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Träfflista för sökning "WFRF:(Frost J A) srt2:(2020-2024)"

Sökning: WFRF:(Frost J A) > (2020-2024)

  • Resultat 1-10 av 45
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1.
  • Bravo, L, et al. (författare)
  • 2021
  • swepub:Mat__t
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  • Tabiri, S, et al. (författare)
  • 2021
  • swepub:Mat__t
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  • 2021
  • swepub:Mat__t
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  • Kanai, M, et al. (författare)
  • 2023
  • swepub:Mat__t
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  • Santangelo, James S., et al. (författare)
  • Global urban environmental change drives adaptation in white clover
  • 2022
  • Ingår i: Science. - : American Association for the Advancement of Science (AAAS). - 0036-8075 .- 1095-9203. ; 375
  • Tidskriftsartikel (refereegranskat)abstract
    • Urbanization transforms environments in ways that alter biological evolution. We examined whether urban environmental change drives parallel evolution by sampling 110,019 white clover plants from 6169 populations in 160 cities globally. Plants were assayed for a Mendelian antiherbivore defense that also affects tolerance to abiotic stressors. Urban-rural gradients were associated with the evolution of clines in defense in 47% of cities throughout the world. Variation in the strength of clines was explained by environmental changes in drought stress and vegetation cover that varied among cities. Sequencing 2074 genomes from 26 cities revealed that the evolution of urban-rural dines was best explained by adaptive evolution, but the degree of parallel adaptation varied among cities. Our results demonstrate that urbanization leads to adaptation at a global scale.
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8.
  • Bar, N., et al. (författare)
  • A reference map of potential determinants for the human serum metabolome
  • 2020
  • Ingår i: Nature. - : Nature Research. - 0028-0836 .- 1476-4687. ; 588:7836, s. 135-140
  • Tidskriftsartikel (refereegranskat)abstract
    • The serum metabolome contains a plethora of biomarkers and causative agents of various diseases, some of which are endogenously produced and some that have been taken up from the environment1. The origins of specific compounds are known, including metabolites that are highly heritable2,3, or those that are influenced by the gut microbiome4, by lifestyle choices such as smoking5, or by diet6. However, the key determinants of most metabolites are still poorly understood. Here we measured the levels of 1,251 metabolites in serum samples from a unique and deeply phenotyped healthy human cohort of 491 individuals. We applied machine-learning algorithms to predict metabolite levels in held-out individuals on the basis of host genetics, gut microbiome, clinical parameters, diet, lifestyle and anthropometric measurements, and obtained statistically significant predictions for more than 76% of the profiled metabolites. Diet and microbiome had the strongest predictive power, and each explained hundreds of metabolites—in some cases, explaining more than 50% of the observed variance. We further validated microbiome-related predictions by showing a high replication rate in two geographically independent cohorts7,8 that were not available to us when we trained the algorithms. We used feature attribution analysis9 to reveal specific dietary and bacterial interactions. We further demonstrate that some of these interactions might be causal, as some metabolites that we predicted to be positively associated with bread were found to increase after a randomized clinical trial of bread intervention. Overall, our results reveal potential determinants of more than 800 metabolites, paving the way towards a mechanistic understanding of alterations in metabolites under different conditions and to designing interventions for manipulating the levels of circulating metabolites. 
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