dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2006 ; Pearson et al. , 2007 ). We adopted the default regularization parameters but restricted MaxEnt to using only linear and quadratic functional forms, which constrains models to produce relatively simple models that do not over-fi t to the training data  ( Merow et al. , 2013 ; Syfert et al. , 2013 ). Sampling bias was controlled by including a sampling bias dataset  ( Phillips et al. , 2009 )  constructed from all georeferenced plant occurrence data from both the GBIF data portal and all georeferenced records from Solanaceae Source (6,200,000 and 47,500 records respectively; downloaded 24 September 2014). Th e spatial extent of the area modeled also infl uences model performance  ( Peterson et al. , 2011 ). In our case, species ranges varied from very small within a single country (e.
A leave-one-out method was performed for species with sample sizes from 5 to 10 data points  ( Pearson et al. , 2007 ). SDM performance was evaluated by using the area under the curve (AUC) in a receiver operating characteristic (ROC) plot; an AUC value of 1. 0 indicates perfect discrimination ability and a value of 0. 5 or less indicates a prediction no better than random. Only models with an average test AUC above 0. 7 were used for further analyses. In the case that the SDM did not have an average test AUC above 0. 7, a convex hull was created and used in place of the SDM for the rest of the ex situ gap analysis. For species with AUC above 0. 7, SDMs were then reconstructed using the complete set of occurrences to develop a more robust predictive model  ( Fielding and Bell, 1997  ) and these models were used for further analyses.
, 2013 ) , and dismo  ( Hijmans et al. , 2014 )  packages. All raster fi les were mapped in ESRI ArcGIS 10 ( ESRI, 2013 ). In situ conservation assessment-We evaluated the preliminary conservation status of the wild relatives of eggplant using the IUCN Red List Categories and Criteria ( IUCN, 2012 ), one of the most widely used systems for assessing the conservation status of species in their natural habitats  ( Rodrigues et al. , 2006 ; Mace et al. , 2008 ). Th reatened species were placed within three categories (critically endangered, endangered, and vulnerable) based on fi ve criteria (A-E; IUCN, 2012 ). A fourth category, near threatened, was used when species partially met the IUCN threat criteria and were present in areas with documented rapid change in land use. Because population data were lacking for the eggplant wild relatives, several of the Red List criteria were inapplicable and we focused on using estimates of geographic range as the key determinant of extinction risk (criterion B).
e. , those in which more than seven under-collected species were estimated to occur in the same pixel) and evaluated regions within and outside of protected areas as defi ned from the World Database on Protected Areas ( WDPA, 2010 ). We also prepared a map of hotspots for further collecting of those species preliminarily assessed at risk for extinction by overlaying the collecting priority map for each species identifi ed in one of the threatened categories (including those in the near threatened category). To further prioritize regions for conservation actions, the extent of area in collecting hotspots for species at risk for extinction was combined with the overall area for general priority collecting hotspots to identify regions with multiple species poorly represented in genebanks that coincide with regions where there are also signifi cant conservation threats.
macrocarpon ) are included in our analysis as members of the Anguivi grade. Occurrence data-A total of 5618 occurrence records from herbarium specimens and 936 germplasm accession records were compiled. Records with geographic coordinates ranged from 1 to 1100 per species ( Table  2  ). All herbarium specimen records for the eggplant genepools defi ned above are mapped in Fig. 1. All occurrence data are available for download from the Natural History Museum Data Portal ( http://dx. doi. org/10. 5519/0006757 ). Species distribution models-SDMs with adequate predictive performance to use in the gap analysis were produced for 37 CWR species [median test AUC was 0. 83 ± 0. 06 SD (from 5-fold crossvalidated models)]. SDMs could not be built for fi ve species with fewer than fi ve georeferenced records ( Solanum aldabrense C.
ACKNOWLEDGEMENTS Th e authors thank the managers curating Solanaceae in both herbaria and genebanks for contributing data for these analyses. Helpful comments by two anonymous reviewers greatly improved the manuscript. Th is work was undertaken as part of " Adapting Agriculture to Climate Change: Conserving, Protecting and Preparing Crop Wild Relatives " ( http://www. cwrdiversity. org/ ) supported by the  Government of Norway  and the  Natural History Museum's Natural Resources Initiative. Taxonomic and systematic work on Solanum and eggplant CWR was supported by  National Science Foundation (NSF)  grant  DEB-0316614  ' PBI Solanum  -a world treatment' to SK. MMS was funded by the  Crop  and  Pest Wild Relatives  project as part of the  NHM Natural Resources Initiative.
