dataesr/scientific-paragraphs-categorization huggingface dataset:
-------------------------------------------------- -Funding information  FONDECYT, Grant/Award Number :  11110410 ,  1151355 ;  Fontagro, Grant / Award Number: FTG/RF-1028-RG; The  Netherlands Ministry of Economic Affairs , Grant/Award Number:  BO-20-007-006 ;  Austin Eugene Lyons Fellowship ;  Academy of Finland , Grant/Award Number: 281337;  USDA National Institute of Food and Agriculture , Grant/Award Number:  2014-67003-21979 ,  NH00616-R ;  Department of Animal Science and College of Agricultural Sciences, Pennsylvania State University ; the  Dutch Dairy Board  (Zoetermeer, The Netherlands);  University of California ,  Davis Sesnon Endowed Chair Program ;  Pennsylvania Soybean Board ;  Agricultural GHG Research Initiative for Ireland ;  Northeast Sustainable Agriculture Research and Education; Swedish Infrastructure for Ecosystem Science (SITES)  at R€ ob€ acksdalen Research Station; Department for Environment, Food and Rural Affairs (Defra; UK); Defra, the  Scottish Government, Department of Agriculture and Rural Development (Northern Ireland) , and the  Welsh Government as part of the UK's Agricultural GHG Research Platform projects ;  Swiss Federal Office of Agriculture, Berne, Switzerland ;  French National Research Agency , Grant/Award Number:  ANR-13-JFAC-0003-01 ;  New Hampshire Agricultural Experiment Station ; the  Product Board Animal Feed  (Zoetermeer, The Netherlands);  PMI Nutritional Additives ;  DSM Nutritional Products ;  German Federal Ministry of Food and Agriculture (BMBL)  through the  Federal Office for Agriculture and Food (BLE) ;  European Commission  through  SMEthane, Grant/Award Number :  FP7-SME-262270 ;  INIA, Spain , Grant/Award Number: MIT01-GLOBALNET-EEZ.
All the variables were categorized into categorical or binary variables (S1 and S2 Tables). A Multi Component Analysis (MCA) was performed (using R [Version 3. 3. 3]) to examine relationships among the various variables extracted from the manuscripts. Used on categorical but non ordered data, MCA does not impose constraints on the data  [36]  as does a Principal Component Analysis (PCA). From the papers reporting a change in LEK, we excluded for the purpose of analysis the papers (1) which findings on LEK were classified as anecdotal, (2) were multi-nation studies under the category Region, and (3) the studies addressing more than one topic, hence making the classification difficult. All the variables used are listed in S1 and S2 Tables. The choice was made to include the primary data contained in the papers (e.
g. Topic, Gender, various drivers, etc) into our analysis whereas the secondary data that we extracted from the papers (e. g. Period, Region) were used as supplementary variables. whereby they don't contribute to the formation of the MCA space but are overlaid to display correspondences. To test dependencies between variables, chi-square statistical tests were performed between pairs of variables (tested at the significance level of 0. 05) and redundant correlated variables were excluded from the analysis. Finally, a clustering was performed using the Hierarchical Clustering on Principal Component function (HCPC) of FactoMiner package and using the Ward classification method.
Finally, 19% of the studies found men to be more knowledgeable especially in Asia and Europe. Over 50% of studies recommended some measure to protect LEK and around 33% of the publications gave some insights of biodiversity implications of changes in LEK. Finally, only 5 diachronic studies were identified, i. e. , analysing two datasets for the same location at two different periods. The MCA was performed on a sample of 75 studies accordingly to the suitability selection criteria (S3 Table ). The MCA was proven to be an efficient way of highlighting the relationships between the LEK status and the various drivers. While it could have added some value to the analysis, the time periods when the studies took place couldn't be integrated to the analysis as this temporal information was missing for 24% of the papers.
2 K for the spectral channels used in temperature and water vapour retrievals  (Fetzer et al. , 2003; Strow et al. , 2006). AIRS TCWV retrievals are mainly limited by the accurate initialisation of the humidity profile  (Fetzer et al. , 2006). Previous versions of AIRS TCWV were validated against radiosondes over oceanic areas  (Fetzer et al. , 2006)  and against reanalysis (ECMWF)  (Susskind et al. , 2006). Gettelman et al. (2006)  showed that AIRS retrievals in polar regions are unbiased relative to in situ radiosondes. Most results indicate a small mean bias that does not exceed 10 % with no significant dependency upon cloud amount. AIRS TCWV data 2 used in this study are from the version 6, monthly weighted means, level 3 product, referenced as AIRX3STM. 006. It presents a standard physical retrieval that includes both AIRS and AMSU radiances  (Susskind et al.
