金钗石斛干鲜品的质量比较研究

[目的] 对金钗石斛干鲜品的化学成分及抗氧化活性进行比较研究,了解两者的差别,为金钗石斛干鲜品的区别应用提供依据。[方法] 采用气相色谱法和紫外-可见分光光度法进行成分含量测定比较。采用比色法及试剂盒测定干鲜品对羟自由基的清除作用及抗氧化能力。利用傅利叶变换红外光谱法对干鲜品粉末样品的全息化学特征谱进行比较。[结果] 金钗石斛鲜品的总生物碱和多糖含量及其对羟自由基的清除率和抗氧化能力均显著高于干品。金钗石斛干品与鲜品的红外谱图相似,在1 630 cm^-1、1 506 cm^-1、1 243 cm^-1处,贵州赤水的官渡、长期与红花3个基地的金钗石斛鲜品峰强均大于干品,各个产地金钗石斛鲜品的图谱与多糖对照品图谱的相关系数均大于干品。[结论] 金钗石斛干品与鲜品在化学成分及抗氧化活性方面存在着一定的差异。     

玉米/鹰嘴豆间作条件下不同施磷量对灌耕灰钙土无机磷组分的影响--测试文章

在甘肃引黄灌区的灌耕灰钙土区域,通过玉米/鹰嘴豆间作种植,采用蒋柏藩、顾益初石灰性土壤无机磷分级方法研究了施磷水平和间作种植方式对玉米、鹰嘴豆土壤无机磷素形态的影响。结果表明：研究区各无机磷形态含量顺序为O-P＞Ca₁₀-P＞Ca₈-P＞Al-P＞Fe-P＞Ca₂-P,施磷能够显著提高玉米和鹰嘴豆土壤中Ca₂-P、Ca₈-P、Al-P和Fe-P的含量,O-P和Ca₁₀-P的含量不随施磷量增加发生显著性变化;与单作相比,间作种植对鹰嘴豆土壤带各无机磷组分含量没有显著影响,但间作玉米种植带土壤各组分无机磷含量均低于单作土壤,其中不施磷肥处理下Fe-P、施纯磷 40 kg·hm^-2处理下Al-P及施纯磷 80 kg·hm^-2处理下Ca₁₀-P的含量显著低于单作土壤。     

Deleting the BAFF receptor TACI protects against systemic lupus erythematosus without extensive reduction of B cell numbers

B cell-activating factor of the TNF family (BAFF) is an essential B cell survival factor. However, high levels of BAFF promote systemic lupus erythematosus (SLE) in mice and humans. Belimumab (anti-human BAFF) limits B cell survival and is approved for use in patients with SLE. Surprisingly, the efficacy of rituximab (anti-human CD20) in SLE remains controversial, despite depleting B cells more potently than belimumab. This raises the question of whether B cell depletion is really the mechanism of action of belimumab. In BAFF transgenic mice, SLE development is T cell-independent but relies on innate activation of B cells via TLRs, and TLR expression is modulated by the BAFF receptor TACI. Here, we show that loss of TACI on B cells protected against BAFF-mediated autoimmune manifestations while preserving B cells, suggesting that loss of BAFF signaling through TACI rather than loss of B cells may underpin the effect of belimumab in the clinic. Therefore, B cell-sparing blockade of TACI may offer a more specific and safer therapeutic alternative to broad B cell depletion in SLE.     

稳态视觉诱发脑机接口特征提取方法的研究

目的为有效提取稳态视觉诱发脑机接口(SSVEP-based brain-computer interface)中的脑电特征,提出一种基于独立成分分析(independent component analysis,ICA)与希尔伯特黄变换(HilbertHuang transform,HHT)的特征提取方法。方法对采集得到的脑电信号进行带通滤波,得到预处理的脑电信号,将滤波后的脑电信号作为ICA的输入,经过ICA实现独立成分的快速获取。引入HHT对独立成分进行经验模态分解(EMD),分解获取固有模态函数(intrinsic mode function,IMF),通过对IMF的频域分析,即可提取出特征。将ICA和HHT法同WT法、ICA法以及HHT法等常用的特征提取方法在频域、功率谱估计、在时间消耗等多方面进行比对分析。结果频域分析和功率谱估计中,本文提出的方法明显优于WT法和ICA法,略优于HHT法。时间消耗方面,本文提出的方法略优于HHT法。结论基于ICA和HHT的特征提取方法在稳态视觉诱发脑机接口的特征提取中是可行的,并有效去除了脑电信号中的噪声。     

Cardiac Genetic Predisposition in Sudden Infant Death Syndrome

Background

Sudden infant death syndrome (SIDS) is a leading cause of postneonatal mortality. Genetic heart diseases (GHDs) underlie some cases of SIDS.

Integrative modeling of multi‐platform genomic data under the framework of mediation analysis

Given the availability of genomic data, there have been emerging interests in integrating multi‐platform data. Here, we propose to model genetics (single nucleotide polymorphism (SNP)), epigenetics (DNA methylation), and gene expression data as a biological process to delineate phenotypic traits under the framework of causal mediation modeling. We propose a regression model for the joint effect of SNPs, methylation, gene expression, and their nonlinear interactions on the outcome and develop a variance component score test for any arbitrary set of regression coefficients. The test statistic under the null follows a mixture of chi‐square distributions, which can be approximated using a characteristic function inversion method or a perturbation procedure. We construct tests for candidate models determined by different combinations of SNPs, DNA methylation, gene expression, and interactions and further propose an omnibus test to accommodate different models. We then study three path‐specific effects: the direct effect of SNPs on the outcome, the effect mediated through expression, and the effect through methylation. We characterize correspondences between the three path‐specific effects and coefficients in the regression model, which are influenced by causal relations among SNPs, DNA methylation, and gene expression. We illustrate the utility of our method in two genomic studies and numerical simulation studies. Copyright © 2014 John Wiley & Sons, Ltd.     

