Suchergebnisse

Ponencia presentada en: VIII Congreso de la Asociación Española de Climatología celebrado en Salamanca entre el 25 y el 28 de septiembre de 2012. ; [EN]Tourism is an important sector of the Spanish economy. The popularity of Mediterranean beaches does not decrease even during the European economic crisis. Rising temperature and extreme weather events may threat the popularity of summer tourism, both directly and indirectly. Unpleasant heat waves and severe persistent droughts can occur even without a systematic change of the climatic characteristics, but the growing frequency and severity of such events are expected due to the global warming process. ; [ES]El turismo es un importante sector de la economía española. La popularidad de las playas mediterráneas no decrece aún en los años de crisis económica europea. El aumento de la temperatura y fenómenos meteorológicos extremos pueden amenazar la popularidad del turismo de verano, tanto directa como indirectamente. Olas de calor y desagradables graves sequías persistentes pueden ocurrir aún sin cambio sistemático de las características climáticas, pero la creciente frecuencia y gravedad de tales eventos se espera debido al proceso de calentamiento global. ; The research was supported by the Spanish project "Tourism, Environment and Politics" ECO 2010-18158.

Póster presentado en: VIII Congreso de la Asociación Española de Climatología celebrado en Salamanca entre el 25 y el 28 de septiembre de 2012. ; The research was supported by the Spanish project "Tourism, Environment and Politics" ECO 2010-18158.

Mieth, Bettina et al. ; The standard approach to the analysis of genome-wide association studies (GWAS) is based on testing each position in the genome individually for statistical significance of its association with the phenotype under investigation. To improve the analysis of GWAS, we propose a combination of machine learning and statistical testing that takes correlation structures within the set of SNPs under investigation in a mathematically well-controlled manner into account. The novel two-step algorithm, COMBI, first trains a support vector machine to determine a subset of candidate SNPs and then performs hypothesis tests for these SNPs together with an adequate threshold correction. Applying COMBI to data from a WTCCC study (2007) and measuring performance as replication by independent GWAS published within the 2008–2015 period, we show that our method outperforms ordinary raw p-value thresholding as well as other state-of-the-art methods. COMBI presents higher power and precision than the examined alternatives while yielding fewer false (i.e. non-replicated) and more true (i.e. replicated) discoveries when its results are validated on later GWAS studies. More than 80% of the discoveries made by COMBI upon WTCCC data have been validated by independent studies. Implementations of the COMBI method are available as a part of the GWASpi toolbox 2.0. ; EF acknowledges support from the advanced ERC grant (ERC-2011-AdG 295642-FEP) on the Foundation of Economic Preferences. MK, BM, and KRM were supported by the German National Science Foundation (DFG) under the grants MU 987/6-1 and RA 1894/1-1. TD and DS were supported by the German National Science Foundation (DFG) under the grants DI 1723/3-1 und SCHU 2828/2-1. GB and TS acknowledge support of the German National Science Foundation (DFG) under the research group grant FOR 1735. MK, DT, KRM, and GB acknowledge financial support by the FP7-ICT Programme of the European Community, under the PASCAL2 Network of Excellence. MK acknowledges a postdoctoral fellowship by the German Research Foundation (DFG), award KL 2698/2-1, and from the Federal Ministry of Science and Education (BMBF) awards 031L0023A and 031B0187B. AN acknowledges support from the Spanish Multiple Sclerosis Network (REEM), of the Instituto de Salud Carlos III (RD12/0032/0011), the Spanish National Institute for Bioinformatics (PT13/0001/0026) the Spanish Government Grant BFU2012-38236 and from FEDER. This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No. 634143 (MedBioinformatics). MK and KRM were financially supported by the Ministry of Education, Science, and Technology, through the National Research Foundation of Korea under Grant R31-10008 (MK, KRM) and BK21 (KRM). ; Peer reviewed

