Latest results from the DAMPE space mission

The space-based DAMPE (DArk Matter Particle Explorer) particle detector has been taking data for more than 7 years since its successful launch in December 2015. Its main scientific goals include the indirect search for dark matter signatures in the cosmic lepton and gamma-ray spectra, the study of galactic cosmic rays up to energies of hundreds of TeV and studies on highenergy gamma ray astronomy. The measurement of galactic cosmic ray spectra are reported here, those being fundamental tools to investigate the mechanisms of acceleration at their sources and propagation through the interstellar medium. Results on proton and helium, which revealed new spectral features, are described. Ongoing analyses on the cosmic ray light, medium and heavy mass nuclei are outlined, together with studies of the so-called secondary cosmic rays. Latest results on gamma-ray astronomy and dark matter search will be also summarized

Large scale multifactorial likelihood quantitative analysis of BRCA1 and BRCA2 variants: An ENIGMA resource to support clinical variant classification

The multifactorial likelihood analysis method has demonstrated utility for quantitative assessment of variant pathogenicity for multiple cancer syndrome genes. Independent data types currently incorporated in the model for assessing BRCA1 and BRCA2 variants include clinically calibrated prior probability of pathogenicity based on variant location and bioinformatic prediction of variant effect, co-segregation, family cancer history profile, co-occurrence with a pathogenic variant in the same gene, breast tumor pathology, and case-control information. Research and clinical data for multifactorial likelihood analysis were collated for 1,395 BRCA1/2 predominantly intronic and missense variants, enabling classification based on posterior probability of pathogenicity for 734 variants: 447 variants were classified as (likely) benign, and 94 as (likely) pathogenic; and 248 classifications were new or considerably altered relative to ClinVar submissions. Classifications were compared with information not yet included in the likelihood model, and evidence strengths aligned to those recommended for ACMG/AMP classification codes. Altered mRNA splicing or function relative to known nonpathogenic variant controls were moderately to strongly predictive of variant pathogenicity. Variant absence in population datasets provided supporting evidence for variant pathogenicity. These findings have direct relevance for BRCA1 and BRCA2 variant evaluation, and justify the need for gene-specific calibration of evidence types used for variant classification

Large scale multifactorial likelihood quantitative analysis of BRCA1 and BRCA2 variants: An ENIGMA resource to support clinical variant classification

Abstract The multifactorial likelihood analysis method has demonstrated utility for quantitative assessment of variant pathogenicity for multiple cancer syndrome genes. Independent data types currently incorporated in the model for assessing BRCA1 and BRCA2 variants include clinically calibrated prior probability of pathogenicity based on variant location and bioinformatic prediction of variant effect, co-segregation, family cancer history profile, co-occurrence with a pathogenic variant in the same gene, breast tumor pathology, and case-control information. Research and clinical data for multifactorial likelihood analysis were collated for 1395 BRCA1/2 predominantly intronic and missense variants, enabling classification based on posterior probability of pathogenicity for 734 variants: 447 variants were classified as (likely) benign, and 94 as (likely) pathogenic; 248 classifications were new or considerably altered relative to ClinVar submissions. Classifications were compared to information not yet included in the likelihood model, and evidence strengths aligned to those recommended for ACMG/AMP classification codes. Altered mRNA splicing or function relative to known non-pathogenic variant controls were moderately to strongly predictive of variant pathogenicity. Variant absence in population datasets provided supporting evidence for variant pathogenicity. These findings have direct relevance for BRCA1 and BRCA2 variant evaluation, and justify the need for gene-specific calibration of evidence types used for variant classification. This article is protected by copyright. All rights reserved.Peer reviewe

Electromagnetic shower identification in the SAND Calorimeter of the DUNE Near Detector

