18525 research outputs found
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Nonlinear Models with Moderated Parameters: New Methods and Software for Social Science Applications
No full textIntrinsically nonlinear models are used rarely in psychology and social science research, despite being well suited for informing theory, directly testing key hypotheses, and identifying intraindividual variation in important psychological processes. Additionally, assessing the presence of moderation is often of key importance for psychological theory testing. However, methods for testing, plotting, and probing moderation were developed for linear models, and currently, their use remains largely restricted to linear models. Psychology researchers seeking to implement nonlinear models with moderated parameters currently face a variety of conceptual and logistical challenges. Therefore, the overarching aims of this dissertation are to establish the utility of nonlinear models for social science, and to derive novel methodological and software tools that facilitate the creation and visualization of nonlinear models with moderated parameters. First, a review of the methodological and applied literature demonstrates the enhanced theoretical and substantive contributions that may be made through use of moderated nonlinear models. Second, guidelines for moderated nonlinear model selection, parameterization, and specification are introduced. Third, conceptual and mathematical extensions of the Johnson-Neyman technique – the state-of-the-art method for probing and visualizing moderation – are derived such that this technique can now be applied to moderated nonlinear models. Finally, a new Shiny app is introduced, which enables researchers to fit, evaluate, and visualize nonlinear models with moderated parameters in a code-free environment. Ideally, the methods and software presented in this dissertation will reduce many of the key conceptual and logistical barriers that social scientists currently face when implementing moderated nonlinear models, thereby increasing the use of such models across psychology and social science
Analyzing Wound Induced Polyploidy
No full textAll organisms have developed strategies to respond to injury. Some tissues induce compensatory proliferation of surrounding cells which migrate into and invade the wound site. However, alternative strategies exist such as endoreplication, or repeated S-phase entry without mitosis, and syncytia formation by cell-cell fusion lead to the formation of polyploid cells that drive wound closure without proliferation. It was believed these alternative repair strategies were restricted to quiescent tissues. On the contrary, in this dissertation I have characterized that the mitotically capable Drosophila pupal notum induces both nuclear polyploidy and syncytia formation in response to wounding. I determined syncytia formed rapidly near wounds by both border breakdown and a novel temporally distinct shrinking cell behavior. Using sparsely labeled cells throughout the notum I was able to confirm cytoplasmic mixing between fusing cells and determined that half of the cells within 70 µm of the wound fused into syncytia. Syncytia then outcompeted unfused neighbors at the leading edge, such that the number of unfused cells at the leading edge always dropped to zero before wound closure. By fusing, syncytia rapidly relocalized resources from distal cells to the leading where they accumulated into large migratory structures, likely contributing to their invasiveness. Additionally, fusion at the leading edge reduced the need for coordinated border exchanges or intercalations at the leading edge likely increasing the rate of wound closure. These studies revealed wound induced polyploidy is not restricted to quiescent tissues and may be present in other mitotic tissues throughout the animal kingdom. Future studies to understand the induction and regulation of wound induced polyploidy will not only further our understanding of wound closure, but shed light on how polyploidy becomes dysregulated in pathological conditions like cancer
A Holistic Approach to Healing Depression: The Synergy of Evidence-Based Practices (EBPs) and Spirituality in Addressing Depression within African American Veterans and Service Members (V/SMs) Community
No full textDivinity School Doctor of Ministry in Integrative Chaplaincy Final ProjectsThis Final Project explains depression from a biopsychosocial and spiritual framework and therefore requires a holistic approach to healing. Research literatures on depression have often assumed a divide between the spiritual realm, where thoughts and ideas are shaped, and the physical realm, where these ideas and thoughts are manifest. To bridge these literatures, I argue we could productively reframe depression as a spiritual matter rather than just biological, biochemical and neurochemical imbalances in the brain. Depression, an existential crisis, is caused by both external and internal factors.
