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A novel series of 2-amino-[1,2,4]triazolo[1,5-a]pyrimidine-6-carboxamide derivatives has been achieved successfully via an efficient one-pot three-component Biginelli-like heterocyclization reaction between different benzaldehydes, 1H-1,2,4-triazole-3,5-diamine, and N-substituted acetoacetamides in the presence of p-toluenesulfonic acid as a catalyst. Moreover, the effects of different conditions on the reaction were well investigated. In addition, cancer cell growth inhibition activity for these target compounds was also explored, and analogue 5l demonstrated the most potent cytotoxic activity against different cancer cells. These finds indicat that 2-amino-[1,2,4]triazolo[1,5-a]pyrimidine-6-carboxamide core could be well worth further optimization as a potential scaffold for development of anticancer agents.

A new series of derivatives characterized by the presence of the 3,4,5-trimethoxylbenzamide substituted benzofurans were synthesized and evaluated for antiproliferative activity against four cancer cell lines and one normal human cell line. Among them, derivative 6g with greatest cytotoxicity significantly inhibited the growth of MDA-MB-231, HCT-116, HT-29 and HeLa cell lines with IC50 values of 3.01, 5.20, 9.13, and 11.09 μM, respectively. Importantly, 6g possessed excellent selectivity over non-tumoral cell lines HEK-293 (IC50 > 30 μM). Moreover, mechanistic studies revealed that 6g induced HeLa cells arrested in G2/M phase in a concentration-dependent manner, and inhibited polymerization of tubulin via a consistent way with CA-4. In general, these observations suggest that 6g is a promising anti-cancer lead and is worth further investigation to generate potential antitumor agents.

Imbalance miRNA target sample data bring about the lower prediction accuracy of SVM(Support Vector Machine). This paper proposes an SVM algorithm to predict the target genes based on biased discriminant idea. This paper selects an optimal feature sets as input data, and constructs a kernel optimization objective function based on the biased discriminant analysis criteria in the empirical feature space. The conformal transformation of a kernel is utilized to gradually optimize the kernel matrix. Through the comparative analysis of the experimental results of human, mouse and rat, the imbalance SVM with biased discriminant has higher specificity, sensitivity and prediction accuracy, which proves that it has stronger generalization ability and better robustness.

To apply needle-knife to treat lumber disc herniation (LDH) and surface electromyography were used to analyze biomechanical characteristic of patient's lumber muscle to make a comprehensive evaluation on its efficacy.Thirty patients who met the inclusive criteria were selected and treated with needle-knife, once a week for 2 weeks. Visual analogue scale (VAS), ASLR and JOA score before and after treatment were observed. Surface electromyography was applied to test the surface electromyography signals. AEMG, MFs and MPF were calculated before and after the treatment.After treatment, VAS was significantly reduced, ASLR, JOA, AEMG and MPF were obviously increased, and the absolute value of MFs was lowed (all P0.01).The needle-knife could significantly relieve lumbar muscle strength, muscle tone and muscle fatigue, improve in the imbalance of lumbar extensor muscle group, leading to the recovery of biomechanical characteristic, and the clinical efficacy is superior.

When rule base is sparse, we cannot get any reasoning result by traditional CRI method, however interpolative reasoning methods can tackle this problem. After analyzing the interpolative reasoning method based on center of gravity by Zhiheng Huang and Qiang Shen, this paper presents a generalized fuzzy interpolative method based on center of gravity (COG). This method allows the conditions appearing in the antecedent part and the consequence of the rules to be represented not only by triangular fuzzy numbers but also by trapezoidal fuzzy numbers; moreover it can keep the convexity of the inferential consequence. In this process, scale and move rate transformation operations are used to support such reasoning.

Traditional TCP congestion control has poor performance in high-speed networks because of its slow response with large congestion windows. HSTCP has been proposed as one of the possible ways to better utilize the networks with high bandwidth-delay products. Experiments revealed that HSTCP can use the bandwidth more sufficient than current TCP, but it has severe RTT unfairness. In this paper, we first investigate the RTT unfairness through mathematical analysis and simulation studies, and then we add a fair factor to the HSTCP to decrease the difference of window increase caused by different RTT. Experiments show that the modified algorithm can guarantee the bandwidth fairness of HSTCP flows and reduce the packets loss rate.

To improve the performance of the existing genetic algorithms for job shop scheduling problem and speed up searching for optimal scheduling solution, this paper analyzes the difficulty and characteristics of the operation-based coding and designs a new crossover, which is based on the job. As illustrative numerical examples, both 6times6 and 10 times 10 job-shop scheduling problems are considered. Through the comparative simulations with position-based crossover, the feasibility and effectiveness of the proposed crossover are demonstrated.