VictorJuiz/thesis huggingface dataset:
From rags to riches : cell-free RNA analysis for non-invasive cancer detection and characterization

Cell-free RNA (cfRNA) analysis could allow non-invasive tumor gene expression profiling and monitoring of cancers. Here, we describe RARE-Seq (Random priming & Affinity capture of cell-free RNA fragments for Enrichment analysis by Sequencing), a novel cfRNA profiling platform optimized for the simultaneous noninvasive detection of tissue-derived transcripts and associated somatic mutations. We show that platelet contamination can substantially confound cfRNA analyses and describe a framework to overcome it. In analytical validations, we find RARE-Seq to be ~50-fold more sensitive for detecting tumor-derived transcriptomes than whole transcriptome strategies, corresponding to a limit of detection of 0.05%. To explore the clinical utility of RARE-Seq, we profiled 269 samples from 201 individuals with cancer or non-malignant conditions and controls. Detection of non-small cell lung cancer (NSCLC) expression signatures in cfRNA increased with stage, ranging from 40% in stage I to 87% in stage IV, with driver somatic mutations detectable in cfRNA from 44% of stage IV patients. When applied to EGFR mutant NSCLC patients who developed resistance to tyrosine kinase inhibitors, RARE-Seq detected both histologic transformation and mutation-based resistance mechanisms. Furthermore, RARE-Seq detected other solid tumor types and identified tissue of origin with high accuracy. Lastly, we demonstrate the potential utility of RARE-Seq for assessing benign pulmonary conditions and tracking response to mRNA vaccines. Together, these results highlight the potential value of ultrasensitive cfRNA analysis and provide proof-of-concept for multiple potential clinical applications.

Keywords: Medicine.
Advisor: Diehn Maximilian
Alizadeh Ash
Gentles Andrew J.
From satellites to stomata : measuring and modeling vegetation responses to water stress at ecosystem scale

The response of plants to water stress is a first-order control on the water and carbon cycles, and plays a key role in wildfire, crop productivity, and forest mortality. While water stress response has been studied extensively in individual plants, ecosystem-scale data and models are largely missing, limiting our ability to understand plant water stress effects at large spatial scales. This dissertation helps address that gap. In Chapter 2, I present the first field experiment directly testing the sensitivity of microwave radiometry (a type of remote sensing) to plant water potential in a forest. I show that vegetation optical depth derived from radiometry mirrors diurnal and seasonal changes in tree leaf water potential. In Chapter 3, I use a simulation experiment to investigate how plant traits describing water stress response could be estimated with microwave radiometry from satellites in various orbits combined with a land surface model. Encouragingly, using two satellites similar to those already in Sun-synchronous orbits yields similar accuracy to using a geostationary satellite observing at all hours of the day, which has been proposed but would be much more expensive in practice. In Chapter 4, I focus on one key mechanism of water stress: the closing and opening of stomata on plant leaves. I introduce a new model of stomatal response to water stress, I empirically estimate the time scales that stomatal responses to water stress appear to be optimized for in a range of ecosystems, and I relate those time scales to ecosystem climate characteristics. Overall, the results presented in my dissertation can help see the forest for the trees - that is, help understand how water stress affects entire ecosystems. In the future, this work could be extended to predict vegetation responses to the increased stresses of climate change and identify hot spots of vulnerability or resilience, among other applications.

Keywords: Sustainability.
Earth System Science.
Advisor: Konings Alexandra
Jackson Rob
Lobell David
Schroeder Dustin
The genetic manipulation of non-model flatworms and its applications

