dataesr/scientific-paragraphs-categorization huggingface dataset:
From the end of August until the beginning of November, all macrocosms were exposed to the 2050 climate conditions. From April 2010 to November 2011, the Ecotron climate-regulation system monitored hourly means of air temperature and humidity, as well as daily means of precipitation and CO 2 concentration. Each of the four experimental treatments combining both CO 2 and ECE treatments was replicated three times. Further details on the experimental conditions can be found in  Roy et al. (2016). In each of the 48 monoliths, SWC was continuously measured at soil depths of 7, 20 and 50 cm using time domain reflectometry (TDR) probes (IMKO, Ettlingen, Germany) and averaged across soil depths and monoliths in order to get one value per macrocosm. In addition, to match with data on forage quality, SWC was averaged across regrowth periods before the cuts, i.
Statistical analysis All variables related to the chemical composition of plant communities and in vitro rumen fermentation parameters were analyzed using a mixed model (MIXED procedure, SAS Enterprise Guide 5. 1, SAS Institute Inc. , Cary, NC). Each macrocosm was considered as an experimental unit. For the April and June cuts, CO 2 concentration was used as a fixed effect, and the effects of ECE treatment and CO 2 -ECE interaction were added as fixed effects for the November cut, with the macrocosm used as a random factor. For each variable analyzed, data were subjected to three covariance structures: compound symmetry, autoregressive order 1 and unstructured covariance. The covariance that resulted in the smallest Akaike's information criterion was retained for analysis. Fractions (relative abundances) were transformed by the arcsine of the square root before the analysis of variance.
Acknowledgements. This study benefited from human resources (CNRS staff) and technical resources allocated to the ECOTRONS Research Infrastructure and from the French government-sponsored " Investissement d'Avenir" funding  ( AnaEE-France ANR-11-INBS-0001 ). We thank the technical staff of  INRAE Herbipole  and UREP groups for extracting the intact soil monoliths; the Ecotron team,  Olivier Darsonville ,  Lionel Thiery  and  Marine Zwicke  for their technical help during this experiment;  Didier Macheboeuf  for his expertise in in vitro rumen fermentation; and  Pierre-Jean Haupais  for his contribution to soluble carbohydrate analysis. Angela Augusti  and  Marie-Lise Benot  received post-doctorate positions through an  INRAE scientific  package (2010)(2011)(2012)(2013)(2014). Angela Augusti  was also supported by the  European FP7 ExpeRT Transnational Access program.
Financial support. This research has been supported by the  FP7 in Food, Agriculture and Fisheries and Biotechnology (Animal Change  project; grant no. 266018 ). This study was supported by the  European Community FP7  funding ( Animal Change  project, grant agreement number  266018 ,[ 2007][2008][2009][2010][2011][2012][2013 ] and an  ANR  project grant ( VALIDATE ). Review statement. This paper was edited by  Paul Stoy  and reviewed by two anonymous referees.
Molecular method The environmental DNA from the roots was extracted with the PowerSoil DNA isolation Kit following the manufacturer's instructions (MoBio Laboratories, Inc. , Carlsbad, CA, USA). The PCR was performed using the specific fungal primers ITS1F (5'-CTTGGTCATTTAGAGGAAGTAA-3') and ITS2 (5'-GCTGCGTTCTTCATCGATGC-3')  (Gardes and Bruns, 1993; White et al. , 1990). We selected these primers to allow for comparison with our previous dataset obtained on a similar plantation, at a different site  (Durand et al. , 2017). These primers has also been found as one of the most competent in uncovering the fungal diversity of plant root  (Bainard et al. , 2017; Huang et al. , 2020). PCR was performed in a reaction volume of 35 µL using 0. 5 µM forward and reverse primer each, 200 µM dNTPs, 1 µL of Phire hot start II DNA polymerase (1 U/µl), 26.
25 µL of 5X Phire reaction buffer (Thermo Fisher Scientific, Inc. USA), 10 ng template DNA and sterilized water to reach a final volume of 35 µl. The PCR conditions were 98 °C for 30 sec, followed by 25 cycles of 98°C for 5 sec, 57. 2 °C for 5 sec and 72 °C for 15 sec, followed by a final extension step at 72 °C for 1 min. These primers targeted a short portion of the fungal ITS region, resulting in amplicons of small size (~300 bp), appropriated for Illumina sequencing. The DNA quality and quantity were assessed by agarose gel electrophoresis using the ImageLab software (Bio-Rad, USA) and with a Qubit TM dsDNA HS Assay Kit (Invitrogen, Carlsbad, CA, USA) using the Qubit 4 fluorometer. The partial fungal ITS region  (Wang et al. , 2015)  of 6 and 7 of nine poplar and nettle samples (i. e. with the concentration of environmental DNA required) were sequenced with the Illumina MiSeq platform (Microsynth AG, Switzerland).