The measurements are performed at the humidity levels listed in Table  1. When applying the WARR technique, a radar antenna is kept in a fixed position and the other antenna is moved on the wood surface with a 1 cm step. The distances between the two antennas vary from 16 to 26 cm and from 11 to 21 cm for orthogonal and parallel polarisations of the electric field, respectively. When the FO method is applied, the distances between the antennas are 16 and 11 cm for orthogonal and parallel polarisation states, respectively. The arrival times are visualised with Radan software and Matlab. Examples of Ascans and B-scans showing the superposition of direct-air wave, direct wave and reflected wave are provided in Figs. 3  and 4 ; here, the polarisation of the electric field is orthogonal to the fibres and the humidity level by mass water is 12 %.
Acknowledgements. The authors are grateful to  COST (European Cooperation in Science and Technology , www. cost. eu)for funding the  Action   TU1208  " Civil Engineering Applications of Ground Penetrating Radar " (www. GPRadar. eu). The experimental results presented in this paper were collected at the  University of Bordeaux, France , during a 1-month short-term scientific mission supported by  TU1208. We acknowledge also the  French National Research Agency (ANR)  for supporting this study through the  Xylo-plate project ,  Equipex XYLOFOREST. The authors are grateful to the  EGU GI Division President ,  Francesco Soldovieri , the chief-executive editor of the Geoscientific Instrumentation, Methods and Data Systems journal,  Jothiram Vivekanandan  and the executive editors,  Ari-Matti Harri  and  Håkan Svedhem , for inviting them to submit this paper, which resumes and extends the presentation (Reci et al.
In this study, we relied on genetic tools to verify the species identity of collected scallops using a DNA barcoding approach  [19]. We also used these genetic data to assess potential long-term (i. e. , intergenerational) effects of a chronic chemical contamination on M. varia by estimating genetic diversity at each sample site. Our integrative, multi-time scale approach is illustrated in Fig 1. To the best of our knowledge, there is no previous information about the bioaccumulation of organic pollutants in M. varia and no previous study assessing the effects of chemical pollutants on the genetic diversity of its populations. Indeed, previous ecotoxicological studies on M. varia in this region have focused on trace metal concentration bioaccumulation processes and correlations with biomarkers.
We explored the multivariate response of biomarkers for our four sampling sites using Canonical Correspondance Analysis (CCA), in which ordination is constrained by environmental data, represented here by trace metal concentration in tissues. Stepwise model selection based on an AIC-like statistic was performed to determine which combination of trace metals optimized the fit of the CCA ordination. Individuals with missing data (n = 11) were removed from the analysis. CCA and its statistical significance (estimated using an ANOVA-like procedure based on 999 permutations) as well as model selection were performed in R using package vegan (functions cca, step and anova. cca; [31] ). Table  1. Summary of our sampling design. Analyses of two types of chemical contaminants (trace metals and organic pollutants), biomarkers, phosphatases and genetic data were conducted on natural populations of variegated scallops (Mimachlamys varia) sampled at four sites.
Species validation All individuals but one (from Loix-en-Ré) successfully amplified, and good quality sequences spanning 461 to 538 bp of the gene were obtained for 118 individuals (Loix-en-Ré: 29; Minimes: 29; Les Palles: 30; Port Neuf: 30). Sequences generated for this study were deposited in Genbank (accession numbers: KU680872-83). Prior to this study, only four cox1 sequences of variegated scallops were available in public databases. All 118 sequences obtained in this study were genetically closest (~98% sequence identity) to the variegated scallop (M. varia) sequences already in Genbank (accession numbers EU523665-66), validating species identifications made in the field. The importance of this taxonomic verification step has been acknowledged by several ecotoxicological studies, for organisms collected in the field (e.
In some datasets (e. g. , Laccase dataset, for which variance at Les Minimes was higher than at other sites), residuals deviated from normality and variances were non homogeneous. Results from one-way ANOVAs were therefore checked using Kruskall-Wallis tests, which provided the same results as the ANOVAs. Except for malondialdehyde, all biomarker levels differed among sites in March (p < 0. 03), while three biomarkers (Laccase, malondialdehyde and superoxyde dismutase) showed significant differences among sites in September (p < 0. 004). Results for each biomarker is further presented and discussed below. The values obtained are in agreement with previous works in other bivalve species Crassostrea gigas and in scallop M. varia  [6, 54]. Laccase proves to be a sensitive biomarker and phenoloxydase seems to play an important role in bivalve immunity when they are activated by ROS  [25].
