In Silico Techniques to Improve Understanding of Gait in Cerebral Palsy

Thesis (Ph.D.)--University of Washington, 2024In this dissertation we focus on utilizing computer-aided engineering techniques to improve our understanding of gait in cerebral palsy (CP). CP is the most common motor disability in children and arises from a non-progressive brain injury at or near the time of birth which alters control (i.e., poor coordination and increased muscle co-contraction). Additionally, individuals with CP often develop secondary, progressive impairments like weakness and contracture. Current treatments to improve mobility in CP primarily target secondary impairments but functional outcomes are inconsistent, leaving treatment efficacy at around 50%. To improve treatment efficacy, clinicians need a better understanding of the complex interactions between, and relative effects of, multi-modal neuromuscular impairments on gait. However, eliciting interactions between, and relative effects of, neuromuscular impairments on gait is difficult or even impossible to do clinically and experimentally. Thus, the goal of this dissertation was to utilize in silico techniques to improve the understanding of gait in CP. Specifically, we use physics-based (i.e., musculoskeletal) modeling, optimal control (i.e., neuromuscular simulation), and data-driven modeling (i.e., machine learning) to investigate the interactions between, and relative effects of, altered control, muscle weakness, and contracture on gait and predict and understand gait energetics in CP which can be used to improve treatment efficacy. The effects of altered motor control on gait are poorly understood because altered control persists post-intervention and its relative effects are difficult to discern amidst secondary impairments, like weakness and contracture. Prior studies have investigated the impacts of weakness, contracture, and altered control on gait, but they have yet to be investigated together. Thus, in this dissertation we sought to understand the effects of, and interactions between, neuromuscular impairments during gait by utilizing a musculoskeletal model and neuromuscular simulation framework. We simulated nondisabled (ND) gait and then perturbed each simulation with altered control, weakness, and contracture of varying severities. We found that altered control exacerbated the restrictions imposed by secondary impairments: ND gait was less robust to, and required more muscle activation to adapt to, weakness and contracture with altered control when compared to unaltered control (Chapter 3). These findings highlight the inimical effects of altered control on gait and emphasize the advantages of in silico techniques to identify specific impairments, such as altered control, that should take treatment precedence (in silico-informed interventions). However, it is unclear if these conclusions extend to different gait patterns like those in CP. Abnormal gait patterns are common for individuals with CP; the most inimical and common of which is crouch gait. Crouch gait is characterized by excessive knee flexion, which increases knee extensor demand while reducing the knee extensor's ability to extend the knee making it inefficient and disadvantageous. In Chapter 4, we extended our prior computational methods to simulate crouch gait of varying severities. By simulating both crouch and ND gait, and incorporating machine learning (ML), we investigated if the interactions between, and relative effects of, neuromuscular impairments are gait pattern-specific. We determined that the interactions between, and relative effects of, neuromuscular impairments are gait pattern-specific highlighting advantages and disadvantages of walking in crouch. Thus, by combining computational techniques like modeling, simulation, and machine learning we elicited rationale for why individuals may select non-normative gait patterns and emphasized the utility of in silico techniques to parse and identify impairments primarily affecting function in CP which could then be used to inform treatment. Individuals with CP consume on average 2x the energy of their ND peers while walking; the origin of which remains unknown. Elevated energy consumption persists post-intervention making it a primary complaint among patients and objective of research in the CP community. We sought to accurately predict and understand energetics in CP with modeling, simulation, and machine learning to reduce clinical collection burden on patients and caregivers and improve identification of effective treatment methods for reducing energetics in CP. In the final study of this dissertation, we first used our modeling and simulation framework to generate and perturb walking simulations from gait data from the largest database of walking data for individuals with CP. Generated simulations then acted as synthetic data within a machine learning algorithm to complement existing clinical data and attempt to improve predictions of energetics in CP. Using simulations generated for 240 children with cerebral palsy we analyzed the energetic discrepancy—difference between measured and predicted—to identify primary mechanisms elevating energetics in CP (Chapter 5). Synthetic data generated from gait simulations marginally improve prediction accuracy of energetics in CP, but augmented discrepancy models—energetic predictions with the reconstructed discrepancy—improved modeling of CP energetics, identifying kinematics at initial contact and contracture as primary mechanisms elevating walking energy in CP. Utilizing in silico techniques can provide additional synthetic data (i.e., data augmentation) to reduce data collection burdens on patients, caregivers, and clinicians while eliciting additional insight in causal mechanisms affecting gait and function. This dissertation supports in silico informed interventions by improving our understanding of gait in CP. By utilizing modeling, simulation, and machine learning we examined the interactions between, and effects of, neuromuscular impairments on gait in both ND and CP individuals and how that information could better predict and understand energetics in CP. This work provides a foundation to utilize modeling, simulation, and machine learning to rapidly evaluate causal mechanisms impacting gait, probe and parse complex relationships between neuromuscular impairments, and incorporate synthetic data to better inform machine learning algorithms and clinical decision making. In conclusion, the work we have completed over the last 4 years highlights the benefits of in silico techniques to understand gait in CP, seeking to support the creation and implementation of in silico informed interventions for individuals with CP

