The anode materials that have been developed for solid oxide fuel cells (SOFCs) are vulnerable to deactivation by carbon buildup (coking) from hydrocarbon fuels or by sulfur contamination (poisoning). We report on a mixed ion conductor, BaZr(0.1)Ce(0.7)Y(0.2-)(x)Yb(x)O(3-delta), that allows rapid transport of both protons and oxide ion vacancies. It exhibits high ionic conductivity at relatively low temperatures (500 degrees to 700 degrees C). Its ability to resist deactivation by sulfur and coking appears linked to the mixed conductor's enhanced catalytic activity for sulfur oxidation and hydrocarbon cracking and reforming, as well as enhanced water adsorption capability.
DNA N6-methyldeoxyadenosine (6 mA) modifications were first found more than 60 years ago but were thought to be only widespread in prokaryotes and unicellular eukaryotes. With the development of high-throughput sequencing technology, 6 mA modifications were found in different multicellular eukaryotes by using experimental methods. However, the experimental methods were time-consuming and costly, which makes it is very necessary to develop computational methods instead. In this study, a machine learning-based prediction tool, named csDMA, was developed for predicting 6 mA modifications. Firstly, three feature encoding schemes, Motif, Kmer, and Binary, were used to generate the feature matrix. Secondly, different algorithms were selected into the prediction model and the ExtraTrees model received the best AUC of 0.878 by using 5-fold cross-validation on the training dataset. Besides, the ExtraTrees model also received the best AUC of 0.893 on the independent testing dataset. Finally, we compared our method with state-of-the-art predictors and the results shown that our model achieved better performance than existing tools.
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