dataesr/scientific-paragraphs-categorization huggingface dataset:
OVIDE data set The   1 ). For both data sets, C T was combined with T , S, nutrients, O 2 and A T to derive the Cant concentration following the ϕC T method. Preindustrial atmospheric CO 2 was fixed at 278. 8 ppm to compute the preindustrial C T  (Pérez et al. , 2008; Vàzquez-Rodrìguez et al. , 2009). This databased diagnostic uses water mass properties of the subsurface layer between 100 and 200 m as reference to evaluate preformed and disequilibrium conditions. An uncertainty of 5. 2 µmol kg -1 on Cant values was estimated from random error propagation in input parameters  (Pérez et al. , 2010). A comparison between different methods used to separate Cant from natural C T in the Atlantic Ocean  (Vàzquez-Rodrìguez et al. , 2009)  and along 24. 5 • N  (Guallart et al. , 2015)  concluded to a good agreement between ϕC T and the other methods.
3 ± 0. 6 µmol kg -1 compared to observation-based estimates (Table  2 ). This deficit is more pronounced in the upper MOC ( Cant model-data = -5. 9 ± 0. 7 µmol kg -1 ) than in the lower MOC ( Cant model-data = -3. 6 ± 0. 6, Table  2 ). The largest difference between model and data (up to -20 µmol kg -1 ; Fig. 6c ) is detected in subsurface waters at the transition between East North Atlantic Central Water (ENACW) and Mediterranean Water (MW) and between the two limbs of the MOC. Figure  6  also reveals an underestimation by the model of Cant levels in lNEADW (below 3500m depth in the western European basin) by 5 to 10 µmol kg -1 , which is in line with a transport of Nordic overflow waters across the OVIDE section close to zero. The variability of the model-data differences at OVIDE (Fig. 6d ) is the largest at the boundary between the upper and lower limbs of the MOC and between 700 and 2000 km off Greenland.
Data availability. All references for the availability of in situ data sets are indicated in the text. A simulation of the model data set can be accessed at: https://vesg. ipsl. upmc. fr/thredds/catalog/ ORCA05-ATLN/catalog. html (ORCA05-PISCES, 2018). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/bg-15-4661-2018-supplement.
We are actually interested not by a strict separation of the two fractions, but rather by an identification of the patches of fines, which are characteristic of active transport. Finally, always using the same cameras, we took pictures for short time intervals at a rate of 50 photos per second, framing a limited area of the flume. Thereby we were able to determine the 2-dimensional water surface velocity field. For this purpose, we dyed water and we employed precise seeding techniques; the method we used to obtain velocity field is the Large Scale Particle Image Velocimetry, in particular we used the software Fudaa-LSPIV  [9, 10]. We conducted an experiment that lasted 60 hours and that was characterized by a configuration of the flume with a slope equal to 3. 18 % and a maximum width of 0.
or Biotrack, weighting 7-11 g, ~9 months of signal). Birds were located by triangulation using a portable Yagi antenna at least twice a week during the breeding period (April to September) and once a week the rest of the year. When individuals were recovered as dead, mortality cause was classified as either due to hunting or other causes. We compiled individual detection histories on a monthly basis to estimate monthly mortality probabilities. Birds recovered dead during the 15 days following the release were excluded from the analysed dataset as this mortality might be induced by the stress of the capture and manipulation  (Bro et al. 1999).
An age effect distinguishing between first-year (FY) and after first-year (AFY) is usually used in survival analysis. However, due to the data collection design (several periods of marking and monthly occasions), we had to use a complex system to code a simple age-effect with two classes. Moreover, the number of FY was relatively low so that complex models may have been over-parametrized. Thus, we decided to make a first model selection without age, to retain the most supported model and then tested for an age-effect only on this selected model. All models were implemented using programme E-SURGE  (Choquet et al. 2009). All parameters were estimated on a monthly basis and are provided with standard errors (± SE). Annual survival (from marking to next breeding season for FY, year-round for AFY) and associated variance were computed based on the delta method  (Seber 1982).
