Emergence of a novel reassortant avian influenza virus(H10N3) in Eastern China with high pathogenicity and respiratory droplet transmissibility to mammals

Decades have passed since the first discovery of H10-subtype avian influenza virus(AIV) in chickens in 1949,and it has been detected in many species including mammals such as minks,pigs,seals and humans.Cases of human infections with H10N8viruses identified in China in 2013 have raised widespread attention.Two novel reassortant H10N3 viruses were isolated from chickens in December 2019 in eastern China during routine surveillance for AIVs.The internal genes of these viruses were derived from genotype S(G57) H9N2 and were consistent with H5N6,H7N9 and H10N8,which cause fatal infections in humans.Their viral pathogenicity and transmissibility were further studied in different animal models.The two H10N3 isolates had low pathogenicity in chickens and were transmitted between chickens via direct contact.These viruses were highly pathogenic in mice and could be transmitted between guinea pigs via direct contact and respiratory droplets.More importantly,these viruses can bind to both human-type SAα-2,6-Gal receptors and avian-type SAα-2,3-Gal receptors.Asymptomatic shedding in chickens and good adaptability to mammals of these H10N3 isolates would make it easier to transmit to humans and pose a threat to public health.

Unsupervised random forest for affinity estimation

This paper presents an unsupervised clustering random-forest-based metric for affinity estimation in large and high-dimensional data.The criterion used for node splitting during forest construction can handle rank-deficiency when measuring cluster compactness.The binary forest-based metric is extended to continuous metrics by exploiting both the common traversal path and the smallest shared parent node.The proposed forest-based metric efficiently estimates affinity by passing down data pairs in the forest using a limited number of decision trees.A pseudo-leaf-splitting(PLS)algorithm is introduced to account for spatial relationships,which regularizes affinity measures and overcomes inconsistent leaf assign-ments.The random-forest-based metric with PLS facilitates the establishment of consistent and point-wise correspondences.The proposed method has been applied to automatic phrase recognition using color and depth videos and point-wise correspondence.Extensive experiments demonstrate the effectiveness of the proposed method in affinity estimation in a comparison with the state-of-the-art.