dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements. We acknowledge  Martine Couapel  for her help with the field experiments. We thank  Nicholas Devert  and  Sabrina Dubois  for performing leaf water extractions at  ISPA, Bordeaux, France. We thank  Michael Staubwasser ,  Daniel Herwartz , and  Mohammed El-Shenawy  for permission and support during leaf water triple oxygen and hydrogen isotope analyses in the Stable Iso-tope Laboratory at the  University of Cologne, Germany. This study was conducted in the framework of the  HUMI-17 project supported by the ANR  ( ANR-17-CE01-0002-01 ), CNRS FR3098 ECCOREV, LABEX OT-Med, and  OSU-Pytheas. It benefited from the  CNRS  resources allocated to the  French ECOTRONS Research Infrastructure , from the  Occitanie Region  and  FEDER  investments as well as from the state allocation " Investissement d'Avenir" AnaEE-France   ANR-11-INBS-0001.
We also thank the editor ( Cindy De Jonge ),  Marco Lehmann , and one anonymous reviewer for their useful comments, which contributed to improving the manuscript. Financial support. This research has been supported by the  ANR  ( ANR-17-CE01-0002-01 ) and benefited from  AnaEE-France   ANR-11-INBS-0001. The O 3 HP platform benefited from the state allocation " Investissement d'Avenir" AnaEE-France   ANR-11-INBS-0001  as well as financial and human resource support from the  IMBE  ( UMR7263   CNRS ,  AMU ,  IRD, UA) ,  OHP  ( UAR3470   CNRS ), and  ECCOREV  ( FR3098 CNRS ). Review statement. This paper was edited by  Cindy De Jonge  and reviewed by  Marco Lehmann  and one anonymous referee.
Data availability. Data are available within the article or its Supplement. Additional data (e. g. , LI-COR measurement data) will be made available on request. Meteorological data can be accessed from the COOPERATE database  (Reiter et al. , 2015). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/bg-20-2161-2023-supplement.
, 2004; Enquist, Allen, et al. , 2007; Savage et al. , 2004). The theoretical expectation of MST assumes that natural selection has universally tended to optimise vascular networks to maximally supply resources to the body but yet, at the same time, minimise the energy required for resource distribution  (West et al. , 1997 (West et al. , , 1999b)). However, tests of MST focused primarily on the interspecific scale, comparing a wide range of highly contrasting species sizes. While this body of work has revealed the global patterns of covariation that generally confirm the predictions of MST  (Enquist, Allen, et al. , 2007; Enquist, Kerkhoff, et al. , 2007; Niklas & Enquist, 2001) , large interspecific datasets are often limited to examine subtle variation in allometric relationships  (Coomes, 2006; Enquist & Bentley, 2012; Killen et al.
Each pot was automatically adjusted to reach the target soil water content by weighing and watering once a day. PPFD was maintained at 190 μmol m -2 s -1 and VPD air was maintained at 0. 7-0. 8 kPa. All detailed meteorological data, including daily soil water content, air temperature and VPD air , are available in the PHENOPSIS database  (Fabre et al. , 2011)  (http://bioweb. supag ro. inra. fr/pheno psis/).
The projected leaf area of the rosette (RA, mm 2 ) was determined every 2-3 days with ImageJ. A sigmoid curve was fitted for each plant using the four-parameter logistic model: where d is the number of days after the emergence of the first two true leaves, a is the maximum vegetative rosette area, d 0 is the time when a/2 leaf area has expanded and b is related to the maximum rate of leaf production. The maximum rate of rosette expansion (R max , mm 2 day -1 ) was calculated from the first derivative of the logistic model at d 0 as R max = a/(4b). We then calculated maximum absolute growth rate (g dry mass daya -1 ) as the product of R max and LMA  (Vasseur et al. , 2012 (Vasseur et al. , , 2014)). Phenotypic data, including rosette pictures, are stored in the PHENOPSIS database (http://bioweb.
ACK N O WLE D G E M ENTS We thank  Myriam Dauzat  for her technical assistance during trait measurement and environmental control of the PHENOPSIS platform. This work was supported by  INRAE ,  CNRS , the  French Agency for Research (ANR  grant  ANR-17-CE02-0018-01 , 'AraBreed' to FV, DV and CV),  Région Occitanie  ( FEDER FSE IEJ 2014-2020 ; Project  PHENOPSIS 2. 0 ) and the  European Research Council (ERC)  (' CONSTRAINTS ': grant  ERC-StG-2014-639706-CONSTRAINTS  to CV), 'PHENOVIGOUR': grant ( ERC-StG-2020-949 , 843  to FV). FV was initially funded by a  PhD   CIFRE grant  ( ANRT ,  French Ministry of Research)  supported by  BAYER Crop Science  (contract  0398/2009-09 42 008 ).
