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

Risk factors for unfavourable postoperative outcome in patients with Crohn's disease undergoing right hemicolectomy or ileocaecal resection. An international audit by ESCP and S-ECCO

Aim: Patient- and disease-related factors, as well as operation technique, all have the potential to impact on postoperative outcome in Crohn's disease. The available evidence is based on small series and often displays conflicting results. The aim was to investigate the effect of preoperative and intra-operative risk factors on 30-day postoperative outcome in patients undergoing surgery for Crohn's disease. Method: This was an international prospective snapshot audit including consecutive patients undergoing right hemicolectomy or ileocaecal resection. The study analysed a subset of patients who underwent surgery for Crohn's disease. The primary outcome measure was the overall Clavien\u2013Dindo postoperative complication rate. The key secondary outcomes were anastomotic leak, reoperation, surgical site infection and length of stay in hospital. Multivariable binary logistic regression analyses were used to produce odds ratios and 95% confidence intervals. Results: In all, 375 resections in 375 patients were included. The median age was 37 and 57.1% were women. In multivariate analyses, postoperative complications were associated with preoperative parenteral nutrition (OR 2.36, 95% CI 1.10\u20134.97), urgent/expedited surgical intervention (OR 2.00, 95% CI 1.13\u20133.55) and unplanned intra-operative adverse events (OR 2.30, 95% CI 1.20\u20134.45). The postoperative length of stay in hospital was prolonged in patients who received preoperative parenteral nutrition (OR 31, 95% CI 1.08\u20131.61) and those who had urgent/expedited operations (OR 1.21, 95% CI 1.07\u20131.37). Conclusion: Preoperative parenteral nutritional support, urgent/expedited operation and unplanned intra-operative adverse events were associated with unfavourable postoperative outcome. Enhanced preoperative optimization and improved planning of operation pathways and timings may improve outcomes for patients