Pathway‐Based Association Study of Multiple Candidate Genes and Multiple Traits Using Structural Equation Models

There is increasing interest in the joint analysis of multiple genetic variants from multiple genes and multiple correlated quantitative traits in association studies. The classical approach involves testing univariate associations between genotypes and phenotypes and correcting for multiple testing that results in loss of power to detect associations. In this paper, we propose modeling complex relationships between genetic variants in candidate genes and measured correlated traits using structural equation models (SEM), taking advantage of prior knowledge on clinical and genetic pathways. We adopt generalized structured component analysis (GSCA) as an approach to SEM and develop a single association test between multiple genetic variants in a gene and a set of correlated traits, taking into account all available data from other genes and other traits. The performance of this test is investigated by simulations. We apply the proposed method to the Quebec Child and Adolescent Health and Social Survey (1999) data to investigate genetic associations with cardiovascular disease‐related traits.     

Global Analysis of Methylation Profiles From High Resolution CpG Data

New high throughput technologies are now enabling simultaneous epigenetic profiling of DNA methylation at hundreds of thousands of CpGs across the genome. A problem of considerable practical interest is identification of large scale, global changes in methylation that are associated with environmental variables, clinical outcomes, or other experimental conditions. However, there has been little statistical research on methods for global methylation analysis using technologies with individual CpG resolution. To address this critical gap in the literature, we develop a new strategy for global analysis of methylation profiles using a functional regression approach wherein we approximate either the density or the cumulative distribution function (CDF) of the methylation values for each individual using B‐spline basis functions. The spline coefficients for each individual are allowed to summarize the individual's overall methylation profile. We then test for association between the overall distribution and a continuous or dichotomous outcome variable using a variance component score test that naturally accommodates the correlation between spline coefficients. Simulations indicate that our proposed approach has desirable power while protecting type I error. The method was applied to detect methylation differences, both genome wide and at LINE1 elements, between the blood samples from rheumatoid arthritis patients and healthy controls and to detect the epigenetic changes of human hepatocarcinogenesis in the context of alcohol abuse and hepatitis C virus infection. A free implementation of our methods in the R language is available in the Global Analysis of Methylation Profiles (GAMP) package at http://research.fhcrc.org/wu/en.html .     

iGWAS: Integrative Genome‐Wide Association Studies of Genetic and Genomic Data for Disease Susceptibility Using Mediation Analysis

Genome‐wide association studies (GWAS) have been a standard practice in identifying single nucleotide polymorphisms (SNPs) for disease susceptibility. We propose a new approach, termed integrative GWAS (iGWAS) that exploits the information of gene expressions to investigate the mechanisms of the association of SNPs with a disease phenotype, and to incorporate the family‐based design for genetic association studies. Specifically, the relations among SNPs, gene expression, and disease are modeled within the mediation analysis framework, which allows us to disentangle the genetic effect on a disease phenotype into two parts: an effect mediated through a gene expression (mediation effect, ME) and an effect through other biological mechanisms or environment‐mediated mechanisms (alternative effect, AE). We develop omnibus tests for the ME and AE that are robust to underlying true disease models. Numerical studies show that the iGWAS approach is able to facilitate discovering genetic association mechanisms, and outperforms the SNP‐only method for testing genetic associations. We conduct a family‐based iGWAS of childhood asthma that integrates genetic and genomic data. The iGWAS approach identifies six novel susceptibility genes (MANEA, MRPL53, LYCAT, ST8SIA4, NDFIP1, and PTCH1) using the omnibus test with false discovery rate less than 1%, whereas no gene using SNP‐only analyses survives with the same cut‐off. The iGWAS analyses further characterize that genetic effects of these genes are mostly mediated through their gene expressions. In summary, the iGWAS approach provides a new analytic framework to investigate the mechanism of genetic etiology, and identifies novel susceptibility genes of childhood asthma that were biologically meaningful.     

北京市房山区气象因素与手足口病发病的关系及预测模型

目的研究北京市房山区气象因素与手足口病发病的关系,为预测北京市房山区手足口病发病情况提供依据。方法收集房山区2009~2013年手足口病月发病数和同期气象因素资料,建立数据库,使用excel2010与SAS9.2相结合进行相关分析、主成分分析和多元线性回归分析。结果手足口病月发病率与平均最低气温、平均气温、平均最高气温、平均相对湿度、平均水汽压、降水量、降水日数呈正相关;与平均气压呈负相关。主成分多元线性回归分析结果显示,对手足口病发病数影响大的气象因素依次为平均气压、气温、日照时数、平均水汽压、降水日数、平均风速、降水量和平均相对湿度。结论房山区手足口病发病率有明显的季节性,降水日数多、降水量大、高温、高湿、高水汽压、低气压气候易引起手足口病的高发;主成分多元线性回归建立的预测方程可预测手足口病的月发病数。