dataesr/scientific-paragraphs-categorization huggingface dataset:
Proteome-Based Analyses As proteome-based analyses rely on genome annotation, they are sensitive to bias introduced by different annotation methods, and the results of such analyses should, therefore, be interpreted with caution. To mitigate this, we reannotated all genomes with the same automated pipeline as described above (M&M, Supplementary File 1 available on Dryad). We inferred a Virus (Phage) Proteomic Tree using only the members of the new family to assess its internal structure (Fig. 3a ). This showed clearly-defined clusters at the subfamily and genus rank, but revealed longer than expected branch lengths for phages that had very similar genomes, implying that this method should not be used for fine-grained taxonomic classification. Among 1296 singleton proteins (proteins without recognizable homologues in the analyzed genomes) and 2070 protein clusters defined using the OPC approach, we identified 14 clusters common for all viruses belonging to the new family "Herelleviridae" (Table  2 , Supplementary Table  S2  available on Dryad).
Classification of the viral proteins using pVOGs showed that 38 pVOGs were shared between all 93 virus genomes, with 14 pVOGs functionally annotated (Table  2 , Supplementary Table  S2  available on Dryad). Upon closer inspection of the gene annotations, we found that these analyses might have been confounded by the presence of introns and inteins in many of the core genes. Indeed, many genes of spounaviruses and related viruses are invaded by mobile introns or inteins  (Goodrich-Blair et al. 1990; Lavigne and Vandersteegen 2013). These gaps in coding sequences challenge standard gene prediction tools and introduce additional bias in similarity-based cluster algorithms. Because of these insertions as confounding factors, we used a subset of 10 core genes for further phylogenetic analysis. Analysis of Gene Synteny Viral genomes are thought to be highly modular, with recombination and horizontal gene transfer potentially resulting in "mosaicism"  (Juhala et al.
Marker Protein Phylogenies We used the amino acid sequences of concatenated marker proteins identified from the OPC analysis (Table  2 ) to generate a phylogenetic tree that is able to identify the evolutionary relationships at the genus and subfamily rank within the new family Herelleviridae (Fig. 4 ). This tree supported all proposed new taxa but was unable to differentiate between the different species. Branches representing subfamilies and genera were particularly well-supported (UFBOOT support above 99%). Additionally, nearly all topologies of single marker trees (Supplementary Fig. S3  available on Dryad) fitted well in the suggested taxonomic structure. The only notable deviation from the proposed classification scheme could be found in the Tail tube protein tree (VOG0068-OPC6141, Supplementary Fig.
S3  available on Dryad). It shuffled members of the genus Silviavirus into the Kayvirus clade and also mixed the genera Nitunavirus and Agatevirus with unclassified phages. This may indicate that the evolutionary signal contained in this marker is insufficient to resolve related genera. Alternatively, the inconsistencies may be explained by the effect of horizontal gene transfer or convergent evolution introducing additional noise in our data. Regardless of the true reason of this inconsistency, it should be stressed that with a small number of available marker loci, additional sources of phylogenetic signal (e. g. , whole genome phylogenies) may be necessary to properly interpret any result.
Comparison of the Results Obtained Using Different Methods Virus classification methods in general suffer from a low signal-to-noise ratio. This "noise" may be introduced in the data by horizontal gene transfer and differences in mutation rates in different viral lineages. To get a measure of the discrepancies between the methods used above, we calculated the normalized Robinson-Foulds distances (representing the fraction of data partitions that are present only in one of the analyzed trees, Supplementary Table  S3  available on Dryad) and created tanglegrams for the visual comparison of topologies (Supplementary Fig. S4  available on Dryad). Trees obtained using different methods differed considerably (normalized Robinson-Foulds metric in range 0. 16-0. 58) but topological distances between them were comparable to distances between single marker trees (and in most cases smaller, see Supplementary Table  S3  available on Dryad).
DISCUSSION The rapid expansion of phage genomics and metagenomics has left taxonomy behind. There are more than 8000 publicly-available caudovirus genomes, but only 873 have been officially classified by the ICTV  (Davison 2017). The remaining genomes are provisionally stashed in the NCBI database within "unclassified" bins attached to the order Caudovirales or its associated families  (Brister et al. 2015; Adams et al. 2017; Simmonds et al. 2017). One of the main problems is that the level of sequence divergence is so high that it often leaves no detectable sequence similarity between disparate members of the same order. Thus, not a single reliable phage-specific or even Caudoviralesspecific marker gene could be defined. In addition, a classification system based on a single marker would be highly prone to instances of horizontal gene transfer.
