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Prism v 6.0g

Manufactured by GraphPad
Sourced in United States

GraphPad Prism v 6.0g is a data analysis and graphing software designed for scientific research. It provides a comprehensive set of tools for data visualization, statistical analysis, and curve fitting. The software is used to create high-quality graphs, analyze experimental data, and interpret scientific results.

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24 protocols using prism v 6.0g

1

Statistical Analysis of Experimental Data

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All statistical tests were performed with Prism v.6.0 g (GraphPad) and data are represented as mean ± SEM (or SD when specified). Unless mentioned otherwise, we used Mann–Whitney U test for two-group comparison. All p-values were calculated with two-tailed statistical tests and 95% confidence intervals. *p < 0.05, **p < 0.01, ***p < 0.001; ns, non-significant.
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2

Statistical Analysis of Experimental Data

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Data analysis was performed using GraphPad Prism v6.0g software (Graphpad). Statistical differences were determined using a Two-tailed Student t test. Data are expressed as mean ± standard deviations (SD) of data from at least three sample replicates. Differences with a P-value of < 0.05 are considered statistically significant.
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3

Statistical Analysis with GraphPad Prism

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GraphPad Prism v6.0g (GraphPad Software, Inc., United States) was used for statistical analysis.
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4

Behavioral Data Analysis using ANOVA

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Data were compiled in and analyzed using GraphPad Prism V6.0g for Mac OSX. Behavioral data were analyzed using a two-way repeated measures analysis of variance (ANOVA) with a Bonferroni post-hoc analysis where appropriate. Critical values reaching a p < 0.05 level were considered to be statistically significant.
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5

Analyzing Diet Impacts on Biological Traits

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Data for diet development experiments were compiled across experimental replicates and examined jointly. All data were examined for normality using the D’Agostino-Pearson omnibus normality test. Data sets that failed the assumption of normality were examined using non-parametric tests. Fecundity and hatch rate data, including those from the CI experiments, were compared by Kruskal-Wallis ANOVA, and by Dunn’s multiple comparison tests. Wolbachia density data were organized by family and diet, and then compared by 2-way ANOVA. ZIKV infection prevalence of infection data were compared via Fisher’s exact test, while ZIKV load data were compared by Mann Whitney U test. Longevity data were compared via Mantel-Cox test. All statistical analyses were performed using Prism V 6.0 g (Graphpad), except for the analysis of the longevity data, which was performed using SPSS V17 (IBM). Statistical output from characterization assays is presented in Supplementary File 1.
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6

Statistical Analysis in Biomedical Research

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All statistical tests were performed with Prism v.6.0g (GraphPad), and data are represented as mean ± SEM. We used alternatively unpaired t test, paired t test, or Mann-Whitney U test for two-group comparison. All p-values were calculated with two-tailed statistical tests and 95% confidence intervals. *, P < 0.05; **, P < 0.01; and ***, P < 0.001.
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7

Survival Analysis of CLL4 Clinical Trial

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Fisher’s exact tests were performed for co-occurrence analysis between mutated genes and clinical features. PFS and OS was assessed from randomisation using Kaplan Meier (KM) and Log rank analysis. PFS was defined as time from randomisation to progression (i.e. relapse needing further treatment) or death, or to last follow-up date (Oct 2010; final CLL4 PFS update). OS was defined as time from randomisation to death or to last follow-up date for survivors (August 2016, final CLL4 OS update). Multivariate Cox Proportional Hazard models were generated for OS and PFS using backwards selection (P<0.05), to test the confounding effect of multiple prognostic variables. The Bland-Altman test was used to test agreement between multiple factors, reporting the agreement bias, which is the mean difference between two measurements. All reported P values were 2-sided and results were considered significant at the 5% level, using multiple hypothesis testing when appropriate (Benjamini and Hochberg method38 ). Statistical analysis was conducted using R v3.3.0, SPSS v23 (IBM), and Prism v6.0g (GraphPad).
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8

Prognostic Factors in Chronic Lymphocytic Leukemia

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Fisher’s exact tests were performed for co-occurrence analysis between mutated genes and clinical features. PFS and OS were assessed from randomisation using Kaplan Meier (KM) and Log rank analysis. PFS was defined as time from randomisation to progression (i.e. relapse needing further treatment) or death, or to last follow-up date (Oct 2010; final CLL4 PFS update). OS was defined as time from randomisation to death or to last follow-up date for survivors (August 2016, final CLL4 OS update). Multivariate Cox Proportional Hazard models were generated for OS and PFS using backwards selection (P < 0.05), to test the confounding effect of multiple prognostic variables. The Bland–Altman test was used to test agreement between multiple factors, reporting the agreement bias, which is the mean difference between two measurements. All reported P values were two-sided and results were considered significant at the 5% level, using multiple hypothesis testing when appropriate (Benjamini and Hochberg method [38 ]). Statistical analysis was conducted using R v3.3.0, SPSS v23 (IBM), and Prism v6.0 g (GraphPad).
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9

Statistical Analysis of Experimental Data

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Statistical analyses were performed with GraphPad Prism v6.0g (GraphPad Software, Inc., La Jolla, CA). Pairwise comparisons were conducted using non-parametric tests (Kruskal-Wallis or Friedman tests), as samples sizes were insufficient to assess normality. Dunnett’s or Holm-Sidak tests were used to adjust p-values for multiple comparisons. Where applicable, results are expressed as mean ± standard error of mean.
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10

Statistical Analyses for Biological Experiments

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Statistical analyses were performed using GraphPad Prism v 6.0g (GraphPad Software Inc.). For results of in vitro experiments, paired t-tests or a one-way ANOVA followed by Dunnett’s multiple comparisons test was used, for two or more comparisons, respectively. For survival analysis, Kaplan-Meier curves were plotted and analyzed by the log rank test. Mouse serum, spleen, and brain viral loads and titers were compared using the Mann-Whitney test.
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