Funding information Grant/Award Number:  ANR-17-CE02-0018-01 ;  Association Nationale de la Recherche et de la Technologie , Grant/ Award Number:  0398/2009-09 42 008 ;  H2020   European Research Council , Grant/Award Number:  ERC-StG-2014-639706-CONSTRAINTS  and  ERC-StG -2020-949843-PHENOVIGOUR;  Région Occitanie Pyrénées-Méditerranée , Grant/ Award Number: FEDER FSE IEJ 2014-2020; Project  PHENOPSIS 2.
In contrast, light scattering devices determine the concentration of PM by directing the air stream to be sampled into a light scattering chamber. A beam of light is then directed onto the particles in the air, and a sensor measures the amount of light scattered by the particles  [8]. The device uses a relationship between the scattered light and the mass concentration of the particles, which is usually pre-set at the factory using a standard type of dust  [9]. Light scattering devices are portable, easy to use, and provide real-time data at a relatively low cost. The aim of the present study was to investigate the validity of existing and new methods of measuring dust levels, which are feasible for use during veterinary inspections of animal welfare. As a reference method, the dust level was collected by the use of a gravimetric measuring device.
Methodology In each barn, the dust level was assessed using six methods (explained in detail in the following sections): light scattering measurement, dust sheet test with a duration of 1 h, dust sheet test with a duration of 2-3 h, tape test, visibility assessment, and deposition assessment. These were compared to a reference method, i. e. , gravimetric measurement. One observer collected all the data during the barn visits. The spots in the barns, where the different types of assessments took place, are indicated in Figure  1.
Statistical Analysis Data were analysed using R Studio Software  [10] , and a significance level of 0. 05 was used for all statistical tests. Gravimetric measurements (i. e. , the reference method) were obtained from only nine of the 11 farm visits due to maloperation of aparatus. In addition, due to restrictions posed as a consequence of COVID-19 and some unexpected events during the visits, some data from methods examined were not obtained. Therefore, the number of pairs between the reference method and each method was: light scattering measurement (n = 7), dust sheet test 1 h (n = 9), dust sheet test 2-3 h (n = 9), tape test (n = 9), visibility assessment (n = 8) and deposition assessment (n = 8). Prior to analysis, the data from the reference method were log-transformed to obtain data normality.
Six methods, considered relatively feasible for dust assessment during veterinary inspections, were examined in the present study for their validity. Although the number of farm visits was reduced compared to the original plan due to the COVID-19 health crisis and an avian influenza outbreak, the study revealed that the methods differed in potential. The most promising method was the dust sheet test, which is simple to perform and requires limited training, preparation, and investment as the only thing needed is pieces of black paper that can be discarded after each inspection. Based on the scattering of the data points closely around the regression line and good performance of all the statistical measures applied, i. e. , significant p-value of the slope, high adjusted R 2, and low RMSE, the dust sheet test with a test duration of 2-3 h was shown to be a valid method of assessing dust levels in poultry barns.
At the end of the experiment, we aseptically sampled each fruit area, crushed it with a sterile pestle, and plated (serially diluted) on appropriate solid growth media as described above. For each Drosophila species, 27 experimental units were created, i. e. , one unit for every possible permutation among the microbial communities associated with the three adults. We measured the male presence on the delimited fruit area for each male on each fruit experimental unit to increase the data resolution (four observations per experimental unit). We found that males can deposit cells of every microbial species studied on the fruit area around the peduncle insertion (Supplemental information). Controls without flies allowed to confirm the absence of cultivable microbial contaminants in our system.
ACKNOWLEDGMENTS We are grateful to  Laure Benoit ,  Marie-Pierre Chapuis ,  Candice Deschamps , and  Romain Gallet  for their help for the molecular identification of the microbial isolates used in this study and during the preliminary experiments, to Francois Leulier for providing the Lactobacillus plantarum strain, and to the  SEPA  technical platform of the  CBGP laboratory  for hosting all experiments presented in this study. We would like to thank  Paul Becher  for constructive criticism of the manuscript and  Lauren Gillespie  for proofreading the manuscript. This research was supported by  French National Research Agency  through the '' SWING '' project [ ANR-16-CE02-0015 ] and by  Agropolis Fondation  under the reference ID 1505-002 through the '' Investissements d'avenir'' program [ Labex Agro :  ANR-10-LABX-0001-01 ].
