dataesr/scientific-paragraphs-categorization huggingface dataset:
The average observation densities are shown in Figure. 1 for precipitation, which illustrates the benefits of using HyMeX observations. The data distribution is such that scores will be dominated by forecast performance over land in Spain and France. Italy features a high The observations used have been quality controlled, in order to weed out erroneous and duplicate data. Excessively dense parts of the network have been thinned down. Data has been further filtered by comparing observed values departures with operational AROME-France forecasts, in order to remove stations that are clearly not suitable for model verification. In this process, called data selection, stations with time-averaged rms (root mean square) errors much larger than usual AROME-France error statistics are removed from the verification dataset.
In summary, using HyMeX-SOP1 observations instead of the routine dataset has the following consequences: (a) a two-to three-fold increase in data volumes, (b) high data density over Spain, France, and Italy (the latter for precipitation only), (c) the HyMeX-specific data appears to have good quality because its data selection statistics are similar to routine data, (d) HyMeX data contains many more high rain observations (more than 10mm in 3 hours) than the routinely available data over the SOP1 period. As will be seen in the score studies presented in the following sections, this data helps with the identification of statistically significant changes to high precipitation scores.
• ICE3, a five-species cloud/precipitation microphysics scheme that manages cloud water, cloud ice, precipitating rain, snow and graupel, with a PDF-based sedimentation scheme. • a 1-D vertical mixing scheme with prognostic turbulent kinetic energy. • a sub-grid shallow convection scheme based on an eddy diffusivity mass flux approach. • SURFEX, a detailed coupled surface model that computes the evolution of independent tiles to model prognostic soil, vegetation, towns, lakes, sea and ice surface; it also contains a multilayer prognostic snow scheme, an interface to the ECOCLIMAP-2 physiographic database, and a 1-D turbulent surface layer scheme to diagnose screen-level variables. The AROME-EPS ensemble setup used as the reference in this paper is called REF. It is similar to  Bouttier et al.
Verification procedures The verification methodology is similar to  Bouttier et al. (2012). Here, we use the larger HyMeX observation database, and the experiments are longer, to facilitate the identification of statistically robust impacts. The ensemble performance is measured using objective scores to compare in situ observations with raw (i. e. uncalibrated) ensemble forecasts for the same parameters. Three-hourly observations of T2m, RH2m, ff10m and rr3 are compared with AROME forecasts horizontally interpolated at station location, using a nearest neighbour method, with a ten-minute tolerance on the observation time. T2m, RH2m and ff10m are vertically interpolated from the model vertical levels using a high resolution, one-dimensional prognostic boundary layer model. The assumed observation errors are listed in Table  1.
A high-resolution ensemble beats both systems despite its relatively small ensemble size. These findings confirm the result of  Clark et al. (2009)  in an independent framework. The visual interpretation of the ROC diagrams is confirmed by the ROC area scores, according to which the superiority of AROME-EPS over both AROME-France and PEARP is significant at the 95% level, at all forecast ranges between 9 and 30 hours. This result is robust with respect to changes to the event thresholds (within values that can be reliably tested in the available dataset). The Brier and CRPS scores (not shown) lead to the same conclusions. An exception is weak precipitation, for which AROME-EPS is not clearly better than PEARP for rr3 thresholds less than 3mm. Both systems mainly differ in the success rates of a user who forecasts rain if any ensemble member predicts it, i.
Comparison with an ensemble data assimilation In an experiment called IC-EDA6, the reference IC perturbation scheme is replaced by an initialisation by the atmospheric and surface analyses of an AROME ensemble data assimilation system (EDA). The EDA algorithm is described in  Brousseau et al. (2012)  and  Vié et al. (2011). Its principle is to run an ensemble of independently perturbed data assimilation systems. The result is an ensemble of analyses that samples the uncertainties on the initial state of the AROME ensemble prediction system. The EDA algorithm tested here is a six-member 3D-Var AROME data assimilation system at the same model resolution as AROME-EPS. The EDA perturbations stem from adding random Gaussian noise to the observations in six instances of the AROME-France 3D-Var assimilation, including the surface analyses.
