VictorJuiz/thesis huggingface dataset:
Deep learning on local sites for protein structure and function analysis
Keywords: Medicine.
Biomedical Data Science.

Understanding how the three-dimensional structure of a protein leads to its function is important for determining disease mechanisms, developing targeted therapeutics, and engineering new proteins with desired functional characteristics. The expansion of protein structure databases due to experimental and computational advances provides an unprecedented opportunity to learn structure-function relationships in a data-driven manner. Deep learning methods that operate on protein structures have shown promise for specific tasks, but their utility for functional analysis has been limited due to inconsistencies in model training and evaluation, lack of labeled protein function data, and an inability to reconcile global predictions with local biochemical mechanisms. In this dissertation, I explore these challenges and propose a framework for protein analysis based on learning on local sites rather than the entire protein structure. First, to establish standards for model development and evaluation, I present work on (1) developing a suite of benchmark datasets, processing tools, and baseline models, and (2) quantifying the effect of differing structure compositions in the training data. I then describe a self-supervised learning method that leverages evolutionary relationships to learn general-purpose representations of local structural sites, and show how these representations enable improved performance on downstream tasks involving classification, search, and annotation of functional sites. By clustering millions of sites, I propose a framework for protein analysis based on conserved structural motifs which enables the discovery of functional relationships across protein classes. Finally, I present a method for explainable function annotation that predicts the overall function of a protein as well as the individual residues which are responsible.
Design technology co-optimization of energy-efficient digital logic using carbon nanotubes
Keywords: Engineering.
Electrical Engineering.

Carbon nanotube (CNT) field-effects transistors (FETs) are promising candidates for high-speed and low-energy digital logic. CNT FETs have been integrated within multiple industrial fabrication facilities at mature technology nodes (e.g., 90/130nm on 200mm silicon wafers). However, understanding CNT energy and delay benefits at extremely scaled nodes (e.g., beyond 2nm) is difficult due to several reasons: 1. CNT FETs have distinct characteristics -- material properties and fabrication techniques -- versus silicon FETs, that directly impact digital logic energy and delay. 2. CNT FET energy efficiency analysis must include CNT FET minimum leakage current. 3. CNT FET digital logic energy and delay exhibit numerous trade-offs, far beyond those for silicon FETs. I address these challenges through new physics-based models using physically meaningful parameters, new CNT FET doping and layout design techniques, and extensive Design Technology Co-Optimization (DTCO). To illustrate new physics-based models, I present the extended scale length theory. It captures the differences between CNT FET and silicon FET electrostatics that are crucial for energy and delay estimation. It is used to quantify CNT FET minimum leakage current within 3× vs. experimentally calibrated Non-Equilibrium Green's Function (NEGF) solvers. In contrast, prior models can underestimate CNFET minimum leakage current by 105×. Understanding CNT FET digital logic energy and delay trade-offs requires extensive DTCO simulations across many design and technology parameters. My new physics-based models enable fast DTCO: over 350,000 simulations in a few days vs. several months using existing Technology CAD simulators. Such extensive DTCO helps derive CNT FET design and technology parameters with up to 7× projected Energy-Delay Product (EDP) benefits vs. silicon FETs at the 2nm technology node. These DTCO simulations also include a new CNT FET extension doping technique based on the concept of a barrier booster. A new logic layout technique called Omni 3D exploits CNFET low-temperature fabrication to further enable up to 1.9× additional projected EDP benefits. Several of my thesis contributions extend beyond CNT FETs, e.g., to FETs based on two-dimensional materials.
Discovery and engineering the TG2/LRP-1 pathway for lysosomal delivery of molecular cargo : implications for celiac disease
Keywords: Engineering.
Chemical Engineering.

