dataesr/scientific-paragraphs-categorization huggingface dataset:
Genetic diversity and parentage analysis Accessions with seven or more missing data were not included in the genetic diversity analysis. Next, two different data sets were prepared: the first set consisted of 394 unique accessions that included interspecific hybrids, European reference winegrape varieties and North American species; there were 380 accessions in the second set after the 14 samples of hybrids, North American species and European wine grape varieties were excluded. Simple matching distance (SMD)  [54]  was calculated with 19 (representing the 19 grape chromosomes) and 34 SSR markers on both data sets. Hierarchical clustering (Ward method) and principal coordinate analysis were carried out with DARWIN V5. 0. 158  [55]  to determine the number of groups. Following these analyses, STRUCTURE V2.
3. 1 was used to infer the number of clusters with 19 and 34 markers, and with both data sets of 394 and 380 accessions  [56]. The membership of each accession was run for a range of genetic clusters with K values of 1 to 10 using the admixture model, and it was replicated 10 times for each K. Each run was implemented with a burn-in period of 100,000 steps followed by 400,000 Monte Carlo Markov Chain replicates using no prior information and assuming correlated allele frequencies. The posterior probability was then calculated for each value of K using the estimated log-likelihood of K to choose the optimal K  [57]. The results from STRUCTURE were displayed by DISTRUCT software  [58]. The microsatellite tool kit software  [59]  was used to calculate standard parameters of genetic variability: expected heterozygosity (He); allele frequencies (AF); and observed heterozygosity (Ho).
The deviation from Hardy-Weinberg equilibrium at each locus was examined by calculating the inbreeding coefficient 'F IS ' for each group, and the overall differentiation index 'F ST ' with FSTAT V2. 9. 3. 2 software  [60]. The probability of identity (PI), probability of exclusion (PE) and LOD likelihood ratios for potential parent-progeny relationships were calculated with FAMOZ software  [61]. The 10,000 simulated pairs were performed to identify a log of the odds ratios (LOD) score threshold to assess a potential parent pair with 34 SSR markers. Only pairs with LOD scores higher than the threshold level were considered. A discrepancy of a maximum of two loci was allowed to cover possible data errors  [62] , null alleles  [63] , and clonal mutations as previously described  [64]. Potential parental pairs were further evaluated if a discrepancy in the allelic data was observed.
Acknowledgements The authors gratefully acknowledge the funding support of the  American Vineyard Foundation  and the Louis P. Martini Endowed Chair research funds. They also gratefully acknowledge the research support of  Rong Hu ,  Nina Romero , and  Daniel Pap. We thank  Dr. John Preece  and  Bernie Prins  of the  National Clonal Germplasm Repository  for their efforts at curating this important collection. The authors also gratefully acknowledge  Dr. Jeffrey L. Dangl  and his review of this manuscript.
Sequences analyses were performed using BioEdit sequence analysis Software. Full-length ORFs were identified using BLAST research in the GenBank database (http://www. ncbi. nlm. nih. gov/blast/Blast. cgi). Amino acid sequences alignment was carried out using ClustalW method as described  [6]. Semi-quantitative RT-PCR experiments were performed to study the tissue distribution pattern of PaTEH1 using the P-S3/R3 primer pair. The experiments were repeated five times.
Electrophysiological recordings and analysis Na + currents were recorded using the two electrode voltageclamp (TEVC) technique. Oocytes were tested using 1 M-KCl/2 M-Kacetate-filled borosilicate glass electrodes connected to a TEVC amplifier (TEV-200, Dagan Corporation, Minneapolis, Minessota, USA). Digidata 1440A interface (Axon CNS Molecular Devices, California, USA) and pCLAMP 10 software (Axon CNS Molecular Devices) were used for acquisition (sampling rate: 10 or 100 kHz; band pass-filter: 5 kHz) and stimulation protocols. All experiments are performed at room temperature (18-21uC) in SOS. Capacitive transient and leak currents were corrected using P/N (N = 6) subtractions. For activation experiments, families of Na + current traces were generated by step depolarisations to test potentials from 270 to +40 mV (10 mV increment) from a holding potential of 2100 mV.
For recovery from inactivation experiments, Na + currents were generated by a two-pulse protocol beginning with a first pulse to 25 mV for 50 ms followed by a second pulse to 25 mV for 14. 5 ms. The duration between the two pulses was increased by 1 ms every set of double pulses and each set was separated by 10 s. Fractional recovery was calculated by dividing the maximum current amplitude the second test pulse by the maximum current amplitude of the corresponding first pulse. The recovery data were fit with the following exponential equation: I = I 0 +(Imax2I 0 )*(12exp (2k*(t2t 0 ))) where k is the rate constant and t is the time (GraphPad Software; equation name: plateau followed by one phase association equation).
