Data are expressed as the mean ± one standard error of the mean. Physiological data were assessed for significance using a t test, a one-way analysis of variance (ANOVA) with a Holm-Sidak post-hoc test, or a two-way repeated measures ANOVA with a Holm-Sidak post-hoc test, as appropriate, using SigmaPlot Version 14.0. For sequencing data, alpha- and beta-diversity metrics were calculated using the q2-diversity plugin in QIIME2. Alpha (within sample)-diversity metrics used Kruskal-Wallis (P values <.05) followed by Benjamini-Hochberg false discovery rate (FDR) correction to examine community richness and abundance. Alpha diversity measured using Shannon's Diversity was plotted in PrismGraphpad v7. Beta (across sample)-diversity metrics used Pairwise PERMANOVA (P values <.05) followed by Benjamini-Hochberg FDR correction to determine if the microbial communities were statistically significantly different. For visualization of β-diversity, we used weighted UniFrac distances72 (link) plotted as a Principal Coordinate Analysis (PCoA). To determine significantly enriched bacteria per group, we use LEFSE analysis (Z-scores > 2.0 and P < .05), balances analysis with the GNEISS plugin in QIIME2.
Sigmaplot version 14
Manufactured by Merck Group
Sourced in United States
SigmaPlot Version 14.0 is a data analysis and graphing software developed by Systat Software, Inc. It is designed to create high-quality plots and graphs for scientific and engineering applications.
Automatically generated - may contain errors
Lab products found in correlation
16 protocols using sigmaplot version 14
1
Microbiome Diversity Analysis Protocol
2
Quantitative Binding Kinetics of Wnt7a Peptides
3
Statistical Analysis of Experimental Data
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The SigmaPlot version 14.0 software was used for statistical analysis, while GraphPad Prism 6.0 software was used for graphical representation. Two-way ANOVA, followed by Bonferroni test, was performed. The results are expressed as mean ± standard error of mean (SEM) and were considered statistically significant if they had a null hypothesis probability of less than 5% (p < 0.05).
4
Comparative Analysis of Pelagic Sargassum
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To assess potential differences in the biochemical and elemental composition between the three pelagic Sargassum morphotypes, statistical analysis was conducted using SigmaPlot version 14.0. For thermogravimetric analysis, as well as for comparison of monosaccharide composition, and of phenolic and phlorotannin contents, values for samples corresponding to the same morphotype, or to the bulk, and harvested at the three sites of collection, were pooled together. However, bulk values were not considered for the statistical tests to focus on comparison between the three morphotypes. Data were first tested for normality and homogeneity of variance using the Shapiro-Wilk test and Brown-Forsythe test, respectively. When these tests were passed, one-way ANOVA was performed, followed by a posthoc Holm-Sidak test for all pairwise multiple comparisons. When the normality test (Shapiro-Wilk) failed, Kruskal-Wallis one-way analysis of variance on ranks was applied, followed by a post hoc Tukey test for all pairwise multiple comparisons. In addition, T-test done in Excel was used to assess the impact of enzymatic treatment on monosaccharide composition of alcohol insoluble residues. The significance level was set at p-value ≤ 0.05 for all the data analysis.
5
Analyzing Treatment Effects with ANOVA
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One-way analysis of variance (ANOVA) was performed using SPSS 26.0 to determine the differences in different treatment levels. Percentage data were subjected to arcsine square root transformation prior to ANOVA. Tukey’s honestly significant difference (HSD) was used as a post hoc test [53 (link)]. Population life table data were analyzed by the TWOSEX-MSChart program (TWOSEX-MSChart is available at http://140.120.197.173/Ecology/ accessed on 6 January 2021) [54 ]. The standard errors of each parameter were estimated by the bootstrap technique, with 100,000 samples; the paired bootstrap test was applied to test the differences among evaluated parameters [55 (link)]; all the graphs were created using SigmaPlot Version 14.0.
6
Comparative Assessment of Wood Types
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Shapiro-Wilk and Brown-Forsythe tests were used to evaluate normality and equality of variance assumptions, respectively (Sigma Plot version 14.0). The seven wood types were compared using the non-parametric Kruskal-Wallis one-way analysis of variance test (ANOVA; α = 0.05) and then further evaluated using pairwise multiple comparison Tukey tests. For comparisons of discrete categorical variables (e.g., DI vs. river water) and changes in DRP concentration over time (initial vs. final concentration), Student t tests were used when assumptions of normality and equal variance were met. Mann-Whitney Rank-Sum test was used when the normality assumption was not met, and Welch’s t test was used when the assumption of equal variances was not met.
7
Statistical Analysis of Experimental Data
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Statistical analysis was performed by SigmaPlot (version 14.0) data analysis software. Descriptive statistics were applied where appropriate. Between groups comparisons were made by a one‐way ANOVA on Ranks or Wilcoxon signed‐rank test with a P ≤ .05 significance level. Post hoc analysis (Dunn's analysis for multiple comparisons) was performed where appropriate.
8
Enzymatic Kinetic Analysis of Plant Biosynthetic Enzymes
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After validating the enzymatic activity for TraesCS2D01G518200/TraesCS2D01G518300 (TDC), TraesCS1B01G312700 (SNAT), and TraesCS1D01G016700 (ASMT), kinetic constants for them were further determined. In brief, varied substrates (Trp for TDC, both of Ser and 5M‐Trm for each of SNAT and ASMT) were supplied into the reaction mixture. After incubation at 37°C (TDC and ASMT) or 45°C (SNAT), amounts of generated products were calculated based on the standard curves for respective chemicals. Kinetic parameters were calculated using a Michaelis–Menten model (SigmaPlot, version 14.0). All the reactions were run in duplicate, and each experiment was repeated twice.
9
Statistical Analysis of Experimental Data
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Statistical analyses were performed with SigmaPlot Version 14.5. Data are expressed as mean ± standard deviation (SD). Differences among groups were assessed by nonparametric Kruskal-Wallis analysis of variance (ANOVA) on ranks or one-/two-way ANOVA. Post hoc comparisons were calculated using the Student-Newman-Keuls test, and the results were considered significant at p < 0.05.
10
Dose-Response Modeling and Herbicide Resistance Evaluation
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The data from dose–response experiments were checked for the assumptions of non‐linear regression prior to a two‐stage meta‐analysis approach using Sigmaplot (version 14.5). The data were fitted to a three‐parameter log‐logistic model:
Y = d / 1 + exp b log x − log LD 50
The segregation of F2 families was tested against a single‐gene model using a two‐sided exact binomial test that used the method of small P‐values. The expected survival values corrected for the survival of the F2 progenies using the method outlined by Busi and Powles.32 In addition, a chi‐square test of homogeneity was conducted to determine if all F2 families had a similar segregation response to dicamba treatments.
The segregation of F2 families was tested against a single‐gene model using a two‐sided exact binomial test that used the method of small P‐values. The expected survival values corrected for the survival of the F2 progenies using the method outlined by Busi and Powles.
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