dataesr/scientific-paragraphs-categorization huggingface dataset:
FUNDING ND was partially supported by the  LABEX PRIMES of Université de Lyon  ( ANR-11-LABX-0063 ) and the  MIC-MAC   JCJC  project ( ANR-19-CE45-0005 ). AK was partially supported by the  Wellcome EPSRC Centre for Medical Engineering at Kings College London  ( WT 203148/Z/16/Z ) and the  UK EPSRC  ( EP/P001009/1  and  EP/R005516/1 ). MD was partially supported by the  European Union Horizon 2020 Programme for Research and Innovation (CardioFunXion ,  H2020-14-MSCA-ITN-642676 ).
Patients Five hundred eighty-two consecutive adults (18 to 85 years old) referred for septic shock  [8]  and treated with norepinephrine and/or epinephrine, were prospectively enrolled after admission on ICU from four tertiary hospitals between 2015 and 2019. End-stage cardiac insufficiency (NYHA class IV), liver cirrhosis (Child-Pugh classification C) or ongoing cancers were excluded. The Strasbourg University Hospital Ethic Committee approved this multicentre study (NCT02391792) and it was registered on clinicaltrial. gov. Informed consent was obtained from the patient or relatives at admission, and confirmed by the patient when possible. Patients were managed following current guidelines without specific therapeutic intervention.
Data sampling and evaluation of patients Blood samples for global coagulation tests (platelet counts, prothrombin time (PT), fibrinogen, antithrombin and D-dimers) were collected within 12 h after the patients were admitted in the ICU (day 1), as well as on day 2. All patients were followed up for 28 days after enrolment in the study, and 7-and 28-day all-cause mortality was assessed. Daily Sequential Organ Failure Assessment (SOFA) score was also documented.
Since there were very few missing data (between 0 and 3% depending on the variable), no specific method for handling missing data was implemented. A p value < 0. 05 was considered statistically significant. All the analyses were realized using R software version 3. 6. 0. R Core Team (2019). R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https :// www. R-proje ct. org/.
The delta SOFA has therefore the strongest association with the 28-day mortality in patients with sepsis and DIC  [17]. Yet, including points for a SOFA ≥ 2 in SIC score looks useless, as in our cohort, all septic shock patients have a SOFA ≥ 2 on ICU admission (11. 0 ± 3. 2 points), including patients who were included before the change of definition of sepsis and septic shock. Our study has several strengths and some limitations. Among the strengths, we have enrolled a large and multicenter cohort of septic shock patients that were well characterized clinically. Then, data collection was prospective, therefore allowing a low rate of missing data. Finally, we have compared the main scores and their revised form, taking into account the recent recommendations for DIC diagnosis. The main limitation of the study is that we do not have the gold standard comparator that could help assess the validity of other scores.
Sampling Juvenile ST sharks were captured in the Revillagigedo Archipelago  (from 2006 to 2016, n=26)  and Clipperton Atoll (2017, n=28) using hook and line (Figure  1 ). Biometric data (e. g. total length [TL], sex) was recorded and a fin clip sample was taken from the free rear tip of the second dorsal fin for each individual. All sharks were immature  (Compagno et al. , 2005) , ranging from 0. 69 to 1. 52 m TL in Revillagigedo and from 0. 7 to 0. 97 m TL in Clipperton, which corresponds to young-of-the-year to 5-year-old sharks  (Smart et al. , 2017). Tissue samples were immediately transferred to a -20 °C freezer onboard the boat. Once at the laboratory, entire (i. e. including skin, subdermal tissue and cartilage) fin clip samples (~1 cm) were lyophilized and homogenized.
Acknowledgments: Gaël Le Croizier  was supported by a postdoctoral grant from the  French National Research Institute for Sustainable Development (IRD). Fieldwork was supported by  Alianza WWF/Telmex-Telcel, Chris Fischer Productions/National Geographic. The authors thank  Southern Sport  and  Quino El Guardián  for providing space in their boats. This work was financially supported by the  French National Research Agency  project  ANR-17-CE34-0010   MERTOX. This research was carried out under permits from the  Secretaría de Agricultura, GanaderÍa, Desarrollo Rural ,  Pesca y Alimentación  ( DGOPA. 06668. 150612. 1691 )and  Comisión Nacional de Areas Naturales Protegidas  ( F00. DRPBCPN-APFFCSL. REBIARRE-102/13 ) of Mexico. We are also grateful to  Secretaría del Medio Ambiente y Recursos Naturales and Dirección del Parque Nacional Revillagigedo  for providing necessary permits to conduct research at the  Revillagigedo National Park , a  UNESCO World Heritage Site.
