dataesr/scientific-paragraphs-categorization huggingface dataset:
Fitting of model to data We used the available public database to collect our data while the formulated model of Equation (1) includes 16 parameters. To treat the waggliness of the reported daily new cases, we smoothed the data to remove noise from the data set so as to make it suitable for our analysis. The total population of the United Kingdom is 68,530,739 (1), which was used for calculating the initial number of susceptible humans, while the initial value for the number of infected humans was calculated from the reported daily new cases. Other initial values were assumed. The link to the data used for this research and the initial values; S h (t) = 68530739; E h (t) = 0; I h (t) = 31412; Q h (t) = 0; S r (t) = 1074103; E r (t) = 1074103; and I r (t) = 1074103; can be found in the Data Availability section.
2 ). Fifteen single nucleotide polymorphisms (SNPs) were detected that significantly correlated with the stained area (%) at 72 hours after TZ staining (Supplementary Table  2 ). Among them, 10 SNPs were located in the promoter region of ARR16 (AT2G40670) on chromosome #2 (Fig. 1e ). A similar peak of SNPs was observed in the dataset at 48 hours after TZ staining (data not shown). Seeds from two loss-of-function mutation lines arr16 #1 and arr16 #2 were significantly less permeable than those of WT when subjected to TZ staining (Fig. 1f, g ). Similar phenotypes were observed in two T-DNA insertion lines having knockdown effects on ARR16 expression during seed development (Supplementary Fig. 3 ), indicating that ARR16 is a causal gene in our GWAS and negatively regulates seed coat permeability.
Expression profile data accessible in a public microarray database indicate that transcript levels for ARR16 are highest during late seed coat development and are also present in the developing endosperm (Supplementary Fig. 4a ). We confirmed that the transcript is most abundant at the mature green phase of seed development (about 15 days after flowering (DAP)) in developing seeds by RT-qPCR analysis (Supplementary Fig. 4b ). Moreover, F1 seeds from reciprocal crosses between WT and arr16 confirmed that the genotype of the maternal seed coat tissue defined the permeability phenotype (Supplementary Fig. 5 ). These results demonstrate that ARR16 expression modulates seed coat permeability, probably during the development of the seed coat. Additionally, seed size and weight were higher in arr16 compared to WT (Fig.
At 7 days, seed survival rates were scored based on the opening of healthy, green cotyledons following radicle emergence, since CDT often inhibits seedling establishment without affecting radicle emergence. The time for survival rate to decrease to 50% (P50) was estimated by the package viabilitymetrics 34 (version 0. 0. 0. 9100) in R 35 (version 3. 6. 1). The speed of cotyledon greening (T50CG) was calculated based on the parameter t50. total with FourPHFfit. bulk function in the R package germinationmetrics 36 (version 0. 1. 3. 9000).
GWAS and haplotype analysis. GWAS was performed with the GWA-Portal (https://gwas. gmi. oeaw. ac. at/)  37  in which values of the stained area (%) of seeds at 72 hours after TZ staining and AF levels in the hilum region of dry seeds for 145 natural accessions were used as phenotypic data without transformation. The 1001 full sequence data set TAIR 9 was set as genotype data, followed by association analyses using the linear regression model. Manhattan and Miami plots were generated using the R package qqman 38 (version 0. 1. 4) with SNPs having minor allele frequencies > 0. 1. The SNPs significantly associated with phenotypic data variations were detected by the Benjamini-Hochberg method (FDR < 0. 01). Linkage disequilibrium and haplotype block analysis in the ARR16 genetic region were performed using the software Haploview (version 4.
2) with the confidence intervals method  39  for 17 SNPs having minor allele frequencies > 0. Supermix Biorad kit (Bio-Rad), according to the manufacturer's protocol. The thermal cycling conditions were: 95°C for 8 min followed by 40 cycles of 10 s at 95°C for denaturation, and 10 s at 60°C for annealing and extension. The primers used for RT-qPCR were designed from the target gene sequences using Primer3 software (http://frodo. wi. mit. edu) and are listed in Supplementary Table  5. A constitutive gene AT5G60390 (EF1α) and AT4G12590 that is stably expressed in seed  42  were used as reference genes to calculate relative mRNA levels for all the samples.
