underscoring the potential of ASC-seeded hydrogels to be used clinically in cell-based therapies for chronic wound healing.
Advanced computing and automation in radiation therapy treatment planning [electronic resource]
Keywords: Electrical Engineering.

\prefacesection{Abstract} The Boltzmann transport equation describes the macroscopic behavior of radiation particles such as neutrons, photons, and electrons as they travel through and interact with matter. The widely known Monte Carlo (MC) method is a general approach to obtaining open form solutions to the linearized Boltzmann transport equation. Monte Carlo algorithm is thte most accurate way to predict how dose is delivered inside a patient. However, the computation time inhibits its routine use in the clinic. For a patient, MC carlo simulation takes anywhere from 10 hours to several days. In this dissertation, we use the state-of-the-art cloud computing technology to accelerate Monte Carlo methods. The approach is evaluated for pencil beams and broad beams of high-energy electrons and photons. The cloud-based Monte Carlo simulation is compared to single-threaded implementation and demonstrates a 47x speed up. Current clinical treatment planning requires multiple trial-and-error adjustments of system model parameters. Producing a treatment plan is time consuming. A team of physician, dosimetrist, and physicist manually adjust parameters in a commercial planning environment. In this dissertation, an autonomous treatment planning technique is implemented in a clinical platform. An outer-loop decision function interacts on-the-fly with an inner-loop clinical treatment planning system (TPS). The approach is applied to 3 head and neck volumetric modulated arc therapy (VMAT) cases and one prostate intensity-modulated radiation therapy (IMRT) case. A strategy of using population-based prior patient data was explored. An upper and lower bound for the dose-volume segments are derived from a group of previously treated patients. The bounds are then used for new case to provide a dosimetric range for acceptability. An heuristic algorithm adjusts the constraints for the optimization using a stochastic approach. Rather than setting a deterministic value for each dose-volume segments, the constraint is changed during each iteration of the outer-loop optimization. The proposed algorithm is applied to a head and neck VMAT case and a prostate IMRT case on a clinical treatment planning system. Results obtained show a comparable dose volume histogram (DVH) compared to manual planning.
Advanced piezoresistive and magnetoresistive sensors [electronic resource]
Keywords: Electrical Engineering.

Smart sensors are now being used almost everywhere, with increasing demands and growing markets across various industrial applications, such as consumer electronics, automotive, healthcare, and power. Among all sensing technologies, resistive sensors, which can convert various forms of environmental energy into resistance changes, are popular and widely employed as critical components and basic building blocks for sensing platforms. Furthermore, data collections with less expensive and more accurate miniature sensors are required, which bring new challenges to engineering researchers in sensor design, fabrication, and integration. In this work, advanced piezoresistive and magnetoresistive sensors for various industrial applications will be discussed. First, a stand-alone stretchable absolute pressure sensor network has been developed for smart vehicle applications. The network with microelectromechanical systems (MEMS) piezoresistive sensing elements can be mounted on various curved surfaces to cover an area that is 100 times larger than its original size. Also, the pressure sensing network can provide good sensitivity and be fabricated in a cost-effective manner on silicon wafers. In addition, low-noise readout electronics was implemented in this stand-alone absolute pressure sensor network for achieving signal conditioning, power management, and wireless communication. Therefore, the sensor network demonstrates the first step towards the development of an absolute pressure distribution monitoring system, and shows great potential to be employed in a variety of smart systems. Second, a multilayer anisotropic magnetoresistive (AMR) sensor has been described for high accuracy angle detection. Intrinsic angular errors of AMR angle sensors can be attributed to two dominant sources: the second harmonic error due to induced anisotropy, and the eighth harmonic error from shape anisotropy. The fabricated quadruple layer AMR angle sensor with correction algorithm reduces mean magnitude of angular errors by a factor of 5 relative to the traditional single-layer device. Moreover, the quadruple layer AMR angle sensor can be operated at low magnetic fields under 100 Oe, enabling the use of cheaper hard magnets for magnetic field sensing. Thus, the proposed quadruple layer AMR angle sensor is promising for many industry applications. Finally, the third part of this dissertation will focus on lowering magnetic 1/f noise in AMR magnetic field sensors, which normally operate in the unsaturated region. IrMn exchange-biased AMR sensors have been optimized for increased magnetoresistive ratio and reduced noise. Sensor noise performance can be further improved by the addition of a thin MgO layer between the NiFe and the Ta capping layer. The decrease in magnetic 1/f noise can be attributed to magnetization stabilization by IrMn and a reduction in pinning sites by MgO. Therefore, the good performance of IrMn exchange-biased AMR sensors render them promising for a wide range of applications.
Advances in CRISPR-Cas9 genome engineering technologies in saccharomyces cerevisiae and tests for cis-regulatory evolution [electronic resource]
Keywords: Genetics.

