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FORCE MODULATION ON MAGNETIC BEADS: BRIDGING FORCE SPECTROSCOPY AND VISUAL DETECTION
No full textThis dissertation focuses on investigating biomolecular interactions via modulating magnetic beads on a macroscopic surface in a massively parallel fashion with well-controlled mechanical forces. It addresses resolving mechanochemical effect of small molecule anticancer drugs on DNA duplex with high force resolution and characterizing controlled multivalent interactions on a millimeter-scale surface by using force-induced remnant magnetization spectroscopy (FIRMS) which is a unique magnetic detection-based force spectroscopy technique recently developed by our group. In addition, a new type of surface-based assay that enables naked-eye detection of biomolecular interactions has been developed via simultaneous force modulation on hundreds of thousands of magnetic beads.
A concept of differential binding force is proposed to achieve precise and label-free measurement of the mechanical effect of a drug molecule binding to a DNA duplex. The high-resolution binding forces measured by FIRMS enable the binding behavior of drug molecules with different chirality and DNA of various sequences to be distinguished.
Using FIRMS, I also resolved the single-, double-, and triple- biotin-streptavidin bonds, multivalent DNA bonds and CXCL12-CXCR4 bonds, on millimeter-scale surfaces. The binding forces obtained by FIRMS can be the discriminating parameter for multivalent bonds formed on macroscopic sizes of surfaces, which makes it valuable for surface-based biological research as well as designing supramolecular materials.
Modulating surface-immobilized beads with well-defined forces in a highly parallel fashion also allows characterization of biomolecules interactions with the naked eye. Two molecular ruler systems were constructed to accurately measure lengths of nucleic acid strands and to quantify molecular binding forces with high resolution, respectively. Additionally, improving the detection sensitivity for particle-based sensing applications was also demonstrated.
This dissertation is organized as follows. In Chapter 1, I will review various multiple-parallel force assays that allow characterization of biomolecules. Chapter 2 will be an overview of scanning atomic magnetometer, which is applied to precisely quantify magnetic beads in the FIRMS technique. Chapters 3 to 5 will be a presentation of the research I have conducted on investigating various biomolecular interactions fulfilled by highly parallel modulation of magnetic beads via well-defined mechanical forces. Finally, a conclusion will be reached in Chapter 6
Differences in Provider Beliefs and Delivery of the 5As for Cigarette and Non-Cigarette Tobacco Use Between Two Types of Healthcare Centers Serving Rural and/or Medically Underserved Areas of Texas, US
No full textBackground/Objectives: Rural populations in the US bear a disproportionate burden of cancer mortality, which may be partly due to their elevated tobacco use and the limited receipt of tobacco use interventions in rural healthcare settings. Here, we examine providers’ use of the 5As (Ask, Advise, Assess, Assist, and Arrange), a brief tobacco cessation intervention, with their patients to assess intervention gaps. Methods: Provider practices in substance use treatment centers (SUTCs) and medical healthcare centers (MHCs), each serving rural and/or medically underserved areas (MUAs) of Texas, were compared. In total, 347 providers from 10 SUTCs (n = 174) and 9 MHCs (n = 173) responded to an anonymized survey about their cigarette and non-cigarette screening and intervention delivery, along with their perceived importance and workforce’s preparedness to help patients stop using tobacco. Linear mixed and generalized linear mixed models were used to assess differences between practices at SUTCs and MHCs. Results: More MHC than SUTC providers reported that cigarette and non-cigarette tobacco use cessation intervention were (respectively) important parts of their job (p = 0.0009; p = 0.0023) and that their workforce was prepared to help their patients quit tobacco (p = 0.0275), although less than half of all respondents endorsed preparedness. Relative to those at SUTCs, MHC providers reported higher rates of asking (SUTCs = 59.57% and MHCs = 77.21%; p = 0.0182) and advising (SUTCs = 45.34% and MHCs = 72.35%; p = 0.0017) their patients to quit cigarette smoking and advising them to quit non-cigarette tobacco products (SUTCs = 43.94% and MHCs = 71.76%; p = 0.0016). Conclusions: Overall, providers in both settings may benefit from greater preparation to deliver tobacco cessation care; needs were more prevalent within SUTCs than MHCs. Our findings can inform strategic planning to improve centers’ capacity to comprehensively address their patients’ tobacco use in rural/MUAs of Texas, US
GPU Accelerated Refined Tensor Voting