This EDA algorithm is similar to the one used in  Bouttier et al. (2012) , with recent improvements to the adaptive ensemble inflation algorithm  (Raynaud et al. , 2012). EDA is a computationally expensive system, so that experiment IC-EDA6 only contains six members from 12 October to 18 November 2012 (36 days). In order to avoid verification biases linked to varying ensemble size, IC-EDA6 scores will be compared to REF6, which comprises the six corresponding members of the REF experiment; these members use the same lateral boundaries, SPPT and surface perturbations patterns. The EDA system is related to PEARP in the sense that they both use the same global ensemble data assimilation scheme, AEARP. EDA relies on AEARP to provide its large scale boundary conditions, and PEARP initial conditions use a combination of singular vectors and AEARP perturbations.
Interaction between initial and surface perturbations The IC-EDA6 experiment has shown that ensemble data assimilation improves the short term ensemble scores, but degrades them at longer ranges. A likely explanation is EDA provides good atmospheric perturbations, but they tend to be advected from the model domain after a few hours, because the AROME only covers a relatively small region. At longer ranges, we hypothesize that the impact of EDA is dominated by slowly-evolving EDA surface perturbations, which are not as good as in REF. To test this hypothesis, an experiment called IC-EDASURF6 has been set up like IC-EDA6, except that the surface conditions are replaced by those from REF6. As expected, the result (Figures  8  and 9 ) combines the short term benefit of EDA with the longer term impact of the direct surface perturbation scheme.
One could try to improve this situation by perturbing the turbulence parametrisation, the humidity field, and model dynamics, for instance using the horizontal diffusion operator. A key objective of AROME-EPS is the prediction of high impact weather events. The HyMeX-SOP1 dataset used here over 75 days was helpful in sampling the overall ensemble performance, but it did not include many intense winds or rain. The results are believed to be an adequate assessment of ensemble performance for average autumn weather in the HyMeX area, but they may not be informative about extreme weather. For instance, the highest rain threshold for which ensemble reliability diagrams can be convincingly constructed here is about 20mm in 3 hours. Catastrophic flooding events in the area are often triggered over small areas by at least twice higher accumulations.
This will be the topic of future work. EDA ensemble data assimilation HARMONIE a generic name for several numerical weather prediction models used in the HIRLAM group, among which the AROME model. HIRLAM a group of European national weather services. HyMeX a field experiment dedicated to hydrology in the Mediterranean area. HyMeX-SOP1 a special observing period of HyMeX that focused on weather prediction during autumn 2012. MOGREPS-UKV a non-hydrostatic limited-area numerical weather prediction model used by the UK Met Office. PEARP a global ensemble prediction system based on the ARPEGE model. RRTM a radiative transfer parametrisation used in the AROME model. SPPT a stochastic physics scheme that involves random perturbations of physics tendencies. SURFEX a surface parametrisation used in the AROME model.
Acknowledgements: This work has been funded by  Météo-France  and  CNRS. It is a contribution to the  HyMeX (HYdrological cycle in Mediterranean EXperiment) program  of the MISTRALS metaprogram. It received support from the  LEFE/MANU/VIPS  contract of the  Institut National des Sciences de l'Univers. Helpful comments from two anonymous reviewers are acknowledged.
Acknowledgements We thank the team of the curators of the  Institut Pasteur MLST system (Paris, France)  for importing novel alleles, profiles and/or isolates at http://www. pasteur. fr/mlst. We are very grateful to all laboratory directors for their collaboration in this study:  Dr. Marcel El Achkar ,  Dr. Fadel Moukkadem ,  Dr. Ibtihal Kassam ,  Dr. Rikardo Sarraf ,  Dr. Nathalie Sapiroua ,  Dr. Gilbert karayakoupoglou ,  Dr. Samir Ghanem. We also thank  Catherine Quinqueneau ,  Catherine Ramont ,  Cyrielle Lebreton ,  Mariam Yehya  and  Taha Abdo  for excellent technical assistance. This work was supported in part by  AZM  and  SAADE Association.
Epidemiological typing Epidemiological typing of all isolates was carried out by bla OXA-51-like sequence based typing (SBT) as described previously  [16]. The sequences were compared to all variants present in BLAST. Each new sequence detected was submitted to GenBank and assigned by Lahey database for beta-lactamase classification (http://www. lahey. org/studies/webt. asp). MLST was performed on a panel of 57 isolates according to the Pasteur scheme (http://www. pasteur. fr/mlst). When a new allele or a new sequence type (ST) was identified, it was submitted to Pasteur Institute MLST Database. eBURST (http:// eburst. mlst. net/) was applied to compare identified ST(s) to available ST(s) present in MLST Database (last update 22. 10. 2014). A clonal complex (CC) was defined as a set formed by the founder ST and its single locus variants (SLV)  [10].
