VictorJuiz/thesis huggingface dataset:
Developing methods for simulating reactive condensed-phase chemical systems via quantum dynamics and machine learning
Keywords: Humanities and Sciences.
Chemistry.

This thesis explores a variety of algorithmic advances that enable the ab initio simulation of condensed-phase chemical systems at reduced computational cost and at the time and length scales required to statistically converge physical properties. We first employ multiple time scale techniques to obtain nanosecond-scale ab initio molecular dynamics simulations of excess protons in two hydrogen-bonded liquids, imidazole and 1,2,3-triazole, using density functional theory (DFT) at the generalized gradient approximation (GGA) level of accuracy. These liquids transport protons via a structural mechanism similar to that of water, and their chemical motifs are present in systems ranging from biological proton channels to proton exchange membrane fuel cells. Using a time correlation function analysis, we decompose the proton transport mechanism into three first-order processes with distinct characteristic timescales, thereby providing a fuller picture of the associated structural transport mechanism. We also uncover two mechanisms that slow down the rate of proton transfer in 1,2,3-triazole relative to imidazole and thus explain their experimentally observed 10-fold difference in proton conductivity. We then develop machine-learned potentials (MLPs) capable of modeling excess protons and hydroxide ions in liquid water at the GGA and hybrid DFT levels of accuracy and use them to perform multi-nanosecond classical and path integral simulations of proton defects in water at a fraction of the cost of the corresponding ab initio simulations. The transport of proton defects in water is of particular interest because it underlies several important chemical and biological processes. Our results show that the MLPs are able to reproduce ab initio trends and converge properties such as the diffusion coefficients of excess protons and hydroxide ions. We use the multi-nanosecond MLP trajectories to study with unprecedented statistical accuracy the role of hypercoordination in the transport mechanism of the hydroxide ion and provide further evidence for the asymmetry in diffusion between excess protons and hydroxide ions. Finally, we develop a computationally efficient quantum-classical Ehrenfest approach based on pure quantum states for the accurate simulation of linear and nonlinear electronic spectra. We demonstrate the performance of our method by showing that it is able to quantitatively capture the exact linear, 2D electronic spectroscopy, and pump--probe spectra for a Frenkel exciton model in slow bath regimes and is even able to reproduce the main spectral features in fast bath regimes.
The development and analysis of United States and India wind energy atlases that account for multiple altitudes, wind speed thresholds, and exclusions
Keywords: Enevoldsen, Peter, degree committee member.
Engineering.

Climate change, air pollution, and energy security are significant challenges that this dissertation aims to help address. It does so by quantifying, for the first time, onshore and offshore wind resources at different relevant altitudes over both the United States and India, two of the largest global carbon emitters. Today, the United States is far short of meeting climate targets despite a rising penetration of onshore wind energy and a rapidly mobilizing offshore wind industry. India faces the challenges of meeting rising energy demand from a large and growing population, providing reliable and fully electrified energy, and eliminating its pollution and carbon emissions. Here, wind energy atlases were developed for the United States and India using Geographic Information System (GIS) mapping that accounts for technical, climate, environmental, and social exclusions. These exclusions delineate areas where wind farm development is not permitted due to land or marine conservation, infrastructure and safety requirements, existing use by other stakeholders, and climate and ocean restrictions such as wind speed and water depth thresholds. Informed by policy and regulation, setback distances are applied from possible turbine locations to fully account for siting constraints. The outcome is a series of maps illustrating the best places to develop wind farms. Wind energy potential is quantified for multiple hub heights between 100-250 m, with several wind speed threshold, wake loss, and turbine scenarios. These maps will increase certainty for resource planning, significantly expedite the wind farm siting process, reduce investment risk, decrease future project costs, and increase access to key data for energy planners. Findings indicate that, with all but the highest wind speed thresholds, more than sufficient area and potential are available to meet U.S. and India demand for all energy purposes by 2050 using wind energy alone. With economically viable wind speed thresholds and hub heights of modern wind turbines, ~27% of onshore and ~64% of offshore U.S. areas are available, while ~23% of onshore areas in India are available. This translates to 32 TW, 27 TW, and 6.5 TW of potential, respectively. Wind power availability is generally more sensitive to increasing wind speed thresholds than hub height. The U.S. offshore analysis presents an additional economic heatmap to indicate low-cost areas and a map of optimal turbine foundation type. The India onshore analysis quantifies maximum area and potential for repowering, as well as the co-locational opportunities for wind with solar PV resources. Atlases with this detail and scope will help bring wind energy ambitions to fruition, driving the U.S. and India closer to a clean, reliable, and equitable renewable energy system.
Development and deployment of machine learning in medicine
Keywords: Engineering.

Recent advances in machine learning have enabled important applications in medicine, where many critical tasks are tedious and time-consuming for clinicians to perform. This dissertation presents work on using machine learning for cardiology, pathology, and RNA sequencing. This dissertation begins with several applications of machine learning in cardiology, focusing on echocardiograms, or ultrasounds of the heart. Conventional assessment of echocardiograms requires tedious annotation by a human expert. First, I introduce EchoNet-Dynamic, an algorithm for assessing cardiac function from echocardiograms. EchoNet-Dynamic is then integrated into a clinical system and evaluated with a blinded randomized clinical trial. Extensions of the algorithm to pediatric patients and emergency department point-of-care echocardiograms are then presented. This dissertation then presents work applying machine learning to pathology and RNA sequencing. First, I present in silico-IHC, which predicts immunohistochemical stains from commonly available histochemically-stained tissue samples. Next, I present ST-Net, which combines RNA sequencing and pathology by estimating spatial transcriptomics measurements from microscopy images. Finally, I present CloudPred, which predicts patient phenotypes from single-cell RNA sequencing data
Development, evaluation, and translation of novel neuroimaging probes and techniques to investigate neurodegenerative diseases
Keywords: Bioengineering.

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
Development, feasibility, and acceptability of a novel augmented reality medical simulator
Keywords: Education.

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
Development of 3D printable hybrid biomaterials for musculoskeletal tissue engineering
Keywords: Engineering.
Mechanical 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.
Development of a Causal Connectome in Major Depressive Disorder Using Machine Learning
Keywords: Bioengineering
Magnetic brain stimulation
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Thesis

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.
Development of Algorithms to Determine Genetic Markers for Immunotherapy Response in Melanoma
Keywords: Melanoma
Immunology
Computational biology
Text
Thesis

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.
The development of entrepreneurial skills and motivation : the role of institutions
Keywords: Engineering.
Management Science and Engineering.

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.
Development of Flux Reconstruction in AD-GPRS
Keywords: flux reconstruction
AD-GPRS
Multiscale methods
Text
Thesis

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.
Diamond : an experimental determination of diamond nucleation, nuclear magnetic resonance spectroscopy of diamondoids, and development of a novel technique for color center fabrication
Keywords: Engineering.
Materials Science and Engineering.

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.
Diasporic Lag in Memory-Making: Chinese American Historiographies of the Second Sino-Japanese War
Keywords: Sino-Japanese War (1937-1945)
Collective memory
Chinese Americans
Collective trauma
World War II
Text
Thesis

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.
Differential carbohydrate metabolism shapes colonization by bacteroidetes
Keywords: Medicine.
Microbiology and Immunology.

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.