Implications of ITS sequences and RAPD markers for the taxonomy and biogeography of the Oxytropis campestris and O. arctica (Fabaceae) complexes in Alaska

Taxonomic consensus is lacking on the Oxytropis arctica and O. campestris species complexes, two polyploid complexes found in the interior and arctic areas of Alaska. One classification has emphasized flower size, whereas flower color is considered a key diagnostic character in another classification. Our analyses of internal transcribed spacer (ITS) sequences and random amplified polymorphic DNA (RAPD) markers provided no support for either classification system. The trees generated from ITS sequences and the phenogram derived from RAPD markers suggest that most recognized taxa in the two complexes are probably polyphyletic, including O. arctica var. barnebyana, which is listed as threatened in Alaska. The only consistent pattern detected by both types of molecular markers was a geographic split dividing the northeastern arctic populations from most other populations (48.60-55.03% in AMOVA analyses). This genetic subdivision probably reflects a Pleistocene barrier formed by the northern coastal ice shield. Our molecular data, in conjunction with the previously reported variation of ploidy levels in these groups, suggest a scenario of recent and multiple origins of polyploidy. It is possible that most Alaskan populations of these two complexes are best referred to as a single taxonomic species despite morphological differentiation within the complexes.     

Ploidy patterns in cervical dysplasia

The ploidy patterns determined for groups of patients with cervical dysplasia (cervical intraepithelial neoplasia [CIN]) were subjected to statistical analysis. The patterns were based on the measurement of at least 100 Feulgen-stained nuclei from 30 patients with normal cervices, 10 cases of CIN I, 18 cases of CIN II and 33 cases of CIN III. The scale of the patterns was a log transformation of the ratio of the total extinction (optical density) of the nuclei to that of the 2N reference; this widens the intervals for higher ploidies, alleviating sampling requirements for intervals in which occurrences are rare and helping to maintain a reasonable sample size-to-dimensionality ratio. Pairwise discriminant analyses showed clear distinctions between the ploidy pattern for normal cases and those for CIN I, CIN II and CIN III. The distinctions between the different grades of CIN, based on these modest sample sizes, were less clearcut, largely due to pronounced patient-to-patient variability. Analysis of variance confirmed that the patient groups constitute statistically distinct entities. An aneuploid pattern did not seem to develop until CIN III lesions were involved. The diagnostic and prognostic significance of these preliminary findings require further study using larger data sets and correlations to patient survival.     

Prognostic significance of image cytometry DNA parameters in tissue sections from breast and gastric cancers.

The ploidy patterns determined for several groups of mammary and gastric carcinomas were subjected to a set of statistical analyses. The DNA distribution patterns were derived from image cytometry measurements of each of at least 150 Feulgen-stained tumour cell nuclei from tissue sections from 84 invasive ductal carcinomas of the breast and from 30 tubular adenocarcinomas of the stomach. Widely used DNA parameters (mean value, standard error of the mean, DNA-malignancy grade, 2c deviation index and the exceeding rate according to B?cking, DNA-histogram types according to Auer, DNA-index according to Atkin) were analysed by univariate and multivariate statistics. The DNA histograms were also analysed multiparametrically. The results showed different prognostic groups of the breast tumours to be distinguishable on single parameters with a reliability of up to 66%. None of these parameters permitted the discrimination of gastric carcinomas. Although the DNA-histogram-analysis increased accuracy by nearly 10%, compared with the classification accuracy of the best single parameters, it is still far from being applicable in clinical diagnostics. The use of further image cytometry parameters will be required for such applications.     

Applications of statistical analysis in diagnostic histopathology and cytopathology

Corresponding to the rapid increase in the amount of data available for use in clinical diagnoses, there is an increased need for procedures that can provide the diagnostician with meaningful statistical summaries of data and with statements concerning the statistical significance associated with a diagnostic evaluation. It has been demonstrated that multivariate statistical assessment of clinical material can provide consistent, reliable and highly sensitive diagnostic clues, even in instances in which trained personnel are unable to see any change. Several examples of applications of statistical analyses in diagnostic cytology and histopathology are given in this paper. The examples were chosen to be illustrative of the different types of problems for which statistical analyses have been found useful. These problems differ with respect to the extent of the statistical methods thus far developed and the difficulty involved in developing further analyses. For many problems, appropriate statistical analyses are readily available; other problems require definition of custom-made test statistics and, in some cases, also definition of new statistical distributions. The problems discussed here are only a small sample of the existing problems, but they provide at least an indication of the scope of the role that statistics plays in cytopathologic and histopathologic diagnosis.     