With this assumption, this project examines mental illness stigma among African American Veterans and Service members (V/SM) by exploring personal narratives, statistics, and historical contexts. As we move forward, it is essential to recognize the unique challenges faced by these minority groups in their journey to overcome depression and stigma. By facilitating open dialogue, offering targeted mental health support like acceptance and commitment therapy (ACT), Play, and other holistic approaches to healing, and leveraging the positive aspects of religious/spiritual formation, mental health providers and chaplains can contribute to a more inclusive and compassionate society for all individuals, regardless of their cultural background
Redefining mechanisms of transcriptional dysregulation in Diffuse Large B cell Lymphoma with chemical-genetics
No full textTranscription is dysregulated in the majority of human cancers. Understanding how normal transcriptional programs are dysregulated in cancers is crucial to understanding how cancers develop and successfully treating them. However, our understanding of the mechanisms controlling transcriptional activation and repression have been hindered by a lack of techniques that can capture transcriptional changes on the appropriate timescale to identify direct mechanisms. Here, I combine cell line models of inducible targeted protein degradation and time-resolved genomic methodologies to study transcriptional dysregulation in the context of diffuse large B-cell lymphomas. Using this approach, I determined that mutant FOXO1 controls the activation of a small gene network that includes known oncogenes and inducers of DNA damage by controlling enhancer activation and accessibility. Under normal conditions, wild type FOXO1 does not exert the same level of transcriptional control. However, if AKT activity is inhibited by a small-molecule, wild type FOXO1 can activate gene targets to the same extent as mutant FOXO1. I also applied this system to study the function of the histone deacetylase, HDAC3. HDAC3 has been implicated to have roles in transcription, chromatin structure, and replication. However, acute degradation of HDAC3 showed very few changes in transcription or global chromatin structure and only modest defects in replication. Unlike chemical inhibition, acute degradation of HDAC3 did not change global levels of histone acetylation suggesting that the characterized effects of HDAC3 inhibition are actually due to off-target inhibition of HDAC1 or HDAC2. Therefore, by combining inducible targeted protein degradation with time resolved genomic techniques, I have been able to identify the direct mechanisms through which FOXO1 controls gene expression and laid the ground work for the identification of the direct mechanisms by which HDAC3 controls DNA-templated processes
COMBINING METHODOLOGIES FOR IMPROVING INTERPRETABILITY AND GENERALIZABILITY OF GENOMIC INVESTIGATIONS
No full textParalleling the expansive growth of large-scale biobanks that contain patient electronic medical records-tied genome wide genetic data, the number of genome-wide association studies (GWASs) and the volume of newly identified disease associated genetic factors have expectedly accelerated in size as well. While this has facilitated an ongoing discovery process of countless disease-associated genetic loci, there remains an enormous gap in understanding the biological consequences and mechanisms related to these genomic areas. To address this gap, many bioinformatic methods including colocalization with functional annotations have been developed to better interpret GWAS findings. In this manner, many methods help identify expression quantitative trait loci (eQTLs), areas of the genome shown to regulate gene expression. Advancing this endeavor even further, several efforts have trained imputed gene expression models that capture the cumulative cis-regulatory effects on gene expression. Because of the immediate interpretability of these models, they can be utilized in regression models for transcriptomic wide association studies (TWAS) to characterize specific gene associations with phenotypes and diseases of interest. The work presented here largely strives to leverage this feature of interpretability by integrating these imputed gene expression models with other modeling methods to help design more interpretable genomic studies. By integrating with Bayesian frameworks, linear mixed effects models, and additive models, both unbiased and hypothesis-driven methods are demonstrated and characterized in the context of both common and rare diseases. This versatile collection of tools hopes to provide diverse means of approaching clinical phenotype by allowing for interrogation of both the entire genome and specific functional partitions of genetic factors. In addition to these transcriptomic-based tools, a method of quantifying cumulative ancestry-driven genetic effects on phenotypes is also demonstrated to help directly in interpretation of existing GWAS studies. Taken together, these methodologies both create approaches to better interpret existing results as well as provide tools for designing genomic investigations that possess immediate interpretability
In Situ Lipidomics of Staphylococcus aureus Osteomyelitis Using Imaging Mass Spectrometry
No full textMALDI IMS is a powerful technology that combines multiplexed omics data with in situ spatial information. Additional pairing with compatible imaging modalities increases the biological context of the biomolecular distributions throughout a tissue sample. This thesis aimed to establish a multimodal MALDI IMS workflow for fresh-frozen bone and to leverage this advancement for understanding the spatially oriented lipidome of a S. aureus-infected femur. Notably, fresh-frozen murine femurs are difficult to prepare for MALDI IMS due to its mineralized and heterogeneous nature. Methods were investigated to cryosection femurs, thaw frozen sections, and deposit MALDI matrices to achieve reproducible lipid signal and dual polarity coverage for 10 µm spatial resolution imaging. The established imaging workflow could then be used to spatially map biomolecules involved in inflammatory bone diseases like S. aureus osteomyelitis. This debilitating infection was of interest due to its spatially defined abscess pathology and its current medical relevancy regarding the high rate of treatment failure. First, molecular profiles or atlases were created for tissue types throughout an infected femur, including surrounding soft tissue, bone-specific structures, tissue altered by