dataesr/scientific-paragraphs-categorization huggingface dataset:
We experimented with specifying other random intercepts that vary with depth and measurement direction, but these resulted in models that were numerically intractable and failed to converge so we omitted them from the final model. As a result, our sampling design may contain some artefacts of pseudoreplication that we have not been able to account for fully in our model specification. Marginal P-values and the exact values of estimated marginal means should therefore be interpreted cautiously. Similarly, all model combinations with random slopes were numerically intractable and failed to converge. The final model includes random intercepts according to tube and month. The R code to fit our final model is available in the Supporting Information. We analysed the GLMM using the ANOVA function from the R package CAR  (Fox & Weisberg, 2019).
Acknowledgements The processing and the analysis of the data, as well as writing the manuscript were self-funded by  M. Arnaud. This research received funding from  National Geographic  ( CP134ER-17 ), the  Royal Geographical Society  with the  Institute of British Geographers , the  Estuarine & Coastal Sciences Association (Charles Boyden Fund) , and the  University of Leeds  ( International Mobility Grant, Climate Bursary Grant and Leeds University PhD Scholarship ), all awarded to  M. Arnaud. We thank  David Ashley  from the  School of Geography at the University of Leeds  for his help with the laboratory analysis and instrumentation support. We also thank the students and staff of the  Vietnamese National University of Ho Ch ı Minh City  for their support in the field. The management committee and staff of the  Cần Giờ Park gave permission  for access to, and use of, the field site and are thanked for their support during the field work, especially  Mr Nghia ,  Mr Sinh ,  Mr Hung ,  Mr Hoan ,  Mr Sang  and  Mr Kiet.
| Allochronic isolation The timing of adult eclosion is an important host-related adaptation that generates ecologically based, allochronic premating isolation between Rhagoletis attacking different host plants  (Feder & Filchak, 1999; Feder et al. , 1997; Inskeep et al. , 2021; Meyers et al. , 2020). To evaluate allochronic isolation, we compared adult eclosion times for 939 flies collected in 2017 from PNW, SW and ENA populations. We recorded the number of days it took for pupae to eclose as adults when incubated at 24°C, 65% relative humidity and 14:10 h light-dark following a standardized 6-month overwintering period at 4°C. Differences in eclosion times among populations were tested for significance by ANOVA (data were normally distributed) using population (PNW, SW, ENA) as the predictor variable and followed by Tukey honest significant difference (HSD) post hoc comparisons.
| Strain verification Sanger sequencing was used to verify that the Wolbachia strains in the mating trials were identical to those identified with TEEseq genotyping. A 550-bp fragment of wsp and a 500-bp fragment of hcpA were PCR-amplified (see Table  S3  for primers and protocols) from 10 frozen parents from each population in the postmating RI assays. Base-calling software does not disentangle two co-occurring strains well  (Tenney et al. , 2007). Thus, we manually searched raw trace data using ugene  (Okonechnikov et al. , 2012)  for the presence of strain-specific polymorphisms. All sequenced flies were additionally genotyped for a 650-bp fragment of the cytochrome c oxidase I (COI) mtDNA gene (Table  S3 ). Reads were quality trimmed, aligned using muscle  (Edgar, 2004) , and manually checked using aliview  (Larsson, 2014).
We added two R. cingulata sequences (GenBank: HQ677090. 1 and HQ677087. 1) for comparison, as well as a Rhagoletis completa (Cresson) sequence as an outgroup (GenBank: HQ677111. 1) (Doellman et al. , 2019). We generated an RAxML  (Stamatakis, 2014)  tree for the COI alignment using a GTRGAMMA model. ggtree  (Yu et al. , 2017)  was used to prepare trees. Fly COI sequences were deposited in GenBank (MZ820172-MZ820235).
(b) Estimated potential for introgression in the event of secondary contact for Wolbachia strain wCin3 from the SW/Mexican populations to the ENA/ PNW populations based on gene flow calculations and critical migration rates (m k ) shown in Table  S18. If p(GH) > m k the Wolbachia strain is expected to introgress and if p(GH) < m k the Wolbachia strain is expected to remain diverged between populations. Note that the probability of introgression varies depending on host lifespan and vertical transmission rate of the Wolbachia strain, wCin3. Data for the Mexican populations were taken from  Tadeo et al. (2015).
