VictorJuiz/thesis huggingface dataset:
Myeloid cells in the tumor: a study on targeting myeloid-derived suppressor cells and activating macrophages for cancer immunotherapy
Keywords:  Department of Biology
Biology
Macrophages
Myeloid-derived suppressor cells
Tumor Immunology
Calreticulin
CD47
Text
Thesis

Colony stimulating factor-1 receptor (CSF1R) signaling is crucial for macrophage differentiation and survival. Inhibition of CSF1R through monoclonal antibodies and antagonists has been widely explored in preclinical studies due to its ability to deplete pro-tumorigenic tumor-associated macrophages. However, CSF1R inhibition has had limited clinical and therapeutic efficacy, necessitating the need for further investigation into the effects of CSF1R blockade on myelopoiesis in the tumor microenvironment. In this study, we report that deletion of CSF1R in an inducible knockout mouse model leads to the expansion of CXCR2+ granulocytic-myeloid derived suppressor cells (G-MDSCs) in both thioglycollate-induced peritonitis and tumorigenesis. Further, we demonstrate that targeting CXCR2+ G-MDSCs could increase macrophage programmed cell removal (PrCR), and combination with anti-CD47 monoclonal antibody treatment could serve as an effective therapeutic strategy for cancer treatment. Lastly, we begin a broader study on tumor clearance by macrophages through the lens of “eat me” signal calreticulin (Calr), ultimately revealing key molecular components of the mechanism of Calr secretion by macrophages for PrCR.
Natalie Cross' NSC ePortfolio
Keywords: Communication in science
Marine biology
Marine ecology
Marine animals
Climatic changes
Nature conservation
Information visualization
Mixed materials
Image
Text
Article
Essay
Geospatial data
Map
Photograph
Presentation slides
Remote sensing imagery
Report
Thesis
Essays
Cartographic dataset
Maps
Photographs
Remote-sensing images
Dataset

This document is an archive of an electronic portfolio (ePortfolio) produced as a capstone requirement for the Notation in Science Communication. In their ePortfolios, NSC students demonstrate how they have met the NSC learning outcomes. These learning portfolios detail students’ growth and development as scientists and science communicators rather than showcasing only their best work. ePortfolios are reviewed and approved by faculty, earning students a special designation on their official Stanford transcript that reflects their science communication acumen.
National Security and Academic Freedom: How American Universities Navigate Great Power Competition
Keywords: Center for International Security and Cooperation
research security
academic freedom
China
Second Red Scare
university
Text
Thesis

How do American universities navigate the tension between national security and academic freedom that arises during periods of great power competition? To answer this question, I examine two time periods when intense rivalry between the US and its rival country drove the American government to scrutinize universities: from 1947 to 1957, which encapsulates the Second Red Scare, and from 2013 to present day, which covers US-China great power competition. Across these two case studies, I find reoccurring patterns in the threat perceptions and mitigating strategies of the US government, university administrators, and professors. US government officials express concerns that foreign rivals are taking advantage of the openness of American universities to steal research and to brainwash students. These threat perceptions have led the government to investigate faculty accused of ties to foreign rivals, create new laws and policies, and publicly communicate to university administrators to try and mitigate these concerns. Much of these government strategies achieve success by pressuring administrators into complying with the government’s goals or by deterring faculty from carrying out certain actions. Some university administrators and faculty, meanwhile, argue that the government’s increased involvement threatens the core values of their institutions, especially academic freedom. A few have publicly expressed these concerns in attempts to pressure the government to limit its policies. Many university administrators, however, choose to cooperate with the government to protect their funding or because they agree with the government’s perspective. To protect themselves, many faculty members also limit their behavior and censor their studies. These trends indicate that balancing national security and academic freedom represents a difficult issue, one for which the US government, university administrators, and faculty have failed to reach a satisfactory solution. Modern developments in dual use technology and globalization only exacerbate this tension. This thesis hopes to inspire greater communication between policymakers, university administrators, and university faculty so all sides understand one another’s concerns.
Natural experiments in economics
Keywords: Economics.

