VictorJuiz/thesis huggingface dataset:
Engineering the dark food chain : the role of methanotrophic bacteria in food production and climate change mitigation

Humanity currently faces the global challenge of providing nutritious food to support our growing population in the face of catastrophic climate change. Current methods for food production contribute to global greenhouse gas emissions through demand for land, fertilizers and energy. Meanwhile, waste management and fossil fuel extraction industries release to the atmosphere large volumes of methane, a potent greenhouse gas. Methanotrophic (methane-oxidizing) bacteria present an opportunity to create low-carbon, sustainable food systems. When methanotrophs grow, they use methane as their primary source of carbon and energy, producing a protein-rich biomass that can serve as a new source of protein, offsetting current use of unsustainable animal feeds. Transforming methane into a high-value animal feed product can incentivize its capture, reducing greenhouse gas emissions. Through a combination of critical review and analysis, laboratory research, and modeling studies, this dissertation investigates the role methanotrophs can play in food production and climate change mitigation. Chapter 2 establishes the framework of a dark food chain, which outlines how bacteria that grow independent of light can add value to human food production. Chapter 3 investigates the ways in which bacteria can add value when used as an aquaculture feed through health benefits derived from bacterial storage polymers. While these laboratory studies are ultimately inconclusive, we uncover and report inconsistencies in an experimental system used in aquaculture research. Finally, Chapter 4 is a techno-economic model that evaluates the market potential for methanotrophic protein to replace unsustainably harvested fishmeal in aquaculture feed. Taken together, these three chapters broaden scientific knowledge of how methanotrophs can contribute to food production while mitigating methane emissions, thus advancing the path towards industrial application

Advisor: Criddle Craig 
Luby Stephen
Boehm Alexandria
Naylor Rosamond
Engineering the microenvironment at the protein-hydrogel interface to investigate the role of the extracellular matrix protein type in single cell cardiomyocyte structure and function

I developed hydrogel platforms to investigate the role of the extracellular matrix (ECM) proteins on cardiomyocyte structure and function. The biochemical and biophysical properties of the microenvironment are known to play a key role in cell structure and function. Human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) hold great potential as a model to expand our knowledge of human heart muscle cells and their interactions with the surrounding microenvironment. In this thesis, I provide an introduction to cardiomyocytes and the role of the microenvironment properties in modulating cardiomyocyte structure and function. I present the gap in technologies that have been used to manipulate the protein-hydrogel interface (Chapter 1). I provide an in-depth review of the changes of native cardiomyocyte adhesion molecules and ECM composition throughout cardiac development and disease (Chapter 2). I provide an in-depth review of in-vitro platforms for cardiomyocyte structural and function assessment (Chapter 3). I discuss the development of our protocol which consists of a combination of lift-off protein patterning, a covalent protein-hydrogel linker, and the copolymerization protein transfer technique. We show that we are able to tune the ECM protein type across a wide variety of proteins including a mixture of ECM proteins, individual proteins, engineered biomaterials, and cell-cell adhesion proteins (Chapter 4). Next, I present traction force microscopy data comparing the hiPSC-CM morphology and contraction force on laminin versus fibronectin, with Matrigel as a positive control (Chapter 5). Last, I provide a summary of my findings, future platform development areas, and discuss future research directions my PhD work enables (Chapter 6). I provide ideas for how my hydrogel platforms could be implemented to study cardiac disease with interstitial fibrosis or cardiomyocytes with mutations in the cell adhesion molecules

Keywords: Mechanical Engineering.
Advisor: Kenny Thomas William
Heilshorn Sarah
Pruitt Beth
Engineering yeast platforms for enhancing biosynthesis and enzyme discovery

