dataesr/scientific-paragraphs-categorization huggingface dataset:
Indeed, they are used to treat snakebites, arthritis, rheumatism, asthma, bronchitis, diarrhea, hemorrhoids, dyspepsia, fever, and many others (Dr. Duke's Phytochemical and Ethnobotanical Databases, 2021). All these traditional uses make these plants interesting candidates in the development of new drug treatments. That is why the Strychnos genus was the subject of numerous research works in our laboratory for more than three decades, especially for its promising antiplasmodial (in vitro) and antimalarial (in vivo) properties. During these investigations, many isolated monoterpene indole alkaloids showed promising activities on various chloroquine-sensitive (CQS) strains as well as on some chloroquine-resistant (CQR) strains  (Wright et al. , 1994; Wright et al. , 1996; Frédérich et al. , 1999; Frédérich et al.
Expanding the therapeutic arsenal would allow to counter the growing resistance of the parasites and potentially eradicate the pathology. In view of their promising antiplasmodial and antimalarial potential, plants of the Strychnos genus are therefore very interesting candidates. In recent years, the field of natural products research has evolved significantly with the development of various dereplication tools, including molecular networking, which allows the quick discrimination of known and unknown metabolites based on comparisons with databases of mass spectra  (Yang et al. , 2013; Wang et al. , 2016; Nothias et al. , 2018; Fox Ramos et al. , 2019a; Fox Ramos et al. , 2019b; Beniddir et al. , 2021). Inspired by these advances, we envisioned that the use of molecular networks would be an efficient approach to both explore the chemodiversity of alkaloids in plants of the Strychnos genus but also to target metabolites that have not yet been identified in the previous studies.
MolNetEnhancer annotation The MS/MS data of the global molecular network were further annotated using MolNetEnhancer workflow exploiting exclusively the GNPS experimental annotations. MolNetEnhancer is a workflow that allow to have a more comprehensive chemical overview of metabolomics and to highlight structural details for each MS/MS spectra. Using the automated chemical classification through ClassyFire, the classifications provided by MolNetEnhancer were of different levels: from kingdoms to subclasses  (Ernst et al. , 2019).
Moreover, some of these alkaloids, present mainly in extracts with promising or good activity, are related to ions of different masses still unknown to date and also present mainly in extracts with the same levels of antiplasmodial activity. However, it is important to note that an alkaloid, not identified in the molecular network, is not necessarily unknown in the literature. Indeed, identifications depend on databases that are not yet complete today. Enriching these databases would allow better identifications and even more efficient targeting of metabolites that were never reported in the literature. As an example, the third framed cluster includes usambarensine, 3′,4′-dihydrousambarensine, and N b -methylusambarensine (Figure  5 ; Supplementary Table  S3 ). The class assigned to this group is harmala alkaloids, which is confirmed because usambarensine and its derivatives include a harmane group in their structure (Figure  6 ).
falciparum. Therefore, the development of mass spectrometry and chemoinformatic techniques, such as molecular networking, allows to have an overview of the metabolites' content of the extracts studied, to quickly identify known ones, and to detect new potentially active ones, even with low quantities. The use of other dereplication tools such as ISDB (In-Silico spectral DataBase)  (Allard, et al. , 2016) , MS2LDA  (Wandy et al. , 2018) , MixONat  (Bruguière et al. , 2020) , and MADByTE  (Egan et al. , 2021) , and the realization of various bio-and mass-guided fractionations would allow to characterize and identify the new active metabolites observed in this study.
In addition, these extracts were tested against the chloroquine-sensitive P. falciparum strain (3D7) in three independent test series. A total of 28% (12 extracts out of 43) showed promising (≤ 5 µg/ml) and good (between 5 and 15 µg/ml) antiplasmodial activities. The active extracts were from leaves and root barks of S. usambarensis, root barks of S. variabilis, leaves of S. phaeotricha, root barks of S. angolensis, trunk barks of S. longicaudata, leaves of S. malchairi, roots and collar barks of S. icaja, root barks of S. nux-vomica, and root barks of S. malacoclados. Some of these activities were never reported in the literature, namely these of trunk barks of S. longicaudata as well as leaves of S. phaeotricha and S. malchairi. These results, implemented as metadata in the molecular network of the forty-three methanolic crude extracts, allowed to highlight the presence of many alkaloids still unknown and potentially active against malaria.
