dataesr/scientific-paragraphs-categorization huggingface dataset:
Clinical samples and gene expression data Affymetrix data from two independent cohorts of previously untreated patients with MM were used. The training cohort consisted of 206 patients with MM and was called Heidelberg-Montpellier (HM) cohort. This cohort also included five BMPC samples from healthy donors. Samples were obtained after signature of a written informed consent form in accordance with the Declaration of Helsinki and after approval by the Ethics Committees of Montpellier (DC-2008-417) and Heidelberg. After Ficoll-density gradient centrifugation, plasma cells were purified using anti-CD138 MACS microbeads (Miltenyi Biotech, Bergisch Gladbach, Germany) as previously described  [66]. These data are publicly available through the ArrayExpress database (E-MTAB-372). The validation cohort (TT2 cohort) included 345 patients with MM from the University of Arkansas for Medical Sciences (UAMS, Little Rock, AR, USA).
These data can be accessed at the online Gene Expression Omnibus (GSE2658). The clinical characteristics of the two cohorts have been previously described  [29, 67]. Gene expression profile data from a cohort of 282 patients with MM included in the Dutch-Belgian/German HOVON-65/GMMG-HAD trial were also used (GSE19784) (HOVON65/GMMGHD4 cohort)  [68]. The clinical characteristics of the this cohort have been previously described  [68]. We also used Affymetrix data of 188 relapsed patients with MM subsequently treated with bortezomib (GSE9782) from the study by Mulligan et al. (Mulligan cohort)   [69]. The XG1, XG2, XG3, XG4, XG5, XG6, XG7, XG10, XG11, XG12, XG13, XG14, XG16, XG19, XG20 and XG21 HMCLs were obtained as previously described  [70]. The AMO-1, LP1, L363, U266, OPM2, and SKMM2 HMCLs were purchased from DSMZ (Braunsweig, Germany) and the RPMI8226 cell line from ATTC (Rockville, MD, USA).
We conclude that the R. stellina specimen is not an oral dental plate (supragnathal) but belongs to part of the dermal skeleton  (Burrow et al. 2016) , and that a flat ASG aboral surface is characteristic of the acanthothoracids, very different to the arthrodire condition  (contra Burrow et al. 2016). Therefore, R€ ucklin & Donoghue's (2015a, 2016) interpretation of the disarticulated R. stellina plate does not inform on the interrelationship of dermal and oral skeletons and hence their conclusions regarding the evolutionary link between teeth and the dermal ornament (non-independence of these) as based on this plate, can no longer be supported. History Museum, London). This data has been deposited in Dryad  (Smith et al. 2017). 3D rendering, presented as selected 'virtual' orthogonal sections (Fig.
CONCLUSIONS We found multiple and distinct differences between the articulated dental plates of the acanthothoracid CPW. 9A and the isolated plate of Romundina stellina described by R€ ucklin & Donoghue (2015a, 2016) from 3D segmentation of data obtained from non-invasive x-ray tomography. These differences were determined from quantitative comparisons of odontogenic populations, vascular spaces within the hard tissues, shape of the plate and shape of the aboral surface, along with the tissue composition of the aboral surfaces. We conclude that, compared with the articulated dental plate in CPW. 9A, the isolated plate of R. stellina is not part of the oral skeleton, therefore, it cannot be relevant to discussions regarding the evolutionary origins of teeth. Moreover, although there is some overlap among the larger denticles and tubercles, in CPW.
Acknowledgements. We thank  Farah Ahmed  and  Amin Garbout  ( Image and Analysis Centre, NHM ) for access and assistance with CT-scanning. This work was financially supported by  NERC  Standard grants  NE/K01434X/1754  (ZJ),  NE/ K014235/1  (MMS). Phil Donoghue  and an anonymous referee are thanked for their comments on an earlier version of the manuscript.