Unfortunately, if the number of available marker loci is small, this method becomes vulnerable to the noise introduced by horizontal gene transfer  (Davidson et al. 2015). More importantly, this approach is heavily influenced by the gene annotation. This may be a crucial disadvantage as the quality of database records is often debatable and computational reannotation of analyzed genomes does not always yield valid, comparable results. On the other hand, these drawbacks can be easily circumvented by methods analyzing whole genome sequences (DICE, VICTOR, BLAST). Obviously, they are annotation-independent and mitigate the effects of horizontal gene transfer by averaging the signal across the total genome length. Unfortunately, if the untranslated nucleotide sequence of the virus is used, rapid decay of the similarity should be expected above the genus rank (e.
g. , Supplementary Fig. S2  available on Dryad). Above that rank, nucleotide sequence similarities were virtually undetectable, but sequence translations (DICE coefficient) or protein sequences (Phage Proteomic Tree) were still considerably similar. Thus, nucleotide sequence-based approaches capture small differences (e. g. , silent mutations) between closely related genomes and may be well suited for species and strain demarcation but gradually lose sensitivity with each consecutive taxonomic rank. To the best of our knowledge, the GOAT algorithm is the only method explicitly aimed at capturing the signal associated with genomic rearrangements in fluid genomes of viruses. Unfortunately, the evolutionary process that is responsible for the observed variations is even less studied than whole genome similarity metrics and we cannot rule out that this algorithm may be disproportionally susceptible to some random rearrangement events.
, Supplementary Figs. S2-S4 available on Dryad, Table1, Supplementary TableS1available on Dryad). The first cluster (here suggested to retain the name Spounavirinae), groups Bacillus-infecting viruses that are similar to Bacillus phage SPO1. The second cluster (Bastillevirinae) includes Bacillus-infecting viruses that most closely resemble phage Bastille. The third cluster (Brockvirinae) comprises viruses of enterococci that are similar to Enterococcus phage ϕEF24C. The fourth cluster (Twortvirinae) gathers staphylococci-infected viruses that are similar to Staphylococcus phage Twort. The remaining cluster (Jasinskavirinae) consists of viruses infecting Listeria that are similar to Listeria phage P100. The classification left a).
, Supplementary Figs. S2-S4 available on Dryad, Table1, Supplementary TableS1available on Dryad). The first cluster (here suggested to retain the name Spounavirinae), groups Bacillus-infecting viruses that are similar to Bacillus phage SPO1. The second cluster (Bastillevirinae) includes Bacillus-infecting viruses that most closely resemble phage Bastille. The third cluster (Brockvirinae) comprises viruses of enterococci that are similar to Enterococcus phage ϕEF24C. The fourth cluster (Twortvirinae) gathers staphylococci-infected viruses that are similar to Staphylococcus phage Twort. The remaining cluster (Jasinskavirinae) consists of viruses infecting Listeria that are similar to Listeria phage P100. The classification left a) Tevenvirinae Ackermannviridae Eucampyvirinae Ounavirinae Vequintavirinae Herelleviridae Unassigned Tunavirinae Guernseyvirinae Autographivirinae Sepvirinae Unassigned Bclasvirinae Dclasvirinae Pclasvirinae Chebruvirinae Nclasvirinae Mclasvirinae Nymbaxtervirinae Ampvirinae Arquatrovirinae Mccleskeyvirinae Picovirinae Tevenvirinae Ackermannviridae Eucampyvirinae Ounavirinae Vequintavirinae Herelleviridae Unassigned Tunavirinae Guernseyvirinae Autographivirinae Sepvirinae Unassigned Bclasvirinae Dclasvirinae Pclasvirinae Chebruvirinae Nclasvirinae Mclasvirinae Nymbaxtervirinae Ampvirinae Arquatrovirinae Mccleskeyvirinae Picovirinae.
MALDI-TOF MS parameters Protein mass profiles were generated using a Microflex LT MALDI-TOF (Bruker Daltonics, Germany) mass spectrometer, with detection in positive ion linear mode at a laser frequency of 50 Hz in a mass range 2-20 kDa. The acceleration voltage was set to 20 kV, and the time of extraction delay was 200 ns. Each spectrum corresponds to the ions obtained from 240 laser shots fired in six regions of the same deposit on the ground plate and acquired automatically using the function of the Flex Control AutoXecute V. 2. 4 software (Bruker Daltonics). The spectra were visualized with Flex analysis v. 3. 3 software and exported to ClinProTools2. 2 and MALDI Biotyper v. 3. 0 (Bruker Daltonics, Germany) for data processing. To obtain usable results, we evaluated the reproducibility of each group using the ClinProTools 2.
