dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGEMENTS The authors would like to thank all members of the  Wanker, Vidal, Jacob , and  Twizere laboratories  for helpful discussions throughout this project. This work was supported by the  Helmholtz Association, iMed and Helmholtz-Israel Initiative on Personalized Medicine (Germany) ; the  Federal Ministry of Education and Research  and e:med Systems Medicine -IntegraMent  01GS0844  (Germany) all to E. E. W. ; as well as by the  CHDI Foundation (USA) ; the  German Cancer Consortium DKTK (Germany)  and the  Deutsche Krebshilfe, ENABLE (Germany)  to E. E. W. and P. T. This work was also supported by a  Claudia Adams Barr Award  to S. G. C. , a  Fonds de la Recherche Scientifique (F. R. S. -FNRS) -Télévie Grant  ( FC27371 , Credit no  7454518F ) and a  Wallonia-Brussels International  ( WBI)-World Excellence Fellowship  to J.
O. Additional supports were provided by  NIH  grants  P50HG004233  and  R01GM130885  awarded to M. V. and  U41HG001715  awarded to M. V. , D. E. H. and M. A. C. This work was also supported by the  LabEx IBEID  (grant  10-LABX-0062 ). M. V. is a  Chercheur Qualifié Honoraire , and J. C. T. a  Maître de Recherche of the Fonds de la Recherche Scientifique (F. R. S. -FNRS, Wallonia-Brussels Federation, Belgium ).
We present results from the FAOSTAT emissions shares database, covering emissions from agri-food systems and their shares to total anthropogenic emissions for 196 countries and 40 territories for the period 1990-2019. We find that in 2019, global agri-food system emissions were 16. 5 (95 %; CI range: 11-22) billion metric tonnes (Gt CO 2 eq. yr -1 ), corresponding to 31 % (range: 19 %-43 %) of total anthropogenic emissions. Of the agri-food system total, global emissions within the farm gate -from crop and livestock production processes including on-farm energy use -were 7. 2 Gt CO 2 eq. yr -1 ; emissions from land use change, due to deforestation and peatland degradation, were 3. 5 Gt CO 2 eq. yr -1 ; and emissions from pre-and post-production processes -manufacturing of fertilizers, food processing, packaging, transport, retail, household consumption and food waste disposal -were 5.
, 2021a, b; Rosenzweig et al. , 2020; Tubiello et al. , 2021a). This larger food system perspective expands the potential for designing GHG mitigation strategies across the entire food system, i. e. , over and above the more traditional focus on agricultural production and land use management that is currently found within countries' nationally determined contributions  (Crumpler et al. , 2021). Significant progress has recently resulted in the development of novel databases with global coverage of countrylevel data on agri-food system emissions  (Crippa et al. , 2021a, b; Tubiello et al. , 2021a). Tubiello et al. (2021a) , in particular, provided a mapping of emission categories of the Intergovernmental Panel on Climate Change (IPCC) -used by countries for reporting their national GHG inventories (NGHGI) to the United Nations Framework Convention on Climate Change (UNFCCC) -unto internationally accepted food and agriculture concepts that are more easily understood by farmers and planners in countries, including in ministries of agriculture.
, 2018; Tubiello et al. , 2021c)  and peat fires  (Prosperi et al. , 2020) , from carbon removals, which are largely associated with processes in managed forests rather than on agricultural land  (Grassi et al. , 2021). We present herein and discuss results from the first agrifood system emissions database in FAOSTAT. The new database covers, as in previous versions  (Tubiello et al. , 2013) , agriculture production activities within the farm gate and associated land use and land use change emissions on agricultural land. Importantly, it also includes estimates of emissions from pre-and post-production processes along food supply chains, including fertilizer manufacturing, energy use within the farm gate, food processing, domestic and international food transport, retail, packaging, household consumption, and food system waste disposal.
The database provides emissions data for four main GHG gases/categories (CO 2 , CH 4 , N 2 O and fluorinated gases) and their combined CO 2 equivalent (CO 2 eq. ) levels. Data are available by country, over the period 1990-2019, as well as by regional and other relevant aggregations. Importantly, data are provided in both IPCC and FAO classifications, facilitating the identification of national mitigation strategies across agri-food systems in countries, regionally and globally.
