dataesr/scientific-paragraphs-categorization huggingface dataset:
Initially, combined error model was used for all analytes, but preliminary investigation indicated that the error models mentioned above provided the same fit as combined error models. Parameters were estimated using the parametric nonlinear mixed-effect modelling software Monolix (2020R1) with stochastic approximation expectation-maximization algorithm (SAEM) combined to Markov Chain Monte Carlo procedure. Relative Standard Errors (RSE) were calculated via estimation of the Fisher Information Matrix and log-likelihood calculation was estimated using importance sampling method. Pre and post data processing were performed with R software (V 3. 6. 1).
Model selection and evaluation The best model was determined by Objective Function value, Bayesian Information Criteria (OF and BIC respectively), physiological relevance of parameter estimates, precision of estimates (RSE), visual inspection of the fitted individual profiles and overall goodness-of-fit (GOF) plots. GOF plots included plots of observed concentrations versus model population and individual predicted concentrations, as well as population and individual weighted residuals (PWRES and iWRES) versus time and versus model predicted concentrations. The final model was qualified by inspection of prediction-corrected visual predictive check (pc-VPC)  [26]  and normal prediction distribution error (NPDE)  [27]  plots. NPDE and pc-VPC were performed with Monte-Carlo simulations. Five hundred datasets, identical in structure to the original dataset, were simulated.
Due to very low levels of DAR7 and DAR8 proportions, k dec7 and k dec8 parameters could not be estimated accurately and were assumed to be equal to k dec6. Measurements of DAR8 relative proportion were excluded from the analysis as the observed data were close to zero in most patients and induced model instability. All k dec,i parameters were assumed to have the same IIV. Attempts to have different IIV parameters for each k dec,i did not improved the fit but resulted in poor precision of IIV estimates. SAR408701 ADC concentration measured in plasma, was defined as follows: C ADC ¼ C DAR1 þ C DAR2 þ C DAR3 þ C DAR4 þ C DAR5 þ C DAR6 þ C DAR7 þ C DAR8 Where C ADC is the estimated SAR408701 plasma concentration and C DARi the individual ADC DAR i estimated plasma concentration. Models with peripheral DM4 deconjugation and combined central and peripheral DM4 deconjugation were tested but did not improve model prediction criteria.
NAB model NAB was represented as the DAR0 species and shared with conjugated antibody the same distribution parameters (V c , V p and Q) but not the same clearance. A specific CL NAB parameter was estimated. SAR408701 and NAB elimination were linear as no evidence of target-mediated drug disposition at the tested doses was found. Non-linear Fig. 1  Semi-mechanistic pharmacokinetic model of tusamitamab ravtansine, a DM4 conjugated ADC elimination and combined linear/non-linear elimination were nevertheless evaluated for each analyte but didn't improved data fitting, thus confirming linear PK of conjugated antibody and NAB. Administered DAR fraction SAR408701 administered dose was fractioned between all DAR compartments. Initially, DAR administered fractions (F DARi ) and there corresponding IIV were fixed at values measured in dose material batches: median values for parameter estimates and coefficients of variation for IIV (Table  3 ).
Data Data from 254 patients of TED13751 FIH study were included in the analysis. PK data were collected over 58 cycles (i. e. 29 months), with a median value of 4 cycles per patient (i. e. 2 months). A total of 3746 SAR408701 plasma concentrations, 3740 DM4 plasma concentrations, 3734 MeDM4 plasma concentrations and 3734 NAB plasma concentrations were analysed. As values below the limits of quantification (BLQ) represented 1%, 36%, 13% and 31% of total SAR408701, DM4, MeDM4 and NAB concentrations respectively, they were included in the analysis and used as censored data. DAR was assessed in 13 patients out of the 254 patients enrolled in TED13751 study. Spaghetti plots of SAR408701, DM4, MeDM4 and NAB observed concentrations over time at cycle 1 are presented in Online Resource 2a. Spaghetti plots of average DAR and proportions of individual DAR species over time after dose are presented in Online Resource 2b and 2c respectively.