DNA ploidy analysis of cervical condyloma and intraepithelial neoplasia in specimens obtained by punch biopsy

This study analyzed the feasibility of, and the strategy for, DNA ploidy analysis of cervical condyloma and intraepithelial neoplasia by a computerized digital imaging system. Paraffin-embedded tissue provided satisfactory single-cell preparations for DNA ploidy analysis after enzyme digestion and additional procedures. Negative endocervical curettings and normal squamous mucosa were used as internal diploid controls. With suitable controls, 21 (81%) of the 26 aneuploid lesions were identified as such in the single-cell preparations. The remaining five lesions (not recognized as aneuploid in the single-cell preparations) had ploidy levels between 2.08n and 2.30n and required DNA measurements on 12-microns sections. Criteria for these DNA measurements were defined: specimens intended for DNA ploidy analysis should contain abnormal epithelium of at least 3 mm to 4 mm in greatest dimension and should be accompanied by diploid controls, such as endocervical curettings or normal ectocervical squamous mucosa. With a combination of single-cell preparations and 12-microns tissue sections, it was possible to accurately determine the DNA ploidy patterns of the cervical lesion specimens obtained by punch biopsies. Available evidence suggests that ploidy analysis can provide useful diagnostic and prognostic information.     

Multinomial distributions in ploidy analysis

Ploidy patterns can be summarized in the form of a vector of proportions representing the frequency of occurrence of DNA contents in specified intervals. Data represented in this way can be analyzed statistically using the multinomial distribution. Properties of the multinomial distribution and computational difficulties that arise in its application are considered. Special problems involved in formulating hypothesis tests and confidence regions for multivariate discrete distributions are discussed in the context of evaluating vectors of proportions representing DNA ploidy patterns. Construction of tailor-made critical regions for detecting specific types of deviations from normal ploidy patterns is proposed, and a detailed example is given. In this example, two critical regions are compared: a standard critical region, consisting of cases whose probability of occurrence is small, and a diagnostic critical region, consisting of cases considered to be clinically indicative of abnormality. Advantages of the diagnostic critical region are noted.     

Using generalized dissimilarity modelling to analyse and predict patterns of beta diversity in regional biodiversity assessment

Generalized dissimilarity modelling (GDM) is a statistical technique for analysing and predicting spatial patterns of turnover in community composition (beta diversity) across large regions. The approach is an extension of matrix regression, designed specifically to accommodate two types of nonlinearity commonly encountered in large-scaled ecological data sets: (1) the curvilinear relationship between increasing ecological distance, and observed compositional dissimilarity, between sites; and (2) the variation in the rate of compositional turnover at different positions along environmental gradients. GDM can be further adapted to accommodate special types of biological and environmental data including, for example, information on phylogenetic relationships between species and information on barriers to dispersal between geographical locations. The approach can be applied to a wide range of assessment activities including visualization of spatial patterns in community composition, constrained environmental classification, distributional modelling of species or community types, survey gap analysis, conservation assessment, and climate-change impact assessment.     

Different opinions on classification of DNA histograms produced from paraffin-embedded tissue

Flow cytometric DNA ploidy determination has been regarded as an objective prognostic parameter in several types of human cancer. To test whether DNA histograms are similarly interpreted, a series of flow cytometric DNA histograms was posted to six investigators working in the field for independent classification. The histograms were produced from paraffin-embedded adrenal adenomas or non-neoplastic tissue and had several different patterns. Only 44% of the histograms were similarly classified by all investigators, and 85% by five of the six participants, when DNA ploidy was evaluated. Different criteria for tetraploidy existed, and also some uncertainty in classifying peridiploid and small aneuploid peaks. It is concluded that lack of consensus on histogram classification may result in widely varying percentages of DNA aneuploid tumors found even if the data are similar. Until general agreement is reached on the definition of DNA aneuploidy and its subclasses, classification of DNA histograms is variable and subjective.     

