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Objective This study was performed to investigate the effectiveness and safety of ultrasound-guided transforaminal nerve block in the treatment of lumbar disc herniation. Methods Sixty patients who underwent treatment for protrusion of a lumbar intervertebral disc in Wangjing Hospital from January 2016 to December 2017 were divided into the study group and the control group. The visual analog scale (VAS) pain scores, the Japanese Orthopaedic Association (JOA) scores of the lumbar vertebra, PRI (pain rating index), and PPI (present pain intensity) were recorded at 30 minutes, 1 week, and 3 months after the operation. Results There were significant differences in the VAS, JOA, PRI, and PPI scores between the study group and control group. Conclusion Ultrasound guidance can improve the efficacy and safety of transforaminal nerve block in the treatment of lumbar disc herniation and shorten the operative duration.

To solve many-objective flow-shop scheduling problems (FSP), a genetic algorithm based on the relative entropy of fuzzy sets (REFS_GA) is proposed. A mathematical model of the many-objective FSP is built, which involves four scheduling criterions of FSP. In REFS_GA, the Pareto front is mapped to fuzzy set, and the relational entropy coefficient of fuzzy sets is used to measure the similarity between the fuzzy sets of Pareto solutions and ideal solution. The coefficient is used as the fitness of genetic algorithm (GA) and to guide algorithm evolution. The performance of REFS_GA is evaluated through compared with GA based on g-dominance (gGA), random weight GA (rwGA) and the third version of non-dominated sorting genetic algorithm (NSGA-III). Experiments are carried out with eight DTLZ benchmark functions, six MaF benchmark functions with 4, 7 or 10 objectives, respectively, nine scheduling problems with four objectives and a real-world many-objective FSP. Experimental results show that REFS_GA can solve may-objective benchmark functions and many-objective FSP. The optimisation solution and performance indicators of REFS_GA are better than gGA, rwGA and NSGA-III. It can be concluded that REFS_GA is an effective method to solve many-objective optimisation problems. The main contributions of the work are that a four-objective model of FSP is built and a priori approach based on fuzzy set is proposed to solve many-objective FSP. [ABSTRACT FROM AUTHOR]

A new mechanism，namely a combination of curve matching method based on the discrete Fréchet distance and evolutionary algorithms，is proposed to solve pick-and-place sequence optimisation problems as a multi-objective optimisation problem. The essence of the mechanism is to accomplish the comparison of objective vectors with curve matching method. The objective vector is mapped into the array of points with a binary mapping operator and the discrete Fréchet distance is utilised to measure the similarity between the reference array of points and the comparison array of points. The genetic algorithm based on the discrete Fréchet distance (FGA) is proposed. To test the new mechanism, together with FGA, three other test algorithms are selected to solve the sequence optimisation problem. The simulation results indicate that FGA outperforms other algorithms. This new mechanism is rational and feasible for multi-objective pick-and-place sequence optimisation problems. [ABSTRACT FROM AUTHOR]

Soil fungi are important components of belowground biodiversity and play important roles in soil carbon and nutrient cycling. We investigated fungal communities in the top soil (0-10 cm) of 22 Pinus massoniana forests in the Three Gorges Reservoir Region using high-throughput sequencing technique. We found that Ascomycota and Basidiomycota were the dominant fungi phyla, and Eurotiales, Russulales, and Tremellales were the most abundant fungi orders. The dominant functional groups in P. massoniana forests were saprophytic fungi, ectomycorrhizal fungi, and ericoid mycorrhizal fungi. Results of redundancy analysis showed that environmental variables but not spatial variables were the main drivers of soil fungal community structure across the 22 P. massoniana forests, which suggested that habitat filtering rather than dispersal limitation shaped soil fungal community structure. Aboveground biomass, soil conductivity, available phosphorus, soil bulk density, carbon to nitrogen ratio, nitrate concentration, and proportion of slit were the main factors explaining the variation in soil fungal community structure. It should be noted that the key factors influencing different fungal functional groups differed across forests. [ABSTRACT FROM AUTHOR]