火麻仁品种与药用部位本草考证

目的:通过对火麻仁的本草考证,明确火麻仁药材品种及入药部位,为临床用药提供文献依据。方法:考证历代本草著作,结合现代研究资料进行分析。结果:火麻仁的基原植物古今一致,均为桑科大麻属植物大麻Cannabis sativa。而入药部位,早期文献中对本品的药用部位并未作严格区分,麻賁(雌花序或未成熟的果实)、麻子(果实)、麻子仁(种仁)三者相互混用;自陶弘景开始,逐渐认识到其果皮的毒性,其后历代本草、方书大都特别指出大麻入药须去除果皮,使用种仁。现代药学文献对火麻仁药用部位的记载,或果实,或种仁,较混乱。结论:火麻仁品种古今一致;种仁比果实更安全,其药用部位应为种仁。     

基于KPCA算法的阿尔茨海默症辅助诊断

阿尔茨海默症（AD）是一种起病隐匿、进行性发展的神经系统退行性疾病,利用磁共振成像和计算机技术对AD患者的辅助诊断是目前不断探索的新课题。本研究先对磁共振图像进行预处理和相关性分析,然后利用核主成分分析法（KPCA）对脑灰质图像进行特征提取,结合Adaboost算法进行分类,并与主成分分析法（PCA）进行对比试验。通过对AD神经影像学计划数据库中的116名AD患者、116名轻度认知障碍患者,以及117名正常对照的脑部功能磁共振成像进行的研究表明,利用机器学习能够很有效地辅助诊断AD脑部疾病,KPCA算法对图像进行特征提取比PCA 算法更加充分完备,分类结果更加精确,能够获得更好的AD辅助诊断结果。     

Enhancing the representation of functional connectivity networks by fusing multi‐view information for autism spectrum disorder diagnosis

Functional connectivity network provides novel insights on how distributed brain regions are functionally integrated, and its deviations from healthy brain have recently been employed to identify biomarkers for neuropsychiatric disorders. However, most of brain network analysis methods utilized features extracted only from one functional connectivity network for brain disease detection and cannot provide a comprehensive representation on the subtle disruptions of brain functional organization induced by neuropsychiatric disorders. Inspired by the principles of multi‐view learning which utilizes information from multiple views to enhance object representation, we propose a novel multiple network based framework to enhance the representation of functional connectivity networks by fusing the common and complementary information conveyed in multiple networks. Specifically, four functional connectivity networks corresponding to the four adjacent values of regularization parameter are generated via a sparse regression model with group constraint ( l_2,1 ‐norm), to enhance the common intrinsic topological structure and limit the error rate caused by different views. To obtain a set of more meaningful and discriminative features, we propose using a modified version of weighted clustering coefficients to quantify the subtle differences of each group‐sparse network at local level. We then linearly fuse the selected features from each individual network via a multi‐kernel support vector machine for autism spectrum disorder (ASD) diagnosis. The proposed framework achieves an accuracy of 79.35%, outperforming all the compared single network methods for at least 7% improvement. Moreover, compared with other multiple network methods, our method also achieves the best performance, that is, with at least 11% improvement in accuracy.     

KPCA和Adaboost算法在阿尔茨海默症功能磁共振影像分类中的 应用

本研究的目的在于使用机器学习方法,对脑部功能磁共振成像数据进行分析与特征提取,完成对阿尔茨海默症 （AD）的辅助诊断与分析。首先对数据进行预处理与去除协变量,并从大脑全局特征出发,根据现有的自动解剖标记模 板,把每个被试的大脑分为116个脑区,通过提取每个脑区的时间序列,构建全脑功能连接矩阵,然后使用核主成分分析 法进行特征提取,最后用Adaboost算法进行分类。在对34名AD患者、35名轻度认知障碍患者和35名正常对照组的功能 磁共振成像数据进行的实验结果表明,利用静息态功能磁共振成像,同时结合机器学习的方法,能够有效地实现AD的正 确分类,准确率可以达到96%,该结果可以为AD患者的临床辅助诊断提供有效的判断依据。     

Neighborhood hash graph kernel for protein-protein interaction extraction

Automated extraction of protein-protein interactions (PPIs) from biomedical literatures is an important topic of biomedical text mining. In this paper, we propose an approach based on neighborhood hash graph kernel for this task. In contrast to the existing graph kernel-based approaches for PPI extraction, the proposed approach not only has the capability to make use of full dependency graphs to represent the sentence structure but also effectively control the computational complexity. We evaluate the proposed approach on five publicly available PPI corpora and perform detailed comparisons with other approaches. The experimental result shows that our approach is comparable to the state-of-the-art PPI extraction system and much faster than all-path graph kernel approach on all five PPI corpora.     

