dataesr/scientific-paragraphs-categorization huggingface dataset:
The observed increase in N e could be driven by the increasing population differentiation between North American and European wolves, which would have explained why is not observed when analysing the European dataset only. However, the wide 95% HPD intervals of reconstructed N e changes do not allow us to draw conclusions about the demographic changes with high certainty. Our analyses using Approximate Bayesian Computations showed that we cannot reject the constant population size model for European wolves, although such result may be caused by difficulties to detect moderate bottlenecks using mtDNA with simulation analyses  (Mourier et al. , 2012 , see detailed discussion in SI). Therefore, it may be important to further assess the demographic changes in European wolves using nuclear genomic data.
| Population history of French wolves Recent population history of French wolves is similar to other Western European populations. In the 19th and 20th centuries, European wolf populations declined dramatically as a result of human persecution and the species was extirpated from most of western and northern Europe  (Baillon, 2016; Krofel et al. , 2017). The Alpine wolf population reportedly disappeared at the beginning of the 20th century  (Valière et al. , 2003 and references therein) , although a recent study showed that a few individuals survived in the French Alps up to the mid-20th century  (Dufresnes et al. , 2019). The youngest samples from our dataset are dated to 1944 CE (Central France) and 1954 CE (Southern France), which is consistent with  Baillon (2016) , who reported that wolves in France were observed until the 1970s.
wolves and modern European and Middle Eastern populations. Mitogenome data showed that Southern and North-Western historical French wolves had the lowest genetic distance from modern populations from Balkans and Eastern Europe (F ST : 0. 08-0. 14). Analyses based on the D-loop dataset (which contained a considerably larger number of samples from European populations available from earlier studies) indicated that historical French wolves were generally less differentiated from other historical European/Middle Eastern populations (F ST : 0. 00-0. 63; mean F ST : 0. 10, 0. 17, 0. 47 for Southern, North-Western and North-Eastern France, respectively) than from modern populations (F ST : 0. 09-0. 95, mean F ST : 0. 51, 0. 57 F I G U R E 3 Results of phylogeographic analyses in BayesTraits and RAPS.
Temporal changes in effective population sizes (N e ) in European (a) and Holarctic wolves (b). A-Skyline plot for European wolves (Haplogroups W1 and W2), B-Skygrid plot for wolves from Eurasia and North America (dataset E). The line represents the median, and the grey area represents 95% HPD intervals. Downloaded from https://onlinelibrary. wiley. com/doi/10. 1111/mec. 17054 by Cochrane France, Wiley Online Library on [20/06/2023]. See the Terms and Conditions (https://onlinelibrary. wiley. com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License | 11 DOAN et al.
The results of the research have been shared with the broader public via public databases. AUTH O R CO NTR I B UTI O N S M. P. and A. S. designed the study; K. D. and F. P. performed research, K. D. analysed data; A. S. , G. L. , F. B. , E. B. , E. C. -B. , S. Frère, S. Foucras, A. G. , Ch. G. , A. L. , W. M. , R. P. , S. P. and J. -H. Y. provided samples; A. S. , M. P. and G. L. acquired funding for the project; K. D and M. P. wrote the manuscript with input from all co-authors. ACK N O WLE D G E M ENTS We thank Fédération Nationale des Chasseurs, France and its branches in Bas-Rhin, Meuse, Aude, Seine-Maritime, and Haute Marne for financial support and samples for the analysis. For providing us with samples for the analyses we thank: In France: Musée de la Cour d'or, Metz; Musée zoologique de Strasbourg; Musées départementaux de la Haute-Saône Champlitte; Musée des Confluences, Lyon; Muséum Gabriel Foucher, Muséum d'histoire naturelle de la ville de Bourges, Musée Requien, Avignon; Musée d'Histoire Naturelle et d'Ethnographie de Colmar; Musée des Beaux-Arts et maison Picassiette, Chartres; Museum d'Histoire Naturelle de Grenoble; Musée de l'Aquarium, Nancy; Musée des Légendes et des Métiers de la Forêt d'Orléans, Loury; Museum d'Histoire Naturelle de Nantes, France; Musée et Parc Buffon Montbard; Museum d'Histoire Naturelle du Mans, France; Ecomusée du Montmorillonnais, Montmorillon, Museum des Sciences Naturelles, Angers, Museum d'Histoire Naturelle de Bourges, Musée d'Art et d'Histoire de Toul, Museum Lecoq Clermont-Ferrand, Museum d'Histoire Naturelle Lille, Musée d'Histoire naturelle du Mans, Musée de Montbeliard, Mairie de Montmédy, Mairie de Dugny sur Meuse; Mairie de Lucheux, Muséum d'Histoire naturelle-Jardin de l'Arquebuse de la ville de Dijon.
