dataesr/scientific-paragraphs-categorization huggingface dataset:
Repeat, gene and functional annotations. Before gene annotation, repeat sequences including TEs on the chromosome-level assembly were de novo predicted using Extensive de novo TE Annotator (EDTA, v. 1. 8. 4) with default settings  77  and annotated by RepeatMasker (http://www. repeatmasker. org). Genes were predicted by combining: (1) RNA-seq evidence, (2) protein homology and (3) ab initio prediction. For gene prediction with transcriptional evidence, RNA-seq reads from our newly sequenced young leaf, old leaf, root and publicly available rhizome and leaf data (accession no. SRR9644796 and SRR9644797) were aligned to the assembly by the HISAT2-StringTie pipeline to obtain transcript-based annotation  78, 79. CDSs were predicted using Transdecoder (https://github. com/ TransDecoder). In addition, the de novo transcriptome was assembled by Trinity with default settings (https://github.
com/trinityrnaseq/trinityrnaseq); PASA, which integrated the de novo transcript assemblies, was applied to further update the assembly with default settings (https://github. com/PASApipeline/PASApipeline). Homology-based gene annotation was conducted using Genewise software with genomic sequences and gene annotations from representative monocots, including Colocasia esculenta (accession no. ASM944546v1), Zea mays (no. B73 RefGen_v4), Oryza sativa (no. GCF_000005425) and Zostera marina (no. GCA_001185155. 1)  80. Ab initio gene prediction was conducted using AUGUSTUS  81  and GeneMark-ES/ ET  82. The final consensus gene annotations were generated by EVidenceModeler with different weights among annotations (RNA-seq > gene homology > ab initio)  83. Finally, protein-coding genes with more than 30% of the CDS overlapping with repeat sequences were considered repeat-or transposon-related genes and were discarded from downstream analyses.
GO functional annotations were inferred using the 'non-redundant' database of plants in eggNOG 4. 5 with default settings  84. AMK reconstruction. Ancestral genomes are reconstructed in a six-step method as illustrated in Fig. 4a. The first step consists of aligning the genes (protein sequences) using BlastP with thresholds for cumulative identity percentage (CIP) ≥50% and cumulative alignment length percentage BLAST parameters (CALP) ≥50% (defined in Salse et al. 85  ) (https://github. com/nelumbolutea/amk_article/blob/main/6. CIP_ CALP. pl), which deliver conserved genes between the investigated species given the following formulas: CIP = ∑ nb ID by (HSP/AL) × 100 where CIP corresponds to the cumulative percentage of sequence identity observed for all the high-scoring pairs (HSPs) divided by the cumulative aligned length (AL) which corresponds to the sum of all HSP lengths.
The third step consists of extracting all combinations of chromosome-to-chromosome relationships (for SBs sharing more than five orthologous genes) from the step 2 output file (or alternatively using tools such as DRIMM synteny software  86  ). In the fourth step, SBs from the previous output file are then merged into ancestral protochromosomes (also referred to as CARs). This step consists of defining independent groups of SBs sharing synteny between the modern species investigated (or alternatively with tools such as MGRA software  87  or ANGES software  88  ). The fifth step corresponds to CAR validation, in which CARs correspond exclusively to diagonals in dotplot-based comparative genomics deconvolutions of the synteny between the investigated species. Finally, the sixth step consists of deriving a parsimonious evolution model by introducing the smallest number of rearrangements (fissions, fusions and translocations) to explain the transition between the ancestral and modern genomes.
All data described in the current study, such as conserved genes, SBs and ancestral chromosome blocks, are available in Supplementary Tables  5  and 6. Gene and WGD analyses. To identify the origins of genes from duplications and WGDs in Acorus, intraspecific and interspecific SBs were identified by MCscan via JCVI  89. To determine the WGDs in relation to the divergence of rice, asparagus and seagrass, raw 4dTv values for all syntenic paralogous pairs or orthologous pairs were estimated and corrected for possible multiple transversions at the same site according to a previous method  90  ; K S values of all syntenic paralogous/orthologous pairs were also calculated by codeML of the PAML package  91. Histograms of 4dTv and K S values for all syntenic paralogues/orthologues were plotted with a bin size of 0.
