dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical analysis First, we evaluated the WHO recommended rule-in thresholds for APRI (1. 5 for ≥F2 and 2. 0 for F4)  8. We then optimised the rule-in thresholds by giving priority to specificity (≥90%), and the rule-out thresholds by giving priority to sensitivity (≥80%). Finally, we assessed thresholds defined by maximising Youden's J, equivalent to maximising the sum of sensitivity and specificity without priority to any of the two. To calculate sensitivity and specificity, data were pooled using a single-stage IPD meta-analysis approach. We used a bivariate Bayesian random-effects meta-analysis model for sensitivity and specificity using patient-level covariates with study-level random effects to account for anticipated variability between sites (full model details are presented in Supplementary Methods 1)  54.
Validation We validated the model using bootstrap resampling, as widely recommended over split-sample approaches for internal validation  58. We obtained 500 bootstrap samples from the original dataset, then fitted the model and estimated rule-in, rule-out and Youden thresholds, sensitivity and specificity at each different thresholds, and overall AUROC. Validation results for the APRI model for cirrhosis are shown in Supplementary Methods 2. Model parameters show unimodal distributions with narrow spread indicating good stability of parameter estimates.
Acknowledgements We would like to thank the staff at all participating centres who contributed to patient follow-and data collection. The research groups of M. L. (PROLIFICA) in The Gambia and Senegal are supported by the  European Commission  ( FP7-HEALTH , grant ID:  265994 ), the  Medical Research Council (MRC)  in the UK (grant ID:  MR/R011117/1 ) and  Gilead Sciences (Inc. ,  Foster City, CA, USA). A. J. S. was funded by the  National Institute for Health Research (UK)  with an  Academic Clinical Lectureship at the University of Liverpool, UK. The statistical work was undertaken on Barkla, part of the High Performance Computing facilities at the  University of Liverpool, UK. The authors assume full responsibility for analyses and interpretation of these data.
(2016). The residence times of reservoirs that linked subnetwork corridor are obtained from the global data databased  (Lehner et al. , 2011). Reservoirs that are linked to main channels are considered as major reservoirs. The operation rules of major reservoirs were adopted from existing algorithms  (Biemans et al. , 2011; Haddeland et al. , 2006; Hanasaki et al. , 2006) , which require a reference run with reservoir operation turned off to provide natural water flows as model input.
Model Forcing A database at a spatial resolution of 0. 5°, consisting of climate variables, land-use change, nitrogen deposition, nitrogen fertilizer, and manure application (Figure  S1  in Supporting Information S1), atmospheric CO 2 concentration, and hydrological data was developed to drive the DLEM-TAC model. The climate variables, which consist of daily precipitation, daily mean temperature, daily maximum temperature, daily minimum temperature, daily wind speed and daily shortwave radiation, were compiled from the CRU-NCEP data set for 1901-2019  (Viovy, 2018). Climate data of each year during 1800-1900 were randomly sampled from the years between 1901 and 1920. The land use data were from HYDE version 3. 2  (Klein Goldewijk et al. , 2017) , and the land cover cohort within a grid cell is composed of four natural vegetation types, one cropland type, and other non-vegetation land-use types  (Tian et al.
, 2018). Detailed information on DLEM land-use input data can be found in M. Liu and Tian (2010). The annual atmospheric CO 2 concentration from 1800 to 2019 was obtained from the NOAA GLOBALVIEW-CO 2 data set (https://www. esrl. noaa. gov). The nitrogen deposition data set was obtained from the Atmospheric Chemistry and Climate Model Intercomparison Project. The N fertilizer data were obtained from  Lu and Tian (2017)  and the spatial manure N application data were adopted from B. Zhang et al. (2017)  (Figure  S2  in Supporting Information S1). The hydrological input data, covering the flow direction, flow distance, and upstream area, were obtained from the Dominant River Tracing (DRT) data set  (Wu et al. , 2012). The bank-full width and bank-full depth data sets were obtained from the Hydrological Modeling and Analysis Platform  (Getirana et al.
, 2012). The channel density and channel slopes of small streams and rivers were derived from the National Hydrography Dataset plus v2 data (available at: http://www. horizon-systems. com/NHDPlus/index. php). Surface area, upstream area, volume, depth, and averaged residence time for lakes were obtained from the Hydrolakes data set  (Messager et al. , 2016). The variables for reservoirs, including built year, height, maximum storage, water surface area, residence time, and upstream area, were obtained from the GRanD database  (Lehner et al. , 2011).
