dataesr/scientific-paragraphs-categorization huggingface dataset:
Phylogenetic inference The Pantagruel pipeline  65  was used to infer a maximum-likelihood (ML) 'core-genome tree' using the '-S' option and otherwise default parameters. Briefly, 291 single-copy core-genome genes were extracted from the 882 assembled V. cholerae genomes; these markers represent a strict definition of the core genome that is those genes occurring once and once only in all genomes of the dataset. This restrictive definition was intended to retain only genes with an expected high degree of sequence conservation and relatively low prevalence of horizontal gene transfer compared with other core genes, towards a robust phylogenetic inference at the species scale. The alignments of these 291 single-copy core-genome genes were concatenated and the resulting supermatrix was reduced to its 37,170 polymorphic positions, from which a ML tree was computed from RAxML 66 v.
8. 2. 11 (model ASC_GTRGAMMAX using Stamatakis' ascertainment bias correction; one starting parsimony tree; rapid bootstraps for estimating branch supports). Phylogenies were also inferred from whole-genome alignments of the concatenated consensus sequences of both chromosomes from the SNP alignment of the 456 mapped 7PET genomes and 33 mapped VcD genomes. These alignments contained 2,092 and 91,312 polymorphic positions, respectively, and were used as input to RAxML-NG 67 v. 1. 0. 1 to build the ML 'mapped genome trees' using the following options: 'all --tree pars{10} --bs-trees 200 --model GTR+G4+ASC_STAM'. Alternative Article https://doi. org/10. 1038/s41564-023-01472-1 topologies were compared using RAxML-NG option '--sitelh' to generate per-site likelihood values and the 'SH. test' function from the 'phangorn' R package  68  to test hypotheses.
9, which corresponds to the 7PET-T13 sub-lineage, were further separated into VcH. 9. a to VcH. 9. h, based on visual examination of the tree structure and aiming to maximize uniformity of the spatio-temporal metadata associated to genomes in each cluster; clusters correspond to clades, either entirely or at the exclusion of another cluster included in the clade; that is, genome clusters can emerge from each other. Final trees for the mapped genome datasets were rooted manually according to the branching pattern in the 882 assembled V. cholerae core-genome tree, the diversity of which encompasses that of the mapped genome trees. From a subset of the 456 mapped 7PET genome alignments (n = 335) corresponding to VcH. 9, a recombination-free phylogeny (Extended Data Fig. 10 ) was inferred using ClonalFrameML 69 v.
1. 11 with default parameters and using the ML mapped genome tree (restricted to the VcH. 9 genome tips) as a starting tree. BactDating 70 v. 1. 1 was then used to estimate a timed phylogeny (using 100,000 Monte Carlo Markov chain iterations and otherwise default parameters) of the Yemen 2016-2019 genomes and relatives using the ClonalFrameML tree and day-resolved dates as input; median day of the year of isolation was used for isolates where these data were missing. Three independent chains were run from different random seeds and yielded close results. Supporting data for phylogenetic analyses of the 882 assembled V. cholerae, 456 mapped 7PET genomes and 33 mapped VcD genomes are available on the figshare repository (Data Availability).
Correlation of spatio-temporal and phylogenetic distances GPS data associated with the site of sample collection (health centres) were used to compute spatial geodetic distances using R script 'gps_coords. r'  71, 72. Temporal distances were computed from the difference between day of collection (available only for 2018 and 2019 Yemen isolates). Phylogenetic distances were computed from the mapped genome tree using the function 'cophenetic' from the core R package 'stats'  73. Spatial, temporal and phylogenetic distances were compared using a Monte Carlo approximation of the Mantel test as implemented in the 'mantel. randtest' function from the R package 'ade4'  74  , using 100,000 permutations to compute the simulated P value. Maps showing the distribution of genomes clusters over the Yemen territory and in the region of Sana'a were obtained using QGIS 3.
Pangenome analysis and clade-specific SNPs On one hand, the synteny-aware pangenome pipeline Panaroo 75 v. 1. 2. 3 was run on the 882 assembled V. cholerae genome set with the option '--clean-mode strict' and default parameters otherwise. On the other hand, a combined VCF file containing information on all SNP variation within the 456 mapped genome set was obtained using the 'bcftools merge' command. To identify clade-specific SNPs and accessory gene presence/absence patterns, we used custom R scripts  58  to compare the combined VCF file and the gene presence/absence table output of Panaroo, respectively, with the mapped genome tree. Based on lists of genomes assigned to various clades and clusters (Results), we identify SNPs or accessory genes that are specific of a focus clade in contrast to a background group or a sister clade, considering the contrast significant when the Bonferroni-corrected P value is below 0.
