VictorJuiz/thesis huggingface dataset:
Specific extrusion of Enterovirus-A71-infected cells from human colonoids and consequences for viral spread
Keywords:  Department of Biology
Biology
Microbiology
Virology
Enteroviruses
Organoid
Extrusion
Text
Thesis

Enterovirus-A71 (EV-A71) is a causative agent of Hand Foot and Mouth Disease and occasionally progresses to life-threatening encephalitis and viral meningitis; it is currently endemic in East Asia. There are no antiviral treatment options and no FDA-approved vaccine. EV-A71 is transmitted via fecal-oral routes and thus first enters the body through the GI tract. The Kirkegaard laboratory has cultured colonic epithelial organoids (colonoids) so as to present their apical surface to the medium, mimicking the lumen of the intestine, to study EV-A71. We have found, after eight hours of infection, infected cells are extruded from the epithelium via mechanosensitive pathways. Given that this both liberates virus-infected cells from the epithelium and is likely to preserve epithelial integrity, we hypothesize that the specific extrusion of infected cells benefits both the virus and the host.
To test this hypothesis, I have employed colonoids to investigate the fate of extruded cells and the virus within, as well as how extrusion affects overall viral burden. Extruded cells initially sequester virus, but significantly release virus by sixteen hours post infection. Additionally, viral Stability was equivalent in cell-associated and free virus, indicating that infected extruded cells can propagate infection. Cell-associated virus displayed decreased levels of secondary infection as compared to equivalent titers of cell free virus; clusters of secondarily-infected cells were observed in RD monolayers when intact extruded cells were used to propagate infection, indicating that extruded cells could propagate infection en bloc. When extrusion was blocked by inhibiting force-sensitive ion channels with GsMTx4, virus was still released as free-virus, which ultimately increased viral burden. Thus, blocking extrusion led to more severe infection. Overall extrusion establishes a limited and persistent infection, which benefits both the host and the virus.
Extrusion of infected cells has shown to be a host response to a growing number of pathogens, including poliovirus, measles, Salmonella enterica serovar Typhimurium, and Listeria. Understanding extrusion and its role in the EV-A71 infectious cycle can illuminate the factors that promote or prevent severe disease and elucidate targets for antiviral treatments.
Spectroscopic modeling and ultraviolet laser absorption spectroscopy measurements for the development of nonequilibrium thermometry and speciation diagnostics of for O2, NO and CN
Keywords: Engineering.
Mechanical Engineering.

Accurate spectroscopic modeling, when coupled with state-of-the-art laser diagnostics, enables quantitative measurements of the often poorly characterized nonequilibrium chemistry distinctive of hypersonic flows. The validation and improvement of existing chemical and fluid mechanical models that attempt to capture the complex molecular dynamics in such high-speed flows rely heavily on the temperature and concentration measurements delivered by laser-based sensors. The nonequilibrium thermochemistry that diatoms such as oxygen (O2), nitric oxide (NO), and the cyano-radical (CN) undergo requires high-fidelity experimental quantification, as these molecules and their internal energy distributions, rotational and vibrational, significantly impact the heat loads experienced by hypersonic vehicles. This dissertation will leverage the strong ultraviolet absorption spectra of the three aforementioned molecules, O2, NO, and CN, to develop a combination of laser diagnostics to probe the rotational and vibrational temperatures and species concentrations, both total and in specific vibrational quantum states. Low uncertainty, time-resolved measurements of such quantities provide invaluable data for the various modeling efforts to compare calculations against and improve model specific parameters. In this work, four sets of studies were undertaken to develop a spectroscopic model, use the model to inform temperature diagnostic wavelengths, perform an experimental spectroscopic validation, and finally demonstrate the accuracy of the diagnostic. Three major spectroscopic systems were modeled for the purposes of these works, the Schumann-Runge system, which give rise to the strong ultraviolet absorption spectrum of O2, the NO γ-bands, one of the strongest ultraviolet systems of NO, and the CN Violet system, one of the strongest molecular absorption systems in the ultraviolet. The developed models informed wavelengths that probed specific quantum states of the aforementioned diatoms and could be used in a two-color thermometry scheme to infer state-specific temperature and number density time histories in a shock tube environment. The experimental validation of the spectroscopic models at novel conditions and wavelengths improved the modeling capabilities and provides a framework for future spectroscopic studies that seek to further quantify the absorption spectra of air diatoms. Upon validating the spectroscopy, the laser diagnostics could be deployed to study the nonequilibrium chemistry of these diatoms behind strong shock waves and yielded quantitative temperature and concentration data critical to modeling heat loads experienced by hypersonic vehicles and the design of the thermal protection systems to withstand such loads.
Spin and surface textures in nanophotonics
Keywords: Engineering.
Materials Science and Engineering.

