dataesr/scientific-paragraphs-categorization huggingface dataset:
0 • ), except two samples with the highest La/Ce ratios (1014-14 and 370-13 with θ = -0. 5 and + 2. 8 • , respectively). While the samples from Europe mostly show ΔεCe/Ga between -0. 7 and 0. 5, and Canadian samples between -1. 5 and -0. 5. Siberian samples deviate more significantly from the mantle array with ΔεCe/Ga from -2. 3 to -0. 3 (except 1014-14 and 370-13 with ΔεCe/Ga = -0. 1 and 0. 8, respectively). To further explore the significance of our data and inferences, and how representative the Siberian xenoliths could be of the CC, we calculated θ and ΔεCe/Ga values using the elemental concentrations of 445 Archean and 1136 post-Archean granulites from the literature. We used the database compiled by  Hacker et al. (2015)  and added new values from the PetDB database. Fig. 6  presents the relative numbers of samples from each region for the Archean and post-Archean datasets.
All cratons are incorporated into both the Archean and post-Archean datasets. Compared to the global ages distribution of the continental lithosphere (Fig. 7  in  Poupinet and Shapiro, 2009)  our database shows an under-representation of post-Archean samples from Siberia relative to their Archean equivalents and an over-representation of West European samples relative to Antarctica, Australia and South America that are under-represented. The calculated θ and ΔεCe/Ga values of the complete database are reported in Supplementary data 2 and shown in Fig. 7a. Archean and post-Archean granulites present similar, normal distributions of θ and ΔεCe/Ga values with peaks at -3 • and -0. 4, respectively. These slightly negative values will result in isotopic C. Israel et al. LITHOS 460-461 (2023)   107369 compositions increasingly deviating to the left of the mantle array over time.
(2015). Their 138 La/ 142 Ce ratios range from 0. 00350 to 0. 00322, in agreement with the peak for Archean granulites but lower than the peak observed for post-Archean granulites (Fig. 7b ). Our observations are based on a comprehensive set of granulite compositions, however, weighting the parent/daughter ratios by the relative surface area of different continents is unlikely to change significantly our observations. In any case, a more mafic LCC than the  Rudnick and Gao (2014)  reference is suggested by the literature on granulites. The granulite database (Fig. 7 ) indicates the most likely parent/daughter ratios of the LCC that result in θ = -3 • and ΔεCe/Ga = -0. 46 ±0. 05 are 138 La/ 142 Ce = 0. 00380 ±0. 00007 and 147 Sm/ 144 Nd = 0. 128 ±0. 007. The atypical isotope evolution of Siberian samples likely results from La/Ce decrease due to enhanced mobility of LREE during high-grade Sm/ 144 Nd diagrams estimated for the European, Canadian and Siberian samples from this study and compared to Archean (n = 445) and post-Archean granulites (n = 1136) from literature.
Willig and Stracke (2019)  and  Willig et al. (2020)  showed similar results for their modeled LCC. The horizontal distances (ΔεCe) of the bulk CC and the deep continental crust from the mantle array are -1. 5 ±0. 8 and -2. 9 ±1. 2, respectively (mean values and two standard deviations). These estimates consider: (1) three reference compositions of the CC after  Rudnick and Gao, 2014 and Hacker et al. , 2015 and  (2) the range of Nd isotopic composition of the bulk CC -εNd = -21 to -10 after  Allègre and Lewin, 1989 and Chauvel et al. , 2014, respectively). Only the Ce-Nd isotopic compositions for the Siberian samples plot near the modeled MCC + LCC reservoir. The peak θ values in the literature dataset (-3 • , Fig. 7a ) and the associated ΔεCe per billion years, enable to estimate the deviation of the Ce isotopic composition from the mantle array for the LCC at about -0.
Impact of lower continental crust recycling on the mass-balance budget of the BSE The massive recycling of LCC proposed by  Tang et al. (2015)  affects the mass-balance calculations for the Ce-Nd systematics. The resultant bulk CC and MCC + LCC εCe-εNd isotopic compositions are closer to the mantle array than the previous reservoir estimates (with ΔεCe ranging from -1. 2 to -1. 6 (±0. 9) -see bulk CC and MCC + LCC represented as red and black fields in Fig. 8 ). The MCC + LCC reservoir fits with the granulite database, suggesting that the Ce-Nd isotopic composition of the LCC is located to the left of the mantle array, but not as far as the Siberian samples. Our estimates indicate a mass fraction of bulk CC relative to the BSE (m bulk CC /m BSE = 0. 024 ±0. 011 and 0. 033 ±0. 022 for depleted and chondritic BSE, respectively) that is 4.
