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Sigmaplot software version 12

Manufactured by Merck Group
Sourced in United States

SigmaPlot software version 12.5 is a data analysis and graphing tool used for scientific and engineering applications. It provides capabilities for data visualization, curve fitting, and statistical analysis.

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12 protocols using sigmaplot software version 12

1

Statistical Comparison Methodology

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Differences between groups or among groups were tested by either paired t-test or by using one-way analysis of variance (ANOVA) with repeated measures over time. The assumption of the ANOVA was examined, and a nonparametric measure based on ranks was used if needed. When ANOVA indicated a significant effect (F-ratio, P<0.05), the Student–Newman–Keuls multirange test was used to compare the means of the individual groups. Statistical analyses were conducted using SigmaPlot software, version 12.5 (San Jose, CA, USA). For all comparisons, P<0.05 was regarded as statistically significant. Values are reported as mean ± standard error of the mean.
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2

Colon Inflammation and Serum IL-6 Analysis

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The data in this paper are expressed as a scatter plot of the data which include the median ± interquartile values. All data were tested for normality with Shapiro–Wilk test. Colon mRNA levels and serum Il-6 level data which passed this test were then further analyzed by one-way analysis of variance (ANOVA), while data which failed this test were analyzed by Kruskal–Wallis test. Tukey’s or Dunnett’s post hoc analysis was used to evaluate differences between groups. Spearman Rank Correlation test was used to determine the correlation between serum level and colon mRNA expression of Il-6. Body weight reduction and DAI were analyzed by repeated measurement ANOVA (RM ANOVA). Histological analysis scores were analyzed with one-way ANOVA, followed by Tukey–Kramer post hoc analysis. Groups were considered to be significantly different at the levels indicated on each figure. Statistical analyses were performed with the SigmaPlot software version 12.5 (San Jose, CA, USA).
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3

Survival Analysis of Experimental Data

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All data except survival were analyzed using SAS software version 9.2 (Cary, NC). Generalized linear modeling was used for single time-point analyses and the mixed procedure was used for repeated measures analyses. Assumptions of normality and equal variance were verified for each experiment. All main effect and interactions were investigated in the initial statistical model. When significant effects were present we performed pre-planned pairwise comparisons. Non-normally distributed (IDO enzymatic activity and gene expression) data was log transformed for analysis. These data are presented as means ± 95% confidence intervals as distributions were significantly right skewed. Results for experiments with small group sizes are presented as individual data points. Unless otherwise indicated in the figure legend, these individual data points represent the average of technical triplicates. Survival data was analyzed using Kaplan-Meier analysis with the Holm-Šidák method for pairwise comparisons with Sigma Plot software version 12.5. P-values less than 0.05 were considered significant.
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4

Evaluating Experimental Treatments

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Data are represented as mean ± standard error of the mean (SEM). Statistical analysis was performed by one-way analysis of variance followed by Tukey’s multiple comparison test. The analysis was performed using SigmaPlot software version 12.5 (San Jose, CA, USA). A P value < 0.05 was considered statistically significant.
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5

Statistical Analyses of Experimental Data

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SigmaPlot software (version 12.5) was used to analyze all experimental data. The statistical analyses performed included the Student’s t-test, one-way analysis of variance (ANOVA) and the Mann–Whitney test. p<0.05 indicated a statistically significant difference. The experimental data are expressed as mean ± standard error of the mean (SEM).
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6

Triplicate Experiments and Statistical Analysis

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All experiments were performed in triplicate and analyzed by ANOVA test and student t-test using the SigmaPlot software version 12.0 (CA, United States).
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7

Quantifying Cartilage Glycosaminoglycan Content

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IBM SPSS Statistics release 20.0.0 was used to calculate linear mixed-effects (LME) models with segment, measurement, and their interaction as fixed effects and subjects as a random effect (variance components). Significance was assumed at p < 0.05. The assumptions made were a normal distribution of the raw data and the residuals. The Kolmogorov-Smirnov test showed a normal distribution of ΔT1 values in the cartilage of the upper ankle joint (p = 0.6, residuals p = 0.9), the lower ankle joint (p = 0.8, residuals p = 0.6), and the knee (p = 0.9, residuals p = 0.4). Data are presented as counts with their SD. Exclusion conditions were ΔT1 times of < 400 ms and > 1,500 ms. SigmaPlot software version 12.0 was used to plot data. ΔT1 was the primary outcome measure of this study. An increase in ΔT1 corresponded to a decrease in the GAG content.
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8

Analysis of Ion Release Kinetics

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SigmaPlot software version 12.0 (SigmaPlot, Systat Software Inc., San Jose, USA) was used, with the significance level set at 5%. Data obtained on the release of F and TMP, as a function of time, were analyzed by two-way, repeated measures analysis of variance (ANOVA) and Student-Newman-Keuls' test. Cumulative ion release data were analyzed by one-way ANOVA followed by Tukey's test.
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9

Cell Viability Bioassay Protocols

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All treatments consisted of four experimental units in triplicate (n = 12). All data were verified for homoscedasticity and normality. The values for cell viability obtained from photoprotection and cytotoxicity bioassays were analyzed using one-way ANOVA followed by the Tukey–Kramer post-hoc test for multiple comparison. Sigma Plot software (version 12) was used for all statistical analyses. A p-value less than 0.05 was considered to indicate significant differences in all statistical analyses.
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10

Comparative Enzymatic Analysis of Trx-Dependent H2O2 Reduction

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The comparison of a selenocysteine-dependent enzyme (TcTGR) with a Cys-dependent enzyme (EcTrxR), regarding its ability to catalyze the Trx-dependent reduction of H2O2, was evaluated by mixing 150 μM NADPH with either 60 μM TsTrx and 11.2 nM TcTGR or 6 μM EcTrx and 83 nM EcTrxR in TE buffer. The reaction was started by adding 1 mM H2O2, and the absorbance at 340 nm was measured. The final volume of the reaction mixture was 0.6 mL.
The kinetic constants Km and kcat of TsPrx1 and TsPrx3 for either H2O2, t-butyl hydroperoxide, or the cumene hydroperoxide substrates were determined by varying the concentration of the corresponding peroxide at a constant concentration of both NADPH (150 μM) and TsTrx (60 μM). To obtain the kinetic parameters for TsTrx, a constant concentration of 50 μM H2O2 was used at varying TsTrx concentrations. In all cases, fixed concentrations of TsPrxs (1.25 μM) and TcTGR (11.2 nM) were used (these last concentrations were previously determined to prevent them being limiting). The kinetic constants of TcTGR toward H2O2 was obtained by varying the concentration of the peroxide at a constant concentration of NADPH (150 μM) and TsTrx (60 μM). All initial velocity data were fitted to the Michaelis–Menten equation through non-linear regression analysis using Sigma-Plot Software version 12.
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