dataesr/scientific-paragraphs-categorization huggingface dataset:
After filtration, the eDNA can be extracted, amplified, and sequenced using metabarcoding while being non-invasive for many macro-organisms. With bioinformatics tools, the DNA sequences can be sorted, cleaned, and compared to a reference genetic database to identify the species present in the environment  (Boyer et al. 2016). There is now a considerable body of literature using eDNA metabarcoding from marine water samples in most ecosystems, from cold Arctic  (Merten et al. 2023)  and Antarctica  (Fonseca et al. 2022)  to temperate  (Rozanski et al. 2022 ) and tropical environments  (Polanco Fernández et al. 2020 ) targeting generally fish but also other groups such as eukaryotes, sharks, mammals. A few tree-of-life approaches have shown that we can recover a large taxonomic breadth of species from just a single water sample using a multi-primer approach  (Stat et al.
Analysis of physical parameters The structure of the water column was characterized using a CTD (Idronaut Ocean 316) equipped with sensors for conductivity, temperature, PAR (Photosynthetic Active Radiation) (sensor xy), and chlorophyll a (chl a; sensor xy). The CTD was left 1 min at the surface to measure incident light out of the water before data acquisition. The conductivity profile for the section of the study site and the temperature salinity plot to identify changes in water masses were done using R by applying a multilevel B-splines method available in the MBA package from the methods of  (Lee et al. 1997).
Bioinformatics The 18S-V9 assay targeting planktonic organisms was processed to generate a list of MOTUs to approximate their richness. We followed the processing pipeline of the TARA studies using the same marker  (de Vargas et al. 2015)  where they used the SWARM clustering algorithm  (Mahé et al. 2015). The entire workflow consisted of aligning pairedend reads with vsearch  (Rognes et al. 2016) , then sample demultiplexing and primer trimming using cutadapt (no mismatch tolerated for demultiplexing and two mismatches tolerated for primer trimming;  (Martin 2011 ). Then we clustered sequences using SWARM (d = 1) and detected chimeras using uchime  (Edgar et al. 2011). We further curated reads by removing all occurrences below 3 reads  (de Vargas et al. 2015). Taxonomic assignments were matched to the protist reference ribosomal databases (PR2;  (Vaulot 2022) , a curated genetic database for the 18S-v9 primers and using the RDP classifier algorithm from the dada2 pipeline with defaults settings  (Callahan et al.
2016). We accounted for tag-jump by removing all intra-library occurrences of less than 0. 001 ratio of the total sequence abundance. Taxonomic assignments were then checked to remove any non-target amplification such as Homo sapiens and all sequences occurings in the blanks were removed from the data set. We also discarded all sequences not assigned to any domain (e. g. , bacteria, archaea, or eukaryotes) and only retained sequences assigned to eukaryotes for analysis. The fish sequence data with the teleo primers was processed to generate a list of taxonomic entities (taxa) corresponding to distinct species. We used a pipeline using the obitools toolkit  (Boyer et al. 2016 ) encapsulated in the snakemake pipeline manager (https:// gitlab. mbb. univ-montp2. fr/ edna/ snake make_ rapid run_ obito ols, version 1.
2. 0). The main steps include the merging of forward and reverse reads using the illuminapairedend function, demultiplexing using nsgfilter, and cleaning using obiclean with default values. Taxonomic assignment was done using ecotag on the EMBL snapshot of May 2021. We investigated all perfect matches corresponding to several species to attempt getting finer taxonomic assignments, if for example, 2 species of the same genus corresponded to a sequence detected by only was known to occur in the Arctic, we replaced the genus-level assignment to the species-level by the species occurring locally. We accounted for tag-jump by removing all sequence occurrences below 0. 001 ratio of their total abundance in a given library  (Schnell et al. 2015). Data was further cleaned by removing all occurrences below 10-reads for a given PCR replicate, and we cleaned taxonomic assignments for taxonomic redundancy to better approximate species identity and richness under an incomplete genetic reference database.
So, for each filter, any detection at a higher taxonomic level that was not a 100% perfect match was removed if a detection was present at a lower level. For example, in a given sample, we detected the species Clupea harengus (100% match) and the genus Clupea (98% match) so we removed the Clupea occurrence to have a representative number of taxa, as the genus detection is likely an error derived from the correct species-level sequence. This approach allows to get a conservative estimate of the number of species, considering the low fish richness in these latitudes, both overall and per clade, together with the coverage of the reference database (48% at least, as shown in GAPeDNA,  (Marques et al. 2021 ). In cases with lower reference database coverage and higher richness, there are good reasons to use clustering, for example to better approximate richness, but in this particular case using a non-clustering pipeline was more appropriate.
