Comparison of raw and processed Radix Polygoni Multiflori (Heshouwu) by high performance liquid chromatography and mass spectrometry

<p>Abstract</p> <p>Background</p> <p><it>Radix Polygoni Multiflori </it>is the dried root tuber of <it>Polygonum multiflorum </it>Thunb. (Fam. Polygonaceae). According to Chinese medicine theory, raw (R-RPM) and processed (P-RPM) <it>Radix Polygoni Multiflori </it>possess different properties. The present study investigates the differences in chemistry between raw and processed <it>Radix Polygoni Multiflori</it>.</p> <p>Methods</p> <p>Five pairs of R-RPM and P-RPM as well as 15 commercial decoction pieces were analyzed with high performance liquid chromatography (HPLC) and mass spectrometry (MS).</p> <p>Results</p> <p>Two anthraquinones, namely emodin-8-<it>O</it>-(6'-<it>O</it>-malonyl)-glucoside and physcion-8-<it>O</it>-(6'-<it>O</it>-malonyl)-glucoside disappeared or decreased significantly and 2,3,5,4'-tetrahydroxystilbene-2-<it>O</it>-<it>β</it>-<it>D</it>-glucopyranoside, emodin-8-<it>O</it>-<it>β</it>-<it>D</it>-glucopyranoside and physcion-8-<it>O</it>-<it>β</it>-<it>D</it>-glucopyranoside decreased after the R-RPM samples being processed. On the other hand, the contents of emodin and physcion generally increased after processing.</p> <p>Conclusion</p> <p>The present study indicates that processing <it>Radix Polygoni Multiflori </it>may change the contents and types of chemicals in it. These changes are probably responsible for the various pharmacological effects of R-RPM and P-RPM as well as hepatotoxicity.</p

Comparison of the Immunoregulatory Function of Different Constituents in Radix Astragali and Radix Hedysari

Radix Astragali (RA), known as “Huangqi” in China, is one of the most popular herbal medicines known worldwide to reinforce “Qi”. RA is traditionally prepared from the dried roots of Astragalus membranaceus (MJHQ) and A. membranaceus var. mongholicus (MGHQ). Radix Hedysari is named “Hongqi” (HQ), which is similar to RA. We assessed and compared the chemical constituents and bioactivity of RA and HQ. Different constituents were extracted into five major parts and were analyzed using different methods. Comparison of the immunological effects of extracts was done by using two immunological models. Results showed that flavonoids and saponins present in RA and HQ were not only structurally significantly different but also different in their immunological effect. Amino acids extract (AE) in MGHQ shows immunological effect while AE in MJHQ and HQ did not. Polysaccharides comprised the major constituents in RA and HQ. All polysaccharides extract (PE) of the three herbs showed similar levels of immunological effect in both immunological assays

Real-time partway deadheading strategy based on transit service reliability assessment

This paper presents a partway deadheading strategy for transit operations to improve transit service of the peak directions of transit routes. This strategy consists of two phases: reliability assessment of further transit service and optimization of partway deadheading operation. The reliability assessment of further transit service, which is based on the current and recent service reliability, is used to justify whether or not to implement a partway deadheading operation. The objective of the second phase is to determine the beginning stop for a new service for the deadheaded vehicle by maximizing the benefit of transit system. A heuristic algorithm is also defined and implemented to estimate reliability of further transit service and to optimize partway deadheading operation. Then, the partway deadheading strategy proposed in this paper is tested with the data from a transit route in Dalian city of China. The results show the partway deadheading strategy with the reasonable parameters can improve transit service

Transport Turnover with Spatial Econometric Perspective under the Energy Conservation and Emissions Reduction in China

The method of spatial econometrics model is used to study the space correlation of road turnover (freight and passenger) among the 31 provinces (municipalities and autonomous regions) in China. Since some factors (such as the level of economic development, industrial structure, and population) may impact the road turnover (freight and passenger), these indexes are used as variables to reflect the influence in this model. The data of 31 provinces (municipalities and autonomous regions) in China in 2012 is collected to analyze the method. The results show that when the characteristics of turnover in a region are analyzed, it is necessary to consider the influence of its surrounding areas in space. Different scenarios were established according to different economic growth and degree of emission reduction to analyze the influence of the reduction of road turnover in regions with high spatial cluster effect to the whole country. We also carried out how to use the space effect of road turnover to the energy saving and emission reduction in the transportation industry

BiFA-YOLO: A Novel YOLO-Based Method for Arbitrary-Oriented Ship Detection in High-Resolution SAR Images

Due to its great application value in the military and civilian fields, ship detection in synthetic aperture radar (SAR) images has always attracted much attention. However, ship targets in High-Resolution (HR) SAR images show the significant characteristics of multi-scale, arbitrary directions and dense arrangement, posing enormous challenges to detect ships quickly and accurately. To address these issues above, a novel YOLO-based arbitrary-oriented SAR ship detector using bi-directional feature fusion and angular classification (BiFA-YOLO) is proposed in this article. First of all, a novel bi-directional feature fusion module (Bi-DFFM) tailored to SAR ship detection is applied to the YOLO framework. This module can efficiently aggregate multi-scale features through bi-directional (top-down and bottom-up) information interaction, which is helpful for detecting multi-scale ships. Secondly, to effectively detect arbitrary-oriented and densely arranged ships in HR SAR images, we add an angular classification structure to the head network. This structure is conducive to accurately obtaining ships’ angle information without the problem of boundary discontinuity and complicated parameter regression. Meanwhile, in BiFA-YOLO, a random rotation mosaic data augmentation method is employed to suppress the impact of angle imbalance. Compared with other conventional data augmentation methods, the proposed method can better improve detection performance of arbitrary-oriented ships. Finally, we conduct extensive experiments on the SAR ship detection dataset (SSDD) and large-scene HR SAR images from GF-3 satellite to verify our method. The proposed method can reach the detection performance with precision = 94.85%, recall = 93.97%, average precision = 93.90%, and F1-score = 0.9441 on SSDD. The detection speed of our method is approximately 13.3 ms per 512 × 512 image. In addition, comparison experiments with other deep learning-based methods and verification experiments on large-scene HR SAR images demonstrate that our method shows strong robustness and adaptability