dataesr/scientific-paragraphs-categorization huggingface dataset:
Data reduction and fit algorithms The Mantid software pacakage  [33]  was used for the initial reduction of the neutron data recorded at IN16B. The Lamp software package 1 provided by the ILL was used for the IN5 data. All fits were carried out using python3 scripts employing the scipy. optimize. curve fit command. Errors in the fit parameters were calculated from the square root of the diagonal of the covariance matrix. The Voigt profiles used to calcu-.
Molecular dynamics simulations MD simulations were performed using Gromacs 2016. 3  [34, 35, 36]  with the Amber99SB-ILDN force field. [37]  Parameters for the D-Serine ligand were derived from the existing L-Serine parameters. Charges for D-serine were obtained by the RESP approach, as described in  [38, 39, 40]. Quantum mechanical calculations prior to RESP calculations were done with TURBOMOLE V7. 1  [41]  on the Hartree-Fock level using the RI-J approximation  [42]  and a 6-31G* basis set. [43, 44, 45]  Two different side-chain conformers of D-Serine were used and charges averaged over these two conformations. For the pyruvate residue, existing force-field parameters from acetate and the amide carbonyl were used. Charges were calculated as described above using a single conformation. 2 or analysis and visualisation of MD trajectories we used self-written Python scripts in combination with the modules MDAnalysis,  [46]  NumPy  [47]  and Matplotlib.
Acknowledgements TR acknowledges a PhD studentship jointly funded by the  ILL  and the  Universität Hamburg (Federal Excellence Cluster Hamburg Centre for Ultrafast Imaging   EXC 1074 ). We thank the RRZ of the  Universität Hamburg  for access to their  HPC cluster ("Hummel") for MD simulations and Partnership for Structural Biology  (PSB), the Partnership for Soft Condensed Matter (PSCM) in Grenoble for access to their facilities, including notably the DLS. We are grateful to  Michael Marek Koza  ( ILL ) for help on IN5 and  Ralf Schweins  ( ILL ) for help with the DLS apparatus. BAY acknowledges the  Wellcome Trust  for supporting this work [ 110296/B/15/Z ].
Supergroup F Wolbachia is, so far, the only clade composed by some strains infecting arthropods and some infecting filarial nematodes  [17, 18]. All other described supergroups exclusively infect arthropods  [19] [20] [21] [22] [23]. The proposed supergroup G members  [24]  and R  [25]  are now considered to be part of supergroup B  [26]  and A  [27] , respectively, and are no longer considered as separate supergroups. In the last few years, the number of published Wolbachia genomes has increased and currently 48 draft and 21 complete genomes of Wolbachia are available at the NCBI database. However, these data are not a good representation of the entirety of Wolbachia diversity. Indeed, among the 21 complete genomes, 15 are symbionts of insects belonging to either supergroup A or B (including 7 strains from species of Drosophila), 2 are symbionts of insects belonging to other supergroups (F and E) and 4 are symbionts of nematodes (supergroups L, C or D).
8-100% identical and match the sequence of Chthonius ischnocheles available at the NCBI database (JN018172 specimen voucher MNHN-JAB62). The existence of cryptic species of Chthonius ischnocheles has been documented  [38]  and the specimens IV-J5 and IV-S1 form a paraphyletic group with the other Chthonius ischnocheles (Fig. 1 ). The COI sequences of the specimens identified as Atemnus politus present more intraspecific variability: calculated pairwise similarities range between 97. 7-99. 8%. The blastn analysis  [40]  does not provide identification of strongly similar sequences in databases; indeed no COI sequences from species related to the Atemnus genus have been deposited in the NCBI database. The most similar COI sequence in the database belongs to Atemnidae sp. JA-2011 voucher (JN018203), showing only 79.