Correspondence between environmental conditions and species composition We used several analytical methods to examine the relationship between environmental parameters and taxa/MOTUs composition in eDNA samples. First, environmental parameter values for each eDNA sample were ordinated with a principal component analysis (PCA) using the FactomineR R package. From the biological compositional data, the taxa and MOTUs composition were ordinated using principal coordinate analysis (PCoA) using the ape R package. We also ordinated the decomposition of the beta-diversity by its total, turnover and nestedness components using betapart  (Baselga and Orme 2012). Then, a co-inertia analysis was conducted using the coinertia function of the ade4 R package to evaluate variance variation between (i) fish and environment, (ii) eukaryotes and environment, and (iii) fish and eukaryotes.
Antibody array analysis of midgestation fetal liver cells Our group has used expression of CD14, in conjunction with CD326, to distinguish hepatoblasts and liver sinusoidal endothelial cells (LSECs) among fetal liver cells lacking the blood cell marker CD45  (9, 20). Using this staining scheme, antibody screening was performed to analyze the expression of a wide array of cell surface antigens on hepatoblasts as well as other fetal liver cell-populations as described in a the following dataset article  (21). Fetal liver cells (21 weeks' gestation) were first depleted of CD235a + erythrocytes by immunomagnetic bead depletion. To define the non-hematopoietic compartment of liver cells, cells were stained with CD45 as well as labeled CD235a antibody to remove residual red cells by gating. The diverse population of hematopoietic cells comprised 71.
Notable loss of CD203c expression was observed after 7 days (Figure  6F ) and after 15 days of culture of sorted CD203c + CD326 ++ cells (Figure  6G ). It is important to note that declines in the S/N ratios occur, in part, due to Immunofluorescence staining of fetal liver. Liver was fixed and frozen sections prepared that were stained with antibodies against CD326 (AlexaFluor 488, green), CD203c (AlexaFluor 568, red), and DAPI to stain nuclei (blue). CD203c was stained using clone 97A6 (A, B) and clone FR3-16A11 (C, D). Images were photographed using a Leica CTR6500 microscope with  200X (A, C ) and 630X (B, D) magnification. Image analysis was performed using Media Cybernetics In Vivo and Apple iPhoto software. Color channels were enhanced individually, with uniform application of all processes, before generating composite, shown in the composite photomicrographs in the left column.
Hepatoblastoma cell lines and RNAseq data Hepatoblastoma cell lines HB214, HB295, HB243, HB279, HB282 and HB284 were maintained as previously described  (40). Additional descriptions of the tumor origins of the hepatoblastoma cell lines are presented in that publication. Briefly, pathology reports indicated that the main cellular components of the tumors from which these cell lines were derived are fetal (HB214, HB295), embryonal (HB243, HB282, HB284), and embryonal and macrotrabecular (HB279). HB-279 and HB-284 were obtained from the same patient at primary and recurrence resection. Raw FASTQ sequencing files were aggregated from sources described in  Kats et.
Data presentation and statistical analysis Flow cytometry data were analyzed using FlowJo software, versions 9 and 10 (FlowJo, Inc. ; Ashland, OR). Statistical analysis and charting were performed using Aabel NG software (Gigawiz Ltd. Co. OK, USA) and GraphPad Prism version 9. 3. 1(GraphPad Software, San Diego, California, USA), both for Macintosh.
Among armadillos, some are relatively myrmecophagous (e. g. , tolypeutines), while others are more omnivorous (e. g. , euphractines). Whereas an extended history of myrmecophagy is universally associated with dental regression  (Charles et al. , 2013; Davit-Béal et al. , 2009; Reiss, 2001) , herbivory and omnivory are not. Accordingly, is the dental regression seen in xenarthrans the result of inheriting regressed teeth from their last common ancestor, which possibly had an insectivorous/myrmecophagous diet followed by subsequent dietary shifts to herbivory and omnivory? Or does it represent parallel events in multiple lineages? A second set of questions concerns the timing and patterns of regressive evolution. First, has the degeneration of teeth in xenarthrans taken place over a short period of time, consistent with selection against their presence, or has it been a gradual process over many millions of years in a manner more in line with relaxed selection and genetic drift? Furthermore, is there any sort of consistency in the sets of genes that are lost and the timing of those losses, or does the regression of teeth occur via divergent genomic patterns? To answer these evolutionary questions, we collected genomic data to study patterns of pseudogenization and selection pressure in 11 core dental genes for most living species of Xenarthra.
