dataesr/scientific-paragraphs-categorization huggingface dataset:
In this paper, we first contextualize the physical background of the GEOVIDE cruise to help in the interpretation of the distribution of TEIs in the eastern SPNA. Other work dealing with TEI distribution is also published in this Biogeosciences GEOVIDE Special Issue:  Cossa et al. (2017) ,  Lemaître et al. (2017 ), García-Ibáñez et al. (2017) , and others. Subsequently, through the analysis of the GEOVIDE cruise data along with altimetry, oceanic database, and airsea flux data, we disentangle the causes of the anomalous thermohaline properties of the surface and intermediate layers of the eastern SPNA in May-June 2014. The paper is organized as follows. Data and methodology are described in Sect. 2. Section 3 displays the main results on the large-scale and mesoscale patterns of the circulation and thermohaline anomalies in 2014, setting the GEOVIDE TEI stations in this context.
GEOVIDE data The GEOVIDE cruise  (Lherminier and Sarthou, 2017)  was the French contribution to the GEOTRACES program (http:// www. geotraces. org/) in the North Atlantic. It was carried out onboard the French R/V Pourquoi Pas? from 15 May 2014 to 30 June 2014. A total of 78 stations were measured and sampled along two hydrographic sections: (i) the seventh repetition of the OVIDE section (from Portugal to Greenland (http://www. umr-lops. fr/Projets/Projets-actifs/OVIDE) and (ii) a section across the southern Labrador Sea between Cape Farewell and Newfoundland. In this paper we only deal with data from the OVIDE section. Because this cruise was inserted into the GEOTRACES project, a large number of parameters were measured, some of them present in the ocean in very low concentrations. Therefore, several rosette casts (up to nine) had to be done at some stations; the full-depth cast with salinity and oxygen samples was always used for the physical characterization of water masses and currents.
The velocity errors were given by the resulting covariance matrix from the box inverse model. For more details about the inverse model configuration specific to OVIDE, see  Lherminier et al. (2007 Lherminier et al. ( , 2010) )  and  Gourcuff et al. (2011). The volume transports were computed by multiplying velocities by the distances between two stations. Their errors were obtained from the full covariance matrix of velocities, taking into account error correlations as explained in  Mercier (1986). For the computation of transport across the OVIDE section from GEOVIDE data, the first challenge was to determine the proper spatial subsampling. In order to select the station positions and minimize the error associated with the subsampling, a sensitivity analysis was performed with the data from the 2010 OVIDE cruise before the GEOVIDE cruise was carried out.
As expected, mesoscale and ageostrophic structures of horizontal sizes smaller than the distances between stations are visible on the S-ADCP section but are not resolved in the inverse model solution (e. g. , between stations 45 and 38 or between stations 32 and 27). However, because the geostrophic velocity is an average between stations, this does not imply any bias in the transports. This outcome is also supported by  Gourcuff et al. (2011) , who compared altimetry and S-ADCP data and showed that the contributions of ageostrophic motions tend to cancel out when averaged over the distance between stations. The inverse model estimates the absolute geostrophic transport and the transport of heat and other tracers. The under-sampling of the GEOVIDE cruise notably increases the errors associated with the transport of tracers because the horizontal gradients of those tracers are less well resolved.
Oceanic database We used the in situ analysis system (ISAS;  Gaillard et al. , 2016) , which based on Argo profiles and other qualified in situ observations (cruises, fixed-point time series, ships of opportunity, etc. ) produced monthly gridded fields of temperature and salinity profiles by using optimal interpolation for the period since 2002. We also used EN4 reanalysis. Similar to ISAS, EN4 reanalysis is an optimal interpolation that incorporates in situ data measured since 1900, filling gaps by extrapolation from the observational data using covariances from the Hadley Centre model  (Good et al. , 2013). We also used the temperature and salinity analysis developed by JAMSTEC  (Hosoda et al. , 2008) , which is also an optimal interpolation based on Argo profiles, the Triangle Trans-Ocean Buoy Network (TRITON), and other in situ observations.
