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The quantification analysis for target gene expression was performed using the relative quantification comparative CT method [ 9].
The samples undergo screening and quantification analysis for legal and illicit drugs and poisonous substances, and the analysis results are then sent to the forensic pathologist and stored in the laboratory's toxicology database.
GUSB, UBC (β2-microglobulin) and SDHA (succinate dehydrogenase) showed the smallest variations in Δ CT levels and were expressed at constant levels in samples regardless of treatment; these genes were therefore used as internal references for normalisation in the real-time RT PCR quantification analysis for the NB cell lines.
The following analyses were performed: (i) changes in DNA methylation at the genome-wide level, (ii) unsupervised cluster analysis for the GNAS locus and comparisons with previous GNAS methylation data used for PHP diagnosis, and (iii) detailed DNA quantification analysis for other imprinted DMRs.
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The method for quantification analysis of the images was the same as reported previously [27].
For quantification analysis of target mRNA, LightCycler was used.
In spite of this uncertainty, qPCR, estimating anammox bacterial RNA or DNA gene abundance, is still an effective and powerful tool for quantification analysis of anammox bacteria.
For quantification analysis of real time RT-PCR data, a standard curve was generated by pooling all the cDNA samples to form one cDNA mixture and then diluting this cDNA mixture 10-, 1000 1000- and 10,000-fold so that expression of the gene of interest was within the range of the standard curve.
Cell lysates for absolute quantification analysis of CYP3A4 were prepared from lyophilized human primary hepatocytes by sonification in the presence of glass beads in buffer containing Complete Mini-Protease Inhibitor Cocktail, and following homogenization.
Complex quantification analysis and sensitivity for interfering compounds are the drawbacks for this technique.
Recurrence quantification analysis is proposed for residual evaluation in order to have the comprehensive methodology of neural model-based fault detection.
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