Light carries information on its multiple degrees of freedom, underlying a variety of optical imaging and characterization methods. To benefit from such methods, it is essential to achieve precise control over light waves based on the intrinsic properties of light. Although this has been typically obtained by using bulky conventional optics, it has recently been recognized that optical resonances in high-index nanostructures can be harnessed to manipulate light waves in a controlled fashion. This novel approach, called flat optics, offers new opportunities for the integration of the ability to control light waves into optoelectronic devices. In this thesis, we illustrate our approaches with thin nanopatterned semiconductor films to achieve improved control over optical degrees of freedom in optoelectronic devices, especially focusing on optical spin and surface textures.
Stability and dynamics at non-aqueous fluid-fluid interfaces
Keywords: Engineering.
Chemical Engineering.

Fluid-fluid interfaces are common in nature and in various industrial processes and products, including personal care, lubricating oils, wildfire fighting, and food science. Understanding the stability of these interfaces is crucial to their morphology and dynamics, and can enable engineering of interfaces towards an end application. Aqueous liquids are ubiquitous and their interfacial behavior has been reasonably well studied. There is much less known about interfaces involving non-aqueous liquids. In this thesis, we are concerned with two classes of phenomena that are constructed of fluid-fluid interfaces with a non-aqueous liquid component: non-aqueous foams and emulsions. Non-aqueous foams are dispersions of air in liquid, and therefore comprised of many air - non-aqueous liquid interfaces. We explore various mechanisms by which the interfaces are stabilized and destabilized, subsequently influencing the resulting foams. These mechanisms can also be engineered to intentionally control stability of foams, stabilizing beneficial and destabilizing disadvantageous foams. Here, we primarily examine systems where foaming is detrimental, with the aim to destabilize the foam by destabilizing the component interfaces. Diesel fuels are one such system, with unique characteristics that make antifoaming additives challenging. Using custom single bubble interferometry, we show experimental validation of conventional antifoams in diesel and, importantly, demonstrate that a globally viable ash-free alternative is effective. Additionally, we present experimental evidence illustrating the likely physical mechanisms behind each antifoam. Widely used antifoam additives are insoluble and present challenges in implementation and robust function over time. Inspired by evaporation-induced stabilization observed in multi-component mixtures such as diesel and lubricant oils, we introduce a novel miscible antifoam mechanism. By strategically inverting the surface tension gradient and thus the direction of induced flows, this mechanism leverages solutocapillary Marangoni flows to destabilize a foam. Emulsions are dispersions of aqueous liquid in non-aqueous liquid or vice versa, and are built of non-aqueous - aqueous interfaces. We focus on double emulsions (DEs) (water-in-oil-in-water 2-interface emulsions), generated via microfluidics. We conduct a systematic characterization of DE size and shell thickness across a wide range of flow rates, fluid composition and properties, and stability and instability. Understanding interface stability in double emulsions allows better control of DE size, uniformity, and robustness, essential for applications in biomedical research. This dissertation corroborates that fluid-fluid interfaces are key to understanding factors influencing the stability and dynamics of numerous foams and emulsions and their applications, illuminating areas of great potential.
The stairway to heaven : EMP2 links cell cycle arrest, lipid metabolism, and ferroptosis sensitivity
Keywords: Medicine.

Ferroptosis is a non-apoptotic form of cell death that can be triggered by inhibiting the system xc- cystine/glutamate antiporter or the phospholipid hydroperoxidase glutathione peroxidase 4 (GPX4). Here, we show that cells arrested in the cell cycle by stabilization of wild type p53 or by inhibition of cyclin-dependent kinase activity have increased sensitivity to GPX4 inhibitors but not system xc- inhibitors. Cell cycle arrest alters the lipidome to increase the abundance of oxidizable polyunsaturated fatty acid-containing phospholipids (PUFA-PLs), which drives sensitivity to GPX4 inhibitors. Changes in PUFA-PL abundance upon cell cycle arrest are linked to reduced expression of membrane bound O-acyltransferase domain containing 1 (MBOAT1) and epithelial membrane protein 2 (EMP2). A candidate orally bioavailable GPX4 inhibitor increased ferroptotic lipid peroxidation and shrunk tumor volumes when combined with a CDK4/6 inhibitor. Thus, cell cycle arrest may make certain cancer cells more susceptible to ferroptosis in vivo.
Standardizing enteroid culture for intestinal radiotherapy modeling
Keywords: Medicine.
Stem Cell Biology and Regenerative Medicine.

