Fine Arts

Authors: Porter Bischoff, Kody Garrett, Clayton Rawson. Mentors: Britt Wyatt, Josh Premo. Insitution: Utah Valley University. This research delves into the underexplored territory of medical experiences and their potential impact on undergraduate students' motivation in STEM courses. While prior studies have focused on factors like gender and ethnicity in STEM, little attention has been given to the influence of medical experiences and chronic conditions on STEM students, despite evidence suggesting that students with medical conditions face unique challenges in completing their degrees.Our study specifically investigates the effects of medical experiences and chronic conditions on students enrolled in science classes at an open enrollment institution. We hypothesize that increased academic interruptions due to medical experiences may lead to decreased science motivation, reduced sense of belonging, self-efficacy, and self-determination.Data was collected from 390 students across 14 biology courses, including non-majors, at a teaching-focused institution, both before and after the courses. Surprisingly, 57% of surveyed students reported having a medical experience, and 22% reported having a chronic condition, highlighting the significance of this identity within the student population.As anticipated, students experiencing more medical interruptions exhibited a notable decrease in their sense of belonging and self-efficacy, albeit with a small effect size. Intriguingly, students with medical experiences who engaged more with science demonstrated significantly higher levels of science immersion and motivation. This suggests that medical experiences can influence student engagement with science, both positively and negatively. The impact of these interruptions on a student's academics is closely linked to their sense of belonging and self-efficacy. However, if medical experiences drive increased engagement with science, students may find themselves more motivated to explore these experiences within the context of scientific inquiry.Understanding how medical experiences can shape students' motivation is essential as science instructors adapt their course content and pedagogy to be more inclusive, embracing the diverse identities within their student population.

Authors: Brett Pickett, Lincoln Sutherland, Jacob Lang. Mentors: Brett Pickett. Insitution: Brigham Young University. Introduction: Breast cancer is one of the most prevalent types of cancer present in society today, and is the second leading cause of cancer death for women. Approximately 13% (1 in 8) of women will develop invasive breast cancer, with 3% of women (1 in 39) dying from this type of cancer. Three important classifications used when formulating a treatment plan for breast cancer are the presence or absence of Estrogen Receptor (ER), Progesterone Receptor (PR), or Hormone Receptor (HR). Treating Estrogen Receptor Positive (ER+) breast cancer with aromatase inhibitors, such as Letrozole, is the current standard treatment for all postmenopausal women. A prior study by Lee et. al. identified PRR11 as the only gene that was significantly overexpressed in resistant vs non-resistant cancers among the 51 genes in chromosome arm 17q23. The goal of the current study is to perform a secondary analysis of this valuable dataset to identify genes, signaling pathways, and biomarkers across the whole human transcriptome that are significantly associated with treatment resistance in ER+ patients.Methods: We retrieved, preprocessed and analyzed 58 ER+ breast cancer samples from patients who had been treated with Letrozole, which are publicly available in the NCBI Gene Expression Omnibus (GEO) database. The Automated Reproducible MOdular Workflow for Preprocessing and Differential Analysis of RNA-seq Data (ARMOR) was used to process our data downloaded from NCBI. This workflow trimmed low quality reads from the RNA-sequence reads, mapped and quantified our data to generate a DEG list. Gene ontology enrichment with camera was also performed. Next, the genes were mapped to common gene identifiers and input to the signaling pathway impact analysis (SPIA) algorithm to identify intracellular signaling pathways that were enhanced by our DEGs. With that information, Pathway2Target was used to identify known drug targets within our identified pathways. Finally, a decision tree-based machine learning approach was used to predict features/expressed genes that could be used to most accurately classify responders vs nonresponders to Letrozole. Results: Our comparison of 36 responders versus 22 non-responders detected a total of 18,735 genes and identified 105 that were statistically significant (p-value < 0.05) after applying a false-discovery rate (FDR) correction, including SOX11, S100A8/S100A8, and IGLV3-25. We then used the Signaling Pathway Impact Analysis (SPIA) algorithm to determine whether any known intracellular signaling pathways were significantly enriched in DEGs (Bonferroni-adjusted p-value < 0.05). This analysis identified 4 pathways that were statistically significant in Non-Responders to Letrozole Treatment. We then used the pathway results to predict 60 existing therapeutic targets that could be repurposed to treat the resistance phenotype. Notably, the predicted targets for the non-response phenotype included VEGFA, a current target for solid tumors as well as ESR1, an Estrogen Receptor. We next wanted to determine whether we could predict transcriptional biomarkers that could aid with identifying patients that do not respond to treatment. To do so, we used the read counts for all samples as the input for this analysis and identified 278 transcriptional biomarkers. Performance metrics for all biomarkers identified yielded an area under the receiver-operator characteristic (AUROC) curve of 0.972 (Figure 2), indicating an exceptional ability to classify Letrozole responders vs non-responders by the transcriptional profile. Sensitivity for all transcriptional biomarkers was measured at 100%, and specificity at 94%. We used the top two biomarkers from our first analysis as input for a second analysis to determine the performance of a smaller subset. Our second analysis determined that PRDX4 and E2F8 together yielded an AUROC of 0.823 and an overall accuracy of 88.2%. Discussion:Our results identify additional DEGs, pathways, targets and biomarkers for further exploration in the treatment and categorization of ER+ breast cancer.

