dataesr/scientific-paragraphs-categorization huggingface dataset:
00001. Taxonomic annotations for each contig were selected based on the higher bit scores for the best hits obtained from the NCBI or JGI databases. The annotated contigs were further separated into three categories (plants, fungi, metazoa) according to their annotations, and unknown contigs were removed. Contigs showing sequence coverage > 80% with a known sequence on NCBI or JGI databases and with average reads > 5 combined from all conditions were selected for further analyses. A summary of the assembly and the contig selection process is shown in Table  S2. Raw read counts were normalized (with the function counts in DESEQ2) and differential transcription levels of the contigs (Bonferroni adjusted P < 0. 05) were calculated with DESEQ2  (Love et al. , 2014). Condition-specific differentially transcribed genes (log 2 (fold-change) > 2 or < À2, FDR < 0.
Acknowledgements The project was funded by the  JGI , a  Department of Energy (DOE) Office of Science User Facility , and supported by the  Office of Science of the US DOE  under contract no. DE-AC02-05CH11231  within the framework of the  Mycorrhizal Genomics Initiative (CSP  no. 305 ),  Metatranscriptomics of Forest Soil Ecosystems  project (CSP no. 570 ), and the  1000 Fungal Genome projects  ( CSP  no. 1974 ). This work has been partially financed by projects  CGL2016-78946-R (AEI/FEDER ,  UE)  and  20866/ PI/18  ( FEDER and Programa Regional de Fomento de la Investigaci on -Plan de Actuaci  on 2019de la Fundaci on S eneca,  Agencia de Ciencia y Tecnolog ıa of the Region of Murcia, Spain). This research was also supported by the  Laboratory of Excellence ARBRE  ( ANR-11-LABX-0002-01 ), the  Region Lorraine , the  European Regional Development Fund , and the  Plant-Microbe Interfaces Scientific Focus Area in the Genomic Science Program , and the  Office of Biological and Environmental Research in the US DOE, Office of Science.
Data availability Full genomes, predicted genes, and transcripts of T. claveryi T7 and T. nivea G3 can be found at the following JGI portals: https://genome. jgi. doe. gov/Tercla1/Tercla1. home. html and https://genome. jgi. doe. gov/Tirniv1/Tirniv1. home. html, respectively. RNA-seq data from T. claveryi 9 H. almeriense mycorrhiza and drought stress assay are available at Gene Expression Omnibus (GEO) with the accession nos. GSE154490 and GSE155042.
2  and 3 ). The final 10 chromosomal pseudomolecules anchored 302 Mb (80%) of the genome (Supplementary Fig. 4 , Supplementary Table  4 ). Protein-coding genes were annotated using a combination of ab initio prediction and transcript evidence gathered from RNA sequenced from nine tissues/developmental stages of nodulation using both RNA-sequencing (RNA-seq) and PacBio isoform sequencing (Iso-Seq) (Supplementary Tables  5  and 6 ). The current annotation contains 32,667 gene models (Fig. 1a , Supplementary Table  7 ). Their expression pattern was also determined by developing a Gene Atlas from the RNA-seq data obtained here (Supplementary Table  8 ) and from an earlier nodulation kinetics  18. The identification of 94. 4% of the 1,440 genes in the Plantae BUSCO dataset (Supplementary Table  9 ) confirmed the high quality of the genome assembly and annotation.
In A. duranensis and A. ipaiensis, no NCRs with the cysteinerich motif 1 could be found, whereas 10 and 5 NCR-like genes, respectively, with the defensin-like motif 2 were identified (Fig. 4a , Supplementary Fig. 15  and 16 ) and the expression of most of them is induced in the nodule (LegumeMines database). These features of Dalbergioid NCRs raise questions as to how they emerged and whether they evolved to symbiotic or defense functions.
5c  and 5d ). In the globin phylogeny, AeLb1 and AeLb2 cluster tightly with certain class 1 Glb genes of Arachis and also of the more distantly related legume Chamaecrista fasciculata. The Arachis genes are also highly expressed in nodules (LegumeMines database) and probably encode Lbs. Among the C. fasciculata genes, one was previously evidenced to be highly expressed in root nodules and to code for a putative ancestral Lb named ppHB  37  (corresponds to the Chafa1921S17684 gene in Fig. 5c ). Sequence and synteny analysis further indicated that A. evenia Lbs and class 1 Glb genes are similar and located in a single locus that is conserved in legumes (SI Appendix, Fig. S18-S20 ). This supports the hypothesis that A. evenia Lbs arose from class 1 Glbs by local gene duplication and the presence of probably such Lbs in Arachis and Chamaecrista legumes, further suggests this evolution to be ancient.
