dataesr/scientific-paragraphs-categorization huggingface dataset:
qiagen. com/ingenuity). Statistical analysis for flow cytometry and Luminex data were performed using Prism ® 6. 0 (Graphpad Software Inc. , La Jolla, CA, USA). Two-sided Friedman tests followed by a Dunn's posttest comparing each time-point with the baseline were performed. Significant correlations between Luminex, transcriptomic, and cytometry variables were identified using the Pearson correlation coefficient based on a correlation threshold of 0. 80 and a q-value threshold of 0. 05. Representation of the co-expression network was generated using Cytoscape ® (42).
co-expression network representation highlights the Predominant Factors involved in the early Vaccine response We integrated our data in a global analysis to provide a comprehensive overview of the early immune reaction following vaccination. We calculated Pearson correlations (cutoff: |R| > 0. 8 and q-value < 0. 05) for the whole dataset to establish links between the parameters of the three studied compartments (Tables  S1  and S2  in Supplementary Material). This led to the construction of a co-expression network where the nodes correspond to the biological variables and the connecting lines correspond to significant correlations (degree of connectivity) between two variables (Figure  6 ). Non-draining LNs were considered to be the untreated conditions of draining LNs. The parameters were organized according to their type (cell, cytokine, or gene) and their origin (skin, blood, or LN).
The innate response was also initiated at the systemic level with rapid and transient granulocyte recruitment and the release of IL-6 and IL-1RA, from 6 to 24 h p. i. , followed by a persistent phase involving inflammatory monocytes. Systemic inflammation was confirmed by molecular signatures, such as those of genes that upregulate the IL-6 and TNF pathways and acute phase response signaling. Finally, integration of the data in a co-expression network allowed visualization of the relationships between the three compartments and the chronology of the events. As a modified form of the vaccinia virus, MVA binds to a wide range of host cells and fuses with the cell membrane without the requirement of specific cell receptors, but the ability to fully complete the replication cycle is dependent on downstream intracellular events  (44).
This work was supported by the French government "Programme d'Investissements d' Avenir" (PIA) under Grant ANR-11-INBS-0008 funding the Infectious Disease Models and Innovative Therapies (IDMIT, Fontenay-aux-Roses, France) infrastructure, the PIA grant ANR-10-EQPX-02-01 funding the FlowCyTech facility (IDMIT, Fontenay-aux-Roses, France), and the PIA ANR-10-LABX-77 grant funding the Vaccine Research Institute (VRI, Créteil, France). The Agence Nationale de Recherche sur le SIDA et les Hépatites Virales (ANRS, Paris, France) supported this work. The European Commission Advanced Immunization Technologies (ADITEC) Grant FP7-HEALTH-2011-280873 also contributed to this research.
Data-assembling is increasingly used to model habitat preferences of cetaceans at the basin scale  (Cañadas et al. , 2018; Roberts et al. , 2016; Rogan et al. , 2017). Due to the various protocols, platform types and observation heights, species detectability and data quality vary with surveys. In addition, each survey may not collect the same information, particularly with regard to observation conditions. Some surveys only record Beaufort sea-state while others record additional parameters that also influence species detection, such as sun glare, cloud coverage or wave height. In the process of synthesizing different datasets, only variables common across all datasets can generally be retained in a broad-scale analysis, which nevertheless needs to account for heterogeneity. Finally, to make basin-wide predictions from the assemblage of a number of local surveys, identifying areas of environmental extrapolations is crucial to bolster confidence in predicted maps  (Mannocci et al.
, 2018). Our study aims to understand how deep-diving cetaceans are distributed at a large scale and to highlight areas of high relative densities for conservation purposes. To model the habitats of deep-diving cetaceans at a large scale, we assembled data from different surveys in the North Atlantic Ocean and the Mediterranean Sea from 15 organizations. To take into account heterogeneity in sighting protocols, we built a hierarchical model to estimate the effective strip width (ESW) across platforms and observation conditions. We then modelled relative densities of three deep-diving cetacean taxa with generalized additive models (GAMs). Finally, we performed a gap analysis  (Jennings, 2000; Mannocci et al. , 2018)  to assess the reliability of the predictions outside the surveyed area.
