dataesr/scientific-paragraphs-categorization huggingface dataset:
Only PGAM1 enzymes contain an annotated phosphoglyceromutase active site (IPR005952), while both PGAM1 and PGAM2 belong to the same PFAM superfamily (histidine phosphatase, PF03000) as inferred using InterProScan and PFAMscan respectively  (46, 89). This isoform was predominantly mitochondria-targeted, with plastid-or dualtargeted isoforms only identified in P. tricornutum (Phatr3_J37201) and a small number of other pennate diatoms (Attheya septentrionalis, Amphiprora coffeaformis, Entomoneis sp. CCMP2396) and haptophytes (Phaeocystis antarctica, Phaeocystis rex sp. CCMP2000) (Fig. S3 ). To confirm plastidial localization of Phaeodactylum enolase and PGAM proteins, three proteins (Phatr3_J41515, ptEnolase; Phatr3_J17086, ptPGAM1A; Phatr3_J37201, ptPGAM2) were cloned into pPhat: eGFP vector sequences and transformed into wild-type P.
tricornutum Pt1. 86 cells by biolistic transformation (Figs. 1D, S4 ). These proteins were selected based on co-regulation analysis, suggesting tight transcriptional linkage to one another and core chloroplast metabolic pathways (Fig. S5 , see below). The observed fluorescence patterns were coincident with chlorophyll, confirming plastid targeting. Physiological roles of diatom ptEnolase and ptPGAM identified from cellular and environmental sequence expression data Next, we considered the mRNA abundances of diatom ptEnolase, ptPGAM1A and ptPGAM2 sequences, both in published microarray (57) and RNAseq data for P. tricornutum  (41)  (Figs. S5-S7, Table  S2 ), and more broadly within the Tara Oceans dataset (64) (Fig. 2 , S8-10, Table  S3 ), as proxies of their physiological function. First, considering general patterns of transcriptional co-regulation across published P.
Genome-scale enrichment analyses of the in silico localizations of proteins encoded by differentially expressed genes revealed distinctive changes in glycolysis knockout organelle metabolism compared to control lines. These effects were most strongly observed in 19C CL conditions, in which of the 239 genes differentially upregulated (mean fold-change >2, P-value < 0. 05) in both ptEnolase and ptPGAM1A knockout lines compared to controls, 85 (36%) were inferred to possess chloroplast targeting peptides based on ASAFind (47) and 63 (26%) were found to inferred to possess chloroplast targeting peptides based on HECTAR predictions  (49) , compared respectively to 13% and 4. 8% of genes globally encoded across the entire genome (chi-squared P < 10 -05 ; Fig. 4A ). Less dramatic changes were evident in 19C LD and 8C CL conditions, reflecting the less exaggerated growth phenotypes of glycolysis-gluconeogenesis knockout lines under each condition, although we noted that 13 of the 51 genes (25%) inferred to be downregulated in both ptEnolase and ptPGAM1A knockout lines under 8C CL conditions were inferred to encode chloroplast-targeted proteins by ASAFind, representing likewise an enrichment compared to all genes within the genome (chi-squared P < 0.
05; Fig. 4A ). Considering the KEGG, PFAM, GO and transcriptional repartition of the differentially expressed genes in each knockout line (Table  S5 , sheets 5-8), we noted clear patterns of differential upregulation of genes encoding photosynthesis-related proteins in both ptEnolase and ptPGAM1A knockout lines, specifically under 19C CL conditions. These included (chi-squared P < 0. 05) GO enrichments in lightharvesting complex (GO:0030076), photosynthesis (GO:0009765) and protein-chromophore linkage (GO:0018298) GO terms, the chlorophyll-binding (PF00504) PFAM domain. We additionally considered the accumulation of genes that are differentially expressed in knockout lines in PhaeoNet, a WGCNA (Weighted Genome Correlation Network Analysis)-based division of the entire Phaeodactylum genome into thirty co-regulated gene modules  (41) ; and noted a chi-squared P < 0.
