VictorJuiz/thesis huggingface dataset:
Accelerated proliferation and aging driven by the prion state of an RNA-modifying enzyme

The term 'prion' is loaded with fear and negative connotation. The reason for this is because the first prions to be described, those of PrP in various species, were agents of death and disease unlike any known to mankind up to that point. The PrP prions captured the attention and fear of the general public and shaped our understanding of what prions are or could be for decades after. But prions can be more than just agents of disease, they can drive paradigm-shifting biology. If we go back to the original definition as coined by Stanley Prusiner himself, the only requirements to be a prion is to be proteinaceous and infectious. There are no thresholds of Q/N enrichments that need to be met or β- amyloid fibers formed in order to be considered a prion. Prions are highly stable protein conformations that can drive new biological phenotypes endowed with certain characteristics unique to prions. These phenotypes are usually dominant, disobey Mendel's law on inheritance, rely on protein chaperones for the faithful propagation across generations, and are encoded not in nucleic acid, but in the protein conformation itself. In fluctuating environments, switching between different growth strategies, such as those affecting cell size and proliferation, can be advantageous to an organism. Trade-offs arise, however. Mechanisms that aberrantly increase cell size or proliferation— such as mutations or chemicals that interfere with growth regulatory pathways—can also shorten lifespan. We report a natural example of how the interplay between growth and lifespan can be epigenetically controlled. We find that a highly conserved RNA-modifying enzyme, the pseudouridine synthase Pus4/TruB, can act as a prion, endowing yeast with greater proliferation rates at the cost of a shortened lifespan. Cells harboring the prion grow larger and exhibit altered protein synthesis. This epigenetic state, [BIG+] (better in growth), allows cells to heritably yet reversibly alter their translational program, leading to the differential synthesis of dozens of proteins, including many that regulate proliferation and aging. Our data reveal a new role for prion-based control of an RNA-modifying enzyme in driving heritable epigenetic states that transform cell growth and survival

Keywords: Chemical and Systems Biology.
Advisor: Jarosz Daniel
Brunet Anne
Ferrell James Ellsworth
Mochly-Rosen Daria
Accelerating pediatric magnetic resonance imaging using deep learning-based image reconstruction

Magnetic resonance imaging (MRI) is a powerful diagnostic tool for visualizing soft tissue anatomy, but physical limits on data acquisition speed result in uncomfortably long MRI exams. This is problematic for many patient populations, but especially for pediatric patients, who often require general anesthesia (GA) to reduce anxiety and body motion. Many attempts at accelerating data acquisition have been made to reduce or eliminate use of GA for pediatric MRI. For example, compressed sensing (CS) methods have been used to iteratively reconstruct rapidly acquired measurements into high-quality images by leveraging sparse priors. More recently, deep learning (DL) methods have been used to train deep neural network models to map the rapidly acquired measurements into even higher-quality images. While DL reconstruction approaches may potentially accelerate data acquisition beyond CS, these approaches have several issues which impede their clinical adoption. First, DL reconstructions require large quantities of high-quality ground truth data for supervised training, which can be costly and time-consuming to acquire. Second, memory requirements during network training limit the applicability of DL reconstruction to low-dimensional MRI data, such as static or dynamic 2-D imaging with limited spatiotemporal resolution. In this thesis, a series of projects demonstrating robust DL reconstruction techniques for acceleration of high-dimensional pediatric MRI will be presented. First, physics-based models are incorporated into deep CNN architectures to enforce consistency between intermediate network outputs and the rapidly acquired measurements in a novel method called DL-ESPIRiT. Physics-based modeling allows DL-ESPIRiT to be trained end-to-end in a supervised fashion with relatively little training data compared to non-physics-driven DL reconstruction. DL-ESPIRiT is applied and validated on 12X prospectively accelerated dynamic 2-D MRI scans acquired at Lucile Packard Children's Hospital. Finally, DL-ESPIRiT is extended to leverage subspace methods within the network to address GPU memory limitations during training. This method, known as deep learning-based subspace reconstruction (DL-Subspace), reconstructs a compressed representation of the MRI data instead of the data directly, thereby reducing the memory footprint during training and accelerating DL inference times. DL-Subspace is demonstrated to reconstruct 2-D dynamic MRI data with 4X higher memory efficiency and inference speed

