VictorJuiz/thesis huggingface dataset:
Unraveling the immunological ecosystem of human tuberculosis

Outcomes in tuberculosis (TB) infection are thought to be driven in part by underlying differences in the phenotype and structure of cells in granulomas, the histological hallmark of TB. However, high-dimensional phenotypic and spatial analysis of TB granulomas has not been conducted. Here, we utilize multiplexed ion beam imaging by time of flight (MIBI-TOF) to construct a map of the composition and architecture of human TB granulomas. MIBI-TOF employs mass spectrometry to image metal-conjugated antibodies, allowing multiplexed single-cell phenotyping that retains the spatial organization of complex tissues. We applied MIBI-TOF to a cohort of clinical specimens from patients with TB disease, revealing the complex single cell composition of granulomas, including a spectrum of myeloid subsets and a highly ordered structure of protein expression. We then used a spatial adaptation of the machine learning algorithm, Latent Dirichlet Allocation (spatial-LDA), to identify cellular microenvironments indicative of spatially coordinated immune responses in TB. This revealed an immunosuppressed microenvironment with spatially coordinated co-expression of IDO1 and PD-L1 by myeloid cells, proliferating regulatory T cells, and high levels of TGFB along with a near absence of IFNg and markers consistent with T-cell activation, supporting a myeloid-mediated mechanism of immune suppression. We observed similar trends in gene expression of an immunoregulatory program in a confirmatory transcriptomic analysis of peripheral blood collected from over 1500 individuals with TB infection and healthy controls across 29 cohorts spanning 14 countries. Overall, this first-of-its-kind systems analysis of human granulomas raises the possibility of myeloid-mediated immune suppression within the granuloma and serves as a framework for leveraging independent cohorts and complementary methodologies to understand how local and systemic immune responses are linked in human health and disease

Keywords: Immunology.
Advisor: D'Angelo Robert
Banaei Niaz
Bendall Sean
Davis Mark M.
Using AI for improving evacuation during compound disasters

Compound disasters have been leading to extensive casualties and property losses around the world as more frequent and intense natural disasters, pandemics, and humanitarian crises have overlapped in recent years. Evacuating people in a more timely, efficient, and safe manner has thus become a complex challenge in disaster response and management. However, very little work has utilized AI to improve evacuation during compound disasters. The work presented here aimed to address this gap. In its three central chapters, this dissertation addresses three aspects of disaster evacuation amidst a changing climate and an emerging pandemic: early warning decision-making prior to an initiation of disaster evacuation, emergency vehicle routing during a disaster evacuation, and pandemic risk mitigation after initiation of a disaster evacuation. After an introductory chapter, in my second chapter, I present data-driven machine learning models that improve upon the existing rainfall-based debris flow warning model in Taiwan. In addition, I identify the rainfall trajectories most strongly related to debris flow occurrences and provide decision-makers with comprehensive trade-offs and multi-objective analysis to balance the risks of missing debris flows versus frequent false alerts. In my third chapter, I discuss whether a DNN (Deep Neural Network)-based solution (Deep Reinforcement Learning) can provide sufficient extra routing efficiency to accommodate increased social distancing in a time-constrained evacuation operation compared to a non-DNN solution (Sweep Algorithm). My fourth chapter shows how I used an age-structured epidemiological model, known as the Susceptible-Exposed-Infectious-Recovered (SEIR) model, to analyze to what extent different disaster evacuation strategies can decrease infections and delay pandemic peak occurrences. In sum, the data-driven findings presented in this dissertation demonstrate the utility of AI and data science as decision-support tools for disaster resilience, and contribute to model-based decision-making for the dynamic compound disaster response and evacuation operations

Advisor: Field Christopher B.
Kitanidis P. K. (Peter K.)
Fletcher Sarah (Sarah Marie)
Using ancient human genomes to uncover temporal and spatial population dynamics