NHMF, Muséum d'Histoire Naturelle de Neuchâtel, Museum of Natural History Chauds de Fonds, Musée Jurassica Porrentry, Museum d'Histoire Naturelle Genève. We thank Patrice Meniel, Université de Bourgogne; Service régional de l'Archéologie PACA; Olivier Lemercier, responsable d'opération du site néolithique de La Fare (Forcalquier; Alpes-de-Haute-Provence); Christophe Maniquet, INRAP, Service Régional de l'Archéologie de Limoges; Thierry Argant, Service archéologique de Lyon. We also thank private owners of wolf specimens: Bernard Barrat, Pierre de Chabot Tramecourt, Denise de Contal, Catherine Boursier Mougenot, Bernadette and Georges de Romémont, Christine de Neuville, François d'Hamonville, and Château de Dinteville. This research was funded by the National Fédération of Hunters, France. KD and MP were supported by the Polish National Agency for Academic Exchange (NAWA, Polish Returns Fellowship PPN/ PPO/2018/1/00037).
French wolves and modern European and Middle Eastern populations. Mitogenome data showed that Southern and North-Western historical French wolves had the lowest genetic distance from modern populations from Balkans and Eastern Europe (F ST : 0. 08-0. 14). Analyses based on the D-loop dataset (which contained a considerably larger number of samples from European populations available from earlier studies) indicated that historical French wolves were generally less differentiated from other historical European/Middle Eastern populations (F ST : 0. 00-0. 63; mean F ST : 0. 10, 0. 17, 0. 47 for Southern, North-Western and North-Eastern France, respectively) than from modern populations (F ST : 0. 09-0. 95, mean F ST : 0. 51, 0. 57 F I G U R E 3 Results of phylogeographic analyses in BayesTraits and RAPS.
F 0 , 0 Temporal changes in effective population sizes (N e ) in European (a) and Holarctic wolves (b). A-Skyline plot for European wolves (Haplogroups W1 and W2), B-Skygrid plot for wolves from Eurasia and North America (dataset E). The line represents the median, and the grey area represents 95% HPD intervals. Downloaded from https://onlinelibrary. wiley. com/doi/10. 1111/mec. 17054 by Cochrane France, Wiley Online Library on [20/06/2023]. See the Terms and Conditions (https://onlinelibrary. wiley. com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License | 11 DOAN et al.
Data set A total of 445 publicly available genomes of Y. pestis sampled between 1908 and 2014 from humans, rodents, fleas and shrews, were recovered from the online database EnteroBase (S1 Table)  [19]. 440 genomes were sampled from Madagascar, three from Turkey and two from India. The sequences were searched using the Fields "Species" (Yersinia pestis), AND "Country" (Madagascar) OR "Country (India) OR "Country (Turkey). Indian sequences that did not belong to the 1. ORI2. a sub-branch were removed, as well as sequences coming from samples that were cultured in laboratories for extended periods of time as they likely present an excess of mutations. All the assembled sequences can be downloaded from EnteroBase after creating an account. All sequence reads are publicly available in the Sequence Read Archive (SRA) of the NCBI (S1 Table ).
Sequence generation The 411 Y. pestis isolates from Madagascar together with epidemiological data were sent to the Institut Pasteur (Paris, France) for genomic sequencing. Isolates were grown in LB broth at 28˚C for 24 h. Genomic DNA was extracted using PureLink Genomic DNA Mini Kit (Ther-moFisher Scientific, Waltham, MA, USA) according to manufacturer's instructions. 100 nucleotides paired-end library were prepared and sequenced on an Illumina platform HiSeq2500 (Illumina, San Diego, CA, USA). The sequences were uploaded to EnteroBase between the years 2015 and 2017. The remaining 34 sequences were generated in previous studies  [3, 9, 24].
SNP analysis The EnteroBase Toolkit (EToKi) was used to align the sequences against the Y. pestis reference genome CO92 (accession number AL590842. 1), to produce a SNP matrix. In brief, EToKi align module adopted minimap2 to align each genome against the CO92 genome, and extracted SNPs in the aligned regions. The same module also identified repeat regions including CRISPR regions, tandem repeats and interspersed repeats in the CO92 genome using PILER-CR, TRF and a self-comparison by minimap2, respectively. Any SNPs in the repeat regions, or in a region that was shared by �95% of genomes were discarded. The resulting core genomic, non-repetitive SNP matrix contained 783 sites representing 143 different genotypes (S2 Table ). While the genotype of the Indian sequences had been determined by a previous study  [3] , the presence in the matrix of 226 previously described SNPs related to 1.
