Necrotic enteritis due to Clostridium perfringens strains harboring the netB gene is a well-known disorder in poultry. The aim of this study was to investigate the association of a novel bacteriocin, perfrin, with netB among isolates from healthy and diseased ostriches and broiler chickens. Forty-six C. perfringens isolates from broiler chickens and ostriches collected from 2010 to 2014 were included in this study and subjected to PCR to detect netB and perfrin genes. Six (60%) and 9 (25%) isolates were positive for both netB and perfrin genes in broilers and ostriches, respectively. Statistical analysis found a significant difference between healthy and diseased flocks for perfrin both in broilers and ostriches. For netB, the significant difference was only found between healthy and diseased ostrich flocks. This is the first report of the presence of perfrin in netB-positive C. perfringens strains in ostriches.

Clostridium (Clostridioides) difficile is a Gram-positive anaerobic rod-shaped bacterium and the main cause of nosocomial diarrhoea in humans. In recent years, the transmission of C. difficile from environmental reservoirs (e.g. food) to humans has become a major focus of research. The aim of this study was to investigate the prevalence and corresponding toxin genes of C. difficile in faecal samples and meat of quails. Thirty samples of packed quail meat in Mashhad, Iran and 500 faecal samples (pooled to n = 5) were collected on quail farms in the Northeastern Khorasan region for further investigation. Of 100 pooled quail faecal samples 10% showed cultural growth of C. difficile. In meat samples two out of 30 specimens (7%) showed cultural growth. In six of ten isolates from faecal samples toxin genes (tcdB and tcdA) were present, while four isolates harboured no toxin genes. However, in meat isolates no toxin genes were present. Mutations in the tcdC gene were not detected, indicating that ‘hypervirulent’ strains such as RT027 and RT078 were not present. The data suggest that quail and quail products might hold a potential for the spread of C. difficile.

Despite complex fluctuations, missing data, and maintenance costs of detectors, traffic volume forecasting at intersections is still a challenge. Moreover, most existing forecasting methods consider an isolated intersection instead of multiple adjacent ones. By accurately forecasting the volume of short-term traffic, a low-cost method can be provided to solve the problems of congestion, delay, and breakdown of detectors in the road transport system. This paper outlines a novel hybrid method based on deep learning to estimate short-term traffic volume at three adjacent intersections. The gated recurrent unit (GRU) and long short-term memory (LSTM) bilayer network with wavelet transform (WL) noise reduction algorithm (WL+GRU-LSTM) are used to analyze raw traffic volume data. The WL+GRU-LSTM is constructed by comparing different machine learning and deep learning methods. A comparative study was used to choose the model's network structure, training technique, and optimizer type. To prove the model's accuracy and resilience, it was compared with the leading short-term traffic forecasting approaches. Experimental results confirm that the WL+GRU-LSTM model can forecast complex traffic volume fluctuations in different approaches of intersections with an accuracy of over 94%. It also shows better results compared to current methods. The proposed model could replace intermediate loop detectors.