dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGMENTS We are grateful to  A. Picciocchi  for his comments on an earlier version of the manuscript and  C. Durmort  for her help with the SpNOX knockout, and we thank  Charlotte Genestet  for technical help. This work used the platforms of the Grenoble Instruct Center (ISBG;  UMS 3518 CNRS-CEA-UGA-EMBL ) and notably the MP3 platform, with support from  FRISBI  ( ANR-10-INSB-05-02 ) and  GRAL  ( ANR-10-LABX-49-01 ) within the  Grenoble Partnership for Structural Biology. This work has been supported by a grant from the  Joseph Fourier University  (AGIR 2013-Grenoble, France). We also acknowledge support from the  French Agence Nationale de la Recherche (ANR) , under contract  ANR-2010-1536-01  ( Prototype Nox ) and to the  Programme Technology pour la Santé du CEA  under contract  A-TSANV-03-26  (BOSS).
Data set No. of sequences Swiss-Prot TrEMBL Total Total 553,474 77,483,538 78,037,012 Filter I (NADPH) 575 252,407 253,164 Filter II (FAD) 64 7,375 7,439 Filter III (HH) 47 4,357 4,404 Eukaryotes 47 3,354 3,401 Prokaryotes 0 996 996 Archaea 0 1 1 Unclassified 0 6 6 a The table shows the total number of sequences in the Swiss-Prot and TrEMBL databases, the number of sequences selected by the software after each filtering step, the number of eukaryotic, prokaryotic, and archaea sequences in each data set.
Idiopathic pulmonary fibrosis (IPF) is a fatal lung disease with an unpredictable course. An observational study was set up using the French hospital discharge database to describe the reasons, outcomes and costs of hospitalisations related to this disease. Patients newly hospitalised for idiopathic pulmonary fibrosis (ICD-10 code: J84. 1) in 2008 were identified and followed for 5 years. As J84. 1 includes other fibrotic pulmonary diseases, an algorithm excluding age<50 years and presence of a differential diagnosis in the following year was defined. Overall, 6,476 patients were identified; of whom 30% were admitted through the emergency unit and 12% died during their first hospitalisation. Most of patients were hospitalised at least once for one or several acute events (n = 5,635; 87. 0% of patients), of whom 36.
Data source Subjects were identified from the French exhaustive national hospital discharge database (Programme de Médicalisation des Systèmes d'Information [PMSI]), which covers all stays in French public and private hospitals involved in medicine, surgery and obstetrics. In 2004, French hospitals adopted a prospective payment system. Since then, the PMSI database has become the basis of hospital funding by third-party payers  [14]. A standard discharge summary report is generated for each hospital stay, and includes information on the patient characteristics (e. g. sex, age, residence code), main diagnosis that led to hospital admission and examinations carried out during hospitalisation. Diagnoses are coded using the International Classification of Diseases, 10 th revision (ICD-10) either as main diagnosis (one per stay), related diagnosis (one per stay) or significant associated diagnosis (as many as necessary).
Competing Interests: The study was supported by Boehringer Ingelheim France. Celine Fernandez-Montoya and Katell Le Lay are employed by Boehringer Ingelheim. Aurelie Schmidt, Laura Catella, Fanny Porte and Stève Benard are employed by st[è]ve consultants. Boehringer Ingelheim is the market authorization holder of Ofev® (pirfenidone) which is indicated in the treatment of IPF. This does not alter our adherence to all the PLOS ONE policies on sharing data and materials. data). Patients with a first admission related to IPF in 2008 were extracted using the existing ICD-10 code "J84. 1: Other interstitial pulmonary diseases with fibrosis" as the main diagnosis, related diagnosis or significant associated diagnosis. In order to ascertain the first hospitalisation related to IPF, patients were excluded if they had been hospitalised for IPF in the 2 years before 2008.
Collected data Age gender and known comorbidities: chronic respiratory insufficiency, chronic obstructive pulmonary disease, heart failure, emphysema, pulmonary hypertension, lung cancer and sleep apnoea were collected at first hospitalisation. For all hospitalisations, we analysed length of stay, admission to intensive care unit, admission to critical care unit, attendance in emergency department, use of ventilation and extracorporeal membrane oxygenation, management in expert centres (S4 Table ) and in-hospital mortality.
