dataesr/scientific-paragraphs-categorization huggingface dataset:
Identification of PUF Family Members in Rosa Chinensis The PUF family is mainly involved in posttranscriptional control by binding to specific regulatory cis-elements that contain a UGUR (R: purine) flanked by an AU-rich sequence. Through this interaction, they govern RNA decay and translational repression  [44]. Based on the presence of 6 putative PUF-binding core motifs in the 3 UTR of RhBRC1 (Figure  1A ), we hypothesized that PUF proteins might mediate the posttranscriptional regulation of RhBRC1 in response to sugar. The phylogenetic analysis of the identified RcPUF (Rosa chinensis PUF) proteins was performed using MEGA7. 0 software. According to previous studies, the PUF family includes 26 members in Arabidopsis, which can be grouped into five subfamilies through phylogenetic analysis  [44]  (Figure  4A ).
In Rosa chinensis, we only identified twelve PUF members based on the genome sequence from GDR database  [57] [58] [59] , which is far less than in Arabidopsis. The PUF protein members can be classified into four groups (Figure  4A ), and their gene length varies from 2000 bp to 5000 bp (Figure  4B ). Moreover, out of these twelve PUF members, eleven of them contain eight PUF repeats, and only RC7G0558100 contains seven PUF repeats (Figure  S3 ).
Taken together, these findings show that RhPUF4 expression is early and highly expressed in non-dormant axillary buds, and is negatively correlated with RhBRC1 expression. proteins might mediate the posttranscriptional regulation of RhBRC1 in response to sugar. The phylogenetic analysis of the identified RcPUF (Rosa chinensis PUF) proteins was performed using MEGA7. 0 software. According to previous studies, the PUF family includes 26 members in Arabidopsis, which can be grouped into five subfamilies through phylogenetic analysis  [44]  (Figure  4A ). In Rosa chinensis, we only identified twelve PUF members based on the genome sequence from GDR database  [57] [58] [59] , which is far less than in Arabidopsis. The PUF protein members can be classified into four groups (Figure  4A ), and their gene length varies from 2000 bp to 5000 bp (Figure  4B ).
RhPUF4 Could Bind to the 3 UTR of RhBRC1 and Promote Plant Growth In order to know whether RhPUF4 could bind to the 3 UTR of RhBRC1, the NCBI database was used to find the homologous gene of RhPUF4 in Arabidopsis. The BLAST result showed that APUM2 (AT2G29190) was a homologous gene of RhPUF4, with a high query cover (99%), a high percent identity (61. 76%), and a low E-value (0. 0). Moreover, our phylogenetic tree also confirmed that RhPUF4 and APUM2 belong to a same cluster (Figure  4A ). APUM2 is involved in cell differentiation and highly expressed in the shoot meristem in Arabidopsis  [60]. It has a high binding affinity to a conserved sequence that contains a core UGUR motif flanked with an NRKR motif  [49, 50]. Moreover, we also found the UGURNRKD motif in the 3 UTR of RhBRC1 (Figure  1A ).
In order to know whether RhPUF4 could also respond to the same motif, we used the SWISS-MODEL database  [61, 62]  to predict the tertiary structure of RhPUF4 and APUM2. The result showed that its structure had a high QMEAN (qualitative model energy analysis) value (-1. 47 and -1. 45, respectively, Figure  7A, B ), and both of them had a conserved eight-pumilio-repeats domain (Figure  7A, B ). Furthermore, the WoLF PSORT database was used to predict the subcellular localization of RhPUF4  [63]. This analysis indicated that RhPUF4 was located in the nucleus and the cytoplasm. Both results supported that RhPUF4 and APUM2 could bind to the same motif in the 3 UTR, probably based on their conserved PUF motif. In order to get an insight into the function of RhPUF4 during plant development, we overexpressed its close homolog APMU2 in Arabidopsis.
RhPUF4 Could Bind to the 3′UTR of RhBRC1 and Promote Plant Growth In order to know whether RhPUF4 could bind to the 3′UTR of RhBRC1, the NCBI database was used to find the homologous gene of RhPUF4 in Arabidopsis. The BLAST result showed that APUM2 (AT2G29190) was a homologous gene of RhPUF4, with a high query cover (99%), a high percent identity (61. 76%), and a low E-value (0. 0). Moreover, our phylogenetic tree also confirmed that RhPUF4 and APUM2 belong to a same cluster (Figure  4A ). APUM2 is involved in cell differentiation and highly expressed in the shoot meristem in Arabidopsis  [60]. It has a high binding affinity to a conserved sequence that contains a core UGUR motif flanked with an NRKR motif  [49, 50]. Moreover, we also found the UGURNRKD motif in the 3′UTR of RhBRC1 (Figure  1A ).