First, we evaluated the temporal and horizontal extension of the potential temperature (θ ) and salinity (S) anomalies detected in the surface layer from ISAS: both properties were averaged between 20 and 500 m at each ISAS grid point in the North Atlantic, and monthly anomalies were then estimated with respect to the 2002-2012 mean values. Second, the ISAS, EN4, and JAMSTEC databases were used to evaluate the heat and freshwater content changes in the upper 1000 m in the region delimited by 40-60 • N and 45-10 • W: for each month the heat content (HC month ) and the freshwater content (FWC month ) of the volume of water in the box previously defined was estimated following Eqs. (  1 ) and (2), respectively: HC month = z=n z=1 i=m i=1 θ z,i • Cp z,i • ρ z,i • V z,i , (1) FWC month = z=n z=1 i=m i=1 (35 -S z,i ) • V z,i 35 , (2) where z and i are the depth levels and grid points of the database, and Cp z,i , ρ z,i , and V z,i are the heat content capacity, density, and volume of each depth level and grid point of the database.
Air-sea flux data In order to evaluate the role of atmospheric forcing on the θ and S anomalies observed during the GEOVIDE cruise, reanalyzed ERA-Interim data  (Berrisford et al. , 2011)  and NCEP data  (Kanamitsu et al. , 2002 , http://www. esrl. noaa. gov/psd/) were processed. In particular, we estimated seasonal anomalies of net air-sea heat flux (and its components: sensible heat, latent heat, net longwave radiation, and net shortwave radiation) and freshwater flux (and its components: precipitation and evaporation) as follows. Firstly, seasonal means were computed by defining winter as DJF, spring as MAM, summer as JJA, and autumn as SON. Secondly, seasonal anomalies were calculated relative to the mean seasonal cycle of 2002-2012. Finally, the anomalies of winter-spring 2014 that preceded the GEOVIDE cruise were estimated.
3. 2. Specifically, from Greenland to Portugal, these stations were located in the East Greenland Coastal Current (EGCC, station 53), the East Greenland-Irminger Current (EGIC, station 60, same position as 51), the Irminger Gyre (station 44, same position as station 46), in the middle of the Iceland Basin (part of the NNAC, station 32), in the SNAC (station 21), in the center of the southward recirculation in the IAP (station 13), on the Iberian Peninsula slope (station 8), and on the Portuguese continental shelf (station 2). Importantly for the GEOTRACES community, although the super stations and XL stations are representative in terms of circulation, the large-scale S-θ anomalies detailed in Sect. 3. 4 need to be taken into account when comparing GEOVIDE data with data from the previous decade.
Concern-ing the freshwater, we detected that, despite the variability in freshwater content change on intra-seasonal and seasonal timescales (Fig. 12 ), there is good agreement between the trends shown by the ocean freshwater content and the airsea freshwater flux over the 2013-2014 period. We are aware of the large uncertainty associated with the air-sea freshwater flux  (Josey and Marsh, 2005; Dee et al. , 2011)  and the ocean freshwater content. Therefore, we estimated both variables from two and three databases, respectively. The difference between the ERA-Interim and NCEP estimates of accumulated air-sea freshwater flux over the two years amount to 0. 4 × 10 12 m 3 , while the ocean freshwater content estimates differ by 0. 3 × 10 12 m 3 (Fig. 12 ). We conclude that between 70 and 100 % of the freshening observed in the considered volume of the SPNA is caused by air-sea freshwater inputs.
Our results elucidate the important role of air-sea flux in the θ -S changes in this region on a short timescale, overcoming the warming induced by the increase in the MOC amplitude and associated heat transport in May-June 2014. Data availability. In addition to the data referenced in the paper that are publicly available, the GEOVIDE CTDO2 hydrographic and SADCP data were published by SEANOE  (Lherminier and Sarthou, 2017). The 2014 Greenland-Portugal GEOVIDE CTDO2 hydrographic and SADCP data (GO-SHIP A25 and GEOTRACES GA01) are available at SEANOE: http://doi. org/10. 17882/52153. Competing interests. The authors declare that they have no conflict of interest. Special issue statement. This article is part of the special issue "GEOVIDE, an international GEOTRACES study along the OVIDE section in the North Atlantic and in the Labrador Sea (GA01)".
Figure9. Heat content anomalies with respect to the mean heat content for the period 2002-2012 in the upper 1000 m of the region 40-60 • N, 45-10 • W: the monthly time series in gray and the 2-year running mean in black. Data source: EN4 database(Good et al. , 2013). The red square highlights the short-term cooling event analyzed in this paper.
