dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2017)  as illustrated by its evaluation at the Collelongo and Le Bray sites. The dynamic allocation scheme implemented in the understorey vegetation results in a temporal dynamics that are consistent with our current understanding of understorey vegetation growth in managed stands but could not be evaluated yet at a large spatial scale because of data paucity. Unfortunately, long time series of the annual tree growth for the entire population of trees are still rare and difficult to obtain. Third, most current practices of forest management in monospecific stands are implemented in the GO+ v3. 0 code and can be combined in a wealth of forest management options, including the drainage (not shown) and mechanical preparation of soil, control of vegetation, thinning, coppicing and clear-cutting of the tree stand.
Nevertheless, the model still has to be evaluated at a large scale. It is worth noting the GO+ code inherently accounts for the light resource competition between trees and understorey. By construction, the access of the vegetation foliage to light is prioritized from the top to the bottom of the canopy allowing the ground vegetation to respond to thinning and to recover first after clear-cutting. The tree layer subsequently dominates when trees have regrown. This version of the GO+ model also suffers from a number of limitations. It does not yet include a biogeochemistry module and does not allow us to simulate mixed-stand forests or stands that are not even-aged; this is because so far very few evaluation datasets are available. The uptake of water from the soil is not prioritized, with understorey vegetation and trees having access to the same soil volume and their transpiration being withdrawn from the soil simultaneously.
g. PnET-BGC for forest hydrology  (Gbondo-Tugbawa et al. , 2001; Pourmokhtarian et al. , 2012) , ANAFORE for cambial growth and carbohydrate storage  (Deckmyn et al. , 2008) , CANOAK, or ORCHIDEE-CAN for light and turbulence attenuation within the canopy  (Harley and Baldocchi 1995; Naudts et al. , 2015) , but are more restricted in terms of temporal scales, process continuity and exhaustiveness. Very few models that can be run over large gridded datasets can implement canopy processes at an hourly timescale: ORCHIDEE-CAN v1. 0  (Naudts et al. , 2015; Luyssaert et al. , 2018) , JULES  (Best et al. , 2011; Clark et al. , 2011)  or, optionally, LPJ-Guess v3. 0  (Smith et al. , 2014). The majority run at a daily timescale; that resolution may impair the sensitivity of non-linear processes to climate and CO 2 such as photosynthesis, respiration or stomatal function.
Acknowledgements. The datasets used for evaluating the GO+ model with stand growth data and tree species phenology were provided by  Céline Meredieu  ( INRAE UEFP0570 ) for the maritime pine permanent plot, the " GIS Coopérative de données " https: //www6. inra. fr/giscoop/(last access: 7 June 2017) (Douglas fir and maritime pine datasets),  French ICP-Forest network (French part of ICP-Forests Level II network Renecofor ), and  Christopher Reyer  ( PIK -Potsdam ) and the  ISI-MIP  project for the beech datasets. The datasets of flux measurements were downloaded from the European Data Cluster and Fluxnet (La Thuile 2015 dataset). The authors thank  John H. C. Gash  for reviewing the English and style of the paper. Financial support. This research has been supported by the  French National Research Agency (ANR , grant nos.
Code and data availability. The GO+V3. 0 Python code (https://doi. org/10. 15454/5K9HCS, Loustau et al. , 2020)  together with a short user manual and example files (parameters for sites and species, output files, meteorological datasets) can be downloaded from https://github. com/DenisLOUSTAU/GOplus_model_INRAE (last access: 20 April 2020). The code is also available from the repository https://data. inrae. fr/dataverse/eos (last access: 18 April 2020) although with fewer example files. The data used for evaluating GO+ were from the Fluxnet database located at the European Fluxes Database Cluster (http://gaia. agraria. unitus. it/home, last access: 14 August 2017). The DOI of the datasets of flux sites used are as follows: -Le Bray: https://doi. org/10. 18140/FLX/1440163 (Berbigier  and Loustau, 1996-2008)    -Collelongo: https://doi.
org/10. 18140/FLX/1440167 (Matteucci, 1996(Matteucci,  -2014)   )    -Soroe: https://doi. org/10. 18140/FLX/1440155 (Ibrom and Pilegaard, 1996-2014). The forest inventory data used for Douglas fir and partly for maritime pine were provided by the "GIS" data cooperative (Seynave et al. , 2018; https://www6. inra. fr/giscoop, last access: 7 June 2017), and by the PROFOUND project database (Reyer et al. , 2019; https://doi. org/10. 5880/PIK. 2019. 008, Reyer et al. , 2019) for beech forests (Soroe, Collelongo, Solling). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/gmd-13-5973-2020-supplement.
