dataesr/scientific-paragraphs-categorization huggingface dataset:
RT-PCR and RT-qPCR RT-qPCR was performed using cDNA from the reverse transcriptase reaction described above and SsoAdvanced Universal SYBR Green Supermix (Bio-Rad, Hercules, CA, USA; 172-5270) on CFX96/CFX384 real-time PCR machine (Bio-Rad) following standard protocol. Data were analyzed using Bio-Rad CFX manager software with the Ct method (2 -DDCt ). ACTIN2 (At3g18780) and UBIQUITIN10 (At4g05320) were used as internal controls. The expression values were normalized to WT controls (expression level was set as 1). Primers for RT-qPCR are listed in (Supplementary Table  S2 ). These experiments were repeated in three independent biological replicates, each with three technical replicates. Statistical significances are determined.
RNA-seq, data analysis, and gene expression analysis Two-week-old Col-0 and coilin1 seedlings were spray inoculated with water or DC3000 hrcC for 24 h. The seedlings were collected for RNA-Seq and 1 lg of total RNA was used for RNA-Seq library preparation using illuminaTruseq Stranded mRNA Sample Preparation LS (low sample) kit following the manufacturer's protocol. Three biological replicates were analyzed for each condition. Sequencing was performed on an Illumina Hi-Seq 4000 system with 150-nucleotide paired-end reads and the sequenced reads were quality controlled using FASTQC version 0. 11. 5  (Wingett and Andrews, 2018). Data processing was performed as described previously  (Abulfaraj et al. , 2018)  and have been deposited in NCBI's Gene Expression Omnibus GEO Series accession number GSE182639.
AS analysis The RNAseq data obtained was used to carry out AS analysis essentially as described in  Bazin et al. (2020). Genes that are alternatively spliced in the Atcoilin-1 background were identified using the rMATS pipeline  (Shen et al. , 2014). Alternative spliced events were validated by both RT-PCR and RT-qPCR using primers designed to show the differences between normally spliced and alternatively spliced genes. The RT-qPCR data analysis and the corresponding statistical analysis were carried out as described in the RT-PCR and RT-qPCR methods section above.
1%-5. 3% B for 5 min, 5. 3%-10. 5% for 15 min, 10. 5%-21. 1% for 70 min, 21. 1%-31. 6% B for 18 min, ramping from 31. 6% to 94. 7% B in 2 min, maintaining at 94. 7% for 5 min, and 4. 7% B for 15-min column conditioning. The electrospray potential was 1. 9 kV and the ion transfer tube temperature was set at 270 C. The MS parameters included application mode as standard for peptide, default charge state of 3 and the use of EASY-IC as internal mass calibration in both precursor ions (MS1) and fragment ions (MS2). For each sample, DIA-MS data were acquired from three injections for three precursor mass ranges 400-650; 650-900, and 900-1,200 m/z, respectively. The DIA isolation windows were between 6 and 8 Da for each precursor mass range and the mass defect was 0. 9995. The HCD collision energy was set at 30%.
DIA-MS data analysis DIA data were analyzed using Spectronaut software version 14 against the Arabidopsis spectral library that was generated inhouse and published recently  (Zhang et al. , 2019)  to identify and quantify peptides and proteins. The Biognosys default settings were applied for identification: excluding duplicate assay; generation decoy based on mutated method at 10% of library size; and estimation of FDRs using Q-value as 0. 01 for both precursors and proteins. The P-value was calculated by kernel-density estimator. Interference correction was activated and a minimum of 3 fragment ions and 2 precursor ions were kept for the quantitation. The area of extracted ion chromatogram (XIC) at MS/MS level was used for quantitation. Peptide (stripped sequence) quantity was measured by the mean of 1-3 best precursors, and protein quantity was calculated accordingly by the mean of 1-3 best peptides.
Experimental Results This section presents two sets of experiments. The first one aims to test the correct implementation of the proposed formulations (Section 4. 1); the second one empirically compares them using random graphs (Section 4. 2). We implemented the proposed mathematical formulations using Gurobi version 9. 5. 1  [27]. This off-the-shelf optimization software executes optimization techniques such as simplex, branch-and-bound, branchand-cut, cutting planes, parallelism, and heuristics to find optimal solutions to mathematical formulations with linear and quadratic constraints. In all the experiments, we used default Gurobi's parameters, including Presolve = 2, Heuristics = 0. 05, and MIPGap = 0. Presolve = 2 generates an equivalent and smaller formulation mapped into the original one; this usually improves the running time.
