dataesr/scientific-paragraphs-categorization huggingface dataset:
The database was further uploaded into the MSI application (https://msi. happy-dev. fr/) to make it available for potential users. Tested panel. The panel of isolates used to test the online and Bruker databases included the remaining 94 isolates from the collection. Table  1  shows the distribution of the isolates selected to be part of the online database and of those used as the test panel. DNA sequencing. All 134 clinical isolates used in the study were identified using hsp60 sequencing. Two PCRs were used: (i) a PCR enabling the sequencing of a 324-bp fragment of the hsp60 gene (from positions 1218 to 1560) was applied to all the isolates following a protocol adapted from reference 5, and (ii) if the first sequencing identified the species E. hormaechei, a second hsp60 fragment (nucleotides 230 to 1027) was sequenced after amplification with primers hsp60_230_F (59-TTGCCTCTAAAGCG AACGAC-39) and hsp60_1027_R (59-GAATAGCGGCTTCTTCACCC-39).
DNA extraction was performed by using the InstaGene matrix (Bio-Rad, Marnes la Coquette, France) following the manufacturer's instructions. PCRs were performed in a 50-ml final volume with 0. 2 mM each primer, 2 ml of DNA, and 25 ml of Qiagen Taq PCR master mix. Amplification conditions were as follows: after 7 min of denaturation at 94°C, we used 35 amplification cycles of denaturation at 94°C for 1 min, annealing at 56°C for 30 s, and extension at 72°C for 30 s, with a final extension step for 5 min at 72°C. Sequence analysis was carried out using BioEdit v. 7. 0. 5. 3. Sutton et al. described 22 clades (A to V) and proposed a type strain for each clade  (10). To evaluate the interclade discriminating power of the hsp60 sequences to identify ECC clades, we determined them for all type strains (Table  3 ) and created a local database.
Then, we constructed phylogenetic trees according to the maximum likelihood method from the alignments of the regions from positions 1218 to 1560 and 230 to 1027. Evolutionary analyses were conducted in MEGA X (26) (see Fig. S1  in the supplemental material). The partial hsp60 sequence of each clinical isolate was compared to the corresponding sequences of the reference strains of the 22 clades. Species or subspecies were assigned if the dissimilarity score between the hsp60 sequence of the studied isolates and of the type strain sequences was inferior to 2%. If this was not the case, the closest identification was returned and named "proxy. " MALDI-TOF MS sample preparation and data acquisition. (i) Sample preparation for the online database. Each isolate was thawed and cultured at 37°C for 18 to 24 h on Columbia blood agar (COH).
An external calibration standard (Bacterial Test Standard; Bruker Daltonics) was used. FlexControl (version 3. 0; Bruker Daltonics) was used for data acquisition. Mass spectrometry reference databases. We used two databases for comparative identification: the Bruker reference database and the MSI online database set up for the study. Using FlexAnalysis (version 4. 2) software (Bruker Daltonics), spectra were visually analyzed. Poorquality spectra were removed, and between 20 and 24 spectra per isolate were retained. A total of 931 spectra from seven ECC species were used to create the online database and included in the MSI software. Mass spectrometry systems for identification. Two identification systems were used for the mass spectrum identification of isolates from the tested panel. The identifications were assigned an identification score.
For the MALDI Biotyper v. 4. 1. 90, 02/2020 (Bruker Daltonics), a log score of. 2 is considered to indicate a high confidence level for identification at the species level, while for MSI, the threshold is a score of 20  (27). Identification of interspecies discriminating peaks was performed with spectra from the online database (42 isolates) using the MALDIquant  (24)  and MALDIrrpa  (28)  packages in the R environment. The main discriminating peaks observed for the 7 ECC species (frequency greater than 95% for one species and absent for at least one species) were listed. An average spectrum containing aligned masses (tolerance = 150 ppm) and the average of intensities for each species was created to visualize peaks using the plotly package  (29) , available online at https://agodmer. github.
io/ECC/. Statistical analysis of identifications. The identification performances of the MSI and MALDI Biotyper systems were compared. The identification score and Cohen kappa coefficient were calculated using the caret package in the R environment (see Text S1 in the supplemental material for calculation details). The rate of correct identification from the tested panel by species from the three replicates was reported. The confidence scores of the identifications generated by each of the two systems (MSI and MALDI Biotyper) associated with incorrect and correct identifications were compared with the nonparametric Mann-Whitney U test. From the confidence scores associated with the identification given by the two identification systems, receiver operating characteristic (ROC) curves were produced to evaluate the rates of correct and incorrect identifications as a function of the threshold scores using XLSTAT (v.