Clinical and radiological assessments Clinical and radiological assessments were performed postoperatively at 45 days, 3 months, 6 months, 1 year, and then every 2 years until the final follow-up. The clinical assessment consisted of functional scores (Merle d'Aubigné-Postel PMA Score  [8] , Harris Hip Score -HHS  [9] ) determined preoperatively and during the follow-up period. Chronic instability was defined by the occurrence of more than two episodes of dislocation and/or subluxation perceived by the patient. The radiological evaluation was carried out using OSIRIX® software (Pixmeo, Bernex, Switzerland). The aim was looking for periprosthetic osteolysis based on the DeLee-Charnley topography  [10] , signs of loosening, and determining whether the cup's position had shifted  [11]. The extent of the allograft's integration was evaluated using the Oswestry classification  [12].
Statistical analysis Statistical analysis was performed using SPSS Statistics (version 23, SPSS Inc. , Chicago, IL, USA). The significance level was set at p < 0. 05. The results were expressed as mean ± standard deviation for quantitative variables and percentages for qualitative variables. After confirming the data were distributed normally, Student's t-test was used to compare quantitative variables (change in clinical outcome scores over time). The cup's survivorship was determined using Kaplan Meier survival curves (with 95% confidence intervals), with revision surgery for aseptic loosening and all-cause revision with implant removal as the main endpoints.
| PATIENTS AND METHODS We included in the analysis patients diagnosed with AML older than 18 years, who underwent an allo-SCT in first complete remission between 2000 and 2018, with a KPS score at the time of transplant ranging from 50% to 80%. Patients received a transplant from a matched sibling donor (MSD), matched unrelated donor (MUD), mismatched unrelated donor at one HLA locus (MMUD), cord blood (CB), or haploidentical (haplo) donor. All transplants from haplo donors were performed using an unmanipulated, T-cell replete graft. Conditioning intensity (myeloablative, MAC; reduced intensity, RIC) was defined according to EBMT standards. 24 A complete list of EBMT centers contributing data to this study is presented in Table  S4 , Supplementary file. Non-relapse mortality (NRM) was defined as death without prior recurrence of the disease.
In order to investigate prognostic factors in this population, all factors associated with one outcome in univariate analysis with a p value less than 0. 05 or variables deemed conceptually important were included in the Cox model. We investigated more specifically conditioning intensity in the population of patients receiving transplant from MSD or MUD. As we found a qualitative interaction between conditioning intensity and KPS, further analyses were stratified on KPS value equal or less than 80. All p-values were two-sided, and p < 0. 05 was considered statistically significant. Statistical analyses were performed with the SPSS 24 (SPSS Inc. /IBM, Armonk, NY, USA) and R 3. 6. 2 (R Development Core Team, Vienna, Austria) software packages.
35 Pain with concomitant pain medication registration in a custom made 1-month pain diary. -Pain NRS of contralateral knee, both hips, both hands and spine. 19 PainDETECT questionnaire used to identify the likelihood of a neuropathic pain component. 36 -Motion analysis (GaitSmart) at 24 months. 28 Performance based tests at 24 months. 29 Physical examination of contralateral knee, hips and hands. Open access ► Optional systemic biochemical (bio)markers -Epigenetic, genomic, transcriptomic, proteomic, lipidomic and metabolomic markers (to be defined). ► General clinical data -History and type of knee traumatism and surgery. -Smoking habits. -Menopausal status. -Concomitant OA treatment. ► Advanced parameters -Bone shape analysis on radiographs of contralateral knee. -Subchondral bone analysis on radiographs of contralateral knee.
