dataesr/scientific-paragraphs-categorization huggingface dataset:
All the 30 sequences were submitted to the GenBank database at NCBI (accession num-bers from MK418498 to MK418527). Phylogenetic analyses revealed four monophyletic ribotypes (Figure  3 ); three of these belonged to the Order Glomerales and one, to Archaeosporales. Concerning Glomerales, 11 of the sequences were grouped with Rhizophagus irregularis/Rh. proliferum, and seven sequences showed affinity with Rhizophagus diaphanum; a group of two sequences were related to Glomus. Estimates of evolutionary divergence between representative sequences of clades observed in the tree confirmed these ribotypes of Glomerales (Table  6 ). Finally, a group of ten sequences were included in the Order Archaeosporales and were closely related with Archaeospora.
Estimates of evolutionary divergence between representative ribotypes observed in the phylogenetic tree representing SSU-ITS-LSU rDNA sequences of Glomerales colonizing Mimosa pigra flooded roots. The number of base substitutions per site between sequences are shown. Analyses were conducted using the Maximum Composite Likelihood model and it involved five nucleotides sequences. All positions containing gaps and missing data were eliminated. There was a total of 517 positions in the final dataset. Best hits were following NCBI or MaarjAM databases.
Acknowledgments J. Santillán-Manjarrez  received a scholarship from  CONA-CyT-Mexico  while studying for a master's degree in science. The authors acknowledge  Ó. Cano-Flores ,  J. A. Viccon-Pale  ( UAM-Xochimilco ), and  M. G. Medina-Canales  ( ENCB-IPN ) for help in soil analysis, and advice during plant sampling and DNA sequence analysis, respectively. A. Meléndez-Herrada  ( UAM-Xochimilco ), and  D. P. Janos  ( University of Miami ) provided critical comments on an early version of the manuscript. We thank two anonymous reviewers and Editors for their careful reading of the manuscript, comments and suggestions.
Downy mildew symptoms (caused by the oomycete pathogen Plasmopara viticola) did not differ significantly between the cropping systems (Table  S2 ). Figure  1  -Experimental design. Foliar fungal communities were characterized in three conventional (CONV) and three organic (ORGA) vineyard plots by a metabarcoding approach. We analyzed 20 foliar samples per plot. For each plot, we thus obtained 20 community profiles (described in terms of amplicon sequence variants (ASV)) and one association network (inferred with the SparCC software developed by  Friedman & Alm, 2012). More networks were then obtained by varying network reconstruction parameters (Figure  5 ). The effects of cropping system (CONV versus ORGA) on the grapevine foliar microbiota were assessed with both community and network α-and β-properties.
Bioinformatic analysis Based on the mock community included in the sequencing run, we found that analyzing single forward (R1) sequences with DADA2  (Callahan et al. , 2016)  was a good option for fungal community characterization  (Pauvert et al. , 2019). Using DADA2 v1. 6, we retained only R1 reads with less than one expected error (based on quality scores;  Edgar & Flyvbjerg, 2015)  that were longer than 100 bp, and we then inferred amplicon sequence variants (ASV) for each sample. Chimeric sequences were identified by the consensus method of the removeBimeras function. Taxonomic assignments were performed with RDP classifier  (Wang et al. , 2007) , implemented in DADA2 and trained with the UNITE database v. 7. 2 (UNITE Community 2017). Only ASVs assigned to a fungal phylum were retained.
The ASV table was then filtered as described by  Galan et al. , (2016)  with a script (https://gist. github. com/cpauvert/1ba6a97b01ea6cde4398a8d531fa62f9) that removed ASVs from all samples for which the number of sequences was below the cross-contamination threshold, defined as their maximum number in negative control samples. Finally, we checked the compositional similarity of the technical replicates (Figure  S1 ), retaining the sample with the largest number of sequences for each replicate. The final ASV table contained 1116 ASVs, 112 samples and 4,760,068 high-quality sequences.
