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Exploring the role of LUCAT1 and RBMX in human macrophages
No full textLong noncoding RNAs (lncRNA) and RNA binding proteins (RBP) impact nearly all biological responses. lncRNAs are transcripts larger than 200 nucleotides that play crucial roles in many cells and tissues by regulating gene expression. RBPs act in parallel and in concert with lncRNA to mediate the fate of RNA. Here we sought to determine the mechanism of action of LUCAT1, a lncRNA, discovered by us to be an important regulator of the macrophage inflammatory response. LUCAT1 promotes the expression of many immune- and inflammation-associated genes including CXCL7, IL-24 and NT5E at the transcriptional level. Using in vitro binding assays with biotinylated LUCAT1 RNA and nuclear lysates, we have identified HNRNPL and MATRIN3 as potential LUCAT1-ineracting proteins. siRNA knockdown of putative LUCAT1-interacting proteins revealed that RBMX promotes expression of LUCAT1 and LUCAT1-regulated genes. RBMX is a nuclear protein expressed in all cells and plays an important role by binding to RNA and regulating splicing, transcription, and stability. Dysregulation of RBMX expression is linked to cancer and neurological disorders, but its function in immune cells is unknown. To uncover the cellular functions of RBMX in human monocytes and macrophages, we used CRISPR/Cas9 editing to generate a monoclonal THP-1 cell line with an in frame internal deletion in RBMX spanning amino acid positions 218-250 (RBMXΔ218-250). Immunofluorescence in PMA-differentiated THP-1 macrophages shows that RBMXΔ218-250 is localized in the nucleus but punctate structures are smaller and diffused compared to WT RBMX. Upon PMA stimulation, RBMXΔ218-250 cells exhibit a defect in macrophage-like morphology. Further, LPS stimulation of PMA-differentiated RBMXΔ218-250 cells resulted in attenuated ERK1/2 signaling. Transcriptome analysis revealed decreases in growth factors and other cytokines in resting and stimulated RBMXΔ218-250 cells compared to control. These results provide insights into the function, mechanism, and potential cooperative relationship of LUCAT1 and RBMX in macrophages
Data Set for Validation of a multiscale, hysteresis mechanics model in predicting oily shoe-floor friction across surfaces with varying finishes
No full textThis data set is described within the manuscript, “Validation of a multiscale, hysteresis mechanics model in predicting oily shoe-floor friction across surfaces with varying finishes” [1].
Shoe-floor friction is an important contributing factor to the risk of experiencing a slip and fall event. A theoretical model by B. Persson has been developed to describe viscoelastic energy dissipation caused by multiscale surface topography of a hard material as it slides against an elastomer with time-dependent material properties. Yet, this model has not been extensively validated for shoe-floor friction. In this data set, we report the time-dependent material properties for the three shoes described in [1], the model predictions for coefficient of friction based on the model when considering all length scales (“full-length-scale”) and when excluding small scale features (“limited-length-scale), and experimentally measured coefficient of friction values. The coefficient of friction values are reported for the three shoe materials and 10 floor surfaces.
[1] Ing, H., Chadha, V., Randolph, A.B., Reifler, K., Jacobs, T.D.B. and Beschorner, K.E., 2025. Validation of a multiscale, hysteresis mechanics model in predicting oily shoe-floor friction across surfaces with varying finishes. Journal of Tribology, in press
High Accuracy Location Tracking for a Hemostasis Stent Achieved by Fusing Magnetic and Inertial Measurements
No full textThis dissertation will introduce a location tracking system that aims to track a stent when it is deployed into the human artery to achieve hemostasis. This system is proposed to be applied in emergent conditions where common surgical devices such as a fluoroscopy system are not available, such as treating injured soldiers on the battlefield. The locating approach is based on both magnetic measurements and inertial measurements. Each approach can work individually to achieve locating. The magnetic locating approach applies a magnetic source, and the sensor can detect its location in a coordinate system centered with the reference magnet. The inertial locating approach integrates the linear acceleration and angular velocity measured by the sensor to obtain the angular and linear displacement in a period. These two approaches are then fused to remove the measurement error and the background noise which are random variables. A Kalman filter is applied in most studies as an effective approach for the fusion. In practice, the accuracy of the locating result can be impaired by many factors, such as the disturbing magnetic sources for the magnetic approach, and accumulated measurement error for the inertial approach. Therefore, the focus of this dissertation is to identify all potential causes of the error especially for this application, and then give solutions to correct the errors in real-time and to enhance the location measurement reliability in all conditions. Validation experiments for each improvement approach and the overall locating performance will be introduced. The feasibility of this locating system to as a rescuing device will be proven at last
Adaptive ensemble learning for anomaly detection in hyperspectral imaging.