K E Y W O R D S beaked whales, data-assembling, deep-diving cetaceans, habitat modelling, kogiids, sperm whales Baltic and Black Seas, the Gulf of Mexico and the Hudson Bay, both because of an absence of effort data and of ecological and environmental differences (Figure  1a ; Supporting Information Appendix S1). Four sub-regions were defined in the study area (Table  1 ; Figure  1a  survey area was used for accurate distance computations (Albers equal-area conic defined from https://projectionwizard. org). Effort data were linearized and divided into 5-km segments using arcGIs 10. 3 (ESRI, 2016) and the MarIne GeospatIal ecoloGy tools software  (Roberts, Best, Dunn, Treml, & Halpin, 2010). The segment length represented a trade-off value across varying survey transect lengths, for example, aerial surveys had transect lengths of up to 100 km while shipboard surveys were often much shorter.
| Effective strip width estimation Line-transect surveys are commonly used to estimate cetacean abundance  (Buckland et al. , 2015; Hammond et al. , 2013). A key parameter to estimate this abundance is the ESW, which corrects the decreasing detection of animals with distance from the trackline. ESW is expected to depend on survey platform height, platform type, sea-state, species, etc…  (Buckland et al. , 2015). ESW estimation was a key step in the data-assembling process to take into account heterogeneity in effort per segment in the models and to directly compare the different surveys  (Hedley & Buckland, 2004). ESWs are generally estimated for each survey (i. e. , no pooling of information) using DIstance software  (Buckland et al. , 2015; Thomas et al. , 2010). However, for deep-diving cetaceans, the majority of surveys contained insufficient sightings to allow surveyspecific detection functions to be fitted.
Consequently, for each species group, we pooled sightings from the various surveys, taking into account survey heterogeneity. We built a hierarchical model in which survey identity was included as a random effect. In conventional distance sampling  (Buckland et al. , 2015; Marques & Buckland, 2003) , factors such as the characteristics of the species being surveyed, search methods, search platform, environmental conditions can all affect ESW estimation. However, the different datasets did not always contain this information, especially regarding observation conditions. All surveys recorded environmental data such as Beaufort sea-state, cloud coverage and sun glare, although Beaufort sea-state was the only parameter recorded by all of them. Platform type, observation height and Beaufort sea-state were used as covariates in the hierarchical model.
Truncation distance w was first determined as the 95th percentile of the set of perpendicular distances for each species group, that is, the 5% most distant sightings were discarded from the analysis  (Buckland et al. , 2001, p. 16 ). Then, we created classes to pool the different surveys; namely platform type (plane or boat), observation height (e. g. , 0-5 m; 5-10 m…) and Beaufort sea-state (0-1; 1-2; 2-3 and 3-4; data collected beyond a Beaufort sea-state 4 being removed from the analysis). Hierarchical modelling was then performed in R 3. 3. 1 (R Core Team, 2016) in a Bayesian framework using jaGs version 4-6 and package "rjags" (jags model in Supporting Information Appendix S4;  Plummer, 2016; Royle & Dorazio, 2008). For each taxon, perpendicular distances of sightings were used to estimate a detection function with a hazard key.
Posterior inferences were based on the pooled sample of 4,000 values (1,000 per chain). The advantage of setting a hierarchical model to estimate detection functions is to borrow strength across the different datasets to increase the precision of estimates. For each combination of surveyplatform type-observation height-Beaufort sea-state, estimated detection functions are shrunk towards a common detection function (itself estimated from the data) according to the available data corresponding to this particular combination of survey-platform type-observation height-Beaufort sea-state. If, for a given combination of parameters, there were few sightings, the estimated detection function was very close to the common detection function, whereas if there were enough data, the estimated detection function could deviate from this common function.
| Habitat modelling To model habitat preferences of deep-divers, we fitted GAMs  (Hastie & Tibshirani, 1986; Wood, 2006)  with a Tweedie distribution to account for over-dispersion  (Foster & Bravington, 2013)  with the "mgcv" R package  (R 3. 3. 1. version; Wood, 2013). GAMs extend generalized linear models to allow for smooth nonlinear functions of predictor variables  (Hastie & Tibshirani, 1986; Wood, 2006). The mean number of individuals per segment µ was modelled with a logarithmic link function: where f(X p ) are nonparametric smooth functions (thin plate regression splines) of the covariates and α is the intercept  (Hastie & Tibshirani, 1986). To attenuate the scope for over-fitting, the maximum number of knots was limited to 4 (mgcv parameter k = 4;  Wood, 2006 ). An offset equal to segment length multiplied by twice the ESW was included (except for the JNCC-ESAS surveys in which only one side of the vessel was surveyed).
