VictorJuiz/thesis huggingface dataset:
From deep sea to deep time : investigations into benthic marine biological nitrogen fixation and the origins of diazotrophy

Nitrogen-fixing microorganisms (i.e., diazotrophs) play a critical role in the marine environment. Through the reduction of nitrogen gas to ammonia, diazotrophs supply bioavailable nitrogen to ecosystems where productivity is nitrogen limited. Although the ecological and biogeochemical importance of diazotrophs in both the surface ocean and shallow marine sediments is well documented, we know comparatively little about diazotrophic communities in deep-sea sediments (greater than 200 m water depth). Recent work, however, suggests diazotrophs are active at some deep-sea sites. These findings call for additional investigations into the phylogenetic diversity and ecology of diazotrophs in the deep-sea benthos to assess the potential environmental controls on their activity. Additionally, since diazotrophy is the only known mechanism by which organisms offset ecosystem fixed nitrogen losses, understanding its evolutionary origins would offer insight into the evolution of a key component of Earth's nitrogen cycle. Interestingly, previous work has found that genes essential for nitrogen fixation are evolutionarily related to those in other geobiologically important metabolisms (e.g., photosynthesis, methanogenesis, the reductive acetyl-CoA pathway), but these evolutionary relationships remain unresolved. In this dissertation, I address these knowledge gaps by: (1) studying the phylogenetic diversity and ecology of diazotrophs (both active and inactive) in deep-sea sediments collected offshore Northern California and (2) investigating the evolutionary history of nitrogenase reductase (NifH), an essential protein in nitrogen fixation. In Chapter 1, I combine stable isotope and molecular techniques to identify active diazotrophs in a deep-sea sediment core collected from Monterey Canyon, California. In Chapter 2, I develop an open-source R package (Phylogenetic Placement for Inferring Taxonomy; PPIT) that assigns taxonomy to nifH sequences using phylogenetic placement and sequence identity approaches. In Chapter 3, I examine the distribution, taxonomic identity, and ecology of potential diazotrophs in sediments along a 267 km transect offshore San Francisco, California. I apply PPIT to nifH amplicon sequences recovered from continental shelf to abyssal plain and leverage paired geochemical measurements to identify environmental parameters influencing the composition of the nifH-containing community. In Chapter 4, I explore the evolutionary history of the protein family containing NifH using publicly available amino acid sequence data in phylogenetic and comparative sequence conservation analyses. Together, these studies significantly advance our understanding of diazotrophy in deep-sea sediments and link its evolution to the origins of ancient metalloclusters, pigments, and a self-organizing molecular system

Keywords: Earth System Science.
Advisor: Dekas Anne
Francis Christopher
Spormann Alfred M.
Welander Paula
From fire to water : how natural and anthropogenic metal(loid)s threaten ecosystem and human health

Metal(loid)s are ubiquitous in the environment, either naturally occurring (geogenic) in soil and sediments or introduced by human activity (anthropogenic), and impose threats to human and ecosystem health globally. Exposure to toxic metal(loid)s may transpire through consumption of contaminated groundwater or food, or through inhalation of metal-bearing particulate matter. In my dissertation, I explore known and unforeseen phenomena that drive physical and chemical changes in the environment and impact exposure risks of communities to metal(loid)s. I employ laboratory, field, and modeling approaches to (1) address the environmental processes that present hazards from geogenic As, U, and Cr exposure and improve predictions of regional threats, and (2) propose rapid field detection methods for anthropogenic Pb contamination to help minimize exposure. In Chapter 2, I demonstrate that wildfires catalyze the widespread production of toxic Cr(VI) in wildland areas depending on fire severity, geologic substrate, and ecosystem type. I found that Cr(VI) persists in wind-dispersible particulates composing smoke and post-fire dust and presents concern for respiratory exposure. In Chapters 3 and 4, I combine environmental variables reflecting fundamental biogeochemical insights and modeling techniques to predict regional groundwater hazards from three major geogenic contaminants: As, U, and Cr. Mobilization of these contaminants in soils and sediments can be triggered by changes in redox conditions, metal speciation, and pH that alter the solid-phase partitioning. Groundwater use can be proactively managed to protect groundwater quality. To this end, my thesis provides an ability to project where and why one or co-occurring contaminants threaten groundwater quality. Lastly, in Chapter 5, I show that a simple colorimetric test and portable X-ray fluorescence analysis effectively detect Pb and its chemical form in seven frequently adulterated spice types. The intentional addition of Pb in food products presents an acute exposure risk and growing public health concern. These methods help improve our ability to rapidly assess Pb adulteration in food and can be used to remove potential Pb sources from the global supply chain. Collectively, this work furthers our understanding of the biogeochemical cycling of metal(loid)s and drivers of contamination, while offering tools to help mitigate exposure to geogenic and anthropogenic metal(loid)s from air, water, and food through improved predictive capacity and rapid detection methods

