The data were evaluated using analysis of variance (ANOVA) followed by Tukey-Kramer post hoc tests. Statistical analyses were performed using JSTAT software (Tokyo, Japan) and KaleidaGraph 3.6 software (Synergy Software, Reading, PA, United States). All values were expressed as the mean ± STD and p-values less than 0.05 were considered statistically significant.
Kaleidagraph 3
Manufactured by Synergy Software
Sourced in United States
KaleidaGraph 3.6 is a data analysis and visualization software. It provides tools for data input, manipulation, and graphical representation.
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Lab products found in correlation
22 protocols using kaleidagraph 3
1
Statistical Analysis of Experimental Data
2
Pharmacokinetics and Anti-inflammatory Assays
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Data are expressed as the mean ± standard deviation (SD, n = 3, for the PK studies and in vitro studies) or the mean ± standard error of the mean (SEM, n = 7 or 8, for the mouse air pouch model). The statistical significance of differences was assessed by using one-way analysis of variance (ANOVA) followed by Dunnett's test. These statistical analyses and the determination of half-maximal inhibitory concentration (IC50) values (using the four-parameter curve fit algorithm) were performed by using the Kaleida Graph 3.6 software (Synergy Software, Reading, PA). P-values of <0.05 were considered significant.
3
Fungal Growth Kinetics Analysis
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Growth curves were generated from experimental data by plotting ln (Dt/Do), where Dt is the average colony diameter at time t (d) and Do (cm) is the average colony diameter at the initial time. Data were fitted using the modified Gompertz model [40 (link)], from which biological parameters were estimated by nonlinear regression; in order to validate the results, the coefficient of determination (R2adj) was determined to each growth curve using KaleidaGraph 3.51 (Synergy Software, Reading, PA, USA):
where A is the maximum mold growth achieved during the stationary phase, υmax is the maximum specific growth rate (cm/d), λ is the lag time (d), and e = 2.7182.
All parameters were analyzed using analysis of variance (ANOVA) with Minitab 17 software (Minitab Inc., State College, PA, USA). A p-value of 0.05 was used to decide significant differences among averages (Tukey’s test).
where A is the maximum mold growth achieved during the stationary phase, υmax is the maximum specific growth rate (cm/d), λ is the lag time (d), and e = 2.7182.
All parameters were analyzed using analysis of variance (ANOVA) with Minitab 17 software (Minitab Inc., State College, PA, USA). A p-value of 0.05 was used to decide significant differences among averages (Tukey’s test).
4
Microbial Resistance Profiling via Weibull Model
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Growth or inhibition curves were generated from experimental data by plotting log(N/No), where N was the average number of CFU/g at time (t) and No was the average number of CFU/g at the initial time point. Microbial growth or inactivation curves were fitted using the Weibull model (Eq. (1) ) through nonlinear regression (KaleidaGraph 3.51, Synergy Software, Reading, PA, USA)
* where b and n are the scale and shape parameters of the model, respectively.
The Weibull model corresponds to an upward concave survival curve when n > 1, a downward concave curve when n < 1, and a linear curve when n = 1. This results in two parameters: b, which indicates the rate of bacterial inactivation, and n, which indicates how the microorganisms survive or die.
The parameters n and b were used to calculate the frequency distribution of resistance. The mode of distribution represents the treatment time at which most of the population dies or is inactivated. The mean value represents the average inactivation time with its variation. Once the values of b and n were determined, the resistance frequency curves were plotted using the following equation (Eq. 2):
* where tc is a measure of the resistance or sensitivity of the organism, and Φ is the fraction of organisms that divide at a given time [46 (link)].
* where b and n are the scale and shape parameters of the model, respectively.
The Weibull model corresponds to an upward concave survival curve when n > 1, a downward concave curve when n < 1, and a linear curve when n = 1. This results in two parameters: b, which indicates the rate of bacterial inactivation, and n, which indicates how the microorganisms survive or die.
The parameters n and b were used to calculate the frequency distribution of resistance. The mode of distribution represents the treatment time at which most of the population dies or is inactivated. The mean value represents the average inactivation time with its variation. Once the values of b and n were determined, the resistance frequency curves were plotted using the following equation (Eq. 2):
* where tc is a measure of the resistance or sensitivity of the organism, and Φ is the fraction of organisms that divide at a given time [46 (link)].
5
Circular Dichroism Analysis of Nucleic Acids
6
DNA Duplex Thermal Melt Analysis
7
Steroid Receptor-Mediated Signaling Analysis
8
Measuring Deactivation and Desensitization of GluA1
9
Estrous Cycle and Hormone Dynamics
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All experimental data are presented as the mean ± SEM. The statistical significance of the differences in the duration of the estrous cycle, before and during intra-uterine administration,
was analyzed using a Student’s t-test. Plasma P4 concentration and CL diameter between control and treated groups were analyzed using two-way repeated measures ANOVA with
Fisher’s PLSD test using SAS 9.4 (SAS Institute Inc., Cary, NC, USA). The statistical significance of the differences in PG and P4 concentrations between the control and treated groups was
analyzed using one-way ANOVA with Dunnett’s multiple comparison post-hoc test using the KaleidaGraph 3.6 (Synergy Software, Reading, PN, USA) software package. Statistical significance was
set at P < 0.05.
was analyzed using a Student’s t-test. Plasma P4 concentration and CL diameter between control and treated groups were analyzed using two-way repeated measures ANOVA with
Fisher’s PLSD test using SAS 9.4 (SAS Institute Inc., Cary, NC, USA). The statistical significance of the differences in PG and P4 concentrations between the control and treated groups was
analyzed using one-way ANOVA with Dunnett’s multiple comparison post-hoc test using the KaleidaGraph 3.6 (Synergy Software, Reading, PN, USA) software package. Statistical significance was
set at P < 0.05.
10
Cell Survival Assay for Radiosensitivity
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To assess radiosensitivity, an appropriate number of prepared single cells following irradiation were plated on dishes and incubated for approximately 10 days. Colonies were fixed and stained with crystal violet. Colonies consisting of more than 50 cells were counted, and surviving fractions (SFs) were determined as described previously.16 Data were fitted to the following equation (linear‐quadratic [LQ] model) by KaleidaGraph 3.6 (Synergy Software, Reading, PA, USA):
Parameters of α, β, and D10 (dose obtaining SF at 10%) were determined by the curve fitting.
Parameters of α, β, and D10 (dose obtaining SF at 10%) were determined by the curve fitting.
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