The use of flux limiters is widespread within the scientific computing community to capture shock dis- continuities and are of paramount importance for the temporal integration of high-speed aerodynamics, multiphase flows and hyperbolic equations in general. Meanwhile, the breakthrough of new computing architectures and the hybridization of supercomputer systems pose a huge portability challenge, particularly for legacy codes, since the computing subroutines that form the algorithms, the so-called kernels, must be adapted to various complex parallel program- ming paradigms. From this perspective, the development of innovative implementations relying on a minimalist set of kernels simplifies the deployment of scientific computing software on state-of-the-art supercomputers, while it requires the reformulation of algorithms, such as the aforementioned flux lim- iters. Equipped with basic algebraic topology and graph theory underlying the classical mesh concept, a new flux limiter formulation is presented based on the adoption of algebraic data structures and kernels. As a result, traditional flux limiters are cast into a stream of only two types of computing kernels: sparse matrix-vector multiplication and generalized pointwise binary operators. The newly proposed formulation eases the deployment of such a numerical technique in massively parallel, potentially hybrid, computing systems and is demonstrated for a canonical advection problem. ; The work of N. V. and X. Á. F. has been supported by the Government of Catalonia, FI AGAUR predoctoral grants 2019FI_B2_ 000104 and 2019FI_B2_00076. N. V., X. Á. F., J. C., A. O. and F. X. T. have been funded by the Spanish Research Agency, ANUMESOL project ENE2017-88697-R. J. C. has also been funded by Spanish Research Agency, GALIFLOW project ENE2015-70662-P. The studies of this work have been carried out using the MareNostrum 4 supercomputer of the Barcelona Supercomput- ing Center, projects IM-2019-3-0026 and IM-2020-1-0006; the TSUBAME3.0 supercomputer of the Global Scientific Information and Computing Center at Tokyo Institute of Technology; the Lomonosov-2 supercomputer of the shared research facilities of HPC computing resources at Lomonosov Moscow State University; the K-60 hybrid cluster of the collective use center of the Keldysh Institute of Applied Mathematics; the JFF cluster of the Heat and Mass Transfer Technological Center at Technical University of Cat- alonia. The authors thankfully acknowledge these institutions for the compute time and technical support. ; Peer Reviewed ; Postprint (published version)

OBJECTIVES: We aimed to investigate the association between polymorphisms located in type I interferon (IFN)-induced genes, genes belonging to the toll-like receptor (TLR) pathway, and genes encoding neurotransmitter receptors and the response to IFN-β treatment in patients with multiple sclerosis (MS). METHODS: In a first or screening phase of the study, 384 polymorphisms were genotyped in 830 patients with MS classified into IFN-β responders (n = 416) and nonresponders (n = 414) according to clinical criteria. In a second or validation phase, the most significant polymorphisms associated with IFN-β response were genotyped in an independent validation cohort of 555 patients with MS (281 IFN-β responders and 274 nonresponders). RESULTS: Seven single nucleotide polymorphisms (SNPs) were selected from the screening phase for further validation: rs832032 (GABRR3; p = 0.0006), rs6597 (STUB1; p = 0.019), rs3747517 (IFIH1; p = 0.010), rs2277302 (PELI3; p = 0.017), rs10958713 (IKBKB; p = 0.003), rs2834202 (IFNAR1; p = 0.030), and rs4422395 (CXCL1; p = 0.017). None of these SNPs were significantly associated with IFN-β response when genotyped in an independent cohort of patients. Combined analysis of these SNPs in all patients with MS (N = 1,385) revealed 2 polymorphisms associated with IFN-β response: rs2277302 (PELI3; p = 0.008) and rs832032 (GABRR3; p = 0.006). CONCLUSIONS: These findings do not support an association between polymorphisms located in genes related to the type I IFN or TLR pathways or genes encoding neurotransmitter receptors and the clinical response to IFN-β. Nevertheless, additional genetic and functional studies of PELI3 and GABRR3 are warranted. ; SM is early stage researcher of the FP7 (2007-2013) Marie Curie Initial Training Network "UEPHA*MS" (2008-2012; Grant Agreement Number 212877). Spanish Ministry of Economy and Competitiveness, SAF/ 2012-34670, MS research, 2013-2015. Ministerio de Economía y Competitividad (MINECO) Convocatoria Proyectos de Investigación 2012 (Madrid, Spain); Funciones intracelulares de tipo-no-citoquina de las subunidades de la familia IL-12; SAF2012-32118. Dirección General de Investigación Científica y Técnica - DGICYT. Spanish Government (BFU2012-38236).