How data were acquired Genotyping: DNA extraction followed by PCR analysis (2720 Thermal Cycler, Applied Biosystems) using 14 microsatellites markers. Total lipids, proteins and fatty acid composition: Near Infra-Red Spectroscopy (NIR-6500 analysis system SYS II, FOSS©, calibration software: WINISI V4. 10. 0. 15326; acquisition software: ISISCAN version 2. 85). Storage protein composition: Solid-liquid extraction with salt buffer followed by gel filtration chromatography analysis (Superdex 75 Increase, GE Healthcare). Polysaccharide composition: alcohol extraction followed by acid hydrolysis. Alditol acetate derivatization and gas chromatography analysis (TRACE GC ULTRA TM , THERMO) for neutral sugars & colorimetric measurement for acidic sugars. Rheological data: seed grinding and soaking in a verjuice-like solution and rheological measurement using a Botswick consistometer (24cm, ENDECOTTS).
a b s t r a c t The data were collected from a brown mustard seeds collection of 18 accessions during two years and in three distinct sites of production in France. The 18 accessions of mustard seeds were selected to be representative of genetic, agronomical and technological variabilities. All accessions were produced in the "Bourgogne " area. This article describes agronomical data (PMG, yield), genotyping data, global composition of mustard seeds (lipids, proteins and polysaccharides) and fine composition of the previous macronutrients potentially involved in the technological properties (fatty acids, storage proteins and osidic composition of polysaccharides). Additional data regarding the potential rheological property of each accessions were also reported. These data can be reused by food industries, breeders and geneticists in order to understand pedoclimatic effects (year and location) and.
However, the availability of genome data covering basal AMF phylogenetic nodes (Archaeosporales, Paraglomerales) is presently based on fragmentary protein coding datasets  [9]. Geosiphon pyriformis (Archaeosporales) is the only fungus known to produce endosymbiosis with nitrogenfixing cyanobacteria (Nostoc punctiforme) presumably representing the ancestral AMF state  [10] [11] [12]. Unlike other AMF, it forms long fungal cells ('bladders') that enclose cyanobacteria. Once in the bladder, the cyanobacteria are photosynthetically active and fix nitrogen, receiving inorganic nutrients and water from the fungus. Arguably, G. pyriformis represents an ideal candidate to investigate the origin of AMS and the emergence of a unique endosymbiosis. Here, we aimed to advance knowledge in these questions by sequencing the genome of G.
Placement of G. pyriformis based on phylogenomics The G. pyriformis genome annotation was used to identify the phylogenetic placement of this species using amino-acid sequences. In this case, we used a set of 434 conserved fungal single copy genes (data available at DOI: 10. 5281/zenodo. 1413687) to construct a phylogenetic tree of the fungal kingdom. Phylogenomics supports the monophyly of Glomeromycotina and its close relationship with Mortierellomycotina within the phylum Mucoromycota  [1]  (Figure  3 ). Within Glomeromycotina, G. pyriformis groups within a monophyletic clade with Ambispora leptoticha and Paraglomus occultum, which diverged around 287 MYA (Figure  S1 ). This clade is distinct from more diverged nodes that contain sequenced representatives from Glomerales and Diversisporales  [27].
Among the 344 orthogroups classified as lost in the MRCA of the Glomeromycotina, we note the missing key enzymes involved in essential metabolic functions, such as sugar and thiamine metabolisms, or in the biosynthesis of fatty acids. These genes are also referred to as "Missing Glomeromycotina Core Genes" (MGCGs; see table of "Orthogroups lost in the MRCA of Glomeromycotina" in Data S1, see table of "Missing MRCA genes across glomeromycotina core genes" in Data S1), and our analysis reveals that these have also been lost by G. pyriformis. Other key losses that affect all sequenced Glomeromycotina include enzymes that actively degrade plant cell wall (see table of "Presence and absence of plant and fungal cell wall related CAZymes" in Data S1). Among the 661 orthogroups gained in the MRCA, most encode for proteins involved in signaling pathways (e.
MRCA of all extant Glomeromycotina carried hallmarks of mutualism and obligate biotrophy Genome data from a representative of the basal node of the AMF phylogeny filled an important gap in understanding the origin of AMS. Specifically, it allowed us to conclude that the MRCA of all extant Glomeromycotina carried hallmarks of mutualism and obligate biotrophyi. e. , a lack of genes for fatty acids and thiamine biosynthesis and nutrition, and a reduced number of genes that actively degrade plant cells. As such, the mechanisms involved in AMS appeared prior to the emergence of Glomeromycotina and a represent a synapomorphy of this sub-phylum. G. pyriformis has also conserved genomic signatures of sexual reproduction, as well as an apparent low nuclear polymorphism. Both traits are thus conserved across Glomeromycotina and are in stark contrast with the notion that these organisms represent an ancient asexual lineage.