74 ± 0. 02 Pailhès first-year † released female 0. 02 ± 0. 01 male < 0. 01 ± 0. 00 wild female 0. 11 ± 0. 07 male 0. 23 ± 0. 08 after first-year released ‡ female 0. 03 ± 0. 02 male < 0. 01 ± 0. 00 wild female 0. 17 ± 0. 09 male 0. 36 ± 0. 07 † 'Annual survival' of first-year individuals is in fact survival from end of August to April, the beginning of the subsequent breeding season. ‡ Due to the lack of information in our dataset to estimate natural mortality rate of released after first-year individuals, we used those of wild individuals to compute their annual survival rates. than for females (Fig. 2 ). Similar results were found by  Sandercock et al. (2011)  in their study on effects of hunting in willow ptarmigan, where females had a higher risk of harvest mortality than males. Hunter selectivity has been suggested to explain this result  (Sandercock et al.
Acknowledgements -We would like to thank all the people involved in the fieldwork among sites and over years and in particular  Jean-Bernard Puchala ,  Jean-François Mathon ,  Tanguy Le Brun ,  Jean-Christian Favas ,  Christophe Pin ,  Michel Le Pley ,  Grégoire Massez  and  Renaud Blanc. We would also like to thank our partners for their support in this study: the members of the  Office National of Forestry in Luberon  ( Régis Gaudin ,  Claude Moutarde  and  Lionel Kmiec ), the  Natural Park of Luberon , the  Natural Park of Port-Cros , the hunter association of Pailhès ( Daniel Armengol  and  Guy Roudier ) and the  Federation of Hunters of Herault  ( FDC 34 ). We are also grateful to  Nigel Yoccoz  for valuable comments on earlier version of the manuscript.
Draft reference genome of Tisochrysis lutea and comparison with other sequenced Haptophytes In order to analyze the strain improvement program at the genetic level, it was necessary to sequence the Tiso genome. Nowadays, a reference genome is necessary to use many molecular approaches and also provides better knowledge of the evolutionary history of the species. A draft Tisochrysis lutea reference genome was obtained from the sequencing of the three Tiso strains in this study (Tiso-Wt, and the two new clonal strains). Sequencing data of these three strains were combined to obtain the Tiso reference genome, which is thus a consensus of these strains. The sequencing was performed using an Illumina Sequencer HiSeq 2000. The library preparation was performed in matepair mode to optimize genome assembly  [30].
B. Numbers of genes in common or not between the three haptophytes sequenced. Genes between two algae are considered homologous if the protein sequence is similar over more than 30% of its total length. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article. ) sequence or protein domain homologous to ones known in databases. In addition, 2096 genes (17. 7%) had an Enzyme Code (EC) identified. The amount of genes with a putative function identified was similar to the amount for other microalgae sequenced to date (approximately 50%). Tisochrysis lutea belongs to the Haptophyta phylum  [31]  (Fig. 4 ). This phylum is of major importance in aquatic ecosystems, being composed of algae species that are dominant either in freshwater, or in the seas and oceans  [32, 33].
Genomic comparison of the two improved clonal strains The draft genomes of Tiso-Wt, Tiso-S2M2-Cl1 and Tiso-S2M2-Cl2 were sequenced and compared to investigate the genomic shift induced by the improvement program and isolation procedure. The genome of each of the three strains was sequenced independently and the sequenced data for each strain was mapped on the reference genome produced in this study. Polymorphisms (SNPs and small or large insertion/deletions) from loci polyallelic within or between strains were identified using a combination of different software, as described in the Materials and methods section, to eliminate false positive polymorphism. First, polyallelic loci were identified in each strain independently. The number of polyallelic loci was 9301 and 12,935 in Tiso-S2M2-Cl2 and Tiso-S2M2-Cl1, respectively, and 21,869 in Tiso-Wt (Fig.
5 ). The number of polyallelic loci is, unsurprisingly, higher in Tiso-Wt (around 2 fold) than in the clonal strains, due to the presence of several individuals in this strain. Among these data, the estimation of haplotype number was performed with HapCut and Freebayes software  [40, 41]. On average, 53 haplotypes were detected in Tiso-Wt. This number is a preliminary estimation because it is difficult to identify haplotypes with short reads. In the future, using long reads will improve accuracy. Concerning the clonal strains (Tiso-S2M2-Cl1 and Cl2), unsurprisingly, two haplotypes were identified confirming the diploid phase in the life cycle of Tiso. Second, the genomic events which had given rise to the lipid-improved clonal strains (Tiso-S2M2-Cl1 and Tiso-S2M2-Cl2) were sought. Polyallelic loci and monoallelic loci of each strain were compared to identify polymorphic loci between strains (SNPs, small and large insertion/deletions).
