dataesr/scientific-paragraphs-categorization huggingface dataset:
Species delimitation The process of delimitation was carried out in a series of steps. We tested our PSH through a range of species delimitation methods that included the Multi-rate Poisson Tree Process analysis ((m)PTP,  Kapli et al. 2017) , statistical parsimony networks (TCS Java program, ver. 1. 21, see https:// bio. tools/tcs; Clement et al. 2000; Hart and Sunday 2007) , the Automatic Barcode Gap Definition (ABGD,  Puillandre et al. 2012)  and Assemble Species by Automatic Partitioning (ASAP,  Puillandre et al. 2021). All delimitation analyses were run without outgroups included in the input files. Multi-rate PTP is designed to better accommodate sampling and population specific characteristics of a broad range of datasets as this incorporates different levels of intraspecific genetic diversity (based on specific branching events or species-specific sampling)  (Kapli et al.
2017). This analysis has been shown to outperform PTP  (Zhang et al. 2013)  in yielding more accurate delimitations with respect to taxonomy (i. e. identifies more taxonomically accepted species). TCS  (Clement et al. 2000)  bins sequences into haplotypes and calculates the frequencies of the haplotypes in the sample  (Clement et al. 2000). Relationships among haplotype networks were explored using this method using 'parsimnet', a function in the R. cran package Haplotypes (ver. 1. 1. 2, C. Aktas, see https://cran. r-project. org/web/packages/ haplotypes/index. html) that finds the most parsimonious networks and is an implementation of the TCS methods proposed by  Templeton et al. (1992). TCS  (Clement et al. 2000)  has been shown to be useful for species delimitation as the parsimony connection limit (95%) successfully delimits the same number of subnetworks as taxa (see  Hart and Sunday 2007).
ABGD and ASAP are both ascending hierarchal clustering programs that merge sequences into groups defined as partitions. The partitions are defined by probability and barcode gap width however, unlike ABGD that is solely based on pairwise distances  (Puillandre et al. 2012) , there is no need for an a priori defined P (when P is the prior maximum divergence of intraspecific diversity) within ASAP's input functions. ASAP produces a scoring system that ranks the partitions based on a combination of both probability and gap width ('asap-score')  (Puillandre et al. 2021). In ABGD and ASAP analyses, there are two available substitution models, Jukes and Cantor (1969) (JC69) and Kimura (1980) (K80). However, the evolutionary model selected by IQ-TREE (a variant of  Tamura and Nei 1993)  is not an available model option when partitioning sequences in these programs, so  Tamura and Nei (1993)  corrected genetic distances were imported into these delimitation analyses for comparison.
We used ABGD alongside ASAP because although ASAP has been developed to replace ABGD, comparisons between the two programs will allow for direct comparision to results from previously published literature. The default parameters employed within ABGD were P min = 0. 001, P max = 0. 10, 10 steps, X = 1. 5, Nb bins of 20. Only the initial partitions were examined from the ABGD algorithm outputs (following  Puillandre et al. 2021). In ASAP, the default parameters were also used. When assessing ABGD and ASAP partitions, we examined a range of partitioning definitions. This was done to encapsulate all inter-and intraspecific variability and accommodate for over-splitting or coalescing of recently radiated taxa, rather than only selecting a single partition to represent species boundaries. ABGD initially divides the data into groups based on a range of prior intraspecific divergences and a statistically inferred barcode gap, and subsequently recursively applies the same procedure to the initial groups  (Puillandre et al.
2012). For this reason, the initial partitions generally represent species or operational taxonomic units (OTUs) whereas the recursive partitions generally reflect population level diversity. Here we only examined the initial partition outputs and only considered ABGD partition 2 (P = 0. 001; to highlight the intraspecific genetic variability) and partition 6 (P = 0. 01), as defining species based on less than 1% sequence divergence is not appropriate. Partition 1 was omitted from reports as the primary partition assumes that a single gap can be defined for the entire dataset however the gap distances are highly likely to differ between groups within a dataset  (Puillandre et al. 2012). We considered these two partitions from both substitution model outputs (JC69 and K80) (Supplementary Fig.
