Radiologic Cerebral Reperfusion at 24 h Predicts Good Clinical Outcome

Cerebral reperfusion and arterial recanalization are radiological features of the effectiveness of thrombolysis in acute ischemic stroke (AIS) patients. Here, an investigation of the prognostic role of early recanalization/reperfusion on clinical outcome was performed. In AIS patients (n = 55), baseline computerized tomography (CT) was performed 64 8 h from symptom onset, whereas CT determination of reperfusion/recanalization was assessed at 24 h. Multiple linear and logistic regression models were used to correlate reperfusion/recanalization with radiological (i.e., hemorrhagic transformation, ischemic core, and penumbra volumes) and clinical outcomes (assessed as National Institutes of Health Stroke Scale [NIHSS] reduction 65 8 points or a NIHSS 64 1 at 24 h and as modified Rankin Scale [mRS] < 2 at 90 days). At 24 h, patients achieving radiological reperfusion were n = 24, while the non-reperfused were n = 31. Among non-reperfused, n = 15 patients were recanalized. Radiological reperfusion vs. recanalization was also confirmed by early increased levels of circulating inflammatory biomarkers (i.e., serum osteopontin). In multivariate analysis, ischemic lesion volume reduction was associated with both recanalization (\u3b2 = 0.265; p = 0.014) and reperfusion (\u3b2 = 0.461; p < 0.001), but only reperfusion was independently associated with final infarct volume (\u3b2 = 12 0.333; p = 0.007). Only radiological reperfusion at 24 h predicted good clinical response at day 1 (adjusted OR 16.054 [1.423\u2013181.158]; p = 0.025) and 90-day good functional outcome (adjusted OR 25.801 [1.483\u2013448.840]; p = 0.026). At ROC curve analysis the AUC of reperfusion was 0.777 (p < 0.001) for the good clinical response at 24 h and 0.792 (p < 0.001) for 90-day clinical outcome. Twenty-four-hour radiological reperfusion assessed by CT is associated with good clinical response on day 1 and good functional outcome on day 90 in patients with ischemic stroke

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

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

The Control Unit of the KM3NeT Data Acquisition System

The KM3NeT Collaboration runs a multi-site neutrino observatory in the Mediterranean Sea. Water Cherenkov particle detectors, deep in the sea and far off the coasts of France and Italy, are already taking data while incremental construction progresses. Data Acquisition Control software is operating off-shore detectors as well as testing and qualification stations for their components. The software, named Control Unit, is highly modular. It can undergo upgrades and reconfiguration with the acquisition running. Interplay with the central database of the Collaboration is obtained in a way that allows for data taking even if Internet links fail. In order to simplify the management of computing resources in the long term, and to cope with possible hardware failures of one or more computers, the KM3NeT Control Unit software features a custom dynamic resource provisioning and failover technology, which is especially important for ensuring continuity in case of rare transient events in multi-messenger astronomy. The software architecture relies on ubiquitous tools and broadly adopted technologies and has been successfully tested on several operating systems

Characterisation of the Hamamatsu photomultipliers for the KM3NeT Neutrino Telescope

