影响小麦粒重的农艺性状,生理指标的主成分分析

本研究对长江中游麦区11个小麦品种（系）的13个与粒重密切相关的农艺性状和生理指标进行了主成分分析。13个原始指标综合成为4个独立的新指标;主成分1,光合同化因子;主成分2,籽粒灌浆速率与构成因子;主成分3,籽粒灌浆持续期与产量构成因子;主成分4,籽粒灌浆参数综合因子。主成分值与粒重相关分析表明,主成分4及主成分2与粒重有极显著的正相关。通过逐步回归分析,建立了小麦粒重与主成分值之间的回归方程。以主成分值为指标的系数聚类,可将十一个品种分为三大类型。不同类型品种主成分值的大小和相互协调方式不同,直接导致品种（系）粒重不同。     

用主成分分析法分析金翅夜蛾亚科各性状对分类的重要性和性状变异方向

本文在“金翅夜蛾亚科的数值分类研究”的基础上用主成分分析的结果对金翅夜蛾亚科分类的性状做进一步分析,说明各性状对分类的重要性和性状的变异方向,并对用协方差矩阵和相关矩阵进行的主成分分析结果进行比较,说明对本问题（分类指标全是定性指标）适宜用协方差矩阵做主成分分析。     

The contribution of size and shape variation to patterns of sexual dimorphism of the human femur

The present research was undertaken to determine the relationship between patterns of generalized intrapopulational variation determined from principal components analysis and patterns of sexual dimorphism determined from Student's t and discriminant function analysis. The analysis was based upon 17 measurements of 97 femurs from a Middle Mississippian Amerindian population. The results of the principal components analysis indicated that the 17 original measurements could be represented as four principal component variates. Inspection of component loadings lent support to the contention that the first principal component reflected variation in general size while components two to four reflected variation in femoral shape. Analysis of the relationship between principal component loading and male-female differences reflected in Student's t demonstrated a high (0.97) positive correlation between absolute magnitude of loading in the first principal component and magnitude of Student's t. As a result, discriminant analyses of the femur, utilizing univariate criteria for the inclusion of variables, have been biased in the direction of size variation. Subsequent stepwise discriminant function analyses demonstrated that an adequate discriminant model must reflect all dimensions of morphological variation at the intrapopulational level.     

Effects of mutation, truncation, and temperature on the folding kinetics of a WW domain

The purpose of this work is to show how mutation, truncation, and change of temperature can influence the folding kinetics of a protein. This is accomplished by principal component analysis of molecular-dynamics-generated folding trajectories of the triple β-strand WW domain from formin binding protein 28 (FBP28) (Protein Data Bank ID: 1E0L) and its full-size, and singly- and doubly-truncated mutants at temperatures below and very close to the melting point. The reasons for biphasic folding kinetics [i.e., coexistence of slow (three-state) and fast (two-state) phases], including the involvement of a solvent-exposed hydrophobic cluster and another delocalized hydrophobic core in the folding kinetics, are discussed. New folding pathways are identified in free-energy landscapes determined in terms of principal components for full-size mutants. Three-state folding is found to be a main mechanism for folding the FBP28 WW domain and most of the full-size and truncated mutants. The results from the theoretical analysis are compared to those from experiment. Agreements and discrepancies between the theoretical and experimental results are discussed. Because of its importance in understanding protein kinetics and function, the diffusive mechanism by which the FBP28 WW domain and its full-size and truncated mutants explore their conformational space is examined in terms of the mean-square displacement and principal component analysis eigenvalue spectrum analyses. Subdiffusive behavior is observed for all studied systems.     

Interspecific relations of African crocidurine shrews (Mammalia: Soricidae) based on multivariate analysis of mandibular data

Eighteen graded non-metric characters and 30 measurements of the mandible and lower dentition were used to investigate the grouping of African species of the genera Crocidura, Suncus and Sylvisorex. Seventy-seven taxa (species, subspecies and synonyms), including four European species, were represented by one to nine mandibles (147 mandibles in all), and the means of four Pleistocene African species were also included. The metric data were subjected to principal component and canonical variate analyses, and the non-metric data to principal component analysis. Grouping was detected by comparing the results of the three analyses. Six groups are recognized within the genus Crocidura , typified by C. fumosa, C. turba, C. russula, C. fuscomurina, C. hirta and C. flavescens. Sylvisorex is divisible into two groups, typified by S. lunaris and S. granti. The African species of Suncus (but not the introduced species, S. murinus ) form a single group, which is linked to the Sylvisorex granti group. Suncus is closely related to, or convergent with, the Crocidura fuscomurina group. The first non-metric principal component, the second metric principal component and the second canonical variate are significantly correlated. It is considered that these together represent a general trend of evolutionary advance. Phylogenetic relations are discussed in the light of this; Crocidura may be diphylefic.     

Harmonic and anharmonic aspects in the dynamics of BPTI: a normal mode analysis and principal component analysis.

