Sas 9

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SAS 9.4 is an integrated software suite for advanced analytics, data management, and business intelligence. It provides a comprehensive platform for data analysis, modeling, and reporting. SAS 9.4 offers a wide range of capabilities, including data manipulation, statistical analysis, predictive modeling, and visual data exploration.

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Lab products found in correlation

Prism 8, by GraphPad (151 mentions) Prism 6, by GraphPad (145 mentions) Prism 7, by GraphPad (136 mentions) Prism 5, by GraphPad (130 mentions) Stata 13, by StataCorp (95 mentions) Prism 9, by GraphPad (93 mentions) R version 3, by R Project for Statistical Computing (92 mentions) Stata 14, by StataCorp (89 mentions) Sas version 9, by SAS Institute (85 mentions) Stata 15, by StataCorp (82 mentions) R software, by R Project for Statistical Computing (66 mentions) Stata 12, by StataCorp (63 mentions) Sas enterprise guide 7, by SAS Institute (62 mentions) Spss 22, by SAS Institute (58 mentions) Stata 16, by StataCorp (56 mentions) Spss 21, by IBM (40 mentions) Spss software, by IBM (40 mentions) Spss 20, by IBM (40 mentions) Proc glimmix, by SAS Institute (38 mentions) S plus 8, by TIBCO Software (37 mentions)

20 063 protocols using sas 9

Syrinx Size and Neurological Correlates

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All analyses were performed by SAS V 9.2.10 Student's t‐tests were used to compare the mean syrinx size and extent values among groups with and without skull abnormalities, spinal pain, and neurologic signs. The folded form F statistic was used to test if variances were equal among groups. If unequal, Satterwaithe's approximation for degrees of freedom for the Student's t‐test was used. Spearman's correlation was used to test for correlations of age with syrinx size and extent values. Analysis of variance (ANOVA) was used to compare the means of syrinx sizes and extent values among all 4 sex and neuter statuses. A chi‐square test was used to test for an association between skull and CCJ abnormalities and presence of spinal pain or neurologic signs. It also was used to test for an association between presence of 1 or 2 skull and craniocervical junction abnormalities and presence of spinal pain or neurologic signs.
All hypothesis tests were 2‐sided and the significance level was α = 0.05. Chi‐square tests were implemented by using PROC FREQ10 in SAS 9.2., Student's t‐tests were implemented using PROC TTEST10 in SAS 9.2., ANOVA in PROC GLM10 in SAS 9.2., and correlations by using PROC CORR10 in SAS 9.2.

Freeman A.C., Platt S.R., Kent M., Huguet E., Rusbridge C, & Holmes S. (2014). Chiari‐Like Malformation and Syringomyelia in American Brussels Griffon Dogs. Journal of Veterinary Internal Medicine, 28(5), 1551-1559.

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Evaluating Gut Microbiome Impacts on Livestock

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Differential analysis of growth performances, carcass performances, and gastrointestinal development parameters was verified through a normal distribution test using SAS procedure “proc univariate data = test normal.” Subsequently, one-way ANOVA S-N-K test was applied to investigate the differences among the 5 treatments. Results were presented as mean ± SEM. OTU abundances of cecal bacteria first conducted a transformation of normal distribution using log₂, and then Student's T-test of SAS 9.2 was applied for the differential analysis. Alpha diversity and beta diversity in our samples were calculated with QIIME (Version 1.7.0) and displayed with R software (Version 3.15.3). PCoA analysis was displayed by WGCNA package, stat packages, and ggplot2 package in R software (Version 3.15.3). The OTU abundance of ruminal bacteria was first transformed into normal distribution using the log₂ transformation, and then the Student's T-test of SAS 9.2 was applied to analyze the differences of bacteria. P < 0.05 was significant. Spearman correlations between bacteria communities and production performances, carcass performances, and intestinal development parameters were assessed using the PROC CORR procedure of SAS 9.2 and then the correlation matrix was created and visualized in a heatmap format using R software (Version 3.15.3).

