dataesr/scientific-paragraphs-categorization huggingface dataset:
The two others, i. e. K352R and P395S, were detected in four and ten accessions, respectively. While located in the MIF4G domain, they did not occur in the 15 aminoacid region which was mutated in the resistance alleles described so far, but instead were detected at least 28 amino-acids downstream. Twenty-three nonsynonymous mutations were detected in the CPR5-1 gene (Additional file 1: Table  S6 ). However, none of them led to an untimely stop codon or frameshift. Similarly to what we observed in our O. glaberrima dataset, O. sativa presented high variability at the NLR RYMV3 locus, with 112 non-synonymous mutations (Additional file 1: Table  S6 ). Eight mutations were detected in 10. 4% of the accessions and resulted in stop codons or frameshifts. The 104 others were in frame mutations, leading to amino-acid substitutions or single amino acid insertions or deletions in the protein.
These results demonstrated that the different truncated forms of CPR5-1 were resistance alleles of the RYMV2 recessive resistance gene. In addition, O. sativa lines mutated in the CPR5-1 gene were characterized. T-DNA insertional mutant lines tagged in the CPR5-1 gene were identified by searching the flanking sequence database  [27]  of the mutant library developed by Jeon et al. [28]  and Jeong et al. [29]. Two independent T-DNA insertions in the CPR5-1 gene were confirmed by sequencing the T-DNA flanking regions. In the 3D-01842 line, T-DNA was inserted 1975 bp downstream of the ATG, in the fourth exon; in the 3A-06612 line, T-DNA was inserted 315 bp downstream of the ATG in the first intron (Fig. 2a ). Phenotyping of these mutants was performed on a minimum of 12 plants homozygous for the insertion.
Genomic data Genomic data on O. glaberrima accessions based on high coverage genomic sequencing (average 35X, range 20-55X) were obtained from Cubry et al. [21]. The IRGSP 1. 0 Nipponbare sequence  [51]  had been used as reference for mapping and SNP calling. The polymorphism database  [52, 53]  included genomic data of 163 O. glaberrima and 83 O. barthii accessions. In a first step, the database was filtered for the 163 O. glaberrima accessions and for the ATG-stop codon regions of target genes. The ORGLA04G0147000. 1, ORGLA01G0359000. 1 and ORGLA11G0175800. 1 gene models established on the CG14 accession  [1] , corresponding to Os04g42140. 1, Os01g68970. 1 and Os11g43700. 1 gene models on the reference Nipponbare sequence, were considered for RYMV1, CPR5-1 and NLR RYMV3 , respectively. The target regions corresponded to positions 24,946,655-24,952,068 on chromosome 4 of the reference Nipponbare sequence for RYMV1, 40,071,092-40,073,727 on Nipponbare chromosome 1 for CPR5-1 and 26,377,263-26,380,577 on Nipponbare chromosome 11 for NLR RYMV3.
Only SNPs and indels that were polymorphic within O. glaberrima accessions were conserved. In a second step, polymorphisms were filtered with GATK 3. 7 VariantFiltration  [54]  using the following filters: QUAL< 200, MQ0 > 4 && MQ0/DP > 0. 1, DP < 10, clusterSize 3 in clusterWindow-Size 10, DP > 20,000. SNPs with more than 10% missing data or heterozygous in more than 10% accessions were filtered out, and genotypes defined based on a single read were considered as missing data. Genomic data of the O. glaberrima CG14  [1]  and Tog5681 accessions  [23]  were included in the dataset. The haplotype of the RYMVsusceptible CG14 accession was used as a reference to describe variants observed in the population. Genomic data from the 3000 Rice Genomes Project  [26]  were retrieved from the Rice SNP-Seek database  [25].
