VictorJuiz/thesis huggingface dataset:
Development, evaluation, and translation of novel neuroimaging probes and techniques to investigate neurodegenerative diseases

Despite decades of research, neurodegenerative diseases remain poorly understood and without effective disease-modifying treatment or prevention. To develop successful treatment options and understand disease pathogenesis, we need better tools and techniques to monitor patients and track disease progression in vivo. Medical imaging, including PET and MRI, can non-invasively help clinicians assess treatment outcomes and diagnose disease earlier while learning more about the relationship between multiple underlying pathologies. To this end, I have worked to develop, evaluate, and translate a novel neuroinflammatory PET imaging probe aimed at visualizing reactive microglia and macrophages in neurodegenerative diseases. Further, I have developed and applied 7T MRI motion correction technology and PET/MRI advanced quantitative image processing techniques towards discovering novel image-based biomarkers of Alzheimer's Disease. Together, these novel efforts will allow scientists and clinicians to noninvasively investigate neurodegenerative disease-specific pathologies in vivo, aiding in patient stratification, earlier diagnosis, and improved treatment outcomes

Keywords: Bioengineering.
Advisor: James Michelle (Michelle Louise)
Zeineh Michael
Camarillo David
Development, feasibility, and acceptability of a novel augmented reality medical simulator

Given the expanding field of education within medicine, the purpose of this dissertation is to introduce an augmented reality (AR) medical simulator to the medical education community. This dissertation will describe the simulator development, feasibility, and acceptability to medical students and medical trainees. The following series of studies is presented about the AR Simulator. First, we review the learning theories behind experiential education that form the foundation for the area of study (Chapter 2). Then, a technical description of the development of the gaze tracking was conducted to validate the software by which future learning outcomes are judged (Chapter 3). Second, a qualitative study announced the acceptability and feasibility of a multiplayer AR medical simulator to medical students (Chapter 4). Following the qualitative study, two mixed method studies of the effectiveness of the simulator were conducted (Chapter 5 and 6). These mixed method investigations explore engagement, limitations, and advantages of the learning tool. The results of the investigations demonstrate that the AR simulator is technically facile, accepted by learnters, and has the potential to transform traditional medical simulation

Keywords: Education.
Advisor: Domingue Ben
Haber Nick
Stevens Mitchell L.
Development of 3D printable hybrid biomaterials for musculoskeletal tissue engineering

This thesis work uses a hybrid tissue engineering approach to improve the biomaterial and biologic delivery for musculoskeletal tissue engineering applications. A novel methodology is first presented for synthesizing a tunable, bioactive polycaprolactone-based composite with nanoparticle features, which can be 3D printed for high customizability. A new substrate coating technology, Hybrid Tissue Engineering Constructs (HyTEC), is then introduced to demonstrate the importance of sustained biologics release in bone tissue repair in both small and large animal models.

Keywords: Engineering.
Mechanical Engineering.
Advisor: Levenston Marc Elliot
Yang Yunzhi Peter
Skylar-Scott Mark
Development of a Causal Connectome in Major Depressive Disorder Using Machine Learning

Major depressive disorder (MDD) is the leading cause of disability worldwide, affecting more than 300 million people [1, 2]. While the first and primary approach to treating MDD is with antidepressant medications, transcranial magnetic stimulation (TMS) is an emerging non-invasive tool for stimulating brain networks and has proven to be effective for treatment of MDD [3, 4]. Currently, TMS for MDD targets the depression pathophysiology at the left DLPFC [12]. However, relatively low response rates have been observed when TMS is administered in the DLPFC, with other brain regions such as the medial prefrontal cortex (MPFC) or the anterior insula showing promise [13-15]. Therefore, we analyze functional connectivity networks to 1) better understand the pathophysiology of MDD and 2) elucidate potential novel treatment targets. We record TMS-evoked potentials (TEP) from 6 regions of interests (ROIs) that broadly covered the brain for 38 medication-free healthy patients and 27 patients with MDD after single pulses of TMS in 26 stimulation sites across the brain. We hypothesized that the local (left prefrontal) early TEP (20-80ms) after stimulation is more effective at differentiating between patients with MDD and healthy controls based on previous studies [30, 31]. However, we found that the early TEPs (20-80ms) were not able to differentiate between the MDD subgroup and the healthy patient subgroup. Furthermore, our results demonstrated that brain responses from TMS were not localized in the DLPFC, but at various regions of the brain such as the central and occipital regions. There was spatial variance identified in these connectivity networks between stimulation sites and ROIs widespread across the brain. Together, this work reveals that there is greater spatial and temporal diversity in connectivity networks of MDD.

