dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments  S. M. Bernasconi  acknowledges instrumentation funding from  ETH Zürich  and support from  Swiss National Science Foundation  grants  200021_143485 ,  200020_160046. M. Daëron acknowl-BERNASCONI ET AL. 10. 1029/2020GC009588 24  of  25 de Paris  for support for the organization of the VIth Clumped Isotope Workshop in Paris in 2017. Funding was provided to  C. M. John  and the  Imperial College Clumped Isotope Laboratory by Qatar Petroleum, Shell , and the  Qatar Science and Technology Panel. Funding was provided to  K. W. Huntington  at  University of Washington  from  NSF   EAR grants   1933130 ,  1713275 , and  1156134. T. Vennemann acknowledges the support of the  Swiss National Science Foundation  grant  206021-164032. A. N. Meckler acknowledges funding from the  Trond Mohn Foundation  and the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  (grant agreement no.
638467 ). Funding was provided to the  Tripati Laboratory  at  UCLA  for these analyses from  DOE BES  grant  DE-FG02-13ER16402. Funding was provided to the  Hyland Laboratory  at  NCSU  for these analyses from  NSF EAR-FRES  grant  1925973. Funding was provided to the  Yoshida Laboratory at Tokyo Tech. for these analyses from  JSPS  grant  JP17H06105. Funding was provided to the  CLIMB Lab at the University of Bergen  for these analyses by the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  (grant agreement No  638467 ) and by the  Trond Mohn Foundation. Funding was provided to the  UU Clumped Laboratory at Utrecht University  by the  Netherlands Research Organization (NWO)  through  VIDI grant   016. 161. 365. Funding was provided to the  CUBES-SIL  at  CU  Boulder by startup funds from the  University of Colorado Boulder  and  NSF   EAR grant   1524785.
The  UM SCIPP Laboratory  was supported by startup funds from the  University of Michigan. J. R. Kelson was supported by  NSF   PRF grant   1854873. HPA acknowledges support by  ERC  (Grant no. 724097 ) and  ISF  (Grant no. 171/16  and  1000/16 ). Funding was provided to the  Eiler lab at Caltech  from the  DOE BES program , award number  DE-SC0016561.
e. , whether clamping and/or declamping times are associated with post-transplant outcomes, independently of ischemia duration. We thus retrospectively studied, using the database of the French National Registry of Organ Transplantation (Cristal, Agence de la Biomédecine), the impact of time-of-theday of clamping and declamping, as well as their interaction with clamping duration, on mortality and graft survival after deceased donor renal transplantation.
Population Patients were selected from the French Registry of Organ Transplantation (Cristal registry), which registers data on all donor and transplant recipients in France  [12]. All first kidney transplant recipients from brain dead donors between January 2006 and December 2017 were included. Exclusion criteria were: Pediatric recipients (<18 yearsold), multiple organ transplanted patients, living-donor recipients, non-heart-beating donor recipients, and re-transplanted patients. From this initial cohort, only patients with reliable cross-validated ischemia duration, clamping and declamping times were kept for analysis. For each patient, the total ischemia duration between clamping and declamping time was calculated. A linear regression model was used to compare the calculated and the reported ischemia duration.
Data Sources Donor characteristics and post-transplant outcomes were retrieved from the Cristal registry. Recipient comorbidities and pretransplant dialysis modalities were extracted from The French Renal Epidemiology and Information Network (REIN) registry  [12]. Since those data are recorded at each annual follow-up for patients with renal replacement therapies (RRT) due to end-stage renal disease (ESRD), the last follow-up before transplantation was selected.
Despite being statistically significant, these differences were of little clinical relevance and survival curves were adjusted for these potential confounders, identified in the Cox model, to limit bias. The study population was restricted to the patients with reliable cross-validated ischemia duration, clamping and declamping times. Although we cannot totally exclude a sample effect, we assumed that coding errors and missing data occur at random. Information on the experience of the medical staff, specifically the surgeon performing the graft transplantations is not available in the Cristal database. A causal relation cannot be demonstrated by our study design. At this stage, although day-time clamping appears to be associated with better results, this does not necessarily imply that a delay in transplant surgery would improve outcomes.