Methods To study patterns of dental gene loss in xenarthrans, we assembled a dataset of 11 dental genes: nine genes have well-characterized functions and/or expression patterns tied to tooth development and are frequently pseudogenized in edentulous and enamelless taxa (ACP4, AMBN, AMELX, AMTN, DSPP, ENAM, MMP20, ODAM, ODAPH;  Choo et al. , 2016; Delsuc et al. , 2015; Gasse et al. , 2012; McKnight and Fisher, 2009; Meredith et al. , 2014 Meredith et al. , , 2013 Meredith et al. , , 2011a Meredith et al. , , 2009;; Mu et al. , 2021; Sharma et al. , 2018; Sire et al. , 2008; Smith et al. , 2017; Springer et al. , 2019 Springer et al. , , 2016)) , and two other genes (DMP1, MEPE) are expressed in dentin  (Sun et al. , 2011; Gullard et al. , 2016). Our taxonomic coverage included 31 xenarthran species (four anteaters, six sloths, seven dasypodid armadillos, 14 chlamyphorid armadillos) plus 25 outgroup species spanning the remaining three superorders of placental mammals.
Target sequence capture and sequencing Baits for DNA sequence exon capture were designed using available xenarthran sequences for the complete CDSs of all focal dental genes except ACP4. This was due to its link to amelogenesis imperfecta being reported after the design and synthesis of these baits. For each gene, 80mer baits were generated with a 4x tiling density and were then BLASTed against the genome assemblies of Choloepus hoffmanni (GCA_000164785. 2) and Dasypus novemcinctus (GCA_000208655. 2). Baits with more than one hit and a Tm outside the range 35-40°C were excluded. This resulted in a final set of 5,262 baits (Supplementary Dataset S1) that were synthesized as part of a myBaits RNA kit by Arbor Biosciences (Ann Arbor, MI, USA). The xenarthran Illumina libraries previously prepared  (Gibb et al.
Whole genome sequencing and assemblies For the southern naked-tailed armadillo (Cabassous unicinctus MVZ 155190), a PCR-free library Illumina library with 450 bp inserts was prepared according to the recommendations of the Broad Institute for subsequent genome assembly with DISCOVAR de novo (https://software. broadinstitute. org/software/discovar/blog/?page_id=375) following the strategy of the Zoonomia project (Zoonomia consortium, 2020). The library was sequenced at the Vincent J. Coates Genomics Sequencing Laboratory (University of California, Berkeley, CA, USA) on an Illumina HiSeq4000 instrument using 250 bp paired-end reads. A draft genome assembly was produced from these data using the DISCOVAR de novo assembler (https://github. com/broadinstitute/discovar_de_novo/releases/tag/v52488) using the following command: DiscovarDeNovo READS=[ReadsFile] OUT_DIR=[OutputDirectory] NUM_THREADS=24 MAX_MEM_GB=700.
We also utilized multiple whole genome assemblies deposited in NCBI Whole Genome Shotgun (WGS) contig database (Supplementary Table  S2 ). To obtain genes on NCBI, we BLASTed human reference nucleotide sequences, composed of exons, introns and flanking sequences, against the WGS database using discontiguous megablast (BLAST+ 2. 8. 0). We then used the NCBI-derived xenarthran sequences to BLAST using discontiguous megablast against de novo assemblies imported into Geneious. Sequences derived from assemblies that contained strings of more than 10 unknown nucleotides (Ns) had these stretches of Ns reduced to 10 for ease of alignment. Raw sequencing reads have been deposited in the Short Read Archive (SRA) at the National Center for Biotechnology Information (NCBI) under Bioproject PRJNA907496.
Dataset assembly Xenarthran sequences were assembled, and occasionally combined, from the various methodologies employed (Supplementary Datasets S2-12). Outgroup sequences were retrieved by BLASTing reference sequences against GenBank using discontiguous megablast and obtaining NCBI annotated gene models (Supplementary Table  S2 ). All orthologs of xenarthrans and outgroup sequences were aligned using MUSCLE  (Edgar, 2004)  in Geneious, examined by eye and adjusted manually to correct for errors in the automated alignments. After characterizing putative inactivating mutations (see below), we prepared codon alignments suitable for subsequent selection pressure analyses based on dN/dS estimations (Supplementary Datasets S13-23) by removing insertions, incomplete codons and stop codons from individual sequences, and any other portions of sequences with dubious homology, including: exons 7-9 of AMBN, given probable exon duplications within the human reference  (Toyosawa et al.