Celiac disease (CeD) is a systemic, inflammatory immune disorder that primarily affects the small intestine. It is triggered by the consumption of dietary gluten, derived from wheat, barley, or rye in certain genetically susceptible individuals. Gluten peptides are rendered highly antigenic to CeD patients through deamidation at certain epitopes by transglutaminase-2 (TG2), a widely expressed and multifunctional enzyme. Over 95% of all CeD patients express HLA-DQ2, and a vast majority of the remaining population express HLA-DQ8, and in these individuals the deamidated peptides tightly bind MHC-II on the surface of antigen presenting cells (APCs) and are then presented to gluten-specific CD4+ T cells. TG2 activity is necessary for peptide deamidation as demonstrated by restoration of a healthy phenotype upon TG2 inhibition in a mouse model of CeD. Thus, understanding the activity and localization of TG2 could help understand the mechanisms underlying CeD, and how APCs take up TG2 and gluten peptides leading to the hallmark T cell response. In my dissertation I discuss a new pathway for gluten antigen uptake and presentation in celiac disease, and how this pathway can be utilized for therapeutic applications. This pathway relies on the protein-protein interactions between three different widely expressed proteins, TG2, α2-macroglobulin and LRP-1. The complex that forms between TG2 and its gluten substrate is recognized by α2- macroglobulin, an abundant plasma protein, which leads to a conformational change. This conformation is recognized by its canonical cell surface receptor, LRP-1, triggering an endocytic event eventually delivering the cargo to the lysosome. In HLA-DQ2 expressing cells the deamidated gluten peptide released in the lysosome binds to MHC-II and is presented on the cell surface. The antigens presented on the cell surface through this pathway efficiently activate gluten-reactive T cells. Finally, this pathway can be hijacked to induce the lysosomal degradation of proteins of interest. A ligand to the protein of interest is conjugated to a gluten peptide, allowing for the entire complex to be endocytosed in an LRP-1 dependent manner. The molecular cargo, including the protein of interest is degraded, and LRP-1 is recycled to the cell surface. This approach could be used to target therapeutically relevant proteins without needing to inhibit them, in a manner analogous to LYTACs, but using only small molecules. Overall, this work offers insight into the molecular mechanisms behind antigen presentation in CeD, and how this knowledge can be used to design novel therapeutics for CeD and other diseases.
Discovery of the lysosomal bis(monoacylglycero)phosphate synthase
Keywords: Medicine.
Biochemistry.

Lysosomes maintain cellular homeostasis in diverse nutrient environments through the degradation and recycling of biological molecules, such as proteins, water-soluble metabolites, and lipids. As such, lysosome dysfunction underlies monogenic and complex neurodegenerative diseases. Strikingly, bis(monoacylglycero)phosphate (BMP) is a lysosome-resident glycerophospholipid that potently stimulates lysosomal catabolism, and its abundance is aberrant in neurodegeneration. Given its essential role in lysosome function, modulating BMP may ameliorate neurodegeneration; however, the enzyme responsible for its synthesis has been unknown for over a half a century, slowing progress in this field. This dissertation employs interdisciplinary tools to identify and validate the elusive BMP synthase (BMPS) and define its role in monogenic neurodegeneration. Chapter 1 explores cytopathology shared between monogenic and complex neurodegenerative diseases and establishes lysosomal storage disorders as a model for understanding neurodegeneration. Chapter 2 chronicles BMP's discovery in 1967, details both its structure-activity relationship and metabolism, and posits hypotheses for how the modulation of BMP may be therapeutically efficacious in neurodegeneration and other lysosome-associated diseases. Chapter 3 demonstrates that the Batten disease gene product CLN5 is the long-sought lysosomal BMPS and how the loss of BMP inhibits lysosomal lipid metabolism. Chapter 4 reveals that glycerophosphodiesters (GPDs) inhibit BMPS, establishing a metabolic connection between CLN3 and CLN5 Batten disease. Chapter 5 engineers an enzyme-based tool to detect and measure GPDs in diverse biological matrices. Chapter 6 looks ahead to the trajectory of the burgeoning field of BMP in health and disease. This work provides a genetic handle to study BMP and pioneers its therapeutic potential in neurodegeneration and beyond.
Dissecting the cellular and molecular basis of photosymbiosis in the acoel convolutriloba longifissura
Keywords: Humanities and Sciences.
Biology.