Interestingly, SNPs remain the main polymorphism present between all strains (47% on average ± 2%), but large indels have a higher importance (38% on average ± 2) (Fig. 5-B ). This is because only 3. 3% of SNPs have a potential impact on the phenotype, whereas 7. 5% of large indels have one. Large indels are less frequent at the scale of the whole genome (20%), but their likelihood of having an impact on the phenotype is higher than for the SNPs. Thus, in the future, large indels will be studied as carefully as SNPs. The origin of these large insertions was searched for using sequence similarity. Mainly transposable elements were identified (48% of large insertions matched with a transposable element known in the RepBase  [45]  database). Transposable elements are a molecular mechanism well known to respond to stress conditions and play an important role in genome evolution  [43] [44] [45].
In stationary phase, when nitrogen was deficient, two samples were taken (Fig. S1 ). Absence of nitrogen in the culture medium was confirmed by spectrophotometry at 220 nm as described in  [57]. The detailed lipid profiles were analyzed using High Performance Thin Layer Chromatography (HPTLC). The harvested samples (approximately 150 million algae cells in 10 mL) were suspended in 6 mL of Folch solution (methanol-chloroform 2:1). The samples prepared were deposited with an automatic TLC sampler onto a silica plate. Migration was realized in solvent (20 mL hexane; 5 mL diethanol ether; 0. 5 mL acetic acid) with standard lipids (Table  S5 ). The revelation was realized in Chromatogram Immersion Device solution (H3Po4-CuSo4) at 180 °C for 30 min. Identification and analysis of bands was performed with WinCats software.
Sequencing and assembly of the reference genome of Tiso Algal cultures were treated with antibiotics (Sigma N°A5955) to minimize bacterial contamination. The DNA extractions for each strain were performed after the 6 months of the clonal strains isolation. DNA was extracted using a phenol-chloroform method, described in detail in  [59]. For each clone, 10 μg of DNA (Tiso-Wt, Tiso-S2M2-Cl1 and Tiso-S2M2-Cl2) were used for the sequencing, which was performed at BioGenOuest Nantes platform using an Illumina HiSeq2000. The libraries were built using the mate-pair method (2 × 100 b sequenced for a DNA fragment of 2000 bases on average) and the genomes of each clone (Tiso-Wt, Tiso-S2M2-Cl1 and Tiso-S2M2-Cl2) were sequenced independently in different lanes. The raw sequencing data obtained were filtered to conserve only reads with a sufficient quality: First, the raw data were treated using CutAdapt software  [60]  to eliminate Illumina residual adapters (Anywhere adapters in sequence).
Second, the reads were filtered with a homemade script used to conserve only read pairs with a mean sequencing quality score higher than Q30, a length higher than 75 bases for both reads. After filtering was complete, 75% of read pairs were conserved (94, 71, 105 millions of read pairs from Tiso-Wt, Tiso-S2M2-Cl1 and Tiso-S2M2-Cl2, respectively, Table  S1 ). The reference genome of the Tiso species was established by the sum of all the sequences. Assembly was performed using CLC Assembly Cell 4. 0 (launched with default parameters with an insert-size between 1500 and 3000 bases), 13,114 contigs were obtained (for an overall size of 53. 73 Mb). N = 33 contigs were deleted because they were not associating with Tiso but with prokaryotic origins (bacteria or virus sequences were identified in these contigs by BLAST on the nr database).
Gene identification and annotation Identification of genes was performed using the MAKER2 training and annotation pipeline  [62]. MAKER2 was employed using several tools: a) BLASTn  [63]  with Arabidopsis thaliana coding sequences (TAIR-10  [64] ); b) Tophat2  [65]  for aligning the RNAseq data available for Tiso  [14] ; c) BLASTx for aligning all CEGMA (core eukaryotic genes)  [66] ; and d) Augustus  [67]  for models based on the gene structures of Chlamydomonas reinhardtii. All results of these different programs were combined by Maker 2, and a total of 20,582 genes were identified. Functional annotation was performed with BLASTx  [63]  on the nonredundant protein database (nr), with a similarity score higher than 30%. In addition, InterProScan software was used to identify functional domains  [68].
