dataesr/scientific-paragraphs-categorization huggingface dataset:
First, the new FAO update reduced emissions from peat fires by nearly 1 Gt CO 2 eq yr -1 , due to a more limited accounting of these emissions, as discussed in section 2. Second, new FAO estimates of forest emissions (Tubiello et al 2021) revised emissions from net forest conversion downwardly by 0. 4 Gt CO 2 eq yr -1. With these new estimates, our results extend the available data to more recent years, while also improving data on key food system components. Our results confirm recent quantifications of the relative contributions of the food system to total anthropogenic emissions. We used an unpublished version of the PRIMAP database (FAO 2020, Gütschow et al 2021) with increased sectoral resolution to compute the relative contribution of food system emissions to total anthropogenic emissions over 1990-2018, globally and for the AI/NAI country groupings.
Acknowledgments This work is the result of a collaboration among lead researchers at  FAO ,  UNSD ,  UNIDO ,  IEA ,  Potsdam Institute for Climate ,  NASA  ( WBS 281945. 02. 80. 01. 13), the  Agricultural Models Intercomparison and Improvement Project (AgMIP) , the  Center on Global Energy Policy, School of International and Public Affairs at Columbia University , the  Center for Climate Systems Research at Columbia University , and  New York University. FAOSTAT, the main database used as data source in this study, is funded by  FAO  member states through their contributions to the  FAO Regular Programme. We thank the  Swiss Federal Office for Agriculture  for support to FAO under the project ' Strengthening Global Assessments of Sustainable Agriculture '. F N T and C R conceived the ideas and together with G C and K K designed the methodology.
Genome assemblies All genome assemblies used in this study can be downloaded from https://raba. ibpc. fr/home/ovallon@ibpc. fr/Briefcase/Chlamy genomes. The assemblies for Chlamydomonas incerta, Chlamydomonas schloesseri and Edaphochlamys debaryana are described in  (64) , that from CC-2931 was obtained by assembly of PacBio reads (Craig et al. , bioRxiv, doi: https://doi. org/10. 1101/2021. 04. 23. 441226). The CC-4532 and CC-503 (v6) assemblies are available on the Phytozome website (https://phytozome-next. jgi. doe. gov/). For strain CC-1690 ('21gr'), recently released Nanopore raw sequencing data  (62)  were base-called and de novo assembled into chromosomes as described in (63)(GenBank accession:JABWPN000000000). For the present work, we used a version prior to the Illimuna polishing step and used linkage data  (65)  to further scaffold the last unplaced contig (unplaced 1) to the end of chromosome 15, forming its right arm.
Repeat consensus sequences were phased and used as blast queries to retrieve the coordinates of the repeat copies. Sequences were extracted using EMBOSS (v6. 4. 0) seqret (Supplemental File F2). Multiple sequence alignments were generated with MAFFT (v7. 130) with iterative refinement method G-INS-i. Pairwise distances were calculated using EMBOSS distmat with the Jukes-Cantor substitution model. Phylogenetic analyses and trees were generated using PhyML with the generalized time-reversal (GTR) model for nucleotide evolution and drawn using Interactive Tree Of Life (https://itol. embl. de/) (69). JAL-view  (70)  and Bioedit  (71)  were used for data visualization and calculation of consensus and logo sequences. Consensus sequences were computed with Advanced Consensus Maker (https://www. hiv.
Transcriptomics The transcript dataset from (72) (accession number: GSE112394; strain CC-5390) was searched using each Spacer sequence as BLAST queries on NCBI server. Duplicate hits were discarded and coverage was computed using bedtools coverage (v2. 29. 2)  (73). Iso-Seq data (accession number: PRJNA670202; multiple laboratory strains) and ChIP-seq data (accession number: PRJNA681680; strain CC-5390) were used to assess transcription and H3K4me3 marks. Circular consensus sequence Iso-Seq reads were mapped against the CC-1690 assembly using minimap2 (parameters: -ax splice:hq -secondary no). ChIP-seq reads were mapped using bwa-mem  (74) , duplicates were removed using the Picard tool MarkDuplicates (http://broadinstitute. github. io/ picard/), and peaks were called with MACS v2 (parameters: callpeak -g 9.
Genomic reads mapping Illumina data for each strain (Supplemental Table  ST3 ) were mapped against the whole genome of CC-1690 using bwa-mem  (74). The bam file was used to calculate the average whole genome coverage and extract all reads mapping to Sultan arrays. This read subset was then aligned against all Sultan consensus sequences from the same strain. The fold increase in median coverage within each consensus, compared to the whole genome, was used as a measure of the number of repeats within each array from which the consensus was derived (Supplemental File F3).