We low-pass filter, zero-path difference, and Hanning apodize the interferogram before Fourier transforming the interferogram into the frequency response S(ν). We use one device for microstrip wave speed measure-ments. The DUT is a microstrip Fabrey-Perot cavity with an impedance intentionally mismatched from the surrounding lines. The standing wave pattern in the spectrum of the bolometer behind the cavity can be used to determine wave speed. (See Figure  8  for a schematic and data sample. ) The wave speed is a function of both the ILD dielectric constant r and the Nb penetration depth Λ. Alternatively, the banddefining filter resonance is determined by series inductance dominated by the CPW magnetic inductance. Data from the band locations and test devices allow us to solve for both r and Λ and we summarize these numbers is Table  2  for different materials.
In order to characterize the noise performance, we have measured noise spectra for the BICEP2 and Keck Array detectors using un-multiplexed data digitized at 400 kHz. Although the frequency range corresponding to degree-scale anisotropies is 0. 05-1 Hz for the BICEP2 and Keck Array scan strategy, these measurements allow us to observe device performance near the typical multiplexing frequency of 25 kHz and thus model noise aliasing due to the readout. These measurements, adjusted for expected aliasing, are in good agreement with noise measured in the science-mode multiplexer configuration. Figure  20  compares the measured noise for a representative detector from the Keck Array to its various modeled components. Photon noise dominates at the low frequencies of interest for sky observations.
The development and testing of focal planes were supported by the Gordon and Betty Moore Foundation at Caltech. Readout electronics were supported by a Canada Foundation for Innovation grant to UBC. The receiver development was supported in part by a grant from the W. M. Keck Foundation. BICEP2 was supported by the US National Science Foundation under grants ANT-0742818 and ANT-1044978 (Caltech/Harvard) and ANT-0742592 and ANT-1110087 (Chicago/Minnesota). The computations in this paper were run on the Odyssey clus-ter supported by the FAS Science Division Research Com-puting Group at Harvard University. Tireless administrative support was provided by Irene Coyle and Kathy Deniston.
We also carefully excluded mutations in eleven genes (AIFM1, ATP1A3, c12orf65, CISD2, FXN, MTPAP, PRPS1, SFXN4, SPG7, TIMM8, and TSFM), for which mutations might cause clinical presentations related to our patients (e. g. , Mohr-Tranebjaerg and CAPOS syndromes). Thus, we progressively filtered WES data. After excluding intronic, 5 UTR, 3 UTR and synonymous variants, we obtained 14,241 exonic and splicing variants. A total of 9,776 variants were selected with a number of call higher than 4 using the GATK, VarScan, SNVer, Lofreq, Platypus softwares. We discarded common variants reported in NHLBI Exome Sequencing Project, Exome Variant Server, NCBI, the 1000 genomes and ExAC ver0. 2, with a frequency greater than 1%, leading to 145 left variants. Then 30 deleterious SNPs predicted by SIFT, PolyPhen-2, and Mutation Taster softwares were retained (see Supplementary Table  S1 ).
Finally, these variants were filtered for heterozygous mutations in gene encoding mitochondrial protein (MitoCarta), as to date all DOA genes are involved in mitochondrial physiology. We only found the c. 1402C>T heterozygous mutation in AFG3L2 exon 11 (Figure  1B ) that was never reported before in any database (see above for a list). It modifies an evolutionary highly conserved arginine 468 in cysteine (p. Arg468Cys) within an arginine-finger motif (RPGRxxR) that is found in the AAA domain of all AFG3L2 orthologs and SPG7 paralogs. The arginine 468 is also conserved in the prokaryote FtsH from E. coli (Figure  1C ). In silico prediction of the p. Arg468Cys pathogenicity by Polyphen2, SIFT, Mutation taster, imutant, Pmut, MetaSVM, MetaLR, FathMM, and PhD-SNP programs gave the highest scores, supporting the pathogenicity of this mutation.
