dataesr/scientific-paragraphs-categorization huggingface dataset:
De novo assembly of mycobacterial genomes was performed with SPAdes v3. 8. 2  (Bankevich et al. , 2012)  using different odd k-mer sizes in the range k = 21 to k = 87. Assembly stats such as statistics such as N50, largest contig, GC-content, genome fraction covered, and genes number were computed with Quast v3. 1  (Gurevich et al. , 2013)  to assess the quality and compare it to the reference genome H37Rv (NC_000962. 3). In addition, correction of assembly errors was carried out using Pilon v1. 22  (Walker et al. , 2014). To call features within contigs, genome annotation was performed using Prokka v1. 11  (Seemann, 2014) , with proteincoding genes predicted with Prodigal  (Hyatt et al. , 2010)  and homology searches done against UniProt (Swiss-prot) database  (Apweiler, 2004)  using BLASTP  (Altschul et al.
Pan-genome construction, core and accessory genome evolution Identification of clusters of homologous genes within the 47 genomes annotated was performed with GET_HOMOLOGUES V09062017 (Contreras-Moreira and Vinuesa, 2013), using BLASTP with a sequence identity of 90% and minimum default query coverage of 75% for paired alignments. Once the local alignments were sorted and indexed, three algorithms, Bidirectional best-hit (BDBH)  (Contreras-Moreira and Vinuesa, 2013) , OrthoMCL (OMCL)  (Fischer et al. , 2011) , COGtriangles (COG)  (Kristensen et al. , 2010)  also included in GET_HOMOLOGUES software, were used to produce sequence clusters (both nucleotide and peptides) according to protein sequence similarity. Single-copy orthologous clusters were defined as those containing only one sequence from each input genome; extra copies were considered paralogous.
Functional pan-genome analysis and pan-genome wide association study of Mtb lineage-4 The data of the pan-genome matrix (PGM) and the GeneBank annotation files were used as input in BPGA v1. 3 software  (Chaudhari et al. , 2016). Identifiers with the best hits were assigned from the KEGG (Kyoto Encyclopedia of Genes and Genomes) and COG (Clusters of Orthologous Groups of proteins) reference databases  (Kanehisa and Goto, 2000; Tatusov et al. , 2000) , for which USEARCH's ublast  (Edgar, 2010)  function was used, with an E-value cutoff of 10E-5, databases and tools were used by default in BPGA V1. 3. This analysis allowed us to annotate metabolic pathways and the functional categories of COGs within the dispensable genome, the core genome, and the unique genes that make up the pan-genome. An association study was conducted based on patterns of gene presence/absence in the pan-genome.
4. 0  (Darling et al. , 2004). This software can also generate a file in tabular format with the SNPs and its coordinates in each genome. This can be used to identify differential SNPs between high and low prevalence strains. Gaps in the alignments were removed, and minor nucleotide frequency in each position computed with a script available at https://github. com/ualonso85/Thesis-pipeline/blob/ master/freqs. sh. To assess the accuracy of our sequencing and bioinformatic approaches, we amplified the sequence region of the variants associated with the high or low prevalence in 18 Mtb strains randomly selected. The PCR products were sequenced on an ABI 3730 sequencer (Applied Biosystems). There was 100% identity between all sequenced regions both by Illumina HiSeq 2500 and ABI 3730.
Phylogenomic analysis The PGM generated from the consensus of orthologs groups with the COG and OMCL algorithms was used for the phylogenomic reconstruction of the 47 genomes. The phylogenetic trees were built with the parsimony method from discrete characters (present/absence genes) using the PARS v3. 69 software include in PHYLIP suite. In addition, a maximum likelihood method was used for phylogenomic analysis with the GTR substitution model and ultrafast bootstrap of 1000 replicates (-bb 1000 -alrt 1000) using IQ-TREE v1. 6. 12  (Nguyen et al. , 2015). The signal of each genetic tree was calculated based on the mean values of branch support with the SH-aLRT and UFBoot tests. The grouping of clades in the phylogenomic tree was verified with 26 Mtb genomes of different lineages (L2, L3, L4, L6) with known tree topology downloaded from the NCBI database.
