dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements. We thank the anonymous referee for comments and suggestions that improved this article. This work has made use of data from the  European Space Agency (ESA) mission Gaia  (https: //www. cosmos. esa. int/gaia),processed by the Gaia Data Processing and Analysis Consortium (DPAC, https://www. cosmos. esa. int/web/gaia/dpac/consortium). Funding for the DPAC has been provided by national institutions, in particular the institutions participating in the Gaia Multilateral Agreement. Further details of funding authorities and individuals contributing to the success of the mission is shown at https://gea. esac. esa. int/archive/documentation/GEDR3/Miscellaneous/sec_acknowl/. We thank the  United Kingdom Particle Physics and Astronomy Research Council (PPARC) , the  United Kingdom Science and Technology Facilities Council (STFC) , and the  United Kingdom Space Agency (UKSA)  through the following grants to the  University of Bristol , the  University of Cambridge , the  University of Edinburgh , the  University of Leicester , the  Mullard Space Sciences Laboratory of University College London , and the  United Kingdom Rutherford Appleton Laboratory (RAL) :  PP/D006511/1 ,  PP/D006546/1 ,  PP/D006570/1 ,  ST/I000852/1 ,  ST/J005045/1 ,  ST/K00056X/1 ,  ST/K000209/1 ,  ST/K000756/1 ,  ST/L006561/1 ,  ST/N000595/1 ,  ST/S000623/1 ,  ST/N000641/1 ,  ST/N000978/1 ,  ST/N001117/1 ,  ST/S000089/1 ,  ST/S000976/1 ,  ST/S001123/1 ,  ST/S001948/1 ,  ST/S002103/1 , and  ST/V000969/1.
This paper made use of the Whole Sky Database (WSDB) created by  Sergey Koposov  and maintained at the  Institute of Astronomy, Cambridge  with financial support from the  Science and Technology Facilities Council (STFC)  and the  European Research Council (ERC). We thank the  William Herschel and Isaac Newton Telescopes on the Roque de los Muchachos Observatory ,  La Palma , Spain, as well as the  Optical Infrared Coordination Network for Astronomy (OPTICON)  for their support of this project through telescope time, especially during the commissioning and verification phases. We thank the Copernico 1. 82m telescope (Mt. Ekar, Asiago Italy) operated by  INAF Padova  for supporting the project through telescope time (under the  Large Programme Tomasella-SNe ) during the verification phases.
AB acknowledges financial support from the  Netherlands Research School for Astronomy (NOVA). AG acknowledges the financial support from the  Slovenian Research Agency  (grants  P1-0031 ,  I0-0033 ,  J1-8136 ,  J1-2460 ). AH was funded in part by the  Leverhulme Trust  through grant  RPG-2012-541  and by the  European Research Council  grant  320360. AP acknowledges support from the  NCN  grant No. 2016/21/B/ST9/01126. CM acknowledges support from  Jim  and  Hiroko Sherwin. DAK  acknowledges support from the  Spanish  research projects AYA 2014-58381-P,  AYA2017-89384-P , from  Juan de la Cierva Incorporación  fellowship  IJCI-2015-26153 , and from  Spanish National Research  Project  RTI2018-098104-J-I00  (GRBPhot). EB and STH are funded by the  Science and Technology Facilities Council  grant  ST/S000623/1.
