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Sigmaplot v12

Manufactured by Merck Group
Sourced in United States

SigmaPlot v12.5 is a data analysis and graphing software developed by Systat Software Inc. It is designed for creating publication-quality graphs and performing advanced data analysis. The software offers a range of features, including curve fitting, statistical analysis, and data manipulation tools. SigmaPlot v12.5 is compatible with various data file formats and provides a user-friendly interface for data visualization and exploration.

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118 protocols using sigmaplot v12

1

Atherosclerosis Progression in Mouse Model

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All mice that survived to termination of the study and developed atherosclerotic plaque were included in the analysis (one mouse was excluded due to no discernable plaque and one was euthanized due to poor health prior to study termination). Each study was performed in 2–3 batches of mice and the histologic analysis data were compared to the average value for the MR-Intact group for each batch. Data are reported as mean ± SEM. Means between two groups were compared with unpaired Student's t-test using GraphPad Prism v.7.01. Normally distributed data in Table 1 (blood pressure, weight, serum cholesterol, and serum aldosterone) were analyzed by 2-factor ANOVA with Holm-Sidak post-test using SigmaPlot v.12.5. Non-normally distributed data (fasting glucose, heart weight, and spleen weight in Table 1 and smooth muscle α-actin quantification in Figure 8B) were analyzed by Kruskal-Wallis ANOVA. The Von Kossa scoring data in Figure 9 was analyzed by Mann-Whitney Rank-Sum test using SigmaPlot v.12.5. Statistical significance was defined as p < 0.05.
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2

Toxicological Parameters and Behavioral Analysis

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Concentration-mortality bioassay data were submitted to probit analysis to estimate toxicological parameters LC5 and LC50 (PROBIT PROC; SAS Institute, 2008). 25 Behavioral data were submitted to analysis of variance (ANOVA) and to Tukey's HSD test when necessary (Sigmaplot v 12.5). Morphometric data were submitted to the Kruskal-Wallis non-parametric test (Sigmaplot v 12.5). The fluorescence data were submitted to Tukey's test (Significance, P < 0.05) performed with the program GraphPad Prism version 4.0 for Windows (GraphPad Software, San Diego, California, USA).
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3

Concentration-Mortality Curves and Oviposition Bioassays

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Concentration-mortality curves were estimated with probit analyses using the PROBIT procedure in the SAS statistical software package. 45 The number of eggs laid, adult emergence, sex ratio, and egg-to-adult viability from each cage in the no-choice oviposition bioassay were analyzed by a Student's t-test or a Mann-Whitney rank sum test when assumptions of normality and homoscedasticity were not satisfied. All comparisons were performed using SigmaPlot v. 12.5 (Systat Software, San Jose, CA, USA). For the free-choice oviposition bioassay, the same parameters were analyzed by a paired Student's t-test on SigmaPlot v. 12.5. Additionally, the daily number of emerged adults and the daily emergence percentage in each treatment were submitted to nonlinear regression analysis using the curve fitting procedure of SigmaPlot v. 12.5. Regression analyses were conducted to detect trends in the pre-imaginal development period that resulted from the lime sulfur treatment or the choice of female oviposition. Regression models for each treatment were considered significantly different when the confidence limits of their parameters did not overlap.
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4

Statistical Analysis of Neuronal Profiles

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Statistical analyses were computed using non-parametric tests. For multiple comparisons, a Kruskal-Wallis ANOVA and Friedman test were performed. To analyze the projection neuron profile in the motor cortex, and for the physiological data, we used the Shapiro-Wilk test to determine in every case if the data were normally distributed, and we applied the appropriate statistical test to establish statistical differences (one-way ANOVA or Kruskal-Wallis and Bonferroni or Tukey post-hoc tests, respectively) with Sigma Plot v12. To analyze the length and spines across the cortical depth we used a Kolmogorov-Smirnov test. The level of confidence was set at 95% (p < 0.05).
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5

Statistical Analysis of Toxicity Data

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Statistical analysis of the data
was performed using SigmaPlot (v. 12). For each compound, data on
burrowing times and percentage weight gain from the toxicity study
were subject to Shapiro–Wilk and Levene–Mediane tests,
to test for normality and equal variance, respectively. If they passed,
then a one-way analysis of variance (ANOVA) was performed to assess
the differences in the values among the treatment groups; where normality
failed, ANOVA was performed using a Kruskal–Wallis analysis
on ranks. Differences between the measured and predicted pore water
concentrations were first tested for normality using a Shapiro–Wilk
test. As normality failed for each API, the difference between measured,
and predicted values was then evaluated by a Mann–Whitney U
Rank test. The relative accuracy of the estimated results was estimated
by calculating proportional deviation from the measured to the estimated
value.
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6

