Kitainik axioms do not characterize the class of inclusion measures based on contrapositive fuzzy implications

In this short communication, we refute the conjecture by Fodor and Yager from [5] that the class of inclusion measures proposed by Kitainik coincides with that of inclusion measures based on contrapositive fuzzy implications. In particular, we show that the conjecture only holds when the considered universe of discourse is finite.The research reported in this paper was conducted with the financial support of the Odysseus programme of the Research Foundation – Flanders (FWO) (grant number G0H9118N) and partially supported by the Spanish Ministry of Science, Innovation and Universities (MCIU), State Agency of Research (AEI), Junta de Andalucía (JA), Universidad de Málaga (UMA) and European Regional Development Fund (FEDER) through the projects PGC2018-095869-B- I00 (MCIU/AEI/FEDER) and UMA2018-FEDERJA-001 (JA/UMA/FEDER). Funding for open access charge: Universidad de Málaga / CBU

Genomic regions influencing intramuscular fat in divergently selected rabbit lines

[EN] Intramuscular fat (IMF) is one of the main meat quality traits for breeding programs in livestock species. The main objective of this study was to identify genomic regions associated with IMF content comparing two rabbit populations divergently selected for this trait, and to generate a list of putative candidate genes. Animals were genotyped using the Affymetrix Axiom OrcunSNP Array (200k). After quality control, the data involved 477 animals and 93,540 single nucleotide polymorphisms (SNPs). Two methods were used in this research: single marker regressions with the data adjusted by genomic relatedness, and a Bayesian multi-marker regression. Associated genomic regions were located on the rabbit chromosomes (OCU) OCU1, OCU8 and OCU13. The highest value for the percentage of the genomic variance explained by a genomic region was found in two consecutive genomic windows on OCU8 (7.34%). Genes in the associated regions of OCU1 and OCU8 presented biological functions related to the control of adipose cell function, lipid binding, transportation and localization (APOLD1, PLBD1, PDE6H, GPRC5D, and GPRC5A) and lipid metabolic processes (MTMR2). The EWSR1 gene, underlying the OCU13 region, is linked to the development of brown adipocytes. The findings suggest that there is a large component of polygenic effect behind the differences in IMF content in these two lines, as the variance explained by most of the windows was low. The genomic regions of OCU1, OCU8 and OCU13 revealed novel candidate genes. Further studies would be needed to validate the associations and explore their possible application in selection programs.The work was funded by project AGL2014-55921-C2-1-P from National Programme for Fostering Excellence in Scientific and Technical Research -Project I+D. BSS was supported by a FPI grant from the Ministry of Economy and Competitiveness of Spain+ (BES-2015-074194). NIB was supported with a "Ramon y Cajal" grant provided by Ministerio de Ciencia e Innovacion (RYC-2016-19764). CSH and PN were supported by the Medical Research Council (United kingdom, grants MC_PC_U127592696 and MC_PC_U127561128). CSH was supported by Biotechnology and Biological Sciences Research Council (United Kingdom, Grant/Award Number: BBS/E/D/30002276).Sosa-Madrid, BS.; Hernández, P.; Blasco Mateu, A.; Haley, CS.; Fontanesi, L.; Santacreu Jerez, MA.; Pena, RN.... (2020). Genomic regions influencing intramuscular fat in divergently selected rabbit lines. Animal Genetics. 51:58-69. https://doi.org/10.1111/age.12873586951Aken, B. L., Ayling, S., Barrell, D., Clarke, L., Curwen, V., Fairley, S., … Searle, S. M. J. (2016). The Ensembl gene annotation system. Database, 2016, baw093. doi:10.1093/database/baw093Aloulou, A., Ali, Y. B., Bezzine, S., Gargouri, Y., & Gelb, M. H. (2012). Phospholipases: An Overview. Methods in Molecular Biology, 63-85. doi:10.1007/978-1-61779-600-5_4Amisten, S., Mohammad Al-Amily, I., Soni, A., Hawkes, R., Atanes, P., Persaud, S. J., … Salehi, A. (2017). Anti-diabetic action of all-trans retinoic acid and the orphan G protein coupled receptor GPRC5C in pancreatic β-cells. Endocrine Journal, 64(3), 325-338. doi:10.1507/endocrj.ej16-0338Astle, W., & Balding, D. J. (2009). Population Structure and Cryptic Relatedness in Genetic Association Studies. Statistical Science, 24(4). doi:10.1214/09-sts307Aulchenko, Y. S., Ripke, S., Isaacs, A., & van Duijn, C. M. (2007). GenABEL: an R library for genome-wide association analysis. Bioinformatics, 23(10), 1294-1296. doi:10.1093/bioinformatics/btm108Barrett, J. C., Fry, B., Maller, J., & Daly, M. J. (2004). Haploview: analysis and visualization of LD and haplotype maps. Bioinformatics, 21(2), 