Statistical analysis Statistical analyses of baseline data for the current manuscript were performed using SPSS Statistics V. 25. For evaluation of differences between included and excluded patients t-tests were used. Analysis of variance was used to compare included patients of the five different centres. P values <0. 05 were considered as statistically significant. Future analysis plan: Statistical analyses will be in line with the objectives of the original project. At time of data analysis the best methods to address the aims of APPROACH will be defined as this systems medicine is a fast evolving field. The final analysis plan will be decided on before database lock. In an overview it will comprise: Validation of the prediction model used in the inclusion process: Model predictions of pain and structural progression will be compared with actual observed progression over 2-year follow-up.
Development and validation of a predictive model for OA progression: Baseline data and/or change over the first six months follow-up will be used to train and test (ML) models for OA progression. External validation of these models will be needed for implementation in practice. Discovery and prediction of phenotypes/endotypes: The dataset will be explored by use of different statistical approaches to define subgroups with common characteristics. Identified phenotypes/endotypes will be be selected in discussion with clinical experts and described and predicted in enough detail to be of use in practical OA diagnosis and patient selection.
To reduce the uncertainty in the black carbon (BC) induced climatic impacts from the global and regional aerosolclimate model simulations, it is a foremost requirement to improve the prediction of modelled BC distribution. And that specifically, over the regions where the atmosphere is loaded with a large amount of BC, e. g. , the Indo-Gangetic plain (IGP) in the Indian subcontinent. Here we examine the wintertime radiative perturbation due to BC with an efficiently modelled BC distribution over the IGP in a high-resolution (0. 1 • ×0. 1 • ) chemical transport model, CHIMERE, implementing new BC emission inventories. The model efficiency in simulating the observed BC distribution was assessed executing five simulations: Constrained and bottomup (Smog, Cmip, Edgar, P ku) implementing respectively, the recently estimated India-based constrained BC emission and the latest bottom-up BC emissions (India-based: Smog-India, and global: Coupled Model Intercomparison Project phase 6 (CMIP6), Emission Database for Global Atmospheric Research-V4 (EDGAR-V4) and Peking University BC Inventory (PKU)).
, 2004; Verma et al. , 2007 Verma et al. , , 2011)). The simulated BC distribution from LMDZT-GCM was also found to match consistently well the available observations at high altitude Himalayan Hindukush stations (e. g. , Hanle, Satopanth)) which are relatively remotely located and mostly influenced by the transport of aerosols  (Santra et al. , 2019). The above evaluations, therefore, suggest that the large underestimation of BC concentration over the India mainland would primarily be due to BC emission dataset, instead of the model configurations. The simulated atmospheric BC burden with atmospheric chemical transport models is related to the BC emission strength as input and simulated atmospheric residence time of  BC (Textor et al. , 2006). While the atmospheric residence time of BC aerosols is independent of the emission strength, it is an indication of model-specific treatments of transport and aerosol processes affecting the simulated BC burden.
This is done executing multiple BC transport simulations with CHIMERE, implementing new BC emission inventories, which included the recently estimated India-based constrained BC emissions and the latest bottom-up BC emissions (India-based: Speciated Multi-pOllutant Generator (Smog-India), and global: Coupled Model Intercomparison Project phase 6 (CMIP6), Emission Database for Global Atmospheric Research-V4 (EDGAR-V4) and Peking University BC Inventory (PKU)). A short description of the five BC emission datasets is provided in Section 2. 1. The bottom-up BC emissions applied in the present study are being widely used in regional and global climate models in the assessment of spatial and temporal distribution of aerosol burden and aerosol-climate interactions  (Eyring et al. , 2016; Zhou et al.
The WRF meteorological model The WRF model is a state-of-the-art numerical weather forecast and atmospheric simulation system designed for both research and operational applications. The initial and boundary meteorological conditions for WRF simulation are obtained from Global Forecast System (GFS) National Center for Environmental Prediction -FINAL operational global analysis data (NCEP-FNL, http://rda. ucar. edu/datasets/ds083. 2/) at a spatial resolution of 1 • × 1 •. Meteorological fields are simulated in WRF at the temporal resolution of one-hour with the horizontal resolution same as that for CHIMERE simulation. The meteorological boundary conditions are updated every six hours. The optimized schemes applied in WRF simulation are as follows: Lin scheme for cloud microphysics  (Lin et al.
