dataesr/scientific-paragraphs-categorization huggingface dataset:
), discharge location (home, another medical unit, death). FOP patients were identified in PMSI by the presence of ICD-10 code M61. 1 (Myositis ossificans progressiva or Fibrodysplasia ossificans progressiva) (http://apps. who. int/classifications/icd10/browse/2010/en#/M60-M63). However, it is essential to note that the PMSI data set has limitations: i) diagnoses are not confirmed or validated; ii) there is no narrative description of the patient's condition or history; and iii) patients are not captured in the data set unless they are hospitalized.
Regulatory authorizations The study was approved by the CCTIRS (Comité consultatif sur le traitement de l'information en matière de recherche) [Advisory Committee on Information Processing in Material Research in the Field of Health] and by the French National Commission on Information Technology and Liberties (CNIL) (authorization number: DR-2016-048). Patients identified in the CEMARA database were individually informed of the study, including the linkage to their PMSI records, and their rights relating to the confidentiality of their data. However, since PMSI data are deidentified, patients suspected of FOP in this resource could not be individually informed of the study. For both datasets, only persons residing in France, including Metropolitan (mainland) France and overseas Departments which were present in PMSI and/or CEMARA database, were included.
Methods The workflow of the study is described in Fig. 1. It is composed of the following phases: 1) identification of FOP cases in PMSI and CEMARA, 2) validation of positive cases in both databases by a FOP expert from the CR-MOC and/ or its regional network and 3) estimation of missing cases using capture-recapture method. All patients recorded in the CEMARA database were diagnosed as FOP patients by expert physicians, meaning that they were seen at least once in CR-MOC clinics, and they were diagnosed either by clinico-radiological hallmarks and/or by molecular testing, available since 2006. Patients included in both CEMARA and PMSI were considered to be true FOP patients because of their presence in the CEMARA database. PMSI records for the remaining patients (coded as FOP in PMSI but not present in the CEMARA database) were reviewed by a physician member of the CR-MOC who is an expert in FOP.
Our capture-recapture methodology uses two sources, so the unbiaised estimator of cases can be calculated as: N ^¼ n1 þ 1 ð Þ n2 þ 1 ð Þ f2 þ 1 ð Þ -1 where n1 is the number of confirmed cases in the first source, n2 the number of confirmed cases in the second source and f2 is the number of common cases. Statistical analysis was performed with SAS software, version 9. 3 (North Carolina, USA).
Results The number of FOP patients in both databases are reported in Fig. 2  and Table  1. In the PMSI and CEMARA databases, respectively 483 and 83 patients have been identified. Among these patients: 1) 41 patients were present in both databases and are all true positive FOP patients; 2) 42 patients were present in the CEMARA database but not in the PMSI and are all true positive FOP patients; and 3) 442 patients were in the PMSI database but not in the CEMARA database. Among the 442 PMSI patients who were not present in CEMARA, 410 were determined to be false positives after the review of their PMSI records, while 32 were still suspected cases, whose history was compatible with FOP but not confirmatory. For these 32 cases, our FOP expert contacted local genetic teams and confirmed 1 patient as a true positive case of FOP and eliminated 7 cases as false positives (not FOP).
The first symptomsbeside the toe malformations-occurred after birth for 97. 3% of the patients. Mean age at first symptoms, besides the hallux valgus, was 7 years. It occurred above 18 years in only 6. 9% of cases. Mean age at diagnosis was 10 years and above 18 years in 14. 9% of cases. The geographical distribution of FOP patients did not show a specific Fig. 2  Result of the linkage of the 2 databases pattern and seemed to be randomly distributed in whole France.
Discussion Accuracy of the method used for approaching the prevalence Our metholodology for estimating the prevalence of FOP in France was based on a capture-recapture method using 2 complementary national databases. Records were linked probabilistically between PMSI and CEMARA. PMSI is the main information system for recording the medical activity of patients in France; potential FOP patients can be identified within it by the ICD-10 diagnostic code M61. 1. CEMARA is a population registry focused on rare diseases which includes FOP. Our study's aim was to identify patients on their main care pathways, from birth to death. Through this method, the prevalence of FOP is estimated to be 1. 36 per million inhabitants (or 1/735,000 births). The independence of the sources of informations could not be estimated, so we applied the Chao estimator which is more appropriate for data sources where probability of cases' inclusion differs for each source.