The imputation method used PRIMAP, a complete dataset of emissions estimates for all IPCC sectors, by country, covering the period 1990-2019  (Gütschow et al. , 2021). The PRIMAP dataset is already available in FAOSTAT for the computation of emissions shares of agriculture to the total anthropogenic total  (FAO, 2021c; Tubiello et al. , 2021a). It compiles all available information on GHG emissions by country, including from official reporting. It was used internationally as the basis for an early, first-order estimate of agri-food system shares in total GHG emissions  (IPCC, 2019). Additionally, it was recently used in a UNFCCC synthesis report (UNFCCC, 2021) to assess world GHG emissions from all sectors in preparation of a stock take exercise that will be undertaken in 2022-2023 to assess countries' performance against their mitigation commitments under the Paris Agreement.
Global trends The FAOSTAT dataset considered in this study estimates in 2019 total anthropogenic emissions at 52 Gt CO 2 eq. yr -1 without land use, land use change and forestry emissions (LULUCF), as well as 54 Gt CO 2 eq. yr -1 with LULUCFconsistently with recent estimates (IPCC, 2019). We use the latter figure to compute emissions shares. In 2019 world total agri-food system emissions, expressed in terms of 95 % confidence intervals (CI) determined using an overall uncertainty of 40 %, were 16. 5 (CI range: 10-23) billion metric tonnes (Gt CO 2 eq. yr -1 ), corresponding to 31 % (range: 19 %-42 %) of total anthropogenic emissions (Table  1 ). Of the food system total, global emissions within the farm gate -from crop and livestock production processes including on-farm energy use -were 7.
Conversely, the latter was the dominant emissions component in Brazil, Indonesia and the DRC. Additionally, the new database allowed for an in-depth analysis by country of pre-and postproduction emissions along the agri-food chain, highlighting a significant variety in most relevant sub-process contribution (Table  3 ). For the year 2019, pre-and post-production emissions were dominated in China by food household consumption processes (463 Mt CO 2 eq. yr -1 ), whereas food waste disposal was the dominant pathway in Brazil, Indonesia (77 Mt CO 2 eq. yr -1 ), DRC (8 Mt CO 2 eq. yr -1 ), Pakistan (33 Mt CO 2 eq. yr -1 ) and Mexico, (56 Mt CO 2 eq yr -1 ). Emissions from food retail dominated the pre-and postproduction component in the USA (292 Mt CO 2 eq. yr -1 ), the Russian Federation (177 Mt CO 2 eq.
1 Gt CO 2 eq. yr -1 ), the USA (0. 7 Gt CO 2 eq. yr -1 ) and the EU-27 (0. 6 Gt CO 2 eq. yr -1 ). This has important repercussions for food-relevant national mitigation strategies, such as those included in countries' nationally determined contributions, considering that until recently these have focused mainly on reductions of non-CO 2 gases within the farm gate and on CO 2 mitigation from land use change  (Hönle et al. , 2019). Importantly, the FAOSTAT database presented here allows for an estimation of the percentage share contribution of food system emissions in total anthropogenic emissions, by country as well as at regional and global levels, over the period 1990-2019. A number of important issues can be highlighted to this end (Table  5  and Fig. 6 ). First, in terms of CO 2 eq. , the share of world total agri-food system emissions decreased from 40 % in 1990 to 31 % in 2019.
, 2022). The noted increase in absolute emissions from pre-and post-production activities was in fact present in all regions, leading to increases in the relative contributions to agri-food systems of this component, except for Africa. An analysis on agri-food system impacts on total GHG emissions would not be complete without a focus on component gases in addition to quantities expressed in CO 2 eq. The FAOSTAT data confirm the trends form 1990 to 2019 seen for total CO 2 eq. emissions, with important features (Table  6 ). First, the impact of agri-food systems on world total CO 2 emissions was 21 % in 2019 (down from 31 % in 1990), a respectable share considering the importance of carbon dioxide in any effective long-term mitigation strategy. While most regions had contributions around this value, ranging 13 %-23 % for North America, Oceania, Europe and Asia, the CO 2 contribution of agri-food systems was highest in Africa (52 %) and South America (  70    North America were the only regions where the CO 2 share of agri-food systems actually increased from 1990 to 2019, confirming the growing weight of pre-and post-production processes, which typically involve fossil-fuel energy use and thus emissions of CO 2 gas through combustion.
