dataesr/scientific-paragraphs-categorization huggingface dataset:
Potential interviewees will be identified through purposive sampling with the goal of interviewing a range of experts with broad knowledge and perspectives on the MHD research landscape in Europe, including leaders of RFOs, researchers and policy experts. The recorded and transcribed interviews will be subject to qualitative analysis using the Framework Method, a highly systematic method for categorizing data with a matrix output that allows descriptive and explanatory conclusions to be organised by theme  [5]. The qualitative data will then be charted into a framework matrix to allow thematic analysis across the interviews.
Thus, we will identify the number of MHD articles and reviews through bibliometric analyses of research outputs across and within the 31 study countries for the period 2002-2013. Papers will be identified by means of a filter for which precision and recall will be tested by MHD experts, who will mark papers as relevant or not. We will analyse the publication and citation of scientific papers using the Thomson Reuters Web of Science (WoS) database and citation indexes. The growth in research output for European MHD papers over the period will be compared to world MHD research output as well as to overall biomedical research output. The output of each country will also be explored, including the growth rate and the extent of international collaboration. These data will be further analysed by Gross Domestic Product (GDP) to determine variation in outputs based on individual countries' economies.
Finally, we will examine the research outputs by individual disorders, both across the 31 European countries and by individual country. Citation analysis is used in allocating research funds, including at a central level to distribute funds across institutions based on aggregate statistics and at an institutional level to evaluate individual researcher and research groups for purposes of distributing funds within an institution. Citation analysis will be undertaken on the identified MHD papers using two overlapping databases, the Science Citation Index Expanded and the Social Sciences Citation Index. To measure the individual impact of research papers, 5-year actual citation impact scores will be used. Fractional counting of citations, such that a citation in a paper with n addresses would count for only (1/n)th of overall citations instead of a full point, will be used in order to facilitate comparison between different disciplinary affiliations at the paper level.
Analysis of papers referenced in clinical guidelines is an indicator of how research can inform health practice. It may also serve as an indicator of the lag time between research expenditure and potential health benefits as well as an attribution of the benefits of a particular country's research  [7]. Papers cited in MHD clinical guidelines in the 31 study countries will be identified using a macro that generates search statements to find the publications in the WoS database. The papers will then be analysed in terms of timeframe, leading country, research type, systematic reviews and mean 5-year citation score. Finally, the results of research may be reported in news stories, which provide an additional perspective regarding the potential impact of research. While medical stories in the press target the general public, impact on health behaviours cannot be assumed and indeed may be positive or negative  [8].
Media stories may also influence healthcare professionals and decision makers and in turn the policy agenda. Key newspapers in the 31 countries will be selected and their online databases and archives searched for the period 2002-2013 using simplified queries to identify stories about research involving one or more of the 10 MHDs. The related scientific publications will be identified in the WoS database. Analyses will include the focus of the research, the timeframe, the extent to which research is reported in multiple countries, particularly outside of the study country, and the journals attracting the most extensive press coverage.
In 2005, the EBC published, for the first time, overall estimates of annual costs for brain disorders in Europe, which were updated and extended to additional disorders in 2010  [15, 16]. The EBC study was based on the best data available at the time, and modelling allowed extrapolation to countries with paucity of data, the results of which were found to be consistent with administrative data on healthcare expenditure in Europe. Cost of illness estimates, including breakdowns by country and type of cost (direct and indirect), were reported for each of the MHDs included in the Map-ping_NCD study except for attention deficit hyperactivity disorder and suicide/self-harm, and we will analyse the MHD research activity and output data collected in light of the estimated cost of these disorders.
Acknowledgements The Mapping_NCD consortium: The project described in this paper relies upon the research assistance of other members of the  Mapping_NCD Consortium  in collecting data on MHD research activity across the European Area. The authors gratefully acknowledge the contributions of the following Consortium members:  Ane Auraaen  ( Université Paris-Est Créteil Val de Marne );  Mursheda Begum  ( King's College London );  Reinhard Busse  ( Berlin University of Technology );  Ludovica Borsoi  ( Università Bocconi );  Oriana Ciani  ( Università Bocconi );  Jaime Espín  ( Escuela Andaluza de Salud Pública );  Diana Gosálvez  ( Escuela Andaluza de Salud Pública );  Hala Hourani  ( London School of Economics );  Anshoo Lumba  ( Université Paris-Est Créteil Val de Marne );  María del Mar Requena  ( Escuela Andaluza de Salud Pública );  Gavin McDonough  ( Université Paris-Est Créteil Val de Marne );  Esther Molina-Montes  ( Escuela Andaluza de Salud Pública );  Davina Nauth  ( London School of Economics );  Elena Pallari  ( King's College London );  María-José Sánchez  ( Escuela Andaluza de Salud Pública );  Silvia Sommariva  ( Università Bocconi );  Argo Soon  ( Estonian Research Council );  Anne Spranger  ( Berlin University of Technology );  Victor Stephani  ( Berlin University of Technology );  Rosanna Tarricone  ( Università Bocconi ); and  Erica Visintin  ( London School of Economics ).
