dataesr/scientific-paragraphs-categorization huggingface dataset:
Three of these clusters included at least 20 TNLs. These data thus confirm the involvement of tandem or segmental gene duplication and recombination as major mechanisms of R gene expansion in peach and other plant species  [42]. In perennial plants, which have a much longer generation time than annuals, and particularly in those of the Rosaceae, these mechanisms may constitute an adapted response to pathogens, contributing substantially to the great diversity of this group of proteins, as observed in apple  [40]. We used the large set of sequences obtained from peach to identify motifs specific to the various TNL domains. Our findings confirm the conserved motifs previously identified for the TIR and NB domains by Meyers et al. [3]  and Jupe et al. [39]  and identify original specific motifs for NLL and LRR sequences.
Conclusions The peach genome contains numbers of TNL genes similar to those in other perennial plants with genomes of similar size. The distribution of the genes of this family and of the variants of its specific domains between peach chromosomes confirmed the hypothetical mechanisms of TNL expansion. PL domains were recovered from about two thirds of the TNLs identified. The PL dataset for the peach genome was used to define specific motifs that will be useful complementary tools for detecting and localising TNLs and their variants in plant genomes. PL domains may be required for the function of some TNL proteins, particularly through specific motifs potentially involved in the recognition of effectors or guard proteins. TNL mapping and the definition of PL domain signature sequences should make it possible to identify functional TNLs from peach or, more widely, in other perennial plants and members of the Rosaceae.
Detection and mapping of the TNL genes TNLs in the Prunus persica v1. 0 genome were detected by Blast P analysis with the NCBI BLAST tool available from the GDR website (https://www. rosaceae. org/tools/ ncbi_blast). The blast analyses were carried out with the default parameters, except for the description and alignment parameters, which were increased to 500 to visualize as many results as possible in the graphical overview. We used the sequences of the TMV resistance protein N (Q40392. 1) [57] , the RKN resistance protein Ma (CAR94514. 1) [14] , the TIR-1, -2, -3, and -4 motifs developed by Meyers et al. [3]  and the TIR motifs 13, 15, and 18 developed by Jupe et al. [39] , as queries, and the peach genome v1. 0 predicted peptides as the database. The N and Ma sequences were chosen because they appeared to be the most relevant for TNL detection.
Indeed the N gene is the reference TNL gene for plants in general, and the Ma gene is the only Prunus TNL R gene to have been functionally validated. The TIR domain is characteristic of the TNL family. We therefore retained the TIR motifs developed from genome-scale data for Arabidopsis and potato. As a complementary tool for the detection of TNL proteins, we used the PL motifs defined in this study to query the database. We retained the predicted peptides with an e-value below 1e-04 for detection with N and Ma, below 0. 068 for detection with the TIR motifs and below eight for detection with the PL motifs. The protein sequences retrieved were subjected to InterProScan analysis to confirm that they corresponded to TNLs. We also cross-referenced the data with the complete list of proteins from the predicted proteins database available from the GDR website (ftp://ftp.
Identification and structure of TNL-related proteins The nucleotide sequences of predicted TNL genes were extracted with 5 kb of upstream sequence and 5 kb of downstream sequence, with the GDR Sequence Retrieval tool available from http://www. rosaceae. org/retrieve/ sequences. Fgenesh, an HMM-based ab initio gene predictor (gene-finding parameters: Solanum lycopersicum, Vitis vinifera, Hevea brasiliensis, and Populus trichocarpa), was used to predict TNL gene structures. The individual analysis and expert annotation of these predicted genes made it possible to assemble their intron-exon structures. The predicted amino-acid sequences were then analysed with InterProScan 5, which was used to identify the various domains with default parameters and with the whole set of available databases. This approach made it possible to associate exon structures with families of domains.
Sequence alignment and generation of a phylogenetic tree for PL domains We trimmed the 124 sequences to obtain a final alignment composed of 103 PL domains (including 9 PL domains from N (1), Ma (5), RPS4 (1), BS4 (1) and Gro1-4 (1)). The full sequence alignment (246 positions, gaps included) included the three PL motifs. Domain sequences from the five cloned functional reference TNLs, RPS4 [GenBank:CAB50708], N [Swiss-Prot:Q40392], BS4 [GenBank:AAR21295], Gro1-4 [Gen-Bank:AAP44390], and Ma [GenBank:CAR94514], were extracted as previously described. The aminoacid sequences of the domains were manually curated and aligned, using MEGA6 and Muscle with default settings. The phylogenetic tree for PL domains was constructed with MEGA6, using a Maximum Likelihood method with 100 Bootstrap replications and the Jones-Taylor-Thornton model.
