dataesr/scientific-paragraphs-categorization huggingface dataset:
Videos analysis Preliminary observations of hornet's behavior showed that in Experiment 1, some food sources could be consumed within 10 minutes of the presentation of the stimuli. Thus, in this experiment, hornet behavior was only analyzed during the first 10 minutes of each recording. In Experiment 2, none of the odorant sources were consumed, so hornet behavior was analyzed during the full 15 min of the experiment. Videos were visualized using Movie Maker V. 2011 software (Microsoft Corporation, Redmond, USA) allowing us to precisely record the number of visits and the amount of time hornets spent on each stimulus. At each separate occasion a hornet entered a Petri dish (at least with its entire head), times of entry and of exit were recorded. Hornet attraction to the different stimuli was measured according to two variables: 1) Visit number: the total number of hornet visits during each session; and 2) Total visit duration: the sum of all individual visit durations performed by hornets during this session.
Even fewer studies have been made to evaluate the quality and accuracy of de novo scaffolds  [29]  (Table  1 ), as the major limiting step is the availability of a high quality reference genome sequence to benchmark an assembly resulting from processing short reads. DNA was extracted from the O. tauri strain in 2001 (OT-2001) and in 2009 (OT-2009) and 40 millions pairedend DNAseq reads were generated from each extraction. These datasets were used to compare the output of three de novo assembly algorithms. The resulting assemblies were benchmarked against the O. tauri sequenced genome to estimate their quality and the percent of the genome covered. Combined with RNAseq data, this data led to a significant improvement of the reference genome sequence by resolving 1194 gaps corresponding to 460 kb and resulting in a remarkable improvement of the 7699 protein coding genes models.
Data We used the O. tauri whole genome sequence as a reference (GenBank accession number CAID01000001 to CAID01000020), sequenced on two BAC and five shotgun libraries  [7]. The scaffolding was improved by using information about the location of each contig in a BAC library hybridized to macroarrays  [7] , leading to 20 scaffolds representing a total of 12. 56 Mb, corresponding to 20 chromosomes. The reference genome assembly contained 1671 gaps as a consequence of low coverage (7X). The culture used for the reference genome sequence came from a natural sample of O. tauri isolated 1995 in the Thau Lagoon  [11, 12]  and maintained by serial subcultures using 50 ml plastic tissue culture flasks in 20 ml K medium at 20°C under 100 μE s - 1  m -2 constant light in  Banyuls sur mer. Every 2 to 3 weeks the cells reach a stationary phase (at a concentration of approximately 3 10 7 cells.
Both 2001 and 2009 cultures were non-axenic and contained bacteria, as judged from the presence of bacterial contigs in the assemblies  [35]. As the OT-2009 dataset corresponded to a clonal strain, this dataset was used for analysis of de novo assemblers and genome update. The clonal strain resequenced in 2009 has been submitted to the Roscoff Culture Collection under accession number RCC4221. The Illumina dataset have been deposited in the SRA archive under accession numbers: SRX026855 and SRX030853.
Improving a historical reference genome Gap closing was performed in 4 steps using the OT-2009 dataset (1) de novo assembly, (2) IMAGE and (3) PCR sequencing (4) CRAC. De novo scaffolds recruitment to close gaps in the reference genome sequence was done as follows. De novo scaffolds were aligned onto the reference genome sequence using blastn. If the scaffold aligned onto the reference over 200 bp with 95% identity and with at least 50 bp on each side of a gap, the sequence of the scaffold was used to close the gap. As de novo assemblers may discard some informative low copy reads, we also used raw reads to improve the reference genome with two further steps. In a second step, we performed local iterative de novo assemblies using IMAGE  [42]  (version 2. 1) and the 41 millions paired-end reads.
We divided the genome into 597 super-contigs corresponding to n = 577 gaps and chromosomes (n = 20). IMAGE aligned the 41 M pairedend reads Illumina dataset against these super contigs using BWA (with default parameters). IMAGE subsequently gathered paired-end reads for which only one of the paired reads mapped at the end of one of two super contigs separated by a gap. If at least 10 paired-end reads were gathered, IMAGE performed a local assembly of these paired-end reads to elongate contigs iteratively. Since the publication of the first version of the genome, primers have been designed manually to fill additional gaps, especially around coding regions. DNA from PCR were sent to sequencing platforms and this enabled 134 additional gaps in the updated genome version to be closed. As a last step we used CRAC, a sensitive mapping method that uses a k-mer profiling approach of reads onto a reference genome  [43].
