dataesr/scientific-paragraphs-categorization huggingface dataset:
Outcome measures Prespecified exploratory endpoints in the study included change from baseline to Week 24 in 6-min walking distance (6MWD), WHO FC, and hemodynamic parameters measured by right heart catheterization (RHC). Patients classified as responders at Week 24 were free from clinical worsening (see Supplement for definition), improved to WHO FC I/II, and had an improvement in 6MWD of ≥30 m  [2]. Pulmonary arterial compliance (PAC) and cardiac function parameters were calculated post hoc using RHC data (see Supplement for details). The relationship between response and stroke volume index (SVI) and right atrial pressure (RAP) was assessed using thresholds shown to be independently associated with long-term outcomes (SVI ≥ 31 mL/beat/m 2 and RAP b 10 mmHg)  [4]. Biomarkers assessed in RESPITE included NT-proBNP, growth/differentiation factor 15 (GDF-15), suppression of tumorigenicity 2 (ST-2), and asymmetric dimethylarginine (ADMA).
The ongoing Phase IV randomized, controlled Riociguat rEplacing PDE5i Therapy evaluated Against Continued PDE5i thErapy (REPLACE) study (ClinicalTrials. gov: NCT02891850) will further assess the strategy of switching to riociguat in patients with PAH and an insufficient response to PDE5i. Strengths of this study include the wide range of biomarkers monitored, hemodynamic data collected at baseline and Week 24, and the use of RRS as a secondary endpoint in the study, meaning that consistent data are available for these analyses. Limitations include that only 84% of patients completed the study as well as the post hoc nature of the assessment and the arbitrary definition of clinical response. A further limitation is the small patient cohort, with only 16 of 51 patients who completed the study being classed as responders.
With the creation of a global fisheries subsidies database for 2018  (Sumaila et al. , 2019a,b) , and in light of the United Nation Sustainable Development Goals and on-going negotiations within the World Trade Organization (WTO) to discipline harmful fisheries subsidies, this paper presents an up-dated estimate of the proportion of fisheries subsidies that are provided to the small-and large-scale fishing sub-sectors at the regional and global scale. Sumaila et al. (2019b)  forms the baseline of the present study because compared to other fisheries subsidy databases, such as the OECD's Fisheries Support Estimates  (OECD, 2006) , it does not only rely on each country's official reporting but explores and analyses a much larger source of information. For example, it includes tax concessions, financial law documents and news articles.
Dividing Total Country Subsidies Between SSF and LSF In order to determine the SSF-LSF split of each subsidy type amount for each country analyzed in this study, reported subsidies information was grouped and assessed based on three different data types; quantitative data, qualitative data, or no data reported (Figure  1  Each source for each country and subsidy type was assessed independently to search for specific evidence regarding SSF and/or LSF, and if available new sources were assessed. The following questions were posed to decide under which data type each source would fall (Figure  1 ): 1. Is quantitative data available regarding the SSF-LSF split? If yes, dollar amount is recorded. If no, the next question follows: 2. Is qualitative information regarding the SSF-LSF split available? If yes, use the qualitative information to estimate how much of the total subsidies amount allocated to the SSF and LSF.
2) is used as an adjustment factor to estimate the SSF subsidies for all countries and subsidy types, which have not been assessed. Adjustmentfactor i,j = 2009 propSSF i , j 2009 propSSF s,j (2) The adjustment factor is then multiplied with the up-dated SSF subsidy per sub-region s and subsidy type j (Eq. 3). propSSFestimate i,j = propSSF s,j × Adjustmentfactor i,j (3) The final proportion of SSF subsidy estimates for each country and subsidy type (propSSFestimate i,j ) from Eq. 3 is then multiplied with the total amount of subsidy per type for each country in 2018 taken from  Sumaila et al. (2019b)  to present the absolute SSF subsidy amount in USD for the year 2018. Completed code for the model and all raw data (input) as well as the resulting output file presenting the split of subsidies into LSF and SSF are publicly available on a Github repository: https: //github.
Categories and Further Analysis To better understand the divide into SSF and LSF not only at a subsidy category, subtype and regional level we also analyzed our data by political groups: (a) United Nation based developed versus developing countries and (b) low and high Human Development Index (HDI) groups. While the UN developed and developing grouping is commonly used when working with global data, original subsidies data  (Sumaila et al. , 2019b)  was grouped using the 2017 United Nation HDI for part of the analysis and presentation of the results  (Sumaila et al. , 2019a  Additionally, to better understand what the percentage of subsidies provided to each sub-sector means in terms socioeconomics, we calculated the subsidy intensity by dividing the amount of subsidies by the number of fishers active in each sub-sector and the amount of subsidies per dollar of landed value for each sector.