ORI3 (S3 Table ) confirmed the genotype of the remaining sequences. This entire matrix was used to generate a time calibrated phylogeny of Y. pestis. For the phylogeographic analysis only sequences sampled from humans were used to avoid possible bias introduced by having samples from hosts with different travel ranges and capacities. This human-specific data set consisted of 300 sequences collected between 1964 and 2012, including 183 from the Central Highlands and 117 from Mahajanga. Sequences with duplicated genotypes were kept in the datasets as they inform the evolutionary and demographic processes that have occurred through time. However, identical sequences do not contain information regarding the topology and branch lengths of a subtree of identical sequences, those are the result of the underlying stochastic process of tree generation combined with the branch rate variation estimated from the entire data set  [25].
Phylogeographic analysis of Y. pestis The phylogeographic analysis was done with the BEAST 2 package bdmm  [30] , which uses a structured (also called "multi-type") birth-death model for the tree-generating process. Under this approach each sequence is assigned to a "type" or subpopulation and the type changes are estimated, following a type change rate m, jointly with the phylogenetic reconstruction. This analysis produces a multi-type tree, in which lineages of different types are annotated by different colors, and the migrations (type changes) are indicated by color changes occurring along the branches. Migration rates per lineage per year and the diversification-extinction ratio, which represents the number of new lineages generated per year and is also known as the reproductive number (R) in epidemiological contexts, are also estimated for each type.
The rate parameter for becoming uninfectious (δ) was fixed to a value of 1 to avoid identifiability issues with the other parameters  [33]. The output files were combined with the software LogCombiner  [34] , after removing a 10% burn-in. This yielded effective sample sizes above the standard threshold of 200 for all parameters, as assessed by the software Tracer v. 1. 7  [35]. The maximum a posteriori tree (MAP), which represents the tree with the greatest posterior probability in the combined runs, was used for the visual representation of the migrations between foci, as it can display the type changes along a branch. The tree was visualized and edited with FigTree 1. 4. 4  [36]. As migration times on the MAP tree represent only point estimates, the median and 95% posterior intervals of migration times were calculated for each migration using a custom R script.
Molecular dating analysis The estimation of divergence times was done using a relaxed lognormal molecular clock, the HKY model of nucleotide substitutions and the Bayesian birth-death skyline tree prior implemented in the BEAST 2 package BDSKY  [33]. The birth-death skyline model follows a forward in time birth-death process with only one lineage existing at time 0 in the past. As we move closer to the present, the lineages can split (birth event) at a rate λ, go extinct (death event) at a rate μ, and be sampled at a rate C. These rates are reparametrized to estimate parameters like the sampling proportion (s), the become uninfectious rate (δ) and the diversification-extinction ratio  [33]. The sampling proportion prior was specified per year, and tip date sampling and the correction for ascertainment bias were also applied in this analysis.
The rate parameter for becoming uninfectious (δ) was also fixed to a value of 1. Three replicate analyses were done with a MCMC length of 480 million states, and output files were later combined with LogCombiner, after removing a 10% burn-in. All parameters obtained effective sample sizes above 200. A maximum clade credibility tree was obtained for the combined runs using the program TreeAnnotator  [37]  with a 10% burn-in. The tree was edited with FigTree 1. 4. 4. The BEAST 2 XML files, output log files and the MAP and MCC tree files are provided as supporting information (S1-S6 Appendices).
While the results found here support previous claims of a reintroduction of Y. pestis to Mahajanga from the Central Highlands, it is also possible that additional migrations of this pathogen occurred from the Northern Highlands, the third plague focus in the country, and from other regions of Madagascar. In this regard, a recent study detected positive samples of Y. pestis in fleas sampled in the southwest part of the country  [40]. While most of the samples were isolated from a flea species (Echidnophaga gallinacea) not suitable to be an efficient vector, the presence of the pathogen in an area far away from the plague foci suggests that the transmission of Y. pestis among non-human hosts is more widespread than previously thought. However, hypotheses of Y. pestis migrations to or from regions other than the Central Highlands couldn't be tested with our dataset given the limited number of available sequences (<10) sampled outside the two main foci discussed here.
Acknowledgments We would like to thank  Mark Achtman  and  Elisabeth Carniel  for supervising the acquisition of the sequences and for their feedback during the project conceptualization. We thank  Michelle O'Reilly  for providing the Madagascar map for Fig 1. We also thank  Johannes Krause ,  Kirsten Bos ,  Alexander Herbig  and  Maria Spyrou  for their valuable inputs and suggestions for the analyses, and  Sanni O ¨versti  and  Ariane Weber  for their comments on the manuscript.
