Development of miracle medicines from sialic acids

Sialic acids are electronegatively charged C9-sugars and are considered to play important roles in higher animals and some microorganisms. Denoting their significance, understanding and exploiting the complexity of the sialic acids has been referred to as the “the third language of life”. In essence, “sialic acid derivatives possess a harmonious shape and good balance between two opposing hydrophilic and hydrophobic parts, meaning that they should display various kinds of potentially unique and possibly conflicting physiological activities (glycolipoids)”. Consequently, there are good omens that unprecedented ‘miracle’ medicines could be developed from sialic acid derivatives. In this review, the first problem, the preparation of sialic acids, is covered, the synthesis of sialic acid derivatives and confirmation of their structures obviously being of critical significance. In addition we needed to confirm their precise stereochemistry and a hydrolysis method has been developed for confirmation of the anomeric position. Several of the compounds have already demonstrated interesting bioactivity

Machine learning for combinatorial optimization of brace placement of steel frames

A method is presented for optimal placement of braces of plane frames using machine learning. The frame is subjected to static horizontal loads representing seismic loads. We consider the process of seismic retrofit by attaching braces. Therefore, the maximum value of additional stresses in the existing beams and columns and the maximum interstory drift angle are incorporated in the optimization problem. Characteristics of approximate optimal solutions and nonoptimal solutions are extracted using machine learning based on support vector machine and binary decision tree. Convolution and pooling are used for defining the features characterizing the solutions while reducing the number of variables. Optimization is carried out using a heuristic algorithm called simulated annealing based on local search. It is shown in the numerical examples that the computational cost is successfully reduced by avoiding costly structural analysis for a solution judged by machine learning as nonoptimal, and the important features in approximate optimal and nonoptimal solutions are identified