18525 research outputs found
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Automated Human Head Model Construction and Fast RF Shimming Design in 7T MRI using Deep Learning
No full textThe design of RF coils for MRI depends on customized head models with extensive tissue labels, significantly enhancing electromagnetic (EM) simulations for RF coil validation and optimization. Open-source segmentation tools have generated head models from MRI scans, but the most advanced automatically segmented brain model for EM simulations lacks muscle, fat, skin, and detailed brain labels, limited by the tissue types detectable in T1-weighted (T1w) images. This study presents an advanced head segmentation suite with 14 distinct tissue labels. Using state-of-the-art segmentation on paired T1w MRI and CT scans, our models demonstrate improved field uniformity at 7 Tesla.
Moreover, Ultrahigh field (UHF) Magnetic Resonance Imaging (MRI) offers an elevated signal-to-noise ratio (SNR), which benefits clinical diagnostics and advanced research. However, the jump to higher fields introduces complications, particularly transmit radiofrequency (RF) field (B1+) inhomogeneities, manifesting as uneven flip angles and image intensity irregularities. These artifacts can degrade image quality and impede broader clinical adoption. Traditional RF shimming methods, such as Magnitude Least Squares (MLS) optimization, effectively mitigate B1+ inhomogeneity but remain time-consuming and typically require the patient’s presence to compute solutions. Although these methods show promise, challenges such as extensive training periods, limited network complexity, and practical data requirements persist. Here, we introduce a holistic learning-based framework called Fast-RF-Shimming, which achieves a 5000× speed-up in RF shimming compared to traditional MLS. It first uses random-initialized Adaptive Moment Estimation (Adam) to derive reference shimming weights from multi-channel B1+ fields, then employs a Residual Network (ResNet) to map these fields to ultimate shimming outputs, incorporating a confidence parameter into its loss function. A Non-uniformity Field Detector (NFD) optionally identifies extreme non-uniform outcomes. Comparative evaluations with standard MLS underscore notable gains in both processing speed and predictive accuracy. As a result, this technique presents a faster, more efficient RF shimming framework for UHF MRI, offering a promising solution for persistent inhomogeneity challenges
Physiological Reactivity in Parents and Adolescents Exposed to Adverse Childhood Experiences
No full textPhysiological synchrony between parent and adolescent is important for children who have been exposed to adverse childhood experiences (ACEs) as it can reveal more about how both the parent and adolescent are regulating their stress. The current study examines two aspects of physiological reactions in response to a stressful task completed by both parents and their adolescent offspring. Both skin conductance (SC) levels, a sympathetic response, and RSA (respiratory sinus arrhythmia), a parasympathetic response, were measured for the parent-adolescent dyads and analyzed using 32-second epochs to create synchrony correlation coefficients. Results demonstrated that adolescents exposed to ACEs experienced a significant difference in SC between baseline and the conflict task, suggesting a heightened physiological response to stress. In addition, there was synchrony between adolescents and parents for SC levels but no synchrony in RSA levels. Furthermore, parenting behaviors, both positive and negative, were not related to child physiological responses. This suggests that the parenting behaviors and physiological synchrony between parent and child do not impact the child’s physiological reactivity. Future studies should look further into specific target areas for intervention, other than parents, for these adolescents and how these interventions could improve dysregulation in physiological reactivity.Thesis completed in partial fulfillment of the requirements of the Honors Program in Psychological Science
State Implementation of Federal Programs: Essays on the Effects of Electoral Incentives, Partisanship, and Special Interests on State Choices
No full textResearchers and policymakers have claimed that the expansion of federal programs over the last several decades has come at the expense of state leeway, while others argue that national leaders cannot hold states accountable to associated federal requirements. Thus, whether federal or state governments claim more power in the implementation of federal programs remains an unanswered question. This debate has significant implications. State leaders, particularly those in the executive branch, are responsible for implementing most federal programs in the United States. They have discretion in doing so due to the constitutional design of American federalism and the political incentives of national leaders to delegate it to them. However, the state leaders' choices on how to use this discretion affect the quality of the schools children attend, the roads Americans drive on, and the healthcare they can access. Therefore, these choices are consequential. Furthermore, since