DISCUSSION Results of this study clearly show that there is an imbalance toward the LSF sub-sector, who receive the vast majority of fisheries subsidies. While the current analysis is based on an updated dataset for 2018, the results are similar to those produced using a subsidies dataset for 2009, where results showed a total of 16% being provided to the SSF  (Schuhbauer et al. , 2017)  compared to the 19% estimated in the present study. As capacity-enhancing subsidies undermine sustainable fishing practices and can promote overfishing, we already know that it is of concern that these subsidies as a percentage of the global total, have increased from 57% in 2009 to 63% 2018  (Sumaila et al. , 2019a). Looking into capacity-enhancing subsidies in terms of SSF, the proportion received by the SSF has increased from 41% in 2009 to 59% in 2018.
Sumaila et al. (2019b)e United Nations Development Programme's (UNDP) cut-off where all countries below 0. 7 HDI are part of the low HDI group and all countries equal or above 0. 7 part of a high HDI group. As our dataset for splitting global subsidies into LSF and SSF is based onSumaila et al. (2019b), we use the same grouping, presenting global subsidies based on subsidy categories and split into High and Low HDI groups.
A single subcutaneous injection of emicizumab provided a linear pharmacokinetic profile with a half-life of approximately 4-5 weeks in healthy adult subjects,  19  thus allowing for infrequent dosing regimen. Initially, once weekly (QW) administration of emicizumab at 0. 3, 1, and 3 mg/kg bodyweight dose-dependently increased plasma emicizumab concentrations, and reached steady-state ~12 weeks after first injection, in HA adult patients with and without FVIII-inhibitors. 20, 21 Pharmacologic modeling determined a novel regimen, which would allow shortening the time to reach steady-state. 22 This regimen was first studied in the phase 3 HAVEN 1 trial (ClinicalTrials. gov number, NCT02622321), which included 109 HA adult patients with FVIII-inhibitors. 23 Mean steady-state plasma emicizumab concentrations were reached after 4 weeks of weekly loading dose of 3 mg/kg bodyweight followed by QW maintenance dose of 1.
25, 26 Interestingly, similar pharmacokinetic profiles and through levels of plasma emicizumab concentrations were observed in the 152 HA adult patients without FVIII-inhibitors who were enrolled in the phase 3 HAVEN 3 study (ClinicalTrials. gov number, NCT02847637), whether they were included in the QW prophylaxis regimen cohort or in the maintenance dose of 3. 0 mg/kg bodyweight emicizumab every 2 weeks cohort (Q2W). Other emicizumab prophylaxis regimens were studied in the HAVEN 4 trial (ClinicalTrials. gov number, NCT03020160), in which HA adult and adolescent patients with and without FVIII-inhibitors received emicizumab subcutaneous injection every 4 weeks (Q4W). In the expansion cohort, patients received the usual 3 mg/kg loading dose per week during 4 weeks followed by a maintenance dose of 6 mg/kg bodyweight Q4W.
Mean trough plasma concentrations reached 50 μg/mL after loading dose and were maintained slightly above 40 μg/mL throughout the trial. 27 However, unlike QW and Q2W regimens, the Q4W dosing regimen resulted in peaks and valleys of emicizumab concentration, which could lessen the efficiency of emicizumab prophylaxis over bleeding protection. 27 9] [30]  However, with the exception of bridging FIXa and FX, emicizumab and FVIII have little in common, and emicizumab pharmacokinetic properties are less likely to be correlated with binding-capacity to VWF or ABO groups, as described recently for FVIII. 31 As a result, same QW, Q2W, and Q4W emicizumab dosing regimens as those used for adults were studied in children with HA. Pharmacokinetic profile of emicizumab prophylaxis in HA pediatric patient was first studied in the HAVEN 2 trial (ClinicalTrials.
