Rice University Theses and Dissertations

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Rice University makes all graduate theses and dissertations (1916-present) available online at no cost to end users. Occasionally a thesis or dissertation may be be missing from the repository. If you are unable to find a specific dissertation, please let us know and we will attempt to make it available through the repository, provided that the author has not elected for it to be embargoed.

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Computational Imaging System for 3D Sensing and Reconstruction
(2024-12-06) Tan, Shiyu; Veeraraghavan, Ashok
The thesis explores three challenges in 3D imaging with different applications: 3D stereo imaging with large depth-of-field, 3D sensing with a compact device, and 3D microscopy of thick scattering samples with fast scanning speed. The first part of this thesis focuses on a stereo imaging system that can get large depth-of-field and high-quality 3D reconstruction in light-limited environments. To overcome the fundamental trade-off between imaging volume and signal-to-noise ratio (SNR) that appears in conventional stereo, a novel end-to-end learning-based technique is proposed by introducing a phase mask at the aperture plane of the cameras in a stereo imaging system. The phase mask creates a depth-dependent yet numerically invertible point spread function, allowing us to recover sharp image texture and stereo correspondence over a significantly extended depth of field (EDOF) than conventional stereo. The second part of the thesis exploits the strongly dispersive property of metasurfaces to propose a compact, single-shot, and passive 3D imaging camera. The proposed device consists of a metalens engineered to focus different wavelengths at different depths and two deep networks to recover depth and RGB texture information from chromatic, defocused images acquired by the system. The third part of the thesis explores a learning-based method that can rapidly capture 3D volumetric images of thick scattering samples using a traditional wide-field microscope. The key idea is to use a 3D generative adversarial network (GAN) based neural network to learn the mapping between the blurry low-contrast image stacks obtained using a wide-field microscope and the sharp, high-contrast image stacks obtained using a confocal microscope. After training the network with widefield-confocal stack pairs, the network can reliably and accurately reconstruct 3D volumetric images that rival confocal images in terms of lateral resolution, z-sectioning , and image contrast.
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Neutron scattering studies of Sr(Co1−xNix)2As2, FeSn, CsV3Sb5, and YbMnBi2
(2024-12-05) Xie, Yaofeng; Dai, Pengcheng
In this thesis, we present several neutron scattering investigations on the complex magnetic and electronic properties of a series of quantum materials, including helical order in Sr(Co1-xNix)2As2, spin excitations in FeSn and CoSn, electron-phonon coupling in charge-density-wave state of CsV3Sb5, vortex lattice in Ta doped CsV3Sb5, and spin chirality in YbMnBi2. Firstly, we investigate magnetic ordering and spin fluctuations in Sr(Co1-xNix)2As2, a quasi-two-dimensional planar magnet. Neutron scattering studies reveal a c-axis incommensurate helical magnetic structure in Sr(Co1-xNix)2As2, with enhanced quasi-2D ferromagnetic spin fluctuations induced by Ni doping. Band structure calculations suggest that this helical order arises from Ruderman-Kittel-Kasuya-Yosida (RKKY) interactions mediated by itinerant electrons, offering insight into the quantum order-by-disorder mechanism near a quantum critical point. Next, we examine spin excitations in the metallic kagome lattice materials FeSn and CoSn. In these systems, destructive quantum interference of electronic hopping paths produces nearly localized electrons, resulting in flat electronic bands. Our neutron scattering measurements uncover well-defined spin waves in FeSn and paramagnetic scattering in CoSn, highlighting the delicate balance between geometric frustration and magnetic order in kagome systems. Furthermore, we observe anomalous non-dispersive excitations, attributed to the scattering from hydrocarbon contamination. We also investigate the electron-phonon coupling in CsV3Sb5, a kagome lattice material exhibiting intertwined CDW and superconductivity. Neutron scattering experiments demonstrate that the CDW in CsV3Sb5 is associated with a static lattice distortion and a sudden hardening of a longitudinal optical phonon mode. This finding underscores the critical role of wave vector-dependent electron-phonon interactions in the CDW order, contributing to our understanding of its coupling with superconductivity in kagome metals. The fourth study focuses on the superconductivity in Ta-doped CsV3Sb5, which exhibits enhanced superconductivity upon suppression of CDW order. Through Small-Angle Neutron Scattering (SANS), we probe the vortex lattice structure and its evolution in the superconducting state of Cs(V0.86Ta0.14)3Sb5. Our results show that the vortex lattice exhibits a strikingly conventional behavior, including a triangular symmetry, conventional 2e pairing, and a field dependent scattering intensity that follows a London model. Our results suggest that optimal bulk superconductivity in Cs(V0.86Ta0.14)3Sb5 arises from a conventional Bardeen-Cooper-Schrieffer electron-lattice coupling. Finally, we investigate the giant anomalous Nernst effect (ANE) and anomalous Hall effect (AHE) in the canted antiferromagnet YbMnBi2. The ab-plane spin canting in YbMnBi2 is believed to break time-reversal symmetry, generating a non-zero Berry curvature that gives rise to the giant ANE and AHE. However, direct evidence for this mechanism has remained elusive, as earlier unpolarized neutron measurements excluded significant moment canting. By leveraging the unique advantages of polarized neutron scattering, which can differentiate magnetic scattering from nuclear scattering, we have uncovered clear evidence of spin chirality persisting at temperatures well above room temperature. Additionally, further neutron scattering measurements have revealed inversion-symmetry breaking and anisotropic spin fluctuations, indicating the presence of Dzyaloshinsky-Moriya interactions that likely drive the observed spin chirality, which in turn underlies the ANE and AHE. Our findings provide a detailed mechanism that directly explains the origins of the giant ANE and AHE in YbMnBi2. Overall, the combination of these works advances the understanding of quantum materials by revealing new insights into the magnetic, electronic, and lattice dynamics of these complex systems. The results presented herein pave the way for future studies on quantum magnetism, unconventional superconductivity, and the development of new materials with novel electronic and magnetic properties.