What this study adds • Correlations between CYP1A2 activity using genetic information alone, correction for the covariates effect and a caffeine probe are evaluated. • The different approaches to estimate CYP1A2 activity with regard to dose-adjusted clozapine concentrations are evaluated. recorded. The data were collected by two different people, then combined to avoid transcription errors. Exclusion criteria were children under 16 years of age and patients who had failed to adhere to caffeine abstinence before phenotype determination.
| CCS calculation CCS was calculated as described by Lesche et al. 18  Briefly, an activity score (AS) of 1. 0 was ascribed to the CYP1A2*1A allele and 1. 5 to the CYP1A2*1F allele, and was then multiplied by 1. 5 for currently smoking patients, or by 0 in the presence of a strong inhibitor (i. e. , fluvoxamine or ciprofloxacin). No individuals in our cohort were taking strong CYP1A2 inhibitors. A posthoc power analysis was performed using the G*Power software (version 3. 1. 9. 7).
| RESULTS Eighty-nine patients aged between 28 and 48 years were included in this study. Participants' characteristics are shown in Table  1. Most patients carried the CYP1A2*1F allele and no deviation from the Hardy-Weinberg equilibrium was observed (P = 0. 63). The median value of the 17X/137X ratio for the overall population was 0. 60 (quartiles: 0. 36-0. 88). As expected, smokers had a higher median ratio (0. 88, quartiles: 0. 61-1. 08) than non-smokers (0. 40, quartiles: 0. 31-0. 60) and this was significant according to the Student's t-test performed after log 10 transformation (P = 6 Â 10 À9 ) (Figure  1A ). We found no correlation between the 17X/137X ratio and the number of cigarettes smoked, but this information was available for a limited number of patients (n = 27) and was based on the patients' own statements, thus limiting its reliability (data not shown).
Prototype Formulations and Primary Packaging Materials Solutions for subcutaneous injection were investigated for their stability for a period of up to three months prior to entry into development. Three compositions were selected based on (i) preliminary chemical stability data as well as (ii) an automated formulation screening with focus on aggregation and physical stability, as exemplified in Refs. [20, 21, 45]. Peptide concentrations of 0. 1, 0. 5, and 2. 5 mg/mL appeared suitable for the intended dosing scheme of phase 1 clinical trials. The lower concentrations were important for the initial dose ramping only. The maximum concentration of 2. 5 mg/mL was most relevant for the final drug product consisting of a ready-to-use solution for daily s. c. application using a multiple-dose pen device.
Stability Monitoring According to the very recently published "good modeling practices" for the generation of kinetic models, it is recommended to produce at least 20-30 experimental stability data points in duplicates, covering (at least) three different incubation temperatures  [39]. For the present study, the accelerated stability data of the drug products required for the kinetic analysis were determined after storing the containers in an upright position at 5 ± 3 • C, 25 ± 3 • C, 30 ± 3 • C, 37 ± 3 • C and 40 ± 3 • C in temperature-controlled incubators for up to three months. The time intervals of accelerated degradation measurements were taken as follows: 0, 13, 33, 46, 60, and 90 days at all five temperatures. Due to constraints of API availability prior to the entry into development, we decided at the initiation of the 3-month stability study to run most analytical methods with single-point measurements (no replicates) in favor of a comprehensive coverage of the temperature range, and three formulations (F1-F3), three peptide concentrations, and two primary packaging materials (PM1 and PM2).
This described stability study was an internal pilot for maximum risk assessment regarding long-term (physical and chemical) stability with the intension to derive valuable conclusions for future projects. The experimental data for chemical degradation of SAR441255 (minimum of 20-30 points for each drug product) were fitted using the AKTS software  [48]  as described below. Long-term experimental data were measured after storage of the peptide formulations at the recommended storage temperature of 5 • C for 6 and for 24 months using the remaining backup samples. Additional time points of analysis were requested for both packaging materials PM1 and PM2 (e. g. , after 9 and 12 months of storage). However, data could only partially be collected and were not uniform for all drug products due to capacity limitations and changed priorities of the analytical lab.
