农业生态系统能量分析

农业生态系统的能量分析从能流着手进行农业生态系统的功能量化分析,是农业生态系统重要的研究方法之一。本文简要回顾讨论了农业生态系统能量分析研究的历史和进展。着重讨论了能值分析方法对传统能量分析方法的新发展,分析了农业生态系统能量分析目前存在的问题,并就其进一步发展方向进行了探讨能值分析方法用生产某种能量或物质所直接或间接耗用的太阳能值量来衡量该能量或物质的价值,不仅在概念上而且在算法上都是原有能量分析方法的新发展．发展到能值分析阶段的农业生态系统的能量分析方法亦存有自身的不足之处,如能值转换率的计算过于繁复,能值指标体系缺乏统一性及系统可持续发展的能值综合评价指标缺乏等。这些问题的解决与能值最大功率的明晰化、能值分析与能量及耗散分析的结合、与物质分析及景观结构分析的结合等研究等共同构成了农业生态系统能量分析的几大发展方向。     

On the Effect of Correlation Between Plots in Randomized Block Experiments

Correlation between adjacent plots in field experiments are common. In this paper the effect of correlation on the usual analysis of variance in a randomized block design is studied. A generalised analysis of variance where correlation is taken into consideration, and the analysis of covariance where the residuals of the neighbouring plots are used as covariates, is discussed. Uniformity trials are used as a basis of a Monte-Carlo study. The generalised analysis improved the power of the tests. The analysis of covariance method was not better than the usual analysis of variance method.     

Biosensors and flow injection analysis.

Combining flow injection analysis with a biosensor is a novel biosensing process which has allowed speedy and accurate analysis. Diagnostic analysis is the most important application for biosensing flow injection analysis, but other applications include bioprocess monitoring, analysis of food and agricultural products, as well as environmental analysis. In addition, the analysis of compounds, such as explosives and abused drugs, and monitoring of Salmonella, the microorganism that causes food poisoning, have been reported.     

A new analysis method for the comparison of 2 temporal patterns,with indices of association

A new method for evaluating the degree of association between the behavioural patterns of 2 individuals is described and illustrated. It is proposed discrimination should be made between the unit for sampling (sample interval) and the unit for analysis. The length of the sample interval affects analysis accuracy, while the length of the analysis unit affects the analysis scale. A new “moving analysis unit (MAU) method” is proposed to divide an observation period into analysis units. The MAU method, with various lengths of analysis units, allows for various aspects of interaction between 2 individuals. As a similarity coefficient between the 2 temporal patterns, Iwao's ω (Iwao 1977) is proposed. Investigation of nonparametric confidence intervals of ω is performed with the bootstrap (Efron 1979), which is also applied for obtaining the null hypothesis distribution of ω. The MAU method, ω and the bootstrap are applied to the analysis of both insect and human communication.     

Recent progress in mass spectrometry for single-cell metabolomics

Metabolites are the end products of cellular vital activities and can reflect the state of cellular to a certain extent. Rapid change of metabolites and the low abundance of signature metabolites cause difficulties in single-cell detection, which is a great challenge in single-cell metabolomics analysis. Mass spectrometry (MS) is a powerful tool that uniquely suited to detect intracellular small-molecule metabolites and has shown good application in single-cell metabolite analysis. In this mini-review, we describe three types of emerging technologies for MS-based single-cell metabolic analysis in recent years, including nano-ESI-MS based single-cell metabolomics analysis, high-throughput analysis via flow cytometry, and cellular metabolic imaging analysis. These techniques provide a large amount of single-cell metabolic data, allowing the potential of MS in single-cell metabolic analysis is gradually being explored and is of great importance in disease and life science research.     