MALDI-TOF MS biomarker mass set To evaluate the reproducibility of the MS profiles, the spectra of the three groups (bed bugs infected with B. quintana, bed bugs infected with B. henselae and bed bug controls) were imported into the ClinProTools 2. 2 software. For the detection of the discriminant peaks associated with infection status, we compared the average spectrum of the three categories of bed bugs. The software was used to generate a peak list for each group in the mass range of 2 to 20 kDa and to identify the discriminant peaks. The parameters of the ClinProTools 2. 2 software for the generation of the spectra were as follows: resolution of 800; noise threshold of 2. 50; maximum peak of 800 ppm and correspondence with calibration peaks of 20%. For the calculation of the peak, the peak was made on single spectra, with a signal-to-noise threshold of 2.
MALDI-TOF MS for detection of B. quintana and B. henselae in bed bugs The spectra sorted by Flex Control AutoXecute V. 2. 4 software were analyzed by ClinProTools 2. 2 software to check the reproducibility of each category (Figure  1 ). This analysis showed the presence of some peaks only in bed bugs infected with B. quintana and other peaks present only in bed bugs infected with B. henselae but absent in controls (Figure  2 ). The ClinProTools 2. 2 software also calculated and created the principal component analysis (PCA) of the three groups of bed bugs. It compared the group of infected bed bugs with B. quintana and the control group (Figure  3. A), as well as the group of infected bed bugs with B. henselae with the control group (Figure  3. B). To better identify discriminating peaks between the infected bed bugs and the control bed bugs, we used the genetic algorithm tool of the ClinProTools 2.
2 software. We compared the spectra of 62 bed bugs infected with B. quintana and 34 spectra of control bed bugs. Similarly with the second pathogen, we compared 61 specimens of infected bed bugs with B. henselae and 34 control bed bugs. The genetic algorithm model and operator peak picking generated 10 discriminating peaks between bed bug controls and bed bugs infected with B. henselae (Table  1 ), with a recognition capability (RC) and cross-validation (CV) of 100% for both, and nine discriminating peaks between bed bug controls and bed bugs infected with B. quintana (Table  2 ), with a recognition capability (RC) of 100% and cross-validation (CV) of 98. 53 % (Figure  4, 5 ). The last step of the MALDI TOF analysis was the blind test of the spectra of bed bugs against our arthropod database, previously upgraded with the reference spectra of each bed bug category (infected with B.
quintana and B. henselae above 90%. In the case of slight heterogeneity within the same group, the more reference spectra one has, the more accurate the blind test analyses become. For this proof of concept step, it is necessary to keep only the best spectra to assess the performance of the tool in a blind test analysis. A validated database following a successful blind test analysis with high quality spectra means that each of these spectra can be used eventually as a reference to encompass all the future diversity encountered. Two groups of bed bugs were fed blood infected with B. henselae or B. quintana separately; the qPCR showed the acquisition of both bacteria by bed bugs with a 100% infection rate, in accordance with what was previously reported  [5]. After its success in microbiology, MALDI-TOF MS has emerged in the field of entomology and identification of arthropods.
5%) on all spectra analyzed. In this study, qPCR allowed proper classification of the three groups of bedbugs according to their infectious status. As demonstrated in previous work, bed bug heads provide reproducible spectra. Therefore, 50 specimens only were selected as controls. The database was upgraded with reference spectra of each category. The blind test showed a percentage of identification ranging from 90. 16% to 91% for all three groups. Regarding the identification of incorrect Bartonella species, we have reported in previous studies that the proteins of the infected arthropod itself could play a role in the differentiation of the two groups (infected/non-infected). Indeed, in Plasmodium-infected mosquitoes, a lower number of peaks and a decreased intensity of others was found compared to control mosquitoes.