Uncertainties in shares (not shown) are the same as those estimated for absolute emissions. See  Crippa et al. (2021a)  for a specific list of hydrofluorocarbons (HFCs) used in agri-food systems, which form the basis of the F-gas emissions data estimated in this  work. 1990 2019 1990 2019 1990 2019 1990 2019 1990   et al. , 2021b)). Our findings are furthermore consistent with the strong growth in F-gas emissions reported in recent studies  (Minx et al. , 2021; Park et al. , 2021). An important aspect of the dataset presented in this study is its provision of information mapped across IPCC and FAO categories alike. Specific IPCC sectors include agriculture and land use, land use change and forestry (LULUCF). The IPCC further considers the agriculture, forestry and other land use (AFOLU). While countries report their agriculture and food emissions to the UNFCCC within national GHG inventories, our findings highlight the importance of expanding that reporting to a fuller agri-food systems view, one that properly weights the contribution of food to the global economy.
Data availability The GHG emission data presented herein cover the period 1990-2019 at the country level, with regional and global aggregates. They are available as open data, with DOI https://doi. org/10. 5281/zenodo. 5615082 (Tubiello et al. , 2021d) , and via the FAOSTAT emissions shares database (https://www. fao. org/faostat/en/#data/EM, FAO, 2021a).
Conclusions This paper provided details of a new FAOSTAT database on GHG emissions along the entire agri-food systems chain, including crop and livestock production processes on the farm, land use change activities from the conversion of natural F. N. Tubiello et al. : GHG emissions from pre-and post-production ecosystems to agricultural land, and processes along food supply chains, from input manufacturing to food processing, transport and retail, including household consumption and waste disposal. The data are provided in open-access mode to users worldwide and are available by country over the time period 1990-2019, with plans for annual updates. The major trends identified in this work help locate GHG emissions hotspots in agri-food systems at the country, regional and global level. This can inform the process of designing effective mitigation actions in food and agriculture.
This work adds to knowledge on GHG emissions from agriculture and land use -generally well established in the literature -by adding critical information on emissions from a range of pre-and post-production processes. The new data highlight the increasingly important role that pre-and post-production processes along supply chains play in the overall GHG footprint of agri-food systems, globally and in most countries, providing new insights into food and agriculture development trends and future mitigation options. The granularity of the dataset allows us, for the first time, to highlight specific processes of importance in specific countries or groups of countries with similar characteristics. The relevance of the information being provided cuts across several national and international priorities, specifically those aiming at achieving more productive and sustainable food systems, including in relation to climate change.
Acknowledgements. FAOSTAT is supported by  FAO 's member countries. We acknowledge the efforts of national experts who provide the statistics on food and agriculture, as well as statistics on energy use, that are the basis of this effort. All authors contributed critically to the drafts and gave final approval for the publication. We are grateful for overall support by the  Food Climate Partnership at Columbia University. Review statement. This paper was edited by  David Carlson  and reviewed by two anonymous referees.
This suggests that mating-type differentiation predates speciation within Ostreococcus, suggesting that Ostreococcus MTþ and MTÀ are remarkably ancient. However, the total number of gametologs, their synteny, and sequence conservation among Mamiellales and Mamiellophyceae remains unknown. Here, we investigated the architecture and phylogenetic profiles of the MTþ and MTÀ alleles to unfold their evolutionary history. We analysed the gene set of the two candidate mating-type loci, and identified the complete set of gametologs between them. This allowed us to define the set of orthologous genes located inside each of the available candidate MT loci in Mamiellales. This data set was then leveraged: 1) to investigate the presence of evolutionary strata, 2) construct gene genealogies to search for trans-specific evolution signatures, and 3) identify the opposite mating types from additional Mamiellophyceae sequence data.
Expanding the Number of Mamiellales Species with Two Mating-Type Alleles Since the core MT GFs allow MTþ and MTÀ delineation in the Mamiellales, we used the sequence data to screen 33 transcriptomes (MMETSP and 1KP data sets) from several Mamiellophyceae species for homologous sequences (listed in supplementary table S5, Supplementary Material online). The taxonomic affiliation of each transcriptome was inferred from 18S rDNA sequences (supplementary table S6 and fig. S2 , Supplementary Material online). The phylogenetic range of the transcriptomes spanned from the early divergent freshwater species, such as Monomastix opisthostigma (Monomastigales), Crustomastix, and Dolichomastix (Dolichomastigales), to early Mamiellales, such as Mantoniella. It also included several Micromonas strains from novel species, such as Micromonas bravo and Micromonas polaris.
