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  • Chk is dramatically induce by the IL family

    2019-12-03

    Chk is dramatically induce by the IL-4 family of cytokines and equally dramatically inhibited by IFN-γ, a regulation that appears to be specific for human monocytes. In primary human T cells, Chk is not normally present, but it can be induced by potent activators of T rosmarinic acid such as PHA or IL-2 ((McVicar et al., 1994) and Fig. 5). In contrast to our data in the human monocytes, Chk induction in primary T cells is not affected by IFN-γ (Fig. 5). These results further establish a unique role for Chk in monocyte differentiation. In addition to differences in cell specific regulation of Chk, there also appears to be species specific effect in the regulation of Chk. To date, three different Chk knockout mice have been generated with no obvious phenotype yet described (unpublished result and (Samokhvalov et al., 1997, Hamaguchi et al., 1996)). These results suggest that the knockout mice have either entirely compensated for the deficit in Chk or that the role of Chk is so refined that it has not yet been identified. Additionally, most studies in the mouse have involved the use of tissue macrophages or peritoneal exudate macrophages rather than blood monocytes. Experiments similar to the ones reported in this manuscript revealed that, in peritoneal exudate macrophages of mice, Chk is constitutively expressed and stimulation with IL-4 family members results in a slight elevation of Chk protein, while treatment with IFN-γ results in a slight reduction of Chk protein (data not shown). Therefore, in addition to the difference in species, another explanation for the difference in phenotype is the differentiation state of the macrophages. It has been well established that monocytes give rise to tissue macrophages, characterized in the mouse by its expression of antigenic markers such as macrosialin (CD68) (Gordon, 1999). This evidence suggests that the phenotype of the tissue macrophages or peritoneal exudate macrophages may be beyond the stage at which Chk induction, signaling, and regulation are involved.
    Acknowledgements
    Introduction The bilevel programming problem (BLP) is a nested optimizations problem with two levels in a hierarchy, the upper and lower level decision-making. Both of them have their own objective functions and constraints. The upper level maker makes his decision firstly, followed by the lower decision make. The objective function and constraint of the upper level programming rely not only on their own decision variables but also on the optimum solution of the lower level programming. The decision maker at the lower level has to optimize its own objective function under the given parameters from decision maker at the upper level, who, in return, with complete information on the possible reactions of the lower, selects the parameters so as to optimize its own objective function. Unlike the multiple objective mathematical programming techniques, the bilevel mathematical programming emphasizes the non-cooperative character of the system. The application of this hierarchical model can be used widely in such areas as resource allocation, decentralized control, network design problem, etc. [1]. The successful application of this hierarchical model depends on how well it is solved in handling realistic complications. A significant amount of effort have been devoted to solving bilevel mathematical programming and many efficient algorithms have been proposed. To date a few algorithms exist to solve BLP, which can be classified into four types: approach of using the Karush–Kuhn–Tucker (K-K-T) condition [2], [3], [4], [5], [6], penalty function approach [7], [8], [9], [10], descent approach [11], [12] and evolutionary approach [13]. Recently, the evolutionary algorithms are widely used to solve different problems in optimal areas and become an alternative for solving bilevel programming for its good characteristics. In 1994, Mathieu etc. [14] firstly developed a genetic algorithm based bilevel programming algorithm. In 1998, Kemal etc. [15] proposed a dual temperature simulated annealing approach for solving bilevel programming problems. In this method, the lower level problem is stochastically relaxed with a parameter that can be used as a temperature scale in rosmarinic acid simulated annealing. Oduguwa etc. [16] proposed a bilevel genetic algorithm, which is an elitist optimization algorithm developed to encourage limited asymmetric cooperation between the two players, to solve different classes of the bilevel problems within a single framework. Wang etc. [17] proposed an evolutionary algorithm for solving nonlinear bilevel programming problem. A specific optimization problem is constructed with two objectives firstly, which then is solved by a new evolutionary algorithm. By solving the specific problem, they decrease the upper objective value, identify the quality of any feasible solution from infeasible solutions, force the infeasible solutions moving toward the feasible region and improve the feasible solutions gradually.