First molecular determination of herpesvirus from two mysticete species stranded in the Mediterranean Sea

BACKGROUND Herpesvirus can infect a wide range of animal species: mammals, birds, reptiles, fish, amphibians and bivalves. In marine mammals, several alpha- and gammaherpesvirus have been identified in some cetaceans and pinnipeds species. To date, however, this virus has not been detected in any member of the Balaenoptera genus. CASE PRESENTATION Herpesvirus was determined by molecular methods in tissue samples from a male fin whale juvenile (Balaenoptera physalus) and a female common minke whale calf (Balaenoptera acutorostrata) stranded on the Mediterranean coast of the Region of Valencia (Spain). Samples of skin and penile mucosa from the fin whale and samples of skin, muscle and central nervous system tissue from the common minke whale tested positive for herpesvirus based on sequences of the DNA polymerase gene. Sequences from fin whale were identical and belonged to the Alphaherpesvirinae subfamily. Only members of the Gammaherpesvirinae subfamily were amplified from the common minke whale, and sequences from the muscle and central nervous system were identical. Sequences in GenBank most closely related to these novel sequences were viruses isolated from other cetacean species, consistent with previous observations that herpesviruses show similar phylogenetic branching as their hosts. CONCLUSIONS To our knowledge, this is the first molecular determination of herpesvirus in the Balaenoptera genus. It shows that herpesvirus should be included in virological evaluation of these animals.

Determination of preventive maintenance lead time using hybrid analysis

The time for conducting Preventive Maintenance (PM) on an asset is often determined using a predefined alarm limit based on trends of a hazard function. In this paper, the authors propose using both hazard and reliability functions to improve the accuracy of the prediction particularly when the failure characteristic of the asset whole life is modelled using different failure distributions for the different stages of the life of the asset. The proposed method is validated using simulations and case studies.