DATA ARCHIVING STATEMENT Synchrotron scans of Romundina stellina are available at https://doi. org/10. 5523/bris. 7h9gynbsui4u1hap471inrlua; mCT-scans of CPW. 9A are available in the Dryad Data Repository, along with supporting information and movies: https://doi. org/10. 5061/dryad. hr718.
Specifically, SMOS-IC is much simpler and does not account for corrections associated with the antenna pattern and the complex SMOS viewing angle geometry. It considers pixels as homogeneous to avoid uncertainties and errors linked to inconsistent auxiliary datasets which are used to characterize the pixel heterogeneity in the SMOS L3 algorithm. SMOS-IC also differs from the current SMOSL3 product (Version 300, V300) in the values of the effective vegetation scattering albedo (ω) and soil roughness parameters. An inter-comparison is presented in this study based on the use of ECMWF (European Center for Medium range Weather Forecasting) SM outputs and NDVI (Normalized Difference Vegetation Index) from MODIS (Moderate-Resolution Imaging Spectroradiometer). A six-year (2010)(2011)(2012)(2013)(2014)(2015) inter-comparison of the SMOS products SMOS-IC and SMOSL3 SM (V300) with ECMWF SM yielded higher correlations and lower ubRMSD (unbiased root mean square difference) for SMOS-IC over most of the pixels.
The soil moisture of the first 2-3 cm soil layer is highly related to the soil emissivity at L-band through the soil permittivity. SMOS uses an interferometric radiometer, which delivers multi-angular brightness temperature measurements at L-band. Currently, various products are derived from the SMOS data at Level 2  (Kerr et al. , 2012   [1] ) and at Level 3 (Al Bitar et al. , 2017  [5] ), such as the SMOSL3 Brightness Temperature (SMOSL3 TB) and the SMOSL3 SM and τ products, with a 625 km 2 sampling. The SMOS SM retrieval algorithm, which is common to both SMOS Level 2 (L2) and Level 3 (L3) products, has been continuously improved since the launch of the satellite in 2009  (Kerr et al. , 2001 [6] ;  Mialon et al. , 2015 [7] ; Al Bitar et al. , 2017  [5] ). It has been evaluated against several datasets from various space-borne sensors such as SMAP, the active Advanced Scatterometer (ASCAT), the Advanced Microwave Scanning Radiometer (AMSR-E) or different versions of the SMOS products (Al-Yaari et al.
II In relation to the above point, in some cases, the Level 2 and Level 3 algorithms use values of LAI derived from MODIS  [30]  to initialize the value of optical depth in the inversion algorithm  (Kerr et al. , 2012 [1] ). In SMOS-IC, this is not implemented, and the initialization of optical depth in the inversion algorithm is based on a very simple approach (given in the following) and is completely independent of the MODIS data. III SMOS-IC uses as input SMOS Level 3 fixed angle bins Brightness Temperature (TB) data at the top of the atmosphere and contains different flags allowing to filter SM retrievals accounting for the quality of the input TB data and for the TB angular range in the L-MEB inversion. SMOS-IC does not make use of the computationally expensive corrections based on angular antenna patterns to account for pixel heterogeneity as in the L2   [31].
, 2009 [35] ), and τ is related to the vegetation water content (VWC, kg/m 2 ) of the whole vegetation layer. However, the NDVI index is a good indicator of the vegetation density and it can be used to interpret the seasonal changes in the SMOS τ product at large scale over low vegetation as found by  Grant et al. (2016)  [25], but with some caveats: saturation effects at high levels of vegetation density, sensitivity to the effects of snow and soil reflectivity  (Qi et al. , 1994 [36] ), etc. It may be noted that NDVI is the proxy used for estimating τ in the current operational algorithm of the SMAP mission (O'Neill et al. , 2012  [37] ). In Section 2, we present a description of both SMOS algorithms (SMOSL3 and SMOS-IC) and of the MODIS NDVI and ECMWF SM datasets. The inter-comparison of the SMOS products in terms of soil moisture and vegetation optical depth is given in Section 3.