Model Evaluation To examine the performance of DLEM-TAC, we compared the simulated terrestrial loading and riverine export with observations for each C species (at 56 land sites and 47 world major rivers. The detailed data used for model calibration and validation are shown in Figure  S3 , Tables  S1  and S2  in Supporting Information S1). The determination coefficient (R 2 ) and Nash-Sutcliffe efficiencies (NSE,  Nash and Sutcliffe (1970) ) were applied to assess the performance of DLEM-TAC. The NSE ranges between 𝐴𝐴 -∞ and 1. An NSE close to 1 means a good match of the simulated to the observed data; NSE = 0 means that the simulation is as skillful as the average of the observed data (M. Li et al. , 2019). Simulated terrestrial loading of DOC, POC, and DIC agreed well with observations (log transformed, R 2 > 0.
7; Figure  3a-3c ). Aquatic module simulations of the observed annual mean discharges and the C export to the coast were consistent with those recorded by the GEMS-GLORI database  (Meybeck & Ragu, 2012) , with R 2 values of the log of discharge and export of DOC, POC, and DIC being 0. 8, 0. 8, 0. 6, and 0. 7, respectively (Figures  3d-3g ), which can be considered as satisfactory  (Moriasi et al. , 2015). Furthermore, the simulated monthly riverine C exports of the major rivers by  We evaluated the simulated performance of seasonal variability on DOC exports at 10 sites, with 3 rivers located in the tropical region, 5 rivers in the temperate region, and 2 rivers in the boreal region. Comparisons between simulated and database observations had R 2 higher than 0. 5 and NSEs higher than zero at most sites.
We did not evaluate monthly POC exports due to lack of data availability. Additionally, simulated total organic carbon exports of 5 temperate rivers and 3 boreal rivers were comparable to observed values with R 2 values in most cases higher than 0. 4 and NSEs higher than zero. Finally, the DIC exports simulated by DLEM-TAC in 3 tropical rivers and 9 temperate rivers were representative of observations with R 2 values higher than 0. 5 and higher than zero at most sites.
Data Availability Statement The CRU-NCEP data are freely available at https://vesg. ipsl. upmc. fr. NOAA GLOBALVIEW-CO2 data are available at https://www. esrl. noaa. gov. The required hydrological data are available at http:// files. ntsg. umt. edu/data/DRT/upscaled_global_hydrography/ (Dominant River Tracing data set), http://www. horizon-systems. com/NHDPlus/index. php (National Hydrography data set plus v2 data), https://www. hydrosheds. org/products/hydrolakes (HydroLAKES data set), and https://sedac. ciesin. columbia. edu/data/collection/grand-v1 (GRanD v1. 01 database), respectively.
Data processing To assess the ocean mass budget, we rely on time series from observations and models. To ensure the consistency between the various datasets, we apply the same processing to each of them. For gridded ocean datasets, a restrictive mask is applied so that all data cover the same spatial extent. This mask excludes polar oceans (above 66°North and below 66°South), marginal seas and a buffer zone of 200 km from the coastlines to minimise leakage effects from land mass variations estimated by  GRACE and GRACE-FO data (Dobslaw et al. , 2020). The mask extent is shown in Figure  SI1  of the supplementary information (SI). The global averages are weighted according to the surface of sea water within each grid cell. For each time series, annual and semi-annual signals are removed by least-square fitting, a three-months low-pass Lanczos filter is applied and the temporal average is removed.
Some components of the budget are assessed from several available estimates. In such cases, the ensemble mean is computed as the average of the considered time series at each time step. Uncertainties are assumed to be Gaussian and are provided as standard uncertainties, corresponding to the 68 % confidence level. When ensemble means are used to assess a component of the budget, the associated standard uncertainty σ is computed by combining the standard deviation σ ens of the datasets included in the ensemble and the standard uncertainties σ 1≤i≤N of the N individual time series when this information is provided, assumed independent: σ = σ 2 ens + N i=1 σ 3 i (3) This approach is used for the contribution of Greenland and Antarctica ice sheets melting and for the thermosteric sea level component.
