dataesr/scientific-paragraphs-categorization huggingface dataset:
Data Country-level data were collated from multiple sources. Historical data on physician and nurse/midwife densities (1990-2013) were obtained from the WHO Global Observatory Health database  [29]. Historical and projected total population and population aged 65 and over were obtained from the United Nations Population Division  [28]. We used the World Bank Development Indicator database to retrieve historical (1995-2013) GDP per capita (in constant 2011 dollars, purchasing power parity (PPP) values), total health expenditures per capita (in constant 2011 international dollars, PPP values), and the share of health spending OOP  [30]. Projected real GDP per capita through 2030 were obtained from the analysis carried out by Patrick Huang-Vu Eozenou (see Additional file 1: Annex B for details).
Historical data on OOP expenditures per capita (1995-2013) were calculated from health expenditure and OOP percentage data. Estimates for future OOP per capita were obtained by projecting the OOP percentage of total health spending from 2014 to 2030 and selecting the models giving the smallest predicted error for each country (see Additional file 1: Annex A for details). Projections of future workforce demand were made for 165 countries for which both demand and supply input data were available. To obtain the estimates for nurses and midwives and all other workers, we use the approach taken by WHO to fill missing data by multiplying the projected physician density for each country by a constant 2. 517 ratio of nurses and midwives to physicians based on the WHO Global Observatory data  [29].
Acknowledgements The authors are grateful to  Edson Araujo  ( Senior Health Economist, HNPGP ),  Caglar Ozden  ( Lead Economist , DECTI), and  Michael Weber  ( Labor Economist , SPLGP) who provided peer review comments on the draft paper as part of the  World Bank  internal review process. The authors would also like to thank the scientific review committee at the  World Health Organization , members of the  Global Health Workforce Alliance , conference attendees at the 2nd Conference on the Economics of the Health Workforce (iHEA 2015), and seminar attendees at the  Health Systems Global Conference , the  Global Health Sciences Department at the University of California, San Francisco , and the  Global Health Economics Consortium Symposium. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors and do not necessarily represent the views of the  World Bank , its Executive Directors, or the governments of the countries they represent.
0-ST-Array, Affymetrix UK Ltd. , High Wycombe, UK) using a GeneChip Fluidics Station 450/250. The signals were measured using a GeneChip Scanner 3000 7G. CEL-files were analysed and normalised by RMA using the open-source 'Altanalyze' software  (Emig et al. , 2010). Results of differentially abundant RNAs in TEB and ducts were ranked according to raw p-value (one-way analysis of variance (ANOVA)) (Table  S1 ). Raw data files have been submitted to GEO with the accession number GSE94371.
For Immunofluorescence, sections were incubated in Mouse on Mouse (M. O. M) Mouse Ig blocking reagent for 60 minutes, followed by overnight incubation in primary antibody at 4°C. Primary antibody was detected with an appropriate Alexafluor-conjugated secondary antibody. Images of stained sections were captured using a Leica TCS-SP2 microscope in three or four channels using Leica LCS software (Leica Microsystems, Milton Keynes, UK). Negative controls were performed using the same protocols with substitution of the primary antibody with IgG matched controls. In double staining experiments, control single stained sections in which either the primary antibody was left out or the primary antibody was combined with the wrong secondary antibody showed no staining. Primary antibodies used were, anti-SMA (clone 1A4, mouse monoclonal, catalogue #A5691, Sigma, UK; diluted 1:500), anti-Ki67 (rabbit polyclonal, catalogue #ab16667, Abcam, UK; diluted 1:300), anticleaved caspase-3 (rabbit polyclonal, catalogue #9661S, Cell signalling, UK; diluted 1:100).
Protein analysis Transduced mammary organoids were serum starved for 12 hours and left unstimulated or stimulated with 10 ng/ml FGF2 for the indicated times. ERK1/2 inhibitor SCH772984 was obtained from Selleckchem (Newmarket, Suffolk, UK; #S7101). Organoids were released from Matrigel using non-enzymatic cell recovery solution (BD Biosciences) and then lysed in Laemmli buffer (2% SDS, 10% glycerol, 1. 25% beta-mercaptoethanol, 0. 002% bromphenol blue, 0. 0625 M Tris pH 6. 8). Following SDS-PAGE, protein extracts were transferred to a PVDF membrane and probed with antibodies to p44/42 MAPK (ERK1/2) (Cell Signalling Technologies, Leiden, The Netherlands, Antibody #9102), phospho-p44/42 MAPK (ERK1/2) (Thr202/Tyr204) (Cell Signalling Technologies, Antibody #9101), phospho-FGF Receptor (Tyr653/654) (Cell Signalling Technologies, Antibody #3471), FGFR1 (9740, Cell Signaling, Technology, Rabbit monoclonal, clone D8E4), FGFR2 (H00002263-M01, Abnova, Mouse monoclonal, clone 1G3), FGFR3 (PA5-34574, Rabbit polyclonal, ThermoFisher Scientific), FGFR4 (HPA028251, Rabbit polyclonal, Sigma Aldrich) or anti-tubulin (clone BM1A), Sigma, antibody #T6199).