ECMWF and MODIS Data The ECMWF dataset used in this study for the SM product inter-comparison was obtained from the SMOSL3 SM pre-processor. This ECMWF product has a spatial resolution of 625 km 2 and 1-day temporal resolution, using the same EASE 2. 0 grid and interpolated in time and space to fit the SMOSL3 sampling resolutions. It is based on the ERA-Interim dataset. ERA-Interim uses a numerical weather prediction (NWP) system (IFS-Cy31r2) to produce reanalyzed data  (Berrisford et al. , 2011)   [53]. The ECMWF soil surface (Level 1, top 0-7 cm soil layer) and soil deep temperature (Level 3, 28-100 cm) are used in the computation of the effective soil temperature for the SMOS-IC and SMOSL3 SM products following the parameterization of  Wigneron et al. (2001)   [54]. It is worth noting that, unlike the SMOSL3 SM product, the SMOS-IC processor does not use the ECMWF SM product to compute contributions from the fixed fractions (i.
Soil Moisture Figure  1  shows the values of the temporal mean SM over the globe and over the period 2010-2015 for the three SM datasets considered in this study: (a) SMOS-IC; (b) SMOSL3 SM; and (c) ECMWF. It should be kept in mind that ECMWF SM is representative of the first 0-7 cm of the soil surface  (Albergel et al. , 2012 [65] ) and the inherent nature of the simulated soil moisture  (Koster et al. , 2009 [66] ) is different to that measured by the SMOS satellite observations, which are sensitive to the first ~0-3 cm of the soil surface  (Escorihuela et al. , 2010 [67] ;  Njoku and Kong et al. , 1977 [68] ). In Figure  1 , Remote Sens. 2017, 9, 457 9 of 21 ECMWF SM must be analyzed in terms of spatial patterns rather than absolute values. Although the images in Figure  1a ,b, have many similarities, some spatial patterns shown by the ECMWF SM product are in better agreement with SMOS-IC than with SMOS L3 SM.
Both products show τ values that are sensitive to vegetation, as the highest τ values were found for the main boreal and tropical forests, and the minimum for dry areas, such as Inner Asia or Australia. It must be noted that the τ values coming from the SMOSL3 product were slightly larger than those obtained by the SMOS-IC product. In order to identify possible patterns, Figure  7  shows a global map which illustrates the differences of τ between both SMOS datasets (SMOSL3 minus SMOS-IC). This result shows that the greatest differences between both τ datasets were found over some regions of the Northern Hemisphere, such as the east of USA, the north of Europe and also in the Amazon basin. It can be noted that slightly higher correlation values are generally obtained with SMOS-IC especially in the west of Mexico, the northeastern regions of Brazil and some parts of the Sahel.
, 2016 [25] ). Figure  10  presents a global map that shows for each pixel which τ dataset (SMOSL3 or SMOS-IC) leads to the largest correlation (R) values with MODIS NDVI. Over northern mid-latitudes, larger correlations were generally obtained with SMOSL3. However, except for these regions, the highest R values were generally obtained with SMOS-IC while no clear patterns were found in terms of longitude. Figure  11  shows a dispersion diagram in order to assess the range of correlation values found for both SMOS τ datasets against MODIS NDVI. The diagram generally yields positive correlations, although a non-negligible number of negative correlations can be noted for both SMOS products. The negative correlation values between τ and NDVI are difficult to explain. In some cases, negative values were found in dense forest areas and can be related to the saturation of NDVI for large biomass values  (Grant et al.