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Quantum Algebraic Geometry Codes
(2024-12-06) Han, Zhengyi; Goldman, Ronald
Quantum error correction is an essential aspect of quantum information theory, providing protection for quantum states against noise and decoherence. This thesis investigates the construction of quantum error correction codes derived from classical algebraic geometry (AG) codes. We present two distinct construction techniques, highlighting the flexibility and self-orthogonality of AG codes, and demonstrate their ability to produce asymptotically good quantum codes. Additionally, we explore strategies to fine-tune the parameters of classical AG codes, ensuring they possess the desired properties for quantum code construction. This work serves as a comprehensive guide to the fundamental concepts and common methodologies underlying quantum algebraic geometry codes.
Plastic Waste-Derived CO2 Sorbents and Methods for Enhanced Selectivity and Stability
(2024-10-29) Savas, Paul E; Tour, James M
Carbon capture will be a key pathway used by heavy carbon dioxide (CO2) emitting industries, such as petroleum refining, electrical power generation, and cement production, to reduce their carbon emissions. Solid sorbent technology has been touted as the most promising method to attain emissions reduction targets; however, many of these sorbents suffer drawbacks related to their cost, performance, or both. Due to these shortcomings, carbon capture solid sorbents have only just now begun to achieve industrial relevance. Chapter 1 will discuss the carbon capture problem and general sorbent performance metrics. At the same time, plastic waste pollution threatens the environment and acts as another significant risk that must be addressed. Current recycling technologies must be improved to limit the buildup of plastic waste. Here, we develop a pyrolytic method producing a highly microporous CO2 sorbent synthesized from plastic waste-salt mixtures. Diverse plastics with applications as packaging, textile, and construction materials were all addressed in this process. Chapters 2 and 3 examine our results regarding plastic-waste sorbents. To enhance the CO2 selectivity performance of plastic-derived sorbents, an amine-containing polymer, polyethylenimine (PEI), was added to the carbon sorbent in Chapter 4. To do this, we first modulated the pore structure of the sorbent to become more mesoporous through a liquid-salt templating mechanism. More mesoporosity allowed better accommodation of PEI into the carbon pore network. The PEI-carbon sorbent, although having better uptake and selectivity performance, showed poor stability especially in the presence of oxygen. Oxidative degradation pathways of PEI lead to imine formation and ammonia evolution; these end-products greatly reduce the sorbent’s CO2 uptake capacity. While strategies to reduce oxidative degradation exist through polymer modification, we show here that oxygen-related PEI destruction could be improved by maintaining some adsorbed CO2 during cycling. This adsorbed CO2 acts as a carbamate-protecting group to the amines and prevents continual polymer degradation. In this regard, the formed carbamates prolong PEI-based sorbent lifetimes.
A School Effects Analysis of First-Generation, Working-Class Students’ Long-Term Outcomes
(2024-10-28) Harvey, Lauren; Fiel, Jeremy
Sociologists have long recognized schools as important factors in student outcomes, but prior work often takes institutional forces for granted when analyzing class inequality in higher education, focusing instead on students’ skills and resources. This study applies the Critical Cultural Wealth Model to argue that institutions differentially impact long-term academic, professional, and social-emotional outcomes of first-generation, working-class (FGWC) students and their peers. School effects analyses of data from the College and Beyond II Study reveal several key findings. First, colleges and universities differentially affect student outcomes. Second, these institutions shape academic, professional, and social-emotional disparities between FGWC students and their peers. Finally, the institutions that most positively affect academic outcomes for FGWC students have more negative impacts on these students’ social psychological outcomes. These results affirm that shows higher education institutions matter for student success and class inequality but show they may do so in contradictory ways for different outcomes.
