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1

Comprehensive Quality Evaluation of CIF

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We used MS Excel 2019 (Microsoft, Redmond, WA, USA) to calculate the relative correlation degree γi of the nine bioactive constituents of CIF based on the method outlined by Hua et al. [15 (link)] to evaluate its comprehensive quality. The Shannon’s Diversity Index H’ of the bioactive constituents, γi and the color parameters (L*, a* and b*) of CIF were calculated using the PCH’ Diversity Index Calculation Tool designed by the researcher Beiluozhongyuan (https://pan.baidu.com/s/1jG3eWKI). We conducted Pearson correlation and multiple linear regression (MLR) analyses using SPSS v21 (IBM, Chicago, IL, USA) to elucidate the relationships between the nine bioactive constituents, γi and their color parameters. We performed cluster analysis on the γi and color parameters using TBtools v1.0983 [16 (link)] and SPSS v21 with the hierarchical clustering method to create a clustering heat map and to calculate rescaled distance cluster combinations, respectively. We analyzed significant differences between the γi and color parameters among different cluster groups using SPSS v21 using one-way ANOVA followed by Fisher’s least significant difference (LSD) test.
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2

Injury Epidemiology in Dance Athletes

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Statistical analyses were undertaken using SPSS V.21.0 software (SPSS Inc., Chicago, IL, USA). Values of anthropometric, injury incidence, severity, absence, dancing history and training volume were reported as median (range), since the data were not normally distributed (Kolmogorov-Smirnov test, SPSS V.21.0 software ((SPSS Inc., Chicago, IL, USA)). Site and type of injury, injury sustained according to the type of activity, perceived cause and history of treatment received were reported as percentage.
A two-way full factorial generalized Poisson loglinear model was used to assess differences and significant interaction effects between gender (males versus females) and age-class (junior, youth, adult and senior) for anthropometric details, injury incidence, severity, absence, type of injury, site, dancing history and training volume. This model was used because the data were frequency counts with skewed distributions. The test statistics for this model is the Wald chi-square with a level of significance set at p < 0.05.
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3

Papillary Thyroid Carcinoma Risk Factors

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All analyses were performed in SPSS v21 software. Compliance with the normal distribution for the age variable was checked using the Shapiro-Wilk test. All analyses were performed in SPSS v21 program. Compliance with the normal distribution for the age variable was checked using the Shapiro-Wilk test. Comparison of the age variable that did not fit the normal distribution between the groups was done using the Mann-Whitney U test. Intergroup evaluation of categorical variables was done using Pearson χ2, Yates corrected χ2, and Fisher’s exact test, whichever was appropriate. Prospective selective logistic regression analysis was performed using variables with a statistically significant difference to determine the most effective variables in determining papillary carcinoma. The performance measurement of the model obtained after the logistic regression analysis was calculated. Evaluation of model performance was made using the receiver operating characteristic (ROC) curve. P-values < 0.05 were considered statistically significant.
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4

Statistical Analysis of In Vitro and In Vivo Data

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In vitro data are presented as means ± SD of at least three independent experiments. Comparison with controls were made using a two-tailed Student’s t-test performed in GraphPad Prism® 5, with P values <0.05 considered significant. Cox regression was performed using SPSS V21.0, where strain was a factor and log10 dose a covariate. In vivo bacterial burden data was transformed to the log10 and then analysed using a two parameter General Linear Model using SPSS V21.0. Bacterial burden data from dead mice was not included in the analysis.
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5

Chronic Diseases and Multimorbidity in Elderly Adults

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Statistical analysis was performed using R and SPSS V.21.0 (SPSS). Sample size was calculated by the formula, n =(Z2a×P0 (1−P0)/d2. With the data of elderly adults in southwest China, P0 was 16.1%,14 (link)
d was 0.1P0, a was 0.05, the minimal sample size of 2084 participants was required. The χ2, t-test and one-way analysis of variance were used to assess the differences in sociodemographic characteristics between subjects. Binary logistic regression analysis was conducted to examine factors associated with chronic diseases and multimorbidity, and a forward stepwise selection strategy was adopted when the regression models performed. To increase the representativeness of the study population, all statistics were calculated by using base weights adjustment (population weight and poststratification sample weights). The complex samples module in SPSS V.21.0 was adopted to account for the multistage sample design. Geographic heat map of chronic diseases was drawn by R V.3.2.1 programme. The association rule mining analysis was used to explore the correlations and patterns of multimorbidity between chronic diseases among community-dwelling elderly people. P<0.05 was considered statistically significant in the present study.
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6