Excel在药学数据分析工作中的应用

目的：采用常用的电子表格处理系统Microsoft Excel解决药学实验过程中遇到的数据分析问题。方法：应用工作表函数中内置的统计函数,以线性回归为例说明源数据的输入与结果返回的具体操作过程;对数据分析工具中的＂描述统计＂工具、t检验与方差分析,结合具体实例对药学实践中遇到的药学统计实际问题进行综合探讨。结果：用Excel表中内置的统计函数工具进行线性回归分析,方法简单、结果可靠;Excel表中的数据分析工具适用于日常药学实验数据分析过程中遇到的描述统计分析、t检验与方差分析。Excel与其它数据处理软件相比具有操作快捷、使用方便、计算精确、易于学习与掌握等优点。结论：Excel友好的界面,清晰的统计分析结果,使医药工作者在使用Excel的数据分析软件时会感到非常的方便快捷,灵活实用,值得在药学实践中应用推广。     

Co-inertia analysis: an alternative method for studying species–environment relationships

• 1 Methods used for the study of species–environment relationships can be grouped into: (i) simple indirect and direct gradient analysis and multivariate direct gradient analysis (e.g. canonical correspondence analysis), all of which search for non-symmetric patterns between environmental data sets and species data sets; and (ii) analysis of juxtaposed tables, canonical correlation analysis, and intertable ordination, which examine species–environment relationships by considering each data set equally. Different analytical techniques are appropriate for fulfilling different objectives.
• 2 We propose a method, co-inertia analysis, that can synthesize various approaches encountered in the ecological literature. Co-inertia analysis is based on the mathematically coherent Euclidean model and can be universally reproduced (i.e. independently of software) because of its numerical stability. The method performs simultaneous analysis of two tables. The optimizing criterion in co-inertia analysis is that the resulting sample scores (environmental scores and faunistic scores) are the most covariant. Such analysis is particularly suitable for the simultaneous detection of faunistic and environmental features in studies of ecosystem structure.
• 3 The method was demonstrated using faunistic and environmental data from Friday (Freshwater Biology 18, 87-104, 1987). In this example, non-symmetric analyses is inappropriate because of the large number of variables (species and environmental variables) compared with the small number of samples.
• 4 Co-inertia analysis is an extension of the analysis of cross tables previously attempted by others. It serves as a general method to relate any kinds of data set, using any kinds of standard analysis (e.g. principal components analysis, correspondence analysis, multiple correspondence analysis) or between-class and within-class analyses.

     

Principles for Griffing's combining ability analysis

Griffing's diallel analysis is used in plant improvement programs to identify superior parents for crossing and for characterizing general, specific, and reciprocal effects. Eight different model/method combinations are commonly used in the analysis. The accuracy of the analysis is improved by using the appropriate model and method. In many instances, Model One with Method Three or Four is the most appropriate for obtaining unbiased estimates of combining abilities and gene action. The effective use of Griffing's analysis and the influence of several factors on this analysis are discussed. A personal computer program on this analysis is also made available to interested readers.     

Using the maximum test statistic in the two-period crossover clinical trial

In a two-period crossover trial where residual carryover is suspected, it is often advised that first-period data only be used in an analysis appropriate for a parallel design. However, it has been shown (Willan and Pater, 1986, Biometrics 42, 593-599) that the crossover analysis is more powerful than the parallel analysis if the residual carryover, expressed as a proportion of treatment effect, is less than 2- square root of 2(1 - rho), where rho is the intrasubject correlation coefficient. Choosing between the analyses based on the empirical evaluation of this condition is equivalent to choosing the analysis with the larger corresponding test statistic. Approximate nominal significance levels are presented that maintain the desired level when basing the analysis on the maximum test statistic. Furthermore, the power and precision of the analysis based on the maximum test statistic are compared to the crossover and parallel analyses.     

A Relational Analysis of American Kinship Terminology

As an alternative to traditional semantic analysis using class-product logic ("Componential analysis"), it is proposed that the possibilities of analysis in terms of relative products be explored in appropriate lexical-taxonomic domains, particularly kinship. In such analyses the order of components is intrinsic to meaning. The process is illustrated by an analysis of American kinship terminology.     