Mutational Profiling of the Adeno-Associated Virus Rep Protein for Gene Therapy Production
(2024-10-24) Azim, Tasfia; Silberg, Jonathan; Thyer, Ross
Adeno-associated virus (AAV), a federally approved gene therapy vector that is currently in clinical trials for hundreds of diseases, often presents suboptimal characteristics for therapeutic applications, such as suboptimal tissue specificity, limited cargo size, undesired immune responses, and costly manufacturing. To improve this gene therapy vector, AAV proteins are frequently studied via rational design and combinatorial engineering. While the latter approach increases the sequence space that can be explored, it also presents unique challenges such as genotype-phenotype mismatches, noise arising from mutational errors in cloning, and bias arising in amplicon preparation and sequencing. In AAV, Rep proteins mediate DNA packaging and virus assembly, suggesting that changes in Rep activity, expression, or DNA binding might affect genome packaging. However, these proteins are not as well-understood as the proteins that make up the virus shell. I sought to understand how mutations in the Rep protein affect activity by selecting a library of Rep mutants for their ability to produce virions. To do this, I designed large protein libraries to examine a broad sequence space, I designed a selection strategy that couples genotype-phenotype characteristics of the mutants, and I designed a single-stranded DNA isolation workflow that enabled me to sequence winning variants in deep sequencing. By sequencing the rep gene following the purification of viruses that package AAV genomes, I identified Rep mutants having non-synonymous mutations with a range of cellular activities. Surprisingly, synonymous mutations within the p19 promoter were enriched to the greatest extent, increasing in abundance by 102 to 104-fold. When the most highly enriched mutant was used to package a synthetic DNA cargo into the AAV capsid, the packaging efficiency could not be differentiated from native Rep. These findings suggest that these synonymous mutations enhance AAV genome packaging into capsids by affecting Rep-genome interactions. They also suggest that silent sequence changes in the DNA cargo packaged by Rep can be used to tune packaging DNA packaging efficiency. Additionally, I designed a sequential cloning method for developing barcoded chimeric protein libraries, which enables easier analysis of deep-sequenced these. This cloning strategy has been partially validated. Future work should be done to optimize this cloning method, as it would be applicable for chimeric library design for any protein engineering experiment. Lastly, this work outlines considerations in high-throughput protein engineering experimental design. I hope this section of my thesis enlightens those who wish to begin high-throughput protein experiments and learn from some of the critiques I have of my own work.
Essays on Financial Market Structure and Shareholder Voting
(2024-12-06) Blonien, Patrick; Back, Kerry
Chapter 1: Size Discovery in Slow Markets Adding a size-discovery trading protocol, where a break in the limit order book occurs to match orders at a fixed price, can increase allocative efficiency in markets with slow trading frequency. A high trading frequency spreads liquidity, resulting in a strong incentive to wait for a size-discovery session. This incentive to delay trade is smaller in slower markets, and its negative effect on efficiency can be offset in slower markets by the positive effect of size discovery. This result rationalizes the empirical fact that size-discovery protocols only exist in slower markets. Potential conflicts of interest between traders and platform operators are identified but seem unlikely to drive the existence of size-discovery trading protocols. Chapter 2: Is 24/7 Trading Better? with Alexander Ober Are daily market closures still needed? In a model of large traders who manage inventory risk, we show that even short market closures can significantly improve liquidity. Anticipating these closures, traders engage in aggressive trading, which concentrates and coordinates liquidity. A market structure with a daily closure improves allocative efficiency relative to a continuously open market, even though traders cannot trade during the closure itself. If traders have heterogeneous information about the asset value, trade is less aggressive on the whole, but closure still retains its substantial welfare benefits. Our findings suggest moving to a longer trading day could be beneficial, but moving to 24/7 trading would harm welfare. Chapter 3: Proxy Advice and Errors in Shareholder Voting with Alan Crane, Kevin Crotty, and David De Angelis How does proxy advice relate to voting mistakes? Structural estimates of latent proposal quality imply advisor ISS’s recommendations are wrong half the time for shareholder proposals. Vote outcomes, however, are correct the vast majority of the time because positive recommendations, which are particularly uninformative, are less influential. Our results support recent theory that proxy advice crowds out information collection by institutional investors and aims to create controversy. Recommendations are less informative than most mutual funds' votes. Vanguard’s votes are a considerably better benchmark for proposal quality than ISS recommendations. Overall, our analysis implies limiting ISS’s influence would improve voting outcomes.
