scholarly journals Assessment of habitat conditions using Self-Organizing Feature Maps for reintroduction/introduction of Aldrovanda vesiculosa L. in Poland

2011 ◽  
Vol 80 (2) ◽  
pp. 139-148
Author(s):  
Piotr Kosiba ◽  
Lucyna Mróz ◽  
Ryszard Kamiński
Keyword(s):  
Cluster Analysis ◽  
Chemical Properties ◽  
Habitat Conditions ◽  
Feature Maps ◽  
Ecological Data ◽  
Statistical Parameters ◽  
Topological Map ◽  
Water Properties ◽  
And Cluster Analysis ◽  
Self Organizing

The study objects were <em>Aldrovanda vesiculosa</em> L., an endangered species and fifty five water sites in Poland. The aim of the present work was to test the Self-Organizing Feature Map in order to examine and predict water properties and type of trophicity for restoration of the rare plant. Descriptive statistical parameters have been calculated, analysis of variance and cluster analysis were carried out and SOFM model has been constructed for analysed sites. The results of SOFM model and cluster analysis were compared. The study revealed that the ordination of individuals and groups of neurons in topological map of sites are similar in relation to dendrogram of cluster analysis, but not identical. The constructed SOFM model is related with significantly different contents of chemical water properties and type of trophicity. It appeared that sites with <em>A. vesiculosa</em> are predominantly distrophic and eutrophic waters shifted to distrophicity. The elevated model showed the sites with chemical properties favourable for restoration the species. Determined was the range of ecological tolerance of the species in relation to habitat conditions as stenotopic or relatively stenotopic in respect of the earlier accepted eutrophic status. The SOFM appeared to be a useful technique for ordination of ecological data and provides a novel framework for the discovery and forecasting of ecosystem properties constituting a validation of the SOFM method in this type of studies.

scholarly journals Modelling of habitat conditions by self-organizing feature maps using relations between soil, plant chemical properties and type of basaltoides

2011 ◽  
Vol 79 (4) ◽  
pp. 315-324 ◽  
Author(s):  
Piotr Kosiba ◽  
Andrzej Stankiewicz ◽  
Lucyna Mróz
Keyword(s):  
Chemical Elements ◽  
Chemical Properties ◽  
Habitat Conditions ◽  
Feature Maps ◽  
Ecological Data ◽  
Statistical Parameters ◽  
Plant Chemical ◽  
Chemical Properties Of Soils ◽  
Lower Silesia

The paper shows the use of Kohonen's network for classification of basaltoides on the base of chemical properties of soils and <em>Polypodium vulgare</em> L. The study area was Lower Silesia (Poland). The archival data were: chemical composition of types of basaltoides from 89 sites (Al<sub>2</sub>O<sub>3</sub>, CaO, FeO, Fe<sub>2</sub>O<sub>3</sub>, K2O, MgO, MnO, Na<sub>2</sub>O, P<sub>2</sub>O<sub>5</sub>, SiO<sub>2</sub> and TiO<sub>2</sub>), elements contents in soils (Cd, Co, Cu, Fe, Mn, Mo, Ni, Pb, S, Ti and Zn) and leaves of <em>P. vulgare</em> (Ca, Cd, Co, Cu, Fe, K, Mg, Mn, Mo, N, Ni, P, Pb, S, Ti and Zn) from 20 sites. Descriptive statistical parameters of soils and leaves chemical properties have been shown, statistical analyses using ANOVA and relationships between chemical elements were carried out, and SOFM models have been constructed. The study revealed that the ordination of individuals and groups of neurons in topological maps of plant and soil chemical properties are similar. The constructed models are related with significantly different contents of elements in plants and soils. These models represent different chemical types of soils and are connected with ordination of types of basaltoides worked out by SOFM model of TAS division. The SOFM appeared to be a useful technique for ordination of ecological data and provides a novel framework for the discovery and forecasting of ecosystem properties.

scholarly journals Chromosomal Regions in Prostatic Carcinomas Studied by Comparative Genomic Hybridization, Hierarchical Cluster Analysis and Self-Organizing Feature Maps

2002 ◽  
Vol 24 (4-5) ◽  
pp. 167-179 ◽  
Author(s):  
Torsten Mattfeldt ◽  
Hubertus Wolter ◽  
Danilo Trijic ◽  
Hans‐Werner Gottfried ◽  
Hans A. Kestler
Keyword(s):  
Cluster Analysis ◽  
Comparative Genomic Hybridization ◽  
Hierarchical Cluster Analysis ◽  
Hierarchical Cluster ◽  
Data Matrix ◽  
Comparative Genomic ◽  
Feature Maps ◽  
Genomic Hybridization ◽  
A Genome ◽  
Self Organizing