Authors: Eden Beazer, Aubree Bench, Ethan Jones, Jared Carter. Mentors: Jeffrey Tessem. Insitution: Brigham Young University. Incidence of diabetes worldwide has grown from 108 million people in 1980 to 422 million people in 2014, nearly tripling in just thirty-four years. Type 2 diabetes (T2D) is characterized by the loss of pancreatic beta cell mass and the failure of the remaining beta cells to provide adequate insulin. Contributing to the development of T2D is glucolipotoxicity (GLT), a condition characterized by the harmful elevation of glucose and fatty acid levels within beta cells. While there are existing treatments for symptoms of diabetes, much remains to be understood about its underlying causes and effective preventative measures. Flavonoids are naturally occurring phenolic compounds found in many fruits and vegetables that have various anti-inflammatory health benefits. Previous studies suggest that epicatechin, a flavonoid present in cocoa, can reduce the effects of diabetes by diminishing insulin desensitization and increasing glucose stimulated insulin secretion (GSIS). Interestingly, the bioavailability of epicatechin is poor, while its metabolites are more easily absorbed in the small intestine. Further studies demonstrated that under non-stressed conditions in beta-cells, hippuric acid, homovanillic acid, and 5-phenylvaleric acid, metabolites of epicatechin, stimulate insulin secretion at concentrations more realistically found in the body. However, the effects of these metabolites in glucolipotoxic conditions are unknown. Here, we present the effects of epicatechin and its metabolites hippuric acid, homovanillic acid, and 5-phenylvaleric acid on beta cell insulin secretion and mitochondrial respiration under GLT culture conditions. This study aimed to contribute to the limited body of knowledge on the bioactivity of flavonoid metabolites on beta cell function under damaging conditions observed with T2D, offering crucial insights for developing effective strategies to harness the health benefits associated with flavonoids.

