dataesr/scientific-paragraphs-categorization huggingface dataset:
Materials and methods Data collection. To estimate the costs of invasive alien reptiles and amphibians worldwide, we used the most recent version (4. 0; released in June 2021) of the InvaCost database (https:// doi. org/ 10. 6084/ m9. figsh are. 12668 570) at the time of writing, per the "getInvaCostVersion" function of the invacost R package  31, 78. This version of the database contains a total of 13,123 cost entries, extracted primarily from four different sources: Web of Science (https:// webof nowl edge. com/), Google Scholar (https:// schol ar. google. com/), Google search engine (https:// www. google. com/) and biological invasion experts, that were contacted as part of this collection and identification process, to gather additional data. The collection and collation of data from these sources were based upon systematic search strings used to identify the costs of biological invasions  5.
The InvaCost database contains entries reported in 15 languages in a sufficiently detailed manner to analyse the cost of invasive alien species  55. Presently, InvaCost is the most complete public database on the economic costs of invasive alien species and whose methodology allows standardising costs over time and across countries. This database is continuously updated either by corrections (e. g. duplicate entries) or by adding new data. The reliability of the data is therefore reliant on the underlying reporting material. InvaCost, however, does not include benefits quantitatively. While these data were also screened according to their method reliability in an objective manner, we could not capture the full spectrum of underlying approaches to cost estimation in a holistic way. Each entry was converted into 2017 US dollars to standardise the cost values, making costs directly comparable.
In addition, each entry has 65 descriptor variables, including, for example, information on species' taxonomy, the type of ecosystem invaded and the type of economic impact caused. The data in InvaCost were carefully screened to minimise spatial overlaps, e. g. at local and national scales over time. Data processing. We performed a series of steps before proceeding with the analysis of the data (Fig. 6 ). First, we refined the database by eliminating data with no information on starting and ending years (i. e. incomplete temporal scale; see later) and no monetised cost in the column "Cost_estimate_per_year_2017_USD_ exchange_rate". Next, we filtered the database to include only the "Amphibia" and "Reptilia" classes (column: "Class", n = 354). Entries belonging to the class "Amphibia/Reptilia" and "Reptilia/Amphibia" were included in a new "Class" group ("Diverse", n = 6).
On the other hand, an entry recorded as "Amphibia/Arthropoda/Mammalia/Diverse" was excluded from the study due to the impossibility of separating the unique costs of amphibians. The total costs of the alien species were calculated with the function "summarizeCosts" of the invacost package  78. This function summarises the cumulative costs and average annual costs of invasive alien species and splits the total costs evenly into annual cost estimates reflecting the respective period over which the cost was incurred, without inflating the cost estimates  78. This final dataset contained 353 entries which were all homogenised and converted on an annual basis, whereby the total cost for each estimate and each species was divided by the duration time of each cost entry. This process of 'expanding' the dataset was performed by the function "expandYearlyCosts'' of the invacost R package  78.
This function expands the estimated costs over the years they occurred or were estimated to have occurred. This means that one cost entry that reports a total cost of 100 million over 10 years, for example, will be transformed into ten cost entries with an annual cost of 10 million dollars each. The duration of the study was determined from the columns "Probable_starting_year_adjusted" for the start of the impact and "Prob-able_ending_year_adjusted" for the end of the impact. All analyses were carried out on this expanded database to reflect the duration of the costs according to each entry. Our final, expanded dataset had 710 entries from 1986 to 2020, thus covering 35 years.
Examples of potential costs are the extrapolation of economic impacts due to the loss of tourist revenue from the presence of R. marina in Australia  19  , or the cost predicted for the removal of B. irregularis in Guam  57. Further, to avoid the overestimation of extrapolated costs, the InvaCost database followed a strict selection and classification of costs, notably excluding previous potential costs that were later incurred and listed as observed costs. III. Species This descriptor shows the name of the species responsible for the incurred costscost totals per species. In addition, entries that contained more than one species that could not be distinguished (column: "Species") were added to the "Species" category "Diverse" (n = 44). IV. Impacted sector market and/or activity sector affected by the costs of invasive herpetofauna, (e.
g. Agriculture, Health, Public and social welfare). All entries with more than one category were pooled into "Diverse". V. Type of costs Type of cost generated by the invasion: Damage (e. g. yield loss, health injury), Management (e. g. control, research) Mixed (indistinguishable damage and management components). VI. Geographic region Refers to the continental region where the cost of alien species invasion was recorded. We also made several changes to the descriptor variable "Geographic_region" to homogenise the subgroup. These changes were the joining of "Pacific Islands" and "Pacific Islands/Oceania" into "Oceania and Pacific Islands", and "Central America" and "South America" into "Central/South America" and "Oceania/South America'' into ''Diverse". We also examined the proportion of invasive alien species for which costs were reported using a database reporting alien herpetofauna worldwide, separately by order and family  26, 40.