Outcomes evaluation All patients were observed in a coronary care unit for at least 24 hours before discharge or transfer. We recorded the incidence of severe clinical events such as death, recurrent myocardial infarction, target vessel revascularisation, stroke, procedure-related complications, acute renal failure and major or clinically relevant bleeding during hospitalisation. Survival data at 6 months and 1 year were collected. A successful procedure was defined as achievement of thrombolysis in myocardial infarction (TIMI) grade 2 or 3 flow, <50% residual stenosis and absence of any major clinical adverse event within 24 hours. A failed procedure was defined as resulting in TIMI grade 0-1 flow regardless of residual stenosis with the absence of any major clinical adverse event within 24 hours.
Statistical analysis Continuous variables are expressed as median and IQR, and discrete variables as absolute values and percentages. The log-rank test was used to compute the significance of time-to-event data, and survival rates were estimated using the Kaplan-Meier method. The Kaplan-Meier curve was plotted through day 730 (2 years). An additional Cox proportional hazard regression analysis was performed to describe the effects of gender and age on survival, with and without adjustment on centre. Analyses were performed using SPSS software V. 23 (IBM, Armonk, New York, USA).
It must be remembered that in some cases oral administration of clopidogrel could be compromised or delayed as a high proportion of the patients may be unable to swallow anything in the context of pulmonary oedema or cardiogenic shock and altered conscious state. Nevertheless, bleeding complications may have occurred after hospital discharge. Prasugrel was rarely administered due to the time period of inclusion and advanced age population. However, new P2Y12 inhibitors appear as an interesting alternative to clopidogrel in selected patients. 28 Further data may shortly be available in this field (SENIOR study, NCT 02099617). The third point of discussion is the delay of primary PCI. We found a mean delay from symptoms to primary PCI at 5. 8 ±7. 6 hours, which is a consequence of multiple factors including difficulties in diagnosis and sometimes refusal from other healthcare organisations to perform interventional procedures in elderly patients based on outdated data and their own perception of the risk-to-benefit ratio.
miR-92a target identification in PMBL To identify miR-92a targets in PMBL, we combined in silico miR-92a target prediction and gene expression profile in PMBL and DLBCL patient samples, and in the four cell lines (Figure  2 ). Analysis of the CoMeTa database, chosen to limit the number of false positive targets likely to result from a ranking method, provided a list of 1815 putative in silico target-genes of miR-92a. The quantile method enabled us to normalise the microarray datasets of PMBL and DLBCL patients and the four cell lines. The normalized datasets were filtered through the CoMeTa miR-92a putative target-gene list. We thus obtained a gene expression profile for 1135 filtered genes. Comparison of the filtered gene lists from PMBL and DLBCL patients enabled us to identify 32 downregulated and 17 up-regulated putative targets of miR-92a, with a FDR<0.
Analysis of the CoMeTa database, chosen to limit the number of false positive targets by a ranking method, provided a list of 1815 putative in silico target-genes of miR-92a. The quantile method enabled us to normalise the microarray datasets for PMBL and DLBCL patients and the four cell lines. The normalized datasets were filtered through the CoMeTa miR-92a putative target-gene list. We thus obtained a gene expression profile of 1135 filtered genes. Comparison of the filtered gene lists from PMBL and DLBCL patients enabled us to identify 32 down-regulated and 1 up-regulated putative targets of miR-92a, with a FDR<0. 05 and a fold change > 2 (q-value < 0. 05). Comparison of cell line with high expression of miR-92a (Karpas hsa-miR-92a-1 and Karpas wt) and of cell line with low expression of miR-92a (Karpas miRZip-92a and SU-DHL-5 wt) enabled us to identify 16 down-regulated and 3 up-regulated putative targets of miR-92a, with a FDR<0.