Acknowledgements We thank  Alex Kehagias  and  Marios Petropoulos  for their collaboration at early stages of the project and  Francesco Alessio ,  Stéphane Detournay ,  Gia Dvali ,  Alavaro Herraez ,  Eran Palti ,  Cumrun Vafa  and  Nick Warner  for useful discussions. We also thank the  Corfu Summer Institute 2019 , during which this project was initiated. Q. B. is supported by the  Deutsche Forschungsgemeinschaft under Germany's Excellence Strategy EXC 2121 "Quantum Universe" -390833306. The work of L. C. is supported by the  ERC Advanced Grant  " High-Spin-Grav ". The work of D. L. is supported by the  Origins Excellence Cluster. The work of S. L. is supported by the  ANR  grant  Black-dS-String  ( ANR-16-CE31-0004 ).
Method: A systematic search of the European Clinical trial registry, the U. S. National Library of Medicine ClinicalTrials. gov, and the World Health Organization's (WHO) International Clinical Trials Registry Platform (ICTRP) was conducted on May 10 th , 2020 to identify the study details of the SOLIDARITY and DisCoVeRy trials. A supplementary search of PubMed, WHO's website, French authorities' websites, and Google search engine was conducted. A critical review was performed on the findings.
Method A systematic search of the European Clinical trial registry, the U. S. National Library of Medicine ClinicalTrials. gov, and the WHO's International Clinical Trials Registry Platform (ICTRP) was conducted on May 10 th , 2020 to identify the study designs of the SOLIDARITY and DisCoVeRy trials. With regard to the SOLIDARITY study, the trials reported at national level in clinical trial registries were also identified. A supplementary search of PubMed, WHO's website, French authorities' websites including MESRI, MSS and Inserm, and Google search engine using the keywords of "SOLIDARITY trial" and "DisCoVeRy trial" was conducted to identify additional information on the progress of the two trials.
Study design DisCoVeRy is a European adaptive, randomized, multicentric open 5 parallel arms trial. Randomization is balanced as 1:1:1:1:1. The target sample size is 3100 patients enrolled, with 620 patients per treatment arm. Patients are stratified according to geography and to the level of severity (moderate or severe) according to well-predefined criteria. It is not clear what elements of the adaptive design will be considered for adjustment of the trial design by the study monitoring committee. Based on the available information on the U. S. National Library of Medicine's ClinicalTrial. gov database, the adjustments are likely related to interventions that may be revised based on milestone results predefined in the adaptive study design. The number of patients and population do not seem to be considered as part of the changes to occur in the adaptive design.
of study design between Solidarity and Discovery trial the trial (i. e. remdesivir, interferon ß-1a, lopinavir/ritonavir, hydroxychloroquine) in the past 29 days 7. Contraindication to any study medication including allergy 8. Use of medications that are contraindicated with lopinavir/ritonavir i. e. drugs whose metabolism is highly dependent on the isoform Solidarity trial Discovery trial CYP3A with narrow therapeutic range (e. g. amiodarone, colchicine, Trial ID PER-010-20 NCT04315948 simvastatine). Trial title Solidarity: An International Randomized Controlled Trial to Evaluate Non- Multi-centre, Adaptive, Randomized Trial of the Safety and Efficacy 9. Use of medications that are contraindicated with Licensed Covid-19 Treatments in Addition to Standard of Care among of Treatments of COVID-19 in Hospitalized Adults (DisCoVeRy) hydroxychloroquine: citalopram, escitalopram, hydroxyzine, Hospitalized Patients domperidone, pipéraquine.
Setup and methods The FALSTAFF spectrometer is meant to provide the full characterization of the fission fragments, i. e. their masses before and after neutron evaporation process, their kinetic energies and their nuclear charges. Using those information, one could deduce the neutron multiplicity as a function of mass, which will provide valuable data about the energy sharing between the two fragments at the scission point. The mass before neutron evaporation is obtained via the 2V (Double Velocity) method. To apply this method the assumption is made that the neutron emission, in average, does not change the velocity of the fragments in the center of mass frame. It requires the measurement of both fragment velocities in coincidence. The velocity is determined with two time-of-flight (ToF) Secondary Electron Detectors (SeD)  [4] , represented in Fig.
