VictorJuiz/thesis huggingface dataset:
Regulatory roles of long non-coding RNA genes in cellular identity and complex traits

Genomic research primarily focuses on protein-coding genes, due to their obvious functional relevance and the well-defined relationships between DNA variation and protein sequence. However, most disease-associated variants are not in protein-coding sequences. This has led to much investigation of non-coding variation as it relates to protein-coding gene processing, but there is also a need to examine non-coding transcripts, and their roles in cellular function and trait outcomes. This dissertation contributes to the latter goal through studies of long non-coding RNA (lncRNA) genes, which are genes that produce RNAs > 200 nucleotides long that are not translated into proteins. There are several examples of lncRNAs with key regulatory roles in processes such as dosage compensation (XIST), embryonic development (HOTAIR), and the immune response (NEAT1). However, it is still unclear how many lncRNA genes are functional, how many are byproducts of surrounding active gene expression, and how many are just transcriptional noise. The objective of this dissertation is to progress our understanding of non-coding genes through both broad analyses of the interplay between genetic variation and lncRNA expression patterns, followed by focused analyses of the involvement of candidate lncRNAs in immune response and other complex traits. In Chapter 2, I use the Genotype-Tissue Expression (GTEx) version 8 data to establish hallmarks of lncRNA gene expression and regulation and identify lncRNAs with robust complex trait associations. In Chapter 3, I examine the effects of the anti-inflammatory mouse lncRNA NeST on gene expression and chromatin accessibility in murine T cells. Finally, in Chapter 4, I profile lncRNA expression patterns in bulk purified immune cell populations, explore the regulatory patterns of the human NEST gene in these cell types, and evaluate this gene's possible relevance to autoimmune diseases. The findings and approaches described in this dissertation add to the existing annotation of functional lncRNA genes, and can help frame future studies of non-coding gene expression

Keywords: Genetics.
Advisor: Kirkegaard Karla
Montgomery Stephen
Fire Andrew Zachary
Pulendran B. (Bali)
Tang Hua (Geneticist)
Reimagining state-society relations through the works of China’s New Documentary movement: West of the Tracks, 24 City, and We the Workers

Through analysis of three “New Documentary” films about labor in post-socialist China, this thesis investigates how Philip Huang’s concept of the third realm appears in both the filmmakers' negotiations with the state and the state-society interactions embedded in the films themselves. Though the Chinese state is frequently seen as authoritarian and its citizens as repressed individuals, in Wang Bing’s West of the Tracks (2003), Jia Zhangke’s 24 City (2008), and Huang Wenhai’s We the Workers (2017), we witness how their relationship can be one of negotiation, rather than simply one of command.

Keywords:  Department of History
Documentary films
Post-communism
Cinéma vérité
New wave films > History
China
Labor
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Thesis
Advisor: Mullaney Thomas
Ma Jean
Reinforcement learning : when can we do sample efficient exploration?

The performance of Reinforcement Learning (RL) agents varies wildly with the underlying problem: sometimes they learn quickly, some other time slowly, and sometime not at all. When can an RL agent quickly learn good policies? To provide a theoretically justified answer, this dissertation investigates four fundamental RL questions: 1) RL agents often learn much faster than what theory predicts. How common are easy instances and how can an algorithm systematically exploit them? We prove that several RL problems of practical interest are learnable much faster than what worst-case analyses suggest. This is achieved by an agnostic, instance-adaptive algorithm that can leverage benign instances to accelerate learning while preserving state-of-the-art worst-case minimax guarantees. 2) Randomized agents often perform better empirically than their deterministic counterpart. Is their performance grounded in solid theoretical roots? We provide an affirmative answer in the context of function approximation, and give the first regret bounds for randomized exploration with function approximation, placing randomized agents on solid theoretical footing. 3) Providing formal online learning guarantees with function approximation requires more stringent assumptions than what is necessary in batch RL; can these additional requirements be relaxed or is such gap fundamental? We give an encouraging answer by designing and analyzing a minimax optimal online algorithm that works under the Bellman closure of the function class, as is commonly needed in offline batch analyses of RL methods with function approximation. As a special case, this gives the first misspecification-robust algorithm and analysis for misspecified linear contextual bandits. 4) What are the fundamental limits of batch reinforcement learning? Is there a fundamental sample complexity separation between online and batch RL? The dissertation establishes the first exponential lower bounds for batch RL even for simple linear predictors, and provide the first, formal exponential separation that shows that batch RL can be exponentially harder than online RL

Advisor: Kochenderfer Mykel J.
Brunskill Emma
Lazaric Alessandro
Sidford Aaron
Replication of RNA by transcription polymerases

