Euclidean Dissimilarity Research Articles

Metabolic and physiological effects of water stress on Moshgak (Ducrosia anethifolia Boiss) populations using GC–MS and multivariate analyses

Ducrosia anethifolia Boiss is a medicinal and aromatic plant that is used as a food additive and drug in the areas of natural distribution. In this study, eight populations from different geographical regions of Iran were evaluated for their essential oil composition, total phenolics and flavonoids as well as for free radical scavenging activity during two consecutive years under water deficit. Analysis of variance was performed using a randomized block design at three levels of irrigation and 2 years, using the GLM procedure of SAS software and cluster analysis was used according to Ward's method using squared Euclidean dissimilarity. The highest essential oil and total phenolics/flavonoids content were obtained in the second year. GC–MS analysis was used to analyze the essential oil components. In normal conditions, cis-chrysanthenyl acetate (39.1–66.4%) and α-pinene (1.02–4.7%) were recognized as major compounds. The essential oil components were elevated under water stress. The highest increase in cis-chrysanthenyl acetate (21.32%) in response to water stress was observed in Abarkuh1. Elevation in total phenolic, flavonoid and antioxidant activity was also observed in plants subjected to water deficit. The highest content of phenolic acids was measured in Semirom (136.74 mg TAE/g DW), while the highest flavonoid content was in Kerman (6.05 mg QUE/g DW) in severe water stress condition. Finally, a high diversity in the studied populations can be used to select advantageous populations for other pharmaceutical and food purposes.

Fatty acid-based index development in estuarine organisms to pinpoint environmental contamination

Estuaries have long been preferred areas of human settlement, where multiple anthropogenic activities take place, which have contributed to a significant decrease in environmental quality of these ecosystems. Accordingly, environmental monitoring and management have long relied on the development of tools that summarize and simplify complex information and provide direct interpretation of quality status. Here, the fatty acid profiles of three abundant estuarine species, namely Hediste diversicolor, Carcinus maenas and Pomatoschistus microps, were used to develop and validate a multimetric index, based on the Euclidean dissimilarities of profiles between sites, in response to contamination gradient in a large urban estuary. Spatial differences were generally related to unsaturated fatty acids (mono- and polyunsaturated, of the n−3 and n−6 series) in all species, albeit more pronounced in P. microps. Multivariate models returned high classification accuracies for the three sampled sites, varying from 73.3% in the invertebrate species to 100.0% in the fish species. Results show the applicability of the developed FA-based index, particularly due to the easy of communication, for managers and the public alike, but also highlight the need for prior validation on species suitability or sensitivity to depict environmental contamination.

EHypertension: A prospective longitudinal multi‐omics essential hypertension cohort

eHypertension: A prospective longitudinal multi‐omics essential hypertension cohort

Reprint: Investigation of Genetic Divergence in Local Sunflower Hybrids and Inbred Lines by Applying Morphological Markers

Knowledge of genetic diversity is the basic requisite for any plant breeding program. Seventeen sunflower hybrids and fifteen inbred lines including ten Cytoplasm male sterile lines and five restorer lines were evaluated at the experimental area of National Agriculture Research centre, Islamabad, Pakistan during autumn-2011. The objective of study was to investigate the genetic diversity, categorize the proficient cross combiners and approximate characters association among sunflower hybrids and inbred lines. Data was estimated on i.e. flower initiation days, full flowering days, full developmental days, height of plant, disk diameter, stem thickness, leaves per plant, hundred achenes weight, achenes yield and oil content percentage. The maximum achenes yield was contributed by Hysun-33 2119 kg ha-1. Followed by SMH-0924 and SMH-0925, SMH-1028 and SMH-0926 were suggested as potential significant hybrids for future breeding plans to incorporate maximum achenes yield and oil content percentage. The CMS-11, CMS-25 and CMS-10 were long statured with vigorous stem and all the restorers were early maturing recommended for including in hybridization program to generate high heterotic factions. Cluster diagram based on Euclidean dissimilarity matrix grouped hybrids as well as inbred lines in four groups. The group I and IV consist of four whereas group II and group III have three and five hybrids respectively. All the restores were clustered in III and IV and CMS in I and II groups respectively. According to principle component analysis the first two components contribute 64% and 89% in total variation in hybrids as well as in inbred lines respectively.

