dataesr/scientific-paragraphs-categorization huggingface dataset:
Phylogenetic analysis revealed that the WNV identified in our study belonged to lineage 1 and was closely genetically related to a WNV isolated on a symptomatic horse the 3 rd of October 2015  13  (GenBank accession number MT863559) (Figure  3 A ). Our WNV small genomic section diverged from the 2015 WNV (GenBank accession number MT863559) by 5 single nucleotide polymorphism (SNPs) and from a WNV isolated in Spain on a horse in 2010  14  (GenBank accession number JF719069) by 4 SNP (Figure  3 B ). was used as an outgroup in the phylogenetic tree. The evolutionary history was inferred using a maximum likelihood method using PhyML. Bootstrap values obtained after 100 replicates are shown at major nodes (percentage of replicate trees in which the associated taxa clustered together). Evolutionary distances, as represented by length of branches, are expressed in number of base substitutions per site.
Taxonomy was next assigned to each ASV using blast on a database gathering Fungi, Protist, and animal COI records. A total of 1,330 (17. 6%), 1,681 (22. 3%), and 53 (0. 7%) ASVs were assigned to the Animalia, Fungi, and Protist kingdoms, respectively, and 4,486 ASVs (59. 4%) were unassigned. Unassigned ASVs corresponded to off-targeted genomic amplifications or assignments falling below the required 80% identity threshold. ASVs count per kingdom represented 17% (N=406,892), 39% (N= 924,375), 0. 1% (N=2,292) and 44% (N= 1,027,285) of the total ASVs count for the Animalia, Fungi, Protist kingdoms and unassigned ASVs, respectively (Supplementary file 2). Fungi from the genera Leohumicola (class: Leotiomycetes, phylum: Ascomycota), Tremella, Cryptococcus and Rhodotorula (phylum: Basidiomycota) were overrepresented.
Six fish's species were also detected in this study. ASVs assigned to these fish's species (class: Actinopterygii) were scarce (3% of the Chordata phylum), and Salmo salar (salmon) was the most represented and present in 3 study sites in both Camargue and Mali. Vertebrates typically associated to the Camargue regions were detected, such as Bos taurus, Equus caballus or birds belonging to the Ardea genus (i. e. herons and egret). While accounting for only 0. 3% of ASVs counts inside the Animalia kingdom, ASVs assigned to the Aves class were the most diverse inside the Chordata phylum with 6 birds identified as the species level (i. e. Sylvia atricapilla, Motacilla flava, Hirundo rustica, Pica pica, and Corvus corax from the Passeriforms order, and Gallus gallus). ASVs were also assigned to the Ardea genus, Passeriformes order and Phasianidae family (Figure  5 ).
Aedes albopictus and Ae. aegypti COI sequences might share a strong homology on our target region with other Aedes species in Camargue that are not present in our database. The identification of fish species in this study might have resulted from any DNA contamination from the environment (e. g. food contamination), laboratory reagents or kits  41. ASVs assigned to these fish species were scarce and filtering out low-abundant and/or lowprevalent features might improve false-identifications, with the trade-off of losing species identification. The Cx. pipiens s. l. complex has two recognized forms -pipiens and molestusthat exhibit behavioral and physiological differences and can form hybrids. Culex pipiens pipiens feed preferentially on birds, and Cx. pipiens molestus on humans but both can transmit WNV  42, 43.
Phylogenetic analyses A background set of 36 full-length WNV genome sequences across different lineages was obtained from GenBank. One full-length genome sequence of Japanese Encephalitis Virus was also downloaded to be used as an outgroup in the phylogenetic tree. Genome sequences were aligned using Clustal Omega and curated by Gblocks software implemented in the seaview v. 5. 0. 4 interface 63 by allowing gap positions within the final block and less strict flanking positions before to generate the best-scoring maximum-likelihood (ML) tree with 100 bootstrap replicates with PhyML (15980534). The GTR + I + G nucleotide substitution model was chosen based on the lowest Akaike Information Criterion (AIC) value using the Smart Model Selection in PhyML (SMS) software  64. Phylogenetic trees were visualized using the ggtree R package  65.