For specimen K5, a 373 contig draft genome of 1,445,964 bp was obtained, with an average G + C content of 35. 6% (largest contig, 25,286 bp, N50 = 5741 bp, average sequencing depth of 420X). For specimen K3, a 200 contig draft genome of 1,404, 177 bp was obtained, with an average G + C content of 35. 49% (largest contig, 40,755 bp, N50 = 10,346 bp, average sequencing depth = 205X) (Table  1 ). Among 221 single-copy orthologue genes conserved among proteobacteria (BUSCOs database), 155 are present in the wApol K5 draft genome and 151 in wApol K3, suggesting 70. 2 and 69% complete BUSCOs, respectively. This percentage can be used to assess the level of completeness of genomes (see Methods). By comparison, the Wolbachia from Drosophila melanogaster, wMel, has a higher level of BUSCOs in the current analysis with 180 genes (81.
Multi-locus phylogenies and phylogenomics The analysis of Wolbachia in Geogarypus minor, wGmin, was more complex. Two genes were sequenced for wGmin, coxA and ftsZ, and only nested PCR provided clean sequences to enable phylogenetic comparisons. The Wolbachia phylogeny, based on the two genes, identified two different sub-groups of Wolbachia in our samples (Fig. 4a ). The Wolbachia infecting Chthonius Fig. 3  Organization and molecular analysis of the biotin operons of Wolbachia. a Organization of the biotin operon. The operons among the Wolbachia and four outgroups were identified and then aligned using MAFFT  [46]. b Phylogeny of the biotin operon (concatenation of contig containing bioA and bioD with the contig containing bioH, bioF and bioB). The length of datasets is 4603 bp. The topology was inferred using Maximum Likelihood (ML) inference using IQ-TREE  [39].
The phylogeny based on six genes (16S rDNA, ftsZ, dnaA, gatB, fbpA and coxA) confirms the topology of wApol and wCisc (wGmin and wCsco not included) (Fig. 4b ). Our analysis shows the same topology for wApol sequences from the two draft genomes (K5 and K3) as well as sequences of wApol K5 amplified by PCR, confirming that this topology is not an artifact of draft missassembly. In addition, to verify the accuracy of the Wolbachia presence in Geogarypus minor, the coxA gene was sequenced from another specimen (III-J5) derived from an independent DNA extraction, and PCR amplification and the sequences were identical to another specimen III-T3 (accession number MT273088). The Wolbachia from Cordylochernes scorpioides had been previously studied using a set of genes not routinely sequenced for Wolbachia evolutionary studies (groEL, fabK, nuoG, NADH dehydrogenase I subunit F, aspS, gltA, coxA, ftsZ, wsp, orpB, nuoD, isocitrate dehydrogenase gene, TPR domain-containing protein gene)  [47].
Source of material, DNA extraction and characterization Specimens were caught in the field using classical soil microfauna recovery methods using a Berlese-Tullgren funnel  [49, 61]. The specimens were collected at Montpellier in 2017 (43°37′52. 0″N 3°52′04. 6″E). DNA from specimens was extracted using the Monarch® Genomic DNA Purification Kit following the recommended protocol for extraction from tissues (New England Biolabs (NEB), USA) with overnight incubation at 56 °C with proteinase K. The quality of the extraction was verified by a PCR amplification of the host COI gene (Table  S6 ). A total of 20 sequences were deposited in the GenBank Data Library: MN923050-MN923069 (Table  S7 ).
De novo assembly pipeline Illumina reads were filtered by quality and "cleaned" using the wrapper Trim Galore! (https://www. bioinfor matics. babraham. ac. uk/projects/trim_galore/), and then merged with PEAR  [62]. PacBio circular consensus sequences (CCS) were generated using SMRT® pipe RS_ ReadsOfInsert Protocol (PacBio) with a minimum 3 full passes and minimum predicted accuracy superior at 90. The adaptors were removed by trimming off the first and last 65 bp of the reads, any reads smaller than 20 bp, or reads containing residual adaptor sequences (potential chimeric reads) using seqkt (github. com/lh3/seqtk) (analyses were performed with an in-house shell script). A first hybrid de novo assembly was done using Unicycler  [63]. The potential contigs belonging to Wolbachia were detected by nucleotide similarity using blastn  [40]  and selected (similarity superior at 80%, bitscore superior at 50).
