VictorJuiz/thesis huggingface dataset:
Integrated analysis of stress stratigraphy and hydraulic fracture propagation : connecting numerical models, laboratory experiments and field studies
Keywords: Geophysics.

The unifying theme of this thesis is that an understanding of the stress variations in the earth's crust and geomechanical modeling are essential for optimization and safe execution of underground fluid injection operations. This thesis integrates field studies, laboratory experiments and numerical modeling to (a) understand how the stress in the earth's crust varies with depth and (b) understand how stress influences injection induced fault slip and hydraulic fracture propagation. This integrated analysis is presented in the context of unconventional oil & gas production, stimulation of enhanced geothermal systems (EGS), underground CO2 storage and induced seismicity applications
An integrated biosynthesis platform for tropane alkaloid drugs using yeast cellular engineering
Keywords: Bioengineering.

Despite their medicinal use by humans long before the development of agriculture, plants remain a major source of bioactive molecules and pharmaceutical leads for modern medicine. One such group of bioactive molecules, the tropane alkaloids (TAs), are acetylcholine receptor inhibitors produced by certain nightshade plants and are classified as essential medicines by the World Health Organization for treating diverse neuromuscular dysfunctions. The current crop-based supply chain for TA drugs is ecologically unsustainable, vulnerable to environmental disturbances, and lacks the manufacturing agility needed to respond to drug shortages and public health crises. To address these limitations, we have developed a proof-of-concept biomanufacturing platform by genetically engineering brewer's yeast to convert sugar and amino acids into the most widely used TA medicines, hyoscyamine and scopolamine. Reconstructing the complete TA biosynthetic pathway in yeast necessitated rewiring yeast nitrogen and polyamine metabolism for overproduction of key precursors and intermediates, discovery of the final missing enzyme in the pathway using a functional genomics approach, and cellular and protein engineering to enable expression of a recalcitrant plant acyltransferase in the yeast vacuole using a novel transmembrane enzyme design. We then developed a neural network-based strategy to identify new TA transporters from a plant RNA sequencing dataset and showed that incorporating these transporters into our engineered yeast platform alleviates intracellular transport restrictions that limit TA production. Finally, we demonstrated the utility of our technology for exploring new drug leads by leveraging computational tools for biosynthetic pathway expansion and enzyme activity prediction to produce two classes of N-substituted TA derivatives in yeast. Our final yeast platform, capable of producing hyoscyamine and scopolamine at titers of 100-500 micrograms per liter, incorporates over 30 additional enzymes expressed across six cellular locations and constitutes a starting point for the development of a new TA supply chain via microbial biomanufacturing
Integrated data-scientific approaches for improved simulations of heterogeneous catalytic reactions
Keywords: Chemical Engineering.

Many interconnected problems underlie the search for new materials for producing renewable fuels. This work studies three such problems: the computational simulation of catalytic materials to predict their activity in reducing CO2 to fuels, the meaning-preserving integration of scientific knowledge represented in differing models produced by different scientists, and the data-driven improvement of exchange functionals for Density Functional Theory (DFT), the primary tool for computational materials chemistry. Solutions to these problems mutually support each other: integration of computational chemistry databases requires a variety of DFT models to be produced as starting material, the data-driven exchange functional development is accelerated by technology that combines multiple datasets in a secure way (and the data-sharing culture that this technology promotes, more generally), and the DFT predictions can provide more value to experimentalists searching for better catalysts when their underlying functionals have been optimized for surface chemistry
Integrated lithium niobate photonics for quantum technologies
Keywords: Electrical Engineering.

We are entering an age where we can engineer and build machines that are enhanced by the principles of quantum mechanics. These new machines will dramatically change the way we compute, sense the world around us, and communicate. Quantum technologies of all kinds face two major challenges: individual device performance and scalability. Device performance improvements have been dramatic in recent years with superconducting qubit coherence times growing by orders of magnitude in the past decade, for example. However, individual device performance alone will not usher in a new era of quantum technologies. Systems of scale require high performing devices that can be combined in new and expansive ways. Large-scale quantum systems promise to revolutionize drug discovery, materials science research, secure communications, and the simulation of quantum physics itself. These applications require systems with orders of magnitude more entanglement than is currently possible. Scale has two parts: the number of devices integrated locally into a system and the number of systems interacting over distance. Moving to chip-scale integration can benefit both scalings since many devices can be integrated on the surface of a single chip, and nanoscale integrated systems promise performance benefits for efficient transduction to connect distant systems. The first part of this dissertation describes my work towards building efficient transducers to coherently connect quantum machines based on superconducting microwave qubits over large distances. This effort used thin-film lithium niobate nanophotonic devices integrated with superconducting circuits on the same chip with the goal of providing a transparent link between quantum computers over standard optical fiber infrastructure. The second part of this dissertation describes a comprehensive demonstration of resonant second-order nonlinear integrated photonic systems based on thin-film lithium niobate. We built an on-chip optical parametric oscillator that improved upon the state of the art by three orders of magnitude in threshold power and sets the standard for device performance in X-cut lithium niobate. The demonstrated chip-scale nonlinear circuits can now be expanded to scalable quantum computing architectures based on entangled optical modes and enhanced quantum sensing with squeezed light
Integrating statistical and multiplex functional genomics to interpret non-coding human genetic variation
Keywords: Genetics.