Keywords: Bioengineering
Magnetic brain stimulation
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Thesis
Advisor: Keller Corey
Deisseroth Karl
Lee Jin Hyung
Development of Algorithms to Determine Genetic Markers for Immunotherapy Response in Melanoma

We hope to characterize response to immune checkpoint inhibitor therapy by distinguishing between genomic correlates of responders versus non responders to this therapy and validating this computational technique with data from robust CRISPR screens in in vivo models. Such characterization seeks to assist in the creation of therapeutic supplements to existing immunotherapies and thus improve response in patients. To that end, we aim to better understand the genetic underpinnings of why some melanoma patients respond to CTLA-4 blockade therapy and use results to guide development of future therapeutics which improve melanoma prognosis. Existing literature characterizes genetic correlates to blockade therapy, but none incorporate in vitro models for response as our methodology does. This work seeks to build an algorithm for prediction of such genetic markers of immune checkpoint inhibitor therapy response and to demonstrate that this methodology is applicable in in vivo contexts. Such an algorithm could then be further applied to a wide variety of diseases and therapies in order to more quickly and accurately characterize genetic markers of the response, and to more rapidly develop targeted therapeutics that improves clinical outcomes. Ultimately, we see that our proposed algorithm for such in silico analysis successfully predicts markers that perform well in in vivo experiments. We hope this leads to more rapid and informed development of potential therapeutics to improve response to immune checkpoint inhibitors, an active area of research in oncology and immunology today.

Keywords: Melanoma
Immunology
Computational biology
Text
Thesis
Advisor: Satpathy Ansuman
Lundberg Emma
The development of entrepreneurial skills and motivation : the role of institutions

Recognizing the pivotal role of entrepreneurship in driving innovation and economic growth, a spectrum of institutions, encompassing government bodies, universities, and business accelerators, has formulated diverse strategies to promote entrepreneurship. These efforts involve various facets, such as formal educational programs, mentoring initiatives, regulatory reforms, and infrastructural support. These institutional endeavors collectively aim to cultivate entrepreneurial skills and motivation among aspiring entrepreneurs. Though extant academic work explores the interaction of institutions and entrepreneurship from diverse angles and provides significant insights, it remains unclear whether and how institutions actually help in developing entrepreneurial skills and motivation. Across three tightly-linked papers, this dissertation addresses this gap. Focusing on accelerators, an emerging institution in the entrepreneurship ecosystem, the first paper explores its impact on an entrepreneur's subsequent professional engagements and long-term impact on entrepreneurial ecosystem. I use a regression discontinuity design on accepted and almost accepted participants from a unique data set of 30,000 applicants from Startup Chile. The second paper explores the impact of bureaucratic institutions on entrepreneurial opportunity recognition in a randomized field experiment in Bangladesh. The third paper explores the strategies and performance of institutional entrepreneurs analyzing more than 8000 entrepreneurs from an Alumni survey dataset. Together, these papers help us address endogeneity issues in prior works and make several contributions to literature on entrepreneurship, and organization theory. It also offers valuable insights for management practitioners and policymakers.