Compared to other arthropod spectra, bed bug samples demonstrate significant intra-group heterogeneity. It is therefore impossible to have a relevant identification with 2 or 3 reference spectra, as was the case for highly reproducible Australian mosquito spectra, for example  [25]. An upgrade of the database with a high number of bed bug reference spectra should be a satisfactory approach to avoid misidentifications. In conclusion, after a preliminary work on fleas and the present study on bed bugs, MALDI-TOF MS appears to be a promising tool for differentiating arthropods infected or not with Bartonella, and further complementary studies are needed to better characterize the differential proteome of infected and non-infected arthropods. Perspectives include testing this tool in the field during entomological studies.
spectra within each cluster, and high quality and reproducible MALDI-TOF MS spectra from the head of 4 C. lectularius controls, 5 C. lectularius infected with B. henselae and 11 C. lectularius infected with B. quintana were introduced into our in-house arthropod database (available on request) (Species of this database are reported in Supplementary data 1). Then, a total of 34 spectra of C. lectularius controls, 61 spectra of C. lectularius infected with B. henselae and 62 spectra of C. lectularius infected with B. quintana were queried against the upgraded database.
Enumeration of T and NK cells in peripheral blood of patients Enumeration of CD4 and CD8 T and NK cells in peripheral blood of patients enrolled in the RELEVANCE trial was performed as previously described  (Plonquet et al, 2007). Briefly, absolute cell counts were derived directly from the flow cytometry data by using fluorescent beads of calibrated concentration ('single platform' technology). Lymphocytes were gated according to high-CD45 fluorescence intensity and low side scatter intensity; CD4 T cells were defined as CD3 + CD4 + lymphocytes, CD8 T cells were defined as CD3 + CD8 + lymphocytes, and NK cells as CD3 À CD16 + and/ or CD56 + lymphocytes. See the Data S1 for additional methods.
Confocal images and quantitative analysis of (A) CD4 + T cell F-actin immune synapse formation (F-actin, red or tyrosine-phosphorylated proteins, P-Tyr, white) interactions, and (B) CD8 + T cell F-actin immune synapse formation (F-actin, red) or granzyme B (green) interactions from representative patient and drug treatment groups. Quantitative image analysis measured the total area (lm 2 ) of F-actin polymerization at CD4 + or CD8 + T cell contact sites and synapses with FL B cells. (C) FL patient CD8 + T-cell killing against autologous tumour B cells (+sAg) as target cells following the drug treatments. Data are presented as mean AE SEM (n = 6). *P < 0Á05 by one-way ANOVA. myeloid stages 7 and 8. To determine the effects on neutrophil maturation, CD34 + cells were treated with bendamustine or lenalidomide for 14 days, followed by 7 days with no drug.
Quantitative image analysis measured the total area (lm 2 ) of F-actin polymerization at CD4 + or CD8 + T cell contact sites and synapses with FL B cells. (C) FL patient CD8 + T-cell killing against autologous tumour B cells (+sAg) as target cells following the drug treatments. Data are presented as mean AE SEM (n = 6). *P < 0Á05 by one-wayANOVA.
Acknowledgements The authors thank the  Nikon Imaging Facility at King's College London  for use of the Point Scanning Confocal A1R microscope, and all facility staff ( Daniel Matthews ,  Isma Ali ,  Benjamin Robinson  and  Daniel Metcalf ) who provided support. Research funding was provided by  Celgene Corporation , Summit, NJ and  San Francisco , CA. Medical writing assistance was provided by  Dorothy Fallows  and  Bio Connections , LLC, and funded by  Celgene Corporation.
Molecular investigation Whole genome sequencing (WGS) and bioinformatic analysis of isolate F91 was performed as previously described  [3]. DNA was extracted using Wizard Genomic DNA Purification Kit (Promega, Madison, United States (US)), and DNA libraries were prepared with Nextera XT (Illumina, San Diego, US). Paired-end, 564,319 150bp indexed reads were obtained on a MiSeq platform (Illumina). De novo assembly was performed using SPAdes software version 3. 13. 0 [4]. Quality Assessment Tool for Genome Assemblies (QUAST) software version 5. 0. 2 showed that the assembly yielded 204 contigs with a N50 of 28,258 nt  [5]. The contigs covered 93. 4% of the GC reference strain FA1090 genome. The complete genome of the F91 isolate is available in deposited at DDBJ/ENA/Genbank under accession number VNGI00000000.