Recent advances in synthetic biology and metabolic engineering have enabled yeast to serve as a favourable platform for expression of multiple heterologous enzymes sourced from plants, fungi and bacteria; thereby, enabling the synthesis of valuable natural and semi-synthetic compounds. However, these heterologous enzymes can suffer from low activity, specificity, stability and solubility in yeast, resulting in arduous iterations of design-build-test-learn cycles to optimize their production, often performed on a single enzyme basis. Laboratory directed evolution has proven to be a powerful and high-throughput method for protein engineering, albeit its limited application for biosynthetic enzymes. Here, we harness RNA-based small molecule switches to develop a generalizable selection method for directed evolution of biosynthetic enzymes. Our design utilizes an RNA switch for detection of intracellular compound production, which then regulates the expression of a selection gene. Our data shows that the auxotrophy selection gene SpHIS5 exhibits the highest selective capability in combination with a theophylline-responsive RNA switch. Using the theophylline-responsive and a (S)-reticuline-responsive RNA switch, we demonstrated the enrichment of a high-producing variant of caffeine demethylase and a variant of norcoclaurine synthase, each in a population size of 10^3. Our work demonstrates the capability of RNA switches for enhancing biosynthetic enzyme activities via selection. In addition to a platform for biosynthesis, we also explored the potential of yeast for enzyme and compound discovery. Here, we characterized the biosynthetic potential of a putative tomato gene cluster computationally predicted from plant genome databases in yeast, and identified two previously unknown compounds from yeast culture, one being a hydroxycinnamic acid amide compound, dihydro-coumaroyl anthranilate amide. Further studies of the enzymes in the gene cluster reveal a previously uncharacterized amide synthesis activity for tomato chalcone synthase, which uses the same active site for synthesis of the amide compound and for its canonical synthesis of naringenin chalcone. Our work demonstrates the potential of yeast as a characterization tool for computationally aided discovery of compound structures and enzymatic activities from plant genomes

Keywords: Bioengineering.
Advisor: Endy Andrew D.
Smolke Christina D.
Covert Markus
Enhancing electromagnetic performance of metal devices : from nanoscale materials studies to device optimization and macroscale reconfiguration

The interaction of electromagnetic waves with metals results in a rich diversity of physical phenomena across the electromagnetic spectrum. Metals are ubiquitous to many applications ranging from antennas for radio frequency communication to plasmonic waveguides designed for subwavelength light confinement, and there exist many varying strategies for improving the performance of these metallic electromagnetic devices ranging from microwave to optical frequencies. This thesis presents five main studies that are linked to the enhancement of electromagnetic performance of metal devices, from investigations of nanoscale metal crystal structure all the way to large-scale metallic antenna reconfiguration. First, we introduce a fabrication technique termed metal-on-insulator rapid melt growth. We demonstrate that rapid melt growth is a high-throughput, high-yield crystal growth process that can be harnessed to produce gold bicrystals on a silicon dioxide substrate to enable fundamental grain boundary studies. Next, we investigate the mechanical properties of gold grain boundaries with tailored misorientation angles produced via rapid melt growth. Through in situ transmission electron microscope tensile testing, we found that grain boundaries are inherently strong and failure occurs transgranularly via twin formation or plastic collapse. Next, we report that inhomogeneous distributions of platinum impurities in nanoscale structured gold has a large impact on the thermoelectric properties of gold. As well, in the absence of platinum, the photothermoelectric response of gold is sensitive to subtle changes in crystal structure, indicating that certain lattice defects should be carefully considered in thermoelectric devices. Next, we demonstrate that inverse design methods can be used to improve the coupling efficiency of plasmonic fiber-to-slot grating couplers through a boundary optimization algorithm. Additionally, we design grayscale, multilayer dielectric metamaterials operating in the radio frequency regime with improved functionality using topology optimization and additive manufacturing techniques. Lastly, we demonstrate the use of pneumatically-driven soft continuum robots to construct deployable monopole antennas that are capable of controlling operating frequency. As well, we show that soft robotic actuation strategies can be exploited to actively control the polarization state of a helical antenna

Keywords: Electrical Engineering.
Advisor: Fan Jonathan Albert
Okamura Allison
Vuckovic Jelena
Ensuring Universal and Equitable Access to the COVID-19 Vaccine

Vianna Vo’s capstone project centered on the issues of human rights and global health related to COVID-19 vaccine distribution and access. More specifically, Vianna examined how a temporary suspension of intellectual property rights pertaining to the development of the COVID-19 vaccine is necessary to protect the human right to health. Through an analysis of human rights law in the 20th century, UN guidelines on health as a human right, and intellectual property practices, Vianna demonstrates how a human rights lens can positively create equitable and universal vaccine distributions.