Keywords: Engineering.
Management Science and Engineering.
Advisor: Eesley Charles
Eisenhardt Kathleen
Katila Riitta
Development of Flux Reconstruction in AD-GPRS

Flux reconstruction is an integral component of multiscale methods in simulating multiphase flow and transport in porous media. This report details the algorithmic development and implementation of flux reconstruction for incompressible multiphase flow and transport in SUETRI-B's research simulator, AD-GPRS. Starting with the reduced pressure system, a splitting scheme is proposed. The scheme forms a local Neumann problem for each coarse node by removing coupling terms between coarse cells. Total fluxes computed using multiscale pressure are enforced on the boundaries of the coarse cells and multiscale pressure increment is fixed at the coarse nodes to have well-posed Neumann problems with a unique solution for each. The algorithm is tested on a homogeneous problem to illustrate the various steps of the flux reconstruction algorithm. Permeability field from the top layer of SPE-10 Model 2 is used to test the applicability of the proposed algorithm for highly heterogeneous porous media. Numerical results demonstrate that fine scale divergence is successfully reduced, up to machine precision in most of the spatial domain, from the total flux field when the given flux reconstruction step is implemented between the flow and transport problems. In AD-GPRS, convergence of the outer sequential loop is enforced by running an extra sequential loop and declaring convergence if both the pressure and transport inner loops converge with zero Newton iterations. However, extremely slow sequential convergence issue is faced when the reconstruction step is used during the extra sequential loop. By skipping the reconstruction step during the extra sequential loops, the convergence issue is resolved. Finally, modifications to the proposed algorithm are put forward to extend application to compressible multiphase flow and transport problems in porous media.

Keywords: flux reconstruction
AD-GPRS
Multiscale methods
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Thesis
Diamond : an experimental determination of diamond nucleation, nuclear magnetic resonance spectroscopy of diamondoids, and development of a novel technique for color center fabrication

Diamond is a useful material with numerous applications, classically owing to its high hardness and strength as a material. However as the understanding of the material has deepened and diamond nanomaterials have been further developed, new applications have been developed such as diamond electronics, magnetometry, and quantum computing. In this work, we utilize several methods to gain a more atomistic understanding of diamond. First, through the use of diamondoid seeds, diamond nanoparticles of known size and shape, the nucleation landscape of diamond growth during chemical vapor deposition (CVD) was experimentally determined. By measuring the resultant nanoparticle density after seeded growths, the critical nucleus size and interfacial energy were calculated and found to be orders of magnitude lower than classical predictions. Secondly, nuclear magnetic resonance (NMR) spectroscopy was performed on pentamantane isomers. NMR results on these diamondoid molecules were able to reveal effects to the electronic structure due to non-covalent interactions that could have important implications for diamond surfaces and surface defects. Lastly, a novel technique for generating nitrogen-vacancy (NV) centers in diamond was demonstrated. Through the use of metal vacancy-injection (VI) films, vacancies were able to be doped into diamond substrates to enhance the NV center density. Furthermore, it was shown that through patterning of these VI films, the areas of vacancy doping and resulting NV centers could be patterned in the diamond substrate. This technique offers a reliable and scalable method to create high-quality color centers while limiting lattice damage.

Keywords: Engineering.
Materials Science and Engineering.
Advisor: Melosh Nicholas A.
Mukherjee Kunal (Researcher in materials science)
Vuckovic Jelena
Diasporic Lag in Memory-Making: Chinese American Historiographies of the Second Sino-Japanese War

