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The interchangeable use of different herbs to prepare the same formulation is a common practice in Traditional Chinese Medicine (TCM). However, this practice would require the component herbs to share similar compositions, at least in terms of the bioactive agents, to ensure they can replace each other in drug preparation. In this study, we developed an effective and comprehensive high-performance liquid chromatography-diode array detector (HPLC-DAD) method for simultaneous analysis of 11 phenolic compounds in the methanol extracts of Dendrobium huoshanense, Dendrobium nobile (D. nobile), Dendrobium chrysotoxum (D. chrysotoxum), and Dendrobium fimbriatum (D. fimbriatum), which have been identified as interchangeable ingredients for the same TCM preparation “Shihu” in the Chinese pharmacopeia (ChP). The consistency of the four Dendrobium species was evaluated on the basis of the presence of the 11 investigated compounds and the HPLC fingerprints of the methanol extracts of the plants. When gradient elution was performed with a solvent system of acetonitrile and water on a Zorbax Eclipse XDB-C18 (150 mm × 4.6 mm, 5 μm) with monitoring at 220 nm, all 11 investigated compounds were isolated at the baseline. The established HPLC method showed excellent linearity (all analytical curves showed relative coefficients [R2] > 0.999), sensitivity, precision (relative standard deviation [RSD] < 2%), and accuracy (recovery, 90.65–99.17%). These findings confirmed that the method we constructed was reliable. Quantification analysis showed significant differences in the contents of the investigated polyphenols in the four Dendrobium species. Evaluations of consistency revealed that the similarities among the four species were 0.299–0.906 in assessments based on the 11 polyphenols and 0.685–0.968 in assessments based on HPLC fingerprints. Thus, the components of the four Dendrobium species may be significantly different, and more experiments are required to determine whether they can be used interchangeably in the same amounts for preparing the formulation according to ChP.

The interchangeable use of different herbs to prepare the same formulation is a common practice in Traditional Chinese Medicine (TCM). However, this practice would require the component herbs to share similar compositions, at least in terms of the bioactive agents, to ensure they can replace each other in drug preparation. In this study, we developed an effective and comprehensive high-performance liquid chromatography-diode array detector (HPLC-DAD) method for simultaneous analysis of 11 phenolic compounds in the methanol extracts of Dendrobium huoshanense, Dendrobium nobile (D. nobile), Dendrobium chrysotoxum (D. chrysotoxum), and Dendrobium fimbriatum (D. fimbriatum), which have been identified as interchangeable ingredients for the same TCM preparation “Shihu” in the Chinese pharmacopeia (ChP). The consistency of the four Dendrobium species was evaluated on the basis of the presence of the 11 investigated compounds and the HPLC fingerprints of the methanol extracts of the plants. When gradient elution was performed with a solvent system of acetonitrile and water on a Zorbax Eclipse XDB-C18 (150 mm × 4.6 mm, 5 μm) with monitoring at 220 nm, all 11 investigated compounds were isolated at the baseline. The established HPLC method showed excellent linearity (all analytical curves showed relative coefficients [R2] > 0.999), sensitivity, precision (relative standard deviation [RSD] < 2%), and accuracy (recovery, 90.65–99.17%). These findings confirmed that the method we constructed was reliable. Quantification analysis showed significant differences in the contents of the investigated polyphenols in the four Dendrobium species. Evaluations of consistency revealed that the similarities among the four species were 0.299–0.906 in assessments based on the 11 polyphenols and 0.685–0.968 in assessments based on HPLC fingerprints. Thus, the components of the four Dendrobium species may be significantly different, and more experiments are required to determine whether they can be used interchangeably in the same amounts for preparing the formulation according to ChP.

