dataesr/scientific-paragraphs-categorization huggingface dataset:
33  68. The phylogenetic tree was obtained based on the concatenated multiple sequence alignments of the 1,415 core gene sequences from the cgMLST_genus scheme; recombination was accounted for using Gubbins. The tree was rooted on the branch leading to isolate FB-8, which was the earliest branching genome in a phylogenetic analysis that included the external group Ralstonia solanacearum isolate IBSBF1503 (Supplementary Fig. 2 ). Leaves are labelled with the identifier of the isolate in the BIGSdb database, followed by the isolate name. Genomic species are labelled as G. S. An interactive iTOL version of the tree can be accessed at: https://itol. embl. de/shared/1l7Fw0AvKOoCF. Source data are provided as a Source Data file. The analysis was performed with 186 B. bronchiseptica genomes, and representatives of the phylogenetic diversity of B.
Acknowledgments We thank  Julien Guglielmini  and  Alexis Criscuolo (Bioinformatics and Biostatistics HUB in Institut Pasteur)  for help with the BIGSdb-Pasteur database configuration and for advice regarding bioinformatics analyses. This work was supported financially by the  French Government 's  Investissement d'Avenir program Laboratoire d'Excellence "Integrative Biology of Emerging Infectious Diseases"  ( ANR-10-LABX-62-IBEID ). BIGSdb development is funded by a  Wellcome Trust Biomedical Resource  grant ( 218205/Z/19/Z ). This work used the computational and storage services provided by the  IT department at Institut Pasteur. The National Reference Center for  Whooping Cough  and other Bordetella Infections is supported by  Institut Pasteur and Santé publique France (Public Health France ).
Data availability BIGSdb Pasteur : https://bigsdb. pasteur. fr BIGSdb Pasteur, Bordetella resource: https://bigsdb. pasteur. fr/bordetella/ The sequence reads data generated in this study for Bordetella bronchiseptica isolates from France, and for B. tumulicola, B. muralis and B. tumbae type strains, have been deposited in the European Nucleotide Archive (ENA) database, part of INSDC (NCBI/ENA/DDBJ), and is accessible under BioProject number PRJEB49946 (https://www. ebi. ac. uk/ena/browser/textsearch?query=PRJEB49946). Bordetella genomes list and accession numbers: Supplementary Data 4.
The dataset included representatives of the 16 currently described Bordetella species, and the 20 additional Bordetella genomic species defined above. Higher-level clades comprising several (genomic) species could be recognized. As expected, the three classical Bordetella species were grouped in a common clade. A second clade comprised B. holmesii, B. avium, B. hinzii and B. trematum, consistent with Linz et al. 4 , and also B. pseudohinzii and Bordetella genomic species 1, 5 and 12 20. A third major clade comprised B. petrii and genomic species 2, 7, 23, 25 and 26. The three novel species B. muralis, B. tumbae and B. tumulicola and genomic species 4 were part of a single clade, which was distantly associated to the B. petrii clade, consistent with previous 16S based phylogenetic analyses 19.
20ilker et al. on the basis of nrdA gene sequences20. The Bordetella diversity dataset is publicly available from the genomic platform as project Bordetella genus phylogeny (project id: 23; URL: https://bigsdb. pasteur. fr/cgi- bin/bigsdb/bigsdb. pl?db=pubmlst_bordetella_isolates&page=query&project_list=23&submit =1).
genus phylogeny dataset (92 isolates), project id 23: https://bigsdb. pasteur. fr/cgi- bin/bigsdb/bigsdb. pl?db=pubmlst_bordetella_isolates&page=query&project_list=23&submit =1 B. bronchiseptica phylogeny dataset (211 isolates), project id 24: https://bigsdb. pasteur. fr/cgi- bin/bigsdb/bigsdb. pl?db=pubmlst_bordetella_isolates&page=query&project_list=24&submit =1 B. pertussis phylogeny (124 isolates), project id 25 : https ://bigsdb. pasteur. fr/cgi- bin/bigsdb/bigsdb. pl?db=pubmlst_bordetella_isolates&page=query&project_list=25&submit =1 Bordetella public genomes dataset (2,085 isolates), project id 27: https://bigsdb. pasteur. fr/cgi- bin/bigsdb/bigsdb. pl?db=pubmlst_bordetella_isolates&page=query&project_list=27&submit =1Bordetella genus nrdA dataset (180 isolates), project id 29: https://bigsdb.