A comparison of two dissimilarity functions for mixed-type predictor variables in the delta -machine

The delta -machine is a statistical learning tool for classification based on dissimilarities or distances between profiles of the observations to profiles of a representation set, which was proposed by Yuan et al. (J Claasif 36(3): 442–470, 2019). So far, the delta -machine was restricted to continuous predictor variables only. In this article, we extend the delta -machine to handle continuous, ordinal, nominal, and binary predictor variables. We utilized a tailored dissimilarity function for mixed type variables which was defined by Gower. This measure has properties of a Manhattan distance. We develop, in a similar vein, a Euclidean dissimilarity function for mixed type variables. In simulation studies we compare the performance of the two dissimilarity functions and we compare the predictive performance of the delta -machine to logistic regression models. We generated data according to two population distributions where the type of predictor variables, the distribution of categorical variables, and the number of predictor variables was varied. The performance of the delta -machine using the two dissimilarity functions and different types of representation set was investigated. The simulation studies showed that the adjusted Euclidean dissimilarity function performed better than the adjusted Gower dissimilarity function; that the delta -machine outperformed logistic regression; and that for constructing the representation set, K-medoids clustering achieved fewer active exemplars than the one using K-means clustering while maintaining the accuracy. We also applied the delta -machine to an empirical example, discussed its interpretation in detail, and compared the classification performance with five other classification methods. The results showed that the delta -machine has a good balance between accuracy and interpretability.

Q-Rung Orthopair Fuzzy Modified Dissimilarity Measure Based Robust VIKOR Method and its Applications in Mass Vaccination Campaigns in the Context of COVID-19

A new inclusion called total inclusion relation has improved the existing dissimilarity measure for q-rung orthopair fuzzy sets (qROFSs). For qROFSs, the modified axiomatic definition of dissimilarity measure is proposed. The modified Hamming and Euclidean dissimilarity measures are defined. An algorithmic procedure for a robust VIKOR method based on modified dissimilarity measures is established. The application of the robust VIKOR method in Mass Vaccination Campaigns (MVCs) in the COVID-19 situation is given.

Assessment of Twitter Data Clusters with Cosine-Based Validation Metrics Using Hybrid Topic Models

Text data clustering is performed for organizing the set of text documents into the desired number of coherent and meaningful sub-clusters. Modeling the text documents in terms of topics derivations is a vital task in text data clustering. Each tweet is considered as a text document, and various topic models perform modeling of tweets. In existing topic models, the clustering tendency of tweets is assessed initially based on Euclidean dissimilarity features. Cosine metric is more suitable for more informative assessment, especially of text clustering. Thus, this paper develops a novel cosine based external and interval validity assessment of cluster tendency for improving the computational efficiency of tweets data clustering. In the experimental, tweets data clustering results are evaluated using cluster validity indices measures. Experimentally proved that cosine based internal and external validity metrics outperforms the other using benchmarked and Twitter-based datasets.

Genetic and Leaf Characteristic Diversity on 10 Mutant Progenies of Patchouli (Pogostemon cablin) Provide Insights to Selection Strategies

The importance of patchouli plants to produce essential oil has established this medicinal herb as one of the Indonesia’s main economic contributor. To date, Indonesia can only provide 5 varieties i.e. Sidikalang, Tapak Tuan, Lhokseumawe, Patchoulina 1 and Patchoulina 2. In order to increase national patchouli production, several efforts needs to be done by providing new superior patchouli lines. This experiment was undertaken to reveal diversity of mutant progenies derived from gamma ray irradiation in patchouli. Six SSR primers were used to determine genetic diversity on MV 3 generation of the mutants. Molecular analysis revealed a moderate to high polymorphism based on tested primers, which amplified 73 bands, and resulting of 0.705 PIC value. Dendrogram analysis based on Euclidean dissimilarity showed broad diversity that ranged from 0.23 to 0.82. Phenotypic tree gave clear separation that all mutant clones were clustered into three different group, with Eucledian dissimilarity coefficient ranging from 0.02 to 0.13. This report indicates that Patchouli vegetatively-propagated mutants were able to maintain their variability continuously from their parental lines (M 0 ). Furthermore, our findings offer comprehensive information on breeding strategies of patchouli plants, giving an opportunity to assess important traits at early generation.