Notably, cytoplasmic phosphogycerate mutase (126-128) and enolase enzymes (129-131) have been identified in proteomic surveys of plant and algal cold-responsive genes, but it remains to be determined whether plastidial versions of these enzymes possess functions relevant to high latitudes. Our data shed light into functions of a poorly understood metabolic resource that may facilitate diatom success at high latitudes, and that may serve as a candidate for metabolic engineering in crop species for enhanced photosynthetic activity under continuous light and low temperatures  (33). No. 835067), and from the ANR 'BrownCut' project (ANR-19-CE20-0020). CB, EM and JJ were supported by ANR 'DIM' (ANR-21-CE02-0021). The lipid analysis were performed at the LIPANG (Lipid analysis in Grenoble) platform hosted by the LPCV (UMR 5168 CNRS-CEA-INRAE-UGA) and supported by the Rhône-Alpes Region, the European Regional Development Fund (ERDF), Institut Carnot 3BCAR and Labex GRAL, financed within the University Grenoble Alpes graduate school (Ecoles Universitaires de Recherche) CBH-EUR-GS (ANR-17-EURE-0003).
Pelagophyte plastid D i a t o m / p e l a g o p h y t e / d i c t y o c h o p h y t e m i t o c h o n d r i o n P h a t r 3 _ d r a f t J 1 1 7 2 ; P h a t r 3 _ d r a f t J 1 1 9 2 Fig. S1. Consensus topology of a 380 taxa x 413 aa alignment of Enolase sequences, sampling all organelle-targeted isoforms from cryptomonads, haptophytes and ochrophytes and representatives from a densely-sampled dataset of 151 taxonomic groups $across the tree of life  (Dorrell et al. , 2021). The tree topology shown is the consensus of the best-scoring RAxML trees identified using three substitution matrices: GTR, JTT, and WAG. Branch thickness corresponds to frequency of topology recovery in the consensus of consenus trees; branches are coloured by taxonomic affiliation; and tips (cryptomonads, haptophytes and ochrophytes only) by predicted in silico localisation.
Phaeodactylum RNAseq datasets subject to nitrate limitation (McCarthy et al. , 2017), phosphate starvation and addition (Cruz de Carvalho et al. , 2016) and Circadian Fe enrichment and limitation (Smith et al. , 2016), respectively. While limited transcriptional responses are identifiable in response to changing N, P or Fe availability, the relative transcription of plastidial to mitchondrial-targeted enolase is substantially greater in RNA sampled in long day (> 12h post-illumination) conditions in C. D: relative fold changes in plastidial enolase and PGAM1 expression in published microarray data assembled in Ashworth et al. , 2016, under different illumination conditions. Both Enolase and.
Acknowledgments RGD acknowledges a  CNRS Momentum Fellowship , awarded from  2019-2021 , an  ANR JCJC Grant  ( « PanArctica  »  ANR-21-CE02-0014-01 ) and an  ANR t-ERC  Grant ("ChloroMosaic"), awarded from  2021-2022. CB acknowledges support from  FFEM -French Facility for Global Environment ,  French Government 'Investissements d'Avenir'  programs  OCEANOMICS  ( ANR-11-BTBR-0008 ),  FRANCE GENOMIQUE  ( ANR-10-INBS-09-08 ),  MEMO LIFE  ( ANR-10-LABX-54 ), and  PSL Research University  ( ANR-11-IDEX-0001-02 ), the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  (Diatomic; grant agreement.
Chloroplast Dual Chloroplast/ Mitochondria Endomembrane Dual Endomembrane/ Mitochondria Mitochondria Phaeodactylum RNAseq datasets subject to nitrate limitation (McCarthy et al. , 2017), phosphate starvation and addition (Cruz de Carvalho et al. , 2016) and Circadian Fe enrichment and limitation (Smith et al. , 2016), respectively. While limited transcriptional responses are identifiable in response to changing N, P or Fe availability, the relative transcription of plastidial to mitchondrial-targeted enolase is substantially greater in RNA sampled in long day (> 12h post-illumination) conditions in C. D: relative fold changes in plastidial enolase and PGAM1 expression in published microarray data assembled in Ashworth et al. , 2016, under different illumination conditions. Both Enolase and A) Enolase Plastid Light-harvesting Protein import Photosynthesis tRNA synthetases PsbM PsbU Psb27 PetB PetJ PsaO PetH AtpC Chl synthesis A C D PsbO PsbP PsbQ GluRS E F G PsbW Alternative electron flow GluRR H I K NDH PTOX SOD GSA L M N Carbon metabolism PMP2 Mpv17 TRF PBS P E* R SBP RPE rpiA PRK TrX HMBS S T V TKT TAL Carbonic anhydrases UPS W Y S* FBP TPI CAB CAT UPD U PGluM FBA ALDO GAPDH PGK Ornithine cycle CPO G6PE PFK1 PGAM ACGS PPO Ribosome pckA Enol PPC AspAT ACGK FcH rps15 rpl15 rpl7 PyrK PPDK NAGPR ChlH rpl17 rpl10 rpl28 MCM DLAD PDH ACOAT ChlD MPoM Fe stress Fatty Acids Lipid synthesis Amino acid assimilation PCR ISIP1 ISIP3 AcCOA GPAT PLA2 Shikimate metabolism GS GOGAT CBR FvDX Fer ACPMT AGPAT LPIN DPHS AAL AlaT DPAR ACP LPAT DGDP DHQS ChlS Riboflavin synthesis ACPS DGAT PLPL DHQD Ser, Thr metabolism DMRS RibS RibK ACPR MGAT PGP SK SOAT CBL Isoprenoid synthesis ACPD PCT SQD1 PSCVT CysS THFCM DXS Carotenoid synthesis EACPR CDGP SQD2 ChrS GHMT DXR HGS PS SCD AS CM ThAL MEPC MBMM PDS1 LCS APRT PpDH DMEK HGPT PDS2 Lysine synthesis IGPS POGTA MECS TocC PLI ASPK PA4H Sulfate assimilation HMPS TocOMT ZEP ASDH TrpS KGTA AdK HMPR NQDH VDE DHPS Branched chain AA SulR IDI Fig.