Host haplotype identification The mitochondrial variable open reading frame (ORF) was amplified with FATP6. 1 (5′-TTTGGGSATTCGTTTA GCAG-3′) and RORF (5′-SCCAATATGTTAAACASCA TGTCA-3′) primers  [38]  and submitted to sanger sequencing (GenBank submission ID 2033564). Protein-coding sequences were analyzed using MEGA version 6  [39]. Sequence alignment was performed using MUSCLE. The best model (Kimura-2 parameters with uniform substitution rates) was selected for the lowest BIC (Bayesian Information Criterion). Maximum-likelihood tree was computed with the best model, and the robustness of the tree was tested with 1000 bootstrap replicates.
For each sample, a first PCR amplification of 35 cycles was performed for the generation of amplicons (96 °C for 30 s, 54 °C for 30 s, 72 °C for 1 min) followed by 15 cycles with Illumina adaptors and sequencing index for the library (95 °C for 15 s, 58 °C for 30 s, 72 °C for 1 min). For both 16S rRNA gene and ITS2 markers, pairedend sequencing with 250 bp read length was performed on two different flow cells using the MiSeq system (Illumina) using the v2 chemistry according to the manufacturer's protocol. Sequencing was performed at the McGill University in the Génome Québec Innovation Centre, Montréal, Canada. Raw sequence data are available in the SRA database (BioProject ID PRJNA393088, BioSample ID SUB2829485).
Sequence analysis of metabarcoding datasets The FROGS pipeline (Find Rapidly OTU with Galaxy Solution) implemented on a galaxy instance (http:// sigenae-workbench. toulouse. inra. fr/galaxy/) was used for data processing  [43]. In brief, paired reads were merged using FLASH  [44]. After denoising and primer/adapters removal with cutadapt  [45] , de novo clustering was done using SWARM that uses a local clustering threshold  [46] , with aggregation distance d = 3 after denoising. Chimera were removed using VSEARCH  [47]. We filtered the dataset for singletons and performed affiliation using Blast+ against the Silva database (release 128, September 2016) for 16S rRNA gene amplicons. For ITS2 metabarcoding, the Symbiodinium clade was assessed using blastn best hit against the nr/nt database of the NCBI  [48].
Statistical analyses All statistical analyses were done using R version 3. 3. 1 (  [49] , http://www. R-project. org). We used the phyloseq R package for community composition analysis  [50]  to infer alpha diversity metrics at the OTU level, as well as beta diversity (between sample distance) from the OTU table. Community similarity was assessed by principal coordinate analysis (PCoA) using the Bray-Curtis dissimilarity index. We performed non-parametric Kruskal-Wallis tests (since normality of residuals and homogeneity of variances were rejected (Shapiro and Bartlett test, respectively)) to compare alpha diversity metrics (Chao1 and Shannon). When Kruskal-Wallis tests were significant, we then computed pairwise comparisons between group levels (post hoc analyses) with Bonferroni corrections for multiple testing using Dunn tests.
Redundancy analysis  [51]  (hereafter named RDA) was used to investigate the variations of the different bacterial OTU/Symbiodinium clades under the constraint of the environmental variables. Bacterial OTUs and Symbiodinium clades were Hellinger-transformed before performing RDAs  [52]  on datasets of geography, temperature, and host genotypes (Additional file 1: Table  S1 ). Then significant variables (i. e. , variables that significantly explained changes in the distribution of OTUs/clades) were identified using a forward-selection procedure (999 permutations), implemented in the R "vegan" package and in the "rda" and "ordiR2step" functions. For all analyses, the threshold significance level was set at 0. 05.
Accordingly, principal coordinate analysis based on Bray-Curtis dissimilarity index followed this partitioning with two main groups (French Polynesia and Djibouti vs. Taiwan and New Caledonia) separated along the first axis (which explained 56. 1% of the total variation) (Fig. 3 ). Samples from Djibouti forming a separate group along the second axis are hosting Symbiodinium clade A1. Samples from different host clades were overlapping on both axes. Bacterial microbiota was highly diverse with a small number of core taxa The bacterial microbiota of each coral sample was analyzed using 16S rRNA gene (V3V4) amplicon sequencing with MiSeq. It yielded a total of 8,198,530 informative clusters. After singleton filtering, we obtained 33,649 OTUs (representing a total of 7,108,067 sequences) that could be annotated using the Silva 16S database.
