dataesr/scientific-paragraphs-categorization huggingface dataset:
More particularly, despite the short period of the IASI data set available up to now, two noticeable statistically significant apparent trends are inferred from the daily IASI measurements: a positive trend in the upper stratosphere (e. g. 1. 74 ± 0. 77 DU year -1 between 30 and 50 • S), which is consistent with other studies suggesting a turnaround for stratospheric O 3 recovery, and a negative trend in the troposphere at the mid-latitudes and high northern latitudes (e. g. -0. 26 ± 0. 11 DU year -1 between 30 and 50 • N), especially during summer and probably linked to the impact of decreasing ozone precursor emissions. The impact of the high tem-poral sampling of IASI on the uncertainty in the determination of O 3 trend has been further explored by performing multivariate regressions on IASI monthly averages and on ground-based Fourier transform infrared (FTIR) measurements.
, 2010; Barret et al. , 2011; Scannell et al. , 2012; Safieddine et al. , 2013). Here, we use the first 6 years  (2008) (2009) (2010) (2011) (2012) (2013)  of the new O 3 data set provided by IASI on MetOp-A to perform a first analysis of the O 3 time development in the stratosphere and in the troposphere. This is achieved globally by using zonal averages in 20 • latitude bands and a multivariate linear regression model which accounts for various natural cycles affecting O 3. We also explore in this paper to which extent the exceptional temporal sampling of IASI can counterbalance the short period of data available for assessing trends in partial columns. In Sect. 2, we give a short description of IASI and of the O 3 retrieved columns used here. Section 3 details the multivariate regression model used for fitting the time series.
(2015)  for the sake of comparison. Note that because we are not interested here in validating the IASI columns, which was achieved in previous papers (e. g. Dufour et al. , 2012; Oetjen et al. , 2014) , but in validating the trends obtained from IASI, we did not correct biases between IASI and FTIR due to different vertical sensitivity and a priori information. The results are given in DU year -1 in Table  4. We see large significant positive total column trends from IASI at middle and polar stations (e. g. 5. 26 ± 4. 72 DU year -1 at Ny-Ålesund), especially during spring. These values are consistent with those reported in  Knibbe et al. (2014)  for the 2001-2010 period in spring in the Antarctic (around 3-5 DU year -1 ). This trend is not obtained from the FTIR data for which trends are found to be mostly non-significant (even not retained in the stepwise elimination procedure in some cases) as reported in  Vigouroux et al.
In all cases, we observe that the fitted trends inferred from both IASI and FTIR with the same temporal samplings are within the uncertainties of each other and that those associated with the subsampled IASI data sets are significantly larger than those obtained with the daily ones, leading to statistically non-significant trends. Even if validating the IASI fitted trends with independent data sets is challenging due to the short-time period of available IASI measurements and the insufficient number of usable correlative measurements over such a short period, the results obtained for IASI vs. FTIR tend to confirm the conclusion drawn in Sects. 4. 3. 1 and 4. 3. 2, that the high temporal sampling of IASI provides good confidence in the determination of the trends even for periods shorter than those usually required from other observational means.
Acknowledgements. IASI has been developed and built under the responsibility of the  Centre National d'Etudes Spatiales (CNES, France). It is flown onboard the MetOp satellites as part of the EUMETSAT Polar System. The IASI L1 data are received through the EUMETCast near-real-time data distribution service. Ozone data used in this paper are freely available upon request to the corresponding author. We acknowledge support from the  O 3 -CCI  project funded by  ESA  and by the  O3M-SAF  project funded by  EUMETSAT. P. -F. Coheur  and  C. Wespes  are, respectively, Senior Research Associate and Postdoctoral Researcher with F. R. S. -FNRS. The research in Belgium was also funded by the  Belgian State Federal Office for Scientific, Technical and Cultural Affairs  and the  European Space Agency (ESA Prodex IASI Flow  and BO 3 MSAF).
