dataesr/scientific-paragraphs-categorization huggingface dataset:
Effect of Sward WC Content on Milk Production The mean effect of GC swards on milk production per cow and per hectare for the WCO database is outlined in Table  3. Within the WCO database, the mean experimental characteristics and milk production per cow and per hectare were as follows: number of cows = 43, stocking rate = 2. 67 cows/ha, experiment length = 126 d, N application = 87 kg/ha, WC content = 21. 9%, daily milk yield/cow = 19. 4 kg, and milk yield/ha = 10,458 kg. The equations that accounted for the greatest proportion of variation in predicted milk production per cow and per hectare according to sward WC content in the WCO database are described in Table  4  and Figure  2. The residual standard error is low at 1. 18 for daily milk yield, indicating a good precision of the predictive equations.
, 2004). Harris et al. (1997a)  compared milk production from mixed GC swards with WC contents of 0, 25, 50, and 75% and reported that milk and MS yields increased with increasing sward WC content up to 50% but that increasing WC content above 50% had no effect on milk or MS yields. In this study, within the WCO database, where milk production from GC swards with differing WC contents were compared, there was a quadratic response (P = 0. 038) for milk and MS yields per cow as sward WC increased (Table  4 ; Figure  4 ). Above a sward WC content of 60% for milk and MS yields, production per cow begins to decline. This may be due to the lack of data above sward WC contents of 60%; however,  Niderkorn and Baumont (2009)  hypothesized that when the legume proportion in the diet is too high (i. e.
, >50%), the benefit of legumes may decrease as excess N results in an intense production of urea and an increase in excreted N. Also, the risk of bloat is increased when sward WC contents are too high  (Clarke and Reid, 1974). When Gd/ha was included (and kept constant at 650 d) in the prediction equations for the per-hectare variables, milk and MS yields per hectare increased linearly as sward WC content increased (Figure  4 ) up to 60% in the WCO database. Milk solids yield per hectare increased as milk fat and protein yields per cow increased as sward WC content increased. Grazing days per hectare had a significant effect on all per-hectare variables with the exception of lactose yield per hectare, as expected, and was included in the model to allow a more precise prediction of the effect of WC content on production per hectare, as the study effect did not account for all of the variation in Gd/ha between experiments.
This is in contrast to the GWC database, where milk yield per hectare was not significantly affected but was reduced numerically when WC was included in a sward as stocking rate and N fertilizer application were reduced and is a reflection of the altered grazing management  practices that have traditionally been associated with GC swards. A balance between the optimum sward WC content for milk production and pasture production must be achieved to optimize both animal and pasture performance. However, within a sward the percentage of PRG and WC will oscillate in time and space and the overall level of control of pasture composition will be lower than what can be achieved with PRG monocultures  (Chapman et al. , 2016). Therefore, it can be difficult to maintain optimum levels of WC in the sward because climatic factors (drought), pests (insects and nematodes), poor soil fertility, and grazing management can all negatively affect sward WC content  (Woodfield and Caradus, 1996).
The most controllable factor that influences sward WC content is N fertilizer application. Several studies have shown a negative relationship between N fertilizer and sward WC content and biological N fixation  (Clark and Harris, 1996; Phelan, 2013) , and within both databases where mean sward WC contents were 33. 6 and 22. 3% on GC swards for the GWC database and the WCO database, respectively, N fertilizer applications were reduced significantly compared with GO swards. Harris et al. (1997b)  reported that an N fertilizer application rate of 200 kg/ha per year may be the most suitable for pasture DM production and WC persistence. However, within grazing systems, the overall N inputs, whether from N fertilizer or biologically fixed by WC, affect the level of nutrients that can potentially be lost to groundwater.
nih. gov/Blast. cgi)  56. Arabidopsis and rice PLA members were searched and the sequences were retrieved from TAIR (https://arabidopsis. org)  57  and RGAP (https://rice. plantbiology. msu. edu)  58 databases, respectively. To find additional Vitis vinifera PLA, a search restricted to "Vitis" was performed on NCBI (https://www. ncbi. nlm. nih. gov/), using the PLA conserved motifs (GxSxG  4, 34  , RYGKYCGxxxxGC, LDACCxxHDxCV  35, 36  , DGGGxRG, GTSTG, SAAPTY, DGGxxANN  32, 33  ) as query (November 2017). The putative grapevine PLA sequences were further confirmed on CRIBI database (V2 annotation) (http://genomes. cribi. unipd. it/grape/)  59.