Definition and analysis of specific PL-domain signatures Using the complete dataset for TNLs, we confirmed the position of each domain by InterProScan analysis, and we extracted each domain individually. For each of the TIR, NB, NLL, and LRR domains, we removed those (i) encoded by more than two exons and (ii) with domain lengths of less than 450, 950, 260, and 750 bp, respectively. We then performed a rough alignment for each domain, to remove potential aberrant sequences. The final set of sequences obtained was analysed with MEME software. As for other TNL domains, we considered a single exon to correspond to a single domain and we arbitrarily decided to consider exons consisting of more than 400 nucleotides as valid PL domain exons. After this initial selection on a size basis, a rough alignment was used to exclude aberrant sequences.
MEME software was used to identify specific signatures from sequence alignments. The settings used were as follows: 0 or 1 occurrence of the motif per sequence, with a maximum of five different motifs and a minimummaximum motif width of 5-30 amino-acids. MAST and BLASTP analyses conducted with default parameters identified PL domains in predicted peptides. BLASTP analyses with the Plant Genome DataBase website (http://www. plantgdb. org/cgi-bin/blast/PlantGDBblast) led to the identification of PL domain-related sequences and, therefore, TNL sequences. The genomes used for this study were those of perennial (peach, poplar, grape) and annual (barrel clover, potato) dicots, monocots (rice, sorghum), a model plant (Arabidopsis) and a monocot (maize) and a dicot (soybean) with large genomes. We analysed the secondary structure of the PL domains with HHpred and the Chou Fasman Secondary Structure Prediction Server available on the Expasy resource portal, for the 124 sequences subjected to MEME analysis and the associated consensus sequence.
Availability of data and material The datasets supporting the conclusions of this article are either available in the DRYAD repository, doi:10. 5061/ dryad. dp20h, http://dx. doi. org/10. 5061/dryad. dp20h, or in https://www. ebi. ac. uk/ena, http://www. ebi. ac. uk/ena/data/ view/LT555556-LT555568 for biological sequences [EMBL accession numbers LT555556 to LT555568] or included within the article and its additional files.
Parameters (units) Depth range (cm) Value Clay <2 μm (g kg - 3 × 1 m 2 of cover crops were sampled each year. Samples were dried in an oven at 65°C for three days and then weighed. The total aboveground biomass of cover crops was ground into powder prior total N measurements. From the beginning of the experiment in 2010, means of N input originating from the cover crops residues in each of the four treatments were (123 kg ha -1 , 127 kg ha -1 , 125 kg ha -1 and 128 kg ha -1 ) under NTN0, NTN1, CTN0 and CTN1 conditions respectively. The amount of N fertilizer applied under N1 conditions was determined according to the N budget method for maize  [52] , based on the predictive balance-sheet method (Software Azobil, INRA, Laon, France) using the following formula: B þ Rf ¼ ðRi À LÞ þ Mn þ X Where B is the N requirement of the crop, Rf is the residual soil mineral nitrogen content at harvest, Ri is the readily available soil mineral nitrogen in a determined depth of soil before maize planting, L is the soil mineral nitrogen potential loss during the period from analysis of soil N to N-fertilizer application, X is N the fertilizer rate and Mn is the net supply of soil mineral nitrogen during the growing season.
To complement this work, we examined the expression level of seven key ripening regulators in ARF2as fruit: RIN; AP2a; NOR; TAGL1; NR; ETHYLENE RESPONSE FACTOR E1 (ERF. E1); and COL-ORLESS NON-RIPENING (CNR; S4 Fig). All of these ripening regulators were downregulated in the ARF2as fruit at the red stage. The gene expression data obtained correlated with the delayed-ripening phenotype observed in the ARF2as fruit. Fruit with reduced ARF2 levels display altered ripening-associated metabolism Metabolite analysis using high-resolution LC-MS was performed with extracts derived from WT and ARF2as fruit at the green and red stages. A PCA plot in which the entire LC-MS profiles of the different samples were represented was generated. While no separation was observed between the green tissues, the WT and the ARF2as fruit samples could be clearly separated from each other at the red stage  (Fig 2F and S1 Table).