Datasources assessement The 2 databases used in this study, CEMARA and PMSI, capture patient data in different ways that result in different coverage and quality. CEMARA captures diagnostic data from expert national centres (Centre of Reference "Maladies Osseuses Constitutionnelles"-CR-MOC) where FOP patients' diagnosis is validated by physicians with expertise in the disease, through their out-patient or inpatient clinics. The PMSI system captures ICD diagnostic codes for in-patient clinics only. Although the capture-recapture analysis predicts a very small number of missed cases, it is difficult to evaluate whether cases are missed from CEMARA because some patients may not reach a center of reference. Non -population -based registries, such as CEMARA, recruit from selected bodies, clinical centers or other types of structures (members of a patient organization, participants registered via an ERN -European Reference Network-or other disease-specific registry,.
Relevance and extrapolation to other countries Most FOP cases arise as a result of a spontaneous (de novo) mutation. Fewer than ten multigenerational FOP families are known worldwide  [7]. Moreover, there are no ethnic, racial, gender or geographic predispositions (  [7, 19]  to FOP. Therefore, the prevalence observed in France can serve as a benchmark for extrapolation to other countries. Variation would still be expected due to differences in access to healthcare services (including in diagnostic capabilities) or differences in the efficiency of health informatics systems. Why was this study conducted in France? France has three important characteristics that make it a good environment for population studies: 1) The healthcare system permits a thorough ascertainment of rare diseases like FOP through a centralized pathway of care for complex and chronic rare diseases (specific Centres of Reference organization); 2) the Centre of Reference network systematically records patient data, including diagnosis and place of residence, for patients with rare diseases in the CEMARA database; and 3) the national healthcare system covers a population large enough to yield a narrow confidence interval for the prevalence estimate.
These countries' relatively small populations and highly structured referral networks support the ascertainment of patients for epidemiology studies  [20] [21] [22] [23]. Our study demonstrates that similar outcomes can be achieved in a larger country where initiatives such as CEMARA and CR-MOC have been implemented. Finally, our study demonstrates the utility of national daatacapture mechanisms in dealing with the challenges of rare disease epidemiology, including the small number of cases and their heterogeneity. As with CEMARA, a data capture system is more valuable for epidemiological analysis and case ascertainment if it is linked to other national initiatives (such as PMSI).
Future research Several sources can be used to ascertain phenotypic patients: patient groups, healthcare providers, registries, administrative databases, health records (digitally or manually), and others. Each source has limits: many can only identify patients who have already been diagnosed (which represents a selection bias), and administrative databases can yield false positives because diagnosis codes are not validated. Our study combines several sources to mitigate these disadvantages using robust capture-recapture estimators. Another question is whether FOP prevalence may rise due to the trend toward an increased paternal age, a known risk factor for FOP as for other conditions due to single-mutation variation  [8]. FOP is rarely detectable in utero. The average paternal age at conception varies considerably over time and across countries.
It will be relevant to evaluate the impact that paternal age may have on genetic risks and on the prevalence of singlemutation diseases such as FOP. This study provides basic characterization of the FOP prevalent population: age distribution, age at onset of symptoms, age at diagnosis, gender, and location of symptoms at onset of disease. Numerous other questions remain to be studied, including a detailed description of the natural history and patient-reported outcomes like quality of life and familial burden. To better assess the epidemiology and natural history of FOP, other systems will be established in the coming years to aggregate various FOP datasets at a national and/or international levels, including the international registry project supported by IFOPA  [24]. These registries could yield insights about this severe disease, including the identification of endpoints for interventional trials, development of multicentric translational research projects, and biobanking.
