dataesr/scientific-paragraphs-categorization huggingface dataset:
Our results, based on an improved gene and taxon sampling, provided strong support for the placement of chytrids as a sister clade to all other Fungi. This solid position of the root on the chytrid branch is additionally consistent with the distribution of so-considered derived characters in Blastocladiomycota, including sporic meiosis, relatively small numbers of carbohydrate metabolism genes and, in some species, hyphal-like apical growing structures (Allomyces) and narrow sporangia exit tubes (e. g. Catenaria spp. )  5, [62] [63] [64]. Despite the use of a large dataset, some branches remained unresolved, in particular the position of Glomeromycota, sister either to Mucoromycota or Dikarya (Fig. 2a ), which has important implications related to their symbiotic adaptation to land plants  24, 65, 66.
Conclusions We generated the first genome sequence data for the two known species of sanchytrids, a group of atypical fungal parasites of algae. The phylogenetic analysis of two independent datasets of conserved proteins showed that they form a new fast-evolving fungal phylum, the Sanchytriomycota, sister to the Blastocladiomycota. Our phylogenetic analyses also provided strong support for Chytridiomycota being the sister group to all other fungi. Sanchytrids have a complex life cycle that includes a flagellated phase (zoospores) with non-motile flagella that are engaged in an ongoing reductive process. The inclusion of sanchytrids and a wide taxon sampling of fungi in our multigene phylogeny allowed the inference of three large independent flagellum loss events across Holomycota. Interestingly, the sanchytrid residual flagellum endowed with a long kinetosome might represent an exaptation of this structure as support for a lipid organelle probably involved in light sensing.
9. 19  108  (Supplementary Fig. 14). Second, open-reading frames were predicted and translated from the assembled contigs using Transdecoder v2 (http:transdecoder. github. io) with default parameters and Cd-hit v4. 6  109  with 100% identity to produce protein sequences for A. gromovi and S. tribonematis. Finally, to remove possible eukaryotic host (Tribonema) contamination, the predicted protein sequences were searched by BLASTp 110 against two predicted yellow-green algae proteomes inferred from the T. gayanum transcriptome  13  and the Heterococcus sp. DN1 genome (PRJNA210954; also a member of the Tribonematales). We excluded sanchytrid hits that were 100% or >95% identical to them, respectively. Statistics of the final assembled genomes were assessed with QUAST v4. 5  111  and Qualimap v2.
2. 1  112  for coverage estimation. In total, we obtained 7220 and 9368 protein sequences for A. gromovi and S. tribonematis, respectively (Table  1 ). To assess genome completeness, we used BUSCO v2. 0. 1  37  on the decontaminated predicted proteomes with the fungi_odb9 dataset of 290 near-universal single-copy orthologs. The inferred proteins were functionally annotated with eggNOG mapper v2  38  using DIAMOND as the mapping mode and the eukaryotic taxonomic scope. This resulted in 3757 (A. gromovi) and 4670 (S. tribonematis) functionally annotated peptides for the predicted proteomes (Supplementary Data 1). To determine the presence of sanchytrid-specific proteins, we used OrthoFinder  43  to generate orthologous groups using the proteomes of the 59 species used in the GBE59 dataset (Supplementary Data 2).
After extracting the orthogroups that were present only in both sanchytrid species, we functionally annotated them with eggNOG mapper v2  38  (Supplementary Data 2). The mitochondrial genomes of the two sanchytrid species were identified in single SPAdes-generated contigs using BLAST  113  and annotated with MITOS v1  114. We confirmed the quality of the obtained mitochondrial genome assemblies by reassembling them with NOVOPlasty  115. Additional BLAST searches were made to verify missing proteins. Phylogenomic analyses and single-gene phylogenies. A revised version of a 264 protein dataset (termed GBE)  49  was used to reconstruct phylogenomic trees  13, 52. This dataset was updated with sequences from the two sanchytrid genomes and all publicly available Blastocladiomycota sequences. Sequences were obtained mainly from GenBank (http://www.
ncbi. nlm. nih. gov/genbank, last accessed November 2019) and, secondarily, the Joint Genome Institute (http://www. jgi. doe. gov/; last accessed May 2017). For details on the origin of sequence data see Supplementary Data 8. Our updated taxon sampling comprises a total of 71 Opisthokonta (2 Holozoa and 69 Holomycota), 2 Amoebozoa and 1 Apusomonadida. Two datasets with two different taxon samplings were prepared, one with all 74 species (GBE74) and one without the long-branching core Microsporidia and metchnikovellids for a total of 59 species (GBE59). In addition, these datasets were also constructed without the sanchytrids to test if they induced LBA artefacts, for a total of 72 (GBE72) and 57 (GBE57) species. Additionally, for the taxon sampling of 59 species we used a second phylogenomic dataset of 53 highly conserved proteins  59  : the BMC dataset (BMC59).