Genome annotation Protein coding genes were predicted using Funannotate V1. 7. 4 (https://funannotate. readthedocs. io/) [DOI:10. 5281/zenodo. 3679386], which automates gene prediction. Assembled transcripts using Trinity  [64]  and Rnaseq reads mapped bam file were used as transcript evidence for gene call. Transposable elements were predicted using TransposonPSI  [65]. Repeat sequences were first identified using RepeatModeler  [66]  with multiple numbers of iterations. The iteration with the most number of repeats were then used for soft-masking the genome with REPEATMASKER (open 4. 0. 646) [16]. Output files generated from above procedures were used to identify repeat along the assembly. The completeness of genome assembly was assessed with BUSCO version 2. 0  [67]  with default parameters using the fungal gene dataset [fungi_odb9] ( see table of "Genome completeness using Busco" in Data S1).
Putative gene functions were identified using Diamond BLASTX  [59]. Pfam domain analysis were performed using Pfamscan  [68, 69]  (Figure  S4 ) (see table "Pfam domain counts from genomes of mucoromycota used in this study" and "pvalues calculated by fisher's test" in Data S1) and Carbohydrate-active enzymes (CAZYme) were identified using the dbCAN CAZy database  [70]  (Table  S2 ) (see table of "Presence and absence of plant and fungal cell wall related CAZymes" in Data S1. Putative CAZymes were further verified through comparisons of data from Morin et al 2019  [17]  (Table  S1 ). Secretory proteins were identified using previously published pipelines  [71, 72]  (see table of "Secretome proteins from genomes" in Data S1), and effectors were identified using EffectorP 2. 0  [73]  ( see table of "effector prediction on G.
Pyriformis genome" in Data S1). The putative MAT loci of Paraglomus sp. , and R. irregularis were identified by BLAST search procedures (see figure "Transcriptional directions of the putative AMF mating-type locus" and table "Identification of Meiosis specific genes" in Data S1). The tests for a two-evolutionary rates analysis was performed in part by measuring intergenic distance among genes in genome using a R script  [22]  ( see figure of "Heatmap showing evidence for a "one speed genome" in Geosiphon Pyriformis" in Data S1). For this study, published genomes of additional Glomeromycotina, Mortierellomycotina, and Mucoromycotina were downloaded from JGI portal MycoCosm database  [74, 75]  [DOI: 10. 1093/nar/gkt1183].
SNP calling and allele frequency analysis Filtered Mate Pair and Paired End reads were mapped onto the assembled G. pyriformis genome using the BWA-MEM v 0. 7. 17 algorithm  [76]  and sorted into a BAM file using samtools (v 1. 9)  [76]. Variants were called using FREEBAYES v1. 2. 0  [77]  and filtered using vcftools  [78]. Filtering cutoffs and procedures were as described by in Ropars et al. 2016 and Morin et al. 2019  [17, 21]  (see figure "Genome wide allele frequency of G. Pyriformis shows it is a homokaryon". Quality filtered variants and SNPs which passed filtering were used for constructing allele frequency plot using a custom R script (code available in GitHub repository).
Phylogenetic analysis and molecular dating The phylogenomic analyses employed a set of 434 generally conserved and single-copy proteins in fungi (data available at DOI: 10. 5281/zenodo. 1413687), which were developed through efforts of the 1000 fungal genomes project and provided in the Joint Genome Institute MycoCosm site  [1, 75, 79]. Profile-Hidden-Markov-Models of these markers were searched in the Geosiphon predicted protein sequences using HMMER3 (v3. 1b2)  [68]  and recovered 393 homologs (out of the 434) in total. The 434 markers in 45 included fungal genomes were further collapsed into 57 partitions using a greedy search embedded in PartitionFinder v. 2. 1. 1 for consistent phylogenetic signals  [80]. Phylogenetic trees were produced using the PHYling pipeline (data available DOI: 10.
5281/zenodo. 1257002) and with maximum likelihood method implemented in IQ-TREE (v. 1. 7-beta9) [81]. Concordance factors across the tree were calculated using the package implemented in IQ-TREE. The divergence time of Geosiphon sp. from the clade of "Ambispora leptoticha and Paraglomus occultum" was estimated using the R8S v1. 81  [82]  with the phylogenetic tree reconstructed from the earlier step. We employed five calibration constraints to calibrate the tree, including the crown groups of Fungi (1100 MYA)  [83] , Dikarya (772 MYA)  [83] , Chytridiomycota (>573 MYA)  [84] [85] [86] , the MRCA of Chytridiomycota and terrestrial fungi (>750 MYA)  [84] [85] [86] , and Glomeromycotina (>460 MYA)  [11]. The divergence time of each clade was inferred using the Langley-Fitch method with Powell algorithm  [87] [88] [89].