What happens when an entire town suddenly receives a huge wealth shock? For over a century, Spain has conducted a national lottery which often results in the random allocation of large cash windfalls to one town. Leveraging data on lottery ticket expenditures, we match winning towns to non-winning towns with equal winning probability. For towns that won in recent decades, consumption of durables increases while employment, businesses, and migration to the town decrease. An analysis of a century of winners reveals a stark and persistent population increase for towns that won after the Civil War. My results suggest a limited role for wealth shocks in spurring economic growth outside of large recessions. In chapter 2, I study the effect of parenthood on the lives of low-socioeconomic-status parents. My data consists of longitudinal, high frequency administrative records from a large urban U.S. county, covering housing, treatment for substance use disorder (SUD), enrollment in government assistance programs, and crime. Using an event study design, I find that new parenthood leads to increases in housing instability and government assistance use. New parenthood also decreases crime and increases take-up of treatment for substance use disorder. The effects of parenthood are heterogeneous by race and vulnerability to mental health disorders. In chapter 3, I examine the extent to which education is a valuable investment for academically marginal students. I ask this question in the context of the Dominican Republic, where schooling is mandatory until eighth grade and many students must decide between continuing on to high school or entering the labor force. I estimate the value of years of secondary education and high-school completion for low-performing students by exploiting the passing cutoffs on the eighth and twelfth grade national exams. I find that students who barely pass the exam in eighth grade complete more years of education, are more likely to enroll in college and experience higher earnings. A broad implication of these facts is that education has large and positive returns even for the lowest part of the ability distribution
Negotiating equality at home : a historical study of informal intimate relationships in 20th century Colombia
Keywords: Law JSD.

This dissertation's main goal is to unearth the history of the legal changes that granted property rights protections to concubine women living in out-of-wedlock families in 20th century Colombia. Through the study of court cases, oral history interviews, newspapers, and legislative initiatives, I discover the agents of change, detail their strategies, and elucidate their losses and victories. I found that the absence of family law regulations deepened structural conditions of gender inequality in out-of-wedlock families, as control over property and economic means in informal relationships fell decidedly on men's side. Facing a total absence of Family Law protections, concubine women turned to the civil and labor courts to fight for their property. Therefore, the study of concubine women's fights reveals how these women attempted to alter the gendered order inside the family, rejecting the role of dependent and unpaid laborer. This dissertation sits at the intersection of legal history, family law, and feminist studies, and contributes to these distinct scholarly fields. First, I shed light on the urgency of studying the relationship between property and gender in informal intimate relationships as a separate study of the role of property within marriage. Second, I propose to add a level of complexity to the feminist critique of marriage. What I find with this research is that marriage is a double-edge sword as both an engine of women's oppression and a guarantor of certain property rights for women who enter it. Third, I propose a revision of the role of the Colombian feminist movement and its involvement with changes to Family Law
Neural Mechanisms of Juvenile Aggression
Keywords: Ludington, Sarah
violence, development, neuroethology, POMC, B-endorphins
Text
Thesis

Aggression in juveniles is an evolutionarily adaptive behavior documented in many species, but the neural mechanisms behind these displays are poorly understood. In humans, the importance of knowing what is happening in the brain during maladaptive aggression is especially key in instances of psychiatric disorders that display as aggressive tendencies in childhood, such as autism spectrum disorders (ASD), schizophrenia, and borderline personality disorder. Mimetic poison frog (Ranitomeya imitator) tadpoles live in small pools with limited resources and have evolved offensive aggressive tendencies towards conspecific tadpoles in defense of these resources. Tadpoles will attack, kill, and cannibalize other tadpoles as a primary resource defense mechanism. I examined the neural basis of neonate aggression in these tadpoles by comparing individuals that were placed in aggressive encounters and individuals placed in an environment with a non-threatening stimulus. I first did a longitudinal study to determine that the tadpoles were most aggressive when they were around five or six weeks old. I then compared patterns of generalized neural activity using immunohistochemical detection of phosphorylated ribosomes and a candidate neuropeptide, arginine vasotocin, which has been implicated in aggression in other vertebrates. Vasotocin cells in the preoptic area of the hypothalamus were not more active in aggressive tadpoles. I then examined neural activation across several brain regions suspected to be involved in aggression, and found that the anterior amygdala had the most neural activation in aggressive tadpoles compared to controls and the other brain regions. Through molecularly profiling active neurons using phosphoTRAP, I identified proopiomelanocortin (POMC), a precursor polypeptide with multiple post-translational peptides, as enriched in active neurons during aggression. Through pharmacological manipulations with the peptides of POMC (aMSH, ACTH, and B-endorphin), I found that a B-endorphin antagonist significantly increased aggressive behavior in tadpoles. Overall, this project is advancing understanding of how aggression is regulated in the juvenile brain.
Neural network approaches to the study of word learning
Keywords: Linguistics.