Keywords: Electrical Engineering.
Advisor: Vasanawala Shreyas
Nishimura Dwight George
Pauly John (John M.)
Accessible platforms for anomalous cell detection beyond laboratory boundaries

Resource limitations and socioeconomic challenges pose obstacles to equitable healthcare access worldwide. Accurate diagnosis is an important early step in which technical innovation can help to bridge some gaps. Towards this goal, I worked on developing anomalous cell detection tools to enable broader access to health information for resource-constrained clinical settings. In this thesis, I explore portable tools for detecting malaria in blood samples and screening forensic swab samples. Quantifying the malaria-causing parasite Plasmodium falciparum relies on isolating the ring-stage infected red blood cells, which are challenging to differentiate from the majority of uninfected cells. I used magnetic levitation to perform label-free biophysical separation and detection of malaria-infected cells, by cumulatively leveraging density and magnetic susceptibility differences in the cells. Next, I built an accessible platform to process, image, and analyse whole blood patient samples in field settings, in a malaria-endemic region of Uganda. I combined cell lysis, suspension, and machine learning on cellphone images to develop an automated morphology-based classifier for blood samples. I also explored a different application for portable image-based screening, to address a sample processing backlog in sequencing forensic samples in the sexual assault justice workflow. I developed an automated algorithm for differentiating sperm from epithelial cells in sexual assault swab samples, using morphology features captured in cellphone imaging of microchips. Overall, this work demonstrates examples of anomalous cell detection technology built specifically for under-resourced settings. Such interdisciplinary techniques could help to overcome existing gaps in access to biological data, with the aims of supporting accurate diagnosis and surveillance of malaria, or screening of backlogged sexual assault samples

Keywords: Bioengineering.
Advisor: Demirci Utkan
Egan Elizabeth S.
Fordyce Polly
Prakash Manu
Accumulation and heritability of mutations in reef-building corals

Mutations are the fundamental source of variation among organisms, and they can have deleterious, neutral, or beneficial effects on the overall fitness of an organism. Whether a mutation affects the organism it arises in, the offspring of that organism, or both depends on what type of cell it arises in. In many animals, mutations in germ cells are inherited by the offspring, whereas somatic cells are generally thought not to be inherited. Somatic mutations can cause cancer, aging, and general genome instability. However, plants and many basal animal taxa may lack embryonic germ-soma distinction. Colonial reef-building corals can grow to be meters across, live for hundreds to thousands of years while maintaining fertility, and almost never develop recognizable tumors. Whether they have embryonic germline segregation is controversial. All of this makes them a pivotal group in which to understand genome maintenance. Corals also create critical ecosystems that are threatened by many stressors, including ocean warming. Mutation research in corals thus has far-reaching implications for the study of genome integrity and cell lineage development, as well as for the capacity of these animals to adapt when faced with selective pressures. In this dissertation I characterize the rates and patterns of somatic, germ, and stem cell mutations in corals. I show that 26% of the post-embryonic mutations found in the parent soma are inherited by their sperm. I also compare the rates of different types of mutations in corals living under normal conditions to those living in former nuclear test sites. The results yield insight into genome maintenance dynamics and somatic mutation inheritance in a colonial organism with a position in the tree of life that suits it to be key in understanding the evolution of germline-soma distinction. It also points to how population diversity arises in these animals, key information when considering how they may adapt in the coming years

Keywords: Biology.
Advisor: Palumbi Stephen R.
Hadly Elizabeth Anne
Petrov Dmitri Alex
Rosenberg Noah
Acousto-optics in thin-film lithium niobate