9% of the overall sequences (Additional file 7: Table  S5 ). Three other genera, Arcobacter, Acinetobacter, and Sphingomonas were present at appreciable relative proportions (6. 6, 1. 9, and 1%, respectively, of total phyla tags). Arcobacter (Campylobacteraceae) was particularly abundant in samples from Djibouti (25% of sequence tags), whereas Acinetobacter (Moraxellaceae) represented 9. 4% of the sequence tags in New Caledonia. Among 227 families and 513 genera in the whole dataset, only the genus Endozoicomonas was common to all samples. We also considered as core phylotypes (at the family and genus level) the taxa shared by 50% of the individuals within each region (Table  3 ). Two additional genera, Arcobacter and Acinetobacter, fall into this category in addition to Endozoicomonas. At the family level, four supplementary families, namely Comamonadaceae, Rhodobacteraceae, Caulobacteraceae, and Sphingomonadaceae, were shared by at least 50% of samples.
In all cases, core taxa were among the 10 most abundant in the dataset (14 families and 6 genera were shared by at least 50% of the overall samples) (Additional file 7: Table  S5 ). Considering each region independently, strikingly much more taxa were shared between at least 50% of samples from Djibouti (27 families and 46 genera) (Additional file 7: Table  S5 ), which could be linked to higher equitability. Twelve families and 5 genera were core to French Polynesia samples, 12 families and 4 genera for New Caledonia, whereas only 7 families and 3 genera were shared by at least 50% of samples from Taiwan (Additional file 7: Table  S5 ). Lastly, principal coordinate analysis based on Bray-Curtis dissimilarity index showed a partitioning of bacterial communities by both host haplotype and region (Fig.
Symbiodinium communities were mostly influenced by minimal temperatures, whereas bacterial community structure was associated to host haplotype and mean temperatures To disentangle the influence of biogeography, temperature, and host haplotype (Additional file 1: Table  S1 ) on coral communities of both Symbiodinium and bacteria, we computed redundancy analysis (RDA) and forward selection procedures. First, we performed RDAs on the whole dataset (geography, thermal regime, and host haplotype). A very high proportion (85%) of Symbiodinium clade distribution was explained by the variables used in this study; this proportion was lower although important (36%) for the bacterial OTU distributions (Fig. 6 , Additional file 8: Table  S6 ). Forward selection procedures were carried-out on the whole dataset to identify the significant variables (Additional file 1: Table  S1 ) constraining the distribution of both communities.
damicornis sensu lato. All core taxa of bacteria were among the most abundant in the dataset, which is in contradiction with previous study identifying rare taxa as ubiquitous endosymbionts  [59]. of the Endozoicomonas genus were present in all samples and were the most abundant in the whole dataset, making up from 33. 3% of the total number of sequence tags in Djibouti (consistent with a higher evenness) to 87. 4% in French Polynesia where rare OTUs were the most diverse. This group has been described for the first time only recently in marine slugs  [60]. So far, they have been identified in numerous coral species and are now considered as ubiquitous endosymbionts of many marine host species (see  [61]  for a review). Notably, comparative genomic analysis of different strains of Endozoicomonas from different hosts suggests a common role in carbohydrate cycling with potential specificities in amino-acid synthesis  [62].
Influence of geography on coral microbiota is very low Because we found different microbiota composition between samples, we used redundancy analyses in order to identify variables that significantly constrained their composition. We first found that the influence of geography was low, since longitude had no significant effect and the effect of latitude in our dataset was null for Symbiodinium assemblages and was marginal on bacterial communities (less than 1% when added to the variance explained by host haplotype and mean temperature). In contradiction with this result, geographic clustering of symbionts is expected to be high in brooding species with vertical transmission, compared to those with horizontal transmission that would in contrary favor propagation of locally adapted symbionts.
Materials and methods a. Animals: Antedon bifida was obtained at the station « Of Biologie Marine of Roscoff » France. b. Obtention of crinoïd mRNA: Digestive coeca were excised from the A. bifida body. A. bifida mRNA was obtained from Uptizol (Interchim). Quality control was operated. c. Sequencing: Sequencing was made on Illumina Next Seq 500 with paired-end : 2. 75 bp Transcriptome was assembled from RNA-Seq fastq files using Trinity v2. 1. 1 ( 8 with default parameters. A BLAST database was created with the assembled transcripts using makeblastdb application from ncbi-blast+ (v2. 2. 31+). The sequences of transcripts of interest were then blasted against this database using blastn application from ncbi--blast+  9  with parameter word_size 7.