Domain Structure Analysis, Sequence Properties, Subcellular Location Prediction and Chromosomal Location. Domain and clan determination was performed using Pfam database (http:// pfam. xfam. org/)  60. Molecular weight (Mw) and isoelectric point (pI) were predicted using the ProtParam tool from ExPASy (http://web. expasy. org/protparam/)  61. Subcellular location was predicted using TargetP (http:// www. cbs. dtu. dk/services/TargetP/)  62 , Localizer (http://localizer. csiro. au/)  63 and Predotar (https://urgi. versailles. inra. fr/predotar/)  64 servers and PLA putative function using Blast2GO version 3. 3 software tool (https://www. blast2go. com/)  65. The Map Viewer tool from NCBI (http://www. ncbi. nlm. nih. gov/mapview/) was used to map PLA genes in V. vinifera chromosomes. The physical map constructed with grapevine phospholipase A genes' location was also compared to a genetic linkage map representing P.
viticola resistance (Resistance to Plasmopara viticola, Rpv) QTLs in grapevine  [66] [67] [68] [69] [70] [71] [72] [73] [74] [75] [76]  to access the location of grapevine PLA within these loci. All the molecular predictions were manually curated and compiled. Phylogenetic Analysis. The MAFFT software, with the L-INS-I option version 7 (http://mafft. cbrc. jp/alignment/software/)  77 , was used for grapevine and Arabidopsis PLA protein sequences alignment. Sequences edition was performed with Jalview software (http://www. jalview. org/)  78. A maximum likelihood (ML) phylogenetic analysis was obtained with RAxML-HPC v. 8, on CIPRES Science Gateway (https://www. phylo. org)  79 , with the following parameters: protein substitution model PROTCAT; protein substitution model + BLOSUM62; bootstrap 1000 iterations with rapid boot strap analysis (-fa).
Statistical analysis. Due to the lack of data normality and homogeneity of variances, the statistical analysis of the data was based on non-parametric tests. In order to compare FA, lipid profile and qPCR data in inoculated and mock inoculated samples, statistical analysis was performed by the Mann-Whitney U test using IBM ® SPSS ® Statistics software (version 23. 0; SPSS Inc. , USA). Results yielding p < 0. 05 were considered statistically significant. In addition to the traditional univariate comparisons between fatty acid contents of each genotype, we also performed the evaluation of the whole lipid profile as one. Thus, the data was analysed using a multivariate approach that already proved to be an efficient tool to evaluate the fatty acid profile of plant ecotypes  24. All statistical analysis were conducted using Primer 6 software  83.
Acknowledgements This work was supported by  Fundação para a Ciência e Tecnologia, FCT/MCTES/PIDDAC , Portugal through the projects  PEst-OE/BIA/UI4046/2014 ,  UID/MULTI/00612/2013  for  BioISI ,  UID/MAR/04292/2013  for  MARE , investigator FCT program  IF/00819/2015  to  AF, PhD  grants  PD/BD/131030/2017  to ARC, PhD grant from  Universidade de Lisboa  to JF and Postdoctoral grant  SFRH/BPD/115162/2016  to BD.
Shotgun proteomic analysis Leaf total proteins were extracted, digested, and analysed by LC-MS/MS using the PAPSSO platform (INRA, Le Moulon, Gif sur Yvette; see Supplementary Method S2 for details). Protein identification was performed using X! Tandem Piledriver (version 2015. 04. 01; http://www. thegpm. org/TANDEM/index. html) by querying the MS/MS data against the TAIR10 protein library together with a custom contaminant database (trypsin, keratins). Identified proteins were filtered and grouped using X! Tandem Pipeline (3. 4. 1; pappso. inra. fr/bioinfo/xtandempipeline/) (Langella et al. , 2017)  according to two criteria: (1) a minimum of two different peptides required with an E-value smaller than 0. 01, and (2) a protein E-value (calculated as the product of unique peptide E-values) smaller than 10 -5.
The false discovery rates (FDRs) at the peptide and protein levels were 0. 03% and 0. 0%, respectively. Relative peptide quantification by peakarea integration on eXtracted ion chromatogram (XICs) was performed using the MassChroQ software (pappso. inra. fr/bioinfo/masschroq/)  (Valot et al. , 2011). Relative protein abundance was calculated and defined as the sum of peptide intensities considering only (1) reproducible peptides, (2) specific peptides, and (3) correlated peptides belonging to the same protein (see Supplementary Method S2 for details). When the peptides of a protein were not present or not reproducibly observed in one or more conditions, spectral counting (SC) was used in place of XICs analysis.
