VictorJuiz/thesis huggingface dataset:
Oncogene Induced Senescence Alters Genomic Susceptibility to UV-Induced DNA Lesions
Keywords: Morrison, Ashby
Department of Biology
Melanoma
Epigenetics
Text
Thesis

Oncogene Induced Senescence (OIS) is a tumor suppressive pathway that halts cell cycle progression in response to oncogene activation. During the development of cutaneous melanomas, mutational escape from OIS is a key contributor to the onset of overt malignancy. However, the origins of the escape process itself remain poorly understood. It is unclear if escape mutations arise solely through the random acquisition of DNA lesions, or if lesion acquisition is predetermined by the extensive epigenetic changes associated with OIS entry. To address this knowledge gap, we applied TRansposase Assisted Damage Sequencing (TRAD-Seq) to characterize changes in genomic susceptibility to UV-induced Cyclobutane Pyrimidine Dimers (CPDs) in a melanoma OIS model. We found a global increase in CPD susceptibility that is particularly pronounced in Differentially Susceptible Regions (DSRs) characterized by depletion of lamin B1 and enrichment of H3K9me3. Top-ranked DSRs encompass a range of cancer-associated genes relevant to melanoma development, including the CDKN2A/p16 locus implicated in OIS-escape. Furthermore, DSRs overlap with structural mutation hotspots identified in an Australian melanoma sequencing cohort, suggesting that differential CPD susceptibility underlies large-scale genomic instabilities seen in clinical melanomas. Together, our results show that OIS-driven epigenomic reorganization alters CPD susceptibility in a manner conductive to melanoma development and senescence escape.
One Man, One Vote: Representational Equality, Prison Gerrymandering, and Reform
Keywords: prison gerrymandering
Text
Thesis

The Census Bureau counts incarcerated Americans at the location of their prison cells, rather than their hometowns. Census data is a central part of redistricting, enabling state and local governments to create roughly equal constituencies. But because felons largely cannot vote, districts containing penitentiaries can be the same size as their neighbor but with a fraction of the voting population. This means that voters in that district have a disproportionately high amount of representation. Patterns of incarceration further mean that people in prison are primarily incarcerated out of urban, Democratic communities of color and held in prisons in rural, white, Republican areas. The result is a transfer of political power from urban hubs to rural areas, with consequences for policy. Thirteen states have taken some level of action to address prison gerrymandering and mitigate its effects. This thesis investigates the factors relating to legislative success, finding that Democratic trifecta governments, mobilizing narratives around racial equity, and a legal foundation based in notions of electoral equality and representational nexuses all correspond to the passage of comprehensive legislation.
Online social network risk management
Keywords: Management Science and Engineering.

As online social networks become more influential in society, a new form of cyber threat continues to impact its users: platform manipulation. This occurs when social network users attempt to exploit other users through their online behavior on a social network platform. Current methods for detecting and eliminating platform manipulation rely on the use of machine learning (ML) and artificial intelligence (AI) models. While these methods are able to parse through very large data sets efficiently, they are often trained to only recognize specific traits within user behavior (e.g., spam, malicious links, hate speech), and may not be able to incorporate other forms of evidence (e.g., human observations) in real time without being re-trained and validated, which can be costly and time-consuming in practice. To make an accurate risk assessment of a user's long-term behavior which can be updated over time, decision-makers need a method for integrating multiple forms of evidence (AI and human) across space (by malicious trait) and time into a probability distribution over possible scenarios, as well as knowledge of the potential consequences to the platform from each scenario. In this research, we use probabilistic risk analysis to combine both AI-generated and human-generated evidence in order to determine the risk of individual users to the platform. This allows decision-makers to not only consider the probability that users are malicious, but also their range of potential consequences. We also develop a decision analysis framework for platform referral decisions (whether to request human intervention) using the value of clairvoyance on human agent observations to determine when to involve human agents in the decision-making process (and when to rely on automated decisions to remove users from the platform)
Operando surface-sensitive UV-Vis methods on nickel-based oxygen evolution reaction electrocatalysts
Keywords: Chemistry.

