ライフサイエンスコーパス: ANOVA

1 ANOVA analysis clearly revealed that there was a signifi

2 ANOVA analysis indicates differences between samples fro

3 ANOVA analysis of the regional (11)C-DED binding data re

4 ANOVA and Bland-Altman analyses were used to determine l

5 ANOVA and multiple regression equations were used in the

6 ANOVA confirmed that subjectively rated hunger (P = 0.56

7 ANOVA followed by Newman-Keuls post-hoc analyses were us

8 ANOVA indicated significant differences (P < .05) betwee

9 ANOVA results demonstrate that a learning effect disting

10 ANOVA results showed that concentration of ethanol and t

11 ANOVA results showed that the experimental data were sat

12 ANOVA revealed a statistically significant difference be

13 ANOVA revealed significant effects (p<0.001) of pH and N

14 ANOVA showed no systematic differences between groups in

15 ANOVA showed significant differences among rosemary hone

16 ANOVA was conducted for each frequency band with the fol

17 ANOVA was used to test 32 planned contrasts in the data,

18 ANOVA-simultaneous component analysis (ASCA) of the ring

19 ANOVAs with repeated-measures showed significant effects

20 tion of significant differences (p < 0.0001; ANOVA) in CD34+ cells mechanical properties throughout t

21 ion (quinolinic acid: F = 21.027, p < 0.001 [ANOVA]; BTP: F = 6.792, p < 0.01 [ANOVA]).

22 uced significant growth inhibition (p=0.001, ANOVA) and enhanced median survival to 27 days over cont

23 tumor growth at study conclusion, P < 0.001; ANOVA on ranks with Dunn test).

24 nt as compared with control mice (P = 0.002; ANOVA on ranks with Dunn test), while standard gemcitabi

25 recorded with the DTL electrode (p < 0.005, ANOVA).

26 p < 0.001 [ANOVA]; BTP: F = 6.792, p < 0.01 [ANOVA]).

27 diabetic mice from baseline scans (P < 0.03, ANOVA), with a subsequent increase of 40% to a level not

28 at a false discovery rate of less than 0.05 (ANOVA), and 15 type I and type III interferon genes were

29 ere prepared to specified lengths (P < 0.05, ANOVA) and proved consistent in size.

30 or (IL-1R)-knockout mouse corneas (P < 0.05, ANOVA).

31 tween anodized and nonanodized Ti (P > 0.05; ANOVA for all cell types).

32 here was no significant difference (P > 0.1; ANOVA) in the mean preintervention serum 25(OH)D in the

33 ted using a voxel-wise, mixed-effects, 2 x 2 ANOVA.

34 T-tests, chi (2), ANOVA and Pearson Correlation tests were performed, with

35 whose milk allergy resolved (eta(2) = 0.43; ANOVA P = .034).

36 with the highest measures of interleukin-6 (ANOVA p<0.05; 4.6 +/- 2.6 pg/mL in patients with AMI vs.

37 100+/-2.96 vs. 68.2+/-7.53 vs. 94.0+/-0.72%; ANOVA, P<0.01).

38 +/- 2.4%; feature-tracking: -28.7 +/- 4.8%) (ANOVA with Tukey post-hoc, F-value 279.93, p < 0.01).

39 6 +/- 1.20, 6.29 +/- 1.12 and 6.52 +/- 0.81 (ANOVA: p = 0.123); before IOL implantation: 5.46 +/- 1.0

40 6 +/- 1.06, 5.83 +/- 1.09 and 6.17 +/- 0.89 (ANOVA: p = 0.0291).No adverse effect related to sponge u

41 2 +/- 1.21, 7.30 +/- 1.55 and 7.99 +/- 0.96 (ANOVA: p = 0.079); after nucleus delivery: 6 +/- 1.20, 6

42 ulated differences between MS and HC using a ANOVA and associations with disability using linear regr

43 In addition, ANOVA analysis showed that, for all the electrodes with

44 nd the smallest amount of stromal admixture (ANOVA p < 2.2e-16).

45 UBE3A (Parkin) increased with E or E+P (all ANOVA, P<0.003).

46 actin increased with E or E+P treatment (all ANOVA, P<0.03).

47 antly increased with E or E+P treatment (all ANOVA, P<0.05).