The database was filtered on the target regions indicated above for indels and non-synonymous SNPs from the base SNP set, which includes about 18 million SNPs. SNP effects were retrieved from the database, while indel effects were manually estimated. The sequence variant nomenclature proposed by Den Dunnen et al. [55]  was used to describe the mutations and their effects on CDS and proteins. Based on the results described in this paper or previously  [12, 14, 19] , dominant alleles were indicated with the first letter in upper case and recessive ones with the first letter in lower case; when there was no preferred hypothesis, allele names were written with all letters in upper case. For the RYMV2 and RYMV3 candidate genes, the different alleles were named depending on their association (R) or not (S) with RYMV resistance.
Acknowledgements We thank  Christine Dubreuil-Tranchant  and  François Sabot  for their help in the bioinformatics analysis,  Gatean Maillot  for technical help in T-DNA mutant characterization and  Harold Chrestin  for seed multiplication. We are also grateful to  Denis Fargette ,  François Anthony ,  Mathias Lorieux  and  Nils Poulicard  for helpful discussions. We thank  Gynheung An  for providing the T-DNA mutant lines.
Availability of data and materials The O. glaberrima genomic dataset analysed in the current study have been described in  [21]  and are available in the IRD Gigwa repository, https:// gigwa. ird. fr/gigwa/. The sequences obtained by Sanger in this study are available in GenBank under reference accessions numbers MT348348 to MT348356 for RYMV2, MT348357 to MT348360 for RYMV1, and MT348361 to MT348369 for NLR RYMV3. All additional data generated in this study are included in the present article and its supplementary information files.
Quantified insecticidal activity of the efficient local Bti strains When tested at more concentrations of δ-endotoxins, the group 3 strains could not kill any larvae of A. aegypti at or below 5 ng/l and killed all the larvae at or above 250 ng/l. These strains killed about 50% of larvae at a concentration of 75 ng/l of δ-endotoxins. At other concentrations of 30 ng/l, 50 ng/l, 100 ng/l and 125 ng/l, they killed 19%, 38%, 66% and 86%, respectively. Among these Group 3 strains, the percentage of larvae killed by QBT220 was slightly higher as shown in Figure  2. Probit analysis software showed that the calculated LC 50 for the reference strain H14 was 95 ng/l. On the other hand, the calculated LC 50 for QBT217, QBT218, QBT220 and QBT221 were 65 ng/l, 66 ng/l, 60 ng/l and 68 ng/l with an error of þ/-20 ng/l, respectively.
senhousia (i. e. , longer at 3′), has been found in Ruditapes philippinarum, but located in the F genome (unpublished GenBank annotation mentioned in Passamonti et al. 25  ). As suggested by Bettinazzi et al. 19  , non-canonical features of the cox2 gene are often coupled with DUI, but it is difficult to propose a general function because each major lineage of bivalves that possesses the DUI system exhibits some novel features. Clearly further analyses involving additional species are required to better understand the relationship between these structural variations in the cox2 gene and DUI, as well as other general features of DUI-exhibiting mitochondrial genomes. To date, DUI has been found in over one hundred bivalve species representing four taxonomic orders and twelve bivalve families (reviewed in Gusman et al.
In both male mitochondrial DNAs, four unassigned regions contained supernumerary ORFs of >150 bp, i. e. between cob-trnW, trnW-rrnS, trnG-cox2 and rrnL-atp6 for S. plana and between cob-cox2, cox2-trnW, trnG-rrnS and rrnS-trnM for L. balthica (Table  3 ). Five ORFs corresponding to the expected profile were found in S. plana, two between trnW-rrnS and three between trnG-cox2, whereas four ORFs with the expected profile were found in L. balthica, one between trnG-rrnS and three between rrnS-trnM (Fig. S2 ). Sequence similarity searches using PSI-BLAST  55  against non-redundant protein sequences and SWISSPROT databases failed to detect significant sequence similarity with known proteins for all these ORFs except one (i. e. mtORFan1 in the unassigned region trnG-rrnS of L. balthica M mtDNA; Fig.