Statistical analyses Statistical analyses were performed with R software v3. 4. 1 (R Core Team, 2017), with the packages lme4  (Bates, Mächler, Bolker, & Walker, 2015) , vegan  (Oksanen et al. , 2018) , permute (G. L. Simpson, 2016) , phyloseq  (McMurdie & Holmes, 2013)  including the DESeq2 extension  (Love, Huber, & Anders, 2014) , and igraph  (Csardi & Nepusz, 2006). Data were manipulated and plots were created with reshape2, plyr and ggplot2  (Wickham, 2007 (Wickham, , 2011 (Wickham, , 2016)) , cowplot (Wilke, 2018), ggraph  (Pedersen, 2018)  and VennDiagram  (Chen, 2018). Effect of cropping system on community α-diversity. Generalized linear mixed models (GLMMs) were used to test the effect of cropping system on the richness, diversity and evenness of fungal communities. The models included the cropping system as a fixed treatment effect, the block and its interaction with the cropping system as random factors, and the sampling depth (defined as the total number of raw sequences per sample) as an offset  (Bálint et al.
Acknowledgements We thank  Lucile Muneret ,  Andreas Makiola  and all members of the  ANR NGB Consortium  ( ANR-17-CE32-0011 ) for helpful scientific exchanges on next-generation biomonitoring and for their comments on the manuscript. We also thank  Gregory Gambetta ,  Guilherme Martins ,  Frédéric Barraquand ,  Isabelle Lesur  and  Adrien Rush  for useful comments on preliminary results. We thank the  Genotoul sequencing facility (Get-PlaGe)  for sequencing and the  Genotoul bioinformatics facility (Bioinfo Genotoul)  for providing computing and storage resources. We also thank  Julie Sappa  from  Alex Edelman & Associates  for English language revision of first version of the manuscript. We thank the  INRA MEM metaprogram (Meta-Omics of Microbial Ecosystems)  for financial and scientific support.
Sequencing was funded by the  INRA MEM   MetaBAR  project and bioinformatic and statistical analyses were performed as part of the  INRA MEM Learn-biocontrol project. Additional funding was received from the  LABEX COTE  ( ANR-10-LABX-45 ), the  LABEX CEBA  ( ANR-10-LABX-25-01 ) and  INRA EcoServ metaprogram on ecosystem services  ( IBISC  project) and the  Aquitaine Region (Athene project ,  n°2016-1R20301-00007218 ). CP's PhD grant was funded by the  INRA and Bordeaux Sciences Agro (BSA). The management of the experimental site was partly funded by the  AFB (French Agency for Biodiversity)  within the  DEPHY network.
Acknowledgments. The paper and its formalization have benefited from detailed comments by six reviewers and two artifact evaluators at POPL and four reviewers at ICFP, whose wit and dedication we highly appreciate. It also benefited from  Mark Summerfield 's many suggestions. Blanchette received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  (grant No. 713999 ,  Matryoshka). Gheri  was partially supported by the  German Federal Ministry of Education and Research (BMBF)  and by the  Hessen State Ministry for Higher Education, Research , and the  Arts (HMWK)  within  CRISP. Popescu received funding from  UK's Engineering and Physical Sciences Research Council (EPSRC)  via grant  EP/N019547/1 , Verification of Web-based Systems (VOWS).
2019a. Bindings as Bounded Natural Functors (Extended Version). https://github. com/dtraytel/Bindings-as-BNFs/blob/master/bindings_extended. pdf. Jasmin Christian Blanchette ,  Lorenzo Gheri ,  Andrei Popescu , and  Dmitriy Traytel. 2019b. Formalization associated with this paper. https://github. com/dtraytel/Bindings-as-BNFs. Jasmin Christian Blanchette ,  Johannes Hölzl ,  Andreas Lochbihler ,  Lorenz Panny ,  Andrei Popescu , and  Dmitriy Traytel. 2014. Truly Modular  (Co)datatypes for Isabelle/HOL. In Interactive Theorem Proving (ITP) 2014,  Gerwin Klein  and  Ruben Gamboa  (Eds. ). LNCS, Vol. 8558. Springer, 93-110. https://doi. org/10. 1007/978-3-319-08970-6_7Jasmin   Christian Blanchette ,  Fabian Meier ,  Andrei Popescu , and  Dmitriy Traytel. 2017. Foundational  Nonuniform (Co)datatypes for Higher-Order Logic.
