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Sökning: WFRF:(Mahoney Lori)

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1.
  • Böhm, Johann, et al. (författare)
  • Mutation spectrum in the large GTPase dynamin 2, and genotype-phenotype correlation in autosomal dominant centronuclear myopathy.
  • 2012
  • Ingår i: Human mutation. - : Hindawi Limited. - 1098-1004 .- 1059-7794. ; 33:6, s. 949-59
  • Tidskriftsartikel (refereegranskat)abstract
    • Centronuclear myopathy (CNM) is a genetically heterogeneous disorder associated with general skeletal muscle weakness, type I fiber predominance and atrophy, and abnormally centralized nuclei. Autosomal dominant CNM is due to mutations in the large GTPase dynamin 2 (DNM2), a mechanochemical enzyme regulating cytoskeleton and membrane trafficking in cells. To date, 40 families with CNM-related DNM2 mutations have been described, and here we report 60 additional families encompassing a broad genotypic and phenotypic spectrum. In total, 18 different mutations are reported in 100 families and our cohort harbors nine known and four new mutations, including the first splice-site mutation. Genotype-phenotype correlation hypotheses are drawn from the published and new data, and allow an efficient screening strategy for molecular diagnosis. In addition to CNM, dissimilar DNM2 mutations are associated with Charcot-Marie-Tooth (CMT) peripheral neuropathy (CMTD1B and CMT2M), suggesting a tissue-specific impact of the mutations. In this study, we discuss the possible clinical overlap of CNM and CMT, and the biological significance of the respective mutations based on the known functions of dynamin 2 and its protein structure. Defects in membrane trafficking due to DNM2 mutations potentially represent a common pathological mechanism in CNM and CMT.
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2.
  • Grossmann, Igor, et al. (författare)
  • Insights into the accuracy of social scientists' forecasts of societal change
  • 2023
  • Ingår i: Nature Human Behaviour. - : Springer Nature. - 2397-3374. ; 7, s. 484-501
  • Tidskriftsartikel (refereegranskat)abstract
    • How well can social scientists predict societal change, and what processes underlie their predictions? To answer these questions, we ran two forecasting tournaments testing the accuracy of predictions of societal change in domains commonly studied in the social sciences: ideological preferences, political polarization, life satisfaction, sentiment on social media, and gender-career and racial bias. After we provided them with historical trend data on the relevant domain, social scientists submitted pre-registered monthly forecasts for a year (Tournament 1; N = 86 teams and 359 forecasts), with an opportunity to update forecasts on the basis of new data six months later (Tournament 2; N = 120 teams and 546 forecasts). Benchmarking forecasting accuracy revealed that social scientists' forecasts were on average no more accurate than those of simple statistical models (historical means, random walks or linear regressions) or the aggregate forecasts of a sample from the general public (N = 802). However, scientists were more accurate if they had scientific expertise in a prediction domain, were interdisciplinary, used simpler models and based predictions on prior data. How accurate are social scientists in predicting societal change, and what processes underlie their predictions? Grossmann et al. report the findings of two forecasting tournaments. Social scientists' forecasts were on average no more accurate than those of simple statistical models.
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