One Plaque at a Time: Investigating Heterogeneities of Amyloid Aggregates and Their Correlation with Alzheimer's Disease Progression

Electronic Thesis or DissertationAlzheimer's disease (AD) is a progressive neurodegenerative disorder pathologically characterized by the accumulation of amyloid beta (Aβ) protein into fibrillar aggregates, known as plaques, within the brain. However, the underlying mechanisms of aggregate formation, their evolution throughout disease progression, and their relationship to AD severity remain largely unknown. To address these gaps in knowledge, we employ infrared (IR) imaging, an analytical technique combining IR spectroscopy with optical microscopy, to investigate the structural heterogeneities of Aβ plaques in human brain tissues. Using this method, significant variations in plaque secondary structural composition are revealed. Amyloid plaques are usually characterized using histological staining. A well-known morphological type, the dense core plaque, has historically been thought to be composed of mature Aβ fibrils. Previous studies have shown that mature Aβ fibrils exhibit a parallel β-sheet secondary structure, while Aβ oligomers may have a differing secondary structure, such as antiparallel β-sheet. Therefore, it has been understood that the fibrillar cores of these plaques are exclusively composed of parallel β-sheets. However, we demonstrate that these cores exhibit a variety of secondary structural conformations, including both parallel and antiparallel β-sheets, and differ in localized β-sheet contribution. These findings suggest that a heterogeneous mixture of Aβ fibrils with varying secondary structures exists within plaques, rather than a singular, distinct conformation. Furthermore, the secondary structure distribution of different Aβ aggregates evolves with disease progression, and we show that specifically the β-sheet content in plaques increases as AD progresses. These results highlight and reemphasize the role of fibrillar aggregates in AD pathology and also point to the possibility of categorizing plaques based on their chemistry, which can pave the way for the development of effective targeted treatment options for AD

The Unique Contributions of Pretend Play Types to Skill Acquisition: a New Curriculum

Electronic Thesis or DissertationResearch supports the notion that play is an important context for learning and development. Participation in play has been linked to developmental gains in cognition, communication, and socio-emotional intelligence (Ashiabi, 2007; Lai et al., 2018; Ogura, 1991). In more recent research, investigators have shifted focus to examine if the type of play (i.e., structured versus unstructured or fantastical versus realistic) matters regarding skill development (e.g., Colliver et al., 2022; Thibodeau et al., 2016; Thibodeau et al., 2020; Veraksa et al., 2022). However, results are mixed and appear to be dependent on the skill or developmental area being targeted. The current research aimed to investigate the unique influence of two common pretend play types (fantastical and sociodramatic) on skill development, specifically, in the promotion of executive functioning and social-emotional development. Participants included 138 preschool children who were assigned by classroom to one of three conditions: fantastical pretend play, sociodramatic pretend play; or business-as-usual (control). The study took place over 5-weeks, with teachers leading 15-minute play sessions outlined in their assigned curriculum every school day. Significant differences emerged between the conditions at posttest and interactions with attendance were found for the social-emotional outcomes. Implications for dissemination are discussed, and suggestions for future directions are given

Leadership Experiences of K-12 Principals in Times of Crisis As Exemplified by the COVID-19 Pandemic

Electronic Thesis or DissertationThe purpose of this study was to understand the leadership experiences of K-12 principals in times of crisis as exemplified by the COVID-19 Pandemic. This phenomenological, qualitative study used semi-structured interviews to understand the crisis leadership experience of ten principals in public, K-12 schools in the state of Alabama. Through this study, macro themes related to the competencies and characteristics necessary for educational leaders to prepare for, navigate, and recover from crisis emerged. COVID-19 has provided the platform to study crisis leadership across all disciplines and especially education. This study hopes to add to the emerging body of research on crisis leadership in education and culminates with a leadership strategy Executive Summary addressing leading through crisis at the district level