A clear separation between the WT and transgenic fruit samples was observed in principle component analysis (PCA) of the metabolite data (Fig 8A ; S9 and S10 Tables). The red pigmented samples (i. e. ARF2-OX-42R, ARF2-OX-53R and WT-53R) clustered together while the green ones (i. e. ARF2-OX-42G and WT-42G) were clearly separated, both from the red samples and from each other. The yellow sectors of the transgenic fruit (ARF2-OX-53Y) clustered separately, in between the red and green clusters. Targeted analysis of selected flavonoids and glycoalkaloids was subsequently performed on the ARF2-OX fruit samples. Flavonoids generally accumulate during ripening as observed for naringenin chalcone, naringenin and phloretin-di-hexose in the WT-53R samples at as compared to WT-42G fruit (Fig 8B  , upper row ).
Gene expression analysis Three biological replicates of each RNA sample (ARF2-OX-42G; ARF2-OX-42R; WT-G; and WT-R) were labeled and hybridized to the 34K gene EUTOM3 Exon array (http://www. eu-sol. net/science/bioinformatics/data-and-databases/all-databases) and data were analyzed as previously described  [48]. For Reverse-transcription and quantitative real-time PCR (qRT-PCR) DNaseI-treated RNA was reverse-transcribed using the High Capacity cDNA reverse transcription kit (Applied Biosystems) and cDNA was used for qRT-PCR analysis using three biological repeats per fruit stage, with three technical repeats for each sample. Gene-specific oligonucleotides were designed using Primer Express 2 software (Applied Biosystems). Expression of the CAC gene was used as an endogenous control, with the exception of S9.
Accession numbers Sequence data from this article can be found in the EMBL/GenBank data libraries under the following accession numbers: ARF2A (Solyc03g118290; DQ340255), ARF2B (Solyc12g042070; HM143940), ARF3 (Solyc02g077560; DQ340254), ARF4 (Solyc11g069190; DQ340259), ARF9 (Solyc01g096070; XM_010328837. 1), ASR1 (Solyc04g071610; AK326001. 1), Arabidopsis ARF2 (At5g62000; gi|30697610), CNR (Solyc02g077920; DQ672601), RIN (Solyc05g012020; AF448523), TAGL1 (Solyc07g055920; AY098735), FUL1 (Solyc06g069430; AY306155), FUL2 (Solyc03g114830; AY306156), AP2a (Solyc03g044300; HQ586952), NOR (Solyc10g006880; AY573802), GLK2 (Solyc10g008160; JX163897), ACO1 (Solyc07g049530; EF501822), ACS2 (Solyc01g095080; AY326958), ACS4 (Solyc05g050010; NM_001247351), ETR1 (Solyc12g011330; AF043084), ETR2 (Solyc07g056580; AF043085), ETR3 (NR; Solyc09g075440; AY600437), ETR4 (Solyc06g053710; AY600438), ETR5 (Solyc11g006180; AF118844), ETR6 (Solyc09g089610; AY600440), PSY (Solyc03g031860; DQ335097), PDS1 (Solyc03g123760; DQ339100), CKX7 (Solyc08g061930; NM_001279287), LOG8 (Solyc08g062820; XM_004245030), ERF.
Acknowledgments We would like to thank  Richard Immink  and  Ruud de Maagd , Plant Research International B. V. ,  Wageningen ,  The Netherlands  for the Y2H library and yeast strains,  Nir Ohad ,  Tel Aviv University, Israel for the BiFC system and Yuval Eshed, Weizmann Institute of Science, Israel  for the transgenic lines. AA is the incumbent of the Peter J. Cohn Professorial Chair.
Funding: AA is supported by the  Minerva Foundation  (www. theminervafoundation. com)and the  European Union   Seventh Framework Program FP7/2007-2013 'SAMIT' ERC  grant # 204575  (http://cordis. europa. eu/fp7/home_en. html). The work in the AA lab was supported by the  Adelis Foundation ,  Leona M. and Harry B. Helmsley Charitable Trust ,  Jeanne  and  Joseph Nissim Foundation for Life Sciences, Tom and Sondra Rykoff Family Foundation Research and the Raymond Burton Plant Genome Research Fund.
What are the main avenues of research on JAs? Our understanding of JA biosynthesis, perception, signaling, and catabolism have progressed considerably over the past 10 years. There are now plenty of transcriptomics datasets from different plant species in response to various stresses available online. From these, several genes have been identified that could potentially be interesting for plant breeders as they might enhance resistance to pathogens and resilience to unfavorable weather. Fundamental research now needs to focus on understanding how plants integrate various types of stresses and how they prioritize their responses as in a real life situation crops will typically not face a single, isolated stress but a combination of stresses that might occur sequentially or at the same time.