No full textGathering and extracting meaningful and reliable information from visual data can be challenging, especially when dealing with noisy or incomplete data. Edge detection algorithms such as the Canny edge detector or the Sobel operator attempt to solve this issue by detecting and isolating image edges. However, they often fail in the presence of noise or extracting higher-level structures. Noise removal techniques such as Gaussian blur exist. Still, they usually come at the cost of losing essential details in the image, leading to a smoothing effect that can obscure critical features such as edges, corners, or textures. This trade-off between noise reduction and feature retention limits their effectiveness in applications where clarity and accuracy are crucial. The Tensor Voting algorithm offers a robust perceptual organization and feature extraction solution by leveraging geometric and contextual information. However, its high computational complexity has limited its adoption in many cases. Tensors do not always fall within what the original framework describes as the "osculating circle." There is no way to refine the width of the vote field to prevent votes from being cast out into regions where no votes should be cast. Finally, iterative voting increases the overall energy in the image causing the structure to distort. The research presented in this paper aims to explain the Tensor Voting algorithm, outline the math involved, provide a way to allow tensor voting to vote orthogonal to the tensor as well as laterally, add method in which the user can control the width of the vote field, normalize tensor voting for use in iterative voting, and address the algorithm's computational complexity by parallelizing the 2D Tensor Voting algorithm using CUDA on NVIDIA GPUs. We discuss the challenges of parallelizing the algorithm, including a discussion between the scatter and gather versions. Experimental results show that parallelizing the Tensor Voting algorithm with CUDA can achieve up to three orders of magnitude over the CPU implementation
Development of the Universal Indentation Platform for Comprehensive Evaluation of Mesoscale Elastic, Strength, and Fracture Properties of Cementitious Composite
No full textAdvances in experimental mechanics have significantly enhanced our understanding of material properties across multiple scales, necessitating the development of versatile methodologies to address the unique challenges posed by complex, heterogeneous materials. This thesis proposes an original characterization approach that extends the capabilities of micro-indentation platforms to flexural testing, enabling the simultaneous evaluation of elastic, flexural, and fracture properties at the mesoscale. By introducing a universal testing setup, the research bridges the gap between small-scale testing and practical applications, ensuring high precision, adaptability, and cost-efficiency. The study builds upon the foundational micro-bending methodology, now adapted to incorporate scratch-mode bending along extension to fracture mechanics analysis. This approach, validated through extensive experimental campaigns, provides robust insights into the fracture mechanics of brittle and quasi-brittle materials. The investigation includes advanced energy decomposition techniques and stress-intensity formulations to characterize crack growth behavior. Furthermore, an extensive characterization of cement-based materials demonstrates the evolution of mechanical properties over time, capturing the effects of hydration and material aging. The methodologies developed in this thesis were validated on diverse material systems, from ceramics to cement composites, highlighting their reliability and scalability. This work establishes a comprehensive framework for integrating fracture mechanics principles with advanced experimental techniques, offering novel pathways for material development and performance optimization
Utilizing Trauma-Informed Compassionate Leader Behaviors for Employees Facing Health-Related Trauma
No full textTrauma-informed compassionate leader behaviors (TICLB) represent an emerging framework that adapts compassionate leadership to address the needs of employees affected by trauma. While prior research has predominantly centered on trauma from human trafficking or military-related PTSD, this study expands the scope of TICLB to encompass employees dealing with trauma resulting from illness. This research advances the TICLB literature by (1) broadening workplace compassion studies to include previously unexamined employee outcomes, (2) extending the application of TICLB to a new category of trauma, and (3) deepening the understanding of TICLB’s theoretical framework by incorporating relevant, unexplored outcomes. The primary objectives of this study were to investigate whether TICLB produces significant outcomes (burnout, engagement, sense of control, and use of accommodations) for employees suffering from health-related trauma and to examine the roles that severity of illness and perceived supervisor support (PSS) play in this relationship. Data were collected from 200 employees who worked at least part-time during their cancer journey. Findings revealed that TICLB was positively associated with engagement, sense of control, and use of accommodations. PSS mediated the relationship between TICLB and engagement and sense of control. Severity of illness moderated the relationship between TICLB and burnout. This research provides valuable insights into the impact of TICLB, a novel and important concept, highlighting its necessity as a critical approach for supporting employees experiencing trauma
