Body image and distrust of science as predictors of dieting and disordered eating

Research has linked the widespread prevalence of dieting, despite its high cost and limited effectiveness, to body image issues, which are exacerbated by the ubiquitous promotion of diet culture. This research explores connections between distrust of science, body image, and the propensity for dieting and disordered eating. Two hundred twenty-five participants from a public university (N = 225, Mage = 20.03, SD = 4.45), had their dieting and disordered eating behaviors assessed using the Weight Control Behavior Scale (French et al., 1995). Body image was measured using the Body Appreciation Scale (Tylka & Wood-Barcalow, 2015) and the Body Surveillance and Shame subscales of the Objectified Body Consciousness Scale for Youth (OBC-Youth) (Lindberg et al., (2006). Distrust of science was evaluated with an adapted version of the Credibility of Science Scale (Hartman et al., 2017). It was found that distrust of science significantly predicted disordered eating behaviors and negative body image perceptions were significantly associated with an increased likelihood of engaging in disordered eating behaviors. These findings support past research that suggests body image predicts dieting behaviors and further indicates that distrust of science is a significant predictor of disordered eating practices.M.A.Includes bibliographical reference

Space-time-modulated metamaterial antenna architectures for wireless communication applications

Over the years, time-modulation technique has been employed to alter the radiation pattern of conventional antenna arrays by periodically connecting and disconnecting the antenna elements from the feed network using RF switches. On the other hand, metamaterials (MTMs), known as artificially engineered electromagnetic structures, consist of several sub-wavelength unit cells with unique properties such as negative propagation constant, which can be leveraged to develop a MTM antenna with appealing beamforming capabilities, offering a compelling alternative to complex phased-array architectures. We develop spacetime-modulated metamaterial (ST-MTM) transceiver architectures capable of generatingcontrollable harmonic frequencies, which facilitate a variety of beneficial functionalities. These include nonreciprocity enabling simultaneous transmission and reception of signals, harmonic beam scanning, and directional modulation to enhance physical-layer (PHY) security, serving as a potential alternative to traditional cryptographic methods in wireless communication systems. In addition, the ST-MTM antenna is leveraged as a beamspace multiple-input multiple-output (MIMO) receiver, which facilitates the retrieval of information from multiple users at the same time. Specifically, the transmitted information of each user, located in a specific direction, can be retrieved from a distinct harmonic frequency component within the received spectrum.Ph.D.Includes bibliographical reference

Explainable CNN-based ADHD detection using EEG data

Attention Deficit Hyperactivity Disorder (ADHD) is a prevalent neurodevelopmental condition marked by persistent symptoms of inattention, hyperactivity, and impulsivity, significantly affecting individuals across all age groups globally. Accurate and timely diagnosis is critical for effective intervention, yet current diagnostic methods often rely on subjective clinical evaluations and behavioral assessments, which can be inconsistent and prone to bias. To address these challenges, this study introduces an innovative data-driven approach for the automated detection of ADHD using Electroencephalography (EEG) data, leveraging Convolutional Neural Network (CNN) models integrated with explainability techniques.The proposed methodology employs advanced preprocessing techniques to extract meaningful features from raw EEG signals, capturing subtle neural activity patterns associated with ADHD. Utilizing a hybrid dataset comprising EEG recordings from both children and adults, the model demonstrates robust performance, achieving an accuracy of 98.91% on unseen test data. These results underscore the model's potential for precise and reliable ADHD detection, offering a significant improvement over traditional diagnostic methods. To ensure transparency and interpretability in clinical applications, two state-of-the-art explainability techniques—Local Interpretable Model-agnostic Explanations (LIME) and SHAPley Additive Explanations (SHAP)—were employed. LIME approximates the model's behavior for specific data instances, identifying influential features in individual predictions, while SHAP provides a global perspective by quantifying feature importance across the dataset. These techniques validated the relevance of specific EEG channels and features in distinguishing ADHD, revealing critical biomarkers and enhancing model interpretability. This study establishes a comprehensive framework for automated ADHD detection, integrating deep learning with robust explainability methods to ensure accuracy and transparency. By bridging the gap between advanced machine learning techniques and clinical applicability, this work promotes objective, early, and reliable ADHD diagnosis. Beyond ADHD detection, the framework's adaptability suggests potential extensions to other neurodevelopmental disorders, highlighting its broader implications in AI-driven healthcare solutions.M.S.Includes bibliographical reference