First evidence of autotriploidization in sterlet (Acipenser ruthenus)

Polyploidization has played an important role in vertebrate evolution. Acipenseridae bring clear examples of polyploidy ancestry and, also, polyploidization seems to be an ongoing process in these fishes. In the present study, the genetic origin of six triploid specimens morphologically determined as Acipenser ruthenus from commercial aquaculture was analyzed using a combination of mitochondrial and nuclear markers. A further five successive statistical analyses including median joining of mitochondrial DNA control region sequences, principal coordinate analysis (PCA), factorial correspondence analysis (FCA), STRUCTURE assignation, and NewHybrids status determination for microsatellite data were applied for the clarification of the origin of one extra chromosome set added in these triploids genomes. Although interspecific hybridization had been suggested as a source of these triploids, the statistical analyses showed that the investigated triploids originate from autotriploidization rather than from interspecific hybridization. Therefore, we conclude that a combination of molecular markers with suitable statistical analyses should be used to verify the origin of unusual ploidy level. Evidently, such an approach is critically essential in aquaculture, where interspecific hybridization is very common and usually detected by changes in ploidy levels only.     

Subspace projection approaches to classification and visualization of neural network-level encoding patterns

Recent advances in large-scale ensemble recordings allow monitoring of activity patterns of several hundreds of neurons in freely behaving animals. The emergence of such high-dimensional datasets poses challenges for the identification and analysis of dynamical network patterns. While several types of multivariate statistical methods have been used for integrating responses from multiple neurons, their effectiveness in pattern classification and predictive power has not been compared in a direct and systematic manner. Here we systematically employed a series of projection methods, such as Multiple Discriminant Analysis (MDA), Principal Components Analysis (PCA) and Artificial Neural Networks (ANN), and compared them with non-projection multivariate statistical methods such as Multivariate Gaussian Distributions (MGD). Our analyses of hippocampal data recorded during episodic memory events and cortical data simulated during face perception or arm movements illustrate how low-dimensional encoding subspaces can reveal the existence of network-level ensemble representations. We show how the use of regularization methods can prevent these statistical methods from over-fitting of training data sets when the trial numbers are much smaller than the number of recorded units. Moreover, we investigated the extent to which the computations implemented by the projection methods reflect the underlying hierarchical properties of the neural populations. Based on their ability to extract the essential features for pattern classification, we conclude that the typical performance ranking of these methods on under-sampled neural data of large dimension is MDA>PCA>ANN>MGD.     

Test statistics for detecting aneuploidy and hyperdiploidy

Possible approaches to the analytical evaluation of ploidy patterns are discussed and two specific problems are considered: detection of early onset of aneuploidy and detection of moderate hyperdiploidy. A statistical model for a euploid DNA pattern is formulated in terms of a mixture distribution. A test statistic for detecting deviations from this pattern is defined, and its performance is evaluated for simulated data representing differing degrees of severity of aneuploidy. An analysis based on a discriminant function using order statistics of the sample cumulative distribution functions is proposed for detecting hyperdiploidy. This procedure has the advantage of being relatively distribution-free; its performance is evaluated for simulated data and is compared with that of its classical counterparts. Although the results reported are only preliminary, they indicate that tailor-made statistical analyses can provide early detection of aneuploidy and hyperdiploidy with known and acceptable error rates using clinically reasonable sample sizes.     

Cladistics and the land plants: A response to Robinson

The reconstruction of phylogenetic relationships should be based not on belief but on an explicit and logical analysis of all available characters. Hennigian phylogenetic systematics (cladistics) provides a framework for evaluating putative homologies characterizing particular hierarchical levels, determining relationships of taxa sharing congruent patterns of homologies, and constructing a classification based on this information. Fossils can and should be included in the analysis if enough of the relevant characters are preserved; this is not currently possible for early land plants because of the fragmentary fossil record. To avoid circularity, adaptive and functional considerations should be addressed only after a phylogenetic hypothesis based on patterns on patterns of shared homologies is available.     

