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Tyrosinase is the rate-limiting enzyme for controlling the production of melanin.

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A rapid and sensitive ultra-high performance liquid chromatography with tandem mass spectrometry approach was established for the simultaneous determination of 4-caffeoylquinic acid, loganic acid, chlorogenic acid, loganin, 3,5-dicaffeoylquinic acid, dipsacoside B, asperosaponin VI, and sweroside in raw and wine-processed Dipsacus asper. Chloramphenicol and glycyrrhetinic acid were employed as internal standards. The proposed approach was fully validated in terms of linearity, sensitivity, precision, repeatability as well as recovery. Intra- and interassay variability for all analytes were 2.8-4.9 and 1.7-4.8%, respectively. The standard addition method determined recovery rates for each analytes (96.8-104.6%). In addition, the developed approach was applied to 20 batches of raw and wine-processed samples of Dipsacus asper. Principle component analysis and partial least squares-discriminate analysis revealed a clear separation between the raw group and wine-processed group. After wine-processing, the contents of loganic acid, chlorogenic acid, dipsacoside B, and asperosaponin VI were upregulated, while the contents of 3,5-dicaffeoylquinic acid, 4-caffeoylquinic acid, loganin, and sweroside were downregulated. Our results demonstrated that ultra-high performance liquid chromatography with tandem mass spectrometry quantification combined with chemometrics is a viable method for quality evaluation of the raw Dipsacus asper and its wine-processed products.

Synthetic Aperture Radar (SAR) raw data simulation is a fundamental problem in radar system design and imaging algorithm research. The growth of surveying swath and resolution results in a significant increase in data volume and simulation period, which can be considered to be a comprehensive data intensive and computing intensive issue. Although several high performance computing (HPC) methods have demonstrated their potential for accelerating simulation, the input/output (I/O) bottleneck of huge raw data has not been eased. In this paper, we propose a cloud computing based SAR raw data simulation algorithm, which employs the MapReduce model to accelerate the raw data computing and the Hadoop distributed file system (HDFS) for fast I/O access. The MapReduce model is designed for the irregular parallel accumulation of raw data simulation, which greatly reduces the parallel efficiency of graphics processing unit (GPU) based simulation methods. In addition, three kinds of optimization strategies are put forward from the aspects of programming model, HDFS configuration and scheduling. The experimental results show that the cloud computing based algorithm achieves 4× speedup over the baseline serial approach in an 8-node cloud environment, and each optimization strategy can improve about 20%. This work proves that the proposed cloud algorithm is capable of solving the computing intensive and data intensive issues in SAR raw data simulation, and is easily extended to large scale computing to achieve higher acceleration.

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Summary In order to discuss the feasibility of using R744/R744 cascade refrigeration system (CRS) instead of R744/R717 CRS, six configurations of R744/R744 CRS assisted with expander and mechanical subcooling system (MS) are analyzed. Based on the thermodynamic analysis, the results show that the high pressure, the condensing temperature of the low‐temperature cycle (LTC), and the degree of subcooling of LTC and the high‐temperature cycle (HTC) are three important operating parameters with an optimum value corresponding to the maximum coefficient of performance (COP). Compared with other CRSs, CRS with HTC throttling valve and MS of HTC (CTSH) and CRS with HTC expander and MS of HTC (CESH) show an excellent performance. CESH has the highest COP and is improved by an average of 13.8% compared with the COP of R744/R717 CRS. The COP of CTSH is improved by an average of 4.2% compared with the COP of R744/R717 CRS. In conclusion, it is an efficient way to improve performance that CRS combines MS in HTC and HTC expander. And it is possible R744/R744 CRS instead of R744/R717 CRS.

The flow directions of the extracted drainage network are more random based on random flow model. Binary linear regression method is applied to calculate the residual value at every point, and then the normalized residuals are used to prune reasonably as the variables. The terrains are distinguished automatically according to the normalized residuals, and the local catchment area threshold is determined based on each terrain feature. When there are two terrains in the experimental area, source density is large on mountainous terrain, and it is small on flat terrain. Eventually, the extracted result is consistent with the actual drainage network.