Natural dermal armors are inspiring the development of advanced engineering materials and next generation flexible armors. Fish scales are an exemplary candidate and consist largely of laminated plies of unidirectional type I collagen fibrils. The mechanical properties of fish scales depend on the interpeptide bonds within the triple helix of the collagen fibrils. Adjusting the strength of these bonds to change the performance of the scales has applications to the design and functionality of bioinspired flexible armors. Here, elasmodine scales were exposed to polar solvents to adjust the extent of intermolecular bonding. Changes in the mechanical properties were evaluated in uniaxial tension and at two different strain rates. Results showed that the constitutive behavior was highly dependent on the intermolecular bonds. A significant increase was observed in elastic modulus (stiffness), strength and toughness as a result of increasing the extent of interpeptide bonding via solvents with low affinity for hydrogen bonding. A 300% increase was seen in the elastic modulus of scales soaked in acetone compared to HBSS at the highest strain rate. Furthermore, the importance of interfibril bonding was dependent on loading rate. Overall, results showed that the “protecto-flexibility” of fibrous armor materials can be improved by activating interfibril bonds and that this could spawn approaches for tuning armor performance.

Despite being individually uncommon, rare diseases are a heterogeneous set of nearly 7,000 diseases estimated to affect 6-8% of the global population, making them collectively common. Rare diseases are infrequent and their determinants are usually unknown which negatively impact clinicians’ ability to make an accurate and timely diagnosis. With the inception of consortia such as the Undiagnosed Diseases Network (UDN) and ontologies such as the Human Phenotype Ontology (HPO) there is now a wealth of information on rare diseases. This rich information has enabled researchers to infer gene-disease relationships and clinicians to assign genetic diagnoses. However, rare diseases that are caused by unique or uncommon gene combinations remain unsolved. In this study, we address this problem by assuming that a disease can be described by a combination of clinical features or phenotypes and, thus, a combination of genes. By developing a pipeline to incorporate known gene-phenotype relationships from publicly available resources such as HPO and using various combinatorial search algorithms, we can prioritize the most likely sets of genes to result in a given set of phenotypes. Integration of our pipeline with individual patient data is expected to further reduce the search space and produce higher quality predictions. We anticipate the results from this study to advance the quality and accuracy of gene-disease relationship predictions for rare and undiagnosed diseases associated with one or more genes. Our pipeline could be used as a tool for the diagnosis and discovery of likely oligogenic, rare and undiagnosed diseases that are normally turned away from consortia like the UDN.

Wildfires play a major role in the structure and composition of landscapes and the general ecology of the Pacific Northwest. The Klamath-Siskiyou eco-region in northern California and southern Oregon has been experiencing an increase in the frequency, scale, and intensity of wildfires in recent years. Understanding the effects of wildfires on small mammal communities is an important, yet understudied, aspect of the response of wildlife to wildfires. In 2014, two wildfires burned areas on and adjacent to a long-term study area of wildlife. We will investigate the effects of these wildfires on the occupancy of small mammals such as Neotoma fuscipes, Glaucomys sabrinus, and Tamiasciuris douglasii, using occupancy analyses of data collected during fall field seasons using track plate stations. The longitudinal data set that we have includes years of data before the fires occurred, allowing us to disentangle any effects of the wildfires from any naturally occurring variation. This research is important in determining what lasting effects these increases in wildfire frequency are going to have on predator-prey dynamics.

For many years, concerted efforts to combat malaria through the use of antimalarial drugs, bed nets, and other public health measures led to marked reductions in morbidity and mortality. Unfortunately, progress has stalled. Reductions in malaria have leveled off and even reversed in certain areas (WHO, 2017). As of 2016, there were 216 million cases and 445,000 deaths annually due to Plasmodium infections (WHO, 2017). To regain momentum and accelerate malaria eradication efforts, an effective and durable vaccine is needed. The Murphy Laboratory focuses on developing novel pre-erythrocytic (PE) malaria vaccines that can effectively stop the Plasmodium sporozoite (spz) before the clinically symptomatic blood stage begins. Identification and inclusion of multiple different protective Plasmodium antigens is thought to be crucial to developing a broad immune response and durable protection against this intracellular parasite. To test and define protective antigens, the Laboratory developed an “Acute Challenge” (AC) model in order to sensitively measure T-cell responses that are completely or partially protective. In this model, DNA vaccines encoding Plasmodium yoelii proteins are delivered by gene gun to induce CD8+ T-cell responses in BALB/c mice. At the peak of the immune response, we challenge the mice with luciferase-expressing P. yoelli sporozoites and measure the parasite burden and protection using IVIS imaging. A known protective epitope derived from P. yoelii circumsporozoite protein (CSP) induces a potent and protective response in this system. My project is to utilize the AC model to assess P. yoelli candidate antigens, of unknown protective potential, that are putatively exported or secreted from the parasite-containing vacuole into the host cell cytoplasm. Confirmed protective antigens will then be assessed for their localization and defined T-cell epitopes. The results will be used to create vaccines designed to maximize such responses and target the responding T-cells to the liver.

