Gamma-ray bursts (GRBs) are the most luminous explosions in the Universe. They are produced during the collapse of massive stellar-sized objects, which create a black hole and eject material at ultra-relativistic speeds. They are unique tools to study the evolution of our Universe, as they are the only objects that, thanks to their extraordinary luminosity, can be observed during the complete history of star formation, from the era of reionisation to our days.One of the main tools to obtain information from GRBs and their environment is optical and near-infrared spectroscopy. After 17 years of studies spectroscopic data for around 300 events that have been collected. However, spectra were obtained by many groups, at different observatories, and using instruments of very different types, making data difficult to access, process and compare.Here we present GRBspec: A collaborative database that includes processed GRB spectra from multiple observatories and makes them available to the community. The website provides access to the datasets, allowing queries based not only on the observation characteristics but also on the properties of the GRB that was observed. Furthermore, the website provides visualisation and analysis tools, that allow the user to asses the quality of the data before downloading and even make data analysis online.
In spite of a trend in favour of the BP-BES, long-term efficacy and safety were similar for both the BP-BES and the DP-EES in this specific population of diabetic patients. Considering the low incidence of the studied clinical events, other studies with larger population sizes are needed to confirm this observation.
<p>Space Surveillance and Tracking (SST) requires the development of observational campaigns to early identify Earth orbiting objects, estimate their orbital elements and monitor the evolution of their trajectories.</p> <p>With a growing number of satellites being launched every year, there is an increasing interest in SST at worldwide level and especially across the EU. In this context, Romania is one of the few EU member states involved in such activities.</p> <p>SST systems must implement an Image Data Reduction Subsystem, able to timely process the continuous exposures taken in an automatic and continuous way by optical telescopes, analyse these data sets to identify objects of interest (target objects) and retrieve accurate measurements of the apparent position and brightness of the detected objects. In the last step, the tool generates tracklet information for the identified target objects.</p> <p>In this context, The <em>Generic Data Reduction Framework for Space Surveillance -&#160; <strong>gendared,</strong></em> prototyped initially by GMV in the frame of an ESA Romanian Industry Incentive Scheme project, is intended to be used in order to support the operational reduction of the image data acquired by optical telescopes. <strong><em>gendared</em></strong> receives as input the raw images taken by the telescope, and generates as output astrometric and photometric data for the target objects detected in the observation images.</p> <p>The key aspects that drove the development of <strong><em>gendared</em></strong> and its advantages include the fact that the software is autonomous and unassisted, can process images in near real-time, can cope with different image acquisition schemas, and is configurable and modular.</p> <p>As of the end of the prototyping phase and the acceptance of the software by ESA, <strong><em>gendared</em></strong> has been successfully tested and validated on Romanian telescopes and developed further for various operational use cases of different telescopes, such as the German Aerospace Centre (DLR) telescopes.</p> <p>Furthermore, since mid-2020, <strong><em>gendared</em></strong> is part of a larger architecture of GMV products supporting the operational data processing activities in Romanian Space Agency&#8217;s SST Operational Centre (COSST), which ensures the Romanian contribution to the EUSST Consortium. In this case <strong><em>gendared</em></strong> is covering, in a centralized architecture, the processing of the data coming from the Romanian optical telescopes involved in EUSST. The software was further upgraded for this purpose, with new algorithms and processing pipelines included.</p> <p><br />Most recently GMV has also won a Romanian Research & Development grant to upgrade <strong><em>gendared</em></strong> with artificial intelligence algorithms. This project involves a consortium led by GMV, with the Faculty of Automatic Control and Computer Science and the Astronomical Institute of the Romanian Academy as partners. By integrating artificial intelligence techniques into the current <strong><em>gendared </em></strong>framework, we aim to improve its performance, reduce user involvement and computational costs, as well as increase pipeline autonomy. The upgraded solution will be tested and validated in representative scenarios based on real telescope data, with the ultimate goal of converting the prototype into a product that will be validated in an SST operational environment on a wide range of telescopes.</p> <p>&#160;</p> <p><img src="data:image/png;base64, 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
Ictal asystole is a rare complication of epileptic seizures and is frequently unrecognized by non-neurologists. We describe a case of ictal asystole as first clinical manifestation of unknown temporal lobe epilepsy and we discuss epidemiologic, pathophysiologic and therapeutic features.
favor of DES implantation (OR 0.79, 95% CI 0.63 to 0.99, chi2 ϭ1.07, pϭ0.04, I2 ϭ0%). Conclusions:Our study confirmed the cerebral vascular benefits of PCI by significantly reducing CVA risks, and the composite outcome was better in patients undergoing PCI with drug-eluting stent despite a higher repeat revascularization rate. It poses imperative demands for future prospective randomized studies to define the optimal strategy in patients with diabetes and left main and/or multivessel disease.
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