Human genetics connects natural genetic variation with molecular and organism phenotypes, yet the space of human genetic variation is vast and almost entirely non-coding with unknown function. Statistical and multiplexed molecular genetics provide distinct and complementary approaches to identify and interpret pervasive genetic complexity, particularly with respect to molecular phenotypes like gene expression. Chapter 2 describes the design and implementation of a massively parallel reporter assay to identify causal genetic variants within tightly linked genome regions that are statistically associated with gene expression. It describes a wide range of molecular properties of regulatory variants, and demonstrates that multiple linked causal variants underlie many human genetic associations. Chapter 3 reports the generation and analysis of high-resolution allele-specific expression data gathered from samples in the Genotype-Tissue Expression Consortium and a set of ovarian cancers, and shows how allele-specific expression is connected to proximal genetic cis-regulation as well as cancer progression. Finally, Chapter 4 uses statistical and molecular genetics to dissect two biological systems, the RNA content of extracellular exosomes and the role of genetic variation in regulating the retinal pigment epithelium. These studies represent substantial new data describing human genetic variation and its molecular or phenotypic effects, as well as extensive statistical and computational interpretation thereof, across a range of cellular contexts
Integrating systems approaches to mammalian cell signaling
Keywords: Chemical and Systems Biology.

Cells are elegant computers that resulted from evolution. They constantly sense information from inside and outside, process them through a large network of molecular interactions, and compute their behavior as an output. They achieve such computation through the integration of a series of molecular functions. Molecular biology is the study of these functions, such as how they diffuse, bind, and catalyze. However, studying each molecular function in detail is not enough to understand their overall outcome. We need a systems approach for studying how molecular interactions are integrated to produce behavior. In my understanding, the concept of Chemical and Systems Biology emerged from such needs. We need biochemical tools for measuring and perturbing a system in parallel. We also need computational tools for facilitating measurements and modeling to account for integrated behavior. This thesis is about systems approaches for studying biology; how to measure, perturb and model biological systems, particularly for studying mammalian cell signaling. I first introduce a molecular and computational tool for measuring dynamic cell signaling states. Next, I describe our effort for extending microscopic imaging to a high-throughput genetic perturbation platform. Then, using adipogenesis as a case example, I present how modeling facilitates our understanding of complex cellular behavior. Taken together, my thesis work contributes to innovation towards a systems-level understanding of biological systems
Integration in cross-boundary creative projects : an empirical study
Keywords: Management Science and Engineering.

Collaboration across boundaries is critical for most creative work. Most research to date has documented the challenges of cross-boundary collaboration and knowledge transfer and translation across boundaries. Limited research has investigated the ways in which ideas are integrated into a cohesive whole. This dissertation addresses this gap by examining how ideas get integrated in creative cross-boundary projects. Through an in-depth field study of a Thai real estate development company, I empirically examine the process of integration and the practices that support integration. I introduce a theory of integration as a complex, dynamic and recursive process that occurs over time and requires idea elaboration. Ideas are first introduced, then elaborated and transformed by the cross-boundary project team and either integrated into the cohesive whole or rejected. As an idea is introduced or transformed it stimulates the transformation of other ideas in a continuous process until the project is complete. I identify four activities that support idea elaboration and integration across boundaries: instantiating ideas, representing others, balancing interests, and anticipatory activities. I also identify the conditions that enable project members to perform these integrative activities: multiple identities, big picture thinking, and a formal coordination role. I propose therefore that integration in creative cross-boundary projects is a complex process requiring idea elaboration and attention to project members' opinions, interests, and perspectives, and that a core activity of creative cross-boundary projects is how team members bring different perspectives to bear. This dissertation advances theory on cross-boundary teams, integration, and creativity by showing how integration occurs and the activities that enable integration
Integration of spin squeezed states into free space atomic sensors
Keywords: Physics.

