VictorJuiz/thesis huggingface dataset:
Longitudinal intestinal microbiome collection and cultivation reveals site-specific compositions and functions
Keywords: Medicine.
Genetics.

The spatiotemporal structure of the human microbiome, proteome, and metabolome reflects and determines regional intestinal physiology and may have implications for disease. Yet, we know little about the distribution of microbes, their environment, and their biochemical activity in the gut because of reliance on stool samples and limited access to only some regions of the gut using endoscopy in fasting or sedated individuals. To address these deficiencies, my thesis work aimed to profile the composition and function of the microbiome along the spatial gradient of the intestinal tract using a variety of different techniques and model systems. In CHAPTER 2, I work with the Tabula Sapiens consortium to collect immune cells and microbial contents from matched regions of the human intestinal tract of organ donors. We found spatially distinct microbial communities and immune transcriptomic profiles. In CHAPTER 3, I work with the Tabula Microcebus consortium to elucidate the composition of the microbiota along the mouse lemur intestinal tract in two lemurs: one with recent antibiotic history and one without recent antibiotics. Similar to human organ donors, we found incredibly diverse and spatially distinct microbial communities in a mouse lemur that did not recently take antibiotics. However, we found profoundly lower diversity all throughout the intestinal tract of the mouse lemur that had recently taken antibiotics, highlighting how perturbances to the microbiota can influence both small intestine and large intestine communities. In CHAPTER 4, I attempt to address many of the limitations of using organ donors by studying the human intestinal microbiota using an ingestible sampling device that allows for noninvasive collection of human intestinal contents during physiological conditions. We were able to profile spatially distinct microbiotas, bile acids, and secreted human proteomes along the intestinal tract of 15 healthy human subjects. Further, we were able to integrate this multi-omics dataset to provide important biological insight into microbial interactions with bile acids and the human proteome that have important implications for human health. In CHAPTER 5, I use cultivation techniques to acquire small intestine and stool-derived in vitro communities, along with a comprehensive library of isolates (> 400), from the human small intestine and stool of a single subject. Mouse colonization of these communities and isolates reveals how taxa maintain a preferential spatial niche, as well as highlight the importance isolate ancestry in undergoing spatially distinct dynamics in response to a diet switch. Ultimately, I was able to annotate compositional and functional differences in the microbiome along the intestinal tract using a variety of different methods, highlighting the need for investigation beyond the stool microbiota to be able to further harness and tune the effects of the microbiota on human health
Losing touch : essays
Keywords: Humanities and Sciences.
Comparative Literature.

"Losing Touch: Essays" is a book about connection, disconnection, and what happens in between. "Losing Touch" makes reference to multiple kinds of contact and distance, from the embodied, literal experience of losing another's physical touch through separation or social distancing, to "losing one's touch" and being more prone to failure, to "losing touch with someone" across physical or virtual time and space, and even becoming "out of touch" with a cultural zeitgeist or "losing touch with reality." The chapters that follow are correspondingly organized into two distinct but related groups, "Missed Connections," centering on the desires wrought by and despite gender and sexuality in the years since the #MeToo movement, and "Connection Failed," devoted to virtual and digital intimacies in the wake of the COVID-19 pandemic.
Lost in translation : investigating new roles for methyl lysine signaling in the regulation of protein synthesis and cancer pathogenesis
Keywords: Humanities and Sciences.
Biology.

Protein lysine methylation is a common type of post-translational modification that plays a crucial role in various cellular processes. The enzymes responsible for catalyzing these methylation reactions are known as lysine methyltransferases (KMTs). While KMTs have been extensively studied in the context of chromatin regulation and histone methylation, it is increasingly recognized that non-histone protein methylation also plays a significant role in fundamental biological and pathological processes. However, numerous human KMTs remain uncharacterized, and the biological significance of many lysine methylation events is still unknown. In this work, I characterize SMYD5 (SET and MYND domain containing 5), an orphan human KMT. I demonstrate that SMYD5 methylation regulates protein synthesis, particularly active ribosomal function, and specifically enhances mRNA translation for oncogenic programming, facilitating the evolution of malignant gastric cancer. I also propose that inhibiting SMYD5 could serve as a potential cornerstone for combination therapy to treat GAC. The following chapter presents rigorously validated resources that enhance our understanding of the physiological functions of eEF1A methylation, a key modulator of the protein synthesis machinery. These tools will facilitate subsequent research aimed at exploring fundamental biological process. In summary, this study broadens our understanding of methyl-lysine signaling by examining an unidentified enzyme's physiological roles in cancer pathology. Moreover, this work provides robust methodological resources that expedite advancements in the study of protein methylation-based cellular signaling, particularly in the context of human health and disease.
Low dimensional motor neural structure of freely moving ambulation and reaching
Keywords: Engineering.
Electrical Engineering.