Acknowledgements. We gratefully acknowledge the crew of the  R/V Pourquoi Pas ? for their help and assistance during the cruise and with winch repairs. The GEOVIDE cruise would not have been achieved without the technical skills and commitment of  Catherine Kermabon ,  Pierre Branellec ,  Philippe Le Bot ,  Olivier Ménage ,  Stéphane Leizour ,  Michel Hamon ,  Floriane Desprez de Gésincourt  (LOPS),  Fabien Pérault , and  Emmanuel de Saint Léger (CNRS). We are particularly grateful to  Géraldine Sarthou  for her persistent dedication to the project and her precious advice as well as to the three anonymous reviewers who greatly helped us in improving the paper. The NCEP Reanalysis 2 data were provided by the  NOAA/OAR/ESRL PSD in Boulder, Colorado, USA  from their website at http://www. esrl.
noaa. gov/psd/. The altimeter products were produced by  Ssalto/Duacs  and distributed by Aviso with support from  CNES. For this work,  Patricia Zunino  was supported by  CNRS  and  IFREMER  within the framework of the projects  AtlantOS (European Union Horizon 2020 , grant no. 633211 ) and  GEOVIDE  ( ANR-13-BS06-0014-02 ). H. Mercier was financed by  CNRS ,  Pascale Lherminier by Ifremer , and  Nathalie Daniault  by the  University of Western Brittany ,  Brest. Maribel I. García-Ibáñez  and  Fiz F. Pérez  were supported by the  Spanish Ministry of Economy and Competitiveness  through the  BOCATS  ( CTM2013-41048-P ) project co-funded by the  Fondo Europeo de Desarrollo Regional   2014-2020  (FEDER). Edited by:  Gilles Reverdin Reviewed  by: three anonymous referees.
4. 2 Figure 12. 212 Figure 12. Monthly time series of the freshwater content change between one month and the month before (in m 3 ) accumulated since 1 February 2013 in the upper 1000 m of the box delimited by 40-60 • N and 45-10 • W computed from three datasets: EN4 (blue), ISAS (red), and JAMSTEC (green). Integrated air-sea freshwater flux, or precipitation minus evaporation, over the same box and accumulated from 16 January 2013 from ERA-Interim (continuous black line) and from NCEP (discontinuous black line) databases.
76: Carmina nostra colunt ~ Prop. 2. 26. 26: carmina tam sancte nulla puella colit (but cf. ecl. 3. 61:illecolitterras;illimeacarminacurae, which is surely more likely the source) and more convincingly, 3. 89: iactat convicia divis ~ Prop. 3. 8. 18quae mulier rabida iactat convicia lingua. antiquepoetsAusoniusandClaudian. 38Besidespoets, he is probably acquainted with Quintilian and certainly with Hyginus' Fabulae. Some of these are more certain than others, such as Seneca, e. g. buc. 1. 47:Quid,quaeso,repetiscurasquerevolvisinanes?~Oed. 764:Curasrevolvitanimusetrepetitmetusand buc. 4. 55: molli. carmine ~ Agam. 361: carmine molli; and Columella, e. g. buc. 4. 1-5EgeratAmphrysi. Huc quoque cum tenera lascivus forte Napea / Pan aderat ~ 10. 264-5 Maenaliosque choros dryadum nymphasque Napaeas, / Quae colitis nemus Amphrysi,the only two texts that put Napaeae by the Amphrysos, and buc.
Vaccine Protection We included studies on killed OCV efficacy and effectiveness against cholera. We defined vaccine efficacy as the protection conferred under ideal conditions of a randomized, controlled trial, whereas vaccine effectiveness is the protection when the vaccine is given under actual public health situations, assessed by observational studies. From the reports on vaccine protection, we tabulated data on study design, site and year, intervention, study population and vaccine coverage, primary assessment of protection (clinical endpoints and definitions), total number of cholera cases, main infection serotypes and biotypes, estimated vaccine efficacy or effectiveness after a single dose with subanalysis by disease severity or age group, and duration of follow-up. We plotted the estimated vaccine efficacy or effectiveness by duration of follow-up.
RESULTS We identified 422 records on killed OCV immunogenicity or protection, 421 through the database search and 1 meeting presentation (Figure  1 ). We removed 228 duplicates and screened the titles and abstracts of 194 articles, of which 145 (75%) were excluded and 49 (25%) full-text articles were downloaded and reviewed. Of these, 23 studies fulfilled the inclusion criteria and were included in the systematic review: 17 articles and 1 presentation on single-dose vaccine immunogenicity and 6 articles on single-dose vaccine protection (see references listed in Supplementary Data). General descriptions, risk of bias within the study  [17] , main biases, and quality grading scores  [18]  of the 23 included studies are shown in Supplementary Table  S1.
