核极限学习机诊断乳腺良恶性肿块样病变

目的 采用核极限学习机（KELM）方法对乳腺良恶性肿块样病变进行分类,并评估其鉴别诊断效能。方法 对93例患者103个经术后病理或长期随访确诊的乳腺肿块样病变行MR检查。由1名低年资和1名高年资乳腺影像学诊断医师参照乳腺影像报告和数据系统（BI-RADS）第2版,选取12个MRI特征及临床特征,分别独立及采用KELM方法对乳腺病变进行良恶性分类,并计算诊断效能。结果 低年资和高年资医师使用KELM方法鉴别诊断乳腺良恶性病变的敏感度、特异度、准确率分别为0.88、0.89、0.91和0.93、0.91、0.92,AUC分别为0.84和0.89。低年资和高年资医师独立诊断的敏感度、特异度、准确率分别为0.91、0.74、0.86和0.90、0.85、0.92,AUC分别为0.83和0.90。结论 基于影像学特征及临床资料特征的KELM方法可辅助临床鉴别诊断乳腺肿块样良恶性病变,具有较理想的敏感度、特异度和准确率。     

杧果核仁的化学成分及体外对弱精症大鼠精子质量的影响

目的研究杧果Mangifera indica核仁的化学成分及体外对弱精症大鼠精子质量的影响。方法采用硅胶柱色谱、SephadexLH-20柱色谱和重结晶等分离纯化手段,分离纯化杧果核仁化学成分,进一步以~1H-NMR、~(13)C-NMR和质谱等有机波谱学方法鉴定各单体化合物的结构;采用雷公藤多苷ig大鼠建立弱精症大鼠模型,以此评价化合物体外对弱精症大鼠的精子活力和精子存活率的影响。结果从杧果核仁中得到9个化合物,分别鉴定为维生素E(1)、没食子酸甲酯(2)、山柰酚(3)、槲皮素(4)、3,4-O-异丙基莽草酸(5)、5-羟基-4-氧代吡喃-2-羧酸(6)、阿魏酸(7)、异槲皮苷(8)、1,2,3,4,6-五-O-没食子酰葡萄糖(9);化合物1、7、9在体外能使弱精症大鼠的精子活力和精子存活率增加。结论化合物1、3~6、8为首次从杧果核仁中分离得到,化合物1、7、9体外能够改善弱精症大鼠的精子质量。     

细辛中马兜铃酸A与黄樟醚的炮制减毒方法研究

目的研究细辛中马兜铃酸A与黄樟醚炮制减毒方法。方法采用HPLC法,对细辛生品及各个炮制品中黄樟醚、马兜铃酸A的量进行检测,以其量变化为评价指标比较不同炮制方法对毒性物质的去除效果。结果黄樟醚进样量在169.2~846.0μg(r=0.999 6)有良好线性关系,平均回收率为99.83%,RSD为1.67%;马兜铃酸A进样量在11.6~58.0 ng有良好线性关系,平均回收率为101.43%,RSD为1.25%。黄樟醚的炮制去除效果的大小顺序为盐制炒焦米泔水制碱制甘草制醋制姜制酒制碱醋制蜜制,其中盐制与炒焦对细辛中黄樟醚的去除率达到55%以上;马兜铃酸A的炮制去除效果的大小顺序为炒焦碱醋制盐制碱制醋制米泔水制甘草制酒制蜜制姜制,其中炒焦对细辛中马兜铃酸A的去除率达到60%以上。结论细辛炮制后,黄樟醚和马兜铃酸A的量都有不同程度的降低,其中以炒焦炮制最优。     