Amplicon-based metagenomic The amplicon-based metagenomic method implemented here was performed on mosquito fragments  15  , was amplified with universal primers BF2/BR2 68 (Supplementary table  1 ) that are routinely used for macroinvertebrate monitoring  38, 39. Illumina Nextera® universal tails sequences were added to the 5' end of each of these primers to facilitate library preparation, as described above for the WNV tiling amplicon-based sequencing procedure. PCR amplifications, library preparation and sequencing were made according to the same protocol described above for WNV sequencing excepting for the annealing temperature that was set to 50°C for 30 sec. All raw sequences have been deposited in the NCBI database under the NCBI BioProject number PRJNA768434.
was used for turning paired-end fastq files into merged reads, filtering out Illumina adapters, denoising and removal of chimeras and filtering out replicates. Taxonomic assignment was carried out for the amplicon sequence variants (ASVs) using the qiime2-feature-classifier classify-consensus-blast plugin using a database of 1 176 764 sequences gathering Fungi, Protist, and Animal COI records, recovered from the Barcode of Life Database Systems (BOLD) the 7 th March 2021. The database was built according to the step-by-step tutorial provided by Devon O'Rourke 69. Only sequences within our primer coordinates were retained in the final database. A percentage identity threshold of 90% was used to assign a taxonomy to an ASV. Shannon's diversity index was used to assess Culicidae and Chordata species richness and evenness (relative abundance of species inside a sample), i.
Benjamini-Hochberg FDR correction was used to adjust the p-values to account for the multiple testing. Phylogenetic distances between species from the Culicidae family and Chordata phylum were calculated with IQ-TREE 72  with the ultrafast bootstrap algorithm and automatic model selection, as implemented in the Qiime2 phylogeny iqtree-ultrafast-bootstrap plugin. Differential ASVs abundances between sampling sites were compared on a nonrarefied dataset using the q2-aldex2 differential abundance package, by modelling the data as a log-ratio transformed probability distribution rather than as counts 73. The R package phyloseq 74  was used to represent phylogenetic relationship among ASVs assigned to Culex pipiens mosquitoes. All further diversity metrics implemented in Qiime2 can be accessed by running Qiime2 diversity commands on data provided on Supplementary files 4.
Acknowledgments We are grateful to  Laurie-Lou Weghel  for her interest and support throughout this project. We also thank  Frédéric Jean  ( EID Méditerranée ) for his thorough involvement on the field work and  Nicolas Benoit  for his help regarding the molecular detection of Plasmodium spp. We also thank  Clément Gendrot  and  Sylvain Buffet  for their help at designing and printing the.
For analysis, a reference index was created based on the Pteropus giganteus annotation GCF_902729225. 1 from the NCBI database, which was assembled on April 15, 2020. FastQ files were pre-processed using TAGADA (https://github. com/FAANG/analysis-TAGADA, 12 October 2021, Genotoul plateform, Auzeville, France ) and the results were visualized with DRAMAA software (https://gitlab. com/hssgenomics/Shiny (accessed on 1 October 2021), version 1. 4. 0). Raw data and gene tables are available through the GSE134585 datasets (Supplementary Table  S2 ). GO annotations were performed (Gene Ontology, http://geneontology. org/, current release 16 November 2021), using the human Gene symbols as references. An ISGs table  [34]  was crossed with (ECM_E2 + ECM_ES2)/2-vs-PTC DEGs (Supplementary Table  S3 ), with an adjusted FDR value lower <0.
Viruses NiV-Malaysia isolate UMMC1; GenBank AY029767, (NiV-M), NiV-Bangladesh isolate SPB200401066, GenBank AY988601, (NiV-B), recombinant NiV-enhanced green fluorescent protein (EGFP) (rNiV-M-eGFP)  [35]  and NiV Cambodia isolate NiV/KHM/CSUR381, GenBank MK801755, isolated from P. lylei bat urine in Cambodia  [26]  (NiV-C) were produced and titrated on Vero E6 cells at the INSERM Jean Mérieux Biosafety Level 4 (BSL-4) laboratory in Lyon, France, with the highest level of biocontainment. Vero E6 cell line (ATCC#CRL-1586) obtained from the kidney of African Green Monkey and cells of the human carcinoma HeLa cell line (Cat# ATCC CCL-2) were cultured in DMEM medium supplemented with GlutaMAX (Life TechnologiesTM, Thermo Fisher Scientific, Illkirch, France, Cat# 61965-026) and 10% of heat-inactivated (30 min at 56 • C) fetal bovine serum (FCS) (Dutscher, Bernolsheim, France, Cat# S1810-500) and 15 µg/mL Gentamicin (Life TechnologiesTM, Thermo Fisher Scientific, Illkirch, France, Cat# 15750-037).
