VictorJuiz/thesis huggingface dataset:
Insights on Alzheimer’s disease etiology from network approaches in healthy aging

The etiology of Alzheimer’s disease involves the presymptomatic development and progression of amyloid-β and tau in healthy aging. Amyloid-β and tau are naturally occurring proteins that can form abnormal aggregates – amyloid-β plaques and neurofibrillary tangles – which constitute the pathological hallmarks of Alzheimer’s disease. The initial formation of these aggregates occurs decades before the onset of cognitive symptoms, in individuals otherwise considered to be healthy and unimpaired. This dissertation hinges on in-vivo PET imaging of amyloid-β and tau in humans using PIB-PET and AV1451-PET to explore this presymptomatic phase of Alzheimer’s disease – when pathology is present without detectable symptoms. I place particular emphasis on amyloid-β pathology – understanding the factors that underlie vulnerability to amyloid-β as well as identifying the initial sources and progressive spread of amyloid-β pathology in healthy aging. My focus on amyloid-β is consistent with the predominant framework for Alzheimer’s disease, the amyloid cascade hypothesis, which contends that amyloid-β initiates a slow and ultimately deadly chain of events that results, decades later, in deteriorating memory and breakdown of cognition. In recognition that Alzheimer’s disease does not reflect a focal disorder, but rather network failure of large-scale brain systems, I adapt a network-based framework to account for the role of the complex interdependencies between distributed brain regions – of glucose metabolism from FDG-PET, of brain activity from resting-state functional MRI, and of amyloid-β from PIB-PET. Examining metabolic brain networks, I reveal widespread, highly systematic reorganization of glucose metabolism in old age – well beyond what has been revealed using other methods – that is more heterogeneous in those possessing both substantial amyloid-β and genetic risk for Alzheimer’s disease. Further, I demonstrate that the topology of early-life “metabolic inefficiency” – a novel metric that removes the potential association of glucose metabolism with highly connected hubs – explains the topology of amyloid-β in healthy aging. Finally, I provide evidence that very early amyloid-β accumulation, in those without substantial amyloid-β pathology, is multifocal and broadly distributed across brain networks – consistent with shared tissue vulnerability, not transneuronal spread, being the driving force of accumulation of amyloid-β pathology. These findings support the notion that shared tissue vulnerability of a metabolic origin drives widespread, systematic accumulation of amyloid-β in healthy aging. Future work should uncover the nature and origin of metabolic tissue vulnerability to amyloid-β, exploring the complex chain of events that drive widespread age-related reorganization – especially of cerebral glucose metabolism – and its links other age-related changes and the onset of pathological accumulation of amyloid-β.

Advisor: Jagust, William J
Post-Conflict Governance and Reconstruction: Public Goods, Policing, and Foreign Aid in Uganda

This dissertation investigates how post-conflict countries manage the reconstruction process to simultaneously consolidate political support and minimize new insurgencies. Focusing on subnational variation in the case of Uganda since 1986, I show how and when the central government has manipulated the provision of public goods (i.e., electricity), security (i.e., police infrastructure), and development (i.e., foreign aid projects) to achieve its twin political objectives of improving political support and minimizing violent threats to its rule. In making these distributive choices, I argue that incumbent rulers face what I call a “victor’s dilemma” in which they often must choose between allocating state resources to improve their short-term electoral interests or to invest in longer-term state-building projects that improve the state’s capacity. When armed and electoral opposition are concentrated in different geographic constituencies, resource-constrained governments will prioritize resource allocation disproportionately towards those areas considered a larger threat to their grip on power. On the whole, I demonstrate that Uganda’s allocation of essential state-related services and activities has been motivated largely by short-term interests to maintain political control rather than long-term state building efforts of reconstruction.

Advisor: Arriola, Leonardo R
Linear and Nonlinear Electromagnetic Responses in Topological Semimetals

The topological consequences of time reversal symmetry breaking in two dimensional electronic systems have been a focus of interest since the discovery of the quantum Hall effects. Similarly interesting phenomena arise from breaking inversion symmetry in three dimensional systems. For example, in Dirac and Weyl semimetals the inversion symmetry breaking allows for non-trivial topological states that contain symmetry-protected pairs of chiral gapless fermions. This thesis presents our work on the linear and nonlinear electromagnetic responses in topological semimetals using both a semiclassical Boltzmann equation approach and a full quantum mechanical approach. In the linear response, we find a ``gyrotropic magnetic effect" (GME) where the current density $j^B$ in a clean metal is induced by a slowly-varying magnetic field. It is shown that the experimental implications and microscopic origin of GME are both very different from the chiral magnetic effect (CME). We develop a systematic way to study general nonlinear electromagnetic responses in the low-frequency limit using a Floquet approach and we use it to study the circular photogalvanic effect (CPGE) and second-harmonic generation (SHG). Moreover, we derive a semiclassical formula for magnetoresistance in the weak field regime, which includes both the Berry curvature and the orbital magnetic moment. Our semiclassical result may explain the recent experimental observations on topological semimetals. In the end, we present our work on the Hall conductivity of insulators in a static inhomogeneous electric field and we discuss its relation to Hall viscosity.