Acknowledgments We acknowledge the  United States DoD DTRA Cooperative Threat Reduction Program 's support of project  HDTRA1-17-1-0060 ,  Strengthening Global Veterinary Epidemiology Capabilities and Rapid Regional Disease Surveillance Information-Sharing. The authors are grateful to the experts attending the two workshops organized by the  FAO  for sharing their knowledge, experience and providing valuable inputs for developing the set of core competencies for field veterinary epidemiology.
Data Collection for Physics and Chemistry of Soil Sampling was conducted in spring 2020 based on seven 20 × 20 m (400 m 2 ) plots for each of the three treatments (OF, TF, and Con), randomly established (in total, 21 plots). Four 1. 5 × 1. 5 m (2. 25 m 2 ) quadrats were established in each plot to assess the vascular herbaceous plant community (grass and forbs), with 84 quadrats in total. Three soil samples per plot were randomly collected at 0-20 cm depth. The three soil samples were mixed together to obtain two sub-samples for soil analyses and one for the arbuscular mycorrhizal fungi (AMF) analysis; one for soil analyses was stored at +4 °C in the refrigerator before measuring microbial and biochemical properties (microbial biomass, basal respiration and urease enzyme), and the second was air-dried and used for the evaluation of physical and chemical properties, such as texture (clay, silts, sands), bulk density, pH, electrical conductivity (EC, proxy for salinity), macro-nutrients (N and P), organic matter, and total neutralizing value (TNV).
Data Collection for Physics and Chemistry of Soil Sampling was conducted in spring 2020 based on seven 20 × 20 m (400 m 2 ) plots for each of the three treatments (OF, TF, and Con), randomly established (in total, 21 plots). Four 1. 5 × 1. 5 m (2. 25 m 2 ) quadrats were established in each plot to assess the vascular herbaceous plant community (grass and forbs), with 84 quadrats in total. Three soil samples per plot were randomly collected at 0-20 cm depth. The three soil samples were mixed together to obtain two sub-samples for soil analyses and one for the arbuscular mycorrhizal fungi (AMF) analysis; one for soil analyses was stored at +4 • C in the refrigerator before measuring microbial and biochemical properties (microbial biomass, basal respiration and urease enzyme), and the second was air-dried and used for the evaluation of physical and chemical properties, such as texture (clay, silts, sands), bulk density, pH, electrical conductivity (EC, proxy for salinity), macro-nutrients (N and P), organic matter, and total neutralizing value (TNV).
Soil Properties and Biodiversity under Different Fire Treatments A holistic analysis of soil and community parameters was conducted thanks to a principal component analysis (PCA). The PCA outputs showed that around 65% of the overall variance was explained by axes 1 and 2 together (46 and 19%, respectively), underscoring that the whole variance of the dataset was well captured by only two axes (Figure  4 , Table  S1 ). We observed a clear distinction between the control area and the two-fire treatment (TF) along axis 1 (PC1), which was driven by soil properties including SR (r = 0. 94, p < 0. 001), N (r = 0. 94, p < 0. 001), urease (r = 0. 93, p < 0. 001), SIR (r = 0. 91, p < 0. 001) and MBC (r = 0. 89, p < 0. 001; Table  S2 ), which is a soil functionality gradient. PC2, which is a biodiversity gradient, is driven by and positively correlated to plant diversity indices, including Shannon (r = 0.
Hybridizations between Musa species and subspecies, enabled by their transport via human migration, were proposed to have played an important role in banana domestication. We exploited sequencing data of 226 Musaceae accessions, including wild and cultivated accessions, to characterize the inter(sub)specific hybridization pattern that gave rise to cultivated bananas. We identified 11 genetic pools that contributed to cultivars, including two contributors of unknown origin. Informative alleles for each of these genetic pools were pinpointed and used to obtain genome ancestry mosaics of accessions. Diploid and triploid cultivars had genome mosaics involving three up to possibly seven contributors. The simplest mosaics were found for some diploid cultivars from New Guinea, combining three contributors, i.
g. , AA, AAA, AB, AAB, ABB;  Baurens et al. , 2019 , Cenci et al. , 2021 , Simmonds & Shepherd, 1955). A third species, Musa schizocarpa (S genome, 2n = 22) from New Guinea, was proposed to be involved in a few minor cultivars (AS genome) and in East African Highland Bananas  (Carreel et al. , 2002; Nemeckova et al. , 2018). Fe'i cultivars found in New Guinea and Polynesia and some rare interspecific cultivars are derived from species from the first clade (former Australimusa section, T genome, 2n = 20; De  Langhe et al. , 2009). The species M. acuminata has diverged into several subspecies, whose numbers and classification status vary throughout the literature and databases  (Häkkinen & Väre, 2008; Perrier et al. , 2009; Shepherd, 1990; Simmonds, 1962). Different molecular marker analyses have shown the involvement of the subspecies M.