Acknowledgements The authors would like to thank  Fredrik Wallberg ,  David Robertson  and the  Breakthrough Histopathology Core Facility  for technical assistance. This study was funded by an  Institute of Cancer Research PhD studentship, Breakthrough Breast Cancer , the  Wales Cancer Research Centre ,  Breast Cancer Now and Cancer Research UK. We acknowledge NHS funding to the  NIHR Biomedical Research Centre. GT is supported by the  Wales Cancer Research Centre. MJS is supported by  Cardiff University, Breast Cancer Now and Cancer Research UK.
The empirical part of this research is built upon a historical timeline analysis of the development of Sahbhagi Dhan and of the research community. Secondary data on the development was gathered first. This comes from a literature review of a range of sources produced by, through or on the organisations and individuals comprising the community, including research papers, scientific blogs, news-letters, annual reports and technical bulletins. After this initial data collection, the first author undertook a fieldwork for three to four months (April-July, 2012) to collect primary data from the scientists associated with the development of the variety in several locations in India and also in the Philippines. Scientists were interviewed on their involvement with the research community, the history of the development of Sahbhagi Dhan, details of the organisational structure of the research community and the role of farmers in the Sahbhagi Dhan development.
Several rounds of interviews were made with the heads of these organisations to get their views on this project, contributing to a total of 45 in-depth interviews made with the scientists, directors and others. The farmers' group associated with the institute CRURRS, at Hazaribagh, Jharkhand, played a crucial role in the variety development, so this group of farmers was interviewed during another visit in October and December 2012. Information and opinions from around 100 farmers, including women farmers, was informally acquired, and several focused group discussions (FDGs) were also held with them. Another 45 farmers who had worked closely with the development of Sahbhagi Dhan were interviewed in-depth. This primary (interview, conversational and FDG) data is mainly qualitative, as we are trying to understand the process of the development of a plant variety from a particular theoretical perspective.
Objective: To describe real-life dosing patterns in children with growth hormone deficiency (GHD), born small for gestational age (SGA) or with Turner syndrome (TS) receiving growth hormone (GH) and enrolled in the NordiNet International Outcome Study (IOS; NCT00960128) between 2006 and 2016. Design: This non-interventional, multicentre study included paediatric patients diagnosed with GHD (isolated (IGHD) or multiple pituitary hormone deficiency (MPHD)), born SGA or with TS and treated according to everyday clinical practice from the Czech Republic (IGHD/MPHD/SGA/TS: n = 425/61/316/119), France (n = 1404/188/970/206), Germany (n = 2603/351/1387/411) and the UK (n = 259/60/87/35). Methods: GH dosing was compared descriptively across countries and indications. Proportions of patients by GH dose group (low/medium/high) or GH dose change (decrease/increase/no change) during years 1 and 2 were also evaluated across countries and indications.
Two previous surveys evaluated differences in the diagnosis and treatment of patients with GHD across countries and national centres  (15, 16). However, both the surveys were published over a decade ago and it is unknown if these differences still exist or if they manifest in other indications such as SGA and TS. The aim of this report is to describe dosing patterns in real-life clinical practice among GH-treated children with GHD, born SGA or with TS enrolled in the NordiNet International Outcome Study (IOS; ClinicalTrials. gov identifier: NCT00960128).
Study design The NordiNet International Outcome study (IOS) is a non-interventional, multicentre study evaluating the long-term effectiveness and safety of Norditropin (somatrophin) (Novo Nordisk A/S) as prescribed by the treating physicians in the real-life clinical setting. The methodology, objectives and study design of NordiNet IOS have been detailed previously  (17). Data for the present report were collected prospectively from April 2006 to July 2016; however, study data continued to be collected until December 2016 when the NordiNet IOS concluded. In Germany, prior to 2006, data were collected by the national Novo Nordisk non-interventional study on Norditropin-treated children, GrowthWin. These data were migrated into NordiNet IOS as described in  Höybye et al. 2013 (17).
