dataesr/scientific-paragraphs-categorization huggingface dataset:
Information on the child's diet at seven years of age was collected using a frequency food questionnaire (28 foodstuffs, frequencies ranging from "never" to "several times a day"). We defined a score of obesogenic food consumption by summing the frequencies of intake of the following foodstuff categories: 'French fries, fried potatoes' , 'pizzas, pies' , 'soft drinks' , 'light soda' , 'pastries, biscuits, cakes' , 'sweets, chocolate' , and 'crackers, snacks'. The score for obesogenic dietary habits was categorized into quartiles. All models were adjusted for total lipids in cord blood (when studying prenatal exposure) or in child blood (when studying postnatal exposure), after log10-transformation  [50, 51]. Missing data for chlordecone concentrations and covariates were accounted for using the Full Information Maximum Likelihood (FIML) method  [52, 53].
Acknowledgements We are grateful to all the families of the Timoun cohort, in particular to those participating in the successive follow-up visits, to the staff of the follow-up at 7 years ( Marie-Fred Noyon ,  Nathalie Surville Barland ,  Annie-Claude Coriolan ,  Chantale Emeville ,  Colette Danquin ,  Sabrina Mimifir ,  Tania Plumain ,  Indira Oujagir ,  Agnès Desiré ,  Katia Galbas ,  Stéphanie Reine ), and to  Catherine Adam , laboratory technician at the  LEAE-CART , for her valuable help in xenobiotics analysis.
5-25 km,  [56] ) and requires grid enhancement to achieve higher spatial resolution  [55]. The use of optical remote sensing to map surface melt, using the response of grain size to the presence of meltwater, can achieve a much higher spatial resolution (10 m-1 km) than from passive microwave observations. Here, we present the d opt retrieved via the Pre-operational Sentinel-3 Snow and Ice (SICE) toolchain [57] from the OLCI instrument onboard the Sentinel-3A satellite. We present the SICE d opt dataset in Greenland for 2017-2019 and evaluate it against ground observations. We describe the response of d opt to surface warming and melt using Automatic Weather Stations (AWS) observations and build a surface melt flag based on d opt at a 1 km spatial resolution. Eventually, we compare our melt flag with available melt maps derived from passive microwave measurements.
Mosaic Construction Swath data are combined into 1 km EPSG:3413 projection daily mosaics over the Greenland ice sheet. When a region is covered by multiple OLCI scenes the same day, the pixel that is cloud-free and presents the minimal solar zenith angle is used in the mosaic. A lookup table identifies the scene ID of each pixel in each daily mosaic. Grids of solar zenith and azimuth angles, viewing zenith and azimuth angles and ozone are extracted from each OLCI scene and assembled according to the same look-up table as the daily reflectance mosaics [57].
e. for clear sky conditions, when remote sensing is possible. The planar albedo can be simplified to a function of the bulk absorption coefficient α(λ) and absorption length l: r p = exp -α(λ) l u(µ 0 ) (14) If the retrieved grain size is smaller than 0. 1 mm, a residual cloud contamination is possible, and we flag the pixel accordingly. Due to limitation of the ART for low sun conditions, pixels that have a solar zenith angle greater than 75 • are not considered for retrieval. We also limit our retrieval to the snow covered part of the ice sheet and for the pixels that have been classified as "clean snow" by the SICE retrieval  [51]. An extension of this retrieval for polluted snow pixels  [50]  is also included in the SICE dataset.
Passive Microwave Remote Sensing of Surface Melt and Melt Flag Comparison To map surface melt over the Greenland ice sheet between 2017 and 2019, we used the MEaSUREs Greenland daily surface melt flag  [12, 83]. It used, for that period, data from the SSM/I F17 and F19 sensors on board the Defense Meteorological Satellite Program (DMSP) satellites  [84]  and a snowpack microwave emission model and dynamic threshold adjusted for each year to detect melting/nonmelting surface conditions. It is available at a 25 km resolution on an EASEv2 grid and only fully glaciated pixels are considered. For each year, between 1st of May and 30th of September, we compared the detected melt from remotely sensed d opt and MEaSUREs melt flag to the reference melt calculated at the PROMICE AWS, using four metrics.