Spearman Correlation Analysis of Geographical Factors

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This analysis, comparing the correlation between the different datasets collected, was carried out with the Spearman method (SPSS, v. 21.0 -https://www-01.ibm.com/support/docview.wss ?uid=swg21608060). P values were set at <0.05 for statistical significance, while P values <0.01 indicated highly significant correlations (Zar, 1972) . Geographical factors constitute an organic whole as they are all connected and the statistical effectiveness can be reduced due to collinearity between the variables involved. Therefore, it was necessary to control for multicollinearity, a significant correlation between explanatory variables caused by the specific characteristics of the variables investigated that reduces the accuracy of the results (Liu and Li, 2009) . This was done using SPSS v. 21.0
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7

Multivariate Analysis of Skin and Hair Traits

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The data obtained for hair fiber, hair cortisol, sweating rate, active sweat gland estimation, histomorphometry, IRT, and qPCR analysis were analyzed using the General Linear Model (GLM) of SPSS V.21 using breed and group as fixed effects. The correlation coefficient between breed, group, and THI with hair characteristics, hair cortisol, hair follicle qPCR, sweating, skin histometry, and skin-surface IRT were assessed by Pearson’s correlation coefficient test using SPSS V.21. Lastly, suitable statistical analysis wherever necessary was performed for the NGS approaches, which are listed in their respective sections. The results are represented as mean ± standard error (SE), with the significance level set at p < 0.05.
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8

Analyzing Microbial Community Structure and Environmental Factors

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All data was tested for normality using Kolmogorov-Smirnov test and log-transformed as necessary76 . Variation in PNR, gene abundances and environmental variables between sites, bays and time-points were analysed using a three-way analysis of variance (ANOVA) followed by a post-hoc Bonferroni test77 (link) in SPSS v21 (IBM, USA). A one-way ANOVA was used on environmental variables between sites in both bays at each time-point in SPSS v21. Analysis of covariance (ANCOVA) was conducted on gene abundance data by time-point in Graph Pad Prism v6 (GraphPad software, USA). A cluster dendrogram was created in Primer 7 (Quest research limited) using a bray-curtis similarity resemblance matrix, analysis of similarities (ANOSIM) was carried out between sites. Canonical correspondence analysis (CCA) using CANOCO 5 (http://www.canoco5.com) was used to explore relationships between community structure and environmental parameters. Explanatory value of environmental factors was determined using forward selection (tested by 499 Montecarlo permutations). Differences and correlation coefficients were considered significant at P < 0.05 unless otherwise stated in the text. Only significant explanatory variables were plotted.
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9

Evaluating Normality and Statistical Analysis

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The data in the figures and tables were presented as mean ± SEM. Experimental data have been analyzed by the Shapiro-Wilk with SPSS v.21 (SPSS Inc., Chicago, IL, USA). The result showed that all the data conform to normal distribution, so the data were analyzed with one-way ANOVA and Tukey’s test used to analyze differences between groups with SPSS v.21. A probability value p < 0.05 was considered as statistically significant.
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10

Quantitative Analysis of Callosal Potentials

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For input-output and refractoriness testing, quantitative analyses were conducted on waveforms which were the average of four successive sweeps. In cases where more than one slice was used from a single rat, those multiple-slice data were averaged for that rat and used for the analysis. Two categories of inferential statistical tests were used to evaluate the effects of injury and CFZ on callosal CAPs. First, analyses conducted at single levels of stimulation were evaluated using ANOVA (SPSS v21) combined with Bonferroni posthoc tests, and where appropriate, effect size was estimated using the Cohen’s D statistic. Secondly, the significance of shifts in curves, measured in input-output or refractory analyses, was evaluated with the Mann-Whitney U test. A nonparametric approach was selected in the latter case, because homogeneity of variance requirements were not met when ranges of stimulus intensities were applied. Statistical analysis of WB data was also performed using SPSS v21. Raw relative optical density measures were first tested for normality (Kolmogorov-Smirnov statistic) and homogeneity of variance. Appropriate data were then analyzed with ANOVA, and Dunnett post hoc tests. The significance level, α = 0.05, was used for all inferential statistics, and averaged values are expressed as mean ± SEM.
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