glacialis as outgroup to Symbiodiniaceae. At each node, the total number of families that include genes from all diverging isolates (and not others) is shown; the proportion of dark genes among all genes in these families are shown as a pie chart. b Number of isolate-specific gene families for each isolate, showing the proportion of families that are dark. c Number of gene families specific to Symbiodiniaceae, Symbiodinium, and other genera the UniProt database. We consider these as "dark" genes, i. e. they are either novel or too highly diverged to identify putative homologues in existing data; some families of dark genes appear to be specific to some isolates (Fig. 5b ). Although proteins of S. microadriaticum CCMP2467 are available in UniProt (to which we expected many Symbiodinium proteins would share significant sequence similarity), the proportion (> 90%) of dark genes among the core genes of Symbiodinium remains higher than those among the core genes for other genera, e.
g. the node encompassing Breviolum, Cladocopium and Fugacium (83. 56% dark genes; Fig. 5a ). This result suggests that the function of core genes in Symbiodinium species is largely unknown and that the divergence of gene sequences has been more extensive among the Symbiodinium isolates than among the other Symbiodiniaceae genera. Although these observations also reflect the dearth of dinoflagellate sequences in UniProt (and public databases in general), the presence of highly divergent homologues implies lineage-specific functional innovation, lending support to the observation of conserved, lineage-specific dark genes in dinoflagellates  [37, 38]. Conserved dark genes were also reported in core genes of other eukaryote lineages including green algae  [39]  and the haptophyte Emiliania  [40].
The species tree inferred using OrthoFinder (see "Methods") based on the homologous protein families (Fig. 5a ) is in complete topological congruence to the accepted Symbiodiniaceae phylogeny reconstructed using large subunit rRNA  [26]. Our alignment-free tree (Fig. 1b ) is also topologically congruent to the two phylogenies (in Fig. 5a  and in  [26] ), except for the branching order of S. natans and S. pilosum basal to the Symbiodinium clade. This result demonstrates, in the case of Symbiodiniaceae and despite the extensive genome divergence, a strong consensus of phylogenetic signal exhibited by the different datasets (i. e. single marker gene, homologous protein families or whole-genome sequences), and the power of scalable alignment-free approaches for inferring phylogenetic relationships comprehensively using whole-genome sequences rather than a small subset of genes.
We consider our results here to be more reliable than those based largely on transcriptome data  [36] , in which gene numbers can be overestimated due to differential transcription, transcript isoforms, RNA editing and intrinsically shorter sequences. The smaller number of gene families shared among Symbiodiniaceae found here (i. e. 18,453 compared to 76,087 in González-Pech et al. [36] ) reflects our more-conservative approach based on whole-genome sequence data, and our use of larger and/or higher-quality datasets. Nonetheless, our observations support the notion that the evolution of gene families has contributed to the diversification of Symbiodiniaceae  [36].
The two genomes are highly dissimilar; we observed a low read-mapping rate (< 15%) of read pairs from one genome dataset against the genome assembly of the counterpart, and vice versa (Fig. 6a ). Only 14. 70 Mbp (1. 33%) of the genome sequence of S. tridacnidorum aligned to 11. 84 Mbp (1. 55%) of S. natans at 90% identity or greater. Figure  6b  shows the most-represented protein domains in the two genomes; gene functions related to methylation and retrotransposition, previously reported in other Symbiodiniaceae  [54] , are more abundant in S. tridacnidorum, whereas functions related to ion transport are more prominent in S. natans (Additional file 1: Supplementary Note  [55] [56] [57]  and Additional file 3: Supplementary Table  7 ). We assessed the genome features distinct to each species that may have contributed to the discrepancy in genome size.
tridacnidorum. In both genomes, we recovered genes that encode relicts of dinoflagellate spliced leader (DinoSL) sequences indicating retroposition of genetic elements. These genes encode various enzymatic functions (Additional file 2: Supplementary Figure  10 ); the presence of DinoSL relicts suggests the importance of high transcription activity of these genes in genome evolution. We recovered putative DinoSL sequences in both genomes (496 on 350 scaffolds of S. tridacnidorum, and 244 on 211 scaffolds of S. natans; see "Methods"), some in DinoSL or DinoSL-5S rRNA tandem arrays, as previously described in dinoflagellates  [58]. We also recovered genes that encode the reverse transcriptase domains indicating retrotransposition (Additional file 3: Supplementary Tables 8 and 9). Thus, gene duplication and repeats likely expanded in S.
