| 1. |
- Abe, H., et al.
(författare)
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Gamma-ray observations of MAXI J1820+070 during the 2018 outburst
- 2022
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Ingår i: Monthly notices of the Royal Astronomical Society. - : Oxford University Press. - 0035-8711 .- 1365-2966. ; 517:4, s. 4736-4751
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Tidskriftsartikel (refereegranskat)abstract
- MAXIJ1820+070 is a low-mass X-ray binary with a black hole (BH) as a compact object. This binary underwent an exceptionally bright X-ray outburst from 2018 March to October, showing evidence of a non-thermal particle population through its radio emission during this whole period. The combined results of 59.5 h of observations of the MAXI J1820+070 outburst with the H.E.S.S., MAGIC and VERITAS experiments at energies above 200 GeV are presented, together with Fermi-LAT data between 0.1 and 500 GeV, and multiwavelength observations from radio to X-rays. Gamma-ray emission is not detected from MAXI J1820+070, but the obtained upper limits and the multiwavelength data allow us to put meaningful constraints on the source properties under reasonable assumptions regarding the non-thermal particle population and the jet synchrotron spectrum. In particular, it is possible to show that, if a high-energy (HE) gamma-ray emitting region is present during the hard state of the source, its predicted flux should be at most a factor of 20 below the obtained Fermi-LAT upper limits, and closer to them for magnetic fields significantly below equipartition. During the state transitions, under the plausible assumption that electrons are accelerated up to similar to 500 GeV, the multiwavelength data and the gamma-ray upper limits lead consistently to the conclusion that a potential HE and very-HE gamma-ray emitting region should be located at a distance from the BH ranging between 10(11) and 10(13) cm. Similar outbursts from low-mass X-ray binaries might be detectable in the near future with upcoming instruments such as CTA.
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| 2. |
- Morales, J. C., et al.
(författare)
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A giant exoplanet orbiting a very-low-mass star challenges planet formation models
- 2019
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Ingår i: Science. - : American Association for the Advancement of Science (AAAS). - 0036-8075 .- 1095-9203. ; 365:6460, s. 1441-1445
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Tidskriftsartikel (refereegranskat)abstract
- Surveys have shown that super-Earth and Neptune-mass exoplanets are more frequent than gas giants around low-mass stars, as predicted by the core accretion theory of planet formation. We report the discovery of a giant planet around the very-low-mass star GJ 3512, as determined by optical and near-infrared radial-velocity observations. The planet has a minimum mass of 0.46 Jupiter masses, very high for such a small host star, and an eccentric 204-day orbit. Dynamical models show that the high eccentricity is most likely due to planet-planet interactions. We use simulations to demonstrate that the GJ 3512 planetary system challenges generally accepted formation theories, and that it puts constraints on the planet accretion and migration rates. Disk instabilities may be more efficient in forming planets than previously thought.
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| 3. |
- Kattge, Jens, et al.
(författare)
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TRY plant trait database - enhanced coverage and open access
- 2020
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Ingår i: Global Change Biology. - : Wiley-Blackwell. - 1354-1013 .- 1365-2486. ; 26:1, s. 119-188
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Tidskriftsartikel (refereegranskat)abstract
- Plant traits-the morphological, anatomical, physiological, biochemical and phenological characteristics of plants-determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait-based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits-almost complete coverage for 'plant growth form'. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait-environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives.
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| 4. |
- Perez-Nadales, Elena, et al.
