VictorJuiz/thesis huggingface dataset:
Detecting adaptive regulatory evolution in cancer using copy number alterations and a generalized sign test

Gene expression changes accompany somatic mutations in cancer, often as their direct consequence. Whether such regulatory mutations increase in frequency reflects the cellular fitness of the associated expression changes. Identifying which regulatory mutations are present in human tumors, as well as their gene targets, is needed to improve our understanding of evolution in cancer and prioritize targets for treatment. After introducing the research question, I describe in Chapter 2 an approach for mapping genomic regions where copy number influences gene expression ("copy number expression quantitative trait loci"; cn-eQTLs) and report widespread associations. In Chapter 3, I introduce the cancer association toolkit software, casskit, written to enable large-scale cancer association studies. casskit wraps performant data science libraries and offers federated data structures and model components for commonly encountered cancer association tasks. In Chapter 4, I introduce the population sign test (PopST), a generalization of QTL-based sign tests for neutral evolution. PopST pools inference across independent evolutionary trajectories, while incorporating covariates and propagating uncertainty from QTL mapping. In chapter 5, I present results from applying PopST to cn-eQTLs mapped in a large, pan-cancer cohort of tumor samples to identify adaptive regulatory evolution. Across cancers, copy number alterations systematically reinforce expression changes in cancer genes and genes involved in proliferation. I highlight how PopST can detect subtype-specific selection and selection at the level of protein complexes, where I find coordinated up-regulation of spindle assembly checkpoint components. Finally, I conclude by discussing how regulatory evolution can provide a framework for interpreting cancerassociated genetic variants through their downstream effects on gene expression

Advisor: Fraser Hunter B.
Jarosz Daniel
Petrov Dmitri Alex
Developing automated tools in radiation therapy to streamline treatment planning and improve plan quality

By recent estimates, just under 2 million cancer cases are diagnosed in the US each year, with a majority of those patients receiving radiation therapy. For each patient, a team of personnel manually designs a treatment plan with the intent to treat diseased tissue while minimizing damage to surrounding healthy organs. Treatment planning for these patients can be both time-consuming and labor intensive, especially for emerging treatment modalities where radiation is delivered from noncoplanar directions or with nonuniform gantry arcs. Radiation therapy treatment planning is a complicated process for determining various machine parameters that allow for the delivery of a desired dose distribution. As treatment planning can involve many conflicting objectives, no single plan can optimize performance on all objectives at once. Instead, planning typically involves the navigation of numerous clinical trade-offs, where planners iteratively adjust certain hyperparameters in order to achieve an acceptable treatment plan. To streamline the treatment planning process, we have developed a fully automated framework, which requires no active planning time. This dissertation will present the components of this framework—including treatment plan utility functions, meta-optimization, content-based image retrieval, etc.—as well as the applications of automated planning to emerging treatment modalities. Were this framework to be adopted in clinical settings, we would expect substantial reductions in planning times and improvements to overall plan quality

Keywords: Bioengineering.
Advisor: Xing Lei
Altman Russ
Marsden Alison (Alison Leslie)
Yang Yong (Clinical professor of radiation)
Developing FPGAs as an acceleration platform for data-intensive applications

FPGAs have grown in popularity as compute accelerators in recent years, being deployed in the clouds of Amazon, Alibaba, and Microsoft for either internal tasks like networking acceleration or directly for customers to rent. As their use has grown, it has become increasingly clear that they are fairly unproductive for developers compared to competing platforms like GPUs and CPUs. We aim to close this gap in this dissertation by taking a domain-specific approach. We argue that general-purpose FPGA development tools are fundamentally limited by the complexity of the platform, and therefore focus on building faster and simpler tools for the specific case of streaming data-intensive applications. We first present Fleet, a system for accelerating massively parallel streaming workloads on FPGAs. Fleet provides a simple language for users to define a compute unit that processes a single stream of data, and then automatically replicates the compute unit many times into a memory controller fabric so that the final design can process many independent streams at once. We next present a fast compilation system for Fleet-like applications. Our system leverages the fact that the memory controller design is fixed across applications, and therefore compiles it ahead of time, leaving empty slots for copies of the user's processing unit. The user-visible compile time is thus reduced only to the time required to compile a few copies of the processing unit and replicate them into the prebuilt memory controller. Finally, we leverage the design patterns of identical compute units and streaming DRAM access to design a FPGA accelerator for the problem of finding interesting subgroups in large tabular datasets. This accelerator is able to outperform GPUs and CPUs on a cost per throughput basis due to its customized partitioning of SRAM resources across compute units