Advisor: Moore, Joel E
Two Shots to Cancer Prevention: Improving Uptake of the Human Papillomavirus (HPV) Vaccine among Preadolescent Patients of a Primary Care Network

The current HPV vaccine uptake rate is not on track to achieve the Healthy People 2020 goal of 80%, nor attain the desired reduction in HPV-related cancer burden that could be achieved through optimal uptake. Increasing the HPV vaccination rates to 80% could prevent an additional 53,000 future cervical cancer cases in the U.S among girls who are 12 years or younger over the course of their lifetime as well as many additional cases of other cancers, precancers, and genital warts in both sexes. Understanding the current HPV vaccination trends, determining predictors of vaccination, and identifying the characteristics of primary care visits that are missed opportunities for HPV vaccination allows for the development of more effective strategies that can accelerate HPV vaccine uptake./nMy dissertation, comprised of three papers, examines the multiple levels of influence associated with uptake of the HPV vaccine among preadolescents ages 11-12 at the provider, patient/parent, and visit levels. The findings will help elucidate salient factors that influence the provider’s decision to recommend the HPV vaccine to their patients, the parent’s decision to accept and follow through with that recommendation, the processes of care that are required to ensure that recommendation is carried out, and the relationships among these various factors. The first and second paper utilizes electronic medical records and administrative data to identify: 1) patient and provider factors associated with HPV vaccine uptake and 2) patient, provider and visit factors associated with missed clinical opportunities to recommend and administer the HPV vaccine. The third paper explores the clinic, primary care team and parent level factors that affect uptake of the HPV vaccine through semi-structured key-informant interviews./nThe primary conclusions from this research highlight the importance of developing and implementing multi-level interventions that engage parents, all clinic staff (e.g. providers and support staff) and health care systems. Education, training and communication for HPV vaccines should focus on emphasizing the importance of timely vaccinations, bundling the HPV vaccines with other vaccines due at the same time, and ensuring that all clinic staff provide a strong and consistent HPV vaccination recommendation.

Advisor: Bloom, Joan R
Foundations of Strongly Lensed Supernova Cosmology

This dissertation presents solutions to some problems associated with using strongly gravitationally lensed supernovae (gLSNe) to measure the cosmological parameters. Chief among these are new methods for finding gLSNe and extracting their time delays in the presence of microlensing. The first of these results increased the expected gLSN yields of the Zwicky Transient Facility and the Large Synoptic Survey Telescope, upcoming wide-field optical imaging surveys, by an order of magnitude. The latter of these results involved performing simulations of radiation transport in supernova atmospheres. These simulations provided evidence that some Type Ia supernovae come from sub-Chandrasekhar mass progenitors.

Advisor: Nugent, Peter E
Kasen, Daniel N
Leveraging deep neural networks to study human cognition

The majority of computational theories of inductive processes in psychology derive from small-scale experiments with simple stimuli that are easy to represent. However, real-world stimuli are complex, hard to represent efficiently, and likely require very different cognitive strategies to cope with. Indeed, the difficulty of such tasks are part of what make humans so impressive, yet methodological resources for modeling their solutions are limited. This presents a fundamental challenge to the precision of psychology as a science, especially if traditional laboratory methods fail to generalize. Recently, a number of computationally tractable, data-driven methods such as deep neural networks have emerged in machine learning for deriving useful representations of complex perceptual stimuli, but they are explicitly optimized in service to engineering objectives rather than modeling human cognition. It has remained unclear to what extent engineering models, while often state-of-the-art in terms of human-level task performance, can be leveraged to model, predict, and understand humans./nIn the following, I outline a methodology by which psychological research can confidently leverage representations learned by deep neural networks to model and predict complex human behavior, potentially extending the scope of the field. In Chapter 1, I discuss the challenges to ecological validity in the laboratory that may be partially circumvented by technological advances and trends in machine learning, and weigh the advantages and disadvantages of bootstrapping from largely uninterpretable models. In Chapter 2, I contrast methods from psychology and machine learning for representing complex stimuli like images. Chapter 3 provides a first case study of applying deep neural networks to predict whether objects in a large database of images will be remembered by humans. Chapter 4 provides the central argument for using representations from deep neural networks as proxies for human psychological representations in general. To do this, I establish and demonstrate methods for quantifying their correspondence, improving their correspondence with minimal cost, and applying the result to the modeling of downstream cognitive processes. Building on this, Chapter 5 develops a method for modeling human subjective probability over deep representations in order to capture multimodal mental visual concepts such as "landscape". Finally, in Chapter 6, I discuss the implications of the overall paradigm espoused in the current work, along with the most crucial challenges ahead and potential ways forward. The overall endeavor is almost certainly a stepping stone to methods that may look very different in the near future, as the gains in leveraging machine learning methods are consolidated and made more interpretable/useful. The hope is that a synergy can be formed between the two fields, each bootstrapping and learning from the other.

