dataesr/scientific-paragraphs-categorization huggingface dataset:
Patient recruitment All definite cases of Bartonella endocarditis diagnosed at Reunion Island University Hospital from 2009 to 2021 were retrospectively included in this study. All patients diagnosed with infective endocarditis (IE) on Reunion Island are treated at this hospital. For each case, medical records were analyzed and various data were extracted, including age, sex, year of diagnosis, country of residence, medical history, clinical presentation, presence of fever, possible exposure, suspected infection pathway, affected valve, surgical intervention, determination of C-reactive protein and hemoglobin levels, antibacterial treatment and outcome.
Characteristics of the patients and epidemiological data (Table 1) In total, 12 cases were included in the study. Most of the infected patients were male (83. 3%) and the median age was 32 years (IQR: 23. 75-40. 5 years). The recorded sites of residence of the patients were: Mayotte (n = 6, 50%), Comoros (n = 5, 41. 7%) and Reunion Island (n = 1, 8. 3%). Five of the six patients from Mayotte had recently traveled to or originated from Comoros. The only patient living on Reunion Island had also recently moved there from Mayotte (Table  1 ). None of the patients was immunocompromised or homeless, although information about living conditions was not available for all patients. However, three patients (25%) reported living in poor socioeconomic conditions: sheet metal house, clay ground and absence of running water.
DISCUSSION As a result of the ASAS-SPEAR project, the term "early axial spondyloarthritis" has for the first time been defined. This covers one of the unmet needs in the field of axSpA  [19]. The aim of this definition is to standardise the use of the term early axSpA in the research setting, enabling the inclusion of homogeneous study populations in studies evaluating research questions in patients with axSpA at an early stage of the disease. Hence, ASAS recommends that from now on, studies referring to early axSpA use this definition. It is important to note that the ASAS-SPEAR initiative also aimed to develop a consensual definition for the term "early pSpA" in addition to developing a consensual definition for the term "early axSpA". Yet, after discussing the results of the SLR, the decision by the working group was not to pursue a definition of early pSpA at this stage, since this term appears to be rarely used with almost no data available in the current medical literature.
Acknowledgements: We would like to thank all ASAS members for their participation in the project. Results of the Delphi Survey from the latest round in which each of the items was included. In total there were 3 rounds, eventually less for one specific item if consensus on it could be achieved earlier, either to include it (complete agreement, score 7-9) or to exclude it (complete disagreement, score 1-3). IBP: Inflammatory Back Pain; CBP: Chronic Back Pain; SIJ: Sacroiliac joints; Rx: radiographic. Regardless of Rx SIJ damage was defined as "damage on radiographs of the sacroiliac joint should not be taken into account". Full description of the items is available on Table  1. Figure legend.
Protocol Cytometry data were retrospectively extracted from the automated hematology analyzer (Sysmex XN-200, Sysmex, France). No blood sampling was mandatory for the study as blood cell count is usually monitored daily after cardiac surgery in our centre. Those data were merged with the data from the prospective cohort (ClinicalTrials. gov Identifier: NCT03281317). Three time points of interest were defined: pre-operative, post-operative (on the day of surgery) and 5 days after surgery. We choose the pre-operative and post-operative time points to capture the modifications of CPD induce by the surgery. Day 5  was chosen because it allowed to describe the evolution of CPD with the resolution of inflammation without too many lost too follow-up patients. Blood cell count (CBC) was measured on an automated Sysmex XN2000 analyzer equipped with impedance and fluorescence flow cytometry devices.
Data collection Baseline demographic data, per-operative and ICU data presented were collected prospectively. The EuroSCORE 2, a validated score for post-operative morbimortality of patients undergoing cardiac surgery was calculated  (10). The American Society of Anesthesiologists physical status (ASA score) has been evaluated during the preoperative consultation  (11). Complications were monitored until hospital discharge. All those data were recorded in a data base (Excel, Microsoft). Cell population data are recorded prospectively by the automated hematology analyzer and were extracted retrospectively. The two data bases were merged in order to perform this analysis.
