Images were analyzed using a standard random-effect procedure.
For analysis of K2P5.1-mRNA expression, RNA was purified using Trizol reagent (Invitrogen, Carlsbad, CA, USA) and cDNA synthesis was performed using a standard protocol with random hexamer primers (all reagents were purchased from Applied Biosystems, Foster City, CA, USA).
For qRT-PCR analysis, total RNA was extracted based on the Trizol protocol (Invitrogen, Carlsbad, CA), treated with DNAase I(Promega, Madison, WI), and reverse-transcribed to cDNA (random priming) by using a standard protocol (SuperScript II reverse-transcriptase, Invitrogen, Carlsbad, CA).
Total RNAs from the control, 50, 150 or 300 mM NaCl treated seedlings were extracted as described above using the TRI solution, treated with DNAase I, and reverse-transcribed to cDNA (random priming) by using a standard protocol (SuperScript II reverse transcriptase, Invitrogen).
Data Extraction and Synthesis Two investigators independently extracted study characteristics using a standard form and pooled data using a random-effects model.
Reverse transcription was performed from 100 ng total RNA using Superscript II reverse transcriptase (Invitrogen) and random hexamers (Applied BioSystems, Warrington, United Kingdom) using a standard RT reaction.
using a standard sizing gradient.
For this experiment, the application produces random context messages (related to traffic information) — we used a standard python random function (within bounds) for each of the context variables: location, speed and number of hazards.
Although use of a standard random intercepts model results in a biased estimate for the effect of the predictor that is correlated with the random effect, Bouwmeester et al. [ 3] explained that, in fact, this correlation is beneficial in terms of prediction and particularly calibration, because inclusion of that predictor partly explains differences in prevalence between clusters.