Alternative topology test and dating analyses To test the likelihood of other possible phylogenetic placements of G. pyriformis, we first reconstructed the associated phylogenetic trees using constraint tree topology as illustrated in Figure  S2  via "-g" option of the IQTREE package (iqtree-1. 7-beta9) [81]  ( see table "Results of alternative tree topology test for phylogenetic tree" in Data S1). We then compared our best tree (shown as Figure  3 ) with alternative topologies to compute the log-likelihoods of the trees using Kishino-Hasegawa test, Shimodaira-Hasegawa test, expected likelihood weight, and approximately unbiased test via "-zb" and "-au" parameters in IQTREE  [90] [91] [92] [93] [94]. All tests were performed with 10,000 resampling estimated log-likelihood (RELL) method for reliable results.
Detection of putative horizontal gene transfers To identify genes in Geosiphon that have potential origin in cyanobacteria, we compared the Geosiphon sp. genome to the available fungal and cyanobacterial genomes. To highlight potential HGT genes, we used a Python script (available in github repository)  [96]  to filter out genome component in G. pyriformis with higher similarity score to cyanobacteria than any fungi, excluding the G. pyriformis itself (see figure of " Phylogenetic tree showing evidence of horizontal gene transfer in selenium binding protein" and "Phylogenetic tree showing evidence of horizontal gene transfer in Molybdenum cofactor carrier" in Data S1) (see table of "HGT containing genes with pfam domains" in Data S1).
Gene orthology and evolution of symbiotic specific genes Orthogroups resulting from the OrthoFinder run were parsed using a custom Python script. To be retained, an orthogroup had to fill the following conditions: any sequence from the non-AMS fungi, at least one sequence of Geosiphon pyriformis, one sequence of either Rhizophagus irregularis or Rhizophagus cerebriforme and at least one sequence of Gigaspora rosea or Diversispora epigaea (see table of "Functions of genes which are gained in orthogroups" in Data S1). Reciprocally, orthogroups that could correspond to gene losses in the AMS fungi were extracted by retaining orthogroups with no sequences of AMS fungi and at least one sequence of each non-AMF fungi (Table  S3 ) and (see table of "Orthogroups lost in the MRCA of Glomeromycotina" and "Missing MRCA genes across glomeromycotina core genes" in Data S1).
6. 1  [81]  with 10,000 replicates of SH-aLRT. Trees were visualized and annotated with the iTOL platform v5. 5 [101,102] (Figure  S3 ). After first round of phylogeny, orthogroups showing an AMF specific pattern were blasted against the full MycoCosm database (1565 proteomes, last accessed: 03/01/2020) to confirm the AMF-specific pattern and a phylogenetic analysis was performed following the procedure described above.
chitololigosaccharides that are known symbiotic signals triggering the activation of the symbiotic program on the host plant [2,51,52]. PRJNA610605, Biosample SAMN14307302. RNA-seq reads are available in SRA with accession of SRR12018969, SRR12018968, SRR12018970. All scripts used to analyze are archived in https://github. com/madhubioinfo/Geosiphon. METHOD DETAILS Cultivation of G. pyriformis samples from natural habitat G. pyriformis was sampled during autumn in the only known stable habitat near the village Bieber in the Spessart (Germany). Active bladders of the Geosiphon-Nostoc endosymbiosis were found in slightly acidic soil (pH 5). The bladders occurred close to the hornwort Anthoceros spp.
Acknowledgements We thank  Vasilis Kokkoris  and  Allison MacLean  for comments on an earlier version of the manuscript. N. C research is funded by the  discovery program  of the Natural Sciences and Engineering Research council ( RGPIN-2020-05643 ) and the  Discovery Accelerator Supplements program  ( RGPAS-2020-00033 ). N. C is a University of Ottawa Research chair in Microbial Genomics. M. K. and C. K. were funded by the  Czech Sciences Foundation (GAČR) as Junior  grant with the project number  GJ16-16406Y. This work was also supported by the  Agence.
Nationale de la Recherche (ANR) grant EVOLSYM (ANR-17-CE20-0006-01) to P. M. D, by the Bill and Melinda Gates Foundation as Engineering the Nitrogen Symbiosis for Africa (OPP1172165) to PM. D and by Laboratoire de Recherche en Sciences Végétales (LRSV) laboratory, which belongs to the TULIP Laboratoire d'Excellence (ANR-10-LABX-41). J. E. S. is a CIFAR Fellow in the program Fungal Kingdom: Threats and Opportunities. Y. W. and J. E. S. were supported by US National Science Foundation grants DEB-1441715 and DEB-1557110.
