dataesr/scientific-paragraphs-categorization huggingface dataset:
Finally, beak size and shape are known to directly influence song characteristics and, in particular, vocal performance. For instance, larger beaks are associated with songs produced at a slower pace in Darwin finches  (Podos 2001)  or in Meliphagidae  (Friedman et al. 2019). Here, we do not have data on beak morphologies, but because beak size has been shown to change in insular environments  (Grant 1998 , Blondel 2000 , Scott et al. 2003 , Eloy de Amorim et al. 2017 ), it will be a factor to explore by future studies trying to understand changes observed on islands. Similarly, body size measures were not taken on the recorded birds, but rather from species means, which could obscure any possible effects of morphology on the song parameters analysed.
Acknowledgments -We are extremely thankful to everyone who made this study possible. Francis Njie  provided invaluable assistance in Cameroon,  Octávio Veiga  on  São Tomé  and  Malicia Besnard in France. Facundo Fernandez-Duque  helped in data analysis. We also thank  Rita Covas  for the discussions around the design of this project and the field work and  Clémentine Vignal ,  Jérome Sueur  and two anonymous reviewers for their constructive comments. Funding -This study was funded by the  INEE-CNRS  ( PEPS  project ' Soundscape ') and the SFE2. AR was funded by the  University of Montpellier ; MM by the  Portuguese Science and Technology Foundation  (FCT:  SFRH/BPD/100614/2014 ) and via the European Union's  Horizon 2020 Research and Innovation Programme  (grant agreement  854248 ); CD, TL and DG by the  CNRS.
Clinical studies and strain collections The strains studied here were collected from blood cultures of hospitalized adult patients suffering E. coli bloodstream infections, enrolled in two large multicentric observational studies, Colibafi (Ethics Committee CPP Hôpital Saint Louis, Paris, France: number 2006-4) and Septicoli (ClinicalTrials. gov: identifier NCT02890901) conducted in 2005 and 2016-7, respectively, in Paris and its close suburbs. Only patients previously included in the study and patients receiving vasopressors before the onset of bloodstream infection were excluded. Both studies aim was to the identify risk factors of mortality at day 28 in E. coli bloodstream infections  [3, 6]. To limit epidemiologic biases, we focused only on strains isolated in teaching hospitals located in Paris and its close suburbs (which accounted for 8 among the 15 hospitals included in the Colibafi study for a total of 3,900 adult acute care beds); the other hospitals were in the rest of France.
Genome global analysis and typing All genomes were assembled with shovill version 1. 0. 4 [25]  using SPAdes v3. 13. 1  [26]  and standard parameters, and then annotated with Prokka 1. 14. 5  [27]. Genome typing was performed as previously described including species identification, phylogrouping, multi-locus sequence type (MLST) determination according to the Warwick scheme and in silico serotyping  [28] [29] [30]. STcs were defined as single or double locus variant based on the MLST data of the Warwick scheme using PHYLOViZ  [31]  in congruence with the core genome based phylogeny (see below). Resistance genes were scanned with Resfinder v3. 2  [32]. Virulome was analyzed with a custom database consisting of VirulenceFinder  [33] , VFDB  [34] , and specific genes from extra-intestinal E.
coli, as previously described in the Table  S2  of  [30]. We also searched for point mutation responsible for betalactam (ampC promoter) and fluroquinolone (gyrA/B, parC/E) resistance  [35]. Virulence genes were classified into 6 families: invasin, protectin, toxin, adhesin, iron acquisition, and miscellaneous  [30]. Contig locations, i. e. , plasmid or chromosome, were predicted using PlaScope  [36]. We search for plasmid replicons using PlasmidFinder database, as previously described  [30, 37]. Integrons were searched using Integronfinder with standard parameter  [38]. A pangenome was computed using Roary v3. 12 with default parameters  [39]. Then, a phylogeny based on a core genome alignment  [40]  was performed with Iqtree v1. 6. 12 following the protocol described in  [41]. Only the top 10 STcs of each collection were further studied.