Multicellular organisms are now commonly regarded as holobionts, collections of species making up an individual. The intimate relationships between symbiotes in a holobiont seem to involve the molecular interdependence of species to varying degrees. Studying the integration of molecular pathways in holobionts has been experimentally challenging, but high-throughput sequencing technologies now allow for the simultaneous evaluation of multiple partners at once. In this dissertation, we use the photosymbiotic acoel Convolutriloba longifissura to evaluate some of the implications of the molecular crosstalk between species. In Chapter 2, we evaluate the responses of algal symbionts to host regeneration. Early post-injury the algal response is defined by a decrease in photosynthetic efficiency and an upregulation of photosynthesis-related genes. A second wave of transcriptional responses takes place two days post-injury, with unique molecular players. We demonstrate that regeneration of the holobiont is dependent on the transcription factor, Cl-runt, a conserved regulator of acoel regeneration. Knockdown of Cl-runt further decreases the operational quantum yield of photosystem II and dampens the early and late transcriptional responses in both the host and the symbionts. This work describes the coordination of molecular responses to regeneration in a photosymbiotic holobiont. In Chapter 3 we characterize the cell type diversity found in C. longifissura and compare their cell type composition to the non-symbiotic acoel Hofstenia miamia. We identify 17 unique cell types belonging to all expected tissue types: epidermal, muscle, neural, endoderm-like, secretory, and stem cells. Based on their relative DNA content and gene expression, four populations are cycling stem cells. Comparison with H. miamia demonstrates high cell type similarity. Muscle and epidermal cell types have the best alignment scores, while stem cells share the highest number of supporting genes. The endoderm-like cell types are identified as homologs but show low similarity. However, we identify five orthologous transcription factors shared between acoel endoderm-like cell types: foxA, ets-2, gsc, hnf4, and elf5. Functional characterization is needed to evaluate if these cell types have shared functions in these acoels. Overall, the high similarity found between acoel cell types supports a limited cell type divergence between acoels.
Dry well performance analysis : efficiency of stormwater capture and emerging contaminant removal
Keywords: Engineering.

Dry wells are vadose zone infiltration wells that are deeper than their widest surface dimension. Dry wells are promising for urban stormwater capture and recharge because of their high infiltration capacity, low cost, and small footprint. However, little is known about how much stormwater can be captured by dry wells. Furthermore, since dry wells bypass the natural contaminant attenuation processes in topsoil, concerns have been raised about potential groundwater contamination by dry wells especially for hydrophilic organic compounds. This study applies field monitoring, mathematical modeling, and laboratory testing to address the abovementioned concerns about dry wells. In the first part of the study, a hydrologic model was constructed using Storm Water Management Model to estimate the stormwater capture efficiency of dry wells under various climate conditions and evaluate different dry well deployment strategies. In the second part of the study, four pilot-scale stormwater treatment systems [i.e., biochar-amended filters, regenerated activated carbon (RAC)-amended filters, UV/H2O2 + biochar-amended filters, UV/H2O2 + RAC-amended filters] were developed and their performance for removal of hydrophilic contaminants of emerging concern (CECs) was tested under high flow rates and continuous CEC dosing. The field lifespan of the developed systems was estimated for a demonstration case study in a Los Angeles neighborhood using a contaminant transport model. Results from this work show that dry wells can be configured to capture about 20% of annual rainfall in Southern California. However, this efficiency will decrease because of climate change and thus better deployment strategies need to be employed. UV light coupled with black carbon-amended filters are cost-effective stormwater treatment systems for the removal of hydrophilic CECs in dry wells. Adopting these systems in dry wells can effectively increase public acceptance for stormwater capture and facilitate future deployment of dry wells.
Efficient learning from 3D molecular structures using equivariant neural networks
Keywords: Engineering.