Statistical analysis Data are presented as mean, standard deviation (s. d. ) and percentages. Descriptive statistics were applied on baseline characteristics (GH dose, duration of GH treatment, age at treatment start, height standard deviation score (HSDS) for national references, body mass index (BMI) standard deviation score (SDS) and IGF-I SDS) by diagnosis and country. Linear regression was performed to analyse the relationship between HSDS at treatment start and GH dose during the full treatment period. Statistical significance was set at P < 0. 05. Statistical analysis was performed using SAS v9. 4 (SAS Institute Inc. , Cary, NC, USA).
2 years, were slightly older at treatment start than those in France, Germany or the UK, where mean age at treatment start was between 9. 1 and 9. 7 years of age. Patients born SGA in France were on average 8. 3 years of age at treatment start, which was older than those in the Czech Republic, Germany and the UK. Girls with TS in the UK, where the mean age at treatment start was 6. 2 years, were on average more than 2 years younger at treatment start than those in the other countries. Across all indications, duration of treatment (follow-up period in the study) tended to be longer in patients in Germany compared with the other countries, despite age at treatment start being similar between Germany and the other countries; this is probably a reflection of the earlier date of inclusion for German patients whose data were migrated into NordiNet IOS from GrowthWin.
Overall, the real-world data collected in NordiNet IOS indicate that there are national specific practices in treating patients with GH, which are only partly explicable (e. g. national guidelines, absence of updated growth charts). It remains to be established whether a proportion of patients receiving lower-thanrecommended GH doses may have benefited from an increase in dose or whether a proportion were dosed appropriately, i. e. meeting their individualised growth target. In addition to height gain, which is recognised to be improved with earlier GH treatment start, the benefits of GH treatment on metabolic and other endpoints  (34, 35) , including cognitive outcomes  (36) , may be dependent on appropriate dosing and age-appropriate treatment start.
Acknowledgements The authors thank all the physicians who contributed patient data to NordiNet IOS. The authors would like to thank  Anita Chudecka  ( Novo Nordisk Health Care AG, Zürich, Switzerland ) for her statistical support and  Gediminas Puras  ( Novo Nordisk ), for his review and input to the manuscript. Medical writing and submission support were provided by  Sam Mason  and  Beverly La Ferla  of  Watermeadow Medical , funded by  Novo Nordisk. 155   Clinical Study O Blankenstein  and others GH dosing in children with GHD, SGA, TS www. eje-online. org.
Objective: The aim of this study was to assess general practitioner (GP) management practices related to skin cancer prevention and screening during standard medical encounters. Setting: Data on medical encounters addressing skin cancer issues were obtained from a French database containing information from 17 019 standard primary care consultations. Participants: Data were collected between December 2011 and April 2012 by 54 trainees who reported the regular practice of 128 GPs using the International Classification of Primary Care. Outcome measures: Reasons for encounters and the following care processes were recorded: counselling, clinical examinations and referral to a specialist. Medical encounters addressing skin cancer issues were compared with medical encounters that addressed other health problems using a multivariate analysis.
However, several authors have described that general practitioners (GPs) are uncomfortable with dermatological reasons for consultations,  21  especially in cases involving suspicious lesions of skin cancer. 14 22 3] [24]  When searching for data on GP involvement in skin disorder management in the literature, it is frustrating to realise the limited number of publications on this topic, which limits the development of guidelines. 1  In the rare published studies, the reported data are sometimes biased due to self-reporting (as many authors have simply questioned GPs about their dermatology practices  12 14  ), thus neglecting the gap between actual practices and those reported by healthcare professionals. 25 Other authors aiming to describe the dermatological activity of GPs have used a database that included the three main reasons for consultations provided by the patients.
Study design The study design was a cross-sectional multicentred observation of GP consultations. 8] [29] [30]  The ICPC is an epidemiological tool produced by the World Organization of National Colleges, Academies and Academic Associates of General Practitioners/Family Physicians. 27 The purpose of the ICPC is to classify data about three elements of a healthcare encounter: (1) the reasons for the encounter, (2) the diagnosis (or managed health problem), and (3) the intervention or 'process of care'. 28 In our study, the data were collected by 54 GP interns who were previously trained in the use of the ICPC for 1. 5 days. Over 20 weeks, they prospectively reported on 2 half-days per week the content of all of the medical encounters they attended using the ICPC. They had a paper checklist to orient the observation.