Most studies recover species that are not represented in genomic databases  [3] [4] [5]. This demonstrates a collectively limited knowledge of the true diversity of fungi, a problem exacerbated when considering early-diverging lineages, which are primarily microscopic and diverse in biotrophic groups 1,6. Among these uncultured fungal lineages are entire hyperdiverse phyla, such as the Cryptomycota 7. Even among the culturable earlydiverging fungi (EDF), such as the Chytridiomycota, environmental DNA studies show almost no overlap with known and sampled species  8, 9. Because genome sequencing has thus far been limited to cultured fungi, the phylogeny of EDF remains poorly resolved. A better understanding of their genomes will provide clues regarding evolutionary history, basic biology and metabolism (for example, ref.
pseudoplumigaleata and T. sphaerospora form a monophyletic group sister to the Kickxellomycotina within the Zoopagomycota (Fig. 1a ). Because single-cell libraries will only recover a partial genome, we determined the effect of genome completeness and input cell number on generating an accurate phylogeny. We annotated all libraries from D. cristalligena, C. protostelioides, R. allomycis and M. bicuspidata and constructed individual phylogenetic trees using each library (Supplementary Data). These topologies were compared to the co-assembly-based tree in Fig. 1a. Only three libraries (7. 7%) were incongruent for that particular taxon, despite libraries having as few as 15 genes belonging to orthologous gene clusters (median: 434): one R. allomycis (1 cell, 1,095 clusters) and two C. protostelioides (100 cell and 10 cell, 1,091 and 1,090 clusters, respectively) (Supplementary Table  3 ).
The sample was obtained from Perch Pond Fen near Old Town, Penobscot County, Maine, USA, in June 2014. This enrichment culture was filtered through a 40-μ m mesh (removing pollen and sporangia) and concentrated by centrifugation to about 5 × 10 4 zoospores per ml. The sample was preserved in 10% glycerol, shipped on ice and stored at -80 °C. M. bicuspidata was isolated from an infected population of the water flea Daphnia dentifera grown under laboratory conditions. D. dentifera samples were rinsed repeatedly with deionized water. Then, insect pins were used to puncture the carapace and a micropipette was used to collect haemolymph, which contained a mixture of M. bicuspidata yeast cells and ascospores. Cells were preserved in 10% glycerol at a concentration of 10 5 spores per ml and stored at -80 °C.
Furthermore, downstream co-assembly of libraries reduces the problem of random bias. As a quality-control step, amplified cells were 18S rDNA screened using the following primers: M13CRYPTO2-2F (5′ -GTTTTCCCAGTCACGACCACAGG GAGGTAGTGACAG-3′ ), M13AU4v2 (5′ -CAGGAAAAGCTATGACGCCTC ACTAAGCCATTC-3′ ), M13DPD360FE (5′ -GTTTTCCCAGTCACGACCGGA GARGGMGCMTGAGA-3′ ), M13DPD1492RE (5′ -CAGGAAACAGCTATGACA CCTTGTTACGRCTT-3′ ), M13SR1RFor (5′ -GTTTTCCCAGTCACGACTA CCTGGTTGATYCTGCCAGT-3′ ) and M13NS4Rev (5′ -CAGGAAACAGCT ATGACCTTCCGTCAATTCCTTTAAG-3′ ). Amplified 18S rDNA fragments were treated with ExoSap-IT (Thermo Fisher Scientific) and Sanger sequenced. Sequences were queried using BLAST against the National Center for Biotechnology Information non-redundant (NCBI nr) and Assembling the Fungal Tree Of Life (AFTOL)  76  databases.
Reads were also normalized to make the coverage more uniform, including removing reads that have very high coverage (> 100). Individual libraries from each species were assembled with SPAdes 77 (v2. 6 or v3. 0), using the 'careful' and 'singlecell' options, and k-mer sizes of 21, 33 or 55. After assembly, scaffolds of < 2 kb in length were removed as, in our experience, they are phylogenetically ambiguous. Assembled contigs were also compared using BLAST to a set of contaminant databases: bacterial, mouse, human, feline and canine. Finally, we performed tetramer principle component analysis and removed any outlier contigs. This extra cautious read usage for assembly probably leads to reduced completeness and removes symbiotic occurrences but guarantees one species genome instead. Supplementary Table  1  presents summaries of HiSeq libraries of variable number of cells for each organism.
25) in their single cells are indicative of heterozygosity. Only SNPs with an allele frequency > 25% were kept for further analysis. Comparisons of ploidy levels between species were performed on four single-cell libraries with the highest completeness and quality. K-mer graphs were plotted using the kmercountexact. sh script of the BBTools package (http://jgi. doe. gov/data-andtools/bbtools/). For intraspecific variation, reads from the eight D. cristalligena one-cell libraries were aligned to the D. cristalligena co-assembled genome using BBMap (v37. 76) (https://sourceforge. net/projects/bbmap/). Variants were identified using FreeBayes 81 (v1. 1. 0-54-g49413aa) with parameters '--min-coverage 5 -F 0. 01 -C 2 -no-mnps -no-complex'. SNPs with 100% disagreement with reference and 100% frequency were kept and mapped to genes for which all libraries had > 90% coverage.
