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We Rise Over Run: Exploring Life Histories of Black Women Secondary Mathematics Teachers
No full textExamining issues of recruiting and retaining Black teachers in secondary mathematics is of particular importance given that they only represent 6% of the high school mathematics teaching force, and an even smaller percentage of these Black high school mathematics teachers are women. The aim of my dissertation study is to explore life histories of Black women mathematics teachers through a gender-sensitive framework utilizing a Black Feminist perspective. Such inquiry is important for addressing a major gap in mathematics education literature, in that the scholarship does not fully attend to the unique complexities of Black women’s experiences in relation to the nature of mathematics and mathematics teaching and learning. Interviews will be conducted and artifacts will be collected through a life history methodological approach that (1) acknowledges the intersectional nuances of racialized and gendered experiences in mathematics, (2) provides insight into the challenges, triumphs, and unique perspectives that Black women secondary mathematics teachers (BWSMTs) encounter and develop in pursuing thriving careers as mathematics teachers, and (3) describes BWSMTs’ hopes for future Black girls in mathematics and the Black mathematics teacher pipeline
The role of networks and serious illness in Medicare Advantage disenrollment
No full textMedicare Advantage (MA) plans provide the Medicare benefit to beneficiaries through private plans. These plans are paid capitated payments to deliver care to their enrollees and thus use various strategies to control the cost of providing care. Understanding whether the MA program meets the needs of enrollees is important for beneficiaries with serious illnesses. We used novel data on MA networks from Ideon and data from Surveillance, Epidemiology, and End Results (SEER)-Medicare to answer various questions about the MA program for enrollees needing specialty care.
We examined how changes in special enrollment rules that gave beneficiaries more flexibility in changing their MA coverage effected MA disenrollment for beneficiaries with new cancer diagnoses. Using 2016-2019 SEER-Medicare data, we compared outcomes among beneficiaries diagnosed with cancer in January and March (treatment groups) to those diagnosed in April (control group). We found that more flexible enrollment rules had no effect on beneficiaries diagnosed with cancer in January but led to small increases in switching coverage for beneficiaries diagnosed in March.
We also wanted to understand how network breadth for specialized cancer care was associated with changes in MA coverage after a new cancer diagnosis. The analysis stratified by three plan types: non-employer coverage; zero premium, non-employer coverage; premium charged, and employer coverage. Using data from SEER-Medicare (2019-2020) and Ideon (2019), we find that increases in network breadth are associated with decreases in the probability of switching coverage for enrollees in non-employer plans that charged a premium. There was no association between network breadth and changes in enrollment for those enrolled in zero premium non-employer plans or employer coverage.
Lastly, we explored how MA plans construct networks for skilled nursing facility (SNF) care. We used 2022 data from Ideon in this analysis and various publicly available data files to contruct measures for network breadth. Our analysis found that plans included less than half of SNFs in the service area in network
A Physical Mechanism for Apical Protein Sorting
No full textDespite decades of research, the apical sorting of epithelial membrane proteins remains incompletely understood. We noted that apical cytoplasmic domains are smaller than those of basolateral proteins; however, the reason for this discrepancy is unknown. We used a synthetic biology approach to investigate whether a size barrier at the Golgi apparatus might hinder apical sorting of proteins with large cytoplasmic tails. We focused on Crb3, Ace2, and Muc1 as representative apical proteins with short cytoplasmic tails. By incorporating a streptavidin-binding peptide, these proteins can be trapped in the endoplasmic reticulum (ER) until addition of biotin, which triggers synchronous release to the Golgi and subsequent transport to the apical cortex. Strikingly, increasing the size of their cytoplasmic domains caused partial mislocalization to the basolateral cortex and significantly delayed Golgi departure. Moreover, N-glycosylation of “large” Crb3 was delayed, and “small” Crb3 segregated into spatially distinct Golgi regions. Biologically, Crb3 forms a complex through its cytoplasmic tail with the Pals1 protein, which could also delay departure, but although associated at the ER and Golgi, Pals1 disassociated prior to Crb3 departure. Notably, a non-dissociable mutant Pals1 hampered exit of Crb3. We conclude that, unexpectedly, a size filter at the Golgi facilitates apical sorting of proteins with small cytoplasmic domains and that timely release of Pals1, to reduce cytoplasmic domain size, is essential for the normal kinetics of apical protein sorting. Additionally, I laid out a synthetic biology framework for performing a pooled genome-wide CRISPR/Cas9 knock-out screen to identify genes which are required sorting and delivery of apical proteins to the appropriate cell surface
Coping Skills Modeling and Proactive Responding Coaching for Teachers