Regeneration is a dynamic biological process that allows organisms to restore lost or injured tissues. Among flatworms, whole-body regeneration is common and has been studied extensively in the planarian Schmidtea mediterranea. However, despite interest in understanding their regenerative capacities, genetic manipulations and live imaging remain challenging. Here, I discuss advances made in the genetic manipulation and live imaging of two highly regenerative flatworm systems, Schmidtea mediterranea and Macrostomum lignano. First, I describe how we developed a novel method of transient mRNA-based expression in S. mediterranea. By leveraging nanostraws, an array of minuscule hypodermic needles, to deliver mRNA encoding nanoluciferase, an extremely bright luminescent enzyme, we observed the first instance of transgene expression in planarian cells. With a positive control for delivery, we identified additional chemical-based transfection methods that functioned both in vitro and in vivo. Using this, we identified post-transcriptional regulators of gene expression and explored sites of translation initiation using synthetic mRNAs. Finally, we constructed a specialized microscope capable of detecting the faint signals emitted by luminescing cells, enabling the quantification of transfection kinetics and permitting live imaging of animals transfected in vivo, thus paving the way for future studies dedicated towards developing longer-term DNA-based expression. Second, I introduce a comprehensive toolkit for live imaging of whole-body regeneration in the flatworm Macrostomum lignano, including a high throughput cloning pipeline, a refined injection protocol, and several advanced microscopy techniques. By driving tissue-specific reporter expression, we examine how various structures regenerate. Using a genetically encoded toxin, we ablate specific cell types to study their functions during regeneration. Through a custom luminescence/fluorescence microscope, we monitor the nervous system after cellular ablation and find it necessary for wound healing post-injury. Finally, we built a tracking microscope to continuously image freely moving animals for over a week, quantifying kinetics of wound healing, nerve cord repair, body regeneration, growth, and behavioral recovery. Notably, our findings suggest that nerve cord reconnection operates independently from primary body axis re-specification and other downstream processes of regeneration. Together, these results demonstrate how expanding the reach of our technological tools to non-model organisms can reveal new insights into their unique biology.

Keywords: Engineering.
Bioengineering.
Advisor: Fire Andrew Zachary
Wang Bo (Researcher in bioengineering)
Bintu Lacramioara
Greenhouse gas detection in the energy system : an integration of remote sensing and GIS database

The oil and gas (O&G) system significantly contributes to global anthropogenic greenhouse gas emissions (GHGs). The rapid development of remote sensing technologies enables direct measuring of point-source GHGs within the O&G system. These technologies, known as "top-down" methods, demonstrated their capabilities in measuring methane, carbon dioxide, and gas flaring by collecting radiance in the shortwave infrared using airborne or spaceborne remote sensors. This study aims to enhance our understanding of GHGs in the O&G system using remote sensing and geographic information system (GIS) techniques. We first estimated field-level gas flaring volumes using the satellite gas flaring data and a global O&G field GIS database. We then introduced an improved method for detecting and quantifying methane emissions based on Sentinel-2 satellite imagery. Finally, we applied imaging spectroscopy methods to measure carbon dioxide emissions within the liquefied natural gas (LNG) supply chain, with a particular focus on LNG terminals.

Keywords: Sustainability.
Energy Resources Engineering.
Advisor: Brandt Adam (Adam R.)
Jackson Rob
Lobell David (David Brian)
Higher-order approximation manifolds for more efficient nonlinear projection-based model order reduction

Solving large-scale parameterized dynamical systems, which may be obtained through, for example, the discretization of partial differential equations, is foundationally important to many fields, including engineering. Oftentimes, these high-dimensional models are expensive to evaluate due, in part, to their large size; this expense may be exacerbated by repeated evaluations in a large-dimensional parameter space. Projection-based model order reduction is a framework that allows us to solve these high-dimensional models at a much lower cost in terms of computational resources. This is accomplished by collecting prior solutions associated with different parameter values obtained by exercising the high-dimensional model and forming a lower-dimensional subspace. Using this subspace, we can compute a new solution associated with an unsampled parameter value at (ideally) a much lower cost. This computational efficiency has significant implications for applications in simulation-driven design, optimal control, and uncertainty quantification, among others, all of which would be impractical, if not impossible, for truly large-scale problems without resorting to some form of surrogate modeling. In practice, however, projection-based reduced order models sometimes struggle to achieve this level of performance in problems that exhibit the well-known Kolmogorov barrier as is often encountered in first-order hyperbolic partial differential equations, e.g., Navier-Stokes equations. This dissertation presents dimension reduction techniques, the problem of the Kolmogorov barrier, why it remains a challenge for model reduction today, and discusses methods to solve it. Among these methods are two novel approaches presented in this dissertation: a data-driven quadratic approximation manifold as well as an arbitrarily nonlinear approximation manifold using artificial neural networks. With no sacrifice in accuracy, both achieve an order of magnitude improvement in wall clock time compared to the current state-of-the-art in projection-based model order reduction for industrial-grade flow problems.