Empirical Validation In order to check the correct implementation of the proposed mathematical formulations, we executed them over a set of graphs with a known burning number. These graphs are paths, and a family of tight examples for the BFF algorithm, a recently published approximation algorithm for the GBP  [8]. Since setting tight lower and upper bounds might help improve the running time, we set L = b(G) -2 and U = b(G) + 2 for all tests. Tables  1  and 2  show two running times for each formulation: t o and t p. The time it takes Gurobi to reach the optimal solution is denoted by t o. The time it takes Gurobi to find the optimal solution and guarantee it is optimal is denoted by t p. In other words, t p is the time to proven optimality. We set a maximum running time of 2000 s; a reported value of >2000 means that an optimal solution could not be found within that time.
Computing Optimal Solutions The previous section shows that the CSP2 + BS seems better suited for solving graphs with a relatively large burning number. Nevertheless, CSP1 + BS seems better for solving graphs with a relatively small burning number. In this section, we executed CSP1 + BS and CSP2 + BS over synthetic and real-world benchmark graphs (See Table  3 ); most of these were taken from the network repository  [29]  and the Stanford large network dataset collection (SNAP)  [30]. Since this experimentation aims to find optimal burning sequences, we set the lower and upper bounds to the tightest known. To find the upper bound, we executed three state-of-the-art heuristics: improved cutting corners heuristic (ICCH), backbonebased greedy heuristic (BBGH), and component-based recursive heuristic (CBRH)  [5] ; their authors kindly provided the implementation of these.
Regarding CSP2 + BS, we executed it over graphs with an upper bound on the burning number of at least seven and order at most 1458 because Gurobi's branch-and-bound algorithm exhausted all available memory when executed over bigger graphs. This way, the results reported in Table  3  confirm the optimality of most previously known solutions. Furthermore, the optimal solution of some graphs is reported for the first time (ca-netscience, web-polblogs, DD68, DD199, DD497, lattice2D, and tech-routers-rf). The TVshow graph could not be solved within ∼48 h using CSP1 + BS; this goes along with the observation that CSP1 + BS does not seem adequate for solving graphs with a relatively large burning number. From Table  3 , we also observe that synthetic graphs with a well-defined structure seem more challenging to solve.
The obtained set of optimal solutions can be helpful as a benchmark dataset for comparing non-exact algorithms for the GBP, i. e. , approximation algorithms, heuristics, and metaheuristics. Finally, as part of the reviewing process, a reviewer pointed to the possibility of reducing the number of variables and constraints from CSP2 by removing variables x i and y i,j. However, we believe such variables cannot be removed because they let us guarantee that the solution has length B and that all vertices are burned. Nevertheless, we agree that mathematical formulations with fewer variables and constraints might exist. Thus, we will seek alternative mathematical formulations of the problem for future work.
Acknowledgments: We thankfully acknowledge  Gurobi  for providing a free-of-charge academic license for Gurobi version 9. 5. 1. We are grateful to anonymous reviewers whose questions, comments, and suggestions helped improve an earlier version of this paper. We also acknowledge  Consejo Nacional de Ciencia y Tecnología (CONACYT)  and  Instituto Nacional de Astrofísica, Óptica y Electrónica (INAOE)  for providing the necessary resources for the development of this research. The authors thankfully acknowledge the computer resources, technical expertise and support provided by the  Laboratorio Nacional de Supercómputo del Sureste de México ,  CONACYT member of the network of national laboratories.
Phages contribute to an important part of the accessory pangenome of D. solani. Gene-replacing events are less frequently observed  [5] , but they highlight the capacity of D. solani to recombine with DNA sequences from genetically related bacteria. For instance, D. solani strains PPO9019 and PPO9134 contain distinctive replacing events with sequences from D. dianthicola, while strain RNS07. 7. 3B contains several replacing events with sequences from a bacterium that is phylogenetically close to the isolate RNS05. 1. 2A [5]. In a previous study, we compared genetic variations (SNPs and Indels) of 76 D. solani genomes, including those available in databases  [4]. Among them, ten isolates showed a high number of SNPs/Indels: RNS05. 1. 2A which is presently the most divergent isolate in D. solani, PPO9134 and PPO9019, which exhibit replacing events from D.