A 20-kb insert SMRTbell library was generated using a BluePippin 15 kb lower-end size selection protocol (Sage Science). 55 Single-Molecule Real-Time (SMRT) cells were run on the PacBio RS II system with collections at 4-hourly intervals and the P6-C4 chemistry  49  by the Norwegian Sequencing Center (CEES, Oslo, Norway). A total of 8,432,354 PacBio post-filtered reads was generated, producing 49 Gb of single-molecule sequencing data, which represented a 78x coverage of the A. evenia genome. PacBio reads were assembled using HGAP (version included in smrtpipe 2. 3. 0), the assembly was polished using the Quiver algorithm (SMRT Analysis v2. 3. 0) and then the SSPACE-LongRead (v1. 1) program scaffolded the contigs when links were found (Supplementary, Table  1 ). MiSeq reads were also generated to correct the sequence and estimate the genome size based on k-mer analysis (SI Appendix, Supplementary Note 1).
The de novo genome assembly contains 1,848 scaffolds, with a scaffold N50 of ~0. 985 Mb and with 90% of the assembled genome being contained in 538 scaffolds Then, we performed the A. evenia chromosomal-level assembly using serial analyses (fully described in Supplementary Note 1). The anchored scaffolds were joined with stretches of 100 Ns to generate 10 pseudomolecules named Ae01 to Ae10 according to the linkage group nomenclature for A. evenia  12  (Supplementary Tables  3  and 4 ). Gene prediction and annotation. First, repeats were called from the assembled genome sequence using RepeatModeler v1. 0. 11 (https://github. com/rmhubley/RepeatModeler) (SI Appendix, Table  S12 ). The genome was then masked using RepeatMasker v4-0-7 (http://www. repeatmasker. org/). Nine tissue specific RNA-Seq libraries (sequenced by the GeT-PlaGe Platform, Toulouse, France) and full-length transcripts generated from Iso-Seq (sequenced by the Cold Spring Harbor Laboratory, New York, USA) (details in SI Appendix, Supplementary Methods) were aligned on the unmasked reference with STAR  50.
The resulting bam files were processed with StringTie 51 v1. 3. 3b to generate gene models in gtf format which were merged with Cuffmerge from Cufflinks 52 v2. 2. 1 to produce a single gtf file. This gtf was used to extract a corresponding transcript fasta file using the gtf_to_fasta program included in the TopHat 52 v2. 0. 14 package. The masked genome, the transcript fasta file and the gff files were used to train a novel AUGUSTUS 53 v3. 2. 3 model. This model was used to call the genes for all chromosomes. The AUGUSTUS prediction and the gtf files were then given to EVM 54 v1. 1. 1 to refine the model and remove wrongly called genes. This produced a new gff file that was used to extract the corresponding transcripts using gtf_to_fasta. These transcripts were processed with TransDecoder 55 in order to validate the presence of an open reading frame.
To check the completeness of the prediction, a master list of 100 nodulation genes was created and used for some additional manual annotation leading to the current annotation containing 32,667 gene models (Supplementary Table  7 ). Alignments of the Illumina RNA-seq clean reads from the nine samples with the STAR software supported 25,301 of the 32,667 predicted genes (Supplementary Table  8 ). Finally, genome assembly and annotation quality was assessed using the Benchmarking Universal Single Copy Orthologs (BUSCO 56 v3) with the BLAST E-value cutoff set to 10 -5 (Supplementary Table  9 ). The BUSCO analysis includes a set of 1,440 genes that are supposed to be highly conserved and single copy genes present in all plants. Gene functions were assigned according to the best match of alignments using BLASP (1e-5) to SwissProt database.
Gene expression analysis. The normalized gene expression counts were computed using Cufflinks package based on the TopHat 51 output results of the RNA-Seq data analysis from the nine samples' (Root N-, Root N+, Nodule 4d, Nodule 7, Nodule 14d, Stems, Leaves, Flowers and Pods) analysis performed for the A. evenia genome annotation. Gene expression was calculated by converting the number of aligned reads into FPKM (Fragments per kilobase per million mapped reads) values based on the A. evenia gene models. RNA-seq data previously obtained from RNA samples of A. evenia IRFL6945  18  were also processed and converted into FPKM. Orthogroup inference. We inferred orthogroups with OrthoFinder 57 v. 0. 4. 0 to determine the relationships between A. evenia, the other diploid Aeschynomene taxa and several legume species.