The enormous mammal's lifespan variation is the result of each species' adaptations to their own biological trade-offs and ecological conditions. Comparative genomics have demonstrated that genomic factors underlying both, species lifespans and longevity of individuals, are in part shared across the tree of life. Here, we compared protein-coding regions across the mammalian phylogeny to detect individual amino acid (AA) changes shared by the most long-lived mammals and genes whose rates of protein evolution correlate with longevity. We discovered a total of 2,737 AA in 2,004 genes that distinguish long- and short-lived mammals, significantly more than expected by chance (P = 0.003). These genes belong to pathways involved in regulating lifespan, such as inflammatory response and hemostasis. Among them, a total 1,157 AA showed a significant association with maximum lifespan in a phylogenetic test. Interestingly, most of the detected AA positions do not vary in extant human populations (81.2%) or have allele frequencies below 1% (99.78%). Consequently, almost none of these putatively important variants could have been detected by genome-wide association studies. Additionally, we identified four more genes whose rate of protein evolution correlated with longevity in mammals. Crucially, SNPs located in the detected genes explain a larger fraction of human lifespan heritability than expected, successfully demonstrating for the first time that comparative genomics can be used to enhance interpretation of human genome-wide association studies. Finally, we show that the human longevity-associated proteins are significantly more stable than the orthologous proteins from short-lived mammals, strongly suggesting that general protein stability is linked to increased lifespan. ; This work was supported by AEI-PGC2018-101927-BI00 (FEDER/UE), the Spanish National Institute of Bioinformatics of the Instituto de Salud Carlos III (PT17/0009/0020), FEDER (Fondo Europeo de Desarrollo Regional)/FSE (Fondo Social Europeo), "Unidad de Excelencia María de Maeztu," funded by the AEI (CEX2018-000792-M) and Secretaria d'Universitats i Recerca and CERCA Programme del Departament d'Economia i Coneixement de la Generalitat de Catalunya (GRC 2017 SGR 880). ; With funding from the Spanish government through the 'Severo Ochoa Centre of Excellence' accreditation (CEX2018-000792-M). ; Peer reviewed

The standard approach to the analysis of genome-wide association studies (GWAS) is based on testing each position in the genome individually for statistical significance of its association with the phenotype under investigation. To improve the analysis of GWAS, we propose a combination of machine learning and statistical testing that takes correlation structures within the set of SNPs under investigation in a mathematically well-controlled manner into account. The novel two-step algorithm, COMBI, first trains a support vector machine to determine a subset of candidate SNPs and then performs hypothesis tests for these SNPs together with an adequate threshold correction. Applying COMBI to data from a WTCCC study (2007) and measuring performance as replication by independent GWAS published within the 2008–2015 period, we show that our method outperforms ordinary raw p-value thresholding as well as other state-of-the-art methods. COMBI presents higher power and precision than the examined alternatives while yielding fewer false (i.e. non-replicated) and more true (i.e. replicated) discoveries when its results are validated on later GWAS studies. More than 80% of the discoveries made by COMBI upon WTCCC data have been validated by independent studies. Implementations of the COMBI method are available as a part of the GWASpi toolbox 2.0. ; EF acknowledges support from the advanced ERC grant (ERC-2011-AdG 295642-FEP) on the Foundation of Economic Preferences. MK, BM, and KRM were supported by the German National Science Foundation (DFG) under the grants MU 987/6-1 and RA 1894/1-1. TD and DS were supported by the German National Science Foundation (DFG) under the grants DI 1723/3-1 und SCHU 2828/2-1. GB and TS acknowledge support of the German National Science Foundation (DFG) under the research group grant FOR 1735. MK, DT, KRM, and GB acknowledge financial support by the FP7-ICT Programme of the European Community, under the PASCAL2 Network of Excellence. MK acknowledges a postdoctoral fellowship by the German Research Foundation (DFG), award KL 2698/2-1, and from the Federal Ministry of Science and Education (BMBF) awards 031L0023A and 031B0187B. AN acknowledges support from the Spanish Multiple Sclerosis Network (REEM), of the Instituto de Salud Carlos III (RD12/0032/0011), the Spanish National Institute for Bioinformatics (PT13/0001/0026) the Spanish Government Grant BFU2012-38236 and from FEDER. This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No. 634143 (MedBioinformatics). MK and KRM were financially supported by the Ministry of Education, Science, and Technology, through the National Research Foundation of Korea under Grant R31-10008 (MK, KRM) and BK21 (KRM).