11 research outputs found
Probing quantum gravity using photons from a flare of the active galactic nucleus Markarian 501 observed by the MAGIC telescope
We analyze the timing of photons observed by the MAGIC telescope during a
flare of the active galactic nucleus Mkn 501 for a possible correlation with
energy, as suggested by some models of quantum gravity (QG), which predict a
vacuum refractive index \simeq 1 + (E/M_{QGn})^n, n = 1,2. Parametrizing the
delay between gamma-rays of different energies as \Delta t =\pm\tau_l E or
\Delta t =\pm\tau_q E^2, we find \tau_l=(0.030\pm0.012) s/GeV at the 2.5-sigma
level, and \tau_q=(3.71\pm2.57)x10^{-6} s/GeV^2, respectively. We use these
results to establish lower limits M_{QG1} > 0.21x10^{18} GeV and M_{QG2} >
0.26x10^{11} GeV at the 95% C.L. Monte Carlo studies confirm the MAGIC
sensitivity to propagation effects at these levels. Thermal plasma effects in
the source are negligible, but we cannot exclude the importance of some other
source effect.Comment: 12 pages, 3 figures, Phys. Lett. B, reflects published versio
Insights into the high-energy γ-ray emission of Markarian 501 from extensive multifrequency observations in the Fermi era
We report on the γ-ray activity of the blazar Mrk 501 during the first 480 days of Fermi operation. We find that the average Large Area Telescope (LAT) γ-ray spectrum of Mrk 501 can be well described by a single power-law function with a photon index of 1.78 ± 0.03. While we observe relatively mild flux variations with the Fermi-LAT (within less than a factor of two), we detect remarkable spectral variability where the hardest observed spectral index within the LAT energy range is 1.52 ± 0.14, and the softest one is 2.51 ± 0.20. These unexpected spectral changes do not correlate with the measured flux variations above 0.3 GeV. In this paper, we also present the first results from the 4.5 month long multifrequency campaign (2009 March 15-August 1) on Mrk 501, which included the Very Long Baseline Array (VLBA), Swift, RXTE, MAGIC, and VERITAS, the F-GAMMA, GASP-WEBT, and other collaborations and instruments which provided excellent temporal and energy coverage of the source throughout the entire campaign. The extensive radio to TeV data set from this campaign provides us with the most detailed spectral energy distribution yet collected for this source during its relatively low activity. The average spectral energy distribution of Mrk 501 is well described by the standard one-zone synchrotron self-Compton (SSC) model. In the framework of this model, we find that the dominant emission region is characterized by a size ≲0.1 pc (comparable within a factor of few to the size of the partially resolved VLBA core at 15-43 GHz), and that the total jet power (≃1044 erg s-1) constitutes only a small fraction (∼10-3) of the Eddington luminosity. The energy distribution of the freshly accelerated radiating electrons required to fit the time-averaged data has a broken power-law form in the energy range 0.3 GeV-10 TeV, with spectral indices 2.2 and 2.7 below and above the break energy of 20 GeV. We argue that such a form is consistent with a scenario in which the bulk of the energy dissipation within the dominant emission zone of Mrk 501 is due to relativistic, proton-mediated shocks. We find that the ultrarelativistic electrons and mildly relativistic protons within the blazar zone, if comparable in number, are in approximate energy equipartition, with their energy dominating the jet magnetic field energy by about two orders of magnitude. © 2011. The American Astronomical Society
Parasitologia en el sur-oeste de Europa
Available from Centro de Informacion y Documentacion Cientifica CINDOC. Joaquin Costa, 22. 28002 Madrid. SPAIN / CINDOC - Centro de Informaciòn y Documentaciòn CientìficaSIGLEESSpai
Pilot multi-omic analysis of human bile from benign and malignant biliary strictures: A machine-learning approach
Cholangiocarcinoma (CCA) and pancreatic adenocarcinoma (PDAC) may lead to the
development of extrahepatic obstructive cholestasis. However, biliary stenoses can also be caused
by benign conditions, and the identification of their etiology still remains a clinical challenge.
