dataesr/scientific-paragraphs-categorization huggingface dataset:
However, the degradation in the quality of the solutions is faster for ML than for TrivialRand, consistent with the fact that ML was trained on exhaustive trees only: when the difference between the training data and the input data becomes too large, the behaviour Fig. 13  The running time of ML on the real dataset described in Sect. "Experiments on real data" compared with the running time of the exact methods Hybroscale and TreeChild on the same dataset. The solid lines represent the average running times within each instance group. The shaded areas represent 95% confidence intervals Fig. 14  Ratio between the number of reticulations outputted by ML and TrivialRand Best and the reference value for an increasing percentage of removed leaves on LGT data. Each point on the horizontal axis corresponds to a certain percentage of leaves removed from each tree; each line represents the average, within the instances of a group (L, R) with a certain percentage of removed leaves, of the output reticulation number divided by the reference value.
Effect of the threshold on ML We tested the effectiveness of adding a threshold τ > 0 to ML on the same datasets of Sects. "Experiments on normal data", "Experiments on LGT data" and "Experiments on ZODS data" (normal, LGT and ZODS). Recall that each instance group consists of 48 instances. We ran ML ten times for each threshold τ ∈ {0, 0. 1, 0. 3, 0. 5, 0. 7} on each instance, took the lowest output reticulation number and averaged these results within each instance group. The results are shown in Fig. 15. For all types of data, a threshold τ ≤ 0. 3 is beneficial, intuitively indicating that when the probability of a pair being reducible is small it gives no meaningful indication, and thus random choices among these pairs are more suited. The seemingly best value for the threshold, though, is different for different types of instances.
To compute and update these quantities we proceed as described in Sect. "Time complexity" and Appendix A. The general idea of the function PickNext used in FeatImp is to mimic the first splits of the classification tree by progressively discarding the candidate reducible pairs that are not among the top α% scoring for each of the considered features, for some input parameter α. We implemented FeatImp and test it on the same instances as Sects. "Experiments on normal data", "Experiments on LGT data" and "Experiments on ZODS data" with α = 20. The results are shown in Figure  16. As expected, FeatImp works consistently worse than ML on all the tested datasets, and it also worse than TrivialRand on most instance groups. However, it is on average 12% better than TrivialRand on the LGT instance group having 50 leaves and 30 reticulations and on all the LGT instance groups with 100 leaves, which are the most difficult for the randomised heuristics, as already noticed in Sect.
g. local pressure threshold  [15]  ) that need to be adjusted for different flame types and conditions, limiting their application to specific flame configurations. With the increase in computational cells, the need for three-dimensional simulations, incorporating detailed chemistry, and developing novel postprocessing tools capable of extracting key information from the dataset might be necessary. Previous studies have also suggested DDT might be a spatially and temporally stochastic process even when the flow is subjected to minimal fluctuations of initial conditions  [16, 17]. Therefore, it is of great interest to develop a universal data diagnostics tool for delineating DDT mechanisms while conventional diagnostic methods may fail. Among others, eigenanalysis-based methods have been used to resolve complex physiochemical couplings between flows and chemical kinetics in incompressible turbulent reactive flows  [18, 19].
Acknowledgments This study was supported by the  National Natural Science Foundation of China  (Grant No. 12002207  ) and  Shanghai Pujiang Program  ( 20PJ408500 ). All the computations were carried out on SJTU supercomputing resources ( https://hpc. sjtu. edu. cn/). Dr. XiaoHang Fang  gratefully acknowledges the financial support from the  John Fell Oxford University Press Research Fund  ( 0011348 ) and the  VPR Catalyst Grant  ( 10040436 ) from the  University of Calgary. The authors are particularly grateful to  Professor Elaine Oran  for developing and sharing the computing code for DDT simulations.
We then screened our collection for VAGs and antimicrobial resistance genes (ARGs). As reported previously  19  , the ST58/24 subgroup exhibited significantly more VAGs classified in toxin, protectin and iron acquisition system categories (Fig. 2a , Supplementary Data 2) and was predicted to be more antibiotic (ampicillin and trimethoprim) resistant (Fig. S2 , Supplementary Data 3) than the other subgroups. Moreover, almost all strains in this subgroup carry the HPI and the ColV plasmids. Of note, whereas this subgroup represented 27% of our collection, it encompassed two-third of the strains isolated from human extra-intestinal infections (Fig. S3 ), an over-representation also observed by Reid et al. 19. Altogether, these first analyses confirmed that virulence and resistance genes within the E.
