dataesr/scientific-paragraphs-categorization huggingface dataset:
Raw image files were processed by Illumina basecalling Software 1. 7 for with default parameters and the sequences of each individual were generated as 90-bp pair-end reads. Read Mapping on the Human Reference Genome SOAPaligner (soap2. 21) was used to align the cleaned reads to the human reference genome (hg19) with a maximum of three mismatches. The full set of options set was: SOAPaligner -a -b -D -o -u -p -2 -m -x -s 40 -l 35 -v 32. Based on results from SOAPaligner, SOAPsnp was used to assemble the consensus sequence and call genotypes in the target regions. The following options for SOAPsnp were used: SOAPsnp -i -d -o -r 0. 0005 -e 0. 001 -u -L 150 -T -s -2. For detailedinformationonthemeaningof eachof the above options for SOAPaligner and SOAPsnps, we refer to http://soap. genomics.
This implies to study how water penetrates this degraded geometry, which can be described as a hot porous medium. Many analytical experimental programs have been, and still are dedicated to that question. Tutu et al. (1984)  and  Ginsberg et al. (1986)  performed the first small scale experiments (18 kg of debris) on reflooding of single-size spherical particle beds, i. e. , monodisperse beds. Experiments of  Wang and Dhir (1988) , and, later, the SILFIDE program by EdF  (Atkhen and Berthoud, 2006) , allowed to reach larger scales (70 kg of debris) and to simulate residual power by an induction heating. During the late-2000s, more representative conditions were investigated during experiments of DEBRIS  (Rashid et al. , 2008) , POMECO  (Li and Ma, 2011a,b)  and PRELUDE  (Repetto et al. , 2011)  programs, where higher temperatures, up to 1000 • C, were investigated, as well as multi-dimensional effects, for example by use of a lateral by-pass to simulate the effect of nondegraded assemblies in the outer parts of the core.
Nevertheless, and given the high precision of the instrumentation of the CALIDE facility, the existence of Weak Inertial regime is worth investigating, especially considering the lack of experimental data concerning that question in the literature. Quantitative prediction of pressure losses necessitates expressions for coefficients of linear and non-linear terms. In this work, expressions for Darcy (linear) and Strong Inertial (quadratic) terms will be determined. Most often, as it will be discussed later, cubic deviations do not play a significant role, given their magnitude and the range of Reynolds numbers encountered in practice. For that reason, no expression will be sought for coefficient of the cubic term in Weak Inertial regime. In the case of monodisperse beds, a validated model for pressure losses is the Ergun's law  (Ergun, 1952) , widely used in chemical and petroleum engineering  (Nemec and Levec, 2005; Shaban and Khan, 1995) : - ∂P ∂z + g = K U + U 2 , (6) where is the "passability" of the medium, permeability and passability being calculated by: K = ε 3 d 2 h K (1 -ε) 2 (7) = ε 3 d h (1 -ε) , ( 8 ) where ε is the porosity of the bed, d the diameter of the particles and h K and h are the Ergun constants.
Weak inertial regime As explained in the introduction, first pore-scale inertial effects should result in a cubic deviation to Darcy's law, which corresponds to the Weak Inertial regime: -∂P ∂z + g = K U + bU 3. ( 13 ) The Weak Inertial regime has been theoretically derived from Navier-Stokes equations using the Homogenization method  (Mei and Auriault, 1991; Wodié and Levy, 1991; Firdaouss et al. , 1997)  and reproduced by numerical simulations  (Lasseux et al. , 2011; Yazdchi and Luding, 2012) , but never observed experimentally in disordered particle beds. Indeed, experimental observation is very difficult, because of the very low magnitude of the cubic deviation, which is lost in measurement noise. Nevertheless, and given the high precision of the instrumentation of the CALIDE facility, the existence of Weak Inertial regime in tested particle beds is worth investigating, especially considering the lack of experimental data on that question.
Conclusions Motivated by uncertainty reduction in nuclear debris beds coolability, experiments have been conducted on the CALIDE facility in order to investigate single phase pressure losses in representative debris beds. In this paper, experimental results obtained on the CALIDE facility have been presented and analyzed in order to identify a simple single-phase flow pressure loss correlation for debris-bed-like particle beds covering various reflooding flow conditions. On the basis of current literature, Reynolds number in singlephase parts of a nuclear debris bed was estimated to range from Re = 15 to more than Re = 1000, and debris bed granulometry was estimated between a few hundreds microns to 10 mm, with convex high sphericity shapes and porosity of the order of 40%. Experimental data obtained on the CALIDE facility have been interpreted to determine the macro-scale behavior of pressure losses in particle beds.
