dataesr/scientific-paragraphs-categorization huggingface dataset:
Data extraction We developed, piloted and revised a data extraction template in Microsoft Excel at the beginning of the study. One reviewer extracted data from each included study Children and adolescents and SARS-COV-2 variants of concern: a systematic review and a second reviewer verified the extraction. Extracted data included information on the publication characteristics, study design, study setting, study population overall and those younger than 20 years old, age groups, prevalent VOC(s) at the time of study, and outcomes for each relevant review question.
Data synthesis We synthesised the information from the included studies descriptively for each question. We tabulated summaries of the data extracted, including the number of studies identified for each VOC, the age groups studied and the outcomes reported. The presence of a VOC was determined by genetic sequencing or by assumptions based on authors' reporting, the dominant lineage at the time of testing, and the study location. Definitions of disease severity differed between studies. To allow comparisons, we applied the following categorisation: mild or asymptomatic disease (patients were not hospitalised), moderate disease (patients were hospitalised, but did not require intensive care), severe disease (patients were hospitalised and intensive care was necessary). If we did not have sufficient information to apply these categories, we used the authors' definitions and reported these in the text.
The alpha variant did not appear to result in more severe COVID-19 disease than disease caused by non-VOCs in children and adolescents in five studies. [17] [18] [19] [20] [21]  In two studies reporting on paediatric inpatients and outpatients one from Brazil and one from the USA, the overall hospitalisation rate as well as the ICU admission rate of children infected with the gamma variant was higher than for non-VOCs (Gamma vs. non-VOC: 28. 3% vs. 24. 9%. 47  , Gamma vs. non-VOC: 5. 2% vs. 1. 2% 48 ). 47, 48  Severity of delta variant infection was compared with the pre-delta period (including periods where previous VOCs where predominant) in eight studies but the reported data were inconsistent, and differences in disease severity could not be assessed. 26, [28] [29] [30] [32] [33] [34] [35]  Studies of omicron variant infection described less severe disease than infection with the delta variant in children and adolescents <18 years old, based on data from nine studies from South Africa, Qatar and the USA.
Strength and limitations We used a systematic approach to search different databases, covering the period of VOC circulation that includes the first omicron variants (BA. 1 and BA. 2) in 2022. We included a broad selection of study designs in various languages and screened reference lists of included studies. Our search strategy might have missed relevant studies, although we only excluded three records for language reasons. A limitation of the review is that misclassification of the variant could have occurred as only a minority of study samples from all cases was sequenced in several studies and we relied on dates during which a variant was dominant. Non-differential misclassification would reduce the magnitude of associations with VOC infection status.
Acknowledgements We would like to thank all members of  WHO's Maternal, Newborn, Child and Adolescent Health and Ageing department , who contributed to the development of this review:  Kathleen Strong ,  Theresa Diaz ,  Anshu Banerjee ,  Maria van Kerkhove ,  Abdi Mahamud ,  Valentina Baltag ,  Judith Ann Mandelbaum-Schmid.
To simulate the next stage of fracture network development, we model fracture propagation and interaction using the most efficient fracture geometry identified in Figure  4  with the software GROW  [43]. This software determines the direction of growth by optimizing work, that is, by identifying the fracture geometry that maximizes (or minimizes) ∆𝑊 ),- divided by the fracture area produced by fracture propagation, ∆𝐴, for stress (or displacement) loading conditions. Because the properties of the intact rock near the edges of growing fracture tips influence the direction of growth, we show the results of four combinations of shear strength, 𝜏. , tensile strength, 𝑡. , and internal friction coefficient, 𝜇 /. Three of the scenarios use the same 𝜏. and 𝑡. with varying internal friction, 𝜇 / , while the fourth uses higher 𝜏.
During reconstruction, we apply algorithms to remove noise, such as ring artefacts. We remove remaining noise in the three-dimensional data using the software Avizo3D TM , including the application of a non-local means filter  [58]. We then segment the solid rock from the pores and fractures using an algorithm similar to Otsu's thresholding technique to identify a global threshold between the solid material and the fractures and pores  [59]. This thresholding technique is not strongly influenced by noise  [59]  and produces segmented scans with porosity similar to values measured with sample imbibition  [60]. We then apply several processing techniques to the segmented scans in Avizo3D TM , including Label Analysis, that identify individual fractures using connected clusters of voxels, and calculate the geometric characteristics and orientation of fractures relative to the vertical and 𝜎 ! direction.
