scholarly journals Metabolic Capacity Differentiates Plenodomus lingam from P. biglobosus Subclade ‘brassicae’, the Causal Agents of Phoma Leaf Spotting and Stem Canker of Oilseed Rape (Brassica napus) in Agricultural Ecosystems

Pathogens ◽  
2022 ◽  
Vol 11 (1) ◽  
pp. 50
Author(s):  
Magdalena Frąc ◽  
Joanna Kaczmarek ◽  
Małgorzata Jędryczka

In contrast to the long-lasting taxonomic classification of Plenodomus lingam and P. biglobosus as one species, formerly termed Leptosphaeria maculans, both species form separate monophyletic groups, comprising sub-classes, differing considerably with epidemiology towards Brassicaceae plants. Considering the great differences between P. lingam and P. biglobosus, we hypothesized their metabolic capacities vary to a great extent. The experiment was done using the FF microplates (Biolog Inc., Hayward, CA, USA) containing 95 carbon sources and tetrazolium dye. The fungi P. lingam and P. biglobosus subclade ‘brassicae’ (3 isolates per group) were cultured on PDA medium for 6 weeks at 20 °C and then fungal spores were used as inoculum of microplates. The test was carried out in triplicate. We have demonstrated that substrate richness, calculated as the number of utilized substrates (measured at λ490 nm), and the number of substrates allowing effective growth of the isolates (λ750 nm), showed significant differences among tested species. The most efficient isolate of P. lingam utilized 36 carbon sources, whereas P. biglobosus utilized 60 substrates. Among them, 25–29 carbon sources for P. lingam and 34–48 substrates for P. biglobosus were efficiently used, allowing their growth. Cluster analysis based on Senath criteria divided P. biglobosus into two groups and P. lingam isolates formed one group (33% similarity). We deduce the similarities between the tested species help them coexist on the same host plant and the differences greatly contribute to their different lifestyles, with P. biglobosus being less specialized and P. lingam coevolving more strictly with the host plant.

2006 ◽  
Vol 37 (01) ◽  
Author(s):  
W Hermann ◽  
T Villmann ◽  
HJ Kühn ◽  
P Baum ◽  
G Reichel ◽  
...  

Author(s):  
Hyeuk Kim

Unsupervised learning in machine learning divides data into several groups. The observations in the same group have similar characteristics and the observations in the different groups have the different characteristics. In the paper, we classify data by partitioning around medoids which have some advantages over the k-means clustering. We apply it to baseball players in Korea Baseball League. We also apply the principal component analysis to data and draw the graph using two components for axis. We interpret the meaning of the clustering graphically through the procedure. The combination of the partitioning around medoids and the principal component analysis can be used to any other data and the approach makes us to figure out the characteristics easily.


Crop Science ◽  
1994 ◽  
Vol 34 (4) ◽  
pp. 852-865 ◽  
Author(s):  
Rita Hogan Mumm ◽  
Lawrence J. Hubert ◽  
J. W. Dudley

2011 ◽  
Vol 8 (1) ◽  
pp. 201-210
Author(s):  
R.M. Bogdanov

The problem of determining the repair sections of the main oil pipeline is solved, basing on the classification of images using distance functions and the clustering principle, The criteria characterizing the cluster are determined by certain given values, based on a comparison with which the defect is assigned to a given cluster, procedures for the redistribution of defects in cluster zones are provided, and the cluster zones parameters are being changed. Calculations are demonstrating the range of defect density variation depending on pipeline sections and the universal capabilities of linear objects configuration with arbitrary density, provided by cluster analysis.


1990 ◽  
Vol 104 (3) ◽  
pp. 443-453 ◽  
Author(s):  
L. Dijkshoorn ◽  
A. Van Ooyen ◽  
W. C. J. Hop ◽  
M. Theuns ◽  
M. F. Michel

SUMMARYA quantitative carbon source growth assay, comprising ten carbon sources, was used to compare acinetobacter strains from three hospitals. The strains had been obtained during episodes of increased prevalence of isolations and were, for each hospital, assumed to be epidemiologically related. This assumption was supported by the electrophoretic protein profiles of the strains. Univariate analysis of growth data showed significant differences between strains from the three hospitals. Moreover, cluster analysis revealed that the major pattern in the data was related to the epidemiological origin of the strains. Exceptions to the epidemic-related pattern were observed. Thus, apart from epidemiological factors, other factors might contribute to carbon source growth profiles of the strains. It is concluded that the carbon growth assay may be useful to distinguish roughly between acinetobacter strains from different sites of origin. Further studies are required to analyse additional factors which influence carbon source growth of strains.


2003 ◽  
Vol 17 (1) ◽  
pp. 111 ◽  
Author(s):  
Jeremy D. Holloway ◽  
Scott E. Miller

The biosystematic position of the Parallelia generic complex is reviewed and a revised generic classification of its component taxa is presented. Bastilla Swinhoe (= Xiana Nye, syn. nov., Naxia Guenée, syn. nov.) is identified as the most appropriate genus for a large number of these taxa, including the joviana-group, which is reviewed in detail, with description of two new species, B. nielseni, sp. nov. and B. binatang, sp. nov. Parallelia prouti Hulstaert, syn. nov. and P. cuneifascia Hulstaert, syn. nov. are recognised as junior synonyms of Bastilla vitiensis Butler and two newly described Tahitian taxa are transferred into the joviana-group. Larval host records are examined in relation to this new generic system and significant preference for the Euphorbiaceae is noted for several groups: Bastilla, Buzara Walker (= Caranilla Moore, syn. nov., another segregate from Parallelia) and an Australian group within Grammodes Guenée.


2016 ◽  
Vol 8 (3) ◽  
pp. 32 ◽  
Author(s):  
Olivier K. Bagui ◽  
Kenneth A. Kaduki ◽  
Edouard Berrocal ◽  
Jeremie T. Zoueu

<p class="1Body">Most commercially available ground coffees are processed from Robusta or Arabica coffee beans. In this work, we report on the potential of Structured Laser Illumination Planar Imaging (SLIPI) technique for the classification of five types of Robusta and Arabica commercial ground coffee samples (Familial, Belier, Brazil, Colombia and Malaga). This classification is made, here, from the measurement of the extinction coefficient µ<sub>e</sub> and of the optical depth OD by means of SLIPI. The proposed technique offers the advantage of eliminating the light intensity from photons which have been multiply scattered in the coffee solution, leading to an accurate and reliable measurement of µ<sub>e</sub>. Data analysis uses the chemometric techniques of Principal Component Anaysis (PCA) for variable selection and Hierarchical Cluster Analysis (HCA) for classification. The chemometric model demonstrates the potential of this approach for practical assessment of coffee grades by correctly classifying the coffee samples according to their species.</p>


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