The SICE Snow Optical Grain Diameter Dataset The SICE toolchain provides daily snapshots of optical snow grain diameter at a 1 km resolution. The main challenge for shortwave optical remote sensing is the impossibility of retrieval during cloudy conditions. The average cloud-free coverage was 45% in 2017, 31. 8% in 2018 and 40% in 2019 (Figure  2 ). The ice-sheet-wide snow-covered area-only mean d opt was around 0. 3 mm at the beginning of the season and peaked above 0. 6 mm around late July or early August, when surface melt is widespread. The smaller standard deviation in May indicates the relative homogeneity of cold, dry snow grains at the surface in the early melt season. As melt starts on the southern and low-lying ice sheet, the standard deviation increases and reaches its maximum synchronously with the average d opt , at the peak of the melt season.
On the 12th and the following days, the warming continues and the AWS, the  -based flag and the MEaSUREs flag all indicate melt at KPC_U (Figures  6  and 7 ). However, different spatial patterns can be seen in the two melt flags. On the 12th, MEaSUREs flag reaches altitudes up to 2000 m a. s. l. The  -based flag shows a main melting area reaching up to 1600 m a. s. l. and isolated melting patches up to 1800 m a. s. l. On the 13th and 14th, it is in turn the  -based melt flag that reaches the highest altitudes (up to 2400 m a. s. l. ) while the MEaSUREs melting area does not reach higher than 2200 m a. s. l. (Figure  6 ). On the 18th, the melt stops at higher elevation and continues at below 1600 m a. s. l. According to the MEaSUREs flag and below 1200 m a. s. l. for the  -based flag. During the entire period, the EGP AWS did not show any sign of melt either as calculated from the AWS data (Figure  1 ) or according to the two melt flags (Figure  6 ).
Unfortunately, no in situ melt data is available in the area where the two melt flags disagree. Additionally, the KPC_U AWS quickly loses its snowpack which limits the number of days for which both d opt -based and MEaSUREs melt flags are available. At KAN_U however, melt is frequent, and the snow never melts out, making it an appropriate location to evaluate the two melt flags against the melt estimated from AWS data (Figure  8 ). As expected, there are more days where retrieval was impossible for the d opt -based flag because of cloud cover: retrieval was possible for only 45% of days during May-September of 2017-2019. Considering the AWS-derived melt estimate as ground truth, accurate melt flagging, false positives and false negatives can be found for both melt flags (Figure  8 , Table  3 ).
In theory, it is possible that large melt-affected snow grains remain at the surface after the melt stops and consequently that our melt flag would show false positives after the end of the melt event. Although it is a plausible scenario, we do not see this occurring often in our dataset, potentially for two reasons. First, the pervasive action of wind on the ice sheet may quickly erode the surface layer. Small windblown snow grains then cover the melted snow and the d opt -based flag is reset to nonmelting. Second, the end of the melt period coincides with cooler temperatures and consequently solid precipitation. The reflective fresh snow deposited at the surface has the potential to shut down the melt. In that situation, once the site becomes cloud-free again, our retrieval will identify fine grained fresh snow and nonmelting conditions.
In this absence of ground truth, the grain shape could only be used as a tuning parameter within radiative transfer models to minimize the difference between calculated and observed surface reflectances. A recent report from the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) evaluated the accuracy of the OLCI instruments onboard Sentinel-3A and B when measuring ocean reflectance  [93]. They concluded that the OLCI measurements of surface reflectance at wavelength 865 nm were within the 2% accuracy defined in the mission requirements. However, the measured reflectances at 1020 nm were ~9% and ~6% too bright for Sentinel-3A and Sentinel-3B, respectively. These biases have been derived over dark ocean surfaces and cannot be used over bright snowy surfaces. No reference dataset is currently available for a vicarious calibration of OLCI over bright surfaces.
