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Acenes comprise an important class of organic semiconducting materials. As graphene nanoribbons of ultimate width, they are valuable atom-precise model systems for studying the properties of this form of nanoscale carbon materials. Heptacene is the smallest member of the acene series that could only be studied under matrix isolation conditions. Its existence in bulk had never been positively confirmed, despite efforts dating back more than 70 years. We report that the reduction of 7,16-heptacenequinone produces a mixture of two diheptacene molecules. The diheptacenes undergo thermal cleavage to heptacene at high temperatures in the solid state. Monitoring this cycloreversion by solid state C cross-polarized magic angle spinning NMR reveals that solid heptacene has a half-life time of several weeks at room temperature. The diheptacenes are valuable precursors for generating films of heptacene by vapor phase deposition that can be studied below or at room temperature.

The oxidation and reduction of water is a key challenge in the production of chemical fuels from electricity.

BackgroundPseudouridylation is the most prevalent type of posttranscriptional modification in various stable RNAs of all organisms, which significantly affects many cellular processes that are regulated by RNA. Thus, accurate identification of pseudouridine (Ψ) sites in RNA will be of great benefit for understanding these cellular processes. Due to the low efficiency and high cost of current available experimental methods, it is highly desirable to develop computational methods for accurately and efficiently detecting Ψ sites in RNA sequences. However, the predictive accuracy of existing computational methods is not satisfactory and still needs improvement.ResultsIn this study, we developed a new model, PseUI, for Ψ sites identification in three species, which are H. sapiens, S. cerevisiae, and M. musculus. Firstly, five different kinds of features including nucleotide composition (NC), dinucleotide composition (DC), pseudo dinucleotide composition (pseDNC), position-specific nucleotide propensity (PSNP), and position-specific dinucleotide propensity (PSDP) were generated based on RNA segments. Then, a sequential forward feature selection strategy was used to gain an effective feature subset with a compact representation but discriminative prediction power. Based on the selected feature subsets, we built our model by using a support vector machine (SVM). Finally, the generalization of our model was validated by both the jackknife test and independent validation tests on the benchmark datasets. The experimental results showed that our model is more accurate and stable than the previously published models. We have also provided a user-friendly web server for our model at http://zhulab.ahu.edu.cn/PseUI, and a brief instruction for the web server is provided in this paper. By using this instruction, the academic users can conveniently get their desired results without complicated calculations.ConclusionIn this study, we proposed a new predictor, PseUI, to detect Ψ sites in RNA sequences. It is shown that our model outperformed the existing state-of-art models. It is expected that our model, PseUI, will become a useful tool for accurate identification of RNA Ψ sites.Electronic supplementary materialThe online version of this article (10.1186/s12859-018-2321-0) contains supplementary material, which is available to authorized users.

The cycle life of LiCoO 2 cathodes cycled at a high cutoff voltage can be largely improved by surface modification with ZnO coating. The bright-field transmission electron microscopy ͑TEM͒ images and selected area diffraction patterns of as-coated LiCoO 2 particles reveal the existence of continuous ZnO films with a 10-nm thickness deposited on LiCoO 2 surfaces, indicating a Li-Zn-O phase formed on the surface region. Besides high-voltage cycleability, cycle-life degradation caused by inappropriate conductive carbon can also be moderated by ZnO coating. Furthermore, the rate capability at high current density can also be notably improved. The role ZnO coating played in the charge-discharge process is discussed from the viewpoint of surface chemistry with the aid of data from scanning electron microscopy, TEM, energy-dispersive X-ray, and impedance spectra. Moreover, the correlation between electrochemical performance and surface properties of ZnO-coated LiCoO 2 is explored.

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5-methylcytosine (m5C) is one of the most common and abundant post-transcriptional modifications (PTCMs) in RNA. Recent studies showed that m5C plays important roles in many biological functions such as RNA metabolism and cell fate decision. Because most experimental methods that determine m5C sites across the transcriptome are time-consuming and expensive, it is urgent to develop accurate computational methods to identify m5C sites effectively. A benchmark dataset is important for developing and evaluating computational methods. In this work, we constructed four different datasets according to the data redundancy and imbalance. Based on these datasets, we generated three different kinds of features, i.e., KNFs (K-nucleotide frequencies), KSNPFs (K-spaced nucleotide pair frequencies), and pseDNC (pseudo-dinucleotide composition), and then used a support vector machine (SVM) to build our models. Based on the imbalanced and nonredundant dataset, Met935, we extensively studied the three kinds of features and determined an optimal combination of the features. Based on the feature combination, we built models on the three different datasets and compared them with state-of-the-art models. According to the predictive results of the stringent jackknife test, the models based on the three features, 4NF, 1SNPF, and pseDNC, are superior or comparable to other methods. To determine the best model between the models based on the imbalanced dataset Met935 and the balanced dataset Met240, we further evaluated the two models on an independent test set Test1157. Our results demonstrate that the model based on the balanced dataset Met240 achieved the highest recall (68.79%) and the highest Matthews correlation coefficient (MCC) (0.154). In addition, the model is also superior to other state-of-the-art methods according to the integrated parameter MCC on the independent test set. Thus, we selected the model based on Met240 as our final model, which was named RNAm5CPred. In addition, a web server for RNAm5CPred (http://zhulab.ahu.edu.cn/RNAm5CPred/) has been provided to facilitate experimental research.

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