8 0. 6-0,6 0. 4-0,6 0. 2-0,4 0-0,2 < -0. 2 Fig. S6. Transcriptional coregulation of plastid-targeted Enolase and PGAM1 to other core organelle metabolism pathways. These heatmaps show the Spearman correlation coefficients calculated between ptEnolase and ptPGAM1 against genes encoding other core Phaeodactylum plastid-and mitochondrial-targeted proteins, as annotated in Ait-Mohamed et al. 2020, considering a merged and ranked RNAseq and microarray dataset. Where more than one homologue of a particular enzyme exist, the highest correlation coefficient is shown. Complete gene lists, including abbreviations, are provided in TableS2.
, 2012). Furthermore, by combining accurate species range maps with knowledge on the geographical patterns of specific threats (e. g. climate change, human activities), the conservation status of species can be quantified (e. g. International Union for Conservation of Nature (IUCN) Red List of Threatened Species;  Bland et al. , 2015). Full documentation of species ranges is challenging, as the ecology of many species remains unknown or poorly documented  (Pimm et al. , 2014) , global distribution information is often missing or incomplete  (Wisz et al. , 2008; Duputi e et al. , 2014) , and regional information is scattered across diverse datasets and sources  (Serra-Diaz et al. , 2017). Data collection is especially challenging for diverse taxa with many (but poorly monitored) species, such as plants  (Butchart et al.
, 2005). As a result, plant species ranges are much less documented compared with many animal clades, and existing documentation is often restricted to specific regions or clades  (Miller et al. , 2012). This lack of documentation limits the study of global macroecological factors shaping plant diversity and slows down the process of designing global conservation priority settings  (Miller et al. , 2012; Bland et al. , 2015). The recent sharp increase in freely accessible online data opens the possibility for increased automation in the production of global distribution maps  (W€ uest et al. , 2020). For example, the Global Biodiversity Information Facility (GBIF; http://www. gbif. org/) is the largest database for species occurrence records  (Beck et al. , 2014)  and contains data from diverse sources, including museum records, inventory campaigns, and citizen science projects such as iNaturalist (http://www.
inaturalist. org/) and Les Herbonautes (http://lesherbonautes. mnhn. fr/). Furthermore, an increasing proportion of natural history collections are being digitized and integrated into data networks (e. g. Botanical Information and Ecology Network (BIEN);  Maitner et al. , 2018)  and the construction of regional atlases and datasets (e. g. Global Inventory of Floras and Traits (GIFT);  Weigelt et al. , 2020)  continues, building up extensive records of (past) specimen occurrences, national forest inventories, and species checklists. This digitization process is ongoing and some of these datasets are far from being complete, with urban regions or areas along roads more likely to be surveyed than more remote or inaccessible areas  (Kadmon et al. , 2004; Araujo & Guisan, 2006). Furthermore, the different datasets are not integrated and available data formats vary.
Nevertheless, these growing public databases are providing useful highquality data, which may be used to map species ranges, especially when datasets are combined  (Duputi e et al. , 2014). Given the large amount of available data and the ongoing improvement in the quantity and quality of these datasets over time, the generation of accurate and open-access range maps will benefit from the development of an automated mapping pipeline. Current estimates list over 380 000 species of vascular plants  (Cheek et al. , 2020) , which is far more than any other existing group for which mapping efforts have been attempted. Because mapping so many species at the global scale is challenging, previous mapping attempts have mainly focused on direct mapping of species richness using statistical models  (Kier et al.