263-265. doi:10.1093/bioinformatics/bth457Beissinger, T. M., Rosa, G. J., Kaeppler, S. M., Gianola, D., & de Leon, N. (2015). Defining window-boundaries for genomic analyses using smoothing spline techniques. Genetics Selection Evolution, 47(1). doi:10.1186/s12711-015-0105-9Blasco, A., & Pena, R. N. (2018). Current Status of Genomic Maps: Genomic Selection/GBV in Livestock. Animal Biotechnology 2, 61-80. doi:10.1007/978-3-319-92348-2_4Browning, B. L., & Browning, S. R. (2016). Genotype Imputation with Millions of Reference Samples. The American Journal of Human Genetics, 98(1), 116-126. doi:10.1016/j.ajhg.2015.11.020Carneiro, M., Afonso, S., Geraldes, A., Garreau, H., Bolet, G., Boucher, S., … Ferrand, N. (2011). The Genetic Structure of Domestic Rabbits. Molecular Biology and Evolution, 28(6), 1801-1816. doi:10.1093/molbev/msr003Carneiro, M., Rubin, C.-J., Di Palma, F., Albert, F. W., Alföldi, J., Barrio, A. M., … Andersson, L. (2014). Rabbit genome analysis reveals a polygenic basis for phenotypic change during domestication. Science, 345(6200), 1074-1079. doi:10.1126/science.1253714Cesar, A. S., Regitano, L. C., Mourão, G. B., Tullio, R. R., Lanna, D. P., Nassu, R. T., … Coutinho, L. L. (2014). Genome-wide association study for intramuscular fat deposition and composition in Nellore cattle. BMC Genetics, 15(1). doi:10.1186/1471-2156-15-39Chaves, V. E., Frasson, D., & Kawashita, N. H. (2011). Several agents and pathways regulate lipolysis in adipocytes. Biochimie, 93(10), 1631-1640. doi:10.1016/j.biochi.2011.05.018Chen, W.-M., & Abecasis, G. R. (2007). Family-Based Association Tests for Genomewide Association Scans. The American Journal of Human Genetics, 81(5), 913-926. doi:10.1086/521580Claire D’Andre, H., Paul, W., Shen, X., Jia, X., Zhang, R., Sun, L., & Zhang, X. (2013). Identification and characterization of genes that control fat deposition in chickens. Journal of Animal Science and Biotechnology, 4(1). doi:10.1186/2049-1891-4-43Do, D. N., Strathe, A. B., Ostersen, T., Pant, S. D., & Kadarmideen, H. N. (2014). Genome-wide association and pathway analysis of feed efficiency in pigs reveal candidate genes and pathways for residual feed intake. Frontiers in Genetics, 5. doi:10.3389/fgene.2014.00307Do, D. N., Schenkel, F. S., Miglior, F., Zhao, X., & Ibeagha-Awemu, E. M. (2018). Genome wide association study identifies novel potential candidate genes for bovine milk cholesterol content. Scientific Reports, 8(1). doi:10.1038/s41598-018-31427-0Fan, B., Du, Z.-Q., Gorbach, D. M., & Rothschild, M. F. (2010). Development and Application of High-density SNP Arrays in Genomic Studies of Domestic Animals. Asian-Australasian Journal of Animal Sciences, 23(7), 833-847. doi:10.5713/ajas.2010.r.03Gao, Y., Zhang, R., Hu, X., & Li, N. (2007). Application of genomic technologies to the improvement of meat quality of farm animals. Meat Science, 77(1), 36-45. doi:10.1016/j.meatsci.2007.03.026Garrick, D. J. (2011). The nature, scope and impact of genomic prediction in beef cattle in the United States. Genetics Selection Evolution, 43(1). doi:10.1186/1297-9686-43-17Garrick, D. J., & Fernando, R. L. (2013). Implementing a QTL Detection Study (GWAS) Using Genomic Prediction Methodology. Genome-Wide Association Studies and Genomic Prediction, 275-298. doi:10.1007/978-1-62703-447-0_11Gotoh, T., Takahashi, H., Nishimura, T., Kuchida, K., & Mannen, H. (2014). Meat produced by Japanese Black cattle and Wagyu. Animal Frontiers, 4(4), 46-54. doi:10.2527/af.2014-0033Gotoh, T., Nishimura, T., Kuchida, K., & Mannen, H. (2018). The Japanese Wagyu beef industry: current situation and future prospects — A review. Asian-Australasian Journal of Animal Sciences, 31(7), 933-950. doi:10.5713/ajas.18.0333Hocquette, J. F., Gondret, F., Baéza, E., Médale, F., Jurie, C., & Pethick, D. W. (2010). Intramuscular fat content in meat-producing animals: development, genetic and nutritional control, and identification of putative markers. Animal, 4(2), 303-319. doi:10.1017/s1751731109991091Hopkins, D. L., Fogarty, N. M., & Mortimer, S. I. (2011). Genetic related effects on sheep meat quality. Small Ruminant Research, 101(1-3), 160-172. doi:10.1016/j.smallrumres.2011.09.036Jiao, X., Sherman, B. T., Huang, D. W., Stephens, R., Baseler, M. W., Lane, H. C., & Lempicki, R. A. (2012). DAVID-WS: a stateful web service to facilitate gene/protein list analysis. Bioinformatics, 28(13), 1805-1806. doi:10.1093/bioinformatics/bts251Jin, C., Wang, W., Liu, Y., & Zhou, Y. (2017). RAI3 knockdown promotes adipogenic differentiation of human adipose-derived stem cells by decreasing β-catenin levels. Biochemical and Biophysical Research Communications, 493(1), 618-624. doi:10.1016/j.bbrc.2017.08.142Kass, R. E., & Raftery, A. E. (1995). Bayes Factors. Journal of the American Statistical Association, 90(430), 773-795. doi:10.1080/01621459.1995.10476572Kim, E.