Significant differences among three or more groups were evaluated by Tukey-Kramer test or Dunnett's test. All statistical tests were performed using R. Expression profile of ARR16 gene in developing seed tissues based on the microarray analysis 45 was obtained from the Arabidopsis eFP Browser 46 (https://bar. utoronto. ca/efp_arabidopsis/ cgi-bin/efpWeb. cgi). programme under the Marie Skłodowska-Curie grant agreement No [846387]. The IJPB benefits from the support of Saclay Plant Sciences-SPS (ANR-17-EUR-0007). The authors thank the Bordeaux-Metabolome Facility, supported by MetaboHUB (ANR-11-INBS-0010), where GC-based suberin analyses were performed.
Acknowledgments We thank  D. C. Bergmann  ( Stanford University ) for the gift of arr16#1 and arr16#2 seeds, and  T. Schmülling  ( Freie Universität Berlin ) for the gift of ahk2/3, ahk2/4 and ahk3/4 seeds. We are also grateful to  A. Berger ,  C. Sallé ,  A. Frey ,  A. To  and  I. Debeaujon  in our institute for their technical aid and advice. N. S. received funding from the  Japanese Society for the Promotion of Science (JSPS) Overseas Research Fellowship  and the  European Union 's  Horizon 2020 research and innovation.
NATURAL GAPS IN GENETIC DIVERSITY SEPARATE THE THREE RSSC SPECIES Average nucleotide identity (ANI) is now an accepted way to use whole-genome sequences to measure relationships between strains and propose species delineations  (Oren et al. 2023). The ANI between any pair of genomes can be calculated based on different algorithms, such as BLAST comparisons ("ANIb") or the MUMMER index ("ANIm"). We used pyani  (Pritchard et al. 2016 ), a Python-based ANIb software, to calculate pairwise ANIb values for 300 RSSC genomes, including genomes of 11 phylotype III strains and 148 phylotype I strains. When the resulting 90,000 ANIb values are hierarchically clustered and visualized as a heatmap, three obvious clusters correspond to the three accepted RSSC species (Fig. 2A ).
AN ANI THRESHOLD OF 96% IS NOT THE APPROPRIATE CUTOFF FOR DELINEATING SPECIES IN THE RSSC Depending on the taxon, bacterial species borders can be drawn using ANI threshold values of 95 to 96%  (Chun et al. 2018; Oren et al. 2023 ). However, an ANI <95% is the most widely used cutoff for dividing species. This threshold has been applied across the bacterial domain in the Genome Taxonomy Database  (Parks et al. 2020). We investigated the distributions of 90,000 ANIb comparisons among 300 RSSC genomes to determine if there is a biologically relevant cutoff that separates RSSC species. The R. nicotianae proposal applied an 96% ANI species threshold value. However, our analysis of 300 RSSC genomes suggests that 95% is the appropriate threshold for delineating species within the RSSC (Fig. 2 ).
The R. nicotianae proposal calculated ANI with three methods: FastANI using the Genome Taxonomy Database website interface, ANIb using the JSpeciesWS website interface, and MUMMER-based ANI (ANIm) using the JSpeciesWS website interface. The authors then carried out 434 FastANI comparisons (LMG9673 T and RS proposed_T vs. 204 phylotype I and 11 phylotype III strains), 24 ANIb comparisons (LMG9673 T and RS proposed_T vs. 1 phylotype I and 11 phylotype III strains), and 24 ANIm comparisons (LMG9673 T and RS proposed_T vs. 1 phylotype I and 11 phylotype III strains). Comparing RS proposed_T to the 11 phylotype III genomes yielded FastANI values from 95. 85 to 96. 06%, ANIm values from 96. 12 to 96. 26%, and ANIb values from 94. 95 to 95. 33% as described in the R. nicotianae proposal. We also computed ANIb values, but we used the Python-based pyani tool over a larger sample size of phylotype I and III genomes (Fig.
Q9 Proposing new names without careful consideration can create confusion in the research community and potentially in the published literature. For example, the widely used NCBI genome database transiently adopted the R. nicotianae proposal. Within two weeks of the publishing of the R. nicotianae proposal in Frontiers in Microbiology, we noticed that NCBI had renamed the genome of the much studied model R. pseudosolanacearum strain GMI1000 as "Ralstonia nicotianae. " This occurred before the IJSEM list editors had the opportunity to consider this proposal and issue a decision about publishing the new name. Although GMI1000 is a phylotype I R. pseudosolanacearum strain, the GMI1000 genome was still labeled in NCBI as "Ralstonia solanacearum" for historical reasons: The genome was sequenced and deposited 14 years before the RSSC was formally divided into three species  (Salanoubat et al.