This specific approach may lead to dry SM bias in forested regions, as noted by  Wigneron et al. (2012)   [29]. Currently, in the SMOSL3 V300 retrieval algorithm, the values of the vegetation and soil roughness parameters differ mainly between forest and low vegetation categories. The SMOSL3 and SMOS-IC soil moisture retrievals were compared globally against ECMWF SM data for the period 2010-2015. This evaluation extends the work of Fernandez-Moran et al. (2016)  [31] who evaluated a preliminary version of the SMOS-IC product at local scale using numerous in situ SM stations from ISMN and found higher R and lower ubRMSE with SMOS-IC than with the SMOSL3 V300 product. At global scale, both the SMOS-IC and SMOSL3 SM products were generally found to be drier than the ECMWF SM product. However, the different layers considered in the modeled ECMWF SM (top 0-7 cm soil layer) with respect to the remotely sensed SMOS SM (~top 0-3 cm soil layer), as well as the inherently different nature of simulated soil moisture  (Koster et al.
, 2009 [66] ), makes it difficult to truly assess the performance of the SMOS products in terms of bias at global scale. In terms of temporal variations, higher correlation values and lower ubRMSD values were generally found between SMOS-IC SM and ECMWF SM, than between SMOSL3 SM and ECMWF SM. The ECMWF SM dataset is not "truth", and a larger inter-comparison of SMOS-IC and SMOSL3 against other modeled SM products such as MERRA (Modern-Era Retrospective analysis for Research and Applications)  (Reichle et al. , 2011 [75] ) or remotely sensed data (such as SMAP, AMSR-E, and ASCAT) should be made in the future to confirm the very preliminary results found here. In terms of τ values, the SMOS-IC and SMOSL3 τ products were compared to MODIS NDVI values over 2010-2015 in terms of correlation values.
Thus, at L-band, the NDVI index (as the LAI index) is only a proxy, which is used to provide an estimate of τ over rather low vegetation covers during the vegetation growth (O'Neill et al, 2012  [37] ;  Wigneron et al. , 2007 [24] ;  Lawrence et al. , 2014 [76] ;  Grant et al. , 2016 [25] ). A larger inter-comparison of the SMOS-IC and SMOSL3 τ products against different vegetation datasets (remotely sensed products, LAI, forest biomass) should be made in the future to confirm the results found in this study. As for the Level 2 and 3 algorithms, based on rather complex and detailed concepts and auxiliary datasets, the simple SMOS-IC algorithm will be improved regularly and will be used to improve the L2 and L3 SMOS retrieval algorithms. These different approaches are complementary and a regular inter-comparison analysis between them should be of great benefit to improve the L-MEB inversion, and ultimately the SM and τ products retrieved from the SMOS observations.
Figure8shows the correlations obtained by comparing the SMOSC-IC and SMOSL3 τ datasets to MODIS NDVI. All correlations values are presented here including those not significant as done by Grant et al. (2016)  [25]. It can be noted that slightly higher correlation values are generally obtained with SMOS-IC especially in the west of Mexico, the northeastern regions of Brazil and some parts of the Sahel. Conversely, slightly higher R values were obtained in western and central Europe with SMOSL3. The lowest correlations were found generally over forests for both SMOS products; a result which can be partly related to the tendency of NDVI to saturate for high biomass and LAI values. However, higher R values were obtained with SMOS-IC for some areas of the boreal forests and the tropical forests of Africa.
Figure8shows the correlations obtained by comparing the SMOSC-IC and SMOSL3 τ datasets to MODIS NDVI. All correlations values are presented here including those not significant as done byGrant et al. (2016) [25]. It can be noted that slightly higher correlation values are generally obtained with SMOS-IC especially in the west of Mexico, the northeastern regions of Brazil and some parts of the Sahel. Conversely, slightly higher R values were obtained in western and central Europe with SMOSL3. The lowest correlations were found generally over forests for both SMOS products; a result which can be partly related to the tendency of NDVI to saturate for high biomass and LAI values. However, higher R values were obtained with SMOS-IC for some areas of the boreal forests and the tropical forests of Africa.