2014; Durand et al. , 2017; Foulon et al. , 2016b; Huang et al. 2020; Nguyen et al. , 2016). We removed singleton OTUs to avoid technical artefacts and overestimation of the number of species  (Tedersoo et al. , 2010). The taxonomic assignment of OTUs was performed using the UNITE  (Kõljalg et al. , 2013)  database by a naïve Bayesian approach  (Wang et al. , 2007). The presence of plant sequences was verified using the BLAST tool of the GenBank database and removed. Each fungal OTU was further assigned to functional or morphological groups of fungi using FUNguild  (Nguyen et al. , 2016). For each assignment, three confidence rankings (i. e. , "possible": suspected, "probable": fairly certain, "highly probable": absolutely certain) referring to previously peer reviewed data were given. We only considered the functional and morphological assignation with at least a "probable" confidence ranking.
Data analysis Alpha diversity indices (OTU richness, Chao estimation, Shannon diversity index, inverse of the Simpson diversity index) were calculated using MOTHUR. Samples were rarefied at 33 000 sequences, corresponding to the smallest number of sequences detected in a sample. No sample was excluded. To verify that sequencing depth allowed covering most of the fungal diversity of our root samples, Good's coverage was calculated as C = [1 -(n / N)] × 100 (%), where "n" is the number of OTUs, and "N" is the number of sequences  (Good, 1953). Statistical analyses were performed with R software v. 3. 6. 1 (R Development Core Team, 2019). All the considered variables were tested for their homoscedasticity (Bartlett or Levene tests) and normal distribution (Shapiro-Wilk test) and compared using analysis of variance (ANOVA) followed by a Tukey HSD test.
Diversity of mycobiome associated with nettle and poplar roots Illumina MiSeq sequencing of ITS1 amplicons obtained from 26 root samples of poplar and 25 root samples of nettle generated a total of 3,481,223 non-chimeric fungal sequences. The initial total number of sequences obtained per sample ranged from 33,615 to 140,409. The sample with the lowest number of sequences was selected for subsampling, revealing a total number of 1833 non-singleton OTUs for 1,683,000 fungal reads, and constituting our final dataset. Rarefaction curves calculated for each experimental condition of nettle and poplar tended to reach an asymptotic profile, suggesting that the sequencing depth was sufficient to cover the fungal diversity of our root samples. Additionally, the measured Good's coverage values were greater than 99% for each condition and in every sample, suggesting a representative sampling.
)  (Foulon et al. , 2016b; Hrynkiewicz et al. , 2010; Lacercat-Didier et al. , 2016). Hebeloma or Inocybe species, which were well represented at the studied site, are "early stage" fungi capable of colonizing roots of trees established in virgin or disturbed habitats  (Smith and Read, 2010). In our dataset, only 7% of the variability were explained by the cultivar factor when performing a PERMANOVA on the poplar dataset (data not shown), indicating that the mycobiomes of Skado and I214 roots were comparable, consistently our previous results  (Foulon et al. , 2016a) : in our previous work, the mycobiome of eight poplar cultivars grown at a TM-contaminated site, exhibited slight differences, especially between I214 and Skado roots. Together with the present study, the only discrepancy between the two communities concerned the Laccaria genus, only recorded under the I214 cultivar in small abundance.
Results of the Illumina MiSeq sequencing of ITS1 amplicons, including richness and diversity indices of the mycobiomes associated to I214 and Skado poplar roots, or to nettle roots growing in the I214 or Skado plots, at low trace-metal (LTM) and the high trace-metal (HTM) areas. Mean values and standard deviations (mean ± SD) are provided for each experimental condition. The "total number of sequences" corresponds to datasets before subsampling at 33,000 sequences, while the other parameters are calculated after subsampling. Values designated with the same letters are not significantly different (Kruskal-Wallis test, P < 0. 05).
An aliquot of these samples was grounded < 200 µm and scanned with a spectrometer (FTIR 660, Agilent ex-Varian) in the mid-infrared spectral range from 4,000 to 400 cm -1 with a KBr separator and a silicon detector. The diffuse reflectance measurements were made at 2 cm -1 intervals and was converted to absorbance. The raw spectra were subjected to two different pre-processing steps. First, the spectral signature was measured from the dataset after taking the second derivative to remove baseline shifts and separate overlapping absorption. Absorbance measurements were selected each 20 cm interval in order to reduce the number of independent variables (from 1766 to 176) and bands associated with CO2 interference (2500-2300 cm -1 ) were removed. The data were then used for multivariate statistical analysis.