Numerical model design To identify the arrangement of fractures in the first stage of fracture growth, we use the software Fric2D  [62]. Fric2D uses continuum mechanics to calculate the stresses and strains within a homogeneous linear elastic material containing fractures and frictional faults in twodimensional plane strain  [62]. The two-dimensional plane strain models are one meter in the direction out of the board/screen, or z-direction. The x-direction is parallel to the 𝜎 & direction and y-direction is parallel to the 𝜎 ! direction. We select the loading conditions of the first set of numerical models to match the effective stress of the majority of the experiments (𝜎 & =5 MPa), and to produce slip along the fractures with the lowest amount of loading (e. g. , 𝜎 ! =50 MPa for the lowest strength parameters of the intact rock).
We use a rectangular model geometry with a side length of 8 mm perpendicular to the 𝜎 ! direction (horizontal) and 4 mm parallel to the 𝜎 ! direction (vertical) to ensure that the system shape does not promote the development of fractures at 20-30° from the 𝜎 ! direction, as suggested by previous work  [27] [28]. To simulate the growth of fractures, we use the software GROW  [43] , which calls Fric2D. GROW accurately simulates the propagation path and linkage of two nearby fracture tips separated by a releasing step  [63] , the propagation of wing cracks from inclined fractures observed in uniaxial and biaxial compression experiments  [46] , the coalescence of hundreds of fractures observed in uniaxial compression experiments  [64] , and the development of thrust faults in accretionary prisms  [65].
parallel and sub-parallel to the 𝜎 ! direction, to the volume of fractures aligned along the orientations expected for shear following the Mohr-Coulomb criterion, about 17-32° from the 𝜎 ! direction, depending on the internal friction coefficient, throughout six X-ray tomography triaxial compression experiments on intact and damaged Westerly granite cylinders with confining stresses, 𝜎 & , of 5-20 MPa, fluid pressures, 𝑃 ( , of zero to 10 MPa, and effective stresses, confining stress minus fluid pressure, of 5 MPa and 10 MPa. This suite of experiments provides an unparalleled dataset that reveals differences in the deformation mechanisms of intact and damaged low-porosity crystalline rock, and rocks deformed with pore fluid at different confining stresses and the same effective stress.
Objective Current management of patients with pediatric rheumatic diseases (PRD) should aim at achieving the best possible well-being. To identify sociodemographic/clinical characteristics, needed paramedical services and school accommodations associated with well-being in patients at inclusion in a French health network Réseau pour les Rhumatismes Inflammatoires Pédiatriques (RESRIP) that supports coordination of the patient's health pathway. To evaluate the evolution of well-being over time in this patients benefiting from such support. Methods Patients > 3 years old enrolled in RESRIP (2013-2020) were included. At enrollment, data were collected on sociodemographic/clinical characteristics, ongoing medications, and paramedical and educational actions to be implemented by RESRIP. Well-being during the last 6 months was reported with a standardized questionnaire at enrollment and every 6 months.
RESRIP acts as a link with the patient's various medical, para-medical and social health professionals. It does not provide care but day-to-day support, particularly in terms of education. It establishes contact with schools or professional establishments to ensure that its pupils/students are properly integrated, and organizes multidisciplinary meetings with school nurses and/or doctors, as well as with teachers, to explain the pathology or resolve certain tensions or misunderstandings. Finally, RESRIP has a cross-sectional PTE program (www. resrip. fr) for children with CRDs and their families to help them manage their disease on a daily basis. Understanding the factors associated with well-being seems therefore essential today. Thus, we first sought to identify the sociodemographic/clinical characteristics, medical and paramedical therapy needs and schooling accommodations associated with well-being in patients at inclusion in RESRIP.
