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Sökning: WFRF:(Wagner Ludwig) > Övrigt vetenskapligt/konstnärligt

  • Resultat 1-3 av 3
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1.
  • de Jong, R. S., et al. (författare)
  • 4MOST : Project overview and information for the First Call for Proposals
  • 2019
  • Ingår i: The Messenger. - : European Southern Observatory. - 0722-6691. ; 175, s. 3-11
  • Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
    • We introduce the 4-metre Multi-Object Spectroscopic Telescope (4MOST), a new high-multiplex, wide-field spectroscopic survey facility under development for the four-metre-class Visible and Infrared Survey Telescope for Astronomy (VISTA) at Paranal. Its key specifications are: a large field of view (FoV) of 4.2 square degrees and a high multiplex capability, with 1624 fibres feeding two low-resolution spectrographs (R = λ/Δλ ~ 6500), and 812 fibres transferring light to the high-resolution spectrograph (R ~ 20 000). After a description of the instrument and its expected performance, a short overview is given of its operational scheme and planned 4MOST Consortium science; these aspects are covered in more detail in other articles in this edition of The Messenger. Finally, the processes, schedules, and policies concerning the selection of ESO Community Surveys are presented, commencing with a singular opportunity to submit Letters of Intent for Public Surveys during the first five years of 4MOST operations.
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2.
  • Augusto Silva, Iran, et al. (författare)
  • Deep learning for rapid and reproducible histology scoring of lung injury in a porcine model
  • 2023
  • Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
    • Acute respiratory distress syndrome (ARDS) is a life-threatening condition with mortality rates between 30-50%. Although in vitro models replicate some aspects of ARDS, small and large animal models remain the primary research tools due to the multifactorial nature of the disease. When using these animal models, histology serves as the gold standard method to confirm lung injury and exclude other diagnoses as high-resolution chest images are often not feasible. Semi-quantitative scoring performed by independent observers is the most common form of histologic analysis in pre-clinical animal models of ARDS. Despite progress in standardizing analysis procedures, objectively comparing histological injuries remains challenging, even for highly-trained pathologists. Standardized scoring simplifies the task and allows better comparisons between research groups and across different injury models, but it is time-consuming, and interobserver variability remains a significant concern. Convolutional neural networks (CNNs), which have emerged as a key tool in image analysis, could automate this process, potentially enabling faster and more reproducible analysis. Here we explored the reproducibility of human standardized scoring for an animal model of ARDS and its suitability for training CNNs for automated scoring at the whole slide level. We found large variations between human scorers, even for pre-clinical experts and board-certified pathologies in evaluating ARDS animal models. We demonstrate that CNNs (VGG16, EfficientNetB4) are suitable for automated scoring and achieve up to 83% F1-score and 78% accuracy. Thus, CNNs for histopathological classification of acute lung injury could help reduce human variability and eliminate a time-consuming manual research task with acceptable performance.
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3.
  • Kehoe, Laura, et al. (författare)
  • Make EU trade with Brazil sustainable
  • 2019
  • Ingår i: Science. - : American Association for the Advancement of Science (AAAS). - 0036-8075 .- 1095-9203. ; 364:6438, s. 341-
  • Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)
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  • Resultat 1-3 av 3

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