Altitude-Constrained Occlusion-Aware UAV Coverage Planning for Autonomous Wilderness Search and Rescue

Thesis (Ph.D.)--University of Washington, 2024Autonomous Unoccupied Aerial Vehicles (UAVs) have transformed how search and rescue (SAR) missions operate, enabling rapid real-time video feedback over large and difficult-to-access areas. However, ensuring full search coverage in wilderness environments is challenging, and current UAV path planning solutions do not account for occlusion caused by complex terrains and dense vegetation. In wilderness settings, UAV operators often rely on manual control to mitigate the effects of occlusion, performing on-the-fly position adjustments to achieve less-occluded viewing angles. To address the challenge of autonomous occlusion-aware coverage path planning, this dissertation presents VWSGA+, a waypoint path planning algorithm that guarantees complete coverage in occluded environments. The practicality and theoretical guarantees of VWSGA+ are demonstrated in a full-scale simulation and real-world experiments, in which the VWSGA+ is compared against state-of-the-art methods and is shown to be superior in providing complete coverage using battery-efficient paths. This dissertation advances the state of autonomy for UAV-assisted wilderness SAR operations by producing high-confidence aerial search coverage paths, ultimately enhancing the search team’s ability to provide this life-saving service to society

Feasibility and acceptability of an innovative hospital-based educational training for caregivers of patients with severe stroke at the Instituto Nacional de Ciencias Neurológicas in Perú in 2023

Thesis (Master's)--University of Washington, 2024Background: Patients with severe stroke have high mortality and morbidity, and often lower quality of life. Stroke affects not only patients, but also family members and caregivers who assume new responsibilities and burdens such as emotional and psychological distress when caring for patients with new neurologic disabilities. Gaps in health care access in Lima, Perú include financial barriers for those of low socioeconomic status, the lack of a national palliative care training program, and insufficient caregiver assistance, which affects the continuity of care at a health facility as well as for home-based caregivers. All these factors negatively affect quality of life, mortality, and morbidity of patients with stroke. Caregiving training status is one of the main root causes for poor outcomes in patients with stroke. Therefore, we designed an innovative hospital-based training intervention to caregivers of patients with severe stroke. Methods: This implementation pilot study hybrid 3 type occurred from June to November 2023 and consisted of randomizing caregivers to two intervention arms of the hospital-based training intervention (online vs in-person). We evaluated implementation outcomes in accordance with Proctor et al. to evaluate feasibility, acceptability, and fidelity, and also employed the RE-AIM framework to evaluate reach, adoption and implementation of a hospital-based caregiving training for patients with severe stroke at the Instituto Nacional de Ciencias Neurológicas in Peru. Using a convergent mixed-methods design, we simultaneously collected quantitative and qualitative data to understand and explain findings on quantitative phase. Data were analyzed separately and then merged and triangulated for results, discussion, and conclusions. The educational training included nasogastric (NG) and foley tube management, bathing and cleaning, and mobilization. The quantitative phase consisted of a randomized 1:1 allocation of participants assigned to the training intervention: in-person versus website arms. For the qualitative phase, we administered 19 questionnaires through semi-structured interviews to caregivers and nursing stroke staff who participated in training and who cared for stroke patients to gain in-depth understanding of the intervention at one-month follow-up. Results: We recruited 38 participants and randomly assigned 19 to each intervention arm: in-person and virtual. The percentage of eligible participants enrolled in the study was 86%. All enrolled participants were retained in their assigned intervention arm. Seventy-nine percent of participants completed training, including 100% in the in-person arm compared to 57.9% of the virtual arm. Two validated questionnaires to measure caregiver’s burden were applied in more than 80% of the cases. The total acceptability reported by participants was 82.9%, (in-person training 94.4% versus virtual 70.6%). Fidelity of the intervention was 95% for in-person training and for virtual training the Median website log-in counts was 2, and median time spent by each participant at one-month follow-up was 284 minutes. We identified the following main themes from our qualitative interviews: (1) willingness to participate actively in the educational training from caregivers and nursing stroke staff (SNS); (2) barriers to training, including caregiver burden and family issues; (3) continuing care at home; (4) applying intervention components and performing follow-up; and (5) suggestions made by caregivers and SNS to add additional intervention components such as psychological support, rehabilitation, nutrition, and administrative counseling after patients’ discharge. Conclusions: The hospital-based educational training for caregivers was feasible and acceptable. Main facilitators identified were a positive influence and support for caregivers and nursing staff caring for patients with stroke. Family issues and caregiver burden were the main barriers. Further caregiver training should include additional components such as psychological support, rehabilitation and nutrition training, and administrative counseling after patient’s discharge