48 s significantly higher in patients with AMI (ANOVA p<0.05; 304 +/- 116 pg/mL in AMI vs. 265 +/- 86 pg

49 An ANOVA analysis combined with the Tukey test was used to

50 An ANOVA test for all the probes showed that at a 95% level

51 A design of experiments strategy and an ANOVA analysis are used to establish the effect of the a

52 Finally, as revealed by an ANOVA test, some volatiles differed markedly in content

53 We used an ANOVA to test for the heterogeneity of the effect on bir

54 Statistical analysis (ANOVA with post hoc two-sided t tests, P < 0.05) reveale

55 n) and the results of a univariate analysis (ANOVA), allowed the identification of potential volatile

56 hrough the application of variance analysis (ANOVA) factors critical to removing of lead were identif

57 nalysis was done by Cox regression analysis, ANOVA, and chi(2).

58 Kaplan-Meier survival analysis and ANOVA were performed.

59 emometric techniques as cluster analysis and ANOVA were used to classify honeys according to their bo

60 aluated using multivariate data analysis and ANOVA.

61 Matrix eQTL supports additive linear and ANOVA models with covariates, including models with corr

62 VA), principal component analysis (PCA), and ANOVA simultaneous component analysis (ASCA), were used

63 sed a significant reduction in angiogenesis (ANOVA, p<0.05), demonstrating its ex vivo efficacy.

64 Different statistical approaches (ANOVA, Principal Components Analysis, Cluster Analysis)

65 apply diverse higher level analyses, such as ANOVA and clustering.

66 A 2-way balanced ANOVA was used to determine whether differences in micro

67 Anyway, both ANOVA and PCA analyses have highlighted the low influenc

68 and ADAM10 (alpha-secretase) increased (both ANOVA, P<0.02) but PSEN1 (presenilin1) decreased (ANOVA,

69 Whole-brain ANOVA revealed that AS and MSV interacted in the inferio

70 le subtype showed highest mutational burden (ANOVA p < 0.01) and the smallest amount of stromal admix

71 with higher scar transmurality (P<0.0001 by ANOVA) and in regions with patchy scar (versus endocardi

72 +/- standard error of the mean, p = 0.040 by ANOVA).

73 cardial, midwall, epicardial scar; P<0.05 by ANOVA).

74 26 weeks (non-inferiority limit of 0.4%) by ANOVA in an intent-to-treat analysis (full analysis set)

75 o week 52 (non-inferiority limit of 0.4%) by ANOVA in the full analysis set.

76 uestionnaire-core 30 at week 54, analysed by ANOVA and adjusted for baseline score.

77 Results were analysed by ANOVA and the optimal condition was determined through r

78 Data were analysed by ANOVA and Tukey's post-hoc tests or a linear mixed-effec

79 Differences among lines were analyzed by ANOVA and shown to be significant (P <0.05), for BW, WC,

80 The experimental data were analyzed by ANOVA method and a well-predictive, second order polynom

81 the effects of rearing on H3K4me3 binding by ANOVA.

82 rades, there were significant differences by ANOVA for PAF (P < .0001).

83 ormed probes were significantly different by ANOVA with 0.01 q-value threshold reduced to zero signif

84 4 studies, n = 156), which was not shown by ANOVA at the end of the intervention, largely because of

85 analysed by the chemometric technique called ANOVA-simultaneous component analysis (ASCA).

86 exploratory data analytical technique called ANOVA-Simultaneous Component Analysis (ASCA).

87 o variation in plasma exosome concentration (ANOVA, P < 0.05).

88 , P<0.02) but PSEN1 (presenilin1) decreased (ANOVA, P<0.02) with treatment.

89 ed in significantly lower tooth deformation (ANOVA/Student-Newman-Keuls post hoc, p = 0.05).

90 According to a mixed design ANOVA, this was statistically significant (P < 0.001).

91 and who were antibody positive for dUTPase (ANOVA p=0.008; 369 +/- 183 pg/mL in AMI and positive for

92 assessed using a whole-brain, mixed-effects ANOVA with correction for multiple comparisons at curren

93 using POD) was estimated using mixed-effects ANOVA.

94 red using a fixed effects and random effects ANOVA, respectively.

95 e compared between groups with mixed effects-ANOVA and generalized linear models.

96 DP, ADC, and PRM gas trapping and emphysema (ANOVA, P < .001) measurements were significantly differe

97 es, such as those provided by the extendable ANOVA framework.