Gels were stained with silver nitrate and bands with a molecular mass between 25 and 40 kDa, corresponding to bands observed in the activity-based protein profiling (ABPP) experiment, were excised with a OneTouch GridCutter (2. 0 × 7. 0 mm; Gel Company). Each slice was placed into a 0. 5ml tube, digested, and analysed by LC-MS/MS as detailed in Supplementary Method S3. Proteomics data analyses were performed as explained in Supplementary Method S3. Identified proteins were filtered and grouped using X! Tandem Pipeline (3. 4. 2) according to the following criteria: (1) a minimum of two different peptides required with an E-value smaller than 0. 05, and (2) a protein E-value smaller than 10 -4.
Bioinformatics and statistical analyses All statistical analyses were performed using the R software (https://www. r-project. org/). Data were analysed using two-way ANOVA with genotype and nutrition conditions as variable factors, followed by a Tukey's post-hoc test. For the relative quantification of proteins by the XICs method, statistical analyses were carried out on log 10 -transformed protein abundance. Proteins corresponding to proteases with a Tukey's P-value ≤0. 05 were considered to be significantly differentially accumulated. Fold-change (FC) ratios were determined as abundance ratios of atg5 versus Col and of atg5/sid2 versus sid2. No FC threshold was applied as even a small change in protease abundance can have a strong impact on the proteome due to post-translational regulation and the large substrate spectrum of proteases.
In order to identify proteases among the proteins detected by LC-MS/ MS, Gene Ontology and functional information for each protein were analysed using the following databases: MEROPS (https://www. ebi. ac. uk/merops/), UniProt (www. uniprot. org/), agriGO (bioinfo. cau. edu. cn/agriGO), and Mapman (mapman. gabipd. org/).
Accession numbers AT4G38220; AT2G27020; AT2G24200; AT4G20850; AT5G35590; AT4G31300; AT3G22110; AT2G05840; AT4G14800; AT3G60820; AT1G53750; AT3G05530; AT4G17510; AT5G05780; AT5G58290; AT5G10540; AT1G21720; AT1G56450; AT5G42790; AT4G01610; AT1G47128; AT1G53850; AT5G45890; AT5G51070; AT3G13235; AT1G79340; AT1G50380; AT3G51260; AT5G23540; AT4G38630; AT1G51710; AT5G66140; AT4G30910; AT4G39090; AT5G43060; AT5G10760; AT1G16470; AT1G79210; AT2G14260; AT5G23140; AT4G16190; AT5G26860; AT3G14290; AT3G20630; AT2G41790; AT4G36760; AT5G36210; AT1G50250; AT5G42270; AT4G36195; AT1G09750; AT3G54400; AT3G02110; AT2G39850; AT1G52510; AT2G47940; AT2G35780; AT5G39830; AT3G27925; AT3G14067; AT3G19170; AT5G42390; AT3G24590; AT1G13270; AT5G05740; AT2G30950; AT4G18370; AT3G52500; AT5G42240; AT1G06430; AT5G23210; AT3G05350; AT5G65760; AT1G67700; AT3G61820; AT1G01300; AT4G34980; AT2G05920; AT3G18490; AT2G33530; AT4G21650; AT5G08260; AT1G09130; AT1G21750; AT1G47710; AT4G16500; AT517290; AT5G60360; AT5G50260; AT1G02305; AT4G35350.
It is thus likely that the decrease in these chloroplast-predicted proteases was not correlated to the slight senescence phenotype observed in atg5 under lownitrate conditions (Fig. 1b ), but constitutes a more robust phenotype that was shared by the two autophagy-defective lines studied, independently of the nitrate conditions. In order to determine whether changes in protease contents were linked to a modification of the transcription of their genes or to modifications in their protein turnovers, gene expression levels were evaluated using the transcriptomic data obtained by Masclaux-Daubresse et al. (  2014 ), (Supplementary Dataset S1). Only the CLPD, RD21B, AED1, and SAG12 genes were up-regulated in atg5 and/or atg5/sid2 (Table  1 ), and only SBT3. 13 was repressed in atg5 under low nitrate (Table  2 ).
Inhibitor proteins of cysteine proteases are differentially accumulated in autophagy-defective lines Several protease inhibitors are small proteins that can form stable complexes with target proteases and block or prevent access to the active site. Among the long list of protease inhibitors available in the MEROPS database, three were found to be differentially accumulated in autophagy-defective   S1. lines compared to the controls (Table  3 ). PDI5 and Serpin-1, that can inhibit RD21A, had increased accumulation in atg5 under both LN and HN conditions. PDI5 also accumulated in atg5/sid2. The extracellular CYS4 cysteine protease inhibitor was less abundant in atg5/sid2 under LN.
