dataesr/scientific-paragraphs-categorization huggingface dataset:
Other examples of coupled model applications that use OASIS3-MCT can be found on the OASIS3-MCT coupled model page 2. The underlying software was refactored significantly in OASIS3-MCT to improve parallel performance and coupling capabilities. MCT serves as a key part of the OASIS3-MCT implementation and provides parallel capabilities for coupling operations. OASIS3-MCT_3. 0 also provides new capabilities to couple fields within a single component running on concurrent, overlapping, or partially overlapping processes. This increases the flexibility of OASIS3-MCT significantly and provides a mechanism for coupling data between different decompositions or grids within a single model, among many other things. OASIS3-MCT can now be used as a 2 https://portal. enes. org/oasis/oasis-dedicated-user-support-1/ survey-on-coupled-models-using-oasis-march-2016/ coupled-models-using-oasis coupling layer for components running sequentially, concurrently, or both; for single or multiple executable execution; to exchange coupling fields defined on a subset of the component tasks; and to support features like a separate I/O component included in the executable but not involved in the coupling.
In the current version, I/O is done via a gather and/or scatter to/from a root task and data are written in serial from the root task. This is likely to eventually lead to memory and performance issues. Finally, better support within OASIS3-MCT for shared memory threading (i. e. , OpenMP) and on various multi-core architectures is likely to become more important in the future. In summary, OASIS3-MCT_3. 0 is the latest released version of the OASIS coupler. OASIS3-MCT extends the wellused OASIS software with backwards compatibility with regard to usage, but has an entirely new implementation internally. It provides the functional capability to couple highresolution structured or unstructured grids at high core counts successfully and should serve the community well for the next several years.
Acknowledgements. This research was supported by the  ESi-WACE H2020   European  project, grant agreement no. 675191  (www. esiwace. eu),the IS-ENES2 FP7 European project, contract number  312979  (https://verc. enes. org/ISENES2),and the  CON-VERGENCE  project funded by the  French National Research Agency :  ANR-13-MONU-0008. Edited by: Claire Levy Reviewed by:  Moritz Hanke  and two anonymous referees.
Code availability. The OASIS3-MCT source code is available for use and testing after registration at https://portal. enes. org/oasis/download. The SVN command line to download OASIS3-MCT_3. 0 is "svn checkout https://oasis3mct. cerfacs. fr/ svn/branches/OASIS3-MCT_3. 0_branch/oasis3-mct" (last access: September 2017). The OASIS3-MCT_3. 0 source code is also available as a tar file at ftp://ftp. cerfacs. fr/pub/globc/exchanges/ distrib-oasis/oasis3-mct. tar. gz (last access: September 2017).
ACKNOWLEDGMENTS The authors are grateful to  Dr. Rémy Bordonné  for his valuable collaboration and comments on the manuscript. Work in the authors' laboratory is supported by the  University of Malta , the  ALS Malta Foundation , and the  Malta Council for Science and Technology. ML is supported by the  Endeavor Scholarship Scheme (Malta) , part-financed by the  EU-European Social Fund  under Operational Programme II-Cohesion Policy  2014-2020 , "Investing in human capital to create more opportunities and promote the well-being of society. ".
The latter two isolates exhibited resistance to gentamicin and kanamycin which was not observed with the four isolates harbouring OXA-68. Sequence-based typing using multiplex PCR  (Turton et al. , 2007)  and rep-PCR analyses  (Zander et al. , 2012)  did not suggest a close relationship of the chicken isolates to any of the international clonal lineages (ICs) highly prevalent in hospitals worldwide (data not shown). In addition, 22 pooled faecal samples representing 10 flocks of chicken were sampled in Germany. None of these faecal samples yielded A. baumannii.
Isolation of A. baumannii from geese We took choana swabs from 40 goslings from a single farm in Germany in the year 2013, and isolated A. baumannii from three of these chicks (7. 5%). The three isolates (designated 3. 3A, 3. 5D and 4. 1V) carried different variants of the bla OXA-51-like gene encoding OXA-314, OXA-71 and OXA-95, respectively (Table  1 ). ApaI macrorestriction analysis and sequence-based typing according to  Turton et al. (2007)  did not indicate a close relationship to international clones IC1-3 (data not shown). Antimicrobial resistance testing of one of the isolates (4. 1V) showed intermediate susceptibility to kanamycin and resistance to sulfamerazine and sulfamerazine-trimethoprim suggesting a history of selection pressure of this strain in the context of livestock production.