The proteins for these datasets were searched in the new species with tBLASTn  110  , incorporated into the individual protein datasets, aligned with MAFFT v7  116  and trimmed with TrimAl v1. 2 with the automated1 option  117. Alignments were visualized, manually edited and concatenated with Geneious v6. 0. 6  118  and single-gene trees obtained with FastTree v2. 1. 7  119  with default parameters. Single-gene trees were manually checked to identify and remove paralogous and/or contaminating sequences. The concatenation of the clean trimmed 264 proteins of the GBE dataset resulted in alignments containing 93,743 (GBE59) and 86,313 (GBE74) amino acid positions. The alternative alignment without the sanchytrids contained 93,421 (GBE57) and 84,949 (GBE72) amino acid positions. The concatenation of the 53 proteins of the BMC59 dataset resulted in an alignment of 14,965 amino acid positions.
The BI phylogenetic trees of the GBE59 and BMC59 datasets were reconstructed using PhyloBayes-MPI v1. 5  120  under the CAT-Poisson and CAT-GTR models, respectively, with two MCMC chains and run for more than 15,000 generations, saving one for every ten trees. Analyses were stopped once convergence thresholds were reached (i. e. maximum discrepancy <0. 1 and minimum effective size >100, calculated using bpcomp) and consensus trees constructed after a burn-in of 25%. ML phylogenetic trees were inferred with IQ-TREE v1. 6 under the PMSF approximation of the LG + C20 + R8 (GBE59 and GBE57), LG + C20 + F + R9 (GBE72), LG + C20 + F + R10 (GBE74) or LG + C60 + R7 (BMC59) models with guide trees inferred with the LG + R8, LG + F + R9, LG + F + R10 or LG + R7 models, respectively, selected with the IQ-TREE TESTNEW algorithm as per the Bayesian information criterion (BIC).
AU tests were carried out on the resulting trees for each taxon sampling with the -z and -au options of IQ-TREE. Additionally, to minimize possible systematic bias due to the inclusion of fastevolving sites in our GBE59 dataset, we progressively removed the fastest-evolving sites at 5% of sites at a time. For that, among-site substitution rates were inferred using IQ-TREE under the -wsr option and the best-fitting model for a total of 19 new data subsets (Supplementary Data 3). We then reconstructed phylogenetic trees for all these subsets using IQ-TREE with the same best-fitting model as for the whole dataset. To assess the support of the alternative topologies in the bootstrapped trees, we used CONSENSE from the PHYLIP v3. 695 package  125  and interrogated the UFBOOT file using a Python script (M.
From these categories, we identified 1158 orthologs (non-redundant among categories) in the sanchytrid genomes that were shared among a set of 43 species, including 38 Holomycota, 2 Holozoa, 2 Amoebozoa and 1 apusomonad (for the complete list, see Supplementary Data 4). We annotated the protein sets of these 43 species using eggNOG mapper v2  38  with DIAMOND as mapping mode and the eukaryotic taxonomic scope. All ortholog counts were transformed into a presence/absence matrix (encoded as 0/1) and analysed with the R script  126  detailed in Torruella  et al. (2018), in which similarity values between binary COG profiles of all species were calculated to create a complementary species-distance matrix. We then analysed this distance matrix using a principal coordinate analysis and plotted binary COG profiles in a presence/absence heatmap.
Clustering of the species and orthologs was done by Ward hierarchical clustering (on Euclidean distances for orthologs) of the interspecific Pearson correlation coefficients. The raw species clustering was also represented in a separate pairwise correlation heatmap colourcoded to display positive Pearson correlation values (0-1). Finally, COG categories of each primary metabolism were also analysed separately (categories C, E, F, G, H, I, P and Q) using the same workflow (for more details, see Torruella et al. (2018)  and https://github. com/xgrau/paraphelidium2018). We compared in more detail the inferred metabolism of a subset of species (the two sanchytrids, R. allomycis and Allomyces macrogynus). The annotation of these proteomes was done using BlastKOALA v2. 2  127  , with eukaryotes as taxonomy group and the genus_eukaryotes KEGG GENES database, and the annotations were uploaded in the KEGG Mapper Reconstruct Pathway platform 71 by pairs.