Two plasmidic constructions were necessary to cover the whole FOXP1 3'UTR. When luciferase activity of these cells was assessed 72h after the miR-92a transfection, a significant reduction in luminescence was measured with the Gluc-FOXP1-3'UTR-2 construction, while no significant difference was observed with the Gluc-FOXP1-3'UTR-1 construction (Figure  4D ). We used the miRNA target prediction database TargetScan to identify a 7mer-A1 miR-92a-3p binding site on the FOXP1 3'UTR, which is conserved across vertebrates (Supplementary figure  6 ). This site was located at position 4502-4508 of FOXP1 3'UTR cloned on the Gluc-FOXP1-3'UTR-2 construct. These results showed the presence of a functional miR-92a binding site on the 3' extremity of the FOXP1 3'UTR, and thus demonstrated that FOXP1 was a target of miR-92a.
Using this list to filter our transcriptomic datasets of PMBL versus DLBCL patient samples, we identified 9 FOXP1 targets: 3 downregulated by FOXP1 (DERL3, TTC28, and TPRA1), and 6 upregulated by FOXP1 (FMNL3, DNAJB6, KMO, SNX29, ERP44, and CD80). Since the originality of our study is the use of patient samples that were carefully laser-microdissected to obtain high quality RNAs, we also performed a systematic review of databases presently available on FOXP1 targets in human samples. In the "transcription factor encyclopedia" (http://cisreg. cmmt. ubc. ca/) database, this enabled us to find 8 FOXP1 targets. Two of them were overexpressed in PMBL versus DLCBL human samples, as expected. In addition, the upregulation of CDKN1A, one of these two FOXP1 target genes is coherent with the clinical data.
Quantification of expression of the miR-17~92 cluster and its paralogs Laser-microdissection was performed on each tissue sample using a PALM-Microbeam/Zeiss system (Zeiss, Wetzlar, Germany). On 7-μm-thick sections of formalinfixed paraffin-embedded samples, tumor cells were identified using anti-CD20-antibody (Clone L26, Dako, France) for PMBL and DLBCL, and anti-CD30-antibody (Clone Ber-H2, Dako, France) for cHL. In each PMBL and DLBCL sample, approximately 1500 tumor cells were microdissected, corresponding to a mean area of 301. 440 System. TBP (Hs99999910_m1) (Life-Technologies) was used as the endogenous control for normalization. Data were normalized on the reference gene, using CFX manager software and expression levels were calculated using the 2 -ΔCq method. Immunohistochemistry was performed on 5µmthick snap-frozen sections from patient samples, and smears from the four cell lines studied, using anti-human FOXP1 antibody (HPA003876-100UL, 1/10, Sigma, FRANCE).
Western blots 10 7 cells of each of the four cell lines studied were lysed in 200µL lysis buffer (0. 5M Tris-HCl, pH 6. 8, 2mM EDTA, 10% glycerol, 2% SDS and 5% b-mercaptoethanol). Whole lysates (20µg) were separated on a 10% SDS-PAGE and immunoblotted using antihuman FOXP1 (HPA003876-100UL, 1/2500, Sigma, FRANCE) or anti-human beta-actin (AC-74 clone, 1/5000, Sigma, France). Bands were visualized by chemiluminescence on an LAS-3000 analyser (Fujifilm, France) and analyzed using Multi-Gauge-V3. 0 software (Fujifilm, France).
Statistical analyses Analyses were performed with GraphPad and SAS 8. 2 software (SAS Institute Inc, USA). MicroRNA expression levels were expressed as the median fold change +/-Standard Deviation. mRNA expression levels were expressed as the mean fold change +/-Standard Deviation. The Mann-Whitney test was used to compare expression levels in PMBL, DLBCL and cHL samples, and in the four cell lines studied. The results of these comparisons were considered significant when the two-sided p-value was <0. 05. This significance threshold was corrected for multiple comparisons using the Bonferroni method. Droplet-digital-PCR data were analysed using the QuantaSoft software to measure the fraction of positive droplets and calculate the amount of template per droplet based on a Poisson distribution.