Advisor: Griffiths, Thomas L
A Material Point Method Framework for the Simulation of Extrusion-Based Additive Manufacturing Processes

Additive Manufacturing (AM), also known as 3D Printing (3DP) or rapid prototyping, is defined as building three-dimensional objects by adding materials in a layer-by-layer fashion. Although AM technologies have been developed since the late 1980's, they have garnered academic and industrial interest in the past ten years due to its wide application in different industrial fields such as automotive, aerospace, biomedical, architecture, and medicine. Currently, AM is seen as a clever solution in a more and more complex manufacturing environment, specifically in terms of customization, multifunctionality, innovative design, and geometry. Many industrial companies are now trying to improve their manufacturing capabilities by adopting different AM technologies to fabricate their products./nIn this study, a computational framework is developed to optimize process parameters and simulate residual stresses in extrusion-based additive manufacturing processes. A particle-based method, namely the material point method (MPM) is adopted for simulations. The proposed numerical framework has two main parts. In the first part, the extrusion behavior of print material is simulated. Here, the fluid flow is modeled as an incompressible viscous fluid with the thermal changes due to cooling accounted for the simulations. Moreover, the main MPM code is coupled with the level set method and the ghost fluid method to track the free surface and apply the free surface boundary conditions. In the second part, a thermo-mechanical model is solved to investigate the cooling-induced residual stresses of extruded material. The applicability of the proposed numerical framework is first validated by solving select representative problems and comparing with other numerical methods. Then, it is applied to the extrusion-based manufacturing processes. The effects of different process parameters (nozzle velocity, extrusion velocity, nozzle temperature, etc.) are investigated by adopting a parametric study.

Advisor: Zohdi, Tarek I
Identification of Olfactory Stem Cell Lineage Trajectories and Cell State Transitions

Adult stem cells maintain structure and function of tissues under widely varying conditions, but the molecular and cellular mechanisms that underlie the development of diverse cell types during either tissue maintenance or injury-induced regeneration remain incompletely understood. This dissertation combines lineage tracing with single-cell RNA sequencing and assay for transposase-accessible chromatin (ATAC) sequencing to address the regulation of these phenomena in the olfactory epithelium, one of only a few sites of ongoing adult neurogenesis./nOlfactory stem cells, known as horizontal basal cells (HBCs), contribute to both maintenance and regeneration of the tissue. First, we consider the role of HBCs in homeostatic conditions. Classifying cells on the basis of gene expression, we identify three lineage trajectories arising from HBCs via two developmental bifurcations. We find that sustentacular cells arise by direct fate conversion of HBCs without cell division, as predicted by single-cell RNA-sequencing and validated by clonal lineage tracing. We also establish that multipotency of the HBC population arises from unipotent fate decisions of individual HBCs. Finally, single-cell RNA-sequencing indicates that canonical Wnt signaling promotes neuronal fate choices in HBCs, which we validate in vivo with gain-of-function and loss-of-function experiments./nSecond, we consider the contribution of HBCs to injury-induced regeneration of the olfactory epithelium, and whether the HBC injury response differs from homeostatic maintenance. We discovered activated olfactory stem cell states that are both transient and unique to regeneration. The activated stem cells express genes associated with epithelial wound repair in other stem cell niches. These cells are also heterogeneous, giving rise to multiple lineages, including renewed HBCs. Moreover, renewed HBCs are themselves competent to produce differentiated cell types, and do so in a second wave of differentiation several days after injury, bypassing the transient activated states associated with the earliest stages of regeneration./nFinally, we begin investigation of the priming of quiescent HBCs for rapid transcriptional response to injury, using ATAC sequencing to query chromatin accessibility. We find the HBCs to have broadly accessible chromatin at gene promoters corresponding to more differentiated cells in the olfactory lineage, but they maintain a closed conformation over the coding regions of genes that are not actively expressed./nTaken together, these findings contribute to our understanding of how neurogenesis and regeneration are accomplished in a fully-developed, adult sensory tissue. Our integration of single-cell genomics with in vivo lineage tracing lays the groundwork for investigating outstanding questions relating to the development, maintenance, and repair of other tissues. This work may ultimately aid in the development of stem-cell based therapies to replace specialized cell types and tissues lost to damage and disease.