Keywords: COVID-19 vaccines
Health and human rights
Intellectual property (International law)
Text
Thesis
Entrepreneurial finance : from accelerators to IPOs

Entrepreneurial finance is a cornerstone of economic growth. Prior work has done much to explore the financial outcomes of entrepreneurial financing. However, many gaps remain in our understanding of the organizational effects of these events. This dissertation addresses these gaps by applying organizational theory lenses to entrepreneurial finance in three tightly-linked papers. The first is a literature review on entrepreneurial finance, with a focus on IPOs and emerging fundraising mechanisms. The second paper uses over 1 million reviews from Glassdoor.com to assess how the addition of public shareholders affects employees when an organization goes public. Finally, the third paper examines how the composition of accelerator cohorts affects entrepreneurial learning and startup performance. Together, these papers contribute several findings to the literature on organizations and entrepreneurship, as well as insights for managers and entrepreneurs

Keywords: Management Science and Engineering.
Advisor: Eesley Charles
Byers Thomas (Thomas H.)
Eisenhardt Kathleen M.
Katila Riitta
Entrepreneurial strategies in institutional changes : tackling the conflicts between new and old rules

Prior literature emphasizes that aligning with regulatory, normative, and cognitive institutional arrangements aids organizational formation, resource gathering, and performance. However, during institutional changes, the old and the new rules often coexist and interact, resulting in conflicting institutional arrangements. These conflicts create strategic dilemmas for entrepreneurs, but we don't have a systematic understanding of entrepreneurial strategies to tackle the conflicts. To address this gap, my dissertation focuses on three aspects: (1) adapting to the transition from old rules to new rules, (2) leveraging the new rules to replace the old rules, and (3) combining the new rules and the old rules. I examine these aspects in three empirical settings respectively: marketization, digitization, and tokenization. My first paper analyzes how entrepreneurs change growth strategies during China's institutional change from a government-dominated to a more market-based economy. My second paper draws on institutional intermediary and network tie formation literature to examine entrepreneurial fundraising strategies on online platforms rather than offline. My third paper draws on optimal distinctiveness theory to explore how blockchain entrepreneurs combine new and old elements in framing to balance differentiation and legitimation. Empirically, I use machine learning models to create measures from big data and econometric models to identify causal relationships. Overall, my dissertation contributes to institutional theory by examining entrepreneurial agency in tackling institutional pressure and contributes to strategy literature by analyzing the institutional effects on entrepreneurial strategies

Keywords: Management Science and Engineering.
Advisor: Eesley Charles
Eisenhardt Kathleen M.
Katila Riitta
Environmental arsenic exposure and the human gut microbiome

Arsenic contamination of drinking water is a global environmental issue and public health crisis. In the US, 13 million people are exposed to levels exceeding US Water Quality Standards, and in Bangladesh, more than 25 million people are chronically exposed. Chronic ingestion can lead to arsenicosis, a disease with no effective treatment. By studying the role of the microbiome in arsenic metabolism, we aim to broaden traditional risk assessment to incorporate consideration of host-associated microbiota. Our hypothesis is that arsenic ingestion alters the structure and function of gut microbiota and that differences in the microbiota contribute to observed variations in presence of arsenicosis, given similar exposures. Anaerobic bioreactors were employed to characterize the effects of exogenous arsenic on gut microbiota samples and their transformations of arsenic. We observed interindividual variability in As(V) to As(III) conversion rates, methylation, and thiolation, with differences between Bangladeshi and Palo Alto cohorts. These results illustrate the potential biotransformative capability of the gut microbiome and variation within and across cohorts. PCoA of 16S rDNA sequencing data shows clustering by subject and cohort, with the exception of a Palo Alto Nepali male whose samples cluster with the Bangladeshi cohort. There is a temporal gradient in taxonomic composition over time for some individuals' bioreactors. Taxa were identified that drive variance within cohort groups, with variance in Bangladeshi stool correlated with exposure and arsenic disease state, nail arsenic, and urinary arsenic. Correlations between microbiome structure and differences in biotransformation and/or disease state could lead to novel management strategies and interventions for communities exposed to arsenic