Enteroids are self-organizing, three-dimensional (3D) cell clusters that mimic native intestinal tissue and offer an alternative to traditional two-dimensional (2D) and in vivo models. Enteroids contain a variety of intestinal cell types and mimic tissue architecture. While similarly expandable and affordable to traditional 2D cell culture, enteroids better recapitulate the 3D microenvironment. Enteroids can also be derived from patient-biopsies, providing a human-specific alternative to animal models. The current standard of culturing enteroids is within basement membrane extract (BME) that is derived from the matrices surrounding mouse sarcoma. Although enteroids form and grow efficiently within BME matrices, these matrices are inherently variable and cannot be easily tuned. In the second chapter of this thesis, we present a defined, engineered matrix that overcomes these limitations. Specifically, we pair Hyaluronan (HA) with Elastin-Like Protein (ELP) to form the HELP matrix. The HELP matrix supports the formation, growth, and long-term culture of primary murine and human enteroids. We demonstrate the independent tuning of the following matrix properties in HELP: 1) ligand concentration (specifically the fibronectin mimicking ligand RGD), 2) stiffness, and 3) viscoelasticity. In the third chapter of this thesis, we leverage the enteroid model to explore the effects of conventional (CONV) and ultra-high dose rate (FLASH) radiation on healthy intestinal tissue. Radiation is especially damaging to intestinal tissue due to its highly proliferative nature. Fortunately, recent work has demonstrated the potential for ultra-high dose rate delivery to effectively ablate cancerous tissue, while mitigating damage to healthy tissue. While promising, much of this work has relied on murine or 2D cell culture models. In this thesis, we demonstrate the radiation of enteroids derived from healthy primary human intestinal tissue and observe cellular and molecular level differences between CONV- and FLASH-irradiated groups. In summary, this thesis describes: an engineered matrix that was designed to standardize enteroid culture (Chapter 2) and the application of enteroids to investigate intestinal response to radiation (Chapter 3).
State law and tribal justice in Iraq, (1914-2022)
Keywords: Humanities and Sciences.
History.

Scholars of the British Empire lavish great attention on structural changes brought on by the imposition of the Mandate in Iraq in 1921. Exploring the Tribal Criminal and Civil Disputes Regulation (TCCDR) archives----over twenty thousand files of judicial proceedings from across Iraq covering the 1925 -- 1958 period----gave me reason to believe that many more stories remained untold. The TCCDR provided a legal framework for the prosecution of tribal disputes through a combination of state magistrates and customary authorities. It offered a full-fledged appellate system by way of petitions. In impressive displays of resiliency and agency, Iraqis manipulated often complacent authorities to set the terms of interactions, thereby tempering the overarching impact of state consolidation. The voluminous number of petitions shows the proficiency with which Iraqis appropriated legal tools available to them. More so, my dissertation breaks new ground as the first in-depth examination of the TCCDR's legacy in post-1958 Iraqi legislation. The main overarching claim of the final chapters is, contrary to the recurring paradigm that tribes are empowered by state weakness, that customary justice mechanisms are profoundly crippled by government failure. After the collapse of the Iraqi State in 2003, the withdrawal of security forces and ensuing corruption resulted in the dislocation and fragmentation of traditional power structures. This translated into the disruption of tribal justice. Ultimately, the recurrence of protracted vendetta cycles, punctuated by outbreaks of armed confrontation, is not a sign of tribal vigor. Rather, such violence should be understood as one of the ramifications of war impacting Iraqi society, and the resulting state failures
Statistical analysis of scientific machine learning
Keywords: Engineering.