In order to know whether RhPUF4 could also respond to the same motif, we used the SWISS-MODEL database  [61, 62]  to predict the tertiary structure of RhPUF4 and APUM2. The result showed that its structure had a high QMEAN (qualitative model energy analysis) value (-1. 47 and -1. 45, respectively, Figure  7A, B ), and both of them had a conserved eight-pumilio-repeats domain (Figure  7A, B ). Furthermore, the WoLF PSORT database was used to predict the subcellular localization of RhPUF4  [63]. This analysis indicated that RhPUF4 was located in the nucleus and the cytoplasm. Both results supported that.
Callus Treatment and GFP Quantification The transformed calluses were placed in liquid basic medium (Murashige and Skoog [MS] containing salts and vitamins) containing the different treatments for 8 h at 22 • C under light. GFP intensity was assessed under a fluorescence microscope, and quantification was performed on 2D images using ImageJ software  [96]. The integrated density of gray values was determined on 30 randomly selected spots on a representative part of each sample. Each modality was replicated three to eight times.
When the use of one or the other nostril could not be clearly stated by the experimenters, the data point was discarded. Five odour stimuli were used in the study: (1) vanilla; (2) odour of female NMRI mice or female C56BL/6J mice (according to the strain of the tested mouse); (3) garlic; (4) rat odour; (5) distilled water. The vanilla odour was prepared by mixing five drops of concentrated vanilla flavouring (Déco'Relief ® , France) in 20 mL of distilled water. Garlic odour was prepared by mixing 2. 4 g of mashed garlic in 60 mL of distilled water for 10 min. Female mice odour was prepared by mixing 35 g of female-soiled bedding with 300 mL of distilled water for 30 min. Rat odour was prepared by mixing 45 g of rat-soiled bedding with 300 mL of distilled water for 30 min. All odour stimuli (pure distilled water included) were aliquoted in Eppendorf tubes (0.
Materials and Methods All scripts and instructions required to reproduce the analysis are implemented in a pipeline available on Github at https://github. com/cmdoret/CSD_Lfabarum; last accessed October 16, 2019. For all analyses, we use contigs from the latest version of the L. fabarum reference genome (Dennis AB, in preparation) (Lfab v1. 0, Available on request: https://bipaa. genouest. org/is/parwaspdb; last accessed October 16, 2019) which have been anchored onto six chromosomes (supplementary table  S2 , Supplementary Material online) in line with the six chromosomes that were deduced from karyotyping  (Belshaw and Quicke 2003). Raw reads are available on NCBI SRA database under bioproject PRJNA505237, while the anchored genome used here along with all files required to reproduce the analysis are available on Zenodo at doi: 10.
STACKS Pipeline We used the STACKS software (version 1. 48)  (Catchen et al. 2013)  to process RADseq data. Following quality control using fastqc (https://www. bioinformatics. babraham. ac. uk/projects/ fastqc; last accessed October 16, 2019, version 0. 11), the raw reads were trimmed and demultiplexed using the "process radtags" module from the STACKS suite and two mismatches were allowed to detect adapters. The 93-bp trimmed, demultiplexed reads were aligned to the latest assembly of the L. fabarum genome using BWA-aln (version 0. 7. 2)  (Li and Durbin 2009)  with four mismatches allowed. Only uniquely aligned reads were extracted using samtools (version 1. 4)  (Li et al. 2009). Stacks were then generated from SAM files of unique hits using the Pstacks module, requiring a minimum read depth (-m) of 3 to consider a stack.
