An NMR-based metabonomic investigation of the toxic effects of 3-trifluoromethyl-aniline on the earthworm Eisenia veneta

¹H NMR spectra of earthworms Eisenia veneta treated with 3-trifluoromethyl-aniline in a 72-h contact filter paper test have been analysed using pattern recognition techniques to determine the biochemical response. Various strategies for data reduction of the metabolite profile, and illustration by principal components analysis are applied and discussed. The use of mean principal components plots in simplifying group data representation and highlighting the dose-response function is demonstrated. Hierarchical cluster analysis, and cluster significance analysis of the principal components were also used to examine the relative distribution of dose groups. Identification and assignment of metabolite responses to toxicity were found via correlation coefficient-shift plots. As measured by the correlation coefficients alanine was the most significant metabolite, but increased levels of other amino acids such as glycine and asparagine were also observed. Further, elevated levels of glucose, and the citric acid cycle intermediates citrate and succinate were noted as potential biomarkers of toxicity. This work provides a basis for examining the biochemical response of invertebrates to toxins. This should provide a framework to examine toxicity effects of other halogenated aromatic pollutants to earthworms used as environmental monitors.     

An NMR-based metabonomic investigation of the toxic effects of 3-trifluoromethyl-aniline on the earthworm Eisenia veneta

¹H NMR spectra of earthworms Eisenia veneta treated with 3-trifluoromethyl-aniline in a 72-h contact filter paper test have been analysed using pattern recognition techniques to determine the biochemical response. Various strategies for data reduction of the metabolite profile, and illustration by principal components analysis are applied and discussed. The use of mean principal components plots in simplifying group data representation and highlighting the dose-response function is demonstrated. Hierarchical cluster analysis, and cluster significance analysis of the principal components were also used to examine the relative distribution of dose groups. Identification and assignment of metabolite responses to toxicity were found via correlation coefficient-shift plots. As measured by the correlation coefficients alanine was the most significant metabolite, but increased levels of other amino acids such as glycine and asparagine were also observed. Further, elevated levels of glucose, and the citric acid cycle intermediates citrate and succinate were noted as potential biomarkers of toxicity. This work provides a basis for examining the biochemical response of invertebrates to toxins. This should provide a framework to examine toxicity effects of other halogenated aromatic pollutants to earthworms used as environmental monitors.     

DISSIMILARITIES IN COGNITION OF FLAVOUR TERMS RELATED TO VARIOUS SENSORY LABORATORIES IN A MULTIVARIATE STUDY

A study of dissimilarities in cognitive perception of 20 common flavor terms was carried out by the Sensory Group of Norway. An average cognitive pattern of the flavor terms was revealed from multidimensional scaling (MDS) and cluster analysis (CLU). In general, small but interesting deviations between different sensory laboratories working with various food products were found by a multivariate pattern recognition technique based on principal component analysis (PCA). Suggestions for finding general reference standards for flavor terms are discussed.     

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。     

Synchronization regulation in a model of coupled neural masses

A model of coupled neural masses can generate seizure-like events and dynamics similar to those observed during interictal to ictal transitions and thus can be used for theoretical study of the control of epileptic seizures. In an effort to understand the mechanisms underlying epileptic seizures and how to avoid them, we added a control input to this model. Epileptic seizures are always accompanied by hypersynchronous firing of neurons, so research on synchronization among cortical areas is significant for seizure control. In this study, principal component analysis (PCA) was used to identify synchronization clusters composed of several neural masses. A method for calculating the synchronization cluster strength and participation rate is presented. The synchronization cluster strength can be used to identify synchronization clusters and the participation rate can be employed to identify neural masses that participate in the clusters. Each synchronization cluster is controlled as a whole using a proportional-integral-derivative (PID) controller. We illustrate these points using coupled neural mass models of synchronization to show their responses to increased (between node) coupling with and without control. Experiment results indicated that PID control can effectively regulate synchronization between neural masses and has the potential for seizure prevention.     