Comparing the measured parent/daughter ratios with a large set of granulites from the literature, we found that only Siberian xenoliths deviate significantly to the left of the εCe-εNd mantle array with time (high negative θ and high negative ΔεCe/Ga, representing the degree and rate of deviation, respectively). As a comparison, Archean and post-Archean literature granulites also have negative θ and ΔεCe/Ga but essentially to a lesser extent than the Siberian samples, making the latter exceptional cases in the LCC database. Most worldwide granulites exhibit a θ of -3 • and can deviate from the εCe-εNd mantle array by ≈0. 46 epsilon Ce units per billion years. Using this compilation, we propose new parent/daughter ratios for the LCC: 138 La/ 142 Ce = 0. 00380 ±0. 00007 and 147 Sm/ 144 Nd = 0.
128 ±0. 007. These findings suggest that high-grade metamorphism and/or reworking occurring in LCC conditions may lead to enhanced mobility of certain LREE in LCC rocks and deviation of the La-Ce and Sm-Nd isotopic systems from the mantle array over time. The LCC represented by the granulite database cannot be reproduced by a typical mass-balance budget of the BSE. The later results in the isotope composition of the deep continental crust (MCC + LCC) far to the left of the εCe-εNd mantle array; it would require to incorporate a substantial proportion of Siberian-like granulites within the continents, although they are rare in the LCC database. Alternatively, incorporating a substantial proportion of LCC in mass-balance calculations, considering the massive recycling of LCC back into the mantle, provides a more reliable explanation.
Acknowledgments We thank  D. Laporte  for providing Beaunit,  Bournac , and Ivrea samples,  C. Fonquernie ,  C. Bosq , and  D. Auclair  for technical support,  K. R. Ludwig  for the use of his program IsoplotR,  E. Hasenstab ,  A. Stracke  and  H. Downes  as well as two anonymous reviewers for helpful comments that have improved the quality of this manuscript and the  Geological Survey of Japan  for providing the isotope reference material JNdi-1. Reference material Ce LMV is available upon request. This project received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  (Grant Agreements No. 682778 -ISOREE  and No. 817934 -MILE-STONE ). A. V. G. was supported by the state assignment project of  IGM SB RAS  (No. 122041400157-9 ).
The burden of sickle cell disease (SCD) in France has been difficult to apprehend due to the paucity of reliable nationwide epidemiological data. We aimed to describe the epidemiology of SCD and evaluate its burden and costs. Patients with SCD and most severely affected patients were identified between 2012 and 2018 from the French National Health Data System database (SNDS, Système national des données de santé). Outcomes of interest included rates of acute and chronic complications, healthcare resource utilization and associated costs, and were compared in subpopulations of patients before and after hematopoietic stem cell transplantation, initiating hydroxyurea or a chronic transfusion program. Between 2012 and 2018, 22,619 patients with SCD were identified, among which 4,270 patients were defined as most severely affected.
Methods The French healthcare system offers universal coverage for nearly 99% of French residents in mainland France and overseas territories. 6 Reimbursed medical services are captured in an exhaustive pseudonymized patient-level collection of claims data: the French National Health Data System database (SNDS, Système national des données de santé). SNDS is a suitable tool for epidemiological and population-based medical resource utilization research. 6, 7 he study population consisted of patients identified in SNIIRAM (Système national d'information inter-régimes de l'Assurance maladie) from the French general healthcare insurance system, alive on or born after January 1 st , 2012, with at least one SCD-related record between 2009 and 2018 (Figure  1 ): -hospitalization for SCD (ICD-10 code D57 excluding D57.
Results The prevalence of SCD was 15,203 patients in 2012 and slightly increased over the years to 21,668 patients alive in 2018 (Online Supplementary Figure  S2 ). Overall, between 2012 and 2018, a total of 22,619 patients with SCD were identified in the national database. Among these, 2,207 patients were not covered by the general health insurance system and were excluded, resulting in a study population of 20,412 patients with SCD. A subpopulation of 4,270 most severely affected patients was identified at the index date. Male/female sex ratio was 0. 74, with 8,702 (42. 6%) male and 11,710 (57. 4%) female patients. Mean (standard deviation [SD]) age at the index date was 24. 0 (21. 0) years, and 45% of patients were younger than 18 years old. Most severely affected patients tended to be younger, with a mean age at the index date of 21.
