| 1. |
- Aad, G, et al.
(author)
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- 2015
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swepub:Mat__t
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| 4. |
- Blösch, Günter, et al.
(author)
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Twenty-three unsolved problems in hydrology (UPH) - a community perspective
- 2019
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In: Hydrological Sciences Journal. - : Informa UK Limited. - 0262-6667 .- 2150-3435. ; 64:10, s. 1141-1158
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Journal article (peer-reviewed)abstract
- This paper is the outcome of a community initiative to identify major unsolved scientific problems in hydrology motivated by a need for stronger harmonisation of research efforts. The procedure involved a public consultation through online media, followed by two workshops through which a large number of potential science questions were collated, prioritised, and synthesised. In spite of the diversity of the participants (230 scientists in total), the process revealed much about community priorities and the state of our science: a preference for continuity in research questions rather than radical departures or redirections from past and current work. Questions remain focused on the process-based understanding of hydrological variability and causality at all space and time scales. Increased attention to environmental change drives a new emphasis on understanding how change propagates across interfaces within the hydrological system and across disciplinary boundaries. In particular, the expansion of the human footprint raises a new set of questions related to human interactions with nature and water cycle feedbacks in the context of complex water management problems. We hope that this reflection and synthesis of the 23 unsolved problems in hydrology will help guide research efforts for some years to come.
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| 5. |
- Brunelli, M., et al.
(author)
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Experimental Determination of Irreversible Entropy Production in out-of-Equilibrium Mesoscopic Quantum Systems
- 2018
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In: Physical Review Letters. - 1079-7114 .- 0031-9007. ; 121:16
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Journal article (peer-reviewed)abstract
- By making use of a recently proposed framework for the inference of thermodynamic irreversibility in bosonic quantum systems, we experimentally measure and characterize the entropy production rates in the nonequilibrium steady state of two different physical systems a micromechanical resonator and a Bose-Einstein condensate each coupled to a high finesse cavity and hence also subject to optical loss. Key features of our setups, such as the cooling of the mechanical resonator and signatures of a structural quantum phase transition in the condensate, are reflected in the entropy production rates. Our work demonstrates the possibility to explore irreversibility in driven mesoscopic quantum systems and paves the way to a systematic experimental assessment of entropy production beyond the microscopic limit.
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| 6. |
- Frisell, T, et al.
(author)
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Comparative analysis of first-year fingolimod and natalizumab drug discontinuation among Swedish patients with multiple sclerosis
- 2016
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In: Multiple sclerosis (Houndmills, Basingstoke, England). - : SAGE Publications. - 1477-0970 .- 1352-4585. ; 22:1, s. 85-93
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Journal article (peer-reviewed)abstract
- Natalizumab (NTZ) and fingolimod (FGL) are mainly used second line in relapsing–remitting multiple sclerosis (MS), although pivotal trials included mainly treatment-naïve patients. Objective: This study aims to provide real-world data on safety and discontinuation rates. Methods: Using IMSE, a drug monitoring registry for all newer MS drugs in Sweden, we analysed differences in baseline characteristics and 1-year drug survival for patients registered 2011–2013, initiating treatment with NTZ ( n=640) or FGL ( n=876). Among FGL initiators, n=383 (44%) had previously used NTZ (FGLafterNTZ). Results: Compared with NTZ, the FGL cohort was older and more often male (36/38 years, 24%/33% males). Baseline Expanded Disability Status Scale was similar across groups, but MS Severity Score was higher in NTZ patients, and Symbol Digit Modalities Test and MS Impact Scale (MSIS-29) was higher in FGLafterNTZ versus FGLNTZ-naïve patients. Proportion on drug after 1 year was high, NTZ=87%, FGLNTZ-naïve=83% and FGLafterNTZ=76%. Adverse events was the most frequent reason for discontinuing FGL (FGLNTZ-naïve=9%, FGLafterNTZ=12%), and was significantly higher than on NTZ (3%). In contrast, the proportion of patients stopping treatment due to lack of effect was more similar: NTZ=4%, FGLNTZ-naïve=3%, FGLafterNTZ=8%. Conclusion: FGL and NTZ were both well tolerated, but FGL less so than NTZ, especially in patients switching to FGL from NTZ. Group differences were not explained by differences in recorded baseline characteristics.
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| 9. |
- Bouget, D., et al.
(author)
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Preoperative Brain Tumor Imaging: Models and Software for Segmentation and Standardized Reporting
- 2022
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In: Frontiers in Neurology. - : Frontiers Media SA. - 1664-2295. ; 13
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Journal article (peer-reviewed)abstract
- For patients suffering from brain tumor, prognosis estimation and treatment decisions are made by a multidisciplinary team based on a set of preoperative MR scans. Currently, the lack of standardized and automatic methods for tumor detection and generation of clinical reports, incorporating a wide range of tumor characteristics, represents a major hurdle. In this study, we investigate the most occurring brain tumor types: glioblastomas, lower grade gliomas, meningiomas, and metastases, through four cohorts of up to 4,000 patients. Tumor segmentation models were trained using the AGU-Net architecture with different preprocessing steps and protocols. Segmentation performances were assessed in-depth using a wide-range of voxel and patient-wise metrics covering volume, distance, and probabilistic aspects. Finally, two software solutions have been developed, enabling an easy use of the trained models and standardized generation of clinical reports: Raidionics and Raidionics-Slicer. Segmentation performances were quite homogeneous across the four different brain tumor types, with an average true positive Dice ranging between 80 and 90%, patient-wise recall between 88 and 98%, and patient-wise precision around 95%. In conjunction to Dice, the identified most relevant other metrics were the relative absolute volume difference, the variation of information, and the Hausdorff, Mahalanobis, and object average symmetric surface distances. With our Raidionics software, running on a desktop computer with CPU support, tumor segmentation can be performed in 16-54 s depending on the dimensions of the MRI volume. For the generation of a standardized clinical report, including the tumor segmentation and features computation, 5-15 min are necessary. All trained models have been made open-access together with the source code for both software solutions and validation metrics computation. In the future, a method to convert results from a set of metrics into a final single score would be highly desirable for easier ranking across trained models. In addition, an automatic classification of the brain tumor type would be necessary to replace manual user input. Finally, the inclusion of post-operative segmentation in both software solutions will be key for generating complete post-operative standardized clinical reports.
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| 11. |
- Helland, Ragnhild Holden, et al.
(author)
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Segmentation of glioblastomas in early post-operative multi-modal MRI with deep neural networks.
- 2023
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In: Scientific reports. - 2045-2322. ; 13:1
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Journal article (peer-reviewed)abstract
- Extent of resection after surgery is one of the main prognostic factors for patients diagnosed with glioblastoma. To achieve this, accurate segmentation and classification of residual tumor from post-operative MR images is essential. The current standard method for estimating it is subject to high inter- and intra-rater variability, and an automated method for segmentation of residual tumor in early post-operative MRI could lead to a more accurate estimation of extent of resection. In this study, two state-of-the-art neural network architectures for pre-operative segmentation were trained for the task. The models were extensively validated on a multicenter dataset with nearly 1000 patients, from 12 hospitals in Europe and the United States. The best performance achieved was a 61% Dice score, and the best classification performance was about 80% balanced accuracy, with a demonstrated ability to generalize across hospitals. In addition, the segmentation performance of the best models was on par with human expert raters. The predicted segmentations can be used to accurately classify the patients into those with residual tumor, and those with gross total resection.
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