dataesr/scientific-paragraphs-categorization huggingface dataset:
Here, we showed similar synovial pathotypes distribution between OA and early treatment-naïve RA Fig. 3  Quantitative and semi-quantitative scoring of immune cells infiltration distinguish osteoarthritis (OA) synovial pathotypes. (A) Digital image analysis was performed on OA synovial tissue sections using QuPath software. 22  A representative picture of both the immunohistochemistry original (upper panel; 3,3′-Diaminobenzidine (DAB)-positive cells in brown, nuclei in blue) and segmented (lower panel; DAB-positive cells are circled in light blue, DAB-negative cells in pink) images is shown. Scale bar = 50 µm. (B) Correlation between the percentage of CD3 + , CD20 + , CD138 + , and CD68 + positive cells and the semi-quantitative scores for inflammatory cells markers CD3, CD20, CD138, and CD68 (total score of both lining and sublining monocytes/macrophages), respectively.
This work was supported by departmental funds (Queen Mary University of London, Centre for Experimental Medicine and Rheumatology). M. -A. B. is funded by the "Fondation pour la recherche médicale" [grant number ARF202004011786] and by the Inserm "ATIP-Avenir" program. A. N. is funded by "Versus Arthritis" Clinical Lectureship in Experimental Medicine and Rheumatology [grant number 21890]. We would also like to thank "Versus Arthritis" for infrastructure support (Experimental Arthritis Treatment Centre [grant number 20022]). The NIHR supported F. R. 's fellowship [TRF-2018-11-ST2-002]. Medical Research Council (MRC) support has been obtained for the development of the PEAC cohort [grant number 36661]. The R4RA trial was funded by the Efficacy and Mechanism Evaluation (EME) Program, a partnership between the MRC and the National Institute for Health and Care Research (NIHR) [grant 11/100/76].
STRAP clinical trial (EudraCT reference 2014-003529; STRAP-EU reference 2017-004079 www. matura-mrc. whri. qmul. ac. uk) was funded by the MRC [grant MR/K015346/1]. The MRC also funded the Immune-Mediated inflammatory Disease Biobanks in the UK (IMIDBio-UK) [grant MR/R014191/1].
Study area: the Seine hydrosystem The Seine basin (≃78,650 km 2 ) is entirely located within the Paris sedimentary basin in northern France. The geological settings consists of concentric tertiary sedimentary formations (alternating clay, sandstone and limestone), Cretaceous Chalk and Jurassic limestones lying on a basement of Hercynian crystalline rock formations, mainly outcropping at the extreme south-east (Morvan) and north-east (Ardennes) limits of the basin. The highest altitude is 856 m adove sea level (asl) and 90% of the basin is below 300 m. The river network shows gentle slopes and climate does not exhibit sharp geographical gradients. The climatic regime of the basin is pluvial oceanic, modulated by seasonal variations in evapotranspiration. The mean annual precipitation rate is 825 mm over the 1980-2020 period [SAFRAN dataset, Vidal   et al.
, 2010]. Flow at the downstream Seine river was determined at the gauging station of Vernon before the estuary. The mean discharge at the station was 490 m 3 •s -1 over the 1980-2020 period (HYDRO database). Furthermore, variographic studies have shown the stationarity of both groundwater and river water stock over this time period  [Flipo et al. , 2012]. Currently, the Seine basin is the most urbanized and industrialized basin in France  [Flipo et al. , 2020]. It includes 17 million inhabitants (25% of the national population), with 10 million in the Paris conurbation alone, and 40% of national industrial activities, while a large zone around the huge Paris conurbation is oriented towards the mass production of cereals and industrial crops (Figure  1 ). This scale of activity creates an enormous energy demand, which can be partially offset by exploiting geothermal energy  [Bayer et al.
These budgets depend strongly on land cover, described in each grid-cell as a mosaic of 15 plant functional types (PFTs) including bare soil, evergreen and deciduous trees, C3 and C4 grasses and crops. All PFTs share the same equations but with different parameters. The proportion of each PFT is prescribed in each grid cell from land cover maps derived from satellite observations, with yearly updates  [Lurton et al. , 2020]. Two options are compared in this work regarding the evolution of leaf area index (LAI), which can be either prescribed via input maps of monthly LAI, or calculated dynamically at a daily time step by the so-called STOMATE module, which redistributes the net primary production (photosynthesis minus autotrophic respiration) owing to PFT dependent rules of allocation and phenology.
