dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements This work was supported by grants from  INSERM (France) , the  Université Paris Cité, Labex Inflamex  (to S. L. ),  Inserm-Transfert  ( CoPOC MAT-PI-17217-A-01  to S. L. ),  Agence Nationale pour la Recherche  ( ANR19-CE14-0041-02  to S. L. ) and  Fondation pour la Recherche Médicale (Equipe FRM   EQU202203014642  to S. L. ). We are grateful to  Drs G. Gauthier  and  V. Gratio  and to  J. Da Silva  from the flow cytometry platform,  N. Sorhaindo  from the  Biochemistry Platform (Center de Recherches sur l'Inflammation, INSERM U1149, Paris) , and  O. Thibaudeau  from the  Plateau de Morphologie  ( INSERM UMR1152 , Paris), for their help in flow cytometry, liver function tests, and histology respectively. This work benefited from equipment and services from the iGenSeq core facility (genotyping and sequencing) at  Institut du Cerveau et de la Moëlle épinière, Paris.
2 To quantify zebrafish neutrophils rapidly and precisely from large datasets, we developed an ImageJ macro called 'LenCell'. LenCell automatically applies a band-pass filter to remove high and low background frequencies, and to detect bright spots (in this case fluorescent neutrophils) in the cleaned images. To characterize the efficiency of LenCell at whole-animal level, transgenic larvae with DsRed-neutrophils were imaged on a single plane during a nonlethal S. flexneri infection (2500 CFUs; Fig. 2A ). We then assessed the efficiency of both the Ellett and Lieschke method (without data normalisation) and the LenCell macro (optimized by iteratively identifying image analysis parameters) by comparing leukocyte units with neutrophil numbers counted by hand (Fig. 2B ). By determining the mean error between both methods from 132 images of S.
Concluding remarks In the future, our automated microscopy workflow can help screen drug libraries, study clinically relevant Shigella strains and test the role of host genes underlying neutrophil biology. It also has the great potential to define efficacy of antibiotics in vivo, as well as the concentration needed to reach the MIC in the zebrafish HBV by immersion. In this way, our workflow may help to define new therapeutic strategies to combat Shigella infection in humans. Our workflow can be viewed as an important step towards the development of personalised medicine and may, one day, be useful to generate a database of antibiotics and their efficacies against different bacterial pathogens in vivo.
The parameters that provide results closest to those obtained by hand counting are then considered as 'optimized parameters'. The macro to count and detect neutrophils in an HBV z-stack image is very similar; however, it includes several pre-processing steps. First, the image is cleaned by using a very large band pass filter (first RBA: 100, second RBA: 200). The two resulting images are added up, and the resulting image is processed through a z-projection by standard deviation, which makes the ROIs stand out. Finally, the z-projection is processed through the same pipeline as described before for whole-zebrafish images but with slightly differing parameters (first RBA: 9, second RBA: 10, minimum size 0). Although a correction factor can be calculated to limit the error rate and determine absolute neutrophil numbers, we avoid post-processing correction of the data and only present the raw, uncorrected results (leukocyte units) throughout this manuscript for both the LenCell macro and 'Ellett and Lieschke' method.
Statistical analysis and data processing Statistical significance was determined using GraphPad Prism v9. For comparisons of the different analysis methods, a two-way ANOVA with post-hoc Sidak's multiple comparisons test was performed. For experiments occurring over time, i. e. bacterial burden and neutrophil counts over time, values are presented as normalised to the first time point for every larva (4 hpi). Two-way ANOVA with post-hoc Dunnett's multiple comparisons test was performed for these experiments. For quantification of spreading events, unpaired two-tailed Student's t-test was used. Outlier larvae (dead, uninfected) were removed from the dataset. The last timepoint typically contains more dead larvae and, therefore, fewer replicates. In figures, Pvalues are as follows: P>0. 05, non-significant (ns); *P<0.
