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We compared the results of scenario one with scenario two that used the average values for the time periods covered by data for chlorophyll a, copepods, anchovies and sardines, there were high correlation of selection frequencies (Spearman's rank correlation of 0.88, p<0.0001) (Fig 5a&c).
In contrast, all correlations of the repeated control samples with the corresponding original measurements were high (correlation coefficients: 0.910 0.987).
The correlations between change in DHI-N scores and those of the other self-report measures were high, correlation coefficients (r) ranging 0.50-0.57 (Table 4).
Although the correlations were high (correlation coefficients ranging from 0.886 to 0.980 for the different parameters), the EDV, ESV and LVEF calculated by the two software packages differed significantly (p < 0.001).
There were high correlation coefficients between the real-time PCR and microarray data (R = 0.9817, P < 0.05 under normal growth conditions; R = 0.9764, P < 0.05 under salt stress conditions) (Additional file 5: Figure S3), which validates the reliability of the microarray results.
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There were high correlations between LV-functional parameter (EDV, ESV and LVEF) measurements within the entire study group (Figure 2A,B,C).
There were high correlations amongst them so principal component analysis (PCA) was first applied to the raw data, which was standardised to have unit variance.
There were high correlations between the CDDS-17 and CDDS-15 versions (r = 0.994, P < 0.001).
There were high correlations between the overall ChQOL score and the three domain scores.
There were high correlations between anxiety and depression, BMI and FFMI, ISWT and ESWT.
As expected there were high correlations between PM10 and PM2.5 (Table 3).
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CEO of Professional Science Editing for Scientists @ prosciediting.com