Genome evolution between 2001 and 2009 OT-2001 and OT-2009 reads were aligned on the reference genome with BWA with default parameters  [34]. We used custom C scripts to scan the pileup files to call variants. There were 11 760 029 sites covered by a minimum of 10 reads and a maximum, which was chosen as 220 for OT-2001 and 256 for OT-2009 reads were retained for the analysis of the OT-2001 (corresponding to 125% of the average genome coverage for each library), to discard low covered regions and possible duplicated regions in the reference genome. Candidate substitutions were identified when 99% of the OT-2001 reads were consistent with the reference nucleotide and 99% of the OT-2009 reads were consistent with the variant. This led to 12 candidate substitutions. In order to confirm each of these substitutions, we designed primers (Additional file 1: Table  S1 ) to sequence 100 bp each side of the substitution in the OT-2001 and the OT-2009 samples.
The position of the substitution within the gene and the type of mutations (non-synonymous, synonymous, non-coding, nonsense) was obtained from manual inspection of the alignments of the 2001 and 2009 coding sequences. We used TMHMM to identify transmembrane domains (http://www. cbs. dtu. dk/services/TMHMM-2. 0/)  [44]. The information about the gene families (number and presence of homologous genes) within sequenced green alga and land plants genomes, corresponding to the genes containing non-sense or frameshift mutations, were retrieved from the pico-PLAZA database  [45]. The absence of a homologous gene was further confirmed by a tblastn against the genome sequence. To investigate copy number variations between 2001 and 2009, we analysed the coverage over 50 bp windows along the chromosomes.
Updated genome sequence annotation RNAseq data was obtained from cells grown under diurnal LD cycles (12L12D). As most genes are expressed rhythmically in these conditions  [46] , we isolated RNA every 3 hours over a 24 hours cycle and pooled the samples for sequencing. RNA was extracted using the RNeasy-Plus Mini kit (Qiagen, Hilden, Germany) following the manufacturer's instructions. Contaminating DNA was removed using RQ1 RNAse-free DNAse (Promega Corporation, Madison, US). Poly-A RNA was isolated and paired-end librairies were generated following the protocol from the Illumina mRNA-Seq Sample Prep Kit. Sequencing was carried out on a single lane of Illumina GIIx and 76 bp paired-end reads were obtained. RNAseq data was used to guide the annotation procedure using the annotation pipeline developed at Gent University.
Similar scripts can be downloaded from https://mulcyber. toulouse. inra. fr/projects/eugene/. The updated genomic sequence of O. tauri was annotated by using the EuGene  [47, 48]  gene finding system. Both Eugene (ab initio) as well as Splice-Machine  [49]  were specifically trained for O. tauri datasets. This pipeline integrates homology information derived from proteins sets of other microalgae from the Mamiellophyceae family; Bathycoccus prasinos RCC4222 (a clonal lineage re-isolated from RCC1105,  [10] ), Micromonas pusilla RCC299 and CCMP1545  [9] , Ostreococcus lucimarinus  [8] , ESTs and full-length transcripts from Ostreococcus tauri that could be collected from NCBI, and all junctions present from mapping the present RNAseq dataset. Given the high density of the gene content in O.
De novo assemblies of O. tauri's genome We generated de novo assemblies of the O. tauri genome using 41 million paired-end 76 bp Illumina reads from the OT-2009 strain. The three different assembly algorithms produced between 1402 to 2080 scaffolds, with a weighted median size length (N50) of 9,539 to 14,550 bp (Table  2 ). The total assembly size varied from 12. 3 to 12. 8 Mb and corresponds to 94 to 96% of the complete Genbank reference genome sequence. Among the three assemblies, ABySS and CLCbio produced assemblies with better contiguity; they had fewer scaffolds (CLC: 1402 and Ab: 1490) with greater N50 (Ab 14550, CLC 14519) and both covered 96% of the Genbank reference genome sequence. ABySS produced the longest assembly of 12. 8 Mb, closest to the expected O. tauri genome size. The alignment of the de novo assembly generated by ABySS on to the O.