ACKNOWLEDGMENTS This work was supported by  Université-Paris-Diderot ,  Région Île-de-France , and  Institut National Cancer (INCa). We thank pathologists and haematologists from Colombia hospitals:  Marly-Hospital ,  Fundación-Cardio-Infantil-Hospital, Country-Hospital, San-Jose-Hospital ,  UIS-Hospital, Neiva-Hospital, Mederic-Hospital, Imbanaco-Hospital , for kindly providing PMBL cases. We also thank  Dr G. Bousquet  for thorough critical review of the manuscript. A Swaine reviewed the English language.
Net primary production (NPP) is an important ecological metric for studying forest ecosystems and their carbon sequestration, for assessing the potential supply of food or timber and quantifying the impacts of climate change on ecosystems. The global MODIS NPP dataset using the MOD17 algorithm provides valuable information for monitoring NPP at 1-km resolution. Since coarse-resolution global climate data are used, the global dataset may contain uncertainties for Europe. We used a 1-km daily gridded European climate data set with the MOD17 algorithm to create the regional NPP dataset MODIS EURO. For evaluation of this new dataset, we compare MODIS EURO with terrestrial driven NPP from analyzing and harmonizing forest inventory data (NFI) from 196,434 plots in 12 European countries as well as the global MODIS NPP dataset for the years 2000 to 2012.
Our definition for top-down differs from traditional carbon cycle modelling  [16]. This study wants to extend and test this concept for Europe on a continental scale. For this purpose, we obtain two wall-to-wall spatially-explicit and consistent MODIS NPP datasets by acquiring the global dataset using global climate driver and by creating a regional dataset MODIS EURO using 1-km European climate data. We evaluate these two datasets by comparing with the NPP derived from forest inventory data from 12 European countries. We assess the reliability and potential discrepancies of the MODIS satellite-driven top-down versus the terrestrial bottom-up NPP estimates from continental to national scale and across different gradients like location, elevation or stand density. This will provide a better understanding of the reliability of remote sensing based NPP estimates, which could be used also for regions, where no terrestrial measurements are available.
Thus, we first had to develop a harmonized and consistent terrestrial dataset for estimating Terrestrial NPP. We calculated NPP using the forest inventory data according to Equation  (3). NPP " CARB INC `FR TO `CLF (3) CARB INC is the carbon increment of trees (gC¨m ´2¨year ´1). FR TO is the carbon used for fine root turnover  [28, 29]. Fine root turnover FR TO is assumed to be equal to the carbon flow into litter C LF  [27, 30]. Both processes are controlled by the same factors and the assumption of similarity between the above-and belowground turnover of short-living plant organs is supported by recently collected European data on fine root turnover  [29]  and litter fall  [31]. C LF is the flow of carbon into litter (gC¨m ´2¨year ´1) estimated using a climate-sensitive and species-dependent model  [31]  and is calculated as: Broadleaf-dominated : C LF " CF expp2.
Analysis of NPP Results We thus have three NPP sources: two using the MOD17 algorithm with different daily climate data: (i) MODIS GLOB produced by the Numerical Terradynamic Simulation Group (NTSG) at University of Montana and (ii) MODIS EURO by running the original MOD17 algorithm and the latest BPLUTs parametrized by  [9]  with downscaled daily climate data from Europe  [25]  as well as (iii) Terrestrial NFI NPP using forest inventory data from the 12 countries (Table  S1 ) and local carbon estimation methods  [32]. We compared the three NPP datasets across Europe, by our 4 regions (Figure  1 ) and the 12 countries to analyze our results across different spatial scaling. We extracted for each forest inventory plot at the corresponding MODIS cell the average NPP from MODIS GLOB and MODIS EURO for 2000 to 2012.
Results NPP estimated using the MOD17 algorithm has the advantage of providing spatial-and temporal-continuous NPP estimates across Europe on a 1-km resolution and Figure  2    Our NFI dataset covers the full elevational and latitudinal range of forest conditions in Europe including different site conditions, tree species, development stages or management practices. For most countries we have more than 5000 inventory plots (exception: Belgium with 512 plots) and in most cases a plot spacing of at least 4 by 4 km (Table  S1 ). This dataset also provides information on forest properties such as tree age, carbon stocks or stand density and Table  2  indicates that these characteristics vary across Europe. Terrestrial NFI NPP is driven by forest information collected by field crews. Thus it provides NPP and the carbon accumulation by forest stands during a certain time period.
