Compact and accurate linear and nonlinear autoregressive moving average model parameter estimation using Laguerre functions

A linear and nonlinear autoregressive moving average (ARMA) identification algorithm is developed for modeling time series data. The algorithm uses Laguerre expansion of kernals (LEK) to estimate Volterra-Wiener kernals. However, instead of estimating linear and nonlinear system dynamics via moving average models, as is the case for the Volterra-Wiener analysis, we propose an ARMA model-based approach. The proposed algorithm is essentially the same as LEK, but this algorithm is extended to include past values of the ouput as well. Thus, all of the advantages associated with using the Laguerre function remain with our algorithm; but, by extending the algorithm to the linear and nonlinear ARMA model, a significant reduction in the number of Laguerre functions can be made, compared with the Volterra-Wiener approach. This translates into a more compact system representation and makes the physiological interpretation of higher order kernels easier. Furthermore, simulation results show better performance of the proposed approach in estimating the system dynamics than LEK in certain cases, and it remains effective in the presence of significant additive measurement noise.     

A unified method for rare variant analysis of gene-environment interactions

Advanced technology in whole-genome sequencing has offered the opportunity to comprehensively investigate the genetic contribution, particularly rare variants, to complex traits. Several region-based tests have been developed to jointly model the marginal effect of rare variants, but methods to detect gene-environment (GE) interactions are underdeveloped. Identifying the modification effects of environmental factors on genetic risk poses a considerable challenge. To tackle this challenge, we develop a method to detect GE interactions for rare variants using generalized linear mixed effect model. The proposed method can accommodate either binary or continuous traits in related or unrelated samples. Under this model, genetic main effects, GE interactions, and sample relatedness are modeled as random effects. We adopt a kernel-based method to leverage the joint information across rare variants and implement variance component score tests to reduce the computational burden. Our simulation studies of continuous and binary traits show that the proposed method maintains correct type I error rates and appropriate power under various scenarios, such as genotype main effects and GE interaction effects in opposite directions and varying the proportion of causal variants in the model. We apply our method in the Framingham Heart Study to test GE interaction of smoking on body mass index or overweight status and replicate the Cholinergic Receptor Nicotinic Beta 4 gene association reported in previous large consortium meta-analysis of single nucleotide polymorphism-smoking interaction. Our proposed set-based GE test is computationally efficient and is applicable to both binary and continuous phenotypes, while appropriately accounting for familial or cryptic relatedness.     

An enhanced machine learning tool for cis-eQTL mapping with regularization and confounder adjustments

Many expression quantitative trait loci (eQTL) studies have been conducted to investigate the biological effects of variants in gene regulation. However, these eQTL studies may suffer from low or moderate statistical power and overly conservative false-discovery rate. In practice, most algorithms for eQTL identification do not model the joint effects of multiple genetic variants with weak or moderate influence. Here we present a novel machine-learning algorithm, lasso least-squares kernel machine (LSKM-LASSO) that model the association between multiple genetic variants and phenotypic traits simultaneously with the existence of nongenetic and genetic confounding. With a more general and flexible framework for the estimation of genetic confounding, LSKM-LASSO is able to provide a more accurate evaluation of the joint effects of multiple genetic variants. Our simulations demonstrate that our approach outperforms three state-of-the-art alternatives in terms of eQTL identification and phenotype prediction. We then apply our method to genotype and gene expression data of 11 tissues obtained from the Genotype-Tissue Expression project. Our algorithm was able to identify more genes with eQTL than other algorithms. By incorporating a regularization term and combining it with least-squares kernel machine, LSKM-LASSO provides a powerful tool for eQTL mapping and phenotype prediction.     

离子法测定中蒙药材苦杏仁中六种阴离子的含量

目的: 建立一种同时测定中蒙药苦杏仁药材中F^－、Cl^－、Br^－、NO^－₃ 、SO2^－₄ 、PO^3－₄ 阴离子的含量测定方
法。方法:采用离子色谱法、阴离子色谱柱、氢氧化钠梯度淋洗、电导检测苦杏仁6 种无机阴离子。结果: F^－、
Cl^－、Br^－、NO^－₃ 、SO₂^－4 、PO^3－₄ 的加样回收率介于97%～ 102%,苦杏仁中F－、Cl－、Br－、NO－3 、SO2－4 、PO3－
4 重复性良好,含量分别为0．057%0．084%,0．063%,0．070%,0．30%,0．12%。结论: 离子色谱法可以同时检测苦杏仁中6 种阴离
子,灵敏度高,结果准确可行,为苦杏仁的质量控制提供了一种简便快速的方法．     