-S., Ros-Freixedes, R., Pena, R. N., Baas, T. J., Estany, J., & Rothschild, M. F. (2015). Identification of signatures of selection for intramuscular fat and backfat thickness in two Duroc populations1. Journal of Animal Science, 93(7), 3292-3302. doi:10.2527/jas.2015-8879Kuleshov, M. V., Jones, M. R., Rouillard, A. D., Fernandez, N. F., Duan, Q., Wang, Z., … Ma’ayan, A. (2016). Enrichr: a comprehensive gene set enrichment analysis web server 2016 update. Nucleic Acids Research, 44(W1), W90-W97. doi:10.1093/nar/gkw377Lander, E., & Kruglyak, L. (1995). Genetic dissection of complex traits: guidelines for interpreting and reporting linkage results. Nature Genetics, 11(3), 241-247. doi:10.1038/ng1195-241Lionikas, A., Meharg, C., Derry, J. M., Ratkevicius, A., Carroll, A. M., Vandenbergh, D. J., & Blizard, D. A. (2012). Resolving candidate genes of mouse skeletal muscle QTL via RNA-Seq and expression network analyses. BMC Genomics, 13(1), 592. doi:10.1186/1471-2164-13-592López de Maturana, E., Ibáñez-Escriche, N., González-Recio, Ó., Marenne, G., Mehrban, H., Chanock, S. J., … Malats, N. (2014). Next generation modeling in GWAS: comparing different genetic architectures. Human Genetics, 133(10), 1235-1253. doi:10.1007/s00439-014-1461-1Marras, G., Rossoni, A., Schwarzenbacher, H., Biffani, S., Biscarini, F., & Nicolazzi, E. L. (2016). zanardi: an open-source pipeline for multiple-species genomic analysis of SNP array data. Animal Genetics, 48(1), 121-121. doi:10.1111/age.12485Martínez-Álvaro, M., Hernández, P., & Blasco, A. (2016). Divergent selection on intramuscular fat in rabbits: Responses to selection and genetic parameters1. Journal of Animal Science, 94(12), 4993-5003. doi:10.2527/jas.2016-0590Mateescu, R. G., Garrick, D. J., Garmyn, A. J., VanOverbeke, D. L., Mafi, G. G., & Reecy, J. M. (2015). Genetic parameters for sensory traits in longissimus muscle and their associations with tenderness, marbling score, and intramuscular fat in Angus cattle1. Journal of Animal Science, 93(1), 21-27. doi:10.2527/jas.2014-8405McLarenD.G.&SchultzC.M.(1992)Genetic Selection to Improve the Quality and Composition of Pigs. In45th Reciprocal Meat Conferences Proceedings. Colorado State University pp.115–21.Migdał, Ł., Kozioł, K., Pałka, S., Migdał, W., Otwinowska-Mindur, A., Kmiecik, M., … Bieniek, J. (2018). Single nucleotide polymorphisms within rabbits ( Oryctolagus cuniculus ) fatty acids binding protein 4 ( FABP4 ) are associated with meat quality traits. Livestock Science, 210, 21-24. doi:10.1016/j.livsci.2018.01.018Miller, I., Rogel-Gaillard, C., Spina, D., Fontanesi, L., & de Almeida, A. (2014). The Rabbit as an Experimental and Production Animal: From Genomics to Proteomics. Current Protein & Peptide Science, 15(2), 134-145. doi:10.2174/1389203715666140221115135Mortimer, S. I., van der Werf, J. H. J., Jacob, R. H., Hopkins, D. L., Pannier, L., Pearce, K. L., … Pethick, D. W. (2014). Genetic parameters for meat quality traits of Australian lamb meat. Meat Science, 96(2), 1016-1024. doi:10.1016/j.meatsci.2013.09.007Nyima, T., Müller, M., Hooiveld, G. J. E. J., Morine, M. J., & Scotti, M. (2016). Nonlinear transcriptomic response to dietary fat intake in the small intestine of C57BL/6J mice. BMC Genomics, 17(1). doi:10.1186/s12864-016-2424-9Ochsner, K. P., MacNeil, M. D., Lewis, R. M., & Spangler, M. L. (2017). Economic selection index development for Beefmaster cattle I: Terminal breeding objective1. Journal of Animal Science, 95(3), 1063-1070. doi:10.2527/jas.2016.1231Pannier, L., Gardner, G. E., O’Reilly, R. A., & Pethick, D. W. (2018). Factors affecting lamb eating quality and the potential for their integration into an MSA sheepmeat grading model. Meat Science, 144, 43-52. doi:10.1016/j.meatsci.2018.06.035Peña, F., Juárez, M., Bonvillani, A., García, P., Polvillo, O., & Domenech, V. (2011). Muscle and genotype effects on fatty acid composition of goat kid intramuscular fat. Italian Journal of Animal Science, 10(3), e40. doi:10.4081/ijas.2011.e40Pena, R., Ros-Freixedes, R., Tor, M., & Estany, J. (2016). Genetic Marker Discovery in Complex Traits: A Field Example on Fat Content and Composition in Pigs. International Journal of Molecular Sciences, 17(12), 2100. doi:10.3390/ijms17122100Purcell, S., Neale, B., Todd-Brown, K., Thomas, L., Ferreira, M. A. R., Bender, D., … Sham, P. C. (2007). PLINK: A Tool Set for Whole-Genome Association and Population-Based Linkage Analyses. The American Journal of Human Genetics, 81(3), 559-575. doi:10.1086/519795Ros-Freixedes, R., Gol, S., Pena, R. N., Tor, M., Ibáñez-Escriche, N., Dekkers, J. C. M., & Estany, J. (2016). Genome-Wide Association Study Singles Out SCD and LEPR as the Two Main Loci Influencing Intramuscular Fat Content and Fatty Acid Composition in Duroc Pigs. PLOS ONE, 