Alleles were scored with GENEMAPPER 4. 0 (Applied Biosystems). For the anther-smut samples, we used a previously published dataset of genotypes produced with a set of 11 microsatellite markers  (Vercken et al. , 2010). The Microbotryum dataset was rendered haploid by randomly removing one allele in the rare cases of heterozygosity  (Vercken et al. , 2010). Indeed, individuals are almost completely homozygous because of high selfing rates  (Hood & Antonovics, 2000; Giraud, 2004; Giraud et al. , 2005; Giraud et al. , 2008) , and this may bias analyses of population subdivision using Bayesian clustering methods assuming random mating. Apparent heterozygosity may in principle result from co-infection of the plant by multiple genotypes  (Tollenaere et al. , 2012) , but previous studies have shown that a given S.
latifolia bud typically hosts a single M. lychnidis-dioica genotype, even in cases of multiple infections at the plant scale  (Lopez-Villavicencio et al. , 2007). For all the analyses we kept only the individuals (both for the fungus and the plant) successfully genotyped at eight loci or more. Our plant dataset contained individuals that were identified by the samplers as S. latifolia (n=592 individuals), S. dioica (n=74) and putative hybrids between these species (n=50). The anther-smut fungi dataset initially contained samples that were identified by the samplers as collected on S. latifolia (n=669), on S. dioica (371) and on hybrids between the two plant species (n=30). We first ran analyses to ensure that our samples all belonged to the species studied, S. latifolia for the plant and M.
Population structure We investigated the population structure within the M. lychnidis-dioicae and S. latifolia species. First, we used the model-based Bayesian clustering methods implemented in TESS v2. 3  (Guillot, 2009). Analyses were performed anew on the genetic marker dataset created for the fungus in our previous study  (Vercken et al. , 2010)  in order to have the same software tools applied on the plant and fungal datasets. We only kept one representative of each genotype in the dataset for clustering analyses and assigned the repeated genotypes to the same cluster as the corresponding representative genotypes. We used the conditional auto-regressive (CAR) Gaussian model of admixture with 200,000 MCMC iterations after a burn-in of 100,000 iterations. We used the CAR Gaussian model of admixture with linear trend surface, setting the spatial interaction parameter (ρ) to 0.
6. These parameters (ρ and trend) affect the weight given to spatial distance when clustering genotypes. TESS outputs were processed with CLUMPP v1. 1. 2 (Jakobsson & Rosenberg, 2007) , to identify distinct clustering solutions in the replicated runs for each K value. We considered that an individual belonged to a given genetic cluster if its membership coefficient for this cluster was superior to 0. 8. As described above, repeated genotypes were not included in the analyses that established cluster assignment per population, but they were added back for subsequent analyses. We also performed population structure analyses not relying on any population genetic model and therefore not affected by deviations from Hardy-Weinberg assumptions, such as those caused by selfing. Principal component analyses (PCA) were performed using the ade4 R package and discriminant analyses of principal component (DAPC) using the adegenet R package with the R software (find.
clusters function). For both the host and the parasite, we tested the existence of isolation by distance (IBD) patterns by assessing the significance of the correlations between matrices of genetic and geographic distances, using a Mantel test (100,000 resamples for the null distribution) as implemented in the ncf R package  (Paradis et al. , 2004). Genetic distances between individuals were calculated using Nei's Da estimator as implemented in POPULATION v1. 2. 32  (Langella, 2002). Geographic distances were computed with TESS (with the option Compute Great Circle Distances). A Mantel test was also performed to evaluate the significance of the correlation between the genetic distances between pathogen individual pairs and plant individual pairs. To investigate the congruence of genetic structure between the host and the pathogen, we performed partial Mantel tests (100,000 resamples for the null distribution) using the ncf R package to assess the significance of the correlation of pairwise genetic distances between pathogen individuals and plant individuals, as above, but removing the effect of the correlation between genetic and geographic distances.