Acknowledgments: The authors thank  C. Lorentz  for her help with the NMR analyses. The authors gratefully acknowledge the  National Agency of Research  ( CHEMLIVAL N • ANR-12-CDII-0001_01 ) for funding. C. C. A. thanks the  National Agency of Research  ( CHEMLIVAL N • ANR-12-CDII-0001_01 ) for grant. A. K. thanks  GDRI (International Research Network) "Catalytic upgrading of Biomass " for grant. The authors thank AXELERA, cluster for the chemical and environmental sectors, for support.
Results To accomplish this goal, the proteome from mature and ripe fruit was assessed from the variety O'Henry through shotgun proteomics using 1D-gel (PAGE-SDS) as fractionation method followed by LC/MS-MS analysis. Data from the 131,435 spectra could be matched to 2,740 proteins, using the peach genome reference v1. After data pre-treatment, 1,663 proteins could be used for comparison with datasets assessed using transcriptomic approaches and for quantitative protein accumulation analysis. Close to 26% of the genes that code for the proteins assessed displayed higher expression at ripe fruit compared to other fruit developmental stages, based on published transcriptomic data. Differential accumulation analysis between mature and ripe fruit revealed that 15% of the proteins identi ed were modulated by the ripening process, with glycogen and isocitrate metabolism, and protein localization overrepresented in mature fruit, as well as cell wall modi cation in ripe fruit.
2 ), with the most substantial differences being related to charge and hydrophobicity, using the Guy scale  (Guy HR, 1985). In terms of data dispersion, the P. persica primary transcripts proteome displayed a higher dispersion for 5 of the 6 parameters assessed, when compared to the datasets from the current proteome and the proteome from Spring Lady (Supplementary Table  2 , Supplementary Fig. 2 ). The functional analysis of the 1,663 proteins identi ed in this work, performed using Gene Ontology (GO) analysis, indicated that this protein set was enriched in processes related to carboxylic acid metabolism and intracellular transport, and to a lesser extent to protein folding (Fig. 2A ). We next asked if the genes encoding the proteins present in the mesocarp of mature and ripe fruits were more expressed at this stage, or if they were expressed at the same levels in any developmental stage of the fruit development.
In order to answer this question, a contrast between the transcriptional expression was performed of 18,074 P. persica genes (herein "full-dataset", variety Fantasia, GEO dataset GSE71561) at 125 days after full bloom (DAFB) against its expression at 41, 54, 69, 83, 111 DAFB. Close to 32% of the "full-dataset" did not display any expression level difference when contrasting its expression at 125 DAFB, corresponding to ripe fruit stage against any other stage ( rst lane, dark bar, Fig. 2B ). This proportion was 5. 9%, when considering the protein-coding genes assessed in this study (1,136 genes that matched our dataset, herein "proteomics-dataset", rst lane, grey bar, Fig. 2B ). When assessing the number of genes that displayed its highest expression levels at 125 DAFB (second lane, Fig. 2B ), 14.
6% of the "full-dataset" displayed this behavior, compared to 25. 8% of the "proteomics-dataset. " Finally, when assessing the number of genes that displayed its lowest expression levels at 125 DAFB (third lane, Fig. 2B ), 14. 1% of the "full-dataset" displayed this behavior, compared to 17. 6% of the "proteomicsdataset". This result indicates that an important proportion of the protein-coding genes characterized in the mesocarp of the mature-ripe fruit reached their highest expression levels at the ripening stage of the peach fruit developmental curve. Similar to the previous analysis, we evaluated if the protein-coding genes expressed at the fruit mesocarp during the fruit ripening were mainly expressed at this tissue or if they had an even expression across leaves, immature and ripe fruit.
Therefore, a transcriptomic dataset derived from the variety Babygold, consisting of 20,149 genes, whose expression was characterized by means of RNA-seq at leaves, 2 cm immature and ripe fruit mesocarp (Fig. 2C ,  Lü et al. , 2018) , was assessed. Gene expression at leaves was characterized by close to 35% of the genes being more expressed at this tissue, compared to 16% in the current "proteomics-dataset. " Gene expression in immature fruit displayed 38% of the genes being more expressed at this tissue, whereas in the "proteomics-dataset" this number was of 29%. Finally, at ripe fruit, 26% of the genes displayed a higher expression in this tissue, whereas in the "proteomics-dataset" this number doubled to 52%. This result indicates that half of the protein-coding genes characterized in the mesocarp of the mature-ripe fruit reached their highest expression in ripe fruit.