CaWaQS (v3. 17) Mostly relying on the former MODCOU-NEWSAM software  [Ledoux et al. , 1989]  based on the blueprints of de  Marsily et al. [1978] , the physically-based CaWaQS3. x [Flipo et al. , 2022 [Flipo et al. , , 2007 ] computes, at a daily time-step, the main physical processes controlling the water budget and flow dynamics in each compartment of a hydrosystem. Each CaWaQS library deals with a specific compartment of a hydrosystem. This feature allows users to bypass certain modules when necessary and makes CaWaQS suitable for coupled applications. CaWaQS is capable of distributing forcing fluxes over its own grid, and accepting forcing fluxes at various grid sizes. Forcing runoff fluxes are aggregated by CaWaQS to local sub-catchment areas and directly routed to the river network in which river discharges are simulated using a Muskingum scheme and water levels are simulated using a Manning-Strickler approach  [Saleh et al.
Numerical experiment design The coupled tool was applied over the 2001-2018 period, which was noteworthy for remarkable flood events (e. g. winter/spring 2001, winter 2016-2018) as well as extremely low-flow/drought periods and heat-waves (e. g. summer  2003, summer 2006-2007). ORCHIDEE setup. ORCHIDEE is run over the Seine basin in stand-alone mode, forced with the SAFRAN meteorological data set  [Vidal et al. , 2010]. It provides the required atmospheric variables at an hourly time step, over a regular 8 × 8 km grid, which is therefore the horizontal resolution of the simulations. A total of 1490 cells are used to described the simulated domain. A constant carbon dioxide concentration (370 ppm) over the period 1980-2018 was applied. The vegetation distribution map was derived at 0. 5°resolution from the ESA-CCI Land Cover dataset at 300 m resolution, for the 1992-2015 period [see details in  Lurton et al.
, 2020]. Vegetation distribution outside this period was set to that of the nearest available year with a constant vegetation distribution. For simulations with prescribed LAI, the monthly values were obtained from an existing 40-year global simulation performed in stand-alone mode at 0. 5°( 55 km resolution). The soil background albedo map was derived from the MODIS albedo dataset aggregated at 0. 5°resolution. Soil texture distribution maps were obtained from four sources: Reynolds  [Reynolds et al. , 2000] , SoilGrids  [Hengl et al. , 2017] , LUCAS  [Ballabio et al. , 2016] , and INRA  [Jolivet et al. , 2006]. The initial state was obtained after a five-year warm-up period  (1980) (1981) (1982) (1983) (1984) (1985)  in order to reach the land surface model equilibrium state for soil moisture and soil temperature.
, 2008]  database. River network water inputs account for subsurface runoff, overflows from aquifer to soil surface as well as river-aquifer exchanges. Mainly due to computation time concerns, river-aquifer exchange calculations were restrained to the main hydraulic network, which corresponds to river reaches with a Strahler order above or equal to 3 (Figure  1 ). The thermal parameterization (thermal conductivities, heat capacities and porosities) of the Seine basin was extracted from  Dentzer [2016]  and  Rivière [2012]. Heat transport was simulated in the river reaches with a Strahler order above or equal to 3 (Figure  1 ). Soil surface temperatures obtained from ORCHIDEE were prescribed to river reaches below Strahler order 3. The initial temperature state of the aquifer system was obtained by running the coupled model for 40 years by re-cycling 2 sets of 20-year simulations in the period 1985-2005.
Previous studies  [Flipo et al. , 2022]  indicate that around two-thirds of effective rainfall contributes to aquifer recharge in the basin, and this ratio was also considered as a qualitative target for the calibration. A set of 10 ORCHIDEE simulations were chosen to illustrate this iterative process (Table  1 ). First, EXP1-EXP4 allow us to assess the influence of the soil texture map (with a prescribed LAI map and default parameters, as used at global scale for CMIP6). Then, the two available options to account for LAI variations (prescribed map vs dynamic simulation by the STOMATE module) can be assessed by comparing EXP4 and EXP5. Finally, EXP5-EXP10 only differ by some ORCHIDEE parameters found to be effective by several sensitivity analyses [e. g. Raoult et al. , 2021] : the saturated hydraulic conductivity (K sat ) assigned to the dominant soil textures in the Seine River basin (loamy sand, loam, clay loam) and its decay factor c K (1); the visible and near-infrared leaf albedo and maximum LAI of the main PFTs in the basin.