4000129910. MF is supported by a  Royal Society -Science Foundation Ireland University Research Fellowship  NB acknowledges support from the research programme VENI, with project number  016. 192. 277 , which is (partly) financed by the  Netherlands Organisation for Scientific Research (NWO). NI is partially supported by  Polish NCN DAINA  grant No. 2017/27/L/ST9/03221. PAW acknowledges research funding from the  South African National Research Foundation. RWW was funded by the  Science and Technology Facilities Council  grant  ST/P000541/1. V. A. R. M. R. acknowledges financial support from  Radboud Excellence Initiative , the  Fundação para a Ciência e a Tecnologia (FCT)  in the form of an exploratory project of reference  IF/00498/2015/CP1302/CT0001 ,  FCT  and the  Ministério da Ciência, Tecnologia e Ensino Superior (MCTES)  through national funds and when applicable cofunded  EU  funds under the project  UIDB/EEA/50008/2020 , and supported by  Enabling Green E-science for the Square Kilometre Array Research Infrastructure (ENGAGE-SKA) ,  POCI-01-0145-FEDER-022217 , and  PHOBOS ,  POCI-01-0145-FEDER-029932 , funded by  Programa Operacional Competitividade e Internacionalização  ( COMPETE 2020 ) and FCT, Portugal.
Metagenomic analysis We trimmed adaptors using bbduk from bbmap 38. 22  [26]  after which we processed reads using a snakemake  [27]  workflow implemented in anvi'o v5 (http:// merenlab. org/2018/07/09/anvio-snakemake-workflows/) [28]. Briefly, we used illumina-utils v1. 4  [29]  with the "iu-filter-quality-minoche" program with default parameters for sequence quality filtering. We co-assembled two metagenomic datasets per site (that is, RE5 and RE6 for TAG; RE12 and RE13 for Rainbow; and RE3 and RE7 for Snake Pit) using Megahit 1. 1. 3 [30]  and the --metasensitive mode, discarding contigs smaller than 1000 bp. We performed read recruitment analyses with Bowtie v2. 3. 4  [31]. We used the option "all-against-all" to map reads from each of the six individual samples onto each of the three co-assemblies.
We trimmed multiple sequence alignments using trimAl v1. 4. 1  [36]  with the setting "-gt 0. 5" to remove positions with gaps in more than 50% of sequences. We reconstructed a Maximum Likelihood (ML) phylogenetic tree using IQ-TREE v1. 6. 12  [37]  with the "WAG" general matrix model  [38]  and 1000 bootstrap replicates that we visualized using anvi'o and FigTree v1. 4. 4. Finally, we made a finer GTDB reference taxa filtration after a first visualization, providing a better representation of our MAG genera. For this purpose, we removed references from distant families and added some from the closest genera or families in a supervised manner. We calculated average nucleotide identity (ANI) online using the ANI calculator (https://www. ezbiocloud. net/ tools/ani)  [39]. As for functional analyses, we extracted KO assignations from the anvi'o database for each MAG and used the KEGG Decoder v 1.
0. 8. 2 (www. github. com/bjtully/BioData/tree/master/KEGGDecoder)  [40]  to determine the completeness of various metabolic pathways based on a key set of genes. In addition, we used the RAST platform to provide a more detailed analysis of the different Zetaproteobacteria genomes (from this study and their free-living counterparts). The schematic representation of the predicted metabolic potentials within Zetaproteobacteria MAGs was constructed based on the KEGG annotations and the Reconstruct Pathway tool (https://www. genome. jp/kegg/tool/map_pathway. html). To explore the taxonomic composition of each samples based on the small-subunit rRNA, the qualityfiltered reads were analyzed using the phyloFlash v3. 4 pipeline  [41]  with the option "almost everything" and the SILVA database release 138.
Code and data availability The metagenome raw reads are available in the European Nucleotide Archive under Bioproject Accession Number PRJEB37577. We also made the FASTA files available for individual metagenomic co-assemblies and for the 49 MAGs at DOI (10. 12770/4186eeef-32b1-4ffb-9a40-61a02a3852b7), as well as the anvi'o merged profile databases for each co-assembly and MAG. The URL https://gitlab. ifremer. fr/rimicaris/rimicaris-exoculatacephalothoracic-epibionts-metagenomes provides access to a detailed reproducible bioinformatics workflow for all the computational analyses.