Ploidy Separation and Filtering To determine the ploidy of all individuals, we rely on genomewide homozygosity. Haploid samples are hemizygous and should have extremely high homozygosity levels compared with diploid ones. We included only high-confidence SNPs in the STACKS populations module by using a stringent cutoff of 20 reads for the minimum sequencing depth (-m parameter). 899 high-confidence SNPs passed the quality filters and on an average, each sample presented 853 of these polymorphic sites with a (high) mean coverage of 132Â. We then computed the proportion of homozygous SNPs per individual using VCFtools (version 0. 1. 13) (Danecek et al. 2011 ) on the output VCF file from populations. To account for sequencing errors and paralog merging, a conservative threshold of 90% homozygosity among polymorphic sites was determined empirically based on the bimodal distribution of homozygosity (supplementary fig.
Collinearity Analyses Collinearity blocks were defined using the default parameters of MCScanX: A collinearity block is called if two genomic segments share 5 homologous genes in conserved order with at most other 25 genes inserted in between. Gene coordinates were defined by merging maker gene prediction tracks and transcripts assembled from reference-aligned RNAseq reads from 5-day-old larvae (Dennis AB, K€ ach H, Vorburger C, in revision) (SRA accession numbers SAMN10024115-SAMN10024165). Gene sequences were extracted at the merged coordinates using bedtools2 (Quinlan and Hall 2010) and homologous genes were defined by all versus all BlastN, using the BLASTþ command line tools  (Camacho et al. 2009 ), selecting matches with an e-value inferior to 10e-05.
Collinearity across CSD Regions The molecular mechanisms underlying ml-CSD have not been studied thus far, but a verbal model suggested that ml-CSD may derive from a sl-CSD system via duplication of the original CSD locus  (Boer et al. 2008; Schmieder et al. 2012). We therefore evaluated whether the multiple CSD regions of L. fabarum could have evolved via duplication. A common approach to infer gene duplications across different genomic regions is to look for collinearity; the conserved order of homologous genes between regions. We used MCScanX  (Wang et al. 2012)  to investigate genome-wide patterns of collinearity in genes and transcripts. The coordinates used were generated by combining gene tracks from the MAKER annotation pipeline  (Dennis AB, in preparation)  and coordinates from aligned transcripts from larvae (Dennis AB, K€ ach H, Vorburger C, in revision) (SRA accession numbers SAMN10024115-SAMN10024165).
Acknowledgments We thank  Paula Rodriguez  for her work with the lab rearings and  John Wang  for discussions. All computations were performed at the  Vital-IT  (http://www. vital-it. ch;last accessed October 16, 2019) Center for high-performance computing of the  SIB Swiss Institute of Bioinformatics. This study was supported by the  Swiss National Science Foundation  (http:// www. snf. ch; last accessed October  16 ,  2019 ), grants  PP00P3_139013  and  PP00P3_170627  to T. S. , and  SNSF  Professorship number  PP00P3_146341  and Sinergia grant number  CRSII3_154396  to C. V. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. All authors have read and approved the manuscript and it has not been accepted or published anywhere else.
Sampling and measurements Representative samples of harvested ryegrass, white clover and chicory were collected daily and oven dried at 60°C for 72 h to determine DM. In all, three dried samples of offered and refused feed were composited by week and ground through a 1-mm sieve before analysis for organic matter (OM), CP, NDF, ADF and ADL. Voluntary intake and intake behaviour were measured daily during the measuring period as described by  Baumont et al. (2004). In brief, the daily pattern of feed intake and the time spent eating and ruminating were monitored individually using an automated system. The trough was continuously weighed by sensors fitted with strain gauges wired up to a data processing system, enabling the recording of intake over the day on 1-min timescale. Jaw movements were recorded by a polyurethanefoam-filled balloon placed in the submandibular space and connected to the microcomputer via a pressure transducer.
The reliability of the SF 6 technique on rumen-cannulated sheep had been validated (data not shown) to ensure that potential non-uniform leakage of the different gasses (SF 6 and CH 4 ) via the cannula does not bias the results. On day 5, 2 h before morning feeding, animals were given a dose of Cr-EDTA (300 ml, 2. 77 g/l Cr diluted fourfold in water) through the rumen cannula to determine liquid dilution rate in the rumen. Samples of ruminal fluid were taken at 2, 4, 6, 8, 24, 26, 28 and 30 h following marker administration, and pH was immediately measured with a SevenEasy S20 pH meter (Mettler-Toledo GmbH, Schwerzenbach, Switzerland). For subsequent analysis of volatile fatty acids (VFA), 0. 8 ml of ruminal fluid was mixed with 0. 5 ml of a 0. 5 N HCl solution containing 20 g/l metaphosphoric acid and 4 g/l crotonic acid.