Kinetic Analysis of SP_0166 Enzyme

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Kinetic parameters of SP_0166 were analyzed by detecting the amount of the product generated over a range of substrate concentrations from 0.01 mM to 30 mM at pH 7.5 for 1 h. For quantification, calibration standards were prepared for agmatine, cadaverine, and putrescine. The nonlinear regression method of the Michaelis–Menten equation with Sigma Plot v.12 was used to estimate the kinetics parameters (kcat, Km, and kcat/Km). All experiments were performed with three independent replicates and the values represent the mean ± the standard deviation of three separate measurements.
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7

Quantifying Cellular Responses to Stimuli

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Data are presented as means ± standard deviation for at least 3 independent experiments. The significance of differences between groups was determined using a Student's t-test with statistical significance defined as p < 0.05. Statistical analyses were performed using SigmaPlot v12.
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8

Morphine-Induced Opioid Withdrawal in Mice

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Opioid dependence was induced in mice by repeated intraperitoneal (i.p.) injection of morphine at 12h intervals for 6 days6 (link). Mice were treated with escalating doses of morphine as follows: day 1: 20 mg/kg, day 2: 40 mg/kg, day 3: 60 mg/kg, day 4: 80 mg/kg, day 5: 100 mg/kg; day 6, only one injection in the morning, 100 mg/kg. Withdrawal was precipitated by injecting naloxone (1 mg/kg, s.c.) 2h after the last administration. Somatic signs of withdrawal were evaluated immediately for 30 min. The following somatic signs were monitored: jumps, wet-dog shakes, paw tremor bouts, sniffs teeth chattering and ptosis. A global score was calculated by using a coefficient for each sign (0.8; 1; 0.35; 0.5; 7.5; 4.5 and 1.5, respectively)6 (link). Statistical analysis: Data are expressed as mean ± SEM and analyzed by two-way ANOVA using Sigmaplot (v12) followed by Bonferroni tests for tests for post hoc pairwise comparisons. For all tests significance was accepted at p < 0.05.
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9

Simulating LDL-C Adherence Thresholds

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The PK/PD simulations produced LDL-C concentration profiles consisting of 1000 data points over 30 days (720 h). The increment of time associated with each data point (approximately 0.7 h) was calculated, and a binary (yes/no) determination was made of whether that increment was spent at the LDL-C goal (< 70 mg/dL). Summing the ‘yes’ increments and dividing by the total time course gave the percentage of time for which the serum LDL-C level was at goal. The adherence rate threshold was defined as the lowest adherence rate that facilitated attainment of the LDL-C goal for the same amount of time as perfect adherence (i.e., 1.0), subject to a z test of proportions. Z tests were performed in SigmaPlot v. 12.
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10

Measuring ALDH Enzyme Inhibition Kinetics

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Human ALDH1A1, ALDH1A2, ALDH1A3, ALDH2, ALDH1B1, ALDH3A1, ALDH4A1, ALDH5A1 and rat ALDH1L1 were prepared and purified as previously described. 27 (link), 63 (link)–66 (link)Inhibition of ALDH activity by compounds and IC50 curves were determined by measuring the formation of NAD(P)H spectrophotometrically at 340 nm (molar extinction coefficient of 6200 M−1 cm−1) on the Beckman DU-640 and Spectramax 340PC spectrophotometers using purified recombinant enzyme. Reaction components for ALDH1A and ALDH2 assays consisted of 100–200 nM enzyme, 200 μM NAD+, 100 μM propionaldehyde, and 1% DMSO in 25 mM BES buffer, pH 7.5. For ALDH1B1, the assay included 500 μM NAD+ and 200μM propionaldehyde. For ALDH3A1, the assay included 300μM NADP+, 20nM enzyme, and 300μM benzaldehyde. For ALDH4A1 and ALDH5A1, the assay included 1.5mM NAD+, 100nM enzyme, with 20mM propionaldehyde for ALDH4A1 and 2mM propionaldehyde for ALDH5A1. For rat ALDH1L1, the assay included 0.5mM NADP+, 200nM enzyme ad 4mM propionaldehyde. All assays were performed at 25°C and were initiated by addition of substrate after a 2 min incubation period. IC50 curves were collected for compounds which substantially inhibited ALDH1A activity at 20 μM compound. Data were fit to the four parameter EC50 equation using SigmaPlot (v12) and the values represent the mean/SEM of three independent experiments (each n=3).
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