Long-Context Sequence Models for Image Retrieval
(2024-10-25) Xiao, Zilin; Ordóñez-Román, Vicente
Image retrieval is an important problem in computer vision with many applications. In general, retrieval is usually cast as a metric learning problem where a model is trained under a distance or similarity objective to compare pairs of inputs. In this thesis, we introduce Extractive Image Re-ranker, a solution that takes as input local features corresponding to an image query and a group of gallery images, and outputs a refined ranking list through a single forward pass. This model can be used for image retrieval where typically a query image is compared to a large database of images using global features, and then a retrieved gallery of images is re-ranked based on more refined local features. ExtReranker formulates the re-ranking problem as a span extraction task analogous to the text span extraction problem in natural language processing. In contrast to pair-wise correspondence learning, our approach leverages long-context sequence models to effectively capture the list-wise dependencies between query and gallery images at the local-feature level. Our approach achieves superior performance compared with other re-rankers on established image retrieval benchmarks (CUB-200, SOP, and In-Shop). ExtReranker also achieves state-of-the-art re-ranking performance to alternative methods on ROxford and RParis while using 10X fewer local descriptors and having 5X lower forward latency.
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Healthcare Access and Equity
(2024-12-05) Mishra, Dibya Deepta; Coughlin, Maura; Tang, Xun
This dissertation presents three essays examining healthcare access and equity in developing contexts, with a focus on India. The first essay investigates patient behavior in hospital selection following the implementation of a universal insurance program, shedding light on factors influencing healthcare utilization patterns. The second essay evaluates the multifaceted impacts of a large-scale subsidized menstrual hygiene product distribution scheme on women's health outcomes and educational attainment. The final essay assesses the effects of a nutrition intervention program targeting adolescent girls on both educational performance and health indicators. Together, these studies contribute to our understanding of how targeted health interventions and policy changes can address disparities in healthcare access and improve overall well-being in resource-constrained settings.
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Leveraging Multipath to Increase Radar Field-of-View and Sensing Performance
(2024-11-15) Mehrotra, Nishant; Sabharwal, Ashutosh
Radars are an indispensable sensing modality for autonomous navigation, vehicular networking and beyond, with features complementary to visible light sensing systems. Traditional radar signal processing estimates the range and radial velocity of objects in direct line-of-sight to the radar, i.e., objects directly illuminated by the radar that scatter the illumination back to the radar. However, line-of-sight signal processing limits radar performance in three ways. First, in radar systems with highly directional signal transmissions, e.g., those in the millimeter-wave and terahertz frequency bands, line-of-sight processing limits the field-of-view over which objects can be detected/sensed. Second, real-world signal propagation is rarely limited to line-of-sight propagation, and signals undergo significant multipath due to secondary reflections in the environment. Line-of-sight processing in presence of multipath results in the formation of false targets, a.k.a. ``ghosts,'' at physically incorrect locations, degrading accurate target detection and localization capabilities. Third, line-of-sight Doppler processing prevents radars from estimating the tangential velocities of moving objects, making it challenging to distinguish between objects that are stationary versus those that are moving tangentially to the radar. This thesis tackles all three limitations by rethinking the role of multipath in radar signal processing. The three parts of this thesis demonstrate how the three limitations can be overcome by treating multipath as an opportunity to leverage - by explicitly incorporating multipath into radar signal processing - rather than as a nuisance. The first part of this thesis theoretically demonstrates that leveraging multipath for radar imaging can improve radar resolution when multipath provides new ``looks'' of the imaging scene beyond those provided by line-of-sight, effectively forming a multi-``look'' synthetic aperture without requiring any physical aperture extension. The second and third parts of this thesis translate this theoretical idea into practice. The second part of this thesis utilizes the additional ``looks" provided by multipath to sense beyond-field-of-view objects that are imperceptible with line-of-sight processing, e.g., objects behind the radar or around-corners, without having to contend with the problem of multipath ``ghosts''. The final part of this thesis in turn uses multipath from static features in the environment (building pillars, walls, etc.), that may be known a-priori or estimated via beyond-field-of-view processing, to estimate both the tangential and radial velocities of line-of-sight moving objects. Overall, this thesis advocates for novel modeling and signal processing approaches to improve and unlock new sensing capabilities with existing radar systems. The methods proposed in this thesis are implementation-agnostic and are compatible with existing radar sensing and communication pipelines across different waveform choices and frequency bands. Hence, the results presented in this thesis are applicable to multiple use-cases, such as autonomous navigation, vehicular networking, emergency services, spatial computing, joint radar sensing and cellular communication, etc.
