dataesr/scientific-paragraphs-categorization huggingface dataset:
The tree with the highest log likelihood (-1732. 50) is shown. Evolutionary analyzes were conducted in MEGA X software. A total of 30 sequences obtained from specimens of this study are shown in bold, including 3 randomly selected sequences for the species for which we obtained a number of sequences ≥3, all the sequences for the specimens for which other species <3 obtained. acquisition by laboratories with insufficient means  (Sevestre et al. , 2021a). However, the cost for analyzes is very low, and these machines may be purchased for technological platforms and used in the fields of microbiology, mycology, and agricultural entomology (medical, entomological, and even agricultural). The arthropod spectra database must be large enough and regularly updated to correctly discriminate between certain closely related species.
The creation of the database requires expertise in entomology and molecular biology analyzes, the development of the protocol (body part of the arthropod, procedure) is essential when it has never been done for this arthropod. On the other hand, the preservation mode (ethanol, frozen, fresh) and storage duration of the specimen  (Benyahia et al. , 2021) , the compartment of the arthropod used can impact the appearance of the profile within the same species  (Ngoy et al. , 2021; Nebbak et al. , 2017; Kumsa et al. , 2016). In a MALDI-TOF MS database, it is not necessary to have a library of spectra of local tick species. Once available and trained, the method can recognize the species from anywhere in its distribution. For example, an R. sanguineus s. l. collected in Algeria or in Cameroon will be correctly identified using a database containing spectra of R.
sanguineus s. l. from another country, such as in our work, even without adding spectra of this species in the database. However, the identification score may be lower. It is therefore important to continuously enrich a database with spectra of new specimens from different geographical origins. Moreover, to compare to an existing database, the same method for obtaining the MS spectra from arthropods must be followed including, the same preservation mode of the specimens, same body part used, same grinding condition. , which are parameters that can vary the MS profiles within species. One other potential limit is how the presence of microorganisms (microbiome or contamination) in ticks can affect the results of species identification. For example, the differences observed in bed bugs of different geographical origin, are not definitively explained but could be also due to the microbiome or bacterial carriage.
(Yssouf et al. , 2015). Another limitation of this tool observed in the study is the inability to separate the species of the Ha. leachi group. After sequencing the five Ha. leachi specimens added to the MALDI-TOF MS database, we obtained two different groups of sequences. This difference made us recheck the morphological identifications in our collection. The electron microscope allowed us to identify two different species: Ha. muhsamae and Ha. paraleachi. Seven other specimens were randomly selected and submitted to molecular biology to identify species in this group and to assess the ability of a MALDI-TOF blind test in the differentiation between these two species (Ha. muhsamae and Ha. paraleachi). Blind test results did not reveal a clear distinction among the Ha. leachi group. But this could be the result of the low number of the specimens tested.
However, some specimens of engorged Rhipicephalus spp. showed a similarity of 95. 31% with the GenBank sequence of R. sanguineus (KR870984). This suggests that it may be a different species from the one to which the sequence corresponds. Indeed, it is different from the remaining sequence of R. sanguineus already available in the GenBank (KX793744/KT382447), for which our own R. sanguineus sequences had 99. 51% identity  (Hornok et al. , 2017; Zemtsova et al. , 2016). However, in some cases, it is known that the threshold of variability of the 16S gene should not reach 5% within a species  (Dantas-Torres, 2018). Some studies show that there is at least three lineages of R. sanguineus: temperate lineage, tropical lineage and south-east lineage  (Bakkes et al. , 2020; Nava et al. , 2018). Although our MALDI-TOF database does not have the spectra of all R.
sanguineus lineages, before adding in the database, the R. sanguineus s. l. from Cameroon (identified by molecular biology as belonging to the tropical lineage) matches with those from Mali and Vietnam, of tropical lineage, with low scores. Thus, the MSP might be useful to differentiate R. sanguineus lineages, although more work is needed. In addition, interrogating the GenBank database with 16S and 12S rRNA genes from the specimen identified as Ha. paraleachi (OP698036, OP698031, OP698032, OP698033) revealed, respectively, 96. 57% and 97. 05% identity with the sequences of Ha. elliptica (HM068961 and HM068953). Such a result must be interpreted in the absence of Ha. paraleachi sequences in the GenBank. Interestingly, according to  Camicas et al. (1998) , Ha. elliptica and Ha. paraleachi belong to the same Ha.