Acknowledgments: The authors would like to thank the  TOSCA (Terre Océan Surfaces Continentales et Atmosphère) CNES program  and the  European Space Agency (ESA)  for funding this study. The authors acknowledge CATDS for the SMOSL3 dataset (http://catds. ifremer. fr)and the cooperation of the  different soil moisture in situ networks of the International Soil Moisture Network (ISMN)  project.
However, a few recent publications report the occurrence of QS in marine bacterial strains isolated in oligotrophic environments. In their pioneering work,  Moran et al. (2004)  sequenced the full genome of Silicibacter pomeroyi, an oligotrophic Roseobacter and detected two QS systems. Similarly, in a previous study, we reported many translated gene sequences affiliated to Roseobacters luxI and hdtS from oligotrophic environments in the predicted proteome of Global Ocean Sampling metagenomic dataset  (Doberva et al. , 2015). Collectively, these preliminary observations suggest that QS could constitute an important physiological trait of Rhodobacteraceae in all types of aquatic environments. New AHLs are regularly described in this group  (Schaefer et al. , 2008; Thiel et al. , 2009; Ziesche et al.
Culture of Strain MOLA 401 The strain MOLA 401 is from the MOLA culture collection (WDCM911 1  ) and available (strain code BBCC401) upon request 2. This strain was isolated on December 3, 2004 at 4 m depth from marine oligotrophic waters in the southwest lagoon of New Caledonia (France; 22 • 21. 23 S/166 • 23. 43 E)  (Conan et al. , 2008). Sampled waters harbored a Chl a concentration of 1. 07 µg ml -1 (F. Joux, pers. communication). All culturing steps were performed using Marine Broth (MB) 2216 (BD Difco, Sparks, MD, United States of America). The draft genome sequence has been published under accession number JQEY00000000 and revealed a quorum-sensing dependent physiology  (Doberva et al. , 2014).
Phylogenetic Analyses of the Strain MOLA 401 16S rRNA and Quorum Sensing Genes Phylogenetic analyses were conducted using MEGA 6 software  (Tamura et al. , 2013)  to assess the position of strain MOLA 401 QS genes with respect to already published phylogenies and to infer their putative functional role. Four different genes were considered: 16S rRNA, luxR (an AHL receptor), luxI (an AHL synthase), and hdtS (another AHL synthase). All alignments were performed using ClustalW  (Larkin et al. , 2007)  and trimmed manually. Phylogenetic trees were constructed using the Neighbor-Joining (NJ) method with p-distance correction and 1000 bootstrap replicates.
LC-MS Analyses UHPLC-MS analyses were performed with a Waters (Milford, CT, United States) Acquity UPLC-TQD (Triple Quadrupole Detector) system controlled by the MassLynx 4. 1 software. Column was an Acquity HSS C18 (2. 1 × 50 mm) with 1. 8 µm particle size  (Waters). The column oven was set to 40 • C. The flow rate was maintained at 0. 6 mL min -1 and the injection volume was 2 µL. The mobile phase was composed of 0. 1% formic acid in water (eluent A) and 0. 1% formic acid in acetonitrile (B). A gradient profile was used, starting with 95% of A, keeping this composition constant for 0. 5 min. Proportion of B was linearly increased to 100% in 6. 5 min, and was left at 100% for 3 min. The T. Q. Detector operated in ElectroSpray Ionization (ESI) in the positive and negative modes. First, the third quadrupole (Q3) has been used in scanning mode on the m/z 50-800 mass range in order to confirm the molecular weight and the purity of our 26 standard AHLs (2 mg mL -1 in DMSO, 2 µL injected), but also to determine their retention time (RT) under our chromatographic conditions.
