dataesr/scientific-paragraphs-categorization huggingface dataset:
Southern blot hybridization Southern blotting followed the protocol described by  Koukalova et al. (2010)  using rDNA probes, A-genome-specific IGS probe (IGS-A) and C-genome-specific IGS probe (IGS-C) labelled with 32 P (DekaPrime kit, Fermentas, Lithuania). The hybridization signals were visualized by Phosphor imaging (Typhoon 9410, GE Healthcare, PA, USA) and signals were quantified using ImageQuant software (GE Healthcare).
The fragments were ligated to a pDrive vector and sequenced. Sequences were submitted to GenBank under accession numbers KT008109 ('Darmor' L), KT008110 ('Darmor' S), KT008111 ('Asparagus kale') and KT008112 ('Yudal'). Subrepeats were analysed using the YASS genomic similarity search tool  (Noe and Kucherov, 2005)  and Tandem Repeats Finder  (Benson, 1999). A phylogenetic tree was reconstructed using the Seaview program  (Gouy et al. , 2010). The A-genome-specific probe (IGS-A) was prepared by digestion of a plasmid DNA of a B. rapa IGS clone (GenBank: KT008109) with Tru1I. The resulting 1Á4-kb Tru1I fragment was cloned and sequenced (pBrapsr2 clone). Its 1436-insert was found to contain the majority of the B. rapa-specific C-subrepeats and part of the B-subrepeats. We checked the specificity by mapping the next-generation sequencing (NGS) reads to the IGS-A sequence.
In silico rDNA sequence assembly Regions (18S, ITS, 26S) of 35S rDNA of B. napus, B. oleracea and B. rapa were assembled into contigs by mapping of Illumina reads to reference 18S-ITS1-5Á8S-ITS2-26S sequences obtained from GenBank (AF128100. 1, KM538957, KF704394, GQ891874, X51576. 1, X13557. 1). The 35S rDNA sequence of Arabidopsis thaliana (accession number: X52322) was used as a reference to aid assembly. The following Illumina reads archives were used: B. napus 'Darmor' (ERR457740, ERR457754 and ERR457738), B. napus 'Yudal' (ERR457757), B. rapa (SRR1296482 and SRR5479996) and B. oleracea (SRR074124). Sequence downloads and basic read manipulations of genomic reads were done with the aid of the Galaxy Server  (Goecks et al. , 2010). Different regions of 35S rDNA sequences (contigs) were ultimately assembled into one single consensus sequence for each species using CLC Genomics Workbench 6.
Sequence polymorphisms of rDNA units The availability of genomic reads allowed us to reconstruct 18S-5Á8S-26S operons in B. napus and the diploid species B. rapa and B. oleracea (Table  1 ). The reads were mapped to the reference sequence which had been assembled from sequences in GenBank (for accession numbers, see Methods). All three rRNA genes (18S, 5Á8S and 26S) were recovered including intervening ITS1 and ITS2 sequences. From the mapped reads, we generated a single rDNA consensus sequence for each diploid species and two cultivars of B. napus ('Darmor' and 'Yudal'). The total length of the recovered 18S-5Á8S-26S region was 5803 bp in all samples. Pairwise alignment of consensus sequences revealed 72 polymorphic sites (1Á24 % divergence) between B. rapa and B. oleracea units. Brassica rapa had a slightly higher intragenomic variation [ten high-(!20 %) frequency SNPs per unit] than B.
Acknowledgements We thank  Dr Zuzana Tesa  rikov a and Kv eta Dofkov a (both from  Institute of Biophysics ASCR ) for the help with DNA extractions. We acknowledge the  BrACySol BRC (INRA Ploudaniel, France) , which provided most of the seeds of B. napus cultivars as well as the B. rapa and B. oleracea lines used in this study. We thank  Dr Ian Bancroft  ( John Innes Centre, Norwich, UK. ) for a gift of B. napus 'Tapidor' seeds. The work was funded by the  Czech Science Foundation  ( P501/13/10057S ), the  French National Research Institute  for.
Root hairs are involved in water and nutrient uptake, and thereby in plant autotrophy. In legumes, they also play a crucial role in establishment of rhizobial symbiosis. To obtain a holistic view of Medicago truncatula genes expressed in root hairs and of their regulation during the first hours of the engagement in rhizobial symbiotic interaction, a high throughput RNA sequencing on isolated root hairs from roots challenged or not with lipochitooligosaccharides Nod factors (NF) for 4 or 20 h was carried out. This provided a repertoire of genes displaying expression in root hairs, responding or not to NF, and specific or not to legumes. In analyzing the transcriptome dataset, special attention was paid to pumps, transporters, or channels active at the plasma membrane, to other proteins likely to play a role in nutrient ion uptake, NF electrical and calcium signaling, control of the redox status or the dynamic reprogramming of root hair transcriptome induced by NF treatment, and to the identification of papilionoid legume-specific genes expressed in root hairs.