Atomic sensors utilize cold atom ensembles to measure a variety of observables, including magnetism, time, acceleration, and gravity, with high precision. Each type of sensor has a specific sequence of laser light to manipulate the quantum states of the atoms such that they measure the desired observable. For well optimized sensors, the ultimate resolution is determined by the quantum projection noise (QPN), a limit set by the Heisenberg uncertainty principle. The QPN is typically reduced by either increasing the number of atoms or by increasing the time between laser pulses. Application parameters, such as physical size or repetition rate, limit the extent of these improvements. Quantum entanglement through spin squeezing provides a third avenue for increasing the resolution of sensors. In this thesis, I will describe the methods I developed to utilize cavity generated spin squeezed states in free space atomic clocks and gravimeters. First, I will detail the fluorescence detection procedure, including both hardware and software techniques, with noise low enough to readout states with variance below the QPN. The ultimate resolution limit of our system is 690 microradians. I will then present the results of a successful integration of these states into a Ramsey sequence capable of reducing the averaging time of an atomic clock by three times. Finally, I will report the status of the laser system constructed to drive velocity sensitive Raman transitions and its ability to create useful beamsplitter operations
An integrative approach to understanding how body size affects physiology, ecology, and conservation
Keywords: Biology.

Body size can be a strong predictor of morphological traits, physiological performance, ecological function, and even anthropogenic impacts. Variation in body size creates many different challenges and opportunities for organisms that arise from scaling relationships. Rorqual whales are the largest of all animals and thus represent a unique study system to understand how animals function at the extreme of body size. Rorquals exist sympatrically, have the same foraging mechanism, and hunt similar prey. The major difference between rorqual species is their range of body sizes that spans an order of magnitude. Using CATS tags, which record rorqual kinematics and behavior in nature, I can ask questions about how physiological and ecological functions scale with body size, using the best-suited organism. In this dissertation, I determine the allometric relationships of rorqual morphological traits (Chapter 1) and use these relationships along with biologging data to determine that morphology predicts the behavior and performance of rorquals in their natural environment (Chapter 2). I apply the results of the morphology and performance studies to a conservation issue, microplastic pollution, to a generate a high-resolution quantitative estimate of potential plastic ingestion by baleen whales (Chapter 3). Finally, I integrated the results from Chapters 1 and 2 to determine that rorqual stomach content data is driven by morphological variations that affect maneuverability and speed across rorquals. Finally, my conclusion describes how integrating the major themes in my dissertation provided me with insights to how body size affects physiology, ecology, and conservation
Interpreting drilling records to predict well success
Keywords: Oil well drilling
Geothermal well drilling
Machine learning
Neural networks (Computer science)
Natural language processing
Petroleum engineers
Geothermal engineering
Cluster analysis
Text
Thesis

For the last decade, modern machine learning, especially the deep neural network, has made tremendous impacts to all facets of life. The energy industry is not oblivious to the trend, and machine learning has found its applications in all of kind of operations. Often, the most cost-intensive operation that is carried out in the energy industry is the well drilling operation. This report is an extensive study on the application of deep neural networks in optimizing geothermal well drilling operation, but can be readily applied to oil and gas well due to their similarities.
This study shows that drilling optimization by optimizing rate-of-penetration from past drilling records is a highly delicate process. Not only a sufficient amount of data from multiple sources of information is required, the data also must be properly recorded and standardized, or it must go through preprocessing to filter out invalid entries and to standardize. In this study, a R2 score of 0.53 has been achieved for the rate-of-penetration prediction deep neural network model with random train/validation splitting scheme. However, as the rate-of-penetration prediction model trained with random splitting scheme has limited usage in production, an alternative splitting scheme called well-by-well train/validation splitting is used. With the well-by-well train/validation splitting scheme, the rate-of-penetration prediction deep neural network model was only able to achieve a R2 score of 0.1
Rate-of-penetration is not the only criterion used in drilling optimization. This study also considered optimization by reducing nonproductive times. The most common reasons for nonproductive times are tripping, and the drillers encountering problems. Therefore, having a model that can forecast these nonproductive times is helpful to the overall quest of minimizing the total costs. In this study, machine learning models that can predict potential tripping/problems from drilling records have been developed, and both models have satisfactory accuracy to be used in real life situations.
The problem of incorporating nonnumerical entries in drilling records was also studied in this study. In addition to the standard numerical-type entries, textural-type entries are often found in drilling records. These textural-type entries are often nonstandardized written English remarks/comments from the drillers, which may carry useful information about the condition of the well that does not available elsewhere in the records. However, these remarks/comments require expensive and time-consuming manual data preprocessing in order to incorporate them into machine learning models. Bidirectional Encoder Representations from Transformers (BERT) provides a solution for automating preprocessing of textural data. Using textual information, together with numerical information, has improved the quality of predictions in most cases.
Interrogating a new HIV-1 vaccine target in the gp41 C-heptad repeat
Keywords: Molecular and Cellular Physiology.