Acknowledgements This research was funded in whole, or in part, by the  Wellcome Trust  (grant nos  206194  and  108413/A/15/D ). This work was supported by  Institut Pasteur, Santé publique France , and by the  French Government 's  Investissement d'Avenir programme ,  Laboratoire d'Excellence 'Integrative Biology of Emerging Infectious Diseases'  (grant no. ANR-10-LABX-62-IBEID ). F. L. , M. J. D. , G. A. B. , A. T. -B. , M. A. B. , A. C. and N. R. T. were supported by  Wellcome  funding to the  Sanger Institute  (grant nos  206194  and  108413/A/15/D ). E. N. , J. R. , M. -L. Q. and F. -X. W. were supported by  French Government  funding to  Institute Pasteur  (grant no. ANR-10-LABX-62-IBEID ). We thank  J. Woolfolk ,  S. Clare  and  C. Tolley  for their support in sample management at  Wellcome Sanger Institute , as well as the  Sanger Pipelines team  for support.
Data availability Short-read genomic data sequenced at the WSI were deposited at the European Nucleotide Archive (ENA) under the BioProject PRJEB34436. Four of the resulting assemblies comprised a single 123-kb contig corresponding to the ICP1-like phage; these assemblies were deemed uncontaminated and complete ICP1-like phage genomes and were deposited to GenBank under the accessions MW911612-MW911615. Complete hybrid genome assemblies for reference strains CNRVC019243 and CNRVC019247 were deposited to the ENA under the BioProject accessions PRJEB52123 and PRJEB47951 (Assemblies GCA_937000105 and GCA_937000115), respectively. Supplementary data are available online on the Figshare repository, under the following digital object identifiers (doi): https://doi. org/10. 6084/m9. figshare. 16595999, https:// doi.
Patients and Study Design This retrospective intra-individual study involved 10 patients who underwent bilateral cochlear implantation at a tertiary referral center. The study included 2 infants and 8 adults. Manual cochlear implantations were performed between January 2006 and June 2020, while robot-assisted implantations took place between September 2020 and May 2022. Sequential implantation was applied to 9 of the patients, while 1 patient underwent both manual and robot-assisted cochlear implantations in a single procedure. The unique composition of this cohort enabled us to utilize each patient as their own control. The local ethics committee approved the study (Brest CHRU: 29BRC23. 0107; 22 June 2023). All patients consented to the use of their data. The study followed the French General Data Protection Regulation (GDPR).
Radiological Analysis The position of the electrode array was studied. Pre-implantation computed tomodensitometry (CT) and post-implantation CT (within 24 h after surgery) were performed in all patients. Using 3D multiplanar reconstruction of the images with Horos v. 2. 2. 0 open-source software (https://horosproject. org/, accessed on 14 September 2023), we determined the intrascalar position of each electrode according to the basilar membrane  [13]. They have been classified as "all in ST" when they were located in the scala tympani, "translocated" if they were partly or completely in the scala vestibuli, and "BMR" in cases of basilar lamina rupture.
Statistical Analysis Statistical analyses were performed using SPSS 25 software. Non-parametric tests were performed to assess the association between hearing performance and robotic/manual insertion. Categorical and quantitative paired variables were analyzed using the Fisher's exact test and the Wilcoxon signed rank test, respectively. Quantitative unpaired variables were analyzed using the Mann-Whitney test. No regression models were used for missing data. Data were saved onto our hospital's database server using a password-protected spreadsheet per the Data Protection Act. The significance threshold was set at 0. 001, and the suggestiveness threshold was set at 0. 05  [15, 16].
