不同产地脱脂乳木果仁总酚含量测定及其生物活性

测定了26个不同产地脱脂乳木果仁甲醇提取物的总酚含量(TPC)、抗氧化、细胞毒、EB病毒早期抗原(EBV-EA)抑制等活性,并评价了TPC与各生物活性间的相关性。采用紫外分光光度法测定TPC;采用1,1-二苯基-2-苦基肼(DPPH)自由基清除法、2,2′-连氮基-双(3-乙基苯并噻唑啉-6-磺酸)二铵盐(ABTS)自由基清除法和铁离子还原法(FRAP)3种方法评价提取物抗氧化活性;3-(4,5-二甲基噻唑-2)-2,5-二苯基四氮唑溴盐(MTT)法评价提取物对人白血病细胞HL60,肺癌细胞A549和乳腺癌细胞SK-BR-3的细胞毒性;间接免疫酶法评价提取物对EBV-EA抑制活性。结果表明:不同产地乳木果仁总酚含量相差较大(25.8~265.7 mg/g,以没食子酸计);部分产地提取物或有较好的抗氧化活性［DPPH:IC₅₀ 3.4~54.9 μg/mL,ABTS:1.11~4.09 mmol/g(以Trolox计),FRAP:1.24~1.93 mmol/g(以Trolox计)］、较强的细胞毒性(对不同细胞)(IC₅₀ 25.1~95.5 μg/mL)和EBV-EA抑制活性(100 μg/mL:85.8%~94.9%),其中TPC只与抗氧化活性有显著的相关关系(r=0.750~0.837,P<0.01)。     

Volumetric magnetic resonance imaging classification for Alzheimer’s disease based on kernel density estimation of local features

Background The classification of Alzheimer's disease (AD) from magnetic resonance imaging (MRI) has been challenged by lack of effective and reliable biomarkers due to inter-subject variability.This ar...     

BCYB代数的理想和同构定理

本文使用一种新的方法引入了BCYB代数的理想的概念,并由此引入了BCYB代数的商代数,进而又定义了BCYB代数的同态、同构、同态的核等术语,最终导出了BCYB代数的第一同构定理和双商定理。     

Quantitative magnetic resonance spectroscopy: semi-parametric modeling and determination of uncertainties.

A semi-parametric approach for the quantitative analysis of magnetic resonance (MR) spectra is proposed and an uncertainty analysis is given. Single resonances are described by parametric models or by parametrized in vitro spectra and the baseline is determined nonparametrically by regularization. By viewing baseline estimation in a reproducing kernel Hilbert space, an explicit parametric solution for the baseline is derived. A Bayesian point of view is adopted to derive uncertainties, and the many parameters associated with the baseline solution are treated as nuisance parameters. The derived uncertainties formally reduce to Cramér-Rao lower bounds for the parametric part of the model in the case of a vanishing baseline.The proposed uncertainty calculation was applied to simulated and measured MR spectra and the results were compared to Cramér-Rao lower bounds derived after the nonparametrically estimated baselines were subtracted from the spectra. In particular, for high SNR and strong baseline contributions the proposed procedure yields a more appropriate characterization of the accuracy of parameter estimates than Crémer-Rao lower bounds, which tend to overestimate accuracy.     

Semiparametric estimation of single‐index hazard functions without proportional hazards

Summary This research develops a semiparametric kernel‐based estimator of hazard functions which does not assume proportional hazards. The maintained assumption is that the hazard functions depend on regressors only through a linear index. The estimator permits both discrete and continuous regressors, both discrete and continuous failure times, and can be applied to right‐censored data and to multiple‐risks data, in which case the hazard functions are risk‐specific. The estimator is root‐n consistent and asymptotically normally distributed. The estimator performs well in Monte Carlo experiments.     

乳熟期鲜食玉米穗不同部位碳水化合物的变化

对乳熟期鲜食玉米穗不同部位碳水化合物的变化研究表明，籽粒中可溶性总糖（TSC）质量分数为先增加后降低，蔗糖（SUC）和TSC的变化趋势相似，淀粉质量分数逐渐增加，可溶性酸性蔗糖转化酶的活性先增加后降低．穗轴中SUC及TSC质量分数在授粉后任何时期总是高于籽粒，淀粉质量分数远低于籽粒．苞皮中TSC与SUC质量分数缓慢减少，在籽粒灌浆初期积累有大量的TSC，为籽粒迅速灌浆做出了贡献，淀粉质量分数远低于穗轴．鲜食玉米采收后应低温放置或及时加工，以抑制糖快速转化为淀粉，防止鲜食玉米品质下降．     

核桃仁泥外敷治疗肌肉注射后皮下硬结的疗效观察

采用核桃仁泥外敷治疗１３８例（实验组）肌肉注射后皮下硬结，并与４０例（对照组）采用新鲜土豆片外敷硬结法比较。结果表明：实验组患者治疗１５天后Ⅰ度和Ⅱ度硬结治愈率分别为８１．１３％和４２．２５％，总有效率达９２．０３％，明显优于对照组（Ｐ＜０．００１）。     

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.     

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.     

最小二乘支持向量机算法在中医临床脉图参数-血压预测模型的应用

目的 提出基于最小二乘支持向量机（LSSVM）算法的学习模型,以提高中医临床血压数据预测的准确度和效率。方法 将LSSVM学习模型应用于中医临床血压数据预测。用LSSVM等式约束代替支持向量机不等式约束,将二次规划问题转化为线性方程求解问题,降低计算复杂性,加快算法收敛速度。收集320例患者的临床脉图参数及血压数据,以其中300例样本作为训练样本,训练得到LSSVM学习模型,以其余20例样本作为测试数据,用得到的LSSVM学习模型根据患者的脉图参数预测血压数据。结果 实验证明,LSSVM学习模型对血压数据有较好的预测准确度。其中基于多项式核函数的LSSVM学习模型较基于径向基核函数LSSVM学习模型表现出更好的学习和预测能力,基于多项式核函数的LSSVM学习模型中收缩压、舒张压、平均动脉压预测结果的平均预测误差分别为7.88%、8.40%、6.67%,低于基于径向基核函数的LSSVM学习模型的预测误差（分别为7.95%、9.70%、7.48%）。结论 本实验提出的基于LSSVM的学习模型仅通过患者的临床脉图参数就可预测患者血压数据,对中医学临床诊断有一定的参考价值。