Funding A. T. L. -J. is funded by the  European Union 's  Horizon 2020 framework research and innovation program  under the  Marie Sklodowska -Curie grant  agreement (grant no. H2020-MSCA-IF-2020-895330 ). Research in S. M. 's laboratory is supported by a  European Research Council Consolidator Grant  (grant no. 772853 -ENTRAPMENT ) and a  Wellcome Trust   Senior Research Fellowship  (grant no. 206444/Z/17/Z ). Open Access funding is provided by the  London School of Hygiene and Tropical Medicine. Deposited in PMC for immediate release.
| 16S rRNA amplicon sequencing and processing of raw sequences A single lane of Illumina MiSeq system was used to generate 251-base paired-end reads from the V4 variable region of the 16S rRNA gene using bar-coded universal primers (515F/806R) in ticks. Two extractions reagent controls were set, in which the different DNA extraction steps were performed using the same conditions as for the samples but using water as a template. DNA amplification was then performed on the extraction control in the same conditions as for any other sample. The raw 16S rRNA paired sequences obtained from tick samples were deposited at the SRA repository (Bioproject No. PRJNA865094). 16S rRNA sequences were analysed using QIIME2 pipeline  (v. 2019. 7; Bolyen et al. , 2019). The demultiplexed raw sequences (obtained in fastq files) were denoised, quality trimmed and merged using DADA2 software  (Callahan et al.
, 2016)  implemented in QIIME 2  (Bolyen et al. , 2019). The amplicon sequence variants (ASVs) obtained were aligned with q2-alignment of MAFFT  (Katoh et al. , 2002)  and used to construct a phylogeny with q2-phylogeny of FastTree 2  (Price et al. , 2010). Taxonomy was assigned to ASVs using a classify-sklearn naïve Bayes taxonomic classifier based on SILVA database (release 138;  Bokulich et al. , 2018). Only the target sequence fragments were used in the classifier (i. e. classifier trained with the 515F/806R primers)  (Ren & Wu, 2016; Werner et al. , 2012). Taxa that persisted across serial fractions of the samples using QIIME 2 plugin feature-table (core-features) were considered ubiquitous. To determine the impact of Rickettsia taxon in the microbial community, the taxon was putted to 0 in the ASVs table, and the following tests were realised with the same procedure.
05) as implemented in QIIME 2  (Bolyen et al. , 2019). Dispersion, which measures bacterial variability between samples within the population, was calculated using 'betadisper' function (script available, File S1) and Vegan R package implemented in Rstudio (RStudio  Team, 2020). The dispersion between groups was compared using the ANOVA test (p <. 05). The clustering analysis, which measures the similarity between the tick microbial samples, was calculated for microbial samples of the two tick species to test for a clustering pattern according to the species, as well as within each tick species individually to test for a clustering pattern according to the pathogen infection status. The cluster analysis was conducted with the Jaccard coefficient of similarity (script available, File S1) with the use of Vegan  (Oksanen et al.
, 2021)  implemented on R studio  (RStudio Team, 2020). The differences in bacterial taxa abundance between the two species were tested using a t-test and performed with the ANOVA-Like Differential Expression (ALDEx2; script available, File S1) package  (Fernandes et al. , 2013). Only taxa with significant differences (p <. 05) were used for representation of the differential taxa relative abundance. Relative abundance was measured as centred log ratio (clr) transformation which uses the geometric mean of the read counts in the sample. The advantage of the clr transformation is that it makes the quantification scale free and therefore comparable between conditions  (Fernandes et al. , 2014). The resulting data were used to construct the heatmap with the heatmap. 2 function (script available, File S1), implemented in R studio environment (RStudio Team, 2020).
| Inference of bacterial co-occurrence networks Here we used co-occurrence network to compared the architecture (i. e. network topology), node hierarchy (i. e. centrality values and keystone taxa), and robustness between networks of the two tick species with and without Rickettsia. Co-occurrence networks were built for each dataset using the bacterial taxonomic profiles at genera level. The networks allow the graphic visualisation of the bacterial community assemblies. Bacterial taxa are represented by nodes and the significant correlations between taxa are represented by edges. Analyses of significant positive (weight >0. 75) or negative (weight < -0. 75) correlations were performed using the Sparse Correlations for Compositional data (SparCC) method (script available, File S1)  (Friedman & Alm, 2012)  implemented in R studio environment  (RStudio Team, 2020).