Ancient DNA research in the past decade has revealed that European genetic population structure changed dramatically in the prehistoric period (14,000-3,000 years before present, YBP), reflecting the widespread introduction of Neolithic farmer and Bronze Age Steppe ancestries. However, little is known about how population structure changed in the historical period onward (3,000 YBP - present). This dissertation addresses this question through both spatial and temporal dimensions. First, we showed that the population in the city of Rome alone was highly dynamic over time, and remarkably heterogeneous at the height of the Roman Empire, including 40% of the population having Near Eastern ancestry. To understand the genetic structure of contemporaneous populations outside of Rome and how they interacted, we collected whole genomes from 204 European and Mediterranean individuals, many of which are the first historical period genomes from their region (e.g. Armenia, France). Similar to our findings in Rome, we found that most regions show remarkable inter-individual heterogeneity. Around 8% of historical individuals carry ancestry uncommon or non-existent in the region where they were sampled, some indicating cross-Mediterranean contacts. Despite this high level of mobility, overall population structure across western Eurasia is relatively stable through the historical period up to the present, mirroring the geographic map. We show that, under standard population genetics models with local panmixia, the observed level of dispersal would lead to a collapse of population structure. Persistent population structure thus suggests a lower effective migration rate than indicated by the observed dispersal. This phenomenon can be explained by extensive transient dispersal arising from drastically improved transportation networks and the Roman Empire's mobilization of people for trade, labor, and military. Together, these works highlight the utility of ancient DNA in elucidating finer scale human population dynamics in recent history

Keywords: Medicine. Department of Biomedical Data Science
Advisor: Pritchard Jonathan D.
Altman Russ
Rosenberg Noah
Schumer Molly
Using natural language processing on social media to understand psychological processes

Because of their global reach, social media platforms provide a treasure trough of naturalistic language data for examining social and psychological phenomena. In this work, we use social media language to examine affective processes and measure population well-being and health. Regarding the examination of affective processes, in Chapter 2, we found that Twitter users in the US (in 55,867 tweets from 1,888 users) and Japan (in 63,863 tweets from 1,825 users) are more likely to produce affect that supports cultural values, but are more influenced by affect that violates cultural values. In the process, we also built a new sentiment analysis tool for Japanese to better facilitate research in this area. In Chapter 3, we examined these patterns specifically in U.S. news media (in 30 million tweets posted by 182 news sources). We showed that across a decade (2011-2020), politically biased news sources produced posts with more high arousal negative affect (e.g., anger, hate) on Twitter and that these posts were more likely to spread. Regarding the measurement of population well-being and health, in Chapter 4, we combined advances that have evolved between 2013 and 2021 that incrementally improved data pre-processing, natural language processing, and machine-learning methods using social media language to measure population well-being. Using 1.3 billion Twitter posts and 1.9 million Gallup respondents in the U.S., we establish the current state-of-the-art (as of 2022) to measure population well-being and demonstrate its performance and validity across several validity domains, including test-retest reliability and face and external convergent validity. In Chapter 5, we study the psychological adaptation to the COVID-19 pandemic by leveraging 620,191 Twitter posts from the early stages of the pandemic (March to May 2020) that were geolocated to 889 US counties, finding that people switched from more self-referential language (I, me, mine) to more group-referential language (we, us, ours) following shared trauma, similar to what has been observed during prior crises. In the same study, we also found that overall use of self-referential language in a county predicted future COVID-19 mortality rates. Together, these studies show the potential of social media language data, combined with psychology and computer science methods, to help us understand and monitor psychological and societal processes in the digital age

Keywords: Psychology.
Advisor: Tsai Jeanne Ling
Eichstaedt Johannes C.
Knutson Brian
Markus Hazel Rose
Using natural language processing to support student-centered education

Language is central to education, being the core medium of instruction. Researchers and practitioners have long used manual methods to analyze instructional language like classroom discourse and instructional texts, with the goal of facilitating student-centered instruction. In this dissertation, I offer three studies demonstrating how natural language processing (NLP) can expand the possibilities for student-centered educational research and practice. First, I illustrate how computational methods can help analyze textbook content. Applying lexicons, word embeddings and topic models, I study the depiction of historically marginalized groups in U.S. history textbooks. I find that Latinx individuals are rarely discussed and African Americans are described with low agency and power. The quantification of these inequities can aid researchers, teachers, publishers and other stakeholders make informed decisions when developing and choosing textbooks. Second, I demonstrate how NLP can identify teachers' student-centered talk moves in classroom discourse. I develop an unsupervised machine learning measure for teachers' uptake of student contributions, a high-leverage teaching practice that supports dialogic instruction and makes students feel heard. I extensively validate this measure by analyzing the linguistic phenomena it captures, such as repetition and elaboration, and by demonstrating its correlation with positive educational outcomes across three datasets of student-teacher interaction. Third, I demonstrate how computational research on classroom discourse can be translated into improving educational practice. Building on my uptake measure, I develop a web application that provides teachers with consistent, individualized feedback on their uptake of student contributions. In a large-scale randomized controlled trial in an online computer science course, Code in Place (n=1,136 instructors), I find that the tool improves instructors' uptake of student contributions by 27% and I present suggestive evidence that it also improves students' satisfaction with the course and assignment completion. Together, these studies demonstrate the possibilities for natural language processing methods to measure and to improve key aspects of student-centered instruction