Authors: Zach Fiore. Mentors: Jared Nielsen. Insitution: Brigham Young University. Gait apraxia is a type of apraxia that affects lower limb use in walking. It is characterized by difficulty initiating gait, freezing of gait, and other gait disturbances that cannot be attributed to complications affecting sensory, motor, or cerebellar function, psychiatric disease, nor ataxia. Symptoms often present following brain trauma. Previous research has indicated that gait apraxia may be linked to lesions in the frontal lobes, basal ganglia and supplementary motor area. However, the specific cerebral location has been debated with minimal research done on the symptom’s implicated neural circuits. The purpose of this study is to determine the networks in the brain that are involved in the pathophysiology of gait apraxia. To determine this, we used the lesion network mapping method. A systematic literature review was performed, with specific inclusion criteria, to find case studies of patients presenting with gait apraxia stemming from acquired brain injury (n=15). Lesion network mapping analysis (Fox et al., 2018) was performed on 15 cases with a large cohort of healthy control resting-state scans (n=1000). The analysis showed that lesions exhibited functional connectivity to the bilateral medial dorsal and pulvinar nuclei of the thalami (n=15), which supports previous associations of basal ganglia damage contributing to gait apraxia. A novel region, the cingulate cortex (n=15), was also found to be functionally connected to the lesion networks. This region is a part of the cingulo-opercular network, responsible for many functions, including action. This network has recently been found to display strong functional connectivity with the somato-cognitive action network, responsible for coordinating movements with cognitive processes. Further research is necessary to determine the mechanism of how these networks interact in contributing to gait apraxia.

Authors: Hannah Smith. Mentors: Jeff Parker. Insitution: Brigham Young University. The Arabic language uses a rich system of patterns called verb forms to create verbs with related meanings from roots made of consonants. For example, the root k-t-b, in form I, kataba, means ‘to write.’ When it appears in form II as kattaba, it means ‘to make someone write.’ In form III, kaataba, it means ‘to exchange letters,’ ‘to correspond’ (Wehr, 2019). There are ten commonly used verb forms, although not every root is used in every form. As Arabic has been exposed to English and speakers borrow verbs from English into their Arabic, they make choices about how and whether to integrate these foreign words into the verb forms. For example, the verb ‘to block (on social media),’ with the derived root b-l-k, is used in form II, ballaka, ‘to block.’ Other borrowed verbs, however, don’t get integrated in this way. Instead, they are used as a noun with a native Arabic verb, usually ‘amila, ‘to do.’ For example, English ‘to delete’ becomes ‘amila daliit, literally ‘to do a delete.’ This study aims to understand what factors influence when and how completely borrowings from English like these are integrated into the verb forms in Jordanian Arabic (JA) by interviewing university students in Amman, Jordan. The interviews are primarily focused on four forms in which borrowings in JA frequently appear (Salem, 2015). Initial data from a group of eight participants shows a general acceptance for the borrowings in their base forms, with much more variation in speakers’ intuitions regarding their usage in other forms. For example, all eight participants perceive ‘to block,’ ballaka, as a completely acceptable word used by most people. However, only two participants felt the same way about its counterpart form, taballaka, ‘to be blocked,’ whereas two other participants perceived it as something no one would ever use. Furthermore, some participants perceive verbs that rely on ‘to do,’ such as ‘to delete,’ to be in the beginning stages of integration, with three of eight interviewees stating that some of these verbs actually are acceptable in verb form II. Thus far, the perceptions of all the borrowings seem to indicate that speakers believe that integration of borrowed verbs is a gradual process. Furthermore, in addition to other factors, the extent to which the verbs are integrated in JA seems to be affected in large part by cultural perceptions of how foreign the verbs are, a factor that has not been noted in previous studies on the subject.

