dataesr/scientific-paragraphs-categorization huggingface dataset:
In contrast, the BLUE model includes sub-grid-scale transitions at the grid level among all vegetation types as indicated by the harmonised landuse change data (LUH2) data set (https://doi. org/10. 22033/ ESGF/input4MIPs. 1127; Hurtt et al. , 2011 Hurtt et al. , , 2018)). Furthermore, H&N2017 assume conversion of natural grasslands to pasture, while BLUE allocates pasture proportionally on all natural vegetation that exists in a grid cell. This is one reason for generally higher emissions in BLUE. H&N2017 add carbon emissions from peat burning based on the Global Fire Emission Database (GFED4s; van der Werf et al. , 2017) and peat drainage based on estimates by  Hooijer et al. (2010)  to the output of their bookkeeping model for the countries of Indonesia and Malaysia. Peat burning and emissions from the organic layers of drained peat soils, which are not captured by bookkeeping methods directly, need to be included to represent the substantially larger emissions and interannual variability due to synergies of land use and climate variability in Southeast Asia, in particular during El Niño events.
Similarly to H&N2017, peat burning and drainage-related emissions are also added to the BLUE estimate. The two bookkeeping estimates used in this study also differ with respect to the land-use change data used to drive the models. H&N2017 base their estimates directly on the Forest Resource Assessment of the FAO, which provides statistics on forest area change and management at intervals of 5 years currently updated until 2015  (FAO, 2015). The data are based on country reporting to the FAO and may include remote-sensing information in more recent assessments. Changes in land use other than forests are based on annual national changes in cropland and pasture areas reported by the FAO  (FAOSTAT, 2015). BLUE uses the harmonised land-use change data LUH2 (https://doi. org/10. 22033/ESGF/input4MIPs.
1127, Hurtt et al. , 2011 Hurtt et al. , , 2018)) , which describe land-use change, also based on the FAO data, but downscaled at a quarter-degree spatial resolution, considering sub-grid-scale transitions among primary forest, secondary forest, cropland, pasture, and rangeland. The LUH2 data provide a new distinction between rangelands and pasture. To constrain the models' interpretation on whether rangeland implies the original natural vegetation to be transformed to grassland or not (e. g. browsing on shrubland), a new forest mask was provided with LUH2; forest is assumed to be transformed, while all other natural vegetation remains. This is implemented in BLUE. The estimate of H&N2017 was extended here by 2 years (to 2017) by adding the anomaly of total tropical emissions (peat drainage from  Hooijer et al.
The DGVMs used the HYDE land-use change data set  (Klein Goldewijk et al. , 2017a, b) , which provides annual half-degree fractional data on cropland and pasture. These data are based on annual FAO statistics of change in agricultural land area available until 2012. The FAOSTAT land use database is updated annually, currently covering the period of 1961-2016 (but used here until 2015 because of the timing of data availability). HYDE-applied annual changes in FAO data to the year 2012 from the previous release are used to derive new 2013-2015 data. After the year 2015 HYDE extrapolates cropland, pasture, and urban land use data until the year 2018. Some models also use an update of the more comprehensive harmonised land-use data set  (Hurtt et al. , 2011) , which further includes fractional data on primary and secondary forest vegetation, as well as all underlying transitions between land-use states  (Hurtt et al.
GOBM evaluation and uncertainty assessment for S OCEAN The mean ocean CO 2 sink for all GOBMs falls within 90 % confidence of the observed range, or 1. 6 to 2. 8 GtC yr -1 for the 1990s. Here we have adjusted the confidence interval to the IPCC confidence interval of 90 % to avoid rejecting models that may be outliers but are still plausible. The GOBMs and flux products have been further evaluated using f CO 2 from the SOCAT v6 database. We focused this initial evaluation on the interannual mismatch metric proposed by  Rödenbeck et al. (2015)  for the comparison of flux products. The metric provides a measure of the mismatch between observations and models or flux products on the x axis as well as a measure of the amplitude of the interannual variability on the y axis. A smaller number on the x axis indicates a better fit with observations.
