Regional comparison of absolute gravimeters SIM.M.G-K1 key comparison

Twelve absolute gravimeters were compared during the regional Key Comparison SIM.M.G-K1 of absolute gravimeters. The four gravimeters were from different NMIs and DIs. The comparison was linked to the CCM.G-K2 through EURAMET.M.G-K2 via the DI gravimeter FG5X-216. Overall, the results and uncertainties indicate an excellent agreement among the gravimeters, with a standard deviation of the gravimeters' DoEs better than 1.3 μGal. In the case of the official solution, all the gravimeters are in equivalence well within the declared uncertainties. == Main text To reach the main text of this paper, click on Final Report [http://www.bipm.org/utils/common/pdf/final_reports/M/G-K1/SIM.M.G-K1.pdf] . Note that this text is that which appears in Appendix B of the BIPM key comparison database kcdb.bipm.org/ [http://kcdb.bipm.org/] . The final report has been peer-reviewed and approved for publication by the CCM, according to the provisions of the CIPM Mutual Recognition Arrangement (CIPM MRA)

Using verbal autopsy to measure causes of death: the comparative performance of existing methods

Background: Monitoring progress with disease and injury reduction in many populations will require widespread use of verbal autopsy (VA). Multiple methods have been developed for assigning cause of death from a VA but their application is restricted by uncertainty about their reliability. Methods: We investigated the validity of five automated VA methods for assigning cause of death: InterVA-4, Random Forest (RF), Simplified Symptom Pattern (SSP), Tariff method (Tariff), and King-Lu (KL), in addition to physician review of VA forms (PCVA), based on 12,535 cases from diverse populations for which the true cause of death had been reliably established. For adults, children, neonates and stillbirths, performance was assessed separately for individuals using sensitivity, specificity, Kappa, and chance-corrected concordance (CCC) and for populations using cause specific mortality fraction (CSMF) accuracy, with and without additional diagnostic information from prior contact with health services. A total of 500 train-test splits were used to ensure that results are robust to variation in the underlying cause of death distribution. Results: Three automated diagnostic methods, Tariff, SSP, and RF, but not InterVA-4, performed better than physician review in all age groups, study sites, and for the majority of causes of death studied. For adults, CSMF accuracy ranged from 0.764 to 0.770, compared with 0.680 for PCVA and 0.625 for InterVA; CCC varied from 49.2% to 54.1%, compared with 42.2% for PCVA, and 23.8% for InterVA. For children, CSMF accuracy was 0.783 for Tariff, 0.678 for PCVA, and 0.520 for InterVA; CCC was 52.5% for Tariff, 44.5% for PCVA, and 30.3% for InterVA. For neonates, CSMF accuracy was 0.817 for Tariff, 0.719 for PCVA, and 0.629 for InterVA; CCC varied from 47.3% to 50.3% for the three automated methods, 29.3% for PCVA, and 19.4% for InterVA. The method with the highest sensitivity for a specific cause varied by cause. Conclusions: Physician review of verbal autopsy questionnaires is less accurate than automated methods in determining both individual and population causes of death. Overall, Tariff performs as well or better than other methods and should be widely applied in routine mortality surveillance systems with poor cause of death certification practices. © 2014 Murray et al.; licensee BioMed Central Ltd

High-rate tests on Resistive Plate Chambers operated with eco-friendly gas mixtures

Results obtained by the RPC ECOgas@GIF++ Collaboration, using Resistive Plate Chambers operated with new, eco-friendly gas mixtures, based on Tetrafluoropropene and carbon dioxide, are shown and discussed in this paper. Tests aimed to assess the performance of this kind of detectors in high-irradiation conditions, analogous to the ones foreseen for the coming years at the Large Hadron Collider experiments, were performed, and demonstrate a performance basically similar to the one obtained with the gas mixtures currently in use, based on Tetrafluoroethane, which is being progressively phased out for its possible contribution to the greenhouse effect. Long term aging tests are also being carried out, with the goal to demonstrate the possibility of using these eco-friendly gas mixtures during the whole High Luminosity phase of the Large Hadron Collider.Comment: Submitted to European Physical Journal C on October 24, 2023, 15 pages, 14 figure