Acknowledgments References Acknowledgements The authors acknowledge the financial and operational support of  INRAE divisions ACT and AgroEcoSystem  for organizing the webinar. Several studies mentioned in the review have benefitted from the  dynamics of IDEAS (Initiative for Design in Agrifood Systems , https:// www6. inrae. fr/ ideas-agrif ood). The authors deeply thank all participants of the webinar for their input, the editor and the reviewers for their constructive comments, and  Michelle  and  Michael Corson  for proofreading the English.
Amplicons were sequenced in an ABI PRISMI 3130XL (Thermo Fisher) using the forward and reverse primers of each gene. Sequences were manually edited with Geneious Pro 5. 4. 6 (Biomatters Ltd. ) , then subjected to BLAST (https://blast. ncbi. nlm. nih. gov/Blast. cgi;  Altschul et al. , 1997)  to compare with sequences of other Pristimantis. Newly generated sequences were deposited in GenBank. Accession numbers are available in Appendix 1. According to BLAST, sequences of the new species were highly similar to species currently assigned to the Pristimantis unistrigatus species group. To infer the phylogenetic relationships among the new species and its close relatives, newly generated sequences were inserted into a data set retrieved from GenBank containing selected homologous sequences (Appendix 1).
Sequence selection in GenBank was focused on specimens of the P. unistrigatus species group from the Andes, Pantepui and Amazonian lowlands. Additionally, sequences of two species of the genus Oreobates were retrieved to root the tree. In total, 265 sequences (16S = 136; COI = 70; RAG1 = 59) corresponding to 137 specimens were selected. We aligned sequences of each gene using MAFFT online server (https://mafft. cbrc. jp/alignment/server/) with default parameters, except by the use of the E-INS-i strategy for the 16S and G-INS-i strategy for protein-coding genes  (Katoh & Standley, 2013). The final matrix was concatenated in Mesquite  (Maddison & Maddison, 2021)  and composed of 137 terminals with 1,827 bp (16S = 561 pb; COI = 636 pb; RAG1 = 630 pb). Best-fit evolutionary models and partition schemes were determined through ModelFinder  (Kalyaanamoorthy et al.
Measurements were taken to the nearest 0. 01 mm using a Leica stereomicroscope (model S8APO) coupled to a Leica DFC295 camera, except for SVL, measured to the nearest 0. 01 mm with a digital caliper. Raw data are provided in Table  S1. Format of the description and terminology of morphological characters follow  Kok & Kalamandeen (2008) ,  Duellman & Lehr (2009) ,  and Kok, Means & Bossuyt (2011). Color in life was described based on photographs taken in the field, following the color catalog provided by  Köhler (2012).
Call description follows the call centered approach of  Köhler et al. (2017). Spectrogram and oscillogram were generated in R environment (R Core Team, 2019) through the 'seewave' package 2. 0. 5  (Sueur, Aubin & Simonis, 2008)  using a Hanning window, 256 points of resolution (Fast Fourier Transform) and an overlap of 85%. Bioacoustic raw data are provided in Tables  S2.
ACKNOWLEDGEMENTS We thank  Igor Y. Fernandes  and  Anthony S. Ferreira  for fieldwork assistance; to  Esteban D. Koch  for fieldwork assistance and philosophical contributions to the manuscript; to  Pilar L. Maia  for logistic support in the fieldwork at the municipality Iranduba; to  Instituto Nacional de Pesquisas da Amazônia (INPA)  for logistic assistance (specially to  Andresa Viana ) and for supporting molecular data acquisition in the  Laboratório Temático de Biologia Molecular facility ; to  Fernanda P. Werneck  ( INPA-H ),  Ariane Silva  ( INPA-H ),  Ana L. C. Prudente  (MPEG),  João F. M. Sarmento  (MPEG),  Felipe Toledo  ( FNJV ) and  Simone Dena  ( FNJV ) for access to collections; to  Laboratório de Entomologia Sistemática Urbana e Forense(LESUF-INPA ; specially to  Marcelo Cutrim ) for assistance with photos of the lateral head, hand and foot of holotype; to  Edgar Lehr ,  H.
Soil aggregate stability is an important indicator of soil condition and is directly related to soil degradation processes such as erosion and crusting. Aggregate stability is conventionally measured by testing the aggregate resistance to water disturbance mechanisms. Such measurements, however, are costly and time-consuming, which make them difficult to implement at a regional or country scale. In this study, we explore two different approaches to estimate soil aggregate stability by means of commonly-measured soil properties or mid-infrared spectroscopy measurements. The first approach relies on land use and soil properties. In the second approach aggregate stability is estimated by a model fitted with mid-infrared spectroscopic data. We tested the two approaches with a dataset composed of 202 soil samples from mainland France, in which aggregate stability was measured with a fast wetting test.
