dataesr/scientific-paragraphs-categorization huggingface dataset:
Subjects and clinical assessments Fifteen retired rugby players (all males; 7 professionals and 8 amateurs) without known neurodegenerative disease and with no prior cervical spine surgery were enrolled in the study, together with 15 aged-and sex-matched healthy controls. Demographic data are summarized in Table  2. The local ethics committee of our institution approved the protocol and written informed consent was obtained from each participant. Modified Japanese Orthopedic Association score (mJOA)  28  and neck pain (Neck Disability Index (NDI)  29  questionnaires were filled by the participants to evaluate motor and sensory dysfunction.
g. the free pool magnetization right after a dual-offset RF pulse (MT ± ') is related to the magnetization right before (MT ± ) by MT ± '=(1-δ M± )×MT ± ). B 1 and T 1 maps were used to determine the δ factors that fit the attenuation of each MT-w relative to the unsaturated M 0 image, given the ihMTRAGE sequence parameters. The ihMTsat metric was finally derived from to the formulae  21  : , Where the term that accounts for the quadratic B 1 dependence of ihMT with the saturation parameters used  50   https://github. com/lsoustelle/ihmt_proc (hash f3f49e0)). The T 1 maps were then non-linearly registered to the ICBM-MNI-152  51, 52  template on brain and PAM50 template on SC  53. On SC, the PAM50 regions of interest including WM Corticospinal Tracts (CST), Lateral Sensory Tracts (LST), Posterior Sensory Tracts (PST), Rubro/Reticulospinal Tracts (RST), and anterior and intermediate GM (ant-int) were warped back into the subject space and used for quantification of the quantitative maps.
Statistical analyses The statistical analyses were performed using JMP Version 9 (SAS Institute Inc. , Cary, NC). The mJOA and NDI scores were compared between the rugby players and controls using the non-parametric Steel-Dwass all pairs test, separately, and a p-value<0. 05 was considered as statistically significant. For SC, T 1 and ihMTsat parameters were compared between Rugby players and healthy controls using ANOVA, looking at the (R vs. HC) group effect when accounting for age and cervical levels. P values were corrected for multiple comparisons (5 ROIs, 2 parameters, p corrected <0. 005). For the brain, T 1 and ihMTsat parameters were also compared between Rugby players and healthy controls in the three compartments using ANOVA, looking at the (R vs. HC) group effect when accounting for age.
P values were corrected for multiple comparisons (3 compartments, 2 parameters, p corrected <0. 008). To investigate the evolution of metrics with age, linear regressions were performed. Finally, R 1 (1/T 1 ) and ihMTsat maps on brain and SC were used in voxel-wise multi-variate analyses using the Permutation Analysis of Linear Models (PALM) tool available in the FSL package (version alpha119)  56. The Non-Parametric Combination (NPC) option  55  , which allows joint inference over multiple modalities, was more particularly used in this study. To benefit from the multi-parametric MR protocol, R 1 and ihMTsat maps were thus combined using NPC in order to locate potential tissue abnormalities and investigate whether R 1 and ihMTsat were significantly lower in players as compared to HC. For both brain and SC, the analysis was performed using WM mask, GM mask, and the whole structure.
Data/code availability statements Data can be made available via a request to the authors and will be shared through a formal data sharing agreement. The post-processing code has been well described in the Materials & Methods section and Figure  1 , with all the software and toolboxes used (SCT, FSL, SPM, ANTS, etc. ). The ihMTsat map derivation code is also available at: https://github. com/lsoustelle/ihmt_proc (hash f3f49e0)).
Quantitative imaging results Representative images and quantitative T 1 and ihMTsat maps of both brain and SC obtained from one rugby player are provided on Figure  3. IhMT data biased from PVE due to cord curvature (visual assessment) were removed from the analysis (2, 2, 8, and 9 images for the C1, C2, C6, and C7 levels, respectively, for rugby players and 1, 1, 1, 7 and 8 images at C1, C2, C5, C6, and C7 levels for the HC). Brain ihMTsat maps of one rugby player and one HC were also removed because of motion artifacts. Conversely, brain and SC T 1 maps of all subjects were kept. Mean maps of R 1 (1/ T 1 ) and ihMTsat for both rugby players and HC groups (obtained by averaging all individual maps co-registered in the PAM50 and MNI-152 template spaces) are presented on Figure  4. Rugby players globally presented with lower R 1 (i.