We first reconstructed the evolution of the dN/dS ratio (ω) for each gene using the Bayesian approach implemented in Coevol 1. 4b  (Lartillot and Poujol, 2011). Coevol provides a visual representation of the variation in dN/dS ratio estimates across a phylogeny (e. g. Lartillot and Delsuc, 2012; Springer et al. , 2019). We used the dsom procedure to jointly estimate branch specific dN/dS ratios, divergence times, body sizes, generation times, and ages at sexual maturity modeled as a multivariate Brownian diffusion process across the phylogeny. We used the same composite placental mammal topology for all analyses  (Emerling et al. , 2015; Gibb et al. , 2016) , assumed fossil calibrations from previous studies  (Meredith et al. , 2011b; Emerling et al, 2015; Foley et al. , 2016) , and extracted the three life history traits (body size, generation time, and age at sexual maturity) from the PanTHERIA database  (Jones et al.
, 2009). We set the prior on the root node to 97 Ma with a standard deviation of 20 Ma following the molecular dating estimates of  Meredith et al. (2011b). For each dataset, we ran two independent MCMC for a total of 1000 cycles, sampling parameters every cycle. MCMC convergence was checked by monitoring the effective sample size of the different parameters using the tracecomp command of Coevol. The first 100 points of each MCMC were excluded as burnin and posterior inferences were made from the remaining 900 sampled points of each chain. Next, based on the patterns of inactivating mutations, we performed branch model dN/dS ratio analyses  (Yang 1998; Yang and Nielsen, 1998)  using codeml in PAML v4. 8  (Yang, 2007)  to estimate the selection pressure experienced by the different tooth genes throughout xenarthran history.
Selection pressure analyses Given that shared inactivating mutations provide only a minimum probable date for inactivation, they may underestimate the timing of the onset of relaxed selection on a gene. Gene dysfunction may predate the fixation of a frameshift indel or premature stop codon, e. g. , due to disruption of non-coding elements. In order to evaluate changes in selection pressure resulting from dental regression, we first reconstructed the global variation in dN/dS across the placental phylogeny (Figure  4 ) using the program Coevol, which implements a form of rate smoothing through the incorporation of a Brownian motion model of continuous trait evolution. These analyses allow for the visualization and localization of shifts in selection pressure that have occurred within xenarthrans, likely corresponding to relaxed selection and pseudogenization events in the 11 focal dental genes.
Acknowledgements We would like to thank  Anaïs Tibi  and  Delphine Sérol  for their contribution to this study. We also thank  Sérgio Ferreira-Cardoso  and  Lionel Hautier  for helpful discussions. Jeremy Johnson  ( Broad Institute, Cambridge, MA, USA ) kindly provided early access to xenarthran genome assemblies. This work would not have been possible without the help of the following individuals and institutions in accessing xenarthran tissue samples:  Mariella Superina ,  Jim Loughry ,  Agustín Jiménez ,  Rodolfo Rearte ,  Guido Valverde , and  Guillermo Pérez-Jimeno ;  Philippe Gaucher ,  Roxane Schaub ,  Gérard Lievin ,  Erika Taube ,  Philippe Cerdan ,  Michel Blanc ,  Maël Dewynter ,  Sébastien Barrioz ,  Rodolphe Paowé ,  Jean-François Mauffrey ,  Eric Hansen ,  François Ouhoud-Renoux , and  Jean-Christophe Vié  ( French Guiana );  Baptiste Chenet  and  David Gomis  ( Zoo de Lunaret, Montpellier, France );  John Trupkiewich  ( Philadelphia Zoo, USA );  Daniel Hernández  ( Facultad de Ciencias, Universidad de la República, Montevideo, Uruguay );  Sergio Vizcaíno  ( Museo de La Plata, La Plata, Argentina );  Ross MacPhee  ( American Museum of Natural History, New York, USA );  Jonathan Dunnum  and  Joseph Cook  ( Museum of Southwestern Biology, Albuquerque, USA );  Jake Esselstyn ,  Donna Dittman , and  Mark Hafner  ( Louisiana State University Museum of Natural Science, Baton Rouge, USA );  Darrin Lunde.
