All data are presented as means ± SEM. For statistical analysis, longitudinal data were square-root transformed to approximate normal distribution. Since significant deviations from normal distribution were only rarely detected (Shapiro-Wilk test in the Univariate Procedure; SAS 8.2), transformed data for body weight, food intake, glucose, insulin and glucagon levels were evaluated by ANOVA (Linear Mixed Models; SAS 8.2) taking the fixed effects of Group (liraglutide-/placebo-treated), Time (relative to glucose administration/treatment duration), and the interaction Group*Time into account. Transformed data of clinical-chemical analyses were evaluated by ANOVA (General Linear Models; SAS 8.2) taking the fixed effects of Group, Age, and the interaction Group*Age into account. AUC insulin/glucose was calculated using GraphPad Prism® software (version 5.02). AUCs and all remaining parameters were tested for significance by Mann-Whitney-U-test using GraphPad Prism® software. Results of Western blot analyses were related to the mean value of the placebo group (set to 1) and presented as box-plots with median. P values less than 0.05 were considered significant. Net glucose elimination rates [29 ] were calculated and slopes were compared using GraphPad Prism® software (version 5.02).
Sas 8
Manufactured by SAS Institute
Sourced in United States, Japan
SAS 8.2 is a software package designed for advanced data analysis and statistical modeling. It provides a comprehensive suite of tools for data management, manipulation, and analysis. The core function of SAS 8.2 is to enable users to efficiently and accurately process, analyze, and interpret complex data sets, supporting a wide range of industries and research applications.
Automatically generated - may contain errors
Lab products found in correlation
Prism 7, by GraphPad (8 mentions)
Prism 8, by GraphPad (7 mentions)
Sigmaplot 12, by Grafiti LLC (6 mentions)
Prism 5, by GraphPad (5 mentions)
Prism 6, by GraphPad (3 mentions)
Excel 2019, by SAS Institute (3 mentions)
Sybr green pcr master mix, by Takara Bio (2 mentions)
Originpro 8, by OriginLab (2 mentions)
Sas software, by SAS Institute (2 mentions)
Origin 8, by OriginLab (2 mentions)
Excel 2003, by SAS Institute (2 mentions)
Sas software version 9, by SAS Institute (2 mentions)
Spss version 16.0 statistical package for windows, by IBM (2 mentions)
Spss 19, by IBM (2 mentions)
Spss statistics 19, by IBM (2 mentions)
Prism 5, by SAS Institute (2 mentions)
Spss 20, by IBM (2 mentions)
I sanger cloud platform, by Majorbio (2 mentions)
Spss statistics 20, by IBM (2 mentions)
Excel 2007, by SAS Institute (2 mentions)
473 protocols using sas 8
1
Liraglutide Effects on Glucose Metabolism
2
Comparative Analysis of Carcass Traits and Meat Quality in Shaziling and Yorkshire Pigs
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Carcass traits and meat quality in the same breed at different ages were expressed as mean ± SEM and analyzed by one-way ANOVA in SAS 8.2. Different lowercases/uppercases denote significant differences among Shaziling/Yorkshire pigs at different ages (P < 0.05). The unpaired t-test in SAS 8.2 was applied to compare the above results in the 2 breeds at the same age. Significant differences from the t-test were marked as ∗ for P < 0.05, ∗∗ for P < 0.01, and ∗∗∗ for P < 0.001.
Principle component analysis (PCA) and orthogonal partial least squares discriminate analysis (OPLS-DA) were performed using ropls (Version1.6.2,http://bioconductor.org/packages/release/bioc/html/ropls.html ) R package on Majorbio Cloud Platform (https://cloud.majorbio.com ). Variable importance in the projection (VIP) was calculated in the OPLS-DA model. Differential metabolites were identified according to the standard of VIP >1, P < 0.05, and FC < 0.5 or >2. Then the differential metabolites were summarized and mapped into biochemical pathways through KEGG enrichment analysis. The significantly enriched pathways were identified by Fisher's exact test. The correlational heatmaps were generated according to the result of Pearson correlation analysis. These 2 processes are both conducted in the environment of Python package named Scipy.stats (https://docs.scipy.org/doc/scipy/ ).
Principle component analysis (PCA) and orthogonal partial least squares discriminate analysis (OPLS-DA) were performed using ropls (Version1.6.2,
3
Magnolol and Honokiol Effects Analysis
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Data, not including MC, PC, and NC groups, were statistically analyzed using the procedures of SAS (8.2) software (2000; SAS Inc., Cary, NC, USA). The model included the fixed effects of magnolol, honokiol, and magnolol × honokiol with the animal as the random effect. All data (including MC, PC, and NC groups) were subsequently examined to compare the treatment differences by using the General Linear Models procedure of SAS (8.2), and the statistical model only included the fixed effects of treatments. Least-squares means were reported throughout, and differences at P<0.05 were considered statistically significant.
4
Glucose Metabolism in GHR-KO Mice
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All data are displayed as mean ± SEM. PROC GLM (General Linear Models, SAS 8.2) was used to analyze the body weight at the day of necropsy, the area under the curve (AUC) values for glucose and insulin and all assessed parameters concerning quantitative stereology of the pancreas. Effects of group (GHR-KO, WT), age (young, adult), sex (male and female adults) and the interaction group*age were taken into consideration. None of the investigated parameters was significantly affected by sex. Results of ivGTTs (glucose and insulin levels) were analyzed using PROC MIXED (Linear Mixed Models; SAS 8.2) considering effects of group (GHR-KO, WT), age (young, adult) as well as the interaction group*age. The Graph Pad Prism software (version 5.02 and 10.1.2) was used to generate figures and calculate AUC values, means and SEMs. P values < 0.05 were defined as significant.