Phylogenomic reconstruction with SNPs was performed by converting the eXtended Multi-Fasta (XMFA) alignment file from Mauve to fasta format with a script available at https://github. com/eead-csic-compbio/eead-csic-compbio. gith ub. io/blob/master/scripts/xmfa2fasta. pl. Trimal software v1. 2rev59  (Capella-Gutierrez et al. , 2009)  was used to removed gaps leaving only the nucleotide positions that were present in all Mtb genomes (core). The concatenated sequences from each genome that contained the SNPs were used to build the phylogeny using the maximum likelihood method with the same parameters in IQ-TREE described above but using TVM (transversional model) such as nucleotide substitution model. A strain of Mycobacterium canettii 140010059 Accession NC_015848 was included as root. This study was approved and carried out in accordance with guidelines and regulations by the Ethics Review Committee of Coporación para Investigaciones Biológicas.
Prevalence of Mtb lineage-4 in NE area of Medellín In the Medellín city, 135 Mtb isolates were recovered from Popular, Santa Cruz, Manrique, and Aranjuez communes, from 2005 to 2006. The spoligotype of these isolates were represented by patterns of presence or absence of some of the 43 spacers in the Direct Repeat (DR) locus. Sublineage classification was performed by assessing the SIT code retrieved from the SPOLDB4 database, as well as with 24 MIRU-VNTR analyzed online with MIRU-VNTR plus tool (Supplementary Table  1 ). In total 99 (73. 3%) of the 135 isolates were classified as high prevalence due to the high frequency of the Haarlem1 SIT62 and LAM9 SIT42 sublineages, with a relative frequency of 0. 37 and 0. 36, respectively. The remaining 26. 7% corresponded to 34 isolates of different sublineages with low frequency (<0.
M. tuberculosis isolates and general genomic features Once the 47 Mtb isolates were defined according to the high or low prevalence, each isolate was sequenced. A total of 156,746,390 reads of the isolates were obtained after filtering by quality parameters, with an average of 3,335,030 reads per sample. The average genome size of the 47 isolates was 4. 348 Mb, G + C 65. 5%, genome fraction 98. 6%, with coverage depth of 102X (ranging from 30X in UT331 to 198X in UT86), and N50 was 114,855 (ranging from 78,270 in UT414 to 166,979 in UT86) with 105 contigs on average (ranging from 66 in UT105 to 140 in UT487) (Supplementary Table  3 ). Where possible, assembly errors were corrected by remapping reads, which considers alignment differences and reads coverage for each genome. Isolates UT487 and UT30 were taken as an example to show the most identified types of assembly errors in the data set (Supplementary Table  4 ).
Functional pan-genome analysis A functional profile of the core-genome was obtained by annotating COG categories. The [R] general function was the most frequently observed (16%), followed by  [I]  Lipid transport and metabolism (8%), [S] Function unknown (8%), [Q] Secondary metabolites biosynthesis, transport and catabolism (7%), [K] Transcription (7%), [C] Energy production and conversion (7%), [E] Amino acid transport and metabolism (7%), and [L] Replication, recombination and repair (5%). Other functional categories were found related to processes of the central metabolism of mycobacteria such as translation, transport biogenesis and metabolism of carbohydrates and coenzymes, among others (Supplementary Figure  4 ). Metabolic pathways were identified in Mtb pan-genome using the KEGG databases.
Phylogenomic analysis of the Mtb lineage-4 The analysis of the discrete characters of the PGM allowed the phylogenomic reconstruction of the isolates of Mtb, capturing the phylogeny implicit in the matrix. The tree showed high branch support values. However, some branches in each of the three main clades showed support values lower than 70% (see Figure  7 ). The tree was rooted with the Mycobacterium canetti species that was used as an "outgroup" genome. The topologies suggest three main clades that represent the evolutionary relationships of the 47 Mtb genomes based on their gene content (Figure  7 ). As the phylogenomic reconstruction was conducted only with genomes of isolates that belonged to the Mtb lineage-4, we wanted to confirm whether the topology of the branches was conserved when adding to the analysis 26 complete Mtb genomes from different lineage [11 lineage-4 (Euro-American), 8 lineage-2 (East-Asian), 4 Lineage-3 (East-African-Indian) y 3 Lineage-6 (West-Africa)] previously downloaded from NCBI database.
Phylogenetic analysis of the pan-genome and SNPs Comparing the topology of the phylogenomic tree from PGM with the genotyping data generated by spoligotyping and 24-loci MIRU-VNTR, the 16 isolates previously genotyped as Haarlem1 SIT62 were part of the same clade, together with a Haarlem1 SIT3022, 14 isolates between Haarlem1 and Haarlem3 with different SIT were part of the second clade and 16 LAM isolates with different SIT made up the third clade (Figure  7 ). The only isolate that did not coincide with genotyping was UT277 Haarlem1 SIT3022, because it was located in the same clade of Haarlem1 SIT62. As spoligotyping and 24-loci MIRU-VNTR tests have a lower level of resolution than whole-genome analysis, the information they provide at the clade and family level is less reliable  (Tatusov et al.