2014a). β Pictoris b was discovered northeast of the star in data obtained in 2003 with NaCo. All available images in addition to this discovery image were obtained in 2009 and later, as the planet orbited southwest of the star, after it passed behind the star  (Lagrange et al. 2010). The orbital plane is close to equatorial, and the disk of β Pictoris is seen almost edge-on. This geometrical configuration prevented following the planet in direct imaging when it was projected too close (typically less than 120 mas) to the star. Altogether, only about 50% of the planetary orbit had been monitored thus far, including the 2003 epoch, which suffers from relatively large uncertainties and thus limits the precision on the orbital parameters of the planet. We here present a homogeneous set of planet observations obtained with the Spectro-Polarimetric High-Contrast Exoplanet Research instrument (SPHERE), and in particular, the recent recovery of the planet in September 2018.
25 mas. IFS data were also recorded, but they are not analyzed here. We used apodized Lyot coronagraphs that include either a 185 mas diameter focal mask (N ALC YJH S) or, when the planet was closer to the star, a smaller (145 mas) mask (N ALC YJ S), combined to an apodizer as well as a pupil stop  (Carbillet et al. 2011). All coronagraphic data were recorded in stabilized pupil mode so as to apply angular differential imaging (ADI) post-processing techniques to remove the stellar halo, as described in  Marois et al. (2006). Most coronagraphic data were also recorded while four satellite footprints of the point spread function (PSF) had been created by the deformable mirror of the instrument and were used for fine monitoring of the frame centering and for photometric purposes. The FoV rotation during the coronagraphic observations varied between 16 • and 54 • (see Table  1 ).
Each observing sequence was obtained with the following pattern: PSF -coronagraphic observations -PSF -sky. The PSF data correspond to non-saturated exposures of the star placed out of the coronagraphic mask and obtained using a neutral density filter. They are used for relative photometric reference and to estimate the image quality at the beginning and end of the observations. The sky data were recorded at the end of the coronagraphic sequence to estimate the background level and hot pixels in the science images. Finally, an astrometric field, either Orion or 47 Tuc, was observed with IRDIS for each run (see  Maire et al. 2016 ). In the case of Orion, the sub-field we used was chosen to be part of the one considered with NaCo since 2008 to allow the best match between the astrometric calibrations of NaCo and SPHERE.
Acknowledgements. We acknowledge financial support from the  Programme National de Planétologie (PNP)  and the  Programme National de Physique Stellaire (PNPS)  of  CNRS-INSU. This work has also been supported by a grant from the  French Labex OSUG@2020  (Investissements d'avenir  ANR10 LABX56 ). The project is supported by  CNRS , by the  Agence Nationale de la Recherche  ( ANR-14-CE33-0018 ). This work has made use of the the SPHERE Data Centre, jointly operated by  OSUG/IPAG (Grenoble), PYTHEAS/LAM/CESAM (Marseille) ,  OCA/Lagrange (Nice)  and  Observatoire de Paris/LESIA (Paris). AML thanks  Julian Mejia  for his help during the September 2018 DVM observations,  Nadège Meunier ,  Eric Lagadec , and  Philippe Delorme  for their assistance in the DC process, and  Pascal Rubini  for his help in the scheduling of the observations.