11(3), e0152496. doi:10.1371/journal.pone.0152496Sahana, G., Guldbrandtsen, B., & Lund, M. S. (2011). Genome-wide association study for calving traits in Danish and Swedish Holstein cattle. Journal of Dairy Science, 94(1), 479-486. doi:10.3168/jds.2010-3381Schmid, M., & Bennewitz, J. (2017). Invited review: Genome-wide association analysis for quantitative traits in livestock – a selective review of statistical models and experimental designs. Archives Animal Breeding, 60(3), 335-346. doi:10.5194/aab-60-335-2017Song, H., Sun, B., Liao, Y., Xu, D., Guo, W., Wang, T., … Deng, J. (2018). GPRC5A deficiency leads to dysregulated MDM2 via activated EGFR signaling for lung tumor development. International Journal of Cancer, 144(4), 777-787. doi:10.1002/ijc.31726Spencer, C. C. A., Su, Z., Donnelly, P., & Marchini, J. (2009). Designing Genome-Wide Association Studies: Sample Size, Power, Imputation, and the Choice of Genotyping Chip. PLoS Genetics, 5(5), e1000477. doi:10.1371/journal.pgen.1000477Stephens, M., & Balding, D. J. (2009). Bayesian statistical methods for genetic association studies. Nature Reviews Genetics, 10(10), 681-690. doi:10.1038/nrg2615Sukegawa, S., Miyake, T., Ibi, T., Takahagi, Y., Murakami, H., Morimatsu, F., & Yamada, T. (2013). Multiple marker effects of single nucleotide polymorphisms in three genes,AKIRIN2,EDG1andRPL27A, for marbling development in Japanese Black cattle. Animal Science Journal, 85(3), 193-197. doi:10.1111/asj.12108Sul, J. H., Martin, L. S., & Eskin, E. (2018). Population structure in genetic studies: Confounding factors and mixed models. PLOS Genetics, 14(12), e1007309. doi:10.1371/journal.pgen.1007309Swierczynski, J. (2014). Role of abnormal lipid metabolism in development, progression, diagnosis and therapy of pancreatic cancer. World Journal of Gastroenterology, 20(9), 2279. doi:10.3748/wjg.v20.i9.2279Toosi, A., Fernando, R. L., & Dekkers, J. C. M. (2018). Genome-wide mapping of quantitative trait loci in admixed populations using mixed linear model and Bayesian multiple regression analysis. Genetics Selection Evolution, 50(1). doi:10.1186/s12711-018-0402-1Uemoto, Y., Nakano, H., Kikuchi, T., Sato, S., Ishida, M., Shibata, T., … Suzuki, K. (2011). Fine mapping of porcine SSC14 QTL and SCD gene effects on fatty acid composition and melting point of fat in a Duroc purebred population. Animal Genetics, 43(2), 225-228. doi:10.1111/j.1365-2052.2011.02236.xVisscher, P. M., Wray, N. R., Zhang, Q., Sklar, P., McCarthy, M. I., Brown, M. A., & Yang, J. (2017). 10 Years of GWAS Discovery: Biology, Function, and Translation. The American Journal of Human Genetics, 101(1), 5-22. doi:10.1016/j.ajhg.2017.06.005Vitti, J. J., Grossman, S. R., & Sabeti, P. C. (2013). Detecting Natural Selection in Genomic Data. Annual Review of Genetics, 47(1), 97-120. doi:10.1146/annurev-genet-111212-133526Wahl, S., Drong, A., Lehne, B., Loh, M., Scott, W. R., Kunze, S., … Yang, Y. (2016). Epigenome-wide association study of body mass index, and the adverse outcomes of adiposity. Nature, 541(7635), 81-86. doi:10.1038/nature20784Wang, W., & Seale, P. (2016). Control of brown and beige fat development. Nature Reviews Molecular Cell Biology, 17(11), 691-702. doi:10.1038/nrm.2016.96Wang, B., Yang, Q., Harris, C. L., Nelson, M. L., Busboom, J. R., Zhu, M.-J., & Du, M. (2016). Nutrigenomic regulation of adipose tissue development — role of retinoic acid: A review. Meat Science, 120, 100-106. doi:10.1016/j.meatsci.2016.04.003Wang, X., Tucker, N. R., Rizki, G., Mills, R., Krijger, P. H., de Wit, E., … Boyer, L. A. (2016). Discovery and validation of sub-threshold genome-wide association study loci using epigenomic signatures. eLife, 5. doi:10.7554/elife.10557Wang, J., Shi, Y., Elzo, M. A., Su, Y., Jia, X., Chen, S., & Lai, S. (2017). Myopalladin gene polymorphism is associated with rabbit meat quality traits. Italian Journal of Animal Science, 16(3), 400-404. doi:10.1080/1828051x.2017.1296333Won, S., Jung, J., Park, E., & Kim, H. (2018). Identification of genes related to intramuscular fat content of pigs using genome-wide association study. Asian-Australasian Journal of Animal Sciences, 31(2), 157-162. doi:10.5713/ajas.17.0218Zhang, H., Wang, Z., Wang, S., & Li, H. (2012). Progress of genome wide association study in domestic animals. Journal of Animal Science and Biotechnology, 3(1). doi:10.1186/2049-1891-3-26Zhang, G.-W., Gao, L., Chen, S.-Y., Zhao, X.-B., Tian, Y.-F., Wang, X., … Lai, S.-J. (2013). Single nucleotide polymorphisms in the FTO gene and their association with growth and meat quality traits in rabbits. Gene, 527(2), 553-557. doi:10.1016/j.gene.2013.06.024Zomeño, C., Hernández, P., & Blasco, A. (2013). Divergent selection for intramuscular fat content in rabbits. I. Direct response to selection1. Journal of Animal Science, 91(9), 4526-4531. doi:10.2527/jas.2013-636