The reads were trimmed (quality score threshold 0. 05) and assembled using CLC genomics workbench V12 (Qiagen, Denmark). The annotation of the strains was performed using Prokka software V1. 14. 6  [8]  with the following parameters (--addgenes --genus Dickeya --species solani --kingdom Bacteria --gcode 11 --usegenus). In the case of D. solani RNS05. 1. 2A, a draft genome (37 contigs, JWMJ00000000. 1) was previously generated from Illumina HiSeq 2000 version 3, sequencing paired-pair libraries  [5]. To achieve the complete assembly of the genome, the genomic DNA was sequenced using the PacBio RS II sequencing platform (Pacific Biosciences, Menlo Park, CA, USA) and Illumina reads were used to correct the assembled PacBio genome. The phages content analysis was performed using the platform PHASTER  [9, 10].
Phylogenomics To highlight phylogenetic relationships between the D. solani isolates, we used a coreproteome phylogenomics approach. In addition to the predicted proteomes of RNS10-105-1A, RNS13-30-1B, RNS13-31-1A, RNS13-48-1A, RNS15-102-1A and RNS05. 1. 2A, we retrieved those of 34 D. solani (Table  S1 ) available at GenBank (https://www. ncbi. nlm. nih. gov/genome/, accessed on 13 January 2022). The 40 proteomes were clustered using OrthoFinder software version 2. 4. 0  [11]  with the following command: orthofinder -t 56 -M msa -A mafft -T iqtree -f orthofineder/. The phylogenetic tree was inferred from concatenated core-proteomes using IQTREE version 2. 1. 4-beta [12]  with the following command: iqtree -s SpeciesTreeAlignement. phylip -nt AUTO -ntmax 50 -seed 12345 -bb 1000. The species Dickeya dadantii was used as an outgroup.
Variant Calling and Analysis We conducted a variant calling analysis of D. solani strains RNS10-105-1A, RNS13-30-1B, RNS13-31-1A, RNS13-48-1A and RNS15-102-1A, as well as seven other D. solani strains: IPO2222 T , Ds0432. 1, RNS07. 7. 3B, RNS05. 1. 2A, A623S-20A-17, PPO9019 and PPO9134. These strains were included in this study because they exhibited a variable degree of divergence according to phylogenetic analysis. The variant calling was performed using a Snippy pipeline (version 4. 6. 0) [15]  and a complete genome of the strain RNS08. 23. 3. 1A (NZ_CP016928. 1/CP016928. 1) as a reference sequence. The variants' densities were calculated and plotted using a karyoploteR package  [16].
Phylogenetic Analysis and Annotation of Variants' Hotspot Regions within D. solani Strains To analyze the phylogenetic relationship of the variants' hotspot within D. solani strains, we extracted the 1 kbp window sequences harboring those variants based on a cut-off of variant density above 9 variants/1kbp. To do so, we retrieved the coordinates of the 1 kbp windows based on the predefined cut-off, then using these coordinates to obtain the corresponding sequences for each species from the whole genome SNP alignment including invariant sites generated by Snippy. In this analysis, we included all the 40 D. solani strains used in phylogeny and the D. dadantii 3937 sequence as an outgroup. The workflow and commands are available at: https://github. com/SolayMane/Dsolani_HGT_paper. md/blob/main/hgt_regions.
md, accessed on 13 January 2022. For each alignment extracted, corresponding to the 45 regions that were identified, a phylogenetic tree was inferred using FastTree software version 2. 1. 11 [17]  with the following parameters: -nt -boot 1000 -gtr. Furthermore, to investigate the functional characteristics of the genes with variations in the four strains RNS13-48-1A, RNS13-30-1B, RNS15-102-1A and RNS13-31-1A, we extracted the genes of the 45 identified regions. Functional annotation of the 63 unique genes was performed using emapper-2. 1  [18]  based on EggNOG orthology data  [19]. Sequence searches were performed using Diamond software  [20].
Interestingly, we identified a fragment of 347,817 bp inverted within the D. solani RNS05. 1. 2A chromosome. Using Illumina reads, we verified the correct mapping at the junction of this inversion. The flanking region 5′ of this fragment harbors a gene coding for a phage tail length tape measure protein (GpT), which is known to dictate the tail length and facilitate DNA transit to the cell cytoplasm during infection. The flanking region 3′ encodes a phage integrase that is known to mediate unidirectional site-specific recombination between the phage attachment site and the bacterial attachment site  [21]. The genome sequence was deposited at NCBI with the accession number CP104920. S4 ).