This is likely to provide the first RNAseq dataset from M. truncatula root hairs, thereby the most exhaustive transcriptome available to date for this cell type, and thus a valuable resource to investigate root hair functions in legumes. Expert gene analyses have been focused in order to uncover ROS networks and pump, transporter and channel machineries likely to play a role in two major functions of root hairs, namely nutrient ion uptake and early molecular dialogue with rhizobia following NF perception. Moreover, using whole genome comparisons, we identified legume-specific genes expressed in root hairs, responding or not to NF. Altogether, these results constitute a promising dataset for researchers interested in plant mineral nutrition, rhizobial symbiosis, and legume specificities.
General Features of the M. truncatula Root Hair Transcriptome RNA sequencing was carried out on two independent biological replicates for each of the three treatments (H 2 O, NF 4 h, NF 20 h), yielding six root hair transcriptomic datasets. We generated ∼366 million high-quality paired-end reads, which were mapped to the M. truncatula predicted transcriptome (Mt4. 0v1). About 266 million (ca. 73%) of paired-end reads were unambiguously mapped (Table  S1 ). In order to assess the experimental variability, the two replicates obtained for a given treatment were compared by plotting the expression levels (expressed in Count per Million Reads) found in one dataset against those found in the second dataset (Figure  S1 ). Linear regression analysis yielded R 2 coefficient of 0. 99, 0. 99, and 0. 97 for the control, NF 4 h, and NF 20 h treatments, respectively.
For a large majority of genes (≥65% whatever the treatment), the fold change in expression level between the two repetitions was lower than 1. 5. As could be anticipated, we noticed that the biological variability in terms of expression level fold change was larger for genes displaying low expression levels. In order to further assess consistency among biological replicates for the transcriptome datasets, we subjected the expression data derived from the six datasets to principal component analysis (PCA; Figure  S2 ). The first component (Dim1) clearly separated the control from the NF treated samples (Figure  S2A ) and the overall distribution confirmed that there was no biological repetition bias (Figure  S2B ). To validate RNA-seq based differential gene expression in response to NF treatments, quantitative real-time RT-PCR (RT-qPCR) tests were also carried out.
" It is also worth to note that for both treatments (NF 4 h and NF 20 h), the analysis of the downregulated gene list gave rise to about twice more significantly enriched GO terms than the up-regulated gene list. Overrepresented semantic terms in the description of differentially expressed genes list was generated using the Gene Cloud software  (Krouk et al. , 2015). This analysis also identified the words "transporters" (or transport, uniport, symport) and "redox" (or oxydation, reduction, oxydoreductase) as amongst the enriched terms, especially in the description list of down-regulated genes at time 4 h with respect to the terms related to membrane transport (Figure  S4 ).
(A) Up-regulated genes. (B) Down-regulated genes. One thousand seventy genes were annotated using agriGO toolkit (http://bioinfo. cau. edu. cn/agriGO/index. php). GO terms identified with an adjusted p < 0. 05 are enclosed into boxes. Red and blue boxes refer to the changes in expression levels induced by the NF 4 h and NF 20 h treatments, respectively. Dashed arrow indicates that all the GO levels have not been represented. hairs are listed in Table  S5. Arabidopsis-Medicago phylogenetic relationships obtained for the Shaker, AHA, SLAC, ALMT, MSL, MCA, CSC/OSCA, CNGC, GLR, and ANN families are displayed in panel A of Figures  S12-S19 , respectively. The expression levels (in FPKM, obtained from the control root hair dataset) of genes belonging to these families are provided in panel B of these figures.
Further, comparison with the single previous report on root hairs  (Breakspear et al. , 2014)  is not straightforward since both the transcriptomic methods (gene chips vs. RNA-seq) and plant culture conditions were different. Indeed,  Breakspear et al. (2014)  used culture medium supplemented with aminoethoxyvinylglycine (AVG), an inhibitor of ethylene biosynthesis, probably leading to changes in hormone balance and root growth when compared with our experimental conditions. For instance, the two cytokinin response regulator genes MtRR4 (Medtr5g036480) and MtRR9 (Medtr3g015490) have been recently shown to be induced by NF treatment in presence of AVG, while only MtRR9 is induced in absence of AVG  (Van Zeijl et al. , 2015). In agreement with this result, only MtRR9 is identified as induced by NF in our transcriptome dataset (Table  S2 ).