5-Methylcytosine detection Raw 40× Nanopore reads previously used for genome assembly were downsampled to 10× using filtlong (https://github. com/rrwick/Filtlong); fast5 files were subset accordingly. The subsets of fastq and fast5 were then used in the default deepsignal pathway for 5mC-CpG prediction using model 'model. CpG. R9. 4 1D. human hx1. bn17. sn360. v0. 1. 7+' (77) (Supplemental File F4).
Importantly, to verify that the number of Sultan repeats was correctly assessed in class A and B subtelomeres, we manually verified the colinearity between individual long reads from the raw unassembled dataset (Supplemental Figure  S3 ). Southern blotting and analysis of read mapping were also used to confirm the structure of class C and D subtelomeres (see below). In 29 out of the 31 Sultan-containing subtelomeres, we found a non-repetitive sequence adjacent to the most centromere-proximal Sultan that we called 'Spacer' (Figure  1 , blue boxes), since it seemed to connect the Sultan array to a (GGGA) n microsatellite (Figure  1 , yellow boxes). Most Spacer sequences were 450-550 bp long and on the Sultan repeat side the first dozen nucleotides were highly conserved across subtelomeres (Supplemental File F1,TG GTGAGAGCAAAC found in 24 subtelomeres andTGGT GCGGGCAAACATTT found in 4, the two least conserved nucleotides are in bold).
The Sultan repeat itself is not a TE: it was detected neither in a previous large-scale survey of TEs, including the terminal repeat in miniature (TRIM) retrotransposons that are shorter than 1000 bp and form long arrays  (79) , nor in a recent annotation of Chlamydomonas TEs  (64) , nor in a search against Pfam databases. In class B subtelomeres, Sultan repeats were longer than in class A, due to the presence of large insertions homologous to various TEs (Figure  2C  and Supplemental Table  ST2 ). On a given class B subtelomere, all Sultan repeats   4 ). Arrays of ribosomal DNA (purple) compose class D subtelomeres (Supplemental Figure  S1 ). The display of the longest class C and D subtelomeres is not at scale and interrupted by '//', while '?' marks the only elusive molecule end due to assembly collapse.
The transcripts were generally characterized by a conserved 5 -splice site (G ∧ GTAG), with the (GGGA) n repeat positioned at the beginning of the first, usually extremely long, intron. Sequence similarity was limited to the first exon. The transcripts originating from the Spacer only contained very short putative coding sequences (average of 124 aa). BLASTx-based comparison of the transcribed sequences to Chlorophytes proteins only yielded some hits to TE genes, suggesting that they may represent long non-coding RNAs. Interestingly, the expression of Spacer sequences peaked at dusk in synchronous diurnal cultures (Supplemental Figure  S7B ), correlating with tran-scription of genes associated with DNA replication  (72). We also observed transcription from the RAE sequences following the rDNA arrays on 8 R and 14 R (Supplemental Figure  S1B ).
The subtelomeres of CC-503 all belong to haplotype 1, while 8 L in CC-1690 and 6 L in CC-4532 belong to haplotype 2. A maximum likelihood phylogenetic tree was built from the aligned Sultan consensus sequences from each subtelomere (Figure  6A ). When the sequence data were available, the Sultan repeats of the same chromosome end but from the different laboratory strains generally grouped together, suggesting that most Sultan arrays have not relocated since these strains were genetically separated in the laboratory. A notable exception is the grouping of CC-503 2 R with 9 L in other strains, due to a documented reciprocal translocation between these chromosome arms that occurred in the laboratory history of CC-503. We also found that the 6 L Sultan of CC-4532 (haplotype 2) was highly similar to that found on 11 L in all three laboratory strains, including CC-4532 itself.
This length variation between the haplotypes again supports substantial subtelomere allelic variation within the species. For Suber and Subtile repeats, we observed mapped Illumina reads from all laboratory strain datasets, but only in some of the available wild isolates, indicating that their presence might not be fully conserved in the species. Accordingly, the CC-2931 assembly lacked both Suber and Subtile repeats. Interestingly, we found that the 1 L subtelomere in the CC-2931 assembly featured an rDNA array with similar organization to 8 R and 14 L, i. e. complete rDNA copies followed by a full-length rDNA-associated element (RAE) and MSAT-10 cRei. In contrast, CC-503 and CC-4532 showed a truncated array as in CC-1690 (Supplemental Figure  S1D ). The unusual 1 L rDNA organization is thus a characteristic feature of C.
