Chemoenzymatic synthesis of amaryllidaceae alkaloids and their C-1 analogues : symmetry based approach to total synthesis of thebaine

Described herein is the chemoenzymatic total synthesis of several Amaryllidaceae constituents and their unnatural C-I analogues. A new approach to pancratistatin and related compounds will be discussed along with the completed total synthesis of 7 -deoxypancratistatin and trans-dihydrolycoricidine. Evaluation of all new C-l analogues as cancer cell growth inhibitory agents is described. The enzymatic oxidation of dibromobenzenes by Escherichia coli 1M 109 (pDTG60 1) is presented along with conversion of their metabolites to (-)-conduritol E. Investigation into the steric and functional factors governing the enzymatic dihydroxylation of various benzoates by the same organism is also discussed. The synthetic utility of these metabolites is demonstrated through their conversion to pseudo-sugars, aminocyclitols, and complex bicyclic ring systems. The current work on the total synthesis of some morphine alkaloids is also presented. Highlighted will be the synthesis of several model systems related to the efficient total synthesis of thebaine.

GA approach for finding Rough Set decision rules based on bireducts

Feature selection plays an important role in knowledge discovery and data mining nowadays. In traditional rough set theory, feature selection using reduct - the minimal discerning set of attributes - is an important area. Nevertheless, the original definition of a reduct is restrictive, so in one of the previous research it was proposed to take into account not only the horizontal reduction of information by feature selection, but also a vertical reduction considering suitable subsets of the original set of objects. Following the work mentioned above, a new approach to generate bireducts using a multi--objective genetic algorithm was proposed. Although the genetic algorithms were used to calculate reduct in some previous works, we did not find any work where genetic algorithms were adopted to calculate bireducts. Compared to the works done before in this area, the proposed method has less randomness in generating bireducts. The genetic algorithm system estimated a quality of each bireduct by values of two objective functions as evolution progresses, so consequently a set of bireducts with optimized values of these objectives was obtained. Different fitness evaluation methods and genetic operators, such as crossover and mutation, were applied and the prediction accuracies were compared. Five datasets were used to test the proposed method and two datasets were used to perform a comparison study. Statistical analysis using the one-way ANOVA test was performed to determine the significant difference between the results. The experiment showed that the proposed method was able to reduce the number of bireducts necessary in order to receive a good prediction accuracy. Also, the influence of different genetic operators and fitness evaluation strategies on the prediction accuracy was analyzed. It was shown that the prediction accuracies of the proposed method are comparable with the best results in machine learning literature, and some of them outperformed it.