Algorithm of Barrier Objective Penalty Function

In this paper, an algorithm of barrier objective penalty function for inequality constrained optimization is studied and a conception–the stability of barrier objective penalty function is presented. It is proved that an approximate optimal solution may be obtained by solving a barrier objective penalty function for inequality constrained optimization problem when the barrier objective penalty function is stable. Under some conditions, the stability of barrier objective penalty function is proved for convex programming. Specially, the logarithmic barrier function of convex programming is stable. Based on the barrier objective penalty function, an algorithm is developed for finding an approximate optimal solution to an inequality constrained optimization problem and its convergence is also proved under some conditions. Finally, numerical experiments show that the barrier objective penalty function algorithm has better convergence than the classical barrier function algorithm.     

Synthesis,Biochemical Characterization,and Genetic Encoding of a 1,2,4-Triazole Amino Acid as an Acetyllysine Mimic for Bromodomains of the BET Family

Lysine acetylation is a charge-neutralizing post-translational modification of proteins bound by bromodomains (Brds). A 1,2,4-triazole amino acid (ApmTri) was established as acetyllysine (Kac) mimic recruiting Brds of the BET family in contrast to glutamine commonly used for simulating this modification. Optimization of triazole substituents and side chain spacing allowed BET Brd recruitment to ApmTri-containing peptides with affinities similar to native substrates. Crystal structures of ApmTri-containing peptides in complex with two BET Brds revealed the binding mode which mirrored that of Kac ligands. ApmTri was genetically encoded and recombinant ApmTri-containing proteins co-enriched BRD3(2) from cellular lysates. This interaction was blocked by BET inhibitor JQ1. With genetically encoded ApmTri, biochemistry is now provided with a stable Kac mimic reflecting charge neutralization and Brd recruitment, allowing new investigations into BET proteins in vitro and in vivo.     

A Versatile Strategy for Screening Custom-Designed Warhead-Armed Cyclic Peptide Inhibitors

Demand for peptide-based pharmaceuticals has been steadily increasing, but only limited success has been achieved to date. To expedite peptide-based drug discovery, we developed a general scheme for cell-based screening of cyclic peptide inhibitors armed with a user-designed warhead. We combined unnatural amino acid incorporation and split intein-mediated peptide cyclization techniques and integrated a yeast-based colorimetric screening assay to generate a new scheme that we call the custom-designed warhead-armed cyclic peptide screening platform (CWCPS). This strategy successfully discovered a potent inhibitor, CY5-6Q, that targets human histone deacetylase 8 (HDAC8) with a K_D value of 15 nM. This approach can be a versatile and general platform for discovering cyclic peptide inhibitors.     

Cr(VI) and Cu(II) removal from aqueous solution in fixed bed column using rice bran; experimental,statistical and GA modelling

Rice bran, a green and low-cost adsorbent, is used for Chromium (VI) and Copper (II) remotion from its aqueous solution. The influence of different process parameters in a fixed-bed on the removing efficiency has been investigated. The results show that the removal efficiency is higher at a minimum flow rate, low metal ion concentration, and higher bed height. The adsorption studies show that the rice bran has a better affinity to Chromium (VI) than Copper (II). Different kinetic models are used for the prediction of the column performance. This study shows that rice bran could be a potential and eco-friendly adsorbent for chromium (VI) and copper (II) removal and is suitable for developing countries like India. Multiple linear regression and ANN-based genetic algorithm modelling have been applied successfully to predict both metal ions' percentage removal separately.     

Optimal design of non-equilibrium experiments for genetic network interrogation

Many experimental systems in biology, especially synthetic gene networks, are amenable to perturbations that are controlled by the experimenter. We developed an optimal design algorithm that calculates optimal observation times in conjunction with optimal experimental perturbations in order to maximize the amount of information gained from longitudinal data derived from such experiments. We applied the algorithm to a validated model of a synthetic Brome Mosaic Virus (BMV) gene network and found that optimizing experimental perturbations may substantially decrease uncertainty in estimating BMV model parameters.     

深水多波束测深系统脉冲压缩实时算法

线性调频信号是大时宽带宽积信号,通过脉冲压缩可提高探测距离和分辨率,已在深水多波束测深系统中得到广泛应用。针对深水多波束测深系统中脉冲压缩运算量大的问题,提出了一种基于分组的脉冲压缩实时算法。该算法对需要进行脉冲压缩的波束进行分组,每个节拍只计算一组波束,可大幅减少深水多波束测深系统脉冲压缩的运算时间,节省了处理器资源。该算法已成功应用于某深水多波束测深系统,经多次海试证明,工作稳定可靠。     

Improved neural networks based on genetic algorithm for pulse recognition

Pulse diagnosis is an important part of Chinese medicine and has played an important role in the development of Chinese medical science. However, the pulse is traditionally determined by cutting it off, which leads to a lack of objective standard pulse identification methods and affects their accuracy and feasibility. This research has studied and discussed the processing and identification of four kinds of pulse: normal pulse, wiry pulse, smooth pulse, and thready pulse. Four frequency-domain characteristics of the pulse wave and six kinds of wavelet scale energy characteristic information were extracted, and a three-layer BP (backprocessing) neural network was established. The LM (Levenberg–Marquard) algorithm and a genetic algorithm were used to improve the BP neural network, to train on and predict experimental samples, and to obtain classification accuracies of 90% and 95% respectively. Moreover, improved BP neural network based on a genetic algorithm has shown highly superior performance in terms of convergence speed and low error rate.