Keywords: Earth System Science.
Advisor: Fendorf Scott
Dekas Anne
Gorelick Steven M.
Hansel Colleen
From Genomic Sequencing to Cerebral-Based Weaponry: The New Frontier of the People’s Liberation Army’s Military Technology

The People Republic of China’s 2020 “14th Five Year Plan” – the cornerstone for Chinese national defense posture and economic planning – is the first guiding document to highlight the strategic importance of biotechnology in China’s history. Yet there has been very little awareness or response among U.S. policymakers and experts to the Chinese military’s rapidly evolving biotechnology programs. This thesis is the first study to comprehensively evaluate the scope and status of the People’s Liberation Army’s (PLA) biotechnology capabilities, ranging from emerging biotechnologies to advanced tools already deployed from the Chinese arsenal. To understand the evolution of the PLA’s biotechnology programs and milestones, this thesis collected an original dataset of over 500 open-source Chinese language documents, ranging from Chinese Communist Party (CCP) white papers to Chinese military scientific journals and Chinese news publications. Through two case studies – an investigation of the PLA’s brain-computer interface capabilities and a study of China’s large-scale genomic sequencing programs – this thesis examines the PLA’s strategies for developing and deploying biotechnologies at scale. This analysis first concludes that late-stage partnerships between commercial Chinese biotechnology organizations and military research groups have been an effective avenue for the Chinese military to pursue rapid development of militarized biotechnology. Second, the CCP’s efforts to concentrate technological capabilities under centralized umbrella organizations have proven to be an efficient strategy for deployment. By more fully understanding the PLA’s biotechnology capabilities and deployment strategies, U.S. policymakers and research institutions can work together to better respond to this growing national security risk.

Keywords:  Center for International Security and Cooperation
Genomic Sequencing
Academy of Military Medical Sciences
Academy of Military Sciences
Military Technology
Biotechnology
Brain-Computer Interface
Command and Control
Command, Control, Communications, Computers
Cyberspace Agency of China
Chinese Communist Party
China Electronics Technology Corporation
Chinese Military Commission
Defense Advanced Research Projects Agency
Deep Brain Stimulation
Defense Intelligence Agency
Electroencephalogram
Integrated Joint Operations Platform
Intelligence, Surveillance, and Reconnaissance
Military-Civil Fusion
Next-Generation Sequencing
National University of Defense Technology
People’s Liberation Army
People’s Republic of China
Research and Development
State-Owned Enterprise
Strategic Support Force
Science and Technology Commission
Transcranial Magnetoelectric Stimulation
Text
Thesis
Advisor: Skylar Mastro Oriana
Palmer Megan J.
From haptic illusions to beyond-real interactions in virtual reality