This interview allows RESRIP team to know the patient and the family and understand their needs in order to set up targeted actions. RESRIP's purpose is to facilitate the daily life of families by finding trained health professionals and setting up social and educational actions. It also aims to support the medical, paramedical and school professionals of these children/adolescents  [17]. Actions to be implemented by RESRIP are then put in place. Every 6 months, the same questionnaire is completed face-to-face or by phone. For families who cannot be reached, the questionnaire is sent directly by mail and completed by the parents/guardians and/or the child (> 7 years old). Responses are stored in a medical file by using ICT-Chorus software certified in accordance to the RGPD law (HDS certification as well as ISO 27001).
Sociodemographic characteristics At the enrollment visit, data were collected on sex, age, date of disease onset and of disease diagnosis. According to the age at the time of inclusion patients were classified into 3 groups: preschooler (3-5 years old), middle schooler (6-11 years old) and adolescent (≥ 12 years old). Disease duration at the time of inclusion was calculated and grouped into 2 categories: < 1-year evolution and ≥ 1-year evolution. Information regarding patients' number of school days' absenteeism over the last 6 months were collected at EV and every 6 months. We then dichotomized school absenteeism into two groups: non-absenteeism (less than 12 days per 6 months) and absenteeism (12 days or more per 6 months) since 4 half days per month were considered as regulatory threshold of absenteeism as defined by the French national education system (https:// www.
The BOUSSOLE observatory consists of a mooring where a buoy, specifically designed for collecting radiometric and bio-optical quantities, collects optical data at a high temporal resolution. Oceanographic cruises visit the site monthly to collect a complementary dataset of algal pigment concentrations and IOPs, which include light absorption coefficients of phytoplankton, detritus, and CDOM  (Antoine et al. , 2006). Numerous studies combining optics and biochemistry have exploited these two complementary sets of observations collected at the BOUS-SOLE site, e. g. fluorescence to infer Chl a (e. g. Bayle et al. , 2015; Mignot et al. , 2011)  and phytoplankton community composition  (Sauzède et al. , 2015) , to infer the size structure of the phytoplankton community from measured light spectra  (Organelli et al.
Data from satellites Satellite-retrieved IOPs and Chl a were obtained from the OCEANCOLOUR_MED_BGC_L3_MY_009_143 product (EU Copernicus Marine Service Information, 2022). The product provides multi-sensor R RS (λ) spectra (SeaW-iFS, MODIS-AQUA, NOAA20-VIIRS, NPP-VIIRS, and Sentinel3A-OLCI) merged using the state-of-the-art multisensor merging algorithms and shifted to the standard Sea-WiFS wavelengths  (412, 443, 490, 555, and 670 nm). IOPs (a PH (443) and a DG (443)) are derived from R RS (λ) via the Quasi-Analytical Algorithm version 6 (QAAv6) model  (Lee et al. , 2002). Chl a concentration is determined using the Mediterranean regional algorithm, an updated version of MedOC4  (Volpe et al. , 2019)  and AD4  (Berthon and Zibordi, 2004) , merged according to the water type present  (Mélin and Vantrepotte, 2015; Volpe et al.
Code availability. The GOTM code is available at https://doi. org/10. 5281/zenodo. 4896611  (Bolding et al. , 2021). The FABM code available at https://doi. org/10. 5281/zenodo. 8424553,  (Bruggeman and Bolding, 2021). The BFM code adapted to work under the FABM convention is available at https://doi. org/10. 5281/zenodo. 10075551 (Álvarez  et  al. , 2023), and the bio-optical model is available at https://doi. org/10. 5281/zenodo. 10075682  (Lazzari, 2023). ParSAC is available from PyPI and can be installed by executing the command pip install parsac < -user >; it requires Parallel Python. The 1D setup where the simulations presented can be run and the configuration files for the optimization can be obtained from https://doi. org/10. 5281/zenodo. 10067424 (Álvarez, 2023a). Data availability.