NAturE MicrobioloGy Functional analyses. Subtilase cluster analysis was achieved using CLANS  83  and a list of identified protein sequences described in Li et al. 45. Metallopeptidase and hydrophobin trees were constructed using RAxML 84 (v8. 2. 2; GAMMA + WAGF substitution model; 100 bootstrap replicates) and PFAM seed set sequences: PF02128 and PF01185 + PF06766, respectively. Trees were visualized using the Interactive Tree of Life (iTOL) website  85  (http://itol. embl. de). For phylogenetic analysis of identified NRPS genes, adenylation domains were mined from proteomes using HMMER  86  (v3. 1b2) with a hidden Markov model based on the fungal and bacterial domains identified by Bushley and Turgeon  53. MUSCLE  87 (v3. 8. 31)  was used to align extracted domains with those used for the hidden Markov model creation and gaps were removed manually.
RAxML 84 (v8. 2. 8) was used for phylogenetic reconstruction using the gamma model of rate heterogeneity and the RTREV substitution matrix with 100 bootstrap replicates. Phylogenetic analysis. All-v-all blastp  88  (v2. 2. 26) was run using a cut-off of 1 × 10 -5 on the predicted proteomes of the representative set of fungal and outgroup species provided in Supplementary Table  2. Clusters were predicted using MCL  89  (v1. 008) with an inflation value of 2. A python script identified clusters containing at most one gene copy per genome, allowing for up to 8 missing taxa per cluster, which resulted in 805 total clusters selected. Each cluster was aligned using MAFFT  90  (v7. 047) on each cluster, with a multiple sequence alignment algorithm detected automatically (using the -auto flag). Alignments were trimmed with Gblocks 91 (v0.
91b) and concatenated, followed by rapid bootstrap maximum likelihood tree building with RAxML 84 , 27,751 distinct alignment patterns, 100 bootstrap replicates and using the GAMMA + WAGF protein model. For all 29,255 positions in the alignment, the target genomes had a median of 14% missing, compared to the median of 2. 5% missing of others. Orthogroup reconstruction. Using MCL clustering  89  , orthologues were collected using a minimum of three taxa per orthogroup, ignoring single-cell C. protostelioides and R. allomycis in favour of the respective co-assemblies. The R package APE  92  was used for ancestral-state reconstruction. Protein functions predicted using PRIAM  93  were mapped to each internal node of the phylogenetic tree presented in Fig. 1. Gains and losses in single-cell lineages were considered as relative to the ancestral fungal node (Supplementary Fig.
Acknowledgements The work conducted by the  US Department of Energy JGI , a  DOE Office of Science User Facility , is supported by the  Office of Science of the US Department of Energy  under contract no. DE-AC02-05CH11231. The authors thank  M. Duffy  ( University of Michigan, MI, USA ) for providing the sample of M. bicuspidata,  J. Longcore  ( University of Maine, ME, USA ) for B. helicus and  N. Ivanova  for metabolic biochemistry insights. S. R. A. , I. V. G. , T. Y. J. and C. A. Q. were supported by the  NSF  grant  DEB-1354625. M. E. S. , T. Y. J. , N. K. R. and G. L. B. were supported by the  NSF  grant  DEB-1441677  ( Genealogy   of Life (ZyGoLife) the conundrum of Kingdom Fungi ).
Data availability The co-assembled genomes and annotations of the target species are available through MycoCosm (https://genome. jgi. doe. gov/fungi) and Genbank using the following MycoCosm URLs and NCBI accessions, respectively: R. allomycis CSF55 single cell (https://genome. jgi. doe. gov/Rozal_SC1; QUVT00000000), B. helicus Perch Fen single cell (https://genome. jgi. doe. gov/Blyhe1; QPFV00000000), C. protostelioides ATCC 52028 single cell (https://genome. jgi. doe. gov/Caupr_ SCcomb; QUVS00000000), D. cristalligena RSA 468 single cell (https://genome. jgi. doe. gov/DimcrSC1; QRFA00000000), P. cylindrospora RSA 2659 single cell (https:// genome. jgi. doe. gov/Pipcy3_1; QPFT00000000), T. sphaerospora RSA 1356 single cell (https://genome. jgi. doe. gov/Thasp1; QUVU00000000), S. pseudoplumigaleata Benny S71-1 single cell (https://genome.