Statistical analysis Patient socio-demographic characteristics and relevant comorbidities were analysed during the first hospitalisation. Data from the first hospitalisation and all subsequent hospitalisations were included in the clinical and economic analyses. The same method was used for the sensitivity analysis. Quantitative data were expressed as median and interquartile range (IQR) and ordinal data were described by their numbers and percentages. The descriptive statistical analyses were performed using SAS V9. 3 1 software (SAS Institute Inc. Cary, NC, USA).
Hospitalisations Over a 5-year maximum follow-up period, 21,660 hospitalisations (including the first hospitalisation) were extracted from the database, corresponding to a median of 2. 0 (IQR, 1. 0-4. 0) hospital stays per patient. Median length of stay was 8. 3 days (IQR, 4. 3-14. 0 days) and median cumulative length of stay was 20. 0 days (IQR, 9. 0-39. 0 days) per patient. Among patients, 19. 4% were hospitalised at least once in a reference/expert centre. During the first hospitalisation, the median length of stay was 8. 0 days (IQR, 2. 0-15. 0 days), with 85. 3% lasting for more than 24 hours. These long lengths of stay were due largely to acute events that involved 4,729 patients (73. 0%). The reasons of hospitalisation in the remaining patients were various: scheduled examination, related respiratory conditions (e.
Discussion This nationwide study shows that patients with pulmonary fibrosis, mostly idiopathic, are hospitalised for a variety of serious causes. The first hospitalisation was related to an acute event for nearly 75% of the population. When hospitalised, patients face a poor prognosis, with an in-hospital mortality of 12. 8% during the first hospitalisation and 36. 8% at 3 years. It should be noted that the PMSI database collects only secondary care data, which covers the mortality occurring during hospitalisations. As patients dying outside hospitals are not counted by the PMSI, the mortality, hereby reported, corresponds to the minimal estimate. This study is the first to provide extensive data on the hospital management of patients with pulmonary fibrosis, mostly idiopathic, in France.
Limited data are currently available to provide an overview of routine practice in patients with IPF. In a recent study based on data collected between 1997 and 2012 in the US, Brown et al. reported the incidence and outcomes of hospitalisations related to IPF. Although management in the US may differ from the one in Europe, some similarities with our findings are apparent  [13]. Overall, they observed a mortality of 22. 4% in a cohort of 97 IPF patients with a respiratory hospitalisation compare to an inhospital mortality ranging from 17. 0% to 32. 6% for patients experiencing respiratory-related events in our study. Furthermore mean lengths of stay were similar (8. 6 vs 6. 9 days in our study). In 2013, Navaratnam et al. assessed IPF-related hospitalisations in England from 1998 to 2010 through the Hospital Episode Statistics database, but the analysis was limited to sociodemographic characteristics of patients (notably age and sex) and length and cost of stays  [17].
Patients hospitalised for IPF had a high risk of emergency room visits (39. 6%). In our study, attendance in emergency departments ranged from 20. 4% for acute respiratory worsening to 50. 3% for respiratory infection. However, the study periods differ, as the study by Collard et al. included patients since diagnosis whereas we started to follow our patients after their first hospitalisation. Our study provides extensive data on hospital management for patients with IPF in France, which can be used for future economic analyses in France. Discharge databases permit estimations of the cost of a disease with a relatively high confidence, especially when few data are available, as for IPF. In this way, Collard et al. estimate a total medical cost per patient from the Medicare database, without further description by cost category  [20].
Other studies have attempted to characterise the cost of IPF with less accuracy, using Delphi panel  [21]  or extrapolation  [17]. Our study was based on a comprehensive national hospital database. However, this database was not developed for epidemiological purposes. Indeed, it is a tool built to measure hospital activity and to determine the cost of hospital stays according to the diagnosis-related group system. As the rate is related to the patients' characteristics, clinical diagnoses and procedures, the collection of data over the hospital stay is accurate. A national agency (Agence Technique de l'Information sur l'Hospitalisation) manages the PMSI database and regularly publishes guidelines for coding at the hospital level under the supervision of a physician. This database is increasingly being used as a reference for epidemiological, medical and economic studies in France, including by health authorities.
Furthermore, in order to assess the impact of possible misclassification, we performed a sensitivity analysis limited to patients with at least two hospitalisations related to IPF and found consistent results in the patients' characteristics. Moreover, the patients studied and presented in the manuscript have been hospitalised, which likely explains the older age and the balanced sex ratio compared to what is reported in cohort studies. Indeed, this population is slightly different from that of patients who are followed in clinical practice with less advanced disease and not experiencing severe acute events leading to acute deterioration. Other discharge database studies  [17, [23] [24] [25] [26]  have also been performed using the ICD-10 code "J84. 1" or the ICD-9 code "516. 3" with satisfying results.