Conclusions We presented the optical snow grain diameter (d opt ) retrieved for 3 years of Sentinel-3 OLCI observations over the Greenland ice sheet. After ensuring that the remotely sensed d opt matches with in situ observations at EastGRIP, we analyzed the response of retrieved d opt to surface warming and melt at four PROMICE automatic weather stations where melt can be calculated from meteorological measurements. We established that the retrieved d opt and the in situ estimation of melt covary, which leads to a binary melt flag that can be constructed around the threshold d opt of 0. 64 mm with a high likelihood of melting above this threshold. We applied this threshold to our d opt dataset and derived melt maps for daily 1 km mosaics of Greenland. We found that the spatiotemporal evolution of our melt flag compares well with the passive microwave MEaSUREs melt flag during a heat wave in Northeast Greenland and at the KAN_U PROMICE AWS.
Data Availability Statement: The daily mosaics of retrieved optical grain diameter, SSA and albedo are available at https://doi. org/10. 22008/FK2/OIAJVO [97]. The scripts of the retrievals are available at https://doi. org/10. 5281/zenodo. 5179529 [57]. The PROMICE AWS data are available at https: //doi. org/10. 22008/promice/data/aws [78]  and the scripts of the SEB model are available at https: //doi. org/10. 5281/zenodo. 4542767 [98].
Methods and analysis For this ongoing work, four databases were searched: PubMed, Embase, WHO COVID-19 Global literature on coronavirus disease and PsycINFO, for the period between January 2020 and April 7 2021. Peer-reviewed published literature in English and all types of population-based studies will be considered. Studies using standard tools to assess multimorbidity such as disease count, comorbidity indices or disease combinations will be retained, as well as studies with standard scales and scores for frailty or measurement of a socioeconomic gradient. Initial search included 10 139 articles, 411 for full-text reading. Results will be summarised by risk factor, objective and outcome. The feasibility of meta-analysis will be determined by the findings and will aim to better understand uncertainties of the results.
Data management The literature will be managed using Rayyan,  43  EndNote and Excel. Records retrieved from the four databases will be exported in EndNote for deduplication. Title and abstract screening will be performed in Rayyan, a web and mobile app for systematic reviews. Full-text articles will be read in EndNote, and data will be extracted in customised Excel tables. Two researchers will perform the screening process and data extraction. Considering the actuality of the topic, the quantity of the literature envisaged and the need for timely information, several researchers will be involved in the screening process. For example, two researchers will assume the role of the first reviewer while several colleagues will act as the second reviewer. Weekly meetings will ensure a clear understanding of the study process.
Data synthesis The literature review is ongoing. The search included 10 139 articles in title and abstract screening phase, of which 411 were retained for full-text reading. The screening process results will be presented with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) diagram. 50 Given the interest in several risk factors, the PRISMA diagram may be adjusted, as suggested in figure  1. The final set of retained studies will be described using standardised tables. They will be summarised within each objective, by type of risk factor and study outcome and presented in tabulated form. Study characteristics will be described as well as their main findings. When possible, meta-analysis models will be performed by each objective, for each risk factor and for a specific outcome with the aim to better understand the uncertainties (random error and bias) of the obtained results.
The feasibility of performing meta-analyses will depend on additional criteria, such as number of studies applying the same measurement tools for risk factors and evaluating the same outcomes, as well as reporting OR, HR, risk ratio or slope. For the meta-analysis, a minimum of four studies will be required. In studies assessing the role of socioeconomic inequalities on health outcomes, unadjusted studies stratified by age group will be sought to avoid overadjustment. 51 For studies assessing the roles of biomedical factors, adjusted analyses will be preferred. Adjustment factors will be thoroughly reported. Random effects meta-analyses will be performed with R software using the metafor package. 52 Findings will be presented with forest plot(s). Publication bias will be visually assessed using funnel plots and formally tested using Begg and Mazumdar's rank correlation test  53  and Egger's regression test.