Evaluation of the sensitivity of a medical data-mining application to the number of elements in small databases

Data-mining methods can be used to generate rules, or identify patterns, from medical data to assist clinical diagnosis and decision-making. However, in the initial stages of a clinical study on a new diagnostic approach, there could be a limited medical dataset available; or the medical characteristics could mean that the number of patients involved in the study will never be large. Diagnoses made using the rules discovered from such small medical databases should be considered suspect unless a confidence range for a particular diagnosis can be established. A method to evaluate the sensitivity and reliability of data-mining with small databases is presented in this paper. Efron's bootstrap method for statistical testing was used to assess the accuracy of the rules produced during the training step of the data-mining algorithm. The case study for validating this new approach was based on a limited-sized mammographic database previously used to discover associations between the diagnostic features of breast masses in mammograms and the biopsy-based classification of the masses. Using the new approach, it was possible to distinguish between the association rules that were sensitive to the size of the training datasets from those that were not. The methods proposed should lead to an efficient way for validating the patterns discovered in medical data-mining applications using small datasets.     

Simulation model for gynecologic specimen classification in a high-resolution prescreening system

Presentation is made of the design of a statistical model for the generation of "artificial specimens" to be used in the development and testing of a high-resolution prescreening system for gynecologic specimen classification. The model is based on two considerations: (1) the nature of the biologic material to be examined and (2) the system to be studied, which in this case is the FAZYTAN cervical prescreening system. Since gynecologic specimens that belong to the same clinical class (Papanicolaou group) have similar compositions of the different cytologic cell types, the simulation model presented is based on the close relationship between the degree of cancer suspiciousness expressed in the clinical diagnostic group and the composition of the cellular samples on a specimen. Statistically, the model considered here is based on an analysis of the single-cell classification (SCC) output process, taking the inherent system properties into account. The statistical information obtained by evaluating large sets of labelled cells is then used to produce artificially generated point distributions in the SCC decision space ("artificial specimens"), which can be used for examination of system reactions under controlled conditions. False-positive and false-negative error rates and system operation characteristics can be measured, and the effects of varying cell compositions as well as the relative performance of different specimen classifiers can be investigated. Although the "artificial specimens" thus created allow the investigation of system reactions with respect to a great variety of input processes, they cannot replace experiments on thousands of original specimens in order to measure system quality under realistic conditions.     

Discrimination of DNA ploidy patterns by order statistics

The use of order statistics to discriminate and classify DNA ploidy patterns is proposed, especially for the classification of additional observations: whether a given sample is more likely to have come from a normal or an abnormal tissue, and with what probability, based on its ploidy pattern. The method involves the order of observations within each of several samples (e.g., euploid and aneuploid DNA patterns) and the use of subsets of the obtained order statistics as independent variables in a linear discriminant analysis. It thus replaces univariate observations by (some of) their order statistics, which are then used as the variables in the discriminant analysis. The procedure does not require normality of distributions or the transformation of nonnormal distributions, as do many discriminant functions; order statistics are usually distribution-free and thus are particularly useful for nonparametric inference. Preliminary simulation studies verified the potential usefulness of the order statistics discriminant function method as applied to DNA ploidy analysis. Its advantages as compared to the usual methods for hypothesis testing, e.g., the use of the chi-square or Kolmogorov-Smirnov tests to as certain "goodness-of-fit," is discussed. The proposed method is easy to implement and easy to interpret; it is also applicable to the study of distributions of other types of measurements.     

Classification of vegetation sequences in Toohey Forest,Queensland

In this paper we consider one method of mapping larger units identified from the spatial pattern of sequences of vegetation types. The basic data were presence/absence data for 6450 stands arranged in 90 transects. A second set of data was derived by averaging the species occurrences in non-overlapping groups of 5 stands. A divisive numerical classification was used to determine the primary vegetation units. In all, 5 different sets of primary types were derived, using different species suites, different sample sizes and different numerical methods. We briefly discuss the types identified and their spatial patterns in the area.Each of these types was then used to define a string of type-codes for every transect so that each transect represents a sample from the landscape containing information on the frequency and spatial distribution of the primary vegetation types. The transects may be classified using a Levenshtein dissimilarity measure and agglomerative hierarchical classification, giving 5 analyses of transects, one for each of the primary types discussed above. We then examine these transect classifications to investigate the stability of the vegetation landspace patterns under changes in species used for the primary classification, in size of sample unit and in method of primary classifications. There is a considerable degree of stability in the results. However it seems with this vegetation that the tree species and non-tree species have considerable independence. We also indicate some problems with this approach and some possible extensions.     