Greenland and Antarctica ice sheet data The amount of ice mass changes from Greenland and Antarctic ice sheets can be estimated from three independent approaches (e. g. Shepherd et al. , 2012; Hanna et al. , 2013; Cazenave and the WCRP Global Sea Level Budget Group, 2018) : (1) volume change estimated using altimetry data, (2) mass change estimated from gravimetric data, and (3) mass change estimated by the input-output method (IOM) using the surface mass balance from models and the ice discharge measured by InSAR data. In this study, we consider mostly IOM and altimetry products from different datasets to estimate ice sheet mass loss independently from GRACE and GRACE-FO data. For Greenland, we use: (1) IOM estimate from Velicogna et al. (  2020   2020 ) and (  2 ) multi-approach IMBIE estimate  (Shepherd et al.
To obtain the sea level contribution from ice mass change, we assume that water is evenly redistributed over the global ocean. Considering a global ocean surface of 361. 4 × 10 8 km 2 , 1 Gt of ice is equivalent to 1/361. 4 mm of sea level change. In the following, we use ensemble mean time series from the above listed datasets, following the processing described in section 2. 2. Figure  2  shows the time series of ocean mass contribution for the individual datasets as well as the ensemble means for Greenland and Antarctica. The ensemble means compared with a purely gravimetry estimate from  Velicogna et al. (2020)  shows that GRACE and GRACE-FO observations allow a better time sampling rate, hence allow capturing a stronger temporal variability (Figure  SI3 ). SI1.
Altimetry-based GMTSL data We compute the GMSL time series from the vDT2021 sea level product operationally generated by the Copernicus Climate Change Service (C3S). This dataset is dedicated to the sea level stability for climate studies  (Legeais et al. , 2021). It provides daily sea level anomalies grids at a 1/4 degree spatial resolution from January 1993 until August 2021, based at any time on a reference altimeter mission (TOPEX/Poseidon, Jason-1, Jason-2, Jason-3 and Sentinel-6 Michaël Freilich), plus a complementary mission (ERS-1,2, Envisat, Cryosat or SARAL/AltiKa depending on the time frame) to increase the spatial coverage. The GMSL time series is corrected for the GIA effect considering a value of -0. 3 ± 0. 05 mm/yr  (Peltier, 2004)  as well as for the sea floor subsidence due to the present-day ice melting with a rate of -0.
Thermosteric sea level data The global mean thermosteric sea level (GMTSL) is computed from seven in-situ oceanographic datasets: (a) EN4. SI1. (Argo, 2021). In addition, EN4 and IAP datasets also include MBT and XBT data. JAMSTEC includes Triangle Trans-Ocean Buoy Network (TRITON) data and additional conductivity-temperature-depth profiler data from ships. From these datasets, we compute the thermosteric sea level change due to temperature variations between 0 and 2000 m depth. A linear trend of 0. 12 ± 0. 03 mm/yr is added to the GMTSL to take into account the contribution of the deep ocean  (Chang et al. , 2019). Figure  5  shows the individual GMTSL time series as well as the ensemble mean. 4 Resulting global mean ocean mass budgets 4. 1 Budget from the sum of mass contributions Figure  6a  shows the ocean mass budget comparing the GRACE-based GMOM to the sum of its contributions from ice sheets, ice caps, glaciers and terrestrial water storage.
The Argo Program is part of the Global Ocean Observing System. The NOAA Argo data are available from https://www. ncei. noaa. gov/access/global-ocean-heat-content/. The SIO Argo climatology data are available from http://sio-argo. ucsd. edu/RG_Climatology. html. The JAMSTEC MOAA GPV data are available from http://www. jamstec. go. jp/ ARGO/argo_web/argo/?page_id=83&lang=en. The EN4 Argo data are available from http://hadobs. metoffice. com/en4/download. html. The IAP data are available from http://159. 226. 119. 60/cheng/. The IFREMER ISAS20 data are available from https://www. seanoe. org/data/ 00412/52367/. The Ishii v7. 3. 1 data are available from https://climate. mri-jma. go. jp/pub/ocean/ts/v7. 3. 1/. The TWS WGHM v2. 2d data are available from https://doi. pangaea. de/10. 1594/PANGAEA.