1; SAMN06767 132 Bordetella g Bordetella geno 8 2 AU8256 ASM226134 7 Spilker enomic group 2 (271) v1; conti et al , species 2 GCF_00226 gs 2014 1345. 1; SAMN06767 130 Bordetella g Bordetella geno 3 2 AU1437 ASM226129 19 Spilker enomic group 4 8 (287) v1; conti et al , species 4 GCF_00226 gs 2014 1295. 1; SAMN06767 136 Bordetella g Bordetella geno 2 2 AU1464 ASM226147 13 Spilker enomic group 5 6 (290) v1; conti et al , species 5 GCF_00226 gs 2014 1475. 1; SAMN06767 137 Bordetella g Bordetella geno 4 3 AU1808 ASM226126 8 Spilker enomic group 7 9 (299) v1; conti et al , species 7 GCF_00226 gs 2014 1265. 1; SAMN06767 141 Bordetella g Bordetella geno 1 1 AU1915 ASM211968 comp Spilker enomic group 8 7 (302) v1; lete et al , species 8 GCF_00211 2014 9685. 1; SAMN06767 142.
STATISTICAL AND MACHINE LEARNING ANALYSES RStudio 1. 3. 1093(www. r-project. org) software was used to perform the statistical analyses. First, an exploratory analysis (EDA) was carried out (using "tidyverse", "ggplot2", "GGally" packages in R). All the variables were scaled before any analysis to ensure that they were not dependent on body size. In the exploratory analysis, multivariate normality was tested using the Mardia test ("QuantPsyc" R package), and the impact of three predictor variables, habitat (mountain, boreal forest, tundra), mobility (migratory and sedentary) and subspecies (Rangifer tarandus granti, groenlandicus, pearyi, and caribou) was tested. After exploratory analysis and based on the natural distribution of the data, the habitat category was simplified to include two classes: "open" for tundra and "closed" for mountain and boreal forest in the predictive analyses.
One of the advantages of stacking is the avoidance of overfitting when sample size is relatively small and of course, it improves the overall predictive performance of each of the models included in the stack  (Nti et al. 2020 ). Here, "caret" and "caretEnsemble" R libraries were used. Blending. This ensemble approach is very similar to stacking technique. The only difference is that, while stacking uses a test dataset for prediction, blending uses a holdout dataset from the training set to make predictions. That is predictions take place on only the validation dataset from the training set  (Nti et al. 2020). The outcome of the predicted dataset and validation dataset is used for building the final model for predictions on the test dataset. Same models as explained above were also implemented in this case.
Anterior and posterior second phalanges were successfully separated in 94% of the cases using all the variables in the Blending test. Proximal or distal measurements tested separately did not produce satisfactory results. A two-way permutation Manova was then carried out on anterior and posterior first and second phalanges respectively (Table  6 ) and third phalanges altogether reporting significant values for subspecies predictor in the six cases, mobility in almost all cases (except posterior first phalanx and anterior second phalanx, and almost significant for posterior second phalanx). Habitat was not significant in second (anterior and posterior) phalanx. PCAs were carried out on the metacarpals and metatarsals using all the variables in order to explore data and morphological characteristics.
ACKNOWLEDGEMENTS This project has received funding from the  European Union 's  Horizon 2020 Research and Innovation Program (Marie Sklodowska Curie Actions , Grant Agreement n ° 794925) and  Marie Curie Alumni Association  through a  Micro-Media Grant. We would like to thank  Claire Saint-Germain  ( Ostéoteque de l'Université de Montréal, Quebec, Canada ),  Maeve Winchester  ( Ministry of Forests, Lands, Natural Resources Operations and Rural Development of British Columbia, Canada ),  Kamal Khidas  ( Canadian Museum of Nature, Ottawa, Canada ),  Guillaume Gingras  ( Forest and Wildlife Department of the Government of Quebec, Canada ) and  James Woollett  and  Steeve Côté  ( Laval University, Quebec, Canada ) who provided us with samples of caribou to complete this study. We also thank the reviewers of this manuscript.
We present two novel shape signature-based reflection symmetry detection methods with their theoretical underpinning and empirical evaluation. LIP-signature and R-signature share similar beneficial properties allowing to detect reflection symmetry directions in a high-performing manner. For the shape signature of a given shape, its merit profile is constructed to detect candidates of symmetry direction. A verification process is utilized to eliminate the false candidates by addressing Radon projections. The proposed methods can effectively deal with compound shapes which are challenging for traditional contour-based methods. To quantify the symmetric efficiency, a new symmetry measure is proposed over the range [0, 1]. Furthermore, we introduce two symmetry shape datasets with a new evaluation protocol and a lost measure for evaluating symmetry detectors.