I present three approaches to using neural network models for studying word learning in children. In the process, I show what we can conclude from these different approaches about how words are learnt by humans as well as by neural networks. The first approach takes neural networks to be cognitive models, allowing for stronger conclusion about the connection between model and human word learning outcomes. I show that the predictability of words in the linguistic contexts children are exposed to is a good predictor of the age at which children will begin to produce these words themselves. Additionally, I demonstrate that language models share similarities with children when it comes to the order in which they learn new words, but only if these words are predicates or function words. The second approach considers neural networks to be independent learners from humans and uses them as a "proof of concept" tool. These model learners can exemplify what behaviors are in practice learnable rather than inherent. Here, I show that the meanings of logical operators like "and" and "or" are in fact learnable by models that are exposed to visually grounded language, and therefore, could also be learnt without prior biases by human learners, supporting existing usage-based theories of their acquisition. I also find that these visually-grounded models can learn the meanings of other function words, favoring gradient semantic representations over threshold based semantics for comparative quantifiers and spatial prepositions. The third and final approach, like the second, considers models as independent learners, but instead of using them to differentiate between existing proposals, it considers them as a tool to generate novel hypotheses about what drives language learning in both models and children. We can establish novel causal relationships about what allows models, and by extension possibly humans, to learn a given outcome. I show that neural agents exposed to social interaction in addition to visual and linguistic input can acquire a shape bias when learning new lexical categories, and furthermore, that this bias follows from efficient communication strategies. I propose that the shape bias seen in humans could also follow from these same communication strategies
Neural network models of visual learning
Keywords: Psychology.

Humans show remarkable ability in not only recognizing the complicated visual environment surrounding them but also efficiently learning from this environment. The ventral visual stream underlies this critical ability and is currently best modeled by deep neural networks both quantitatively and qualitatively. However, such networks have remained implausible as a model of the development of the ventral stream, in part because they are trained with supervised methods requiring many more labels than are accessible to infants during development. In this dissertation, we first propose strong learning algorithms that learn from totally unlabelled or only partially labelled data. Then, we show that these algorithms together have largely closed this gap. We find that neural network models learned with deep unsupervised contrastive embedding methods achieve neural prediction accuracy in multiple ventral visual cortical areas that equals or exceeds that of models derived using today's best supervised methods, even when trained solely with real human child developmental data collected from head-mounted cameras, despite the fact that these datasets are noisy and limited. The proposed semi-supervised method has also proven to leverage small numbers of labelled examples to produce representations with substantially improved error-pattern consistency to human behavior. Furthermore, we propose two learning benchmarks measuring how well unsupervised models are able to predict human visual learning effects in both real-time and life-long timescales. Testing multiple high-performing unsupervised learning algorithms at both time-scales, we show how specific algorithm designs are helping matching the human learning results. Taken together, these results illustrate one of the first uses of unsupervised learning to provide a quantitative model of a multi-area cortical brain system, and present a strong candidate for a biologically-plausible computational theory of primate sensory learning. In addition to this, we also present models of other functions or species, serving as pre-steps of extending the models of visual learning to these domains
Neutrino driven outflows in supernovae : from hydrodynamics to nucleosynthesis
Keywords: Physics.

Core-collapse supernovae (CCSNe) have shaped our galaxy, played an essential role in the formation of the solar system, and created many elements found on Earth. Some of the outstanding questions in the field of core collapse supernovae are intimately tied to neutrinos. Neutrinos are produced in copious numbers in a CCSNe explosion and are messengers of the physical processes occurring in and around the hot and dense supernova core where matter reaches nuclear densities. In the supernova explosion detected in 1987, known as SN1987A, only 25 neutrino events were detected, yet this already provided vital clues to the explosion mechanism. With the next-generation neutrino detectors such as DUNE and Hyper-Kamiokande, which will have the capacity to detect many thousands of neutrino events from the next Galactic supernova explosion, qualitatively new understanding of the supernova phenomenon will be achieved. The question is then what features one may expect to see in these detectors and how can one reconstruct the explosion dynamics from the neutrino observations. In this thesis, we will focus on the physics that takes place several seconds after the explosion is launched. We will focus on a region known as the `hot bubble' of supernova explosion, which hosts a plethora of interesting phenomena, ranging from non-trivial neutrino oscillations to nucleosynthesis of heavy elements. We will show that the hydrodynamics of this hot bubble can crucially impact neutrino oscillations and that the imprints of the resulting density features on the neutrino spectrum can be detected in DUNE. We will also show that the hot bubble can be a site of a certain nucleosynthesis process, known as the $\nu p-$ process, which helps resolve a 30 year old puzzle regarding the origin of certain proton-rich isotopes observed in the Solar System, such as $^{92,94}$Molybdenum and $^{96,98}$Ruthenium
Neutron and X-ray scattering studies of disorder in quantum materials
Keywords: Applied Physics.