ACKNOWLEDGMENTS We are indebted to INSERM,  CNRS , the  University of Montpellier , the  University of Angers , the  CHU d'Angers, the region Pays de la Loire, Angers Loire Metropole , the  Fondation pour les Maladies Rares and Fondation pour la Recherche Médicale , and the following patient associations:  Retina France ,  UNADEV ,  Ouvrir Les Yeux  for their financial the support. Drs. B. Wissinger  and  C. de Layon  are acknowledged for constructive discussions.
Data analysis Data are reported as frequencies (percent) and number. Potential associations with experimentation of e-cig. were studied using chi-square tests. Variables that were significant in univariate analyses at p ≤ 0. 05 were included in a stepwise logistic regression model. The alpha level was set at 0. 05. All analyses were performed with the SAS software version 8. 2 (SAS Institute®, Cary, NC).
Use of electronic cigarette by non-smokers In the present study we identified 56 non-smokers who experimented e-cig. (9. 9 % of e-cig. experimenters and 1. 7 % of the study population). These non-smokers were younger than those young smokers who were e-cig. experimenters. Previous studies report only on very small proportions of non-smoker e-cig. experimenters: 1. 6 % in a 2011 and 4. 4 % in 2012 in the US study  [13] , 0. 1 % in a Korean study  [12] , 1. 4 % among Polish girls, 3. 2 % among Polish boys of 15 to 19 years old (data of 2011)  [14]. Our data shows higher non-smokers e-cig. experimentation rate. This suggests that we urgently need prospective studies to learn whether and to what extent non-smoker e-cig. experimenters become conventional cigarette smokers, chronic e-cig. users or mixed users to elaborate more on place of e-cigarette in promotion or prevention of tobacco use in adolescents.
Methods The INITIATIVES BPCO cohort COPD subjects included in the present analysis were recruited in the INITIATIVES BPCO cohort between January 2005 and August 2009. The INITIATIVES BPCO cohort is a real-world cohort of clinically and spirometry-diagnosed COPD patients identified in 17 pulmonary units of university hospitals located throughout France. Data are recorded in a standardized case report form but, due to the real-world nature of patient care, datasets do not have to be complete to include a patient. Only demographic characteristics and spirometry are mandatory. Detailed information about this cohort can be found in a previous report. 16 Respiratory physicians prospectively recruited subjects in stable condition (no history of exacerbation requiring medical treatment for the previous 4 weeks) with a diagnosis of COPD based on a post-bronchodilator FEV 1 /FVC (forced vital capacity) ratio 70%.
17 Subjects with a main diagnosis of bronchiectasis, asthma, or any other significant respiratory diseases were carefully excluded. At the time of the analyses, the INITIA-TIVES BPCO cohort contained data on 633 COPD subjects. Because our goal was to study the impact of both lung function and comorbidities on dyspnea scores, we selected subjects with complete data for spirometry, plethysmography, comorbidities (coronary artery disease, chronic heart failure, thromboembolic history, diabetes, hypertension), Hospital Anxiety-Depression (HAD) Scale, Saint George's Respiratory Questionnaire (SGRQ) score to assess health-related quality of life (HRQoL), and numbers of acute exacerbations of COPD during the previous year. The study was approved by the Ethics Committee of Versailles (France), and all subjects provided informed written consent.
Data collection We used a standardized characterization process that covered demographic data, risk factors including cumulative tobacco smoking, and COPD characteristics (including symptoms and spirometry) in stable condition. Pulmonary function tests were performed according to international standards,  18  and included a spirometry and an optional plethysmographic assessment of lung volumes. Severity of airflow obstruction was evaluated according to GOLD classification. 17 he number of acute exacerbations of COPD during the previous year was determined according to patient's selfreport. Comorbidities (including congestive heart failure, coronary artery disease, hypertension, and diabetes mellitus) were identified from the patient files (physician-diagnosed comorbidities). Malnutrition was defined by body mass index (BMI) 18.
Multivariate analysis Stepwise ordinal logistic regressions were performed in order to find parameters that explain mMRC and BDI scores, using the LOGISTIC procedure from SAS ® 9. 2 statistical software, with a significant level of entry of 15% and a significant level of stay of 10% as criteria for introducing or removing a covariate. For each score, two stepwise regressions were performed: -Starting from no variable. -Starting from all variables. Each time, the two stepwise regressions converged to the same model. Twelve variables were used as covariates. Lung function variables were included as continuous (FEV 1 % pred, FVC% pred, functional residual capacity [FRC]% pred) or categorical (inspiratory capacity/total lung capacity ratio [IC/TLC], 25% or 25%) variables. Comorbidities were included as individual variables.