Keywords: Engineering.
Mechanical Engineering.
Advisor: Farhat Charbel
Iaccarino Gianluca
Kochenderfer Mykel
Impact of climate variability on landslide hazard in the western United States

Landslides broadly include debris flows, rock falls, avalanches, mudslides, and any other down-gradient movement of soil, rock, or debris under the direct influence of gravity. Although landslides occur over a broad range of lithologies, climatologies, hydrological regimes, and land use types, the majority are caused by precipitation. However, for most precipitation-triggered landslides, other complex atmospheric, surface, and subsurface conditions also play a role in predisposing a slope to landslides by increasing the effects of down-gradient forces and/or reducing the strength of the underlying slope. While there is "high confidence" that climate change will affect landslide hazard in some regions, several limitations persist in attributing the unique influence of a climate variable — like precipitation — to a given inventory of landslides. Key challenges broadly include: the short length of the landslide record, geographic reporting bias, and the complexity of the physical system. This dissertation develops strategies for overcoming these attribution challenges, while enhancing fundamental understanding of landslide response climate variability. Chapter 1 applies panel regression with fixed effects to quantify the impact of daily, ten-day, and thirty-day precipitation accumulation on landslide hazard in urbanized and non-urbanized areas. Chapter 2 evaluates the effect of wildfire severity on precipitation-triggered landslide hazard across the western United States. In addition to developing empirical frameworks for analyzing causal relationships between land use, climate variability, and landslides, the chapters enhance fundamental understanding of landslide response to climate variability.

Keywords: Sustainability.
Earth System Science.
Advisor: Diffenbaugh Noah S.
Burke Marshall
Caers Jef
Willenbring Jane K.
Improving access to untranscribed speech corpora using AI

How easily speech corpora can be indexed and searched has a direct impact on how effectively its contents can be used by many interested parties — from linguists, to language teachers, to community members. As transcribing speech is much more time consuming than recording it, large parts of speech corpora typically remain untranscribed, making it difficult to index and search these sub-parts. While searchable transcriptions can be automatically derived using a speech-to-text system for major languages like English, such technologies are typically unavailable for smaller languages, especially those typical in language documentation work. For documentation projects, this difficulty creates a bottleneck for creating language learning materials for language revitalisation and maintenance as well as linguistic analyses. In this dissertation, I propose four approaches to widen this bottleneck to enable some form of search or indexing, or accelerate the time-consuming process of transcription. Each chapter addresses a common but distinct scenario within language documentation projects according to the types and amounts of available data. For each scenario, I propose a context-appropriate, data-efficient solution that leverages AI speech models as well as external resources where appropriate.

Keywords: Humanities and Sciences.
Linguistics.
Advisor: Jurafsky Dan
Anttila Arto
Manning Christopher D.
Integrated optics as a toolbox for communication, quantum science, and sensing

Integrated optics promises to bring the rich world of optical technology to the nanoscale; the fabrication of bulk optics on chips shows great promise in applications across communications, sensing, augmented and virtual reality, and even quantum computing. However, multitudinous challenges must be overcome to make these applications a reality. In this thesis, I will present new experimental and theoretical techniques to tackle four such challenges. First, I will describe how nonlinear optics can be used to generate passive optical isolators - filling a critical gap in integrated photonic infrastructure. Second, I will show how photonic inverse design can be applied to mode multiplexing to enable higher bandwidth optical communications. Third, I will describe new techniques for the scalable integration of 2D materials into photonic devices, allowing us to take advantage of their unique material process in a way that is compatible with mass manufacturing. Finally, I will show how inverse design techniques can be used to optimize modulatory signals in solid-state quantum systems to engineer their spectra and interactions.

