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On Controlling a Pediatric Lower-Limb Exoskeleton Using Finite-State Machine and Electroencephalography
No full textThere are approximately 330,000 American children aged 5 to 17 who suffer from ambulatory difficulties that adversely impact their quality of life. Robotic-Assisted Gait Training (RAGT) has been shown to be an effective intervention for motor-related conditions in children with mobility disabilities. Compared to traditional therapy, RAGT enables longer duration, higher intensity training sessions, more precise movement patterns, and decreased physical demands on therapists. Furthermore, the advent of portable, powered exoskeletons, while still relatively rare in pediatric applications, has significantly increased accessibility to treatment, allowing for more frequent therapy sessions. Integrating that with a brain-computer interface (BCI) could further optimize the learning process and accelerate motor function recovery. To address this need, the Laboratory for Non-Invasive Brain-Machine Interface Systems at the University of Houston in collaboration with Center for Wearable Exoskeletons at TIRR Memorial Hermann has developed a Pediatric Lower-Extremity Gait System (P-LEGS). This dissertation aims at building a control system for P-LEGS that can utilize explicit manual input or electroencephalography (EEG) signals to command the device to generate movement trajectories. The process of developing, evaluating, and prototyping the various levels of control for the system, as well as the proposed movement intent decoding pediatric BCI, is delineated herein. This dissertation contributes to the pediatric rehabilitation community by introducing a novel exoskeleton system that can provide customizable Robotic-Assisted Gait Training and function as a mobility assistive device. Furthermore, it advances the fields of neural engineering and neuroscience through the development of a pediatric brain-computer interface capable of decoding movement intent
A Novel Interleukin-15 Receptor Antagonist for the Treatment of Rheumatoid Arthritis
No full textRheumatoid arthritis (RA) affects approximately 1.3 million adults in the United States, causing debilitating joint inflammation, pain, and potential disability. Current treatment options, including methotrexate and biologics like adalimumab, have limited efficacy, leaving a substantial number of patients without adequate relief. Interleukin-15 (IL-15), a cytokine pivotal in RA pathogenesis, promotes inflammatory responses and is elevated in RA patients, highlighting it as a compelling therapeutic target. This study explores the potential of IFRA3Q1, a novel peptoid antagonist, designed to inhibit IL-15 by selectively binding to its receptor, IL-15Rα. Peptoids offer several advantages over peptides, such as increased stability, enhanced bioavailability, and reduced immunogenicity, making IFRA3Q1 a promising therapeutic candidate. The primary objectives of this research are to validate IFRA3Q1’s efficacy in reducing RA symptoms in preclinical models and to thoroughly evaluate its pharmacokinetic properties and safety profile. In Aim 1, we evaluated the therapeutic efficacy of IFRA3Q1 using the collagen antibody-induced arthritis (CAIA) model in BALB/c mice. Mice are treated with IFRA3Q1 following arthritis induction, and IFRA3Q1 treatment significantly reduced clinical arthritis scores, paw swelling, and histopathological markers of inflammation and joint damage in CAIA mice. Immune profiling via flow cytometry revealed that IFRA3Q1 decreased immune cell populations, including natural killer (NK) cells, natural killer T (NKT) cells, and memory CD8+ T cells, while also diminishing pro-inflammatory cytokines such as TNF-α, IL-6, and IFN-γ. Immunofluorescence analysis further confirmed reduced infiltration of CD3+ T cells and CD19+ B cells into joint tissues, highlighting the compound’s immunomodulatory effects. In Aim 2, we investigate the plasma stability of IFRA3Q1 and further evaluate the pharmacokinetic properties and toxicity profile of IFRA3Q1 in mouse model. High-performance liquid chromatography (HPLC) and LC-MS/MS analyses successfully identified IFRA3Q1 in both mice and human plasma, confirming its molecular integrity and recovery. Plasma recovery studies demonstrated the critical role of formic acid (FA) in enhancing analyte detection, with 0.5% FA providing optimal recovery by disrupting plasma protein binding. Plasma stability studies further confirmed that IFRA3Q1 remains stable under physiological conditions in both mice and human plasma over 72h periods. Additionally, systemic toxicity evaluations indicated that IFRA3Q1 was well-tolerated in treated mice, with stable body weight and no adverse effects observed. This research demonstrates that IFRA3Q1 effectively mitigates inflammation, preserves joint integrity, and offers a favorable pharmacological profile, positioning it as a promising therapeutic candidate for RA. These findings pave the way for further preclinical development and potential clinical translation of IFRA3Q1 as a novel IL-15-targeted therapy for autoimmune diseases