federal resources represent more than 30% of state budgets, this discretion over the execution of national programs is an important power of state executives. Thus, this dissertation examines how state leaders make such choices, predominantly in the context of the choice of program recipients. I show that while state leaders have strong incentives to implement federal programs to advance national goals, the extent to which they do so varies across states due to differences in the political environment in which state leaders operate. The choice of whether to support the intended outcomes of federal policymakers is not a politically neutral choice. I show that differences in how the electoral supporters of state leaders are geographically distributed, in their partisanship, and in their relationship with special interests shape their electoral incentives. Furthermore, these differences have implications for state leaders' support of the advancement of federal goals to the extent that these forces incentivize state leaders to prioritize a set of constituents other than those preferred by national leaders. Thus, this dissertation expands our understanding of the consequences of the American federalist system by demonstrating how variation in state political environments can shape how federal programs are used to advance state and national goals
The Pathway to a Diverse Pipeline: Recruitment, Retention, and Belonging in a Welding Skilled Trade Training Program with Memphis Welding School
No full textLeadership and Learning in Organizations capstone projectThe Memphis Welding School (MWS) is the first black-owned welding school in Memphis, aiming to empower individuals from diverse backgrounds through skilled trade workforce development in welding and boilermaking. The project addresses a pressing labor shortage in the skilled trades, particularly welding, while highlighting barriers to recruitment and retention of underrepresented groups, including women and BIPOC, in a predominantly white male industry. Using a mixed-methods approach, the research incorporated quantitative surveys and qualitative interviews to gather insights from MWS students and industry professionals regarding their experiences, perceptions of belonging, and engagement within the welding and boilermaking programs. The findings reveal significant recruitment challenges, a positive environment at MWS fostering student satisfaction and retention, and a notable desire for career advancement among diverse participants, ultimately suggesting actionable recommendations for improving recruitment and professional development in the welding and boilermaking trades
Reframing What it Means to Pay Attention: Leveraging Discourse to Improve Student Practice
No full textLeadership and Learning in Organizations capstone projectFlorence Middle School (FMS) is a public, community-based school located in Florence, Alabama. It serves approximately 683 seventh- and eighth-graders from diverse backgrounds. Evidence of FMS administrators' and teachers' commitment to providing students with learning experiences (academic and social) that prepare them to thrive in high school can be observed in the school’s culture and in students' overall yearly improvement on the state assessment. However, historically, its students have struggled to meet the state assessment’s math proficiency benchmarks. Therefore, the FMS administrators and I decided to focus on adjustments that math teachers can make to their facilitation of discourse during lessons to support students in improving their math proficiency. The purpose of the project was to identify discourse facilitation strategies that engage students in knowledge acquisition, critical thinking, and practice that align with Alabama state standards for math. I implemented a single-case study design to analyze the discourse facilitation practices of three seventh-grade math teachers. Throughout the project, I examined lesson plans, teacher-submitted videos, and student work to gain insight into their instructional practices for facilitating discourse during lessons. My analysis these data sources revealed the following findings: Student engagement in metacognition (thinking) co-occurred with instances in which teachers emphasized metacognitive discourse during guided practice, students application of thinking strategies coincided with moments when teachers modeled metacognitive skills during guided practice, and when teachers explicitly communicate the goals for learning and practice, students show interest and eagerness to demonstrate their understanding of math concepts
Mitochondria, Fueling a Novel Extracellular Vesicle and Shaping the Heart