gov number, NCT02795767). HAVEN 2 is a non-randomized, open-label, multicenter, phase 3 clinical trial designed to evaluate the efficacy, safety, and pharmacokinetics of emicizumab prophylaxis in children <12 years of age (12 to 17 years, if bodyweight < 40 kg) with HA and high-titer FVIII-inhibitors (≥5 BU/mL), previously treated with BPAs. 32 First cohort of patients (n=68) received subcutaneous injections of loading dose emicizumab 3 mg/ kg QW for 4 weeks, followed by maintenance dose 1. 5 mg/kg QW thereafter. Two additional nonrandomized cohorts were subsequently opened to enroll patients to receive similar loading dose regimen, followed by maintenance dose 3 mg/kg Q2W (n=10 patients), or 6 mg/kg Q4W (n=10 patients). Results were concordant with those observed in adult patients, revealing that through plasma emicizumab concentrations were maintained at approximately 50 µg/mL, 45 µg/mL, and 38 µg/mL with QW, Q2W, and Q4W dosing, respectively (Figure  1 ).
0001) intra-individual reduction of ABR among patients who were previously receiving prophylaxis with BPAs. 23 Such efficacy was recently confirmed in pediatric patients (<12 years of age), who are the most prone to develop FVIII-inhibitors and to receive ITI. 36  Data about emicizumab prophylaxis clinical efficiency in HA children with FVIII-inhibitors are arising from single centers case series and the phase 3 HAVEN 2 trial (ClinicalTrials. gov number NCT02795767; Table  1 ). In both case series, no spontaneous bleeds were reported for up to 52 weeks after initiation of QW emicizumab prophylaxis in a total of 15 male HA children with high-titer FVIII-inhibitors. 37, 38 In the phase 3 HAVEN 2 trial, enrolling 88 HA children (2-12 years of age) with high-titer FVIII-inhibitors, 68 patients received the QW dosing regimen for a median efficacy period of 57.
6 weeks (range 17. 9-92. 6). Among them, 94. 7% had zero treated bleeds, and 72. 3% experienced zero bleeds, whether they were treated or not. Onceweekly administration of emicizumab resulted in an ABR for treated bleeds of 0. 3 (95% CI, 0. 17; 0. 5), with a median ABR of 0 for all treated bleed endpoints. An intra-individual comparison was performed in a total of 18 patients <12 years of age who had previously participated in the non-interventional study (ClinicalTrials. gov identifier NCT02476942) prior to enrolling in HAVEN 2, and received treatment with QW emicizumab prophylaxis. Intra-individual comparison analysis showed that emicizumab prophylaxis resulted in a 99% reduction in treated bleeds compared to previous episodic/prophylaxic treatment with BPA. 39 Among 15 patients who received prior prophylactic BPA treatment, QW emicizumab prophylaxis at a median duration of 89.
The observational MOTIVATE study (ClinicalTrial. gov number, NCT04023019) and the prospective Emicizumab PUP and Nuwiq ITI study (ClinicalTrial. gov number, NCT03344003), will attempt to address these concerns. However, other issues are not resolved yet for HA patients with FVIII-inhibitors receiving emicizumab, such as: i) how to monitor and define ITI success?, ii) how to adapt ITI regimen?, iii) when to stop ITI?, iv) what to do after ITI completion?, etc. In case ITI was successful, should emicizumab be continued or should it be replaced by a return to FVIII prophylaxis? (Table  2 ).
Since then, no new potentially emicizumab-related serious TE and TMA have been recorded in clinical trials. 53 However, four additional TEs had been reported in the postmarketing setting, among which three were venous TE, and one was arterial TE. 54  Although few data are available with regards to these recent TE, it seems that emicizumab was used alone. Patients had received (offlicense) emicizumab prophylaxis for treating acquired hemophilia. However, it is known that acquired haemophilia may be at risk of thrombosis because of older age, autoimmunity, and cancer;  55  and it is unlikely to occur in children. 55 In addition, non-hemophilia children are globally less at risk for thrombosis compared to adults, even in the presence of inherited thrombophilia. 56 Interestingly, neither TMA nor thromboembolic events have been reported to date in HA pediatric patients <12 years of age with or without FVIIIinhibitors,  27, 33, 37, 38, 47  thus suggesting that emicizumab prophylaxis is relatively safer in this age group.
To do this, we first describe the model and the data that comprise the test bed used to drive the model. This test bed includes distributions of plant traits, most of which are based directly on observations across research sites in Panama. We then describe a series of numerical experiments aimed at exploring the structural and parametric uncertainty in the model. These include (1) assessing direct control of trait uncertainty on model predictions using an ensemble of model runs with only one PFT per ensemble member, (2) separating ensembles where we embed FATES within two related but divergent land surface models, the Energy Exascale Earth System Model (E3SM) Land Model (ELM) and the Community Land Model (CLM), (3) ensembles where we add greater numbers of competing PFTs (from 1 to 2 to 10) into each ensemble member, and (4) a set of ensembles where we compete two PFTs against each other in each ensemble member while also varying a set of ecosystem-level parameters that govern competition and disturbance in the model.
