Consequences of a dot-1.1 Deletion on Germ Cell Components in Caenorhabditis Elegans

Overview

The evolutionarily conserved DOT1 family of proteins is essential for development in higher mammals. However, overactive DOT1L acts as an oncoprotein, most notably in childhood leukemia. Although deleting DOT1 genes leads to excessive cell death, down-regulation may be a promising treatment if potential negative impacts and apoptosis can be avoided. Consequently, our study focused on the outcomes of a DOT1 gene knockout and on the molecular components that cause DOT1 gene deletion lethality. Using Caenorhabditis elegans (C. elegans) as a convenient system, we bred a nematode strain with a dot-1.1 deletion in concert with a ced-3 mutation to minimize the typical side effects of a dot-1.1 knockout. We hypothesized that (1) a dot-1.1 deletion would impact the Pgranule morphology and localization due to the similar consequences that occur with a dot-1.1 deletion and Pgranule function inhibition, and (2) dot-1.1 deletion lethality is linked to irregular pgl-1 expression because faulty gene expression can lead to embryonic lethality and dot-1.1 and pgl-1 have similar characteristics. In this study, we found no change in Pgranule structure in mutants. However, we discovered that the dot-1.1 deletion occasionally caused Pgranules expression in a third cell in embryos and larvae along with the two expected germ cells, indicating an additional cell with germline potential. Furthermore, we found no significant difference in levels of PGL -1::GFP expression after dot-1.1 was deleted, suggesting that pgl-1 expression functions properly in a background without dot-1.1 and therefore is not the cause of embryo death in dot-1.1 mutants. Yellow to Green: An Unsupervised Machine Learning Approach to Bus Stop Redistribution Grace Yan Morgantown High School, Morgantown, WV The transportation sector is the largest source of greenhouse gas emissions, making up 29% of US emissions, with school buses being a significant contributor. Last year, this study aimed to lower carbon emissions at a local high school by rearranging stops within existing routes using the Google Maps API. This year’s study builds upon previous work by validating the optimization results with statistical tests and extending the optimization to other schools. T o further optimize the bus system, bus stop consolidation was considered to reduce the total number of buses, particularly, as bus driver shortages continue to worsen across the nation. 106 This study also presents a new approach to bus route optimization by reducing the number of routes through bus stop recombination with an unsupervised machine learning approach– the k-means algorithm. Testing the k-means algorithm on the initial 30 routes indicated that the process worked best on smaller, more centralized areas. So, k-means was applied to two smaller areas each with 7 routes to regroup the stops while removing a bus. This reduced the travel distance by 22% and 12% for the two areas. Overall, the optimization would save 9.6% in total route distance and a calculated $15,198 on fuel each year (at a fuel cost of $4.50/gallon). The optimization would be able to prevent 35 metric tons from being emitted into the atmosphere each year. T o further improve efficiency, other clustering algorithms can be explored such as graph-based clustering, density- based clustering, and fuzzy k-means clustering.

Competition history

• JSHS 2023 Category not listed

Resources

• National JSHS abstract book ↗

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