Robots with dynamics: efficient motion planning and analysis of controllers via machine learning

This thesis aims to improve the efficiency and robustness of motion planning for robots with significant dynamics, leveraging both advances in machine learning as well as contributions in algorithmic and foundational techniques. The key objectives are to (a) efficiently compute safe open-loop trajectories that obey non-trivial robot dynamics so that they are easy to follow with closed-loop controllers, and (b) efficiently analyze and characterize the capabilities of closed-loop robot controllers to enable safe real-world deployment.This effort starts by exploring alternatives to the standard methodology of generating control sequences in sampling-based planning for systems with dynamics. Typically, these methods rely on random controls, which are useful to argue desirable properties, but which lead to slow convergence and low-quality solutions in practice.To address this, the thesis first proposes using machine learning to train goal-reaching controllers via reinforcement learning. Such learned controllers can be integrated with sampling-based planners and help guide the expansion of the underlying planning structure towards the global goal. This is shown to lead to the faster discovery of high-quality trajectories on mobile robot navigation problems, including for physically-simulated challenges with uneven terrains.In addition, this thesis proposes the offline construction of a “roadmap with gaps” data structure for systems with dynamics, which can express the learned controller's reachability capabilities in a target environment. Online, the sampling-based planner uses the “roadmap with gaps” to promote the fact discovery of high-quality trajectories to the goal. The overall approach enhances the efficiency of motion planning in various benchmarks, including physics-based simulations of vehicular systems and aerial robots.The open-loop solutions generated by sampling-based planners require closed-loop feedback control for reliable real-world execution. To this end, the thesis first integrates techniques for identifying approximate analytical models of the robot's dynamics that allow fast motion planning and reduce the model gap. It then focuses on achieving closed-loop operation at both the planning and control levels by proposing a safe replanning framework for kinodynamic motion planning and integrating feedback controllers that reason about robot dynamics. These contributions allow for safe and efficient tracking of planned trajectories on a physical platform.Concurrently, the thesis also addresses the challenge of understanding the global dynamics of robot controllers, including learned ones, which is crucial for safe deployment of such solutions and the composition of controllers. A topological framework (Morse Graphs) is leveraged, and data-driven modeling approaches are proposed to enable data-efficient characterization of controller attractors and their regions of attraction, even for high-dimensional systems.Finally, the thesis contributes an open-source software library, which provides a flexible and efficient framework for integrating machine learning methods into kinodynamic planning and control.Ph.D.Includes bibliographical reference

A novel ionizer design for detector length compensation in time-of-flight energy analysis of molecular beams

This dissertation presents preliminary analysis of the cyclooctatetraene (COT) on Cu(001) adsorption system with angle-resolved helium atom scattering (HAS) and scanning tunneling microsopy (STM). A novel ionizer design and mass spectrometer orientation concept for time-of-flight (TOF) analysis of helium atoms is introduced. Implementation of the novel detector and optimization of TOF resolution is shown. Studies of the COT on Cu(001) adsorption system with HAS initially revealed a high-order commensurate superstructure with unit cell vectors of [6 1, 1 3] relative to the 4-fold substrate periodicity. Desorption measurements using HAS showed COT undergoes first order desorption at 570 K. Further analysis with HAS and STM at various adsorbate coverages, as well as DFT calculations, suggest that COT has one allowed adsorption site which is centered between four substrate atoms. At high coverages, COT primarily forms short-ranged ordered domains with unit cells of [3 1, 1 3] with domain boundaries of [3 0, 1 3]. The highly sensitive ionizer design achieves high TOF energy resolution with a long ionization region through a new mass spectrometer orientation and compensation of neutral arrival times of the scattered helium beam. A mass resolving quadrupole mass spectrometer orientation is reversed from the conventional sense, such that the neutral beam first passes the electron multiplier, then travels through the quadrupole region before finally passing through the ionization region. The concept is illustrated here with application to the TOF He atom detector. A carefully selected compensating variable potential curve is applied to the ionization region so that as helium progresses through the ionization region the ions are created with ever increasing ion energies. Thus, all ions from the same section of the helium beam will be detected at the same time. Compensation is also possible with high electron space charge, allowing for increased ionization efficiency through both a larger ionization volume and higher electron densities within the ionizer.Ph.D.Includes bibliographical reference