2  Static image from the anvi'o interactive display for the Rimicaris datasets with the 49 genome bins retained for this study. From inner to outer layers: phylogenomic tree based on concatenated marker proteins according to GTDB-Tk, length layer (shows the actual length of a genome), auxiliary layer with information about contigs stored in the contig database (GC-content), four view layers with information about MAGs across samples stored in the profile database (mean coverage), completion, redundancy, genome class based on GTDB-Tk, and bacterial genome bin layers. The horizontal layers show genome bin taxonomy based on GTDB-Tk for the ten most abundant families, total number of reads mapped and corresponding mapping percentage of reads, and total number of reads for each sample. Relative abundances of families are noted as the percentages of reads recruited to the bins for each sample distribution of CPR organisms.
These lineages are often reported in association with abundant autotrophic taxa involved in nitrogen, sulfur, and iron cycling  [51, 52]  as well as arsenic in contaminated sediments  [53]. We also observed several MAGs which abundance was not significantly different at both sites (abs(log2Fold-Change)<2) like TAG_MAG_00006, Desulfobulbia (Desulfocapsaceae family), and RB_MAG_00001, Bacteroidia (Flavobacteriaceae family). Like for its counterpart RB_MAG_00025, TAG_MAG_00019 Gammaproteobacteria (Thiotrichaceae family) was not differentially abundant at both Rainbow and TAG shrimps. These data provide further evidence for the occurrence of similar genomes in each host individual regardless of the site of sampling, yet with varying abundance partly reflecting contrasting environmental conditions.
3a, b ). These observations are in agreement with previous TEM observations  [24]. Unfortunately, we were able to retrieve one 16S rRNA gene for Zetaproteobacteria TAG_MAG_00014 only, and not for RB_MAG_00008. A blast analysis between the 16S rRNA gene full-length sequence extracted from TAG_ MAG_0014 and the ones retrieved using PhyloFlash from TAG (RE5 and RE6) and Rainbow (RE12 and RE13) showed 100% and 97. 7% identity, respectively (Additional File 8). Overall, data did not allow designing specific probes for each symbiont, nor did previous studies, which led to a single lineage sequence  [16, 21]  or unpublished data. Therefore, we cannot yet fully describe the distribution of either recovered Zetaproteobacteriarelated MAGs. We were nevertheless able to perform a phylogenomic analysis using 120 bacterial marker genes from our 49 MAGs, as well as 600 closely related genomes from GTDB, to infer how Zetaproteobacteria and other MAGs reconstructed in this study are distributed relative to known taxa (Additional File 9A).
Acknowledgements We thank chief scientists of the  MOMAR2008-Leg2 ,  BioBaz 2013 , and BICOSE 2014 cruises ( J. Dyment ,  F. Lallier , and  M. A. Cambon-Bonavita ) and the captains and crew of R/Vs Pourquoi pas? and Atalante and  ROV Victor  for logistic assistance in collecting samples. We also thank  Cyrielle Jan  for FISH experiments and pictures,  Hilary Morrison  for assistance with sequencing, and  A. Murat Eren ,  Florian Trigodet ,  Cyril Noël , and  Loïs Maignien  for their insights on the manuscript and on bioinformatics and statistical analyses. We are really grateful to two anonymous reviewers for their constructive comments, which helped us to improve the manuscript.
The quality of the DNAs was controlled determining the DNA Integrity Number (DIN) analyzing 20 ng of DNA on a TapeStation 4200 (Agilent). One µg genomic DNA was used to prepare a library for whole genome sequencing on an automated platform, using the Illumina "TruSeq DNA PCR-Free Library Preparation Kit", according to the manufacturer's instructions. After normalization and quality control, qualified libraries were sequenced on a HiSeqX5 platform from Illumina (Illumina Inc. , CA, USA) at the Centre National de Recherche en Génétique Humaine (CEA, Evry, France), generating paired-ended, 150-bp reads. Sequence quality parameters were assessed throughout the sequencing run. Standard bioinformatics analysis of sequencing data was based on the Illumina pipeline to generate a FASTQ file for each sample.
