dataesr/scientific-paragraphs-categorization huggingface dataset:
We investigate the opposite approach and shows its advantage in terms of inference power. Our analysis is based on more than 10,000 RABV partial and whole-genome sequences available in the NCBI Virus database  5. Our pipeline employs a concatenated alignment of the ve RABV gene fragments, with gaps for the regions absent in partial sequences. The phylogenetic analyses were performed with time-e cient inference methods suited for large datasets: an approximate maximum-likelihood phylogenetic analysis using FastTree, time-scaling with the least-square dating method of LSD2, and ancestral geographic character inference using a maximum-likelihood method implemented in PastML. We validated our pipeline by comparing its results to those from previous studies, to phylogenetic analyses using subsampled data sets and to those utilizing more complex evolutionary models accounting for potential evolutionary rate changes between genes and for potential selection pressure 26,29,59,60 : Using a larger data set, we obtained a compatible tree topology and compatible (between different trees and evolutionary models) but more precise dating (Fig.
Methods Data Set Compilation A total of 25,787 sequences of rabies virus (RABV) were obtained from the NCBI Virus database 5 in September 2021. A quality check was conducted to remove sequences that were (1) missing date and country information, (2) older than 1972 (for sequencing quality), (3) identi ed as vaccine or laboratory strains, (4) or shorter than 200 nucleotides in length. As a result, 14,752 sequences were retained for this study. Sequence ltering is demonstrated in Supp. Figure  1 , and a complete list of sequences with criteria for exclusion can be found in Supp. Table  1  ('exclusion' column).
Sequence Alignment Sequence les were grouped by which gene they belonged to according to a custom script (https://github. com/amholtz/GlobalRabies/blob/main/R/clean_RABV. R). Whole genome sequences were separated and assigned to all ve gene groups. Each gene group was aligned using the MAFFT (v7. 505) with FFT-NS-1 strategy, addfragments, and keep-length method of the reference sequence  31. This resulted in ve multiple sequence alignments for each gene coding region: N gene (1,353nt), P gene (894nt), M gene (609nt), G gene (1,575nt), and L gene (6,429nt). Sequences were subsequently concatenated in gene order (noncoding regions removed) using Goalign (v0. 3. 5)  32  , introducing gaps in non-sequenced locations in the genome, for a total length of 10,860 nucleotides.
Subsampling The full-canine tree was subsampled to reduce geographic bias towards countries with a disproportionally high number of sequences by employing a custom script (https://github. com/amholtz/GlobalRabies/blob/main/python/py_subsampling. py) which removes sequences from over-sampled countries by phylogenetic diversity  38  given an input tree. At each step of the algorithm, the tree tip with the shortest branch among those sequences corresponding to overrepresented countries is removed. The process is repeated until a desired target number of sequences is reached for all the countries. As several candidates for removal might exist at each step (i. e. tips with the same, shortest, branch length), the tip to be removed is chosen randomly in such cases. The subsampling was repeated ve times, generating slightly different (due to stochasticity) trees with 5500 sequences each.
Purifying Selection Model To quantify the effect of purifying and diversifying selection on the time calibration, we used the software package HyPhy 45 on the smaller data set of 236 whole-genome sequence subset from Troupin et al. 26  to lighten the computational load 26. This subset is made of only WGS and its dating has been previously analyzed, such that it is useful to assess model variations. Site-speci c rates of the numbers of nonsynonymous and synonymous (dN and dS) substitutions per set were inferred in each codon using the Mixed Effects Model of Evolution (MEME)  46  , Fixed Effects Likelihood (FEL)  47  and the adaptive Branch-Site Random Effects model (aBSREL)  48  in HyPhy. The dN/dS ratio was then used by each model to optimize and re-estimate branch lengths of the tree with a xed topology (Supp.
Phylogeographic Reconstruction and Subsampling Consensus Ancestral geographic characters of canine RABV transmission were using the fast likelihood method available in PastML version 1. 9. 34  49  to reconstruct the historical spread of canine RABV. Marginal probabilities of locations of ancestral nodes were estimated given the geographic location of sampled sequences in a rooted time-calibrated tree. To assess the con dence in state estimates of internal nodes with respect to potential geographic sampling bias, we repeated PastML on each of the ve independent and uniquely subsampled reconstructed and time-calibrated trees and compared the ancestral state estimates. A custom script (https://github. com/amholtz/GlobalRabies/blob/main/R/ACR_Sub_comparison. R) was used to identify and compare nodes with identical cluster compositions between the subsampled and full-canine trees to create an aggregated consensus country-annotation (Supp.