Comparative genomic hybridization (CGH) is an established genetic method which enables a genome‐wide survey of chromosomal imbalances. For each chromosome region, one obtains the information whether there is a loss or gain of genetic material, or whether there is no change at that place. Therefore, large amounts of data quickly accumulate which must be put into a logical order. Cluster analysis can be used to assign individual cases (samples) to different clusters of cases, which are similar and where each cluster may be related to a different tumour biology. Another approach consists in a clustering of chromosomal regions by rewriting the original data matrix, where the cases are written as rows and the chromosomal regions as columns, in a transposed form. In this paper we applied hierarchical cluster analysis as well as two implementations of self‐organizing feature maps as classical and neuronal tools for cluster analysis of CGH data from prostatic carcinomas to such transposed data sets. Self‐organizing maps are artificial neural networks with the capability to form clusters on the basis of an unsupervised learning rule. We studied a group of 48 cases of incidental carcinomas, a tumour category which has not been evaluated by CGH before. In addition we studied a group of 50 cases of pT2N0‐tumours and a group of 20 pT3N0‐carcinomas. The results show in all case groups three clusters of chromosomal regions, which are (i) normal or minimally affected by losses and gains, (ii) regions with many losses and few gains and (iii) regions with many gains and few losses. Moreover, for the pT2N0‐ and pT3N0‐groups, it could be shown that the regions 6q, 8p and 13q lay all on the same cluster (associated with losses), and that the regions 9q and 20q belonged to the same cluster (associated with gains). For the incidental cancers such clear correlations could not be demonstrated.

scholarly journals Spatial distribution and cluster analysis of dengue using self organizing maps in Andhra Pradesh, India, 2011–2013

2018 ◽  
Vol 3 (1) ◽  
pp. 52-61 ◽  
Author(s):  
Srinivasa Rao Mutheneni ◽  
Rajasekhar Mopuri ◽  
Suchithra Naish ◽  
Deepak Gunti ◽  
Suryanarayana Murty Upadhyayula
Keyword(s):  
Cluster Analysis ◽  
Spatial Distribution ◽  
Andhra Pradesh ◽  
Self Organizing Maps ◽  
And Cluster Analysis ◽  
Self Organizing

scholarly journals Water trophicity of Utricularia microhabitats identlfied by means of SOFM as a tool in ecological modeling

2011 ◽  
Vol 76 (3) ◽  
pp. 255-261 ◽  
Author(s):  
Piotr Kosiba ◽  
Andrzej Stankiewicz
Keyword(s):  
Water Quality ◽  
Cluster Analysis ◽  
Ecological Modeling ◽  
The Self ◽  
Topological Map ◽  
Upper Silesia ◽  
Hierarchical Tree ◽  
Feature Map ◽  
Self Organizing

The study objects were 48 microhabitats of five <em>Utricularia</em> species in Lower and Upper Silesia (POLAND). The aim of the paper was to focus on application of the Self-Organizing Feature Map in assessment of water trophicity in <em>Utricularia</em> microhabitats, and to describe how SOFM can be used for the study of ecological subjects. This method was compared with the hierarchical tree plot of cluster analysis to check whether this techniques give similar results. In effect, both topological map of SOFM and dendrogram of cluster analysis show differences between <em>Utricularia</em> species microhabitats in respect of water quality, from eutrophic for <em>U. vulgaris</em> to dystrophic for <em>U. minor</em> and <em>U. intermedia</em>. The used methods give similar results and constitute a validation of the SOFM method in this type of studies.

scholarly journals Chemical properties and similarity of habitats of Utricularia species in Lower Silesia, Poland

2011 ◽  
Vol 73 (4) ◽  
pp. 335-341 ◽  
Author(s):  
Piotr Kosiba
Keyword(s):  
Cluster Analysis ◽  
Organic Substance ◽  
Chemical Properties ◽  
Total Hardness ◽  
Extreme Position ◽  
Water Properties ◽  
Lower Silesia

The study object consisted of 28 microhabitats of five <em>Utricularia</em> species localized in the Province of Lower Silesia, Poland. The aim of the study was to analyse the chemical properties of water and to present the differentiation of microhabitats in respect of their chemism, i.e., whether there are differences between the microhabitats, and which of the <em>Utricularia</em> species show the highest tolerance to the chemical properties of water. Analysed were the contents of NO<sup>-<sub>2</sub></sup>, NO<sup>-<sub>3</sub></sup>, NH<sup>+</sup><sub>4</sub>, PO<sup>-2</sup><sub>4</sub>, K<sup>+</sup>, Ca<sup>+2</sup>, Mg<sup>+2</sup>, Na<sup>+</sup>, Fe<sup>+3</sup>, SO<sup>-2</sup><sub>4</sub>, total hardness of water, organic substance, pH and trophicity of water. The differentiation of microhabitats of <em>Utricularia intermedia</em> and <em>U. minor</em> appeared to be small, but much higher in case of <em>U. vulgaris</em>, <em>U. australis</em> and <em>U. ochroleuca</em>. The similarity of microhabitats has been determined by cluster analysis. The tree plot showed the least similarity of <em>U. minor</em> and <em>U. intermedia</em>, which occupy an extreme position in relation to microhabitats of the remaining species. Such a grouping suggests that this species is clearly distinct because of its connection with water properties.

scholarly journals Expanding Self-Organizing Map for data visualization and cluster analysis

2004 ◽  
Vol 163 (1-3) ◽  
pp. 157-173 ◽  
Author(s):  
Huidong Jin ◽  
Wing-Ho Shum ◽  
Kwong-Sak Leung ◽  
Man-Leung Wong
Keyword(s):  
Cluster Analysis ◽  
Data Visualization ◽  
Self Organizing Map ◽  
And Cluster Analysis ◽  
Self Organizing
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