Quantum materials generally refer to condensed matter systems whose properties are dictated by strongly interacting quantum mechanical effects. This includes many classes of materials at the forefront of condensed matter research, for example high temperature superconductors, quantum spin liquids, and many others. In addition to hosting their eponymous phases, frequently these materials also exhibit competing ground states, where minute parameter changes lead to qualitatively different phases. The existence of atomic scale disorder is often sufficient to alter the ground state of many such systems. Experimentally, disorder is an inherent component of real (and in particular, bulk) materials, commonly due to imperfections in synthesis and intentional introduction of substitution/dopant atoms. It is therefore important to characterize and model the disorder in such materials to fully understand their physics. The nature and time/length scales of disorder varies significantly between different materials, requiring specifically tailored techniques. This thesis details innovative neutron and x-ray scattering studies on several quantum materials of current interest with distinct types of disorder relevant to high temperature superconductivity and quantum spin liquids
New constructions in Ramsey theory
Keywords: Mathematics.

Ramsey theory is the study of the patterns and structures that must arise in any sufficiently large system. Many important results in Ramsey theory rely on certain constructions of large systems with special properties, such as Erdős's random construction of a graph with no large clique or independent set. In this thesis, we use probabilistic and combinatorial tools to find new constructions, and thus make progress on multiple Ramsey-theoretic problems
New metrics for evolving risks to human and wildlife health
Keywords: Biology.

Humans are altering the global environment at a rapid pace, which impacts ecosystems and creates opportunities for the spread of infectious diseases to new species. Healthy ecosystems are often dependent on healthy human populations and vice versa. However, resolving risks to human and wildlife populations requires insights provided by a scale of data that is not often available. New modeling approaches can be used to maximize the use of available data to achieve critical insights. In this dissertation, my collaborators and I develop new analytical and statistical approaches to understand the magnitude of risks to the health of human or wildlife populations and provide practical knowledge to mitigate those risks. We first examine the seasonal risk of spillover of a pathogenic acanthocephalan parasite to southern sea otters, which we find is driven by intermediate host population dynamics and sea otter foraging behavior. Then, we investigate the relationship between the genome sizes of viruses and their potential to spillover into new species by comparing the genomes of zoonotic and non-zoonotic viruses. We find that larger genomes increase spillover risk. The risk of death from viruses is difficult to discern due to incomplete sampling of affected individuals. We develop a statistical method to calculate the true risk of death for COVID-19 across countries and find this risk in the early stages of the pandemic was approximately 1\%. Lastly, we investigate through a diverse set of population genetic and epidemiological modeling how emerging gene drive technology can mitigate human risk to schistosomiasis. We find that schistosomiasis can be reduced under select conditions, such as low selfing rate in snail intermediate hosts. Together these chapters demonstrate how to leverage new quantitative approaches to guide conservation and disease management decisions, each important aspects of human and wildlife health
Next generation cell therapies : from genome to receptor to vector engineering
Keywords: Bioengineering.

The next generation of cell therapies will be leveraged to target solid tumors, clear viral infections, repair wounds, overcome autoimmunity, reverse aging, and beyond. To achieve this goal, diverse immune cell types need to respond to clinician- or disease-derived inputs and generate novel therapeutic responses. However, multiple challenges exist that inhibit the development of these next generation cell therapies including 1) primary immune cells silencing delivered transgenes, 2) engineered receptors lacking response to soluble inputs or activation of therapeutic pathways, and 3) clinically approved viral vectors that are incapable of engineering diverse immune cell types. To overcome these challenges, we expanded the cell therapy toolbox by developing a suite of technologies for genome, receptor, and vector engineering. Leveraging genome engineering, we develop a CRISPR-based virus-like particle (VLP) method to knockin very large genetic payloads into essential genomic loci. This methodology stabilizes expression of large and difficult to express transgenes in primary human T cells over a long period. With receptor engineering, we generate a chimeric transmembrane protein that oligomerizes in response to a small molecule drug or protein antigen to activate Toll-like Receptor (TLR) signaling. The receptor controllably increases the cytotoxicity of natural killer (NK) cell to enhance their anti-cancer capacity. With vector engineering, we reprogram lentiviruses to target specific cell types quickly and easily. This is leveraged to engineer cells that normally cannot be engineered by lentiviruses like NK cells or selectively engineer cell subtypes in a mixture. Together, these technologies create a new foundation for generating cell therapies that move beyond T cells targeting blood cancers and maximize the therapeutic benefit of this new drug class