It should be noted that for all of the above-mentioned treatments, cost comparison was restricted to a few years after treatment intensification and does not reflect the lifetime cost of patients undergoing HSCT or initiating HU and CTP. Cost distribution was skewed, with a small number of patients with very high costs, driving mean costs to €23,347. 75 after HSCT, €45,075. 85 after CTP initiation and €20,752. 06 after HU initiation. Even though the median gives a better indication of cost distribution among patients, this difference between the two measures highlights a high variability in HCRU within these subpopulations, notably among HSCT patients. Mean costs might be considered a better measure of the economic burden since the costs of these outliers are also presented for reimbursement The use of the SNDS database allowed to include a majority of patients with SCD in France and enabled adequate power for analysis of this population, but our study has some limitations inherent to secondary database analysis.
This difference results from the lack of clinical and biological data in SNDS that allows to precisely estimate disease severity in clinical practice. Consequently, the definition of most severely affected patients relied on acute events and treatment utilization in a reallife setting rather than chronic comorbidities. Altogether, this resulted in a possible inclusion of non-severe patients in the most severely affected patients subgroup. The availability of both genotype data and biological variables would have allowed a more precise identification of severity profiles and assessment of the burden related to severe SCD and provided additional insights into the therapeutic management of the disease and overall burden reported in this study. Our results nevertheless confirm the severity subgroup definition and are coherent with existing literature that highlights that the most severely affected patients share characteristics that are associated with more acute and chronic pain, more hospital re-admissions and an accumulation of organ damage.
39, 40 Because this study is descriptive, the relationship between the severity and outcomes is yet to be demonstrated. Results for the age at death are challenged by the study design that does not allow the longitudinal follow-up of patients, causing a possible overestimation of life expectancy. Furthermore, severity status was defined on index date only, resulting in a potential underestimation of the number of most severely affected patients by not including patients whose severity status appeared during follow-up. Finally, French medicaladministrative databases do not allow to control the data validity and quality. Some studies have reported some unreliability in the ICD-10 diagnoses coded in the PMSI database. 6, 41 There is a risk of information bias related to coding errors in hospitalization diagnosis; however, considering the important number of patients included, this bias is expected to have a limited impact.
, 2012; Crisp et al. , 2017). We use the version 10 OCO-2 Level 2 bias-corrected XCO2 data product from Goddard Earth Sciences Data and Information Services Center (GES 105 DISC) Archive: https://disc. gsfc. nasa. gov/datasets/OCO2_L2_Lite_FP_10r/summary), which has been validated with collocated ground-based measurements from the Total Carbon Column Observing Network (TCCON; discussed in more detail in Section 2. 2). After filtering and bias correction, the OCO-2 XCO2 retrievals agree well with TCCON in nadir, glint, and target observation modes, and generally have absolute median differences less than 0. 4 ppm and Root Mean Square differences less than 1. 5 ppm  (O'Dell et al. , 2012; Wunch et al. , 2017). 110 https://doi. org/10. 5194/egusphere-2022-1022 Preprint. Discussion started: 15 December 2022 c Author(s) 2022.
1  and Table. 2) to assess IAV patterns among the datasets. Keppel-Aleks et al. , (2014)  showed that drivers of IAV (i. e. , temperature, drought stress, or fire) could be attributed when surface CO2 were aggregated into similar broad zonal belts, whereas process-level attribution was not possible with global averaging. We therefore analyze broad zonal belts to gain a large-scale understanding of how three CO2 datasets are similar and where differences lie. 190.
XCO2 IAV compared to surface and TCCON ground-based sites 290 Given that the small IAV signal (up to 1 ppm over land, and smaller over ocean) is similar in magnitude to noise and systematic bias in  OCO-2 soundings (Torres et al. , 2019) , we corroborate patterns of IAV from OCO-2 with other datasets. The OCO-2 IAV timeseries in broad latitudinal belts share similarities with those of TCCON XCO2 and MBL surface CO2 IAV timeseries, with all timeseries showing similar relationships to MEI. Especially striking is that all timeseries capture the lagged response in the NH midlatitude belt to the strong 2015/16 El Nino (Fig. 4a-c ). 295 Although the patterns are similar, the magnitude of IAV at the MBL sites is almost double the IAV in the OCO-2 XCO2 timeseries. Given that the atmospheric boundary layer, where surface observations are made, is on average 10% of the total column, this suggests that much IAV in total column observations is present within the free troposphere.