Mitochondrial genome sequencing. Total DNA extractions from S. plana male and female gonads were respectively used to prepare two DNA libraries that were paired-end sequenced (2 × 100 bp) using an Illumina HiSeq platform (Génome Québec Innovation Centre, Montréal, QC, Canada). Paired reads were trimmed with Trimmomatic 0. 32  68  and merged with Pear 0. 9. 6  69  on the Galaxy online platform  70  , checking the quality of reads at every step with FastQC 0. 11. 5  71. MITObim 1. 8  72  was used to assemble the complete genome sequence, starting from a S. plana partial cox1 sequence (GenBank accession number KX447421 for the male mtDNA and KX447423 for the female mtDNA) as an initial seed. For L. balthica, the library preparation was done following the SQK-LSK108. Nanopore protocol. A DNA shearing step (using Covaris gTubes) was included to linearize circular mtDNA molecules (1 min at 11k RPM in a 5415 R Eppendorf centrifuge).
The resulting library was sequenced on the Minion MIN-101B sequencer using a R9 flowcell. Basecalling was performed using Albacore 2. 1. 3  73 , followed by quality control using Minion_QC 1. 0  74. Nanopore adapters were removed and chimeric reads were discarded using Porechop 0. 2. 3 (Wick R. Porechop, available at: https://github. com/rrwick/Porechop). Reads with quality scores <9 were removed using Nanofilt 2. 0. 0  75. Cleaned reads were assembled using Canu 1. 6  76  considering an approximate genome size of 2 Gb and disregarding all reads smaller than 500 bp. Resulting unitigs were blasted locally using blastn  55  , using male and female rrnL sequences (Genbank accession numbers KX831969 and KX831970, respectively)  42. Based on these results, we identified tig00000009 (length of 26,120 bp, assembly based on 61 nanopore reads) as the male mitogenome.
The two F-type contigs (tig00000885 and tig00000888) were then merged. The quality of the mitogenome assembly was checked at each step of the pipeline (initial Canu assembly, Nanopolish polishing, Pilon polishing) by calculating the percent identity between our F-type unitigs and a published F-type mitogenome from the same sampling locality (KM373201)  43  using MUMmer 3. 23  81. Mitochondrial genome sequences were deposited in GenBank (accession codes MN528026 and MN528027 for the F and M mtDNAs of S. plana, respectively, and accession codes MN528028 and MN528029 for the F and M mtDNAs of L. balthica, respectively). Characterization of mtDNAs and sequence analyses. The male and female mitogenome sequences obtained were annotated with MITOS2 82. Protein-coding genes (PCG) were also predicted using NCBI′s Open Reading Frame Finder, choosing the invertebrate mitochondrial genetic code and alternative start codons.
Amino acid sequences were determined using EMBOSS Transeq and by assuming an invertebrate mitochondrial code  85. Codon-alignments were generated by aligning M and F amino acid sequences for each gene using MUSCLE  86  in the online portal found at http://www. ebi. ac. uk/Tools/msa/ muscle/84. Using these protein alignments as references, nucleotide sequences for each respective gene were then codon-aligned via PAL2NAL v14  87. The exceptionally large indel within the Mcox2 gene was manually excluded from the gene alignments. MEGA was also used to conduct a Z-test of Selection [via the Nei-Gojobori method] for each gene  52, 88. All plots relating to gene conservation were generated in R version 3. 4. 1 (R Core Team 2017) using the package ggplot2 v3. 2. 0  89. For comparative purpose, p-distances of individual genes between F and M mtDNAs were also calculated for three species known for having the greatest F versus M DNA divergences in the families Mytilidae, Veneridae and Unionidae: i.
