Relationship between molecular and enological features of Patagonian wine yeasts: relevance in selection protocols

Summary In this paper, a study of the relationship between genetic patterns, obtained by the combination of mtDNA-RFLP and PCR-amplified inter-δ sequence DNA polymorphism analysis, and relevant enological phenotypic data (fermentative power, specific productivity, volatile and total acidity) was carried out on Argentinean Saccharomyces cerevisiae isolates from north Patagonia. The use of a powerful statistical tool, Generalized Procrustes analysis, allowed us to weigh the relationship for each isolate in particular, denoting a good enough degree of agreement between molecular and physiological data for most of the population analysed. The inclusion of a physiological feature, as the killer sensitivity biotype, within identification methods resulted in a higher degree of discrimination among isolates and in better correlation between both characterizations. The combined use of methods based on molecular polymorphisms and killer biotype could be applied so as not to miss any isolate with differential enological properties in selection protocols.     

Advances in Geometric Morphometrics

Geometric morphometrics is the statistical analysis of form based on Cartesian landmark coordinates. After separating shape from overall size, position, and orientation of the landmark configurations, the resulting Procrustes shape coordinates can be used for statistical analysis. Kendall shape space, the mathematical space induced by the shape coordinates, is a metric space that can be approximated locally by a Euclidean tangent space. Thus, notions of distance (similarity) between shapes or of the length and direction of developmental and evolutionary trajectories can be meaningfully assessed in this space. Results of statistical techniques that preserve these convenient properties—such as principal component analysis, multivariate regression, or partial least squares analysis—can be visualized as actual shapes or shape deformations. The Procrustes distance between a shape and its relabeled reflection is a measure of bilateral asymmetry. Shape space can be extended to form space by augmenting the shape coordinates with the natural logarithm of Centroid Size, a measure of size in geometric morphometrics that is uncorrelated with shape for small isotropic landmark variation. The thin-plate spline interpolation function is the standard tool to compute deformation grids and 3D visualizations. It is also central to the estimation of missing landmarks and to the semilandmark algorithm, which permits to include outlines and surfaces in geometric morphometric analysis. The powerful visualization tools of geometric morphometrics and the typically large amount of shape variables give rise to a specific exploratory style of analysis, allowing the identification and quantification of previously unknown shape features.     

Effects of detrending and rescaling on correspondence analysis: solution stability and accuracy

Detrending and non-linear axis rescaling potentially improve the accuracy of gradient recovery in correspondence analyses but also reduce the stability or consistency of solutions. Variation among bootstrapped ordination solutions was compared across methods in analyses of both field and simulated data. Solution accuracy, measured with mean squared errors from Procrustes analysis, was compared using simulated data with known structure.Standard detrending-by-segments combined with non-linear rescaling entailed some cost in solution stability, but could improve the accuracy of solutions for long gradients. Without non-linear rescaling these solutions were usually less stable and less accurate. Although detrending-by-polynomials might be preferable on other grounds, it did not produce more accurate or stable solutions than detrending-by-segments.Abbreviations CA = correspondence analysis - DCA = detrended correspondence analysis - MSE = Procrustes mean squared error statistic - SD = standard deviation units of species turnover - SRV = scaled variance in species ranks     

Comparison of ordinations and classifications of vegetation data

The methodology of comparing the results of multivariate community studies (resemblance matrices, ordinations, hierarchical and nonhierarchical classifications) is reviewed from two viewpoints: basic strategy and measure employed. The basic strategy is determined by 7 choices concerning the type of results, consensus methods or resemblance measures, hypothesis testing or exploratory analysis, lack or presence of reference basis, data set congruence or algorithmic effects, number of factors responsible for differences among results, and the number of properties considered in the comparison. Included is a brief summary of methods applicable to vegetation studies. Examples from a grassland survey demonstrate the utility of comparisons in evaluating the effects of plot size, data type, standardization, taxonomic level and number of species on classifications and ordinations.Abbreviations OUC = Operational Unit of Comparison - PCA = Principal Components Analysis - PCoA = Principal Coordinates Analysis - SSA = Incremental Sum of Squares Agglomeration     