Deep learning methods operating on three-dimensional (3D) molecular structures show promise in addressing vital challenges in biology and chemistry. The scarcity of experimentally determined structures, however, poses a significant hurdle in many machine learning applications. The incorporation of equivariance into deep learning models, leveraging inherent symmetries in structural biology problems, is essential for efficient learning from limited data. This dissertation delves into the utilization of rotationally and translationally equivariant neural networks in various structural biology problems. These include protein model quality assessment, the development of a machine learning--based scoring function for protein-ligand docking that considers protein flexibility, and the implementation of pocket-aware 3D fragment-based ligand optimization.
Electrochemical sulfur recovery from sulfide-containing wastewaters
Keywords: Engineering.

Shifting paradigms from wastewater treatment to resource recovery requires innovative processes for converting contaminants to products to address water scarcity, combat resource depletion, and promote circular economies. Sulfide, a contaminant in wastewater, can be recovered as sulfur-containing products that add value to various industrial manufacturing processes. Electrochemical sulfur recovery from wastewaters exhibits advantages in terms of modularity and tunability, but further insights are required to transform this opportunity into a robust and stable practice that yields a diverse portfolio of recovered sulfur products from wastewaters. Therefore, this dissertation explores mechanistic- and process- level understanding of both bulk and interfacial reactions during electrochemical sulfur recovery to guide the design and optimization of process with enhanced performance and feasibility. Specifically, the rate-limiting steps and reaction barriers of electrochemical sulfide oxidation were identified by product quantification and electrode characterization. Then, the performance of electrochemical sulfate production was evaluated in a broad range of sulfide-containing wastewaters. Integration of sulfate production and nitrogen recovery was also demonstrated via electrochemical stripping. Finally, gas evolution reactions at electrode-electrolyte interfaces were probed using scanning electrochemical microscopy to inform electrode design for electrochemical sulfur recovery and, more broadly, for electrochemical water treatment. The insights from this dissertation will advance water reuse and a circular sulfur economy by enabling sulfur recovery and informing the incorporation of electrochemical unit processes into wastewater treatment trains.
Enabling agile hardware development with the PEak programming language
Keywords: Engineering.
Computer Science.

Domain-specific languages for hardware can significantly enhance designer productivity, but sometimes at the cost of ease of verification. On the other hand, ISA specification languages are too static to be used during early stage design space exploration. This dissertation presents PEak, an open-source hardware design and specification language, which aims to improve both design productivity and verification capability. PEak does this by providing a single source of truth for functional models, formal specifications, and RTL. PEak has been used in several academic projects, and PEak-generated RTL has been included in multiple fabricated hardware accelerators. In these projects, the formal capabilities of PEak were crucial for enabling both novel design space exploration techniques and automated compiler synthesis.
Engineered proteins to NKp46 for cancer therapeutics
Keywords: Engineering.
Chemical Engineering.

NKp46 (NCR1, CD335) is an activating receptor expressed on the surface of natural killer (NK) cells in the immune system. NKp46 has been the target of NK cell engaging bispecific antibodies in recent years, with multiple studies demonstrating the potential of engaging NKp46 for antitumor responses in cancer. In this thesis, I discuss the discovery and engineering of antibodies and fibronectin domain proteins that bind to either human NKp46, mouse NKp46, or both using phage display and yeast surface display. One species cross-reactive antibody was epitope mapped to conserved mouse and human amino acid residues and could induce NK cell proliferation and activation. A panel of NK cell engager (NKCE) bispecific antibody constructs was created from binders of both NKp46 and the tumor antigen CD20 to explore NCKE configurations, valency, and Fc effector functions. NKp46 engagement with the bispecific antibodies was shown to increase NK cell cytotoxicity against tumor cells as compared to the anti-CD20 antibody alone in an Fc-independent manner. This thesis contributes to a body of work related to NKp46, NCKEs, and cancer therapeutics, and provides a number of proteins as useful tools for basic science and translational applications.
Engineering catalyst interfaces for carbon dioxide utilization
Keywords: Engineering.
Chemical Engineering.