Jupiter and Saturn are our solar system’s largest gas giants with some of the most popular features of any known planet: Jupiter’s Great Red Spot (GRS) and Saturn’s rings. Over the summer of 2018, we analyzed these characteristics at Pacific Lutheran University’s W. M. Keck Observatory. Closer to the Earth, Jupiter’s atmosphere is subject to differential rotation in which the atmosphere of the planet rotate at different speeds. We use feature tracking and 2D to 3D mapping techniques to observationally determine the angular rotation of the GRS and compare it to the expected rotation of 11.5 km/s determined by the magnetosphere. Through our analysis we observe the movement of the GRS over multiple nights and determine the average speed to be around 10.97 km/s, a 4.60% difference from the expected value. Further beyond, Saturn’s rings are composed of particles of ice and dust that are thought to be remnants of comets, asteroids, or moons that collided in orbit around the planet. Since these rings are not single structures, their particles feature non-uniform spacing. The light intensity of the rings increase as you approach the B ring from either direction (with the exceptions of the Cassini Division, Encke, and Keeler gaps). Our research focused on determining the spatial variation of these intensities as observed from our land-based observatory and comparing this data to Hubble Space Telescope data quantifying atmospheric scattering in Tacoma.

Pain is a major contributor to disability, and is more prevalent among low income groups. Rehabilitation care can be an effective treatment, although access and improvement may be associated with social factors. We examined the association of social factors (English proficiency and income) with rehabilitation use, improvement in rehabilitation, and meeting treatment goals among older adults in bothersome pain. We hypothesized: 1) participants with lower English proficiency will experience less improvement and achieve treatment goals less often than participants who are proficient in English and 2) participants with lower incomes will experience less improvement and achieve their treatment goals less often than higher income participants. This is a secondary analysis of the National Health and Aging Trends Study (NHATS), a cohort study representative of Medicare beneficiaries. Participants are interviewed yearly with a self-reported health and socioeconomic survey. We included community dwelling participants who indicated being bothered by pain in the last month. Participants reported how well they understood or spoke English. “Not well” or “not at all” was considered not proficient. Income was self-reported as total household income. Outcomes reported are rehabilitation use for pain related reasons in the next year, improvement with rehabilitation, and meeting treatment goals. Descriptive analysis suggests participants with lower English proficiency had higher rates of rehabilitation improvement. Rehabilitation users have a median income about $6,000 higher than non-users, however there is no difference in income between improvement groups or those that do or do not meet rehabilitation goals. We found lower income is associated with lower rehabilitation use but not improvement among participants in bothersome pain. These results indicate improvement is consistent among rehabilitation users however, access is limited for lower income participants. More work is required to determine how barriers can be eliminated to improve access to rehabilitation care for people in pain.

About 3.1 million adolescents are diagnosed yearly with depression. Adolescent onset of depression is associated with acute or chronic difficulties in physical, mental, and psychosocial functioning. However, over 60% of adolescents with depression do not receive mental health care, and, among those who do, treatment engagement is low. Behavioral Activation (BA) is an evidence-based psychosocial intervention for individuals with depression. While BA holds promise as an effective treatment, researchers have found that adolescents may be better reached and engaged through social and mobile technologies. In addition, BA requires frequent interaction from patients over time, which can be difficult and costly to administer in-person. There is an opportunity to improve the usability of and engagement with EBPIs via online technologies. Asynchronous Remote Communities (ARC) is a promising technology-based approach for engaging adolescents that leverages technology’s reach while providing support, social interactions, and motivation. ARCs are private online groups on which researchers can deliver weekly research tasks to participants and gather information about their perceptions in a format that is lightweight, accessible, usable, and low burden. We have used ARC to both discover design requirements and to design/build a platform for administering BA, which we have tested with clinicians and adolescents. We used ARC with 10 mental health clinicians specializing in treating teens with depression to discover their needs. Specifically, we worked with mental health clinicians to better understand their needs and to identify facilitators and barriers to adapting BA to ARC. We used the Slack online platform to create an accessible, anonymous environment where we posted 20-minute long design activities each week for 10 weeks to be completed asynchronously. We were then able to recruit teenagers to understand their needs. Based on the results, we are adapting BA to ARC settings and then testing out the feasibility.

Electronic health records (EHRs) are often used by clinical and data researchers in numerous ways for various scientific investigations. When sharing patient information, certain precautions must be followed as to prevent the risk of a malicious actor being able to extract sensitive information. This study examines an experimental method of removing potentially identifiable information from free text medical notes by finding and removing phrases which are statistically uncommon. Furthermore, this study assesses if this method reduces risk of identification while also maintaining the utility of the data. The method involves analyzing a free-text dataset by first breaking all text up into fixed length phrases. The frequencies of these phrases are then tracked across the entire dataset on a per-patient, per-note, and dataset-wide basis. To benchmark the method, notes are de-identified using the method and privacy and utility are tested under different conditions. The results from using this method on real clinical notes are expected to produce text that will not only be more secure but will also retain information useful for applications such as machine learning, natural language processing, and data analysis. If this method proves to be successful, it could lead to institutions being able to share medical notes with researchers more easily. This in turn would eliminate a major obstacle which medical researchers face, as it would give them access to more data. Finally, when data are shared between institutions for research, the risk of identification can be represented as an objective and quantifiable metric.