Keywords: Engineering.
Electrical Engineering.
Advisor: Vuckovic Jelena
Fan Shanhui
Soh H. Tom
Low energy inference for always-on tinyML applications

Internet of Things (IoT) devices and sensors are generating data at an unprecedented scale. Processing all of this data in the cloud is infeasible due to the energy and latency cost of sending it over long distances. tinyML seeks to deploy deep neural networks (DNNs) on low-cost IoT devices in order to alleviate these challenges by distilling the data into just the necessary and useful information. This is particularly crucial in always-on scenarios where tinyML systems continuously analyze sensor data, necessitating low-energy processing to extend battery life and minimize overall energy consumption. This thesis studies the optimization of inference energy for always-on tinyML processors, covering insights across the stack from the network architecture, to the hardware architecture, to the circuit design of memories and compute. Our contributions include: (1) A study on tinyML network architectures and precision, assessing model size, accuracy, energy, and latency trade-offs, validated by silicon measurements. (2) 6T-latch-based Inner Loop Memories (ILMs) optimized for tinyML with an access cost of only 15 fJ/Byte. (3) A Pipelined Pixel Streaming (PPS) system architecture and dataflow that leverages ILMs to reduce memory access overhead for memory-intensive bottleneck layers. (4) Full-custom bit-serial multipliers and adder trees integrated with ILMs to reduce compute area and energy. These contributions culminate in Medusa, a programmable 8b digital processor fabricated in 28 nm CMOS for always-on tinyML applications that achieves an image-to-class inference energy of 0.83 (4.6) µJ at an accuracy of 86.2% (91.6%) on CIFAR-10 with all parameters stored on chip. Medusa achieves a 3.0x lower energy per inference at iso-latency when compared with the current state of the art. Medusa achieves the goal of sub-nJ-per-pixel always-on tinyML inference across the Image Recognition, Visual Wake Words, and Audio Keyword Spotting workloads from the MLPerf Tiny Inference Benchmark deployed in this work.

Keywords: Engineering.
Electrical Engineering.
Advisor: Horowitz Mark (Mark Alan)
Murmann Boris
Arbabian Amin
Microfluidic methods to probe basophils for food allergy diagnostic applications

Basophils are rare (~1% of peripheral blood leukocytes), yet potent effector cells that initiate and perpetuate a wide range of immunological responses. In particular, the role of basophils in food allergy is widely studied and has led to the emergence of a highly accurate ex vivo diagnostic test for food allergy: the basophil activation test (BAT). In contrast to commonly used food allergy diagnostic tests, i.e., skin prick test (SPT), allergen specific IgE (sIgE) measurement, and oral food challenge (OFC), the BAT provides highly sensitive and specific results (more than 90% across a range of allergens) while not subjecting individuals to potentially unsafe outcomes (e.g., anaphylaxis). Furthermore, basophil reactivity measured with BAT correlates well with severity and can be used to predict allergen thresholds that elicit a reaction in OFC. Despite the advantages of BAT over other methods, translating it to common clinical practice has been hampered. Motivated by the potential that BAT presents for changing the paradigm of food allergy diagnostics, my thesis dissertation focuses on various microfluidic approaches and aspects of measuring basophil activation that aim to increase clinical accessibility to BAT and to other future assays that probe basophil function. First, I investigated the utility of avidin as a basophil activation marker to evaluate whether avidin could enhance the predictive capability of BAT. Next, I developed various methods for rapid immunomagnetic isolation of basophils from whole blood to enable BAT-based diagnostic approaches and research that require purified basophils. Finally, I developed and characterized a novel device and analysis pipeline for increasing the clinical accessibility to BAT. Where applicable, I used computational methods to inform the design of the devices and analyzed data with machine learning.