CO2 hydrogenation provides the unique opportunity of valorizing this greenhouse gas to a variety of chemicals, and the selectivity towards a specific product relies in first place on the heterogeneous catalysts used to accelerate the rate of the reactions, whose properties are further dictated by their structure. Conventional heterogeneous catalysts consist of multiple elements in their metal and/or oxide form supported on oxide materials, and the interface between the metal(s) and the supporting oxide has been identified as a crucial aspect in achieving reactivity and selectivity beyond what can be obtained with individual components. This dissertation focuses on three types of catalyst interfaces, and the impact of their structure on CO2 hydrogenation reactions. This knowledge acquired during my PhD was utilized to design novel catalyst structures that achieved unprecedented activity and selectivity for CO2 hydrogenation towards valuable products.
Engineering self-assembling superantigen as cancer immunotherapy
Keywords: Engineering.
Bioengineering.

Superantigens are the most potent immune activating molecules known in nature. As such, they hold unique potential in the field of cancer immunotherapy, where treatments demand vigorous stimulation of the immune system. Studies in immunotherapy point to a tension between two opposing factors: the need to increase effectiveness while decreasing adverse reactions. Superantigens provide an avenue for a magnified boosting of the immune system, but likewise necessitate greater molecular control and precision to leverage this capacity. This thesis focuses on utilizing computational tools to propose a novel method for introducing molecular logic gate control into an existing superantigen, and consequently harness the unique ability of these proteins while providing a mechanism for control. In the first chapter, we begin with a review of the molecular interactions and strategies behind existing superantigen therapies. We then leverage the computational protein design program RosettaRemodel in chapter two in order to design the molecular logic gates AND and NOT into a superantigen through creating a novel fusion protein between the Mycoplasma Arthritidis Mitogen superantigen and the TIM (triose-phosphate isomerase) barrel. Next, in chapter three we express a focused library of computational solutions and identify the optimal conditions for production and characterization of an ideal design. Finally, in chapter four we experimentally test and quantify the anti-tumor activity of our designed superantigen through both in vitro and in vivo studies, ending with conclusion of our work in chapter five.
Enhancing machine learning with data-efficient methods
Keywords: Engineering.

Supervised deep learning techniques have made a tremendous and unprecedented impact in all segments of our lives, including finance, healthcare, social networks, and more. However, the progress is hindered by a substantial challenge: the dependence on large, high-quality labeled datasets. This issue is particularly acute in areas such as biomedicine, where the procurement and annotation of data are not only costly but also intricate. In response to these challenges, this thesis introduces innovative machine learning strategies that are data-efficient, aiming to reduce the dependence on extensive labeled datasets while either preserving or improving the efficacy of deep learning models. The thesis is systematically divided into two primary sections, each targeting key aspects of data-efficient machine learning. Part I is dedicated to the development of advanced algorithms optimized for existing datasets, particularly under the constraint of limited labeling. This section introduces a novel machine learning setting for enhancing generalization and robustness in low-label scenarios, proposes an innovative open-world semi-supervised learning framework, and adapts this framework to real-world applications. Part II focuses on augmenting training resources by incorporating supplementary knowledge. It explores the integration of auxiliary tasks to enhance training, examines the use of historical data to improve AutoML search efficiency, and introduces methods for including large datasets that were previously unmanageable due to memory constraints.
Essays in political economy : status perceptions and survey experiments
Keywords: Business.

This dissertation contains three chapters each of which addresses topics related to status perceptions and survey experiments. The first chapter provides a new theory that clarifies the mechanism through which low perceptions of subjective status can lead to increased support for populist and populist-right candidates. The second chapter is the experimental extension of the first chapter, and it implements a survey experiment with a treatment that induces respondents to hold lower perceptions of their socioeconomic status. The last chapter is a methodological investigation of adaptive algorithms' relative performance in efficiently identifying the best arm compared to uniform randomization, the most widely adopted experimental design in social science, in a setting with multiple treatments and a limited budget.