1% FA just before LC-MS/MS injection. All analyses were performed on a Q Exactive TM Plus Mass Spectrometer (Thermo Fisher Scientific) coupled with a Proxeon EASY-nLC 1000 (Thermo Fisher Scientific). One µg of peptides was injected into a home-made 50 cm C18 column (1. 9 μm particles, 100 Å pore size, ReproSil-Pur Basic C18, Dr. Maisch GmbH, Ammerbuch-Entringen, Germany). Column equilibration and peptide loading were performed at 900 bars in buffer A (0. 1% FA). Peptides were separated with a multi-step gradient of 2 to 5% buffer B (80% ACN, 0. 1% FA) for 5 min, 5 to 22% buffer B for 150 min, 22 to 45% buffer B for 60 min, 45 to 80% buffer B for 10 min at a flow rate of 250 nL/min over 240 min. Column temperature was set to 60°C. MS data were acquired using Xcalibur software using a data-dependent method.
Data analyses WGS analysis: Genomic DNA reads were aligned to the L. donovani Ld1S reference genome (Bussotti & Myler unpublished, https://www. ncbi. nlm. nih. gov/bioproject/PRJNA396645) with BWA mem (version 0. 7. 12) with the flag -M to mark shorter split hits as secondary. Samtools fixmate, sort, and index (version 1. 3) were used to process the alignment files and turn them into bam format  [24]. RealignerTargetCreator and IndelRealigner from the GATK suite were run to homogenize indels  [25]. Eventually, PCR and optical duplicates were labeled with Picard MarkDuplicates [version 1. 94(1484)] (https://broadinstitute. github. io/picard/) using the option "VALIDATION_STRINGENCY=LENIENT". For each read alignment file, Samtools view (version 1. 3) and BEDTools genomecov (version 2. 25. 0) were used to measure the sequencing depth of each nucleotide  [26].
Reads were cleaned of adapter sequences and low-quality sequences using cutadapt version 1. 11  [29]. Only sequences of at least 25 nucleotides in length were considered for further analysis. STAR version 2. 5. 0a [30] , with default parameters, was used for alignment on the reference genome (GCA_002243465. 1). Genes were counted using featureCounts version 1. 4. 6-p3 [27]  from Subreads package (parameters: -t gene -g gene_id -s 1). Count data were analyzed using R version 3. 6. 1  [31]  and the Bioconductor package DESeq2 version 1. 24. 0  [32]. The normalization and dispersion estimation were performed with DESeq2 using the default parameters and statistical tests for differential expression were performed applying the independent filtering algorithm. A generalized linear model including the replicate effect as blocking factor was set in order to test for the differential expression between EP.
1 log, EP. 1 stat, EP. 1 meta, LP. 1 log and LP. 1 stat. For each pairwise comparison, raw p-values were adjusted for multiple testing according to the Benjamini and Hochberg (BH) procedure  [33]  and genes with an adjusted p-value lower than 0. 05 were considered differentially expressed. The RNAseq data have been deposited in NCBI's Gene Expression Omnibus  (Edgar et al. , 2002)  and are accessible through GEO Series accession number GSE165615 (https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE165615). For small RNome analysis, Ψ-seq and detection of pseudouridylated sites, the 42 bp sequence reads obtained from the Illumina Genome Analyzer were first trimmed of Illumina adapters using the FASTX toolkit (http://hannonlab. cshl. edu/fastx_toolkit), and reads of 15 nucleotides or less were discarded from subsequent analysis.
The remaining reads were mapped to the reference genome (GCA_002243465. 1) using SMALT v0. 7. 5 (https://www. sanger. ac. uk/tool/smalt-0/) with the default parameters. Only properly paired partners were retained. Each read pair was "virtually" extended to cover the area from the beginning of the first read to the end of its partner. For each base, the number of reads initializing at that location as well as the number of reads covering the position were calculated. A combination of BEDTools v2. 26. 0 Suite (http://bedtools. readthedocs. io/en/latest/) and in-house Perl scripts was used to calculate the Ψratio and Ψ-fc (fold change), as previously described  [21, 22]. Proteomics analysis: Raw data were analyzed using MaxQuant software version 1. 5. 3. 8  [34]  using the Andromeda search engine  [35].
