SM-17

Porcine circovirus type 3 (PCV3) was firstly detected in 2016 in USA. Later PCV3 was discovered in Asia, Europe, and South America. The present investigation demonstrates for the first time the circulation of PCV3 among pigs in Russia. The viruses were detected at two geographically distant unrelated commercial farms with records of reproductive failure (abortions, stillbirth), porcine dermatitis, and nephropathy syndrome (PDNS). The two farms were located in the region of Smolensk (western part of Russia) and the region of Tyumen (West Siberia, Russia). We investigated samples collected from pigs of different ages. We performed PCR for the PCV3 DNA detection. The DNA of PCV3 was detected in serum, kidney, heart, spleen, pleural effusion, and peritoneal cavity fluid samples. Two viral genomes were sequenced and the corresponding strains were named PCV3-RU/SM17 (the strain from Smolensk region) and PCV3-RU/TY17 (the strain from Tyumen region). The full genome sequences of both strains were 2000 nucleotides in length and contained at least two ORFs, encoding the Cap and Rep proteins. Full sequence alignment revealed a 99.3% identity between the PCV3-RU/SM17 and PCV3-RU/TY17 strains. Molecular analysis showed that the two strains from Russia are highly homologous to viruses identified in other countries, with a 98.5-99.6% homology for PCV3-RU/TY17 and 97.9-99.0 for PCV3-RU/SM17. The PCV3-RU/SM17 and PCV3-RU/TY17 strains were found to form a monophyletic group in a phylogenetic tree based on PCV3 complete genome sequences.

Various chemometric methods were used to analyze data sets of marine water quality for 19 parameters measured at 16 different sites of southern Hong Kong from 2000 to 2004 (18,240 observations), to determine temporal and spatial variations in marine water quality and identify pollution sources. Hierarchical cluster analysis (CA) grouped the 12 months into three periods (January-April, May-August and September-December) and the 16 sampling sites into two groups (A and B) based on similarities in marine water-quality characteristics. Discriminant analysis (DA) was important in data reduction because it used only eight parameters (TEMP, TURB, Si, NO(3)(-)-N, NH(4)(+)-N, NO(2)(-)-N, DO, and Chl-a) to correctly assign about 86% of the cases, and five parameters (SD, NH(4)(+)-N, TP, NO(2)(-)-N, and BOD(5)) to correctly assign >81.15% of the cases. In addition, principal component analysis (PCA) identified four latent pollution sources for groups A and B: organic/eutrophication pollution, natural pollution, mineral pollution, and nutrient/fecal pollution. Furthermore, during the second and third periods, all sites received more organic/eutrophication pollution and natural pollution than in the first period. SM5, SM6, SM17, SM10, SM11, SM12, and SM13 (second period) were affected by organic and eutrophication pollution, whereas SM3 (third period) and SM9 (second period) were influenced by natural pollution. However, differences between mineral pollution and nutrient/fecal pollution were not significant among the three periods. SM17 and SM10 were affected by mineral pollution, whereas SM4 and SM9 were highly polluted by nitrogenous nutrient/fecal pollution.