However, the 16S gene sequence of I. rasus (KY296972) is present in GenBank  (D'Amico et al. , 2018) , but our sequence did not match it because the primer sequence is not the same and therefore does not target the same genome fragment. Within 2 other ticks morphologically identified as Ixodes spp. , one was recognised as I. rasus (OP718638) both by MS and molecular biology. The last one was not recognised by MS but had a sequence different from the sequence of I. rasus and cannot be definitively identified. This result is not surprising, especially since, Afrotropical Ixodes sequence representation in GenBank is severely lacking. Which limits any kind of data integration effort to evaluate taxonomic accuracy using multiple lines of evidence.
Conclusion MALDI-TOF has once again shown in this study its reliability as a rapid and efficient tool for the identification of the alcohol-preserved tick. This emerging tool is of particular interest as it acts as one of the multiple lines of evidence in the "integrative taxonomy". This tool was used to correct the morphological identification of seven ticks, and to identify some engorged ticks that were not morphologically identifiable. Three specimens of the 31 engorged Rhipicephalus spp. , and one of the 2 Ixodes spp. could not be identified by morphology and molecular biology, were identified by MALDI-TOF by adding one spectrum of the three in the database. This highlights capital importance of morphological identification in an integrative approach to taxonomy. After this work, the spectra of the ticks which have been identified by MALDI-TOF MS could used to update the DB as Creating MSP using higher number of spectra would generate more reliable and robust reference.
Acknowledgments This study received financial support from the  Institute Hospitalo-Universitaire (IHU) Méditerranée Infection. We express our thanks to the  Vectors Borne Diseases Laboratory of the Research Unit for Biology and Applied Ecology (VBID-RUBAE) of the University of Dschang  for their assistance. We are grateful to the  Divisional Delegation of the Ministry of Livestock, Fisheries and Animal Industries, West Region  for connecting us with farmers and facilitating access. We also thank the availability of breeders who kindly helped us to access the cattle and local guides to facilitate the work on the field.
Funding information This study was supported by the  Institut Hospitalo-Universitaire (IHU) Méditerranée Infection , the  National Research Agency  under the " Investissements d'avenir" programme , reference  ANR-10-IAHU-03 , the  Région Provence Alpes Côte d'Azur and European ERDF PRIMI  funding. YNY received a grant of PhD scholarship from  IHU Méditerranée Infection. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
A HMM hypothesizes that the observable states of the system being modeled, which can be described by empirical measurements, evolve according to an underlying unobservable hidden stochastic process. Moreover, the transition from one state to the next is assumed to depend only on the current state and a set of fixed probabilities, with no influence from the history of the system. Here, our observables are the step lengths (ℓ) and turning angles (θ), characteristic of the individual parasite trajectories. The hidden processes can be regarded as the behavioral (inner) state of the individual, expected in the present case to reflect the reported high-motility and low-motility modes of sporozoites. The trajectories of INV and NINV were analyzed using the moveHMM package in R  17. Two hidden states were identified independently of the invasive phenotype (Fig.
Of note, in our experimental setup, while searching for a BV, sporozoites move in a nearly two-dimensional path in the upper dermis, with thickness (30 µm) considerably smaller than the tissue surface extension (320-664 × 320-901 µm). Taking into account all the above considerations, the HMM track analysis was further refined to test our hypothesis (see ref. 28 and "Methods"). For each INV and NINV trajectory, segment stretches presenting high-and low-motility modes as assigned by the HMM state analysis were identified (Fig. 2a ). The corresponding state alternations over time are depicted in Fig. 2b  for four illustrative examples. Step lengths, defined as the distance traveled between two consecutive direction changes in one dimension, were next computed for each track  22  and combined in four groups to generate larger datasets for statistical analysis (INV St1/low-and St2/high-motility and NINV St1/ low-and St2/high-motility).