(B, C) Deep sequencing data were mapped to genome assembly of laboratory strain CC-1690 (Supplemental File F3). Estimates of Sultan repeat count in each subtelomere are calculated from the median read depth of Sultan consensus sequences. (B) Plot of Sultan repeat count estimates for CC-1690 (blue) and CC-503 (orange) against the actual repeat count observed in Nanopore sequencing of CC-1690. Shown are the median depth (±SD) and trend lines using the least-squares method. (C) Boxplot distribution of repeat count in laboratory strains for each subtelomere (see Supplemental Figure  S9  for strain-specific count, including more distant laboratory strains). Subtelomeres potentially affected by the distribution of haplotype blocks among these strains are highlighted. this analysis. The closest known relatives of C.
FUNDING Agence Nationale de la Recherche  grants ' AlgaTelo ' [ ANR-17-CE20-0002-01 ]; 'PhenoVar' [ ANR16-CE12-0019 ]; ' Initiative d'Excellence' program  from the  French State  [' DY-NAMO ',  ANR-11-LABX-0011-01 ];  Ville de Paris  ( Programme Émergence (s)); R. C. is supported by a  BBSRC   EASTBIO Doctoral Training Partnership grant. Funding for open access charge:  Agence Nationale de la Recherche  [ ANR-17-CE20-0002-01 ].
DATA AVAILABILITY All alignments can be found in Supplemental File F1. The scripts used in this work are included in Supplemental files F2, F3 and F4. The genomes used in this study are accessible at this secure self-certified URL: https://raba. ibpc. fr/home/ovallon@ibpc. fr/Briefcase/ Chlamy genomes They include the assembly for CC-1690 (Gen-Bank accession: JABWPN000000000) with a correction for subtelomere  9  R, for CC-4532 and CC-503 (v6) (https://phytozome-next. jgi. doe. gov/), for C. incerta (https://phycocosm. jgi. doe. gov/Chlin1; NCBI accession: GCA 016834605. 1), C. schloesseri (https://phycocosm. jgi. doe. gov/Chlsc1; NCBI accession: GCA 016834595. 1) and E. debaryana (https://phycocosm. jgi. doe. gov/Edade1; NCBI acces-.
9 Figure 5. 95 Figure 5. Chromatin modifications and transcription at subtelomeres. (A) DNA methylation (5mC at CpG context) at subtelomeres representative of the 4 A to D classes, detected from the Nanopore reads. The location of the Sultan, Suber, Subtile and rDNA arrays (with the NTS marked as an open circle) are depicted in colour on the x-axis. The 14 R subtelomere being truncated, DNA methylation at the telomere-rDNA junctions is shown in (Supplemental Figure S6), along with other subtelomeres. (B) Plot of nucleotide coverage of RNA-seq reads along the 11 most expressed Spacers, starting from the Sultan/Spacer junction (0) towards the G-rich repeats. Transcript dataset from (72). (C) H3K4me3 enrichment values extracted for the Spacer sequences, all gene promoters and 20 000 random genomic regions of 500 bp (size similar to the Spacers), using H3K4me3 ChIP-seq data (accession number: PRJNA681680) mapped to the CC-1690 assembly.
Data and methodologies To address the four terms of reference, the MC WG followed a low extent of planning developed according to the 'Draft framework for protocol development for EFSA's scientific assessments' (EFSA, 2020) (Section 3). It should be noted that according to the background information and the ToRs provided by the European Commission, the MC WG was not mandated to provide risk assessment considerations on in vivo and in vivo random mutagenesis techniques in plants.
Literature search The MC WG with the support of an information specialist designed literature searches to identify possible books, chapter of books or reviews published on random mutagenesis techniques as applied in vivo and in vitro in plants. The GMO panel considered that taking into account very extensive history of random mutagenesis and available original research, it is reasonable to restrict the search to book chapters and reviews. The search was not limited by publication year. The bibliographic databases listed in Table  1  were searched in order to identify relevant studies. The databases have been identified in line with the defined scope of the review. The search strings as run in the sources of information are available in the Appendix. They are structured as a combination of three searches: search 1 combines terms for chemical or physical mutagenesis and plants; search 2 combines terms for random mutagenesis and plants; search 3 combines terms for mutagenesis in plants and generic terms for techniques.