e. the mytilid Modiolus modiolus (GenBank accession: F, KX821782; M, KX821783)  53  , the venerid Ruditapes philippinarum (GenBank accession: F, AB065375; M, AB065374)  17  , and the unionid Quadrula quadrula (GenBank accession: F, FJ809750; M, FJ809751)  14. These comparisons facilitated a better understanding of the selection pressures acting on the PCGs in these species. Finally, the presence of supernumerary mitochondrial protein-coding genes (and mtORFans) in unassigned regions was examined using ORF Finder (NCBI). We retained only ORFs with a minimal length of 150 bp and localized on the coding strand, as mitochondrial genes are only encoded on one strand in our two species. Transmembrane domains were predicted using the TMHMM server v2. 0 (http://www. cbs. dtu. dk/services/TMHMM/). Alignments between predicted ORFs were done with MUSCLE  86  and the percentage of identity was visualized with MView 90 , via the EMBL-EBI online web services  85  keeping default parameters.
Acknowledgements EP thanks the  Molecular Core Facility  at the  University of La Rochelle and the bioinformatics platform Toulouse Midi-Pyrenees (Bioinfo GenoToul ). This work was funded by the  French Agence Nationale de la Recherche  ( HySea  project  ANR-12-BSV7-0011 ;  DRIVE  project  ANR-18-CE02-0004-01 ),  La Rochelle Université , the  CPER/ FEDER  ( ECONAT  project), and by funding from the  Natural Sciences and Engineering Research Council of Canada  (grant no. RGPIN/435656-2013  to SB).
Data Collection Data for this study were collected from 17 dairy farms (7 organic and 10 LI-conventional) throughout England and Wales between November 2011 and October 2012. All herds were a mix of both purebred and different crossbred cows (Table  1 ). Herd sizes ranged from 150 to 550 cows, and a total of 1,070 cows were recorded to encompass a broad range of breeds and crosses from each farm. A one-off questionnaire was completed to gain information on pre-survey health and parity as well as a breeding pedigree for all individual cows (according to the farmers' records). Milk from each cow was sampled over four dates: autumn 2011 (D1), spring (D2), summer (D3), and autumn 2012 (D4). A corresponding questionnaire for each farm and cow was used to record husbandry practices on all sampling dates, including milk yield, disease incidence, health treatments, cow diet, calving intervals, milking, and grazing management.
Low-Input-Production and Principal Components Analysis The initial data collection involved 1,070 cows, but for some farms and/or cows on some sampling dates, there are missing and incomplete records. For the observational statistics, the cows selected had records on any given date for production, health, and FA composition results (explained later). This resulted in 299, 757, 772, and 613 cows on D1, D2, D3, and D4, respectively. Focusing on the available data, using a combination of farm records and results from milk analysis and the priorities of typical LI practices, the variables selected to define LI-P were split into three main criteria: 1. Production: i. Milk yield (L/day). ii. Total fat and protein solids (kg/day).
Fatty acid profile: i. Percentage of total profile with desirable FAs (n-3, OA, CLA9, EPA+ DPA+ DHA). ii. Percentage of total profile with FAs often consumed in excess and undesirable (C12:0, C14:0, C16:0, n-6, and n-6/n-3 ratio). The elements of LI-P had different units (FAs were proportional, yield: liters/cow/day, SCC: ×10 3 cells/ml milk, etc. ); thus, the data was standardized (normalization to mean of zero and standard deviation of one)  (53)  to give each element of LI-P the same weight. Principal components analysis (PCA) in the package "vegan" (54) was used to aid visualization of the effects of Farm ID (2-17) on LI-P. Two sets of graphs were produced from the PCA. First, graphs (Figure  1 ) in which points represent samples/records from cows (at each farm, one graph for each the four dates), where the closer two points are to each other in PCA ordination space, the more similar their characteristics (in terms of production, health, and milk FA profile).