Advanced Kinetic Modeling and Stability Predictions The AKTS-Thermokinetics software (version 5. 3, AKTS AG, Advanced Kinetics and Technology Solutions, Siders, Switzerland), combining advanced kinetics and statistical analyses of the accelerated stability datasets obtained from 5 • C to 40 • C over a period of three months, was used to identify kinetic models which best describe the rates of purity loss and HMWP increase for each peptide formulation. Except for the use of replicates, our procedure used "good modeling practices" recently recommended for accelerated stability predictions of bioproducts  [39, 49, 50]. Briefly, the software screened and compared the variety of kinetic models, including one-step and two-step models (Equation (  1 )), describing the reaction progresses (i. e. , purity loss and HMWP formation) independent of the complexity of their molecular mechanisms as a function of time and temperature in a manner as previously described  [34, 35, 51].
dα dt = A × exp - Ea RT (1 -α) n α m (1) with α: the reaction progress, A: pre-exponential factor, Eα: activation energy, n: reaction order, m: a parameter introduced considering the possible autocatalytic behavior of the reaction. Long-term predictions of purity loss and HMWP formation as a function of time and temperature were subsequently performed using the best identified models with the optimized kinetic parameters. Subsequently, 99. 9% percentile prediction intervals (PIs) were calculated using bootstrap analysis (resampled > 100 times with replacement). Finally, the predicted long-term stability data (purity loss and HMWP formation) after 24 months at 5 • C plus 28 days at 30 • C were experimentally analyzed. To assess prediction accuracy, the difference (as percentage point) between experimentally and predicted purity along with HMWP values were investigated.
Long-Term Prediction of Purity Loss For each combination of the three peptide formulations and two primary packaging materials, a large variety of models from the simplest (zero and first-order) to the more complex were screened to fit the three-month experimental data using AKTS. The software compared and ranked these models. The best models were selected based on their Akaike and Bayesian information criterion (AIC and BIC) scores, and Residual Sum of Squares (RSS) values. Equations (  2 )-(  7 ), Figure  2 dα dt = exp(16. 1) × exp - 85. 1E3 RT × (1 -α) 6 × α 0. 3 (7) Table  3. Combinations of three different peptide formulations and two different packaging materials that were used for experimental and in silico assessment of accelerated and long-term stability. Details about the formulations (F1, F2, F3) and packaging materials (PM1, PM2) are provided in Tables  1  and 2.
Therefore, it is highly important to ensure that the parameters chosen for the accelerated stability studies are most relevant for the use in clinical trials and for the intended commercial drug product. (3) The present study is the first report of long-term stability predictions on a therapeutic peptide in liquid formulations. Of course, further studies will be required to demonstrate general applicability of this approach for therapeutic peptides. However, successful applications of long-term predictions using advanced kinetic modeling for vaccines  [34] [35] [36] [37] [38] [39] [40]  and antibodies using Arrhenius kinetics  [25]  suggest that these approaches are generally applicable for a broader range of modalities. The AKTS software can, in principle, operate on any kind of data that follow a reaction progress without the need to know the exact molecular mechanism and kinetic model.
Acknowledgments: S. P. -M. thanks  Tan Minh Tran  and  Rüdiger Hoffmann  for supporting sample manufacturing. The authors would like to thank  Mike Hillebrand  and  Anna-Ariana Stipić  for performing the HPLC and SEC analyses of the samples,  Martin Bossart  and  Michael Wagner  for their continuous support of the presented studies, as well as  Norbert Pleuss  for synthesizing and providing the required peptide amounts.
RESULTS Pangenome of M. avium subsp. paratuberculosis isolates and comparison to the M. avium subsp. hominissuis pangenome. To investigate the genomic structure and gene content of M. avium subsp. paratuberculosis isolates, we performed a pangenome analysis. Of the 182 genomes of field M. avium subsp. paratuberculosis strains circulating in France (Fig. 1 ; see Data Set 1 in Table  S1  in the supplemental material) (20), 137 were selected for pangenome analysis. Genomes with less than 20Â coverage or whose assembly yielded more than 400 contigs or ,69. 2% GC content were excluded from the analysis. For genome assembly, as described in Materials and Methods, the number of contigs ranged from 123 to 358, with an average of 178, and N 50 ranged from 30,069 to 97,349, with an average of 67,901.
e. , 98% of genes) in the core genome and 55 genes in the accessory genome (Fig. 2a  and b ). Among the 123 redundant gene clusters, 5 belong to the core genome (4. 07%) and 118 belong to the accessory genome, including 28 soft-core genes (22. 76%), 37 shell genes (30. 08%), and 53 cloud genes (43. 09%). Also, 85 of these genes were annotated as hypothetical protein genes. A BLAST database was constructed containing the nucleotide sequences of the 153 genomes included in the analysis to verify the presence of the remaining 55 accessory genes. Searches against this database revealed that the majority of genes are present partially at the ends of assembled contigs in draft genomes. These analyses confirm that M. avium subsp. paratuberculosis has high genetic stability regardless of time and country of isolation.