No full textMitochondria are dynamic organelles that influence cellular processes beyond energy production. My dissertation explores two aspects of mitochondrial biology: the characterization of blebbisomes, a novel class of extracellular vesicles (EVs) containing functional mitochondria, and the role of mitochondrial dynamics in cardiac myocyte development. Blebbisomes are large EVs (up to 20 µm) with continuous membrane blebbing, released via a unique, myosin IIB-dependent process independent of the ESCRT-III complex. They can initiate apoptosis autonomously and contain immune checkpoint inhibitors like HLA-E, VISTA, and PD-L2, suggesting roles in immune evasion and intercellular communication. In cardiac myocytes, disrupting mitochondrial fission through DRP1 knockdown resulted in hyperfused mitochondria and impaired sarcomere assembly, resembling dilated cardiomyopathy. Conversely, MFN2 knockdown, inhibiting mitochondrial fusion, caused mitochondrial fragmentation and enhanced myofibril formation. Using DeAct plasmids to selectively disassemble actin on mitochondria rescued these defects, indicating actin’s critical role in coordinating mitochondrial dynamics and sarcomere formation. Together, these findings demonstrate that mitochondria actively shape cellular organization and communication, with implications for understanding cardiomyopathy, immune regulation, and disease progression
From Patterns to Pathways: Configurational Analysis of Client Management Practices – A Co-Occurrence and CAM Framework Application
No full textLeadership and Learning in Organizations capstone projectThe ALL IN Company is a boutique consultancy that supports small businesses and nonprofits in strengthening recruitment and retention through its proprietary Five-Star Hiring System. The purpose of this study was to examine how the organization could codify and scale its client-management practices—spanning marketing, onboarding, and engagement—without sacrificing the relational personalization that defines its brand. Using a sequential mixed-methods design grounded in the Configurational Approach to Marketing and probabilistic co-occurrence analysis, the investigation integrated CEO interviews, employee surveys, client and proxy-client survey data, and testimonial/document analysis collected between August and October 2025. Findings show that reliability, transparency, onboarding clarity, and perceived organizational fit consistently co-occur as the configuration predicting satisfaction, retention, and advocacy, leading to recommendations for implementing a lightweight CRM backbone, standardizing onboarding, formalizing transparency practices, conducting brand-alignment audits, and institutionalizing ongoing engagement rhythms to support sustainable, scalable growth
Data Modeling for System Design
No full textSoftware systems are often composed of components and subsystems from diverse, independently developed sources. Integration and interoperability require correct interpretation of data exchanged between those components, which can result in increased development efforts and a risk of system failure due to data misinterpretation, especially as systems scale.
Many have reached for data modeling as a tool to support systems integration by providing a common semantics and structure for documenting exchanged data, which can be reasoned over to determine the interoperability of software components. The Future Airborne Capability Environment (FACE) and Fast Healthcare Interoperability Resources (FHIR) are standards in military avionics and healthcare that exemplify this approach. Each relies on data modeling to aid in the integration task, but key limitations prevent automation, ultimately placing the burden of data model reasoning on a human, which is error-prone and does not scale.
This dissertation examines these limitations and builds on existing research in model-driven engineering, conceptual data modeling languages, ontologies, and healthcare interoperability to propose solutions for automating the integration task. First, an OWL-based data modeling language (“DMSDL”) is presented, providing well-defined data compatibility semantics. Second, analysis techniques are proposed to automate incompatibility detection at scale. Finally, model transformations from FACE and FHIR are developed to demonstrate automation in those standards. These contributions are implemented in a proof-of-concept software tool (“pydmsd”), which provides the foundation for both a FACE Model Compatibility Tool and a FHIR Profile Interoperability Tool
Three Essays on How Students with Disabilities Experience the Transition from Secondary to Postsecondary Life
No full textPaper 1. Existing research shows that participation in secondary CTE can improve transition outcomes for SWD (Baer et al., 2003; Harvey, 2002; Lee et al., 2016; Plasman, 2019; Wagner et al., 2017). While ample research connects CTE with improved employment opportunities, research linking CTE to college-going is thin (Cellini, 2006; Gottfried & Bozick, 2016; Plasman et al., 2017). Further, while existing work on the impact of CTE on adult outcomes focuses on SWD in general, less is known about the effect of CTE completion on students in specific disability categories (Baer, Daviso, Flexer, et al., 2011; Lombardi et al., 2018; Mazzotti et al., 2013; Test et al., 2009; Theobald, Goldhaber, et al., 2019; Trainor et al., 2020). Thus, Paper 1 uses quantitative methods to determine the association between CTE and employment and postsecondary enrollment for students across multiple disability categories. To further stratify disability type, we use additional Individualized Education Program (IEP) elements (number of special services and level of inclusion in general education) as controls in our model.
