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Blood parasite (haemosporidian) infections are conventionally detected using blood samples; this implies capturing and handling birds to obtain them, which induces stress and causes pain. Feathers have blood vessels, and some blood could be preserved in the feather's shaft after moulting. We used feather DNA for detecting haemosporidians by PCR testing in diverse scenarios. First, haemosporidian DNA was detected in feathers from carcasses of infected birds, proving the feasibility of the approach. Storage temperature affected DNA recovery, with maximum retrieval and haemosporidian detection at the lowest temperature (− 20 °C). All feather types from infected birds kept at optimal conditions yielded haemosporidian DNA. Parasite detection by PCR was correlated with DNA yield, which was significantly higher in heavier birds, flight feathers, and more feathers per pool. Lastly, haemosporidians were detected employing feathers moulted from wild and captive birds to estimate infection prevalence. We show for the first time that using blood from feather shafts for haemosporidian detection can be an advantageous and less invasive alternative to blood sampling if feathers are optimally preserved. This method could contribute to uncovering haemosporidian infections in endangered and elusive birds, and it might facilitate routine screening in captive birds, thereby improving infection detection, prevention, and control. [ABSTRACT FROM AUTHOR]

Haemosporidians are a widespread group of blood parasites transmitted by vectors. Despite their relevance for bird conservation, few studies have been conducted in the Amazonia and even less in terrestrial wild birds. We analysed blood samples from 168 game birds, collected from 2008 to 2015 by subsistence hunters of an indigenous rural community in the Peruvian Amazonia. DNA was tested for Haemoproteus spp., Plasmodium spp. and Leucocytozoon spp. and positive amplicons were sequenced and curated for phylogenetic analysis. Haemosporidian prevalence was 72% overall, 66.7% for Haemoproteus spp. and 5.4% for Plasmodium spp. and respectively by bird species: Spix's Guan (Penelope jacquacu,n = 72) 87.5% and 0%, Razor-billed Curassow (Mitu tuberosum,n = 45) 77.8% and 6.7%, White-winged Trumpeter (Psophia leucoptera,n = 20) 6.3% and 12.5%, Blue-throated Piping-guan (Pipile cumanensis,n = 16) 73.3% and 6.7%, and Great Tinamou (Tinamus major,n = 15) 10% and 15%. Leucocytozoon spp. was not found. P. leucoptera and T. major were less likely to be infected with Haemoproteus spp. Fruit abundance had a negative association with Haemoproteus spp. prevalence and precipitation was negatively associated with Plasmodium spp. prevalence. The 106 sequences examined represented 29 lineages, 82.8% of them were new lineages (Plasmodiumn = 3, Haemoproteusn = 21). Novel host-parasite associations and lineages were unveiled, including probably new species of Plasmodium spp. Our results highlight the scientific value of alternative sampling methods and the collaboration with local communities. [ABSTRACT FROM AUTHOR]

To achieve this task, we separated the prediction problem into three perspectives: that of the mammal, the coronavirus, and the network linking known coronaviruses with their hosts. B replying to b Rasmussen et al. I Nature Communications i https://doi.org/10.1038/s41467-022-32746-7 (2022) We appreciate the comments made by Rasmussen et al.[1] regarding our recent study, Wardeh et al.[2], which uses machine learning to predict hosts that may become co-infected with multiple different coronaviruses, and hence may be the site of recombination and generation of novel future strains. [Extracted from the article]