No full textSupporting the development of children’s social-emotional and regulatory skills is of increasing importance in early elementary school settings but is also increasingly difficult to accomplish given teacher-to-child ratios and instructional expectations. This study aimed to develop an intervention to support teachers to integrate modeling of coping skills throughout the school day by labeling emotions that arise in the classroom environment, identifying possible coping strategies for those emotions, and providing guided practice with those strategies for children. We also aimed to train and coach teachers to attend to a target child’s precursor behaviors and respond proactively when they began demonstrating indicators of distress or social-emotional support needs. A concurrent multiple baseline across participants design was used to evaluate the impacts of teacher training and weekly coaching on teachers’ use of these skills. Overall, teachers demonstrated more frequent modeling of coping skills and proactive response techniques during intervention compared to baseline, though attrition precluded the determination of a functional relation. Results for teachers’ integration of component skills (such as labeling emotions and labeling coping strategies) are also presented. Teachers found the training procedures to be socially valid and meaningful for the needs of their students
Integrative Computational Approaches to Drug Repurposing for Alzheimer’s Disease: Leveraging Multi-Omics, Electronic Health Records, and Generative AI
No full textAlzheimer's disease (AD) involves complex, multifactorial brain changes that begin decades before symptom onset, making the development of therapeutic and preventive interventions extremely challenging. With AD prevalence increasing worldwide, the development of safe and effective therapies has become an urgent public health priority. Drug repurposing, the identification of new therapeutic uses for existing drugs, represents a promising strategy for accelerating drug development in AD, offering advantages through established drug safety profiles, lower costs, and reduced development timelines. Although hundreds of AD repurposing candidates have been proposed over the past decade, few have undergone rigorous validation, making it difficult to prioritize candidates for clinical investigation. To bridge this gap, we applied three approaches integrating diverse sources of -omics and clinical data to suggest promising drug repurposing candidates for AD: (1) leveraging large language models to rapidly mine and synthesize the biomedical literature, (2) performing transcriptomic analysis to identify drugs capable of reversing AD-associated changes in gene expression, and (3) using Mendelian randomization to identify drugs acting on proteins causally associated with AD. We then investigated the real-world effects of the candidate drugs using data from electronic health records and national health insurance claims. Our findings support aspirin, metformin, losartan, and simvastatin as high-priority candidates warranting further evaluation in randomized clinical trials for AD. This work not only advances our understanding of AD repurposing opportunities, but also presents a flexible, generalizable computational framework applicable to a broad range of complex diseases
Squeegees and Square Inches: Differential Instructional Design and Enactments for Student Explorations of Area Measure and What They May Reveal About Teacher Perspectives on Equity
No full textThis work is a comparative case analysis of two elementary mathematics teachers and their attempts to help their third and fourth-grade students understand the foundations of area measure from a spatial reasoning perspective: that area is a two-dimensional space formed as the product of lengths with associated implications for the properties of area units and their symbolizations, and how the area formula for a rectangle (length x width) relates to its dynamic formation. Through my analysis, the teachers’ design and instructional enactment is shown to influence student mathematical meanings as evidenced by in-the-moment activity. And I highlight how the teacher activity in the classroom might be revealing something about the teachers’ tacit assumptions and goals around equity that were shared in 1:1 interviews with me as the researcher.
I used a combination of phenomenological thematic analysis of interview transcripts and interaction analysis of classroom instruction to identify teacher’s perspectives on equitable mathematics instruction, examine students’ opportunities to learn in the classroom based on the form and function of teacher assistance, and articulate the mathematical meanings students develop in the two classrooms attributed to different forms of teacher assistance
Deep-Learning-Enhanced Atlas-Based Preoperative and Intraoperative Registration for Cochlear Implant Surgery Navigation
No full textThis dissertation provides the groundwork for intraoperative registration in cochlear implant surgery though the developed Vision6D pose annotation tool and a series of deep-learning-based methods. The primary contributions can be summarized as follows. First, self-supervised ossicles registration and segmentation, as detailed in Chapter 2. Second, the development of the Vision6D software and its comprehensive user study using two public 6D pose estimation datasets, as introduced in Chapter 3. Third, 2D monocular microscope views to 3D CT registration using the incus of the ossicles as a landmark, which is described in Chapter 4. Fourth, mastoidectomy shape prediction to extract the postmastoidectomy mesh directly from preoperative CT scans, as shown in Chapters 5, 6, and 7. Fifth, postmastoidectomy surface multi-view synthesis from a single microscope image is proposed in Chapter 8. Sixth, surgical scene completion for the synthetic postmastoidectomy surface multi-views through single-step denoising diffusion GAN, as illustrated in Chapter 9. Finally, Chapter 10 utilizes the prior contributions from Chapters 5 to 8 to perform the monocular patient-to-image intraoperative registration for cochlear implant surgery that leverages the synthetic surgical views. These combined components provide numerous opportunities for future intraoperative navigation systems and surgical applications
Prostaglandin E2 Metabolite and Colorectal Cancer: Genetic Determinants and Lifestyle Modifiers