First, we compared the two sanchytrid proteomes to confirm their similarity; second, we compared them with the proteomes of R. allomycis and A. macrogynus to study their metabolic reduction. To explore if the long branch observed in sanchytrids could be explained by the absence of DNA repair and genome maintenance genes, we used a dataset of 47 proteins involved in these functions from a previous study on fast-evolving budding yeasts  60. These proteins were blasted  113  against the proteomes of the two sanchytrid species, the blastoclad A. macrogynus and four species of fast-and slowevolving yeasts from that study. Homology searches and phylogenetic analysis of specific proteins. To assess the evolution of the flagellum in holomycotan lineages, we used a dataset of over 60 flagellum-specific proteins  77, [128] [129] [130]  to examine a total of 43 flagellated and nonflagellated species within and outside the Holomycota.
The flagellar toolkit proteins were identified using Homo sapiens sequences as Blast queries. Candidate proteins were then blasted against the non-redundant GenBank database to confirm their identification and submitted to phylogenetic analysis by multiple sequence alignment with MAFFT  116  , trimming with TrimAl 117 with the automated1 option and tree reconstruction with FastTree 119. After inspection of trees, we removed paralogs and other non-orthologous protein sequences. We excluded the proteins with no identifiable presence in any of the 43 species used in the analysis and encoded the presence/absence of the remaining ones in a 1/0 matrix. The native R heatmap function  126  was used to plot the flagellar proteome comparison between all species according to their presence/absence similarity profiles.
To study the presence or absence of the fusion of the BeGC1 and BeCNG1 light-sensing proteins, we blasted them against the proteomes of S. tribonematis and A. gromovi using the B. emersonii sequences (BeGC1: AIC07007. 1; BeCNG1: AIC07008. 1) as queries. We used the protein dataset of Avelar et al. 96  in which the authors blasted both the guanylyl-cyclase GC1 and the rhodopsin domains of the BeGC1 fusion protein of B. emersonii and the BeCNG1 proteins against a database with >900 genomes from eukaryotic and prokaryotic species all across the tree of life (see Table  S1  of their manuscript). We used MAFFT to include the new sequences in a multiple sequence alignment for the BeCNG1 protein channel and separately for both domains of the BeGC1 fusion protein  96. After trimming with TrimAl, we reconstructed phylogenetic trees for the three datasets using IQ-TREE with the best-fitting models: LG + F + I + G4 for the rhodopsin domain and BeCNG1, and LG + G4 model for GC1 guanylyl-cyclase domain.
The resulting trees were visualized with FigTree 123. To study the possible presence of a carotenoid synthesis pathway for retinal production in sanchytrids, we combined previous datasets for carotenoid biosynthesis and cleavage enzymes from one giant virus (ChoanoV1), two choanoflagellates and two haptophytes  100  , together with the carotenoid biosynthesis enzymes found in B. emersonii  96. This dataset also included three enzymes for early sterol and carotenoid biosynthesis (isoprenoid biosynthesis steps). The protein sequences from this dataset were blasted (BLASTp) against the proteomes of the two sanchytrids and their host T. gayanum. Results were confirmed by blasting the identified hits to the NCBI non-redundant protein sequence database. To study the presence of proteins involved in cell wall penetration in sanchytrids (including cellulases, hemicellulases, chitinases and pectinases), we used the complete mycoCLAP v1 database of carbohydrate-degrading enzymes (https://mycoclap.
fungalgenomics. ca/ mycoCLAP/). We blasted this database against our two sanchytrid proteomes and other two representatives of Blastocladiomycota and four Chytridiomycota. We then screened for canonical cellulose-, hemicellulose-, pectin-and chitin-degrading enzymes identified in Fungi from the previous studies  13, 131, 132. The identified hits were blasted back to both the NCBI non-redundant protein sequence database and the CAZy database (cazy. org) to create a dataset for phylogenetic reconstruction for each enzyme. We added the identified sequences to specific protein datasets from previous studies in the case of cellulases  13  and of the chitin degradation proteins GH20 β-N-acetylhexosaminidase  75  and GH18 chitinase  74. Proteins were aligned with MAFFT  116  and trimmed from gaps and ambiguously aligned sites using TrimAl  117  with the automated1 option.