Polymorphism identification Sequence reads obtained independently from each clone (Tiso-Wt, Tiso-S2M2-Cl1 and Tiso-S2M2-Cl2) were mapped onto the Tiso reference genome using Mosaik software (Percentage mismatch: 0. 15; Hash size: 15; Insert size: 1500-300 bases; Algorithm: very sensitive)  [69]. On average, 80% of read-pairs were aligned on the reference genome, 14% were aligned in multi-loci and 6% were not aligned (Table  S2 ). First of all, polymorphisms were identified to determine polyallelic loci from reads mapped independently on reference genome for each strain. An extrapolation at the whole genome level was realized for each strain to compare their numbers. Secondly, polyallelic loci detected in each strain and monoallelic loci in each strain were compared to identify polymorphic loci between strains.
A search for SNPs and small indels was performed using Freebayes software and these alignment data  [41]  (minimal total coverage: 40; minimal allele coverage: 10; pooled-continuous option because we did not know the number of individuals in the Wt strain). In addition, a search for SNP and small indels was also performed using DiscoSNP software  [70]  directly from the reads data (Size of Kmers:21; minimal coverage per read set: 10). DiscoSNP is not biased by an alignment step but it produces more errors in polymorphism positions in the genome. An SNP or small indel in or between strains was validated if it was identified with both programs (74% of SNPs identified by Freebayes and 67% of SNPs identified by DiscoSNP). Putative impacts of SNPs and small indels on gene function were evaluated using SnpEff  [71].
Only the missense variant, codon STOP variant and frameshift variant were considered to have a potential impact on genes. The large insertion polymorphism was identified by alignment of the genomes of each strain on the consensus reference using a parse script to extract each genomic sequence specific to one or two strains. In complement, MiNTheGap software  [72]  (size of Kmers: 21; minimal coverage 10) was used. The origin of the large insertion sequences identified between the strains was searched for on several databases (TAIR-10  [64] , Uniprot  [73] , RepBase  [45] ) by similarity (BLAST software, e-value E -20). All data (annotation, contigs, proteins, and raw data) are available in the SEANOE databank DOI: https://doi. org/10. 17882/47171. Raw data for Tiso-Wt is available in SRR3156597 in NCBI.
Funding This work was funded by the  Agence National de la Recherche  ( ANR-12-BIME-0004 ), Facteur 4 project and the  INRIA  Project  Lab Algae in silico. C. Baroukh was supported by a "Contrat Jeune Scientifique" (CJS) INRA-INRIA  fellowship. There are no non-financial competing interests. The funders had no role in study design, data collection and analysis, the decision to publish or preparation of the manuscript.
A similar ENAD mode is also found in other data sets such as NIWA  41  and ECMWF-Interim 42 ozone (Supplementary Fig. 3 ). Both EOF1 and ENAD mode (and their PC time series) are well reproduced by the SLIMCAT off-line chemical transport model simulation (Fig. 1b, d, f , and Supplementary Fig. 2b, d, f ; see Methods section for the details of the simulations). The correlation coefficient between the simulated PC of the ENAD mode and the vortex shift index (Fig. 2e ) is 0. 75, larger than that derived from observations. The spatial correlation between the ENAD mode and the TCO regressed against the polar vortex shift is up to 0. 90 in the model simulations (Fig. 2b, h ). These similarities allow us to use the SLIMCAT model to attribute the ENAD-related ozone change to dynamical and chemical ozone changes.
Methods Data sets. The TCO and clear-sky UV index data for the period 1980-2012 used in this study are derived from the multi-sensor reanalysis version 2 (MSR2) data set, which assimilates data from several total ozone satellite instruments, including the total ozone mapping spectrometer, the solar backscatter UV instrument and the global ozone monitoring experiment  40. Note that the MSR2 data set only provides public TCO data until 2012, which limits the analysis period in this study to 1980-2012. The National Institute of Water and Atmospheric Research (NIWA) assimilated ozone column data, which has a horizontal resolution of 1°latitude × 1. 25°longitude and covers the time period from January in 1979 to November in 2012, is also employed. More details about the NIWA data can be found in Bodeker et al.
41  For the atmospheric dynamical fields, ERA-Interim reanalysis data sets  42  from the European Centre for Medium-Range Weather Forecasts (ECMWF) are used. The ERA-Interim TCO data are also used in this study. SLIMCAT model. To simulate the stratospheric ozone distribution and its variations under a range of forcings, we use the TOMCAT/SLIMCAT off-line threedimensional chemical transport model  39. This model uses horizontal winds and temperatures from the ERA-Interim reanalysis data set  42. Vertical advection is calculated from the divergence of the horizontal mass flux 39 , and chemical tracers are advected conserving second-order moments  49. Supplementary Figure  1  shows that the time series of model-calculated monthly mean of TCO anomaly averaged over the Eurasian continent is in very good agreement with that derived from the MSR2 data, giving confidence in the ability of the SLIMCAT model to reproduce the long-term variability and trend of TCO over Eurasia.
