dataesr/scientific-paragraphs-categorization huggingface dataset:
e. species with shallower H-D scaling relationships dominating arid environments and vice versa) as opposed to plasticity and local adaptation within species  (Lines et al. , 2012 ). To answer this question, we selected a subset of species in the Tallo database that had been sampled at multiple sites spanning a gradient in aridity (defined and quantified in the same way as the previous case study). Specifically, we only included records for species that (i) were found at two or more distinct sites with at least 10 individual trees sampled at each site, (ii) were recorded at locations with at least a 20% difference in aridity index between their most arid and humid site and (iii) spanned a size range of at least 20 cm in stem diameter. This left us with 155,002 trees belonging to 342 species (303 angiosperms and 39 gymnosperms).
, 2018; Stovall et al. , 2019) , making it critical to identify the environmental conditions under which tall trees can thrive. Most efforts to tackle this challenge have used global or regional maps of forest canopy height derived from remote sensing as a starting point and then worked backward to infer the environmental drivers that shape the distribution of tall forests  (Gorgens et al. , 2021; Scheffer et al. , 2018; Zhang et al. , 2016). But an alternative bottom-up approach to answering this question is to build an allometric model that predicts a tree's potential height anywhere in the world based on current-day and future environmental conditions  (Chave et al. , 2014). To trial this approach, we used the entire Tallo database to fit a multiple regression model in which we expressed tree height Climate data were obtained at 30-arc second resolution and assigned to each tree based on their geographical coordinates.
AI values were taken from the Global Aridity Index and PET Database, while all other climatic predictors were obtained from WorldClim2  (Fick & Hijmans, 2017). As our aim was to provide a proof of concept, climatic predictors were selected based on those identified by previous studies as playing a role in modifying H-D scaling relationships  (Chave et al. , 2014; Hulshof et al. , 2015; Lines et al. , 2012) , rather than through an extensive model selection process. et al. , 2020) , any predicted declines in tree height may therefore be conservative. However, it is also important to note that our model predictions do not account for the effects of rising atmospheric CO 2 on plant water use efficiency, which may offset some of the impacts of rising aridity on tree hydraulic function under global warming  (Rifai et al.
to decrease by an average of 5. 4% globally when CO 2 fertilization is not taken into account. However, projected changes varied substantially among biomes and biogeographical regions (Figure  4b ), ranging anywhere between -20. 1% and +18. 8%. Trees in Mediterranean woodlands are predicted to show the strongest decreases in height, with an average projected height F I G U R E 4 Global variation in the predicted height of large trees under current-day climate (a) and projected relative changes in height under a future climate scenario (b). For each biome, the size threshold for 'large trees' was defined as the 99th percentile stem diameter value of trees in the Tallo database. Both current-day and future climate data were obtained from the WorldClim2 database at 5-minute resolution  (Fick & Hijmans, 2017).
Overall, the Tallo database provides a new way to explore how global patterns of forest canopy structure can be reconciled with the processes that constrain the allometry of individual trees. For instance, model predictions could be compared to canopy height maps derived from remote sensing to identify areas of agreement and discrepancy between the two, providing new clues on the processes that shape variation in tree height across the world's forests. Moreover, spatially explicit maps of potential tree height generated in this way could also be used to benchmark the outputs of dynamic global vegetation models.
| FUTURE DE VELOPMENTS Looking ahead, we intend to continue curating and expanding the scope and scale of the Tallo database. In addition to increasing the geographical and taxonomic coverage of the database, we also plan to source new data capturing additional axes of crown size. In particular, we aim to incorporate data on crown depth for as many trees as possible. In addition to being interesting in its own right  (Shenkin et al. , 2020; Vermeulen, 2014) , information on crown depth would allow users to calculate more realistic estimates of crown surface area and volume  (Jucker et al. , 2015; Loubota Panzou et al. , 2021; Shenkin et al. , 2020). Additionally, we also plan to augment the database by adding information on local competitive environment (e. g. stand basal area, tree density or cover), as it is well known that tree crown architecture is strongly influenced by competition for light with neighbouring trees  (Jucker et al.
, 2015; Lines et al. , 2012; Purves et al. , 2007). As part of these efforts, we will also look to expand Tallo beyond its initial focus on seed plants with a single self-supporting dominant stem that undergoes secondary growthbetter capturing multi-stemmed trees, as well as other life forms such as shrubs, tree ferns, palms and climbers. Finally, as the database expands, we also plan to begin incorporating more data on crown dimensions and tree height derived from remote sensing platforms such as airborne and terrestrial laser scanning and structure-from-motion UAV photogrammetry. These emerging technologies allow for much more accurate and comprehensive measurements of different crown attributes  (Disney, 2019) , as well as capturing data on the crown dimensions of large, canopy dominant trees which tend to be disproportionately underrepresented in traditional field-based surveys  (Fischer et al.
