Selection and Validation of Reference Genes for Quantitative Real-Time PCR in Buckwheat (Fagopyrum esculentum) Based on Transcriptome Sequence Data

Quantitative reverse transcription PCR (qRT-PCR) is one of the most precise and widely used methods of gene expression analysis. A necessary prerequisite of exact and reliable data is the accurate choice of reference genes. We studied the expression stability of potential reference genes in common buckwheat (Fagopyrum esculentum) in order to find the optimal reference for gene expression analysis in this economically important crop. Recently sequenced buckwheat floral transcriptome was used as source of sequence information. Expression stability of eight candidate reference genes was assessed in different plant structures (leaves and inflorescences at two stages of development and fruits). These genes are the orthologs of Arabidopsis genes identified as stable in a genome-wide survey gene of expression stability and a traditionally used housekeeping gene GAPDH. Three software applications – geNorm, NormFinder and BestKeeper - were used to estimate expression stability and provided congruent results. The orthologs of AT4G33380 (expressed protein of unknown function, Expressed1), AT2G28390 (SAND family protein, SAND) and AT5G46630 (clathrin adapter complex subunit family protein, CACS) are revealed as the most stable. We recommend using the combination of Expressed1, SAND and CACS for the normalization of gene expression data in studies on buckwheat using qRT-PCR. These genes are listed among five the most stably expressed in Arabidopsis that emphasizes utility of the studies on model plants as a framework for other species

Autonomous on-board data processing and instrument calibration software for the Polarimetric and Helioseismic Imager on-board the Solar Orbiter mission

A frequent problem arising for deep space missions is the discrepancy between the amount of data desired to be transmitted to the ground and the available telemetry bandwidth. A part of these data consists of scientific observations, being complemented by calibration data to help remove instrumental effects. We present our solution for this discrepancy, implemented for the Polarimetric and Helioseismic Imager on-board the Solar Orbiter mission, the first solar spectropolarimeter in deep space. We implemented an on-board data reduction system that processes calibration data, applies them to the raw science observables, and derives science-ready physical parameters. This process reduces the raw data for a single measurement from 24 images to five, thus reducing the amount of downlinked data, and in addition, renders the transmission of the calibration data unnecessary. Both these on-board actions are completed autonomously. (C) The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License