天然5,8过氧化甾醇的结构、生理活性、波谱特征和合成方法

本文对目前已知的天然5,8内过氧化物甾醇的结构、生理活性、波谱性质作了综述,并对其合成方法作了讨论.     

分子生物学技术在中药鉴定中的应用及探讨

对PCR及其延伸技术在中药鉴定中的应用作了总结,对该方法的优势和注意事项作了说明,并对该技术在中药品种和质量鉴定中的应用前景作了展望。     

谷胱甘肽测定方法研究进展

对分光光度法、酶法、荧光法、电化学法以及和HPLC等测定方法的原理、发展和应用作了详细叙述,同时对其它方法也作了简单介绍,最后对谷胱甘肽测定方法的发展作了展望。     

RNA扩增的研究进展

RNA扩增是伴随着基因芯片技术的应用而发展起来的一项技术,但不仅仅应用于芯片杂交。本文对RNA扩增的技术方法进行了综述,重点介绍了mRNA的线性扩增、指数扩增以及两者相结合的扩增方法以及优缺点,对microRNA的扩增方法也作了介绍,并对将来的发展作了展望。     

植物组织培养物的超低温保存

对超低温保存技术的研究历史、基本原理、方法、影响因素和国内外植物组织培养物超低温保存的研究进展作了介绍,并对这一问题的前景作了展望.     

抗体酶研究进展

本文回顾了抗体酶的研究历史,讨论了诱导法、基因工程法、拷贝法和化学修饰法等制备抗体酶的方法,并重点论述了生物筛选法和纯化学筛选法等抗体酶的筛选方法。对抗体酶研究的应用性前景,抗体酶催化效率的理论基础和天然抗体酶作了探讨。对抗体酶研究取得的成果作了介绍。     

仙客来的生物学特性和施肥管理

对仙客来的生物学特性、需肥特性。以及影响仙客来吸收养分的因素和施肥方法作了系统的介绍，对仙客来盆花在施肥管理方面作了系统的分析研究，对高品质仙客来的生产栽培具有重要的指导意义。     

基因间互作、孟德尔遗传规律及高考命题分析

基因间互作是高考的常见考点,相关题目难度较大。统计了近几年高考对基因间互作的考查,介绍了基因间互作与孟德尔遗传规律的关系及控制相关试题难度的方法,并就基因间互作的教学提出了建议。     

磷脂酰甘油合成代谢研究

从磷脂酰甘油分子的结构特点和研究方法出发,重点介绍了磷脂酰甘油合成代谢近年来的研究进展,对磷脂酰甘油在光合作用中的作用作了综述,并对磷脂酰甘油的研究问题和前景作了展望。     

双列杂交F1代加倍单倍体的遗传模型

双列杂交法已广泛地应用于性状的遗传研究,但均局限于无非等位基因互作的性状．本文在前人研究的基础上提出了应用双列杂交F_1代加倍单倍体检验双基因互作模型适合性的方法,并对遗传参数的估算作了进一步的研究．     

Fourier methods for biosequence analysis.

Novel methods are discussed for using fast Fourier transforms for DNA or protein sequence comparison. These methods are also intended as a contribution to the more general computer science problem of text search. These methods extend the capabilities of previous FFT methods and show that these methods are capable of considerable refinement. In particular, novel methods are given which (1) enable the detection of clusters of matching letters, (2) facilitate the insertion of gaps to enhance sequence similarity, and (3) accommodate to varying densities of letters in the input sequences. These methods use Fourier analysis in two distinct ways. (1) Fast Fourier transforms are used to facilitate rapid computation. (2) Fourier expansions are used to form an 'image' of the sequence comparison.     

Ensemble Methods for MiRNA Target Prediction from Expression Data

Background

microRNAs (miRNAs) are short regulatory RNAs that are involved in several diseases, including cancers. Identifying miRNA functions is very important in understanding disease mechanisms and determining the efficacy of drugs. An increasing number of computational methods have been developed to explore miRNA functions by inferring the miRNA-mRNA regulatory relationships from data. Each of the methods is developed based on some assumptions and constraints, for instance, assuming linear relationships between variables. For such reasons, computational methods are often subject to the problem of inconsistent performance across different datasets. On the other hand, ensemble methods integrate the results from individual methods and have been proved to outperform each of their individual component methods in theory.

Results

In this paper, we investigate the performance of some ensemble methods over the commonly used miRNA target prediction methods. We apply eight different popular miRNA target prediction methods to three cancer datasets, and compare their performance with the ensemble methods which integrate the results from each combination of the individual methods. The validation results using experimentally confirmed databases show that the results of the ensemble methods complement those obtained by the individual methods and the ensemble methods perform better than the individual methods across different datasets. The ensemble method, Pearson+IDA+Lasso, which combines methods in different approaches, including a correlation method, a causal inference method, and a regression method, is the best performed ensemble method in this study. Further analysis of the results of this ensemble method shows that the ensemble method can obtain more targets which could not be found by any of the single methods, and the discovered targets are more statistically significant and functionally enriched. The source codes, datasets, miRNA target predictions by all methods, and the ground truth for validation are available in the Supplementary materials.     

Comparing and predicting between several methods of measurement

In studies designed to compare different methods of measurement where more than two methods are compared or replicate measurements by each method are available, standard statistical approaches such as computation of limits of agreement are not directly applicable. A model is presented for comparing several methods of measurement in the situation where replicate measurements by each method are available. Measurements are viewed as classified by method, subject and replicate. Models assuming exchangeable as well as non-exchangeable replicates are considered. A fitting algorithm is presented that allows the estimation of linear relationships between methods as well as relevant variance components. The algorithm only uses methods already implemented in most statistical software.     