Limitations & Perspectives This exploratory study was performed on a retired group of rugby players and the HC group was selected to match the age of players but other parameters such as profession, lifestyle, or hours of sports practice were not considered. More importantly, this investigative study was conducted on a limited number of participants and the effects should now be investigated in a larger cohort of players. Analyses based on the players experience in professional or amateur leagues could not be considered in this study due to statistical power, but similar trends were observed for both sub-populations in the cohort (data not shown). This would nonetheless require further investigations. The position of the players (forwards, especially front row, vs. backs) could not be considered either due to statistical power, but such distinction would be interesting in the future.
Acknowledgements This work was performed within a laboratory member of  France Life Imaging network  (grant  ANR-11-INBS-0006 ) and was supported by the  Institut Carnot Star , the  ARSEP Foundation  ( Fondation pour l'Aide à la Recherche sur la Sclérose en Plaques ) and the  CNRS  ( Centre National de la Recherche Scientifique ). The project also received funding from the  European Union 's  Horizon 2020 research and innovation program  under the  Marie Sk odowska-urie  grant agreement o 1 0 with the financial support of the egional ouncil of Provencelpes-te d' zur and * MIDEX  (n°  ANR-11-IDEX-0001-02 ), funded by the  Investissements d' Avenir  project funded by the  French Government , managed by the  French National Research Agency (ANR). The authors would like to thank  T. Kober  from  Siemens Healthcare  for MP2RAGE sequence support,  A.
The project is a joint effort of 30 European countries, and the European Environment Agency, co-funded by the European Commission under Horizon 2020  [4]. As part of the HBM4EU project, the HBM4EU-aligned studies were set up to collect harmonized and quality controlled recent internal exposure data from European citizens to environmental pollutants. The aim of these HBM4EU-aligned studies is to collect a population sample for children  (6-11 years) , teenagers  (12-19 years) , and adults (20-39 years) that is representative of sex (males, females) and includes individuals from cities, towns/suburbs, and rural areas, with different socio-economic status (SES), and recruited from the four geographical regions of Europe (north, east, south, and west) as defined by the UN geoscheme. Since some European countries already have a recurrent HBM program, i.
The HBM data can also be input into models to estimate external intakes or the dose to target organs. Furthermore, connecting HBM data to biomarkers of early effects may increase knowledge of the underlying processes that could eventually lead to adverse health outcomes  [20]. All this information supports policymakers in designing and evaluating measures or regulations such as REACH to protect the population against adverse effects of environmental pollutants. This paper provides a transparent description of the characteristics of the studies and the population sample in which exposure to the first set of priority substances was measured. It compares the obtained population sample with the general European population for the following characteristics: sex, residential degree of urbanization, and educational level of the household, based on EUROSTAT data (see Section 3 results).
Materials and Methods The aim of the HBM4EU-aligned studies is to collect comparable internal exposure data for priority substances in a sample of the European population that is representative of sex (males, females) and includes individuals from cities, towns/suburbs, and rural areas, with different socio-economic status (SES), and recruited from the four geographical regions of Europe (north, east, south, and west) as defined by the UN geoscheme. The population sample was selected by utilizing a sampling scheme designed to guide the inclusion of eligible studies. The scheme is based on the inclusion of 11 to 12 primary sampling units (PSUs) per age group (children 6-11 years, teenagers 12-19 years, and adults 20-39 years), distributed proportionally to the number of inhabitants in the four geographical regions (north, east, south, west) of Europe, with a maximum contribution of 300 participants per PSU.