While both DNA and RNA can serve as hereditary material in viruses, DNA is considered the carrier of genetic information in cellular life. For RNA to serve as a genetic material in cells, enzymatic machinery to replicate RNA (i.e., synthesize full-length copies of RNA) would be required. However, RNA replicase enzymes that are well-known to replicate RNA have so far only been found in RNA viruses such as influenza and dengue but not in cellular organisms. Interestingly, numerous lines of evidence suggest that two classes of pathogenic RNAs (hepatitis delta virus in animals and viroids in plants) replicate their RNA genetic material by redirecting cellular DNA-dependent RNA polymerases (also called transcription polymerases) to serve as RNA-dependent RNA polymerases. This noncanonical RNA replication activity of transcription polymerases could provide a general means for propagating RNA as hereditary material in cellular contexts because transcription polymerases are found across the tree of cellular life. But a molecular-level understanding of the diversity, mechanisms, and origins of RNAs that can be replicated by transcription polymerases was lacking. By integrating next-generation sequencing, microfluidics, bioinformatics, and biochemistry approaches, I have investigated in vitro RNA replication by a model transcription polymerase enzyme from T7 bacteriophage called T7 RNAP. There were three previous papers on RNA replication by T7 RNAP, all published more than two decades ago, which together described five replicating RNA sequences. The origins of the RNAs replicated by T7 RNAP and requirements for RNA replication by T7 RNAP were unclear. I identified hundreds of new replicating RNAs, defined three mechanisms involved in replication (subterminal de novo initiation, RNA shape-shifting, and interrupted rolling circle synthesis), and described emergence from DNA seeds as a mechanism for the origin of novel RNA replicons. These results inform models for the replication of naturally occurring RNA genetic elements and demonstrate how replicating RNAs can originate from non-replicating materials

Keywords: Bioengineering.
Advisor: Fire Andrew Zachary
Bryant Zev David
Li Jin (Billy)
Representation learning and algorithms in hyperbolic spaces

Graph embedding methods aim at learning representations of nodes that preserve graph properties (e.g., graph distances). These embeddings can then be used in downstream applications such as clustering, visualization, nearest neighbor search and classification tasks. Most machine learning algorithms learn embeddings in standard Euclidean spaces. Recent research shows promise for more faithful embeddings by leveraging non-Euclidean geometries, such as hyperbolic or spherical geometries. In particular, trees can be embedded almost perfectly into two-dimensional hyperbolic spaces, while this is not possible in Euclidean spaces of any dimension. In this thesis, we develop machine learning models that operate in hyperbolic spaces. We start by introducing hyperbolic representation learning methods for hierarchical graphs, including methods for multi relational graphs or graphs with node features. We demonstrate the benefits of these hyperbolic representations compared to their Euclidean counterparts on a variety of link prediction benchmark datasets. We then design algorithms that operate on these hyperbolic representations. We first propose a method for hierarchical clustering in hyperbolic space, which can be used to solve any similarity-based hierarchical clustering problem with gradient-descent. We then propose a generalization of Principal Component Analysis for hyperbolic inputs, and show that it yields improved low-dimensional representations and data visualizations compared to previous manifold dimensionality reduction methods

Keywords: Computational and Mathematical Engineering.
Advisor: Ré Christopher
Guibas Leonidas J.
Leskovec Jurij
Resolving structural changes in photoinduced reactions in solution with X rays and electrons

The need for efficient, low-cost solar energy capture has driven interest in mechanistic understanding of light-induced chemical reactions in solution, for instance, natural and artificial photosynthesis. Resolving the structural changes of a molecule during a photoinduced reaction in solution can be challenging, however, with many interdependent intra- and inter-molecular degrees of freedom. This thesis reports experiments using three techniques that can be used to disentangle structural changes during photoinduced reactions: x-ray absorption spectroscopy, valence-to-core x-ray emission spectroscopy, and ultrafast electron diffraction

Keywords: Physics.
Advisor: Church Sarah Elizabeth
Gaffney Kelly
Bucksbaum Philip H.
Reis David A.
Resource and data efficient deep learning