The visualisation and measures of topological features (i. e. number of nodes and edges, network diameter, modularity, average degree, weighted degree and clustering coefficient) of the networks were performed using the software Gephi 0. 9. 2  (Bastian et al. , 2009). Topological features can provide information about the bacterial community stability and robustness. Core co-occurrence analysis was conducted to determine the strongest co-occurrence associations in the bacterial microbiota using the same technic as standard co-occurrence networks, but with a higher SparCC threshold for positive (weight > 0. 9) or negative (weight < 0. 9) correlations.
| Comparative network analysis The differential network represents the correlations that are different between the same taxa in two bacterial networks. The statistical network estimation was realised using Network Construction and Comparison for Microbiome Data (NetComi; script available, File S1)  (Peschel et al. , 2021)  on Rstudio  (RStudio Team, 2020). Each node represents a bacterial taxon shared by the two networks. Each edge represents a type of connection. Jaccard index was calculated to test for dissimilarities in local centrality measures (degree, betweenness centrality, closeness centrality and eigenvector centrality) between nodes in networks of the two tick species with and without Rickettsia. The Jaccard index tests the similarity between sets of 'most central nodes' of networks, which are defined as those nodes with a centrality value above the empirical 75% quartile.
The last type is a random removal of nodes. The robustness of networks is calculated with Network Strengths and Weaknesses Analysis (NetSwan; script available, File S1;  Lhomme, 2015)  in Rstudio  (RStudio Team, 2020). A statistical unpaired t-test was conducted between networks with and without Rickettsia for each species and each node removal condition with GraphPad Prism version 8. 0. 1 (GraphPad Software).
0686). (d) Jaccard clusterisation of H. marginatum and R. bursa samples. The Jaccard distance is represented between 0 and 2, the lines are proportional to this distance. Each sample is represented by a violet dot for H. marginatum and a green one for R. bursa. (e) Venn diagram displaying the comparison of taxa composition for all taxa. The purple and green circles represent H. marginatum and R. bursa samples, respectively. Numbers represent the amount of taxa found in each dataset and those shared by the two groups. (f) Dendrogram heatmap resulting from the heatmap. 2 function implemented on R studio (RStudio Team, 2020). Taxa were clustered based on relative abundance (calculated as clr transformed values). Each column represents the CLR values for bacterial taxa per sample and per group.
AUTH O R CO NTR I B UTI O N S ACC and AM conceived the idea and designed experiments. AM, LMH, EPS, AFS, and VC conducted the experiments. ACC, AWC, and DO supervised data analysis. AM, and ACC analysed and/or interpreted the results. AM visualised the results. ACC, SM, JCP, and AF provided resources and input on the experiments and data interpretation. AM, and ACC drafted the first version of the manuscript. AM, AWC, LMH, EPS, AFS, VC, SM, JCP, AF, DO and ACC reviewed, edited, and approved the manuscript. ACK N O WLE D G E M ENTS UMR BIPAR is supported by the French Government's Investissement d'Avenir program, Laboratoire d'Excellence 'Integrative Biology of Emerging Infectious Diseases' (grant no. ANR-10-LABX-62-IBEID). Alejandra Wu-Chuang is supported by Programa Nacional de Becas de Postgrado en el Exterior 'Don Carlos Antonio López' (grant no.
Bioinformatics Analysis The HLA-G, LILRB1, and LILRB2 mapping; genotype calling; and haplotyping strategies used here have already been published elsewhere  [23, 24]. Briefly, CutAdapt  [25] , hlamapper version 2. 2, function DNA, database version 2. 1 [26] , and GATK v. 3. 7 Haplotype-Caller in -ERC GVCF mode  [27]  were used for trimming adaptor sequences, alignment to the reference genome sequence (GRCh38/hg38) and genotype calling, respectively. Uncertainly, genotypes were interrogated using VCFx checkpl (www. castelli-lab. net/apps/vcfx (accessed on 1 October 2021)), with the minimum genotype likelihood set to 99. 9% to retain only high-quality genotypes. GATK routine ReadBackedPhasing, using a minimal phase quality threshold of 500, coupled with a Bayesian probabilistic model implemented in the PHASE software  [28] , enabled taking some phase information from the paired-end reads, adding efforts in the identification of the most probable haplotypes.
The phased VCF file was converted into HLA-G CDS sequences by using the hg38 reference sequence as a draft and replacing the correct nucleotide at each position, two sequences per sample, using the application vcfx (function fasta) (www. castelli-lab. net/apps/vcfx (accessed on 1 October 2021)). Using a local BLAST server with databases containing all known class I and II HLA CDS sequences described so far, downloaded from the IPD-IMGT/HLA database (https://www. ebi. ac. uk/ipd/imgt/hla/ (accessed on 1 October 2021)) version 3. 31. 0, the closest known HLA-G coding allele was defined for each haplotype. The procedures applied for genotyping LILRB1/2 exonic regions and the SNPforID 34-plex AIMs were very similar to those used to HLA-G, except that BWA-MEM (Burrows-Wheeler)  [29]  instead of hla-mapper [26] was employed to the reference genome (GRCh38/hg38) alignment and that for SNPforID 34-plex only genotype calling was performed.