Materials and Methods The workflow includes five main parts (Fig. 1 ): data collection, data cleaning, parameter optimization, mapping by integration of SDM and polygons, and map validating. The working environment is in R  (R Core Team, 2013) , and the scripts for data cleaning, parameter optimization, and mapping are accessible online (https://gitlab. ethz. ch/gdplants/gdplants/). This code can be flexibly applied to any plant clade or region of interest. Illustrated here for Fagales and Pinales, the species range and richness maps can be efficiently constructed for other clades following the data science workflow.
Data collection and merging We retrieved occurrence information for Fagales (including Betulaceae, Casuarinaceae, Fagaceae, Juglandaceae, Myricaceae, Nothofagaceae, and Ticodendraceae families) and Pinales (including Araucariaceae, Cephalotaxaceae, Cupressaceae, Phyllocladaceae, Pinaceae, Podocarpaceae, Sciadopityaceae, and Taxaceae families) from 48 databases (see Supporting Information Table  S1  for details). To reduce the risk of underestimation of species ranges in regions for which observational data are scarce, we included not only text-based datasets but also existing distribution maps, which were either already available in the form of raster or shape files or were digitized by our team. The 48 databases used in this study consist of online data sources, journal articles, and books containing regional checklists, expert-drawn maps, and occurrence points.
For two large online data sources, specific packages in the R environment are available: we used the RGBIF package  (Chamberlain et al. , 2017)  to access the GBIF and the BIEN package  (Maitner et al. , 2018)  for the BIEN (data downloaded in October 2018). We retrieved data from all other sources manually. We converted all occurrence data into decimal longitude/latitude format in the World Geodetic System 1984 (EPSG 4326).
Data cleaning To account for synonymous, unresolved, misspelled, or wrong species names and wrong or missing family names, we We kept only records with standardized species names, and we removed all duplicate records. We attributed all subspecies to species, and we removed hybrid species. To account for the records of cultivated species or records that were assigned to incorrect coordinates, we removed records falling within a 10 km radius around country capitals, within a 5 km radius around country centres, within a 1 km radius around biodiversity institutions, within a 1°radius around the GBIF headquarters (Copenhagen, Denmark), and within a 0. 5°radius around longitudinal/ latitudinal coordinates 0,0 using the R-package COORDINATE-CLEANER  (Zizka et al. , 2019). We evaluated whether observations were made in the species' native range using the regional-level distribution database, Royal Botanic Gardens, Kew, UK (POWO, 2019; accessed in February 2019), which includes most families of Fagales (except Juglandaceae and Myricaceae) and all Pinales families.
For each species, we generated a 2°buffer around the Kew distribution range and removed records outside this buffer. We manually checked species for which the cleaning process resulted in more than 50% of records being deleted, and we manually retrieved erroneous treatments. As uneven distribution of occurrence records may increase the uncertainty on both the shapes and connectedness of hulls, and may cause underestimation of species ranges in regions with sparse occurrences due to the deviation of weight in SDM mapping, for species with > 50 occurrences, we removed occurrences closer to each other than 0. 1°using the 'desaggregation' function in the R-package ECOSPAT  (Di Cola et al. , 2017).
Adding environmentallystratified pseudo-absences guaranteed that the entire environmental space was considered for model training, and that uninformed model extrapolations were avoided. We combined presences and pseudo-absences of each species into a presence-absence dataset. For all presences and for all pseudo-absence sampling methods, we ensured that the final points selected were at least 5 arc-min apart from each other to avoid spatial autocorrelation and bias from overly dense sampling. We used Kew's regional-level distribution maps as a reference, randomly drawing 2000 presence points (inside the distribution ranges) and 10 000 absence points (as described earlier), which we used as independent validation data to evaluate the various parameterization settings. We assessed the results of different ensembles of pseudo-absence strategies and modelling strategies based on TSS values.
Producing species distribution maps and lineage richness maps, and evaluation We selected the best-performing parameter combination as the optimal combination. For species with > 20 occurrences, we obtained the final distribution map by determining the overlap between the polygon map and the SDM map. For species with fewer than 20 occurrences, the final distribution map was equal to the polygon map. Finally, we generated lineage richness maps by stacking the final species distribution maps. We evaluated species distribution maps and lineage richness maps separately. We manually checked all distribution maps using resources including Flora of China (eFloras, 2020), The PLANTS Database of the US Department of Agriculture (USDA & NRCS, 2020), PlantZAfrica (http://pza. sanbi. org/), Flora Malesiana (http://portal.