Stork sampling in western Poland in the year 2014 To confirm our findings we repeated sampling of white stork nestlings in western Poland in the year 2014. We collected 87 choana swabs from 87 individuals and isolated A. baumannii from 11 nestlings (13%) (Table  1 ). A. variabilis was isolated from 24 individuals (30%). In addition, we identified four isolates of Acinetobacter pittii, another clinically relevant species which we had not identified in the previous year. Again, we observed a distinct diversity among the 11 isolates of A. baumannii (Table  1 , no. 45-55) as indicated by the finding of nine different bla OXA-51-like variants at the translational level. All 11 isolates including six isolates recovered from three pairs of siblings were distinguishable on the basis of ApaI macrorestriction analysis (data not shown).
Diversity of the bla OXA-51-like gene Overall, the diversity of the bla OXA-51-like gene sequenced from stork isolates was high. Among the 47 isolates (including those obtained from pellets) isolated in 2013 and 2014 we identified a total of 30 variants of bla OXA-51-like on the translational level and 19 of these were novel in a sense that they were not represented by any database entry accessible via BLAST (see Table  1  for GenBank accession numbers). Only a single bla OXA-51-like gene was found to be non-functional due to introduction of an internal stop codon (Table  1 , isolate 261/1C).
Avian isolates exhibit OXA variants known from international clonal lineages It was described that typing of clinical isolates of A. baumannii belonging to international clones (IC) by rep-PCR and multilocus sequence typing (MLST) correlates well with specific variants of bla OXA-51-like  (Zander et al. , 2012; Pournaras et al. , 2014). Here, we have identified a number of bla OXA-51-like genes in avian isolates encoding protein variants such as  265, 268, 269, 271  and 290 known to be associated with clinical A. baumannii lineages IC1, 3-6 and 8  (Zander et al. , 2012). However, on the nucleotide level none of the avian isolates carried a bla OXA- 51-like gene identical to any database entry. Next, we have typed representatives of these OXA-variants by rep-PCR but found no significant similarity to any of the clinical A.
Bacterial species identification Speciation of isolates recovered from CHROMagar Acinetobacter was based on PCR detection of bla OXA-51-like  (Turton et al. , 2006a) , partial 16S rRNA gene sequencing  (Weisburg et al. , 1991)  and partial rpoB sequencing using primers Ac696F and Ac1598R as described  (Nemec et al. , 2009). To determine bla OXA-51-like variation the coding region was fully sequenced as described  (Zander et al. , 2012). New OXA-51 variants were deposited at GenBank (see Table  1 ) and registered at Lahey database now hosted at NCBI (https://www. ncbi. nlm. nih. gov/pathogens/beta-lactamasedata-resources/).
Illumina sequencing Libraries for Illumina short read sequencing were prepared from 1 ng of extracted DNA utilizing the Nextera XT DNA Library Prep Kit according to the manufacturer's recommendations (Illumina Inc. , USA). Sequencing was carried out in paired-end (2 3 300 bp) on a MiSeq benchtop instrument. The whole genome shotgun project of 18 avian isolates has been deposited at DDBJ/ENA/GenBank under the accession of bioprojects PRJNA326058 and PRJNA390481.
rep-PCR Epidemiological typing of isolates was performed by rep-PCR (DiversiLab System; bioM erieux, N€ urtingen, Germany) following the manufacturer's instructions. Results were analysed with the DiversiLab software using the Pearson correlation statistical method to determine distance matrices and the unweighted pair group method with arithmetic averages (UPGMA) to create dendrograms. Isolates that clustered below the threshold of 95% were considered unrelated  (Higgins et al. , 2012). Representative isolates belonging to A. baumannii international clones 1-8 were included as controls  (Higgins et al. , 2010).
