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- All Subjects: Biology
- Creators: College of Integrative Sciences and Arts
- Member of: Barrett, The Honors College Thesis/Creative Project Collection
Purpose—Use a framework of genetic knowledge to investigate the association between the genotypes of various genes with phenotypes, specifically the traits of elite athletes, in order to establish a personal opinion on their relevance to athletic performance.
Methods—Assemble and analyze selected published scientific studies on genotype and athletic performance and lastly to formulate a personal opinion on the value of genetic testing of athletes. ACTN3, ACE, MSTN, and apoE were the genes selected for analyses.
Results—Two genes, ACTN3 and ACE, showed a significant relationship of genotype to phenotypic traits related to athletic performance. ApoE did not demonstrate a phenotypic association with athletic performance, however it showed a correlation with injury susceptibility leading to traumatic brain injury (TBI). MSTN did not show a phenotypic association with athletic performance.
Conclusion—When considering the multifactorial nature of athletics, each sport must be investigated individually due to the different individual requirements. ACTN3 and ACE are the most widely studied genes, therefore, considerable data on their relevance to athletic performance was easily obtained and supported a relationship between genotype and athletic performance.
During the global COVID-19 pandemic in 2020, many universities shifted their focus to hosting classes and events online for their student population in order to keep them engaged. The present study investigated whether an association exists between student engagement (an individual’s engagement with class and campus) and resilience. A single-shot survey was administered to 200 participants currently enrolled as undergraduate students at Arizona State University. A multiple regression analysis and Pearson correlations were calculated. A moderate, significant correlation was found between student engagement (total score) and resilience. A significant correlation was found between cognitive engagement (student’s approach and understanding of his learning) and resilience and between valuing and resilience. Contrary to expectations, participation was not associated with resilience. Potential explanations for these results were explored and practical applications for the university were discussed.
We attempted to apply a novel approach to stock market predictions. The Logistic Regression machine learning algorithm (Joseph Berkson) was applied to analyze news article headlines as represented by a bag-of-words (tri-gram and single-gram) representation in an attempt to predict the trends of stock prices based on the Dow Jones Industrial Average. The results showed that a tri-gram bag led to a 49% trend accuracy, a 1% increase when compared to the single-gram representation’s accuracy of 48%.
In order to further compare porcine and human-derived enzymes, a determination of the enzyme effectiveness was done via digestion simulation. The digestion for both the human and porcine-derived enzymes consisted of three steps: oral, gastric, and intestinal. After the digestion, the absorbance for each enzyme class as well as a dilution curve of the formula used was read and recorded. Using the standard dilution curve and the absorbance values for each unknown, the formula and thus enzyme concentration that was lost through the reaction was able to be calculated.
The effectiveness of both the human and porcine enzymes, determined by the percent of formula lost, was 18.2% and 19.7%, respectively, with an error of 0.6% from the spectrophotometer, and an error of about 10% from the scale used for measuring the enzymes. This error was likely due to the small mass required of the enzymes and can be prevented in the future by performing the experiment at a larger scale.