Oocytes were placed in dishes (ibidi, Martinsried, Germany) in PBS/glycerol 5:1 solution and imaged through the 610/0. 3 numerical aperture objective of a Nikon (Nikon Instruments, Melville, NY) A1S1 confocal laser scanning microscope equipped with argon-ion (488 nm) and diode (561 nm) lasers. Alexa FluorH 488 was excited at 488 nm and the fluorescence emission was collected using a 500-550 nm filter; Alexa FluorH 546 was excited at 561 nm and the fluorescence emission was collected using a 570-600 nm filter. ImajeJ software (version 1. 4. 3. 67) was used to measure relative fluorescence. To quantify relative fluorescence intensity for each tested oocyte, an identical area of 5002. 2 mm 2 was delimited and the total number of pixels corresponding to AlexaFluorH 488 or AlexaFluorH 546 in this area was determined.
Statistical analysis Statistical analysis of all data was performed using GraphPad Prism software (version 5). All data are presented as means 6 standard error of the mean (SEM). Significance tests between groups of data were performed using a variance analysis (one-way ANOVA) followed by a Tukey post hoc test for comparison of all groups, or the unpaired Student's t-test. p values are specified. Statistical probabilities are expressed as * p,0. 05, ** p,0. 01 and *** p,0. 001.
Analysis of cDNA clones unmasks possible intron retention in PaTEH1 transcript Since the genome sequence of Periplaneta americana is not available in databases, a conventional RT-PCR strategy was performed to clone the cDNAs encoding the homolog of DmTEH1 in the nerve chord of P. americana. Briefly, using degenerated primers P-S1 and P-R1 (Table  1 ) designed from conserved amino acid regions deduced from an alignment of TEH1 subunits of Drosophila melanogaster (Genbank accession number NP_649959), Anopheles gambiae (accession number XP_310354), Apis mellifera (accession number XP_001120804), Nasonia vitripennis (accession number XP_001606835) and Tribolium castaneum (number XP_969821), a 561 bp cDNA fragment was amplified. Blast analysis indicated that the deduced amino acid sequence of this cDNA shares 51% of sequence identity with the extracellular loop of DmTEH1 and it was thus identified as TEH1-like protein of P.
Finally, using a primer pair encompassing the ORF, we amplified two different cDNAs. Ten clones contained an ORF of 843 bp encoding a 280 amino acids protein, named PaTEH1A (accession number KC206367). Six other clones displayed a longer insert (937 bp), containing an ORF of 840 bp encoding a 279 amino acids protein, named PaTEH1B (accession number KC206368). The nucleotide sequence of PaTEH1B clones corresponded to PaTEH1A but with an insertion of 94 bp after the position 807. This 94 bp insert in PaTEH1B sequence contained a novel stop codon and modified the coding sequence of the 39end, leading to a deduced amino acid protein, PaTEH1B that diverged from PaTEH1A sequence only by the last 11 residues of their Ctermini (Figure  1A ). These data indicated that PaTEH1A and PaTEH1B likely result from an alternative splicing mechanism of a single gene.
Activation end of PaTEH1A variant. The retention of intron 1 results in a short coding sequence with an early stop codon in PaTEH1B, leading to a novel C-terminal end. To determine whether a similar intron retention splicing mechanism targeting the C-terminal end of TEH1 occurs in other insect species, we examined available genomic sequences in the GenBank database. teh1 gene of D. melanogaster contains an intron of variable length that disrupts the end of the coding sequence (accession number Dmel_CG12806). Bioinformatic analysis of the genome of N. vitripennis (accession number NC_015869) led us to the same conclusion. Two transcript variants have been annotated and they only differ by the end of their 39 coding sequence (accession number XM_003425719, transcript variant 1 and XM_003425720, transcript variant 2), resulting from the presence of the first 72 bp of the intron 1 of the gene (accession number LOC100123225), and leading to a different 24-amino-acid Cterminal end.
Since only one copy of teh1-like gene has been found in N. vitripennis genome, TEH1-like variants 1 and 2 reported in the GenBank may result from a retention intron mechanism. Blast analysis also revealed the existence of two teh1 bicistronic genes in the genome of the mosquito, Culex quinquefasciatus (accession numbers CpipJ_CPIJ006103 and CpipJ_CPIJ006104). Both genes differ by the length of exon 2 (13 bp and 85 bp). Accordingly, the deduced amino acid sequences of both transcripts (accession numbers XM_001847934 and XM_001847935) differ in their length and C-terminal end. Concerning D. melanogaster and Anopheles gambiae, only one gene and one transcript have been described (accession numbers Dmel_CG12806 and AgaP_A-GAP003797). Nevertheless, the presence of a short coding sequence with an early stop codon in the intron sequences of both genes predicts the possibility of a conserved intron retention process in these insects.
