dataesr/scientific-paragraphs-categorization huggingface dataset:
Hierarchical clustering analysis Clustering was performed using the Heatmap function of the Complex-Heatmap library in the Bioconducter package in the R program  [17]. The matrix was built with normalized, logtransformed ratio values for each spot obtained by the analysis with the ImageMaster 2D Platinum 7. 0 software. Euclidean distance was used to calculate the distance or dissimilarity between conditions, and the Complete Link plugin of Cluster 3. 0 was used for clustering.
Mass spectrometry and protein identification The exclusive and differential spots selected for protein identification by mass spectrometry were excised from the 2-D gel and subjected to tryptic digestion as previously described  [18]. The sample containing the proteolytic digestion was fractionated by phase-reversed chromatography on nanoAcquity UPLC coupled to a Q-Tof micro mass spectrometer (Waters) as previously described  [19]. The obtained spectra were analyzed using the ProteinLynx v2. 3 software and compared with the NCBI non-redundant database, setting the taxonomy parameter to green plants, and with the SWISSPROT database setting the parameter to Citrus sinensis. The protein identification was obtained using the MASCOT MS/MS IonSearch (www. matrixscience. com) with the following parameters: trypsin enzyme digestion, 1-cleavage site lost, cysteinecarbamidomethylation (Cys) as fixed modification and as methionine oxidation (Met) modification, error tolerance of 30 ppm, tolerance for mass error equal to 0.
Protein-protein interaction network (interactomics) Proteins are in constant interaction in a living cell. They do not perform their functions alone but integrate a series of networks and protein-protein interactions that are fundamental in almost all cellular processes (Pallas and García 2011). To understand how A. alternata interacts with susceptible and resistant plant proteins to affect cell functions, the proteins identified as differentially expressed were analyzed by searching the String database and the protein-protein interaction network. The abbreviations of the specific protein names related to A. thaliana in the network for 'Minneola' are shown in Additional File 1, Table  S3 , and the relation between proteins of 'Clemenules' and orthologous proteins in A. thaliana are shown in Table  S4.
Funding MSD and EMAS were funded by  Coordenação de Aperfeiçoamento Pessoal de Nível Superior (CAPES). TMO was funded by  Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq). This research was supported by  CNPq  (Pesquisador Visitante Especial call coordinated by FM). CPP and FM received a Productivity Grant from  CNPq (PQ). This work was made in the frame of the  International Consortium in Advanced Biology (CIBA).
Phylogenetic Analyses. SSU-and LSU rRNA gene sequences of Centrodinium spp. were analysed using Basic Local Search Tool (BLAST, http://blast. ncbi. nlm. nih. gov/Blast. cgi) against databases in GenBank. e closest matches to these searches were sequences in the genus Alexandrium (primarily A. affine) and the sequences reported as "Alexandrium sp. ZL2017" that were later identified as Centrodinium punctatum in Li et al. [11]. Based on these results, rRNA gene sequence data were compiled from similar sequences identified using BLAST. Sequence alignments of available SSU-and D1-D2 LSU rRNA gene sequences of Centrodinium spp. , representatives of each species of Alexandrium, other gonyaulacoid dinoflagellates, and other dinokaryotic dinoflagellates were accomplished by Clustal W  [16]  and the evolutionary history was inferred by using the Maximum Likelihood method based on the General Time Reversible model with Gamma distributed with Invariant sites and the default settings in MEGA7 so ware  [17].
In the LSU rRNA gene phylogeny, Centrodinium spp. also clustered with sequences retrieved from GenBank as Alexandrium affine and A. concavum (Figure  5 ). In an additional LSU rRNA tree more reference sequences were added from GenBank within the A. affine clade to include sequences identified as A. affine, A. tamarense, and A. concavum (Figure  S1  as Supplementary material). e strains CAWD51-52 diverged from the other sequences of A. affine. In the SSU-and LSU rRNA gene phylogenies (Figures  4 5 ), the species of the tamarense/catenella/fraterculus groups of Alexandrium clustered with high support as a sister group to the Centrodinium spp. and A. affine clades. e clade of Alexandrium sensu stricto (s. s. ) containing the type, A. minutum, and four divergent species (A. diversaporum, A. leei, A.
at clade includes sequences retrieved from GenBank under the names   A. a ne, A. tamarense, and A. concavum. Two sequences from New Zealand, the strains CAWD51 named A. a ne (accession number AY338753) and CAWD52 named A. concavum (accession number AF032348) were identical and diverged from the main group of A. a ne. e latter subdivided into two groups, one for strains isolated exclusively from Japan and China, and other group for strains from diverse world regions (Figure  S1 ). e cells of the strain CAWD52 illustrated in MacKenzie et al. [19]  corresponded to A. gaarderae as de ned by Larsen and Nguyen-Ngoc  [20]. e species Alexandrium a ne was rst described as Protogonyaulax a nis  [21] , and since the earlier molecular phylogenies the sequences of A. a ne have always diverged from the members of the tamarense/catenella group  [22].
