VictorJuiz/thesis huggingface dataset:
Engineering designer biomaterials from topologically diverse polyacrylamide derivatives
Keywords: Materials Science and Engineering.

The translational pharmaceutical landscape is rapidly changing and demands new polymers for both drug stability and delivery. Polyacrylamide derivatives have demonstrated a lack of immunogenicity, water solubility, chemical stability, and are both commercially available and inexpensive, making them attractive candidates for translational use. Moreover, polyacrylamides with the chemical, size, and topological control needed for translation can be synthesized via reverse addition fragmentation transfer (RAFT) polymerization, a scalable, robust, and coming-off-patent polymerization technique. This research in this dissertation highlights the promise of polyacrylamide derivatives, synthesized via RAFT, as translational biomaterials. The first portion of this dissertation will describe new developments in insulin formulation technology. I propose the use of polyacrylamide amphiphilic carrier/dopant copolymers (AC/DC) as novel excipients to occupy the air-water interface I will cover the synthesis of a library of AC/DC excipients, the proposed mechanism of action, and formulation work to generate both the (1) fastest acting, stable, injectable insulin formulation and (2) a commercial cold chain resilient insulin formulation. The second portion of the dissertation will describe a new adjuvant platform for subunit vaccines enabled by a hyperbranched polyacrylamide derivative. I will (1) highlight a universal scaling behavior uncovered during the synthesis of these materials, (2) a processing step to generate low-dispersity hyperbranched polymers, and (3) the implementation of adjuvant-conjugated hyperbranched polymers as adjuvants for vaccines. In summary, in this dissertation I will explain how novel polyacrylamide derivative biomaterials can be used to address the need of rapidly changing pharmaceutical landscape
Engineering reagents to target the sialic acid glyco-immune checkpoint
Keywords: Chemistry.

Sugar molecules decorate the human cell surface, creating a dense network of glycoproteins, glycolipids, and proteoglycans known as the glycocalyx. One "hallmark" of cancer cells is an overrepresentation of sialic acid-containing sugar structures (sialoglycans) in the glycocalyx. These sialoglycans bind to the "Sialic acid binding Ig-like Lectin" (Siglec) inhibitory receptor family expressed on both innate and adaptive immune cells. Immune inhibitory signaling of sialoglycans is therefore exploited by cancer cells to prevent an antitumor immune response and promote cancer cell survival. Because of this, sialic acids can be considered glyco-immune checkpoint ligands and represent promising therapeutic targets. Blocking sialoglycan interactions with typical antibody reagents is challenging, however, due to their vast chemical diversity; sialic acids are a family of ~50 sugar molecules in humans that can be chemically linked in multiple ways to numerous glycoproteins and glycolipids. In addition, there is a relative scarcity of high-affinity, glycoform-specific antibodies, and the precise structures of the most potent Siglec ligands remain unclear. To address this, this work describes the development of unique biological reagents to block the sialoglycan-Siglec inhibitory checkpoint interaction for cancer therapy
Engineering reduced precious metal catalysts for proton exchange membrane water electrolyzers
Keywords: Chemical Engineering.

As a renewable chemical and fuel, hydrogen produced from water electrolyzers is playing an increasingly important role across many sectors of the economy. However, proton exchange membrane (PEM) water electrolyzers are still at an economic disadvantage relative to the competing methane-derived hydrogen production process, in part due to the expensive precious metal catalysts, Pt and IrOx. The aim of this dissertation is to design and develop hydrogen evolution reaction (HER) and oxygen evolution reaction (OER) catalysts with reduced precious metal content, while maintaining or improving efficiency and stability. In the first section, we rationalize the need to diversify catalysts beyond Pt and IrOx using market-based analysis and we use technoeconomic analysis to develop a cost-based framework for establishing novel catalyst performance targets. We then demonstrate the feasibility to utilize non-precious metal HER catalysts in a commercial PEM electrolyzer, highlighting its remarkable stability. We then investigate the fundamental properties of reduced precious metal OER catalysts. We combine lab-scale aqueous electrolyte experiments, computational material methods, and advanced characterization techniques to explain the high OER activity of Ru- and Ir-based pyrochlores. While these materials did not demonstrate greater stability than commercially-used IrOx, this wholistic approach to studying catalysts is important for determining new design principles for OER catalyst stability. We further show that these OER catalysts can be integrated into a PEM electrolyzer configuration and maintain high activity and stability at moderate current densities
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
Keywords: Mechanical Engineering.

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
Engineering yeast platforms for enhancing biosynthesis and enzyme discovery
Keywords: Bioengineering.

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
Enhancing electromagnetic performance of metal devices : from nanoscale materials studies to device optimization and macroscale reconfiguration
Keywords: Electrical Engineering.

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
Ensuring Universal and Equitable Access to the COVID-19 Vaccine
Keywords: COVID-19 vaccines
Health and human rights
Intellectual property (International law)
Text
Thesis

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.
Entrepreneurial finance : from accelerators to IPOs
Keywords: Management Science and Engineering.

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
Entrepreneurial strategies in institutional changes : tackling the conflicts between new and old rules
Keywords: Management Science and Engineering.

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
Environmental relevance and physiological role of 3-methylhopanoids in anoxygenic phototrophic bacteria
Keywords: Earth System Science.

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
Equivariant machine learning for macromolecules
Keywords: Applied Physics.

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
ESG and cost of debt
Keywords: Business.

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
Essays in asset pricing and machine learning
Keywords: Management Science and Engineering.

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
Essays in corporate finance and mobile application market
Keywords: Economics.

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
Essays in corporate governance
Keywords: Business.

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
Essays in housing markets
Keywords: Business.

This dissertation studies inefficiencies and riskiness in the US housing market. In Chapter I, coauthored with Anthony Lee Zhang, we study liquidity in residential real estate markets and show that market illiquidity is a key determinant of individual house price risk. In Chapter II, coauthored with Zi Yang Kang, we study how the quality of houses traded in a market evolves in the presence of predictable cyclical changes in market conditions. Chapter III studies how industrial concentration creates risk concentration through amplified pass-through of industry-specific productivity shocks into local house prices, wages, and employment