Red knots in Europe: a dead end host species or a new niche for highly pathogenic avian influenza?

The 2020/2021 epidemic in Europe of highly pathogenic avian influenza virus (HPAIV) of subtype H5 surpassed all previously recorded European outbreaks in size, genotype constellations and reassortment frequency and continued into 2022 and 2023. The causative 2.3.4.4b viral lineage proved to be highly proficient with respect to reassortment with cocirculating low pathogenic avian influenza viruses and seems to establish an endemic status in northern Europe. A specific HPAIV reassortant of the subtype H5N3 was detected almost exclusively in red knots (Calidris canutus islandica) in December 2020. It caused systemic and rapidly fatal disease leading to a singular and self-limiting mass mortality affecting about 3500 birds in the German Wadden Sea, roughly 1 % of the entire flyway population of islandica red knots. Phylogenetic analyses revealed that the H5N3 reassortant very likely had formed in red knots and remained confined to this species. While mechanisms of virus circulation in potential reservoir species, dynamics of spill-over and reassortment events and the roles of environmental virus sources remain to be identified, the year-round infection pressure poses severe threats to endangered avian species and prompts adaptation of habitat and species conservation practices.

Combining scale-space and similarity-based aspect graphs for fast 3D object recognition.

This paper describes an approach for recognizing instances of a 3D object in a single camera image and for determining their 3D poses. A hierarchical model is generated solely based on the geometry information of a 3D CAD model of the object. The approach does not rely on texture or reflectance information of the object's surface, making it useful for a wide range of industrial and robotic applications, e.g., bin-picking. A hierarchical view-based approach that addresses typical problems of previous methods is applied: It handles true perspective, is robust to noise, occlusions, and clutter to an extent that is sufficient for many practical applications, and is invariant to contrast changes. For the generation of this hierarchical model, a new model image generation technique by which scale-space effects can be taken into account is presented. The necessary object views are derived using a similarity-based aspect graph. The high robustness of an exhaustive search is combined with an efficient hierarchical search. The 3D pose is refined by using a least-squares adjustment that minimizes geometric distances in the image, yielding a position accuracy of up to 0.12 percent with respect to the object distance, and an orientation accuracy of up to 0.35 degree in our tests. The recognition time is largely independent of the complexity of the object, but depends mainly on the range of poses within which the object may appear in front of the camera. For efficiency reasons, the approach allows the restriction of the pose range depending on the application. Typical runtimes are in the range of a few hundred ms.