| 61. |
- Acciari, V. A., et al.
(författare)
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Gamma-Ray Observations of the Be/Pulsar Binary 1A 0535+262 During a Giant X-Ray Outburst
- 2011
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Ingår i: Astrophysical Journal. - 0004-637X .- 1538-4357. ; 733:2
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Tidskriftsartikel (refereegranskat)abstract
- Giant X-ray outbursts, with luminosities of about 1037 erg s–1, are observed roughly every five years from the nearby Be/pulsar binary 1A 0535+262. In this article, we present observations of the source with VERITAS at very high energies (VHEs;E >100 GeV) triggered by the X-ray outburst in 2009 December. The observations started shortly after the onset of the outburst and provided comprehensive coverage of the episode, as well as the 111 day binary orbit. No VHE emission is evident at any time. We also examined data from the contemporaneous observations of 1A 0535+262 with the Fermi/Large Area Telescope at high-energy photons (E > 0.1 GeV) and failed to detect the source at GeV energies. The X-ray continua measured with theSwift/X-Ray Telescope and the RXTE/PCA can be well described by the combination of blackbody and Comptonized emission from thermal electrons. Therefore, the gamma-ray and X-ray observations suggest the absence of a significant population of non-thermal particles in the system. This distinguishes 1A 0535+262 from those Be X-ray binaries (such as PSR B1259-63 and LS I +61°303) that have been detected at GeV-TeV energies. We discuss the implications of the results on theoretical models.
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| 62. |
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| 63. |
- Aliu, E., et al.
(författare)
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VERITAS Observations of the Unusual Extragalactic Transient Swift J164449.3+573451
- 2011
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Ingår i: Astrophysical Journal Letters. - 2041-8205 .- 2041-8213. ; 738:2
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Tidskriftsartikel (refereegranskat)abstract
- We report on very high energy (>100 GeV) gamma-ray observations of Swift J164449.3+573451, an unusual transient object first detected by the Swift Observatory and later detected by multiple radio, optical, and X-ray observatories. A total exposure of 28 hr was obtained on Swift J164449.3+573451 with the Very Energetic Radiation Imaging Telescope Array System (VERITAS) during 2011 March 28-April 15. We do not detect the source and place a differential upper limit on the emission at 500 GeV during these observations of 1.4 × 10–12 erg cm–2 s–1 (99% confidence level). We also present time-resolved upper limits and use a flux limit averaged over the X-ray flaring period to constrain various emission scenarios that can accommodate both the radio-through-X-ray emission detected from the source and the lack of detection by VERITAS.
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| 64. |
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| 65. |
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| 66. |
- Sumaila, U. Rashid, et al.
(författare)
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WTO must ban harmful fisheries subsidies
- 2021
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Ingår i: Science. - : American Association for the Advancement of Science (AAAS). - 0036-8075 .- 1095-9203. ; 374:6567, s. 544-544
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)
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| 67. |
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| 68. |
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| 69. |
- Ge, R, et al.
(författare)
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Normative Modeling of Brain Morphometry Across the Lifespan Using CentileBrain: Algorithm Benchmarking and Model Optimization
- 2023
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Ingår i: bioRxiv : the preprint server for biology. - : Cold Spring Harbor Laboratory.
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- Background: Normative modeling is a statistical approach to quantify the degree to which a particular individual-level measure deviates from the pattern observed in a normative reference population. When applied to human brain morphometric measures it has the potential to inform about the significance of normative deviations for health and disease. Normative models can be implemented using a variety of algorithms that have not been systematically appraised. Methods: To address this gap, eight algorithms were compared in terms of performance and computational efficiency using brain regional morphometric data from 37,407 healthy individuals (53% female; aged 3-90 years) collated from 87 international MRI datasets. Performance was assessed with the mean absolute error (MAE) and computational efficiency was inferred from central processing unit (CPU) time. The algorithms evaluated were Ordinary Least Squares Regression (OLSR), Bayesian Linear Regression (BLR), Generalized Additive Models for Location, Scale, and Shape (GAMLSS), Parametric Lambda, Mu, Sigma (LMS), Gaussian Process Regression (GPR), Warped Bayesian Linear Regression (WBLG), Hierarchical Bayesian Regression (HBR), and Multivariable Fractional Polynomial Regression (MFPR). Model optimization involved testing nine covariate combinations pertaining to acquisition features, parcellation software versions, and global neuroimaging measures (i.e., total intracranial volume, mean cortical thickness, and mean cortical surface area). Findings: Statistical comparisons across models at PFDR<0.05 indicated that the MFPR-derived sex- and region-specific models with nonlinear polynomials for age and linear effects of global measures had superior predictive accuracy; the range of the MAE of the models of regional subcortical volumes was 70-520 mm3 and the corresponding ranges for regional cortical thickness and regional cortical surface area were 0.09-0.26 mm and 24-560 mm2, respectively. The MFPR-derived models were also computationally more efficient with a CPU time below one second compared to a range of 2 seconds to 60 minutes for the other algorithms. The performance of all sex- and region-specific MFPR models plateaued at sample sizes exceeding 3,000 and showed comparable MAEs across distinct 10-year age-bins covering the human lifespan. Interpretation: These results provide an empirically benchmarked framework for normative modeling of brain morphometry that is useful for interpreting prior literature and supporting future study designs. The model and tools described here are freely available through CentileBrain (https://centilebrain.org/), a user-friendly web platform.
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| 70. |
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