Our knowledge of viral host ranges remains limited. Completing this picture by identifying unknown hosts of known viruses is an important research aim that can help identify and mitigate zoonotic and animal-disease risks, such as spill-over from animal reservoirs into human populations. To address this knowledge-gap we apply a divide-and-conquer approach which separates viral, mammalian and network features into three unique perspectives, each predicting associations independently to enhance predictive power. Our approach predicts over 20,000 unknown associations between known viruses and susceptible mammalian species, suggesting that current knowledge underestimates the number of associations in wild and semi-domesticated mammals by a factor of 4.3, and the average potential mammalian host-range of viruses by a factor of 3.2. In particular, our results highlight a significant knowledge gap in the wild reservoirs of important zoonotic and domesticated mammals' viruses: specifically, lyssaviruses, bornaviruses and rotaviruses. A more comprehensive map of viral host ranges can help identify and mitigate zoonotic and animal-disease risks. A divide-and-conquer approach which separates viral, mammalian and network features predicts over 20,000 unknown associations between known viruses and susceptible mammalian species. [ABSTRACT FROM AUTHOR]

Novel pathogenic coronaviruses – such as SARS-CoV and probably SARS-CoV-2 – arise by homologous recombination between co-infecting viruses in a single cell. Identifying possible sources of novel coronaviruses therefore requires identifying hosts of multiple coronaviruses; however, most coronavirus-host interactions remain unknown. Here, by deploying a meta-ensemble of similarity learners from three complementary perspectives (viral, mammalian and network), we predict which mammals are hosts of multiple coronaviruses. We predict that there are 11.5-fold more coronavirus-host associations, over 30-fold more potential SARS-CoV-2 recombination hosts, and over 40-fold more host species with four or more different subgenera of coronaviruses than have been observed to date at >0.5 mean probability cut-off (2.4-, 4.25- and 9-fold, respectively, at >0.9821). Our results demonstrate the large underappreciation of the potential scale of novel coronavirus generation in wild and domesticated animals. We identify high-risk species for coronavirus surveillance. Homologous recombination between co-infecting coronaviruses can produce novel pathogens. Here, Wardeh et al. develop a machine learning approach to predict associations between mammals and multiple coronaviruses and hence estimate the potential for generation of novel coronaviruses by recombination. [ABSTRACT FROM AUTHOR]

Bluetongue, a devastating disease of ruminants, has historically made only brief, sporadic incursions into the fringes of Europe. However, since 1998, six strains of bluetongue virus have spread across 12 countries and 800 km further north in Europe than has previously been reported. We suggest that this spread has been driven by recent changes in European climate that have allowed increased virus persistence during winter, the northward expansion of Culicoides imicola, the main bluetongue virus vector, and, beyond this vector's range, transmission by indigenous European Culicoides species — thereby expanding the risk of transmission over larger geographical regions. Understanding this sequence of events may help us predict the emergence of other vector-borne pathogens. [ABSTRACT FROM AUTHOR]

We investigate the restriction of animal movements as a method to control the spread of bluetongue, an infectious disease of livestock that is becoming increasingly prevalent due to the onset of climate change. We derive control policies for the UK that minimise the number of infected farms during an outbreak using Bayesian optimisation and a simulation-based model of BT. Two cases are presented: first, where the region of introduction is randomly selected from England and Wales to find a generalised strategy. This "national" model is shown to be just as effective at subduing the spread of bluetongue as the current strategy of the UK government. Our proposed controls are simpler to implement, affect fewer farms in the process and, in so doing, minimise the potential economic implications. Second, we consider policies that are tailored to the specific region in which the first infection was detected. Seven different regions in the UK were explored and improvements in efficiency from the use of specialised policies presented. As a consequence of the increasing temperatures associated with climate change, efficient control measures for vector-borne diseases such as this are expected to become increasingly important. Our work demonstrates the potential value of using Bayesian optimisation in developing cost-effective disease management strategies. [ABSTRACT FROM AUTHOR]

Reports on the identification of new strain of virus that causes scrapie in sheep in European Union countries. Determination of the presence of atypical forms of scrapie in resistant sheep; Details of measures planed by the Union in eradicating the disease; Use of nucleotide sequence in determining the susceptibility of sheep to the disease.

A correction to the article "Climate Change and the Recent Emergence of Bluetongue in Europe." that the was published in the previews issue of Nature Reviews Microbiology.