General characteristics of the medical encounters A total of 123 skin cancer-related encounters were identified in the database, corresponding to 0. 7% of the overall number of encounters reported in the database (table  2 ). The characteristics of these encounters were compared with those of the 1521 (8. 9%) encounters that addressed regular dermatology issues and the 15 375 (90. 3%) that did not address dermatology issues. In the skin cancer group, the mean duration of the consultation was significantly longer (18. 41 vs 17. 80 min (regular dermatology problems) vs 17. 11 min (non-dermatology problems); p=0. 003), and there were more reasons for the encounter (3. 74 compared with 3. 49 (regular dermatology) and 2. 69 (non-dermatology); p<0. 001). Encounters that addressed skin cancer problems Table  3  shows that certain care processes were conducted less frequently during encounters that addressed skin cancer issues: patient counselling (5.
Acknowledgements The authors wish to thank the 54 trainees for their participation in data collection, the 128 GPs who agreed to take part in the study, and the  French medical schools involved in the ECOGEN study  ( Amiens, Angers, Besançon, Bordeaux, Brest, Clermont Ferrand, Dijon, Grenoble, Lille, Limoges, Lyon, Marseille, Montpellier, Nancy, Nantes, Nice, Paris Descartes, Paris Pierre et Marie Curie, Paris Diderot, Paris Est Créteil, Paris Ile-de-France Ouest, Poitiers, Rennes, Rouen, St-Etienne, Strasbourg , and Tours). Contributors CR conceived the study, was responsible for its supervision and was responsible for drafting the manuscript. SH participated in the database extraction and participated in drafting the manuscript. AG performed the statistical analysis and helped draft the manuscript.
4 ). Several bands with molecular masses in the range of ~130 kDa to ~15 kDa were detected in the membrane fraction of AnxA5-CF770 ligand blot (Fig. 4 ). Proteins of corresponding bands that were competed in presence of unconjugated AnxA5, suggesting for putative AnxA5-binding proteins, were isolated from cytotrophoblasts (CT) and syncytiotrophoblasts (ST) membrane fractions and subjected them to size-fractionated SDS-PAGE bands before tryptic digestion to nanoLC-LTQ Orbitrap mass spectrometry analysis. Database searches against proteins in UniProt with Mascot scores > 30 identified AnxA proteins (A1, A2, A5 and A6), E-cadherin/catenin complex (β and δ ) and ERM proteins (ezrin, radixin and moesin) (Table  1 ).
S3F. The quantification of protein proximity was performed by using ImageJ and by normalizing the fluorescence spots generated to the number of nuclei. FLIM/FRET experiments. FLIM measurements were done in frequency domain by phase modulation on a custom system based on a commercial module (Lifa, Lambert Instruments, Netherlands) as described previously  51. The module was attached to aNikonTE2000 TIRF inverted microscope equipped with a Coolsnap HQ CCD camera (Photometrics, USA), a 473 nm modulated laser diode (Omicron, Germany) used for excitation of the donor fluorophore and a 100 × 1. 49 NA TIRF objective. The laser light was coupled using an optical fiber to the TIRF illumination arm. Fluorescence emission was selected by a band-pass filter (500-550 nm). Transfected cells were imaged in wide field illumination by a mercury lamp with standard Nikon filter cubes and a Coolsnap Ez CCD camera (Photometrics, USA) prior to FLIM measurements in order to estimate the expression level of both EGFP-and mCherry-tagged proteins.
Multi-dimensional acquisitions were performed in streaming mode using Metamorph 7. 7. 6 software (Molecular Devices, France). A single region (ROI) of the membrane was photobleached per cell to avoid non-specific bleaching. FRAP analyses on E-cadherin-GFP, thus trophoblasts were maintained in culture medium in IBIDI μ -Slide 8 well and imaged with a FRAP head (Errol and Roper, France), 18 h after transfection. Fluorescence recovery images were monitored by scanning the bleached area (3. 2 μ m region of the plasma membrane) with an attenuated laser beam (0. 9 mW from the pupil of the objective). After photobleaching, images were acquired every 500 ms during 5 s then every 40 s for 270 s. To increase the signal/noise ratio, denoising was proceed by ND-Safir software  52  and the eventual shift was corrected by Motion 2D  53.