AUTH O R S' CO NTR I B UTI O N S T. J. conceived the idea for the Tallo database and led the aggregation of the data with the assistance of J. Ch. , D. A. C. , J. Ca. , A. A. , G. J. L. P. , T. R. F. , D. F. and V. A. U. and all co-authors contributed to the data. T. J. performed the analyses with the assistance of F. J. F. T. J. wrote the first draft of the manuscript, with all authors providing editorial input.
I G U R E 1 Overview of the Tallo database, including (a) geographical coverage, (b-c) size range of sampled trees, (d) climatic range of the data and (e) taxonomic coverage in phylogenetic space. Panel (a) shows the total number of trees recorded in grid cells of approximately 200 × 200 km. In (b-d), the density of overlapping points is reflected by a colour gradient ranging from black (low point density) to yellow (high point density). Data on mean annual rainfall and temperature show in (d) were obtained from WorldClim2 database.
The fitted model was used to generate spatially explicit predictions of tree height at global scale under both current and future climate scenarios at 5-arc minute spatial resolution (approximately 10 km) obtained from WordClim2. Rather than using a single size threshold for defining 'large trees' across all ecosystem types, predictions were made using the 99th percentile stem diameter value of trees from each biome class in the Tallo database. These ranged from 33 cm in drylands to 95 cm in tropical rainforests. To explore how climate change might affect the distribution of tall trees, we used CMIP6 future climate projections for the period of 2061-2080 derived from the CNRM-ESM2-1 global climate model run under the shared socio-economic pathway (SSP) 245 (equivalent to RCP 4. 5). To simplify the analysis, stem diameter values used to define 'large trees' for each biome type were kept the same under both current and future climate scenarios.
ACK N OWLED G EM ENTS We are indebted to the countless researchers and field assistants who helped collect the field data compiled in the Tallo database and without whom this work would not have been possible. T. J. was supported by a  UK NERC   Independent Research Fellowship  (grant:  NE/S01537X/1 ). J. Ch. acknowledges an ' Investissement d'Avenir' grant  managed by the  Agence Nationale de la Recherche  ( CEBA  grant:  ANR-10-LABX-25-01  and  TULIP  grant:  ANR-10-LABX-0041 ). A. A. is currently supported by  Hebei University  (grant:  521100221033 ) and was previously supported by the  Jiangsu Science and Technology Special Project  (grant:  BX2019084 ) and  Metasequoia Faculty Research Startup Funding at Nanjing Forestry University  (grant:  163010230 ). G. J. L. P. was supported by projects  DynAfFor  (grant:  CZZ1636.
01D ) and  P3FAC  (grant:  CZZ1636. 02D ) and by the  International Foundation for Science  (grant:  D/5822-1 ). T. R. F. was funded by  NERC  (grant:  NE/N011570/1 ). L. F. A was supported by  CAPES  and  ABC-CNPq  (grant:  004/96 ). B. B. L. was supported by  COMPASS-FME , a multiinstitutional project supported by the  U. S. Department of Energy, Office of Science, Biological and Environmental Research  as part of the  Environmental System Science Program. M. v. B. acknowledges funding from the  Agua Salud Project , a collaboration between the  Smithsonian Tropical Research Institute (STRI) , the  Panama Canal Authority (ACP)  and the  Ministry of the Environment of Panama (MiAmbiente) , the  Smithsonian Institution Forest Global Earth Observatory (ForestGEO) ,  Heising-Simons Foundation ,  HSBC Climate Partnership ,  Stanley Motta ,  Small World Institute Fund, Frank  and  Kristin Levinson , the  Hoch family , the  U Trust , the  Working Land and Seascapes Program of the Smithsonian , the  National Science Foundation  (grant:  EAR-1360391 ),  Singapore's Ministry of Education and Yale-NUS College  (grant:  IG16-LR004 ).
S. Department of Energy ; CAPES;  International Foundation for Science , Grant/Award Number: D/5822-1; P3FAC, Grant/Award Number:  CZZ1636. 02D ; DynAfFor, Grant/Award Number :  CZZ1636. 01D ; Nanjing Forestry University , Grant/Award Number:  163010230 ;  Jiangsu Science and Technology Special Project , Grant/Award Number:  BX2019084 ;  Hebei University , Grant/Award Number:  521100221033 ;  Agence Nationale de la Recherche , Grant/ Award Number:  ANR-10-LABX-0041  and  ANR-10-LABX-25 -.
