NEMO-SN1 Abyssal Cabled Observatory in the Western Ionian Sea

The NEutrinoMediterranean Observatory—Submarine Network 1 (NEMO-SN1) seafloor observatory is located in the central Mediterranean Sea, Western Ionian Sea, off Eastern Sicily (Southern Italy) at 2100-m water depth, 25 km from the harbor of the city of Catania. It is a prototype of a cabled deep-sea multiparameter observatory and the first one operating with real-time data transmission in Europe since 2005. NEMO-SN1 is also the first-established node of the European Multidisciplinary Seafloor Observatory (EMSO), one of the incoming European large-scale research infrastructures included in the Roadmap of the European Strategy Forum on Research Infrastructures (ESFRI) since 2006. EMSO will specifically address long-term monitoring of environmental processes related to marine ecosystems, marine mammals, climate change, and geohazards

Low in‑hospital mortality rate in patients with COVID‑19 receiving thromboprophylaxis: data from the multicentre observational START‑COVID Register

Abstract COVID-19 infection causes respiratory pathology with severe interstitial pneumonia and extra-pulmonary complications; in particular, it may predispose to thromboembolic disease. The current guidelines recommend the use of thromboprophylaxis in patients with COVID-19, however, the optimal heparin dosage treatment is not well-established. We conducted a multicentre, Italian, retrospective, observational study on COVID-19 patients admitted to ordinary wards, to describe clinical characteristic of patients at admission, bleeding and thrombotic events occurring during hospital stay. The strategies used for thromboprophylaxis and its role on patient outcome were, also, described. 1091 patients hospitalized were included in the START-COVID-19 Register. During hospital stay, 769 (70.7%) patients were treated with antithrombotic drugs: low molecular weight heparin (the great majority enoxaparin), fondaparinux, or unfractioned heparin. These patients were more frequently affected by comorbidities, such as hypertension, atrial fibrillation, previous thromboembolism, neurological disease,and cancer with respect to patients who did not receive thromboprophylaxis. During hospital stay, 1.2% patients had a major bleeding event. All patients were treated with antithrombotic drugs; 5.4%, had venous thromboembolism [30.5% deep vein thrombosis (DVT), 66.1% pulmonary embolism (PE), and 3.4% patients had DVT + PE]. In our cohort the mortality rate was 18.3%. Heparin use was independently associated with survival in patients aged ≥ 59 years at multivariable analysis. We confirmed the high mortality rate of COVID-19 in hospitalized patients in ordinary wards. Treatment with antithrombotic drugs is significantly associated with a reduction of mortality rates especially in patients older than 59 years

Event reconstruction for KM3NeT/ORCA using convolutional neural networks

The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino detector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower- or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches

Event reconstruction for KM3NeT/ORCA using convolutional neural networks

The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino de tector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower-or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches

A km3 detector in the Mediterranean: Status of NEMO

The status of the NEMO project, which aims at characterizing and monitoring the Capo Passero (Sicily, Italy) candidate site for the km3 underwater Mediterranean detector and developing and testing the related key technological solutions, is described. NEMO Phase 1, which is a technological demonstrator aiming towards the Mediterranean km3 telescope, is under realization and is reported. © 2005 Elsevier B.V. All rights reserved

NEMO-SN1 (Western Ionian Sea, off Eastern Sicily): Example of architecture of a cabled observatory

NEMO-SN1, located in the central Mediterranean Sea, Western Ionian Sea, off Eastern Sicily Island (Southern Italy) at 2100 m water depth, 25 km from the harbour of the city of Catania, is a prototype of a cabled deep-sea multiparameter observatory and the first operating with real-time data transmission in Europe since 2005. NEMO-SN1 is also the first-established node of EMSO (European Multidisciplinary Seafloor Observatory, http://emso-eu.org), one of the incoming European large-scale research infrastructure included since 2006 in the Roadmap of the ESFRI (European Strategy Forum on Research Infrastructures, http://cordis.europa.eu/esfri/roadmap.htm), which will specifically address long-term monitoring of environmental processes related to Marine Ecosystems, Climate Change and Geo-hazards. NEMO-SN1 has been deployed and developed over the last decade thanks to Italian resources and to the EC project ESONET-NoE (European Seas Observatory NETwork Network of Excellence, 20072011) that funded the LIDO-DM (Listening to the Deep Ocean Demonstration Mission) and a technological interoperability test (http://www.esonet-emso.org/ esonet-noe/). NEMO-SN1 is performing geophysical and environmental long-term monitoring by acquiring seismological, geomagnetic, gravimetric, accelerometric, physico-oceanographic, hydro-acoustic, bio-acoustic measurements specifically related to earthquakes and tsunamis generation and ambient noise characterisation in term of marine mammal sounds, environmental and anthropogenic sources. A further main feature of NEMO-SN1 is to be an important test-site for the construction of KM3NeT (Kilometre-Cube Underwater Neutrino Telescope, http://www.km3net.org/), another large-scale research infrastructure included in the ESFRI Roadmap constituted by a large volume neutrino telescope. The description of the observatory and the most recent data acquired will be presented and framed in the general objectives of EMSO. © 2011 IEEE