Acknowledgements We wish to thank  Population Health Information Research Infrastructure (PHIRI)  coordination team for initiating and financing this project. We would like to thank documentation experts from  Santé Publique France ,  Laetitia Haroutunian  and  Sandra Kerzanet , for their valuable advice on formulating the search strategy. Contributors LC-B, JCos, BD, PN,  NS  and  MJF  identified the research questions. All authors were involved in the conceptualisation of the search strategy. TTM and JG will share the role of a first reviewer, each reviewing half of the retrieved articles; the role of a second reviewer will be divided between LC-B, SMB, RH, RF-S, FPB, JCot, MA, BV, PB and PN. Acting first and the second reviewer will independently evaluate retrieved records for eligibility and inclusion in the systematic reviews and they will independently extract relevant data.
The third-party (JCos), if necessary, will resolve any disagreement between the two reviewers. TTM, JG, JCos,  BD  and  LC-B  formulated the first version of this article. All authors revised and approved the final version of the article. Funding These systematic reviews are part of  PHIRI  project (available from: https://cordis. europa. eu/project/id/101018317/fr). PHIRI is funded by the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement no:  101018317. The protocol was developed in collaboration with all listed institutions. The funding body was not involved in the design of the study; it will not be involved in the collection, analysis and interpretation of data or in writing the manuscript. The content of this publication will represent the views of the authors only and not of the European Commission or any other body of the  European Union.
On the contrary, sorghum landraces showed a more variable response to total water transpired: positive for S8 (from Pakistan) and negative for S6 (from Sudan). The three remaining landraces did not show significant interaction with the total water transpired. Since the relationship between total water transpired and total silica extracted (%) was significant but not high, we decided to compare different predictors to observe which had the strongest effect on biosilica accumulation. The model that explained the highest variability in the dataset included the interaction effect of water treatment and genotype (Table  2 ). When the dataset with all the accessions was tested for the effect of water treatment alone, the explained variability was too low (5%) to accept the model, showing that biosilica accumulation was not exclusively associated with the water regime.
The explained variability increased substantially when the species was used as an explanatory variable. The value of the adjusted R-squared increased by 10% when the genotypes were used as predictors and by 26% when the interactive effect of genotypes and treatment was used, showing that there was a high variability among genotypes and in how genotypes responded to water treatment. Table  2. Comparison of linear and multiple linear regression models to account for silica accumulation variability (% of silica extracted) in the dataset, including the three species of finger millet, pearl millet and sorghum.
The strong effect that genotypes have in explaining the variability in the dataset (Table  2 ) could also be related to the specific genetic mechanism that sorghum landraces display for biosilica accumulation. The wide expression profile of Lsi1 and Lsi2 transporter proteins and their regulation, which can be related to different absorption rates of monosilicic acid, is known in the literature  [58]. Furthermore, the presence of mutants unable to absorb high quantities of silica in sorghum has been suggested by Markovitch and colleagues  [59] , which possibly justifies the low biosilica accumulation of the landrace S6 (Figure  4 ). While silica accumulation in millets responds to the water availability, in sorghum, there is an extra variable that could play a role in biosilica formation and that could account for the high variability rate: the genetics of the silicon transporter.
Selection of Landraces To exclude the possibility of changes in the genetics of Si absorption introduced in the breeding process, we decided to work with traditional landraces to observe the variability in biosilica production in un-improved genotypes. Landraces were chosen according to two criteria: (a) area of origin, under the assumption that we need to investigate dryland crops which might have suffered different local adaptations and evolution processes, and (b) climate of origin, to cover all the possible variability within an arid environment. Thus, samples were chosen by selecting their coordinates of origin using the Climatic Research Unit TS3. 10 Dataset  [66]  and https://en. climate-data. org/ (accessed on 1 October 2018), which provide high resolution data on world temperature, precipitation and relative humidity.
The databases provide five independent climate variables (mean temperature, average sun hours, precipitation, rainy days and relative humidity) covering the global land surface. They also estimate maximum and minimum temperatures as well as secondary variables, such as potential evapotranspiration. CRU TS3. 10 has been used to intersect the climate variables (provided in the form of grids) with the origin coordinates of the varieties on Google Earth. We obtained landraces of pearl millet (Pennisetum glaucum L. R. Br), finger millet (Eleusine coracana L. Gaertn) and sorghum (Sorghum bicolor L. Moench) seeds from the collection of the ICRISAT (Hyderabad, India) gene bank (Table  3 ). Table  3. Selected landraces from the ICRISAT genebank with a. the acronym used to identify them and b. their accession number.