Phylogenomics of D. solani Strains RNS10-105-1A, RNS13-30-1B, RNS13-31-1A, RNS13-48-1A and RNS15-102-1A The draft genome sequences of D. solani strains RNS10-105-1A, RNS13-30-1B, RNS13-31-1A, RNS13-48-1A and RNS15-102-1A were assembled using CLC Genomics V12 and then annotated using Prokka. The assembly and annotation statistics are provided in Table  S2. In addition to these five strains, we retrieved 35 Dickeya solani genome sequences available at GenBank (https://www. ncbi. nlm. nih. gov/genome/, accessed on 13 January 2022) to perform core-genome based phylogenetic analysis. The predicted proteomes of the 40 strains were clustered using OrthoFinder software. The analysis showed that 99. 6% of the genes are clustered into orthogroups with a total of 4600 genes that represent the pan-genome of D.
SNP and Indel Variations in D. solani Strains To uncover the genetic variability within D. solani strains, the variants (Complex, Del, INS, MNP, SNP) against the D. solani RNS08. 23. 3. 1A genome (CP016928. 1) were identified using Snippy software (Table  S5 ). We analyzed twelve strains: RNS10-105-1A, A623-S20-A17 and RNS05. 1. 2A (from the RNS05. 1. 2A subgroup), RNS07. 7. 3B, RNS13-30-1B, RNS13-31-1A, RNS13-48-1A and RNS15-102-1A (all five in the same phylogenetic cluster), strains PPO9019 and PPO9134 (with HGT from D. dianthicola) and IPO2222 T and Ds0432. 1 exhibiting a low number of variations. As compared to RNS08. 23. 3. 1A, the number of variants ranged from 9 (Ds0432. 1) to 39,743 (RNS05. 1. 2A). Table  1  summarizes the variant analysis. The RNS05. 1. 2A subgroup (RNS10-105-1A, A623-S20-A17 and RNS05.
Functional Annotation of the Variable Regions in D. solain Strains In strains RNS13-48-1A, RNS13-30-1B, RNS15-102-1A and RNS13-31-1A, we investigated gene function within the 45 identified regions with variations. In these four strains, 63 unique genes exhibited variations. The genes were scanned against the EggNOG database (v5. 0) in order to categorize them according to their Gene Ontology (GO) category. Out of these 63 genes, the seed orthologs search resulted in 59 genes with significant hits that were used to annotate the genes (Table  S6 ). In total, 62 COGs were assigned to 59 genes where the COG annotations were classified into four general functional categories including Metabolism, Poorly Characterized, Cellular Processes and Signaling and Information Storage and Processing, with 31 (50%), 13 (21%), 9 (15%) and 9 (14%) hits, respectively (Figure  6 ).
The top three COG functional categories were 1-Unknown function, 2-Inorganic ion transport and metabolism and 3-Transcription with, respectively, 13, 10 and 6 genes. In addition, among the 59 analyzed genes, 35 were also assigned to at least one KEGG Orthology identifier. These genes were used to query the KEGG ORTHOLOGY Database to highlight the pathways in which those genes are involved. The majority of the genes were involved in Metabolic Pathways (map01100), ABC transporters (map02010), Quorum Sensing (map02024), the Bacterial secretion system (map03070) and the Biosynthesis of secondary metabolites (map01110). The results are detailed in Tables  S7  and S8. Figure  7  exemplifies region 26 in which the ddpF and ddpD genes, which display 5 and 16 variants, respectively, encode components of a putative peptide/nickel ABCtransport system.
Discussion This work highlighted different patterns of variations in the emerging pathogen D. solani. D. solani isolates distribute into two different subgroups, diverging with more than 30,000 variations (SNPs and Indels) along the chromosome. In the first subgroup is the strain type IPO2222 T , as well as most of the isolated strains such as Ds0432. 1 and RNS08. 23. 3. 1A, while in the second one are RNS101-05-1A, RNS05. 1. 2A and A623-S20-A17. We are proposing the strain RNS05. 1. 2A as a reference strain for this subgroup, as we obtained and deposited in GenBank its complete genome sequence (CP104920). The strains RNS08. 23. 3. 1A and RNS05. 1. 2A reached the same level of aggressiveness in tuber maceration assay  [5]. Aside SNP and Indels, we observed that integrated phage genes also contribute to the variability of D.