The amplitude of the interannual variability in S OCEAN (y axis) is calculated as the temporal standard deviation of the CO 2 flux time series. The calculation for the x axis is carried out as follows: (1) the mismatch between the observed and the modelled f CO 2 is calculated for the period 1985 to 2017 (except for the IPSL model, which uses 1985 to 2015 due to data availability), but only for grid points for which actual observations exist. (2) The interannual variability in this mismatch is calculated as the temporal standard deviation of the mismatch. (3) To put numbers into perspective, the interannual variability in the mismatch is reported relative to the interannual variability in the mismatch between a benchmark f CO 2 field and the observations. The benchmark f CO 2 field is designed to have no interannual variability, i.
ILAMB variables cover key processes that are relevant for the quantification of S LAND and resulting aggregated outcomes. The selected variables are vegetation biomass, gross primary productivity, leaf area index, net ecosystem exchange, ecosystem respiration, evapotranspiration, and runoff (see Fig. B2  for the results and for the list of observed databases). Results are shown in Fig. B2  and discussed in Sect. 3. 1. 3. For the uncertainty, we use the standard deviation of the annual CO 2 sink across the DGVMs, which averages to ±0. 8 GtC yr -1 for the period from 1959 to 2017. We attach a medium confidence level to the annual land CO 2 sink and its uncertainty because the estimates from the residual budget and averaged DGVMs match well within their respective uncertainties (Table  5 ).
Model evaluation The evaluation of ocean estimates (Fig. B1 ) shows a relative interannual mismatch of 15 % and 17 % for the two pCO 2based flux products over the globe, relative to the pCO 2 observations from the SOCAT v6 database for the period 1985-2017. A 0 % mismatch would indicate a perfect model, and a field with no interannual variability would result in a 100 % mismatch. A mismatch larger than 100 % is possible when the method produces a larger mismatch than the benchmark field with no interannual variability (see  Sect. 2. 5. 3). This mismatch by the pCO 2 -based flux products is improved compared with earlier published versions of these two flux products of around 20 %-25 % for the 1992-2009 time period  (Rödenbeck et al. , 2015) , likely because of the larger data availability after 2009.
Notable was Germany, whose emissions did not decrease significantly. In contrast, there is no apparent trend in CO 2 emissions from land-use change (Fig. 6 ), though the data are very uncertain, with the two bookkeeping estimates showing opposite trends over the last decade. Larger emissions are expected increasingly over time for DGVM-based estimates as they include the loss of additional sink capacity, while the bookkeeping estimates do not. The LUH2 data set also features large dynamics in land use in particular in the tropics in recent years, causing higher emissions in DGVMs and BLUE than in H&N.
Discussion Each year when the global carbon budget is published, each flux component is updated for all previous years to consider corrections that are the result of further scrutiny and verification of the underlying data in the primary input data sets. Annual estimates may improve with improvements in data quality and timeliness (e. g. to eliminate the need for extrapolation of forcing data such as land use). Of the various terms in the global budget, only the fossil CO 2 emissions and the growth rate in atmospheric CO 2 concentration are based primarily on empirical inputs supporting annual estimates in this carbon budget. Although it is an imperfect measure, the carbon budget imbalance provides a strong indication of the limitations in observations, in understanding or full representation of processes in models, and/or in the integration of the carbon budget components.
Data availability The data presented here are made available in the belief that their wide dissemination will lead to greater understanding and new scientific insights into how the carbon cycle works, how humans are altering it, and how we can mitigate the resulting human-driven climate change. The free availability of these data does not constitute permission for publication of the data. For research projects, if the data are essential to the work, or if an important result or conclusion depends on the data, co-authorship may need to be considered. Full contact details and information on how to cite the data shown here are given at the top of each page in the accompanying database and summarised in National emission data are also available from the Global Carbon Atlas (http://www. globalcarbonatlas.
Acknowledgements. We thank all people and institutions who provided the data used in this carbon budget;  Richard Betts ,  Erik Buitenhuis ,  Jinfeng Chang ,  Shijie Shu , and  Naomi Smith  for their involvement in the development, use and analysis of the models and data products used here; and  Fortunat Joos ,  Samar Khatiwala , and  Timothy DeVries  for providing historical data. We thank  Rob Jackson  and the Global Carbon Project members for their input throughout the development of this update. We thank  Ed Dlugokencky  for providing atmospheric CO 2 measurements;  Camilla Stegen Landa ,  Christophe Bernard , and  Steve Jones  of the  Bjerknes Climate Data Centre  and the  ICOS Ocean Thematic Centre data management at the University of Bergen , who helped with gathering information from the SOCAT community; and  Vassilis Kitidis ,  Pedro M.
