Environmental and Occupational Health Sciences Institute
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Saving two birds with one facade: landscape design for urban wildlife habitat and bird-building collision prevention
No full textHuman-centric design often ignores the importance of ecosystem function. This omission extends beyond the lack of diverse, valuable habitat in urban centers. Regarding bird-window collisions specifically, traditional architectural and landscape design can lead to the inadvertent decimation of species, and greatly impact systems beyond the local scope. This thesis reveals locations on the Rutgers University campuses with high rates of bird-building collisions, and recommends dynamic mitigation approaches dependent on such characteristics present at each site. Window reflectivity, lighting, spatial patterns, vegetation placement, and much more can be attributed to such collisions. At priority intervention sites, Dana Library in Newark and Nelson Biology Laboratories in New Brunswick, a second-story overhang and a multi-story glass-enclosed walkway are additional features hypothesized to be contributing to collisions at the site level. At both sites a façade is proposed, primarily for its potential use as a buffer to window glass. The seasonality and dietary needs of nine species most disproportionately affected across the campuses modeled approaches in vegetation selection. Rutgers contributes significantly to this global issue, and should look to examples of other universities to create a bird-friendly campus.M.L.A.Includes bibliographical referencesIncludes vit
The association between binge eating and alcohol use in a mouse model
No full textBecause alcohol use disorder (AUD) and binge eating disorder (BED) are often comorbid, it is likely that they share common underlying neurocircuitry. Interestingly, both AUD and BED have known effects on the brain’s reward center, the striatum. Both disorders are known to affect dopamine levels in this region. The aim of the current study is to determine if binge eating behavior cross sensitizes individuals to alcohol use and vice versa. To test this, we used rodent models. C57BL/6J mice were allowed to binge eat a high fat diet for several weeks and were then exposed to alcohol to evaluate their sensitivity to the locomotor response to alcohol and later their sensitivity to alcohol consumption. A complementary experiment exposed mice to binge levels of alcohol for several weeks, and then determined their propensity to binge eat a high fat diet. Interestingly male mice, but not female mice, with previous binge exposure were more sensitive to the stimulatory properties of alcohol. Further, male mice with previous alcohol exposure consumed less palatable food when allowed to binge eat. Together, these results suggest behavioral cross sensitization from binge eating to alcohol, in a sex-specific manner, but not alcohol to binge eating. These results provide a model in which to explore the similarities and differences in neural circuitry underlying these behaviors. Further, it could help inform the clinical treatment of concomitant binge eating and alcohol use.M.S.Includes bibliographical reference
Computational modeling and design strategies for biocatalytic and therapeutic proteins
No full textThis dissertation explores the physics-based protein modeling approaches and data-driven deep learning protein design models, and their applications on the development of novel biocatalysts and therapeutics, focusing on three key applications: biocatalysis, covalent antibody engineering, and antiviral drug resistance analysis. In the first study, we address the challenge of modeling and designing hemoproteins for olefin cyclopropanation reactions. Hemoproteins, particularly engineered myoglobins (Mb), have shown promise in asymmetric cyclopropanation, allowing access to different diastereo- and enantiomeric forms of cyclopropanes. However, achieving fully stereodivergent cyclopropanation remains challenging due to the inherent stereopreference of the scaffold proteins. Using a multi-scale density functional theory (DFT) and Rosetta-based multi-state design approach, we elucidated the structural determinants of stereoselectivity and identified key mutations that enhance the catalytic activity and stereoselectivity of Mb variants across various olefin substrates, providing a systematic framework for optimizing enzyme-substrate interactions. The second study focuses on covalent antibody-antigen crosslinking using the reactive non-canonical amino acid O-(2-bromoethyl)-tyrosine (ObeY). Covalent crosslinking offers a means to create irreversible sidechain ligations across the antibody-antigen interface and leverage thehighly specific antibody-antigen interactions, enhancing the