Funding This research was supported by a  European Research Council  consolidator grant ( ConvergeAnt ERC-2015-CoG-683257 ; FD); the  Centre National de la Recherche Scientifique (CNRS ; FD); the  Scientific Council of the Université de Montpellier (FD) ;  Investissements d'Avenir  grants managed by  Agence Nationale de la Recherche  ( CEBA :  ANR-10-LABX-25-01 ;  CEMEB :  ANR-10-LABX-0004 ; FD); a  National Science Foundation Postdoctoral Research Fellowship in Biology  (award no. 1523943 ; CAE); a  National Science Foundation   Postdoctoral Fellow Research Opportunities in Europe award  (CAE); the  People Programme (Marie Curie Actions)  of the  European Union's Seventh Framework Programme  ( FP7/2007-2013 ) under  REA  grant agreement no. PCOFUND-GA-2013-609102 , through the  PRESTIGE programme coordinated  by  Campus France (CAE) ; the  France-Berkeley Fund  (FD and MWN); and the  Natural Sciences and Engineering Research Council of Canada (NSERC , no.
Reverse transcription and genes amplification by PCR cDNAs were synthesized from total RNAs in 96-well plates with the RevertAid H Minus Reverse Transcriptase and the oligo(dT) 18 primer according to the manufacturer's protocol (www. thermoscientific. com). Specific primers for the amplification of the full-length opening reading frames of the candidate genes were designed according to the published peach genome sequence in the Genomic Database for Rosaceae (GDR, www. rosaceae. org). The candidate genes are referenced as Prupe. 4G072600. 1 for eIF4E (coding sequence length: 705 bp), and Prupe. 1G046600. 1 for eIFiso4E gene (645 bp). For both genes and each individual, polymerase chain reactions (PCRs) were performed in a final volume of 25 µL using the Taq DNA Polymerase from Qiagen (www. qiagen.
Allelic sequencing, phasing and alignment PCR products were sequenced with the Sanger method by the Genewiz company, following recommendations available on the www. genewiz. com website. Quality of sequences was verified from the chromatograms using Chromas v. 2. 5. 1 (https://technelysium. com. au/wp/chromas/). Sequences of individuals were all classified in a data file corresponding to the appropriate main group and sub-group in which they belonged (see Plant material and sampling section). As the Prunus species used in the current study are all diploid, each gene can have up to two alleles. In this case, heterozygous allelic forms with unphased genotypic data were rebuilt with the ELB algorithm method implemented in Arlequin v. 3. 5. 2. 2 as a pseudo-Bayesian approach to specifically estimate gametic phase in recombining sequences  (Excoffier et al.
Genetic diversity parameters Genetic diversity estimates were calculated using DNAsp v. 5. 10. 01 software  (Librado and Rozas 2009). The haplotype diversity (Hd), the nucleotide diversity (π), the ratio of nonsynonymous to synonymous substitutions (dn/ds ratio) and the Tajima's D statistic were calculated from allelic data for each main group and sub-group. The Datamonkey online database (www. datamonkey. org) was used to test the selection pressure on (i) the whole genes sequences with the RELAX test to analyze whether the strength of the selection has been relaxed or intensified along the phylogeny of the sequences and the BUSTED model (Branch-site Unrestricted Statistical Test for Episodic Diversification) to provide a gene-wide test for positive selection at at least one site on at least one phylogenetic branch, and (ii) at individual sites with the FUBAR test (Fast Unconstrained Bayesian AppRoximation) for large data sets with a Bayesian approach to infer the dn/ds ratio on a per-site basis and detect positive or negative pervasive selection at the amino acid level assuming the selection pressure for each site is constant along the entire phylogeny  (Weaver et al.
Acknowledgements The authors wish to pay their respects to the late  Dr Raul Karychev , former scientist of the  Kazakh Research Institute of Horticulture and Viticulture (Almaty ) who helped tremendously We acknowledge the European FP7  IRSES-246795  " STONE ", the  ANR-13-KBBE-0006  " COBRA " and the  PRIMA FREECLIMB  (#  1813-2 ) grants. S. L. was recipient of a  Chinese Scholarship Council PhD  grant. V. D. and S. L. thank the  French Embassy in Beijing  for accommodation and research support in China ( Xu Guangqi  program  2016-2018 ) and the  Research Federation on Integrative Biology and Ecology of Bordeaux University  for a travel grant. The authors wish to acknowledge all the people who helped in collecting the samples. We thank the curators of the  French Genetic Resources Centre (Marine Delmas) , of the  US ARS-USDA  repository ( John Preece ) and of the  Czech Horticultural  repository of Lednice.