Keywords have been selected with the help of the members of the WG, consulting thesaurus (e. g. CAB Thesaurus) and dictionaries. The searches are limited to documents published in the English language and to books, book chapters or reviews. When the bibliographic databases did not have filter for reviews, i. e. CAB Abstracts, a search string was designed to identify this type of publication. Searches were run on 22 December 2020: 344 documents were retrieved in CAB Abstracts, 215 in Scopus and 202 in the databases of the Web of Science Core Collection. The output of the searches, that is, records retrieved from bibliographic databases was exported into an EndNote library (Clarivate Analytics). After removing the duplicate references in EndNote, 517 unique documents were identified by the databases.
6 Since then, the number of mutated plant varieties steadily increased at least until the 1980s when new and more advanced technologies in molecular biology became available. To date, over 3,200 plant varieties which have been produced by mutagenesis approaches are registered in the FAO/IAEA database with more than half of the registered varieties coming from rice (Oryza sativa), barley (H. vulgare), Chrysanthemum spp. , wheat (Triticum spp. ), soybean (Glycine max) and maize (Zea mays) (Jankowicz-Cieslak and  Till, 2016b; Jankowicz-Cieslak et al. , 2016b; Spencer-Lopes et al. , 2018 ; FAO/IAEA, 2020).
Bioinformatical and Statistical Analyses Analyses were performed using the R software  [35]. The dada2 workflow (v. 1. 8, "dada2" R package;  [36] ), based on Divisive Amplicon Denoising Algorithm ("DADA") was adapted (trimming, error rate learning, and sequence length inspection) as previously described  [33]  and used on the 47 samples to obtain an amplicon sequence variant (ASV) table. ASV identified fine-scale variations down to the genus level compared to operational taxonomic units.
Data Cleaning Raw data were handled as previously described  [33]  using the R software  [35] , with the "phyloseq" and "microbiome" packages  [37, 38]  and the "ggplot2" package for the construction of the plots  [39]. Data were rarefied at a sample size of 3500, then expressed in per mille proportions and filtered from the proportions lower than 1/1000.
Bacterial Diversity and Structure The alpha diversity of the bacterial communities was analyzed using rarefied and proportion-expressed data by calculating the number of observed ASVs, the Shannon index, and the evenness (i. e. , Pielou index). The effect of Wolbachia, the gender of individuals, and the interaction between both factors on the alpha diversity was tested using linear models (type II ANOVA, F-test). The beta diversity of the bacterial communities was assessed using rarefied, proportionexpressed and filtered data. Beta diversity analyses were performed on both presence/ absence data and abundance data. The community structure was analyzed: using (i) the Hellinger distance and a transformation-based redundancy analysis (tb-RDA) on the presence/absence data and (ii) the Bray-Curtis dissimilarity matrix and a distance-based redundancy analysis (db-RDA) on the abundance data.
Network Analysis A network analysis was conducted with the SparCC method (Sparse Correlations for Compositional data) and the thresholds of -0. 5 and 0. 5 for the Pearson correlation coefficient. To determine how Wolbachia is correlated to other bacterial genera, we used the "SpiecEasi"  [42] , "igraph"  [43] , and "qgraph"  [44]  packages, as previously described  [45]. This analysis was performed on the whole rarefied dataset (W-and W+ individuals together), as well as on W-and W+ samples separately. Several network features were calculated:.
Assessment of Wolbachia The Wolbachia genus was confirmed to be absent from all W-female and male samples while it was detected in 100% of the W+ female and 83% of the W+ male samples (Figure  S1 ). However, one female and two male W+ samples had a very low abundance of Wolbachia (<3‰) and were thus removed from the dataset before further analysis, along with ASVs only detected in these three samples.
In addition to Wolbachia, Serratia was always present, but only in W+ females. A network analysis of the whole dataset showed that the bacterial communities formed a small-world network, with a small-worldness score of 1. 32 and that only a subset of bacteria were connected to each other as the network modularity value was of 0. 035, thus rather low (Figure  S5 ). The largest subnetwork (Figure  5 ) contained two clusters of 19 and 2 bacterial genera. The subnetwork was characterized by the presence of 10 hub bacteria (ANPR, Caulobacter, Sphingomonas, Flavobacterium, Sphingobacterium, Acinetobacter, Alkanindiges, Cellvibrio, Erwinia, Trabulsiella) and 4 articulation points (Brevundimonas, Sphingobacterium, Acinetobacter, and Trabulsiella). In the network, the most interconnected bacteria was Wolbachia, which was significantly and positively correlated to 2 bacteria (Providencia and Serratia) and negatively correlated to 8 bacteria (Staphylococcus, Flavobacterium, Sphingobacterium, ANPR, Acinetobacter, Alkanindiges, Cellvibrio, and Erwinia in bacterial phyla order).
