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- Alqasim, A., et al.
(författare)
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TOI−757 b: an eccentric transiting mini−Neptune on a 17.5−d orbit
- 2024
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Ingår i: Monthly Notices of the Royal Astronomical Society. - 0035-8711 .- 1365-2966. ; 533:1, s. 1-26
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Tidskriftsartikel (refereegranskat)abstract
- We report the spectroscopic confirmation and fundamental properties of TOI−757 b, a mini−Neptune on a 17.5−d orbit transiting a bright star (V = 9.7 mag) discovered by the TESS mission. We acquired high−precision radial velocity measurements with the HARPS, ESPRESSO, and PFS spectrographs to confirm the planet detection and determine its mass. We also acquired space−borne transit photometry with the CHEOPS space telescope to place stronger constraints on the planet radius, supported with ground−based LCOGT photometry. WASP and KELT photometry were used to help constrain the stellar rotation period. We also determined the fundamental parameters of the host star. We find that TOI−757 b has a radius of Rp = 2.5 ± 0.1R. and a mass of Mp = 10.5+−2212M, implying a bulk density of ρp = 3.6 ± 0.8 g cm−3. Our internal composition modelling was unable to constrain the composition of TOI−757 b, highlighting the importance of atmospheric observations for the system. We also find the planet to be highly eccentric with e = 0.39+−000708, making it one of the very few highly eccentric planets among precisely characterized mini−Neptunes. Based on comparisons to other similar eccentric systems, we find a likely scenario for TOI−757 b’s formation to be high eccentricity migration due to a distant outer companion. We additionally propose the possibility of a more intrinsic explanation for the high eccentricity due to star−star interactions during the earlier epoch of the Galactic disc formation, given the low metallicity and older age of TOI−757.
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| 2. |
- Attia, Zachi I., et al.
(författare)
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Rapid Exclusion of COVID Infection With the Artificial Intelligence Electrocardiogram
- 2021
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Ingår i: Mayo Clinic proceedings. - : ELSEVIER SCIENCE INC. - 0025-6196 .- 1942-5546. ; 96:8, s. 2081-2094
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Tidskriftsartikel (refereegranskat)abstract
- Objective: To rapidly exclude severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection using artificial intelligence applied to the electrocardiogram (ECG). Methods: A global, volunteer consortium from 4 continents identified patients with ECGs obtained around the time of polymerase chain reaction-confirmed COVID-19 diagnosis and age- and sex-matched controls from the same sites. Clinical characteristics, polymerase chain reaction results, and raw electrocardiographic data were collected. A convolutional neural network was trained using 26,153 ECGs (33.2% COVID positive), validated with 3826 ECGs (33.3% positive), and tested on 7870 ECGs not included in other sets (32.7% positive). Performance under different prevalence values was tested by adding control ECGs from a single high-volume site. Results: The area under the curve for detection of acute COVID-19 infection in the test group was 0.767 (95% CI, 0.756 to 0.778; sensitivity, 98%; specificity, 10%; positive predictive value, 37%; negative predictive value, 91%). To more accurately reflect a real-world population, 50,905 normal controls were added to adjust the COVID prevalence to approximately 5% (2657/58,555), resulting in an area under the curve of 0.780 (95% CI, 0.771 to 0.790) with a specificity of 12.1% and a negative predictive value of 99.2%. Conclusion: Infection with SARS-CoV-2 results in electrocardiographic changes that permit the artificial intelligence-enhanced ECG to be used as a rapid screening test with a high negative predictive value (99.2%). This may permit the development of electrocardiography-based tools to rapidly screen individuals for pandemic control. (C) 2021 Mayo Foundation Medical Education and Research
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