VictorJuiz/thesis huggingface dataset:
Elucidating the early transcriptional response to the systemic acquired resistance-activating metabolite N-hydroxy-pipecolic acid in arabidopsis
Keywords: Humanities and Sciences.
Biology.

Systemic acquired resistance (SAR) is a robust immune response in plants that confers long-lasting and broad-spectrum disease resistance throughout the body of a plant. Establishment of SAR begins at the site of an initial pathogen infection where mobile signals are sent out from infected tissue to uninfected tissue, initiating the priming of systemic immune responses. In part, this priming is carried out through accumulation of the defense hormone salicylic acid (SA) and through substantial transcriptional reprogramming to prioritize defense. N-hydroxy-pipecolic acid (NHP) has emerged as a powerful mobile signal required for the initiation and amplification of SAR. In the model plant Arabidopsis thaliana (Arabidopsis), exogenous NHP is sufficient to activate SAR-like immune responses, including SA accumulation and transcriptional changes. Previous studies have tracked these changes 1-2 days after an initial NHP treatment, when such responses could be detected in untreated, distal tissues. As a result, little was known about the very early phase of NHP signaling including the immediate transcriptional changes and physiological events that occur once NHP levels increase in tissues and how these impact the development of SAR. Furthermore, key players that orchestrate NHP signaling and the impact of SA accumulation on early NHP signaling were unknown. Here, I show that NHP induces genome-wide transcriptional changes in Arabidopsis seedlings within minutes of treatment and these distinct patterns of expression, over the course of minutes to hours, provide evidence toward the primary and secondary targets of NHP-driven signaling. My findings indicate that members of the WRKY transcription factor family are among the primary targets of NHP signaling, these in turn act as executors of secondary NHP transcriptional reprogramming. Additionally, through study into the early NHP transcriptomic response in the SA accumulation deficient mutant (sid2), I identified NHP responsive genes which are SA-independent and partially SA-independent, revealing that NHP can drive early signaling in a low-SA environment. Unexpectedly, I also uncovered preliminary evidence suggesting the relationship between early NHP and SA signaling is not only synergistic but also antagonistic in certain conditions. Using this transcriptomic dataset, I performed a reverse genetic screen to identify early NHP responsive genes. This screen revealed that the transcription factor WRKY70 is required for full NHP-elicited SAR. Overall, my thesis work is first to investigate and characterize the early time frame of NHP-induced transcriptional reprogramming. My research has helped to elucidate the progression of NHP signaling in the absence and presence of accumulating SA levels and provides a strong foundation for the discovery of key players involved in NHP signal transduction pathways important to the establishment of immune priming.
Embedding spaces and implicit representations for recovering high frequency cloth geometry
Keywords: Engineering.