During in situ counts, ticks were found by direct observation following parting of the hair and by palpation. During removal counts the same procedure was followed but ticks were removed upon counting and the dogs were also combed to ensure that all ticks had been counted and removed. The primary assessment criteria was the number of ticks counted on the control and the treated groups of dogs on the various assessment times and days, with efficacy calculations based on geometric (GM) and arithmetic (AM) means. Geometric means were calculated using the tick count data + 1, and 1 was subsequently subtracted from the result to obtain a meaningful mean value for each group. Efficacy of the DPP formulation against adult R. sanguineus (s. l. ) at 2, 8 and 48 h after treatment or infestation was calculated as follows: Efficacy % ð Þ ¼ 100 Â M c -M t ð Þ= M c where: M c = Mean number of live ticks (categories 1, 2, 3 and 6) on dogs in the untreated control group (Group 1) at a specific time point.
By Day 7 after treatment the active ingredients had spread throughout the hair-coat and on the skin and between 63 and 81. 7 % of ticks were killed within 2 h after being released onto dogs from Day 7 through Day 35, dropping to 51. 8 % on Day 42 (it should be noted that the DPP formulation is labelled for monthly reapplication). The rapid acaricidal efficacy observed at 2 h increased to > 90 % by 8 h after each infestation from Day 7 through Day 35. The acaricidal efficacy recorded 48 h after infestation was in concordance with previous measurements performed against R. sanguineus (s. l. ) adult ticks with the same product for 1 month after treatment  [9]. In the present experiment, residual efficacy was assessed for 6 weeks and was above 90 % (GM) for 5 weeks. 0 The earliest speed of kill data against R.
Phylogenetic Data To estimate phylogenetic diversity we used the plastid and nuclear phylogeny of Sarcolaenaceae produced by  Haevermans et al. ( in prep ). The taxon sampling includes 47 species belonging to the family, for a total of 91 Operational Taxonomic Units (OTUs). All ten genera were represented and 66 % of the species were sampled, thereby capturing most of the taxonomic and morphological/ecological diversity and covering the full geographic distribution of Sarcolaenaceae. In addition to the 47 Sarcolaenaceae sampled, 6 species were selected from their sister group, Dipterocarpaceae, and 1 species from the next most-closely related family, Cistaceae  (Dayanandan et al. 1999 ; Ducousso et al. 2004 ; Haevermans et al. in prep ) , all of which served as outgroup taxa. Sequence data were obtained from one nuclear (ITS) and three plastid ( rbcL, psbA -trnH , and psaA -ORF170 ) markers  (Haevermans et al.
( 2001 ) , and another for the age of the stem-group of the clade comprising Leptolaena , Mediusella , Sarcolaena , Xerochlamys and Xyloolaena based on the estimated age of a fossil pollen attributable to this group  (Coetzee and Muller 1984 ). In order to assess the cladogenesis process in Sarcolaenaceae , we measured the degree of imbalance of the Sarcolaenaceae consensus tree topology using the R package apTreeshape  (Bortolussi et al. 2006 ) , in conjunction with the R package ape  (Paradis et al. 2004  ). The imbalance was estimated by calculating the Colless's index  (Mooers and Heard 1997 ). We compared this experimental value against those obtained for 500 simulated trees built under the Equal Rate Markov (ERM) Yule model or the PDA (Proportional to Distinguishable Arrangement) model in which each tree is equally probable  (Mooers and Heard 1997 ) , using the function colless.
PD is a group measure of phylogenetic diversity given by the minimum spanning path along the tree linking all species occurring in a grid cell (see Faith chapter " The PD Phylogenetic Diversity Framework: Linking Evolutionary History to Feature Diversity for Biodiversity Conservation "). For cells with only one species, the PD value corresponds to the branch length from the tip to the root of the tree. MPD is the mean distance (i. e. , mean branch length) between all pairs of species occurring in a given grid cell; this measure provides information on phylogenetic relatedness of the set of species occurring in that cell, controlling for species richness. These two measures were computed using the R package picante  (Kembel et al. 2010 ). The distributions of the three measures (species richness, PD and MPD) were then overlaid on the polygon of Sarcolaenaceae occurrence and plotted on a map of Madagascar, which results in parts of the island not being represented in the overall polygon.