Deploying Usability: Ensuring Trust in Electronic Voting for Military Absentee Voters
(2024-11-13) Kim, Nessa; Kortum, Philip; Byrne, Michael
U.S. uniformed service members deployed overseas face unique challenges in exercising their right to vote, often showing lower voter confidence due to difficulties in updating registration, requesting absentee ballots, and meeting tight voting deadlines. The current research evaluated the usability and trustworthiness of “CACvote,” an absentee voting system designed to enhance voting access and security for military personnel. CACvote allows voters to instantly request ballots and verify the legitimacy of their mail-in votes using secure authentication. Through a series of three experiments, the research sought to determine whether military voters find CACvote intuitive and trustworthy. First, a baseline experiment was conducted to establish usability and trust metrics with eligible military voters for a traditional electronic voting system (“Baseline”), simulating the core functions of electronic ballot-marking. Building on this model and insights from the experiment, a second formative experiment explored how additional features of the user interface—including support for authentication, ballot verification, and secure mailing processes—impact usability and trust. These findings informed subsequent system iterations. Finally, the third experiment evaluated the refined voting system’s overall usability and trustworthiness with military voters. CACvote demonstrated increased vote-casting rates and reduced assistance requests compared to the Baseline system, while maintaining similarly high levels of user satisfaction and trustworthiness. No significant differences were found in perceived workload or satisfaction between the two systems, suggesting that CACvote’s novel features integrate effectively with traditional electronic voting methods. This research also explored the relationship between trust and voting system usability, highlighting the distinct roles of past voting experience and perceived workload in shaping the voter’s trust in the voting system. These findings contribute to the exploration of how voting system usability influences voter confidence, highlighting the role of trust as a key indicator of usable security.
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Job Attitudes of Full-Time Teaching Faculty: A Qualitative Exploration of Professional Self-Concept, Psychological Contract, and Workplace Justice Perceptions
(2024-12-05) McSpedon, Megan Rose; Beier, Margaret E.
A growing proportion of American higher-education faculty are ineligible for tenure, hired through term-limited contracts (Drake et al., 2019). These work arrangements provide universities the ability to grow or shrink their faculty workforce in response to shifting institutional priorities and student enrollment (GAO, 2017; Kezar, 2013). These work arrangements are particularly common in teaching-focused faculty positions (Baldwin & Chronister, 2001); recent estimates suggest 61% of teaching faculty at four-year institutions are hired on term-limited contracts (e.g., one-to-five year contracts; GAO, 2017). Teaching faculty may receive successive term-limited contracts, creating a career-long relationship with an institution. Although the growth of this role at four-year universities is well-documented, less is known about how these work arrangements impact the psychological experiences of these organizationally embedded teaching faculty. Building on prior quantitative findings (Quenemoen et al., 2023), the qualitative study applied an abductive model (Timmermans & Tavory, 2012) to explore the experiences and perceptions that shape teaching faculty’s job attitudes and behaviors. Semi-structured interviews with 22 full-time teaching faculty explored participants’ professional self-concept (Beach & Mitchell, 1987; Lee & Mitchell, 1994), psychological contract (Rousseau, 1989), and justice beliefs (Colquitt et al., 2001). Findings, including the role of job crafting (Wrzesniewski & Dutton, 2001) in reducing participants’ perceptions of job insecurity, the psychological effects of ambiguous policies and performance standards, and teaching faculty’s responses to experiences of injustice and psychological contract breach are discussed.
Entropy stable reduced order modeling of nonlinear conservation laws using discontinuous Galerkin methods
(2024-10-31) Qu, Ray; Chan, Jesse
Reduced order models (ROMs) construct inexpensive surrogate models to reduce costs associated with many-query scenarios. Current methods for constructing entropy stable ROMs for nonlinear conservation laws utilize full order models (FOMs) based on finite volume methods (FVMs) on uniform grids. This master's thesis describes how to generalize the construction of entropy stable ROMs from finite volume FOMs to high-order discontinuous Galerkin (DG) FOMs. Significant innovations of this thesis include the introduction of new test basis involving DG weight matrix for accuracy, a dimension-by-dimension hyper-reduction strategy, and the simplification of the hyper-reduction step, which is achieved by employing the Carathéodory pruning technique specifically tailored for the hyper-reduction of boundary terms.
How African Immigrants Interpret The Connection Between Their Religion and Health.