To our knowledge, this is the most complete database published on invasive herpetofauna records, whereby each entry represents the occurrence of an alien established species in a country, federal state or biogeographically-separate island or archipelago. Furthermore, we examined whether species with costs were more studied than those without costs, and correlated the magnitude of costs with the number of studies (Supplementary Appendix A). We used an unpaired two-sample Wilcoxon test with an alpha level of 0. 05 to compare study numbers between species with (i. e. , in InvaCost) and without (i. e. , not in InvaCcost) costs. We then assessed whether the magnitude of total and observed costs of invasive alien species in InvaCost was correlated to their respective numbers of associated.
Acknowledgements The authors acknowledge the  French National Research Agency  ( ANR-14-CE02-0021 ) and the  BNP-Paribas Foundation Climate Initiative  for funding the  InvaCost  project that allowed the construction of the InvaCost database. The present work was conducted following a workshop funded by the  AXA Research Fund Chair of Invasion Biology  and is part of the  AlienScenarios  project funded by  BiodivERsA and Belmont-Forum  call 2018 on biodiversity scenarios. C. C. was supported by  Portuguese National Funds  through FCT, I. P. , under the  programme of "Stimulus of Scientific Employment-Individual Support " within the contracts " CEECIND/02037/2017 ". RNC acknowledges funding from the  Leverhulme Trust   Early Career Fellowship  ( ECF-2021-001 ).
trichocarpa × P. maximowiczii (clone OP42) which we defined as 'easy-to-root from woody stem cuttings', and the hybrid aspen P. tremula × P. tremuloides (clone T89) which we defined as 'difficult-to-root from woody stem cuttings'. OP42 is one of the poplar clones used most widely, both in Northern Europe and worldwide  (Taeroe et al. , 2015). It can easily be propagated from dormant stem cuttings. In contrast, the hybrid aspen T89 cannot be propagated via dormant stem cuttings but can be easily propagated in vitro, and is very amenable to genetic transformation  (Nilsson et al. , 1992). The analysis of the transcriptomic dataset showed more differentially expressed genes encoding transcription factors (TFs) in OP42 than in T89. We identified several TFs that could explain differences in ability to produce adventitious roots.
Pre-processing of RNA-seq data The data pre-processing was performed as described in  Delhomme et al. (2014). Briefly, the quality of the raw sequence data was assessed using FastQC (http://www. bioinformatics. babraham. ac. uk/projects/fastqc/). Residual ribosomal RNA (rRNA) contamination was assessed and filtered using SortMeRNA (v2. 1; Kopylova et al. , 2012 ; settings --log -paired in --fastx--sam --num_alignments 1) using the rRNA sequences provided with SortMeRNA (rfam-5s-database-id98. fasta, rfam-5. 8s-database-id98. fasta, silva-arc-16s-database-id95. fasta, silva-bac-16s-database-id85. fasta, silva-euk-18s-database-id95. fasta, silva-arc-23s-database-id98. fasta, silva-bac-23s-database-id98. fasta and silva-euk-28s-database-id98. fasta). Data were then filtered to remove adapters and trimmed for quality using  Trimmomatic (v0.
Differential gene expression analysis Statistical analysis of single-gene differential expression between conditions was performed in R (v3. 4. 0; Team, 2018) using the  Bioconductor (v3. 5; Gentleman et al. , 2004) DESeq2 package (v1. 16. 1; Love et al. , 2014). FDR adjusted P values were used to assess significance; a common threshold of 1% was used throughout. For the data quality assessment and visualization, the read counts were normalized using a variance stabilising transformation (vst) as implemented in DESeq2. The biological relevance of the data, such as similarity of biological replicates (Supplementary Fig. S3A, B ) and other visualizations (e. g. heat maps), were obtained using custom R scripts, available at https://github. com/nicolasDelhomme/ poplarcambium. The gene list encoding P.
trichocarpa transcription factors was downloaded from the plant transcription factor database v4. 0 (http://planttfdb. gaolab. org/). Dendrograms and heat maps were generated using the function heatmap. 2 from the gplots R library. Heat maps of differentially expressed genes (DEGs, DE cut-offs of FDR ≤0. 01 and |LFC| ≥0. 5), were generated using the function heatmap. 2 from the gplots R library. The 17 997 genes, which were detected in all biological replicates, were used for further analysis. Genes which were expressed only in one or two biological replicates for each genotype, but which were significant for differential expression between T89 and OP42, were analysed separately. The gene expression mean values are listed in Supplementary Dataset S3 (sheet 6).
