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      Eco-Virological Approach for Assessing the Role of Wild Birds in the Spread of Avian Influenza H5N1 along the Central Asian Flyway

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          A unique pattern of highly pathogenic avian influenza (HPAI) H5N1 outbreaks has emerged along the Central Asia Flyway, where infection of wild birds has been reported with steady frequency since 2005. We assessed the potential for two hosts of HPAI H5N1, the bar-headed goose ( Anser indicus) and ruddy shelduck ( Tadorna tadorna), to act as agents for virus dispersal along this ‘thoroughfare’. We used an eco-virological approach to compare the migration of 141 birds marked with GPS satellite transmitters during 2005–2010 with: 1) the spatio-temporal patterns of poultry and wild bird outbreaks of HPAI H5N1, and 2) the trajectory of the virus in the outbreak region based on phylogeographic mapping. We found that biweekly utilization distributions (UDs) for 19.2% of bar-headed geese and 46.2% of ruddy shelduck were significantly associated with outbreaks. Ruddy shelduck showed highest correlation with poultry outbreaks owing to their wintering distribution in South Asia, where there is considerable opportunity for HPAI H5N1 spillover from poultry. Both species showed correlation with wild bird outbreaks during the spring migration, suggesting they may be involved in the northward movement of the virus. However, phylogeographic mapping of HPAI H5N1 clades 2.2 and 2.3 did not support dissemination of the virus in a northern direction along the migration corridor. In particular, two subclades (2.2.1 and 2.3.2) moved in a strictly southern direction in contrast to our spatio-temporal analysis of bird migration. Our attempt to reconcile the disciplines of wild bird ecology and HPAI H5N1 virology highlights prospects offered by both approaches as well as their limitations.

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          RAxML-VI-HPC: maximum likelihood-based phylogenetic analyses with thousands of taxa and mixed models.

          RAxML-VI-HPC (randomized axelerated maximum likelihood for high performance computing) is a sequential and parallel program for inference of large phylogenies with maximum likelihood (ML). Low-level technical optimizations, a modification of the search algorithm, and the use of the GTR+CAT approximation as replacement for GTR+Gamma yield a program that is between 2.7 and 52 times faster than the previous version of RAxML. A large-scale performance comparison with GARLI, PHYML, IQPNNI and MrBayes on real data containing 1000 up to 6722 taxa shows that RAxML requires at least 5.6 times less main memory and yields better trees in similar times than the best competing program (GARLI) on datasets up to 2500 taxa. On datasets > or =4000 taxa it also runs 2-3 times faster than GARLI. RAxML has been parallelized with MPI to conduct parallel multiple bootstraps and inferences on distinct starting trees. The program has been used to compute ML trees on two of the largest alignments to date containing 25,057 (1463 bp) and 2182 (51,089 bp) taxa, respectively.
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            TNT, a free program for phylogenetic analysis

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              What's wrong with Bonferroni adjustments.

               T Perneger (1998)

                Author and article information

                Role: Editor
                PLoS One
                PLoS ONE
                Public Library of Science (San Francisco, USA )
                7 February 2012
                : 7
                : 2
                [1 ]EMPRES Wildlife Unit, Emergency Centre for Transboundary Animal Diseases, Animal Production and Health Division, Food and Agriculture Organization of the United Nations, Rome, Italy
                [2 ]United States Geological Survey, Western Ecological Research Center, San Francisco Bay Estuary Field Station, Vallejo, California, United States of America
                [3 ]Wildlife Health Center, School of Veterinary Medicine, University of California, Davis, California, United States of America
                [4 ]Department of Biomedical Informatics, The Ohio State University, Columbus, Ohio, United States of America
                [5 ]United States Geological Survey, Patuxent Wildlife Research Center, Beltsville Lab, Beltsville, Maryland, United States of America
                [6 ]Computer Network Information Center, Chinese Academy of Sciences, Beijing, China
                [7 ]Institute of Zoology, Chinese Academy of Sciences, Beijing, China
                [8 ]Wildlife Science and Conservation Center, Bayanzurkh District Ulaanbaatar, Mongolia
                [9 ]Ornithological Laboratory, Institute of Biology, Mongolian Academy of Sciences, Ulaanbaatar, Mongolia
                [10 ]Bangor University, School of Biological Sciences, Brambell Laboratories, Bangor, Gwynedd, United Kingdom
                [11 ]School of Biosciences, University of Birmingham, Birmingham, United Kingdom
                [12 ]Max Planck Institute for Ornithology, Radolfzell, Germany
                [13 ]Bombay Natural History Society, Hornbill House, Mumbai, India
                [14 ]Wetlands International, 471, 6700 AL, Wageningen, The Netherlands
                [15 ]United States Geological Survey, Alaska Science Center, Juneau, Alaska, United States of America
                University of Durham, United Kingdom
                Author notes

                Conceived and designed the experiments: SHN JYT NJH. Performed the experiments: SHN JYT NJH KAS DJP BPY FML NB TN CMB PJB MW SB TM. Analyzed the data: NJH KAS DJ IOV. Contributed reagents/materials/analysis tools: JYT DJ DCD. Wrote the paper: SHN NJH JYT.

                Newman et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
                Page count
                Pages: 12
                Research Article
                Veterinary Science
                Animal Types
                Veterinary Diseases



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