TE sequence proportions between collinear genes in LF and MF syntenic blocks were compared with sliding windows in gene-flanking regions (±5 kb) and gene bodies (from the translation start site to the stop site). Any genomic positions overlapping between the flanking region and gene were discarded during analysis of the flanking regions. For both flanking regions, a 100-bp sliding window with a 10-bp step was used, whereas 40 evenly divided windows were applied for genes  93. Furthermore, for each sliding window, the proportion of the sequence belonging to TEs was summarized. The average proportion in each sliding window was calculated for genes in LFs and MFs. These averaged proportions represent the TE density in the flanking regions and genes in LFs and MFs. Moreover, to investigate subgenome dominance (biased expression levels between LFs and MFs)  94  , all five Acorus RNA-seq datasets used for gene annotation were surveyed.
Gene expression levels (FPKMs) were calculated by HISAT2-StringTie pipeline  60, 61. For each RNA-seq dataset, log 2 -transformed FPKM values for anchor genes from LFs and MFs were compared using the one-sided paired t tests in GraphPad Prism v. 9. Sequence substitutions and synteny retention among monocots. To compare the relative sequence substitutions among monocots, we surveyed 42 monocots with available genome assemblies, including Acorus, and six outgroup taxa (Fig. 2a  and Supplementary Table  13 ). The species tree was constructed based on 104 strict single-copy orthologous genes using OrthoFinder  95. We concatenated single-copy genes and generated a phylogenetic tree by IQ-TREE2 under the optimal substitution model JTT + F + I + G4 according to the Bayesian information criterion scores of 144 tested models  96.
Synteny retention among different genes. To estimate the variation in synteny retention among different genes during monocot radiation, we used the outgroup taxon Nelumbo nucifera because of its greatest similarity of syntenic structure in relation to monocots, and the availability of required datasets including whole-genome methylation, population resequencing and expression profiles of all organs and tissues  12, 24. The 29,582 Nelumbo genes sharing homologue(s) (BlastP E value <10 -6 ) with at least 1 of the 42 monocots were used for the following analysis of synteny retention rates. For each Nelumbo nucifera gene, the number of monocots showing a syntenic relationship was used to represent its relative synteny retention rate during monocot radiation. To gain insight into different factors related to synteny retention rates among genes, data including the types of gene duplications, nucleotide diversity, CDS length, gene length, the number of predicted PPIs, the average expression level, expression specificity, TE density and methylation levels on genes and flanking regions were obtained from our previous study  24.
To estimate OG gain and loss along the ultrametric tree, we applied Dollo-Parsimony via COUNT software with default settings  97. To estimate the number of OGs with significant expansion and contraction along the ultrametric tree, CAFE was applied with a P value threshold of 0. 05 (ref. 98 ). In parallel, to better detect OGs with significant copy number differences between monocots and non-monocot angiosperms, the copy numbers of these two clades were compared using the two-sided Mann-Whitney U-test for each OG. OGs with a P value <0. 01 and fold change of the average copy number ≥2 were considered significantly different. Reporting summary. Further information on research design is available in the Nature Research Reporting Summary linked to this article.
acknowledgements This work was supported by grants from the  Strategic Priority Research Program of Chinese Academy of Sciences  (No. XDB31000000 ), the  National Natural Science Foundation of China  (Nos 32170240,  31570220  and  31870208 ), the  Youth Innovation Promotion Association of Chinese Academy of Sciences  (No. 2019335 ). Completion of this article was also supported by the  Institut Carnot Plant2Pro  (# 0001455  project  SyntenyViewer 2017 ) and the  ISITE CAP2025  (# 00002146 SRESRI 2015  ' Pack Ambition Recherche Project '  TransBlé 2018 ). We thank  C. Dai  and  T. Wan  for the discussion, and  Z. Gao  for figure editing.
Data availability The datasets generated and analysed during the current study including PacBio Sequel II, Illumina, Hi-C data, genome assembly, annotation and RNA-seq reads have been deposited in China National GeneBank (CNGB, https://db. cngb. org/) under accession number CNP0001708. Public transcriptomes used in this study are available from NCBI under the accession number SRR9644796 and SRR9644797. Source data are provided with this paper.
Current database for estimating inhalation exposure This section reviews the history and data behind the current default air concentration values in the EFSA (2014a) used for inhalation exposure estimation. It will become evident that the underlying studies,  Siebers et al. (2003)  and California EPA (1998), and subsequent guidance interpretation are incongruent with exposure assumptions according to  EFSA (2014a). This illustrates that inhalation exposure estimation would benefit from a revised approach or database. The application parameters and conditions of field studies underpinning the default values are presented in Table  2.