Fitness was determined using flow cytometry to measure the proportion of YFP tagged and untagged cells in each competition, as described in the electronic supplementary material. This allowed us to scale up our fitness assay and gave equivalent fitness estimates to estimating fitness by plating out colonies overnight. Values of fitness are represented as relative to the ancestral WT fitness level. To test for differences in evolvability between the two genotypes (fitness through time), we analysed our complete fitness dataset using linear mixed-effects models (LME). In this model, fitness was the response value and genotype, antibiotic and time were included as a fixed categorical predictors. Note that time was not considered as a continuous variable as we only sampled four time points. To account for the nonindependence between replicate populations and successive sampling in time, replicate was included as a random component of the model.
In mixed models, model fit was checked by visual inspection of the residuals. We also wanted to know if the levels of antibiotic resistance acquired by P. aeruginosa were affected by the presence of the SOS response, in the short-and the long term. To do so, we challenged initial (ancestral) and final (transfer 23) populations of the WT and LexA strains with increasing (twofold) concentrations of ciprofloxacin (minimum of 50 mg l 21 and maximum of 1600 mg l 21 ) and determined the MIC. The analysis was performed by measuring the optical density at 600 nm (OD 600 ) in a spectrophotometer (BioTek Synergy 2) and recording the minimum antibiotic concentration at which the average of the populations were not able to produce any growth (OD 600 , 0. 1) after 24 h. We analysed MIC differences with a non-parametric Kruskal-Wallis test, all data together, or in pairs when considered interesting and correcting p-values with the Bonferroni correction method for multiple comparisons.
T he Atlantic Meridional Overturning Circulation (AMOC) plays a key role in the meridional heat transport, and in heat and carbon storage in the ocean  1. Changes in the AMOC affect surface oceanic conditions in the North Atlantic (salinity, temperature and sea level), with impacts on marine ecosystems and regional climate (for example, Greenland glaciers  2  ). Understanding the mechanisms driving AMOC decadal variability is therefore critical for climate predictability, particularly in the Northern Hemisphere  [3] [4] [5]. In response to increased greenhouse gas concentrations, climate models project a gradual AMOC slowdown during the 21st century  6, 7. AMOC strength has only recently been monitored through observation networks. Existing data sets do not reveal a strong trend in the 2000s  8, 9  , although a declining tendency appears from the early 2010s  10, 11.
For this purpose, we use available outputs from different climate models using the Coupled Model Intercomparison Project Phase 5 (CMIP5) database  35  complemented by additional simulations performed using one model, and in situ recent oceanic observations as well as longer paleoclimate proxy records of the last millennium. We find that moderate volcanic eruptions may reset a 20-year intrinsic variability mode in the North Atlantic both in model simulations as well as in the data analyzed, leading to interference patterns over the recent period and in the near future.
Results Analysis of the CMIP5 database. We investigate the mechanisms involved in bidecadal North Atlantic and AMOC variations and reduced AMOC variability around the trend during the 2000s using CMIP5 simulations and sensitivity tests conducted specifically with the IPSL-CM5A-LR model  36. Historical climate simulations (1870-2005) driven by natural and anthropogenic forcing archived in the CMIP5 database exhibit a large spread in the simulated AMOC at 48°N (Fig. 1a ). Out of 19 available model simulations, we select the subset of 9 (8 þ IPSL-CM5A-LR called 'Bi-Dec' ensemble) which exhibit peaks of spectral energy in the The black curve is the natural forcing including solar and volcanic eruptions and the red curve represents the anthropogenic forcing including greenhouse gas changes and the anthropogenic aerosols effects.
43) data set (Fig. 2b ). These data sets are compared with the IPSL-CM5A-LR ensemble of historical simulations. Figure  2  shows that the IPSL-CM5A-LR simulations capture the observed 1970 and 1990s GSAs in the Labrador Sea and Eastern subpolar gyre. In this model, an increase in the EGC leads to a decreased salinity in the Labrador Sea, followed by AMOC (in blue) averaged over the eastern subpolar gyre (magenta region). In b,c, the 5-member ensemble mean outputs from historical simulations using IPSL-CM5A-LR are also displayed (black, mean value; grey shaded, s. d. envelope). The horizontal black lines at the top of b,c delimit the 20-year sliding windows for which significant correlation is detected (at the 95% confidence level) between the simulations and the observations (using the Bedford Institute data only in b.