S3 ). ASAP-scores are the average of the P-value and relative barcode gap width of the partitions. In terms of the ASAP partition outputs, three input files were examined. The original, uncorrected alignment file was entered into the webserver (https://bioinfo. mnhn. fr/abi/public/asap/ asapweb. html#) and both substitution models were applied to the file (=uncorrected p-distances). The  Tamura and Nei (1993)  corrected MEGA X CSV genetic distance file was imported and tested against both JC69 and K80. A MEGA X CSV TN93 corrected distance file with the gamma shape α (0. 692) and base frequencies (A = 0. 245, C = 0. 174, G = 0. 189 and t = 0. 391) specified, was also tested with both substitution models. ASAP 1st and ASAP 2nd partitions were considered due to the low (and thus better supported) ASAP scores of between 5.
Species diversity estimates Considering the large percentage of species clades consisting of single specimens (Fig. 3 ), we assessed whether further diversity remained hidden due to undersampling, by generating species rarefaction-extrapolation (R-E) curves and the associated 95% confidence intervals, using iNEXT (ver. 2. 0. 2, see https://cran. r-project. org/web/packages/iNEXT/ index. html; Hsieh et al. 2016). Through iNEXT (interpolation and extrapolation), an R. cran package  (Hsieh et al. 2016) , our diversity estimates used abundance-based data  (Gotelli and Colwell 2011 ) that tally the abundance of each species (SSH results were utilised as input) across predetermined geographic regions (Table  1 ). A sample-size R-E curve was produced to determine whether the rate of discovery of new species slowed down or reached saturation with an increase in sample size.
To avoid discarding data, a sample-size R-E curve that rarefies to smaller sample sizes or extrapolates to larger sample sizes (i. e. plots with respect to sample size) was utilised  (Colwell et al. 2012; Hsieh et al. 2016). We conducted both whole dataset and region-based R-E curves that are represented by the means of repeated resampling (1000 bootstrap replicates). Examining the slope of these curves allowed us to assess the impact of undersampling on estimating diversity.
475, the gamma shape α distribution parameter was 0. 692 and the base frequencies were A = 0. 245, C = 0. 174, G = 0. 189, t = 0. 391 (unequal base frequencies). Rates for the six substitution types estimated from the dataset were AC = 1. 0000, AG = 10. 6386, AT = 1. 0000, CG = 1. 0000, CT = 7. 1609 and GT = 1. 0000 (unequal transition rates, equal transversion rates and unequal purine and pyrimidine rates). To assess the robustness of the PSH, results were compared to those from four delimitation methods (mPTP, TCS, ABGD and ASAP) (Fig. 2 , 4, Supplementary Table  S1 , Supplementary Fig. S1-S4 ). The Multi-rate Poisson Tree Process (mPTP) yielded 62 taxonomic units and TCS recovered 56 groups. Species delimitation based on genetic distance using ABGD analysis detected between 49 and 52 groups respectively (initial partitions 6 (P = 0.
One of the most well-sampled species, species 29 (n = 280), has a circum-Antarctic distribution (collected from over 11 000 km) and contains samples collected from Prydz Bay, the Ross Sea and the Antarctic Peninsula. An additional six species have very large distributions (defined here as >2000 km) and data show that at least four species distributions span the Polar Front (clades 14, 24, 26 and 42) (grey highlighted boxes within the PSH, Fig. 3 ). Thirteen clades, including the aforementioned four species  (1, 2, 12, 28, 35, 44, 47, 48 and 50)  have been collected above the Polar Front (Falkland Islands, Burdwood Bank, Bouvet Island and Kerguelen Plateau) and three of these species have an Antarctic continental shelf sister-species (1 with 10 + 55, 2 with 29 and 44 with 30, Supplementary Table  S1 ).
Even more new species delimited The primary species hypotheses (PSH) outlined 59 species within the Doris kerguelenensis complex, of which 27 were new species-level clades. Subsequent species delimitation analyses showed highly congruent results (48 -63 total range), with the best supported ASAP scores converging on 59 species. Our results corroborated the D. kerguelenensis clades that were previously detected through mitochondrial, nuclear and metabolomic datasets  (Wilson et al. 2009 (Wilson et al. , 2013)) , with one exception (the original clade 12,  Wilson et al. 2009). As expected, species delimitation methods based on different underlying assumptions rendered varying species hypotheses. Overall, ASAP 2nd (K80) showed the highest evidence for over-splitting (63 species), relative to the clades recognised in the PSH and all other partition analyses.