The dissimilarity of spectral signatures was calculated by the Euclidean distance between samples. Spearman correlations were performed between physical and chemical properties. Differences were declared significant at the 0. 05 probability level. All statistical calculations and plots were executed using R software (3. 5. 2 version) with the following packages: "car", "ade4", "ggplot2", "spectacles", "signal", "geosphere" and "vegan".
Acknowledgements This project was financially supported by the  CNRS/INSU (VINAWORM)  research programme under the framework of the  EC2CO programme. We are very grateful to  Kim Yen Do  for her invaluable help in organizing the experiment. We also acknowledge the technical assistance of the farmers in Dong Cao village for their participation in the sampling of soil samples. Thanks, are also extended to  L. Ganesha  for his unconditional support.
, 2017). Seven consensus questions on the influence of viticulture practices on vine health were addressed during the consensus statement construction workshop  (Soustre-Gacougnolle et al. , 2018). Training sessions were conducted from 2014 to 2016 to ensure that everyone understood the nature of the experiments that would be conducted and the type of data that would be collected. For example, participants learned how to crush vine leaves in liquid nitrogen, extract ribonucleic acids (RNA), perform transcriptomic molecular analyses and interpret the results, as well as receiving instruction in secondary metabolites extraction and how to use gas chromatography followed by mass spectrometry (GC-MS) to characterise them. We visited the vineyards to assess soil quality, to carry out spade tests, and estimate vine vigour as well as disease pressure levels.
All participants obtained training in biometrics and statistics. Depending on the topic, researchers, winegrowers, NGO members, or advisers were alternately learners and trainers in the laboratories and/or the vineyards. To illustrate data, we used images with a blue background for vines grown in conventional practices and a brown for bio/biodynamics (Fig. 1 ). During the period 2014-2016, workshops covered vineyard climate, vine plant development and health status (i. e. presence of downy mildew, powdery mildew, and five major vine viruses), and the activity of natural defences based on data collected during the three seasons. Each workshop table had a 2. 5 × 1 m paper support with the different phenological stages of the vine during the year positioned on the abscissa and a scale of values (low-medium-high) on the ordinate.
On 29 March 2018, a workshop was held for graphics construction, during which the graphics were projected in real time, made visible to the other tables, and used for intra-and inter-tables discussions and debates. The scientific article was written after this workshop and published in November 2018  (Soustre-Gacougnolle et al. , 2018). Data collection. All workshop discussions were filmed and audio-recorded. We also conducted open individual interviews on life-path and experience-building with 20 winegrowers at each place: Westhalten in 2014-2019 and Dambach-La-Ville, Muttenz, and Tullinger-Berg in 2018-2019. An interview focusing on vine health and viticulture practices was carried out at Westhalten in 2019.
During the workshop on vine health, levels of plant infection, and biochemical and molecular data on vine defence systems as well as winegrowers' assessments of disease symptoms in the vineyards and the intensity of leaves' greenness were discussed before laying out the corresponding images (Fig. 1 ). When all the data were obtained for the consensus questions of the project on vine health, we favoured the same input as for the meteorological data, proposing to the participants to explain how each had made their observations and obtained results. In this way, we again legitimised both the qualitative and quantitative dimensions of participants' experiences. These discussions evinced the difficulties that everyone, whether winegrowers, advisers, or researchers, had to overcome in order to produce useful data, and a better understanding of each other's' work emerged.
Acknowledgements Many thanks to all the participants from the  Westhalten group (GIEE) :  M. Boesch ,  M. Breuzard ,  M. Burgenath ,  A. Bursin ,  E. Dauer ,  S. Diringer ,  R. Hetsch ,  B. Huntzinger ,  P. Isner ,  B. Kaepfer ,  C. Koehler ,  J. F. Lallemand ,  H. Lallemand ,  R. Lassablière ,  Y. Miclo ,  C. Rominger ,  P. Rué ,  P. Schäfer ,  G. Schatz ,  V. Schatz ,  F. Schermesser ,  J. L. Schlegel ,  D. Schmitt ,  F. Wisselmann ,  T. Muré ,  F. Cousin , and  M. Mermet. Many thanks to all participants for engaging in meetings from  French, Swiss , and German locations and their involvement in their site-specific  REPERE  project-building. Thanks to  F. Weibel ,  H. Roemer , and  J. Schneider  for their help in building group projects in Switzerland and Germany, respectively. Thanks to  Y.
