URCAF Abstracts 2021

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https://hdl.handle.net/10057/19836

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    20th Annual Undergraduate Research and Creative Activity Forum
    (Wichita State University, 2021-04-09) WSU Undergraduate Students
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    Thermal performance analysis of bacteria and bacterivore interactions
    (Wichita State University, 2021-04-09) Bristow, Stephanie Allison; Luhring, Thomas M.
    Microbiomes have a significant role in global carbon emissions and can potentially shift in response to climate change. Bacterivores play a strong role in regulating soil biomes and therefore could alter the structure of these microbial communities if theywere impacted by a slight change in temperature. We hope to estimate how bacterivores and their prey could shift in their relationships in the presence of one another across a thermal gradient. We cultured the bacterivore protist Paramecium aurelia on 3 bacterial treatments (E. Coli, S. enteritidis, and their combination). We then subjected each combined P. aurelia and bacterial treatment to a 12-37°C gradient of temperatures for 18-24 hours under exponential growth conditions. Each bacterial treatment was divided into a bacteria control, and 2 P. aurelia + bacteria plates (one for counting protists, one for measuring the effects of protists on bacteria) and replicated 5 times across 8 temperatures. Paramecium were counted individually after being pipetted and bacterial counts were conducted by hemocytometer. We predict inherent differences in temperature-dependent bacterial growth rates will have cascading impacts on the fitness of their predators. We also predict that differences in the number of bacterial cells in suspension versus in biofilms will affect the growth rates of Paramecium. The outcomes of this project will be critical first steps for elucidating the joint impacts of shifting climates and bacterivore communities on microbiomes.
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    Macrophomina phaseolina hot spots and correlations with soil and plant factors
    (Wichita State University, 2021-04-09) Houchen, Barrett; Houseman, Gregory R.
    Macrophomina phaseolina is a fungal pathogen capable of infecting over 500 plant species across the world and one of the most important pests of soybeans in Kansas. Investigation has been conducted on M. phaseolina's presence, due to its effects on agriculture and crop yield. Conversely, minimal research has examined the fungal pathogen's significance in native prairie communities, its correlation with environmental factors, or its spatial structure. Our goals are to better understand M. phaseolina's behavior in native prairies in hopes to apply these insight to agricultural systems. In the summer of 2020, we quantified the spatial structure of M. phaseolina in a 15 x 15 m grid of untilled tallgrass prairie and correlated M. phaseolina's abundance with soil and plant characteristics. We found a high variability in the density of M. phaseolina and limited evidence for spatial aggregation of pathogen abundance. Additionally, bivariate analysis revealed weak correlations between pathogen abundance and individual soil properties and no correlation between pathogen abundance and plant variables. The results found rule out several key factors and suggest a better understanding of how physical disturbance and the mechanism of spread for M. phaseolina contribute to the large differences in density observed.
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    PARROT: An orofacial myofunctional imaging and pressure mapping device
    (Wichita State University, 2021-04-09) Chastain, Hanna; Bell, Heidi
    Orofacial Myofunctional Disorders (OMDs) are characterized by abnormal movement patterns of the mouth. Complications from OMDs include problems with talking, swallowing, and breathing. The tongue is a commonly assessed structure within the mouth to monitor complications from OMDs. The tongue's placement within the oral cavity, however, limits the accessibility to observe and record objective lingual behaviors such as spatial positioning, placement (passive, active), movement, and performance simultaneously. PARROT is a wireless orofacial myofunctional imaging and pressure device to objectively measure tongue behaviors such as spatial positioning, placement, and movement without impeding natural movement. Further development and refinement of PARROT as a wearable mouthpiece with integrated sensors continues, in addition to exploring PARROT's viability for telemedicine capabilities alongside clinical guidance. Results obtained from this study will assist in the advancement of PARROT and current clinical practices addressing functional complications such as dysphagia and sleep apnea.
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    Smart wireless, flexible hybrid electronic for fall risk monitoring
    (Wichita State University, 2021-04-09) Nguyen, Tommy; Lee, Yongkuk
    Falls give a serious public health issue among the elderly (people aged 65 years or older) since they not only cause significant inquires and even mortality, but also result in enormous costs for healthcare services. In general, about 28~35% of older adults 65 years old or over and 32~42% of older adults 70 years old or over experience fall-related injury more than one time each year based on the World Health Organization (WHO) Global Report. The number of older adults suffered from fall-related injury will gradually increase as time goes on since their population is growing faster than any other age group; their population was 49.2 million in 2016 (about 15% of U.S. population) and is expected to reach 98 million by 2060 (about 25% of U.S. population). In addition, the medical costs associated with falls for older adults was estimated as $56 billion dollars by 2020. In order to minimize adverse consequences of falls and provide adequate medical response and care, a cost-effective, reliable and immediate fall detection system is essential. Therefore, this research focuses on the development of a skin-wearable hybrid electronic system for fall risk monitoring, which offers signal fidelity for accurate fall detection and user comfort for long-term use. We have designed and fabricated the skin-wearable device including a 6-axis motion sensor to collect motion data for different human activities (e.g., walking, running, and falls). A number of deep-learning algorithms were tested using collected data to identify an optimized fall detection algorithm. The collective results will significantly improve the life quality and independence of older adults as minimizing adverse consequences of falls and fall-related injuries.