He C., Wu H., Lv Y., You H., Zha L., Li Q., Huang Y., Tian J., Chen Q., Shen Y., Xiong S, & Xue F. (2021). Gastrointestinal Development and Microbiota Responses of Geese to Honeycomb Flavonoids Supplementation. Frontiers in Veterinary Science, 8, 739237.

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Predicting Energy Values of Corn Grains

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Data were checked for normality using the UNIVARIATE procedure of SAS 9.2 (SAS Inst. Inc., Carry, NC, USA). No outliers were identified. Then data were analyzed using the PROC MIXED procedure of SAS. Dietary treatment was the only fixed effect and period was the random effect included in the model and the individual pig was treated as the experimental unit. There was no interaction between treatment and period. The LSMEANS statement was used to calculate the least squares means for each treatment with Tukey’s adjustment. The correlation coefficients (r) among the chemical compositions and energy values were calculated using the CORR procedure of SAS 9.2. Prediction equations for the DE and ME contents of corn grains were developed using the REG procedure of SAS 9.2. Stepwise regression was used with chemical compositions as independent variables. The R², p-value, the Mallows statistic (C(p)), Bayesian information criterion (BIC), root mean square error (RMSE) and Akaike’s information criterion (AIC) were used to identify the best-fit equations. In all analyses, the differences were considered significant if p<0.05.

Dong W., Li J., Li Z., Zhang S., Li X., Yang C., Liu L, & Zhang S. (2019). Physicochemical properties and energy content of yellow dent corn from different climatic origins in growing pigs. Asian-Australasian Journal of Animal Sciences, 33(11), 1787-1796.

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Rumen Microbiome and Fermentation Dynamics

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Data were checked for normal distribution using PROC Univariate Normal in SAS 9.2 (SAS Institute Inc., Cary, NC). Differences of ruminal pH, rumen thiamine, rumen VFAs and rumen NH₃-N concentrations were analyzed using PROC GLM of SAS 9.2. P-value < 0.05 was considered as significance and 0.05 ≤ P < 0.10 was considered as a tendency. Barplot, pieplot, principal coordinate analysis (PCoA), hierarchical clustering analysis (HCA) and heat map for different rumen non-methanogens were conducted using R package version 3.3.1. Spearman correlation analysis between non-methanogens and ruminal fermentation variables or thiamine content was assessed using the PROC CORR procedure of SAS 9.2. A correlation matrix was created and visualized in a heatmap format using R package version 3.3.1. The abundances of non-methanogens and ruminal variables were considered to be correlated with each other when the absolute values of correlation coefficients (r) were above 0.55 and P-values were below 0.05.

Xue F., Nan X., Li Y., Pan X., Guo Y., Jiang L, & Xiong B. (2019). Metagenomic insights into effects of thiamine supplementation on ruminal non-methanogen archaea in high-concentrate diets feeding dairy cows. BMC Veterinary Research, 15, 7.

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Soil Methane Flux Dynamics

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To test for significant differences of in situ CH 4 fluxes, volumetric soil moisture content and soil temperature (at 5 cm depth), respectively, between soil types (wetland and upland) a 1-way repeated measures ANOVA using the Mixed procedure in SAS 9.2 (SAS Institute Inc., USA) with soil type as class variable and investigation site (Betula, Salix, Fen1 and Fen2) as a random factor was applied.
Using a 1-way ANOVA with the GLM procedure in SAS 9.2 it was tested whether mean values of soil pH H2O , log 10 -transformed concentrations of TON, NH 4
? , %C and soil C:N ratio for the entire active layer (A, B and C horizons) were significantly different between upland and wetland soils. A nonparametric analysis using the Wilcoxon test was applied using the NPAR1WAY procedure in SAS 9.2 for mean values of NO 3 -, DOC and %N concentrations due to non-normal distribution. In the manuscript, average values are presented with standard error of the mean as uncertainty measure.
Q 10 values of microbial growth, e.g. the rate of 3 H -leucine incorporation, for the permafrost upland and wetland sites were calculated using the rates at 5 and 15 °C with the following formula:
Differences in Q 10 values between active layer, TP and DP for upland and wetland sites were tested using t test assuming equal variances.