Statistical analysis Normality was assessed graphically; quantitative data were presented as means (standard deviation/SD) or medians (interquartile range/IQR) and were compared using the Student ttest and the Kruskall-Wallis nonparametric test. Categorical, ordinal, and binary data were presented as frequencies and percentages and were compared using Chi-squared or Fisher's exact tests if the conditions of validity were not fulfilled. A Bonferroni correction was applied to compensate for alpha risk inflation with repeated measurements. For longitudinal data, changing patterns of CPD with time were assessed using mixed linear modeling. ROC curve analyses were carried out for cell line parameters of interest. Optimal cut-off values were calculated using Youden's method. An adjusted logistic regression analysis based on the EuroSCORE 2 was also carried out in order to determine whether those associations were independent of classical risks factors of post-operative morbi-mortality included in this validated score.
Last, we developed a multivariable model combining the CPD variable of interest with the EuroSCORE 2 to predict post-operative complications. Data were split into a training set (60%) and a validation set (40%). The model was developed using logistic regression on the training data set. Variable selection was stepwise, AIC based. Then the model accuracy and area under the curve were calculated on the validation set. The Hosmer-Lemeshow Goodness-of-fit Test was also carried out. Missing data were considered to be missing at random and were omitted from the analysis. All analyses were performed using R software. Cell population data parameters as visualized on the analyzer SCC: Granularity, SFL: Fluorescence.
Carotid plaques, defined as c-IMT ≥1. 5 mm, were included in the measurement. The protocols for ultrasound measurements of the carotids in the MDCÀ ÀCC, PIVUS and UK Biobank have been previously described  [20] [21] [22]. The c-IMT mean-max measure was available in the PIVUS and UK Biobank but not in the MDCÀ ÀCC dataset. Supplemental Table  I  summarizes the ultrasonographic measures available in each study.
Data mining To investigate whether the SNPs of interest have been implicated in any other relevant phenotypes as well as if other genes had been associated with circulating IL-8 levels, we used multiple publicly available databases: the LDtrait  [32]  (https://ldlink. nci. nih. gov/? tab=ldtrait) from the GWAS catalog (https://www. ebi. ac. uk/gwas/), the open GWAS project (https://gwas. mrcieu. ac. uk/, accessed 20212209) and the PhenoScanner (http://www. phenoscanner. medschl. cam. ac. uk/, accessed 20211511). We also explored the possible genotype-specific gene expression patterns using the GTEx dataset (https://www. gtexportal. org/home/, accessed 20211207) in the arterial wall. Data sharing statement. Legal obligations prevent us from sharing individual level data for the IMPROVE cohort.
While no pattern is reported for rs117518778 on chromosome 8, rs8057084 and rs4888378 on chromosome 16 were reported to be associated with changes in the expression of RP11-252K23. 2, a long non-coding transcript lying in the CFDP1 intron and with opposite changes in the expression pattern of BCAR1 in the artery wall (Supplemental Table  X ). In addition, while no association between IL-8 and circulating IL-8 levels was observed in the IMPROVE, this association has been shown in other populations included in a recently performed meta-analysis  [34]. At the same time, SNPs located at multiple loci have been identified as associated with IL-8 in independent cohorts  [35] [36] [37].
Both lead SNPs seem to regulate the expression of BCAR1-CFDP1-TMEM170A with both effect alleles associated with a lower expression of BCRA1 and higher expression of a novel intron at CFDP1 according to the data from the GTex dataset as well as published data  [39]. In particular, it has been shown that rs4888378 regulates the activity of an enhancer acting on different promoters possibly mediating a reduced expression level of BRCA1 in the presence of the effect allele A. Experiments performed in an experimental model of carotid artery ligation indicate that down regulation of BCAR1 expression is associated with a reduced migration of vascular smooth muscle cells after PDGF-BB stimulation and with a reduced neointima formation after carotid artery ligation  [40]. Notably, the association of the BCAR1-CFDP1-TMEM170A locus with c-IMT has been confirmed in a large meta-analysis  [4].