Mosquitoes play a crucial role as primary vectors for various infectious diseases in Thailand. Therefore, accurate distribution information is vital for effectively combating and better controlling mosquito-borne diseases. Here, we present a curated dataset of the mosquito distribution in Thailand comprising 12,278 records of at least 117 mosquito species (Diptera: Culicidae). The main genera included in the dataset are Aedes, Anopheles, Armigeres, Culex, and Mansonia. From 2007 to 2023, data were collected through routine mosquito surveillance and research projects from 1,725 locations across 66 (out of 77) Thai provinces. The majority of the data were extracted from a Thai database of the Thailand Malaria Elimination Program. To facilitate broader access to mosquito-related data and support further exploration of the Thai mosquito fauna, the data were translated into English.
To construct a dataset of mosquito distribution in Thailand, a comprehensive and meticulous review of information was conducted. No specific temporal range limitations were imposed to include the maximum amount of available data. Most of the data presented in this dataset has been extracted from the Thailand Malaria Elimination Program database  [3]  by the Division of Vector Borne Diseases, Department of Disease Control, Ministry of Public Health, Thailand. While the Thailand Malaria Elimination Program database has undergone digitization, it is important to note that the information was deposited in the Thai language only, thus restricting access to a wider audience. Therefore, our objective was to address and overcome this limitation by expanding the accessibility of mosquito-related data, thus supporting further exploration of Thailand's mosquito fauna from a broader audience.
The data were also curated from various scientific studies carried out by the Department of Entomology, Faculty of Agriculture, Kasetsart University (KU), and the Department of Medical Technology, School of Allied Health Sciences, Walailak University. For each recode of mosquito occurrence, our dataset includes fields describing their: (i) taxonomy (scientific Name, kingdom, phylum, class, order, family, genus, specific epithet, scientific Name Authorship, taxonRank); (ii) collection details (event ID, occurrence ID, event date, sampling protocol); (iii) geolocation data (county, country Code, locality, location ID, decimal Latitude, decimal Longitude, geodetic Datum). The temporal coverage of the database spans from February 19, 2007, to April 4, 2023, encompassing a significant timeframe.
The dataset contains 12,278 records of at least 117 mosquito species, mainly of the genera Aedes, Anopheles, Armigeres, Culex, and Mansonia. Also, the mosquitos were collected from 1,725 locations in 66 provinces out of 77 across Thailand. The data are provided in the Darwin Core format  [4]. Our data are published in the Integrated Publishing Toolkit of the Global Biodiversity Information Facility (GBIF)  [5]  and are publicly available for use by others (https://doi. org/10. 15468/tbd7fz)  [6].
Larval collection As part of 'baseline surveys', the collection of mosquito immature stages aimed to gather comprehensive information on the distribution of vector species in a specific area. This involved exploring and identifying larval habitats and seasonal patterns to understand mosquito populations more broadly. Unfortunately, the data from the field studies lacked specific information about the larval habitat. Both larvae and pupae were collected using the dipping technique and/or water-holding container inspection. A thorough inspection was conducted on all indoor and outdoor artificial water containers within a household, as well as other containers within 10-20 m from the house. When mosquito larvae or pupae were detected, they were carefully transferred into a collection bottle using a Pasteur pipette.
Thailand Malaria Elimination Program dataset Most of the adult mosquito data were extracted from the public domain of the Thailand Malaria Elimination Program database  [3]. Data were deposited and digitized only in Thai. Briefly, the data was exported into Microsoft Excel files, and all Thai information (such as subdistrict and village names) was then transliterated using either The Royal Institute of Thailand's Transliterated Words Database System [20] or the thai2english database [21]. Mosquitoes were collected as part of routine mosquito surveys of the Thailand Malaria Elimination Program using the OHLC technique across Thailand. Both indoor and outdoor collections were performed by experienced researchers and skilled public health professionals. Mosquitoes were collected using a flashlight and a mouth aspirator every hour from 6 p.
m. to 6 a. m. , and each collection required 50 min. There was a resting period of 10 min between each collection. Collectors were assigned to perform OHLC for a total of 6 h. Two nights of collection were conducted at each location. However, the collected mosquitoes were primarily identified using adult morphological keys. No molecular analysis was performed as the essential tools and reagents required to perform molecular testing were unavailable at each Office of Disease Prevention and Control. Unfortunately, geographic coordinates were not provided for the entirety of the data from the public domain of the Thailand Malaria Elimination Program database. The village/sub-district name listed in the original dataset file was then utilized along with administrative division details to establish a precise location by leveraging Google Earth.