S2B ) and T 1 (24 h after transfer in hydroponic conditions; Supplementary Fig. S2C ). We mapped the RNA-seq reads to the P. trichocarpa reference genome (Supplementary Dataset S1, sheet1) and classified 17 997 genes in the current annotation as being expressed significantly in all biological replicates in both genotypes at times T 0 and T 1 (Supplementary Dataset S1, sheet 2). These 17 997 genes represent approximately 43% of the annotated genes in the Populus genome (poplar v3 assembly version;  Tuskan et al. , 2006). Interestingly, there were more DEGs in OP42 after 24 h in hydroponic conditions than in T89 (Fig. 3 ). In the case of T89, a total of 1198 (6. 6% of the 17 997) genes were differentially expressed; 824 were up-regulated and 374 were down-regulated at T 1 compared with T 0 (Fig.
3A ; Supplementary Dataset S2, sheets 11-13). GO enrichment analysis of DEGs showed a significant enrichment of GO terms related to biological processes, and molecular functions related to carbohydrate catabolism or redox mechanisms, regulation of transcription, response to abiotic stresses, cation binding, nucleic acid binding activity, or electron carrier activity (Supplementary Dataset S3, sheets 4, 5). In contrast, in OP42, a total of 5464 genes (30% of the 17 997 genes) were found to be differentially expressed, among which 3242 were up-regulated, and 2222 down-regulated at time T 1 compared with T 0 (Fig. 3A, C ; Supplementary Dataset S2, sheets 8-10). Interestingly, among the 3242 DEGs, 2420 (74. 6%) were exclusively up-regulated in OP42 at T 1 (Fig. 3B ), suggesting a specific remodulation of the transcriptome in OP42 during the 24 h timeframe that did not occur in T89.
The GO enrichment analysis of these up-regulated DEGs showed a significant enrichment of GO in cellular components, biological processes or molecular functions related to cell metabolism or cell biology, such as transcription regulation, translation and post translation regulation (Supplementary Dataset S3, sheet 4). Similarly, 66% of the 2222 DEGs that were down-regulated in OP42 at T 1 compared with T 0 were specifically differentially expressed in OP42 (Fig. 3C ). In contrast to the up-regulated genes, the GO enrichment analysis showed a significant enrichment of GO in cellular components, biological processes or molecular functions related to abiotic stress responses (Supplementary Dataset S3, sheet 5). When the two genotypes were compared with each other, 25% of the 17 997 genes were differentially expressed between OP42 and T89 at T 0 (Fig.
3A ; Supplementary Dataset S2) among which, 2007 were up-regulated in T89 compared with OP42 (Fig. 3A ) while 2533 were down-regulated (Fig. 3A ; Supplementary Dataset S2, sheets 2 to 4). This difference between the two genotypes was reduced to 14% 24 h after  transfer into hydroponic conditions, with 1156 up-regulated and 1330 down-regulated in T89 compared with OP42 (Fig. 3A ; Supplementary Dataset S2, sheets 5 to 7). The genes that were differentially expressed between T89 and OP42 are mostly involved in cellular and chemical homeostasis, photosynthesis, dioxygenase activity and protein synthesis (Supplementary Dataset S3, sheets 4 and 5). (C) (B) (D) (E) (G) (F) ( H) (I) (K) (L) (M) (N) (O) (P) (Q) ARP P CZ X.
Genes related to cambium or vascular tissues behave similarly in both genotypes After checking the similarity of the biological replicates of RNA-seq data (Supplementary Fig. S3A, B ), we also confirmed the quality and the specificity of the datasets. For this, we selected a list of 40 Arabidopsis genes described as being expressed in the cambium or vascular tissues, and checked the expression of their putative Populus orthologues in our data (Supplementary Fig. S3C ; Supplementary Dataset S3, sheet 1). All were found to be expressed (and most behaved similarly) in the two genotypes, showing a slight up-regulation or down-regulation in both OP42 and T89 between T 0 and T 1 (Supplementary Fig. S3C ; Supplementary Dataset S3, sheet 1). A few exceptions to this general pattern included Potri.