The gridded product of temperature and salinity at DYFAMED was obtained from the OceanSITES Global Data Assembly Centre via ftp by choosing the option "Connect as Guest". (ftp://ftp. ifremer. fr/ifremer/oceansites, last access: 22 November 2021). Nitrate, nitrite, and phosphate concentrations at DYFAMED were obtained from https://doi. org/10. 17882/ 43749  (Coppola et al. , 2021). Chl a and HPLC data at BOUS-SOLE are available from http://www. obs-vlfr. fr/Boussole/html/ boussole_data/other_useful_files. php (last access: 29 November 2022,  Antoine and Vellucci, 2022). IOP data at BOUSSOLE are available upon request to IMEV. Satellite data are available from the EU Copernicus Marine Service at https://doi. org/ 10. 48670/moi-00299 (E. U. Copernicus Marine Service Information, 2022). The results of the numerical simulations can be found at https://doi.
org/10. 5281/zenodo. 7628060 (Álvarez, 2023b), and scripts for data analysis and visualization are available through https://doi. org/10. 5281/zenodo. 10068885 (Álvarez, 2023c). The analysis of model output has been performed with R 4. 0. 5 using the packages RNetCDF 2. 5-2 and RSQLite 2. 2. 12 for importing data; abind 1. 4-5, akima 0. 6-2. 1, chron 2. 3-56, lubridate 1. 8. 0, Hmisc 4. 5-0, and stringr 1. 4. 1 for data analysis; and plot3D 1. 3, unikn 0. 6. 0, and viridisLite 0. 4. 1 for visualization (all publicly available in CRAN).
, 2021). An approximation of a constant average amplitude is used outside of the spatial coverage of the source model. Then it computes at the same time frequency, 210 sets of diffusion coefficients that will be used in Salammbô-EnKF simulations, enabling a dynamical description of wave particle interactions. FARWEST relies on the same theoretical frame (based on the quasi-linear theory) followed in the PADIE code  (Glauert & Horne, 2005)  and the WAPI code  (Sicard et al. , 2008)  and assuming a dipole magnetic field. As the computational cost required to evaluate one configuration of diffusion coefficients was too prohibitive for realtime forecasting, especially given the large number of ensemble members used in the SafeSpace pipeline, we have developed an innovative computing logic (N. Dahmen et al.
EMERALD Radial-Diffusion Model The Electric and MagnEtic RAdiaL Diffusion (EMERALD, Aminalragia-Giamini et al. , 2023) model uses an ensemble of neural networks for the derivation and nowcasting of the radial diffusion coefficient   *  * in the outer radiation belt. The model uses solely solar wind parameters as input. Specifically, it depends on the solar wind speed and density (V sw and N sw , respectively), and the interplanetary magnetic field (IMF). The development of the model is based on the extensive SafeSpace database of radial diffusion coefficients (Katsavrias   and E ) and assuming the same approach as  Fei et al. (2006). EMERALD outputs are expected values and confidence distributions for the electric and magnetic component of the   *  *. Using these distributions, confidence levels in the form of percentiles can be directly derived for each component at each time-step.
3) and considering as an initial state, the assimilated state on 17 March from the first simulation. The latter two ensemble forecast simulations represent the forecast that would have been provided the day before St. Patrick's Day storm. The first one represents the forecast that would have been computed by the SafeSpace pipeline, and the second one, using the reference model, is provided here as a comparison point to evaluate the impact of the SafeSpace developments on the forecast. We note that for both forecasts, we don't assimilate any observation data from on 17 March on. In the prototype pipeline, we usually have a few hours of observations available at the beginning of the day of the run, but here we assume conservatively that only data about previous days are available. Figure  8  presents the obtained electron fluxes on the RBSP B satellite trajectory for 460 keV, 1.
The two different forecasts mostly differ on the 2. 23 MeV energy channel, for which the SafeSpace forecast seem to better reproduce the behavior of the observed fluxes after 18 March. For this high energy channel, both forecasts overestimate significantly the fluxes at the beginning of the storm (during most of 17 March and 18 March), which we can also impute to the lack of dropouts in the models. Figure  9  presents the same simulations results for the 1. 11 MeV energy fluxes, both on RBSP A and B, while accounting for the L* dependency. The lack of dropouts model in the forecast simulations can be seen between 17 March and 18 March. Hence, thanks to the GPS and GOES observations, the data assimilation simulation is able to partially reproduce the flux depletion above L* = 4. At this energy, it is readily apparent that both the Safespace and reference forecasts overshoot the depth of the injection during the storm, with large enhancements of the fluxes below L* = 2.