(2024-10-22) Biney, Moses Ohene; Howard Ecklund, Elaine; Diaz, Christina J
Religion can positively and negatively influence individuals’ health behaviors. While religion can deter risky behaviors like alcohol abuse, it can sometimes discourage seeking healthcare. Religion has primarily been presented as a barrier to seeking healthcare. Additionally, African immigrants in the United States of America have received less coverage in research about their religion and health despite being part of a demographic group (Blacks) that has developed a mistrust of the medical health system in the U.S. due to historical treatment. This thesis examines the health experiences of African immigrants in Houston, Texas, focusing on how they interpret the connection between their religion and physical health. It also explores the perceived role that religious congregations play in the health experiences of African immigrants. Drawing on in-depth interviews of 37 Christian African immigrants living in Houston, I find that religion acts as a pathway to healthy living and seeking healthcare among African immigrants. Thus, religion provides a framework for a positive perspective on medical healthcare. By focusing on African immigrants, this study serves as a case for understanding the health experience and behaviors of highly educated and religious populations.
Pseudomonas aeruginosa Strategies in Infections and Intraspecies Competition
(2024-12-03) Xu, Qi; Kirienko, Natasha V; Grande-Allen, Jane
Pseudomonas aeruginosa is a Gram-negative, opportunistic human pathogen responsible for a variety of nosocomial infections, including bloodstream infections, ventilator-associated pneumonia, and urinary tract infections. These infections pose significant challenges in healthcare settings due to P. aeruginosa’s ability to gradually develop resistance to a wide range of antibiotics, including β-lactams, aminoglycosides, and fluoroquinolones. Consequently, understanding the mechanisms of P. aeruginosa infections is crucial for developing new modalities treatments, such as antivirulence therapy. A key aspect of its pathogenicity lies in the production of numerous virulence factors, including exotoxins, proteases, and quorum-sensing molecules, which enable it to damage host tissues and evade the immune system. Additionally, P. aeruginosa exhibits a remarkable ability to adapt to polymicrobial environments, often outcompeting other microorganisms by utilizing several secretion systems or quorum sensing systems to gain a fitness advantage. This adaptability not only enhances its survival but also makes it a formidable pathogen in chronic infections, particularly in immunocompromised patients. Here we demonstrated that a class of glycolipids called rhamnolipids predominantly drive P. aeruginosa acute virulence against murine macrophages. Secreted rhamnolipids can form micelles that exhibit acute cytotoxicity, rupturing the macrophage plasma membrane and damaging intracellular organellar membranes within minutes. We also examine these rhamnolipid micelles’ structural and biochemical properties via transmission electron microscopy and liquid chromatography-mass spectrometry. While these micelles are particularly toxic to macrophages, they are also capable of damaging a wide range of other cells, including human bronchial epithelial cells, red blood cells, and even Gram-positive bacteria. Finally, we reported that rhamnolipid production in various panels of clinical isolates strongly correlates with P. aeruginosa virulence. In addition, we also examined the consequences of pyoverdine production during P. aeruginosa lung infection, using an adapted in vitro pyoverdine virulence model in human bronchial epithelial cells (16HBE). Conditioned medium from P. aeruginosa caused acute cell death and severe damage to the epithelial monolayer in a pyoverdine-, but not pyochelin-, dependent manner. Interestingly, pyoverdine production is associated with secretion of cytotoxic rhamnolipids. Consistent with this observation, chemical depletion of lipids or genetic disruption of rhamnolipid production was sufficient to abrogate toxicity from conditioned medium on 16HBE cells. Altogether, these findings suggest that pyoverdine and pyochelin play distinct roles in virulence during acute P. aeruginosa lung infections. In terms of intraspecies competition strategies employed by P. aeruginosa, we used a genome-wide transposon insertion library screen to discover that ST111 strains outcompete multiple non-ST111 strains through production of R pyocin. We confirmed this finding by showing that competitive dominance in vitro was lost by ST111 mutants with R pyocin gene deletions. Further investigation showed that sensitivity to ST111 R pyocins (specifically R5 pyocin) and R1 pyocins is caused by deficiency in the O-antigen ligase waaL, which leaves lipopolysaccharide (LPS) bereft of O antigen, enabling pyocins to bind the LPS core. Analysis of 5,135 typed P. aeruginosa strains revealed that majority of international, high-risk sequence types (including ST111, ST175, and ST235) are enriched for R5 pyocin production, indicating a correlation between these phenotypes and suggesting a novel approach for evaluating risk from emerging prevalent P. aeruginosa strains. Overall, our study sheds light on the mechanisms underlying the dominance of ST111 strains, highlighting the role of waaL in R pyocin susceptibility.