inflammatory cell influx, and SACs. In all, nearly 250 lipids were mapped to morphological features throughout the femur using IMS and supplementary LC-MS/MS. Next, significant lipid alterations were identified between two global regions of the abscess: the inner neutrophilic tissue and outer fibrous tissue. Ether lipids were discovered to closely associate with leukocytes throughout the intramedullary cavity, and lipids with PUFAs, like arachidonic acid, were most abundant in the fibrous tissue. These data allude to the unique membrane composition of innate leukocytes following a myelopoietic response and provide information on potential lipid substrates for lipid-mediated inflammatory signaling between cells. Finally, the lipidome was characterized for a unique population of cells in the fibrotic border of abscesses. In situ lipid distributions suggest a lipid-laden phenotype for macrophages in chronic bone marrow abscesses, highlighting cellular functions that cannot be discerned with traditional microscopy
The Origins of the Evolution of Thermal Conductivity of Perovskite Superlattices
No full textThe thermal conductivities of (SrTiO3)n/(CaTiO3)n and other such superlattices have been measured for several layer thicknesses, revealing a trend that dips to a minimum and then rises as the interface density approaches the unit-cell scale. We employ density-functional-theory (DFT)-based machine-learning molecular-dynamics simulations to investigate the thermal conductivities in (SrTiO3)n/(CaTiO3)n superlattices, examining how interface phonons influence the thermal and vibrational properties for n = 1, 2, 4, 6, and 8. The simulations reproduce the thermal conductivity trends with n values and reveal the absence of true interfacial phonons in SL1 and SL2. This study sheds light on the potential for tailoring thermal transport in nanoscale materials by understanding and manipulating the role of interface-specific phonons in superlattices.College of Arts and ScienceDepartment of Physics and Astronom
State of the Art in Super-Resolution: A 2024 Comparison of Techniques and Models
No full textThis is a comprehensive comparison study of contemporary super-resolution (SR) techniques and models as of 2024. This Thesis highlights the significant advancements in computer vision and image processing. Also, It include the Traditional methods such as interpolation based and frequency domain approaches. With the introduction of deep learning, particularly the Convolutional Neural Networks (CNNs) which transformed the SR landscape by automating complex feature extraction and enabling more accurate image reconstruction. Key models discussed in this dissertation are SRCNN (which introduced end to end mapping for SR), VDSR and FSRCNN. This Thesis also explores advancements in real time applications with ESPCN and the multi scale capabilities of LapSRN
Disruptive STEM Literacy with Multilingual Immigrant Children and Youth
No full textThis dissertation presents a framework for designing and analyzing learning environments that aim to disrupt oppressive educational settings for students from racially and linguistically diverse backgrounds. The problem, referred to as “business-as-usual,” this dissertation addresses is three-fold: limited identities are welcomed STEM and literacy, disciplines are strictly siloed by subjects, and students often have little agency of what and how they learn. The framework for this dissertation offers three themes of disruption: welcoming more expansive identity resources through multilingualism, multimodality, and multicultural education; transdisciplinary curriculum through culturally relevant, arts, and civics education; and enhancing students’ agency through choice, roles and affect. Video data was collected of two iterations of co-design out-of-school curriculum with a non-profit organization that serves refugee and immigrant children and youth in Summer 2023 and Spring 2024. Iterative content logging, analytical memos, and interaction analysis sessions led to three findings that provide design principles for disruption. First, the transdisciplinary curriculum was responsive to children’s interests, resulting in opportunities for children to shape the learning trajectory of the community. Second, the cameras were repositioned not only as research tools, but also as “princess camera," a disruptive audience for the students who wanted to do something different form the main activity. Princess Camera allowed for students to invent new roles and exercise a new form of agency where they invited adults into their imaginary play. The third design principles focused on the co-construction of a welcoming environment by teachers and students. This environment opened up opportunities for students to shape the overall atmosphere and therefore the learning processes of the knowledge-building community. This dissertation concludes with a call for researchers, educations, and policy makers to consider how the disruptive framework could help with the design of more transformative environments where multilingual students are authorized to shape the knowledge of their learning communities
Dissecting Mechanisms of Proinflammatory Transcriptional Control in the Vascular Endothelium
No full textCombinatorial signaling by proinflammatory cytokines synergizes to exacerbate toxicity to cells and tissue injury during acute infections. To explore synergism at the gene-regulatory level, we investigated the dynamics of transcription and chromatin signaling in response to dual cytokines by integrating nascent RNA imaging mass spectrometry, RNA sequencing, amplification-independent mRNA quantification, assay for transposase-accessible chromatin using sequencing (ATAC-seq), and transcription factor profiling. Costimulation with interferon-gamma (IFNg) and tumor necrosis factor alpha (TNFa) synergistically induced a small subset of genes, including the chemokines CXCL9, -10, and -11. Gene induction coincided with increased chromatin accessibility at non-coding regions enriched for p65 and STAT1 binding sites. To discover coactivator dependencies, we conducted a targeted chemogenomic screen of transcriptional inhibitors followed by modeling of inhibitor dose response curves. These results identified high efficacy of either p300/CREB-binding protein (CBP) or bromodomain and extra-terminal (BET) bromodomain inhibitors to disrupt induction of synergy genes. Combination p300/CBP and BET bromodomain inhibition at half-maximal inhibitory concentrations (subIC 50) synergistically abrogated IFNg/TNFa induced chemokine gene and protein levels