Authors: Alberto Miranda, Samannoy Ghosh, Yong Lin Kong. Mentors: Yong Lin Kong. Insitution: University of Utah. Microscale robots can impart a broad range of functionalities in the biomedical domain that can be leveraged to address unmet clinical needs, including noninvasive surgery and targeted therapies. Conventional robot navigation methods typically involve specific gaits suited for certain environmental conditions. However, implementing the same conventional methods inside a human body is highly challenging. As the human body is a complex and dynamic environment, a microrobot must adapt to these complex and challenging environments to perform targeted studies. Previous research demonstrated an integration of an untethered, 3D-printed three-linked-sphere crawler with a model-free reinforcement algorithm. The work done with the theoretical Najafi–Golestanian three-linked-sphere mechanism was its first experimental integration with a reinforcement learning algorithm as a relatively simple and highly scalable self-learning robot that can navigate in unconfined and confined spaces. The progress presented in the current research is a direct continuation of the previous work on the 3-linked-sphere crawler. While the previous work focused on developing a proof of concept for adaptive gait learning for the crawler, the current work focuses more on the challenges of implementing the robot in a low Reynolds number fluid medium. Our current research hypothesizes that a self-learning autonomous system could demonstrate successful gait adaptation in a low Reynold’s flow environment. The design of our robot has been significantly improved to make it sustainable for extended use under viscous fluids. The research presented outlines the work that has been done to transition the robot from a crawler into a swimmer, the challenges that have been faced, and how they have been addressed. Successful implementation of this 3-sphere-swimmer will be a step forward in integrating machine learning tools into microswimmers for autonomous gait adaptation inside the human body.

Authors: Josh D Gubler, Connor D Olsen, Fredi R Mino, Mingchuan Cheng, Jacob A George. Mentors: Connor Olsen. Insitution: University of Utah. The long-term goal of this research is to investigate how lower sampling rates of electromyographic (EMG) signals affect the performance of classification and regression algorithms. EMG signals measure the electrical activity of muscle contractions. Myoelectric interfaces can classify or regress features generated from the EMG signal to control devices like prostheses, exoskeletons, robotic systems, or human-computer interfaces. Most of the power of the EMG signal is contained between 50 and 500 Hz, and most recording devices sample EMG at 1 kHz with a 5-15 Hz high-pass filter and a 375-500 Hz low-pass filter. As myoelectric devices become wireless and integrated with wearable technology, reducing the sampling rate can substantially reduce battery consumption and processing power. We sampled EMG data at 30 kHz from the forearms of three participants while they performed six gestures. We then downsampled to rates ranging between 50-1000 Hz and calculated various EMG features from the downsampled data. We found significant effects for both EMG feature and sampling rate on regression performance of a modified Kalman Filter (p < 0.05, two-way ANOVA). The mean-absolute-value and waveform-length EMG features performed significantly better at low frequencies (<250 Hz) in contrast to zero-crossing, slope-sign-change, and mean-frequency EMG features (p < .05, multiple pairwise comparisons). Sampling rate also had a significant impact on the classification accuracy of a k-nearest neighbors algorithm (p < 0.05, two-way ANOVA). However, sampling rate had no impact on classification accuracy for a continuous Convolutional Neural Network (CNN) (p > 0.05, two-way ANOVA). Future work will validate the effectiveness of this CNN as a control modality when using downsampled EMG from wearable sensors. If proficient control can be achieved from down sampled EMG, this could substantially improve battery life and make EMG a more practical biosensor for wearable devices.