In total, at least one homologous gene was recovered for each GF (with an average of 11 GFs per transcriptome) in 28 of 33 transcriptomes (fig. 5 ). The most striking pattern came from O. mediterraneus MMETSP0929 (strain RCC2572) and O. lucimarinus MMETSP0939 (strain BCC118000) transcriptomes. Although both data sets displayed hits for almost all core genes (17 out of 23), the taxonomic affiliation inferred for these genes by best BLAST hit (BBH) was not consistent with the 18S taxonomic affiliation. Instead, it suggested affiliation to a different species of the opposite mating type (supplementary fig. S3 , Supplementary Material online). In O. mediterraneus MMETSP0929, 14 of 17 genes were affiliated to species from the opposite MT groups (MTÀ), such as O. tauri and O. lucimarinus, not to the reference genome O.
To this end, we selected Micromonas transcriptomes with more than one positive hit with the GFs, and the highest number of hits in the majority of transcriptomes (nine transcriptomes), together with one outgroup from the genus (Mantoniella sp. MMETSP1468). Finally, we built individual GF phylogenies from these sequences and the core genes GF data set (supplementary fig. S4 , Supplementary Material online). A consistent subclustering of strains within the M. commoda group was observed. MMETSP 1084, 1387, 1403, and 1400 clustered together in 11 of 13 phylogenies, whereas MMETSP1404 and 1393 clustered with genes from the reference genome of M. commoda RCC299 (fig. 7A  and supplementary fig. S4 , Supplementary Material online). In only two phylogenies, there was no apparent subclustering (fig.
Discussion Direct evidence of meiosis is not available for most marine planktonic microbial eukaryotes. This is either due to the difficulty in culturing certain species, or because experimental studies are hampered by a lack of knowledge about sex determination and the conditions required to induce a sexual cycle. In the case of haploid green picoalgae (cell diameter <2 mm) of the Mamiellales lineage, population genomics data in one species allowed the identification of two candidate mating-type alleles with suppressed recombination  (Blanc-Mathieu et al. 2017 ). Here, comparative genomics of seven related species within the Mamiellales lineage unraveled different facets in the mode and tempo of evolution in this enigmatic locus. First, although no MTþ and MTÀ specific genes could be identified for all seven species, MTþ and MTÀ specific genes could be identified within the Ostreococcus genus.
MTÀ specific genes may be implicated in mating-type differentiation, such as the previously identified gene encoding an RWP-RK domain  (Worden et al. 2009 ). The two MTþ specific genes that have been identified in Ostreococcus encode for unknown proteins. One of these proteins (gm1. 767_g) harbors WD40 repeats and is predicted to bind to other proteins. The second protein has a DNA binding domain, which is also found in DNA mismatch repair proteins (gm1. 689_g, PF00488). A WD40 protein has been shown to regulate mating in the fungus Ustilago maydis  (Wang et al. 2011 ). Nevertheless, the functional range of WD40 proteins is too wide to confidently infer a role of the Ostreococcus protein to act as a MTþ signal protein. Second, comparative phylogenetics of core gametologs allowed the identification the opposite mating types in two additional species for which transcriptomes were available: the MTÀ in O.
mediterraneus and the MTþ in O. lucimarinus. This mating-type profiling is made possible by the high divergence between the MTþ and MTÀ regions, as gametologs cluster by MT and not by species. By screening available environmental data from the TARA Oceans project for the and M pusilla reference genomes and homologous genes retrieved from diverse Micromonas spp. transcriptomes. In the phylogeny "A," two M. commoda subclusters are highlighted in dark green (MMETSP1403, 1400, 1084, 1387) and light green (MMETSP1393, 1404, and the reference M. commoda RCC299). In phylogeny "C," there is no "A type" subclustering. In the actin and b-tubulin trees, subclusters are absent and branch lengths are shorter than "A," but parallel with phylogeny "C. " presence of these gametologs, we previously found that, in fact, both mating types of O.
and supplementary table S7 and fig. S2, Supplementary Material online). Third, analyzing additional transcriptome data from early diverging branches of the Mamiellophyceae class, we could detect orthologous genes to the Mamiellales gametologs in eight additional transcriptomes. However, we could not detect any significant difference in GC3 signatures in the earliest Mamiellophyceae, as would be expected under suppressed recombination; on the contrary, GC3 values appear to be higher in homologous genes in Dolichomastigales. This suggests the Mamiellales gametologs are not part of a lower GC region in earlier branching Mamiellophyceae. The conservation of 23 gametologs within the Mamiellales lineage prompted us to investigate the dynamic of these genes. The additional gametologs within the Ostreococcus lineage support an ancient large translocation event.
