dataesr/scientific-paragraphs-categorization huggingface dataset:
Data availability. Hg stable isotope data are available in the Supplement. Metadata and atmospheric concentration data are available from the NSF Arctic Data Center (urn:uuid:e58b2369-0701-4dac-b3c6-b8e15a0cd187, https://arcticdata. io/catalog/view/ urn:uuid:e58b2369-0701-4dac-b3c6-b8e15a0cd187, last access: 16 October 2019;  Helmig et al. , 2018). Flux data are available from Daniel Obrist (daniel_obrist@uml. edu) upon request. Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/bg-16-4051-2019-supplement. Author contributions. MJ conceived the study and led the Hg stable isotope sampling and analysis with support from JES and DO. YA led the data analysis of flux data. MJ wrote the paper with input from all authors.
Remotely sensed imagery is a common tool for straightforward land cover classification. The main difficulty is that the land cover classes in forest-agriculture mosaics are distributed along a landscape gradient, which results in misclassification. Images with high and very high spatial resolutions provide greater spatial detail and precise information  [10] [11] [12] [13]. However, the low temporal resolution of such data does not enable monitoring vegetation dynamics to better discriminate land cover classes that are spectrally similar. In contrast, satellite time series provide great opportunities for this purpose. MODIS and Landsat optical time series have been used extensively for land cover classification since the 1970s. Despite a 1-2-day revisit time, however, land cover derived from MODIS has low local accuracy due to the maximum spatial resolution of 250 m.
They are freely available under an open license, unlike most SAR data (e. g. , TerraSAR-X, ALOS2, RADARSAT-2, COSMO-Sky Med) and optical data (e. g. , SPOT, Quickbird, WorldView, Geo-Eye, Ikonos). Recent studies have demonstrated the potential of S-2 data for mapping 6-12 land cover classes from a single-date image  [23] [24] [25] [26] [27]. Studies that use S-2 time series focus on one or a few specific land cover classes, usually on homogeneous agricultural or forested landscapes located in the same climatic region  [28] [29] [30] [31] [32] [33] [34]. Use of S-1 data has been limited to combining them with S-2 or Landsat data, which increases classification accuracy  [35] [36] [37] [38] [39]. Hence, the use of S-1 time series alone for mapping land cover classes has not yet been evaluated.
Preprocessing The S-1 images were preprocessed using the Sentinel-1 Toolbox (Sentinel-1 toolbox, ESA, http: //step. esa. int/main/toolboxes/sentinel-1-toolbox/). The images were first radiometrically calibrated to extract the backscattering coefficients (σ 0 VV, σ 0 V H) (Figure  2 ). A speckle filter was then applied (Lee refined, with a 7 ˆ7 window,  [52] ). The images were geo-coded using Shuttle Radar Topography Mission data to correct topographic deformations (geometric correction accuracy < 1 pixel) and were projected onto the ETRS89-TM3/ETRS0 system (EPSG 3042) and the WGS84/UTM 22S system (EPSG 32722) for the Spanish and the Brazilian study areas, respectively. A ratio channel was produced (VV/VH) from the backscattering coefficient images. All images were then converted from linear to decibel (dB).
[63]  demonstrated that it can be set to 100 without a major loss in accuracy. The experiments were performed using the randomForest package of R software (randomForest package version 4. 6-14, Andy Liaw, https://cran. r-project. org/web/packages/randomForest/index. html). We used the OrfeoToolbox to calculate the final RF prediction (OrfeoToolbox version 6. 6. 1. , CNES, https://www. orfeo-toolbox. org/).
Methods Satellite-derived products. A satellite-derived Level-3 data set of Chl a concentration (mg m -3 ) was obtained from the European Space Agency's GlobColour project (http://www. globcolour. info). The 8-day composite Chl a concentrations using standard Case 1 water algorithms were used (i. e. , OC4Me for Medium-Resolution Imaging Spectrometer, and OC3v5 for Moderate Resolution Imaging Spectroradiometer/Visible Infrared Imaging Radiometer Suite sensors). The altimetry-derived geostrophic velocities (AVISO MADT daily product) were produced by CLS/AVISO (Collecte Localization Satellites/Archiving, Validation, and Interpretation of Satellite Oceanographic data), with the support from the CNES (Centre National d'Etudes Spatiales; http://www. aviso. altimetry. fr/duacs/). BGC-Argo network.
