UARK (University of Arkansas )
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Rumination and Inhibitory Control in Suicidal Ideation: The Role of Explicit and Implicit Emotional Processing
No full textSuicide is the tenth leading cause of death in the United States, and suicidal ideations (SI; thinking, considering, or planning suicide) are a strong predictor of suicide attempts. Prior literature describes inhibitory control as an important facet of regulating negative thoughts, and studies suggest that inhibitory control deficits and engagement in brooding rumination are risk factors of SI. Recent studies have found implicit cognitive markers of suicidality suggesting that level of processing may variably be linked to SI. This study examined the relation between aspects of inhibitory control across explicit and implicit emotional contexts and levels of brooding rumination among individuals with and without SI. Participants included 49 adults (21 ideators and 28 controls, M age=29.63, SD=10.07) recruited from the community. They completed a suicide risk interview, measures of rumination and suicidality, and a modified affective go/no-go task assessing response inhibition across emotional word stimuli in explicit and implicit contexts. Results indicated that ideators reported significantly higher brooding rumination than controls. Generalized linear mixed modeling revealed three main effects: ideators had faster RTs than controls, RTs were faster across implicit trials than explicit trials, and more commission errors and omission errors were made on explicit trials than implicit trials. No significant interactions emerged. Findings suggest that ideators exhibit heightened reactivity to stimuli, regardless of valence or level of processing. Explicit emotional processing is likely imposing a higher cognitive load, thereby disrupting inhibitory functioning. While brooding rumination was elevated across ideators, it did not directly impact aspects of response inhibition
Constraining the Properties and Formation Pathways of Low Mass X-ray Binaries in Early-Type Galaxies
No full textIn this dissertation, we explore how low-mass X-ray binaries (LMXBs) form and evolve in nearby early-type galaxies. We investigate X-ray binary (XRB) luminosity function (XLF) scaling relations for Chandra-detected populations of LMXBs within the footprints of 24 early-type galaxies. Our sample includes Chandra and Hubble Space Telescope observed galaxies at D ≲ 25Mpc that have estimates of the globular cluster (GC) specific frequency (SN) reported in the literature. As such, we are able to directly classify X-ray-detected sources as being coincident with unrelated background/foreground objects, GCs, or sources that are within the fields of the galaxy targets. We model the GC and field LMXB popu- lation XLFs for all galaxies separately and then construct global models characterizing how the LMXB XLFs vary with galaxy stellar mass and SN . We find that our field LMXB XLF models require a component that scales with SN and has a shape consistent with that found for the GC LMXB XLF. We take this to indicate that GCs are “seeding” the galactic field LMXB population, through the ejection of GC LMXBs and/or the diffusion of the GCs in the galactic fields themselves. However, we also find that an important LMXB XLF com- ponent is required for all galaxies that scales with stellar mass, implying that a substantial population of LMXBs are formed “in situ,” which dominates the LMXB population emission for galaxies with SN ≲ 2. For the first time, we provide a framework quantifying how directly associated GC LMXBs, GC-seeded LMXBs, and in situ LMXBs contribute to LMXB XLFs in the broader early-type galaxy population. We further analyze the host GC properties of LMXBs and find statistically significant trends in host cluster mass and color, consistent with theoretical expectations that metal-rich and massive clusters are more efficient at retaining and forming LMXBs. Finally, in a new case study, we present a broadband X-ray spec- tral energy distribution (SED) for Maffei 1, a nearby elliptical galaxy. Using Chandra and NuSTAR data, we construct a 0.5–20 keV spectral model and fit an absorbed broken power law plus hot gas component. Our results suggest the hard X-ray emission is background- dominated, and that the XRB population is consistent with a mixture of neutron star and black hole binaries. Scaling relations suggest the adopted distance to Maffei 1 may be un- derestimated. Together, these results offer new insights into the formation pathways and emission properties of LMXBs across ETGs, both in population-level scaling and detailed spectral behavior
Tree-Based Differential Item Functioning Detection Methods: Exploring Their Performance in Diverse Measurement Scenarios