IKONOS影像景观格局特征尺度的小波与半方差分析

建立不同方向和尺度下景观格局的小波分析方法,并以IKONOS PAN 1m 影像为例,对城市景观和农田景观格局进行了实例研究,并与半方差函数的分析结果进行比较分析。试验结果发现试验区的城市景观图像表现出不同层次和方向的结构特征,主要是阴影－单个楼房－楼房与道路的组合3个尺度的特征,分别出现在2m,16m和128m以及256m分析尺度上,农田景观主要是树木行－农田2个层次和方向尺度特征组合,分别为2m,128m和256m。半方差分析结果对试验区景观结构的多层次和方向不敏感,但可发现景观结构的总体平均特征。城市景观总体结构在空间上的变程为135m,反映的是楼房与道路组合的总体平均特征;农田景观总体结构在空间上的变程为152m。通过改变半方差函数的分析范围和计算间距可以克服半方差对多尺度分析的不足,如该试验通过缩小城市景观和农田分析范围,也发现城市景观有一个在空间结构上15m的变程,与小波分析的特征尺度16m非常接近,而农田景观出现空穴效应的周期一半长度大约为2m,正好是小波分析出种植树木的行距结构特征尺度的大小。这要分析者对研究有一定的先验知识才能够做到选择合适的分析范围,而这恰是景观格局特征尺度分析服务的目的。通过试验区研究证明小波分析是一非常有应用前景的景观格局研究方法,与半方差分析相比,对在解释多尺度结构和不同方向的结构具有明显的优势,且不受分析数据统计平稳假设的约束,但由于对图像分解是基于2进制,导致分析尺度以2的幂函数变化,不像半方差分析可通过理论模型拟合,对特征尺度的估计可以是连续的。     

生态模型的灵敏度分析

灵敏度分析用于定性或定量地评价模型参数误差对模型结果产生的影响,是模型参数化过程和模型校正过程中的有用工具,具有重要的生态学意义．灵敏度分析包括局部灵敏度分析和全局灵敏度分析．局部灵敏度分析只检验单个参数的变化对模型结果的影响程度;全局灵敏度分析则检验多个参数的变化对模型运行结果总的影响,并分析每一个参数及其参数之间相互作用对模型结果的影响．目前,在对生态模型的灵敏度分析中,越来越倾向于使用全局灵敏度分析的方法．但国内仍多采用局部灵敏度分析方法,很少采用全局灵敏度分析方法．文中详细论述了局部灵敏分析和全局灵敏度分析的主要方法(一次变换法、多元回归法、Morris法、Sobol’法、傅里叶幅度灵敏度检验法和傅里叶幅度灵敏度检验扩展法),希望能为国内生态模型的发展提供一个比较完善的灵敏度分析方法库．结合国内外的灵敏度分析发展现状,指出联合灵敏度研究、灵敏度共性研究及空间直观景观模型的灵敏度分析将为生态模型灵敏度分析研究中的热点和难点．     

群落二维格局分析的两种方法

群落二维空间格局研究能够更好地揭示群落的特征,但在分析方法上有较大的困难。用垂直相交的两条样带在两个方向上同时取样的二维取样法,获得数据,用DCA排序与格局分析相结合的方法,得到群落不同格局规模斑块的长、宽及面积,实现二维格局研究。在亚高山草甸群落格局研究中应用表明,两个方法均是有效的。     

Wavelet analysis of ecological time series

Wavelet analysis is a powerful tool that is already in use throughout science and engineering. The versatility and attractiveness of the wavelet approach lie in its decomposition properties, principally its time-scale localization. It is especially relevant to the analysis of non-stationary systems, i.e., systems with short-lived transient components, like those observed in ecological systems. Here, we review the basic properties of the wavelet approach for time-series analysis from an ecological perspective. Wavelet decomposition offers several advantages that are discussed in this paper and illustrated by appropriate synthetic and ecological examples. Wavelet analysis is notably free from the assumption of stationarity that makes most methods unsuitable for many ecological time series. Wavelet analysis also permits analysis of the relationships between two signals, and it is especially appropriate for following gradual change in forcing by exogenous variables.     