Motor systems neuroscience is an interdisciplinary field that studies how the brain controls voluntary movement. Mechanistic and correlational understandings gained from decades of animal studies have been translated into brain machine interfaces, devices that can potentially help individuals with motor injury. However, most of these studies have been done in classically constrained settings. There is growing theoretical evidence, but limited experimental proof, that suggests that these models of neural control may not generalize to naturalistic settings. Thus, there is a knowledge gap in both mechanistic understanding and clinically translatable findings of how the brain controls unconstrained behavior. My PhD work focuses on bridging this knowledge gap by exploring the neural control of free locomotion and unconstrained reaching, broken up into 3 main projects: 1. Building a platform that collects wireless neural data and full-body markerless kinematics to study naturalistic behavior. 2. Performing computational analyses on a reach-walk task to quantify the neural differences of similar behaviors performed under different levels of cognitive difficulty. 3. Decoding locomotion to quantify the neural relationships between the gait cycle and total body position. My experimental work and computational analysis advance the field of motor systems neuroscience by representing a novel experimental platform to uncover the structure of ambulatory control. Many results from my freely moving experiments can only be captured with this platform, and show that previous constrained experiments are limited in their ability to capture the full activity of the motor cortex. My research is foundational for subsequent studies that may inform human clinical studies leveraging new insight about free movement.
The LZ dark matter WIMP search and treatment of fundamental signals
Keywords: Humanities and Sciences.
Physics.

While we can observe dark matter gravitationally, we have yet to determine its exact physical properties through non-gravitational means. One of the most historically favored models for dark matter is the weakly-interacting massive particle (WIMP). The LUX-ZEPLIN (LZ) dark matter experiment seeks to detect WIMPs through the use of a dual-phase time projection chamber (TPC) filled with a 7 tonne active xenon target. LZ published world-leading limits on WIMP-nucleon cross-sections for WIMP masses above 9 GeV/c^2 following its first WIMP search. In addition to summarizing the full WIMP search, this work discusses the development of key efforts including the treatment of fixed and time-varying conditions within both reconstruction and analysis frameworks, studies of high-activity periods likely resulting from fluorescence of the TPC walls, and the development of novel methods to model backgrounds and test analysis cut efficiencies. LZ's future WIMP searches will incorporate a form of bias mitigation known as "salting" in which unknown quantities of fake signal events are injected into WIMP-search data. This work discusses the methods used to reliably model, create, and inject fake yet unidentifiable signal events into LZ's data stream. This work concludes with results from the application of autoencoders on PMT waveforms to remove unknown backgrounds.
Machine learning and natural language processing for code quality analysis in introductory programming courses
Keywords: Engineering.
Electrical Engineering.

The employment rate of software developers has risen significantly over the last 30 years. As a result, more students are considering computer science as a potential career path. Over the last 15 years, introductory programming course (CS1) enrollment has been increasing at a much faster rate than the increase in the number of CS faculty, with no apparent signs of slowing. Thus, a scalability issue clearly exists. Technology has opened up learning opportunities to a wide audience. Millions of people use Massive Open Online Course (MOOC) providers, while hundreds of thousands enroll in online CS courses and coding boot camps. Automated assessment helps instructors to maintain pace with the overwhelming workload. Moreover, emphasis is mostly placed on functionality. However, software development is much more than just writing working programs. Code quality assessment remains a manual process. This dissertation focuses on critical CS1 qualitative aspects as well as how to use technology to take on time-consuming human tasks. First, I cover one of the fundamental code quality standards, readability, and focus on its corner- stone in CS1 programs, namely function names. An identifier that captures the intended task with clarity makes code readable and self-documenting. I present and examine a semi-automated software system that I built to improve function names. It uses a variation of the Na ̈ıve Bayes classifier to assess the quality of the identifiers and then suggests alternatives for the poor ones. Second, I study the relationship between problem-related entities and functional decomposition. I introduce a method for quantifying how broad a student's view of the problem is by the time they jump into coding. I proceed with software implementation and explain how I used natural language processing (NLP) to detect problem-related entities, which is a key stage in this process. Finally, I use the system to classify students at scale and determine how the broadness of the problem's view affects learners' performance, the time required to solve a programming challenge, and the complexity of the solution. Third, I introduce a systematic approach to detecting when novice programmers decompose their code and identify what drives their decision. I detail a software system that I built to implement these tasks. Next, I use the system to classify students and explore their relationship with program complexity and student performance. Lastly, I introduce an alternative to the standard testing approaches for functionality validation. My solution depends on code instrumentation. Its main advantages are that it takes substantially less code and can test programs with nondeterministic behavior or user input. Then, I present Delve, an educational tool that I created for instructors and teaching assistants in introductory programming courses. Delve integrates many ideas from my previous systems and bundles them into an easy-to-use graphical user interface
Machine learning and statistical methods for physical systems
Keywords: Engineering.