2b ). We therefore hypothesize that this smaller GSA may not have strongly affected convection in the subpolar gyre, with therefore a very weak signature on the AMOC variability, in contrast with the two other GSA. This is however not fully supported by the fingerprint of the 1980s GSA in the EN3 data set, challenging this interpretation. Last millennium perspective. To further assess the validity of the excitation of bidecadal variations by volcanic eruptions, we investigate two high-resolution proxy records and a new IPSL-CM5A-LR simulation spanning the last millennium, which includes several earlier Agung-like volcanic eruptions. This simulation also exhibits a clear spectral peak at the 20-year time scale for the 48°N AMOC variability (Supplementary Fig. 6 ). The first proxy record is an updated compilation of six annually resolved d 18 O reconstructions from Greenland ice cores (see Methods and Supplementary Fig.
While the exact processes at play may be model dependent, the coherence of the Bi-Dec model responses suggest a key role for the propagation of salinity and temperature anomalies as observed during GSA events. The exact path and pattern for this propagation may vary among the models that exhibit bidecadal AMOC variability, including, for example, the strength of the amplifying mechanisms through sea ice and atmosphere interactions, clearly depicted by the IPSL-CM5A-LR model. Further intercomparisons of the precise mechanisms at play within the different models are needed to understand this potential diversity. Nevertheless, the present results provide a good working hypothesis based on the data that is currently available. Indeed, this impact of volcanic eruptions explains several aspects of recent AMOC variations, and associated patterns (for example, GSAs).
Methods CMIP5 analysis. We use the CMIP5 database of historical simulations for the period 1850-2005 (http://cmip-pcmdi. llnl. gov/cmip5/), performed using solar, volcanic and anthropogenic forcings, and pre-industrial control simulations, performed without changes in forcing, to explore the variability of the simulated subpolar AMOC. This analysis is restricted to the 19 models for which the meridional stream function output is available (Supplementary Table  1 ). Based on the information from several paleoclimate archives from the North Atlantic, which consistently show bidecadal variability (see main text), we select the subset of models for which a peak of energy significantly larger than a red noise process is present in the spectral band of 10 to 30 years. A power spectrum analysis (Supplementary Fig.
Salinity data. Observational salinity data have been downloaded from the Bedford Institute of Oceanography website (http://www. bio. gc. ca/science/data-donnees/ base/start-commencer-eng. php). Their analysis is focused on the Labrador Sea area, which is a key for the processes involved in the 20-year variability  20. Since changes in deep convection driving the AMOC can be triggered by salinity anomalies in the Labrador Sea as deep as 300 m, we also consider subsurface data to this depth  20. To evaluate the observational uncertainty, we also use the version 2a of data compilation from EN3 (http://www. metoffice. gov. uk/hadobs/en3), and a SSS data compilation from ships of opportunity and oceanographic cruises  42  for the eastern part of the subpolar gyre. In situ temperature anomalies.
In the IPSL-CM5A-LR climate model, the salinity anomalies are associated with temperature anomalies in the Labrador Sea and all along the subpolar gyre and Nordic Seas  20. This is related to variations of the EGC, which brings cold and fresh water from the Arctic. We check in Supplementary Fig. 4  that, in the data analyzed, the salinity anomalies shown in Fig. 2  have a temperature counterpart (using the same source of data, that is, Bedford Institute of Oceanography and EN3). It is clearly the case in the Labrador Sea, with once again an agreement between historical simulations and temperature data, following the GSAs of the 1970 and 1990s. We note that the GSA of the 1980s is not clear in the Bedford Institute of Oceanography data set and absent in the model. We argue that this GSA is primarily driven by exceptional atmospheric forcing  22.
Our PC is closely related, during the overlapping period, (R ¼ 0. 67, Po0. 05) to the average of five Arctic and Greenland d  18  O ice core records previously used to characterize the bidecadal SST variability in the North Atlantic during the past 700 years  16. Bivalve proxy records. The bivalve data used are described in the study by Butler et al. 49  This proxy is a unique record of bivalve growth north of Iceland, annually resolved over a period of 1,357 years. Radiocarbon measurements taken on absolutely dated shell material have been used to show the co-varying influence of Atlantic and Arctic waters on the North Icelandic Shelf over the past millennium  67. The correlation between growth and observed temperature is positive and weak (0. 217, Po0. 05 using HadISST1 gridded data), suggesting that the bivalves are responding to a nutrient signal that is only partially temperature driven.