E. S. , R. G. , D. M. , S. D. and  R. U. C. acknowledge support from the  "Progetti Premiali" funding scheme of the Italian Ministry of Education, University, and Research. SPHERE is an instrument designed and built by a consortium consisting of  IPAG (Grenoble,France ),  MPIA (Heidelberg, Germany) ,  LAM (Marseille, France), LESIA (Paris, France), Laboratoire Lagrange (Nice, France), INAF Osservatorio Astronomico di Padova (Italy) ,  Observatoire de Genève (Switzerland) ,  ETH Zurich (Switzerland) ,  NOVA (Netherlands) ,  ONERA (France)  and  ASTRON (Netherlands)  in collaboration with  ESO. SPHERE was funded by  ESO , with additional contributions from  CNRS (France) ,  MPIA (Germany) ,  INAF (Italy) ,  FINES (Switzerland)  and  NOVA (Netherlands). SPHERE also received funding from the  European Commission Sixth and Seventh Framework Programmes as part of the Optical Infrared Coordination Network for Astronomy (OPTICON)  under grant number  RII3-Ct-2004-001566  for  FP6  ( 2004-2008 ), grant number  226604  for FP7 (2009)(2010)(2011)(2012) and grant number  312430  for FP7 (2013)(2014)(2015)(2016).
The Xist RNA levels recovered from Xist DB+C were higher than from Xist FL-induced cells ( Previously described Xist protein interactors  [13]  were found among the top hits in Xist FL RNA (and also in Xist DB+C RNA) confirming the success of the ChIRP-MS experiment (Dataset EV1). Indeed, considering the 20 top hits for Xist FL RNA after filtering out weakly annotated protein isoforms, 18 of them are in the Chu et al list  [13]. Of these top 20 hits, all were shared with Xist DB+C RNA, with the notable exception of the PRC1 component RNF2/ RING1B (Fig 2B; Dataset EV1). Overall, higher fold enrichment for the remaining factors in Xist DB+C compared to Xist FL is consistent with the increased yield of Xist RNA and proteins retrieved from mutant cells (Figs 2B and EV3A-C; Dataset EV1).
C By focusing on the 81 hits previously identified by Chu et al  [13] , we compared the protein interactomes of Xist FL and Xist DB+C RNAs (Fig  2C ). Consistently with our previous analysis, we detected the majority of published hits to interact with both Xist FL and Xist DB+C (74 out of 81 with a minimum of 2. 5 DOX/noDOX fold change in one of the samples) (Dataset EV1). SPEN and many other proteins with a proposed role in long-range gene silencing were present in the Xist DB+C interactome, such as members of the m6A RNA methyltransferase machinery (RBM15, WTAP, and YTHDC1) and proteins involved in Xist spreading such as the hnRNPU matrix attachment protein (Fig  2C ). In contrast, five proteins were absent from the Xist DB+C interactome (Fig 2C ), including the three members of non-canonical PRC1 present in Chu et al's list-RNF2/RING1B, RYBP, and PCGF5.
We also found that PCGF3, which was not in the original Chu et al's list, was present in the Xist FL interactome, but lacking from the Xist DB+C interactome in our ChIRP-MS experiment (Dataset EV1). Furthermore, hnRNPK RBP previously linked to Xist-induced PCGF3/5-PRC1 recruitment, was also not detected in Xist DB+C ChIRP-MS (with the exception of two poorly annotated isoforms) (Dataset EV1), corroborating findings from others using in vitro pull-down experiments  [14, 15]. The histone deacetylase complex subunit SAP18 was also lacking from the Xist DB+C interactome, while three other proteins were found to bind more weakly to Xist DB+C than to Xist FL RNA: TRIM71 (an E3 ubiquitin-protein ligase); SRSF2 (serine and arginine-rich splicing factor 2); and hnRNPA1 (heterogeneous nuclear ribonucleoprotein A1; Fig 2C; Dataset EV1).
In conclusion, Xist DB+C and Xist FL RNAs share most of their protein interactome with few exceptions such as proteins of the PCGF3/5-PRC1 complex. Combined with previous results on Xist DA  [13] , these data illustrate the modular organization of Xist lncRNA, with RNA motifs interacting independently with different proteins and possibly performing distinct functions. The absence of PCGF3/5-PRC1 from the Xist DB+C interactome explains the global lack of H2AK119ub and concomitant loss of H3K27me3 enrichment over the X chromosome (Fig  1B  and C ), consistent with the hierarchical model proposed for PRC1/PRC2 recruitment  [9, 10].