Methods 2). The consensus tree was created by pruning the full tree to keep the union of all sequences in subsampled trees, yielding a nal tree with 6,096 sequences (Fig. 3 ), and the aggregated ACR result was used as a new annotation for the consensus tree using PastML. Regional and Colonization History Ancestral Character Reconstruction ACR using PastML version 1. 9. 34  49  was repeated on the 6096-sequence aggregated tree for two geographic characters. The rst de ned the character using a World Bank regional grouping variable using R library country code 50 (Fig. 4 ) which re ects cultural and geographic similarities. This resulted in the grouping of 120 countries in the original data set into 18 regions. The second de ned the character according to the colonial history of the sequence country of isolation (Fig.
Introduction Event Exploration To visualize human-mediated transmissions of RABV, long geographic distances over short time intervals were located on the phylogeny using a custom script (https://github. com/amholtz/GlobalRabies/blob/main/R/introduction_events_FullTree. R). The package cepiigeodist  51  was used to calculate geographic distances between each most-populated city in the country. Character states from the phylogeographic analysis were considered only if there was a consensus between the aggregated subsamples and the full-canine tree. A branch of length Y with a resolved consistent parent node state A and a resolved consistent child node state B was considered as a transmission from country A to country B over Y years. Transmissions within countries and between neighboring countries were discarded, resulting in 232 long-range transmissions.
Acknowledgements The authors thank  S. L. Kosakovsky Pond  for his help running HyPhy and interpreting the results. We would also like to thank  Edward Holmes ,  Jake Faber  and  Joël Brehin  for their helpful reading and critical comments in the review of the manuscript. G. B. acknowledges support from the  Research Foundation -Flanders (Fonds voor Wetenschappelijk Onderzoek -Vlaanderen, FWO, Belgium ; grant n°  G098321N ,  G0E1420N ) and from the  Internal Funds KU Leuven  (Grant No. C14/18/094 ). A. H. acknowledges École doctorale  Frontières de l'Innovation en Recherche et Education-Programme Bettencourt. A. H is funded by the  INCEPTION programme (Investissements d'Avenir  grant  ANR-16-CONV-0005 ). This work was funded by  Institut Pasteur.
All Lung Tranplant Recipeints from the French cohort (COLT, Cohort in Lung Transplantation, l'Institut du Thorax, INSERMUMR1087/CNRS UMR 6291, CNIL 911142) were recruited in this study and gave their written informed consent to participate to the study in accordance with the Declaration of Helsinki. These are patients sampled between 2009 and 2014, with clinical data collected in 2017.
Sandflies (Diptera; Psychodidae) are medical and veterinary vectors that transmit diverse parasitic, viral, and bacterial pathogens. Their identification has always been challenging, particularly at the specific and sub-specific levels, because it relies on examining minute and mostly internal structures. Here, to circumvent such limitations, we have evaluated the accuracy and reliability of Wing Interferential Patterns (WIPs) generated on the surface of sandfly wings in conjunction with deep learning (DL) procedures to assign specimens at various taxonomic levels. Our dataset proves that the method can accurately identify sandflies over other dipteran insects at the family, genus, subgenus, and species level with an accuracy higher than 77. 0%, regardless of the taxonomic level challenged.
Specimen selection and storage The database of WIP from Psychodidae insects, comprising 1673 pictures, gathers samples belonging to the Phlebotominae family in the majority from well-established laboratory breeds, limiting potential intrapopulation WIP variations, but also from specimens collected in natura whose identification was performed at the time of their catch with available regional identification keys. Laboratory-reared representatives were provided using a standard method of sand fly breeding  17. The description of the samples used in this study is given in Table  1.