Morpho-physiological Diversity to Evaluate Dry Summer Adaptability of Pearl Millet

Pearl millet germplasm lines, viz., 17 cytoplasmic male sterile lines and 83 inbred lines, were evaluated for 15 different morpho-physiological traits. The correlation analysis revealed strong positive association of days to 50% flowering and maturity (0.66, 0.15) with grain yield. Significant positive association between water-soluble carbohydrates (WSC) and grain yield (0.17) depicted that WSC is a good indicative for selecting genotypes for summer cultivation. Principal component (PC) analysis showed five PCs having eigenvalue >1 explaining 74.72% of the total variation with number of tillers, WSC, stover yield, days to 50% flowering and days to maturity being the most important characters in PC1. Dendrogram based on Euclidean dissimilarity revealed two main groups at linkage distance of 354.80. The group A consisted of all CMS lines, and group B consisted of all the Punjab Inbred Bajra (PIB) lines. The germplasm lines were grouped into 11 clusters. All the test CMS lines and inbreds BDRC-1, PIB-200-P2, PIB-680, PIB-802, PIB-920 were identified as potential genotypes for summer cultivation.

Analysis of financial stock markets through multidimensional scaling based on information measures

In this paper, a novel multidimensional scaling (MDS) based on information measures method is proposed to analyze financial stock markets. In order to examine the effectiveness of this method, we applied it to the classification of two types of artificial series, the logistic map model and the cubic map model, as well as stock time series. Moreover, the traditional MDS using Euclidean dissimilarity is also provided as a reference for comparisons. The results show that the MDS based on information measures can give us more detailed, exact and clearer information on the classification of simulation series and stock time series than the MDS using Euclidean dissimilarity. In addition, the proposed graphical method may also assist in the construction of multivariate econometric models.

Morphological Characterization of Two Fresh Water Eels Monopterus cuchia (Hamilton, 1822) and Ophisternon bengalense (McClelland, 1844)

Morphometric and meristic characters and truss measurements of 32 Monopterus cuchia, and of 17 Ophisternon bengalense were compared to know the population status of two fresh water eels of Bangladesh. The mean numbers of line below head were significantly different between two species. Significant differences were observed in 11 morphometric characters: Pre dorsal length (PDL), Post dorsal length (PoDL), Post anal length (PoAL), Head length (HL), Snout length (SnL), Upper jaw length (UJL), Lower jaw length (LJL), Head width (HW), Pre orbital length (PrOrL), Least body diameter (LBD) and Highest body diameter (HBD) and one truss measurement (3-5) between two species in varying degrees. For both morphometric and landmark measurements, the first and second DF (discriminant function) accounted 64.8% and 33.2% of among group variability, explaining 98% of total group variability. M. cuchia collected from Mymensingh and from Dinajpur constructed one sub-cluster and O. bengalense collected from Sathkhira and from Bagerhat constructed another sub-cluster based on the Distance of squared Euclidean dissimilarity. A correct classification of individuals into their original population from leave-one-out-classification varied between 93.3% and 94.1% by discriminant analysis. The results of the present study suggested that there was limited intermingling among populations and the populations of the species were separated from one another.J. Environ. Sci. & Natural Resources, 9(1): 127-137 2016