ML trees were inferred using IQ-TREE  133  with the best-fitting model selected with the IQ-TREE TESTNEW algorithm as per BIC. The best-scoring tree was searched for up to 100 iterations, starting from 100 initial parsimonious trees; statistical supports were generated from 1000 ultrafast bootstrap replicates and 1000 replicates of the SH-like approximate likelihood ratio test. Trees were visualized with FigTree  123. Analyses of hyphae multicellularity-related genes. We used a dataset of 619 hyphal multicellularity-related proteins belonging to 362 gene families  103. These gene families were grouped into ten functional categories: actin cytoskeleton (55 proteins), adhesion (30), polarity maintenance (107), cell wall biogenesis/remodelling (92), septation (56), signalling (82), transcriptional regulation (51), vesicle transport (103), microtubule-based transport (32) and cell cycle regulation (11).
The 619 proteins were searched by BLAST in the GBE59 dataset proteome and later incorporated into individual gene family protein alignments with MAFFT v7  116. After trimming with TrimAl 117 with the automated1 option, alignments were visualized with Geneious v6. 0. 6  118  and single-gene trees were obtained with FastTree 119 using default parameters. Single-protein trees were manually checked to identify paralogous sequences and confirm the presence of genes within large gene families. The presence/absence of genes was binary coded (0/1) for each species and the resulting matrix was processed with a specific R script  13. Finally, we reconstructed the multicellularity-related gene gain/loss dynamics by applying the Dollo parsimony method implemented in Count v10. 04  134. These analyses were done on both the complete dataset of all hyphae multicellularity-related genes and on ten separate datasets corresponding to the functional categories mentioned above.
Acknowledgements We thank the UNICELL single-cell genomics platform (https://www. deemteam. fr/en/unicell). This work was funded by the  European Research Council   Advanced Grants ProtistWorld  (No. 322669 , to P. L. -G. ) and  Plast-Evol  (No. 787904 , to D. M. ) and the  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie ITN  project  SINGEK  (http://www. singek. eu/;grant agreement no. H2020-MSCA-ITN-2015-675752 , to L. J. G. and P. L. -G. ). The contribution by S. K. was supported by the  RSF  Grant No. 16-14-10302. We also thank  Christina Cuomo  and the  Broad Institute  for allowing us to use the unpublished fungal genome sequences in this study.
Data availability The raw sequence data and assembled genomes generated in this study have been deposited at the National Center for Biotechnology Information (NCBI) sequence databases under Bioproject accession codes PRJNA668693 and PRJNA668694. Additional data generated in this study (including alignments, phylogenetic trees and assembled genomes) are available in the Figshare repository project 91439. DNA and protein sequences of the species used in this study were downloaded from the GenBank public databases [https://www. ncbi. nlm. nih. gov/nucleotide/ and https:// www. ncbi. nlm. nih. gov/protein/], genome [https://www. ncbi. nlm. nih. gov/genome/], SRA [https://www. ncbi. nlm. nih. gov/sra/], the JGI genome database [https:// genome. jgi. doe. gov/portal/], the CAZy database [cazy. org] and the mycoCLAP database [https://mycoclap.
Acknowledgements In 2020, the  OHBM   Open Science Special Interest group  was supported by  Openneuro  (https://openneuro. org/),INCF (https://ww w. incf. org/),Neuromod (https://www. cneuromod. ca/), CONP  (ht tps://conp. ca/), the  Wellcome Centre for Integrative Neuroimaging  (https://www. win. ox. ac. uk/), UNIQUE  (https://www. unique. quebec/),QBIN (https://www. rbiq-qbin. qc. ca/Accueil),OHBM Australian chapter (https://ohbmaustralia. wordpress. com/), and OHBM (https://www. humanbrainmapping. org). We would also like to thank all the participants, speakers, and volunteers of the  2020 Open Science Room and OHBM Brainhack.
1D correlation function (the probability of finding a scatterer at position r in the sample if another scatterer is located at the origin) from small angle X-ray scattering (SAXS) is used for the determination of the crystalline lamellar thickness, l, and the lamellar long period, L. 2. 6. Temperature dependent 1D Wide-angle X-Rays (WAXS) WAXS measurements at different temperatures were obtained using a Panalytical Empyrean diffractometer equipped with a PIXcel 1D detector and chopped specimens loaded into a 1 mm Quartz capillary, which was rotated during data collection. The initial measurements at 30 °C and 50 °C resulted in comparatively high intensities because they were carried out with a 10 mm beam mask. However, the mask size was reduced to about 7 mm at 75 °C using lead tape to ensure that the signal originated from the heated area of the specimen, assumed to be commensurate with the diameter of the modified nozzle of the Leister heat blower used to control the temperature, or about 10 mm.