Image acquisition and database construction The protocol used to capture sandfly WIPs was as already described for Glossina sp. WIPs acquisition  15. Briefly, dissected wings were deposited on a glass slide and covered by a small cover slide. A Keyence™ VHX 1000 microscope, using the VH-Z20r camera, and a Keyence VH K20 adapter set for an illumination incidence of 10° were used. All pictures were enlarged to a maximal occupancy, and the High Dynamic Range (HDR) function was used for all photos. Shots were then filled in a database with their taxonomic information, sex, date of capture, country of collection, and name of the entomologist that has undergone morphological identification. All pictures were filled in the database. See Fig. 1  for an example of images gathered during the study. Collected dataset, image pre-processing, and dataset splitting for training/learning and validation.
The Phlebotomine dataset includes 1673 pictures of 17 sandfly species  18. Underrepresented sandfly species (less than ten samples/pictures) were discarded from the training dataset to prevent overfitting. Processed images were then resized to 256 width and 116 height pixels, and pixel values were normalized within the (0,1) range. The dataset was prepared for k-fold cross-validation, with k = 5, shuffled randomly, and partitioned into k equalsize subsets having a similar class distribution. A separately learned classifier was evaluated for each subgroup using kth of the whole dataset for validation and the remaining k-1 as training data (see Fig. 2A  for illustration). This strategy allowed measuring the mean accuracy of the five distinct classifiers. Among all existing machine learning methods, Deep Convolutional Neural Networks and their different architectures have shown in the last decade to be the most adapted for image classification.
Training of the neural network (CNN) CNN architecture of MobileNet  19  , ResNet  20  , and YOLOv2 21 were deemed for automatically classifying sandfly specimens with the dataset. Compared to classic Deep Learning, ours is more compact to cope with the specificity of our dataset in terms of size; therefore, thinner image recognition and classification architecture were developed to consider its reduced size. Inspired by MobileNet, the first takes advantage of depth-wise convolution  19  , with only one depth-wise convolution per layer of the CNN architecture that reduces the complexity and number of features extracted. A batch normalization was set to speed up and stabilize the training process  22. In addition to this first compact CNN architecture, two interconnected layers like VGG  23  for YOLOv2 were applied with a DarkNet-19  21  architecture with 1 or 2 scales less than the original network.
Test for accuracy at the family/subfamily taxonomic level The accuracy of the classifier was tested at various taxonomic levels ranging from the family (Psychodidae) to the genera (Phlebotomus, Lutzomyia (Lutzomyia & Migonemyia, if taking into account revised taxonomy  25, 26  ), Sergentomyia) and the species level (12). The Psychodidae family encompasses about 2600 species; however, only specimens belonging to the Phlebotominae subfamily are included in the dataset due to their medical importance as pathogen vectors. We first explored the training classifier accuracy on the Phlebotomine dataset and other non-Psychodidae specimens from Calliphoridae, Culicidae, Glossinidae, Muscidae, and Tabanidae datasets  18. We trained the CNN on such a combination to improve the model's accuracy. The dataset was filled with 1673 pictures of Phlebotomine WIPs.
Still, five species were unsatisfactorily covered in terms of WIP pictures and were discarded from the training dataset of the Phlebotomine subset. Using this pictures-set, we ascertain the accuracy of the process to discriminate the Psychodidae family from other non-Psychodidae. From our dataset and method, the automatic classification process accuracy is an astonishing 99. 8% (Table  2 ). Knowing that the wing size doesn't belong to the descriptor selected during the training process, our classification accuracy would rely on other descriptors more specific to the WIPs.
Test for accuracy at the subgenus taxonomic level To further investigate the taxonomic congruence of our methodology with the already proposed one, we assess its classification reliability at the subgenus level. At the generic level, the subgenera of sandflies have been intensively studied over many decades, with taxonomists providing varying views about their number and designation. The genus Phlebotomus currently encompasses 13 subgenera. For the genus Sergentomyia, ten subgenera are proposed. The Chinus genus is not further divided into subgenera. The taxonomic subdivisions in Neotropical Phlebotominae are rather complex and remain debatable  26, 28. A checklist of American sandflies is available  25. We use the classification provided by Akhoundi et al. 1. Our dataset does not fully cover the biodiversity of sandflies, particularly for New World sandfly species, at the subgeneric level; however, we provide data on four subgenera of the genus Phlebotomus, namely Adlerius, Larroussius, Paraphlebotomus and Phlebotomus, that in total harbor 30 species proven or suspected as vectors or many human-infecting Leishmania  4.