(författare)
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Predictors of mortality in solid organ transplant recipients with bloodstream infections due to carbapenemase-producing Enterobacterales : The impact of cytomegalovirus disease and lymphopenia
- 2020
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Ingår i: American Journal of Transplantation. - : WILEY. - 1600-6135 .- 1600-6143. ; 20:6, s. 1629-1641
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Tidskriftsartikel (refereegranskat)abstract
- Treatment of carbapenemase-producing Enterobacterales bloodstream infections in solid organ transplant recipients is challenging. The objective of this study was to develop a specific score to predict mortality in solid organ transplant recipients with carbapenemase-producing Enterobacterales bloodstream infections. A multinational, retrospective (2004-2016) cohort study (INCREMENT-SOT, ClinicalTrials.gov NCT02852902) was performed. The main outcome variable was 30-day all-cause mortality. The INCREMENT-SOT-CPE score was developed using logistic regression. The global cohort included 216 patients. The final logistic regression model included the following variables: INCREMENT-CPE mortality score >= 8 (8 points), no source control (3 points), inappropriate empirical therapy (2 points), cytomegalovirus disease (7 points), lymphopenia (4 points), and the interaction between INCREMENT-CPE score >= 8 and CMV disease (minus 7 points). This score showed an area under the receiver operating characteristic curve of 0.82 (95% confidence interval [CI] 0.76-0.88) and classified patients into 3 strata: 0-7 (low mortality), 8-11 (high mortality), and 12-17 (very-high mortality). We performed a stratified analysis of the effect of monotherapy vs combination therapy among 165 patients who received appropriate therapy. Monotherapy was associated with higher mortality only in the very-high (adjusted hazard ratio [HR] 2.82, 95% CI 1.13-7.06, P = .03) and high (HR 9.93, 95% CI 2.08-47.40, P = .004) mortality risk strata. A score-based algorithm is provided for therapy guidance.
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| 5. |
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| 6. |
- Lazaridis, Iosif, et al.
(författare)
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Ancient human genomes suggest three ancestral populations for present-day Europeans
- 2014
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Ingår i: Nature. - : Springer Science and Business Media LLC. - 0028-0836 .- 1476-4687. ; 513:7518, s. 409-
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Tidskriftsartikel (refereegranskat)abstract
- We sequenced the genomes of a similar to 7,000-year-old farmer from Germany and eight similar to 8,000-year-old hunter-gatherers from Luxembourg and Sweden. We analysed these and other ancient genomes(1-4) with 2,345 contemporary humans to show that most present-day Europeans derive from at least three highly differentiated populations: west European hunter-gatherers, who contributed ancestry to all Europeans but not to Near Easterners; ancient north Eurasians related to Upper Palaeolithic Siberians(3), who contributed to both Europeans and Near Easterners; and early European farmers, who were mainly of Near Eastern origin but also harboured west European hunter-gatherer related ancestry. We model these populations' deep relationships and show that early European farmers had similar to 44% ancestry from a 'basal Eurasian' population that split before the diversification of other non-African lineages.
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| 9. |
- Bouakaze, Caroline, et al.
(författare)
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Predicting haplogroups using a versatile machine learning program (PredYMaLe) on a new mutationally balanced 32 Y-STR multiplex (CombYplex) : Unlocking the full potential of the human STR mutation rate spectrum to estimate forensic parameters
- 2020
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Ingår i: Forensic Science International. - : Elsevier BV. - 1872-4973 .- 1878-0326. ; 48
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Tidskriftsartikel (refereegranskat)abstract
- We developed a new mutationally well-balanced 32 Y-STR multiplex (CombYplex) together with a machine learning (ML) program PredYMaLe to assess the impact of STR mutability on haplogourp prediction, while respecting forensic community criteria (high DC/HD). We designed CombYplex around two sub-panels M1 and M2 characterized by average and high-mutation STR panels. Using these two sub-panels, we tested how our program PredYmale reacts to mutability when considering basal branches and, moving down, terminal branches. We tested first the discrimination capacity of CombYplex on 996 human samples using various forensic and statistical parameters and showed that its resolution is sufficient to separate haplogroup classes. In parallel, PredYMaLe was designed and used to test whether a ML approach can predict haplogroup classes from Y-STR profiles. Applied to our kit, SVM and Random Forest classifiers perform very well (average 97 %), better than Neural Network (average 91 %) and Bayesian methods (< 90 %). We observe heterogeneity in haplogroup assignation accuracy among classes, with most haplogroups having high prediction scores (99-100 %) and two (E1b1b and G) having lower scores (67 %). The small sample sizes of these classes explain the high tendency to misclassify the Y-profiles of these haplogroups; results were measurably improved as soon as more training data were added. We provide evidence that our ML approach is a robust method to accurately predict haplogroups when it is combined with a sufficient number of markers, well-balanced mutation rate Y-STR panels, and large ML training sets. Further research on confounding factors (such as CNV-STR or gene conversion) and ideal STR panels in regard to the branches analysed can be developed to help classifiers further optimize prediction scores.
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