Evaluation strategy. To validate the coupled model performance, the Seine basin hydrogeological and thermal application results were compared to a set of river discharge and water level monitoring wells as well as river and aquifer temperature data, distributed among the different aquifer units (Figure  1 ). The Kling-Gupta Efficiency coefficient (KGE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Percent bias (PBIAS) and the Pearson correlation coefficient (CC) were used to quantitatively evaluate the misfit between simulated and observed values and dynamics. Field data were collected between 2001-2018. The variables collected are discharge data from 183 gauging stations; hydraulic head data from 267 piezometers were used to evaluate the performance statistics of the hydrology part.
Discharge and hydraulic head time series data were obtained from publicly available ADES and HYDRO national datasets. River water temperature data were assembled from various agencies operating in the basin; the dataset comprises hourly and daily observations from 62 sites  [Rivière et al. , 2021]. During the simulation period  (2001) (2002) (2003) (2004) (2005) (2006) (2007) (2008) (2009) (2010) (2011) (2012) (2013) (2014) (2015) (2016) (2017) (2018) , 48 stations with hourly observations were selected. The aquifer temperature data used were obtained from the national ADES database. While  providing a comprehensive amount of data, records are often incomplete and heterogeneous. The majority of the temperature data were collected on site while pumping, hence the depths of the temperature observations represent the depth of the screened well.
A pre-processing was therefore applied to the dataset using the interquartile range (IQR) method, as described in  Hemmerle et al. [2019]. c K 2 - - - - 4 - - 10 - K sat (m•s -1 ) Loamy sand 4. 05 × 10 -5 - - - - - -1. 73 × 10 -5 - - Loam 2. 88 × 10 -6 - - - - - -1. 50 × 10 -6 - - Clay loam 7. 22 × 10 -7 - - - - - -4. 62 × 10 -7 - - Near-infrared leaf albedo TeBE 0. 22 - - - - - 0. 2 - - - TeBS 0. 22 - - - - - 0. 2 - - - NC3 0. 3 - - - - - 0. 25 - - - AC3 0. 3 - - - - - 0. 1 - - - Visible-light leaf albedo TeBE 0. 06 - - - - - 0. 04 - - - TeBS 0. 06 - - - - - 0. 04 - - - NC3 0. 1 - - - - - 0. 05 - - - AC3 0. 1 - - - - - 0. 04 - - - Maximum LAI TeBE 4. 5 - - - - - - - - 5. 5 TeBS 4 - - - - - - - - 5 NC3 2 - - - - - - - - 3 AC3 2 - - - - - - - - 3 From the initial data set of 9179 stations over the Seine-Normandy region, 212 stations with a minimum of 40 observations over the simulation period remained after the pre-processing.
, 2020]. Eventually, the Vernon discharge bias decreases from 39. 8% in EXP1 to 25. 9% in EXP4, so we kept the Reynolds soil texture map for further optimization. The dynamic calculation of the vegetation dynamics by the STOMATE module (EXP5) allows a further increase in AET (+26 mm•a -1 ) compared to simulations with prescribed LAI (EXP1-4). The reason is that STOMATE calculates a higher LAI than the prescribed one, increasing the AET via a larger biomass during the growth season. The resulting decrease in total runoff leads to a further reduction of the Vernon discharge bias decreases, from 25. 9% to 14. 2 %. The small decrease in the contribution of surface runoff to total runoff (-3%) can be attributed to the trade off between surface runoff and increased vegetation activity and water abstraction of roots in the topsoil.
The coupled ORCHIDEE-CaWaQS framework is designed to take advantage of broadly available gridded datasets from catchment to regional scales to estimate hydrosystem water and energy budgets. The transport module estimated the mean river temperatures and aquifer temperatures adequately. The findings presented herein have the potential to inform biological or water quality studies  [Flipo et al. , 2020]  on the Seine basin or engineering applications such as ATES to reduce the dependency on fossil fuel  [Lee, 2010]  by providing insights into key physical processes driving the heat fluxes in the river network and the aquifer system. The impact of climate change on the share of energy fluxes at the regional basin scale will also be studied and provide useful information for adaptation and mitigation strategies.
Acknowledgments This work is a contribution to the  PIREN Seine research program , which is part of the  Zone Atelier Seine , included in the e-LTER network. We gratefully acknowledge the river temperature dataset provided by  William Thomas. Groundwater  withdrawals were obtained from  Seine-Normandy Water Agency , over the 1994-2012 period. We would like to thank two anonymous reviewers for their input that improved the final article's quality.
