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The data presented here provide additional genes to be tested for normalization of qPCR, an essential procedure to avoid misinterpretation when measuring gene expression [ 53].
The results of the merged data revealed that the most adequate of the genes tested for normalization in Mitchell are EF1α, SAND and RPS13; the three showing the lowest reliability are TUB, ACT and GAPDH.
Three miRNAs (miR-93, -103, -191) were tested for normalization according to the manufacturer's recommendations for serum/plasma applications [ 9]. 250 μL of serum was used for measurement of hK11 and hK13 with sandwich ELISA (human KLK-11 and KLK-13 pair sets ELISA kits, Sino Biological Inc., Beijing, China).
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To test for normalization efficiency, a fraction of the full-length-enriched cDNAs were cut with SfiI and ligated to pDNR-Lib plasmid (Clontech).
The peach reference gene was selected starting from Arabidopsis homologous genes [ 50], tested for transcript normalization in peach (Tadiello and Trainotti, unpublished results) and chosen to normalize qRT-PCR data because of its superior result compared to the previously used Internal Transcribed Spacer of the ribosomal RNA [ 10].
To test for potential normalization effects, ANOVAs compared performance and brain activation between ADHD boys under either drug condition with healthy control subjects.
To test for potential normalization effects of MPH, brain activation in ADHD patients under either drug condition was compared with that of 13 healthy age-matched boys.
Raw data were subjected to Z normalization and tested for significant changes as previously described (Minor et al., 2011).
To gauge whether muscle normalization would reduce inter-individual variability in brain data, we tested for a positive correlation between pre-normalization SIs for the pituitary and the adjacent muscle (linear regression).
After normalization, transcripts were tested for significant expression changes among sample groups via one-way ANOVA (α = 0.05) and for correlation with their corresponding EDGE-tag using a Spearman's rank correlation test.
were selected for analysis using the R software version 2.11.1 (R Development Core Team, 2010) and the Lowess test for normalization.
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