Acknowledgements This work was mainly supported by the financial support of  ANR-12-ADAPT-0010-0  to MH and AL. The work of PR was supported by a Ph. D. scholarship from the  German Academic Exchange Service (DAAD)  and by the grant  DI 1794/3-1  of the  German Research Foundation (DFG)  to ND. The authors are grateful to  Renier van der Hoorn  ( Oxford University, Oxford, UK ) for providing MVB072, to  Marine Poret  ( University of Caen, France ) for help, and to  David Lawrence  for English editing.
Sequence data In total, we amplified and sequenced the mitochondrial COI (up to 658bp) and 16S rDNA (c. 440 bp), and the nuclear ITS2 (290-419 bp) and 28S rDNA (c. 760 bp) from 513 specimens of Terebellides spp from the North East Atlantic. Final data coverage was as follows: COI, 462 spms (90%) (GenBank accession numbers: MG024894-MG025355), 16S rDNA, 75 spms (15%) (GenBank accession numbers: MG025443-MG025517), ITS2, 402 spms (90%)  (GenBank accession numbers: MG024492-MG024893), and 28S rDNA, 86 spms (17%) (Gen-Bank accession numbers: MG025356-MG025441) (S36 Appendix and Table  2 ). Sequences from individual specimens can be identified by the extraction number and an appended clade-number (S36 Appendix), preliminary circumscribed from statistical parsimony haplotype networks  [35] , also known as TCS-analyses, of COI-data (see below).
One other member of Trichobranchidae, Trichobranchus roseus (Malm, 1874), and two representatives of Terebellidae, Polycirrus Grube, 1850 and Pista cristata (Mu ¨ller, 1776) were selected to root the tree  [17]. Outgroups were used when assessing the general phylogeny of the Terebellides lineages, but not in the species delimitation analyses. Molecular data for outgroups were either retrieved as above (Trichobranchus roseus: COI (GenBank accession number MH1139.
Alignments We used MAFFT version 7. 017  [36]  within Geneious version 7. 0. 6 with the following settings: algorithm = E-INS-i, scoring matrix = 200PAM / k = 2, gap open penalty = 1. 53, to align 16S rDNA and 28S rDNA. Aligning was unproblematic since the sequences were of similar length and resulting alignments had a moderate number of indels. The ITS2-region was challenging to align due to a high number of indels, and we proceeded with aligning using two approaches. In the first approach, we removed identical haplotypes with the uniqhaplo. pl script (S35 Appendix) leaving a data set with 136 unique ITS2-sequences. As we experienced problems with two sequences that were shorter due to incomplete 3'-end, these sequences were first removed (1999_13 and 2865_24), and the remaining 134 complete, or nearly complete, sequences were aligned with the X-INS-i algorithm in MAFFT that takes into account the secondary structure of the sequence.
Subsequently the short excluded sequences were reincluded with the mafft-add command. The resulting alignment is referred to as ITS2x-unique. In the second approach, the sequences in the ITS2x-unique alignment were realigned using the software RNAsalsa  [37] , using the secondary structure of ITS2 modeled for Eumida ockelmanni Eibye-Jacobsen, 1987 (GenBank accession number HM358782)  [38]  as a constraint, and implementing default parameters. The resulting alignment is referred to as ITS2s-unique. Identical sequences removed in the first step with the uniqhaplo. pl script were then added back to the two alignments by hand in Geneious version 7. 0. 6 mimicking the gaps present in those identical sequences aligned. The two resulting alignments with all 402 ITS2-sequences are referred to as ITS2x-all, and ITS2s-all.
Finally, we used the MUSCLE alignment option in Geneious version 7. 0. 6 to align all 462 COI-sequences (COI-all) which was trivial due to the absence of indels. Identical COI-sequences were removed using uniqhaplo. pl script creating an alignment with 271 unique COI-sequences (COI-unique). Where relevant, aligned gene partitions were concatenated using Mesquite v. 2. 75  (Maddison and Maddison 2008 )  [39]. For the statistical parsimony haplotype analyses, we used COI-all, and the two ITS2-all alignments as a starting point. Sequences of each haplotype network were extracted separately, and subsequently these clade data sets were pruned to remove gaps in flanking positions that was caused by incomplete sequencing. The purpose of this was to obtain the same data coverage for all included specimens in each haplotype network, and allowing for an unambiguous assessment of haplotypes.