3c, d, c3 ). Cluster 2 (c2) parasites displayed a mixed behavior of c1 and c3 parasites (Fig. 3c, d, c2 ). Altogether, the data reveal a strong superdiffusive component in a long-, medium-and short-time scale range, respectively, for the forward-moving (c1), forward-meandering (c2) and circularly meandering (c3) sporozoite populations. For extra analysis regarding the MSD, see Supplementary Figs. 5, 6 , and 7 and the accompanying discussion in Supplementary Note 3. The superdiffusive Lévy-like highmotility mode allows for probing a large portion of the search space, compatible with a global search exploration for BV. On the other hand, subdiffusive behavior is clearly associated with circular motility as observed in INV sporozoite moving around vessels or c3 NINV, suggesting that parasites are performing local exploitation, which allows refining the search to detect targets scattered in a restricted area.
Identification of high-and low-motility states Each individual x and y time series were independently analyzed with the moveHMM package (version 1. 0. 1) of the R software  17  with the goal of identifying two different motility states. For each state, the initial reference parameters of mean step length, standard deviation, mean turning angle and angle concentration, drawn from a von Mises distribution of turning angles, are taken as: i. Mean step length: 0. 1 (St1), 0. 5 (St2); ii. Standard deviation of step length: 0. 1 (St1), 0. 5 (St2); iii. Mean turning angle: pi (St1), 0 (St2); iv. Angle concentration: 1 (St1), 1 (St2). The initial parameters are not critical in obtaining the results. In fact, they are progressively fitted by the HMM algorithm until reaching a convergence from the maximum likelihood function.
1. Datasets were tested either with a Kruskall-Wallis test followed by Dunn's multiple comparison, a two-tailed Fisher's exact test or twotailed Mann-Whitney U test with a confidence level of 95%. Graphs were generated with the softwares GraphPad Prism (version 9. 5. 1) and R (version 4. 2. 2). Illustrative simulations were obtained with the R adehabitatHR package (version 0. 4. 20), and cartoons were created with BioRender. com. Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article. Open Access This article is licensed under a Creative Commons Attribution 4. 0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Acknowledgements We would like to thank the team of the  Center for Production and Infection of Anopheles (CEPIA, C2RA, Institut Pasteur)  for providing mosquitoes, the staff of the  Central Animal Facility (C2RA, Institut Pasteur)  for animal care and breeding, the staff of the  Photonic BioImaging platform (UTechS PBI ,  C2RT ,  Institut Pasteur)  for providing training and access to spinning-disk microscopes. We are grateful to  Joana Tavares  for sharing reagents and expertise in intravital imaging,  Jean-Yves Tinevez  for insights on image analysis automation,  Dr Gandhi Viswanathan  for extensive discussions and critical reading of the manuscript and  Dr Robert Ménard  for continuous support. This work was supported by funds from the  Institut Pasteur , the  Agence National de la Recherche (ANR, French National Research Agency)/Deutsche Forschungsgemeinschaft (DFG, German Research Foundation )-project number SporoSTOP  ANR-19-CE15-0027 , and the  French Government 's  Investissement d'Avenir program ,  Laboratoire d'Excellence Integrative Biology of Emerging Infectious Diseases- project number  ANR-10-LABX-62-IBEID (R.
We will validate presumed gene product function from reviews and Gene Ontology on the UniProt platform  [33]  and the National Center for Biotechnology Information (NCBI) Gene database  [34]  before assigning their function (e. g. , negative regulator) and level of action (e. g. , downstream to receptors). Discrepancies will be resolved through consensus by three authors with expertise in interferon biology (BR, SM, DH). From this preliminary list of genes, we will only include those with ≥1 variant associated with a Mendelian disease through any effect on protein function to strengthen their biological relevance. We will search the Online Mendelian Inheritance in Man (OMIM)  [35]  and the NCBI ClinVar  [36]  clinical annotation databases for genotype-phenotype correlations. We will validate that all top 21 type I interferon-inducible genes in systemic lupus erythematosus are included in our list, and add missing items  [37].
