dataesr/scientific-paragraphs-categorization huggingface dataset:
Analysis https://doi. org/10. 1038/s41893-022-00970-0 densities (particularly for rangers; one per 26 km 2 ) should serve to update and refine current recommendations and guide the recruitment and deployment of site-based personnel at the national level. Quantified workforce data could also support the proposed recognition of the ranger profession by the International Labour Organization  14. Changes in personnel numbers and densities have the potential to be reliable general indicators of progress towards adequate levels of management effectiveness of protected areas, both within national systems and on broader geographic scales, and provide a baseline for determining the impact of staff increases. To enable accurate workforce monitoring, requirements for providing data on personnel could be included in national planning and reporting requirements for the Convention on Biological Diversity, in monitoring of United Nations Educational, Scientific and Cultural Organization World Heritage Sites, in reporting to the World Databases on Protected Areas and on OECMs, and in protected area management effectiveness assessments.
Data collection In phase 1 (2017), we first circulated a comprehensive multi-language questionnaire and associated guidelines on protected area personnel numbers to major national protected area agencies, focusing on the 50 countries listed in the WDPA as having the most protected areas. The questionnaire requested information on personnel numbers, type of employers and management levels (from executive to skilled practical workers). Protected area personnel were defined as those spending at least 50% of their work time on protected area-related tasks. The questionnaire also requested information about job titles used for personnel equivalent to rangers. This phase produced usable data for 28 countries/territories. In phase 2 (2018 onwards), we conducted online searches for published data on protected area personnel numbers in the countries/ territories not included in the questionnaire survey or where questionnaire responses were incomplete or unclear.
Because the format, completeness and level of detail of the data varied widely, from comprehensive personnel lists to single figures, we restricted our raw dataset to six variables that could be consistently extracted from data obtained for each country/territory: 1. Total number of non-ranger personnel (if known) 2. Total number of rangers (if known) 3. Total number of protected area personnel (either the sum of 1 and 2 or provided as an undifferentiated total) 4. Terrestrial area of protected areas covered by surveyed personnel (km 2 ) 5. Total terrestrial area of protected areas of the country/territory (km 2 ) 6. Year of the data We used the WDPA, official publications and websites to determine (or verify) the area of terrestrial protected areas covered by the personnel listed for each country/territory, using WDPA data if there were discrepancies.
Candidate predictors To predict the number of rangers and non-rangers in countries and territories for which we had no data (Statistical analysis), we collected information on the following set of variables, hereafter referred to as candidate predictors: Location data The WGS84 latitude and longitude of the centroid of the largest land mass associated with each country/ territory (to obtain the polygons defining the land masses, we used the R package rnaturalearth version 0. 1. 0; https://github. com/ropensci/rnaturalearth) 2020 data from the World Bank (https://data. worldbank. org/ indicator). • Area of the country/territory • Population density: the mid-year population divided by land area • Environmental Performance Index (EPI): a composite index using 32 performance indicators across 11 categories • Ecosystem Vitality Index (EVI): an indicator of how well countries preserve, protect and enhance ecosystems and the services they provide • Species Protection Index (SPI): an indicator of the species-level ecological representativeness of each country's/territory's protected area network Not all this information was available for all countries/territories.
Software We performed all the data analyses using the free open-source statistical software R version 4. 1  57. We used the R package spaMM version 3. 9. 13 to implement LMMs  58  and the R package ranger version 0. 13. 1 to implement RF/ETs 59. To reformat and plot the data, we used the Tidyverse suite of packages  60. Details are provided in an R package we specifically developed so that findings presented in this paper can readily be reproduced (see Code availability). Using a workstation with an AMD Ryzen Threadripper 3990 × 64-core processor and 256 GB of RAM, our complete workflow ran in ~3,000 CPU hours.
Estimation of required numbers and densities of personnel To estimate the numbers of personnel and rangers required for effective management of existing protected areas, we referred to ref. 25. This estimates that the minimum budget needed to adequately manage the existing protected area system is US$67. 6 billion per year and that current annual expenditure is US$24. 3 billion. From these figures, we can calculate that resources invested in the current global system of protected areas are approximately 36% of what is required. We consulted data from https://ourworldindata. org to determine that the proportion of global public expenditure on employee compensation has remained between 21. 01% and 23. 33% in the years from 2006 to 2019. We obtained these figures from the 'Government Spending' section of the site, consulting the chart 'Share of employee compensation in public spending, 2002 to 2019' and selecting data for 'World'.
