VictorJuiz/thesis huggingface dataset:
Unraveling the immune response to vaccination in mice and humans
Keywords: Medicine.
Microbiology and Immunology.

The fundamental goal of vaccinations is the induction of robust and durable protective immunity against pathogens. The emergence of the SARS-CoV-2 virus, and its rapid mutation into immune-evasive variants, underscored the necessity for vaccines to confer long-lasting and cross-protective memory responses. While current vaccination strategies for respiratory viruses such as SARS-CoV-2 and influenza virus provide moderate protection, achieving durable sterilizing immunity against these pathogens has remained a major challenge. This dissertation explores two vaccination strategies that have the potential to enhance current immunization approaches. First, we investigate the potential of vaccine adjuvants as a strategy to augment the magnitude, durability and breadth of vaccine-induced immunity. In Chapter 2, using mouse models, we perform a comparative evaluation of the immune responses induced by diverse vaccine adjuvants in combination with a candidate COVID-19 vaccine antigen. In Chapter 3, we delve deeper into the adaptive immune responses induced by an adjuvanted COVID-19 vaccine in humans, with a particular focus on the breadth of the antibody responses in the presence and absence of the adjuvant. Our findings presented in these two chapters highlight the crucial role of vaccine adjuvants in enhancing the immune response to vaccination, elucidate the diversity of different adjuvant formulations, and provide evidence for the enhancement of cross-reactive immunity by investigating the clonal dynamics of B cells over the course of vaccination in humans. Second, we examine the incorporation of T cell memory in addition to humoral immunity as a vaccination strategy to enhance protection against infection. In Chapter 4, using influenza as a model, we investigate the role of T cell memory and antibody responses in contributing to protection either individually or in combination. We find that combining T cell memory with humoral immunity provides with superior protection against infection, highlighting T cells as attractive targets of vaccination. In Chapter 5, using a novel vaccination platform utilizing circular RNA encoding antigenic sequences, we induce robust CD8 T cell responses in mice, elucidating a novel candidate platform for T cell vaccines. Collectively, the findings in this thesis significantly enhance our understanding of the adaptive immune responses induced by vaccination. Our results underscore the pivotal role of adjuvants in shaping the immune response to vaccination, and reveal the contribution of T cell memory to protection against infection, presenting two promising strategies to address current challenges in vaccinology.
Upscaling and automation : symbolic computational methods for multiscale model discovery
Keywords: Sustainability.
Energy Resources Engineering.

Despite current modeling capabilities and the availability of computational power, complex multi-physical, multiscale systems continue to challenge our physical intuitions due to their elusive behaviors. While high-fidelity simulations have paved the way for understanding these systems, the associated computational costs prohibit such tools from being used to inform experimental investigations in a timely manner and facilitate swift technological advancements. As a result, multiscale models have been increasingly relied upon for efficiently predicting large-scale, or average, multi-physical behaviors of complex systems, and how they are affected by intricate fine-scale variations. Such models can be systematically generated through rigorous upscaling techniques, which provide a priori estimates of modeling error and conditions under which models are valid (i.e., applicability conditions). However, the derivations required in upscaling techniques are time-consuming, prone to analytical error, and become quickly intractable for complex, multi-physical systems. In this dissertation, we advance the paradigm of multiscale modeling beyond these human-centered limitations by developing a symbolic computational framework for automating and accelerating upscaled model development via homogenization theory. By automating the required analytical derivations, we democratize the utilization of upscaling techniques in practical applications and enable model development in a feasible amount of time (i.e., seconds) with no requirements in analytical tractability, nor specialized expertise in mathematical model formulation from users. We demonstrate our software prototype, Symbolica, in both canonical and complex systems by developing models for a variety of advection-diffusion-reaction systems and Li-ion battery modules undergoing thermal runaway. Novel model development strategies are formulated, encoded in Symbolica, and used to discover nontrivial modeling scenarios and multiscale models with 1.) emergent terms, 2.) nonlinear effective parameters, 3.) macroscopic advection that is independent of pore-scale advection, and 4.) effective parameters that couple geometric and physical effects. Ultimately, our work demonstrates how Symbolica can be used to reduce the level of effort in rigorous multiscale modeling and model implementation for real-world applications
Upskilled and reskilled : how gender shapes career transitions in the new economy
Keywords: Humanities and Sciences.
Sociology.