Case study and data The dataset is composed of 202 soil samples from the French national soil monitoring network (Réseau de Mesures de la Qualité des Sols, RMQS)  (Arrouays et al. , 2003)  (Fig. 1 ). The RMQS is based on a regular grid of 16 km × 16 km which leads to good spatial coverage, i. e. the sites are uniformly spread over the study area. This is profitable for PTF modelling, mapping and estimation of spatial means  (Brus and Saby, 2016). The selection of the sites of this study was purposely done to built as much as possible a representative dataset of the RMQS, and therefore of the diversity of mainland French soils. The distribution of the three land use for the 202 sites is: 129 cropland sites, 52 grassland sites and 21 woodland sites. For all 202 soil samples, we measured in the laboratory the aggregate stability and six other soil properties: texture (i.
The final cubist model is a set of rules based on the explanatory variables, which are then applied for prediction of the aggregate stability. The model was built using the cubist function from the R package Cubist (Kuhn and Quinlan, 2021) using a optimized number of committees. The number of committees is the number of cubist models that are fitted and then combined for prediction in an ensemble of cubist models. It improves stability of the models and reduces the variance of the prediction. We explored a range of committees between 0 (i. e. a single cubist model) to 30. The optimal number of committees was determined by inspection of the RMSE values. The two models were fitted to explain the aggregate stability index, MWD FW , using elementary soil properties (i. e. OC, pH water , CaCO 3 and texture) and the land use as explanatory variables.
The optimal number of PLS components is determined by a K-fold cross-validation strategy, using the root mean square error as criterion. The PLSR model was implemented using the plsr function from the R package pls  (Liland et al. , 2021). Cubist and PLSR for spectroscopic modelling were fitted with either MIR (MIR cubist and MIR PLSR ) or MIR and land use (hereafter LU) (MIR cubist+LU and MIR PLSR+LU ). Land use was added to the spectroscopic modelling because it is an easy-to-access information in field surveys. The number of components for the PLSR model was selected by visual inspection of the RMSE values, by testing between 1 to 20 components, whereas the number of committees of the cubist model was tested for values between 1 to 30. We selected a number of components and committees as small as possible, and for which larger number would show only a marginal improvement in the RMSE value.
Accuracy assessment For every model, the prediction performance was assessed based on the results of a K-fold cross-validation. The dataset was randomly split into K = 10 approximately same size folds. At each step, the soil samples of one fold were kept apart for the validation and the remaining soil samples (K -1 folds) were used as calibration dataset. The results of the K-fold predictions were collected all together and used to calculate the prediction performance statistics. Folds were defined previously to the models construction to ensure that each models use the same folds to evaluate the prediction accuracy of LB and spectroscopic models and make the statistics comparable. Models were compared using the mean error (ME), the root mean square error (RMSE), the squared Pearson's r correlation coefficient (r 2 ), and the modelling efficiency coefficient (MEC), defined with the following equations: Mean error: ME = 1 n ∑ n i=1 z i -ẑi , ( 1 ) where z i and ẑi denote the measured and predicted values of soil aggregate stability for site i, respectively, and n is the total number of measured values.
Soil characteristics Descriptive statistics of the conventional laboratory soil analyses and MWD FW measured in laboratory are summarized in Table  2. The physico-chemical properties of the soils collected in this study showed great variations. In particular, organic carbon ranged from 5. 37 to 79. 70 g⋅kg -1 with an average value of about 21 g⋅kg -1. This is similar to the values reported for the whole RMQS dataset in France (Institut National De La Recherche Agronomique, 2021, see in the Supplementary Materials, Table  1 ). However, our dataset showed a lower organic matter content than RMQS (median is 17. 00<19. 60 g⋅kg -1 , mean: 20. 79<25. 59 g⋅kg -1 ) (Table  2  and Table 1  in the Supplementary Materials). This can be explained by the over-representation of cropland in our dataset, as cropland have usually a lower organic matter content than other land uses, i.