Resistance phenotype prediction Beta-lactamase types and alleles were controlled based on the Beta-Lactamase DataBase  [42]  and the Bacterial Antimicrobial Resistance Reference Gene Database when necessary  [43]. From these results, we predicted phenotypic resistance to clinically relevant antibiotics of 4 classes: (i) betalactams including ampicillin (AMP), piperacillin/tazobactam (TZP), cefotaxime/ceftazidime (CTX/CAZ), cefepime (FEP), carbapenems (CARB); (ii) fluoroquinolones (FQ); (iii) aminoglycosides including gentamicin (GEN) and amikacin (AMK); and (iv) cotrimoxazole (SXT), as previously described  [44]. The predicted phenotypes arising from presence of genes or mutations are described in Additional file 1: Table  S1. This approach was validated on the 2016-7 collection where phenotypic antibiograms (susceptible/resistant) were available as a correlation of 96.
These genes are almost exclusively found on plasmidic contigs, iro genes and iss_12 being almost always co-localized on the same contig, and iuc genes on another. We also found less VFs of the miscellaneous class (adjusted p value = 0. 04), partly due to the less frequent presence of etsC gene encoding a putative type I secretion outer membrane protein. As these iron acquisition and miscellaneous genes are typically carried by the pS88 plasmid (accession number: CU928146), we searched for its presence in our strains using blastN alignments. The plasmid was detected in nearly all strains, including subgroup D, but was not found in the emerging subgroup A. The antigenic diversity is d Predicted phenotypes of the strains. The results are presented as percentage of resistant strains for eight antibiotics of clinical importance (no carbapenem-resistant strain has been found).
Acknowledgements We are particularly grateful to  François Blanquart ,  Julie Marin , and  Marie-Hélène Nicolas-Chanoine  for their useful comments on the manuscript and to  Marie Petitjean  for the EnteroBase genomes. The Colibafi Group is composed of  Michel Wolff ,  Loubna Alavoine ,  Xavier Duval ,  David Skurnik ,  Paul-Louis Woerther ,  Antoine Andremont ,  Etienne Carbonnelle ,  Olivier Lortholary ,  Xavier Nassif ,  Sophie Abgrall ,  Françoise Jaureguy ,  Bertrand Picard ,  Véronique Houdouin ,  Yannick Aujard ,  Stéphane Bonacorsi ,  Agnès Meybeck ,  Guilène Barnaud ,  Catherine Branger ,  Agnès Lefort ,  Bruno Fantin ,  Claire Bellier ,  Frédéric Bert ,  Marie-Hélène Nicolas-Chanoine ,  Bernard Page ,  Julie Cremniter ,  Jean-Louis Gaillard ,  Françoise Leturdu ,  Jean-Pierre Sollet ,  Gaëtan Plantefève ,  Xavière Panhard ,  France Mentré ,  Estelle Marcault , and  Florence Tubach.
Funding This work was partially funded by a  Translational Research Grant  from the  Agence Nationale de la Recherche (ANR) ,  Ministry of Higher Education and Research, France 2015  (grant  n°ANR-15-CE-17-0019-01 ). ED was partially supported by the  "Fondation pour la Recherche Médicale " (Equipe FRM 2016, grant number  DEQ20161136698 ). GR was supported by a " Poste d'accueil " funded by the  "Assistance Publique-Hôpitaux de Paris" (AP-HP)  and the " Commissariat à l'énergie atomique et aux énergies alternatives" (CEA ) personal grant for his PhD.
Availability of data and materials The whole-genome sequences of the 912 strains studied have been deposited under the Bioprojects PRJEB35745 (https://www. ncbi. nlm. nih. gov/ bioproject/?term=PRJEB35745) [6]  and PRJEB39260 (https://www. ncbi. nlm. nih. gov/bioproject/?term=PRJEB39260)  [24].
2005). In the context of tree quality assessment, the first objective of this paper was to propose a method for automatically measuring the dimensions of the defects after detection  (Nguyen et al. 2016a ) and identification  (Nguyen et al. 2020a). The second objective was to verify the efficiency of the whole data processing. For this, we compared the defect attributes automatically estimated with field measurements achieved by a forest expert. The third and last objective was to assess the computing time in relation to the main algorithm parameters.
TLS data collection and preprocessing A Faro Focus 3D X130 scanned the sampled trees in the Champenoux forest close to Nancy, France (48° 43′ 30. 1″ N, 6° 20′ 52. 1″ E). The database consists of point clouds of trees of two species: sessile oak (Quercus petraea (Matt. ) Liebl. ) and European beech (Fagus sylvatica L. ). In order to detect the small defects, we used a high-resolution setting and the distance from the scanner to the trunk was approximately 3-4 m. The angular resolution was 0. 018° on both horizontal and vertical directions. Each tree was scanned from two points of view towards the target side to describe half of the trunk circumference. Scans of each tree were coregistered in FARO SCENE software (Faro Technologies Inc. , Lake Mary, FL) and exported to a unique file to recover the 3D view of one side of the trunk.
