AEIV结合AHSI用于饱和脂肪酮类物质的13C NMR谱模拟

应用一种反映分子局部微环境描述子--原子电性相互作用矢量（vector of atomic electronegative interaction,AEIV）和原子杂化状态指数（Atomic Hybridation State Index, AHSI）对饱和脂肪酮类化合物的55种分子中的153个¹³C NMR谱建模模拟,应用多元线性回归方法得到定量结构波谱关系(QSSR)模型的复相关系数R_MM=0.997, 标准偏差为SD_MM=7.155. 采用留一法交互检验的结果是R_CV=0.993,SD_CV=10.195. 并随机抽出三部分分子进行检验,得到的相关系数分别是R_MM1=0.996,R_MM2=0.996,R_MM3=0.999. 研究结果表明使用AEIV和AHSI所建模型预测能力是相当稳定的.     

Discrimination of Beers by Cyclic Voltammetry Using a Single Carbon Screen-printed Electrode

A fast, simple and costless methodology without sample pre-treatment is proposed for the discrimination of beers. It is based on cyclic voltammetry (CV) using commercial carbon screen-printed electrodes (SPCE) and includes a correction of the signals measured with different SPCE units. Data are submitted to partial least squares discriminant analysis (PLS−DA) and support vector machine discriminant analysis (SVM−DA), which allow a reasonable classification of the beers. Also, CV data from beers can be used to predict their alcoholic degree by partial least squares (PLS) and artificial neural networks (ANN). In general, non-linear methods provide better results than linear ones.     

Rapid and label-free classification of pathogens based on light scattering,reduced power spectral features and support vector machine

The rapid identification of pathogens is crucial in controlling the food quality and safety. The proposed system for the rapid and label-free identification of pathogens is based on the principle of laser scattering from the bacterial microbes. The clinical prototype consists of three parts: the laser beam, photodetectors, and the data acquisition system. The bacterial testing sample was mixed with 10 mL distilled water and placed inside the machine chamber. When the bacterial microbes pass by the laser beam, the scattering of light occurs due to variation in size, shape, and morphology. Due to this reason, different types of pathogens show their unique light scattering patterns. The photo-detectors were arranged at the surroundings of the sample at different angles to collect the scattered light. The photodetectors convert the scattered light intensity into a voltage waveform. The waveform features were acquired by using the power spectral characteristics, and the dimensionality of extracted features was reduced by applying minimal-redundancy-maximal-relevance criterion (mRMR). A support vector machine (SVM) classifier was developed by training the selected power spectral features for the classification of three different bacterial microbes. The resulting average identification accuracies of E. faecalis,E. coli and S. aureus were 99%, 87%, and 94%, respectively. The overall experimental results yield a higher accuracy of 93.6%, indicating that the proposed device has the potential for label-free identification of pathogens with simplicity, rapidity, and cost-effectiveness.     

基于人工神经网络研究芳胺喹唑啉衍生物的抗胃癌活性

基于分子电性距离矢量(M_t)表征21个芳胺喹唑啉衍生物的分子结构,并与其对人胃癌细胞的抗癌活性(pI)关联。通过最佳变量子集回归方法建立上述化合物抗胃癌活性的三参数(M_(15)、M_(18)、M_(82))定量构效关系模型。其交叉验证系数(R_(cv)~2)、非交叉验证系数(R~2)依次为0.386和0.737。通过R、R_(cv)~2、V_(IF)等检验,模型具有较好的相关性、稳健性和预测能力。结果显示,-CH_2-、-CH、-O-、-S-和-NH-等分子结构单元直接影响这些化合物的抗胃癌活性。将M_(15)、M_(18)、M_(82)作为人工神经网络的输入层结点,采用3∶3∶1的网络结构,利用BP算法获得了令人满意的pI模型,其R~2和标准偏差S_D分别为0.992和0.080,表明pI与三参数呈现优异的非线性关系。     

An ensemble learning framework for potential miRNA-disease association prediction with positive-unlabeled data