Removal of putative microbial contaminants To identify putative sequences from bacteria, archaea and viruses in the genome scaffolds, we followed the two-phase approach of Chen et al. [32]. In brief, we first searched the scaffolds (BLASTn) against a database of bacterial, archaeal and viral genomes from RefSeq (release 88); hits with E ≤ 10 -20 and alignment bit score ≥ 1000 were considered as significant. We then calculated the proportion of bases in each scaffold covered by significant hits. Next, we assessed the added length of implicated genome scaffolds across different thresholds of these proportions, and the corresponding gene models in these scaffolds as predicted from available transcripts using PASA v2. 3. 3  [83]  (see below), with a modified script available at https://github.
0. 2 (https://bitbucket. org/arobinson/quadtrim) with the flags -m 2 and -g to remove homopolymeric guanine repeats at the end of the reads (a known bias of Illumina NovaSeq 6000 data). Transcriptome assembly was done with Trinity v2. 1. 1  [84]  in de novo and genome-guided modes. De novo transcriptome assembly was done using default parameters and the trimmed read pairs. For genome-guided assembly, high-quality read pairs were aligned to the preliminary de novo genome assembly using Bowtie v2. 2. 7  [85]. Transcriptomes were then assembled with Trinity in the genome-guided mode using the alignment information, and setting the maximum intron size to 100,000 bp. Both de novo and genome-guided transcriptome assemblies from each sample were used for scaffolding (see above) and gene prediction.
Generation of full-length transcript data Full-length transcripts for S. tridacnidorum and S. natans were generated using the PacBio IsoSeq technology (Additional file 3: Supplementary Table  13 ). All sequencing was conducted using the PacBio Sequel platform at the Institute for Molecular Bioscience (IMB) Sequencing Facility, The University of Queensland (Brisbane, Australia). Full-length cDNA was first synthesised and amplified using the TeloPrime Full-Length cDNA Amplification Kit (Lexogen) and TeloPrime PCR Addon Kit (Lexogen) following the protocols provided in the product manuals. One synthesis reaction was performed for each sample using 821 ng from S. tridacnidorum and 1. 09 μg from S. natans of total RNA as starting material. Next, 25 (S. tridacnidorum) and 23 (S. natans) PCR cycles were carried out for cDNA amplification.
natans) AMPure PB beads. The above-described PCR purification and sequencing library preparation methods were used for the DinoSL transcript libraries; cDNA from these libraries was sequenced in one SMRT cell per sample. Due to the abundance of undesired 5′-5′ and 3′-3′ pairs, and to recover as much transcript evidence as possible for gene prediction, we adopted two approaches for processing IsoSeq data (Additional file 2: Supplementary Figure  13 ). First, the IsoSeq 3. 1 workflow (https://github. com/PacificBiosciences/IsoSeq) was followed. Briefly, circular consensus sequences (CCS) were generated from the subreads of each SMRT cell with ccs v3. 1. 0 without polishing, and by setting the minimum number of subreads to generate CCS (--minPasses) to 1. Removal of primers was done with lima v1.
8. 0 in the IsoSeq mode, with a subsequent refinement step using IsoSeq v3. 1. 0. At this stage, the refined full-length transcripts of all SMRT cells (excluding those from the DinoSL library) were combined to be then clustered by similarity and polished with IsoSeq v3. 1. 0. High-and low-quality transcripts resulting from this approach were further used for gene prediction. Second, we repeated the IsoSeq workflow with some modifications. We polished the subreads with the Arrow algorithm and used at least three subreads per CCS with ccs v3. 1. 0 to generate high-accuracy CCS. Primer removal and refinement were done as explained above. The subsequent clustering and polishing steps were skipped. The resulting polished CCS and full-length transcripts were also used for gene prediction. IsoSeq data from the DinoSL library were processed separately following the same two approaches.