Energy Storage Devices: Performance and Regeneration
(2024-09-20) Tariq, Ayesha; Lou, Jun
This thesis presents a deep understanding of a layered material, Chromium Germanium Telluride (CrGeTe3) as a potential electrode material for energy storage devices. We conclude that CrGeTe3 crystals possess the ability to intercalate lithium ions with a high specific discharge capacity of 537.5 mAh/g. We also investigate the direct regeneration of degraded graphite anode by washing it with various organic solvents under different temperatures. We successfully revived the spent graphite from a specific capacity of 188.23 mAh/g to 224.66 mAh/g at 0.02 C rate and from 41.53 mAh/g to 157.36 mAh/g at 0.1 C rate, by washing with a solution of iodine in ethanol. Washing in anhydrous acetonitrile at 50 °C also restores the capacity of spent graphite to 226.5 mAh/g at 0.02 C rate. Washing the spent graphite in acetonitrile containing 0.5% water content at 70 °C exhibited a high restored specific capacity of 215.9 mAh/g at 0.02 C rate and 168.36 mAh/g at 0.1 C rate. These results pave way for ecofriendly full-body regeneration of lithium-ion batteries.
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Chronic large-scale recording with ultraflexible electrode arrays for studying neural codes and their stability
(2024-11-07) Zhu, Hanlin; Xie, Chong
A central question in neuroscience is identifying neural codes that stably represent external variables across time. Using mice visual perception as the experimental paradigm, I focused on the debate between rate code versus temporal code based neural representation and depicted their differential contribution to the duality of neural representation stability and drift. Despite the pivotal distinction between spike counting based rate code and spike timing aware temporal codes, previous studies have yet to unveil the role of temporal code in long-term visual representation due to technical constraints. Past reports on drift in visual code over time predominantly relied on calcium imaging, which lacked the temporal resolution to capture fast-spiking dynamics and were further confounded by interferences such as photobleaching, leaving a gap in our comprehension of these complexities in neural code. While such investigation could have been carried out with electrophysiological recordings that resolves fast spiking dynamics, the scale and longevity necessary to study representation stability has not been achieved with conventional rigid electrodes. Our group overcomes these hurdles with large scale implantation of ultraflexible nanoelectronic threads (NETs) electrodes, which provide unprecedented longitudinal recordings across many neurons, while minimizing tissue-electrode interface instability. In this thesis, I a) established a platform to map visual response properties of neural units from > 1000 channels of ultraflexible electrodes. b) developed a method to track same units recorded by these ultraflexible electrode arrays. c) compared the stability of different neural codes by longitudinally tracking > 1000 single neuron units from 5 mice over 15 consecutive days from animals subjected to repeated, diverse visual stimuli every day. Our result reveals that considering the fast temporal dynamics of neuronal spikes (temporal code) enhances the stability of individual neuron tuning, neuronal population representation, and decoding accuracy compared to rate code. Thus, temporal coding, a mechanism that operates on the millisecond scale of neural communication, might be a fundamental principle that supports the consistency of sensory experiences, amidst the ever-changing brain states and synaptic strengths.
Towards All-Optical Circuit-Switched Datacenter Network Architectures with Low Energy and High Performance
(2024-09-24) Das, Sushovan; Ng, T. S. Eugene
Since the genesis of the “cloud”, network infrastructure has become ubiquitous across the globe and is expected to support highly customized, fine-grained applications (e.g., HPC, distributed ML, DNN, etc.) with stringent performance requirements. However, as we move to the “post-Moore’s law era” of networking, CMOS-based electrical packet-switch ASICs are struggling to cope up with the increasing capacity while maintaining low power consumption and cost. Moreover, environmental awareness makes green and long-term sustainable cloud infrastructure design an absolute necessity. This energy-critical situation has led to several recent proposals regarding all-optical circuit-switched network core design for sustainable future-generation clouds. Optical circuit-switching (OCS) technologies are the key components that make those proposals fundamentally promising, as OCS is inherently eco-friendly along with the unique advantages: a) agnostic to data rate, b) negligible/zero power consumption, c) negligible forwarding latency, and d) no need for a frequent upgrade. However, the existing OCS core-based cloud architectures pose several challenges such as a) lack of native multicast capability, b) inability to handle traffic skewness, and c) terrible tail performance of individual flows. Fundamentally, these challenges are inherent to the OCS properties and operational abstraction of existing OCS cores. First, OCS can only provide point-to-point circuits and hence cannot have multicast support. Second, round-robin OCS core architectures lack the freedom of path diversity, leading to poor performance under skewed traffic. Third, the flows incur subsequent disruption due to periodic OCS downtime, leading to unpredictable tail performance. In my thesis, I envision a holistic low-energy and high-performance cloud architecture, capable of addressing all three challenges. To address the first challenge, I propose Shufflecast: a separate optical core to support energy-efficient high-performance multicast, complementing the existing unicast-capable all-optical core. Shufflecast leverages small fanout, inexpensive, passive optical splitters to connect the Top-of-rack (ToR) switch ports, ensuring data-rate agnostic, low-power, physical-layer multicast. To address the second challenge, I propose OSSV: a combination of OCS-based core (between ToR switches) and OCS-based reconfigurable edge (between servers and ToR switches). While the OCS core is traffic agnostic and realizes reconfigurably non-blocking ToR-level connectivity, the OSSV edge reconfigures itself to rectify the incoming traffic skewness. Such spatial flexibility to reorganize the flows can largely compensate for the lack of core-level path diversity. To address the third challenge, I propose Phoenix: a more flexible OCS core and OCS edge-based architecture with precise space and time-domain control. Apart from the suitable locations, Phoenix edge can also find opportunistic moments for the flow reorganization that can minimize the OCS downtime-induced disruption. Overall, the highly flexible optical edge with both space and time-domain flexibility can significantly improve the tail performance of individual flows under realistic workloads. We extensively evaluate several aspects of these architectures with large-scale simulations and testbed implementation. We believe such holistic system design can make all-optical circuit-switched network cores widely acceptable and adoptable to the community.