Descriptive Statistical Analysis For the descriptive statistical analysis, additional inclusion criteria were considered: on any sampling date, records existing   3. The "R" package "nlme" (55) was used to model "Breed" against the variables described for the LI-P, with Season and Farm ID as random factors. The linear mixed-effects model accounts for variation explained by the fixed effects (Breed) and random effects (Season and Farm ID). As farms were observed across the four sampling dates, these related measures would violate the independence assumptions made by a linear model, hence, the use of "Farm ID" and "Season" as random factors. Days in milk did not differ between the breeds (Fstatistic = 1. 50, p = 0. 165), allowing the breed to be compared without differentiating or adjusting for the stage of lactation.
DISCUSSION The data collected for this paper provides valuable information from commercial farms of direct practical application for farmers, in an area lacking in the scientific literature. As a study monitoring on-farm activities, many variables are not controlled, but the statistical model mitigates some of these effects. The data collected is of sufficient quality and range to provide invaluable insights into LI-P systems in the UK. This includes the effects of breed combinations on LI-P and determining how and why breeds are suited to different farms. Although this paper does not draw definitive conclusions, it explores the current status of dairy breeding strategies and highlights how farmer's decision-making should direct future LI (cross) breeding research.
1A , arrowhead). Thus, the distal limb expression of Dbx2 was delayed by approximately 24h when compared to the onset of Hoxa13 and Hoxd13 in the autopod (Fig. 1A -C;  10, 38  ). At E12. 5, Dbx2 mRNA spread to the entire distal-most portion of the autopod, both in digit and interdigit mesoderm and in a nested domain within the Hoxa13/Hoxd13 expressing cells (Fig. 1A, B ). However, Dbx2 transcripts were rapidly downregulated in the interdigit region and, from E13 onwards, they were detected in sequentially formed domains reminiscent of the forming digit joints. To assess which cell type(s) express the Dbx2 gene, we used known markers of tendon and cartilage precursors expressed in digit joints. We also re-analyzed single cell-RNA sequencing (scRNAseq) datasets from E11, E13 and E15 mouse hindlimbs  39  (Fig.
Identification of HOX13-bound sequences regulating Dbx2 expression in digits. Dbx2 expression in distal limbs is strongly compromised in the absence of HOX13 proteins 23. To further evaluate whether HOX13 paralogs could act as direct regulators of Dbx2 expression, we set up to characterize the extent of the Dbx2 regulatory landscape both by analyzing available Hi-C datasets and by performing 4C-seq experiments (Fig. 2 ). TADs are megabase-scale structures that constitute a unit of 3D genome organization  37, 46. Thus, they often coincide with and delimit the extent of gene regulatory landscapes  47, 48. TADs are generally independent from the transcriptional status of the gene(s) inside and can be identified across different cell types or tissues. We analyzed high-resolution (5Kb bin size) Hi-C data from ES cells and embryonic cortex  49  , as well as 40Kb-resolution Hi-C profiles from E12 mouse distal limbs 50 (Fig.
2B , asterisk and Fig  S3A ). These results suggested that Dbx2 expression in the developing limbs is mostly driven by mid and short-range regulatory interactions within its immediate 150Kb surroundings. To identify putative regulatory sequences controlling Dbx2 expression in developing digits, we analyzed H3K27 acetylation datasets  23  , a histone modification specifically enriched in active enhancers and promoters  51. In mouse E12 PFL and DFL cells, within the 800Kb spanned by Dbx2 and its flanking TADs, we identified only five non-coding regions specifically enriched in H3K27ac (Fig. 2C  and Fig. S3B ). These were located within the Dbx2-interTAD domain, suggesting that they could correspond to Dbx2 regulatory elements. Of these, two were located in the intergenic region on the 5' side of the Dbx2 locus, two others mapped within Dbx2 intronic sequences and one overlapped with the first Dbx2 exon and TSS.