HIV-1 infection requires viral and cell membrane fusion, a process which relies on the gp41 N-heptad repeat (NHR) and C-heptad repeat (CHR) of the viral glycoprotein Env. When the CHR adopts an α‑helical conformation, one face is composed of highly conserved residues and is a validated target for HIV-1 inhibition, suggesting that this conserved α-helical face might be an effective vaccine target. No neutralizing antibodies targeting the highly conserved α-helical face of the CHR have been described, although an engineered protein targeting this epitope, 5-Helix, shows potent neutralizing activity against multiple viral strains. We have identified the first such examples of neutralizing antibodies against this epitope through parallel antibody selections, an important proof-of-principle for CHR-targeting vaccine efforts. First, we use mutagenesis and selection of a yeast surface-display library to isolate two novel antibodies that target this epitope, A24.3 and A25.2. Surprisingly, the X-ray crystal structure of the A24.3 antigen-binding fragment (Fab) indicates that CHR binding is concomitant with displacement of the variable light domain and is mediated by germline-conserved framework residues on the variable heavy (VH) domain. We next designed a stapled CHR peptide to identify novel antibodies targeting this highly conserved epitope from a ~7.6 x 10^10-member single-chain variable fragment (scFv) library designed by Distributed Bio: the SuperHuman Library 2.0. We identified 52 novel anti-CHR scFv clones, many of which bind multiple CHR peptides representing the consensus sequences of distinct clades of HIV-1. In order to functionally evaluate these novel antibodies, we assessed their viral neutralization activity using Env-pseudotyped lentivirus. We first assessed the efficacy of 5-Helix against a panel of HIV-1 strains representing circulating strains worldwide and found 5-Helix shows broad neutralization activity across viral clades and neutralization tiers. We next found both A2 antibodies and Distributed Bio scFvs neutralize the tier-1 HXB2 strain, the first examples of neutralizing antibodies targeting this conserved epitope in the CHR. Additionally, A25.2 VH antibody shows cross-clade neutralization, inhibiting HIV-1 strains representing diverse CHR sequences. Taken together, these neutralization studies combined with our biochemical and structural characterization provide proof-of-concept that the conserved α-helical face of the CHR could be effectively targeted by antibodies, potentially opening new avenues in vaccine development for HIV-1 and viruses with closely related class-I fusion mechanisms
Interspecies organogenesis
Keywords: Stem Cell Biology and Regenerative Medicine.

Advances in stem cell technology and tissue engineering promise clinical applications. Although much progress is being made, developing whole organs from in vitro cultured cells proves difficult. The spatial and temporal intricacies that occur during organogenesis ensure adequate perfusion, organ size, tissue distribution and function. Since many developmental properties are not cell intrinsic, proper development requires rapidly evolving extrinsic cues that may act locally or in gradients. This environment is difficult to model in vitro. To generate fully functional organs from patient-specific induced pluripotent stem cells (iPSCs), in vivo approaches have been attempted in which an animal is used as a bioreactor. In this approach, development of a target organ is genetically disabled in a host embryo, and injected donor cells (often from a different species) are coaxed into generating the target organ by the surrounding host cells. Although this interspecies developmental niche is likely more natural than an in vitro model, the in vivo approach to organogenesis comes with its own challenges
Invariants of codimension 2 contact submanifolds
Keywords: Mathematics.

We construct new invariants of codimension 2 contact submanifolds. Our main invariant can be viewed as a deformation of the contact homology algebra of the ambient manifold. We describe various applications of these invariants to contact topology. In particular, we exhibit examples of codimension 2 contact embeddings into overtwisted and tight contact manifolds which are formally isotopic but fail to be isotopic through contact embeddings. We also give new obstructions to certain relative symplectic and Lagrangian cobordisms