These R values are, as expected, smaller than the zonal averages shown in Fig. 11b , which average some of the site-level noise for TCCON and gridcell-level noise for OCO-2. The detailed XCO2 IAV 370  or MBL surface CO2 IAV to ENSO, and track the CO2 IAV against the positive/negative phase of ENSO, together with the transport of the signal from South to North (Fig. 4 ). All datasets show consistent patterns in the response to 385 the El Nino periods, although we note that the IAV amplitude is a factor of almost two smaller in the column average mole fraction, which reflects the fact that IAV variations emerge due to surface fluxes in the lower part of the atmosphere  (Olsen & Randerson, 2004) , but are efficiently transported into the free troposphere which comprises the bulk of the column.
Conclusions 430 We examined IAV in OCO-2 data to determine whether the small variations that result from interannual flux variations can be detected in light of other sources of variance in the space-based dataset. Our results show that zonal averages reveal relationships with ENSO that are consistent with those from established ground-based monitoring network. Zonal averages greatly reduce random noise in XCO2 compared to 5x5 o averages. In general, OCO-2 can successfully monitor CO2 IAV over both land and ocean, contributing important spatial coverage beyond inferences of IAV from 435 existing ground-based networks.
Acknowledgements 455 The authors thank the participants of the  NASA OCO-2 mission  for providing the OCO-2 data product (From GES DISC Archive: https://disc. gsfc. nasa. gov/datasets/OCO2_L2_Lite_FP_10r/summary)used in this study. We thank TCCON partners for providing total column data. We acknowledge NASA support through the  OCO Science team  and grants  80NSSC18K0897 ,  80NSSC21K1071 ,  80NSSC18K0900 , and  80NSSC21K1070  to the  University of Michigan  and  University of Virginia. The Paris TCCON site has received funding from  Sorbonne Université , the 460 French research center  CNRS , the  French space agency CNES , and  Région Île-de-France. TCCON sites at Tsukuba, Rikubetsu and Burgos are supported in part by the  GOSAT  series project. Burgos is supported in part by the  Energy Development Corp.
Philippines , the TCCON site at Reunion Island has been operated by the  Royal Belgian Institute for Space Aeronomy  with financial support since 2014 by the  EU  project  ICOS-Inwire and the ministerial decree for ICOS  ( FR/35/IC1  to  FR/35/C6 ) and local activities supported by LACy /UMR8105  and by  OSU-R/UMS3365 -465   Université de La Réunion. The TCCON stations at Garmisch and Zugspitze have been supported by the  Helmholtz Society  via the research program " Changing Earth -Sustaining our Future ". The Eureka TCCON measurements were made at the  Polar Environment Atmospheric Research Laboratory (PEARL)  by the  Canadian Network for the Detection of Atmospheric Change (CANDAC) , primarily supported by the  Natural Sciences and Engineering Research Council of Canada, Environment and Climate Change Canada , and the  Canadian Space Agency.
Data availability The version 10 OCO-2 Level 2 bias-corrected XCO2 data product is available from Goddard Earth Sciences Data and Information Services Center Archive: https://disc. gsfc. nasa. gov/datasets/OCO2_L2_Lite_FP_10r/summary. TCCON Data is publicly available from the TCCON Data Archive (https://tccondata. org/) hosted by the California Institute of Technology. MBL dry air mole fraction data is available from NOAA Global Monitoring Laboratory Earth System Research Laboratories Archive: https://gml. noaa. gov/ccgg/mbl/data. php.
Study Variables The predictor variable was specialty background (OMFS/ CFPS-Rs vs. TS-Rs). The main outcome variables were LHS and CFRCs until postoperative month six. The data were collected from surgical and/or intensive medical records in both electronic and handwriting forms. CFRCs were classified into seven parameters: (1) large incision or hypertrophic scar ≥ five mm width in dorsoventral direction, (2) hair loss, (3) sensory deficit, (4) facial motor deficit, (5) temporal fossa depression, (6) hematoma or prolonged bleeding, and (7) infections requiring prolonged antibiotics > one weeks. All of these seven parameters were recorded in binary (yes/no). The necessity or patient's demand for plate and screw removal after midfacial and/or mandibular fracture repair, which were concomitantly performed with upper facial fractures, were not regarded as a complication, if this material removal was performed after six months since the fracture repair.