Contributing studies are listed alphabetically in Table  1  and a short description with additional information per study is provided in the Supplementary Materials. The data from the PSUs are pooled into a European sample of children, teenagers, and adults. The baseline characteristics of the sampled population of each age group (children, teenagers, and adults) are described per study (PSU) (Tables  S1-S3 ), per European region (north, east, south, west), and for the total sampled population (Tables  1 2 3 ). Verification of the contributing studies was undertaken to ascertain whether they met the predefined criteria for representativeness: sex, attained educational level (ISCED), and degree of urbanization (DEGURBA). Furthermore, the characteristics of the HBM4EU-aligned studies sample of each age group were compared to those of the European general population based on EUROSTAT's tables for EU-28, 2017 (see Section 3).
To safeguard the comparability of the data, analysis had to be performed in laboratories that successfully participated in the HBM4EU QA/QC program  [21]. The laboratories could choose for which analytes they participated in the QA/QC program and the analytical qualification was evaluated at the metabolite level  [42, 43]. As a result, the biomarkers for which data are available differ somewhat per PSU. The biomarker data for each of the data collections have a data quality label assigned. A detailed overview of analytes measured per study is available in Tables S10-S12. The detection limit (LOD) and quantification limit (LOQ) reported vary among the studies, as well as the methods used for the determination of LOD/LOQ. The LOQ is used as a cut-off to report quantifiable data, except when a lab only reported a LOD, then the LOD was used as a cut-off to report quantifiable data.
Consequently, some children were already 12 years when urine samples were collected. It was decided to accept these deviations and not exclude those individuals and their data from the HBM4EU-aligned studies. Depending on the research objective, it will be evaluated whether these subjects should be included or excluded from the dataset. Not all ages are equally represented in the HBM4EU children's group (see Table  S1 ). Some studies recruited children over the entire age range from 6 to 11 years i. e. , CROME whereas other studies include a narrower age range, e. g. , NAC II, OCC (Table  S1 ). This leads to an overrepresentation of specific ages (see Figure  2 ). Most children (N = 978) are 7 years old (31. 2%) while 6 years old children are underrepresented (4. 4%). Also, the sampling dates varied among PSUs.
Questionnaires As part of the HBM4EU project, new harmonized questionnaires have been developed based on knowledge and experience in different EU countries (Pack et al. in preparation). As the HBM4EU-aligned studies combine ongoing and newly planned studies, the questionnaires used by the studies differ. Four studies, that were still in the planning phase, adopted the HBM4EU questionnaires or developed their own questionnaire based on the HBM4EU questionnaire (CROME, HBM4EU-study for Switzerland, Diet_HBM, and INSEF-ExpoQuim). The HBM4EU questionnaires are available online: https://doi. org/10. 5281/zenodo. 6414615. Questionnaires had different components and were differently administered (self-administered, telephone, in person, and computer assisted or on paper). In some studies, different collection methods were applied for separate questionnaire modules.
Difficulty with these FPQ and FFQ are the differences in foods listed, length of the reference period, response intervals for specifying the frequency of use, and the procedure for estimating portion size. For example, some questionnaires asked for dietary habits over the last year, others over the last 3 months. In addition to FFQ and FPQ, some studies (26%) also requested information on recent dietary exposure through a 24 h recall questionnaire. This 24 h recall method was also recommended by EFSA for the harmonized collection of food consumption data in adults  [47]. To harmonize the available data across the studies a post-harmonization approach was followed which is described elsewhere  [13]. The final codebooks with harmonized variables are available online: HBM4EU Harmonized Codebook Adults-Aligned studies|Zenodo (https://doi.
Strengths and Limitations Overall, the adopted approach resulted in a sample of 3137 children (6-12 years), 2950 teenagers (12-18 years), and 3522 adults (20-39 years) which is comparable to the general European population (EUROSTAT EU-28, 2017). The HBM4EU-aligned studies provide a large coherent dataset that allows making robust conclusions regarding internal exposure to pollutants in European children, teenagers, and adults, as well as stratified by sex, educational level, degree of urbanization, and European region. Furthermore, the quality and comparability of analytical results are guaranteed through the extensive QA/QC scheme organized as part of HBM4EU  [21]. Working with a network of multiple laboratories provided a large capacity and therefore samples could be analyzed in a rela-tively short timeframe.
