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Sökning: WFRF:(Martelli M)

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  • Föregående 123[4]
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  • Allocca, Giancarlo, et al. (författare)
  • Validation of 'Somnivore', a Machine Learning Algorithm for Automated Scoring and Analysis of Polysomnography Data
  • 2019
  • Ingår i: Frontiers in Neuroscience. - : Frontiers Media S.A.. - 1662-4548 .- 1662-453X. ; 13
  • Tidskriftsartikel (refereegranskat)abstract
    • Manual scoring of polysomnography data is labor-intensive and time-consuming, and most existing software does not account for subjective differences and user variability. Therefore, we evaluated a supervised machine learning algorithm, Somnivore (TM), for automated wake-sleep stage classification. We designed an algorithm that extracts features from various input channels, following a brief session of manual scoring, and provides automated wake-sleep stage classification for each recording. For algorithm validation, polysomnography data was obtained from independent laboratories, and include normal, cognitively-impaired, and alcohol-treated human subjects (total n = 52), narcoleptic mice and drug-treated rats (total n = 56), and pigeons (n = 5). Training and testing sets for validation were previously scored manually by 1-2 trained sleep technologists from each laboratory. F-measure was used to assess precision and sensitivity for statistical analysis of classifier output and human scorer agreement. The algorithm gave high concordance with manual visual scoring across all human data (wake 0.91 +/- 0.01; N1 0.57 +/- 0.01; N2 0.81 +/- 0.01; N3 0.86 +/- 0.01; REM 0.87 +/- 0.01), which was comparable to manual inter-scorer agreement on all stages. Similarly, high concordance was observed across all rodent (wake 0.95 +/- 0.01; NREM 0.94 +/- 0.01; REM 0.91 +/- 0.01) and pigeon (wake 0.96 +/- 0.006; NREM 0.97 +/- 0.01; REM 0.86 +/- 0.02) data. Effects of classifier learning from single signal inputs, simple stage reclassification, automated removal of transition epochs, and training set size were also examined. In summary, we have developed a polysomnography analysis program for automated sleep-stage classification of data from diverse species. Somnivore enables flexible, accurate, and high-throughput analysis of experimental and clinical sleep studies.
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  • Wilder-Smith, Annelies, et al. (författare)
  • The legacy of ZikaPLAN: a transnational research consortium addressing Zika
  • 2021
  • Ingår i: Global Health Action. - : Taylor & Francis. - 1654-9716 .- 1654-9880. ; 14
  • Forskningsöversikt (refereegranskat)abstract
    • Global health research partnerships with institutions from high-income countries and low- and middle-income countries are one of the European Commission's flagship programmes. Here, we report on the ZikaPLAN research consortium funded by the European Commission with the primary goal of addressing the urgent knowledge gaps related to the Zika epidemic and the secondary goal of building up research capacity and establishing a Latin American-European research network for emerging vector-borne diseases. Five years of collaborative research effort have led to a better understanding of the full clinical spectrum of congenital Zika syndrome in children and the neurological complications of Zika virus infections in adults and helped explore the origins and trajectory of Zika virus transmission. Individual-level data from ZikaPLAN`s cohort studies were shared for joint analyses as part of the Zika Brazilian Cohorts Consortium, the European Commission-funded Zika Cohorts Vertical Transmission Study Group, and the World Health Organization-led Zika Virus Individual Participant Data Consortium. Furthermore, the legacy of ZikaPLAN includes new tools for birth defect surveillance and a Latin American birth defect surveillance network, an enhanced Guillain-Barre Syndrome research collaboration, a de-centralized evaluation platform for diagnostic assays, a global vector control hub, and the REDe network with freely available training resources to enhance global research capacity in vector-borne diseases.
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  • Föregående 123[4]

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