No full textColorectal cancer (CRC) is a major global health burden. Urinary prostaglandin E2 metabolite (PGE-M) has been associated with CRC risk in observational studies. However, causal interpretation has been limited by potential confounding, selection bias, and reverse causation. In this dissertation, I examined the potential causal role of PGE-M in CRC development and the influence of modifiable lifestyle factors on PGE-M levels. In Aim 1, I conducted a genome-wide association study (GWAS) of urinary PGE-M levels among approximately 8,200 cancer-free participants from eight studies and identified two genome-wide significant loci represented by rs9302064 near the ABCC4 and rs1501101 in the EMCN genes previously implicated in PGE2 signaling and CRC development. In addition, I identified suggestive association of PGE-M levels with 54 variants across 13 loci in Asian participants, 80 variants across 11 loci in Europeans, and 145 variants across 22 loci in the combined analysis of both populations at P < 10-5. Mendelian randomization (MR) analyses using the PGE-M associated variants identified in Aim 1 and data from the largest CRC GWAS conducted to date (100,204 cases; 154,587 controls) provided a suggestive evidence for potential causal association between PGE-M levels and CRC risk. In Aim 2, I developed dietary and lifestyle scores predictive of urinary PGE-M using Elastic Net regression models. These dietary and lifestyle scores were significantly associated with CRC risk and mortality, especially among Asian men. Together, these findings provide new insights into a potential etiologic role of PGE-M in CRC and suggested that urinary PGE-M could be targeted for CRC prevention via lifestyle modifications
Biomarkers for Predicting Immunotherapy Response in Breast Cancer
No full textRecent clinical studies have demonstrated that combining neoadjuvant chemotherapy with immune checkpoint inhibitors can improve the response rate in early-stage breast cancer patients. Despite these advances, most patients do not respond to immunotherapy, highlighting the need for better biomarkers to optimize treatment benefit. This thesis aims to identify peripheral blood and tumor biomarkers that predict breast cancer immunotherapy outcomes. Using patient derived biopsy, we measured the expression of antigen presentation protein MHC-I on tumor cells and identified a positive correlation between MHC-I expression and immunotherapy response. We also observed significant MHC-I expressional heterogeneity across races and breast cancer subtypes. In addition, systemic immunity also significantly affects immunotherapy response, making peripheral immune biomarkers potential candidates for immunotherapy prediction. Using >500 peripheral blood RNA sequenced transcriptomes collected longitudinally from over 150 patients in the control and pembrolizumab arms of the ISPY-2 trial, we demonstrated the interconnected nature of systemic and tumoral immune response, and showed that the dynamics of immune gene expression patterns derived from peripheral blood as baseline and on-therapy can predict clinical outcomes. These findings highlight the potential of integrating peripheral and tumoral biomarkers to guide immunotherapy decisions, advancing the field of precision oncology
Insights Into the Barrier Function Of The Stratum Corneum Lipid Matrix Using A Multiscale Molecular Simulation Approach
No full textThe stratum corneum (SC), the outermost layer of the skin, serves as a crucial barrier, protecting against environmental stressors and preventing excessive water loss. It consists of corneocytes—dead skin cells—embedded in a dense lipid matrix. This matrix, primarily composed of cholesterol, free fatty acids, and ceramides (CERs), is essential for the skin's barrier function. CERs, with their diverse headgroup chemistries and chain lengths, are particularly important for maintaining the SC's structural organization and, consequently, its barrier properties. Disruptions in lipid composition, as seen in skin conditions like atopic dermatitis (eczema) and psoriasis, can compromise this barrier. Such disruptions often lead to altered lateral (2-D) and lamellar (layered) lipid organization, reduced lipid packing, and increased permeability. Understanding the molecular mechanisms behind these changes is critical for developing effective skincare treatments and strategies to repair compromised skin barriers.
This work addresses key gaps in the SC lipid research by leveraging molecular dynamics (MD) simulations to explore the role of CERs in the structural and functional organization of SC lipids. MD simulations complement experimental methods by providing molecular-level detail, allowing researchers to study nanoscale interactions and dynamic processes that are often difficult to capture experimentally. To tackle the complexity of lipid organization in the SC, a multiscale simulation approach was used, combining all-atom and coarse-grained (CG) models. By integrating these two modeling approaches, the research focuses on the contribution of CERs to the lateral and lamellar organization of lipids in the SC. A key emphasis was placed on understanding the short periodicity phase—a specific lipid arrangement critical to the SC’s barrier function—and its relationship to overall barrier properties. This work delves into how the chemical structure of CER headgroups, the dynamics of lipid tails, and variations in lipid composition influence key properties such as lipid packing, hydrogen bonding networks, and permeability. Notable outcomes include the development of new CG models for CERs and simulations of complex lipid mixtures that mimic the structural differences between healthy and diseased SC. These models provide valuable insights into the molecular basis of SC lipid organization and dysfunction, offering a foundation for developing strategies to repair and improve compromised skin barriers. This research bridges the gap between detailed molecular understanding and practical applications in skincare and barrier repair