Implementation of BC emissions and multiple CHIMERE simulations In the present study, five simulations are carried out subjected to the same model processes with CHIMERE but implementing different BC emission inventories. The BC inventories include recently estimated India-based-(i) constrained and (ii) bottomup BC emissions (Smog-India), including the bottom-up BC emissions from global datasets extracted over India-(iii) EDGAR-V4 (EDGAR), (iv) CMIP6 and (v) PKU. Spatially and temporally resolved constrained BC emission over India is taken as per  Verma et al. (2017). The observationally-constrained BC emissions were estimated over the Indian region constraining the simulated BC concentration in a general circulation model (Laboratoire de Météorologie Dynamique atmospheric General Circulation Model (LMDZT-GCM)) with the observed BC by combining forward and receptor modelling approaches  (Kumar et al.
, 2018; Verma et al. , 2017). BC emission inventory based on bottom-up approach is generally compiled using information on activity data and generalised emission factors (see the references for bottom-up emissions, Table  1 (top) ). The recent bottomup BC emission database over India implemented is from Smog-India  (Pandey et al. , 2014; Sadavarte and Venkataraman, 2014). The CMIP6 BC emission used in the model simulations of CMIP6 is a combination of regional and global emission inventories and re-gridded as per EDGAR-V4  (Eyring et al. , 2016). In the present study, global BC emission inventories utilised, viz. Besides BC emission, emission of aerosol species such as OC, SO 2 , primary particulate matter (PPM) are also implemented in CHIMERE. This implementation is done to perform atmospheric aerosol transport simulation for atmosphere with abundant aerosol species (including BC), and that for atmosphere without BC.
Uncertainties in activity data and emission factors have been inferred leading to uncertainty in bottom-up BC inventories of about greater than 200% over India and Asia  (Bond et al. , 2004; Streets et al. , 2003; Lu et al. , 2011). One of the drawbacks of the bottom-up approach is its inability to take into account possible unknown or missing emission sources. Bottom-up BC emissions are thus found to be often lower than the actual  (Rypdal et al. , 2005; Johnson et al. , 2011; Zhang et al. , 2005; Reid et al. , 2009). Bottom-up BC emission over India includes a large missing source of BC emitted over India  (Venkataraman et al. , 2006). Hence, the divergence in emission data (refer Figure  1b ) using five emission datasets (observationally-constrained and bottom-up BC emissions) is indicative of inadequacy in BC emission source strength suggesting specific improvement required in bottom-up BC emission tabulation over the IGP and that at specific locations where the divergence is typically noted to be large.
This approach is further strengthened, implementing BC emissions from five new BC emission inventory databases and simulating BC transport subjected to the same aerosol physical and chemical processes with CHIMERE. Bias in simulated estimates (X modelled ) from simulations at stations mentioned above for hourly, all-day and daytime hours is estimated with respect to observed data (X obs ) with the equation as follows, Bias = (X modelled -X obs ) X obs × 100% (1) where, X = BC concentration, temperature, relative humidity, wind speed and PBLH. Statistical analyses are carried out corresponding to daytime and all-day winter monthly mean to evaluate the normalised mean bias (NMB, equation 2) and root mean square error (RMSE, equation 3) from the simulated results for N (=10 in this study) number of stations.
In contrast, a cooling effect at TOA (-10 to -20 W m -2 ) is exhibited considering the atmosphere without BC aerosols (Figure  6e ). It is also seen that the patch with the most substantial value (>15 W m -2 ) of the net radiative forcing due to BC is observed in and around megacities and is extended to the eastern coast. A comparison of the radiative effect due to BC from Constrained estimates with that from Smog estimates shows that bottom-up BC emissions (e. g. , Smog-India) lead to a relatively lower wintertime radiative warming at ATM and TOA by 30%-50% than the constrained emissions over most of the IGP and that by more than 80% over northern IGP (in and around Delhi). The comparison between the bottomup and the Constrained estimates, thus, indicates the potential underestimation of wintertime radiative perturbation due to BC aerosols over the IGP attributable to the low BC emission strength in the bottom-up BC emission database.
https://doi. org/10. 5194/acp-2020-511 Preprint. Discussion started: 25 September 2020 c Author(s) 2020. CC BY 4. 0 License. emission Database for EDGAR, CMIP6, and PKU are re-gridded to the resolution as per the Smog-India database. The BC transport simulation in CHIMERE corresponding to emission database-Constrained, Smog-India, EDGAR, CMIP6, PKU are referred to as, respectively, Constrained, Smog, Edgar, Cmip and P ku. The annual BC emission strength over the study domain as estimated from the implemented inventories lies in the range 415-1517 Gg yr -1. Details of simulation experiment and BC emission inventories are summarised inTable 1 (top).