The genetic variation for both plants and pathogens was quantified using the allelic richness, Ar, calculated with ADZE1. 0  (Szpiech et al. , 2008). Expected heterozygosity (H E ), observed heterozygosity (H O ) and F IS were computed using GENEPOP 4. 3  (Rousset, 2008). The statistical analyses on allelic richness and H E (correlations and Wilcoxon rank sign tests) were performed with JMP v9 (SAS Institute) using the value of the statistics per marker.
Population structure and isolation by distance in the fungal pathogen Microbotryum lychnidisdioicae Genetic structure patterns for M. lychnidis-dioicae (n=679) was also investigated using TESS, and gave results very close to the clustering results found in the host plant. Three clusters were differentiated at K=3: one in Western (n=308), one in Southern (mainly Italy, n=160) and one in Eastern Europe (n=178) (Figs. 2a  and 2b ). Only 33 individuals could not be assigned to any cluster, half of which coming from contact areas between two clusters. The Deviance Information Criterion (DIC) decreased steadily with increasing number of K, with no sharp drop (Fig. S2b ). The PCA however showed that the structure at K=3 was the strongest, with three distinct clusters corresponding to those obtained with the TESS software and a separation of the Western cluster by the first axis, then a subsequent split into Southern and Eastern clusters along the second axis (Fig.
Population structure of S. latifolia has previously been investigated using microsatellite markers across Europe, but only the East/West structure had been highlighted  (Keller et al. , 2012). This previous study nevertheless suggested that a finer genetic subdivision was present. In fact, re-analysing this independent dataset, that includes different markers and samples from ours, also reveal the Italian cluster at K>2 (not shown). The biogeographic pattern we found in S. latifolia is typical of the genetic structure originating from contracted range in the south of Europe during the last glacial maximum followed by recolonization northward after climate warming at the beginning of the Holocene. The structure thus indicates that, during the last glaciation, S. latifolia likely survived in three main glacial refugia (the Iberian, the Italian and the Balkan refugia), and then recolonized Northern Europe without much large-scale eastern or western dispersal that would have admixed the different clusters.
Costructure between the host Silene latifolia and pathogen Microbotryum lychnidis-dioicae In M. lychnidis-dioicae, the strongest genetic structure divided the samples into three genetic clusters, as previously reported using the same microsatellite dataset as well as using nuclear gene sequences  (Vercken et al. , 2010; Gladieux et al. , 2011; Gladieux et al. , 2013). These three genetic clusters closely matched those observed in S. latifolia, with the same Southern, Western and Eastern clusters. This indicates that the anther-smut pathogen has remained during the last glaciation in all the three refugia of its host, i. e. , in the Iberian, Italian and Balkan Peninsula, leaving no major disease-free area. Our extensive sampling of host-pathogen pairs from the same individual plants allowed comparing the population structures of the two interacting species at a very fine level across Europe.
Acknowledgments We thank the  Plateforme de Génotypage GENTYANE INRA UMR1095  and  William Fontanaud  for assistance with genotyping. We thank all those who helped with sampling seeds, plant material and pathogens (Vercken et al. , 2010;Gladieux et al. , 2015). This work was supported by the  European Research Council  (starting grant  GenomeFun 309403  to TG), the  Agence Nationale de la Recherche  (grants GANDALF  ANR-12-ADAP-0009  and  Emerfundis 07-BDIV-003 ), and the  National Science Foundation  (grant  DEB-1115765  to MEH).
These sensing elements are embedded into a film of insulating silicon rubber in order to form a soft, sensing layer with a thickness of 0. 9 mm. The sensing layer is then sandwiched between two electrodes; all the electrodes, as well as the conductive trace for signal transmission, are printed on the substrate layer by the screen printing technique. Variations of resistance induced by mechanical stimuli are transformed into voltage output by means of a resistance to voltage converter, digitalised and sent to a PC where they are processed with a MATLAB interface. The sensorised film was tested statically by applying forces perpendicular to its surface in the range between 0 and 9. 8 N, showing a resolution of 0. 15 V/N, an almost linear behaviour and hysteresis between force uploading and downloading of less than 15%.