Advisor: Hanrahan P. M. (Patrick Matthew)
Zaharia Matei
Horowitz Mark (Mark Alan)
Development and characterization of dispersed alkali carbonates as solid bases for C-H carboxylation reactions

Transitioning to a more sustainable chemical industry requires developing syntheses that minimize energy input and waste and can utilize waste materials such as carbon dioxide (CO2). A major contributor to the energy input of processes is the upstream energy required to synthesize any stoichiometric reagents used. Therefore, avoiding stoichiometric use of energy-intensive resources is imperative. One approach to CO2 utilization is the formation of carboxylic acids and derivatives, a valuable set of materials with applications in polymers, solvents, and fine chemicals. Synthesizing these from CO2 and un-activated C-H bonds would incorporate CO2 as a C1 feedstock and could displace conventional methods that rely on harsh oxidations. Due to the stability of the reactants, developing a process that avoids stoichiometric reagents that are energy-intensive to produce has been a continuing challenge. Prior work in the Kanan lab involved developing conditions for the C-H carboxylation of monocarboxylate salts (pKa > 35) to form the corresponding dicarboxylates, using carbonate as a base under high CO2 pressures. Carbonate is an attractive base because it can be easily regenerated from its conjugate acid, bicarbonate, by thermal decomposition. More recently, we have expanded the scope of this reaction to neutral substrates using a high surface area super basic carbonate formed by dispersing alkali carbonate salts into the pores of an inert mesoporous support. The carbonate can be regenerated after methylation and removal of the carboxylate product and used for multiple cycles. This dissertation will describe the early development of this reaction for arene esterification, further improvements in the carbonate base by moving to high pore volume mesoporous carbon supports, and efforts to better understand the carbonate structure and origin of the super-basicity at high temperatures. The first chapter will be a brief review of solid base chemistry the use of solid base catalysts in industrial setting. The second chapter will introduce the development of mesoporous dispersed carbonates and their application for a closed, two-step cycle for arene esterification with CO2 and methanol. The third chapter will describe how mesoporous carbon supports can be used to increase the reactive fraction of the material by increasing both the carbonate loading and conversion. The fourth and final chapter will describe studies to better understand the carbonate structure and origin of the super-basicity through high-temperature characterization by IR spectroscopy

Keywords: Chemistry.
Advisor: Kanan Matthew William
Solomon Edward I.
Xia Yan
Development and characterization of small molecule responsive RNA switches

RNA is more than a messenger -- advances in the last decade have shown RNA playing a key role beyond the central dogma in cellular and organismal regulation. Inspired by these breakthroughs researchers have developed a new generation of RNA-based devices for use in manufacturing, human health and basic research. In the span of two years mRNA vaccines were designed, tested, manufactured and distributed to hundreds of millions of humans worldwide. Improving RNA technologies requires the creation of new RNA sensors, specifically for small molecules. My thesis work covers two approaches to help improve our development of small molecule RNA sensors. We built on previous studies from our lab that detailed a novel method, DRIVER, for the enrichment of a large number small-molecular RNA aptamers. Through our work we ran a selection against 5,120 small molecules and developed new RNA aptamers against over 200 small molecules, tripling the number of small molecular RNA aptamers. Additionally, using chemical mapping techniques we characterized RNA devices developed in our lab to understand tertiary RNA-ligand interactions and improve our selection methods

