dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments For the samples collected as part of the  EU-CONTRAST  project we would like to thank  Dr. Oumar T. Diaw  and  Dr. Moumoudane M. Seye  ( Institut Senegalais de Recherches Agricoles, ISRA, route des Hydrocarbures, Bel Air, Dakar, Senegal ) for their coordination of the field work and the collections in Senegal and  Dr. Mariama Lamine  for fieldwork coordination and the RISEAL team for collections in Niger. Also, we would like to thank  Dr. Fiona Allan  and  Ms. Muriel Rabone  for sample and data curation and preparation for material held in SCAN. For the collection of the Tanzania SCORE samples we would also like to acknowledge the hard work of  Ms. Teckla Angelo  for the fieldwork coordination, and  Mr. Honest Nagai ,  Mr. Boniface Emmanuel ,  Mr. John Igogote ,  Dr. Sarah Buddenborg  and  Mr.
Flow cytometry samples were measured on an LSR II cytometer (BD Biosciences) and analyzed using FlowJo software (Tree Star, Inc. , Ashland, OR). The following antibodies were used for confocal microscopy: PIP4K2B (sc-393246, Santa Cruz Biotechnology, Inc. , Heidelberg, Germany), Vinculin (V9131, Sigma-Aldrich), EEA1 (ab2900, Abcam, Cambridge, UK), Ezrin (#3145, Cell Signaling, Danvers, MA), ArfGAP1 (sc-271303, Santa Cruz), Lysozyme (A0099, Dako, Agilent Technologies, Inc. , Santa Clara, CA), ATP6V1A (17115-1-AP, Proteintech, Rosemont, IL) and LAMP-1 (13-1071, eBioscience). Goat anti-rabbit and anti-mouse Alexa Fluor 488 antibodies were purchased from ThermoFisher Scientific. The following antibodies were used for Western blotting: EEA1 (ab2900), CATH D (ab6313), YKT6 (ab77150), IRG1 (ab122624, all from Abcam), RAB5 (C8B1), RAB7 (D95F2), NF-B2 (#4882, all from 1 The abbreviations used are: M⌽, macrophage; BMDC, bone marrow-derived dendritic cell; CATH, cathepsin; CANX, calnexin; CALR, calreticulin; DAVID, Database for Annotation, Visualization and Integrated Discovery; DC, dendritic cell; EEA1, early endosome antigen 1; GO, gene ontology; IRG1, immune-responsive gene 1; LAMP, lysosome-associated membrane protein; LBP, latex bead-containing phagosome; LPS, lipopolysaccharide; MHC, major histocompatibility complex; Mtb, Mycobacterium tuberculosis; NOS, nitric oxide synthase; NOX, NADPH oxidase; OVA, ovalbumin; PCV, pathogen-containing vacuole; ROS, reactive oxygen species; SLC, solute carrier protein; STX, syntaxin; STXBP, syntaxin-binding proteins; TFR, transferrin receptor; ZBP1, Z-DNA-binding protein 1.
4 -20% (acetonitrile, 0. 1% formic acid) over 175 min followed by a step from 20 -28% acetonitrile, 0. 1% formic acid over 30 min at 300 nl/min. Mass spectrometric identification was performed on an Orbitrap Fusion Tribrid mass spectrometer (Thermo-Fisher Scientific) operated in "Top Speed" data dependent and positive ion mode. FullScan spectra were acquired ranging from 400 m/z to 1600 m/z, at a resolution of 120,000 (at 200 m/z), with an automated gain control (AGC) of 300,000 and a maximum injection time of 50 ms. Charge state screening was enabled to exclude precursors with a charge state of 1. The intensity threshold for a MS/MS fragmentation was set to 104 counts. The most intense precursor ions are isolated with a quadrupole mass filter width of 1. 6 m/z and HCD fragmentation was performed in one-step collision energy of 35% and activation Q of 0.
25. MS/MS fragment ions were analyzed in the segmented linear ion trap with a normal scan range in rapid mode. The detection of MS/MS fragments was set up as the "Universal Method," using a maximum injection time of 300 ms and a maximum AGC of 2000 ions. Data Processing and Quantitative Analysis of Label-free Data-Protein identification and label-free quantification were performed using MaxQuant Version 1. 5. 1. 7  (31)  with the following parameters: stable modification carbamidomethyl (C); variable modifications oxidation (M), acetylation (protein N terminus) and deamidation (NQ). Search was conducted using the Uniprot-Trembl Mouse database (54505 entries, downloaded 2015), using Trypsin/P as a specific protease, accepting a maximum of two missed cleavages and including common contaminants.