Despite PvETRAMP11. 2 being a well characterized antigen, its genetic variability has not been studied. Therefore, the aim of this study was to characterize the genetic variability of pvetramp11. 2 in P. vivax field samples of parasite populations collected from two endemic areas of the Brazilian and Peruvian Amazon. This study was conducted within the framework of a project supported by the European Commission project (EC_EraNet FP7 ELAC2015/T08-1061; acronym SEROVIVAX), aimed at developing inter-country standardized serosurveillance tools to accurately monitor changes in P. vivax transmission intensity in Amazonian regions. In addition, we analyzed allelic variants, using pvetramp11. 2 from publicly available sequences of clinical isolates from different malaria endemic regions worldwide.
Sequence data set The sequence data set included in the present study comprises novel sequences from Brazilian (n = 68) and Peruvian parasite samples (n = 36) and additional sequences of pvetramp11. 2 (n = 104) from parasite samples collected in different geographical areas (Peru, Mexico, Colombia, China, Myanmar, Thailand and Papua New Guinea) that were downloaded from the PlasmoDB  [25]  database, with the overall aim to provide an overview of pvetramp11. 2 genetic polymorphisms in populations from different malaria endemic areas of the world (Additional file 1: Text S1). Thepvetramp11. 2 sequence of the curated reference strain PvP01 (PVP01_0422600) was used as the query to retrieve the FASTA files from the plasmoDB database, in the section "genetic variation. " Only pvetramp11. 2 sequences with no ambiguity in terms of nucleotides or gaps were selected, including from Peru (n = 26), China (n = 10), Mexico (n = 15), Papua New Guinea (n = 4), Colombia (n = 23), Thailand (n = 14) and Myanmar (n = 12).
The Peruvian sequences retrieved from Plas-moDB were kept separate from those of the Peruvian population sequenced in the present study and named Peru_PDB. The P. vivax Salvador-1 etramp11. 2 sequence (Sal-1; PVX_003565) was used as reference for the alignments. A total data set of 208 sequences was generated. Nucleotide sequences were aligned using the ClustalW multiple sequence aligner in Accelrys Gene v2. 5 (Accelrys Inc. , BIOVIA, San Diego, CA, USA) and CLC-Main workbench v21 software (Qiagen, Hilden, Germany).
Population genetic analysis Statistical analyses were carried out using DnaSP v6  [26] , DnaSP v5  [27]  and GenAlEx v6. 5. software  [28, 29]. The number of alleles (H), segregating sites (Ss), allele diversity (Hd), singletons (Si), nucleotide diversity (Pi), average number of nucleotide differences (K), synonymous (Syn) and non-synonymous (N-Syn) mutations were calculated to infer population diversity. Sliding window analysis of nucleotide diversity (Pi), with a window size of 10 bp and step size of 5 bp, was performed for all the populations (Papua New Guinea represented by only 4 samples and Thailand with only 1 allele were excluded from the analysis). Tajima's D, Fu and Li's D* and F* test statistics, carried out using DnaSP v5, were used to assess the selective pressure on the gene.
Protein analysis The sequences from the total data set (n = 208) were translated into amino acid sequences applying the standard genetic code with DnaSP v6. Deduced proteins from reference sequences Brazil I, India VII, Mauritania, Chesson, Belem, IQ07 and PVP01 and field samples from parasite populations collected in Madagascar (M08, M15, M19) and Cambodia (C08, C15, C127) retrieved from the PlasmoDB database were added to the analysis.
Codon usage analysis The extent of codon preference was estimated by the effective number of codons value (ENC), the measure that quantifies the effective number of codons used in a gene  [34] , and the Codon bias index (CBI), the measure of the extent to which preferred codons are used in a gene  [35] , using DnaSP v6 software. ENC values range from 20 to 61 (from maximum codon bias to no codon preference). CBI values range from 1 (maximum codon bias) and 0 (uniform use of synonymous codons). An ENC value < 40 is commonly treated as evidence of a strong codon usage bias  [36]. The Fast Unconstrained Bayesian AppRoximation (FUBAR) method  [37]  implemented in the Data Monkey Web Server was applied to identify codons targeted by selection, setting a posterior probability of 0. 9 (default value) for significant levels of codon deviation from neutrality.