No full textDespite the availability of numerous methods for detecting differential item functioning (DIF), the continued development and evaluation of innovative, data-driven approaches remains essential. Tree-based methods, in particular, represent a significant advancement in DIF detection. Unlike some traditional techniques, they can simultaneously screen multiple variables for DIF without discretizing continuous variables, and do not require the pre-specification of focal and reference groups; capabilities that are especially valuable in today’s diverse and multifaceted assessment contexts. However, research systematically examining the performance of these methods under realistic measurement conditions is limited. This dissertation, in three simulation studies, critically examines the robustness and practical limitations of global and item-focused recursive partitioning approaches across a broad range of empirically realistic conditions. Additionally, effect size measures are incorporated alongside statistical significance, another understudied topic with tree-based methods, to identify DIF on the item-level with the global methods, and to enhance practical interpretation of DIF detection for both methods. In the first study, the Rasch tree method is evaluated for dichotomous items, demonstrating strong detection capabilities under balanced DIF conditions and large DIF magnitudes, but reduced accuracy with unbalanced DIF and higher contamination rates. The second study compares two item-focused trees for dichotomous data, Rasch-IFT and Logistic-IFT methods, finding that the methods generally yield high item-level true positive rates, though with a tendency to overidentify negligible and moderate DIF underscoring the importance of integrating an effect size criterion. The final study extends this work to polytomous items, comparing the global partial credit tree, PCM-TREE, to the item-focused partial credit tree, PCM-IFT. While PCM-TREE offers conservative and reliable DIF screening in terms of identifying which covariates induce DIF, PCM-IFT excels at identifying item-level DIF, particularly with continuous covariates. Collectively, these studies provide evidence supporting the validity of tree-based methods as flexible tools for DIF detection, while also identifying conditions in which their effectiveness is limited. The studies provide guidance for practitioners in selecting appropriate methods for complex, real-world testing scenarios and contribute to advancing fairness in educational and psychological assessment
Understanding Bacterial Interactions and Motility in Complex Porous Micro-Environments
No full textBacterial motility is crucial for survival, adaptation and disease progression across environments such as soil, host tissues, and biofilm. Bacterial motility in such complex porous microenvironments not only governs how bacteria move through the physical barriers but also plays an important role in processes that are significant to the human health and environment, like improved drug delivery, enhanced treatment efficacy, and more effective bioremediation in contained and structured environments. Despite the importance, how bacteria behave and navigates in a media, where physical confinement and spatial heterogeneity dominates, is not fully understood. This dissertation studies the motility of Escherichia coli in microscale porous environments by using both natural systems and synthetic biomimicking systems, to systematically investigate the effects of confinement on the bacterial movement and flagellar dynamics. Three different experimental systems were studied: (i) Two dimensional porous media mimicked by microspheres, (ii) aqueous micro-environments with natural soil particles, and (iii) biologically relevant hydrogel that simulate mucosal layer or the extracellular matrix. Quantitative imaging methods and trajectory analysis were used to study the bacterial motion and filament behavior. In synthetic porous media mimicked by polystyrene microspheres, bacterial velocity decreased and directional reorientation increased with higher microsphere density. In natural soil microenvironments, bacterial movement was further influenced by factors such as particle size, void fraction and proximity to soil particles. Bacterial velocities showed positive correlation with particle size and a negative correlation with void fraction, while directional changes increased near the soil surface, emphasizing the ecological relevance of soil structure in microbial transport. In hydrogel environment mimicking host associated viscoelastic barriers, direct visualization of fluorescently labelled flagellar filaments revealed confinement induced structural transitions like unbundling, looping, kinking, and curling, which were associated with distinct motility types termed as SWIM, TRAP, and STALL. These motility types were marked with progressive reductions in translational motion and coordination of the flagella bundle. Together, these findings highlight how physical constraints imposed by pore-scale architecture influence bacterial behavior at both whole-cell and flagellar levels, revealing that while the intrinsic motility mechanisms in E. coli remains intact, their mobility and flagellar configuration are strongly influenced by the geometry of the surrounding microstructure. This work provides an integrated, multiscale understanding of bacterial motility in complex porous systems, with its applications in microbial ecology, infection biology, and design of biomimetic porous materials
From Stem Cells to Smooth Muscle Cells: Characterizing Secretome Profiles and Evaluating Regenerative Potentials