No full textHyperspectral images capture detailed electromagnetic radiation across ultraviolet, visible, and infrared wavelengths, providing critical data for remote sensing tasks where visible light alone is insufficient. These images have applications in domains such as agriculture, surveillance, disaster recovery, and environmental monitoring. A key challenge in leveraging hyperspectral data is anomaly detection, which aims to identify spectral signatures that differ from the background. Hyperspectral anomaly detection (HAD) has been the focus of extensive research, resulting in a variety of algorithms designed to exploit the rich information within hyperspectral images.
Most HAD algorithms stem from a limited set of modeling biases, leading to three key challenges: selection bias, performance disparities, and singular modeling bias. The restricted range of modeling biases results in correlations between datasets and algorithms, making it easier for certain algorithms to perform better on specific datasets. This creates a risk of skewed results if the datasets are not selected carefully, providing an inherent advantage to some algorithms. Performance disparities exist where some algorithms excel on individual datasets while others generalize better across multiple datasets. Lastly, using a singular modeling bias can limit model flexibility, leading to issues such as underfitting or overfitting depending on the scenario.
To address these challenges, this thesis focuses on three main research tasks. First, I develop a framework to identify significant correlations between HAD modeling biases and datasets. This framework helps predict which biases are likely to perform best on a given dataset, reducing selection bias when choosing datasets and algorithms. Next, I design an adaptive ensemble learning algorithm that integrates multiple HAD modeling biases. This ensemble approach bridges the gap between specialized and generalized performance by combining diverse biases, thus reducing disparities. Finally, I conduct a systematic study of how different error quantification methods in ensemble learning influence the contribution of each modeling bias to the final solution. This study provides valuable insights into the utility of various modeling biases across different datasets.
Together, these contributions highlight the importance of incorporating diverse modeling biases in HAD and demonstrate how ensemble learning can effectively integrate them for better performance across hyperspectral datasets
Transcription factor regulation in pulmonary vascular disease and dermal fibrosis in Systemic Sclerosis
No full textSystemic Sclerosis (SSc) is a rare autoimmune disorder characterized by vasculopathy and fibrosis of the skin and internal organs that lead to multi organ dysfunction. In SSc, changes in micro vessels, initiated by damage and apoptosis of endothelial cells is thought to trigger reduced blood flow and tissue ischemia, leading to vascular manifestations such as Raynaud’s phenomenon, digital ulcers, interstitial lung disease (ILD), and pulmonary hypertension (PH). In the skin, dermal fibroblasts secrete large amounts of extra cellular matrix proteins and can also form myofibroblasts which can drive pathogenic dermal fibrosis.
The interplay between chromatin landscape and transcription factors has an important role in governing the gene regulatory networks and transcriptome of cells, which in turn dictate the cell state as healthy or diseased. The origin of fibrosis and inflammatory pathways in the lungs and skin of SSc patients are not well established and through our study we have found FOSL2 expression in endothelial cells and TGF-β1 in dermal fibroblasts to be a key driver in initiating and maintaining the pathogenesis and inflammation observed in the vasculature and skin of SSc patients respectively.