Acknowledgments F. G. was partly supported by a convention (no:  2101893310 ) between  CNRS INSU  and the  French Ministry of Ecology (MTES)  for the implementation of the Monitoring Program of the  European Marine Strategy Framework directive (MSFD)  for pelagic habitats and the descriptor "biodiversity". Samples were collected during the  IFREMER "Etoile" cruise onboard R/V Côtes de la Manche (CNRS-INSU)  in the frame of the  Joint European Research Infrastructure network for Coastal Observatory -Novel European eXpertise for coastal observa-Tories (JERICO-Next )  H2020   INFRAIA  project no. 654410. We thank  I. Puillat , project manager, and  P. Lazure , scientific coordinator of the Etoile cruise. We thank  L. Courcot  for the SEM assistance.
The area west of the Kerguelen Islands (20-70°E/45-60°S) is characterized by a weak mesoscale activity except for a standing meander region of the Antarctic Circumpolar Current (ACC) localized between 20 and 40°E. A unique bio-physical dataset at high-resolution collected by a southern elephant seal (Mirounga leonina) reveals a conspicuous increase in foraging activity at the standing meander site up to 5 times larger than during the rest of her three-month trip west of the Kerguelen Islands. Here, we propose a physical explanation for such high biological activity based on the study of small-scale fronts with scales of 5 to 20 km, also called submesoscales. The standing meander is associated with intensified frontal dynamics at submesoscale, not observed in the rest of the region. Results shed new light on the spatial distribution of submesoscale fronts in the under-sampled area west of the Kerguelen plateau and emphasize their importance for upper trophic levels.
9°S, 37. 7°E), including elephant seals  25, 26  and macaroni penguins  27. The present work complements the research of other tagging programs that use animals as oceanographers to study animal behaviour in relation to characteristic water masses  28  and large-to meso-scale oceanic features  [29] [30] [31] [32]. However, this study focuses on submesocale physics thanks to a unique dataset at unprecedented high-resolution collected in situ by a female southern elephant seal from the Kerguelen Islands. Unlike previous tags mounted on marine mammal, the one used in this study recorded for the first time every single dive realized by a seal during its journey (or over 6900 dives). This dataset advantageously contains both physical and behavioural data, which allow us to explore the submesoscale dynamics of the region west of the Kerguelen Islands as well as its relation with the foraging behaviour of the tagged seal.
Southern elephant seal observations at unprecedented high-resolution. Results are inferred from a unique dataset of physical and biological observations collected by a female southern elephant seal (Fig. 2b ). The seal was equipped with sensors measuring temperature, conductivity and pressure at a continuous high-frequency of 0. 5 Hz and its travel was tracked through the Argos satellite system. Buoyancy, which is of opposite sign to the fluid density, and spiciness, which indicates thermohaline variations along constant density surfaces  33  (see Methods), are estimated from temperature and salinity observations. Both fields have a final vertical resolution of 1 m and a horizontal resolution of 1 km (see Methods). This gives access to a unique dataset of vertical sections (x-z) of buoyancy and spiciness at very high-resolution over a long distance (>5000 km) and down to 600 m in the ocean interior.
In addition, the map of Chlorophyll a concentration level 2 data in Fig. 2b  has been processed by CLS for the Kerguelen area. It corresponds to a time average of Chlorophyll a concentration over the 85 days of the seal's journey. Southern Elephant Seal dataset. High-resolution data was recorded by a seal-borne Conductivity-Temperature-Depth Satellite Relay Data Logger (CTD SRDL, Sea Mammal Research Unit, University of St Andrews) deployed on a post-breeding female southern elephant seal from the Kerguelen Islands (49°20′S, 70°20′E). The seal is tracked by the Argos satellite system and is equipped with sensors recording conductivity, temperature and pressure at a continuous frequency of 0. 5 Hz between 27 October 2014 and 20 January 2015, with an accuracy of ±0. 02 °C for temperature and ±0.