A comparison is made between a 200-ps molecular dynamics simulation in vacuum and a normal mode analysis on the protein bovine pancreatic trypsin inhibitor (BPTI) in order to elucidate the dual aspects of harmonicity and anharmonicity in the dynamics of proteins. The molecular dynamics trajectory is analyzed using principal component analysis, an effective harmonic analysis suited for comparison with the results from the normal mode analysis. The results suggest that the first principal component shows qualitatively different behavior from higher principal components and is associated with apparent barrier crossing events on an anharmonic conformational energy surface. The higher principal components appear to have probability distributions that are well approximated by Gaussians, indicating harmonicity. Eliminating the contribution from the first principal component reveals a great deal of correspondence between the 2 methods. This correspondence, however, involves a factor of 2, as the variances of the distribution of the higher principal components are, on average, roughly twice those found from the normal mode analysis. A model is proposed to reconcile these results with those from previous analyses.     

A multivariate study of the external measurements of the Sperm whale (Physeter catodon)

The methods of principal component analysis and of canonical analysis are applied to data of the external measurements of the Sperm whale (Physeter catodon). The principal component analysis summarizes the main features of the data in the first four components and suggests those characters of prime importance while the canonical analysis permits comparisons between those whales caught at several locations.     

GeoPCA: a new tool for multivariate analysis of dihedral angles based on principal component geodesics

The GeoPCA package is the first tool developed for multivariate analysis of dihedral angles based on principal component geodesics. Principal component geodesic analysis provides a natural generalization of principal component analysis for data distributed in non-Euclidean space, as in the case of angular data. GeoPCA presents projection of angular data on a sphere composed of the first two principal component geodesics, allowing clustering based on dihedral angles as opposed to Cartesian coordinates. It also provides a measure of the similarity between input structures based on only dihedral angles, in analogy to the root-mean-square deviation of atoms based on Cartesian coordinates. The principal component geodesic approach is shown herein to reproduce clusters of nucleotides observed in an η-θ plot. GeoPCA can be accessed via http://pca.limlab.ibms.sinica.edu.tw.     

Quantification of sleepiness through principal component analysis of the electroencephalographic spectrum

Although circadian and sleep research has made extraordinary progress in the recent years, one remaining challenge is the objective quantification of sleepiness in individuals suffering from sleep deprivation, sleep restriction, and excessive somnolence. The major goal of the present study was to apply principal component analysis to the wake electroencephalographic (EEG) spectrum in order to establish an objective measure of sleepiness. The present analysis was led by the hypothesis that in sleep-deprived individuals, the time course of self-rated sleepiness correlates with the time course score on the 2nd principal component of the EEG spectrum. The resting EEG of 15 young subjects was recorded at 2-h intervals for 32-50 h. Principal component analysis was performed on the sets of 16 single-Hz log-transformed EEG powers (1-16 Hz frequency range). The time course of self-perceived sleepiness correlated strongly with the time course of the 2nd principal component score, irrespective of derivation (frontal or occipital) and of analyzed section of the 7-min EEG record (2-min section with eyes open or any of the five 1-min sections with eyes closed). This result indicates the possibility of deriving an objective index of physiological sleepiness by applying principal component analysis to the wake EEG spectrum.     

Free energy landscape of a biomolecule in dihedral principal component space: sampling convergence and correspondence between structures and minima

Dihedral principal component analysis (dPCA) has recently been developed and shown to display complex features of the free energy landscape of a biomolecule that may be absent in the free energy landscape plotted in principal component space due to mixing of internal and overall rotational motion that can occur in principal component analysis (PCA) [Mu et al., Proteins: Struct Funct Bioinfo 2005;58:45-52]. Another difficulty in the implementation of PCA is sampling convergence, which we address here for both dPCA and PCA using a tetrapeptide as an example. We find that for both methods the sampling convergence can be reached over a similar time. Minima in the free energy landscape in the space of the two largest dihedral principal components often correspond to unique structures, though we also find some distinct minima to correspond to the same structure.     

An approach to incorporate linkage disequilibrium structure into genomic association analysis

In this study, we propose to use the principal component analysis (PCA) and regression model to incorporate linkage disequilibrium (LD) in genomic association data analysis. To accommodate LD in genomic data and reduce multiple testing, we suggest performing PCA and extracting the PCA score to capture the variation of genomic data, after which regression analysis is used to assess the association of the disease with the principal component score. An empirical analysis result shows that both genotype-basod correlation matrix and haplotype-based LD matrix can produce similar results for PCA. Principal component score seems to be more powerful in detecting genetic association because the principal component score is quantitatively measured and may be able to capture the effect of multiple loci.     

