A small NGS–SNP panel of ancestry inference designed to distinguish African,European, East,and South Asian populations

In this study, a small set of ancestry informative SNPs was selected to differentiate African, European, East and South Asian samples, which was detected by the next-generation sequencing technology. A total of 127 Chinese Shaanxi Han individuals were collected as test samples. No statistically significant linkage disequilibrium of any pair of loci or departure from Hardy–Weinberg equilibrium of each locus was observed in the test population. To evaluate the performance of ancestry assignment using this panel, admixture analysis, principal component analysis, and likelihood ratio calculations were conducted based on the 1000 genome data and test samples. All populations were clustered into four groups, African, European, South and East Asian populations, which were consistent with their geographical origins. The pairwise fixation index (F_ST) between populations from different continental groups ranged from 0.140 to 0.621 with average 0.415, and the pairwise F_ST between populations from the same continent ranged from 0.000 to 0.056 with average 0.012. The likelihood ratio results of 125 test individuals indicated that their ancestry components were highly possible from East Asia. In conclusion, this small set of ancestry informative SNPs can be used as a reliable tool to identify and quantify ancestry components of unknown samples.     

基于微流控芯片的72重单核苷酸多态性族群推断系统的构建

建立了常染色体单核苷酸多态性（SNPs）复合检测芯片体系,用于未知个体的族群来源推断。基于前期筛选的74-SNPs组合,采用竞争性等位基因特异性聚合酶链式反应（PCR）的原理构建SNPs的扩增体系,在微流控芯片的每个反应孔内完成一个SNP的检测,通过高通量PCR微流控芯片实现了其中72个SNPs的同步检测。芯片的扩增由平板PCR仪完成,反应孔的荧光信号通过激光共聚焦扫描仪检测,最终通过提取的荧光值进行结果分析。使用该芯片检测获得52份样本的SNPs分型,分型结果的准确率为100%。以57个人群的3628个样本为参考人群数据库,进行20份样本的族群来源推断,推断结果与样本的实际来源一致。本研究建立的常染色体72个SNPs微流控芯片体系可以有效地进行SNP多态性分析检测,基于参考数据库,20份检测样本族群推断的准确性为100%。     

Neural Network Inference of Molar Mass Distributions of Peptides during Tailor-Made Enzymatic Hydrolysis of Cheese Whey: Effects of pH and Temperature

The fine-tuning of the enzymatic hydrolysis of proteins may provide a pool of peptides with predefined molar mass distributions. However, the complex mixture of molecules (peptides and amino acids) that results after the proteolysis of cheese whey turns unfeasible the assessment of individual species. In this work, a hybrid kinetic model for the proteolysis of whey by alcalase, multipoint-immobilized on agarose, is presented, which takes into account the influence of pH (8.0-10.4) and temperature (40-55 degrees C) on the activity of the enzyme. Five ranges of peptides' molar mass have their reaction rates predicted by neural networks (NNs). The output of NNs trained for constant pH and temperatures was interpolated, instead of including these variables in the input vector of a larger NN. Thus, the model complexity was reduced. Coupled to differential mass balances, this hybrid model can be employed for the online inference of peptides' molar mass distributions. Experimental kinetic assays were carried out using a pH-stat, in a laboratory-scale (0.03 L) batch reactor. The neural-kinetic model was integrated to a supervisory system of a bench-scale continually stirred tank reactor (0.5 L), providing accurate predictions during validation tests.     

高中生如何解元素推断题

研究高中生如何解元素推断题能加深对化学问题解决的认识.用整群随机抽样法,以海南省某中学高二和高三年级各一个班的学生为被试,以改编的高考元素推断题为工具,用NVivo分析学生解题的书面报告,发现元素的解法和顺序有多种类型.这种多样性由不同的问题表征方式和解决策略引起.对课程与教学的启示:采用科学表征,并促进学生科学表征建...     

Objective Bayesian analysis of the Frechet stress–strength model

Several reference priors and a general form of matching priors are derived for a stress–strength system, and it is concluded that none of the reference priors is a matching prior. The study shows that the matching prior performs better than Jeffreys prior and reference priors in meeting the target coverage probabilities.     