8, 84. 9, 87. 2 for ABySS, Velvet and CLCbio respectively, Fisher exact test: p-value < 2. 2x10 -16 for all 3 assemblers). The number of CDS included from start to stop codon within a scaffold was 3101 (41. 5%) for Velvet, 3363 (42. 7%) for ABySS and 3274 (41. 5%) for CLCbio. To estimate the impact of sequencing depth on reference genome coverage and de novo assembly, we randomly sampled paired-end reads from our dataset to produce seven subsets corresponding to a 10, 25, 80, 125, 200, 225 and 250 fold sequencing depth. The different sampled paired-end reads sets were aligned against the reference genome using BWA and reassembled de novo. The obtained scaffolds were aligned against the reference genome using NUCmer. Figure  2  shows the relationship between raw reads and scaffolds genome coverage, and sequencing depth.
Genome evolution between 2001 and 2009 Comparison of the OT-2001 and OT-2009 datasets enabled us to identify 8 nucleotide substitutions, 2 deletions : percentage of aligned bases against the reference genome sequence and against the CDS sequences. 3 : percentage of complete CDS within a single scaffold. and 1 insertion that had occurred in this strain between the 2001 and 2009 cultures (Table  4 ). All except the insertion were confirmed by independent Sanger sequencing on the OT-2001 DNA and the OT-2009 DNA. The predicted insertion in the first 145 bp of chromosome 9 could not be amplified because of its proximity to the telomere CCCTAAA repeats. One substitution is synonymous, 6 are non-synonymous and one corresponds to a nonsense mutation (Table  4 ). In total, two substitutions result in the introduction of a stop codon in a coding sequence (the nonsense mutation and one of the deletions).
Annotation update Gene prediction from the updated genome sequence, followed by manual editing by experts, led to the annotation of 7699 protein coding genes, 39 tRNA and 319 transposable elements (TE) (Table  5 ). Compared to the annotation of the historical genome sequence, (proteincoding) genes are longer and contain fewer introns (Table  5 ), while the proportion of validated introns -as measured by RNAseq -has risen drastically from 7% to 89%. The complete updated genome sequence has been submitted to Genbank and is available under accession numbers CAID01000001 to CAID01000020. Old gene model names are provided as synonyms in new gene models and the link between updated gene and the previous annotations can be browsed via ORCAE (bioinformatics. psb. ugent. be/orcae/overview/OsttaV2).
Acknowledgements We would like to thank the GenoToul bioinformatic platform (bioinfo. genotoul. fr),the  ATGC Bioinformatics Platform  and the computing center  HPC@LR  for access to computing facilities. The Illumina sequence data analyzed in this study were produced by the  US Department of Energy Joint Genome Institute  http://www. jgi. doe. gov/within the  Community Sequencing Program   CSP-129. PCR Sequencing was performed on the sequencing platform of the  LGDB Lab in Perpignan (Richard Cooke ) and on the BIO2MAR platform in Banyuls sur mer. We are grateful to  Sebastien Peuchet  for help with the PCR resequencing of predicted substitutions,  Elodie Desgranges  and  Jérémie Heurtault  for help with manual annotations. Part of this work was funded by  ANR-12-BSV7-0006-01 "REVIREC" (NG ) and the  European Community 's  7th Framework program FP7  under grant agreement  n°254619  (GP and AEW).
BV, SR and YVDP would like to acknowledge the support of  Ghent University (Multidisciplinary Research Partnership 'Bioinformatics : from nucleotides to networks'; http://www. nucleotides2networks. be). ER and AC acknowledge the support from the  Défi MASTODONS SePhHaDe CNRS , the  Labex NUMev , and the  Projet Investissements d'Avenir France Génomique. RBM acknowledge Nice-Sophia University  for funding.
Ground data collection Inside each ESU, three different techniques were applied following the sampling scheme described in Figure  2 , consisting of randomly selecting 11 points within each 20 × 20 m ESU. All of these techniques are based on gap fraction measurements: DHP, ceptometer, and the novel PASTIS-57 technique  (Baret, Lecerf, et al. 2010 ). The spatial variability was taken into account by averaging over the three ESUs sampled within each site. The DHP technique uses a digital camera with a fish-eye objective to measure gap fraction over a wide range of viewing angles  (Weiss et al. 2007 ). Using a professional camera, Nikon Coolpix 995, true-colour high-resolution images were taken 15 cm from the ground level, looking upwards, level and aligned with the magnetic south at the same location as the PASTIS-57 sensors.