Keywords: Bioengineering.
Advisor: Bryant Zev David
Smolke Christina D.
Fire Andrew Zachary
Fordyce Polly
Development of a live-cell HIF-1α reporter and characterization of single-cell HIF-1α signaling

Hypoxia inducible factor-1α (HIF-1α) is a crucial transcription factor required to regulate cellular responses to hypoxia, as well as large-scale metabolic changes in rapidly dividing cells and immune cells. Yet, despite the importance of HIF-1α, little is known about the dynamics of its activation at a single cell level. Here, I present my work to address this shortcoming. First, I review in detail some of the key characteristics of HIF-1α and its regulation of metabolism, as well as techniques for studying how single cells decode dynamic information. Next, I present my work developing a computational pipeline for high-throughput analysis of live-cell microscopy data, which was used to analyze all of the data presented here. Then I discuss the development and validation of a single cell HIF-1α reporter and present results characterizing the HIF-1α response of single cells through the lens of information theory. Further, I present observations of heterogeneous, dynamic patterns of HIF-1α in response to a physiological stimulus, interferon gamma. Finally, I present results from mathematical modeling of the downstream metabolic changes, revealing heterogeneity in the predicted metabolic phenotype of individual cells. In sum, my work contributes a valuable tool to the scientific community in the form of an optimized HIF-1α reporter, as well as novel observations and insights about the activity of HIF-1α at a single cell level

Keywords: Bioengineering.
Advisor: Covert Markus
Bintu Lacramioara
Bryant Zev David
Development of CRISPR-based barcoding tools for single cell genomics in cancer

A thorough investigation of the evolution of specialized cellular functions is critical to fully understand how tumors evolve. The genetic alterations that drive cancer growth have been well characterized through large scale genome sequencing projects and have led to significant therapeutic advances like targeted small molecule inhibitors. However, the genetic alterations that drive cancer growth are not predictive of the underlying transient gene expression or transcriptional states that occur within a tumor or population of cancer cells. These states are critical to the adaptation of tumors to therapeutic interventions and to tumor microenvironmental changes. With the advent of high-throughput single-cell RNA-sequencing technologies, thorough characterization of the heterogeneity of cell states within a tumor is now possible. However, the extent to which these cell states are inherited cannot be understood with single snapshot measurements of transcript abundance levels. This work first harnesses the Type V CRISPR/Cas12a system to engineer compact barcode sequences with optimized capacity to enable tracking of ~700 subclonal populations within a single clone. With this added resolution, we integrated the technology with single-cell RNA-sequencing to investigate the heritability of gene expression, or transcriptional memory, in melanoma cells. We initially identified EZH2, a member of the polycomb repressive complex 2 (PRC2), as a putative regulator of transcriptional memory. This work has implications for identifying the epigenetic regulators of gene expression programs that may control therapeutically relevant phenotypes like drug resistance and metastasis

Keywords: Genetics.
Advisor: Cong Le 
Winslow Monte
Fire Andrew Zachary
Development of larval and adult body plans at a cellular resolution : insights from the hemichordate schizocardium californicum

