Familial hypercholesterolaemia in children and adolescents from 48 countries: a cross-sectional study

Background: Approximately 450 000 children are born with familial hypercholesterolaemia worldwide every year, yet only 2·1% of adults with familial hypercholesterolaemia were diagnosed before age 18 years via current diagnostic approaches, which are derived from observations in adults. We aimed to characterise children and adolescents with heterozygous familial hypercholesterolaemia (HeFH) and understand current approaches to the identification and management of familial hypercholesterolaemia to inform future public health strategies. Methods: For this cross-sectional study, we assessed children and adolescents younger than 18 years with a clinical or genetic diagnosis of HeFH at the time of entry into the Familial Hypercholesterolaemia Studies Collaboration (FHSC) registry between Oct 1, 2015, and Jan 31, 2021. Data in the registry were collected from 55 regional or national registries in 48 countries. Diagnoses relying on self-reported history of familial hypercholesterolaemia and suspected secondary hypercholesterolaemia were excluded from the registry; people with untreated LDL cholesterol (LDL-C) of at least 13·0 mmol/L were excluded from this study. Data were assessed overall and by WHO region, World Bank country income status, age, diagnostic criteria, and index-case status. The main outcome of this study was to assess current identification and management of children and adolescents with familial hypercholesterolaemia. Findings: Of 63 093 individuals in the FHSC registry, 11 848 (18·8%) were children or adolescents younger than 18 years with HeFH and were included in this study; 5756 (50·2%) of 11 476 included individuals were female and 5720 (49·8%) were male. Sex data were missing for 372 (3·1%) of 11 848 individuals. Median age at registry entry was 9·6 years (IQR 5·8-13·2). 10 099 (89·9%) of 11 235 included individuals had a final genetically confirmed diagnosis of familial hypercholesterolaemia and 1136 (10·1%) had a clinical diagnosis. Genetically confirmed diagnosis data or clinical diagnosis data were missing for 613 (5·2%) of 11 848 individuals. Genetic diagnosis was more common in children and adolescents from high-income countries (9427 [92·4%] of 10 202) than in children and adolescents from non-high-income countries (199 [48·0%] of 415). 3414 (31·6%) of 10 804 children or adolescents were index cases. Familial-hypercholesterolaemia-related physical signs, cardiovascular risk factors, and cardiovascular disease were uncommon, but were more common in non-high-income countries. 7557 (72·4%) of 10 428 included children or adolescents were not taking lipid-lowering medication (LLM) and had a median LDL-C of 5·00 mmol/L (IQR 4·05-6·08). Compared with genetic diagnosis, the use of unadapted clinical criteria intended for use in adults and reliant on more extreme phenotypes could result in 50-75% of children and adolescents with familial hypercholesterolaemia not being identified. Interpretation: Clinical characteristics observed in adults with familial hypercholesterolaemia are uncommon in children and adolescents with familial hypercholesterolaemia, hence detection in this age group relies on measurement of LDL-C and genetic confirmation. Where genetic testing is unavailable, increased availability and use of LDL-C measurements in the first few years of life could help reduce the current gap between prevalence and detection, enabling increased use of combination LLM to reach recommended LDL-C targets early in life

Search for light long-lived neutral particles that decay to collimated pairs of leptons or light hadrons in pp collisions at √s = 13 TeV with the ATLAS detector

A search for light long-lived neutral particles with masses in the O(MeV–GeV) range is presented. The analysis targets the production of long-lived dark photons in the decay of a Higgs boson produced via gluon–gluon fusion or in association with a W boson. Events that contain displaced collimated Standard Model fermions reconstructed in the calorimeter or muon spectrometer are selected in 139 fb−1 of √s = 13 TeV pp collision data collected by the ATLAS detector at the LHC. Background estimates for contributions from Standard Model processes and instrumental effects are extracted from data. The observed event yields are consistent with the expected background. Exclusion limits are reported on the production cross-section times branching fraction as a function of the mean proper decay length cτ of the dark photon, or as a function of the dark-photon mass and kinetic mixing parameter that quantifies the coupling between the Standard Model and potential hidden (dark) sectors. A Higgs boson branching fraction above 1% is excluded at 95% CL for a Higgs boson decaying into two dark photons for dark-photon mean proper decay lengths between 10 mm and 250 mm and dark photons with masses between 0.4 GeV and 2 GeV

Deep generative models for fast photon shower simulation in ATLAS

The need for large-scale production of highly accurate simulated event samples for the extensive physics programme of the ATLAS experiment at the Large Hadron Collider motivates the development of new simulation techniques. Building on the recent success of deep learning algorithms, variational autoencoders and generative adversarial networks are investigated for modelling the response of the central region of the ATLAS electromagnetic calorimeter to photons of various energies. The properties of synthesised showers are compared with showers from a full detector simulation using geant4. Both variational autoencoders and generative adversarial networks are capable of quickly simulating electromagnetic showers with correct total energies and stochasticity, though the modelling of some shower shape distributions requires more refinement. This feasibility study demonstrates the potential of using such algorithms for ATLAS fast calorimeter simulation in the future and shows a possible way to complement current simulation techniques

