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Recent studies have shown that RNA methylation modification can affect RNA transcription, metabolism, splicing and stability. In addition, RNA methylation modification has been associated with cancer, obesity and other diseases. Based on information about human genome and machine learning, this paper discusses the effect of the fusion sequence and gene-level feature extraction on the accuracy of methylation site recognition. The significant limitation of existing computing tools was exposed by discovered of new features. (1) Most prediction models are based solely on sequence features and use SVM or random forest as classification methods. (2) Limited by the number of samples, the model may not achieve good performance. In order to establish a better prediction model for methylation sites, we must set specific weighting strategies for training samples and find more powerful and informative feature matrices to establish a comprehensive model. In this paper, we present HSM6AP, a high-precision predictor for the Homo sapiens N6-methyladenosine ( m 6 A) based on multiple weights and feature stitching. Compared with existing methods, HSM6AP samples were creatively weighted during training, and a wide range of features were explored. Max-Relevance-Max-Distance (MRMD) is employed for feature selection, and the feature matrix is generated by fusing a single feature. The extreme gradient boosting (XGBoost), an integrated machine learning algorithm based on decision tree, is used for model training and improves model performance through parameter adjustment. Two rigorous independent data sets demonstrated the superiority of HSM6AP in identifying methylation sites. HSM6AP is an advanced predictor that can be directly employed by users (especially non-professional users) to predict methylation sites. Users can access our related tools and data sets at the following website: The codes of our tool can be publicly accessible at [ABSTRACT FROM AUTHOR]

Motivation mRNA location corresponds to the location of protein translation and contributes to precise spatial and temporal management of the protein function. However, current assignment of subcellular localization of eukaryotic mRNA reveals important limitations: (1) turning multiple classifications into multiple dichotomies makes the training process tedious; (2) the majority of the models trained by classical algorithm are based on the extraction of single sequence information; (3) the existing state-of-the-art models have not reached an ideal level in terms of prediction and generalization ability. To achieve better assignment of subcellular localization of eukaryotic mRNA, a better and more comprehensive model must be developed. Results In this paper, SubLocEP is proposed as a two-layer integrated prediction model for accurate prediction of the location of sequence samples. Unlike the existing models based on limited features, SubLocEP comprehensively considers additional feature attributes and is combined with LightGBM to generated single feature classifiers. The initial integration model (single-layer model) is generated according to the categories of a feature. Subsequently, two single-layer integration models are weighted (sequence-based: physicochemical properties = 3:2) to produce the final two-layer model. The performance of SubLocEP on independent datasets is sufficient to indicate that SubLocEP is an accurate and stable prediction model with strong generalization ability. Additionally, an online tool has been developed that contains experimental data and can maximize the user convenience for estimation of subcellular localization of eukaryotic mRNA. [ABSTRACT FROM AUTHOR]

This letter presents a novel approach for extracting accurate outlines of individual buildings from very high-resolution (0.1–0.4 m) optical images. Building outlines are defined as polygons here. Our approach operates on a set of straight line segments that are detected by a line detector. It groups a subset of detected line segments and connects them to form a closed polygon. Particularly, a new grouping cost is defined first. Second, a weighted undirected graph $\textit {G(V,E)}$ is constructed based on the endpoints of those extracted line segments. The building outline extraction is then formulated as a problem of searching for a graph cycle with the minimal grouping cost. To solve the graph cycle searching problem, the bidirectional shortest path method is utilized. Our method is validated on a newly created data set that contains 123 images of various building roofs with different shapes, sizes, and intensities. The experimental results with an average intersection-over-union of 90.56% and an average alignment error of 6.56 pixels demonstrate that our approach is robust to different shapes of building roofs and outperforms the state-of-the-art method. [ABSTRACT FROM AUTHOR]

Wuzhuyu decoction (WZYD) is a classic traditional Chinese medicine (TCM) formula. It has been extensively used for treating migraine for thousands of years in TCM. Four potential active ingredients from WZYD, ginsenoside-Rg (Rg), ginsenoside-Rb (Rb), evodiamine (Ev) and rutaecarpine (Ru), were found to have positive correlations with pharmacodynamic indicators involving mouse migraine in our previous study. To find a better therapeutic effect on migraine, this research was carried out to optimize the combinations of Rg, Rb, Ev and Ru using the uniform design method. The results showed that Rb and Ev played key roles in improving the therapeutic effect on mouse migraine by strongly ameliorating pharmacodynamic indicators associated with migraine. They significantly increased the contents of 5-hydroxytryptamine, noradrenaline and dopamine in brain tissues, and reduced the content of nitric oxide in brain tissues and the activities of nitric oxide synthase in both brain tissues and blood serum. The optimal concentrations of Rb and Ev were 1057.4 mg/L and 312.5 mg/L, respectively. Rg and Ru contributed less to the overall desirability, suggesting that they had reverse effects on some pharmacodynamic indicators of this type of migraine. The verification test demonstrated by the immunohistochemical method that the optimal combination inhibited the expression of c- fos and c- jun in periaqueductal gray of mice, and strongly ameliorated pharmacodynamic indicators. These results suggested that the therapeutic effect of the optimal combination of the four ingredients was strong, and the optimal results were proven to be reliable and accurate. [ABSTRACT FROM AUTHOR]

Objective: To investigate the effect of compound 962 capsule (abbre. as 962) on liver lipid peroxidation and indexes of thymus and spleen in aged rats. Methods: Rats were divided into young control, aged model, Piracetam, 962 middle dose (0.9 g/kg) and high dose (1.8 g/kg) groups. All test drugs were administrated for 1 month by gastrogavage. The liver lipid peroxidation was determined by thiobarbituric (TBA) method. The thymus index and spleen index were determined by weighing method. Results: 962 (middle and high dose) attenuated liver lipid peroxidation, increased the thymus index of aged rats. There was no effect on spleen index in all drug-treated groups. Conclusion: 962 could decrease liver lipid peroxidation and increase thymus index. It suggested that 962 might be beneficial in retarding aging process. [ABSTRACT FROM AUTHOR]