Air sampling was done throughout, both during and after applications for various durations. Besides sampling during application, the pump flow rates were set at a very high flow rate, 14. 5-14. 7 L/min, incomparable to any other available study or current approaches in regulatory studies, which typically use flow rates of 0. 5-2 L/min. Using the data from California EPA (1998), PSD (  2008 ) derived a highest TWA estimate of 15 μg/m 3 including the samples taken during the application with an air-assisted sprayer. Note that measurements taken during the application are not a true measure of volatilisation and samples would also have captured airborne aerosols from spray drift. Additionally, this value includes an outlier sample, which was suggested in the report to be excluded due to a sampling problem (e.
93 μg/m 3 /AR 6. 73 kg AI/ha = 0. 73254 μg/m 3 /kg a. s. /ha The normalised data show that air concentrations are in the same range for both studies and the 15-fold difference between VP grouping is not justified based on this data, which is suggested by  EFSA (2014a). This shows that the grouping has been applied arbitrarily in  Koerdel et al. (1999)  and that the current underlying data do not support it. In the studies cited above, instead of averaging samples from all sides of the field, the sample from the side of the plot with the maximum residue is used for each sampling period as this represents the worst-case for potential inhalation exposure depending on the wind direction (i. e. exposure is always measured downwind from the application source). The 24-h average air concentrations are determined at a static location directly adjacent to the field and it is extremely unlikely that any person, be it bystander or resident, would spend 24 h in a single outdoor location which incidentally is also constantly downwind from the treated field.
Conclusion and outlook A revision of the currently applied approach is recommended along with a revision of the currently used default values for bystander and resident exposure assessment  (EFSA, 2014a; EFSA et al. , 2022). The reasons are laid out in the current manuscript and relate to limitations of the underlying air concentration data and the associated arbitrary grouping according to vapour pressure. As a way forward, all stakeholders should be encouraged to assess whether novel data could be used to derive revised and more applicable air concentration values for bystander and resident exposure to vapour. CropLife Europe companies have generated air concentration data in regulatory processes that could be a suitable resource for the generation of a novel database. Further, an adaptation of the existing vapour pressure grouping should be reviewed and revised based on novel data.
Oscillation amplitude sweep was performed by varying the stress from 0. 1 to 1000 Pa logarithmically, at a constant frequency and temperature of 1 Hz and 60 • C respectively. Paraffin oil was used to avoid evaporation from sample while performing measurements. Stress corresponding to cross over point of the storage modulus (G ′ ) and loss modulus (G ′′ ) curves was determined, which is described as flow point or flow stress  (Mezger, 2011; Zhu et al. , 2019) , using the rheometer software (TA Instruments Trios V5. 0. 0. 44616). Cohesion was determined by measuring axial force required to separate the top plate from the bottom plate of the rheometer (similar to Probe Tack test described by Malvern Instruments limited, 2021). Sample was loaded on the bottom plate and the top plate was lowered to a gap of 1 mm after which excess product was carefully trimmed.
Flow properties of superconcentrated paste using a shear cell measurement Flow properties of superconcentrated pastes were described by cohesiveness and flowability function (ffc) using shear cell tests. A ringshear tester (RST-XS, Dr. Dietmar Schulze Schüttgutmesstechnik, Wolfenbüttel, Germany) was used to characterize flow properties of superconcentrated pastes at various DM contents at 25 • C. Superconcentrated paste was filled in the shear cell (24 cm 3 ) and excess material was scraped off. Shear test measured yield locus of a pre-consolidated bulk solid sample, by applying shear stress until failure. Cohesiveness and ffc were determined by analyzing the yield locus measurements using RST control software (RSV 95). All yield locus measurements were performed at a normal pre-shear stress of 2000 Pa using four shear points (400, 1000, 1600 and 400 Pa).
Acknowledging cultural acceptability and other intangible human needs associated with meat consumption Cultural food adequacy is receiving increasing attention in the literature on food systems  (Bené et al. 2019). While the criteria to qualify this dimension are still unclear, they require, as a minimum, the recognition of social and cultural values associated with food, which go far beyond nutrition  (Béné et al. 2019). This is important for the case of meat, as often the normative standards and beliefs about the levels and types of meat consumption are culturally loaded and need to be contextualized  (Nguyen and Platow 2021 ). Yet, it has been acknowledged that there is no scientific way to determine the boundaries of meat consumption based on cultural preferences  (Buckwell and Nadeu 2018).
