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- Rohringer, N., et al.
(författare)
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Stimulated X-Ray Raman Scattering with Free-Electron Laser Sources
- 2016
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Ingår i: X-Ray Lasers 2014. - Cham : Springer. - 0930-8989. - 9783319195216 - 9783319195209 ; , s. 201-207
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Bokkapitel (refereegranskat)abstract
- Stimulated electronic x-ray Raman scattering is the building block for several proposed x-ray pump probe techniques, that would allow the study of electron dynamics at unprecedented timescales.We present high spectral resolution data on stimulated electronic x-ray Raman scattering in a gas sample of neon using a self-amplified spontaneous emission x-ray free-electron laser. Despite the limited spectral coherence and broad bandwidth of these sources, high-resolution spectra can be obtained by statistical methods, opening the path to coherent stimulated x-ray Raman spectroscopy. An extension of these ideas to molecules and the results of a recent experiment in CO are discussed.
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- Buddenkotte, Thomas, et al.
(författare)
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Deep learning-based segmentation of multisite disease in ovarian cancer
- 2023
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Ingår i: EUROPEAN RADIOLOGY EXPERIMENTAL. - : Springer Nature. - 2509-9280. ; 7:1
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Tidskriftsartikel (refereegranskat)abstract
- Purpose: To determine if pelvic/ovarian and omental lesions of ovarian cancer can be reliably segmented on computed tomography (CT) using fully automated deep learning-based methods.Methods: A deep learning model for the two most common disease sites of high-grade serous ovarian cancer lesions (pelvis/ovaries and omentum) was developed and compared against the well-established “no-new-Net” framework and unrevised trainee radiologist segmentations. A total of 451 CT scans collected from four different institutions were used for training (n = 276), evaluation (n = 104) and testing (n = 71) of the methods. The performance was evaluated using the Dice similarity coefficient (DSC) and compared using a Wilcoxon test.Results: Our model outperformed no-new-Net for the pelvic/ovarian lesions in cross-validation, on the evaluation and test set by a significant margin (p values being 4 × 10–7, 3 × 10–4, 4 × 10–2, respectively), and for the omental lesions on the evaluation set (p = 1 × 10–3). Our model did not perform significantly differently in segmenting pelvic/ovarian lesions (p = 0.371) compared to a trainee radiologist. On an independent test set, the model achieved a DSC performance of 71 ± 20 (mean ± standard deviation) for pelvic/ovarian and 61 ± 24 for omental lesions.Conclusion: Automated ovarian cancer segmentation on CT scans using deep neural networks is feasible and achieves performance close to a trainee-level radiologist for pelvic/ovarian lesions.Relevance statement: Automated segmentation of ovarian cancer may be used by clinicians for CT-based volumetric assessments and researchers for building complex analysis pipelines.Key points:The first automated approach for pelvic/ovarian and omental ovarian cancer lesion segmentation on CT images has been presented.Automated segmentation of ovarian cancer lesions can be comparable with manual segmentation of trainee radiologists.Careful hyperparameter tuning can provide models significantly outperforming strong state-of-the-art baselines. Graphical Abstract: [Figure not available: see fulltext.]
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- Han, L., et al.
(författare)
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Cell transcriptomic atlas of the non-human primate Macaca fascicularis
- 2022
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Ingår i: Nature. - : Springer Nature. - 0028-0836 .- 1476-4687. ; 604:7907, s. 723-731
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Tidskriftsartikel (refereegranskat)abstract
- Studying tissue composition and function in non-human primates (NHPs) is crucial to understand the nature of our own species. Here we present a large-scale cell transcriptomic atlas that encompasses over 1 million cells from 45 tissues of the adult NHP Macaca fascicularis. This dataset provides a vast annotated resource to study a species phylogenetically close to humans. To demonstrate the utility of the atlas, we have reconstructed the cell–cell interaction networks that drive Wnt signalling across the body, mapped the distribution of receptors and co-receptors for viruses causing human infectious diseases, and intersected our data with human genetic disease orthologues to establish potential clinical associations. Our M. fascicularis cell atlas constitutes an essential reference for future studies in humans and NHPs.
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