Recovering cloth geometry from simulation or real world data using machine learning has shown promise, but existing methods still struggle to capture high-frequency folds/wrinkles and generalize beyond the limited 3D data currently available. To address these challenges, this dissertation explores a general paradigm whereby high-frequency information is procedurally embedded into low-frequency data so that when the latter is smoothed by the network the former still retains its high-frequency detail. This paradigm is applied to various aspects of the cloth capture pipeline, ranging from simulation to data acquisition to reconstruction. The first part of my Ph.D. dissertation addresses the challenge of choosing the right data representations for inferring high-frequency geometry; how data is parameterized is crucial to effectively training learning-based models and improving generalizability to unseen distributions. The first parameterization we developed is texture sliding, which we define as the changing of 2D texture coordinates on a per-camera basis such that any point which is visible from some stereo pair of cameras can be triangulated back to its ground truth 3D position. The second parameterization we developed is a skinned tetrahedral mesh framework for virtual cloth, which we demonstrate improves the prediction of high-frequency wrinkles and folds. Intrinsically, a tetrahedral mesh provides a more robust parameterization of three-dimensional space, since it contains a true extra degree of freedom as compared to the degenerate co-dimension one body surface. The second part of my Ph.D. dissertation investigates how to enable democratization of clothed human reconstruction by moving from models only trained on limited scans of people to weakly supervised methods that can make use of large-scale image datasets that are readily available. To alleviate dependency on labeled 3D data, we present a weakly supervised approach to monocular clothed human reconstruction using 2D normal maps as ground truth labels (only) during training. Given a single RGB image, our neural network infers a signed distance function (SDF) discretized on a tetrahedral mesh surrounding the body. Subsequently, estimated skinning and camera parameters are used to generate a triangle mesh and then a normal map from the SDF. Additionally, we propose a number of SDF regularizers to aid in silhouette matching as well as losses based on motion by mean curvature to encourage smoothness. The system is trained end-to-end, and we analytically compute backpropagation gradients for Marching Tetrahedra, rasterization, and the SDF regularizers.
EMERGENT ANTI-DE SITTER SPACETIME FROM BOUNDARY QUANTUM PHASE TRANSITIONS IN 1+1D
Keywords: Quantum gravity
Phase transformations (Statistical physics)
AdS/CFT
Quantum entanglement
Space and time
Holography
Quantum mechanics
Random matrices
Duality
Measurement
Geometry
Text
Thesis

In this thesis, we explore the implications of a recent algorithm by Scott Aaronson and Jason Pollack showing that it is possible to reconstruct a bulk AdS geometry given contiguous entropies from a CFT on the boundary. We consider this in light of the area law/volume law phase transition in monitored circuit models, and show that volume law entanglement corresponds to the formation of large-scale structure in the bulk, while area law entanglement fails to do so. We also investigate behavior near the quantum critical point, finding preliminary evidence that a dual phase transition in the bulk may coincide with the formation of a nontrivial bulk geometry.
An empire of clocks in disarray : time coordination and adventure fiction in the British Empire, 1880-1890
Keywords: Humanities and Sciences.

Drawing on work in history of science and imperial history as well as literary studies, this dissertation explores the interrelation of adventure fiction, time coordination, and the British Empire. It argues that adventure fiction was the preeminent form for representing not just the entirety of the British Empire, but also the interconnectedness of late-nineteenth-century globalisation. To undertake this dream of global coordination, adventure fiction relied on the technologies of time coordination that were expanding and approaching maturity in this period. One difficulty, however, was that time coordination was far less settled, far less dispersed, and far more unreliable than has often been assumed. The dissertation investigates global coordination through readings of, amongst others, Robert Louis Stevenson, Anthony Trollope, Thomas Hardy, H. Rider Haggard, and Henry James, and tries to show how contemporaries tried to make sense of their bewildering, globalised, imperial world.
Empowering machine learning systems for drug discovery with mechanistic biological knowledge
Keywords: Engineering.
Bioengineering.

Machine learning has the potential to solve critical tasks in drug discovery, from identifying new therapeutic uses for drugs to personalizing treatment plans for patients. However, datasets in drug discovery are often small due to the high time and labor cost of experiments, limiting the applicability of machine learning systems. Here, our key insight is to infuse machine learning systems with prior information about biology, so they can learn efficiently from small, labeled datasets. We find a way to structure diverse prior information, from molecular interactions between proteins to clinical annotations about diseases, into a heterogeneous knowledge graph. We then develop two machine learning systems that use the knowledge graph to mechanistically model biological phenomena and achieve strong performance. First, we develop the multiscale interactome, a machine learning system that uses a knowledge graph to model how drugs treat diseases across multiple scales of biology. The multiscale interactome predicts which drugs treat a disease up to 40% more effectively than the prior state-of-the-art, identifies proteins and biological functions relevant to treatment, and predicts genes that alter the treatment's efficacy and side effects. Second, we develop PLATO, a deep learning system that uses a knowledge graph to achieve strong performance on tabular datasets with orders of magnitude more features than samples (i.e. "small" labeled datasets). In PLATO, the knowledge graph is auxiliary to the tabular dataset and describes input features, like genes. PLATO uses the knowledge graph to infer the weights of a multilayer perceptron, thereby using prior information to learn efficiently from a small, labeled dataset. Across 6 datasets, PLATO outperforms the prior state-of-the-art by up to 10.19%. Ultimately, we provide a general framework to empower data-driven, machine learning systems with an extensive, mechanistic knowledge of biology
Enabling low voltage electronics for ultrasonic structural health monitoring
Keywords: Engineering.
Electrical Engineering.