98 mpared to naive control and TA-treated eyes (ANOVA P < 0.001).

99 tistical analyses were conducted by 1-factor ANOVA and post hoc Tukey honestly significant difference

100 In addition, a 1-factor ANOVA was run to discover any main effect of lifestyle.

101 ween surveys were compared by using 1-factor ANOVA.

102 n intent-to-treat analysis by using 2-factor ANOVA and with longitudinal mixed-effects models.

103 asting triglyceride concentrations (2-factor ANOVA) in plasma (P = 0.023) and large very-low-density

104 and a few forest honeys based on two-factor ANOVA and cluster analysis.

105 Two-factor ANOVA was used to examine the difference between protein

106 istical analyses were performed by factorial ANOVA.

107 dent variables were compared using factorial ANOVA.

108 tion in patients with chronic heart failure (ANOVA; P<0.001 for all).

109 icles ingested and retained more frequently (ANOVA, P < 0.01).

110 erentially expressed between the two groups (ANOVA, P<0.05) and further analyzed by hierarchical clus

111 During submaximal LBNP, FVR increased in HT (ANOVA P < 0.05) but not in LT (ANOVA P > 0.05), and stro

112 tients who received more than 12 injections (ANOVA P = .001).

113 oup was noted for implicit grammar learning (ANOVA, p = 0.021).

114 lower in patients with extensive bone loss (ANOVA, P = 0.030).

115 reased in HT (ANOVA P < 0.05) but not in LT (ANOVA P > 0.05), and stroke volume was lower in LT relat

116 , E2 administration shifted FVR lower in LT (ANOVA P < 0.05), with no effect in HT.

117 Multivariate repeated-measure ANOVA for mixed models was employed.

118 oducibility was examined by repeated-measure ANOVA.

119 variable SEM contains the repeated measures ANOVA (both the univariate and the multivariate models)

120 Data were analyzed by repeated measures ANOVA and post hoc Bonferroni test.A total of 19 patient

121 ng the tests using one-way repeated measures ANOVA on ranks.

122 Repeated measures ANOVA revealed that the percentage of voxels with increa

123 operfusion was analyzed by repeated measures ANOVA with post hoc Bonferroni-Dunn test.

124 conditions, assessed with repeated measures ANOVA, in all patients who completed assessments during

125 and compared to sham using repeated measures ANOVA.

126 ficance using a t-test and repeated measures ANOVA.

127 d data were compared using repeated measures ANOVA.

128 and altered images, using repeated measures ANOVA.

129 e tested with the use of a repeated-measures ANOVA and paired sample t tests.

130 Groups were compared with repeated-measures ANOVA for fractional anisotropy (FA), and magnetization

131 One-way/mixed-effects repeated-measures ANOVA models were used to determine changes of CSF (Delt

132 A repeated-measures ANOVA showed there was no significant (P = 0.2) time x v

133 change values; and used a repeated-measures ANOVA to assess pre- to postintervention changes.

134 Repeated-measures ANOVA was performed to determine whether significant dif

135 A two-factor repeated-measures ANOVA was used to identify interactions between meditati

136 p = 0.021; ODI, p = 0.205, repeated-measures ANOVA).

137 t metabolite identified by repeated-measures ANOVA, followed by eicosapentaenoate (P-interaction = 4.

138 l between reporting years (repeated-measures ANOVA, P = 0.985).

139 riation was assessed using repeated-measures ANOVA.

140 Repeated measures ANOVAs were performed on the: (a) coefficient of variati

141 rences were analysed using repeated measures ANOVAs.

142 Repeated-measures ANOVAs indicated a main effect of intensity for both ano

143 repeated-measures analysis of variance (MMRM ANOVA).

144 atment groups using the general linear model ANOVA.

145 A mixed-model ANOVA was used to determine the effect of race and dieta

146 xamined with the use of a linear mixed-model ANOVA.

147 d calcium were compared by using mixed-model ANOVA.

148 Multifactor ANOVA revealed that levels of those volatiles changed si

149 Multifactor ANOVA, considering the content of total anthocyanins as

150 re subjected to evaluation using multifactor ANOVA and principal component analysis (PCA), both showi

151 l component analysis (PCA), and multivariate ANOVA (MANOVA).

152 el geometric representation and multivariate ANOVA were used to analyze the T(H) cytokine profile.

