Spss v24

The experimental data were statistically processed using Microsoft Excel 2010 software and analyzed by SPSS v24.0. SPSS v24.0 was used for PCA, modeling and best-fit analysis, and Pearson’s method was used for linear and partial correlation analysis. Taking harvest days as x and fruit growth and quality indicators as y, the optimal model was determined by linear equations, one-dimensional polynomial equations and logistic equations fitting analysis. The Z-core standardization method was adopted to non-dimensionally process the original data. he raw data of all measured indicators was imported into SPSS for analysis. All data were used as variables, and correlation analysis was undertaken. The extraction factor was based on the eigenvalue. After outputting the results, the results of kmo and Bartlett’s test were verified to determine whether the data were suitable for principal component analysis. Fit analysis was performed by Origin Pro 2021, testing with multiple fitting methods, screened and judged by the value of R². Finally, it was considered that the multiple regression equation was more suitable for the data of this research. Analysis of variance (ANOVA) and Duncan’s multiple analysis were carried out to determine significant (p = 0.05) differences between the two groups. Trends and figures within the data were described graphically using Origin 2021 pro and R Studio.

In order to determine the LC values and sublethal concentrations, we used IBM SPSS v.24.0. The data obtained from F₁T. urticae were analyzed by one-way ANOVA followed by Tukey's honestly significant difference (HSD) test. Development duration, longevity, fecundity and demographic parameters of F₁T. urticae individuals were analyzed according to the two-sex life table procedure by using the Bootstrap method with 100,000 resamplings (Chi and Liu 1985 ; Chi 1988 ; Huang and Chi 2012 (link)). The paired bootstrap test was used to compare differences (Chi 2018 ). The computer program TWOSEX-MSChart (Chi 2018 ) was used to analyze the raw data. The survival rate curve was constructed using Kaplan–Meier test in IBM SPSS v.24.0.

During the toxicity assay, conducted over a period of 11 days, the adult weevil survival rate was monitored daily, and Kaplan-Meier survival analysis with the log rank test was performed using SPSS (v24) to determine the survival function in the treated and control groups. cDNAs prepared from RNA extracted from the gut and fat body of each individual insect (surviving) in the experimental (n-trt and survived adult-trt) and sus, ind and res RPW groups were also used as templates for RT-qPCR. Reactions were carried out using SYBR Green PCR Master Mix (Life Technologies, USA) according to the manufacturer’s instructions, with six biological and three technical replicates. Tubulin and β-actin primers were used to normalize gene expression (Additional file 3: Table S1). The relative expression levels of P450s in the silenced vs. control groups were measured via the 2^−ΔΔC_T method [71 (link), 73 (link)]. PCR amplification, data analysis and gel evaluation were performed as described above. Difference analysis was performed by using Student’s t test, followed by Duncan’s multiple comparison test, with SPSS (v24) software. A value of p < 0.05 was considered significant.

Hierarchical cluster analysis was performed on the RT-qPCR data using Cluster software [74 (link)]. The 2^-ΔCт values from the RT-qPCR analysis were log transformed, and data from each sample and tissue were clustered on the basis of the average linkage distance between the median values [71 (link), 74 (link)]. The significance of differences in P450 transcript expression were calculated between ind and res samples compared to sus using the paired t-test, with an alpha significance level of 0.05 (p < 0.05), using SPSS (v24). Multiple-comparison testing with the least significant difference (LSD) test was performed to assess the differential expression of transcripts within each CYP family (p < 0.05) using SPSS (v24).

Statistical analyses were performed using SPSS v.24 (SPSS, Chicago, IL, United States). We first explored dependent variables to determine missing data points, normality of distributions (with Kolmogorov–Smirnov tests), and outliers (using the Explore command of SPSS v.24). An alpha level of 0.05 was used for all statistical tests. Comparisons of continuous variables between the two groups were conducted using analyses of variance (ANOVAs), and categorical variables were compared using the chi-square test.

Baseline characteristics were presented as mean and SD for continuous variables and median and IQR for non-parametric data. Normality was assessed by Shapiro-Wilk test. For categorical and ordinal variables with non-parametric distribution, Fisher’s exact test and Mann-Whitney U test were used respectively for comparisons between two groups. Age-adjusted and sex-adjusted mortality were calculated by logistic regression analyses. Multivariate analysis to predict mortality was performed using stepwise logistic regression with conservative criteria for entry or exit from the model of 0.1. Variables listed in online supplemental 3 were included in multivariate analysis. The Hosmer and Lemeshow goodness-of-fit test was performed to evaluate logistic regression model adequacy. Matched case–control analyses (1:1) using IBM SPSS V.24 were implemented to explore underlying multimorbidity among ethnic minorities; controls were White patients matched by age, gender and deprivation subdomains. Performance of the CURB65 and ISARIC 4C tools among individual ethnic groups were assessed using receiver operating characteristic curves Area Under the Receiver Operator Curve (AUROC). Statistical analyses were carried out using SPSS V.24.

Quantitative data analysis will be conducted using SPSS V.24. Descriptive statistics will be used to report on baseline demographic, clinical data, and program usability. Categorical data will be presented as frequencies and percentages, whilst continuous data is presented as means and standard deviations. Numerical data will be analysed using SPSS V.24. Repeated measures ANOVA will be used to assess the preliminary efficacy between groups across three time points for physical activity, clinical indices, and psychosocial status.
As a feasibility randomised controlled trial, and therefore not statistically powered, results will be interpreted with caution. Recruitment and retention rates will be reported on and presented using the CONSORT flow diagram [21 (link)].