Pasteurella multocida is an important respiratory pathogen of cattle. OmpH Protein is a major protective antigen of bacteria has been well studied in avian strains. In the literature there are no data available for the study of a variety of sequence of this protein among isolates with cattle respiratory pathology. There have been described several genes associated with the virulence of the bacterium in respiratory disease of cattle, but none of the authors compared the frequency of detection of these genes with the pathogenicity for laboratory animals. The aim of our study was the development of allele-specific PCR to determine Omph-types of Pasteurella multocida and the search for new approaches to assess the pathogenicity of isolates of bacteria. Total amount of 83 isolates allocated from the lungs of calves with respiratory pathology was investigated. All isolates belonged to groups A or D (isolates 63 and 20, respectively). Among isolates of capsular serogroup A we revealed 6 types, most propagation types were A1 and A2. All isolates of capsular serogroup D were one omph- type. In 16 out of 23 farms there were identified isolates of only one omph-type, 4 - 2 types, 3 - three types. The frequency of gene hgbb - hemoglobin binding protein correlated with pathogenicity of isolates for white mice. The developed allele-specific PCR along with hgbb gene detection can be used for screening and studying the properties of antigen and circulating pathogenic isolates and selecting a candidate vaccine strains
In the article, on a small numerical example, we consider the similarity and difference of symptoms and syndromes according to their diagnostic meaning, i.e. according to the information they contain about the belonging of conditionals of animals to different nosological images. This problem can be solved for veterinary with the use of a new method of agglomerative cognitive clustering, implemented in Automated System-Cognitive analysis (ASC-analysis). This method of clustering differs from the known traditional methods in: a) in this method, the parameters of the generalized image of the cluster are calculated not as averages from the original objects (symptoms) or their center of gravity, but are determined using the same basic cognitive operation of ASC-analysis, which is used to form generalized images of the classes based on examples of objects and which really correctly provides a generalization; b) the similarity criterion is not the Euclidean distance or its variants, but the integral criterion of non-metric nature: "the total amount of information", the application of which is theoretically correct and gives good results in unortonormated spaces, which are usually found in practice; c) cluster analysis is carried out not on the basis of initial variables, frequency matrices or matrix of similarity (differences), depending on the units of measurement on the axes (measurement scales), but in cognitive space, in which one unit of measurement is used for all axes: the amount of information, and therefore the results of clustering do not depend on the initial units of measurement of features of objects. All this allows us to get the results of clustering, understandable to specialists and amenable to meaningful interpretation, well-consistent with the experts ' assessments, their experience and intuitive expectations, which is often a problem for classical clustering methods
The article deals with the similarity and difference of nosological images in veterinary medicine using a new method of agglomerative clustering implemented in Automated system-cognitive analysis (ASC-analysis) on a small numerical example. This method is called Agglomerative cognitive clustering. This method differs from the known traditional facts: a) parameters of a generalized image of the cluster are computed not as averages from the original objects (classes) or their center of gravity, and are defined using the same underlying cognitive operations of ASC-analysis, which is used for the formation of generalized images of the classes on the basis of examples of objects and which is really correct and provides a synthesis; b) as a criterion of similarity we do not use Euclidean distance or its variants, and the integral criterion of non-metric nature: "the total amount of information", the use of which is theoretically correct and gives good results in non-orthonormal spaces, which are usually found in practice; c) cluster analysis is not based on the original variables, matrices of frequency or a matrix of similarities (differences) dependent on the measurement units of the axes, and in the cognitive space in which all the axes (descriptive scales) use the same unit of measurement: the quantity of information, and therefore, the clustering results do not depend on the original units of measurement features. All this makes it possible to obtain clustering results that are understandable to specialists and can be interpreted in a meaningful way that is in line with experts' assessments, their experience and intuitive expectations, which is often a problem for classical clustering methods