2021-2022 Research Paper

Abstract

This research paper is an analytical explorative of a particle distribution technique known as laser diffraction, and an image analysis method known as a time series. Laser diffraction is a particle sizing technique for materials ranging from hundreds of nanometers up to several millimeters in size. A variety of analytical techniques exist that are often used to process laser diffraction data; one of these methods being a time series. C. elegans, a freely swimming nematode, rely on mechanosensory neurons to perform locomotion. Using laser diffraction, mechanosensory data can be gathered on C. elegans because of the fast rate of data collection. Using the time series, a control is established by using a human hair in place of the nematodes. The goal of this study is to determine whether the use of a human hair is viable for accurately predicting the sinusoidal locomotion patterns of the C. elegans nematode, by comparing previously gathered data on the oscillatory frequencies of nematodes to data gathered on the oscillatory frequencies of the human hair. Additionally, a Convolutional Neural Network will be used to predict movement outcomes in both the nematodes and the human hair, and compare them using regression testing. Data found that both R values demonstrate strong linear relationships, being R=0.7876, R=0.99982, respectively. These statistical analyses prove that both correlations are statistically significant, thus upholding the research question and hypothesis. This paper ultimately provides a framework for future research on nematode locomotion and their predictable response to external stimuli.