The camera was set to automatic exposure as recommended by  Garrigues et al. (2006)  to prevent saturation. Although the DHP technique can be performed under varying illumination conditions (direct and diffuse light;  Garrigues et al. 2006 ), all DHP measurements were performed under similar conditions (morning and/or afternoon) over the same ESU and field campaigns. The images were processed using CAN-EYE software (www. avignon. inra. fr\can-eye) to derive actual PAI (true PAI) and FAPAR values. In the computation of the actual PAI, the leaf clumping is taking into account according to the method developed by  Lang and Xiang (1986). The ceptometer uses a line quantum sensor (Cavadevices, Buenos Aires, Argentina) that measures the photon flux between 300 and 1000 nm, and up to 3000 µmol m -2 s -1 , over a 1 m linear probe.
FAPAR was estimated by measuring the incident PAR i at the top of the canopy and PAR t transmitted beneath the canopy, at the soil level. Based on these two measurements, FAPAR can be calculated as follows: FAPAR = 1 -PAR t /PAR i. Measurements were taken throughout the day. PASTIS-57 (PAI Autonomous System from Transmittance Instantaneous Sensed from 57°) systems allow continuous monitoring of PAI  (Baret, Lecerf, et al. 2010). Each system consists of six photodiodes that measure the light in the blue spectral domain (where the vegetation is almost black and multiple scattering is minimal) plugged into one logger that records data at a sampling rate of five minutes. Sensors positioned directly on the soil measure the light penetrating inside the canopy without interception by canopy elements, and at least one sensor located above the top of the canopy monitors the incoming solar radiation.
826 and 0. 724 for LAI and FAPAR, respectively. These results are fairly lower than those reported by  Camacho et al. (2013)  who found a correlation of 0. 89 and 0. 92 for LAI and FAPAR respectively over the shrubland sites included in their  DIRECT comparison database during 2003 DIRECT comparison database during -2004. This difference can be related to (i) the fact that only a few DIRECT sites correspond to shrublands in 2003 and 2004, which are exclusively composited of low vegetation and poorly represent the vegetation type of our studied site, (ii) the design of GEOV1 products, which was learned on CYCLOPES products for low LAI values and, conversely to the MOD15A2 LAI product, does not take into account the canopy structure and clumping effect. This may not have a significant impact on shrublands similar to grasslands, but can lead to larger deviations for forested ecosystems.
Validation of biophysical parameters The ground data acquired in 2011 for the three sites were used to evaluate the accuracy of the GEOV1 LAI and FAPAR products. As shown in the homogeneity analysis, the ground measurements only partially represent the site's 3 × 3 km main area of interest and as such International Journal of Remote Sensing 2541 only the selection of GEOV1 pixels, as indicated in Figure  4  by the blue rectangles, was used further in the validation. The GEOV1 LAI product was compared with the PASTIS-57 and DHP PAI measurements. The regression analysis shows that GEOV1 products are correlated with ground measurements with an R 2 of 0. 61 and 0. 35 for PASTIS-57 and DHP, respectively (Figures  8(a ) and (b)). PAI estimates from PASTIS-57 ground measurements are somewhat higher than the GEOV1 LAI product values.
Background: Legumes form root nodules to house nitrogen fixing bacteria of the rhizobium family. The rhizobia are located intracellularly in the symbiotic nodule cells. In the legume Medicago truncatula these cells produce high amounts of Nodule-specific Cysteine-Rich (NCR) peptides which induce differentiation of the rhizobia into enlarged, polyploid and non-cultivable bacterial cells. NCRs are similar to innate immunity antimicrobial peptides. The NCR gene family is extremely large in Medicago with about 600 genes. Results: Here we used the Medicago truncatula Gene Expression Atlas (MtGEA) and other published microarray data to analyze the expression of 334 NCR genes in 267 different experimental conditions. We find that all but five of these genes are expressed in nodules but in no other plant organ or in response to any other biotic interaction or abiotic stress tested.
Global analysis of NCR gene expression The probe-sets and expression data corresponding to the NCRs described before  [14, 16]  were searched in MtGEA (version 3) using the BLAST search option of the database. Expression profiles of 334 probe-sets were obtained (Additional file 1: Table  S1 ) and their expression patterns in 267 different experimental conditions (254 from MtGEAv3, five unpublished conditions, three from  [22]  and five from  [23] ) are summarised in Figure  1. The transcriptome compendium is mostly derived from the M. truncatula genotype 'Jemalong' but also contains data sets obtained from the genotypes 'R108' and 'F83005. 5' although all specific experiments discussed below were obtained with the 'Jemalong' genotype. As specified in more detail below, the compendium covers the plant's major organs, various kinds of abiotic and biotic stresses and data from specific cell and tissue types.