UV-and MS-based dereplication and isolation Extraction of the dried lichen thallus with dichloromethane by maceration at room temperature gave a brown extract enriched in quinoid pigments easily visualized on TLC. Fractionation of the extract by silica gel chromatography yielded several fractions. These fractions were analyzed by HPLC-DAD and showed two main groups of compounds with distinct UV absorptions. The first group displays a UV spectrum with four main bands at 210-232; 271-280; 308-310 and 420-490 nm characteristic of anthraquinones  [11]. The second group displays a different UV spectrum with two main bands at 278-280 and 358-360 nm strongly suggesting the presence of bianthronic compounds  [12]. In an attempt to map the chemical diversity of the dichloromethane extract, the LC-MS/MS data were processed using the feature-based molecular networking workflow  [13]  and subsequently dereplicated against the Lichen Database (LDB)  [14] , hosted by GNPS  [15].
The molecular networking-based dereplication process afforded only two hits (emodine and 7-O-methylfragilin). These results reveal the lack of lichen compounds references in MS/MS databases and the high potential for new compounds discovery in N. laevigatum. The quest for tackling the issue of molecular networking annotation has fueled a number of creative and successful endeavors from the community, including our group  [16] [17] [18] [19]. Hence, as part of our continuous interest in the discovery of new chlorinated quinoids pigments, the molecular network was colorcoded (Fig. 1  and S1 in supporting information for the whole molecular network) based on the halogenation pattern (1 × Cl, 2 × Cl) of the fragmented parent masses. This annotation revealed that there were over 50 metabolites possessing at least a single chlorine in their chemical structure.
The probability that experimental and theoretical data match is then calculated  [25]. For compounds 5 and 6, two sets of resonance signals were obtained, to which two diastereoisomers must be assigned. CP3 parameters were then determined for all possible pairs of diastereoisomers of the gauche conformers of 5 and 6. Unfortunately, we were unable to exploit the 1 H NMR chemical shifts, because they were too close for both compounds 5a and 5b. Furthermore, 13 C NMR data were not available for compound 5b. Therefore, we give only results for the isomers of compounds 6a and 6b. The CP3 results allowed to attribute the (10R*, 10 ′ S*) relative configuration isomer for compound 6a and the (10R*, 10 ′ R*) one for compound 6b (Table  3 ). In addition, the results are in favor of the gauche-2 conformation for 6b which is indeed the most stable conformer (Table  2 ).
Their biosynthesis implies the polyketide pathway  [32]. The metabolism of halogenated compounds is restricted to fungi or lichenized fungi. In lichens, parietin is the most frequent anthraquinone biosynthesized by Xanthoria or Caloplaca species, but anthraquinones pigments are particularly prominent in the families of Nephromataceae, Physciaceae and Telochistaceae  [2, 21, 33, 34]. Bianthrones are more scarcely ever isolated. As demonstrated in this work, it could be explained by the difficulty to access to these compounds through purification process due to their instability and rapid degradation. Beside flavoobscurins isolated from the lichen H. obscurata, two chlorinated bianthrones, were isolated from the endophytic fungus Penicillium sp. (accession number HQ112180)  [24]  and from Aspergillus alliaceus  [35].
Data dependent LC-ESI-HRMS 2 analysis LC-ESI-HRMS 2 analyses were achieved by coupling the LC system to a hybrid quadrupole time of-flight mass spectrometer Agilent 6530 (Agilent Technologies, Massy, France) equipped with an ESI source, operating in positive-ion mode. Source parameters were set as followed: capillary temperature at 320 • C, source voltage at 3500 V, sheath gas flow rate at 10 L. min -1. The divert valve was set to waste for the first 3 min. MS scans were operated in full-scan mode from m/z 100 to 1700 (0. 1 s scan time) with a mass resolution of 11,000 at m/z 922. MS1 scan was followed by MS2 scans of the five most intense ions above an absolute threshold of 5000 counts. Selected parent ions were fragmented at a collision energy fixed at 45 eV and an isolation window of 1.
3 amu. Calibration solution, containing two internal reference masses (purine, C 5 H 4 N 4 , m/z 121. 050873, and HP-921 [hexakis-(1H,1H,3H-tetrafluoropentoxy)phosphazene], C 18 H 18 O 6 N 3 P 3 F 24 , m/z 922. 0098). A permanent MS/MS exclusion list criterion was set to prevent oversampling of the internal calibrant. LC-UV and MS data acquisition and processing were performed using MassHunter Workstation software (Agilent Technologies, Massy, France).