Thin-film lithium niobate has opened the doors to new opportunities in integrated acousto-optics. Low-loss, wavelength-scale waveguides enable devices of an unprecedented size and efficiency. In this thesis, I develop a general framework for treating acousto-optic waveguides and design tools for the piezoelectric transducers that drive them. This work culminates in two demonstrations: a collinear acousto-optic modulator in suspended lithium niobate with a record figure-of-merit, and an integrated acousto-optic deflector for on-chip beam-steering in lithium niobate-on-sapphire

Keywords: Applied Physics.
Advisor: Safavi-Naeini Amir H.
Digonnet Michel J. F.
Schleier-Smith Monika
Adaptability and regulation of the ubiquitin-proteasome system

Aging, disease, and environmental stressors are associated with failures in the ubiquitin-proteasome system (UPS), yet a quantitative understanding of how the UPS senses and addresses proteotoxic stressors is lacking. We assessed UPS performance and adaptability in yeast under stressors using quantitative measurements of the proteolytic stress response (PSR), a transcriptional program driven by the protein Rpn4 that expresses the UPS. We found that the UPS productively adapts to different proteotoxic stressors using separate mechanisms. Proteolytic stressors cause Rpn4 stabilization while folding stressors increase RPN4 transcription without increasing UPS demand. We also probed the genome for modulators of the PSR using the reverse genetic method ReporterSeq. We identified several PSR regulators, including thioredoxin reductase (Trr1), which had no known role in UPS regulation. Unlike all known mechanisms for Rpn4 regulation, which regulate Rpn4 levels, we found that Trr1 modulates the molecular activity of Rpn4 and does so in response to oxidative stress. Together, this work reveals the distinct effects of proteotoxic stressors on the UPS and illuminates the genetic landscape through which cells signal it

Keywords: Biochemistry.
Advisor: Brandman Onn
Ferrell James Ellsworth
Jarosz Daniel
Krasnow Mark
Adaptable stem cell differentiation in the adult Drosophila intestinal epithelium

Adult organs must be able to adapt rapidly to physiological changes including injury and changes to food availability. Resident stem cells replace damaged and dying cells by dividing and differentiating, stimulated by signals emanating from the dying cells themselves. How stem cells precisely tune their differentiation to match division and death rates in real time remains a largely open question. First, I present a novel method for live imaging the adult Drosophila midgut while still intact in the living organism. With this method I am able to monitor in real time the dynamics of stem cell divisions, differentiation, and loss in the midgut. I am able to precisely measure Notch activity over time and find that a threshold level of Notch defines stem cells from their intermediate progenitors, enteroblasts. Second, I find that injury alters differentiation rates and blurs normally distinct features of stem cells, enteroblasts and terminally differentiated enterocytes. I measure rates of Notch activation and deactivation, indicators of where cells are in the differentiation process and find that both are accelerated in injured midguts. Injury raises Notch activity in mitotic stem cells beyond the enteroblast threshold and maintains Notch activity longer in maturing cells. The injury response pathway JAK-STAT appears to play a role in the acceleration of Notch deactivation kinetics and blurring between enteroblast and enterocyte characteristics, suggesting that JAK-STAT plays more of a role in regulating enterocyte maturation than enteroblast specification. Finally, I find that food consumption affects population wide Notch activity levels and excessive activation of insulin signaling might also lead to blurring between stem and enteroblast identities

Keywords: Developmental Biology.
Advisor: O'Brien Lucy Erin
Fuller Margaret T.
Nusse Roel
Wang Bo (Researcher in bioengineering)
Adaptive and efficient batch reinforcement learning algorithms

