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Small mammals such as rodents can to carry zoonotic pathogens. Currently, there is impaired knowledge on zoonotic pathogens in rodents and insectivores in the Netherlands. This limits opportunities for preventive measures and complicates risk‐assessments for zoonotic transmission to humans. Leptospira spp. and Toxoplasma gondii are present on a list of prioritized emerging pathogens in the Netherlands and were therefore the focus of this study. Both pathogens have the ability to survive under moist environmental conditions. In total, a group of 379 small mammals (rodents & insectivores) were tested on pathogenic Leptospira spp., and 312 on T. gondii. Rodents and insectivores were trapped at various sites, but mostly on pig and dairy farms throughout the country. Over five percent of the animals (5.3%, n = 379) tested positive for Leptospira DNA, and five of the animals (1.6%, n = 312) tested were positive for T. gondii DNA. The animals positive for T.gondii were all brown rats and the ones for Leptospira spp. were various species. Our results show that insectivores and rodents might be used as an indicator for the environmental contamination and/or the contamination in wildlife for Leptospira spp. [ABSTRACT FROM AUTHOR]

In this paper, we developed a robust learning method for animal classification from camera-trap images collected in highly cluttered natural scenes and annotated with noisy labels. We proposed two different network structures with and without clean samples to handle noisy labels. We use k-means clustering to divide the training samples into groups with different characteristics, which are then used to train different networks. These networks with enhanced diversity are then used to jointly predict or correct sample labels using max voting. We evaluate the performance of the proposed method on two public available camera-trap image datasets: Snapshot Serengeti and Panama-Netherlands datasets. Our experimental results demonstrate that our method outperforms the state-of-the-art methods from the literature and achieved improved accuracy on animal species classification from camera-trap images with high levels of label noise. • Dealing with label noise in camera-trap images. • Partitioning noisy training set to train the independent networks. • Using multiple stages to retrain the networks on the updated labels. [ABSTRACT FROM AUTHOR]

Background:  Brown rats (Rattus norvegicus) may carry pathogens that can be a risk for public health. Brown rats in the Netherlands were tested for the zoonotic pathogens Leptospira spp. and Seoul hantavirus (SEOV), in order to obtain insight in their prevalence. Methods and results:  Cross-sectional studies were performed at four locations from 2011 to 2015. The rats were tested for Leptospira spp. using real-time PCR and/or culture resulting in a prevalence ranging between 33-57%. Testing for SEOV was done through an adapted human Seoul hantavirus ELISA and real-time RT-PCR. Although at several locations the ELISA indicated presence of SEOV antibodies, none could be confirmed by focus reduction neutralization testing. Conclusion:  The results indicate a widespread presence of Leptospira spp. in brown rats in the Netherlands, including areas with a low leptospirosis incidence in humans. No evidence for circulation of SEOV was found in this study. [ABSTRACT FROM AUTHOR]