No full textThis dissertation examines the role of vascular smooth muscle cells (VSMCs) in vascular disease and the potential of stem cell-derived VSMCs (SC-SMCs) for regenerative medicine. VSMCs are crucial for maintaining function; however, their dysregulation contributes to the progression of diseases like atherosclerosis, vascular calcification, and aneurysms. Disease progression is influenced by various environmental factors, prompting VSMCs to switch phenotypes, leading to adverse vascular remodeling. This research focuses on adult bone marrow mesenchymal stem cells (BM-MSCs) and adipose-derived stem cells (ADSCs) as sources for generating reparative VSMCs. Through comparative analysis, it investigates the effects of SC-SMCs on VSMC biomarker expression and ECM composition, considering different differentiation conditions and serum presence. The findings highlight distinct functional capacities and biomarker profiles of VSMCs derived from BM-MSCs versus ADSCs. The dissertation further explores molecular mechanisms behind VSMC phenotypic changes and the reparative properties of SC-SMC CM in ECM repair and anti-inflammatory actions. It discusses optimizing SC differentiation to improve VSMC functionality and standardizing protocols for vascular tissue engineering (VTE). The role of mechanobiology, miRNA, and epigenetics in SMC phenotype regulation is also analyzed, providing a holistic approach to vascular repair. The dissertation outlines the challenges of translating these cellular and molecular insights into clinical strategies for vascular pathologies. It highlights the need for advanced materials and scaffolds to support vascular tissue regeneration and repair. Future research directions include longitudinal studies to monitor VSMC phenotypes, non-invasive imaging for in vivo tracking, and systems biology to enhance VTE therapies. In conclusion, the dissertation contributes to the understanding of VSMCs in vascular health and disease, advocating for the use of stem cell technologies in developing novel vascular therapies. A multi-disciplinary approach is called for to address the challenges in the field and improve the treatment options for vascular diseases
Understanding Bias and Fairness in Large Language Models: An Empirical Study
No full textThis thesis investigates demographic bias in large language models (LLMs) through the use of evaluating outcome disparities when utilized in decision making tasks as well as underlying associations that could contribute to furthering these disparities. Using profiles from the Adult dataset, we analyze how Gemini 2.0 Flash performs in an income prediction task using zero-shot and few-shot prompting methods. Our findings show that models exhibit measurable differences in demographic parity and false positive rates, with the use of few-shot prompting reducing these disparities. Alongside this line of testing, we tested associational bias in Qwen 2.5 using probability based association tests that compare log probabilities of occupations and adjectives in prompts that contain gendered pronouns. These tests revealed measurable gender-linked associations, with male prompts more probable to display high status occupations and competence based adjectives. Female prompts were found to be more likely to show caregiving occupations as well as warmth based adjectives. Using both tests, we see that outcome disparities and internal associations of the model align which suggests that these patterns could affect downstream decision based tasks that these LLMs are used for. While the difference in prompting strategies helped mitigate some bias, the persistence of gender based patterns helps highlight the importance of evaluating LLMs for fairness as they become heavily utilized within applications that could have social consequences
A Dynamic Hierarchical Attention Framework for Multimodal Malware Detection
No full textThe increasing use of Android in the worldwide mobile ecosystem has come along with a significant increase in advanced malware, highlighting the critical necessity for efficient, scalable, and adaptable detection systems. Despite recent advancements in machine learning improving malware detection, the majority of current solutions are limited to one, two, or three data modalities, hence neglecting the comprehensive behavioral spectrum of contemporary multi-vector threats. This thesis presents the first comprehensive multimodal framework for Android malware detection, which combines textual, time-series (temporal), graph-based (structural), and visual information using an innovative hierarchical attention mechanism and Dynamic Fusion Controller(DFC). Our methodology consistently classifies and processes modalities as either sequential or structural, facilitating content-adaptive weighting and resilient cross-modal representation learning. We advance the implementation of cutting-edge time series techniques, such as MiniRocket, for malware detection, hence creating new opportunities for temporal analysis in cybersecurity. Comprehensive experimental assessment shows that our framework performs exceptionally well, with 99.46% classification accuracy and 97.15% detection accuracy, significantly outperforming existing approaches through effective multimodal integration and hierarchical attention mechanisms
Modeling U.S. Corn Production Archetypes in the Agricultural Policy/Environmental eXtender Model to Generate Life Cycle Assessment Inventory Data
No full textCorn (Zea mays) is an important crop grown across the US for many uses such as grain for animal feed, bio-ethanol production, food products, and industrial products. Corn is produced in almost every state in the US, but most corn production is concentrated in the Corn Belt Region including Iowa, Illinois, Ohio, Indiana, Minnesota, and Nebraska. Although technologies and production practices are improving corn production efficiency primarily through increased yields (improved land use), corn production at this scale greatly impacts the environment through emissions from fertilizer and pesticide runoff and leaching, soil degradation due to improper field practices, as well as greenhouse gas (GHG) emissions associated with pumping large amounts of water for irrigation. Since the 1990’s, LCA has become a valuable tool for providing an overall framework for quantifying sustainability characteristics of agricultural production and consumption patterns. Crop