Despite recent advances in technology, current Virtual Reality (VR) experiences have important limitations, including users' inability to walk around in large virtual environments or receive realistic haptic feedback. In this dissertation, I argue that we do not need to strive for perfect VR hardware---hardware that can closely replicate users' real-world experiences---to address these challenges. Rather, I argue that we can take advantage of the users' perceptual limits and motor control adaptability to improve the user experience instead. VR provides a unique opportunity to programmatically overwrite users' sensory signals and transform how movements are visually rendered. We can leverage this property and tap into the human sensorimotor control loop to not only overcome the current limitations of VR technology, but also to extend our abilities beyond what we can do in the real world. In part I of this dissertation, I explore illusory interactions that apply subtle remappings to users' movements in VR. While users do not notice the resulting sensory discrepancy, they alter their movements in response. I demonstrate how we can intentionally use these unnoticeable changes in movements to improve the perceived performance of encountered-type haptic devices, such as drones and shape displays. In part II, I highlight that we can take these remappings one step further and apply noticeable transformations, which I refer to as ``beyond-real.'' I present a conceptual framework for describing these interactions and conduct a survey to categorize the types of transformations explored in prior research. I then demonstrate that these interactions can be utilized to address the challenge of locomotion in VR. While users likely have not experienced such transformations in the real world before, they can learn to adapt to the novel dynamics and remain in control of their movements. Throughout the dissertation, I demonstrate that by understanding the limits of human sensory integration and motor control, we can design effective VR interactions that create an illusion of improved haptics and extend users' abilities beyond what is possible in the real world

Advisor: Follmer Sean
Landay James A.
Agrawala Maneesh
From structure to function : learning principles of tissue behavior from architecture

Immunological processes occur in microenvironments; local milieus of cells, molecules and other factors that drive and tune the process of interest. If an immunological process is essential, its microenvironment will develop into a more complex tissue structure. Tissue imaging lets us observe these structures directly and the new wave of highly multiplexed imaging platforms paints their cellular and molecular composition in unprecedented detail. Early highly-multiplexed tissue imaging data revealed that the microenvironments in healthy and diseased tissues were much more diverse and complex than we had anticipated. This presents the opportunity to develop new classes of therapeutics and biomarkers targeting microenvironments rather than molecules. However, first we must improve our ability to interpret the biology underlying the complex structure of tissue microenvironments. We developed a framework for using spatial data to elucidate the specific tissue behavior and misbehavior of disease processes while also learning general principles, such as how tissues reuse useful programs or are co-opted by malignancy. Here, we introduce a theoretical biological model of tissue behavior and an accompanying computational framework, termed Tissue Schematics. We develop and apply this framework in human lymphoid tissue, the immune tumor microenvironment, and autoimmune type 1 diabetes. Lastly, we discuss how the computational analyses will lead to hypothesis-driven experimentation. This framework aids in generating hypotheses surrounding disease origins and avenues for early diagnostics and therapies

Keywords: Microbiology and Immunology.
Advisor: Bollyky Paul
Nolan Garry P.
Bendall Sean
Meyer Everett
From the front line to the living room and back again : pedagogical pleasure in board game play

Through an exploration of the pleasure derived from playing Eurogames, a subset of board games that gained popularity in the mid to late 20th century in the geographic West, gaming is a performative act that not only relies upon pedagogical and ideological structures of meaning-making, which influences the way players see and interact with the world around them. Ideologies configure the behaviors of those within and under it, influencing how one constructs their ideas and values. Through an analysis of mechanical and narrative components of the game themselves as well as the theoretical frameworks that surround critical pedagogy, pleasure activism, and ideological value formation, play is essential to understanding the self, the Other, and the world, but within a space dedicated to exploration rather than outcome (i.e. a platform such as Eurogames). Play is often relegated to spaces outside of what the West considers "work" and due to this designation, game play allows people to explore the world around them without the larger ideological frameworks at work outside of the game. Eurogames, through an exploration of how they work upon the psyche and sociality of players, participate in a process where players can instill justice and liberation into the things that we enjoy, the things they find pleasure in. Pleasure, in this sense, is also an activist framework in which players have the potential to create liberatory spaces through an engagement with games, but only within the limitations that both the game and the social world around the game provide. Ultimately, board games, particularly Eurogames, can be analyzed through the lens of pedagogical pleasure in play to explore the ways that these cultural materials participate in performances of social justice, community-building, and equity-making