RESULTS Species-wide genomic genetic diversity. To evaluate the genetic diversity and evolution of the S. pyogenes four HMM PBPs (1A, 1B, 2A, and 2X) species-wide, publicly available whole-genome sequencing (WGS) data were obtained for 26,465 samples (see Table  S1  in the supplemental material). The 26,465 genome sequences are of 1571 emm types ("1" indicates some samples were nontypeable since they were variants not present in emm typing databases) and 6731 multilocus sequence types (MLSTs) (Fig. 1A ). To establish a genetic framework within which the evolution of the HMM PBPs could be interpreted, the population structure of the cohort was evaluated by densitybased spatial clustering (DBSCAN) for lineages of core genomes significantly more closely genetically related to each other than at random.
Along with orthologues of PBP1A, PBP1B, PBP2A, and PBP2X, these streptococci also encode PBP2B, a second class B PBP not native to S. pyogenes. Given our identification of the interspecies HGT acquisition of SDSE-like pbp1b and pbp2x sequences (Fig. 2B  and D ), we analyzed the 26,465 genome assemblies for evidence of acquisition of PBP2B using BLAST. This analysis identified 22 sequence assemblies from 18 unique isolates (4 isolates had sequences deposited in both the SRA and MGDB) with a pbp2b xenologue having 100% nucleotide identity with pbp2b of SDSE strain NCTC7136. These 18 isolates (Fig. 7 ) were of 8 different genetic lineages and 9 different emm-types: 22, 44, 58, 63, 66, 74, 75, 90, 106, differing lineage to lineage on average by 14,860 core SNPs (range = 7,844 to 17,143). Inspection of the assemblies for these 18 isolates showed the PBP2B gene to be integrated into the S.
S. pyo. , S. pyogenes; SDSE, Streptococcus dysgalactiae subsp. equisimilis; SDSD, Streptococcus dysgalactiae subsp. dysgalactiae. synthesis D-alanyl-D-alanine ligase Ddl, folate transport protein FolT, and PG precursor synthesis UDP-MurNAc-tripeptide-D-alanyl-D-alanine ligase MurF (Fig. 8 ). The donated DNA appears to be an evolutionary mosaic stemming from more than a single S. dysgalactiae donor subspecies. The 59 end of the donated sequence encoding pbp2b-recR-ddl has high identity with SDSE strain NCTC7136 sequence (Fig. 8B ), whereas the 39 end encoding folT-murF has high identity (. 98%) with S. dysgalactiae subsp. dysgalactiae strain DB31752-13 sequence (Fig. 8C ). In comparing the 5. 9-kb donated segment with the NBCI sequence database, only SDSD strain DB31752-13 had a full-length match containing all five genes.
DISCUSSION Here, we defined the genetic diversity present in the most comprehensive collection of S. pyogenes whole-genome sequences studied to date. The population genomic data set facilitated development of an integrative reverse genotype-to-phenotype investigative strategy that was exploited to yield new knowledge about genetic polymorphisms that decrease antimicrobial agent susceptibility, a global public health problem of immense magnitude. Given the technical ease and low cost of large-scale microbial whole-genome sequencing and access to large, publicly available genome databases, the strategy we used is broadly applicable to other pathogens. Despite the cohort encompassing much of the currently available WGS data for the species, 26,465 samples constitute only a very small fraction (0.
Four key substituted sites located in one of the three transpeptidation conserved catalytic motifs of PBP2X (Fig. 5 ) are of special interest: (i) T 341 A and M 342 I adjacent to the catalytic serine within the S 340 XXK motif, (ii) S 400 R in the S 399 XN motif, and (iii) a second example of the T 553 K substitution in the conserved K 550 SGT motif. This second example of the T 553 K substitution was found in an emm6 strain (sample ERR1516025) that differs in PBP2X allele/variant from the two emm43 isolates identified by Vannice et al. (28). WGS data for this emm6 strain were deposited in the SRA in 2016 and therefore its isolation predates the 2017-2018 identification of the isolates documented by Vannice et al. Two substitutions, T 459 A and K 461 N, were identified in the S 458 XXK motif of PBP2A.