The ability to detect, assess, and predict damage is key to ensuring the safety and long-term maintainability of critical aerospace, civil and mechanical structures. Traditional maintenance methods are increasingly limited in their effectiveness due to their use of bulky equipment, reliance on a schedule and incompatibility with new materials such as composites. Ultrasonic structural health monitoring (SHM) addresses these challenges by employing networks of piezoelectric sensors that are embedded within the structure under test. Ideally, the accompanying actuation and data acquisition electronics have a compact form factor that enables localized data collection and consolidation before communicating with the centralized processing unit. However, despite advances in the sensors, the electronics have remained relatively bulky thereby greatly limiting the deployment of real-time SHM. A key limiter to miniaturization is the use of high actuation voltages (50 Vpp to 200 Vpp) to ensure that the received signals have sufficient SNR to localize defects accurately and reliably. With aerospace applications as our target, we explore pathways to enable the design and implementation of integrated interface electronics to actuate and receive the ultrasonic guided waves used for SHM. First, we present a frequency-domain S-parameter approach to SHM that allows us to trade off longer measurement times with lower actuation voltages to achieve high SNR transfer function measurements of the structures under test. Using this method, we successfully perform end-to-end SHM at actuation voltages as low as 1.26 Vpp, an improvement of up to 160 times. We then present a method to simplify and speed up the measurements using broadband actuation signals. Next, we develop a framework for determining the SNR requirements of a widely used SHM detection and localization algorithm. Finally, we specify CMOS-compatible hardware requirements derived from these earlier insights along with experimental results obtained from a printed circuit board (PCB) prototype.
Enabling wireless low-power ultrasound imaging systems for implantable and wearable applications
Keywords: Engineering.
Electrical Engineering.

Therapeutic and diagnostic ultrasound (US) imaging has seen significant growth over the past decades, becoming an essential part of healthcare systems and emergency medicine. Keeping up with current trends, we present the development of two wireless US imaging platforms to expand the capabilities of implantable devices and create new opportunities for clinical and research applications. The first platform introduces deep-tissue cellular-level thermoacoustic (TA) imaging, enabling long-term monitoring and diagnosis of diseases. It features a mm-sized ultrasound receiver array imager, allowing for imaging deep inside the body. The second platform builds upon the first one and integrates a full end-to-end US pulse-echo imaging system with increased US channels, enabling high-resolution anatomical and blood flow imaging. Both platforms utilize ultrasound for power transfer and wireless communication, achieving performance comparable to state-of-the-art imaging systems while maintaining small size and low power consumption. The systems incorporate advanced imaging reconstruction and compression techniques, significantly reducing data volume while maintaining a high imaging rate. This thesis provides a detailed analysis of the developed systems, including the fabricated chips and preliminary testing results, highlighting the motivations, challenges, and potential contributions of this research in advancing the field of implantable US imaging
Energy-performance tradeoffs in data centers and machine learning
Keywords: Engineering.
Electrical Engineering.