KAAS assigned orthologue genes by blast comparison against KEGG genes database using BBH (bi-directional best hit) method. The same assignment analysis was performed for the wApol draft genome, the set of 14 complete or draft genomes and 1 supplementary draft genome: wNleu, Wolbachia from Nomada eucophthalma (LYUV00000000). The assigned KOs were ordered in 165 different KEGG pathways (Table  S5 ). For the biotin operon, the amino-acid sequences were identified with the KAAS assignments of wApol and other studied Wolbachia genomes and the complete operons were identified using tblastn and then aligned. The nucleotide sequences were aligned using MAFFT  [46] , concatenated using Seaview  [68]  and a phylogenetic analysis was performed with Maximum Likelihood inference inference using IQ-TREE  [39].
For the later, the poorly aligned positions of the produced orthologue genes alignments were eliminated using Gblocks  [71]. The phylogenetic analyses were performed with Maximum Likelihood inference using IQ-TREE  [39]. The most appropriate model of evolution was evaluated by Modelfinder  [72]  (implemented as functionality of IQ-TREE). The robustness of each node was evaluated by a bootstrap test (1000 replicates). The phylogenetic trees were edited by FigTree (https:// github. com/rambaut/figtree/) and Inkscape (https:// inkscape. org/).
Acknowledgments We thank  Mark Judson  from  MNHN  for the confirmation of the morphological identification of the pseudoscorpion specimens. We thank  Andy Gardner ,  Tom Evans ,  Rich Roberts ,  Jim Ellard  and  Don Comb  from  New England Biolabs  for their support. We thank  Laurie Mazzola ,  Danielle Fuchs  and and  Kristen Augulewicz  from the  NEB Sequencing Core. We also thank anonomous reviewers for helpful and constructive comments.
Availability of data and materials Data generated are available in GenBank: BioProject PRJNA593570; BioSample SAMN13481355 for Wolbachia endosymbiont strain of Atemnus sp. Specimen K5 (genome: WQMQ00000000); BioSample SAMN14519337 for Wolbachia endosymbiont strain of Atemnus sp. Specimen K3 (genome: JAAXCS000000000). The raw data are available in GenBank as Sequence Read Archive (SRA): SRX7550679 to SRX7550680, SRX8097933 to SRX8097936. In addition, a total of 34 sequences were deposited in the GenBank: MN923050 to MN923069, MN931247 to MN931248, MN931689 to MN931700 and MT273088.
Image analyses All images were analyzed blind to the experimental condition. Optical images were acquired with an Axioplan2 ApoTome microscope (Zeiss, Germany) using ZEN software (Zeiss, RRID:SCR_013672), and processing was performed using Fiji software (RRID:SCR_002285,  [Schindelin et al. , 2012] ) or ImageJ (NIH, https://imagej. nih. gov/ij/, RRID:SCR_003070). ISH/IHC double staining experiments were analyzed by counting labelled SVZ cells along the lateral ventricular wall on 40X magnification images (n = 3 SVZ for Ki67 and Dlx2, n = 8 SVZ for Ascl1). For cell counting in V-SVZ and OB tissues from electroporation experiments, a minimum of three sections per brain (n = animal) were analyzed and the totality of GFP+ or Tomato+ cells of each section were counted. To measure mean intensity of fluorescence after antibody staining, ROI were applied on individual electroporated cells.
Statistical analysis Control and Vax1 electroporation assays were performed from the same litter and were reproduced in two or three independent experiments for lateral or dorsal electroporation, respectively. Histograms were drawn with GraphPad Prism version 8 (GraphPad Software, San Diego, CA). Data are presented as mean ± SD. Each sample (n = animal/brain) is represented on histograms by dot. Statistical analyses were performed using R software (RRID:SCR_001905) and R Commander Package (https://CRAN. R-project. org/package=Rcmdr). The non-parametric two-tailed Mann Whitney U test was performed for all in vivo experiments, on pooled experimental repetitions when appropriate. Differences were considered statistically significant when p0. 05.