Data availability. The GRACE/GRACE Follow-On JPL, CSR and GFZ Level 2 (Stock's coefficients) and Level 3 (mascon) Release 6 data are available from https://podaac. jpl. nasa. gov. The GRACE/GRACE Follow-On GSFC data are available from https://earth. gsfc. nasa. gov/ geo/data/grace-mascons. The C3S altimetry data are available from https://cds. climate. copernicus. eu/cdsapp#!/dataset/satellite-sea-level-global? tab=overview. The AVISO altimetry along-track data for Jason-3, SARAL/AltiKa and Sentinel-3A are available from https://www. aviso. altimetry. fr/. The total column water vapour REMSS climate data records are available at https://wui. cmsaf. eu/safira/action/viewProduktDetails? eid=21864&fid=23. Argo data were collected and made freely available by the international Argo Program and the national programs that.
2. 2 data from the Met Office Hadley Center(Good et al. , 2013)  with Gouretski and Reseghetti (2010)  correction applied, (b) IAP (Institute of Atmospheric Physics from the Chinese Academy of Sciences) data(Cheng et al. , 2017(Cheng et al. ,  , 2020)), (c) the IFREMER (Institut Français de Recherche pour l'Exploitation de la Mer) ISAS (In Situ Analysis System) 20 dataset (Gaillard et al. , 2016), (d) Ishii et al. (2017) data, (e) JAMSTEC (Japan Agency for Marine-Earth Science and Technology) MOAA GPV (Grid Point Value of the Monthly Objective Analysis using the Argo data) version 2021 product data set (Hosoda et al. , 2010), (f) NOAA (National Oceanic and Atmospheric Administration) data (Levitus et al. , 2012; Garcia et al. , 2019) and (g) SIO (Scripps Institute of Oceanography) data (Roemmich and Gilson, 2009).
For the aerial part, the 30 m resolution SRTM1 and ASTER global databases are sufficient to capture the geometry of volcanic islands with a diameter larger than 1 km. On the contrary, global bathymetric databases, such as GEBCO, only offer a horizontal resolution of approximately 500 m, which is insufficient to account for the submarine geometry of the edifice and to clearly identify an insular shelf. High resolution bathymetric data (a horizontal resolution of at least 50 m) around the island are therefore necessary to clearly identify the boundary between the insular shelf and the non-eroded submarine slopes of the volcanic edifice.
Data The method has been designed to be broadly applicable. We consequently preferred the use of global topographic data. Therefore, we compared the results obtained with GDEM SRTM1 or ASTER topographic data, both of which have a horizontal resolution of 1 arc second, or about 30 m, which seems sufficient for Corvo. Regarding the bathymetry, the global GEBCO data do not allow to clearly identify the contours of an insular shelf because of their insufficient horizontal resolution. As a result, we used the EMODnet database. This database covers the whole European territory, of which the Azores are part, and offers aerial and submarine topographic data around Corvo with a horizontal resolution of 50 m per pixel, which is sufficient for our analysis. These different datasets are initially delivered in longitude/latitude in the WGS84 datum.
To rectify the deformations induced by this coordinate system, we have re-projected the data into UTM (zone 25N, WGS72 datum; Fig. 3 ). In order to choose the best dataset between SRTM1 and ASTER, we compared these data with the EMODnet dataset. It appears that the SRTM1 data are more accurate than the ASTER data  (Kervyn et al. , 2008). However, these data indicate an upward shift of several tens of meters compared to EMODnet, which breaks up the topographic continuity between these two datasets. In further detail, the SRTM1 data are quite precise but lack accuracy. In contrast, the ASTER data is noisier, indicating lower accuracy. However, the ASTER data diverge less from EMODnet than SRTM, indicating better accuracy. In the context of our study, which is based on a surface fitting, it seems more relevant to focus on accuracy rather than precision.
Data availability Raw data for our study come from open access topographic data. They can be freely downloaded from the website https://search. earthdata. nasa. gov/search for ASTER data and from the website https://portal. emodnet-bathymetry. eu for https://doi. org/10. 5194/esurf-2022-18 Preprint. Discussion started: 17 June 2022 c Author(s) 2022. CC BY 4. 0 License.
Covariates We used the mean of two previous HSCL-5 measures as an indicator of average levels of mental distress across pre-pandemic years. These included responses on the first available measure, collected between October 1999 and July 2009 (in the mother's 15 th week of gestation) and on the most recent measure before the pandemic (collected when the children were 8 years for the mothers and in year 2015 for the fathers). If data for one measure point was missing, we used the score on the other as indicator of prepandemic mental distress. Baseline sociodemographic characteristics included sex, age, education level, and living condition in Wave 1, i. e. , living alone. Health factors included current Body Mass Index (BMI), which was assessed during the pandemic; history of psychiatric disorders; and chronic medical conditions, that were reported in Wave 1-3.