Acknowledgements Funding for this project was provided by the  National Natural Science Foundation of China  ( 41630421 ,  41575039 ,  41705022  and  41575038 ). J. Z. is supported by the  Fundamental Research Funds for the Central Universities  (lzujbky-2017-4). S. -W. S. was partly supported by  'Climate Change Correspondence Program' of Korea Ministry of Environment. We acknowledge the data sets from the MSR2 and ECMWF. We would like to thank  Dr. Greg Bodeker  of  Bodeker Scientific , funded by the  New Zealand Deep South National Science Challenge , for providing the combined TCO (NIWA ozone) database. The SLIMCAT modelling work was supported by the  UK National Centre for Atmospheric Science (NCAS). W. F. acknowledges the support of  NCAS  fund. We also acknowledge the CCM modelling groups for making their simulations available for this analysis, the joint  WCRP SPARC/IGAC Chemistry-Climate Model Initiative  ( CCMI ) for organizing and coordinating the model data analysis activity, and the  British Atmospheric Data Centre (BADC)  for collecting and archiving the CCMI model output.
N. L. A. and A. T. A. acknowledge that their work used the  ARCHER UK National Supercomputing Service  (http://www. archer. ac. uk) and the MONSooN system, a collaborative facility supplied under the  Joint Weather and Climate Research Programme , which is a strategic partnership between the UK Met Office, and N. B. and F. M. C. were supported by the  Joint UK BEIS/Defra Hadley Centre Climate Programm  ( GA01101 ). N. B. and S. B. were also supported by the  European Commission's Seventh Framework Programme , under grant agreement no. 603557 ,  StratoClim  project. S. B. acknowledges funding by the LABEX L- IPSL  project (grant  ANR-10-LABX-18-01 ). The EMAC simulations have been performed at the  German Climate Computing Centre (DKRZ)  through support from the  Bundesministerium für Bildung und Forschung (BMBF) ,  Germany.
Code availability. The code of TOMCAT/SLIMCAT is available through the URL: http://www. see. leeds. ac. uk/research/icas/research-themes/atmospheric-chemistryand-aerosols/groups/atmospheric-chemistry/tomcatslimcat. Maps and plots were made with the NCAR Command Language (version 6. 4. 0) (software), 2017. Boulder, Colorado: UCAR/NCAR/CISL/TDD. https://doi. org/10. 5065/ D6WD3XH5. Data availability. The original observational data are publicly available and can be downloaded from the corresponding websites (MSR2 TCO data: http://www. knmi. nl/kennis-en-datacentrum/publicatie/multi-sensor-reanalysis-of-total-ozone; http://www. temis. nl/protocols/O3global. html; NIWA TCO data: http://www. bodekerscientific. com/data/total-column-ozone; ERA-Interim data: https://www. ecmwf. int/en/forecasts/datasets/reanalysis-datasets/era-interim; Hadley Centre Sea Ice data set: https://www.
Conclusion In this work, torsion fatigue behavior of 100Cr6 steel in very high cycle regime was studied using an ultrasonic torsional fatigue machine. The fatigue test results indicate high scatter of obtained fatigue data due to the presence of a large number of inclusions in MnS. Fatigue crack initiation started at the periphery surface at higher stress amplitudes. However, there is a transition of crack initiation site from surface to subsurface when the stress level was decreased and just slightly higher than very high cycle fatigue limit. Fatigue crack initiation of most specimens is linked to subsurface inclusion failure. As for the fatigue crack propagation, the fracture surface is rather smooth during the very first slow crack propagation stage. The overall crack orientation appears to follow a spiral shape on a plane with an orientation of 45º with respect to the loading direction, which corresponds to the maximum principal stress plane.
Acknowledgements This data article is issued from  M. Kakou Charles  KINIMO's Thesis. Part of sample storage and pretreatment was performed at  Centre de Recherches Oceanologigues (Abidjan, Cote d'Ivoire). Authors are thankful to  Emmanuelle Viard  and  Christine Devouge-Boyer ,  Normandie Université, COBRA ,  UMR CNRS 6014 et FR 3038 ,  Université de Rouen et INSA de Rouen , 1 rue Tesnière, 76821  Mont Saint-Aignan Cedex, France  for laboratory analyses.