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

Effect of angiotensin-converting enzyme inhibitor and angiotensin receptor blocker initiation on organ support-free days in patients hospitalized with COVID-19

IMPORTANCE Overactivation of the renin-angiotensin system (RAS) may contribute to poor clinical outcomes in patients with COVID-19. Objective To determine whether angiotensin-converting enzyme (ACE) inhibitor or angiotensin receptor blocker (ARB) initiation improves outcomes in patients hospitalized for COVID-19. DESIGN, SETTING, AND PARTICIPANTS In an ongoing, adaptive platform randomized clinical trial, 721 critically ill and 58 non–critically ill hospitalized adults were randomized to receive an RAS inhibitor or control between March 16, 2021, and February 25, 2022, at 69 sites in 7 countries (final follow-up on June 1, 2022). INTERVENTIONS Patients were randomized to receive open-label initiation of an ACE inhibitor (n = 257), ARB (n = 248), ARB in combination with DMX-200 (a chemokine receptor-2 inhibitor; n = 10), or no RAS inhibitor (control; n = 264) for up to 10 days. MAIN OUTCOMES AND MEASURES The primary outcome was organ support–free days, a composite of hospital survival and days alive without cardiovascular or respiratory organ support through 21 days. The primary analysis was a bayesian cumulative logistic model. Odds ratios (ORs) greater than 1 represent improved outcomes. RESULTS On February 25, 2022, enrollment was discontinued due to safety concerns. Among 679 critically ill patients with available primary outcome data, the median age was 56 years and 239 participants (35.2%) were women. Median (IQR) organ support–free days among critically ill patients was 10 (–1 to 16) in the ACE inhibitor group (n = 231), 8 (–1 to 17) in the ARB group (n = 217), and 12 (0 to 17) in the control group (n = 231) (median adjusted odds ratios of 0.77 [95% bayesian credible interval, 0.58-1.06] for improvement for ACE inhibitor and 0.76 [95% credible interval, 0.56-1.05] for ARB compared with control). The posterior probabilities that ACE inhibitors and ARBs worsened organ support–free days compared with control were 94.9% and 95.4%, respectively. Hospital survival occurred in 166 of 231 critically ill participants (71.9%) in the ACE inhibitor group, 152 of 217 (70.0%) in the ARB group, and 182 of 231 (78.8%) in the control group (posterior probabilities that ACE inhibitor and ARB worsened hospital survival compared with control were 95.3% and 98.1%, respectively). CONCLUSIONS AND RELEVANCE In this trial, among critically ill adults with COVID-19, initiation of an ACE inhibitor or ARB did not improve, and likely worsened, clinical outcomes. TRIAL REGISTRATION ClinicalTrials.gov Identifier: NCT0273570

On-ground calibrations of XGRE: An ultrafast gamma-ray spectrometer onboard the TARANIS mission for TGF studies

International audienceXGRE (X-ray, Gamma-ray and Relativistic Electrons detector) was one of the main instruments onboard the TARANIS satellite. It is an ultra-fast gamma-ray and electron detector, with a 350 ns dead time, built for measuring Terrestrial Gamma-ray Flashes (TGF). In this paper, we will shortly present the TARANIS mission, the design of the XGRE instrument and the measured performances during the instrument calibration at APC, LESIA and payload calibrations done onboard the satellite at CNES

TARANIS XGRE and IDEE detection capability of terrestrial gamma-ray flashes and associated electron beams

International audienceWith a launch expected in 2018, the TARANIS microsatellite is dedicated to the study of transient phenomena observed in association with thunderstorms. On board the spacecraft, XGRE and IDEE are two instruments dedicated to studying terrestrial gamma-ray flashes (TGFs) and associated terrestrial electron beams (TEBs). XGRE can detect electrons (energy range: 1 to 10 MeV) and X- and gamma-rays (energy range: 20 keV to 10 MeV) with a very high counting capability (about 10 million counts per second) and the ability to discriminate one type of particle from another. The IDEE instrument is focused on electrons in the 80 keV to 4 MeV energy range, with the ability to estimate their pitch angles. Monte Carlo simulations of the TARANIS instruments, using a preliminary model of the spacecraft, allow sensitive area estimates for both instruments. This leads to an averaged effective area of 425 cm2 for XGRE, used to detect X- and gamma-rays from TGFs, and the combination of XGRE and IDEE gives an average effective area of 255 cm2 which can be used to detect electrons/positrons from TEBs. We then compare these performances to RHESSI, AGILE and Fermi GBM, using data extracted from literature for the TGF case and with the help of Monte Carlo simulations of their mass models for the TEB case. Combining this data with the help of the MC-PEPTITA Monte Carlo simulations of TGF propagation in the atmosphere, we build a self-consistent model of the TGF and TEB detection rates of RHESSI, AGILE and Fermi. It can then be used to estimate that TARANIS should detect about 200 TGFs yr-1 and 25 TEBs yr-1

How Islam influences women’s paid non-farm employment: evidence from 26 Indonesian and 37 Nigerian provinces

Studies on women’s employment in Muslim countries often mention Islam, but its influence is undertheorized and tests simply compare ‘Muslim’ women and areas to ‘non-Muslim’ women and areas. Here, multilevel analyses of Indonesia and Nigeria show this focus is not tenable: non-farm employment of Muslim women is not consistently lower than that of non-Muslim women, nor is it lower in Muslim-dominated provinces than in other provinces. A new theoretical frame conceptualizes religion’s influence in terms message and messenger. It is shown how different manifestations of Islam influence women’s non-farm employment, inside and outside the home. Empirically, the ideological strand of Islam is more important than differences between Islam and Christianity. In addition, when a conservative Islam is codified through Shari’a-based law women’s employment outside the home seems to be lower, but the presence of Islamic political parties seems to foster women’s access to the labor market through their focus on support for the poor