On all those figures, data are normalized to their integral. The LoF (Length-of-Flight) is calculated from the positions measured in the Start and Stop detectors. The angle between the track of the fragments and the emissive foils is also calculated providing the thickness crossed by the fragments. This information is needed to calculate the energy loss by the fragments. The left panel of Fig. 2  shows the comparison between experimental and simulated LoF distributions. One can observe that the simulation has the good angular parameters since the LoF is well reproduced. One of the improvements made in 2019 was the replacement of the Start detector as the previous one was too noisy which resulted in the inability to distinguish the less energetic events from the electronic noise. As the heavy fission fragments deposit less energy in matter than the light ones, this resulted in an unbalance in the number of fragments detected which was detrimental to all the other observables.
The difference between the time of arrival of the fragments at the stop and the new Start detector is presented in Fig. 2  (right pannel). The noise issue was solved and the balance between light and heavy fragments restored. The time was calibrated thanks to the measurement with an alpha source and the agreement with the simulations data is excellent. Based on the ToF and LoF measurements, the velocity is calculated event-by-event. Fig. 3  shows the experimental (red curve) and the simulated (G4-GEF (blue curve)) distributions. Agreement between simulation and experiment is very good although the resolution for the light fragments seems slightly worse in the experiment. The kinetic energy distribution is presented in Fig. 4 , after having been corrected from the energy lost in the entrance window of the ionisation chamber and the emissive foil of the Stop detector.
The calibration procedure is based on data and GEANT4 tables. In order to perform this calibration, the Cf source was placed at three different positions: target position, between Start and Stop detector and in front of the ionisation chamber. Due to the low energy of fission fragments (60-100 MeV), energy losses in the materials are important. The kinetic energy measured in the chamber has to be corrected for energy losses suffered by the fragment in the emissive foil and in the ionisation chamber entrance window. One obtains the energy at the point where the veloc-  ity is measured. One can then apply the method described in Section 2 to obtain the mass distribution of the fission fragments after neutron evaporation. The experimental and simulated distributions obtained for the fragment mass after the iterative procedure are shown in Fig.
Acknowledgement This work was partially supported by  CEA ,  NEEDS  and within the  Seventh Frame-work Programme through CHANDA  ( EURATOM  contract no. FP7-605203 ). The authors would like to specially thank the  P2IO LabEx  ( ANR-10-LABX-0038 ) in the framework " Investissements d'Avenir"  ( ANR-11-IDEX-0003-01 ) managed by the  Agence Nationale de la Recherche  ( ANR, France ) for making this work possible.
Recent research shows increasing decadal ice mass losses from the Greenland and Antarctic Ice Sheets and more generally from glaciers worldwide in the light of continued global warming. Here, in an update of our previous ISMASS paper  (Hanna et al. , 2013) , we review recent observational estimates of ice sheet and glacier mass balance, and their related uncertainties, first briefly considering relevant monitoring methods. Focusing on the response to climate change during 1992-2018, and especially the post-IPCC AR5 period, we discuss recent changes in the relative contributions of ice sheets and glaciers to sea-level change. We assess recent advances in understanding of the relative importance of surface mass balance and ice dynamics in overall ice-sheet mass change. We also consider recent improvements in ice-sheet modelling, highlighting data-model linkages and the use of updated observational datasets in ice-sheet models.
In addition, radar altimeter surveys do not adequately sample relatively steeper-sloping ice-sheet margins and require correction for the highly-variable radar-reflection depth that has strong seasonal variations and interannual trends and complex interactions between linearlypolarized radar signals and the direction of the surface slope. Successful corrections have been developed and applied to radar altimeter data from ERS1 and ERS2 using crossover analysis data  (Wingham et al. , 1998; Davis and Ferguson, 2004; Zwally et al. , 2005; Yi et al. , 2011; Khvorostovsky, 2012)  and to Envisat data using repeat track analysis and an advanced correction algorithm  (Filament and Remy, 2012). However, the corrections applied by others to Envisat and CryoSat data have been questioned due to complex interaction of the crosstrack linearly-polarized radar signal of Envisat and CryoSat with the surface slope that affects the highly-variable penetration/reflection depth  (Zwally et al.