Acknowledgements This research was partly funded by the  ANR CEPS Green Greenland  project and by  ANR   MORDICUS  project ( ANR-13-SENV-0002-02 ). It is also funded by the  SPECS  project funded by the  European Commission 's  Seventh Framework Research Programme  under the grant agreement  308378  and by the  EMBRACE  project with Research Number  282672. To analyze the CMIP5 data, this study benefited from the  IPSL Prodiguer-Ciclad facility , which is supported by  CNRS ,  UPMC ,  Labex  L-IPSL, which is funded by the  ANR  ( Grant  # ANR-10-LABX-0018 ) and by the  European FP7   IS-ENES2  project (Grant # 312979 ). We are grateful to the  Bedford Institute of Oceanography ,  EN3  and  Gilles Reverdin  for freely providing their ocean data. We also thank  Laurent Terray  and  Aurelien Ribes  for interesting discussions around this paper and  Vincent Hanquiez  and  Patrick Brockmann  for help with the figures.
IMGT ® is specialized in the immunoglobulins or antibodies, T cell receptors, major histocompatibility proteins of humans and other vertebrate species, and in the proteins of the immunoglobulin superfamily and major histocompatibility superfamily of any species. IMGT ® has been built on the IMGT-ONTOLOGY axioms and concepts, which bridged the gap between genes, sequences and three-dimensional structures. The concepts include the IMGT ® standardized keywords (identification), IMGT ® standardized labels (description), IMGT ® standardized nomenclature (classification), IMGT unique numbering and IMGT Colliers de Perles (numerotation). IMGT ® comprises seven databases, 15,000 pages of web resources and 17 tools, and includes data from fishes to humans, for basic to veterinary, medical and translational research.
IMGT®: the Birth of Immunoinformatics IMGT®, the international ImMunoGeneTics information system®  [1, 2] , was created in 1989 by Marie-Paule Lefranc at Montpellier, France (CNRS and Montpellier University). The founding of IMGT® marked the advent of immunoinformatics, a new science, which emerged at the interface between immunogenetics and bioinformatics. For the first time, immunoglobulin (IG) or antibody and T cell receptor (TR) variable (V), diversity (D), joining (J) and constant (C) genes were officially recognized as "genes" as well as the conventional genes  [3] [4] [5] [6]. This major breakthrough allowed genes and data of the complex and highly diversified adaptive immune responses to be managed in genomic databases and tools. The adaptive immune response was acquired by jawed vertebrates (or gnathostomata) more than 450 million years ago and is found in all extant jawed vertebrate species from fishes to humans.
IMGT® provides a common access to standardized data from genome, proteome, genetics, and two-dimensional (2D) and three-dimensional (3D) structures. IMGT® is the acknowledged high-quality integrated knowledge resource in immunogenetics for exploring immune functional genomics. IMGT® comprises seven databases (for sequences, genes, and 3D structures)  [7] [8] [9] [10] [11] [12]  and 17 online tools  [13] [14] [15] [16] [17] [18] [19] [20] [21] [22] [23] [24] [25] [26] [27] [28] , as well as more than 15,000 pages of web resources (e. g. , IMGT Scientific chart, IMGT Repertoire, IMGT Education > Aide-mémoire  [29] , the IMGT Medical page, the IMGT Veterinary page, the IMGT Biotechnology page, the IMGT Immunoinformatics page)  [2]. IMGT® is the global reference in immunogenetics and immunoinformatics  [30] [31] [32] [33] [34] [35] [36] [37] [38] [39] [40] [41] [42] [43] [44] [45].
(NUMEROTATION). With a focus on IG and TR immune repertoires, we first review the fundamental information generated from these IMGT-ONTOLOGY concepts which led to the IMGT Scientific chart rules, and then IMGT/ HighV-QUEST  [18, 23, 24] , the online web portal for the analysis of rearranged IG and TR repertoire nucleotide sequences from next generation sequencing (NGS). IMGT/HighV-QUEST is the high throughput version of IMGT/V-QUEST  [13] [14] [15] [16] [17] [18]  with the results of the integrated IMGT/ JunctionAnalysis  [19, 20]  and of IMGT/Automat  [21, 22]. IMGT/HighV-QUEST runs against IMGT reference directories built from sequences annotated in IMGT/LIGM-DB  [7] , the IMGT® nucleotide database (176,948 sequences from 347 species in July 2015), and from IMGT/GENE-DB  [8] , the IMGT® gene database (3567 genes and 5258 alleles from 22 species, of which there were 715 genes and 1469 alleles for Homo sapiens and 869 genes and 1319 alleles for Mus musculus in July 2015).
