All statistical analyses were performed using SAS Studio (version 3.2, SAS institute, Cary, NC). The group of calves housed in one respiration chamber were considered the experimental unit with treatment and batch as fixed effects. Continuous variables (i.e., figures presented of HP, activity-related heat production, RMR, RQ, FHP, and the net rates of COX and FOX) were analyzed using mixed-model analysis with PROC MIXED in SAS (SAS 9.4M6, SAS Studio, SAS Institute). Time entered the model as a repeated statement in case of repeated measurements, and then the interactions between time and treatment and the SLICE command from SAS Studio (version 3.2, SAS Institute, Cary, NC) to control Type I error were included. The effect of the diet on daily averages over the entire experimental period of HP, activityrelated HP, RMR, 13 C recovery, RQ, FHP, and the net rates of COX and FOX were analyzed by ANOVA using a general linear model with treatment, batch, chamber, and their interactions as fixed effects. The normal distribution of the residuals was checked to verify the model assumptions. Treatment effects were studied by pairwise comparisons using the Tukey method. All values are presented as LSM ± SEM. Significance was declared when P ≤ 0.05 and trends were declared when P < 0.10.
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SAS Studio is a web-based interactive development environment (IDE) for executing SAS programming code. It provides a user-friendly interface for writing, running, and debugging SAS programs.
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Sas ondemand for academics, by SAS Institute (3 mentions)
Glimmix, by SAS Institute (2 mentions)
Sas version 9, by SAS Institute (2 mentions)
Trizol reagent, by Thermo Fisher Scientific (2 mentions)
Sas 9, by SAS Institute (2 mentions)
Sas viya, by SAS Institute (1 mentions)
Spss software, by IBM (1 mentions)
Rstudio, by R Project for Statistical Computing (1 mentions)
R statistical package, by SAS Institute (1 mentions)
Stata 14, by StataCorp (1 mentions)
Prism 7, by GraphPad (1 mentions)
Stata version 16, by StataCorp (1 mentions)
Spss statistics 28, by IBM (1 mentions)
Sas studio 3, by SAS Institute (1 mentions)
Sas enterprise guide, by SAS Institute (1 mentions)
Stata version 15, by StataCorp (1 mentions)
Spss statistics for macintosh, by IBM (1 mentions)
Clocklab, by Actimetrics (1 mentions)
Spss version 26, by IBM (1 mentions)
Spss statistics v25, by SAS Institute (1 mentions)
175 protocols using sas studio
1
Metabolic Responses in Calves
2
Hazelnut Phytohormone Bioassay Protocol
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Preliminary bioassay experiments were a completely randomized design, with zeitgebers being the main factors of interest whereas, for the 2019–2020 experiments, treatments (SER or TRP) and selections (‘Alex’, ‘Delta’ or native hazelnuts) and kind of bud (vegetative or flower) were the main factors. Data were analyzed using Proc GLIMMIX in SAS® Studio (SAS Institute, Cary, NC, USA). Residuals were used to verify the assumptions of normal error distribution and constant variance. For main factors or interactions, if the means were significantly different, they were separated using Tukey’s test (α = 0.05). In the phytohormone analyses, there were 3 replicates per bud type per selection or natives (flowering and vegetative). Source of hazelnuts, kind of bud and treatment (SER or TRP) for hazelnuts, whereas concentrations of SER or TRP were considered as fixed factors and the metabolite responses (µg/g FW) were subjected to ANOVA using PROC GLIMMIX in SAS® Studio (SAS Institute Inc., Cary, NC, USA). For all responses, the normal distribution and constant variance assumptions on the error terms were verified by examining the residuals. When the effects were significant, least square (LS) means were separated at α = 0.05 level. Mean ± SEM (µg/g FW) responses per each metabolite are presented in a graphical format.
3
Statistical Analysis of Research Data
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For CK measurements, the significance of differences between control and PLP samples was calculated with a paired Student's t-test in Microsoft Excel ® . For statistical analysis of all other data SAS ® Studio (https://odamid.oda.sas.com/SASStudio) was used. Homogeneity and homoscedasticity were tested by Shapiro-Wilk and Levene tests (p ≥ 0.01) before ANOVA testing was performed followed by Tukey post hoc test. If assumptions were not met, transformations (log 2 , log 10 , sqrt, n 0.1 , n 0.4 , n 1.5 , n 7 , n 25 ) were performed. Paired Wilcoxon test was performed if assumptions were still not met after transformation.