In addition, the use of a large-scale candidate locus approach means that there is prior probability for immunological and cardiometabolic traits, therefore our use of a suggestive a priori threshold to define significance in IMPROVE was appropriate. Genetic instrumental variables are robust, but typically demonstrate modest effect sizes, as observed here. An additional limitation is the non-standard methods for measuring c-IMT. We acknowledge that the differences in measurements might influence the findings, however the results in the different datasets are convergent, with gives us confidence in the comparability of the findings. In addition, different c-IMT measures have previously been combined in meta-analyses  [28, 43] , with some results being externally validated in UK Biobank  [22].
An estimated depth of coverage was computing using the genome L. interrogans sv Copenhageni strain Fiocruz L1-130 as a reference. A taxonomic analysis based on Kraken2 [28] using human, bacterial and viral databases allowed us to classify 99% of the reads on average with a minimum of 97. 2% in one sample. We generated a database of 273 genome sequences of representative pathogenic Leptospira strains from our publicly accessible genome database online https://bigsdb. pasteur. fr/leptospira/, which is based on the software framework Bacterial Isolate Genome Sequence Database  [4]. Our allele database includes genomes from 8 pathogenic species from 23 serogroups and 59 serovars isolated from patients from 40 countries (supplementary Table  2 ). The variant calling analysis was performed with the variant calling pipeline (v0.
10. 0) from the Sequana project  [29]  (https://github. com/sequana/variant_calling). The mapping step was performed with BWA (v0. 7. 17)  [30]  and the variant calling was done with freebayes (v1. 3. 2)  [31]. All VCF (Variant Calling Format) les (20 samples times 273 strains) were processed to gather the number of SNPs, INDELs, and MNPs in each sample and each strain. Scripts are available as notebooks on https://github/biomics-pasteur-fr/manuscript_capture_leptospira. Variants were removed from subsequent analysis if one of the following conditions was met: (i) a frequency of the alternate below 0. 5 (minor variants), (ii) a strand balance below 0. 2 (or > 0. 8) indicating an unbalance count of forward and reverse reads supporting the variant, (iii) a coverage below 10. In the case of sample 18 (highest Ct), we allowed the depth to be as low as 4.
3 ). Thus, the 6 samples with more than 90% Leptospira reads (samples 4, 7, 10, 11, 13 and 17) have Ct values < 32 (Table  1 ). Variant calling approach was performed in order to identify each sample's genotype. Given a database of 273 genomes of pathogenic Leptospira strains from different species, serogroups and serovars and originated from geographic areas (sup. Table  2 ), we searched for the closest genome to a given sample by minimizing the distance between the raw sequencing data of the sample and the Leptospira reference genomes. The distance used is the count of high-quality variants found in a given sample as compared to the different strains, as explained hereafter. Since the capture was designed with probes covering the core genomes only, we consider the core genome of the 273 strains; the average core genome length is 574.
kirschneri sv Grippotyphosa (id117) (Table  2 ). Sample 18 was excluded from the analysis due to a low average coverage of 1X. Indeed, with our stringent lter, less than 1 SNP was identi ed per strain. Nevertheless, we decreased the required depth to be as low as 4. In such case, the number of SNPs was about 20, on average, across all genomes (sup. Figure  5 ). One had no SNPs (L. mayottensis sg Mini; id149) although 10-15 other strains had only 2-3 SNPs. These 10-15 strains are all close to each other in the phylogenetic tree (Fig. 5 ) and belong to L. kirschneri. In particular, id117 has only 2 SNPs and was also the best hit in sample 6 and 7 (Table  2 ). Table  2  : SNPs, INDELs and MNPs found in the 20 samples. The Leptospira sequences from samples was compared with a database of 273 core genomes of pathogenic Leptospira strains to nd the closest homologue.
Figures Figure  1  Schematic representation of the target enrichment method used in this study. For all samples, genomic libraries were prepared following the Sureselect Xt HS Target Enrichment System for Illumina. A library of pre-designed RNA probes based on 130 L. interrogans genomes was added to the genomic libraries. The target DNA (here the Leptospira core genome) hybridizes with the probes, but other DNA (human DNA) does not, allowing the uncaptured DNA to be removed during wash steps. The probe-bound DNA is then eluted and the enriched genomic libraries are collected for Illumina sequencing. Phylogeny and single nucleotide polymorphisms of the Leptospira sequences among patient samples is then performed by comparing with a database of 273 core genomes of pathogenic Leptospira strains to identify the infecting strain.