therapeutic potential of antibodies. Through Rosetta-based modeling and molecular dynamics simulations, we investigated the formation mechanisms of covalent crosslinks and their conformational dynamics. Our findings elucidate the structural factors influencing crosslinking efficiency and inform the rational design of covalent antibody-based therapeutics. The third study explores drug resistance mechanisms in the SARS-CoV-2 main protease, a critical target for antiviral drug development. Resistance mutations can compromise the efficacy of protease inhibitors, posing a significant challenge in the treatment of COVID-19. We employed Rosetta and deep-learning models to perform computational active site mutation scanning calculations to identify potential resistance mutations and assess their impact on inhibitor binding. This study provides valuable insights into the molecular basis of drug resistance, guiding the design of more robust antiviral therapeutics. Together, these studies demonstrate the power of computational design approaches in rational creation of novel functional proteins, offering new strategies for developing efficient biocatalysts and therapeutics. By integrating physics-based modeling and data-driven deep learning approaches, this dissertation contributes to the advancement of computational protein design, paving the way for future innovations in biocatalysis and drug development.Ph.D.Includes bibliographical reference
Describing lights: love in victorian prose
No full textThis dissertation examines a nineteenth-century British literary phenomenon wherein authors attempting to indicate the merit of a beloved object describe not the object itself, but light on its periphery. The passages in question, while ostensibly descriptions of objects of affection, are best read as representations of the process by which attention rescues something from lusterless anonymity and ignites it with specialness. This project argues that because that rescue is both the condition of possibility for love and an inevitable result of describing, the images of light around beloved things we find in Victorian literature articulate philosophies of love that are also theories of description. Each chapter focuses on an author and his or her favored light source, demonstrating that source’s prominence in the author’s oeuvre and detailing how its distinguishing features embody the author’s particular notion of love and unique descriptive style. The chapters cover George Eliot’s realist dawns, Thomas Hardy’s distant stars, Joseph Conrad’s flickering similes, and Vernon Lee’s sentimental halos. “Describing Lights” reveals Victorian descriptions of light to be sites of dense conceptual work—aesthetic manifestos that are also philosophies of love. In the process, it makes the case for taking love seriously as an object of study within the history of ideas. Love in the addressed texts is never ahistorical or apolitical. It is something whose history and politics literary texts are uniquely well-equipped to explore.Ph.D.Includes bibliographical reference
Computational modeling of infrared spectroscopy for nucleic acids and fatty acids
No full textLinear and two-dimensional infrared (IR) spectroscopy provides powerful tools for probing the structure, dynamics, and interactions of biomolecules. This dissertation focuses on the theoretical modeling of IR spectroscopy to unravel the molecular-level details of DNA duplexes, G-quadruplexes, and omega-3 fatty acids. By integrating molecular dynamics (MD) simulations with spectroscopy modeling methods, this work establishes a robust framework to interpret the vibrational signatures of complex systems under varying environmental conditions.For nucleic acids, this research highlights the sensitivity of IR spectroscopy to hydrogen bonding, base stacking interactions, and solvation effects, particularly in the 1600–1800 cm−1 region. Our computational methods, combined with isotope labeling techniques, enable the disentanglement of overlapping vibrational modes and reveal site-specific structural dynamics of DNA double helices. These approaches are extended to G-quadruplexes, providing insights into their stability and ion-mediated interactions. For omega-3 fatty acids, the study utilized frequency maps and coupling models tailored to ester carbonyl vibrations, elucidating how molecular conformation and packing influence their spectral features.
By bridging experimental IR spectroscopy with atomistic MD simulations, this dissertation work achieves quantitative agreement with measured spectra across a range of biomolecular systems. The results demonstrate how structural changes, electrostatic environments, and intermolecular interactions manifest in IR spectra, providing a detailed understanding of biomolecular behavior.