Genes encoding reactive oxygen species scavenging proteins are mostly up-regulated in OP42 compared with T89 Reactive oxygen species (ROS) are signalling molecules involved in the response to biotic and abiotic stresses as well as many aspects of plant development, including AR formation, as shown by recent studies (reviewed in  Nag et al. , 2013; Li et al. , 2017; Velada et al. , 2018). We therefore searched for genes encoding ROS scavenging proteins among all DEGs in T89 and OP42. We identified 43 DEGs encoding ROS scavenging proteins, 33 of which belong to the GLUTATHIONE S-TRANSFERASE superfamily (GSTs) and 10 to the PEROXIDASE superfamily (Supplementary Dataset S3 sheet 3). Twenty of these genes were up-regulated at T 1 compared with T 0 in both genotypes, but on average the fold change was higher in OP42 than in T89 (Supplementary Fig.
S5 ; Dataset S3, sheet 3); nine genes were repressed 24 h after cutting in both genotypes. The most striking observation was that 32 out of 43 genes were significantly upregulated in OP42 compared with T89 at T 1 , and 21 of those were also up-regulated in OP42 at T 0 (Supplementary Dataset S3, sheet 3); only six were up-regulated in T89 compared with OP42 at T 0 and T 1 ; four were up-regulated in T89 compared with OP42 at T 0 , but down-regulated in T89 compared with OP42 at T 1 ; and five were up-regulated in OP42 compared with T89 at T 0 -but by contrast, up-regulated in T89 at T 1.
The easy-to-root OP42 shows increased transcriptional activity in the cambium compared with the difficult-to-root T89 The different stages of AR initiation (ARI) in Populus are associated with substantial remodelling of the transcriptome  (Ramirez-Carvajal et al. , 2009; Rigal et al. , 2012). We therefore focused our analysis on the expression of TFs. From the 58 families of TFs identified in Populus, 49 families were represented in the DEG list (Table  1 ; Supplementary Dataset S2, Dataset S3, sheet 2) and most of the DEGs were observed in OP42 (Table  1 ). Furthermore, 24 h after cutting, 210 and 209 TFs were up-or down-regulated respectively in OP42, while in T89 there were only 89 up-regulated and 43 down-regulated DEGs (Table  1 ). The most represented DEGs belong to the ARF, bHLH, bZIP, C2H2-and C3H-type zinc-finger family, ERF, LBD, MYB, MYB-related, NAC and WRKY families.
Several genes belonging to these TF families have been shown to be involved in the control of adventitious rooting in Populus species (reviewed in  Legue et al. , 2014). Genes from the LATERAL BOUNDARY (LBD) gene family have been shown to be involved in the development of lateral organs in Arabidopsis (reviewed in  Matsumura et al. , 2009). In particular AtLBD16, AtLBD17, AtLBD18 and AtLBD29 were shown to be involved in lateral root, adventitious root or regeneration processes in Arabidopsis  (Okushima et al. , 2007; Liu et al. , 2018). Interestingly we observed that 10 PtrLBD genes were specifically up-regulated at T 1 in OP42, among which the putative orthologue of AtLBD16, PtrLBD16 (Potri. 002G041200), was up-regulated in OP42 at T 1 with a log 2 FC of 4. 3 (Supplementary Dataset S2, sheet 6).
In addition, PtrLBD11 (Potri. 010G217700) was also up-regulated in OP42 at T 1 with a log 2 FC of 8. 5 (Supplementary Dataset S2, sheet 6). PtrLBD11 is the putative orthologue of AtLBD11 which was shown to be involved in secondary growth and stem cell maintenance in the cambium during root development  (Ye et al. , 2021). The expression of other genes involved in the control of vascular differentiation that could contribute to the rooting difference between T89 and OP42 were specifically up-or down-regulated in OP42. The NAC family of transcription factors is one of the largest plant-specific families of transcriptional regulators involved in various aspects of plant development and responses to biotic and abiotic stresses (reviewed in  Olsen et al. , 2005). Twenty-four genes from the NAC family were differentially expressed in OP42 at T 1 compared with T 0 (Table  1 ; Supplementary Dataset S2, sheet 6).
Among the up-regulated genes encoding NAC transcription factors in OP42, Potri. 001G080900 (log 2 FC of 7. 5) and Potri. 002G057200 (log 2 FC of 9) encode putative orthologues of AtJUNGBRUNNEN1 (AtJUB1/AtNAC042), a transcription factor induced by ROS, and that represses senescence in Arabidopsis  (Wu et al, 2012). This up-regulation could be related to the up-regulation of genes encoding ROS scavenging proteins, as described above. Fifteen NAC genes were specifically down-regulated in OP42 (Supplementary Dataset S2, sheet 6). Potri. 001G404400 and Potri. 017G063300 were down-regulated with a log 2 FC of -3 and -2, respectively. These two genes encode putative orthologues of the Arabidopsis VND-INTERACTING2 (AtVNI2/AtNAC83) protein which was shown to interact with the AtVASCULAR-RELATED NAC-DOMAIN7 (AtVND7) transcription factor regulating the differentiation of xylem vessels  (Yamaguchi et al.
