Relation of genetic phylogeny and geographical distance of tick-borne encephalitis virus in central Europe

Tick-borne encephalitis virus (TBEV) is the most important arboviral agent causing disease of the central nervous system in central Europe. In this study, 61 TBEV E gene sequences derived from 48 isolates from the Czech Republic, and four isolates and nine TBEV strains detected in ticks from Germany, covering more than half a century from 1954 to 2009, were sequenced and subjected to phylogenetic and Bayesian phylodynamic analysis to determine the phylogeography of TBEV in central Europe. The general Eurasian continental east-to-west pattern of the spread of TBEV was confirmed at the regional level but is interlaced with spreading that arises because of local geography and anthropogenic influence. This spread is reflected by the disease pattern in the Czech Republic that has been observed since 1991. The overall evolutionary rate was estimated to be approximately 8x10(-4) substitutions per nucleotide per year. The analysis of the TBEV E genes of 11 strains isolated at one natural focus in Zd`ar Kaplice proved for the first time that TBEV is indeed subject to local evolution.

HIERARCHICAL DECOMPOSITION OF MULTICLASS PROBLEMS

Several popular Machine Learning techniques are originally designed for the solution of two-class problems. However, several classification problems have more than two classes. One approach to deal with multiclass problems using binary classifiers is to decompose the multiclass problem into multiple binary sub-problems disposed in a binary tree. This approach requires a binary partition of the classes for each node of the tree, which defines the tree structure. This paper presents two algorithms to determine the tree structure taking into account information collected from the used dataset. This approach allows the tree structure to be determined automatically for any multiclass dataset.