Mitochondrial DNA variation in cultivated and wild potato species (Solanum spp.).

The European cultivated potato, Solanum tuberosum subsp. tuberosum, has 6 related cultivated species and more than 200 wild relatives. In Solanum spp., studies of cytoplasmic organelles have been mainly confined to the plastid DNA composition of cultivated and wild species. In this study, 53 genotypes of 30 potato species belonging to the subsections Estolonifera and Potatoe, 2 tomato species, and a black nightshade genotype were examined using PCR markers to evaluate mitochondrial DNA diversity and assess whether mtDNA variability was correlated with series classification, geographical origin, ploidy, and endosperm balance number (EBN). The markers used revealed interspecific mtDNA variability in Solanum spp. and identified 13 different haplotypes. Intraspecific variability was also observed in a few species and genomic regions. Cluster analysis allowed arrangement of the 13 haplotypes into 7 subgroups, and statistical association tests showed significant relationships between mitochondrial patterns detected by molecular analysis and ploidy, EBN, and geographical origin. On the whole, the evolutionary patterns for the genomic regions analyzed reflected the species relationships established on the basis of morphological and molecular (nuclear and plastidial DNA) data. The mtDNA variability shown is also important for better characterization of genetic resources for potato breeding.     

Developing a classifier for the Habitats Directive grassland types in Denmark using species lists for prediction

Abstract. This paper describes the use of supervised methods for the classification of vegetation. The difference between supervised classification and clustering is outlined, with reference to their current use in vegetation science. In the paper we describe the classification of Danish grasslands according to the Habitats Directive of the European Union, and demonstrate how a supervised classification can be used to achieve a standardized and statistical interpretation within a local flora. We thereby offer a statistical solution to the legal problem of protection of certain selected habitat types. The Habitats Directive protects three types of Danish grassland habitats, whereas two remaining types fall outside protection. A classification model is developed, using available Danish grassland data, for the discrimination of these five types based on their species composition. This new Habitats Directive classification is compared to a previously published unsupervised classification of Danish grassland vegetation. An indicator species analysis is used to find significant indicator species for the three protected habitat types in Denmark, and these are compared to the characteristic species mentioned in the interpretation manual of the Habitats Directive. Eventually, we discuss the pros and cons of supervised and unsupervised classification and conclude that supervised methods deserve more attention in vegetation science.     

Studies on Cell Number and Nuclei in Spores and on Ploidy Level in Ascochyta rabiei Isolates

Isolates of the phytopathogenic ascomycete Ascochyta rabiei (Pass.) Labr. were stained with the DNA-specific fluorochrome 4',6-diamidino-2-phenylindole (DAPI) and compared for differences in number of nuclei per pycnidiospore and the ploidy level. Microscopic analyses revealed that within the examined isolates five different combinations of cell number and number of nuclei in spores exist. A one-celled spore may contain one, two and four nuclei, respectively, and in the case of two-celled spores there exist types with one and two nuclei in one cell. Microfluorometric analyses of wild types and benomyl-treated isolates revealed differences in ploidy level among the wild types.     

Standardization and denoising algorithms for mass spectra to classify whole-organism bacterial specimens

MOTIVATION: Application of mass spectrometry in proteomics is a breakthrough in high-throughput analyses. Early applications have focused on protein expression profiles to differentiate among various types of tissue samples (e.g. normal versus tumor). Here our goal is to use mass spectra to differentiate bacterial species using whole-organism samples. The raw spectra are similar to spectra of tissue samples, raising some of the same statistical issues (e.g. non-uniform baselines and higher noise associated with higher baseline), but are substantially noisier. As a result, new preprocessing procedures are required before these spectra can be used for statistical classification. RESULTS: In this study, we introduce novel preprocessing steps that can be used with any mass spectra. These comprise a standardization step and a denoising step. The noise level for each spectrum is determined using only data from that spectrum. Only spectral features that exceed a threshold defined by the noise level are subsequently used for classification. Using this approach, we trained the Random Forest program to classify 240 mass spectra into four bacterial types. The method resulted in zero prediction errors in the training samples and in two test datasets having 240 and 300 spectra, respectively.