7, where both the observation and the data assimilation runs show the belt ends. We note that Figure  9  only presents the median flux for each simulation, and that in each case some ensemble members do predict the correct injection depth. The median estimation might be off because of a bias in the radial diffusion estimations in our models, or more probably because of the lack of dropouts in the models. Indeed, the fluxes at lower energies and higher L* values, over-estimated due to the lack of dropouts at the beginning of the storm, can be easily and rapidly pushed by radial diffusion, which dominates other processes during the storm, to these lower L* values. This also explain why both forecasts seem to predict a enhancement ahead of the measurements. After the storm, above L* = 3, the SafeSpace forecast is qualitatively reasonable, while over-estimating the fluxes up to L* = 5.
Conclusions In this article, we presented the new implementation proposed by the SafeSpace project pipeline to model the outer radiation belt dynamics. To do so, solar wind driven codes produce time updated estimations of radial diffusion and wave particle interactions. The latter then serve as input data for Salammbô-EnKF that provides a forecast of the outer radiation belt fluxes. To evaluate the physical models involved in the SafeSpace pipeline, we conducted data assimilation and ensemble simulations of the CME driven St. Patrick's Day storm (18 March 2015) and compared them to our reference Salammbô model. In the case of St Patrick's Day storm, the SafeSpace physical description improves the forecasting accuracy of the electron radiation belts. Thanks to its design focused on the uncertainty propagation, the inner magnetosphere section of the SafeSpace modeling pipeline is able to produce a global forecast of the outer electron radiation belt, given the assumed solar wind parameter uncertainty distributions.
Assimilating measurements from highly elliptical orbits, such as the orbits of the Van Allen Probes and Arase  (Mitani et al. , 2018) , would allow for a very good global radiation belt nowcast, but near real-time data on such orbit is not readily available. Future missions focused on providing such data sets in real time, or provisions to make existing data available faster on flying missions, would greatly help improve the nowcasting and forecasting frameworks, such as SafeSpace. the formula in Equation  3. The GPS-NS54/CXD electron fluxes are publicly available at https://www. ngdc. noaa. gov/stp/space-weather/satellite-data/satellite-systems/gps/data/ns54. The Van Allen Probes RBSP A and RBSP B MagEIS Level 2 datasets used in this study are directly available at https://rbsp-ect. newmexicoconsortium.
org/ data_pub/rbspa/mageis/level2/ and https://rbsp-ect. newmexicoconsortium. org/data_pub/rbspb/mageis/level2/. The solar wind and geomagnetic indices from the OMNIWeb database are publicly available at https://omniweb. gsfc. nasa. gov/. All magnetic fields models evaluation and geomagnetic coordinates computations in this study were performed using the IRBEM  (Boscher et al. , 2022)  and SpacePy  (Morley et al. , 2022)  libraries.
Data Availability Statement The computing method followed in FARWEST is described in N. Dahmen et al. (2022)  and based on  Glauert and Horne (2005). The   *  * data set used to train the EMERALD model can be found in https://synergasia. uoa. gr/modules/document/index. php?course=PHYS120. The GEO model regression weights are provided in  Katsavrias et al. (2021). The plasma densities, VLF wave intensities, the synthetic solar wind forecast, as well as the SafeSpace fluxes forecast values are available on Zenodo in  Brunet et al. (2023) , which can be found at https://doi. org/10. 5281/zenodo. 7786270. The NOAA GOES-15/MAGED unidirectional electron fluxes data are publicly available at https://www. ncei. noaa. gov/data/goes-space-environment-monitor/access/full/2015/03/ goes15/. From these unidirectional fluxes, we have computed the omnidirectional fluxes used in this study using.
Inclusive and differential measurements of the top-antitop (t t) charge asymmetry A t t C and the leptonic asymmetry A ¯ C are presented in proton-proton collisions at √ s = 13 TeV recorded by the ATLAS experiment at the CERN Large Hadron Collider. The measurement uses the complete Run 2 dataset, corresponding to an integrated luminosity of 139 fb -1 , combines data in the single-lepton and dilepton channels, and employs reconstruction techniques adapted to both the resolved and boosted topologies. A Bayesian unfolding procedure is performed to correct for detector resolution and acceptance effects. The combined inclusive t t charge asymmetry is measured to be A t t C = 0. 0068 ± 0. 0015, which differs from zero by 4. 7 standard deviations. Differential measurements are performed as a function of the invariant mass, transverse momentum and longitudinal boost of the t t system.