To explore the pathogenic mechanisms of MicroRNA (miRNA) on diverse diseases, many researchers have concentrated on discovering the potential associations between miRNA and disease using machine learning methods. However, the prediction accuracy of supervised machine learning methods is limited by lacking of experimentally-validated uncorrelated miRNA-disease pairs. Without these negative samples, training a highly accurate model is much more difficult. Different from traditional miRNA-disease prediction models using randomly selected unknown samples as negative training samples, we propose an ensemble learning framework to solve this positive-unlabeled (PU) learning problem. The framework incorporates two steps, i.e., a novel semi-supervised Kmeans (SS-Kmeans) to extract reliable negative samples from unknown miRNA-disease pairs and subagging method to generate diverse training sample sets to make full use of those reliable negative samples for ensemble learning. Combined with effective random vector functional link (RVFL) network as prediction model, the proposed framework showed superior prediction accuracy comparing with other popular approaches. A case study on lung and gastric neoplasms further confirms the framework’s efficacy at identifying miRNA disease associations.     

Development of models for prediction of the antioxidant activity of derivatives of natural compounds

Antioxidants are important for maintaining the appropriate balance between oxidizing and reducing species in the body and thus preventing oxidative stress. Many natural compounds are being screened for their possible antioxidant activity. It was found that a mushroom pigment Norbadione A, which is a pulvinic acid derivative, shows an antioxidant activity; the same was found for other pulvinic acid derivatives and structurally related coumarines. Based on the results of in vitro studies performed on these compounds as a part of this study quantitative structure–activity relationship (QSAR) predictive models were constructed using multiple linear regression, counter-propagation artificial neural networks and support vector regression (SVR). The models have been developed in accordance with current QSAR guidelines, including the assessment of the models applicability domains. A new approach for the graphical evaluation of the applicability domain for SVR models is suggested. The developed models show sufficient predictive abilities for the screening of virtual libraries for new potential antioxidants.     

Classification of nasopharyngeal cell lines (C666-1, CNE2, NP69) via Raman spectroscopy and decision tree

Researchers have demonstrated that Raman spectroscopy can be used for characterization of tumor cells with excellent spatial resolution. However, performance evaluation of different algorithms in classifying multiclass of Raman spectra has not been reported yet. In this work, we present Raman spectra of nasopharyngeal carcinoma and nasopharyngeal normal cell lines. Combined with student’s t-test and several multivariate approaches, including decision tree, support vector classification, and linear discriminant analysis, our work shows that the relative content of two histological abnormality sensitive bands at 1449 and 1658 cm⁻¹ in tumor cells is significantly different from that of normal cells (p = 0.0132), and can be a biomarker to classify these cells. This difference is confirmed by importance analyses in the decision tree model. Furthermore, performances of statistical methods are compared with one another to explore the ability in classification. Results show that the decision tree can be more capable for classification between tumorous and normal cell lines with sensitivity and specificity of 99.0% and 96.9%, respectively. Findings of this work further support our previous work and indicate that the decision tree performs more robustly in cell classification. Our work will prove helpful to the early diagnosis of nasopharyngeal carcinoma, and will indicate the decision tree to be the primary algorithm in tumor-cell classification.     

随机结构正交展开分析的Ritz动力聚缩法

针对随机结果正交展开理论计算上的弱点 ,本文在分析扩阶矩阵特性的基础上 ,于 Ritz模态向量子空间中对扩阶方程实现动力聚缩 ,大大提高了正交展开理论对实际工程问题的分析能力。分析实例表明 :即使结构参数具有很大变异性 (如δ =0 .4 )时 ,该算法依然能理想地与 Monte Carlo法模拟结果相吻合 ,计算时间则远远小于 Monte Carlo模拟法。同时 ,分析例证再一次强化了在结构动力分析中考虑结构参数随机性的必要性     

Equivariant Sternberg–Chen Theorem

Smooth (C ) vector fields with linear symmetries and anti-symmetries are considered. We prove that provided symmetry group is compact then a smooth conjugacy in Sternberg–Chen Theorem can be chosen symmetric.     

Unique Orbital Normal Form for Vector Fields of Hopf-Zero Singularity

Normal forms for vector fields of Hopf-zero singularity in R³ are studied. Multiple Lie bracket method is used to give unique normal forms under both conjugacy and orbital equivalence for such vector fields with a generic quadratic part.Dedicated to Professor Shui-Nee Chow on the occasion of his 60th birthday.