Henipavirus Infections of bPCs and bRSCs For NiV-M, NiV-B, and NiV-C, bPCs, and bRSCs were plated in 12-well plates and upon reaching 80% of confluence were infected for 1 h at 37 • C with the virus at an MOI of 0. 1 (as the infection was saturating at an MOI of 1) using the highest biocontainment environment (BSL4). Next, the virus-containing medium was removed and cells were washed once with PBS. Finally, fresh medium was added to cells, followed by incubation at 37 • C for 0, 6, 16, 24, or 48 h. Cell morphology was observed under a Zeiss Axiovert 100 M microscope and photos were taken at 24 h post-infection using ImageJ software. For each time point, infected cell lysate was prepared using RLT buffer (Qiagen, Les Ulis, France, Cat# 79216) prior to RT-qPCR analysis according to a validated BSL-4 procedure.
Data were analyzed using StepOne v. 2. 3 software (Applied Biosystems, Thermo Fisher Scientific, Illkirch, France,) and calculations were done using the 2∆∆CT method. Expression was normalized to that of glyceraldehyde 3-phosphate dehydrogenase (GAPDH), as described previously  [36]. The primers used are pGAPDH forward: ATCATCCCTGCTTCTACT and reverse: AGGTCAGATCCACAACT; and NiV-N forward: GGCAGGATTCTTCGCAACCATC and reverse: GGCTCTTGGGCCAATTTCTCTG.
Data were analyzed using StepOne v. 2. 3 software (Applied Biosystems,Thermo Fisher Scientific, Illkirch, France,) and calculations were done using the 2ΔΔCT method. Expression was normalized to that of glyceraldehyde 3-phosphate dehydrogenase (GAPDH), as described previously  [36]. The primers used are pGAPDH forward: ATCATCCCTGCTTCTACT and reverse: AGGTCAGATCCACAACT; and NiV-N forward: GGCAGGATTCTTCGCAACCATC and reverse: GGCTCTT-GGGCCAATTTCTCTG.
Acknowledgments: The authors thank  Doris Preininger ,  Anton Weissenbacher , and  Tiergarten Schönbrunn  ( Vienna, Austria ) for Pteropus samples and  Jean-Marc Reynes  and  Institut Pasteur  for providing us with  NiV Cambodia Isolate and Pierre E. Rollin and Centre for Disease Control and Prevention, Atlanta, USA , for providing us with  NiV Bangladesh  isolate. We also thank  F. Enchery ,  K. Dhondt ,  J. Fouret , and  C. Legras-Lachuer  for help in initiating and realizing this work.
Funding: The study was supported by LABEX ECOFECT (ANR-11-LABX-0048) of Lyon University, within the program "Investissements d'Avenir" (ANR-11-IDEX-0007), operated by the French National Research Agency (ANR), and by ANR-18-CE11-0014-02, by grants from the "Finovi Foundation" (AO13) and Aviesan Sino-French agreement on Nipah virus study.
In addition to the adjustment of standard binning quality assessment metrics, we computed metrics specific to covers (i. e. overlapping clusters). In order to further explore the three binning approaches, we compared the results of the best-performing binner of each category with our expected community structures (species and strain levels), taking into account the characteristics of the genes (e. g. 16S ribosomal RNA-16S rRNA, prophage and plasmid genes) and corresponding genomes (e. g. GC content, closely related genomes) included in our SGC. Finally, we evaluated the usability and scalability of the best-performing binners on the IGC. We believe that our results as well as our methodology will be of great use to the community to gain insight into which binner is the most adequate to handle specific cases, thereby to bin the sequences of a given dataset such as a non-redundant microbial gene catalog, and on how to evaluate overlapping binning results.
Closely related genomes  (40)  were also added by identifying known strains with a tetranucleotide frequency correlation (TETRA) above 0. 99 and an Average Nucleotide Identity (ANI) higher than 95-96% with JSPECIES  (41). A description of the 47 strains included in the SGC and their taxonomy, as well as a representation of the corresponding taxonomic tree are available in Supplementary Tables S1, S2 and Figure  S1. DNA, RNA sequences and non-redundant gene catalog. For each genome (contigs or complete sequence), we retrieved the NCBI ftp links for the genomic, coding and RNA sequences using the R package REUTILS (version 0. 2. 3, Gerhard Sch öfl 2016) based on the RefSeq or GenBank accession number and we downloaded the corresponding files. We then concatenated the 16S rDNA sequences with the coding sequences (CDS) from all genomes to obtain a single FASTA file.