Data Management and Statistical Analysis Data were collected and entered into a Microsoft Excel 2007 worksheet (Microsoft Inc. , WA, USA), and statistical software MedCalc® (MedCalc Software Ltd. , Ostend, Belgium) for analysis. Descriptive and binary statistics were compiled to provide an overview of the sample and identify significant outcome differences between both trainee groups (OMFS/CFPS-Rs vs. TS-Rs). Parameters associated with the outcomes of interest in bivariate analysis (P < 0. 15) were included in a multiple logistic regression model. For all analyses, a P ≤ 0. 05 was considered statistically significant. To identify the benefit-risk profile of the surgery by OMFS/CFPS-Rs over the surgery by TS-Rs, we computed the number needed to treat (NNT, i. e. , LHS < nine days [median]), to harm (NNH, i.
Authors' Contributions Conception and design: PP, KS, CT, NS, J-PM, AN Acquisition, analysis and interpretation of (blinded) data: PP, KS, CT, NS Drafting and revising the work: PP, KS, CT, NS, J-PM, AN Final approval of the work: PP, KS, CT, NS, J-PM, AN. Agreement to all aspects of the work: PP, KS, CT, NS, J-PM, AN. Funding Open Access funding enabled and organized by Projekt DEAL. There was no further external funding. Data Availability Deidentified individual participant data are not available. Based on the current patient data protection law in Germany, open access to the raw data is not allowed. The datasets generated and analyzed during this study are available from the corresponding author upon reasonable request.
, 2023). In the present study, we intend to replicate and to extend previous findings using epigenetic clocks in individuals with BD. We chose to use six epigenetic clocks: five that are predictive of accelerated aging, mortality and morbidity  (Hannum, Horvath, EN, GrimAge, PhenoAge)  and one associated with telomere length (DNAmTL) to estimate the epigenetic age of 184 individuals with BD using DNAm data obtained from whole blood samples. In addition, we investigated the associations between epigenetic age accelerations and basic socio-demographic and clinical variables (including age, sex, body mass index, tobacco use, childhood maltreatment, current use of psychotropic drugs and several indicators of BD course).
Sample description and clinical assessment This study was approved by the Comité de Protection des Personnes -La Pitié-Salpêtrière hospital -Paris -France (ClinicalTrials. gov Identifier: NCT02572674). All participants provided written informed consent prior to inclusion. The sample consisted of 184 euthymic individuals with a diagnosis of BD according to the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM IV) criteria. Participants were recruited in eight university-affiliated psychiatric departments in France. Inclusion criteria for this study were as follow: Caucasian origin; age between 18 and 55 years; and scores below 10 at the Montgomery-Asberg depression rating scale  (Montgomery and Asberg, 1979 ) and 12 at the Young Mania Rating Scale  (Young et al. , 1978)  at inclusion.
The SCID (Structured Clinical Interview for DSM Disorders) was used to confirm the diagnosis of BD. Childhood traumatic events exposure was assessed with the French validated version of the Childhood Trauma Questionnaire (CTQ)  (Bernstein and Fink, 1998; Paquette et al. , 2004). For all participants, the database contained basic information such as age, sex, Body Mass Index (BMI), smoking status. Current alcohol misuse and past or current substance misuse were exclusion criteria. Information about the number of major mood episodes, age at onset of BD, current prescribed medications (antidepressants, atypical antipsychotics, lithium and anticonvulsants) were also recorded into the database.
DNA extraction and methylation measurement A blood sample was collected at inclusion and whole blood DNA was extracted using the automated Maxwell 16 DNA Purification Instrument and the dedicated Maxwell® RSC Blood DNA Kit (Promega). Whole blood DNA samples were treated with sodium bisulfite using the EZ DNA methylation kit (Zymo Research, CA, USA) following the manufacturer's standard protocol. Methylome analysis was performed using Illumina Infinium MethylationEPIC Arrays (Illumina, Inc. San Diego, USA) at Diagenode facilities (Diagenode, Belgium). The Illumina GenomeStudio software was used to extract the raw signal intensities of each probe (raw beta values) from the array. These beta values were used for further analyses of epigenetic clocks, as recommended  (Higgins-Chen et al. , 2022; Pelegí-Sisó et al.