Sequence alignments were visualized with Jalview 61 v2. 11. 0. Microsynteny analysis was performed using the Legume Information System with the Genome Context Viewer (https://legumeinfo. org/lis_context_viewer) and the CoGe Database (https://genomevolution. org/coge/), using the GEvo (genome evolution analysis) tool to visualize the gene collinearity in syntenic regions. Nodulation mutants. Nodulation mutants were obtained for A. evenia and characterized as fully described in the Supplementary Note 3. Briefly, a large scale mutagenesis was performed by treating 9,000 seeds from the CIAT22838 line with 0. 30% EMS incubated overnight under gentle agitation. Germinated M1 seedlings were transferred in pots filled with attapulgite. M1 plants were allowed to self and 4-6 M2 pods corresponding to approximately 40 seeds were collected from individual M1 plants.
The A. evenia genome was indexed with BWA 63 index (v0. 7. 12-r1039, using standard parameter). Reads were assessed for quality using the FastQC software (https://www. bioinformatics. babraham. ac. uk/projects/fastqc/) and aligned on the reference genome with bwa mem using "M" option. The alignment file was compressed, sorted and indexed with Samtools  64 (v1. 3. 1). Variations were called with Freebayes 65 (v1. 1. 0, with "-p 100 --use-best-n-alleles 2 -pooled-discrete"). The resulting variation file was annotated using SnpEff 62 (v4. 3t and "eff -c snpEff. config transcript" parameters) and SNP indexes corresponding to mutant allele frequencies were calculated. SNP plots with the SNP index and their chromosomal positions were obtained to identify genetic linkages visible as clusters of SNPs with an SNP index of 1.
Data availability The data reported in this study are tabulated in Datasets S1-S9 and SI Appendix. Genome assembly and annotation, accession resequencing and RNA-seq data for A. evenia are deposited at NCBI under BioProject ID: PRJNA448804. RNA-seq data for other Aeschynomene species are available under the BioProject ID: PRJNA459484. Resequencing data for A. evenia nodulation mutants are available under the BioProject ID: PRJNA590707 and PRJNA590847. Accession numbers for all deposited data are given in Supplementary Dataset 9. Genome assembly and annotation for A. evenia can also be accessed at the AeschynomeneBase (http://aeschynomenebase. fr) and at the Legume Information System (https://legumeinfo. org). Additional data were obtained from Legume Mines (https://mines. legumeinfo. org) and CoGe (https://genomevolution.
In silico bioinformatic analysis The subcellular localization of CP12 from ve species of diatoms was predicted using a signal peptide predictor dedicated to diatoms, HECTAR (https://webtools. sb-roscoff. fr/) [27]. The signal peptides of CP12 from other organisms were predicted using ChloroP (http://www. cbs. dtu. dk/services/ChloroP/, [28]), SignalP (http://www. cbs. dtu. dk/services/SignalP/ [29] ) and TargetP (http://www. cbs. dtu. dk/services/TargetP/ [30] ). These enabled the N-terminus of the mature chloroplast CP12 proteins to be determined. Disordered regions were predicted using PONDR VL-XT (http://www. pondr. com/) [31] , DisEMBL Remark-465 (http://dis. embl. de/) [32] , IUPred2A Loading [MathJax]/jax/output/CommonHTML/jax. js (https://iupred2a. elte. hu/) [33]  and s2D (http://www-mvsoftware.
ch. cam. ac. uk/index. php/s2D) [34]. Coiled coil regions were predicted using Paircoil (http://cb. csail. mit. edu/cb/paircoil2/paircoil2-like. html), with a minimum search window of 21 residues  [35]. Amino acid frequency in the algal and diatom CP12 was analyzed by composition pro ler (http://www. cpro ler. org/cgi-bin/pro ler. cgi) [36] against the Protein Data Bank (PDB) (https://www. rcsb. org/). Overexpression and puri cation of CP12 from T. pseudonana Primers containing the NdeI and BamHI restriction sites were used to amplify and clone the CP12 gene in frame with the N-terminal histidine tag of the pET28a expression vector (Novagen) (forward primer 5′ CATATGGCTGCCATTGAAGCTGCTCT 3′ and reverse primer 5′ GGATCCCTAACGGGAACCAAGGGCC 3′). This plasmid was used to transform Escherichia coli BL21(DE3) pLysS.
Frames of 990 ms with dead-time of 10 ms were recorded throughout the elution, with 100 frames recorded at the very rst minutes of the elution to measure the buffer background. The protein concentration was monitored via the absorbance at 280 nm with an in situ spectrophotometer. The experiment was performed in 30 mM Tris, 50 mM NaCl, 2 mM EDTA, 1 mM Tris(2-carboxyethyl)phosphine (TCEP), pH 7. 5. Data reduction to absolute units and solvent subtraction were performed using FOXTROT, a dedicated inhouse application. The frames recorded during the elution peak were carefully compared with each other, and data corresponding to identical scattering pro les and radius of gyration (Rg) were averaged to increase the signal-to-noise ratio. Data analysis was performed using the ATSAS suite of software  [44].