Examining the effect of terpene exposure on stress-related outcomes in adults

Thesis (Master's)--University of Washington, 2024Nature contact has been shown to improve psychological and physiological stress-related outcomes, although the underlying mechanisms are less understood. The purpose of this study was to determine the effect of terpene exposure on stress-related outcomes in adults following a seated forest exposure. In a double-blind, randomized crossover trial, participants were exposed to a seated forest intervention with terpenes filtered out of the air and an identical session without the filtration of terpenes. The sessions were separated by an eight-day washout period. The primary outcome measured was the high frequency (HF) component of heart rate variability (HRV). Secondary outcomes included measures of blood pressure, skin conductance levels, heart rate, self-reported stress and affect, and levels of inflammatory cytokines in serum. Outcomes were measured via mobile physiology equipment, self-report questionnaires, and serum samples. This is the first study to investigate the effect of terpene exposure during a seated forest exposure in adults in a randomized crossover trial and furthers scientific understanding of the role that olfactory stimuli and terpene exposure play in the multisensory pathways that link well-being and forest exposure

High-throughput analysis of the antigenic effects of mutations to influenza hemagglutinin

Thesis (Ph.D.)--University of Washington, 2024Influenza virus rapidly evolves to escape neutralization by polyclonal antibodies. However, we have a limited understanding of how the effects of viral mutations on antibody neutralization vary across the human population, and how this heterogeneity might affect virus evolution. In my dissertation, I address this question by mapping the antigenic effects of influenza mutations against sera from defined age cohorts. First, I describe the development of an improved deep mutational scanning system to measure how mutations in hemagglutinin (HA) affect neutralization by human sera. I engineer a chimeric, barcoded HA construct, which both improves sample throughput while also allowing for absolute quantification of both escape and sensitizing mutations. I show that the resulting barcoded libraries can be used to map the HA epitopes targeted by monoclonal antibodies, antibody cocktails, and polyclonal sera, and that these measurements are consistent with results from traditional neutralization assays. In the remainder of my thesis, I use a barcoded library in the background of the A/Hong Kong/45/2019 H3 HA protein to analyze heterogeneity in serum antibody targeting across individuals and age cohorts. I find that the effects of HA mutations on serum neutralization differ across age groups, and that these differences can be partially rationalized in terms of exposure histories. For instance, mutations that revert to amino acids found in the HAs of older viral strains often increase neutralization sensitivity in older individuals, but not young children. I incorporate data from similar experiments using the earlier, non-barcoded A/Perth/16/2009 H3 HA library, which also found substantial differences between child and adult escape maps. Natural mutations that fixed in influenza variants after 2020 cause the greatest escape from sera from children and teenagers, suggesting that antigenic pressure from younger age groups play a more prominent role in driving viral evolution. Overall, my graduate research demonstrates that influenza faces distinct antigenic selection regimes from different age groups, and that this heterogeneity may have a substantial impact on viral evolution. More rigorous characterization of this immune heterogeneity has the potential to improve both evolutionary forecasting and vaccine effectiveness