MATERIALS AND METHODS Genomic sequencing data. Genomic sequencing read sets were obtained from the National Center for Biotechnology Information Sequence Read Archive (NCBI SRA; https://www. ncbi. nlm. nih. gov/sra) by searching with the terms S. pyogenes, whole-genome sequencing (WGS), and Illumina platform. This identified 49 BioProjects (all but PRJNA559889 having WGS runs for 10 or more isolates) containing 25,045 runs of Illumina WGS single or paired-end reads. The NCBI Microbial Genome Database (MGDB; https://www. ncbi. nlm. nih. gov/genome/browse#!/prokaryotes/) was searched for genome assemblies (partial and complete) with the term S. pyogenes. This identified 2,123 WGS assemblies (210 complete closed and 1,913 partial contigs/scaffolds). Sequences were downloaded using SRAToolkit fasterqdump (https://github.
com/ncbi/sra-tools). There is some redundancy in the WGS data cohort, stemming from isolates for which sequence data were deposited in both the SRA and MGDB. No attempt was made to cross reference the databases and remove redundant samples which, if entirely overlapping, would maximally represent 15. 6% of the cohort. Retention of this redundant WGS data provided an important internal control for subsequent genetic characterizations (i. e. , emm type, MLST, core SNPs, lineage, PBP alleles, etc. ) as the same determinations should be made for the duplicated sequence samples. Variation in the PBPs, largely focused on the transpeptidase domain of PBP2X, has been studied previously for some of these samples  (5, (28) (29) (30) (31). Whole-genome sequence assembly. Unassembled WGS read sets obtained from the NCBI SRA (n = 25,045) were filtered to remove low-quality, adapter, and library generation artifact sequences using Trimmomatic (72) and were base call error corrected using Musket  (73).
Emm/M type determination from short reads is problematic since some lineages of S. pyogenes encode in addition to Emm/M protein, an M-related protein (Mrp) and an Emm-like protein (Enn), whose genes can share sequence similarity confounding read mapping and assembly. To enhance determination of these epidemiological markers, both read mapping-based, with unassembled reads and ShortReadSequenceTyper2  (76) , and BLAST-based, with assembled reads and EmmTyper (https:// github. com/MDU-PHL/emmtyper) and MLSTcheck (https://github. com/sanger-pathogens/mlst_check), approaches were used. Assignments of emm type were made relative to the CDC emm database (https://www2. cdc. gov/vaccines/biotech/strepblast. asp) and MLST relative to the PubMLST database (https://pubmlst. org/organisms/streptococcus-pyogenes).
Samples for which an emm type and MLST could not be determined were suspected of being contaminated or not S. pyogenes and were purged from the cohort. SNP discovery and phylogenetic reconstruction. SNPs were identified by comparison of each genomic assembly to reference genomes, M89 strain MGAS23530 and M1 strain MGAS2221, using the MUMmer dnadiff function  (77). SNPs were concatenated and converted to an aligned multifasta Antimicrobial Agent Susceptibility in S. pyogenes ® January/February 2022 Volume 13 Issue 1 e03618-21 mbio. asm. org 21 sequence file using Prephix and Phrecon (https://github. com/codinghedgehog) and were annotated using snpEFF  (78). Phylogeny for the cohort was inferred by neighbor-joining using RapidNJ (79), and trees were generated using Dendroscope  (80). Population structure determination.
Pseudo core genomes were generated for all 26,465 samples of the curated cohort using SNPswapper (https://github. com/codinghedgehog) with SNPs identified relative to the genome of reference M89 strain MGAS23530 substituted back into the reference genome. Strain MGAS23530 lacks phages and ICEs, and its sequence is therefore essentially limited to the core genome. This process results in a pseudo core genome alignment preserving the core genome genetic differences between the sequences and excluding potentially erroneous SNPs identified relative to quasirepetitive mobile genetic element sequences. Distinct genetic lineages within the population were assessed among the aligned pseudo core genomes using PopPUNK (81) by density-based spatial clustering. To facilitate some analyses that were computationally problematic/intractable when applied to the entire cohort of 26,465 WGS read sets, a reduced minimal set of 595 samples broadly representative of the population structure was generated based on the following selection criteria: (i) include samples of each different genetic cluster identified; (ii) if a given cluster included samples of more than one emm type or previously determined genetic lineage (for example, preepidemic and epidemic emm1 lineages), then additional samples with these characteristics were included; and (iii) preference was given to samples with complete closed WGS assemblies or, if incomplete, a higher N 50 and a lower number of contigs.
Thus, the stability of the interaction between an outer filament and the central filament would be a function of these many interactions. Due to the bipolar nature of the bundle, this interface has a local pseudo-C2 symmetry (Fig. 3E ) and repeats every five subunits. With this bundling mechanism, one P. calidifontis cell can form a robust bridge with another cell and have the potential to connect up to five different cells simultaneously (Fig. 3F ). AbpA Is a Homolog of Bacterial TasA. We next assessed the evolutionary provenance of AbpA by performing sensitive profileprofile comparisons using HHpred  (51) , whereby a profile hidden Markov model (HMM) of a query sequence was compared against the profile HMMs of the protein family database (Pfam). The search yielded a highly significant (97% probability) hit to a profile HMM of Peptidase_M73 protein family (PF12389), which includes camelysin, a secreted metalloendopeptidase  (52) , and TasA, a key component of bacterial biofilms  (18, (53) (54) (55) (56) (57) (58).