Some points that can be improved are lowering and harmonization of detection and quantification limits (LOD, LOQ) across laboratories and additional laboratory training to laboratories across Europe to analyze the same broad set of metabolites including newer (emerging) chemicals. This will in turn improve the potential for exploring co-occurrent internal exposures (i. e. , personal mixtures). With the current dataset, this is already possible, but to a limited extent because the combination of available data varies per study (Supplementary Material Tables  S10-S12 ). The HBM4EU-aligned studies dataset combines information on socio-economic characteristics, health status, lifestyle, food habits, and residential environment, from the different studies into a large EU-wide and comprehensive dataset for the interpretation of internal exposure levels.
The post-harmonization approach applied, is a time-consuming process and also presents some limitations. For example, information on the consumption of canned food items was available for only 4/11 studies in adults. Missing variables in some studies will reduce the sample size available for the analysis of exposure determinants. Upfront alignment of questionnaires in future surveys will increase the comparability and use of the European pooled dataset. Pooling data from different studies increases the sample size and statistical power to study exposure-effect associations. For a European study that is only partly financed with EU funds, and which requires financial commitments from the member states, it is not feasible to impose a fully harmonized questionnaire. It was emphasized by the participating countries that flexibility with regard to questionnaires is necessary, on the one hand, to guarantee continuity with previous campaigns, for those countries that already have a recurrent HBM program, but on the other hand, also to include culturally and nationally bound information.
The HBM4EU-aligned studies dataset provides a good picture of the internal exposure of European citizens. However, there are some points that can be improved. Individuals with lower secondary education attainment are underrepresented in our population sample. This has been observed previously in epidemiological studies  [52]. Future surveys should include additional efforts to attempt to engage individuals attaining up to lower secondary education (ISCED level 0-2). Furthermore, the final sampling period of the HBM4EU-aligned studies is fairly wide 2014-2021, spanning a time period of eight years. In addition, the time periods differ per PSU (Tables  S4-S6 ). Such a broad sampling period may introduce a time trend effect within the sample. For the next European HBM program, it is recommended to shorten the sampling period to a three-year period, to improve on this aspect.
With regard to the age of the participants, there are also differences per PSU (Tables  S4-S6 ) resulting in an over-or underrepresentation of specific ages. For example, no teenagers of 19 years were included. Ideally, each PSU collects individuals of each age within the defined age range, resulting in an equal distribution of age. To address these limitations, when comparing the data from the studies or pooling the data, the influence of those factors (age, sex, sampling years, season, smoking and educational level) on the concentration levels will be investigated using linear regression models per dataset. Significantly influencing factors will be taken into account in the further statistical analysis, by including these variables in the models. Comparison of results from the individual contributing studies (PSUs) should be performed with caution as not all studies are representative on the national level and therefore results may not in all cases be generalized to the particular country in which they were collected.
In addition, heterogeneity among PSUs in sampling years, age, season of sampling, and number of subjects within an age group should be considered if exposure data between studies are compared. More upfront alignment will improve the potential of the European HBM dataset to extrapolate exposure results to the entire European population. To compare exposure levels between European countries, stricter criteria will need to be applied to achieve an equal age distribution per PSU, a shorter time frame, and representativeness on the national level for the collected subsample per PSU. In practice, these criteria are difficult to achieve within the context of alignment of HBM studies where the studies are only partially funded through European projects, and thus national or regional interests in terms of target population and time frame cannot simply be ignored.
Acknowledgments: The authors would like to thank everybody who contributed to the HBM4EUaligned studies: the participating children, adolescents, adults and their families, the fieldworkers that collected the samples, and database managers that made the information available to HBM4EU, the HBM4EU project partners, especially those from WP7 for developing all materials supporting the fieldwork, WP9 for organizing the QA/QC scheme under HBM4EU and all laboratories who performed the analytical measurements. HBM4EU is co-financed under Horizon 2020 (grant agreement No 733032). The authors thank all principal investigators of the contributing studies for their participation and contribution to the HBM4EU-aligned studies and the national program owners for their financial support.