Economic calculations We based our calculations on published data from 2020  25  , which estimate that expanding the protected areas to 30% would generate higher overall output (revenues) than non-expansion (an extra US$64-454 billion per year by 2050). This figure is only an indicative, partial estimate, generated for the purposes of comparison and to illustrate the substantial return on investment that protected area staff investments imply. Using these figures and our estimates of personnel requirements to ensure effective management of 30% coverage, we calculated the range of sums that each additional protected area staff member has the potential to generate (Supplementary Table  8 ). For clarity, we rounded these figures to the nearest hundred US dollars in the main text. Our estimates of the gross value added per worker in forestry and agriculture (sectors responsible for similar proportions of the world as protected areas) are included to provide a point of comparison for the figures showing the economic benefit generated per protected area personnel member (see the preceding).
The data for the gross annual value of world agricultural production (US$3,550,231,736,000) and the number of workers employed in agriculture (343,527,711) come from the Food and Agriculture Organization of the United Nations 30 , providing an average gross value of annual agricultural production per worker of US$10,335. We adjusted these 2018 data to 2020 price levels using a deflator based on the US consumer price index (CPI) from the World Economic Outlook database 61 (Supplementary Table  9 ). This ensures that all the economic value data we present are directly comparable for protected area, agricultural and forestry workers. We calculated the gross value of forest production per worker on the basis of direct contribution of forestry of more than US$539 billion to world GDP in 2011  62  and total forest-sector employment of 11.
Acknowledgements We are extremely grateful to more than 150 individuals and their colleagues and employers who gave their time to obtain or locate the data used for this study. Staff of the  UN Environment Programme World Conservation Monitoring Centre  processed data from the World Database on Protected Areas for use in this study. C. Stengel prepared Figs. 4 and5. Finally, we acknowledge the following organizations whose support to the author team enabled this study to take place: Re:wild, the  IUCN World Commission on Protected Areas , the  International Ranger Federation , the  Game Rangers Association of Africa, WWF  and the  Leibniz Institute for Zoo and Wildlife Research.
1J , Fig. S2D ). This result shows that the transcripts upregulated in LSK of tumor-bearing mice are enriched in myeloid-lineage genes. We next analyzed the repartition of LSK from tumor-free and tumor-bearing mice across clusters by performing a downsampled density plot to obtain an equal cell number in each condition (Fig. 1K ). The density plot obtained con rmed that MPP3 cluster 4 was enriched in the PyMT dataset (Fig. S2E ). This suggests that myeloid differentiation potential of LSK is increased in tumor-bearing mice. We validated the increase of FLT3 -CD150 -CD48 + MPP3 frequency among LSK in PyMT and Py230 tumorbearing mice by ow cytometry  17  (Fig. 1L , Fig. S2F, S2G ). In addition, we observed that HSC from WT or PyMT mice were differentially represented in HSC clusters 0 and 1 (Fig.
To validate the cellular perturbation affecting the BM stroma, we established a gating strategy enabling to distinguish the different BM niche cell subsets. Global consensus to identify subsets of BM stromal cells by ow cytometry is lacking, in particular for MSC, with some studies using reporter mice based on the expression of Nestin  34  , Neuron/Glia antigen 2 (NG2)  37  , LepR  50  , or expression of surface molecules including Sca1 or PDGFRα  49, 52. Thus, we took advantage of our scRNAseq to identify speci c markers to distinguish different niche cell subsets. We searched for transcripts encoding surface proteins differentially expressed between the different clusters. We found that in the Cdh5 expressing EC clusters, SEC expressed Vcam1, while AEC expressed high levels of Cd200 (Fig.
Protein array To evaluate changes in circulating protein content of tumor-bearing mice, WT and PyMT serum was collected and incubated on Proteome Pro ler Mouse XL Cytokine Array (R&D), according to manufacturer instructions. Membrane images were analyzed using ImageJ software and pixel intensity ratio of PyMT vs WT sample was measured. OPG concentration was measured by ELISA (R&D) in 24h culture supernatant of small pieces (20mg) of PyMT tumors (tumor-conditioned media), supernatant of Py230 cell lines, and mice serum.