The point cloud was preprocessed to enhance the outcome of the process. The noise was reduced by using an algorithm based on Euclidian distance clustering  (Nguyen et al. 2020a). Then the point cloud with its reduced noise was processed in the Graphite software (https:// gforge. inria. fr/ frs/? group_ id= 1465) including the following steps: (1) smoothing of the point cloud, (2) meshing of the point cloud, and (3) smoothing the resulting mesh. Additional details regarding the process were presented in  Nguyen et al. (2020a) ). The data that support the findings of this study are openly available in  (Nguyen et al. 2020b).
Implementation of the software The input of the software is a mesh derived from TLS data of the trunk (the preprocess and creation of the mesh were presented in the section "Preprocessing" or in  Nguyen et al. (2020a). The program was implemented in C++ language using several libraries: DGtal (DGtal) to represent 3D volumic data and for the computation of the trunk centerline, OpenCV  (Bradski 2000)  for the machine learning, GNU GSL  (Gough 2009)  for the linear regression, and PCL  (Rusu and Cousins 2011)  for normal computation and clustering. The final objective of the project was to provide a graphical tool for grading trunks or logs. The graphic environment was provided by the platform Computree (http:// compu tree. onf. fr), an open-source software dedicated to processing LiDAR data in forestry.
Branch diameter measurement Our test database mainly focused on naturally pruned parts of the trunks, where only two branches were measured: one in an oak tree and another on a beech tree. The estimated diameters of the branches were 8. 3 mm (oak) and 20. 3 mm (beech) compared with field measures of 8 mm and 20 mm, respectively. The reconstruction of the two branches is illustrated in Fig. 7. is Branch scar, is Burl, and is Small defect including bud cluster, sphaeroblast, and picot 1 3.
Processing time The running time was recorded by the "time" program provided in the GNU Linux operation system. The experiments were carried out on a laptop running GNU Linux with a 4th generation 2. 7 GHz Intel Core i7 processor, 32 GB of RAM, and a Quadro K610M graphics card. Figure  11  shows the running time of the program for 6 trunks and different settings of the main parameters, which impact the running time of the different algorithmic steps. They the number of points (vertices) in the meshes, the voxel size in the centerline computation, and the patch size in segmentation. If other parameters were fixed, the general trend is that the execution time increased when the number of points increased (Fig. 11a ). However, the factors that had the strongest impact on execution time were the voxel size and the patch size of the segmentation algorithm.
These results need to be improved through the different possibilities already mentioned. Furthermore, the two methods mentioned are based on the evaluation of external defects, the correctness of the assessment needs thus to be validated by a measurement of knot inside the wood, and the coupling between the outside and internal characteristics of the defect can be improved by building a learning database for classification making use of internal information coming from X-ray CT scanning. An important point in grading log quality is the identification between a sound knot and an unsound knot. To our knowledge, there was no research about this aspect of using TLS. For a completed automatic procedure of grading log quality using TLS, a study of the feasibility of the identification of these two-knot types is still necessary.
This is the function of the Random Forests classification step to re-examine all detected singularities and to decide, which is the most probable class 1 3 of defects it corresponds to. The classification was rather successful and failed mainly for small defects but as a supervised method it is highly dependent on the quality and quantity of data in the learning database, and some improvement of the current database could be looked by structuring it not only through the biological definition of the defects, but also through size criteria, and by additional data. Then, the last step looking for characterizing each defect according to its type by relevant dimensional characteristics gave encouraging results. Nevertheless, for some flat defects like old branch scars, some inconvenience is due to the definition of the defect borders coming from their different perceptions by the algorithm or by a human operator.
Finally, we use measurements of yaw misalignment and wind speed from the nacelle lidar to determine the relationship between yaw misalignment and power production as a function of wind speed. The rest of the paper is organized as follows. Section 2 provides an overview of the field experiment, including the wind plant and turbine specifications, instrumentation, control strategy, and wind resource information. The FLORIS wind farm control model and the wind direction variability model used to predict wake-steering performance are presented in Sect. 3. Data processing steps performed before analyzing wake-steering performance are described in Sect. 4. Section 5 compares the expected and measured yaw offsets during the experiment as a function of wind direction and wind speed. Suggested corrections to the wind vane measurements, estimated using nacelle lidar measurements, are described in Sect.