Preliminary results on the long term operation of RPCs with eco-friendly gas mixtures under irradiation at the CERN Gamma Irradiation Facility

Since 2019 a collaboration between researchers from various institutes and experiments (i.e. ATLAS, CMS, ALICE, LHCb/SHiP and the CERN EP-DT group), has been operating several RPCs with diverse electronics, gas gap thicknesses and detector layouts at the CERN Gamma Irradiation Facility (GIF++). The studies aim at assessing the performance of RPCs when filled with new eco-friendly gas mixtures in avalanche mode and in view of evaluating possible ageing effects after long high background irradiation periods, e.g. High-Luminosity LHC phase. This challenging research is also part of a task of the European AidaInnova project. A promising eco-friendly gas identified for RPC operation is the tetrafluoruropropene (C$_{3}$H$_{2}$F$_{4}$, commercially known as HFO-1234ze) that has been studied at the CERN GIF++ in combination with different percentages of CO$_2$. Between the end of 2021 and 2022 several beam tests have been carried out to establish the performance of RPCs operated with such mixtures before starting the irradiation campaign for the ageing study. Results of these tests for different RPCs layouts and different gas mixtures, under increasing background rates are presented here, together with the preliminary outcome of the detector ageing tests

International study to evaluate PCR methods for detection of Trypanosoma cruzi DNA in blood samples from Chagas disease patients