It is also worth to note that M. truncatula root hairs expressed two homologs (Medtr6g034030 and Medtr6g045200) of an Arabidopsis anion channel (AtSLAH3) known to play a role in pollen tube tip growth  (Gutermuth et al. , 2013). Altogether, these results provide evidence that the present transcriptome datasets constitute a valuable resource to further investigate major functions of root hair biology in M. truncatula.
A Panoply of Nutrient Ion Transporters that Includes Unexpected Systems In silico mining of the transcriptome dataset in order to identify plasma membrane transport systems likely to play a role in plant mineral nutrition revealed candidate genes displaying significant expression levels in root hairs, whatever the nutrient ion we considered. The present results complete previous analyses establishing a genomic inventory of M. truncatula transporters  (Benedito et al. , 2010). Altogether, the results indicate that the M. truncatula root hair is a cell type particularly dedicated to plant mineral nutrition. Candidate genes likely to play a role in uptake from the soil solution of the macronutrients potassium, nitrate, ammonium, phosphate, and sulfate are listed in Figure  7 ; Table  S5. Two families of K + transport systems, named Shaker and HAK/KUP/KT, have been identified in Arabidopsis as playing a major role in K + uptake from the soil solution  (Véry and Sentenac, 2003).
The Shaker K + channel family is strongly conserved in plants, as well as its regulatory network that comprises protein kinase from the CIPK (CBL-Interacting Protein Kinase) family  (Cuellar et al. , 2013; Sharma et al. , 2013; Véry et al. , 2014). In Arabidopsis, two Shaker inward K + channel genes are expressed in root hairs, AtAKT1 and AtKC1, giving rise to heteromeric K + channels involved in passive "low affinity" K + uptake from the soil solution  (Sharma et al. , 2013). The present transcriptome datasets indicate that only two inward Shaker channel genes are expressed in M. truncatula root hairs too, Medtr4g113530 and Medtr3g108320, which are the closest relatives of the Arabidopsis AtAKT1 and AtKC1 (Figure  S11 ). This suggests that these two genes encode Shaker subunits able to form heteromeric K + channels like in Arabidopsis.
This activation would occur by direct Ca 2+ -binding to its EFhands and Ca 2+ -induced phosphorylation by CBL1/9-CIPK26 complexes  (Drerup et al. , 2013). It is tempting to speculate that similar regulation mechanisms occur in M. truncatula root hairs too, giving rise to interactions between ROS and Ca 2+ signaling pathways, since close homologs of Arabidopsis CBL1/9 and CIPK26 can be identified in the present transcriptome dataset (Table  S2 ; Figure  8A ). This would make sense with the fact that ROS signals and Ca 2+ influx are very rapidly induced upon NF treatment, within a few seconds and then decreased (for review, see  Felle et al. , 1998; Puppo et al. , 2013). Except within the peroxidase family, only few genes involved in the ROS network as described by  Mittler et al. (2004)  are identified by the present data as sensitive to NF treatment (Table  S6 ).
RNA-seq Bioinformatic Treatment and Analysis Each RNA-seq sample was subject to the same pipeline from trimming to count of transcript abundance as follows. The raw data (fastq) were trimmed to keep only bases with Phred Quality Score >20 and sequence read length >30 bases. Bowtie v2 was used to map reads to the M. truncatula transcriptome (Mt4. 0v1 with-local option). The abundance of mRNAs was calculated by a local script which parses SAM files and counts only pairedend reads for which both reads map unambiguously to the same gene (i. e. , reads mapping to multiple positions were discarded). According to these rules, around 73% of PE reads aligned to transcripts, in each RNA seq sample (Table  S1 ). Differential gene expression across samples was analyzed using the EdgeR package (Version 2.
4. 6) in the statistical software "R" (Version 2. 15. 0). Raw read counts normalization was carried out with the EdgeR (v 2. 4. 6) software [with the Counts Per Million (CPM) function]. This method allows normalizing the data as a function of the size of the library. For each experimental treatment (control, NF 4 h, and NF 20 h) the libraries corresponding to the two replicates were normalized together, both libraries having the same normalization factor. For variability analyses, only genes with an average CPM >0 were considered. To control the false discovery rate, adjusted p-values were calculated using the Benjamini and Hochberg procedure  (Storey and Tibshirani, 2003). Genes with an adjusted p < 0. 05 were considered as being differentially expressed. In a complementary, we also wanted to explore the relative expression levels of the different genes with FPKM values.