The mass-budget method involves subtracting two large quantities (SMB and discharge) and needs detailed and complete regional information on these components, which is recently available from satellite radar data for discharge. SMB cannot be directly measured at the ice-sheet scale but is instead estimated using regional climate models that are evaluated and calibrated using in-situ climate and SMB observations. These RCM/SMB models can have significant uncertainties in derived accumulation and runoff (of the order of 15%, e. g. Fettweis, 2018). Deriving discharge requires knowledge of bathymetry and the assumption of an internal velocity profile in order to determine ice flux across the grounding line, and there are also errors in determining the position of the grounding line. Further uncertainty arises in estimating the discharge from the areas where the ice velocity is not measured.
Rignot et al. (2019)  used the mass budget method to compare Antarctic snow accumulation with ice discharge for 1979-2017, using improved, high-resolution datasets of ice-sheet velocity and thickness, topography and drainage basins and modelled SMB. Within uncertainties their total mass balance estimates for WAIS and the Antarctic Peninsula agreed with those of  Shepherd et al. (2018)  but they derived a -57±2 Gt yr -1 mass balance for East Antarctica for 1992-2017, compared with the +5±46 Gt yr -1 for the same period derived in IMBIE2. Possible reasons for this difference include uncertainties in ice thickness and modelled SMB in the mass budget method, together with further uncertainties in the IMBIE-2 EAIS mass estimates arising from volume to mass conversions within the altimetry data processing and significantly uncertain GIA corrections when processing GRACE data.
Greenland Ice Sheet According to several recent estimates, the GrIS lost 257±15 Gt yr -1 of mass during 2003-2015  (Box et al. , 2018) , 262±21 Gt yr -1 during  2007 -2011 (Andersen et al. , 2015)) , 269±51 Gt yr -1 during  2011 -2014 (McMillan et al. , 2016)) , 247 Gt yr -1 of massrepresenting 37% of the overall land ice contribution to global sea-level riseduring 2012-2016  (Bamber et al. 2018) , and 286±20 Gt yr -1 during 2010-2018  (Mouginot et al. , 2019). A slightly greater mass loss of 308±12 Gt yr -1 based on GRACE gravimetric satellite data for 2007-2016 was given by  Zhang et al. (2019). Some of the difference between these numbers can be attributed to different methods considering either just the contiguous ice sheet or also including disconnected peripheral glaciers and ice caps, the latter being the case for GRACE-based estimates.
(2016)  also found an atmospheric warming signal on mass balance in the northernmost reaches of the ice sheet. Taking a longer perspective from 1972-2018, using extended datasets of outlet glacier velocity and ice thickness, improved bathymetric and gravity surveys and newly-available high resolution SMB model output,  Mouginot et al. (2019)  reported that dynamical losses from the GrIS have continuously increased since 1972, dominating mass changes except for the last 20 years, estimating that over this longer period 66±8% of the overall mass losses were from dynamics and 34±8% from SMB. They concluded that dynamics are likely to continue to be important in future decades, apart from the southwest where runoff/SMB changes predominate, and that the northern parts of GrISwhere outlet glaciers could lose their buttressing ice shelvesare likely to be especially sensitive to future climate warming.
, 2012; Ivins et al. , 2013; Argus et al. , 2014)  or it can be inferred via data inversion  (Gunter et al. , 2014; Martín-Español et al. , 2016; Sasgen et al. , 2017). Both approaches would benefit from better spatial coverage of GPS observations of land deformation, while the first approach strongly depends on past ice sheet change, for which constraints are severely lacking, particularly across the interior of the Greenland and Antarctic ice sheets. Both approaches also typically rely on the assumption that mantle viscosity beneath the major ice sheets is spatially uniform and high enough that the signal due to past ice-mass change is constant in time. However, recent work has revealed regions in both Greenland and Antarctica where mantle viscosity is much lower than the global average (e.