Changing Population of Aphidius colemani in Relation to Environmental Factors

Morphometric and meristic characters and truss measurements of 32 Monopterus cuchia, and of 17 Ophisternon bengalense were compared to know the population status of two fresh water eels of Bangladesh. The mean numbers of line below head were significantly different between two species. Significant differences were observed in 11 morphometric characters: Pre dorsal length (PDL), Post dorsal length (PoDL), Post anal length (PoAL), Head length (HL), Snout length (SnL), Upper jaw length (UJL), Lower jaw length (LJL), Head width (HW), Pre orbital length (PrOrL), Least body diameter (LBD) and Highest body diameter (HBD) and one truss measurement (3-5) between two species in varying degrees. For both morphometric and landmark measurements, the first and second DF (discriminant function) accounted 64.8% and 33.2% of among group variability, explaining 98% of total group variability. M. cuchia collected from Mymensingh and from Dinajpur constructed one sub-cluster and O. bengalense collected from Sathkhira and from Bagerhat constructed another sub-cluster based on the Distance of squared Euclidean dissimilarity. A correct classification of individuals into their original population from leave-one-out-classification varied between 93.3% and 94.1% by discriminant analysis. The results of the present study suggested that there was limited intermingling among populations and the populations of the species were separated from one another.J. Environ. Sci. & Natural Resources, 9(1): 139-141 2016

Ageing bias and precision for deep-water snappers: evaluating nascent otolith preparation methods using novel multivariate comparisons among readers and growth parameter estimates

Tropical deep-water snappers (Etelinae) support valuable fisheries across the Indo-Pacific, with stock assessments reliant on age-based information in the absence of reliable catch and effort statistics. These long-lived species have been considered notoriously difficult to age. However, nascent developments in ageing protocols, particularly thinner transverse sections of otoliths (∼180–200 µm), are providing improvements in growth zone clarity, interpretation and repeatability of annuli counts. At a recent international workshop, thin sectioned otoliths from three deep-water snappers were read under reflected light by eight fisheries scientists from across the Indo-Pacific, with various levels of fish-ageing experience. Precision and bias were assessed using traditional ageing precision metrics (index of average percent error, IAPE; and coefficient of variation, CV), and a novel approach using multivariate analyses (metric multidimensional scaling, mMDS) based on Euclidean dissimilarity among readers’ counts and subsequent von Bertalanffy (vB) growth parameter estimates. Annuli counts between the primary reader and all other readers were within two for 80% of estimates, with uniform variation across a wide age range for Etelis carbunculus (intraclass correlation coefficient (ICC) = 0.924, n = 20, 3–25 annuli) and Etelis sp. (ICC = 0.933, n = 15, 2–27 annuli). In contrast, annuli counts for Pristipomoides filamentosus (n = 14, 4–49) were less precise (i.e. ICC = 0.835, 66% of counts within two of primary reader) with a bias toward greater variation in younger, pre-maturational life stages (≤5 annuli). Traditionally accepted ageing precision (IAPE ≤ 5.5%, CV ≤ 7.6%) was achieved for each species, but was commensurate with reader experience. The multivariate mMDS ordination was more informative in identifying both distance (i.e. dissimilarity) and direction (i.e. form) of variations in annuli counts and vB growth parameter estimates among readers. The acceptable level of ageing precision and bias achieved among most readers indicated that deep-water snappers can be aged precisely when appropriate otolith preparation methods are used. This study contributes towards ageing protocols that can be used to (i) interrogate bias and precision of age data for stock assessments, and (ii) standardise comparisons of age-based life history characteristics across a species range.

Ricci flow embedding for rectifying non-Euclidean dissimilarity data

Pairwise dissimilarity representations are frequently used as an alternative to feature vectors in pattern recognition. One of the problems encountered in the analysis of such data is that the dissimilarities are rarely Euclidean, while statistical learning algorithms often rely on Euclidean dissimilarities. Such non-Euclidean dissimilarities are often corrected or a consistent Euclidean geometry is imposed on them via embedding. This paper commences by reviewing the available algorithms for analysing non-Euclidean dissimilarity data. The novel contribution is to show how the Ricci flow can be used to embed and rectify non-Euclidean dissimilarity data. According to our representation, the data is distributed over a manifold consisting of patches. Each patch has a locally uniform curvature, and this curvature is iteratively modified by the Ricci flow. The raw dissimilarities are the geodesic distances on the manifold. Rectified Euclidean dissimilarities are obtained using the Ricci flow to flatten the curved manifold by modifying the individual patch curvatures. We use two algorithmic components to implement this idea. Firstly, we apply the Ricci flow independently to a set of surface patches that cover the manifold. Second, we use curvature regularisation to impose consistency on the curvatures of the arrangement of different surface patches. We perform experiments on three real world datasets, and use these to determine the importance of the different algorithmic components, i.e. Ricci flow and curvature regularisation. We conclude that curvature regularisation is an essential step needed to control the stability of the piecewise arrangement of patches under the Ricci flow.