Acknowledgements. We thank staff and students at these institutions for helping make samples available:  Western Australian Museum (WAM) , the  Baker Laboratory; University of South Florida (USF) ,  Scripps Institution of Oceanography (SIO-BIC) , the  Australian Antarctic Division (AAD) , the  Alfred Wegener Institute (AWI) , the  Yale Peabody Museum (YPM) , the  California Academy of Sciences (CAS)  and the  National Museum of Natural History, Smithsonian Institution (USNM)  for making samples available. In particular, we appreciate the efforts of  Charlotte Seid  ( SIO-BIC ),  Sarah Dietrick  and  Nicole Avalon (USF) ,  Elie Poulin  ( UCHILE ),  Javier Naretto  ( Costa Humboldt ) and  Christoph Held (AWI)  in supporting this work. We are grateful to  Mia Hillyer ,  Alex Hickling  and  Linette Umbrello  (all  WAM ) for extraction and sequencing assistance, and also to  Linette Umbrello  for analytical advice and assistance.
1e ), whereas GMV and Guadeloupe Culex rhabdovirus were only identified in two (with low abundance) and zero samples, respectively (Data Set S1). Notably, Wenzhou sobemo-like virus 3 (WSLV3), which was not identified in our previous study, showed high abundance in 54% of the mosquitoes fed with WNV-spiked blood without infectious WNV in their heads at 14 dpe, whereas this virus was absent in the plaque assay positive samples (except for one sample with only 80 reads). Due to the relatively low number of viral species in Cx. quinquefasciatus, no alpha and beta diversity analyses were performed. Presence of arboviruses in blood meal modulates viral loads of specific ISVs, and vice versa. Since metagenomics is less sensitive compared to targeted PCR assays, and the abundance data from viral metagenomics are not fully quantitative, the genome copies of selected core viruses in the head and body of mosquitoes were further determined by qRT-PCR.
Hence, the earlier time points would be more informative to understand the effect of diet on the virome. In nature, mosquitoes undergo successive gonotrophic cycles described by getting a blood meal and laying eggs, which could result in cyclic alterations in the replication and suppression of ISV, and hence their potential vector competence for arboviruses (see above). Since the abundance data obtained from viral metagenomics is not fully quantitative and the positive results from plaque assay need to be further confirmed, qRT-PCRs were performed to obtain the absolute load of ZIKV, WNV, and the selected core ISV genomes to better study their differential abundances among the different conditions. All selected mosquito-specific viral genomes (GMV, PCLPV, ATV, AANV, GCTLV, and WSLV3) could be detected in both head and body of most mosquitoes, sometimes reaching 10 8 copies (Fig.
MATERIALS AND METHODS Mosquito populations and viral strain. Field Ae. aegypti at larval or pupal instar and Cx. quinquefasciatus imagos from Guadeloupe collected in 2018 were used for this study. Ae. aegypti and Cx. quinquefasciatus were reared by the Institut Pasteur of Guadeloupe and Institut Pasteur in Paris, respectively. The first generation (F1) of Ae. aegypti and Cx. quinquefasciatus was used in this study. Lyophilized ZIKV strain Martinique (GenBank: KU647676) was isolated in 2015 and provided by the Emergence Virus Unit (Marseille). The WNV strain used belongs to lineage 1a and was isolated from a horse in France (Camargue) in 2000  (28). The hatched condition and virus isolation method were described in detail in Text S1. Mosquito oral infections. Seven-day-old Ae. aegypti and Cx.
Arbovirus and Microbiota Interactions in Mosquitoes mBio Month YYYY Volume XX Issue XX 10. 1128/mbio. 01021- 22 15  applied for 16S rRNA sequencing using standard lab protocols as optimized in a previous study  (47). Sequencing was performed on the Illumina MiSeq platform. Bioinformatic analysis for viral metagenomics and 16S rRNA sequencing data. The obtained raw paired-end reads were trimmed for quality and adapters. Reads mapping to a set of contaminating contigs known to be present in reagents were removed, and the remaining reads were de novo assembled. Contigs from all pools longer than 1,000 bp were clustered for redundancy. The representative contigs were taxonomically annotated using DIAMOND  (48)  against the nr database. All contigs annotated as eukaryotic virus were extracted using an in-house python script.