Data georeferencing process The World Geodetic System 1984 served as the standard reference for determining the precise geographic coordinates (latitude and longitude) associated with any given location in the dataset. In case geographic coordinates were not provided, as mentioned above, the village/sub-district name listed in the original dataset file was utilized along with administrative division details to establish a representative location by leveraging Google Earth. As a result, these coordinates do not provide precise but representative geographic locations within the corresponding village/sub-district (Figure  1 ).
DATA VALIDATION AND QUALITY CONTROL For the Minimus and Dirus complexes and the Maculatus Group, mosquitoes were identified by experienced taxonomists using well-established morphological keys  [7, 8, [11] [12] [13] [14]  and molecular techniques  [15] [16] [17] [18]. Data validation was thoroughly screened using the Integrated Publishing Toolkit of GBIF  [5]. This toolkit, which utilizes the GBIF software, ensures data validation through its network. Additionally, metadata fields are accessible on the online pages  [6].
Acknowledgements The authors would like to thank everybody who contributed to the creation of these datasets and paper, including the  Division of Vector Borne Diseases, Department of Disease Control, Ministry of Public Health, Thailand , the  Department of Entomology, Faculty of Agriculture, Kasetsart University , and the  Department of Medical Technology, School of Allied Health Sciences, Walailak University. Special gratitude to  Paloma Helena Fernandes Shimabukuro  and  Melissa Liu  for their help in the suggestion of dataset and manuscript preparation.
Sample composition, data collection, and phylogenetic tree The analyzed sample is composed of 121 naviculars belonging to 46 extant and 17 extinct primate species (alongside one specimen attributed to Omomyidae indet. [UCMP V 134984]). The extant taxa represent nearly the entire genus diversity of strepsirrhines (cheirogaleids: n ¼ 8; galagids: n ¼ 2; indriids: n ¼ 8; lemurids: n ¼ 15; lorisids: n ¼ 10; daubentoniids: n ¼ 5; and lepilemurids: n ¼ 2) and a representative sample of extant noncatarrhine haplorrhines (atelids: n ¼ 10; callitrichids: n ¼ 10; cebids: n ¼ 5; pitheciids: n ¼ 9; and tarsiids: n ¼ 1). Due to the semi(terrestrial) locomotor behavior of several catarrhine species, which is not adequate for comparison with early euprimates, this group has not been included in the sample.
05. 2. 2. Quantification of shape, impact of size, and navicular shape variation Quantification of shape using three-dimensional geometric morphometrics To accurately depict navicular shape, a series of 16 homologous anatomical landmarks (Fig. 1 ; Table  1 ) was placed on the 3D surface of each specimen using the software Landmark v. 3. 0. 0. 6  (Wiley, 2006). For those specimens belonging to the left foot, a mirroring of the mesh was performed before placing the landmarks in order to have the same orientation for all specimens. A generalized Procrustes analysis was carried out with the function 'gpagen' of the package geomorph v. 4. 0. 3 (Adams and Ot arola-Castillo, 2013)  to obtain the Procrustes shape coordinates  (Rohlf and Slice, 1990; Mitteroecker and Gunz, 2009). A mean shape was calculated for each species, and species means were used as input in all further analyses.
Impact of size on navicular shape To assess the impact of navicular size on navicular shape in the analyzed sample, we first examined the correlation between navicular size and body mass (SOM Fig. S2 ). After that, we computed a series of allometric regressions of species-mean Procrustes shape coordinates vs. log 10transformed centroid size (CS;  Goodall, 1991)  in the entire sample and two subsets: only euprimates (excluding plesiadapiforms) and euprimates except for the small vertical clingers and leapers (Tarsius spectrum, Galago moholi, and Sciurocheirus alleni). We fitted a multivariate phylogenetic linear model with a Pagel's lambda by penalized likelihood using the 'mvgls' function of the package mvMORPH v. 1. 1. 6 (Clavel et al. , 2015; Clavel and Morlon, 2020). A penalized-likelihood approach is a modern formulation of the regularization/shrinkage approaches, which are commonly used to constrain the estimates of parameters such as the maximum likelihood error, to address analyses in which the number of variables exceed the sample number  (Clavel et al.