Keywords: Linguistics.
Advisor: Jurafsky Dan
Hill Heather C.
Piech Chris (Christopher)
Using the Metabolic Index to Elucidate the Relative Predictive Capacities of Environmental Indicators on Marine Invertebrate Biogeography in the Southern California Bight

Under a changing climate, the marine life in the global ocean is facing a variety of stressors. Hypoxia (low oxygen conditions), warming, and acidification are of particular concern. While studies have examined the effects of these various stressors, little research has explored synergistic effects of multiple stressors and the role of physiological parameters on invertebrate biogeography. Here, I calculate the metabolic index, an ecophsyiological framework that incorporates both temperature and oxygen in order to understand how an organism’s environment affects its phenology and biogeography. Employing machine learning techniques, naïve Bayes and full Bayes models, I investigate the relative capacity of various environmental parameters–temperature, oxygen, metabolic index (phi), salinity, and pH–for predicting the occurrence of Kellet’s whelk (Kelletia kelletii) and white urchin (Lytechinus pictus) in the Southern California Bight. Although Kellet’s whelk occurrence data was too sparse for machine learning techniques to produce viable results, Bayesian models did indeed produce predictive results for white urchin analysis. I ultimately find that although there seems to be no single environmental parameter that wholly accounts for marine invertebrate occurrence, full Bayes models indicate that oxygen leads as the dominant parameter with the highest predictive power for white urchin in the study area. This finding agrees with existing literature on the importance of oxygen in predicting biogeographic distribution in marine environments. Ultimately, these results contribute to a growing body of research indicating hypoxia is a major threat to marine ecosystems and highlight the need for expanded policy efforts to mitigate greenhouse gas emissions driving oxygen depletion in global oceans.

Keywords: metabolic index
Sea urchins
Kellet's whelk
Oceanography
deoxygenation
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Thesis
Advisor: Sperling Erik
Payne Jonathan
Marquez Andy
Using the prognostic score to design stronger observational studies

The discipline of causal inference has risen to great importance - particularly in medicine - because of the increasing abundance of passively-collected observational data and the urgent burden of causal questions too numerous to be addressed with individual randomized experiments. For decades, popular causal inference approaches have involved matching or adjustment based on a propensity score, which summarizes measured baseline variation associated with treatment assignment. However, the propensity score does not capture all of the baseline variation which may be important to a causal question. The prognostic score, encapsulating baseline variation associated with the potential untreated outcome presents a promising -- though underdeveloped -- complement to the propensity score. I present several methods which leverage the prognostic score alongside the propensity score to facilitate stronger study designs: After addressing prognostic score estimation using a new pilot design approach, I introduce and characterize (1) a study design for jointly matching subjects on propensity and prognostic scores, (2) prognostic score stratification approach for large matching studies, and (3) the assignment-control plot, a versatile visualization tool for causal inference study design, methodological research, and education. The more clearly we characterize meaningful aspects of baseline variation -- such as the prognostic score -- the more effectively we can design and communicate about observational causal inference studies

Keywords: Biomedical Informatics.
Advisor: Baiocchi Michael
Chen Jonathan H.
Simard Julia
Validation of the Alliance Sleep Questionnaire for Screening Obstructive Sleep Apnea Compared to Gold Standard In-Lab Polysomnography Study

Background: Obstructive sleep apnea (OSA) is the most common type of sleep-disordered breathing disorder and is associated with various health repercussions. Given the high prevalence of OSA, diagnosis needs to be better streamlined to get individuals the care they need to improve their overall wellbeing. The current gold standard for diagnosing sleep disordered breathing requires patients to have an overnight in-lab polysomnography (PSG). The purpose of this validation study was to evaluate which questions from the self-reported Alliance Sleep Questionnaire (ASQ) have diagnostic utility for screening sleep apnea.
Methods: The sample used a research study cohort of individuals who completed a PSG and filled out the ASQ (n = 735 females, n = 631 males) of who were n = 439 no apnea, n = 443 mild apnea, n = 262 moderate apnea and n = 222 severe sleep apnea. Logistic regression models were used to identify relevant ASQ questions associated with apnea to find correlations, create regression models, classification trees and the area under the curve (AUC) for the final models (backward selection).
Results: The results of this study found that sex, BMI, and the primary complaint of loud snoring were the most predictive variables for sleep apnea. For all severities, (mild, moderate, and severe) being male was a strong predictor with odds ratios (OR) of 2.31, 3.92 and 4.69 respectively. BMI was split into three groups: normal weight, overweight and obese. Across all severities the odds ratios increased with weight category. The overweight group had OR’s of 1.44 for mild, 1.75 for moderate and 2.87 for severe. Similarly, the obese group had OR’s of 1.60 for mild, 2.79 for moderate and 8.68 for severe. Lastly, the primary complaint of snoring had OR’s of 1.89, 3.73 and 7.93 for mild,
moderate, and severe apnea. AUC for the final models were 0.69, 0.79 and 0.88 (mild, moderate, and severe).
Conclusions: Using the ASQ questionnaire to screen for sleep apnea and predict severity is possible using a specific subset of questions. There is need to replicate these findings in a community-based sample before it can be implemented into clinical practice.