Bivariate genome scans incorporating factor and principal component analyses to identify common genetic components of alcoholism, event-related potential, and electroencephalogram phenotypes

Genetic components significantly contribute to the susceptibilities of alcoholism and its endophenotypes, such as event-related potential measures and electroencephalogram. An endophenotype is a correlated trait which identifies individuals at risk. Correlated traits could be influenced by shared genes. This study is intended to identify chromosome regions that may harbor common genetic loci contributing to alcoholism, event related potential measures and electroencephalogram. All 143 Collaborative Study on the Genetics of Alcoholism families with 1,614 individuals provided by the Genetic Analysis Workshop 14 were used for the analysis with aldx1 as an alcoholism diagnosis. We carried out factor and principal component analyses on the 12 event-related potentials, then bivariate genome scans on aldx1 and electroencephalogram (ecb21), as well as alcoholism and the principal component scores of the event-related potential measures. A univariate genome scan was also carried out on each trait. Factor and principal component analysis on the event-related potential measures showed that the 4 ttths and 4 ntths belong to one cluster (cluster 1), while the 4 ttdts belonged to another (cluster 2). From each cluster, one principal component was extracted and saved as pc1 (for cluster 1) and pc2 (for cluster 2). The results of genome scans revealed only one chromosome region, chromosome 4 q at about 100 cM, identified by several univariate genome scans including aldx1, ecb21, and pc2, and the evidence of linkage increased significantly in the bivariate genome scans of aldx1 and ecb21 and aldx1 and pc2. Our study suggests that the same quantitative trait locus on the chromosome 4 q region, where ADH3 is located, may influence the risk of alcoholism, variations of electroencephalogram, and the 4 ttdts of the event-related potential measures.     

Source Apportionment and Risk Assessment of Emerging Contaminants: An Approach of Pharmaco-Signature in Water Systems

This paper presents a methodology based on multivariate data analysis for characterizing potential source contributions of emerging contaminants (ECs) detected in 26 river water samples across multi-scape regions during dry and wet seasons. Based on this methodology, we unveil an approach toward potential source contributions of ECs, a concept we refer to as the “Pharmaco-signature.” Exploratory analysis of data points has been carried out by unsupervised pattern recognition (hierarchical cluster analysis, HCA) and receptor model (principal component analysis-multiple linear regression, PCA-MLR) in an attempt to demonstrate significant source contributions of ECs in different land-use zone. Robust cluster solutions grouped the database according to different EC profiles. PCA-MLR identified that 58.9% of the mean summed ECs were contributed by domestic impact, 9.7% by antibiotics application, and 31.4% by drug abuse. Diclofenac, ibuprofen, codeine, ampicillin, tetracycline, and erythromycin-H2O have significant pollution risk quotients (RQ>1), indicating potentially high risk to aquatic organisms in Taiwan.     

Principal Component Analysis on Morphological Variation in the Saussurea nipponica Complex (Compositae)

Abstract For the understanding of morphological differentiation and recognition of natural groups in the Saussurea nipponica complex, 440 individuals from 19 populations were examined, especially by using statistical methods. The variation range of 16 morphological characters within and between populations were analyzed not only separately but also synthetically by Duncan's multiple range test, principal component analysis, and cluster analysis. Of the 16 characters examined, characters concerning plant size (height and diameter of stem, size of involucre, etc.) and involucral bract (length of involucral bract and recurved part of involucral bract) are suggested to be important to recognize natural groups. Five groups are recognized by a complex pattern of the morphological characters. They can be defined multivariately as natural groups having indegenous habitat and distribution range, and considered as subspecies of S. nipponica.     