An investigation of microtubule-kinetochore attachment mechanisms

Thesis (Ph.D.)--University of Washington, 2024The ability to replicate is a defining feature of life. At the center of eukaryotic cell division are a set of protein machines responsible for pulling apart the chromosomes before cells divide. Spindle microtubules grow from the poles of the cell and connect to chromosomes via protein complexes called kinetochores. Kinetochores must maintain tenacious attachments to microtubule tips, even as they assemble and disassemble underneath their grip. Additionally, kinetochores mediate an error correction process to ensure the proper attachments to microtubules are formed before separation of the chromosomes commences. Here, I work to understand how the proteins in the kinetochore work together to maintain attachments to microtubules. I investigate two different mechanisms for microtubule-kinetochore attachment: the conformational wave mechanism and the biased diffusion mechanism. I developed a new optical trapping assay, using it to show that microtubule protofilament morphological and energetic properties can be measured and changed. I investigate the role of protofilament curl enlargement in the attachment and motility of the kinetochore. I develop theoretical models that show that the biased diffusion mechanism can fit experimentally measured detachment rates for assembling and disassembling kinetochores. Finally, I show kinetochores exhibit asymmetry in their sliding friction when they are dragged along microtubule lattices, a new phenomenon for microtubule-kinetochore biophysics. I argue this sliding friction forms the basis for a new mode of error correction during cell division, one that likely holds across most eukaryotic organisms

Observational epidemiology for evaluating respiratory virus vaccine impact using population surveillance data

Thesis (Ph.D.)--University of Washington, 2024Observational studies of vaccine preventable diseases (VPDs) are needed to answer questions not addressed by clinical trials and inform policy decisions. In addition, data from community, rather than clinical, settings are essential as clinical surveillance largely does not capture individuals at low risk for severe disease and may not be representative of subclinical disease. Also, mild illness can contribute to onward community transmission and result in missed work or school. Such observational data from community settings is needed both for diseases with licensed vaccines in routine use and diseases with vaccines in clinical development that are expected to be licensed. This dissertation aims to address two epidemiologic gaps for respiratory viruses: (1) estimating effectiveness of COVID-19 booster vaccination among healthy, young adult populations and (2) describing genomic diversity of respiratory syncytial virus (RSV) to inform future vaccine impact studies.In Chapter 1, we provide a rationale for the use of observational epidemiology in the study of VPDs and provide a brief introduction of the specific aims of this dissertation. In Chapter 2, we used data from the Husky Coronavirus Testing Study (HCT), a large SARS-CoV-2 university testing program, to estimate relative vaccine effectiveness (VE) of COVID-19 mRNA vaccine primary series plus monovalent booster dose versus primary series only against symptomatic SARS-CoV-2 infection. Data are from September 2021 to July 2022 in a community-based university population. Relative VE was estimated using the test-negative design and adjusted logistic regression implemented via generalized estimating equations (GEE). Analyses included 2,218 test-positive cases (59% received monovalent booster dose) and 9,615 test-negative controls (62%) from 9,066 individuals, with median age of 21 years. Estimated adjusted relative VE of primary series plus monovalent booster dose versus primary series only against symptomatic SARS-CoV-2 infection was 40% (95% CI: 33–47%) during the overall analysis period and 46% (39–52%) during a period of Omicron circulation. In this relatively young and healthy adult population, an mRNA monovalent booster dose provided increased protection against symptomatic SARS-CoV-2 infection. In Chapter 3, we used data from the Seattle Flu Study (SFS), a community-based respiratory virus surveillance study in Seattle, USA and publicly available RSV genomes to assess genomic diversity of RSV over four respiratory virus seasons (2019–2020, 2020–2021, 2021–2022, and 2022–2023). SFS nasal swabs were collected and tested for RSV by RT-qPCR, with whole genome sequencing (WGS) performed for a subset. Among SFS samples collected from children and adults, 1.8% (232/13014) and 0.2% (86/46042) respectively tested positive for RSV-A and 1.1% (139/13016) and 0.3% (119/46043) for RSV-B. In Washington, USA during 2019–2020, RSV-A and RSV-B co-circulated (majority clades A.D.1 and B.D.4.1.1). No RSV was observed in 2020–2021 following implementation of nonpharmaceutical interventions to reduce SARS-CoV-2 transmission. Subsequently, RSV re-emerged off-season in 2021–2022 and shifted to mostly RSV-B (clade B.D.E.1) and then RSV-A (clade A.D.5.2) in 2022–2023. Shifts in genomic diversity over time were similar for all ages. We used community-based respiratory virus surveillance data from two studies to answer questions regarding two VPDs, COVID-19 and RSV. Results may inform the evidence base for policy recommendations for COVID-19 and RSV vaccines as well as provide a comparison for future monitoring of SARS-CoV-2 and RSV using similar surveillance data