A century after its discovery, Chagas disease, caused by the parasite Trypanosoma cruzi, still represents a major neglected tropical threat. Accurate diagnostics tools as well as surrogate markers of parasitological response to treatment are research priorities in the field. The polymerase chain reaction (PCR) has been proposed as a sensitive laboratory tool for detection of T. cruzi infection and monitoring of parasitological treatment outcome. However, high variation in accuracy and lack of international quality controls has precluded reliable applications in the clinical practice and comparisons of data among cohorts and geographical regions. In an effort towards harmonization of PCR strategies, 26 expert laboratories from 16 countries evaluated their current PCR procedures against sets of control samples, composed by serial dilutions of T.cruzi DNA from culture stocks belonging to different lineages, human blood spiked with parasite cells and blood samples from Chagas disease patients. A high variability in sensitivities and specificities was found among the 48 reported PCR tests. Out of them, four tests with best performance were selected and further evaluated. This study represents a crucial first step towards device of a standardized operative procedure for T. cruzi PCR.Fil: Schijman, Alejandro G. Instituto de Investigaciones en Ingeniería Genética y Biología Molecular (INGEBI-CONICET). Laboratorio de Biología Molecular de la Enfermedad de Chagas (LabMECh); Argentina.Fil: Bisio, Margarita. Instituto de Investigaciones en Ingeniería Genética y Biología Molecular (INGEBI-CONICET). Laboratorio de Biología Molecular de la Enfermedad de Chagas (LabMECh); Argentina.Fil: Orellana, Liliana. Universidad de Buenos Aires. Instituto de Cálculo; Argentina.Fil: Sued, Mariela. Universidad de Buenos Aires. Instituto de Cálculo; Argentina.Fil: Duffy, Tomás. Instituto de Investigaciones en Ingeniería Genética y Biología Molecular (INGEBI-CONICET). Laboratorio de Biología Molecular de la Enfermedad de Chagas (LabMECh); Argentina.Fil: Mejia Jaramillo, Ana M. Universidad de Antioquia. Grupo Chagas; Colombia.Fil: Cura, Carolina. Instituto de Investigaciones en Ingeniería Genética y Biología Molecular (INGEBI-CONICET). Laboratorio de Biología Molecular de la Enfermedad de Chagas (LabMECh); Argentina.Fil: Auter, Frederic. French Blood Services; Francia.Fil: Veron, Vincent. Universidad de Parasitología. Laboratorio Hospitalario; Guayana Francesa.Fil: Qvarnstrom, Yvonne. Centers for Disease Control. Department of Parasitic Diseases; Estados Unidos.Fil: Deborggraeve, Stijn. Institute of Tropical Medicine; Bélgica.Fil: Hijar, Gisely. Instituto Nacional de Salud; Perú.Fil: Zulantay, Inés. Facultad de Medicina; Chile.Fil: Lucero, Raúl Horacio. Universidad Nacional del Nordeste; Argentina.Fil: Velázquez, Elsa. ANLIS Dr.C.G.Malbrán. Instituto Nacional de Parasitología Dr. Mario Fatala Chaben; Argentina.Fil: Tellez, Tatiana. Universidad Mayor de San Simon. Centro Universitario de Medicina Tropical; Bolivia.Fil: Sanchez Leon, Zunilda. Universidad Nacional de Asunción. Instituto de Investigaciones en Ciencias de la Salud; Paraguay.Fil: Galvão, Lucia. Faculdade de Farmácia; Brasil.Fil: Nolder, Debbie. Hospital for Tropical Diseases. London School of Tropical Medicine and Hygiene Department of Clinical Parasitology; Reino Unido.Fil: Monje Rumi, María. Universidad Nacional de Salta. Laboratorio de Patología Experimental; Argentina.Fil: Levi, José E. Hospital Sirio Libanês. Blood Bank; Brasil.Fil: Ramirez, Juan D. Universidad de los Andes. Centro de Investigaciones en Microbiología y Parasitología Tropical; Colombia.Fil: Zorrilla, Pilar. Instituto Pasteur; Uruguay.Fil: Flores, María. Instituto de Salud Carlos III. Centro de Mahahonda; España.Fil: Jercic, Maria I. Instituto Nacional De Salud. Sección Parasitología; Chile.Fil: Crisante, Gladys. Universidad de los Andes. Centro de Investigaciones Parasitológicas J.F. Torrealba; Venezuela.Fil: Añez, Néstor. Universidad de los Andes. Centro de Investigaciones Parasitológicas J.F. Torrealba; Venezuela.Fil: De Castro, Ana M. Universidade Federal de Goiás. Instituto de Patologia Tropical e Saúde Pública (IPTSP); Brasil.Fil: Gonzalez, Clara I. Universidad Industrial de Santander. Grupo de Inmunología y Epidemiología Molecular (GIEM); Colombia.Fil: Acosta Viana, Karla. Universidad Autónoma de Yucatán. Departamento de Biomedicina de Enfermedades Infecciosas y Parasitarias Laboratorio de Biología Celular; México.Fil: Yachelini, Pedro. Universidad Católica de Santiago del Estero. Instituto de Biomedicina; Argentina.Fil: Torrico, Faustino. Universidad Mayor de San Simon. Centro Universitario de Medicina Tropical; Bolivia.Fil: Robello, Carlos. Instituto Pasteur; Uruguay.Fil: Diosque, Patricio. Universidad Nacional de Salta. Laboratorio de Patología Experimental; Argentina.Fil: Triana Chavez, Omar. Universidad de Antioquia. Grupo Chagas; Colombia.Fil: Aznar, Christine. Universidad de Parasitología. Laboratorio Hospitalario; Guayana Francesa.Fil: Russomando, Graciela. Universidad Nacional de Asunción. Instituto de Investigaciones en Ciencias de la Salud; Paraguay.Fil: Büscher, Philippe. Institute of Tropical Medicine; Bélgica.Fil: Assal, Azzedine. French Blood Services; Francia.Fil: Guhl, Felipe. Universidad de los Andes. Centro de Investigaciones en Microbiología y Parasitología Tropical; Colombia.Fil: Sosa Estani, Sergio. ANLIS Dr.C.G.Malbrán. Centro Nacional de Diagnóstico e Investigación en Endemo-Epidemias; Argentina.Fil: DaSilva, Alexandre. Centers for Disease Control. Department of Parasitic Diseases; Estados Unidos.Fil: Britto, Constança. Instituto Oswaldo Cruz/FIOCRUZ. Laboratório de Biologia Molecular e Doenças Endêmicas; Brasil.Fil: Luquetti, Alejandro. Laboratório de Pesquisa de Doença de Chagas; Brasil.Fil: Ladzins, Janis. World Health Organization (WHO). Special Programme for Research and Training in Tropical Diseases (TDR); Suiza

International Study to Evaluate PCR Methods for Detection of Trypanosoma cruzi DNA in Blood Samples from Chagas Disease Patients