Moreover, the Enterobacteriaceae family was the only taxon shared by both sexes of both W-and W+ treatments (Figure  S4A ). Seven bacterial genera (i. e. , the γ-proteobacterial Erwinia, Trabulsiella, Pseudomonas, Acinetobacter, Alkanindiges, Cellvibrio, and the α-proteobacterial Sphingomonas) were shared between all W-female and male flies, while Wolbachia itself was the only genus shared by all W+ flies of both sexes (Figure  S4B ). In addition to Wolbachia, Serratia was always present, but only in W+ females. A network analysis of the whole dataset showed that the bacterial communities formed a small-world network, with a small-worldness score of 1. 32 and that only a subset of bacteria were connected to each other as the network modularity value was of 0. 035, thus rather low (Figure  S5 ).
The largest subnetwork (Figure  5 ) contained two clusters of 19 and 2 bacterial genera. The subnetwork was characterized by the presence of 10 hub bacteria (ANPR, Caulobacter, Sphingomonas, Flavobacterium, Sphingobacterium, Acinetobacter, Alkanindiges, Cellvibrio, Erwinia, Trabulsiella) and 4 articulation points (Brevundimonas, Sphingobacterium, Acinetobacter, and Trabulsiella). In the network, the most interconnected bacteria was Wolbachia, which was significantly and positively correlated to 2 bacteria (Providencia and Serratia) and negatively correlated to 8 bacteria (Staphylococcus, Flavobacterium, Sphingobacterium, ANPR, Acinetobacter, Alkanindiges, Cellvibrio, and Erwinia in bacterial phyla order). When comparing the W-and W+ datasets separately, both also had bacterial communities forming small-world networks respectively with a score of 3.
The model species Arabidopsis thaliana (L. ) Heynh. , for which both functional traits  (Lasky et al. , 2012; Vile et al. , 2012; Vasseur et al. , 2018ab; Sartori et al. , 2019; Exposito-Alonso, 2020)  as well as biogeographic history  (Lee et al. , 2017; Hsu et al. , 2019)  are well studied, presents a unique opportunity to test above hypotheses  (Takou et al. , 2019). The native distribution of this annual selfing species extends from north Africa to the north of Norway and thus its populations experience dramatically-different environmental conditions  (Hoffmann, 2002). Thanks to an international effort of sampling, seeds from more than a thousand of fully sequenced accessions are available  (1001 Genomes Consortium, 2016). Taking advantage of this unique genomic database,  Lee et al. (2017)  reconstructed the recent history of colonization of Europe of A.
Near-infrared spectra predictions At the end of the treatment, we acquired spectra of near-infrared reflectance of green leaves, non-destructively, using a portable spectrometer (ASD LabSpec, Malvern Panalytical, Holland, wavelength range: [780; 2500 nm]). Spectra were taken on leaves dedicated for leaf traits measurements just before harvesting and on additional individuals in order to get 12 spectra per accession and per treatment. Acquired spectra were used to predict SLA, LDMC, leaf nitrogen concentration (LNC, %), R scores and C scores for 2,160 individuals. Predictive models based on convolutional neural networks (CCNs) were developed using an independent database gathering more than 20,000 spectra and their respective reference. We evaluated the robustness of our predictions by testing their correlation with values obtained with the traditional destructive methods [Supplementary Material S1; TableS2].
Statistical analyses We compared means of traits observed in control condition and coefficients of variation of NIRS-predicted values of traits in all treatments thanks to Tukey tests ('Multcomp' package,  Hothorn et al. , 2008). We compared cross-treatment plasticity of geographical groups through Tukey tests comparisons of the coefficient of variation across all treatments. The coefficient of variation (CV) is calculated as the total standard deviation of traits of each group across treatments divided by the cross-treatment mean. We analyzed trait plasticity in response to the treatments using linear mixed-effects models that test log-response ratios (log ratios hereafter) of traits and CSR scores as a function of geographical groups, treatments, and their interactions. Accession identity and plate identity were considered as random effects in the models.
Log ratios were calculated as the logarithm of the ratio of an individual value in a given treatment and the mean value of its accession in control condition. We analyzed the variability of performance traits (survival and number of fruits) using generalized mixed models ('lme4' package,  Bates et al. , 2015). We performed a binomial regression for survival models (logit as a link function) and a Poisson regression for fecundity models (log as a link function). We considered three fixed effects in these models: geographical origin (3 levels), treatment effect (5 levels) and their interaction. Two random effects were considered: accession identity and plate. Only one plant died in the control condition. Because most values were at the extreme of the binomial distribution in this condition, the model suffered from convergence issues.