3%. Alignment of the four genomes with respect to the reference strain K-10 shows that the genomic organization is remarkably stable, with only one large inversion shared among the four strains, corresponding to a previous observation by Talaat and coworkers (23) (Fig. S1 ). Whole-genome SNP phylogeny. A maximum likelihood (ML) tree was reconstructed using an alignment of 2,786 nonhomoplasic SNPs, including 193 M. avium subsp. paratuberculosis isolates (the 192 French isolates in Data Sets 1 and 2 and the K-10 genome in Data Set 4 in Table  S1 ). The ML phylogenetic tree (Fig. 3 ) shows two dominant well-supported monophyletic lineages (100% bootstrap support at basal nodes) and a minor well-supported monophyletic lineage, called clades A, B, and C for clarity only. This population structure was confirmed by fastBAPS analysis.
(ii) Data Set 2. To investigate French M. avium subsp. paratuberculosis population structure changes over a long period, 10 M. avium subsp. paratuberculosis strains collected in 2003 were sequenced, and the sequences were deposited in the NCBI public database. (iii) Data Set 3. To broaden comparative genomic analysis and provide a global vision of M. avium subsp. paratuberculosis phylogeny, we included all whole-genome sequences available for M. avium subsp. paratuberculosis in NCBI SRA, GenBank, and ENA, for which all information regarding country of origin and year of isolation was available. This added 826 genomes, which were downloaded in September 2021. (iv) Data Set 4. For synteny studies and genetic characterizations, the 12 complete genomes recently available for M. avium subsp. paratuberculosis were downloaded from NCBI RefSeq in August 2021.
The same procedure was used to annotate the sequences of the 13 complete and 110 draft M. avium subsp. hominissuis genomes from Data Set 5 (Table  S1 ). Next, panaroo-qc was used to check the quality of M. avium subsp. hominissuis draft genomes. Outlier genomes in terms of number of contigs and number of genes were excluded from the analysis, which excluded 9 genomes and left 114 genomes for analysis. Panaroo (v. 1. 2. 8)  (31)  was used to delineate the pangenomes of both subspecies independently. The pipeline generates initial gene clusters using a greedy incremental clustering approach that processes sequences based on length (as implemented in CD-HIT). Next, neighborhood information is generated by constructing a graph of the pangenome where nodes are gene clusters and edges connect genes adjacent on contigs.
The graph and neighborhood information are then used to identify and merge fragmented or mistranslated genes and identify genes missed by the gene-calling algorithm. To detect any redundant gene clusters that can inflate the estimated pangenome's size, consensus sequences of all gene clusters constituting the pangenome were aligned to a complete genome-i. e. , FDAARGOS_305 (also known as the K-10 strain) (NCBI accession no. NZ_CP022095. 2) (45) for M. avium subsp. paratuberculosis and 104 (NCBI accession NC_008595. 1) for M. avium subsp. hominissuis-using blastn  (46)  with an identity cutoff of 90% and a similarity E value cutoff of 1 Â 10 210. Redundant gene clusters were labeled using the R IRanges (v. 2. 28. 0) package  (47)  if the best blastn result from one cluster overlapped another cluster by at least 75%, creating a matching network.
3. Gene families with partial sequence from one assembly were excluded. Concatenated core gene alignment was the first test for the presence of recombinant sequences using the pairwise homoplasy index (PHI) test in SplitTree (v. 4. 18. 0)  (48) , with a significance threshold of P # 0. 05. An ML tree was inferred based on the core gene alignments using IQ-Tree (v. 1. 6. 9) with 100 bootstraps as pseudoreplicates. Selection of the best-fit model of evolution, K3Pu1F1I, was performed in IQ-Tree based on the lowest Bayesian information criterion. The phylogenetic tree was visualized using Interactive Tree of Life. Python script roary_plots. py (https://github. com/sanger-pathogens/Roary/ blob/master/contrib/roary_plots/roary_plots. py) was used to plot core gene phylogeny beside the pangenome matrix.