Keywords: Engineering.
Mechanical Engineering.
Advisor: Tang Sindy (Sindy K. Y.)
Galli Stephen J.
Santiago Juan G.
Mill of this world : studies in the ecology of circular motion in the age of Cervantes

Miguel de Cervantes famously wrote the scene in which Don Quixote charges against windmills as though they were giants, while Sancho Panza sees them plainly as windmills. On the one hand, the object is understood as something else; on the other, it is a simple identity that hides any further understanding of the object. My dissertation takes this scene as a point of departure to inquire how language in the time of Cervantes captured understandings of technology, addressing the polysemy of circular motion embodied in and performed by machines such as mills, cranes and wheels. The results suggest that tensions between identity and difference like that in Don Quixote existed across literary genres and were part of the material and symbolic environment in which machines existed. This theme is approached variously. In Chapter 1, Juan de Herrera's manuscript on the principle of the crane used at El Escorial suggests mental automation of a geometrical idealism even in the empirical context of machine-building. Chapter 2 considers machine-building in the relatively new context of patent-granting, juxtaposing understudied descriptions of mills by inventors Ayanz, Garay and Lobato del Canto and those of nontechnical writers such as Polidoro and Pineda. I argue that these inventors approach machines from the point of view of how motions relate to output; linguistically, a mill is more than simply a mill, it is that entire relation. In contrast, mythographic and encyclopedic historiography consider machines primarily in terms of their functions, indexing them to various extrinsic elements. Finally, the materials assembled in Chapter 3 constitute a first survey of how, in Spanish (and some Italian) emblem books, machines with rotary elements, mills among them, are charged with moral elements and are made to stand for unresolvable aspects of existence itself.

Keywords: Humanities and Sciences.
Comparative Literature.
Advisor: Eshel Amir
Greene Roland
Huber Marie
Multi-pole NEM relays and multiple-bits-per-cell RRAM for efficient 3-D ICs

In this dissertation, I present techniques for improving the power, performance, and area of integrated circuits (ICs) through 3-D integration of two emerging nanotechnologies: (1) resistive random-access memory (RRAM), a non-volatile memory with multiple-bits-per-cell storage capability, and (2) nanoelectromechanical (NEM) relays, nano-scale mechanical relays that can be actuated electrostatically. In modern ICs for edge computing, data movement between on and off-chip memories typically consumes a large fraction of the total power. Dense, non-volatile embedded memory can reduce/eliminate off-chip data movement by keeping frequently-read application data always on chip. RRAM is a good candidate for such a memory, especially because it can store multiple bits per cell, achieving high density on-chip storage. However, efficient and reliable operation with multiple-bits-per-cell RRAM has been a challenge due to (1) stochastic device behavior during programming that results in large pulse counts with traditional write-verify methods, and (2) reliability issues arising from resistance relaxation. Towards the goal of achieving efficient and reliable multiple-bits-per-cell RRAM, I present three contributions: (1) range-dependent adaptive resistance (RADAR) tuning, a fast and energy-efficient programming method for multiple-bits-per-cell RRAM that uses an adaptive combination of coarse- and fine-grained cell resistance tuning, yielding a 2.4x reduction in pulse count over prior methods, (2) characterization of resistance relaxation behavior in three RRAM technologies and analysis of its implications for multiple-bits-per-cell storage, and (3) efficient multiple-bits-per-cell embedded RRAM (EMBER), the first demonstration of a fully-integrated multiple-bits-per-cell RRAM macro. EMBER contains a multiple-bits-per-cell read and write controller with a high degree of flexibility that enables good level allocation (mitigating reliability issues from resistance relaxation) and programming scheme optimization (yielding low-energy, low-latency multiple-bits-per-cell writes). Finally, in reconfigurable ICs, in addition to the memories, the routing fabric consumes a large fraction of the overall area and power. I demonstrate that replacing CMOS routing switches with 3-D integrated multi-pole nanoelectromechanical (NEM) relays in a coarse-grained reconfigurable array (CGRA) can achieve 19% lower area and 10% lower power at iso-performance.

Keywords: Engineering.
Electrical Engineering.
Advisor: Raina Priyanka (Assistant Professor of Electrical Engineering)
Howe Roger Thomas
Wong Hon-Sum Philip