Supplementary Materials: The following are available online at https://www. mdpi. com/article/10. 3390/jcm10112322/s1. Table  S1 : Cause of graft failure and death according to time-of-the day of declamping. No statistical difference was observed between the 2 groups. Table  S2 : Baseline characteristics of the sub-population of renal transplanted patients paired on the same donor (n = 1436) and grouped according to declamping time-of-the-day. BMI, body mass index; CPRA, calculated panel reactive antibody. p-value for night-time vs day-time declamping by t-test or Chi-square test. Institutional Review Board Statement: Ethical review and approval were waived for this study. The data in our study came from the CRISTAL french national cohort: CRISTAL is a national database initiated in 1996 and administered by the Agence de la biomédecine that prospectively collects data on all ogan transplant candidates in France along with their outcomes.
Data are entered into the registry by each center. Data collection is mandatory. Withdrawal from the waiting list and patient death are prospectively notified. The study was conducted according to the French law indicating that research studies based on the national registry CRISTAL are part of the transplant assessment activity and do not require institutional review board approval. Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. Data Availability Statement: Data availability is under the responsibility of C. J. (Agence de la Biomedecine, France).
BS and DM are supported by grants from Agence Nationale pour la Recherche (ANR-16-RHUS-0006-PreciNASH, ANR-10-LABX-0046, ANR TOMIS-Leukocyte: ANR-CE14-0003-01 and ANR CALMOS: ANR-18-CE17-0003-02), the Leducq Foundation LEAN Network 16CVD01 and the National Center for Precision Diabetic Medicine-PreciDIAB (ANR-18-IBHU-0001; 20001891/NP0025517; 2019_ESR_11). BS is a recipient of an Advanced ERC Grant (694717).
The protein thermal stability was evaluated using the simplified Thermofluor assay available on the High Throughput Crystallization Laboratory (HTX Lab) of the EMBL Grenoble  48. BRCA2 peptide structural analysis by NMR. The 15 N/ 13 C-labeled F0 fragment was analyzed by 3D heteronuclear NMR. in order to assign its Hn, N, Cα, Cβ, and Co chemical shifts, and identify its binding site to HSF2BP. Therefore, 3D NMR HNCA, CBCACONH, HNCACB, HNCANNH, HNCO, and HNCACO experiments were performed on a 700 MHz Bruker AVANCE NEO spectrometer equipped with a triple resonance cryogenic TCI probe at 283 K. The data were processed using Topspin 4. 0 (Bruker) and analyzed using CCPNMR 2. 4  49. Sodium trimethyl-silyl-propane-sulfonate (DSS) was used as a chemical shift reference. Experiments were performed on a 5-mm-diameter Shigemi sample tube containing the 500 μM uniformly 15 N/ 13 C-labeled protein, in 50 mM Hepes pH 7.
The first 2 μL injection was ignored in the final data analysis. The integration of the peaks corresponding to each injection. the correction for the baseline and the fit were performed using the Origin 7. 0 software provided by the manufacturer, to obtain the stoichiometry (N), dissociation constant (K d ), and enthalpy of complex formation (ΔH) for each interaction. These data are indicated in Table  1. Two replicates were performed for each experiment. Size-exclusion chromatography (SEC) coupled to MALS was used in order to measure the molecular masses of the complexes in solution. Therefore, HSF2BP and ARM proteins were loaded in the presence and absence of the BRCA2 fragment F15X on a Superdex 200 10/300 GL (GE Healthcare) using a HPLC Shimadzu system coupled to MALS/QELS/UV/RI (Wyatt Technology).
Crystals were prepared for X-ray diffraction experiments using the CrystalDirect  51. Crystals were obtained using the hanging-drop vapor diffusion method at 291 K. One μl of protein and reservoir solution containing 100 mM MgCl 2 , 100 mM MES pH 6 and 16% (w/v) PEG 3350 were mixed. Needle crystals appeared within 3 days, were grown for 1-2 weeks and were frozen in liquid nitrogen after cryoprotection using the reservoir solution supplemented with 20% glycerol. X-ray diffraction data were collected on the beamlines PROXIMA-1 and PROXIMA-2 at the SOLEIL synchrotron (St Aubin, France) and reduced using the XDS package  52. First phases for a triclinic crystal form (Table  2 ) were obtained by molecular replacement using the program MOLREP version 11. 7. 03 from CCP4  53  and different homologous models.