Reinforcement learning (RL) focuses on solving the problem of sequential decision-making in an unknown environment and achieved many successes in domains with good simulators (Atari, Go, etc), from hundreds of millions of samples. However, real-world applications of reinforcement learning algorithms often cannot have high-risk online exploration. To bridge this gap, this dissertation investigates how to perform reinforcement learning from an offline dataset, named batch reinforcement learning methods. We provide theoretically justified new algorithms, as well as empirical validation in simulation and real datasets. More specifically, this dissertation studies the two main aspects of batch reinforcement learning. How to evaluate a policy given a fixed dataset collected by other policies? Offline policy evaluation is a challenging counterfactual reasoning problem, in these partial-information and sequential settings. The solution to this problem relies on two types of estimators: direct model-based estimators and importance re-weighing estimators. We propose a model-based estimator with mean-square error bound and analyze a variance reduction heuristic for importance sampling. How to learn a new policy from a fixed dataset collected by other policies? Learning policy using function approximation from a fixed dataset suffers from overestimating a counterfactual policy by empirical reward/value maximization. Prior theoretical justification relies on strong assumptions about the data distribution and thus is less informative to guide practice. We propose the idea of pessimism to constrain the policy search space and avoid instability. Following this intuition, we proposed three new algorithms: the first convergent policy gradient, value function learning with finite sample error bounds, and avoiding the overfitting in direct policy gradient. The primary contribution of this dissertation is advancing the foundations of batch RL and develop batch RL algorithms with provable guarantees under realistic assumptions. One of the driving goals is finite-sample error bounds for algorithms in function approximation settings. To that end, this dissertation makes progress in both policy evaluation and policy learning problems by studying the theory of these problems and using the theoretical insights to derive new algorithms with strong empirical performance as well

Advisor: Brunskill Emma
Ma Tengyu
Van Roy Benjamin
Wager Stefan
Adaptive Conservative Local Time-stepping Scheme for Multiphase Flow and Transport In Porous Media

Accurate and efficient simulation of the fluid flow and transport in the subsurface remains a challenging task. In implicit pressure explicit saturation (IMPES) formulation, the elliptic flow equation is discretized implicitly, whereas the hyperbolic transport equation is explicit. To ensure stability, the latter requires the choice of a sufficiently small timestep size, such that the local Courant-Friedrichs-Lewy (CFL) condition is not violated. This restriction leads to a large number of small global timestep sizes which drives the simulation very expensive. In order to take advantage of the local nature of the CFL condition, many local time-stepping methods were proposed. In this thesis we extend the use of the Adaptive Conservative Time Integration (ACTI) scheme devised by Jenny [1] for applications in reservoir simulation. We eliminate the most expensive step of the original algorithm and describe the details of local timestep size assignment to ensure stability for non-convex non-monotone flux functions. We investigate the performance of the scheme for a number of model problems with various physics including the tracer problem, immiscible two-phase flow in presence of buoyancy and the black oil model. We show that with modified ACTI, the size of the global timestep for explicit update of the transport can be chosen several orders of magnitude larger than that of standard IMPES. As a result, a speed-up of an order of magnitude is achieved in the number of flux and cell updates while maintaining negligible discrepancies in the solution when compared to conventional IMPES.
[1] P. Jenny, Time adaptive conservative finite volume method," Journal of Computational Physics, vol. 403, p. 109067, 2020.

Keywords:  Energy Resources Engineering
School of Earth Energy &amp; Environmental Sciences
multiphase flow and transport
adaptive local time integration
reservoir simulation
Thesis
Advisor: Tchelepi Hamdi
Adaptive Evolution in Cancer Metastasis