03 g/kg for salinity  57. Only the ascending phase of a dive is used because it is more uniform in speed and direction compared to the descent when the seal dives sinuously to forage. The dataset is comprised of 6942 dives, or over eighty dives per day, which corresponds to a cumulative length of 5665 km with a median spacing between two dives of 700 m (Supplementary Fig. S3 ). Dives can be as deep as 500 to 1000 m. They generally last less than 25 minutes and are separated by a few minutes surfacing, where the seal breathes without transiting. More than 85% of the dives reach a depth of at least 100 m, 45% reach 300 m and 25% are 400 m or deeper. To ensure a better accuracy of the conductivity-derived salinity data, two additional steps are applied to the temperature and salinity fields. First temperature and salinity are corrected for a thermal cell effect, and then a density inversion removal algorithm is applied to the salinity field.
Buoyancy. Along-track time series of buoyancy (in s -2 ), b = g(1 -ρ/ρ 0 ), where g is gravity, ρ is potential density, and ρ 0 = 1025 kg m -3 is a reference density reveals variability covering both meso-and submesoscales. For the analysis, in particular the calculation of lateral buoyancy gradients (b x ), buoyancy was first linearly interpolated along the seal's path onto a regular grid of 100 m resolution, corresponding to the shortest along-track distance between two dives (Fig. S1 ). A moving average with a 1 km window was then applied such that the final dataset has a vertical resolution of 1 m and a horizontal resolution of 1 km. Buoyancy anomalies are resolved by multiple vertical profiles, such that the structures are not related to aliasing of the along-track data. Spiciness. Spiciness (in kg m -3 ) is a state variable most sensitive to isopycnal thermohaline variations and least correlated with the density field  33.
Mixed layer depth. The mixed layer depth (in m) is defined as the level of a 0. 03 kg. m -3 density increase with respect to the density at 15 m depth  61. Quantify seal foraging activity. Foraging activity is estimated from high-resolution dive data, recorded at the continuous sampling frequency of 0. 5 Hz, by taking into account both the horizontal and vertical sinuosity of a dive. Indeed, a seal is expected to decrease its speed when feeding and move more sinuously along the horizontal axis, displaying what is effectively area-restricted search behaviour (ARS)  13, 62. However, adding the vertical dimension is also important  36  , and high-resolution dive data of female SES demonstrates how vertical sinuosity can significantly improve the predictive capacity of ARS as a proxy for foraging success  24.
This is backed by simulated diving tracks of beluga whales (Delphinapterus leucas) used to refined ARS in a 3D space  63. Here, we develop an index, the First-Passage Hunting Time (FPHT), which indicates the amount of time spent hunting in a region of given radius and includes the following steps. First, we compute the Hunting Time (HT) via an automated broken stick algorithm, which summarizes the vertical sinuosity of the dive data based on the optimized number of segments within each dive. Further details outlining this step are provided in Heerah et al. 37. A behavioural state is then assigned to each dive segment based on visual inspection of dive segment sinuosity distribution: low sinuosity values (>0. 9) represent transit behaviour and high sinuosity values (<0. 9) represent hunting behaviour.
To validate this method, prey encounter events were detected from an accelerometer  64  that, unfortunately, only recorded during the first three weeks of the seal's trip due to limited storage capacity. However, evidence suggests that elephant seals even exhibit high foraging success during the outward transit when movement was relatively rapid and direct  65. Furthermore, most prey encounter events (79%) recorded during these initial three weeks occurred within hunting segments, which comforts the use of the accelerometer data as validation data thoughouth the entire seal's journey. This result is also consistent with the analyses of two post-breeding elephant seal trips in Heerah et al. 37. Next, we modify the First-Passage Time approach (FPT)  66  , which is a scale-dependent foraging metric (i.
The dataset has been made available to the community and can be found on the Marine Mammals Exploring the Oceans Pole to Pole database (http://www. meop. net/). The animal in this study was handled in accordance with the Institut polaire francais Paul-Emile Victor (IPEV) ethical and Polar Environment Committees guidelines as part of the SNO-MEMO and IPEV program 109 (PI. H. Weimerskirch). The experimental protocols were approved by the Ethics Committee of IPEV and Polar Environment Committees.
