Summary
The NADPH oxidase enzyme complex, NOX2, is responsible for reactive oxygen species (ROS) production in neutrophils and has been recognized as a key mediator of inflammation. Here, we have performed rational design and in silico screen to identify a small molecule inhibitor, Phox-I1, targeting the interactive site of p67phox with Rac GTPase that is a necessary step of the signaling leading to NOX2 activation. Phox-I1 binds to p67phox with a submicromolar affinity and abrogates Rac1 binding, and is effective in inhibiting NOX2-mediated superoxide production dose-dependently in human and murine neutrophils without detectable toxicity. Medicinal chemistry characterizations have yielded promising analogs and initial information of the structure-activity relationship of Phox-I1. Our studies suggest the potential utility of Phox-I class inhibitors in NOX2 oxidase inhibition and present the first application of rational targeting of a small GTPase - effector interface.
We present a complete, fully automatic solution based on genetic algorithms for the optimization of discrete product placement and of order picking routes in a warehouse. The solution takes as input the warehouse structure and the list of orders and returns the optimized product placement, which minimizes the sum of the order picking times. The order picking routes are optimized mostly by genetic algorithms with multi-parent crossover operator, but for some cases also permutations and local search methods can be used. The product placement is optimized by another genetic algorithm, where the sum of the lengths of the optimized order picking routes is used as the cost of the given product placement. We present several ideas, which improve and accelerate the optimization, as the proper number of parents in crossover, the caching procedure, multiple restart and order grouping. In the presented experiments, in comparison with the random product placement and random product picking order, the optimization of order picking routes allowed the decrease of the total order picking times to 54%, optimization of product placement with the basic version of the method allowed to reduce that time to 26% and optimization of product placement with the methods with the improvements, as multiple restart and multi-parent crossover to 21%.
In this paper we propose and discuss several new approaches to noise-resistant training of multilayer perceptron neural networks. Two groups of approaches: input ones, based on instance selection and outlier detection, and output ones, based on modified robust error objective functions, are presented and compared. In addition we compare them to some known methods. The experimental evaluation of the methods on classification and regression tasks and comparison of their performances for different amounts of noise in the training data, proves the effectiveness of the proposed approaches.
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