A century after its discovery, Chagas disease, caused by the parasite Trypanosoma cruzi, still represents a major neglected tropical threat. Accurate diagnostics tools as well as surrogate markers of parasitological response to treatment are research priorities in the field. The polymerase chain reaction (PCR) has been proposed as a sensitive laboratory tool for detection of T. cruzi infection and monitoring of parasitological treatment outcome. However, high variation in accuracy and lack of international quality controls has precluded reliable applications in the clinical practice and comparisons of data among cohorts and geographical regions. In an effort towards harmonization of PCR strategies, 26 expert laboratories from 16 countries evaluated their current PCR procedures against sets of control samples, composed by serial dilutions of T.cruzi DNA from culture stocks belonging to different lineages, human blood spiked with parasite cells and blood samples from Chagas disease patients. A high variability in sensitivities and specificities was found among the 48 reported PCR tests. Out of them, four tests with best performance were selected and further evaluated. This study represents a crucial first step towards device of a standardized operative procedure for T. cruzi PCR

Deep-learning based reconstruction of the shower maximum Xmax using the water-Cherenkov detectors of the Pierre Auger Observatory

The atmospheric depth of the air shower maximum Xmax is an observable commonly used for the determination of the nuclear mass composition of ultra-high energy cosmic rays. Direct measurements of Xmax are performed using observations of the longitudinal shower development with fluorescence telescopes. At the same time, several methods have been proposed for an indirect estimation of Xmax from the characteristics of the shower particles registered with surface detector arrays. In this paper, we present a deep neural network (DNN) for the estimation of Xmax. The reconstruction relies on the signals induced by shower particles in the ground based water-Cherenkov detectors of the Pierre Auger Observatory. The network architecture features recurrent long short-term memory layers to process the temporal structure of signals and hexagonal convolutions to exploit the symmetry of the surface detector array. We evaluate the performance of the network using air showers simulated with three different hadronic interaction models. Thereafter, we account for long-term detector effects and calibrate the reconstructed Xmax using fluorescence measurements. Finally, we show that the event-by-event resolution in the reconstruction of the shower maximum improves with increasing shower energy and reaches less than 25 g/cm2 at energies above 2×1019 eV

Multi-messenger observations of a binary neutron star merger

On 2017 August 17 a binary neutron star coalescence candidate (later designated GW170817) with merger time 12:41:04 UTC was observed through gravitational waves by the Advanced LIGO and Advanced Virgo detectors. The Fermi Gamma-ray Burst Monitor independently detected a gamma-ray burst (GRB 170817A) with a time delay of ~1.7 s with respect to the merger time. From the gravitational-wave signal, the source was initially localized to a sky region of 31 deg2 at a luminosity distance of 40+8-8 Mpc and with component masses consistent with neutron stars. The component masses were later measured to be in the range 0.86 to 2.26 Mo. An extensive observing campaign was launched across the electromagnetic spectrum leading to the discovery of a bright optical transient (SSS17a, now with the IAU identification of AT 2017gfo) in NGC 4993 (at ~40 Mpc) less than 11 hours after the merger by the One- Meter, Two Hemisphere (1M2H) team using the 1 m Swope Telescope. The optical transient was independently detected by multiple teams within an hour. Subsequent observations targeted the object and its environment. Early ultraviolet observations revealed a blue transient that faded within 48 hours. Optical and infrared observations showed a redward evolution over ~10 days. Following early non-detections, X-ray and radio emission were discovered at the transient’s position ~9 and ~16 days, respectively, after the merger. Both the X-ray and radio emission likely arise from a physical process that is distinct from the one that generates the UV/optical/near-infrared emission. No ultra-high-energy gamma-rays and no neutrino candidates consistent with the source were found in follow-up searches. These observations support the hypothesis that GW170817 was produced by the merger of two neutron stars in NGC4993 followed by a short gamma-ray burst (GRB 170817A) and a kilonova/macronova powered by the radioactive decay of r-process nuclei synthesized in the ejecta

Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks for 195 countries and territories, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017