Acknowledgements We would like to thank our colleagues at the central DNA sequencing lab (ZBS 1) as well as  Sybille M€ uller-Bertling  for excellent technical assistance. G. W. , P. G. H. and H. S. acknowledge financial support from the  Deutsche Forschungsgemeinschaft  ( DFG-FOR 2251 ). S. G. and S. P. S. were funded by a  R egion Rhône-Alpes   ARC1  Sant e fellowship and a  FINOVI Young Researcher grant. This publication made use of the Acinetobacter baumannii MLST website (http:// pubmlst. org/abaumannii/)hosted at the  University of Oxford (Jolley and Maiden , 2010). The development of this site has been funded by the  Wellcome Trust.
MATERIALS & METHODS Table  1  summarizes host species of biting females of species of Culicoides identified using molecular methods and animal baits. In order to exclude variability of morphological characters, we use an Interactive Identification Key for Culicoides  (Mathieu et al. , 2012). The raw dataset included 12 morphological characters (Table  2)   12 ) Eyes-Inter Ocular-Space-Shape (H02). For the size of the maxillary palpus, only a single sensory pit was used. However, if a Culicoides specimen had multiple irregular pits then we classified them as a small opening. All specimens in this study present a sensilla coeloconica in segment III. Therefore, a new group (Segment-IV-X) has been drawn according to  Talavera et al. (2015). Finally, Culicoides species were classified into ornithophilic (O) and mammalophilic (M) or ornithophilic/mammalophilic (O, M) according to their host species (Table  1 ).
For the statistical analysis, the morphological characteristics and species classification are coded as qualitative variables (see Data S1). Therefore, quantitative methods such as the Principal Coordinates Analysis (PCO) or Nonmetric Multidimensional Scaling (NMDS) are not applicable here. Multiple correspondence analysis (MCA) is a data analysis technique for qualitative variables  (Greenacre & Jörg, 2006) , to obtain maps showing the distances between the qualitative variables and the observations. MCA was performed with XLSTAT software and used to explore the correlation structure between morphological characteristics and host preference. Within the indicator matrix, the rows represented individuals and the columns represented categories of the variables. Correspondence   Viennet et al.
The first axis was found to be the most important dimension, the second axis the second most important, and so on, in terms of the amount of variance accounted for. All Culicoides species (Data S1) were first coded into a 12, 10 or 7 morphological characteristics indicator matrix and analyzed by MCA to obtain the discriminant factors. All species were then projected in order to indicate the species preferences. The MCA map showed that the inertia for the two first dimensions is >70%. So, 3 groups were obtained into three ellipses, based on the F1 and F2 axis. The ellipses were built with the average of each group ± 1 SD (standard deviation), includes about 68% of the observations. A student test was used to compare the several categories (M vs. M/O and O vs. M/O).
RESULTS Overall, 53 species were investigated. Five species  (C. duddingstoni, C. minutissimus, C. reconditus, C. salinarius and C. truncorum)  are ornithophilic, 27 are mammalophilic and 21 are ornithophilic/mammalophilic (Fig. 1 ). Multiple correspondence analysis locates all the categories in a Euclidean space. The MCA map showed that the inertia for the two first dimensions is ranging to 74% and 90% (Fig. 2 ). The first dimension explained more of 59% of data variability, and the categories are mainly organized along this axis. In the Fig. 2 , each point (red, green, blue) corresponds to a Culicoides species category and several species could be plotted in the same point. A first MCA allowed a separation between ornithophilic (O), mammalophilic (M) and ornithophilic/mammalophilic (O/M) (results not shown but very similar to those of the Fig.
blinding The patients and the technicians will know the order of the tests (HFNT or control) at the first CWRET. The analysis of the data recorded continuously during the tests (RR, CR, SpO 2 , PtCO 2 , muscle oxygenation) will be carried out by technicians who will be blinded to the group. The final statistical analysis will be carried out by a person who will be blinded to group allocation. data management and statistical analysis Prism 5 (GraphPad, La Jolla, California, USA) software will be used for the statistical analysis. The results will be expressed as means with SD or medians with IQRs depending on their distribution (respectively normal or not normal). Comparison of the TLim (primary outcome) and physiological responses (secondary outcomes) between the two conditions will be carried out using the method described by Wellek and Blettner 20 in order to evaluate (1) the effect of treatments (HFNT vs control), (2) the effect of treatment sequence (HFNT first vs control first) and (3) the firstorder carryover risk.