广西英罗港红树植物群落的非线性排序

采用主分量分析 (PCA)、无偏主分量 (DPC)和非度量多维调节 (NMDS)等方法对广西英罗港 2 2个红树植物群落样地进行了排序。PCA和 DPC分析结果表明 ,取样数据具有明显的非线性结构。通过 NMDS分析 ,得到二维 NMDS排序格局 ,它能较好地反映了红树植物群落与环境因子之间的相互关系。     

木耳栽培种质资源的数量分类研究

在木耳栽培种质资源农艺性状调查的基础上,应用数量分类学中的Q型聚类分析法对20个木耳菌株进行分类研究,并对14个农艺性状进行R型聚类分析和主成分分析。结果表明：Q型聚类将20个木耳菌株在欧氏距离6.29处依据子实体朵型性状分为簇生型菌株和菊花型菌株两大类群,菊花型类群在欧式距离4.79处依据生育期性状的原基发生类型划分为分散型和集中型两个亚群;R型聚类表明菌丝体性状（1个）、生育期性状（2个）、子实体性状（8个）等11个农艺性状间相关性较强;主成分分析中,发现子实体背面皱褶、耳片数、原基发生时间、子实体朵型、干耳背面颜色等5个性状是14个农艺性状的第1主成分,贡献率高达62.26%,把第1主成分命名为朵型-生育期构成因子,作为种质评价的指标。     

Visualisation of synchronous firing in multi-dimensional spike trains

The gravity transform algorithm is used to study the dependencies in firing of multi-dimensional spike trains. The pros and cons of this algorithm are discussed and the necessity for improved representation of output data is demonstrated. Parallel coordinates are introduced to visualise the results of the gravity transform and principal component analysis (PCA) is used to reduce the quantity of data represented whilst minimising loss of information.     

Characterization of exopolymers of aquatic bacteria by pyrolysis-mass spectrometry

Exopolymers from a diverse collection of marine and freshwater bacteria were characterized by pyrolysis-mass spectrometry (Py-MS). Py-MS provides spectra of pyrolysis fragments that are characteristic of the original material. Analysis of the spectra by multivariate statistical techniques (principal component and canonical variate analysis) separated these exopolymers into distinct groups. Py-MS clearly distinguished characteristic fragments, which may be derived from components responsible for functional differences between polymers. The importance of these distinctions and the relevance of pyrolysis information to exopolysaccharide function in aquatic bacteria is discussed.     

Characterization of exopolymers of aquatic bacteria by pyrolysis-mass spectrometry.

Exopolymers from a diverse collection of marine and freshwater bacteria were characterized by pyrolysis-mass spectrometry (Py-MS). Py-MS provides spectra of pyrolysis fragments that are characteristic of the original material. Analysis of the spectra by multivariate statistical techniques (principal component and canonical variate analysis) separated these exopolymers into distinct groups. Py-MS clearly distinguished characteristic fragments, which may be derived from components responsible for functional differences between polymers. The importance of these distinctions and the relevance of pyrolysis information to exopolysaccharide function in aquatic bacteria is discussed.     

夜蛾科(鳞翅目)卵分类性状的定量分析

本文用主成分分析法对夜蛾科卵的分类性状进行定量分析,用以说明各主成分的生物含义和各变量对分类的重要性．     

Quantification of Sleepiness Through Principal Component Analysis of the Electroencephalographic Spectrum

Although circadian and sleep research has made extraordinary progress in the recent years, one remaining challenge is the objective quantification of sleepiness in individuals suffering from sleep deprivation, sleep restriction, and excessive somnolence. The major goal of the present study was to apply principal component analysis to the wake electroencephalographic (EEG) spectrum in order to establish an objective measure of sleepiness. The present analysis was led by the hypothesis that in sleep-deprived individuals, the time course of self-rated sleepiness correlates with the time course score on the 2nd principal component of the EEG spectrum. The resting EEG of 15 young subjects was recorded at 2-h intervals for 32–50?h. Principal component analysis was performed on the sets of 16 single-Hz log-transformed EEG powers (1–16?Hz frequency range). The time course of self-perceived sleepiness correlated strongly with the time course of the 2nd principal component score, irrespective of derivation (frontal or occipital) and of analyzed section of the 7-min EEG record (2-min section with eyes open or any of the five 1-min sections with eyes closed). This result indicates the possibility of deriving an objective index of physiological sleepiness by applying principal component analysis to the wake EEG spectrum. (Author correspondence: putilov@ngs.ru)     

Quantitative analysis of correlations among flower traits in Gerbera hybrida, Compositae. III. Genetic variability and structure of principal component traits

 A sample of 36 flower traits consisting of six morphological categories in the Davis population of gerbera was restructured into phenotypic and genetic principal component traits. The first 5 phenotypic principal component traits accounted for 62% of the total phenotypic variance of the 36 traits and have moderate to high heritablities. The first 5 genetic principal component traits account for 97% of total genetic variance and all have high heritability. Morphological structure of these component traits suggest an underlying process identified by the first genetic principal component involving largely trans and disk floret traits. The results of this study indicate that the quantitative genetic structure of the gerbera flower is controlled by a few independent components and that principal component analysis is a useful tool to reveal variation in this structure. These composite traits are heritable and are expected to respond to selection. Received: 20 September 1997 / Accepted: 19 January 1998