Paper 2. Disability diagnosis plays a critical role in defining identity and life quality (Cadwgan, & Goodwin, 2018). Still, we know little about how students and their parents experience their disability diagnosis (Ash et al., 2020; Dale et al., 2006; Gillman et. al, 2000; Kenyon et al., 2006, 2014; Roulstone, 2015; Ruggero et al., 2012). Currently, existing literature categorizes the diagnosis experience as either positive or negative and predominantly focuses on the experiences of parents. In response, Paper 2 uses generalized inductive methodology and critical realist methodology to support a more nuanced inquiry into disability diagnosis. I identify six typologies of disability diagnosis: (1) the Missed Diagnosis, (2) the Misdiagnosis, (3) the Unwelcomed Diagnosis, (4) the Welcomed diagnosis, (5) the Unsurprising Diagnosis, and (6) the Surprising Diagnosis. Paper 2 centers findings on the experiences of disabled students and their parents to understand the ever-complex and context-specific nature of learning about one’s disability.
Paper 3. Parents’ involvement and high expectations are established as hallmarks of transition services; both are repeatedly linked to enhanced outcomes for SWD in the areas of high school graduation, employment, and PSE enrollment (Doren et al., 2012; Mazzotti et al., 2016; Papay & Bambara, 2014; Test et al., 2009; Wagner et al., 2014). Yet, limited involvement in IEP meetings, inaccessible information, and negative teacher attitudes often lead to exclusion of parents/guardians during transition planning (Hirano et al., 2018; Miller-Warren, 2016; Wilt & Morningstar, 2018). Like their parents, SWD frequently cite minimal engagement with their postsecondary transition planning process (Hetherington et al., 2010). As a result, existing research shows that parents and SWD experience feelings of ambivalence, stress, and anxiety during the transition planning process. Still, little is known about how this transition planning context impacts dynamics in the parent-child relationship and vice versa (Van Hees et al., 2018). In response, Paper 3 uses phenomenology to prioritize and privilege the self-told stories of students with disabilities and their parents to explore the lived postsecondary transition planning experience.
Collectively, these papers aim to enhance our understanding of how disabled students and their parents experience secondary special education and the impact postsecondary transition planning has on the development of self and postsecondary success
Essays on High-Dimensional Econometrics without Sparsity and Bounds on Standard Errors
No full textThis dissertation develops econometric methods to address key challenges in inference and estimation, particularly in high-dimensional settings. The first two chapters introduce inference without restrictive sparsity assumptions, employing the Orthogonal Greedy Algorithm with High-Dimensional AIC (OGA+HDAIC) for model selection. The third chapter focuses on standard error estimation when empirical moments are derived from multiple data sources. Chapter 1 presents a Local Projections (LP) framework for estimating dynamic responses to shocks with high-dimensional controls. Traditional methods like LASSO rely on sparsity conditions, limiting their effectiveness in dense settings. I propose an OGA+HDAIC-based approach that enhances robustness, interpretability, and efficiency. Simulations and empirical applications illustrate its advantages. Chapter 2, coauthored with Harold D. Chiang and Yuya Sasaki, develops a novel inference method for high-dimensional regression and instrumental variable models in a cross-sectional setting. Unlike existing methods, it remains valid without sparsity constraints. Simulations highlight its advantages over LASSO- and random forest-based methods. Chapter 3, coauthored with Yuya Sasaki, examines inference challenges when combining empirical moments from dependent data sources, such as surveys and administrative records. This chapter constructs both lower and upper bounds on standard errors using best-possible distributional bounds, accounting for finite-sample randomness and broadening their applicability in empirical research. Together, these chapters contribute to econometric inference by developing techniques robust to high dimensionality and improving standard error estimation in modern data environments