This work contributes to advancing computational spectroscopy by offering new tools and methodologies to study complex biomolecular systems. The developed framework is generalizable and paves the way for future investigations of protein-nucleic acid interactions, lipid assemblies, and other biologically relevant complexes, with applications spanning structural biology, biomedical research, and materials science.Ph.D.Includes bibliographical reference
Enhancing quantum computing efficiency: compilation strategies leveraging algorithmic and hardware insights
No full textQuantum computing has rapidly advanced, with diverse quantum devices such as superconducting qubits, trapped ions, neutral atoms, and photonic chips. Since Richard Feynman’s 1981 proposal, significant algorithms—including Shor’s algorithm, Grover’s search, and Variational Quantum Algorithms (VQAs)—have been developed, underscoring the need for efficient systems that bridge high-level algorithms and hardware implementations. Quantum algorithms, typically expressed in high-level languages, are transformed into logical circuits, then mapped onto physical circuits using hardware-specific basis gates via qubit mapping and routing, and finally executed through control pulses. Future quantum systems are expected to incorporate error correction codes to enhance computational reliability. My research focuses on algorithm-specific compilation with cross-stack optimization to enhance the efficiency of quantum program execution on existing hardware. I explore optimization opportunities arising from gate commutativity in algorithms like the Quantum Approximate Optimization Algorithm (QAOA) and Quantum Fourier Transform (QFT), as well as flexibility in circuit synthesis for Variational Quantum Eigensolver (VQE) algorithms. Additionally, I analyze often-overlooked hardware characteristics, such as the regularity of qubit connectivity in modern quantum devices, to inform compilation strategies. By studying gate commutativity and qubit connectivity, we discovered a compilation pattern for QAOA achieving linear circuit depth for clique graphs. Building upon this, we developed a general framework adaptable to practical cases, effectively handling sparsity of problem graphs and hardware noise variability. This led to up to 72% reduction in circuit depth and 66% reduction in gate count on IBM and Google architectures with up to 1,024 qubits, outperforming baselines in experiments on IBM Mumbai. We extended this to QFT compilation, resulting in the first linear-depth QFT circuits for architectures like Google Sycamore, IBM heavy-hex, and 2D grids with arbitrary qubit counts. Our methods overcome limitations of techniques relying on SAT solvers or heuristics, which often suffer from long compilation times or suboptimal outcomes due to large search spaces. In another contribution, we introduced Tetris, a compilation framework for VQA applications. Tetris focuses on reducing two-qubit gates during compilation, as these have higher error rates and execution times. By exploiting opportunities in circuit synthesis and using a refined intermediate representation of Pauli strings, Tetris reduces two-qubit gate counts and mitigates hardware mapping costs through a fast bridging approach. Overall, Tetris achieves reductions of up to 41.3% in CNOT gate counts, 37.9% in circuit depth, and 42.6% in circuit duration across molecular simulations compared to state-of-the-art approaches. The methodologies and insights from my research are not limited to these three scenarios; they can be applied to future quantum program compilation tasks. By focusing on cross-stack optimization and leveraging both algorithmic properties and hardware characteristics, my work contributes to bridging the gap between quantum algorithms and hardware, significantly improving the efficiency and scalability of quantum computing implementations.Ph.D.Includes bibliographical reference
A persistent yet dangerous game of solitaire: how the neoliberal university invisibilizes single student-mothers
No full textUnmarried student-mothers are a specific college student population whose enrollment numbers continue to increase on American campuses, yet only approximately 28% of student-mothers graduate within six years (United States Government Accountability Office, 2019). With fierce determination, student-mothers enter universities out of necessity with promises of upward socioeconomic mobility; however, they soon encounter institutional campus barriers influenced by neoliberalism which expect student-mothers to conform to certain traditional college student standards. Extant literature reveals how campus cultures invisibilize student-mothers through flickering or absent services that hinder their degree-completion successes, yet there is a paucity of research dedicated to student-mothers’ experiences within college classrooms. Grounded in a three-pronged theoretical approach, this study examines three areas of inequitable college campus practices: 1) how the neoliberal university’s campus services neglect student-mothers; 2) how matricentric feminism’s call for agency and flexibility illuminates institutional barriers student-mothers face which silence their voices; and 3) how sociocultural learning theory can redirect professors’ classroom policies and practices to provide student-mothers with inclusive epistemic credibility. Six student-mothers who graduated from four New Jersey universities guide this study, which utilizes a Participatory Action Research methodology to actively elevate the participants as co-researchers and generate decolonizing new knowledge (Fine & Torre, 2021; Lennette, 2022). Their narratives foreground their lived experiences to offer recommendations that can inform universities’ future policies and practices at the administrative level, on campus at large, and within the classroom. Keywords: student-mothers, college, university, higher education, neoliberalism, matricentric feminism, communities of practice, invisibilization, sense of belonging, qualitative, participatory action research, non-traditional studentsEd.D.Includes bibliographical reference