Recruitment The first patient was randomised on October 2014. Enrolment is ongoing at the time of publication. The recruitment process is planned to fit with routine practice. Potential participants to the trial are identified at the time they attend for diagnosis and treatment choice for their breast cancer in one of the four involved tertiary-care centres. Patients who meet selection criteria receive a brief study presentation and full participant information sheet by a clinician. After selection criteria confirmation and answering to potential further patient questions about the trial, written informed consent is obtained before surgery by the patient's surgeon. Baseline data are collected following consent during the preoperative period.
DATA MANAGEMENT Data is recorded on study specific case report forms (CRFs) via an electronic data capture system (CS Online). To maintain participant's anonymity, CRFs are identified only by a coded patient number and initials. All records that contain patient names or other identifying information will be stored separately from the study records and can be identified only by the coded patient number and initials. A data manager from the INSERM CIC 1415 biometry unit verifies the data and sends queries for missing or inconsistent data.
Economic evaluation A cost-effectiveness study will be performed on the basis of resource use and HRQOL data collected alongside the trial. Direct medical costs will be assessed from the hospital and the payer perspectives in both groups and during the whole follow-up period, that is, 9 months after surgery. For each patient, we will collect the healthcare resource use both in the hospital setting and primary care services. This covers the initial surgical stay (duration of the surgical procedure, number of consumables (drains and sutures), length of stay), subsequent hospital stays due to complications/infections, general practitioners and gynaecologist visits (over a 21-day period following surgery only) and home nursing care visits (over a 21-day period only). To value resources, we will use the following unit costs information: ▸ Hospital stays: diagnosis-related group payment per discharge in the French prospective payment scheme.
▸ Visits: general fee classification (Nomenclature Générale des Actes Professionnels) and the reimbursement rate at the date of analysis. Health states will be valued into utility coefficients using data from the EuroQoL group (European value set). It will allow computing QALYs for each patient in both groups. Costs and QALYs will be compared between the two groups using non-parametric tests. Means and 95% CIs for costs, QALYS and incremental net monetary benefit will be estimated using the non-parametric bootstrap method. Differences in costs and differences in QALYs observed in the bootstrap replicates will be represented in the cost-effectiveness plane. A cost-effectiveness acceptability curve will be computed.
Ethics and dissemination In conformity with the Declaration of Helsinki, all participants will sign a written informed consent form that describes this study and provides sufficient information for patients to make an informed decision about their participation. Consent will be obtained from patients before they undergo any study procedure. Participants may withdraw from the study at any time during the clinical trial without any impact on their care. In that event, data collected prior to participant withdrawal will be used in the trial analysis except if a participant requests removal of all her data from the database. Sponsor of the study may audit trial conduct as deemed appropriate. A formal amendment to the local research ethics committee will be required for any amendments to the study protocol which may impact the conduct of the study, or the potential safety of or benefits to patients will require, if needed an amendment will also be required from the National regulatory Agency for Security of Medicines and healthcare products.
Any protocol amendments will be communicated to investigators and oversight authority but also to trial participants and registries, if deemed necessary. The chief investigator will be given an access to the cleaned data set. Reports will follow international guidelines: CONSORT Statement and Extension of the CONSORT Statement to Randomised Trials of Non pharmacologic Treatment. Research findings will be submitted for publication in peer-reviewed journals regardless of whether or not they are statistically significant. Authors will be individuals who have made key contributions to study design and conduct. Trial findings will also be submitted for presentation at scientific meetings. The study findings will also be presented at relevant national and international breast cancer conferences.
7712 Å. Five initial sulfur sites (three individual methionines plus two cysteines involved in a disulfide bond) were located with the SHELXC/D programs  (69). Using the program Phaser (70), the heavy-atom model was then completed up to 7 sulfur sites, and phases were refined before being improved by density modification using Parrot  (71). The first steps of model building were performed automatically using Buccaneer (72) and completed manually with Coot  (73). The model was finally refined using the high-resolution data set collected at a 1. 1-Å resolution with refmac and Phenix  (74, 75). A crystal of the complex nano-L06/RBP was cryocooled in trimethylamine N-oxide (TMAO) or 10% PEG 600, and a data set was collected at Soleil Proxima 1 (Soleil synchrotron, Saint-Aubin, France) up to a 2.