Acknowledgments We thank all the families,  FOP France  and the  French FOP Network of physicians  involved in this study and in FOP monitoring  JP. Alberti (Saint Gély du Fesc), H. Angibaud (Arès), P. Bercegeay (Orvault), O. Boccara (Paris) ,  C. Bodemer  (Paris),  D. Castanet  (Toulouse),  JC. Colombani (Ajaccio), V. Despert (Rennes) ,  A. Coeslier-Dieux  (Lille),  G. Conte  (Nice),  P. Dolhem  ( Saint Quentin ),  JP. Fizaine  ( Dieuc s/Meuse ),  V. Forin  (Paris),  T. Funck-Bretanno  (Paris),  B. Gallot  (Venansault),  V. Gasnier  ( Eury Gregy s/ Yerres ),  X. Grattepanche  (Avesnes),  D. Geneviève  (Montpellier),  M. Gérard  (Caen),  JL Ginies  (Angers),  A. Goldenberg  ( Rouen) ,  L. Goumy  (Angers),  V. Guigonis  (Limoges),  C. Grimault  (Nantes),  S. Hadj-Rabia  (Paris),  D. Haye  (Tours),  E.
Annotation and Genome Comparisons Genome annotation, including manual curation, and comparisons were performed using the web interface MicroScope  (Vallenet et al. , 2013)  which allows graphic visualization enhanced by a synchronized representation of synteny groups 1. Predictions of repeated sequences were performed using Repseek  (Achaz et al. , 2007)  and those of genomic islands (GIs) using SIGI-HMM  (Waack et al. , 2006)  and Alien hunter  (Vernikos and Parkhill, 2006). The dbCAN database was used to identify carbohydrate active enzymes (CAZymes) 2   (Yin et al. , 2012). The genomic sequence reported in this article has been deposited in the EMBL database under the accession number LT634361.
Quorum sensing is a bacterial communication process that controls a range of functions at the population level. In Gram-negative bacteria, the most studied quorum sensing system comprises the production and detection of acyl homoserine lactones (AHLs), diffusible compounds that act as signaling molecules between cells  (Garg et al. , 2014). Though AHL production was previously reported in T. maritimum  (Romero et al. , 2010) , no homologous gene for AHL biosynthesis was detected in its genome. In contrast, quorum quenching refers to all processes involved in the inhibition of bacterial communication  (Kalia, 2013). A N-acyl homoserine lactonase encoding gene (GenBank: KR232938. 1) belonging to the metallo-β-lactamase family has been proposed to be the quorum quencher of T. maritimum  (Mayer et al.
ACKNOWLEDGMENTS We are thankful to  Institut Français de Bioinformatique (IFB) ,  V. Barbe  ( CEA/Genoscope ) for the optical mapping,  V. Loux  (MaIage) from the  INRA MIGALE  bioinformatics platform (http://migale. jouy. inra. fr)for help with bioinformatics and  C. Klopp  ( GenoToul Bioinfo ) for his contribution to genome assembly.
FUNDING This work was supported by the  Agence Nationale pour la Recherche  (contract  ANR-14-CE19-0020 ). It has benefited from the expertise of the High-throughput Sequencing platform of I2BC (http://www. i2bc. paris-saclay. fr) and GeT core facility platform (http://get. genotoul. fr)and was supported by the  France Génomique national infrastructure , funded as part of " Investissement d'avenir" program  managed by  Agence Nationale pour la Recherche  (contract  ANR-10-INBS-09 ).
2 ). All trends are evaluated from annual mean data over the full period length (see Methods section). A map showing the spatial distribution of the ocean temperature profiles used to compute the observation-based correlation is given in Supplementary Fig. 1. As expected, ice concentration and sea surface temperature trends are negatively correlated, meaning that an increase (decrease) in ice concentration is associated with a surface cooling (warming). Nonetheless, the correlation becomes positive as depth increases and reaches a maximum at a depth of nearly 120 m in observations and 150 m in the model: this entails that an increase (decrease) in ice concentration is also associated with heat gain (loss) at depth. Both peaks in positive correlation (from observational and model data sets) are significant at the 95% confidence level, despite weaker correlation values in the observations.
2 forcing set starts in 1958, a simulation using the model in a former configuration (described and evaluated previously  35  ) was performed to study the changes over the period 1952-1978 (Supplementary Figs. 5  and 6 ). Diagnostics. In order to produce the correlations displayed in Fig. 2 , the following steps were applied independently to the observational and model data sets. Sea-ice concentration and ocean temperature trends were first calculated from annual data and only the statistically significant ones were considered for correlation calculations. Regarding model trends, significance was assessed by testing the null hypothesis with a Student's t-test, taking into account a reduced sample size due to lag-1 autocorrelation  36. For observation-based data, however, a simple test without adjustment for autocorrelation was performed.