Gene prediction and function annotation We adopted a comprehensive, customised approach for ab initio gene prediction from dinoflagellate genomes, following earlier studies  [32, 33] ; the workflow is available at https://github. com/TimothyStephens/ Dinoflagellate_Annotation_Workflow. For a detailed overview of this workflow, see Figure  S1  in Chen et al. [32]. A de novo repeat library was first derived for the genome assembly using RepeatModeler v1. 0. 11 (http://www. repeatmasker. org/RepeatModeler/). All repeats (including known repeats in RepeatMasker database release 20180625) were masked using Repeat-Masker v4. 0. 7 (http://www. repeatmasker. org/); masked sequences were used in the subsequent steps for gene prediction. We used scripts available from RepeatMasker to calculate Kimura distances for sequences of each repeat type (calcDivergenceFromAlign.
pl) and to generate a repeat landscape for each genome (createRepeatLandscape. pl). As direct transcript evidence, we used the de novo and genome-guided transcriptome assemblies from Illumina short-read sequence data. For S. necroappetens CCMP2469, we used transcriptome data of the other six Symbiodinium isolates for gene prediction, as well as other available transcriptome datasets of Symbiodinium: S. microadriaticum CassKB8  [86] , S. microadriaticum CCMP2467  [87] , and S. tridacnidorum Sh18  [88]. We also combined the S. microadriaticum CassKB8 transcriptome data generated here with those from a previous study  [86]. For each sample, we concatenated all RNA-Seq transcripts and "cleaned" them using SeqClean (https://sourceforge. net/projects/seqclean/) and the Uni-Vec database build 10.
0. For S. natans and S. tridacnidorum, we also incorporated the PacBio IsoSeq fulllength transcript data (above) as evidence to guide gene prediction. We used PASA v2. 3. 3  [83] , customised to recognise dinoflagellate alternative splice donor sites (see above), and TransDecoder v5. 2. 0  [83]  to predict coding sequences (CDS). These CDS were searched (BLASTp, E ≤ 10 -20 ) against a protein database that consists of RefSeq proteins (release 88) and a collection of available and predicted (with TransDecoder v5. 2. 0  [83] ) proteins of Symbiodiniaceae (total of 111,591,828 sequences; Additional file 3: Supplementary Table  14 ). We used the analyze_blastPlus_topHit_coverage. pl script from Trinity v2. 6. 6  [84]  to retrieve only those CDS having a hit with > 70% coverage of the database protein sequence (i.
e. nearly full-length) in the database for subsequent analyses. The near full-length gene models were checked for TEs using HHblits v2. 0. 16 (probability = 80% and Evalue = 10 -5 ), searching against the JAMg transposon database (https://sourceforge. net/projects/jamg/files/ databases/) and TransposonPSI (http://transposonpsi. sourceforge. net/). Gene models containing TEs were removed from the gene set, and redundancy reduction was conducted using cd-hit v4. 6  [89, 90]  (ID = 75%). The remaining gene models were processed using the prepare_golden_genes_for_predictors. pl script from the JAMg pipeline (altered to recognise GA donor splice sites; http://jamg. sourceforge. net/). This script produces a set of "golden genes" that was used as training set for the ab initio gene prediction tools AUGUSTUS v3.
3. 1  [91]  (customised to recognise the non-canonical splice sites of dinoflagellates; https:// github. com/chancx/dinoflag-alt-splice) and SNAP v2006-07-28  [92]. Independently, the soft-masked genome sequences were passed to GeneMark-ES v4. 32  [93]  for unsupervised training and gene prediction. UniProt-Swiss-Prot proteins (downloaded on 27 June 2018) and predicted proteins of Symbiodiniaceae (Additional file 3: Supplementary Table  14 ) were used to produce a set of gene predictions using MAKER v2. 31. 10 [94]  protein2genome; the custom repeat library was used by RepeatMasker as part of MAKER prediction. A primary set of predicted genes was produced using EvidenceModeler v1. 1. 1  [95] , modified to recognise GA donor splice sites. This package combined the gene predictions from PASA, SNAP, AU-GUSTUS, GeneMark-ES and MAKER protein2genome into a single set of evidence-based predictions.
