Investigating the Effect of Plastic Mulches on Agroinfiltration

Overview

Agroinfiltration, a simple and efficient method to transiently express recombinant proteins in plants using Agrobacterium bacteria, is a popular method for biological research. However, few studies have tried to increase the efficiency of the process. This project aims to raise the efficiency of agroinfiltration in Nicotiana benthamiana using colored plastic mulches which have been found to increase plant growth by affecting plant conditions like the soil or light. Four groups of plants were investigated: a control without any plastic coverings on plants, red plastic, black plastic, and silver plastic mulch covered groups. A RUBY reporter was used to express the red pigment betalain via agroinfiltration. The concentration of betalain pigment was measured with spectrophotometry. A two sample t-test showed a significant difference between having plastic mulch versus not having any. Therefore, there was evidence that plastic mulches made a difference in agroinfiltration efficiency. Identification of Pancreatic Cancer Driver Genes with a Novel Machine Learning Approach: Principal Features Minnie Liang West Lafayette Jr/Sr High School, West Lafayette, IN With the lowest 5-year survival rate among all cancers, pancreatic cancer is considered one of the most lethal diseases in the world. Currently, there are very few reliable genes to target in pancreatic cancer therapies; hence, identifying new biomarkers is crucially needed. This study develops a state-of-the-art machine-learning workflow for cancer biomarker discovery: first, we detect the cancerous gene expression patterns through random forest modeling of the principal components of the single-cell RNA-sequencing (scRNA-seq) data and 42 then second, we identify the key genes driving these patterns. This method accounts for the effects of complex gene-gene interactions through random forest modeling and, thus, enables the genes driving cancer cell growth to be accurately identified. With a scRNA-seq dataset of pancreatic cancer, our workflow identifies several genes, including KRT17 and PTGS2, which have been validated as potential therapeutic targets in the literature. Our workflow also identifies novel genes that have never been studied, including MXRA5 and NDUFA6, while showing great potential as therapeutic targets. Their overexpression is linked with inferior survival in pancreatic cancer patients. My workflow potentially accelerates discoveries of therapeutic targets for genetic diseases, and for pancreatic cancer, my newly-discovered genes provide promising directions for advancing its treatments.

Competition history

• JSHS 2023 Category not listed

Resources

• National JSHS abstract book ↗

Related projects

Closest projects by meaning, across every fair and year in the corpus.