Moreover, gene presence/absence also points to the HPI with 12 genes over the 13 significantly associated with virulence related to this virulence determinant (Fig. 2d ). In contrast, no unitig or gene related to the ColV plasmids were significantly associated with virulence (Fig. 2c ). At the species level, both aer and sit operons, but not the plasmid by itself, are involved in the mouse sepsis phenotype We then wanted to evaluate the role of the VAGs used to infer the presence of ColV plasmids in extra-intestinal virulence, but this time at the species level. For this purpose, we used the data of a GWAS analysis performed on 370 strains of the genus Escherichia that have been tested in the mouse sepsis assay, including 24 strains of the CC87 (see above). In this work, the HPI was the strongest genetic element associated with virulence in mice, followed by the aerobactin (iucABCD/ iutA) and sit (sitABCD) operons  15.
S6 ). If we consider the presence of the ColV plasmid as a covariate in the GWAS analysis, the unitigs associated with iucABCD/iutA and sitABCD remain significant, arguing for a role beyond their simple location on the plasmid (Fig. S7 ). Overall, these data indicate that within the Escherichia genus, the sole presence of the ColV plasmid does not explain virulence in our mouse sepsis model, even though aerobactin and sit operons can be found as plasmid borne. The HPI needs other VAGs to express full virulence Next, we assessed the respective role of the iron acquisition-related operons aer, sit and iro as compared to the higher predictor of mouse death, the HPI, in the same 370 strains' dataset. We found high odds ratios for the status "killer" (i. e. at least 9/10 mice killed per strain)  11  as a function of the presence of the HPI in strains carrying different VAG combinations.
The HPI co-occurs with aer, sit and iro operons in E. coli genomes at different frequencies depending on the phylogeny of the strain To get a broader view of VAG co-occurrences and their genomic location, we used the 2302 high-quality circularized E. coli genomes available in RefSeq (https://www. ncbi. nlm. nih. gov/refseq/). Using this dataset prevented us from the potential bias resulting from the Illumina short-read sequencing when we analysed the genomic location of VAGs among the 370 Escherichia strains. Nonetheless, we confirmed that PlaScope enables a classification of chromosomal and plasmidic contigs with high performances (Fig. S9 ). First, we focused on the five main ST responsible for bacteremia in France  23  [ST131 (n = 123), ST73 (n = 41), ST69 (n = 33), ST95 (n = 46), ST10 (n = 48)] as well as the STc58 (n = 37) to quantify co-occurrence frequencies and prevalence of VAG pairs.
E. coli CC87 dataset The collection is composed of strains belonging to the CC87 (Institut Pasteur scheme numbering) which gathered strains from both ST58 (n = 139) and its sister group ST155 (n = 93), all from phylogroup B1. The strains were sampled from humans (n = 125), domestic (n = 66) and wild (n = 21) animals and environment (n = 20) (Supplementary Data 1). Human strains corresponded to commensals (n = 64), ExPEC (n = 60) and InPEC (n = 1) whereas animal strains were all commensals (Fig. S3 ). The geographic origin was diverse (Europe n = 91, America n = 59, Australia n = 50, Africa n = 30, and Asia n = 2). Of note, 26 ST58 strains previously described by Reid et al. 19  were also included. All our strains were sequenced on Illumina platforms. Genomes were annotated with Prokka using standard parameters  78.
Bioprojects and accession numbers of the genome are detailed in Supplementary Data 1. Fasta and gff files are available on figshare  79, 80. The O-type, H-type and fimH of the strains were determined with Abricate  85  with 90% identity and 90% coverage based on ecoh  86  , serotypefinder  87  and fimTyper 88 databases. We searched for virulence associated genes (VAGs) and antibiotic resistance genes (ARGs) as previously described using Abricate with 90% identity and 90% coverage  23. Our virulence database was composed of VirulenceFinder 89 , VFDB  90  and specific genes from extra-intestinal E. coli. The VAGs were classified into 6 main families, namely invasin, protectin, toxin, adhesin, iron acquisition, and miscellaneous. The number of VAGs per strain was compared between the CC87 subgroups using Kruskal-Wallis test.