5 https://doi. org/10. 5194/acp-2020-511 Preprint. Discussion started: 25 September 2020 c Author(s) 2020. CC BY 4. 0 License. emission Database for EDGAR, CMIP6, and PKU are re-gridded to the resolution as per the Smog-India database. The BC transport simulation in CHIMERE corresponding to emission database-Constrained, Smog-India, EDGAR, CMIP6, PKU are referred to as, respectively, Constrained, Smog, Edgar, Cmip and P ku. The annual BC emission strength over the study domain as estimated from the implemented inventories lies in the range 415-1517 Gg yr -1. Details of simulation experiment and BC emission inventories are summarised inTable 1 (top).
In 2009, the newly elected FMLN government of El Salvador launched a comprehensive health reform, which gave the National Health Forum (NHF) a key role in developing community participation. This study aims to examine and analyze the content and impact of this social movement during the study period 2009-2018. The context was analyzed through relevant documents, which helped identify key stakeholders. Semi-structured interviews took place at 3 levels of decision making: the political level, the health professionals, and the community. Data were analyzed using a qualitative methodology. Participation is carried by a structure created by the Ministry of Health, the National Health Forum. The NHF developed 3 strategies: (1) leadership building in the communities through the strengthening of social, political, and economic skills, where they developed social accountability mechanisms at all decision-making levels of the health system; (2) the strategy carried by the sectoral working groups, where they responded to prioritized needs identified by the community; and (3) advocacy for human rights-based policies.
Methods The study is a descriptive case study, using qualitative methods. We first conducted a document analysis to gather information on the context of El Salvador. We searched PubMed to identify epidemiological data and community participation strategies. Our search strategy was ("Community Health Services" [MeSH]) OR "Community Participation" [MeSH]) AND El Salvador [tiab]. We retrieved 91 articles, of which 8 were full-text reviewed. We identified 3 relevant studies from the included reference list. We developed a second search in Google Academy to identify any kind of document which described the functioning of the Salvadoran health system or community health interventions during the civil war, as those reporting during and after the health reform: 20 references were included. Once identified and contacted with key stakeholders, they shared with us other relevant documents to be able to have a complete understanding of the context in El Salvador.
This was developed through the application of a reflexive approach and the triangulation of data. Data collection: 3 interviewers conducted 10 interviews during fieldwork: 3 members from the NHF; 2 cadres of the Vice-Ministry of Public Health Policies; one primary health care unit coordinator; one frontline professional; and 3 community leaders. Participants were encouraged to speak freely and allowed to lead the interview in the direction they found most interesting and important. Interviews lasted from 30 to 60 minutes and were recorded with participants' written informed consent. The participants were free to leave at any time of the interview. After transcribing the content of audio-recorded interviews, data was coded using Nvivo V R version 10 and then analyzed. Ethical standards were compliant with those set by the National Health Ethics Committee of El Salvador (CNEIS/2018/005_A).
Acknowledgments This research was possible thanks to support from members of the  Ministry of Public Health Policies , the  National Health Form (FNS) , and the  Public Health Policies Observatory, University of El Salvador , which facilitated access to different communities that might otherwise be difficult to reach because of ongoing social violence. We appreciate all the help and support given by  Queen Margaret University specially Claire Mc Lellan ,  Jone Garc ıa , and  Alastair Ager. The authors would especially like to thank  Eduardo Espinoza ,  Margarita Posada ,  Ma Isabel Rodriguez ,  Angela Elıas ,  Rina Abrego ,  Morena Murillo ,  Henry Palacios ,  Rene Domınguez ,  Alex Martınez ,  Emma Cortez ,  Moises Garcıa ,  Juana Rivas ,  Jorge Irazola ,  Luzmila Argueta ,  Guillermo Argueta , and community members for their warm welcome.