Keywords: Sleep apnea syndromes
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The value of reproduction in American photography, 1969-1974 : authorship, ownership, and culpability

The art historian Douglas Crimp wrote in 1984: "photography may have been invented in 1839, but it was only discovered in the 1970's." He refers to a story of photography's assimilation into art history: this dissertation shows how the 'discovery' of photography in 1970's America was not appropriation but literal remaking--old photographs were reproduced to make photography cohere as an autonomous art. Further, the technical details of this remaking were connected to literary, economic, and legal debates over the meaning of reproduction. These debates spawned theoretical models of reproduction that photographers drew upon to portray the technical details of their work to favorable effect, selectively highlighting aspects of the reprinting process to create new value for the resulting photographs. Centering the period around how reproduction changes the value of a photograph, I show how Lee Friedlander, Diane Arbus, and Michael Lesy negotiated misperceptions and generalizations about photographic reproduction to position their respective work as intellectual property, unique but reproducible art, and convincing forensic evidence

Keywords: Art and Art History.
Advisor: Nemerov Alexander
Kwon Marci
Meyer Richard
Values-driven decision support for cadaveric kidney transplant decisions

Although the demand for kidney transplants continues to greatly exceed supply, most cadaveric kidneys are offered to multiple patients before being accepted or discarded. The decision of whether to accept or decline an offered kidney is difficult because of uncertainty about the quality and length of the patient's remaining life. If they decline the offer, then it is uncertain whether they will ever receive a kidney, let alone one of higher quality than the one currently available. This thesis develops a decision-analytical approach to model the situation facing patients waiting for kidney transplants, based on twenty years of data on patients waiting for transplants in the US. When they are offered a matching kidney, they and their surgeons must decide whether to accept the offer. We model each patient's situation as a one-step decision-making problem that compares the observed remaining lifetime or landmark survival for similar patients who accepted an offered kidney of similar quality and those who declined such an offer. This information can facilitate a meaningful conversation between a patient and their surgeon that can address the particular preferences and circumstances of that patient. Our analysis shows that for patients in almost every situation, the observed remaining lifetime for similar patients was longer if they accepted the offer than if they declined it, and sometimes significantly longer. Because accepting an offer almost always reduces the time a patient will spend on dialysis, it also improves the quality of life for most patients. Therefore, we believe that our results should encourage patients to accept many more offered kidneys than they have in the past

Keywords: Management Science and Engineering.
Advisor: Shachter Ross D.
Kochenderfer Mykel J.
Van Roy Benjamin
Variations in Russian Support for Far-Right Actors

This paper analyzes variation in Russian support for far-right organizations, which has increased since the return of Vladimir Putin to the Russian presidency in 2012. Building on previous research into state sponsorship of terrorist groups and support for political movements across state borders, this study seeks to determine how a state may select which groups to sponsor or support. This paper consists of a comparative case study of Russian support for two terrorist organizations––The Base and the Russian Imperial Movement––and two political parties––the Northern League in Italy and the National Rally in France. My analysis finds that ideology is a key differentiating factor in motivating support for terrorist organizations, while propensity to succeed is a differentiating factor for political parties. This study seeks to determine how these organizations are able to operate in order to diminish the harm they are able to inflict on minority groups in their regions of operation.

Keywords: Stanford Global Studies
Center for Russian, East European Studies
Right-wing extremists
Russia
Le Pen, Marine
Salvini, Matteo, 1973-
Russian Imperial Movement
The Base
Lega nord
Northern League
National Rally
Front national (France : 1972- )
Terrorism
Putin, Vladimir Vladimirovich, 1952-
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Thesis