Acknowledgements We thank the members of the  Cremer lab  for support and critical reading of the manuscript. We are grateful to  S Wild  and  A Bosio  from  Miltenyi Biotec  for providing CRE-mRNA. We thank  Kapil Bharti  ( NIH, Bethesda, USA ) for sharing Vax1 cDNA. We thank the local  PiCSL-FBI core facility (IBDM, AMU-Marseille ) supported by the  French National Research Agency  through the  « Investments for the Future' program  ( France-BioImaging ,  ANR-10-INBS-04 ) as well as the IBDM animal facility. We are grateful to AMUTICYT Cytometry and Cell Sorting Core facility,  AMU ,  UMR-S 1076. This work was supported by the  Agence National pour la Recherche  (grants  ANR-13-BSV4-0013  and  ANR-17-CE16-0025 ),  Fondation pour la Recherche Medicale (FRM)  ' Label Equipe FRM ' and  Fondation de France (FDF)  grant  FDF70959  to HC.
4a-f ). Hierarchical clustering of RNA-seq data revealed that subsets generated in culture maintain a strong "culture imprinting" (Supplementary Fig. 4a ). Indeed, we could identify a 2000 genes signature (1000 genes up-and 1000 genes downregulated), which clearly separates in vitro-generated cells from circulating blood subsets regardless of their subset identity (Supplementary Fig. 4b ). The majority of these genes were associated to cell cycle and metabolism as shown by pathways enrichment analysis (Supplementary Fig. 4c ). Nonetheless, once this "in vitro culture signature" was subtracted from the total protein coding genes, CD141 + Clec9A + and CD1c + CD206 +/-cells generated in culture transcriptionally align to blood cDC1 and cDC2, respectively (Fig. 4a ). To further validate the similarity of in vitro generated cells with physiologically circulating subsets, we performed GSEA  58  using the BubbleGum software  59.
This methodology enables to score the enrichment of a signature in a pairwise comparison of two transcriptomes. We scored cDC1 alignment using gene signatures specific for blood cDC1 obtained from published datasets (cDC1>CD1c  [+ 60  and DC1>ALL  20  ). CD14 -CD1c + cells have recently been shown to contain two distinct subsets termed as cDC2 and cDC3  20. Alignment of cultured cells was probed towards total CD1c + cells (CD1c>cDC1), cDC2 (cDC2>ALL and cDC2>DC3), and DC3 signatures (DC3>cDC2 and DC3>ALL). We found that in vitro-generated CD141 + Clec9A + and CD1c + CD206 +/-cells are enriched in genes defining circulating blood cDC1 and cDC2, respectively (Fig. 4b ). The expression of the top 50 genes for each signature in the differentiated subsets further supports this conclusion (Fig. 4c ).
7a  and Supplementary Fig. 7a ). Third, unlike cDC1, cDC2 generated in vivo exhibit noticeable phenotypic differences. In vivo-generated cDC2 express Fig. 4  Human DC generated in vitro align with circulating blood DC. a Hierarchical clustering of primary (n = 3 healthy donors) vs. in vitro-generated (n = 3 cord blood donors) cDCs based on 17,791 genes after removing the "in vitro culture signature" (2000 genes) defined by pairwise comparison of primary versus in vitro generated subsets. b GSEA using blood cDC1s (DC1>CD1c + ) and CD1c + cells (CD1c + >DC1) signatures generated from published datasets  60  , as well as previously published signatures of blood cDC1 (DC1>ALL), cDC2 (DC2>ALL), and cDC3 (DC3>ALL)  20. BubbleMap shows the enrichment of each gene signature in the pairwise comparison of CD141 + Clec9A + , CD1c + CD206 -, and CD1c + CD206 + cells generated in vitro (FDR false detection rate, NES normalized enrichment score).