WGS data were analysed in silico to determine the sequence types as previously described  [3, 6, 7]. Additional resistance markers were sought using ResFinder version 3. 1. 1 (https://cge. cbs. dtu. dk/ services/ResFinder/). The genomes of three available French MDR-GC isolates (F89  [8] , F90  [3]  and F91), as well as those from recent descriptions in Europe (H18-502, H18-209  [9]  and IR72  [10] ) and Japan (FC428  [11] ), were aligned using Parsnp software version 1. 2 and corrected from recombination  [12]. A maximum likelihood phylogeny was then built to compare these seven core-genomes and visualised using iTOL version 4  [13]. Relatedness between F91 and previous ceftriaxone-resistant GC isolates was observed in the phylogenetic tree (Figure ), and isolate F91 appeared to be distinct from F90.
piercenet. com, Rockford, Il, USA). Both assays were run on a THERMOmax microplate reader using SOFTmax software (Molecular Devices, www. moleculardevices. com, Menlo Park, CA, USA). Identification of tshβ paralogs in the Atlantic salmon. Gene and transcript names are in lower case and italics (e. g. tshβ) and protein names are in upper case (e. g. TSHβ). Atlantic salmon tshβ loci were identified in the recent Atlantic salmon genome assembly (ICSASG_v2, GCA_000233375. 4)  24  after interrogation of the Atlantic salmon annotated gene database in GenBank. The presence of additional tshβ genes was investigated by blasting salmon tshβa and tshβb against the Atlantic salmon genome. Gene sequences were examined with CLC Main Workbench 8 (Qiagen Bioinformatics, Hilden, Germany) for prediction of exons, introns, coding sequence (CDS) and signal peptide.
75 Gb of sequencing data and up to ~686. 5 million reads. Nanopore libraries and sequencing. Genomic DNA (gDNA) was extracted from snap-frozen liver, spleen and kidney from a juvenile Loire-Allier Atlantic salmon using the Genomic tip-100/G (Qiagen). gDNA size was inspected in the TapeStation Genomic DNA system (Agilent) and found to be >60 Kb. The gDNA was subsequently sheared to 10-20 Kb fragments using a g-tube (Covaris, Woburn, MA) before library preparation. The library preparation was performed using 1D Genomic DNA by ligation for either SQK-LSK108 or SQK-LSK109 kit (Oxford Nanopore technologies, Oxford, UK). All nanopore libraries were sequenced in a FLO-MIN106 R9. 4. 1 SpotON Flow Cell attached to either MinION or GridION devices (Oxford Nanopore Technologies), generating 28. 3 Gb of nanopore sequencing data divided over ~3.
21 million reads. Alignment of Illumina and nanopore reads to tshβ paralogs. Illumina reads were aligned to the tshb paralogs using bowtie2 (version 2. 2. 5)  56  , while nanopore reads were aligned using minimap2 (version 2. 5-r572)  57  with the default Oxford nanopore parameters (map-ont). SAMtools (version 1. 2)  58  was used to remove unmapped reads from the SAM file, convert to BAM, sort and generate the index. The resulting BAM files were visualized using Integrative Genomics Viewer (IGV) (version 2. 3. 83)  59. phylogeny analysis. Phylogeny analysis of 39 vertebrate TSHβ amino-acid sequences was performed using a part of dataset from  21  , enriched with additional teleost TSHβ sequences, including TSHβ paralogs of salmonids identified in this study. New tshβ genes were either retrieved from GenBank or were identified by blasting (TBLASTN algorithm) genome assembly databases when genes were not annotated in GenBank.
All samples were analysed in duplicates and each qPCR run contained a negative control using water in substitute for template cDNA. Calculations of sample concentrations were made using the Roche LightCycler 1. 2 manufacturer's software. Fluorescence in situ hybridization (FIsH). Antisense and sense cRNA probes for FISH were synthesized by in vitro transcription from tshβa or tshβb plasmids using T7 and SP6 RNA polymerase (Promega, Madison, Wisconsin USA) and labelled with digoxigenin-11 UTP (Roche Diagnostics) at 37 °C for 2 h. Probes were purified using Nucleospin RNA clean-UP kit (Machere-Nagal, Hoerdt, France) and controlled by gel electrophoresis. Probe length for tshβa was 401 bp which nearly covers the 420 bp CDS sequence. Probe length for tshβb was 512 bp which covers the entire CDS sequence (Supplementary Fig.
Acknowledgements This work was supported by grants from the  European Community H2020   Marie Sklodowska-Curie ITN IMPRESS  no  642893 , the  Agence Nationale de la Recherche ANR SALTEMP  no  ANR-12-ADAP-0021  and the  Research Council of Norway DigiBrain  no  248828. We thank the team from  CNSS  for raising the fish, and the team from  BIOM, Banyuls , for the help with tissue samplings. Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the  U. S. Government.