The ability to scale computing over the coming decade is limited by power and energy: whether that be the power limits of individual processor chips due to heating effects, the ability of the power grid to supply a certain amount of power relative to the massive demand of hyperscale data centers, or the growing emphasis on reducing the carbon impact of computing. All of these causes benefit not only from revolutionary technological improvements, but also from fine-grained power consumption control. In this thesis, a general three tiered approach to control the performance--energy tradeoff in computing is presented, which combines tools from stochastic modeling, dynamic programming, and operating systems. A concrete instance is developed for the processor speed control setting, where a higher speed improves a program's slowdown performance at the convex cost of unit energy or power. For the modern slowdown performance metric, I show that the processor queue state must be modeled with its underlying multi-level structure otherwise the control policy will be sub-optimal even in expectation. To avoid the complexity of the complete multi-level policy solution, I develop an approximate control policy that accounts for the multi-level state and functions under any scheduling policy. While in some settings a clear performance--energy tradeoff is possible, in modern machine learning (and deep neural networks in particular) large gains can be achieved by actually redesigning the training algorithms themselves for energy efficiency. Distributed Distillation is one example of this in the small, power-limited devices setting. Presented in this thesis, Distributed Distillation achieves a 10,000x reduction in the power-hungry communication required for distributed on-device training compared to the vanilla distributed stochastic gradient decent algorithm. Both of these approaches (improved algorithms and tradeoff control policies) can be combined to produce even further improvement.
An engineered interleukin-11 decoy cytokine as a cancer therapeutic
Keywords: Medicine.

Cytokines are key signaling molecules in the body and can be exploited by cancer to drive unregulated growth. Chapter 1 focuses on the role of engineered cytokines as therapeutics and their benefits and drawbacks compared to more traditional antibody therapeutics. Chapter 2 focuses on the interleukin (IL)-6 family cytokine, IL-11, which has been shown to be highly expressed in lung adenocarcinoma among other cancer types. As the main goal of this thesis, an engineered high affinity IL-11 antagonist was developed using yeast surface display and directed evolution. This IL-11 variant binds with high affinity to human and mouse IL-11 receptor while lacking binding to the signaling co-receptor gp130. Furthermore, the engineered IL-11 variant potently blocks downstream phospho-STAT3 signaling in cancer cells and significantly reduced tumor growth in a xenograft mouse model of lung cancer. Chapter 3 describes additional efforts to develop a cross-reactive IL-11 antibody that could be developed as a parallel therapeutic approach to the engineered IL-11 cytokine. This antibody work provides a starting point for further antibody campaigns against IL-11 and highlights some of the benefits that engineering cytokines have over antibodies.
Engineering biomimetic models of human brain development
Keywords: Medicine.

Human brain development is the product of spatiotemporally conserved signaling between cells and their microenvironment. Human pluripotent stem cells (hPSCs) have the potential to recapitulate brain development in vitro as cells, either in two-dimensional monolayers or three-dimensional organoids, are differentiated from neuroepithelial stem cells to mature neural circuits. These hPSC-derived models are guided by our understanding of neurodevelopment yet remain limited by our inability to expose cells to the myriad cell-intrinsic and cell-extrinsic signaling cues present in the neurodevelopmental niche. In this thesis, I explore how biomaterials and biofabrication can be leveraged to engineer hPSC-derived models of human brain development which better incorporate biomimetic spatial and temporal signaling cues. .
Engineering enzymes for selective detection and degradation of mucins
Keywords: Humanities and Sciences.
Chemistry.