All these sequences were strongly contacted by the Dbx2 promoter (Fig. 2B ). Other H3K27ac-positive regions were identified within the Ano6/Arid2/Scaf11 TAD, yet they were not specifically enriched in this epigenetic mark in DFL cells, arguing against a specific involvement of these sequences in Dbx2 regulation. We also analyzed HOXA13 and HOXD13 ChIP seq datasets  28  to determine whether these proteins would directly interact with the Dbx2 locus (Fig. 2C  and Fig. S3 ). We observed HOXA13/HOXD13 binding at several locations within the Dbx2 genomic region. While most of these HOX13 bound sequences were not located in H3K27ac-positive and Dbx2 interacting regions, we nevertheless observed strong binding of these proteins in three of the DFL-specific H3K27ac-positive regions showing an interaction with Dbx2.
Nell2/Dbx2/Ano6 coregulation in developing limbs. The absence of limb alterations in Dbx2 null mice raised the question of whether the neighboring Nell2 and Ano6 genes may contribute to limb development. In fact, the entire Dbx2 genomic region has a syntenic interval in humans and other tetrapods (Fig. 6A ) and the deletion involved in hand-foot defects in humans also contains the NELL2 and ANO6 genes  36. WISH analysis as well as mining a scRNA-seq dataset  39  revealed that Nell2 and Ano6 are specifically expressed in the distal portion of mouse developing limbs, in a population of Hoxa13/Hoxd13 double-positive cells, part of which also express Dbx2 (Fig. 6B, C ). In both cases, their transcripts were distributed on both sides of the developing digits, displaying an indentation (Nell2) or a faint band (Ano6) corresponding to the joints of the forming phalanges (Fig.
Hi-C/ ChIP seq/ scRNA-seq data analysis 4C-seq interaction profiling All scripts used to analyze data and generate figures are available at https://github. com/lldelisle/scriptsForBeccariEtAl2021. The calculations were performed using the facilities of the Scientific IT and Application Support Center of EPFL. For the chicken Hi-C analysis, the raw forelimb and hindlimb data were extracted from GEO (see Table  II ) and processed independently using HiCUP v0. 8. 0 76 on galGal6. Valid pairs were obtained using a custom python script. Both valid pair files were merged before analysis. Valid pairs from Hi-C carried out on mouse material were downloaded from GEO (see Table  II ). Valid pairs of each study were loaded in a cool file using cooler version 0. 8. 10 77 using a resolution of 5Kb, 20Kb or 40Kb.
9] [80]  with --minBoundaryDistance 100000 and either default parameters for the choice of window size or a fixed window size of 120Kb. Plots were obtained using pyGenomeTracks version 3. 6  78, 81. ChIP-seq paired-end (PE) fastq of HOXA13 and HOXD13 data, as well as single-read (SR) fastq from H3K27me3 and H3K27ac and corresponding inputs were downloaded from GEO (see Table  II ). Adapter sequences and bad quality bases were removed with Cutadapt 82 version 1. 16 with options -a GATCGGAAGAGCACACGTCTGAACTCCAGTCAC -A GATCGGAAGAGCGTCGTGTAGGGAAAGAGTGTAGATCTCGGTGGTCGCCGTATCATT -q 30m 15 (-A being used only in PE datasets). Reads were mapped with bowtie 2. 3. 5  83  with default parameters on mm10. Alignments with a mapping quality below 30 were discarded with samtools view version 1. 9  84, 85.
For HOXA13 and HOXD13, coverage and peak calling were computed by macs2 version 2. 1. 1. 20160309 with options --call-summits -f BAMPE -B. Coverage was then normalized by the number of million fragments used in macs2 coverage. For histone marks, coverage and peak calling were computed by macs2 with options -f BAM --nomodel --extsize 200 --broad using the BAM of input in -c. The coverage was then normalized by the number of million tags used in macs2 coverage. Plots were obtained using pyGenomeTracks version 3. 6  78, 81. For DFL_E12_H3K27ac, the two replicates were averaged. For the scRNA-seq, matrices with counts were downloaded from GEO (see Table  II ). UMAP and expression plots were obtained using Seurat package version 3. 2. 2 86 on each dataset individually. We performed our 4C-seq experiments according to 87.