Discovery of Immunogenic Neo-peptides from Actionable RNA Fusion CD74-NRG1 for Developing Cancer Vaccines
No full textThis study investigates the CD74-NRG1 fusion protein as a tumor-specific neoantigen source and presents a comprehensive pipeline for identifying and validating immunogenic neoantigens from chimeric RNAs, with promising implications for personalized cancer vaccines. The CD74-NRG1 fusion, resulting from a translocation between chromosomes 5 and 8, creates a unique fusion junction that disrupts cell signaling and drives tumorigenesis. This novel antigenic sequence, absent in healthy cells, represents an attractive target for tumor-specific immunotherapy with minimal side effects. To evaluate the neoantigenic potential of CD74-NRG1, 36 peptides (9, 10, and 11 amino acids) derived from the fusion junction and wild-type sequences were tested for HLA binding using in-silico tools (NetMHCpan4.1, MixMHCpred3.0, BigMHC) and an in-vitro flow-cytometry based biochemical HLA assay. NetMHCpan4.1 showed a 88% overlap with the 10 neopeptides with strong binding to HLA-B*07:02 established through the flow-cytometry based assay and was therefore found to have the best predictive accuracy of these computational methods. Further immunogenicity testing of the 36 peptides using an ELISpot assay revealed that 7 of 10 validated binders to HLA-B*07:02 effectively activated CD8 T cells, indicating their potential to elicit robust immune responses. The dCODE Dextramer assay was used to identify specific immune cell states induced by these peptides, including naïve, early activated, early exhausted, and effector memory CD8 T cells. Clonal analysis found 21 distinct CD8 T cell clones expanded by 6 of the top binding neopeptides which were validated to be immunogenic through the ELISpot assay. Conserved amino acid residues in the CDR3 sequences of these TCRs supported their structural stability and specificity. Additionally, CrossDome bioinformatics analysis evaluated off-target effects, identifying APPKATSTS as having the highest off-target potential with 13 self-antigen matches. Overall, these findings underscore the CD74-NRG1 fusion as a promising neoantigen for TCR-based immunotherapy. Six fusion-derived peptides, APPKATSTS, PPKATSTST, QKPTDAPPKA, KPTDAPPKATS, DAPPKATSTST, and APPKATSTSTT emerged as potential vaccine candidates, though further validation is recommended to assess off-target risks
Architects of Community: An Examination of How Interscholastic Sport Coaches Promote a Sense of Community
No full textThis study examined how interscholastic sport coaches promote a sense of community (SOC) within their programs and identified the strategies employed to achieve this outcome. While research demonstrates that sport participation facilitates SOC, limited evidence exists regarding the specific mechanisms through which coaches promote SOC and the identified strategies employed to achieve this outcome in interscholastic settings. Using Warner's Sport and Sense of Community Theory as a conceptual framework, this basic qualitative study employed semi-structured interviews with nine interscholastic sport coaches. Data analysis utilized a hybrid thematic analysis approach to identify patterns in coaching practices that promote SOC. Findings revealed that coaches could unknowingly foster SOC through team policies, coaching methodologies, and communication protocols. Coaches explanation of their team management approach indicated that they offered administrative considerations, competition, equitable decision-making, leadership opportunities, and social spaces to their players. The research demonstrated that offering the possibility to experience an SOC required deliberate design and ongoing decision-making rather than emerging as an incidental outcome. This study advances both theoretical and practical understanding of SOC in interscholastic sport contexts. Theoretically, it extends Sport and Sense of Community Theory by examining its application in secondary school-sponsored athletics. Practically, it provides evidence-based strategies for coaches to intentionally design programs that enhance felt community as a viable outcome of sport participation. The findings suggest that fostering SOC should be considered a measurable indicator of program success, particularly for novice coaches. Moreover, this research reinforces interscholastic sport's potential as a vehicle for promoting belonging among adolescents, emphasizing the need for intentional program design as a pathway to positive sport experiences
Modeling Nanofiltration Separation Mechanisms in Covalent Organic Framework Membranes