production simulation models such as APEX have been increasingly used to analyze the impact of agricultural management at the field and watershed-level. Average regional and state-level corn production practices vary. Existing models of corn production in the United States do not account for differences in soil type and climate condition. Because production practices vary across the US, a framework for this project was developed to differentiate between production practices and include climate (precipitation, minimum and maximum temperature) and soil type. This framework included modeling county-level corn production using the APEX model and integrating results from APEX and other data into the LCA models to assess environmental impacts, such as global warming potential. When used in combination with LCA, the crop production model provides LCI across a variety of production practices and regions, allowing us to estimate potential regional environmental impacts of crop production more accurately. The body of work discussed in this article is an overview of the modeling process for a much broader research project performing a Life Cycle Assessment of the production of corn in the United States (Thoma et al., 2018). The Agricultural Policy/Environmental eXtender (APEX) model was used to simulate 90% of the US corn production by using 73 different archetypical farm models from the top 15 corn producing states. We created lifecycle inventory data sets based on county-level survey data, state budgets, and model simulation data. We succeeded in calibrating 98% of our models to within our desired accuracy. During the calibration portion of this process, it was determined that the denitrification subprogram within APEX led to ammonia loss values that were higher than expected and nitrogen dioxide emissions that were lower than expected given the model inputs. The LCA developed from this modeling effort provides a basis for regional comparisons of corn production
Lifetime Stressor Exposure, Race and Ethnicity, and Habitual Stress Processes: An Evaluation of Repetitive Negative Thinking and Hair Cortisol Concentration
No full textHabitual stress processes are thought to influence acute stress reactivity and stress-related disease. Habitual stress processes have been shown to adapt in response to stressor exposure experienced across the lifespan. The current study seeks to explore whether two habitual stress processes, hypothalamic-pituitary-adrenal (HPA) axis activity and repetitive negative thinking (RNT), were associated with lifetime stressor exposure (self-reported and objective neighborhood-level stressors). Additionally, the current study attempts to quantify how this process may be moderated by race and ethnicity. Participants (N = 134, Mage = 18 [SDage= 1.69], 56.7% female) consisting of White (N = 52), Black (N = 37 ), Latinx (N = 45) young adults, completed a battery of questionnaires including the Stress and Adversity Inventory (STRAIN), Repetitive Negative Thinking Questionnaire (RNTQ-10), Experiences of Discrimination Scale (EODS), and a residential history to estimate cumulative neighborhood- level stressors (linked to Area Deprivation Index). Participants were also asked to provide a 3- cm hair sample for analysis of hair cortisol concentration (HCC). Separate multiple linear regressions were conducted to assess the independent and interactive effects of stressor exposure and race and ethnicity on habitual processes (HCC and RNT). No significant main effect emerged for lifetime stressor exposure or cumulative ADI on RNT or HCC. No interaction effects of race and ethnicity were found. EODS scores were significantly associated with RNT. Race and ethnicity did emerge as a significant predictor of HCC, with Black and Latinx participants displaying significantly greater HCC than their white counterparts. The findings from this study suggest that lifetime stressor exposure (both individual and neighborhood-level) may not be related to HCC or RNT. While no moderating effects of race and ethnicity were found, results did show that racial and ethnic differences in HCC occur and may be driven by factors other than lifetime stressor exposure
Public Responses to Sanitation Interventions in Ghana: From 1987 to Present Times.
No full textSince 1987 Ghana\u27s government administrations have implemented a number of sanitation programs to improve environmental quality and public health, These included the establishment of National Sanitation Day, the launch of the Community-Led Total Sanitation program, the introduction of the Kumasi Ventilated Improved Pit latrine, and major urban sanitation initiatives such as those in the Greater Accra Metropolitan Area and Greater Kumasi Metropolitan Area. Few academics have examined how the Ghanaian people responded to these projects, despite the fact that many have examined their physical and policy components. By investigating public responses, modes of opposition, and adherence to sanitation measures over the past four decades, this study seeks to close that void. The research uses personal accounts and official documents to illustrate how economic struggles, cultural values, and a heavy dependence on government resources influenced public attitudes toward sanitation efforts. A noticeable pattern of public detachment emerged throughout the evolution of sanitation policies. This estrangement was not arbitrary; rather, it reflected broader economic, social, and historical patterns. According to the report, these public responses continuously hampered the implementation of sanitation initiatives and shaped Ghana\u27s public health policies