Next Generation Very Large Array Memo No. 6, Science Working Group 1: The Cradle of Life

This paper discusses compelling science cases for a future long-baseline interferometer operating at millimeter and centimeter wavelengths, like the proposed Next Generation Vary Large Array (ngVLA). We report on the activities of the Cradle of Life science working group, which focused on the formation of low- and high-mass stars, the formation of planets and evolution of protoplanetary disks, the physical and compositional study of Solar System bodies, and the possible detection of radio signals from extraterrestrial civilizations. We propose 19 scientific projects based on the current specification of the ngVLA. Five of them are highlighted as possible Key Science Projects: (1) Resolving the density structure and dynamics of the youngest HII regions and high-mass protostellar jets, (2) Unveiling binary/multiple protostars at higher resolution, (3) Mapping planet formation regions in nearby disks on scales down to 1 AU, (4) Studying the formation of complex molecules, and (5) Deep atmospheric mapping of giant planets in the Solar System. For each of these projects, we discuss the scientific importance and feasibility. The results presented here should be considered as the beginning of a more in-depth analysis of the science enabled by such a facility, and are by no means complete or exhaustive.Comment: 51 pages, 12 figures, 1 table. For more information visit https://science.nrao.edu/futures/ngvl

ASIME 2018 White Paper. In-Space Utilisation of Asteroids: Asteroid Composition -- Answers to Questions from the Asteroid Miners

In keeping with the Luxembourg government's initiative to support the future use of space resources, ASIME 2018 was held in Belval, Luxembourg on April 16-17, 2018. The goal of ASIME 2018: Asteroid Intersections with Mine Engineering, was to focus on asteroid composition for advancing the asteroid in-space resource utilisation domain. What do we know about asteroid composition from remote-sensing observations? What are the potential caveats in the interpretation of Earth-based spectral observations? What are the next steps to improve our knowledge on asteroid composition by means of ground-based and space-based observations and asteroid rendez-vous and sample return missions? How can asteroid mining companies use this knowledge? ASIME 2018 was a two-day workshop of almost 70 scientists and engineers in the context of the engineering needs of space missions with in-space asteroid utilisation. The 21 Questions from the asteroid mining companies were sorted into the four asteroid science themes: 1) Potential Targets, 2) Asteroid-Meteorite Links, 3) In-Situ Measurements and 4) Laboratory Measurements. The Answers to those Questions were provided by the scientists with their conference presentations and collected by A. Graps or edited directly into an open-access collaborative Google document or inserted by A. Graps using additional reference materials. During the ASIME 2018, first day and second day Wrap-Ups, the answers to the questions were discussed further. New readers to the asteroid mining topic may find the Conversation boxes and the Mission Design discussions especially interesting.Comment: Outcome from the ASIME 2018: Asteroid Intersections with Mine Engineering, Luxembourg. April 16-17, 2018. 65 Pages. arXiv admin note: substantial text overlap with arXiv:1612.0070

