Spss 16.0 statistic

The fruit of the plants were bagged on the 5^th day after flowering and the fruit color and anthocyanin content were investigated on the 14^th day under bagging condition. Color differences were assessed by the color index of red grapes (CIRG). A CR-400 colorimeter (Konica Minolta, Chroma Meter, Osaka, Japan) was used to measure the values of L*, a*, and b*. CIRG was calculated with the equation CIRG = [(180-H)/(L* + C*)], H=arctan (b*/a*), C=(a*² + b*²)^0.5 (Carreno et al., 1995 (link); Zhang et al., 2008 (link)).
The total anthocyanin content in the peel was quantified using the pH differential spectroscopic method (Cheng and Patrick, 1991 (link)).
One-way analysis of variance (ANOVA) was conducted on the CIRG value and anthocyanin content, and significant differences between groups were assessed by Duncan’s multiple range tests (p < 0.05) using SPSS 16.0 Statistics (SPSS Inc., Chicago, IL, USA).

Fourteen genes were chosen for the validation of RNA-seq using qRT-PCR. Primer Premier 5.0 software was used to design the primers, which are listed in Table S2. A total of 1 μg RNA per sample was reverse transcribed using a PrimeScript™ reagent Kit with gDNA Eraser (TaKaRa, Beijing, China) in 20 μL of reaction mixture. The qRT-PCR was performed on a LightCycler^® 96 instrument (Roche, Basel, Switzerland), using THUNDERBIRD SYBR qPCR Mix (TOYOBO, Shanghai, China) with the following reactions: 95°C 2 min; 95°C 30s, 60°C 10s, and 68°C 10s for 40 cycles. The PCR products were quantified by 2^−△△CT method (Livak and Schmittgen, 2001 (link)) with normalization to the expression level of SmGAPDH (EGP1067575). Significant differences between groups were assessed by Student’s t-test (p < 0.05) using SPSS 16.0 Statistics (SPSS Inc., Chicago, IL, USA).

Statistical analyses were performed using SPSS 16.0 statistics software (SPSS Inc., Chicago, IL). Continuous data were presented as mean and standard deviation (SD). The differences in continuous data were compared using independent two-sample t-test. One-way ANOVA together with Scheffe post-hoc tests were applied for comparing differences among groups. The associations between categorical variables presented as count and percentage were evaluated with Chi-square tests. The associations between ACR and metabolic risk factors were evaluated by linear regression analyses. Univariate and multivariable logistic regression models were performed to yield the odds ratios (ORs) of variables for the presence of albuminuria.
Receiver operating characteristic (ROC) analysis and the area under the ROC curve (AUC) were used to assess the accuracy and discriminatory ability of MS risk factors in detecting albuminuria. The optimal cut-point for each MS risk factor associated with CKD was established based on the AUC and Youden’s index. All statistical assessments were two-sided, and a p value <0.05 was considered statistically significant.