B3:  Toshinobu Machida ,  Guangsheng Chen ,  Steven C. Wofsy ,  Ken Davis ,  Joshua DiGangi ,  Jeff Peischl ,  Thomas B. Ryerson ,  Britton Stephens ,  Colm Sweeney ,  Kathryn McKain , and  Luciana V. Gatti ;  University of Colorado/CIRES  for the NOAA WP-3D aircraft vertical profile data; and the  Japan Meteorological Agency. We thank the individuals and institutions that provided the databases used for the model evaluations introduced here and  Nigel Hawtin  for producing Fig. 2. Finally, we thank all funders who have supported the individual and joint contributions to this work (see Table  A5 ), as well as the reviewers of this paper and previous versions, and the many researchers who have provided feedback. Edited by: David Carlson Reviewed by:  H. Damon Matthews ,  Albertus J. Dolman , and one anonymous referee.
Genetic testing for BRCA1 and BRCA2 has been part of genetic counseling in European Union countries and North America since their discovery in the 90's, and has greatly improved recommendations about clinical management options and the most appropriate treatments. Nonetheless, both retrospective and prospective studies on large datasets of BRCA1/2 PV carrier families are still very much needed to refine individual cancer risk estimates by considering other genetic and lifestyle/environmental factors, and they will also contribute to a better understanding of the correlation between mutant BRCA1/2 alleles and phenotype. In particular, accurate age-specific risk estimates for the different types of cancer would be useful when choosing risk reduction strategies such as prophylactic bilateral mastectomy or salphingo-oophorectomy.
Protocol, Data Collection, and Database The GEMO coordinating center was located at Centre Léon Bérard (Lyon) until September 2015 and is currently held at Institut Curie (Paris). All data and biospecimens are stored without personal identifiers. The GEMO case report form (CRF) includes information on participants' family history, gyneco-obstetrics risk factors (age at menarche, number of pregnancies, age at menopause), preventive surgery and tumor pathology (histology, grade, tumor size, hormone receptors status). Data on socio-demographic variables (age at inclusion, sex, ethnicity/population ancestry) and medical history of cancer (laterality, other cancer prior recruitment into study) are also collected. Geneticists invite BRCA1/2 PV carriers, whether affected with cancer or not, to participate in GEMO during the consultation informing them of their BRCA1/2 positive test results.
After completing the CRF with the participant, the geneticist sends it to the coordinating center, and requests that an aliquot of the blood DNA sample (at least 10 µg) that was used for genetic testing is shipped from the testing laboratory to the coordinating center. The study protocol is illustrated in Supplementary Figure  1. Recently, an upgraded electronic database on FileMaker Pro 16 (FileMaker Inc. , Santa Clara, California, USA) was developed to collate, manage and distribute core data and DNA samples, and to facilitate inter-operability with the GGC BRCA1/2 (ex-UMD-BRCA1/BRCA2) database  (20)  and that of the prospective cohort on BRCA1/2 PV carriers GENEPSO  (21).
Ethics The study is performed in compliance with the Helsinki Declaration and received a favorable review of the French National Committees for personal data protection in medical research (CCTIRS N • 07223 and CNIL agreement N • 1245228). GEMO has human ethics approval at all the participating institutions where subjects are recruited. All research projects making use of data and/or materials collected by GEMO are required to have independent ethical approval from their host institutions. Participants give written informed consent during genetic counseling sessions and understand that as a result of participation, personal details will be recorded and stored in a coded format on a database. They consent to samples of DNA material prepared from blood cells being stored in a central location and to de-identified information and samples being made available for scientifically and ethically approved research projects.
Access to DNA Samples and to Family and Clinical Data Investigators wishing to use the GEMO DNA collection and related clinical and family data submit a brief expression of interest to principal investigators (gemo@curie. fr) who then circulate the proposal to the GEMO steering committee with a 10-day opportunity given to highlight any major issues, especially duplication of, or complementarity to, existing projects. If favorably reviewed, a full application is then submitted and verified to ensure that sufficient resources to conduct the project exist, the amount of DNA requested is appropriate, and that the proposal has any required ethics approvals. When the project is accepted, a material transfer agreement and/or a data transfer agreement are signed between the coordinating center and the research institution of the applicant.
