Climate is a critical determinant of agricultural productivity, and the ability to accurately predict this productivity is necessary to provide guidance regarding food security and agricultural management. Previous predictions vary in approach due to the myriad of factors influencing agricultural productivity but generally suggest long-term declines in productivity and agricultural land suitability under climate change. In this paper, I relate predicted climate changes to yield for three major United States crops, namely corn, soybeans, and wheat, using a moderate emissions scenario. By adopting data-driven machine learning approaches, I used the following machine learning methods: random forest (RF), extreme gradient boosting (XGB), and artificial neural networks (ANN) to perform comparative analysis and ensemble methodology. I omitted the western US due to the region's susceptibility to water stress and the prevalence of artificial irrigation as a means to compensate for dry conditions. By considering only climate, the model's results suggest an ensemble mean decline in crop yield of 23.4\% for corn, 19.1\% for soybeans, and 7.8\% for wheat between the years of 2017 and 2100. These results emphasize potential negative impacts of climate change on the current agricultural industry as a result of shifting bio-climactic conditions.
The 5-year survival rate for late-stage metastatic melanoma is only ~30%. A major reason for this low survival rate is that one of the most commonly mutated genes in melanoma, NRAS, has no FDA-approved targeted therapies. Because the RAS protein does not have any targeted therapies, patients with RAS mutant tumors have an ongoing need for treatments that indirectly target RAS. This thesis project aims to identify expression and phosphorylation levels of proteins downstream of RAS in melanoma cell lines with the most common driver mutations. By analyzing the protein-level differences between these genetic mutants, we hope to identify additional indirect RAS protein targets for the treatment of NRAS mutant melanoma. RAS has several downstream effector proteins involved in oncogenic signaling pathways including FAK, Paxillin, AKT, and ERK. 5 melanoma cell lines (2 BRAF mutant, 2 NRAS mutant, and 1 designated wildtype) were analyzed using western bloting for FAK, Paxillin, AKT, and ERK phosphorylation and total expression levels. The results of western blot analysis showed that NRAS mutant cell lines had increased expression of phosphorylated Paxillin. Increased Paxillin phosphorylation corresponds to increased Paxillin binding at the FAT domain of FAK. Therefore, cell lines with increased FAK FAT – Paxillin interaction would be more sensitive to FAK FAT domain inhibition. The data presented provide an an explanation for the reduction in cell viability in NRAS mutant cell lines infected with Ad-FRNK. This information also has significant clinical relevance as researchers work to develop synthetic FAK FAT domain inhibitors, such as cyclic peptides. Additionally, cell lines with high levels of phosphorylated AKT showed a significant reduction in the amount of phosphorylated ERK. The identification of this inverse relationship may help to explain why BRAF and NRAS mutations are mutually exclusive. To conclude, NRAS mutant cell lines have increased expression of phosphorylated Paxillin and AKT which may explain why NRAS mutant cell lines are more sensitive to FAK FAT domain inhibition.