Funding This was supported by the  CNRS. BG is supported by the  Agence Nationale de la Recherche  grant  ANR-15-CE05-0021-03. HL is supported by the  A*MIDEX  grant  AAP-IM2B-NE-2020-02-Launay. HS scholarship was founded by the  Chinese Scholarship Council  ( 201704910795 ). WH was supported by a fellowship for a visiting scholar from the  Chinese Academy of Sciences.
Statement of need Having a properly oriented MRI dataset is a fundamental step in any neuroimaging workflow. For example, manual segmentation is greatly simplified by the symmetry obtained by aligning brains with respect to the perpendicular stereotaxic planes. In human neuroimaging MRI orientation is often done automatically, using atlas registration to generate an affine matrix representing the rotation and translation necessary to align the superior/inferior, anterior/posterior and left/right directions. However, this is not always the case in other species, or in developmental data. For most species there is no reference atlas, and especially in ex vivo neuroimaging, brains are often oriented in arbitrary ways with respect to the stereotaxic planes, and may have large non-tissue space surrounding the brain.
Methods Reorient was coded in JavaScript and runs as a GitHub Web page. Code style was verified using eslint (https://eslint. org). Unit tests and end-to-end tests were implemented using mocha. js (https://mochajs. org) and puppeteer (https://pptr. dev). Modifications in the code are continuously tested using CircleCI (https://circleci. com). Interactive stereotaxic viewers with orientation information panel on the side. Dragging is used to easily translate, rotate and crop the data. Save the reorientation matrix and the crop box along with the reoriented Nifti volume for a reproducible workflow. Middle: Reorientation example using a macaque from Prime-DE site "amu"  (Brochier et al. , 2019; Milham et al. , 2020). Left: Center brain by dragging in the interface. Middle: Rotate brain with respect to the standard view axes.
The map was created using QGIS version 2. 18  41  (URL: https ://qgis. org/). (unpublished data) and coupled with information from prior studies  36. Laying dates were estimated by subtracting the average incubation duration from the estimated hatching date. Egg-laying, hatching, and fledging dates were estimated similarly in burrows without camera traps, but with less accuracy, ranging from 2 to 3 weeks. For burrows containing a chick during our last visit (i. e. March 2020), the laying date was estimated by subtracting the incubation duration from the estimated hatching date. A total of 86 breeding events were observed during the study period. Egg-laying, hatching, and fledging dates were estimated and visually represented using 1d Kernel Density Estimate with the stat_density function implemented in the package 'ggplot2'  44.
Foraging trip characteristics. Data for a total of 40 TP foraging trips were collected in 2017 (number of trips: n = 8), 2018 (n = 12), and 2019 (n = 20) from 21 GPS-tracked individuals, and 57 WTS foraging trips were obtained in 2017 (n = 45), and 2018 (n = 12) from 38 individuals. Multiple trips were sometimes recorded from the same individuals (Table  1 ). The following metrics were calculated for each trip, using the R package "trip" 45 : (1) foraging trip duration from the departure to the return to the colony, (2) cumulative distance travelled between all locations assuming straight-line Euclidean distances between 2 successive locations, (3) maximum distance from the colony (hereafter "maximum range"), (4) average travel speed along the trip at sea (i. e. total distance travelled divided by the trip duration), and (5) maximum travel speed during the trip, computed between two successive locations, and assuming straight-line Euclidean distances.
This method has been used to assess ecologically meaningful behaviors from geolocation data for a range of seabird species, including Procellariiforms  [47] [48] [49] [50] [51]. In order to estimate daily activity patterns of each species, the total number of each behavioral type identified by the EMbC was summed per hour of the day, and divided by the total number of behaviors per hour, thus representing the relative proportion of each behavior according to the time of the day. Daily distribution of each behavior were compared using Watson-Wheelers test for homogeneity on two samples of circular data, implemented in the 'circular' package  52. Proportion of each behavior was also calculated per individual in order to compare the time spent on each behavior between species. This comparison was computed using a multivariate analysis of variance (MANOVA), including the proportional use of each behavior per individual as response variables, and species, colony type (i.