Relationship among biomarker responses and contaminants CCA were run on each sampling season separately prior to attempting analyses pooling the two sampling dates. For the month of September, constrained analyses had a poor fit and were therefore not pursued, and consequently an analysis pooling both sampling seasons was not attempted. For the month of March, a model including Cd, As, Pb and Cr was selected. The ANOVA-like function of the R package vegan suggested that this constrained analysis was statistically significant (anova. cca function; 999 permutations, p < 0. 001). This model, explaining 44% of the variance in the dataset (axis 1: 40%; axis 2: 4%), suggested that Cd was the most structuring factor of the environmental matrix (anova. cca permutation test: p < 0. 001), and most particularly the variation in the Laccase and MDA biomarkers (Fig  5 ).
Indeed, both Laccase and MDA levels were positively correlated with Cd concentrations in the tissues of M. varia (linear models: R 2 : 18%, F 1,36 = 9. 0, p = 0. 005 for LAC and R 2 : 12%, F 1,36 = 5. 9, p = 0. 021 for MDA), although variance in the MDA dataset was higher than in the Laccase dataset. Loix-en-Ré and Les Minimes displayed the lowest and the highest biomarkers and Cd concentrations, respectively. Enzymatic activities of Laccase and MDA are involved in immune response and lipidic peroxidation, respectively. Thus, lipidic peroxidation, which acts on the plasma membranes by destabilizing fatty acid poly-insaturated, could be notably due to accumulation of Cd. Our results are in accordance with  [6] , which reported the induction of Laccase activity in harbour areas. An increase in Laccase activity reflects the synthesis of melanins from phenolic substrates, which plays a decisive role of cellular response to metal and in particular to Cd in M.
varia. Contrary to inorganic pollutants, it was impossible to explore the multivariate response of biomarkers using CCA with ordination constrained by organic pollutants concentrations in digestive glands. Indeed, whole digestive glands were used for organic contaminant measurements to maximize the sensitivity of the methodology, and biomarkers were assayed in other individuals. Therefore we can only highlight trends of simultaneous modulation of biomarkers and organic contaminants. Our study of organic contaminants in digestive glands of M. varia showed, for the first time, a high bioaccumulation capacity of this marine organism for this type of contaminants. In parallel, significant changes were observed in activities of enzymes involved in the modulation of oxidative stress (GST, SOD), immune system alteration (Laccase), mitochondrial respiration (CS) and general metabolism (phosphatase) in scallops collected from impacted sites compared with less exposed scallops (data from March).
Genetic diversity assessment The final alignment used for genetic diversity assessments was 477 bp including seven sites with missing data that were excluded from analyses. The alignment included 12 variable sites that were all synonymous substitutions. A total of 12 haplotypes were found within the dataset, including one central haplotype that was the most common at all sites (Fig  6 ). The greatest genetic diversity was observed in Loix-en-Ré (the reference site for inorganic contaminants), particularly in terms of nucleotide diversity. Conversely, the lowest genetic diversity was measured in Les Minimes (Table  4 ). The difference in nucleotide diversity observed between the reference site (Loix-en-Ré) and the other three sites was statistically significant (one sample ttest: t 2 = -5. 3, p = 0.
This study was supported by a Master grant from the  Port de Plaisance de La Rochelle  and an  ATER grant  from  Université de la Rochelle  for  Marine Breitwieser  and  Amelia Viricel , respectively. Additional funding was provided by the  Ministère des Affaires étrangères-Royal Norwegian Embassy , the  Centre National pour la Recherche Scientifique , the  Port de Plaisance de La Rochelle. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Mapping of the opossum NEAT1 gene To characterize the NEAT1 gene in the opossum genome, we first analyzed its genetic structure at the syntenic locus, which in eutherian mammals, contains the FRMD8, NEAT1, MALAT1 and SCYL1 genes in this order and in the same transcriptional orientation. Comparison of the opossum, human and mouse genomic sequences revealed significant conservation of the FRMD8, MALAT1 and SCYL1 gene regions but, as previously reported,  12  no sequence similarity in the NEAT1 region (Fig. 3A ). For instance, while an RNase P consensus sequence was easily recognizable at the 3 0 -end of the opossum MALAT1 gene, no such sequence was found within the putative opossum NEAT1 region extracted from the UCSC genomic database. 12, 18 Because of the ancient divergence around 190 Mya between marsupials and eutherian mammals, a homology-based search of non-coding genes with low sequence conservation is challenging.