The BGC-Argo dataset used in the present study is publicly available at ftp://ftp. ifremer. fr/ifremer/argo/dac/coriolis/, an Argo Global Data Assembly Center. This dataset represents an international initiative by compiling 132 BGC-Argo floats (a total of 14,415 stations) from AOML-NOAA, BODC, CORIOLIS, CSIRO, and INCOIS. It covers the time period from July 2010 to June 2017 and involves a variety of float platforms (NKE's PROVOR, Webb's APEX, and SeaBird's NAVIS) performing different missions (in dive depths, frequency of profiling, and data acquisition) and equipped with different sensors for measuring Chl a and backscattering at 700 nm (Seabird MCOMS, WETLabs Eco-Triplet, and Eco-FLBB). A quality control procedure was achieved on the CTD  22  , Chl a 23 and backscattering  24  data. Fluorescence data were corrected for non-photochemical quenching on daytime profiles following the method of Xing et al.
The average F 490 within provinces ranges between 0. 26 and 0. 33 (Supplementary Fig. 1 , analysis of 3321 profiles), which translates into an overestimation factor of 3 to 4 with respect to the factory calibration. This value is actually lower than overestimates derived from HPLC Chl a  26, 27. We note here that these HPLC-based estimates (1) are relevant to a spatio-temporal domain restricted to the float deployment (the estimated correction factor might change as environment and community composition change during the float journey) and (2) present a large (yet unexplained) variability. Here, we use a conservative value of 0. 3 for F 490 (corresponding to a factory calibration overestimation of 3. 3). This value has the advantage of integrating a broad spatio-temporal domain (e. g. , winter conditions) and a large dataset for the estimation of this correction (more than 3300 profiles).
ANDRO dataset. The ANDRO atlas ASCII file (available at http://www. coriolis. eu. org/) contains the float parking pressure (actually a representative parking pressure which is generally an average of the measured pressures during float drift at depth) and temperature, deep and surface displacements, and associated times, deep and surface associated velocities with their estimated errors (see Ollitrault and Rannou  28  ). ANDRO data originate from AOML, Coriolis, JMA, CSIRO, BODC, MEDS, INCOIS, KORDI, KMA, and CSIO and represent a total of 6271 floats contributing to 612,462 displacements. Each float cycle (deep displacement between two profiles) provides an estimate of the zonal and meridional current velocities at their drifting depth (mostly around 1000 m). From 2002 to 2016, around 21,300 cycles were available close to the SWIR (Supplementary Fig.
2 ). These velocities were binned into 1°by 1°boxes and then averaged in space and time. Only the box containing more than 5 data points were kept. Standard deviation of the zonal (u') and meridional (v') velocity components in each box were used to calculate the mean deep eddy kinetic energy (EKE) as follows: EKE ¼ 1 2 u′ 2 þ v′ 2 À Á ð1Þ where the overbar denotes the time average over the whole period (2002-2016). EKE was then interpolated on a finer grid by a Gaussian correlation function, weighted by the local number of data, with a decorrelation radius of 100 km. Helium dataset. The helium data were extracted from the GLobal Ocean Data Analysis Project (GLODAP) Version 2, a cooperative effort to coordinate global synthesis projects funded through NOAA/DOE and NSF as part of the Joint Global Ocean Flux Study-Synthesis and Modeling Project (JGOFS-SMP)  29.
The GLODAP Version 2 data product (available at https://www. glodap. info) is composed of data from 724 scientific cruises covering the global ocean. Here, two different expeditions (06AQ19960317; March 23-31, 1996 on the Polarstern) and (35MF19960220; February 28-March 25, 1996 on the Marion Dufresne) were used to generate the two sections of δ 3 He. Isopycnals between meridional section. A combination of hydrographic profiles from different sources are used to construct a 3-D climatology of potential density in the region 0-55°E and 55-45°S, with a half degree horizontal resolution, and a 25 m vertical resolution. We use three distinct sources of observations to maximize the number of profiles. The first set of observations is conductivity-temperature-depth (CTD) data from ship-recorded observations during the period 1906-2016 from the NOAA World Ocean Database (https://www.
nodc. noaa. gov/OC5/SELECT/dbsearch/ dbsearch. html). We only use profiles that have a quality control flag of 1, containing information on their position, date, temperature, and salinity. The second set of observations we use is float observations from the Argo international program. The Argo float profiles of pressure, salinity, and temperature used in this study were gathered in the period 2002-2016. They provide temperature and salinity between 0 and 2000 m. We only use profiles that have a quality control flag of 1, and contain information on their position, date, temperature, and salinity. As a final data set, we use profiles derived from the animal-borne sensor program MEOP (http://www. meop. net/; Treasure et al. 30  ). Similar to the other datasets, we only use profiles with control flag of 1, and that contains position, date, temperature, and salinity.