Three-Dimensional Printable Magnetic Hydrogels with Adjustable Stiffness and Adhesion for Magnetic Actuation and Magnetic Hyperthermia Applications
No full textStimuli-responsive hydrogels hold immense promise for biomedical applications, but conventional gelation processes often struggle to achieve the precision and complexity required for advanced functionalities such as soft robotics, targeted drug delivery, and tissue engineering. This study introduces a class of 3D-printable magnetic hydrogels with tunable stiffness, adhesion, and magnetic responsiveness, prepared through a simple and efficient “one-pot” method. This approach enables precise control over the hydrogel’s mechanical properties, with an elastic modulus ranging from 43 kPa to 277 kPa, tensile strength from 93 kPa to 421 kPa, and toughness from 243 kJ/m3 to 1400 kJ/m3, achieved by modulating the concentrations of acrylamide (AM) and Fe3O4 nanoparticles. These hydrogels exhibit rapid heating under an alternating magnetic field, reaching 44.4 °C within 600 s at 15 wt%, demonstrating the potential for use in mild magnetic hyperthermia. Furthermore, the integration of Fe3O4 nanoparticles and nanoclay into the AM precursor optimizes the rheological properties and ensures high printability, enabling the fabrication of complex, high-fidelity structures through extrusion-based 3D printing. Compared to existing magnetic hydrogels, our 3D-printable platform uniquely combines adjustable mechanical properties, strong adhesion, and multifunctionality, offering enhanced capabilities for use in magnetic actuation and hyperthermia in biomedical applications. This advancement marks a significant step toward the scalable production of next-generation intelligent hydrogels for precision medicine and bioengineering
Teacher Perceptions of the Impact of Instructional Coaching on Tiered Instruction
No full textBackground: The primary role of an instructional coach on a campus is to provide professional learning opportunities, both individually and campus wide, for teachers through a variety of methods. These models include instructional coaching cycles, presentation of new, relevant research, and modeling of instructional and behavioral strategies during collaborative team meetings. Effective coaches tend to impact not only teacher practices and student learning, but also move the system in positive directions. Purpose: This qualitative study sought to understand the impact that an instructional coach can have on the tiered instruction of campus teachers and the resulting perceptions of those interactions. This study focused on the qualitative data gathered from an interview with teachers who have participated in an instructional coaching cycle to help the instructional coach understand their role, job duties, and potential impact on the instructional performance of teachers by investigating the following questions: Q1: What are teacher perceptions surrounding the role of the instructional coach? Q2: What are teacher perceptions of the impact of the instructional coach on helping implement instructional strategies that assist in the improvement of tiered instruction? Methods: This research used a basic qualitative study to first gather participant feedback via an electronic survey. Next, a single, virtual interview of four participants by the participant researcher was conducted to clarify the information gathered from the survey and to collect additional data regarding teacher perceptions about the impact of an instructional coach and instructional coaching cycles. These interviews were with four teachers who had previously participated in an instructional coaching cycle with the participant researcher and were followed by a member check with each participant to confirm the answers of the interview questions. The member checks ensured accuracy of and validated the results of the answers collected. The researcher then used a coding system to analyze themes that were developed to answer the research question. Results: The data collected helped outline three themes that emerged through the thematic analysis: Theme 1) When given the opportunity to participate in an instructional coaching cycle with the campus instructional coach, teachers agree that their tiered instruction was improved following the coaching cycle. Theme 2) Teachers are not aware of the vast array of roles of the instructional coach and agree campus administration can help define and clarify the various ways an instructional coach can contribute to campus improvement and both teacher and student learning and success. Theme 3) After collaborating with the campus instructional coach, teachers have continued to use best practices and implement new instructional strategies in their classrooms. Conclusion: Teacher perceptions surrounding the role of the instructional coach and the impact of instructional coaching cycles are positive and teachers involved in this study agree that the instructional coach and resulting instructional coaching cycles improved their tiered instruction. Instructional coaches can contribute not only to the growth and success of the teachers on their campuses but also help improve overall student learning outcomes and achievement