Animal life begins as a single cell, the embryo. What then proceeds are a series of processes that result in the familiar and sometimes unfamiliar animals on our planet. How embryos form adult animals has been at the heart of the field of developmental biology. While the origin of animal body plans has long fascinated biologists, most of what we know about adult body plan development comes from species that are formed directly from an embryo, a strategy called direct development. As a result, many questions remain about the formation of adult body plans from species with a distinct larval phase. New tools and techniques, where cellular transformations associated with changes in gross morphology can be studied in detail, have invigorated these lines of investigation in indirect developing species. In this thesis, I utilize the enteropneust hemichordate Schizocardium californicum to characterize the transformation of a larva into an adult. The catastrophic metamorphosis observed in animals such as fruit flies and sea urchins led to general hypotheses that adults form from cell populations separate from the larva. However, I present descriptive and molecular evidence that metamorphosis in some species, like S. californicum studied here, metamorphosis is far more transformational than catastrophic. The origin of adult body plan structures prior to the start of metamorphosis features prominently in theories of adult body plan development, but the detailed patterns of cell proliferation and cell death that give rise to the adult remains uncertain. Here, I use a combination of cell-labeling strategies in S. californicum to identify the distribution of proliferating and dying cells in larval and adult body plans and through metamorphosis. I found that adult tissues proliferate during the late larval phase prior to the start of overt metamorphosis and that cell death increases with the onset of metamorphosis. Investigating cellular composition provides another avenue for comparing larval and adult body plans. With single-cell RNA sequencing I defined larval and adult cell states by transcriptional similarity. Then, with a new form of in-situ hybridization called hybridization chain reaction (HCR), we returned to the organism and discovered when and where important transcripts that describe the transcriptional identity of cells were expressed. While previous data of metamorphosis have been descriptive at a morphological level, this dataset provides descriptions at a cellular level and a framework for future studies to test the implications of this work

Keywords: Biology.
Advisor: Lowe Christopher (Associate professor of biology)
Bergmann Dominique
Palumbi Stephen R.
Wang Bo (Researcher in bioengineering)
Development of lipid kinase inhibitors as novel broad-spectrum antivirals

Viral diseases are an unpredictable and growing threat to global health, as exemplified by recent outbreaks of Ebola virus, Zika virus, and of course the pandemic precipitated by SARS-CoV-2. These outbreaks highlight the fact that the modern medical arsenal, advanced as it is, has few options to fight viral infections. The current approach of developing a specific vaccine or antiviral for each virus also forces us to react to new threats as they arise, rather than having options ready to prevent a pandemic before it starts. To address this critical unmet need, I sought to develop and evaluate novel antiviral compounds with broad-spectrum activity against known viruses, as well as the potential to combat unexpected future threats. Our group's approach to developing novel antivirals was to target a host factor necessary for viral replication, rather than directly targeting the virus itself. This approach would present a higher barrier to resistance, as the drug target would not be under viral genetic control; if the host factor were critical to many viruses, this approach could also lead to broad-spectrum activity. We identified phosphatidylinositol-(4,5)-bisphosphate (PIP2) as host phospholipid necessary for the replication of hepatitis C virus, rhinovirus, and several enteroviruses, among other pathogens. I therefore worked to develop inhibitors of the kinases responsible for PIP2 biosynthesis, PI4K and PIP5K, and test their antiviral efficacy against these viruses. My work has resulted in novel highly-potent inhibitors of these lipid kinases, with excellent activity against a broad range of viruses. Several inhibitors have also shown unprecedented activity in animal models of enteroviruses. These inhibitors have the potential to progress to clinical use down the road and address a critical unmet medical need

Keywords: Chemistry.
Advisor: Glenn Jeffrey S.
Khosla Chaitan
Bertozzi Carolyn R.
Differential Treatment of Politically Connected Labor Traffickers: Evidence from the Brazilian Dirty List of Slave Labor

Over 40 million people worldwide were victims of modern slavery in 2016, 369,000 in Brazil alone. As part of an ambitious anti-trafficking agenda, the Government of Brazil implemented the Dirty List of Slave Labor, a name-and-shame policy that imposes financial and reputational sanctions on traffickers. Despite its relevance, there have been no attempts to identify sources of bias in the listing criteria nor to evaluate the influence of rent-seeking on the List's outcomes. Using novel data on campaign finance of traffickers from 2004 to 2018, I document compelling evidence that politically connected perpetrators are not less likely to be listed unless they are large donors, but they benefit from early removal and stay in the List 5.5 fewer months than non-connected exploiters. Being linked to ruralist legislators intensifies these effects, long-term connections matter, and there are increasing marginal benefits to donations. Tests that consider variation in the List's visibility support the hypothesis that campaign finance is used as evasion tactics. Money-giving is significantly more generous and frequent in election cycles leading up to inspection and listing years, but it drops afterwards. Although the present results are not causal, they inform a comprehensive research agenda and policy design and evaluation.