ISGF3 regulates versican expression in macrophages

Supplemental Figure 1Growing evidence supports a role for versican as an important component of the inflammatory response, with both pro- and anti-inflammatory roles depending on the specific context of the system or disease under investigation. Our goal is to understand the regulation of macrophage-derived versican and the role it plays in innate immunity. In previous work, we showed that LPS triggers a signaling cascade involving TLR4, the Trif adaptor, type I interferons, and the type I interferon receptor, leading to increased versican expression by macrophages. In the present study, we used a combination of chromatin immunoprecipitation, siRNA, chemical inhibitors, and mouse model approaches to investigate the regulatory events downstream of the type I interferon receptor to better define the mechanism controlling versican expression. Results indicate that transcriptional regulation by canonical type I interferon signaling via the heterotrimeric transcription factor, ISGF3, controls versican expression in macrophages exposed to LPS. This pathway is not dependent on MAPK signaling, which has been shown to regulate versican expression in other cell types. The stability of versican mRNA may also contribute to prolonged versican expression in macrophages. These findings strongly support a role for macrophage-derived versican as a type I interferon-stimulated gene and further our understanding of versican’s role in regulating inflammation.NIH grants: R01AI130280, R01AI136468, and R21AI14753

When Harry Met Sidney: A Celebration of Friendship through Stories and Songs

Thesis (Master's)--University of Washington, 2024Far too many black trailblazing entertainers are mere footnotes in history. Contemporary American show business was shaped and refined by the talents of black artists, many of whom were not permitted to eat or drink where they performed for a sea of white faces. These early entertainers often suffered silently and elegantly onstage while they fought for civil rights, equality, fair wage, and dignity backstage. Harry Belafonte and Sidney Poitier are perhaps two of the more distinguished and decorated performers of the mid twentieth century, but few people really know the depth of their relationship or that Sidney got his start as Harry’s understudy. When Harry Met Sidney: A Celebration of Friendship through Stories and Songs is an ode to the memory of these legendary figures. This monodrama details their early years at the American Negro Theatre in New York City, their paralleling rises to fame, and their harrowing journey from Newark, New Jersey to Greenwood, Mississippi at the dangerous height of the Civil Rights Movement to transport $70,000 in cash to the Student Nonviolent Coordinating Committee, a protest organization on the frontlines

3D Printed Engineered Living Materials with Genetically Programmed Mechanical Properties and Bioproduction Performance for the Design of Functional Objects and Therapeutic Delivery Platforms

Thesis (Ph.D.)--University of Washington, 2024The synergy of synthetic biology and materials science yields innovative strategies to find alternative approaches for environmental, medical, and manufacturing challenges. Among these approaches, Engineered Living Materials (ELMs) stand out as a promising platform. ELMs, are a distinctive class of smart materials, which are synthetic living systems where genetically modified microorganisms are integrated into a polymer network, forming functional objects. The material properties and applications are determined by the cellular platform and the encapsulating polymer network. Nevertheless, gaps still exist in the seamless integration of biotic (cellular) and abiotic (polymer) components into a singular material, followed by their assembly into devices and machines. Herein, two different biocompatible polymer networks were developed including (i) a protein-based composite, bovine serum albumin (BSA) – poly (ethylene glycol) diacrylate (PEGDA), and (ii) a synthetic matrix comprising PEGDA-glycerol. These photocurable polymer networks were designed for processing ELMs in light-based 3D printing technologies. The relationships between embedded microorganisms and surrounding polymer matrices were investigated with respect to microbial viability, microbial proliferation behavior, bioproduction capacity, and mechanical properties of ELMs. Subsequently, the interactions between engineered microbial metabolites (L-dopa, betaxanthin, and proteinase A) and protein-based (BSA-PEGDA) polymer matrix were utilized to program mechanical stiffness and degradation time points as desired of 3D printed ELM objects. In an alternative strategy, the polymer concentration of the synthetic matrix (PEGDA-Glycerol) was adjusted to tune the toughness and moduli of 3D printed ELM bioreactors. Finally, an innovative approach toward ELMs for advanced drug delivery was developed using metabolically engineered probiotic strains and 3D printed medical stents. These ELM stents were designed to detect inflammatory biomarkers and initiate responses through the secretion of anti-inflammatory small molecules. This strategy presents a substantial opportunity for facilitating long-term, localized delivery