Identifying the ancestral genetic groups that contribute to cultivars and their informative alleles We generated DNA sequencing data from 150 Musaceae accessions and 12 F1 diploid hybrids. High-coverage genome sequencing data of 25 banana accessions were retrieved from the National Center for Biotechnology Information (NCBI) to complement this dataset (Table  S1 ). The diversity sample of 175 accessions (excluding F1 individuals) included 63 representatives of wild Musa species and subspecies (i. e. , seedy bananas), 62 diploid and 49 triploid or tetraploid cultivars (i. e. , parthenocarpic and seedless) representing different areas of origin or economically important subgroups, and one outgroup species (Ensete ventricosum). A total of 42 011 682 biallelic SNP variant sites were obtained after read mapping on the M.
The M. acuminata ssp. truncata accession usually clustered at the base of the group comprised of M. acuminata ssp. banksii, M. acuminata ssp. microcarpa ('Borneo'), and M. acuminata ssp. errans (Figure  4 ; Figure  S6 ). For one phylogeny on chromosome 10, it clustered at the base of the group containing M. schizocarpa accessions and the M_1 haplotype. We extended our search for M_1 and M_2 wild representatives by using low-coverage genome skimming sequencing data for 39 Musa species or subspecies available in the NCBI database (Table  S1 ). The analysis of profiles of normalized ancestry informative allele ratios obtained for the 39 accessions suggested that M_1 corresponded to M. acuminata ssp. halabanensis (Figure  S7 ). No wild representative for M_2 was identified.
However, the origins of a few chromosomal segments could not be determined, particularly in accessions of a dominant malaccensis, zebrina, or M_2 origin. Some might originate from more divergent forms of these contributors, while others could correspond to M_2 regions not represented in the sample used to determine M_2 alleles. Phylogenetic analysis suggested a close relationship between M_1 and two geographically distant groups M. schizocarpa (PNG) and M. acuminata ssp. sumatrana (Sumatra, Indonesia). The analysis of low-coverage sequencing samples from public databases suggested that M_1 corresponded to M. acuminata ssp. halabanensis. This wild banana was described in Sumatra  (Hotta, 1989; Meijer, 1961; Nasution, 1989)  and was accepted as an M. acuminata subspecies  (Häkkinen & Väre, 2008).
aspx), except for one accession for which DNA was processed with the Dovetail™ Hi-C protocol and sequenced using HiSeqX at Dovetail Genomics (https://dovetailgenomics. com/). For 64 additional accessions, whole genome sequencing data were collected from the NCBI public database (Table  S1 ), including 39 accessions with low sequencing coverage that we only used to search for wild representatives of ancestries of unknown origins (Table  S1 ). Reads were quality-filtered and adapters were removed using Cutadapt  (Martin, 2011). Quality filtering and adapter removal were already performed for reads provided by Genoscope.
Detection of aneuploidy To detect large Indels or potential aneuploidy, the SNP coverage was plotted for each position in the vcf file along chromosomes of M. acuminata reference sequence V4  (Belser et al. , 2021)  using vcf2allPropAndCov. py and vcf2allPropAndCovByChr. py from the vcf toolbox (https://github. com/SouthGreenPlatform/VcfHunter) (Baurens et al. , 2019). The size of large regions showing dosage change was estimated visually based on the graphical representation. The most common event was a 4-Mb duplication in the pericentromeric region of chromosome 8 that was shared by 23 accessions and considered as a single event (Table  S2 ). Thus, a total of 21 deletions (estimated size range 0. 1-25 Mb, average 8. 3 Mb) and 10 duplications (estimated size range 0. 1-4 Mb, average 3. 8 Mb) were identified.
