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In Jeju island of South Korea, a classical swine fever (CSF) non-vaccinated region, many pig farmers insisted on abortion and stillbirth in pregnant sows and high mortality of suckling/weaning piglets by circulating CSF virus from 2014 to 2018. We investigated whether CSF viruses isolated from pigs in Jeju Island (Jeju LOM) have recovered their pathogenicity by conducting experiments using pregnant sows and specific pathogen-free (SPF) pigs. The CSF modified live LOM vaccine (MLV-LOM) and Jeju LOM strains induced abortion and stillbirth in pregnant sows. Viral antigens were detected in the organs of fetuses and stillborn piglets in the absence of specific pathological lesions associated with the virulent CSF virus in both groups (MLV-LOM and Jeju LOM strain). However, antigen was detected in one newborn piglet from a sow inoculated with a Jeju LOM strain, suggesting that it may cause persistent infections in pigs. SPF pigs inoculated with the MLV-LOM or Jeju LOM strains were asymptomatic, but virus antigen was detected in several organ and blood samples. Virus shedding in both groups of animals was not detected in the feces or saliva until 21 days post inoculation. The serum concentration of the three major cytokines, IFN-α, TNF-α, and IL-10, known to be related to lymphocytopenia, were similar in both groups when the MLV-LOM or Jeju LOM strains were inoculated into SPF pigs. In conclusion, Jeju LOM strains exhibited most of the characteristics of the MLV-LOM in pigs and resulted in the same adverse effects as the MLV-LOM strain.

Predictive business process monitoring aims at providing predictions about running instances by analyzing logs of completed cases in a business process. Recently, a lot of research focuses on increasing productivity and efficiency in a business process by forecasting potential problems during its executions. However, most of the studies lack suggesting concrete actions to improve the process. They leave it up to the subjective judgment of a user. In this paper, we propose a novel method to connect the results from predictive business process monitoring to actual business process improvements. More in detail, we optimize the resource allocation in a non-clairvoyant online environment, where we have limited information required for scheduling, by exploiting the predictions. The proposed method integrates the offline prediction model construction that predicts the processing time and the next activity of an ongoing instance using Bayesian Neural Networks (BNNs) with the online resource allocation that is extended from the minimum cost and maximum flow algorithm. To validate the proposed method, we performed experiments using an artificial event log and a real-life event log from a global financial organization.

Atypical porcine pestivirus (APPV), currently classified as pestivirus K, causes congenital tremor (CT) type A-II in piglets. Eighteen APPV strains were identified from 2297 South Korean wild boars captured in 2019. Phylogenetic analysis of the structural protein E2 and nonstructural proteins NS3 and Npro classified the APPV viruses, including reference strains, into Clades I, II and III. Clade I was divided into four subclades; however, the strains belonging to the four subclades differed slightly, depending on the tree analysis, the NS3, E2, and Npro genes. The maximum-likelihood method was assigned to South Korean wild boar APPV strains to various subclades within the three trees: subclades I.1 and I.2 in the E2 tree, subclade I.1 in the Npro tree, and subclades I.1 and I.4 in the NS3 ML tree. In conclusion, APPV among South Korean wild boars belonging to Clade I may be circulating at a higher level than among the South Korean domestic pig populations.

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