F I G U R E 1 Overview of the Tallo database, including (a) geographical coverage, (b-c) size range of sampled trees, (d) climatic range of the data and (e) taxonomic coverage in phylogenetic space. Panel (a) shows the total number of trees recorded in grid cells of approximately 200 × 200 km. In (b-d), the density of overlapping points is reflected by a colour gradient ranging from black (low point density) to yellow (high point density). Data on mean annual rainfall and temperature show in (d) were obtained from WorldClim2 database.
Using a smaller database (KL65) representative of the diversity of Klebsiella and curated for strain origin (see methods) evidenced no significant association between the origin of the strain and the prevalence of T6SS (Fisher's Exact Test, P = 0. 13) (Fig. 1b ). In the laboratory model strain CH1157, two T6SS-apparented pathogenicity islands were detected, hereafter referred to as T6SS-1 and -2 (27 and 21 kb, respectively). T6SS-1 is closely related to T6SS-1 of reference hypervirulent NTUH-K2044 strain owing their genomic organization and the synteny between the two T6SS core gene loci (Fig. 1c ). In contrast, the regions between vgrG and tssM (corresponding to the toxin-antitoxin system) are highly specific to each strain and likely possess specific T6SS dependent effector encoding genes.
T6SS-1 of strain CH1157 encompasses a potentially phospholipase encoding gene (tle1) comprising a DUF2235 domain and its cognate three antitoxin genes named tli1 whereas NTUH-K2044 possesses a LysM gene with a peptidoglycan binding domain and an MltD gene (murein lytic transglycosylase). In the KL65 database, the presence of Tle1 phospholipase toxic effector seems to be specific to the K. pneumoniae species complex, since it was detected only in 18 K. pneumoniae strains, 2 K. variicola strains (342 and At-22) and 1 K. quasivariicola (Fig. 1b ). The CH1157 T6SS-2 cluster differs slightly from T6SS-1, having fewer core genes (tssBC, hcp, PAAR , clpV missing) and a different cluster organization. However, synteny was observed between T6SS-2 locus and the second T6SS cluster of K. variicola 342 (Fig.
The diagram was built with the genoplotR package  64. The identity percentages were calculated using blastp (v BLAST 2. 7. 1+, default parameters). Membrane permeability associated with periplasmic expression of Tle1 phospholipase. The Tle1 effector antibacterial effect has been previously reported  30. To assess if this phenotype was associated with the effector of strain CH1157, heterologous expression of Tle1 in E. coli DH5α was performed. Addressing of Tle1 specifically to the periplasm significantly affected bacterial growth, as evidenced by spectrometric measurement of the growth of E. coli DH5α/pBAD33-ssOmpA-tle1 compared to that of the control strain (E. coli DH5α/pBAD33-ssOmpA) (Fig. 2a ). In contrast, cytoplasmic addressing of Tle1 (E. coli DH5α recombinant strain harboring pBAD33-RBS-tle1) did not affect bacterial growth compared to that of the control strain (E.
This toxic effect was first assessed by in vitro competition assays in i) an interspecies manner, by co-cultivating a CH1157 attacker strain and a variety of Gram-negative potential prey strains representative of those found in mice microbiota (E. coli, Bacteroides thetaiotaomicron, Bacteroides fragilis, Parabacteroides dystasonis) and ii) in an intraspecies manner, by co-cultivating a CH1157 attacker strain and its T6SS-derived mutant strains impaired either for T6SS core gene synthesis (CH1476-ΔtssB) or for toxin-antitoxin system production (CH1476-ΔTA), respectively (data not shown). No antibacterial effect was observed in the tested conditions. A different genetic background between the cKp CH1157 and the MDR HS11286 strains could also explain the difference in the in vitro effect of Tle1.
1c ) and its putative protein (Tke) contains a N-terminal LysM peptidoglycan binding domain and a M23 putative catalytic site in the C-terminal part. No cognate immune protein was identified around the Tke potential effector, likely due to the lack of data in the immune protein database  36. Bioinformatic analyses have shown that CH1157 Tke are mainly found in K. pneumoniae species and share specific domains of other T6SSs effectors (Type VI Amidase Effector (Tae) and Type VI Metallopeptidase Effector (Tpe))  47. Moreover, an SLT transglycosylase domain (PF 01464. 23) previously identified in Tke was shown to belong to Lyz-like superfamily (cl00222) and to share domains with 11th effector group in K. pneumoniae described by Li et al. 36. T6SS targeted cells could therefore be affected in their capacity to survive in a competitive and stressful environment such as the intestinal tract.