This dissertation broadly explores how stereotypes about gender shape career transitions. Using two online survey experiments, Chapter 1 examines how hiring discrimination against stay-at-home fathers who reskill using career re-entry assistance reinforces cultural expectations around gender and caregiving. Chapter 2 investigates how employers' treatment of stay-at-home fathers (identified in Chapter 1) shifted during the COVID-19 pandemic, and the implications for gendered norms around work and family. Chapter 3 leverages rare pre- and post-COVID-19 survey and administrative data to determine whether an online intervention improved early-career women's confidence in their interpersonal skills at work, an important predictor of workplace retention among women. Together, these papers shed light on the structural and social barriers to gender equality, and the types of interventions that might be used to help mitigate their effects
Using a systems approach to improve WASH services in primary government schools in Sitapur, India
Keywords: Engineering.

Background: Clean and functional Water, Sanitation, and Hygiene (WASH) infrastructure promotes children's health and learning in primary schools. However, contextual complexities and priorities of stakeholders often hinder WASH infrastructure from effectively contributing to better health outcomes. Methods: This qualitative study uses a systems perspective to investigate the drivers behind current public service successes or failures in primary government schools in Sitapur district, Uttar Pradesh, India to inform the development of an incentive-compatible and contextually relevant WASH service delivery system. Using a grounded theory approach, we conducted 76 in-depth interviews with key informants, including headmasters, village leaders, ration distributors, and government officials at local and district levels. Results: While our analysis is ongoing and will be completed in the first quarter of 2024, preliminary observations, particularly of the Mid-day Meal and NIPUN Bharat schemes, suggest clear information exchange and strong accountability mechanisms within/between stakeholder categories involved in public service delivery. Current program efforts primarily manage immediate risks without addressing underlying vulnerabilities, contributing to short-term solutions. Conclusions: Our research preliminarily provides insights into successful service delivery mechanisms across systems and, by transcending traditional water sector boundaries, potentially bring new solutions applicable within the water sector. We also anticipate that our research will provide insights to bolster existing WASH service deliveries in Uttar Pradesh and potentially institutionalize effective practices more widely in India. This study contributes to global literature by unraveling intricate cause-effect relationships and sociopolitical dynamics influencing successful service deliveries and proposes adaptable strategies for wider application.
Using chemical tools to identify and inhibit serine hydrolases in commensal bacteria
Keywords: Medicine.
Chemical and Systems Biology.

With rising rates of antimicrobial resistance and an increasing understanding of how commensal bacteria in the human microbiota affect a wide variety of diseases, it is of utmost importance to discover novel bacterial drug targets. In particular, microbiota-related diseases are often characterized by a dysbiotic microbial community and a bloom of pathobiont bacteria. Thus, identifying selective targets that can decrease the fitness of a subset of bacterial species within a community without having broad-spectrum effects will be critical. Previous work has utilized a chemoproteomics technique called activity-based protein profiling (ABPP), which uses chemical probes that can covalently modify catalytic active enzymes, to identify relevant druggable enzymes in several bacterial pathogens. This approach is especially powerful for identifying serine hydrolases, a broad superfamily of enzymes with functions in metabolism and cell signaling in mammals. While they have not previously been studied in commensal bacteria, these functions suggest serine hydrolases may be important for bacteria to maintain themselves in complex communities, and their diversity hints at their potential as selective drug targets. In Chapter 2, a combination of bioinformatics and ABPP were utilized to identify serine hydrolases in the skin commensal bacterium Staphylococcus epidermidis, most of which had close homologs in other staphylococcal species including the pathobiont Staphylococcus aureus. In particular, the lipase SeFphB is a functional homolog of the virulence factor SaFphB in S. aureus and has varying activity levels across clinical isolates of S. epidermidis. While loss of SaFphB has been previously shown to be important in infection, SeFphB does not play a role in commensal skin colonization by S. epidermidis. These results suggest that SaFphB is still a viable drug target and potential off-target effects on SaFphB homologs in the skin microbiota likely will not be detrimental. In Chapter 3, a pipeline to predict serine hydrolases bioinformatically across gut commensal bacteria was paired with ABPP to identify homologs of the human protease DPP4 (hDPP4) in the model symbiont Bacteroides thetaiotaomicron. One enzyme, BT4193, was demonstrated to be a functional homolog of hDPP4 and could be inhibited by hDPP4-targeting drugs that are used for the treatment of type 2 diabetes. Furthermore, BT4193 plays an important role in envelope integrity and in B. thetaiotaomicron fitness in complex bacterial communities, indicating that off-target inhibition of bacterial enzymes by existing FDA-approved drugs can affect the gut microbiota. In totality, this work explores the diversity of serine hydrolases across commensal bacteria, characterizes the function of a skin commensal lipase and a gut commensal protease, and highlights the potential of serine hydrolases in commensal bacteria as selective drug targets
Using family sequencing data to understand sequencing errors, meiotic crossovers, and disease risk
Keywords: Medicine.
Biomedical Informatics.