Acknowledgements This work was partly funded by the  CNES (OSTST-OSIW)  and the  Laboratoire d'Excellence LabexMER  ( ANR-10-LABX-19 ). L. S. is a NASA-JPL JVSRP affiliate and is supported by a joint  CNES-Région Bretagne doctoral grant. M. F. is supported by  NASA  grant  NNX15AG42G. P. K. is supported by the  NASA-CNES SWOT mission  and a  NASA Senior Fellowship. This study was also supported by the  CNES - TOSCA  project Elephant seals as Oceanographic Samplers of submesoscale features led by  C. Guinet. The authors would like to thank the  French Polar Institute (Institut Paul Emile Victor, IPEV)  ( Progams 109  and  1201 ) and CNES-TOSCA as well as  Christophe Guinet  and all the volunteers involved in the research program on southern elephant seals in Kerguelen. The authors would also like to thank  Baptiste Picard  for providing the elephant seal data,  Tandi Reason Dahl  for realizing the summary schematic (Figure 4) and two anonymous reviewers who considerably helped improve the manuscript.
To assess whether the observed induction of the circular transcripts was simply the consequence of a general increase of transcription in the relevant genomic region in DM1 patients, modulation of the ratios between the circular and the linear isoforms was calculated. We identified four circRNAs (circCDYL, circHIPK3, circRTN4_03, and circZNF609) with a significantly increased circular-to-linear ratio in DM1 muscles (Figure  1a ), implying a de-regulation of the circular transcript independent from its linear counterpart. Accordingly, a similar trend was also observed in the RNAseq data, where the circular-to-linear ratios were higher in the DM1-affected muscles compared to the controls (Table  S3 ). As additional validation, published RNA-seq datasets of five controls and nine DM1 quadriceps muscle biopsies  [37, 38]  were analyzed with the stringent criteria described for tibialis anterior.
DM1-circRNA levels in PBMCs and plasma of DM1 patients Since peripheral blood can be obtained with a minimally invasive procedure, it represents a potentially interesting tissue for biomarker identification. Thus, we measured DM1-circRNA expression in PBMCs and plasma of DM1 patients and sex-and age-matched controls. Diseased and healthy subjects were chosen with the same criteria adopted for the harvesting of skeletal muscle biopsies (Table  S10 ). In PBMCs all tested DM1-circRNAs were readily detectable, but none of them showed a significant modulation (Figure  S5 ). In plasma samples only circCDYL and circRTN4 were readily detectable. For both, circCDYL and circRTN4, a small but not significant induction in DM1 patients could be observed. This trend is in line with the data obtained in biopsies (Figure  S6 ).
RNAseq and Bioinformatics Analysis Taking advantage of the GEO (www. ncbi. nlm. nih. gov/geo/) and DMseq (http://dmseq. org/) repositories, we analyzed RNAseq data-sets derived from tibialis anterior muscle biopsies taken from DM1 and control patients (GSE86356)  [37, 38]. In order to ensure sufficient sequencing depth for the identification of the expectedly rare back-splicing events, we used only data-sets containing more than 75 million reads (Table  S1 ). In this way, raw reads in fastq format from five control and 25 DM1 ribo-depleted libraries were aligned to a hg19 reference genome with BWA software (version 0. 7. 12, Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Cambridge, UK), with options chosen according to a CIRI2 manual  [39, 40]. Subsequently, circRNAs were identified by detecting back-splice events in each of the aligned samples using CIRI2 (version 2.
e. , reads spanning a splice junction. Additionally, RNAseq data obtained from quadriceps muscle biopsies included in the same study (GSE86356)  [37, 38]  were used for circRNA identification. Among the available data sets, only libraries prepared with high-quality RNA were used (RIN ≥ 7). Identification, quantification of circRNAs, and estimation of circular-to-linear ratios were performed as described above. Data on alternative splicing events in DM1 available on the DM-seq platform  [38]  were downloaded for the same tibialis anterior data sets used for circRNA analysis. In detail, PSI values determined by MISO software (hg19 v2. 0 MISO version, Airoldi and Burge, Cambridge, MA, USA) of a subset of 10 splicing biomarkers of disease severity in DM1 were correlated to circular-to-linear ratios of DM1-circRNAs.