Structural studies of hiv-1 reverse transcriptase inhibition, drug resistance, and function: novel inhibitor discovery and nnrti design, second-strand initiation mechanisms, and host-factor complex formation

HIV-1 reverse transcriptase (RT) is the enzyme that converts the single-stranded viral RNA genome into double-stranded DNA, which is irreversibly integrated into the host chromosome. HIV is the etiological agent of acquired immunodeficiency syndrome (AIDS), which kills ~630,000 people each year, making effective treatment and prevention of critical. RT is the target of nearly half of the FDA-approved antiretroviral therapies. Despite decades of research, many questions remain regarding drug resistance, fundamental mechanistic understanding, and the role of host factors in reverse transcription. We leverage structural biology (X-ray crystallography, cryo-EM) to answer these questions. First, we describe the rational design and structure determination of novel non-nucleoside reverse transcriptase inhibitors (NNRTIs) that potently inhibit a clinically significant drug-resistant triple-mutant RT. These inhibitors show no phenotypic cross-resistance with FDA-approved NRTIs and some NNRTIs, indicating the potential for synergistic use in antiretroviral therapy. Next, we determined the first known structures of an HIV-1 second-strand reverse transcription initiation complex. These complexes are unique in their selective use of a polypurine tract (PPT) RNA primer, which, unlike typical RNA, is not cleaved by RT during first-strand synthesis, making it a unique substrate for second-strand initiation. Moreover, they are uniquely susceptible to drug inhibition relative to other functional states. Our structures of the PPT RNA, pre-incorporation, and NNRTI-bound second-strand initiation complexes reveal a unique architecture. In these structures, the primer adopts unusual placement and induces the opening of the cryptic NNRTI binding pocket, even in the absence of an NNRTI. Together, these structures provide a basis for understanding the unusual sensitivity of second-strand initiation to NNRTI inhibition and fill a critical mechanistic gap in our knowledge of the reverse transcription cycle. Finally, we characterize the binding of HIV-1 RT to host factor eukaryotic translation elongation factor 1 alpha 1 (eEF1A1), which is highly abundant in the cell and required for reverse transcription in vivo as part of the reverse transcription complex (RTC). Preliminary cryo-EM experiments provide insight into the spatial arrangement of the RT/eEF1A1 complex. Disruption of the binding interface by small molecules is an attractive means of developing a new class of HIV-1 inhibitors. These three projects contribute significantly to our fundamental understanding of HIV-1 reverse transcriptase structure, function, and inhibition and advance our collective knowledge of this incredible molecular machine of HIV.Ph.D.Includes bibliographical reference

Examining Romina’s Growth in Reasoning and Collaboration in 10th & 12th Grades

This is Analytic 2 (of 3) of "Tracing Romina’s Growth In Reasoning And Sense-Making about Math Problems and Development of Beliefs about Math Teaching/Learning"This analytic examines the development of Romina’s problem-solving heuristics and examples of her behaviors in collaborative settings in tenth and twelfth grades. We watch Romina argue, make meaning, and generalize as she revisits the Tower Problem through an extension known as “Ankur’s Challenge.” Two years later, we witness her engagement with the Taxicab Geometry task as a senior in high school and ultimate connection to the isomorphism of this task with Pascal’s Triangle and Towers Problem. As examined by Steffero (2010), Romina’s mathematical behaviors in these high school episodes include a variety of questioning in collaborative settings that seek information, make suggestions, ask for explanation, or reiterate others’ ideas. We also watch Romina’s justification and generalization develop as she incorporates other’s ideas through collaboration to refine her problem-solving strategies and representations as well as ultimately make convincing arguments for her peers.References Steffero, M. (2010). Tracing beliefs and behaviors of a participant in a longitudinal study for the development of mathematical ideas and reasoning: A case study. Rutgers The State University of New Jersey, School of Graduate Studies

Genomic resources for the study of anisogramma anomala and the Eastern filbert blight pathosystem