In order to reduce the evolutionary distance, we then compared the opossum and Tasmanian devil (TD) genomic sequences (Fig. 3B ). As expected, conservation was increased for the FRMD8, MALAT1 and SCYL1 genes. Interestingly, it also revealed an approximately 5-kb long repeat-free region, downstream of the FRMD8 gene, with significant homologies between the 2 marsupial genomes. This region resembled, in size and positioning, the homologous repeat-free region shared by the human and mouse NEAT1_1 loci. 12, 13 In addition, in a region yet only partially assembled in the opossum genome, a downstream RNase P consensus sequence was found in the corresponding region of the TD genome, suggesting conservation of the 3 0 -end cleavage site of the long NEAT1_2 isoform. To reconstitute a full-length NEAT1 opossum sequence, 3 gaps of undefined nucleotides (stretches of N's within the UCSC genomic database) of 87, 532 and 951 nt were PCRamplified and sequenced (Fig.
3C ), resulting in a continuous 31-kb long stretch of genomic sequence starting at nt 13 631 789 of the opossum chromosome 8. Within the reconstituted sequence corresponding to the longest gap, a typical RNase P consensus cleavage site was uncovered. [18]  Evidence for 2 opossum NEAT1_1 isoforms Using this information and in the absence of reported ESTs in opossum or TD databases, we determined the sequence of the 5 0 -and 3 0 -ends of the short opossum NEAT1_1 isoform by RACE experiments. By 5 0 -RACE with cDNAs from uterine/placental tissues at 13 dpc, a single transcription initiation site was found 30 bp downstream of a presumptive TATA box. Interestingly, the nucleotide sequence of both the TATA box and the orthologous transcription initiation site were conserved in both TD and wallaby (Macropus eugenii) NEAT1 loci (Fig.
S2 ), that have been shown to regulate the balance between the short and long NEAT1 isoforms in human cells. 19 In addition, the opossum NEAT1_1b isoform and the mouse NEAT1_1 transcript are very similar in size (3 177 and 3 194 nt, respectively). Finally, the mapping of the NEAT1 transcription initiation site and of the RNase P cleavage consensus sequence in the opossum genome defines an 18 607 nt long NEAT1_2 isoform, as compared to the 20 771 and 22 743 nt long mouse and human transcripts. Collectively, these data demonstrate a global structural conservation of the NEAT1 gene in mammals despite an overall lack of sequence similarity between the marsupial and eutherian orthologs.
Specificity of NEAT1 gene expression in opossum A singularity of the opossum NEAT1 gene is the expression of 2 poly-adenylated NEAT1_1 isoforms with canonical PAS (AATAAA) immediately upstream of the poly(A). Quantitation by qRT-PCR of RNA extracted from a mixed uterus/placenta sample in late gestation demonstrate similar abundance for both NEAT1_1 isoforms in opossum, indicating that neither of these 2 PAS corresponds to a cryptic 3 0 -end. Moreover, both PAS DNA sequences  (B) Level of NEAT1 identity between species was defined by measuring the % of sequence aligned (cov) and the % of identity within aligned segments (id), using BLASTN. These values are reported on the graph with the identity color code shown on the left scale to underline the higher conservation of NEAT1_1 as compared to NEAT1_2 and the overall low conservation of both NEAT1 isoforms between marsupial and eutherian mammals.
Database screening and sequence analyses Genomic sequences from the FRMD8-SCYL1 locus were extracted from the opossum (6. 8X coverage assembly of the Monodelphis domestica genome, Broad monDom5, Oct. 2006), Tasmanian devil (Sarcophilus harrisii, UCSC/NCBI, sarHar1, Feb. 2011), human (Homo sapiens, UCSC GRCh37/hg19, 2009) and mouse (Mus musculus, UCSC GRCm38, 2011) genomes from the UCSC website. Analyses of syntenic conserved sequences were performed using the MultiPipMaker alignment tool (http://pipmaker. bx. psu. edu) using either the human or the opossum genome sequences as a reference. Multiple alignments of the NEAT1 transcripts were performed using the EMBL-EBI web_server and the Clustal Omega package (http:// www. ebi. ac. uk/Tools/msa/clustalo/). tRNA-like structures were predicted with Quickfold (http://unafold.