Although these 2 codes slightly differ (S1 Appendix), we can assume that the results are generally comparable as differences represent very rare diseases. In order to validate the accuracy of the ICD-9 code "516. 3", Agabiti et al. cross-checked their data against a chart review of 404 individual IPF cases collected from six Italian hospitals. They found no significant difference in the distribution of patient demographics and clinical characteristics between the subsample and the database population  [23] , thus validating the proper selection of IPF patients using this code. Very recently, a study presented another method to assess the performance of algorithms applied to identify IPF patients including validation by calculating positive predictive value using medical records  [27]. This approach, which may be more accurate, could not be applied in our study, due to the difficulty of obtaining nominative patient-level data in France.
2c ). I L C 3 N C R + I L C 2 I L C 1 I L C 3 N C R - I L C 3 N C R + I L C 2 I L C 1 I L C 3 N C R - I L C 3 N C R + I L C 2 I L C 1 I L C 3 N C R - I L C 3 N C R + I L C 2 I L C 1 K i -6 7 To further verify the consistency of our observations, we retrieved mRNA expression data from The Cancer Genome Atlas (TCGA) for treatment-naive AML patients, including APL. TCGA data confirmed the specific upregulation of PGD synthase (but not PGE and PGI2), CRTH2 as well as B7H6 (but no other B7H molecules, except B7H3) in APL as compared to the other AML subtypes (Supplementary Fig. 2d ). Together, these findings suggest that circulating ILC2s in APL patients are primed to secrete IL-13 in response to malignant cells. ILC2-derived IL-13 promotes functional M-MDSC. To understand the circuitries established by ILC2s through the secretion of IL-13, we screened different cell populations from APL patients and healthy donors for the expression of IL-13Rα1.
6g, h ). Thus, we assessed the presence of M-MDSCs in the same tumours and observed that M-MDSCs frequencies positively correlated with tumour-infiltrating ILC2s (Fig. 6i ). In addition, we quantified T, NKT, B cells, as well as eosinophils in the prostate, blood and spleen of tumour-free and tumour-bearing TRAMP mice (i. e. , before and after 12 weeks of age) and we found no difference in the blood and in the spleen. However, in the prostate, beside the enhancement in M-MDSCs already described above, we found a reduction of NKT cells (Supplementary Fig. 6a-c ). Overall, these data in prostate cancer, although only correlative, suggest that the expansion in M-MDSCs within the tumour might be driven by the increase in ILC2s responding to tumoursecreted PGD2.
001 or 0. 0001 were labelled respectively with **, *** or ****. Sample size was estimated with G*power software in order to obtain relevant statistical analysis (p < 0. 05) with a power ≥ 0. 8. For human experiments, the effect size was derived based on our previously published cohort of AML patients  22  focusing on ILC3 NCR + frequencies in patients vs. HD (d = 1. 79). Thus, we estimated a minimum sample size of 10 patients at diagnosis and of 4 patients in remission (d = 3. 22). For mice experiments, the effect size was estimated based on a pilot experiment comparing ILC2 increase between PML-RARA APL mice and CTR mice (d = 3. 06). Thus, we predicted that each group had to contain at least four mice per group. Mice developing unrelated diseases were excluded from the analysis. Statistical analysis of survival curves was performed with Log-rank (Mantel-Cox) test.
Acknowledgements We are grateful to the patients for their dedicated collaboration and to healthy donors for their blood or CD34 + HSC donation. We thank  Prof. P. Romero  for insightful discussions and support of this study. We thank  Prof. H. de Thé  for providing the PML-RARA APL mice,  Dr A. Donda  for help with the TRAMP model,  P. Reichenbach  and  S. Ferreira Lopes  for technical help. We thank the caretakers of the animal facility for their excellent assistance. We thank  Daniela Pende  ( Istituto di Ricovero e Cura a Carattere Scientifico Azienda Ospedaliera Universitaria San Martino-Istituto Nazionale per la Ricerca sul Cancro, Genoa, Italy ),  Alessandro Moretta  ( Dipartimento di Medicina Sperimentale, Università degli Studi di Genova, Genoa, Italy ) and  Silvia Parolini  ( Sezione di Oncologia e Immunologia Sperimentale, Dipartimento di Medicina Molecolare e Traslazionale, Università di Brescia ) for providing masking antibodies against NKp30 (clone F252) and NKp46 (clone  KL247 ).