How do diaspora communities construct histories of their homelands? My thesis compares diaspora and homeland understandings of a single historical moment: Japanese atrocities committed on Chinese soil and to Chinese peoples during the Second Sino-Japanese War. While the literature on collective memory and intergenerational trauma focuses attention on temporal inheritances of memory, few have considered how memory may be crystallized and transmitted across space, from homelands to diaspora. While traveling through China in 2021, I observed that second-generation Chinese Americans (those born in the United States) harbored greater and less nuanced forms of anti-Japanese anger than mainland Chinese youth of the same age, despite the former’s spatial and temporal distance from the mainland. Through both an original survey study and in-depth interviews, this thesis assesses whether these differences in historical memory are observed across a broader and more representative population, and also investigates the causes behind these differences in historical memory. Survey data demonstrates that third-generation Chinese Americans tend to hold greater degrees of anti-Japanese anger than do their first- and second-generation counterparts, and that second-generation Chinese Americans tend to hold different forms of anti-Japanese anger according to the year of their parents’ migration. Interview data suggests that several factors contribute to this difference, including a drive among second- and third-generation Chinese Americans to retrieve knowledge of Chinese history and heritage; a tendency among first-generation parents to educate their second-generation children with outdated historical narratives; and the surprising salience of mainland war films in Chinese American understandings of the war. However, survey and interview data indicate that Chinese Americans’ experiences with alienation, racism, and outsidership in the U.S. lead them to question both U.S. and Chinese narratives of the war, and to extend sympathy towards the contemporary Japanese people. Ultimately, this thesis documents how diasporic communities preserve, distort, and construct memories, with an agency that is often discounted in broader geopolitical discourse.

Keywords: Sino-Japanese War (1937-1945)
Collective memory
Chinese Americans
Collective trauma
World War II
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Thesis
Differential carbohydrate metabolism shapes colonization by bacteroidetes

Industrialization has transformed the gut microbiota, reducing the prevalence of Prevotella relative to Bacteroides. Here, we isolate Bacteroides and Segatella strains from the microbiota of Hadza hunter-gatherers of Tanzania, a population with high levels of Prevotella. We demonstrate that plant-derived microbiota-accessible carbohydrates (MACs) are required for persistence of Segatella copri but not Bacteroides thetaiotaomicron in vivo. Differences in carbohydrate metabolism gene content, expression, and in vitro growth reveal that Hadza Segatella strains specialize in degrading plant carbohydrates, while Hadza Bacteroides isolates use both plant and host-derived carbohydrates, a difference mirrored in Bacteroides from non-Hadza populations. When competing directly, S. copri requires plant-derived MACs to maintain colonization in the presence of Bacteroides thetaiotaomicron, as a no MAC diet eliminates S. copri colonization. Segatella's reliance on plant-derived MACs and Bacteroides' ability to use host mucus carbohydrates could explain the reduced prevalence of Prevotella in populations consuming a low-MAC, industrialized diet.

Keywords: Medicine.
Microbiology and Immunology.
Advisor: Sonnenburg Erica
Sonnenburg Justin
Bhatt Ami
Monack Denise
Schneider David
Digital Panopticons: The Carceral Logics of Surveillance in Neighborhood Safety Apps

As technology continues to evolve and become more accessible, home security systems have become increasingly common. These systems offer homeowners a sense of safety and peace of mind, as they provide real-time monitoring and the ability to respond to potential threats. With the advent of smart home technology, homeowners can control their security systems remotely, receiving alerts and even controlling access to their homes through their mobile devices. In recent years, one notable evolution in home security is the rise in popularity of neighborhood social networking sites that promise to help make the user feel safer. The most notable apps in this category include Nextdoor, Neighbors by Ring, and Citizen. These social networking apps allow users to share videos, photos, and text posts with other camera owners that live nearby. 
In this thesis, I will be exploring how this genre of consumer surveillance fits into a broader history of surveillance being used as a tool for the control and classification of marginalized communities. Specifically, my research seeks to highlight the carceral logic underlying these technologies and the assumptions they create about what is needed to ensure true public safety. The purpose is to analyze how surveillance practices in the US have become necessitated through inflated fears of crime and what implications this offers as we consider how technology industries continue to innovate and develop new apparatuses for surveillance.
My research analyzes marketing videos by Ring used to promote its reveals three key findings. First, Neighbors seeks to amplify fear of crime, regardless of actual crime rates in a neighborhood and causes hyper-vigilance. Second, the marketing of Ring’s Neighbors app encourages an incarceration mindset in the domestic spheres of the home and neighborhood that is accompanied by unnecessary policing and criminal punishment. Finally, Amazon’s marketing of Ring frames surveillance as a public service.