These samples correspond to the nodule tissues from the most apical part of the nodule to the most proximal part and are approximately overlapping with the nodule zones I, II, II-III, III and IV. Cluster analysis of the NCR abundance profiles in these five samples distinguishes groups of NCR genes that have preferential expression in defined zones of the nodule and that are sequentially activated from nodule apex to proximal tissues (Figure  3A  and Additional file 2: Figure  S4 ). The transcriptome in specific tissues and cells of nodules was also obtained by LCM  [23]. In this experiment, the transcriptome was analyzed in nodule meristems, the distal and the proximal infection zone, the infected cells of the fixation zone and the uninfected cells of the fixation zone (Figure  3B ). Although the type of samples are not entirely overlapping, a good correspondence can be observed between the LCM dataset and the handdissected dataset (Figure  3A, B ).
The LCM dataset shows that NCR genes are not expressed in the nodule meristem which is free of rhizobia (Figure  3B ). This observation is in agreement with their activation by the infecting rhizobia. It also indicates that the expression of the early NCR genes in the hand-dissected sample I results from the significant presence of cells from the infection zone in that sample. NCR genes were reported to be expressed in infected cells  [7]. Unexpectedly, the LCM dataset revealed a relatively high expression of several NCR genes in the uninfected cells (Figure  3B , sample UC). However, among them were the genes NCR001, NCR084, NCR035 and NCR247 for which in situ hybridization, promoter-GUS or -mCherry fusions or immunolocalization of the peptide demonstrated a specific expression in the infected symbiotic nodule cells and no expression in uninfected cells  [8, 10, 14].
Promoter-GUS analysis and immunolocalization of selected NCRs in nodules In order to confirm the expression data from MtGEA, stable transgenic M. truncatula R108 lines were generated carrying promoter-GUS fusion constructs for 3 different NCR genes, representing different temporal classes of NCRs and inoculated with Sinorhizobium meliloti strain 1021 or Sinorhizobium arboris strain B554. NCR001 is not activated before the late stages of the nodule formation, NCR084 is slightly induced in early time points (4 dpi) and fully activated at the mature stage of the nodule and finally NCR121 is an early gene which is already fully activated at 4 dpi. GUS expression in the 3 transgenic lines was not detected in root tips or other root parts (Additional file 2: Figure  S6 ). In agreement with Clustering was performed using Pearson correlation.
Expression of NCR genes in plant organs Previously, NCR expression in conditions other than the symbiosis with rhizobium was tested by EST analysis  [14]  and with dedicated microarrays  [31] , indicating the absence of expression. The MtGEA database offers the possibility to extend this analysis to more plant organs and biotic and abiotic stress conditions. Besides nodules, 8 other plant organs  [20]  were interrogated for NCR expression (Additional file 2: Figure  S8 ) as well as plant treatments with the phytohormones auxin, cytokinin and auxin transport inhibitors  [32, 33]  (Additional file 2: Figure  S9 ). Interestingly, treatment of roots with the auxin transport inhibitors TIBA or NPA leads to the formation of uninfected nodule-like structures  [33]. However, in none of these conditions were NCR genes expressed except for the NCR genes with relaxed expression described above.
Promoter-GUS analysis of NCR expression during pathogenic interactions We used the 3 NCR promoter-GUS reporter lines to confirm the absence of NCR expression during pathogenic responses and to complement these observations. The MtGEA dataset includes M. truncatula responses to root pathogens and therefore we analyzed leaf or stem pathogens that encompass also other trophic interactions and infection strategies. Inoculation of M. truncatula leaflets with the necrotrophic soft rotting bacterium Dickeya dadantii 3937 induced maceration symptoms from 1 dpi on, but failed to induce NCR expression (Additional file 2: Figure  S12 ). Similarly infiltration of the virulent strain Pseudomonas syringae pv. tomato DC3000 (Pst) induced necrosis in the infiltrated zone within 2 dpi, whereas the hrcC mutant strain that is unable to form a functional type three secretion system (TTSS) did not induce any visible reaction (Additional file 2: Figure  S12 ).