Somatic Cell Count (SCC) The somatic cell counts (cell/ml) for the milk samples were determined using NucleoCounter SCC-100 (coulter electronic-Chemometec A/s, Denmark). As the SCC values are log-normal  (Shook, 1982) , means and S. D were calculated after logarithm transformation of the raw data (Neperian logarithm base e=2. 718). However, the values were expressed in cells/ml to be more explicit. To get values in cells/ml, the inverse transformation according to the function e x was applied.
Acknowledgements This study has been achieved within  FAO  project  UTF/SAU/021/SAU  with the support of  Camel and range research Center (CRRC). The authors thank  Mr. Sallal Issa Al-Mutairi ,  Head of the CRRC  for his support, and  Dr. M. Bengoumi , regional supervisor of the  FAO  project for his encouragements. We express our grateful to the  Prince Mansour  who give us the opportunity to collect milk in his camel farm.
, 2005). For each step of the cell wall proteomics flowchart, the specificities of CWPs must be taken into account: plant fractionation, protein extraction, protein separation, protein identification by mass spectrometry (MS), and bioinformatics. Indeed, CWPs can be tightly trapped into the extracellular matrix and escape the extraction procedure. They may not be resolved at the step of separation by two-dimensional electrophoresis (2D-E) because they are mainly basic glycoproteins  (Jamet et al. , 2008). Finally, the databases used for protein identification using MS data contain no information about PTMs such as glycosylation, thus preventing the identification of some of them. In this review, we will give a survey of plant cell wall proteomics studies with a focus on A. thaliana because this plant provides the best documented cell wall proteomes.
MATERIALS AND METHODS All the A. thaliana proteins reported in this review have been analyzed with different bioinformatics software to predict their sub-cellular localization and their functional domains using ProtAnnDB (http://www. polebio. lrsv. ups-tlse. fr/ProtAnnDB/ index. php) as previously described  (San Clemente et al. , 2009). Briefly, the following programs have been used for prediction of sub-cellular localization: TargetP (http://www. cbs. dtu. dk/ services/TargetP/), SignalP (http://www. cbs. dtu. dk/services/ SignalP/), Predotar (http://urgi. versailles. inra. fr/predotar/ predotar. html), Aramemnon (http://aramemnon. botanik. uni-koeln. de/) and TMHMM (http://www. cbs. dtu. dk/services/ TMHMM-2. 0/). The programs used for prediction of functional domains were PROSITE (http://prosite.
Only those having a bona fide predicted signal peptide are named CWPs in this review. This point will be discussed below. From the fifth step of the proteomics flowchart (Figure  2 ), bioinformatics analyses lead to group proteins in families either by sequence comparison to proteins present in databases and already annotated, or by search for functional domains as defined in domain repertoires like PROSITE, Pfam, or InterProScan. Two types of classification have then been proposed. Both of them have drawbacks and suffer from ambiguity. The first type is based on the physiological processes in which the proteins are assumed to be involved, like growth and development, stress or defense against pathogens. The drawback is that it can be difficult to sort the proteins. For example, glycoside hydrolases (GHs) could be involved in both plant development and defense  (Kasprzewska, 2003).
WallProtDB, A CELL WALL PROTEOMICS DATABASE The classification of proteins described above has been used to build up the WallProtDB database (http://www. polebio. scsv. ups-tlse. fr/WallProtDB/) (Pont-Lezica et al. , 2010; Ligat et al. , 2011). The 20 published A. thaliana cell wall proteomes listed in Table  1  (described in more details in Table  A1 ) have been subjected to the same bioinformatics software pipeline (ProtAnnDB) in order to compare them more accurately. The number of CWPs identified in these proteomes is very variable, ranging from 6 to 137. The less populated proteomes are a leaf apoplast proteome  (Haslam et al. , 2003) , the AGP proteome  (Schultz et al. , 2004 ) and those focused on proteins which level of accumulation changes in response to a treatment  (Ndimba et al.