Physical systems often exhibit highly complex, non-linear behaviors, making their characterization without a profound understanding of underlying dynamics a challenging endeavor. In this presentation, I will explore several methodologies for integrating valuable prior knowledge into machine learning-based and statistical methods, aimed at predicting physical system behaviors and bolstering the control of engineering systems. The talk will be divided into three main parts. In the first part, I will present several deep-learning-based interpolation methods for estimating nearshore bathymetry with sparse measurements. By applying deep learning-based models from a Bayesian perspective, we provide uncertainty quantification for the underlying bathymetry profile and compare our predictions with those from Gaussian Process Regression (GPR). Results show that our approach provides more accurate posterior estimates, particularly when encountering patterns with sharp gradients, such as those found around sandbars and submerged objects. In the second part, I will introduce a novel framework, namely physics-based Parameterized Neural Ordinary Differential Equations, for predicting laser ignition inside a rocket combustor. A 0D reacting flow model is combined with neural ordinary differential equations to deliver accurate solutions, even with limited training samples, that adhere to physical constraints. We validate our physics-based PNODE using solution snapshots of high-fidelity Computational Fluid Dynamics (CFD) simulations, demonstrating its capability to accurately learn complex combustion dynamics with high-dimensional parameters of laser ignition. In the third part, we consider the use of a periodic Martingale Model for Forecast Evolution (MMFE) to integrate weather forecasts into decision-making processes for energy system controls. We demonstrate that, when combined with a linear state space model, our periodic MMFE yields computationally tractable Markov Decision Processes (MDPs) with low-dimensional state variables. We provide algorithms for the maximum likelihood estimation of model parameters for our periodic MMFE and showcase the corresponding asymptotic convergence. Furthermore, we validate the generative power of our framework for weather forecast errors by calibrating our periodic MMFE using real data collected from hourly commercial weather forecasts.
Magnetic field amplification and radiation emission in relativistic beam-plasma systems
Keywords: Humanities and Sciences.
Physics.

Magnetic field amplification by relativistic plasma instabilities is crucial to many beam-plasma systems. In high-energy astrophysical environments, these instabilities can mediate collisionless shock formation, particle acceleration, and radiation emission, while in the laboratory they can affect laser-matter interaction related to compact radiation sources and inertial fusion. In this Thesis, we report the discovery of a new nonlinear plasma streaming instability which generates much stronger, larger-scale magnetic fields than previously thought possible in dilute, relativistic beam-plasma systems. Our kinetic theory for the growth and saturation of this instability is validated in plasmas of varying composition by multidimensional particle-in-cell simulations and indicates that it could lead to significantly enhanced particle acceleration in gamma-ray bursts. We further show that modern high-power laser and accelerator facilities can drive lepton beams large enough to probe multiple relativistic instabilities. We derive requirements on the lepton beam characteristics needed to study these instabilities in astrophysically-relevant laboratory conditions and show that they can enable unprecedented x-ray flux needed for high energy density science experiments
Making small embedded systems secure and dependable
Keywords: Engineering.
Electrical Engineering.