153 distribution-Wachter's MANOVA (multivariate ANOVA) spectral distribution, a phenomenon that was prev

154 f farmers (P for multifactorial multivariate ANOVA= .041), significantly so for TLR7 (adjusted geomet

155 vealed outliers in performance, and a nested ANOVA model revealed the extent to which all metrics or

156 We performed an adjusted 2-factor nested ANOVA mixed-effects model procedure on the postintervent

157 influence on LLGR were analyzed by means of ANOVA and the Levene test of homogeneity.

158 The results of ANOVA showed that variety, location, production year, an

159 s Met genotype in COMT Met homozygotes only (ANOVA, p = 0.027).

160 data were compared by the Student t test or ANOVA, and categoric variables were compared by the chi(

161 cal analysis was performed with t testing or ANOVA.

162 Our ANOVA analysis indicated that nine variables contributed

163 lis test to eQTLs detected by the parametric ANOVA and linear model methods.

164 larval age and size of polystyrene particle (ANOVA, P < 0.01), and surface properties of the plastic,

165 compared to non-AMD and early AMD patients (ANOVA, P < .01).

166 test, address reduction, stable persistence, ANOVA, and F24.

167 Data were analysed using a split plot ANOVA and least significant test.

168 principal component analysis and Procrustes ANOVA indicate that symmetric variation accounts for mos

169 ficantly more reproducible than an OECD RBT (ANOVA, P < 0.05), with more consistent rates and extent

170 : P < 0.01, female CON vs. CF: P < 0.001; RM ANOVA) accompanied by increased abdominal adiposity in m

171 Repeated measures analysis of variance (RM-ANOVA) showed that chicken litter leachate stimulated ph

172 the findings were statistically significant (ANOVA, post hoc P<0.01).

173 We build a Smoothing Spline ANOVA (SS-ANOVA) model for predicting death age based on

174 an lifetime is modeled with smoothing-spline ANOVA given the covariates information including sex, li

175 We build a Smoothing Spline ANOVA (SS-ANOVA) model for predicting death age based on four majo

176 genes, that are missed by both the standard ANOVA method as well as SVA, but may be relevant to this

177 formance of our method SVA-PLS with standard ANOVA and a relatively recent technique of surrogate var

178 s were analysed using parametric statistics (ANOVA).

179 T testing, ANOVA, and regression analyses are reviewed.

180 Paired t-tests, ANOVA and generalized-estimating-equations models were u

181 using statistical methods including t-tests, ANOVA and the Kruskal-Wallis analysis of variance test.

182 We find that ANOVA, F24, and JTK_CYCLE consistently outperform the ot

183 The ANOVA and response surfaces analysis showed that the mos

184 The ANOVA results confirmed that all prices had risen over t

185 The ANOVA test was used to compare the refractive results am

186 The ANOVA/Tukey test was used for statistical analysis (alph

187 and genes with a P < 0.01, according to the ANOVA models, and a log(2)-fold change >2.5 were conside

188 ct sizes; performing moderator tests akin to ANOVA style analyses; and analyzing data with fixed- and

189 )) after 1 week and 3 weeks of TA treatment (ANOVA P < 0.01, P < 0.001, respectively).

190 Univariate ANOVA revealed atrophy and CagA-status as the only indep

191 mTLE) on the volume of the left (univariate ANOVA F=29.6, p<0.001) and right (F=8.3, p<0.001) entorh

192 ion of a dual criterion based on univariate (ANOVA) and multivariate analyses (OPLS-DA) allowed us to

193 Analysis of the expression data used ANOVA and Bayesian estimation of temporal regulation.

194 Data were analysed using ANOVA at p=0.05.

195 ces between the 2 groups were analyzed using ANOVA, Wilcoxon Rank Sum, chi, and Fisher Exact tests.

196 e three outcome measures were assessed using ANOVA and response surface models.

197 ession of innate immunity receptors by using ANOVA and multivariate regression analysis.

198 ions across diet groups were tested by using ANOVA, and a false discovery rate-controlling procedure

199 ically, and differences were tested by using ANOVA.

200 Group means were compared using ANOVA with significance set at P < 0.01.

201 en alpha- and beta-cells were detected using ANOVA and in silico replications of mouse and human isle

202 ferences between means were determined using ANOVA and least significant difference with hay (5), bal

203 Statistical analysis was performed using ANOVA, chi-square or Fisher's test, and logistic regress

204 analysis were evaluated statistically using ANOVA one-way and three-way analysis of variance, varian

205 nvironmental and anthropogenic drivers using ANOVAs.