The bacterial genera with differential abundance among three food source groups were determined by R package DESeq2 with padj ,0. 05 and baseMean. 10 as cutoff. Statistical analyses. All statistical analyses were performed and visualized in R using the phyloseq (52), vegan  (53) , and ggplot2 (54) packages. To test median difference between two groups of continuous variables (alpha diversity measures, abundances, etc. ), a pairwise Wilcoxon test was performed. Multiple testing correction was performed where appropriate using the Benjamini-Hochberg procedure (false-discovery rate [FDR] adjustment set at ,0. 05). Observed richness was calculated by using the phyloseq (52) package. A PERMANOVA test was used to compare beta diversity among groups. qRT-PCR to determine the viral genome copies in mosquito heads and bodies.
The extractions for the NGS analyses were used to determine the viral genome copies in the mosquito bodies. Viral RNA from the heads were extracted with the Qiagen Viral RNA minikit. qRT-PCRs were performed on the extractions to determine genome copies of ZIKV, PCLPV, GMV, AATV, and AANV in both head and body of Ae. aegypti, whereas we tested Cx. quinquefasciatus for WNV, GCTLV, and WSLV3. The reagents and analysis methods of the qRT-PCR are described in Text S1. The specific primers, probes, and qPCR conditions of each virus are in Data Set S4. Bioinformatic analysis of phageome. The host prediction of phage contigs combined the results of multiple methods as described in Text S1, including blastn, CRISPR spacer arrays, and tRNA sequences. CheckV  (55)  was used to estimate the completeness of phage contigs.
ACKNOWLEDGMENTS We thank  Chloë Verspecht  and  Raul Yhossef Tito Tadeo  for their help during 16S rRNA sequencing and data analysis. We thank  Antoine Boullis  for his technical and logistical support during mosquito oral infections. This work was supported by the  fellowship of China Postdoctoral Science Foundation  ( 2021M691238 ), by the  French Government 's  Investissement d'Avenir program ,  Laboratoire d'Excellence "Integrative Biology of Emerging Infectious Diseases " (grant no. ANR-10-LABX-.
1 M in the second wash. Cells were mounted with fluoromount and imaged directly in the cell imaging plates. All images were acquired using an LSM 880 Confocal Scanning Microscope housed at the Imaging Platform of the Institut Pasteur de Lille using the ZEISS ZEN Imaging Software. Image acquisition was done at 40X for the various cellular markers in Fig. 5. For EEA1 quantifications, we selected 10 random regions positive for MAP2 in 2-3 different wells from 3 independent cell culture batches. Images were taken using a 63X objective and zoom of 2.
snRNA-seq analysis and differential expression analysis UMI Count Matrices for gene expression and for HTO libraries were generated using the CellRanger software (10 × Genomics). After filtering for low quality cells according to the number of RNA, genes detected, and percentage of mitochondrial RNA, and normalizing the HTO matrix using centered log-ratio (CLR) transformation, 2,990 cells were assigned back to their sample of origin using HTODemux function of the SeuratV3 R Package (Satijalab), resulting to 1,794 and 1,196 cells for BIN1 KO and WT, respectively. Then, Seurat Workflow with SCTransform normalization was used to cluster the cells according to their transcriptome similarities. Each cluster was annotated using cell type specific markers. Finally, differential expression analysis between BIN1 KO and WT cells within each identified cell type was performed using DESeq2 package  [38].
Statistical analyses Statistical information are available in the method section and in the figure legends. Two-tailed statistical tests were used. Hypothesis testing was carried out with Kruskal Wallis test followed by Mann-Whitney comparison and ANOVA with post-hoc Tukey. Statistical analyses were performed using R 3. 6. 0 (R Core Team (2019). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https:// www. R-proje ct. org/), RStudio 1. 2. 1335 and GraphPad Prism. For boxplots, the bold segment, lower and upper hinges represent the median, first quartile and third quartile respectively. The upper whisker extends from the hinge to the largest value no further than 1. 5 * IQR from the hinge (where IQR is the inter-quartile range, or distance between the first and third quartiles).