Acknowledgements We thank  J. Dunn  and  D. Monselesan  for providing and documenting the BOA data. Computational resources for this simulation have been provided by the supercomputing facilities of the  Université catholique de Louvain (CISM/UCL ) and the  Consortium des Equipements de Calcul Intensif en Fédération Wallonie Bruxelles (CECI)  funded by the  Fonds de la Recherche Scientifique de Belgique (FRS-FNRS ). O. L. and H. G. are, respectively, Postdoctoral Researcher and Research Director within the  Fonds National de la Recherche Scientifique (F. R. S. -FNRS-Belgium). This work was supported by  FNRS   Research Project  " Amélioration de la représentation de la glace de mer antarctique dans les modèles climatiques grâce à une meilleure compréhension des processus gouvernant son état moyen et sa variabilité ", under Grant Agreement no.
Data availability. All observational data supporting the findings of this study are publicly available online. BOA hydrographic data are available at https://researchdata. ands. org. au/bluelink-ocean-archive/692138? source=suggested_datasets, and the OSISAF sea-ice concentration satellite product at http://osisaf. met. no. The MATLAB code produced in order to perform all the data processing and analyses of this study is available upon request to the authors.
35  14 (2014). This figure is very close to the one obtained in our own study (€146. 12). Yazici et al  16  showed the duration of biological therapy in patients with RA in the US. This study estimates infusion times of 130, 216, and 409 minutes per patient for abatacept, infliximab, and rituximab, respectively. These data are in line with the results of our study, with regard to the "ranking" of times per treatment, but they are a little higher with regard to the total time for each treatment. These differences could well be related to possible differences between the health care processes in the Spanish and US health systems. Of particular interest is the waiting time recorded in the American study, in which the average for the three treatments analyzed is 17. 8 minutes. Our results fix the length of the waiting phase at an average of 47 minutes.
This work does confirm that all these variables have an influence on the unit cost of administering medicines, and this invites us to believe that the optimization of resources would help to reduce costs and thus achieve greater efficiency (Figure  5 ). It is clear that the vast majority of the works published in our country use costs of administering biological therapies to patients with RA, which are taken from medical tariffs published by the autonomous communities that are themselves taken from the e-Salud database. This study was a pioneering study in Spain because it made an estimate of the cost of the  biological agents applied to patients with RA, starting from primary data. So, in view of the results obtained in this work, it may be inferred that the data found in secondary sources are an estimate, on the high side, of the cost of administering biological agents in RA and, therefore, do not reflect the true cost of these treatments with any exactitude.
Acknowledgments Our sincere acknowledgment to  María Aparicio ,  Hospital Universitario Barcelona, María de los Ángeles Macheles, Hospital Universitario Miguel Zaragoza ;  Yoana Ivanova ,  Max Weber Institute ;  Lucía Martínez ,  Max Weber Institute , Madrid; and  Neboa Zozaya ,  Max Weber Institute  for the good work and support they have given us in the fieldwork for data collection. The working group includes  A Hidalgo  ( University of Castilla -La Mancha, Toledo ),  LM Longobardo  ( University of Castilla -La Mancha ),  PA Cabello  ( Servicio de Salud de Castilla La Mancha -SESCAM ), and  G Nocea  ( MSD, Madrid, Spain ).
Using the semi-classical perturbation approach in the impact approximation, full width at half maximum and shift have been determined for eight spectral lines of Ar VII, for broadening by electron-, proton-, and He III-impacts. The results are provided for temperatures from 20,000 K to 500,000 K, and for an electron density of 10 18 cm -3. The obtained results will be included in the STARK-B database, which is also in the virtual atomic and molecular data center (VAMDC).