, 2012 (Pattyn et al. , , 2013)). These numerical experiments demonstrated that in order to resolve grounding-line migration in marine icesheet models, a sufficiently high spatial resolution needs to be applied, since membrane stresses need to be resolved across the grounding line to guarantee mechanical coupling. The inherent change in basal friction occurring across the grounding line -zero friction below the ice shelf -requires high spatial resolution (e. g. , <1 km for Pine Island Glacier;  Gladstone et al. , 2012)  for an accurate representation of grounding-line migration. Therefore, a series of ice-sheet models have implemented a spatial grid refinement, mainly for the purpose of accurate data assimilation  (Cornford et al. , 2015; Gillet-Chaulet et al. , 2012; Morlighem et al. , 2010) , but also for further transient simulations where the adaptive mesh approach enables the finest grid to follow the grounding-line migration  (Cornford et al.
Model initialisation, uncertainty and inter-comparison Despite major improvements in ice-sheet model sophistication, major uncertainties still remain pertaining to model initialisation as well as the representation of critical processes such as basal sliding and friction, ice rheology, ice damage (such as calving and MICI) and sub-shelf melting. New developments in data assimilation methods led to improved initialisations in which the initial ice-sheet geometry and velocity field are kept as close as possible to observations by optimising other unknown fields, such as basal friction coefficient and ice stiffness (accounting for crevasse weakening and ice anisotropy;  Arthern and Hindmarsh, 2006; Arthern and Gudmundsson, 2010; Cornford et al. , 2015; MacAyeal, 1992; Morlighem et al. , 2010 Morlighem et al.
, 2018a; Seroussi et al. , 2019) , which has seen an unprecedented return from ice-sheet modelling groups globally. With ISMIP6, the ice-sheet modelling community has engaged to evolve to new standards in availability, accessibility and transparency of ice-sheet model output data (e. g. Goelzer et al. , 2018b) , facilitating model-model and data-model comparison and analysis. ISMIP6 has strengthened the links between the ice-sheet modelling community and other communities of global and regional climate modellers, ocean modellers and remote sensing and observations of ice, ocean and atmosphere.
Marzeion et al. (2017) , using a yet more extended set of glaciological and geodetic measurements  (Zemp et al. , 2015) , gave a global glacier mass-change rate estimate of -0. 61±0. 07 mm SLE yr -1 for 2003-2009 (including Greenland peripheral glaciers, but not those of the Antarctic periphery), obtained by averaging various recent GRACE-based studies  (Jacob et al. , 2012; Chen et al. , 2013; Yi et al. , 2015; Schrama et al. , 2014)  and several studies combining GRACE with other datasets  (Gardner et al. , 2013 , and an update of it;  Dieng et al. , 2015; Reager et al. , 2016; Rietbroek et al. , 2016). The studies based on GRACE data consistently give less negative glacier mass balances than those obtained using other methods. Uncertainties in the GRACE-derived estimates remain important especially due to the small size of glaciers compared with the GRACE footprint of ∼300 km.
(2018) , and the mass budget of -6 Gt yr -1 for the Antarctic peripheral glaciers over 2003-2009 estimated by  Gardner et al. (2013) , we get an estimate of the current global glacier mass budget of -266±33 Gt yr -1 (0. 73 ± 0. 09 mm SLE yr -1 ). The most recent studies to highlight are those of  Zemp et al. (2019)  and  Wouters et al. (2019). The former is based on glaciological and geodetic measurements but uses a muchextended dataset (especially for the geodetic measurements), the most updated glacier inventory (RGI 6. 0) and a novel approach. The latter combines, for each glacier region, the temporal variability from the glaciological sample with the glacier-specific values of the geodetic sample. The calibrated annual time series is then extrapolated to the whole set of regional glaciers to assess regional mass changes, considering the rates of area change in the region.
Both models are highly sensitive to the initial ice volume. Regarding  Marzeion et al. (2018) , while they use basically the same statistical model as in  Marzeion et al. (2012 Marzeion et al. ( , 2014a Marzeion et al. ( ,b, 2015 Marzeion et al. ( , 2017)) , the use of a newer version (5. 0) of the RGI, as well as updated DEMs and SMB calibration datasets, led to lower SLR contributions from glacier wastage to the end of the 21 st century, similar to those by  Huss and Hock (2015) : 84   A recent intercomparison of six global-scale glacier mass-balance models, GlacierMIP  (Hock et al. , 2019) , has provided a total of 214 projections of annual glacier mass and area, to the end of the 21 st century, forced by 25 GCMs and four RCPs. Global glacier mass loss (including Greenland and Antarctic peripheries) by 2100 relative to 2015, averaged over all model runs, varies between 94±25 (RCP2.