, 2008)  and to repress its activity  (Yamaguchi et al. , 2010). A third putative orthologue of AtVNI2 (Potri. 003G166500) was in contrast up-regulated with a log 2 FC of 2. 6 (Supplementary Dataset S2, sheet 6). Potri. 001G404400 and Potri. 011G121300 encode two other NAC transcription factors involved in vascular development, both of which were also down-regulated in OP42 at T 1 (Supplementary Dataset S2, sheet 6). Potri. 001G404400 is a putative ortholog of AtNAC-REGU-LATED SEED MORPHOLOGY 1 (AtNARS1/AtNAC2) which was shown to be involved in the regulation of asymmetric cell divisions of sieve element precursors in the phloem downstream of AtSHORTROOT (AtSHR), a GRAS family TF  (Kim et al. , 2020). Potri. 011G121300 encodes a putative orthologue of AtNAC86 involved in the differentiation of sieve elements  (Furuta et al.
, 2014). Interestingly Potri. 007G132000, the orthologue of AtSHR, was up-regulated in OP42 at T 1 with log 2 FC of 3 (Supplementary Dataset S2, sheet 6). In addition, seven SCARECROW-Like (SCL) genes of unknown function were down-regulated in OP42 at T 1 (Supplementary Dataset S2, sheet 6). In Arabidopsis, AtSHR together with its closely related member AtSCARECROW (AtSCR) controls radial patterning during root development  (Nakajima et al. , 2001). They are also important for the maintenance of the root apical meristem and the quiescent centre (reviewed in  Vernoux and Benfey, 2005)  as well as the positioning of the stem cell niche  (Lucas et al. , 2011). In Pinus radiata the expression of several PrSCL genes was associated with the maturation-related decline of competence to develop adventitious roots  (Abarca et al.
, 2014). In addition, several SCARECROW-LIKE (SCL) family genes, such as PrSCL1 from Pinus radiata and CsSCL1 from Castanea sativa  (Sanchez et al. , 2007; Solé et al. , 2008; Vielba et al. , 2011) , PrSHR from Populus radiata  (Solé et al. , 2008) , and PtrSCR from Populus trichocarpa  (Rigal et al. , 2012) , were shown to be induced during the earliest stages of AR formation in cuttings generated in vitro. In OP42, Potri. 001G242000, which is similar to AtSCL30/SCL14, an essential gene for the activation of stress induced response  (Fode et al. 2008) , was up-regulated with log 2 FC of 9 (Supplementary Dataset S2, sheet 6). In T89, three SCL genes encoding DELLA proteins involved in the gibberellic acid signalling pathway were upregulated compared with OP42 at T 1 (Supplementary Dataset S2, sheet 6).
Gibberellic acid has been shown to be a negative regulator of adventitious root development in Populus  (Mauriat et al. , 2014). Whether a difference in the regulation of gibberellic acid signalling pathway explains the rooting difference between OP42 and T89, requires further investigation. The APETALA2/ETHYLENE RESPONSE FACTOR (AP2/ERF) family was the most represented, with 21 and 42 ERF genes up-regulated at T 1 in T89 and OP42, respectively (Table  1 ; Supplementary Dataset S3, sheet 2). Twenty of the ERFs up-regulated in T89 were also up-regulated in OP42 at 24 h after cutting. Among the 22 genes specifically up-regulated in OP42, we found PtrERF003 (Potri. 018G085700; log 2 Table  1. Numbers of differentially expressed transcription factors which were either up-or down-regulated in T89 and OP42.
Downregulated Upregulated Downregulated     (Trupiano et al. , 2013) , and PtrERF39 (Potri. 003G071700), a likely orthologue of the oxygen sensing AtRAP2. 12 (At1g53910) which has recently been shown to be involved in primary root growth inhibition upon oxygen deficiency in Arabidopsis  (Shukla et al. , 2020). Several WUSHEL-Like Homeobox genes, have been shown to positively control AR development in Populus species  (Li et al. , 2018; J. Liu et al. , 2014; B. Liu et al. , 2014; Xu et al. , 2015). More specifically, the P. tomentosa PtoWOX5a (Potri. 008G065400) gene  (Li et al. , 2018) , and the Populus × euramericana PeWOX11/12ba (Potri. 013G066900) and PeWOX11/12b (Potri. 019G040800) genes  (Xu et al. , 2015)  are involved in AR development in poplar; nevertheless, they were not expressed in the cambium cells of OP42 or T89 (Supplementary Dataset S1).