Altogether, we gathered 33096 profiles in the region 0-55°E-55-45°S. From this dataset, we computed potential density for each 25 m vertical interval between the sea surface and 2000 m. Then, for each interval, we produced maps of climatological fields of potential density using an Optimal Interpolation procedure. The Optimal Interpolation and gridding method are described in detail in Schmidtko et al. 31. As a brief summary, we interpolate onto a 0. 5°grid in the region 0-55°E-55-45°S. We used a 550 km isotropic decorrelation scale, incorporating an anisotropic isobath-following component using a "Fast Marching" algorithm, as well as front-sharpening components. In addition, recent data are emphasized in the mapping, which produces a climatology typical of the years 2000-2010 (see Schmidtko et al.
This approach was initially developed for predicting the development of the Kerguelen phytoplanktonic plume  19  and thereafter extended to the entire Southern Ocean 8. The model has been calibrated and validated in the Crozet and Kerguelen regions by combining satellite data (altimetry and ocean color), lithogenic isotopes, iron measurements, and drifters  [19] [20] [21]  (see d'Ovidio et al. 19  for further details). Here the advection scheme is applied to the period 2010-2015 and for the planktonic bloom season, November to March, in order to obtain a mean climatological signal. Bloom characterization. To determine the bloom magnitude, each float time series was divided into individual annual cycles, starting on 1 July. Cycles that do not cover the theoretical bloom period (from early November to late February the next year) with at least eight float profiles were discarded.
Acknowledgements M. A. was supported by a  CNES  ( Centre National d'Etudes Spatiales)   Postdoctoral Fellowship  and by a  European Union 's  Horizon 2020 Marie Sklodowska-Curie grant  (no. 746748 ). This work represents a contribution to the remOcean project (REMotely sensed biogeochemical cycles in the OCEAN, GA 246777) funded by the  European Research Council  and to the project  SOCLIM  ( Southern Ocean  and climate) supported by the  French research program LEFE-CYBER of INSU-CNRS , the  Climate Initiative of the foundation BNP Paribas , the  French polar institute (IPEV) , the  CNES Tosca/OSTST  (project  LAECOS ) and  Sorbonne Université. J. -B. S. was funded by the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  (grant agreement  637770 ).
Hox genes and GT development To precisely assess Hox genes transcription during GT development, we initially quantified their expression levels by using RNA-sequencing (RNA-seq). We analyzed datasets from three different stages of GT embryonic development starting from embryonic day 12. 5 (E12. 5), E16. 5 and E18. 5. We observed that genes positioned in the 5' portion of both HoxA (Hoxa7 to Hoxa13) and HoxD (Hoxd8 to Hoxd13) clusters were expressed at all developmental stages (Figure  1A  and Figure  1 -figure supplement 1). Furthermore, with the exception of Hoxc11 and Hoxc10, only basal levels of mRNAs were scored for the HoxC and HoxB clusters (Figure  1 -figure supplement 1), consistent with previous observations  (Hostikka and Capecchi, 1998; Montavon et al. , 2008). Overall, we detected a general decrease in the amount of Hox mRNAs during GT development, in particular for Hoxd12 and Hoxd13 (Figure  1A ).
5, and E17. 5, and used forebrain at E12. 5 as a control tissue lacking Hoxd mRNA. As a baseline to our temporal series, we used a mouse embryonic stem cells (mESC) dataset  (Noordermeer et al. , 2014)  assuming that these cells somehow reflect the ground-state 3D architecture of the gene cluster. In mESC, contacts between Hoxd13 and the C-DOM were mainly scored in the island II and V regions. A large proportion of the interactions was scored in the cluster itself (Figure  2B , top, red lines) where they were likely driven by H3K27me3 marks  (Vieux-Rochas et al. , 2015). This 3D architecture was altogether quite comparable to that found in forebrain cells with discrete contacts established between Hoxd13 and island II and V (Figure  2B , bottom, red lines). These two profiles likely reflected the 3D chromatin state of C-DOM in the complete absence of transcription.
Upon transcriptional activation, however, frequencies of contacts with the C-DOM increased and interactions between Hoxd13 and previously characterized enhancers (Prox, GT2)  (Gonzalez et al. , 2007; Lonfat et al. , 2014)  became visible (Figure  2B , second track, blue lines). Quantification of these interactions revealed a 22% increase in overall contacts over this regulatory region, when the CR at E10. 5 was compared with ES cells (Figure  2B ). This dataset showed a C-DOM specific chromatin architecture that is organized before the emergence of the genital bud. In subsequent stages of GT development (E12. 5 or E13. 5), contacts between various enhancer regions and Hoxd13 continued to increase to reach a maximum at E13. 5 with an additional 35% of overall interaction when compared to the CR sample (Figure  2B ).