8 The relationship between depression and dyspnea has, however, been less investigated than for anxiety. Sanchez et al recently performed a principal component analysis including dyspnea (MRC and BDI), HRQoL, HAD score, and plethysmography data in a large population of 328 patients with altered ventilatory capacity, including 128 COPD patients. Surprisingly, psychological status and dyspnea appeared as separate dimensions. 28 he authors hypothesized that BDI and MRC only reflected the sensory dimension of dyspnea, thus neglecting its affective component. Conversely, another study also found that depression was a significant contributor to low HRQoL (SGRQ) in multivariate analysis. 23 In our patients, depression appeared much more significantly associated with dyspnea assessed by BDI in multivariate analysis than by mMRC.
Acknowledgments The  Initiatives BPCO study group  consisted of  G Brinchault-Rabin  (Rennes),  P-R Burgel  (Paris),  D Caillaud  ( Clermont-Ferrand ),  P Carré  (Carcassonne),  P Chanez  ( Marseille) ,  A Chaouat  ( Vandoeuvre les Nancy ),  I Court-Fortune  ( Saint-Etienne ),  A Cuvelier  ( Rouen) ,  R Escamilla  (Toulouse),  C Gut-Gobert  (Brest),  G Jebrak  (Paris),  F Lemoigne  (Nice),  P Nesme-Meyer  (Lyon),  T Perez  and the late  I Tillie-Leblond  (Lille),  C Perrin  (Cannes),  C Pinet  (Toulon),  C Raherison  (Bordeaux), and  N Roche  (Paris). This work was funded by unrestricted grants from  Boehringer Ingelheim France  and  Pfizer France.
The so-called MyoTools protocol was approved by the Local Ethics Committee (CPP-Ile de France VI; La Pitié-Salpêtrière). Data of patients with Limb Girdle Muscular Dystrophy type C (LGMD2C) caused by γ-sarcoglycan deficiency were also analysed in order to demonstrate the feasibility and sensitivity of using the dynamometer in subjects with strength weakness. Limb girdle muscle dystrophy was chosen for proof of concept of the use of the device in patients with neuromuscular disease as the disease is characterized by progressive proximal muscle weakness  [15]. Strength of distal muscles was therefore expected to remain stable in a one month period for testretest measurements. The patient data were taken from the protocol Eudract no. 2006-005132-24, ISRCTN no. 22225367 and clinicalTrials. gov no.
Statistical analyses Statistical analyses were performed with the SPSS version 19 software. Results are presented as means ± standard deviation and the limit of significance was set at p < 0. 05. In order to determine whether norms should be specified for each upper limb side or not, significant differences between dominant and non-dominant sides were analysed by a paired t-test in right-and left-handers. Coefficients of predictive equations were determined separately on children younger than 18 years old and adults over 18 years old. In both groups, a stepwise linear regression selected the best predicting variables among sex, age, body mass, height, body mass index, fat mass percentage, forearm circumference, hand circumference and hand length for the natural logarithm of wrist flexion and extension torque.
Normative data Torques on the dominant and on the non-dominant sides were compared to determine whether norms should be specified for each upper limb side or not. The wrist flexion torque was significantly stronger on the dominant side than on the non-dominant side in left-handers but not in right-handers, while wrist extension torque was significantly higher on the dominant side than on the nondominant side in right-handers but not in left-handers (Table  2 ). Because the dominant side was not systematically the strongest side for each function in left-and righthanders, further analyses were performed without taking into account hand dominance and results are presented for each side.
Feasibility in a small patient group Torque measurement of wrist extension was measured in 9 patients with LGMD2C to illustrate the feasibility of the measurements in weak subjects. All patients were wheelchair-bound and right-handed. Patients' age, gender and body mass are presented with height and dominant side data in Table  6. Mean test and retest measurements of wrist extension strength were previously published (Day -30 and Day 0 in Figure  1  of  [16] ). Thirty three percent of the retest measures could not be performed by the same evaluator as the test measurements. Retest values were significantly higher than the test measures with a mean torque difference of 12% (p < 0. 05, n = 18 pooling right and left side measurements). However, the ICC of 0. 879 between retest and test suggests a good correlation of both measurements.