Low-power, low-cost embedded microcontrollers (MCUs) are frequently used in security-critical settings. Recent systems advances, such as type-safe languages and standardized protocols, improve security and dependability, but add additional software to a system. MCU-based embedded systems, however, have limited flash memory, so minimizing code size is extremely important. As a result, code size limits lead many embedded systems to use C code and custom protocols, with all of the security implications that brings. This dissertation shows that new techniques and principles allow embedded systems to have the security and dependability benefits of modern "big system" programming practices and protocols while keeping code size small. First, this dissertation focuses on the use of Rust --- a modern, memory-safe programming language --- in embedded systems. I present evidence that idiomatic Rust code generates binaries 50-100% larger than similar C code. I characterize and quantify several major sources of this increase. I describe a set of Rust programming principles that reduce the size of Rust binaries, and evaluate these on an industrial Rust firmware application. I also discuss a collection of compiler optimizations that could further reduce Rust binary sizes. Second, this dissertation examines 6LoWPAN, the standard IP protocol design for low-power devices. Despite 6LowPAN being an IETF standard, different 6LoWPAN implementations often fail to communicate with each other. Their failures stem from 6LoWPAN's design, particularly the implications code size limits have on implementations. I propose three design principles for Internet protocols on low-power networks that prevent such failures, and evaluate these by using them to design a modified 6LoWPAN specification. This modified 6LoWPAN enables interoperable implementations across a wide variety of code size budgets
Making video traffic a friendlier internet neighbor
Keywords: Engineering.

Streaming video traffic from services like Netflix and YouTube accounts for the vast majority internet traffic today---recent estimates put the fraction as high as 60-75\% of all bytes sent over the internet. Given video traffic is such a large fraction of the internet, this thesis makes video traffic friendlier to neighboring applications that share the same networks. The thesis begins with Sammy, a system that smooths out video traffic so that the throughput of a video session is close to the minimum the video session needs for good performance. By judiciously picking throughput based on the needs of the video application, Sammy is able to substantially smooth out video traffic. In internet-scale experiments, Sammy reduces throughput of video traffic by more than half and dramatically reduce congestion, all while slightly improving video quality of experience over today's top production algorithms. The thesis next considers how new algorithms that affect congestion on the internet (like Sammy) are evaluated in networking research today. The gold standard is to run large-scale A/B tests, to understand at how an algorithm actually performs in practice. I show in experiments run at scale that typical A/B tests can lead to biased results, even to the point of switching the direction of results---an algorithm that appears worse in an A/B test might actually improve performance when deployed, and vice versa. I discuss the implications of this and suggest how researchers can deal with this bias. Finally, I revisit the long-standing problem of sizing buffers in routers. Prior work suggested that buffers can be reduced by a factor of square root of the number of flows using the router, offering dramatic buffer reductions in networks carrying many flows. I revisit these results, removing assumptions and showing that they hold for modern congestion control algorithms. Importantly, I discuss how our approach of smoothing video traffic with Sammy challenges the assumptions of the classic buffer sizing problem, potentially allowing for a future with much smaller buffers. Overall, this thesis invites us to reconsider how we deal with network congestion at scale. Rather than focusing on congestion control algorithms that maximize throughput, we should prioritize the ways we actually use the internet---applications like video streaming and web browsing and gaming and so on. If we do so, we can reduce congestion and improve the performance of all applications that share the internet
Making waves! Acoustic and nanophotonic platforms for amplification-free cellular and molecular diagnostics
Keywords: Engineering.
Mechanical Engineering.

Infectious diseases are one of the leading causes of death in the 21st century, claiming the lives of over 16 million individuals each year. These staggering numbers highlight the need for rapid, sensitive, and multiplexed diagnostics to minimize the spread of infectious disease and guide appropriate treatment. Existing diagnostic methods often suffer from a need for sample culturing or molecular amplification, leading to long diagnostic times or high limits of detection when amplification is not possible. Here, I combine acoustic bioprinting with nanophotonics for 1) culture-free detection and identification of bacteria from a blood sample using surface enhanced Raman Spectroscopy and 2) amplification-free, multiplexed detection of proteins using high-Q metasurfaces. First, I describe how acoustic bioprinting combined with surface enhanced Raman spectroscopy (SERS) can be used to rapidly detect and identify pathogens from complex multicellular samples. Next, I describe a high quality-factor ("high-Q") dielectric metasurface assay that, when coupled with our home-built acoustic bioprinter for site-specific surface chemistry, allows for amplification-free detection of proteins down to femtomolar concentrations. Together, my work provides a foundation for faster, higher-throughput, and more sensitive cellular and molecular diagnostics.