Keywords: Consumer surveillance
Carceral
Text
Thesis
Advisor: King Jennifer
Dimensionality reduction of embedded boundary models for nonlinear fluid-structure interaction

Embedded (sometimes immersed) boundary methods (EBMs) for CFD and fluid-structure interaction (FSI) -- also known as immersed boundary methods, Cartesian methods, or fictitious domain methods -- are the most robust solution methods for flow problems past obstacles that undergo large motions, deformations, shape changes, and/or surface topology changes. They can also introduce a high degree of automation in the task of mesh generation and significant flexibility in the meshing of complex geometries. However, despite massive advancements in compute power, the application of CFD- or FSI-based EBMs to the simulation of many realistic engineering systems can still take days or weeks on many cores of a supercomputer, rendering the use of such models in many-query applications challenging, impractical or simply infeasible. The application of projection-based model order reduction (PMOR) to nonlinear CFD and FSI models has been shown in recent years to produce low-dimensional projection-based reduced order models (PROMs) which can be queried in real-time, without sacrificing the accuracy of the underlying high-fidelity, high-dimensional computational model. However, the application of PMOR to EBMs for CFD and FSI remains a challenge, primarily because EBMs dynamically partition the computational fluid domain into real and fictitious subdomains. Consequently, despite their myriad benefits, EBMs hamper the acceleration of the solution of computationally intensive problems by these types of physics-based surrogate models. This dissertation addresses several issues stemming from the real/fictitious partitioning of the computational fluid domain to pave the way for the PMOR of EBMs. Specifically, it motivates, presents, and illustrates a computational framework for constructing EBM-PROMs that incorporates state-of-the-art methodologies for mitigating computational bottlenecks associated with strong nonlinearities in the underlying high-dimensional model. This framework includes a weighted low-rank matrix factorization approach for computing suitable reduced-order bases; and adaptations of the energy-conserving sampling and weighting hyperreduction procedure as well as the piecewise-affine approximation method that account for the real/fictitious dynamic partitioning of the nodes of the embedding CFD mesh. It also employs an adaptive sampling approach for choosing an optimal set of parameter points on which to train a parametric EBM-PROM, based on a novel error indicator. Finally, the performance of the proposed computational framework for the PMOR of embedded boundary models is investigated for two representative, unsteady, nonlinear, turbulent flow problems that necessitate the use of an EBM; as well as two prototypical steady-state, shape-parametric studies of relevance to MDAO.

Keywords: Engineering.
Aeronautics and Astronautics.
Advisor: Farhat Charbel
Alonso Juan
Kochenderfer Mykel
Direct & indirect effects of extreme weather in a warming world

Extreme weather seems to be becoming more common and more extreme. A new literature on production networks shows that direct effects from extreme weather spread over supply chains and lead to indirect effects at distant companies. However, existing papers have not driven home their relevance for climate damage estimates. We examine the direct and indirect effects of extreme heat and extreme precipitation on public companies in the U.S. We use 20 years of quarterly financial data and more than 600,000 firm location records to econometrically estimate the direct effect of extreme weather at a company's establishments. We next estimate the indirect effect of extreme weather at a company's suppliers' establishments. We use data from the latest Climate Model Intercomparison Project, CMIP6, to project changes in the incidence of extreme heat and extreme precipitation at company establishments over the next twenty years. We find that the projected indirect effects are roughly half the size of the projected direct effects. We then estimate the direct effects for different subgroups, and we compare the effects of extremes with the effects of warmer or wetter weather than average. We find substantial heterogeneity in how different types of firms are affected by extreme weather. We close by reviewing climate modeling for near-term predictions as the climate changes from its historical norms. We use the climate projections data from our firm analysis to review the accuracy and uncertainty of these models, with the goal of helping provide an operational guide to individuals who will be using these climate data for adaptation and planning

Keywords: Engineering.
Management Science and Engineering.
Advisor: Weyant John P. (John Peter)
Gillingham Kenneth
Sweeney James L.