Advisor: Ngai, John
Characterizing rare and transient conformations of proteins using amide hydrogen exchange and thiol exchange

The dynamic nature of proteins is often an underappreciated aspect of biology necessary for understanding protein function and turnover in the cell. This is in part because within the multitude of states that a protein occupies throughout its lifetime, at most a few are significantly populated and have long enough lifetimes to study using traditional structural biology methods. In this thesis, I develop and explore experimental methods (amide hydrogen-deuterium exchange and cysteine labeling) to selectively label and characterize these rare and transient conformations methods. Specifically, I used cysteine labeling as a means to validate computational predictions of rare conformational fluctuations that expose potential drug-binding sites, I used pulse-labeling amide hydrogen exchange coupled with mass spectrometry detection to identify intermediates formed during protein folding and developed new data analysis procedures for analyzing hydrogen exchange rates measured determined by mass spectrometry on proteolytic fragments./nThe first project used thiol labeling rates to validate computational predictions of cryptic binding sites. Analysis of millisecond-long molecular dynamics (MD) simulations of β-lactamase uncovered several potential conformational fluctuations that expose hidden, or cryptic binding pockets. Targeting these rare conformations presents a potential avenue for drug development. I validated the existence of these pockets by introducing cysteine residues in select positions and characterizing their accessibility to chemical modifications. In addition to validating the existence of these fluctuations, modification of cysteines at these sites modulates activity allosterically./nIn the second project, I used pulsed labeling hydrogen exchange to follow the folding pathway of a protein family over evolutionary time (>3 billion years). I determined the conformations populated during folding (folding intermediates) for a family of RNase H proteins, including two extant and seven ancestral proteins. Each of these proteins was shown to populate a similar folding intermediate prior to the rate-limiting step in folding; however, the details of the steps leading up to this intermediate varied. We further showed that we can alter these early folding steps for a given protein using rationally designed mutations./nThe third project compared and characterized the refolding and co-translational folding pathway of the protein HaloTag. We found that HaloTag aggregates during refolding but not during cotranslational folding, and it adopts at least one intermediate during refolding that is suppressed during cotranslational folding./nFinally, in the fourth project I developed a modification of a recently published approach for analyzing data from mass spectrometry-based detection of hydrogen exchange. This modification allows me to obtain multiple protection factors per peptide monitored and obtain a quantitative measurement of the free energy of hydrogen exchange in different regions of the protein.

Advisor: Marqusee, Susan
High Dimensional Statistical and Computational Methods for Knowledge Discovery and Data Mining in Biomedical Data

Biomedical sciences have seen radical growth in recent decades, inspired by a plethora of technological breakthroughs, of which sequencing and imaging are two particular technologies whose advancements have enabled scientists to explore areas that were previously impossible. High-throughput sequencing, for instance, is perhaps one of the most groundbreaking advancements in biology; it allows genetic material (e.g DNA, RNA, proteins) to be identified cheaply and accurately, granting investigators unprecedented insight into the inner workings of the genome—the blueprint of all living organisms. Therefore, high-throughput technology, and in recent years single cell sequencing in particular, has become the cornerstone of genetics research. Sequencing can reveal the genomic location of a gene, but often times the physical locations where a gene is expressed in a cell are also biologically meaningful, and with imaging technologies like florescent tagging and powerful electronic microscopes, this information is now possible to ascertain. Of course, the field of imaging technology is vast, and other areas have also seen tremendous leaps forward. For instance, with the development of CT scans and better PET tracers, researchers now have an in vivo view of the metabolic activities in organs, allowing researchers to monitor and study diseases as they progress, thus generating an unprecedented level of understanding of devastating conditions such as Alzheimer’s./nIn response to the profusion of quality data, statistical techniques that attempts to analyze/nthese data have also flourished into the field of computational biology and statistical/ngenomics, which has since emerged as an indispensable part of scientific discovery pipeline as well as an important interface between statistics/machine learning and biomedical sciences. In this thesis we examine applications of statistical techniques to three vastly different data sets. In the first work we analyze data from PET brain scans of Alzheimer’s Disease patients and explore how linear mixed effect model offers a powerful and flexible alternative for gauging β-Amyloid accumulation. The data we study in the second work consists of singlecell RNAseq data from mouse embryonic, human embryonic, and human cancer cells, from which we introduce a biclustering method to simultaneously extract biologically relevant cell clusters and genes that are active in those clusters. In the third work, multiple sources of biological databases consisting of both imaging and sequencing data were leveraged into a machine learning problem, on which random forest is applied to mine organogenesis master regulators.

Advisor: Huang, Haiyan