Statistical analyses Piecewise latent growth modelling based on data from the eight waves during the pandemic was performed to allow for a nonlinear pattern across time. As a result, our model has one intercept and three slopes, one for each period (see Figure  1  and model illustration in Figure  S1 ). Individual characteristics and health factors were included as predictors of variation in the intercept and all slopes in a single model, equivalent to a multiple regression. In addition, the period-specific Covid-19-related variables were included as predictors for relevant slopes. We ran our model both with and without adjusting for prepandemic mental distress, to investigate which predictors were general rather than specific to mental health changes in a pandemic context. All data analyses were performed using lavaan version 0.
Acknowledgements This talk was dedicated to the memory of our long-time collaborator  Franz Käppeler  whose enthusiasm and approach to laboratory nuclear astrophysics remain for us as a beacon of guidance. We gratefully acknowledge the  Discovery Science program  of the  NIF facility (LLNL)  for the approval of a high-power laser shot for this research and for the laboratory support of the experiment. Support of Pazy Foundation (Israel)  and  USA-Israel Binational Science Foundation  ( BSF  Grant Nr. 2020136 ) and support of  Israel Science Foundation  ( ISF Grant  Nr. 876/19 ) for the building of the MONICA detector are acknowledged. This work was supported in part by the  U. S. Department of Energy, Office of Nuclear Physics , under Contract No. DE-AC02-06CH11357. This research used resources of ANL's ATLAS facility, which is a  DOE Office of Science User Facility.
| Molecular analysis DNA and RNA from 54 formalin-fixed, paraffin-embedded (FFPE) and two fresh frozen (FF) diagnostic (D) samples, and from 11 FFPE and one FF relapsed (R) samples were extracted using the AllPrep DNA/RNA FFPE Kit (Qiagen, Germany) or the QIAmp DNA/RNA Mini Kit (Qiagen). CNA were analyzed in 56 diagnostic and 12 relapse samples using the Oncoscan CNV FFPE assay (ThermoFisher Scientific). GISTIC was used to identify the targets of focal somatic CNA. The mutational status of 121 genes recurrently altered in B cell lymphoma (Supplementary Table  S3 ) was examined in 55 of 56 diagnostic and 10 of 12 relapse samples (NGS data for diagnostic sample FL027, and relapse samples FL027 and FL034 were not available) using a custom targeted NGS panel and sequenced in a MiSeq instrument (Illumina).
FigureS8B]. Univariable Cox regression showed a negative impact on OS for FOXO1 mutations [HR = 4. 9 (95% CI 1. 1-22. 8), p = 0. 042, Supplementary FigureS8C], as well as a trend toward a poorer OS for the 8 cases with TMEM30A deletion [HR = 2. 9 (95% CI 0. 9-9. 5), p = 0. 076, Supplementary FigureS8D]. Our dataset was not powered to perform multivariable analyses, due to the low number of cases and the heterogeneity of baseline features.
Selected protein altering variants on known/predicted structures. (A), Domain schema (top) of human CD79B protein showing the location of variants identified in the present study. The schema also shows the location of phosphosites and disulphides below the domains. The structure below shows the dimeric structure of the CD79B Immunoglobulin-like domains with the location of the mutated residues as spheres and their interacting residues as sticks. The insets on the right show the regions around the two variants inside this domain. (B), Domain schema (top) of the human PLCG2 protein showing the location of variants identified. The complete structure of PLCG2 (modeled based on the PLCG1 structure PDB:6pbc) and its variants. The inset structures zoom in on the location of specific mutated amino acids (labeled with numbers) and their interacting residues (labeled without numbers).
Domain schema (middle) of the human PIM1 protein showing the location of variants identified. The schema also shows the location of phosphosites below the domains. The structure to the right (PDB code:3dcv) is that of the catalytic domain and shows the location of Ser97 and Gln127 in addition to the two known phosphosites (pSer89 and pSer261) that lie in this domain. The location of an inhibitor (ATP analog) is also shown in yellow. Positions of known kinase activating mutations are shown as spheres; those lying in the same sequence region as the two PIM1 variants are shown in yellow; the other colors denote other sequence regions; green variants are in the second major region containing in excess of 5 known activating mutations (from UniProt). (D), Domain schema (top) of the human MCL1 protein showing the location of variants identified.