Protein-coding genes In addition to the expression data, gene models were informed by protein homology using a combined database of Viridiplantae from UniProtKB/Swiss-Prot  (Boutet et al. , 2007)  and selected Brassicaceae from RefSeq  (Pruitt et al. , 2012). Following initial training and annotation by ab initio gene predictors, proteincoding loci were further annotated with InterPro to provide PFAM domains, which were combined with a BLAST search to the UniProtKB/Swiss-Prot Viridiplantae database to infer gene ontology (GO) terms. In accordance with MAKER-P recommendations  (Campbell et al. , 2014) , the final set of 27 128 protein-coding loci was obtained by filtering out those with an annotation edit distance (AED) score of 1 unless they also contained a PFAM domain. Approximately 95% of loci had an AED score <0.
Analysis of single-copy orthologs from the Eudicotyledons odb10 database with BUSCO v3. 0. 2  (Simão et al. , 2015)  revealed a high completeness of 98. 4% and a duplication level of 23. 6% (Table  S6 ). Subsequent alignment of the reads to the assembly using minimap2 v2. 17  (Li, 2018)  and purge_dups v1. 0. 1  (Guan et al. , 2020)  presented bimodal peaks in the read depth distribution, indicative of a large duplicated fraction within the assembly (Figure  S17 ). As efforts to collapse this duplicated fraction using assembly parameters were unsuccessful, and purge_dups is intended to correct duplication arising from heterozygosity (which does not apply in T. arvense), the fraction was reduced by manual curation instead. Contigs starting from the left-hand side of the read depth distribution were consecutively removed until reaching an approximation of the estimated genome size, with any contigs containing non-duplicated predicted BUSCO genes kept preferentially in favour of discarding the next contig with lower read depth in the series.
The deduplicated assembly from Canu was polished with the PacBio Sequel II HiFi CCS reads using two iterations of RACON v1. 4. 3  (Vaser et al. , 2017) , prior to repeat reassembly. Bionano maps were used to build de novo scaffolds using the polished assembly; hybrid scaffolds were generated using the de novo Bionano maps and the assembly (https://bionanogenomics. com/ support-page/data-analysis-documentation/). To further resolve repetitive regions and improve assembly contiguity, the bionanoscaffolded assembly was integrated into the HERA pipeline  (Du and Liang, 2019). The Hi-C data were aligned with bwa-mem v0. 7. 17  (Li and Durbin, 2009) , PCR duplicates were marked with picard tools v1. 83 (http://broadinstitute. github. io/picard), and the quality was assessed with the hic_qc. py tool of Phase Genomics (https://github.
Each sample was digested with the BtgI_BgLII restriction enzyme combination, barcoded and sequenced on the Illumina NovaSeq S1 (single-end 101 bp) yielding 1 237 890 mean reads per sample. The raw reads were demultiplexed based on the barcode information and aligned to the most recent iteration of the pennycress genome using bwa. Sequence-aligned files were processed through samtools v1. 9  (Li et al. , 2009)  and picard tools to sort the files and remove group identifiers. Variants were called using GATK HaplotypeCaller v3. 3. 0. SNPs identified among these 94 lines were used for the development of genetic maps. The second linkage map was derived from a cross between MN106-Ref and a mutant line '2019-M2-111'. To identify the variant alleles in 2019-M2-111, we performed whole-genome resequencing using paired-end reads on the Illumina Platform.
Genome sequences Arabidopsis thaliana (Araport 11), Schrenkiella parvula (v2. 2) and Arabidopsis lyrata (v2. 1) genome sequences and gene annotation were downloaded from Phytozome  (Goodstein et al. , 2012). The Eutrema salsugineum gene annotation was obtained from Phytozome and lifted over the assembly GenBank GCA_000325905. 2.