One of the models obtained by the Robetta server  54  gave the best correlation in the final translation function. These phases allowed to find the selenium substructure from a SeMet SAD dataset from the same crystal form with only ARM protein-containing selenomethionines. Later a monoclinic crystal form (Table  2 ) was obtained from a complex with both ARM and F15X containing selenomethionines. The collected SeMet SAD dataset (wavelength of data collection: lambda = 0. 97918 Å) allowed to directly calculate phases, without external model contributions, and confirmed the initial model built in the triclinic crystal form. Se sites were found using the SHELX C/D/E suite of programs. These sites were refined using PHASER version 2. 8. 2 in EP mode. The resulting Se SAD phases were improved by density modification using PARROT version 1.
0. 4 and a model automatically build using BUCCANEER version 1. 6. 10 confirming the sequence attribution for ARM and F15X. The resulting model underwent iterative cycles of manual reconstruction in COOT  55  and refinement in BUSTER version 2. 10. 3 56 (Table  2 ). At the end of the refinement, 90% and 3. 5% of the residues were in favored and outlier regions of the Ramachandran plot, respectively. Few residues were not visible in the electron density (L55 in chain B, V52 and Ala53 in chain C, loop 51-55, F169, and R208 in chain D, and H33 in chain F). These residues were included in the pdb file, but with an occupancy of 0. The final pdb file and monoclinic dataset have been deposited in the Protein Data Bank (entry code 7BDX [https://doi. org/10. 2210/pdb7bdx/pdb]). Meiotic spread nuclei preparations and immunocytochemistry.
All images within one analysis were taken with the same intensity. Images were analyzed using ImageJ (Fiji) software. BRME1, RAD51, and DMC1 foci quantification was performed using the ImageJ function "Analyze particles" in combination with a manual threshold and particles smaller than 0. 0196 µm 2 and larger than 0. 98 µm 2 were excluded. MLH1 foci were counted manually and blind by three individual researchers. HSF2BP foci quantification was performed using the ImageJ function "Analyze particles" in combination with a manual threshold. Since HSF2BP intensity is variable between slides and this influences the foci count because foci tend to merge as the intensity increases, size of particles was taken into account by adjusting the foci count in each nucleus in such a way that all large (equal to or larger than twice the average) HSF2BP-positive areas were divided by the average area size of 0.
25 µm 2 to obtain more accurate foci number (this average focus size was calculated from particles analyzed of 15 wild-type and 16 Brca2 Δ12/Δ12 nuclei with various HSF2BP intensities). Particles smaller than 0. 0294 µm 2 were excluded. For both BRME1 and HSF2BP a mask of SYCP3 was used to reduce the background signal. Statistical analysis. Statistical analysis was performed using GraphPad Prism software version 9. Statistical significance was determined using one-way ANOVA with Tukey test for comparisons involving more than two groups (genotypes, treatments) or using unpaired two-tailed t-test for comparison between +/+ and Δ12/Δ12 only in the experiments where the Δ12/+ group was not equally sampled (n = 2). Statistical details of the experiments can be found in the figure, figure legend or in the text of the results section, where n represents the number of animals.
Acknowledgements We thank  Ambre Petitalot  for the first purification of the BRCA2 F0 fragment, Christophe Velours on the I2BC protein-protein interaction facility for the SEC-MALS experiments,  Guillaume Hoffmann  and  Jose A. Marquez  for their advices during crystallogenesis assays on the HTX platform, and  Virginie Ropars  for her great help during crystallogenesis assays, crystal freezing, and X-ray crystallography data collection. We acknowledge SOLEIL for the provision of synchrotron radiation facilities and we would like to thank the respective staffs for assistance in using PROXIMA-1, PROXIMA-2, and SWING beamlines. We thank  Nicole van Vliet  for the assistance with the initial mouse experiments and  Prof. Hyunsook Lee  (Dept. of  Biological Sciences, Seoul National University ) for generously sharing the anti-BRCA2 antibody.
We thank the  Josephine Nefkens Cancer Progam  for infrastructure support. The research leading to these results has received funding from the  European Community 's  Seventh Framework Program H2020  under iNEXT (grant agreement  N°653706 ). It was also supported by the  French Infrastructure for Integrated Structural Biology  (https:// www. structuralbiology. eu/networks/frisbi, ANR-10-INSB-05-01 ), by the  CNRS IR-RMN-THC   Fr3050  and by the  CEA. This research was also funded by the  Dutch Cancer Society  and by the  Gravitation program  CancerGenomiCs. nlfrom the  Netherlands Organization for Scientific Research (NWO)  and is part of the  Oncode Institute , which is partly financed by the  Dutch Cancer Society.