Observer-Based Simultaneous States and Parameters Estimation Method with Application to System Heath Monitoring
No full textJoint state and parameter estimation is paramount in many engineering and scientific fields, as it involves determining the internal states of a system and the estimation of its time-varying / unknown parameters simultaneously. This twin estimation aspect is crucial for real-time system monitoring, fault diagnosis and the optimization of system control strategies. Provided in this thesis is a comprehensive review of the existing simultaneous states and parameters estimation techniques from the literature, discussing the strengths and limitation of each approach. As an industrial application, an augmented extended Kalman filter is implemented to estimate the substrate temperature in the Advanced Metal Organic Chemical Vapor Deposition (AMOCVD) process, used for High-Temperature Superconductor tapes manufacturing. Precise temperature control in this process is crucial to minimize nano-scale defect growth during production. However, accurate substrate temperature measurement is hindered by sensor drift caused by the deposition of precursor gases over the crystal rod, obstructing the area between the pyrometers and the substrate. To overcome this challenge, a physics-based heating model for the substrate is developed and an augmented extended Kalman filter is implemented for the simultaneous estimation of the substrate temperature and heating model parameters in real-time. A particle swarm optimization algorithm is employed to systematically tune the filter’s noise covariance matrices and avoid the ad-hoc manual selection of those matrices. The estimated temperature is then fed into the control system to regulate the flow of current through the tape, driving the substrate temperature to the desired set point. Inspired by the latter work, a novel simultaneous state and parameter estimation algorithm based on Luenberger observer is proposed. In this approach, a modified Luenberger observer is employed for system states estimation and a recursive least squares estimation is used to estimate the unknown time-varying parameters of the model, in real-time. In the formulation of the novel approach, the unknown parameters of the model are represented as additive uncertainty matrices to the state and input matrices in the state space system representation. The utility of this formulation is that the model uncertainties are confined to the structure of the state matrices and thus allows the identification of the location(s) within the state matrices requiring parameter adaptation. Based on the location(s) and size of these adaptations, real-time health monitoring and health degradation isolation for a system can be realized thereby enabling prognostics, remaining useful life estimation, and forecasting
A Contrastive Learning Approach for Automated Identification of Anatomical Landmarks in 3D Human Torso Surface Scans
No full textThe utilization of three-dimensional (3D) imaging is expanding for quantifying breast morphology, particularly for assessing breast position and volume symmetry and for longitudinal monitoring of changes in breast appearance during reconstructive surgery. Breast morphometry from 3D photographs has been reported, and several studies utilize manual identification of anatomical landmarks (fiducial points), which is time-consuming and subject to operator bias. Using machine learning (ML) algorithms to automate the identification of fiducials would mitigate operator bias and yield reliable, objective measurements. Here, we propose an ML framework to automate the identification of thirteen anatomical landmarks in 3D photographs of female torsos using supervised contrastive learning. The framework includes a feature extractor utilizing a convolutional neural network backbone and a multi-layer perceptron to generate and condense a feature map for each image. Specifically, we utilized a feature bank that is updated regularly during training to optimize the representation of fiducial points and filter out background (clutter) points. This framework ensures that features of the same fiducial point are similar to each other but distinct from features of other fiducial points and background clutter. Inference is performed by computing a score map for each fiducial point, where the highest score indicates the predicted point location. Extensive experiments were conducted on a breast reconstruction dataset and a public dataset to rigorously validate the effectiveness of the proposed method. The evaluation employed a qualitative and quantitative analysis to assess various performance aspects of the model. The proposed approach demonstrated competitive performance in comparison to existing techniques for automated fiducial points identification in 3D images. Furthermore, the proposed framework can be integrated into various medical applications, such as the registration of 3D torso images from different clinical visits and the evaluation of breast symmetry in plastic surgery