Statistical analysis Data relative to chemical composition of individual forages were analysed by ANOVA using the GLM procedure of SAS Enterprise Guide 5. 1  (SAS, 2013). Data relative to voluntary intake, feeding behaviour, digestive parameters, N balance and CH 4 emissions were analysed as a repeated Latin square design using the PROC MIXED procedure. The statistical unit was the individual sheep. The diet, the experimental period and their interaction were considered as fixed effects and sheep as a random effect. The Tukey-Kramer test was used to compare means. The REPEATED statement with the most desirable covariance structure was used to analyse the kinetics of fermentation end-products concentrations in the rumen fluid. Normality of data distribution was evaluated using PROC UNIVARIATE procedure.
Simulated datasets used to develop the meta-models The dataset used to develop the meta-models includes 106,092 NH3 emission simulations performed within the CADASTRE_NH3 framework using the Volt'Air model  (Ramanantenasoa et al. , 2018). These simulations encompass three types of fertilizer, five geographical areas, and weather conditions corresponding to three contrasted crop years. Simulation units of CADASTRE_NH3 are the Small Agricultural Areas (SAR): a SAR is considered to be geographical entity with homogeneous characteristics of agricultural activities, weather and soil conditions (http://agreste. agriculture. gouv. fr). SAR sizes range from 1,096 to 440,650 ha, and the number of SAR per region varies from 6 to 74. Within our dataset, each SAR is characterized each year by the area of the cultivated crop species, the weather conditions, and soil types.
Fertilization management is characterized at the regional scale. Areas of cultivated crop species were derived from the European Land Parcel Identification System for France  (Martin et al. , 2014). Data on weather conditions were provided by the French national meteorological service "Météo-France". Data on the spatial distribution of soils were derived from the European Soil DataBase (ESDB)  (Panagos et al. , 2012)  and the soil properties were derived from the Harmonized World Soil Database  (FAO et al. , 2012). Data on cultural practices were derived from the survey conducted in 2005-06 by the Service de la Statistique et de la Prospective of the French Ministry of Agriculture, for 10 main arable crops (soft wheat, durum wheat, barley, grain maize, forage maize, oilseed rape, sunflower, peas, sugar beet and potato) and two types of grasslands (grass leys and intensive permanent grasslands) in 21 French regions (Corse region was excluded)  (AGRESTE, 2006) , following the methodology of  Mignolet et al.
(2007). Fertilization was defined as a succession of fertilizations on a given crop species. Each fertilizer application was characterized by the date of application, type of fertilizer (3 mineral fertilizers: ammonium nitrate, N solution, urea; 5 organic manure: cattle farm yard manure (FYM), cattle slurry, sheep FYM, pig slurry, and vinasse), and dose applied. For the simulations with Volt'Air, fertilizers were assumed to be applied on the surface without abatement techniques, at a single time, 8:00 am UTC. Each simulation covered the cumulative NH3 emissions over 30 days after a specific fertilizer application on a specific SAR*crop*fertilization*year combination. A more detailed description of the dataset and of the simulation procedure may be found in  Ramanantenasoa et al. (2018). Part of the dataset including the simulations of Volt'Air was made freely available in the related data paper  (Génermont et al.
In order to capture the possible effect of weather variables between years, weather conditions of two contrasting crop years were added: 2007-08 and 2010-11. Crop year 2005-06 was characterized by a dry spring and a hot summer (AGRESTE, 2010); 2007-08 enjoyed favorable conditions for crop growth, i. e. , a humid spring with temperatures close to the seasonal average and a hot summer with rainfall close to the seasonal average  (JRC, 2008) ; 2010-11 was characterized by a very dry, hot spring and a rainy summer  (AGRESTE, 2014). The same sites and cultural practices were analyzed for the three years; only the weather conditions were changed. The sizes of national and regional datasets per fertilizer type and per geographical area and the numbers of SAR per geographical area are given in Table  1.
