No abstract

Tumor-associated carbohydrate antigens (TACA) result from the aberrant glycosylation that is seen with transformation to a tumor cell. The carbohydrate antigens that have been found to be tumor-associated include the mucin related Tn, Sialyl Tn, and Thomsen-Friedenreich antigens, the blood group Lewis related LewisY, Sialyl LewisX and Sialyl LewisA, and LewisX, (also known as stage-specific embryonic antigen-1, SSEA-1), the glycosphingolipids Globo H and stage-specific embryonic antigen-3 (SSEA-3), the sialic acid containing glycosphingolipids, the gangliosides GD2, GD3, GM2, fucosyl GM1, and Neu5GcGM3, and polysialic acid. Recent developments have furthered our understanding of the T-independent type II response that is seen in response to carbohydrate antigens. The selection of a vaccine target antigen is based on not only the presence of the antigen in a variety of tumor tissues but also on the role this antigen plays in tumor growth and metastasis. These roles for TACAs are being elucidated. Newly acquired knowledge in understanding the T-independent immune response and in understanding the key roles that carbohydrates play in metastasis are being applied in attempts to develop an effective vaccine response to TACAs. The role of each of the above mentioned carbohydrate antigens in cancer growth and metastasis and vaccine attempts using these antigens will be described.

No abstract

Traditional power grid and its demand-side management (DSM) techniques are centralized and mainly focus on industrial consumers. The ignorance of residential and commercial sectors in DSM activities degrades the overall performance of a conventional grid. Therefore, the concept of DSM and demand response (DR) via residential sector makes the smart grid (SG) superior over the traditional grid. In this context, this paper proposes an optimized home energy management system (OHEMS) that not only facilitates the integration of renewable energy source (RES) and energy storage system (ESS) but also incorporates the residential sector into DSM activities. The proposed OHEMS minimizes the electricity bill by scheduling the household appliances and ESS in response to the dynamic pricing of electricity market. First, the constrained optimization problem is mathematically formulated by using multiple knapsack problems, and then solved by using the heuristic algorithms; genetic algorithm (GA), binary particle swarm optimization (BPSO), wind driven optimization (WDO), bacterial foraging optimization (BFO) and hybrid GA-PSO (HGPO) algorithms. The performance of the proposed scheme and heuristic algorithms is evaluated via MATLAB simulations. Results illustrate that the integration of RES and ESS reduces the electricity bill and peak-to-average ratio (PAR) by 19.94% and 21.55% respectively. Moreover, the HGPO algorithm based home energy management system outperforms the other heuristic algorithms, and further reduces the bill by 25.12% and PAR by 24.88%.