Seroprevalence and molecular detection of Toxoplasma gondii and Neospora caninum in beef cattle and goats in Hunan province, China.

BACKGROUND: Toxoplasma gondii and Neospora caninum are closely related protozoan parasites that are considered important causes of abortion in livestock, causing huge economic losses. Hunan Province ranks 12th in the production of beef and mutton in China. However, limited data are available on the seroprevalence, risk factors and molecular characterization of T. gondii and N. caninum in beef cattle and goats in Hunan province, China. METHODS: Sera of 985 beef cattle and 1147 goats were examined for the presence of specific antibodies against T. gondii using indirect hemagglutination test (IHAT) and anti-N. caninum IgG using competitive-inhibition enzyme-linked immunoassay assay (cELISA). Statistical analysis of possible risk factors was performed using PASW Statistics. Muscle samples of 160 beef cattle and 160 goats were examined for the presence of T. gondii DNA (B1 gene) and N. caninum DNA (Nc-5 gene) by nested PCR. The B1 gene-positive samples were genotyped at 10 genetic markers using the multilocus nested PCR-RFLP (Mn-PCR-RFLP). RESULTS: Specific IgG against T. gondii were detected in 8.3% (82/985) and 13.3% (153/1147) and against N. caninum in 2.1% (21/985) and 2.0% (23/1147) of the beef cattle and goats, respectively. Based on statistical analysis, the presence of cats, semi-intensive management mode and gender were identified as significant risk factors for T. gondii infection in beef cattle. Age was a significant risk factor for T. gondii infection in goats (P < 0.05), and age > 3 years was a significant risk factor for N. caninum infection in beef cattle (P < 0.05). PCR positivity for T. gondii was observed in three beef samples (1.9%; 3/160) and seven chevon samples (4.4%; 7/160). Genotyping of PCR positive samples identified one to be ToxoDB#10. The N. caninum DNA was observed in one beef sample (0.6%; 1/160) but was negative in all chevon samples. CONCLUSIONS: To our knowledge, this is the first large-scale serological and molecular investigation of T. gondii and N. caninum and assessment of related risk factors in beef cattle and goats in Hunan Province, China. The findings provide baseline data for executing prevention and control of these two important parasites in beef cattle and goats in China.

Molecular Detection of Toxoplasma gondii and Neospora caninum in Domestic Ducks in Hunan Province, China.

Toxoplasma gondii and Neospora caninum are protozoan parasites that infect warm-blooded animals, and cause major economic losses in livestock industries worldwide. However, little is known about the genotypes of T. gondii and N. caninum in domestic ducks in China. Herein, brain samples from 588 domestic ducks from Hunan province in China were examined for the presence of T. gondii and N. caninum. Polymerase chain reaction (PCR) was used to detect T. gondii B1 gene and N. caninum NC-5 gene. Forty-five DNA samples (7.7%; 95% CI: 5.5-9.9) were positive for B1 gene, and two (0.3%; 95% CI: 0-0.7) were positive for NC-5 gene. The risk factors significantly associated with T. gondii infection were age and sex. The 45 samples positive for T. gondii were genotyped using multi-locus PCR-RFLP analysis and only one sample was fully genotyped as ToxoDB#9 (Chinese I). These results provide new information about the epidemiology of T. gondii and N. caninum in ducks in Hunan province in China. The data also highlight the importance of a "One Health" approach to dealing with toxoplasmosis.

Learning Rich Part Hierarchies with Progressive Attention Networks for Fine-Grained Image Recognition.

We investigate the localization of subtle yet discriminative parts for fine-grained image recognition. Based on the observation that such parts typically exist within a hierarchical structure (e.g., from a coarse-scale "head" to a fine-scale "eye" when recognizing bird species), we propose a novel progressive-attention convolutional neural network (PA-CNN) to progressively localize parts at multiple scales. The PA-CNN localizes parts in two steps, where a part proposal network (PPN) generates multiple local attention maps, and a part rectification network (PRN) learns part-specific features from each proposal and provides the PPN with refined part locations. This coupling of the PPN and PRN allows them to be optimized in a mutually reinforcing manner, leading to improved pinpointing of fine-grained parts. Moreover, the convolutional parameters for a PPN at a finer scale can be inherited from the PRN at a coarser scale, enabling a rich part hierarchy (e.g., eye and beak in a bird's head) to be learned in a stacked fashion. Case studies show that PA-CNN can precisely identify parts without using bounding box/part annotations. In addition, quantitative evaluations demonstrate that PA-CNN yields state-of-the-art performance in three challenging fine-grained recognition tasks. i.e., CUB-200-2011, FGVC-Aircraft, and Stanford Cars.