Introduction: The presence of high-risk chromosomal abnormalities [t(4;14), del(17p), and t(14;16)] has been linked with inferior outcomes in patients with multiple myeloma (MM). A prespecified interim analysis of the Phase 3 IKEMA study (NCT03275285) demonstrated that isatuximab (Isa) + carfilzomib (K) and dexamethasone (d; Isa-Kd) significantly improved progression-free survival (PFS) versus Kd in patients with relapsed MM. This prespecified subgroup analysis of IKEMA examined efficacy and safety in patients with high-risk cytogenetics.
DATA AVAILABILITY STATEMENT Qualified researchers can request access to patient-level data and related study documents including the clinical study report, study protocol with any amendments, blank case report forms, statistical analysis plan, and dataset specifications. Patient-level data will be anonymized, and study documents will be redacted to protect the privacy of trial participants. Further details on Sanofi's data-sharing criteria, eligible studies, and process for requesting access are at: https://www. vivli. org.
De-novo identification of motifs using RIMS-seq Programs and a detailed manual for the de-novo identification of motifs in RIMS-seq are available on github (https: //github. com/Ettwiller/RIMS-seq/). Using the mpileup files, positions and 14bp flanking genomic regions for which a high quality (base quality score ≥ 35) C to T in R1 or G to A in R2 was found, were extracted for the foreground. Positions and 14bp flanking regions for which a high quality (base quality score ≥ 35) G to A in R1 or C to T in R2 was found, were extracted for the background. C to T or G to A in the first position of reads were ignored. If the percentage of C to T or G to A are above 5% for at least 5 reads at any given position, the position was ignored (to avoid considering positions containing true variants). Motifs that are found significantly enriched (P-value < 1e -100 ) in the foreground sequences compared to background sequences were found using mosdi pipeline mosdi-discovery with the following parameters: 'mosdi-discovery -v discovery -q x -i -T 1e-100 -M 8,1,0,4 8 occ-count' using the foreground sequences with x being the output of the following command : 'mosdi-utils count-qgrams -A 'dna'' using the background sequences.
Phylogeny of the ATCC synthetic microbiomes and visualization The phylogenetic trees of both ATCC synthetic gut and skin microbiomes were done using OrthoFinder version 2. 3. 11 (33)  using the MSA workflow and MAFFT for the multiple sequence alignment program. The program options are available at https://github. com/davidemms/ OrthoFinder. The phylogenetic tree and abundance data obtained from DNA-seq, RIMS-seq and bisulfite-seq were visualized using iTOL  (34)  for each synthetic community (see Supplementary Figure  S5 ).
Quality control of the data The insert size for each downsampled filtered bam file was calculated using Picard version 2. 20. 8 using the default parameters and the option CollectInsertSizeMetrics ('Picard Toolkit. ' 2019. Broad Institute, GitHub Repository. http: //broadinstitute. github. io/picard/; Broad Institute). The GC bias for each downsampled filtered bam file was calculated and plotted using Picard version 2. 20. 8 using the default parameters and the option CollectGcBiasMetrics.
Intact mass LC-MS Intact mass analysis was performed by tandem liquid chromatography-mass spectrometry (LC-MS/MS) on an Vanquish Horizon UHPLC System equipped with a diode array detector and a Thermo Q-Exactive Plus mass spectrometer operating under negative electrospray ionization mode (-ESI). UHPLC was performed using a Thermo DNAPac™ RP Column (2. 1 × 50 mm, 4 m) at 70 • C and 0. 3 ml/min flow rate, with a gradient mobile phase consisting of hexafluoroisopropanol (HFIP)-N,N-diisopropylethylamine (DIEA) aqueous buffer and methanol. UV detection was performed at 260 nm. Intact mass analysis was performed under Full MS mode, and ESI-MS raw data was deconvoluted using Promass HR (Novatia Inc. ).
In addition, we performed a control experiment corresponding to the standard DNA-seq. Resulting libraries were paired-end sequenced using Illumina and mapped to their corresponding genomes (Methods). For comparison, all datasets were downsampled to 5 million reads corresponding to 200× coverage of the E. coli genome and instances of high confidence C to T variants (Q score > 35) on either R1 or R2 were identified. As expected, control DNA-seq experiments show comparable numbers of C to T read variants between R1 and R2, indicating true C to T variants or errors during amplification and sequencing (Figure  2A ). On the other hand, the overall number of C to T read variants in R1 is progressively elevated for 10 min, 1 h and 3 h of heat-alkaline treatment of the E. coli K12 samples with an overall 4-fold increase after 3 htreatment compared to no treatment; heat-alkaline treatments did not increase the rate of C to T read variants in R2 (Figure  2A ).