Modified multidimensional scaling approach to analyze financial markets.

Detrended cross-correlation coefficient (σDCCA) and dynamic time warping (DTW) are introduced as the dissimilarity measures, respectively, while multidimensional scaling (MDS) is employed to translate the dissimilarities between daily price returns of 24 stock markets. We first propose MDS based on σDCCA dissimilarity and MDS based on DTW dissimilarity creatively, while MDS based on Euclidean dissimilarity is also employed to provide a reference for comparisons. We apply these methods in order to further visualize the clustering between stock markets. Moreover, we decide to confront MDS with an alternative visualization method, "Unweighed Average" clustering method, for comparison. The MDS analysis and "Unweighed Average" clustering method are employed based on the same dissimilarity. Through the results, we find that MDS gives us a more intuitive mapping for observing stable or emerging clusters of stock markets with similar behavior, while the MDS analysis based on σDCCA dissimilarity can provide more clear, detailed, and accurate information on the classification of the stock markets than the MDS analysis based on Euclidean dissimilarity. The MDS analysis based on DTW dissimilarity indicates more knowledge about the correlations between stock markets particularly and interestingly. Meanwhile, it reflects more abundant results on the clustering of stock markets and is much more intensive than the MDS analysis based on Euclidean dissimilarity. In addition, the graphs, originated from applying MDS methods based on σDCCA dissimilarity and DTW dissimilarity, may also guide the construction of multivariate econometric models.

Genetic and chemo-divergence in eighteen core collection of Zingiber officinale from North-West Himalayas

Eighteen Zingiber officinale germplasm, collected from North-Western Himalayan region and showing significant differences in morphology and aroma, were characterized both chemically and genetically. Molecular analyses employing ISSR, SSR and IRAP techniques revealed moderate to high variations in clonally propagated ginger crop. ISSR analysis showed primers ISSR 4 and 8, having 9 polymorphic loci each, exhibiting 100% polymorphism while in SSR and IRAP primers RM153, RM278 and primer combinations of RT6 with RT3, RT4 and RT7, respectively showed 100% polymorphism. Essential oil evaluation revealed 31 major components based on GC analysis of hydro-distilled volatile oil extracted from rhizome of the collected population. Pair-wise Euclidean dissimilarity distance ranged from 3.37 to 16.93 revealing moderate variation in oil profile. The multivariate analyses of the oil based data could explain 99.9% of cumulative variation. Hierarchical analyses of molecular and oil profiles, grouped all the 18 genotypes into two major clusters, clearly distinguishing two germplasm (161 and 179) genetically and chemically both. The study also revealed two genetically similar germplasm from Theri (146 and 147) based on all the three techniques. This distinctness in oil and molecular profiling of the germplasm points towards distinct genotypes from different regions of North Western Himalayas. Further, ISSR profiling and essential oil array based classification produced geographically concordant groupings. The study revealed existence of moderate diversity in the unexplored North Western Himalayas.

Translation Invariance in the Polynomial Kernel Space and Its Applications in kNN Classification

In this paper, a new technique is presented to measure dissimilarity in kernel space providing scaling and translation invariance. The motivation comes from signal/image processing, where classifiers are often required to ensure invariance against linear transforms, since in many cases linear transforms do not affect the content of a signal/image for a human observer. We examine the theoretical background of linear invariance in the polynomial kernel space, introduce the centered correlation and centered Euclidean dissimilarity in kernel space, deduce formulas to compute it efficiently and test the proposed dissimilarity measures with the kNN classifier. The experimental results show that the presented techniques are highly competitive in similarity or dissimilarity based classification methods.