NEAT1 mRNA expression was determined by qRT-PCR. Reverse transcription was performed with 100 ng of DNasetreated RNA. PCR was carried out with 5 ml of diluted (1:20) cDNA in a final volume of 25 ml using the FastSYBR Green PCR Master Mix (Qiagen) in an ABI PRISM 7000 sequence detection system. Transcript levels were normalized relative to the amount of the housekeeping RPL0 gene (ribosomal protein L0). Samples (3 samples per organ unless otherwise stated) were assayed in duplicate. Primers are listed in Table  S1  and mapped on Fig 4A. NEAT1 genomic and transcripts sequences have been submitted to the GenBank database under accession KX036207 and KX036208-210 respectively.
Acknowledgments We thank  Professor Ulrich Zeller  ( Humbolt University, Berlin ) for providing opossum tissue samples at precisely defined time points during gestation;  A. Leskowicz  and  V. Marquis ,  UMR5503 , for the gift of OK cells;  Anne Dupressoir ,  Christian Lavialle  and  Dominique Weil  for discussion and critical reading of the manuscript.
The location on chromosome 8 of the opossum genome (UCSC database) of 3 gaps that were PCR-amplified and sequenced is shown as red boxes. The triple-helix forming motifs (green) and modeled structure of the downstream tRNA-like element found in gap 3 are depicted. The putative RNase P and RNase Z cleavage sites at the 3 0end of the opossum NEAT1_2 isoform are inferred by comparison with the tRNA-like structure in the human locus.
IEC-SAXS data collection SAXS data were collected on BM29 at ESRF  (Pernot et al. , 2013)  using a PILATUS 1M detector (Dectris) at a distance of 2. 864 m from the 1. 8 mm diameter flowthrough capillary. The scattering of pure water was used to calibrate the intensity to absolute units  (Orthaber et al. , 2000). The intensities were scaled such that the forward scattering corresponds directly to the concentration (in mg ml À1 ) times the molar mass (in kDa) of idealized proteins, i. e. 1 a. u. = 8. 03 Â 10 À4 cm À1 , unless explicitly stated otherwise. Data collection was performed continuously throughout the chromatography run at a frame rate of 1 Hz. The X-ray energy was 12. 5 keV and the accessible q-range was 0. 032-4. 9 nm À1. The incoming flux at the sample position was of the order of 5 Â 10 11 photons s À1 in 700 Â 700 mm.
A summary of the acquisition parameters is given in Table  1. All images were automatically azimuthally averaged with pyFAI  (Ashiotis et al. , 2015; Kieffer & Wright, 2013; Kieffer & Karkoulis, 2013). Online purification was performed with a high-pressure liquid-chromatography (HPLC) system (Shimadzu, France) consisting of an inline degasser (DGU-20A5R), a binary pump (LC-20ADXR), a valve for buffer selection and gradients, an auto-sampler (SIL-20ACXR), a UV-Vis array spectrophotometer (SPD-M20A) and a conductimeter (CDD-10AVP). The HPLC system was directly coupled to the flowthrough capillary of the SAXS exposure unit  (Round et al. , 2015). The flow rate for all online experiments was 1 ml min À1 , resulting in a mean passage time of material through the X-ray beam of 0. 1 s. Initial evaluation of the data quality was performed using the automatic SEC-SAXS processing pipeline available at the BM29 beamline  (Brennich et al.
SAXS data analysis To compare the scattering from different frames, the ratio of scattering in the low-q region (0. 11-0. 5 nm À1 ) to the high-q region (1. 5-2. 5 nm À1 ) was calculated in the same manner as in  Hynson et al. (2015). For easier comparison between different buffer subtractions, the result was normalized to give the same mean over the region of interest. Radii of gyration were calculated using AUTORG from the ATSAS package  (Petoukhov et al. , 2007) , P(r) functions were calculated using GNOM  (Svergun, 1992)  and Porod volumes were estimated using the Volume interface of SCA ˚TTER (available at http:// www. bioisis. net/tutorial/9). The estimation of molecular mass based on the Porod analysis was performed by dividing the Porod volume by 1. 7 nm 3 kDa À1  (Petoukhov et al.