By analyzing changes in chromatin accessibility and gene expression using single-cell technologies, we show here that FOSL2 is predicted to regulate the altered state of endothelial cells from SSc-ILD patients with PH. Re-examining the endothelial cell states in mice overexpressing Fosl2 using sing-cell RNA sequencing and comparing the gene expression in murine and human patient models indicates that Fosl2 drives downstream gene expression seen in both human and murine PH, and when overexpressed binds to cis elements showing upregulated transcription factor binding in human SSc-ILD-PH lungs. Similarly, using single-cell technologies in the dermal fibroblasts of SSc patients we have identified open chromatin regions in fibrotic genes, which are concordant with transcriptional upregulation in myofibroblasts from SSc skin due to TGF-β1 activity. Through this study, we present the role of transcription factors in influencing cell type specific gene regulation and maintaining disease activity in SSc
The Wireless Inductive Coupling and Linear Variable Differential Transformer: Analysis, Experiment, and Design
No full textWireless inductive coupling can offer significant advantages for in-reactor applications. The technology can avoid electrical feedthroughs penetrating fuel cladding or other metal barriers in fuel performance testing to improve the experiment safety and simplify the design. To alleviate the coupling attenuation caused by metal barriers, coaxially coupled solenoid coils working at low frequencies are used. The linear variable differential transformer (LVDT) is integrated into the technology because it has been successfully demonstrated in reactor applications and can be used to measure various parameters. However, research on wireless inductive coupling with metal layers in between coaxial solenoid coils is limited. At high temperatures, current LVDT analytical models can't predict the LVDT performance accurately because these models ignore the magnetic flux in the air.
The truncated region eigenfunction expansion method based on the magnetic vector potential is adopted to build analytical models for LVDT and coaxial solenoid coil with metal layers. Experiments are performed to validate finite element method (FEM) simulation and analytical models. FEM results are used to help analytical models' validation in a broader scope. High temperature wireless coupling experiments are conducted to explore the influence of temperature. Based on Kirchhoff's voltage law, wireless coupling for LVDT sensor experiments is performed to test the feasibility of the technology.
These models agree with experimental results. The LVDT model is a useful tool to explore the LVDT performance at high temperatures. Compared with FEM, the LVDT model is faster and more adaptable for different LVDT structures. Solutions for wireless inductive coupling coils with metal layers can help to investigate the coupling under different conditions. A higher temperature will enhance the coupling because the conductivity of the metal is reduced. Wireless inductive coupling for LVDT has been demonstrated to be feasible. The non-linearity caused by the wireless coupling for the LVDT sensor is limited. This work can benefit irradiation experiments and LVDT application in high temperatures. Wireless inductive coupling with metal layers is better understood, facilitating its design and optimization for different applications
Computational Methods to Determine Mechanisms Associated with Respiratory Infection
No full textRespiratory viral infections pose a serious threat to public health, with seasonal influenza infection alone causing anywhere between 290,000 and 650,000 deaths each year and SARS-CoV-2 (COVID-19) infection resulting in over 7 million deaths since its emergence in late 2019. Children are uniquely susceptible to severe influenza infection with over 20 million children hospitalized and approximately one million children experiencing severe, life-threatening disease each year. Unfortunately, many of the underlying host mechanisms that result in severe disease remain unclear, especially in pediatric populations.
Computational methods have emerged as a critical tool for revealing underlying immune response mechanisms of action, identifying key biomarkers and drug targets, and predicting complex longitudinal outcomes of infection. Here, we focus on three methods to elucidate the regulatory processes and host immune factors associated with enhanced pathology during respiratory infection. Machine learning models are applied to patient data to identify novel biomarker combinations that can accurately classify influenza and SARS-CoV-2 infection. Network-based algorithms are developed and applied to whole genome data from juvenile mice infected with influenza to identify new pathways involved in regulating host responses and viral replication. Lastly, a mechanistically derived ordinary differential equation (ODE) model of the innate immune response is constructed to elucidate differential host immune response mechanisms between juveniles and adults. Together, these methods will aid in understanding host immune responses to respiratory infections and aid in the development of improved therapeutics
Measuring the effects of social, physical, and chemical environments in pregnancy: a biomarker approach to reduce disparities in maternal cardiovascular health
No full textThis dissertation develops a framework for measuring the placenta as a critical link between environmental exposures and maternal health, with implications for long-term maternal cardiovascular outcomes and the Black-White disparities in these outcomes in the U.S.