Dissecting the regulatory potential of the C-DOM TAD We next explored the functional dynamics of C-DOM during GT development. A detailed analysis of our CR ATAC-seq and ChIP-seq datasets revealed several accessible chromatin sites, some of which correspond to previously identified GT enhancers such as GT2  (Lonfat et al. , 2014)  (Figure  3A , black arrow). Noteworthy, the GT and limb enhancer sequence Prox was not yet accessible at this stage (Figure  3A , red arrow). At E13. 5, when C-DOM is fully active, both chromatin accessibility peaks and H3K27ac marks were scored over previously characterized enhancers within this region, including Prox and GT2 (Figure  3A ). As development progressed, in E17. 5 GT, H3K27ac marks were lost in C-DOM (Figure  3A ) correlating with the loss of both Hoxd transcripts and chromatin interactions (see above).
Identification of GT-specific enhancers In order to identify other elements acting in GT, we then focused on the genomic interval positioned between the SB and the rel5 breakpoints (Figure  5A ), since this region accounted for 76% of Hoxd13 expression in the incipient bud (see Figure  3C ). Based on ATAC-seq, H3K27ac ChIP-seq, 4C-seq datasets and on DNA sequence conservation, we selected five sub-regions of approximately 30kb in size and tested them for enhancer activity in transgenic assays (Figure  5B, C ). Each region was cloned upstream of a LacZ reporter gene driven by a minimal beta-globin promoter and integrated at random positions in the mouse genome. X-gal staining of E13. 5 transgenic embryos revealed enhancer activity in the GT for the IIIE and IVE sequences (Figure  5C ), in cellular territories included within the wider expression domain of Hoxd13 in this tissue.
Amongst them, the possibility that the functional contribution of GT2 is required at a particular stage of GT development, which was not considered in our analyses. To explore this possibility, we used RT qPCR to measure the Hoxd13 mRNA level in the CR at E10. 5, a developmental stage where this enhancer is already accessible, as seen in our ATAC-seq dataset (Figure  6 -figure supplement 3C, black arrow), and capable of triggering lacZ transcription  (Lonfat, 2013). At this early stage, we observed a slight (27%), but significant (p=0. 0152) decrease in the expression of Hoxd13 (Figure  6 -figure supplement 3D), suggesting that GT2 alone may have a role in controlling Hoxd13 expression prior to GT formation.
Group 13 HOX proteins access the TAD structure Our datasets on single enhancer deletions raise several potential hypotheses (see the discussion). Amongst them the possibility that the transcriptional outcome of the C-DOM regulation may rely upon an unspecific, global effect of accumulating various factors within the landscape architecture, thus licensing the TAD for activation of the target genes. The same C-DOM TAD was previously shown to regulate Hoxd13 and neighboring Hoxd genes during distal limb bud development, a structure that resembles in many respects the developing genitals  (Cobb and Duboule, 2005; Cohn, 2011; Infante et al. , 2015; Tschopp et al. , 2014). In this case, the products of both Hoxa13 and Hoxd13 were shown to bind to most of those C-DOM regulatory sequences specific for distal limb buds.
From this observation, it was concluded that HOX13 proteins themselves were instrumental in activating or re-enforcing transcription of the Hoxd13 gene in this developmental context, by accumulating at this landscape and binding to many accessible sites due to their low binding specificity  (Beccari et al. , 2016; Sheth et al. , 2016). In this context, we used an antibody against the HOXA13 product in a CnR approach, with either CR cells at E10. 5 or GT cells at E13. 5, i. e. before GT formation and during its emergence, respectively. Previous work has shown both redundancy of binding to limb regulatory elements and similarity of DNA binding motifs between HOXA13 and HOXD13  (Sheth et al. , 2016). As such, and because of the HOXA13 binding profile in our dataset, we consider that this dataset reflects the binding of either HOXA13, HOXD13 or of both proteins and is thus referred to as 'HOX13' (Figure  8 ).