Christiansen J.R., Romero A.J.B., Jørgensen N.O.G., Glaring M.A., Jørgensen C.J., Berg L.K, & Elberling B. (2015). Methane fluxes and the functional groups of methanotrophs and methanogens in a young Arctic landscape on Disko Island, West Greenland. Biogeochemistry., 122(1).

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Analyzing Livestock Feed Efficiency

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The average cycle threshold (Ct) values were calculated using the SAS version 9.4 (SAS Institute Inc., USA). The expression level of each target gene was generated with reference to β-actin housekeeping genes. All data were checked for quantile-quantile plots, normality, and homogeneity of variance by generating histograms and through formal statistical tests as part of the UNIVARIATE procedure of SAS 9.4. Measurements followed a completely randomized design, and data were analyzed using the general linear model procedure of SAS 9.4. The difference of gene expression and phenotypic data between the two RFI groups were analyzed using Student’s t-test of SAS 9.4. The correlation between the expression of individual genes, and between gene expression and feed efficiency traits were calculated by estimating Pearson’s product-moment correlation using PROC CORR of SAS 9.4. All data shown in tables are expressed as mean±standard deviation (SD). Differences were considered statistically significant at p<0.05.

Jin S., Xu Y., Zang H., Yang L., Lin Z., Li Y, & Geng Z. (2019). Expression of genes related to lipid transport in meat-type ducks divergent for low or high residual feed intake. Asian-Australasian Journal of Animal Sciences, 33(3), 416-423.

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Analyzing Genotypic Variance in Crop Traits

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Test for normality of phenotypic data was first conducted using PROC UNIVARIATE of SAS (SAS 9.4, SAS Institute Inc., Cary, NC, USA) using a Q-Q plot of residuals. Analysis of variance (ANOVA) was conducted to test the effects of genotype, year, and genotype by year interactions on the measured traits using PROC MIXED of SAS (SAS 9.4, SAS Institute Inc., Cary, NC, USA). Genotype, year, and genotype by year interaction were treated as fixed effects, while replications within years were treated as a random effect. Broad sense heritability for each trait was calculated using the formula:

H^{2} = \frac{V_{g}}{V_{g} + \frac{V_{gy}}{y} + \frac{V_{e}}{ry}}

where V_g = Genotypic variance, V_gy = Genotype by year variance, V_e = Error variance, r = number of replications, and y = number of years. Variance components were generated using PROC VARCOMP of SAS (SAS 9.4, SAS Institute Inc., Cary, NC, USA) using the restricted maximum likelihood (REML) method. Correlation among traits and with biomass weight was estimated by Pearson’s correlation coefficient using PROC CORR of SAS (SAS 9.4, SAS Institute Inc., Cary, NC, USA). BLUP value of individual genotypes was used in the calculation of the correlation.

M. Razar R, & Missaoui A. (2020). QTL mapping of winter dormancy and associated traits in two switchgrass pseudo-F1 populations: lowland x lowland and lowland x upland. BMC Plant Biology, 20, 537.

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Dry Matter Intake and Performance in Late-Lactating Cows

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Pearson correlation coefficients were calculated (SAS 9.4; SAS Inst. Inc., Cary, NC) to determine the relationships between late-lactation performance characteristics, energy partitioning, and subsequent nonlactating voluntary dry matter intake (VDMI). Dependent variables used to compute ME_m were investigated for multicollinearity using multiple linear regression and evaluating variance inflation factor, tolerance, and collinearity diagnostics (SAS 9.4; SAS Inst. Inc.). Forward stepwise linear regression was used to explore the influence of each of the four independent variables used to compute ME_m. At each step, variables were chosen according to their contribution to the model’s coefficient of determination (R²). Residual average daily gain (RADG) was computed for each cow as the residual from mixed model regression (SAS 9.4; SAS Inst. Inc.) of shrunk BW average daily gain (SADG) on MEI, study-average BCS, and milk yield (kg/day). The average number of days each cow was pregnant during the trial was included as a random variable. The effects of time on calf feed intake, scaled to BW, were characterized using a spline regression model (NLIN procedure, SAS 9.4; SAS Inst. Inc.) to determine whether a break point in time existed, and if so, the slope of the two resulting regression lines.