Background: Childhood brain tumours (CBTs) are the leading cause of cancer death in children under the age of 20 years globally. Though the aetiology of CBT remains poorly understood, it is thought to be multifactorial. We aimed to synthesize potential risk factors for CBT to inform primary prevention. Methods: We conducted a systematic review and meta-analysis of epidemiological studies indexed in the PubMed, Web of Science, and Embase databases from the start of those resources through 27 July 2023. We included data from case-control or cohort studies that reported effect estimates for each risk factor around the time of conception, during pregnancy and/or during post-natal period. Random effects meta-analysis was used to estimate summary effect sizes (ES) and 95% confidence intervals (CIs).
Search strategy and eligibility criteria We searched PubMed, Web of Science, and Embase databases for articles without restriction on publication date and language and extracted information from original articles published in peer-reviewed journals from 1976 to 2022. The studies were included if they 1) were case-control or cohort studies, 2) reported effect estimates for specific exposure time window: preconception, prenatal or postnatal, and 3) reported risks of CBT for children below 20 years old at diagnosis. Only studies that provided estimates of the Relative Risk (RR), such as Odds Ratio (OR), Hazard Ratio (HR), Standardized Mortality Ratio (SMR), Mortality Rate Ratio (MRR), Standard Incidence Ratio (SIR) or Incidence Rate Ratio (IRR) with 95% confidence intervals (CIs) were included.
Data collection process Following removal of duplicates and screening, we extracted the following data from the full-text articles: authors' name, year of publication, study location (city, country and continent), period of diagnosis, age range at diagnosis, exposures, exposure assessment methods, outcome ascertainment, number of CBT or, if not available, of childhood cancer cases and controls/study population, follow-up duration, as well as risk estimates with their respective CIs. Information on study design (case-control and cohort or nested case-control) was also extracted. Registry-based case-control studies (with exposure data from censuses, hospital records, and other register data) and Nested case-control studies were considered as cohort studies. Among exposures extracted were birth and parental characteristics, pesticides and other chemicals, radiation, and lifestyle factors.
The I 2 statistic was assessed to quantify the heterogeneity of the results between studies. I 2 values of 0% were considered to represent "no heterogeneity", from 1% to 35% "low heterogeneity", from 36% to 55% as "moderate", from 56% to 70% as "substantial" and above 71% as "considerable" heterogeneity  [41]. As standard requirement, a minimum of 2 studies were needed for the meta-analysis and 3 studies for the bias analysis. Furthermore, we also performed sensitivity analyses by stratifying the studies by: 1) publication year to explore any time trend and 2) geographical region where the studies were conducted. A nominal significance level of 0. 05 was used for heterogeneity and Egger's tests. Analyses were conducted using STATA® software, version 15. 1 (College Station, TX, USA).
, Israel 1991-2014 < 19 Gestational age Hospital records Cancer registry 35 231344 2020a[123] Kessous et al. , Israel 1991-2014 < 19 Maternal pre- Hospital records Cancer registry 38 238 005 2020b[124] pregnancy obesity Cha et al. , Korea 1995-2006 < 12 Birth characteristics Birth database Death database 344 6479,406 2011[125] Hong et al. , South Korea 2006-2015 < 20 Postnatal exposure to National health National Health 2872 12 068 821 2019[126] diagnostic low-dose insurance system Insurance System ionizing radiation Australia /Oceania Stavrou et al. , Australia-New 1994-2005 < 13 Maternal smoking Midwives data Cancer registry 20 979,809 2009[127] South Wales during pregnancy collection Mathews et al. , Australia 1985-2005 < 20 Postnatal exposure to Health services Cancer registry 283 10. 9 million 2013[128] computed records tomography (CT) scans Europe Yeh et al.
Denmark, Finland, 1985-2006 < 15 Postnatal exposure to Childcare database Cancer registry 3600 17 848 2010b[144] Sweden, and infections records and Nordic Society Norway of Paediatric Haematology and Oncology Bjørge et al. , Denmark, Finland, 1967-2010 < 15 Perinatal risk factors Face-to-face Cancer registries 5163 172 422 2013[16] Norway, and interview and in direct Sweden reports from pediatric, oncology, and neurosurgery hospitals Sundh et al. , Denmark, Finland, 1982-2012 < 20 ART Birth registers Cancer registries 156 450,215 2014[145] Sweden and Norway Hakulinen Finland 1965-1970 < 15 Parental Antenatal records Cancer registry 219 219 et al. , 1976 occupational [146] exposure to hydrocarbons Seppälä et al. , Finland 1996-2014 < 20 Gestational diabetes Birth registry Cancer registry 484 2407 2020[147] Verkasalo Finland 1970-1989 0-19 Postnatal exposure to Calculated Cancer registry 39 134 800 et al.