ACKNOWLEDGMENTS We thank the  Soleil synchrotron (France)  for the beam time allocation (proposal 20140754). This work was supported by grants from the  Agence Nationale de la Recherche  (grants  ANR-11-BSV8-004-01  " Lactophages " and  French Infrastructure for Integrated Structural Biology [FRISBI]). D. van Sinderen is supported by a  Principal Investigator award  (reference no. 13/IA/1953 ) through  Science Foundation Ireland (SFI). J. Mahony  is the recipient of a  Technology Innovation Development Award (TIDA)  (reference no. 14/ TIDA/2287 ) funded by  Science Foundation Ireland (SFI).
e. the cancer-specific hazards λ Ci are the same. The data sets are large (20000 patients each) to enable clear distinction between the random variation and true differences. Since our examples are here to highlight the fact that the measures are not equal, we have chosen the simulation parameters so that these differences are rather obviousthe cancer specific hazard in our sample is not homogeneous, but depends rather strongly on age (excess hazard ratio for age equals 1. 1), the age of the patients is asymetrically distributed between 50 and 80 with the older patients being more common in the sample. The information on the hazard of dying from other causes is obtained from the official Slovene life tables split by sex, age and calendar year.
Further work may be needed to clarify these issues. In practice, a common criterion in choosing a certain method is also the availability of the software. In the relative survival setting, the most recently introduced method is the PP estimator and the inexistence of this method in some of the standard software (e. g. SAS) can be a clear reason for the age-stratified Ederer II method to be used. However, the command availability is not a problem for the R or Stata users. All the methods mentioned in this paper are available in R package relsurv  [15] , in Stata, the commands stns  [16] , strs  [17]  and stnet  [18]  include the required options. In SEER*Stat the PP estimator is under development.
The observations are typically decadal time series of ground-based data from the international Network for the Detection of Atmospheric Composition Change (NDACC) and the CCM simulations are taken from the CCMVal-2 REF-B1 simulations database. The chemical species considered are column O 3 , HCl, NO 2 , and N 2 O. We check the consistency between observations and model simulations in terms of the forced and internal components of the total interannual variability (externally forced variability and internal variability) and identify the driving factors in the interannual variations of stratospheric chemical composition over NDACC measurement sites. Overall, there is a reasonably good agreement between regression results from models and observations regarding the externally forced interannual variability.
Introduction The variability of stratospheric chemical composition occurs on a broad spectrum of time scales, ranging from hours to decades. Some of this variability involves couplings between chemistry and dynamics and, more generally, chemistry-climate interactions and is driven by external forcings (e. g. volcanic aerosols, solar activity, halogen loading (e. g. CFCs and halons), levels of greenhouse gases (GHG)) and modes of climate variability (e. g. QBO, ENSO). These forcings are thought to be responsible for most of the interannual variability in stratospheric chemical composition. Many observational studies have attempted to link variations in stratospheric chemical composition, in particular ozone, to these forcings. Although several studies have considered vertically resolved ozone datasets (e.
g. Zawodny et al. , 1991; Brunner et al. , 2006; Randel et al. , 2011) , the most commonly analysed datasets are satellite column ozone data. Studies of global satellite data records such as TOMS (Total Ozone Mapping Spectrometer) or SAGE (Stratospheric Aerosol and Gas Experiment) have documented the impact of the QBO on column ozone  (Bowman et al. , 1989; Zawodny et al. , 1991; Randel et al. , 1994; Yang et al. , 1994; Baldwin et al. , 2001; Randel et al. , 2007). Tropical column ozone variations were found to be approximately in phase with equatorial zonal wind near 30 hPa, an indicator of the QBO, whereas the extra-tropical column ozone anomalies tended to be out of phase with the tropical signal. Linear regressions showed that the mean amplitude of the QBO signal is about 2-4% of the mean column ozone  (WMO, 1999).