Foraging areas and their overlap. To deal with data heterogeneity related to the use of various GPS devices with differences in the acquisition frequency of GPS locations (between 15 and 60 min, average 23. 7 min for WTS, 27. 0 min for TP), all tracks were interpolated at a regular interval of 15 min using the function redisltraj from the R package adehabitatLT  53. Interpolated tracks were used to identify the main foraging areas of both species, by computing the Kernel Utilization Distribution of the GPS locations identified as "intensive foraging" or "extensive search" with a smoothing parameter h = 0. 2° to avoid over-fragmentation, using the R package adehabitatHR  53. Main foraging areas were defined as 90% Utilization Distributions (UDs), representing the 90 percent volume contours of the Kernel Utilization Distribution.
Spatial overlap of colony foraging areas was determined by overlapping the UDs 90% of the different colonies for each species, and calculating the percent of area shared, ranging from 0-100% following this equation  54  : With A 0 = the area of 90% UD intersection between colonies, and A Colony = the area of the 90% UD of the colony, calculated with the package rgeos  55. Depth of foraging bouts. The depth of the ocean where intensive foraging or extensive search bouts were performed was determined using the 'ETOPO180' variable downloaded from https ://www. ngdc. noaa. gov/mgg/ globa l/globa l. html. Values were compared between species using Wilcoxon-Mann-Whitney tests.
g. neritic vs. oceanic for instance) between the two seabird species. Stable isotope values were compared between species using a PERMANOVA, with δ 15 N and δ  13  C values as response variables, and species, colony type, year and as explanatory variables. As mentioned above, we did not expect an impact of colony type on stable isotope values, but this variable was kept in the analyses to ensure the veracity of our assumptions. The PERMANOVA was performed using the function adonis from the vegan 58 package. SIA of carbon and nitrogen were used together to estimate and compare isotopic niche width between the two species using Stable Isotope Bayesian Ellipses In R (R package SIBER  59  ). The standard ellipse area corrected for small sample sizes (SEA C ), % Shared Area = [A 0 ] ÷ A Colony1 -A 0 + A Colony2 -A 0 + A 0 containing 40% of the bivariate δ  13  C and δ  15  N data, and the convex hull areas (TA) were computed for each species, giving an estimation of their isotopic niche width.
The Trimmomatic v. 0. 36  66  and OBITOOLS software package  67  were used for subsequent filtering of the reads following the general workflow described in De Barba et al. 68. Taxonomic assignments were performed using both the approach available within the OBITOOLS pipeline and a BLAST search on the NCBI nucleotide database. A consensus taxonomic assignment was obtained considering only family and genus level assignments and used for further analyses. Negative extraction and PCR controls were used and carried through the workflow to assess potential cross-contamination and set minimal threshold values for species detections. Each prey taxa identified was categorized according to its habitat, depth range and migrating behavior, i. e. , pelagic, benthic, benthopelagic, reef-associated, based on Fishbase  69  and SeaLifeBase  70  information.
DNA metabarcoding. When considering the reads assigned to a family or genus level taxonomy, we obtained a total of 249,918 and 478,790 fish sequences, 146,962 and 55,068 cephalopod sequences and 94,046 and 284,686 crustacean sequences for TP and WTS respectively (see Supplementary Information S1 for summary data of the DNA metabarcoding analyses). A total of 18 taxa were identified in the 3 TP regurgitates with 6 fish families, 4 cephalopod families and 1 crustaceans family. Most of the prey were deep pelagic organisms migrating at the surface at night (Gempylidae, Myctophidae, Chiroteuthidae, Enoploteuthidae, Histopteuthidae, Onychoteuthidae, Stylocheiron sp. ), but some of the prey were non-migrating deep pelagic organisms (Neoscopelidae, Sternoptychidae, Euphausia sp. ), and three taxa of deep benthopelagic organisms were also identified (Macrouridae, Trichiuridae).
Alpheus sp. , Dynomene sp. , Atergatis sp. ). Of the limited number of regurgitate samples available, and over a total of 41 taxa identified overall, only 2 taxa (the cephalopod Abralia  sp. and the krill Euphausia sp. ) were found in the 2 seabird species regurgitates; the NMDS-plot showed a clear absence of overlap in prey species composition between TP and WTS (Fig. 12 ). The two-dimensional Bray Curtis dissimilarity index indicated a stress value of 9. 72 × 10 -5. Figure  8. 90% Utilization distributions (UDs) of foraging GPS locations of Tahiti petrels (TP) and wedge-tailed shearwater (WTS). The overlap between UDs 90% is represented in purple. Bathymetry data were extracted from 'ETOPO1 Global ReliefModel' from 'National Oceanic and Atmospheric Administration'. The maps were created using the package ggplot2 version 3.