Function Transcript Effect Estimator (LOFTEE) and the Mendelian Clinically Applicable Pathogenicity score (M-CAP v1. 4). The LOFTEE filtering criteria will be used to annotate non-missense predicted LOF variants, as it provides a conservative filtering strategy to increase specificity (e. g. , removal of variants predicted to escape nonsense-mediated decay) and was used to annotate variants in the Genome Aggregation Database (gnomAD; a public resource of ~126k high-quality exomes from around the world that does not include UKB data)  [41]  and Genebass (a public resource of exome-based genotype-phenotype associations within the UKB)  [42]. The M-CAP score will be used to interpret pathogenicity and nominate missense variants for inclusion  [43]. This supervised learning classifier incorporates nine established pathogenicity likelihood scores (namely SIFT and PolyPhen-2) and achieves substantial reduction in the misclassification rate of known pathogenic variants (<5%) as compared to other existing methods (26-38%)  [43].
The SAIGE-GENE+ method builds upon SKAT-O to reduce type I error inflation for very rare variants in large biobanks with unbalanced case-control ratios, reduce computational resources and account for sample relatedness  [57]. We will use a relatedness coefficient cut-off of ≥0. 125 (up to third-degree relatedness) in SAIGE-GENE+, and perform our analyses using the open-source R package SAIGE (https://github. com/saigegit/SAIGE). We will include the first 10 genetic principal components in the gene-level (combined with the generalized mixed model approach in SAIGE-GENE+) and RVGRS models to control for population structure  [58] , in addition to adjusting for age and sex  [59]. We will also adjust for scanner site in neuroradiological analyses to control for potential technical confounding  [60].
Study Design. A cross-sectional study was conducted in women aged 16-45 years, attending ANC for the first time and the maternity ward for delivery in AH and BH from November 2016 to March 2019. Study participants had positive urine pregnancy test, ultrasound scan of foetus, and were HIV negative. Women with history of a debilitating condition in pregnancy and those receiving anthelmintics and malaria treatment prior to enrolment at ANC were exempted from our study. After consent was given by participants, questionnaire data was captured using CSPro (V6. 2)-based electronic tablets while other relevant disease prevention tools that guided the study were gathered from hospital records. Each participant provided urine and blood samples for S. haematobium and P. falciparum diagnosis, respectively.
Acknowledgments We thank the  Ghana Health Service  for granting us the permission to work in the study hospitals. Our gratitude goes to administrative and medical staff of the  Battor Catholic Hospital and Adidome Government Hospital  for their support in recruiting participants for this study. We are indebted to our study participants for their cooperation. The team members of the  MAHEVA  project at the  Noguchi Memorial Institute for Medical Research, College of Health Sciences, University of Ghana (NMIMR) , are also acknowledged for their contribution towards the success of the project. Our sincerest appreciation goes to the  NMIMR  for providing the laboratory space and logistics to conduct this research. We acknowledge funding support from  IRD  (JEAI Malaria-helminth co- infection in Southern Ghana: Issues and challenges in vaccine development against placental malaria (MAHEVA) ),  International Joint Laboratory (LMI- CONSHELM) , and the  French Embassy in Ghana.
Based on the clinical and laboratory findings, an NIH score was determined for each patient according to the National Institutes of Health (NIH) HIES rating system  [18]. The DNA samples were sent to the Laboratory of Human Genetics of Infectious Diseases, France, and Research Center for Immunodeficiencies, Iran, and genetic studies by whole-exome sequencing (WES) were performed to identify underlying genetic defects. A questionnaire was prepared and filled with patients' data including demographics, molecular and clinical features. All statistical analyses were performed using SPSS software (v. 26. 0, Chicago, IL). Written informed consent for participation in this study was obtained from patients or their parents. This study was performed in accordance with the Declaration of Helsinki and was approved by the Ethics Committee of Shahid Beheshti University of Medical Sciences (Approval Code: IR.