In accordance with this, we found that hnRNPK, alongside with non-canonical PRC1 members, is lost from the Xist DB+C protein interactome as revealed by ChIRP-MS. Interestingly, SAP18 was another protein lacking from the Xist DB+C interactome (Fig 2C ; Dataset EV1). The potential role for this histone deacetylase complex subunit in XCI remains to be addressed. However, SAP18 is unlikely to affect PcG recruitment. Indeed, recent knockdown experiments showed that SAP18 does not affect SMCHD1 recruitment to the Xi, which is a secondary event to H2AK119ub deposition  [53]. The Xist DB+C mutant cannot bind PCGF3/5-PRC1 (Fig  2B  and C ) or induce global PRC1/PRC2 recruitment to the X chromosome (Figs  1B  and C,  and 3A ). This provide us with a model to appreciate the role of Xist-mediated PcG recruitment on the initiation of transcriptional silencing without affecting pre-marked PcG sites on the X chromosome prior Xist expression  [9, 51]  or the other PcG functions throughout the genome.
Another possibility is that low levels of PcG proteins may simply be recruited to promoters and gene bodies as a consequence of gene silencing following Xist DB+C RNA coating. Taking advantage of comparable nChIP-seq and RNA-seq datasets in Xist FL and Xist DB+C, we detected multiple genes which have been silenced and yet with negligible accumulation of H3K27m3 or H2AK119ub at their promoter regions in Xist DB+Cinduced cells (Fig  4F ). This suggests that Xist-mediated gene silencing can occur in the absence of PcG recruitment, at least, for a subset of X-linked genes. The other subset of X-linked genes has detectable residual accumulation of PcG marks, which could play a role in the initiation of their silencing. Alternatively, the recruitment of PcG at X-linked genes could be secondary to transcriptional silencing in PCGF3/5-PRC1-unbound Xist DB+C.
Xist hits from ChIRP-MS were ranked according to Xist FL DOX/ Xist FL noDOX fold change in peptide counts. To calculate this and Xist DB+C/Xist FL noDOX ratios, when peptide counts for Xist FL noDOX samples were 0, it was considered 1 (Dataset EV1). For comparison with Chu et al list  [13] , only annotated protein isoforms with an Annotation score in UniProtKB ≥ 3 (out of 5) were considered with a minimum of 2. 5 DOX/noDOX fold change in one of the samples. Proteins present in the Chu's list with DOX/no DOX ratio inferior in Xist DB+C than Xist FL were considered underrepresented in Xist DB+C protein interactome (Fig  2C ). Proteins with no peptide counts for Xist DB+C or with equal peptide counts to Xist FL noDOX, which had a Xist FL DOX/noDOX ratio ≥ 4, were considered not to be part of the Xist DB+C protein interactome (Fig 2C ).
Acknowledgements We thank  Elphège Nora ,  Rafael Galupa ,  Martin Escamilla-Del-Arenal , and  Sérgio de Almeida  for critical reading of the manuscript. We are also grateful to members of the  Heard Lab  ( Curie Institute ), members of the  Carmo-Fonseca's Lab  (iMM JLA), in particular to  Kenny Rebelo , and also to  Pierre Gestraud  and  Nicolas Servant  ( U900 ,  Curie Institute ) and  Aurélie Teissandier  ( U934/UMR3215 ,  Curie Institute ) for their help and critical input to this project. We also want to thank the Bioimaging facility, in particular  José Rino , at iMM JLA for their technical support with fluorescent light microscopy and imaging analysis. This work was supported by  Fundação para a Ciência e Tecnologia (FCT) , project grants  PTDC/BEX-BCM/2612/2014  (A. C. R. and S. T.