1 ). • Another efficient method is introduced by exploiting new theoretical results on LIPsignature (see Theorem 2 and Section 4. 2 ). • A verification process for these proposed methods is theoretically justified in Theorem 4 (see Section 4. 4 ) to remove the false candidates. • The proposed methods also provide a symmetry measure ranging from 0 to 1, where 1 indicates the perfect symmetry. • Two novel public benchmark datasets are designed and named UTLN-SRA for the compound shapes with single reflection and UTLN-MRA for those with the multiple reflections. • A new evaluation protocol, which only needs one parameter instead of two in the previous protocol, is presented to evaluate symmetry detectors. • A novel lost measure is also introduced for comprehensive comparisons of the competence of the reflection symmetry detectors.
MPEG-7 dataset MPEG-7  [38]  was generally composed for shape analysis techniques: shape matching, classification, etc. The dataset consists of 70 classes, 20 binary shapes for each class. In 2019, Gonzalez et al. [2]  attempted to address several shapes of MPEG-7 for reflection symmetry detection. Accordingly, the selected set includes both symmetrical and quasi-symmetrical shapes which their groundtruth (GT) symmetrical axes were indicated by a group of concerned researchers. Fig. 9  (a) illustrates several symmetrical shapes, while Fig. 9 (b ) is for some of the quasi-symmetrical shapes, where their GT axes are drawn in red lines.
Our UTLN reflection datasets It should be noted that the shapes of MPEG-7  [38]  are not ideal for evaluating detectors of reflection symmetry. Indeed, they are not actually symmetrical shapes in many cases. Therefore, the corresponding ground truth, which is manually defined by human vision, may not be unique and disputable in several cases. This remark can be evidently observed in Fig. 9 (b). Furthermore, because those of MPEG-7 are single contour shapes, they are not challenging definitely. It is necessary to consider how well a symmetry detector works on complex/compound shapes which the traditional contour-based methods  [2]  either have not been validated on or could not perform well. On the other hand, there is only one symmetrical axis for every shape of MPEG-7. Therefore, this fails to evaluate how a symmetry detector works on a multiple symmetric shape and how good the detection is when the number of symmetrical axes is unknown.
Addressing those above shortcomings, we introduce in this paper a new dataset for evaluation of reflectional symmetry detection, called "UTLN Reflection dataset"  4  , which is created by collecting free images on the Internet. Our dataset contains two test suites: UTLN-SRA (Single Reflection Axis) and UTLN-MRA (Multiple Reflection Axes). The first one, which contains shapes having a single reflection symmetry (see Fig. 10 (a) ), is designed to evaluate the detection of the best reflection axis. The second one, which addresses shapes of multiple reflection axes (see Fig. 10 (b) , is served for evaluating the detection of multiple reflection axes. Moreover, UTLN-SRA and UTLN-MRA contain both simple and com-   pound shapes. We asked five experts in the Computer Vision field to annotate the datasets.
Let us consider the loss of symmetry detection in the k th shape image of a dataset. We suppose that { θ j,k D } L k j=1 is a set of L k directions of symmetrical axes resulted from the k th shape, while { θ i,k G } M k i =1 is a set of M k directions of the ground-truth axes. Each detected axis is assigned to the closest ground-truth axis in terms of direction difference. We then calculate the loss for each ground-truth axis. If none of the detected axes is assigned to any ground-truth axes, the loss is maximal (i. e. , equal to 1). In other words, the loss is the total of angle differences between the ground-truth axis and its corresponding detected axes which are normalized by the number of the ground-truth axes. Eq. (  11 ) , based on Eqs. (  8 ) and (  9 ) , defines the angle loss for the i th ground-truth axis of the k th shape ( GT i k ).
Since the shape contains M k ground-truth axes, the maximal angle difference is then calculated by 180 2 M k = 90 M k. Therefore, the angle difference between the ground-truth axis and every its detected axis is then weighted by M k 90. Similarly, Eq. (  12 ) , which is based on Eqs. (  8 ) and (  10 ) , defines the distance loss d j,k for the distance from the j th detected axis of the k th shape to the center/position of the ground-truth axis. It is then weighted by 2 √ W 2 + H 2 since the maximal distance loss is √ W 2 + H 2 2. GT i k = { j} 1 ≤ j≤L k , where i = arg min m | θ j,k D -θ m,k G | (8) δ j,k θ = | θ j,k D -θ i,k G | M k 90 (9) σ j,k d = d j,k 2 √ W 2 + H 2 (10) The losses of angle and distance for each ground-truth axis are defined as i,k θ = ⎧ ⎨ ⎩ j∈ GT i k δ j,k θ , if | GT i k | > 0 1 , otherwise (11) i,k d = ⎧ ⎨ ⎩ j∈ GT i k σ j,k d , if | GT i k | > 0 1 , otherwise (12) So, we propose to define hereunder the total losses on a dataset with N shapes.