Data availability The coordinates and structure factors file for the HSF2BP-BRCA2 complex described in the study were deposited in Protein Data Bank  62  , entry code 7BDX [https://doi. org/ 10. 2210/pdb7bdx/pdb]. Source data are provided with this paper. The microscopy data generated in this study have been deposited in BioImage Archive 63 in BioStudies 64 database under accession code S-BIAD166 [https://www. ebi. ac. uk/biostudies/BioImages/ studies/S-BIAD166]. Source data are provided with this paper.
Here we provide a proof-of-concept for the use of virtual reality (VR) goggles to assess reading behavior in beginning readers. Children performed a VR version of a lexical decision task that allowed us to record eye-movements. External validity was assessed by comparing the VR measures (lexical decision RT and accuracy, gaze durations and refixation probabilities) to a gold standard reading fluency test-the One-Minute Reading test. We found that the VR measures correlated strongly with the classic fluency measure. We argue that VR-based techniques provide a valid and childfriendly way to study reading behavior in a school environment. Importantly, they enable not only the collection of a richer dataset than standard behavioral assessments but also the possibility to tightly control the environment.
Apparatus The VR environment was created using the software Unity (Unity Technologies ApS) and displayed on a WQHD OLED screen (2,560 × 1,440 pixels) covering up to 100 °of visual angle with a refresh rate of 70 Hz. Eye movements were recorded using the infra-red eye-tracker in the virtual reality headset Fove 0 HMD (FOVE, Inc. ). The headset size was adapted to children with a strap at the back of the device in order to make this comfortable for the children and to achieve good immersion (i. e. , no lights from the classroom). Children were free to move the head and the design of the experiment allow them to continually see the stimulus in front of their head location. Recording was binocular with a high spatial accuracy (<1 °) and a sampling rate of 120 Hz (however, we recorded at 70 Hz in order to match the refresh rate of the screen).
Analysis We used Linear Mixed-Effects models (LMEs) to analyze our data, with items and participants as crossed random effects, including by-item and by-participant random intercepts  (Baayen et al. , 2008). Items in these analyses were the words/ pseudowords. LMEs were used to analyze response time and fixation durations while Generalized (logistic) LMEs were used to analyze error and refixation rates. The models were fitted with the lmer (for LMEs) and glmer (for GLMEs) functions from the lme4 package  (Bates et al. , 2015)  in R. We report regression coefficients (b), standard errors (SE) and |t-values| (for LMEs) or |z-values| (for GLMEs) for all factors. Fixed effects were deemed reliable if |t| or |z| > 1. 96  (Baayen et al. , 2008). All durations were inverse transformed (-1,000/duration) prior to analysis.
Quantification of CO 2 fluxes at the Earth's surface is required to evaluate the causes and drivers of observed increases in atmospheric CO 2 concentrations. atmospheric inversion models disaggregate observed variations in atmospheric CO 2 concentration to variability in CO 2 emissions and sinks. they require prior constraints fossil CO 2 emissions. Here we describe GCP-GridFED (version 2019. 1), a gridded fossil emissions dataset that is consistent with the national CO 2 emissions reported by the Global Carbon Project (GCP). GCP-GridFEDv2019. 1 provides monthly fossil CO 2 emissions estimates for the period 1959-2018 at a spatial resolution of 0. 1°. Estimates are provided separately for oil, coal and natural gas, for mixed international bunker fuels, and for the calcination of limestone during cement production.
ATM F F L UC OCEAN L AND I M Inversion models use an integrated approach to simultaneously quantify all fluxes of the global carbon budget and they are, by design, constrained by observations of atmospheric CO 2 mole fraction, or satellite derived products of column CO 2. 2] [23] [24] [25] [26] [27] [28] [29] [30] [31] [32]  and studies cited therein). By synchronously quantifying E FF , F LAND and S OCEAN , inversion models avoid budget imbalance and hence the global carbon budget equation is closed without a B IM term as follows: = + + G E F S ( 2 ) ATM F F inv LAND inv OCEAN inv Inversion models require prior constraints on the regional distribution of the CO 2 fluxes that they seek to disaggregate. Here we describe our development of the Global Carbon Budget Gridded Fossil Emissions Dataset (GCP-GridFED; version 2019.