d. R),  PTDC/BIA-MOL/29320/2017  IC&DT (C. G. and S. T. d. R), and  IF/00242/2014  (V. P. and S. T. d. R), by an  ERC  Advanced Investigator award ERC-ADG-2014 671027 attributed to E. H. and  Sir Henry Wellcome Postdoctoral Fellowship  (J. J. Z. ), by the  Scleroderma Research Foundation  (Y. Q. , H. Y. C) and by  US National Institutes of Health   NIH P50-HG007735  (H. Y. C. ). H. Y. C. is an Investigator of the  Howard Hughes Medical Institute. Publication costs were supported by  UID/BIM/ 50005/2019 , project funded by  Fundação para a Ciência e a Tecnologia (FCT)/ Ministério da Ciência, Tecnologia e Ensino Superior (MCTES)  through  Fundos do Orçamento de Estado.
Data availability The datasets produced in this study are available in the following databases: • nChIP-seq and RNA-seq data: Gene Expression Omnibus GSE123743 (https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi? acc=GSE123743). • ChIRP-MS Proteomic data: PRIDE PXD014287 or https://doi. org/ 10. 6019/PXD014287 (https://www. ebi. ac. uk/pride/archive/pro jects/PXD014287). Expanded View for this article is available online.
Moreover, beyond target screening, additional information can be obtained, exploiting the same analytical data for suspect and NTS. Because analytes are not selected a priori in HRMS methods, data files from full scan HRMS/MS analyses can be archived and screened retrospectively for thousands of suspects. "Suspect screening", in particular, is a recently developed approach that consists of screening large lists of chemical compounds of interest (the "suspects" or "known unknowns") in complex samples using their molecular formula (and the resulting calculated exact mass) at first without reference standards available  [19]. Subsequently, other mass spectral information such as MS/MS spectra is used for tentative identification of suspects' hits. Sometimes, suspect screening is directly performed against MS/ MS databases so that fragmentation (in addition to the exact molecular mass) is taken into account, rendering the hits with a higher degree of confidence.
It should be noted that only qualitative or semi-quantitative data are obtained with the suspect screening workflows. However, this information for hundreds of chemicals can be more valuable than exact concentrations for only a few compounds. In fact, it is possible to prioritize compounds (positive hits) on which to concentrate future target analysis, when standards can be purchased for identity confirmation and quantification. If no reference standard can be obtained, the confidence of identification should be clearly reported (see below). Suspect screening is becoming increasingly popular and is especially attractive where lists of chemicals exist, as in chemical registration. To help laboratories make suspect screening comprehensive, in 2016 the NORMAN community launched the Suspect List Exchange initiative where lists of compounds from monitoring campaigns, research projects, substance classes and market lists, contributed by different NOR-MAN partners and NORMAN-connected initiatives are collected, aggregated and curated.
Besides the individual lists, a merged list, the NORMAN Suspect List Exchange Database (SusDa t), was created. This list contains for each compound the exact mass and possible ions of the molecule that are needed for exact mass screening (MS ready form), together with the additional information for identification with MS, i. e. substance classifiers and predicted retention time index (RTI) for liquid chromatography. Additionally, information on predicted physico-chemical properties and toxicity is provided and can be used for prioritization. At the moment this merged suspects list contains more than 40,000 chemicals and the database is growing continuously. Recently, NORMAN ran a suspect screening of mass spectrometric raw data from environmental samples for a list of REACH chemicals provided by the European Chemicals Agency.
Future needs: quality assurance for NTS (item 3) Any information from NTS-based monitoring needs to be accompanied by clear statements about the NTS data quality when feeding into regulatory processes. This helps to clarify what information NTS data can provide about the presence of the compound in the environment and what are the data limitations. Harmonized NTS protocols and minimum quality requirements are needed to provide high quality data useful for regulatory processes. The first draft of a national guideline for NTS is available in Germany (Germa n Chemi cal Socie ty), with a specific focus on surface water monitoring. The NOR-MAN network aims to provide a more general guideline based on the experiences gained through different collaborative trials and other activities for water, indoor dust and biota.