Reads were mapped to the mouse genome (mm10) using Bowtie2 (v2. 3. 4. 1)  (Langmead and Salzberg, 2012) , with standard settings. The coverage was obtained as the output of MACS2 (v2. 1. 1. 20160309) (Zhang et al. , 2008). Peak calling in Figure  5  was done using MACS2 (v2. 1. 0. 20160309) call peak (--gsize 1870000000) using the corresponding input data as control BAM (-c). CTCF motif orientation was assessed using the CTCFBSDB 2. 0 database  (Ziebarth et al. , 2012) , with EMBL_M1 identified motifs.
ATAC-seq reads processing was done on the Duboule lab local Galaxy server  (Afgan et al. , 2016). Reads were mapped to the mouse genome (mm10) using Bowtie2 (v2. 3. 4. 1)  (Langmead and Salzberg, 2012) , (-I 0 -X 2000 --fr --dovetail --very-sensitive-local). Reads with mapping quality below 30, mapping to mitochondria, or not properly paired were removed from the analysis. PCR duplicates were filtered using Picard (v1. 56. 0). Peak calling was done using MACS2 (v2. 1. 0. 20151222) call peak (--nomodel --shift -100 --extsize 200 -call-summits). The coverage was done using the center of the Tn5 insertion and extended on both sides by 20bp (script developed by L. Lopez-Delisle). When indicated, coverage profiles represent an average of the replicates, this was done by dividing each replicate by the number of million reads that fall within peaks in each sample (for normalization) and calculating the average coverage.
Acknowledgements We thank all members of the  Duboule laboratories  for insightful comments and discussion. We are grateful to  Sandra Gitto  and  Thi Hanh Nguyen Huynh  for their help with mice breeding and genotyping, as well as  Mylène Docquier ,  Brice Petit  and  Christelle Barraclough  ( University of Geneva ) and  Bastien Mangeat  and the gene expression core facility ( GECF, EPFL Lausanne ) for DNA sequencing. We also thank  Marion Leleu  for her help with data analysis.
Data availability All raw and processed RNA-seq, 4C-seq, ChIP-seq, Cut & Run, and ATAC-seq datasets are available in the Gene Expression Omnibus (GEO) repository under accession number GSE138514. Design of experiments, RA, CB, BM and DD; Bench work, RA, BM; Computing analysis, RA, LL-D; Analysis of results, RA, LL-D, DD; Manuscript writing, RA, CB, LL-D and DD; Funding acquisition, DD, CB. All authors read and approved the final manuscript.
Results: To test this hypothesis, isopod host CAZomes (i. e. the entire CAZyme repertoire) were characterized in marine, freshwater and terrestrial species through a transcriptomic approach. Many CAZymes were identified in 64 isopod transcriptomes, comprising 27 de novo datasets. Our results show that marine, freshwater and terrestrial isopods exhibit different CAZomes, illustrating different strategies for lignocellulose degradation. The analysis of variations of the size of CAZy families shows these are expanded in terrestrial isopods while they are contracted in aquatic isopods; this pattern is probably resulting from the evolution of the host CAZomes during the terrestrial adaptation of isopods. We show that CAZyme gene duplications and horizontal transfers can be involved in adaptive divergence between isopod CAZomes.
These enzymes, identified as Carbohydrate Active enZymes (CAZymes) and classified as GH7 and GH9 in the families of the CAZy database  [24] , belong to a complex pathway involving many CAZymes that can degrade and release monosaccharides from lignocellulose. Recently, Bredon et al. [25]  have identified 17 endogenous lignocellulose-degrading CAZy families in the common pill-bug Armadillidium vulgare, which illustrates the complexity of this process in this terrestrial isopod. Furthermore, some marine isopods could use hemocyanins in their digestive tract to modify lignin during lignocellulose digestion, thus facilitating access to cellulose and hemicellulose  [26, 27]. However, there is so far no animal genome known to encode all necessary CAZymes to degrade lignocellulose, and in most cases, animals benefit from mutualistic associations with microbial symbionts allowing an efficient degradation of lignocellulose  [28].
In the present study, we identified and annotated the host CAZomes of many isopod species belonging to different families to better understand their successful colonization of all environments. To this end, we identified CAZymes from 64 isopod transcriptomes, including both publicly available transcriptomes and 27 de novo sequenced and assembled datasets. These transcriptomes were obtained from a wide range of isopod species among Oniscidea, Asellota, Valvifera, Sphaeromatidea and Limnoriidea. Our analysis of the CAZomes from this large dataset highlights the different strategies for lignocellulose digestion in isopods, according to their adaption to marine, freshwater and terrestrial environments. In addition, we assessed the molecular evolution of some lignocellulose-degrading key genes, suggesting an ancient origin and acquisition of these genes during the conquest of land.