Mass accuracy was set to 4. 5 ppm for precursor ions and 0. 5 Da for ion trap MS/MS data. The "match between runs" option enabled transfer of identifications across samples within a time window of 2 min of the aligned retention times. Identifications were filtered at a 1% false-discovery rate (FDR) at the protein level, accepting a minimum peptide length of 7 amino acids. Label-free quantification of identified proteins referred to razor and unique peptides and required a minimum ratio count of two and were calculated based on the raw spectral protein intensity of the Max-Quant software. For each condition, raw intensities were logarithmized and then normalized to the calculated average and used for downstream analyses in Perseus 1. 5. 3. 1  (32). Student's t test (twotailed, homoscedastic) was performed on the normalized protein intensities, and proteins with p Ͻ 0.
05 and a fold change Ͼ 1. 5 were considered significantly altered in abundance between the samples. All raw data sets as well as the normalized data set can be accessed in PRIDE (PXD012155). Clustering of Phagosomal Proteins-Phagosomal proteins were clustered according to their log2 [LPS/resting] during the three different time points (15 min, 60 min and 120 min) in Perseus  (33). The data were clustered hierarchically in 8 clusters, based on the k-means of cosine similarity. LBP Lysis and Confirmation of Phagosomal Proteins by Western Blotting-Phagosome pellets were lysed in 2% (v/v) Triton X-100, 50 mM Tris, pH 8. 0, 10 mM dithiothreitol, 2ϫ protease inhibitor mixture (Roche SAS, Boulogne-Billancourt, France) for 30 min on ice. Next, phagosomal proteins were recovered after spinning down the beads twice at 16,100 ϫ g for 4 min at 4 °C.
In all samples, maximum projections of three focal planes were generated using Fiji software (ImageJ 1. 51, NIH) to display the localization of labeled proteins of the phagosomes that are displayed in insets. Experimental Design and Statistical Rationale-For LC-MS/MS analysis, LBPs were isolated from BMDCs in independent experiments of four biological replicates (15 min time point of both, resting and LPS-treated cells) and three biological replicates (60 min and 120 min time point of both, resting and LPS-treated cells). Therefore, a total of 20 samples was analyzed by LC-MS/MS. Protein identification was performed using MaxQuant (v 1. 5. 1. 7). Identifications were filtered at a 1% false-discovery rate (FDR) at the protein level, accepting a minimum peptide length of 7 amino acids. Label-free quantification of identified proteins referred to razor and unique peptides and required a minimum ratio count of two and were calculated based on the raw spectral protein intensity of the MaxQuant software.
For each condition, raw intensities were logarithmized and then normalized to the calculated average and used for downstream analyses in Perseus 1. 5. 3. 1  (32). Student's t test (two-tailed, homoscedastic) was performed on the normalized protein intensities, and proteins with p Ͻ 0. 05 and a fold change Ͼ 1. 5 were considered significantly altered in abundance between the samples. Further details can be found above in the section Data Processing and Quantitative Analysis of Label-free Data. For WB validation, LBPs were isolated from BMDCs in two independent experiments comprising all time points (15 min, 60 min, 120 min) and both conditions (resting and LPS-treated). Therefore, a total of 12 samples was analyzed by Western blotting. For analysis by confocal microscopy, images of BMDCs of two independent experiments comprising all time points (15 min, 60 min, 120 min) and both conditions (resting and LPS-treated) were acquired.
Replicates of phagosomal protein extracts were digested with trypsin and analyzed by liquid chromatography-tandem mass spectrometry (LC-MS/MS) on an Orbitrap Fusion Tribrid mass spectrometer (Fig. 1 ). Data derived from three to four biological replicates were used to identify and quantify phagosomal proteins that were detected at least twice in each condition. Two thousand eight hundred forty-three proteins could be identified in the phagosomal proteome (supplemental Table  S1 ), which is in the same range as previously reported for the phagosome proteome of macrophages  (18). In resting DCs, 80, 15, and 233 proteins were enriched at least 1. 5-fold in 15 min LBPs, 60 min LBPs and 120 min LBPs (log2 [LPS/resting] Յ Ϫ0. 58, p value Ͻ 0. 05), respectively (Fig. 1 ). Moreover, we found 113, 37, and 80 proteins to be significantly enriched in 15 min LBPs, 60 min LBPs and 120 min LBPs of LPS-treated BMDCs (log2 [LPS/resting] Ն 0.
