Application of linear and machine learning models to genomic prediction of fatty acid composition in Japanese Black cattle.

We collected 3180 records of oleic acid (C18:1) and monounsaturated fatty acid (MUFA) measured using gas chromatography (GC) and 6960 records of C18:1 and MUFA measured using near-infrared spectroscopy (NIRS) in intermuscular fat samples of Japanese Black cattle. We compared genomic prediction performance for four linear models (genomic best linear unbiased prediction [GBLUP], kinship-adjusted multiple loci [KAML], BayesC, and BayesLASSO) and five machine learning models (Gaussian kernel [GK], deep kernel [DK], random forest [RF], extreme gradient boost [XGB], and convolutional neural network [CNN]). For GC-based C18:1 and MUFA, KAML showed the highest accuracies, followed by BayesC, XGB, DK, GK, and BayesLASSO, with more than 6% gain of accuracy by KAML over GBLUP. Meanwhile, DK had the highest prediction accuracy for NIRS-based C18:1 and MUFA, but the difference in accuracies between DK and KAML was slight. For all traits, accuracies of RF and CNN were lower than those of GBLUP. The KAML extends GBLUP methods, of which marker effects are weighted, and involves only additive genetic effects; whereas machine learning methods capture non-additive genetic effects. Thus, KAML is the most suitable method for breeding of fatty acid composition in Japanese Black cattle.

Molecular typing of Salmonella enterica serovar 4,[5],12:i:- isolates from humans, animals and river water in Japan by multilocus variable-number tandem repeat analysis and pulsed-field gel electrophoresis.

Fifty-one Salmonella enterica serovar 4,[5],12:i:- (S. 4, [5],12:i:-) isolates (14 human strains, 34 animal strains and 3 river water strains) which are assumed to be monophasic variants of S. Typhimurium were analyzed using pulsed-field gel electrophoresis (PFGE) and multiple-locus variable-number tandem repeat analysis (MLVA) in order to investigate their genetic diversities and relationships. PFGE, MLVA and combination of them identified 28, 27 and 34 profiles (Simpson's diversity indices [DI]=0.94, 0.96 and 0.97), respectively. No correlations were detected between MLVA clustering and PFGE clustering or phage typing. These results suggested that S. 4,[5],12:i:- originated from multiple S. Typhimurium ancestors. Two cattle and one pig isolates showing identical phage types as well as PFGE and MLVA profiles to human isolates S. 4,[5],12:i:- suggested the existence of the links between human infections and animal reservoirs.

Field application of the combination of neutralizing test and virus isolation, so-called spot test, to detect herds including cattle persistently infected with bovine viral diarrhea virus.

To detect herds including cattle persistently infected (PI) with bovine viral diarrhea virus (BVDV), application of the combination of neutralizing antibody detection and virus isolation, so-called spot test, were performed on sera of 3 calves selected from each of 26 farms. Nine farms were judged as positive because 64 or more antibody titers were detected from 2 or more calves or BVDV was isolated from one or more calves. PI cattle were detected from 8 of the 9 farms. The positive judgment on one farm was obtained only when the indicator virus used on the neutralizing test was genotypically identical with the isolate from the farm. These results suggest that the spot test can be effective in detecting herds with PI cattle and that the accuracy may be influenced by the genotypes of the indicator viruses.