Metastasis is the spread of cancer from primary sites to distant regions of the body, called metastatic sites, and is responsible for nearly 90% of cancer deaths. Nevertheless, there exist few therapeutic options for the treatment of metastatic cancer. Thus, understanding of metastasis is critical to developing new clinical insights and therapeutic options. Cancer and metastasis are evolutionary processes driven at least in part by genetic changes that are generated under drift and selection. Somatic copy number alterations (SCNAs), the most common genetic change in the cancer genome, occur when multiple copies of the genome are gained or lost and have been shown to strongly influence cancer gene expression. While previous studies have predominantly focused on SCNAs and gene expression patterns in primary cancer, fewer studies have investigated their role in metastatic cancer. This study focused on SCNAs and identified gene expression patterns under selection during the process of metastasis, in which the corresponding genes were hypothesized to underlie metastasis. We developed a statistical approach based on the quantitative trait loci sign test, a well-established method to detect selection, to identify SCNAs and gene expression patterns under selection specifically in metastasis. We found 4,588 genes under selection more in metastatic cancer than primary cancer, including SMAD9, PLAC1, TTC28, TSTD3, and SMO. Importantly, we found that the genes under selection more in metastatic cancer than primary cancer were up-enriched for tolerance induction, a process that inhibits the immune system, and down-enriched for opsonization, a process that marks targets of the immune system. This suggests that the evasion of the immune system may be a critical process that allows metastasis to occur. In sum, this study identified candidate driver genes that underlie metastasis. Identifying such driver genes may contribute to the development of new clinical insights and therapeutic options, and ultimately reduce the massive contribution of metastasis to cancer morbidity and mortality.

Keywords:  Department of Biology
Biology
Cancer
Metastasis
Genetics
Evolution
Thesis
Advisor: Fraser Hunter
Winslow Monte
Adaptive experiments and a rigorous framework for type I error verification and computational experiment design

What wouldn't we give for faster access to life-saving drugs, cancer cures, or pandemic-ending vaccines? In recent decades, modern statistics has found something to trade: at the price of additional complexity and the loss of Gaussian behavior of our estimators, we can get faster, more robust, more flexible, and more efficient experiments through the use of adaptive designs. This thesis covers breakthroughs in several areas of adaptive experiment design: (i) (Chapter 2) Novel clinical trial designs and statistical methods in the era of precision medicine. (ii) (Chapter 3) Multi-armed bandit theory, with applications to learning healthcare systems and clinical trials. (iii) (Chapter 4) Bandit and covariate processes, with finite and non-denumerable set of arms. (iv) (Chapter 5) A rigorous framework for simulation-based verification of adaptive design properties

Keywords: Statistics.
Advisor: Lai T. L.
Lavori Philip W.
Lu Ying
Adaptive games for education: performance and engagement in preschoolers

Active learning—allowing learners to direct their own studies—is a key part of some educational theories and practices but is not well-understood in terms of its underlying mechanisms or effectiveness at different ages. Meanwhile, cognitive psychology studies have found that adults can benefit from being given self-direction in some types of tasks, and school-aged children can also benefit in simplified versions of these paradigms. Here I extend these past studies in two ways: studying younger children, whose metacognitive skills (which likely play a role in effective self-directed learning) are still developing, and using real-world stimuli for which children may have varying degrees of prior knowledge. In this multi-session study, I explore whether an active learning advantage can be found in preschoolers (ages 3-4) using a tablet game that teaches letter and number recognition over a week. I compare performance and engagement in two conditions: active learners are able to control time allocation between letter and number games that adapt to their level of knowledge; control learners follow a standard curriculum with no adaptive game dynamics. Despite the challenges presented by the adaptive curriculum, I find that active learners performed just as well as their peers who followed a standard curriculum.

Keywords:  Symbolic Systems Program
active learning
adaptive games
Department of Psychology
Symbolic Systems Program
Thesis
Advisor: Frank Michael C.
Kachergis George
Adaptivity and efficiency in the design of sequential experiments

Sequential experiments that take place in many practical settings are often characterized by an exploration-exploitation tradeoff that is captured by the multi-armed bandit framework. This framework has been studied extensively in the literature and applied in various application domains including revenue management, service operations, online retail, recommender systems, and healthcare. We study fundamental problems where there are uncertainties about key characteristics governing the sequential experimentation environment, such as the information acquisition process, the payoff structure, and the feedback structure. Furthermore, we design and analyze near-optimal dynamic optimization policies that are adaptive with respect to these characteristics

Keywords: Electrical Engineering.
Advisor: Gur Yonatan
Boyd Stephen P. 
Lall Sanjay
Spiess Jann 
Wager Stefan