py. This added a set of private alleles for M. acuminata ssp. sumatrana, M. acuminata ssp. truncata, Australimusa, and Ensete as outgroup. For the M. acuminata subspecies of step one and M. schizocarpa, the alleles attributed using the CoA approach were kept because they introgress each other and thus a private allele approach is not appropriate. The M. balbisiana alleles that were kept, were those of the private allele strategy that allowed eliminating basal alleles shared with Australimusa and Ensete. Normalized ancestral allele ratios were calculated for each private allele of the 10 resulting genetic groups in the dataset of 187 individuals and a mean value was calculated on sliding windows of 5601 SNPs (allele_ratio_per_acc. py and PaintArp. py). The mean normalized ratios along chromosomes were drawn to visualize local ancestry profiles (plot_allele_normalized_mean_ra-tio_per_acc.
py). In the fourth step, iterative approaches were used to select private alleles of the M_1 and then the M_2 unknown contributors (Figure  S2 ; Method S4). Phased data (haplotypes) used in this process were obtained from parent-child trios with PhaseInVcf. py. Globally, iterations were a three-stage process (Figure  S2 ): (i) identification and selection of accessions/haplotypes with large regions of unknown ancestry, (ii) determination of private alleles for M_1 or M_2 from selected accessions/haplotypes using all defined ancestral genetic groups, (iii) the arbitrary attribution or calculation of a level of allele fixation for the M_1 or M_2 set of alleles, (iv) the calculation of normalized allele ratio profiles on the complete dataset with all defined ancestral groups, and (v) drawing of ancestry plots to identify other accessions or parent-child trios that would allow improvement of the M_1 or M_2 private allele set.
Chromosome painting Identified ancestry informative alleles were used to analyze ancestral contributions along chromosomes of the 226 accessions. For this, representative accessions of ancestral groups have been used to infer, for each position j corresponding to an informative allele, the observed proportion A ijk of reads supporting an allele n ΣA ijk ). This was performed using the allele_ra-tio_group. py script added to vcfhunter toolbox. The P jk value was arbitrarily set to 0. 47 or 0. 66 for the M_1 origin in regions in which three or two accessions were used to identify specific alleles of this origin (Method S4 for detailed calculation). The P jk value was arbitrarily set to 0. 8 for M_2 origins. The observed proportion of reads (O ijk , similar to A ijk but for accessions to be characterized) was then calculated using the script allele_ratio_per_acc.
Breakpoint determination Recombination/introgression breakpoints, corresponding to regions along chromosomes where there is a switch between two distinct ancestral origins, were determined by visual inspection of graphic representations depicting, for each position with an assigned ancestral origin, the proportion of reads supporting this origin in the studied accession. This was done using the DrawRatioDetailInterractive. py script with data generated during the chromosome painting step. A graph was generated for each origin along each chromosome of the accession. These graphs showed a succession of clusters of colored dots corresponding to each origin, the ends of cluster blocks thus locating the position of recombination regions (Figure  S4 ). By clicking on the first or the last point of the introgressed block, we obtained breakpoint coordinates which were then compared between accessions.
2. 0. py (Table  S4 ). (iv) Phylogenetic analysis using PHYML v3. 1  (Guindon et al. , 2010)  with the GTR + Γ model (-m GTR -b -5 -v e -c 4 -a e -s BEST options) and tree visualization using FigTree v1. 4. 4 (http://tree. bio. ed. ac. uk/ software/figtree/) with trees manually re-rooted with E. ventricosum and colored using ReformatTree. py. Maximum likelihood phylogenetic analysis was performed based on chromosomal regions of 52 to 55 accessions from which representative consensus or haplotypes were reconstructed from concatenated polymorphous sites. Regions of 2. 5 to 15. 0 Mb were used, with a total of 87 827 to 498 129 sites and between 72 785 and 410 907 polymorphic sites (Table  S4 ). SM1. Accessions used to select private alleles in order to identify accessions for the multivariate approach.
ACKNOWLEDGMENTS This work was supported by the  France G énomique  ( ANR-10-INBS-09-08 ) project  DYNAMO , the  Centre de coop ération Internationale en Recherche Agronomique pour le D éveloppement (CIRAD) , and the  Agropolis Fondation  ( ID 1504-006 ) ' GenomeHarvest ' project through the  French Investissements d'Avenir program  ( Labex Agro :  ANR-10-LABX-0001-01 ). The authors wish to thank  Francoise Carreel  and  Xavier Perrier  for critical reading of the manuscript,  Céline Cardi  for technical assistance,  Xavier Perrier  for providing two DNA samples, and  Christophe Jenny  for discussions on different banana accessions.