, 2022) , the relative expression of the following cytokine and antimicrobial peptide genes were determined by qPCR using the 7500 real-time PCR system (Applied Biosystems): Interferon (IFN)-g, IL-1b, IL-4, IL8like(L)1, IL8L2, IL-17A, IL-10, and avian beta defensin AvBD10 and AvBD12. The qPCR reaction was performed with 4 ng of cDNA in duplicate using the SYBRGreen Master mix (Applied Biosystems) following 40 amplification cycles (95°C/ 15 s, 60°C/1 min). The relative amount of target gene expression was determined by the 2 ÀDDCt method with beta-actin as the reference gene  (Livak and Schmittgen, 2001). The absence of any genomic DNA contamination was checked by a qPCR on the total RNA. PCR efficiency was between 90 and 110%. Statistical tests were performed using duplicates of the 2 ÀDCt data for each gene in the vaccinated and placebo groups.
Data collected The following variables were extracted: age, gender, place of birth, HIV transmission route, date of HIV diagnosis, Center for Diseases Control (CDC) stage, height, weight measurements, history of combination antiretroviral therapy, CD4 nadir, HIV viral loads. BMI values were calculated for each available weight using the baseline height (weight/height*height, in kg/m 2 ) and classified as follows: underweight (<18), normal (18-24. 9), overweight (25-29. 9) and obese (≥30).
Statistical analysis Descriptive analyses were performed using paired or unpaired t-tests for continuous variables and Fischer's exact test or McNemar test for categorical variables, according to the data and groups compared. Continuous variables are presented as means with standard deviations (SD) or 95% confidence intervals (95% CI), and categorical variables as numbers with percentages for each category. To limit the imbalance between PLWH who switched to TAF and those who remained on TDF, each participant in the Switch group was matched (1:1) on baseline characteristics using a propensity score with a logit link function, using a nearest-neighbour distance with one who remained on TDF  [7]. The baseline characteristics used for the propensity score model were selected according to their clinical interest: gender (man/woman), age (continuous), place of birth (sub-Saharan Africa; France; other), risk factor for HIV infection (heterosexual; homosexual/bisexual; people who inject drugs; other), duration since HIV diagnosis (continuous), duration with TDF-based regimen (continuous), baseline regimen (TDF/FTC/RPV; TDF/FTC/EVG/c), plasma viral load (continuous), BMI (continuous).
These data suggest that multiple immune mechanisms elicited by PvDBPII immunization are associated with protection. The identified immune correlates could guide the development of an effective vaccine for P. vivax malaria. Importantly, all the polyfunctional antibody features that correlated with protection cross-reacted with both PvDBPII SalI and PvW1 variants, suggesting that immunization with PvDBPII should protect against diverse P. vivax isolates.
Both datasets corresponding to either antibody functions specific to PvDBPII SalI (Figure  3A ) or to PvDBPII PvW1 (Figure  3C ) showed significant correlations between most of the variables. We observed positive correlations between C1q binding, FcγR binding, isotypes, antibody recognition titres and binding inhibitory titres in both datasets (Figure  3A  and 3C ). Negative correlations were observed for the affinity and dissociation constants, K D and k dis , in the PvDBPII SalI dataset (Figure  3A ), whereas all three PvDBPII-antibody binding kinetic constants in the PvDBPII PvW1 dataset showed negative correlations with the rest of the dataset (Figure  3C ). The IgG3 and IgM readouts did not show correlations to most of the features of both datasets (Figure  3A  and 3C ). PMR reduction showed a significant correlation in the PvDBPII SalI dataset with antibody recognition titre, binding inhibitory titre, k dis , IgG1, IgG2, IgG4, IgA, C1q binding and FcγR binding (Figure  3A ).
In case of the PvDBPII PvW1 dataset, the PMR reduction showed significant correlations with the variables mentioned above for PvDBPII SalI dataset, K D and avidity (Figure  3C ). We proceeded to identify the variables that have a significant contribution to the PMR reduction outcome in both datasets. The Boruta algorithm selected as important the same 11 readouts in both datasets (Figure  3B  and D ). These important variables include binding inhibitory titres, FcγR binding and C1q binding, IgG1, IgG2, IgG4, k dis and antibody titres. Binding inhibitory titre appeared as the variable with the highest importance in the PvDBPII SalI dataset (Figure  3B ). In case of the PvDBPII PvW1 dataset, this variable also had high importance in the dataset (Figure  3D ). Correlations of the PvDBPII SalI and PvW1 binding inhibitory titres to PMR reduction showed high significance (p ≤ 0.