Antimicrobial resistance among migrants in Europe: a systematic review and meta-analysis

BACKGROUND: Rates of antimicrobial resistance (AMR) are rising globally and there is concern that increased migration is contributing to the burden of antibiotic resistance in Europe. However, the effect of migration on the burden of AMR in Europe has not yet been comprehensively examined. Therefore, we did a systematic review and meta-analysis to identify and synthesise data for AMR carriage or infection in migrants to Europe to examine differences in patterns of AMR across migrant groups and in different settings. METHODS: For this systematic review and meta-analysis, we searched MEDLINE, Embase, PubMed, and Scopus with no language restrictions from Jan 1, 2000, to Jan 18, 2017, for primary data from observational studies reporting antibacterial resistance in common bacterial pathogens among migrants to 21 European Union-15 and European Economic Area countries. To be eligible for inclusion, studies had to report data on carriage or infection with laboratory-confirmed antibiotic-resistant organisms in migrant populations. We extracted data from eligible studies and assessed quality using piloted, standardised forms. We did not examine drug resistance in tuberculosis and excluded articles solely reporting on this parameter. We also excluded articles in which migrant status was determined by ethnicity, country of birth of participants' parents, or was not defined, and articles in which data were not disaggregated by migrant status. Outcomes were carriage of or infection with antibiotic-resistant organisms. We used random-effects models to calculate the pooled prevalence of each outcome. The study protocol is registered with PROSPERO, number CRD42016043681. FINDINGS: We identified 2274 articles, of which 23 observational studies reporting on antibiotic resistance in 2319 migrants were included. The pooled prevalence of any AMR carriage or AMR infection in migrants was 25·4% (95% CI 19·1-31·8; I2 =98%), including meticillin-resistant Staphylococcus aureus (7·8%, 4·8-10·7; I2 =92%) and antibiotic-resistant Gram-negative bacteria (27·2%, 17·6-36·8; I2 =94%). The pooled prevalence of any AMR carriage or infection was higher in refugees and asylum seekers (33·0%, 18·3-47·6; I2 =98%) than in other migrant groups (6·6%, 1·8-11·3; I2 =92%). The pooled prevalence of antibiotic-resistant organisms was slightly higher in high-migrant community settings (33·1%, 11·1-55·1; I2 =96%) than in migrants in hospitals (24·3%, 16·1-32·6; I2 =98%). We did not find evidence of high rates of transmission of AMR from migrant to host populations. INTERPRETATION: Migrants are exposed to conditions favouring the emergence of drug resistance during transit and in host countries in Europe. Increased antibiotic resistance among refugees and asylum seekers and in high-migrant community settings (such as refugee camps and detention facilities) highlights the need for improved living conditions, access to health care, and initiatives to facilitate detection of and appropriate high-quality treatment for antibiotic-resistant infections during transit and in host countries. Protocols for the prevention and control of infection and for antibiotic surveillance need to be integrated in all aspects of health care, which should be accessible for all migrant groups, and should target determinants of AMR before, during, and after migration. FUNDING: UK National Institute for Health Research Imperial Biomedical Research Centre, Imperial College Healthcare Charity, the Wellcome Trust, and UK National Institute for Health Research Health Protection Research Unit in Healthcare-associated Infections and Antimictobial Resistance at Imperial College London

Brief Report: Suboptimal Lopinavir Exposure in Infants on Rifampicin Treatment Receiving Double-dosed or Semisuperboosted Lopinavir/Ritonavir: Time for a Change.

BACKGROUND: Although super-boosted lopinavir/ritonavir (LPV/r; ratio 4:4 instead of 4:1) is recommended for infants living with HIV and receiving concomitant rifampicin, in clinical practice, many different LPV/r dosing strategies are applied due to poor availability of pediatric separate ritonavir formulations needed to superboost. We evaluated LPV pharmacokinetics in infants with HIV receiving LPV/r dosed according to local guidelines in various sub-Saharan African countries with or without rifampicin-based tuberculosis (TB) treatment. METHODS: This was a 2-arm pharmacokinetic substudy nested within the EMPIRICAL trial (#NCT03915366). Infants aged 1-12 months recruited into the main study were administered LPV/r according to local guidelines and drug availability either with or without rifampicin-based TB treatment; during rifampicin cotreatment, they received double-dosed (ratio 8:2) or semisuperboosted LPV/r (adding a ritonavir 100 mg crushed tablet to the evening LPV/r dose). Six blood samples were taken over 12 hours after intake of LPV/r. RESULTS: In total, 14/16 included infants had evaluable pharmacokinetic curves; 9/14 had rifampicin cotreatment (5 received double-dosed and 4 semisuperboosted LPV/r). The median (IQR) age was 6.4 months (5.4-9.8), weight 6.0 kg (5.2-6.8), and 10/14 were male. Of those receiving rifampicin, 6/9 infants (67%) had LPV Ctrough <1.0 mg/L compared with 1/5 (20%) in the control arm. LPV apparent oral clearance was 3.3-fold higher for infants receiving rifampicin. CONCLUSION: Double-dosed or semisuperboosted LPV/r for infants aged 1-12 months receiving rifampicin resulted in substantial proportions of subtherapeutic LPV levels. There is an urgent need for data on alternative antiretroviral regimens in infants with HIV/TB coinfection, including twice-daily dolutegravir

