The genome of Border disease virus genotype 8 from chamois by next generation sequencing

Machine Learning and Geo-Based Multi-Criteria Decision Support Systems in Analysis of Complex Problems

Many complex problems require a multi-criteria decision, such as the COVID-19 pandemic that affected nearly all activities in the world. In this regard, this study aims to develop a multi-criteria decision support system considering the sustainability, feasibility, and success rate of possible approaches. Therefore, two models have been developed: Geo-AHP (applying geo-based data) and BN-Geo-AHP using probabilistic techniques (Bayesian network). The ranking method of Geo-APH is generalized, and the equations are provided in a way that adding new elements and variables would be possible by experts. Then, to improve the ranking, the application of the probabilistic technique of a Bayesian network and the role of machine learning for database and weight of each parameter are explained, and the model of BN-Geo-APH has been developed. In the next step, to show the application of the developed Geo-AHP and BN-Geo-AHP models, we selected the new pandemic of COVID-19 that affected nearly all activities, and we used both models for analysis. For this purpose, we first analyzed the available data about COVID-19 and previous studies about similar virus infections, and then we ranked the main approaches and alternatives in confronting the pandemic of COVID-19. The analysis of approaches with the selected alternatives shows the first ranked approach is massive vaccination and the second ranked is massive swabs or other tests. The third is the use of medical masks and gloves, and the last ranked is the lockdown, mostly due to its major negative impact on the economy and individuals.

Epidemiology of Bovine Pestiviruses Circulating in Italy

Pestiviruses are widespread and economically important pathogens of cattle and other animals. Pestivirus A (formerly known as Bovine viral diarrhea virus 1, BVDV-1), Pestivirus B (Bovine viral diarrhea virus 2, BVDV-2), and Pestivirus H (HoBi-like pestivirus, HoBiPeV) species are infecting primarily cattle. Like other RNA viruses, pestiviruses are characterized by a high degree of genetic variability. This high rate of variability is revealed by the existence of a number of viral subgenotypes within each species. In cattle, the highest number of pestivirus subgenotypes has been documented in European countries, particularly in Italy. The aim of this review is to report an up-to-date overview about the genetic diversity of pestiviruses in Italian cattle herds. All three bovine pestiviruses species have been identified in cattle population with variable frequency and geographical distribution. The genetic diversity of Italian pestiviral strains may have diagnostic and immunological implications, affecting the performance of diagnostic tools and the full cross-protection elicited by commercially available vaccines. Implementation and strengthening of coordinated approaches for bovine pestivirus control in Italy are recommended. Therefore, it would be extremely important to increase control and restriction measures to the trade of cattle and biological products of bovine origin, including those containing fetal bovine serum.