The weightings used for the package were as follows: PASA 10, MAKER protein 8, AUGUSTUS 6, SNAP 2 and GeneMark-ES 2. Only gene models with transcript evidence (i. e. predicted by PASA) or supported by at least two ab initio prediction programmes were kept. We assessed completeness by querying the predicted protein sequences in a BLASTp similarity search (E ≤ 10 -5 , ≥ 50% query/target sequence cover) against the 458 core eukaryotic genes from CEGMA  [96]. Transcript data support for the predicted genes was determined by BLASTn (E ≤ 10 -5 ) similarity search, querying the transcript sequences against the predicted CDS from each genome. Genes for which the transcripts aligned to their CDS with at least 50% of sequence cover and 90% identity were considered as supported by transcript data. Following Liu et al.
[35] , functional annotation of the predicted genes was conducted based on sequence similarity searches against known proteins, in which the predicted protein sequences were used as query (BLASTp, E ≤ 10 -5 , minimum query or target cover of 50%) against the manually curated Swiss-Prot database, and those with no Swiss-Prot hits were subsequently searched against TrEMBL (both databases from UniProt, downloaded 27 June 2018). The best UniProt hit with associated Gene Ontology (GO, http://geneontology. org/) terms was used to annotate the query protein with those GO terms using the UniProt-GOA mapping (downloaded 3 June 2019). Pfam domains  [97]  were searched in the predicted proteins of all samples using PfamScan  [98]  (E ≤ 0. 001) and the Pfam-A database (release 30 August 2018)  [97]. Tests for enrichment of domains were done with one-tailed Fisher's exact tests, independently for over-and under-represented features; domains with Benjamini and Hochberg  [99]  adjusted p ≤ 0.
05 were considered significant. Enrichment of GO terms was performed using the topGO Bioconductor package  [100]  implemented in R v3. 5. 1, applying Fisher's exact test with the 'elimination' method to correct for the dependence structure among GO terms. GO terms with a p ≤ 0. 01 were considered significant. To assess the potential impact of organellar genome sequences on our analysis, we used available sequences of plastid genomes  [101] [102] [103]  and mitochondrial genes  [104] [105] [106] [107]  from other dinoflagellates as query to search (BLASTn, E ≤ 10 -10 ) against the seven de novo assembled genomes from this study. For each genome scaffold that shares significant similarity to known organellar sequences, we assessed if the scaffold contains other protein-coding genes (predicted above) that encode nonorganellar functions.
Gene Ontology (GO) enrichment of genes in families core to Symbiodiniaceae and to Symbiodinium (defined as those common to all isolates in, and exclusive to, each group) was conducted using the topGO Bioconductor package  [100]  implemented in R v3. 5. 1, implementing Fisher's exact test and the 'elimination' method; the GO terms associated to the genes of all isolates surveyed here were used as the background for each comparison. We considered a p ≤ 0. 01 as significant. The significance of size differences of the gene families shared by S. tridacnidorum and S. natans was assessed with a two-tailed Fisher's exact test correcting p values for multiple testing  [99] ; difference in size was considered significant for gene families with adjusted p ≤ 0. 05. We assessed the relative abundance of gene functions based on the annotated GO terms and protein domains in all predicted genes.
Funding R. A. G. P. was supported by an  International Postgraduate Research Scholarship  and a  University of Queensland Centenary Scholarship. This work is supported by two  Australian Research Council  grants ( DP150101875  awarded to M. A. R. , C. X. C. and D. B. , and  DP190102474  awarded to C. X. C. and D. B. ). D. W. B and  Y. Cheng  were supported by a  Human Frontier Science Program  grant ( RGP0030 ). D. B. was also supported by a research grant from the  National Aeronautics and Space Administration (NASA ;  80NSSC19K0462 ) and a  NIFA-USDA   Hatch grant  ( NJ01180 ).
Data collection: We carried out social network interviews as part of the ENDOW project in an attempt to capture full networks of households in one matrilineal (N = 40 households) and one patrilineal (N = 30 households) community of Mosuo in the summers of 2017 and 2018. Accompanied by local guides, we walked from house to house and asked any available adult member of the household, man or woman, who could also comment on the networks and kin relatedness of other adult members of the household, to participate. We explained the study to potential participants and addressed their questions before obtaining their informed consent for the interview (UNM IRB 06915). We employed a name generator approach in which respondents were asked to free list individuals with whom they had various kinds of social ties  (Marsden 2005).