0014) (Figure  4A ). This feature was also highly correlated when both PvDBPII alleles were compared (Figure  4B ), indicating that immunization with PvDBPII SalI elicits antibodies that cross-react with PvDBPII PvW1. We modeled PMR reduction using a Random Forest regression with binding inhibitory titres specific to the two PvDBPII alleles. The importance of the two features as a PMR reduction predictor was similar and no statistical difference was observed (Figure  4C ). Correlations to PMR reduction for the rest of the variables considered as important by the Boruta algorithm in the PvDBPII SalI and PvW1 datasets are shown in Supplementary Figures  S3  and S4 , respectively. All these correlations to PMR reduction were statistically significant (Supplementary Figures  S3  and S4 ). The unimportant variables of both datasets are also shown in Suplementary Figures  S5  and S6  (PvDBPII SalI and PvW1 datasets, respectively).
In case of the variables specific to PvDBPII SalI, IgA was significantly correlated to PMR reduction but it was not considered as an important predictor by the Boruta algorithm (Supplementary Figure  S5 ). In case the variables specific to PvDBPII PvW1, avidity, K D and IgA also correlated to the PMR reduction but they were not significant predictors as per the Boruta algorithm (Supplementary Figure  S6 ). Next, we compared individually the 10 variables selected as important by the Boruta algorithm between the PvDBPII SalI and PvW1 datasets, with the exception of the binding inhibitory titres, already shown in Figure  4B. We observed significant correlations for all the variables (p ≤ 0. 001) (Supplementary Figure  S7 ), indicating strong cross-reactivity of polyfunctional PvDBPII-specific antibodies that correlate with PMR reduction.
We found that in addition to antibody recognition and binding inhibitory titres, PMR reduction is correlated with k dis , IgG1, IgG2, IgG4, IgA, C1q binding and FcγR binding for antibodies against both PvDBPII SalI and PvW1 variants (Figure  3A  and 3C ). Using the Boruta classifying algorithm, we obtained the same 11 variables that significantly contribute to PMR reduction in both PvDBPII SalI and PvW1 datasets (Figure  3B  and 3D ). Although a statistical analysis cannot definitively prove that a biomarker is causally associated with protection rather than just correlated, the variable that showed the highest importance in the PvDBPII SalI dataset was the binding inhibitory titre (Figure  3B ), which also had high importance in the PvDBPII PvW1 dataset (Figure  3D ). The binding inhibitory titres specific to the two PvDBPII variants were highly correlated (Figure  4B ) and modeled PMR reduction similarly when combined (Figure  4C ).
The optical density was measured immediately at a wavelength of 450 nm (OD 450 ). The reference serum was assigned 200 arbitrary units, AU, and the standard curve from the reference serum was used to fit a four-parameter logistic model using ADAMSEL version 3. 0 software (Ed Remarque© 2021). OD 450 values were later converted to AU using the standard curve and antibody titres of test sera were reported in AU for each sample.
PvDBPII variant SalI or PvDBPII PvW1 were immobilized via His tag at 5 μg/ml for 600 s. A mammalian cytosolic protein, peroxiredoxin 6 (PRDX6)  (49) , was loaded at 3 μg/ml to precharged NTA biosensors for 600 s as a reference biosensor (negative control). Loaded biosensors were tested for binding to test sera in the following steps: baseline (60 s in buffer), association step (600 s in serum dilutions) and dissociation step (600 s in buffer). Two wells containing only buffer instead of sera were assigned as reference wells. Signals from reference wells and reference biosensors were later subtracted. Affinity, association and dissociation constants (K D , k a and k dis , respectively) were calculated by Data Analysis HT software version 10 (Fortebio) using kinetic analysis and a 1:1 binding model.