Fundamental IMGT-ONTOLOGY Concepts for NGS IDENTIFICATION: IMGT® Standardized Keywords More than 325 IMGT® standardized keywords (189 for sequences and 137 for 3D structures) were precisely defined  [57]. They represent the controlled vocabulary assigned during the annotation process and allow standardized search criteria for querying the IMGT® databases and for the extraction of sequences and 3D structures. They have been entered in BioPortal [87] at the National Center for Biomedical Ontology (NCBO) in 2010. Standardized keywords are assigned at each step of the molecular synthesis of an IG. Those assigned to a nucleotide sequence are found in the "DE" (definition) and "KW" (keyword) lines of the IMGT/ LIGM-DB files  [7]. They characterize, for instance, the gene type, the configuration type and the functionality type  [57].
DESCRIPTION: IMGT® Standardized Labels More than 560 IMGT® standardized labels (277 for sequences and 285 for 3D structures) were precisely defined  [58]. They are written in capital letters (no plural) to be recognizable without creating new terms. Standardized labels assigned to the description of sequences are found in the "FT" (feature) lines of the IMGT/LIGM-DB files  [7]. Querying these labels represent a big plus compared to the generalist databases (GenBank/European Nucleotide Archive (ENA)/DNA Data Bank of Japan (DDBJ)). Thus it is possible to query for the "CDR3-IMGT" of the human rearranged productive sequences of IG-Heavy-Gamma (e. g. , 1828 CDR3-IMGT obtained, with their sequences at the nucleotide or amino acid level). The core labels include V-REGION, D-REGION, J-REGION, and C-REGION which correspond to the coding region of the V, D, J and C genes.
IMGT structure labels for IG chains and domains allow describing the chain and domains of 3D structures (see for example http://www. imgt. org/3Dstructure-DB/cgi/details. cgi?pdbcode=1HZH). Correspondence between human IG structure labels and sequence labels, links and correspondence between standardized keywords for IDENTIFICATION and standardized labels for DESCRIPTION in IMGT/PROTEIN-DB and IMGT/3Dstructure-DB are detailed in IMGT Scientific chart (see http://www. imgt. org/IMGTScientificChart/SequenceDescription/ IMGT3Dkeywords. html). These labels are necessary for a standardized description of the IG sequences and structures in databases and tools  [58]. Highly conserved amino acids at a given position in a domain have IMGT labels  [58]. Thus three amino acid labels are common to the V and C domains: 1st-CYS (cysteine C at position 23), CONSERVED-TRP (tryptophan W at position 41), and 2nd-CYS (C at position 104)  [60] [61] [62] [63] 65].
IMGT-ONTOLOGY concepts of classification have been entered in the NCBO BioPortal  [87]. The IMGT® IG and TR gene names  [3] [4] [5] [6]  were approved by the Human Genome Organization (HUGO) Nomenclature Committee (HGNC) in 1999  [88, 89]  and were endorsed by the WHO-IUIS Nomenclature Subcommittee for IG and TR  [46, 47]. The IMGT® IG and TR gene names are the official international reference and, as such, have been entered in IMGT/GENE-DB  [8] , in the Genome Database (GDB)  [90] , in LocusLink at the National Center for Biotechnology Information (NCBI) USA  [91] , in Entrez Gene (NCBI) when this database (now designated as "Gene") superseded LocusLink  [92] , in NCBI MapViewer, in Ensembl at the European Bioinformatics Institute (EBI)  [93] , and in the Vertebrate Genome Annotation (Vega) Browser  [94]  at the Wellcome Trust Sanger Institute (UK).
HGNC, Gene NCBI, Ensembl, and Vega have direct links to IMGT/GENE-DB  [8]. IMGT® human IG and TR genes were also integrated in IMGT-ONTOLOGY on the NCBO BioPortal and, on the same site, in the HUGO ontology and in the National Cancer Institute (NCI) Metathesaurus. The Mus musculus IG and TR genes were provided in 2005 to the Mouse Genome Database (MGD)  [95] , following the IMGT® presentation of the 7 mouse IG and TR loci at the 19th International Mouse Genome Conference (IMGC) in Strasbourg, France. Amino acid sequences of human IG and TR constant genes (e. g. , Homo sapiens IGHM, IGHG1, IGHG2) were provided to UniProt  [96]  in 2008. Since 2007, IMGT® IG gene and allele names have been used for the description of the therapeutic monoclonal antibodies (-mab suffix) and fusion proteins for immune applications (FPIA) (-cept suffix) of the WHO-INN programme  [48, 49].