ColV plasmid inference and plasmidic sequences prediction To infer the presence of ColV plasmids we searched for VAGs from 6 gene sets (cvaABC/cvi, iroBCDEN, icuABCD/iutA, etsABC, ompT/hlyF, sitABCD) using Abricate with 90% identity and 95% coverage as proposed by Reid et al. 19. Gene sequences were retrieved from the plasmid pAPEC-O2-ColV (RefSeq: NC_007675. 1). Then, from these results, we inferred the presence of ColV plasmids on the basis of Liu's criteria  49  , i. e. if at least one gene from 4 of the 6 gene sets was present. We also predicted chromosomal and plasmidic sequences using PlaScope  22  , which is a targeted approach to classify contigs based on a database of chromosome and plasmid sequences of E. coli (database available on Zenodo  92  ).
The p-value threshold for each analysis (i. e. unitigs and gene presence/absence) was corrected using Bonferroni method considering the number of unique variant patterns as the number of multiple tests. These corrections resulted in p-values of 6. 09E-07 and 1. 56E-05 for unitigs and genes, respectively. Finally, to identify their location, we mapped back the statistically significant unitigs using bwa 0. 7. 17  100  and bedtools 2. 30. 0 101 to 3 fully sequenced reference genomes of E. coli: CVM_N16EC0879 (ST WU 58-ST IP 24-like, phylogroup B1, GenBank: CP043744. 1, https://www. ncbi. nlm. nih. gov/assembly/GCA_008386415. 1/), IAI1 (ST WU 1128-ST IP 294, non-CC87 phylogroup B1, Refseq: NC_011741. 1, https://www. ncbi. nlm. nih. gov/assembly/GCF_000026265. 1/), S88 (ST WU 95-ST IP 1, phylogroup B2, Genbank: CU928161.
Role of VAGs from ColV plasmid in virulence at the species level To assess the role of each VAG used to infer the presence of ColV plasmids, we retrieved the genomes of 370 strains representative of the diversity of the Escherichia genus as well as the results of the GWAS previously done on these strains  15. First, we screened the genomes for ColV plasmid VAGs as described above and classify contigs of each genome using PlaScope. Then, we constructed a blastN database from nucleic sequences of all ColV plasmid VAGs found among the 370 genomes. We ran a blastN alignment using this database and the unitigs as query and considered only perfect matches (i. e. 100% identity over 100% of the unitig length). From this analysis we were able to associate each unitig and its p-value/beta-value to a given VAG.
Distribution and location of VAGs at the species level among fully sequenced genomes of E. coli To analyse more thoroughly the distribution of VAGs used to infer ColV plasmids, we determined their prevalence among the 2302 genomes of E. coli (taxid 562) available on Refseq on September 19th 2022. Both fasta and genbank files were retrieved using ncbi-genomedownload  109. Accession numbers of assemblies and replicons (chromosomes and plasmids) are available in Supplementary Data 11. ColV plasmid VAGs were screened as described above using Abricate. Then, for each couple of VAG/location in a given genome, we searched for other co-occurrent VAGs. Results are presented as heatmaps and colored according to the frequency of the co-occurrences and prevalence of VAG pairs. To test for association between chromosomal and/or plasmidic VAGs (fyuA, iroN, iucA and sitA) in a given ST/STc or phylogroup, we computed odds ratios from a contingency table constructed from the presence-absence data of each gene pair to be compared.
When the results are significant, a 0 < OR < 1 reflects a negative association between two VAGs and an OR > 1 a positive association. We also tested the association between the presence of HPI and another iron capture system at the whole species level using Pagel's model  29  from the R package phytools  110. This method uses a likelihood ratio test to compare two models in which binary traits evolve independently or in a dependent manner along a phylogenetic tree. We considered a system as present in a strain when all the genes of the focal system were detected. The phylogenetic tree was generated from the core gene alignment of the 2302 genomes of RefSeq using Iqtree v1. 6. 12  111  under the GTR + F + I + G4 model. In a second step, we determined the site of insertion for chromosomal VAGs.