This thesis is organized into two primary areas of research: cis-regulatory evolution and genome engineering. It is now known that noncoding regions comprise the vast majority of genomic regions under selective constraint in mammals. Despite this, the ability to detect natural selection on noncoding regions has lagged behind the ability to detect selection on coding regions. In Chapter 1, we introduce a new test to detect selection on cis-regulatory elements, and demonstrate its utility on three mammalian transcriptional enhancers. In Chapter 2, we investigate the evolution of resistance to the mycotoxin citrinin by comparing two closely related species of budding yeast, Saccharomyces paradoxus and Saccharomyces cerevisiae. Applying a genome-wide test for selection on cis-regulation, we identified five genes involved in resistance in S paradoxus, four of which are necessary for resistance and increase resistance in S cerevisiae when over-expressed. In the second half of this dissertation, I discuss my work with genome engineering and CRISPR-Cas9. In Chapter 2, we build an improved CRISPR activator (CRISPRa) to simultaneously induce overexpression (in S cerevisiae) of the four genes identified to confirm their role in citrinin resistance. In Chapter 3, we build an inducible CRISPR interference (CRISPRi) system and and designed gRNA libraries to determine design rules for CRISPRi gRNA design in S cerevisiae. We determined that gRNAs targeted to a region with low nucleosome occupancy and high chromatin accessibility within a window of 0 to 200bp upstream of the transcription start site (TSS) are most likely to be effective. In Chapter 4 we confirm and refine these rules in a much larger library consisting of ~9000 unique strains. Additionally, we present a novel method for parsing complex oligonucleotide libraries into single, sequence verified DNA sequences using high throughput sequencing and yeast synthetic biology. We utilize this technology to create and characterize a collection of ~9000 individual inducible CRISPRi strains to the vast majority of essential and respiratory essential genes in S cerevisiae. In Chapter 5, we compare mismatch tolerance for Cas9 in vitro and in vivo (in S cerevisiae) and test a variety of truncated and full-length gRNAs (with 17, 18, and 20 nucleotides of complementarity sequence). We observed notable differences between in vitro and in vivo Cas9 cleavage specificity profiles, with in vivo cleavage being more sensitive/less tolerant to mismatches.
Advances in flight safety analysis for commercial space transportation [electronic resource]
Keywords: Aeronautics and Astronautics.

The diversity in the kinds of vehicles that are appearing in the commercial space transportation sector raises questions regarding the applicability of the licensing procedures and methodologies that are in place to protect public safety. These licensing procedures are designed to limit risks to public safety in case of a space vehicle explosion. Concerns arise because the methods currently used are derived from expendable launch vehicles (ELVs) developed during the Space Shuttle era, and thus they might not be fully applicable to future vehicles, which include new types of ELVs, suborbital vehicles, reusable launch vehicles (RLVs), and a number of hybrid configurations. This dissertation presents a safety analysis tool, called the Range Safety Assessment Tool (RSAT), that quantifies the risks to people on the ground due to a space vehicle explosion or breakup. This type of problem is characterized by the complexity and uncertainty in the physical modeling. RSAT has been used to analyze both launch and reentry scenarios and can be applied to many possible vehicle configurations. The Space Shuttle Columbia accident was modeled with RSAT, and the results were compared with simulations performed by the Columbia Accident Investigation Board (CAIB). A methodology to perform sensitivity and optimization studies is also presented. This methodology leverages previous work done in active subspaces and Gaussian process regression to generate surrogate models. The proposed sensitivity and optimization methodologies were used to analyze a commercial ELV. The results show that the methodology can handle a large number of stochastic inputs and identify opportunities to decrease risk.
Advances in structure-based modeling of turbulent flows [electronic resource]
Keywords: Aeronautics and Astronautics.