Genetic diversity of P. vivax etramp11. 2 in different geographical regions A panel of 208 sequences was used to evaluate the worldwide genetic variation of pvetramp11. PlasmoDB confirmed the presence of all nine SNPs detected in the Brazilian and Peruvian samples evaluated in this study. Only two additional synonymous mutations were observed, C66T in samples from Colombia and Peru_PDB and A96G in samples from Colombia, which defined two additional alleles, H4 restricted to Colombia (13%) and H5 shared among Peru_PDB (11. 5%) and Colombia (8. 7%) (Table  1 ). The analysis of the total data set showed the presence of 11 alleles. Samples from the Latin American region included all alleles, while those from the SEA/PNG region included five alleles. The reduced allele diversity found in the SEA/PNG populations could also be a consequence of the low number of samples available for the analysis.
Funding This work was done in the framework of and supported by the  SeroVivax  project,  European Commission   FP7   ELAC2015/T08-1061. Field work in Brazil was supported by the  Ministry of Health of Brazil  ( 404067/2012-3 ) and the  Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP ,  05/51988-0 ,  07/51199-0  and  16/50108-0 ). Field work in Peru was supported by the  Consejo Nacional de Ciencia, Tecnología e Innovación Tecnológica  (Grant Number  008-2014-FONDECYT ) and the  Académie de Recherche et d'Enseignement Supérieur-Commission de la Coopération au Développement of Belgium  (Grant Number  ARES-CCD ,  PRD-Peru 2014-2019  to NS, ALC, and ARA). ARA is a Postdoctoral Researcher of the  Fonds de la Recherche Scientifique (FNRS, Belgium).
2. The data set included pvetramp11. 2 sequences from Brazil (n = 68) and Peru (n = 36) generated in the present study, as well as additional pvetramp11. 2 sequences (n = 104) retrieved from the PlasmoDB database, including sequences from Peru_PDB (n = 26), Colombia (n = 23), Mexico (n = 15), Thailand (n = 14), China (n = 10), Papua New Guinea (n = 4) and Myanmar (n = 12). Analysis of the sequences retrieved from.
For additional insights, we used AlphaFold2 (AF2)  17  to predict the structure of the ACAD9-ECSIT complex using sequences matching our constructs. The AF2 model predicted the binding site to contain ECSIT residues 316-338 and closely resembled the experimental structure (RMSD of 1. 635 Å over 552 Cα atoms; Supplementary Fig. 6A-C ). No additional ECSIT residues were predicted to contact ACAD9, suggesting that most, if not all, of the specific interactions between these proteins were captured in the cryo-EM structure. Indeed, we found that a synthetic peptide spanning ECSIT residues 318-336 was sufficient to cause the deflavination of ACAD9 (Supplementary Fig. 2B ), confirming that these residues are crucial for complex formation. Notably, the full length AF2 model available from the database predicts that ECSIT comprises two flexibly linked globular domains, an N-terminal domain (res.
G ACAD9 S191A appears a more stable dimer than ACAD9 WT (Supplementary Fig. 1B ) and forms an ACAD9 S191A -ECSIT CTER complex. Experiments were repeated thrice with similar results. H DLS assays show that all ECSIT mutants show a reduced average MW (130-150 kDa) in comparison to the ACAD9 WT -ECSIT CTER complex, indicating partial complex formation but a reduction in stability. ACAD9 S191A has a MW very similar to ACAD9 WT , however, in complex with ECSIT CTER , there is the formation of higher MW species. Blue and red dashed lines indicate the expected MW for ACAD9 WT -homodimer and ACAD9 WT -ECSIT CTER complex, respectively. Data are presented as mean values ± SD of at least n = 3 biological replicas. I ITC binding assay for the binding affinity between ACAD9 S191A and ECSIT CTER. The equilibrium dissociation constant (K D ) of the ACAD9 S191A -ECSIT CTER complex is 3.
93 μM, ~3-fold lower than that of ACAD9 WT -ECSIT CTER (Supplementary Fig. 2A ). The experiment was repeated thrice with similar results. Source data are provided as a Source Data file. essential for ECSIT binding and MCIA formation (Supplementary Videos 1 and 2). Similarly to the ACAD9 WT -ECSIT CTER dataset, residues 455-485 could not be built into the ACAD9 S191A map. However, increasing the threshold of the unsharpened ACAD9 WT map reveals some density at the base of ACAD9 S191A model, hinting at their possible position. Thus, we fitted the missing residues into this signal, using the coordinates of the ACAD9 AF2 model where residues 455-464 and 475-492 form flexible loops, sandwiching a short α-helix (res. 465-474). The lack of definition in the density points to a mobility of this region (Supplementary Fig.