124 research outputs found

    Genomic regions influencing intramuscular fat in divergently selected rabbit lines

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    [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

    Removal efficiency for emerging contaminants in a WWTP from Madrid (Spain) after secondary and tertiary treatment and environmental impact on the Manzanares River

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    The effluents from wastewater treatment plants (WWTPs) can be an important contamination source for receiving waters. In this work, a comprehensive study on the impact of a WWTP from Madrid on the aquatic environment has been performed, including a wide number of pharmaceuticals and pesticides, among them those included in the European Watch List. 24-h composite samples of influent (IWW) and effluent wastewater after secondary (EWW2) and after secondary + tertiary treatment (EWW3) were monitored along two campaigns. Average weekly concentrations in IWW and EWW2 and EWW3 allowed estimating the removal efficiency of the WWTP for pharmaceutical active substances (PhACs). In addition, the impact of EWW3 on the water quality of the Manzanares River was assessed, in terms of PhAC and pesticide concentrations, through analysis of the river water collected upstream and downstream of the discharge point. After a preliminary risk assessment, a detailed evaluation of the impact on the aquatic environment, including a toxicological study and screening of pharmaceutical metabolites, was made for the seven most relevant PhACs: sulfamethoxazole, azithromycin and clarithromycin (antibiotics), metoprolol (antihypertensive), diclofenac (anti-inflammatory/analgesic), irbesartan (antihypertensive), and the antidepressant venlafaxine. Among selected PhACs, irbesartan, clarithromycin and venlafaxine presented moderate or high risk in the river water downstream of the discharge. Albeit no acute toxicity was detected, more detailed studies should be carried out for these substances, including additional toxicological studies, to set up potential sublethal and chronic effects on aquatic organisms.This work was developed under the financial support of DRACE INFRAESTRUCTURAS S.A. as a part of the project Estudio de contaminantes emergentes en aguas residuales y superficiales de Madrid. The authors acknowledge the support of Jose Ramon Rodriguez from DRACE INFRAESTRUCTURAS S.A., for collection of wastewater and surface water samples, as well as the discussion and useful suggestions from Jesus Angel López, Pedro Miguel Catalinas and Maria Elvira Benito, from Sub-Direccion General de Aguas, Ayuntamiento de Madrid. The University Jaume I of Castellón, Spain (project UJI-B2018-55), the Ministry of Science, Innovation and University, Spain (Ref RTI2018-097417-B-I00) and Generalitat Valenciana, Spain (research group of excellence PROMETEO 2019/040) are also acknowledged. The authors are very grateful to the Serveis Centrals d'Instrumentació Científica (SCIC) of University Jaume I for the use of LC-MS/MS instrumentation

    The timing of the deglaciation in the Atlantic Iberian mountains: Insights from the stratigraphic analysis of a lake sequence in Serra da Estrela (Portugal).

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    Understanding the environmental response to the last glacial termination in regions located in transitional climate zones such as the Atlantic Iberian mountains is crucial to estimate potential changes in regions affected by current glacial melting. We present an 8.5 m-long, solid last deglaciation and Holocene chronostratigraphic record including detailed sediment analysis from Lake Peix¿ao, a pro-glacial lake in the Serra da Estrela (Central Portugal). The age-depth model relies on a Bayesian approach that includes 16 AMS 14C dates and 210Pb-137CS measurements, robustly dating the lake formation at 14.7 ± 0.32 cal. ka BP. This chronological reconstruction shows an average sedimentation rate of ca. 0.07 cm yr-1 (15 yr cm-1), enabling proxy analyses at decadal timescales. The sediment sequence is composed of five lithological units: (U1) coarse and unsorted fluvioglacial lacustrine deposits; (U2) massive fluvioglacial lacustrine deposits (863-790 cm below surface [bsf]; 14.7 ± 0.32-13.8 ± 0.12 cal. ka BP); (U3) water current fluvioglacial lacustrine deposits (790-766 cm bsf; 13.8 ± 0.12-12.9 ± 0.29 cal. ka BP); (U4) laminated/banded lacustrine deposits characterized by terrigenous deposits from ice-covered lake periods and episodic events of iceand snow melting (766-752 cm bsf; 12.9 ± 0.29-11.7 ± 0.15 cal. ka BP); and (U5) massive muddy lacustrine deposits (752-0 cm bsf; 11.7 ± 0.15 cal. ka BP-present). The occurrence of U2 to U4 deposits defines the transition from glacial cold (U1) to net warm postglacial conditions (U5). These climate transitions are marked bychanges in sediments and the presence of very low sedimentation rate periods, possibly related to the Intra-Allerød Cold Period and the coldest phase of the Younger Dryas. Our results support the previously proposed timing of the retreat of the Serra da Estrela glaciers ca. 13.8 ± 0.12 cal. ka BP. The robust chronology of Lake Peixao highlights the potential of Iberian pro-glacial lakes for dating deglaciation processes and will lead to unprecedented decadal-to-centennial timescale palaeoclimate reconstructions in this region since the last glacial-interglacial transition

    Effectiveness of telephone monitoring in primary care to detect pneumonia and associated risk factors in patients with SARS-CoV-2

