Reading and Writing Difficultiesin Third and Sixth-Grade Students: A Cross-Sectional Survey

Background: In southern Italy and, specifically, in the region of Campania, many surveys show that the average of students with reading difficulties is much higher than in northern Italy and abroad. On the other hand, specific learning disorders (SLDs) in Campania are much less certified. Since there are no etiological reasons that can explain this apparent inconsistency, an objective of this cross-sectional study was to evaluate the extent of reading/writing difficulties in students from a province of Campania and then to assess the ability of teachers to identify such difficulties in their students. Methods: Of a total of 241 enrolled students, 155 (64.31%), including 73 from primary school and 82 from secondary school, belonging to 5 schools in the province of Salerno (Italy), took part in the survey. Students' reading and writing skills were assessed through standardized tests. The tests results were then compared with teacher judgments and context-related variables. Results: At the reading test, 28.7% of primary school and 13.4% of lower secondary school students fell below the 5th percentile for age. Results of the writing test were even more significant: almost half of the students of both levels of education performed below the 5th percentile. Teacher judgments showed higher agreement with standardized assessments in primary (88%, K of Cohen = 0.68) than in secondary school (78%, K = 0.23). Conclusions: Reading and writing difficulties were common in our sample. While reading skills tended to improve with age, writing difficulties apparently persisted to some extent in third and sixth-grade classes. The accuracy of teacher judgments on reading skills is relatively high, but teachers seem to hardly report reading difficulties "requiring attention". Although less "severe" than others, such difficulties should be taken into account, mainly because of their potential developmental trajectories

Evidence for collectivity in pp collisions at the LHC

Measurements of two- and multi-particle angular correlations in pp collisions at s=5,7, and 13TeV are presented as a function of charged-particle multiplicity. The data, corresponding to integrated luminosities of 1.0pb−1 (5 TeV), 6.2pb−1 (7 TeV), and 0.7pb−1 (13 TeV), were collected using the CMS detector at the LHC. The second-order ( v2 ) and third-order ( v3 ) azimuthal anisotropy harmonics of unidentified charged particles, as well as v2 of KS0 and Λ/Λ‾ particles, are extracted from long-range two-particle correlations as functions of particle multiplicity and transverse momentum. For high-multiplicity pp events, a mass ordering is observed for the v2 values of charged hadrons (mostly pions), KS0 , and Λ/Λ‾ , with lighter particle species exhibiting a stronger azimuthal anisotropy signal below pT≈2GeV/c . For 13 TeV data, the v2 signals are also extracted from four- and six-particle correlations for the first time in pp collisions, with comparable magnitude to those from two-particle correlations. These observations are similar to those seen in pPb and PbPb collisions, and support the interpretation of a collective origin for the observed long-range correlations in high-multiplicity pp collisions

ISARIC-COVID-19 dataset: A Prospective, Standardized, Global Dataset of Patients Hospitalized with COVID-19

The International Severe Acute Respiratory and Emerging Infection Consortium (ISARIC) COVID-19 dataset is one of the largest international databases of prospectively collected clinical data on people hospitalized with COVID-19. This dataset was compiled during the COVID-19 pandemic by a network of hospitals that collect data using the ISARIC-World Health Organization Clinical Characterization Protocol and data tools. The database includes data from more than 705,000 patients, collected in more than 60 countries and 1,500 centres worldwide. Patient data are available from acute hospital admissions with COVID-19 and outpatient follow-ups. The data include signs and symptoms, pre-existing comorbidities, vital signs, chronic and acute treatments, complications, dates of hospitalization and discharge, mortality, viral strains, vaccination status, and other data. Here, we present the dataset characteristics, explain its architecture and how to gain access, and provide tools to facilitate its use