Advisor: Criddle Craig 
Fendorf Scott
Relman David A.
Environmental contaminants and health in early life : studies on air pollution and drinking water quality

Environmental pollution is damaging to human health worldwide. Yet the links between contaminants and health outcomes are difficult to quantify. This dissertation aims to uncover associations between contamination and health in two settings: Dhaka, Bangladesh, and California, USA. In Chapter 1, I find that ambient particulate matter air pollution in Dhaka is associated with child pneumonia, and that this association is dependent on the source composition of particulate matter. In Chapters 2 and 3, I investigate whether the presence of contaminants in drinking water during pregnancy increases risk of preterm birth in California. Using a sibling-matched design, I find that high levels of nitrate in drinking water more than double the odds of early preterm birth (20-31 weeks). Using a survival analysis framework, I show that modest associations between arsenic in drinking water and preterm birth vary throughout gestation. Finally, Chapter 4 combines machine learning and community-based surveys to characterize exposure to nitrate and 1,2,3-trichloropropane, and emerging contaminant, among domestic well users in California's Central Valley. In each chapter, I aim to improve understanding of environmental health risks and provide methodological contributions to overcome existing research barriers

Advisor: Fendorf Scott
Luby Stephen
Baiocchi Michael
Shaw Gary M.
Environmental relevance and physiological role of 3-methylhopanoids in anoxygenic phototrophic bacteria

This dissertation research seeks to utilize laboratory and field techniques to uncover meaning behind the occurrence of 3-methylhopanoids in an anoxygenic phototroph. I am interested in how the environmental context of a new 3-methylhopanoid source organism isolated from an extreme environment will influence future interpretations of both rock record and modern ecosystems. I chose to study this new lineage of 3-methyhopanoid producers in laboratory cultures (CHAPTER 1), in situ from the environments that this strain was directly isolated from (CHAPTER 2), and in silico through the metagenomic data of other hot springs and an expanding list of environments in which the purple non-sulfur bacteria (PNS) call home (CHAPTER 3). This work indicates that PNS bacteria are potential sources of 3-methylhopanoids and thus should be considered along with methanotrophs, and the more rarely indicated acetic acid bacteria, when interpreting modern and ancient sedimentary biomarker records

Keywords: Earth System Science.
Advisor: Dekas Anne
Francis Christopher
Welander Paula
Equivariant machine learning for macromolecules

Predicting the structure of macromolecules and macromolecular complexes is a grand challenge in biochemistry, with major implications for basic science and drug discovery. Computational methods for structure prediction generally leverage pre-defined structural features to distinguish accurate structural models from less accurate ones. This raises the question of whether a method that has full access to the three-dimensional (3D) coordinates of each atom can yield improved results. I present a machine learning method that learns to identify accurate models of protein complexes directly from 3D atomic coordinates, leveraging underlying physical symmetries in lieu of hand-crafted features. The neural network architecture combines multiple ingredients that together enable end-to-end learning from molecular structures containing tens of thousands of atoms: a point-based representation of atoms, equivariance with respect to rotation and translation, local convolutions, and hierarchical subsampling operations. In addition to protein complexes, I present results on RNA structure prediction

Keywords: Applied Physics.
Advisor: Dror Ron
Schnitzer Mark Jacob
Brünger Axel T.
ESG and cost of debt