Structure-based modeling, introduced by Kassinos and Reynolds (1995), uses one-point tensors to provide a more detailed description of the turbulent fluctuations. This thesis describes further developments to two structure-based models, namely the Algebraic Structure-based Model (ASBM), which belongs to the RANS category, and the Interacting Particle Representation Model (IPRM), which can been interpreted as a PDF model. The ASBM is an engineering model of turbulence for wall-bounded flows. A new variant of the model has been formulated, which exhibits (1) a segregated near-wall correction that leads to a new paradigm for model development and comparison, (2) a set of fully-explicit equations that replaces the original formulation and reveals the highly nonlinear nature of the ASBM, and (3) a new coupling with transport equations that improves the accuracy of the model. The original and newer variants of the ASBM are then applied in the simulation of separated flows, so as to obtain a comprehensive assessment of their predictive capabilities. This is then followed by a thorough study on the ability of the model to provide well-converged solutions. The IPRM is a stochastic structure-based model of homogeneous turbulence. This thesis documents a new formulation based on an Eulerian reference frame that replaces the original Lagrangian framework and thus avoids the slow convergence and bias of statistical estimators. The derivation of the Eulerian formulation, its solution through radial basis functions, and a comparison against the original solution methods are reported in detail. Taken together, the work performed on both models advances the applicability and understanding of structure-based modeling for turbulent flows.
Advances on chip inside a cell for monitoring physiological cell parameters [electronic resource]
Keywords: Electrical Engineering.

Continuous monitoring of physiological parameters inside a living cell will lead to major advances in understanding of biology and complex diseases, such as cancer. It also enables us to develop new medical diagnostics and therapeutics. Here we propose a technique to measure the pH level inside a cell using a wireless implantable passive sensor, which consists of capacitive and inductive components. Even a slight change of cellular pH level can reveal substantial information about the health of a cell. Only a highly sensitive pH sensor can detect this slight variation. Building such a wireless implantable sensor platform is the main goal of this thesis. Our designed and fabricated pH sensor is basically an extended gate ISFET (Ion Sensitive Field Effect Transistor) structure. It is compatible with CMOS (Complementary Metal Oxide Silicon) fabrication processes and can be miniaturized as an implantable device. To fully understand the parameters that give rise to high pH sensitivity, we have investigated the effect of solution molecular size on enhancing the sensitivity of this type of sensor. Our simulation and experimental results show that buffered solutions with larger counter ion size can induce significantly higher hydrogen ion activity at the sensor surface. The larger buffer counter ion size results in higher externally measured sensitivity, which even exceeds the Nernst limit (59mV/pH). For each pH buffer solution there is a specific size of the counter ion, which is necessary to achieve this high sensitivity. In addition, progress in nanofabrication and wireless communication has opened up the potential of making our wireless chip small enough that it can be wholly inserted into a cell. To investigate how the chip could be internalized into the cell and how the chip would affect cell physiology, we designed and fabricated a series of 3D multilayer structures with different sizes as a potential RFID (Radio Frequency IDenentification) cell tracker. Our experiments show that the chips with smaller sizes than 21 µm × 9 µm × 1.5 µm can be easily internalized by various types of living cells, such as macrophages, cancer cells, and normal/healthy cells. The incubated cells with internalized chips stayed alive during the 7 days of monitoring. Also, we observed successful cell division from these incubated cells. These results are the first steps towards long-term, wireless, intracellular physiologic monitoring.