Keywords: Theater and Performance Studies.
Advisor: Robinson Aileen
Elam Harry Justin 
Looser Diana
Thiranagama Sharika
Full waveform inversion by model extension : a robust method to estimate the seismic propagation velocity in the subsurface from recorded seismograms

Seismic imaging is an effective method to produce accurate maps of the Earth's subsurface, and has been employed for decades in global seismology, hydrocarbon exploration, geothermal energy production, and more recently CO2 sequestration and monitoring. In complex geological settings, the quality of such maps highly depends on having a reliable seismic velocity model, which can be difficult to obtain. In this thesis, I develop a novel method, namely full waveform inversion by model extension (FWIME), designed to produce accurate acoustic velocity models of the subsurface from seismic recordings when conventional methods fail. I leverage the robust convergence properties of wave-equation migration velocity analysis (WEMVA) with the accuracy and high-resolution nature of acoustic full waveform inversion (FWI) by combining these techniques into a compact, mathematically consistent, and user-friendly workflow. By doing so, I mitigate the need for accurate initial models and the presence of coherent long-offset and/or low-frequency energy within the recorded data, which are difficult and costly to acquire but often necessary for conventional methods to succeed. The novelty of my method resides in the design of a custom loss function and the optimization strategy I develop to pair WEMVA with FWI, which is more efficient and powerful than applying each method separately or sequentially. I illustrate the potential of my proposed method by accurately inverting datasets generated by realistic 2D benchmark models which simulate complex and challenging geological scenarios encountered in field applications. In each scenario, the dataset lacks low-frequency energy and the initial velocity model is inaccurate, which prevents conventional methods from recovering useful solutions. In addition, I develop an efficient 3D numerical implementation of FWIME with the use of general-purpose graphics processing units (GPU) to handle 3D field datasets containing tens of terabytes of information, and to recover billions of unknown parameters. I successfully apply FWIME to a 3D ocean-bottom-node dataset acquired by Shell in the Gulf of Mexico. I show that my method outperforms conventional FWI and manages to improve the velocity model and the resulting subsurface image quality

Keywords: Geophysics.
Advisor: Biondi Biondo
Clapp Robert G. (Robert Graham)
Dunham Eric
Functional genomic analysis identifies novel regulators of germline development using flatworm models

Schistosomes are parasitic flatworms causing one of the most prevalent neglected tropical diseases, schistosomiasis, from which over 250 million people are suffering. With a further 800 million at risk of infection, schistosomiasis imposes a global socioeconomic burden comparable to that of tuberculosis, HIV/AIDS, and malaria. Only a single, partially effective drug is available to treat the disease. These parasites have high fecundity and their eggs are both the transmissible agents and the cause of the infection-associated pathology. Despite the biomedical significance, molecular mechanisms that regulate schistosome reproduction and germline development are only now being understood. Through single-cell RNA sequencing and functional genomic analysis, we first discover that the schistosome germline stem cells (GSCs) derive from a population of somatic stem cells in juvenile parasites. This cell lineage expresses a schistosome specific factor, which we name eled. Through loss-of-function studies, we find that eled prevents the premature differentiation of GSCs. Second, we identify a gene regulatory circuit that balances the output of proliferation and differentiation of the schistosome male GSCs. This program is controlled by onecut, a homeobox transcription factor, and boule, a highly conserved mRNA binding protein essential for germline development in many animals. The germline specific expression of these regulators is conserved in the planarian, the free-living evolutionary cousin of the schistosome, but the function of onecut and boule has been modified to support GSC maintenance. Altogether, my thesis provides important insights to understand the fate regulation and evolutionary adaptation of germline development

Keywords: Bioengineering.
Advisor: Wang Bo (Researcher in bioengineering)
Garten Matthias
Prakash Manu
Functional neural network surrogates trained on scarce data