Among this set of best multifactor models, the combination of polymorphisms that maximizes both the TBA and the CVC was selected. Although very similar to the original MDR method, GMDR v. 0. 9  [40]  classifies the individuals based on a residual-based score and provides the signal test indicating the significance of the identified model  [40]. In contrast, some modifications are observed in the MB-MDR method  [41]. First, the method does not split the data into training and learning sets. Model selection is based on the strength of association between a set of genotypes and the binary outcome of interest, using all the data, with the statistical significance of the models being assessed through a permutation testing strategy by randomizing the case/control status in the original dataset. In addition, risk categories are defined using a regression model, which allows adjustment for population stratification.
Transcriptional analysis of EV-defective mutants To decipher the link between the expression of these TF and the EV production phenotype of their corresponding mutants, we performed the RNA-seq analysis of the hap2Δ, gat5Δ, and WT strains grown for 18 h on SD plates at 30°C, a condition which corresponds to the maximum EV production (see Fig. 1C ). Differential expression gene analysis identified 538 genes commonly upregulated in both hap2Δ and gat5Δ strains (Fig. 4A ) (Table  S1 ). Gene ontology (GO) analysis using the FungiDB database (https:// fungidb. org/fungidb/app) revealed that genes associated with ribosome biogenesis, transmembrane transport, and protein-containing complex assembly were enriched in this set (Fig. 4B ). This suggests that the downregulation of the translation machinery expected to occur during the transition from the exponential phase to the stationary phase  (31)  is impaired in both mutant strains.
Quantification of EV particles by nanoFCM A NanoAnalyzer (nanoFCM) instrument equipped with a 488-nm laser at 50 mW and single-photon counting avalanche photodiodes detectors (SPCM APDs) was used for the detection of the EV particles. Band-pass filters allowed for the collection of light in specific channels (488/10 nm). Light scattering and fluorescence of individual EVs were collected on single-photon counting avalanche photodiodes detectors on three channels: side scatter (SSC)-488/10 (trigger channel). Sample fluid was focused to approximately 1. 4 µm using an HPLC-grade water filter and de-gas as the sheath fluid via gravity feed. Data were generated through the Nanoanalyser Professional Suite V 2. 0 software, 0. 02-μm-filtered PBS was used to define the event triggering threshold. Measurements were taken over 1-min periods at a sampling pressure of approximately 1.
0 kPa, modulated, and maintained by an air-based pressure module. Samples were diluted in 0. 02-μm-filtered PBS as required to allow 3,000-13,000 counts to be recorded during this time. During data acquisition, the sample stream is completely illuminated within the central region of the focused laser beam, resulting in approximately 100% detection efficiency, which leads to accurate particle concentration measurement via single-particle enumeration. Optical alignment was tested and calibrated using fluorescent 250-nm silica nanoparticles. Further calibration measurements were taken prior to analysis using 250-nm silica nanoparticles of known concentration (for EV concentration calculation). Isolated EV samples were sized according to standard operating procedures using the proprietary four-modal silica nanosphere cocktail generated by nanoFCM to allow for a standard curve to be generated based on the four sizes of the nanosphere populations of 68, 91, 113, and 155 nm in diameter.
Silica provides a stable and monodisperse standard with a refractive index of approximately 1. 43-1. 46, which is close to the range of refractive indices reported in the literature for EVs (n = 1. 37-1. 42). Using such a calibration standard enabled accurate flow cytometry size measurements, as confirmed when comparing flow cytometry with cryo-TEM results. The laser was set to 10 mW and 10% SSC decay. Data reported in the figure were handled within the nanoFCM Professional Suite v2. 0 software to analyze particles between 40 and 155 nm.
Analysis of differential expression data was performed in DeSeq2  (62). DNA read alignment, variant detection, and ploidy analysis were performed as previously described  (63). Illumina reads were aligned to the C. neoformans H99 reference genome using Minimap2 aligner v. 2. 9 (64) with the "-ax sr" parameter. BAM files were sorted and indexed using SAMtools (65) version 1. 9. Picard version 2. 8. 1 (http:// broadinstitute. github. io/picard) tools were used to identify duplicate reads and assign correct read groups to BAM files. SAMtools version 1. 9 and Picard version 2. 8. 1 were then used to filter, sort, and convert SAM files and assign read groups, and mark duplicate reads. Single-nucleotide polymorphisms (SNPs) and insertions/deletions (indels) were called using Genome Analysis Toolkit version 3.