Search for light long-lived neutral particles that decay to collimated pairs of leptons or light hadrons in pp collisions at s√ = 13 TeV with the ATLAS detector

A search for light long-lived neutral particles with masses in the O(MeV–GeV) range is presented. The analysis targets the production of long-lived dark photons in the decay of a Higgs boson produced via gluon–gluon fusion or in association with a W boson. Events that contain displaced collimated Standard Model fermions reconstructed in the calorimeter or muon spectrometer are selected in 139 fb−1 of s√ = 13 TeV pp collision data collected by the ATLAS detector at the LHC. Background estimates for contributions from Standard Model processes and instrumental effects are extracted from data. The observed event yields are consistent with the expected background. Exclusion limits are reported on the production cross-section times branching fraction as a function of the mean proper decay length cτ of the dark photon, or as a function of the dark-photon mass and kinetic mixing parameter that quantifies the coupling between the Standard Model and potential hidden (dark) sectors. A Higgs boson branching fraction above 1% is excluded at 95% CL for a Higgs boson decaying into two dark photons for dark-photon mean proper decay lengths between 10 mm and 250 mm and dark photons with masses between 0.4 GeV and 2 GeV

Deep generative models for fast photon shower simulation in ATLAS

The need for large-scale production of highly accurate simulated event samples for the extensive physics programme of the ATLAS experiment at the Large Hadron Collider motivates the development of new simulation techniques. Building on the recent success of deep learning algorithms, variational autoencoders and generative adversarial networks are investigated for modelling the response of the central region of the ATLAS electromagnetic calorimeter to photons of various energies. The properties of synthesised showers are compared with showers from a full detector simulation using geant4. Both variational autoencoders and generative adversarial networks are capable of quickly simulating electromagnetic showers with correct total energies and stochasticity, though the modelling of some shower shape distributions requires more refinement. This feasibility study demonstrates the potential of using such algorithms for ATLAS fast calorimeter simulation in the future and shows a possible way to complement current simulation techniques

Investigación, reflexión y acción de la realidad socio-educativa a principios del siglo XXI : vol. III

Consciente de la importancia de la investigación educativa para promover las transformaciones que permitan ofrecer a la sociedad nacional e internacional una educación pertinente y relevante que al mismo tiempo, contribuya con el desarrollo humano, en febrero del 2011 y en el marco de la celebración del 30 aniversario de su fundación, el Instituto de Investigación en Educación (INIE) organiza el II Congreso Internacional de Investigación Educativa: Su Incidencia en la Realidad Social. Los aportes presentados durante este II Congreso Internacional de Investigación Educativa, se han recopilado en la publicación del libro digital: Investigación, reflexión y acción de la realidad socio-educativa a principios del siglo XXI. Estos trabajos tienen por objetivo ofrecer propuestas para repensar la educación y los procesos educativos que se desarrollan en las aulas, así como valorar la importancia de realizar las transformaciones que se requieren a partir de los resultados de las investigaciones

Search for light long-lived neutral particles that decay to collimated pairs of leptons or light hadrons in pp collisions at √s = 13 TeV with the ATLAS detector

A search for light long-lived neutral particles with masses in the O(MeV–GeV) range is presented. The analysis targets the production of long-lived dark photons in the decay of a Higgs boson produced via gluon–gluon fusion or in association with a W boson. Events that contain displaced collimated Standard Model fermions reconstructed in the calorimeter or muon spectrometer are selected in 139 fb−1 of s = 13 TeV pp collision data collected by the ATLAS detector at the LHC. Background estimates for contributions from Standard Model processes and instrumental effects are extracted from data. The observed event yields are consistent with the expected background. Exclusion limits are reported on the production cross-section times branching fraction as a function of the mean proper decay length cτ of the dark photon, or as a function of the dark-photon mass and kinetic mixing parameter that quantifies the coupling between the Standard Model and potential hidden (dark) sectors. A Higgs boson branching fraction above 1% is excluded at 95% CL for a Higgs boson decaying into two dark photons for dark-photon mean proper decay lengths between 10 mm and 250 mm and dark photons with masses between 0.4 GeV and 2 GeV. [Figure not available: see fulltext.]

Deep Generative Models for Fast Photon Shower Simulation in ATLAS

AbstractThe need for large-scale production of highly accurate simulated event samples for the extensive physics programme of the ATLAS experiment at the Large Hadron Collider motivates the development of new simulation techniques. Building on the recent success of deep learning algorithms, variational autoencoders and generative adversarial networks are investigated for modelling the response of the central region of the ATLAS electromagnetic calorimeter to photons of various energies. The properties of synthesised showers are compared with showers from a full detector simulation using geant4. Both variational autoencoders and generative adversarial networks are capable of quickly simulating electromagnetic showers with correct total energies and stochasticity, though the modelling of some shower shape distributions requires more refinement. This feasibility study demonstrates the potential of using such algorithms for ATLAS fast calorimeter simulation in the future and shows a possible way to complement current simulation techniques.</jats:p