Briggs E.A., Holder A.L., Gross M.A., Moehlenpah A.N., Taylor J.D., Reuter R.R., Foote A.P., Goad C.L, & Lalman D.L. (2022). Retained energy in lactating beef cows; effects on maintenance energy requirement and voluntary feed intake. Translational Animal Science, 6(3), txac120.

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Quantifying Bone Microstructure Dynamics

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The relationships between rate constants and morphometric data were determined using Spearman correlation coefficients (r; SAS 9.4) in each ROI. The relationships between rate constants and the natural logarithm of the rate constants (ln(k_i)) to exercise variables were determined using robust linear regressions with CTRL-PSB and CLI-PSB data (SAS 9.4; mm-method)^{40 (link)}. A linear mixed model with horse as a random variable was performed to determine the effects of Group (FX, CLI, and CTRL) and ROI (Damaged, Non-Damaged) on the calculated rate constants (k₁, k₂, k₄, k₅) and morphometric data (BVF, TMD, Cr.Af; SAS 9.4; proc mixed). Ranked data were used to construct the linear mixed models when models built with raw data had residuals that were not normally distributed (W < 0.90). Comparisons of model means were performed with a Tukey–Kramer correction. In all analyses, p ≤ 0.05 was considered statistically significant.
Additional analyses were performed to check model assumptions. A linear mixed model, with horse as a random variable, was performed to determine if the smallest feasible k₃ was different among Groups (CTRL, CLI, FX) within the Non-Damaged ROI (SAS 9.4; proc mixed). Additionally, the Borgonovo sensitivity of k₁ to k_m, TMD_ROI, and TMD_max was determined for the Non-Damaged ROI (Supplementary Information 5)^{41 (link),42}.

Shaffer S.K., Stover S.M, & Fyhrie D.P. (2023). Training drives turnover rates in racehorse proximal sesamoid bones. Scientific Reports, 13, 205.

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Evaluating Optimal Egg Treatment Dose

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Hatchability data were analyzed and compared between treatments using a logistic regression model due to its binary nature. PROC LOGISTIC in SAS9.4 was used, and pairwise comparisons were performed at the P < 0.05 level. In addition, polynomial regression of second degree or quadratic regression model (Equation 2) was fitted using PROC GLM in SAS9.4 to predict the optimal dose of OEO.

y_{i} = β_{0} + β_{1} x_{i} + β_{2} x_{i}^{2} + ε_{i} i = 1, \dots, n

Navel score data were analyzed by chi-squared test in SAS9.4 using PROC FREQ. The average navel score in each treatment was compared to the noninjected group at the P < 0.05 level.
For body weight at hatch and organ weight, analysis of covariance (ANCOVA) was performed. ANCOVA analyses were feasible because the experimental design allowed for individual recording of egg weight, body weight and all the other measurements. ANCOVA was done to adjust for the effect of egg weight variation on the body weight at hatch considering the egg weight as a covariate while comparing different treatments. The same concept was applied for organ weight considering body weight at hatch as covariate in the statistical model. ANCOVA analyses were performed in SAS9.4 using PROC GLM and treatments were compared using Tukey multicomparison method at the P < 0.05 level.

Niknafs S., Meijer M.M., Khaskheli A.A, & Roura E. (2023). In ovo delivery of oregano essential oil activated xenobiotic detoxification and lipid metabolism at hatch in broiler chickens. Poultry Science, 103(2), 103321.

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