The blurring of the assessment of vaccine immunogenicity by prevaccination titers has also been reported for other vaccines  [30]. However, we could not detect any clear patterns in the responses to the first and second dose by study location. Age group and setting may similarly affect vaccine protection estimates due to immune status and the presence or absence of ongoing natural exposure. Second, the immunogenicity data were analyzed in overlapping age distributions. Ideally, discrete age groups should have been used, but this was not possible due to data presented by varying age groups in the publications. Third, although seroepidemiological and human challenge studies have shown the association of serum antibody levels with protection  [15] , there is no established immunologic correlate of protection (a "protection threshold") for OCVs.
Cell culture, DNA extraction and sequencing The isolates and sources of C. abortus used in this work are summarized in Additional file 1. Cell culture and DNA extraction was performed as previously described  [9]  and genomes were sequenced using the Illumina Genome Analyzer II or Hiseq 2000 with paired end 75, 100 or 108 bp reads. Read data for all samples sequenced has been submitted to the European Nucleotide Archive (ENA, http://www. ebi. ac. uk/ena/) under accession numbers given in Additional file 1.
Mapping, assembly, annotation and plasmid detection The genome of C. abortus S26/3 (GenBank accession CR848038) was used as a reference for the mapping of all C. abortus sample reads using SMALT [https://sourceforge. net/projects/smalt/] and a minimum identity threshold for mapping of 80%, resulting in the coverage statistics detailed in Additional file 1. GATK was used for indel realignment and single nucleotide polymorphisms (SNPs) were called using SAM/BCFtools. No mixed infections were identified, following analysis of heterogeneous variant sites. The avian C. psittaci strain 6BC (accession CP002586) was used to define the root of the phylogeny. Assembly of all genomes was performed using a high throughput assembly method  [24] , producing contigs of both the C. abortus genome and remnant cell line contamination (submitted with read data as above).
Annotation from S26/3 was transferred to the genomes of AB7 and 1B using AnnotateBacteria [https://github. com/sangerpathogens/Bio-AutomatedAnnotation] and manually curated using Artemis and ACT  [25, 26]. The published genome and annotation of LLG  [6]  was updated with the current Illumina data, which provided greater coverage (140×). All annotated genomes (S26/3, LLG, AB7, 1B) [Seth-Smith, H. M. B. et al: Genomic evidence that the live Chlamydia abortus vaccine strain 1B is not attenuated and has the potential to cause disease, in preparation] were curated for the presence of pseudogenes. Pseudogenes were defined as having one or more mutations that would ablate expression (i. e. indel causing frameshift or substitution causing premature stop codon). Functional versions of the 51 identified pseudogenes were used to create a database against which all sequenced genomes with paired-end data were queried for the presence of the 51 identified pseudogenes using ARIBA v2.
4 [https:// github. com/sanger-pathogens/ariba], with manual curation of some lower coverage genomes, especially with respect to phase-variable genes. To identify any reads mapping to the chlamydial plasmid, the plasmids of Chlamydia caviae strain GPIC (accession AE015926) and Chlamydia felis strain Fe/C-56 (accession AP006862) were used as references against which to map all C. abortus sequence data, as well as read data from C. psittaci strain 01 DC12  [27]  as a positive control. C. caviae and C. felis should be equal phylogenetic distances from both C. psittaci and C. abortus  [28]. While mapping was seen from the C. psittaci sequence data (of 89. 1% coverage at 423× and 89. 6% coverage at 406× against C. caviae and C. felis respectively), none of the C. abortus isolate sequencing data produced coverage of either plasmid.
SNP detection, phylogenetic reconstruction and clustering A SNP alignment was obtained from the multiple genome alignment generated from mapping. A phylogenetic reconstruction of the data was carried out using RAxML  [29]  using a Generalized Time Reversible (GTR) model of evolution with a γ correction for among-site rate variation with four rate categories and 100 bootstraps. Strains were clustered into genetically close groups using hierBAPS  [30]  by performing two clustering iterations.
