Mostrando 2,521 - 2,540 Resultados de 2,741 Para Buscar '"CPU"', tiempo de consulta: 0.19s Limitar resultados
  1. 2521
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  3. 2523
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  8. 2528
    “…To investigate the genetic basis of natural variation in sorghum grain color, GWAS with 2,760,264 SNPs was conducted in four environments using multiple models (Blink, FarmCPU, GLM, and MLM). Seventy-three QTL were identified to be associated for the color of exocarp, mesocarp, testa, and tannin content on all chromosomes except chromosome 5, of which 47 might be novel QTL. …”
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  9. 2529
    “…In total, we identified 11 significant SNPs in association with body size traits at the threshold of Bonferroni correction (5.90 × 10(−7)) using the fixed and random model circulating probability unification (FarmCPU) model. Several genes within 200 kb distances (upstream or downstream) of the significant SNPs were identified as candidate genes, including MYH15, KHDRBS3, AIP, DCC, SQOR, and UBAP1L. …”
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  10. 2530
    “…However, compared to the rTMS group without the shielding application, more subcortical regions, like the striatum (CPu), the hippocampus, the thalamus, and the hypothalamus were also activated in the shielding group. …”
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  11. 2531
    “…Association mapping was performed in the genotyped portion of the collection (n = 381) and in each subpopulation (n = 204 and 174) independently via a single-locus mixed-linear model, and two multi-locus models, FarmCPU, and BLINK. A large number (195) of markers were significantly associated with resistance to at least one of 10 rust pathogen races evaluated, nine of which are key candidate markers for further investigation due to their detection via multiple models and/or their association with resistance to more than one pathogen race. …”
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    Online Artículo Texto
  12. 2532
    “…Four statistical models, including the single-locus model GEMMA and the multi-locus models FarmCPU, mrMLM, and 3VmrMLM, were assessed to identify quantitative trait nucleotides (QTNs) associated with pasmo resistance. …”
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  13. 2533
    “…The difference in computing CPU time was 2.89% ± 0.22 on average. CONCLUSIONS: The present study proposed and verified a Python‐based method for converting CAD mesh format files into VGMs and establishing them in FLUKA simply as well as accurately.…”
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  14. 2534
    por Goovaerts, Pierre, Gebreab, Samson
    Publicado 2008
    “…At first glance, Poisson kriging represents an attractive alternative to increasingly popular Bayesian spatial models in that: 1) it is easier to implement and less CPU intensive, and 2) it accounts for the size and shape of geographical units, avoiding the limitations of conditional auto-regressive (CAR) models commonly used in Bayesian algorithms while allowing for the creation of isopleth risk maps. …”
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  15. 2535
    por Jin, Zheming, Bakos, Jason D
    Publicado 2013
    “…This represents a ~40X speedup when compared with BEAGLE’s CPU implementation on a dual Xeon 5520 and 3X speedup versus BEAGLE’s GPU implementation on a Tesla T10 GPU for very large data sizes. …”
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  16. 2536
  17. 2537
    “…For this purpose we introduce mrsFAST-Ultra, a fast, cache oblivious, SNP-aware aligner that can handle the multi-mapping of HTS reads very efficiently. mrsFAST-Ultra improves mrsFAST, our first cache oblivious read aligner capable of handling multi-mapping reads, through new and compact index structures that reduce not only the overall memory usage but also the number of CPU operations per alignment. In fact the size of the index generated by mrsFAST-Ultra is 10 times smaller than that of mrsFAST. …”
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  18. 2538
    por Pissis, Solon P
    Publicado 2014
    “…MoTeX-II comes in three flavors: a standard CPU version; an OpenMP-based version; and an MPI-based version. …”
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  19. 2539
    “…The large informative power and a convenient computational CPU-time for moderately sized networks may make Ndim a valuable tool for the analysis of biological networks.…”
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  20. 2540
    por Hejase, Hussein A., Liu, Kevin J.
    Publicado 2016
    “…None of the probabilistic methods completed analyses of datasets with 30 taxa or more after many weeks of CPU runtime. CONCLUSIONS: We conclude that the state of the art of phylogenetic network inference lags well behind the scope of current phylogenomic studies. …”
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