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Missing data were treated with maximum likelihood imputation.
The adsorbtion data were treated with common kinetic and isotherm models.
All the data were treated with the aid of design expert from Stat-Ease (8.0.7.1).
Missing data were treated with the last observation carried forward method.
Due to the extended number and the heterogeneous composition of the PU coating samples investigated in this study, the Py-GC/MS data were treated with an expert system for data processing.
Data were treated with artificial neural networks (ANNs) and it was found that using suitable architecture of a supervised artificial neural network, the limpets can be successfully distinguished (classified) up to 98%.
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We recommend that real-time QPCR data be treated with a high degree of caution and suggest that if it is used for the analysis of multi-allelic CNPs, that it is accompanied by independent genotyping measures such as MLPA or PRT.
All data were treated and analyzed with the aid of the Minitab v.14 statistical package (Minitab Inc., PA, USA).
Data were treated as cumulative with additional new patients and patient-year exposure providing increased power to the analyses.
Even though data were treated in accordance with the intention-to-treat principle [ 42], this only helped when individual items were analysed.
Biochemical data were treated by multivariate analysis with the SIMCA-P (V12)software (UMETRICS AB Umea, Sweden).
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