[EN] The Hamamatsu R12199-02 3-inch photomultiplier tube is the photodetector chosen for the first phase of the KM3NeT neutrino telescope. About 7000 photomultipliers have been characterised for dark count rate, timing spread and spurious pulses. The quantum eÿciency, the gain and the peak-to-valley ratio have also been measured for a sub-sample in order to determine parameter values needed as input to numerical simulations of the detector.The authors acknowledge the financial support of the funding agencies: Agence Nationale de la Recherche (contract ANR-15-CE31-0020), Centre National de la Recherche Scientifique (CNRS), Commission Europeenne (FEDER fund and Marie Curie Program), Institut Universitaire de France (IUF), IdEx program and UnivEarthS Labex program at Sorbonne Paris Cite (ANR-10-LABX-0023 and ANR-11-IDEX-0005-02), France; 'Helmholtz Alliance for Astroparticle Physics' funded by the Initiative and Networking Fund of the Helmholtz Association, Germany; The General Secretariat of Research and Technology (GSRT), Greece; Istituto Nazionale di Fisica Nucleare (INFN), Ministero dell'Istruzione, dell'Universita e della Ricerca (MIUR), Italy; Agence de l'Oriental and CNRST, Morocco; Nederlandse organisatie voor Wetenschappelijk Onderzoek (NWO), the Netherlands; National Authority for Scientific Research (ANCS), Romania; Plan Estatal de Investigacion (refs. FPA2015-65150-C3-1-P, -2-P and -3-P, (MINECO/FEDER)), Severo Ochoa Centre of Excellence and MultiDark Consolider (MINECO), and Prometeo and Grisolia programs (Generalitat Valenciana), Spain.Aiello, S.; Akrame, SE.; Ameli, F.; Anassontzis, EG.; Andre, M.; Androulakis, G.; Anghinolfi, M.... (2018). Characterisation of the Hamamatsu photomultipliers for the KM3NeT Neutrino Telescope. Journal of Instrumentation. 13:1-17. https://doi.org/10.1088/1748-0221/13/05/P05035S11713Adrián-Martínez, S., Ageron, M., Aharonian, F., Aiello, S., Albert, A., Ameli, F., … Anghinolfi, M. (2016). Letter of intent for KM3NeT 2.0. Journal of Physics G: Nuclear and Particle Physics, 43(8), 084001. doi:10.1088/0954-3899/43/8/084001Adrián-Martínez, S., Ageron, M., Aharonian, F., Aiello, S., Albert, A., Ameli, F., … Anvar, S. (2014). Deep sea tests of a prototype of the KM3NeT digital optical module. The European Physical Journal C, 74(9). doi:10.1140/epjc/s10052-014-3056-3Adrián-Martínez, S., Ageron, M., Aharonian, F., Aiello, S., Albert, A., Ameli, F., … Anton, G. (2016). The prototype detection unit of the KM3NeT detector. The European Physical Journal C, 76(2). doi:10.1140/epjc/s10052-015-3868-9Herold, B., Kalekin, O., & Reubelt, J. (2011). PMT characterisation for the KM3NeT project. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 639(1), 70-72. doi:10.1016/j.nima.2010.09.018Timmer, P., Heine, E., & Peek, H. (2010). Very low power, high voltage base for a Photo Multiplier Tube for the KM3NeT deep sea neutrino telescope. Journal of Instrumentation, 5(12), C12049-C12049. doi:10.1088/1748-0221/5/12/c12049Mollo, C. M., Bozza, C., Chiarusi, T., Costa, M., Capua, F. D., Kulikovskiy, V., … Vivolo, D. (2016). A new instrument for high statistics measurement of photomultiplier characteristics. Journal of Instrumentation, 11(08), T08002-T08002. doi:10.1088/1748-0221/11/08/t08002Adrián-Martínez, S., Ageron, M., Aiello, S., Albert, A., Ameli, F., Anassontzis, E. G., … Anton, G. (2016). A method to stabilise the performance of negatively fed KM3NeT photomultipliers. Journal of Instrumentation, 11(12), P12014-P12014. doi:10.1088/1748-0221/11/12/p12014Lubsandorzhiev, B. K., Vasiliev, R. V., Vyatchin, Y. E., & Shaibonov, B. A. J. (2006). Photoelectron backscattering in vacuum phototubes. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 567(1), 12-16. doi:10.1016/j.nima.2006.05.04