206 rapy (R(2)Y=0.67; Q(2)=0.54; cross-validated ANOVA P=2.6x10(-5)).

207 eeks (R(2)Y=0.65; Q(2)=0.61; cross-validated ANOVA P=5.4x10(-8)).

208 rapy (R(2)Y=0.74; Q(2)=0.66; cross-validated ANOVA P=7.0x10(-8)) and combination therapy (R(2)Y=0.67;

209 ML-IRIS latency; (2) an analysis of variance ANOVA to investigate their impact on IRIS duration; and

210 en compared with 1-way analysis of variance (ANOVA) and chi-square test.

211 The results of analysis of variance (ANOVA) and correlation showed that the second-order poly

212 re analysed using both analysis of variance (ANOVA) and heritability adjusted-genotype main effect pl

213 ults were evaluated by analysis of variance (ANOVA) and principal component analysis (PCA).

214 Analysis of variance (ANOVA) and receiver operating characteristic (ROC) analy

215 techniques along with Analysis of variance (ANOVA) and Taguchi optimization method for optimizing th

216 We used the analysis of variance (ANOVA) and the Student's t-test.

217 best-fit model were by analysis of variance (ANOVA) and Tukey-Kramer post hoc tests, evaluations of r

218 subjected to a one-way analysis of variance (ANOVA) and, if significant differences were revealed, th

219 requencies, we applied ANalysis Of VAriance (ANOVA) based on a linear mixed model and found that cons

220 Univariate statistical analysis of variance (ANOVA) established that individual patterns positively c

221 y by repeated-measures analysis of variance (ANOVA) for intra-observer repeatability, inter-observer

222 s by repeated-measures analysis of variance (ANOVA) for intra-observer repeatability.

223 veloped an easy-to-use analysis of variance (ANOVA) framework for quantifying the performance of gene

224 e developed a Bayesian analysis of variance (ANOVA) model that decomposes these 8mer data into backgr

225 was estimated through analysis of variance (ANOVA) modeling and the use of backward selection of fac

226 a comprehensive set of analysis of variance (ANOVA) models is employed to analyze the data.

227 An analysis of variance (ANOVA) reveals significant differences (p < 0.05) among

228 epeated-measures 2-way analysis of variance (ANOVA) showed this can be attributed to type of surgery

229 ent analysis (PCA) and analysis of variance (ANOVA) tests of the peak areas and migration times are u

230 s) and we performed an analysis of variance (ANOVA) to investigate how the selection of the effect mo

231 e evaluated through an analysis of variance (ANOVA) tool revealing the next steps toward optimizing t

232 An analysis of variance (ANOVA) was performed to compare mean IOP values.

233 Analysis of variance (ANOVA) was used to determine which elemental concentrati

234 ent analysis (PCA) and analysis of variance (ANOVA) were performed to evaluate the differences betwee

235 Two-way analysis of variance (ANOVA) with Bonferroni's post-test, Pearson correlation,

236 s were determined with analysis of variance (ANOVA), Holm-Bonferroni corrected Pearson correlations,

237 or E+P treatment (all analysis of variance (ANOVA), P<0.01).

238 ometric tools, such as analysis of variance (ANOVA), principal component analysis (PCA), and ANOVA si

239 Kruskal-Wallis analysis of variance (ANOVA)-on-Ranks with post-hoc Mann-Whitney U-tests showe

240 eaks were subjected to analysis of variance (ANOVA)-simultaneous component analysis (ASCA).

241 ed using a statistical analysis of variance (ANOVA).

242 sing repeated measures analysis of variance (ANOVA).

243 tively assessed by the analysis of variance (ANOVA).

244 tively assessed by the analysis of variance (ANOVA).

245 of variation (CVs) and analysis of variance (ANOVA).

246 Commonly, standard analysis of variance (ANOVA)/regression is implemented to identify the relativ

247 nalyzed with a one way analysis of variance (ANOVA); differences between groups in proportions were a

248 Univariate analyses of variance (ANOVAs) revealed a significant effect of SNP rs4576072 i

249 om 4 to 12 injections (analysis of variance [ANOVA] P = .027) and compared with patients who received

250 al size did not (2-way analysis of variance [ANOVA] size versus frequency P = 0.03).