Genome alignments and synteny analysis The genome alignments between the different versions of the T. arvense assembly to E. salsugineum were done using MUMmer v4. 0. 0 (Marc ßais et al. , 2018) with a minimal length of 200 nt and followed by filtering for 1:1 matches and removing alignments smaller than 1000 bp. To identify the interspecies gene orthologs and syntenic relationships between T. arvense and other species, we used MCScan in the JCVI utility library (https://github. com/ tanghaibao/jcvi;  Tang et al. , 2008). The ortholog relationships were obtained using the proteinic translation of the CDS and using the argument --cscore=0. 99. To define the syntenic blocks and the corresponding genomic coordinates, we used the parameters --minspan=15 and --minsize=5. The genomic coordinates from the syntenic blocks were parsed to draw the syntenic relationships using Circos v0.
Transcriptome assembly Following quality control and adapter clipping with cutadapt  (Martin, 2011) , biological replicates for each of eleven tissue types from Illumina mRNA-seq libraries were aligned independently using STAR v2. 5. 3a  (Dobin et al. , 2013) , then merged according to tissue type, prior to assembly by a reference-based approach. Each assembly was performed using Ryuto v1. 3m  (Gatter and Stadler, 2019) , and consensus reconstruction was then performed using TACO v0. 7. 3  (Niknafs et al. , 2017)  to merge tissuespecific transcriptome assemblies. PacBio Iso-seq libraries from MN106-Ref were refined, clustered and polished following the Iso-seq3 pipeline (https://github. com/PacificBiosciences/IsoSeq), prior to alignment with STARlong and isoform collapsing using the cDNA_Cupcake (https://github.
, 2014). Putative gene fragments were filtered out following BLAST search to the combined Swiss-Prot + RefSeq protein plant database after exclusion of hits from RepetDB. The de novo library was combined with a manually curated library of plant sequences derived from repbase  (Bao et al. , 2015). Genome masking is performed with RepeatMasker v4. 0. 9  (Smit, 2004)  as part of the MAKER-P pipeline. Proteincoding genes, non-coding RNAs and pseudogenes were annotated with the MAKER-P pipeline following two iterative rounds under default settings, using (i) transcript isoforms from Illumina mRNA-seq and PacBio Iso-seq data, (ii) protein homology evidence from the custom Swiss-Prot + RefSeq plant protein database and (iii) the repeat library and TE sequences for masking. The initial results were used to train gene models for ab initio predictors SNAP v2006-07-28  (Korf, 2004) and Augustus v3.
Transposable element annotation Two de novo annotation tools, EDTA v1. 7. 0  (Ou et al. , 2019)  and RepeatModeler v2. 0  (Flynn et al. , 2020) , were used to annotate TEs independently. For EDTA, the following parameters were used in addition to defaults: --species others, -step all, --sensitive 1, --anno 1, and -evaluate 1. For RepeatModeler2, the additional parameters were -engine ncbi and -LTRStruct. The outputs of both tools were evaluated by manual curation. First, we used tblastn to align each TE consensus with the transposase database obtained from repbase, and the retrotransposon domains  (GAG, Pol, Env, etc. )  were viewed one by one with dotter  (Sonnhammer and Durbin, 1995). Sequences with multiple paralogs were mapped back to the genome and manually extended to determine the full-length boundary of each TE.
A total of 107 fulllength, representative Copia and Gypsy families were successfully evaluated. The TE consensus from RepeatModeler2 was selected as the most accurate model based on full-length paralogs. RepeatMasker was then used to construct the GFF3-like file from the FASTA file from RepeatModeler2, with the optional settings: -e ncbi -q -no_is -norna -nolow -div 40cutoff 225. The perl script rmOutToGFF3. pl was used to generate the final GFF3 file.
Expression atlas Gene expression was measured from the same tissue-specific STAR alignments taken prior to merging biological replicates for transcript assembly, excluding coverage outliers 'mature seed' and 'green old silique'. A total of 27 samples from 9 tissues were therefore considered for gene expression analysis. Raw counts were generated using subread featureCounts v2. 0. 1  (Liao et al. , 2014)  and subsequently normalized using the trimmed mean of M-values (TMM)  (Robinson and Oshlack, 2010)  derived from edgeR v3. 34  (Robinson et al. , 2010). Averaged expression counts by group were taken for tissue specificity evaluation using the Tau (s) algorithm  (Yanai et al. , 2005) , as implemented in the R package tispec v0. 99. 0 (https://rdrr. io/github/roonysgalbi/tispec/), which provides a measure of s in the range of 0 -1, where 0 is non/low specificity, and 1 indicates high/absolute specificity.