CD31 + endothelial cells were subdivided in sinusoidal endothelial cells SEC (VCAM1 + CD200 low ) and AEC arteriolar endothelial cells (VCAM1 -CD200 Hi ). CD31 -CD51 + stromal cells were further divided in MSC (VCAM1 + ), broblasts (VCAM1 -PDPN + ) and osteoblasts (VCAM1 -PDPN -CD200 + ). Mature myeloid cells were gated as CD45 + Lin -(CD3, CD19, NkP46, B220). We further identi ed neutrophils (MHCII -Ly6G + CD11b + ), Ly6C Hi monocytes (MHCII -Ly6G -Ly6C Hi CD11b + ). Cells were acquired using LSRII and/or Fortessa and sorted using FACSAriaIIu (BD Biosciences). Data were analyzed using FlowJo software (Tree star). scRNAseq analysis of hematopoietic stem and progenitor cells For cell sorting experiments, progenitors were enriched by negative selection according to manufacturer instructions (Miltenyi, 130-110-470).
The droplet-based approach of 10X Genomics platform was used to perform scRNAseq. LSK cells of 4 individual mice were sorted in PBS containing 0,04%BSA. Cell concentration was adjusted to 500 cells/uL to target 5 000 cells. Cells were loaded on a Chromium Controller using the Chromium Next GEM Single Cell 3 Reagent Kits v3. 1 according to manufacturer instructions. Raw sequencing reads were processed using the 10x Genomics software Cellranger. To obtain a cell count matrix, reads were mapped to the mouse GRCm38. 84 reference genome. scRNAseq analysis was performed using Seurat v4. During ltering, Gm, Rik, and Rp genes were removed from the count matrix. Cells with less than 1,000 genes per cell and with a high percentage of mitochondrial genes were removed from downstream analyses. Following data QC we obtained a matrix with 14842 cells and 11992 genes.
Of the 14842 cells in our scRNAseq analysis, 5632 cells were from WT mice and 9210 cells were from PyMT mice. On average each cell in the dataset had 2707 unique genes and 6638 UMIs. Data normalization was performed using the default Seurat approach and differentially expressed genes were determined using a logistic regression approach as implemented in Seurat. Speci cally, we construct a logistic regression model predicting group membership based on each feature individually and compares this to a null model with a likelihood ratio test. Dimensionality reduction was performed using PCA on the top 5000 variably expressed genes, as determined using Seurat's variance stabilizing transformation method. The top 20 principle components were considered informative (visualized using an elbow plot) and were used for the non-linear dimensionality reduction technique UMAP.
Louvain clustering of the data was performed across a range of resolution parameters and chose the resolution value using a supervised annotation approach based on the expression score of previously published signatures, calculated using the AddModuleScore function of Seurat. When visualizing the distribution of LSK from both conditions onto the UMAP embedding of the data PyMT cells were randomly downsampled to 5632 cells to correct for any differences in cell counts between the conditions. Gene-set enrichment analysis was performed in R using the enrichR package. Cell cycle status was assigned using the cyclone method implemented in the scran package. In this scheme, a supervised learning approach was used to identify pairs of markers for each cell cycle phase. To generate our PyMT response signature, we rst performed differential expression analysis between our control and treated groups.
We then converted genes that were signi cantly (adjusted p-value< 0. 05) upregulated in the PyMT group into a response signature. An Rmarkdown le with the full scRNAseq analysis pipeline is available at (https://github. com/TeamPerie/Gerber_et_al_2022) scRNAseq analysis of HSC niche Digested BM single cell suspension was enriched for HSC niche cells using CD45 negative expression before cell sorting (Miltenyi, 130-052-301). Lin -(Ter119, CD71, CD3, CD19, NkP46, Ly6G, B220) CD45 - CD31 + endothelial and Lin -CD45 -CD31 -CD51 + stromal cells were subsequently FACS sorted and processed similarly as for LSK scRNAseq.
For the volcano plots only the rst condition was kept. At the end, only genes with a signi cative adjusted p-value (p<0. 05, false discovery rate (FDR) adjusted p-value) were kept and used to de ne each cluster. We used the package ReactomePA for pathway annotation. Bulk RNAseq analysis 1x10  4  MSC of 4 tumor-free or 4 Py230 tumor-bearing mice were sorted in lysing TCL buffer (QIAGEN) containing 1% of b-mercaptoethanol (Sigma) before storage at -80°C. RNA was extracted and isolated using the Single Cell RNA puri cation kit (Norgen) according to the manufacturer's instructions. After extraction, total RNA was analyzed using Agilent RNA 6000 Pico Kit on the Agilent 2100 Bioanalyzer System. RNA quality was estimated based on capillary electrophoresis pro les using the RNA Integrity Number (RIN).
