• Journal of Veterinary Science
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• v.23 no.1
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• pp.6.1-6.8
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• 2022

Background: With limited information available, the association among urinary tract infections, urease-producing bacteria and the presence of magnesium ammonium phosphate (MAP) urolithiasis in canines in Thailand requires more study. Objectives: This study aimed to investigate the association between demographic characteristics of canines and the presence of MAP urolithiasis in canines, and to evaluate antimicrobial susceptibility patterns of bacteria isolated from canine uroliths. Methods: A total of 56 canines admitted for treatment with surgical removal of uroliths were recruited. Demographic characteristics and clinical chemistry data were recorded. Bacteria isolated from the removed uroliths were identified. Chemical compositions of the uroliths were analyzed by Fourier transform infrared spectrometer. Potential risk factors were determined with univariable and multivariable logistic regression analyses. Results: Of 56 canine urolithiasis, bacteria were isolated from uroliths of 38 canines (27 MAP and 11 non-MAP) but not from uroliths of 18 canines (5 MAP and 13 non-MAP). The most common bacteria found in nidus of MAP uroliths was Staphylococcus pseudintermedius (approximately 51%). An antimicrobial resistance was frequently found in Staphylococci isolates (42.86%). Multivariate logistic regression analysis showed that the predictors of MAP urolith in canine urolithiasis were being female (p = 0.044; adjusted odds ratio [OR], 10.22; 95% confidence interval [CI], 1.06-98.24) and the positive urolith culture (p = 0.012; adjusted OR, 8.60; 95% CI, 1.60-46.30). Conclusions: Our results indicate that S. pseudintermedius (a urease-producing bacterium) is the major causative bacteria of MAP uroliths. A positive urolith culture and being female are risk factors of MAP urolithiasis in canines.

• The Plant Pathology Journal
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• v.26 no.1
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• pp.37-48
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• 2010

This paper describes a web-based information system for plant disease forecast that was developed for crop growers in Gyeonggi-do, Korea. The system generates hourly or daily warnings at the spatial resolution of $240\;m{\times}240\;m$ based on weather data. The system consists of four components including weather data acquisition system, job process system, data storage system, and web service system. The spatial resolution of disease forecast is high enough to estimate daily or hourly infection risks of individual farms, so that farmers can use the forecast information practically in determining if and when fungicides are to be sprayed to control diseases. Currently, forecasting models for blast, sheath blight, and grain rot of rice, and scab and rust of pear are available for the system. As for the spatial interpolation of weather data, the interpolated temperature and relative humidity showed high accuracy as compared with the observed data at the same locations. However, the spatial interpolation of rainfall and leaf wetness events needs to be improved. For rice blast forecasting, 44.5% of infection warnings based on the observed weather data were correctly estimated when the disease forecast was made based on the interpolated weather data. The low accuracy in disease forecast based on the interpolated weather data was mainly due to the failure in estimating leaf wetness events.