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Background: MUC1 is a transmembrane glycoprotein, aberrantly glycosylated and overexpressed in a variety of epithelial cancers, and plays a crucial role in cancer progression, especially in breast cancer. It is also an essential regulator for immune functionality, but the mechanisms whereby it effects immune infiltration in breast cancer remain uncertain. Methods: In this research, MUC1 expression was analyzed by the Oncomine and TIMER database. The association between MUC1 and prognosis was evaluated by Kaplan-Meier plotter database. The correlations of MUC1 with immune infiltration and immunological markers were assessed by TIMER database. Results: We found that MUC1 expression was significantly correlated with outcomes in multiple cancers, with the effect being particularly pronounced in breast cancer. Pathologically, elevated MUC1 expression was related with worse prognosis depending on intrinsic subtypes, ER status, patient stage, lymph node and TP53 mutation status in breast cancer. Specifically, low level of MUC1 seemed to be more favorable to luminal B patients with systemic treatment. MUC1 expression had significant negative correlations with infiltrating levels of CD8+ T cells, CD4+ T cells, B cells, macrophages, neutrophils, monocytes and dendritic cells (DCs) in breast cancer. Besides, MUC1 displayed strong regulations on macrophage polarization and diverse immunological gene markers. The patients with lower MUC1 and deeper immune infiltration predicted better prognosis. Conclusions: MUC1 is significantly associated with clinical prognosis and potentially plays an essential role in modulating cytotoxic T lymphocytes (CTLs), tumor associated macrophages (TAMs), natural killer cells (NK cells) and DCs in breast tumors. Collectively, MUC1 could be served as a valuable biomarker of predicting prognosis and immune infiltration for breast cancer patients.

We introduce a stochastic dynamic programming (SDP) model that co-optimizes multiple uses of distributed energy storage, including energy and ancillary service sales, backup capacity, and transformer loading relief, while accounting for market and system uncertainty. We propose an approximation technique to efficiently solve the SDP. We also use a case study with high residential loads to demonstrate that a deployment consisting of both storage and transformer upgrades decreases costs and increases value relative to a transformer-only deployment.

We study decentralized plug-in electric vehicle (PEV) charging control, wherein the system operator (SO) sends price-based signals to a load aggregator (LA) that optimizes charging of a PEV fleet. We study a pricing scheme that conveys price and quantity information to the LA and compare it to a simpler price-only scheme. We prove that the price/quantity-based mechanism can yield a socially optimal solution. We also examine several numerical case studies to demonstrate the superior performance of the price/quantity-based scheme. The price/quantity scheme yields nearly identical PEV charging costs compared to the social optima, whereas the price-only scheme is highly sensitive to the choice of a regularization penalty term that is needed to ensure convergence. We also show that the time to compute an equilibrium with the price-only mechanism can be up to two orders of magnitude greater than with the price/quantity scheme and can involve 24 times more information exchange between the SO and LA.

We develop a stochastic dynamic programming model that co-optimizes the use of energy storage for multiple applications, such as energy, capacity, and backup services, while accounting for market and system uncertainty. Using the example of a battery that has been installed in a home as a distributed storage device, we demonstrate the ability of the model to co-optimize services that ‘compete’ for the capacity of the battery. We also show that these multiple uses of a battery can provide substantive value.

The paper develops a simulation–optimization model that determines where to locate electric vehicle chargers to maximize their use by privately owned electric vehicles. Applying this model to the central-Ohio region, we demonstrate that a combination of level-one and -two chargers is preferable to level-two chargers only. We further explore interactions between the optimization criterion used and the budget available. We finally show that although the optimal location is sensitive to the specific optimization criterion considered, overall service levels are less sensitive to the optimization strategy.