, 2019). Next, we tested for significant correlation between shape and centroid size using the function 'manova. gls'. Navicular shape variation To assess navicular shape variation across the sample, a principal component analysis (PCA) was performed using the species-mean Procrustes shape coordinates. The phylogeny was plotted along with the results of the PCA, building a phylomorphospace using the function 'phylomorphospace' of the package phytools v. 0. 7e20  (Revell, 2012).
S3 ). A two-block partial least squares (2B-PLS) analysis  (Rohlf and Corti, 2000)  was performed to study the covariation between navicular shape and quantified locomotor data (SOM Table  S2 ) in extant taxa. To do so, the function 'two. b. pls' from the package geomorph v. 4. 0. 3 (Adams and Ot arola-Castillo, 2013)  was used. This method uses 3D landmark data and performs a singular value decomposition, which decomposes the covariance matrix of the two blocks of variables into two matrices of eigenvectors (each one for one block of data) and a matrix of eigenvalues (the square-roots of eigenvectors;  Rohlf and Corti, 2000; Bookstein et al. , 2003). The significance of each linear combination is assessed by comparing the singular value to those obtained after resampling. If the partial least squares (PLS) covariation coefficient (r-PLS) is higher than the ones obtained from permutated blocks, its associated p-value is significant.
Phylogenetic multivariate analysis of variance To assess the differences between navicular shape and foot type, taxonomic groups, and locomotor categories, we used a type II phylogenetic multivariate analysis of variance (MANOVA) with the function 'manova. gls' of the package mvMORPH v 1. 1. 6 (Clavel et al. , 2015). The multivariate phylogenetic linear models were fitted with a Pagel's lambda by penalized likelihood using the 'mvgls' function of the package mvMORPH v1. 1. 6 (Clavel et al. , 2015). In comparison to other modes, the advantage of using Pagel's lambda is that it provides more flexibility in estimating the error structure, as if we were fitting a phylogenetic mixed model while accounting for departure from Brownian motion  (Clavel and Morlon, 2020). Significance was assessed using a Pillai statistic and 1000 permutations.
Phylogenetic discriminant function analysis To predict the locomotor category of fossil euprimates, we carried out a recently developed phylogenetic discriminant analysis. This consists of fitting a generalized least-squares model on the extant species data (training data set) to predict the locomotor category of each extinct euprimate species using both its Procrustes shape coordinates and its phylogenetic position. This new method is the first linear discriminant analysis which is both phylogenyinformed and applicable to high dimensional data sets (i. e. , data sets containing more traits than individuals). The fitting of the model on the training data set was performed using the function 'mvgls. dfa' from the package mvMORPH v. 1. 1. 6 (Clavel et al. , 2015) , and the performance (misclassification rate) of the phylogenetic discriminant function analysis (pDFA) was assessed by performing a LOOCV on the training set.
The three-fold random subsampling was repeated 200 times. The K-fold approach tends to highlight more uncertainty in the predictions because it destabilizes more consequently the training set. Convergence analyses To identify morphological convergence depending on similar locomotor behaviors, we utilized a phylogenetic ridge regression method of the package RRPhylo v. 2. 5. 8  (Castiglione et al. , 2019). First, a phylogenetic ridge regression was performed on the tree with the species-means Procrustes shape coordinates of the navicular, using the 'RRPhylo' function to obtain the branch-wise evolutionary rates and the ancestral character estimates at each node. After that, morphological convergence was tested using the function 'search. conv' of the package RRPhylo v. 2. 5. 8. Finally, the mean angle and the p-value of the mean angle were retrieved.
Navicular morphological evolution Ancestral state reconstruction To assess the evolutionary model that best depicts the evolution of the first two principal components (PCs) of the PCA performed on the species-means Procrustes shape coordinates (Fig. 2 ), a maximum-likelihood approach was used with the functions 'mvBM,' 'mvOU,' 'mvEB,' and 'aicw' of the package mvMORPH v. 1. 1. 6 (Clavel et al. , 2015). The ancestral morphotype for each node of the phylogenetic tree was estimated with the function 'estim' of the package mvMORPH v. 1. 1. 6 (Clavel et al. , 2015). Finally, a phenogram plot, a projection of the phylogenetic tree in a space defined by a shape variable (y axis) and time (x axis), was performed using the function 'pheno' of the package phytools v. 0. 7e20  (Revell, 2012)  for each PC.