Indeed, it has been suggested that TasA has evolved from an inactivated camelysin-like protease  (18). The match encompassed the N-terminal signal sequence and the C-terminal β-strand rich ectodomain of both proteins (SI Appendix, Fig. S2 ). While the Dali server (59) failed to find significant structural similarity between AbpA and TasA, possibly, at least in part, due to donor-strand insertion in the ABP subunit but not in the crystalized monomeric TasA (PDB: 5OF1)  (18) , manual comparison of the two proteins revealed a high degree of structural similarity in the jelly-roll domain (Fig. 4 ). Furthermore, a very recent cryo-EM study reveals globular TasA subunits arranged into a filament with a donor strand from one subunit inserted into a sheet in the next subunit  (60) , very similar to what we report herein for ABP filaments.
To this end, we assessed the distribution of the Pfam PF12389 family in bacterial and archaeal genomes available in the Genome Taxonomy Database (GTDB) using AnnoTree (61, 62) (SI Appendix, Supplementary Data 1). In bacteria, TasA homologs are widespread but not universally conserved. They are readily identifiable in different Firmicutes lineages as well as in several other large phyla, including Actinobacteriota, Chloroflexota, Fusobacteriota, and Patescibacteria, whereas diderm bacteria of the phylum Proteobacteria nearly completely lack TasA homologs (Fig. 5A ). Notably, we also found TasA homologs to be relatively widespread in archaea, especially, in several lineages previously constituting the phylum Euryarchaeota (recently redistributed into phyla Halobacteriota, Methanobacteriota, and Thermoplasmatota)  (63).
Notably, the donor-strand (strand B) not only reconstitutes the BIDG β-sheet of the canonical jelly-roll fold but also extends this β-sheet to include a β-hairpin formed by B 0 and B 00 β-strands (Fig. 4D ), further stabilizing the ABP filament. The archaeal protein CanA (65) might also provide some insights into assembly of ABP filaments. It has been shown using a purely in vitro system that CanA self-assembles into filaments (PDB: 7UII) in the absence of any chaperone or catalyst. In the filament, a donor strand from one subunit is inserted into a β-sheet of another subunit. Thus, CanA might serve as a model for a self-assembly process that does not require a chaperone or catalytic usher as is required in the bacterial chaperone-usher pathway. Our results have uncovered an unexpected evolutionary relationship between the loci encoding ABP and TasA, a major matrix protein orchestrating biofilm formation in bacteria.
The refinement statistics are shown in Table  1. Sequence Analyses. Profile-profile comparisons were performed using HHpred against the Pfam database (51). Signal peptides and transmembrane domains were predicted using SignalP v5  (45)  and TMHMM  (82) , respectively. Genomic neighborhoods were analyzed using the enzyme function initiative-genome neighborhood tool (EFI-GNT)  (83)  and SyntTax  (84). The distribution of TasA-like proteins (Pfam PF12389) in the bacterial and archaeal genomes available in the Genome Taxonomy Database (GTDB) was assessed using AnnoTree  (61, 62). Data Availability. The cryo-EM atomic model and map have been deposited in the Protein Data Bank (7UEG )  (85) , and Electron Microscopy Database (EMD-26474)  (86) , respectively. ACKNOWLEDGMENTS. The cryo-EM imaging was done at the Molecular Electron Microscopy Core Facility at the University of Virginia, which is supported by the School of Medicine.
This work was supported by NIH (GM122510 E. H. E. and K99GM138756 to F. W. ). The work in the M. K. laboratory was supported by grants from l'Agence Nationale de la Recherche (ANR-20-CE20-009-02 and ANR-21-CE11-0001-01) and Ville de Paris (Emergence(s) project MEMREMA). We are grateful to Ultrastructural BioImaging Core Facility of Institut Pasteur for the access to electron microscopes and Christine Schmitt for help with sample preparation for SEM.