Materials and methods Identification of T6SS clusters in Klebsiella genus. The list of genomes used in this study is presented in Supplementary Table  S1a. (i) KL427 database. All fully sequenced Klebsiella genomes from NCBI RefSeq were downloaded and analyzed (ftp:// ftp. ncbi. nih. gov/ genom es/, accessed in April 2019). Species assignation was corrected using Kleborate and virulence score assignation according to the presence of well-described virulence factors  57. The species was assigned with a strong level of confidence for 414 genomes, and considered weak for 10 (Supplementary Fig. S2a ). Three strains were discarded due to wrongful genus assignation. Additional K. quasipneumoniae subsp similpneumoniae genomes were downloaded from NCBI on July 6th 2022 (Supplementary Table  S1b ), and verified with Kleborate for correct species assignation.
(ii) KL65 database. This database reflects Klebsiella diversity in terms of species and STs and was manually curated based on the availability of reliable isolation data. Identification of T6SS systems. MacSyFinder  37  , an open-source software that detects macromolecular systems, was used to identify T6SS systems in Klebsiella genomes. As input, it requires: (i) a proteome, (ii) a set of Hidden Markov Model (HMM) protein profiles, and (iii) a model describing the number of genetic components and their organization. (i) Definition of HMM profiles. The HMM protein profiles have been described elsewhere  38. Profiles for TssA, TssB, TssC, TssD (Hcp), TssE, TssF, TssG, TssH (ClpV), TssI (VgrG), TssJ, TssK, TssL, TssM and EvpJ, were used. (ii) Model. To identify bona fide T6SS, in Klebsiella genomes, the model corresponding to T6SSi was applied  38  , which sequentially requires 3 to 13 minimum mandatory proteins (biologically essential components for a putative functional system), associated with the T6SS.
Models were written in plain text using a specific XML grammar and can be modified by the user (see http:// macsy finder. readt hedocs. io/ en/ latest/ for details). (iii) Identification of effectors. BLASTP (v BLAST 2. 7. 1+, default parameters) were performed to identify homologous Tle1-like proteins in genomes of KL65 database to Tle1 protein encoded in the first T6SS locus of CH1157. At least 80% of protein identity was required to consider Tle1-like presence. Similarly, the presence of Tke-like proteins was evaluated by BLASTP (v BLAST 2. 7. 1+, default parameters) using the sequence of the endopeptidase of CH1157 as query against the RefSeq database (April 2019). The latter comprises a total of 14,363 chromosomes and 11,806 plasmids, representing 13,514 complete genomes from 4232 bacterial species.
Klebsiella phylogeny (KL65 database). (i) Identification of the persistent genome. To build the phylogeny, we first identified the persistent genome using PanACoTA  58. The pan genome was inferred with the connected-component clustering algorithm of MMSeqs2 with pairwise bidirectional coverage > 0. 8 and sequence identity > 0. 8. The persistent genome was built from the pangenome, with a persistence threshold of 95%, meaning that a gene family had to be present in a single copy in at least 62 out of the 65 genomes to be considered persistent. Of the 22,288 gene families of the Klebsiella pangenome, 2969 were present in 95% of the genomes. (ii) Construction of the phylogenetic tree. To compute the phylogenetic tree, families of the persistent genome were individually aligned with mafft (v7.
Total nucleic acid extracts were eluted in 50µL of DES Buffer. DNA content of each sample was assessed with Qubit (THER-MOFISHER). Samples were stocked at -20 °C until 16S DNA sequencing. V3 and V4 regions of 16S rRNA gene were amplified using PCR1F_460-PCR1R_460 primer pair (Supplementary Table  S2 ). The amplicons were sequenced using Illumina Miseq (ILLUMINA INC; Waltham, San Diego, CA, USA) at the INRAE GeT platform (GenoToul, Castanet-Tolosan, France), generating 250 bp paired-end reads. Raw data were processed with the rANOMALY  63  which relies on the dada2 R package to produce amplicon sequence variants (ASV) as taxonomic units. The resulting ASV table was filtered with a minimum relative abundance of 5 × 10 -5 and used to perform statistical and diversity analyses. Statistical analysis and data representation.
