Glioblastoma multiforme (GBM) is the most common and lethal primary malignant brain tumor in adults. Despite the multimodal standard treatments for GBM, the median survival is still about one year. Analysis of brain tissues from GBM patients shows that lipid droplets are highly enriched in tumor tissues while undetectable in normal brain tissues, yet the identity and functions of lipid species in GBM are not well understood. The aims of the present work are to determine how GBM utilizes fatty acids, and assess their roles in GBM proliferation. Treatment of U138 GBM cells with a monounsaturated fatty acid, oleic acid, induces accumulation of perilipin 2-coated lipid droplets containing triglycerides enriched in C18:1 fatty acid, and increases fatty acid oxidation. Interestingly, oleic acid also increases glucose utilization and proliferation of GBM cells. In contrast, pharmacologic inhibition of monoacylglycerol lipase attenuates GBM proliferation. Our findings demonstrate that monounsaturated fatty acids promote GBM proliferation via triglyceride metabolism, suggesting a novel lipid droplet-mediated pathway which may be targeted for GBM treatment.

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In highly connected financial networks, the failure of a single institution can cascade into additional bank failures. This systemic risk can be mitigated by adjusting the loans, holding shares, and other liabilities connecting institutions in a way that prevents cascading of failures. We are approaching the systemic risk problem by attempting to optimize the connections between the institutions. In order to provide a more realistic simulation environment, we have incorporated nonlinear/discontinuous losses in the value of the banks. To address scalability challenges, we have developed a two-stage algorithm where the networks are partitioned into modules of highly interconnected banks and then the modules are individually optimized. We developed a new algorithms for classical and quantum partitioning for directed and weighed graphs (first stage) and a new methodology for solving Mixed Integer Linear Programming (MILP) problems with constraints for the systemic risk context (second stage). We compare classical and quantum algorithms for the partitioning problem. Experimental results demonstrate that our two-stage optimization with quantum partitioning is more resilient to financial shocks, delays the cascade failure phase transition, and reduces the total number of failures at convergence under systemic risks with reduced time complexity.