Statistical analysis The sample size was calculated using nQuery software (version 7. 0) based on the following assumptions: a mean total APSI score of 2. 18±0. 56 among women without prior SM, based on the study by Razurel et al;  16  and an expected mean total APSI score of 2. 68 among women with a history of at least one prior SM, an alpha risk of 5%, power of 90%, in a bilateral situation. A total of 28 women per group were estimated to be required. Data are described as mean±standard deviation, median and range, or number and percentage. Factors significantly associated with the stress levels overall and in each dimension of the APSI were studied by univariate analyses using the Student's t, Wilcoxon, χ 2 , or Fisher's exact tests, as appropriate. Variables with a P-value <0. 10 by univariate analysis were included in multiple linear regression models.
Exclusion criteria were pregnancy, institutionalized patients and inability to obtain informed consent. In the present work, IPP was tested at Day 0. Patients were 65. 0 years old [IQR, 57. 0-72. 0] and presented a disparate distribution of males and females (68/32). The RICO study protocol was approved by ethics committee (Comitéde Protection des Personnes Ile de France 1 -N°IRB/ IORG #: IORG0009918) under agreement number 2020-A01079-30. This clinical study was registered at ClinicalTrials. gov (NCT04392401). The committee waived the need for written informed consent because the study was observational, with a low risk to patients, and no specific procedure, other than routine blood sampling, was required. The RICO cohort comply with the Declaration of Helsinki, principles of Good Clinical Practice and the French personal data protection act.
Healthy donors Concomitantly, blood samples from 49 healthy volunteers were independently obtained from EFS (Etablissement Francais du Sang, Lyon, France). Briefly, healthy donors were 40 years old [IQR,  and were heterogeneously distributed between males and females (73/27). Samples were collected in April 2020 and November 2021. We used the Etablissement Francais du Sang standardized procedures for blood donation and followed provisions of articles R. 1243-49 and the French public health code to obtain written non-opposition to the use of donated blood for research purposes from healthy volunteers. The blood donors' personal data were deidentified before transfer to our research laboratory.
Statistical analysis The study cohort was split randomly in a 70/30 manner to obtain two datasets balanced on three parameters: age, sex and mortality. This resulted in a dataset of 216 patients used for machine learning training purposes and an independent test set of 93 patients used for performances validation. For datasets description, qualitative data were reported as counts and frequencies and quantitative data were reported as median [IQR range]. Clinical characteristics were compared with nonparametric Mann-Whitney-Wilcoxon test for continuous variables and a Fisher's exact test or chi-squared test (as appropriate) for categorical variables. The level of significance was set at 5% two-sided tests. Statistical analyses were performed with R software version 3. 6. 2. Data were centered and scaled to perform non-supervised principal components analysis using FactoMineR package (version 2.
4). Genes that were significantly associated with 28-day mortality in a univariate logistic regression model were used to build multivariate models for prediction of the 28-day survival status. Trained models were logistic regression with L1 (Lasso), L2 (Ridge) and mixed (ElasticNet) regularization, Partial Least Squares-discriminant (PLS) analysis and Support Vector Machines with linear kernels (linear SVM) using CARET package (version 6. 0-84). To compensate the imbalanced repartition of mortality in our datasets, the Synthetic Minority Oversampling Technique (SMOTE) was applied for hyper parameters tuning  (9). Models hyper parameters were chosen corresponding to the largest mean Area Under Precision Recall Curve (AUPRC) value among test folds from repeated cross-validation (k-fold=5, number of repeats=10) in the RICO training cohort (10) as sensitivity and Positive Predictive Values (PPV) are parameters of interest.
Briefly, among the 5 machine learning algorithm evaluated, the hyper parameters selected were as follow, Lasso (a=1, l=0. 031), Ridge (a=0, l=0. 556), ElasticNet (a=0. 35, l=0. 37), PLS (ncomp=1) and linear SVM (C=0. 367). AUPRC and their bootstrap 95% confidence interval was obtained using PRROC (version 1. 3. 1) and boot (version 1. 3-28) packages. Number of bootstrap resamples has been set to N=1000. Variables relative importance in the linear SVM model was calculated using the FIRM method from vip package (version 0. 3. 2)  (11). Area Under the ROC Curve (AUROC) and bootstrap 95% confidence interval and diagnostic performances (sensitivity, specificity, positive and negative predictive values and F1 score) at optimal cut-offs for the 9-mRNA panel as well as others individual parameters were obtained considering respective Youden values from cutpointr package (version 1.