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Sökning: WFRF:(Gustavsson I.)

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  • Gustavsson, Larisa, et al. (författare)
  • Resistance to Neonectria ditissima in apple : insights from metabolomics and lipidomics analyses
  • 2023
  • Ingår i: Xxxi international horticultural congress (ihc2022). - : International Society for Horticultural Science. - 9789462613614 ; , s. 329-335, s. 329-336
  • Konferensbidrag (refereegranskat)abstract
    • European canker, caused by the necrotrophic fungus Neonectria ditissima, is the most serious disease in apple production in Sweden. The disease is favored by a relatively cool and rainy climate. The canker damages have a significant economic impact due to reduced bearing surface and increased orchard management costs. The possibilities for chemical and biological control are very limited. Therefore, directed breeding for new resistant cultivars is urgently needed. Knowledge of inheritance of canker resistance and understanding of molecular mechanisms involved in resistant and susceptible responses to fungal attacks would facilitate breeding. In this study, we evaluated the tempo-spatial differences in plant-pathogen interactions in a set of partially resistant and susceptible cultivars by conducting metabolomic and lipidomic analyses. The major trends in metabolomics and lipidomic profiles were common among cultivars, irrespective of the degree of susceptibility. Several metabolites and lipids varied with time point and cultivar under N. ditissima infection. Putative key metabolites such as suberic acid and jasmonic acid were upregulated in all cultivars upon infection. Additionally, several lipids exhibited changes 30 to 45 days post-inoculation. Thus, the approach used seems to have resulted in a rich data set to be further analyzed in light of ongoing QTL-mapping efforts.
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  • Hartley, Philippa, et al. (författare)
  • SKA Science Data Challenge 2: analysis and results
  • 2023
  • Ingår i: Monthly Notices of the Royal Astronomical Society. - 0035-8711 .- 1365-2966. ; 523:2, s. 1967-1993
  • Tidskriftsartikel (refereegranskat)abstract
    • The Square Kilometre Array Observatory (SKAO) will explore the radio sky to new depths in order to conduct transformational science. SKAO data products made available to astronomers will be correspondingly large and complex, requiring the application of advanced analysis techniques to extract key science findings. To this end, SKAO is conducting a series of Science Data Challenges, each designed to familiarize the scientific community with SKAO data and to drive the development of new analysis techniques. We present the results from Science Data Challenge 2 (SDC2), which invited participants to find and characterize 233 245 neutral hydrogen (H i) sources in a simulated data product representing a 2000 h SKA-Mid spectral line observation from redshifts 0.25-0.5. Through the generous support of eight international supercomputing facilities, participants were able to undertake the Challenge using dedicated computational resources. Alongside the main challenge, 'reproducibility awards' were made in recognition of those pipelines which demonstrated Open Science best practice. The Challenge saw over 100 participants develop a range of new and existing techniques, with results that highlight the strengths of multidisciplinary and collaborative effort. The winning strategy - which combined predictions from two independent machine learning techniques to yield a 20 per cent improvement in overall performance - underscores one of the main Challenge outcomes: that of method complementarity. It is likely that the combination of methods in a so-called ensemble approach will be key to exploiting very large astronomical data sets.
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