To this end, we performed a pangenome analysis of the 2302 genomes, using Ppanggolin v1. 2. 74  112  and the annotations from RefSeq as input. The gene families were computed by clustering protein sequences with 90% identity and 90% coverage. Then, we built the pangenome graph and partitioned the pangenomes. Finally, we used PanRGP  27  to determine the regions of genome plasticity (RGP), which can be considered as genomic islands in the cases of chromosomal insertions. The RGP containing iro, aer and sit operons were identified using the function "align". We extracted the sequences and annotations of these RGP as well as 3000 pb upstream and downstream to determine the sites of chromosomal insertion. The pangenome file from Ppanggolin/PanRGP is available on figshare  113. Third, we also took advantage of this high-quality dataset to check for the performances of PlaScope.
Phylogenic incongruence of VAGs among the 2302 genomes of RefSeq We extracted the nucleic sequences of three operons involved in iron acquisition: iroBCDEN, iucABCD and sitABCD. We aligned the sequences of these operons according to their location (i. e. chromosomal or plasmidic) using Mafft v7. 310  114  and computed phylogenetic trees with FastTree v2. 1. 11  95 with the GTR + gamma substitution model. From these trees, we computed patristic distances between all possible pairs of sequences according to their location and ST/phylogroup using "cophenetic" from the R package ape  115. The trees were also annotated and visualized with Itols  96. Open Access This article is licensed under a Creative Commons Attribution 4. 0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Acknowledgements We are grateful to  Laurence Armand  and  Milen Milenkov  for the access to the Madagascar strains and to  Lucile Vigué  for fruitful discussions on selection and epistasis. This work was partially supported by the  "Fondation pour la Recherche Médicale " Equipe FRM 2016, grant number  DEQ20161136698  to E. D. M. G. was funded by the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation ) under Germany's Excellence Strategy -EXC 2155 -project number  390874280. G. R. was partially supported by the  Agence Nationale de la Recherche  under the  French national action Plan on AntiMicrobial Resistance (PAMR) project  SEQ2DIAG  ( ANR-20-PAMR-0010 ).
Data availability The bioprojects and accession numbers of the CC87 genomes are available in Supplementary Data 1. The assemblies and annotation of the 370 strains of Escherichia are available on figshare (https://doi. org/10. 6084/m9. figshare. 11879340. v1, https://doi. org/10. 6084/m9. figshare. 19536163. v1). The 2302 complete genomes of E. coli can be obtained from RefSeq (https://www. ncbi. nlm. nih. gov/assembly) using the accession numbers available in Supplementary Data 11. Source data are provided with this paper.
The approach, henceforth, referred to as GeoGEDI, should benefit from high-resolution DEM increasing availability and temporal stability, thus enabling much broader use. GeoGEDI was tested on v1 and v2 data for different forest and terrain conditions. Its performance was evaluated by analyzing magnitude and direction of the corrections and the impact on GEDI ground elevation and canopy height errors. The rest of the article is organized as follows. Section II introduces the data used to test and evaluate GeoGEDI. In Section III (method), GeoGEDI algorithm is detailed, prior to the presentation of the experimental setup and statistical analyses. Results are reported and discussed in Sections IV and V, respectively. Finally, Section VI concludes this article.
B. GEDI L2A Data The GEDI instrument is composed of three lasers emitting 14 ns long near-infrared laser pulses at high frequency (242 Hz). One laser is split into two coverage beams, while the other two lasers produce two full-power beams. Each beam is deflected every other shot by the beam dithering units, which results in eight parallel ground tracks. Tracks are spaced 600 m apart and composed of 25 m diameter circular footprints 60 m apart along-track. For each footprint, the lidar waveform backscattered by the Earth's surface is recorded  [2]. The recorded waveforms are processed to provide GEDI data products at footprint level. In GEDI L2A products, ground elevation, top of canopy, and RH metrics are derived from geolocated waveforms (L1B product). RHs correspond to cumulative waveform energy from bottom (0%) to top (100%), in 1% increments (RH0 to RH100)  [27].