Multiple linear regression models were used to evaluate the variable/s with the strongest effect on bioslica accumulation. Redundancy analysis (RDA) was applied to summarize the variation of the dataset (including the physiological parameters measured and biosilica accumulation) using water treatment as an explanatory variable and conditioned by species and genotypes (covariables). Since the physiological parameters have different units, and as a consequence they are distributed along specific ranges, they have been scaled to unit variance to avoid variables with high values to have a stronger effect on the analysis  [79, 80]. Statistical analyses and data visualization were conducted in R (version 3. 5. 1) using standard functions of base, ggplot2 (version 3. 3. 5) [81]  and vegan (version 2.
The following are available online on https://doi. org/10. 5281/zenodo. 6320474, File S1: Experiment dataset, File S2: R script, FigureS1: Biomass boxplots, FigureS2: Linear regressions. Author Contributions: Conceptualization, F. D. , C. L. , M. M. , V. V. ; methodology, V. V. , J. K. ; formal analysis, F. D. , J. R. -P. ; investigation, F. D, J. K. ; resources, V. V. , J. K. ; writing-original draft preparation, F. D. ; writing-review and editing, C. L. , M. M. , V. V. , J. R. -P. ; visualization, F. D. , J. R. -P. ; supervision, C. L. , V. V. , M. M. ; project administration, C. L. ; funding C. L. , M. M. All authors have read and agreed to the published version of the manuscript. Funding: This research and the APC were funded by the European Research Council under grant number ERC-Stg 759800.
Acknowledgments: We would like to thank all the  GEMS team (ICRISAT)  who helped during the fieldwork, particularly  Rekha Baddam  and  Suresh Naidu , as well as  Krithika Anbazhagan  and  Sunita Choudhary , whose suggestions have been invaluable and benefited the success of the study. Thanks are due to  Guillermo Marin Garcia  for his help in processing the samples in the laboratory and to  Alessandra Varalli  for her support and guidance. Universitat Pompeu Fabra health and safety measurements have been followed during both the fieldwork and the laboratory work. At the time of working on this project, F. D. , J. R. P. , M. M. and C. L. were members of the  Culture and Socio-Ecological Dynamics (CaSEs-UPF ), a recognised Research Group of the  Catalan Agency for Research  ( AGAUR SGR-212 ).
Supplementary Materials: The following are available online on https://doi. org/10. 5281/zenodo. 6320474, File S1: Experiment dataset, File S2: R script, FigureS1: Biomass boxplots, FigureS2: Linear regressions. Author Contributions: Conceptualization, F. D. , C. L. , M. M. , V. V. ; methodology, V. V. , J. K. ; formal analysis, F. D. , J. R. -P. ; investigation, F. D, J. K. ; resources, V. V. , J. K. ; writing-original draft preparation, F. D. ; writing-review and editing, C. L. , M. M. , V. V. , J. R. -P. ; visualization, F. D. , J. R. -P. ; supervision, C. L. , V. V. , M. M. ; project administration, C. L. ; funding C. L. , M. M. All authors have read and agreed to the published version of the manuscript. Funding: This research and the APC were funded by the European Research Council under grant number ERC-Stg 759800.
CRITICAL NEED TO IMPROVE RHEUMATIC AND MUSCULOSKELETAL DISEASE (RMD) RESEARCH CAPACITY IN EUROPE 2] [3]  These debilitating conditions limit the individual, leading to inability to work and enjoy optimal quality of life, significant healthcare expenditures and related societal costs. 3 Despite progress in the last two decades with improved therapeutics in several RMDs, there are no cures, and their collective impact remains substantial. Access to RMD-related care, policies prioritising RMDs and related research, education/ training, and funding differ between European countries. 4 5 5] [6] [7] [8]  A review of scientific congress abstracts submitted to the EULAR between 2012 and 2016 supported these findings. The data demonstrated underrepresentation (<5%), particularly of central European countries such as Croatia, the Czech Republic, Hungary, Poland, Slovakia and Slovenia.