The Leptospira sequences from samples was compared with a database of 273 core genomes of pathogenic Leptospira strains (sup. Table  2 ) to nd the closest homologue. Sample 1-4, 8-17, 19, 20 have minimal number of single nucleotide polymorphisms (SNPs), deletions, insertions and multiple nucleotide polymorphisms (MNPs) on id246 (L. interrogans sv Icterohaemorrhagiae). Sample 6 and 7 are close to id117 (L. kirschneri sv Grippotyphosa) and Sample 5 is close to id11 (L. interrogans sv Pyrogenes). The phylogenetic tree was generated using multiple alignment (mafft v7. 490) and RaxML software to infer the underlying phylogenetic tree. Visualisation was performed via the iTOL web service (https://itol. embl. de).
While the malaria parasite P. falciparum has low average genome-wide diversity levels, likely due to its recent introduction from a gorilla-infecting ancestor (~10,000-50,000 years ago), some genes display extremely high diversity levels. In particular, certain proteins expressed on the surface of human red-blood-cell-infecting merozoites (merozoite surface proteins, MSPs) possess exactly two deeply-diverged allelic forms that have not recombined. This phenomenon, called allelic dimorphism, is of considerable interest, but its origin and maintenance remains unknown. In this study, we analysed the dimorphism in two highly-variable and paralogous MSPs, DBLMSP and DBLMSP2. Despite thousands of available Illumina WGS datasets from malaria-endemic countries, diversity in these genes has been hard to fully characterise as reads containing highly-diverged alleles fail to align to the reference genome.
Recombination and gene conversion in the DBL domain To detect recombination in our DB protein sequences, we used a method developed by Zilversmit et al. (34)  for studying the var genes in Pf. Briefly, each sequence in a panel is aligned to all others using a HMM-based model that performs pairwise alignment between target and donor while allowing for switching between donors (i. e. recombination). Given our high number of sequences we first clustered them into 35 representatives (at 96% identity), as sequences that are too closely identical would only get aligned to each other, obscuring more distant recombination. We ran Zilversmit et al. 's implementation MosaicAligner on this panel, and built visual representations of the outputs to verify each inferred breakpoint (code available with this paper, see data availability).
Testing for direct evidence of evolution in the DBs The recombination and gene conversion events studied so far were inferred indirectly from population-level data. To test for direct evidence of these events in the DBs, we also analysed data from repeatedly-sequenced isolates through time. We looked for mutations in the DBs in two sources: the 'clone trees' from Hamilton et al. (36) , who repeatedly cloned, cultured and sequenced individual isolates (spanning ~700 erythrocytic life cycles in total), and four experimental genetic crosses between different strains of Pf (142 sequenced parents and progeny in total  (21, 37) ; see data availability and methods for details). Overall we found just two point mutations in one of the genetic cross progeny and no direct evidence of recombination or gene conversion in the DBs (supp.
Genotyping evaluation and performance We provide only a brief summary of genotyping evaluation and performance here and refer the reader to the supplementary for full details. We used two approaches to evaluate the genotype calls made by both pipelines. The first used 14 independent samples with both Illumina and PacBio data, from which high-quality truth assemblies were built in  (31). Using the Illumina data only we genotyped these samples with both pipelines and compared the calls to the truth assemblies. The second relied on remapping the sequencing reads of our 3,589 analysed samples to an 'induced reference genome', made by applying all the calls made by a pipeline to the 3D7 reference genome. We then measure genotyping quality through the level of agreement between reads and the induced genome.