6 with ploidy = 1 according to the GATK Best Practices. HaploScore parameters used to filter SNPs and indels included VariantFiltration, QD < 2. 0, LowQD, ReadPosRankSum <-8. 0, LowRankSum, FS >60. 0, HightFS, MQRankSum <-12. 5, MQRankSum, MQ < 40. 0, LowMQ, and HaplotypeScore >13. 0. To examine variations in ploidy across the genome, the sequencing depth at all positions was computed using SAMtools  (65) , and then the average depth was computed for 1 kb windows across the genome. GO analyses were performed using the FungiDB database (https://fungidb. org/fungidb/app).
Synthesis of cDNA was performed using the Thermo Scientific Maxima First Strand cDNA Synthesis Kit for RT-qPCR, following the manufacturer's instructions. The expression levels of ERG11 and AFR1-AFR3 genes were determined by qPCR using 5 µL of the appropriate dilutions of cDNA and 1 µL of each primer of interest (10 µM), listed in Table  S4. The reactions were performed using the SsoAdvanced Universal SYBR Green Supermix in Hard Shell qPCR plate 96-well thin wall (Bio-Rad). Amplification reactions were performed using an RT-PCR Detection System thermal cycler (Bio-Rad). The Ct values obtained in triplicates were averaged, and the number of molecules was measured by standard curves and normalized to that of the housekeeping gene ACT1 (CNAG_00483). All RT-qPCR data were from three biological replicates (three independent RNA preparations).
Statistical analysis All statistical analyses were performed using GraphPad Prism 9 software (GraphPad Software Inc. ). Data sets were tested for normal distribution using Shapiro-Wilk or Kolmogorov-Smirnov normality tests. In the cases in which the data passed the normality test, they were further analyzed using the unpaired Student's t test or ordinary one-way analysis of variance. When at least one data set was non-normally distributed, we used the nonparametric Kolmogorov-Smirnov or Kruskal-Wallis test.
ACKNOWLEDGMENTS J. R. was supported by the  Pasteur-Roux-Cantarini fellowship of Institut Pasteur  and by  FAPERJ -Fundação de Amparo à Pesquisa do Estado do Rio de Janeiro  ( E-26/204. 456/2021 ). This work was supported by a  CAPES COFECUB  Grant no. 39712ZK  to G. J. , M. L. R. , and L. N. The work was supported by an  ANR  grant (Projet ResistEV  AAPG2021 CE35 ) to G. J. , I. V. E. , and A. A. , T. T. V. D. was supported by the  Pasteur -Paris University (PPU)   International PhD Program. I. V. E. is supported by a  CIFAR   Azrieli Global Scholar Award. We would like to thank in particular  Laëtitia Viengsavanh  for the help with the initial steps of TF mutant collection screening, and some members of the  French Cryptococcosis Study Group: Christine Bonnal (Hôpital Bichat-Claude Bernardn Paris) ,  Marie-Elisabeth Bougnoux  ( Hôpital Necker-Enfants Malades, Paris ),  Nathalie Bourgeois  ( CHU Montpellier ),  Sophie Cassaing  ( CHU Toulouse ),  Lilia Hasseine  ( CHU Nice ), and  Stéphane Ranque  ( IHU Méditerranée Infection, Marseille ).
Determination of the epitopes for the panel of PD-L1 D1 binding VHHs To identify the binding sites for the panel of VHHs on PD-L1 D1, NMR chemical shift perturbation mapping was used. We have previously reported sequence-specific 15 N, 13 C, and 1 H backbone NMR assignments for PD-L1 D1  (39) , which enabled 2D 15 N/ 1 H transverse relaxation optimized spectroscopy (TROSY) and/or 15 N/ 13 C/ 1 H HNCO spectra to be collected for PD-L1 D1 in complex with each inhibitory VHH and compared to the spectra of free PD-L1 D1 (Fig. S1 ). Minimal shift analysis of the spectra was used to identify backbone amide-associated peaks that showed a significant change in position due to either direct interaction with a VHH or due to induced conformational change, as described previously  (40). The observed chemical shift perturbations for backbone amide associated peaks were mapped onto the reported PD-L1 D1 crystal structure (Protein Data Bank [PDB]:5C3T (  6 )) for each of the VHHs.