We performed metabolomic and proteomic analyses of bile from patients with benign (n = 36)
and malignant conditions, CCA (n = 36) or PDAC (n = 57), undergoing endoscopic retrograde
cholangiopancreatography with the aim of characterizing bile composition in biliopancreatic disease
and identifying biomarkers for the differential diagnosis of biliary strictures. Comprehensive analyses
of lipids, bile acids and small molecules were carried out using mass spectrometry (MS) and nuclear
magnetic resonance spectroscopy (1H-NMR) in all patients. MS analysis of bile proteome was
performed in five patients per group. We implemented artificial intelligence tools for the selection
of biomarkers and algorithms with predictive capacity. Our machine-learning pipeline included
the generation of synthetic data with properties of real data, the selection of potential biomarkers
(metabolites or proteins) and their analysis with neural networks (NN). Selected biomarkers were
then validated with real data. We identified panels of lipids (n = 10) and proteins (n = 5) that when
analyzed with NN algorithms discriminated between patients with and without cancer with an
unprecedented accurac
Pilot multi-omic analysis of human bile from benign and malignant biliary strictures: A machine-learning approach
Cholangiocarcinoma (CCA) and pancreatic adenocarcinoma (PDAC) may lead to the
development of extrahepatic obstructive cholestasis. However, biliary stenoses can also be caused
by benign conditions, and the identification of their etiology still remains a clinical challenge.
We performed metabolomic and proteomic analyses of bile from patients with benign (n = 36)
and malignant conditions, CCA (n = 36) or PDAC (n = 57), undergoing endoscopic retrograde
cholangiopancreatography with the aim of characterizing bile composition in biliopancreatic disease
and identifying biomarkers for the differential diagnosis of biliary strictures. Comprehensive analyses
of lipids, bile acids and small molecules were carried out using mass spectrometry (MS) and nuclear
magnetic resonance spectroscopy (1H-NMR) in all patients. MS analysis of bile proteome was
performed in five patients per group. We implemented artificial intelligence tools for the selection
of biomarkers and algorithms with predictive capacity. Our machine-learning pipeline included
the generation of synthetic data with properties of real data, the selection of potential biomarkers
(metabolites or proteins) and their analysis with neural networks (NN). Selected biomarkers were
then validated with real data. We identified panels of lipids (n = 10) and proteins (n = 5) that when
analyzed with NN algorithms discriminated between patients with and without cancer with an
unprecedented accurac
A guide to the BRAIN initiative cell census network data ecosystem
Characterizing cellular diversity at different levels of biological organization and across data modalities is a prerequisite to understanding the function of cell types in the brain. Classification of neurons is also essential to manipulate cell types in controlled ways and to understand their variation and vulnerability in brain disorders. The BRAIN Initiative Cell Census Network (BICCN) is an integrated network of data-generating centers, data archives, and data standards developers, with the goal of systematic multimodal brain cell type profiling and characterization. Emphasis of the BICCN is on the whole mouse brain with demonstration of prototype feasibility for human and nonhuman primate (NHP) brains. Here, we provide a guide to the cellular and spatial approaches employed by the BICCN, and to accessing and using these data and extensive resources, including the BRAIN Cell Data Center (BCDC), which serves to manage and integrate data across the ecosystem. We illustrate the power of the BICCN data ecosystem through vignettes highlighting several BICCN analysis and visualization tools. Finally, we present emerging standards that have been developed or adopted toward Findable, Accessible, Interoperable, and Reusable (FAIR) neuroscience. The combined BICCN ecosystem provides a comprehensive resource for the exploration and analysis of cell types in the brain.Horizon 2020 (H2020)R01 NS096720Radiolog
Improving the performance of the single-dish Cherenkov telescope MAGIC through the use of signal timing.
The Cherenkov light flashes produced by Extensive Air Showers are very short
in time. A high bandwidth and fast digitizing readout, therefore, can minimize
the influence of the background from the light of the night sky, and improve
the performance in Cherenkov telescopes. The time structure of the Cherenkov
image can further be used in single-dish Cherenkov telescopes as an additional
parameter to reduce the background from unwanted hadronic showers. A
description of an analysis method which makes use of the time information and
the subsequent improvement on the performance of the MAGIC telescope
(especially after the upgrade with an ultra fast 2 GSamples/s digitization
system in February 2007) will be presented. The use of timing information in
the analysis of the new MAGIC data reduces the background by a factor two,
which in turn results in an enhancement of about a factor 1.4 of the flux
sensitivity to point-like sources, as tested on observations of the Crab
Nebula.Comment: 27 pages, 11 figures, accepted by Astroparticle Physic