High-quality RNA extraction from copepods for Next Generation Sequencing: A comparative study

Despite the ecological importance of copepods, few Next Generation Sequencing studies (NGS) have been performed on small crustaceans, and a standard method for RNA extraction is lacking. In this study, we compared three commonly-used methods: TRIzol®, Aurum Total RNA Mini Kit and Qiagen RNeasy Micro Kit, in combination with preservation reagents TRIzol® or RNAlater®, to obtain high-quality and quantity of RNA from copepods for NGS. Total RNA was extracted from the copepods Calanus helgolandicus, Centropages typicus and Temora stylifera and its quantity and quality were evaluated using NanoDrop, agarose gel electrophoresis and Agilent Bioanalyzer. Our results demonstrate that preservation of copepods in RNAlater® and extraction with Qiagen RNeasy Micro Kit were the optimal isolation method for high-quality and quantity of RNA for NGS studies of C. helgolandicus. Intriguingly, C. helgolandicus 28S rRNA is formed by two subunits that separate after heat-denaturation and migrate along with 18S rRNA. This unique property of protostome RNA has never been reported in copepods. Overall, our comparative study on RNA extraction protocols will help increase gene expression studies on copepods using high-throughput applications, such as RNA-Seq and microarrays.

Modelling and Prediction of NOx Emission in a Coal-Fired Power Generation Plant

In this paper NOx emissions modelling for real-time operation and control of a 200 MWe coal-fired power generation plant is studied. Three model types are compared. For the first model the fundamentals governing the NOx formation mechanisms and a system identification technique are used to develop a grey-box model. Then a linear AutoRegressive model with eXogenous inputs (ARX) model and a non-linear ARX model (NARX) are built. Operation plant data is used for modelling and validation. Model cross-validation tests show that the developed grey-box model is able to consistently produce better overall long-term prediction performance than the other two models.