We observed that for the gramtools-based pipeline results, a further 200 DB sequences (92 DBLMSP and 108 DBLMSP2) had no coverage gaps and < 15% reads with large insert sizes, and a single majority-pileup difference. We corrected these single-SNP sequences using a custom script (available with this paper), and added them to our analysis set, and also added the 28 DB sequences from the 14 samples assembled by Otto et al. (31) , giving a total of 6,123 analysed sequences. For results section 2 Translation to protein DB gene sequences were translated to protein using seqkit  (57) , and any resulting sequence with two or more stop codons excluded, as DBLMSP and DBLMSP2 are single-exon genes so that a single stop codon at the end of the sequence is expected (i. e. , there is no need to consider stop codons in introns).
The ML (Viterbi) path for each of the 35 representatives was then obtained given the inferred ML parameter values. From the textual output from MosaicAligner, visual representations of the alignments (e. g. Figure  4  panel b) were produced using custom code available with this paper. All mosaic alignments included at least one recombination breakpoint. To validate these, we compared the edit distance of each target to its MosaicAligner-inferred donor path with the edit distance to the single closest donor. The former was always smaller than the latter (supplementary fig. 3. 1).
We provide both uncorrected and corrected tables in the repository associated with this paper (see data availability). We downloaded all available read accessions (284 clone tree samples in six clone trees, and 142 samples in four crosses) from the ENA. For each sample, we performed preprocessing as above (trimmomatic + rasusa) and then genotyped each sample with gramtools on the graph built from the 3,589 'analysis-set' samples above. To discover any missed variation (as these samples were not part of the 3,589 in the graph), or mutational events in progeny samples, we then ran all steps of our gramtools-based pipeline up to and including gapfiller (Figure  1 ). By our evaluation pipeline standards, all samples were confidently-resolved. We then aligned all pairs of progeny and parent samples (in the clone trees, aligning to the only parent, and in the crosses, aligning to both parents and looking for the closest parent (the parents being usually highly diverged)) to infer any mutations.
Data availability and reproducibility All code and input data tables are publicly-available on github at https://github. com/iqbal-laborg/paper_pfalciparum_DBs, and frozen on zenodo at https://doi. org/10. 5281/zenodo. 7677548. The github repository implements our genotyping pipeline and our analysis of DBLMSP and DBLMSP2 sequences, and contains Snakemake workflows to reproduce all steps, including downloading the input data using input tsv tables. The input tsv files are located under analysis/input_data/sample_lists (including ENA run accessions; see repository README. md file for details), and the main ones are also directly provided on zenodo. All MosaicAligner images are also available on the github repository at plasmo_paralogs/docs/latex_recomb_images/doc. pdf and on zenodo. All software used and versions are stored in the reproducibility/container folder of the github repository, including a definition file for building a singularity image used by all Snakemake workflows.
Background: Cancer cells activate different immune checkpoint (IC) pathways in order to evade immunosurveillance. Immunotherapies involving ICs either block or stimulate these pathways and enhance the efficiency of the immune system to recognize and attack cancer cells. In this way, the development of monoclonal antibodies (mAbs) targeting ICs has significant success in cancer treatment. Recently, a systematic description of the mechanisms of action (MOA) of the mAbs has been introduced in IMGT/mAb-DB, the IMGT ® database dedicated to mAbs for therapeutic applications. The characterization of these antibodies provides a comprehensive understanding of how mAbs work in cancer. Methods: In depth biocuration taking advantage of the abundant literature data as well as amino acid sequence analyses from mAbs managed in IMGT/ 2Dstructure-DB, the IMGT ® protein database, allowed to define a standardized and consistent description of the MOA of mAbs targeting immune checkpoints in cancer therapy.
Given the significance of having quick and easy access to reliable information about therapeutic mAbs for the scientific community, IMGT ® , the international ImMunoGeneTics information system ® (http://www. imgt. org) (6) , since 2010, has offered to the community a unique and valuable resource concerning monoclonal antibodies with therapeutic application through its database, IMGT/mAb-DB. This database provides a one-of-a-kind resource on mAbs, fusion protein for immune application (FPIA), composite protein for clinical application (CPCA), related protein of the immune system (RPI), and T cell receptors (TR) with clinical indications. It includes INN names and definitions, sequence analysis, amino acid mutations and therapeutic metadata. The HGNC official names are also provided with a link to the target names  (7).