Acknowledgements Development of PvDBPII as a vaccine candidate was supported by grants from the  Biotechnology Industry Research Assistance Council (BIRAC), New Delhi  and  PATH Malaria Vaccine Initiative. MVDP was supported by grants from the  Bill and Melinda Gates Foundation and Department of Biotechnology (DBT), Government of India. This work was also supported in part by grants from  Agence Nationale de Recherche to CEC  [ ANR-18-CE15-0026  and  ANR-21-CE15-0013-01 ]. CEC is supported by the  French Government 's  Laboratoire d'Excellence  " PARAFRAP " [ ANR-11-LABX-0024-PARAFRAP ] in addition to core funding from  Institut Pasteur. FJM was supported by a  Fellowship  from  Ecole Doctorale BioSPC  ( ED562 ),  F-75006 ,  Université Paris Cité. The authors are grateful for the assistance of the  VAC069 ,  VAC071  and  VAC079  clinical trial teams and all the study volunteers.
The VAC069 and VAC071 trials were funded by the  European Union 's  Horizon 2020 research and innovation program  under grant agreement  733073  for MultiViVax. The  VAC079  trial was funded by the  Wellcome Trust Malaria Infection Study in   Thailand (MIST) program  [ 212336/Z/18/Z ]. For the purpose of open access, the author has applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission. This work was also supported in part by the  UK Medical Research Council (MRC)  [ G1100086 ] and the  National Institute for Health Research (NIHR) Oxford Biomedical.
Patients We undertook a cross-sectional analysis of 1061 patients with CP in this study. This cohort was obtained from the Shanghai Changhai CP Database, in which the patients were prospectively enrolled from 2010 to 2015. 4  Clinical characteristics, which were collected through face-to-face conversations during patients' hospital stays, had been stored in our electronic medical record system. According to the Asia-Pacific consensus report, the diagnosis of CP was based on at least one of the following findings: (1) typical CP histology of pancreatic tissue; (2) pancreatic calcification confirmed by contrast-enhanced computer tomography, magnetic resonance imaging, or endoscopic ultrasound; (3) moderate-to-marked pancreatic ductal lesions on pancreatography obtained by endoscopic retrograde or magnetic resonance pancreatography; and (4) abnormal results of pancreatic function tests.
Statistical Analysis The differences in continuous variables were assessed by means of the Shapiro-Wilk test, which examines whether or not a given variable is normally distributed in a given population. Nonnormally distributed data were presented in terms of the median and interquartile range, and all groups were compared using the Kruskal-Wallis test. Categorical data were presented as frequencies, the differences between groups being compared by means of the c 2 test. Bonferroni adjustment was used to allow for the performance of multiple comparisons. Odds ratios (ORs) with 95% confidence interval (CI) were calculated. All significance tests were 2 sided, a P value <. 05 being considered statistically significant. All statistical analyses were performed using SPSS v22. 0 (International Business Machines Corp [IBM], NY).
However, aBMD is a limited surrogate for femoral strength since it does not account for 3D bone geometry and density distribution. QCT scans allow such 3D reconstruction to build a finite element (FE) model that can deliver improved femoral strength predictions. However, QCT implies a higher radiation dose and higher costs that prevent a systematic usage of this technique for screening purposes. The recently launched 3D-Shaper ® software reconstructs the 3D shape and density distribution from 2D DXA scans and could be a valuable tool if proven reliable. In this Research Topic Dudle et al. presented the first independent evaluation of the software. The authors showed that 3D-Shaper generates an altered BMD distribution compared to QCT but shows an interesting potential for deriving a standardized femoral strength from a 2D DXA scan.
We performed a literature review of studies assessing CYP1A2, CYP2B6, CYP2C9, CYP2C19, CYP2D6, CYP3A, and/or P-gp using drugs from the Geneva cocktail: caffeine, bupropion, flurbiprofen, omeprazole, dextromethorphan, midazolam, and/or fexofenadine. We searched the PubMed database up until April 2021, using logical combinations of the following terms: phenotyping, drug cocktails, clinical pharmacokinetics, each drug of the Geneva cocktail. Case reports were not included.
95%/95%, Tolerance Intervals of Metabolic Indices The PK data reviewed in the literature were used to establish 95%,95% tolerance intervals of phenotyping indices. These tolerance intervals allowed for the determination of "normal" CYP450 and P-gp activities and are presented in Table  1. For fexofenadine, data in the literature showed differences depending on whether it was administered alone or in the Geneva cocktail. Two 95%,95% tolerance intervals were then defined based on whether the phenotyping of P-gp was assessed using fexofenadine alone or in the Geneva cocktail. 2 ). Twenty patients received all the phenotyping probe drugs, while twenty-six patients received only some of them. Most patients (89%, n = 41) had mood disorders. One-fourth were smokers (n = 11), and 40% were obese (n = 18, BMI ≥ 30 kg/m 2 ).