5
Flower Diameter Optimization Protocol
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All the exhibited data are the means ± SE of no less than three biological replications (n ≥ 3). The data were subjected to a one-way ANOVA according to Duncan’s multiple comparison range test or unpaired two-tailed Student’s t test for the significant differences at p = 0.05 with SAS 8.2 and GraphPad Prism 8.0.2, respectively. The main and interactive effects of the solution treatments and longevity of the vase life on the maximum flower diameters are determined by a two-way ANOVA ‘Linear models’ method of the F-test subjected to Fisher’s least significant difference (LSD) test with an SAS 8.2 program, where four treatment solutions and the longevity of the vase life of the flowers were regarded as the independent variable, while the maximum flower diameter was regarded as the dependent variable. A map of the study area was drawn with DIVA-GIS7.5. The bar graphs were plotted with GraphPad Prism 8.0.2. The PCA (principal component analysis) was performed using Origin 2022.
6
Cry1Ac Protein Effects on Diatraea
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Experiments were arranged under a completely randomized design, with a factorial structure considering Diatraea species and treatments (Cry1Ac protein and control) as fixed effects and analyzed using generalized linear mixed models (PROC GLIMMIX, SAS 8.2) [34 ]. The 16 wells within each of the eight replicates were considered subsamples, and well effects were considered random. Each combination of Diatraea species and the protein was made performed once using a single borer generation. Mortality was analyzed as a proportion of dead larvae within each replicate, assuming binomial distribution, whereas indexes of the stage of development (GIS and RGI) and weight (%GI) were analyzed with available (alive) individuals, assuming Gaussian distribution. The mean weight of surviving larvae was also analyzed assuming Gaussian distribution. Fungi contamination of wells was estimated as the proportion of wells contaminated from the total 16 wells per replicate (subsample), and analyzed assuming a binomial distribution. Means were separated by Tukey tests (α = 0.05). In addition, linear regressions comparing the proportion of contaminated wells with the proportion of dead larvae in the controls and the proportion of contaminated wells with the proportion of larvae killed in the treatment were analyzed by linear regression (PROC REG, SAS 8.2) [34 ].
7
Reproductive Performance and Biomarkers in Sows
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The sow or litter was the experimental unit for reproductive performance and biomarkers in serum of sows as well as piglets' growth performance. All data were processed preliminary with Excel (2010), and statistical analysis was performed with the general linear model procedures of SAS 8.1 (SAS Inst. Inc, Cary, NC). Prior to analysis, all data were tested for normality (Shapiro–Wilk test) and homogeneity of variance (Levene's test). If the data failed to fit the normal distribution or the homogeneity of variance was not found, the Kruskal–Wallis test was carried out. Differences for the data of piglets' performance, including litter weight, ADG, litter weight gain and average piglet weight at 9 and 20 d of lactation were examined by PROC MIXED (SAS 8.1, Cary, NC). The treatment was specified as a fixed effect and the parity and piglet numbers were considered as random effects. The birth weight or litter weight after cross-fostering, as well as piglet weight or litter weight at 9 d postpartum, were considered as a covariate. Differences for the other data of sows or piglets were tested by PROC ANOVA (SAS 8.1, Inst. Inc, Cary, NC). All data were expressed as means ± SE; significance was assessed by a P-value of less than 0.05; 0.05 < P ≤ 0.10 was considered as marginally significant.
8
Heritability Estimation of Phenotypic Traits
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Various modules of the Statistical Analysis System (SPSS 19.0) software package were used to analyse the phenotypic data. Broad-sense heritability (h 2 ) was calculated as σ 2 g / (σ 2 g + σ 2 ge / n + σ 2 e / nr) × 100%, where σ 2 g is the genetic variance, σ 2 e is the error variance, σ 2 ge is the interaction variance between genotypes and environments, n is the number of environments, and r is the number of replications in each environment. The estimates of σ 2 g , σ 2 ge and σ 2 e were obtained from a two-way ANOVA using the general linear model (GLM) procedure in SAS 8. 1. In the variance analysis model, the variances of the genotypes, environments and interactions between the genotypes and environments were considered random effects. Correlations were calculated for the owering time between the four environments by SAS 8.1 software.
9
Comparative Analysis of Metabolic Traits
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The differences in total sugar, trehalose, glycerol, lipid, and proline contents in F0 generation between the Beijing and Laibin populations or hybridization populations (F1 generation) were analysed using a t-test with SAS 8.1 (SAS Institute, Cary, NC, USA). Gene expression and bioassays were analysed using one-way analysis of variance followed by a least signi cant difference test with SAS 8.1 (SAS Institute, Cary, USA) to evaluate the signi cant differences among treatments.
10
Statistical Analysis of Behavioral Data
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SAS 8.2 software was used for the statistical analysis. All data are presented as mean ± standard deviation (S.D.). When the data in each group conformed to a normal distribution and had equal variance, a one-way analysis of variance was applied, and the LSD method was used to compare groups. Behavioral test data were analyzed using repeated measures ANOVA. A nonparametric test was used when the sample did not conform to a normal distribution. The confidence interval was set to 0.05. p < 0.05 was considered statistically significant, and p < 0.01 was considered significantly different.
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