Exploring Fatigue Severity and Sensitivity as Indirect Effects of Racial/Ethnic Discrimination on Mental Health Among Racial/Ethnic Young Minority Adults
No full textAlthough ‘racial battle fatigue’ has been observed as an important construct in the context of racism and racial/ethnic discrimination,(Hernández & Villodas, 2020) there has been little empirical work focused on fatigue-related processes in this context. To help address this gap in the literature, the current study examined fatigue severity and sensitivity as simultaneous indirect explanatory constructs in terms of the relation between perceived racial/ethnic discrimination and negative emotional health among a large sample of racial/ethnic minority individuals. Participants (N=1,324) were racial/ethnic minority young adults recruited from the University of Houston between September 2019 through May 2021. Primarily they self-reported identifying as female (80.7%) and Non-Hispanic/Latino Asian (36.4%). Three independent models were conducted to examine the simultaneous role of fatigue sensitivity and fatigue severity in the association between perceived racial/ethnic discrimination and three distinct facets of emotional functioning: general depression (Model 1); anxious arousal (Model 2); and emotional dysregulation (Model 3). Higher amounts of perceived racial/ethnic discrimination were related to increased levels of fatigue sensitivity and fatigue severity, which in turn, were related to depression and emotion dysregulation; larger effect sizes were evident for fatigue sensitivity relative to fatigue severity. For anxious arousal, only fatigue sensitivity exerted a statistically significant indirect effect. All models adjusted for age, race, ethnicity, gender, level of education and subjective social status. The present study offers novel empirical insight into the complexities by which perceived racial/ethnic discrimination is related to negative emotional functioning among racial/ethnic minority young adults. Theoretically, by reducing fatigue sensitivity and severity it may be possible to advert the pernicious impact of racial/ethnic discrimination on mental health
Key Postural Control Metrics and the Influence of Physical Characteristics on Balance After Spaceflight and Bed Rest
No full textThis research aimed to explore why some astronauts experience more severe balance decrements following short-duration spaceflight compared to others. Using archived data, three retrospective analyses were conducted to examine the influence of center of mass (COM), balance metrics, and physical characteristics on postural control outcomes in both spaceflight and head-down bed rest (HDBR) conditions. The first study evaluated the assumption that COM is 55% of a person’s height and found that COM as a percentage of height is significantly higher. This was true for different arm positions (arms at the sides and arms crossed), and sexes with men having significantly higher percent COM than women. While using accurate COM measurements significantly impacted computerized dynamic posturography balance scores, the differences were not clinically meaningful. This suggests that while using an individual’s accurate COM may be more precise, it is unnecessary for most settings. The second study used receiver operating characteristic (ROC) analysis to compare the sensitivity and specificity of postural control metrics. A comparison of area under the curves found that mean path velocity and continuous equilibrium score were the best at detecting post-spaceflight balance decrements. Anteroposterior minimum time to contact and mediolateral peak-to-peak sway were found to be the top performers for HDBR. This highlights the importance of selecting population-specific balance metrics. The third study utilized mixed-effects models to investigate the impact of physical characteristics and anthropometrics on postural control outcomes. While there was no pre-spaceflight difference observed between sexes, women performed significantly better than men following spaceflight. Height was shown to influence pre-spaceflight balance, with shorter individuals performing better, but had no post-spaceflight effect. The study also found that adding additional days to mission durations resulted in significantly worse post-flight balance. Similarly, HDBR data showed pre-HDBR sex differences with men performing significantly better, but no post-HDBR differences, indicating that the men experienced a steeper decline in performance. These findings provide insights into how individual characteristics influence post-spaceflight and post-HDBR balance outcomes
Design of Magnetic Particles and Applications in Force-Based Biosensing