Instrumentation Some additional instrumentation was set up on the wind plant for the wake-steering experiment. First, all turbines were equipped with a supervisory control and data acquisition (SCADA) system allowing 1 Hz data for the most critical variables to be recorded. A Vaisala Triton sodar was installed in the proximity of turbines SMV5 and SMV6, and a Leosphere WindCube v1 profiling lidar was installed between turbines SMV2 and SMV3. Their precise locations are presented in Fig. 1. Although data from the sodar and profiling lidar were not used extensively during the analysis, they were used to cross-check and validate wind measurements from the turbines and to identify the best references for assessing the ambient wind conditions. Additionally, measurements from the WindCube lidar were used to estimate the turbulence intensity distribution at the site.
Compared to the long-term wind speed distribution illustrated in Fig. 2 , the wind speed histogram provided in Fig. 7b  indicates above-average wind speeds during the experiment period; however, higher wind speeds are expected because most of the data were collected during the winter (in February and early March, as illustrated in Fig. 4 ), when the wind resource is strong at the site. Figure  7c  shows that similar turbulence intensity distributions were sampled during the baseline and the controlled periods. Last, the joint distribution of wind direction and wind speed during the experiment period is shown in Fig. 8 , considering the total amount of data analyzed for the baseline and the controlled periods. For almost the entire wind direction sector, data were collected for wind speeds from 3-13 m/s.
g. , from a cup or sonic anemometer) because of volume averaging and line-of-sight measurement limitations  (Kelberlau and Mann, 2020)  and (2) further work is required to validate the relationship between turbulence intensity and wake deficits  (Niayifar and Porté-Agel, 2015)  that is used in the GCH model. Last, the power loss suffered as a result of yaw misalignment is modeled in FLORIS by scaling the rotor-averaged wind speed, v avg , used to determine power and thrust as follows: v avg = v avg cos (γ ) p v /3 , ( 1 ) where γ is the yaw misalignment, following the approach suggested by  Bossanyi (2019). We use a cosine exponent of p v = 1. 61, estimated using WindCube ground-based lidar measurements and power data for SMV6 from previous experiments at the wind plant  (Duc et al. , 2017).
Energy improvement from wake steering To assess the impact of wake steering on the performance of the test turbines SMV5 and SMV6, we measure the change in energy production between the baseline and the wakesteering control periods as a function of wind direction. Specifically, after dividing the measurement data into 2 • wide wind direction bins, we quantify the ratio between the energy produced by the test turbines and the reference turbines for the baseline and the wake-steering control periods using the balanced energy ratio method introduced by Fleming et al. (  2019 ). For each wind direction bin, the energy ratio is calculated as R Energy = N WS i=1 w i P Test,i N WS i=1 w i P Ref,i , (4) where P Test,i and P Ref,i are the mean test and reference powers, respectively, in wind speed bin i; the weighting factor w i is defined as the total number of samples in wind speed bin i for the baseline and the controlled periods combined; and N WS indicates the number of 1 m/s wide wind speed bins used in the calculation.
21 , outweigh the relatively high losses at SMV6, as illustrated in Fig. 22. Note that the individual changes in energy ratio from wake steering for SMV5 and SMV6 closely match the FLORIS-based predictions using the expected yaw offsets for this wind speed bin as well. For the 10-12 m/s wind speed bin, the energy gains at SMV5 are slightly greater than the improvements predicted using FLORIS with all three yaw offset calculation methods, yet very little change in energy is observed at SMV6 from yaw misalignment. Consequently, a greater-than-predicted net energy gain is observed for the wind turbine pair. Last, the performance of wake steering for the 12-14 m/s wind speed bin is unclear based on the data collected. achieved for wind directions between 213-221 •. But these gains appear to be largely canceled by losses for wind directions from 201-211 • , which could be caused by wrongway steering.