Background The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2017 comparative risk assessment (CRA) is a comprehensive approach to risk factor quantification that offers a useful tool for synthesising evidence on risks and risk–outcome associations. With each annual GBD study, we update the GBD CRA to incorporate improved methods, new risks and risk–outcome pairs, and new data on risk exposure levels and risk–outcome associations. Methods We used the CRA framework developed for previous iterations of GBD to estimate levels and trends in exposure, attributable deaths, and attributable disability-adjusted life-years (DALYs), by age group, sex, year, and location for 84 behavioural, environmental and occupational, and metabolic risks or groups of risks from 1990 to 2017. This study included 476 risk–outcome pairs that met the GBD study criteria for convincing or probable evidence of causation. We extracted relative risk and exposure estimates from 46 749 randomised controlled trials, cohort studies, household surveys, census data, satellite data, and other sources. We used statistical models to pool data, adjust for bias, and incorporate covariates. Using the counterfactual scenario of theoretical minimum risk exposure level (TMREL), we estimated the portion of deaths and DALYs that could be attributed to a given risk. We explored the relationship between development and risk exposure by modelling the relationship between the Socio-demographic Index (SDI) and risk-weighted exposure prevalence and estimated expected levels of exposure and risk-attributable burden by SDI. Finally, we explored temporal changes in risk-attributable DALYs by decomposing those changes into six main component drivers of change as follows: (1) population growth; (2) changes in population age structures; (3) changes in exposure to environmental and occupational risks; (4) changes in exposure to behavioural risks; (5) changes in exposure to metabolic risks; and (6) changes due to all other factors, approximated as the risk-deleted death and DALY rates, where the risk-deleted rate is the rate that would be observed had we reduced the exposure levels to the TMREL for all risk factors included in GBD 2017. Findings In 2017, 34·1 million (95% uncertainty interval [UI] 33·3–35·0) deaths and 1·21 billion (1·14–1·28) DALYs were attributable to GBD risk factors. Globally, 61·0% (59·6–62·4) of deaths and 48·3% (46·3–50·2) of DALYs were attributed to the GBD 2017 risk factors. When ranked by risk-attributable DALYs, high systolic blood pressure (SBP) was the leading risk factor, accounting for 10·4 million (9·39–11·5) deaths and 218 million (198–237) DALYs, followed by smoking (7·10 million [6·83–7·37] deaths and 182 million [173–193] DALYs), high fasting plasma glucose (6·53 million [5·23–8·23] deaths and 171 million [144–201] DALYs), high body-mass index (BMI; 4·72 million [2·99–6·70] deaths and 148 million [98·6–202] DALYs), and short gestation for birthweight (1·43 million [1·36–1·51] deaths and 139 million [131–147] DALYs). In total, risk-attributable DALYs declined by 4·9% (3·3–6·5) between 2007 and 2017. In the absence of demographic changes (ie, population growth and ageing), changes in risk exposure and risk-deleted DALYs would have led to a 23·5% decline in DALYs during that period. Conversely, in the absence of changes in risk exposure and risk-deleted DALYs, demographic changes would have led to an 18·6% increase in DALYs during that period. The ratios of observed risk exposure levels to exposure levels expected based on SDI (O/E ratios) increased globally for unsafe drinking water and household air pollution between 1990 and 2017. This result suggests that development is occurring more rapidly than are changes in the underlying risk structure in a population. Conversely, nearly universal declines in O/E ratios for smoking and alcohol use indicate that, for a given SDI, exposure to these risks is declining. In 2017, the leading Level 4 risk factor for age-standardised DALY rates was high SBP in four super-regions: central Europe, eastern Europe, and central Asia; north Africa and Middle East; south Asia; and southeast Asia, east Asia, and Oceania. The leading risk factor in the high-income super-region was smoking, in Latin America and Caribbean was high BMI, and in sub-Saharan Africa was unsafe sex. O/E ratios for unsafe sex in sub-Saharan Africa were notably high, and those for alcohol use in north Africa and the Middle East were notably low. Interpretation By quantifying levels and trends in exposures to risk factors and the resulting disease burden, this assessment offers insight into where past policy and programme efforts might have been successful and highlights current priorities for public health action. Decreases in behavioural, environmental, and occupational risks have largely offset the effects of population growth and ageing, in relation to trends in absolute burden. Conversely, the combination of increasing metabolic risks and population ageing will probably continue to drive the increasing trends in non-communicable diseases at the global level, which presents both a public health challenge and opportunity. We see considerable spatiotemporal heterogeneity in levels of risk exposure and risk-attributable burden. Although levels of development underlie some of this heterogeneity, O/E ratios show risks for which countries are overperforming or underperforming relative to their level of development. As such, these ratios provide a benchmarking tool to help to focus local decision making. Our findings reinforce the importance of both risk exposure monitoring and epidemiological research to assess causal connections between risks and health outcomes, and they highlight the usefulness of the GBD study in synthesising data to draw comprehensive and robust conclusions that help to inform good policy and strategic health planning

Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019

Background: In an era of shifting global agendas and expanded emphasis on non-communicable diseases and injuries along with communicable diseases, sound evidence on trends by cause at the national level is essential. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) provides a systematic scientific assessment of published, publicly available, and contributed data on incidence, prevalence, and mortality for a mutually exclusive and collectively exhaustive list of diseases and injuries. Methods: GBD estimates incidence, prevalence, mortality, years of life lost (YLLs), years lived with disability (YLDs), and disability-adjusted life-years (DALYs) due to 369 diseases and injuries, for two sexes, and for 204 countries and territories. Input data were extracted from censuses, household surveys, civil registration and vital statistics, disease registries, health service use, air pollution monitors, satellite imaging, disease notifications, and other sources. Cause-specific death rates and cause fractions were calculated using the Cause of Death Ensemble model and spatiotemporal Gaussian process regression. Cause-specific deaths were adjusted to match the total all-cause deaths calculated as part of the GBD population, fertility, and mortality estimates. Deaths were multiplied by standard life expectancy at each age to calculate YLLs. A Bayesian meta-regression modelling tool, DisMod-MR 2.1, was used to ensure consistency between incidence, prevalence, remission, excess mortality, and cause-specific mortality for most causes. Prevalence estimates were multiplied by disability weights for mutually exclusive sequelae of diseases and injuries to calculate YLDs. We considered results in the context of the Socio-demographic Index (SDI), a composite indicator of income per capita, years of schooling, and fertility rate in females younger than 25 years. Uncertainty intervals (UIs) were generated for every metric using the 25th and 975th ordered 1000 draw values of the posterior distribution. Findings: Global health has steadily improved over the past 30 years as measured by age-standardised DALY rates. After taking into account population growth and ageing, the absolute number of DALYs has remained stable. Since 2010, the pace of decline in global age-standardised DALY rates has accelerated in age groups younger than 50 years compared with the 1990–2010 time period, with the greatest annualised rate of decline occurring in the 0–9-year age group. Six infectious diseases were among the top ten causes of DALYs in children younger than 10 years in 2019: lower respiratory infections (ranked second), diarrhoeal diseases (third), malaria (fifth), meningitis (sixth), whooping cough (ninth), and sexually transmitted infections (which, in this age group, is fully accounted for by congenital syphilis; ranked tenth). In adolescents aged 10–24 years, three injury causes were among the top causes of DALYs: road injuries (ranked first), self-harm (third), and interpersonal violence (fifth). Five of the causes that were in the top ten for ages 10–24 years were also in the top ten in the 25–49-year age group: road injuries (ranked first), HIV/AIDS (second), low back pain (fourth), headache disorders (fifth), and depressive disorders (sixth). In 2019, ischaemic heart disease and stroke were the top-ranked causes of DALYs in both the 50–74-year and 75-years-and-older age groups. Since 1990, there has been a marked shift towards a greater proportion of burden due to YLDs from non-communicable diseases and injuries. In 2019, there were 11 countries where non-communicable disease and injury YLDs constituted more than half of all disease burden. Decreases in age-standardised DALY rates have accelerated over the past decade in countries at the lower end of the SDI range, while improvements have started to stagnate or even reverse in countries with higher SDI. Interpretation: As disability becomes an increasingly large component of disease burden and a larger component of health expenditure, greater research and developm nt investment is needed to identify new, more effective intervention strategies. With a rapidly ageing global population, the demands on health services to deal with disabling outcomes, which increase with age, will require policy makers to anticipate these changes. The mix of universal and more geographically specific influences on health reinforces the need for regular reporting on population health in detail and by underlying cause to help decision makers to identify success stories of disease control to emulate, as well as opportunities to improve. Funding: Bill & Melinda Gates Foundation. © 2020 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 licens