No full textThis dissertation focuses on the design of magnetic particles and the investigation of their use in protein-protein interactions by atomic magnetometer-based biosensing. Magnetic particles are widely used for labeling biomolecules to study ligand-receptor interactions and to achieve molecule-specific detections. Initial studies in this dissertation focused on resolving quantitatively the binding force between anti-CD4 antibodies attached to commercially available magnetic beads (M280) and surface-bound CD4+ T cell via Force Induced Remnant Magnetization Spectroscopy (FIRMS) and resolving three different interactions between the particles and the surface receptors in this system. The binding force of the CD4 antibody-antigen bonds was determined to be 75 ± 3 pN. Additionally, the same antibody-antigen pair was evaluated on antigen-functionalized surfaces, and the results showed that the CD4 antibody-antigen bond on the cell surface is 15 pN weaker than that on the antigen-functionalized surface due to differences in the binding environment. For magnetic based biosensing, magnetic particles play an important role in manipulating the sensitivity and detection limit and in the exploration of different applications. To design the ideal magnetic particles, several parameters need to be considered such as material, size, functionalizability, and targeted applications. We synthesized three different sizes of magnetite particles (i.e., average diameter 120 (1MP), 440 (4MP), and 700 nm (7MP)) with narrow size distributions and subjected them to thorough characterization, including elemental analysis and quantification of the magnetic properties down to one particle. In addition, we functionalized the magnetite particles by coating them with and subsequently organic silanes for use in applications involving the study of protein-protein interactions. We demonstrated the application of the magnetic particles for specific protein detection using the FIRMS technique and protein interactions via the Exchange-Induced Remnant Magnetization (EXIRM) technique. We used the EXIRM technique to detect magnetic signals arising from the change in the exchange reaction of the higher affinity targeted protein IgG2a with IgG1 on surface-immobilized receptors (i.e., the protein A surface). Furthermore, we compared our synthesized magnetic particles with commercially available particles (M280) in these protein studies. The size effects and magnetic effects were investigated, which led to the conclusion that the 7MP particles, which exhibited the greatest magnetic moment, offer the highest potential for improved sensitivity in biosensing applications
The Interaction Between Cognitive and Motor Functioning In Predicting 1-Year Functional Outcomes Among People With Traumatic Brain Injury
No full textPredicting functional and participation outcomes for people with traumatic brain injury (TBI) is of the utmost importance to patients and families. Existing research has long demonstrated the relationship between neuropsychological test performance and functional outcomes among adults with TBI; however, the impact of motor functioning on this relationship is unclear. To address this gap, the current study examined the interaction between cognition and motor functioning at inpatient rehabilitation discharge when predicting functional independence, community participation, homeboundness, and employment 1 year after TBI. Data from 101 participants across two TBI Model Systems sites were analyzed. Motor functioning at discharge, assessed using the FIM-Motor score and the Continuity Assessment Record and Evaluation (CARE) total score, significantly predicted 1-year functional independence on the Glasgow Outcome Scale – Extended, whereas cognitive performance, assessed using the Brief Test of Adult Cognition by Telephone, significantly predicted 1-year community participation on the Participation Assessment with Recombined Tools-Objective. Rehabilitation length of stay emerged as the only relevant predictor of return to work 1 year after injury. Contrary to study hypotheses, the interaction between cognition and motor functioning did not meaningfully contribute to predicting functional independence, community participation, homeboundness, or employment at 1 year post injury. These study findings indicate that cognition and motor functioning offer distinct and independent contributions to later rehabilitation outcomes among people with TBI. Our findings underscore the importance of interventions that address post-TBI cognitive impairments, including cognitive rehabilitation, as an avenue to improve participation after TBI. Critically, this study demonstrates that such interventions may be equally impactful or beneficial for people with TBI across varying levels of motor functioning. Future studies should further examine the impact of rehabilitation interventions on the predictive relationship between early cognition and later functional and participation outcomes among people with TBI