Bayesian inference for an emerging arboreal epidemic in the presence of control

The spread of Huanglongbing through citrus groves is used as a case study for modeling an emerging epidemic in the presence of a control. Specifically, the spread of the disease is modeled as a susceptible-exposed-infectious-detected-removed epidemic, where the exposure and infectious times are not observed, detection times are censored, removal times are known, and the disease is spreading through a heterogeneous host population with trees of different age and susceptibility. We show that it is possible to characterize the disease transmission process under these conditions. Two innovations in our work are (i) accounting for control measures via time dependence of the infectious process and (ii) including seasonal and host age effects in the model of the latent period. By estimating parameters in different subregions of a large commercially cultivated orchard, we establish a temporal pattern of invasion, host age dependence of the dispersal parameters, and a close to linear relationship between primary and secondary infectious rates. The model can be used to simulate Huanglongbing epidemics to assess economic costs and potential benefits of putative control scenarios.Under the threat of an emerging epidemic, it is imperative to estimate the key epidemiological parameters to predict the likelihood and extent of further spread, as well as to quantify the effectiveness of different strategies for disease control. In recent years, there has been an increasing emphasis on the use of spatiotemporal dynamical models to represent the dispersal and transmission processes of epidemics (1–4). Such models have an advantage over analyses that simply look at spatiotemporal associations in that inferences can be related directly to the underlying processes.The parameters for dynamical models are often estimated, however, from relatively limited and imprecise data. The data are limited because many disease events, for example, exposed or infectious status, are unobservable, and the data are imprecise because even observable events, such as symptom detection, are typically censored in time. In this context, Bayesian inference, supported by modern computational methods, is particularly tractable, and is increasingly the approach of choice (1, 5–9).There are several additional challenges in estimating the key epidemiological parameters for a previously unknown, emerging pathogen. For example, the patterns of spread used to estimate the dispersal and transmission parameters may also reflect the impacts of disease or vector control measures. Allowance for such measures, often applied empirically to manage an emerging pathogen, requires inferences to be made about the effects of control on pathogen spread and disease expression. Furthermore, there is likely to be unknown heterogeneity in the susceptibility of the host population through which the epidemic is spreading. There may also be a need to include seasonal forcing in the model and any effect of vector population dynamics. The host pathogen introduced in the next section exemplifies all these issues.     

Region‐based association tests for sequencing data on survival traits

Family‐based designs enriched with affected subjects and disease associated variants can increase statistical power for identifying functional rare variants. However, few rare variant analysis approaches are available for time‐to‐event traits in family designs and none of them applicable to the X chromosome. We developed novel pedigree‐based burden and kernel association tests for time‐to‐event outcomes with right censoring for pedigree data, referred to FamRATS (family‐based rare variant association tests for survival traits). Cox proportional hazard models were employed to relate a time‐to‐event trait with rare variants with flexibility to encompass all ranges and collapsing of multiple variants. In addition, the robustness of violating proportional hazard assumptions was investigated for the proposed and four current existing tests, including the conventional population‐based Cox proportional model and the burden, kernel, and sum of squares statistic (SSQ) tests for family data. The proposed tests can be applied to large‐scale whole‐genome sequencing data. They are appropriate for the practical use under a wide range of misspecified Cox models, as well as for population‐based, pedigree‐based, or hybrid designs. In our extensive simulation study and data example, we showed that the proposed kernel test is the most powerful and robust choice among the proposed burden test and the existing four rare variant survival association tests. When applied to the Diabetes Heart Study, the proposed tests found exome variants of the JAK1 gene on chromosome 1 showed the most significant association with age at onset of type 2 diabetes from the exome‐wide analysis.     

Infectious disease prediction with kernel conditional density estimation

Creating statistical models that generate accurate predictions of infectious disease incidence is a challenging problem whose solution could benefit public health decision makers. We develop a new approach to this problem using kernel conditional density estimation (KCDE) and copulas. We obtain predictive distributions for incidence in individual weeks using KCDE and tie those distributions together into joint distributions using copulas. This strategy enables us to create predictions for the timing of and incidence in the peak week of the season. Our implementation of KCDE incorporates 2 novel kernel components: a periodic component that captures seasonality in disease incidence and a component that allows for a full parameterization of the bandwidth matrix with discrete variables. We demonstrate via simulation that a fully parameterized bandwidth matrix can be beneficial for estimating conditional densities. We apply the method to predicting dengue fever and influenza and compare to a seasonal autoregressive integrated moving average model and HHH4, a previously published extension to the generalized linear model framework developed for infectious disease incidence. The KCDE outperforms the baseline methods for predictions of dengue incidence in individual weeks. The KCDE also offers more consistent performance than the baseline models for predictions of incidence in the peak week and is comparable to the baseline models on the other prediction targets. Using the periodic kernel function led to better predictions of incidence. Our approach and extensions of it could yield improved predictions for public health decision makers, particularly in diseases with heterogeneous seasonal dynamics such as dengue fever.     