Collection of DNA Samples and Data As of April 2018, 5,303 participants with available DNA sample had been enrolled in GEMO. Participants included 3,087 BRCA1 PV carriers (2,877 women and 210 men) and 2,216 BRCA2 PV carriers (2,005 women and 211 men) belonging to 2,190 and 1,544 families, respectively. The mean number of participants per family was 1. 4 (range: 1-11). For 600 families, DNA samples were collected from three or more family members. While no individuals in the dataset carried more than a single PV, four families segregated two PV in two branches of the family (family 1: BRCA1:c. 5137del and BRCA2:c. 2808_2811del; family 2: BRCA1:c. 1480C>T and BRCA1:c. 3839_3843delinsAGGC; family 3: BRCA1:c. 3841C>T and BRCA2:c. 4889C>G; family 4: BRCA1:c. 4391_4393delinsTT and BRCA2:c. 7680dup).
BRCA1 and BRCA2 Variants Currently, 506 BRCA1 and 494 BRCA2 unique PVs are described in the GEMO database. The number of families in which each PV was observed is shown in Supplementary Table  2  and the distribution of PVs across the gene sequences is shown in Figure  1. The five most common PVs accounted for 21. 3% of all PVs in BRCA1 and 14. 9% of all BRCA2 PVs. The most common BRCA1 PVs were c. 5266dup (7. 5%) and c. 68_69del (3. 9%), originally described as founder PVs in the AJ population  (30) , the c. 3481_3491del founder PV from North-Eastern France (4. 9%)  (31, 32) , and the two common European PVs c. 4327C>T (2. 7%) and c. 3839_3843delinsAGGC (2. 2%)  (33). The most common BRCA2 PVs were c. 2808_2811del (3. 3%), c. 5946del (3. 2%), a Western European PV of AJ origin  (34) , c. 4889C>G (2.
Representativeness of the GEMO Population The GGC database was designed to compile information on all BRCA1/2 variants (pathogenic, neutral and VUS), except common polymorphisms, identified probands in the 17 French licensed laboratories  (20). This database is therefore considered as the reference database for BRCA1/2 variants in France. In June 2018, it contained PV from 6,385 BRCA1 and 4,839 BRCA2 families (Sandrine Caputo, personal communication), and about one third of the population recorded in the GGC database had been enrolled in GEMO. The distribution of PVs along the genes sequence in GEMO and the GGC BRCA1/2 database overlaps (Supplementary Table  2 ), although a few variants were underrepresented in GEMO reflecting a recruitment bias in the study due to the absence of participating cancer clinics in some regions (e.
DISCUSSION Over 5,300 participants have been enrolled in GEMO to date, which provides an overview of BRCA1/2 PVs in a wellcharacterized sample of French counseled HBOC families. The GEMO resource is available to internal and external researchers who can apply for blood DNA and data for use in ethically approved, peer reviewed collaborative and interdisciplinary projects on the genetic epidemiology of cancer in BRCA1/2 families. Its overall goal is to facilitate the translation of research results to the clinical setting. As an example, GEMO contributes massively to the CIMBA effort involving centers on six continents that have recruited BRCA1/2 PV carriers with associated clinical, risk factors, and genetic data  (24). GEMO is one of the three most important contributors to CIMBA projects in terms of number of samples, phenotypic and pathology data.
GEMO study collaborators co-authored 43 CIMBA publications. Publications and summary results for iCOGS SNPs are accessible via http://cimba. ccge. medschl. cam. ac. uk/. At the national level, the GEMO group is aiming to develop specific PRS in the French counseled families in order to assess the clinical utility of incorporating such scores in risk prediction models. Indeed, improvement in the performance of such models for risk stratification and personalized decision-making (e. g. , prophylactic mastectomy/salphingo-oophorectomy or frequency of BC screening) has important clinical implications. Efforts are also being made to render the GEMO database interoperable with other national databases including that of GENEPSO, which is a prospective cohort initiated in 1999, where BRCA1/2 PV carriers are followed over time to observed prospectively characteristics of subjects who develop either primary or secondary cancers  (5).
AUTHOR CONTRIBUTIONS FL, FD, SM, and DS-L coordinated the GEMO study. CN, MiL, IM, SF-F, EM-F, PP, LV-B, Y-JB, DL, IC, PB, VM, CD, PG, M-AC-R, SG, VB, LF, BB, CL, and MG-V invited GEMO participants. CH, BB-dP, DV, HS, NM, LB, MéL, FD, and FL managed the DNA samples. NM, LB, MB, FD, FL, MaL, and YJ managed family and clinical data. NM, LB, MéL, FD, YJ, SMC, and FL curated the variants databases. FL and YJ analyzed the data. FL and NA wrote the paper. All authors read and approved the final manuscript.