Neural networks (NNs) are often used as surrogates or emulators of partial differential equations (PDEs) that describe the dynamics of complex systems. A virtually negligible computational cost of such surrogates renders them an attractive tool for ensemble-based computation, which requires a large number of repeated PDE solves. Since the latter are also needed to generate sufficient data for NN training, the usefulness of NN-based surrogates hinges on the balance between the training cost and the computational gain stemming from their deployment. To reduce this cost, we propose to use multifidelity simulations in order to increase the amount of data one can generate during the allocated computing time. High-, and low-fidelity images are generated by solving PDEs on fine and coarse meshes, respectively; these multifidelity data are then patched together in the process of training a deep convolutional NN (CNN) using transfer learning. This strategy is further generalized to incorporate three levels of multifidelity data. We use theoretical results for multilevel Monte Carlo to guide our choice of the numbers of simulations (and resultant images) of each kind. This multifidelity training is used to estimate the distribution of a quantity of interest, whose dynamics is governed by a system of nonlinear PDEs with uncertain/random coefficients (e.g., parabolic PDEs governing the dynamics of multiphase flow in heterogeneous porous media). Our numerical experiments demonstrate that a mixture of a comparatively large amount of low-fidelity data and a much smaller amount of high-fidelity data provides an optimal balance between computational speedup and prediction accuracy. When used in the context of uncertainty quantification, our multifidelity strategy is several orders of magnitude faster than either CNN training on high-fidelity images only or Monte Carlo solution of the PDEs. This computational speedup is achieved while preserving the accuracy of the estimators of the distributions of the quantities of interest, as expressed in terms of both the Wasserstein distance and the Kullback--Leibler divergence. To further reduce the cost of data generation, we demonstrate that one can start the CNN training for a new task (a given set of PDEs describing, e.g., reactive transport) from a CNN that was originally trained for a different task (another set of PDEs, e.g., for multiphase flow). Our numerical experiments show that this transfer learning approach yields a considerable speedup when applied to the problem of the estimation of the distribution of a quantity of interest, whose dynamics is prescribed by a system of nonlinear PDEs for advection-dispersion transport in porous media with uncertain/random conductivity field. For a given amount of training data, the method has equal or greater prediction accuracy and generalizability to unseen inputs than a CNN whose training is initialized randomly

Keywords: Energy Resources Engineering.
Advisor: Tartakovsky Daniel
Horne Roland N.
Kovscek Anthony R. (Anthony Robert)
Fuzzy Nature of AD Risk Gene Assignments to Brain Cell Types

As AD pathophysiology remains elusive, risk genes offer valuable information lending clinical and pathophysiological insights into the disease. Assignment of cell type for the AD risk genes proves crucial for understanding underlying pathophysiology. This cell type assignment will often have to be granular, necessitating the use of marker and algorithmic cell type information transfer between datasets. We train and validate a cell type labeling neural network on a scRNA dataset and demonstrate its poor generalization to labeling an snRNA dataset. We extend this analysis to show that for the purpose of assigning cell type labels, markers and the neural network approach respectively lose information when compared to the dataset’s ground truth labels. We systematically analyze these AD risk genes by their cell type assignment, and detail cortical regional effects on cell type assignment. We show that while ANK3, CLU, APP, EPDR1, PLEKHA1, and ICA1 are neuronally dominant in their expression, only CLU, and PLEKHA1 have expression profiles that appear to vary by neuronal subtype. Lastly, we demonstrate that AD candidate drug targets (APOE, GRN) are likely functionally expressed in multiple cell types and thus pose risks, while suggesting other candidates that are functionally expressed in only one cell type.

Keywords:  Department of Biology
Biology
Transcriptomics
Neural networks (Computer science)
GWAS
Cell Type Assignment
Alzheimer's disease
Text
Thesis
Advisor: Südhof Thomas
Fraser Hunter