The empirical process on Gaussian spherical harmonics

We establish weak convergence of the empirical process on the spherical harmonics of a Gaussian random field in the presence of an unknown angular power spectrum. This result suggests various Gaussianity tests with an asymptotic justification. The issue of testing for Gaussianity on isotropic spherical random fields has recently received strong empirical attention in the cosmological literature, in connection with the statistical analysis of cosmic microwave background radiation

Anosmia and ageusia as predictive signs of COVID-19 in healthcare workers in Italy. A prospective case-control study

The aim of this study was to investigate the diagnostic accuracy of symptoms and signs in healthcare workers (HCW) with Sars-CoV-2

Epidemiology and Microbiology of Skin and Soft Tissue Infections: Preliminary Results of a National Registry

Skin and soft tissue infections (SSTIs) represent a wide range of clinical conditions characterized by a considerable variety of clinical presentations and severity. Their aetiology can also vary, with numerous possible causative pathogens. While other authors previously published analyses on several types of SSTI and on restricted types of patients, we conducted a large nationwide surveillance programme on behalf of the Italian Society of Infectious and Tropical Diseases to assess the clinical and microbiological characteristics of the whole SSTI spectrum, from mild to severe life-threatening infections, in both inpatients and outpatients. Twenty-five Infectious Diseases (ID) Centres throughout Italy collected prospectively data concerning both the clinical and microbiological diagnosis of patients affected by SSTIs via an electronic case report form. All the cases included in our database, independently from their severity, have been managed by ID specialists joining the study while SSTIs from other wards/clinics have been excluded from this analysis. Here, we report the preliminary results of our study, referring to a 12-month period (October 2016–September 2017). During this period, the study population included 254 adult patients and a total of 291 SSTI diagnoses were posed, with 36 patients presenting more than one SSTIs. The type of infection diagnosed, the aetiological micro-organisms involved and some notes on their antimicrobial susceptibilities were collected and are reported herein. The enrichment of our registry is ongoing, but these preliminary results suggest that further analysis could soon provide useful information to better understand the national epidemiologic data and the current clinical management of SSTIs in Italy

Timing of surgery following SARS‐CoV‐2 infection: an international prospective cohort study

Peri-operative SARS-CoV-2 infection increases postoperative mortality. The aim of this study was to determine the optimal duration of planned delay before surgery in patients who have had SARS-CoV-2 infection. This international, multicentre, prospective cohort study included patients undergoing elective or emergency surgery during October 2020. Surgical patients with pre-operative SARS-CoV-2 infection were compared with those without previous SARS-CoV-2 infection. The primary outcome measure was 30-day postoperative mortality. Logistic regression models were used to calculate adjusted 30-day mortality rates stratified by time from diagnosis of SARS-CoV-2 infection to surgery. Among 140,231 patients (116 countries), 3127 patients (2.2%) had a pre-operative SARS-CoV-2 diagnosis. Adjusted 30-day mortality in patients without SARS-CoV-2 infection was 1.5% (95%CI 1.4–1.5). In patients with a pre-operative SARS-CoV-2 diagnosis, mortality was increased in patients having surgery within 0–2 weeks, 3–4 weeks and 5–6 weeks of the diagnosis (odds ratio (95%CI) 4.1% (3.3–4.8), 3.9% (2.6–5.1) and 3.6% (2.0–5.2), respectively). Surgery performed ≥ 7 weeks after SARS-CoV-2 diagnosis was associated with a similar mortality risk to baseline (odds ratio (95%CI) 1.5% (0.9– 2.1%)). After a ≥ 7 week delay in undertaking surgery following SARS-CoV-2 infection, patients with ongoing symptoms had a higher mortality than patients whose symptoms had resolved or who had been asymptomatic (6.0% (95%CI 3.2–8.7) vs. 2.4% (95%CI 1.4–3.4) vs. 1.3% (95%CI 0.6–2.0%), respectively). Where possible, surgery should be delayed for at least 7 weeks following SARS-CoV-2 infection. Patients with ongoing symptoms ≥ 7 weeks from diagnosis may benefit from further delay

Firm efficiency, foreign ownership and CEO gender in corrupt environments

We study the effects of corruption on firm efficiency using a unique dataset of private firms from 14 Central and Eastern European countries from 2000 to 2013. We find that an environment characterized by a high level of corruption has an adverse effect on firm efficiency. This effect is stronger for firms with a lower propensity to behave corruptly, such as foreign-controlled firms and firms managed by female CEOs, while local firms and firms with male CEOs are not disadvantaged. We also find that an environment characterized by considerable heterogeneity in the perception of corruption is associated with an increase in firm efficiency. This effect is particularly strong for foreign-controlled firms from low corruption countries, while no effect is observed for firms managed by a female CEO. © 2016 The Authors

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