Statistical analysis of unlabeled point sets: comparing molecules in chemoinformatics

We consider Bayesian methodology for comparing two or more unlabeled point sets. Application of the technique to a set of steroid molecules illustrates its potential utility involving the comparison of molecules in chemoinformatics and bioinformatics. We initially match a pair of molecules, where one molecule is regarded as random and the other fixed. A type of mixture model is proposed for the point set coordinates, and the parameters of the distribution are a labeling matrix (indicating which pairs of points match) and a concentration parameter. An important property of the likelihood is that it is invariant under rotations and translations of the data. Bayesian inference for the parameters is carried out using Markov chain Monte Carlo simulation, and it is demonstrated that the procedure works well on the steroid data. The posterior distribution is difficult to simulate from, due to multiple local modes, and we also use additional data (partial charges on atoms) to help with this task. An approximation is considered for speeding up the simulation algorithm, and the approximating fast algorithm leads to essentially identical inference to that under the exact method for our data. Extensions to multiple molecule alignment are also introduced, and an algorithm is described which also works well on the steroid data set. After all the steroid molecules have been matched, exploratory data analysis is carried out to examine which molecules are similar. Also, further Bayesian inference for the multiple alignment problem is considered.     

Geometric morphometries and the analysis of higher taxa: a case study based on the metendosternite of the Scarabaeoidea (Coleoptera)

A geometric morphometric analysis of families belonging to the Scarabaeoidea, using landmarks from three two-dimensional views (frontal, dorsal and lateral) of an internally situated rigid structure, the metendosternite, was carried out. Procrustes distances matrices were obtained to produce phenograms from the three analyses. Relative warp analyses were also performed and the first two relative warps for the three data sets plotted against each other. The metendosternite is an internally situated anatomical structure that has largely been neglected in past studies. However, it proved to be useful in geometric morphometric studies, because it is rigid and landmarks can be easily identified. Geometric morphometries is shown to be a powerful tool to identify phenetic relationships between higher level taxa, and the metendosternite is a new structure available for beetle systematisists.     

Landmark superimposition for taxonomic identification

The identification of organisms is a time-consuming task even for highly specialized researchers. Many ways to accelerate the identification process have been devised – from pictorial keys to automatic, machine identification – with different degrees of success. This paper explores landmark configurations as an aid to taxonomic identification. The basic hypothesis rests on the analogy between human fingerprints and organism landmark configuration. Translated into biological terms, it asks whether individual landmark configurations can be used as diagnostic characteristics for species identification. Water mites of the genus Torrenticola were used as test organisms. The results show that identification can be made simpler through the use of landmark configurations.  © 2004 The Linnean Society of London, Biological Journal of the Linnean Society , 2004, 81 , 267–274.     

Testing different 3D techniques using geometric morphometrics: Implications for cranial fluctuating asymmetry in humans

This study aimed to test the performance of 3D digitizer, CT scanner, and surface scanner in detecting cranial fluctuating asymmetry. Sets of 32 landmarks (6 in the midline and 13 bilateral) were acquired from 14 archeological crania using a 3D digitizer, and from 3D models generated from a CT scanner and surface scanner using Viewbox 4. Levels of shape variation were analyzed in MorphoJ using Procrustes analysis of variance and Principal component analysis. Intra-observer error accounted for 1.7%, 1.8%, and 4.5% of total shape variation for 3D digitizer, CT scanner, and surface scanner respectively. Fluctuating asymmetry accounted for 15%–16% of total shape variation. Variation between techniques accounted for 18% of total shape variation. We found a higher level of missing landmarks in our surface scan data than for both 3D digitizer and CT scanner data, and both 3D model-based techniques sometimes obscured taphonomic damage. All three 3D techniques are appropriate for measuring cranial fluctuating asymmetry. We advise against combining data collected with different techniques.