The global oceans also play a significant role in modulating the observed seasonal rainfall and the recent changes in the past climate signal  31, 32. The greenhouse gases are the main factors explaining the future climate patterns of the region  32. Other observations include gridded mean monthly precipitation (P) and minimum and maximum temperature (Tmin and Tmax, respectively) datasets for the historical period (1981-2010). They were extracted from the WATCH Forcing Data methodology applied to the ERA-Interim Data (WFDEI)  97  meteorological forcing dataset at a grid resolution of 0. 5° × 0. 5°. The WFDEI data set merged with the in-situ data from synoptic stations to update the baseline status's temperature maps and times series (Supplementary Fig. S2a, b ).
Moreover, the selected GCMs represent the whole CMIP6 ensemble, including three models with low climate sensitivity (GFDL-ESM4, MPI-ESM1-2-HR, MRI-ESM2-0) and two models with high climate sensitivity (IPSL-CM6A-LR, UKESM1-0-LL). Other datasets are based on Weather Research and Forecasting (WRF) and the Consortium for Small-scale MOdelling in CLimate Mode (CCLM) regional climate models (RCMs). These RCMs were used to downscale the GCMs under RCP 4. 5 for the West African region  101, 102. Bias correction was also performed on the outputs using a multivariate bias correction (MBC), considering the interdependency between climate variables in the historical and future periods  103. The MBC data sets were generated at a horizontal resolution of 0. 11 × 0. 11 degrees. Future projections focus on two different combination scenarios representing different shared socioeconomic pathways (SSPs) and Representative Concentration Pathways (RCPs).
Impact assessments. Crop model simulation design. CERES and CROPGRO, embedded in the Decision Support System for Agrotechnology Transfer version 4. 7 (DSSAT4. 7, www. dssat. net)  104  , and interfaced with R software (DSSAT-R)  105  , were used to perform spatial yield simulations for millet, maize, rice and cotton productions in a changing climate. The climate forcing data files (WTH. LST) were extracted from historical (In situ observations & model outputs) and projected SSP126 & SSP370 datasets. The soil input data was extracted and formatted from ISRIC (Global Soil Information Based on Automated Mapping  106, 107  , and HC27 (Global highresolution soil profile database for crop modeling applications) generic soil profiles, soil hydraulic properties derived from pedotransfer functions  107  , and soil physical and chemical properties required by the crop models.
The basic physical-geological parameters include sea level rise, geomorphology, coastal slope, regional elevation, shoreline change, significant wave height, and tidal range taken from ISMI2b and ISIMIP3b datasets. Wave energy is related to erosion capacity, where relief and vertical land movements are considered flood risk indicators. The CVI is described as the square root of the product of these physical-geological units divided by the number of variables (n). a: Geomorphology; b: shoreline change rates; c: Coastal slope/relief; d: Relative Sea-level rate; e: Significant wave height (Hs); f: Tidal range. Factors a and b are obtained from the literature, c, d, and f are derived from satellite data, while e is obtained from outputs computed by the WAVEWATCH III model (https:// github. com/ noaa-emc/ ww3) forced by the wind from coupled global models.
Three of the six variables will change to calculate the CVI in the future. These variables are Hs, the sea level rise rate, and the shoreline erosion/acceleration rate. The geomorphology is obtained from literature and verified with Google Earth (https:// earth. google. com/ web/). Landsat images (https:// www. usgs. gov/ lands at-missi ons/ lands at-data-access# C2L1) were used to calculate shoreline erosion/accretion. Sea level rise and tide data were obtained from AVISO altimetry (https:// www. aviso. altim etry. fr). All these images were processed using ArcGIS 10. 2 software (https:// www. esri. com/ about/ newsr oom/ arcwa tch/ the-best-of-arcgis-10-2/) and the Digital Shoreline Analysis System (https:// www. usgs. gov/ cente rs/ whcmsc/ scien ce/ digit al-shore line-analy sis-system-dsas).