VictorJuiz/thesis huggingface dataset:
Insights into metabolism and signaling at small scale

Mass spectrometry is a powerful technique for the study of biological systems because it enables untargeted analysis of endogenous molecules, giving us insight into metabolism and signaling. Here we focus on ambient ionization methods for sampling, primarily desorption electrospray ionization imaging (DESI-MSI). We describe the adaptation of DESI-MSI for the imaging of small tissues, in particular murine adrenal glands under 2 mm in diameter and maize root primary tips less than 1 mm in diameter. We discuss the elements of probe construction, set-up, and image acquisition that influence the quality of measured DESI images. This contextualizes the modifications to the DESI probe and method that were made in order to resolve fine sub-tissue regions in these small tissues. The lateral spatial resolutions were also retroactively calculated for all imaged tissue sets in order to assess the influence of probe, step size, and sample material on spatial resolution. This work then describes two applications of DESI-MSI to small tissues. One is for targeted detection of succinate accumulation in the adrenal medulla in the development of a mouse model for succinate dehydrogenase (SDHx)-deficient pheochromocytoma. In this project, DESI-MSI detects a metabolic change that results from epigenetic silencing of genes which were not shown to be mutated by a genetic screen, suggesting further clinical applications of this technique. The second application of DESI-MSI is to the imaging of maize root cryosections, demonstrating this technique's potential for identifying small molecule regulators of root development. DESI imaging reveals not only metabolite gradients along the developmental axis, but also unique spatial patterning that correlates to known tissue sub-structures, including the inner vasculature and endodermis

Keywords: Chemistry.
Advisor: Zare Richard N.
Dai Hongjie
Moerner W. E. (William Esco)
Integrated analysis of stress stratigraphy and hydraulic fracture propagation : connecting numerical models, laboratory experiments and field studies

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

Keywords: Geophysics.
Advisor: Beroza Gregory C. (Gregory Christian)
Ellsworth William L.
Mukerji Tapan
Zoback Mark D.
An integrated biosynthesis platform for tropane alkaloid drugs using yeast cellular engineering

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

Keywords: Bioengineering.
Advisor: Sattely Elizabeth
Smolke Christina D.
Swartz James R.
Integrated data-scientific approaches for improved simulations of heterogeneous catalytic reactions

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

Keywords: Chemical Engineering.
Advisor: Jaramillo Thomas Francisco
Noerskov Jens
Reed Evan J.
Integrated lithium niobate photonics for quantum technologies

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

Keywords: Electrical Engineering.
Advisor: Safavi-Naeini Amir H.
Fejer Martin M. (Martin Michael)
Miller D. A. B.
Vuckovic Jelena
Integrating physical and data-driven perspectives on building energy performance : a tale of two cities

Cities are the epicenters of our everyday lives. As they account for over 75% of primary energy consumption and 80% of all greenhouse gas emissions globally, the need to decarbonize the urban built environment is becoming increasingly dire. It is well-established that with a better understanding of how and when buildings consume energy, "low-hanging" solutions in the form of building retrofits can provide key energy, environmental, and economic improvements to cities. Fortunately, rapid growth in sensing technologies and smart city initiatives has led to a windfall of structured and unstructured data streams increasingly available to describe building energy consumption. Despite this broad availability of information describing our cities, we lack tools capable of making sense of this data. But when combined with interpretable visualization and computational techniques, they can produce once hidden insights to improve urban building energy performance. Thus, the overarching motivation for this dissertation is to propose new strategies that leverage emerging data sources to empower urban sustainability stakeholders to make informed decisions regarding their energy systems and built environments. To do so, this work introduces computational tools for two types of cities: data-rich and data-sparse cities. The first context area of study -- data-rich cities -- explores the capability of using high-fidelity data streams to predict building energy consumption while considering the impacts of the surrounding urban context. And the second area -- data-sparse cities -- looks to utilize limited observational and sensor data to evaluate how building design decisions influence the onset of heat stress and the demand for energy-intensive space cooling. Despite the unique energy challenges facing every city, creating interpretable, data-driven solutions can inform the future planning of the built environment to improve energy efficiency and human well-being. The results and methods I introduce in this dissertation contribute theoretical and practical knowledge to decision makers determining the energy sustainability future of our world

Advisor: Jain Rishee
Fischer Martin A.
Gorle Catherine
Integrating statistical and multiplex functional genomics to interpret non-coding human genetic variation

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

Keywords: Genetics.
Advisor: Montgomery Stephen
Bassik Michael
He Zihuai (Researcher in biostatistics)
Sidow Arend
Integrating systems approaches to mammalian cell signaling

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

Keywords: Chemical and Systems Biology.
Advisor: Covert Markus
Cimprich Karlene
Ferrell James Ellsworth
Meyer Tobias
Integration in cross-boundary creative projects : an empirical study

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

Keywords: Management Science and Engineering.
Advisor: Hinds Pamela
Sutton Robert I.
Valentine Melissa (Melissa A.)
Integration of spin squeezed states into free space atomic sensors

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

Keywords: Physics.
Advisor: Kasevich Mark A.
Hogan Jason
Schleier-Smith Monika
An integrative approach to understanding how body size affects physiology, ecology, and conservation

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

Keywords: Biology.
Advisor: Goldbogen Jeremy
Denny Mark W.
Hazen Elliott Lee
Thompson Stuart (Professor of biology)
An integrative deep learning framework reveals a dynamic, combinatorial cis-regulatory lexicon in epidermal differentiation

Transcription factors (TFs) bind DNA sequence motif vocabularies in cis-regulatory elements (CREs) to modulate chromatin state and gene expression during cell state transitions. A quantitative understanding of how motif lexicons influence dynamic regulatory activity has been elusive due to the combinatorial nature of the cis-regulatory code. To address this, we undertook multi-omic data profiling of chromatin and expression dynamics across epidermal differentiation to identify 40,103 dynamic CREs associated with 3,609 dynamically expressed genes, then applied an interpretable deep learning framework to model the cis-regulatory logic of chromatin accessibility. This analysis framework identified cooperative DNA sequence rules in dynamic CREs regulating synchronous gene modules with diverse roles in skin differentiation. Massively parallel reporter analysis validated temporal dynamics and cooperative cis-regulatory logic. Variants linked to human polygenic skin disease were enriched in these time-dependent combinatorial motif rules. This integrative approach reveals the combinatorial cis-regulatory lexicon of epidermal differentiation and represents a general framework for deciphering the organizational principles of the cis-regulatory code of dynamic gene regulation

Advisor: Khavari Paul A.
Kundaje Anshul
Chang Howard Y. (Howard Yuan-Hao)
Greenleaf William James
Interaction between dense gravity currents and internal waves

Gravity currents are flows driven by horizontal density gradients. A dense gravity current is a specific type of gravity current where the density of the fluid is higher than that of the ambient, making the gravity current negatively buoyant. When the receiving ambient is stratified, the ultimate fate and transport of a dense gravity current becomes more complex. We consider a two-layer stratification, representative of a stratified water column in lakes and in the near-coastal environment. Using laboratory experiments, the interaction between dense gravity currents and internal waves are considered. Internal waves are observed to be generated by the gravity current, and their behavior in at shorter and longer time scales are quantified. Oncoming internal waves were also introduced, which acted to hinder the mass flux of a portion of the split gravity current

Advisor: Koseff Jeffrey Russell
Fringer Oliver B. (Oliver Bartlett)
Ouellette Nicholas (Nicholas Testroet)