Effect measures in non-parametric regression with interactions between continuous exposures

In many biomedical studies, interest is often attached to calculating effect measures in the presence of interactions between two continuous exposures. Traditional approaches based on parametric regression are limited by the degree of arbitrariness involved in transforming these exposures into categorical variables or imposing a parametric form on the regression function. In this paper, we present: (a) a flexible non-parametric method for estimating effect measures through generalized additive models including interactions; and (b) bootstrap techniques for (i) testing the significance of interaction terms, and (ii) constructing confidence intervals for effect measures. The validity of our methodology is supported by simulations, and illustrated using data from a study of possible risk factors for post-operative infection. This application revealed a hitherto unreported effect: for patients with high plasma glucose levels, increased risk is associated, not only with low, but also with high percentages of lymphocytes.     

Bayesian semi-parametric ROC analysis

This paper describes a semi-parametric Bayesian approach for estimating receiver operating characteristic (ROC) curves based on mixtures of Dirichlet process priors (MDP). We address difficulties in modelling the underlying distribution of screening scores due to non-normality that may lead to incorrect choices of diagnostic cut-offs and unreliable estimates of prevalence of the disease. MDP is a robust tool for modelling non-standard diagnostic distributions associated with imperfect classification of an underlying diseased population, for example, when a diagnostic test is not a gold standard. For posterior computations, we propose an efficient Gibbs sampling framework based on a finite-dimensional approximation to MDP. We show, using both simulated and real data sets, that MDP modelling for ROC curve estimation closely parallels the frequentist kernel density estimation (KDE) approach.     

Fried foods: a risk factor for laryngeal cancer?

The role of fried foods on laryngeal cancer risk was investigated in a case-control study from Italy and Switzerland on 527 cases and 1,297 hospital controls. A significant increased risk was found for high consumption of fried meat, fish, eggs and potatoes, with odds ratios of 1.6, 3.1, 1.9 and 1.9, respectively.     

不同产地枳壳麸炒品HPLC指纹图谱研究

目的:建立枳壳麸炒品HPLC指纹图谱分析方法。方法:用HPLC测定,以醋酸乙酯超声法提取样品,梯度洗脱,紫外检测波长320 nm,柱温30℃,流速1 mL.min^-1。以柚皮苷为参照物分析了10批不同产地的枳壳麸炒品,采用药典委员会颁布的中药色谱指纹图谱相似度评价系统2004 A进行评价,建立了共有模式,并以10批枳壳麸炒品相关系数评价指纹的相似性。结果:枳壳麸炒品液相色谱指纹图由11个特征峰构成指纹图谱的特征。所建立的指纹图谱具有稳定、重复的特点。结论:该方法准确、简单,适用于枳壳麸炒品分析,色谱指纹图谱可用于枳壳麸炒品的鉴别和质量控制。     

无机陶瓷膜微滤技术精制麻杏石甘汤和热毒净颗粒的研究

目的：对无机陶瓷膜微滤技术精制中药复方水提液的效果进行初步评价。方法：用孔径为0．2μm的无机陶瓷膜对麻木叶石甘汤和热净颗粒的提液进行微滤,对水提液微滤前后在性状、总固体、指标成分等方面的变化进行对比分析。结果：复方水提液微滤前均为浑浊液体,微滤后成为颜色变浅的澄明液体,麻杏石甘汤总固体去除率为16．12％,麻黄碱和苦杏仁甙的损失率分别为20．11％和18．16％。热毒净颗粒总固体去除率为27．58％,绿原酸和大黄酸的损失率分别为18．28％和22．86％。结论：无机陶瓷膜微滤技术对中药复方水提液具有较好的澄清除杂效果。     