Analysis of the polymer models. Comparison with Hi-C data. Three Hi-C datasets for A. thaliana Col-0 seedlings were downloaded from the sequence read archive (SRA) ( Supplementary Table  S2  ) using fastq-dump (https://github. com/ncbi/sra-tools/wiki). Each experiment was processed through the TADbit pipeline (66) (https://github. com/3DGenomes/tadbit). Briefly, the pipeline consists of (i) Checking the quality of the FASTQ files; (ii) Mapping of the paired-end reads to the A. thaliana reference genome (release TAIR10 ftp://ftp. ensemblgenomes. org/pub/release-40/plants/fasta/arabidopsis_thaliana/dna/) using GEM  (67)  taking into account the DpnII restriction enzyme cut-site using fragment-based mapping (66) ; (iii) Filtering to remove non-informative reads using the following (default) TADbit filters: self-circle, dangling-ends, error, extra-dangling-ends, duplicated and random-breaks (66) ; (iv) Merging of the datasets into a single one for the restriction enzyme DpnII; (v) Normalization of the merged datasets using the OneD (68) method at 3 kbp and 30 kbp resolution.
Before merging the datasets (point (iv)), their mutual consistency was verified using the reproducibility score (R-score)  (69). The obtained R-score values ranged between 0. 56 and 0. 84 indicating consistency between the merged datasets  (69). For each simulated configuration, contacts were computed using a cut-off distance of 200 nm which characterized roughly the spatial resolution of Hi-C experiments  (70, 71). From these contacts, we then built contact maps for the single-chromosome ( Figure  2M -R ; Supplementary Figure  S3  ) and genome-wide ( Figure  3D  , 3I , and 3N ; Figure  5I-J  ; Figure  6B-F  ; Supplementary Figure  S6G-H  ; Supplementary Figure  S9B-D  ) cases. In the latter, we also estimated the average contact probability P(s) by averaging the predicted contact frequency over all the pairs of loci at the same genomic distance s ( Figure  3E  , 3J , and 3O ).
Interestingly, our optimal pulling protocol resembles to some extent the large-scale dynamics during cell division in A. thaliana when the chromosomes of each daughter cell, after the first transient dynamics, move in parallel, centromere first, towards opposite poles of the mother cell  (52, 79). We also wish to point out that we gathered this insight by analysing the P(s) from the Hi-C datasets. This fact is a clear example that the Hi-C contact patterns, once they are properly interpreted, can inform on the large scale chromosomes organization and that 3D modelling approaches offer a viable bridge to contextualize and quantitatively reconcile imaging and Hi-C results  (70, 93). It is likely that this partial memory of the initial V-shape applies to other species with (sub)metacentric chromosomes like humans and flies where P(s) also exhibits an increase at genomic scales corresponding to inter-arm contacts  (17, 94, 95).
FUNDING This research was partially funded by the  European Union 's  H2020 Framework Programme  through the  ERC  [grant agreement  609989  to MAM-R]. We also acknowledge the support of  Spanish Ministry of Science and Innovation  through  BFU2017-85926-P  to MAM-R CRG thanks the support of the  Spanish Ministry of Science and Innovation  to the EMBL partnership, the  'Centro de Excelencia Severo Ochoa   2013-2017' [SEV-2012-0208 ], the  CERCA Programme/Generalitat de Catalunya ,  Spanish Ministry of Science and Innovation  through the  Instituto de Salud Carlos III , the  Generalitat de Catalunya through Departament de Salut and Departament d'Empresa i Coneixement  and the Co-financing by the  Spanish Ministry of Science and Innovation  with funds from the  European Regional Development Fund (ERDF)  corresponding to the 2014-2020 Smart Growth Operating Program.
DATA AVAILABILITY The Hi-C datasets analysed during the current study are available in the Sequence Read Archive (SRA) repository with accession numbers SRR1029605 from Ref. (44)  , SRR2626429, SRR2626163 from Ref. (45). Epigenomic data were extracted from the Supplementary Table  4  of Ref. (44). All LAMMPS, Bash, R and Python in-house scripts for simulations and data analysis used for this study will be available upon publication.
The diagnosis of TS was based on karyotype. This PREDICT study was conducted in compliance with ethical principles based on the Declaration of Helsinki, the International Conference on Harmonization Tripartite Guideline for Good Clinical Practice and all applicable regulatory requirements. The PREDICT (NCT00256126) and PREDICT Long-term Follow-up (NCT00699855) studies were approved by the Scotland Medical Research and Ethics Committee (reference 05/MRE10/61) and the North West Research Ethics Committee (reference 08/H1010/77), respectively. Informed consent was obtained from parents for all study participants.