Core and pangenome curves were plotted using a modified version of Python script from https:// github. com/rotheconrad/00_Pangenome_Analysis/blob/master/04b_Pangenome_Calculate_Model_Plot. py with 1,000 permutations. The openness of the pangenome was tested using home-made Python script by fitting the Heaps' law model according to  Tettelin et al. (22)  to the mean curve of the new genes using the curve_fit function of the scipy. optimize package  (49). Single-molecule real-time sequencing and assembly. Four genomes of French M. avium subsp. paratuberculosis strains (Data Set 1 in Table  S1 ) were selected on the basis of phylogeny clustering to create complete genomes of reference for M. avium subsp. paratuberculosis strains currently circulating in France: strains PICSAR51, PICSAR68, PICSAR77, and PICSAR142.
For complete genomes (Table  S1 , Data Set 4), pairedend reads (250 bp) were simulated using ART software (v. 2. 5. 8) (59) based on the HiSeq 2500 platform. Illumina reads for Data Set 3 (Table  S1 ) were downloaded from NCBI's SRA database using fasterq-dump (v. 2. 10. 1). Raw sequencing data were processed as previously described  (20). Briefly, short reads were trimmed using fastp v. 0. 20. 1 and a Phred scale base quality threshold of Q30. Kraken2 v. 2. 0. 9-beta with a bacterial database was used to identify potential contamination. Only short reads assigned to the Mycobacteriaceae family were retained in the analyses. To avoid potential alignment error from underlying structural variation compared to the reference genome and to define phylogenetic relationships as precisely as possible (60), reads were mapped to a local reference from the panel, namely, PICSAR77 (GenBank accession no.
CP091844 [Table  S1 , Data Set 1), using BWA-MEM (v. 0. 7. 17-r1188). SNPs were detected with GNU parallel (https://zenodo. org/record/ 3841377) and FreeBayes (v. 1. 3. 2-dirty) (https://arxiv. org/abs/1207. 3907) with -minqual and -minmapq options set to 20 and 30, respectively, for each strain. SNPs were filtered out based on four criteria: (i) the quality of the SNP is more than 300, (ii) at least 2 reads support the variant on the forward and reverse strands, (iii) at least 2 reads support the variant placed on the left and right, and (iv) there is a mean mapping quality at a site of 60 to avoid read mapping error. VCF files from each strain were pulled together to list all detected positions in the population, and variant recall was performed only on these positions. Recall VCF files were merged to obtain a global VCF encompassing all strains.
A global VCF file was functionally annotated with the snpEff tool (v. 4. 5covid19)  (61). In-house script was developed to build the SNP concatenate. SNPs were classified at positions where $ 90% of reads support the alternative allele (ALT). When reads' support values for the ALT were >10% and ,90% (defined as heterozygous SNP) or depth was lower than 4, an ambiguous character, N, was called in the alignment. Positions with an ALT reads' support of #10% were classified as "reference. " Strains for which half of the cladespecific SNPs (see the phylogeny sections) were assigned to an ambiguous character, N, were excluded from the analysis. Only PICSAR126 was excluded, with 68% of ambiguous positions. Homoplasic positions in the alignment were removed using HomoplasyFinder  (62). Homoplasy-removed SNP alignments were used to generate Bayesian and maximum likelihood (ML) trees.
IQ-Tree (v. 1. 6. 9) (63) was used to infer the ML tree with 100 bootstraps as pseudoreplicates. Selection of the best-fit model of evolution, K3Pu1F or TVM1F, was performed in IQ-Tree based on the lowest Bayesian information criterion. MrBayes  (64)  was used to infer the Bayesian tree, with 1,500,000 generations and 25% burn-in with the GTR1G1I substitution model. Trees were midpoint rooted. The phylogenetic tree was visualized using Interactive Tree of Life  (65). Population structure was validated using a Bayesian clustering/assignment approach, as implemented in fastBAPS software (66) using the homoplasy-removed SNP alignments and ML phylogeny as the input. A regression of the sampling date (Table  S1 , Data Sets 1 and 2) against the root-to-tip genetic distances performed with Tempest  (67)  showed that the data set contains insufficient temporal signal for a molecular clock analysis (data not shown).