Commercial production of European hazelnut (Corylus avellana) in the United States is severely limited by Anisogramma anomala, the causal agent of Eastern Filbert Blight (EFB). Research efforts have identified a resistance gene from ‘Gasaway’ that confers complete resistance to EFB. Hazelnut breeding programs have since integrated the so-called ‘Gasaway’ R gene into a range of EFB resistant C. avellana cultivars despite concerns over single gene resistance. In the years since, these ‘Gasaway’ protected hazelnut trees have held up poorly in New Jersey and succumbed to EFB. Despite the economic importance of A. anomala, little is known about this pathogen, largely due to methodological difficulties associated with laboratory-based experimentation on biotrophic pathogens. The purpose of the following works is to apply genomics to elucidate the biological properties of A. anomala and the EFB pathosystem and determine the cause of ‘Gasaway’ resistance breakdown in New Jersey. The genome of a strain of A. anomala isolated from Oregon was sequenced and annotated to provide a reference genome for future study of EFB. The massive 350 Mb genome reveals features that are consistent with other biotrophic plant pathogens, including the proliferation of transposable elements, species-specific gene families with functions related to pathogenesis, and a large cache of putative effector genes. A. anomala also exhibits a genome with bipartite architecture, fitting the “two-speed” genome model, providing a mechanism by which genome evolution occurs. Simple sequence repeat (SSR) markers derived from the genome sequence were used for genetic fingerprinting of a controlled inoculation evaluating the breakdown of ‘Gasaway’ resistance in New Jersey. The results indicate a genetic shift in the population of A. anomala, suggesting the emergence of a novel pathotype of the fungus that is responsible for the compatible plant pathogen interaction and the incidence of EFB observed in New Jersey. Finally, the genome of the ‘Gasaway breaker’ pathotype was sequenced and utilized in a comparative genomics approach with other pathotypes, including the reference strain. The results of the comparative genomics study reveal the expansion of gene families that encode putative effectors with novel functions in the ‘Gasaway breaker’. Evidence of transposable element insertion into genomic regions corresponding to genes encoding effectors suggests that transposon activity may contribute to the mechanisms by which A. anomala is evolving to overcome ‘Gasaway’ mediated resistance. Taken together, these works provide a genetic basis for the breakdown of ‘Gasaway’ resistance in New Jersey and the biological features of A. anomala that enable the pathogen to evolve and adapt to a resistant host population.Ph.D.Includes bibliographical reference

Promising brighter futures: a mixed-methods analysis of the impact of promise programs on BIPOC student outcomes in California and New Jersey

With increasing concerns about rising college costs, policymakers have sought to identify ways to make college affordable. College affordability is particularly important to address equity concerns in both higher education and the workforce, as studies consistently demonstrate disparities in outcomes by race/ethnicity and socioeconomic status. One policy intervention to alleviate the financial burden of college for students has gained traction: tuition-free college—commonly known as "promise programs.” Promise programs are now present in all fifty states, each with its own criteria and benefits. Researchers have paid particular attention to two types of promise programs, first-dollar and last-dollar, due to their different structures. Focusing on concerns around racial/ethnic equity in higher education, this study examined how program structure influences enrollment and credential attainment for students who identify as Black, Indigenous, or People of Color (BIPOC). A mixed-methods design was used to determine the impact of two statewide promise programs—the California College Promise (CCP) and the New Jersey Community College Opportunity Grant (CCOG). Semi-structured interviews were conducted with BIPOC community college students in California and New Jersey to set a baseline understanding of how each promise program influences the student experience. Quantitative enrollment and graduation rate data from the federal Integrated Postsecondary Education Data System (IPEDS) was then analyzed to inform the qualitative themes identified from the interviews. Interview data demonstrated that students in California had a better understanding of financial aid, lower levels of financial stress, and higher campus engagement than students in New Jersey. IPEDS graduation rate data showed that while California has a better overall BIPOC graduation rate, the impact of New Jersey’s program was greater. The results are examined through a conceptual framework that pairs critical race theory and student development theory.Ph.D.Includes bibliographical reference

Evaluating pharmacokinetics and dosing strategies of antibiotics and biologics in lean and obese animal models