Operando surface-sensitive measurements are crucial for understanding the dynamics of the electrode surface chemistry. However, existing methods, such as adopting the meniscus-method with the ambient pressure X-ray photoelectron spectroscopy, are often inaccessible or expensive, impeding the development of advanced electrodes used in electrocatalysts and batteries. In this thesis, UV-Vis- based operando surface methods are developed to study the dynamic surface of nickel-based electrocatalysts. First, this study applies direct current-based UV-Vis spectroscopy on epitaxial lanthanum nickel oxide (LNO) to study the active surface layer for oxygen evolution reaction (OER). Through careful control of the epitaxial film thickness and terminating atomic layer, this study deconvolutes the surface UV-Vis spectrum from the bulk one and proves the active OER surface phase on Ni-terminating LNO to be a single layer of nickel (oxy)hydroxide. The lack of such a surface layer explains the poor OER activity on La-terminating LNO. Next, the thesis proposes a color impedance spectroscopy (CIS)-based technique that unlocks more accessible, lab- based operando surface measurements on a broader range of systems. The setup controls the depth of charge carriers' movement within the electrode by tuning the frequency of the applied AC signal. Theoretically, any coupled light source yields a surface-sensitive spectrum at higher AC frequencies. A physics-based transmission line impedance model for general mixed ionic-electronic conductors is built to simulate both the EIS and CIS outcome. The impedance model provides insights on EIS results analysis that is crucial for interpreting impedance data. Then, the model validates the idea through forward simulation and demonstrates that bulk kinetics strongly affects the CIS spectral outcome. Adopting nickel (oxy)hydroxide film as a model system presents how CIS turns UV-Vis spectroscopy surface-sensitive. Analysis of samples with varying thicknesses deconvolutes the spectrum and reveals a highly oxidized surface layer with strong absorptivity at redox potential (~1.4 V) and OER conditions (1.6 V vs. RHE). This study expects the CIS experiment and model analysis combination to enable a wide range of novel operando electrode surface studies
Opium, economic thought, and the making of Britain's free trade empire, 1773-1839
Keywords: History.

This dissertation provides a history of the East India Company's opium trade in Asia from the creation of a monopoly in Bengal in 1773 to the outbreak of the First Opium War in 1839. It examines debates over the commodification of opium in Bengal to explain the origins of the "imperialism of free trade.".
Optical fiber-based silicon sensors and systems
Keywords: Electrical Engineering.

Modern sensors optimized for a small footprint, high resolution, scalable production, and networkability are typically microelectromechanical systems. However, optical fiber-based sensor systems offer improvements in all of these metrics with the added benefit of immunity to electromagnetic interference. I have developed an optical fiber-based sensor platform that uses standard communications hardware for wavelength-multiplexed, optically-balanced, and thermally-compensated measurements of sensors arrays. I have also developed system-compatible silicon pressure, temperature, and acoustic sensors that I nanofabricate at low-cost on the wafer scale. I focus on the design, fabrication, characterization, and system-integration of these optical fiber sensors that are among the world's smallest and most sensitive for their target phenomena. For aerospace, cellular detection, and acoustic localization applications, I show that my optical fiber sensor systems have sufficient performance to outcompete even microelectromechanical systems
Optimal substrate for direct detection of light dark matter
Keywords: Physics.

The topic of dark matter (DM) and its possible interaction with ordinary matter has occupied the central stage of the field of particle physics in recent decades. One line of research attempts to directly detect such interaction in a controlled lab environment with extremely sensitive (and in some cases even quantum-limited) sensors. SuperCDMS is one such experiment that utilizes transition-edge sensors (TES) fabricated on large silicon or germanium crystals cooled to cryogenic temperatures (around 50 mK). Silicon and germanium are typically used as the substrate material since they are cost-efficient and well-understood. Numerous alternative substrates have been proposed over the years but these novel substrates are often complex and difficult/costly to grow or make. In other cases they may require significant improvement in detector technology in order to work as intended. As such silicon and germanium remain the dominant substrate material in DM direct detection experiments. Expanding the repertoire of substrate materials is valuable. It can lead to better sensitivity to DM interactions, and a higher degree of material complementarity across different detectors and experiments. The latter point is especially important as we move deeper into the low mass region and look for lighter and lighter DM particles. We can begin to encounter backgrounds caused by non-perturbative many-body effects that are highly material-specific. Having substrates of different materials operating under similar conditions provides information about the nature of such backgrounds as we can compare the response in different materials to break any potential degeneracy not resolved by just having silicon and germanium. The work presented in this thesis is an attempt to systematically survey and understand the various solid state physics pertinent to the DM direct detection, and as application suggest criteria for the optimal substrate material to be used for various interaction channels. Towards the end of this work we will present concrete examples of substrates that can potentially be used in the next generation of DM detection experiments
Optimization and high-dimensional loss landscapes in deep learning
Keywords: Physics.