58, p value Ͻ 0. 05) (Fig. 1 ). Using the Database for Annotation, Visualization, and Integrated Discovery  (36, 37) , we determined in a gene ontology (GO) analysis the major biological processes that are predominantly modulated in LBPs of resting and LPS-treated BMDCs during the different stages of phagosome maturation (shown in Fig. 1 ). For this analysis, all proteins with a significant 1. 5-fold change were included. Early (15 min), intermediate (60 min), and late (120 min) phagosomes of resting BMDCs were enriched for proteins that function in different types of localization processes and membrane fusion. In 15 min LBPs, these proteins were associated with phagosomal acidification (ATP6v1b2, ATP6v1h), phagosomal antigen degradation (CATH S) and phagosomal fusion with endosomes and lysosomes (syntaxin (STX) 8, STX12, VTI1b).
In addition, we detected several proteins on the phagosome that are involved in ubiquitination and SUMOylation, such as ubiquitin ligases (CBL, MARCH7, RNF13), as well as, the SUMO ligase RANBP2 and ubiquitin hydrolase FAM63 (supplemental Fig. S1 ). significantly altered in abundance between samples with a p value Ͻ 0. 05 and a fold change Ͼ 1. 5. In the volcano plots, the number of significantly enriched proteins in LBPs of resting BMDCs (blue) and in LBPs of LPS-treated BMDCs (orange) is shown. Enrichment of biological processes was determined by gene ontology analysis using the Database for Annotation, Visualization and Integrated Discovery (DAVID). LPS: lipopolysaccharide; OVA: ovalbumin; LBP: latex bead-containing phagosome.
Acknowledgments-We thank  Kris Gevaert ,  Adriana Mantegazza  and  Sabrina Marion  for scientific discussions,  Marnik Vuylsteke  for statistical support and  Nathalie Deboosere ,  Amanda Gonc  ¸alves, and  Gert Van Isterdael  for technical assistance. We acknowledge the support of the flow cytometry facility and the  Bioimaging core facility  at the  VIB-UGent Center for Inflammation Research.
* AMP was supported by fellowships from  Fonds Wetenschappelijk Onderzoek (FWO)  ( 11W8415N ,  11W8417N ) and EH by fellowships of the omics@VIB program (co-financed by the  Marie Curie   FP7 People Cofund ) and by a research grant of  FWO  ( 1526615N ). EH is supported by the  French government  through the  Program "Investissements d'avenir" (I-SITE ULNE/ ANR-16-IDEX-0004  ULNE) managed by the  National Research Agency. □ S This article contains supplemental Figures and Tables.
Study Population SERAPHIN and REVEAL were both conducted in accordance with the amended Declaration of Helsinki and the protocols reviewed by local institutional review boards (Tables I and II in the Data Supplement) with written informed consent obtained from all patients. SERAPHIN was a global, double-blind, randomized, placebo-controlled event-driven, phase 3 study (NCT00660179). 11 atients were enrolled between May 2008 and December 2009 and were randomized (1:1:1) to placebo, macitentan 3 mg, or macitentan 10 mg. Concomitant treatment with a stable dose of phosphodiesterase type 5 inhibitors, oral/ inhaled prostanoids, calcium channel blockers, or l-arginine was allowed. Double-blind treatment continued until patients experienced a primary end point event or until 285 events had accrued.
Patients were followed until withdrawal from the study or until the end of follow-up (March 2012). Patients who experienced a nonfatal primary end point event and terminated double-blind treatment were eligible to receive another PAH therapy including open-label macitentan 10 mg. REVEAL was a multicenter, observational, US-based registry designed to provide information about patient characteristics, and disease course and management from 3515 consecutively enrolled patients with newly or previously diagnosed PAH (NCT00370214). 8 The registry design and inclusion criteria have been described previously. 20 In brief, patients aged ≥3 months with PAH confirmed by right heart catheterization were enrolled at 55 US centers from March 2006 to December 2009. Patients were followed until December 2012 or until time of death or withdrawal from the study.
Survival Prediction Model for the RAC Of the 3515 patients enrolled in REVEAL, 734 (20. 9%) met the main SERAPHIN eligibility criteria. These patients were included in the RAC (Figure  1A ; Table  2 ). Of the 2781 (79. 1%) patients excluded from the RAC, 1466 (41. 7%) were excluded as they did not meet at least one SERAPHIN eligibility criteria and 1315 (37. 4%) were excluded as they had one or more missing values for the eligibility criteria. Excluded patients had a worse survival prognosis compared to those included in the RAC (Figure I in the Data Supplement). This was not unexpected given that the RAC selection variables excluded patients with baseline characteristics likely to be associated with an increased mortality risk, including comorbid lung condi-tions, renal insufficiency, and portopulmonary hypertension (Table  III  in the Data Supplement).