Comparison of Algorithms for an Electronic Nose in Identifying Liquors

When the electronic nose is used to identify different varieties of distilled liquors, the pattern recognition algorithm is chosen on the basis of the experience, which lacks the guiding principle. In this research, the different brands of distilled spirits were identified using the pattern recognition algorithms （principal component analysis and the artificial neural network）. The recognition rates of different algorithms were compared. The recognition rate of the Back Propagation Neural Network （BPNN） is the highest. Owing to the slow convergence speed of the BPNN, it tends easily to get into a local minimum. A chaotic BPNN was tried in order to overcome the disadvantage of the BPNN. The convergence speed of the chaotic BPNN is 75.5 times faster than that of the BPNN.     

Analytical pyrolysis-pattern recognition for the characterisation of leafy spurge (Euphorbia esula L.) biotypes

Curie-point pyrolysis-gas chromatography-pattern recognition was used to elucidate chemical variations within leafy spurge (Euphorbia spp.). Hierarchical cluster analysis (HCA) readily identified two major clusters corresponding to E. esula and E. cyparissias. The E. esula cluster further separated into three distinct subclusters. Results from principal components analysis (PCA) and fuzzy c-varieties (FCVPC) pattern recognition were similar, verifying the presence of three biotypes among the E. esula samples studied. It is suggested that analytical pyrolysis in combination with pattern recognition may predict the behaviour of biocontrol agents introduced into fields to control leafy spurge.     

Antioxidative defence and photoprotection in pine needles under field conditions. A multivariate approach to evaluate patterns of physiological responses at natural sites

Measurements of antioxidants and chloroplast pigments have been widely used as markers of stress and vitality of conifers in the field. However, due to the high variability of these data and the multiple environmental influences tress are exposed to, a quantification of physiological stress responses has only scarcely been possible. Physiological stress responses cannot be monitored by single stress markers, but are governed by many different interactions. The objective of this study was to evaluate patterns of biochemical stress markers in an objective and repeatable manner. For this purpose, a data set of 12 stress-physiological variables (chloroplast pigments, epoxidation state of the xanthophyll cycle, α-tocopherol, ascorbate and dehydroascorbate, GSH and GSSG) measured on field-grown Pinus canariensis needles (n = 90) was subjected to explorative statistical techniques. Four principal components (PC), which explained 80% of the variance of the original data, were extracted by principal component analysis. According to stress-physiological principles, complex responses were assigned to these PCs. Principal component 1 was positively affected by concentrations of a-tocopherol and total ascorbate, and negatively by the proportion of epoxides in the xanthophyll cycle and by α-carotene contents. Principal component 2 was composed of chlorophyll, lutein, neoxanthin, and β-carotene contents, PC 3 contained information about GSH concentrations and the proportions of GSSG and dehydoascorbate; and PC 4 mainly comprised the pool size of the xanthophyll cycle. These components could be ascribed physiological principles such as antioxidative response in chloroplasts (PC 1), pigment content (PC 2), or antioxidant regeneration (PC 3). Via cluster analysis a classification of samples was made based on the patterns of their PC scores. The resulting clusters represented typical physiological response patterns: Cluster 1 was related to initial stages of oxidative damage, cluster 2 to antioxidative responses, whereas cluster 3 represented healthy trees. The spatial distribution of members of these clusters among field plots revealed that different response patterns could be observed at the same plot, a fact that might be ascribed to small scale differences and/or individually differing resistances, and something that is frequently overlooked in the field.     

Biological guidelines for freshwater sediment based on BEnthic Assessment of SedimenT (the BEAST) using a multivariate approach for predicting biological state