| Gap analysis Even though more than 1,240,000 km of effort was pooled, extensive geographical gaps remained. Predictions in the middle of the Atlantic Ocean are from geographical extrapolation (Figure  1a ) but not necessarily environmental extrapolations. The latter depends on the selected habitat models and covariates therein. We conducted a gap analysis on environmental space coverage to identify areas where habitat models could produce reliable predictions outside survey blocks, that is, geographical extrapolation, whilst remaining within the ranges of surveyed conditions for the combinations of covariates selected by the models, that is, areas of environmental interpolation  (Jennings, 2000; Mannocci et al. , 2018). From the selected models for each taxon, we estimated the convex hull defined by the environmental data used to fit habitat models (hereafter the calibration data).
The convex hull of a set of points is the smallest convex envelop that contains all these points. We then assessed whether a prediction from a set of environmental covariates with a given model fell inside or outside this convex hull (Authier, Saraux, & Péron, 2016;  King & Zeng, 2007). We used climatological predictors instead of monthly predictors to lessen the computational burden. Due to the large amount of data (more than 280,000 points in the calibration dataset), convex hulls were estimated by random subsampling with the "WhatIf" R package  (Stoll, King, & Zeng, 2014). We randomly extracted a fraction of the calibration dataset (10,000 points) to estimate a convex hull and assess environmental extrapolation in the prediction dataset. A combination of climatological predictor values that fall inside the convex hull corresponds to an interpolation.
Combinations of climatological predictor values that were classified as interpolations were set aside but other combinations were retained and further tested against another random sample of 10,000 points from the calibration data. This procedure was carried out until the complete calibration dataset was examined. ( ) log( ) = + ∑ p f(X p ) 3 The full procedure was conducted twice. In a simple approach, the full range of sampled variables was considered to identify all points of the whole study area where the actual combinations of environmental variables had been sampled in survey blocks. In a more "precautionary approach," we excluded 5% of the extreme values of the sampled environmental variables to include in the interpolation areas only the points whose associated combinations of covariates fell within 95% of the core ranges sampled.
| Methodological considerations Over the past few years, data-assembling has been increasingly used for the study of top marine predators  (Cañadas et al. , 2018; Roberts et al. , 2016; Rogan et al. , 2017). Due to the very low sighting rates of deep-diving cetaceans, each survey taken separately cannot provide enough data to investigate the habitats of these rare species. In contrast to  Rogan et al. (2017) , we did not assemble data collected with similar protocols but data collected with different variants of the line-transect distance sampling protocol, which meant standardizing the data according to their core communalities before developing a single spatial model. Ideally, at a time when shared databases are becoming increasingl y important (e. g. , obIs seaMap -https://seamap. env. by enhancing the level of communalities in shared datasets, and helping to describe large-scale habitats and distributions of marine species.
| CON CLUS ION Habitat modelling of rare species is particularly challenging because habitat models require large datasets, yet rare species typically yield low numbers of sightings. As a result, combining datasets is a useful strategy to model the large-scale habitats of deep-divers (beaked whales, sperm whales and kogiids) across the North Atlantic Ocean and the Mediterranean Sea. At a local scale, predicted relative densities of deep-diving cetaceans were consistent with previous studies. At a larger scale, a gradient in predicted relative densities emerged, with the highest relative densities predicted on the western side of the study area. This pattern was evidenced thanks to assembling a large dataset and had not been detected previously. It highlighted the pronounced influence of active frontal zones, such as the Gulf Stream, on deep-diving cetaceans.
) and northern bottlenose whales (Hyperoodon ampullatus); (b) sperm whales (Physeter macrocephalus); and (c) kogiids, including pygmy (Kogia breviceps) and dwarf sperm whales (K. sima). All survey datasets were standardized for units and formats (e. g. , date, time and coordinates) and aggregated into a single common dataset. A specific coordinate projection encompassing the entire F I G U R E 1 Study area divided into sub-regions showing assembled survey effort (a), along with the beaked whale (b), sperm whale (c) and kogiid (d) sightings recorded during all surveys. The blue polygon delineates overall study area and other polygons delineate sub-regions. Surveys were carried out along transects following a line-transect methodology (survey details in Supporting Information Appendix S1). Sightings were classified by group sizes with each point representing one group of individuals and point size representing the number of animals in a group.