For single pairwise comparisons (top), statistical significance is defined by the FDR q-value calculated by the GSEA software (www. broad. mit. edu/gsea) using default parameters. For multiple pairwise comparisons (bottom), the statistical significance was further corrected for multiple testing by the BubbleMap module of BubbleGUM software. c Heatmaps of RNA-seq data displaying the expression of the top 50 genes of blood cDC1 and CD1c + cells signatures in CD141 + Clec9A + , CD1c + CD206 -, and CD1c + CD206 + cells generated in vitro. Genes shared with previously published signatures  20  are highlighted in bold. d UMAP (Uniform Manifold Approximation and Projection) plots of CyTOF data from CD45 + HLA-DR + cells differentiated in vitro using MS5_FS12 and MS5 _ CTRL as compared with cord blood PBMCs.
Pie charts indicate the frequency of each subset among the CD45 + HLA-DR + cells (mean of n = 2 cord blood donors in two independent experiments). e Relative expression of selected markers in UMAP plots of CyTOF data from cells differentiated in vitro with MS5_FS12. f Heatmap of markers mean intensity in each subset identified in MS5_FS12 cultures. higher levels of FcεRIa, CD172a, and CD5, while showing lower expression of HLA-DR and CD86 (Fig. 7b  and Supplementary Fig. 7b ). The specific phenotype conferred by the MS5_FS12 niche education renders cDC2s more akin to their blood counterparts. In order to compare extensively the transcriptional landscape of in vivo (NSG organoids) generated DCs with primary DCs found in human blood, we performed RNA-seq analysis on fluorescence-activated cell sorting (FACS)-sorted cDC2s obtained after the enzymatic digestion of MS5_FS12-containing plugs or purified from human blood.
f Visualization of mouse CD31 + endothelial cells by immunofluorescence. Fixed sections were stained for human CD45 (green) and mouse CD31 (red). Nuclei were stained with Hoechst (blue). Scale bar represents 100 μm (left panel) and 50 μm (right panel). Similar results were observed in n = 5 Matrigel organoids. The presence of mouse CD31 + cells was further confirmed by flow cytomery. Data are presented as floating bars ranging from min to max and line represents median (c, d) or scatter plots with mean ± SEM (e). establishment of a functional niche in murine bone marrow  21, 22  , whereas the expression of human membrane-bound SCF is sufficient to support human myeloid development in humanized mice  66. We therefore wanted to test whether a similar relationship might exist between the soluble and membrane-bound forms of human FLT3L.
Middle: Gating strategy discriminating AXL -CD327 lo/-pDC and AXL + CD327 + pre/AS-DC within CD123 + CD45RA + cells in MS5 _ FS12 organoids. Bar graph illustrates frequency of each subset within CD123 + CD45RA + cells (n = 4 donors). Right: Frequency of pre/AS-DC within CD45 + cells in MS5_CTRL vs. MS5_FS12 organoids (n = 7 donors in 4 independent experiments). *p < 0. 05, two-tailed paired Student's t-test. d GSEA of pDC and pre/AS-DC using published gene signatures  20. Statistical significance defined by the FDR q-value calculated by GSEA software (www. broad. mit. edu/gsea) using default parameters. e CyTOF analysis comparing CD45 + HLA-DR + cells in MS5_FS12 and MS5 _ CTRL organoids. Pie charts display frequency within CD45 + HLA-DR + cells (mean of n = 2 donors in 2 independent experiments).
h Frequency of differentiated subsets within huCD45 + cells in MS5_FS12 plugs at day 12. The number of biological replicates (n) is reported. Data presented as floating bars ranging from min to max and line represents median (a, c, f, h) or as bar graphs with mean ± SEM (c, g). Supplementary Fig. 2a. Cells expressing human membrane-bound FLT3L and SCF were sorted according to antibody staining of the transmembrane proteins (antibodies listed in Supplementary Table  3 ). TPO-expressing cells were sorted according to the expression of mCherry reporter.