ACKNOWLEDGMENTS We thank all members of the  Translational Immunotherapy team  and U932 for their support. We thank  S. Lameiras ,  M. Bohec  and  B. Albaud  for their help with sequencing data. We thank  N. Bendriss-Vermare  and  E. Gomez-Perdiguero  for their insightful advices. We thank  David Gros  and  Lindsay Jean-Pierre. J. H. team is supported by  INSERM ,  Université Paris Cité ,  Fondation ARC ,  Ligue Nationale contre le cancer  and INCA. E. P. team is supported by the  SiRIC-Curie Program  (grant  INCa-DGOS-12554 ), the  Labex DCBIOL  ( ANR-10-IDEX-0001-02 PSL  and  ANR-11-LABX-0043 ), and the  Center of Clinical Investigation (CIC   IGR-Curie 1428 ). This work was also supported by the  Fondation pour la Recherche Médicale , grant number  FDT202106013018 , to Y. G-F. High-throughput sequencing was performed by the  ICGex NGS  platform of the  Institut Curie  supported by the grants  ANR-10-EQPX-03  and  ANR-10-INBS-09-08 , by the  ITMO-Cancer Aviesan  ( Plan Cancer III ), and by the  SiRIC-Curie program  ( SiRIC  Grant  INCa-DGOS-4654 ).
L. P. laboratory is supported by  ATIP-AVENIR ,  ANR-10-LABX-31 ,  ANR-10-IDEX-0001-02 PSL ,  H202 ERC. D. P. laboratory is supported by  CNRS ,  INSERM  and  Universite Paris Cité , and grants from  Fondation pour la Recherche Médicale ,  ATIP-AVENIR ,  Fondation ARC pour la recherche sur le cancer , " Ville de Paris Emergence " and  INCA.
For this, we searched a large number of P-Ps, plasmids, and phages from reference databases for bona fide ARGs. We found many ARGs, and their acquisition seems to have been driven by transposable elements and integrons. To test whether the P-Ps can be induced, we scanned a collection of carbapenem-resistant strains for putative P-Ps. The tested cases showed an almost systematic induction of P-Ps. We then tested whether the induced P-Ps were able to convert a panel of sensitive strains into bacteria that were resistant to broadspectrum cephalosporins.
RESULTS Antibiotic resistance genes are common in phage-plasmids but are rare in other phages. To quantitatively assess the distribution of ARGs in plasmids, phages, and P-Ps, we searched for these genes in the complete bacterial and phage genomes of the RefSeq database. For this, we first updated our database of P-Ps by using a previously described detection method (23) (Fig. S1A ). The novel P-Ps were classified into groups based on their similarities to previously classified elements, as measured by the weighted gene repertoire relatedness (wGRR) (see Materials and Methods) (Fig. S1B ). This led to an almost doubling of the database of P-Ps to a total of 1,416 P-Ps. These elements represent 5. 6% of the 25,275 phages and plasmids. We searched for genes in phages, plasmids, and P-Ps with high sequence similarity (at least 99% identity and 99% coverage) to verified ARGs from three reference databases (ARG-ANNOT, ResFinder, and CARD).
In agreement with previous studies  (8) , ARGs were frequently found in plasmids (20. 8%) and were almost never found in phages (2 out of 3,585 genomes, ,1%) (Fig. 1 ). A total of 4. 2% of the P-Ps carried ARGs, a frequency that is intermediate between that of phages (approximately 76. 0 times greater) and plasmids (4. 9 times lesser). To further validate the annotations of the ARGs, we compared the results of the three databases with the analysis of our data using the NCBI AMRFinderPlus software package  (29). We found similar ARGs in P-Ps and phages as well as an increase in the number of plasmids with ARGs of about 13. 5% (Fig. S1C ). In P-Ps, the ARGs encode a variety of enzymes, such as b-lactamases, dihydrofolate reductases, and aminoglycosides-modifying enzymes. We also identified a few genes encoding efflux pumps (Table  S1 ).