We retained factors with eigenvalue greater than 1. Rotation was performed by the varimax method. Linear regression was performed using V'O 2 peak as the dependent variable and PCA-extracted factors as covariates. Linear regression was adjusted for age, gender, weight, height, active smoking (yes/no) and duration of disease. The normality of the distribution of residuals and the diagnosis of homoscedasticity were evaluated graphically. Statistical analyses were performed on SPSS (IBM SPSS  Statistics 22) ; the threshold for statistical significance was set to p < 0. 05.
Sociodemographic and clinical characteristics A total of 62 subjects (27 men, 35 women) with a mean age of 37 ± 10 years were included. The diagnosis of PLCH was confirmed by suggestive clinical features associated with typical CT lesions in 63% of cases, surgical lung biopsy in 31% of cases and extrathoracic biopsy in 6% of cases. Subjects were Caucasians in 94% of cases. The study population comprised 66% of smokers, 31% of ex-smokers and 3% of non-smokers. The median interval between the date of diagnosis and the date of CPET was 3 years. No statistically significant difference was observed for sociodemographic data and dyspnea severity between subjects with or without reduced V'O 2 peak (Table  1 ).
Principal component analysis After exclusion of the subjects with any missing data, 45 subjects were included in the principal component analysis. Subjects with and without missing data showed similar sociodemographic, PFT and CPET characteristics apart from higher AaDO 2 at rest and peak and lower PaO 2 peak in the missing data group (S1 Table ). Four factors had eigenvalues greater than 1. The first factor alone explained 48% of total variance. The 4 factors explained 79% of total variance. The component matrix after rotation is shown in Table  4. Principal component 1 (PC1) was associated with variables measuring gas exchange, as it was negatively correlated with AaDO 2 at rest (r = -0. 79) and peak (r = -0. 804) and positively correlated with PaO 2 at rest (r = 0. 775) and peak (r = 0.
Acknowledgments The authors would like to thank  Prof. Vincent Jounieaux ,  Centre Hospitalier Universitaire d'Amiens, Amiens, France, Prof. Gae ¨tan Deslee, Hopital Universitaire de Reims, Reims, France ,  Dr. Catherine Tardif ,  Centre Hospitalier Universitaire de Rouen, Rouen, France ,  Dr. Frederic Bart ,  Centre Hospitalier de Bethune, Bethune, France ,  Dr. Francois Jounieaux ,  Marcq-en-Baroeul , France and  Cyril Bergoin , Tourcoing, France for provision of data.
Both statistics seek to identify a recent change in the frequency of an allele or haplotype, which can reflect the impact of selection in a genomic region. PBS was calculated for 19 Collas in comparison to 19 Siberians  19, 20  and 16 lowlanders from Central and South America 2. The lowland group represented a conservative grouping since it included Calchaquíes, who live at an altitude of 2300 m in Argentina and bear overall genomic similarities to the nearby Collas 2. Hence, any genomic signatures shared by Calchaquíes and Collas would be missed by our approach. These filters yielded 72 SNPs. After excluding SNPs with frequency >10% in the 1000 Genomes database, those without gene annotation, and with combined annotation dependent depletion (CADD) scores ≤5 only 9 SNPs remained (Table  1 ).
We supplemented SIFT and PolyPhen2 scores with CADD scores for our 42 missense hits and defined a high-probability damaging variants as (SIFT: "damaging", PolyPhen2: "possibly damaging" or "probably damaging", and CADD Phred >5). This identified 11 SNPs (Table  2 ). Of these 11 SNPs, two (rs570553380 and rs543290190) are not found in 1000 Genome Phase 1 data and are polymorphic only in Peruvians from Lima in the extended 1000 Genome Phase 3 data set; this was only true for one other variant in our list of 42 missense hits. Tellingly, one of these two variants (rs570553380) is located in the EPAS1 gene, which is a major oxygen sensor and has been identified multiple times as subject to high altitude selection in Tibetans  [11] [12] [13] [14]. The other SNP (rs543290190) is located in AKAP6, which is involved in cardiac myocyte reaction to stresses such as hypoxia, hypertrophic remodelling of the heart  25  and stabilises the hypoxia master regulator HIF-1α during hypoxia while enhancing its degradation at normoxia  26.