Remote protein homology detection and fold recognition using two-layer support vector machine classifiers

Remote protein homology detection and fold recognition refer to detection of structural homology in proteins where there are small or no similarities in the sequence. To detect protein structural classes from protein primary sequence information, homology-based methods have been developed, which can be divided to three types: discriminative classifiers, generative models for protein families and pairwise sequence comparisons. Support Vector Machines (SVM) and Neural Networks (NN) are two popular discriminative methods. Recent studies have shown that SVM has fast speed during training, more accurate and efficient compared to NN. We present a comprehensive method based on two-layer classifiers. The 1st layer is used to detect up to superfamily and family in SCOP hierarchy using optimized binary SVM classification rules. It used the kernel function known as the Bio-kernel, which incorporates the biological information in the classification process. The 2nd layer uses discriminative SVM algorithm with string kernel that will detect up to protein fold level in SCOP hierarchy. The results obtained were evaluated using mean ROC and mean MRFP and the significance of the result produced with pairwise t-test was tested. Experimental results show that our approaches significantly improve the performance of remote protein homology detection and fold recognition for all three different version SCOP datasets (1.53, 1.67 and 1.73). We achieved 4.19% improvements in term of mean ROC in SCOP 1.53, 4.75% in SCOP 1.67 and 4.03% in SCOP 1.73 datasets when compared to the result produced by well-known methods. The combination of first layer and second layer of BioSVM-2L performs well in remote homology detection and fold recognition even in three different versions of datasets.     

枳壳麸炒前、后主要活性成分的含量变化

目的: 考察枳壳饮片麸炒前、后主要活性成分的含量变化。方法: 采用RP-HPLC测定柚皮苷、新橙皮苷、辛弗林、川陈皮素、橘皮素、水合橘皮内酯、橘皮内酯、马尔敏和葡萄内酯的含量,考察10批枳壳饮片麸炒前、后主要活性成分的含量变化。结果: 麸炒后枳壳饮片中柚皮苷、新橙皮苷、辛弗林、川陈皮素、橘皮素、水合橘皮内酯、橘皮内酯和马尔敏含量均略微下降,而葡萄内酯含量明显上升。葡萄内酯小剂量(0.6 mg·kg^-1)对正常小鼠小肠运动具有促进作用,高剂量(9 mg·kg^-1)则具有抑制作用。结论: 麸炒后葡萄内酯含量的升高是枳壳炮制的目的,可通过主要活性成分的变化规律阐述枳壳饮片的炮制机制。     

正交试验优选盐炙续断炮制工艺

目的:研究盐炙工艺对川续断皂苷Ⅵ含量的影响,优选盐炙续断炮制工艺.方法:以HPLC测定川续断皂苷Ⅵ含量为评价指标,采用正交试验法筛选盐炙续断炮制工艺参数.结果:优选的盐炙续断炮制工艺为每500 g续断药材用10 g盐浸润45 min,150℃炒制8 min.结论:优选的盐炙续断炮制工艺可明显提高续断有效成分的溶出率,且工艺稳定、质量可控,为续断炮制规范的研究提供参考.     

Additive survival least‐squares support vector machines

This work studies a new survival modeling technique based on least‐squares support vector machines. We propose the use of a least‐squares support vector machine combining ranking and regression. The advantage of this kernel‐based model is threefold: (i) the problem formulation is convex and can be solved conveniently by a linear system; (ii) non‐linearity is introduced by using kernels, componentwise kernels in particular are useful to obtain interpretable results; and (iii) introduction of ranking constraints makes it possible to handle censored data. In an experimental setup, the model is used as a preprocessing step for the standard Cox proportional hazard regression by estimating the functional forms of the covariates. The proposed model was compared with different survival models from the literature on the clinical German Breast Cancer Study Group data and on the high‐dimensional Norway/Stanford Breast Cancer Data set. Copyright © 2009 John Wiley & Sons, Ltd.     

澳洲坚果果仁营养成分分析

<正>澳洲坚果(Macadamia integrifolia),属山龙眼科(Proteaceae),澳洲坚果属(Macadamia)常绿乔木果树,又称夏威夷果、澳洲核桃、昆士兰坚果。原产于澳大利亚昆士兰州东南部和新南