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    Improved technology facilitates the acceptance of telemedicine. The aim was to analyze the effectiveness of telephone follow-up to detect severe SARS-CoV-2 cases that progressed to pneumonia. A prospective cohort study with 2-week telephone follow-up was carried out March 1 to May 4, 2020, in a primary healthcare center in Barcelona. Individuals aged =15 years with symptoms of SARS-CoV-2 were included. Outpatients with non-severe disease were called on days 2, 4, 7, 10 and 14 after diagnosis; patients with risk factors for pneumonia received daily calls through day 5 and then the regularly scheduled calls. Patients hospitalized due to pneumonia received calls on days 1, 3, 7 and 14 post-discharge. Of the 453 included patients, 435 (96%) were first attended to at a primary healthcare center. The 14-day follow-up was completed in 430 patients (99%), with 1798 calls performed. Of the 99 cases of pneumonia detected (incidence rate 20.8%), one-third appeared 7 to 10 days after onset of SARS-CoV-2 symptoms. Ten deaths due to pneumonia were recorded. Telephone follow-up by a primary healthcare center was effective to detect SARS-CoV-2 pneumonias and to monitor related complications. Thus, telephone appointments between a patient and their health care practitioner benefit both health outcomes and convenience. © 2021 by the authors. Licensee MDPI, Basel, Switzerland

    Latin American Consensus: Children Born Small for Gestational Age

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    72-87Cuatrimestra

    Search for squarks and gluinos in events with isolated leptons, jets and missing transverse momentum at s√=8 TeV with the ATLAS detector

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    The results of a search for supersymmetry in final states containing at least one isolated lepton (electron or muon), jets and large missing transverse momentum with the ATLAS detector at the Large Hadron Collider are reported. The search is based on proton-proton collision data at a centre-of-mass energy s√=8 TeV collected in 2012, corresponding to an integrated luminosity of 20 fb−1. No significant excess above the Standard Model expectation is observed. Limits are set on supersymmetric particle masses for various supersymmetric models. Depending on the model, the search excludes gluino masses up to 1.32 TeV and squark masses up to 840 GeV. Limits are also set on the parameters of a minimal universal extra dimension model, excluding a compactification radius of 1/R c = 950 GeV for a cut-off scale times radius (ΛR c) of approximately 30

    Peabody Picture Vocabulary Test-III: Normative data for Spanish-speaking pediatric population

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    OBJECTIVE: To generate normative data for the Peabody Picture Vocabulary Test-III (PPVT-III) in Spanish-speaking pediatric populations. METHOD: The sample consisted of 4,373 healthy children from nine countries in Latin America (Chile, Cuba, Ecuador, Honduras, Guatemala, Mexico, Paraguay, Peru, and Puerto Rico) and Spain. Each participant was administered the PPVT-III as part of a larger neuropsychological battery. PPVT-III scores were normed using multiple linear regressions and standard deviations of residual values. Age, age2, sex, and mean level of parental education (MLPE) were included as predictors in the analyses. RESULTS: The final multiple linear regression models showed main effects for age in all countries, such that scores increased linearly as a function of age. In addition, age2 had a significant effect in all countries, except Guatemala and Paraguay. Models showed that children whose parent(s) had a MLPE >12 years obtained higher scores compared to children whose parent(s) had a MLPE ≤12 years in all countries, except for Cuba, Peru, and Puerto Rico. Sex affected scores for Chile, Ecuador, Guatemala, Mexico, and Spain. CONCLUSIONS: This is the largest Spanish-speaking pediatric normative study in the world, and it will allow neuropsychologists from these countries to have a more accurate interpretation of the PPVT-III when used in pediatric populations

    Search for squarks and gluinos with the ATLAS detector in final states with jets and missing transverse momentum using √s=8 TeV proton-proton collision data

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    A search for squarks and gluinos in final states containing high-p T jets, missing transverse momentum and no electrons or muons is presented. The data were recorded in 2012 by the ATLAS experiment in s√=8 TeV proton-proton collisions at the Large Hadron Collider, with a total integrated luminosity of 20.3 fb−1. Results are interpreted in a variety of simplified and specific supersymmetry-breaking models assuming that R-parity is conserved and that the lightest neutralino is the lightest supersymmetric particle. An exclusion limit at the 95% confidence level on the mass of the gluino is set at 1330 GeV for a simplified model incorporating only a gluino and the lightest neutralino. For a simplified model involving the strong production of first- and second-generation squarks, squark masses below 850 GeV (440 GeV) are excluded for a massless lightest neutralino, assuming mass degenerate (single light-flavour) squarks. In mSUGRA/CMSSM models with tan β = 30, A 0 = −2m 0 and μ > 0, squarks and gluinos of equal mass are excluded for masses below 1700 GeV. Additional limits are set for non-universal Higgs mass models with gaugino mediation and for simplified models involving the pair production of gluinos, each decaying to a top squark and a top quark, with the top squark decaying to a charm quark and a neutralino. These limits extend the region of supersymmetric parameter space excluded by previous searches with the ATLAS detector
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