Every cell in the human body harbors a dense outer layer of carbohydrates, or glycans, that serves as an interface for cell surface communication. This peripheral structure, known as the glycocalyx, influences cell-cell and cell-matrix interactions through a dense network of glycoproteins, glycolipids, and proteoglycans. These glycans are essential for a wide range of physiological processes, and defects in glycosylation often contribute to human disease. In this work, we focus on mucins, which carry densely O-glycosylated domains found in many cell surface and secreted proteins. As introduced in Chapter 1, alterations in mucin expression and glycosylation are common in a variety of diseases such as cancer and cystic fibrosis. These correlations have motivated efforts towards the therapeutic targeting of disease-associated mucins and their aberrant glycoforms. However, the precise contexts in which mucins and their glycans contribute to disease progression remain unclear, due to difficulties associated with their detection and a lack of tools that specifically probe mucin domains. In Chapter 2, we address this limitation by characterizing a panel of bacterial proteases that cleave mucin domains via distinct peptide- and glycan-based motifs, generating a diverse enzymatic toolkit for mucin-selective proteolysis. We further develop catalytically inactive mutants and demonstrate robust detection of mucin-domain glycoproteins by flow cytometry, Western blot, and immunohistochemistry, enabling a new depth in the analysis of mucins on cells and patient tissues. In Chapter 3, we characterize a mucin-selective metalloprotease from Akkermansia muciniphila via X-ray crystallography and molecular modeling to gain insight into the structural determinants of O-glycan recognition. Finally, in Chapter 4, we develop a targeted degradation strategy to selectively remove cancer-associated mucins by fusing an engineered mucin-selective protease to a cancer antigen-binding nanobody. We show that these targeted proteases reduce cancer cell viability in vitro and blunt primary tumor burden and metastatic outgrowth in murine breast cancer models. As nearly all extracellular proteins are glycosylated and glycosylation status is commonly altered in disease, glycoform-dependent and cell type-selective targeted protein degradation presents a general opportunity for increasing on-target specificity for disease-driving extracellular proteins
Engineering genetic codes to control the evolution of so-encoded biological systems
Keywords: Engineering.
Bioengineering.

All life on Earth reads DNA into proteins using the same genetic code or a close variant. But what if it didn't? Here, I introduce a theory of genetic codes and their effect on the evolution of so-encoded genetic systems. I propose a class of genetic codes that penalize mutations by introducing "null codons" that cannot be readily translated and cause the ribosome to stall. I show that these "fail-safe" codes, in theory, slow the evolution of so-encoded organisms. They are "intrinsically" biocontained, being outcompeted by native species upon release or escape. I show experimentally that we can build decoders capable of reading differently-encoded DNA using defined in vitro translation systems with code-specific synthetic tRNAs. Finally, I learned design principles for encoding DNA in different genetic codes by recoding the φX174 bacteriophage genome. I propose concrete steps to further advance fail-safe code engineering, specifically to routinize the building of defined synthetic biological systems up to whole living cells
Engineering novel chalcogenides and semimetals for energy-efficient memory and interconnects
Keywords: Engineering.
Electrical Engineering.

Today's nanoelectronics systems are approaching fundamental limits of energy and latency for numerous data-intensive Internet of Things devices and artificial intelligence applications. To tackle this challenge, future electronics (whether flexible, 3D, neuro-inspired or not) will require energy-efficiency in computing, data-storage, and their interconnections. This calls for the adoption of new materials and for bridging the advances in their transport physics, electronic devices, and systems to enable breakthroughs in low-power memory and interconnects. In this thesis, I present atomic-scale materials engineering to demonstrate several key advances in nanoscale memory and interconnects. First, I demonstrate how electro-thermal control using atomically thin chalcogenides and their superlattices can enable energy-efficient neuro-inspired phase-change memory for both rigid and flexible electronics, unlike existing data storage technology. I explain how engineering the chalcogenide and superlattice materials properties leads to improved memory device performance and tunability, crucial for robustness and optimization. These efforts have led to the lowest programming current density of 0.1 MA/cm2 to-date, and one of the first reports of sub-1 V switching in phase-change memory technology. Next, I introduce an unconventional resistivity scaling (i.e., resistivity decreases in thinner films, unlike conventional metals) in ultrathin transition metal mono-phosphides NbP and TaP. I further probed the surface-state-dominated conduction in thin films of these semimetals that give rise to such unconventional scaling trends. This could open a new paradigm for ultrathin interconnects, going beyond the metal-resistance bottleneck in traditional metals like copper. Together, these results illustrate how combining versatile material functionalities and probing their transport fundamentals can unlock decade-spanning advances in heterogeneously integrated nanoelectronics.