Abstract This paper describes the first results for an alternative approach to the development of sediment quality criteria in the nearshore areas of the Laurentian Great Lakes. The approach is derived from methods developed in the United Kingdom for establishing predictive relationships between macroinvertebrate fauna and the physico-chemistry of riverine environments. The technique involves a multivariate statistical approach using (i) data on the structure of benthic invertebrate communities, (ii) functional responses (survival, growth and reproduction) in four sediment toxicity tests (bioassays) with benthic invertebrates; and (iii) selected environmental variables at 96 reference (‘clean’) sites in the nearshore areas of all five Great Lakes (Lakes Superior, Huron, Erie, Ontario and Michigan). Two pattern recognition techniques (using the computer software package PATN) are employed in the analysis: cluster analysis and ordination. The ordination vector scores from the original axes of the pattern analysis are correlated (using CORR in SAS) with environmental variables which are anticipated to be least affected by anthropogenic activities (e. g. alkalinity, depth, silt, sodium etc.). Multiple discriminant analysis (MDA) is used to relate the site groupings from the pattern analysis to the environmental variables and to generate a model that can be used to predict community assemblages and functional responses at new sites with unknown but potential contamination. The predicted community assemblages and functional responses are then compared with the actual benthic communities and responses at a site, and the need for remedial action is determined. The predictive capability of the discriminant model was confirmed by performing several validation runs on subsets of the data. An example of the use of the model for sediment in Collingwood Bay (an area of concern designated by the IJC in Georgian Bay, Lake Huron) is presented and the technique is shown to be more precise in determining the need for remediation than the currently used provincial sediment quality criteria based on Screening Level Concentration (SLC) and laboratory toxicity tests. The ultimate goal of the study is the development of a method to determine the need for, and the success of, remedial action and to predict what benthic communities should look like at a site if it were clean and what responses of organisms in sediment toxicity tests constitute an acceptable end-point.     

芦荟的HPLC指纹图谱建立、化学模式识别分析及其含量测定

采用Phenomenex C18色谱柱(250 mm×4.6 mm,5 μm),以甲醇-乙腈-0.3％磷酸水为流动相进行梯度洗脱,建立芦荟的指纹图谱.运用化学模式识别方法对不同产地芦荟药材质量控制方法进行评价.结果表明:12批芦荟HPLC指纹图谱共标定23个共有峰,并通过对照品指认其中6个成分;除了广西的3批药材之外,...     

Application of pattern recognition to monitoring fermentations ofBacillus amyloliquefaciens

Summary Pattern recognition techniques were applied to analytical data to distinguish abnormal from normal microbial fermentations usingBacillus amyloliquefaciens as a model system. Patterns of fermentation end products during growth ofB. amyloliquefaciens were obtained from HPLC analysis of broth samples. Data were also obtained from fermentations using other bacterial species, strains, and environmental conditions, and were compared with the model data set. The bacterial species cultured includedB. subtilus, B. licheniformis, andEscherichia coli. Environmental variables included acration and temperature. The chromatographic patterns were compared by using hierarchical cluster and principal component analysis to obtain a quantitative measure of their similarity and to establish the normal variability within a model data set. Statistical analysis of the data indicated that individual fermentations can be assigned to distinct clusters on the basis of their divergence from the model system. Altered environments and other species can be identified as outliers from the model set. These results show that pattern recognition analysis has direct applicability to monitoring fermentation processes.     

A morphometric analysis of the Bonatea speciosa complex (Orchidaceae) and its implications for species boundaries

Species boundaries in rapidly evolving lineages are often blurred, necessitating analysis of the full range of variation within a complex in order to identify traits that can be used to diagnose taxa. The delimitation of Bonatea speciosa (L. f.) Willd. has been controversial, as most, but not all, recent authors have included within the concept of this species several additional taxa that were formerly recognised at the species level, including Bonatea boltonii (Harv.) Bolus and Bonatea antennifera Rolfe. Morphological variation across the Bonatea speciosa complex (Orchidaceae) was explored using principal component and cluster analysis. Scatter plots were used to identify characters that could potentially be used to diagnose taxa. These data support the recognition of three distinct species, B. speciosa , B. antennifera and B. boltonii , instead of a single entity. A taxonomic account of southern African taxa within the B . speciosa complex is presented.     