碱裂解法快速提取中药炒制品DNA的研究

该文旨在探索一种适用于中药炒制品DNA快速提取的方法。以氢氧化钠,1%PVP40和1%Triton X-100配制成碱裂解缓冲液,Tris-HCl为中和液,经加热裂解和中和2步提取不同炒制方法制备的中药炮制品的DNA,选择2种方法对DNA进行纯化,并以纯化后DNA作为模板利用通用引物进行PCR扩增。结果表明优化碱裂解法可简单快速的提取出药材DNA,槐米炒制品DNA质量浓度为(420.61±123.91)g·L-1,且使用5%Chelex-100树脂纯化可以提高DNA提取浓度。研究结果证明优化碱裂解法适用于中药炒制品的DNA快速提取。     

Design of a multiple kernel learning algorithm for LS-SVM by convex programming

As a kernel based method, the performance of least squares support vector machine (LS-SVM) depends on the selection of the kernel as well as the regularization parameter (Duan, Keerthi, & Poo, 2003). Cross-validation is efficient in selecting a single kernel and the regularization parameter; however, it suffers from heavy computational cost and is not flexible to deal with multiple kernels. In this paper, we address the issue of multiple kernel learning for LS-SVM by formulating it as semidefinite programming (SDP). Furthermore, we show that the regularization parameter can be optimized in a unified framework with the kernel, which leads to an automatic process for model selection. Extensive experimental validations are performed and analyzed.     

A kernel-based framework to tensorial data analysis

Tensor-based techniques for learning allow one to exploit the structure of carefully chosen representations of data. This is a desirable feature in particular when the number of training patterns is small which is often the case in areas such as biosignal processing and chemometrics. However, the class of tensor-based models is somewhat restricted and might suffer from limited discriminative power. On a different track, kernel methods lead to flexible nonlinear models that have been proven successful in many different contexts. Nonetheless, a naïve application of kernel methods does not exploit structural properties possessed by the given tensorial representations. The goal of this work is to go beyond this limitation by introducing non-parametric tensor-based models. The proposed framework aims at improving the discriminative power of supervised tensor-based models while still exploiting the structural information embodied in the data. We begin by introducing a feature space formed by multilinear functionals. The latter can be considered as the infinite dimensional analogue of tensors. Successively we show how to implicitly map input patterns in such a feature space by means of kernels that exploit the algebraic structure of data tensors. The proposed tensorial kernel links to the MLSVD and features an interesting invariance property; the approach leads to convex optimization and fits into the same primal-dual framework underlying SVM-like algorithms.     

化浊解毒消溃煎治疗消化性溃疡的临床研究

目的：观察化浊解毒消溃煎治疗消化性溃疡（peptic ulcer,PU）浊毒内蕴证的临床疗效。方法：将符合纳入标准的病例80例,随机分为治疗组和对照组,治疗组服用化浊解毒消溃煎,对照组服用盐酸雷尼替丁胶囊,两组均服药42 d。观察治疗前后主要症状改善情况及胃镜、幽门螺杆菌（helicobacter pylori,HP）变化情况。结果：①临床症状疗效比较：治疗后治疗组临床症状有效率87.5%,对照组75.0%,两组有效率比较,治疗组优于对照组（P〈0.05）。②治疗前后主要症状积分比较：治疗组和对照组各症状均比治疗前有改善（P〈0.05）;治疗组与对照组症状改善比较,差异有统计学意义（P〈0.05）。③胃镜疗效比较：治疗后治疗组胃镜有效率95%,对照组胃镜有效率72.5%;两组胃镜有效率比较,差异有统计学意义（P〈0.05）,治疗组优于对照组。④对HP影响的比较：治疗组治疗前HP阳性31例,治疗后HP阳性8例,清除率74.2%;对照组治疗前HP阳性30例,治疗后HP阳性16例,清除率46.7%;两组清除率比较,差异有统计学意义（P〈0.05）。⑤复发率比较：治疗组复发率为9.5%,对照组为38.9%,两组复发率比较,差异有统计学意义（P〈0.05）。⑥安全性评估：治疗前后两组患者血、尿、便常规,心电图、肝肾功能均无明显变化,提示两组药物均无明显毒副作用。结论：化浊解毒消溃煎能改善消化性溃疡浊毒内蕴证的临床症状,抑刹HP,提高溃疡愈合质量,降低复发率,其疗效作用优于雷尼替丁胶囊组。