Real-Time Reverse Transcriptase qPCR For validation experiments, total RNA was first retro-transcribed using the SuperScript Reverse Transcriptase kit (Version III or IV) and then investigated by SYBR green qPCR according to the manufacturer's protocol (Thermo Fisher Scientific Inc. ). Primer couples were designed by Primer-BLAST tool (Table  S4 ). The circRNAs primers spanned the back-splice junction, while the primer couples for the linear transcripts crossed the linear junction to a neighboring exon. Primer efficiency analysis was performed for all DM1-circRNAs together with their linear counterparts. Additionally, the specificity of primers was assessed by performing melting curve analysis and verification of expected amplicon size by agarose gel electrophoresis. The relative expression was calculated using the comparative Ct method 2 -∆∆CT  [72] , normalizing to the averaged Cts of RPL13, RPL23, and UBC for tissue RNAs and to the averaged Cts of miR-106a and miR-17-5p for plasma RNAs  [68, 69].
In these conditions, the cycle time was about 496 ms. Analytical background was recorded for 30 s at the beginning of each analysis followed by acquisition of the LA-ICP-MS signal of tourmaline for 40-60 s. Data reduction and absolute quantification of signals were performed using the software StalQuant developed at the ETH Zürich, Switzerland. For each LA-ICP-MS analysis, the Si content previously determined by EMPA was used as internal standard. Limits of detection (LOD) were calculated using the 3σ criterion from  Longerich et al. (1996).
Major and minor elements Representative chemical compositions of tourmaline from Puy-les-Vignes are summarized in Table  1  and the complete dataset is provided in the Electronic Supplementary Material. The different generations of hydrothermal tourmaline have overall similar chemical compositions, with some variations in the major elements Fe (0. 5-1. 7 apfu), Mg (0. 7-2. 1 apfu), and Al (5. 6-6. 9 apfu), as well as for the minor elements Na (0. 4-0. 8 apfu), Ti (<0. 3 apfu), and Ca (<0. 2 apfu). Tourmaline belongs mostly to the alkali group and plots within the field of metasedimentary rocks in the Al-Fe-Mg diagram (Fig. 5 ). Only a minority of tourmaline belongs to the X-vacant group and plots along the field of granites and pegmatites (Fig. 5 ). Tourmaline compositions correspond dominantly to the dravite-schorl solid solution, with a few analyses corresponding to the foitite-Mg-foitite solid solution (Fig.
Trace elements Average trace element compositions of tourmaline from Puy-les-Vignes are summarized in Table  2  and the complete dataset is given in the Electronic Supplementary Material. ppm), total REE (0. 3-15 ppm), Sn (7-147 ppm), and Pb (0. 7-19 ppm) (Fig. 7 ). In multi-element diagrams normalized to the upper continental crust (UCC from  Rudnick and Gao, 2003) , the different tourmaline generations show similar trace element patterns with relative enrichments in Sc, V, Cr, Be, Bi, and Zn between 2 and 100 times higher than the UCC values, and relative depletions in Sr, Ba, Cs, Rb, Y, Nb, Ta, Zr, Ti, U, Th, Mo, Sn, Pb, and Cu between 5 and 100 times lower than the UCC values (Fig. 8 ). Principal component analysis (PCA) has been applied in order to understand the compositional variations of tourmaline and to identify trace element correlations.
PCA is a multivariate statistical technique that provides dimensionality-reduction of correlated variables into a reduced set of orthogonal linear combinations (so-called principal components), maximizing the variance and minimizing information loss  (Izenman, 2008). PCA is a classical method of multivariate statistics that is particularly useful for treating large geochemical datasets such as LA-ICP-MS trace element analyses of minerals (e. g. ,  Winderbaum et al. , 2012; Belissont et al. , 2014).
, 2005; Jiang et al. , 2008; Ribeiro da Costa et al. , 2014; Drivenes et al. , 2015; Codeço et al. , 2017). Recent SIMS data from the Panasqueira W-Sn deposit (Portugal) show that tourmaline has δ 11 B values comprised between -12. 7‰ and -3. 7‰ (average δ 11 B = -9‰), which has been interpreted as a boron source derived from a S-type granite  (Codeço et al. , 2017). Generally, the boron isotopic composition of tourmaline is controlled by (i) the composition of the boron source, (ii) the temperature of formation, and (iii) the fluid/rock ratio  (Palmer and Slack, 1989). Based on existing tourmaline-fluid boron isotope fractionation factors from  Meyer et al. (2008) , valid for T=400-700°C and P=200 MPa, it is possible to estimate the isotopic composition of the hydrothermal fluid in equilibrium with tourmaline depending on temperature.