, 2005)  or (iv) a yeast secretion trap assay  (Lee and Rose, 2012 ). An interesting tool is SUBA3 (SUBcellular localization database for Arabidopsis proteins) which collects bioinformatics and experimental data of sub-cellular localization (http://suba. plantenergy. uwa. edu. au/) (Heazlewood et al. , 2007). The precise identification of secreted proteins devoid of predicted signal peptide will allow the demonstration of the existence of alternative secretion pathways in plant cells and the design of bioinformatics software able to predict non-classical secretion of plant proteins.
They will provide new insight into the structure of CWPs, as recently achieved for the AGP31, an A. thaliana cell wall O-glycoprotein  (Hijazi et al. , 2012). Finally, progresses in bioinformatics will be very helpful to characterize cell wall glycoproteins. Several computer programs like GlycoMod  (Cooper et al. , 2001) , GlysodeIQ TM  (Joshi et al. , 2004) , GlycoMiner  (Ozohanics et al. , 2008) , or Peptoonist  (Goldberg et al. , 2007)  have been developed, but most of them do not consider plant glycan specificities. The ProTerNyc software has been developed in this purpose and efficiently used to predict N-glycan motifs on cell wall glycoproteins  (Albenne et al. , 2009; Zhang et al. , 2011). However, additional bioinformatics tools should be developed to improve automatic data interpretation.
, 2006). The corresponding cDNA of Zinnia elegans encodes a protein of 132 amino acids, but only 12, from His 120 to Asn 131 , match the TDIF sequence with two Hyp residues (Hyp 123 and Hyp 126 ). Second, peptide-encoding genes are frequently overlooked during the annotation of genomes. Indeed, gene prediction programs hardly distinguish between short, often intronless peptideencoding genes and random small open reading frames (ORFs). To minimize incorrect gene predictions, it is common that small ORFs are rejected  (Olsen et al. , 2002). To overcome such a deficiency, a bioinformatics approach has been undertaken to identify candidate peptide-encoding genes in the A. thaliana genome  (Lease and Walker, 2006). It has led to an unannotated secreted peptide database containing 33,809 ORFs. The identified peptides have been characterized by the presence of a predicted N-terminal signal peptide and by the absence of transmembrane domains and ER retention sequences.
ACKNOWLEDGMENTS The authors are thankful to  Université Paul Sabatier (Toulouse, France)  and  CNRS  for supporting their research work. The authors also wish to thank  Hélène San Clemente  for her contribution to the bioinformatics work. This work has been done at LRSV, part of the  "Laboratoire d'Excellence" (LABEX)  entitled  TULIP  ( ANR -10-LABX-41 ). The authors wish to apologize for the papers not quoted in this review, due to lack of space.
mj-far-1 identification and sequence homology BLAST searches against M. javanica ESTs database (www. nematode. net; www. nematodes. org) with the G. pallida Gp-FAR-1 (accession no. CAA70477. 2) revealed the presence of one cDNA's encoding a FAR homolog, designated herein as mj-far-1 (DNA) or Mj-FAR-1 (protein), following the previously suggested nomenclature convention for this group of proteins  [42]. Further extension of mj-far-1 by genome walking identified a 802 bp fragment encompassing an open reading frame (ORF), encoding a predicted Mj-FAR-1 protein of 190 aa with a deduced molecular mass of 21. 2 kDa and a theoretical pI 5. 72. In silico analysis of the deduced Mj-FAR-1 amino acid sequence through SignalP 3. 0  [43] , PSORTII  [44] , and TargetP  [45]  algorithms indicate the presence of twenty amino acids comprising an hydrophobic signal peptide.
This signal peptide contains a predicted cleavage site located between position 20 (alanine) and 21 (alanine), directing the protein to the secretory system, a characteristic of PPN's parasitism gene products  [8, 46]  (Fig. 1A ). Secondary structure analysis of Mj-FAR-1 was conducted by PHD  [47] ; http://www. predictprotein. org) and Jpred  [48] ; http://www. compbio. dundee. ac. uk/www-jpred/) algorithms. These analyses revealed that in common with others FARs of animal and plant parasites  [38, 40]  a predominantly alpha-helical conformation is predicted containing a 81. 6% helix, 18. 4% loop and no beta/extended structure was identified (Fig. 1A ). In addition, the observed coiled-coil structure between positions 606100 and 1206160  [49] ; http://www. ch. embnet. org/software/COILS_form.
html), was another common structural property shared with other parasite origin FAR. Likewise, in common with all other counterparts of this protein, a N-linked glycosylation site (Asn-Leu-Thr-Glu) presented at residues 131 along with conserved consensus casein kinase II phosphorylation sites at residues 31, 52, 71, 110 and 114 predicted by the PHD  [47]  algorithms were observed  [38]  (Fig. 1A ). To study the phylogenetic relationship among the predicted amino acid sequence of Mj-FAR-1 and other nematodes FAR, a BLASTp search through available online databases (www. nematode. net; www. nematodes. org and www. ncbi. nlm. nih. gov) was conducted using the amino acid sequence of Gp-FAR-1 of G. pallida as the query using ClustalW in MEGA5. 0 software (http:// www. megasoftware. net/mega. php).