ing scans and coDa survey data. The thunnus survey was carried out thanks to the collaboration of the General Directorate of Fisheries and Maritime Affairs, Government of Galicia. We thank the Direction Générale de l'Armement (DGA), including Odile Gérard and Carole Nahum, for funding Auriane Virgili's doctoral research grant. ML was funded by a Ramón y Cajal postdoctoral contract of the Spanish Ministry of Economy, Industry and Competitiveness, whereas IGB was supported by a PhD fellowship of the Spanish Ministry of Economy, Industry and Competitiveness. This study is a.
25°, daily D High SSH is associated with high mesoscale Standard deviation of SSH (m) 0. 25°, daily D activity and enhanced prey aggregation or primary production Mean of EKE (m 2 /s 2 ) 0. 25°, daily D High EKE relates to the development of eddies Standard error of EKE (m 2 /s 2 ) 0. 25°, daily D and sediment resuspension which induce a prey aggregation Mean of NPP (mgC/m 2 /day) 9 km, 8 days E Net primary production as a proxy of prey availability Harris, Macmillan-Lawler, Rupp, and Baker (2014)olution. A: Depth and slope were derived from the GEBCO-08 30 arc-second database (https://www. gebco. net/);30arc-seconds is approximately equal to 0. 008°. B: Surface area per cell was calculated in arcGIs 10. 3 from the shapefil e of canyons and seamounts provided byHarris, Macmillan-Lawler, Rupp, and Baker (2014).
C: The mean, standard error and gradient of sea surface temperature (SST) were calculated from the GHRSST Level 4 CMC SST v. 2. 0 model (Canada Meteorological Centre 2012, https://podaac. jpl. nasa. gov/dataset/ CMC0. 2deg-CMC-L4-GLOB-v2. 0). D: The Aviso ¼° DT-MADT geostrophic currents database was used to compute mean and standard deviation of sea surface height (SSH) and eddy kinetic energy (EKE; https://www. aviso. altimetry. fr/en/data/products/sea-surface-height-products/global/madt-h-uv. html). E: Net primary production (NPP) was derived from SeaWIFS and Aqua models using the vertically generalized production model (VGPM; http:// orca. science. oregonstate. edu/1080. by. 2160. 8day. hdf. vgpm. s. chl. a. sst. php).
DATA The data used here were measured by the twin Helios spacecraft, which were operational during the late 1970s and early 1980s. Both spacecraft had an electrostatic analyser for measuring the ion distribution function at 40. 5 second cadence  (Schwenn et al. 1975) , and two different fluxgate magnetometers for measuring the magnetic field  (Musmann et al. 1975; Scearce et al. 1975 ). Here we use a re-analysis of the ion distribution functions which fitted a bi-Maxwellian to the proton core population present in the experimentally measured ion distribution functions. This dataset provides the proton core number density (np), velocity (vp), temperatures parallel (T p ) and perpendicular (T p⊥ ) to the magnetic field, and corresponding magnetic field values (B) at a maximum cadence of 40. 5 seconds.
For more details on the fitting procedure and access to the data see  Stansby et al. (2018a). Note that all the data presented in this article are parameters of the proton core population, and not numerical moments of the overall ion distribution. The total temperature was calculated as Tp = 2T p⊥ + T p /3, the temperature anisotropy as T p⊥ /T p , the parallel plasma beta as βp = 2µ0npkBT p / |B| 2 , and the Alfvén speed as vA = |B| / √ npmpµ0. To avoid contamination of very large transients all of the intervals listed as coronal mass ejections by  Liu et al. (2005)  were removed from the dataset before further analysis. The state of the Sun undergoes an 11 year solar cycle, oscillating between solar minimum and solar maximum. Because the highest quality Helios data were taken early in the mission, only data taken in the years 1974 -1978 inclusive (during the solar minimum between cycles 20 and 21) were used.
COMPARISON WITH OTHER CATEGORISATION SCHEMES Recently several authors have also attempted to categorise sources of the solar wind using in-situ observations, choosing the categories of coronal mass ejection wind, coronal hole wind, and interstream wind (for a summary, see  Neugebauer et al. 2016). In this paper we have deliberately removed coronal mass ejection wind from our dataset, and have used proton temperature anisotropy as the only variable separating coronal hole wind (anisotropic) and interstream wind (isotropic). Zhao et al. (2009)  used only the O 7+ /O 6+ ratio and solar wind speed measured at 1 AU. This method is limited by the slow cadence (1 hour) of charge state ratio measurements available, but has the advantage that the O 7+ /O 6+ ratio is known to be directly related to plasma properties near the Sun.