, 2006 at birth [155] Kollerud et al. , Norway 1967-2009 < 16 Childhood exposure On-site indoor Cancer registry 427 712 674 2014[156] to radon radon concentrations measurements and a buffer model with different radius size Mortensen Norway 1999-2010 < 15 Folic acid intake Birth registry Cancer registry 185 687 406 et al. , 2016 during pregnancy [157] Pershagen Sweden 1982-1987 < 6 Maternal smoking Birth registry Cancer registry 81 148917 et al. , 1992 [158] Hemminki Sweden 1960-1994 < 15 Parental Family cancer Cancer registry 1617 8. 8milion et al. , 1999 characteristics database [159] Feychting Sweden 1976, 1977, < 15 Parental Occupational Cancer registry 162 235,635 et al. , 2000 1981, and occupational history [160] 1982 exposure to magnetic fields (continued on next page).
, 2021 external background exposure models [176] ionizing radiation based on aerial spectrometric gamma-ray measurements Williams et al. , UK, England, 1992-2008 < 15 ART Human Cancer registry 12 12 137 2018[177] Wales, and Fertilization and Scotland Embryology Authority records Keegan et al. , UK 1962-2006 < 15 Paternal occupation Occupational data Cancer registry 10854 10702 2013[178] and social class in birth registry Bhattacharya UK -Aberdeen 1993-2005 < 15 Maternal and Maternity and Cancer registry 176 704 et al. , 2014 perinatal risk factors neonatal databank [179] records Nyari et al. UK-England 1975-1986 < 15 Prenatal exposure to Health records Cancer registry 161 404,106 2003[180] infections Kroll et al. , UK-England and 1962-1995 < 15 Postnatal exposure to Estimate magnetic Cancer registry 6584 6577 2010[181] Wales EMF field from high- voltage overhead power lines (continued on next page).
Acknowledgments We would like to thank  Marita Torrissen Mårli  for help with strain construction, and the  NMBU Imaging Center  for help with transmission electron microscopy. This work was supported by a grant from the  Research Council of Norway  ( 296906 ,  250976 ) to MK, from the  Agence Nationale de la Recherche  ( ANR-19-CE15-0011-01 ) to CG, and from the  Swiss National Science Foundation  ( 310030_207974  and  PP00P3_198903 ) to CP.
Gel assays and analysis 1. 2 % agarose gels (in Tris-acetate-EDTA buffer) were used to follow the kinetic of GGA experiments. Gels were post-stained in ethidium bromide (EtBr, 0. 5 µg/ml) for 15 minutes and destained in water for 10 minutes (twice). The quantification of DNA bands (intensity) was performed automatically using a code written in R (https://github. com/github. com/wilfriedgrange/gel_analysis). Details are also given in Supplementary Figure  S2. To quantify the stoichiometry of GGA experiments, gels contain two lanes with --Õ and -Õfragments at time zero. From those, we calculate the ratio S -defined as 100 ◊ -Õ -/(-Õ -+ --Õ ), where -Õand --Õ are intensities (determined from gel assays). For a stoichiometry of 3 ([--Õ ]/[-Õ -]) and 2 fragments experiments (e. g. those reported in Figure  2 ), S -= 49.
five), fidelity is certainly of less importance than efficiency. As such, online tools such as https://github. com/ scottweis1/iBioCAD on the basis of HamediRad et al. (2019) and https://goldengate. neb. com on the basis ofPryor et al. (2020) may give inappropriate results. For our 3 fragments reaction (single-molecule DNA tether), the proposed overhangs are GAGA and CCGA with overhang stabilities of -3. 88 and -5. 31 kcal/mol (https://github.
PRC2 impairment in mouse striatal neurons induces progressive neurodegeneration by triggering a self-sustaining transcription derailment program over time  88. Furthermore, KO or transient chemical inhibition of PRC2 also led cells to enter a quasi mesenchymal state that depends on ZEB1, the mouse homolog of fly Zfh1, is highly metastatic and associates with poor patient survival  77. Therefore, epigenetic events might play a major role at early stages of oncogenesis or during tumour progression in some mammalian cancers  54, 89, 90. Our survey of a large database of different types of solid cancers (Extended Data Fig. 9 ) as well as of data from several Multiple Myeloma patient cohorts (Extended Data Fig. 10 ) indicates that low expression levels of genes encoding canonical PRC1 subunits is associated with several tumours and with poor patient prognosis, consistent with a putative suppressive role for PRC1 in some tumor types.