Using massive computation, deep learning allows machines to translate large amounts of data into models that accurately predict the real world, enabling powerful applications like virtual assistants and autonomous vehicles. As datasets and computer systems have continued to grow in scale, so has the quality of machine learning models, creating an expensive appetite in practitioners and researchers for data and computation. To address this demand, this dissertation discusses ways to measure and improve both the computational and data efficiency of deep learning. First, we introduce DAWNBench and MLPerf as a systematic way to measure end-to-end machine learning system performance. Researchers have proposed numerous hardware, software, and algorithmic optimizations to improve the computational efficiency of deep learning. While some of these optimizations perform the same operations faster (e.g., increasing GPU clock speed), many others modify the semantics of the training procedure (e.g., reduced precision) and can even impact the final model's accuracy on unseen data. Because of these trade-offs between accuracy and computational efficiency, it has been difficult to compare and understand the impact of these optimizations. We propose and evaluate a new metric, time-to-accuracy, that can be used to compare different system designs and use it to evaluate high performing systems by organizing two public benchmark competitions, DAWNBench and MLPerf. MLPerf has now grown into an industry standard benchmark co-organized by over 70 organizations. Second, we present ways to perform data selection on large-scale datasets efficiently. Data selection methods, such as active learning and core-set selection, improve the data efficiency of machine learning by identifying the most informative data points to label or train on. Across the data selection literature, there are many ways to identify these training examples. However, classical data selection methods are prohibitively expensive to apply in deep learning because of the larger datasets and models. To make these methods tractable, we propose (1) "selection via proxy" (SVP) to avoid expensive training and reduce the computation per example and (2) "similarity search for efficient active learning and search" (SEALS) to reduce the number of examples processed. Both methods lead to order of magnitude performance improvements, making techniques like active learning on billions of unlabeled images practical for the first time

Advisor: Zaharia Matei
Bailis Peter
Li Fei Fei
Respect the land : pathways to resource stewardship and resiliency in a changing Arctic

The Arctic is warming at a rapid rate, much faster than anywhere else on the planet, with profound consequences for social-ecological systems. Changes in the availability and abundance of plants and animals are affecting Indigenous (specifically, Alaska Native) harvesters' access to wild foods for subsistence and cultural uses. Access, while influenced by landscape change, may also be constrained or facilitated by Western systems of resource management. This dissertation centers on Indigenous harvesters' access to, and stewardship of, wild foods—federally designated as subsistence—on National Parklands in northwest Arctic Alaska. The overarching motivational questions of this dissertation are 'How does climate-related warming in the Arctic affect Indigenous harvesters' access to coastal subsistence resources?' and 'How can examining multiple perspectives—Indigenous harvesters and management staff—on resource management better facilitate resiliency and stewardship?' Chapter One focuses on the human access dimension of climate impacts on coastal resources in northwestern Arctic Alaska and resulting implications for food sovereignty and resilience of Alaska Native communities. Chapter Two applies a film-based community engagement approach (Community Voice Method) to illustrate how Indigenous values and knowledge guide subsistence hunting and harvesting practices. Chapter Three provides a broader look at subsistence management in Alaska National Parklands from harvester and National Park Service (NPS) staff members' perspectives

Advisor: Ardoin Nicole M. (Nicole Michele)
Crowder Larry B.
Beaudreau Anne Houston 
Dunbar Robert B.
The rheology of polydisperse colloidal suspensions in spherical confinement

In this work, we present a computational study of the rheology and dynamics of spherically-confined polydisperse suspensions of hydrodynamically interacting colloids, a simple model system that provides an important framework for studying the behavior of living and non-living confined colloidal suspensions. Colloids are microscopically small particles suspended in a solvent. Examples include the cytoplasm of biological cells, micro-reactor vesicles, and particle-laden droplets ubiquitous in additive manufacturing and industrial coatings. Here we present an extension of the Confined Stokesian Dynamics algorithm (CSD) capable of modeling the bulk rheology of confined colloidal suspensions of arbitrary particle-size distribution, at and away from equilibrium. We will present novel hydrodynamic functions we developed that couple particle dynamics to suspension properties and, after a brief overview of the CSD algorithm, the results of a systematic study of the impact of changes in confinement and size polydispersity on equilibrium structure and dynamics. We showed that an interplay between polydispersity and confinement reveals demixing and localized dynamics that help explain our previous discovery that such dynamics control the speedup of protein synthesis in faster-growing bacteria. Next, we will present a new theoretical framework we developed for measuring the intrinsic viscosity and osmotic pressure in confined suspensions. The first significant outcome of this work was that tighter confinement and crowding significantly increase equilibrium viscous dissipation and osmotic pressure compared to unconfined suspensions. This result is important for the rational design of biochemical tests, where up to now, only the effect of passive crowders was taken into account. The second significant outcome of this work was the modeling of strong departures from equilibrium, including a representation of biomolecules localized at one pole of the cavity that subsequently diffuse - mimicking the Min cycle in E. coli our work shows that concentration gradients inside cells increase the residence time of proteins close to the membrane, which is beneficial for Min-protein membrane reaction kinetics that promotes correct cell division. Finally, we will present three other significant theoretical developments in the field of microhydrodynamics done in service of the underlying theoretical framework of our model