The baseline characteristics for the patients included and excluded from the RAC are shown in Table V in the Data Supplement. A survival prediction model was built using the baseline data from the RAC (Table  2 ). After the stepwise regression was performed to evaluate the 18 prognostic variables, 10 of these variables were retained in the survival prediction model (Table  3 ). The variables included were: World Health Organization functional class, PAH classification, 6-minute walk distance, heart rate, systemic blood pressure, sex, age, body mass index, Borg dyspnea score, and mixed venous oxygen saturation. The cross-validated C statistic, which quantifies the goodness of fit of the final model, was 76%, indicating that the model is a reasonable fit.
Sensitivity Analyses For the sensitivity analysis that was conducted on the RAC to include baseline PAH therapy as an additional covariate in the model, baseline PAH therapy was not a significant covariate. When baseline PAH therapy was forced into the model, the results were consistent with the main analysis (macitentan 10 mg versus predicted: hazard ratio, 0. 65; 95% CI, 0. 46-0. 91; P=0. 012; Table VI in the Data Supplement). Sensitivity RAC-A (n=1013) comprised the 734 RAC patients and an additional 279 REVEAL patients who were included after the thresholds for some criteria were adjusted to reflect the enrolled SERAPHIN population, rather than the criteria per se. Sensitivity RAC-B comprised the 391 RAC patients who were not receiving ERA therapy at baseline. For analyses based on both Sensitivity RAC-A and RAC-B, the fit of the sensitivity survival prediction model and the comparisons between predicted and observed survival of SERAPHIN patients were consistent with the findings reported based on the RAC survival prediction model (Table VI in the Data Supplement).
DISCUSSION Using real-world data to complement trial data is one approach to exploring the impact of therapies on survival. This approach was used in the evaluation of alglucosidase alfa (Myozyme®) for the treatment of the fatal infantile-onset Pompe disease, as no active comparator was available. The assessment of alglucosidase alfa using data from 2 single-arm studies and from a separate cohort of untreated patients resulted in the approval of this medication. 21, 22 In PAH, the National Institutes of Health prognostic equation was developed from survival data obtained from untreated PAH patients enrolled in the National Institutes of Health registry between 1981 and 1985. 23 This equation has been applied to randomized clinical trial data sets to estimate survival in comparison with treated patients.
24 n the analyses reported here, we used PAH registry data to develop a survival prediction model, which was applied to SERAPHIN data to estimate the effect of macitentan on survival. Although our approach was conceptually similar to previous studies, the methodology has a number of novel aspects. For example, we selected an appropriate observational data set on which to develop our prediction model. We used data from REVEAL because this registry was conducted contemporaneously to SERAPHIN. Using data from 2 studies conducted during the same time period allows us to minimize the unknown confounders and biases that can be introduced as a result of changes in patient management over time. To further ensure the appropriateness of the observational data set, we selected a subgroup of patients in REVEAL who would have met the eligibility criteria for SERAPHIN.
In the future, analyzing real-world and single-arm trial data in parallel could allow researchers to enrich study populations and gain insights into the survival benefits offered by new therapies. This approach may be particularly valuable for rare diseases, where recruitment of large numbers of patients is not practical and where patients receive active therapy in response to disease progression. Prospective planning should improve the robustness and validity of such future analyses, by ensuring that all relevant variables are collected from both the registry and trial patients to address some of the limitations related to missing data. A prespecified analysis plan should be agreed and finalized before any data collection to ensure the integrity of the analysis. In the categories of female sex, age, SBP, BMI, 6MWD, WHO FC, receipt of treatment for PAH and geographic region, data were missing for 3 patients in the overall population (placebo arm: 1, macitentan 10 mg arm: 0).
Acknowledgments All authors contributed to the conception and design of the analyses and the interpretation of the data. Statistical analyses were performed by  B. Hennessy ,  Dr Bacchi , and  Dr Wei. All  authors reviewed and edited the article for intellectual content and approved the final version for publication. All authors had full access to all the data in the study and take responsibility for its integrity and the data analysis. Medical writing assistance was provided by  Stephanie Carter  ( nspm ltd, Meggen, Switzerland ) and funded by  Actelion Pharmaceuticals Ltd (Allschwil, Switzerland).