The following pre-processing steps were performed on the generated FASTQ files: removal of N-containing sequences using VSEARCH version 2. 8. 0  (44) , removal of single reads (using the python script fastqCombinePairedEnd available at https://github. com/enormandeau/Scripts), quality trimming and filtering using TRIMMOMATIC version 0. 36  (45). We used MOCAT version 2. 0  (46)  to map reads with SOA-PALIGNER2 (47) using the 'allbest' mapping mode and to calculate the gene abundance profiles across samples.
Benchmarking workflow Input data and parameters setting. Each binner was benchmarked on the SGC using gene abundance profiles and/or composition information but required different input types and formats (Table  1  and Supplementary Figure  S3 ). When necessary, we used the python script reorder fasta (available at https://github. com/JLSteenwyk/ Molecular sequence analyses scripts) to sort the genes of the SGC according to the gene abundance profiles and/or composition information. The binners were launched with default parameters with the exception of the settings described in Table  1. For hybrid binners, we essentially adjusted the minimum gene length filter. Indeed, most of these binners recommend discarding short sequences (generally below 1000 bp) to avoid skewed tetranucleotide frequencies  (48).
Moreover, we only present the output of DAS TOOL based on the binning results of abundance-based binners since the results of DAS TOOL were identical when including the binning results of hybrids binners. Output data. We converted the obtained binning results to the Bioboxes format used in CAMI and AMBER for the benchmarking of binners. We used the python script convert fasta bins to biobox format proposed by AMBER v1. 0. 3 to convert the output bins stored in a FASTA format, and an in-house R script and bash commands to convert other output formats (e. g. csv or bin-specific files). For Table  1. Selected taxonomy-independent binners. Binners are organized into three categories based on their approach: abundance-based (Ab. ), hybrid (Hyb. ), integrative (Int. ). For abundance data, we used the raw or reformatted (e.
The final set of bins for each binner was used as output with the exception of MGS-CANOPY for which we kept only bins >700 genes to conform to the definition of an MGS  (20). Each formatted output was then also converted using an in-house R script to a Cluster Node List (CNL) format in order to compute metrics comparing covers. Each line corresponds to a bin and is composed of tab-separated numerical values representing genes; these values range from one to the total number of binned genes. We represented each unassigned gene by a singleton bin to prevent potential biases in the paired comparison of the binning results due to unsynchronized gene sets (i. e. a gene assigned by one binner and not by the other). Application to the integrated gene catalog. We applied the best-performing binner of each category (MSPMINER, MAXBIN, DAS TOOL) to the IGC and compared the obtained binning results with the ones obtained on our SGC.
Evaluation of binners on a non-redundant gene catalog Standard metrics  (26, 52)  and test datasets  (25)  have been proposed to evaluate the recovery of MAGs from the assembled contigs of metagenomes. To our knowledge, there is yet no established consensus on how to compare species-level bins built from a non-redundant set of genes. Moreover, although the CAMI challenge has defined a framework to benchmark metagenomic tools, there are few independent published benchmarking results and still a considerable heterogeneity in the evaluation of binners. Quality assessment results are generally obtained either with commonly used tools based on SCMG  (57, 58)  or by comparing the results to a gold standard using tools like AMBER, which was developed to compute standard metrics during the CAMI challenge.
Nevertheless, even with defined standard quality assessment metrics, benchmarking results are not always comparable. Between the SCMG-based approaches, the implementation of metrics and especially the set of SCMG vary across the studies. As for approaches based on a gold standard, each benchmarking result is specific to the used dataset and in particular to its complexity (e. g. number of sequences, samples, species and strains) and to the microbial ecosystem it represents. In this study, we proposed a global benchmarking analysis of the binning results obtained from a non-redundant gene catalog with a focus on the human gut microbiota. It should, however, be noted that apart from the design of the SGC (selected microbial species and abundance profiles), our way of comparing the community structure at the species and strain levels along with the distribution of annotated genes (16S rRNA, prophage and plasmid genes) could Figure  6.
Complementarity and similarity of the methodological approaches Although each binner implements a different methodological approach, they are all related within their category either through their required input, part of their main algorithm (e. g. bin number estimation, computed distance and clustering method) or their dependencies. Indeed, all of the selected hybrid binners use kmer frequencies and the majority estimates the number of bins by starting with the identification of SCMG (MAXBIN, MYCC, COCACOLA, SOLIDBIN). Nevertheless, while almost all binners achieve a relatively good estimation, they do not always use the same marker set and neither does DAS TOOL that implements an integrative approach using SCMG to compute its scoring function. As for hybrid binners with a methodology independent of SCMG, even though they do not reach high completeness results on our SGC, they have shown good results on other datasets  (25, 26).