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Adapting learning and search algorithms to handle protein structural data with the goal of aiding drug discovery
(2024-09-16) Conev, Anja; Kavraki, Lydia E
Experimental methods for protein structure determination (e.g., x-ray crystallography, NMR, cryoEM) require access to expensive equipment and are not scalable. Computational methods assist protein structure prediction and analysis on a far larger scale. Recent deep learning advances, the most notable being DeepMind’s AlphaFold2.0 release in 2021, have provided a wealth of structural data for further analysis and open new opportunities for algorithmic development. In my work, I address three different tasks that make use of the available protein structure data: (1) system-specific binding-affinity prediction (in the context of the immune-related peptide-HLA system); (2) generation of representative ensembles from generic protein structure datasets; (3) protein-ligand ensemble docking. To this end, I examine and adapt a range of algorithms including random forest regression models, unsupervised learning methods and stochastic global optimization techniques. I validate the resulting pipelines on available experimental data and apply them to different macromolecular contexts such as the immune-related formation of the peptide-HLA complex; flexibility of the signal transducer PI3K lipid kinase; CDK2 protein kinase and estrogen receptor α. Developed pipelines are open source and freely available and can help guide the search for novel therapeutics.
BIOGENESIS OF NATIVE EXTRACELLULAR VESICLES AND GENERATION OF BIOENGINEERED EXTRACELLULAR VESICLES AS THERAPEUTIC AGENTS
(2024-10-02) Luo, Xin; Kalluri, Raghu; Grande-Allen, Jane
Extracellular vesicles (EVs) are small vesicles secreted from presumably all types of body cells naturally. EVs are involved in the bidirectional intercellular communication with functional impact. While the mechanism of EV generation and uptake by recipient cells is not fully understood, which is crucial for understanding their biological impact, EVs have already been considered as a drug delivery system in the context of various pathologies. To better evaluate mechanism involved in the biogenesis of EVs, I focused on the functional role of three EV-enriched tetraspanins, CD9, CD63, and CD8. Employing loss of function studies, the proteomics of cells deficient in CD9, CD63, or CD81, and EVs generated by these cells were functionally investigated. CD9, CD63, and CD81 were found to be important for sorting of specific proteins into the EVs, each one displayed distinct contribution in trafficking of proteins into EVs . Next, to explore how engineered EVs can be involved in the regulation of immunity, I designed an engineered EV-based platform for vaccine development (EVX-M+P) and for cancer immunotherapy (EVmIM). EVs were endogenously loaded with mRNA (M) and protein (P) encoding an antigen (X) for the design of EVX-M+P to induce rapid and robust adaptive immune response and protection from future exposure. As a proof of concept, spike protein of SARS-CoV-2 and human ovalbumin (OVA) were used successfully as antigens for vaccines against a viral disease and melanoma, respectively. Next, EVs were endogenously loaded to harbor multiple surface immunomodulatory proteins of CD80, 4-1BBL, CD40L, CD2, and CD32 to generate EVmIM, which could induce APCs and T cells activation simultaneously to strengthen the antigen presentation and immune response against cancer progression, validated in orthotopic melanoma mouse model. The simplicity of EVs modification and cargo loading, and successful testing of the EVX-M+P and EVmIM platforms, offer new methodologies that can streamline the development of a new class of vaccines and immunotherapies. Collectively, my thesis research opens novel vaccination and cancer immunotherapy strategies that can be developed for human testing.

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