Recombination and temporal analysis To investigate the effect of recombination on the phylogeny, ClonalFrame v1. 2  [31]  and Gubbins  [32]  [https://github. com/sanger-pathogens/gubbins] were used. Results from Gubbins were explored using Phandango [https://github. com/jameshadfield/phandango]. The correlation between root-to-tip distances and date of isolation was used to explore temporal signal in the data for the whole collection and for the two BAPS clusters within the major clade independently. This analysis was performed using the "clustered permutation" approach implemented in R scripts  [33]. Three instances of BEAST v1. 8. 2  [34]  were run in parallel for the cluster with significant temporal signal (PG 2, see Results) using a GTRGAMMA model of evolution, strict molecular clock and constant population size for 100,000,000 generations.
Analysis of population structure and diversity The existence of population structure regarding host, geographical location or date of isolation was explored using the Discriminant Analysis of Principal Components (DAPC) implemented in the adegenet R package  [35]. A randomization of the populations (host, country or date) was performed to assess statistical significance and clustering power. The diversity between and within geographical locations was further assessed using an Analysis of Molecular Variance (AMOVA), implemented in the poppr R package  [36]. A permutation test was used to assess statistical significance (n = 1000). Nucleotide diversity was also calculated for the strains isolated in the different countries, as implemented in the pegas R package  [37].
Recombination and diversity within C. abortus Recently it has been shown that recombination is an important feature of chlamydial evolution  [11] [12] [13]. Recombination was analysed within our C. abortus dataset using Gubbins and ClonalFrame which both utilize SNP density to identify recombination blocks. Both algorithms produced very similar outputs with only three small regions of recombination identified (Fig. 2 ). These regions are located within CAB255, encoding a putative membrane protein (affecting PG C), and the polymorphic membrane protein-encoding genes pmp13G-pmp14G (CAB281-282) and pmp16G (CAB596) (affecting the major clade). This indicates that these latter recombinations are ancestral events that occurred after the divergence of the major clade and cluster 3 (LLG/ POS), but before the expansion of the current major clade.
The SNPs located in pmp genes could possibly result from mis-mapping or intragenomic recombination, and may not therefore represent true recombination within this species. C22_98 Krauss_15 C21_98 AC1 10_2431 AB1 VB1 1B AB7 OC1 iC1 AB13 C1_98 C11_98 03DC37 C7_98 03DC34 99DC2 AB2 10DC86 10DC87 24_26_Joseph AC1_Joseph FAG GR_EV C2_98 C10_98 99DC7 09DC76 10DC90 10DC82 99DC1 10DC85 10DC83 10DC89 09_03 S152_3 EBA_Joseph C20_98 C15_98 A22 T20 99DC4 S124_3A 09_01 8312 S95_3 09_02 09_04 S3_3 11_01 09_05 S26_3 Due to this apparent lack of recombination, this dataset provides an opportunity to investigate SNP distribution in chlamydial isolates that has arisen only through mutation and selection. A very even SNP distribution is apparent across the genome (Fig. 2 ), with elevated SNP densities found at three locations which cover genes only around CAB255, CAB381, CAB596 and CAB673, encoding a putative membrane protein, polymorphic membrane proteins Pmp13G and Pmp16G and a hypothetical protein, respectively.
The background rate strongly depends on the local environment (typically, from tens of Hz/antenna, in the most remote areas, up to kHz or more), but it is always larger by several orders of magnitude than the rate of detectable cosmic rays. A fully efficient, self-triggered radio array will be able to discriminate between these ultradominant backgrounds and EAS events. This requirement can only be fulfilled when efficient discriminating variables are defined. To this data analysis challenge can be added a technical challenge: implementing this background rejection at the antenna level and/or at the data acquisition level without introducing a large dead time of the detector or reducing its efficiency. So far, no radio array has satisfyingly solved this double issue. The current situation thus represents a limitation for the development of the technique, in particular for future self-triggered giant radio arrays such as GRAND  [7].
Principles of GRANDproto35 The GRANDproto35 radio data acquisition will be able to record all transient signals under standard background conditions at the experimental site. An offline selection of EAS candidates from the recorded data through the search of the specific EAS signatures detailed in  [9, 13]  will provide the efficiency of this setup. Radio waves of large duration (>300 ns), curved wavefront events, or events coming from noisy directions will be rejected. A similar treatment performed by TREND allowed to isolate a nearly EAS-pure sample from an initial set of 7. 3 • 10 8 events, at the cost of an estimated ∼50% EAS selection efficiency  [9]. Computation of the electromagnetic wave polarization at each triggered antenna position, made possible by the recording of X, Y, and Z antenna voltages, provides an additional tool to discriminate EAS from background.