1), a new gridded 0. 1° × 0. 1° global dataset of monthly CO 2 emissions resulting from fossil fuel oxidation and the calcination of limestone during cement production. The gridded nation-and source-specific emissions in GCP-GridFED are consistent with the nation-and source-specific emissions inventories compiled for the GCP's 2019 GCB assessment  3, 33  and for version 2019. 1 cover the period 1959-2018. The GCP-GridFED will be updated each year for use by inversion models contributing to the annual updates of the GCB, thus aligning the prior constraints on top-down estimates of fossil CO 2 emissions with the bottom-up estimates used by the GCP. Gridded estimates of uncertainty in CO 2 emissions are provided as an additional layer of GCP-GridFED and are based on the relative uncertainties (1σ) in fossil CO 2 presented in the uncertainty assessment of the GCB  3  and the relative uncertainties amongst emission sectors  34.
Methods Overview. GCP-GridFED was produced by scaling monthly gridded emissions for the year 2010, from the Emissions Database for Global Atmospheric Research (EDGAR; version 4. 3. 2)  41  , to the national annual emissions estimates compiled as part of the 2019 global carbon budget (GCB-NAE) for the years 1959-2018 (ref. 3 ). EDGAR data for the year 2010 is used because monthly gridded data was only available for this year at the time of product development (new data for 2015 was published recently and will be adopted in future versions of GCP-GridFED)  42. We describe the key features of the EDGAR and GCB-NAE datasets below (see 'Input Datasets'). GCB-NAE and EDGAR provide information regarding the global emission of CO 2 through the combustion of fossil fuels, industrial processes and cement production, and some other minor sources (e.
g. consumption of lubricants and paraffin waxes, solvent use, agricultural liming); nonetheless, their merits differ. GCB-NAE provides a consistent long-term dataset of annual national CO 2 emissions (1750-2018), however this dataset is not spatially-explicit below the country level and does not include sub-annual variability in CO 2 emissions. EDGAR provides estimates at high spatial resolution for specific fuels and sectors with a representation of the monthly distribution of emissions. However the EDGARv4. 3. 2 estimates are only available for 1970-2010 and a constant monthly distribution, matching the year 2010, is used throughout the time series  41. Our approach merged these two complementary datasets to create a long-term (1959-2018) and gridded (0. 1° × 0. 1°) dataset of global monthly CO 2 emissions.
The start year of 1959 aligns with the period of direct atmospheric measurements of CO 2 concentration  43. Our approach is to scale EDGAR's 2010 monthly gridded CO 2 emissions to match the annual gridded CO 2 emissions from GCB-NAE on a nation-and fuel-specific basis (see 'emissions scaling protocol' , Fig. 1 ; Table  1 ). GCP-GridFED includes additional data layers that are beneficial to inversion models. Gridded uncertainty in CO 2 emissions from each nation and emissions sector is also propagated to our nation-, year-and fuel-specific emissions estimates (Table  2 ). Gridded estimates of the uptake of O 2 related to oil, coal and natural gas use are also made using the literature-based oxidative ratios presented in the CO 2 release and Oxygen uptake from Fossil Fuel Emission Estimate (COFFEE) dataset  36.
Input datasets. National annual emissions from the global carbon budget 2019 (GCB-NAE). The GCB estimates national annual emissions of CO 2 due to coal, oil and natural gas combustion, the oxidative use of these fuels in non-combustive industrial processes, and the production of cement clinker  3, [14] [15] [16] [17] 44. Nat onal CO 2 emissions are preferentially taken from the country submissions to the United Nations Framework Convention on Climate Change (UNFCCC) for 42 "Annex I" countries over the period 1990-2018  44. These countries were members of the Organisation for Economic Co-operation and Development (OECD) in 1992, plus 16 non-OECD European countries and Russia, and contributed ~60% of total global emissions in 1990. Emissions in other countries and in Annex I countries prior to 1990 derive from the Carbon Dioxide Information Analysis Center (CDIAC)  15  and are rooted in energy statistics published by the United Nations (UN)  16, 45.
For recent years not covered by either the UNFCCC or CDIAC datasets, the national emissions are predicted using national or regional energy growth rates from the annual BP Statistical Review of World Energy  14. National cement emissions are based on national inventories of cement production and ratios of clinker production from officially reported clinker production data and emission factors, IPCC default emission factors, industry-reported clinker production, and survey-based clinker ratios  16. Gridded monthly emissions from EDGAR. The Emissions Database for Global Atmospheric Research (EDGAR) version 4. 3. 2  41 is a dataset of global emissions of gases and particulates, including CO 2 , based on available national statistics, default emission factors and methods recommended by IPCC  46, 47.