Molecular Formula Determination and High Resolution MS/MS High-resolution MS/MS analyses were conducted with a Thermo UHPLC-HRMS system. Analyses of microfractions and standards (1. 0 µL injected) were performed in electrospray positive ionization mode in the 133. 4-2000 Da range in centroid mode. The mass detector was an Orbitrap MS/MS FT Q-Exactive focus mass spectrometer. The analysis was conducted in FullMS data dependent MS2 mode. In FullMS, resolution was set to 70,000 and AGC target was 3. 10 6. In MS2, resolution was 17,500, AGC target 10 5 , isolation window 0. 4 Da, normalized collision energy 30, with 15 s dynamic exclusion. UHPLC column was a Phenomenex Luna Omega polar C-18 150 × 2. 1 mm, 1. 6 µm. The column temperature was set to 42 • C, and the flow rate was 0. 5 mL min -1. The solvent system was a mixture of water (A) with increasing proportions of acetonitrile (B), both solvents modified with 0.
Two different methods can be used to screen the metagenomes. The function-driven strategy is performed by a biological activity-screening of expression libraries  18. The sequence-driven approach is based on the direct sequencing of all genetic material from a target environment and on the homology analysis in comparison with sequences already present in the databases  18. The increasing number of works focusing on the study of microbiota from guts of wood-eating insects  19  , cow  20  , green-waste compost  21  shows the relevance of the research for new lignocellulolytic microorganisms and enzymes. At the present, among natural environments, decaying lignocellulosic materials could represent an important reservoir of novel genes encoding enzymes involved in (hemi)cellulose degradation, necessary for the development of eco-compatible and economically favorable industrial processes.
Results Data Statistics. The microbiota of three different lignocellulosic biomasses were analysed by Illumina sequencing of the metagenomic DNAs. A total of 11,208,388,400, 11,274,127,600 and 2,392,000 raw reads for A. donax, E. camaldulensis and P. nigra, respectively, were obtained. Sequence reads accounting for around 10. 0 Gb, for A. donax and E. camaldulensis samples, and 2 Gb, for P. nigra, were selected (Table  1 ). The reads were assembled into 95,292, 159,184 and 33,805 contigs (cut-off value 500 bp) for A. donax, E. camaldulensis and P. nigra biomasses, respectively (Table  1 ). The N50 and N90 contig lengths ranged from 914 to 1,452 and from 546 to 583 bases, respectively. The longest contig was 49,245, 650,642 and 85,030 bases in A. donax, E. camaldulensis and P. nigra, respectively (Table  1 ).
Microbial community composition of lignocellulosic biomasses. The reads were compared against sequences in the NCBI NR database and the results processed by MEGAN version 4. 70. 4 to determine the composition of the microbial communities. The three lignocellulosic biomass samples were shown dominated by Proteobacteria and Actinobacteria. These phyla together accounted for approximately 87. 5%, 87. 2% and 89. 4% of the total biodiversity in A. donax, E. camaldulensis and P. nigra, respectively (Table  2 ). In P. nigra biomass, Firmicutes, and in particular Bacilli, were detected at a high incidence (approximately 10%). A low percentage of reads matched fungal species in A. donax and E. camaldulensis (2. 7% and 5. 3%, respectively) (Table  2 ). The relative abundances of microbial taxa were examined at the level of genera to determine the dominant taxa within the bacterial communities degrading biomass from the different investigated plant species.
camaldulensis biomass, Penicillium strongly dominated the eukaryotic biodiversity showing a relative abundance of 3. 2% (Fig. 1 ). eggNOG and KEGG functional profiling of lignocellulosic biomass. With the aim to investigate the functional diversity in the three samples, the predicted amino acid sequences were also aligned to the databases Evolutionary genealogy of Genes non-supervised orthologous groups-eggNOG-and Kyoto Encyclopedia of Genes and Genomes-KEGG-by using BLAST. As shown in Fig. 2 , the data revealed a prevalence of poorly characterized genes belonging to S (function unknown) or R (general function prediction only) eggNOG category. Moreover, for all three samples, a high        *  The total numbers of CAZYmes is less than the sum (AAs + CBMs + CEs + GHs + GTs + PLs) due to the fact that some multimodular predicted proteins were detected.