Test for automatic classification of the 12 sandfly species filled in the dataset At the species level, our data set is filled with pictures of 17 species, but only 12 provided enough images to encompass a training process. Even if limited in terms of species richness, considering the vast number of sandfly species described (900-1000), our dataset is composed of primary proven vectors of pathogens causing cutaneous and visceral leishmaniasis in the New and Old World, which highlighted the interest for medical entomology purposes. Among the 12 species in the dataset that have undergone a learning process, the best accuracy score was for P. papatasi (100%), the proven vector of L. major (an agent of cutaneous leishmaniasis). The lower recorded accuracy score (77. 8%) is computed for P. perniciosus, a proven vector of L.
infantum (an agent of visceral leishmaniasis). The overall accuracy scores remain astonishing and are always higher than 77% for all the species filled in the database (Table  5 ). The exactness of the method to assign sandfly species must now be probed on a larger in natura collected sandfly sample covering a wider geographic area, including New World species. In addition, with the methods proposed, WIPs variation at the populational level can be investigated with a proper sampling strategy. Morphological species identification remains a golden standard in sandfly taxonomy; however, it is prone to various limitations, including compromised state of decisive structures in the field-collected specimens, intraspecific variability among populations, laborious sample preparation and declining entomological expertise among taxonomists.
So far, only in-house databases of sandfly reference protein spectra have been established, further limiting the applications of this approach. In addition, the interoperability of MALDI-TOF requires a standardized procedure in the conservation of samples, the choice of the adult specimen body part or even the trapping method  37  , and the standardization of procedures for preparation and reproducibility between instruments and homemade databases is desirable  33. Hence, an alternative method for species identification of adult sandflies performed under conditions that do not allow costly and highly sophisticated infrastructures is highly desirable. The application of Deep learning leads to robust results in terms of classification performance. The proposed method has the potential to be used in real-life scenarios since the proposed architecture ends up with a good compromise compared to other methodologies reviewed in Cannet et al.
15. Future development and technical  implementation of this methodology include strengthening the database in terms of Phlebotomine species and population representation, the use of GANs (Generative adversarial network) allowing to fill up the database with new species, even with a low number of representatives. From an application point of view, previous works  15, 16, 24  and this study add evidence to the generic potential of the method for dipteran insect identification. Implementing a SaaS platform would offer a complete service for remotely localized computers with an internet connection.
Acknowledgements We thank  Pr. P. Marty  and  P. Delaunay  ( CHU Nice ) for gaining access to the microscopic facility of the  CHU. Dr. D. Fontenille  ( UMR MIVEGEC, Montpellier, France ) for his support and fruitfully scientific discussions on medical entomology aspects. Mr JP Commes , former  CEO  of  2CSI , for his enthusiasm and advice on the digital aspect of the project.
The cDNA libraries were enriched and sequenced using the Ion Proton Sequencer and an Ion PI Chip v2 (Life Technologies). The resulting reads were cleaned with Trimmomatic 0. 32 software, and then a Bowtie 2 alignment was performed on Lyssavirus genome references. The reads were down-sampled to fit a global coverage estimation of 80 x and were submitted to the SPAdes 3. 1. 1 de novo assembler. The de novo contigs were then submitted to BLAST on a local nucleotide (nt) database. For each segment, the best matches were selected for a Bowtie 2 alignment, which produces very clean and robust 5' and 3' ends, contrary to de novo assemblies of viral genomes, for which 5' and 3' ends are sometimes incomplete. Finally, the de novo assemblies and the alignment on the references were compared, and the strict identities of the de novo and aligned sequences were assessed  [25].
Briefly, the resulting reads were aligned with Bwa software (http://bio-bwa. sourceforge. net/) on the GenBank reference sequence KF154997. The algorithm used was suitable for reads over 70 bp from Illumina technology. The consensus sequence was generated after cleaning the reads to keep only reads correctly appearing: both reads of a pair are aligned to the reference sequence, and the insert length is as expected. Variant calling was performed on the filtered reads using SAMtools (Tools for alignments in the SAM format, v1. 3) and BCFtools (Tools for variant calling and manipulating VCFs, v1. 3).