Despite widespread sequencing efforts, the genetic etiologies of many complex diseases remain poorly understood. In this work, we show that family-based linkage methods, when adapted to handle the large sample sizes and dense marker sets that are now available, provide an opportunity to explore the disease impact of variants with low linkage disequilibrium with their neighbors, such as rare variants. Linkage methods do not rely on linkage disequilibrium in a population, and instead exploit genetic inheritance in families to identify risk regions. We have developed a series of methods that use large cohorts of family-based sequencing datasets to better understand sequencing error rates, meiotic crossovers, and disease risk. First, we show that familial relationships can be leveraged to estimate sample-level estimates of sequencing error rates. Next, we develop a hidden Markov model that identifies meiotic crossovers, shared genetic material between siblings, and inherited deletions in families. Finally, we develop a genome-wide sibling-pair linkage test which leverages sibling IBD to identify genomic regions harboring risk variants. This method not only increases detection power for rare risk variants, but also enables the use of microarrays which are widely and affordably available in the consumer market. Applying our method to crowdsourced autism families who have taken Ancestry.com DNA tests, we identify two significant autism risk regions which we validate with a separate and independent microarray dataset. We show how turning our attention to families provides the power to uncover key events in the genome that cannot be detected otherwise. This thesis provides a framework for extending family-based linkage analysis into the era of next-generation sequencing in order to increase our understanding of genetic risk factors for complex diseases
Using inducible signaling receptors for in vivo fate determination of hematopoietic stem cells to erythroid-specific lineages
Keywords:  Department of Biology
Synthetic biology
CRISPR-associated protein 9
Cell and Gene Therapy
HSPC
Hematopoiesis
Cell receptors
Text
Thesis

Over 7,000 disease-causing mutations have been identified to date, with this number increasing as more are identified. Cell and gene therapies (CGTs) represent a promising therapeutic avenue for generating definitive cures for a myriad of genetic and congenital disorders. However, CGTs are classically limited by editing and delivering efficiencies. This is especially true within the context of hematopoietic stem and progenitor cell (HSPC) gene therapy, where the degree of phenotypic effect is directly related to the degree of differentiation into disease-relevant lineages and the quantitative ability of the engineered cell and its progeny to engraft.
This study presents a set of first-in-class chimeric, synthetic receptors that support HSPC output down towards the erythroid lineage via a non-immunogenic, non-toxic small molecule.
Through functional assays and quantifying edited allele frequency changes during differentiation, this study has demonstrated these synthetic receptors— termed “erythroid cell drives”— that mimic the native erythropoietin/erythropoietin receptor signaling cascade with high fidelity and sensitivity. Additionally, these erythroid cell drives are demonstrated to be erythropoietin independent, dose responsive, and tunable via expression from different loci and promoters.
In sum, erythroid cell drives represent a powerful new tool for clinicians with which to alter the cellular dynamics of CGTs post transplantation and boost the production of clinically relevant red blood cells. The work demonstrated in this study has the potential to dramatically improve the efficacies of existing CGTs as well as offer a new tool by which to better understand stem cell biology.
Using layer information to improve control of image generation models
Keywords:  Department of Computer Science
Artificial intelligence
Generative AI
Text
Thesis

While generative models can produce photorealistic images resembling a prompt, text is an ambiguous medium to describe an artist's intent. Specifically, text is an imprecise way to describe complex scene compositions: it is difficult to describe where objects are spatially and how they should appear visually. Inspired by techniques from graphics systems, we introduce the abstraction of a layer to image generation models and explore how to leverage layer-based information to enable greater control over the generative process. We allow users to define output targets by expressing the spatial layout of layers and specify per-layer constraints that control the layer's appearance. To enable artists to create layer-based compositions, we provide a functioning implementation of an easy-to-use and highly performant web interface that enables users to iteratively generate complex images with diffusion models. We demonstrate through a user study that layer-based information allows novice users of the technology to quickly arrive at images to their liking.
Using marketing technology to grow small businesses : evidence from field experiments in emerging markets
Keywords: Business.