To achieve this, we aligned the quality checked reads to the M. truncatula annotated genome (Mt4. 0v1) using TopHat (version tophat-2. 0. 10). Here again, reads mapping to multiple positions were discarded. As many as 85% of the read pairs aligned unambiguously to the genome. We then used Cufflinks  (Trapnell et al. , 2012)  to estimate transcript abundance and generate FPKM values for all the corresponding genes (Table  S2 ). Assignment of Gene Ontology (GO) terms to differentially expressed genes was performed using agriGO (http://bioinfo. cau. edu. cn/agriGO/index. php) toolkit  (Du et al. , 2010). GO enrichment analysis of differentially expressed genes at 4 and 20 h following NF treatment was implemented using Singular Enrichment Analysis (SEA) with default parameters and M. truncatula genome locus (v4) as a background  (Du et al.
Phylogeny Analyses Except for the MSL family, polypeptide sequences were first aligned with Muscle (V3. 8. 31) and phylogenetic tree was generated with PhyML software (substitution model, LG matrix) using maximum-likelihood method and 1000 bootstrap replicates on Mobyle Pasteur website (http://mobyle. pasteur. fr/ cgi-bin/portal. py#welcome). For MSL family, sequences were first aligned with Muscle using the conserved MscS domain as template to force the alignment around it  (Jensen and Haswell, 2012). Phylogenetic trees were drawn with Dendroscope (http:// ab. inf. uni-tuebingen. de/software/dendroscope/). Bootstrap values are indicated in gray at the corresponding nodes. The length of scale bar indicates the number of nucleotide substitutions per site.
Data Deposition RNA-seq data from this article were deposited at Gene Expression Omnibus (http://www. ncbi. nlm. nih. gov/geo/, accession no. GSE67921) and at CATdb (http://urgv. evry. inra. fr/CATdb/; Project: NGS2013_04_POIL) according to the "Minimum Information About a Microarray Experiment" standards.
Reactions were run on the Chromo4 Real-Time PCR Detection System (Bio-Rad; www. qiagen. com), and quantification was performed with the Opticon Monitor analysis software version 3. 1 (Bio-Rad). Data were analyzed with RqPCRBase, an R package working in the R computing environment  (Hilliou and Tran, 2013). The mRNA levels were normalized against two constitutively expressed endogenous genes (a38: Medtr4g109650; a39: Medtr4g046877). PCR for each biological replicate was performed in three technical replicates. For each reaction, 5 µl of 60-fold-diluted cDNA and 0. 3 µM primers were used. The initial denaturing time was 10 min, followed by 40 cycles at 95 • C for 10 s and 60 • C for 1 min. Specificity of amplification was confirmed by observing a single peak in the dissociation curves at the end of the PCR procedure.
ACKNOWLEDGMENTS This study was supported by the  ANR (Agence Nationale de la Recherche ). We are grateful to  Marie Le Gleuher-Pacoud  for helpful lab assistance and  Hugues Driguez  for providing initial Nod factors preparation. We are grateful to the  Genotoul bioinformatics platform Toulouse Midi-Pyrenees (Bioinfo Genotoul)  for providing computing resources.
FUNDING This work was funded by an  Agence Nationale de la Recherche (ANR)  project ( CAROLS-Channels and Reactive Oxygen species in Legume root hair: role in Symbiosis with Rhizobium - ANR-11-BSV7-010-02 ). This work was supported by the  "Institut National de la Recherche Agronomique , " the  "Centre National de la Recherche Scientifique , " the  University of Nice Sophia Antipolis  and the  French Government (National Research Agency, ANR)  through the  LABEX SIGNALIFE program  ( ANR-11-LABX-0028-01 ). ID was supported by a post-doctoral fellowship from the  ANR  ( CAROLS :  ANR-11-BSV7-010-02 ). AD was supported by  doctoral fellowship  ( PER, Prix d'Encouragement à la Recherche ) from the  "Province Sud de la Nouvelle-Calédonie. "SC, SF, ER are grateful to the  Labex Arcane  ( ANR-11-LABX-0003-01 ) and the  Carnot Polynat Institute  for partial support of this study and the  NMR and Mass Spectrometry Platforms of the ICMG  ( FR 2607 ).
