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Highly pathogenic avian influenza (HPAI) H5N1 and H5N8 have become endemic among domestic poultry in Egypt since 2006 and 2016, respectively. In parallel, the low pathogenic avian influenza H9N2 virus has been endemic since 2010. Despite the continuous circulation of these subtypes for several years, no natural reassortant has been detected so far among the domestic poultry population in Egypt. In this study, the HPAI (H5N2) virus was isolated from a commercial duck farm, giving evidence of the emergence of the first natural reassortment event in domestic poultry in Egypt. The virus was derived as a result of genetic reassortment between avian influenza viruses of H5N8 and H9N2 subtypes circulating in Egypt. The exchange of the neuraminidase segment and high number of acquired mutations might be associated with an alteration in the biological propensities of this virus.

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Point cloud data from 3D LiDAR sensors are one of the most crucial sensor modalities for versatile safety-critical applications such as self-driving vehicles. Since the annotations of point cloud data is an expensive and time-consuming process, therefore recently the utilisation of simulated environments and 3D LiDAR sensors for this task started to get some popularity. With simulated sensors and environments, the process for obtaining an annotated synthetic point cloud data became much easier. However, the generated synthetic point cloud data are still missing the artefacts usually exist in point cloud data from real 3D LiDAR sensors. As a result, the performance of the trained models on this data for perception tasks when tested on real point cloud data is degraded due to the domain shift between simulated and real environments. Thus, in this work, we are proposing a domain adaptation framework for bridging this gap between synthetic and real point cloud data. Our proposed framework is based on the deep cycle-consistent generative adversarial networks (CycleGAN) architecture. We have evaluated the performance of our proposed framework on the task of vehicle detection from a bird's eye view (BEV) point cloud images coming from real 3D LiDAR sensors. The framework has shown competitive results with an improvement of more than 7% in average precision score over other baseline approaches when tested on real BEV point cloud images.