Methods Subjects and ethical approval. All participants provided their written informed consent. The study was approved by the by the Ethics Committee at the University of East Anglia and the Ministry of Health of the Province of Salta (Ministerio de Salud Pública, Salta, Argentina). Ethical approval for the whole genome sequencing of the samples was obtained from University of Cambridge Human Biology Research Ethics Committee (HBREC. 2011. 01). All methods were performed in accordance with the relevant guidelines and regulations. High altitude samples were collected from an Andean population, the Colla, living in Northern Argentina at an altitude >3500 m 9. Native American lowland control samples for selection analyses originated from 5 Calchaquí and 4 Wichí from neighbouring regions in Argentina 2 , plus publicly available sequences from the Complete Genomics database including 5 Mexican (NA19648-200-37-ASM, NA19669-200-37-ASM, NA19735-200-37-ASM, NA19649-200-37-ASM, NA19670-200-37-ASM) and 2 Puerto Rican (HG00732-200-37-ASM, GS000015711-ASM) samples  20.
Additional North-East Siberian samples served as controls for threesided population statistics including 2 Chukchi, 4 Eskimo and 13 Koryak individuals  19, 20. Two additional Chukchi individuals were added in the demographic analyses. Whole genome sequences. Whole genome sequences were obtained with the Complete Genomics platform and quality filtered as described elsewhere  20. The newly sequenced Colla genomes were deposited in the European Nucleotide Archive: PRJEB14828. Data are also freely available through the Estonian Biocentre website. All genomes were annotated against the Ensembl GRCh37 database and compared to dbSNP Human Build 141 and Phase 1 of the 1000 Genomes Project dataset  41  as described previously  20. For 1000 Genomes variant frequency Phase 3 data  42  was used. We focused on variants annotated as "exonic" in Ensembl occurring in Hardy-Weinberg equilibrium in our sample resulting in 402,227 autosomal variants.
Variants with a combined annotation dependent depletion (CADD) score >5 were investigated further. The CADD score was calculated to estimate impact of synonymous and non-synonymous variable alike  43. Missense variants were assessed with the in silico prediction programs SIFT  44  and Polyphen2  45. Tests of positive selection. Two statistics were calculated for each SNP -the population branch statistic (PBS)  14  and the number of segregating sites by length (nSL)  18. The PBS score was based on pairwise F ST values for the three populations using a weighted analysis of variance  46  with the program GENEPOP'007  47. PBS was estimated as in Yi and colleagues  14. The nSL scores were calculated only on the Colla sample. Two statistics were window based. We calculated Tajima's D on the Colla sample using non-overlapping 200 kb windows  17.
Acknowledgements We would like to thank all participants of this study. This work was supported by the  European Research Council  with a  ERC Starting Investigator grant  (grant number  FP7 -261213  to T. K. ); the  Singapore Ministry of Education  (grant number  MOE2015-T2-1-127 , supporting G. S. J. ); the  Estonian Research Council  with an  Estonian Institutional Research grant  (grant number  IUT24-1  to E. M. , G. H. , M. Me. ,  M. Mi. , R. M. , T. K. ) and an  Estonian Research Infrastructure Roadmap grant for sequencing  (grant number  3. 2. 0304. 11-0312 ); the  European Commission  with an  European Regional Development Fund through the Centre of Excellence in Genomics  to the  Estonian Biocentre  (to E. M. , G. H. , M. Me. ,  M. Mi. , R. M. , T. K. ); the  European Union  with a  New Zealand Estonian French Research Exchange Scheme grant  ( People Marie Curie Actions ),  International Research Staff Exchange Scheme , call FP7-PEOPLE-2012-IRSES-number (grant number  318979  to  M.
Imaging Data Analyses Neuroimaging data were analyzed by means of SPM 12 (Wellcome, London, UK) within the MatLab framework (MathWorks, Ismaning, Germany). Pre-processing steps included realignment and unwarping, co-registration of functional with anatomical images, segmentation into compartments of white and gray matters and cerebro-spinal fluid, smoothing (with full width at half maximum of the Gaussian kernel of 7 mm in each direction) and normalization with respect to the Montreal Neurological Institute (MNI) space. Using the canonical hemodynamic function and its derivative set available in SPM12, the pre-processed functional data for the single subject analyses were inserted into six separate general linear models for the six odors, each including two experimental conditions (odor ON period, odor OFF period) and movement parameters.