2010; Parfrey et al. 2011; Blank 2013) , much earlier than the diversification within Volvocales where deep homology of mating-type loci has been reported  (Ferris et al. 2010) , and with a higher upper limit to the estimated 370 Myr divergence of the STE3-like pheromone receptors from basidiomycete fungi  (Devier et al. 2009). Therefore, our data suggest the Mamiellales matingtype sex-determining region to be among the oldest mating type locus reported. In conclusion, we analyzed the phylogenetic profiles of the GFs within the Ostreococcus mating types, and gained insights into the evolutionary history of this sex-determining region in one of the earliest diverging orders of Chlorophytes. The identification of strains from the two opposite mating types in three species will guide future experimental approaches for mating and strain crossing, since a highly efficient transformation protocol is now available in Ostreococcus  (Sanchez et al.
We edited the multiple sequence alignments (MSAs) using several filters on both sequences and positions, implemented in the PLAZA framework and described by Proost  (Proost et al. 2009 ). Briefly, highly divergent and partial sequences were filtered out, and positions containing gaps in minimum 10% of the sequences or containing potentially misaligned amino acids removed. We also applied a minimum length cut-off to the edited MSA: the edited MSA had to be 50 amino acid long at least, otherwise we ignored it. In case the original unedited MSA was shorter, we used this length as a cut-off value instead. Finally, we retained only MSAs that showed at least 50% alignment of amino acid identity in half of the sequences of the MSA. The circular plots depicting the location of homologous genes from GFs having copies outside of the O.
tauri MT loci (fig. 2B  and C ) were generated with the circlize package in R  (Gu et al. 2014 ; https://r-project. org/). To test different gene order rearrangement scenarios between the MTþ and MTÀ regions, we defined S dist , which is the absolute value of the difference of the position of orthologous genes on the MTþ and MTÀ regions. If there are n orthologous genes between the two loci with piÀ the position (in rank) of gene i on MTÀ and piþ the position of its ortholog on MTþ, S dist ¼ P n i¼1 p iÀ À p iþ. S dist ¼0 if all orthologs are perfectly collinear. The expected S dist under random position of orthologous genes in the two mating types was assessed by simulations. If there has been an inversion of gene order between the two regions, S dist is maximal, S dist ¼z(2nÀ2z), with z ¼ n/2 if n is even, and z ¼ (nÀ1)/2 if z is odd.
g. , transcripts from MMETSP samples). The divergent gene removal criterion was based on the results of the all-against-all protein sequence similarity search performed using data from the eight reference genomes only (supplementary table S1, Supplementary Material online). Therefore, it was not used to filter out these sequences from the MSAs. Phylogenetic trees were built for full alignments in case the editing was deemed too stringent, for instance discarding transcripts flagged as partial sequences. Finally, when investigating the molecular phylogeny of the 18S rDNA genes, we used IQ-TREE's ModelFinder Plus parameter to select the best DNA substitution model.
Searching for Homologs in Publicly Available Transcriptomes We used sequences of core MT GF members as queries to search for homologs in Mamiellophyceae transcriptomes (33 transcriptomes in total, listed in supplementary table S5, Supplementary Material online). Transcriptomes were retrieved from the MMETSP  (Keeling et al. 2014; Johnson et al. 2019 ) and 1KP data sets  (Matasci et al. 2014). Reassembled MMETSP transcriptomes were downloaded from https://doi. org/10. 5281/zenodo. 251828 (version 1; January 2017) and 1KP transcriptomes via 1KP's R interface (https://github. com/ropensci/onekp). CDS from each Mamiellophyceae MMETSP transcriptome were predicted using TransDecoder  (Haas et al. 2013)  with default parameters. Sequence similarity searches were performed using TBLASTX (maximum E-value threshold 1e-4) and results were filtered to retain hits with alignment length >50 and amino acid identity >60%.