Massive data collection and computational capabilities have enabled data-driven scientific discoveries and control of engineering systems. However, there are still several questions that should be answered to understand the fundamental limits of just how much can be discovered with data and what is the value of additional information. For example, 1) How can we learn a physics law or economic principle purely from data? 2) How hard is this task, both computationally and statistically? 3) What's the impact on hardness when we add further information (e.g., adding data, model information)? I'll answer these three questions in this thesis in two learning tasks. A key insight in both two cases is that using direct plug-in estimators can result in statistically suboptimal inference. For the first learning task, the thesis focus on variational formulations for differential equation models. I discuss a prototypical Poisson equation. I provide a minimax lower bound for this problem. Based on the lower bounds, I discover that the variance in the direct plug-in estimator makes sample complexity suboptimal. I also consider the optimization dynamic for different variational forms. Finally, based on our theory, I explain an implicit acceleration of using a Sobolev norm as the objective function for training. The second learning task this thesis discuss is (linear) operator learning, which has wide applications in causal inference, time series modeling, and conditional probability learning. I build the first min-max lower bound for this problem. The min-max rate has a particular structure where the more challenging parts of the input and output spaces determine the hardness of learning a linear operator. Analysis also shows that an intuitive discretization of the infinite-dimensional operator could lead to a sub-optimal statistical learning rate. Then, I'll discuss how, by suitably trading-off bias and variance, I can construct an estimator with an optimal learning rate for learning a linear operator between infinite dimension spaces. I also illustrate how this theory can inspire a multilevel machine-learning algorithm of potential practical use.
Statistical analysis supports pervasive RNA subcellular localization and alternative UTR regulation
Keywords: Medicine.
Biochemistry.

Understanding the subcellular localization of RNA molecules across different cell-types and tissues provides a window into previously unknown biology and disease. Low-plex RNA imaging technologies, where only a handful of RNA species could be observed in a single experiment, have been transformed by novel spatially resolved imaging and sequencing techniques which can simultaneously investigate thousands of genes. Named ``Method of the Year'' in 2019 by Nature Methods, the field of spatially resolved transcriptomics continues to accelerate in resolution, sensitivity, and ease of use. Large and exciting datasets have been produced from these efforts, but have been underutilized to discover subcellular RNA localization. We introduce a novel statistical framework to identify RNA subcellular localization patterns in publicly available datasets. We detect that a majority of investigated genes have non-random RNA distribution, and often differential distribution between cell-types. We've combined our analyses of spatial datasets with standard, spatially-naive, single-cell RNA sequencing to further identify genes which have subcellular localization patterning correlated with RNA isoform usage to generate testable hypotheses which we've collaborated with others to successfully validate. Spatial Transcriptomics is rapidly evolving, and we expect that our contribution of a flexible and statistically-sound algorithm will be applicable for the impending influx of spatial datasets
Statistical and computational problems in low-rank matrix estimation
Keywords: Humanities and Sciences.
Statistics.

This dissertation explores several problems in the realm of low-rank matrix estimation. A primary focus is on understanding the statistical and computational limitations. From a practical perspective, understanding such limitations not only provides practitioners with guidance on algorithm selection, but also in some cases spurs the development of cutting-edge methodologies which improve on the state of the art. Within this theme, this dissertation explores and partially answers the following two questions: (1) Given a large-scale low-rank matrix corrupted by random noise, how much information can we accurately infer from the limited observations? (2) How do restrictions on computational resources affect information retrieval? A secondary focus of this dissertation is on developing algorithms that sample from the posterior in the context of low-rank matrix estimation. A standard machinery to fulfill this task is based on Markov Chain Monte Carlo (MCMC) algorithms. However, rigorous guarantees are often difficult to obtain for MCMC algorithms of common use. This dissertation contributes to this line of work from an alternative perspective: We propose an alternative class of efficient algorithms based on diffusion processes that come with rigorous guarantee. This dissertation is organized as follows: We describe the problem in Chapter 1. Chapter 2 studies low-rank matrix estimation from an information-theoretic perspective, and Chapter 3-4 analyzes the effects of limited computational resource. In Chapter 5, we design a sampling algorithm that works well with the low-rank model. Standalone versions of each chapter can be found in [154, 50, 151]
Statistical explorations of deterministic functions
Keywords: Engineering.

In this thesis, we discuss several ways that statistical approaches can be used to investigate black box functions. In the first part of the thesis, we consider the mean dimension, a statistical property of functions concerning how important the interactions between the inputs matter. We show that the mean dimension for some functions, like Gaussian radial basis functions or ridge functions, can vary widely while other functions, like multiquadric radial basis functions, converge to one under weak conditions. In the second part of the talk, we consider probing neural networks, an strategy in which we examine the intermediate outputs as a way to learn more about the mechanics of the overall network. By using tSNE and a class-specific analogue of principle components, we can visualize the progression of how the classifications are made. Further examination are made with tours, an visualization technique that animates interpolations between pairs of projections