No full textSolute transport in nanofiltration (NF) membrane systems is described with the Donnan Steric Pore Model with Dielectric Exclusion (DSPM-DE), which couples size- and charge-based solute partitioning mechanisms into and out of membrane pores with flow through pores, as described by the Extended Nernst-Planck equation. If membrane structural and chemical characteristics are well defined, the DSPM-DE can theoretically be used to identify solute rejection mechanisms, predict NF performance, and guide membrane design. However, the presence of additional separation mechanisms, like adsorption, and the heterogeneous, convoluted characteristics of traditional NF membranes challenge these goals. In this work, we apply covalent organic frameworks (COFs) as model NF membrane materials to demonstrate control over partitioning and transport mechanisms. We experimentally isolate and quantify steric and non-steric contributions to solute partitioning and transport in NF via application of the DSPM-DE to COF membranes fabricated with tailored pore sizes and charge properties. We also demonstrate enhanced non-steric solute rejection by changing COF membrane chemistry, and we highlight the significant impact of adsorption on measured solute rejection by COF membranes. Next, the knowledge gained through this modeling work is applied to estimate the removal of environmentally persistent pharmaceutical pollutants from a simulated domestic wastewater effluent by COF NF membranes with different charge properties, a relevant separation for water recycling. Size-based rejection of uncharged pharmaceutical solutes is well-predicted by the DSPM-DE. Charge-based exclusion mechanisms are relevant for the negatively charged solute, and its rejection by the membrane with fixed negative charges (TpPa-SO3H COF) is accurately predicted. For the nominally uncharged membrane (TpPa-1 COF), however, rejection is underpredicted. Membrane characterizations reveal that the fixed negative charge of the TpPa-SO3H COF can be effectively screened by feed solution ionic strength. In contrast, the persistent negative charge of the TpPa-1 COF is not screened by solution ionic strength and results in enhanced non-steric rejection over model predictions. Quantifying relative contributions of solute separation mechanisms via the DSPM-DE allows for strategic NF membrane design. Combined with independent control over pore properties afforded by COF membranes, this knowledge drives the development of tunable, fit-for-purpose NF membranes with predictable performance for challenging separations
Machine Learning-Based Quantification of Lateral Flow Assay Using Smartphone-Captured Images
No full textLateral flow assay has been extensively used for at-home testing and point-of-care diagnostics in rural areas. Despite its advantages as convenient and low-cost testing, it suffers from poor quantification capacity where only yes/no or positive/negative diagnostics are achieved. In this study, machine learning and deep learning models were developed to quantify the analyte load from smartphone-captured images of the lateral flow assay test. The comparative analysis identified that random forest and convolutional neural network (CNN) models performed well in classifying the lateral flow assay results compared to other well-established machine learning models. When trained on small-size images, random forest models excelled CNN models in image classification. Contrarily, CNN models outperformed random forest models in classifying noisy images
High-Intensity Focused Ultrasound Ablation for Primary or Salvage Prostate Cancer Therapy: Initial Outcomes in the Veteran Healthcare Setting
No full textHigh-Intensity Focused Ultrasound (HIFU) provides comparable oncologic, erectile, and urinary outcomes to standard-of-care options for localized prostate cancer. This study reports the largest United States series of HIFU in veterans for both primary and salvage therapies. We retrospectively analyzed the outcomes of 43 veterans treated at the Michael E. DeBakey Veterans Affairs Medical Center from 2018 to 2022. Primary endpoints included prostate-specific antigen (PSA) reduction and local recurrence rates. Secondary endpoints included 30-day complications, Sexual Health Inventory for Men (SHIM), and American Urological Association Symptom Score (AUASS). In our study, 31 veterans (72.1%) received primary treatment and 12 (27.9%) received salvage therapy, with a median follow-up of 23 and 25 months, respectively. Median PSA nadir was 0.16 for primary and 0.12 for salvage groups, with PSA reduction stable over 30 months. Local recurrence occurred in 16.1% of primary and 16.6% of salvage patients. SHIM scores and AUASS were not statistically different before and after HIFU therapy. Short- and intermediate-term results suggest HIFU is a safe and effective treatment option with excellent potency and preserved urinary function, as well as adequate oncological control for primary and salvage therapies for localized prostate cancer in veterans