The preferential selection of clustered public TCRs that could represent over 8% of the sampled repertoire (Fig. 1g ) raises the question of their specificities  13. As the main function of CD8 + T cells is cytotoxicity towards virally infected cells, we investigated whether the clustered TCRs could be associated with virus recognition. We curated databases of βCDR3s specific for human infectious pathogens  14, 15  to retain only those 5,437 that had been identified by tetramer-based selection, i. e. binding to a soluble HLA bound to a defined peptide. We detected an enrichment of these βCDR3s in CD8 + versus DPCD3 + thymocytes (p<0. 0001) and in clustered versus dispersed CD8 + thymocytes (p<0. 0001) (Fig. 2a  and b , Supplementary Figure  10. , Supplementary table  2 ). Moreover, these virus-specific βCDR3s were significantly enriched within βCDR3s with the highest Pgen and node degree (Fig.
2g , Supplementary table  3 ). Altogether, these results indicate that the selection of clustered TCRs with high generation probabilities corresponds, at least in part, to the selection of virusassociated TCRs whose CDR3s are remarkably conserved between individuals independently of their HLA. (12), in grey for βCDR3s with unknown specificity or in red for those with a virus specificity. e-g. Identification of virus-specific TCRs from single-cell sequencing dataset  16. e. Mean percentages of virus-specific aCDR3s (*p=0. 0496, Mann-Whitney test, mean ± s. e. m. ). f. Mean percentages of virusspecific βCDR3s (****p<0. 0001, Mann-Whitney test, mean ± s. e. m. ). g. Overlay of the βCDR3 network of CD8 + thymocytes from one individual with virus-specific βCDR3s identified in single-cell sequencing datasets  16.
3a ), while some others did not (Fig. 3e , red arrow). As for the HLA-matched dextramers, single TCRs were found to bind HLA-mismatched dextramers loaded with distinct unrelated peptides from EBV (Fig. 3f ) and some TCRs even bound HLA-mismatched dextramers loaded with epitopes from different viruses such as CMV, HIV, HPV, HTLV-1 or influenza (Fig. 3g ). There were also mostly positive correlations for the binding to different HLA-mismatched dextramer specificities (Fig. 3h ). Altogether, within a dataset of >160,000 single CD8 + T cells, among the 66,191 that did bind dextramers, 24,083 could bind more than one viral-derived peptide from either the same or different viruses, and presented by HLA-matched or even HLA-mismatched dextramers. Thus, there are pleiospecific CD8 + T cells (psT cells) whose TCRs are diverse and bind HLA class-I based dextramers, but are not strictly constrained by HLA matching and the presented peptide.
4 c  & d ); (ii) they are highly connected to virus-specific sequences from databases (Fig. 4  e & f) and (iii) many harbour a TCR assigned to at least two specificities from CMV, EBV or Flu. Thus, psT cells migrate to the sites of primary antiviral immune responses. Identification of unconventional T cells responding to influenza vaccination. The identification of a and β CDR3 significantly recruited following influenza vaccination was performed using the TCRNET algorithm  22, 23  for one representative individual. e. Node degree a and β CDR3 of TCRs from bronchoalveolar lavage (BAL) of COVID-19 patients  24  and from control repertoire clustered with virusspecific a and β CDR3 from databases  [14] [15] [16]. Each node represents a single CDR3, the colour of which represents its degree (log scale).
Sequencing was performed with Hiseq 2500 (Illumina®) SR-300 protocols using the LIGAN-PM Genomics platform (Lille, France). TCR deep sequencing data processing FASTQ raw data files were processed for TRB sequence annotation using MiXCR 37 software (v2. 1. 10) with RNA-Seq parameters. MiXCR extracts TRBs and provides corrections of PCR and sequencing errors.
Network generation and representation To construct a network, we computed a distance matrix of pairwise Levenshtein distances between CDR3s using the "stringdist" 38 R package. When two sequences were similar under the defined threshold, LD>1 (i. e. , at most one amino acid difference), they were connected and designated as "clustered" nodes. CDR3s with more than one amino acid difference from any other sequences are not connected and were designated as "dispersed" nodes. Layout of networks for Fig. 1b  and Supplementary Fig. 9a  were obtained by using the graphopt algorithm of the "Igraph" 39 R package and plotted in 2D with "ggplot2" to generate figures  40. Only clustered nodes are represented, edges are not shown and colours represent the node degree (log scale). 4b ) and multiple t-test (Supplementary Fig.