Authors: Rui Jin, Lindsay C Rupp, Anna Busatto, Rob S MacLeod. Mentors: Rob S. MacLeod. Insitution: University of Utah. Introduction: The electrocardiogram is the most common tool to diagnose and assess cardiac conditions, such as rhythm abnormalities, myocardial ischemia, and heart failure. However, clinical diagnosis and management of heart disease are challenging due to the remote nature of body-surface electrocardiogram measurements, with a median accuracy of 67% among physicians. One approach to improve the accuracy of electrocardiography is to conduct mapping studies in which 10-100 catheter-based electrodes are inserted within the heart. The recorded signals provide more proximity and thus accuracy, but they also require specialized software to analyze, quantify, and visualize. We developed the Signal Processor for Electrogram and Electroanatomic Data (SPEED), a new, open-source, unified pipeline to facilitate effective signal processing and visualization of such cardiac-mapping signals.Materials and Methods: Our pipeline is based on two existing toolboxes, the Preprocessing Framework for Electrograms Intermittently Fiducialized from Experimental Recordings (PFEIFER) and OpenEP. PFEIFER is a toolset for sophisticated signal-processing of cardiac electrograms that allows the user to select semi-automatically fiducial markers, which are time points and intervals of interest within a heartbeat. OpenEP primarily accepts as input complete electroanatomic data, including both processed cardiac electrograms and spatial geometry; OpenEP also provides built-in functions for analyzing and visualizing cardiac electrograms, such as displaying potentials on the cardiac geometry. Since both software packages provide complementary workflows for managing electrograms, our goal was to integrate the two software packages and present it to the user as a new Graphic User Interface utilizing both applications simultaneously.Results: It was natural to develop SPEED in MATLAB as this is also the language used for both PFEIFER and OpenEP. The primary interface to SPEED incorporates a data-centric design such that the user can provide the electrogram and geometry files to be processed, and the algorithm automatically determines the applicable functions based on the input type. Since both PFEIFER and OpenEP can parse data into more interpretable open-source formats, the user can also export the processed data for further analysis in addition to visualizing and quantifying the data features. Through integrating both software packages, SPEED can support the following main functionalities: (1) in-depth filtering and processing of electrogram signals, (2) visualizing anatomic geometry and electrode locations, and (3) mapping three-dimensional potential and activation of cardiac electrophysiology.Discussion: SPEED offers the user a more thorough and unified workflow in the analysis of cardiac-mapping signals than either of its components. The user can utilize the functionalities of both PFEIFER and OpenEP simultaneously, allowing for a versatile and powerful processing pipeline. For instance, the user can extract key features from the recorded electrograms and visualize the location of the corresponding electrodes, a feature that was previously not possible. In addition, the open-source nature of the software packages allows the user to modify or expand the functions to better suit their individual needs. The software design of SPEED is still in the early stages; thus, as with most software, further development and user testing will follow to make the algorithms compatible with more data types and implement additional features. Conclusion: SPEED processes and displays the complex information in a clear and accessible way, allowing the user to perform subsequent interpretations and analyses more easily. SPEED can be used by research cardiologists to facilitate a more efficient workflow, as well as to improve the efficiency and accuracy of clinical diagnosis of heart diseases.

Authors: Megan Sorensen, Lianna Olsen, Maleah Bowen. Mentors: Ryan Kellems, Cade Charlton. Insitution: Brigham Young University. This research explores how virtual reality can help children with autism. It explores the effects of an intervention using virtual reality to assess the usability of virtual reality for children with autism.There are more than 200 studies that show that video modeling is a viable way to teach students with autism various skills. Research has started to explore if virtual reality is also a successful way to teach students with autism similar skills. Different studies have been conducted regarding social skills and cognitive skills and how they can be taught using virtual reality. There is very little research about fine-motor skills and virtual reality for individuals with disabilities. Our research adds to that research, but specifically explores fine-motor skills and the feasibility of using virtual reality with children with autism. The study uses a single subject research design with 5-10 participants with autism. Before starting data collection, the participant completes a brief tutorial for how to use virtual reality. One tutorial is for how to use the controllers and the other is for how to use the hand-tracking feature that allows the participant to use their hands instead of the controllers. The study is an alternating-treatment design. The individual is given a specific task to complete using the playroom items found in the “First Steps” application on the Oculus Quest. For example, they are asked to stack blocks, throw paper airplanes, etc. They either complete the task using the controllers or with the handtracking feature based upon a random schedule and the latency of the trial is measured. The data compares hand-tracking and controllers and measures whether the participant is getting faster with every trial. It was hypothesized that the students will be able to learn how to use the virtual reality interface and that they will be able to generalize the skills learned to other settings and/or skills, and that the interventions will be viewed by the participant and parents as socially valid ways to learn. It was anticipated that the participant will get increasingly faster at completing the tasks for both hand-tracking and with the controllers. It was also anticipated that the rate of completing tasks with controllers will be faster than that of hand-tracking. Preliminary date has shown this relationship to be true. Future research will explore the specific practices of how virtual reality can be best used for children with autism.