To improve the accuracy of fault location system, several short-circuit tests need to be conducted before being brought into service in autotransformer (AT) feeding systems for high-speed railways in China. However, no systematic algorithm yet exists to evaluate the consistency of the current distribution of short-circuit tests. A methodology is proposed in this paper to address this problem. Based on Kirchhoff’s current law and the generalized method of symmetrical components, the current deviations of the AT feeding systems are analysed and then normalized with the short-circuit current as they vary greatly with systems and short-circuit sites. It is also found that the short-circuit current varies with the calculation methods, and its unbiased standard deviation also reflects the consistency of the short-circuit test. The mean and maximum of the current deviations, as well as the unbiased standard deviation of the short-circuit current, show the consistency of the short-circuit test from different aspects, although the last two items are highly relevant. Therefore, a unified evaluation index is defined as the sum of the three items, and then applied in two case studies to test its performance. The results show that, the proposed index can clearly distinguish the consistency of the short-circuit tests and may be used to sort the short-circuit tests for fault location systems. Besides, some short-circuit tests may have very poor consistency indices, and thus are not applicable to the tuning of fault location systems. In the authors’ opinion, the determination of the threshold of the proposed index needs further investigation.

Background Cohen’s κ coefficient is often used as an index to measure the agreement of inter-rater determinations. However, κ varies greatly depending on the marginal distribution of the target population and overestimates the probability of agreement occurring by chance. To overcome these limitations, an alternative and more stable agreement coefficient was proposed, referred to as Gwet’s AC1. When it is desired to combine results from multiple agreement studies, such as in a meta-analysis, or to perform stratified analysis with subject covariates that affect agreement, it is of interest to compare several agreement coefficients and present a common agreement index. A homogeneity test of κ was developed; however, there are no reports on homogeneity tests for AC1 or on an estimator of common AC1. In this article, a homogeneity score test for AC1 is therefore derived, in the case of two raters with binary outcomes from K independent strata and its performance is investigated. An estimation of the common AC1 between strata and its confidence intervals is also discussed. Methods Two homogeneity tests are provided: a score test and a goodness-of-fit test. In this study, the confidence intervals are derived by asymptotic, Fisher’s Z transformation and profile variance methods. Monte Carlo simulation studies were conducted to examine the validity of the proposed methods. An example using clinical data is also provided. Results Type I error rates of the proposed score test were close to the nominal level when conducting simulations with small and moderate sample sizes. The confidence intervals based on Fisher’s Z transformation and the profile variance method provided coverage levels close to nominal over a wide range of parameter combination. Conclusions The method proposed in this study is considered to be useful for summarizing evaluations of consistency performed in multiple or stratified inter-rater agreement studies, for meta-analysis of reports from multiple groups and for stratified analysis.

Background Cohen’s κ coefficient is often used as an index to measure the agreement of inter-rater determinations. However, κ varies greatly depending on the marginal distribution of the target population and overestimates the probability of agreement occurring by chance. To overcome these limitations, an alternative and more stable agreement coefficient was proposed, referred to as Gwet’s AC1. When it is desired to combine results from multiple agreement studies, such as in a meta-analysis, or to perform stratified analysis with subject covariates that affect agreement, it is of interest to compare several agreement coefficients and present a common agreement index. A homogeneity test of κ was developed; however, there are no reports on homogeneity tests for AC1 or on an estimator of common AC1. In this article, a homogeneity score test for AC1 is therefore derived, in the case of two raters with binary outcomes from K independent strata and its performance is investigated. An estimation of the common AC1 between strata and its confidence intervals is also discussed. Methods Two homogeneity tests are provided: a score test and a goodness-of-fit test. In this study, the confidence intervals are derived by asymptotic, Fisher’s Z transformation and profile variance methods. Monte Carlo simulation studies were conducted to examine the validity of the proposed methods. An example using clinical data is also provided. Results Type I error rates of the proposed score test were close to the nominal level when conducting simulations with small and moderate sample sizes. The confidence intervals based on Fisher’s Z transformation and the profile variance method provided coverage levels close to nominal over a wide range of parameter combination. Conclusions The method proposed in this study is considered to be useful for summarizing evaluations of consistency performed in multiple or stratified inter-rater agreement studies, for meta-analysis of reports from multiple groups and for stratified analysis.