Robust discriminant analysis and its application to identify protein coding regions of rice genes

Identification of protein coding regions is fundamentally a statistical pattern recognition problem. Discriminant analysis is a statistical technique for classifying a set of observations into predefined classes and it is useful to solve such problems. It is well known that outliers are present in virtually every data set in any application domain, and classical discriminant analysis methods (including linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA)) do not work well if the data set has outliers. In order to overcome the difficulty, the robust statistical method is used in this paper. We choose four different coding characters as discriminant variables and an approving result is presented by the method of robust discriminant analysis.     

微弱发光分析技术原理及应用实例(一)

微弱发光分析技术近年得到迅速发展,在自由基、活性氧分析、化学发光分析、生物的超微弱发光分析、发光免疫分析、生物发光分析等领域得到广泛应用.简要介绍了微弱发光分析技术的测量原理,并以一些研究成果为实例讲解如何应用微弱发光分析技术进行研究和实践.     

新基因功能研究的策略与方法

开展新基因功能研究在当前生物医学研究中变得日益重要.基因功能的研究策略主要包括新基因的生物信息学及体内表达规律分析、功能获得与功能失活策略研究、基因编码产物相互作用蛋白的研究.同时,结合近年来国内外的一些新进展,对与各种策略相关的一些技术方法也进行了介绍.     

MAGMA: Generalized Gene-Set Analysis of GWAS Data

By aggregating data for complex traits in a biologically meaningful way, gene and gene-set analysis constitute a valuable addition to single-marker analysis. However, although various methods for gene and gene-set analysis currently exist, they generally suffer from a number of issues. Statistical power for most methods is strongly affected by linkage disequilibrium between markers, multi-marker associations are often hard to detect, and the reliance on permutation to compute p-values tends to make the analysis computationally very expensive. To address these issues we have developed MAGMA, a novel tool for gene and gene-set analysis. The gene analysis is based on a multiple regression model, to provide better statistical performance. The gene-set analysis is built as a separate layer around the gene analysis for additional flexibility. This gene-set analysis also uses a regression structure to allow generalization to analysis of continuous properties of genes and simultaneous analysis of multiple gene sets and other gene properties. Simulations and an analysis of Crohn’s Disease data are used to evaluate the performance of MAGMA and to compare it to a number of other gene and gene-set analysis tools. The results show that MAGMA has significantly more power than other tools for both the gene and the gene-set analysis, identifying more genes and gene sets associated with Crohn’s Disease while maintaining a correct type 1 error rate. Moreover, the MAGMA analysis of the Crohn’s Disease data was found to be considerably faster as well.     

Identifying key factors using λ contribution analysis

1. Key factor analysis is widely used as the first step in analysing census data to identify factors responsible for population change, but is generally considered to be flawed. The conceptual problems can be overcome by assessing the effects of variation in the life-history parameters on population growth rate, λ. We refer to this as λ-contribution analysis. The difference from key factor analysis is that now each life history parameter is weighted by the sensitivity of λ to that parameter. The rationale for this modification is that population growth rate is the best available measure of population change.
2. The advantages of the new method are: that it correctly assesses the effects of life history parameters on population growth rate; that birth rates are included in the analysis in a natural way without making arbitrary assumptions about birth rate mortalities; that post-reproductive individuals who do not contribute to population growth rate are zero-weighted; and that the analysis can be applied to populations with overlapping generations.
3. It is proposed that λ-contribution analysis should replace conventional key-factor analysis as the first step in a wider analysis of population change and density dependence. λ-contribution analysis also links census studies of natural populations with the use of life-table response experiments.     

Fundamentals of cDNA microarray data analysis

Microarray technology is a powerful approach for genomics research. The multi-step, data-intensive nature of this technology has created an unprecedented informatics and analytical challenge. It is important to understand the crucial steps that can affect the outcome of the analysis. In this review, we provide an overview of the contemporary trend on various main analysis steps in the microarray data analysis process, which includes experimental design, data standardization, image acquisition and analysis, normalization, statistical significance inference, exploratory data analysis, class prediction and pathway analysis, as well as various considerations relevant to their implementation.