In our case, this filter leads to the loss of 60. 9% of the SGC genes, making it difficult to compare the results of hybrid binners with the output of abundancebased binners. This has led us to try to lower the gene length filter for hybrid binners when it was possible. Nevertheless, we would like to point out that apart from this parameter and the setting of the kmer size for MYCC that was advised by the authors, we did not try to optimize the other parameters of each binner. Although it could certainly improve their results, we believe the ones we obtained along with our comprehensive evaluation are enough to make an informed decision on which binners are more adequate to handle specific cases and therefore more suitable to be applied to a given dataset. Besides, our evaluation aims to be representative of the common usage, using either default parameters or setting the main parameters essentially following the recommendations of the authors.
Participants From 2005 to 2016, 32 patients were assessed for initial eligibility and completed the study. Six patients were excluded from the analysis because of an absent index test in one (i. e. a different diffusion sequence had been acquired), missing data on histological analysis in one, biopsy-proven telangiectatic osteosarcoma in three and periosteal osteosarcoma in one. In total, 26 patients were analysed. Figure  2  shows the flow of participants through the study. The age of patients ranged from 4. 7 to 19. 6 years (median: 13 years), with a male:female ratio of 11:15. Tumours were localized in decreasing order to the distal femur (14/26, 54%), proximal tibia (8/26, 31%), femoral diaphysis (2/26, 8%) and proximal humerus (2/26, 8%). Histology demonstrated 14 good responders (54%) and 12 poor responders to chemotherapy (46%) with a similar distribution between the subtypes of conventional osteosarcomas.
The main weakness of previous studies is that measurements obtained at the end of the neoadjuvant regimen are too close to surgery to allow any treatment optimization. Therefore, ADC measurements assessed earlier during the course of therapy should be considered. An emerging and promising approach for the development of imaging biomarkers could rely on radiomics based on MRI. The mining of advanced quantitative features from osteosarcoma combined with clinical and genomic data in high-order statistics may be able to define predictive models for risk stratification of patients before initiating neoadjuvant treatment  [25].
A value of soil VWC per probe was recorded on a CR800 datalogger every 30 minutes from November 26 th 2019 to March 16 th 2020. In one instance, the soil VWC was transformed into water potential to compare our results with other reported results. To obtain the pedotransfer function it was adopted the model by  Van Genuchten (Genuchten, 1980). The parametrization of the model was done with the R package soilphysics 4. 0  (Silva and de Lima, 2015)  and the soil properties measured with the soil analysis.
The extinction coefficient (K), necessary to calculate LAI from PAR data (Eq. (  1 )), depends on the solar angle (ϕ) and a leaf angle distribution coefficient (x) and was calculated applying Eq. (  2 )  (Campbell, 1986). The parameter x was calculated as the ratio of vertical to horizontal projections of the canopy  (Campbell, 1986). Based on field measurements, x was evaluated at 1. 2, corresponding to an ellipsoid leaf angle distribution. A reliable estimation of K is possible only when the solar angle is close to the zenith  (Flerchinger and Yu, 2007; Thomas and Winner, 2000) , therefore, we estimated LAI from PAR measurements collected between 10:30 and 14:30 (solar time). Furthermore, to avoid overestimation of LAI due to excess direct radiation reaching the sensors under the canopy, data from each sensor were smoothed and interpolated with the Daniell modified Fourier method  (Brockwell et al.
Atmospheric conditions Air temperature and relative humidity were logged hourly in each plot, with with two iButtons (DS1923-F5: Hygrochron, iButtonLink LLC, Whitewater, WI,USA) above the canopy at c. a. 5 m height. To protect the sensors from direct radiation and precipitation, these were installed facing the ground, glued to the internal part of bottle caps (Fig. 1, (3) ). As temperature and relative humidity were logged hourly and as they represent continuous variables, a linear interpolation was applied in order to obtain half-hourly time series that matched those of the other measured variables in the data set. The vapour pressure deficit (VPD) and night-time potential evapotranspiration (required to calibrate the zero point for sap velocity) were calculated following the FAO-56 Penman-Monteith method  (Allen et al.