Obesity is a global health concern impacting over 890 million adults and poses significant challenges to healthcare systems worldwide. Despite its prevalence, our understanding of how obesity influences pharmacokinetics and drug biodisposition remains limited. Understanding how obesity affects drug disposition is crucial, as traditional dosing strategies are often based on studies conducted in non-obese individuals. This reliance can result in suboptimal therapeutic outcomes for obese patients, who may experience altered pharmacokinetics due to the physiological changes associated with increased body fat. This dissertation investigates the pharmacokinetic alterations associated with obesity, focusing on antibiotics such as cefoxitin, cefazolin, and piperacillin, along with biologics including nivolumab and recombinant human erythropoietin. By examining obesity-induced changes in the pharmacokinetics of these commonly used drugs, this research aims to determine whether current dosing regimens need to be adjusted for the obese population. In Chapter 1, the introduction provides an overview of the obesity epidemic, discusses the impact of obesity on drug pharmacokinetics, and highlights the importance of body composition in pharmacokinetics. Additionally, it covers the general pharmacokinetic profiles of cefoxitin, cefazolin, piperacillin, nivolumab, and recombinant human erythropoietin. Chapter 2 presents a fully validated LC-MS/MS method for the simultaneous quantitation of cefoxitin, cefazolin, and piperacillin in rat plasma and twelve tissues including abdominal adipose tissue, brain, heart, kidney, liver, lungs, muscle, subcutaneous adipose tissue, skin, small intestine, spinal cord, and spleen. This method demonstrates efficiency through its sensitivity and high throughput, requiring only a minimal sample volume of 5 μL for plasma and 50 – 100 μL for tissue homogenates. Building on the validated bioanalytical method presented in Chapter 2, Chapter 3 quantifies the plasma and tissue concentrations of cefoxitin, cefazolin, and piperacillin collected from lean and obese rats collected at 5, 15, 30, 45, 60, 90, and 120 minutes following intravenous administration of these antibiotics according to body weight. Comprehensive experimental data are collected, including body measurements (total body weight, body length, abdominal circumference), body composition (fat mass and lean mass), and tissue weights from both lean and obese rats. Noncompartmental analysis is performed to compare pharmacokinetics parameters between lean and obese animals. The results indicated that mg/kg dosing is required for obese animals to achieve drug exposure levels comparable to those in lean animals. A whole-body physiologically based pharmacokinetic model is then developed to describe these concentrations, integrating physiological parameters such as tissue weight and blood flow rates for both groups. This model provides a mechanistic framework that enhances our understanding of drug distribution across various tissues. The data generated from this study is anticipated to inform future translational research and support the optimization of antibiotic dosing strategies for the obese population in clinical settings. In Chapter 4, the dissertation further investigates the pharmacokinetics of biologics, specifically focusing on nivolumab and recombinant human erythropoietin using a diet-induced obese rat model. Male Long-Evans rats were fed a high-fat diet to induce obesity, with their progress monitored through various body metrics. The rats were then received nivolumab or recombinant human erythropoietin via intravenous or subcutaneous injection. The collected serum samples were analyzed using ELISA, and pharmacokinetic parameters were calculated using noncompartmental pharmacokinetics analysis. While our previous studies indicated significant differences in pharmacokinetics of human IgG in lean and obese rats, this study found no observable differences in nivolumab and recombinant human erythropoietin in pharmacokinetics between lean and obese rats. The results that mg/kg dosing of these 2 biologics is necessary for obese animals to achieve comparable drug exposure to lean animals. Further research with other biologics is needed in both preclinical and clinical settings to identify optimal dosing strategies for obese populations. Male Long-Evans rats were fed a high-fat diet to induce obesity, with their progress monitored through various body metrics. The rats then received either nivolumab or recombinant human erythropoietin via intravenous or subcutaneous injection. Serum samples were collected and analyzed using ELISA, and pharmacokinetic parameters were calculated using noncompartmental analysis. While our previous studies indicated significant pharmacokinetic differences for human IgG between lean and obese rats after intravenous and subcutaneous injection, this study found no significant differences in the pharmacokinetics of nivolumab and recombinant human erythropoietin between the two groups. These findings suggest that mg/kg dosing of nivolumab and recombinant human erythropoietin is necessary for obese animals to achieve drug exposure comparable to that of lean animals. The differing conclusions between our previous findings on human IgG and the current study also highlight that the pharmacokinetics of each biologic may vary in obese populations. Therefore, further research with other biologics currently in use is essential in both preclinical and clinical settings to establish optimal dosing strategies for obese individuals. Overall, this dissertation explores the impact of obesity on the pharmacokinetics of both small and large molecules, emphasizing the necessity for tailored dosing strategies to improve therapeutic efficacy using a diet-induced animal model. These findings aim to enhance our understanding of drug biodisposition in obese populations and provide a foundation for optimizing therapeutic approaches in this growing demographic.Ph.D.Includes bibliographical reference