Physiological variables will be calculated by subtracting the baseline value from the value at the end of each session (HFNT and control). For all tests, significance will be set to p≤0. 05. Data will be analysed as intention to treat. In the Intention-totreat analysis, if the percentage of missing data is ≤5%, data will be replaced by simple imputation (replacement of the missing value by the median of the available values for that variable). If 5%-20% of data are missing, they will be replaced by multiple imputations using a regression model. 21 hIcs And dIssEMInAtIon Ethical approval to conduct the study has been obtained from the Ethics Committees Nord-Ouest III, Caen, France. In conformity with the Declaration of Helsinki, all participants will be recruited to participate voluntarily and they will sign a written informed consent form.
By employing approximations derived from more detailed numerical simulations, and empirical calibrations from data, the semi-analytical approach has offered a computationally inexpensive route to probing the phenomenology of galaxy formation and, in particular, the theoretical analysis of today's large survey datasets (e. g. Cole et al. 2000; Hatton et al. 2003; Bower et al. 2006; Somerville et al. 2012; Overzier et al. 2013; Bernyk et al. 2016). A significant recent advance has been the advent of full hydro-dynamical simulations in cosmological volumes which, while more computationally demanding than the semi-analytical approach, evolve the dark matter and baryons self-consistently. These simulations provide predictions for the gas and baryonic components of galaxies at high spatial resolution (on ∼kpc scales) and, while sub-grid prescriptions are still needed, these are applied on ∼kpc scales rather than at ∼ 100 kpc scales as in the semi-analytical approach.
The cosmological box sizes of the current generation of hydro-dynamical simulations offer, for the first time, detailed survey-scale predictions that can be compared to contemporary datasets across a large fraction of cosmic time (e. g. Devriendt et al. 2010; Schaye et al. 2010; Dubois et al. 2014; Vogelsberger et al. 2014; Schaye et al. 2015; Khandai et al. 2015). In concert with current and forthcoming observational data, these simulations will, over the next few years, play a central role in advancing our understanding of the key processes that drive galaxy evolution, particularly in the (still poorly understood) high-redshift Universe. It is, therefore, important to study the reproduction of galaxy properties in such models to establish whether they provide a reliable framework for interpreting current and future observational datasets.
in prep). In this work, we study the predicted evolution, in Horizon-AGN, of quantities that are sensitive to the aggregate star formation history of the galaxy population: luminosity and stellar mass functions, the star formation main sequence, rest-frame UV-optical-near infrared colours and the cosmic star formation history. By comparing these predictions to an array of corresponding observational data in the redshift range 0 < z < 6, we explore how well the simulation captures the evolutionary trends of observed galaxies, and probe its usefulness as a tool to investigate the processes that drive that evolution. This paper is organized as follows. In Section 2, we de-scribe the simulation and the methodology used for the prediction of galaxy luminosities, stellar masses and rest-frame colours.
2001; Graham et al. 2005). While many observational studies use Petrosian apertures, other variants, such as  Kron (e. g. Kron 1980)  or modified Sersic (e. g. Bernardi et al. 2013 ) apertures can also be employed, leading to small shifts in galaxy luminosities (e. g. Graham & Driver 2005). However, these offsets are typically lower than the statistical uncertainties. We note that, ideally we would like to quantify the effect of dust on galaxy SEDs via a full radiative transfer approach. While processing all model galaxies using this approach is prohibitively time-consuming, we check, in Section 3 below, whether using a dust screen is a reasonable assumption to make in our calculations. We perform this check by using SUNRISE  (Jonsson 2006; Jonsson et al. 2010 ), a Monte-Carlo radiative transfer code applicable to arbitrary dust geometries, on a small random sample of galaxies.
However, some points of tension with the data are worth noting. In the local Universe (z ∼ 0. 1), the simulation does not reproduce all observational datasets equally well, overshooting the  Eke et al. (2005)  data at both the low and high luminosity ends. While the predicted data points generally fall within the observed ranges, the predicted luminosity functions are slightly steeper than their observed counterparts Figure  2. Comparison of the predicted K (left-hand column) and r-band (right-hand column) luminosity functions from Horizon-AGN to observational data. The solid lines show dust-attenuated luminosity functions predicted by Horizon-AGN, while the greydotted curves show their unattenuated counterparts. Note that, given its low sensitivity to dust, the rest-frame K-band luminosity function with and without extinction are almost identical -we only show the dust-reddened K-band luminosities here.