Overall, our analysis shows that P-Ps encode ARGs much more often than do the remaining phages. In some cases, they encode resistance genes to last-line antibiotics, such as the mcr-1 against colistin and various blaKPC (types 2, 3, 4, and 33) and blaNDM-1 genes against carbapenems (Table  S1 ). Resistance genes are found in specific types and loci of phage-plasmids. Most of the P-Ps carrying ARGs (47 of the 60 P-Ps) were found in the genomes of just four species: Acinetobacter baumannii (n = 8), Escherichia coli (n = 20), Klebsiella pneumoniae (n = 14), and Salmonella (spp. and enterica) (n = 5). This is not overly surprising; our previous study showed that these species had many P-Ps  (23) , many of the genomes of these species are available in the database, and these are all pathogenic bacteria that are known to develop antibiotic resistance  (30).
About half of the sequenced phages are virulent  (53). They are not expected to carry ARGs because they do not produce lysogens, although this cannot be completely excluded, since they may produce pseudolysogens  (54). The causes of the differences in frequencies of ARGs in P-Ps and other temperate phages are less obvious. It could be argued that the frequency of ARGs in P-Ps is caused by their abundance in bacterial pathogens. However, most sequenced phages in the database are also from a few genera, though many of them are Enterobacteria, and this does not seem sufficient to explain the difference in the number of ARGs present in the two types of elements. For example, the naturally occurring integrative prophages of E. coli analyzed in this study are completely devoid of identifiable ARGs.
MATERIALS AND METHODS Genomic data. We used the completely assembled genomes of 8,399 bacterial strains, including their 21. 550 plasmids, and the completely assembled genomes of 3,725 phages. All genome data were retrieved from the nonredundant NCBI RefSeq database  (55)   (March 2021). Similarity between mobile genetic elements. The weighted gene repertoire relatedness (wGRR) assesses the similarity of gene repertoires between pairs of mobile genetic elements by taking into account their numbers of bi-directional best hits (BBH) and their sequence identities. It is computed as described previously  (23)  for all genomes and contigs of phages, plasmids, and P-Ps. Briefly, MMseqs2 (v. 13  A i and B i are the ith BBH pair of the P total pairs. The gene number of the smaller genome is min(#A,#B), and the sequence identity between the BBH pair is id(A i ,B i ).
This resulted in 1,416 putative P-Ps, 740 of which were previously identified. The classification of novel P-Ps is based on the similarity to previously identified P-P groups. P-Ps that were not identified in our previous study  (23) , typically because they correspond to more recent genome sequences, were assigned to defined P-P groups when they had wGRR values of $0. 5 and at least half of their genes were homologous to a previously classified P-P. When there were multiple hits, the P-P was classified according to the classification of the element with the highest wGRR. Identification of antibiotic resistance genes (ARGs), IS elements, and integrons. We searched genomes for ARGs using the CARD  (64) , ResFinder  (65) , and ARG-ANNOT (66) databases as references. We searched for sequence similarity between the genes of a MGE (phage, plasmid, P-P) and these databases using BLASTP (v.
2. 12. 01) (67) (to compare with the protein sequences of CARD and ARG-ANNOT) and BLASTX (v. 2. 12. 01) (67) (for the nucleotide sequences of ResFinder). We collected all hits in all databases, respecting the following constraints: E value of ,10 25 , sequence identity of $99%, and alignment covering sequences by $99%. The results were compared with the output of AMRFinderPlus (3. 10. 18) (tool from NCBI for ARG detection  [29] ) (Fig. S1C ). IS elements were identified using ISEScan (v. 1. 7. 2. 3, default parameters)  (68). Integrons were identified using IntegronFinder (v. 2. 0rc6, default parameters)  (35). Pangenome graphs. To compute the pangenomes of the P-P groups (including newly assigned members), we followed the same workflow as described previously  (23). We computed the pangenome with PPanGGOLiN (v.
Because the PCR can lead to false-positives, we confirmed the presence of the P-Ps by resequencing the whole-genome (P-P and host chromosome). The genomic DNA was isolated as described in the subsection on ARG-encoding P-Ps in CRE strains. Library preparation (Illumina TruSeq DNA PCR-Free) and sequencing were done on the C2RT Biomics platform of the Institut Pasteur (using short reads, paired-end, 250 bp) on an Illumina MiSeq system. The resulting reads were mapped on the P-Ps and on the host genomes (E. coli 55989: NC_011748, E. coli CIP 105917: NZ_CP041623, E. coli CIP 53. 126: NZ_CP022959, E. coli CIP 76. 24: NZ_CP009072). Coverage was extracted as described in the subsection on processing sequencing data. Data processing, storage and availability. If not otherwise stated, all analysis and illustrations were done in the R environment (https://www.