Occurrence data collection We collected 5934 880 valid occurrence records from 48 databases (Table  S1 ) for the 15 families in the two lineages, including 6065 different species names. After correcting species names using the Catalogue of Life (2021) and data cleaning, we retained 1932 species, covering all families and genera of Fagales and Pinales (Table  S2 ). There were 1318 species with > 20 records, 84 species (e. g. Quercus robur, Pinus sylvestris, Juniperus communis) of which had more than 10 000 records (Pinus halepensis had the largest number of records: 242 561). There were 402 species with 4-20 records, which was insufficient for SDM; and 208 rare species with fewer than four records, which was insufficient for polygon mapping (Fig. 2 ; Table  S3 ). For 84 species, > 50% of the records were removed during data cleaning (Table  S3 ).
By manually checking these species, we determined that most of them represent widely cultivated or rare species with only few occurrence records available, implying that the applied cleaning procedures were justified. All occurrence records of three species, Quercus bawanglingensis, Quercus obconicus, and Betula glandulosa, were mistakenly removed and we manually retained these records. Specifically, for Fagales, we originally collected 3134 710 records. S2-S4 ). For Pinales, we originally collected 2800 170 records. After the final data cleaning, the dataset encompassed 2246 672 records with all 606 Pinales species (except hybrid species) of the Catalogue of Life (Tables  S2-S4 ). For the missing 25 species, we searched the literature for their occurrence information and added them into the database (Table  S5 ).
Discussion Species range maps are central for fundamental research in macroecology and biogeography  (Rocchini et al. , 2011) , as well as for conservation and restoration programmes  (Ferrier, 2002; Miller et al. , 2012). However, detailed distribution maps are still lacking for most vascular plant species at a global scale. Previous efforts to map plant species ranges and diversity properties were limited to a few taxa (e. g. Pinus;  Critchfield & Little, 1966) , a specific geographic extent (e. g. China;  Wang et al. , 2010) , or used often simple polygon mapping at coarser resolution when applied globally (e. g. 1°,  Guo et al. , 2020). Integration of different datasets including the increasing online databases (as listed in Table  S1 ;  Serra-Diaz et al. , 2017)  opens possibilities for novel data science approaches to map plants globally.
Here, we demonstrate that based on global database compilations, our comprehensive pipeline can transform the scattered distribution information into global distribution maps in batch-mode processing (Fig. 1 ). Notably, comparing to previous approaches, our algorithm can automatically identify and map ranges of different populations for species with disjunct distribution, by appropriate parameter settings, as well as optional filters such as bioregion partitions and environmental associations. Moreover, we propose a comprehensive SDM mapping algorithm composed of four modelling methods of differing complexity and seven pseudoabsence sampling strategies. Our open pipeline helps to map distribution ranges more accurately and allows to define settings that are specific to the characteristics of the target clades.
Data quality and mapping validation We have acquired a unique collection of occurrence data from various data sources (Fig. 2 ; Table  S1 ) to produce a compilation of species range maps for two important temperate tree clades. Among the compiled occurrences, about 9% were removed due to invalid species names and afterwards about 8% were removed due to incorrect distributions (Tables  S3, S4 ), indicating the importance of data cleaning. The R-package COORDINATE-CLEANER helped us to clean about 20% of the invalid occurrences, removing dubious records without the need of a distribution reference checklist (e. g. species in Juglandaceae and Myricaceae), providing a useful tool for data cleaning. After the data cleaning and mapping steps, the validation with Quercus and Pinus indicated overall good performance of our mapping approach at a large scale.
Further, a large value for the minimum size of a cluster may mistakenly remove the occurrences from a disjunct small population, while a small value may fail to remove outliers. Therefore, the optimization of these parameters was important in generating accurate maps. Since the majority of our species have relatively few occurrences (Fig. 2 ; Table  S3 ) and the datasets were cleaned thoroughly, we ended up using an outlier distance of 5°and a small minimum cluster size of two, thereby keeping a large number of occurrence points (Table  S7 ). However, for species with a large number of occurrences, this parameter set sometimes failed to remove outliers. For example, for Abies balsamea, a minimum cluster size set to < 7 would produce a polygon that includes the outliers in the western coast outside its natural range in eastern North America  (POWO, 2019).