Novel ideas for the comprehensive evaluation of varus knee osteoarthritis: radiological measurements of the morphology of the lateral knee joint

BackgroundThe lateral anatomical and morphological characteristics of knees with varus knee osteoarthritis (OA) have not received sufficient attention. This study used several radiological parameters to describe the morphological characteristics of the lateral knee with OA to determine whether there are relationships between varus knee OA and parameters such as lateral plateau widening (LPW), proximal fibula curvature (PFC), and fibula height (FH).MethodsThe study retrospectively analyzed 1072 subjects [376 males, 696 females; mean age 66.84 ± 7.04 (range 46–83) years; mean body mass index (BMI) 26.98 ± 3.22 kg/m²] who underwent standard radiography for diagnosing or evaluating symptomatic knee joint disease. The 163 Kellgren and Lawrence (K-L) grades 0 and I knees were categorized into the no-knee-OA group, and the 909 K-L grades II–IV knees were classified into the knee-OA group. The medial proximal tibial angle (MPTA), joint line convergence angle (JLCA), and hip-knee-ankle angle (HKAA) were measured to investigate varus knee deformity. The LPW, PFC, and FH were measured. T-tests and chi-square tests were used to compare each index between the two groups. Binary logistic regression was performed to examine the correlation between indexes and knee OA occurrence. Principal component analysis was used to calculate the comprehensive principal component score of varus deformity, which was used to comprehensively evaluate the knee varus deformity degree by reducing the original data's dimension. Multiple logistic and linear regression analyses were performed to examine the correlations between the three parameters and K-L grades and the comprehensive principal component score of varus deformit.ResultsLPW and PFC were significantly greater and FH was significantly smaller in the knee-OA group than in the no-knee-OA group. LPW, PFC, and FH were correlated with knee OA occurrence. One principal component, named the comprehensive principal component score of varus deformity, was extracted from the three indexes, and the total variance of the principal component interpretation was 76.60%. Multivariate logistics and linear regression analysis showed that after adjusting for age and BMI, LPW and PFC were positively correlated with K-L grading and varus deformity. FH was significantly and negatively associatedwith K-L grading and varus deformity (all P < 0.05).ConclusionsRegular morphological changes take place in the lateral knee with varus OA, including lateral dislocation of the tibial plateau, proximal fibula bending, and upward movement of the fibular head. Changes in LPW, PFC, and FH could enable a more comprehensive assessment of varus knee OA occurrence, severity, and deformity.Level of EvidenceRetrospective Study Level III

Capillary Electrophoresis Mass Spectrometry-Based Metabolomics of Plasma Samples from Healthy Subjects in a Cross-Sectional Japanese Population Study

For large-scale metabolomics, such as in cohort studies, normalization protocols using quality control (QC) samples have been established when using data from gas chromatography and liquid chromatography coupled to mass spectrometry. However, normalization protocols have not been established for capillary electrophoresis–mass spectrometry metabolomics. In this study, we performed metabolome analysis of 314 human plasma samples using capillary electrophoresis–mass spectrometry. QC samples were analyzed every 10 samples. The results of principal component analysis for the metabolome data from only the QC samples showed variations caused by capillary replacement in the first principal component score and linear variation with continuous measurement in the second principal component score. Correlation analysis between diagnostic blood tests and plasma metabolites normalized by the QC samples was performed for samples from 188 healthy subjects who participated in a Japanese population study. Five highly correlated pairs were identified, including two previously unidentified pairs in normal healthy subjects of blood urea nitrogen and guanidinosuccinic acid, and gamma-glutamyl transferase and cysteine glutathione disulfide. These results confirmed the validity of normalization protocols in capillary electrophoresis–mass spectrometry using large-scale metabolomics and comprehensive analysis.

Short-term wind speed interval prediction by improved regularized extreme learning machine based on attribute reduction

Accurate wind speed forecasting is important for stable operation of the power system when large-scale wind power is connected to the grid. According to the randomness of wind speed caused by the interaction of weather attributes, this article presents a new wind speed interval prediction method by improved regularized extreme learning machine based on attribute reduction. First, the principal component analysis is used to extract the principal component score sequences of multi-dimensional meteorological attribute factors, and the principal component score sequences are weighted by the variance contribution rate. Then, the original wind speed series is processed by fuzzy information granulation to obtain three components, which represent the minimum value, maximum value, and variation trend of the wind speed interval. The weighted principal component score sequence and the wind speed fuzzy granulation component are used as the input model of the prediction model, and the gradient prediction is performed using the improved regularized extreme learning machine of the gravity search algorithm. Finally, the prediction effect of the proposed method is simulated and analyzed based on the measured data of the wind farm. The results show that the combination prediction method can effectively improve the operational efficiency and accuracy of wind speed prediction, and has strong generalization ability.

Integrating principal component score strategy with power core method for development of core collection in Indian soybean germplasm

Soybean is a leading oilseed crop in India, which contains about 40% of protein and 20% of oil. Core collection will accelerate the management and utilization of soybean genetic resources in breeding programmes. In the present study, eight agromorphological traits of 3443 soybean germplasm were analysed for the development of core collection using the principal component score (PCS) strategy and the power core method. The PCS strategy yielded core collection (CC1) of 576 accessions, which accounted for 16.72% of the entire collection (EC). The analysis based on the power core programme resulted in CC2 of 402 accessions, which accounted for 11.67% of the EC. Statistical analysis showed similar trends for the mean and range estimated in both core collections and EC. In addition, the variance, standard deviation and coefficient of variance were in general higher in core collections than in the EC. The correlations observed in the EC in general were preserved in core collections. A total of 311 and 137 unique accessions were found in CC1 and CC2 in addition to 265 accessions that were found to be common in both core collections. These 265 common accessions were the most diverse core sets, which accounted for 7.64% of the EC. We proposed to constitute an integrated core collection (ICC) by integrating both common and unique accessions. The ICC comprised 713 accessions, which accounted for about 20.62% of the EC. Statistical analysis indicated that the ICC captured maximum variation than CC1 and CC2. Therefore, the ICC can be extensively evaluated for a large number of economically important traits for the identification of desirable genotypes and for the development of mini core collection in soybean.

An Approach for Data Analysis of Multi-Group Metabonomics Base on Hierarchical-PCA

Datasets of metabolomics of multi-group are becoming increasingly complex, hard to summarize and visualize. Hierarchical Modeling makes the data dimensionality reduction and interpretation much easier by principal component analysis (PCA). Dose-response curve is drawed with the principal component score values. As an example, dataset from Ma Xin Shi Gan Tang (MXSGT) water extract administrated rats plasma collected by LC/MS/MS was used to demonstrate this method. As a result, Hierarchical Modeling based on PCA was proved to be an effective, time saving method for data purification.

Evaluation and Statistical Analysis on the Centrality Indexes of Tourism Central City in the Central Liaoning Urban Clusters

This paper took 8 tourism central cities in central Liaoning urban clusters as an example, chose 7 indicators to analyze the centrality indexes of the tourism destinations and study the development of regional tourism industry. It firstly made a principal component analysis, then used the extracted principal components as a new integrated variable, the principal component score matrix as the new integrated variable data to make a cluster analysis through the software SPSS. From the perspective of tourism planning, the paper finally determines to establish a system of tourism central cities: Shenyang isⅠ-class tourism central city, Anshan, Fushun and Benxi are Ⅱ-class tourism central cities, Yingkou, Fuxin, Liaoyang and Tieling are Ⅲ-class tourism central cities, and provides the basis for distribution of the regional tourism economy in central Liaoning.