Keywords:  Department of Economics
labor trafficker, political connection, Dirty List, human trafficking, Brazil, campaign finance, corruption
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Thesis
Advisor: Miller Grant
DIGGING UP OLD WOUNDS OR FIXING FLAWS?WHITE RURAL US HISTORY TEACHER’S RESPONSIBILITIES AROUND RACE

In today’s social political climate, teachers, and especially history teachers, are simultaneously being called on to implement anti-racist education and to avoid teaching critical race theory. However, while there is much debate about what teachers should be teaching students, and especially white students, about race, there is relatively little understanding of how these lessons actually unfold in classrooms. Using semi structured zoom interviews, I set out to answer the question: what are the perceived responsibilities of nine white, rural US history teachers in teaching about the history and legacy of racism in the United States? All the teachers interviewed expressed a strong sense of responsibility to help their students understand what it means to be American. Five main sub-responsibilities emerged: 1) Help students develop either a proud attitude or a critical towards their Americanness, 2) Prepare students for future civic engagement either through voting or through protest, 3) Use the past to contextualize current events and explain either the existence, or absence of, systems of racial oppression in the present. 4) Provide students with diverse materials and perspectives that either uphold or challenge dominant/hegemonic narratives of history. 5) Create a safe space for students to learn how to interact with racial others. Notably, these responsibilities had very little to do with the past; instead, what mattered most were students’ current identities, realities and possible futures. Where teachers varied most was not in their responsibilities themselves but in their sense of what kind of Americans they hoped to foster in their classroom. The pasts that teachers chose to teach were not as important as the reasons that teachers chose to teach them. This insight highlights the need to center and examine teacher motivation in teacher preparation programs and to focus on teacher intention over classroom content.

Keywords: Race
Education
History
Civics
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Thesis
Digital tools to enable large-scale access to biomechanical assessment

How we move and how much we move profoundly affect our health and wellbeing. In biomechanics, we analyze movement to help identify, monitor, and treat diseases and disorders that impact movement. However, biomechanical assessments and interventions have historically been restricted to expensive laboratory settings. My thesis work includes three studies that leverage advancements in computer science, biomechanics, and psychology to translate biomechanical interventions to a clinical or home setting. First, we developed a machine learning model to predict knee loading from inputs that could be extracted from 2D video and demonstrate the feasibility of prescribing personalized biomechanical interventions with a smartphone camera. Second, we evaluated the psychological construct of mindset in people with knee osteoarthritis and found that mindset relates to physical activity levels and the use of exercise for symptom management. Finally, we developed a platform to deploy a nationwide at-home biomechanics study that included an order of magnitude more participants than traditional laboratory studies. With this large dataset, we explored new relationships between biomechanical parameters and measures of health and wellbeing. Together, these studies contribute to a future where simple, scalable movement assessments are used to evaluate health and treat musculoskeletal diseases

Keywords: Bioengineering.
Advisor: Delp Scott
Crum Alia
Giori Nicholas John
A dimension formula for relative symplectic field theory

This dissertation is devoted to proving virtual dimension formulas for the moduli spaces of holomorphic curves which appear in relative Symplectic Field Theory. The crucial ingredients are a generalization of the large antilinear deformation argument introduced by Taubes and Gerig to the case when the domain of the curve has boundary and punctures, and an exponential convergence result generalizing the 3-dimensional results proved by Abbas

Keywords: Mathematics.
Advisor: Eliashberg Y.
Ionel Eleny
Manolescu Ciprian