2015 ). In that respect, we focused on Sarcolaenaceae in order to help provide a better understanding of the potential value of PD for conserving Madagascar's biodiversity. A robust analysis of PD requires a dated phylogeny based on dense taxon sampling, as well as reliable data on the distribution of each species. For Sarcolaenaceae , our sampling comprised nearly 70 % of the total species diversity , with good representation from each of the ten genera in the family. We used the most up-to-date and reliable distributional information, based on more than 2000 occurrence points from the collections kept in the herbaria of the Paris Museum and of the Missouri Botanical Garden examined for recent taxonomic revisions and data from the ca. 40-50 new collections made each year since. The result presented in Fig.
Conclusion As indicated earlier, the type of analysis presented here requires a dated phylogeny based on sampling that is representative of the study group, as well as reliable data on the distribution of each species. For Sarcolaenaceae , our sampling comprised ca. 70 % of the total species diversity , with good representation from each of the 10 genera in the family. We were also able to access reliable distributional information from recent taxonomic revisions augmented by ongoing identifi cations made of subsequently collected material. Our study has shown the potential value of determining the spatial distribution of species richness , PD and MPD for understanding whether the current network of protected areas provides adequate conservation of these important biodiversity values and for identifying gaps in the existing network that should be targeted for the establishment of new PAs.
Acknowledgments Anaëlle Soulebeau is funded by a doctoral grant from  Doctoral School ED227, Ministère de la Recherche , at the  Muséum National d'Histoire Naturelle (Paris, France ). The laboratory work was supported by the  ANR -funded project  EVORANGE  ( ANR-09-PEXT-011 ) and agreement No. 2005/67  between Genoscope and the  MNHN  under the project " Macrophylogeny of life " directed by  Guillaume Lecointre. We thank  Nicolas Chazot  and  Benoit Lefeuvre  for their help with the R script and GIS analysis, and  Philippe Grandcolas  and  Sven Buerki  for comments that helped to improve this manuscript.
Microsatellite genotyping of fish individuals Microsatellite markers developed for the Barbus species  [34] [35] [36]  were used to identify the fish species in our study. Genomic DNA was isolated from fin clip samples stored in 96% ethanol using DNeasy Blood & Tissue Kit (Qiagen GmbH, Hilden, Germany). Further diluted DNA (approx. 10 ng/l) served as a template in the following multiplex PCR analysis of microsatellite loci following the protocol described in  [34]. Amplicons were analyzed on an ABI PRISM 3130 Genetic Analyzer (Applied Biosystems) using 500LIZ® Size Standard (Applied Biosystems) and Hi-Di™ Formamid (Applied Biosystems), and genotypes were finally scored using GeneMapper Software version 4. 0 (Applied Biosystems). Due to the tetraploid genome of the investigated Barbus spp. , only selected microsatellite loci with probable disomic inheritance  [34]  were applied in our study.
The MICRO-CHECKER program  [37]  was used to check for microsatellite null alleles in the Barbus populations and, subsequently, locus Barb21 was excluded from our study. Overall, 19 loci (Barbus2, Barbus22, Barbus26, Barbus28, Barbus31, Barbus32, Barbus36, Barbus37, Barbus40, Barbus41, Barbus47, Barbus49, Barbus50, Barbus56, Bar-bus62, Barbus63, Barbus65, Barb59 and Barb79) were used afterwards following a Bayesian clustering approach implemented in the STRUCTURE software  [38]. The program was run for five independent runs assuming an admixture model and the model of correlated allele frequencies, using 1,000,000 iterations after a burn-in period of 100,000 iterations for K = 2 clusters. The Introgress package  [39]  implemented in the R statistical software was used to calculate the hybridization index (h-index) in sympatric populations.
e. prevalence and abundance of metazoan parasites were calculated according to Bush et al. [47]. The individual abundance of the myxozoan parasites was not taken into consideration (because these parasites cannot be quantified as in the case of other metazoan parasites) and only the prevalence of this parasitic group was taken into account in the analyses. The effect of sampling effort on parasite species richness was corrected using the Chao1 estimator  [48] , and was calculated using the EstimateS program  [49]  on the basis of abundance data excluding data on myxozoan parasites. Similarities between metazoan parasite communities based on presence/absence data (Jaccard index) were computed in PAST  [50]. Table  1  Characteristics of Barbus populations and their metazoan parasite communities.