Observational data is shown using the coloured hatched regions (see legend for the individual datasets used). c 0000 RAS, MNRAS 000, 000-000 The Horizon-AGN simulation 7 c 0000 RAS, MNRAS 000, 000-000 The Horizon-AGN simulation 9 at all redshifts. The difference in steepness is most apparent at z ∼ 1. 7 where it causes the predictions to overshoot the data at high luminosities and undershoot them at low luminosities. Indeed, the tendency to overshoot at low luminosities mirrors a trend seen in the mass-function analysis for low-mass galaxies, and we return to this point in Section 4 below. Nevertheless, the generally good reproduction of the evolving K-band luminosity function, within the observational uncertainties, indicates that, on average, the aggregate mass growth of galaxies over cosmic time is well reproduced by the simulation (this is also borne out by the mass function analysis presented later in this study).
Similar trends are seen in the comparison of the predictions to the observed r-band luminosity functions. While the model predictions agree with data within observational uncertainties, the overproduction of galaxies at the low-luminosity end is also apparent in the r-band filter. We proceed by comparing the 'star-formation main sequence' (i. e. the star formation rate plotted against stellar mass) predicted by Horizon-AGN to observational data in the redshift range 0 < z < 6 (Figure  3 ). Comparison to the observed main sequence probes whether the instantaneous star-formation activity in the simulation is consistent with observations. Performing this exercise over a large redshift range then offers insights into how well Horizon-AGN predicts stellar mass growth over cosmic time, compared to what is observed in the real galaxy population.
We next compare the predicted evolution of rest-frame colours in Horizon-AGN to observational data. To perform this exercise in a consistent way across redshift, we use observational data from a new homogeneous multi-wavelength catalogue of a single area of sky: COSMOS2015 (Laigle et al. submitted). The COSMOS2015 catalog contains 30 bandphotometry, from UV to IR (0. 25-7. 7µm) of more than half a million objects in the 2 deg 2 COSMOS field. It includes the optical datasets from previous releases  (Capak et al. 2007; Ilbert et al. 2009) , the new Y -band data taken with the Subaru Hyper-Suprime-Cam (PI: Guenther), new nearinfrared (NIR) data from the UltraVISTA-DR2 survey and IR data from the SPLASH program (P. L. Capak et al. in prep. ). The photometry for the COSMOS2015 catalog is extracted using SExtractor  (Bertin & Arnouts 1996)  in dual image mode.
The detection image is the chi-squared sum of four NIR images from UltraVISTA-DR2 (Y, J, H, Ks) Figure  5. Comparison of the predicted magnitudes in the N U V (top), r (middle) and J (bottom) band filters, using SUNRISE (full radiative transfer) and SUNSET (dust screen in front of each star particle), on a small random sample of Horizon-AGN galaxies at z ∼ 0. 1. The offset in these predicted magnitudes (∆) is shown in the top section of each panel, with ∆ ≡ MSUNRISE -MSUNSET. and the optical z ++ band image from Subaru Suprime-Cam. Photometric redshifts, rest-frame magnitudes and stellar masses are computed using SED fitting via the LePhare code  (Arnouts et al. 1999 (Arnouts et al. , 2002;; Ilbert et al. 2006 ), following the methodology described in  Ilbert et al. (2013). Comparison to published spectroscopic redshifts of galaxies in the COSMOS field indicates a precision of 0.
2014; Herrera-Camus et al. 2012). In a similar vein, the extinction law (assumed in this study to be Milky-Way-like), may also vary as a function of galaxy properties like age and metallicity (e. g. Buat et al. 2012). The potentially large unknowns in the treatment of dust makes the prediction of colours, especially those involving shorter wavelengths, uncertain. In particular, the current dearth of observational data at high redshift makes it difficult to ascertain whether the properties of dust in our local neighbourhood can be blindly extrapolated to early epochs. In that sense, the rest-frame UV-optical-near infrared colour space may not be the best test of the reliability of the model. Nothwithstanding the dust-related uncertainties outlined above, our analysis indicates that Horizon-AGN produces good agreement with the redshift-evolution of galaxy luminosity functions, the star-formation main sequence and the bulk of the rest-frame UV-optical-near infrared colour space.