Differentially private auditing and monitoring
No full textMachine learning methods and algorithms are an increasingly large component of modern data analytic practices and many businesses and institutions have a natural interest in using collected data for training these algorithms. However, while having appropriate data is important for many of these learning applications, issues can arise when this underlying data is sensitive. Data contributing individuals may not feel comfortable having their data used in for machine learning tasks. At the same time, privacy enhancing technologies (PETS) such as differential privacy may be difficult to understand for both data holders and data analysts. One example application is anomaly detection, in which an auditor could use privacy preserving learning algorithms to provide privacy protections/guarantees. This thesis describes three research contributions aimed at groups seeking to incorporate privacy protection in problems related to search and monitoring. First, we propose the concept of anomaly-restricted differential privacy and provide a privacy preserving anomaly detection algorithm. The goal is to provide potential auditors a way to perform anomaly detection while protecting the privacy of non-anomalous individuals. Second, we provide a differentially-private active learning algorithm and a web-based machine learning tool that implements the algorithm in an online stream-based environment. Analysts can use such a system to understand privacy/utility tradeoffs for anomaly detection or other learning tasks in active learning environments. Finally, we show that sensing systems proposed for “smart buildings” can reveal private information about an individual’s movements, even when the reported data are room occupancy counts. The attack strategy demonstrates some of the privacy challenges facing infrastructure-based sensing systems, where data is only indirectly collected from individuals.Ph.D.Includes bibliographical reference
Effect of Laban Movement on gesture recognition and response of seventh- and eighth-grade band students
No full textMultiple movement methods have been used in the music classroom for educational and research purposes. Laban Movement Analysis (LMA) is a movement framework that has been used with conductors, but there is limited research using Laban movements with K–12 band ensemble students. The purpose of this study was to investigate the effect of Laban instruction on the recognition and performance response to movement gestures among seventh- and eighth-grade band students (N = 44). I used a quantitative one-group pretest posttest design with Laban movement sessions as the treatment. The study took place during normal class time in the Fall of 2023 at a mid-Atlantic region public school. Data collected included an eight-question written assessment administered to all subjects in a group setting, an individual performance test completed one-on-one, and a group ensemble performance test administered to all subjects in a group setting. Each assessment was analyzed using a paired samples t-test to compare paired sample differences between pretest and posttest scores. The results of this study indicated significant effects of Laban movement instruction on middle school instrumentalists’ gesture recognition, particularly evident in total scores in the written assessment (p = .016), individual performance assessment (p < .001), and group ensemble performance assessment (p = .001). The findings suggest that middle school students can recognize Laban movements when used by a conductor and respond appropriately to the gestures on their instruments. Given this study’s findings, ensemble music educators, conductors, and preservice music teachers may find value in training with Laban Efforts, as this approach could support the development of ensemble performance skills and deepen students’ comprehension of conducting gestures.D.M.A.Includes bibliographical reference
Neural object-centric scene representation and generation
No full textAlthough deep learning has achieved remarkable success, it still falls short in robustness, systematic generalization, interpretability, reasoning, and creating new knowledge from limited experience. Addressing these limitations requires learning representations that understand the underlying causal structure of the data. A key step in this direction is discovering hidden generative causal variables, such as objects and other scene factors. This dissertation develops architectures and algorithms to infer object-centric representations of visual scenes without human supervision or labels. Building on the idea of perception as inverse graphics, existing approaches rely on inverting renderers that are brittle, cumbersome, and limited to simple visual scenes. In Part One, we propose, for the first time, the idea of taking an expressive decoder and inverting it to learn object-centric representations. We show that this achieves an unprecedented scene decomposition ability in visually complex scenes. It gracefully handles aspects of raytracing like shadows and reflections that are poorly handled by existing decoders. We also show evidence of systematic generalization by decoding novel object combinations. Next, to extend these benefits from images to videos, we explore two routes: a recurrent route and a parallelizable route; and analyze their trade-offs. In Part Two, we build on our previous success and move beyond monolithic object representations. We introduce a novel method that discovers not only objects but also intra-object factors, crucially, for the first time in complex scenes.Ph.D.Includes bibliographical reference