1 ). C. Reference Datasets 1) High-Resolution DEM and DSM: DEMs at a spatial resolution of 1 m were downloaded from the BD ALTI product of the National Institute of Geographic and Forest Information (IGN)  [30]. For both study sites, the DEMs were derived from ALS acquisitions and delivered with altimetric and planimetric mean quadratic errors within 0. 2 and 0. 6 m, respectively  [31]. Digital surface models (DSMs) representing the top of canopy, top of buildings, or other first return objects were also acquired from IGN, at the same spatial resolution. They were generated from either photogrammetric or ALS point clouds. DSMs were chosen in order to have a minimal temporal acquisition difference with GEDI data. For the Landes, the chosen DSM was produced using a photogrammetric point cloud generated using aerial photographs acquired in summer 2018 at a 35-cm resolution and processed using MicMac dense matching algorithm  [32].
For the Vosges, the DSM was computed using ALS data acquired in winter 2020, and characterized by an average first return point density of 4. 8 pt/m 2. On both sites, a DHM was obtained by subtracting ALS DEM from DSM. To allow for comparison with GEDI products, DEMref and DHMref, a 1-m resolution focal mean DEM and focal maximum DHM, were computed by using a sliding 25-m diameter circular window at each pixel. 2) Forest Database: BD Forêt v2  [33]  provides information about the composition and density for forest stands which have areas of greater than 5000 m 2. The open-source database was used to classify footprints as forest or nonforest. The different datasets are summarized in Table  I.
III. METHOD In this section, the GeoGEDI method is presented (in Section III-A) and the experimental setup is designed (in Section III-B). The latter includes parameter settings and filtering criteria used before analyzing algorithm outputs. The statistical analyses used to assess the algorithm performance are presented in Section III-C. The official French coordinate system, Lambert 93, was used during all the processing steps and analyses. While all IGN datasets were given in Lambert 93, GEDI data had to be transformed from WGS84 to Lambert 93. GEDI's latitude and longitude coordinates were transformed to Lambert 93 coordinates and GEDI's ellipsoidal heights were transformed to fit Lambert-93 altitude system by applying an altimetric conversion grid  [34].
The different GeoGEDI outputs based on v1 or v2, using either the single-beam or four-beam approach, will be referred to as v1_1, v1_4, v2_1, and v2_4. 2) Data Filtering: Once the shifts were computed, several filters were applied. First, footprints associated to too small clusters were discarded. Indeed, cluster size (n i ) can be lowered due to removing low-quality footprints (see Section II-B). Threshold value was set to 1/4 of the theoretical maximum number of footprints for the considered time interval, corresponding to 13 and 50 footprints for the single-beam and four-beam approaches, respectively. All footprints that did not meet one of the abovementioned criteria, with either v1 or v2 dataset, were excluded. From the 151 999 footprints, 150 093 were kept for further analysis. Second, in each data set, i.
e. , v1_1, v1_4, v2_1, or v2_4, footprints where the shift in X or Y for Δ opt reached shift max (i. e. , 50 m) were discarded. Finally, some footprints were discarded due to issues identified in GEDI ground elevation assessment. Six waveform interpretation algorithms (01 to 06) were defined by the GEDI science team to identify the ground peak from GEDI waveforms, with different thresholds and smoothing settings  [7] ,  [27]. In GEDI L2A data v1, the default algorithm for all footprints was algorithm 01. In v2, the presumed best ground elevation is provided for each footprint along with the corresponding algorithm. This leads to possible changes in best algorithm choice and in differences in ground detection and elevation between the two GEDI versions. For v1, the default choice is always algorithm 01.
For v2, the selected optimal algorithm was either 01 or 02 for our study sites. A comparison with DEMref revealed great ground elevation underestimation for some footprints where algorithm 02 was selected, probably due to faulty ground peak detection (Fig. 3  and Appendix A,  Table V ). To eliminate these misestimations in further analyses, footprints having a ground elevation difference between v1 and v2 of more than 1. 5 m were discarded. This concerned 26. 9% and 39. 3% of footprints processed with algorithm 02, corresponding to 3. 4% and 13. 8% of footprints, for Landes and Vosges, respectively. Please note: this source of error was identified after processing footprints; and the footprints were used during the georeferencing process. To limit influence of erroneous ground peak detection when comparing error estimates for different datasets, they were discarded regarding ground elevation and surface height estimation analyses.