However, in the near future we will have access to simultaneous in-situ measurements of protons in the inner heliosphere, in-situ measurements of solar wind composition, and a wide range of remote sensing data. We finish by describing how our new categorisation scheme can be applied to data from upcoming missions to the inner heliosphere. Parker Solar Probe (PSP,  Fox et al. 2016)  will make in-situ measurements of the solar wind at heliocentric distances inside 0. 3 AU, and the first comprehensive in-situ solar wind measurements inside 1 AU since Helios. Proton and magnetic field measurements made by PSP will allow us to perform the categorisation scheme outlined in this paper. Advances in modelling and remote sensing since the Helios era mean that it will then be possible to backmap the solar wind measured by PSP to a predicted source location on the Sun.
ACKNOWLEDGEMENTS D. Stansby is supported by  STFC  studentship  ST/N504336/1 , and thanks  Trevor Bowen ,  Allan Mac-Neil ,  Denise Perrone  and  Alexis Roulliard  for helpful discussions. T. S. Horbury is supported by  STFC  grant  ST/N000692/1. This work was supported by the  Programme National PNST of CNRS/INSU  co-funded by  CNES.
Data were retrieved using HelioPy v0. 5. 3 (Stansby et al. 2018b) and processed using astropy v3. 0. 3 (The Astropy Collaboration et al. 2018). Figures were produced using Matplotlib v2. 2. 2  (Hunter 2007; Droettboom et al. 2018). Code to reproduce the figures presented in this paper is available at https://github. com/dstansby/ publication-code.
Skin microbiomes of a few fish species (see Additional file 1: S1 and S7) were dominated by other prokaryotic classes (Fig. 2 ). For instance, Chaetodon auriga hosted the highest relative abundance in Alphaproteobacteria (64. 6 ± 29% of relative abundance) and the lowest relative abundance in Gammaproteobacteria (2. 8 ± 0. 07%) in the entire dataset. Corythoichthys flavofasciatus, the only member of order Syngnatiformes, was the most enriched in Cyanobacteria (46. 9 ± 13. 1%) and second most depleted in Gammaproteobacteria (7. 5 ± 0. 03%). Pomacanthus imperator was the most enriched in Clostridia (16. 5 ± 4. 7%) and the most depleted in Alphaproteobacteria (1. 0 ± 1. 1%). Naso unicornis was the second most enriched in Clostridia (15. 2%), and the most depleted in Cyanobacteria and Alphaproteobacteria (0% and 2.
To our knowledge, this is the first study investigating a possible phylosymbiosis pattern for the skin microbiome of marine fishes. The two statistical methods used identified contrasting results. The first one (method A) involving repeated random subsampling of one individual per species, revealed no significant phylosymbiosis pattern. The second one, involving averaging microbial relative abundances of prokaryotic OTUs found on individuals of the same species (method B), did detect a significant phylosymbiosis pattern (Fig. 4 ). Fish skin microbiome is characterized by an important intraspecific microbial variability  [22]. In our dataset, while being 1. 3 times lower than the interspecific variability (Fig. 3 ), intraspecific variability might have blurred phylosymbiosis signal detection in method A, by considering one individual per species per subsample.
Sequence processing to define OTUs and their phylogenetic relationships Sequence processing was performed following the SOP of  Kozich et al. for MiSeq  [67] , https://www. mothur. org/ wiki/MiSeq_SOP, 2017) using Mothur  [68]. After assembly of paired reads in each run, sequences of both runs were merged and sequences with an abnormal length (outside a range of 250-300 bp) were removed. Sequences were aligned along the SILVA reference database  [69]  release 128. Chimeras were removed using UCHIME  [70]. Filtered sequences were then classified using the SILVA reference taxonomy and the non-prokaryotic ones were removed. 10,877 sequences from 173 samples were kept after the cleaning process, ranging from 2450 to 43,306 sequences per sample. After this, 2000 sequences were sub-sampled within each sample in order to correct the uneven sequencing efficiency among samples.