Statistical analyses were performed with GraphPad Prism 9 (GraphPad Software; La Jolla, CA, USA). Amplicon sequencing data were explored with R version 3. 5. 3 and Explore Metabar, accessible from the Migale server (https:// shiny. migale. inrae. fr/ app/ explo remet abar)  63. Alpha diversity was explored with the Observed index for richness and the Shannon index for evenness. Pairwise comparison between time points were tested with TukeyHSD test and adjusted P-values were reported. Distances between samples (Beta diversity) were measured with unweighted unifrac distances and projected on a two dimensional spaces by Non-metric Multidimensional Scaling (NMDS). Group of samples are compared by a pairwise Adonis test. A P-value of less than 0. 05 was considered as significant.
The tree was built using the packages ggtree and treeio for R. KLPN : K. pneumoniae ; KLVA : K. variicola ; KP05 : K. pneumoniae 05 ; KLQV : K. quasiivariicola ; KLQS : K. qs similipneumoniae ; KLQQ : K. qs quasipneumoniae ; KLOX : K. oxytoca ; KLMI : K. michiganensis ; KLGR : K. grimontii (c) Diagram of the two clusters of CH1157 strain, one of strain NTUH-K2044 and another of K. variicola 342. White arrows indicate unknown function, and gray arrows indicate genes that are core to the T6SS. The diagram was built with the genoplotR package 64. The identity percentages were calculated using blastp (v BLAST 2. 7. 1+, default parameters).
Acknowledgements We thank members of the PhD Committee of T. M:  D. Boucher  ( M2iSH laboratory, UCA ) and  M. Meynier  ( NeuroDol laboratory, UCA ), and  E. Cascales  ( Laboratoires d'Ingénierie des Sytèmes Macromoléculaires, Aix-Marseille University ) as well as  P. Langella  and  G. Orianne  ( MICALIS, INRAe ) for his expertise in the field. We also thank  D. Jurenas  for providing pBAD33 plasmids,  N. Charbonnel  for technical support in mice experiments and in molecular biology. We would like to thank  H. Billard  and  J. Colombet ,  Plateforme CYSTEM -UCA PART-NER  ( Clermont-Ferrand, France ), for their technical support and expertise. We would like to thank  M. Boyer ,  C. Besnard  and  T. Fleury  for their implication in the project during their internship. This work was supported by a fellowship from the  Ministère de l'Enseignement Supérieur et de la Recherche et de l'Innovation  awarded to T.
Acknowledgements This research has received funding from the  « Investissements d'Avenir » French Government program  managed by the  French National Research Agency ANR  ( ANR-16-CONV-0001 ), from  Excellence Initiative of Aix-Marseille University A*MIDEX  ( ANR-11-IDEX-0001 ),  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Sklodowska-Curie  grant agreement No  713750 , and the  SEPTIMORF  and  3DPolariSR   ANR  grants ( ANR-17-CE13-0014 ;  ANR-20-CE42-0003 ). This work was also supported by  INRIA  in the frame of  NAVISCOPE-IPL (INRIA Project Lab ). The STED Team is associated to the  France BioImaging National Infrastructure  ( ANR-10-INBS-04 ). The authors are grateful to  Arita Silapetere  ( Humboldt Universitat zu Berlin, Germany ) for the assistance in the initiation of the project and  Louwrens Van Dellen  ( Purplecode Aix en Provence, France ) for help in the experimental acquisition programming.
Data availability. The 4polar-STORM raw image stacks are available on request from the corresponding author, for their large size reason. A subset of data (2Go) as well as processed localization data in polarized channels are available at https://doi. org/10. 6084/m9. figshare. 17167004. Processed data of example ROIs generated in this study are available for download at https://gitlab. fresnel. fr/mosaic/4polarSTORM (TestData folder) with explanations of use in the README. md file. All processed orientation/detection-parameters from single molecule data generated in this study are provided in the Source Data file available at https://doi. org/10. 6084/m9. figshare. 17167001. Code availability. The code used to analyse the data is available in the Gitlab webpage https://gitlab. fresnel. fr/mosaic/4polarSTORM, which contains a manual and installation instructions.
We termed our computational framework as DepInfeR and implemented it as an R package (https://github. com/Huber-group-EMBL/DepInfeR). As similar ex-vivo assays can be readily performed on cell lines and primary samples, with established platforms and ready to use datasets for both, drug sensitivity profiling  (Lu et al, 2021; Dietrich et al, 2017; Iorio et al, 2016; Yang et al, 2012)  and drug-protein profiling  (Klaeger et al, 2017) , we expect our approach to be adaptable to different diseases.