Land cover plays an important role in the climate and biogeochemistry of the Earth system. It is of great significance to produce and evaluate the global land cover (GLC) data when applying the data to the practice at a specific spatial scale. The objective of this study is to evaluate and validate the consistency of the Moderate Resolution Imaging Spectroradiometer (MODIS) land cover product (MCD12Q1) at a provincial scale (Anhui Province, China) based on the Chinese 30 m GLC product (GlobeLand30). A harmonization method is firstly used to reclassify the land cover types between five classification schemes (International Geosphere Biosphere Programme (IGBP) global vegetation classification, University of Maryland (UMD), MODIS-derived Leaf Area Index and Fractional Photosynthetically Active Radiation (LAI/FPAR), MODIS-derived Net Primary Production (NPP), and Plant Functional Type (PFT)) of MCD12Q1 and ten classes of GlobeLand30, based on the knowledge rule (KR) and C4.5 decision tree (DT) classification algorithm. A total of five harmonized land cover types are derived including woodland, grassland, cropland, wetland and artificial surfaces, and four evaluation indicators are selected including the area consistency, spatial consistency, classification accuracy and landscape diversity in the three sub-regions of Wanbei, Wanzhong and Wannan. The results indicate that the consistency of IGBP is the best among the five schemes of MCD12Q1 according to the correlation coefficient (R). The “woodland” LAI/FPAR is the worst, with a spatial similarity (O) of 58.17% due to the misclassification between “woodland” and “others”. The consistency of NPP is the worst among the five schemes as the agreement varied from 1.61% to 56.23% in the three sub-regions. Furthermore, with the biggest difference of diversity indices between LAI/FPAR and GlobeLand30, the consistency of LAI/FPAR is the weakest. This study provides a methodological reference for evaluating the consistency of different GLC products derived from multi-source and multi-resolution remote sensing datasets on various spatial scales.

Quality assessment of medical images is highly related to the quality assurance, image interpretation and decision making. As to magnetic resonance (MR) images, signal-to-noise ratio (SNR) is routinely used as a quality indicator, while little knowledge is known of its consistency regarding different observers. In total, 192, 88, 76 and 55 brain images are acquired using T2*, T1, T2 and contrast-enhanced T1 (T1C) weighted MR imaging sequences, respectively. To each imaging protocol, the consistency of SNR measurement is verified between and within two observers, and white matter (WM) and cerebral spinal fluid (CSF) are alternately used as the tissue region of interest (TOI) for SNR measurement. The procedure is repeated on another day within 30 days. At first, overlapped voxels in TOIs are quantified with Dice index. Then, test-retest reliability is assessed in terms of intra-class correlation coefficient (ICC). After that, four models (BIQI, BLIINDS-II, BRISQUE and NIQE) primarily used for the quality assessment of natural images are borrowed to predict the quality of MR images. And in the end, the correlation between SNR values and predicted results is analyzed. To the same TOI in each MR imaging sequence, less than 6% voxels are overlapped between manual delineations. In the quality estimation of MR images, statistical analysis indicates no significant difference between observers (Wilcoxon rank sum test, p w ≥ 0.11; paired-sample t test, p p ≥ 0.26), and good to very good intra- and inter-observer reliability are found (ICC, p icc ≥ 0.74). Furthermore, Pearson correlation coefficient (r p ) suggests that SNRwm correlates strongly with BIQI, BLIINDS-II and BRISQUE in T2* (r p ≥ 0.78), BRISQUE and NIQE in T1 (r p ≥ 0.77), BLIINDS-II in T2 (r p ≥ 0.68) and BRISQUE and NIQE in T1C (r p ≥ 0.62) weighted MR images, while SNRcsf correlates strongly with BLIINDS-II in T2* (r p ≥ 0.63) and in T2 (r p ≥ 0.64) weighted MR images. The consistency of SNR measurement is validated regarding various observers and MR imaging protocols. When SNR measurement performs as the quality indicator of MR images, BRISQUE and BLIINDS-II can be conditionally used for the automated quality estimation of human brain MR images.