Acknowledgements The authors thank the  BICeL platform of the Institut Biologie de Lille  and the  Vect'UB viral platform (INSERM US 005 -CNRS 3427 -TBMCore ,  Université de Bordeaux, France ). The authors also thank the  UMR 8199 LIGAN-PM Genomics platform (Lille, France). We thank  A. Zelhof ,  G. L. Boulianne  and  J Laporte  for antibody, fly stock and cDNA. The a5 and 4C5 antibodies developed by  Fambrough ,  D. M. (The  Johns Hopkins University ) and de Couet,  H. G. /Tanimura ,  T. ( University of Hawaii ) were obtained from the  Developmental Studies Hybridoma Bank , created by the  NICHD  of the  NIH  and maintained at The  University of Iowa, Department of Biology, Iowa City ,  IA 52242.
Cost of remdesivir and hospital stay Remdesivir costs were considered for an average duration of treatment, taking into account discontinuation due to hospital discharge. Based on the results of the simulation, the average duration was 4 days (€1725, €345 x 5 doses), which is in accordance with what was observed in clinical trials  [21]. We also included an analysis with 5 full days of treatment (€2070, €345 x 6 doses) as recommended on the label. We used diagnosis-related group (DRG)-based national hospital tariffs to estimate the daily cost of inpatient care in conventional and ICU wards  [22]. Because COVID-19 is an emergent disease, there were no data on costs of care during hospitalization. For reimbursement of COVID-19-related stays, hospitals have used a generic DRG covering respiratory inflammation and infections (GHM 04M07) with an average daily cost of €550.
For validation, curves of model-predicted and observed daily hospital and ICU admissions, as well as hospital and ICU-bed occupancy and daily mortality, were compared visually (S1 Fig in  S1  Appendix). The model was implemented using C++. The source code is on a public repository on GitHub (https://github. com/henrileleu/covid19). In addition, for comparison with other countries, epidemiological curves of cases, tests and positivity rates from the start of the epidemic to 01/02/2021 are provided in S3 Fig in  S1  Appendix.
Dataset We extracted data from the datasets published by  Cäsar et al. (2013)  and  Berthet et al. (2019). Both datasets were collected at the Reserva Particular do Patrimônio Natural Santuário do Caraça, MG, Brazil (20°05′S, 43°29′W) from six wild groups of black-fronted titi monkeys (Callicebus nigrifrons) habituated to human observers (see more details about the field site and the population in  Berthet et al. 2021). Monkey groups were presented with three stuffed predators: one aerial predator (caracara, Caracara plancus), and two terrestrial predators (tayra Eira barbara and oncilla Leopardus guttulus). Each species of predator was presented twice to each group: once at its usual location (i. e. , in the tree canopy for the aerial predators, on the ground for terrestrial predators) and once at its unusual location (i.
, its distance from the ground), (6) identity of the first individual to call at the predator, (7) height of the first individual to call (i. e. , its distance from the ground), (8) distance between the first individual to call and the predator, (9) number of individuals composing the group, and (10-18) demographic features of the group, namely the number of infants, of female and male juveniles, of female and male subadults, of female and male adults, of paired adults and of non-paired adults. We considered individuals as adults from the age of 30 months, sub-adults between 18 and 30 months, juveniles between six and 18 months, and infants if less than 6 months old  (Cäsar 2011). The final dataset comprised 58 sequences: 23 sequences were collected during the first field period (August 2008-July 2010), and 35 sequences collected during the second field period (May 2015-August 2016).
Sequence coding We used the vocal repertoire described by  Cäsar et al. (2012b) , which comprises two alarm call types: A-call and B-call. To assure that call types were reliability coded, L. Narbona Sabaté and M. Berthet performed an inter-observer reliability test: they both coded 199 calls from 12 randomly selected sequences, comprising about 7% of the final dataset. According to standards  (Landis & Koch 1977; Hallgren 2012) , agreement was almost perfect (Cohen's k = 0. 81). L. Narbona Sabaté extracted the first calls up to 50, and labelled each of them as A-, B-call, or 'other' (undetermined call or other call type), based on listening and visual inspection of the spectrograms. L. Narbona Sabaté also measured each inter-call silence interval. Labelling and measurements were conducted using the PRAAT acoustic analysis software (version 6.
1,  Boersma & Weenink 2009). The final dataset comprised 58 sequences. Fifty-two sequences were 50-call long. Five sequences were shorter than 50 calls, because monkeys stopped calling before the emission of 50 calls (four sequences were one-call long and one was 43-call long). Finally, one sequence was only 20-call long because it could not be entirely recorded due to logistic issues (Appendix I in Supplemental Data).