Comparison of freshwater flux by FOG with previous estimates. Comparing FOG to previous estimates for the Black Sea (BLS in Table  1 ) shows good agreement, especially with Centre de Formation et de Recherche sur les Environnements Méditerranéens' (CEFREM's) estimates  14  , which have been used extensively for oceanic modelling and water balance studies  6. This can be explained by the fact that a few large and well-gauged rivers dominate the total fluxes in both datasets. The estimate by Kara et al. 22  is lower, but the range proposed by Jaoshvili et al. 23  is larger than the uncertainty of FOG. The difference in the periods considered can probably explain this. For the MED, FOG has higher values than all previous estimates (MED in Table  1 ), except when compared to the values derived from national water statistics and which include SGD  24.
14. For a more recent period (1980 to 2000) no significant change is reported by Bouraoui et al. 8  as we observe in the FOG dataset. Over the Black Sea, the trend in FOG is not significant, which is consistent with the results of Ludwig et al. 14  over a longer period. If we assume that time-dependent biases are negligible within ORCHIDEE, we can use the reference simulation (i. e. , without data fusion) to estimate climate-driven trends in river discharges. We find that none of the sub-basins has a significant climatic trend for the 1980-2008 period (Table  2 , Fig. 4 ). It is known that the MED has experienced decreasing precipitation during the 20th century  38  , but this does not necessarily lead to reduced streamflows as evaporation changes with climate as well. However, the non-climatic trends, which can be estimated by subtracting ORCHIDEE from the FOG ensemble, have significant values for a majority of members in several sub-basins (Table  2 ).
Possible factors that contributed to the significant non-climatic trend. Since the data fusion tends to decrease the riverine flow into the Mediterranean, it is unlikely that the decreasing number of stations in the GRDC database leads to the significant non-climatic trend that is identified. The reduced flow to the ocean in the eastern Mediterranean points to increased evaporation over the continents. Our result is consistent with the increased vegetation activity over the Balkan, southern Italy, and Turkey detected by Garcia-Ruiz et al. 3  using the Normalized Difference Vegetation Index. The decreasing flows into the NLE and AEG can be explained by increases in irrigated areas and cultivation of more water intensive crops in Turkey and Greece  39, 40  which lead to higher evaporation and biomass production.
Conclusions The proposed freshwater flux from the continents into the Mediterranean Sea, combines the quality of discharge observations with the predictive capabilities of an LSM. This estimate will help better constrain the water cycle of the Mediterranean Sea 6 and improve our ability to predict the response of the Mediterranean Sea to the evolution of climate and to human water and land use. Since the intensification of agriculture began before the period considered here, it would be desirable to reconstruct freshwater fluxes into the sea over a longer period. This will be difficult because of the lower quality of atmospheric re-analyse (the basis for driving LSMs) before 1979 and the degrading density of the gauging station network available after 1990. This is a call to the community to come together to consolidate the observational databases and implement long-term re-analysis of the continental water cycle.
Method The model and assimilation methodology. The ORCHIDEE land surface model (LSM)  18, 19  was used to simulate the continental water cycle over the period 1979-2013 using the WFDEI-GPCC atmospheric conditions  41. These conditions were derived from the ERA-Interim re-analysis  42  and bias corrected with in-situ observations. In particular, precipitation was adjusted with the Global Precipitation Climatology Centre's (GPCC's) dataset  43. Using the methodology described in Wang et al. 21 , the observations of rivers' discharges collected by the Global Runoff Data Center (GRDC), 56068 Koblenz, Germany were assimilated to correct the simulated runoff and drainage, or equivalently, the moisture convergence (precipitation minus evaporation), as the fusion preserves the water conservation of the model.
To explore the uncertainty of FOG, the error variance defined above was used to generate annual perturbations to the correction factor, x, assuming a normal distribution. Running ORCHIDEE with 30 of the perturbed corrections allowed us to predict as many discharges into the Mediterranean that are within the residual error www. nature. com/scientificreports www. nature. com/scientificreports/ or inter-annual variability, depending on the information used for FOG. The uncertainty of FOG was estimated using the 95% confidence interval of the 30 ensemble members and it is essentially driven by the availability of GRDC stations (Fig. S2 ). This ensemble of solutions was the basis for the statistical evaluation of the results. The dataset. Of the 792 GRDC stations over the domain that was studied (19.