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The list of standard abbreviations for JDS is available at adsa.org/jds-abbreviations-24. Nonstandard abbreviations are available in the Notes. Bovine mastitis is a prevalent infectious disease in dairy herds worldwide, resulting in substantial economic losses. Staphylococcus aureus is a major cause of mastitis in animals, and its antibiotic resistance poses challenges for treatment. Recently, renewed interest has focused on the development of alternative methods to antibiotic therapy, including bacteriophages (phages), for controlling bacterial infections. In this study, 2 lytic phages, vB_SauM_JDYN (JDYN) and vB_SauM_JDF86 (JDF86), were isolated from the cattle sewage effluent samples collected from dairy farms in Shanghai. The 2 phages have a broad bactericidal spectrum against Staphylococcus of various origins. Genomic and morphological analyses revealed that the 2 phages belonged to the Myoviridae family. Moreover, JDYN and JDF86 remained stable under a wide temperature and pH range and were almost unaffected in chloroform. In this study, we prepared a phage cocktail (PHC-1) which consisted of a 1:1:1 ratio of JDYN, JDF86, and SLPW (a previously characterized phage). We found that PHC-1 showed the strongest bacteriolytic effect and the lowest frequency of emergence of bacteriophage insensitive mutants compared with monophages. Bovine mammary epithelial cells and lactating mice mastitis models were used to evaluate the effectiveness of PHC-1 in vitro and in vivo, respectively. The results demonstrated that PHC-1 treatment significantly reduced bacterial load, alleviated inflammatory response, and improved mastitis pathology. Altogether, these results suggest that PHC-1 has the potential to treat S. aureus -induced bovine mastitis and that phage cocktails can combat antibiotic-resistant S. aureus infections. [ABSTRACT FROM AUTHOR]

Accurate and fine-grained individual air quality index (IAQI) prediction is the basis of air quality index (AQI), which is of great significance for air quality control and human health. Traditional approaches, such as time series, recurrent neural network or graph convolutional network, cannot effectively integrate spatial-temporal and meteorological factors and manage the dynamic edge relationship among scattered monitoring stations. In this paper, a ST-CCN-IAQI model is proposed based on spatial-temporal causal convolution networks. Both the spatial effects of multi-source air pollutants and meteorological factors were considered via spatial attention mechanism. Time-dependent features in the causal convolution network were extracted by stacked dilated convolution and time attention. All the hyper-parameters in ST-CCN-IAQI were tuned by Bayesian optimization. Shanghai air monitoring station data were employed with a series of baselines (AR, MA, ARMA, ANN, SVR, GRU, LSTM and ST-GCN). Final results showed that: (1) For a single station, the RMSE and MAE values of ST-CCN-IAQI were 9.873 and 7.469, decreasing by 24.95% and 16.87% on average, respectively. R2 was 0.917, with an average 5.69% improvement; (2) For all nine stations, the mean RMSE and MAE of ST-CCN-IAQI were 9.849 and 7.527, respectively, and the R2 value was 0.906. (3) Shapley analysis showed PM10, humidity and NO2 were the most influencing factors in ST-CCN-IAQI. The Friedman test, under different resampling, further confirmed the advantage of ST-CCN-IAQI. The ST-CCN-IAQI provides a promising direction for fine-grained IAQI prediction. [ABSTRACT FROM AUTHOR]