Medium and Substituent Effects on Dynamic Self-Assembly of Nitrosobenzenes

Electronic Thesis or DissertationNitrosobenzenes (NBs), with the nitroso (-N=O) functionality, often exist as dipolar, colorless, azodioxide dimers (AD) in the solid state. When dissolved, these ADs reversibly dissociate to blue-green colored monomers in solution, demonstrating their dynamic covalent bonding (DCB) ability at nitrogen. This DCB proceeds with no catalyst or external stimulus, which is attractive for potential molecular docking applications where an AD linkage might facilitate bringing together two payloads. This work focuses on DCB studies of o-nitrosocumene (o-NC), o- and p-methylnitrosobenzene (o-MeNB and p-MeNB), 2,6-difluoronitrosobenzene (2,6-DFNB), and p-t-butylnitrosobenzene (p-t-BuNB). Systematic variable temperature (VT) NMR spectroscopic studies in various media have been conducted for each NB system to obtain thermodynamic parameters for the reversible monomerization equilibria of (Z)- and (E)-ADs in solution. 2D NMR exchange spectroscopy (EXSY) experiments aid in identification and analysis of the exchange (interchange) of the various forms of NBs in solution. In water, a novel NMR-aggregation is documented, where NBs self-assemble into a new ensemble possessing distinct, sharp, shielded NMR signals. DOSY (diffusion ordered spectroscopy) NMR analyses reveal diffusion coefficients consistent with aggregates of nm sizes. In addition to NBs, a series of various substituted benzenes is surveyed for nanoaggregation in water to ascertain the scope of this nanoassembly process for small organic molecules. Interestingly, we observe coaggregation of isomeric nitrotoluenes, which suggests these aggregates might function as a new type of host or vessel to recruit organic molecules. This work aims to probe and characterize aqueous aggregate species and measure medium effects (temperature, solvent polarity, hydrophobic effect) on the extent of aqueous self-assembly of NBs both inside and outside of the aggregate environment. Lastly, our collaboration with the Ramamurthy group (University of Miami) utilizes supramolecular hosts (octa acid (OA), cyclodextrins (CDs), cucurbiturils (CBs), and the Fujita Pd cage) to employ small space confinement as a tool to further coerce self-assembly of NBs in water, with the aim of exerting control over AD and nanoaggregate formation for the first time. These hosts can promote NB disassembly and assembly in water, as a function of the cavity spaces and properties

Lower Limb Muscle Activities and Contractility during Stepping Response to Unexpected Waist-Pull Perturbations

Conference ProceedingReactive balance responses, which involve corrective and protective strategies, are highly dependent on rapid muscle activation to restore postural stability. Although electromyography (EMG) is commonly used to measure muscle activity, it has limitations such as signal interference, particularly during fast responses to external disturbances. Ultrasound imaging (US), in contrast, provides visualization of both superficial and deep muscles. Combining EMG and US imaging offers complementary insight into muscle behavior during reactive balance tasks. In this study, we investigated muscle activation and fascicle length changes in the medial gastrocnemius (MGS), lateral gastrocnemius (LGS), and soleus (SOL) muscles of the dominant (stepping) leg during stepping responses to unexpected low-amplitude (57.6 ± 5.8 N) and high-amplitude (123.4 ± 11.1 N) waist-pull perturbations in the anterior and posterior directions. Five young male adults (age: 25.2 ± 5.5 years) participated in the study. Results showed that perturbation amplitude significantly affected the EMG activation of both the MGS and SOL muscles in both directions, consistent with previous studies. Similarly, perturbation amplitude impacted fascicle length shortening in the LGS and SOL muscles. Significant differences in MGS and SOL activation were observed between high-amplitude and lowamplitude perturbations in both directions. Fascicle shortening in the LGS also differed significantly between perturbation amplitudes, whereas SOL fascicle shortening did not. By combining EMG and US imaging within the same participants, this study provides new insights into the neuromuscular mechanisms underlying balance control. These findings may inform the development of improved control strategies for neurorehabilitation devices and fall-prevention systems.This work was supported by the FOAP at the University of Alabama #13009-214271-200

A Modular Cable-Driven Pelvic Robot for Rehabilitation Training: Design, Adaptive Control, and Characterization

Author Accepted ManuscriptAccurate monitoring and control of applied forces on human subjects are essential for safe and effective cable-driven robot-assisted rehabilitation. Managing external disturbances in these interventions, especially with human subjects as end-effectors, is challenging. This study presents a novel L1 adaptive controller (L1AC) for a cable-driven pelvic robot (CPR) capable of applying waist-pull perturbations up to 120 N. The modular design allows for easy setup in both anterior-posterior and medial-lateral directions. The L1AC , designed to minimize external disturbances, was rigorously tested in simulations and experiments. Results showed improved force tracking for a 60 N, 0.5 Hz sinusoidal force profile. In a fixed rigid object scenario, the controller achieved an RMSE of 6.20 ± 0.26 N, compared to 7.55 ± 0.33 N with the full-state feedback reference input (FSFRI) controller, and 6.83 ± 0.11 N with the H-infinity (H∞) controller. For the fixed semi-rigid object scenario, the RMSE was 7.58 ± 0.07 N, compared to 9.80 ± 0.10 N with FSFRI controller, and 8.38 ± 0.16 N with H∞ controller. In human-standing scenarios, it achieved an RMSE of 5.84 ± 0.34 N, compared to 6.20 ± 0.44 N with the FSFRI controller, and 7.42 ± 0.12 N with H∞ controller. The proposed controller was further tested during gait-synchronized waist-pull perturbation walking experiments under five conditions. Electromyographic signals from the calf muscles and lower limb motion capture data from all walking conditions revealed reduced muscle activation and joint motion as perturbation forces increased, demonstrating subjects’ adaptive responses. These findings emphasize the controller’s robustness in force trajectory tracking and its potential to facilitate human motor adaptation, offering significant promise for enhancing rehabilitation strategies using cable-driven robotic systemsThis work was supported by the FOAP at the University of Alabama #13009-214271-200