Phylogenetic analysis The phylogenetic analysis of the whole genome sequences was performed with a dataset of sequences constituted by 12 sequences from Romania (8 wild and 4 domestic animals) and 6 sequences from Moldova (6 domestic animals) and 37 referenced GenBank sequences (S5 Table ). The dataset of sequences constituted with the samples from Romania and Moldova was cleaned to remove ambiguities (N), concatenated and trimmed using MEGA-X. After alignment of the dataset of sequences, phylogenetic analysis was assessed using SeaView software and PhyML, general time reversible (GTR) calculation and 100 bootstraps, respectively. The tree was then vizualised and annotated in FigTree 1. 4. 2 to include bootstrap values. The 37 SANGER N gene consensus sequences of this study were analyzed and edited using Vector NTI software (Invitrogen, France).
The dataset of sequences constituted for the phylogeny of the full length gene (n = 69) was then aligned and trimmed with MEGA-X. A phylogenetic tree was constructed for this analysis with 23 sequences (12 wild and 11 domestic animals) from Romania, 14 sequences (one wild and 13 domestic animals) from Moldova and 32 referenced GenBank sequences of Asian, Arctic-Related and Europe (C, D, SF, CE, WE, EE and NEE) phylogenetic groups. Phylograms was carried out using PhyML of the software Seaview with the GTR parameter and 100 boostraps. The alignment and analysis of the amino acid (aa) and nucleotide (nt) similarities of each gene were undertaken using the Sequence Identity Matrix programs within the BioEdit software.
Bayesian phylogenetic analysis: evolution and dates of divergence A data set of N gene sequences was built using 37 sequences from this study (14 from Moldova and 23 from Romania) and 14 partial N gene sequences from Romania extracted from Genbank, previously published by Turcitu et al. [26]. The data set of partial N gene sequences represented the following RABV lineages: RO#1 (n = 5) and RO#2 (n = 3) clustering with the Russian D group, RO#5 (n = 4), RO#6 (n = 34) and RO#7 (n = 5) clustering with the NEE group. The set of sequences was aligned and trimmed using MEGA V 10. 1. 8, resulting in a 493 bp region of N gene for BEAST analysis. The alignment of sequences was imported into BEAST V 1. 8. 4 to carry out Bayesian phylogenetic analysis and estimate sample dates of divergence. jMo-delTest  [27]  was used to determine the best fitting nucleotide substitution model based on the Bayesian Information Criterion (BIC) value.
Phylogenetic and Bayesian analysis: Evolution and time scale A total of 37 RABV whole genomes and 32 RABV full-length N gene representative of the RABV variants circulating across the region were recovered from GenBank for the purpose of phylogenetic comparison with the study samples (S4 and S5 Tables). The phylogenetic analysis performed using the PhyML and Seaview software, with GTR calculation and 100 bootstraps, is presented for the full-length N gene in S1  Bayesian phylogenetic analysis undertaken on the 37 partial N gene sequences from Romania and Moldova (S6 Table ) revealed that all samples in the study clustered into two assigned  The three lineages RO#5, RO#6 and RO#7 appeared to diversify in the late 1990s early 2000s with RO#5 in ~2000 (95% HPD 1988-2022), RO#6 in ~2001 (95% HPD 1990-2022) and the more recent RO#7 in ~2010 (95% HPD 2006-2019).
Acknowledgments Our sincere thanks go to the molecular biology and rabies diagnosis teams from the  ANSES Laboratory  for rabies and wildlife (Nancy, France), particularly to  Jean-Luc Schereffer ,  Melanie Badre-Biarnais ,  Sebastien Kempf ,  Estelle Litaize  and  Valère Brogat , for providing us with the perfect framework in which to conduct this research and also for their excellent technical contribution and support. We would also like to thank our colleagues from the  Romanian regional laboratories ,  Dr Baranguta ,  Dr Ciornohac ,  Dr Damaceanu ,  Dr Busuioc ,  Dr Anderco  and  Dr Ciuhureanu , who kindly provided us with the samples used in this study.