This paper studies the extent to which ESG (environmental, social, and governance) performance is priced into corporate loans. I construct a firm-specfic ESG measure from 136 raw metrics related to the fundamentals of ESG components. The constructed ESG scores provide measurement transparency and exclude all traditional corporate governance factors that have been established to exert financial impact on firms. Through identification within lender-borrower's persistent relationships over time, I find that a one standard deviation increase in a borrowing company's ESG score leads to a 6.3 basis-point decrease in its loan spread. The reduction in loan spread can be explained through the credit risk channel, where highly rated ESG companies have a 4% lower default probability in the future. Better firm ESG scores also predict fewer financial restatements due to fraud, clerical error, and SEC investigation

Keywords: Business.
Advisor: Seru Amit 
Buchak Gregory 
Krishnamurthy Arvind 
Robles Garcia Claudia
Essays in asset pricing and machine learning

In this thesis we study two applications of machine learning to estimate models that explains asset prices by harnessing the vast quantity of asset and economic information while also capturing complex structure among sources of risk. First we show how to build a cross-section of asset returns, that is, a small set of basis or test assets that capture complex information contained in a given set of characteristics and span the Stochastic Discount Factor (SDF). We use decision trees to generalize the concept of conventional sorting and introduce a new approach to robustly recover the SDF, which endogenously yields optimal portfolio splits. These low-dimensional investment strategies are well diversified, easily interpretable, and reflect many characteristics at the same time. Empirically, we show that traditional cross-sections of portfolios and their combinations, especially deciles and long-short anomaly factors, present too low a hurdle for model evaluation and serve as the wrong building blocks for the SDF. Constructed from the same pricing signals, our cross-sections have significantly higher (up to a factor of three) out-of-sample Sharpe ratios and pricing errors relative to the leading reduced-form asset pricing models. In the second part of the thesis, I present deep neural networks to estimate an asset pricing model for individual stock returns that takes advantage of the vast amount of conditioning information, while keeping a fully flexible form and accounting for time-variation. The key innovations are to use the fundamental no-arbitrage condition as criterion function to construct the most informative test assets with an adversarial approach and to extract the states of the economy from many macroeconomic time series. Our asset pricing model outperforms out-of-sample all benchmark approaches in terms of Sharpe ratio, explained variation and pricing errors and identifies the key factors that drive asset prices

Keywords: Management Science and Engineering.
Advisor: Pelger Markus
Bryzgalova Svetlana 
Giesecke Kay
Essays in corporate finance and mobile application market

In Chapter 1, I examine how VC investors choose startups based on different kinds of information and examine the use and impacts of VC funding in startups. In Chapter 2, I empirically examine whether corporate boards benchmark performance against peer firms to evaluate and fire CEOs. In Chapter 3, I explore adoption and impacts of monetization strategy innovations in the mobile app market to better understand what types of apps choose which monetization strategies

Keywords: Economics.
Advisor: Taylor John B.
Admati Anat R.
Larsen Bradley J.
Essays in corporate governance

This thesis studies several topical areas in empirical corporate governance research. In the first chapter, I investigate the impact of hedge fund activism on firm operating performance. This is the first of a planned series of papers exploring how modern governance regimes interact with non-equity outcomes. In the second chapter, I connect the results of recent econometric work on challenges with difference-in-differences designs when the treatment timing is staggered to governance research, demonstrating the source of the bias and how it impacts the results of many published studies. This chapter is coauthored with my advisor David Larcker and Charles Wang of Harvard Business School, and is undergoing revisions at The Journal of Financial Economics. In the third chapter, I apply alternative estimation techniques to a long-running question in corporate governance---whether state antitakeover affect managerial incentives, finding that, contrary to much prior work (but consistent with legal argument), they do not

Keywords: Business.
Advisor: Larcker David F.
Gelbach Jonah B.
Gipper Brandon
Seru Amit