Pitfalls in the statistical analysis of microbiome amplicon sequencing data

Microbiome data are characterized by several aspects that make them challenging to analyse statistically: they are compositional, high dimensional and rich in zeros. A large array of statistical methods exist to analyse these data. Some are borrowed from other fields, such as ecology or RNA-sequencing, while others are custom-made for microbiome data. The large range of available methods, and which is continuously expanding, means that researchers have to invest considerable effort in choosing what method(s) to apply. In this paper we list 14 statistical methods or approaches that we think should be generally avoided. In several cases this is because we believe the assumptions behind the method are unlikely to be met for microbiome data. In other cases we see methods that are used in ways they are not intended to be used. We believe researchers would be helped by more critical evaluations of existing methods, as not all methods in use are suitable or have been sufficiently reviewed. We hope this paper contributes to a critical discussion on what methods are appropriate to use in the analysis of microbiome data.     

Philosophy and phylogenetic inference: a comparison of likelihood and parsimony methods in the context of Karl Popper's writings on corroboration

Advocates of cladistic parsimony methods have invoked the philosophy of Karl Popper in an attempt to argue for the superiority of those methods over phylogenetic methods based on Ronald Fisher's statistical principle of likelihood. We argue that the concept of likelihood in general, and its application to problems of phylogenetic inference in particular, are highly compatible with Popper's philosophy. Examination of Popper's writings reveals that his concept of corroboration is, in fact, based on likelihood. Moreover, because probabilistic assumptions are necessary for calculating the probabilities that define Popper's corroboration, likelihood methods of phylogenetic inference--with their explicit probabilistic basis--are easily reconciled with his concept. In contrast, cladistic parsimony methods, at least as described by certain advocates of those methods, are less easily reconciled with Popper's concept of corroboration. If those methods are interpreted as lacking probabilistic assumptions, then they are incompatible with corroboration. Conversely, if parsimony methods are to be considered compatible with corroboration, then they must be interpreted as carrying implicit probabilistic assumptions. Thus, the non-probabilistic interpretation of cladistic parsimony favored by some advocates of those methods is contradicted by an attempt by the same authors to justify parsimony methods in terms of Popper's concept of corroboration. In addition to being compatible with Popperian corroboration, the likelihood approach to phylogenetic inference permits researchers to test the assumptions of their analytical methods (models) in a way that is consistent with Popper's ideas about the provisional nature of background knowledge.     

Spatial analysis of two-species interactions

Summary In this paper, I present and discuss some methods for the analysis of univariate and bivariate spatial point pattern data. Examples of such data in ecology include x-y coordinates of organisms in mapped field plots. I illustrate the methods with analyses of data from mapped field plots on Mount St. Helens, Washington state, USA. The statistical methods I emphasize are graphical methods that rely on analysis of distances between organisms. Hypothesis testing for methods like these is easily done using Monte Carlo methods, which I also discuss. For both univariate and bivariate analyses, I find that second-order methods such as K-function plots are often preferable to first-order methods (i.e., QQ-plots). However, for multivariate analyses, these second-order methods are more sensitive to small sample sizes than first-order analyses.     

Cytochemistry of mature angiosperm pollen

The problems involved in applying histochemical and cytochemical methods to mature angiosperm pollen for bright light and fluorescence microscopy are discussed. These methods can be used for general examination or to reveal particular structures or groups of substances. The main methods of testing pollen viability and germinability based on stains and semiquantitative methods are also reviewed. The main methods of staining and their applications are summarised.     

蛋白质质谱分析的无标记定量算法研究进展

作为发现疾病相关生物标志物的重要途径,定量研究已成为蛋白质组学的热点问题.随着实验方法的发展和改进,定量数据处理算法也在不断更新和完善.将现有的无标记定量方法归纳为需要/不需要鉴定结果两类方法,分析比较了两类方法的异同及优缺点,详细讨论了所涉及的主要算法,总结了一些常用的无标记定量软件及对应的网络资源.展望了无标记定量数据分析的未来研究方向.     

A Benchmark of Parametric Methods for Horizontal Transfers Detection

Horizontal gene transfer (HGT) has appeared to be of importance for prokaryotic species evolution. As a consequence numerous parametric methods, using only the information embedded in the genomes, have been designed to detect HGTs. Numerous reports of incongruencies in results of the different methods applied to the same genomes were published. The use of artificial genomes in which all HGT parameters are controlled allows testing different methods in the same conditions. The results of this benchmark concerning 16 representative parametric methods showed a great variety of efficiencies. Some methods work very poorly whatever the type of HGTs and some depend on the conditions or on the metrics used. The best methods in terms of total errors were those using tetranucleotides as criterion for the window methods or those using codon usage for gene based methods and the Kullback-Leibler divergence metric. Window methods are very sensitive but less specific and detect badly lone isolated gene. On the other hand gene based methods are often very specific but lack of sensitivity. We propose using two methods in combination to get the best of each category, a gene based one for specificity and a window based one for sensitivity.     

Caveat emptor: rank transform methods and interaction

When distributional assumptions for analysis of variance are suspect, and nonparametric methods are unavailable, ecologists frequently employ rank transformation (RT) methods. The technique replaces observations by their ranks, which are then analysed using standard parametric tests. RT methods are widely recommended in statistics texts and in manuals for packages like SAS and IMSL. They are robust and powerful for the analysis of additive factorial designs. Recently, however, RT methods have been found to be grossly inappropriate for use with non-additive models. This severe limitation remains largely unreported outside of the theoretical statistics literature. Our goal is to explain this shortcoming of RT methods.