光皮树不同无性系主要农艺性状多样性分析

依据光皮树(Cornus wilsoniana)丰产育种目标,对7个光皮树优良无性系的28个农艺性状进行观测,并进行方差、主成分、聚类及相关性分析。方差分析结果显示28个农艺性状的变异系数为804%~9430%,其中叶形态（叶形、叶色、叶缘）的变异系数最高;表型分化系数为4527%~9640%,即各性状的遗传较为稳定;主成分分析结果显示花序、果实、含油率以及果穗果数因子是主要的遗传因子,是光皮树无性系性状遗传的主要来源;依据主成分因子得分将7个光皮树优良无性系聚为4个类群,其中第Ⅱ类群与第Ⅲ类群杂交可以获得高产高含油率的目标资源。     

MATLAB 7.X生物信息工具箱的应用———基因表达图谱分析（4）*

基因表达图谱原则上可了解整体细胞基因表达的信息,是基因组功能分析的重要研究手段。MATLAB 7.X生物信息工具箱为基因表达谱数据的分析和处理提供了一个综合环境,通过众多统计函数和绘图函数的结合使用,过滤不合格的基因数据和噪声数据,从而对基因表达数据进行聚类分析和主成分分析,绘制相关的基因表达图谱,完成基因芯片数据表达图谱的分析,分析结果可视化程度高,图表清晰、直观。本文主要以酿酒酵母Saccharomyces cerevisiae为例,详细描述了利用MATLAB 7.X生物信息工具箱对其基因表达图谱进行分析的过程。     

Role of cuticular hydrocarbons in the chemical recognition between ant species in the Pachycondyla villosa species complex

Cuticular hydrocarbons (HCs) play important roles in insect communication but few studies clearly demonstrate the direct link between HCs and nestmate recognition. Therefore, cuticular lipids were extracted from ants, their HC and non-HC fractions as well as the three principal classes of HCs (n-alkanes, branched alkanes and alkenes) were purified and tested using an immobilizing "joust" device which allowed quantification of early pairwise behavioural responses, mandibular opening and antennal retraction, without occurrence of subsequent damages as in classic dyadic encounters. Chemical recognition of ants was studied at three levels of interactions (intra-colonial, intra-specific and inter-specific). Three closely related species already chemically characterized were used: Pachycondyla villosa (Pv), P. inversa (Pi) and P. subversa (Ps). Each species had its own behavioural responses. Moreover, responses of Pi and Ps towards Pv were significantly longer, than they were between themselves whereas Pv ants were equally aggressive towards Pi and Ps. These differences are in agreement with the results of the cluster analysis of the cuticular HCs profiles that place Pi closer to Ps. In support of the idea that components of cuticular lipids profiles are important for recognition, we found that only the HC fraction and its branched subfraction elicited a behavioural response of Ps workers. It is suggested that internally branched methyl- and dimethylalkanes are involved in recognition behaviour.     

基于主成分特征投影法建立利用16S rRNA基因序列进行物种分类方法的研究

提出一种有别于系统发育树的根据16S rRNA基因序列进行物种分类的新方法。首先将基因的碱基字母形式转换成数字形式,构建多维向量。然后根据主成分分析方法将该向量向数据分布最大方向投影,将原数据用几个“主成分”线性表出,而不丢失原数据的信息,采用主成分的显示功能作出三维主成分特征投影视图,达到分类的目的。在双歧杆菌和肠球菌的分类识别中得到较好的应用。     

一种快速非比对的蛋白质序列相似性与进化分析方法

本文提出了一种新的快速非比对的蛋白质序列相似性与进化分析方法。在刻画蛋白质序列特征时,首先将氨基酸的10种理化性质通过主成分分析浓缩为6个主成分,并且将每条蛋白质序列里的氨基酸数目作为权重对主成分得分值进行加权平均,然后再融合氨基酸的位置信息构成一个26维的蛋白质序列特征向量,最后利用欧式距离度量蛋白质序列间的相似性及进化关系。通过对3个蛋白质序列数据集的测试表明,本文提出的方法能将每条蛋白质序列准确聚类,并且简便快捷,说明了该方法的有效性。