| 1. |
- Geng, Xiongfei, et al.
(författare)
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Exploring High-energy Emission from the BL Lacertae Object S5 0716+714 with the Fermi Large Area Telescope
- 2020
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Ingår i: Astrophysical Journal. - : American Astronomical Society. - 0004-637X .- 1538-4357. ; 904:1
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Tidskriftsartikel (refereegranskat)abstract
- We present the results of an extensive gamma-ray data analysis of the emission from the blazar S5 0716+714 with the primary motivation to study its temporal and spectral variability behavior. In this work, we extract a 10 days binned gamma-ray light curve from 2008 August 4 to 2016 April 27 in the energy range of 0.1-300 GeV and identify six outburst periods with peak flux of >4 x 10(-7) ph cm(-2) s(-1) from this highly variable source. The brightest flares are identified by zooming in these outburst periods to 1 day binning and using the Bayesian Blocks algorithm. The fastest variability timescale is found to be 1.5 0.3 hr at MJD 57128.01 0.01 with a peak flux above 100 MeV of (26.8 6.9) x 10(-7) ph cm(-2) s(-1). No hint of periodic modulations has been detected for the light curve of S5 0716+714. During the outburst phases, the gamma-ray spectrum shows an obvious spectral break with a break energy between 0.93 and 6.90 GeV energies, which may be caused by an intrinsic break in the energy distribution of radiating particles. The five highest-energy photons, with E > 100 GeV, imply that the high-energy emission from this source may originate from a moving emission region in a helical path upstream in the jet. The spectral behavior and temporal characteristics of the individual flares indicate that the location of the emission region lies in the sub-parsec scale (r(gamma) < 0.85 pc).
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| 2. |
- Wuttke, Matthias, et al.
(författare)
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A catalog of genetic loci associated with kidney function from analyses of a million individuals
- 2019
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Ingår i: Nature Genetics. - : NATURE PUBLISHING GROUP. - 1061-4036 .- 1546-1718. ; 51:6, s. 957-972
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Tidskriftsartikel (refereegranskat)abstract
- Chronic kidney disease (CKD) is responsible for a public health burden with multi-systemic complications. Through transancestry meta-analysis of genome-wide association studies of estimated glomerular filtration rate (eGFR) and independent replication (n = 1,046,070), we identified 264 associated loci (166 new). Of these,147 were likely to be relevant for kidney function on the basis of associations with the alternative kidney function marker blood urea nitrogen (n = 416,178). Pathway and enrichment analyses, including mouse models with renal phenotypes, support the kidney as the main target organ. A genetic risk score for lower eGFR was associated with clinically diagnosed CKD in 452,264 independent individuals. Colocalization analyses of associations with eGFR among 783,978 European-ancestry individuals and gene expression across 46 human tissues, including tubulo-interstitial and glomerular kidney compartments, identified 17 genes differentially expressed in kidney. Fine-mapping highlighted missense driver variants in 11 genes and kidney-specific regulatory variants. These results provide a comprehensive priority list of molecular targets for translational research.
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| 3. |
- Yan, Su-Rong, et al.
(författare)
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A graph-based comprehensive reputation model : exploiting the social context of opinions to enhance trust in social commerce
- 2015
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Ingår i: Information Sciences. - : Elsevier BV. - 0020-0255 .- 1872-6291. ; 318, s. 51-72
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Tidskriftsartikel (refereegranskat)abstract
- Social commerce is a promising new paradigm of e-commerce. Given the open and dynamic nature of social media infrastructure, the governance structures of social commerce are usually realized through reputation mechanisms. However, the existing approaches to the prediction of trust in future interactions are based on personal observations and/or publicly shared information in social commerce application. As a result, the indications are unreliable and biased because of limited first-hand information and stake-holder manipulation for personal strategic interests. Methods that extract trust values from social links among users can improve the performance of reputation mechanisms. Nonetheless, these links may not always be available and are typically sparse in social commerce, especially for new users. Thus, this study proposes a new graph-based comprehensive reputation model to build trust by fully exploiting the social context of opinions based on the activities and relationship networks of opinion contributors. The proposed model incorporates the behavioral activities and social relationship reputations of users to combat the scarcity of first-hand information and identifies a set of critical trust factors to mitigate the subjectivity of opinions and the dynamics of behaviors. Furthermore, we enhance the model by developing a novel deception filtering approach to discard "bad-mouthing" opinions and by exploiting a personalized direct distrust (risk) metric to identify malicious providers. Experimental results show that the proposed reputation model can outperform other trust and reputation models in most cases. (C) 2014 Elsevier Inc. All rights reserved.
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