Statistical Analysis All data were analyzed according to an analysis of variance using PASW Statistics 18 (version 18. 0. 2, SPSS Inc. , Chicago, IL). For all data, the individual duck was used as the experimental unit except for feed intake and FCR during the experimental period (58 to 87 d) in which the pen (n = 4/group), which was distributed according to a block model, was the experimental unit. Data of bird performance (BW, FCR, feed intake, and carcass composition) were analyzed including the experimental group in the model (3 levels: C, SFC, or SFT). To normalize the values of relative weight expressed as percentage, such as liver, which is expressed as % BW, data were transformed as log(x) or arcsin √ (x) when ranging between 0 and 10% prior to analysis. Means between treatments were compared using the Tukey's HSD test, except the means of intake of whole cereal and protein-rich pellets intake, which were compared using the Paired t-test.
For this purpose, the CO 2 and water vapor mole fractions are measured at the inlet and outlet (infrared gas analyzers, Licor 7000; Licor), and the mass flow of air at the inlet thermal mass flow meter (Sensiflow iG; ABB). Water vapor mole fraction measurements are used to correct for the dilution of CO 2 and the variation of airflow rate at the outlet due to the vapor added or removed in the dome by ecosystem transpiration and humidity control. A custom-made manifold system (interconnected solenoid valves, 750 series; Matrix) allows for switching the air fluxes to the infrared gas analyzers from one dome to the other. It takes 12 min to analyze the atmospheric CO 2 concentration of the 12 domes. Missing values or known inaccurate NEE values during unavoidable experimental work (manual watering, mowing, sampling, checks) caused by the respiration of the persons entering the domes were gap-filled by using the equation NEE = f(photosynthetically active radiation), where f is a rectangular hyperbola fitted with the data of the day before or after the disturbance  (53).
To provide conservative results, all statistics were performed using only days with less than 33% of gap-filled NEE data. The C flux partitioning algorithm Reichstein et al. (54)  (www. bgc-jena. mpg. de/∼MDIwork/ eddyproc/index. php) was used to estimate the daytime R eco. Other Measurements. Ecosystem evapotranspiration was measured continuously by the lysimeter weight changes over time. Four shear beam load cells with an accuracy of ±200 g (CMI-C3 5,000 kg, Precia-Molen) are used for each lysimeter. WUE was calculated as mg of CO 2 fixed per g of H 2 O lost. Soil water content was continuously measured at three soil depths (7, 20, and 50 cm) with TDR probes (TRIME Pico 32, IMKO Micromodul-technik, Germany Ettlingen). Reported data relate to the 0-60 cm depth and are expressed relative to soil water content at field capacity.
Microbial nitrification potential was determined on soil samples collected at the end of the experiment, according to the method of Lensi et al. (55)   Statistical Analysis. Linear mixed effects models as available in the R "nlme" package (57) were used to perform repeated-measures ANOVAs on the effects of CO 2 , ECE, Time, and their interactions on fortnightly averaged values of carbon and water fluxes, with the Ecotron's macrocosms as a random factor accounting for temporal pseudoreplication. The statistical models were then simplified to reach the most parsimonious models by using the automatic model simplification "step" procedure based on Akaike's Information Criterion. Planned pairwise comparisons between the means of the treatments at different time periods were performed by using ANOVAs.
ACKNOWLEDGMENTS. Ch. Collin and the experimental field team at Centre d'Ecologie Fonctionnelle et Evolutive (CEFE)-CNRS as well as the technical staff of INRA Grassland Ecosystem UREP and UERT groups are thanked for extracting the intact soil monoliths. We also thank B. Buatois and the Plateforme d'Analyses Chimiques en Ecologie (CEFE and LabEx CEMEB) for the nitrification analyses. This study was supported by the ANR VALIDATE project grant and the AnimalChange project funded by the European Community (FP7/2007-2013, Grant 266018). This study benefited from the CNRS human and technical resources allocated to the ECOTRONS Research Infrastructure as well as from the state allocation "Investissement d'Avenir" AnaEE-France ANR-11-INBS-0001. The Languedoc Roussillon Region and the Conseil Général de l'Hérault also participated in the funding of the Ecotron.
Genes coding for TFs are typically obtained by homology between the genome sequence and a database of TFs (such as RegulonDB  [8]  or Prodoric  [9] ). In these databases, each TF has an associated position weighted matrix (PWM), which estimates the affinity between the TF and a potential binding site (BS) in a promoter region. The low specificity of current methods for identifying transcriptional regulations means that the number of TF/BS affinities found is usually huge, while, in practice, only a few of them correspond to observed regulatory interactions. Nevertheless, even if many of the predicted interactions have a low probability of occurrence, or are never observed, it is reasonable to assume that the TRN reconstructed from them contains most of the physical interactions that occur for a given process and therefore the network should be able to explain co-expression of genes.