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Quercus fenchengsis is a rare Chinese oak species sporadically recorded in Fengcheng, Liaoning, Yuntai Mountain, Henan, and Qinling Mountains, Shaanxi. Here, the complete chloroplast (cp) genome of Q. fenchengensis was first reported. The cp genome is 161,296 bp in length with a GC content of 36.81%, and encodes 134 genes (86 protein-coding genes, 40 tRNA genes and eight rRNA genes). The phylogenetic tree based on the cp genome confirmed that Q. fenchengensis has a close relationship with Quercus aliena acutiserrata and Quercus dentata , consistent with the previous morphology-based suggestion that it would be a hybrid of Q. aliena acutiserrata and Q. dentata ., Competing Interests: The authors declare no competing financial interest. The authors alone are responsible for the content and writing of the paper., (© 2019 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.)

In the present study, we report the complete mitochondrial genome of Antheraea proylei strain In981, a hybrid of Chinese oak silkmoth ( A. pernyi ) and Indian oak silkmoth ( A. roylei ). The circular molecule is 15,573 bp in length, with 37 typical coding genes (13 protein-coding genes, 2 ribosomal RNA genes, and 22 transfer RNA genes) and one non-coding A + T-rich region of 552 bp long. Its gene components and gene order are identical to the common type found in Bombycoidea species. Phylogenetic analyses revealed that In981 is closely related to A. pernyi rather than A. roylei . This is the first report on the complete mitochondrial genome of A. proylei ., Competing Interests: The authors declare no competing financial interest. The authors alone are responsible for the content and writing of the paper., (© 2019 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.)

Prediction of toxicity of 203 nitro- and cyano-aromatic chemicals to Tetrahymena pyriformis was carried out by radial basis function neural network, general regression neural network and support vector machine, in non-linear response surface methodology. Toxicity was predicted from hydrophobicity parameter (log Kow) and maximum superdelocalizability (Amax). Special attention was drawn to prediction ability and robustness of the models, investigated both in a leave-one-out and 10-fold cross validation (CV) processes. The influence that the corresponding changes in the learning sets during these CV processes could have on a common external test set including 41 compounds was also examined. This allowed us to establish the stability of the models. The non linear results slightly outperform (as expected) multilinear relationships (MLR) and also favourably compete with various other non linear approaches recently proposed by Ren (J. Chem. Inf. Comput. Sci., 43 1679 (2003)).

A large data set of 146 natural, synthetic and environmental chemicals belonging to a broad range of structural classes have been tested for their relative binding affinity (expressed as log (RBA)) to the androgen receptor (AR). These chemicals commonly termed endocrine disrupting compounds (EDCs) present a variety of adverse effects in humans and animals. As assays for binding affinity remains a time-consuming task, it is important to develop predictive methods. In this work, quantitative structure-activity relationships (QSARs) were determined using three methods, multiple linear regression (MLR), radical basis function neural network (RBFNN) and support vector machine (SVM). Five descriptors, accounting for hydrogen-bonding interaction, distribution of atomic charges and molecular branching degree, were selected from a heuristic method to build predictive QSAR models. Comparison of the results obtained from three models showed that the SVM method exhibited the best overall performances, with a RMS error of 0.54 log (RBA) units for the training set, 0.59 for the test set, and 0.55 for the whole set. Moreover, six linear QSAR models were constructed for some specific families based on their chemical structures. These predictive toxicology models, should be useful to rapidly identify potential androgenic endocrine disrupting compounds.

It is proposed for the first time a method of prediction of the programmed-temperature retention times of components of naphthas in capillary gas chromatography using artificial neural networks. People are used to predict the programmed-temperature retention time using many formulas such as the integral formula, which requires that four parameters must be determined by calculation or experiments. However the results obtained by the formula are not so good to meet the demand of industry. In order to predict retention time accurately and conveniently, artificial neural networks using five-fold cross-validation and leave-20%-out methods have been applied. Only two parameters: density and isothermal retention index were used as input vectors. The average RMS error for predicted values of five different networks was 0.18, whereas the RMS error of predictions by the integral formula was 0.69. Obviously, the predictions by neural networks were much better than predictions by the formula, and neural networks need fewer parameters than the formula. So neural networks can successfully and conveniently solve the problem of predictions of programmed-temperature retention times, and provide useful data for analysis of naphthas in petrochemical industry.