The MS/MS spectra were searched against the Ld1S database (Bussotti et al. , in preparation). The settings for the search included (i) trypsin digestion with a maximum of two missed cleavages, (ii) variable modifications for methionine oxidation and Nterminal acetylation, and (iii) fixed modification for cysteine carbamidomethylation. The minimum peptide length was set to 7 amino acids and the false discovery rate (FDR) for peptide and protein identification was set to 0. 01. The main search peptide tolerance was set to 4. 5 ppm and to 20 ppm for the MS/MS match tolerance. The setting 'second peptides' was enabled to identify cofragmentation events. Quantification was performed using the XIC-based Label-free quantification (LFQ) algorithm with the Fast LFQ mode as previously described  [36].
Unique and razor peptides, including modified peptides, with at least two ratio counts were accepted for quantification. The mass spectrometry proteomics data were deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD020236  [37]. For the differential analyses, proteins categorized as 'reverse', 'contaminant' and 'only identified by site' were discarded from the list of identified proteins. After log2 transformation, LFQ values were normalized by median centering within conditions (normalizeD function of the R package DAPAR  [38] ). Remaining proteins without any LFQ value in one of the conditions (either EP or LP) and at least two values in the other condition were considered as exclusively expressed proteins. Missing values across the four biological replicates were imputed using the imp.
norm function of the R package norm (norm: Analysis of multivariate normal datasets with missing values. 2013 R package version 1. 0-9. 5). A limma t test was applied to determine proteins with a significant difference in abundance while imposing a minimal fold change of 2 between the conditions to conclude that they are differentially abundant  [39, 40]. An adaptive Benjamini-Hochberg procedure was applied on the resulting p-values using the function adjust. p of R package cp4p  [41]  and the robust method described in Pounds et al. [42]  to estimate the proportion of true null hypotheses among the set of statistical tests. The proteins associated to an adjusted p-value inferior to a False Discovery Rate (FDR) of 0. 01 have been considered as significant and differentially abundant proteins.
Gene Ontology (GO)-enrichment analyses: The Biological Networks Gene Ontology tool (BiNGO) plugin of the Cytoscape software package (version 3. 8. 2) was used. A Benjamini & Hochberg false discovery rate with a significance level of 0. 05 was applied. The lists of L. donovani GO terms were built in house. In order to assign the Gene Ontology Identifiers (GO IDs) we combined the GO-derived identifiers with the ones available from the corresponding orthologs in target species: LdBPK, L. infantum, L. major, L. mexicana, Typanosoma brucei brucei 927 (Tbru) and Typanosoma cruzi (Tcru). For each target species we retrieved both the "curated" and "computed" GO IDs from TriTrypDB on the 11/09/2019. OrthoFinder with the DIAMOND search program was applied to establish orthology between the genes in Ld1S and in target species.
In "one-to-many" orthology relations we concatenated all the non-redundant GO IDs from all the homologs. The GO IDs were then assigned based on the hierarchy: LdBPK curated > LdBPK GO > L. infantum curated > L. major curated > L. mexicana curated > Tbru curated > Tcru curated > LdBPK computed > L. infantum computed > L. major computed > L. mexicana computed > Tbru computed > Tcru computed. The GO IDs were assigned if not present in any higher rank GO ID data set. The GO IDs of snoRNAs, UsnRNA, SLRNA and 7SL classes defined by homology with L. major Friedlin genes were manually attributed. Overall, we assigned biological process (BP), molecular function (MF) and cellular component (CC) GO IDs to 5,246, 4,521 and 7,236 Ld1S genes.