We provide in Table  2  the median NPP for the three NPP sources (MODIS GLOB, MODIS EURO and NFI NPP) and the differences between MODIS and NFI NPP (∆NPP GLOB and ∆NPP EURO ), both in absolute values in gC¨m ´2¨year ´1 and normalized in relation to NFI NPP (Rel. ∆NPPi in %). Results are given in Table  2  for Europe, by country and for the four eco-regions  [7]. At the European level, the MODIS GLOB gives an NPP of 680 gC¨m ´2¨year ´1, the MODIS EURO resulted in 577 gC¨m ´2¨year ´1, and the NPP from the NFI data exhibit a value of 539 gC¨m ´2¨year ´1. The differences in NPP (∆NPP GLOB ) using the global dataset MODIS GLOB are larger than ∆NPP EURO using the regional dataset MODIS EURO (+26% vs. +7%). The same pattern is evident across all four regions and most countries. Only for Poland and Germany ∆NPP GLOB is smaller than ∆NPP EURO.
We provide in Table  2  the median NPP for the three NPP sources (MODIS GLOB, MODIS EURO and NFI NPP) and the differences between MODIS and NFI NPP (∆NPPGLOB and ∆NPPEURO), both in absolute values in gC•m -2 •year -1 and normalized in relation to NFI NPP (Rel. ∆NPPi in %). Results are given in Table  2  for Europe, by country and for the four eco-regions  [7]. At the European level, the MODIS GLOB gives an NPP of 680 gC•m -2 •year -1 , the MODIS EURO resulted in 577 gC•m -2 •year -1 , and the NPP from the NFI data exhibit a value of 539 gC•m -2 •year -1. The differences in NPP (∆NPPGLOB) using the global dataset MODIS GLOB are larger than ∆NPPEURO using the regional dataset MODIS EURO (+26% vs. +7%). The same pattern is evident across all four regions and most countries. Only for Poland and Germany ∆NPPGLOB is smaller than ∆NPPEURO.
Stand Density Effects We analyzed ∆NPPEURO (differences in NPP between MODIS EURO versus NFI NPP) by SDI (Stand Density Index  [36]  calculated with Equation (S10) in the Supplementary Material) for all of Europe (Figure  6 ). ∆NPP shows in Figure  6  a significant trend by stand density index SDI (using linear regression; R 0. 31; ∆NPP = 103. 1 -0. 247 × SDI; p < 0. 001), which confirms that differences in stand density have an effect in our data from the 12 European countries. MODIS EURO NPP estimates are higher than NFI NPP at low SDI classes, while at intermediate SDI classes no discrepancies are evident (Figure  6 ). At high SDI classes MODIS EURO are lower than NFI NPP. We analyzed the effect of SDI for each country, since SDI could be an explanation for the discrepancies visible in Table  2 , Figures  4  and 5.
At high SDI classes MODIS EURO are lower than NFI NPP. We analyzed the effect of SDI for each country, since SDI could be an explanation for the discrepancies visible in Table  2 , Figures  4  and 5. Local effects of forest management intensity, disturbances or differences in the local inventory data design and methodology (Table  S1 ) could lead to differences in SDI. We performed similar graphical analysis as shown in Figure  6  for each country and present here as examples two "extreme" countries: (i) France-positive ∆NPP +10%, with MODIS EURO overestimating NFI NPP; and (ii) Germany-negative ∆NPP ´16%, where MODIS EURO underestimates NFI NPP. For France, MODIS EURO and NFI NPP results agree at high stand density and show discrepancies at low stand density (Figure  7a ). Apparently, MODIS EURO does well in capturing the NPP of stands with high densities, but does not agree with NFI NPP from very open stands.
Both NPP estimates are also consistent across various gradients (elevation, latitude and longitude in Figure  5  and tree age, tree height, MODIS Land cover type and dominant species in Figures  S6-S9 ). In this study we evaluated MODIS EURO in comparison to the global MODIS NPP dataset [17] using our terrestrial NFI NPP. The specific methodologies and differences of our forest inventory data sets (Table  S1 ) and missing information on fine roots and litter fall do not permit a proper validation of NPP. Since the forest inventory data was collected with a different purpose  [10] , it contains a different error structure due to the small sample plot size and large grid spacing (one or very few plots within a MODIS pixel) as compared to the continuous 1-km MODIS grid. The large variations and local discrepancies apparent in this study (Figure  3 ; Table  2 ) are also reflected in a study on evaluating NPP and GPP (Gross Primary Production) from the MOD17 algorithm for North and South America  [37].