Efficient strategies of elimination and verification As mentioned in Sections 4. 4 and 4. 5 , we located the threshold τ ≥ 0. 65 for the refining strategies to point out the right reflection symmetries from the detected candidates. Hereafter, we investigate its influence on the processes of elimination and verification. Accordingly, we address different values τ ∈ { 0. 65 , 0. 7 , 0. 75 , 0. 8 , 0. 85 , 0. 9 , 0. 95 } for reflection symmetry detection on binary shapes of datasets: MPEG-7  [38]  , UTLN-SRA, and UTLN-MRA. Table  3  shows the detection performances using Rsignature and LIP -signature. It can be observed that the reflection symmetry detection with the R -signature generally outperforms that with the LIP -signature on the simple shapes of MPEG-7 and the compound shapes with a single reflection axis (i.
It can be seen in Fig. 13  that our algorithm can detect better than that of Gonzalez et al. [2]. The contour-based analysis could not do exactly due to the fact that just one part of the shape's contour was located and segmented by the Slope-Chain-Code (SCC) theory  [29, 30]. Indeed, the compound shapes (a,b,c,e) are established by many disjointed contours. These shapes are inflectional symmetry if all of the disjointed contours have to be analyzed together. Here, there was an accidentally correct detection for shape (b) because of its inner decorative contour with natural reflection symmetry. Due to the inadequacy of the contour-based approach  [2]  , we would like to omit its evaluation on the compound shapes in UTLN-SRA and UTLN-MRA datasets. Consequently, it could be asserted that our proposal is more adaptive than the recent contour-based analysis  [2].
3. 99 Note: The complexity is estimated in the worst case. "-" means not available. could be due to the better representation power of LIP -signature than R -signature. In addition, since the F1-score measures of all methods on UTLN-MRA are at the modest levels (e. g. , F1-score of LIP -detector and R -detector is 0. 93135 and 0. 89865, respectively), it could assure that the multiple-reflection shapes of UTLN-MRA have been challenging for the state-of-the-art detectors and perspectives. This restates the significant contributions of our proposed datasets for reflection symmetry detection. For estimating the complexity of the previous works, because their detailed algorithms are not available, it is difficult to make an adequate complexity estimation. Therefore, we would like to conduct the complexity based on the primary detection steps in terms of the worst case (refer to the works  [2, 4, 7, 8, 10, 39]  for more detail of formulations and implementations).
Our main idea is to consider a deep-learning-based segmentation to extract the objects of interest and then to address our Algorithm 2 to detect symmetric axes on every extracted object. Concretely, for the first problem, U2-Net  [41]  is used to segment the main object of the image thanks to its high performance in background removal, while an instant segmentation or a panoptic segmentation  [42]  is addressed for the second one. These solutions allow obtaining principle shapes in a real image for the symmetry detection. The last row of Figs. 17   Use Algorithm 2 to detect symmetric axis of D 8: end for tection. The first two rows of Figs. 17 and 18 (a)  show results of our detectors on several natural images in the single and multiple ICCV'17 dataset  [1]  by considering their corresponding segmented shapes resulted by the pre-trained models of U2-Net and the panoptic segmentation, respectively.
Conclusions and perspectives We have introduced two novel methods for reflection symmetry detection of shapes. Our proposals, which are robust against noise and deformation, can effectively deal with the compound shapes, which are challenging for the traditional contour-based methods. Also, two new datasets and a novel metric ( Uloss ) have been proposed for evaluating symmetry detectors. For future works, we will extend this framework to effectively deal with reflection symmetry in 3D shapes and in natural images, and also address the robustness of our proposal against affine, perspective, and non-rigid transformations.
Acknowledgments We would like to thank  Dr. Thai V. Hoang  for the fruitful discussions. We express our gratitude to authors of the competing methods for sharing their codes. Also, we would like to send many thanks to those in Faculty of  IT, HCMC University of Technology and Education, Thu Duc City, Ho Chi Minh City, Vietnam , who gave us crucial supports in high-performing computers for the experiments. Finally, we would like to thank the valuable remarks of the reviewers and editors. Y. G. Kim  and  H. P. Truong  were supported by the  Institute of Information & communications Technology Planning & Evaluation (IITP) , grant funded bythe  Korea government (MSIT)  (No. 2019-0-00231 ) as well as by the  Basic Science Research Program  through the  National Research Foundation of Korea (NRF)  funded by the  Ministry of Education  (  2020R1A6A1A03038540  ).