Global burden of respiratory infections associated with seasonal influenza in children under 5 years in 2018: a systematic review and modelling study

Background: Seasonal influenza virus is a common cause of acute lower respiratory infection (ALRI) in young children. In 2008, we estimated that 20 million influenza-virus-associated ALRI and 1 million influenza-virus-associated severe ALRI occurred in children under 5 years globally. Despite this substantial burden, only a few low-income and middle-income countries have adopted routine influenza vaccination policies for children and, where present, these have achieved only low or unknown levels of vaccine uptake. Moreover, the influenza burden might have changed due to the emergence and circulation of influenza A/H1N1pdm09. We aimed to incorporate new data to update estimates of the global number of cases, hospital admissions, and mortality from influenza-virus-associated respiratory infections in children under 5 years in 2018. Methods: We estimated the regional and global burden of influenza-associated respiratory infections in children under 5 years from a systematic review of 100 studies published between Jan 1, 1995, and Dec 31, 2018, and a further 57 high-quality unpublished studies. We adapted the Newcastle-Ottawa Scale to assess the risk of bias. We estimated incidence and hospitalisation rates of influenza-virus-associated respiratory infections by severity, case ascertainment, region, and age. We estimated in-hospital deaths from influenza virus ALRI by combining hospital admissions and in-hospital case-fatality ratios of influenza virus ALRI. We estimated the upper bound of influenza virus-associated ALRI deaths based on the number of in-hospital deaths, US paediatric influenza-associated death data, and population-based childhood all-cause pneumonia mortality data in six sites in low-income and lower-middle-income countries. Findings: In 2018, among children under 5 years globally, there were an estimated 109·5 million influenza virus episodes (uncertainty range [UR] 63·1–190·6), 10·1 million influenza-virus-associated ALRI cases (6·8–15·1); 870 000 influenza-virus-associated ALRI hospital admissions (543 000–1 415 000), 15 300 in-hospital deaths (5800–43 800), and up to 34 800 (13 200–97 200) overall influenza-virus-associated ALRI deaths. Influenza virus accounted for 7% of ALRI cases, 5% of ALRI hospital admissions, and 4% of ALRI deaths in children under 5 years. About 23% of the hospital admissions and 36% of the in-hospital deaths were in infants under 6 months. About 82% of the in-hospital deaths occurred in low-income and lower-middle-income countries. Interpretation: A large proportion of the influenza-associated burden occurs among young infants and in low-income and lower middle-income countries. Our findings provide new and important evidence for maternal and paediatric influenza immunisation, and should inform future immunisation policy particularly in low-income and middle-income countries. Funding: WHO; Bill & Melinda Gates Foundation.Fil: Wang, Xin. University of Edinburgh; Reino UnidoFil: Li, You. University of Edinburgh; Reino UnidoFil: O'Brien, Katherine L.. University Johns Hopkins; Estados UnidosFil: Madhi, Shabir A.. University of the Witwatersrand; SudáfricaFil: Widdowson, Marc Alain. Centers for Disease Control and Prevention; Estados UnidosFil: Byass, Peter. Umea University; SueciaFil: Omer, Saad B.. Yale School Of Public Health; Estados UnidosFil: Abbas, Qalab. Aga Khan University; PakistánFil: Ali, Asad. Aga Khan University; PakistánFil: Amu, Alberta. Dodowa Health Research Centre; GhanaFil: Azziz-Baumgartner, Eduardo. Centers for Disease Control and Prevention; Estados UnidosFil: Bassat, Quique. University Of Barcelona; EspañaFil: Abdullah Brooks, W.. University Johns Hopkins; Estados UnidosFil: Chaves, Sandra S.. Centers for Disease Control and Prevention; Estados UnidosFil: Chung, Alexandria. University of Edinburgh; Reino UnidoFil: Cohen, Cheryl. National Institute For Communicable Diseases; SudáfricaFil: Echavarría, Marcela Silvia. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. CEMIC-CONICET. Centro de Educaciones Médicas e Investigaciones Clínicas "Norberto Quirno". CEMIC-CONICET; ArgentinaFil: Fasce, Rodrigo A.. Public Health Institute; ChileFil: Gentile, Angela. Gobierno de la Ciudad de Buenos Aires. Hospital General de Niños "Ricardo Gutiérrez"; ArgentinaFil: Gordon, Aubree. University of Michigan; Estados UnidosFil: Groome, Michelle. University of the Witwatersrand; SudáfricaFil: Heikkinen, Terho. University Of Turku; FinlandiaFil: Hirve, Siddhivinayak. Kem Hospital Research Centre; IndiaFil: Jara, Jorge H.. Universidad del Valle de Guatemala; GuatemalaFil: Katz, Mark A.. Clalit Research Institute; IsraelFil: Khuri Bulos, Najwa. University Of Jordan School Of Medicine; JordaniaFil: Krishnan, Anand. All India Institute Of Medical Sciences; IndiaFil: de Leon, Oscar. Universidad del Valle de Guatemala; GuatemalaFil: Lucero, Marilla G.. Research Institute For Tropical Medicine; FilipinasFil: McCracken, John P.. Universidad del Valle de Guatemala; GuatemalaFil: Mira-Iglesias, Ainara. Fundación Para El Fomento de la Investigación Sanitaria; EspañaFil: Moïsi, Jennifer C.. Agence de Médecine Préventive; FranciaFil: Munywoki, Patrick K.. No especifíca;Fil: Ourohiré, Millogo. No especifíca;Fil: Polack, Fernando Pedro. Fundación para la Investigación en Infectología Infantil; ArgentinaFil: Rahi, Manveer. University of Edinburgh; Reino UnidoFil: Rasmussen, Zeba A.. National Institutes Of Health; Estados UnidosFil: Rath, Barbara A.. Vienna Vaccine Safety Initiative; AlemaniaFil: Saha, Samir K.. Child Health Research Foundation; BangladeshFil: Simões, Eric A.F.. University of Colorado; Estados UnidosFil: Sotomayor, Viviana. Ministerio de Salud de Santiago de Chile; ChileFil: Thamthitiwat, Somsak. Thailand Ministry Of Public Health; TailandiaFil: Treurnicht, Florette K.. University of the Witwatersrand; SudáfricaFil: Wamukoya, Marylene. African Population & Health Research Center; KeniaFil: Lay-Myint, Yoshida. Nagasaki University; JapónFil: Zar, Heather J.. University of Cape Town; SudáfricaFil: Campbell, Harry. University of Edinburgh; Reino UnidoFil: Nair, Harish. University of Edinburgh; Reino Unid