In this dissertation, I study the process and impact of customer feedback on small businesses in emerging markets. In order to do so, I design, execute, and analyze a field experiment with 274 small firms in Rwanda. The resulting seven chapters can be summarized with the following abstract. Academic researchers, as well as industry practitioners, recognize that the voice of the customer is a powerful tool that businesses can leverage to enhance their performance. Thus companies routinely solicit and obtain feedback from a subset of their customers. Whether the feedback then has any effect on the non-solicited customers depends both on whether the company makes organization-level changes, as opposed to responding just to the solicited customers, and whether these changes, if any resonate with those customers. This research seeks to assess the impact of customer feedback on firm learning about dimensions to improve and as a result, understand the spillovers it can cause to non-solicited customers. We conduct a randomized controlled field experiment in Rwanda to study the impact of customer feedback on a sample of small-scale entrepreneurs. We hypothesize that customer feedback could operate through two broad mechanisms - (1) the act of seeking feedback itself could have a direct effect on the customers who were reached out to, or (2) the feedback could cause firms to learn from it and respond by improving the products and experiences they offer, which in-turn could cause spillovers across customers. Our study attempts to tease these effects apart. The results from the experiment show that customer recall of the business increases by about 38% and purchase increases by 77% for customers who were not engaged in the feedback process. We present evidence to suggest that these spillover effects of feedback are driven by changes made by the firms in response to the customer feedback that they were provided. Finally, we also show that customer feedback led to an overall positive and significant impact on the performance of the treatment firms with a 62% improvement in sales and 54% improvement in profits relative to the control firms while showing suggestive evidence of diminishing marginal returns to feedback. Through this study we also elucidate a simple feedback seeking process which can be adopted by small businesses to improve their performance. These findings can guide firms in leveraging the information available with their customer base to improve the products/experiences they offer and drive greater profits
Using synchronized clocks to improve temporal fairness, congestion control, and load balancing in datacenter networks
Keywords: Engineering.
Electrical Engineering.

Datacenter networks have long operated under a clockless assumption: network devices cannot rely on timestamps from local clocks for global network-wide decisions. This assumption has served network architects and distributed systems developers well for the past 30 years. However, with the emergence of cheap, scalable, and host-based clock synchronization solutions with nanosecond-level synchronization accuracy, it is now possible to relax this assumption. In this dissertation, I will explore the new potential that accurate clock synchronization unlocks in datacenter networks. First, I will present CloudEx, a fair-access financial exchange system in the public cloud. CloudEx provides fairness for order processing and market data dissemination by compensating for the variable delays in the cloud using accurately synchronized timestamps. CloudEx has been used as a research tool for trading infrastructure and algorithms and has also been deployed as a teaching tool in a financial technology course at Stanford. Second, I will discuss On-Ramp. On-Ramp uses synchronized clocks to measure accurate one-way delays as a signal for congestion, then enforces precise pausing of packet transmission on flows when it senses congestion. I will present my contributions on testing On-Ramp in Meta to reduce the tail latency of a production service by 2.5-3x for the same throughput. Last, I will present Reactive Subflow Spraying (RSS), a multipath and reactive load balancing algorithm that uses synchronized clocks to measure accurate one-way delays as a signal for network load imbalance. I show how the performance of RSS in terms of flow completion times is within 5-10% of the optimal performance and is 2-3.5x better than other mechanisms in asymmetric setups, e.g., in the presence of link failures
Using ultrafast infrared spectroscopy to study free volume in polymer materials
Keywords: Humanities and Sciences.
Chemistry.

The study of the microscopic structure of polymer membranes has been the subject of research efforts for many decades. This research field has seen the development of both theoretical and experimental techniques that seek to understand the connection between molecular-level structure and macroscopic material properties. In parallel, ultrafast infrared spectroscopic techniques were developed to study the structure and dynamics of various molecular systems. Because infrared spectroscopy directly probes molecular vibrations, it is especially suited to provide structural information, and the development of high power and ever shorter laser pulses allowed the extraction of molecular dynamical information with remarkable time resolution. However, despite the many successes of time-resolved infrared spectroscopy with various chemical systems, with some exceptions, this technique has remained mostly absent from the field of polymer research. This work presents research performed to bridge this gap and provide a fundamentally new spectroscopic technique capable of extracting structural, dynamical, and electrostatic information of polymer membranes with great sensitivity, and in a non-destructive and minimally perturbative manner. This work focuses on different aspects of polymer research. It begins with a study of long-lived first vibrational excited sates and their importance as probes for the extraction of polymer structural information. Next, a study on new notions regarding the use of the first-order Stark effect to study environment specific dynamics -- which is crucial to the study of structurally heterogeneous polymer membranes - is presented. Then, a new technique called Restricted Orientation Anisotropy Method (ROAM) is developed, which uses a polarization-selective pump-probe spectroscopy experiment to extract free volume element sizes, size distributions, dynamic time scales, and intrinsic electric fields from polymer membranes. ROAM is then used to study the connection between free volume and polymer dielectric breakdown. Finally, ROAM is used to successfully probe physical aging in a polymer membrane. Together these studies provide an experimental framework for a fundamentally new way of studying polymers, and a direct connection between their macroscopic properties and microscopic structure