Measuring biological diversity using Euclidean metrics

We study the complementary use of Rao's theory of diversity (1986) and Euclidean metrics. The first outcome is a Euclidean diversity coefficient. This index allows to measure the diversity in a set of species beyond their relative abundances using biological information about the dissimilarity between the species. It also involves geometrical interpretations and graphical representations. Moreover, several populations (e.g., different sites) can be compared using a Euclidean dissimilarity coefficient derived from the Euclidean diversity coefficient. These proposals are used to compare breeding bird communities living in comparable habitat gradients in different parts of the world.

General method for the description, visualization and comparison of metal coordination spheres: geometrical preferences, deformations and interconversion pathways.

The coordination sphere geometry of metal atoms (M) in their complexes with organic and inorganic ligands (L) is often compared with the geometry of archetypal forms for the appropriate coordination number, n in ML(n) species, by use of the k = n( n- 1)/2 L-M-L valence angles subtended at the metal centre. Here, a Euclidean dissimilarity metric, R(c)(x), is introduced as a one-dimensional comparator of these k-dimensional valence-angle spaces. The computational procedure for R(c)(x), where x is an appropriate archetypal form (e.g. an octahedron in ML(6) species), takes account of the atomic permutational symmetry inherent in ML(n) systems when no distinction is made between the individual ligand atoms. It is this permutational symmetry, of order n!, that precludes the routine application of multivariate analytical techniques, such as principal component analysis (PCA), to valence angle data for all but the lowest metal coordination numbers. It is shown that histograms of R(c)(x) values and, particularly, scatterplots of R(c)(x) values computed with respect to two or more different appropriate archetypal forms (e.g. tetrahedral and square-planar four-coordinations), provide information-rich visualizations of the observed geometrical preferences of metal coordination spheres retrieved from, e.g. the Cambridge Structural Database. These mappings reveal the highly populated clusters of similar geometries, together with the pathways that map their geometrical interconversions. Application of R(c)(x) analysis to the geometry of four- and seven-coordination spheres provides information that is at least comparable to, and in some cases is more complete than, that obtained by PCA.

Ribosomal, mitochondrial and amplified DNA polymorphisms in Verticillium albo-atrum pathogenic to hops, lucerne and other plants

Forty-seven isolates of Verticillium albo-atrum, 35 from hop (Humulus lupulus), seven from lucerne (alfalfa, Medicago sativa) and five from four other hosts, were analysed for DNA polymorphisms. Restriction fragment length polymorphisms (RFLPs) were detected in ribosomal RNA genes (rDNA) using Southern hybridization. Polymorphisms in mitochondrial DNA (mtDNA) were detected in ethidium bromide stained gels after digestion of total genomic DNA with restriction enzymes which recognize four bases containing only G and C. Amplified polymorphic DNA (APD) was analysed using primers based on rDNA sequences from the intergenic spacer (IGS) and 25S regions. These data were used to construct phenograms using either squared Euclidean dissimilarity coefficients (SEDC) and cluster analysis, or unweighted pair grouping with arithmetic averaging (UPGMA). rDNA RFLPs revealed one group with 44 isolates, a second group with two atypical hop isolates, and a third group containing a single avirulent lucerne isolate. mtDNA RFLPs separated rDNA group one into two subgroups, one group containing 38 isolates from different hosts and the other containing all six virulent lucerne isolates. APD analysis divided the isolates into 16 phenotypes, 12 of which contained most of the hop isolates, but there was no correlation with origin, hop cultivar, pathogenicity or year of isolation. One APD phenotype contained all six virulent lucerne isolates, indicating the genetic differentiation between hop and lucerne isolates. Two further APD phenotypes coincided with the second atypical group containing two hop isolates and a distinct avirulent lucerne isolate, respectively. The three methods revealed that three isolates differed markedly from those of the main group.