4
Structural Brain Differences Exploration
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General linear models were calculated to explore the differences in SWC and DTI indices (FA, MD, RD, and AD) among the groups. We utilized non-parametric permutation-based statistics with the “randomise” tool (p < 0.05) in FSL.46 (link) Five thousand permutations were performed, and threshold-free cluster enhancement (TFCE) was applied to correct for multiple comparisons.47 (link) JHU ICBM-DTI-81 White-Matter Labels and the JHU White-Matter Tractography Atlas were used to identify the location of significant clusters.48 (link),49 (link) All analyses were adjusted for sex and WASO. The Bonferroni correction method was applied. A p-value <0.05 (corrected for multiple comparisons) was considered statistically significant. Statistical analyses were conducted with SAS® Studio (SAS Institute Inc. Cary, NC, USA.).
5
Exercise Biomarker Evaluation Protocol
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Data are reported as mean ± standard deviation for pre- and post-exercise time points. The primary objective of our statistical analyses was to evaluate if there was a relative change in each biomarker concentration following the exercise. Paired sample t-tests were performed to observe differences between test points, and variance comparisons were analyzed between device values. Results were classified as statistically significant when the p-value was 0.05 or less based upon a priori criteria. All analyses were performed using SAS Studio (SAS Institute Inc., Cary, NC, USA).
6
Progesterone Levels and Correlations
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Continuous data are summarized according to mean, standard deviation, minimum and maximum. Confidence intervals at 95% are presented for the mean values (95% CI). Progesterone levels are represented as a box plot to describe the variable distribution according to time measurements. The absolute difference between values in P4 based on pair difference for the four measurements were calculated to test mean differences. Since measures are evaluated in a single patient four times but compared two by two a dependent paired T-test was used to test for mean differences. Pearson's correlation coefficient (ρ) was calculated to find the correlation between the continuous parameters and the progesterone levels at the different time points. GLM(General Linear Model) procedure was applied to evaluate the relationship of categorical variables and progesterone levels at different time points.
A p < 0.05 is statistically significant. All analyses were performed using SAS studio (SAS® Studio).
A p < 0.05 is statistically significant. All analyses were performed using SAS studio (SAS® Studio).
7
Diallel Analysis of Crop Traits
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Data collected from individual plant which constitute a family, were averaged for statistical analysis using SAS studio (University edition). The GCA and SCA effects were estimated according to Griffing's (1956b) Model 1, Method 4 using the DIALLEL-SAS05 program developed by Zhang et al. (2005) . The significance was expressed at p < 0.05, 0.01 and 0.001. The GCA and SCA effects for each trait were determined from the percentage of families' sum of squares (SS) due to GCA and SCA (Tumuhimbise et al., 2014 ). The relative importance of the GCA and SCA effects for each trait was determined from the percentage of the families' sum of squares (SS) (Tumuhimbise, 2013 ; Were et al., 2012 ). The mid-parent (MP) and best parent (BP) heterosis was analysed, using the formula , and . The selection of the best clones for advancement was done by using the selection index (SI) proposed by Ceballos et al. (2004) (link), with some modifications. , and the variables were standardized, using the following formula: .
8
Genetic Factors Influence Infliximab Response
9
Repeated Measures Analysis of Behavioral Data
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Statistical analyses were performed with SAS studio (SAS Institute Inc., Cary, NC, USA). A two-way analysis of variance (ANOVA; PROC GLM) was performed on all data collected on the first trial (combined for daily and weekly interval testing), with sex and apparatus as between-subjects factors. Data from each apparatus and testing interval were then tested separately with mixed model ANOVAs (PROC MIXED), with sex as a between-subjects factor and trial as a repeated measures factor. The Kenward-Roger degrees of freedom approximation and an autoregressive covariance structure (Lag-1) were employed for the repeated measures ANOVAs. Where significant main effects of day were found, Bonferroni-corrected post hoc tests (PROC PLM) compared results from day 1 to results from all subsequent days. Where significant day by sex interactions were found, Bonferroni-corrected planned contrasts (PROC PLM) were performed comparing males and females on each testing day. Figures were made using Microsoft Excel 2016 and Daniel’s XL Toolbox 6.60, and data in all figures represent the means ± standard error of the means.
10
Analyzing Salmonella in Poultry Litter and Dust
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The microbial counts from litter and generated dust samples were log transformed to log10 CFU/g and log10 CFU/L, respectively. For Experiment 1, one-way ANOVA was used to analyze treatment effect (Salmonella inoculum levels) on litter and dust bacterial levels. Means were separated using Tukey's HSD test, and level of significance set at P ≤ 0.05. Simple logistic regression was used to analyze the relationship between litter Salmonella and Salmonella in dust. In Experiment 2, data from litter and dust samples were analyzed using one-way ANOVA with the different litter moisture ranges as the treatment. Means were separated using Tukey's HSD test, and the level of significance was set at P ≤ 0.05. Simple logistic regression was used to analyze the relationship between litter moisture levels and Salmonella in dust samples. Prevalence data were statistically analyzed using Fisher's Exact test with significance at P ≤ 0.05. All data were analyzed using SAS Studio, release 3.8 Enterprise Edition.
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