Peste des petits ruminants is an important transboundary disease infecting small ruminants. Genome or gene sequence analysis enriches our knowledge about the evolution and transboundary nature of the causative agent of this disease, peste des petits ruminants virus (PPRV). Although analysis using whole genome sequences of pathogens leads to more precise phylogenetic relationships, when compared to individual genes or partial sequences, there is still a need to identify specific genes/genomic regions that can provide evolutionary assessments consistent with those predicted with full-length genome sequences. Here the virulent Izatnagar/94 PPRV isolate was assembled and compared to all available complete genome sequences (currently in the NCBI database) to estimate nucleotide diversity and to deduce evolutionary relationships between genes/genomic regions and the full length genomes. Our aim was to identify the preferred candidate gene for use as a phylogenetic marker, as well as to predict divergence time and explore PPRV phylogeography. Among all the PPRV genes, the H gene was identified to be the most diverse with the highest evolutionary relationship with the full genome sequences. Hence it is considered as the most preferred candidate gene for phylogenetic study with 93% identity set as a nucleotide cutoff. A whole genome nucleotide sequence cutoff value of 94% permitted specific differentiation of PPRV lineages. All the isolates examined in the study were found to have a most recent common ancestor in the late 19th or in the early 20th century with high posterior probability values. The Bayesian skyline plot revealed a decrease in genetic diversity among lineage IV isolates since the start of the vaccination program and the network analysis localized the ancestry of PPRV to Africa.
Support vector machine (SVMs) is a classical classification tool in face recognition. In ordinary SVM, every input points are considered to have the same commitment to the training model. On the other hand, this is not generally valid due to some challenges in face recognition. Since there may be a few points undermined by commotion so they are less significant and the machine ought to better to toss them which are undecidable. This paper review some methodology to handle this sort of information giving so as to utilize fuzzy methodology them a weight which demonstrate the diverse commitment of every point to the model. The weights are resolved as for their membership function. Such approach is typically called as Fuzzy SVM (FSVM).
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