Types of meta-model For a given type of fertilizer, meta-models were fitted to each regional dataset of simulated NH3 emissions and then to the whole dataset at the national scale. Five different types of metamodel were developed with each dataset. The first three types of model (ordinary linear regression, Least Absolute Shrinkage and Selection Operator, and Partial Least Squares Regression) assumed a linear relationship between N emission rate over 30 days (or its transformed-value) and input variables. The last two types of model (Generalized additive model, Random Forest) dealt with nonlinear relationships between volatilization rates and input variables. The inputs of generalized additive model and random forest were selected using a regression method (ordinary linear regression, Least Absolute Shrinkage and Selection Operator, or Partial Least Squares Regression, see section 2.
5 for details on the selection procedure). Below, we briefly present the main characteristics of each type of model. Details on the meta-model selection procedure are given in Section 0. The equations of the meta-models are described in Table  3  together with the R functions used for their implementation. The R functions mentioned in Table  3  are freely available from https://cran. r-project. org. They can be easily used to develop meta-models from the dataset described in 2. 2. The present study was performed with the 3. 2. 3 version of R. Ordinary linear regression (LM, Linear Model) was used to relate emission rates (noted y in Table  3 ) to quantitative and categorical input variables (noted X1, …, Xp in Table  3 ) through a linear equation. Here the flexibility of linear models was increased through the inclusion of quadratic terms and interactions between inputs.
With this approach, some parameters become equal to zero by tuning the penalty term for minimizing prediction errors, leading to the selection of a reduced subset of input variables. LASSO was implemented here using the R function cv. glmnet() of the package glmnet. Partial Least Squares Regression (PLSR) can improve the accuracy of linear regression models when the input variables are strongly correlated  (Abdi, 2010). PLSR may be seen as an improved version of principal component regression (PCR). In PCR, principal components are expressed as linear combinations of original variables, such that the variability in the data may be described by a small number of these new variables. In PLSR, the original input variables are also replaced by linear combinations of inputs, but these combinations are selected to describe most of the input data variability while showing a strong correlation to the response (Table  3 ).
The tree is built by recursively partitioning the initial data into smaller groups, called nodes, through binary splits based on a single input variable. Examples of regression trees are given in Figure  S3  in the supplementary material. The whole process is repeated several thousand times, to generate many trees. At each iteration, the OOB data are used to evaluate the accuracy of the fitted trees. The output of RF is calculated by averaging the outputs of the individual trees (Table  3 ). Here RF models were fitted with the R function randomForest() of the randomForest package and the R function ranger() of the ranger package. Ranger is a fast implementation of random forests  (Breiman 2001)  and was used here to fit the RF meta-model for N solution in France to reduce computation time.
Two sets of inputs were considered successively: the inputs listed in Table  2 , and an expanded set of inputs including quadratic terms and interactions. With LM, input variables were selected using either a stepwise or a forward selection procedure based on the AIC criterion  (Akaike, 1974). The total number of models tested in Step 1 was 598. In Step 2, the two best linear meta-models were selected using cross-validation. Crossvalidation is a standard method for evaluating the accuracy of the model predictions  (Efron, 1983). It allows one to estimate how the model is expected to perform when used to make predictions on data independent from those used for estimating the model parameters. The general principle is to split the dataset into K groups. The first group is removed from the dataset.
The remaining part of the dataset is used to fit the model. The fitted model is then used to predict the discarded group of data. The whole procedure is repeated over all groups, and the accuracy of the prediction is evaluated by computing the root mean square error of prediction (RMSEP) over the K groups. Here, two procedures were used successively to define the groups of data, leading to two different types of cross validation. The first type of cross-validation was implemented SAR by SAR in order to estimate the root mean square error of prediction of each meta-model (RMSEP_SBS). RMSEP_SBS measures the ability of each meta-model to approximate the emission rates simulated by Volt'Air in a new SAR, i. e. , a SAR not originally included in the dataset used to develop the meta-model considered.
The second type of cross-validation consisted of dividing the dataset by years. This is also referred to as year-by-year cross-validation and the associated RMSEP is denoted RMSEP_YBY. This value of RMSEP measures the ability of each meta-model to approximate the emission rates simulated by Volt'Air for a new year, not originally included in the dataset used to develop the meta-model. In Step 3, two RF and two GAM meta-models were developed. They included the input variables of the two best linear models selected in Step 2, i. e. , the linear models characterized by the lowest values of RMSEP_SBS and RMSEP_YBY (Linear MM_SAR and MM_YEAR). These models were thus developed from the input variables selected in Step 2. The output and input variables were used in their original form, i. e. , without transformation or interaction.