| 51. |
- Abramowski, A., et al.
(author)
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HESS and Fermi-LAT discovery of gamma-rays from the blazar 1ES 1312-423
- 2013
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In: Monthly notices of the Royal Astronomical Society. - : Oxford University Press (OUP). - 0035-8711 .- 1365-2966. ; 434:3, s. 1889-1901
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Journal article (peer-reviewed)abstract
- A deep observation campaign carried out by the High Energy Stereoscopic System (HESS) on Centaurus A enabled the discovery of gamma-rays from the blazar 1ES 1312-423, 2 degrees away from the radio galaxy. With a differential flux at 1 TeV of phi(1 TeV) = (1.9 +/- 0.6(stat) +/- 0.4(sys)) x 10(-13) cm(-2) s(-1) TeV-1 corresponding to 0.5 per cent of the Crab nebula differential flux and a spectral index Gamma = 2.9 +/- 0.5(stat) +/- 0.2(sys), 1ES 1312-423 is one of the faintest sources ever detected in the very high energy (E > 100 GeV) extragalactic sky. A careful analysis using three and a half years of Fermi Large Area Telescope (Fermi-LAT) data allows the discovery at high energies (E > 100 MeV) of a hard spectrum (Gamma = 1.4 +/- 0.4(stat) +/- 0.2(sys)) source coincident with 1ES 1312-423. Radio, optical, UV and X-ray observations complete the spectral energy distribution of this blazar, now covering 16 decades in energy. The emission is successfully fitted with a synchrotron self-Compton model for the non-thermal component, combined with a blackbody spectrum for the optical emission from the host galaxy.
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| 52. |
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| 53. |
- Dunn, R. J. H., et al.
(author)
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GLOBAL CLIMATE : State of the Climate in 2020
- 2021
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In: Bulletin of the American Meteorological Society. - : American Meteorological Society. - 0003-0007 .- 1520-0477. ; 102:8
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Journal article (peer-reviewed)
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| 54. |
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| 55. |
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| 56. |
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| 57. |
- Abramowski, A., et al.
(author)
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HESS observations of the Carina nebula and its enigmatic colliding wind binary Eta Carinae
- 2012
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In: Monthly notices of the Royal Astronomical Society. - : Oxford University Press (OUP). - 0035-8711 .- 1365-2966. ; 424:1, s. 128-135
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Journal article (peer-reviewed)abstract
- The massive binary system Eta Carinae and the surrounding H ii complex, the Carina nebula, are potential particle acceleration sites from which very high energy (VHE; E= 100 GeV) ?-ray emission could be expected. This paper presents data collected during VHE ?-ray observations with the HESS telescope array from 2004 to 2010, which cover a full orbit of Eta Carinae. In the 33.1-h data set no hint of significant ?-ray emission from Eta Carinae has been found and an upper limit on the ?-ray flux of (99 per cent confidence level) is derived above the energy threshold of 470 GeV. Together with the detection of high energy (HE; 0.1 =E= 100 GeV) ?-ray emission by the Fermi Large Area Telescope up to 100 GeV, and assuming a continuation of the average HE spectral index into the VHE domain, these results imply a cut-off in the ?-ray spectrum between the HE and VHE ?-ray range. This could be caused either by a cut-off in the accelerated particle distribution or by severe ?? absorption losses in the wind collision region. Furthermore, the search for extended ?-ray emission from the Carina nebula resulted in an upper limit on the ?-ray flux of (99 per cent confidence level). The derived upper limit of 23 on the cosmic ray enhancement factor is compared with results found for the old-age mixed-morphology supernova remnant W28.
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| 58. |
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| 59. |
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| 60. |
- Nunes, A, et al.
(author)
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Using structural MRI to identify bipolar disorders - 13 site machine learning study in 3020 individuals from the ENIGMA Bipolar Disorders Working Group
- 2020
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In: Molecular psychiatry. - : Springer Science and Business Media LLC. - 1476-5578 .- 1359-4184. ; 25:9, s. 2130-2143
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Journal article (peer-reviewed)abstract
- Bipolar disorders (BDs) are among the leading causes of morbidity and disability. Objective biological markers, such as those based on brain imaging, could aid in clinical management of BD. Machine learning (ML) brings neuroimaging analyses to individual subject level and may potentially allow for their diagnostic use. However, fair and optimal application of ML requires large, multi-site datasets. We applied ML (support vector machines) to MRI data (regional cortical thickness, surface area, subcortical volumes) from 853 BD and 2167 control participants from 13 cohorts in the ENIGMA consortium. We attempted to differentiate BD from control participants, investigated different data handling strategies and studied the neuroimaging/clinical features most important for classification. Individual site accuracies ranged from 45.23% to 81.07%. Aggregate subject-level analyses yielded the highest accuracy (65.23%, 95% CI = 63.47–67.00, ROC-AUC = 71.49%, 95% CI = 69.39–73.59), followed by leave-one-site-out cross-validation (accuracy = 58.67%, 95% CI = 56.70–60.63). Meta-analysis of individual site accuracies did not provide above chance results. There was substantial agreement between the regions that contributed to identification of BD participants in the best performing site and in the aggregate dataset (Cohen’s Kappa = 0.83, 95% CI = 0.829–0.831). Treatment with anticonvulsants and age were associated with greater odds of correct classification. Although short of the 80% clinically relevant accuracy threshold, the results are promising and provide a fair and realistic estimate of classification performance, which can be achieved in a large, ecologically valid, multi-site sample of BD participants based on regional neurostructural measures. Furthermore, the significant classification in different samples was based on plausible and similar neuroanatomical features. Future multi-site studies should move towards sharing of raw/voxelwise neuroimaging data.
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