Despite deep learning's impressive success, many questions remain concerning how training such high-dimensional models behaves in practice and why it reliably produces useful networks. We employ an empirical approach, performing experiments guided by theoretical predictions, to study the following through the lens of the loss landscape. (1) How do loss landscape properties affect the success or failure of weight pruning methods? Recent work on two fronts -- the lottery tickets hypothesis and training restricted to random subspaces -- has demonstrated that deep neural networks can be successfully optimized using far fewer degrees of freedom than the total number of parameters. In particular, lottery tickets, or sparse subnetworks capable of matching the full model's accuracy, can be identified via iterative pruning and retraining of the weights. We first provide a framework for the success of low-dimensional training in terms of the high-dimensional geometry of the loss landscape. We then leverage this framework both to better understand the success of lottery tickets and to predict how aggressively we can prune the weights at each iteration. (2) What are the algorithmic advantages of recurrent connections in neural networks? One of the brain's most striking anatomical features is the ubiquity of lateral and recurrent connections. Yet while the strong computational abilities of feedforward networks have been extensively studied, our understanding of the role of recurrent computations that might explain their prevalence remains an important open challenge. We demonstrate that recurrent connections are efficient for performing tasks that can be solved via repeated, local propagation of information and propose that they can be combined with feedforward architectures for efficient computation across timescales
Optimization of ion acceleration from high-intensity laser-plasma interactions
Keywords: Physics.

High-energy (100s MeV), high spectral quality ion beams are important for many applications like radiography of plasmas, isochoric heating of materials, and tumor therapy. Advances in the development of intense short pulse lasers, recognized with the 2018 Nobel Prize in physics, have been seen as a very promising route to drive compact ion beam sources. However, despite signficant progress over the past two decades, the control of the ion beam properties remains an outstanding challenge. In this Thesis, we discuss two approaches for controlling and optimizing these laser-driven ion beams, using particle-in-cell simulations and theoretical analysis. First, we show that in laser radiation pressure acceleration, the spectral quality of the ion beam is determined by electron heating, which is dictated by the growth of a surface instability. We show that its growth rate imposes an upper limit on the laser pulse duration, and can limit the maximum peak ion beam energy. Next, we explore the development of a hybrid accelerator that combines the advantages of laser-driven beams (compact, high-charge, 10s MeV) with high-gradient RF acceleration in a meter-scale linac, eliminating the large and expensive radio frequency quadrupoles for bunching. Our one-to-one simulations show that the space-charge field plays a critical role in the acceleration effectiveness, and that by tuning the distance at which the laser-driven beam enters the RF, the space-charge field can be controlled such that it actually increases the beam capture. These are important in guiding future experimental developments, for example for the ultrashort laser pulses at state-of-the-art laser facilities and high-gradient linacs, for which we showcase the possibility of a compact (4.5 m) hybrid accelerator that produces a high-quality, high-charge 250 MeV proton beam
Order parameter texture of unconventional superconductors
Keywords: Physics.

A variety of unconventional superconductors have shown closely competitive tendencies towards different orderings. This leads to a rich phase diagram as a function of external tuning parameters, including magnetic fields and disorder. Under proper conditions, these perturbations can lead to inhomogeneous superconducting states, which become particularly important near phase transitions. The consequent inhomogeneity can affect the shape of the phase boundary, and the nature of phase transitions can be significantly altered. This thesis will illustrate these points under three circumstances. The first part of the thesis is about the competition between superconductivity and charge density wave (CDW) order. As local disorder suppresses charge ordering, it effectively enhances superconductivity. In the core of a dislocation of the CDW, local superconducting states can emerge. These superconducting puddles can develop long-range order at low temperatures. Chapter one will describe experimental signatures of this fragile superconducting state. The second part of the thesis is about the effect of inhomogeneous strain. In a clean system, the superconducting critical temperature of a chiral p-wave superconductor is a linear function of the uniaxial strain, and the phase diagram can host a tetracritical point. The shape of the superconducting phase boundaries near this critical point is significantly altered by the local variations of the uniaxial strain. The last part of the thesis is about the effect of quenched disorder near a first-order phase transition between two superconducting orderings. As the local ordering preference is disturbed by the disorder, superconducting inhomogeneity proliferates. The effect of thermal fluctuation is enhanced by inhomogeneity, which reduces the superconducting critical temperature near the first-order phase transition. This eventually leads to a non-monotonic phase boundary. The first-order phase transition itself may be destroyed under proper conditions
Organic mixed ionic electronic conductors for electrochemical devices
Keywords: Materials Science and Engineering.