Keywords: Chemical Engineering.
Advisor: Zia Roseanna
Fuller Gerald G.
Shaqfeh Eric S. G. (Eric Stefan Garrido)
Ribonucleic acids in the microbiome

Much remains to be characterized and identified in the human microbiome, ranging from small proteins to RNAs that form secondary structures. Recent innovations in sequencing technologies and informatics approaches have enabled us to study the microbiome at many new levels of regulation. In this dissertation, I develop new methods and informatics pipelines to study protein synthesis and predict functional elements in the microbiome. Chapter 2 describes the development of MetaRibo-Seq, a technology that enables high-throughput study of translation in microbiomes and applies it to discover new small proteins. Chapter 3 addresses limitations of Ribo-Seq, the prevalence of contaminant Ribo-Seq signal across structured RNAs, and the likely source of such contamination. Chapter 4 describes a large-scale comparative genomics approach to reveal thousands of novel candidate structured RNAs. Overall, these chapters bring new technologies and resources to study small proteins and RNAs in the human microbiome

Keywords: Genetics.
Advisor: Bhatt Ami (Ami Siddharth)
Kundaje Anshul
Sherlock Gavin
Urban Alexander E.
Rights and Resistance: A Comparative Analysis of the Post-Partition Civil Rights Movements in Indian Occupied Kashmir

Muskan Shafat’s capstone project explores the history and politics surrounding the post-colonial Kashmir civil rights movement and how this has been influenced by continued military occupation. Muskan specifically contrasts the civil rights movement in the 1990s with that in the 2010s, highlight the unique role of digital advocacy in raising awareness of Kashmir globally, eventually leading to increasing global pressure on India and raising awareness about human rights violations in Kashmir. She examines various ways the diaspora can aid this political movement by organizing global protests and holding political representatives accountable to speak up for Kashmir, all the while encouraging activists in Kashmir to document their struggle while relying on media outputs to the wider world. With this analysis, Muskan hopes to highlights the Kashmiri struggle, bringing together voices and narratives of Kashmiri artists, to better understand the role of the international community.

Keywords: Kashmir
Civil rights movements
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Thesis
Ripple Effects Mapping in Community-Engaged Research: Capturing the “Ripples” of the Steps for Change Physical Activity Intervention Trial

An emergent evaluation tool known as ripple effects mapping (REM) is a participatory qualitative method that engages program participants and other stakeholders in exploring intended and unintended intervention impacts. REM was used to explore the intervention impacts of the Steps for Change intervention programs. Steps for Change is a group-randomized controlled physical activity intervention trial targeting lower-income midlife and older adults who live in or near 10 senior affordable public housing sites in two San Francisco Bay area counties. The two intervention arms consisted of an evidence-based behavioral intervention targeting individual-level factors influencing regular physical activity vs. the same behavioral intervention in combination with a citizen science intervention, called Our Voice, which targeted local physical and social environmental factors affecting physical activity. Thematic analysis of the intervention impacts obtained during the REM sessions was used to identify major themes, such as COVID-19, physical activity, and social connectedness. The purpose of the present study was to begin to explore the 12-month intervention-derived impacts under the COVID-19 major theme in a housing site (Site A) randomized to the multi-level intervention and its comparison site (Site B) randomized to the individual-level intervention only. The reported impacts under the COVID-19 theme were examined and quantified based on the different organizational levels of the social ecological model. A quantitative comparison of the two sites revealed a statistically significant difference in the relative frequency distribution of impacts based on the levels of the social ecological model (p < 0.01). Using thematic analysis, four subthemes under COVID-19 were identified: (1) meeting physical activity goals, (2) maintaining social connections, (3) balancing health and wellbeing, and (4) learning the Zoom online meeting platform. The content in these subthemes related to challenges and individual adaptations associated with the COVID-19 pandemic and its influence on participant engagement in the intervention programs. Striving to identify the full complement of intervention impacts through novel qualitative approaches such as REM can provide insights related to both expected and unexpected impacts of a community-based health promotion program. REM in community-engaged research can be a valuable evaluation tool to capture broader intervention effects and potentially enhance program implementation and sustainability in a more comprehensive manner.

Keywords: ripple effects mapping
community-engaged research
participatory action research
citizen science
community-engaged evaluation
social ecological model
Science > Citizen participation
COVID-19 (Disease) > Social aspects
COVID-19 Pandemic (2020-)
physical activity
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Thesis
Advisor: King Abby
Rodriguez Espinosa Patricia
Campero Maria