The derived sequences data were used to design the nested primers GSP2-nes-for (CCAGCC-TAAATGAGAGAAAATGCTTTG) and GSP2-nes-rev (GGCGACTTCTTTCAAGATTTTCTTGTC) used to amplify the full length DNA mj-far-1, which was deposited in GenBank under the accession no. JX863901. To get the mj-far-1 full-length cDNA, freshly hatched M. javanica pre-parasitic J2 were first homogenized using glass beads and liquid nitrogen following total RNAs extraction using an InviTrap Spin Plant RNA Mini Kit (Invitek, Robert-Ro ¨ssle-Str, Berlin. Germany). To remove contaminating genomic DNA, RNA samples were incubated in presence of 10 units of TURBO DNA-free TM DNASE (Applied Biosystems, Foster City, CA). DNA-free RNA was converted into first-strand cDNA using the Verso TM cDNA Synthesis Kit (ABgene, Epsom, UK). mj-far-1 cDNA amplification product with the primer pair longFAR-for (CATTCAATCAACAAGCC-TACTTACTCTC) and longFAR-rev (TCTTGCTATTGC-GAATCAAAGCATTTTC) was subsequently cloned into pGEMT-far vector for sequencing confirmation.
Sequence comparisons and secondary structure predictions Sequence analysis was conducted through programs available at the ExPASY molecular biology server (http://expasy. org/ proteomics). The signalP 3. 0  [89] , the PSORT II  [90]  along with the TargetP  [45]  algorithms were used to predict the location of the signal peptide and its cleavage site. Calculation of the predicted Mj-FAR-1 molecular weight and isoelectric point were performed through the ProtParam program  [91]. Secondary structure prediction of the protein sequence was performed using the PHD  [47]  and Jpred algorithms  [48]. FARs sequence of other nematodes species were obtained by performing BLAST searches against a range of databases available online, such as www. nematode. net, www. nematodes. org and www. ncbi. nlm. nih.
gov. The ClustalW program  [92]  was used to generate an alignment of the FARs protein sequences from Caenorhabditis elegans, animal-and plant-parasitic nematodes. Phylogenetic analysis were conducted on the alignment using the maximum likelihood protein sequence parsimony method  [93]  and the unweighted pair group method with arithmetic mean  [94]. Trees were rooted against the FAR protein sequence of the cyst nematode, Globodera pallida Gp-FAR-1. All methods produced trees of the same topology, and a typical resulting tree with bootstrap values is presented.
DNA-free RNA was converted into first-strand cDNA using the Verso TM cDNA Synthesis Kit (ABgene, Epsom, UK) and qRT-PCR reactions were performed using the SYBR-Green ROX Mix (ABgene). qRT-PCR primers were designed using the Primer Express software (Applied Biosystems, Tables  S1  and S2 ). The subsequent real-time PCR reaction contained 3. 4 ml of the cDNA in a total volume of 10 ml, consisting of 16 SYBR-Green ROX Mix (ABgene) and 150 nM forward primer and 150 nM reverse primer subjected in real-time PCR plasticware (Axygen, Union City, CA). All PCR cycles began with 2 min at 50uC then 10 min at 95uC, followed by 40 two-step cycles comprising 10 s at 95uC and 1 min at 60uC. After PCR reaction, a melting curve was generated by gradually increasing the temperature to 95uC to test for amplicon specificity.
For qPCR a mixture of all cDNAs was used for all treatments, as a template for calibration curves designed for each pair of primers. Each reaction was performed in triplicate and the results represented the mean of two independent biological experiments. Two constitutively expressed genes the small ribosomal RNA (18S; GenBank accession no. AF442193. 1) and elongation factor-1a genes (EF-1a; GenBank accession no. U94493. 1) were the endogenous controls for the M. javanica gene expression analysis (Table  S1 ). Three constitutively expressed genes the actin (ACT; GenBank accession no. U60482. 1), b-tubulin (TUB; GenBank accession no. NM_001247878. 1) and 18S gene (GenBank accession no. BH012957. 1) were the endogenous controls for tomato gene expression analysis (Table  S1 ). Transcript levels were normalized for each sample with the geometric mean of the corresponding selected housekeeping genes.