复方金荞麦颗粒治疗肺癌1000例临床疗效观察

目的 总结复方金荞麦颗粒治疗肺癌的疗效。方法 对1000例各种类型的肺癌患者采用复方金荞麦颗粒5g，3次·d^-1 口服，同时辅以中药汤剂治疗，3mo为1疗程，病情稳定后逐渐减量维持。结果本组1000例患者经3mo～15a以上治疗，基本治愈181例（18．1％），显效518例（51．8％），有效204例（20．4％），无效97例（9．7％），总有效率为90．3％。结论 复方金荞麦颗粒为抗肿瘤的纯中药制剂，具有明显抑制肿瘤细胞生长、增强免疫功能、提高患者生存质量等功效，尤其是对失去手术治疗机会、不能接受化疗和放疗的肺癌患者具有较好疗效。     

Variation of multifocal electroretinogram with axial length

INTRODUCTION: The first-order kernel response of multifocal electroretinogram (mfERG) decreases in myopia. A recent study indicates that the flash ERG is also reduced with increased axial length. The aim of this study was to investigate the variations in the first-order response (K1) and the first slice of second-order response (K2.1) across the retina for different axial lengths. METHODS: Thirty healthy subjects with axial length from 23.72 to 28.13 mm (spherical equivalent refractive errors from plano to -10.50 D) were recruited for mfERG measurement using VERIS 4.0. All subjects were fully corrected after cycloplegic refraction and pupils were dilated prior to mfERG recording. There is one trough, n1, and one peak, p1, in the K1 response and three troughs, n1, n2, n3, and three peaks, p1, p2, p3, in the K2.1 response. The amplitudes and implicit times of K1 and K2.1 responses were analysed to determine the characteristic of the responses across retina and the correlation to axial length. RESULTS: The amplitudes of p1 (in the first-order kernel-K1) decreased in the central region and the paracentral region (ring 3) as the axial length increased. The central retinal region showed high rates of reduction in both n1 and p1 (in K1). The amplitudes of n1p1 and n2p2 (in the first slice of the second-order kernel-K2.1) were reduced in the paracentral region (from ring 2 to ring 5) as axial length increased. The average n1 and p1 in K1, and n1p1 and n2p2 in K2.1 mfERG responses are decreased in amplitude by 6-10% per millimetre elongation of axial length. CONCLUSION: Eyes with longer axial lengths, usually with high myopia, have a weaker mfERG response and this attenuation is across the measured retina (from central to paracentral regions) but different kernel responses show a different pattern of attenuation at different retinal eccentricities. The weaker mfERG responses may be related to the morphological changes associated with increased axial length.     

Protective effects of apricot kernel oil on myocardium against ischemia–reperfusion injury in rats

The present study was undertaken to evaluate the potential cardioprotective effects of apricot kernel oil (AO) on the myocardial ischemia–reperfusion (IR) of rat model in vivo. The rats were divided into five groups: sham-operated, IR, low dose AO-treated IR (LD-AO + IR), medium dose AO-treated IR (MD-AO + IR) and high dose AO-treated IR (HD-AO + IR). All rats were provided with food and water ad libitum. The LD-AO + IR, MD-AO + IR and HD-AO + IR groups were given a daily dose of 2, 6 and 10 ml kg⁻¹ BW⁻¹ of AO, respectively, for 14 days prior to the IR operation. Tetrazolium chloride staining revealed that infarct size and the ratio of infarct weight to the total heart weight were decreased significantly in the three AO-treated groups compared to the IR group. The serum creatine kinase and aspartate aminotransferase activities also demonstrated similar beneficial effects. Myocardial catalase, superoxide dismutase, glutathione peroxidase, and constitutive nitric oxide synthase activities, as well as NO concentrations, were all increased, whereas malondialdehyde content and inducible nitric oxide synthase were decreased in AO-treated rats. These findings suggest that apricot kernel oil has potent cardioprotective effects, and could be developed as a nutriment for the treatment and prevention of myocardial infarcts.     

Kernel machine SNP-set analysis for censored survival outcomes in genome-wide association studies

In this article, we develop a powerful test for identifying single nucleotide polymorphism (SNP)-sets that are predictive of survival with data from genome-wide association studies. We first group typed SNPs into SNP-sets based on genomic features and then apply a score test to assess the overall effect of each SNP-set on the survival outcome through a kernel machine Cox regression framework. This approach uses genetic information from all SNPs in the SNP-set simultaneously and accounts for linkage disequilibrium (LD), leading to a powerful test with reduced degrees of freedom when the typed SNPs are in LD with each other. This type of test also has the advantage of capturing the potentially nonlinear effects of the SNPs, SNP-SNP interactions (epistasis), and the joint effects of multiple causal variants. By simulating SNP data based on the LD structure of real genes from the HapMap project, we demonstrate that our proposed test is more powerful than the standard single SNP minimum P-value-based test for association studies with censored survival outcomes. We illustrate the proposed test with a real data application.     

Prediction of muscle stretch receptor behavior using Wiener kernels

The input-output characteristics of muscle stretch receptors can be represented by a set of Wiener kernels estimated from responses to random length stimuli. We use these kernels to predict the responses of the receptor to deterministic length changes and show that significant non-linearities are present in receptor responses to length stimuli of only a fraction of a millimeter.