Quantification of the fluorescence recovery was realized by ImageJ software. The intensity of fluorescence was normalized and plotted on the graph using GraphPad Prism 6 (La Jolla, USA). Kymograms show the FRAP time course. The mobile fraction was determined as (F ∞ -F 0 )/(F i -F 0 ), where F ∞ is the fluorescence in the bleached region after fluorescence recovery at infinite time, F i is the fluorescence before bleaching and F 0 is the fluorescence immediately after bleaching. The recovery curves were fit to non-linear regression and the plateau followed by one-phase association equation using GraphPad Prism 6 (La Jolla, USA). Mobile fraction and diffusion coefficient were obtained by fitting curves with GraphPad Prism 6 (La Jolla, USA).
Acknowledgements We thank  Anthony Bouter  and  Alain Brisson  from  CNRS UMR5248, University of Bordeaux  for recombinant AnxA5 and fruitful discussions,  Dr. Daniele Evain-Brion  for being our scientific mentor,  Dr. Kristine Moltu  for English improvement,  Dr. Sylvie Dufour  ( UMR955 , IMRB) for generous gift of E-Cadherin-GFP construct and  Drs Pierre Vincent  ( CNRS UMR 8256 ) and  Ana-Maria Gomez  ( Inserm UMR-S 1180 ) for optical recording of calcium transient and annexin A5 translocation. We are grateful to  François Waharte  and  Vincent Fraisier  ( PICT-IBiSA @CNRS-Institut Curie ; member of the  French National Research Infrastructure France-BioImaging   ANR-10-INBS-04 ) for technical assistance and helpful comments and input on various parts of the work, and to members of the  Evain-Brion and Brisson laboratories.
This work has benefitted from the facilities and expertise of the  Nikon Center at Institut Curie-CNRS. The project was supported by  Inserm ,  ANR  (grant  ANR-11-BSV1-0035 ) and RSI professions libérales provinces (44-boulevard de la bastille  75578 Paris Cedex 12 ). We are grateful to the maternity units of Port-Royal and Antony hospitals for placenta collection. SD was supported by  ANR  ( ANR-11-BSV1-0035 ) and the fondation PremUP (www. premup. org).
Characterization of the sequence and structural diversity of the MHC region shows the approach accurately determines the sequences of the highly polymorphic HLA class I and HLA class II genes and the complex structural diversity of complement factor C4A/C4B. It has also uncovered extensive and unexpected diversity in other MHC genes; an example is MUC22, which encodes a lung mucin and exhibits more coding sequence alleles than any HLA class I or II gene studied here. More than 60% of the coding sequence alleles analyzed were previously uncharacterized. We have created a substantial database of robust reference MHC haplotype sequences that will enable future population scale studies of this complicated and clinically important region of the human genome.
Enrichment method captures divergent sequences A set of oligonucleotide probes was used to capture the ∼5-Mbp MHC region from sheared genomic DNA libraries. The targeted region is located at Chromosome 6p21 and encompasses all genes from GPX5 to ZBTB9 (Fig. 1A ). The method for sequencing the MHC was optimized using DNA from the COX cell line, which is homozygous throughout the entire genomic region  (Stewart et al. 2004 ) and is the source of one of the two complete MHC haplotype sequences included in the human genome reference (COX_hap2, GenBank: NT_113891). The method yielded 99. 96% coverage of the COX MHC haplotype, with a mean readdepth of 59. 4 (SD 18. 4) and with 99. 6% of the coverage being >10× (Fig. 1B ). A total of 25 gaps in the sequence represent 0. 04% (2414 bp) of the targeted region; they have a size range of 1-509 bp with a median of 49 bp, and 80% of them are outside the bounds of a coding region (Supplemental Fig.
Generation of high-fidelity haplotypes spanning the MHC region We targeted cell lines that are MHC region homozygous so that their haplotypes could be assembled without bias or ambiguity of phase. The assembly pipeline, a composite of established and in-house bioinformatics programs, consists of three stages that are fully described in the section titled "Methods. " Briefly, in the first stage, we generated and filtered multiple contigs, and in the second, we used read-pair information to join these contigs together to form scaffolds. In the third stage, we determined the relative orientation of the scaffolds and joined them where possible with read-pairs to form 'super-scaffolds. ' The process was designed to be conservative in order to limit or eliminate false joins. The assembly pipeline was optimized on sequence data from the COX cell line.