Transition W( Å) = λ 2 2πc W(s -1 ) (4) where c is the speed of light. For the correction of widths and/or shifts for the difference between calculated and experimental wavelength, one can use the expression: W cor = λ exp λ 2 W. (5) The corresponding expressions for the shifts are analogous to Equations (  4 ) and (  5 ). Here, W cor denotes the corrected width, while λ exp is the experimental wavelength, λ the calculated wavelength, and W the width from Table  1. Dividing the parameter C  [17]  from Table  1  by the corresponding full width at half maximum, one obtains the maximal perturber density for which the line may be treated as isolated. The obtained Ar VII Stark broadening parameters shown in Table  1  will be implemented in the STARK-B database  [18, 19]. This database contains Stark widths and shifts needed first of all for the investigations, modelling, and diagnostics of the plasma of stellar atmospheres, but also for diagnostics of laboratory plasmas and investigation of laser produced, inertial fusion plasma, and for plasma technologies.
Conclusions The semiclassical perturbation calculation of Stark broadening parameters, widths, and shifts for spectral lines broadened by collisions of Ar VII ions with electrons, protons, and doubly charged helium ions have been performed for eight multiplets of Ar VII. The obtained values of Stark broadening parameters will be implemented in the STARK-B database-one of the databases included in the virtual atomic and molecular data center (VAMDC). Since Stark broadening data for Ar VII spectral lines considered here do not exist in the literature, we hope that they will be of interest for the relevant problems in astrophysical, laboratory, laser-produced, inertial fusion, and technological plasmas.
Acknowledgments: The support of the  Ministry of Education, Science and Technological Development of the Republic of Serbia  through project  176002  is gratefully acknowledged. This work has also been supported by the  Paris Observatory , the  PADC (Paris Data Center_OV) , and the  CNRS  which are greatly acknowledged. We also acknowledge financial support from " Programme National de Physique Stellaire" (PNPS)  of  CNRS/INSU ,  CEA  and  CNES, France. This work has also been supported by the  VAMDC (Virtual Atomic and Molecular Data Centre ). VAMDC is funded under the  Combination of Collaborative Projects and Coordination and Support Actions Funding Scheme of The Seventh Framework Program. Call topic: INFRA-2008-1. 2. 2 Scientific Data Infrastructure, Grant Agreement number:  239108. A part of this work has also been supported by the  LABEX Plas@par project  and received financial state aid managed by the  Agence Nationale de la Recherche , as part of the  programme "Investissements d'Avenir"  under the reference,  ANR11IDEX000402.
Literature search strategy We conducted a first search in Medline and Embase databases (up to August 2013), without publication date or language restrictions, by combining the medical subject headings (Medline) or indexed terms (Embase) and corresponding entry terms for colorectal cancers, FH (or hereditary syndromes) and nutritional factors (Supplementary Material 1). After removal of duplicates, no additional eligible article has been identified via the Embase database. Therefore an updated search (up to December 2016) was conducted in Medline only. We also hand-searched reference lists from retrieved articles and reviews on the related topic.
Data extraction Using a standardized data collection form, the following information was extracted for each article by one investigator and verified by a second investigator: first author's last name, publication year, study characteristics (design, country, recruitment, and mean follow-up periods for prospective studies), participants characteristics (sample size, mean age, sex, cancer site, number of cases, FH of CRC), exposure (nutritional factor, exposure assessment), comparisons and corresponding ORs, RRs or HRs and 95% CIs, control for confounding (adjustment for covariates), and potential bias. When available, data for subjects without FH within the same population sample was also recorded.
Characteristics of the studies Fig. 1  shows the flow chart of the selection process. From the 5312 abstracts provided by searches in Medline and Embase databases, 184 full text articles were identified and examined. Among the thirty-one potentially relevant full-text publications identified, we excluded one     , 1991-1994  This data set is common to five articles  (Slattery et al. , 1997b , Slattery et al. , 1997a , Slattery et al. , 1997c , Caan et al. , 1998 , Slattery et al. , 2000 and Slattery et al. , 2003) : there is a potential overlap for two articles reporting results on Western diet, but in one article results concerned CRC  (Slattery et al. , 2003)  and in the other one they concerned CC and analyses were stratified by age category  (Slattery et al. , 2000) ; an overlap between results on physical activity from two articles  (Slattery et al.