Stromal cells were plated at a density of 10 5 cells/well and 7 × 10 4 cord blood-derived CD34 + progenitors were added the following day in each well. Half of the medium was replaced in both the top and bottom well at day 5 and 10. For GM-CSF blocking experiments, 2 μg/ml human GM-CSF neutralizing antibody (R&D catalog number #AF-215-SP) and isotype control (R&D catalog number #AB-108-C) were added to the culture medium. The presence of human GM-CSF in the supernatant of eMSCs co-cultures with CD34 + HSPCs was assessed by enzyme-linked immunosorbent assay (ELISA) using the human GM-CSF ELISA MAX kit (Biolegend) as per manufacturer's instructions. RNA-seq and data processing. RNA-seq analysis was performed in two independent experiments. In the first experiment, human cDC1s and cDC2s from peripheral blood of n = 3 healthy individuals and in vitro generated CD141 + Clec9A + , CD14 -CD1c + CD206 -, and CD14 -CD1c + CD206 + cells from n = 3 independent cord blood donors were FACS sorted.
Adapter ligated cDNA were pooled before final amplification to add flow cell primers. Libraries were sequenced on HiSeq2500 (Illumina Cambridge) for 100 cycles PE in Rapid mode. The raw sequencing data was initially processed using open source, web-based platform Galaxy (version 18. 05. rc1) (https://usegalaxy. org). Reads were filtered for quality with more than 80% of the sequence having quality score > 33 using FastQC tool. Mapping against reference genome was performed with Hisat2 to the hg38 human genome. Adapter sequences were detected automatically with TrimGalore!. Reads under 20 bp were discarded. All processed sequencing files were imported in Partek® Flow® software (Partek, Inc. , build 7. 0. 18. 0514) and the gene count data was normalized using FPKM. In the second experiment, human CD5 -cDC2, CD5 + cDC2, pDC, and pre/AS-DC from peripheral blood of n = 3 healthy individuals and cDC2, pDC and pre/ AS-DC generated both in vivo and in vitro from n = 2/3 independent cord blood donors were FACS sorted.
Between 100 and 1000 cells/subset were collected in TRIzol® (Thermo Fisher) and stored at -80°C. Frozen samples were shipped to GENEWIZ® where they were processed. RNA was extracted and libraries were prepared using an ultralow input RNA library preparation kit (Illumina). Libraries were sequenced on HiSeq2500 (Illumina). The raw sequencing data was initially aligned on the human reference genome hg38 using STAR aligner (v2. 5. 3a)  73  (parameters: --outFilterType BySJout --outFilterMultimapNmax 20 --alignSJoverhangMin 8 --alignSJDBoverhangMin 1 --outFilterMismatchNmax 999 --outFilterMismatchNoerLmax 0. 04 --alignIntronMin 20 --alignIntronMax 1000000 --alignMatesGapMax 1000000 --outSAMprimaryFlag OneBestScore --outMultimapperOrder Random --outSAMattributes All). Raw read counts matrix was also made with STAR (using the parameter --quantMode GeneCounts).
RNA-sequencing analysis. The average gene expression of n = 3 blood donors for cDC1s, n = 2 blood donors for cDC2 and n = 3 cord blood units for in vitro generated subsets were used for RNA-seq data analysis. Hierarchical clustering was performed in Morpheus (Broad Institute, https:// software. broadinstitute. org/morpheus/) using one minus Pearson's correlation and average linkage. GSEA (www. broad. mit. edu/gsea)  58 was used to assess the expression of gene signatures specific for blood cDC1, cDC2, DC3, pDC, and AS-DC in in vitro and in vivo generated subsets. To simultaneously visualize pairwise comparisons of transcriptomes from cord blood-derived cDCs, the BubbleMap module of BubbleGum 59 was used. Results were considered significant when the p-value was below 0. 05 and the FDR (q) value was below 0.