In Chapter 2, we establish a foundation by validating molecular approaches to measure placental tissue biomarkers, and their associations with circulating markers. We quantify placental RNA and protein biomarkers and evaluate biologic and technical sources of variability. Validation of circulating biomarkers with their corollaries measured in placental tissue confirm that what we have measured is predominantly placental in origin, rather than maternal or fetal.
Building on this work, Chapter 3 integrates circulating maternal-placental biomarkers with self-reported measures across three maternal environment domains: social and psychosocial, physical, and chemical. Focusing on the placental hormone human chorionic gonadotropin (hCG), we examine associations with stress exposures at four pregnancy timepoints and the postpartum period. We identified the first trimester as a sensitive period for maternal-placental stress, and found associations between neighborhood stress measures (e.g., food environment and green spaces) and biomarkers at all timepoints. This work positions the placenta as central to “prenatal maternal programming” and adds to the growing recognition of pregnancy as a critical window from which we can glean insights into future maternal cardiovascular health.
In Chapter 4, we extend this research into the neighborhood level by identifying a real-world intervention to increase tree canopy coverage. Using the target trial framework, we estimate the potential impact of the intervention on reducing the Black-White disparity in preeclampsia. While causal estimates cannot be obtained, this work points to the importance of shifting focus upstream from individual stress and pregnancy physiology to structural determinants. Greenery was selected for its ongoing evaluation in neighborhood effects research, representing a realistic, scalable intervention with the potential to inform public policy.
We conclude that accurate and precise measurement of placental tissue biomarkers can represent underlying molecular pathways in pregnancy that are altered by the maternal environment. Pilot work to refine measurement strategy and theory building are essential components in my long-term goal to prevent causes of disparities in maternal cardiovascular outcomes
Laminar organization of vocalization processing and attentional modulation in auditory cortex
No full textThis thesis investigates the neural mechanisms underlying vocalization categorization in the auditory system, focusing on feature-based representations and attentional modulation. Using electrophysiology, behavior, and computational modeling in guinea pigs, we examined how vocalizations are encoded along the auditory pathway and how task engagement shapes these representations. Our recordings revealed that while neurons in the thalamus and primary auditory cortex (A1) layer 4 responded broadly to spectral content, a significant proportion of A1 layer 2/3 neurons exhibited highly selective responses to specific vocalization features. This emergence of feature selectivity supports theoretical models of auditory categorization based on intermediate complexity features. We then trained guinea pigs on vocalization categorization tasks and found that our feature-based computational model, trained only on natural calls, successfully predicted animal behavior across various spectro-temporal manipulations. Finally, we investigated how task engagement modulates vocalization processing in A1 using chronic recordings during active and passive listening conditions. We found that attention altered the input-output relationships, particularly in A1 layer 2/3 neurons which can potentially lead to improved separability between target and non-target vocalizations at the population level. Together, these findings demonstrate that feature-based vocalization representations emerge in superficial layers of auditory cortex and are further enhanced by attention, providing new insights into the neural computations underlying robust vocalization categorization in complex acoustic environments
Our Cousins in Ohio: Appendices and Addenda
No full textMary Howitt's Our Cousins in Ohio (1849) was published simultaneously in two slightly different versions in London and New York. This story of a year in the life of her sister's family in America was compiled from several years of letters and journals written by the sister, Emma Alderson, and her nephew, William Charles. Incidents of daily life in mid nineteenth-century America are blended with reflections on matters of historical significance, including slavery, racism, and abolition; Shaker communities; life in Native American boarding schools; the war with Mexico; and more.
The appendices here are a supplement to Our Cousins in Ohio, Ed. Donald Ingram Ulin (Edinburgh: EUP, 2025)(ISBN 978 1 3995 2357 8). This edition, based on the New York, brings Our Cousins back into print for the first time since 1866, with a critical introduction and textual notes covering all significant variants in the London editio