, 2017). The procedure includes the following major steps -the identification of problems in the datasets (QA) and then data cleaning, flagging, and gap filling of missing data (QC). Step 1 (QA) includes an initial visual inspection and cataloging data, determining the temporal frequency of sampling to assess data availability and preliminary assessing data quality. Step 2 (QC) includes prohttps://doi. org/10. 5194/bg-17-3017-2020 Biogeosciences, 17, 3017-3044, 2020 cessing and cleaning raw datasets; formatting timestamps; detecting and removing duplicates, bad data, and outliers; gap filling of missing data; and flagging quality-controlled data. For each simulation, we recycled meteorology over the 1986-2017 period.
Plant trait data and application to FATES PFT definition A key conceptual point in this study is that we define a PFT only as a vector of plant traits; we do not make any further a priori assumptions about the ecological role that a given PFT plays. In some of these experiments, we do diagnose properties of a PFT that allow us to -in certain casesmake post hoc distinctions such as "early-successional" or "late-successional" PFTs, and in this paper all PFTs may be thought of as belonging to tropical forest tree communities, but we essentially take a probabilistic view of PFTs here as being random draws from some continuous trait covariance matrix. To define this matrix, we assemble several datasets and cross-reference them based on mean values per plant species, with Latin binomials used as the reference index.
We start with two datasets describing plant traits at BCI, and at two other sites across a precipitation gradient in Panama, Parque Nacional Metropolitano (PNM) and Fort San Lorenzo (SLZ), which are originally described in Osnas et al. (  2018 ) and  Wright et al. (2010). Data from these sets used here include leaf lifespan, leaf mass per unit area (LMA), wood density, mortality of 10 cm and larger trees, and leaf N content. For these datasets, we only use values for trees in the canopy stratum. Where a given species occurs in more than one site, we use mean values across the sites. Because these are the only datasets that include leaf lifespan estimates, where other datasets also include an estimate of LMA for a given species, we only use the estimates in these datasets, as they will correspond to the specific individuals with which leaf lifespan is also measured.
We add two further datasets on leaf traits, both based on canopy crane measurements at PNM and SLZ sites:  Gu et al. (2016)  and  Rogers et al. (2017)  and  Wu et al. (2019). Each of these contain estimates of V c,max , LMA, wood density, and leaf N content. We use FATES temperature scaling functions to calculate V c,max at the reference temperature (25 • C) based on the temperature at which specific V c,max observations were made. Together these sets of traits describe plant variation along the leaf and wood economic spectra, two critical axes of functional diversity  (Baraloto et al. , 2010; Wright et al. , 2004). Lastly, we add a dataset on crown area allometry from trees at BCI  (Martínez Cano et al. , 2019). The crown area allometry in FATES is defined with crown area, C, set as a power law relationship with diameter, D, as described above, so for each species we use the crown area coefficient g and exponent d as reported in Martínez  Cano et al.
(2019). These crown area traits control the overall light interception ability of plants, and how it changes over plant size, and thus are important determinants of both baseline growth rates (for coefficient g) and the derivative of growth rates with respect to plant size (for exponent d). In total, we thus use eight traits from the observational datasets: V c,max,25,top (µmol CO 2 m -2 s -1 ), wood density (g cm -3 ), LMA (m 2 g -1 ), leaf N per unit area (Leaf N/area; g m -2 ), leaf lifespan (year), background tree mortality (yr -1 ), crown area coefficient (m 2 cm -1 ), and crown area intercept (unitless). We assume lognormal distributions for V c,max,25,top , LMA, Leaf N/area, leaf lifespan, and background tree mortality and normal distributions for wood density, crown area coefficient, and crown area intercept, with correlations between these traits as determined from the data.