Feature-based molecular networking The MS 2 data files, related to the dichloromethane extract of Nephroma laevigatum were converted from the. d (Agilent) standard dataformat to. mzXML format using the MSConvert software, part of the ProteoWizard package  [37]. All. mzXML were then processed using MZmine 2 v52  [38]. The mass detection was realized keeping the noise level at 1. 2E3 at MS1 and at 2E1 at MS2. The ADAP chromatogram builder was used using a minimum group size of scans of 2, a group intensity threshold of 2E3, a minimum highest intensity of 2E3 and m/z tolerance of 10 ppm  [39]. The chromatogram deconvolution was performed using the Local Minimum Search algorithm with the following settings: chromatographic threshold = 1%, search minimum in RT range (min) = 0. 1, minimum relative height = 5%, minimum absolute height = 2E3, min ratio of peak top/edge = 1.
Molecular networking parameters A molecular network was created using the online FBMN workflow (version release_20) at GNPS (http://gnps. ucsd. edu) with a parent mass tolerance of 0. 02 Da and an MS/MS fragment ion tolerance of 0. 02 Da. A network was then created where edges were filtered to have a cosine score above 0. 65 and more than 6 matched peaks. Further edges between two nodes were kept in the network if and only if each of the nodes appeared in each other's respective top 10 most similar nodes. The spectra in the network were then searched against GNPS spectral libraries. All matches kept between network spectra and library spectra were required to have a score above 0. 65 and at least 6 matched peaks. The molecular networking data were analyzed and visualized using Cytoscape (ver.
Computational details for GIAO NMR chemical shifts calculations All DFT calculations were performed using the Gaussian 09. A01 program  [42]. The calculations of the NMR spectra consisted in three successive steps. For each compound 5 and 6, isomers C-10 R/C-10 ′ R and C-10 R/C-10 ′ S were first subjected to a conformational search using the MMFF94 force-field implemented in TINKER software tools. The resulting lowest energy conformations were then subjected to further optimization at the MPW1PW91/6-31 + G(d,p) level of theory. The bulk solvent effects were described with the integral equation formalism polarizable continuum Model (IEFPCM) with DMSO as solvent  [43]. Vibrational frequencies were computed using the gauge-including atomic orbitals (GIAO)  [44, 45]  method with the same level theory.
Acknowledgements This work was supported by  Limousin Region (doctorale fellowship  grant for  A. Lagarde ). This work was also financially supported by the  ANR (Agence Nationale de la Rencherche, France)  ( ANR-17-CE35-0005 ). We are also grateful to  A-L. Rioux ,  M. Arfeuillère  for technical assistance and to  Mrs. E. Pinault  for HR-MS analysis.
211 Da elemental composition: C22H36O15N2 and Glycan 2; 668. 227 Da elemental composition C26H40O18N2) and acetylation of protein N-termini. The precursor mass tolerance was set to 20 parts-per-million (ppm) for the first search and 10 ppm for main search, with a maximum false discovery rate of 1. 0% set for protein and peptide identifications. The resulting outputs were processed within the Perseus (v1. 4. 0. 6)  [28]  analysis environment to remove reverse matches and common proteins contaminate prior to further analysis. Glycopeptide identified were manually assessed according to the guidelines of Chen et al. [29] , annotated manually with the aid of Protein Prospector tool MS-Product (http://prospector. ucsf. edu/prospec tor/cgi-bin/msform. cgi?form=msproduct). The MS proteomics data were deposited in the ProteomeXchange Consortium via the PRIDE partner repository  [30, 31]  with the dataset identifier PXD018788.
Analysis of BCAL2737a degradation in Burkholderia strains As BCAL2737a was not annotated in the reference B. cenocepacia strain J2315 (see Results), we reanalyzed the previously published B. cenocepacia K56-2 peptidome datasets (Pride accession: PXD014614)  [32]  including BCAL2737a. Searches were performed against the predicted amino acid sequence of BCAL2737a and the K56-2Valvano proteome (NCBI: Taxonomy ID: 985,076) with identical setting as above. The resulting outputs were processed in the Perseus (v1. 5. 0. 9) analysis environment to remove reverse matches and common protein contaminates prior to further analysis. Peptides identified across samples were visualized in heat maps using R (https://www. r-project. org/).