Acknowledgements The authors acknowledge the support of the  Spanish Research Agency (AEI) ,  Ministry of Science and Innovation (MCIN)  under contract no. PID2019-111773RB-I00/AEI/10. 13039/501100011033 ,  PGC2018-095032-B-100 , and SEV-2017-0706 Severo Ochoa, and by  PCI2021-122035-2A  funded by MICIN and by the  European Union Next Generation EU/PRTR , and of the European Union's  Horizon 2020  under grant agreement  881603  ( Graphene Flagship ). R. G. acknowledges funding from the  European Union 's  Horizon 2020 research and innovation program  under the  Marie Sklodowska-Curie  grant agreement No  840588  (GRISOTO) and I. F. A from a fellowship from  la Caixa Foundation  ( ID 100010434 ) with code  LCF/BQ/DI18/11660030  and of  H2020 Marie Skłodowska-Curie  grant agreement No. 713673.
| Island selection We focused on oceanic islands larger than 100 km 2. From an initial list of 87 islands  (Weigelt et al. , 2015)  we excluded 13 for which we were unable to obtain a species checklist, or that do not have terrestrial or freshwater breeding bird species (see Table  S1  in Supporting Information). We excluded 10 additional islands that were not in the same archipelago as other islands in the dataset. In total, we analysed 64 islands, belonging to 11 archipelagos, and spread across the Pacific, Atlantic, and Indian Oceans (Table  S1 , Fig. S1 ).
| Bird species database We extracted data for the abovementioned 64 islands from an existing database (a detailed methodology can be found in  Soares et al. , 2022) , which is based on multiple open sources  (i. e. , del Hoyo et al. , 2014; Dyer et al. , 2017a; Lepage, 2018; IUCN, 2020)  and specific literature (i. e. , regional field guides;  Hume, 2017; Paleobiology Database, 2018). Namely, we obtained a list of known breeding bird species for each island, including established introduced species, extant native species, and native extinct species (we use the term 'extinct species' for both globally extinct and locally extirpated species). For each island, we considered all bird species that went extinct after human arrival, and all successfully established introduced bird species, where were intentionally or accidentally transported to the islands by humans.
Extant native species correspond to native species that currently exist on the island. We followed the taxonomy used by Birdlife International (Handbook of the Birds of the World & BirdLife International, 2018). We use the term 'population' to refer to a species occurrence on an island; a species will thus have more than one population if it is present on more than one island in our dataset. We excluded marine birds as well as non-breeding migrants, occasional breeders, vagrant, and accidental species, to focus on regularly breeding terrestrial and freshwater species. These are the ones clearly highly dependent on island resources, also having particularly high rates of extinction and introduction on oceanic islands  (del Hoyo et al. , 2014).
| Quantifying taxonomic homogenization Given a pair of islands, we considered that they homogenized taxonomically if their bird species composition became more similar over time (decreasing beta-diversity), and conversely that they differentiated if it became less similar (increasing beta-diversity). We measured the taxonomic dissimilarity between island pairs using the Jaccard pairwise dissimilarity, calculated by the beta function in the BAT package  (Cardoso et al. , 2015). This index ranges from 0, when two islands have the same species composition, to 1 when they have no species in common. Given that Jaccard index is sensitive to small sample sizes, we assessed the correlation between the changes in taxonomic betadiversity and the original species richness, using Spearman's rank correlation, and plotted these two variables to help visualize their relationship (Appendix S2).
First, we computed the pairwise functional distances between each pair of species using the Gower dissimilarity index, giving the same weight to each trait (range of pairwise functional distances: 0 -0. 887), and calculated the contribution of each trait to the resulting distance matrix, using dist. ktab and kdist. cor in the ade4 package (Table  S3 ;  Dray & Dufour, 2007). Then, to extract orthogonal axes for the hypervolume delineations, we analysed the distance matrix through a principal coordinate analysis (PCoA) with Cailliez correction for negative eigenvalues, using the pcoa function in the ape package  (Paradis et al. , 2004). We retained the first eight PCoA axes, which cumulatively explained 81. 3% of the total variation (Fig. S2 ). The trait space of each assemblage was constructed using a kernel density hypervolume, approximated to a cloud of stochastic points based on the species in the assemblage, positioned based on their traits.
These kernel density hypervolumes were built using the Gaussian method with a 95% bandwidth, so that hypervolumes represent an estimated 95% of the cloud density and even weight across traits  (Blonder et al. , 2018) , using the kernel. build function in BAT package  (Mammola & Cardoso, 2020). In this probabilistic hypervolume approach, the functional richness (i. e. , functional alpha-diversity) of each assemblage is estimated as the volume of the hypervolume delineated by the cloud of stochastic points, and the functional beta-diversity between two assemblages is measured as the dissimilarity between the two hypervolumes (Fig. S3 ;  Mammola et al. , 2021). This approach does not assume a homogeneous trait space, and thus it responds to variations in point density within the multidimensional space.