251 ase (AST) (P < 0.0001, analysis of variance [ANOVA]).

252 ong dialysis patients (analysis of variance [ANOVA], P = 0.007), but no clear dose-response associati

253 ity was determined by analysis of variances (ANOVA), principal component analysis (PCA), and multivar

254 and our statistical treatment of variation (ANOVA) were critical for effective use of high-throughpu

255 cally significant difference (Kruskal-Wallis ANOVA) was found among hyper-autofluorescent, patchy, an

256 Kruskal-Wallis ANOVA-on-ranks with post hoc Mann-Whitney U tests showed

257 6, and 3.66 +/- 1.19, respectively) by 1-way ANOVA (P = 0.74).

258 ere identified by using Kruskal-Wallis 1-way ANOVA with Bonferroni P value adjustment to correct for

259 The 2-way ANOVA model suggested a highly significant effect of bot

260 Repeated-measures 2-way ANOVA showed this can be attributed to type of surgery a

261 lant incorporation and storage time, a 2-way ANOVA was used to process the results, further analysed

262 A 2-way ANOVA with Bonferroni's post-test revealed no significan

263 luated using Spearman correlations and 2-way ANOVA with differences between patients with HF with red

264 thods, such as a Student's t-Test or a 2-way ANOVA, in these situations give flawed results and shoul

265 Following repeated-measures 2-way ANOVA, this can be attributed to type of aftercare and n

266 Following repeated-measures 2-way ANOVA, this can be attributed to type of surgery and not

267 s versus teamwork interventions, using 2-way ANOVA.

268 -oxidant glutathione (n = 6; p<0.01, one way ANOVA); this is suggestive of moderation of an oxidative

269 One-way ANOVA indicated that the mean hSDH measurement of the ey

270 tistical analysis was performed with one-way ANOVA or two-tailed t-tests.

271 One-way ANOVA pointed successfully on the existing statistical d

272 One-way ANOVA was performed with Prism (version 7).

273 t, paired t test, and Kruskal-Wallis one-way ANOVA were used to analyze the data.

274 One-way ANOVA with post-hoc Dunnett's test was used in which eac

275 One-way ANOVA with the post hoc Bonferroni test was used for gro

276 For statistical analysis t-test and one-way ANOVA with Tukey's post hoc test and Bartlett's test for

277 Hierarchical Cluster Analysis (HCA), One-way ANOVA, and calculation of biological accumulation factor

278 using ELISA, and data analyzed with one-way ANOVA, logistic regression analysis and receiver-operati

279 on were not statistically different (one-way ANOVA, p > 0.05).

280 annual average(-)(1), respectively, one-way ANOVA, p = 0.0006).

281 fatigue, or both were assessed with one-way ANOVA.

282 low- and high-grade gliomas based on one-way ANOVA.

283 the disc herniation (VAS, p = 0.043, one-way ANOVA; p = 0.035, Tukey HSD).

284 A three way ANOVA with fatigue (high and low), task (movement time,

285 aries induction were analyzed with three-way ANOVA at alpha = 0.05.

286 Statistical analysis (three-way ANOVA) was used to calculate the interaction for cone ph

287 Furthermore, two-way ANOVA analysis showed that the bioluminescent method and

288 Two-way ANOVA by group and sex (with P < 0.05) indicated ejectio

289 a obtained from the application of a two-way ANOVA evaluation at 95% confidence level.

290 Two-way ANOVA found statistically significant seasonal and solve

291 Two-way ANOVA showed no significant difference between the tradi

292 The results of two-way ANOVA showed that interactions between groups were obser

293 Two-way ANOVA with a Holm-Bonferroni correction was used to dete

294 e two factors, statistical analyses (two-way ANOVA) were performed.

295 There was an interaction effect (two-way ANOVA, P < 0.001) between age and strain for AL, CT, ACD

296 canning microscopy and analyzed with two-way ANOVA.

297 When ANOVA with Bonferroni post-hoc comparisons was used in t

298 The groups were compared with ANOVA, Kruskal-Wallis test, t-test, Mann-Whitney test an

299 , DBP, OPP were calculated and compared with ANOVA.

300 We analysed F0 data with ANOVA and the F1 and F2 data using mixed models, with gr