ACKNOWLEDGMENTS The Genetic Modifiers of Cancer Risk in BRCA1/2 Mutation Carriers (GEMO) study is a study from the  National Cancer Genetics Network UNICANCER Genetic Group, France. We wish to pay a tribute to  Olga M. Sinilnikova , who with DS-L initiated and coordinated GEMO until she sadly passed away on the 30th June 2014. The team in Lyon ( Olga Sinilnikova , ML, LB,  Carole Verny-Pierre , SM, FD,  Valérie Sornin ) managed the GEMO samples until the biological resource center was.
FUNDING The GEMO resource was initially funded by the  French National Institute of Cancer (INCa ,  PHRC Ile de France ,  AOR 01 082 ,  DS-L 2001-2003 ), the  Association Le cancer du sein, parlonsen!Award  (SM,  O. Sinilnikova ,  DS-L 2004 ) and the  Association for International Cancer Research  ( O. Sinilnikova ;  2008-2010 ). It also received support from the  Canadian Institute of Health Research for the CIHR Team in Familial Risks of Breast Cancer program  ( J. Simard ;  2008-2013 ), and the European commission  FP7 , Project  Collaborative Ovarian, breast and prostate Geneenvironment Study (COGS), Large-scale integrating project  ( D. Easton ,  Per Hall ;  2009-2013 ). GEMO is currently supported by the  INCa AAP 2013 Bases Clinico-Biologiques  (CANSOP grant  2013-1-BCB-01-ICH-1 , DS-L) and the  Fondation ARC pour la recherche sur le cancer  (grant PJA 20151203365, FL).
MSD® ELISA tests Triton X-100 soluble proteins were diluted in the diluent provided for the V-PLEX Aβ peptide panel kit (6E10 antibody, MSD®, Rockville, MD). Samples were loaded in triplicate and manufacturer's protocol was followed. Aβ levels were quantified with the Discovery Workbench4. 0, MSD® software thanks to a standard curve, and normalized to the protein content in each well.
Ligation products were amplified with 12 PCR cycles. Stringent purification was done (0. 6X of AMPure XP) to remove small inserts that would be sequenced preferentially. mRNAseq libraries were qualified and quantified with Agilent High Sensitivity DNA kit and Qubit dsDNA HS Assay Kit respectively, before sequencing on a HiSeq 2000 Illumina platform (2 × 100 bp). Quality controls and data analysis was performed by GenoSplice technology® (www. genosplice. com). Sequencing data quality, read repartition and insert size estimation were performed with FastQC, Picard-Tools, Samtools and rseqc. Reads were mapped on the mm10 Mouse genome assembly with STARv2. 4. 0  [17]. Gene expression analysis was performed as described previously  [57]. Briefly, for each gene present in the Mouse FAST DB v2016_1_full annotations, reads aligning on constitutive regions (i.
Gene ontology analysis Analysis for enriched Gene ontology (GO) terms were performed with Database for Annotation, Visualization and Integrated Discovery (DAVID) Functional annotation Tool (v6. 8)  [28]  by Genosplice Technology®. GO terms and pathways were considered as enriched for fold enrichment ≥2. 0, uncorrected p value ≤0. 05 and minimum number of regulated genes in pathway/term ≥2. 0.
Data were acquired with a Multiclamp 700B amplifier (Molecular Devices, Sunnyvale, CA), digitized with a Digidata 1320A digitizer (Axon Instruments Inc. , Sunnyvale, CA), recorded and analyzed off line with pClamp and Clampfit 10. 3 respectively (Molecular Devices). Recordings were low-pass filtered at 2 kHz and digitized at 10 kHz. A stable baseline was recorded for at least 20 min before starting the input/output experiment. The same amplitude of stimulation was applied three times (0. 033 Hz), before being increased by 10 V. Paired-pulse stimulation experiments were performed with two pulses of stimulation induced at 50 or 100 ms interval. The paired pulse ratio was calculated as the slope of the second fEPSP over the slope of the first fEPSP. Long-term potentiation (LTP) was induced after 20 min of baseline recording, by applying a high-frequency stimulation (HFS) protocol (100 Hz train of stimuli for 1 s, repeated three times at 20 s interval).