The development of wearable devices for continuous health monitoring of biomarkers requires materials that are mechanically compliant, biocompatible, and are sensitive to biochemical molecules. One class of materials that show promise are Organic Mixed Ionic Electronic Conductors (OMIECs), commonly semiconducting polymers, which are redox-active and can simultaneously conduct ions and electrons. Electronic charges accumulated on the conjugated polymer backbone result in secondary property changes in electrochemical potential and electronic conductivity, allowing OMIECs to be implemented in a variety of devices such as chemical sensors, organic electrochemical transistors, and energy storage electrodes. Additionally, the mechanical flexibility and biocompatibility of these polymers enable conformable and seamless integration with biological interfaces, enabling bio-interfacing devices for continuous health monitoring. My PhD research investigates how OMIECs can be designed, characterized, and implemented in 1) polymer-based batteries for energy storage, 2) electrochemical sensors for the detection of biological analytes, 3) organic electrochemical transistors that amplify biosensing signals, and 4) tunable antennas for wireless signal transmission in microwave frequencies compatible with consumer electronics. Leveraging fundamental materials characterization to drive the understanding of device physics, these OMIEC-based technologies are integrated into a fully self-powered and wireless glucose sensor
Organism-wide secretome mapping of tissue crosstalk in exercise
Keywords: Biology.

Exercise has long been known as an effective strategy to prevent and combat human diseases. Conversely, physical inactivity is considered to be an essential contributor to a variety of chronic diseases. Because of this, tremendous efforts have been dedicated to understanding the molecular mechanisms by which exercise conveys such profound health benefits. Identifying these molecular mechanisms will facilitate the development of exercise mimetics that serve as potential therapeutics to improve human health. It has been proposed that tissue crosstalk via soluble factors in the blood during exercise mediates the broad health benefits of exercise. Indeed, blood plasma from exercised animals and humans has been shown to contain exercise-inducible molecules that are sufficient to recapitulate some health benefits of exercise. However, the comprehensive identifications of such molecules and their origins in exercise have been poorly studied due to a lack of tools. In this dissertation, I use Mus musculus as the model system to study the fundamental principles of tissue crosstalk in physical activity. The rich genetic toolbox and diverse exercise training regimens of mice make these animals an ideal model for mapping exercise-inducible blood-borne factors and exploring the potential functions of these molecules. The recent advances in proximity labeling in the mammalian system provide a unique opportunity for biochemical labeling of secreted polypeptides in living mice and downstream proteomic identification via mass spectrometry. Collectively, the application of this conditionally expressed proximity labeling strategy in well-established mouse models enables the global identifications of blood-born polypeptides at a cell type, protein, and peptide resolution. This dissertation consists of two main parts: technology development and biological discoveries made with this technology. First, I describe the development and benchmarking of the proximity labeling-based secretome (secreted polypeptides collectively) profiling methodology in living cells in vitro and in mice (Chapter 2). This is, to the best of my knowledge, the first methodology for labeling and tracking secreted polypeptides from a specific cell type in mice. Additionally, the high temporal resolution of this approach enables the capture of dynamic secretome changes in pathophysiologically perturbed animals. Next, using this secretome profiling tool, I report the identifications of exercise-inducible cell type-protein pairs in mice after a 1-week treadmill running exercise (Chapter 3). We uncover fundamental secretome regulation principles in exercise in a cell type, protein, and peptide-specific manner. Furthermore, we identify the unexpected lactate-inducible release of CES2 proteins from the liver that have anti-obesity and anti-diabetic effects. Lastly, I discuss the remaining questions in exercise secretome, future development of the secretome profiling technology, and applications of this methodology to study other intriguing questions in mammalian physiology (Chapter 4)
Organizing for violence : the politics of militant group formation and fragmentation in armed conflict
Keywords: Political Science.

Non-state armed groups fuel much of the world's ongoing armed conflict. Though often weaker than their government adversaries and forced to operate in secret, many armed groups have shown a surprising ability to emerge, survive, and even thrive in challenging environments. What explains the strength and durability of these groups? Drawing on several sources of original data, I show that armed groups and their leaders gain strength from the founding support of international actors and from the consolidation of organizational control. These internal and external sources of strength enable armed groups to emerge and endure in environments where group formation is difficult and organizational fragmentation is likely. In the first paper, I show that a primary threat to the survival of armed groups is leadership transition. To thwart internal challenges to group cohesion, group leaders must actively employ managerial tools to discourage splintering within their organizations. In the second and third papers, I show that international actors play a central role in creating highly violent armed groups. Seeking trustworthy and capable proxies, foreign states can facilitate the formation of new armed groups to serve as their partners. I argue that states engage in this behavior as a way to mitigate the principal-agent problems that often plague sponsor-proxy relationships. I find that states are more likely to employ "founding" support under certain conditions, particularly when the menu of potential partners in a target state is small. Sponsors may perceive their "foreign founded" proxies as more trustworthy and thus may be willing to invest significantly in the military capabilities of these groups. I find that foreign founded groups conduct more attacks in civil war compared to armed groups receiving other types of non-founding state support. Overall, the findings of this dissertation demonstrate that international and organizational politics directly influence the emergence and strength of armed groups and patterns of violence in civil war