The tower used for these measurements is 41 m a. g. l. (meters above ground level) on a plateau on BCI. The eddy-covariance system includes a sonic anemometer (CSAT3, Campbell Scientific, Logan, UT) and an open-path infrared CO 2 -H 2 O gas analyzer (LI-7500, LI-COR, Lincoln, NE). High-frequency (10 Hz) measurements were acquired by a data logger (CR1000, Campbell Scientific) and stored on a local PC. Data were processed with a custom program using a standard routine described in  Detto et al. (2010). GPP was derived from daytime values of net ecosystem exchange (NEE) by adding the corresponding mean daily ecosystem respiration obtained as the intercept of the light response curve  (Lasslop et al. , 2010). The light curve was fitted on a 15 d moving window using a rectangular hyperbolic function (runs with friction velocity less than 0.
6 ), show both the wide spread of ensemble members, as discussed above, and two systematic model-data mismatches. The first of these is in the shape and amplitude of the seasonal cycles: FATES simulations systematically predict a decrease in GPP during the dry season (February-April) as compared to the eddy-covariance data that do not show a systematic decrease in productivity during the dry season. The second bias is that the FATES simulations here systematically predict a lower latent heat flux and a higher sensible heat flux than the observations. Similar biases are also documented in  Huang et al. (2019). In this paper, we do not try to correct these biases, which likely arise from a combination of (a) not including a broader set of plant traits that govern ecohydrological processes, such as those traits that govern stomatal conductance, canopy turbulence, or rooting depth distributions; (b) not using a full plant hydraulics model  (Christoffersen et al.
Acknowledgements. This research was supported as part of the  Next-Generation Ecosystem Experiments -Tropics , funded by the  US Department of Energy, Office of Science, Office of Biological and Environmental Research. Charles D. Koven  also acknowledges support from the  DOE   Early Career Research Program. LBNL is managed and operated by the  Regents of the University of California  under prime contract no. DE-AC02-05CH11231. Alistair Rogers  and  Shawn P. Serbin  were also supported through the  United States Department of Energy  contract no. DE-SC0012704  to  Brookhaven National Laboratory. Michael C. Dietze was supported by  NSF   1458021. Oak Ridge National Laboratory  is operated by  UT-Battelle, LLC , under contract no. DE-AC05-00OR22725  with the  US Department of Energy. Pacific Northwest National Laboratory  is operated by the  DOE  by the  Battelle Memorial Institute  under contract  DE-AC05-76RL01830.
Matteo Detto  was supported by the  Carbon Mitigation Initiative at Princeton University. Financial support. This research has been supported by the  US Department of Energy, Office of Biological and Environmental Research (Next-Generation Ecosystem Experiments -Tropics) , the  DOE   Early Career Research Program , the  United States Department of Energy (Brookhaven National Laboratory  (contract no. DE-SC0012704 )), and the  NSF  (grant no. 1458021 ). Review statement. This paper was edited by  Sönke Zaehle  and reviewed by  Emilie Joetzjer  and one anonymous referee.
Code availability. The FATES model is available at https://github. com/NGEET/fates (last access: 15 May 2020; https://doi. org/10. 5281/zenodo. 3825474, FATES Development  Team, 2020). Experiments here are based on git commit 0bc7a5d on the fork: https://github. com/ckoven/fates (last access: 4 June 2020; https://doi. org/10. 5281/zenodo. 3875687, FATES Development Team, 2019). FATES is run here within two host land surface models, CLM5 and ELMv1, available at https://github. com/ESCOMP/ctsm (git commit b9c92b7, last access: 15 May 2020; https://doi. org/10. 5281/zenodo. 3739617, CTSM Development Team, 2020) and https://github. com/ E3SM-Project/E3SM (git commit 544db3b, last access: 15 May 2020; https://doi. org/10. 11578/E3SM/dc. 20180418. 36, E3SM Project, 2018), respectively. Scripts to initialize parameter files and analyze model output shown here are available at https://github.
com/NGEET/testbeds (last access: 15 May 2020; https://doi. org/10. 5281/zenodo. 3785705, Koven, 2020a), and scripts to run the all model experiments here are available at https://github. com/ckoven/runscripts (last access: 15 May 2020; https://doi. org/10. 5281/zenodo. 3785703, Koven, 2020b). https://doi. org/10. 5194/bg-17-3017-2020 Biogeosciences, 17, 3017-3044, 2020 Data availability. FATES output files from all simulations described here are archived at https://doi. org/10. 15486/ngt/1569647 (Koven, 2019a). Video supplement. SV1 is an animated version of Fig. 15d , showing 600 years of forest development from bare ground for one ensemble member. The video is available at: https://doi. org/10. 5446/43627 (Koven,  2019b).