Acknowledgements. The authors thank  Michiel Baatsen  and an anonymous reviewer for their comments which greatly improved https://doi. org/10. 5194/wcd-3-1359-2022Weather Clim. Dynam. , 3,  1359-1379 ,  2022 1376  E. M. Sainsbury et al. : Future European post-tropical-cyclone risk in CMIP6 models the clarity of this paper. Elliott Michael Sainsbury was funded by the  Natural Environment Research Council (NERC)  via the  SCE-NARIO Doctoral Training Partnership  with additional CASE funding from  BP. Reinhard K. H. Schiemann ,  Kevin I. Hodges ,  Alexander J. Baker , and  Len C. Shaffrey  are supported by the  UK National Centre for Atmospheric Science (NCAS) at the University of Reading. Alexander J. Baker  acknowledges funding from the  PRIMAVERA  project received from the  European Commission  and  NERC  funding through the  North Atlantic Climate System Integrated Study (ACSIS)  grant.
We thank  Olivier Boucher  and  Thibaut Lurton  (IPSL) for re-running the IPSL-CM6A-LR model to provide us with the  SSP5-85  scenario data needed to include the model in this study. The IPSL-CM6 experiments were performed using the HPC resources of  TGCC  (project  gencmip6 ), provided by  GENCI  ( Grand Équipement National de Calcul Intensif ). This work benefited from French state aid, managed by the  ANR  under the " Investissements d'avenir" programme. Stella Bourdin  is supported by public funding from the  CEA  and the  EUR IPSL-Climate. Len C. Shaffrey  and  Reinhard K. H. Schiemann  acknowledge funding from the  NERC  CANARI project. Financial support. This research has been supported by the  Natural Environment Research Council  (grant nos. NE/S0077261/1 ,  NE/N018044/1 , and  NE/W004984/1 ), the  European Commission   Horizon 2020 Framework Programme (PRIMAVERA , grant no.
641727 ), the  Grand Équipement National De Calcul Intensif  (grant no. 2021-A0100107732 ), and the  Commissariat Général à l'Investissement  (grant no. ANR-11-IDEX-0004-17-EURE-0006 ). Review statement. This paper was edited by  Christian M. Grams  and reviewed by  Michiel Baatsen  and one anonymous referee.
Weather, pedological, agricultural and hydrological data The weather variables were retrieved from the SAFRAN database of MeteoFrance  (Durand et al. , 1993) , namely, the hourly rainfall and air temperature and daily ET0 calculated according to Penman's formula, at an 8 km × 8 km resolution. The map of the agricultural plots includes the land use at the field plot level and is available on a yearly basis from the French Land Parcel Identification System  (IGN, 2015). Crop yield data are only available at the Gers department level (6200 km 2 ) from data collected from agricultural cooperatives by public authorities  (DRAAF Occitanie, 2020). No database provides information about the agricultural practices in the Gélon catchment, but specific surveys offer insights on the irrigation practices in the Gélon and Arrats catchments.
The soil data were provided by the Référentiel Régional Pédologique, a French soil database  (Party et al. , 2016). Stream discharge data at the Gélon outlet have only been available since September 14, 2018. We thus estimated the 2014-2015 discharge from the daily specific stream runoff data recorded at the closest station of the French station hydrometric network, assuming that both specific stream runoffs were equal. This assumption was carefully verified over the period September 14, 2018 to September 13, 2019 at a daily time step during which the discharge at both catchment outlets was monitored. The similarity was very high for 47% of stream runoff (i. e. , between 0. 086 and 1. 0 mm/d) encountered in the Gélon catchment, with an r 2 value of 0. 68 considering linear regression, an NSE Q value of 0.
Parametrisation As far as possible, the parameters corresponding to the functional properties (Appendix C) were determined from existing databases, measurements and in situ observations or retrieved from the literature. Values of crop growth parameters were fixed based on previous studies. Indeed, these parameters were determined previously for several field crops in southwestern France and then validated for three rainfed and irrigated spring crops (sunflower, maize, and sorghum)  (Constantin et al. , 2015) , for wheat on 14 experimental sites in France and for rotations on two sites in southwestern France  (Tribouillois et al. , 2018). We grouped crops into classes to limit the number of crop parameter sets, especially for minority crops. For example, the soybean class includes soybeans as the main crop but also peas, chickpeas, flax and lentils as minority crops.