In a few instances, one, or a few of the shortest sequences were removed prior to pruning the sequence ends (Tables  3  and 4 ). In the choice between removing short sequences or pruning we chose the method that kept the maximum number of haplotypes. As there were a few ambiguities assessing number of haplotypes between the two ITS2-alignments, although based on the same data, we decided to realign the ITS2-data from each network separately, using the E-INS-i algorithm in MAFFT, with scoring matrix = 200PAM / k = 2, and gap open penalty = 1. 53. The rational behind this is that aligning more similar sequences will result in a more accurate alignment. For the distance calculations we used COI-all, and ITS2s-all alignments. All different alignments, and data set combinations described above are available as S1-S9 Appendixes.
Data set combinations For a robust assessment of the evolutionary relationships of the Terebellides lineages, specimens for which three or four of the genetic markers were present (i. e. COI, 16S rDNA, ITS2, 28S rDNA), were combined into a data set comprising 91 Terebellides specimens (S36 Appendix and Table  2 , last column) plus three outgroups. This was done by combining COI-all with either ITS2x-all or ITS2s-all, concatenating 16S rDNA and 28S rDNA, but excluding specimens that did not meet the criteria having three or four genetic markers. This resulted in two data set combinations, referred to as concatenated-xinsi-alignment (CONCATx) and concatenated-salsa-alignment (CONCATs). For the three types of species delimitation analyses, we used the following data sets: COI-all, ITS2x-all, and ITS2s-all for TCS; COI-unique, ITS2s-unique, and ITS2x-unique for GMYC  [40, 41] ; the concatenated alignment of COI-all and ITS2s-all, keeping all specimens with both COI and ITS2 data present, resulting in a data set with 351 Terebellides specimens (Table  2 , 5th column) for STACEY  [42].
Phylogenetic analyses Mitochondrial (COI and 16S rDNA) and nuclear data sets (ITS2 and 28S rDNA) were analyzed separately and combined using Bayesian inference (BI), and Maximum Likelihood (ML). This means five different analyses per method; 1) mitochondrial data alone, 2) nuclear data alone with 28S rDNA combined with xinsi-, or 3) salsa-aligned ITS2 sequences, and 4) mitochondrial data combined with nuclear data with 28S rDNA combined with xinsi-, or 5) salsa-aligned ITS2 sequences (S8 and S9 Appendixes). Bayesian analyses of separate and combined data sets were run in MrBayes version 3. 2  [44]. Partitions were unlinked for the parameters statefreq, revmat, shape and pinvar. Rateprior for the partition rate multiplier was set to be variable. Two independent analyses were run for 10 million generations, with four parallel chains (three hot, one cold), that were sampled every 1000th generation.
Species delimitation analyses Minimum spanning haplotype networks were constructed with the software program TCS 1. 2. 1, using a 95% connection limit with gaps = missing. The General Mixed Yule Coalescent model (GMYC) uses a likelihood ratio test to compare a null model assuming a single coalescent branching rate across a clock-like tree (i. e. intraspecific population events) with a complex model including both coalescent and Yule (interspecific diversification events) branching rate models. The later also estimates the threshold time that maximizes the transition between coalescent and Yule branching models, and hence delimiting species boundaries. Species delimitation with the GMYC algorithm was performed with the R library splits v. 1. 0-19 [46]  using a single threshold and the required R packages ape, paran, and MASS.
With lower cut-off value, the number of species will be more similar to the raw species delimitation estimated by GMYC algorithm without taking into account the support. On the other hand, higher cut-off values would reduce the number of species, generally merging closely related GMYC entities (species). We selected an arbitrary, but high, GMYC support value cut-off (0. 9) to ensure that remaining species are discovered by GMYC algorithms (i. e. supported) under most of the different coalescent models tested (90%). The optimal cut-off value should be validated by simulation studies and with several empirical datasets but this is beyond the scope of our study. STACEY is a phylogenetic and a species delimitation method under a multispecies coalescent method (i. e. find the species tree and delimit species but allowing different coalescent gene trees and coalescent times).
Model selection The selected best-fit models were a general time reversible model with a proportion of invariable sites and gamma distributed rate across sites (GTR+I+G) for the partitions 16S rDNA, ITS2, and ITS2 combined with 28S rDNA, and COI-partition with third codon sites only, while a general time reversible model with a proportion of the sites invariable (GTR+I) was selected for the COI-partition including first and second codon positions. In RAxML, the analyses were run with an independent GTRGAMMAI model for each partition, as the program do not allow the assignment of more than one model to different partitions.