Convex and Non-Convex Relaxations for Densest Subgraph and Submatrix Recovery: Theory and Applications in Community Detection and Drug-Target Prediction

Electronic Thesis or DissertationThis dissertation addresses theoretical and computational challenges in recovering clusters, biclusters, and dense subgraphs from large, sparse, and noisy networks. These structures are fundamental to social network analysis, genomics, and drug–target interaction prediction. Two optimization-based frameworks are developed for unsupervised structure recovery. The first uses nuclear norm minimization to construct a convex relaxation of the planted bicluster detection problem, with recovery guarantees when the planted subgraph size scales as the logarithm of the network raised to the 1.5 power. The second introduces a nonconvex quadratic programming formulation with elastic net regularization and derives recovery guarantees using Karush–Kuhn–Tucker conditions. Under a constant signal-to-noise gap, this approach achieves exact recovery when the planted size scales with the square root of the logarithm of the network, demonstrating sharper thresholds than convex methods. These frameworks are benchmarked against traditional unsupervised techniques on three real-world datasets: the Dolphin social network, STRING protein interaction network and the Nuclear Receptor drug–target network. Our Elastic Net model recovers over 50 percent of known drug–target interactions using only binary data, matching the performance of supervised models that rely on chemical or genomic features. To interpret the results biologically, we map recovered targets to KEGG pathways, identifying clusters associated with immune, cancer-related, and hormonal signaling processes. Applied to the STRING protein interaction network, our model recovers dense modules validated using g:Profiler. Overall, this work provides interpretable and scalable tools for unsupervised learning on complex biological networks

The Rhetorical Disengagement Phenomenon of Quiet Quitting in Organizational Behavior: Harmful Leadership, Muteness and Power

Electronic Thesis or DissertationThis dissertation investigates the antecedents, mechanisms and outcomes of quiet quitting, focusing on the role of leadership behaviors and employee burnout. Invoking conservation of resources theory and muted group theory over the course of three empirical studies, this dissertation examines quiet quitting as a potential coping response to resource depletion and workplace muting of voice behaviors. Studies 1 and 2 employed a longitudinal survey design collecting data across four time points. Study 3 utilized the same survey scales to collect a single round of surveys from non-profit workers. Data were collected from U.S. employees identifying as employed full-time via Prolific.com. Moderated mediation analyses were used in all three studies to test proposed hypotheses. Study 1 interrogated the effects of supervisor undermining on job performance, organizational leadership behaviors (OCBs) and turnover intent via burnout with quiet quitting as a moderator between X and Y. Study 2 examined abusive supervision's impact on perceptions of interpersonal justice and quiet quitting with voice behaviors as a moderator of the relationship between X and Y. Study 3 focused on the non-profit sector to examine how supervisor undermining moderates the relationship between intrinsic motivation, affective commitment, burnout and quiet quitting, with transformational leadership added in post hoc analysis to discern any differences in positive vs. negative leadership behaviors. Findings across all studies indicate that harmful leadership behaviors consistently negatively influence burnout. While quiet quitting does not emerge concretely as a mitigating influence, its moderating role is statistically significant in several analyses. All three studies indicate quiet quitting serves as a symptom of disengagement and organizational disfunction. This dissertation contributes to nascent scholarship empirically defining quiet quitting and exploring its manifestation as both a coping mechanism and a muted strategy for resistance

Sufficient Dimension Reduction with Orthogonality Constraint through Manifold Optimization

Electronic Thesis or DissertationSufficient Dimension Reduction (SDR) aims to identify a central subspace (CS), where the projection of predictors onto the subspace retains all information about the response variable without loss of information, thereby achieving dimension reduction. SDR methods typically fall into two main frameworks: the inverse approach and the forward approach. In practice, the basis matrix of the CS is the primary object of interest. The inverse approach naturally fulfills the orthogonality constraint of the basis matrix through eigenvalue decomposition. In contrast, the forward approach often requires extra steps to meet the requirement. We propose new forward SDR methods that preserve orthogonality constraint via manifold optimization. The Grassmann least squares Dimension Reduction (\textbf{glsDR}) algorithm estimates the central mean subspace (CMS) under semiparametric regression framework. Unlike existing forward methods that require re-orthogonalization, our approach is based on adaptive gradient descent on the Grassmann manifold, ensuring orthogonality at each iteration. As a first order method, \textbf{glsDR} is computationally more efficient than the second order alternatives. The Central Subspace Grassmann least squares Dimension Reduction (\textbf{CS-glsDR}) algorithm extends the method to exhaustive estimation of the central subspace (CS) by replacing observed response with an empirical estimate of its conditional distribution. Additionally, we propose the Grassmann ensemble expectile Dimension Reduction (\textbf{geeDR}) algorithm, which employs the ensemble strategy, recovering the CS by modeling a sequence of conditional expectiles. The effectiveness of the proposed methods is demonstrated through extensive simulation studies and real data applications