e. grassland and woodland, median are 14. 63, 25. 60 and 26. 80 g⋅kg -1 , respectively (Table  1  and 2 in the Supplementary Materials). About half of samples in our dataset is poor in CaCO 3 (median is 0 g⋅kg -1 ) while the remaining samples show a large range of calcareous content from 0 to 866 g⋅kg -1. The texture is variable and a wide variety of texture classes are represented. A wide range of pH water is explored, from extremely acidic (4. 0) to moderately alkaline (8. 3), with an average neutral values of pH water (i. e. 6. 7). MWD FW values vary greatly over the studied area (Table  2 , Fig. 3 ). MWD FW ranged from 0. 19 to 3. 32, with a mean value of 0. 88. The five stability classes defined by Le Bissonnais were represented (Le  Bissonnais, 1996b). Most samples were from soil that were categorized under unstable to very unstable soil aggregate stability classes (46%).
, 1997; Le Bissonnais, 1996a). Further, the LB PTF in our study had a negative coefficient associated to pH water , which implies a negative impact of pH water on soil aggregate stability (i. e. the greater the pH, the lower the aggregate stability). In the literature, pH water is usually negatively correlated to aggregate stability  (Amezketa, 1999; Le Bissonnais and Arrouays, 1997). The accuracy statistics and preliminary results did not emphasize the need to use CaCO 3 , and we thus did not proceed any further. The negligible difference in results when adding CaCO 3 to the set of common soil property is also found the literature  (Erktan et al. , 2016b; Gomez et al. , 2013; Le Bissonnais et al. , 2007). Theses results somehow contrasts with the literature on aggregate stability mechanisms  (Amezketa, 1999; Le Bissonnais, 1988) , but can be attributed to the low CaCO 3 content is our dataset (median of CaCO 3 is 1.
These wavenumbers usually refer to SOC  (Fernández-Getino et al. , 2010)  and iron oxydes  (McDevitt and Baun, 1964). The differences between our models and the literature may be related to the large diversity of soils in our dataset. Indeed, the work of Afriyie et al. (  2020   et al. (2022). In  Gomez et al. (2013)  and  Annabi et al. (2017)  the main focus was on pedological or lithological classes and land use types were not listed. In our case, both approaches highlighted the importance of land use for the prediction of aggregate stability. Land use is known to influence soil characteristics, such as organic carbon  (Chenu et al. , 2000; Le Bissonnais et al. , 2007) , which stress the indirect impact of soil management on aggregate stability (through the addition of carbon to the soil or the changes in SOC dynamics).
With the LB MLR approach it was found that woodland had the highest aggregate stability values, whereas cropland had the lowest values. We stress that assessment of interactions between predictor variables was not included in the LB PTF approach using MLR but was indirectly taken into account in the cubist model. By fitting and inspecting the rules produced by a cubist model with 1 committee (not shown), we observed that the nodes of the tree separated the dataset according to land uses into two separate groups (croplands and grassland/woodland). However, as our dataset is unbalanced with a large number of observations for croplands and few data points in woodland areas, these interactions did not produce accurate predictions. Thus, we did not pursue any further with the assessment of interactions between variables but this might be a valuable contribution in future studies if the dataset size is sufficiently large and balanced between land uses.
Inference strategies to estimate aggregate stability According to the validation statistics, both approaches had similar level of reliability to estimate aggregate stability. The LB PTF is an easily understandable approach and relies on easy-to-obtain soil properties. Aggregate stability can be estimated using the LB approach based on MLR, using an existing legacy dataset and the estimated regression coefficients reported in this study (Eq. 5). The MIR approach also appears to be reliable. MIR scans of the soil are easier, faster, cheaper to obtain (5-10 € per soil sample), when compared to estimating soil properties (40-50 € per sample for texture, SOC, pH water and CaCO 3 ) or aggregate stability measurement (70-80 € per sample). However the investment for the acquisition of the spectrometer is significant (about 30-80 k€) although it is not unreasonable when compared to other common laboratory equipment used for the analysis of soil texture, SOC or CaCO 3.
Our dataset allowed us to study the possibility of predicting aggregate stability at a country scale with a wide diversity of soil and land uses. However, to study a wide diversity of soil the dataset was built from samples collected from two different sampling campaigns. These two campaigns were separated by about 10 years. Even if we favour measures with no or the least time difference, some sites were composites of these two campaigns. Nevertheless, to build our models, especially for the approach based on pedotransfer functions, we used perennial properties that do not evolve rapidly over time, so we assume the error in measurement due to sampling in two different dates is negligible. Finally, despite the wide diversity of soils, the CaCO 3 content overall was low (median was 1 g⋅kg -1 ).