Surgical site infection after gastrointestinal surgery in high-income, middle-income, and low-income countries: a prospective, international, multicentre cohort study

Background: Surgical site infection (SSI) is one of the most common infections associated with health care, but its importance as a global health priority is not fully understood. We quantified the burden of SSI after gastrointestinal surgery in countries in all parts of the world. Methods: This international, prospective, multicentre cohort study included consecutive patients undergoing elective or emergency gastrointestinal resection within 2-week time periods at any health-care facility in any country. Countries with participating centres were stratified into high-income, middle-income, and low-income groups according to the UN's Human Development Index (HDI). Data variables from the GlobalSurg 1 study and other studies that have been found to affect the likelihood of SSI were entered into risk adjustment models. The primary outcome measure was the 30-day SSI incidence (defined by US Centers for Disease Control and Prevention criteria for superficial and deep incisional SSI). Relationships with explanatory variables were examined using Bayesian multilevel logistic regression models. This trial is registered with ClinicalTrials.gov, number NCT02662231. Findings: Between Jan 4, 2016, and July 31, 2016, 13 265 records were submitted for analysis. 12 539 patients from 343 hospitals in 66 countries were included. 7339 (58·5%) patient were from high-HDI countries (193 hospitals in 30 countries), 3918 (31·2%) patients were from middle-HDI countries (82 hospitals in 18 countries), and 1282 (10·2%) patients were from low-HDI countries (68 hospitals in 18 countries). In total, 1538 (12·3%) patients had SSI within 30 days of surgery. The incidence of SSI varied between countries with high (691 [9·4%] of 7339 patients), middle (549 [14·0%] of 3918 patients), and low (298 [23·2%] of 1282) HDI (p < 0·001). The highest SSI incidence in each HDI group was after dirty surgery (102 [17·8%] of 574 patients in high-HDI countries; 74 [31·4%] of 236 patients in middle-HDI countries; 72 [39·8%] of 181 patients in low-HDI countries). Following risk factor adjustment, patients in low-HDI countries were at greatest risk of SSI (adjusted odds ratio 1·60, 95% credible interval 1·05–2·37; p=0·030). 132 (21·6%) of 610 patients with an SSI and a microbiology culture result had an infection that was resistant to the prophylactic antibiotic used. Resistant infections were detected in 49 (16·6%) of 295 patients in high-HDI countries, in 37 (19·8%) of 187 patients in middle-HDI countries, and in 46 (35·9%) of 128 patients in low-HDI countries (p < 0·001). Interpretation: Countries with a low HDI carry a disproportionately greater burden of SSI than countries with a middle or high HDI and might have higher rates of antibiotic resistance. In view of WHO recommendations on SSI prevention that highlight the absence of high-quality interventional research, urgent, pragmatic, randomised trials based in LMICs are needed to assess measures aiming to reduce this preventable complication