广义矩阵分布下多元回归模型的贝叶斯推断

考虑具有奇异矩阵椭球等高分布误差的多元线性回归模型的贝叶斯统计推断,在非信息先验下得到了系数矩阵关于Hausdorff测度的后验边缘分布和未来观察值的预测分布,并得到了一类特殊奇异矩阵椭球等高分布下误差协方差矩阵的后验边缘分布.对于具有奇异矩阵正态分布误差的多元线性回归模型,在广义正态-逆Wishart共轭先验下得到了类似的后验边缘分布和预测分布结果.在上述两种先验分布下,回归系数矩阵的后验边缘分布和预测分布是双奇异矩阵t分布,这种分布具有关于Hausdorff测度的精确密度.结果表明,在非信息先验下,回归系数矩阵的后验边缘分布和未来观察值的预测分布在奇异矩阵椭球等高分布类中具有稳健性.     

Mixtures of Gamma Distributions With Applications

This article proposes a Bayesian density estimation method based upon mixtures of gamma distributions. It considers both the cases of known mixture size, using a Gibbs sampling scheme with a Metropolis step, and unknown mixture size, using a reversible jump technique that allows us to move from one mixture size to another. We illustrate our methods using a number of simulated datasets, generated from distributions covering a wide range of cases: single distributions, mixtures of distributions with equal means and different variances, mixtures of distributions with different means and small variances and, finally, a distribution contaminated by low-weighted distributions with different means and equal, small variances. An application to estimation of some quantities for a M/G/1 queue is given, using real E-mail data from CNR-IAMI.     

Bayesian Inversion of Piecewise Affine Operators in a Gaussian Framework

Piecewise affine inverse problems form a general class of nonlinear inverse problems. In particular inverse problems obeying certain variational structures, such as Fermat's principle in travel time tomography, are of this type. In a piecewise affine inverse problem a parameter is to be reconstructed when its mapping through a piecewise affine operator is observed, possibly with errors. A piecewise affine operator is defined by partitioning the parameter space and assigning a specific affine operator to each part. A Bayesian approach with a Gaussian random field prior on the parameter space is used. Both problems with a discrete finite partition and a continuous partition of the parameter space are considered.

The main result is that the posterior distribution is decomposed into a mixture of truncated Gaussian distributions, and the expression for the mixing distribution is partially analytically tractable. The general framework has, to the authors' knowledge, not previously been published, although the result for the finite partition is generally known.

Inverse problems are currently of large interest in many fields. The Bayesian approach is popular and most often highly computer intensive. The posterior distribution is frequently concentrated close to high-dimensional nonlinear spaces, resulting in slow mixing for generic sampling algorithms. Inverse problems are, however, often highly structured. In order to develop efficient sampling algorithms for a problem at hand, the problem structure must be exploited.

The decomposition of the posterior distribution that is derived in the current work can be used to develop specialized sampling algorithms. The article contains examples of such sampling algorithms. The proposed algorithms are applicable also for problems with exact observations. This is a case for which generic sampling algorithms tend to fail.     

Joint Monitoring of Several Types of Shocks in Normal Dynamic Linear Models: A Bayesian Decision Theoretic Approach

In this article we consider the sequential monitoring process in normal dynamic linear models as a Bayesian sequential decision problem. We use this approach to build a general procedure that jointly analyzes the existence of outliers, level changes, variance changes, and the development of local correlations. In addition, we study the frequentist performance of this procedure and compare it with the monitoring algorithm proposed in an earlier article.     

Conditionally Specified Space-Time Models for Multivariate Processes

This article proposes a class of conditionally specified models for the analysis of multivariate space-time processes. Such models are useful in situations where there is sparse spatial coverage of one of the processes and much more dense coverage of the other process(es). The dependence structure across processes and over space, and time is completely specified through a neighborhood structure. These models are applicable to both point and block sources; for example, multiple pollutant monitors (point sources) or several county-level exposures (block sources). We introduce several computational tricks that are integral for model fitting, give some simple sufficient and necessary conditions for the space-time covariance matrix to be positive definite, and implement a Gibbs sampler, using Hybrid MC steps, to sample from the posterior distribution of the parameters. Model fit is assessed via the DIC. Predictive accuracy, over both time and space, is assessed both relatively and absolutely via mean squared prediction error and coverage probabilities. As an illustration of these models, we fit them to particulate matter and ozone data collected in the Los Angeles, CA, area in 1995 over a three-month period. In these data, the spatial coverage of particulate matter was sparse relative to that of ozone.