The Deep Underground Neutrino Experiment (DUNE) is a next generation, long-baseline accelerator experiment designed to significantly contribute to the study of neutrino oscillations with unprecedented level of sensitivity. It envisages to observe neutrinos from a high intensity wide-band neutrino beam with a Near Detector (ND) system at the Fermi National Accelerator Laboratory and a Far Detector (FD) at ∼ 1300 km from the beam source at Sanford Laboratory, in South Dakota. The System for on-Axis Neutrino Detection (SAND) is one of the three detectors of the ND complex which provides continuous on-axis beam monitoring and performs various physics measurements. This work concerns a study on the electromagnetic calorimeter (ECAL) capabilities of the SAND detector to perform e.m. showers and muon tracks discrimination. In particular, a new clustering algorithm to reconstruct events in the ECAL not exploiting at all Monte Carlo truth has been developed and studied for the first time. A multivariate analysis based on machine-learning techniques has been implemented to perform a classification of the clusters reconstructed by the clustering algorithm and study the capabilities in electromagnetic showers and muon tracks discrimination. The classification analysis is operated in two separated steps: (a) the multivariate methods are trained and validated using a dedicated dataset obtained by simulating particle guns of electrons and muons originated close to the ECAL; (b) the selected optimal classifiers are tested with simulated neutrino interactions in the SAND detector considering the case of a ν µ -dominated beam in neutrino mode. Finally, the calorimeter performance in discriminating electromagnetic showers from muons is preliminary evaluated in terms of the product of efficiency times the signal purity

2020 Dataset on local gambling regulations in Italy

The dataset provides the complete enumeration of gambling policies implemented by Italian Municipalities between 2003 and 2021. The dataset comprises information on all municipalities existing in 2017 and following years (thus considering also merging). The following variables are available: Municipality ISTAT Code (ID), Municipality Name (Name) Province, Region, Researcher, and a series of variables identifying the number and the type of rulings adopted ('regolamento', 'ordinanza' and 'delibera'). The rulings are distinguished between identified and downloaded or only identified (because the document is no longer available). Additional variables describe municipal activism with other administrative acts (Anti-gambling Manifesto, events, projects or tax reductions). Overall, the dataset comprises 8031 units

2020 Dataset on local gambling regulations in Italy

The dataset provides the complete enumeration of gambling policies implemented by Italian Municipalities between 2003 and 2021. The dataset comprises information on all municipalities existing in 2017 and following years (thus considering also merging). The following variables are available: Municipality ISTAT Code (ID), Municipality Name (Name) Province, Region, Researcher, and a series of variables identifying the number and the type of rulings adopted (“regolamento”, “ordinanza” and “delibera”). The rulings are distinguished between identified and downloaded or only identified (because the document is no longer available). Additional variables describe municipal activism with other administrative acts (Anti-gambling Manifesto, events, projects or tax reductions). Overall, the dataset comprises 8031 units

Open data from the first and second observing runs of Advanced LIGO and Advanced Virgo

Advanced LIGO and Advanced Virgo are monitoring the sky and collecting gravitational-wave strain data with sufficient sensitivity to detect signals routinely. In this paper we describe the data recorded by these instruments during their first and second observing runs. The main data products are gravitational-wave strain time series sampled at 16384 Hz. The datasets that include this strain measurement can be freely accessed through the Gravitational Wave Open Science Center at http://gw-openscience.org, together with data-quality information essential for the analysis of LIGO and Virgo data, documentation, tutorials, and supporting software

Publisher’s Note: Observing gravitational-wave transient GW150914 with minimal assumptions [Phys. Rev. D 93, 122004 (2016)]

The gravitational-wave signal GW150914 was first identified on September 14, 2015, by searches forshort-duration gravitational-wave transients. These searches identify time-correlated transients in multipledetectors with minimal assumptions about the signal morphology, allowing them to be sensitive togravitational waves emitted by a wide range of sources including binary black hole mergers. Over theobservational period from September 12 to October 20, 2015, these transient searches were sensitive tobinary black hole mergers similar to GW150914 to an average distance of∼600Mpc. In this paper, wedescribe the analyses that first detected GW150914 as well as the parameter estimation and waveformreconstruction techniques that initially identified GW150914 as the merger of two black holes. We find thatthe reconstructed waveform is consistent with the signal from a binary black hole merger with a chirp massof∼30M⊙and a total mass before merger of∼70M⊙in the detector fram