HIGH PREVALENCE OF Blastocystis spp. INFECTION IN CHILDREN AND STAFF MEMBERS ATTENDING PUBLIC URBAN SCHOOLS IN SÃO PAULO STATE, BRAZIL

After a gastroenteritis outbreak of unknown etiology in the municipality ofSebastião da Grama, SãoPaulo, Brazil, we conducted a parasitological survey to establish the epidemiological profile of enteroparasitosis in children and staff members attending the public urban schools in operation in town. The cross-sectional study evaluated 172 children aged 11 months to 6 years old and 33 staff members aged 19 to 58 years old. Overall, 96 (55.81%) children and 20 (60.61%) staff members were mono-parasitized, while 58 (33.72%) children and 4 (12.12%) workers were poly-parasitized. Protozoa (88.37%; 72.73%) was more prevalent than helminthes (3.48%; 0%) in children and staff members respectively.Blastocystis spp. was the most prevalent parasite in children (86.63%) and staff members (66.67%). The age of 1 year old or less was found to be associated with increased prevalence of giardiasis [OR = 13.04; 95%CI 2.89-58.91; p = 0.00] and public garbage collection was identified as a protective factor against intestinal helminth infections [OR = 0.06; 95%CI 0.00-0.79; p = 0.03]. Although most of the children tested positive for Blastocystis spp. and also presented clinical signs/symptoms (62.2%), this association was not statistically significant [OR = 1.35; 95%CI 0.53-3.44; p = 0.51]. Intestinal parasites still represent a public health concern and this study underscores the importance of further investigations to better understand the pathogenic role ofBlastocystis spp

A new calibration method for charm jet identification validated with proton-proton collision events at √s = 13 TeV

ArXiv ePrint: 2111.03027Copyright © 2022 CERN for the benefit of the CMS collaboration. Many measurements at the LHC require efficient identification of heavy-flavour jets, i.e. jets originating from bottom (b) or charm (c) quarks. An overview of the algorithms used to identify c jets is described and a novel method to calibrate them is presented. This new method adjusts the entire distributions of the outputs obtained when the algorithms are applied to jets of different flavours. It is based on an iterative approach exploiting three distinct control regions that are enriched with either b jets, c jets, or light-flavour and gluon jets. Results are presented in the form of correction factors evaluated using proton-proton collision data with an integrated luminosity of 41.5 fb-1 at  √s = 13 TeV, collected by the CMS experiment in 2017. The closure of the method is tested by applying the measured correction factors on simulated data sets and checking the agreement between the adjusted simulation and collision data. Furthermore, a validation is performed by testing the method on pseudodata, which emulate various mismodelling conditions. The calibrated results enable the use of the full distributions of heavy-flavour identification algorithm outputs, e.g. as inputs to machine-learning models. Thus, they are expected to increase the sensitivity of future physics analyses.SCOAP