KM3NeT broadcast optical data transport system

The optical data transport system of the KM3NeT neutrino telescope at the bottom of the Mediterranean Sea will provide more than 6000 optical modules in the detector arrays with a point-to-point optical connection to the control stations onshore. The ARCA and ORCA detectors of KM3NeT are being installed at a depth of about 3500 m and 2500 m, respectively and their distance to the control stations is about 100 kilometers and 40 kilometers. In particular, the two detectors are optimised for the detection of cosmic neutrinos with energies above about 1 TeV (ARCA) and for the detection of atmospheric neutrinos with energies in the range 1 GeV-1 TeV (ORCA). The expected maximum data rate is 200 Mbps per optical module. The implemented optical data transport system matches the layouts of the networks of electro-optical cables and junction boxes in the deep sea. For efficient use of the fibres in the system the technology of Dense Wavelength Division Multiplexing is applied. The performance of the optical system in terms of measured bit error rates, optical budget are presented. The next steps in the implementation of the system are also discussed

Event reconstruction for KM3NeT/ORCA using convolutional neural networks

The authors acknowledge the financial support of the funding agencies: Agence Nationale de la Recherche (contract ANR-15-CE31-0020), Centre National de la Recherche Scientifique (CNRS), Commission Europeenne (FEDER fund and Marie Curie Program), Institut Universitaire de France (IUF), LabEx UnivEarthS (ANR-10-LABX-0023 and ANR-18-IDEX-0001), Paris Ile-de-France Region, France; Shota Rustaveli National Science Foundation of Georgia (SRNSFG, FR-18-1268), Georgia; Deutsche Forschungsgemeinschaft (DFG), Germany; The General Secretariat of Research and Technology (GSRT), Greece; Istituto Nazionale di Fisica Nucleare (INFN), Ministero dell'Universita e della Ricerca (MUR), PRIN 2017 program (Grant NAT-NET 2017W4HA7S) Italy; Ministry of Higher Education, Scientific Research and Professional Training, Morocco; Nederlandse organisatie voor Wetenschappelijk Onderzoek (NWO), the Netherlands; The National Science Centre, Poland (2015/18/E/ST2/00758); National Authority for Scientific Research (ANCS), Romania; Ministerio de Ciencia, Innovacion, Investigacion y Universidades (MCIU): Programa Estatal de Generacion de Conocimiento (refs. PGC2018-096663-B-C41, -A-C42, -B-C43, -B-C44) (MCIU/FEDER), Severo Ochoa Centre of Excellence and MultiDark Consolider (MCIU), Junta de Andalucia (ref. SOMM17/6104/UGR), Generalitat Valenciana: Grisolia (ref. GRISOLIA/2018/119) and GenT (ref. CIDEGENT/2018/034) programs, La Caixa Foundation (ref. LCF/BQ/IN17/11620019), EU: MSC program (ref. 713673), Spain.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.French National Research Agency (ANR) ANR-15-CE31-0020Centre National de la Recherche Scientifique (CNRS), Commission Europeenne (FEDER fund)European Union (EU)Institut Universitaire de France (IUF)LabEx UnivEarthS ANR-10-LABX-0023 ANR-18-IDEX-0001Shota Rustaveli National Science Foundation of Georgia FR-18-1268German Research Foundation (DFG)Greek Ministry of Development-GSRTIstituto Nazionale di Fisica Nucleare (INFN)Ministry of Education, Universities and Research (MIUR) Research Projects of National Relevance (PRIN)Ministry of Higher Education, Scientific Research and Professional Training, MoroccoNetherlands Organization for Scientific Research (NWO)National Science Centre, Poland 2015/18/E/ST2/00758National Authority for Scientific Research (ANCS), RomaniaMinisterio de Ciencia, Innovacion, Investigacion y Universidades PGC2018-096663-B-C41 A-C42 B-C43 B-C44Severo Ochoa Centre of ExcellenceJunta de Andalucia SOMM17/6104/UGRGeneralitat Valenciana: Grisolia GRISOLIA/2018/119 CIDEGENT/2018/034La Caixa Foundation LCF/BQ/IN17/11620019EU: MSC program 71367