Chemical Composition and Physical Characteristics of Unpopped Popcorn Hybrids

Kernel number, individual kernel weight, individual kernel volume, crude fat, crude protein, reducing sugars, starch content, amylose/amylopectin ratio and fatty acid composition were determined for six popcorn hybrids, grown in Colorado or Nebraska and harvested in 1997. The popcorn hybrids were A358W, 353W, BKH, 019, 1601 and 5501. Also, popcorn hybrids were categorized based on their popped color and popped shape as hybrid types, where A358W and 353W were white butterfly-type popcorn hybrids, BKH and 019 were yellow butterfly-type hybrids and 1601 and 5501 were yellow mushroom-type hybrids. Yellow mushroom hybrids showed significantly fewer number of kernels than the white and yellow butterfly-type hybrids. The number of kernels per 250 g of popcorn hybrids at 14% moisture content ranged from 1454 to 2219. Individual kernel weight of the popcorn hybrids ranged from a low mean of 112.6 mg for hybrid 353W to a high mean of 172.0 mg for hybrid 1601. Individual kernel volume of popcorn hybrids containing 14% moisture ranged from a low mean of 0.130 cm³for hybrid 353W to a high mean of 0.200 cm³for hybrid 1601. The popcorn hybrids contained 3.8–4.6% crude fat, 8.1–10.5% crude protein, 0.07–0.23% reducing sugars and 61.0–67.9% starch, in which 27.0–28.5% of the starch was amylose. Individual kernel volume (size of the kernel) was correlated with starch content (r=0.84). Fat content was highly correlated with starch content (r=−0.82). Popcorn hybrids contained on an average approximately 12.6% palmitic, 2.0% stearic, 25.5% oleic, 58.4% linoleic and 1.5% linolenic acids, respectively. The major fatty acids in the popcorn hybrids were linoleic and oleic acids.     

Fast One-Dimensional Convolution with General Kernels Using Sum-of-Exponential Approximation

Based on the recently-developed sum-of-exponential (SOE) approximation, in this article, we propose a fast algorithm to evaluate the one-dimensional convolution potential $φ(x)=K∗ρ=∫^1_{0}K(x−y)ρ(y)dy$ at (non)uniformly distributed target grid points {$x_i$}$^M_{i=1}$, where the kernel $K(x)$ might be singular at the origin and the source density function $ρ(x)$ is given on a source grid ${{{y_i}}}^N_{j=1}$ which can be different from the target grid. It achieves an optimal accuracy, inherited from the interpolation of the density $ρ(x)$, within $\mathcal{O}(M+N)$ operations. Using the kernel's SOE approximation $K_{ES}$, the potential is split into two integrals: the exponential convolution $φ_{ES}$=$K_{ES}∗ρ$ and the local correction integral $φ_{cor}=(K−K_{ES})∗ρ$. The exponential convolution is evaluated via the recurrence formula that is typical of the exponential function. The local correction integral is restricted to a small neighborhood of the target point where the kernel singularity is considered. Rigorous estimates of the optimal accuracy are provided. The algorithm is ideal for parallelization and favors easy extensions to complicated kernels. Extensive numerical results for different kernels are presented.     

Feature selection and multi-kernel learning for sparse representation on a manifold

Sparse representation has been widely studied as a part-based data representation method and applied in many scientific and engineering fields, such as bioinformatics and medical imaging. It seeks to represent a data sample as a sparse linear combination of some basic items in a dictionary. Gao et al. (2013) recently proposed Laplacian sparse coding by regularizing the sparse codes with an affinity graph. However, due to the noisy features and nonlinear distribution of the data samples, the affinity graph constructed directly from the original feature space is not necessarily a reliable reflection of the intrinsic manifold of the data samples. To overcome this problem, we integrate feature selection and multiple kernel learning into the sparse coding on the manifold. To this end, unified objectives are defined for feature selection, multiple kernel learning, sparse coding, and graph regularization. By optimizing the objective functions iteratively, we develop novel data representation algorithms with feature selection and multiple kernel learning respectively. Experimental results on two challenging tasks, N-linked glycosylation prediction and mammogram retrieval, demonstrate that the proposed algorithms outperform the traditional sparse coding methods.