Morphological analyses Parasites were seeded on poly L-lysine treated coverslips and fixed in 2. 5% glutaraldehyde. Coverslips were mounted on glass slides using Mowiol® 4-88 (Sigma-Aldrich). Images were acquired using an Axiophot microscope at 63x magnification and an Andor camera. Length and width of the parasite cell body, and flagellum length were measured for at least 200 promastigotes using the Image J Fidji software package (https://imagej. net/). The ratios flagellum over body length and body length over body width were determined for the 200 parasites and the Kruskal-Wallis test was used for statistical analysis. The experiment was performed in duplicate.
Curien Maimonide 2018 (GFS and SM), the seeding grant from the Institut Pasteur International Department to the LeiSHield Consortium and the EU H2020 project LeiSHield-MATI -REP-778298-1 (GB), the France Génomique National infrastructure, funded as part of the « Investissements d'Avenir » program managed by the Agence Nationale pour la Recherche contract ANR-10-INBS-09 (HV, RL, CP), and the ERD Funds, project CePaViP (CZ. 02. 1. 01/0. 0/0. 0/ 16_019/0000759) (BV, JS, PV). We thank the CEA-CNRGH for its contribution to the sequencing costs and all the CEA-CNRGH staff who contributed to sample preparation and sequencing for their excellent technical assistance.
Acknowledgements This work was supported by the  Fondation de la Recherche Médicale  contract  FDT201805005619  (LP), the  Agence Nationale pour la Recherche Labex  ' Integrative Biology of Emerging Infectious Diseases ' contract  ANR-10-LABX-62-IBEID  and  Labex 'French Alliance for Parasitology and Health Care ' contract  ANR-11-LABX-0024  (GFS, LP, PP, GB), the  Campus France Franco-Israeli Programme.
Hubert Curien Maimonide 2018 (GFS and SM), the seeding grant from the Institut Pasteur International Department to the LeiSHield Consortium and the EU H2020 project LeiSHield-MATI -REP-778298-1 (GB), the France Génomique National infrastructure, funded as part of the « Investissements d'Avenir » program managed by the Agence Nationale pour la Recherche contract ANR-10-INBS-09 (HV, RL, CP), and the ERD Funds, project CePaViP (CZ. 02. 1. 01/0. 0/0. 0/ 16_019/0000759) (BV, JS, PV). We thank the CEA-CNRGH for its contribution to the sequencing costs and all the CEA-CNRGH staff who contributed to sample preparation and sequencing for their excellent technical assistance.
Elucidating this P( | ) relation is the goal of the C3R2 survey. The C3R2 survey strategy follows the method outlined in  (Masters et al. 2015, hereafter M15). M15 illustrated the use of an unsupervised manifold learning algorithm, the selforganizing map (SOM;  Kohonen 1982) , to map the color distribution of galaxies in the high-dimensional color space (u-g, g-r,. , J-H) anticipated for Euclid and Roman photometric redshift estimation. The SOM is a neural network model widely used to map and identify correlations in highdimensional data. The algorithm uses unsupervised, competitive learning of "neurons" to project high-dimensional data onto a lower-dimensional grid. The SOM algorithm can be thought of as a type of nonlinear principal component analysis, and is also similar in some respects to the k-means clustering algorithm.
In M17 and  (Masters et al. 2019, hereafter M19, i. e. DR2) we presented the results of the C3R2 Keck observations taken in the 2016A, 2016B, and 2017A semesters. Here we present results obtained in the 2017B, 2018A, 2018B, and 2019B semesters, comprising 18 Keck nights (some of which were shared, or half-nights), of which ∼11 had good observing conditions. The new data come from nights allocated by NASA to PI D. Stern (2 nights) and to PI D. Masters (16 nights). We refer the reader to M15 for background on the calibration approach, and to M17 and M19 for the observation and data reduction details of the C3R2 survey. Spectrocopic redshifts have also been obtained using the VLT as part of the C3R2 effort  (Guglielmo et al. 2020). This paper is structured as follows. In §2 we describe the observations and data reduction.